From 080e39d0474a9f8b0f95894e1eef00b56022240b Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Thu, 1 Aug 2024 17:02:07 -0400 Subject: [PATCH 001/111] tests for indepepdent and correct --- chirho/explainable/handlers/components.py | 9 +- docs/source/dynamical_intro.ipynb | 2 +- docs/source/explainable_categorical.ipynb | 2 +- docs/source/test_notebook.ipynb | 203 ++++++++++++++++++ .../explainable/test_handlers_explanation.py | 55 +++++ 5 files changed, 265 insertions(+), 6 deletions(-) create mode 100644 docs/source/test_notebook.ipynb diff --git a/chirho/explainable/handlers/components.py b/chirho/explainable/handlers/components.py index 597ca484..f6fdac14 100644 --- a/chirho/explainable/handlers/components.py +++ b/chirho/explainable/handlers/components.py @@ -292,16 +292,17 @@ def _consequent_eq_neq(consequent: T) -> torch.Tensor: FACTUAL_NEC_SUFF = torch.zeros_like(sufficiency_log_probs) + index_keys = set(antecedents) & set(indices_of(consequent, event_dim=support.event_dim)) if indices_of(consequent, event_dim=support.event_dim) else set(antecedents) + + print(index_keys) + print("poorva«") nec_suff_log_probs_partitioned = { **{ factual_indices: FACTUAL_NEC_SUFF, }, **{ IndexSet(**{antecedent: {ind}}): log_prob - for antecedent in ( - set(antecedents) - & set(indices_of(consequent, event_dim=support.event_dim)) - ) + for antecedent in index_keys for ind, log_prob in zip( [necessity_world, sufficiency_world], [necessity_log_probs, sufficiency_log_probs], diff --git a/docs/source/dynamical_intro.ipynb b/docs/source/dynamical_intro.ipynb index 2624e858..27577bde 100644 --- a/docs/source/dynamical_intro.ipynb +++ b/docs/source/dynamical_intro.ipynb @@ -1330,7 +1330,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.10.14" } }, "nbformat": 4, diff --git a/docs/source/explainable_categorical.ipynb b/docs/source/explainable_categorical.ipynb index 93d73df4..b9c14154 100644 --- a/docs/source/explainable_categorical.ipynb +++ b/docs/source/explainable_categorical.ipynb @@ -878,7 +878,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.10.14" } }, "nbformat": 4, diff --git a/docs/source/test_notebook.ipynb b/docs/source/test_notebook.ipynb new file mode 100644 index 00000000..e7b0bb3d --- /dev/null +++ b/docs/source/test_notebook.ipynb @@ -0,0 +1,203 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "\n", + "import pyro\n", + "import pyro.distributions as dist\n", + "import pyro.distributions.constraints as constraints\n", + "import torch\n", + "\n", + "from chirho.counterfactual.handlers.counterfactual import MultiWorldCounterfactual\n", + "from chirho.explainable.handlers.components import undo_split\n", + "from chirho.explainable.handlers.explanation import SearchForExplanation, SplitSubsets\n", + "from chirho.explainable.handlers import ExtractSupports\n", + "from chirho.explainable.handlers.preemptions import Preemptions\n", + "from chirho.indexed.ops import IndexSet, gather\n", + "from chirho.observational.handlers.condition import condition" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'X'}\n", + "poorva\n", + "{'X'}\n", + "poorva\n", + "{'Y'}\n", + "poorva\n", + "testing with tensor([0., 0., 1.])\n", + "tensor([0., 1., 1.])\n", + "tensor([[[[[-inf, 0., 0.]]]]])\n" + ] + } + ], + "source": [ + "# def test_edge_eq_neq():\n", + "\n", + "def model_independent():\n", + " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", + " Y = pyro.sample(\"Y\", dist.Bernoulli(0.5))\n", + "\n", + "def model_connected():\n", + " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", + " Y = pyro.sample(\"Y\", dist.Bernoulli(X))\n", + "\n", + "\n", + "\n", + "with ExtractSupports() as supports_independent:\n", + " model_independent()\n", + "\n", + "with ExtractSupports() as supports_connected:\n", + " model_connected()\n", + "\n", + "with MultiWorldCounterfactual() as mwc_independent: \n", + " with SearchForExplanation(\n", + " supports=supports_independent.supports,\n", + " antecedents={\"X\": torch.tensor(1.0)},\n", + " consequents={\"Y\": torch.tensor(1.0)},\n", + " witnesses={},\n", + " alternatives={\"X\": torch.tensor(0.0)},\n", + " antecedent_bias=-0.5,\n", + " consequent_scale=0,\n", + " ):\n", + " with pyro.plate(\"sample\", size=3):\n", + " with pyro.poutine.trace() as trace_independent:\n", + " model_independent()\n", + "\n", + "with MultiWorldCounterfactual() as mwc_connected: \n", + " with SearchForExplanation(\n", + " supports=supports_connected.supports,\n", + " antecedents={\"X\": torch.tensor(1.0)},\n", + " consequents={\"Y\": torch.tensor(1.0)},\n", + " witnesses={},\n", + " alternatives={\"X\": torch.tensor(0.0)},\n", + " antecedent_bias=-0.5,\n", + " consequent_scale=0,\n", + " ):\n", + " with pyro.plate(\"sample\", size=3):\n", + " with pyro.poutine.trace() as trace_connected:\n", + " model_connected()\n", + "\n", + "with MultiWorldCounterfactual() as mwc_reverse: \n", + " with SearchForExplanation(\n", + " supports=supports_connected.supports,\n", + " antecedents={\"Y\": torch.tensor(1.0)},\n", + " consequents={\"X\": torch.tensor(1.0)},\n", + " witnesses={},\n", + " alternatives={\"Y\": torch.tensor(0.0)},\n", + " antecedent_bias=-0.5,\n", + " consequent_scale=0,\n", + " ):\n", + " with pyro.plate(\"sample\", size=3):\n", + " with pyro.poutine.trace() as trace_reverse:\n", + " model_connected()\n", + "\n", + "\n", + "trace_connected.trace.compute_log_prob\n", + "trace_independent.trace.compute_log_prob\n", + "trace_reverse.trace.compute_log_prob\n", + "\n", + "#print(trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor)\n", + "#print(trace_independent.trace.nodes[\"Y\"][\"value\"])\n", + "\n", + "Y_values_ind = trace_independent.trace.nodes[\"Y\"][\"value\"]\n", + "Y_values_con = trace_connected.trace.nodes[\"Y\"][\"value\"]\n", + "X_values_rev = trace_reverse.trace.nodes[\"X\"][\"value\"]\n", + "\n", + "\n", + "if torch.any(Y_values_ind == 1.):\n", + " print(\"testing with \", Y_values_ind)\n", + " assert trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor[1,0,0,0,:].sum().exp() == 0.\n", + "else:\n", + " assert trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor[1,0,0,0,:].sum().exp() == 1.\n", + " \n", + "\n", + "if torch.any(Y_values_ind == 0.):\n", + " assert trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor[2,0,0,0,:].sum().exp() == 0.\n", + "else:\n", + " assert trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor[2,0,0,0,:].sum().exp() == 1.\n", + "\n", + "assert torch.all(trace_connected.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor.sum() == 0)\n", + " \n", + "\n", + "print(X_values_rev)\n", + "print(trace_reverse.trace.nodes[\"__cause____consequent_X\"][\"fn\"].log_factor)\n", + "\n", + "\n", + "\n", + "# assert torch.all(trace_connected.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor[0,0,0,0,:] == 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'Y'}\n", + "poorva\n" + ] + } + ], + "source": [ + "with MultiWorldCounterfactual() as mwc_reverse: \n", + " with SearchForExplanation(\n", + " supports=supports_connected.supports,\n", + " antecedents={\"Y\": torch.tensor(1.0)},\n", + " consequents={\"X\": torch.tensor(1.0)},\n", + " witnesses={},\n", + " alternatives={\"Y\": torch.tensor(0.0)},\n", + " antecedent_bias=-0.5,\n", + " consequent_scale=0,\n", + " ):\n", + " with pyro.plate(\"sample\", size=3):\n", + " with pyro.poutine.trace() as trace_reverse:\n", + " model_connected()\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tests/explainable/test_handlers_explanation.py b/tests/explainable/test_handlers_explanation.py index 7900acb5..b8675421 100644 --- a/tests/explainable/test_handlers_explanation.py +++ b/tests/explainable/test_handlers_explanation.py @@ -8,6 +8,7 @@ from chirho.counterfactual.handlers.counterfactual import MultiWorldCounterfactual from chirho.explainable.handlers.components import undo_split from chirho.explainable.handlers.explanation import SearchForExplanation, SplitSubsets +from chirho.explainable.handlers import ExportSupports from chirho.explainable.handlers.preemptions import Preemptions from chirho.indexed.ops import IndexSet, gather from chirho.observational.handlers.condition import condition @@ -314,3 +315,57 @@ def test_SplitSubsets_two_layers(): ).item() assert obs_bill_hits == 0.0 and int_bill_hits == 0.0 and int_bottle_shatters == 0.0 + +def test_edge_eq_neq(): + + def model_independent(): + X = pyro.sample("X", dist.Bernoulli(0.5)) + Y = pyro.sample("Y", dist.Bernoulli(0.5)) + + def model_connected(): + X = pyro.sample("X", dist.Bernoulli(0.5)) + Y = pyro.sample("Y", dist.Bernoulli(X)) + + with ExtractSupports() as supports_independent: + model_independent() + + with ExtractSupports() as supports_connected: + model_connected() + + with MultiWorldCounterfactual() as mwc_independent: + with SearchForExplanation( + supports=supports_independent.supports, + antecedents={"X": torch.tensor(1.0)}, + consequents={"Y": torch.tensor(1.0)}, + witnesses={}, + alternatives={"X": torch.tensor(0.0)}, + antecedent_bias=-0.5, + consequent_scale=0, + ): + with pyro.plate("sample", size=3): + with pyro.poutine.trace() as trace_independent: + model_independent() + + with MultiWorldCounterfactual() as mwc_connected: + with SearchForExplanation( + supports=supports_connected.supports, + antecedents={"X": torch.tensor(1.0)}, + consequents={"Y": torch.tensor(1.0)}, + witnesses={}, + alternatives={"X": torch.tensor(0.0)}, + antecedent_bias=-0.5, + consequent_scale=0, + ): + with pyro.plate("sample", size=3): + with pyro.poutine.trace() as trace_connected: + model_connected() + + + trace_connected.trace.compute_log_prob + trace_independent.trace.compute_log_prob + + print(trace_independent.trace.nodes["__cause____consequent_Y"]["fn"].log_factor) + + assert trace_independent.trace.nodes["__cause____consequent_Y"]["fn"].log_factor[1,0,0,0,:].sum() <0 + assert torch.all(trace_independent.trace.nodes["__cause____consequent_Y"]["fn"].log_factor[2,0,0,0,:] == 0) + assert torch.all(trace_connected.trace.nodes["__cause____consequent_Y"]["fn"].log_factor[0,0,0,0,:] == 0) From 7dd59ade1f5a7dd58e19f056f09fb52e62a69d19 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Thu, 1 Aug 2024 17:18:16 -0400 Subject: [PATCH 002/111] added print --- chirho/explainable/handlers/components.py | 59 ++++++++++++++++++++++- 1 file changed, 57 insertions(+), 2 deletions(-) diff --git a/chirho/explainable/handlers/components.py b/chirho/explainable/handlers/components.py index f6fdac14..5a55cd2d 100644 --- a/chirho/explainable/handlers/components.py +++ b/chirho/explainable/handlers/components.py @@ -193,6 +193,8 @@ def consequent_neq( """ def _consequent_neq(consequent: T) -> torch.Tensor: + + indices = IndexSet( **{ name: ind @@ -231,6 +233,7 @@ def consequent_eq_neq( def _consequent_eq_neq(consequent: T) -> torch.Tensor: + print("consequent", consequent) factual_indices = IndexSet( **{ name: ind @@ -242,6 +245,8 @@ def _consequent_eq_neq(consequent: T) -> torch.Tensor: necessity_world = kwargs.get("necessity_world", 1) sufficiency_world = kwargs.get("sufficiency_world", 2) + # print(indices_of(consequent, event_dim=support.event_dim).keys()) + necessity_indices = IndexSet( **{ name: {necessity_world} @@ -265,6 +270,11 @@ def _consequent_eq_neq(consequent: T) -> torch.Tensor: consequent, sufficiency_indices, event_dim=support.event_dim ) + # print("necessity_indices: ", necessity_indices) + # print("sufficiency_indices: ", sufficiency_indices) + # print("necessity_value: ", necessity_value) + # print("sufficiency_value: ", sufficiency_value) + # compare to proposed consequent if provided # as then the sufficiency value can be different # due to witness preemption @@ -284,18 +294,20 @@ def _consequent_eq_neq(consequent: T) -> torch.Tensor: ) ) + # print("necessity_log_probs", necessity_log_probs) + sufficiency_log_probs = ( soft_eq(support, sufficiency_value, proposed_consequent, **kwargs) if proposed_consequent is not None else torch.zeros_like(necessity_log_probs) ) + # print("sufficiency_log_probs", sufficiency_log_probs) + FACTUAL_NEC_SUFF = torch.zeros_like(sufficiency_log_probs) index_keys = set(antecedents) & set(indices_of(consequent, event_dim=support.event_dim)) if indices_of(consequent, event_dim=support.event_dim) else set(antecedents) - print(index_keys) - print("poorva«") nec_suff_log_probs_partitioned = { **{ factual_indices: FACTUAL_NEC_SUFF, @@ -303,6 +315,9 @@ def _consequent_eq_neq(consequent: T) -> torch.Tensor: **{ IndexSet(**{antecedent: {ind}}): log_prob for antecedent in index_keys + for antecedent in ( + index_keys + ) for ind, log_prob in zip( [necessity_world, sufficiency_world], [necessity_log_probs, sufficiency_log_probs], @@ -310,11 +325,51 @@ def _consequent_eq_neq(consequent: T) -> torch.Tensor: }, } + # nec_suff_log_probs_partitioned = { + # **{ + # factual_indices: FACTUAL_NEC_SUFF, + # }, + # **{ + # IndexSet(**{antecedent: {ind}}): log_prob + # for antecedent in ( + # set(antecedents) + # & set(indices_of(consequent, event_dim=support.event_dim)) + # ) + # for ind, log_prob in zip( + # [necessity_world, sufficiency_world], + # [necessity_log_probs, sufficiency_log_probs], + # ) + # }, + # } + new_value = scatter_n( nec_suff_log_probs_partitioned, event_dim=0, ) + # for ind, log_prob in zip([necessity_world, sufficiency_world], [necessity_log_probs, sufficiency_log_probs]): + # print(ind, log_prob) + + # print(set(antecedents)) + # print(set(indices_of(consequent, event_dim=support.event_dim))) + # print(set(antecedents) & set(indices_of(consequent, event_dim=support.event_dim))) + + # for antecedent in ( + # set(antecedents) + # & set(indices_of(consequent, event_dim=support.event_dim)) + # ): + # print("yo") + # print(antecedent) + + # print({factual_indices: FACTUAL_NEC_SUFF}) + # print(**{factual_indices: FACTUAL_NEC_SUFF}) + + + + # print("nec_suff_log_prob_partitioned", nec_suff_log_probs_partitioned) + # print("new_value", new_value) + + # print(necessity_log_probs, sufficiency_log_probs) return new_value return _consequent_eq_neq From 03516b795b62ca1d000af70c89cfd6990c218bfa Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Thu, 1 Aug 2024 17:18:42 -0400 Subject: [PATCH 003/111] extra case --- docs/source/test_notebook.ipynb | 91 ++++++++++++++++++++++++++++----- 1 file changed, 78 insertions(+), 13 deletions(-) diff --git a/docs/source/test_notebook.ipynb b/docs/source/test_notebook.ipynb index e7b0bb3d..88c1bb2c 100644 --- a/docs/source/test_notebook.ipynb +++ b/docs/source/test_notebook.ipynb @@ -24,22 +24,26 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'X'}\n", - "poorva\n", - "{'X'}\n", - "poorva\n", - "{'Y'}\n", - "poorva\n", - "testing with tensor([0., 0., 1.])\n", - "tensor([0., 1., 1.])\n", - "tensor([[[[[-inf, 0., 0.]]]]])\n" + "consequent tensor([0., 0., 0.])\n", + "consequent tensor([[[[[1., 1., 0.]]]],\n", + "\n", + "\n", + "\n", + " [[[[0., 0., 0.]]]],\n", + "\n", + "\n", + "\n", + " [[[[1., 1., 1.]]]]])\n", + "consequent tensor([0., 1., 0.])\n", + "tensor([0., 1., 0.])\n", + "tensor([[[[[-inf, 0., -inf]]]]])\n" ] } ], @@ -142,15 +146,76 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'Y'}\n", - "poorva\n" + "consequent tensor([[[[[1., 1., 1.]]]],\n", + "\n", + "\n", + "\n", + " [[[[0., 0., 0.]]]],\n", + "\n", + "\n", + "\n", + " [[[[1., 1., 1.]]]]])\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 13\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pyro\u001b[38;5;241m.\u001b[39mplate(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample\u001b[39m\u001b[38;5;124m\"\u001b[39m, size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m):\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pyro\u001b[38;5;241m.\u001b[39mpoutine\u001b[38;5;241m.\u001b[39mtrace() \u001b[38;5;28;01mas\u001b[39;00m trace_connected:\n\u001b[0;32m---> 13\u001b[0m \u001b[43mmodel_connected\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[2], line 9\u001b[0m, in \u001b[0;36mmodel_connected\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmodel_connected\u001b[39m():\n\u001b[1;32m 8\u001b[0m X \u001b[38;5;241m=\u001b[39m pyro\u001b[38;5;241m.\u001b[39msample(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m, dist\u001b[38;5;241m.\u001b[39mBernoulli(\u001b[38;5;241m0.5\u001b[39m))\n\u001b[0;32m----> 9\u001b[0m Y \u001b[38;5;241m=\u001b[39m \u001b[43mpyro\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mY\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mBernoulli\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/primitives.py:189\u001b[0m, in \u001b[0;36msample\u001b[0;34m(name, fn, obs, obs_mask, infer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 172\u001b[0m msg \u001b[38;5;241m=\u001b[39m Message(\n\u001b[1;32m 173\u001b[0m \u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 174\u001b[0m name\u001b[38;5;241m=\u001b[39mname,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 186\u001b[0m continuation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 187\u001b[0m )\n\u001b[1;32m 188\u001b[0m \u001b[38;5;66;03m# apply the stack and return its return value\u001b[39;00m\n\u001b[0;32m--> 189\u001b[0m \u001b[43mapply_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 190\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;66;03m# type narrowing guaranteed by apply_stack\u001b[39;00m\n\u001b[1;32m 191\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", + "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/poutine/runtime.py:386\u001b[0m, in \u001b[0;36mapply_stack\u001b[0;34m(initial_msg)\u001b[0m\n\u001b[1;32m 383\u001b[0m default_process_message(msg)\n\u001b[1;32m 385\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m frame \u001b[38;5;129;01min\u001b[39;00m stack[\u001b[38;5;241m-\u001b[39mpointer:]:\n\u001b[0;32m--> 386\u001b[0m \u001b[43mframe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_postprocess_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 388\u001b[0m cont \u001b[38;5;241m=\u001b[39m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontinuation\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 389\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cont \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/poutine/messenger.py:194\u001b[0m, in \u001b[0;36mMessenger._postprocess_message\u001b[0;34m(self, msg)\u001b[0m\n\u001b[1;32m 192\u001b[0m method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_pyro_post_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmsg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 193\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 194\u001b[0m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/s78projects/chirho/chirho/observational/handlers/condition.py:56\u001b[0m, in \u001b[0;36mFactors._pyro_post_sample\u001b[0;34m(self, msg)\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m---> 56\u001b[0m pyro\u001b[38;5;241m.\u001b[39mfactor(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprefix\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mmsg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[43mfactor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvalue\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m)\n", + "File \u001b[0;32m~/s78projects/chirho/chirho/explainable/handlers/components.py:311\u001b[0m, in \u001b[0;36mconsequent_eq_neq.._consequent_eq_neq\u001b[0;34m(consequent)\u001b[0m\n\u001b[1;32m 307\u001b[0m FACTUAL_NEC_SUFF \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mzeros_like(sufficiency_log_probs)\n\u001b[1;32m 309\u001b[0m index_keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m(antecedents) \u001b[38;5;241m&\u001b[39m \u001b[38;5;28mset\u001b[39m(indices_of(consequent, event_dim\u001b[38;5;241m=\u001b[39msupport\u001b[38;5;241m.\u001b[39mevent_dim)) \u001b[38;5;28;01mif\u001b[39;00m indices_of(consequent, event_dim\u001b[38;5;241m=\u001b[39msupport\u001b[38;5;241m.\u001b[39mevent_dim) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mset\u001b[39m(antecedents)\n\u001b[0;32m--> 311\u001b[0m nec_suff_log_probs_partitioned \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 312\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 313\u001b[0m factual_indices: FACTUAL_NEC_SUFF,\n\u001b[1;32m 314\u001b[0m },\n\u001b[1;32m 315\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 316\u001b[0m IndexSet(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{antecedent: {ind}}): log_prob\n\u001b[1;32m 317\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m antecedent \u001b[38;5;129;01min\u001b[39;00m index_keys\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m antecedent \u001b[38;5;129;01min\u001b[39;00m (\n\u001b[1;32m 319\u001b[0m index_keys\n\u001b[1;32m 320\u001b[0m )\n\u001b[1;32m 321\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ind, log_prob \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\n\u001b[1;32m 322\u001b[0m [necessity_world, sufficiency_world],\n\u001b[1;32m 323\u001b[0m [necessity_log_probs, sufficiency_log_probs],\n\u001b[1;32m 324\u001b[0m )\n\u001b[1;32m 325\u001b[0m },\n\u001b[1;32m 326\u001b[0m }\n\u001b[1;32m 328\u001b[0m \u001b[38;5;66;03m# nec_suff_log_probs_partitioned = {\u001b[39;00m\n\u001b[1;32m 329\u001b[0m \u001b[38;5;66;03m# **{\u001b[39;00m\n\u001b[1;32m 330\u001b[0m \u001b[38;5;66;03m# factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 342\u001b[0m \u001b[38;5;66;03m# },\u001b[39;00m\n\u001b[1;32m 343\u001b[0m \u001b[38;5;66;03m# }\u001b[39;00m\n\u001b[1;32m 345\u001b[0m new_value \u001b[38;5;241m=\u001b[39m scatter_n(\n\u001b[1;32m 346\u001b[0m nec_suff_log_probs_partitioned,\n\u001b[1;32m 347\u001b[0m event_dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 348\u001b[0m )\n", + "File \u001b[0;32m~/s78projects/chirho/chirho/explainable/handlers/components.py:311\u001b[0m, in \u001b[0;36mconsequent_eq_neq.._consequent_eq_neq\u001b[0;34m(consequent)\u001b[0m\n\u001b[1;32m 307\u001b[0m FACTUAL_NEC_SUFF \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mzeros_like(sufficiency_log_probs)\n\u001b[1;32m 309\u001b[0m index_keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m(antecedents) \u001b[38;5;241m&\u001b[39m \u001b[38;5;28mset\u001b[39m(indices_of(consequent, event_dim\u001b[38;5;241m=\u001b[39msupport\u001b[38;5;241m.\u001b[39mevent_dim)) \u001b[38;5;28;01mif\u001b[39;00m indices_of(consequent, event_dim\u001b[38;5;241m=\u001b[39msupport\u001b[38;5;241m.\u001b[39mevent_dim) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mset\u001b[39m(antecedents)\n\u001b[0;32m--> 311\u001b[0m nec_suff_log_probs_partitioned \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 312\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 313\u001b[0m factual_indices: FACTUAL_NEC_SUFF,\n\u001b[1;32m 314\u001b[0m },\n\u001b[1;32m 315\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 316\u001b[0m IndexSet(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{antecedent: {ind}}): log_prob\n\u001b[1;32m 317\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m antecedent \u001b[38;5;129;01min\u001b[39;00m index_keys\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m antecedent \u001b[38;5;129;01min\u001b[39;00m (\n\u001b[1;32m 319\u001b[0m index_keys\n\u001b[1;32m 320\u001b[0m )\n\u001b[1;32m 321\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ind, log_prob \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\n\u001b[1;32m 322\u001b[0m [necessity_world, sufficiency_world],\n\u001b[1;32m 323\u001b[0m [necessity_log_probs, sufficiency_log_probs],\n\u001b[1;32m 324\u001b[0m )\n\u001b[1;32m 325\u001b[0m },\n\u001b[1;32m 326\u001b[0m }\n\u001b[1;32m 328\u001b[0m \u001b[38;5;66;03m# nec_suff_log_probs_partitioned = {\u001b[39;00m\n\u001b[1;32m 329\u001b[0m \u001b[38;5;66;03m# **{\u001b[39;00m\n\u001b[1;32m 330\u001b[0m \u001b[38;5;66;03m# factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 342\u001b[0m \u001b[38;5;66;03m# },\u001b[39;00m\n\u001b[1;32m 343\u001b[0m \u001b[38;5;66;03m# }\u001b[39;00m\n\u001b[1;32m 345\u001b[0m new_value \u001b[38;5;241m=\u001b[39m scatter_n(\n\u001b[1;32m 346\u001b[0m nec_suff_log_probs_partitioned,\n\u001b[1;32m 347\u001b[0m event_dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 348\u001b[0m )\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1457\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.SafeCallWrapper.__call__\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:701\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1152\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1135\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:312\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.do_wait_suspend\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/.local/lib/python3.10/site-packages/debugpy/_vendored/pydevd/pydevd.py:2070\u001b[0m, in \u001b[0;36mPyDB.do_wait_suspend\u001b[0;34m(self, thread, frame, event, arg, exception_type)\u001b[0m\n\u001b[1;32m 2067\u001b[0m from_this_thread\u001b[38;5;241m.\u001b[39mappend(frame_custom_thread_id)\n\u001b[1;32m 2069\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_threads_suspended_single_notification\u001b[38;5;241m.\u001b[39mnotify_thread_suspended(thread_id, thread, stop_reason):\n\u001b[0;32m-> 2070\u001b[0m keep_suspended \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_wait_suspend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframe\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msuspend_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrom_this_thread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframes_tracker\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2072\u001b[0m frames_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 2074\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keep_suspended:\n\u001b[1;32m 2075\u001b[0m \u001b[38;5;66;03m# This means that we should pause again after a set next statement.\u001b[39;00m\n", + "File \u001b[0;32m~/.local/lib/python3.10/site-packages/debugpy/_vendored/pydevd/pydevd.py:2106\u001b[0m, in \u001b[0;36mPyDB._do_wait_suspend\u001b[0;34m(self, thread, frame, event, arg, suspend_type, from_this_thread, frames_tracker)\u001b[0m\n\u001b[1;32m 2103\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_input_hook()\n\u001b[1;32m 2105\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprocess_internal_commands()\n\u001b[0;32m-> 2106\u001b[0m \u001b[43mtime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0.01\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2108\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcancel_async_evaluation(get_current_thread_id(thread), \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mid\u001b[39m(frame)))\n\u001b[1;32m 2110\u001b[0m \u001b[38;5;66;03m# process any stepping instructions\u001b[39;00m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "with MultiWorldCounterfactual() as mwc_connected: \n", + " with SearchForExplanation(\n", + " supports=supports_connected.supports,\n", + " antecedents={\"X\": torch.tensor(1.0)},\n", + " consequents={\"Y\": torch.tensor(1.0)},\n", + " witnesses={},\n", + " alternatives={\"X\": torch.tensor(0.0)},\n", + " antecedent_bias=-0.5,\n", + " consequent_scale=0,\n", + " ):\n", + " with pyro.plate(\"sample\", size=3):\n", + " with pyro.poutine.trace() as trace_connected:\n", + " model_connected()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "consequent tensor([1., 0., 1.])\n" ] } ], From a7b3a8a496596a69fe0b82681b1d8f053158b7fe Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Fri, 2 Aug 2024 09:29:37 -0400 Subject: [PATCH 004/111] debugged reverse --- chirho/explainable/handlers/components.py | 13 +++-- docs/source/test_notebook.ipynb | 61 ++++++++--------------- 2 files changed, 31 insertions(+), 43 deletions(-) diff --git a/chirho/explainable/handlers/components.py b/chirho/explainable/handlers/components.py index 5a55cd2d..a31e2db8 100644 --- a/chirho/explainable/handlers/components.py +++ b/chirho/explainable/handlers/components.py @@ -307,17 +307,22 @@ def _consequent_eq_neq(consequent: T) -> torch.Tensor: FACTUAL_NEC_SUFF = torch.zeros_like(sufficiency_log_probs) index_keys = set(antecedents) & set(indices_of(consequent, event_dim=support.event_dim)) if indices_of(consequent, event_dim=support.event_dim) else set(antecedents) + null_index = factual_indices if factual_indices != {} else IndexSet( + **{ + name: {0} + for name in index_keys + } + ) + nec_suff_log_probs_partitioned = { **{ - factual_indices: FACTUAL_NEC_SUFF, + #factual_indices: FACTUAL_NEC_SUFF, + null_index: FACTUAL_NEC_SUFF for antecedent in index_keys }, **{ IndexSet(**{antecedent: {ind}}): log_prob for antecedent in index_keys - for antecedent in ( - index_keys - ) for ind, log_prob in zip( [necessity_world, sufficiency_world], [necessity_log_probs, sufficiency_log_probs], diff --git a/docs/source/test_notebook.ipynb b/docs/source/test_notebook.ipynb index 88c1bb2c..fc23906c 100644 --- a/docs/source/test_notebook.ipynb +++ b/docs/source/test_notebook.ipynb @@ -24,15 +24,15 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "consequent tensor([0., 0., 0.])\n", - "consequent tensor([[[[[1., 1., 0.]]]],\n", + "consequent tensor([1., 1., 1.])\n", + "consequent tensor([[[[[1., 0., 0.]]]],\n", "\n", "\n", "\n", @@ -41,9 +41,18 @@ "\n", "\n", " [[[[1., 1., 1.]]]]])\n", - "consequent tensor([0., 1., 0.])\n", - "tensor([0., 1., 0.])\n", - "tensor([[[[[-inf, 0., -inf]]]]])\n" + "consequent tensor([0., 0., 1.])\n", + "testing with tensor([1., 1., 1.])\n", + "tensor([0., 0., 1.])\n", + "log_factor tensor([[[[[0., 0., 0.]]]],\n", + "\n", + "\n", + "\n", + " [[[[0., 0., -inf]]]],\n", + "\n", + "\n", + "\n", + " [[[[-inf, -inf, 0.]]]]])\n" ] } ], @@ -137,23 +146,22 @@ " \n", "\n", "print(X_values_rev)\n", - "print(trace_reverse.trace.nodes[\"__cause____consequent_X\"][\"fn\"].log_factor)\n", - "\n", + "print(\"log_factor\", trace_reverse.trace.nodes[\"__cause____consequent_X\"][\"fn\"].log_factor)\n", "\n", "\n", - "# assert torch.all(trace_connected.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor[0,0,0,0,:] == 0)" + "assert torch.all(trace_reverse.trace.nodes[\"__cause____consequent_X\"][\"fn\"].log_factor.sum().exp() == 0)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "consequent tensor([[[[[1., 1., 1.]]]],\n", + "consequent tensor([[[[[1., 0., 0.]]]],\n", "\n", "\n", "\n", @@ -163,31 +171,6 @@ "\n", " [[[[1., 1., 1.]]]]])\n" ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 13\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pyro\u001b[38;5;241m.\u001b[39mplate(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample\u001b[39m\u001b[38;5;124m\"\u001b[39m, size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m):\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pyro\u001b[38;5;241m.\u001b[39mpoutine\u001b[38;5;241m.\u001b[39mtrace() \u001b[38;5;28;01mas\u001b[39;00m trace_connected:\n\u001b[0;32m---> 13\u001b[0m \u001b[43mmodel_connected\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[2], line 9\u001b[0m, in \u001b[0;36mmodel_connected\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m 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172\u001b[0m msg \u001b[38;5;241m=\u001b[39m Message(\n\u001b[1;32m 173\u001b[0m \u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 174\u001b[0m name\u001b[38;5;241m=\u001b[39mname,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 186\u001b[0m continuation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 187\u001b[0m )\n\u001b[1;32m 188\u001b[0m \u001b[38;5;66;03m# apply the stack and return its return value\u001b[39;00m\n\u001b[0;32m--> 189\u001b[0m \u001b[43mapply_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 190\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;66;03m# type narrowing guaranteed by apply_stack\u001b[39;00m\n\u001b[1;32m 191\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", - "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/poutine/runtime.py:386\u001b[0m, in \u001b[0;36mapply_stack\u001b[0;34m(initial_msg)\u001b[0m\n\u001b[1;32m 383\u001b[0m default_process_message(msg)\n\u001b[1;32m 385\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m frame \u001b[38;5;129;01min\u001b[39;00m stack[\u001b[38;5;241m-\u001b[39mpointer:]:\n\u001b[0;32m--> 386\u001b[0m \u001b[43mframe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_postprocess_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 388\u001b[0m cont \u001b[38;5;241m=\u001b[39m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontinuation\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 389\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cont \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/poutine/messenger.py:194\u001b[0m, in \u001b[0;36mMessenger._postprocess_message\u001b[0;34m(self, msg)\u001b[0m\n\u001b[1;32m 192\u001b[0m method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_pyro_post_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmsg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 193\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 194\u001b[0m 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\u001b[43mfactor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvalue\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m)\n", - "File \u001b[0;32m~/s78projects/chirho/chirho/explainable/handlers/components.py:311\u001b[0m, in \u001b[0;36mconsequent_eq_neq.._consequent_eq_neq\u001b[0;34m(consequent)\u001b[0m\n\u001b[1;32m 307\u001b[0m FACTUAL_NEC_SUFF \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mzeros_like(sufficiency_log_probs)\n\u001b[1;32m 309\u001b[0m index_keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m(antecedents) \u001b[38;5;241m&\u001b[39m \u001b[38;5;28mset\u001b[39m(indices_of(consequent, event_dim\u001b[38;5;241m=\u001b[39msupport\u001b[38;5;241m.\u001b[39mevent_dim)) \u001b[38;5;28;01mif\u001b[39;00m indices_of(consequent, event_dim\u001b[38;5;241m=\u001b[39msupport\u001b[38;5;241m.\u001b[39mevent_dim) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mset\u001b[39m(antecedents)\n\u001b[0;32m--> 311\u001b[0m nec_suff_log_probs_partitioned \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 312\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 313\u001b[0m factual_indices: FACTUAL_NEC_SUFF,\n\u001b[1;32m 314\u001b[0m },\n\u001b[1;32m 315\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 316\u001b[0m IndexSet(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{antecedent: {ind}}): log_prob\n\u001b[1;32m 317\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m antecedent \u001b[38;5;129;01min\u001b[39;00m index_keys\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m antecedent \u001b[38;5;129;01min\u001b[39;00m (\n\u001b[1;32m 319\u001b[0m index_keys\n\u001b[1;32m 320\u001b[0m )\n\u001b[1;32m 321\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ind, log_prob \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\n\u001b[1;32m 322\u001b[0m [necessity_world, sufficiency_world],\n\u001b[1;32m 323\u001b[0m [necessity_log_probs, sufficiency_log_probs],\n\u001b[1;32m 324\u001b[0m )\n\u001b[1;32m 325\u001b[0m },\n\u001b[1;32m 326\u001b[0m }\n\u001b[1;32m 328\u001b[0m \u001b[38;5;66;03m# nec_suff_log_probs_partitioned = {\u001b[39;00m\n\u001b[1;32m 329\u001b[0m \u001b[38;5;66;03m# **{\u001b[39;00m\n\u001b[1;32m 330\u001b[0m \u001b[38;5;66;03m# factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 342\u001b[0m \u001b[38;5;66;03m# },\u001b[39;00m\n\u001b[1;32m 343\u001b[0m \u001b[38;5;66;03m# }\u001b[39;00m\n\u001b[1;32m 345\u001b[0m new_value \u001b[38;5;241m=\u001b[39m scatter_n(\n\u001b[1;32m 346\u001b[0m nec_suff_log_probs_partitioned,\n\u001b[1;32m 347\u001b[0m event_dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 348\u001b[0m )\n", - "File \u001b[0;32m~/s78projects/chirho/chirho/explainable/handlers/components.py:311\u001b[0m, in \u001b[0;36mconsequent_eq_neq.._consequent_eq_neq\u001b[0;34m(consequent)\u001b[0m\n\u001b[1;32m 307\u001b[0m FACTUAL_NEC_SUFF \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mzeros_like(sufficiency_log_probs)\n\u001b[1;32m 309\u001b[0m index_keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m(antecedents) \u001b[38;5;241m&\u001b[39m \u001b[38;5;28mset\u001b[39m(indices_of(consequent, event_dim\u001b[38;5;241m=\u001b[39msupport\u001b[38;5;241m.\u001b[39mevent_dim)) \u001b[38;5;28;01mif\u001b[39;00m indices_of(consequent, event_dim\u001b[38;5;241m=\u001b[39msupport\u001b[38;5;241m.\u001b[39mevent_dim) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mset\u001b[39m(antecedents)\n\u001b[0;32m--> 311\u001b[0m nec_suff_log_probs_partitioned \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 312\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 313\u001b[0m factual_indices: FACTUAL_NEC_SUFF,\n\u001b[1;32m 314\u001b[0m },\n\u001b[1;32m 315\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 316\u001b[0m IndexSet(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{antecedent: {ind}}): log_prob\n\u001b[1;32m 317\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m antecedent \u001b[38;5;129;01min\u001b[39;00m index_keys\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m antecedent \u001b[38;5;129;01min\u001b[39;00m (\n\u001b[1;32m 319\u001b[0m index_keys\n\u001b[1;32m 320\u001b[0m )\n\u001b[1;32m 321\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ind, log_prob \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\n\u001b[1;32m 322\u001b[0m [necessity_world, sufficiency_world],\n\u001b[1;32m 323\u001b[0m [necessity_log_probs, sufficiency_log_probs],\n\u001b[1;32m 324\u001b[0m )\n\u001b[1;32m 325\u001b[0m },\n\u001b[1;32m 326\u001b[0m }\n\u001b[1;32m 328\u001b[0m \u001b[38;5;66;03m# nec_suff_log_probs_partitioned = {\u001b[39;00m\n\u001b[1;32m 329\u001b[0m \u001b[38;5;66;03m# **{\u001b[39;00m\n\u001b[1;32m 330\u001b[0m \u001b[38;5;66;03m# factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 342\u001b[0m \u001b[38;5;66;03m# },\u001b[39;00m\n\u001b[1;32m 343\u001b[0m \u001b[38;5;66;03m# }\u001b[39;00m\n\u001b[1;32m 345\u001b[0m new_value \u001b[38;5;241m=\u001b[39m scatter_n(\n\u001b[1;32m 346\u001b[0m nec_suff_log_probs_partitioned,\n\u001b[1;32m 347\u001b[0m event_dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 348\u001b[0m )\n", - "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1457\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.SafeCallWrapper.__call__\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:701\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1152\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1135\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:312\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.do_wait_suspend\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m~/.local/lib/python3.10/site-packages/debugpy/_vendored/pydevd/pydevd.py:2070\u001b[0m, in \u001b[0;36mPyDB.do_wait_suspend\u001b[0;34m(self, thread, frame, event, arg, exception_type)\u001b[0m\n\u001b[1;32m 2067\u001b[0m from_this_thread\u001b[38;5;241m.\u001b[39mappend(frame_custom_thread_id)\n\u001b[1;32m 2069\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_threads_suspended_single_notification\u001b[38;5;241m.\u001b[39mnotify_thread_suspended(thread_id, thread, stop_reason):\n\u001b[0;32m-> 2070\u001b[0m keep_suspended \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_wait_suspend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframe\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msuspend_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrom_this_thread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframes_tracker\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2072\u001b[0m frames_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 2074\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keep_suspended:\n\u001b[1;32m 2075\u001b[0m \u001b[38;5;66;03m# This means that we should pause again after a set next statement.\u001b[39;00m\n", - "File \u001b[0;32m~/.local/lib/python3.10/site-packages/debugpy/_vendored/pydevd/pydevd.py:2106\u001b[0m, in \u001b[0;36mPyDB._do_wait_suspend\u001b[0;34m(self, thread, frame, event, arg, suspend_type, from_this_thread, frames_tracker)\u001b[0m\n\u001b[1;32m 2103\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_input_hook()\n\u001b[1;32m 2105\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprocess_internal_commands()\n\u001b[0;32m-> 2106\u001b[0m \u001b[43mtime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0.01\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2108\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcancel_async_evaluation(get_current_thread_id(thread), \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mid\u001b[39m(frame)))\n\u001b[1;32m 2110\u001b[0m \u001b[38;5;66;03m# process any stepping instructions\u001b[39;00m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] } ], "source": [ @@ -208,14 +191,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "consequent tensor([1., 0., 1.])\n" + "consequent tensor([1., 1., 0.])\n" ] } ], @@ -233,7 +216,7 @@ " with pyro.plate(\"sample\", size=3):\n", " with pyro.poutine.trace() as trace_reverse:\n", " model_connected()\n", - " " + "\n" ] }, { From 6ee8651e8b4b72b190000e1d28f45b0de2460a31 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Fri, 2 Aug 2024 13:52:12 -0400 Subject: [PATCH 005/111] debug consequen_eq_neq --- chirho/explainable/handlers/components.py | 2 +- docs/source/test_notebook.ipynb | 196 ++++++++++++------ tests/explainable/test_handlers_components.py | 4 +- .../explainable/test_handlers_explanation.py | 56 ++++- 4 files changed, 185 insertions(+), 73 deletions(-) diff --git a/chirho/explainable/handlers/components.py b/chirho/explainable/handlers/components.py index a31e2db8..a8ae00b8 100644 --- a/chirho/explainable/handlers/components.py +++ b/chirho/explainable/handlers/components.py @@ -233,7 +233,7 @@ def consequent_eq_neq( def _consequent_eq_neq(consequent: T) -> torch.Tensor: - print("consequent", consequent) + # print("consequent", consequent) factual_indices = IndexSet( **{ name: ind diff --git a/docs/source/test_notebook.ipynb b/docs/source/test_notebook.ipynb index fc23906c..85496a95 100644 --- a/docs/source/test_notebook.ipynb +++ b/docs/source/test_notebook.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -14,9 +14,11 @@ "import torch\n", "\n", "from chirho.counterfactual.handlers.counterfactual import MultiWorldCounterfactual\n", - "from chirho.explainable.handlers.components import undo_split\n", + "from chirho.explainable.handlers.components import undo_split, consequent_eq_neq, sufficiency_intervention\n", "from chirho.explainable.handlers.explanation import SearchForExplanation, SplitSubsets\n", "from chirho.explainable.handlers import ExtractSupports\n", + "from chirho.observational.handlers.condition import Factors\n", + "from chirho.interventional.handlers import do\n", "from chirho.explainable.handlers.preemptions import Preemptions\n", "from chirho.indexed.ops import IndexSet, gather\n", "from chirho.observational.handlers.condition import condition" @@ -24,15 +26,15 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "consequent tensor([1., 1., 1.])\n", - "consequent tensor([[[[[1., 0., 0.]]]],\n", + "consequent tensor([1., 1., 0.])\n", + "consequent tensor([[[[[0., 0., 0.]]]],\n", "\n", "\n", "\n", @@ -41,18 +43,9 @@ "\n", "\n", " [[[[1., 1., 1.]]]]])\n", - "consequent tensor([0., 0., 1.])\n", - "testing with tensor([1., 1., 1.])\n", - "tensor([0., 0., 1.])\n", - "log_factor tensor([[[[[0., 0., 0.]]]],\n", - "\n", - "\n", - "\n", - " [[[[0., 0., -inf]]]],\n", - "\n", - "\n", - "\n", - " [[[[-inf, -inf, 0.]]]]])\n" + "consequent tensor([1., 1., 0.])\n", + "testing with tensor([1., 1., 0.])\n", + "testing with tensor([1., 1., 0.])\n" ] } ], @@ -67,8 +60,6 @@ " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", " Y = pyro.sample(\"Y\", dist.Bernoulli(X))\n", "\n", - "\n", - "\n", "with ExtractSupports() as supports_independent:\n", " model_independent()\n", "\n", @@ -122,101 +113,182 @@ "trace_independent.trace.compute_log_prob\n", "trace_reverse.trace.compute_log_prob\n", "\n", - "#print(trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor)\n", - "#print(trace_independent.trace.nodes[\"Y\"][\"value\"])\n", - "\n", "Y_values_ind = trace_independent.trace.nodes[\"Y\"][\"value\"]\n", - "Y_values_con = trace_connected.trace.nodes[\"Y\"][\"value\"]\n", - "X_values_rev = trace_reverse.trace.nodes[\"X\"][\"value\"]\n", - "\n", "\n", "if torch.any(Y_values_ind == 1.):\n", " print(\"testing with \", Y_values_ind)\n", " assert trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor[1,0,0,0,:].sum().exp() == 0.\n", "else:\n", " assert trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor[1,0,0,0,:].sum().exp() == 1.\n", - " \n", "\n", "if torch.any(Y_values_ind == 0.):\n", " assert trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor[2,0,0,0,:].sum().exp() == 0.\n", "else:\n", " assert trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor[2,0,0,0,:].sum().exp() == 1.\n", "\n", + "Y_values_con = trace_connected.trace.nodes[\"Y\"][\"value\"]\n", "assert torch.all(trace_connected.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor.sum() == 0)\n", " \n", + "X_values_rev = trace_reverse.trace.nodes[\"X\"][\"value\"]\n", + "if torch.any(X_values_rev == 1.):\n", + " print(\"testing with \", Y_values_ind)\n", + " assert trace_reverse.trace.nodes[\"__cause____consequent_X\"][\"fn\"].log_factor[1,0,0,0,:].sum().exp() == 0.\n", + "else:\n", + " assert trace_reverse.trace.nodes[\"__cause____consequent_X\"][\"fn\"].log_factor[1,0,0,0,:].sum().exp() == 1.\n", "\n", - "print(X_values_rev)\n", - "print(\"log_factor\", trace_reverse.trace.nodes[\"__cause____consequent_X\"][\"fn\"].log_factor)\n", - "\n", + "if torch.any(X_values_rev == 0.):\n", + " assert trace_reverse.trace.nodes[\"__cause____consequent_X\"][\"fn\"].log_factor[2,0,0,0,:].sum().exp() == 0.\n", + "else:\n", + " assert trace_reverse.trace.nodes[\"__cause____consequent_X\"][\"fn\"].log_factor[2,0,0,0,:].sum().exp() == 1.\n", "\n", "assert torch.all(trace_reverse.trace.nodes[\"__cause____consequent_X\"][\"fn\"].log_factor.sum().exp() == 0)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "consequent tensor([[[[[1., 0., 0.]]]],\n", - "\n", - "\n", - "\n", - " [[[[0., 0., 0.]]]],\n", - "\n", - "\n", - "\n", - " [[[[1., 1., 1.]]]]])\n" + "consequent tensor([1., 0., 1.])\n" ] + }, + { + "data": { + "text/plain": [ + "tensor([[[[[0., 0., 0.]]]],\n", + "\n", + "\n", + "\n", + " [[[[-inf, 0., -inf]]]],\n", + "\n", + "\n", + "\n", + " [[[[0., -inf, 0.]]]]])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "with MultiWorldCounterfactual() as mwc_connected: \n", + "def model_triple():\n", + " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", + " Y = pyro.sample(\"Y\", dist.Bernoulli(X))\n", + " Z = pyro.sample(\"Z\", dist.Bernoulli(Y))\n", + "\n", + "with ExtractSupports() as supports_triple:\n", + " model_triple()\n", + "\n", + "with MultiWorldCounterfactual() as mwc_triple: \n", " with SearchForExplanation(\n", - " supports=supports_connected.supports,\n", - " antecedents={\"X\": torch.tensor(1.0)},\n", - " consequents={\"Y\": torch.tensor(1.0)},\n", + " supports=supports_triple.supports,\n", + " antecedents={\"Z\": torch.tensor(1.0)},\n", + " consequents={\"X\": torch.tensor(1.0)},\n", " witnesses={},\n", - " alternatives={\"X\": torch.tensor(0.0)},\n", + " alternatives={\"Z\": torch.tensor(0.0)},\n", " antecedent_bias=-0.5,\n", " consequent_scale=0,\n", " ):\n", " with pyro.plate(\"sample\", size=3):\n", - " with pyro.poutine.trace() as trace_connected:\n", - " model_connected()" + " with pyro.poutine.trace() as trace_triple:\n", + " model_triple()\n", + "\n", + "trace_triple.trace.compute_log_prob\n", + "\n", + "\n", + "trace_triple.trace.nodes[\"X\"][\"value\"]\n", + "trace_triple.trace.nodes[\"__cause____consequent_X\"][\"fn\"].log_factor" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "event_shape = ()\n", + "plate_size = 3\n", + "\n", + "def test_consequent_eq_neq():\n", + " factors = {\n", + " \"consequent\": consequent_eq_neq(\n", + " support=constraints.independent(constraints.real, len(event_shape)),\n", + " proposed_consequent=torch.Tensor([0.1]), \n", + " antecedents=[\"w\"],\n", + " )\n", + " }\n", + "\n", + " @Factors(factors=factors)\n", + " @pyro.plate(\"data\", size=plate_size, dim=-1)\n", + " def model_ce():\n", + " w = pyro.sample(\n", + " \"w\", dist.Normal(0, 0.1).expand(event_shape).to_event(len(event_shape))\n", + " )\n", + " consequent = pyro.deterministic(\n", + " \"consequent\", w * 0.1, event_dim=len(event_shape)\n", + " )\n", + "\n", + " return consequent\n", + "\n", + " antecedents = {\n", + " \"w\": (\n", + " torch.tensor(0.1).expand(event_shape),\n", + " sufficiency_intervention(\n", + " constraints.independent(constraints.real, len(event_shape)), [\"w\"]\n", + " ),\n", + " )\n", + " }\n", + "\n", + " with MultiWorldCounterfactual() as mwc:\n", + " with do(actions=antecedents):\n", + " with pyro.poutine.trace() as tr:\n", + " model_ce()\n", + " # with pyro.poutine.trace() as tr:\n", + " # model_ce()\n", + "\n", + " tr.trace.compute_log_prob()\n", + " nd = tr.trace.nodes\n", + "\n", + " with mwc:\n", + " eq_neq_log_probs = gather(\n", + " nd[\"__factor_consequent\"][\"log_prob\"], IndexSet(**{\"w\": {1}})\n", + " )\n", + "\n", + " # this asserion no longer makes sense\n", + " # assert eq_neq_log_probs.sum() == 0 \n", + "\n", + " assert eq_neq_log_probs.sum() < 0 " + ] + }, + { + "cell_type": "code", + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "consequent tensor([1., 1., 0.])\n" + "consequent tensor([[[[[ 0.0194, -0.0109, 0.0051]]]],\n", + "\n", + "\n", + "\n", + " [[[[ 0.0100, 0.0100, 0.0100]]]],\n", + "\n", + "\n", + "\n", + " [[[[ 0.0194, -0.0109, 0.0051]]]]])\n", + "consequent tensor([-0.0094, -0.0123, 0.0056])\n" ] } ], "source": [ - "with MultiWorldCounterfactual() as mwc_reverse: \n", - " with SearchForExplanation(\n", - " supports=supports_connected.supports,\n", - " antecedents={\"Y\": torch.tensor(1.0)},\n", - " consequents={\"X\": torch.tensor(1.0)},\n", - " witnesses={},\n", - " alternatives={\"Y\": torch.tensor(0.0)},\n", - " antecedent_bias=-0.5,\n", - " consequent_scale=0,\n", - " ):\n", - " with pyro.plate(\"sample\", size=3):\n", - " with pyro.poutine.trace() as trace_reverse:\n", - " model_connected()\n", - "\n" + "test_consequent_eq_neq()" ] }, { diff --git a/tests/explainable/test_handlers_components.py b/tests/explainable/test_handlers_components.py index f41dbd9c..0cc16f12 100644 --- a/tests/explainable/test_handlers_components.py +++ b/tests/explainable/test_handlers_components.py @@ -407,8 +407,8 @@ def model_ce(): with do(actions=antecedents): with pyro.poutine.trace() as tr: model_ce() - with pyro.poutine.trace() as tr: - model_ce() + # with pyro.poutine.trace() as tr: + # model_ce() tr.trace.compute_log_prob() nd = tr.trace.nodes diff --git a/tests/explainable/test_handlers_explanation.py b/tests/explainable/test_handlers_explanation.py index b8675421..89d067f0 100644 --- a/tests/explainable/test_handlers_explanation.py +++ b/tests/explainable/test_handlers_explanation.py @@ -8,7 +8,7 @@ from chirho.counterfactual.handlers.counterfactual import MultiWorldCounterfactual from chirho.explainable.handlers.components import undo_split from chirho.explainable.handlers.explanation import SearchForExplanation, SplitSubsets -from chirho.explainable.handlers import ExportSupports +from chirho.explainable.handlers import ExtractSupports from chirho.explainable.handlers.preemptions import Preemptions from chirho.indexed.ops import IndexSet, gather from chirho.observational.handlers.condition import condition @@ -317,7 +317,6 @@ def test_SplitSubsets_two_layers(): assert obs_bill_hits == 0.0 and int_bill_hits == 0.0 and int_bottle_shatters == 0.0 def test_edge_eq_neq(): - def model_independent(): X = pyro.sample("X", dist.Bernoulli(0.5)) Y = pyro.sample("Y", dist.Bernoulli(0.5)) @@ -360,12 +359,53 @@ def model_connected(): with pyro.poutine.trace() as trace_connected: model_connected() + with MultiWorldCounterfactual() as mwc_reverse: + with SearchForExplanation( + supports=supports_connected.supports, + antecedents={"Y": torch.tensor(1.0)}, + consequents={"X": torch.tensor(1.0)}, + witnesses={}, + alternatives={"Y": torch.tensor(0.0)}, + antecedent_bias=-0.5, + consequent_scale=0, + ): + with pyro.plate("sample", size=3): + with pyro.poutine.trace() as trace_reverse: + model_connected() + trace_connected.trace.compute_log_prob trace_independent.trace.compute_log_prob - - print(trace_independent.trace.nodes["__cause____consequent_Y"]["fn"].log_factor) - - assert trace_independent.trace.nodes["__cause____consequent_Y"]["fn"].log_factor[1,0,0,0,:].sum() <0 - assert torch.all(trace_independent.trace.nodes["__cause____consequent_Y"]["fn"].log_factor[2,0,0,0,:] == 0) - assert torch.all(trace_connected.trace.nodes["__cause____consequent_Y"]["fn"].log_factor[0,0,0,0,:] == 0) + trace_reverse.trace.compute_log_prob + + Y_values_ind = trace_independent.trace.nodes["Y"]["value"] + + if torch.any(Y_values_ind == 1.): + print("testing with ", Y_values_ind) + assert trace_independent.trace.nodes["__cause____consequent_Y"]["fn"].log_factor[1,0,0,0,:].sum().exp() == 0. + else: + assert trace_independent.trace.nodes["__cause____consequent_Y"]["fn"].log_factor[1,0,0,0,:].sum().exp() == 1. + + assert torch.all(trace_independent.trace.nodes["__cause____consequent_Y"]["fn"].log_factor.sum().exp() == 0) + + if torch.any(Y_values_ind == 0.): + assert trace_independent.trace.nodes["__cause____consequent_Y"]["fn"].log_factor[2,0,0,0,:].sum().exp() == 0. + else: + assert trace_independent.trace.nodes["__cause____consequent_Y"]["fn"].log_factor[2,0,0,0,:].sum().exp() == 1. + + Y_values_con = trace_connected.trace.nodes["Y"]["value"] + assert torch.all(trace_connected.trace.nodes["__cause____consequent_Y"]["fn"].log_factor.sum() == 0) + + X_values_rev = trace_reverse.trace.nodes["X"]["value"] + if torch.any(X_values_rev == 1.): + print("testing with ", Y_values_ind) + assert trace_reverse.trace.nodes["__cause____consequent_X"]["fn"].log_factor[1,0,0,0,:].sum().exp() == 0. + else: + assert trace_reverse.trace.nodes["__cause____consequent_X"]["fn"].log_factor[1,0,0,0,:].sum().exp() == 1. + + if torch.any(X_values_rev == 0.): + assert trace_reverse.trace.nodes["__cause____consequent_X"]["fn"].log_factor[2,0,0,0,:].sum().exp() == 0. + else: + assert trace_reverse.trace.nodes["__cause____consequent_X"]["fn"].log_factor[2,0,0,0,:].sum().exp() == 1. + + assert torch.all(trace_reverse.trace.nodes["__cause____consequent_X"]["fn"].log_factor.sum().exp() == 0) From 513ec6ee7693f693005caab3e8fb88a2f6ccf0d4 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Fri, 2 Aug 2024 14:25:07 -0400 Subject: [PATCH 006/111] fixed test_consequent_eq_neq --- docs/source/test_notebook.ipynb | 85 +++++++++++++++++++-------------- 1 file changed, 48 insertions(+), 37 deletions(-) diff --git a/docs/source/test_notebook.ipynb b/docs/source/test_notebook.ipynb index 85496a95..08ebae7a 100644 --- a/docs/source/test_notebook.ipynb +++ b/docs/source/test_notebook.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 26, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -26,24 +26,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "consequent tensor([1., 1., 0.])\n", - "consequent tensor([[[[[0., 0., 0.]]]],\n", - "\n", - "\n", - "\n", - " [[[[0., 0., 0.]]]],\n", - "\n", - "\n", - "\n", - " [[[[1., 1., 1.]]]]])\n", - "consequent tensor([1., 1., 0.])\n", "testing with tensor([1., 1., 0.])\n", "testing with tensor([1., 1., 0.])\n" ] @@ -146,16 +135,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 3, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "consequent tensor([1., 0., 1.])\n" - ] - }, { "data": { "text/plain": [ @@ -163,14 +145,14 @@ "\n", "\n", "\n", - " [[[[-inf, 0., -inf]]]],\n", + " [[[[-inf, -inf, -inf]]]],\n", "\n", "\n", "\n", - " [[[[0., -inf, 0.]]]]])" + " [[[[0., 0., 0.]]]]])" ] }, - "execution_count": 22, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -207,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -218,7 +200,7 @@ " factors = {\n", " \"consequent\": consequent_eq_neq(\n", " support=constraints.independent(constraints.real, len(event_shape)),\n", - " proposed_consequent=torch.Tensor([0.1]), \n", + " proposed_consequent=torch.Tensor([0.01]), \n", " antecedents=[\"w\"],\n", " )\n", " }\n", @@ -254,27 +236,48 @@ " tr.trace.compute_log_prob()\n", " nd = tr.trace.nodes\n", "\n", - " with mwc:\n", - " eq_neq_log_probs = gather(\n", - " nd[\"__factor_consequent\"][\"log_prob\"], IndexSet(**{\"w\": {1}})\n", - " )\n", "\n", - " # this asserion no longer makes sense\n", - " # assert eq_neq_log_probs.sum() == 0 \n", + " with mwc:\n", + " eq_neq_log_probs_fact = gather(\n", + " nd[\"__factor_consequent\"][\"log_prob\"], IndexSet(**{\"w\": {0}})\n", + " )\n", "\n", - " assert eq_neq_log_probs.sum() < 0 " + " eq_neq_log_probs_nec = gather(\n", + " nd[\"__factor_consequent\"][\"log_prob\"], IndexSet(**{\"w\": {1}})\n", + " )\n", + " \n", + " consequent_suff = gather(\n", + " nd[\"consequent\"][\"value\"], IndexSet(**{\"w\": {2}})\n", + " )\n", + " eq_neq_log_probs_suff = gather(\n", + " nd[\"__factor_consequent\"][\"log_prob\"], IndexSet(**{\"w\": {2}})\n", + " )\n", + " \n", + " assert torch.equal(eq_neq_log_probs_suff, dist.Normal(0.0, .1).log_prob(consequent_suff - torch.tensor(.01)))\n", + " assert eq_neq_log_probs_nec.sum().exp() == 0 \n" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "consequent tensor([[[[[ 0.0194, -0.0109, 0.0051]]]],\n", + "odict_keys(['w', 'consequent', '__factor_consequent'])\n", + "w tensor([[[[[ 0.0679, -0.0206, -0.1378]]]],\n", + "\n", + "\n", + "\n", + " [[[[ 0.1000, 0.1000, 0.1000]]]],\n", + "\n", + "\n", + "\n", + " [[[[ 0.0679, -0.0206, -0.1378]]]]])\n", + "antecedent {'w': (tensor(0.1000), ._sufficiency_intervention at 0x7fa24de345e0>)}\n", + "consequent tensor([[[[[ 0.0068, -0.0021, -0.0138]]]],\n", "\n", "\n", "\n", @@ -282,8 +285,16 @@ "\n", "\n", "\n", - " [[[[ 0.0194, -0.0109, 0.0051]]]]])\n", - "consequent tensor([-0.0094, -0.0123, 0.0056])\n" + " [[[[ 0.0068, -0.0021, -0.0138]]]]])\n", + "log_prob tensor([[[[[0.0000, 0.0000, 0.0000]]]],\n", + "\n", + "\n", + "\n", + " [[[[ -inf, -inf, -inf]]]],\n", + "\n", + "\n", + "\n", + " [[[[1.3831, 1.3764, 1.3554]]]]])\n" ] } ], From ce96b9f83a9885330fe650e61dc2612a5d1baf02 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Fri, 2 Aug 2024 15:29:13 -0400 Subject: [PATCH 007/111] fixed the test with dimensions --- docs/source/test_notebook.ipynb | 274 ++++++++++++++++++++++---------- 1 file changed, 187 insertions(+), 87 deletions(-) diff --git a/docs/source/test_notebook.ipynb b/docs/source/test_notebook.ipynb index 08ebae7a..f17c2640 100644 --- a/docs/source/test_notebook.ipynb +++ b/docs/source/test_notebook.ipynb @@ -21,7 +21,8 @@ "from chirho.interventional.handlers import do\n", "from chirho.explainable.handlers.preemptions import Preemptions\n", "from chirho.indexed.ops import IndexSet, gather\n", - "from chirho.observational.handlers.condition import condition" + "from chirho.observational.handlers.condition import condition\n", + "from chirho.indexed.ops import indices_of" ] }, { @@ -33,8 +34,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "testing with tensor([1., 1., 0.])\n", - "testing with tensor([1., 1., 0.])\n" + "testing with tensor([0., 1., 0.])\n", + "testing with tensor([0., 1., 0.])\n" ] } ], @@ -145,11 +146,11 @@ "\n", "\n", "\n", - " [[[[-inf, -inf, -inf]]]],\n", + " [[[[-inf, 0., -inf]]]],\n", "\n", "\n", "\n", - " [[[[0., 0., 0.]]]]])" + " [[[[0., -inf, 0.]]]]])" ] }, "execution_count": 3, @@ -189,117 +190,216 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "event_shape = ()\n", - "plate_size = 3\n", - "\n", - "def test_consequent_eq_neq():\n", - " factors = {\n", - " \"consequent\": consequent_eq_neq(\n", - " support=constraints.independent(constraints.real, len(event_shape)),\n", - " proposed_consequent=torch.Tensor([0.01]), \n", - " antecedents=[\"w\"],\n", - " )\n", - " }\n", - "\n", - " @Factors(factors=factors)\n", - " @pyro.plate(\"data\", size=plate_size, dim=-1)\n", - " def model_ce():\n", - " w = pyro.sample(\n", - " \"w\", dist.Normal(0, 0.1).expand(event_shape).to_event(len(event_shape))\n", - " )\n", - " consequent = pyro.deterministic(\n", - " \"consequent\", w * 0.1, event_dim=len(event_shape)\n", - " )\n", - "\n", - " return consequent\n", - "\n", - " antecedents = {\n", - " \"w\": (\n", - " torch.tensor(0.1).expand(event_shape),\n", - " sufficiency_intervention(\n", - " constraints.independent(constraints.real, len(event_shape)), [\"w\"]\n", - " ),\n", - " )\n", - " }\n", - "\n", - " with MultiWorldCounterfactual() as mwc:\n", - " with do(actions=antecedents):\n", - " with pyro.poutine.trace() as tr:\n", - " model_ce()\n", - " # with pyro.poutine.trace() as tr:\n", - " # model_ce()\n", - "\n", - " tr.trace.compute_log_prob()\n", - " nd = tr.trace.nodes\n", - "\n", - "\n", - " with mwc:\n", - " eq_neq_log_probs_fact = gather(\n", - " nd[\"__factor_consequent\"][\"log_prob\"], IndexSet(**{\"w\": {0}})\n", - " )\n", - "\n", - " eq_neq_log_probs_nec = gather(\n", - " nd[\"__factor_consequent\"][\"log_prob\"], IndexSet(**{\"w\": {1}})\n", - " )\n", - " \n", - " consequent_suff = gather(\n", - " nd[\"consequent\"][\"value\"], IndexSet(**{\"w\": {2}})\n", - " )\n", - " eq_neq_log_probs_suff = gather(\n", - " nd[\"__factor_consequent\"][\"log_prob\"], IndexSet(**{\"w\": {2}})\n", - " )\n", - " \n", - " assert torch.equal(eq_neq_log_probs_suff, dist.Normal(0.0, .1).log_prob(consequent_suff - torch.tensor(.01)))\n", - " assert eq_neq_log_probs_nec.sum().exp() == 0 \n" - ] - }, - { - "cell_type": "code", - "execution_count": 26, + "execution_count": 110, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "w torch.Size([3, 4, 1, 1, 3]) c torch.Size([3, 4, 1, 1, 3])\n", "odict_keys(['w', 'consequent', '__factor_consequent'])\n", - "w tensor([[[[[ 0.0679, -0.0206, -0.1378]]]],\n", + "IndexSet({'w': {0, 1, 2}})\n", + "IndexSet({'w': {0, 1, 2}})\n", + "IndexSet({'w': {0, 1, 2}})\n", + "tensor([[[[[0.0000, 0.0000, 0.0000]]],\n", + "\n", + "\n", + " [[[0.0000, 0.0000, 0.0000]]],\n", + "\n", + "\n", + " [[[0.0000, 0.0000, 0.0000]]],\n", "\n", "\n", + " [[[0.0000, 0.0000, 0.0000]]]],\n", "\n", - " [[[[ 0.1000, 0.1000, 0.1000]]]],\n", "\n", "\n", + " [[[[ -inf, -inf, -inf]]],\n", "\n", - " [[[[ 0.0679, -0.0206, -0.1378]]]]])\n", - "antecedent {'w': (tensor(0.1000), ._sufficiency_intervention at 0x7fa24de345e0>)}\n", - "consequent tensor([[[[[ 0.0068, -0.0021, -0.0138]]]],\n", "\n", + " [[[ -inf, -inf, -inf]]],\n", "\n", "\n", - " [[[[ 0.0100, 0.0100, 0.0100]]]],\n", + " [[[ -inf, -inf, -inf]]],\n", "\n", "\n", + " [[[ -inf, -inf, -inf]]]],\n", "\n", - " [[[[ 0.0068, -0.0021, -0.0138]]]]])\n", - "log_prob tensor([[[[[0.0000, 0.0000, 0.0000]]]],\n", "\n", "\n", + " [[[[1.3789, 1.3721, 1.3781]]],\n", "\n", - " [[[[ -inf, -inf, -inf]]]],\n", "\n", + " [[[1.3789, 1.3718, 1.3780]]],\n", "\n", "\n", - " [[[[1.3831, 1.3764, 1.3554]]]]])\n" + " [[[1.3789, 1.3719, 1.3778]]],\n", + "\n", + "\n", + " [[[1.3788, 1.3719, 1.3779]]]]])\n", + "what's up IndexSet({'w': {0, 1, 2}})\n" ] } ], "source": [ - "test_consequent_eq_neq()" + "event_shape = (3,) #(3,)\n", + "plate_size = 4\n", + "\n", + "\n", + "factors = {\n", + " \"consequent\": consequent_eq_neq(\n", + " support=constraints.independent(constraints.real, 0),\n", + " proposed_consequent=torch.Tensor([0.01]), \n", + " antecedents=[\"w\"],\n", + " )\n", + "}\n", + "\n", + "\n", + "fake_w = dist.Normal(0, 0.1).expand(event_shape).to_event(len(event_shape)).sample()\n", + "\n", + "@Factors(factors=factors)\n", + "@pyro.plate(\"data\", size=plate_size, dim=-4)\n", + "def model_ce():\n", + " w = pyro.sample(\"w\", dist.Normal(fake_w, .001))\n", + "\n", + " consequent = pyro.deterministic(\"consequent\", w * torch.tensor(0.1))\n", + "\n", + " print(\"w\", w.shape, \"c\", consequent.shape)\n", + " assert w.shape == consequent.shape\n", + "\n", + "\n", + "antecedents = {\n", + " \"w\": (\n", + " torch.tensor(0.1).expand(event_shape),\n", + " sufficiency_intervention(\n", + " constraints.independent(constraints.real, len(event_shape)),\n", + " [\"w\"]\n", + " ),\n", + " )\n", + " }\n", + "\n", + "with MultiWorldCounterfactual() as mwc_ce:\n", + " with do(actions = antecedents):\n", + " with pyro.poutine.trace() as trace_ce: \n", + " model_ce()\n", + " \n", + "print(trace_ce.trace.nodes.keys())\n", + "with mwc_ce:\n", + " print(indices_of(trace_ce.trace.nodes[\"w\"][\"value\"]))\n", + " print(indices_of(trace_ce.trace.nodes[\"consequent\"][\"value\"]))\n", + " print(indices_of(trace_ce.trace.nodes['__factor_consequent'][\"fn\"].log_factor))\n", + " print(trace_ce.trace.nodes['__factor_consequent'][\"fn\"].log_factor)\n", + "\n", + "\n", + "nd = trace_ce.trace.nodes\n", + "\n", + "trace_ce.trace.compute_log_prob\n", + "\n", + "with mwc_ce:\n", + " eq_neq_log_probs_fact = gather(\n", + " nd[\"__factor_consequent\"][\"fn\"].log_factor, IndexSet(**{\"w\": {0}}, event_dim = 0)\n", + " )\n", + "\n", + " eq_neq_log_probs_nec = gather(\n", + " nd[\"__factor_consequent\"][\"fn\"].log_factor, IndexSet(**{\"w\": {1}}, event_dim = 0)\n", + " )\n", + " \n", + " consequent_suff = gather(\n", + " nd[\"consequent\"][\"value\"], IndexSet(**{\"w\": {2}}, event_dim = 0 )\n", + " )\n", + "\n", + "\n", + " print(\"what's up\", indices_of(nd[\"consequent\"][\"value\"]))\n", + "\n", + "\n", + " eq_neq_log_probs_suff = gather(\n", + " nd[\"__factor_consequent\"][\"fn\"].log_factor, IndexSet(**{\"w\": {2}})\n", + " )\n", + "\n", + " assert eq_neq_log_probs_nec.shape == consequent_suff.shape\n", + "\n", + " assert torch.equal(eq_neq_log_probs_suff, dist.Normal(0.0, .1).log_prob(consequent_suff - torch.tensor(.01)))\n", + " assert eq_neq_log_probs_nec.sum().exp() == 0 \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "# event_shape = (3,) #(3,)\n", + "# plate_size = 4\n", + "\n", + "# def test_consequent_eq_neq():\n", + "# factors = {\n", + "# \"consequent\": consequent_eq_neq(\n", + "# support=constraints.independent(constraints.real, len(event_shape)),\n", + "# proposed_consequent=torch.Tensor([0.01]), \n", + "# antecedents=[\"w\"],\n", + "# event_dim=len(event_shape),\n", + "# )\n", + "# }\n", + "\n", + "# @Factors(factors=factors)\n", + "# @pyro.plate(\"data\", size=plate_size, dim=-1)\n", + "# def model_ce():\n", + "# w = pyro.sample(\n", + "# \"w\", dist.Normal(0, 0.1).expand(event_shape).to_event(len(event_shape))\n", + "# )\n", + "# #consequent = pyro.deterministic(\n", + "# # \"consequent\", w * 0.1, event_dim=len(event_shape)\n", + "# #)\n", + "# consequent = pyro.sample(\"consequent\", dist.Delta(w * 0.1).to_event(len(event_shape)))\n", + "\n", + "# return consequent\n", + "\n", + "# antecedents = {\n", + "# \"w\": (\n", + "# torch.tensor(0.1).expand(event_shape),\n", + "# sufficiency_intervention(\n", + "# constraints.independent(constraints.real, len(event_shape)), [\"w\"]\n", + "# ),\n", + "# )\n", + "# }\n", + "\n", + "# with MultiWorldCounterfactual() as mwc:\n", + "# with do(actions=antecedents):\n", + "# with pyro.poutine.trace() as tr:\n", + "# model_ce()\n", + "\n", + "# tr.trace.compute_log_prob()\n", + "# nd = tr.trace.nodes\n", + "\n", + "\n", + "# with mwc:\n", + "# eq_neq_log_probs_fact = gather(\n", + "# nd[\"__factor_consequent\"][\"log_prob\"], IndexSet(**{\"w\": {0}}, event_dim = 0)\n", + "# )\n", + "\n", + "# eq_neq_log_probs_nec = gather(\n", + "# nd[\"__factor_consequent\"][\"log_prob\"], IndexSet(**{\"w\": {1}}, event_dim = 0)\n", + "# )\n", + " \n", + "# consequent_suff = gather(\n", + "# nd[\"consequent\"][\"value\"], IndexSet(**{\"w\": {2}}, event_dim = 0 )\n", + "# )\n", + "\n", + "\n", + "# print(indices_of(nd[\"consequent\"][\"value\"]))\n", + "\n", + "\n", + "# eq_neq_log_probs_suff = gather(\n", + "# nd[\"__factor_consequent\"][\"log_prob\"], IndexSet(**{\"w\": {2}})\n", + "# )\n", + "\n", + "# assert eq_neq_log_probs_nec.shape == consequent_suff.shape\n", + "\n", + "# assert torch.equal(eq_neq_log_probs_suff, dist.Normal(0.0, .1).log_prob(consequent_suff - torch.tensor(.01)))\n", + "# assert eq_neq_log_probs_nec.sum().exp() == 0 \n" ] }, { From 1f8e72af7cacd9bf2be3d5976d15bbe5faf0d331 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Fri, 2 Aug 2024 17:26:55 -0400 Subject: [PATCH 008/111] consequent_eq_neq --- docs/source/test_notebook.ipynb | 116 +++++++----------- tests/explainable/test_handlers_components.py | 66 +++++----- 2 files changed, 82 insertions(+), 100 deletions(-) diff --git a/docs/source/test_notebook.ipynb b/docs/source/test_notebook.ipynb index f17c2640..d8878dd2 100644 --- a/docs/source/test_notebook.ipynb +++ b/docs/source/test_notebook.ipynb @@ -27,15 +27,26 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "testing with tensor([0., 1., 0.])\n", - "testing with tensor([0., 1., 0.])\n" + "consequent tensor([1., 0., 1.])\n", + "consequent tensor([[[[[1., 1., 0.]]]],\n", + "\n", + "\n", + "\n", + " [[[[0., 0., 0.]]]],\n", + "\n", + "\n", + "\n", + " [[[[1., 1., 1.]]]]])\n", + "consequent tensor([1., 0., 1.])\n", + "testing with tensor([1., 0., 1.])\n", + "testing with tensor([1., 0., 1.])\n" ] } ], @@ -190,55 +201,17 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "w torch.Size([3, 4, 1, 1, 3]) c torch.Size([3, 4, 1, 1, 3])\n", - "odict_keys(['w', 'consequent', '__factor_consequent'])\n", - "IndexSet({'w': {0, 1, 2}})\n", - "IndexSet({'w': {0, 1, 2}})\n", - "IndexSet({'w': {0, 1, 2}})\n", - "tensor([[[[[0.0000, 0.0000, 0.0000]]],\n", - "\n", - "\n", - " [[[0.0000, 0.0000, 0.0000]]],\n", - "\n", - "\n", - " [[[0.0000, 0.0000, 0.0000]]],\n", - "\n", - "\n", - " [[[0.0000, 0.0000, 0.0000]]]],\n", - "\n", - "\n", - "\n", - " [[[[ -inf, -inf, -inf]]],\n", - "\n", - "\n", - " [[[ -inf, -inf, -inf]]],\n", - "\n", - "\n", - " [[[ -inf, -inf, -inf]]],\n", - "\n", - "\n", - " [[[ -inf, -inf, -inf]]]],\n", - "\n", - "\n", - "\n", - " [[[[1.3789, 1.3721, 1.3781]]],\n", - "\n", - "\n", - " [[[1.3789, 1.3718, 1.3780]]],\n", - "\n", - "\n", - " [[[1.3789, 1.3719, 1.3778]]],\n", - "\n", - "\n", - " [[[1.3788, 1.3719, 1.3779]]]]])\n", - "what's up IndexSet({'w': {0, 1, 2}})\n" + "w torch.Size([3, 4, 1, 1, 1, 3]) c torch.Size([3, 4, 1, 1, 1, 3])\n", + "odict_keys(['w', 'consequent'])\n", + "IndexSet({'w': {0, 1, 2, 3}})\n", + "IndexSet({'w': {0, 1, 2, 3}})\n" ] } ], @@ -256,12 +229,13 @@ "}\n", "\n", "\n", - "fake_w = dist.Normal(0, 0.1).expand(event_shape).to_event(len(event_shape)).sample()\n", + "# fake_w = dist.Normal(0, 0.1).expand(event_shape).to_event(len(event_shape)).sample()\n", "\n", - "@Factors(factors=factors)\n", + "# @Factors(factors=factors)\n", "@pyro.plate(\"data\", size=plate_size, dim=-4)\n", "def model_ce():\n", - " w = pyro.sample(\"w\", dist.Normal(fake_w, .001))\n", + " w = pyro.sample(\"w\", dist.Normal(0, 0.1).expand(event_shape).to_event(len(event_shape)))\n", + " # w = pyro.sample(\"w\", dist.Normal(fake_w, 0.001))\n", "\n", " consequent = pyro.deterministic(\"consequent\", w * torch.tensor(0.1))\n", "\n", @@ -275,7 +249,7 @@ " sufficiency_intervention(\n", " constraints.independent(constraints.real, len(event_shape)),\n", " [\"w\"]\n", - " ),\n", + " )\n", " )\n", " }\n", "\n", @@ -288,39 +262,39 @@ "with mwc_ce:\n", " print(indices_of(trace_ce.trace.nodes[\"w\"][\"value\"]))\n", " print(indices_of(trace_ce.trace.nodes[\"consequent\"][\"value\"]))\n", - " print(indices_of(trace_ce.trace.nodes['__factor_consequent'][\"fn\"].log_factor))\n", - " print(trace_ce.trace.nodes['__factor_consequent'][\"fn\"].log_factor)\n", + " # print(indices_of(trace_ce.trace.nodes['__factor_consequent'][\"fn\"].log_factor))\n", + " # print(trace_ce.trace.nodes['__factor_consequent'][\"fn\"].log_factor)\n", "\n", "\n", - "nd = trace_ce.trace.nodes\n", + "# nd = trace_ce.trace.nodes\n", "\n", - "trace_ce.trace.compute_log_prob\n", + "# trace_ce.trace.compute_log_prob\n", "\n", - "with mwc_ce:\n", - " eq_neq_log_probs_fact = gather(\n", - " nd[\"__factor_consequent\"][\"fn\"].log_factor, IndexSet(**{\"w\": {0}}, event_dim = 0)\n", - " )\n", + "# with mwc_ce:\n", + "# eq_neq_log_probs_fact = gather(\n", + "# nd[\"__factor_consequent\"][\"fn\"].log_factor, IndexSet(**{\"w\": {0}}, event_dim = 0)\n", + "# )\n", "\n", - " eq_neq_log_probs_nec = gather(\n", - " nd[\"__factor_consequent\"][\"fn\"].log_factor, IndexSet(**{\"w\": {1}}, event_dim = 0)\n", - " )\n", + "# eq_neq_log_probs_nec = gather(\n", + "# nd[\"__factor_consequent\"][\"fn\"].log_factor, IndexSet(**{\"w\": {1}}, event_dim = 0)\n", + "# )\n", " \n", - " consequent_suff = gather(\n", - " nd[\"consequent\"][\"value\"], IndexSet(**{\"w\": {2}}, event_dim = 0 )\n", - " )\n", + "# consequent_suff = gather(\n", + "# nd[\"consequent\"][\"value\"], IndexSet(**{\"w\": {2}}, event_dim = 0 )\n", + "# )\n", "\n", "\n", - " print(\"what's up\", indices_of(nd[\"consequent\"][\"value\"]))\n", + "# print(\"what's up\", indices_of(nd[\"consequent\"][\"value\"]))\n", "\n", "\n", - " eq_neq_log_probs_suff = gather(\n", - " nd[\"__factor_consequent\"][\"fn\"].log_factor, IndexSet(**{\"w\": {2}})\n", - " )\n", + "# eq_neq_log_probs_suff = gather(\n", + "# nd[\"__factor_consequent\"][\"fn\"].log_factor, IndexSet(**{\"w\": {2}})\n", + "# )\n", "\n", - " assert eq_neq_log_probs_nec.shape == consequent_suff.shape\n", + "# assert eq_neq_log_probs_nec.shape == consequent_suff.shape\n", "\n", - " assert torch.equal(eq_neq_log_probs_suff, dist.Normal(0.0, .1).log_prob(consequent_suff - torch.tensor(.01)))\n", - " assert eq_neq_log_probs_nec.sum().exp() == 0 \n", + "# assert torch.equal(eq_neq_log_probs_suff, dist.Normal(0.0, .1).log_prob(consequent_suff - torch.tensor(.01)))\n", + "# assert eq_neq_log_probs_nec.sum().exp() == 0 \n", "\n", "\n" ] diff --git a/tests/explainable/test_handlers_components.py b/tests/explainable/test_handlers_components.py index 0cc16f12..17421186 100644 --- a/tests/explainable/test_handlers_components.py +++ b/tests/explainable/test_handlers_components.py @@ -371,54 +371,62 @@ def model_ce(): assert nd["__factor_consequent"]["log_prob"].sum() < -10 + @pytest.mark.parametrize("plate_size", [4, 50, 200]) @pytest.mark.parametrize("event_shape", [(), (3,), (3, 2)], ids=str) def test_consequent_eq_neq(plate_size, event_shape): factors = { "consequent": consequent_eq_neq( - support=constraints.independent(constraints.real, len(event_shape)), - proposed_consequent=torch.Tensor([0.1]), # added this + support=constraints.independent(constraints.real, 0), + proposed_consequent=torch.Tensor([0.01]), antecedents=["w"], ) } + w_initial = dist.Normal(0, 0.1).expand(event_shape).to_event(len(event_shape)).sample() + @Factors(factors=factors) - @pyro.plate("data", size=plate_size, dim=-1) + @pyro.plate("data", size=plate_size, dim=-4) def model_ce(): - w = pyro.sample( - "w", dist.Normal(0, 0.1).expand(event_shape).to_event(len(event_shape)) - ) - consequent = pyro.deterministic( - "consequent", w * 0.1, event_dim=len(event_shape) - ) + w = pyro.sample("w", dist.Normal(w_initial, .001)) + consequent = pyro.deterministic("consequent", w * torch.tensor(0.1)) + assert w.shape == consequent.shape - return consequent antecedents = { - "w": ( - torch.tensor(5.0).expand(event_shape), - sufficiency_intervention( - constraints.independent(constraints.real, len(event_shape)), ["w"] - ), - ) - } + "w": ( + torch.tensor(0.1).expand(event_shape), + sufficiency_intervention( + constraints.independent(constraints.real, len(event_shape)), + ["w"] + ), + ) + } - with MultiWorldCounterfactual() as mwc: - with do(actions=antecedents): - with pyro.poutine.trace() as tr: + with MultiWorldCounterfactual() as mwc_ce: + with do(actions = antecedents): + with pyro.poutine.trace() as trace_ce: model_ce() - # with pyro.poutine.trace() as tr: - # model_ce() - tr.trace.compute_log_prob() - nd = tr.trace.nodes - - with mwc: - eq_neq_log_probs = gather( - nd["__factor_consequent"]["log_prob"], IndexSet(**{"w": {1}}) + nd = trace_ce.trace.nodes + trace_ce.trace.compute_log_prob + with mwc_ce: + eq_neq_log_probs_fact = gather( + nd["__factor_consequent"]["fn"].log_factor, IndexSet(**{"w": {0}}, event_dim = 0) + ) + eq_neq_log_probs_nec = gather( + nd["__factor_consequent"]["fn"].log_factor, IndexSet(**{"w": {1}}, event_dim = 0) + ) + consequent_suff = gather( + nd["consequent"]["value"], IndexSet(**{"w": {2}}, event_dim = 0 ) + ) + eq_neq_log_probs_suff = gather( + nd["__factor_consequent"]["fn"].log_factor, IndexSet(**{"w": {2}}) ) - assert eq_neq_log_probs.sum() == 0 + assert eq_neq_log_probs_nec.shape == consequent_suff.shape + assert torch.equal(eq_neq_log_probs_suff, dist.Normal(0.0, .1).log_prob(consequent_suff - torch.tensor(.01))) + assert eq_neq_log_probs_nec.sum().exp() == 0 options = [ From bc5ffb6fbfd8094a344d858dc450409ed6899a06 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Fri, 2 Aug 2024 17:48:08 -0400 Subject: [PATCH 009/111] three variable model --- docs/source/test_notebook.ipynb | 127 ++++++++++++-------------------- 1 file changed, 49 insertions(+), 78 deletions(-) diff --git a/docs/source/test_notebook.ipynb b/docs/source/test_notebook.ipynb index d8878dd2..01f1a301 100644 --- a/docs/source/test_notebook.ipynb +++ b/docs/source/test_notebook.ipynb @@ -27,27 +27,28 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "consequent tensor([1., 0., 1.])\n", - "consequent tensor([[[[[1., 1., 0.]]]],\n", - "\n", - "\n", - "\n", - " [[[[0., 0., 0.]]]],\n", - "\n", - "\n", - "\n", - " [[[[1., 1., 1.]]]]])\n", - "consequent tensor([1., 0., 1.])\n", - "testing with tensor([1., 0., 1.])\n", - "testing with tensor([1., 0., 1.])\n" + "IndexSet({'X': {0, 1, 2}})\n", + "IndexSet({'Z': {0, 1, 2}})\n", + "IndexSet({})\n", + "IndexSet({'Z': {0, 1, 2}})\n" ] + }, + { + "data": { + "text/plain": [ + "torch.Size([1, 3, 1, 1, 1, 1])" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -56,34 +57,53 @@ "def model_independent():\n", " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", " Y = pyro.sample(\"Y\", dist.Bernoulli(0.5))\n", - "\n", - "def model_connected():\n", - " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", - " Y = pyro.sample(\"Y\", dist.Bernoulli(X))\n", + " Z = pyro.sample(\"Z\", dist.Bernoulli(0.5))\n", + " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", "\n", "with ExtractSupports() as supports_independent:\n", " model_independent()\n", "\n", - "with ExtractSupports() as supports_connected:\n", - " model_connected()\n", - "\n", "with MultiWorldCounterfactual() as mwc_independent: \n", " with SearchForExplanation(\n", " supports=supports_independent.supports,\n", - " antecedents={\"X\": torch.tensor(1.0)},\n", + " antecedents={\"X\": torch.tensor(1.0), \"Z\": torch.tensor(1.0)},\n", " consequents={\"Y\": torch.tensor(1.0)},\n", " witnesses={},\n", - " alternatives={\"X\": torch.tensor(0.0)},\n", - " antecedent_bias=-0.5,\n", + " alternatives={\"X\": torch.tensor(0.0), \"Z\": torch.tensor(0.0)},\n", + " # antecedent_bias=-0.5,\n", " consequent_scale=0,\n", " ):\n", - " with pyro.plate(\"sample\", size=3):\n", + " with pyro.plate(\"sample\", size=1):\n", " with pyro.poutine.trace() as trace_independent:\n", " model_independent()\n", "\n", - "with MultiWorldCounterfactual() as mwc_connected: \n", + "trace_independent.trace.compute_log_prob\n", + "\n", + "with mwc_independent:\n", + " print(indices_of(trace_independent.trace.nodes[\"X\"][\"value\"]))\n", + " print(indices_of(trace_independent.trace.nodes[\"Z\"][\"value\"]))\n", + " print(indices_of(trace_independent.trace.nodes[\"Y\"][\"value\"]))\n", + " print(indices_of(trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"value\"]))\n", + "\n", + "trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def model_independent():\n", + " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", + " Y = pyro.sample(\"Y\", dist.Bernoulli(0.5))\n", + "\n", + "with ExtractSupports() as supports_independent:\n", + " model_independent()\n", + "\n", + "with MultiWorldCounterfactual() as mwc_independent: \n", " with SearchForExplanation(\n", - " supports=supports_connected.supports,\n", + " supports=supports_independent.supports,\n", " antecedents={\"X\": torch.tensor(1.0)},\n", " consequents={\"Y\": torch.tensor(1.0)},\n", " witnesses={},\n", @@ -92,57 +112,8 @@ " consequent_scale=0,\n", " ):\n", " with pyro.plate(\"sample\", size=3):\n", - " with pyro.poutine.trace() as trace_connected:\n", - " model_connected()\n", - "\n", - "with MultiWorldCounterfactual() as mwc_reverse: \n", - " with SearchForExplanation(\n", - " supports=supports_connected.supports,\n", - " antecedents={\"Y\": torch.tensor(1.0)},\n", - " consequents={\"X\": torch.tensor(1.0)},\n", - " witnesses={},\n", - " alternatives={\"Y\": torch.tensor(0.0)},\n", - " antecedent_bias=-0.5,\n", - " consequent_scale=0,\n", - " ):\n", - " with pyro.plate(\"sample\", size=3):\n", - " with pyro.poutine.trace() as trace_reverse:\n", - " model_connected()\n", - "\n", - "\n", - "trace_connected.trace.compute_log_prob\n", - "trace_independent.trace.compute_log_prob\n", - "trace_reverse.trace.compute_log_prob\n", - "\n", - "Y_values_ind = trace_independent.trace.nodes[\"Y\"][\"value\"]\n", - "\n", - "if torch.any(Y_values_ind == 1.):\n", - " print(\"testing with \", Y_values_ind)\n", - " assert trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor[1,0,0,0,:].sum().exp() == 0.\n", - "else:\n", - " assert trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor[1,0,0,0,:].sum().exp() == 1.\n", - "\n", - "if torch.any(Y_values_ind == 0.):\n", - " assert trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor[2,0,0,0,:].sum().exp() == 0.\n", - "else:\n", - " assert trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor[2,0,0,0,:].sum().exp() == 1.\n", - "\n", - "Y_values_con = trace_connected.trace.nodes[\"Y\"][\"value\"]\n", - "assert torch.all(trace_connected.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor.sum() == 0)\n", - " \n", - "X_values_rev = trace_reverse.trace.nodes[\"X\"][\"value\"]\n", - "if torch.any(X_values_rev == 1.):\n", - " print(\"testing with \", Y_values_ind)\n", - " assert trace_reverse.trace.nodes[\"__cause____consequent_X\"][\"fn\"].log_factor[1,0,0,0,:].sum().exp() == 0.\n", - "else:\n", - " assert trace_reverse.trace.nodes[\"__cause____consequent_X\"][\"fn\"].log_factor[1,0,0,0,:].sum().exp() == 1.\n", - "\n", - "if torch.any(X_values_rev == 0.):\n", - " assert trace_reverse.trace.nodes[\"__cause____consequent_X\"][\"fn\"].log_factor[2,0,0,0,:].sum().exp() == 0.\n", - "else:\n", - " assert trace_reverse.trace.nodes[\"__cause____consequent_X\"][\"fn\"].log_factor[2,0,0,0,:].sum().exp() == 1.\n", - "\n", - "assert torch.all(trace_reverse.trace.nodes[\"__cause____consequent_X\"][\"fn\"].log_factor.sum().exp() == 0)" + " with pyro.poutine.trace() as trace_independent:\n", + " model_independent()" ] }, { From 5aabfc6173946a10ebe6a2bc3f5e3966e6981843 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Mon, 5 Aug 2024 09:34:13 -0400 Subject: [PATCH 010/111] testing three dependent --- docs/source/test_notebook.ipynb | 75 ++++++++++++++++++++++++++++++--- 1 file changed, 69 insertions(+), 6 deletions(-) diff --git a/docs/source/test_notebook.ipynb b/docs/source/test_notebook.ipynb index 01f1a301..4d26efd6 100644 --- a/docs/source/test_notebook.ipynb +++ b/docs/source/test_notebook.ipynb @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -46,7 +46,7 @@ "torch.Size([1, 3, 1, 1, 1, 1])" ] }, - "execution_count": 48, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -54,14 +54,14 @@ "source": [ "# def test_edge_eq_neq():\n", "\n", - "def model_independent():\n", + "def model_three_independent():\n", " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", " Y = pyro.sample(\"Y\", dist.Bernoulli(0.5))\n", " Z = pyro.sample(\"Z\", dist.Bernoulli(0.5))\n", " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", "\n", "with ExtractSupports() as supports_independent:\n", - " model_independent()\n", + " model_three_independent()\n", "\n", "with MultiWorldCounterfactual() as mwc_independent: \n", " with SearchForExplanation(\n", @@ -70,12 +70,12 @@ " consequents={\"Y\": torch.tensor(1.0)},\n", " witnesses={},\n", " alternatives={\"X\": torch.tensor(0.0), \"Z\": torch.tensor(0.0)},\n", - " # antecedent_bias=-0.5,\n", + " antecedent_bias=-0.5,\n", " consequent_scale=0,\n", " ):\n", " with pyro.plate(\"sample\", size=1):\n", " with pyro.poutine.trace() as trace_independent:\n", - " model_independent()\n", + " model_three_independent()\n", "\n", "trace_independent.trace.compute_log_prob\n", "\n", @@ -88,6 +88,69 @@ "trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor.shape" ] }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "unbound method set.union() needs an argument", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[13], line 22\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pyro\u001b[38;5;241m.\u001b[39mplate(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample\u001b[39m\u001b[38;5;124m\"\u001b[39m, size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pyro\u001b[38;5;241m.\u001b[39mpoutine\u001b[38;5;241m.\u001b[39mtrace() \u001b[38;5;28;01mas\u001b[39;00m trace_independent:\n\u001b[0;32m---> 22\u001b[0m \u001b[43mmodel_three_dependent\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 24\u001b[0m trace_independent\u001b[38;5;241m.\u001b[39mtrace\u001b[38;5;241m.\u001b[39mcompute_log_prob\n\u001b[1;32m 26\u001b[0m \u001b[38;5;66;03m# with mwc_independent:\u001b[39;00m\n\u001b[1;32m 27\u001b[0m \u001b[38;5;66;03m# print(indices_of(trace_independent.trace.nodes[\"X\"][\"value\"]))\u001b[39;00m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;66;03m# print(indices_of(trace_independent.trace.nodes[\"Z\"][\"value\"]))\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 31\u001b[0m \n\u001b[1;32m 32\u001b[0m \u001b[38;5;66;03m# trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor.shape\u001b[39;00m\n", + "Cell \u001b[0;32mIn[13], line 3\u001b[0m, in \u001b[0;36mmodel_three_dependent\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmodel_three_dependent\u001b[39m():\n\u001b[1;32m 2\u001b[0m X \u001b[38;5;241m=\u001b[39m pyro\u001b[38;5;241m.\u001b[39msample(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m, dist\u001b[38;5;241m.\u001b[39mBernoulli(\u001b[38;5;241m0.5\u001b[39m))\n\u001b[0;32m----> 3\u001b[0m Y \u001b[38;5;241m=\u001b[39m \u001b[43mpyro\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mY\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mBernoulli\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m Z \u001b[38;5;241m=\u001b[39m pyro\u001b[38;5;241m.\u001b[39msample(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mZ\u001b[39m\u001b[38;5;124m\"\u001b[39m, dist\u001b[38;5;241m.\u001b[39mBernoulli(torch\u001b[38;5;241m.\u001b[39mmin(X,Y)))\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m: X, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mY\u001b[39m\u001b[38;5;124m\"\u001b[39m: Y, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mZ\u001b[39m\u001b[38;5;124m\"\u001b[39m: Z}\n", + "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/primitives.py:189\u001b[0m, in \u001b[0;36msample\u001b[0;34m(name, fn, obs, obs_mask, infer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 172\u001b[0m msg \u001b[38;5;241m=\u001b[39m Message(\n\u001b[1;32m 173\u001b[0m \u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 174\u001b[0m name\u001b[38;5;241m=\u001b[39mname,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 186\u001b[0m continuation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 187\u001b[0m )\n\u001b[1;32m 188\u001b[0m \u001b[38;5;66;03m# apply the stack and return its return value\u001b[39;00m\n\u001b[0;32m--> 189\u001b[0m \u001b[43mapply_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 190\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;66;03m# type narrowing guaranteed by apply_stack\u001b[39;00m\n\u001b[1;32m 191\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", + "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/poutine/runtime.py:386\u001b[0m, in \u001b[0;36mapply_stack\u001b[0;34m(initial_msg)\u001b[0m\n\u001b[1;32m 383\u001b[0m default_process_message(msg)\n\u001b[1;32m 385\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m frame \u001b[38;5;129;01min\u001b[39;00m stack[\u001b[38;5;241m-\u001b[39mpointer:]:\n\u001b[0;32m--> 386\u001b[0m \u001b[43mframe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_postprocess_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 388\u001b[0m cont \u001b[38;5;241m=\u001b[39m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontinuation\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 389\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cont \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/poutine/messenger.py:194\u001b[0m, in \u001b[0;36mMessenger._postprocess_message\u001b[0;34m(self, msg)\u001b[0m\n\u001b[1;32m 192\u001b[0m method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_pyro_post_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmsg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 193\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 194\u001b[0m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/s78projects/chirho/chirho/observational/handlers/condition.py:56\u001b[0m, in \u001b[0;36mFactors._pyro_post_sample\u001b[0;34m(self, msg)\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m---> 56\u001b[0m pyro\u001b[38;5;241m.\u001b[39mfactor(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprefix\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mmsg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[43mfactor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvalue\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m)\n", + "File \u001b[0;32m~/s78projects/chirho/chirho/explainable/handlers/components.py:350\u001b[0m, in \u001b[0;36mconsequent_eq_neq.._consequent_eq_neq\u001b[0;34m(consequent)\u001b[0m\n\u001b[1;32m 318\u001b[0m nec_suff_log_probs_partitioned \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 319\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 320\u001b[0m \u001b[38;5;66;03m#factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 330\u001b[0m },\n\u001b[1;32m 331\u001b[0m }\n\u001b[1;32m 333\u001b[0m \u001b[38;5;66;03m# nec_suff_log_probs_partitioned = {\u001b[39;00m\n\u001b[1;32m 334\u001b[0m \u001b[38;5;66;03m# **{\u001b[39;00m\n\u001b[1;32m 335\u001b[0m \u001b[38;5;66;03m# factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[38;5;66;03m# },\u001b[39;00m\n\u001b[1;32m 348\u001b[0m \u001b[38;5;66;03m# }\u001b[39;00m\n\u001b[0;32m--> 350\u001b[0m new_value \u001b[38;5;241m=\u001b[39m \u001b[43mscatter_n\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 351\u001b[0m \u001b[43m \u001b[49m\u001b[43mnec_suff_log_probs_partitioned\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 352\u001b[0m \u001b[43m \u001b[49m\u001b[43mevent_dim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 353\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 355\u001b[0m \u001b[38;5;66;03m# for ind, log_prob in zip([necessity_world, sufficiency_world], [necessity_log_probs, sufficiency_log_probs]):\u001b[39;00m\n\u001b[1;32m 356\u001b[0m \u001b[38;5;66;03m# print(ind, log_prob)\u001b[39;00m\n\u001b[1;32m 357\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 376\u001b[0m \n\u001b[1;32m 377\u001b[0m \u001b[38;5;66;03m# print(necessity_log_probs, sufficiency_log_probs)\u001b[39;00m\n\u001b[1;32m 378\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_value\n", + "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/poutine/runtime.py:461\u001b[0m, in \u001b[0;36meffectful.._fn\u001b[0;34m(name, infer, obs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 444\u001b[0m msg \u001b[38;5;241m=\u001b[39m Message(\n\u001b[1;32m 445\u001b[0m \u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mtype\u001b[39m,\n\u001b[1;32m 446\u001b[0m name\u001b[38;5;241m=\u001b[39mname,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 458\u001b[0m infer\u001b[38;5;241m=\u001b[39minfer \u001b[38;5;28;01mif\u001b[39;00m infer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m {},\n\u001b[1;32m 459\u001b[0m )\n\u001b[1;32m 460\u001b[0m \u001b[38;5;66;03m# apply the stack and return its return value\u001b[39;00m\n\u001b[0;32m--> 461\u001b[0m \u001b[43mapply_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 462\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m TYPE_CHECKING:\n\u001b[1;32m 463\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/poutine/runtime.py:378\u001b[0m, in \u001b[0;36mapply_stack\u001b[0;34m(initial_msg)\u001b[0m\n\u001b[1;32m 375\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m frame \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mreversed\u001b[39m(stack):\n\u001b[1;32m 376\u001b[0m pointer \u001b[38;5;241m=\u001b[39m pointer \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m--> 378\u001b[0m \u001b[43mframe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 380\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstop\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m 381\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/poutine/messenger.py:189\u001b[0m, in \u001b[0;36mMessenger._process_message\u001b[0;34m(self, msg)\u001b[0m\n\u001b[1;32m 187\u001b[0m method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_pyro_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmsg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 189\u001b[0m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/s78projects/chirho/chirho/indexed/handlers.py:112\u001b[0m, in \u001b[0;36mIndexPlatesMessenger._pyro_scatter_n\u001b[0;34m(self, msg)\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_pyro_scatter_n\u001b[39m(\u001b[38;5;28mself\u001b[39m, msg: Dict[\u001b[38;5;28mstr\u001b[39m, Any]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 112\u001b[0m add_indices(\u001b[43munion\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmsg\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43margs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 113\u001b[0m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstop\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", + "File \u001b[0;32m~/s78projects/chirho/chirho/indexed/ops.py:90\u001b[0m, in \u001b[0;36munion\u001b[0;34m(*indexsets)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21munion\u001b[39m(\u001b[38;5;241m*\u001b[39mindexsets: IndexSet) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m IndexSet:\n\u001b[1;32m 64\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;124;03m Compute the union of multiple :class:`IndexSet` s\u001b[39;00m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;124;03m as the union of their keys and of value sets at shared keys.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;124;03m union(a, union(a, b)) == union(a, b)\u001b[39;00m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 87\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m IndexSet(\n\u001b[1;32m 88\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 89\u001b[0m k: \u001b[38;5;28mset\u001b[39m\u001b[38;5;241m.\u001b[39munion(\u001b[38;5;241m*\u001b[39m[vs[k] \u001b[38;5;28;01mfor\u001b[39;00m vs \u001b[38;5;129;01min\u001b[39;00m indexsets \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m vs])\n\u001b[0;32m---> 90\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43mset\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munion\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mset\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mvs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mindexsets\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 91\u001b[0m }\n\u001b[1;32m 92\u001b[0m )\n", + "\u001b[0;31mTypeError\u001b[0m: unbound method set.union() needs an argument" + ] + } + ], + "source": [ + "def model_three_dependent():\n", + " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", + " Y = pyro.sample(\"Y\", dist.Bernoulli(0.5))\n", + " Z = pyro.sample(\"Z\", dist.Bernoulli(torch.min(X,Y)))\n", + " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", + "\n", + "with ExtractSupports() as supports_independent:\n", + " model_three_dependent()\n", + "\n", + "with MultiWorldCounterfactual() as mwc_independent: \n", + " with SearchForExplanation(\n", + " supports=supports_independent.supports,\n", + " antecedents={\"X\": torch.tensor(1.0), \"Z\": torch.tensor(1.0)},\n", + " consequents={\"Y\": torch.tensor(1.0)},\n", + " witnesses={},\n", + " alternatives={\"X\": torch.tensor(0.0), \"Z\": torch.tensor(0.0)},\n", + " antecedent_bias=-0.5,\n", + " consequent_scale=0,\n", + " ):\n", + " with pyro.plate(\"sample\", size=1):\n", + " with pyro.poutine.trace() as trace_independent:\n", + " model_three_dependent()\n", + "\n", + "trace_independent.trace.compute_log_prob\n", + "\n", + "# with mwc_independent:\n", + "# print(indices_of(trace_independent.trace.nodes[\"X\"][\"value\"]))\n", + "# print(indices_of(trace_independent.trace.nodes[\"Z\"][\"value\"]))\n", + "# print(indices_of(trace_independent.trace.nodes[\"Y\"][\"value\"]))\n", + "# print(indices_of(trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"value\"]))\n", + "\n", + "# trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor.shape" + ] + }, { "cell_type": "code", "execution_count": null, From 776208f071fbd07c1af5f407968f2f3f3ebd51c9 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Mon, 5 Aug 2024 11:07:56 -0400 Subject: [PATCH 011/111] debugging --- docs/source/test_notebook.ipynb | 312 ++++++++++++++++++++++++++++---- 1 file changed, 273 insertions(+), 39 deletions(-) diff --git a/docs/source/test_notebook.ipynb b/docs/source/test_notebook.ipynb index 4d26efd6..ecb17685 100644 --- a/docs/source/test_notebook.ipynb +++ b/docs/source/test_notebook.ipynb @@ -13,7 +13,19 @@ "import pyro.distributions.constraints as constraints\n", "import torch\n", "\n", + "from typing import Callable, Mapping, Optional, TypeVar, Union\n", + "\n", + "\n", + "from chirho.explainable.handlers.components import (\n", + " consequent_eq_neq,\n", + " random_intervention,\n", + " sufficiency_intervention,\n", + " undo_split,\n", + ")\n", + "\n", + "from chirho.observational.handlers.condition import Factors\n", "from chirho.counterfactual.handlers.counterfactual import MultiWorldCounterfactual\n", + "from chirho.counterfactual.handlers.selection import get_factual_indices\n", "from chirho.explainable.handlers.components import undo_split, consequent_eq_neq, sufficiency_intervention\n", "from chirho.explainable.handlers.explanation import SearchForExplanation, SplitSubsets\n", "from chirho.explainable.handlers import ExtractSupports\n", @@ -22,33 +34,26 @@ "from chirho.explainable.handlers.preemptions import Preemptions\n", "from chirho.indexed.ops import IndexSet, gather\n", "from chirho.observational.handlers.condition import condition\n", - "from chirho.indexed.ops import indices_of" + "from chirho.indexed.ops import indices_of\n", + "\n", + "S = TypeVar(\"S\")\n", + "T = TypeVar(\"T\")" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "factual IndexSet({'X': {0}, 'Y': {0}})\n", "IndexSet({'X': {0, 1, 2}})\n", - "IndexSet({'Z': {0, 1, 2}})\n", - "IndexSet({})\n", - "IndexSet({'Z': {0, 1, 2}})\n" + "IndexSet({'Y': {0, 1, 2}})\n", + "IndexSet({})\n" ] - }, - { - "data": { - "text/plain": [ - "torch.Size([1, 3, 1, 1, 1, 1])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -62,17 +67,242 @@ "\n", "with ExtractSupports() as supports_independent:\n", " model_three_independent()\n", + " \n", + "antecedents = {\"X\": (torch.tensor(0.0), torch.tensor(1.0)),\n", + " \"Y\": (torch.tensor(0.0), torch.tensor(1.0))}\n", "\n", - "with MultiWorldCounterfactual() as mwc_independent: \n", + "with MultiWorldCounterfactual() as m_ind_do:\n", + " with do(actions = antecedents):\n", + " with pyro.poutine.trace() as tr_do:\n", + " model_three_independent()\n", + " \n", + "nodes_do = tr_do.trace.nodes\n", + "\n", + "with m_ind_do:\n", + " print(\"factual\", get_factual_indices())\n", + " print(indices_of(nodes_do[\"X\"][\"value\"]))\n", + " print(indices_of(nodes_do[\"Y\"][\"value\"]))\n", + " print(indices_of(nodes_do[\"Z\"][\"value\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "factual IndexSet({'X': {0}, 'Y': {0}})\n", + "IndexSet({'X': {0, 1}})\n", + "IndexSet({'Y': {0, 1}})\n", + "IndexSet({})\n" + ] + } + ], + "source": [ + "antecedents={\"X\": torch.tensor(1.0), \"Y\": torch.tensor(1.0)}\n", + "alternatives={\"X\": torch.tensor(0.0), \"Y\": torch.tensor(0.0)}\n", + "supports=supports_independent.supports\n", + "antecedent_bias = 0.0 #-0.5\n", + "prefix = \"__cause__\"\n", + "consequents={\"Z\": torch.tensor(1.0)}\n", + "consequent_scale=0\n", + "factors = None\n", + "witnesses = {}\n", + "preemptions = None\n", + "witness_bias = 0.0\n", + "\n", + "\n", + "\n", + "alternatives = (\n", + " {a: alternatives[a] for a in antecedents.keys()}\n", + " if alternatives is not None\n", + " else {\n", + " a: random_intervention(supports[a], name=f\"{prefix}_alternative_{a}\")\n", + " for a in antecedents.keys()\n", + " }\n", + " )\n", + "\n", + "with MultiWorldCounterfactual() as m_ind_do:\n", + " with do(actions = alternatives):\n", + " with pyro.poutine.trace() as tr_do:\n", + " model_three_independent()\n", + " \n", + "nodes_do = tr_do.trace.nodes\n", + "\n", + "with m_ind_do:\n", + " print(\"factual\", get_factual_indices())\n", + " print(indices_of(nodes_do[\"X\"][\"value\"]))\n", + " print(indices_of(nodes_do[\"Y\"][\"value\"]))\n", + " print(indices_of(nodes_do[\"Z\"][\"value\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'Z': tensor(1.)}\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[38], line 55\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m antecedent_handler, witness_handler, consequent_handler:\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pyro\u001b[38;5;241m.\u001b[39mpoutine\u001b[38;5;241m.\u001b[39mtrace() \u001b[38;5;28;01mas\u001b[39;00m tr_do:\n\u001b[0;32m---> 55\u001b[0m \u001b[43mmodel_three_independent\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 57\u001b[0m nodes_do \u001b[38;5;241m=\u001b[39m tr_do\u001b[38;5;241m.\u001b[39mtrace\u001b[38;5;241m.\u001b[39mnodes\n\u001b[1;32m 59\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m m_ind_do:\n", + "Cell \u001b[0;32mIn[2], line 6\u001b[0m, in \u001b[0;36mmodel_three_independent\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m X \u001b[38;5;241m=\u001b[39m pyro\u001b[38;5;241m.\u001b[39msample(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m, dist\u001b[38;5;241m.\u001b[39mBernoulli(\u001b[38;5;241m0.5\u001b[39m))\n\u001b[1;32m 5\u001b[0m Y \u001b[38;5;241m=\u001b[39m pyro\u001b[38;5;241m.\u001b[39msample(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mY\u001b[39m\u001b[38;5;124m\"\u001b[39m, dist\u001b[38;5;241m.\u001b[39mBernoulli(\u001b[38;5;241m0.5\u001b[39m))\n\u001b[0;32m----> 6\u001b[0m Z \u001b[38;5;241m=\u001b[39m \u001b[43mpyro\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mZ\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mBernoulli\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m: X, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mY\u001b[39m\u001b[38;5;124m\"\u001b[39m: Y, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mZ\u001b[39m\u001b[38;5;124m\"\u001b[39m: Z}\n", + "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/primitives.py:189\u001b[0m, in \u001b[0;36msample\u001b[0;34m(name, fn, obs, obs_mask, infer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 172\u001b[0m msg \u001b[38;5;241m=\u001b[39m Message(\n\u001b[1;32m 173\u001b[0m \u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 174\u001b[0m name\u001b[38;5;241m=\u001b[39mname,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 186\u001b[0m continuation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 187\u001b[0m )\n\u001b[1;32m 188\u001b[0m \u001b[38;5;66;03m# apply the stack and return its return value\u001b[39;00m\n\u001b[0;32m--> 189\u001b[0m \u001b[43mapply_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 190\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;66;03m# type narrowing guaranteed by apply_stack\u001b[39;00m\n\u001b[1;32m 191\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", + "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/poutine/runtime.py:386\u001b[0m, in \u001b[0;36mapply_stack\u001b[0;34m(initial_msg)\u001b[0m\n\u001b[1;32m 383\u001b[0m default_process_message(msg)\n\u001b[1;32m 385\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m frame \u001b[38;5;129;01min\u001b[39;00m stack[\u001b[38;5;241m-\u001b[39mpointer:]:\n\u001b[0;32m--> 386\u001b[0m \u001b[43mframe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_postprocess_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 388\u001b[0m cont \u001b[38;5;241m=\u001b[39m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontinuation\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 389\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cont \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/poutine/messenger.py:194\u001b[0m, in \u001b[0;36mMessenger._postprocess_message\u001b[0;34m(self, msg)\u001b[0m\n\u001b[1;32m 192\u001b[0m method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_pyro_post_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmsg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 193\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 194\u001b[0m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/s78projects/chirho/chirho/observational/handlers/condition.py:56\u001b[0m, in \u001b[0;36mFactors._pyro_post_sample\u001b[0;34m(self, msg)\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m---> 56\u001b[0m pyro\u001b[38;5;241m.\u001b[39mfactor(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprefix\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mmsg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[43mfactor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvalue\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m)\n", + "File \u001b[0;32m~/s78projects/chirho/chirho/explainable/handlers/components.py:350\u001b[0m, in \u001b[0;36mconsequent_eq_neq.._consequent_eq_neq\u001b[0;34m(consequent)\u001b[0m\n\u001b[1;32m 318\u001b[0m nec_suff_log_probs_partitioned \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 319\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 320\u001b[0m \u001b[38;5;66;03m#factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 330\u001b[0m },\n\u001b[1;32m 331\u001b[0m }\n\u001b[1;32m 333\u001b[0m \u001b[38;5;66;03m# nec_suff_log_probs_partitioned = {\u001b[39;00m\n\u001b[1;32m 334\u001b[0m \u001b[38;5;66;03m# **{\u001b[39;00m\n\u001b[1;32m 335\u001b[0m \u001b[38;5;66;03m# factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[38;5;66;03m# },\u001b[39;00m\n\u001b[1;32m 348\u001b[0m \u001b[38;5;66;03m# }\u001b[39;00m\n\u001b[0;32m--> 350\u001b[0m new_value \u001b[38;5;241m=\u001b[39m \u001b[43mscatter_n\u001b[49m(\n\u001b[1;32m 351\u001b[0m nec_suff_log_probs_partitioned,\n\u001b[1;32m 352\u001b[0m event_dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 353\u001b[0m )\n\u001b[1;32m 355\u001b[0m \u001b[38;5;66;03m# for ind, log_prob in zip([necessity_world, sufficiency_world], [necessity_log_probs, sufficiency_log_probs]):\u001b[39;00m\n\u001b[1;32m 356\u001b[0m \u001b[38;5;66;03m# print(ind, log_prob)\u001b[39;00m\n\u001b[1;32m 357\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 376\u001b[0m \n\u001b[1;32m 377\u001b[0m \u001b[38;5;66;03m# print(necessity_log_probs, sufficiency_log_probs)\u001b[39;00m\n\u001b[1;32m 378\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_value\n", + "File \u001b[0;32m~/s78projects/chirho/chirho/explainable/handlers/components.py:350\u001b[0m, in \u001b[0;36mconsequent_eq_neq.._consequent_eq_neq\u001b[0;34m(consequent)\u001b[0m\n\u001b[1;32m 318\u001b[0m nec_suff_log_probs_partitioned \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 319\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 320\u001b[0m \u001b[38;5;66;03m#factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 330\u001b[0m },\n\u001b[1;32m 331\u001b[0m }\n\u001b[1;32m 333\u001b[0m \u001b[38;5;66;03m# nec_suff_log_probs_partitioned = {\u001b[39;00m\n\u001b[1;32m 334\u001b[0m \u001b[38;5;66;03m# **{\u001b[39;00m\n\u001b[1;32m 335\u001b[0m \u001b[38;5;66;03m# factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[38;5;66;03m# },\u001b[39;00m\n\u001b[1;32m 348\u001b[0m \u001b[38;5;66;03m# }\u001b[39;00m\n\u001b[0;32m--> 350\u001b[0m new_value \u001b[38;5;241m=\u001b[39m \u001b[43mscatter_n\u001b[49m(\n\u001b[1;32m 351\u001b[0m nec_suff_log_probs_partitioned,\n\u001b[1;32m 352\u001b[0m event_dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 353\u001b[0m )\n\u001b[1;32m 355\u001b[0m \u001b[38;5;66;03m# for ind, log_prob in zip([necessity_world, sufficiency_world], [necessity_log_probs, sufficiency_log_probs]):\u001b[39;00m\n\u001b[1;32m 356\u001b[0m \u001b[38;5;66;03m# print(ind, log_prob)\u001b[39;00m\n\u001b[1;32m 357\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 376\u001b[0m \n\u001b[1;32m 377\u001b[0m \u001b[38;5;66;03m# print(necessity_log_probs, sufficiency_log_probs)\u001b[39;00m\n\u001b[1;32m 378\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_value\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1457\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.SafeCallWrapper.__call__\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:701\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1152\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1135\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:312\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.do_wait_suspend\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/.local/lib/python3.10/site-packages/debugpy/_vendored/pydevd/pydevd.py:2070\u001b[0m, in \u001b[0;36mPyDB.do_wait_suspend\u001b[0;34m(self, thread, frame, event, arg, exception_type)\u001b[0m\n\u001b[1;32m 2067\u001b[0m from_this_thread\u001b[38;5;241m.\u001b[39mappend(frame_custom_thread_id)\n\u001b[1;32m 2069\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_threads_suspended_single_notification\u001b[38;5;241m.\u001b[39mnotify_thread_suspended(thread_id, thread, stop_reason):\n\u001b[0;32m-> 2070\u001b[0m keep_suspended \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_wait_suspend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframe\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msuspend_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrom_this_thread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframes_tracker\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2072\u001b[0m frames_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 2074\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keep_suspended:\n\u001b[1;32m 2075\u001b[0m \u001b[38;5;66;03m# This means that we should pause again after a set next statement.\u001b[39;00m\n", + "File \u001b[0;32m~/.local/lib/python3.10/site-packages/debugpy/_vendored/pydevd/pydevd.py:2106\u001b[0m, in \u001b[0;36mPyDB._do_wait_suspend\u001b[0;34m(self, thread, frame, event, arg, suspend_type, from_this_thread, frames_tracker)\u001b[0m\n\u001b[1;32m 2103\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_input_hook()\n\u001b[1;32m 2105\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprocess_internal_commands()\n\u001b[0;32m-> 2106\u001b[0m \u001b[43mtime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0.01\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2108\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcancel_async_evaluation(get_current_thread_id(thread), \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mid\u001b[39m(frame)))\n\u001b[1;32m 2110\u001b[0m \u001b[38;5;66;03m# process any stepping instructions\u001b[39;00m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "print(consequents)\n", + "\n", + "sufficiency_actions = {\n", + " a: (\n", + " antecedents[a]\n", + " if antecedents[a] is not None\n", + " else sufficiency_intervention(supports[a], antecedents=antecedents.keys())\n", + " )\n", + " for a in antecedents.keys()\n", + "}\n", + "\n", + "antecedent_handler = SplitSubsets(\n", + " {a: supports[a] for a in antecedents.keys()},\n", + " {a: (alternatives[a], sufficiency_actions[a]) for a in antecedents.keys()}, # type: ignore\n", + " bias=antecedent_bias,\n", + " prefix=f\"{prefix}__antecedent_\",\n", + ")\n", + "\n", + "\n", + "witness_handler = Preemptions(\n", + " (\n", + " {w: preemptions[w] for w in witnesses}\n", + " if preemptions is not None\n", + " else {\n", + " w: undo_split(supports[w], antecedents=antecedents.keys())\n", + " for w in witnesses\n", + " }\n", + " ),\n", + " bias=witness_bias,\n", + " prefix=f\"{prefix}__witness_\",\n", + " )\n", + "\n", + "\n", + "consequent_handler: Factors[T] = Factors(\n", + " (\n", + " {c: factors[c] for c in consequents.keys()}\n", + " if factors is not None\n", + " else {\n", + " c: consequent_eq_neq(\n", + " support=supports[c],\n", + " proposed_consequent=consequents[c], # added this\n", + " antecedents=antecedents.keys(),\n", + " scale=consequent_scale,\n", + " )\n", + " for c in consequents.keys()\n", + " }\n", + " ),\n", + " prefix=f\"{prefix}__consequent_\",\n", + " )\n", + "\n", + "\n", + "with MultiWorldCounterfactual() as m_ind_do:\n", + " with antecedent_handler, witness_handler, consequent_handler:\n", + " with pyro.poutine.trace() as tr_do:\n", + " model_three_independent()\n", + " \n", + "nodes_do = tr_do.trace.nodes\n", + "\n", + "with m_ind_do:\n", + " print(\"factual\", get_factual_indices())\n", + " print(indices_of(nodes_do[\"X\"][\"value\"]))\n", + " print(indices_of(nodes_do[\"Y\"][\"value\"]))\n", + " print(indices_of(nodes_do[\"Z\"][\"value\"]))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IndexSet({'X': {0}, 'Y': {0}})\n", + "IndexSet({'X': {0, 1, 2}})\n", + "IndexSet({})\n", + "IndexSet({'Y': {0, 1, 2}})\n", + "IndexSet({'Y': {0, 1, 2}})\n" + ] + }, + { + "data": { + "text/plain": [ + "torch.Size([3, 3, 1, 1, 1, 1])" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "antecedents={\"X\": torch.tensor(1.0), \"Y\": torch.tensor(1.0)}\n", + "alternatives={\"X\": torch.tensor(0.0), \"Y\": torch.tensor(0.0)}\n", + "supports=supports_independent.supports\n", + "antecedent_bias = 0.0 #-0.5\n", + "prefix = \"__cause__\"\n", + "consequents={\"Z\": torch.tensor(1.0)}\n", + "consequent_scale=0\n", + "factors = None\n", + "witnesses = {}\n", + "preemptions = None\n", + "witness_bias = 0.0\n", + "\n", + "\n", + "with MultiWorldCounterfactual() as mwc_independent:\n", " with SearchForExplanation(\n", " supports=supports_independent.supports,\n", - " antecedents={\"X\": torch.tensor(1.0), \"Z\": torch.tensor(1.0)},\n", - " consequents={\"Y\": torch.tensor(1.0)},\n", + " antecedents={\"X\": torch.tensor(1.0), \"Y\": torch.tensor(1.0)},\n", + " consequents={\"Z\": torch.tensor(1.0)},\n", " witnesses={},\n", - " alternatives={\"X\": torch.tensor(0.0), \"Z\": torch.tensor(0.0)},\n", + " alternatives={\"X\": torch.tensor(0.0), \"Y\": torch.tensor(0.0)},\n", " antecedent_bias=-0.5,\n", " consequent_scale=0,\n", - " ):\n", + " ): \n", + " # with SearchForExplanation(\n", + " # supports=supports_independent.supports,\n", + " # antecedents={\"X\": torch.tensor(1.0), \"Z\": torch.tensor(1.0)},\n", + " # consequents={\"Y\": torch.tensor(1.0)},\n", + " # witnesses={},\n", + " # alternatives={\"X\": torch.tensor(0.0), \"Z\": torch.tensor(0.0)},\n", + " # antecedent_bias=-0.5,\n", + " # consequent_scale=0,\n", + " # ):\n", " with pyro.plate(\"sample\", size=1):\n", " with pyro.poutine.trace() as trace_independent:\n", " model_three_independent()\n", @@ -80,39 +310,43 @@ "trace_independent.trace.compute_log_prob\n", "\n", "with mwc_independent:\n", + " print(get_factual_indices())\n", " print(indices_of(trace_independent.trace.nodes[\"X\"][\"value\"]))\n", " print(indices_of(trace_independent.trace.nodes[\"Z\"][\"value\"]))\n", " print(indices_of(trace_independent.trace.nodes[\"Y\"][\"value\"]))\n", - " print(indices_of(trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"value\"]))\n", + " print(indices_of(trace_independent.trace.nodes[\"__cause____consequent_Z\"][\"value\"]))\n", "\n", - "trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor.shape" + "trace_independent.trace.nodes[\"__cause____consequent_Z\"][\"fn\"].log_factor.shape" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 33, "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "unbound method set.union() needs an argument", + "ename": "KeyboardInterrupt", + "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[13], line 22\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pyro\u001b[38;5;241m.\u001b[39mplate(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample\u001b[39m\u001b[38;5;124m\"\u001b[39m, size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pyro\u001b[38;5;241m.\u001b[39mpoutine\u001b[38;5;241m.\u001b[39mtrace() \u001b[38;5;28;01mas\u001b[39;00m trace_independent:\n\u001b[0;32m---> 22\u001b[0m \u001b[43mmodel_three_dependent\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 24\u001b[0m trace_independent\u001b[38;5;241m.\u001b[39mtrace\u001b[38;5;241m.\u001b[39mcompute_log_prob\n\u001b[1;32m 26\u001b[0m \u001b[38;5;66;03m# with mwc_independent:\u001b[39;00m\n\u001b[1;32m 27\u001b[0m \u001b[38;5;66;03m# print(indices_of(trace_independent.trace.nodes[\"X\"][\"value\"]))\u001b[39;00m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;66;03m# print(indices_of(trace_independent.trace.nodes[\"Z\"][\"value\"]))\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 31\u001b[0m \n\u001b[1;32m 32\u001b[0m \u001b[38;5;66;03m# trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor.shape\u001b[39;00m\n", - "Cell \u001b[0;32mIn[13], line 3\u001b[0m, in \u001b[0;36mmodel_three_dependent\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmodel_three_dependent\u001b[39m():\n\u001b[1;32m 2\u001b[0m X \u001b[38;5;241m=\u001b[39m pyro\u001b[38;5;241m.\u001b[39msample(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m, dist\u001b[38;5;241m.\u001b[39mBernoulli(\u001b[38;5;241m0.5\u001b[39m))\n\u001b[0;32m----> 3\u001b[0m Y \u001b[38;5;241m=\u001b[39m \u001b[43mpyro\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mY\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mBernoulli\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m Z \u001b[38;5;241m=\u001b[39m pyro\u001b[38;5;241m.\u001b[39msample(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mZ\u001b[39m\u001b[38;5;124m\"\u001b[39m, dist\u001b[38;5;241m.\u001b[39mBernoulli(torch\u001b[38;5;241m.\u001b[39mmin(X,Y)))\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m: X, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mY\u001b[39m\u001b[38;5;124m\"\u001b[39m: Y, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mZ\u001b[39m\u001b[38;5;124m\"\u001b[39m: Z}\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[33], line 22\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pyro\u001b[38;5;241m.\u001b[39mplate(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample\u001b[39m\u001b[38;5;124m\"\u001b[39m, size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pyro\u001b[38;5;241m.\u001b[39mpoutine\u001b[38;5;241m.\u001b[39mtrace() \u001b[38;5;28;01mas\u001b[39;00m trace_independent:\n\u001b[0;32m---> 22\u001b[0m \u001b[43mmodel_three_dependent\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 24\u001b[0m trace_independent\u001b[38;5;241m.\u001b[39mtrace\u001b[38;5;241m.\u001b[39mcompute_log_prob\n\u001b[1;32m 26\u001b[0m \u001b[38;5;66;03m# with mwc_independent:\u001b[39;00m\n\u001b[1;32m 27\u001b[0m \u001b[38;5;66;03m# print(indices_of(trace_independent.trace.nodes[\"X\"][\"value\"]))\u001b[39;00m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;66;03m# print(indices_of(trace_independent.trace.nodes[\"Z\"][\"value\"]))\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 31\u001b[0m \n\u001b[1;32m 32\u001b[0m \u001b[38;5;66;03m# trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor.shape\u001b[39;00m\n", + "Cell \u001b[0;32mIn[33], line 4\u001b[0m, in \u001b[0;36mmodel_three_dependent\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m X \u001b[38;5;241m=\u001b[39m pyro\u001b[38;5;241m.\u001b[39msample(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m, dist\u001b[38;5;241m.\u001b[39mBernoulli(\u001b[38;5;241m0.5\u001b[39m))\n\u001b[1;32m 3\u001b[0m Y \u001b[38;5;241m=\u001b[39m pyro\u001b[38;5;241m.\u001b[39msample(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mY\u001b[39m\u001b[38;5;124m\"\u001b[39m, dist\u001b[38;5;241m.\u001b[39mBernoulli(\u001b[38;5;241m0.5\u001b[39m))\n\u001b[0;32m----> 4\u001b[0m Z \u001b[38;5;241m=\u001b[39m \u001b[43mpyro\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mZ\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mBernoulli\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43mY\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m: X, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mY\u001b[39m\u001b[38;5;124m\"\u001b[39m: Y, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mZ\u001b[39m\u001b[38;5;124m\"\u001b[39m: Z}\n", "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/primitives.py:189\u001b[0m, in \u001b[0;36msample\u001b[0;34m(name, fn, obs, obs_mask, infer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 172\u001b[0m msg \u001b[38;5;241m=\u001b[39m Message(\n\u001b[1;32m 173\u001b[0m \u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 174\u001b[0m name\u001b[38;5;241m=\u001b[39mname,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 186\u001b[0m continuation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 187\u001b[0m )\n\u001b[1;32m 188\u001b[0m \u001b[38;5;66;03m# apply the stack and return its return value\u001b[39;00m\n\u001b[0;32m--> 189\u001b[0m \u001b[43mapply_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 190\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;66;03m# type narrowing guaranteed by apply_stack\u001b[39;00m\n\u001b[1;32m 191\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/poutine/runtime.py:386\u001b[0m, in \u001b[0;36mapply_stack\u001b[0;34m(initial_msg)\u001b[0m\n\u001b[1;32m 383\u001b[0m default_process_message(msg)\n\u001b[1;32m 385\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m frame \u001b[38;5;129;01min\u001b[39;00m stack[\u001b[38;5;241m-\u001b[39mpointer:]:\n\u001b[0;32m--> 386\u001b[0m \u001b[43mframe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_postprocess_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 388\u001b[0m cont \u001b[38;5;241m=\u001b[39m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontinuation\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 389\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cont \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/poutine/messenger.py:194\u001b[0m, in \u001b[0;36mMessenger._postprocess_message\u001b[0;34m(self, msg)\u001b[0m\n\u001b[1;32m 192\u001b[0m method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_pyro_post_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmsg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 193\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 194\u001b[0m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/s78projects/chirho/chirho/observational/handlers/condition.py:56\u001b[0m, in \u001b[0;36mFactors._pyro_post_sample\u001b[0;34m(self, msg)\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m---> 56\u001b[0m pyro\u001b[38;5;241m.\u001b[39mfactor(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprefix\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mmsg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[43mfactor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvalue\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m)\n", - "File \u001b[0;32m~/s78projects/chirho/chirho/explainable/handlers/components.py:350\u001b[0m, in \u001b[0;36mconsequent_eq_neq.._consequent_eq_neq\u001b[0;34m(consequent)\u001b[0m\n\u001b[1;32m 318\u001b[0m nec_suff_log_probs_partitioned \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 319\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 320\u001b[0m \u001b[38;5;66;03m#factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 330\u001b[0m },\n\u001b[1;32m 331\u001b[0m }\n\u001b[1;32m 333\u001b[0m \u001b[38;5;66;03m# nec_suff_log_probs_partitioned = {\u001b[39;00m\n\u001b[1;32m 334\u001b[0m \u001b[38;5;66;03m# **{\u001b[39;00m\n\u001b[1;32m 335\u001b[0m \u001b[38;5;66;03m# factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[38;5;66;03m# },\u001b[39;00m\n\u001b[1;32m 348\u001b[0m \u001b[38;5;66;03m# }\u001b[39;00m\n\u001b[0;32m--> 350\u001b[0m new_value \u001b[38;5;241m=\u001b[39m \u001b[43mscatter_n\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 351\u001b[0m \u001b[43m \u001b[49m\u001b[43mnec_suff_log_probs_partitioned\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 352\u001b[0m \u001b[43m \u001b[49m\u001b[43mevent_dim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 353\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 355\u001b[0m \u001b[38;5;66;03m# for ind, log_prob in zip([necessity_world, sufficiency_world], [necessity_log_probs, sufficiency_log_probs]):\u001b[39;00m\n\u001b[1;32m 356\u001b[0m \u001b[38;5;66;03m# print(ind, log_prob)\u001b[39;00m\n\u001b[1;32m 357\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 376\u001b[0m \n\u001b[1;32m 377\u001b[0m \u001b[38;5;66;03m# print(necessity_log_probs, sufficiency_log_probs)\u001b[39;00m\n\u001b[1;32m 378\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_value\n", - "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/poutine/runtime.py:461\u001b[0m, in \u001b[0;36meffectful.._fn\u001b[0;34m(name, infer, obs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 444\u001b[0m msg \u001b[38;5;241m=\u001b[39m Message(\n\u001b[1;32m 445\u001b[0m \u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mtype\u001b[39m,\n\u001b[1;32m 446\u001b[0m name\u001b[38;5;241m=\u001b[39mname,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 458\u001b[0m infer\u001b[38;5;241m=\u001b[39minfer \u001b[38;5;28;01mif\u001b[39;00m infer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m {},\n\u001b[1;32m 459\u001b[0m )\n\u001b[1;32m 460\u001b[0m \u001b[38;5;66;03m# apply the stack and return its return value\u001b[39;00m\n\u001b[0;32m--> 461\u001b[0m \u001b[43mapply_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 462\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m TYPE_CHECKING:\n\u001b[1;32m 463\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/poutine/runtime.py:378\u001b[0m, in \u001b[0;36mapply_stack\u001b[0;34m(initial_msg)\u001b[0m\n\u001b[1;32m 375\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m frame \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mreversed\u001b[39m(stack):\n\u001b[1;32m 376\u001b[0m pointer \u001b[38;5;241m=\u001b[39m pointer \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m--> 378\u001b[0m \u001b[43mframe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 380\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstop\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m 381\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/poutine/messenger.py:189\u001b[0m, in \u001b[0;36mMessenger._process_message\u001b[0;34m(self, msg)\u001b[0m\n\u001b[1;32m 187\u001b[0m method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_pyro_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmsg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 189\u001b[0m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/s78projects/chirho/chirho/indexed/handlers.py:112\u001b[0m, in \u001b[0;36mIndexPlatesMessenger._pyro_scatter_n\u001b[0;34m(self, msg)\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_pyro_scatter_n\u001b[39m(\u001b[38;5;28mself\u001b[39m, msg: Dict[\u001b[38;5;28mstr\u001b[39m, Any]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 112\u001b[0m add_indices(\u001b[43munion\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmsg\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43margs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 113\u001b[0m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstop\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", - "File \u001b[0;32m~/s78projects/chirho/chirho/indexed/ops.py:90\u001b[0m, in \u001b[0;36munion\u001b[0;34m(*indexsets)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21munion\u001b[39m(\u001b[38;5;241m*\u001b[39mindexsets: IndexSet) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m IndexSet:\n\u001b[1;32m 64\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;124;03m Compute the union of multiple :class:`IndexSet` s\u001b[39;00m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;124;03m as the union of their keys and of value sets at shared keys.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;124;03m union(a, union(a, b)) == union(a, b)\u001b[39;00m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 87\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m IndexSet(\n\u001b[1;32m 88\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 89\u001b[0m k: \u001b[38;5;28mset\u001b[39m\u001b[38;5;241m.\u001b[39munion(\u001b[38;5;241m*\u001b[39m[vs[k] \u001b[38;5;28;01mfor\u001b[39;00m vs \u001b[38;5;129;01min\u001b[39;00m indexsets \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m vs])\n\u001b[0;32m---> 90\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43mset\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munion\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mset\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mvs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mindexsets\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 91\u001b[0m }\n\u001b[1;32m 92\u001b[0m )\n", - "\u001b[0;31mTypeError\u001b[0m: unbound method set.union() needs an argument" + "File \u001b[0;32m~/s78projects/chirho/chirho/explainable/handlers/components.py:350\u001b[0m, in \u001b[0;36mconsequent_eq_neq.._consequent_eq_neq\u001b[0;34m(consequent)\u001b[0m\n\u001b[1;32m 318\u001b[0m nec_suff_log_probs_partitioned \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 319\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 320\u001b[0m \u001b[38;5;66;03m#factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 330\u001b[0m },\n\u001b[1;32m 331\u001b[0m }\n\u001b[1;32m 333\u001b[0m \u001b[38;5;66;03m# nec_suff_log_probs_partitioned = {\u001b[39;00m\n\u001b[1;32m 334\u001b[0m \u001b[38;5;66;03m# **{\u001b[39;00m\n\u001b[1;32m 335\u001b[0m \u001b[38;5;66;03m# factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[38;5;66;03m# },\u001b[39;00m\n\u001b[1;32m 348\u001b[0m \u001b[38;5;66;03m# }\u001b[39;00m\n\u001b[0;32m--> 350\u001b[0m new_value \u001b[38;5;241m=\u001b[39m \u001b[43mscatter_n\u001b[49m(\n\u001b[1;32m 351\u001b[0m nec_suff_log_probs_partitioned,\n\u001b[1;32m 352\u001b[0m event_dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 353\u001b[0m )\n\u001b[1;32m 355\u001b[0m \u001b[38;5;66;03m# for ind, log_prob in zip([necessity_world, sufficiency_world], [necessity_log_probs, sufficiency_log_probs]):\u001b[39;00m\n\u001b[1;32m 356\u001b[0m \u001b[38;5;66;03m# print(ind, log_prob)\u001b[39;00m\n\u001b[1;32m 357\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 376\u001b[0m \n\u001b[1;32m 377\u001b[0m \u001b[38;5;66;03m# print(necessity_log_probs, sufficiency_log_probs)\u001b[39;00m\n\u001b[1;32m 378\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_value\n", + "File \u001b[0;32m~/s78projects/chirho/chirho/explainable/handlers/components.py:350\u001b[0m, in \u001b[0;36mconsequent_eq_neq.._consequent_eq_neq\u001b[0;34m(consequent)\u001b[0m\n\u001b[1;32m 318\u001b[0m nec_suff_log_probs_partitioned \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 319\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 320\u001b[0m \u001b[38;5;66;03m#factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 330\u001b[0m },\n\u001b[1;32m 331\u001b[0m }\n\u001b[1;32m 333\u001b[0m \u001b[38;5;66;03m# nec_suff_log_probs_partitioned = {\u001b[39;00m\n\u001b[1;32m 334\u001b[0m \u001b[38;5;66;03m# **{\u001b[39;00m\n\u001b[1;32m 335\u001b[0m \u001b[38;5;66;03m# factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[38;5;66;03m# },\u001b[39;00m\n\u001b[1;32m 348\u001b[0m \u001b[38;5;66;03m# }\u001b[39;00m\n\u001b[0;32m--> 350\u001b[0m new_value \u001b[38;5;241m=\u001b[39m \u001b[43mscatter_n\u001b[49m(\n\u001b[1;32m 351\u001b[0m nec_suff_log_probs_partitioned,\n\u001b[1;32m 352\u001b[0m event_dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 353\u001b[0m )\n\u001b[1;32m 355\u001b[0m \u001b[38;5;66;03m# for ind, log_prob in zip([necessity_world, sufficiency_world], [necessity_log_probs, sufficiency_log_probs]):\u001b[39;00m\n\u001b[1;32m 356\u001b[0m \u001b[38;5;66;03m# print(ind, log_prob)\u001b[39;00m\n\u001b[1;32m 357\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 376\u001b[0m \n\u001b[1;32m 377\u001b[0m \u001b[38;5;66;03m# print(necessity_log_probs, sufficiency_log_probs)\u001b[39;00m\n\u001b[1;32m 378\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_value\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1457\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.SafeCallWrapper.__call__\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:701\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1152\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1135\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:312\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.do_wait_suspend\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/.local/lib/python3.10/site-packages/debugpy/_vendored/pydevd/pydevd.py:2070\u001b[0m, in \u001b[0;36mPyDB.do_wait_suspend\u001b[0;34m(self, thread, frame, event, arg, exception_type)\u001b[0m\n\u001b[1;32m 2067\u001b[0m from_this_thread\u001b[38;5;241m.\u001b[39mappend(frame_custom_thread_id)\n\u001b[1;32m 2069\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_threads_suspended_single_notification\u001b[38;5;241m.\u001b[39mnotify_thread_suspended(thread_id, thread, stop_reason):\n\u001b[0;32m-> 2070\u001b[0m keep_suspended \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_wait_suspend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframe\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msuspend_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrom_this_thread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframes_tracker\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2072\u001b[0m frames_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 2074\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keep_suspended:\n\u001b[1;32m 2075\u001b[0m \u001b[38;5;66;03m# This means that we should pause again after a set next statement.\u001b[39;00m\n", + "File \u001b[0;32m~/.local/lib/python3.10/site-packages/debugpy/_vendored/pydevd/pydevd.py:2106\u001b[0m, in \u001b[0;36mPyDB._do_wait_suspend\u001b[0;34m(self, thread, frame, event, arg, suspend_type, from_this_thread, frames_tracker)\u001b[0m\n\u001b[1;32m 2103\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_input_hook()\n\u001b[1;32m 2105\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprocess_internal_commands()\n\u001b[0;32m-> 2106\u001b[0m \u001b[43mtime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0.01\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2108\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcancel_async_evaluation(get_current_thread_id(thread), \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mid\u001b[39m(frame)))\n\u001b[1;32m 2110\u001b[0m \u001b[38;5;66;03m# process any stepping instructions\u001b[39;00m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], @@ -129,11 +363,11 @@ "with MultiWorldCounterfactual() as mwc_independent: \n", " with SearchForExplanation(\n", " supports=supports_independent.supports,\n", - " antecedents={\"X\": torch.tensor(1.0), \"Z\": torch.tensor(1.0)},\n", - " consequents={\"Y\": torch.tensor(1.0)},\n", + " antecedents={\"X\": torch.tensor(1.0), \"Y\": torch.tensor(1.0)},\n", + " consequents={\"Z\": torch.tensor(1.0)},\n", " witnesses={},\n", - " alternatives={\"X\": torch.tensor(0.0), \"Z\": torch.tensor(0.0)},\n", - " antecedent_bias=-0.5,\n", + " alternatives={\"X\": torch.tensor(0.0), \"Y\": torch.tensor(0.0)},\n", + " #antecedent_bias=-0.5,\n", " consequent_scale=0,\n", " ):\n", " with pyro.plate(\"sample\", size=1):\n", From c0a22c0ffcfebe5f2e1b2b51fbde241bb0d06781 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Mon, 5 Aug 2024 14:30:29 -0400 Subject: [PATCH 012/111] minimal example for three independent variables --- docs/source/test_three_independent.ipynb | 129 +++++++++++++++++++++++ 1 file changed, 129 insertions(+) create mode 100644 docs/source/test_three_independent.ipynb diff --git a/docs/source/test_three_independent.ipynb b/docs/source/test_three_independent.ipynb new file mode 100644 index 00000000..5723df52 --- /dev/null +++ b/docs/source/test_three_independent.ipynb @@ -0,0 +1,129 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "\n", + "import pyro\n", + "import pyro.distributions as dist\n", + "import pyro.distributions.constraints as constraints\n", + "import torch\n", + "\n", + "from typing import Callable, Mapping, Optional, TypeVar, Union\n", + "\n", + "\n", + "from chirho.explainable.handlers.components import (\n", + " consequent_eq_neq,\n", + " random_intervention,\n", + " sufficiency_intervention,\n", + " undo_split,\n", + ")\n", + "\n", + "from chirho.observational.handlers.condition import Factors\n", + "from chirho.counterfactual.handlers.counterfactual import MultiWorldCounterfactual\n", + "from chirho.counterfactual.handlers.selection import get_factual_indices\n", + "from chirho.explainable.handlers.components import undo_split, consequent_eq_neq, sufficiency_intervention\n", + "from chirho.explainable.handlers.explanation import SearchForExplanation, SplitSubsets\n", + "from chirho.explainable.handlers import ExtractSupports\n", + "from chirho.observational.handlers.condition import Factors\n", + "from chirho.interventional.handlers import do\n", + "from chirho.explainable.handlers.preemptions import Preemptions\n", + "from chirho.indexed.ops import IndexSet, gather\n", + "from chirho.observational.handlers.condition import condition\n", + "from chirho.indexed.ops import indices_of\n", + "\n", + "S = TypeVar(\"S\")\n", + "T = TypeVar(\"T\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def model_three_independent():\n", + " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", + " Y = pyro.sample(\"Y\", dist.Bernoulli(0.5))\n", + " Z = pyro.sample(\"Z\", dist.Bernoulli(0.5))\n", + " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", + "\n", + "with ExtractSupports() as supports_independent:\n", + " model_three_independent()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "consequent tensor([1.])\n", + "IndexSet({'X': {0}})\n", + "dict_keys(['X', 'Z'])\n", + "IndexSet({})\n" + ] + }, + { + "ename": "AssertionError", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[16], line 17\u001b[0m\n\u001b[1;32m 15\u001b[0m trace_independent\u001b[38;5;241m.\u001b[39mtrace\u001b[38;5;241m.\u001b[39mcompute_log_prob\n\u001b[1;32m 16\u001b[0m nodes \u001b[38;5;241m=\u001b[39m trace_independent\u001b[38;5;241m.\u001b[39mtrace\u001b[38;5;241m.\u001b[39mnodes\n\u001b[0;32m---> 17\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m nodes[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__cause____consequent_Y\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfn\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mlog_factor\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mSize([\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m])\n", + "\u001b[0;31mAssertionError\u001b[0m: " + ] + } + ], + "source": [ + "with MultiWorldCounterfactual() as mwc_independent: \n", + " with SearchForExplanation(\n", + " supports=supports_independent.supports,\n", + " antecedents={\"X\": torch.tensor(1.0), \"Z\": torch.tensor(1.0)},\n", + " consequents={\"Y\": torch.tensor(1.0)},\n", + " witnesses={},\n", + " alternatives={\"X\": torch.tensor(0.0), \"Z\": torch.tensor(0.0)},\n", + " antecedent_bias=-0.5,\n", + " consequent_scale=0,\n", + " ):\n", + " with pyro.plate(\"sample\", size=1):\n", + " with pyro.poutine.trace() as trace_independent:\n", + " model_three_independent()\n", + "\n", + "trace_independent.trace.compute_log_prob\n", + "nodes = trace_independent.trace.nodes\n", + "assert nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor.shape == torch.Size([3, 3, 1, 1, 1, 1])\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 1911de24a5747d704ece23486127ff4182071cd7 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Mon, 5 Aug 2024 16:14:40 -0400 Subject: [PATCH 013/111] more three variable models --- docs/source/test_three_variables.ipynb | 245 +++++++++++++++++++++++++ 1 file changed, 245 insertions(+) create mode 100644 docs/source/test_three_variables.ipynb diff --git a/docs/source/test_three_variables.ipynb b/docs/source/test_three_variables.ipynb new file mode 100644 index 00000000..883d36c3 --- /dev/null +++ b/docs/source/test_three_variables.ipynb @@ -0,0 +1,245 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "\n", + "import pyro\n", + "import pyro.distributions as dist\n", + "import pyro.distributions.constraints as constraints\n", + "import torch\n", + "\n", + "from typing import Callable, Mapping, Optional, TypeVar, Union\n", + "\n", + "\n", + "from chirho.explainable.handlers.components import (\n", + " consequent_eq_neq,\n", + " random_intervention,\n", + " sufficiency_intervention,\n", + " undo_split,\n", + ")\n", + "\n", + "from chirho.observational.handlers.condition import Factors\n", + "from chirho.counterfactual.handlers.counterfactual import MultiWorldCounterfactual\n", + "from chirho.counterfactual.handlers.selection import get_factual_indices\n", + "from chirho.explainable.handlers.components import undo_split, consequent_eq_neq, sufficiency_intervention\n", + "from chirho.explainable.handlers.explanation import SearchForExplanation, SplitSubsets\n", + "from chirho.explainable.handlers import ExtractSupports\n", + "from chirho.observational.handlers.condition import Factors\n", + "from chirho.interventional.handlers import do\n", + "from chirho.explainable.handlers.preemptions import Preemptions\n", + "from chirho.indexed.ops import IndexSet, gather\n", + "from chirho.observational.handlers.condition import condition\n", + "from chirho.indexed.ops import indices_of\n", + "\n", + "S = TypeVar(\"S\")\n", + "T = TypeVar(\"T\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "necessity_log_probs tensor([0.])\n", + "sufficiency_log_probs tensor([-inf])\n", + "IndexSet({'X': {0}, 'Y': {0}})\n", + "nec_suff_log_prob_partitioned {IndexSet({'X': {0}}): tensor([0.]), IndexSet({'Y': {0}}): tensor([0.]), IndexSet({'X': {1}}): tensor([0.]), IndexSet({'X': {2}}): tensor([-inf]), IndexSet({'Y': {1}}): tensor([0.]), IndexSet({'Y': {2}}): tensor([-inf])}\n", + "new_value tensor([[[[[[-inf]]]],\n", + "\n", + "\n", + "\n", + " [[[[-inf]]]],\n", + "\n", + "\n", + "\n", + " [[[[-inf]]]]]])\n", + "tensor([1., 0., 1.])\n", + "tensor([1., 0., 1.])\n", + "tensor(0.)\n", + "tensor([-inf, -inf, -inf])\n" + ] + } + ], + "source": [ + "def model_three_independent():\n", + " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", + " Y = pyro.sample(\"Y\", dist.Bernoulli(0.5))\n", + " Z = pyro.sample(\"Z\", dist.Bernoulli(0.5))\n", + " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", + "\n", + "with ExtractSupports() as supports_independent:\n", + " model_three_independent()\n", + "\n", + "with MultiWorldCounterfactual() as mwc_independent: \n", + " with SearchForExplanation(\n", + " supports=supports_independent.supports,\n", + " antecedents={\"X\": torch.tensor(1.0), \"Y\": torch.tensor(1.0)},\n", + " consequents={\"Z\": torch.tensor(1.0)},\n", + " witnesses={},\n", + " alternatives={\"X\": torch.tensor(0.0), \"Y\": torch.tensor(0.0)},\n", + " antecedent_bias=-0.5,\n", + " consequent_scale=0,\n", + " ):\n", + " with pyro.plate(\"sample\", size=1):\n", + " with pyro.poutine.trace() as trace_independent:\n", + " model_three_independent()\n", + "\n", + "trace_independent.trace.compute_log_prob\n", + "nodes = trace_independent.trace.nodes\n", + "# assert nodes[\"__cause____consequent_Z\"][\"fn\"].log_factor.shape == torch.Size([3, 3, 1, 1, 1, 1])\n", + "\n", + "print(trace_independent.trace.nodes[\"X\"][\"value\"].squeeze())\n", + "print(trace_independent.trace.nodes[\"Y\"][\"value\"].squeeze())\n", + "print(trace_independent.trace.nodes[\"Z\"][\"value\"].squeeze())\n", + "print(trace_independent.trace.nodes[\"__cause____consequent_Z\"][\"fn\"].log_factor.squeeze())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "necessity_log_probs tensor([0.])\n", + "sufficiency_log_probs tensor([-inf])\n", + "tensor([[[[[[0.]]]],\n", + "\n", + "\n", + "\n", + " [[[[0.]]]],\n", + "\n", + "\n", + "\n", + " [[[[-inf]]]]],\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[0.]]]],\n", + "\n", + "\n", + "\n", + " [[[[0.]]]],\n", + "\n", + "\n", + "\n", + " [[[[0.]]]]],\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[-inf]]]],\n", + "\n", + "\n", + "\n", + " [[[[-inf]]]],\n", + "\n", + "\n", + "\n", + " [[[[-inf]]]]]])\n" + ] + } + ], + "source": [ + "# X -> Y, X -> Z\n", + "\n", + "def model_three_diverge():\n", + " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", + " Y = pyro.sample(\"Y\", dist.Bernoulli(X))\n", + " Z = pyro.sample(\"Z\", dist.Bernoulli(X))\n", + " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", + "\n", + "with ExtractSupports() as supports_diverge:\n", + " model_three_diverge()\n", + "\n", + "with MultiWorldCounterfactual() as mwc_diverge: \n", + " with SearchForExplanation(\n", + " supports=supports_independent.supports,\n", + " antecedents={\"Y\": torch.tensor(1.0), \"Z\": torch.tensor(1.0)},\n", + " consequents={\"X\": torch.tensor(1.0)},\n", + " witnesses={},\n", + " alternatives={\"Y\": torch.tensor(1.0), \"Z\": torch.tensor(1.0)},\n", + " antecedent_bias=-0.5,\n", + " consequent_scale=0,\n", + " ):\n", + " with pyro.plate(\"sample\", size=1):\n", + " with pyro.poutine.trace() as trace_diverge:\n", + " model_three_diverge()\n", + "\n", + "trace_diverge.trace.compute_log_prob\n", + "nodes = trace_diverge.trace.nodes\n", + "print(nodes[\"__cause____consequent_X\"][\"fn\"].log_factor)\n", + "assert nodes[\"__cause____consequent_X\"][\"fn\"].log_factor.shape == torch.Size([3, 3, 1, 1, 1, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# X -> Y -> Z X -> Z\n", + "\n", + "def model_three_complete():\n", + " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", + " Y = pyro.sample(\"Y\", dist.Bernoulli(X))\n", + " Z = pyro.sample(\"Z\", dist.Bernoulli(max(X, Y)))\n", + " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", + "\n", + "with ExtractSupports() as supports_complete:\n", + " model_three_complete()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# X -> Y Z\n", + "\n", + "def model_three_isolate():\n", + " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", + " Y = pyro.sample(\"Y\", dist.Bernoulli(X))\n", + " Z = pyro.sample(\"Z\", dist.Bernoulli(0.5))\n", + " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", + "\n", + "with ExtractSupports() as supports_isolate:\n", + " model_three_isolate()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 84381e25885db2ed42a472aeb8adf4555fbdedb6 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Mon, 5 Aug 2024 17:06:58 -0400 Subject: [PATCH 014/111] diverge --- docs/source/test_three_variables.ipynb | 32 ++++++++++++-------------- 1 file changed, 15 insertions(+), 17 deletions(-) diff --git a/docs/source/test_three_variables.ipynb b/docs/source/test_three_variables.ipynb index 883d36c3..0d47eb54 100644 --- a/docs/source/test_three_variables.ipynb +++ b/docs/source/test_three_variables.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -105,33 +105,31 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "necessity_log_probs tensor([0.])\n", - "sufficiency_log_probs tensor([-inf])\n", "tensor([[[[[[0.]]]],\n", "\n", "\n", "\n", - " [[[[0.]]]],\n", + " [[[[-inf]]]],\n", "\n", "\n", "\n", - " [[[[-inf]]]]],\n", + " [[[[0.]]]]],\n", "\n", "\n", "\n", "\n", - " [[[[[0.]]]],\n", + " [[[[[-inf]]]],\n", "\n", "\n", "\n", - " [[[[0.]]]],\n", + " [[[[-inf]]]],\n", "\n", "\n", "\n", @@ -140,7 +138,7 @@ "\n", "\n", "\n", - " [[[[[-inf]]]],\n", + " [[[[[0.]]]],\n", "\n", "\n", "\n", @@ -148,7 +146,7 @@ "\n", "\n", "\n", - " [[[[-inf]]]]]])\n" + " [[[[0.]]]]]])\n" ] } ], @@ -157,8 +155,8 @@ "\n", "def model_three_diverge():\n", " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", - " Y = pyro.sample(\"Y\", dist.Bernoulli(X))\n", - " Z = pyro.sample(\"Z\", dist.Bernoulli(X))\n", + " Y = pyro.sample(\"Y\", dist.Bernoulli(0.5))\n", + " Z = pyro.sample(\"Z\", dist.Bernoulli(torch.min(X, Y)))\n", " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", "\n", "with ExtractSupports() as supports_diverge:\n", @@ -167,10 +165,10 @@ "with MultiWorldCounterfactual() as mwc_diverge: \n", " with SearchForExplanation(\n", " supports=supports_independent.supports,\n", - " antecedents={\"Y\": torch.tensor(1.0), \"Z\": torch.tensor(1.0)},\n", - " consequents={\"X\": torch.tensor(1.0)},\n", + " antecedents={\"Y\": torch.tensor(1.0), \"X\": torch.tensor(1.0)},\n", + " consequents={\"Z\": torch.tensor(1.0)},\n", " witnesses={},\n", - " alternatives={\"Y\": torch.tensor(1.0), \"Z\": torch.tensor(1.0)},\n", + " alternatives={\"Y\": torch.tensor(1.0), \"X\": torch.tensor(1.0)},\n", " antecedent_bias=-0.5,\n", " consequent_scale=0,\n", " ):\n", @@ -180,8 +178,8 @@ "\n", "trace_diverge.trace.compute_log_prob\n", "nodes = trace_diverge.trace.nodes\n", - "print(nodes[\"__cause____consequent_X\"][\"fn\"].log_factor)\n", - "assert nodes[\"__cause____consequent_X\"][\"fn\"].log_factor.shape == torch.Size([3, 3, 1, 1, 1, 1])" + "print(nodes[\"__cause____consequent_Z\"][\"fn\"].log_factor)\n", + "assert nodes[\"__cause____consequent_Z\"][\"fn\"].log_factor.shape == torch.Size([3, 3, 1, 1, 1, 1])" ] }, { From bbc121c326d0dd86d3181030065cc37c34d1dab2 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Mon, 5 Aug 2024 18:13:15 -0400 Subject: [PATCH 015/111] debugged --- chirho/explainable/handlers/components.py | 45 ++++++++++++---- docs/source/test_three_independent.ipynb | 66 ++++++++++++++++------- 2 files changed, 82 insertions(+), 29 deletions(-) diff --git a/chirho/explainable/handlers/components.py b/chirho/explainable/handlers/components.py index a8ae00b8..c48480b3 100644 --- a/chirho/explainable/handlers/components.py +++ b/chirho/explainable/handlers/components.py @@ -234,13 +234,13 @@ def consequent_eq_neq( def _consequent_eq_neq(consequent: T) -> torch.Tensor: # print("consequent", consequent) - factual_indices = IndexSet( - **{ - name: ind - for name, ind in get_factual_indices().items() - if name in antecedents - } - ) + # factual_indices = IndexSet( + # **{ + # name: ind + # for name, ind in get_factual_indices().items() + # if name in antecedents + # } + # ) necessity_world = kwargs.get("necessity_world", 1) sufficiency_world = kwargs.get("sufficiency_world", 2) @@ -307,7 +307,14 @@ def _consequent_eq_neq(consequent: T) -> torch.Tensor: FACTUAL_NEC_SUFF = torch.zeros_like(sufficiency_log_probs) index_keys = set(antecedents) & set(indices_of(consequent, event_dim=support.event_dim)) if indices_of(consequent, event_dim=support.event_dim) else set(antecedents) - null_index = factual_indices if factual_indices != {} else IndexSet( + # null_index = factual_indices if factual_indices != {} else IndexSet( + # **{ + # name: {0} + # for name in index_keys + # } + # ) + + null_index = IndexSet( **{ name: {0} for name in index_keys @@ -315,14 +322,28 @@ def _consequent_eq_neq(consequent: T) -> torch.Tensor: ) + # nec_suff_log_probs_partitioned = { + # **{ + # #factual_indices: FACTUAL_NEC_SUFF, + # null_index: FACTUAL_NEC_SUFF for antecedent in index_keys + # }, + # **{ + # IndexSet(**{antecedent: {ind}}): log_prob + # for antecedent in index_keys + # for ind, log_prob in zip( + # [necessity_world, sufficiency_world], + # [necessity_log_probs, sufficiency_log_probs], + # ) + # }, + # } + nec_suff_log_probs_partitioned = { **{ #factual_indices: FACTUAL_NEC_SUFF, - null_index: FACTUAL_NEC_SUFF for antecedent in index_keys + null_index: FACTUAL_NEC_SUFF # for antecedent in index_keys }, **{ - IndexSet(**{antecedent: {ind}}): log_prob - for antecedent in index_keys + IndexSet(**{antecedent: {ind} for antecedent in index_keys}): log_prob for ind, log_prob in zip( [necessity_world, sufficiency_world], [necessity_log_probs, sufficiency_log_probs], @@ -330,6 +351,8 @@ def _consequent_eq_neq(consequent: T) -> torch.Tensor: }, } + + # nec_suff_log_probs_partitioned = { # **{ # factual_indices: FACTUAL_NEC_SUFF, diff --git a/docs/source/test_three_independent.ipynb b/docs/source/test_three_independent.ipynb index 5723df52..48542cd2 100644 --- a/docs/source/test_three_independent.ipynb +++ b/docs/source/test_three_independent.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -58,31 +58,61 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "consequent tensor([1.])\n", - "IndexSet({'X': {0}})\n", - "dict_keys(['X', 'Z'])\n", - "IndexSet({})\n" - ] - }, - { - "ename": "AssertionError", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[16], line 17\u001b[0m\n\u001b[1;32m 15\u001b[0m trace_independent\u001b[38;5;241m.\u001b[39mtrace\u001b[38;5;241m.\u001b[39mcompute_log_prob\n\u001b[1;32m 16\u001b[0m nodes \u001b[38;5;241m=\u001b[39m trace_independent\u001b[38;5;241m.\u001b[39mtrace\u001b[38;5;241m.\u001b[39mnodes\n\u001b[0;32m---> 17\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m nodes[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__cause____consequent_Y\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfn\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mlog_factor\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m==\u001b[39m torch\u001b[38;5;241m.\u001b[39mSize([\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m])\n", - "\u001b[0;31mAssertionError\u001b[0m: " + "IndexSet({'X': {0, 1, 2}, 'Y': {0, 1, 2}})\n", + "0.0\n" ] } ], + "source": [ + "with MultiWorldCounterfactual() as mwc_independent: \n", + " with SearchForExplanation(\n", + " supports=supports_independent.supports,\n", + " antecedents={\"X\": torch.tensor(1.0), \"Y\": torch.tensor(1.0)},\n", + " consequents={\"Z\": torch.tensor(1.0)},\n", + " witnesses={},\n", + " alternatives={\"X\": torch.tensor(0.0), \"Y\": torch.tensor(0.0)},\n", + " antecedent_bias=-0.5,\n", + " consequent_scale=0,\n", + " ):\n", + " with pyro.plate(\"sample\", size=1):\n", + " with pyro.poutine.trace() as trace_independent:\n", + " model_three_independent()\n", + "\n", + "trace_independent.trace.compute_log_prob\n", + "nodes = trace_independent.trace.nodes\n", + "\n", + "log_probs = nodes[\"__cause____consequent_Z\"][\"fn\"].log_factor\n", + "\n", + "assert log_probs.shape == torch.Size([3, 3, 1, 1, 1, 1])\n", + "\n", + "with mwc_independent:\n", + " print(indices_of(log_probs))\n", + " nec_lp = gather(log_probs, IndexSet(**{\"X\":{1}, \"Y\":{1}} ))\n", + " print(nodes[\"Z\"][\"value\"].item())\n", + " if nodes[\"Z\"][\"value\"].item() == 1:\n", + " assert nec_lp.exp().item() == 0.0 \n", + " else:\n", + " assert nec_lp.exp().item() == 1.0\n", + "\n", + " suff_lp = gather(log_probs, IndexSet(**{\"X\":{2}, \"Y\":{2}} ))\n", + " if nodes[\"Z\"][\"value\"].item() == 1:\n", + " assert suff_lp.exp().item() == 1.0\n", + " else:\n", + " assert suff_lp.exp().item() == 0.0\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], "source": [ "with MultiWorldCounterfactual() as mwc_independent: \n", " with SearchForExplanation(\n", From 498f070429165f6350db6938d936d0c78c68da3f Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Tue, 6 Aug 2024 14:50:21 -0400 Subject: [PATCH 016/111] notebook tested three variable models --- chirho/explainable/handlers/components.py | 4 +- docs/source/test_three_independent.ipynb | 85 ++-- docs/source/test_three_variables.ipynb | 502 +++++++++++++++++----- 3 files changed, 424 insertions(+), 167 deletions(-) diff --git a/chirho/explainable/handlers/components.py b/chirho/explainable/handlers/components.py index c48480b3..8fac00fc 100644 --- a/chirho/explainable/handlers/components.py +++ b/chirho/explainable/handlers/components.py @@ -306,7 +306,8 @@ def _consequent_eq_neq(consequent: T) -> torch.Tensor: FACTUAL_NEC_SUFF = torch.zeros_like(sufficiency_log_probs) - index_keys = set(antecedents) & set(indices_of(consequent, event_dim=support.event_dim)) if indices_of(consequent, event_dim=support.event_dim) else set(antecedents) + # index_keys = set(antecedents) & set(indices_of(consequent, event_dim=support.event_dim)) if indices_of(consequent, event_dim=support.event_dim) else set(antecedents) + index_keys = set(antecedents) # null_index = factual_indices if factual_indices != {} else IndexSet( # **{ # name: {0} @@ -314,6 +315,7 @@ def _consequent_eq_neq(consequent: T) -> torch.Tensor: # } # ) + null_index = IndexSet( **{ name: {0} diff --git a/docs/source/test_three_independent.ipynb b/docs/source/test_three_independent.ipynb index 48542cd2..34dbdad2 100644 --- a/docs/source/test_three_independent.ipynb +++ b/docs/source/test_three_independent.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -58,26 +58,32 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "IndexSet({'X': {0, 1, 2}, 'Y': {0, 1, 2}})\n", - "0.0\n" + "X Y Z\n", + "X Z Y\n" ] } ], "source": [ - "with MultiWorldCounterfactual() as mwc_independent: \n", + "antecedent_sets = [{\"X\": torch.tensor(1.0), \"Y\": torch.tensor(1.0)}, {\"X\": torch.tensor(1.0), \"Z\": torch.tensor(1.0)}]\n", + "consequent_sets = [{\"Z\": torch.tensor(1.0)}, {\"Y\": torch.tensor(1.0)}]\n", + "\n", + "tests = [(\"X\", \"Y\", \"Z\"), (\"X\", \"Z\", \"Y\")]\n", + "for antecedent1, antecedent2, consequent in tests:\n", + " print(antecedent1, antecedent2, consequent)\n", + " with MultiWorldCounterfactual() as mwc_independent: \n", " with SearchForExplanation(\n", " supports=supports_independent.supports,\n", - " antecedents={\"X\": torch.tensor(1.0), \"Y\": torch.tensor(1.0)},\n", - " consequents={\"Z\": torch.tensor(1.0)},\n", + " antecedents={antecedent1: torch.tensor(0.0), antecedent2: torch.tensor(0.0)},\n", + " consequents={consequent: torch.tensor(1.0)},\n", " witnesses={},\n", - " alternatives={\"X\": torch.tensor(0.0), \"Y\": torch.tensor(0.0)},\n", + " alternatives={antecedent1: torch.tensor(0.0), antecedent2: torch.tensor(0.0)},\n", " antecedent_bias=-0.5,\n", " consequent_scale=0,\n", " ):\n", @@ -85,53 +91,30 @@ " with pyro.poutine.trace() as trace_independent:\n", " model_three_independent()\n", "\n", - "trace_independent.trace.compute_log_prob\n", - "nodes = trace_independent.trace.nodes\n", + " trace_independent.trace.compute_log_prob\n", + " nodes = trace_independent.trace.nodes\n", "\n", - "log_probs = nodes[\"__cause____consequent_Z\"][\"fn\"].log_factor\n", + " log_probs = nodes[f\"__cause____consequent_{consequent}\"][\"fn\"].log_factor\n", "\n", - "assert log_probs.shape == torch.Size([3, 3, 1, 1, 1, 1])\n", + " assert log_probs.shape == torch.Size([3, 3, 1, 1, 1, 1])\n", "\n", - "with mwc_independent:\n", - " print(indices_of(log_probs))\n", - " nec_lp = gather(log_probs, IndexSet(**{\"X\":{1}, \"Y\":{1}} ))\n", - " print(nodes[\"Z\"][\"value\"].item())\n", - " if nodes[\"Z\"][\"value\"].item() == 1:\n", - " assert nec_lp.exp().item() == 0.0 \n", - " else:\n", - " assert nec_lp.exp().item() == 1.0\n", + " nec_worlds = IndexSet(**{name : {1} for name in [antecedent1, antecedent2]})\n", + " suff_worlds = IndexSet(**{name : {2} for name in [antecedent1, antecedent2]})\n", "\n", - " suff_lp = gather(log_probs, IndexSet(**{\"X\":{2}, \"Y\":{2}} ))\n", - " if nodes[\"Z\"][\"value\"].item() == 1:\n", - " assert suff_lp.exp().item() == 1.0\n", - " else:\n", - " assert suff_lp.exp().item() == 0.0\n" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "with MultiWorldCounterfactual() as mwc_independent: \n", - " with SearchForExplanation(\n", - " supports=supports_independent.supports,\n", - " antecedents={\"X\": torch.tensor(1.0), \"Z\": torch.tensor(1.0)},\n", - " consequents={\"Y\": torch.tensor(1.0)},\n", - " witnesses={},\n", - " alternatives={\"X\": torch.tensor(0.0), \"Z\": torch.tensor(0.0)},\n", - " antecedent_bias=-0.5,\n", - " consequent_scale=0,\n", - " ):\n", - " with pyro.plate(\"sample\", size=1):\n", - " with pyro.poutine.trace() as trace_independent:\n", - " model_three_independent()\n", + " with mwc_independent:\n", + " nec_lp = gather(log_probs, nec_worlds)\n", + " if nodes[consequent][\"value\"].item() == 1:\n", + " assert nec_lp.exp().item() == 0.0 \n", + " else:\n", + " assert nec_lp.exp().item() == 1.0\n", + "\n", + " suff_lp = gather(log_probs, suff_worlds)\n", + " if nodes[consequent][\"value\"].item() == 1:\n", + " assert suff_lp.exp().item() == 1.0\n", + " else:\n", + " assert suff_lp.exp().item() == 0.0\n", "\n", - "trace_independent.trace.compute_log_prob\n", - "nodes = trace_independent.trace.nodes\n", - "assert nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor.shape == torch.Size([3, 3, 1, 1, 1, 1])\n", - "\n" + " assert torch.allclose(log_probs.squeeze().fill_diagonal_(0.0), torch.tensor(0.0))" ] } ], diff --git a/docs/source/test_three_variables.ipynb b/docs/source/test_three_variables.ipynb index 0d47eb54..b696e3c7 100644 --- a/docs/source/test_three_variables.ipynb +++ b/docs/source/test_three_variables.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 5, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -42,149 +42,204 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "necessity_log_probs tensor([0.])\n", - "sufficiency_log_probs tensor([-inf])\n", - "IndexSet({'X': {0}, 'Y': {0}})\n", - "nec_suff_log_prob_partitioned {IndexSet({'X': {0}}): tensor([0.]), IndexSet({'Y': {0}}): tensor([0.]), IndexSet({'X': {1}}): tensor([0.]), IndexSet({'X': {2}}): tensor([-inf]), IndexSet({'Y': {1}}): tensor([0.]), IndexSet({'Y': {2}}): tensor([-inf])}\n", - "new_value tensor([[[[[[-inf]]]],\n", - "\n", - "\n", - "\n", - " [[[[-inf]]]],\n", - "\n", - "\n", - "\n", - " [[[[-inf]]]]]])\n", - "tensor([1., 0., 1.])\n", - "tensor([1., 0., 1.])\n", - "tensor(0.)\n", - "tensor([-inf, -inf, -inf])\n" - ] - } - ], + "outputs": [], "source": [ - "def model_three_independent():\n", + "# X -> Z, Y -> Z\n", + "\n", + "def model_three_converge():\n", " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", " Y = pyro.sample(\"Y\", dist.Bernoulli(0.5))\n", - " Z = pyro.sample(\"Z\", dist.Bernoulli(0.5))\n", + " Z = pyro.sample(\"Z\", dist.Bernoulli(torch.min(X, Y)))\n", " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", "\n", - "with ExtractSupports() as supports_independent:\n", - " model_three_independent()\n", + "with ExtractSupports() as supports_converge:\n", + " model_three_converge()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "with MultiWorldCounterfactual() as mwc_converge: \n", + " with SearchForExplanation(\n", + " supports=supports_converge.supports,\n", + " antecedents={\"Y\": torch.tensor(1.0), \"X\": torch.tensor(1.0)},\n", + " consequents={\"Z\": torch.tensor(1.0)},\n", + " witnesses={},\n", + " alternatives={\"Y\": torch.tensor(1.0), \"X\": torch.tensor(1.0)},\n", + " antecedent_bias=-0.5,\n", + " consequent_scale=0,\n", + " ):\n", + " with pyro.plate(\"sample\", size=1):\n", + " with pyro.poutine.trace() as trace_converge:\n", + " model_three_converge()\n", "\n", - "with MultiWorldCounterfactual() as mwc_independent: \n", - " with SearchForExplanation(\n", - " supports=supports_independent.supports,\n", - " antecedents={\"X\": torch.tensor(1.0), \"Y\": torch.tensor(1.0)},\n", - " consequents={\"Z\": torch.tensor(1.0)},\n", - " witnesses={},\n", - " alternatives={\"X\": torch.tensor(0.0), \"Y\": torch.tensor(0.0)},\n", - " antecedent_bias=-0.5,\n", - " consequent_scale=0,\n", - " ):\n", - " with pyro.plate(\"sample\", size=1):\n", - " with pyro.poutine.trace() as trace_independent:\n", - " model_three_independent()\n", + "trace_converge.trace.compute_log_prob\n", + "nodes = trace_converge.trace.nodes\n", + "\n", + "values = nodes[\"Z\"][\"value\"]\n", + "log_probs = nodes[\"__cause____consequent_Z\"][\"fn\"].log_factor\n", + "assert values.shape == log_probs.shape\n", + "\n", + "nec_worlds = IndexSet(**{name : {1} for name in [\"X\", \"Y\"]})\n", + "suff_worlds = IndexSet(**{name : {2} for name in [\"X\", \"Y\"]})\n", + "\n", + "with mwc_converge:\n", + " nec_value = gather(values, nec_worlds)\n", + " nec_lp = gather(log_probs, nec_worlds)\n", + " assert nec_lp.exp().item() == 1 - nec_value.item()\n", + "\n", + " suff_value = gather(values, suff_worlds)\n", + " suff_lp = gather(log_probs, suff_worlds)\n", + " assert suff_lp.exp().item() == suff_value.item()\n", + "\n", + "assert torch.allclose(log_probs.squeeze().fill_diagonal_(0.0), torch.tensor(0.0))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# X -> Y, X -> Z\n", + "\n", + "def model_three_diverge():\n", + " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", + " Y = pyro.sample(\"Y\", dist.Bernoulli(X))\n", + " Z = pyro.sample(\"Z\", dist.Bernoulli(X))\n", + " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", + "\n", + "with ExtractSupports() as supports_diverge:\n", + " model_three_diverge()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "with MultiWorldCounterfactual() as mwc_diverge: \n", + " with SearchForExplanation(\n", + " supports=supports_diverge.supports,\n", + " antecedents={\"Y\": torch.tensor(1.0), \"X\": torch.tensor(1.0)},\n", + " consequents={\"Z\": torch.tensor(1.0)},\n", + " witnesses={},\n", + " alternatives={\"Y\": torch.tensor(0.0), \"X\": torch.tensor(0.0)},\n", + " antecedent_bias=-0.5,\n", + " consequent_scale=0,\n", + " ):\n", + " with pyro.plate(\"sample\", size=1):\n", + " with pyro.poutine.trace() as trace_diverge:\n", + " model_three_diverge()\n", + "\n", + "trace_diverge.trace.compute_log_prob\n", + "nodes = trace_diverge.trace.nodes\n", + "\n", + "values = nodes[\"Z\"][\"value\"]\n", + "log_probs = nodes[\"__cause____consequent_Z\"][\"fn\"].log_factor\n", + "\n", + "assert log_probs.shape == torch.Size([3, 3, 1, 1, 1, 1])\n", + "\n", + "nec_worlds = IndexSet(**{name : {1} for name in [\"X\", \"Y\"]})\n", + "suff_worlds = IndexSet(**{name : {2} for name in [\"X\", \"Y\"]})\n", + "\n", + "with mwc_diverge:\n", + " nec_value = gather(values, nec_worlds)\n", + " nec_lp = gather(log_probs, nec_worlds)\n", + " assert nec_lp.exp().item() == 1 - nec_value.item()\n", "\n", - "trace_independent.trace.compute_log_prob\n", - "nodes = trace_independent.trace.nodes\n", - "# assert nodes[\"__cause____consequent_Z\"][\"fn\"].log_factor.shape == torch.Size([3, 3, 1, 1, 1, 1])\n", + " suff_value = gather(values, suff_worlds)\n", + " suff_lp = gather(log_probs, suff_worlds)\n", + " assert suff_lp.exp().item() == suff_value.item()\n", + "\n", + "assert torch.allclose(log_probs.squeeze().fill_diagonal_(0.0), torch.tensor(0.0))" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# X -> Y -> Z\n", + "\n", + "def model_three_chain():\n", + " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", + " Y = pyro.sample(\"Y\", dist.Bernoulli(X))\n", + " Z = pyro.sample(\"Z\", dist.Bernoulli(Y))\n", + " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", "\n", - "print(trace_independent.trace.nodes[\"X\"][\"value\"].squeeze())\n", - "print(trace_independent.trace.nodes[\"Y\"][\"value\"].squeeze())\n", - "print(trace_independent.trace.nodes[\"Z\"][\"value\"].squeeze())\n", - "print(trace_independent.trace.nodes[\"__cause____consequent_Z\"][\"fn\"].log_factor.squeeze())" + "with ExtractSupports() as supports_chain:\n", + " model_three_chain()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor([[[[[[0.]]]],\n", - "\n", - "\n", - "\n", - " [[[[-inf]]]],\n", - "\n", - "\n", - "\n", - " [[[[0.]]]]],\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[-inf]]]],\n", - "\n", - "\n", - "\n", - " [[[[-inf]]]],\n", - "\n", - "\n", - "\n", - " [[[[0.]]]]],\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[0.]]]],\n", - "\n", - "\n", - "\n", - " [[[[-inf]]]],\n", - "\n", - "\n", - "\n", - " [[[[0.]]]]]])\n" + "tensor([0., 0., 1.])\n", + "tensor([[0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.]])\n", + "tensor([[[[[0.]]]]]) tensor([[[[[[0.]]]]]])\n" ] } ], "source": [ - "# X -> Y, X -> Z\n", + "with MultiWorldCounterfactual() as mwc_chain: \n", + " with SearchForExplanation(\n", + " supports=supports_chain.supports,\n", + " antecedents={\"X\": torch.tensor(1.0), \"Z\": torch.tensor(1.0)},\n", + " consequents={\"Y\": torch.tensor(1.0)},\n", + " witnesses={},\n", + " alternatives={\"X\": torch.tensor(0.0), \"Z\": torch.tensor(0.0)},\n", + " antecedent_bias=-0.5,\n", + " consequent_scale=0,\n", + " ):\n", + " with pyro.plate(\"sample\", size=1):\n", + " with pyro.poutine.trace() as trace_chain:\n", + " model_three_chain()\n", "\n", - "def model_three_diverge():\n", - " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", - " Y = pyro.sample(\"Y\", dist.Bernoulli(0.5))\n", - " Z = pyro.sample(\"Z\", dist.Bernoulli(torch.min(X, Y)))\n", - " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", + "trace_chain.trace.compute_log_prob\n", + "nodes = trace_chain.trace.nodes\n", "\n", - "with ExtractSupports() as supports_diverge:\n", - " model_three_diverge()\n", + "values = nodes[\"Y\"][\"value\"]\n", + "log_probs = nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor\n", "\n", - "with MultiWorldCounterfactual() as mwc_diverge: \n", - " with SearchForExplanation(\n", - " supports=supports_independent.supports,\n", - " antecedents={\"Y\": torch.tensor(1.0), \"X\": torch.tensor(1.0)},\n", - " consequents={\"Z\": torch.tensor(1.0)},\n", - " witnesses={},\n", - " alternatives={\"Y\": torch.tensor(1.0), \"X\": torch.tensor(1.0)},\n", - " antecedent_bias=-0.5,\n", - " consequent_scale=0,\n", - " ):\n", - " with pyro.plate(\"sample\", size=1):\n", - " with pyro.poutine.trace() as trace_diverge:\n", - " model_three_diverge()\n", + "print(values.squeeze())\n", + "print(log_probs.squeeze())\n", "\n", - "trace_diverge.trace.compute_log_prob\n", - "nodes = trace_diverge.trace.nodes\n", - "print(nodes[\"__cause____consequent_Z\"][\"fn\"].log_factor)\n", - "assert nodes[\"__cause____consequent_Z\"][\"fn\"].log_factor.shape == torch.Size([3, 3, 1, 1, 1, 1])" + "assert log_probs.shape == torch.Size([3, 3, 1, 1, 1, 1])\n", + "\n", + "nec_worlds = IndexSet(**{name : {1} for name in [\"X\", \"Z\"]})\n", + "suff_worlds = IndexSet(**{name : {2} for name in [\"X\", \"Z\"]})\n", + "\n", + "with mwc_chain:\n", + " nec_value = gather(values, nec_worlds)\n", + " nec_lp = gather(log_probs, nec_worlds)\n", + " assert nec_lp.exp().item() == 1 - nec_value.item()\n", + "\n", + " suff_value = gather(values, suff_worlds)\n", + " suff_lp = gather(log_probs, suff_worlds)\n", + " assert suff_lp.exp().item() == suff_value.item()\n", + "\n", + "assert torch.allclose(log_probs.squeeze().fill_diagonal_(0.0), torch.tensor(0.0))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -193,7 +248,7 @@ "def model_three_complete():\n", " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", " Y = pyro.sample(\"Y\", dist.Bernoulli(X))\n", - " Z = pyro.sample(\"Z\", dist.Bernoulli(max(X, Y)))\n", + " Z = pyro.sample(\"Z\", dist.Bernoulli(torch.max(X, Y)))\n", " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", "\n", "with ExtractSupports() as supports_complete:\n", @@ -202,7 +257,66 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[1., 0., 1.],\n", + " [1., 0., 1.],\n", + " [1., 1., 1.]])\n", + "tensor([[0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.]])\n" + ] + } + ], + "source": [ + "with MultiWorldCounterfactual() as mwc_complete: \n", + " with SearchForExplanation(\n", + " supports=supports_complete.supports,\n", + " antecedents={\"X\": torch.tensor(1.0), \"Y\": torch.tensor(1.0)},\n", + " consequents={\"Z\": torch.tensor(1.0)},\n", + " witnesses={},\n", + " alternatives={\"X\": torch.tensor(0.0), \"Y\": torch.tensor(0.0)},\n", + " antecedent_bias=-0.5,\n", + " consequent_scale=0,\n", + " ):\n", + " with pyro.plate(\"sample\", size=1):\n", + " with pyro.poutine.trace() as trace_complete:\n", + " model_three_complete()\n", + "\n", + "trace_complete.trace.compute_log_prob\n", + "nodes = trace_complete.trace.nodes\n", + "\n", + "values = nodes[\"Z\"][\"value\"]\n", + "log_probs = nodes[\"__cause____consequent_Z\"][\"fn\"].log_factor\n", + "\n", + "print(values.squeeze())\n", + "print(log_probs.squeeze())\n", + "\n", + "assert log_probs.shape == torch.Size([3, 3, 1, 1, 1, 1])\n", + "\n", + "nec_worlds = IndexSet(**{name : {1} for name in [\"X\", \"Y\"]})\n", + "suff_worlds = IndexSet(**{name : {2} for name in [\"X\", \"Y\"]})\n", + "\n", + "with mwc_complete:\n", + " nec_value = gather(values, nec_worlds)\n", + " nec_lp = gather(log_probs, nec_worlds)\n", + " assert nec_lp.exp().item() == 1 - nec_value.item()\n", + "\n", + " suff_value = gather(values, suff_worlds)\n", + " suff_lp = gather(log_probs, suff_worlds)\n", + " assert suff_lp.exp().item() == suff_value.item()\n", + "\n", + "assert torch.allclose(log_probs.squeeze().fill_diagonal_(0.0), torch.tensor(0.0))" + ] + }, + { + "cell_type": "code", + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -217,6 +331,164 @@ "with ExtractSupports() as supports_isolate:\n", " model_three_isolate()" ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([1., 0., 1.])\n", + "tensor([[0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.]])\n" + ] + } + ], + "source": [ + "with MultiWorldCounterfactual() as mwc_isolate: \n", + " with SearchForExplanation(\n", + " supports=supports_isolate.supports,\n", + " antecedents={\"X\": torch.tensor(1.0), \"Z\": torch.tensor(1.0)},\n", + " consequents={\"Y\": torch.tensor(1.0)},\n", + " witnesses={},\n", + " alternatives={\"X\": torch.tensor(0.0), \"Z\": torch.tensor(0.0)},\n", + " antecedent_bias=-0.5,\n", + " consequent_scale=0,\n", + " ):\n", + " with pyro.plate(\"sample\", size=1):\n", + " with pyro.poutine.trace() as trace_isolate:\n", + " model_three_complete()\n", + "\n", + "trace_isolate.trace.compute_log_prob\n", + "nodes = trace_isolate.trace.nodes\n", + "\n", + "values = nodes[\"Y\"][\"value\"]\n", + "log_probs = nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor\n", + "\n", + "print(values.squeeze())\n", + "print(log_probs.squeeze())\n", + "\n", + "assert log_probs.shape == torch.Size([3, 3, 1, 1, 1, 1])\n", + "\n", + "nec_worlds = IndexSet(**{name : {1} for name in [\"X\", \"Z\"]})\n", + "suff_worlds = IndexSet(**{name : {2} for name in [\"X\", \"Z\"]})\n", + "\n", + "with mwc_isolate:\n", + " nec_value = gather(values, nec_worlds)\n", + " nec_lp = gather(log_probs, nec_worlds)\n", + " assert nec_lp.exp().item() == 1 - nec_value.item()\n", + "\n", + " suff_value = gather(values, suff_worlds)\n", + " suff_lp = gather(log_probs, suff_worlds)\n", + " assert suff_lp.exp().item() == suff_value.item()\n", + "\n", + "assert torch.allclose(log_probs.squeeze().fill_diagonal_(0.0), torch.tensor(0.0))" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "import pytest\n", + "\n", + "def model_three_converge():\n", + " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", + " Y = pyro.sample(\"Y\", dist.Bernoulli(0.5))\n", + " Z = pyro.sample(\"Z\", dist.Bernoulli(torch.min(X, Y)))\n", + " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", + "\n", + "def model_three_diverge():\n", + " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", + " Y = pyro.sample(\"Y\", dist.Bernoulli(X))\n", + " Z = pyro.sample(\"Z\", dist.Bernoulli(X))\n", + " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "@pytest.mark.parametrize(\"model\", [model_three_converge, model_three_diverge])\n", + "def test_three_variables(model):\n", + " with ExtractSupports() as supports:\n", + " model()\n", + "\n", + " with MultiWorldCounterfactual() as mwc: \n", + " with SearchForExplanation(\n", + " supports=supports.supports,\n", + " antecedents={\"X\": torch.tensor(1.0), \"Z\": torch.tensor(1.0)},\n", + " consequents={\"Y\": torch.tensor(1.0)},\n", + " witnesses={},\n", + " alternatives={\"X\": torch.tensor(0.0), \"Z\": torch.tensor(0.0)},\n", + " antecedent_bias=-0.5,\n", + " consequent_scale=0,\n", + " ):\n", + " with pyro.plate(\"sample\", size=1):\n", + " with pyro.poutine.trace() as trace:\n", + " model()\n", + "\n", + " trace.trace.compute_log_prob\n", + " nodes = trace.trace.nodes\n", + "\n", + " values = nodes[\"Y\"][\"value\"]\n", + " log_probs = nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor\n", + "\n", + " print(values.squeeze())\n", + " print(log_probs.squeeze())\n", + "\n", + " assert log_probs.shape == torch.Size([3, 3, 1, 1, 1, 1])\n", + "\n", + " nec_worlds = IndexSet(**{name : {1} for name in [\"X\", \"Z\"]})\n", + " suff_worlds = IndexSet(**{name : {2} for name in [\"X\", \"Z\"]})\n", + "\n", + " with mwc:\n", + " nec_value = gather(values, nec_worlds)\n", + " nec_lp = gather(log_probs, nec_worlds)\n", + " assert nec_lp.exp().item() == 1 - nec_value.item()\n", + "\n", + " suff_value = gather(values, suff_worlds)\n", + " suff_lp = gather(log_probs, suff_worlds)\n", + " assert suff_lp.exp().item() == suff_value.item()\n", + "\n", + " assert torch.allclose(log_probs.squeeze().fill_diagonal_(0.0), torch.tensor(0.0))\n", + "\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([0., 0., 1.])\n", + "tensor([[0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.]])\n" + ] + } + ], + "source": [ + "test_three_variables(model_three_diverge)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 1e65c7d59635a295aa2bea4a66668dd5f5dcfea9 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Tue, 6 Aug 2024 15:02:55 -0400 Subject: [PATCH 017/111] three variable test cases aded --- docs/source/test_three_variables.ipynb | 23 ++--- .../explainable/test_handlers_explanation.py | 84 +++++++++++++++++++ 2 files changed, 96 insertions(+), 11 deletions(-) diff --git a/docs/source/test_three_variables.ipynb b/docs/source/test_three_variables.ipynb index b696e3c7..848a3c64 100644 --- a/docs/source/test_three_variables.ipynb +++ b/docs/source/test_three_variables.ipynb @@ -181,18 +181,19 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor([0., 0., 1.])\n", + "tensor([[0., 0., 1.],\n", + " [0., 0., 0.],\n", + " [1., 1., 1.]])\n", "tensor([[0., 0., 0.],\n", " [0., 0., 0.],\n", - " [0., 0., 0.]])\n", - "tensor([[[[[0.]]]]]) tensor([[[[[[0.]]]]]])\n" + " [0., 0., 0.]])\n" ] } ], @@ -200,10 +201,10 @@ "with MultiWorldCounterfactual() as mwc_chain: \n", " with SearchForExplanation(\n", " supports=supports_chain.supports,\n", - " antecedents={\"X\": torch.tensor(1.0), \"Z\": torch.tensor(1.0)},\n", - " consequents={\"Y\": torch.tensor(1.0)},\n", + " antecedents={\"X\": torch.tensor(1.0), \"Y\": torch.tensor(1.0)},\n", + " consequents={\"Z\": torch.tensor(1.0)},\n", " witnesses={},\n", - " alternatives={\"X\": torch.tensor(0.0), \"Z\": torch.tensor(0.0)},\n", + " alternatives={\"X\": torch.tensor(0.0), \"Y\": torch.tensor(0.0)},\n", " antecedent_bias=-0.5,\n", " consequent_scale=0,\n", " ):\n", @@ -214,16 +215,16 @@ "trace_chain.trace.compute_log_prob\n", "nodes = trace_chain.trace.nodes\n", "\n", - "values = nodes[\"Y\"][\"value\"]\n", - "log_probs = nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor\n", + "values = nodes[\"Z\"][\"value\"]\n", + "log_probs = nodes[\"__cause____consequent_Z\"][\"fn\"].log_factor\n", "\n", "print(values.squeeze())\n", "print(log_probs.squeeze())\n", "\n", "assert log_probs.shape == torch.Size([3, 3, 1, 1, 1, 1])\n", "\n", - "nec_worlds = IndexSet(**{name : {1} for name in [\"X\", \"Z\"]})\n", - "suff_worlds = IndexSet(**{name : {2} for name in [\"X\", \"Z\"]})\n", + "nec_worlds = IndexSet(**{name : {1} for name in [\"X\", \"Y\"]})\n", + "suff_worlds = IndexSet(**{name : {2} for name in [\"X\", \"Y\"]})\n", "\n", "with mwc_chain:\n", " nec_value = gather(values, nec_worlds)\n", diff --git a/tests/explainable/test_handlers_explanation.py b/tests/explainable/test_handlers_explanation.py index 89d067f0..7e2b0b7d 100644 --- a/tests/explainable/test_handlers_explanation.py +++ b/tests/explainable/test_handlers_explanation.py @@ -4,6 +4,7 @@ import pyro.distributions as dist import pyro.distributions.constraints as constraints import torch +import pytest from chirho.counterfactual.handlers.counterfactual import MultiWorldCounterfactual from chirho.explainable.handlers.components import undo_split @@ -409,3 +410,86 @@ def model_connected(): assert trace_reverse.trace.nodes["__cause____consequent_X"]["fn"].log_factor[2,0,0,0,:].sum().exp() == 1. assert torch.all(trace_reverse.trace.nodes["__cause____consequent_X"]["fn"].log_factor.sum().exp() == 0) + + +def model_three_converge(): + X = pyro.sample("X", dist.Bernoulli(0.5)) + Y = pyro.sample("Y", dist.Bernoulli(0.5)) + Z = pyro.sample("Z", dist.Bernoulli(torch.min(X, Y))) + return {"X": X, "Y": Y, "Z": Z} + +def model_three_diverge(): + X = pyro.sample("X", dist.Bernoulli(0.5)) + Y = pyro.sample("Y", dist.Bernoulli(X)) + Z = pyro.sample("Z", dist.Bernoulli(X)) + return {"X": X, "Y": Y, "Z": Z} + +def model_three_chain(): + X = pyro.sample("X", dist.Bernoulli(0.5)) + Y = pyro.sample("Y", dist.Bernoulli(X)) + Z = pyro.sample("Z", dist.Bernoulli(Y)) + return {"X": X, "Y": Y, "Z": Z} + +def model_three_complete(): + X = pyro.sample("X", dist.Bernoulli(0.5)) + Y = pyro.sample("Y", dist.Bernoulli(X)) + Z = pyro.sample("Z", dist.Bernoulli(torch.max(X, Y))) + return {"X": X, "Y": Y, "Z": Z} + +def model_three_isolate(): + X = pyro.sample("X", dist.Bernoulli(0.5)) + Y = pyro.sample("Y", dist.Bernoulli(X)) + Z = pyro.sample("Z", dist.Bernoulli(0.5)) + return {"X": X, "Y": Y, "Z": Z} + +def model_three_independent(): + X = pyro.sample("X", dist.Bernoulli(0.5)) + Y = pyro.sample("Y", dist.Bernoulli(0.5)) + Z = pyro.sample("Z", dist.Bernoulli(0.5)) + return {"X": X, "Y": Y, "Z": Z} + +@pytest.mark.parametrize("ante_cons", [("X", "Y", "Z"), ("X", "Z", "Y")]) +@pytest.mark.parametrize("model", [model_three_converge, model_three_diverge, model_three_chain, model_three_complete, model_three_isolate, model_three_independent]) +def test_eq_neq_three_variables(model, ante_cons): + ante1, ante2, cons = ante_cons + with ExtractSupports() as supports: + model() + + with MultiWorldCounterfactual() as mwc: + with SearchForExplanation( + supports=supports.supports, + antecedents={ante1: torch.tensor(1.0), ante2: torch.tensor(1.0)}, + consequents={cons: torch.tensor(1.0)}, + witnesses={}, + alternatives={ante1: torch.tensor(0.0), ante2: torch.tensor(0.0)}, + antecedent_bias=-0.5, + consequent_scale=0, + ): + with pyro.plate("sample", size=1): + with pyro.poutine.trace() as trace: + model() + + trace.trace.compute_log_prob + nodes = trace.trace.nodes + + values = nodes[cons]["value"] + log_probs = nodes[f"__cause____consequent_{cons}"]["fn"].log_factor + + assert log_probs.shape == torch.Size([3, 3, 1, 1, 1, 1]) + + fact_worlds = IndexSet(**{name : {0} for name in [ante1, ante2]}) + nec_worlds = IndexSet(**{name : {1} for name in [ante1, ante2]}) + suff_worlds = IndexSet(**{name : {2} for name in [ante1, ante2]}) + with mwc: + fact_lp = gather(log_probs, fact_worlds) + assert fact_lp.exp().item() == 1 + + nec_value = gather(values, nec_worlds) + nec_lp = gather(log_probs, nec_worlds) + assert nec_lp.exp().item() == 1 - nec_value.item() + + suff_value = gather(values, suff_worlds) + suff_lp = gather(log_probs, suff_worlds) + assert suff_lp.exp().item() == suff_value.item() + + assert torch.allclose(log_probs.squeeze().fill_diagonal_(0.0), torch.tensor(0.0)) From 405866069b0dd7a8f80d06ef40f38a1fb7a1cca5 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Tue, 6 Aug 2024 15:24:16 -0400 Subject: [PATCH 018/111] clean up --- chirho/explainable/handlers/components.py | 94 +-- docs/source/test_notebook.ipynb | 676 ------------------ docs/source/test_three_independent.ipynb | 142 ---- docs/source/test_three_variables.ipynb | 516 ------------- .../explainable/test_handlers_explanation.py | 7 +- 5 files changed, 7 insertions(+), 1428 deletions(-) delete mode 100644 docs/source/test_notebook.ipynb delete mode 100644 docs/source/test_three_independent.ipynb delete mode 100644 docs/source/test_three_variables.ipynb diff --git a/chirho/explainable/handlers/components.py b/chirho/explainable/handlers/components.py index 8fac00fc..c4b04e92 100644 --- a/chirho/explainable/handlers/components.py +++ b/chirho/explainable/handlers/components.py @@ -233,20 +233,9 @@ def consequent_eq_neq( def _consequent_eq_neq(consequent: T) -> torch.Tensor: - # print("consequent", consequent) - # factual_indices = IndexSet( - # **{ - # name: ind - # for name, ind in get_factual_indices().items() - # if name in antecedents - # } - # ) - necessity_world = kwargs.get("necessity_world", 1) sufficiency_world = kwargs.get("sufficiency_world", 2) - # print(indices_of(consequent, event_dim=support.event_dim).keys()) - necessity_indices = IndexSet( **{ name: {necessity_world} @@ -270,14 +259,6 @@ def _consequent_eq_neq(consequent: T) -> torch.Tensor: consequent, sufficiency_indices, event_dim=support.event_dim ) - # print("necessity_indices: ", necessity_indices) - # print("sufficiency_indices: ", sufficiency_indices) - # print("necessity_value: ", necessity_value) - # print("sufficiency_value: ", sufficiency_value) - - # compare to proposed consequent if provided - # as then the sufficiency value can be different - # due to witness preemption necessity_log_probs = ( soft_neq( support, @@ -294,28 +275,15 @@ def _consequent_eq_neq(consequent: T) -> torch.Tensor: ) ) - # print("necessity_log_probs", necessity_log_probs) - sufficiency_log_probs = ( soft_eq(support, sufficiency_value, proposed_consequent, **kwargs) if proposed_consequent is not None else torch.zeros_like(necessity_log_probs) ) - # print("sufficiency_log_probs", sufficiency_log_probs) - FACTUAL_NEC_SUFF = torch.zeros_like(sufficiency_log_probs) - # index_keys = set(antecedents) & set(indices_of(consequent, event_dim=support.event_dim)) if indices_of(consequent, event_dim=support.event_dim) else set(antecedents) index_keys = set(antecedents) - # null_index = factual_indices if factual_indices != {} else IndexSet( - # **{ - # name: {0} - # for name in index_keys - # } - # ) - - null_index = IndexSet( **{ name: {0} @@ -323,26 +291,9 @@ def _consequent_eq_neq(consequent: T) -> torch.Tensor: } ) - - # nec_suff_log_probs_partitioned = { - # **{ - # #factual_indices: FACTUAL_NEC_SUFF, - # null_index: FACTUAL_NEC_SUFF for antecedent in index_keys - # }, - # **{ - # IndexSet(**{antecedent: {ind}}): log_prob - # for antecedent in index_keys - # for ind, log_prob in zip( - # [necessity_world, sufficiency_world], - # [necessity_log_probs, sufficiency_log_probs], - # ) - # }, - # } - nec_suff_log_probs_partitioned = { **{ - #factual_indices: FACTUAL_NEC_SUFF, - null_index: FACTUAL_NEC_SUFF # for antecedent in index_keys + null_index: FACTUAL_NEC_SUFF }, **{ IndexSet(**{antecedent: {ind} for antecedent in index_keys}): log_prob @@ -353,53 +304,10 @@ def _consequent_eq_neq(consequent: T) -> torch.Tensor: }, } - - - # nec_suff_log_probs_partitioned = { - # **{ - # factual_indices: FACTUAL_NEC_SUFF, - # }, - # **{ - # IndexSet(**{antecedent: {ind}}): log_prob - # for antecedent in ( - # set(antecedents) - # & set(indices_of(consequent, event_dim=support.event_dim)) - # ) - # for ind, log_prob in zip( - # [necessity_world, sufficiency_world], - # [necessity_log_probs, sufficiency_log_probs], - # ) - # }, - # } - new_value = scatter_n( nec_suff_log_probs_partitioned, event_dim=0, ) - - # for ind, log_prob in zip([necessity_world, sufficiency_world], [necessity_log_probs, sufficiency_log_probs]): - # print(ind, log_prob) - - # print(set(antecedents)) - # print(set(indices_of(consequent, event_dim=support.event_dim))) - # print(set(antecedents) & set(indices_of(consequent, event_dim=support.event_dim))) - - # for antecedent in ( - # set(antecedents) - # & set(indices_of(consequent, event_dim=support.event_dim)) - # ): - # print("yo") - # print(antecedent) - - # print({factual_indices: FACTUAL_NEC_SUFF}) - # print(**{factual_indices: FACTUAL_NEC_SUFF}) - - - - # print("nec_suff_log_prob_partitioned", nec_suff_log_probs_partitioned) - # print("new_value", new_value) - - # print(necessity_log_probs, sufficiency_log_probs) return new_value return _consequent_eq_neq diff --git a/docs/source/test_notebook.ipynb b/docs/source/test_notebook.ipynb deleted file mode 100644 index ecb17685..00000000 --- a/docs/source/test_notebook.ipynb +++ /dev/null @@ -1,676 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "\n", - "import pyro\n", - "import pyro.distributions as dist\n", - "import pyro.distributions.constraints as constraints\n", - "import torch\n", - "\n", - "from typing import Callable, Mapping, Optional, TypeVar, Union\n", - "\n", - "\n", - "from chirho.explainable.handlers.components import (\n", - " consequent_eq_neq,\n", - " random_intervention,\n", - " sufficiency_intervention,\n", - " undo_split,\n", - ")\n", - "\n", - "from chirho.observational.handlers.condition import Factors\n", - "from chirho.counterfactual.handlers.counterfactual import MultiWorldCounterfactual\n", - "from chirho.counterfactual.handlers.selection import get_factual_indices\n", - "from chirho.explainable.handlers.components import undo_split, consequent_eq_neq, sufficiency_intervention\n", - "from chirho.explainable.handlers.explanation import SearchForExplanation, SplitSubsets\n", - "from chirho.explainable.handlers import ExtractSupports\n", - "from chirho.observational.handlers.condition import Factors\n", - "from chirho.interventional.handlers import do\n", - "from chirho.explainable.handlers.preemptions import Preemptions\n", - "from chirho.indexed.ops import IndexSet, gather\n", - "from chirho.observational.handlers.condition import condition\n", - "from chirho.indexed.ops import indices_of\n", - "\n", - "S = TypeVar(\"S\")\n", - "T = TypeVar(\"T\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "factual IndexSet({'X': {0}, 'Y': {0}})\n", - "IndexSet({'X': {0, 1, 2}})\n", - "IndexSet({'Y': {0, 1, 2}})\n", - "IndexSet({})\n" - ] - } - ], - "source": [ - "# def test_edge_eq_neq():\n", - "\n", - "def model_three_independent():\n", - " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", - " Y = pyro.sample(\"Y\", dist.Bernoulli(0.5))\n", - " Z = pyro.sample(\"Z\", dist.Bernoulli(0.5))\n", - " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", - "\n", - "with ExtractSupports() as supports_independent:\n", - " model_three_independent()\n", - " \n", - "antecedents = {\"X\": (torch.tensor(0.0), torch.tensor(1.0)),\n", - " \"Y\": (torch.tensor(0.0), torch.tensor(1.0))}\n", - "\n", - "with MultiWorldCounterfactual() as m_ind_do:\n", - " with do(actions = antecedents):\n", - " with pyro.poutine.trace() as tr_do:\n", - " model_three_independent()\n", - " \n", - "nodes_do = tr_do.trace.nodes\n", - "\n", - "with m_ind_do:\n", - " print(\"factual\", get_factual_indices())\n", - " print(indices_of(nodes_do[\"X\"][\"value\"]))\n", - " print(indices_of(nodes_do[\"Y\"][\"value\"]))\n", - " print(indices_of(nodes_do[\"Z\"][\"value\"]))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "factual IndexSet({'X': {0}, 'Y': {0}})\n", - "IndexSet({'X': {0, 1}})\n", - "IndexSet({'Y': {0, 1}})\n", - "IndexSet({})\n" - ] - } - ], - "source": [ - "antecedents={\"X\": torch.tensor(1.0), \"Y\": torch.tensor(1.0)}\n", - "alternatives={\"X\": torch.tensor(0.0), \"Y\": torch.tensor(0.0)}\n", - "supports=supports_independent.supports\n", - "antecedent_bias = 0.0 #-0.5\n", - "prefix = \"__cause__\"\n", - "consequents={\"Z\": torch.tensor(1.0)}\n", - "consequent_scale=0\n", - "factors = None\n", - "witnesses = {}\n", - "preemptions = None\n", - "witness_bias = 0.0\n", - "\n", - "\n", - "\n", - "alternatives = (\n", - " {a: alternatives[a] for a in antecedents.keys()}\n", - " if alternatives is not None\n", - " else {\n", - " a: random_intervention(supports[a], name=f\"{prefix}_alternative_{a}\")\n", - " for a in antecedents.keys()\n", - " }\n", - " )\n", - "\n", - "with MultiWorldCounterfactual() as m_ind_do:\n", - " with do(actions = alternatives):\n", - " with pyro.poutine.trace() as tr_do:\n", - " model_three_independent()\n", - " \n", - "nodes_do = tr_do.trace.nodes\n", - "\n", - "with m_ind_do:\n", - " print(\"factual\", get_factual_indices())\n", - " print(indices_of(nodes_do[\"X\"][\"value\"]))\n", - " print(indices_of(nodes_do[\"Y\"][\"value\"]))\n", - " print(indices_of(nodes_do[\"Z\"][\"value\"]))" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'Z': tensor(1.)}\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[38], line 55\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m antecedent_handler, witness_handler, consequent_handler:\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pyro\u001b[38;5;241m.\u001b[39mpoutine\u001b[38;5;241m.\u001b[39mtrace() \u001b[38;5;28;01mas\u001b[39;00m tr_do:\n\u001b[0;32m---> 55\u001b[0m \u001b[43mmodel_three_independent\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 57\u001b[0m nodes_do \u001b[38;5;241m=\u001b[39m tr_do\u001b[38;5;241m.\u001b[39mtrace\u001b[38;5;241m.\u001b[39mnodes\n\u001b[1;32m 59\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m m_ind_do:\n", - "Cell \u001b[0;32mIn[2], line 6\u001b[0m, in \u001b[0;36mmodel_three_independent\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m X \u001b[38;5;241m=\u001b[39m pyro\u001b[38;5;241m.\u001b[39msample(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m, dist\u001b[38;5;241m.\u001b[39mBernoulli(\u001b[38;5;241m0.5\u001b[39m))\n\u001b[1;32m 5\u001b[0m Y \u001b[38;5;241m=\u001b[39m pyro\u001b[38;5;241m.\u001b[39msample(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mY\u001b[39m\u001b[38;5;124m\"\u001b[39m, dist\u001b[38;5;241m.\u001b[39mBernoulli(\u001b[38;5;241m0.5\u001b[39m))\n\u001b[0;32m----> 6\u001b[0m Z \u001b[38;5;241m=\u001b[39m \u001b[43mpyro\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mZ\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mBernoulli\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m: X, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mY\u001b[39m\u001b[38;5;124m\"\u001b[39m: Y, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mZ\u001b[39m\u001b[38;5;124m\"\u001b[39m: Z}\n", - "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/primitives.py:189\u001b[0m, in \u001b[0;36msample\u001b[0;34m(name, fn, obs, obs_mask, infer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 172\u001b[0m msg \u001b[38;5;241m=\u001b[39m Message(\n\u001b[1;32m 173\u001b[0m \u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 174\u001b[0m name\u001b[38;5;241m=\u001b[39mname,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 186\u001b[0m continuation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 187\u001b[0m )\n\u001b[1;32m 188\u001b[0m \u001b[38;5;66;03m# apply the stack and return its return value\u001b[39;00m\n\u001b[0;32m--> 189\u001b[0m \u001b[43mapply_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 190\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;66;03m# type narrowing guaranteed by apply_stack\u001b[39;00m\n\u001b[1;32m 191\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", - "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/poutine/runtime.py:386\u001b[0m, in \u001b[0;36mapply_stack\u001b[0;34m(initial_msg)\u001b[0m\n\u001b[1;32m 383\u001b[0m default_process_message(msg)\n\u001b[1;32m 385\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m frame \u001b[38;5;129;01min\u001b[39;00m stack[\u001b[38;5;241m-\u001b[39mpointer:]:\n\u001b[0;32m--> 386\u001b[0m \u001b[43mframe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_postprocess_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 388\u001b[0m cont \u001b[38;5;241m=\u001b[39m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontinuation\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 389\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cont \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/poutine/messenger.py:194\u001b[0m, in \u001b[0;36mMessenger._postprocess_message\u001b[0;34m(self, msg)\u001b[0m\n\u001b[1;32m 192\u001b[0m method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_pyro_post_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmsg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 193\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 194\u001b[0m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/s78projects/chirho/chirho/observational/handlers/condition.py:56\u001b[0m, in \u001b[0;36mFactors._pyro_post_sample\u001b[0;34m(self, msg)\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m---> 56\u001b[0m pyro\u001b[38;5;241m.\u001b[39mfactor(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprefix\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mmsg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[43mfactor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvalue\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m)\n", - "File \u001b[0;32m~/s78projects/chirho/chirho/explainable/handlers/components.py:350\u001b[0m, in \u001b[0;36mconsequent_eq_neq.._consequent_eq_neq\u001b[0;34m(consequent)\u001b[0m\n\u001b[1;32m 318\u001b[0m nec_suff_log_probs_partitioned \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 319\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 320\u001b[0m \u001b[38;5;66;03m#factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 330\u001b[0m },\n\u001b[1;32m 331\u001b[0m }\n\u001b[1;32m 333\u001b[0m \u001b[38;5;66;03m# nec_suff_log_probs_partitioned = {\u001b[39;00m\n\u001b[1;32m 334\u001b[0m \u001b[38;5;66;03m# **{\u001b[39;00m\n\u001b[1;32m 335\u001b[0m \u001b[38;5;66;03m# factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[38;5;66;03m# },\u001b[39;00m\n\u001b[1;32m 348\u001b[0m \u001b[38;5;66;03m# }\u001b[39;00m\n\u001b[0;32m--> 350\u001b[0m new_value \u001b[38;5;241m=\u001b[39m \u001b[43mscatter_n\u001b[49m(\n\u001b[1;32m 351\u001b[0m nec_suff_log_probs_partitioned,\n\u001b[1;32m 352\u001b[0m event_dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 353\u001b[0m )\n\u001b[1;32m 355\u001b[0m \u001b[38;5;66;03m# for ind, log_prob in zip([necessity_world, sufficiency_world], [necessity_log_probs, sufficiency_log_probs]):\u001b[39;00m\n\u001b[1;32m 356\u001b[0m \u001b[38;5;66;03m# print(ind, log_prob)\u001b[39;00m\n\u001b[1;32m 357\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 376\u001b[0m \n\u001b[1;32m 377\u001b[0m \u001b[38;5;66;03m# print(necessity_log_probs, sufficiency_log_probs)\u001b[39;00m\n\u001b[1;32m 378\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_value\n", - "File \u001b[0;32m~/s78projects/chirho/chirho/explainable/handlers/components.py:350\u001b[0m, in \u001b[0;36mconsequent_eq_neq.._consequent_eq_neq\u001b[0;34m(consequent)\u001b[0m\n\u001b[1;32m 318\u001b[0m nec_suff_log_probs_partitioned \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 319\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 320\u001b[0m \u001b[38;5;66;03m#factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 330\u001b[0m },\n\u001b[1;32m 331\u001b[0m }\n\u001b[1;32m 333\u001b[0m \u001b[38;5;66;03m# nec_suff_log_probs_partitioned = {\u001b[39;00m\n\u001b[1;32m 334\u001b[0m \u001b[38;5;66;03m# **{\u001b[39;00m\n\u001b[1;32m 335\u001b[0m \u001b[38;5;66;03m# factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[38;5;66;03m# },\u001b[39;00m\n\u001b[1;32m 348\u001b[0m \u001b[38;5;66;03m# }\u001b[39;00m\n\u001b[0;32m--> 350\u001b[0m new_value \u001b[38;5;241m=\u001b[39m \u001b[43mscatter_n\u001b[49m(\n\u001b[1;32m 351\u001b[0m nec_suff_log_probs_partitioned,\n\u001b[1;32m 352\u001b[0m event_dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 353\u001b[0m )\n\u001b[1;32m 355\u001b[0m \u001b[38;5;66;03m# for ind, log_prob in zip([necessity_world, sufficiency_world], [necessity_log_probs, sufficiency_log_probs]):\u001b[39;00m\n\u001b[1;32m 356\u001b[0m \u001b[38;5;66;03m# print(ind, log_prob)\u001b[39;00m\n\u001b[1;32m 357\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 376\u001b[0m \n\u001b[1;32m 377\u001b[0m \u001b[38;5;66;03m# print(necessity_log_probs, sufficiency_log_probs)\u001b[39;00m\n\u001b[1;32m 378\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_value\n", - "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1457\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.SafeCallWrapper.__call__\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:701\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1152\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1135\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:312\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.do_wait_suspend\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m~/.local/lib/python3.10/site-packages/debugpy/_vendored/pydevd/pydevd.py:2070\u001b[0m, in \u001b[0;36mPyDB.do_wait_suspend\u001b[0;34m(self, thread, frame, event, arg, exception_type)\u001b[0m\n\u001b[1;32m 2067\u001b[0m from_this_thread\u001b[38;5;241m.\u001b[39mappend(frame_custom_thread_id)\n\u001b[1;32m 2069\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_threads_suspended_single_notification\u001b[38;5;241m.\u001b[39mnotify_thread_suspended(thread_id, thread, stop_reason):\n\u001b[0;32m-> 2070\u001b[0m keep_suspended \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_wait_suspend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframe\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msuspend_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrom_this_thread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframes_tracker\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2072\u001b[0m frames_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 2074\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keep_suspended:\n\u001b[1;32m 2075\u001b[0m \u001b[38;5;66;03m# This means that we should pause again after a set next statement.\u001b[39;00m\n", - "File \u001b[0;32m~/.local/lib/python3.10/site-packages/debugpy/_vendored/pydevd/pydevd.py:2106\u001b[0m, in \u001b[0;36mPyDB._do_wait_suspend\u001b[0;34m(self, thread, frame, event, arg, suspend_type, from_this_thread, frames_tracker)\u001b[0m\n\u001b[1;32m 2103\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_input_hook()\n\u001b[1;32m 2105\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprocess_internal_commands()\n\u001b[0;32m-> 2106\u001b[0m \u001b[43mtime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0.01\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2108\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcancel_async_evaluation(get_current_thread_id(thread), \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mid\u001b[39m(frame)))\n\u001b[1;32m 2110\u001b[0m \u001b[38;5;66;03m# process any stepping instructions\u001b[39;00m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "print(consequents)\n", - "\n", - "sufficiency_actions = {\n", - " a: (\n", - " antecedents[a]\n", - " if antecedents[a] is not None\n", - " else sufficiency_intervention(supports[a], antecedents=antecedents.keys())\n", - " )\n", - " for a in antecedents.keys()\n", - "}\n", - "\n", - "antecedent_handler = SplitSubsets(\n", - " {a: supports[a] for a in antecedents.keys()},\n", - " {a: (alternatives[a], sufficiency_actions[a]) for a in antecedents.keys()}, # type: ignore\n", - " bias=antecedent_bias,\n", - " prefix=f\"{prefix}__antecedent_\",\n", - ")\n", - "\n", - "\n", - "witness_handler = Preemptions(\n", - " (\n", - " {w: preemptions[w] for w in witnesses}\n", - " if preemptions is not None\n", - " else {\n", - " w: undo_split(supports[w], antecedents=antecedents.keys())\n", - " for w in witnesses\n", - " }\n", - " ),\n", - " bias=witness_bias,\n", - " prefix=f\"{prefix}__witness_\",\n", - " )\n", - "\n", - "\n", - "consequent_handler: Factors[T] = Factors(\n", - " (\n", - " {c: factors[c] for c in consequents.keys()}\n", - " if factors is not None\n", - " else {\n", - " c: consequent_eq_neq(\n", - " support=supports[c],\n", - " proposed_consequent=consequents[c], # added this\n", - " antecedents=antecedents.keys(),\n", - " scale=consequent_scale,\n", - " )\n", - " for c in consequents.keys()\n", - " }\n", - " ),\n", - " prefix=f\"{prefix}__consequent_\",\n", - " )\n", - "\n", - "\n", - "with MultiWorldCounterfactual() as m_ind_do:\n", - " with antecedent_handler, witness_handler, consequent_handler:\n", - " with pyro.poutine.trace() as tr_do:\n", - " model_three_independent()\n", - " \n", - "nodes_do = tr_do.trace.nodes\n", - "\n", - "with m_ind_do:\n", - " print(\"factual\", get_factual_indices())\n", - " print(indices_of(nodes_do[\"X\"][\"value\"]))\n", - " print(indices_of(nodes_do[\"Y\"][\"value\"]))\n", - " print(indices_of(nodes_do[\"Z\"][\"value\"]))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "IndexSet({'X': {0}, 'Y': {0}})\n", - "IndexSet({'X': {0, 1, 2}})\n", - "IndexSet({})\n", - "IndexSet({'Y': {0, 1, 2}})\n", - "IndexSet({'Y': {0, 1, 2}})\n" - ] - }, - { - "data": { - "text/plain": [ - "torch.Size([3, 3, 1, 1, 1, 1])" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "antecedents={\"X\": torch.tensor(1.0), \"Y\": torch.tensor(1.0)}\n", - "alternatives={\"X\": torch.tensor(0.0), \"Y\": torch.tensor(0.0)}\n", - "supports=supports_independent.supports\n", - "antecedent_bias = 0.0 #-0.5\n", - "prefix = \"__cause__\"\n", - "consequents={\"Z\": torch.tensor(1.0)}\n", - "consequent_scale=0\n", - "factors = None\n", - "witnesses = {}\n", - "preemptions = None\n", - "witness_bias = 0.0\n", - "\n", - "\n", - "with MultiWorldCounterfactual() as mwc_independent:\n", - " with SearchForExplanation(\n", - " supports=supports_independent.supports,\n", - " antecedents={\"X\": torch.tensor(1.0), \"Y\": torch.tensor(1.0)},\n", - " consequents={\"Z\": torch.tensor(1.0)},\n", - " witnesses={},\n", - " alternatives={\"X\": torch.tensor(0.0), \"Y\": torch.tensor(0.0)},\n", - " antecedent_bias=-0.5,\n", - " consequent_scale=0,\n", - " ): \n", - " # with SearchForExplanation(\n", - " # supports=supports_independent.supports,\n", - " # antecedents={\"X\": torch.tensor(1.0), \"Z\": torch.tensor(1.0)},\n", - " # consequents={\"Y\": torch.tensor(1.0)},\n", - " # witnesses={},\n", - " # alternatives={\"X\": torch.tensor(0.0), \"Z\": torch.tensor(0.0)},\n", - " # antecedent_bias=-0.5,\n", - " # consequent_scale=0,\n", - " # ):\n", - " with pyro.plate(\"sample\", size=1):\n", - " with pyro.poutine.trace() as trace_independent:\n", - " model_three_independent()\n", - "\n", - "trace_independent.trace.compute_log_prob\n", - "\n", - "with mwc_independent:\n", - " print(get_factual_indices())\n", - " print(indices_of(trace_independent.trace.nodes[\"X\"][\"value\"]))\n", - " print(indices_of(trace_independent.trace.nodes[\"Z\"][\"value\"]))\n", - " print(indices_of(trace_independent.trace.nodes[\"Y\"][\"value\"]))\n", - " print(indices_of(trace_independent.trace.nodes[\"__cause____consequent_Z\"][\"value\"]))\n", - "\n", - "trace_independent.trace.nodes[\"__cause____consequent_Z\"][\"fn\"].log_factor.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[33], line 22\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pyro\u001b[38;5;241m.\u001b[39mplate(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample\u001b[39m\u001b[38;5;124m\"\u001b[39m, size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pyro\u001b[38;5;241m.\u001b[39mpoutine\u001b[38;5;241m.\u001b[39mtrace() \u001b[38;5;28;01mas\u001b[39;00m trace_independent:\n\u001b[0;32m---> 22\u001b[0m \u001b[43mmodel_three_dependent\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 24\u001b[0m trace_independent\u001b[38;5;241m.\u001b[39mtrace\u001b[38;5;241m.\u001b[39mcompute_log_prob\n\u001b[1;32m 26\u001b[0m \u001b[38;5;66;03m# with mwc_independent:\u001b[39;00m\n\u001b[1;32m 27\u001b[0m \u001b[38;5;66;03m# print(indices_of(trace_independent.trace.nodes[\"X\"][\"value\"]))\u001b[39;00m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;66;03m# print(indices_of(trace_independent.trace.nodes[\"Z\"][\"value\"]))\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 31\u001b[0m \n\u001b[1;32m 32\u001b[0m \u001b[38;5;66;03m# trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor.shape\u001b[39;00m\n", - "Cell \u001b[0;32mIn[33], line 4\u001b[0m, in \u001b[0;36mmodel_three_dependent\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m X \u001b[38;5;241m=\u001b[39m pyro\u001b[38;5;241m.\u001b[39msample(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m, dist\u001b[38;5;241m.\u001b[39mBernoulli(\u001b[38;5;241m0.5\u001b[39m))\n\u001b[1;32m 3\u001b[0m Y \u001b[38;5;241m=\u001b[39m pyro\u001b[38;5;241m.\u001b[39msample(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mY\u001b[39m\u001b[38;5;124m\"\u001b[39m, dist\u001b[38;5;241m.\u001b[39mBernoulli(\u001b[38;5;241m0.5\u001b[39m))\n\u001b[0;32m----> 4\u001b[0m Z \u001b[38;5;241m=\u001b[39m \u001b[43mpyro\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mZ\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mBernoulli\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43mY\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m: X, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mY\u001b[39m\u001b[38;5;124m\"\u001b[39m: Y, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mZ\u001b[39m\u001b[38;5;124m\"\u001b[39m: Z}\n", - "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/primitives.py:189\u001b[0m, in \u001b[0;36msample\u001b[0;34m(name, fn, obs, obs_mask, infer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 172\u001b[0m msg \u001b[38;5;241m=\u001b[39m Message(\n\u001b[1;32m 173\u001b[0m \u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msample\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 174\u001b[0m name\u001b[38;5;241m=\u001b[39mname,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 186\u001b[0m continuation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 187\u001b[0m )\n\u001b[1;32m 188\u001b[0m \u001b[38;5;66;03m# apply the stack and return its return value\u001b[39;00m\n\u001b[0;32m--> 189\u001b[0m \u001b[43mapply_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 190\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;66;03m# type narrowing guaranteed by apply_stack\u001b[39;00m\n\u001b[1;32m 191\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", - "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/poutine/runtime.py:386\u001b[0m, in \u001b[0;36mapply_stack\u001b[0;34m(initial_msg)\u001b[0m\n\u001b[1;32m 383\u001b[0m default_process_message(msg)\n\u001b[1;32m 385\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m frame \u001b[38;5;129;01min\u001b[39;00m stack[\u001b[38;5;241m-\u001b[39mpointer:]:\n\u001b[0;32m--> 386\u001b[0m \u001b[43mframe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_postprocess_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 388\u001b[0m cont \u001b[38;5;241m=\u001b[39m msg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontinuation\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 389\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cont \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/miniconda3/envs/chirho/lib/python3.10/site-packages/pyro/poutine/messenger.py:194\u001b[0m, in \u001b[0;36mMessenger._postprocess_message\u001b[0;34m(self, msg)\u001b[0m\n\u001b[1;32m 192\u001b[0m method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_pyro_post_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmsg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 193\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 194\u001b[0m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/s78projects/chirho/chirho/observational/handlers/condition.py:56\u001b[0m, in \u001b[0;36mFactors._pyro_post_sample\u001b[0;34m(self, msg)\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m---> 56\u001b[0m pyro\u001b[38;5;241m.\u001b[39mfactor(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprefix\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mmsg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[43mfactor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvalue\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m)\n", - "File \u001b[0;32m~/s78projects/chirho/chirho/explainable/handlers/components.py:350\u001b[0m, in \u001b[0;36mconsequent_eq_neq.._consequent_eq_neq\u001b[0;34m(consequent)\u001b[0m\n\u001b[1;32m 318\u001b[0m nec_suff_log_probs_partitioned \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 319\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 320\u001b[0m \u001b[38;5;66;03m#factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 330\u001b[0m },\n\u001b[1;32m 331\u001b[0m }\n\u001b[1;32m 333\u001b[0m \u001b[38;5;66;03m# nec_suff_log_probs_partitioned = {\u001b[39;00m\n\u001b[1;32m 334\u001b[0m \u001b[38;5;66;03m# **{\u001b[39;00m\n\u001b[1;32m 335\u001b[0m \u001b[38;5;66;03m# factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[38;5;66;03m# },\u001b[39;00m\n\u001b[1;32m 348\u001b[0m \u001b[38;5;66;03m# }\u001b[39;00m\n\u001b[0;32m--> 350\u001b[0m new_value \u001b[38;5;241m=\u001b[39m \u001b[43mscatter_n\u001b[49m(\n\u001b[1;32m 351\u001b[0m nec_suff_log_probs_partitioned,\n\u001b[1;32m 352\u001b[0m event_dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 353\u001b[0m )\n\u001b[1;32m 355\u001b[0m \u001b[38;5;66;03m# for ind, log_prob in zip([necessity_world, sufficiency_world], [necessity_log_probs, sufficiency_log_probs]):\u001b[39;00m\n\u001b[1;32m 356\u001b[0m \u001b[38;5;66;03m# print(ind, log_prob)\u001b[39;00m\n\u001b[1;32m 357\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 376\u001b[0m \n\u001b[1;32m 377\u001b[0m \u001b[38;5;66;03m# print(necessity_log_probs, sufficiency_log_probs)\u001b[39;00m\n\u001b[1;32m 378\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_value\n", - "File \u001b[0;32m~/s78projects/chirho/chirho/explainable/handlers/components.py:350\u001b[0m, in \u001b[0;36mconsequent_eq_neq.._consequent_eq_neq\u001b[0;34m(consequent)\u001b[0m\n\u001b[1;32m 318\u001b[0m nec_suff_log_probs_partitioned \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 319\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{\n\u001b[1;32m 320\u001b[0m \u001b[38;5;66;03m#factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 330\u001b[0m },\n\u001b[1;32m 331\u001b[0m }\n\u001b[1;32m 333\u001b[0m \u001b[38;5;66;03m# nec_suff_log_probs_partitioned = {\u001b[39;00m\n\u001b[1;32m 334\u001b[0m \u001b[38;5;66;03m# **{\u001b[39;00m\n\u001b[1;32m 335\u001b[0m \u001b[38;5;66;03m# factual_indices: FACTUAL_NEC_SUFF,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[38;5;66;03m# },\u001b[39;00m\n\u001b[1;32m 348\u001b[0m \u001b[38;5;66;03m# }\u001b[39;00m\n\u001b[0;32m--> 350\u001b[0m new_value \u001b[38;5;241m=\u001b[39m \u001b[43mscatter_n\u001b[49m(\n\u001b[1;32m 351\u001b[0m nec_suff_log_probs_partitioned,\n\u001b[1;32m 352\u001b[0m event_dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 353\u001b[0m )\n\u001b[1;32m 355\u001b[0m \u001b[38;5;66;03m# for ind, log_prob in zip([necessity_world, sufficiency_world], [necessity_log_probs, sufficiency_log_probs]):\u001b[39;00m\n\u001b[1;32m 356\u001b[0m \u001b[38;5;66;03m# print(ind, log_prob)\u001b[39;00m\n\u001b[1;32m 357\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 376\u001b[0m \n\u001b[1;32m 377\u001b[0m \u001b[38;5;66;03m# print(necessity_log_probs, sufficiency_log_probs)\u001b[39;00m\n\u001b[1;32m 378\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_value\n", - "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1457\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.SafeCallWrapper.__call__\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:701\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1152\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:1135\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.trace_dispatch\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m_pydevd_bundle/pydevd_cython.pyx:312\u001b[0m, in \u001b[0;36m_pydevd_bundle.pydevd_cython.PyDBFrame.do_wait_suspend\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m~/.local/lib/python3.10/site-packages/debugpy/_vendored/pydevd/pydevd.py:2070\u001b[0m, in \u001b[0;36mPyDB.do_wait_suspend\u001b[0;34m(self, thread, frame, event, arg, exception_type)\u001b[0m\n\u001b[1;32m 2067\u001b[0m from_this_thread\u001b[38;5;241m.\u001b[39mappend(frame_custom_thread_id)\n\u001b[1;32m 2069\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_threads_suspended_single_notification\u001b[38;5;241m.\u001b[39mnotify_thread_suspended(thread_id, thread, stop_reason):\n\u001b[0;32m-> 2070\u001b[0m keep_suspended \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_wait_suspend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframe\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msuspend_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrom_this_thread\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mframes_tracker\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2072\u001b[0m frames_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 2074\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keep_suspended:\n\u001b[1;32m 2075\u001b[0m \u001b[38;5;66;03m# This means that we should pause again after a set next statement.\u001b[39;00m\n", - "File \u001b[0;32m~/.local/lib/python3.10/site-packages/debugpy/_vendored/pydevd/pydevd.py:2106\u001b[0m, in \u001b[0;36mPyDB._do_wait_suspend\u001b[0;34m(self, thread, frame, event, arg, suspend_type, from_this_thread, frames_tracker)\u001b[0m\n\u001b[1;32m 2103\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_input_hook()\n\u001b[1;32m 2105\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprocess_internal_commands()\n\u001b[0;32m-> 2106\u001b[0m \u001b[43mtime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0.01\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2108\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcancel_async_evaluation(get_current_thread_id(thread), \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mid\u001b[39m(frame)))\n\u001b[1;32m 2110\u001b[0m \u001b[38;5;66;03m# process any stepping instructions\u001b[39;00m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "def model_three_dependent():\n", - " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", - " Y = pyro.sample(\"Y\", dist.Bernoulli(0.5))\n", - " Z = pyro.sample(\"Z\", dist.Bernoulli(torch.min(X,Y)))\n", - " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", - "\n", - "with ExtractSupports() as supports_independent:\n", - " model_three_dependent()\n", - "\n", - "with MultiWorldCounterfactual() as mwc_independent: \n", - " with SearchForExplanation(\n", - " supports=supports_independent.supports,\n", - " antecedents={\"X\": torch.tensor(1.0), \"Y\": torch.tensor(1.0)},\n", - " consequents={\"Z\": torch.tensor(1.0)},\n", - " witnesses={},\n", - " alternatives={\"X\": torch.tensor(0.0), \"Y\": torch.tensor(0.0)},\n", - " #antecedent_bias=-0.5,\n", - " consequent_scale=0,\n", - " ):\n", - " with pyro.plate(\"sample\", size=1):\n", - " with pyro.poutine.trace() as trace_independent:\n", - " model_three_dependent()\n", - "\n", - "trace_independent.trace.compute_log_prob\n", - "\n", - "# with mwc_independent:\n", - "# print(indices_of(trace_independent.trace.nodes[\"X\"][\"value\"]))\n", - "# print(indices_of(trace_independent.trace.nodes[\"Z\"][\"value\"]))\n", - "# print(indices_of(trace_independent.trace.nodes[\"Y\"][\"value\"]))\n", - "# print(indices_of(trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"value\"]))\n", - "\n", - "# trace_independent.trace.nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def model_independent():\n", - " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", - " Y = pyro.sample(\"Y\", dist.Bernoulli(0.5))\n", - "\n", - "with ExtractSupports() as supports_independent:\n", - " model_independent()\n", - "\n", - "with MultiWorldCounterfactual() as mwc_independent: \n", - " with SearchForExplanation(\n", - " supports=supports_independent.supports,\n", - " antecedents={\"X\": torch.tensor(1.0)},\n", - " consequents={\"Y\": torch.tensor(1.0)},\n", - " witnesses={},\n", - " alternatives={\"X\": torch.tensor(0.0)},\n", - " antecedent_bias=-0.5,\n", - " consequent_scale=0,\n", - " ):\n", - " with pyro.plate(\"sample\", size=3):\n", - " with pyro.poutine.trace() as trace_independent:\n", - " model_independent()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[[[[0., 0., 0.]]]],\n", - "\n", - "\n", - "\n", - " [[[[-inf, 0., -inf]]]],\n", - "\n", - "\n", - "\n", - " [[[[0., -inf, 0.]]]]])" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def model_triple():\n", - " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", - " Y = pyro.sample(\"Y\", dist.Bernoulli(X))\n", - " Z = pyro.sample(\"Z\", dist.Bernoulli(Y))\n", - "\n", - "with ExtractSupports() as supports_triple:\n", - " model_triple()\n", - "\n", - "with MultiWorldCounterfactual() as mwc_triple: \n", - " with SearchForExplanation(\n", - " supports=supports_triple.supports,\n", - " antecedents={\"Z\": torch.tensor(1.0)},\n", - " consequents={\"X\": torch.tensor(1.0)},\n", - " witnesses={},\n", - " alternatives={\"Z\": torch.tensor(0.0)},\n", - " antecedent_bias=-0.5,\n", - " consequent_scale=0,\n", - " ):\n", - " with pyro.plate(\"sample\", size=3):\n", - " with pyro.poutine.trace() as trace_triple:\n", - " model_triple()\n", - "\n", - "trace_triple.trace.compute_log_prob\n", - "\n", - "\n", - "trace_triple.trace.nodes[\"X\"][\"value\"]\n", - "trace_triple.trace.nodes[\"__cause____consequent_X\"][\"fn\"].log_factor" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "w torch.Size([3, 4, 1, 1, 1, 3]) c torch.Size([3, 4, 1, 1, 1, 3])\n", - "odict_keys(['w', 'consequent'])\n", - "IndexSet({'w': {0, 1, 2, 3}})\n", - "IndexSet({'w': {0, 1, 2, 3}})\n" - ] - } - ], - "source": [ - "event_shape = (3,) #(3,)\n", - "plate_size = 4\n", - "\n", - "\n", - "factors = {\n", - " \"consequent\": consequent_eq_neq(\n", - " support=constraints.independent(constraints.real, 0),\n", - " proposed_consequent=torch.Tensor([0.01]), \n", - " antecedents=[\"w\"],\n", - " )\n", - "}\n", - "\n", - "\n", - "# fake_w = dist.Normal(0, 0.1).expand(event_shape).to_event(len(event_shape)).sample()\n", - "\n", - "# @Factors(factors=factors)\n", - "@pyro.plate(\"data\", size=plate_size, dim=-4)\n", - "def model_ce():\n", - " w = pyro.sample(\"w\", dist.Normal(0, 0.1).expand(event_shape).to_event(len(event_shape)))\n", - " # w = pyro.sample(\"w\", dist.Normal(fake_w, 0.001))\n", - "\n", - " consequent = pyro.deterministic(\"consequent\", w * torch.tensor(0.1))\n", - "\n", - " print(\"w\", w.shape, \"c\", consequent.shape)\n", - " assert w.shape == consequent.shape\n", - "\n", - "\n", - "antecedents = {\n", - " \"w\": (\n", - " torch.tensor(0.1).expand(event_shape),\n", - " sufficiency_intervention(\n", - " constraints.independent(constraints.real, len(event_shape)),\n", - " [\"w\"]\n", - " )\n", - " )\n", - " }\n", - "\n", - "with MultiWorldCounterfactual() as mwc_ce:\n", - " with do(actions = antecedents):\n", - " with pyro.poutine.trace() as trace_ce: \n", - " model_ce()\n", - " \n", - "print(trace_ce.trace.nodes.keys())\n", - "with mwc_ce:\n", - " print(indices_of(trace_ce.trace.nodes[\"w\"][\"value\"]))\n", - " print(indices_of(trace_ce.trace.nodes[\"consequent\"][\"value\"]))\n", - " # print(indices_of(trace_ce.trace.nodes['__factor_consequent'][\"fn\"].log_factor))\n", - " # print(trace_ce.trace.nodes['__factor_consequent'][\"fn\"].log_factor)\n", - "\n", - "\n", - "# nd = trace_ce.trace.nodes\n", - "\n", - "# trace_ce.trace.compute_log_prob\n", - "\n", - "# with mwc_ce:\n", - "# eq_neq_log_probs_fact = gather(\n", - "# nd[\"__factor_consequent\"][\"fn\"].log_factor, IndexSet(**{\"w\": {0}}, event_dim = 0)\n", - "# )\n", - "\n", - "# eq_neq_log_probs_nec = gather(\n", - "# nd[\"__factor_consequent\"][\"fn\"].log_factor, IndexSet(**{\"w\": {1}}, event_dim = 0)\n", - "# )\n", - " \n", - "# consequent_suff = gather(\n", - "# nd[\"consequent\"][\"value\"], IndexSet(**{\"w\": {2}}, event_dim = 0 )\n", - "# )\n", - "\n", - "\n", - "# print(\"what's up\", indices_of(nd[\"consequent\"][\"value\"]))\n", - "\n", - "\n", - "# eq_neq_log_probs_suff = gather(\n", - "# nd[\"__factor_consequent\"][\"fn\"].log_factor, IndexSet(**{\"w\": {2}})\n", - "# )\n", - "\n", - "# assert eq_neq_log_probs_nec.shape == consequent_suff.shape\n", - "\n", - "# assert torch.equal(eq_neq_log_probs_suff, dist.Normal(0.0, .1).log_prob(consequent_suff - torch.tensor(.01)))\n", - "# assert eq_neq_log_probs_nec.sum().exp() == 0 \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "# event_shape = (3,) #(3,)\n", - "# plate_size = 4\n", - "\n", - "# def test_consequent_eq_neq():\n", - "# factors = {\n", - "# \"consequent\": consequent_eq_neq(\n", - "# support=constraints.independent(constraints.real, len(event_shape)),\n", - "# proposed_consequent=torch.Tensor([0.01]), \n", - "# antecedents=[\"w\"],\n", - "# event_dim=len(event_shape),\n", - "# )\n", - "# }\n", - "\n", - "# @Factors(factors=factors)\n", - "# @pyro.plate(\"data\", size=plate_size, dim=-1)\n", - "# def model_ce():\n", - "# w = pyro.sample(\n", - "# \"w\", dist.Normal(0, 0.1).expand(event_shape).to_event(len(event_shape))\n", - "# )\n", - "# #consequent = pyro.deterministic(\n", - "# # \"consequent\", w * 0.1, event_dim=len(event_shape)\n", - "# #)\n", - "# consequent = pyro.sample(\"consequent\", dist.Delta(w * 0.1).to_event(len(event_shape)))\n", - "\n", - "# return consequent\n", - "\n", - "# antecedents = {\n", - "# \"w\": (\n", - "# torch.tensor(0.1).expand(event_shape),\n", - "# sufficiency_intervention(\n", - "# constraints.independent(constraints.real, len(event_shape)), [\"w\"]\n", - "# ),\n", - "# )\n", - "# }\n", - "\n", - "# with MultiWorldCounterfactual() as mwc:\n", - "# with do(actions=antecedents):\n", - "# with pyro.poutine.trace() as tr:\n", - "# model_ce()\n", - "\n", - "# tr.trace.compute_log_prob()\n", - "# nd = tr.trace.nodes\n", - "\n", - "\n", - "# with mwc:\n", - "# eq_neq_log_probs_fact = gather(\n", - "# nd[\"__factor_consequent\"][\"log_prob\"], IndexSet(**{\"w\": {0}}, event_dim = 0)\n", - "# )\n", - "\n", - "# eq_neq_log_probs_nec = gather(\n", - "# nd[\"__factor_consequent\"][\"log_prob\"], IndexSet(**{\"w\": {1}}, event_dim = 0)\n", - "# )\n", - " \n", - "# consequent_suff = gather(\n", - "# nd[\"consequent\"][\"value\"], IndexSet(**{\"w\": {2}}, event_dim = 0 )\n", - "# )\n", - "\n", - "\n", - "# print(indices_of(nd[\"consequent\"][\"value\"]))\n", - "\n", - "\n", - "# eq_neq_log_probs_suff = gather(\n", - "# nd[\"__factor_consequent\"][\"log_prob\"], IndexSet(**{\"w\": {2}})\n", - "# )\n", - "\n", - "# assert eq_neq_log_probs_nec.shape == consequent_suff.shape\n", - "\n", - "# assert torch.equal(eq_neq_log_probs_suff, dist.Normal(0.0, .1).log_prob(consequent_suff - torch.tensor(.01)))\n", - "# assert eq_neq_log_probs_nec.sum().exp() == 0 \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/source/test_three_independent.ipynb b/docs/source/test_three_independent.ipynb deleted file mode 100644 index 34dbdad2..00000000 --- a/docs/source/test_three_independent.ipynb +++ /dev/null @@ -1,142 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "\n", - "import pyro\n", - "import pyro.distributions as dist\n", - "import pyro.distributions.constraints as constraints\n", - "import torch\n", - "\n", - "from typing import Callable, Mapping, Optional, TypeVar, Union\n", - "\n", - "\n", - "from chirho.explainable.handlers.components import (\n", - " consequent_eq_neq,\n", - " random_intervention,\n", - " sufficiency_intervention,\n", - " undo_split,\n", - ")\n", - "\n", - "from chirho.observational.handlers.condition import Factors\n", - "from chirho.counterfactual.handlers.counterfactual import MultiWorldCounterfactual\n", - "from chirho.counterfactual.handlers.selection import get_factual_indices\n", - "from chirho.explainable.handlers.components import undo_split, consequent_eq_neq, sufficiency_intervention\n", - "from chirho.explainable.handlers.explanation import SearchForExplanation, SplitSubsets\n", - "from chirho.explainable.handlers import ExtractSupports\n", - "from chirho.observational.handlers.condition import Factors\n", - "from chirho.interventional.handlers import do\n", - "from chirho.explainable.handlers.preemptions import Preemptions\n", - "from chirho.indexed.ops import IndexSet, gather\n", - "from chirho.observational.handlers.condition import condition\n", - "from chirho.indexed.ops import indices_of\n", - "\n", - "S = TypeVar(\"S\")\n", - "T = TypeVar(\"T\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "def model_three_independent():\n", - " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", - " Y = pyro.sample(\"Y\", dist.Bernoulli(0.5))\n", - " Z = pyro.sample(\"Z\", dist.Bernoulli(0.5))\n", - " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", - "\n", - "with ExtractSupports() as supports_independent:\n", - " model_three_independent()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X Y Z\n", - "X Z Y\n" - ] - } - ], - "source": [ - "antecedent_sets = [{\"X\": torch.tensor(1.0), \"Y\": torch.tensor(1.0)}, {\"X\": torch.tensor(1.0), \"Z\": torch.tensor(1.0)}]\n", - "consequent_sets = [{\"Z\": torch.tensor(1.0)}, {\"Y\": torch.tensor(1.0)}]\n", - "\n", - "tests = [(\"X\", \"Y\", \"Z\"), (\"X\", \"Z\", \"Y\")]\n", - "for antecedent1, antecedent2, consequent in tests:\n", - " print(antecedent1, antecedent2, consequent)\n", - " with MultiWorldCounterfactual() as mwc_independent: \n", - " with SearchForExplanation(\n", - " supports=supports_independent.supports,\n", - " antecedents={antecedent1: torch.tensor(0.0), antecedent2: torch.tensor(0.0)},\n", - " consequents={consequent: torch.tensor(1.0)},\n", - " witnesses={},\n", - " alternatives={antecedent1: torch.tensor(0.0), antecedent2: torch.tensor(0.0)},\n", - " antecedent_bias=-0.5,\n", - " consequent_scale=0,\n", - " ):\n", - " with pyro.plate(\"sample\", size=1):\n", - " with pyro.poutine.trace() as trace_independent:\n", - " model_three_independent()\n", - "\n", - " trace_independent.trace.compute_log_prob\n", - " nodes = trace_independent.trace.nodes\n", - "\n", - " log_probs = nodes[f\"__cause____consequent_{consequent}\"][\"fn\"].log_factor\n", - "\n", - " assert log_probs.shape == torch.Size([3, 3, 1, 1, 1, 1])\n", - "\n", - " nec_worlds = IndexSet(**{name : {1} for name in [antecedent1, antecedent2]})\n", - " suff_worlds = IndexSet(**{name : {2} for name in [antecedent1, antecedent2]})\n", - "\n", - " with mwc_independent:\n", - " nec_lp = gather(log_probs, nec_worlds)\n", - " if nodes[consequent][\"value\"].item() == 1:\n", - " assert nec_lp.exp().item() == 0.0 \n", - " else:\n", - " assert nec_lp.exp().item() == 1.0\n", - "\n", - " suff_lp = gather(log_probs, suff_worlds)\n", - " if nodes[consequent][\"value\"].item() == 1:\n", - " assert suff_lp.exp().item() == 1.0\n", - " else:\n", - " assert suff_lp.exp().item() == 0.0\n", - "\n", - " assert torch.allclose(log_probs.squeeze().fill_diagonal_(0.0), torch.tensor(0.0))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/source/test_three_variables.ipynb b/docs/source/test_three_variables.ipynb deleted file mode 100644 index 848a3c64..00000000 --- a/docs/source/test_three_variables.ipynb +++ /dev/null @@ -1,516 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "\n", - "import pyro\n", - "import pyro.distributions as dist\n", - "import pyro.distributions.constraints as constraints\n", - "import torch\n", - "\n", - "from typing import Callable, Mapping, Optional, TypeVar, Union\n", - "\n", - "\n", - "from chirho.explainable.handlers.components import (\n", - " consequent_eq_neq,\n", - " random_intervention,\n", - " sufficiency_intervention,\n", - " undo_split,\n", - ")\n", - "\n", - "from chirho.observational.handlers.condition import Factors\n", - "from chirho.counterfactual.handlers.counterfactual import MultiWorldCounterfactual\n", - "from chirho.counterfactual.handlers.selection import get_factual_indices\n", - "from chirho.explainable.handlers.components import undo_split, consequent_eq_neq, sufficiency_intervention\n", - "from chirho.explainable.handlers.explanation import SearchForExplanation, SplitSubsets\n", - "from chirho.explainable.handlers import ExtractSupports\n", - "from chirho.observational.handlers.condition import Factors\n", - "from chirho.interventional.handlers import do\n", - "from chirho.explainable.handlers.preemptions import Preemptions\n", - "from chirho.indexed.ops import IndexSet, gather\n", - "from chirho.observational.handlers.condition import condition\n", - "from chirho.indexed.ops import indices_of\n", - "\n", - "S = TypeVar(\"S\")\n", - "T = TypeVar(\"T\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# X -> Z, Y -> Z\n", - "\n", - "def model_three_converge():\n", - " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", - " Y = pyro.sample(\"Y\", dist.Bernoulli(0.5))\n", - " Z = pyro.sample(\"Z\", dist.Bernoulli(torch.min(X, Y)))\n", - " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", - "\n", - "with ExtractSupports() as supports_converge:\n", - " model_three_converge()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "with MultiWorldCounterfactual() as mwc_converge: \n", - " with SearchForExplanation(\n", - " supports=supports_converge.supports,\n", - " antecedents={\"Y\": torch.tensor(1.0), \"X\": torch.tensor(1.0)},\n", - " consequents={\"Z\": torch.tensor(1.0)},\n", - " witnesses={},\n", - " alternatives={\"Y\": torch.tensor(1.0), \"X\": torch.tensor(1.0)},\n", - " antecedent_bias=-0.5,\n", - " consequent_scale=0,\n", - " ):\n", - " with pyro.plate(\"sample\", size=1):\n", - " with pyro.poutine.trace() as trace_converge:\n", - " model_three_converge()\n", - "\n", - "trace_converge.trace.compute_log_prob\n", - "nodes = trace_converge.trace.nodes\n", - "\n", - "values = nodes[\"Z\"][\"value\"]\n", - "log_probs = nodes[\"__cause____consequent_Z\"][\"fn\"].log_factor\n", - "assert values.shape == log_probs.shape\n", - "\n", - "nec_worlds = IndexSet(**{name : {1} for name in [\"X\", \"Y\"]})\n", - "suff_worlds = IndexSet(**{name : {2} for name in [\"X\", \"Y\"]})\n", - "\n", - "with mwc_converge:\n", - " nec_value = gather(values, nec_worlds)\n", - " nec_lp = gather(log_probs, nec_worlds)\n", - " assert nec_lp.exp().item() == 1 - nec_value.item()\n", - "\n", - " suff_value = gather(values, suff_worlds)\n", - " suff_lp = gather(log_probs, suff_worlds)\n", - " assert suff_lp.exp().item() == suff_value.item()\n", - "\n", - "assert torch.allclose(log_probs.squeeze().fill_diagonal_(0.0), torch.tensor(0.0))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# X -> Y, X -> Z\n", - "\n", - "def model_three_diverge():\n", - " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", - " Y = pyro.sample(\"Y\", dist.Bernoulli(X))\n", - " Z = pyro.sample(\"Z\", dist.Bernoulli(X))\n", - " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", - "\n", - "with ExtractSupports() as supports_diverge:\n", - " model_three_diverge()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "with MultiWorldCounterfactual() as mwc_diverge: \n", - " with SearchForExplanation(\n", - " supports=supports_diverge.supports,\n", - " antecedents={\"Y\": torch.tensor(1.0), \"X\": torch.tensor(1.0)},\n", - " consequents={\"Z\": torch.tensor(1.0)},\n", - " witnesses={},\n", - " alternatives={\"Y\": torch.tensor(0.0), \"X\": torch.tensor(0.0)},\n", - " antecedent_bias=-0.5,\n", - " consequent_scale=0,\n", - " ):\n", - " with pyro.plate(\"sample\", size=1):\n", - " with pyro.poutine.trace() as trace_diverge:\n", - " model_three_diverge()\n", - "\n", - "trace_diverge.trace.compute_log_prob\n", - "nodes = trace_diverge.trace.nodes\n", - "\n", - "values = nodes[\"Z\"][\"value\"]\n", - "log_probs = nodes[\"__cause____consequent_Z\"][\"fn\"].log_factor\n", - "\n", - "assert log_probs.shape == torch.Size([3, 3, 1, 1, 1, 1])\n", - "\n", - "nec_worlds = IndexSet(**{name : {1} for name in [\"X\", \"Y\"]})\n", - "suff_worlds = IndexSet(**{name : {2} for name in [\"X\", \"Y\"]})\n", - "\n", - "with mwc_diverge:\n", - " nec_value = gather(values, nec_worlds)\n", - " nec_lp = gather(log_probs, nec_worlds)\n", - " assert nec_lp.exp().item() == 1 - nec_value.item()\n", - "\n", - " suff_value = gather(values, suff_worlds)\n", - " suff_lp = gather(log_probs, suff_worlds)\n", - " assert suff_lp.exp().item() == suff_value.item()\n", - "\n", - "assert torch.allclose(log_probs.squeeze().fill_diagonal_(0.0), torch.tensor(0.0))" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# X -> Y -> Z\n", - "\n", - "def model_three_chain():\n", - " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", - " Y = pyro.sample(\"Y\", dist.Bernoulli(X))\n", - " Z = pyro.sample(\"Z\", dist.Bernoulli(Y))\n", - " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", - "\n", - "with ExtractSupports() as supports_chain:\n", - " model_three_chain()" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[0., 0., 1.],\n", - " [0., 0., 0.],\n", - " [1., 1., 1.]])\n", - "tensor([[0., 0., 0.],\n", - " [0., 0., 0.],\n", - " [0., 0., 0.]])\n" - ] - } - ], - "source": [ - "with MultiWorldCounterfactual() as mwc_chain: \n", - " with SearchForExplanation(\n", - " supports=supports_chain.supports,\n", - " antecedents={\"X\": torch.tensor(1.0), \"Y\": torch.tensor(1.0)},\n", - " consequents={\"Z\": torch.tensor(1.0)},\n", - " witnesses={},\n", - " alternatives={\"X\": torch.tensor(0.0), \"Y\": torch.tensor(0.0)},\n", - " antecedent_bias=-0.5,\n", - " consequent_scale=0,\n", - " ):\n", - " with pyro.plate(\"sample\", size=1):\n", - " with pyro.poutine.trace() as trace_chain:\n", - " model_three_chain()\n", - "\n", - "trace_chain.trace.compute_log_prob\n", - "nodes = trace_chain.trace.nodes\n", - "\n", - "values = nodes[\"Z\"][\"value\"]\n", - "log_probs = nodes[\"__cause____consequent_Z\"][\"fn\"].log_factor\n", - "\n", - "print(values.squeeze())\n", - "print(log_probs.squeeze())\n", - "\n", - "assert log_probs.shape == torch.Size([3, 3, 1, 1, 1, 1])\n", - "\n", - "nec_worlds = IndexSet(**{name : {1} for name in [\"X\", \"Y\"]})\n", - "suff_worlds = IndexSet(**{name : {2} for name in [\"X\", \"Y\"]})\n", - "\n", - "with mwc_chain:\n", - " nec_value = gather(values, nec_worlds)\n", - " nec_lp = gather(log_probs, nec_worlds)\n", - " assert nec_lp.exp().item() == 1 - nec_value.item()\n", - "\n", - " suff_value = gather(values, suff_worlds)\n", - " suff_lp = gather(log_probs, suff_worlds)\n", - " assert suff_lp.exp().item() == suff_value.item()\n", - "\n", - "assert torch.allclose(log_probs.squeeze().fill_diagonal_(0.0), torch.tensor(0.0))" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# X -> Y -> Z X -> Z\n", - "\n", - "def model_three_complete():\n", - " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", - " Y = pyro.sample(\"Y\", dist.Bernoulli(X))\n", - " Z = pyro.sample(\"Z\", dist.Bernoulli(torch.max(X, Y)))\n", - " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", - "\n", - "with ExtractSupports() as supports_complete:\n", - " model_three_complete()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[1., 0., 1.],\n", - " [1., 0., 1.],\n", - " [1., 1., 1.]])\n", - "tensor([[0., 0., 0.],\n", - " [0., 0., 0.],\n", - " [0., 0., 0.]])\n" - ] - } - ], - "source": [ - "with MultiWorldCounterfactual() as mwc_complete: \n", - " with SearchForExplanation(\n", - " supports=supports_complete.supports,\n", - " antecedents={\"X\": torch.tensor(1.0), \"Y\": torch.tensor(1.0)},\n", - " consequents={\"Z\": torch.tensor(1.0)},\n", - " witnesses={},\n", - " alternatives={\"X\": torch.tensor(0.0), \"Y\": torch.tensor(0.0)},\n", - " antecedent_bias=-0.5,\n", - " consequent_scale=0,\n", - " ):\n", - " with pyro.plate(\"sample\", size=1):\n", - " with pyro.poutine.trace() as trace_complete:\n", - " model_three_complete()\n", - "\n", - "trace_complete.trace.compute_log_prob\n", - "nodes = trace_complete.trace.nodes\n", - "\n", - "values = nodes[\"Z\"][\"value\"]\n", - "log_probs = nodes[\"__cause____consequent_Z\"][\"fn\"].log_factor\n", - "\n", - "print(values.squeeze())\n", - "print(log_probs.squeeze())\n", - "\n", - "assert log_probs.shape == torch.Size([3, 3, 1, 1, 1, 1])\n", - "\n", - "nec_worlds = IndexSet(**{name : {1} for name in [\"X\", \"Y\"]})\n", - "suff_worlds = IndexSet(**{name : {2} for name in [\"X\", \"Y\"]})\n", - "\n", - "with mwc_complete:\n", - " nec_value = gather(values, nec_worlds)\n", - " nec_lp = gather(log_probs, nec_worlds)\n", - " assert nec_lp.exp().item() == 1 - nec_value.item()\n", - "\n", - " suff_value = gather(values, suff_worlds)\n", - " suff_lp = gather(log_probs, suff_worlds)\n", - " assert suff_lp.exp().item() == suff_value.item()\n", - "\n", - "assert torch.allclose(log_probs.squeeze().fill_diagonal_(0.0), torch.tensor(0.0))" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "# X -> Y Z\n", - "\n", - "def model_three_isolate():\n", - " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", - " Y = pyro.sample(\"Y\", dist.Bernoulli(X))\n", - " Z = pyro.sample(\"Z\", dist.Bernoulli(0.5))\n", - " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", - "\n", - "with ExtractSupports() as supports_isolate:\n", - " model_three_isolate()" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([1., 0., 1.])\n", - "tensor([[0., 0., 0.],\n", - " [0., 0., 0.],\n", - " [0., 0., 0.]])\n" - ] - } - ], - "source": [ - "with MultiWorldCounterfactual() as mwc_isolate: \n", - " with SearchForExplanation(\n", - " supports=supports_isolate.supports,\n", - " antecedents={\"X\": torch.tensor(1.0), \"Z\": torch.tensor(1.0)},\n", - " consequents={\"Y\": torch.tensor(1.0)},\n", - " witnesses={},\n", - " alternatives={\"X\": torch.tensor(0.0), \"Z\": torch.tensor(0.0)},\n", - " antecedent_bias=-0.5,\n", - " consequent_scale=0,\n", - " ):\n", - " with pyro.plate(\"sample\", size=1):\n", - " with pyro.poutine.trace() as trace_isolate:\n", - " model_three_complete()\n", - "\n", - "trace_isolate.trace.compute_log_prob\n", - "nodes = trace_isolate.trace.nodes\n", - "\n", - "values = nodes[\"Y\"][\"value\"]\n", - "log_probs = nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor\n", - "\n", - "print(values.squeeze())\n", - "print(log_probs.squeeze())\n", - "\n", - "assert log_probs.shape == torch.Size([3, 3, 1, 1, 1, 1])\n", - "\n", - "nec_worlds = IndexSet(**{name : {1} for name in [\"X\", \"Z\"]})\n", - "suff_worlds = IndexSet(**{name : {2} for name in [\"X\", \"Z\"]})\n", - "\n", - "with mwc_isolate:\n", - " nec_value = gather(values, nec_worlds)\n", - " nec_lp = gather(log_probs, nec_worlds)\n", - " assert nec_lp.exp().item() == 1 - nec_value.item()\n", - "\n", - " suff_value = gather(values, suff_worlds)\n", - " suff_lp = gather(log_probs, suff_worlds)\n", - " assert suff_lp.exp().item() == suff_value.item()\n", - "\n", - "assert torch.allclose(log_probs.squeeze().fill_diagonal_(0.0), torch.tensor(0.0))" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "import pytest\n", - "\n", - "def model_three_converge():\n", - " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", - " Y = pyro.sample(\"Y\", dist.Bernoulli(0.5))\n", - " Z = pyro.sample(\"Z\", dist.Bernoulli(torch.min(X, Y)))\n", - " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n", - "\n", - "def model_three_diverge():\n", - " X = pyro.sample(\"X\", dist.Bernoulli(0.5))\n", - " Y = pyro.sample(\"Y\", dist.Bernoulli(X))\n", - " Z = pyro.sample(\"Z\", dist.Bernoulli(X))\n", - " return {\"X\": X, \"Y\": Y, \"Z\": Z}\n" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "@pytest.mark.parametrize(\"model\", [model_three_converge, model_three_diverge])\n", - "def test_three_variables(model):\n", - " with ExtractSupports() as supports:\n", - " model()\n", - "\n", - " with MultiWorldCounterfactual() as mwc: \n", - " with SearchForExplanation(\n", - " supports=supports.supports,\n", - " antecedents={\"X\": torch.tensor(1.0), \"Z\": torch.tensor(1.0)},\n", - " consequents={\"Y\": torch.tensor(1.0)},\n", - " witnesses={},\n", - " alternatives={\"X\": torch.tensor(0.0), \"Z\": torch.tensor(0.0)},\n", - " antecedent_bias=-0.5,\n", - " consequent_scale=0,\n", - " ):\n", - " with pyro.plate(\"sample\", size=1):\n", - " with pyro.poutine.trace() as trace:\n", - " model()\n", - "\n", - " trace.trace.compute_log_prob\n", - " nodes = trace.trace.nodes\n", - "\n", - " values = nodes[\"Y\"][\"value\"]\n", - " log_probs = nodes[\"__cause____consequent_Y\"][\"fn\"].log_factor\n", - "\n", - " print(values.squeeze())\n", - " print(log_probs.squeeze())\n", - "\n", - " assert log_probs.shape == torch.Size([3, 3, 1, 1, 1, 1])\n", - "\n", - " nec_worlds = IndexSet(**{name : {1} for name in [\"X\", \"Z\"]})\n", - " suff_worlds = IndexSet(**{name : {2} for name in [\"X\", \"Z\"]})\n", - "\n", - " with mwc:\n", - " nec_value = gather(values, nec_worlds)\n", - " nec_lp = gather(log_probs, nec_worlds)\n", - " assert nec_lp.exp().item() == 1 - nec_value.item()\n", - "\n", - " suff_value = gather(values, suff_worlds)\n", - " suff_lp = gather(log_probs, suff_worlds)\n", - " assert suff_lp.exp().item() == suff_value.item()\n", - "\n", - " assert torch.allclose(log_probs.squeeze().fill_diagonal_(0.0), torch.tensor(0.0))\n", - "\n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([0., 0., 1.])\n", - "tensor([[0., 0., 0.],\n", - " [0., 0., 0.],\n", - " [0., 0., 0.]])\n" - ] - } - ], - "source": [ - "test_three_variables(model_three_diverge)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tests/explainable/test_handlers_explanation.py b/tests/explainable/test_handlers_explanation.py index 7e2b0b7d..ffb61e2c 100644 --- a/tests/explainable/test_handlers_explanation.py +++ b/tests/explainable/test_handlers_explanation.py @@ -411,37 +411,42 @@ def model_connected(): assert torch.all(trace_reverse.trace.nodes["__cause____consequent_X"]["fn"].log_factor.sum().exp() == 0) - +# X -> Z, Y -> Z def model_three_converge(): X = pyro.sample("X", dist.Bernoulli(0.5)) Y = pyro.sample("Y", dist.Bernoulli(0.5)) Z = pyro.sample("Z", dist.Bernoulli(torch.min(X, Y))) return {"X": X, "Y": Y, "Z": Z} +# X -> Y, X -> Z def model_three_diverge(): X = pyro.sample("X", dist.Bernoulli(0.5)) Y = pyro.sample("Y", dist.Bernoulli(X)) Z = pyro.sample("Z", dist.Bernoulli(X)) return {"X": X, "Y": Y, "Z": Z} +# X -> Y -> Z def model_three_chain(): X = pyro.sample("X", dist.Bernoulli(0.5)) Y = pyro.sample("Y", dist.Bernoulli(X)) Z = pyro.sample("Z", dist.Bernoulli(Y)) return {"X": X, "Y": Y, "Z": Z} +# X -> Y, X -> Z, Y -> Z def model_three_complete(): X = pyro.sample("X", dist.Bernoulli(0.5)) Y = pyro.sample("Y", dist.Bernoulli(X)) Z = pyro.sample("Z", dist.Bernoulli(torch.max(X, Y))) return {"X": X, "Y": Y, "Z": Z} +# X -> Y Z def model_three_isolate(): X = pyro.sample("X", dist.Bernoulli(0.5)) Y = pyro.sample("Y", dist.Bernoulli(X)) Z = pyro.sample("Z", dist.Bernoulli(0.5)) return {"X": X, "Y": Y, "Z": Z} +# X Y Z def model_three_independent(): X = pyro.sample("X", dist.Bernoulli(0.5)) Y = pyro.sample("Y", dist.Bernoulli(0.5)) From 34d0fafef329008562276a7fdbeb18240556636b Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Tue, 6 Aug 2024 15:33:21 -0400 Subject: [PATCH 019/111] test for factual log probs --- tests/explainable/test_handlers_components.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/explainable/test_handlers_components.py b/tests/explainable/test_handlers_components.py index 17421186..2bdb1e78 100644 --- a/tests/explainable/test_handlers_components.py +++ b/tests/explainable/test_handlers_components.py @@ -424,6 +424,7 @@ def model_ce(): nd["__factor_consequent"]["fn"].log_factor, IndexSet(**{"w": {2}}) ) + assert torch.equal(eq_neq_log_probs_fact, torch.zeros(eq_neq_log_probs_fact.shape)) assert eq_neq_log_probs_nec.shape == consequent_suff.shape assert torch.equal(eq_neq_log_probs_suff, dist.Normal(0.0, .1).log_prob(consequent_suff - torch.tensor(.01))) assert eq_neq_log_probs_nec.sum().exp() == 0 From 9326a5214876efaf68e5c4dd7ed0eb23b9abca9e Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Tue, 6 Aug 2024 15:35:14 -0400 Subject: [PATCH 020/111] more clean up --- tests/explainable/test_handlers_explanation.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/tests/explainable/test_handlers_explanation.py b/tests/explainable/test_handlers_explanation.py index ffb61e2c..73f1208b 100644 --- a/tests/explainable/test_handlers_explanation.py +++ b/tests/explainable/test_handlers_explanation.py @@ -382,7 +382,6 @@ def model_connected(): Y_values_ind = trace_independent.trace.nodes["Y"]["value"] if torch.any(Y_values_ind == 1.): - print("testing with ", Y_values_ind) assert trace_independent.trace.nodes["__cause____consequent_Y"]["fn"].log_factor[1,0,0,0,:].sum().exp() == 0. else: assert trace_independent.trace.nodes["__cause____consequent_Y"]["fn"].log_factor[1,0,0,0,:].sum().exp() == 1. @@ -399,7 +398,6 @@ def model_connected(): X_values_rev = trace_reverse.trace.nodes["X"]["value"] if torch.any(X_values_rev == 1.): - print("testing with ", Y_values_ind) assert trace_reverse.trace.nodes["__cause____consequent_X"]["fn"].log_factor[1,0,0,0,:].sum().exp() == 0. else: assert trace_reverse.trace.nodes["__cause____consequent_X"]["fn"].log_factor[1,0,0,0,:].sum().exp() == 1. From 45e75d64f93967e9a8f2317705b62328ddd03bc0 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Tue, 6 Aug 2024 15:55:42 -0400 Subject: [PATCH 021/111] fixed a lint error --- tests/explainable/test_handlers_explanation.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/explainable/test_handlers_explanation.py b/tests/explainable/test_handlers_explanation.py index 73f1208b..49f17c19 100644 --- a/tests/explainable/test_handlers_explanation.py +++ b/tests/explainable/test_handlers_explanation.py @@ -3,13 +3,13 @@ import pyro import pyro.distributions as dist import pyro.distributions.constraints as constraints -import torch import pytest +import torch from chirho.counterfactual.handlers.counterfactual import MultiWorldCounterfactual +from chirho.explainable.handlers import ExtractSupports from chirho.explainable.handlers.components import undo_split from chirho.explainable.handlers.explanation import SearchForExplanation, SplitSubsets -from chirho.explainable.handlers import ExtractSupports from chirho.explainable.handlers.preemptions import Preemptions from chirho.indexed.ops import IndexSet, gather from chirho.observational.handlers.condition import condition From 075c33a21955144c2dc85e799be2096b24aef8d2 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Tue, 6 Aug 2024 16:34:29 -0400 Subject: [PATCH 022/111] lint clean --- chirho/explainable/handlers/components.py | 16 +- tests/explainable/test_handlers_components.py | 50 ++--- .../explainable/test_handlers_explanation.py | 181 +++++++++++------- 3 files changed, 147 insertions(+), 100 deletions(-) diff --git a/chirho/explainable/handlers/components.py b/chirho/explainable/handlers/components.py index c4b04e92..4db8fb42 100644 --- a/chirho/explainable/handlers/components.py +++ b/chirho/explainable/handlers/components.py @@ -193,8 +193,6 @@ def consequent_neq( """ def _consequent_neq(consequent: T) -> torch.Tensor: - - indices = IndexSet( **{ name: ind @@ -232,7 +230,6 @@ def consequent_eq_neq( """ def _consequent_eq_neq(consequent: T) -> torch.Tensor: - necessity_world = kwargs.get("necessity_world", 1) sufficiency_world = kwargs.get("sufficiency_world", 2) @@ -243,7 +240,6 @@ def _consequent_eq_neq(consequent: T) -> torch.Tensor: if name in antecedents } ) - sufficiency_indices = IndexSet( **{ name: {sufficiency_world} @@ -274,7 +270,6 @@ def _consequent_eq_neq(consequent: T) -> torch.Tensor: **kwargs, ) ) - sufficiency_log_probs = ( soft_eq(support, sufficiency_value, proposed_consequent, **kwargs) if proposed_consequent is not None @@ -284,17 +279,10 @@ def _consequent_eq_neq(consequent: T) -> torch.Tensor: FACTUAL_NEC_SUFF = torch.zeros_like(sufficiency_log_probs) index_keys = set(antecedents) - null_index = IndexSet( - **{ - name: {0} - for name in index_keys - } - ) + null_index = IndexSet(**{name: {0} for name in index_keys}) nec_suff_log_probs_partitioned = { - **{ - null_index: FACTUAL_NEC_SUFF - }, + **{null_index: FACTUAL_NEC_SUFF}, **{ IndexSet(**{antecedent: {ind} for antecedent in index_keys}): log_prob for ind, log_prob in zip( diff --git a/tests/explainable/test_handlers_components.py b/tests/explainable/test_handlers_components.py index 2bdb1e78..4b0916cf 100644 --- a/tests/explainable/test_handlers_components.py +++ b/tests/explainable/test_handlers_components.py @@ -371,63 +371,69 @@ def model_ce(): assert nd["__factor_consequent"]["log_prob"].sum() < -10 - @pytest.mark.parametrize("plate_size", [4, 50, 200]) @pytest.mark.parametrize("event_shape", [(), (3,), (3, 2)], ids=str) def test_consequent_eq_neq(plate_size, event_shape): factors = { "consequent": consequent_eq_neq( support=constraints.independent(constraints.real, 0), - proposed_consequent=torch.Tensor([0.01]), + proposed_consequent=torch.Tensor([0.01]), antecedents=["w"], ) } - w_initial = dist.Normal(0, 0.1).expand(event_shape).to_event(len(event_shape)).sample() + w_initial = ( + dist.Normal(0, 0.1).expand(event_shape).to_event(len(event_shape)).sample() + ) @Factors(factors=factors) @pyro.plate("data", size=plate_size, dim=-4) def model_ce(): - w = pyro.sample("w", dist.Normal(w_initial, .001)) + w = pyro.sample("w", dist.Normal(w_initial, 0.001)) consequent = pyro.deterministic("consequent", w * torch.tensor(0.1)) assert w.shape == consequent.shape - antecedents = { - "w": ( - torch.tensor(0.1).expand(event_shape), - sufficiency_intervention( - constraints.independent(constraints.real, len(event_shape)), - ["w"] - ), - ) - } + "w": ( + torch.tensor(0.1).expand(event_shape), + sufficiency_intervention( + constraints.independent(constraints.real, len(event_shape)), ["w"] + ), + ) + } with MultiWorldCounterfactual() as mwc_ce: - with do(actions = antecedents): - with pyro.poutine.trace() as trace_ce: + with do(actions=antecedents): + with pyro.poutine.trace() as trace_ce: model_ce() nd = trace_ce.trace.nodes trace_ce.trace.compute_log_prob with mwc_ce: eq_neq_log_probs_fact = gather( - nd["__factor_consequent"]["fn"].log_factor, IndexSet(**{"w": {0}}, event_dim = 0) + nd["__factor_consequent"]["fn"].log_factor, + IndexSet(**{"w": {0}}, event_dim=0), ) eq_neq_log_probs_nec = gather( - nd["__factor_consequent"]["fn"].log_factor, IndexSet(**{"w": {1}}, event_dim = 0) + nd["__factor_consequent"]["fn"].log_factor, + IndexSet(**{"w": {1}}, event_dim=0), ) consequent_suff = gather( - nd["consequent"]["value"], IndexSet(**{"w": {2}}, event_dim = 0 ) + nd["consequent"]["value"], IndexSet(**{"w": {2}}, event_dim=0) ) eq_neq_log_probs_suff = gather( - nd["__factor_consequent"]["fn"].log_factor, IndexSet(**{"w": {2}}) + nd["__factor_consequent"]["fn"].log_factor, IndexSet(**{"w": {2}}) ) - assert torch.equal(eq_neq_log_probs_fact, torch.zeros(eq_neq_log_probs_fact.shape)) + assert torch.equal( + eq_neq_log_probs_fact, torch.zeros(eq_neq_log_probs_fact.shape) + ) assert eq_neq_log_probs_nec.shape == consequent_suff.shape - assert torch.equal(eq_neq_log_probs_suff, dist.Normal(0.0, .1).log_prob(consequent_suff - torch.tensor(.01))) - assert eq_neq_log_probs_nec.sum().exp() == 0 + assert torch.equal( + eq_neq_log_probs_suff, + dist.Normal(0.0, 0.1).log_prob(consequent_suff - torch.tensor(0.01)), + ) + assert eq_neq_log_probs_nec.sum().exp() == 0 options = [ diff --git a/tests/explainable/test_handlers_explanation.py b/tests/explainable/test_handlers_explanation.py index 49f17c19..259d2e36 100644 --- a/tests/explainable/test_handlers_explanation.py +++ b/tests/explainable/test_handlers_explanation.py @@ -317,14 +317,17 @@ def test_SplitSubsets_two_layers(): assert obs_bill_hits == 0.0 and int_bill_hits == 0.0 and int_bottle_shatters == 0.0 + def test_edge_eq_neq(): def model_independent(): X = pyro.sample("X", dist.Bernoulli(0.5)) Y = pyro.sample("Y", dist.Bernoulli(0.5)) + return {"X": X, "Y": Y} def model_connected(): X = pyro.sample("X", dist.Bernoulli(0.5)) Y = pyro.sample("Y", dist.Bernoulli(X)) + return {"X": X, "Y": Y} with ExtractSupports() as supports_independent: model_independent() @@ -332,48 +335,47 @@ def model_connected(): with ExtractSupports() as supports_connected: model_connected() - with MultiWorldCounterfactual() as mwc_independent: - with SearchForExplanation( - supports=supports_independent.supports, - antecedents={"X": torch.tensor(1.0)}, - consequents={"Y": torch.tensor(1.0)}, - witnesses={}, - alternatives={"X": torch.tensor(0.0)}, - antecedent_bias=-0.5, - consequent_scale=0, - ): - with pyro.plate("sample", size=3): - with pyro.poutine.trace() as trace_independent: - model_independent() - - with MultiWorldCounterfactual() as mwc_connected: - with SearchForExplanation( - supports=supports_connected.supports, - antecedents={"X": torch.tensor(1.0)}, - consequents={"Y": torch.tensor(1.0)}, - witnesses={}, - alternatives={"X": torch.tensor(0.0)}, - antecedent_bias=-0.5, - consequent_scale=0, - ): - with pyro.plate("sample", size=3): - with pyro.poutine.trace() as trace_connected: - model_connected() - - with MultiWorldCounterfactual() as mwc_reverse: - with SearchForExplanation( - supports=supports_connected.supports, - antecedents={"Y": torch.tensor(1.0)}, - consequents={"X": torch.tensor(1.0)}, - witnesses={}, - alternatives={"Y": torch.tensor(0.0)}, - antecedent_bias=-0.5, - consequent_scale=0, - ): - with pyro.plate("sample", size=3): - with pyro.poutine.trace() as trace_reverse: - model_connected() + with MultiWorldCounterfactual(): + with SearchForExplanation( + supports=supports_independent.supports, + antecedents={"X": torch.tensor(1.0)}, + consequents={"Y": torch.tensor(1.0)}, + witnesses={}, + alternatives={"X": torch.tensor(0.0)}, + antecedent_bias=-0.5, + consequent_scale=0, + ): + with pyro.plate("sample", size=3): + with pyro.poutine.trace() as trace_independent: + model_independent() + + with MultiWorldCounterfactual(): + with SearchForExplanation( + supports=supports_connected.supports, + antecedents={"X": torch.tensor(1.0)}, + consequents={"Y": torch.tensor(1.0)}, + witnesses={}, + alternatives={"X": torch.tensor(0.0)}, + antecedent_bias=-0.5, + consequent_scale=0, + ): + with pyro.plate("sample", size=3): + with pyro.poutine.trace() as trace_connected: + model_connected() + with MultiWorldCounterfactual(): + with SearchForExplanation( + supports=supports_connected.supports, + antecedents={"Y": torch.tensor(1.0)}, + consequents={"X": torch.tensor(1.0)}, + witnesses={}, + alternatives={"Y": torch.tensor(0.0)}, + antecedent_bias=-0.5, + consequent_scale=0, + ): + with pyro.plate("sample", size=3): + with pyro.poutine.trace() as trace_reverse: + model_connected() trace_connected.trace.compute_log_prob trace_independent.trace.compute_log_prob @@ -381,33 +383,68 @@ def model_connected(): Y_values_ind = trace_independent.trace.nodes["Y"]["value"] - if torch.any(Y_values_ind == 1.): - assert trace_independent.trace.nodes["__cause____consequent_Y"]["fn"].log_factor[1,0,0,0,:].sum().exp() == 0. + log_probs_ind = trace_independent.trace.nodes["__cause____consequent_Y"][ + "fn" + ].log_factor + if torch.any(Y_values_ind == 1.0): + assert log_probs_ind[1, 0, 0, 0, :].sum().exp() == 0.0 else: - assert trace_independent.trace.nodes["__cause____consequent_Y"]["fn"].log_factor[1,0,0,0,:].sum().exp() == 1. + assert log_probs_ind[1, 0, 0, 0, :].sum().exp() == 1.0 - assert torch.all(trace_independent.trace.nodes["__cause____consequent_Y"]["fn"].log_factor.sum().exp() == 0) + assert torch.all(log_probs_ind.sum().exp() == 0) - if torch.any(Y_values_ind == 0.): - assert trace_independent.trace.nodes["__cause____consequent_Y"]["fn"].log_factor[2,0,0,0,:].sum().exp() == 0. + if torch.any(Y_values_ind == 0.0): + assert log_probs_ind[2, 0, 0, 0, :].sum().exp() == 0.0 else: - assert trace_independent.trace.nodes["__cause____consequent_Y"]["fn"].log_factor[2,0,0,0,:].sum().exp() == 1. + assert log_probs_ind[2, 0, 0, 0, :].sum().exp() == 1.0 + + assert torch.all( + trace_connected.trace.nodes["__cause____consequent_Y"]["fn"].log_factor.sum() + == 0 + ) - Y_values_con = trace_connected.trace.nodes["Y"]["value"] - assert torch.all(trace_connected.trace.nodes["__cause____consequent_Y"]["fn"].log_factor.sum() == 0) - X_values_rev = trace_reverse.trace.nodes["X"]["value"] - if torch.any(X_values_rev == 1.): - assert trace_reverse.trace.nodes["__cause____consequent_X"]["fn"].log_factor[1,0,0,0,:].sum().exp() == 0. + if torch.any(X_values_rev == 1.0): + assert ( + trace_reverse.trace.nodes["__cause____consequent_X"]["fn"] + .log_factor[1, 0, 0, 0, :] + .sum() + .exp() + == 0.0 + ) else: - assert trace_reverse.trace.nodes["__cause____consequent_X"]["fn"].log_factor[1,0,0,0,:].sum().exp() == 1. + assert ( + trace_reverse.trace.nodes["__cause____consequent_X"]["fn"] + .log_factor[1, 0, 0, 0, :] + .sum() + .exp() + == 1.0 + ) - if torch.any(X_values_rev == 0.): - assert trace_reverse.trace.nodes["__cause____consequent_X"]["fn"].log_factor[2,0,0,0,:].sum().exp() == 0. + if torch.any(X_values_rev == 0.0): + assert ( + trace_reverse.trace.nodes["__cause____consequent_X"]["fn"] + .log_factor[2, 0, 0, 0, :] + .sum() + .exp() + == 0.0 + ) else: - assert trace_reverse.trace.nodes["__cause____consequent_X"]["fn"].log_factor[2,0,0,0,:].sum().exp() == 1. + assert ( + trace_reverse.trace.nodes["__cause____consequent_X"]["fn"] + .log_factor[2, 0, 0, 0, :] + .sum() + .exp() + == 1.0 + ) + + assert torch.all( + trace_reverse.trace.nodes["__cause____consequent_X"]["fn"] + .log_factor.sum() + .exp() + == 0 + ) - assert torch.all(trace_reverse.trace.nodes["__cause____consequent_X"]["fn"].log_factor.sum().exp() == 0) # X -> Z, Y -> Z def model_three_converge(): @@ -416,6 +453,7 @@ def model_three_converge(): Z = pyro.sample("Z", dist.Bernoulli(torch.min(X, Y))) return {"X": X, "Y": Y, "Z": Z} + # X -> Y, X -> Z def model_three_diverge(): X = pyro.sample("X", dist.Bernoulli(0.5)) @@ -423,6 +461,7 @@ def model_three_diverge(): Z = pyro.sample("Z", dist.Bernoulli(X)) return {"X": X, "Y": Y, "Z": Z} + # X -> Y -> Z def model_three_chain(): X = pyro.sample("X", dist.Bernoulli(0.5)) @@ -430,6 +469,7 @@ def model_three_chain(): Z = pyro.sample("Z", dist.Bernoulli(Y)) return {"X": X, "Y": Y, "Z": Z} + # X -> Y, X -> Z, Y -> Z def model_three_complete(): X = pyro.sample("X", dist.Bernoulli(0.5)) @@ -437,6 +477,7 @@ def model_three_complete(): Z = pyro.sample("Z", dist.Bernoulli(torch.max(X, Y))) return {"X": X, "Y": Y, "Z": Z} + # X -> Y Z def model_three_isolate(): X = pyro.sample("X", dist.Bernoulli(0.5)) @@ -444,6 +485,7 @@ def model_three_isolate(): Z = pyro.sample("Z", dist.Bernoulli(0.5)) return {"X": X, "Y": Y, "Z": Z} + # X Y Z def model_three_independent(): X = pyro.sample("X", dist.Bernoulli(0.5)) @@ -451,14 +493,25 @@ def model_three_independent(): Z = pyro.sample("Z", dist.Bernoulli(0.5)) return {"X": X, "Y": Y, "Z": Z} + @pytest.mark.parametrize("ante_cons", [("X", "Y", "Z"), ("X", "Z", "Y")]) -@pytest.mark.parametrize("model", [model_three_converge, model_three_diverge, model_three_chain, model_three_complete, model_three_isolate, model_three_independent]) +@pytest.mark.parametrize( + "model", + [ + model_three_converge, + model_three_diverge, + model_three_chain, + model_three_complete, + model_three_isolate, + model_three_independent, + ], +) def test_eq_neq_three_variables(model, ante_cons): - ante1, ante2, cons = ante_cons + ante1, ante2, cons = ante_cons with ExtractSupports() as supports: model() - with MultiWorldCounterfactual() as mwc: + with MultiWorldCounterfactual() as mwc: with SearchForExplanation( supports=supports.supports, antecedents={ante1: torch.tensor(1.0), ante2: torch.tensor(1.0)}, @@ -480,9 +533,9 @@ def test_eq_neq_three_variables(model, ante_cons): assert log_probs.shape == torch.Size([3, 3, 1, 1, 1, 1]) - fact_worlds = IndexSet(**{name : {0} for name in [ante1, ante2]}) - nec_worlds = IndexSet(**{name : {1} for name in [ante1, ante2]}) - suff_worlds = IndexSet(**{name : {2} for name in [ante1, ante2]}) + fact_worlds = IndexSet(**{name: {0} for name in [ante1, ante2]}) + nec_worlds = IndexSet(**{name: {1} for name in [ante1, ante2]}) + suff_worlds = IndexSet(**{name: {2} for name in [ante1, ante2]}) with mwc: fact_lp = gather(log_probs, fact_worlds) assert fact_lp.exp().item() == 1 From dde4d36324055d76ad08d4d5e715b714cae9caa7 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Wed, 7 Aug 2024 10:06:50 -0400 Subject: [PATCH 023/111] reverted metadata --- docs/source/dynamical_intro.ipynb | 2 +- docs/source/explainable_categorical.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/dynamical_intro.ipynb b/docs/source/dynamical_intro.ipynb index 27577bde..2624e858 100644 --- a/docs/source/dynamical_intro.ipynb +++ b/docs/source/dynamical_intro.ipynb @@ -1330,7 +1330,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.8.16" } }, "nbformat": 4, diff --git a/docs/source/explainable_categorical.ipynb b/docs/source/explainable_categorical.ipynb index b9c14154..93d73df4 100644 --- a/docs/source/explainable_categorical.ipynb +++ b/docs/source/explainable_categorical.ipynb @@ -878,7 +878,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.10.9" } }, "nbformat": 4, From 75e9f05a705e23bc3901ab345589daf5ad75ea5f Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Thu, 8 Aug 2024 16:17:21 -0400 Subject: [PATCH 024/111] ground truth for conditioning on deterministic node --- docs/source/explainable_categorical.ipynb | 94 +- .../explainable_categorical_alternate.ipynb | 899 ++++++++++++++++++ 2 files changed, 931 insertions(+), 62 deletions(-) create mode 100644 docs/source/explainable_categorical_alternate.ipynb diff --git a/docs/source/explainable_categorical.ipynb b/docs/source/explainable_categorical.ipynb index 93d73df4..2bae0454 100644 --- a/docs/source/explainable_categorical.ipynb +++ b/docs/source/explainable_categorical.ipynb @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 111, "metadata": {}, "outputs": [ { @@ -108,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 112, "metadata": {}, "outputs": [], "source": [ @@ -160,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 113, "metadata": {}, "outputs": [ { @@ -169,88 +169,57 @@ "\n", "\n", - "\n", - "\n", - "\n", - "\n", - "%3\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", "\n", "u_match_dropped\n", - "\n", - "u_match_dropped\n", + "\n", + "u_match_dropped\n", "\n", "\n", "\n", "match_dropped\n", - "\n", - "match_dropped\n", - "\n", - "\n", - "\n", - "u_match_dropped->match_dropped\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "forest_fire\n", - "\n", - "forest_fire\n", - "\n", - "\n", - "\n", - "u_match_dropped->forest_fire\n", - "\n", - "\n", + "\n", + "match_dropped\n", "\n", "\n", "\n", "u_lightning\n", - "\n", - "u_lightning\n", + "\n", + "u_lightning\n", "\n", "\n", "\n", "lightning\n", - "\n", - "lightning\n", - "\n", - "\n", - "\n", - "u_lightning->lightning\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "u_lightning->forest_fire\n", - "\n", - "\n", + "\n", + "lightning\n", "\n", "\n", "\n", "smile\n", - "\n", - "smile\n", + "\n", + "smile\n", "\n", - "\n", - "\n", - "smile->forest_fire\n", - "\n", - "\n", + "\n", + "\n", + "forest_fire\n", + "\n", + "forest_fire\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 3, + "execution_count": 113, "metadata": {}, "output_type": "execute_result" } @@ -306,14 +275,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 114, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.3002)\n" + "tensor(0.3102)\n" ] } ], @@ -340,14 +309,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 129, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(1.0013e-05)\n" + "tensor(0.4234)\n" ] } ], @@ -358,9 +327,10 @@ " consequents={\"forest_fire\": torch.tensor(1.0)},\n", " witnesses={}, # potential context elements, we leave them empty for now\n", " alternatives={\"match_dropped\": torch.tensor(0.0)},\n", + " antecedent_bias=-0.5,\n", " consequent_scale=1e-5,\n", ")(condition(\n", - " data={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)}\n", + " data={\"u_match_dropped\": torch.tensor(1.0)}\n", ")(forest_fire_model))\n", "\n", "logp, trace, mwc = importance_infer(num_samples=10000)(query)()\n", diff --git a/docs/source/explainable_categorical_alternate.ipynb b/docs/source/explainable_categorical_alternate.ipynb new file mode 100644 index 00000000..d3b81248 --- /dev/null +++ b/docs/source/explainable_categorical_alternate.ipynb @@ -0,0 +1,899 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Explainable reasoning with ChiRho (categorical variables)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The **Explainable Reasoning with ChiRho** package aims to provide a systematic, unified approach to causal explanation computations in terms of different probabilistic queries over expanded causal models that are constructed from a single generic program transformation applied to an arbitrary causal model represented as a ChiRho program. The approach of reducing causal queries to probabilistic computations on transformed causal models is the foundational idea behind all of ChiRho. The key strategy underlying \"causal explanation\" queries is their use of auxiliary variables representing uncertainty about what the proposed interventions are and which interventions or preemptions to apply, implicitly inducing a search space over counterfactuals.\n", + "\n", + "The goal of this notebook is to illustrate how the package can be used to provide an approximate method of answering a range of causal explanation queries with respect to models in which categorical variables play the key role. As the key tool will involve sampling-based posterior probability estimation, a lot of what will be said *mutatis mutandis* applies to more general settings where variables are continuous (to which we will devote another tutorial).\n", + "\n", + "In yet [another notebook](https://basisresearch.github.io/chirho/actual_causality.html) we illustrate how the module allows for a faithful reconstruction of a particular notion of local explanation (the so-called Halpern-Pearl modified definition of actual causality [(J. Halpern, MIT Press, 2016)](https://mitpress.mit.edu/9780262537131/actual-causality/)), which inspired some of the conceptual steps underlying the current implementation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Outline**\n", + "\n", + "[Causal explanation and counterfactual thinking](#causal-explanation-and-counterfactual-thinking) \n", + "\n", + "\n", + "[Witness nodes and context sensitivity](#witness-nodes-and-context-sensitivity)\n", + "\n", + "[Probability of causation and responsibility](#probability-of-causation-and-responsibility)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Causal explanation and counterfactual thinking" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Consider the following causality-related queries:\n", + "\n", + "- **Friendly Fire:** On March 24, 2002, A B-52 bomber fired a Joint Direct Attack Munition at a US battalion command post, killing three and injuring twenty special forces soldiers. Out of multiple potential contributing factors, which were actually responsible for the incident?\n", + "\n", + "- **Overshoot:** In dealing with an epidemic, multiple different policies were imposed, leading to the overshoot (the number of those who became infected after the peak of the epidemic) rising from around 15% in the unintervened model to around 25%. Which of the policies caused the overshoot and to what extent?\n", + "\n", + "- **Explainable AI:** Your pre-trial release has been refused based on your [COMPAS score](https://en.wikipedia.org/wiki/COMPAS_(software)). The decision was made using a proprietary predictive model. All you have access to is the questionnaire that was used, and perhaps some demographic information about a class of human beings subjected to this evaluation. But which of these factors resulted in your score being what it is, and what were their contributions?\n", + "\n", + "\n", + "Questions of this sort are more local than those pertaining to average treatment effects, as they pertain to actual cases that come with their own contexts. Being able to answer them is useful for understanding how we can prevent undesirable outcomes similar to ones that we have observed, or promote the occurrence of desirable outcomes in contexts similar to the ones in which they had been observed. These context-sensitive causality questions are also an essential element of blame and responsibility assignments. If the phenomenon we're trying to explain is the behavior of a predictive model, we are dealing with a problem in explainable AI; but the underlying intuition behind the workings of **Explainable Reasoning with ChiRho** is that causally explaining the behavior of an opaque model is not that much different from providing a causal explanation of other real-world phenomena: we need to address such queries in a principled manner employing some approximate but hopefully reliable causal model of how things work (be that events outside of computers, or a predicitive model's behavior). **Explainable Reasoning with ChiRho** package aims to provide a unified general approach to the relevant causal explanation computations.\n", + "\n", + "At some level of generality, a useful point of departure is a general counterfactual one. On one hand, we can ask whether the event would have occurred had a given candidate cause not taken place. This is sometimes called the *but-for test*, has a tradition of being used as a tool for answering causality and attribution queries. \n", + "\n", + "- It is often used in [the law of torts](https://plato.stanford.edu/entries/causation-law/) to determine if a defendant's conduct was the cause of a particular harm. The test is often formulated as follows: \"But for the defendant's conduct, would the harm have occurred?\" \n", + "- A major philosophical position in the analysis of causality is that the definition of causal dependence should be formulated in terms of counterfactual conditionals (Lewis, 1973. “Causation”, Journal of Philosophy, 70: 556–67). On this approach, $e$ causally depends on $c$ if and only if, if $c$ were not to occur $e$ would not occur. (The view does not remain uncontested, see the [SEP entry on counterfactual theories of causation](https://plato.stanford.edu/entries/causation-counterfactual/)).\n", + "- At least a few major approaches to explainable AI (such as [LIME](https://arxiv.org/abs/1602.04938), or [Shapley values](https://papers.nips.cc/paper_files/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html)) are based on the idea that explanations can be obtained by perturbing or shifting the input values and observing the changes in the output. This to a large extent can be thought of as a way of evaluating the but-for condition: if the input value was different, would the output value change? \n", + " \n", + "More generally, we can ask about the probability with which an alterantive intervention would lead to a cahnge in the outcome (perhaps while conditioning on other items of information), in line with the ideas present in Pearl's *Probabilities of causation...* and Chapter 9 of Pearl's *Causality*. While immensely useful, the but-for condition is not fine-grained enough to answer all the questions we are interested in or to give us the intended answers in cases in which the underlying causal model is non-trivial. We will illustrate this observation in this tutorial. \n", + "\n", + "\n", + "On the other hand, we can ask whether given our model (and perhaps conditioning on other pieces of information we posses), intervening on a given candidate cause to have a given value results in the outcome being as observed (or, more generally, the probability of that outcome being as observed) - this is conceptually similar to Pearl's probability of sufficiency. \n", + "\n", + "We will start with these two approaches, but soon we will notice that often our explanatory questions are more local and a more fine-grained tool is needed. The general intuition (inspired by Halpern's *Actual Causality*) that we implemented is that when we ask local explanatory questions, we need to keep some part of the actual context fixed and consider alternative scenarios insofar as potential causes are involved. That is, we (i) search through possible alternative interventions that could be performed on the candidate cause nodes, (ii) search through possible context nodes that are to be intervened to be at their factual values even in the counterfactual worlds, (iii) see how these options play out in intervened worlds, and (iv) investigate and meaningfully summarize what happens with the outcome nodes of interest in all those counterfactual worlds. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start with a very simple model, in which a forest fire can be caused by exactly one of two things: a match being dropped (`match_dropped`), or a lightning strike (`lightning`), and either of these factors alone is already deterministically sufficient for the `forest_fire` to occur. A match being dropped is more likely than a lightning strike (we use fairly large probabilities for the sake of example transparency). For the sake of illustration, we also include a causally irrelevant site representing whether a ChiRho developer smiles, `smile`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: CUDA_VISIBLE_DEVICES=-1\n" + ] + } + ], + "source": [ + "%env CUDA_VISIBLE_DEVICES=-1\n", + "from typing import Callable, Dict, List, Optional\n", + "\n", + "import math\n", + "import pyro\n", + "import pyro.distributions as dist\n", + "import pyro.distributions.constraints as constraints\n", + "import torch\n", + "from chirho.counterfactual.handlers.counterfactual import \\\n", + " MultiWorldCounterfactual\n", + "from chirho.explainable.handlers import ExtractSupports, SearchForExplanation\n", + "from chirho.indexed.ops import IndexSet, gather\n", + "from chirho.observational.handlers import condition\n", + "\n", + "pyro.settings.set(module_local_params=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def importance_infer(\n", + " model: Optional[Callable] = None, *, num_samples: int\n", + "):\n", + " \n", + " if model is None:\n", + " return lambda m: importance_infer(m, num_samples=num_samples)\n", + "\n", + " def _wrapped_model(\n", + " *args,\n", + " **kwargs\n", + " ):\n", + "\n", + " guide = pyro.poutine.block(hide_fn=lambda msg: msg[\"is_observed\"])(model)\n", + "\n", + " max_plate_nesting = 9 # TODO guess\n", + "\n", + " with pyro.poutine.block(), MultiWorldCounterfactual() as mwc:\n", + " log_weights, importance_tr, _ = pyro.infer.importance.vectorized_importance_weights(\n", + " model,\n", + " guide,\n", + " *args,\n", + " num_samples=num_samples,\n", + " max_plate_nesting=max_plate_nesting,\n", + " normalized=False,\n", + " **kwargs\n", + " )\n", + "\n", + " return torch.logsumexp(log_weights, dim=0) - math.log(num_samples), importance_tr, mwc\n", + "\n", + " return _wrapped_model" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "u_match_dropped\n", + "\n", + "u_match_dropped\n", + "\n", + "\n", + "\n", + "match_dropped\n", + "\n", + "match_dropped\n", + "\n", + "\n", + "\n", + "u_lightning\n", + "\n", + "u_lightning\n", + "\n", + "\n", + "\n", + "lightning\n", + "\n", + "lightning\n", + "\n", + "\n", + "\n", + "smile\n", + "\n", + "smile\n", + "\n", + "\n", + "\n", + "forest_fire\n", + "\n", + "forest_fire\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def forest_fire_model():\n", + " u_match_dropped = pyro.sample(\"u_match_dropped\", dist.Bernoulli(0.7))\n", + " match_dropped = pyro.deterministic(\n", + " \"match_dropped\", u_match_dropped, event_dim=0\n", + " ) # notice uneven probs here\n", + "\n", + " u_lightning = pyro.sample(\"u_lightning\", dist.Bernoulli(0.4))\n", + " lightning = pyro.deterministic(\"lightning\", u_lightning, event_dim=0)\n", + "\n", + " # this is a causally irrelevant site\n", + " smile = pyro.sample(\"smile\", dist.Bernoulli(0.5))\n", + "\n", + " forest_fire = pyro.deterministic(\n", + " \"forest_fire\", torch.max(match_dropped, lightning) + (0 * smile), event_dim=0\n", + " )\n", + "\n", + " return {\n", + " \"match_dropped\": match_dropped,\n", + " \"lightning\": lightning,\n", + " \"forest_fire\": forest_fire,\n", + " }\n", + "\n", + "with ExtractSupports() as extract_supports:\n", + " forest_fire_model()\n", + " forest_fire_supports = {k: constraints.boolean for k in extract_supports.supports}\n", + "\n", + "pyro.render_model(forest_fire_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Throughout this tutorial, we assume all nodes are binary and use $'$ as negation. Once we specify (i) the distributions for the nodes we use (`supports`), (ii) candidate causes $X_i = x_i$ (`antecedents`) (iii) their alternative values ($X_i = x_i'$), (iv) elements of the current context (`witnesses`), and (v) the `consequents` of interest $Y=y$. The `SearchForExplanation` handler transforms the original model into one in which interventions and alternative interventions on the antecedents are applied in parallel counterfactual worlds stochastically preempted and context elements are stochastically selected and preempted to be kept at the factual values in all counterfactual worlds.\n", + "\n", + "First, let's go back to our original query. Let $F$ be the `forest_fire`, $f$ stand for $F=1$, $f'$ for $F=0$, $M$ stand for `match_dropped`, with analogous conventions. We also place interventions conditioned on in subscripts, so that, for example\n", + "$P(f_{m'})$ stands for $P(F=1\\vert do(M=0))$.\n", + "\n", + "We are currently interested in $P(f'_{m'}, f_m)$, that is the probability of both forest fire not occurring if we intervene on the match to not be dropped, and forest fire occurring if we intervene on the match to be dropped." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, suppose we are interested in asking the question of whether dropping a match has causal power over whether the forest fire occurs. We assume all relevant nodes are binary. The potential cause (`antecedent`) we're considering is `match_dropped=1`, we contrast it with what would happen if we intervened on it to not happen (`alternatives`). We are interested in whether an outcome variable (`consequent`) has value 1 under these two interventions. The counterfactual world in which we intervene with `alternatives` is world 1, and the counterfactual world in which we intervene with `antecedents` is world 2. We will be interested in cases in which none of these interventions have been preempted (more about this later), so we will sample with appropriate masks as well." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.2998)\n" + ] + } + ], + "source": [ + "query = SearchForExplanation(\n", + " supports=forest_fire_supports,\n", + " antecedents={\"match_dropped\": torch.tensor(1.0)},\n", + " consequents={\"forest_fire\": torch.tensor(1.0)},\n", + " witnesses={}, # potential context elements, we leave them empty for now\n", + " alternatives={\"match_dropped\": torch.tensor(0.0)},\n", + " consequent_scale=1e-5,\n", + ")(forest_fire_model)\n", + "\n", + "logp, trace, mwc = importance_infer(num_samples=10000)(query)()\n", + "print(torch.exp(logp))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "More interestingly, in cases of overdetermination, a similar estimation would lead us to assign no causal role to any of to co-contributing factors. This can be seen in the context in which both causes occurred. Trivially, if lightning occurred, then had no match been dropped, the forest fire, caused by lighning, would still occur (a symmetric reasoning goes through for the lightning as well), $P(f'_{m'}\\vert m, l) = P(f'_{l'}\\vert m, l)=0$. Intuitively, these quantities are not good guides to the causal role of `match_dropped` and `lightning`, as we think they did played a causal role. This is the first illustration of why the but-for analysis is not fine-grained enough." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.2059)\n" + ] + } + ], + "source": [ + "query = SearchForExplanation(\n", + " supports=forest_fire_supports,\n", + " antecedents={\"match_dropped\": torch.tensor(1.0)},\n", + " consequents={\"forest_fire\": torch.tensor(1.0)},\n", + " witnesses={}, # potential context elements, we leave them empty for now\n", + " alternatives={\"match_dropped\": torch.tensor(0.0)},\n", + " consequent_scale=1e-5,\n", + ")(condition(\n", + " data={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)}\n", + ")(forest_fire_model))\n", + "\n", + "logp, trace, mwc = importance_infer(num_samples=10000)(query)()\n", + "print(torch.exp(logp))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comment: The ground truth example for correct intervention. This gives 0.42 as the answer which is the expected answer." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.4262)\n" + ] + } + ], + "source": [ + "query = SearchForExplanation(\n", + " supports=forest_fire_supports,\n", + " antecedents={\"match_dropped\": torch.tensor(1.0)},\n", + " consequents={\"forest_fire\": torch.tensor(1.0)},\n", + " witnesses={}, # potential context elements, we leave them empty for now\n", + " alternatives={\"match_dropped\": torch.tensor(0.0)},\n", + " antecedent_bias=-0.5,\n", + " consequent_scale=1e-5,\n", + ")(condition(\n", + " data={\"u_match_dropped\": torch.tensor(1.0)}\n", + ")(forest_fire_model))\n", + "\n", + "logp, trace, mwc = importance_infer(num_samples=10000)(query)()\n", + "print(torch.exp(logp))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Witness nodes and context sensitivity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some of these intuitions in the forest fire example may be salvaged by considering a two-membered antecedent set, estimating $P(f'_{m',l'}, f_{m,l})$. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.4375)\n" + ] + } + ], + "source": [ + "query = SearchForExplanation(\n", + " supports=forest_fire_supports,\n", + " antecedents={\"match_dropped\": 1.0, \"lightning\": 1.0},\n", + " consequents={\"forest_fire\": torch.tensor(1.0)},\n", + " witnesses={},\n", + " alternatives={\"match_dropped\": 0.0, \"lightning\": 0.0},\n", + ")(forest_fire_model)\n", + "\n", + "logp, trace, mwc = importance_infer(num_samples=10000)(query)()\n", + "print(torch.exp(logp))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This already suggests a more complicated picture, as it turns out that we need to pay attention to membership in larger antecedent sets that would make a difference (that is one reason why we need stochasticity in antecedent candidate preemption: to search for such sets).\n", + "\n", + "But even then, the but-for analysis does not pay sufficient attention to the granularity of a given problem and its causal structure. There are asymmetric cases where the efficiency of one cause prevents the efficiency of another, in which our causal attributions should also be asymmetric, but \"being a member of the same larger antecedent set\" isn't.\n", + "\n", + "A simple example is breaking a bottle. Suppose Sally and Bob throw a rock at a bottle, and Sally does so a little earlier than Bob. Suppose both are perfectly accurate, and the bottle shatters when hit. Sally hits, and the bottle shatters, but Bob doesn't hit it because the bottle is no longer there. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "%3\n", + "\n", + "\n", + "\n", + "prob_sally_throws\n", + "\n", + "prob_sally_throws\n", + "\n", + "\n", + "\n", + "sally_throws\n", + "\n", + "sally_throws\n", + "\n", + "\n", + "\n", + "prob_sally_throws->sally_throws\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "prob_bill_throws\n", + "\n", + "prob_bill_throws\n", + "\n", + "\n", + "\n", + "bill_throws\n", + "\n", + "bill_throws\n", + "\n", + "\n", + "\n", + "prob_bill_throws->bill_throws\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "prob_sally_hits\n", + "\n", + "prob_sally_hits\n", + "\n", + "\n", + "\n", + "sally_hits\n", + "\n", + "sally_hits\n", + "\n", + "\n", + "\n", + "prob_sally_hits->sally_hits\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "prob_bill_hits\n", + "\n", + "prob_bill_hits\n", + "\n", + "\n", + "\n", + "bill_hits\n", + "\n", + "bill_hits\n", + "\n", + "\n", + "\n", + "prob_bill_hits->bill_hits\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "prob_bottle_shatters_if_sally\n", + "\n", + "prob_bottle_shatters_if_sally\n", + "\n", + "\n", + "\n", + "bottle_shatters\n", + "\n", + "bottle_shatters\n", + "\n", + "\n", + "\n", + "prob_bottle_shatters_if_sally->bottle_shatters\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "prob_bottle_shatters_if_bill\n", + "\n", + "prob_bottle_shatters_if_bill\n", + "\n", + "\n", + "\n", + "prob_bottle_shatters_if_bill->bottle_shatters\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sally_throws->sally_hits\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "bill_throws->bill_hits\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sally_hits->bill_hits\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sally_hits->bottle_shatters\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "bill_hits->bottle_shatters\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def stones_model():\n", + " prob_sally_throws = pyro.sample(\"prob_sally_throws\", dist.Beta(1, 1))\n", + " prob_bill_throws = pyro.sample(\"prob_bill_throws\", dist.Beta(1, 1))\n", + " prob_sally_hits = pyro.sample(\"prob_sally_hits\", dist.Beta(1, 1))\n", + " prob_bill_hits = pyro.sample(\"prob_bill_hits\", dist.Beta(1, 1))\n", + " prob_bottle_shatters_if_sally = pyro.sample(\n", + " \"prob_bottle_shatters_if_sally\", dist.Beta(1, 1)\n", + " )\n", + " prob_bottle_shatters_if_bill = pyro.sample(\n", + " \"prob_bottle_shatters_if_bill\", dist.Beta(1, 1)\n", + " )\n", + "\n", + " sally_throws = pyro.sample(\"sally_throws\", dist.Bernoulli(prob_sally_throws))\n", + " bill_throws = pyro.sample(\"bill_throws\", dist.Bernoulli(prob_bill_throws))\n", + "\n", + " # if Sally throws, she hits with probability prob_sally_hits\n", + " # hits with pr=0 otherwise\n", + " new_shp = torch.where(sally_throws == 1, prob_sally_hits, 0.0)\n", + "\n", + " sally_hits = pyro.sample(\"sally_hits\", dist.Bernoulli(new_shp))\n", + "\n", + " # if Bill throws, he hits with probability prob_bill_hits\n", + " # if sally doesn't hit sooner,\n", + " # misses otherwise\n", + " new_bhp = torch.where(\n", + " bill_throws.bool() & (~sally_hits.bool()),\n", + " prob_bill_hits,\n", + " torch.tensor(0.0),\n", + " )\n", + "\n", + " bill_hits = pyro.sample(\"bill_hits\", dist.Bernoulli(new_bhp))\n", + "\n", + " # you can use a analogous move to model the bottle shattering\n", + " # if being hit by a stone doesn't deterministically\n", + " # shatter the bottle\n", + " new_bsp = torch.where(\n", + " bill_hits.bool(),\n", + " prob_bottle_shatters_if_bill,\n", + " torch.where(\n", + " sally_hits.bool(),\n", + " prob_bottle_shatters_if_sally,\n", + " torch.tensor(0.0),\n", + " ),\n", + " )\n", + "\n", + " bottle_shatters = pyro.sample(\"bottle_shatters\", dist.Bernoulli(new_bsp))\n", + "\n", + " return {\n", + " \"sally_throws\": sally_throws,\n", + " \"bill_throws\": bill_throws,\n", + " \"sally_hits\": sally_hits,\n", + " \"bill_hits\": bill_hits,\n", + " \"bottle_shatters\": bottle_shatters,\n", + " }\n", + "\n", + "\n", + "with ExtractSupports() as extract_supports:\n", + " stones_model()\n", + " stones_supports = {k: constraints.boolean if not k.startswith(\"prob_\") else v for k, v in extract_supports.supports.items()}\n", + "\n", + "pyro.render_model(stones_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For now let us assume that the relevant probabilities are 1 (this forces both Sally and Bill to throw stones, makes them perfectly accurate and makes the bottle always shatter if hit). Let us start with the type of analysis we performed for the forest fire case. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(1.0013e-05)\n" + ] + } + ], + "source": [ + "query = SearchForExplanation(\n", + " supports=stones_supports,\n", + " antecedents={\"sally_throws\": torch.tensor(1.0)},\n", + " consequents={\"bottle_shatters\": torch.tensor(1.0)},\n", + " witnesses={},\n", + " alternatives={\"sally_throws\": torch.tensor(0.0)},\n", + " consequent_scale=1e-5,\n", + ")(condition(\n", + " data={\n", + " \"prob_sally_throws\": torch.tensor(1.0),\n", + " \"prob_bill_throws\": torch.tensor(1.0),\n", + " \"prob_sally_hits\": torch.tensor(1.0),\n", + " \"prob_bill_hits\": torch.tensor(1.0),\n", + " \"prob_bottle_shatters_if_sally\": torch.tensor(1.0),\n", + " \"prob_bottle_shatters_if_bill\": torch.tensor(1.0),\n", + " }\n", + ")(stones_model))\n", + "\n", + "logp, trace, mwc = importance_infer(num_samples=10000)(query)()\n", + "print(torch.exp(logp))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sally's throw does not satisfy the but-for condition: if she hadn't thrown the rock, the bottle would still have shattered. Of course, the combined event of Sally throwing a rock and Bob throwing a rock is a but-for cause of the bottle shattering. But that doesn't capture the clear asymmetry at work here. Intuitively, Sally's throw is the (actual) cause of the bottle breaking in a way that Bob's throw isn't. Sally's throw actually caused the bottle to shatter and Bob's throw didn't, in part because Bob's stone didn't actually hit the bottle." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An intuitive solution to the problem, inspired by the Pearl-Halpern definition of actual causality (which we discuss in [another notebook](https://basisresearch.github.io/chirho/actual_causality.html)) is to say that **in answering actual causality queries, we need to consider what happens when part of the actual context is kept fixed.** For instance, in the bottle shattering example, given the observed fact that Bob’s stone didn’t hit, in the counterfactual world in which we keep this observed fact fixed, if Sally nad not thrown the stone, the bottle in fact would not have shattered. \n", + "\n", + "\n", + "For this reason, our handler allows not only stochastic preemption of interventions (to approximate the search through possible antecedent sets) but also stochastic witness preemption of those nodes that are considered part of the context (these needn't exclude each other). In a witness preemption, we ensure that the counterfactual value is identical to the factual one (and by applying it randomly to candidate witness nodes, we approximate a search through all possible context sets)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.2521)\n" + ] + } + ], + "source": [ + "query = SearchForExplanation(\n", + " supports=stones_supports,\n", + " antecedents={\"sally_throws\": torch.tensor(1.0)},\n", + " consequents={\"bottle_shatters\": torch.tensor(1.0)},\n", + " witnesses={\"bill_hits\": None},\n", + " alternatives={\"sally_throws\": torch.tensor(0.0)},\n", + ")(condition(\n", + " data={\n", + " \"prob_sally_throws\": torch.tensor(1.0),\n", + " \"prob_bill_throws\": torch.tensor(1.0),\n", + " \"prob_sally_hits\": torch.tensor(1.0),\n", + " \"prob_bill_hits\": torch.tensor(1.0),\n", + " \"prob_bottle_shatters_if_sally\": torch.tensor(1.0),\n", + " \"prob_bottle_shatters_if_bill\": torch.tensor(1.0),\n", + " }\n", + ")(stones_model))\n", + "\n", + "logp, trace, mwc = importance_infer(num_samples=10000)(query)()\n", + "print(torch.exp(logp))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Admittedly, our search through contexts is very simple and degenerate, as the only part of the actual context which stochastically is kept fixed at the factual value is `bill_hits`. But already with this search, sally throwing is diagnosed as having non-null probability. In fact, the definition of actual causality in Halpern's book (*Actual causality*) contains an existential quantifier: a variable is an actual cause if there is at least one context in which a change in the outcome variable would result from changing the antecedent to have an alternative value, so our search provides a correct diagnosis here.\n", + "\n", + "Crucally, as intended, an analogous inference for whether `bill_throws` is a cause of the bottle shattering, yields a different\n", + "result and assigns null causal role to bill." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(1.0013e-05)\n" + ] + } + ], + "source": [ + "query = SearchForExplanation(\n", + " supports=stones_supports,\n", + " antecedents={\"bill_throws\": torch.tensor(1.0)},\n", + " consequents={\"bottle_shatters\": torch.tensor(1.0)},\n", + " witnesses={\"sally_hits\": None},\n", + " alternatives={\"bill_throws\": torch.tensor(0.0)},\n", + " consequent_scale=1e-5,\n", + ")(condition(\n", + " data={\n", + " \"prob_sally_throws\": torch.tensor(1.0),\n", + " \"prob_bill_throws\": torch.tensor(1.0),\n", + " \"prob_sally_hits\": torch.tensor(1.0),\n", + " \"prob_bill_hits\": torch.tensor(1.0),\n", + " \"prob_bottle_shatters_if_sally\": torch.tensor(1.0),\n", + " \"prob_bottle_shatters_if_bill\": torch.tensor(1.0),\n", + " }\n", + ")(stones_model))\n", + "\n", + "logp, trace, mwc = importance_infer(num_samples=10000)(query)()\n", + "print(torch.exp(logp))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Probability of causation and responsibility" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We might use non-trivial probabilities and be interested in more involved queries. Suppose we aren't sure what part of the context we want to hold fixed, allowing both `sally_hits` and `bill_hits` to be witness candidates, so we attach equal weights to all four possible context sets. \n", + "\n", + "Suppose also that beyond knowing the non-degenerate probabilities involved, we don't know who threw the stone, and we only observed the bottle has been shattered. We can use the handler to estimate the answer to a somewhat different question involving the probabilities that changing the value of `sally_throws` or changing the value of `billy_throws` (whatever these are in the factual world) would lead to a change in the outcome variables, and that fixing them to be at the factual values would result in the outcome variable having the same value. We also allow both `sally_hits` and `bill_hits` as potential witnesses.\n", + "\n", + "For example, we can sample to estimate quantities such as the fraction of possible causes of the bottle shattering in which Sally and Billy are each responsibile:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Degree of responsibility of Sally: tensor(0.7581)\n", + "Degree of responsibility of Billy: tensor(0.6069)\n" + ] + } + ], + "source": [ + "query = SearchForExplanation(\n", + " supports=stones_supports,\n", + " antecedents={\"sally_throws\": None, \"bill_throws\": None},\n", + " consequents={\"bottle_shatters\": torch.tensor(1.0)},\n", + " witnesses={\"sally_hits\": None, \"bill_hits\": None},\n", + ")(condition(\n", + " data={\n", + " \"prob_sally_throws\": torch.tensor(0.8),\n", + " \"prob_bill_throws\": torch.tensor(0.7),\n", + " \"prob_sally_hits\": torch.tensor(0.9),\n", + " \"prob_bill_hits\": torch.tensor(0.8),\n", + " \"prob_bottle_shatters_if_sally\": torch.tensor(0.9),\n", + " \"prob_bottle_shatters_if_bill\": torch.tensor(0.8),\n", + " \"bottle_shatters\": torch.tensor(1.0),\n", + " }\n", + ")(stones_model))\n", + "\n", + "logp, trace, mwc = importance_infer(num_samples=20000)(query)()\n", + "\n", + "nodes = trace.nodes[\"_RETURN\"][\"value\"]\n", + "with mwc:\n", + " st_responsible = gather(nodes[\"sally_throws\"], IndexSet(sally_throws={1})) != \\\n", + " gather(nodes[\"sally_throws\"], IndexSet(sally_throws={2}))\n", + " bt_responsible = gather(nodes[\"bill_throws\"], IndexSet(bill_throws={1})) != \\\n", + " gather(nodes[\"bill_throws\"], IndexSet(bill_throws={2}))\n", + "\n", + "print(\"Degree of responsibility of Sally:\", st_responsible.sum() / st_responsible.numel())\n", + "print(\"Degree of responsibility of Billy:\", bt_responsible.sum() / bt_responsible.numel())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we assumed Sally to be more likely to throw, more likely to hit, and more likely to shatter the bottle if she hits. For this reason, we expect her to be more likely to be causally responsible for the outcome. Conceptually, these estimates are impacted by some hyperparameters, such as witness preemption probabilities, so perhaps a bit more clarity on can be gained if we think we have a complete list of potential causes and normalize. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pyro1.9", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 7e2501a8eb6ebb1b9751f79e0d48d72e1f44543b Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Thu, 8 Aug 2024 18:48:15 -0400 Subject: [PATCH 025/111] responsibility debug --- .../explainable_categorical_alternate.ipynb | 451 ++++++++++++++---- 1 file changed, 354 insertions(+), 97 deletions(-) diff --git a/docs/source/explainable_categorical_alternate.ipynb b/docs/source/explainable_categorical_alternate.ipynb index d3b81248..2c6f8bab 100644 --- a/docs/source/explainable_categorical_alternate.ipynb +++ b/docs/source/explainable_categorical_alternate.ipynb @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 154, "metadata": {}, "outputs": [ { @@ -102,13 +102,14 @@ "from chirho.explainable.handlers import ExtractSupports, SearchForExplanation\n", "from chirho.indexed.ops import IndexSet, gather\n", "from chirho.observational.handlers import condition\n", + "from chirho.observational.handlers.soft_conditioning import soft_eq, KernelSoftConditionReparam\n", "\n", "pyro.settings.set(module_local_params=True)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 250, "metadata": {}, "outputs": [], "source": [ @@ -139,14 +140,30 @@ " **kwargs\n", " )\n", "\n", - " return torch.logsumexp(log_weights, dim=0) - math.log(num_samples), importance_tr, mwc\n", + " # # resample using importance weights to get posterior samples\n", + " # idx = dist.Categorical(logits=log_weights).sample((num_samples,))\n", + " # for name, node in importance_tr.nodes.items():\n", + " # if node[\"type\"] != \"sample\" or pyro.poutine.util.site_is_subsample(node) or node[\"is_observed\"]:\n", + " # continue\n", + " # importance_tr.nodes[name][\"value\"] = torch.index_select(\n", + " # importance_tr.nodes[name][\"value\"],\n", + " # -max_plate_nesting - 1 - len(importance_tr.nodes[name][\"fn\"].event_shape),\n", + " # idx,\n", + " # )\n", + "\n", + " print(log_weights)\n", + "\n", + " # with pyro.poutine.replay(trace=importance_tr), mwc:\n", + " # trace = pyro.poutine.trace(model).get_trace(*args, **kwargs)\n", + "\n", + " return torch.logsumexp(log_weights, dim=0) - math.log(num_samples), importance_tr, mwc, log_weights\n", "\n", " return _wrapped_model" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 156, "metadata": {}, "outputs": [ { @@ -202,10 +219,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 13, + "execution_count": 156, "metadata": {}, "output_type": "execute_result" } @@ -261,14 +278,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 157, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2998)\n" + "tensor(0.3000)\n" ] } ], @@ -282,10 +299,29 @@ " consequent_scale=1e-5,\n", ")(forest_fire_model)\n", "\n", - "logp, trace, mwc = importance_infer(num_samples=10000)(query)()\n", + "logp, trace, mwc = importance_infer(num_samples=10)(query)()\n", "print(torch.exp(logp))" ] }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([10, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n", + "torch.Size([10, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n" + ] + } + ], + "source": [ + "print(trace.nodes[\"match_dropped\"][\"value\"].shape)\n", + "print(trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"].shape)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -295,14 +331,14 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 159, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2059)\n" + "tensor(1.0013e-05)\n" ] } ], @@ -331,18 +367,48 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 160, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.4262)\n" + "{'u_match_dropped': Boolean(), 'match_dropped': Boolean(), 'u_lightning': Boolean(), 'lightning': Boolean(), 'smile': Boolean(), 'forest_fire': Boolean()}\n" ] } ], "source": [ + "print(forest_fire_supports)" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.4186)\n" + ] + } + ], + "source": [ + "\n", + "import math\n", + "# reparam_config = AutoSoftConditioning(scale=math.sqrt(1/(2*math.pi)))\n", + "# def _soft_eq(v1: torch.Tensor, v2: torch.Tensor) -> torch.Tensor:\n", + "# return soft_eq(constraints.boolean, v1, v2, scale=scale)\n", + "# reparam_config = {\"match_dropped\": KernelSoftConditionReparam(_soft_eq)}\n", + "\n", + "def _soft_eq(v1: torch.Tensor, v2: torch.Tensor) -> torch.Tensor:\n", + " return soft_eq(constraints.boolean, v1, v2, scale=0.001)\n", + "\n", + "reparam_config = {\"match_dropped\": KernelSoftConditionReparam(_soft_eq)}\n", + "\n", + "\n", "query = SearchForExplanation(\n", " supports=forest_fire_supports,\n", " antecedents={\"match_dropped\": torch.tensor(1.0)},\n", @@ -351,11 +417,13 @@ " alternatives={\"match_dropped\": torch.tensor(0.0)},\n", " antecedent_bias=-0.5,\n", " consequent_scale=1e-5,\n", - ")(condition(\n", - " data={\"u_match_dropped\": torch.tensor(1.0)}\n", - ")(forest_fire_model))\n", + ")(\n", + " pyro.poutine.reparam(config=reparam_config)(\n", + " condition(data={\"match_dropped\": torch.tensor(1.0)})\n", + " (forest_fire_model)\n", + "))\n", "\n", - "logp, trace, mwc = importance_infer(num_samples=10000)(query)()\n", + "logp, trace, mwc = importance_infer(num_samples=100000)(query)()\n", "print(torch.exp(logp))" ] }, @@ -375,14 +443,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 97, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.4375)\n" + "tensor(0.4616)\n" ] } ], @@ -412,7 +480,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 98, "metadata": {}, "outputs": [ { @@ -421,154 +489,153 @@ "\n", "\n", - "\n", - "\n", - "\n", + "\n", + "\n", "\n", - "%3\n", - "\n", + "\n", "\n", "\n", "prob_sally_throws\n", - "\n", - "prob_sally_throws\n", + "\n", + "prob_sally_throws\n", "\n", "\n", "\n", "sally_throws\n", - "\n", - "sally_throws\n", + "\n", + "sally_throws\n", "\n", "\n", "\n", "prob_sally_throws->sally_throws\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "prob_bill_throws\n", - "\n", - "prob_bill_throws\n", + "\n", + "prob_bill_throws\n", "\n", "\n", "\n", "bill_throws\n", - "\n", - "bill_throws\n", + "\n", + "bill_throws\n", "\n", "\n", "\n", "prob_bill_throws->bill_throws\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "prob_sally_hits\n", - "\n", - "prob_sally_hits\n", + "\n", + "prob_sally_hits\n", "\n", "\n", "\n", "sally_hits\n", - "\n", - "sally_hits\n", + "\n", + "sally_hits\n", "\n", "\n", "\n", "prob_sally_hits->sally_hits\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "prob_bill_hits\n", - "\n", - "prob_bill_hits\n", + "\n", + "prob_bill_hits\n", "\n", "\n", "\n", "bill_hits\n", - "\n", - "bill_hits\n", + "\n", + "bill_hits\n", "\n", "\n", "\n", "prob_bill_hits->bill_hits\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "prob_bottle_shatters_if_sally\n", - "\n", - "prob_bottle_shatters_if_sally\n", + "\n", + "prob_bottle_shatters_if_sally\n", "\n", "\n", "\n", "bottle_shatters\n", - "\n", - "bottle_shatters\n", + "\n", + "bottle_shatters\n", "\n", "\n", "\n", "prob_bottle_shatters_if_sally->bottle_shatters\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "prob_bottle_shatters_if_bill\n", - "\n", - "prob_bottle_shatters_if_bill\n", + "\n", + "prob_bottle_shatters_if_bill\n", "\n", "\n", - "\n", + "\n", "prob_bottle_shatters_if_bill->bottle_shatters\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sally_throws->sally_hits\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "bill_throws->bill_hits\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "sally_hits->bill_hits\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sally_hits->bottle_shatters\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "bill_hits->bottle_shatters\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" } @@ -646,7 +713,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 186, "metadata": {}, "outputs": [ { @@ -699,14 +766,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 292, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2521)\n" + "tensor([-1.1512e+01, -1.1512e+01, -1.1512e+01, ..., -2.3024e+01,\n", + " -1.1512e+01, -2.0027e-05])\n", + "tensor(0.2495)\n" ] } ], @@ -717,6 +786,7 @@ " consequents={\"bottle_shatters\": torch.tensor(1.0)},\n", " witnesses={\"bill_hits\": None},\n", " alternatives={\"sally_throws\": torch.tensor(0.0)},\n", + " consequent_scale=1e-5\n", ")(condition(\n", " data={\n", " \"prob_sally_throws\": torch.tensor(1.0),\n", @@ -728,10 +798,38 @@ " }\n", ")(stones_model))\n", "\n", - "logp, trace, mwc = importance_infer(num_samples=10000)(query)()\n", + "logp, trace, mwc, logw = importance_infer(num_samples=100000)(query)()\n", "print(torch.exp(logp))" ] }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(1.)\n", + "torch.Size([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n", + "torch.Size([1, 1, 1, 1, 1, 3, 1, 1, 1, 1])\n", + "torch.Size([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n", + "tensor([1., 0., 1.])\n", + "tensor([ 0.0000, -0.0101, -0.0101])\n" + ] + } + ], + "source": [ + "trace.nodes.keys()\n", + "print(trace.nodes['sally_throws'][\"value\"].squeeze())\n", + "print(trace.nodes['__cause____antecedent_sally_throws'][\"value\"].shape)\n", + "print(trace.nodes['bill_hits'][\"value\"].shape)\n", + "print(trace.nodes['__cause____witness_bill_hits'][\"value\"].shape)\n", + "print(trace.nodes['bottle_shatters'][\"value\"].squeeze())\n", + "print(trace.nodes['__cause____consequent_bottle_shatters'][\"log_prob\"].squeeze())" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -744,7 +842,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 203, "metadata": {}, "outputs": [ { @@ -798,15 +896,27 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 300, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Degree of responsibility of Sally: tensor(0.7581)\n", - "Degree of responsibility of Billy: tensor(0.6069)\n" + "tensor([-11.5116, -23.0245, -23.0245, -11.5116, -23.0245, -23.0245, -11.5116,\n", + " -23.0245, -11.5116, -23.0245])\n", + "tensor(-12.4279)\n", + "tensor(4.0055e-06)\n", + "torch.Size([10, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n", + "torch.Size([10, 1, 1, 1, 1, 3, 1, 1, 1, 1])\n", + "tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])\n", + "tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])\n", + "tensor([1., 0., 0., 0., 0., 0., 1., 0., 0., 0.])\n", + "tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])\n", + "tensor([1., 0., 1., 0., 1., 1., 1., 1., 1., 0.])\n", + "tensor([0., 1., 0., 1., 0., 1., 0., 0., 1., 1.])\n", + "Degree of responsibility of Sally: tensor(0.8000)\n", + "Degree of responsibility of Billy: tensor(0.8000)\n" ] } ], @@ -816,24 +926,41 @@ " antecedents={\"sally_throws\": None, \"bill_throws\": None},\n", " consequents={\"bottle_shatters\": torch.tensor(1.0)},\n", " witnesses={\"sally_hits\": None, \"bill_hits\": None},\n", + " consequent_scale=1e-5\n", ")(condition(\n", " data={\n", - " \"prob_sally_throws\": torch.tensor(0.8),\n", - " \"prob_bill_throws\": torch.tensor(0.7),\n", - " \"prob_sally_hits\": torch.tensor(0.9),\n", - " \"prob_bill_hits\": torch.tensor(0.8),\n", - " \"prob_bottle_shatters_if_sally\": torch.tensor(0.9),\n", - " \"prob_bottle_shatters_if_bill\": torch.tensor(0.8),\n", + " \"prob_sally_throws\": torch.tensor(1.0),\n", + " \"prob_bill_throws\": torch.tensor(1.0),\n", + " \"prob_sally_hits\": torch.tensor(1.0),\n", + " \"prob_bill_hits\": torch.tensor(1.0),\n", + " \"prob_bottle_shatters_if_sally\": torch.tensor(1.0),\n", + " \"prob_bottle_shatters_if_bill\": torch.tensor(1.0),\n", " \"bottle_shatters\": torch.tensor(1.0),\n", " }\n", ")(stones_model))\n", "\n", - "logp, trace, mwc = importance_infer(num_samples=20000)(query)()\n", + "logp, trace, mwc, logw = importance_infer(num_samples=10)(query)()\n", + "\n", + "print(logp)\n", + "\n", + "print(torch.exp(logp))\n", "\n", "nodes = trace.nodes[\"_RETURN\"][\"value\"]\n", + "\n", + "print(trace.nodes[\"sally_throws\"][\"value\"].shape)\n", + "print(nodes[\"sally_throws\"].shape)\n", "with mwc:\n", + " print(gather(nodes[\"sally_throws\"], IndexSet(sally_throws={0})).squeeze())\n", + " print(gather(nodes[\"sally_throws\"], IndexSet(sally_throws={1})).squeeze())\n", + " print(gather(nodes[\"sally_throws\"], IndexSet(sally_throws={2})).squeeze())\n", + "\n", + " print(gather(nodes[\"bill_throws\"], IndexSet(bill_throws={0})).squeeze())\n", + " print(gather(nodes[\"bill_throws\"], IndexSet(bill_throws={1})).squeeze())\n", + " print(gather(nodes[\"bill_throws\"], IndexSet(bill_throws={2})).squeeze())\n", + "\n", " st_responsible = gather(nodes[\"sally_throws\"], IndexSet(sally_throws={1})) != \\\n", " gather(nodes[\"sally_throws\"], IndexSet(sally_throws={2}))\n", + "\n", " bt_responsible = gather(nodes[\"bill_throws\"], IndexSet(bill_throws={1})) != \\\n", " gather(nodes[\"bill_throws\"], IndexSet(bill_throws={2}))\n", "\n", @@ -850,17 +977,147 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 298, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(0.8100)" + ] + }, + "execution_count": 298, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "st_responsible2 = st_responsible.float()\n", + "st_responsible2[st_responsible2 == 0.0] = 9.2\n", + "st_responsible2[st_responsible2 == 1.0] = 1.0\n", + "# st_responsible2\n", + "logp = torch.logsumexp(st_responsible2.squeeze().float() * logw, dim=0) - torch.log(torch.tensor(1000))\n", + "torch.exp(logp)" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 261, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([False, False, True, True, True, True, True, False, True, False,\n", + " True, True, False, True, True, False, True, True, True, True,\n", + " True, True, False, True, True, True, True, True, True, True,\n", + " False, True, True, False, False, True, True, True, True, True,\n", + " True, True, True, True, True, True, True, True, True, False,\n", + " True, False, True, True, True, True, False, True, True, False,\n", + " False, False, True, True, True, True, True, False, True, True,\n", + " False, True, True, True, True, False, True, True, True, True,\n", + " True, True, True, False, True, True, False, True, False, True,\n", + " 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--git a/docs/source/explainable_categorical_alternate.ipynb b/docs/source/explainable_categorical_alternate.ipynb index 2c6f8bab..339e19df 100644 --- a/docs/source/explainable_categorical_alternate.ipynb +++ b/docs/source/explainable_categorical_alternate.ipynb @@ -11,17 +11,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The **Explainable Reasoning with ChiRho** package aims to provide a systematic, unified approach to causal explanation computations in terms of different probabilistic queries over expanded causal models that are constructed from a single generic program transformation applied to an arbitrary causal model represented as a ChiRho program. The approach of reducing causal queries to probabilistic computations on transformed causal models is the foundational idea behind all of ChiRho. The key strategy underlying \"causal explanation\" queries is their use of auxiliary variables representing uncertainty about what the proposed interventions are and which interventions or preemptions to apply, implicitly inducing a search space over counterfactuals.\n", + "The **Explainable Reasoning with ChiRho** package aims to provide a systematic, unified approach to causal explanation computations. The package provides a single generic program transformation that can be applied to any arbitrary causal model representable as a Chirho program. This program transformation allows several causal explanation queries to be modeled in terms of probabilistic queries. This approach of reducing causal queries to probabilistic computations on transformed causal models is the foundational idea behind all of ChiRho and has been leveraged for causal explanations in this module as well.\n", "\n", - "The goal of this notebook is to illustrate how the package can be used to provide an approximate method of answering a range of causal explanation queries with respect to models in which categorical variables play the key role. As the key tool will involve sampling-based posterior probability estimation, a lot of what will be said *mutatis mutandis* applies to more general settings where variables are continuous (to which we will devote another tutorial).\n", + "The goal of this notebook is to illustrate how the package can be used to provide an approximate method of answering a range of causal explanation queries in causal models with only categorical variables. As the key tool will involve sampling-based posterior probability estimation, a lot of what will be said *mutatis mutandis* applies to more general settings where variables are continuous (to which we will devote another tutorial).\n", "\n", - "In yet [another notebook](https://basisresearch.github.io/chirho/actual_causality.html) we illustrate how the module allows for a faithful reconstruction of a particular notion of local explanation (the so-called Halpern-Pearl modified definition of actual causality [(J. Halpern, MIT Press, 2016)](https://mitpress.mit.edu/9780262537131/actual-causality/)), which inspired some of the conceptual steps underlying the current implementation." + "In yet [another notebook](https://basisresearch.github.io/chirho/actual_causality.html) we illustrate how the module allows for a faithful reconstruction of a particular notion of local explanation (the so-called Halpern-Pearl modified definition of actual causality [(J. Halpern, MIT Press, 2016)](https://mitpress.mit.edu/9780262537131/actual-causality/)), which inspired some of the conceptual steps underlying the current implementation.\n", + "\n", + "Before proceeding, the readers should go through the introductory tutorials on [causal reasoning in Chirho](https://github.com/BasisResearch/chirho/blob/master/docs/source/tutorial_i.ipynb) and [actual causality](https://github.com/BasisResearch/chirho/blob/master/docs/source/tutorial_i.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "[Poorva: come back to this outline for better headlines]\n", + "\n", "**Outline**\n", "\n", "[Causal explanation and counterfactual thinking](#causal-explanation-and-counterfactual-thinking) \n", @@ -72,12 +76,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's start with a very simple model, in which a forest fire can be caused by exactly one of two things: a match being dropped (`match_dropped`), or a lightning strike (`lightning`), and either of these factors alone is already deterministically sufficient for the `forest_fire` to occur. A match being dropped is more likely than a lightning strike (we use fairly large probabilities for the sake of example transparency). For the sake of illustration, we also include a causally irrelevant site representing whether a ChiRho developer smiles, `smile`." + "# Setup" ] }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -107,9 +111,16 @@ "pyro.settings.set(module_local_params=True)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first setup the essentials for performing probabilistic inference on the transformed causal models. We have a function for performing importance sampling on a model and few other utility functions." + ] + }, { "cell_type": "code", - "execution_count": 250, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ @@ -140,30 +151,27 @@ " **kwargs\n", " )\n", "\n", - " # # resample using importance weights to get posterior samples\n", - " # idx = dist.Categorical(logits=log_weights).sample((num_samples,))\n", - " # for name, node in importance_tr.nodes.items():\n", - " # if node[\"type\"] != \"sample\" or pyro.poutine.util.site_is_subsample(node) or node[\"is_observed\"]:\n", - " # continue\n", - " # importance_tr.nodes[name][\"value\"] = torch.index_select(\n", - " # importance_tr.nodes[name][\"value\"],\n", - " # -max_plate_nesting - 1 - len(importance_tr.nodes[name][\"fn\"].event_shape),\n", - " # idx,\n", - " # )\n", - "\n", - " print(log_weights)\n", + " return torch.logsumexp(log_weights, dim=0) - math.log(num_samples), importance_tr, mwc, log_weights\n", "\n", - " # with pyro.poutine.replay(trace=importance_tr), mwc:\n", - " # trace = pyro.poutine.trace(model).get_trace(*args, **kwargs)\n", + " return _wrapped_model\n", "\n", - " return torch.logsumexp(log_weights, dim=0) - math.log(num_samples), importance_tr, mwc, log_weights\n", + "def _soft_eq(v1: torch.Tensor, v2: torch.Tensor) -> torch.Tensor:\n", + " return soft_eq(constraints.boolean, v1, v2, scale=0.001)\n", "\n", - " return _wrapped_model" + "def reparam_config(data):\n", + " return {i: KernelSoftConditionReparam(_soft_eq) for i in data}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start with a very simple model, in which a forest fire can be caused by any of the two things: a match being dropped (`match_dropped`), or a lightning strike (`lightning`), and either of these factors alone is already deterministically sufficient for the `forest_fire` to occur. A match being dropped is more likely than a lightning strike (we use fairly large probabilities for the sake of example transparency). For the sake of illustration, we also include a causally irrelevant site representing whether a ChiRho developer smiles, `smile`." ] }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -219,10 +227,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 156, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -261,31 +269,44 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Throughout this tutorial, we assume all nodes are binary and use $'$ as negation. Once we specify (i) the distributions for the nodes we use (`supports`), (ii) candidate causes $X_i = x_i$ (`antecedents`) (iii) their alternative values ($X_i = x_i'$), (iv) elements of the current context (`witnesses`), and (v) the `consequents` of interest $Y=y$. The `SearchForExplanation` handler transforms the original model into one in which interventions and alternative interventions on the antecedents are applied in parallel counterfactual worlds stochastically preempted and context elements are stochastically selected and preempted to be kept at the factual values in all counterfactual worlds.\n", - "\n", - "First, let's go back to our original query. Let $F$ be the `forest_fire`, $f$ stand for $F=1$, $f'$ for $F=0$, $M$ stand for `match_dropped`, with analogous conventions. We also place interventions conditioned on in subscripts, so that, for example\n", - "$P(f_{m'})$ stands for $P(F=1\\vert do(M=0))$.\n", + "Before we further go into causal queries, let us describe some notation. Let $F$ refer to the `forest_fire`, $f$ stand for $F=1$, $f'$ for $F=0$. The notation $M$ stands for `match_dropped`, with analogous conventions. We also place interventions conditioned on in subscripts. As an example, $f_{m'}$ stands for $F=1$ when $do(M=0)$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Throughout this tutorial, we consider different kinds of causal queries and compute them using a unified program transforamtion. This program transformation takes place using the handler `SearchForExplanation`. It takes the following inputs:\n", + "1. the distributions for the variables we use (`supports`),\n", + "2. the candidate causes $X_i = x_i$ (`antecedents`),\n", + "3. their alternative values ($X_i = x_i'$) (`alternatives`),\n", + "4. the elements of the current context (`witnesses`), and \n", + "5. the `consequents` of interest $Y=y$. \n", "\n", - "We are currently interested in $P(f'_{m'}, f_m)$, that is the probability of both forest fire not occurring if we intervene on the match to not be dropped, and forest fire occurring if we intervene on the match to be dropped." + "The `SearchForExplanation` handler then takes these arguments and transforms the original model into another model in which interventions on antecedents and witnesses are applied stochastically. Once the antecedents `A` and witnesses `W` are chosen, parallel counterfactual worlds are created to condition on `A` being sufficient and necessary causes for the consequent with the context `W`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "First, suppose we are interested in asking the question of whether dropping a match has causal power over whether the forest fire occurs. We assume all relevant nodes are binary. The potential cause (`antecedent`) we're considering is `match_dropped=1`, we contrast it with what would happen if we intervened on it to not happen (`alternatives`). We are interested in whether an outcome variable (`consequent`) has value 1 under these two interventions. The counterfactual world in which we intervene with `alternatives` is world 1, and the counterfactual world in which we intervene with `antecedents` is world 2. We will be interested in cases in which none of these interventions have been preempted (more about this later), so we will sample with appropriate masks as well." + "**Causal Query 1** Is dropping a match a cause of forest fire?\n", + "\n", + "To answer the above question, we compute the probability of both forest fire not occurring if we intervene on the match to not be dropped, and forest fire occurring if we intervene on the match to be dropped. i.e. $P(f'_{m'}, f_m)$. this computation can be carried out using `SearchForExplanation`.\n", + "\n", + "The potential cause (`antecedent`) we're considering is `match_dropped=1`, we contrast it with what would happen if we intervened on it to not happen (`alternatives`). We are interested in whether an outcome variable `forest_fire` (`consequent`) has value 1 under these two interventions. " ] }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.3000)\n" + "tensor(0.2951)\n" ] } ], @@ -299,46 +320,52 @@ " consequent_scale=1e-5,\n", ")(forest_fire_model)\n", "\n", - "logp, trace, mwc = importance_infer(num_samples=10)(query)()\n", + "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", "print(torch.exp(logp))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To answer our causal query, it is enough that the probability above is greater than 0 --- dropping match does have a causal effect on forest fire. But this is not exactly $P(f'_{m'}, f_m)$. Remember that interventions on antecedents are chosen stochastically which induces need for post-processing the samples." + ] + }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "torch.Size([10, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n", - "torch.Size([10, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n" + "tensor(0.5970)\n" ] } ], "source": [ - "print(trace.nodes[\"match_dropped\"][\"value\"].shape)\n", - "print(trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"].shape)" + "mask_intervened = trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "More interestingly, in cases of overdetermination, a similar estimation would lead us to assign no causal role to any of to co-contributing factors. This can be seen in the context in which both causes occurred. Trivially, if lightning occurred, then had no match been dropped, the forest fire, caused by lighning, would still occur (a symmetric reasoning goes through for the lightning as well), $P(f'_{m'}\\vert m, l) = P(f'_{l'}\\vert m, l)=0$. Intuitively, these quantities are not good guides to the causal role of `match_dropped` and `lightning`, as we think they did played a causal role. This is the first illustration of why the but-for analysis is not fine-grained enough." + "A similar estimation as above would not work in case of overdetermination where one of the two factors are enough to cause the outcome. Consider the case where both `match_dropped` and `lightning` did occur. In this case, if we try to determine the causal role of `match_dropped`, it would come out to be zero. And a symmetric reasoning works for lightning as well. This results in $P(f'_{m'}, f_m, m, l) = P(f'_{l'}, f_l, m, l)=0$" ] }, { "cell_type": "code", - "execution_count": 159, + "execution_count": 76, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(1.0013e-05)\n" + "tensor(2.8615e-06)\n" ] } ], @@ -350,83 +377,97 @@ " witnesses={}, # potential context elements, we leave them empty for now\n", " alternatives={\"match_dropped\": torch.tensor(0.0)},\n", " consequent_scale=1e-5,\n", - ")(condition(\n", - " data={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)}\n", - ")(forest_fire_model))\n", + ")(\n", + " pyro.poutine.reparam(config=reparam_config([\"match_dropped\", \"lightning\"]))(\n", + " condition(data={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)})\n", + " (forest_fire_model)\n", + " ))\n", "\n", - "logp, trace, mwc = importance_infer(num_samples=10000)(query)()\n", + "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", "print(torch.exp(logp))" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Comment: The ground truth example for correct intervention. This gives 0.42 as the answer which is the expected answer." - ] - }, { "cell_type": "code", - "execution_count": 160, + "execution_count": 77, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'u_match_dropped': Boolean(), 'match_dropped': Boolean(), 'u_lightning': Boolean(), 'lightning': Boolean(), 'smile': Boolean(), 'forest_fire': Boolean()}\n" + "tensor(2.8286e-06)\n" ] } ], "source": [ - "print(forest_fire_supports)" + "mask_intervened = trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But if we consider both `match_dropped` and `lightning` to be possible causes, we can estimate $P(f'_{m',l'}, f_{m,l}, m, l)$ to determine their causal role." ] }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 79, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.4186)\n" + "tensor(0.0692)\n" ] } ], "source": [ - "\n", - "import math\n", - "# reparam_config = AutoSoftConditioning(scale=math.sqrt(1/(2*math.pi)))\n", - "# def _soft_eq(v1: torch.Tensor, v2: torch.Tensor) -> torch.Tensor:\n", - "# return soft_eq(constraints.boolean, v1, v2, scale=scale)\n", - "# reparam_config = {\"match_dropped\": KernelSoftConditionReparam(_soft_eq)}\n", - "\n", - "def _soft_eq(v1: torch.Tensor, v2: torch.Tensor) -> torch.Tensor:\n", - " return soft_eq(constraints.boolean, v1, v2, scale=0.001)\n", - "\n", - "reparam_config = {\"match_dropped\": KernelSoftConditionReparam(_soft_eq)}\n", - "\n", - "\n", "query = SearchForExplanation(\n", " supports=forest_fire_supports,\n", - " antecedents={\"match_dropped\": torch.tensor(1.0)},\n", + " antecedents={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)},\n", " consequents={\"forest_fire\": torch.tensor(1.0)},\n", " witnesses={}, # potential context elements, we leave them empty for now\n", - " alternatives={\"match_dropped\": torch.tensor(0.0)},\n", - " antecedent_bias=-0.5,\n", + " alternatives={\"match_dropped\": torch.tensor(0.0), \"lightning\": torch.tensor(0.0)},\n", " consequent_scale=1e-5,\n", ")(\n", - " pyro.poutine.reparam(config=reparam_config)(\n", - " condition(data={\"match_dropped\": torch.tensor(1.0)})\n", + " pyro.poutine.reparam(config=reparam_config([\"match_dropped\", \"lightning\"]))(\n", + " condition(data={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)})\n", " (forest_fire_model)\n", - "))\n", + " ))\n", "\n", - "logp, trace, mwc = importance_infer(num_samples=100000)(query)()\n", + "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", "print(torch.exp(logp))" ] }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.2796)\n" + ] + } + ], + "source": [ + "mask_intervened = (trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_lightning\"][\"value\"] == 0)\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "DONE TILL HERE" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -480,7 +521,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -563,7 +604,7 @@ "bill_hits\n", "\n", "\n", - "\n", + "\n", "prob_bill_hits->bill_hits\n", "\n", "\n", @@ -581,7 +622,7 @@ "bottle_shatters\n", "\n", "\n", - "\n", + "\n", "prob_bottle_shatters_if_sally->bottle_shatters\n", "\n", "\n", @@ -593,7 +634,7 @@ "prob_bottle_shatters_if_bill\n", "\n", "\n", - "\n", + "\n", "prob_bottle_shatters_if_bill->bottle_shatters\n", "\n", "\n", @@ -605,25 +646,25 @@ "\n", "\n", "\n", - "\n", + "\n", "bill_throws->bill_hits\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "sally_hits->bill_hits\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "sally_hits->bottle_shatters\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "bill_hits->bottle_shatters\n", "\n", "\n", @@ -632,10 +673,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 98, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -896,76 +937,263 @@ }, { "cell_type": "code", - "execution_count": 300, + "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor([-11.5116, -23.0245, -23.0245, -11.5116, -23.0245, -23.0245, -11.5116,\n", - " -23.0245, -11.5116, -23.0245])\n", - "tensor(-12.4279)\n", - "tensor(4.0055e-06)\n", - "torch.Size([10, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n", - "torch.Size([10, 1, 1, 1, 1, 3, 1, 1, 1, 1])\n", - "tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])\n", - "tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])\n", - "tensor([1., 0., 0., 0., 0., 0., 1., 0., 0., 0.])\n", - "tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])\n", - "tensor([1., 0., 1., 0., 1., 1., 1., 1., 1., 0.])\n", - "tensor([0., 1., 0., 1., 0., 1., 0., 0., 1., 1.])\n", - "Degree of responsibility of Sally: tensor(0.8000)\n", - "Degree of responsibility of Billy: tensor(0.8000)\n" + "tensor([-2.3024e+01, -2.3024e+01, -2.0385e-05, -2.3024e+01, -2.3024e+01,\n", + " -2.3024e+01, -2.3024e+01, -2.3024e+01, -2.3024e+01, -2.3024e+01,\n", + " -2.3024e+01, -2.3024e+01, -2.3024e+01, -2.3024e+01, -2.3024e+01,\n", + " -2.3024e+01, -2.3024e+01, -2.0385e-05, -2.3024e+01, -2.3024e+01,\n", + " -2.3024e+01, -2.3024e+01, -2.0385e-05, -2.3024e+01, -2.3024e+01,\n", + " -2.3024e+01, -2.0385e-05, -2.0385e-05, -2.3024e+01, -2.0385e-05,\n", + " -2.3024e+01, -2.3024e+01, -2.0385e-05, -2.3024e+01, -2.3024e+01,\n", + " -2.3024e+01, -2.3024e+01, -2.3024e+01, -2.3024e+01, -2.0385e-05,\n", + " -2.0385e-05, -2.3024e+01, -2.3024e+01, -2.3024e+01, 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"source": [ "query = SearchForExplanation(\n", " supports=stones_supports,\n", - " antecedents={\"sally_throws\": None, \"bill_throws\": None},\n", + " antecedents={\"sally_throws\": torch.tensor(1.0), \"bill_throws\": torch.tensor(1.0)},\n", " consequents={\"bottle_shatters\": torch.tensor(1.0)},\n", - " witnesses={\"sally_hits\": None, \"bill_hits\": None},\n", + " witnesses={\"bill_hits\": None, \"sally_hits\": None},\n", + " alternatives={\"sally_throws\": torch.tensor(0.0), \"bill_throws\": torch.tensor(0.0)},\n", " consequent_scale=1e-5\n", ")(condition(\n", " data={\n", " \"prob_sally_throws\": torch.tensor(1.0),\n", - " \"prob_bill_throws\": torch.tensor(1.0),\n", + " \"prob_bill_throws\": torch.tensor(0.0),\n", " \"prob_sally_hits\": torch.tensor(1.0),\n", - " \"prob_bill_hits\": torch.tensor(1.0),\n", + " \"prob_bill_hits\": torch.tensor(0.0),\n", " \"prob_bottle_shatters_if_sally\": torch.tensor(1.0),\n", - " \"prob_bottle_shatters_if_bill\": torch.tensor(1.0),\n", + " \"prob_bottle_shatters_if_bill\": torch.tensor(0.0),\n", " \"bottle_shatters\": torch.tensor(1.0),\n", " }\n", ")(stones_model))\n", "\n", - "logp, trace, mwc, logw = importance_infer(num_samples=10)(query)()\n", + "logp, trace, mwc, logw = importance_infer(num_samples=1000)(query)()\n", "\n", - "print(logp)\n", + "# print(logp)\n", "\n", - "print(torch.exp(logp))\n", + "# print(torch.exp(logp))\n", "\n", - "nodes = trace.nodes[\"_RETURN\"][\"value\"]\n", + "# nodes = trace.nodes[\"_RETURN\"][\"value\"]\n", "\n", - "print(trace.nodes[\"sally_throws\"][\"value\"].shape)\n", - "print(nodes[\"sally_throws\"].shape)\n", - "with mwc:\n", - " print(gather(nodes[\"sally_throws\"], IndexSet(sally_throws={0})).squeeze())\n", - " print(gather(nodes[\"sally_throws\"], IndexSet(sally_throws={1})).squeeze())\n", - " print(gather(nodes[\"sally_throws\"], IndexSet(sally_throws={2})).squeeze())\n", + "# print(trace.nodes[\"sally_throws\"][\"value\"].shape)\n", + "# print(nodes[\"sally_throws\"].shape)\n", + "# with mwc:\n", + "# print(gather(nodes[\"sally_throws\"], IndexSet(sally_throws={0})).squeeze())\n", + "# print(gather(nodes[\"sally_throws\"], IndexSet(sally_throws={1})).squeeze())\n", + "# print(gather(nodes[\"sally_throws\"], IndexSet(sally_throws={2})).squeeze())\n", "\n", - " print(gather(nodes[\"bill_throws\"], IndexSet(bill_throws={0})).squeeze())\n", - " print(gather(nodes[\"bill_throws\"], IndexSet(bill_throws={1})).squeeze())\n", - " print(gather(nodes[\"bill_throws\"], IndexSet(bill_throws={2})).squeeze())\n", + "# print(gather(nodes[\"bill_throws\"], IndexSet(bill_throws={0})).squeeze())\n", + "# print(gather(nodes[\"bill_throws\"], IndexSet(bill_throws={1})).squeeze())\n", + "# print(gather(nodes[\"bill_throws\"], IndexSet(bill_throws={2})).squeeze())\n", "\n", - " st_responsible = gather(nodes[\"sally_throws\"], IndexSet(sally_throws={1})) != \\\n", - " gather(nodes[\"sally_throws\"], IndexSet(sally_throws={2}))\n", + "# st_responsible = gather(nodes[\"sally_throws\"], IndexSet(sally_throws={1})) != \\\n", + "# gather(nodes[\"sally_throws\"], IndexSet(sally_throws={2}))\n", "\n", - " bt_responsible = gather(nodes[\"bill_throws\"], IndexSet(bill_throws={1})) != \\\n", - " gather(nodes[\"bill_throws\"], IndexSet(bill_throws={2}))\n", + "# bt_responsible = gather(nodes[\"bill_throws\"], IndexSet(bill_throws={1})) != \\\n", + "# gather(nodes[\"bill_throws\"], IndexSet(bill_throws={2}))\n", "\n", - "print(\"Degree of responsibility of Sally:\", st_responsible.sum() / st_responsible.numel())\n", - "print(\"Degree of responsibility of Billy:\", bt_responsible.sum() / bt_responsible.numel())" + "# print(\"Degree of responsibility of Sally:\", st_responsible.sum() / st_responsible.numel())\n", + "# print(\"Degree of responsibility of Billy:\", bt_responsible.sum() / bt_responsible.numel())" ] }, { @@ -1119,6 +1347,9545 @@ "st_responsible.squeeze()" ] }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "query = SearchForExplanation(\n", + " supports=stones_supports,\n", + " antecedents={\"sally_throws\": torch.tensor(1.0), \"bill_throws\": torch.tensor(1.0)},\n", + " consequents={\"bottle_shatters\": torch.tensor(1.0)},\n", + " witnesses={\"sally_hits\": torch.tensor(0.0)},\n", + " alternatives={\"sally_throws\": torch.tensor(0.0), \"bill_throws\": torch.tensor(0.0)},\n", + " antecedent_bias=-0.5,\n", + " witness_bias=0.5,\n", + " consequent_scale=1e-5\n", + ")(condition(\n", + " data={\n", + " \"prob_sally_throws\": torch.tensor(1.0),\n", + " \"prob_bill_throws\": torch.tensor(0.0),\n", + " \"prob_sally_hits\": torch.tensor(1.0),\n", + " \"prob_bill_hits\": torch.tensor(0.0),\n", + " \"prob_bottle_shatters_if_sally\": torch.tensor(1.0),\n", + " \"prob_bottle_shatters_if_bill\": torch.tensor(1.0),\n", + " \"bottle_shatters\": torch.tensor(1.0),\n", + " }\n", + ")(stones_model))" + ] + }, + { + "cell_type": "code", + "execution_count": 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1.0014e-10, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10, 1.0014e-10,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 9.9998e-01, 9.9998e-01,\n", + " 0.0000e+00, 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00,\n", + " 1.0014e-10, 0.0000e+00, 1.0014e-10, 0.0000e+00, 9.9998e-01, 1.0014e-10,\n", + " 1.0014e-10, 9.9998e-01, 1.0014e-10, 1.0014e-10, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 1.0014e-10, 9.9998e-01, 0.0000e+00, 0.0000e+00,\n", + " 1.0014e-10, 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 1.0014e-10, 9.9998e-01, 1.0014e-10, 0.0000e+00,\n", + " 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10,\n", + " 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00, 1.0014e-10,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00,\n", + " 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10, 1.0014e-10, 1.0014e-10,\n", + " 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 9.9998e-01,\n", + " 1.0014e-10, 9.9998e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 1.0014e-10,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00,\n", + " 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00, 1.0014e-10,\n", + " 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00, 9.9998e-01, 1.0014e-10,\n", + " 1.0014e-10, 0.0000e+00, 1.0014e-10, 1.0014e-10, 9.9998e-01, 0.0000e+00,\n", + " 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00, 9.9998e-01, 0.0000e+00,\n", + " 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00, 9.9998e-01, 1.0014e-10,\n", + " 1.0014e-10, 1.0014e-10, 9.9998e-01, 1.0014e-10, 1.0014e-10, 0.0000e+00,\n", + " 1.0014e-10, 0.0000e+00, 1.0014e-10, 9.9998e-01, 1.0014e-10, 0.0000e+00,\n", + " 9.9998e-01, 1.0014e-10, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10,\n", + " 9.9998e-01, 1.0014e-10, 0.0000e+00, 9.9998e-01, 9.9998e-01, 1.0014e-10,\n", + " 1.0014e-10, 0.0000e+00, 1.0014e-10, 9.9998e-01, 1.0014e-10, 0.0000e+00,\n", + " 0.0000e+00, 9.9998e-01, 0.0000e+00, 1.0014e-10, 9.9998e-01, 1.0014e-10,\n", + " 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10,\n", + " 1.0014e-10, 0.0000e+00, 9.9998e-01, 0.0000e+00, 0.0000e+00, 1.0014e-10,\n", + " 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00, 0.0000e+00, 0.0000e+00, 9.9998e-01, 1.0014e-10, 0.0000e+00,\n", + " 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10,\n", + " 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10])\n", + "tensor(117.9976)\n" + ] + } + ], + "source": [ + "mask_intervened = (trace.nodes[\"__cause____antecedent_bill_throws\"][\"value\"] == 0)\n", + "log_weight_vector=logw\n", + "a = mask_intervened.float().sum()\n", + "print(mask_intervened)\n", + "# mask_intervened = mask_intervened.float()\n", + "# mask_intervened[mask_intervened == 0.0] = 9.2\n", + "# mask_intervened[mask_intervened == 1.0] = 1.0\n", + "print(log_weight_vector)\n", + "print(mask_intervened.squeeze())\n", + "print(torch.exp(log_weight_vector) * mask_intervened.squeeze())\n", + "\n", + "print(torch.sum(torch.exp(log_weight_vector) * mask_intervened.squeeze()))" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'Trace' object has no attribute 'n'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[62], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mtrace\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn\u001b[49m)\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(trace\u001b[38;5;241m.\u001b[39mnodes[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msally_throws\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;241m2\u001b[39m]\u001b[38;5;241m.\u001b[39msqueeze())\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(trace\u001b[38;5;241m.\u001b[39mnodes[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbottle_shatters\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;241m2\u001b[39m]\u001b[38;5;241m.\u001b[39msqueeze())\n", + "\u001b[0;31mAttributeError\u001b[0m: 'Trace' object has no attribute 'n'" + ] + } + ], + "source": [ + "print(trace.n)\n", + "print(trace.nodes[\"sally_throws\"][\"value\"][2].squeeze())\n", + "print(trace.nodes[\"bottle_shatters\"][\"value\"][2].squeeze())\n", + "trace.nodes[\"__cause____consequent_bottle_shatters\"][\"log_prob\"][2]" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1126,6 +10893,15 @@ "outputs": [], "source": [] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# References\n", + "\n", + "1. " + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/docs/source/responsibility.ipynb b/docs/source/responsibility.ipynb new file mode 100644 index 00000000..a562f3de --- /dev/null +++ b/docs/source/responsibility.ipynb @@ -0,0 +1,499 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: CUDA_VISIBLE_DEVICES=-1\n" + ] + } + ], + "source": [ + "%env CUDA_VISIBLE_DEVICES=-1\n", + "from typing import Callable, Dict, List, Optional\n", + "\n", + "import math\n", + "import pyro\n", + "import pyro.distributions as dist\n", + "import pyro.distributions.constraints as constraints\n", + "import torch\n", + "from chirho.counterfactual.handlers.counterfactual import \\\n", + " MultiWorldCounterfactual\n", + "from chirho.explainable.handlers import ExtractSupports, SearchForExplanation\n", + "from chirho.indexed.ops import IndexSet, gather\n", + "from chirho.observational.handlers import condition\n", + "from chirho.observational.handlers.soft_conditioning import soft_eq, KernelSoftConditionReparam\n", + "\n", + "pyro.settings.set(module_local_params=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "def importance_infer(\n", + " model: Optional[Callable] = None, *, num_samples: int\n", + "):\n", + " \n", + " if model is None:\n", + " return lambda m: importance_infer(m, num_samples=num_samples)\n", + "\n", + " def _wrapped_model(\n", + " *args,\n", + " **kwargs\n", + " ):\n", + "\n", + " guide = pyro.poutine.block(hide_fn=lambda msg: msg[\"is_observed\"])(model)\n", + "\n", + " max_plate_nesting = 9 # TODO guess\n", + "\n", + " with pyro.poutine.block(), MultiWorldCounterfactual() as mwc:\n", + " log_weights, importance_tr, _ = pyro.infer.importance.vectorized_importance_weights(\n", + " model,\n", + " guide,\n", + " *args,\n", + " num_samples=num_samples,\n", + " max_plate_nesting=max_plate_nesting,\n", + " normalized=False,\n", + " **kwargs\n", + " )\n", + "\n", + " # # resample using importance weights to get posterior samples\n", + " # idx = dist.Categorical(logits=log_weights).sample((num_samples,))\n", + " # for name, node in importance_tr.nodes.items():\n", + " # if node[\"type\"] != \"sample\" or pyro.poutine.util.site_is_subsample(node) or node[\"is_observed\"]:\n", + " # continue\n", + " # importance_tr.nodes[name][\"value\"] = torch.index_select(\n", + " # importance_tr.nodes[name][\"value\"],\n", + " # -max_plate_nesting - 1 - len(importance_tr.nodes[name][\"fn\"].event_shape),\n", + " # idx,\n", + " # )\n", + "\n", + " print(log_weights)\n", + "\n", + " # with pyro.poutine.replay(trace=importance_tr), mwc:\n", + " # trace = pyro.poutine.trace(model).get_trace(*args, **kwargs)\n", + "\n", + " return torch.logsumexp(log_weights, dim=0) - math.log(num_samples), importance_tr, mwc, log_weights\n", + "\n", + " return _wrapped_model" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "u_match_dropped\n", + "\n", + "u_match_dropped\n", + "\n", + "\n", + "\n", + "match_dropped\n", + "\n", + "match_dropped\n", + "\n", + "\n", + "\n", + "u_lightning\n", + "\n", + "u_lightning\n", + "\n", + "\n", + "\n", + "lightning\n", + "\n", + "lightning\n", + "\n", + "\n", + "\n", + "smile\n", + "\n", + "smile\n", + "\n", + "\n", + "\n", + "forest_fire\n", + "\n", + "forest_fire\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def forest_fire_model():\n", + " u_match_dropped = pyro.sample(\"u_match_dropped\", dist.Bernoulli(0.7))\n", + " match_dropped = pyro.deterministic(\n", + " \"match_dropped\", u_match_dropped, event_dim=0\n", + " ) # notice uneven probs here\n", + "\n", + " u_lightning = pyro.sample(\"u_lightning\", dist.Bernoulli(0.4))\n", + " lightning = pyro.deterministic(\"lightning\", u_lightning, event_dim=0)\n", + "\n", + " # this is a causally irrelevant site\n", + " smile = pyro.sample(\"smile\", dist.Bernoulli(0.5))\n", + "\n", + " forest_fire = pyro.deterministic(\n", + " \"forest_fire\", torch.max(match_dropped, lightning) + (0 * smile), event_dim=0\n", + " )\n", + "\n", + " return {\n", + " \"match_dropped\": match_dropped,\n", + " \"lightning\": lightning,\n", + " \"forest_fire\": forest_fire,\n", + " }\n", + "\n", + "with ExtractSupports() as extract_supports:\n", + " forest_fire_model()\n", + " forest_fire_supports = {k: constraints.boolean for k in extract_supports.supports}\n", + "\n", + "pyro.render_model(forest_fire_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([-2.0027e-05, -1.1512e+01, -2.3024e+01, ..., -2.0027e-05,\n", + " -2.0027e-05, -2.0027e-05])\n", + "tensor(0.4743)\n" + ] + } + ], + "source": [ + "query = SearchForExplanation(\n", + " supports=forest_fire_supports,\n", + " antecedents={\"match_dropped\": 1.0, \"lightning\": 1.0},\n", + " consequents={\"forest_fire\": torch.tensor(1.0)},\n", + " witnesses={},\n", + " alternatives={\"match_dropped\": 0.0, \"lightning\": 0.0},\n", + " consequent_scale=1e-5\n", + ")(forest_fire_model)\n", + "\n", + "logp, trace, mwc, log_weight_vector = importance_infer(num_samples=10000)(query)()\n", + "print(torch.exp(logp))" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(4937.)\n", + "tensor([[[[[[[[[[ True]]]]]]]]],\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[[[[[False]]]]]]]]],\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[[[[[False]]]]]]]]],\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " ...,\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[[[[[ True]]]]]]]]],\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[[[[[ True]]]]]]]]],\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[[[[[ True]]]]]]]]]])\n", + "tensor([-2.0027e-05, -1.1512e+01, -2.3024e+01, ..., -2.0027e-05,\n", + " -2.0027e-05, -2.0027e-05])\n", + "tensor([1.0000, 0.0000, 0.0000, ..., 1.0000, 1.0000, 1.0000])\n", + "tensor(0.7991)\n" + ] + } + ], + "source": [ + "mask_intervened = trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0\n", + "print(mask_intervened.float().sum())\n", + "print(mask_intervened)\n", + "print(log_weight_vector)\n", + "print(torch.exp(log_weight_vector) * mask_intervened.squeeze())\n", + "\n", + "print(torch.sum(torch.exp(log_weight_vector) * mask_intervened.squeeze())/mask_intervened.float().sum())\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(5130.)\n", + "tensor([[[[[[[[[[ True]]]]]]]]],\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[[[[[ True]]]]]]]]],\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[[[[[False]]]]]]]]],\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " ...,\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[[[[[ True]]]]]]]]],\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[[[[[False]]]]]]]]],\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[[[[[ True]]]]]]]]]])\n", + "tensor([-2.0027e-05, -1.1512e+01, -2.3024e+01, ..., -2.0027e-05,\n", + " -2.0027e-05, -2.0027e-05])\n", + "tensor([9.9998e-01, 1.0013e-05, 0.0000e+00, ..., 9.9998e-01, 0.0000e+00,\n", + " 9.9998e-01])\n", + "tensor(0.7025)\n" + ] + } + ], + "source": [ + "mask_intervened = trace.nodes[\"__cause____antecedent_lightning\"][\"value\"] == 0\n", + "print(mask_intervened.float().sum())\n", + "print(mask_intervened)\n", + "print(log_weight_vector)\n", + "print(torch.exp(log_weight_vector) * mask_intervened.squeeze())\n", + "\n", + "print(torch.sum(torch.exp(log_weight_vector) * mask_intervened.squeeze())/torch.sum(torch.exp(log_weight_vector)))" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(2467.)\n", + "tensor([[[[[[[[[[False]]]]]]]]],\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[[[[[False]]]]]]]]],\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[[[[[ True]]]]]]]]],\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " ...,\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[[[[[False]]]]]]]]],\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[[[[[False]]]]]]]]],\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[[[[[False]]]]]]]]]])\n", + "tensor([-2.0027e-05, -1.1512e+01, -2.3024e+01, ..., -2.0027e-05,\n", + " -2.0027e-05, -2.0027e-05])\n", + "tensor([0.0000e+00, 0.0000e+00, 1.0014e-10, ..., 0.0000e+00, 0.0000e+00,\n", + " 0.0000e+00])\n", + "tensor(1.7309e-06)\n" + ] + } + ], + "source": [ + "mask_intervened = (trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 1) & (trace.nodes[\"__cause____antecedent_lightning\"][\"value\"] == 1)\n", + "print(mask_intervened.float().sum())\n", + "print(mask_intervened)\n", + "print(log_weight_vector)\n", + "print(torch.exp(log_weight_vector) * mask_intervened.squeeze())\n", + "\n", + "print(torch.sum(torch.exp(log_weight_vector) * mask_intervened.squeeze())/mask_intervened.float().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 92545291559fb19a292c2dc8d39f2ed1c16435a3 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Mon, 12 Aug 2024 13:40:56 -0400 Subject: [PATCH 027/111] responsibility example --- .../explainable_categorical_alternate.ipynb | 9 +- docs/source/responsibility.ipynb | 381 ++---------------- 2 files changed, 30 insertions(+), 360 deletions(-) diff --git a/docs/source/explainable_categorical_alternate.ipynb b/docs/source/explainable_categorical_alternate.ipynb index 339e19df..6896e229 100644 --- a/docs/source/explainable_categorical_alternate.ipynb +++ b/docs/source/explainable_categorical_alternate.ipynb @@ -147,7 +147,7 @@ " *args,\n", " num_samples=num_samples,\n", " max_plate_nesting=max_plate_nesting,\n", - " normalized=False,\n", + " normalized=True,\n", " **kwargs\n", " )\n", "\n", @@ -475,13 +475,6 @@ "## Witness nodes and context sensitivity" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Some of these intuitions in the forest fire example may be salvaged by considering a two-membered antecedent set, estimating $P(f'_{m',l'}, f_{m,l})$. " - ] - }, { "cell_type": "code", "execution_count": 97, diff --git a/docs/source/responsibility.ipynb b/docs/source/responsibility.ipynb index a562f3de..a203dbc4 100644 --- a/docs/source/responsibility.ipynb +++ b/docs/source/responsibility.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 30, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -65,22 +65,6 @@ " **kwargs\n", " )\n", "\n", - " # # resample using importance weights to get posterior samples\n", - " # idx = dist.Categorical(logits=log_weights).sample((num_samples,))\n", - " # for name, node in importance_tr.nodes.items():\n", - " # if node[\"type\"] != \"sample\" or pyro.poutine.util.site_is_subsample(node) or node[\"is_observed\"]:\n", - " # continue\n", - " # importance_tr.nodes[name][\"value\"] = torch.index_select(\n", - " # importance_tr.nodes[name][\"value\"],\n", - " # -max_plate_nesting - 1 - len(importance_tr.nodes[name][\"fn\"].event_shape),\n", - " # idx,\n", - " # )\n", - "\n", - " print(log_weights)\n", - "\n", - " # with pyro.poutine.replay(trace=importance_tr), mwc:\n", - " # trace = pyro.poutine.trace(model).get_trace(*args, **kwargs)\n", - "\n", " return torch.logsumexp(log_weights, dim=0) - math.log(num_samples), importance_tr, mwc, log_weights\n", "\n", " return _wrapped_model" @@ -88,391 +72,84 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 26, "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "u_match_dropped\n", - "\n", - "u_match_dropped\n", - "\n", - "\n", - "\n", - "match_dropped\n", - "\n", - "match_dropped\n", - "\n", - "\n", - "\n", - "u_lightning\n", - "\n", - "u_lightning\n", - "\n", - "\n", - "\n", - "lightning\n", - "\n", - "lightning\n", - "\n", - "\n", - "\n", - "smile\n", - "\n", - "smile\n", - "\n", - "\n", - "\n", - "forest_fire\n", - "\n", - "forest_fire\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "def forest_fire_model():\n", - " u_match_dropped = pyro.sample(\"u_match_dropped\", dist.Bernoulli(0.7))\n", - " match_dropped = pyro.deterministic(\n", - " \"match_dropped\", u_match_dropped, event_dim=0\n", - " ) # notice uneven probs here\n", - "\n", - " u_lightning = pyro.sample(\"u_lightning\", dist.Bernoulli(0.4))\n", - " lightning = pyro.deterministic(\"lightning\", u_lightning, event_dim=0)\n", - "\n", - " # this is a causally irrelevant site\n", - " smile = pyro.sample(\"smile\", dist.Bernoulli(0.5))\n", - "\n", - " forest_fire = pyro.deterministic(\n", - " \"forest_fire\", torch.max(match_dropped, lightning) + (0 * smile), event_dim=0\n", - " )\n", - "\n", - " return {\n", - " \"match_dropped\": match_dropped,\n", - " \"lightning\": lightning,\n", - " \"forest_fire\": forest_fire,\n", - " }\n", + "def example():\n", + " A = pyro.sample(\"A\", dist.Bernoulli(0.5))\n", + " B = pyro.sample(\"B\", dist.Bernoulli(0.5))\n", + " C = pyro.sample(\"C\", dist.Bernoulli(A))\n", + " return {\"A\": A, \"B\": B, \"C\": C}\n", "\n", "with ExtractSupports() as extract_supports:\n", - " forest_fire_model()\n", - " forest_fire_supports = {k: constraints.boolean for k in extract_supports.supports}\n", - "\n", - "pyro.render_model(forest_fire_model)" + " example()" ] }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor([-2.0027e-05, -1.1512e+01, -2.3024e+01, ..., -2.0027e-05,\n", - " -2.0027e-05, -2.0027e-05])\n", - "tensor(0.4743)\n" + "tensor(0.4971)\n" ] } ], "source": [ "query = SearchForExplanation(\n", - " supports=forest_fire_supports,\n", - " antecedents={\"match_dropped\": 1.0, \"lightning\": 1.0},\n", - " consequents={\"forest_fire\": torch.tensor(1.0)},\n", + " supports=extract_supports.supports,\n", + " antecedents={\"A\": 1.0, \"B\": 1.0},\n", + " consequents={\"C\": torch.tensor(1.0)},\n", " witnesses={},\n", - " alternatives={\"match_dropped\": 0.0, \"lightning\": 0.0},\n", - " consequent_scale=1e-5\n", - ")(forest_fire_model)\n", + " alternatives={\"A\": 0.0, \"B\": 0.0},\n", + " consequent_scale=1e-5,\n", + ")(example)\n", "\n", - "logp, trace, mwc, log_weight_vector = importance_infer(num_samples=10000)(query)()\n", + "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", "print(torch.exp(logp))" ] }, { "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(4937.)\n", - "tensor([[[[[[[[[[ True]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[False]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[False]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " ...,\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[ True]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[ True]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[ True]]]]]]]]]])\n", - "tensor([-2.0027e-05, -1.1512e+01, -2.3024e+01, ..., -2.0027e-05,\n", - " -2.0027e-05, -2.0027e-05])\n", - "tensor([1.0000, 0.0000, 0.0000, ..., 1.0000, 1.0000, 1.0000])\n", - "tensor(0.7991)\n" - ] - } - ], - "source": [ - "mask_intervened = trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0\n", - "print(mask_intervened.float().sum())\n", - "print(mask_intervened)\n", - "print(log_weight_vector)\n", - "print(torch.exp(log_weight_vector) * mask_intervened.squeeze())\n", - "\n", - "print(torch.sum(torch.exp(log_weight_vector) * mask_intervened.squeeze())/mask_intervened.float().sum())\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 103, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(5130.)\n", - "tensor([[[[[[[[[[ True]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[ True]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[False]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " ...,\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[ True]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[False]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[ True]]]]]]]]]])\n", - "tensor([-2.0027e-05, -1.1512e+01, -2.3024e+01, ..., -2.0027e-05,\n", - " -2.0027e-05, -2.0027e-05])\n", - "tensor([9.9998e-01, 1.0013e-05, 0.0000e+00, ..., 9.9998e-01, 0.0000e+00,\n", - " 9.9998e-01])\n", - "tensor(0.7025)\n" + "tensor(0.5016)\n" ] } ], "source": [ - "mask_intervened = trace.nodes[\"__cause____antecedent_lightning\"][\"value\"] == 0\n", - "print(mask_intervened.float().sum())\n", - "print(mask_intervened)\n", - "print(log_weight_vector)\n", - "print(torch.exp(log_weight_vector) * mask_intervened.squeeze())\n", - "\n", - "print(torch.sum(torch.exp(log_weight_vector) * mask_intervened.squeeze())/torch.sum(torch.exp(log_weight_vector)))" + "mask_intervened = (trace.nodes[\"__cause____antecedent_B\"][\"value\"] == 0)\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.float().squeeze())/mask_intervened.float().sum())\n", + "# Marginalizing over the fact that B was intervened on gives the following answer which accounts for the causal role of the set {A = 1, B = 1}" ] }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(2467.)\n", - "tensor([[[[[[[[[[False]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[False]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[ True]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " ...,\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[False]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[False]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[False]]]]]]]]]])\n", - "tensor([-2.0027e-05, -1.1512e+01, -2.3024e+01, ..., -2.0027e-05,\n", - " -2.0027e-05, -2.0027e-05])\n", - "tensor([0.0000e+00, 0.0000e+00, 1.0014e-10, ..., 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00])\n", - "tensor(1.7309e-06)\n" + "tensor(5.1220e-06)\n" ] } ], "source": [ - "mask_intervened = (trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 1) & (trace.nodes[\"__cause____antecedent_lightning\"][\"value\"] == 1)\n", - "print(mask_intervened.float().sum())\n", - "print(mask_intervened)\n", - "print(log_weight_vector)\n", - "print(torch.exp(log_weight_vector) * mask_intervened.squeeze())\n", - "\n", - "print(torch.sum(torch.exp(log_weight_vector) * mask_intervened.squeeze())/mask_intervened.float().sum())" + "mask_intervened = (trace.nodes[\"__cause____antecedent_B\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_A\"][\"value\"] == 1)\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.float().squeeze())/mask_intervened.float().sum())\n", + "# Marginalizing over the fact that B was intervened on and A was not gives the following answer which agrees with the fact that B has no causal role\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 61aa26b59a8a574526ec5967071da3a06e6ddbcc Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Mon, 12 Aug 2024 15:29:40 -0400 Subject: [PATCH 028/111] documentation completed --- .../explainable_categorical_alternate.ipynb | 10151 +--------------- docs/source/responsibility.ipynb | 9 +- 2 files changed, 170 insertions(+), 9990 deletions(-) diff --git a/docs/source/explainable_categorical_alternate.ipynb b/docs/source/explainable_categorical_alternate.ipynb index 6896e229..52f944e3 100644 --- a/docs/source/explainable_categorical_alternate.ipynb +++ b/docs/source/explainable_categorical_alternate.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The **Explainable Reasoning with ChiRho** package aims to provide a systematic, unified approach to causal explanation computations. The package provides a single generic program transformation that can be applied to any arbitrary causal model representable as a Chirho program. This program transformation allows several causal explanation queries to be modeled in terms of probabilistic queries. This approach of reducing causal queries to probabilistic computations on transformed causal models is the foundational idea behind all of ChiRho and has been leveraged for causal explanations in this module as well.\n", + "The **Explainable Reasoning with ChiRho** package aims to provide a systematic, unified approach to causal explanation computations. The package provides a single generic program transformation that can be applied to any arbitrary causal model representable as a Chirho program. This program transformation allows several causal explanation queries to be modeled in terms of probabilistic queries. This approach of reducing causal queries to probabilistic computations on transformed causal models is the foundational idea behind all of ChiRho and in this module, has been leveraged for causal explanations as well.\n", "\n", "The goal of this notebook is to illustrate how the package can be used to provide an approximate method of answering a range of causal explanation queries in causal models with only categorical variables. As the key tool will involve sampling-based posterior probability estimation, a lot of what will be said *mutatis mutandis* applies to more general settings where variables are continuous (to which we will devote another tutorial).\n", "\n", @@ -24,14 +24,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "[Poorva: come back to this outline for better headlines]\n", - "\n", "**Outline**\n", "\n", - "[Causal explanation and counterfactual thinking](#causal-explanation-and-counterfactual-thinking) \n", + "[Overview](#overview)\n", + "\n", + "[Setup](#setup)\n", "\n", + "[But-for Causal Explanations](#but-for-causal-explanations) \n", "\n", - "[Witness nodes and context sensitivity](#witness-nodes-and-context-sensitivity)\n", + "[Context-sensitive Causal Explanations](#witness-nodes-and-context-sensitivity)\n", "\n", "[Probability of causation and responsibility](#probability-of-causation-and-responsibility)" ] @@ -40,7 +41,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Causal explanation and counterfactual thinking" + "## Overview" ] }, { @@ -76,12 +77,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Setup" + "## Setup" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -120,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -147,7 +148,7 @@ " *args,\n", " num_samples=num_samples,\n", " max_plate_nesting=max_plate_nesting,\n", - " normalized=True,\n", + " normalized=False,\n", " **kwargs\n", " )\n", "\n", @@ -162,6 +163,13 @@ " return {i: KernelSoftConditionReparam(_soft_eq) for i in data}" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## But-for Causal Explanations" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -171,9 +179,16 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 20, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'u_match_dropped': Boolean(), 'match_dropped': IndependentConstraint(Real(), 0), 'u_lightning': Boolean(), 'lightning': IndependentConstraint(Real(), 0), 'smile': Boolean(), 'forest_fire': IndependentConstraint(Real(), 0)}\n" + ] + }, { "data": { "image/svg+xml": [ @@ -227,10 +242,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 64, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -283,7 +298,9 @@ "4. the elements of the current context (`witnesses`), and \n", "5. the `consequents` of interest $Y=y$. \n", "\n", - "The `SearchForExplanation` handler then takes these arguments and transforms the original model into another model in which interventions on antecedents and witnesses are applied stochastically. Once the antecedents `A` and witnesses `W` are chosen, parallel counterfactual worlds are created to condition on `A` being sufficient and necessary causes for the consequent with the context `W`." + "The `SearchForExplanation` handler then takes these arguments and transforms the original model into another model in which interventions on antecedents and witnesses are applied stochastically. Once the antecedents `A` and witnesses `W` are chosen, parallel counterfactual worlds are created to condition on `A` being sufficient and necessary causes for the consequent with the context `W`.\n", + "\n", + "And now, we are ready to use `SearchForExplanation` for answering but-for causal questions." ] }, { @@ -299,14 +316,14 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2951)\n" + "tensor(0.2947)\n" ] } ], @@ -333,14 +350,14 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.5970)\n" + "tensor(0.5993)\n" ] } ], @@ -353,35 +370,33 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "A similar estimation as above would not work in case of overdetermination where one of the two factors are enough to cause the outcome. Consider the case where both `match_dropped` and `lightning` did occur. In this case, if we try to determine the causal role of `match_dropped`, it would come out to be zero. And a symmetric reasoning works for lightning as well. This results in $P(f'_{m'}, f_m, m, l) = P(f'_{l'}, f_l, m, l)=0$" + "**Causal Query 2** Is a Chirho developer smiling a cause of forest fire?\n", + "\n", + "The intuitive answer is obviously no and we show that the same conclusion can be drawn using `SearchForExplanation` handler." ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(2.8615e-06)\n" + "tensor(1.0011e-05)\n" ] } ], "source": [ "query = SearchForExplanation(\n", " supports=forest_fire_supports,\n", - " antecedents={\"match_dropped\": torch.tensor(1.0)},\n", + " antecedents={\"smile\": torch.tensor(1.0)},\n", " consequents={\"forest_fire\": torch.tensor(1.0)},\n", " witnesses={}, # potential context elements, we leave them empty for now\n", - " alternatives={\"match_dropped\": torch.tensor(0.0)},\n", + " alternatives={\"smile\": torch.tensor(0.0)},\n", " consequent_scale=1e-5,\n", - ")(\n", - " pyro.poutine.reparam(config=reparam_config([\"match_dropped\", \"lightning\"]))(\n", - " condition(data={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)})\n", - " (forest_fire_model)\n", - " ))\n", + ")(forest_fire_model)\n", "\n", "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", "print(torch.exp(logp))" @@ -389,19 +404,19 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(2.8286e-06)\n" + "tensor(1.0011e-05)\n" ] } ], "source": [ - "mask_intervened = trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0\n", + "mask_intervened = trace.nodes[\"__cause____antecedent_smile\"][\"value\"] == 0\n", "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" ] }, @@ -409,29 +424,29 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "But if we consider both `match_dropped` and `lightning` to be possible causes, we can estimate $P(f'_{m',l'}, f_{m,l}, m, l)$ to determine their causal role." + "A similar estimation as above would not work in case of overdetermination where one of the two factors are enough to cause the outcome. Consider the case where both `match_dropped` and `lightning` did occur. In this case, if we try to determine the causal role of `match_dropped`, it would come out to be zero. And a symmetric reasoning works for lightning as well. This results in $P(f'_{m'}, f_m, m, l) = P(f'_{l'}, f_l, m, l)=0$. This is a canonical example that shows the limitations of but-for analysis." ] }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.0692)\n" + "tensor(2.8025e-06)\n" ] } ], "source": [ "query = SearchForExplanation(\n", " supports=forest_fire_supports,\n", - " antecedents={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)},\n", + " antecedents={\"match_dropped\": torch.tensor(1.0)},\n", " consequents={\"forest_fire\": torch.tensor(1.0)},\n", " witnesses={}, # potential context elements, we leave them empty for now\n", - " alternatives={\"match_dropped\": torch.tensor(0.0), \"lightning\": torch.tensor(0.0)},\n", + " alternatives={\"match_dropped\": torch.tensor(0.0)},\n", " consequent_scale=1e-5,\n", ")(\n", " pyro.poutine.reparam(config=reparam_config([\"match_dropped\", \"lightning\"]))(\n", @@ -445,19 +460,19 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2796)\n" + "tensor(2.7553e-06)\n" ] } ], "source": [ - "mask_intervened = (trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_lightning\"][\"value\"] == 0)\n", + "mask_intervened = trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0\n", "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" ] }, @@ -465,47 +480,70 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "DONE TILL HERE" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Witness nodes and context sensitivity" + "But if we consider both `match_dropped` and `lightning` to be possible causes, we can estimate $P(f'_{m',l'}, f_{m,l}, m, l)$ to determine their causal role that comes out to be greater than 0." ] }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.4616)\n" + "tensor(0.0682)\n" ] } ], "source": [ "query = SearchForExplanation(\n", " supports=forest_fire_supports,\n", - " antecedents={\"match_dropped\": 1.0, \"lightning\": 1.0},\n", + " antecedents={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)},\n", " consequents={\"forest_fire\": torch.tensor(1.0)},\n", - " witnesses={},\n", - " alternatives={\"match_dropped\": 0.0, \"lightning\": 0.0},\n", - ")(forest_fire_model)\n", + " witnesses={}, # potential context elements, we leave them empty for now\n", + " alternatives={\"match_dropped\": torch.tensor(0.0), \"lightning\": torch.tensor(0.0)},\n", + " consequent_scale=1e-5,\n", + ")(\n", + " pyro.poutine.reparam(config=reparam_config([\"match_dropped\", \"lightning\"]))(\n", + " condition(data={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)})\n", + " (forest_fire_model)\n", + " ))\n", "\n", - "logp, trace, mwc = importance_infer(num_samples=10000)(query)()\n", + "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", "print(torch.exp(logp))" ] }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.2793)\n" + ] + } + ], + "source": [ + "mask_intervened = (trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_lightning\"][\"value\"] == 0)\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Context-sensitive Causal Explanations" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "This already suggests a more complicated picture, as it turns out that we need to pay attention to membership in larger antecedent sets that would make a difference (that is one reason why we need stochasticity in antecedent candidate preemption: to search for such sets).\n", + "As the previous example showed, but-for analysis is not sufficient for our analysis. It induces the need to pay attention to membership of variables in larger antecedent sets that would make a difference (that is one reason why we need stochasticity in antecedent candidate preemption: to search for such sets).\n", "\n", "But even then, the but-for analysis does not pay sufficient attention to the granularity of a given problem and its causal structure. There are asymmetric cases where the efficiency of one cause prevents the efficiency of another, in which our causal attributions should also be asymmetric, but \"being a member of the same larger antecedent set\" isn't.\n", "\n", @@ -514,7 +552,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -597,7 +635,7 @@ "bill_hits\n", "\n", "\n", - "\n", + "\n", "prob_bill_hits->bill_hits\n", "\n", "\n", @@ -615,7 +653,7 @@ "bottle_shatters\n", "\n", "\n", - "\n", + "\n", "prob_bottle_shatters_if_sally->bottle_shatters\n", "\n", "\n", @@ -627,7 +665,7 @@ "prob_bottle_shatters_if_bill\n", "\n", "\n", - "\n", + "\n", "prob_bottle_shatters_if_bill->bottle_shatters\n", "\n", "\n", @@ -645,13 +683,13 @@ "\n", "\n", "\n", - "\n", + "\n", "sally_hits->bill_hits\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "sally_hits->bottle_shatters\n", "\n", "\n", @@ -666,10 +704,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 3, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -747,7 +785,7 @@ }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -777,7 +815,7 @@ " }\n", ")(stones_model))\n", "\n", - "logp, trace, mwc = importance_infer(num_samples=10000)(query)()\n", + "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", "print(torch.exp(logp))" ] }, @@ -792,7 +830,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "An intuitive solution to the problem, inspired by the Pearl-Halpern definition of actual causality (which we discuss in [another notebook](https://basisresearch.github.io/chirho/actual_causality.html)) is to say that **in answering actual causality queries, we need to consider what happens when part of the actual context is kept fixed.** For instance, in the bottle shattering example, given the observed fact that Bob’s stone didn’t hit, in the counterfactual world in which we keep this observed fact fixed, if Sally nad not thrown the stone, the bottle in fact would not have shattered. \n", + "An intuitive solution to the problem, inspired by the Pearl-Halpern definition of actual causality (which we discuss in [another notebook](https://basisresearch.github.io/chirho/actual_causality.html)) is to say that **in answering actual causality queries, we need to consider what happens when part of the actual context is kept fixed.** For instance, in the bottle shattering example, given the observed fact that Bob’s stone didn’t hit, in the counterfactual world in which we keep this observed fact fixed, if Sally had not thrown the stone, the bottle in fact would not have shattered. \n", "\n", "\n", "For this reason, our handler allows not only stochastic preemption of interventions (to approximate the search through possible antecedent sets) but also stochastic witness preemption of those nodes that are considered part of the context (these needn't exclude each other). In a witness preemption, we ensure that the counterfactual value is identical to the factual one (and by applying it randomly to candidate witness nodes, we approximate a search through all possible context sets)." @@ -800,16 +838,14 @@ }, { "cell_type": "code", - "execution_count": 292, + "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor([-1.1512e+01, -1.1512e+01, -1.1512e+01, ..., -2.3024e+01,\n", - " -1.1512e+01, -2.0027e-05])\n", - "tensor(0.2495)\n" + "tensor(0.2513)\n" ] } ], @@ -832,46 +868,17 @@ " }\n", ")(stones_model))\n", "\n", - "logp, trace, mwc, logw = importance_infer(num_samples=100000)(query)()\n", + "logp, trace, mwc, log_weights = importance_infer(num_samples=100000)(query)()\n", "print(torch.exp(logp))" ] }, - { - "cell_type": "code", - "execution_count": 151, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(1.)\n", - "torch.Size([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n", - "torch.Size([1, 1, 1, 1, 1, 3, 1, 1, 1, 1])\n", - "torch.Size([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n", - "tensor([1., 0., 1.])\n", - "tensor([ 0.0000, -0.0101, -0.0101])\n" - ] - } - ], - "source": [ - "trace.nodes.keys()\n", - "print(trace.nodes['sally_throws'][\"value\"].squeeze())\n", - "print(trace.nodes['__cause____antecedent_sally_throws'][\"value\"].shape)\n", - "print(trace.nodes['bill_hits'][\"value\"].shape)\n", - "print(trace.nodes['__cause____witness_bill_hits'][\"value\"].shape)\n", - "print(trace.nodes['bottle_shatters'][\"value\"].squeeze())\n", - "print(trace.nodes['__cause____consequent_bottle_shatters'][\"log_prob\"].squeeze())" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Admittedly, our search through contexts is very simple and degenerate, as the only part of the actual context which stochastically is kept fixed at the factual value is `bill_hits`. But already with this search, sally throwing is diagnosed as having non-null probability. In fact, the definition of actual causality in Halpern's book (*Actual causality*) contains an existential quantifier: a variable is an actual cause if there is at least one context in which a change in the outcome variable would result from changing the antecedent to have an alternative value, so our search provides a correct diagnosis here.\n", + "Admittedly, our search through contexts is simple as the only part of the actual context which stochastically is kept fixed at the factual value is `bill_hits`. But already with this search, sally throwing is diagnosed as having non-null probability. In fact, the definition of actual causality in Halpern's book (*Actual causality*) contains an existential quantifier: a variable is an actual cause if there is at least one context in which a change in the outcome variable would result from changing the antecedent to have an alternative value, so our search provides a correct diagnosis here.\n", "\n", - "Crucally, as intended, an analogous inference for whether `bill_throws` is a cause of the bottle shattering, yields a different\n", - "result and assigns null causal role to bill." + "Crucially, as intended, an analogous inference for whether `bill_throws` is a cause of the bottle shattering, yields a different result and assigns null causal role to bill." ] }, { @@ -914,7 +921,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Probability of causation and responsibility" + "## Probability of Causation and Responsibility" ] }, { @@ -930,213 +937,14 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - 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" -2.3024e+01, -2.3024e+01, -2.3024e+01, -2.3024e+01, -2.3024e+01,\n", - " -2.3024e+01, -2.0385e-05, -2.0385e-05, -2.3024e+01, -2.3024e+01,\n", - " -2.3024e+01, -2.3024e+01, -2.0385e-05, -2.3024e+01, -2.3024e+01,\n", - " -2.3024e+01, -2.3024e+01, -2.3024e+01, -2.3024e+01, -2.3024e+01])\n" + "tensor(0.1439)\n" ] } ], @@ -1150,9755 +958,126 @@ " consequent_scale=1e-5\n", ")(condition(\n", " data={\n", - " \"prob_sally_throws\": torch.tensor(1.0),\n", - " \"prob_bill_throws\": torch.tensor(0.0),\n", - " \"prob_sally_hits\": torch.tensor(1.0),\n", - " \"prob_bill_hits\": torch.tensor(0.0),\n", - " \"prob_bottle_shatters_if_sally\": torch.tensor(1.0),\n", - " \"prob_bottle_shatters_if_bill\": torch.tensor(0.0),\n", + " \"prob_sally_throws\": torch.tensor(0.8),\n", + " \"prob_bill_throws\": torch.tensor(0.7),\n", + " \"prob_sally_hits\": torch.tensor(0.9),\n", + " \"prob_bill_hits\": torch.tensor(0.8),\n", + " \"prob_bottle_shatters_if_sally\": torch.tensor(0.9),\n", + " \"prob_bottle_shatters_if_bill\": torch.tensor(0.8),\n", " \"bottle_shatters\": torch.tensor(1.0),\n", " }\n", ")(stones_model))\n", "\n", - "logp, trace, mwc, logw = importance_infer(num_samples=1000)(query)()\n", - "\n", - "# print(logp)\n", - "\n", - "# print(torch.exp(logp))\n", - "\n", - "# nodes = trace.nodes[\"_RETURN\"][\"value\"]\n", - "\n", - "# print(trace.nodes[\"sally_throws\"][\"value\"].shape)\n", - "# print(nodes[\"sally_throws\"].shape)\n", - "# with mwc:\n", - "# print(gather(nodes[\"sally_throws\"], IndexSet(sally_throws={0})).squeeze())\n", - "# print(gather(nodes[\"sally_throws\"], IndexSet(sally_throws={1})).squeeze())\n", - "# print(gather(nodes[\"sally_throws\"], IndexSet(sally_throws={2})).squeeze())\n", - "\n", - "# print(gather(nodes[\"bill_throws\"], IndexSet(bill_throws={0})).squeeze())\n", - "# print(gather(nodes[\"bill_throws\"], IndexSet(bill_throws={1})).squeeze())\n", - "# print(gather(nodes[\"bill_throws\"], IndexSet(bill_throws={2})).squeeze())\n", - "\n", - "# st_responsible = gather(nodes[\"sally_throws\"], IndexSet(sally_throws={1})) != \\\n", - "# gather(nodes[\"sally_throws\"], IndexSet(sally_throws={2}))\n", - "\n", - "# bt_responsible = gather(nodes[\"bill_throws\"], IndexSet(bill_throws={1})) != \\\n", - "# gather(nodes[\"bill_throws\"], IndexSet(bill_throws={2}))\n", - "\n", - "# print(\"Degree of responsibility of Sally:\", st_responsible.sum() / st_responsible.numel())\n", - "# print(\"Degree of responsibility of Billy:\", bt_responsible.sum() / bt_responsible.numel())" + "logp, trace, mwc, log_weights = importance_infer(num_samples=1000)(query)()\n", + "print(torch.exp(logp))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Note that we assumed Sally to be more likely to throw, more likely to hit, and more likely to shatter the bottle if she hits. For this reason, we expect her to be more likely to be causally responsible for the outcome. Conceptually, these estimates are impacted by some hyperparameters, such as witness preemption probabilities, so perhaps a bit more clarity on can be gained if we think we have a complete list of potential causes and normalize. " + "Now we show how our earlier analysis on the `stones_model` can be carried out through some analysis on the samples we get through this model where we have both `sally_throw` and `bill_throws` as candidate causes and both `bill_hits` and `sally_hits` as context nodes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first compute the probability of causation for `sally_throws`. We compute the probability that the set {sally_throws=1} is the cause of bottle shattering." ] }, { "cell_type": "code", - "execution_count": 298, + "execution_count": 50, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "tensor(0.8100)" - ] - }, - "execution_count": 298, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.1956)\n" + ] } ], "source": [ - "st_responsible2 = st_responsible.float()\n", - "st_responsible2[st_responsible2 == 0.0] = 9.2\n", - "st_responsible2[st_responsible2 == 1.0] = 1.0\n", - "# st_responsible2\n", - "logp = torch.logsumexp(st_responsible2.squeeze().float() * logw, dim=0) - torch.log(torch.tensor(1000))\n", - "torch.exp(logp)" + "mask_intervened = (trace.nodes[\"__cause____antecedent_sally_throws\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_bill_throws\"][\"value\"] == 1)\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.float().squeeze())/mask_intervened.float().sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We similarly compute this probability for `bill_throws`." ] }, { "cell_type": "code", - "execution_count": 261, + "execution_count": 51, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "tensor([False, False, True, True, True, True, True, False, True, False,\n", - " True, True, False, True, True, False, True, True, True, True,\n", - " True, True, False, True, True, True, True, True, True, True,\n", - " False, True, True, False, False, True, True, True, True, True,\n", - " True, True, True, True, True, True, True, True, True, False,\n", - " True, False, True, True, True, True, False, True, True, False,\n", - " False, False, True, True, True, True, True, False, True, True,\n", - " False, True, True, True, True, False, True, True, True, True,\n", - " True, True, True, False, True, True, False, True, False, True,\n", - " True, True, False, True, False, True, True, True, True, True,\n", - " True, False, True, False, True, False, False, True, True, True,\n", - " True, False, True, True, True, True, True, False, False, True,\n", - " False, True, True, True, True, True, True, True, True, True,\n", - 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"st_responsible.squeeze()" + "mask_intervened = (trace.nodes[\"__cause____antecedent_sally_throws\"][\"value\"] == 1) & (trace.nodes[\"__cause____antecedent_bill_throws\"][\"value\"] == 0)\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.float().squeeze())/mask_intervened.float().sum())" ] }, { - "cell_type": "code", - "execution_count": 60, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "query = SearchForExplanation(\n", - " supports=stones_supports,\n", - " antecedents={\"sally_throws\": torch.tensor(1.0), \"bill_throws\": torch.tensor(1.0)},\n", - " consequents={\"bottle_shatters\": torch.tensor(1.0)},\n", - " witnesses={\"sally_hits\": torch.tensor(0.0)},\n", - " alternatives={\"sally_throws\": torch.tensor(0.0), \"bill_throws\": torch.tensor(0.0)},\n", - " antecedent_bias=-0.5,\n", - " witness_bias=0.5,\n", - " consequent_scale=1e-5\n", - ")(condition(\n", - " data={\n", - " \"prob_sally_throws\": torch.tensor(1.0),\n", - " \"prob_bill_throws\": torch.tensor(0.0),\n", - " \"prob_sally_hits\": torch.tensor(1.0),\n", - " \"prob_bill_hits\": torch.tensor(0.0),\n", - " \"prob_bottle_shatters_if_sally\": torch.tensor(1.0),\n", - " \"prob_bottle_shatters_if_bill\": torch.tensor(1.0),\n", - " \"bottle_shatters\": torch.tensor(1.0),\n", - " }\n", - ")(stones_model))" + "We can also use the same model as above to compute the degree of responsibility for bill and sally as follows. We interpret the degree of responsbility asisgned to sally for bottle shattering as the probability that `sally_throws=1` is part of the cause." ] }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor([[[[[[[[[[False]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[ True]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[ True]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[False]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[ True]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[ True]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[False]]]]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[[[[ True]]]]]]]]],\n", - 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" 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10, 9.9998e-01,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 9.9998e-01,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00,\n", - " 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10, 1.0014e-10,\n", - " 9.9998e-01, 1.0014e-10, 1.0014e-10, 9.9998e-01, 0.0000e+00, 0.0000e+00,\n", - " 9.9998e-01, 1.0014e-10, 9.9998e-01, 0.0000e+00, 1.0014e-10, 0.0000e+00,\n", - " 9.9998e-01, 0.0000e+00, 9.9998e-01, 1.0014e-10, 1.0014e-10, 1.0014e-10,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10,\n", - " 0.0000e+00, 1.0014e-10, 9.9998e-01, 1.0014e-10, 0.0000e+00, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10, 9.9998e-01,\n", - " 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 9.9998e-01, 0.0000e+00,\n", - " 1.0014e-10, 9.9998e-01, 9.9998e-01, 1.0014e-10, 1.0014e-10, 9.9998e-01,\n", - " 9.9998e-01, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10, 1.0014e-10,\n", - " 0.0000e+00, 0.0000e+00, 9.9998e-01, 0.0000e+00, 1.0014e-10, 1.0014e-10,\n", - " 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10, 9.9998e-01, 0.0000e+00,\n", - " 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00,\n", - " 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 9.9998e-01, 1.0014e-10, 1.0014e-10, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10, 1.0014e-10,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 9.9998e-01, 9.9998e-01,\n", - " 0.0000e+00, 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 1.0014e-10, 0.0000e+00, 9.9998e-01, 1.0014e-10,\n", - " 1.0014e-10, 9.9998e-01, 1.0014e-10, 1.0014e-10, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 1.0014e-10, 9.9998e-01, 0.0000e+00, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 1.0014e-10, 9.9998e-01, 1.0014e-10, 0.0000e+00,\n", - " 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10,\n", - " 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00, 1.0014e-10,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10, 1.0014e-10, 1.0014e-10,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 9.9998e-01,\n", - " 1.0014e-10, 9.9998e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 1.0014e-10,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00,\n", - " 0.0000e+00, 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00, 1.0014e-10,\n", - " 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00, 9.9998e-01, 1.0014e-10,\n", - " 1.0014e-10, 0.0000e+00, 1.0014e-10, 1.0014e-10, 9.9998e-01, 0.0000e+00,\n", - " 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00, 9.9998e-01, 0.0000e+00,\n", - " 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00, 9.9998e-01, 1.0014e-10,\n", - " 1.0014e-10, 1.0014e-10, 9.9998e-01, 1.0014e-10, 1.0014e-10, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 1.0014e-10, 9.9998e-01, 1.0014e-10, 0.0000e+00,\n", - " 9.9998e-01, 1.0014e-10, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10,\n", - " 9.9998e-01, 1.0014e-10, 0.0000e+00, 9.9998e-01, 9.9998e-01, 1.0014e-10,\n", - " 1.0014e-10, 0.0000e+00, 1.0014e-10, 9.9998e-01, 1.0014e-10, 0.0000e+00,\n", - " 0.0000e+00, 9.9998e-01, 0.0000e+00, 1.0014e-10, 9.9998e-01, 1.0014e-10,\n", - " 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10,\n", - " 1.0014e-10, 0.0000e+00, 9.9998e-01, 0.0000e+00, 0.0000e+00, 1.0014e-10,\n", - " 1.0014e-10, 1.0014e-10, 1.0014e-10, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00, 0.0000e+00, 9.9998e-01, 1.0014e-10, 0.0000e+00,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10, 0.0000e+00, 1.0014e-10,\n", - " 1.0014e-10, 0.0000e+00, 0.0000e+00, 1.0014e-10])\n", - "tensor(117.9976)\n" + "tensor(0.2683)\n" ] } ], "source": [ - "mask_intervened = (trace.nodes[\"__cause____antecedent_bill_throws\"][\"value\"] == 0)\n", - "log_weight_vector=logw\n", - "a = mask_intervened.float().sum()\n", - "print(mask_intervened)\n", - "# mask_intervened = mask_intervened.float()\n", - "# mask_intervened[mask_intervened == 0.0] = 9.2\n", - "# mask_intervened[mask_intervened == 1.0] = 1.0\n", - "print(log_weight_vector)\n", - "print(mask_intervened.squeeze())\n", - "print(torch.exp(log_weight_vector) * mask_intervened.squeeze())\n", - "\n", - "print(torch.sum(torch.exp(log_weight_vector) * mask_intervened.squeeze()))" + "mask_intervened = (trace.nodes[\"__cause____antecedent_sally_throws\"][\"value\"] == 0)\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.float().squeeze())/mask_intervened.float().sum())" ] }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 53, "metadata": {}, "outputs": [ { - "ename": "AttributeError", - "evalue": "'Trace' object has no attribute 'n'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[62], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mtrace\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn\u001b[49m)\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(trace\u001b[38;5;241m.\u001b[39mnodes[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msally_throws\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;241m2\u001b[39m]\u001b[38;5;241m.\u001b[39msqueeze())\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(trace\u001b[38;5;241m.\u001b[39mnodes[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbottle_shatters\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;241m2\u001b[39m]\u001b[38;5;241m.\u001b[39msqueeze())\n", - "\u001b[0;31mAttributeError\u001b[0m: 'Trace' object has no attribute 'n'" + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.2004)\n" ] } ], "source": [ - "print(trace.n)\n", - "print(trace.nodes[\"sally_throws\"][\"value\"][2].squeeze())\n", - "print(trace.nodes[\"bottle_shatters\"][\"value\"][2].squeeze())\n", - "trace.nodes[\"__cause____consequent_bottle_shatters\"][\"log_prob\"][2]" + "mask_intervened = (trace.nodes[\"__cause____antecedent_bill_throws\"][\"value\"] == 0)\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.float().squeeze())/mask_intervened.float().sum())" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# References\n", - "\n", - "1. " + "Note that we assumed Sally to be more likely to throw, more likely to hit, and more likely to shatter the bottle if she hits. For this reason, we expect her to be more likely to be causally responsible for the outcome and that is the result we got. Conceptually, these estimates are impacted by some hyperparameters, such as witness preemption probabilities, so perhaps a bit more clarity on can be gained if we think we have a complete list of potential causes and normalize. " ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] } ], "metadata": { diff --git a/docs/source/responsibility.ipynb b/docs/source/responsibility.ipynb index a203dbc4..5607419f 100644 --- a/docs/source/responsibility.ipynb +++ b/docs/source/responsibility.ipynb @@ -88,14 +88,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.4971)\n" + "tensor(0.1057)\n" ] } ], @@ -106,6 +106,7 @@ " consequents={\"C\": torch.tensor(1.0)},\n", " witnesses={},\n", " alternatives={\"A\": 0.0, \"B\": 0.0},\n", + " antecedent_bias=0.4,\n", " consequent_scale=1e-5,\n", ")(example)\n", "\n", @@ -115,14 +116,14 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.5016)\n" + "tensor(0.1077)\n" ] } ], From 58842f6cad7075a2ea85d8fb2ff3e3066008d309 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Mon, 12 Aug 2024 19:02:29 -0400 Subject: [PATCH 029/111] small typos --- .../explainable_categorical_alternate.ipynb | 118 +++++++++++------- 1 file changed, 71 insertions(+), 47 deletions(-) diff --git a/docs/source/explainable_categorical_alternate.ipynb b/docs/source/explainable_categorical_alternate.ipynb index 52f944e3..e7469571 100644 --- a/docs/source/explainable_categorical_alternate.ipynb +++ b/docs/source/explainable_categorical_alternate.ipynb @@ -65,7 +65,7 @@ "- A major philosophical position in the analysis of causality is that the definition of causal dependence should be formulated in terms of counterfactual conditionals (Lewis, 1973. “Causation”, Journal of Philosophy, 70: 556–67). On this approach, $e$ causally depends on $c$ if and only if, if $c$ were not to occur $e$ would not occur. (The view does not remain uncontested, see the [SEP entry on counterfactual theories of causation](https://plato.stanford.edu/entries/causation-counterfactual/)).\n", "- At least a few major approaches to explainable AI (such as [LIME](https://arxiv.org/abs/1602.04938), or [Shapley values](https://papers.nips.cc/paper_files/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html)) are based on the idea that explanations can be obtained by perturbing or shifting the input values and observing the changes in the output. This to a large extent can be thought of as a way of evaluating the but-for condition: if the input value was different, would the output value change? \n", " \n", - "More generally, we can ask about the probability with which an alterantive intervention would lead to a cahnge in the outcome (perhaps while conditioning on other items of information), in line with the ideas present in Pearl's *Probabilities of causation...* and Chapter 9 of Pearl's *Causality*. While immensely useful, the but-for condition is not fine-grained enough to answer all the questions we are interested in or to give us the intended answers in cases in which the underlying causal model is non-trivial. We will illustrate this observation in this tutorial. \n", + "More generally, we can ask about the probability with which an alterantive intervention would lead to a change in the outcome (perhaps while conditioning on other items of information), in line with the ideas present in Pearl's *Probabilities of causation...* and Chapter 9 of Pearl's *Causality*. While immensely useful, the but-for condition is not fine-grained enough to answer all the questions we are interested in or to give us the intended answers in cases in which the underlying causal model is non-trivial. We will illustrate this observation in this tutorial. \n", "\n", "\n", "On the other hand, we can ask whether given our model (and perhaps conditioning on other pieces of information we posses), intervening on a given candidate cause to have a given value results in the outcome being as observed (or, more generally, the probability of that outcome being as observed) - this is conceptually similar to Pearl's probability of sufficiency. \n", @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -116,12 +116,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We first setup the essentials for performing probabilistic inference on the transformed causal models. We have a function for performing importance sampling on a model and few other utility functions." + "We first setup the essentials for performing probabilistic inference on the transformed causal models. We have a function for performing importance sampling on a model and a few other utility functions." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -179,73 +179,97 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 4, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'u_match_dropped': Boolean(), 'match_dropped': IndependentConstraint(Real(), 0), 'u_lightning': Boolean(), 'lightning': IndependentConstraint(Real(), 0), 'smile': Boolean(), 'forest_fire': IndependentConstraint(Real(), 0)}\n" - ] - }, { "data": { "image/svg+xml": [ "\n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "%3\n", + "\n", "\n", "\n", "u_match_dropped\n", - "\n", - "u_match_dropped\n", + "\n", + "u_match_dropped\n", "\n", "\n", "\n", "match_dropped\n", - "\n", - "match_dropped\n", + "\n", + "match_dropped\n", + "\n", + "\n", + "\n", + "u_match_dropped->match_dropped\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "forest_fire\n", + "\n", + "forest_fire\n", + "\n", + "\n", + "\n", + "u_match_dropped->forest_fire\n", + "\n", + "\n", "\n", "\n", "\n", "u_lightning\n", - "\n", - "u_lightning\n", + "\n", + "u_lightning\n", "\n", "\n", "\n", "lightning\n", - "\n", - "lightning\n", + "\n", + "lightning\n", + "\n", + "\n", + "\n", + "u_lightning->lightning\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "u_lightning->forest_fire\n", + "\n", + "\n", "\n", "\n", "\n", "smile\n", - "\n", - "smile\n", + "\n", + "smile\n", "\n", - "\n", - "\n", - "forest_fire\n", - "\n", - "forest_fire\n", + "\n", + "\n", + "smile->forest_fire\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 20, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -300,7 +324,7 @@ "\n", "The `SearchForExplanation` handler then takes these arguments and transforms the original model into another model in which interventions on antecedents and witnesses are applied stochastically. Once the antecedents `A` and witnesses `W` are chosen, parallel counterfactual worlds are created to condition on `A` being sufficient and necessary causes for the consequent with the context `W`.\n", "\n", - "And now, we are ready to use `SearchForExplanation` for answering but-for causal questions." + "Now we are ready to use `SearchForExplanation` for answering but-for causal questions." ] }, { @@ -316,14 +340,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2947)\n" + "tensor(0.2959)\n" ] } ], @@ -372,12 +396,12 @@ "source": [ "**Causal Query 2** Is a Chirho developer smiling a cause of forest fire?\n", "\n", - "The intuitive answer is obviously no and we show that the same conclusion can be drawn using `SearchForExplanation` handler." + "The intuitive answer is obviously no, and we show that the same conclusion can be drawn using `SearchForExplanation`." ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -393,7 +417,7 @@ " supports=forest_fire_supports,\n", " antecedents={\"smile\": torch.tensor(1.0)},\n", " consequents={\"forest_fire\": torch.tensor(1.0)},\n", - " witnesses={}, # potential context elements, we leave them empty for now\n", + " witnesses={}, \n", " alternatives={\"smile\": torch.tensor(0.0)},\n", " consequent_scale=1e-5,\n", ")(forest_fire_model)\n", @@ -424,19 +448,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "A similar estimation as above would not work in case of overdetermination where one of the two factors are enough to cause the outcome. Consider the case where both `match_dropped` and `lightning` did occur. In this case, if we try to determine the causal role of `match_dropped`, it would come out to be zero. And a symmetric reasoning works for lightning as well. This results in $P(f'_{m'}, f_m, m, l) = P(f'_{l'}, f_l, m, l)=0$. This is a canonical example that shows the limitations of but-for analysis." + "A similar estimation as above would not work in a case of overdetermination, in which each of the two factors can alone cause the outcome. Consider the case where both `match_dropped` and `lightning` did occur. In this case, if we try to determine the causal role of `match_dropped`, it would come out to be zero. And a symmetric reasoning works for lightning as well. This results in $P(f'_{m'}, f_m, m, l) = P(f'_{l'}, f_l, m, l)=0$. This is a canonical example that shows the limitations of the but-for analysis." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(2.8025e-06)\n" + "tensor(2.7596e-06)\n" ] } ], @@ -445,7 +469,7 @@ " supports=forest_fire_supports,\n", " antecedents={\"match_dropped\": torch.tensor(1.0)},\n", " consequents={\"forest_fire\": torch.tensor(1.0)},\n", - " witnesses={}, # potential context elements, we leave them empty for now\n", + " witnesses={}, \n", " alternatives={\"match_dropped\": torch.tensor(0.0)},\n", " consequent_scale=1e-5,\n", ")(\n", @@ -543,9 +567,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As the previous example showed, but-for analysis is not sufficient for our analysis. It induces the need to pay attention to membership of variables in larger antecedent sets that would make a difference (that is one reason why we need stochasticity in antecedent candidate preemption: to search for such sets).\n", + "As the previous example showed, the but-for analysis is not sufficient for identifying causal roles. This induces the need to pay attention to the membership of variables in larger antecedent sets that would make a difference (that is one reason why we need stochasticity in the antecedent candidate preemption: to search for such sets).\n", "\n", - "But even then, the but-for analysis does not pay sufficient attention to the granularity of a given problem and its causal structure. There are asymmetric cases where the efficiency of one cause prevents the efficiency of another, in which our causal attributions should also be asymmetric, but \"being a member of the same larger antecedent set\" isn't.\n", + "But even then, the but-for analysis does not pay sufficient attention to the granularity of a given problem and its causal structure. There are asymmetric cases where the efficiency of one cause prevents the efficiency of another, in which our causal attributions should a be asymmetric, but \"being a member of the same larger antecedent set\" isn't.\n", "\n", "A simple example is breaking a bottle. Suppose Sally and Bob throw a rock at a bottle, and Sally does so a little earlier than Bob. Suppose both are perfectly accurate, and the bottle shatters when hit. Sally hits, and the bottle shatters, but Bob doesn't hit it because the bottle is no longer there. " ] From 9ee306892ec5a49fb7bec556bbfb603f80142cbb Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Tue, 13 Aug 2024 13:51:33 -0400 Subject: [PATCH 030/111] small changes --- .../explainable_categorical_alternate.ipynb | 56 ++++++++++--------- 1 file changed, 30 insertions(+), 26 deletions(-) diff --git a/docs/source/explainable_categorical_alternate.ipynb b/docs/source/explainable_categorical_alternate.ipynb index 52f944e3..bb553c23 100644 --- a/docs/source/explainable_categorical_alternate.ipynb +++ b/docs/source/explainable_categorical_alternate.ipynb @@ -41,13 +41,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Overview" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ + "# Motivation\n", + "\n", "Consider the following causality-related queries:\n", "\n", "- **Friendly Fire:** On March 24, 2002, A B-52 bomber fired a Joint Direct Attack Munition at a US battalion command post, killing three and injuring twenty special forces soldiers. Out of multiple potential contributing factors, which were actually responsible for the incident?\n", @@ -56,21 +51,7 @@ "\n", "- **Explainable AI:** Your pre-trial release has been refused based on your [COMPAS score](https://en.wikipedia.org/wiki/COMPAS_(software)). The decision was made using a proprietary predictive model. All you have access to is the questionnaire that was used, and perhaps some demographic information about a class of human beings subjected to this evaluation. But which of these factors resulted in your score being what it is, and what were their contributions?\n", "\n", - "\n", - "Questions of this sort are more local than those pertaining to average treatment effects, as they pertain to actual cases that come with their own contexts. Being able to answer them is useful for understanding how we can prevent undesirable outcomes similar to ones that we have observed, or promote the occurrence of desirable outcomes in contexts similar to the ones in which they had been observed. These context-sensitive causality questions are also an essential element of blame and responsibility assignments. If the phenomenon we're trying to explain is the behavior of a predictive model, we are dealing with a problem in explainable AI; but the underlying intuition behind the workings of **Explainable Reasoning with ChiRho** is that causally explaining the behavior of an opaque model is not that much different from providing a causal explanation of other real-world phenomena: we need to address such queries in a principled manner employing some approximate but hopefully reliable causal model of how things work (be that events outside of computers, or a predicitive model's behavior). **Explainable Reasoning with ChiRho** package aims to provide a unified general approach to the relevant causal explanation computations.\n", - "\n", - "At some level of generality, a useful point of departure is a general counterfactual one. On one hand, we can ask whether the event would have occurred had a given candidate cause not taken place. This is sometimes called the *but-for test*, has a tradition of being used as a tool for answering causality and attribution queries. \n", - "\n", - "- It is often used in [the law of torts](https://plato.stanford.edu/entries/causation-law/) to determine if a defendant's conduct was the cause of a particular harm. The test is often formulated as follows: \"But for the defendant's conduct, would the harm have occurred?\" \n", - "- A major philosophical position in the analysis of causality is that the definition of causal dependence should be formulated in terms of counterfactual conditionals (Lewis, 1973. “Causation”, Journal of Philosophy, 70: 556–67). On this approach, $e$ causally depends on $c$ if and only if, if $c$ were not to occur $e$ would not occur. (The view does not remain uncontested, see the [SEP entry on counterfactual theories of causation](https://plato.stanford.edu/entries/causation-counterfactual/)).\n", - "- At least a few major approaches to explainable AI (such as [LIME](https://arxiv.org/abs/1602.04938), or [Shapley values](https://papers.nips.cc/paper_files/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html)) are based on the idea that explanations can be obtained by perturbing or shifting the input values and observing the changes in the output. This to a large extent can be thought of as a way of evaluating the but-for condition: if the input value was different, would the output value change? \n", - " \n", - "More generally, we can ask about the probability with which an alterantive intervention would lead to a cahnge in the outcome (perhaps while conditioning on other items of information), in line with the ideas present in Pearl's *Probabilities of causation...* and Chapter 9 of Pearl's *Causality*. While immensely useful, the but-for condition is not fine-grained enough to answer all the questions we are interested in or to give us the intended answers in cases in which the underlying causal model is non-trivial. We will illustrate this observation in this tutorial. \n", - "\n", - "\n", - "On the other hand, we can ask whether given our model (and perhaps conditioning on other pieces of information we posses), intervening on a given candidate cause to have a given value results in the outcome being as observed (or, more generally, the probability of that outcome being as observed) - this is conceptually similar to Pearl's probability of sufficiency. \n", - "\n", - "We will start with these two approaches, but soon we will notice that often our explanatory questions are more local and a more fine-grained tool is needed. The general intuition (inspired by Halpern's *Actual Causality*) that we implemented is that when we ask local explanatory questions, we need to keep some part of the actual context fixed and consider alternative scenarios insofar as potential causes are involved. That is, we (i) search through possible alternative interventions that could be performed on the candidate cause nodes, (ii) search through possible context nodes that are to be intervened to be at their factual values even in the counterfactual worlds, (iii) see how these options play out in intervened worlds, and (iv) investigate and meaningfully summarize what happens with the outcome nodes of interest in all those counterfactual worlds. " + "Questions of this sort are more specific and local as they pertain to actual cases that come with their own contexts, unlike average treatment effects discussed in an earlier [tutorial](https://github.com/BasisResearch/chirho/blob/master/docs/source/tutorial_i.ipynb). Being able to answer such context-sensitive questions is useful for understanding how we can prevent undesirable outcomes similar to ones that we have observed, or promote the occurrence of desirable outcomes in contexts similar to the ones in which they had been observed. Moreover, these context-sensitive causality questions are also an essential element of blame and responsibility assignments. " ] }, { @@ -156,6 +137,7 @@ "\n", " return _wrapped_model\n", "\n", + "# The following functions are needed for conditioning on random variables defined using pyro.deterministic\n", "def _soft_eq(v1: torch.Tensor, v2: torch.Tensor) -> torch.Tensor:\n", " return soft_eq(constraints.boolean, v1, v2, scale=0.001)\n", "\n", @@ -298,7 +280,7 @@ "4. the elements of the current context (`witnesses`), and \n", "5. the `consequents` of interest $Y=y$. \n", "\n", - "The `SearchForExplanation` handler then takes these arguments and transforms the original model into another model in which interventions on antecedents and witnesses are applied stochastically. Once the antecedents `A` and witnesses `W` are chosen, parallel counterfactual worlds are created to condition on `A` being sufficient and necessary causes for the consequent with the context `W`.\n", + "The `SearchForExplanation` handler then takes these arguments and transforms the original model into another model in which interventions on antecedents and witnesses are applied stochastically. Once the antecedents $A \\subseteq$ `antecedents` and witnesses $W \\subseteq$ `witnesses` are chosen via sampling, parallel counterfactual worlds are created to condition on `A` being sufficient and necessary causes for the consequent with the context `W`. For more details on `SearchForExplanation`, please refer to the [documentation](https://basisresearch.github.io/chirho/explainable.html#chirho.explainable.handlers.explanation.SearchForExplanation).\n", "\n", "And now, we are ready to use `SearchForExplanation` for answering but-for causal questions." ] @@ -307,11 +289,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Causal Query 1** Is dropping a match a cause of forest fire?\n", + "**Causal Query 1** What is the probability that dropping a match has a causal role over forest fire?\n", "\n", "To answer the above question, we compute the probability of both forest fire not occurring if we intervene on the match to not be dropped, and forest fire occurring if we intervene on the match to be dropped. i.e. $P(f'_{m'}, f_m)$. this computation can be carried out using `SearchForExplanation`.\n", "\n", - "The potential cause (`antecedent`) we're considering is `match_dropped=1`, we contrast it with what would happen if we intervened on it to not happen (`alternatives`). We are interested in whether an outcome variable `forest_fire` (`consequent`) has value 1 under these two interventions. " + "The potential cause (`antecedent`) we're considering is `match_dropped=1`, we contrast it with what would happen if we intervened on it to not happen (`alternatives`). We are interested in whether an outcome variable `forest_fire` (`consequent`) has value 1 under these two interventions. Note that these two inteventions correspond to `match_dropped=1` being a sufficient and necessary cause for `forest_fire=1`. These notions have a correspondence to Pearl's notion of probability of necessity and sufficiency in this simple model. But we later extend these notions to be context-sensitive." ] }, { @@ -370,7 +352,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Causal Query 2** Is a Chirho developer smiling a cause of forest fire?\n", + "**Causal Query 2** What is the probability that a Chirho developer has a causal role over forest fire?\n", "\n", "The intuitive answer is obviously no and we show that the same conclusion can be drawn using `SearchForExplanation` handler." ] @@ -1078,6 +1060,28 @@ "source": [ "Note that we assumed Sally to be more likely to throw, more likely to hit, and more likely to shatter the bottle if she hits. For this reason, we expect her to be more likely to be causally responsible for the outcome and that is the result we got. Conceptually, these estimates are impacted by some hyperparameters, such as witness preemption probabilities, so perhaps a bit more clarity on can be gained if we think we have a complete list of potential causes and normalize. " ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Discussion\n", + "\n", + "If the phenomenon we're trying to explain is the behavior of a predictive model, we are dealing with a problem in explainable AI; but the underlying intuition behind the workings of **Explainable Reasoning with ChiRho** is that causally explaining the behavior of an opaque model is not that much different from providing a causal explanation of other real-world phenomena: we need to address such queries in a principled manner employing some approximate but hopefully reliable causal model of how things work (be that events outside of computers, or a predicitive model's behavior). **Explainable Reasoning with ChiRho** package aims to provide a unified general approach to the relevant causal explanation computations.\n", + "\n", + "At some level of generality, a useful point of departure is a general counterfactual one. On one hand, we can ask whether the event would have occurred had a given candidate cause not taken place. This is sometimes called the *but-for test*, has a tradition of being used as a tool for answering causality and attribution queries. \n", + "\n", + "- It is often used in [the law of torts](https://plato.stanford.edu/entries/causation-law/) to determine if a defendant's conduct was the cause of a particular harm. The test is often formulated as follows: \"But for the defendant's conduct, would the harm have occurred?\" \n", + "- A major philosophical position in the analysis of causality is that the definition of causal dependence should be formulated in terms of counterfactual conditionals (Lewis, 1973. “Causation”, Journal of Philosophy, 70: 556–67). On this approach, $e$ causally depends on $c$ if and only if, if $c$ were not to occur $e$ would not occur. (The view does not remain uncontested, see the [SEP entry on counterfactual theories of causation](https://plato.stanford.edu/entries/causation-counterfactual/)).\n", + "- At least a few major approaches to explainable AI (such as [LIME](https://arxiv.org/abs/1602.04938), or [Shapley values](https://papers.nips.cc/paper_files/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html)) are based on the idea that explanations can be obtained by perturbing or shifting the input values and observing the changes in the output. This to a large extent can be thought of as a way of evaluating the but-for condition: if the input value was different, would the output value change? \n", + " \n", + "More generally, we can ask about the probability with which an alterantive intervention would lead to a change in the outcome (perhaps while conditioning on other items of information), in line with the ideas present in Pearl's *Probabilities of causation...* and Chapter 9 of Pearl's *Causality*. While immensely useful, the but-for condition is not fine-grained enough to answer all the questions we are interested in or to give us the intended answers in cases in which the underlying causal model is non-trivial. We will illustrate this observation in this tutorial. \n", + "\n", + "\n", + "On the other hand, we can ask whether given our model (and perhaps conditioning on other pieces of information we posses), intervening on a given candidate cause to have a given value results in the outcome being as observed (or, more generally, the probability of that outcome being as observed) - this is conceptually similar to Pearl's probability of sufficiency. \n", + "\n", + "We will start with these two approaches, but soon we will notice that often our explanatory questions are more local and a more fine-grained tool is needed. The general intuition (inspired by Halpern's *Actual Causality*) that we implemented is that when we ask local explanatory questions, we need to keep some part of the actual context fixed and consider alternative scenarios insofar as potential causes are involved. That is, we (i) search through possible alternative interventions that could be performed on the candidate cause nodes, (ii) search through possible context nodes that are to be intervened to be at their factual values even in the counterfactual worlds, (iii) see how these options play out in intervened worlds, and (iv) investigate and meaningfully summarize what happens with the outcome nodes of interest in all those counterfactual worlds. " + ] } ], "metadata": { From 55f1782773b296e37fde2943d68f2c378c592511 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Tue, 13 Aug 2024 16:02:31 -0400 Subject: [PATCH 031/111] improved readability --- .../explainable_categorical_alternate.ipynb | 165 ++++++++++++------ 1 file changed, 113 insertions(+), 52 deletions(-) diff --git a/docs/source/explainable_categorical_alternate.ipynb b/docs/source/explainable_categorical_alternate.ipynb index 7c4aa9c2..a0c22e40 100644 --- a/docs/source/explainable_categorical_alternate.ipynb +++ b/docs/source/explainable_categorical_alternate.ipynb @@ -26,7 +26,7 @@ "source": [ "**Outline**\n", "\n", - "[Overview](#overview)\n", + "[Motivation](#motivation)\n", "\n", "[Setup](#setup)\n", "\n", @@ -34,7 +34,9 @@ "\n", "[Context-sensitive Causal Explanations](#witness-nodes-and-context-sensitivity)\n", "\n", - "[Probability of causation and responsibility](#probability-of-causation-and-responsibility)" + "[Probability of causation and responsibility](#probability-of-causation-and-responsibility)\n", + "\n", + "[Further Discussion](#further-discussion)" ] }, { @@ -51,7 +53,9 @@ "\n", "- **Explainable AI:** Your pre-trial release has been refused based on your [COMPAS score](https://en.wikipedia.org/wiki/COMPAS_(software)). The decision was made using a proprietary predictive model. All you have access to is the questionnaire that was used, and perhaps some demographic information about a class of human beings subjected to this evaluation. But which of these factors resulted in your score being what it is, and what were their contributions?\n", "\n", - "Questions of this sort are more specific and local as they pertain to actual cases that come with their own contexts, unlike average treatment effects discussed in an earlier [tutorial](https://github.com/BasisResearch/chirho/blob/master/docs/source/tutorial_i.ipynb). Being able to answer such context-sensitive questions is useful for understanding how we can prevent undesirable outcomes similar to ones that we have observed, or promote the occurrence of desirable outcomes in contexts similar to the ones in which they had been observed. Moreover, these context-sensitive causality questions are also an essential element of blame and responsibility assignments. " + "Questions of this sort are more specific and local as they pertain to actual cases that come with their own contexts, unlike average treatment effects discussed in an earlier [tutorial](https://github.com/BasisResearch/chirho/blob/master/docs/source/tutorial_i.ipynb). Being able to answer such context-sensitive questions is useful for understanding how we can prevent undesirable outcomes similar to ones that we have observed, or promote the occurrence of desirable outcomes in contexts similar to the ones in which they had been observed. Moreover, these context-sensitive causality questions are also an essential element of blame and responsibility assignments. \n", + "\n", + "In this notebook, we demonstrate the use of `SearchForExplanation` that provides a unified approach to answer such questions on all levels of granularity." ] }, { @@ -317,7 +321,7 @@ "\n", "To answer the above question, we compute the probability of both forest fire not occurring if we intervene on the match to not be dropped, and forest fire occurring if we intervene on the match to be dropped. i.e. $P(f'_{m'}, f_m)$. this computation can be carried out using `SearchForExplanation`.\n", "\n", - "The potential cause (`antecedent`) we're considering is `match_dropped=1`, we contrast it with what would happen if we intervened on it to not happen (`alternatives`). We are interested in whether an outcome variable `forest_fire` (`consequent`) has value 1 under these two interventions. Note that these two inteventions correspond to `match_dropped=1` being a sufficient and necessary cause for `forest_fire=1`. These notions have a correspondence to Pearl's notion of probability of necessity and sufficiency in this simple model. But we later extend these notions to be context-sensitive." + "The potential cause (`antecedent`) we're considering is `match_dropped=1`, we contrast it with what would happen if we intervened on it to not happen (`alternatives`). We are interested in whether an outcome variable `forest_fire` (`consequent`) has value 1 under these two interventions. Note that these two inteventions correspond to `match_dropped=1` being a sufficient and necessary cause for `forest_fire=1`. These notions have a correspondence to Pearl's notion of probability of necessity and sufficiency in this simple model. But we later extend these notions to be context-sensitive as well." ] }, { @@ -351,7 +355,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To answer our causal query, it is enough that the probability above is greater than 0 --- dropping match does have a causal effect on forest fire. But this is not exactly $P(f'_{m'}, f_m)$. Remember that interventions on antecedents are chosen stochastically which induces need for post-processing the samples." + "It might seem that we have our answer but that is not the case. Remember that interventions on antecedents are chosen stochastically. This induces a need to post-process the samples to only allow those samples where `match_dropped` was intervened on. Thus, to compute $P(f'_{m'}, f_m)$, we run the following code." ] }, { @@ -372,13 +376,20 @@ "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the result above matches our intuition. `match_dropped` has a causal effect on `forest_fire` only when `lightning` is not there and the corresponding probability is $0.6$" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Causal Query 2** What is the probability that a Chirho developer has a causal role over forest fire?\n", "\n", - "The intuitive answer is obviously no, and we show that the same conclusion can be drawn using `SearchForExplanation`." + "The intuitive answer is obviously zero, and we show that the same conclusion can be drawn using `SearchForExplanation`." ] }, { @@ -408,6 +419,13 @@ "print(torch.exp(logp))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above probability is already 0, and the following post-processing does not affect the result. We still provide the following code snippet for the sake of completeness." + ] + }, { "cell_type": "code", "execution_count": 26, @@ -430,7 +448,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "A similar estimation as above would not work in a case of overdetermination, in which each of the two factors can alone cause the outcome. Consider the case where both `match_dropped` and `lightning` did occur. In this case, if we try to determine the causal role of `match_dropped`, it would come out to be zero. And a symmetric reasoning works for lightning as well. This results in $P(f'_{m'}, f_m, m, l) = P(f'_{l'}, f_l, m, l)=0$. This is a canonical example that shows the limitations of the but-for analysis." + "The examples above show how `SearchForExplanation` can be used for but-for analysis but such analysis would not work in a case of overdetermination, where each of the two factors can alone cause the outcome. Consider the case where both `match_dropped` and `lightning` did occur. In this case, if we try to determine the causal role of `match_dropped`, it would come out to be zero. And a symmetric reasoning works for lightning as well. This results in $P(f'_{m'}, f_m, m, l) = P(f'_{l'}, f_l, m, l)=0$. This is a canonical example that shows the limitations of the but-for analysis." ] }, { @@ -454,7 +472,7 @@ " witnesses={}, \n", " alternatives={\"match_dropped\": torch.tensor(0.0)},\n", " consequent_scale=1e-5,\n", - ")(\n", + ")( # We need to reparametrize as we are conditioning on deterministic nodes\n", " pyro.poutine.reparam(config=reparam_config([\"match_dropped\", \"lightning\"]))(\n", " condition(data={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)})\n", " (forest_fire_model)\n", @@ -486,19 +504,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "But if we consider both `match_dropped` and `lightning` to be possible causes, we can estimate $P(f'_{m',l'}, f_{m,l}, m, l)$ to determine their causal role that comes out to be greater than 0." + "But if we consider both `match_dropped` and `lightning` to be possible causes, we can estimate $P(f'_{m',l'}, f_{m,l}, m, l)$ to determine their causal role that comes out to be greater than 0 as follows." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 129, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.0682)\n" + "tensor(0.0719)\n" ] } ], @@ -538,6 +556,13 @@ "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The probability above again matches our intuition since the probability of `match_dropped=1` and `lightning=1` is $0.28$ where the set $\\{m, l\\}$ is a cause. Now, we move to context-sensitive causal explanations." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -549,16 +574,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As the previous example showed, the but-for analysis is not sufficient for identifying causal roles. This induces the need to pay attention to the membership of variables in larger antecedent sets that would make a difference (that is one reason why we need stochasticity in the antecedent candidate preemption: to search for such sets).\n", + "As the previous example showed, the but-for analysis is not sufficient for identifying causal roles. This induces the need to pay attention to the membership of variables in larger antecedent sets that would make a difference (that is one reason why we need stochasticity in the antecedent candidate preemption: to search for such sets). But even then, the but-for analysis does not pay sufficient attention to the granularity of a given problem and its causal structure. There are asymmetric cases where the efficiency of one cause prevents the efficiency of another, in which our causal attributions should a be asymmetric, but \"being a member of the same larger antecedent set\" isn't. We illustrate using a simple example.\n", "\n", - "But even then, the but-for analysis does not pay sufficient attention to the granularity of a given problem and its causal structure. There are asymmetric cases where the efficiency of one cause prevents the efficiency of another, in which our causal attributions should a be asymmetric, but \"being a member of the same larger antecedent set\" isn't.\n", - "\n", - "A simple example is breaking a bottle. Suppose Sally and Bob throw a rock at a bottle, and Sally does so a little earlier than Bob. Suppose both are perfectly accurate, and the bottle shatters when hit. Sally hits, and the bottle shatters, but Bob doesn't hit it because the bottle is no longer there. " + "Consider the example of breaking a bottle. Suppose Sally and Bob throw a rock at a bottle, and Sally does so a little earlier than Bob. Suppose both are perfectly accurate, and the bottle shatters when hit. Sally hits, and the bottle shatters, but Bob doesn't hit it because the bottle is no longer there." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 130, "metadata": {}, "outputs": [ { @@ -710,10 +733,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 130, "metadata": {}, "output_type": "execute_result" } @@ -791,7 +814,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 131, "metadata": {}, "outputs": [ { @@ -825,6 +848,24 @@ "print(torch.exp(logp))" ] }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(1.0013e-05)\n" + ] + } + ], + "source": [ + "mask_intervened = trace.nodes[\"__cause____antecedent_sally_throws\"][\"value\"] == 0\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -844,14 +885,14 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 133, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2513)\n" + "tensor(0.2494)\n" ] } ], @@ -878,6 +919,24 @@ "print(torch.exp(logp))" ] }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.4987)\n" + ] + } + ], + "source": [ + "mask_intervened = trace.nodes[\"__cause____antecedent_sally_throws\"][\"value\"] == 0\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -889,7 +948,7 @@ }, { "cell_type": "code", - "execution_count": 203, + "execution_count": 136, "metadata": {}, "outputs": [ { @@ -919,38 +978,49 @@ " }\n", ")(stones_model))\n", "\n", - "logp, trace, mwc = importance_infer(num_samples=10000)(query)()\n", + "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", "print(torch.exp(logp))" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 137, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(1.0013e-05)\n" + ] + } + ], "source": [ - "## Probability of Causation and Responsibility" + "mask_intervened = trace.nodes[\"__cause____antecedent_bill_throws\"][\"value\"] == 0\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We might use non-trivial probabilities and be interested in more involved queries. Suppose we aren't sure what part of the context we want to hold fixed, allowing both `sally_hits` and `bill_hits` to be witness candidates, so we attach equal weights to all four possible context sets. \n", + "## Probability of Causation and Responsibility\n", "\n", - "Suppose also that beyond knowing the non-degenerate probabilities involved, we don't know who threw the stone, and we only observed the bottle has been shattered. We can use the handler to estimate the answer to a somewhat different question involving the probabilities that changing the value of `sally_throws` or changing the value of `billy_throws` (whatever these are in the factual world) would lead to a change in the outcome variables, and that fixing them to be at the factual values would result in the outcome variable having the same value. We also allow both `sally_hits` and `bill_hits` as potential witnesses.\n", + "In the examples above, we have shown how `SearchForExplanation` can be used to perform but-for analysis and context-sensitive analysis. In this section, we extend how we can combine these queries ina single model and perform more involved queries about probabilities of causation and responsibility.\n", "\n", - "For example, we can sample to estimate quantities such as the fraction of possible causes of the bottle shattering in which Sally and Billy are each responsibile:" + "We take the earlier defined `stones_model` with non-trivial probabilities and the only observation that the bottle was shattered. We do not know who threw the stone and thus it is not obvious what context to hold fixed. We can capture all these different possibilities using the single program transformation performed by `SearchForExplanation` and post-process the samples to answer different queries." ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 138, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.1439)\n" + "tensor(0.1558)\n" ] } ], @@ -994,14 +1064,14 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 139, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.1956)\n" + "tensor(0.2173)\n" ] } ], @@ -1019,14 +1089,14 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 140, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.0567)\n" + "tensor(0.0769)\n" ] } ], @@ -1039,19 +1109,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can also use the same model as above to compute the degree of responsibility for bill and sally as follows. We interpret the degree of responsbility asisgned to sally for bottle shattering as the probability that `sally_throws=1` is part of the cause." + "We can also use the same model as above to compute the degree of responsibility for bill and sally as follows. We interpret the degree of responsibility assigned to sally for bottle shattering as the probability that `sally_throws=1` is part of the cause. Similarly, the degree of responsibility assigned to billy for shattering the bottle is the probability that `billy_throws=1` is a part of the cause." ] }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 141, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2683)\n" + "tensor(0.2650)\n" ] } ], @@ -1062,14 +1132,14 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 142, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2004)\n" + "tensor(0.1965)\n" ] } ], @@ -1089,22 +1159,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Discussion\n", - "\n", - "If the phenomenon we're trying to explain is the behavior of a predictive model, we are dealing with a problem in explainable AI; but the underlying intuition behind the workings of **Explainable Reasoning with ChiRho** is that causally explaining the behavior of an opaque model is not that much different from providing a causal explanation of other real-world phenomena: we need to address such queries in a principled manner employing some approximate but hopefully reliable causal model of how things work (be that events outside of computers, or a predicitive model's behavior). **Explainable Reasoning with ChiRho** package aims to provide a unified general approach to the relevant causal explanation computations.\n", - "\n", - "At some level of generality, a useful point of departure is a general counterfactual one. On one hand, we can ask whether the event would have occurred had a given candidate cause not taken place. This is sometimes called the *but-for test*, has a tradition of being used as a tool for answering causality and attribution queries. \n", - "\n", - "- It is often used in [the law of torts](https://plato.stanford.edu/entries/causation-law/) to determine if a defendant's conduct was the cause of a particular harm. The test is often formulated as follows: \"But for the defendant's conduct, would the harm have occurred?\" \n", - "- A major philosophical position in the analysis of causality is that the definition of causal dependence should be formulated in terms of counterfactual conditionals (Lewis, 1973. “Causation”, Journal of Philosophy, 70: 556–67). On this approach, $e$ causally depends on $c$ if and only if, if $c$ were not to occur $e$ would not occur. (The view does not remain uncontested, see the [SEP entry on counterfactual theories of causation](https://plato.stanford.edu/entries/causation-counterfactual/)).\n", - "- At least a few major approaches to explainable AI (such as [LIME](https://arxiv.org/abs/1602.04938), or [Shapley values](https://papers.nips.cc/paper_files/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html)) are based on the idea that explanations can be obtained by perturbing or shifting the input values and observing the changes in the output. This to a large extent can be thought of as a way of evaluating the but-for condition: if the input value was different, would the output value change? \n", - " \n", - "More generally, we can ask about the probability with which an alterantive intervention would lead to a change in the outcome (perhaps while conditioning on other items of information), in line with the ideas present in Pearl's *Probabilities of causation...* and Chapter 9 of Pearl's *Causality*. While immensely useful, the but-for condition is not fine-grained enough to answer all the questions we are interested in or to give us the intended answers in cases in which the underlying causal model is non-trivial. We will illustrate this observation in this tutorial. \n", + "# Further Discussion\n", "\n", + "In this notebook, we have shown how `SearchForExplanation` can be used for fine-grained causal queries for discrete causal models. We further elaborate on its application in for different queries. \n", "\n", - "On the other hand, we can ask whether given our model (and perhaps conditioning on other pieces of information we posses), intervening on a given candidate cause to have a given value results in the outcome being as observed (or, more generally, the probability of that outcome being as observed) - this is conceptually similar to Pearl's probability of sufficiency. \n", + "**Explainable AI**: If the phenomenon we're trying to explain is the behavior of a predictive model, we are dealing with a problem in explainable AI; but the underlying intuition behind the workings of **Explainable Reasoning with ChiRho** is that causally explaining the behavior of an opaque model is not that much different from providing a causal explanation of other real-world phenomena: we need to address such queries in a principled manner employing some approximate but hopefully reliable causal model of how things work (be that events outside of computers, or a predicitive model's behavior). **Explainable Reasoning with ChiRho** package aims to provide a unified general approach to the relevant causal explanation computations. At least a few major approaches to explainable AI (such as [LIME](https://arxiv.org/abs/1602.04938), or [Shapley values](https://papers.nips.cc/paper_files/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html)) are based on the idea that explanations can be obtained by perturbing or shifting the input values and observing the changes in the output. This to a large extent can be thought of as a way of evaluating the but-for condition: if the input value was different, would the output value change? \n", "\n", - "We will start with these two approaches, but soon we will notice that often our explanatory questions are more local and a more fine-grained tool is needed. The general intuition (inspired by Halpern's *Actual Causality*) that we implemented is that when we ask local explanatory questions, we need to keep some part of the actual context fixed and consider alternative scenarios insofar as potential causes are involved. That is, we (i) search through possible alternative interventions that could be performed on the candidate cause nodes, (ii) search through possible context nodes that are to be intervened to be at their factual values even in the counterfactual worlds, (iii) see how these options play out in intervened worlds, and (iv) investigate and meaningfully summarize what happens with the outcome nodes of interest in all those counterfactual worlds. " + "**Other Applications**: Causal queries, specifically but-for tests are often used in [the law of torts](https://plato.stanford.edu/entries/causation-law/) to determine if a defendant's conduct was the cause of a particular harm. The test is often formulated as follows: \"But for the defendant's conduct, would the harm have occurred?\". A major philosophical position in the analysis of causality is that the definition of causal dependence should be formulated in terms of counterfactual conditionals (Lewis, 1973. “Causation”, Journal of Philosophy, 70: 556–67). On this approach, $e$ causally depends on $c$ if and only if, if $c$ were not to occur $e$ would not occur. (The view does not remain uncontested, see the [SEP entry on counterfactual theories of causation](https://plato.stanford.edu/entries/causation-counterfactual/))." ] } ], From 8676cd778ef4204b05b36847b87a13ff21b857ba Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Tue, 13 Aug 2024 20:55:53 -0400 Subject: [PATCH 032/111] fixed links in intro, made ac optional in description --- docs/source/explainable_categorical_alternate.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/explainable_categorical_alternate.ipynb b/docs/source/explainable_categorical_alternate.ipynb index a0c22e40..54227d86 100644 --- a/docs/source/explainable_categorical_alternate.ipynb +++ b/docs/source/explainable_categorical_alternate.ipynb @@ -17,7 +17,7 @@ "\n", "In yet [another notebook](https://basisresearch.github.io/chirho/actual_causality.html) we illustrate how the module allows for a faithful reconstruction of a particular notion of local explanation (the so-called Halpern-Pearl modified definition of actual causality [(J. Halpern, MIT Press, 2016)](https://mitpress.mit.edu/9780262537131/actual-causality/)), which inspired some of the conceptual steps underlying the current implementation.\n", "\n", - "Before proceeding, the readers should go through the introductory tutorials on [causal reasoning in Chirho](https://github.com/BasisResearch/chirho/blob/master/docs/source/tutorial_i.ipynb) and [actual causality](https://github.com/BasisResearch/chirho/blob/master/docs/source/tutorial_i.ipynb)" + "Before proceeding, the readers should go through the introductory tutorials on [causal reasoning in Chirho](https://basisresearch.github.io/chirho/tutorial_i.html). They might also find a notebook on [actual causality](https://basisresearch.github.io/chirho/actual_causality.html) helpful." ] }, { From 773d085b9a07ef5ffd182c40ee58a4dfdca0d6f3 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Tue, 13 Aug 2024 20:57:11 -0400 Subject: [PATCH 033/111] fixed link failure in TOC --- docs/source/explainable_categorical_alternate.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/explainable_categorical_alternate.ipynb b/docs/source/explainable_categorical_alternate.ipynb index 54227d86..1d831e02 100644 --- a/docs/source/explainable_categorical_alternate.ipynb +++ b/docs/source/explainable_categorical_alternate.ipynb @@ -32,7 +32,7 @@ "\n", "[But-for Causal Explanations](#but-for-causal-explanations) \n", "\n", - "[Context-sensitive Causal Explanations](#witness-nodes-and-context-sensitivity)\n", + "[Context-sensitive Causal Explanations](#context-sensitive-causal-explanations)\n", "\n", "[Probability of causation and responsibility](#probability-of-causation-and-responsibility)\n", "\n", From a0158f4dc6b974179b5dec3d71d13276ea8a6fcd Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Tue, 13 Aug 2024 20:58:50 -0400 Subject: [PATCH 034/111] style changes in Moivation --- docs/source/explainable_categorical_alternate.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/explainable_categorical_alternate.ipynb b/docs/source/explainable_categorical_alternate.ipynb index 1d831e02..736d44f4 100644 --- a/docs/source/explainable_categorical_alternate.ipynb +++ b/docs/source/explainable_categorical_alternate.ipynb @@ -55,7 +55,7 @@ "\n", "Questions of this sort are more specific and local as they pertain to actual cases that come with their own contexts, unlike average treatment effects discussed in an earlier [tutorial](https://github.com/BasisResearch/chirho/blob/master/docs/source/tutorial_i.ipynb). Being able to answer such context-sensitive questions is useful for understanding how we can prevent undesirable outcomes similar to ones that we have observed, or promote the occurrence of desirable outcomes in contexts similar to the ones in which they had been observed. Moreover, these context-sensitive causality questions are also an essential element of blame and responsibility assignments. \n", "\n", - "In this notebook, we demonstrate the use of `SearchForExplanation` that provides a unified approach to answer such questions on all levels of granularity." + "In this notebook, we demonstrate the use of `SearchForExplanation`, a handler that provides a unified approach to answering such questions on a wide range of levels of granularity." ] }, { From c4d8ed406f3836823950efa34d63dff572f956ed Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Tue, 13 Aug 2024 20:59:46 -0400 Subject: [PATCH 035/111] style changes in Setup --- docs/source/explainable_categorical_alternate.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/explainable_categorical_alternate.ipynb b/docs/source/explainable_categorical_alternate.ipynb index 736d44f4..4b6bc7c9 100644 --- a/docs/source/explainable_categorical_alternate.ipynb +++ b/docs/source/explainable_categorical_alternate.ipynb @@ -101,7 +101,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We first setup the essentials for performing probabilistic inference on the transformed causal models. We have a function for performing importance sampling on a model and a few other utility functions." + "We first setup the essentials for performing probabilistic inference on the transformed causal models. We start a function for performing importance sampling on a model and a few other utility functions." ] }, { @@ -141,7 +141,7 @@ "\n", " return _wrapped_model\n", "\n", - "# The following functions are needed for conditioning on random variables defined using pyro.deterministic\n", + "# The following functions are needed for conditioning on random variables defined using `pyro.deterministic`\n", "def _soft_eq(v1: torch.Tensor, v2: torch.Tensor) -> torch.Tensor:\n", " return soft_eq(constraints.boolean, v1, v2, scale=0.001)\n", "\n", From ff3727877b4ce1079bcc232ace8531dc68fcfb7d Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Tue, 13 Aug 2024 21:12:10 -0400 Subject: [PATCH 036/111] small fixes in Causal query 1 description --- .../explainable_categorical_alternate.ipynb | 20 +++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/docs/source/explainable_categorical_alternate.ipynb b/docs/source/explainable_categorical_alternate.ipynb index 4b6bc7c9..1d00a389 100644 --- a/docs/source/explainable_categorical_alternate.ipynb +++ b/docs/source/explainable_categorical_alternate.ipynb @@ -160,12 +160,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's start with a very simple model, in which a forest fire can be caused by any of the two things: a match being dropped (`match_dropped`), or a lightning strike (`lightning`), and either of these factors alone is already deterministically sufficient for the `forest_fire` to occur. A match being dropped is more likely than a lightning strike (we use fairly large probabilities for the sake of example transparency). For the sake of illustration, we also include a causally irrelevant site representing whether a ChiRho developer smiles, `smile`." + "Let's start with a very simple model, in which a forest fire can be caused by any of the two things: a match being dropped (`match_dropped`), or a lightning strike (`lightning`), and either of these factors alone is already deterministically sufficient for the `forest_fire` to occur. A match being dropped is more likely than a lightning strike (we use fairly large probabilities for the sake of example transparency). For illustration, we also include a causally irrelevant site representing whether a ChiRho developer smiles, `smile`." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -252,10 +252,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 4, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -301,14 +301,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Throughout this tutorial, we consider different kinds of causal queries and compute them using a unified program transforamtion. This program transformation takes place using the handler `SearchForExplanation`. It takes the following inputs:\n", + "Throughout this tutorial, we consider different kinds of causal queries and compute them using a unified program transformation, which takes place using the handler `SearchForExplanation`. It takes the following inputs:\n", "1. the distributions for the variables we use (`supports`),\n", "2. the candidate causes $X_i = x_i$ (`antecedents`),\n", "3. their alternative values ($X_i = x_i'$) (`alternatives`),\n", - "4. the elements of the current context (`witnesses`), and \n", + "4. the candidate elements of the current context (`witnesses`), and \n", "5. the `consequents` of interest $Y=y$. \n", "\n", - "The `SearchForExplanation` handler then takes these arguments and transforms the original model into another model in which interventions on antecedents and witnesses are applied stochastically. Once the antecedents $A \\subseteq$ `antecedents` and witnesses $W \\subseteq$ `witnesses` are chosen via sampling, parallel counterfactual worlds are created to condition on `A` being sufficient and necessary causes for the consequent with the context `W`. For more details on `SearchForExplanation`, please refer to the [documentation](https://basisresearch.github.io/chirho/explainable.html#chirho.explainable.handlers.explanation.SearchForExplanation).\n", + "The `SearchForExplanation` handler then takes these arguments and transforms the original model into another model in which interventions on antecedents and witnesses are applied stochastically. Once the antecedents $A \\subseteq$ `antecedents` and witnesses $W \\subseteq$ `witnesses` are chosen from the candidates via sampling, parallel counterfactual worlds are created to condition on `A` being sufficient and necessary causes for the consequent with the context `W`. For more details on `SearchForExplanation`, please refer to the [documentation](https://basisresearch.github.io/chirho/explainable.html#chirho.explainable.handlers.explanation.SearchForExplanation).\n", "\n", "Now we are ready to use `SearchForExplanation` for answering but-for causal questions." ] @@ -321,7 +321,7 @@ "\n", "To answer the above question, we compute the probability of both forest fire not occurring if we intervene on the match to not be dropped, and forest fire occurring if we intervene on the match to be dropped. i.e. $P(f'_{m'}, f_m)$. this computation can be carried out using `SearchForExplanation`.\n", "\n", - "The potential cause (`antecedent`) we're considering is `match_dropped=1`, we contrast it with what would happen if we intervened on it to not happen (`alternatives`). We are interested in whether an outcome variable `forest_fire` (`consequent`) has value 1 under these two interventions. Note that these two inteventions correspond to `match_dropped=1` being a sufficient and necessary cause for `forest_fire=1`. These notions have a correspondence to Pearl's notion of probability of necessity and sufficiency in this simple model. But we later extend these notions to be context-sensitive as well." + "The potential cause (`antecedent`) we're considering is `match_dropped=1`, we contrast it with what would happen if we intervened on it to not happen (`alternatives`). We are interested in whether an outcome variable `forest_fire` (`consequent`) has value 1 under these two interventions. Note that these two inteventions correspond to `match_dropped=1` being a sufficient and necessary cause for `forest_fire=1`. In this simple case, the notion corresponds to Pearl's notion of probability of necessity and sufficiency - although what `SearchForExplanation` can do goes beyond it, for instance, by allowing for the estimands to be context-sensitive, as we will illustrate later in this notebook." ] }, { @@ -355,7 +355,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "It might seem that we have our answer but that is not the case. Remember that interventions on antecedents are chosen stochastically. This induces a need to post-process the samples to only allow those samples where `match_dropped` was intervened on. Thus, to compute $P(f'_{m'}, f_m)$, we run the following code." + "The above, strictly speaking, is not our answer yet. Remember that interventions on antecedents are chosen stochastically (with default probability $0.5$ for each candidate node). Thus the above is rather $P(f'_{m'}, f_m)P(m\\,\\, intervened)$. To obtain $P(f'_{m'}, f_m)$ we therefore need to multiply the result by 2, obtaining $0.6$. In general, we don't need to keep track of the analytic solutions, and we can reach the similar conclusion by post-processing the samples to reject those where `match_dropped` was not intervened on. So, without knowing or using an analytic form (which in general will not be manageable), to compute $P(f'_{m'}, f_m)$, we can subselect the samples as follows:" ] }, { @@ -380,7 +380,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note that the result above matches our intuition. `match_dropped` has a causal effect on `forest_fire` only when `lightning` is not there and the corresponding probability is $0.6$" + "Note that the result above matches our intuition. `match_dropped` has a causal effect on `forest_fire` only when `lightning` is not there and the corresponding probability is $0.6$." ] }, { From 1269aa16ed887842f8786dd4438d5791eefc8085 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Tue, 13 Aug 2024 21:24:36 -0400 Subject: [PATCH 037/111] causal query 2 small fixes, one oustanding comment --- .../explainable_categorical_alternate.ipynb | 53 +++++++++++-------- 1 file changed, 31 insertions(+), 22 deletions(-) diff --git a/docs/source/explainable_categorical_alternate.ipynb b/docs/source/explainable_categorical_alternate.ipynb index 1d00a389..40ee27c3 100644 --- a/docs/source/explainable_categorical_alternate.ipynb +++ b/docs/source/explainable_categorical_alternate.ipynb @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -252,10 +252,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -317,7 +317,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Causal Query 1** What is the probability that dropping a match has a causal role over forest fire?\n", + "**Causal Query 1** What is the probability that dropping a match has a causal impact on the forest fire?\n", "\n", "To answer the above question, we compute the probability of both forest fire not occurring if we intervene on the match to not be dropped, and forest fire occurring if we intervene on the match to be dropped. i.e. $P(f'_{m'}, f_m)$. this computation can be carried out using `SearchForExplanation`.\n", "\n", @@ -326,14 +326,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2959)\n" + "tensor(0.3035)\n" ] } ], @@ -360,14 +360,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.5993)\n" + "tensor(0.6100)\n" ] } ], @@ -387,7 +387,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Causal Query 2** What is the probability that a Chirho developer has a causal role over forest fire?\n", + "**Causal Query 2** What is the probability that a Chirho developer has a causal impact on the forest fire?\n", "\n", "The intuitive answer is obviously zero, and we show that the same conclusion can be drawn using `SearchForExplanation`." ] @@ -428,7 +428,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -448,19 +448,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The examples above show how `SearchForExplanation` can be used for but-for analysis but such analysis would not work in a case of overdetermination, where each of the two factors can alone cause the outcome. Consider the case where both `match_dropped` and `lightning` did occur. In this case, if we try to determine the causal role of `match_dropped`, it would come out to be zero. And a symmetric reasoning works for lightning as well. This results in $P(f'_{m'}, f_m, m, l) = P(f'_{l'}, f_l, m, l)=0$. This is a canonical example that shows the limitations of the but-for analysis." + "The examples above show how `SearchForExplanation` can be used for but-for analysis. Note, however, that such analysis would not work in a case of overdetermination, where each of the two factors can alone cause the outcome. Consider the case where both `match_dropped` and `lightning` did occur. In this case, if we try to determine the causal role of `match_dropped`, it would come out to be zero (a symmetric reasoning works for lightning as well). This results in $P(f'_{m'}, f_m, m, l) = P(f'_{l'}, f_l, m, l)=0$. This is a canonical example of the limitations of the but-for analysis." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(2.7596e-06)\n" + "tensor(2.8355e-06)\n" ] } ], @@ -484,14 +484,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(2.7553e-06)\n" + "tensor(2.8904e-06)\n" ] } ], @@ -504,19 +504,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "But if we consider both `match_dropped` and `lightning` to be possible causes, we can estimate $P(f'_{m',l'}, f_{m,l}, m, l)$ to determine their causal role that comes out to be greater than 0 as follows." + "One thing we can do, is to consider the set containing both `match_dropped` and `lightning`. Then we can estimate $P(f'_{m',l'}, f_{m,l}, m, l)$ to determine their joint causal role, which comes out to be greater than 0, as follows." ] }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.0719)\n" + "tensor(0.0730)\n" ] } ], @@ -525,7 +525,7 @@ " supports=forest_fire_supports,\n", " antecedents={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)},\n", " consequents={\"forest_fire\": torch.tensor(1.0)},\n", - " witnesses={}, # potential context elements, we leave them empty for now\n", + " witnesses={},\n", " alternatives={\"match_dropped\": torch.tensor(0.0), \"lightning\": torch.tensor(0.0)},\n", " consequent_scale=1e-5,\n", ")(\n", @@ -538,16 +538,23 @@ "print(torch.exp(logp))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now to get our estimand, we would need to multiply our result by four, as we now have made two stochastic decisions about interventions, each with probability $0.5$. Or, we can post-process the sample:" + ] + }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2793)\n" + "tensor(0.2845)\n" ] } ], @@ -560,7 +567,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The probability above again matches our intuition since the probability of `match_dropped=1` and `lightning=1` is $0.28$ where the set $\\{m, l\\}$ is a cause. Now, we move to context-sensitive causal explanations." + "This again matches our intuition, since the probability of `match_dropped=1` and `lightning=1` is $0.28$ where the set $\\{m, l\\}$ is a cause. Now, we move to context-sensitive causal explanations.\n", + "\n", + "TODO_R: this needs to be better explained, something's off with this argument." ] }, { From 411a5018635c083f4314d26609e21c0bb93f2ae1 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Tue, 13 Aug 2024 21:26:55 -0400 Subject: [PATCH 038/111] style changes in contex-sensitive... --- .../explainable_categorical_alternate.ipynb | 147 +++++++++--------- 1 file changed, 74 insertions(+), 73 deletions(-) diff --git a/docs/source/explainable_categorical_alternate.ipynb b/docs/source/explainable_categorical_alternate.ipynb index 40ee27c3..99f5031b 100644 --- a/docs/source/explainable_categorical_alternate.ipynb +++ b/docs/source/explainable_categorical_alternate.ipynb @@ -252,7 +252,7 @@ "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -333,7 +333,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.3035)\n" + "tensor(0.3006)\n" ] } ], @@ -367,7 +367,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.6100)\n" + "tensor(0.6001)\n" ] } ], @@ -460,7 +460,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "tensor(2.8355e-06)\n" + "tensor(2.8425e-06)\n" ] } ], @@ -491,7 +491,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "tensor(2.8904e-06)\n" + "tensor(2.7806e-06)\n" ] } ], @@ -516,7 +516,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.0730)\n" + "tensor(0.0694)\n" ] } ], @@ -547,14 +547,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2845)\n" + "tensor(0.2744)\n" ] } ], @@ -590,7 +590,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -599,153 +599,154 @@ "\n", "\n", - "\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", + "%3\n", + "\n", "\n", "\n", "prob_sally_throws\n", - "\n", - "prob_sally_throws\n", + "\n", + "prob_sally_throws\n", "\n", "\n", "\n", "sally_throws\n", - "\n", - "sally_throws\n", + "\n", + "sally_throws\n", "\n", "\n", "\n", "prob_sally_throws->sally_throws\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "prob_bill_throws\n", - "\n", - "prob_bill_throws\n", + "\n", + "prob_bill_throws\n", "\n", "\n", "\n", "bill_throws\n", - "\n", - "bill_throws\n", + "\n", + "bill_throws\n", "\n", "\n", "\n", "prob_bill_throws->bill_throws\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "prob_sally_hits\n", - "\n", - "prob_sally_hits\n", + "\n", + "prob_sally_hits\n", "\n", "\n", "\n", "sally_hits\n", - "\n", - "sally_hits\n", + "\n", + "sally_hits\n", "\n", "\n", "\n", "prob_sally_hits->sally_hits\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "prob_bill_hits\n", - "\n", - "prob_bill_hits\n", + "\n", + "prob_bill_hits\n", "\n", "\n", "\n", "bill_hits\n", - "\n", - "bill_hits\n", + "\n", + "bill_hits\n", "\n", "\n", "\n", "prob_bill_hits->bill_hits\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "prob_bottle_shatters_if_sally\n", - "\n", - "prob_bottle_shatters_if_sally\n", + "\n", + "prob_bottle_shatters_if_sally\n", "\n", "\n", "\n", "bottle_shatters\n", - "\n", - "bottle_shatters\n", + "\n", + "bottle_shatters\n", "\n", "\n", - "\n", + "\n", "prob_bottle_shatters_if_sally->bottle_shatters\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "prob_bottle_shatters_if_bill\n", - "\n", - "prob_bottle_shatters_if_bill\n", + "\n", + "prob_bottle_shatters_if_bill\n", "\n", "\n", - "\n", + "\n", "prob_bottle_shatters_if_bill->bottle_shatters\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sally_throws->sally_hits\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "bill_throws->bill_hits\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sally_hits->bill_hits\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "sally_hits->bottle_shatters\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "bill_hits->bottle_shatters\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 130, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -823,7 +824,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -859,7 +860,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -894,14 +895,14 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2494)\n" + "tensor(0.2508)\n" ] } ], @@ -930,14 +931,14 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.4987)\n" + "tensor(0.5009)\n" ] } ], @@ -950,14 +951,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Admittedly, our search through contexts is simple as the only part of the actual context which stochastically is kept fixed at the factual value is `bill_hits`. But already with this search, sally throwing is diagnosed as having non-null probability. In fact, the definition of actual causality in Halpern's book (*Actual causality*) contains an existential quantifier: a variable is an actual cause if there is at least one context in which a change in the outcome variable would result from changing the antecedent to have an alternative value, so our search provides a correct diagnosis here.\n", + "Admittedly, our search through contexts is simple as the only part of the actual context which stochastically is kept fixed at the factual value is `bill_hits`. But already with this search, Sally's throw is diagnosed as having impact on the bottle shattering with non-null probability. In fact, the definition of actual causality in Halpern's book (*Actual causality*) contains an existential quantifier: a variable is an actual cause if there is at least one context in which a change in the outcome variable would result from changing the antecedent to have an alternative value, so our search provides a correct diagnosis here.\n", "\n", - "Crucially, as intended, an analogous inference for whether `bill_throws` is a cause of the bottle shattering, yields a different result and assigns null causal role to bill." + "Crucially, as intended, an analogous inference for whether `bill_throws` is a cause of the bottle shattering, yields a different result and assigns null causal role to Bill's throw." ] }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 17, "metadata": {}, "outputs": [ { From 61bb476435228843fda406a83989e42cee2d9708 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Wed, 14 Aug 2024 13:58:51 -0400 Subject: [PATCH 039/111] computation of P(f_{m, l}, f'_{m', l'} | m, l) --- .../explainable_categorical_alternate.ipynb | 151 +++++++++--------- 1 file changed, 78 insertions(+), 73 deletions(-) diff --git a/docs/source/explainable_categorical_alternate.ipynb b/docs/source/explainable_categorical_alternate.ipynb index 99f5031b..17e5a5c7 100644 --- a/docs/source/explainable_categorical_alternate.ipynb +++ b/docs/source/explainable_categorical_alternate.ipynb @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 144, "metadata": {}, "outputs": [ { @@ -106,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 145, "metadata": {}, "outputs": [], "source": [ @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 146, "metadata": {}, "outputs": [ { @@ -174,88 +174,57 @@ "\n", "\n", - "\n", - "\n", - "\n", - "\n", - "%3\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", "\n", "u_match_dropped\n", - "\n", - "u_match_dropped\n", + "\n", + "u_match_dropped\n", "\n", "\n", "\n", "match_dropped\n", - "\n", - "match_dropped\n", - "\n", - "\n", - "\n", - "u_match_dropped->match_dropped\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "forest_fire\n", - "\n", - "forest_fire\n", - "\n", - "\n", - "\n", - "u_match_dropped->forest_fire\n", - "\n", - "\n", + "\n", + "match_dropped\n", "\n", "\n", "\n", "u_lightning\n", - "\n", - "u_lightning\n", + "\n", + "u_lightning\n", "\n", "\n", "\n", "lightning\n", - "\n", - "lightning\n", - "\n", - "\n", - "\n", - "u_lightning->lightning\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "u_lightning->forest_fire\n", - "\n", - "\n", + "\n", + "lightning\n", "\n", "\n", "\n", "smile\n", - "\n", - "smile\n", + "\n", + "smile\n", "\n", - "\n", - "\n", - "smile->forest_fire\n", - "\n", - "\n", + "\n", + "\n", + "forest_fire\n", + "\n", + "forest_fire\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 3, + "execution_count": 146, "metadata": {}, "output_type": "execute_result" } @@ -326,14 +295,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 147, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.3006)\n" + "tensor(0.3016)\n" ] } ], @@ -355,19 +324,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The above, strictly speaking, is not our answer yet. Remember that interventions on antecedents are chosen stochastically (with default probability $0.5$ for each candidate node). Thus the above is rather $P(f'_{m'}, f_m)P(m\\,\\, intervened)$. To obtain $P(f'_{m'}, f_m)$ we therefore need to multiply the result by 2, obtaining $0.6$. In general, we don't need to keep track of the analytic solutions, and we can reach the similar conclusion by post-processing the samples to reject those where `match_dropped` was not intervened on. So, without knowing or using an analytic form (which in general will not be manageable), to compute $P(f'_{m'}, f_m)$, we can subselect the samples as follows:" + "The above, strictly speaking, is not our answer yet. Remember that interventions on antecedents are chosen stochastically (with default probability $0.5$ for each candidate node). Thus the above is rather $P(f'_{m'}, f_m)P(m \\text{ was intervened on})$. To obtain $P(f'_{m'}, f_m)$ we therefore need to multiply the result by 2, obtaining $0.6$. In general, we don't need to keep track of the analytic solutions, and we can reach the similar conclusion by post-processing the samples to reject those where `match_dropped` was not intervened on. So, without knowing or using an analytic form (which in general will not be manageable), to compute $P(f'_{m'}, f_m)$, we can subselect the samples as follows:" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 148, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.6001)\n" + "tensor(0.6030)\n" ] } ], @@ -394,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 149, "metadata": {}, "outputs": [ { @@ -428,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 150, "metadata": {}, "outputs": [ { @@ -453,14 +422,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 151, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(2.8425e-06)\n" + "tensor(2.8435e-06)\n" ] } ], @@ -484,14 +453,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 152, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(2.7806e-06)\n" + "tensor(2.8377e-06)\n" ] } ], @@ -509,14 +478,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 153, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.0694)\n" + "tensor(0.0754)\n" ] } ], @@ -542,19 +511,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now to get our estimand, we would need to multiply our result by four, as we now have made two stochastic decisions about interventions, each with probability $0.5$. Or, we can post-process the sample:" + "Now to get our estimand of $P(f_{m, l}, f_{m', l'}, m, l)$, we would need to multiply our result by four, as we now have made two stochastic decisions about interventions, each with probability $0.5$. Or, we can post-process the sample:" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 154, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2744)\n" + "tensor(0.2900)\n" ] } ], @@ -572,6 +541,42 @@ "TODO_R: this needs to be better explained, something's off with this argument." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One can also compute $P(f_{m, l}, f'_{m', l'} | m, l)$ as follows by subselecting the samples with `match_dropped=1` and `lightning=1`. Since {`match_dropped=1`, `lightning=1`} always leads to `forest_fire=1` and {`match_dropped=0`, `lightning=0`} always leads to `forest_fire=0`, we have $P(f_{m, l}, f'_{m', l'} | m, l) = 1$ that we get as a result of the following code snippet." + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(1.0000)\n" + ] + } + ], + "source": [ + "query = SearchForExplanation(\n", + " supports=forest_fire_supports,\n", + " antecedents={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)},\n", + " consequents={\"forest_fire\": torch.tensor(1.0)},\n", + " witnesses={},\n", + " alternatives={\"match_dropped\": torch.tensor(0.0), \"lightning\": torch.tensor(0.0)},\n", + " consequent_scale=1e-5,\n", + ")(forest_fire_model)\n", + "\n", + "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", + "\n", + "mask_intervened = (trace.nodes[\"match_dropped\"][\"value\"] == 1) & (trace.nodes[\"lightning\"][\"value\"] == 1) & (trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_lightning\"][\"value\"] == 0)\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" + ] + }, { "cell_type": "markdown", "metadata": {}, From 228eebc7253477145b11bd4b3d9d5fc7cbfcf1b2 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Wed, 14 Aug 2024 14:50:42 -0400 Subject: [PATCH 040/111] double checked things --- .../explainable_categorical_alternate.ipynb | 149 +++++++++--------- 1 file changed, 74 insertions(+), 75 deletions(-) diff --git a/docs/source/explainable_categorical_alternate.ipynb b/docs/source/explainable_categorical_alternate.ipynb index 17e5a5c7..e35a16d0 100644 --- a/docs/source/explainable_categorical_alternate.ipynb +++ b/docs/source/explainable_categorical_alternate.ipynb @@ -53,7 +53,7 @@ "\n", "- **Explainable AI:** Your pre-trial release has been refused based on your [COMPAS score](https://en.wikipedia.org/wiki/COMPAS_(software)). The decision was made using a proprietary predictive model. All you have access to is the questionnaire that was used, and perhaps some demographic information about a class of human beings subjected to this evaluation. But which of these factors resulted in your score being what it is, and what were their contributions?\n", "\n", - "Questions of this sort are more specific and local as they pertain to actual cases that come with their own contexts, unlike average treatment effects discussed in an earlier [tutorial](https://github.com/BasisResearch/chirho/blob/master/docs/source/tutorial_i.ipynb). Being able to answer such context-sensitive questions is useful for understanding how we can prevent undesirable outcomes similar to ones that we have observed, or promote the occurrence of desirable outcomes in contexts similar to the ones in which they had been observed. Moreover, these context-sensitive causality questions are also an essential element of blame and responsibility assignments. \n", + "Questions of this sort are more specific and local as they pertain to actual cases that come with their own contexts, unlike average treatment effects discussed in an earlier [tutorial](https://github.com/BasisResearch/chirho/blob/master/docs/source/tutorial_i.ipynb). Being able to answer such context-sensitive questions is useful for understanding how we can prevent undesirable outcomes and promote desirable ones in contexts similar to the ones in which they had been observed. Moreover, these context-sensitive causality questions are also an essential element of blame and responsibility assignments. \n", "\n", "In this notebook, we demonstrate the use of `SearchForExplanation`, a handler that provides a unified approach to answering such questions on a wide range of levels of granularity." ] @@ -101,7 +101,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We first setup the essentials for performing probabilistic inference on the transformed causal models. We start a function for performing importance sampling on a model and a few other utility functions." + "We first setup the essentials for performing probabilistic inference on the transformed causal models. We define a function for performing importance sampling on a model and a few other utility functions." ] }, { @@ -143,10 +143,10 @@ "\n", "# The following functions are needed for conditioning on random variables defined using `pyro.deterministic`\n", "def _soft_eq(v1: torch.Tensor, v2: torch.Tensor) -> torch.Tensor:\n", - " return soft_eq(constraints.boolean, v1, v2, scale=0.001)\n", + " return soft_eq(constraints.boolean, v1, v2, scale=0.001)\n", "\n", "def reparam_config(data):\n", - " return {i: KernelSoftConditionReparam(_soft_eq) for i in data}" + " return {i: KernelSoftConditionReparam(_soft_eq) for i in data}" ] }, { @@ -595,7 +595,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 160, "metadata": {}, "outputs": [ { @@ -604,154 +604,153 @@ "\n", "\n", - "\n", - "\n", - "\n", + "\n", + "\n", "\n", - "%3\n", - "\n", + "\n", "\n", "\n", "prob_sally_throws\n", - "\n", - "prob_sally_throws\n", + "\n", + "prob_sally_throws\n", "\n", "\n", "\n", "sally_throws\n", - "\n", - "sally_throws\n", + "\n", + "sally_throws\n", "\n", "\n", "\n", "prob_sally_throws->sally_throws\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "prob_bill_throws\n", - "\n", - "prob_bill_throws\n", + "\n", + "prob_bill_throws\n", "\n", "\n", "\n", "bill_throws\n", - "\n", - "bill_throws\n", + "\n", + "bill_throws\n", "\n", "\n", "\n", "prob_bill_throws->bill_throws\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "prob_sally_hits\n", - "\n", - "prob_sally_hits\n", + "\n", + "prob_sally_hits\n", "\n", "\n", "\n", "sally_hits\n", - "\n", - "sally_hits\n", + "\n", + "sally_hits\n", "\n", "\n", "\n", "prob_sally_hits->sally_hits\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "prob_bill_hits\n", - "\n", - "prob_bill_hits\n", + "\n", + "prob_bill_hits\n", "\n", "\n", "\n", "bill_hits\n", - "\n", - "bill_hits\n", + "\n", + "bill_hits\n", "\n", "\n", "\n", "prob_bill_hits->bill_hits\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "prob_bottle_shatters_if_sally\n", - "\n", - "prob_bottle_shatters_if_sally\n", + "\n", + "prob_bottle_shatters_if_sally\n", "\n", "\n", "\n", "bottle_shatters\n", - "\n", - "bottle_shatters\n", + "\n", + "bottle_shatters\n", "\n", "\n", - "\n", + "\n", "prob_bottle_shatters_if_sally->bottle_shatters\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "prob_bottle_shatters_if_bill\n", - "\n", - "prob_bottle_shatters_if_bill\n", + "\n", + "prob_bottle_shatters_if_bill\n", "\n", "\n", - "\n", + "\n", "prob_bottle_shatters_if_bill->bottle_shatters\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sally_throws->sally_hits\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "bill_throws->bill_hits\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "sally_hits->bill_hits\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "sally_hits->bottle_shatters\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "bill_hits->bottle_shatters\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 160, "metadata": {}, "output_type": "execute_result" } @@ -829,7 +828,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 161, "metadata": {}, "outputs": [ { @@ -1023,19 +1022,19 @@ "\n", "In the examples above, we have shown how `SearchForExplanation` can be used to perform but-for analysis and context-sensitive analysis. In this section, we extend how we can combine these queries ina single model and perform more involved queries about probabilities of causation and responsibility.\n", "\n", - "We take the earlier defined `stones_model` with non-trivial probabilities and the only observation that the bottle was shattered. We do not know who threw the stone and thus it is not obvious what context to hold fixed. We can capture all these different possibilities using the single program transformation performed by `SearchForExplanation` and post-process the samples to answer different queries." + "We take the earlier defined `stones_model` with non-trivial probabilities and the single observation that the bottle was shattered. We do not know who threw the stone and thus it is not obvious what context to hold fixed. We can capture all these different possibilities using the single program transformation performed by `SearchForExplanation` and post-process the samples to answer different queries." ] }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 173, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.1558)\n" + "tensor(0.1550)\n" ] } ], @@ -1059,7 +1058,7 @@ " }\n", ")(stones_model))\n", "\n", - "logp, trace, mwc, log_weights = importance_infer(num_samples=1000)(query)()\n", + "logp, trace, mwc, log_weights = importance_infer(num_samples=100000)(query)()\n", "print(torch.exp(logp))" ] }, @@ -1074,19 +1073,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We first compute the probability of causation for `sally_throws`. We compute the probability that the set {sally_throws=1} is the cause of bottle shattering." + "We first compute the probability of causation for `sally_throws`. We compute the probability that the set {`sally_throws=1`} is the cause of bottle shattering." ] }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 174, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2173)\n" + "tensor(0.2194)\n" ] } ], @@ -1104,14 +1103,14 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 175, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.0769)\n" + "tensor(0.0647)\n" ] } ], @@ -1129,14 +1128,14 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 176, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2650)\n" + "tensor(0.2767)\n" ] } ], @@ -1147,14 +1146,14 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 177, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.1965)\n" + "tensor(0.1999)\n" ] } ], From fc199ccd85e19945f4363ccaf3676bc4d7d96550 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Thu, 15 Aug 2024 11:19:25 -0400 Subject: [PATCH 041/111] clean_notebooks.sh run --- .../explainable_categorical_alternate.ipynb | 38 ++-- docs/source/responsibility.ipynb | 177 ------------------ docs/source/tutorial_i.ipynb | 44 +++-- 3 files changed, 51 insertions(+), 208 deletions(-) delete mode 100644 docs/source/responsibility.ipynb diff --git a/docs/source/explainable_categorical_alternate.ipynb b/docs/source/explainable_categorical_alternate.ipynb index e35a16d0..a9e6938b 100644 --- a/docs/source/explainable_categorical_alternate.ipynb +++ b/docs/source/explainable_categorical_alternate.ipynb @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 178, "metadata": {}, "outputs": [ { @@ -106,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 179, "metadata": {}, "outputs": [], "source": [ @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 180, "metadata": {}, "outputs": [ { @@ -221,10 +221,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 146, + "execution_count": 180, "metadata": {}, "output_type": "execute_result" } @@ -295,14 +295,14 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 184, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.3016)\n" + "tensor(0.2989)\n" ] } ], @@ -329,20 +329,26 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 187, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.6030)\n" + "tensor(5.7317e-06)\n", + "tensor(2988.9890)\n", + "tensor(5026.)\n", + "tensor(9.6378e-06)\n" ] } ], "source": [ - "mask_intervened = trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0\n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" + "mask_intervened = trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 1\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))\n", + "print(torch.sum(torch.exp(log_weights)))\n", + "print(mask_intervened.float().sum())\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(torch.exp(log_weights)))" ] }, { @@ -550,14 +556,15 @@ }, { "cell_type": "code", - "execution_count": 159, + "execution_count": 194, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(1.0000)\n" + "tensor(1.0000)\n", + "tensor(0.5258)\n" ] } ], @@ -573,8 +580,9 @@ "\n", "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", "\n", - "mask_intervened = (trace.nodes[\"match_dropped\"][\"value\"] == 1) & (trace.nodes[\"lightning\"][\"value\"] == 1) & (trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_lightning\"][\"value\"] == 0)\n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" + "mask_intervened = (trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_lightning\"][\"value\"] == 0)\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(torch.exp(log_weights)))" ] }, { diff --git a/docs/source/responsibility.ipynb b/docs/source/responsibility.ipynb deleted file mode 100644 index 5607419f..00000000 --- a/docs/source/responsibility.ipynb +++ /dev/null @@ -1,177 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "env: CUDA_VISIBLE_DEVICES=-1\n" - ] - } - ], - "source": [ - "%env CUDA_VISIBLE_DEVICES=-1\n", - "from typing import Callable, Dict, List, Optional\n", - "\n", - "import math\n", - "import pyro\n", - "import pyro.distributions as dist\n", - "import pyro.distributions.constraints as constraints\n", - "import torch\n", - "from chirho.counterfactual.handlers.counterfactual import \\\n", - " MultiWorldCounterfactual\n", - "from chirho.explainable.handlers import ExtractSupports, SearchForExplanation\n", - "from chirho.indexed.ops import IndexSet, gather\n", - "from chirho.observational.handlers import condition\n", - "from chirho.observational.handlers.soft_conditioning import soft_eq, KernelSoftConditionReparam\n", - "\n", - "pyro.settings.set(module_local_params=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "def importance_infer(\n", - " model: Optional[Callable] = None, *, num_samples: int\n", - "):\n", - " \n", - " if model is None:\n", - " return lambda m: importance_infer(m, num_samples=num_samples)\n", - "\n", - " def _wrapped_model(\n", - " *args,\n", - " **kwargs\n", - " ):\n", - "\n", - " guide = pyro.poutine.block(hide_fn=lambda msg: msg[\"is_observed\"])(model)\n", - "\n", - " max_plate_nesting = 9 # TODO guess\n", - "\n", - " with pyro.poutine.block(), MultiWorldCounterfactual() as mwc:\n", - " log_weights, importance_tr, _ = pyro.infer.importance.vectorized_importance_weights(\n", - " model,\n", - " guide,\n", - " *args,\n", - " num_samples=num_samples,\n", - " max_plate_nesting=max_plate_nesting,\n", - " normalized=False,\n", - " **kwargs\n", - " )\n", - "\n", - " return torch.logsumexp(log_weights, dim=0) - math.log(num_samples), importance_tr, mwc, log_weights\n", - "\n", - " return _wrapped_model" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "def example():\n", - " A = pyro.sample(\"A\", dist.Bernoulli(0.5))\n", - " B = pyro.sample(\"B\", dist.Bernoulli(0.5))\n", - " C = pyro.sample(\"C\", dist.Bernoulli(A))\n", - " return {\"A\": A, \"B\": B, \"C\": C}\n", - "\n", - "with ExtractSupports() as extract_supports:\n", - " example()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(0.1057)\n" - ] - } - ], - "source": [ - "query = SearchForExplanation(\n", - " supports=extract_supports.supports,\n", - " antecedents={\"A\": 1.0, \"B\": 1.0},\n", - " consequents={\"C\": torch.tensor(1.0)},\n", - " witnesses={},\n", - " alternatives={\"A\": 0.0, \"B\": 0.0},\n", - " antecedent_bias=0.4,\n", - " consequent_scale=1e-5,\n", - ")(example)\n", - "\n", - "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", - "print(torch.exp(logp))" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(0.1077)\n" - ] - } - ], - "source": [ - "mask_intervened = (trace.nodes[\"__cause____antecedent_B\"][\"value\"] == 0)\n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.float().squeeze())/mask_intervened.float().sum())\n", - "# Marginalizing over the fact that B was intervened on gives the following answer which accounts for the causal role of the set {A = 1, B = 1}" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(5.1220e-06)\n" - ] - } - ], - "source": [ - "mask_intervened = (trace.nodes[\"__cause____antecedent_B\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_A\"][\"value\"] == 1)\n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.float().squeeze())/mask_intervened.float().sum())\n", - "# Marginalizing over the fact that B was intervened on and A was not gives the following answer which agrees with the fact that B has no causal role\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/source/tutorial_i.ipynb b/docs/source/tutorial_i.ipynb index ca704abb..34dc10e0 100644 --- a/docs/source/tutorial_i.ipynb +++ b/docs/source/tutorial_i.ipynb @@ -1553,7 +1553,9 @@ " return bayesian_population_intervened_causal_model(n_individuals), context\n", "\n", "\n", - "results, counterfactual_context = bayesian_population_counterfactual_model(n_individuals)\n", + "results, counterfactual_context = bayesian_population_counterfactual_model(\n", + " n_individuals\n", + ")\n", "\n", "with counterfactual_context:\n", " # ChiRho's `MultiWorldCounterfactual` effect handler automatically constructs named index sites\n", @@ -1565,14 +1567,10 @@ " # world given by the specific `IndexSet`. Here, `smokes=0` refers to the counterfactual\n", " # world in which `smokes` was not intervened on.\n", " smokes_factual = gather(results[\"smokes\"], IndexSet(smokes={0})).squeeze()\n", - " smokes_counterfactual = gather(\n", - " results[\"smokes\"], IndexSet(smokes={1})\n", - " ).squeeze()\n", + " smokes_counterfactual = gather(results[\"smokes\"], IndexSet(smokes={1})).squeeze()\n", "\n", " cancer_factual = gather(results[\"cancer\"], IndexSet(smokes={0})).squeeze()\n", - " cancer_counterfactual = gather(\n", - " results[\"cancer\"], IndexSet(smokes={1})\n", - " ).squeeze()\n", + " cancer_counterfactual = gather(results[\"cancer\"], IndexSet(smokes={1})).squeeze()\n", "\n", "print(\"smokes_factual --- \", smokes_factual)\n", "print(\"smokes_counterfactual --- \", smokes_counterfactual)\n", @@ -1647,29 +1645,39 @@ }, "outputs": [], "source": [ - "counterfactual_model_conditioned = condition(bayesian_population_counterfactual_model, data)\n", + "counterfactual_model_conditioned = condition(\n", + " bayesian_population_counterfactual_model, data\n", + ")\n", "\n", - "counterfactual_conditioned_results, counterfactual_conditioned_context = counterfactual_model_conditioned(\n", - " n_individuals\n", + "counterfactual_conditioned_results, counterfactual_conditioned_context = (\n", + " counterfactual_model_conditioned(n_individuals)\n", ")\n", "\n", "with counterfactual_conditioned_context:\n", " # ChiRho's `condition` only conditions the model on the observational part\n", " # of the model, not the counterfactual part.\n", " assert torch.allclose(\n", - " gather(counterfactual_conditioned_results[\"smokes\"], IndexSet(smokes={0})).squeeze(),\n", + " gather(\n", + " counterfactual_conditioned_results[\"smokes\"], IndexSet(smokes={0})\n", + " ).squeeze(),\n", " data[\"smokes\"],\n", " )\n", " assert not torch.allclose(\n", - " gather(counterfactual_conditioned_results[\"smokes\"], IndexSet(smokes={1})).squeeze(),\n", + " gather(\n", + " counterfactual_conditioned_results[\"smokes\"], IndexSet(smokes={1})\n", + " ).squeeze(),\n", " data[\"smokes\"],\n", " )\n", " assert torch.allclose(\n", - " gather(counterfactual_conditioned_results[\"cancer\"], IndexSet(smokes={0})).squeeze(),\n", + " gather(\n", + " counterfactual_conditioned_results[\"cancer\"], IndexSet(smokes={0})\n", + " ).squeeze(),\n", " data[\"cancer\"],\n", " )\n", " assert not torch.allclose(\n", - " gather(counterfactual_conditioned_results[\"cancer\"], IndexSet(smokes={1})).squeeze(),\n", + " gather(\n", + " counterfactual_conditioned_results[\"cancer\"], IndexSet(smokes={1})\n", + " ).squeeze(),\n", " data[\"cancer\"],\n", " )" ] @@ -1771,11 +1779,15 @@ "predictive_counterfactual_posterior = pyro.infer.Predictive(\n", " bayesian_population_counterfactual_model, guide=guide, num_samples=num_samples\n", ")\n", - "predictions_counterfactual_posterior = predictive_counterfactual_posterior(n_individuals)\n", + "predictions_counterfactual_posterior = predictive_counterfactual_posterior(\n", + " n_individuals\n", + ")\n", "\n", "with counterfactual_conditioned_context:\n", " predictions_int_posterior = {\n", - " k: gather(predictions_counterfactual_posterior[k], IndexSet(smokes={1})).squeeze()\n", + " k: gather(\n", + " predictions_counterfactual_posterior[k], IndexSet(smokes={1})\n", + " ).squeeze()\n", " for k in predictions_counterfactual_posterior.keys()\n", " }\n", "\n", From 3034bd6e9810038b73068738972b10fe675fea60 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Thu, 15 Aug 2024 15:22:23 -0400 Subject: [PATCH 042/111] rough --- docs/source/counterfactual_sir.png | Bin 0 -> 126305 bytes docs/source/counterfactual_sir_search.png | Bin 0 -> 42433 bytes .../explainable_categorical_alternate.ipynb | 44 +- docs/source/explainable_sir.ipynb | 1524 +++++++++++++++++ 4 files changed, 1556 insertions(+), 12 deletions(-) create mode 100644 docs/source/counterfactual_sir.png create mode 100644 docs/source/counterfactual_sir_search.png 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b/docs/source/explainable_categorical_alternate.ipynb @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -106,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 180, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -221,10 +221,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 180, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -295,14 +295,14 @@ }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2989)\n" + "tensor(0.3010)\n" ] } ], @@ -329,17 +329,17 @@ }, { "cell_type": "code", - "execution_count": 187, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(5.7317e-06)\n", - "tensor(2988.9890)\n", - "tensor(5026.)\n", - "tensor(9.6378e-06)\n" + "tensor(5.7879e-06)\n", + "tensor(3009.9888)\n", + "tensor(5003.)\n", + "tensor(9.6203e-06)\n" ] } ], @@ -351,6 +351,26 @@ "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(torch.exp(log_weights)))" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lw = log_weights\n", + "print(lw.squeeze())\n", + "\n", + "mask_intervened = (tr.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0) \n", + "\n", + "with mwc:\n", + " oth = gather(tr.nodes[\"os_too_high\"][\"value\"], IndexSet(**{\"lockdown\": {2}, \"mask\": {2}}))\n", + " print(oth.shape)\n", + " os = gather(tr.nodes[\"overshoot\"][\"value\"], IndexSet(**{\"lockdown\": {2}, \"mask\": {2}}))\n", + "\n", + "denom = torch.sum(torch.exp(lw.squeeze()) * mask_intervened.squeeze().float()) / torch.sum(torch.exp(lw.squeeze()))\n", + "print(denom/0.25)" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb new file mode 100644 index 00000000..41fe28a9 --- /dev/null +++ b/docs/source/explainable_sir.ipynb @@ -0,0 +1,1524 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "import numbers\n", + "import os\n", + "from typing import Tuple, TypeVar, Union\n", + "from typing import Callable, Dict, List, Optional\n", + "import math\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import pyro.distributions as dist\n", + "import seaborn as sns\n", + "import torch\n", + "from pyro.infer import Predictive\n", + "\n", + "import pyro\n", + "from chirho.counterfactual.handlers.counterfactual import \\\n", + " MultiWorldCounterfactual\n", + "from chirho.dynamical.handlers.interruption import StaticEvent\n", + "from chirho.dynamical.handlers.solver import TorchDiffEq\n", + "from chirho.dynamical.handlers.trajectory import LogTrajectory\n", + "from chirho.dynamical.ops import Dynamics, State, on, simulate\n", + "from chirho.explainable.handlers import SearchForExplanation\n", + "from chirho.explainable.handlers.components import ExtractSupports\n", + "from chirho.indexed.ops import IndexSet, gather, indices_of\n", + "from chirho.interventional.ops import Intervention, intervene\n", + "from chirho.observational.handlers import condition\n", + "\n", + "R = Union[numbers.Real, torch.Tensor]\n", + "S = TypeVar(\"S\")\n", + "T = TypeVar(\"T\")\n", + "\n", + "\n", + "sns.set_style(\"white\")\n", + "\n", + "seed = 123\n", + "pyro.clear_param_store()\n", + "pyro.set_rng_seed(seed)\n", + "\n", + "smoke_test = \"CI\" in os.environ\n", + "num_samples = 10 if smoke_test else 300\n", + "exp_plate_size = 10 if smoke_test else 2000" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "class SIRDynamics(pyro.nn.PyroModule):\n", + " def __init__(self, beta, gamma):\n", + " super().__init__()\n", + " self.beta = beta\n", + " self.gamma = gamma\n", + "\n", + " def forward(self, X: State[torch.Tensor]):\n", + " dX: State[torch.Tensor] = dict()\n", + " dX[\"S\"] = -self.beta * X[\"S\"] * X[\"I\"]\n", + " dX[\"I\"] = self.beta * X[\"S\"] * X[\"I\"] - self.gamma * X[\"I\"]\n", + " dX[\"R\"] = self.gamma * X[\"I\"]\n", + "\n", + " return dX\n", + "\n", + "\n", + "# TODO add running overshoot to states?\n", + "\n", + "\n", + "class SIRDynamicsLockdown(SIRDynamics):\n", + " def __init__(self, beta0, gamma):\n", + " super().__init__(beta0, gamma)\n", + " self.beta0 = beta0\n", + "\n", + " def forward(self, X: State[torch.Tensor]):\n", + " self.beta = (1 - X[\"l\"]) * self.beta0\n", + " dX = super().forward(X)\n", + " dX[\"l\"] = torch.zeros_like(X[\"l\"])\n", + " return dX" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.15116800367832184\n" + ] + } + ], + "source": [ + "init_state = dict(S=torch.tensor(99.0), I=torch.tensor(1.0), R=torch.tensor(0.0))\n", + "start_time = torch.tensor(0.0)\n", + "end_time = torch.tensor(12.0)\n", + "step_size = torch.tensor(0.1)\n", + "logging_times = torch.arange(start_time, end_time, step_size)\n", + "init_state_lockdown = dict(**init_state, l=torch.tensor(0.0))\n", + "\n", + "# We now simulate from the SIR model\n", + "beta_true = torch.tensor([0.03])\n", + "gamma_true = torch.tensor([0.5])\n", + "sir_true = SIRDynamics(beta_true, gamma_true)\n", + "with TorchDiffEq(), LogTrajectory(logging_times) as lt:\n", + " simulate(sir_true, init_state, start_time, end_time)\n", + "\n", + "sir_true_traj = lt.trajectory\n", + "\n", + "\n", + "def get_overshoot(trajectory):\n", + " t_max = torch.argmax(trajectory[\"I\"].squeeze())\n", + " S_peak = torch.max(trajectory[\"S\"].squeeze()[t_max]) / 100\n", + " S_final = trajectory[\"S\"].squeeze()[-1] / 100\n", + " return (S_peak - S_final).item()\n", + "\n", + "\n", + "print(get_overshoot(sir_true_traj))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def bayesian_sir(base_model=SIRDynamics) -> Dynamics[torch.Tensor]:\n", + " beta = pyro.sample(\"beta\", dist.Beta(18, 600))\n", + " gamma = pyro.sample(\"gamma\", dist.Beta(1600, 1600))\n", + " sir = base_model(beta, gamma)\n", + " return sir\n", + "\n", + "\n", + "def simulated_bayesian_sir(\n", + " init_state, start_time, logging_times, base_model=SIRDynamics\n", + ") -> State[torch.Tensor]:\n", + " sir = bayesian_sir(base_model)\n", + "\n", + " with TorchDiffEq(), LogTrajectory(logging_times, is_traced=True) as lt:\n", + " simulate(sir, init_state, start_time, logging_times[-1])\n", + " return lt.trajectory" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "def MaskedStaticIntervention(time: R, intervention: Intervention[State[T]]):\n", + "\n", + " @on(StaticEvent(time))\n", + " def callback(\n", + " dynamics: Dynamics[T], state: State[T]\n", + " ) -> Tuple[Dynamics[T], State[T]]:\n", + "\n", + " with pyro.poutine.block():\n", + " return dynamics, intervene(state, intervention)\n", + "\n", + " return callback" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "overshoot_threshold = 20\n", + "lockdown_time = torch.tensor(1.0)\n", + "mask_time = torch.tensor(1.5)\n", + "\n", + "\n", + "def policy_model():\n", + "\n", + " lockdown = pyro.sample(\"lockdown\", dist.Bernoulli(torch.tensor(0.5)))\n", + " mask = pyro.sample(\"mask\", dist.Bernoulli(torch.tensor(0.5)))\n", + "\n", + " lockdown_efficiency = pyro.deterministic(\n", + " \"lockdown_efficiency\", torch.tensor(0.6) * lockdown, event_dim=0\n", + " )\n", + "\n", + " mask_efficiency = pyro.deterministic(\n", + " \"mask_efficiency\", (0.1 * lockdown + 0.45 * (1 - lockdown)) * mask, event_dim=0\n", + " )\n", + "\n", + " joint_efficiency = pyro.deterministic(\n", + " \"joint_efficiency\",\n", + " torch.clamp(lockdown_efficiency + mask_efficiency, 0, 0.95),\n", + " event_dim=0,\n", + " )\n", + "\n", + " lockdown_sir = bayesian_sir(SIRDynamicsLockdown)\n", + " with LogTrajectory(logging_times, is_traced=True) as lt:\n", + " with TorchDiffEq():\n", + " with MaskedStaticIntervention(lockdown_time, dict(l=lockdown_efficiency)):\n", + " with MaskedStaticIntervention(mask_time, dict(l=joint_efficiency)):\n", + " simulate(\n", + " lockdown_sir, init_state_lockdown, start_time, logging_times[-1]\n", + " )\n", + "\n", + " trajectory = lt.trajectory\n", + "\n", + " t_max = torch.max(trajectory[\"I\"], dim=-1).indices\n", + " S_peaks = pyro.ops.indexing.Vindex(trajectory[\"S\"])[..., t_max]\n", + " overshoot = pyro.deterministic(\n", + " \"overshoot\", S_peaks - trajectory[\"S\"][..., -1], event_dim=0\n", + " )\n", + " os_too_high = pyro.deterministic(\n", + " \"os_too_high\",\n", + " (overshoot > overshoot_threshold).clone().detach().float(),\n", + " event_dim=0,\n", + " )\n", + "\n", + " return overshoot, os_too_high\n", + "\n", + "\n", + "with ExtractSupports() as s:\n", + " one_run = policy_model()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def importance_infer(\n", + " model: Optional[Callable] = None, *, num_samples: int\n", + "):\n", + " \n", + " if model is None:\n", + " return lambda m: importance_infer(m, num_samples=num_samples)\n", + "\n", + " def _wrapped_model(\n", + " *args,\n", + " **kwargs\n", + " ):\n", + "\n", + " guide = pyro.poutine.block(hide_fn=lambda msg: msg[\"is_observed\"])(model)\n", + "\n", + " max_plate_nesting = 9 # TODO guess\n", + "\n", + " with pyro.poutine.block(), MultiWorldCounterfactual() as mwc:\n", + " log_weights, importance_tr, _ = pyro.infer.importance.vectorized_importance_weights(\n", + " model,\n", + " guide,\n", + " *args,\n", + " num_samples=num_samples,\n", + " max_plate_nesting=max_plate_nesting,\n", + " normalized=False,\n", + " **kwargs\n", + " )\n", + "\n", + " return torch.logsumexp(log_weights, dim=0) - math.log(num_samples), importance_tr, mwc, log_weights\n", + "\n", + " return _wrapped_model" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(1.)\n" + ] + } + ], + "source": [ + "print(torch.exp(logp))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# conditioning (as opposed to intervening) is sufficient for\n", + "# propagating the changes, as the decisions are upstream from ds\n", + "\n", + "# no interventions\n", + "policy_model_none = condition(\n", + " policy_model, {\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(0.0)}\n", + ")\n", + "unintervened_predictive = Predictive(\n", + " policy_model_none, num_samples=num_samples, parallel=True\n", + ")\n", + "unintervened_samples = unintervened_predictive()\n", + "\n", + "# both interventions\n", + "policy_model_all = condition(\n", + " policy_model, {\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(1.0)}\n", + ")\n", + "intervened_predictive = Predictive(\n", + " policy_model_all, num_samples=num_samples, parallel=True\n", + ")\n", + "intervened_samples = intervened_predictive()\n", + "\n", + "policy_model_mask = condition(\n", + " policy_model, {\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(1.0)}\n", + ")\n", + "mask_predictive = Predictive(policy_model_mask, num_samples=num_samples, parallel=True)\n", + "mask_samples = mask_predictive()\n", + "\n", + "policy_model_lockdown = condition(\n", + " policy_model, {\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(0.0)}\n", + ")\n", + "lockdown_predictive = Predictive(\n", + " policy_model_lockdown, num_samples=num_samples, parallel=True\n", + ")\n", + "lockdown_samples = lockdown_predictive()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def add_pred_to_plot(preds, axs, coords, color, label):\n", + " sns.lineplot(\n", + " x=logging_times,\n", + " y=preds.mean(dim=0).squeeze().tolist(),\n", + " ax=axs[coords],\n", + " label=label,\n", + " color=color,\n", + " )\n", + " axs[coords].fill_between(\n", + " logging_times,\n", + " torch.quantile(preds, 0.025, dim=0).squeeze(),\n", + " torch.quantile(preds, 0.975, dim=0).squeeze(),\n", + " alpha=0.2,\n", + " color=color,\n", + " )\n", + "\n", + "\n", + "fig, axs = plt.subplots(4, 2, figsize=(12, 6))\n", + "\n", + "colors = [\"orange\", \"red\", \"green\"]\n", + "\n", + "add_pred_to_plot(\n", + " unintervened_samples[\"S\"], axs, coords=(0, 0), color=colors[0], label=\"susceptible\"\n", + ")\n", + "add_pred_to_plot(\n", + " unintervened_samples[\"I\"], axs, coords=(0, 0), color=colors[1], label=\"infected\"\n", + ")\n", + "add_pred_to_plot(\n", + " unintervened_samples[\"R\"], axs, coords=(0, 0), color=colors[2], label=\"recovered\"\n", + ")\n", + "\n", + "axs[0, 1].hist(unintervened_samples[\"overshoot\"].squeeze())\n", + "axs[0, 0].set_title(\"No interventions\")\n", + "axs[0, 1].set_title(\n", + " f\"Overshoot mean: {unintervened_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {unintervened_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", + ")\n", + "\n", + "\n", + "add_pred_to_plot(\n", + " intervened_samples[\"S\"], axs, coords=(1, 0), color=colors[0], label=\"susceptible\"\n", + ")\n", + "add_pred_to_plot(\n", + " intervened_samples[\"I\"], axs, coords=(1, 0), color=colors[1], label=\"infected\"\n", + ")\n", + "add_pred_to_plot(\n", + " intervened_samples[\"R\"], axs, coords=(1, 0), color=colors[2], label=\"recovered\"\n", + ")\n", + "axs[1, 0].set_title(\"Both interventions\")\n", + "axs[1, 0].legend_.remove()\n", + "\n", + "\n", + "axs[1, 1].hist(intervened_samples[\"overshoot\"].squeeze())\n", + "axs[1, 1].set_title(\n", + " f\"Overshoot mean: {intervened_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {intervened_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", + ")\n", + "\n", + "\n", + "add_pred_to_plot(\n", + " mask_samples[\"S\"], axs, coords=(2, 0), color=colors[0], label=\"susceptible\"\n", + ")\n", + "add_pred_to_plot(\n", + " mask_samples[\"I\"], axs, coords=(2, 0), color=colors[1], label=\"infected\"\n", + ")\n", + "add_pred_to_plot(\n", + " mask_samples[\"R\"], axs, coords=(2, 0), color=colors[2], label=\"recovered\"\n", + ")\n", + "axs[2, 0].set_title(\"Mask only\")\n", + "axs[2, 0].legend_.remove()\n", + "\n", + "axs[2, 1].hist(mask_samples[\"overshoot\"].squeeze())\n", + "axs[2, 1].set_title(\n", + " f\"Overshoot mean: {mask_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {mask_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", + ")\n", + "\n", + "add_pred_to_plot(\n", + " lockdown_samples[\"S\"], axs, coords=(3, 0), color=colors[0], label=\"susceptible\"\n", + ")\n", + "add_pred_to_plot(\n", + " lockdown_samples[\"I\"], axs, coords=(3, 0), color=colors[1], label=\"infected\"\n", + ")\n", + "add_pred_to_plot(\n", + " lockdown_samples[\"R\"], axs, coords=(3, 0), color=colors[2], label=\"recovered\"\n", + ")\n", + "axs[3, 0].set_title(\"Lockdown only\")\n", + "axs[3, 0].legend_.remove()\n", + "\n", + "axs[3, 1].hist(lockdown_samples[\"overshoot\"].squeeze())\n", + "axs[3, 1].set_title(\n", + " f\"Overshoot mean: {lockdown_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {lockdown_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", + ")\n", + "\n", + "\n", + "fig.tight_layout()\n", + "fig.suptitle(\"Trajectories and overshoot distributions\", fontsize=16, y=1.05)\n", + "sns.despine()\n", + "\n", + "plt.savefig(\"counterfactual_sir.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['lockdown', 'mask', 'lockdown_efficiency', 'mask_efficiency', 'joint_efficiency', 'beta', 'gamma', 'S', 'I', 'R', 'l', 'overshoot', 'os_too_high'])\n" + ] + } + ], + "source": [ + "with ExtractSupports() as s:\n", + " policy_model()\n", + "\n", + "supports = s.supports\n", + "print(supports.keys())\n", + "\n", + "antecedents = {\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(1.0)}\n", + "alternatives = {\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(0.0)}\n", + "witnesses = {key: s.supports[key] for key in [\"lockdown_efficiency\", \"mask_efficiency\"]}\n", + "consequents = {\"os_too_high\": torch.tensor(1.0)}\n", + "\n", + "# with MultiWorldCounterfactual() as mwc:\n", + "# query = with SearchForExplanation(\n", + "# supports=supports,\n", + "# alternatives=alternatives,\n", + "# antecedents=antecedents,\n", + "# antecedent_bias=0.0,\n", + "# witnesses=witnesses,\n", + "# consequents=consequents,\n", + "# consequent_scale=1e-8,\n", + "# witness_bias=0.2,\n", + "# ):\n", + "# with pyro.plate(\"sample\", exp_plate_size):\n", + "# with pyro.poutine.trace() as tr:\n", + "# policy_model_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "query = SearchForExplanation(\n", + " supports=supports,\n", + " alternatives=alternatives,\n", + " antecedents=antecedents,\n", + " antecedent_bias=0.0,\n", + " witnesses=witnesses,\n", + " consequents=consequents,\n", + " consequent_scale=1e-8,\n", + " witness_bias=0.2,\n", + " )(policy_model_all)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "logp, tr, mwc, lw = importance_infer(num_samples=10000)(query)()" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([ -inf, -inf, -inf, ..., 16.1154, -inf, -inf])\n", + "tensor(2432)\n", + "torch.Size([10000, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n", + "tensor(0.6594)\n" + ] + } + ], + "source": [ + "print(lw.squeeze())\n", + "\n", + "mask_intervened = (tr.nodes[\"__cause____antecedent_lockdown\"][\"value\"] == 1) & (tr.nodes[\"__cause____antecedent_mask\"][\"value\"] == 0)\n", + "print(mask_intervened.sum())\n", + "\n", + "with mwc:\n", + " oth = gather(tr.nodes[\"os_too_high\"][\"value\"], IndexSet(**{\"lockdown\": {2}, \"mask\": {2}}))\n", + " print(oth.shape)\n", + " os = gather(tr.nodes[\"overshoot\"][\"value\"], IndexSet(**{\"lockdown\": {2}, \"mask\": {2}}))\n", + "\n", + "denom = torch.sum(torch.exp(lw.squeeze()) * mask_intervened.squeeze().float()) / torch.sum(torch.exp(lw.squeeze()))\n", + "print(denom/0.25)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 0.0000e+00, 0.0000e+00, 0.0000e+00],\n", + " [ 0.0000e+00, -inf, 0.0000e+00],\n", + " [ 0.0000e+00, 0.0000e+00, -5.0000e+15]])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tr.trace.nodes[\"__cause____consequent_os_too_high\"][\"fn\"].log_factor[:, :, :, :, :, 6].squeeze()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def get_table(\n", + " trace, mwc, antecedents, witnesses, consequents, others=None, world: int = 1\n", + "):\n", + "\n", + " values_table = {}\n", + " nodes = trace.trace.nodes\n", + " witnesses = [key for key, _ in witnesses.items()]\n", + "\n", + " with mwc:\n", + "\n", + " for antecedent_str in antecedents.keys():\n", + "\n", + " obs_indices = IndexSet(\n", + " **{\n", + " name: {0}\n", + " for name, ind in indices_of(nodes[antecedent_str][\"value\"]).items()\n", + " }\n", + " )\n", + " obs_ant = gather(\n", + " nodes[antecedent_str][\"value\"],\n", + " obs_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " int_indices = IndexSet(\n", + " **{\n", + " name: {world}\n", + " for name, ind in indices_of(nodes[antecedent_str][\"value\"]).items()\n", + " }\n", + " )\n", + " int_ant = gather(\n", + " nodes[antecedent_str][\"value\"],\n", + " int_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " values_table[f\"{antecedent_str}_obs\"] = obs_ant.squeeze().tolist()\n", + " values_table[f\"{antecedent_str}_int\"] = int_ant.squeeze().tolist()\n", + "\n", + " apr_ant = nodes[f\"__cause____antecedent_{antecedent_str}\"][\"value\"]\n", + " values_table[f\"apr_{antecedent_str}\"] = apr_ant.squeeze().tolist()\n", + "\n", + " if witnesses:\n", + " for candidate in witnesses:\n", + " obs_indices = IndexSet(\n", + " **{\n", + " name: {world}\n", + " for name, ind in indices_of(nodes[candidate][\"value\"]).items()\n", + " }\n", + " )\n", + " obs_candidate = gather(\n", + " nodes[candidate][\"value\"],\n", + " obs_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " int_indices = IndexSet(\n", + " **{\n", + " name: {world}\n", + " for name, ind in indices_of(nodes[candidate][\"value\"]).items()\n", + " }\n", + " )\n", + " int_candidate = gather(\n", + " nodes[candidate][\"value\"],\n", + " int_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " values_table[f\"{candidate}_obs\"] = obs_candidate.squeeze().tolist()\n", + " values_table[f\"{candidate}_int\"] = int_candidate.squeeze().tolist()\n", + "\n", + " wpr_con = nodes[f\"__cause____witness_{candidate}\"][\"value\"]\n", + " values_table[f\"wpr_{candidate}\"] = wpr_con.squeeze().tolist()\n", + "\n", + " if others:\n", + " for other in others:\n", + " obs_indices = IndexSet(\n", + " **{\n", + " name: {0}\n", + " for name, ind in indices_of(nodes[other][\"value\"]).items()\n", + " }\n", + " )\n", + "\n", + " obs_other = gather(\n", + " nodes[other][\"value\"],\n", + " obs_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " int_indices = IndexSet(\n", + " **{\n", + " name: {world}\n", + " for name, ind in indices_of(nodes[other][\"value\"]).items()\n", + " }\n", + " )\n", + "\n", + " int_other = gather(\n", + " nodes[other][\"value\"],\n", + " int_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " values_table[f\"{other}_obs\"] = obs_other.squeeze().tolist()\n", + " values_table[f\"{other}_int\"] = int_other.squeeze().tolist()\n", + "\n", + " for consequent in consequents.keys():\n", + "\n", + " obs_indices = IndexSet(\n", + " **{\n", + " name: {0}\n", + " for name, ind in indices_of(nodes[consequent][\"value\"]).items()\n", + " }\n", + " )\n", + " obs_consequent = gather(\n", + " nodes[consequent][\"value\"],\n", + " obs_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " int_indices = IndexSet(\n", + " **{\n", + " name: {world}\n", + " for name, ind in indices_of(nodes[consequent][\"value\"]).items()\n", + " }\n", + " )\n", + " int_consequent = gather(\n", + " nodes[consequent][\"value\"],\n", + " int_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " values_table[f\"{consequent}_obs\"] = obs_consequent.squeeze().tolist()\n", + " values_table[f\"{consequent}_int\"] = int_consequent.squeeze().tolist()\n", + "\n", + " values_df = pd.DataFrame(values_table)\n", + "\n", + " return values_df\n", + "\n", + "\n", + "table = get_table(\n", + " tr,\n", + " mwc,\n", + " antecedents,\n", + " witnesses,\n", + " consequents,\n", + " others=[\"joint_efficiency\", \"overshoot\"],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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153 rows × 18 columns

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" + ], + "text/plain": [ + " lockdown_obs lockdown_int apr_lockdown mask_obs mask_int apr_mask \\\n", + "7 1.0 0.0 0 1.0 1.0 1 \n", + "12 1.0 0.0 0 1.0 1.0 1 \n", + "17 1.0 0.0 0 1.0 1.0 1 \n", + "44 1.0 0.0 0 1.0 1.0 1 \n", + "68 1.0 0.0 0 1.0 1.0 1 \n", + "... ... ... ... ... ... ... \n", + "1932 1.0 0.0 0 1.0 1.0 1 \n", + "1940 1.0 0.0 0 1.0 1.0 1 \n", + "1949 1.0 0.0 0 1.0 1.0 1 \n", + "1984 1.0 0.0 0 1.0 1.0 1 \n", + "1986 1.0 0.0 0 1.0 1.0 1 \n", + "\n", + " lockdown_efficiency_obs lockdown_efficiency_int \\\n", + "7 0.0 0.0 \n", + "12 0.0 0.0 \n", + "17 0.0 0.0 \n", + "44 0.0 0.0 \n", + "68 0.0 0.0 \n", + "... ... ... \n", + "1932 0.0 0.0 \n", + "1940 0.0 0.0 \n", + "1949 0.0 0.0 \n", + "1984 0.0 0.0 \n", + "1986 0.0 0.0 \n", + "\n", + " wpr_lockdown_efficiency mask_efficiency_obs mask_efficiency_int \\\n", + "7 0 0.10 0.10 \n", + "12 0 0.10 0.10 \n", + "17 0 0.45 0.45 \n", + "44 0 0.45 0.45 \n", + "68 0 0.10 0.10 \n", + "... ... ... ... \n", + "1932 0 0.10 0.10 \n", + "1940 0 0.10 0.10 \n", + "1949 0 0.45 0.45 \n", + "1984 0 0.45 0.45 \n", + "1986 0 0.10 0.10 \n", + "\n", + " wpr_mask_efficiency joint_efficiency_obs joint_efficiency_int \\\n", + "7 1 0.7 0.10 \n", + "12 1 0.7 0.10 \n", + "17 0 0.7 0.45 \n", + "44 0 0.7 0.45 \n", + "68 1 0.7 0.10 \n", + "... ... ... ... \n", + "1932 1 0.7 0.10 \n", + "1940 1 0.7 0.10 \n", + "1949 0 0.7 0.45 \n", + "1984 0 0.7 0.45 \n", + "1986 1 0.7 0.10 \n", + "\n", + " overshoot_obs overshoot_int os_too_high_obs os_too_high_int \n", + "7 27.413948 20.081240 1.0 1.0 \n", + "12 28.143654 18.176426 1.0 0.0 \n", + "17 23.878532 29.118336 1.0 1.0 \n", + "44 32.594929 25.202908 1.0 1.0 \n", + "68 30.271990 18.733744 1.0 0.0 \n", + "... ... ... ... ... \n", + "1932 33.913177 16.335846 1.0 0.0 \n", + "1940 18.856628 23.330811 0.0 1.0 \n", + "1949 32.299339 26.959858 1.0 1.0 \n", + "1984 21.073696 29.347139 1.0 1.0 \n", + "1986 32.659847 15.735228 1.0 0.0 \n", + "\n", + "[153 rows x 18 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "factual = table[\n", + " (table[\"lockdown_int\"] == 1)\n", + " & (table[\"mask_int\"] == 1)\n", + " & (table[\"wpr_lockdown_efficiency\"] == 0 & (table[\"wpr_mask_efficiency\"] == 0))\n", + "]\n", + "\n", + "\n", + "counterfactual_lockdown = table[\n", + " (table[\"lockdown_int\"] == 0)\n", + " & (table[\"mask_int\"] == 1)\n", + " & (table[\"wpr_lockdown_efficiency\"] == 0)\n", + "]\n", + "\n", + "display(counterfactual_lockdown)\n", + "\n", + "counterfactual_mask = table[\n", + " (table[\"lockdown_int\"] == 1)\n", + " & (table[\"mask_int\"] == 0)\n", + " & (table[\"wpr_mask_efficiency\"] == 0)\n", + "]\n", + "\n", + "\n", + "fig, axs = plt.subplots(1, 3, figsize=(18, 6))\n", + "\n", + "factual_mean = factual[\"overshoot_int\"].mean().item()\n", + "axs[0].hist(factual[\"overshoot_int\"])\n", + "axs[0].set_title(\n", + " f\"Factual\\n overshoot mean: {factual_mean:.2f}, Pr(too high): {factual['os_too_high_int'].mean().item():.2f}\"\n", + ")\n", + "axs[0].axvline(x=factual_mean, color=\"grey\", linestyle=\"--\")\n", + "\n", + "counterfactual_lockdown_mean = counterfactual_lockdown[\"overshoot_int\"].mean()\n", + "axs[1].hist(counterfactual_lockdown[\"overshoot_int\"])\n", + "axs[1].set_title(\n", + " f\"Counterfactual_lockdown\\n overshoot mean: {counterfactual_lockdown_mean:.2f}, Pr(too high): {counterfactual_lockdown['os_too_high_int'].mean():.2f}\"\n", + ")\n", + "axs[1].axvline(x=counterfactual_lockdown_mean, color=\"grey\", linestyle=\"--\")\n", + "\n", + "counterfactual_mask_mean = counterfactual_mask[\"overshoot_int\"].mean()\n", + "axs[2].hist(counterfactual_mask[\"overshoot_int\"])\n", + "axs[2].set_title(\n", + " f\"Counterfactual_mask\\n overshoot mean: {counterfactual_mask_mean:.2f}, Pr(too high): {counterfactual_mask['os_too_high_int'].mean():.2f}\"\n", + ")\n", + "axs[2].axvline(x=counterfactual_mask_mean, color=\"grey\", linestyle=\"--\")\n", + "\n", + "for i in range(3):\n", + " axs[i].set_xlim(5, 40)\n", + " axs[i].axvline(x=overshoot_threshold, color=\"red\", linestyle=\"-\")\n", + "\n", + "plt.savefig(\"counterfactual_sir_search.png\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_counterfactual_by_context(data, name, other):\n", + "\n", + " grouped_data = data.groupby([\"wpr_lockdown_efficiency\", \"wpr_mask_efficiency\"])\n", + "\n", + " fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n", + "\n", + " for (lockdown_efficiency, mask_efficiency), ax in zip(\n", + " grouped_data.groups.keys(), axs.flatten()\n", + " ):\n", + " data_subset = grouped_data.get_group((lockdown_efficiency, mask_efficiency))\n", + " mean_overshoot = data_subset[\"overshoot_int\"].mean().item()\n", + "\n", + " fixed = mask_efficiency if name == \"lockdown\" else lockdown_efficiency\n", + " ax.hist(data_subset[\"overshoot_int\"])\n", + " ax.set_title(\n", + " f\"{other} eff fixed: {fixed}\\nOvershoot mean: {mean_overshoot:.2f}, Pr(too high): {data_subset['os_too_high_int'].mean().item():.2f}\"\n", + " )\n", + " ax.set_xlim(5, 35)\n", + " ax.axvline(x=mean_overshoot, color=\"grey\", linestyle=\"--\")\n", + " ax.axvline(x=overshoot_threshold, color=\"red\", linestyle=\"-\")\n", + "\n", + " plt.suptitle(\n", + " f\"Counterfactual {name} by {other.lower()} efficiency contexts\",\n", + " fontsize=16,\n", + " y=1,\n", + " )\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "plot_counterfactual_by_context(counterfactual_lockdown, \"lockdown\", \"Mask\")\n", + "\n", + "plot_counterfactual_by_context(counterfactual_mask, \"mask\", \"Lockdown\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "sufficiency_table = get_table(\n", + " tr,\n", + " mwc,\n", + " antecedents,\n", + " witnesses,\n", + " consequents,\n", + " world=2,\n", + " others=[\"joint_efficiency\", \"overshoot\"],\n", + ")\n", + "\n", + "\n", + "factual_sufficiency = sufficiency_table[\n", + " (sufficiency_table[\"lockdown_int\"] == 1)\n", + " & (sufficiency_table[\"mask_int\"] == 1)\n", + " & (\n", + " sufficiency_table[\"wpr_lockdown_efficiency\"]\n", + " == 0 & (sufficiency_table[\"wpr_mask_efficiency\"] == 0)\n", + " )\n", + "]\n", + "\n", + "counterfactual_sufficiency_lockdown = sufficiency_table[\n", + " (sufficiency_table[\"lockdown_int\"] == 0)\n", + " & (sufficiency_table[\"mask_int\"] == 1)\n", + " & (sufficiency_table[\"wpr_lockdown_efficiency\"] == 0)\n", + "]\n", + "\n", + "counterfactual_sufficiency_mask = sufficiency_table[\n", + " (sufficiency_table[\"lockdown_int\"] == 1)\n", + " & (sufficiency_table[\"mask_int\"] == 0)\n", + " & (sufficiency_table[\"wpr_mask_efficiency\"] == 0)\n", + "]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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v+DX3coB044HfjW7sGXujO8lzLpdL9erVu+mBTZkyZdwe39gT6HY+/vhjTZw4Ue3bt9fAgQPl6+sru92u0aNHZ1gOvtHPP/+svn37qnbt2ho+fLj8/Pzk5eWlFStWaN26dekeT1p4+OGHtWPHDs2cOVN169ZNcSzqtFazZk3Fx8drz549+vnnn83e0TVr1tTPP/+so0eP6uLFi249yJPc+BvoRhnx9weAtETOv7WsmPNvluNul/sSExP17LPP6vLly+rdu7fKlSunnDlz6ty5cxoyZIjbzS579uypJk2aaMuWLfr22281ceJEffLJJ5o7d64qV65szvfggw8qMjJSn3/+uTp27KiSJUumyTbC81GsBiywadMmPfnkk253Uo6NjXXrVS1JpUqVuu0Pj1v92PD19U3xbHZYWJhboti0aZNKliypKVOmuC3vxpsoAADuH40bN9bSpUu1Z88eBQUF3XS+4sWLy+Vy6eTJkypfvrzZfuHCBUVGRqp48eJmW0o5JS4uTuHh4Xcd581yWKlSpRQTE6OQkJC7XvbNbNq0SXXr1tXo0aPd2iMjI5U/f363GH799VfFx8ff9KZ7t8vBKZ3wTeo9fGM8Pj4+mjVrljkklyStWLHijrYntYoVK6bjx48naz927Jj5vHRn25+kevXq6tSpk1544QUNHDhQU6ZMceudVqxYMZ08eTLZ6/4dR9K6jx075vY7JS4uTmfOnHHbH6pVqyYvLy/t3r1bu3fvVq9evSRJtWvX1meffaYffvhBklIsVgOAJyHn31xWz/mp8eeff+rEiRMaO3asnnzySbP9u+++S3H+UqVK6bnnntNzzz2nEydO6Mknn9Snn36q8ePHm/Pkz59fkyZNUpcuXdSzZ08tWrTIvKklcCuMWQ1YIKWzmvPnz0925vXRRx/VH3/8oS+//DLZ/ElnQHPkyCEp5UusSpYsqV9//dXt8qKtW7fqr7/+SjGeG88u//rrr+aYjwCA+0vv3r2VM2dO/ec//9GFCxeSPX/q1CnNnTtXDRs2lCTNnTvX7fnZs2dLkvm8dD2nJI0RnGTZsmU37WV1J3LkyJFi/mrRooX27NmjHTt2JHsuMjJSCQkJd71Oh8ORrDfVhg0bdO7cObe2Rx99VP/8848WLlyYbBl3moOPHTvmdvnyH3/8oV9++SVZPDabze19PHPmjL766qtUbtmdadiwofbt26c9e/aYbTExMVq2bJmKFy+uChUqSLqz7b9RSEiIPvzwQ+3YsUNvvPGGWw+shg0bau/evdq3b5/ZdvHiRa1duzbZMry8vDR//ny3dSxfvlxRUVFu+6OPj4+qVq2qdevWKSwszCxK16pVS9euXdO8efNUqlQpFS5cOLVvEQDcV8j5N5fVc35qJPWOv/H9MgxD8+bNc5vv6tWrio2NdWsrVaqUcuXK5VZ3SFKkSBHNnj1bsbGxeu655/TPP/+kQ/TwNPSsBizQqFEjff7558qdO7cqVKigvXv36vvvv3cbN0uSevXqpU2bNmngwIFq3769qlSposuXL+vrr7/WiBEjVLFiRfPGjEuWLFGuXLmUM2dOVatWTSVLltTTTz+tTZs2qXfv3mrRooVOnTqltWvXJhunrFGjRtq8ebNefvllNWrUSGfOnNGSJUtUoUIFxcTEZOA7AwBIC6VKldL48eP16quvqmXLlnriiSfkdDoVFxenPXv2aOPGjWrXrp169Oihtm3baunSpYqMjFTt2rW1f/9+rVq1Ss2aNdNDDz1kLvPpp5/W8OHD1b9/f4WEhOiPP/7Qt99+69YzKbWqVKmixYsX66OPPlLp0qVVoEABBQcHq1evXvr666/14osvqm3btqpSpYquXr2qP//8U5s2bdJXX311xzdX+rdGjRpp6tSpGjp0qIKCgvTnn39q7dq1yS5NffLJJ7V69WqFhoZq3759qlmzpq5evaqdO3eqc+fOatasmbJnz64KFSpow4YNKlOmjPLly6cHH3xQTqdTTz31lObMmaNevXrpqaeeUkREhJlbbxxbtGHDhpo9e7Z69+6t1q1bKyIiQosWLVKpUqV06NChu35vb+b555/X+vXr1adPH3Xr1k2+vr5avXq1zpw5o8mTJ5sHq3ey/f/WrFkzjR49WoMHD1bu3Lk1cuRISdcLKZ9//rl69+6t7t27K0eOHFq2bJmKFSvmto0FChTQCy+8oClTpqh3795q0qSJjh8/rkWLFqlq1ap6/PHH3dZXq1YtffLJJ8qTJ4+cTqckqWDBgipbtqyOHz+udu3apfn7BwCZDTn/5rJ6zk+NcuXKqVSpUho7dqzOnTun3Llza9OmTcmK8ydOnFDPnj312GOPqUKFCnI4HNqyZYsuXLigVq1apbjs0qVLa9asWerevbt69eqlefPmud3sEfg3itWABd58803Z7XatXbtWsbGxqlGjhpm0bpQrVy4tXLhQkydP1pdffqlVq1apYMGCCg4ONi+f8fLy0pgxY/TBBx/o7bffVkJCgkJDQ1WyZEk9/PDDGjJkiGbPnq3Ro0crICBAH3/8scaOHeu2nnbt2unChQtaunSpvv32W1WoUEHvvfeeNm7cqF27dmXY+wIASDtNmzbVmjVrNGvWLH311VdavHixvL295e/vryFDhqhDhw6SpFGjRqlEiRJatWqVtmzZokKFCumFF15Qv3793JbXoUMHnTlzRsuXL9eOHTtUs2ZNzZ49Wz179rzrGF9++WWFhYVp5syZunLliurUqaPg4GDlyJFD8+fP1/Tp07Vx40atXr1auXPnVpkyZdS/f3/zpkB348UXX9TVq1e1du1affHFF6pcubKmT5+u999/320+h8OhGTNmaNq0aVq3bp02b96sfPnyqUaNGvL39zfnGzVqlN555x2FhoYqPj5e/fr1k9PpVPny5TV27FhNmjRJoaGhqlChgsaNG6d169a55dbg4GC9++67mjFjhkaPHq0SJUpo0KBBOnv2bLocuBYqVEhLlizRe++9pwULFig2Nlb+/v76+OOP1ahRo1Rv/7898cQTunLlikaMGKFcuXJp8ODBKly4sObNm6dRo0bpk08+Ub58+dSpUycVLlxYb775ptvr+/fvrwIFCmjBggUKDQ2Vr6+vOnTooNdeey3ZpdlJxeqgoCC38VJr1aql48ePq2bNmmnzpgFAJkfOT1lWz/mp4eXlpY8//lijRo3S9OnT5ePjo0ceeURdu3bVE088Yc5XpEgRtWrVSjt37tSaNWvkcDhUrlw5TZgwQc2bN7/p8v39/TVjxgz17NlTL774ombOnJmq8ceRtdgM7iQCAAAAAAAAALAYY1YDAAAAAAAAACzHMCAAAADAfSY8PPyWz2fPnv2eLp0GAACZAzkfWQ3DgAAAAAD3mVuNGy1Jbdu21ZgxYzIoGgAAkF7I+chqKFYDAAAA95nvv//+ls8XLlxYFSpUyKBoAABAeiHnI6uhWA0AAAAAAAAAsBw3WAQAAAAAAAAAWI5itQdauXKl/P39tX//fqtDAe6Ky+VS69atNW3aNKtDMU2ePFn+/v66ePHibedt0qSJhgwZclfradKkiV544YXbzvfNN98oKCjojuIBkDmRr3G/u3LlioKDg7VmzRqrQzENGTJEQUFBdzSvv7+/Jk+efFfr8ff318iRI2873+LFi9WoUSPFxcXd1XoAZA7kbNzvyNnk7PsJxWqkqe3bt9/1F4gn27x5s1555RU1bdpU1atXV/PmzTVmzBhFRkamOH90dLTGjRunJk2aKCAgQA8//LAGDBigq1ev3nI9586d06BBg9S8eXMFBQWpVq1aeuqpp7Rq1SqlNOLP999/r27duqlu3brmvKtXr77r7Uwq6Cb9q169ulq2bKkPP/xQ0dHRd7ycdevW6a+//tIzzzxjtv3yyy+aPHnyTd+zrKZBgwYqVaqUpk+ffs/LOnr0qHr16qWgoCDVqVNHr7/++h0XwWNjYzV9+nS1bNlS1atXN/fVw4cPu83XrVs3t33jxn9VqlS5520AkDrk65SlNl9/9dVXatu2rapWrapGjRpp0qRJSkhISPV616xZI39//5sesC1YsEAtWrQwfxOEhoYqJiYm1etJMmTIELfv4Ro1aujxxx/Xp59+mqoDtHnz5ilXrlxq1aqV2ca+5a5du3aKj4/XkiVL7nlZv/zyizp37qzq1aurXr16GjVqlK5cuXJHr71ZDv7kk09SnP+LL75Qx44dFRgYqFq1aqlTp07auXPnPW8DgNTjezVlmTFn3+y71t/fX88++2yq1yWRszNSZsnZUVFRGjdunB599FFVq1ZNjRs31rBhwxQWFuY237/rL0n/qlates/xWy2b1QHAs2zfvl0LFy5U//79rQ4lU/nvf/+rwoUL6/HHH1exYsV06NAhLViwQNu3b9eqVauUPXt2c96oqCg988wz+vvvv9WxY0eVKlVKFy9e1O7duxUXF6ccOXLcdD3//POPzp07p8cee0xFixZVQkKCvvvuOw0ZMkTHjx/Xa6+9Zs771Vdf6eWXX1ZgYKD69+8vm82mDRs2aPDgwbp06ZJ69ux519v79ttvK2fOnIqJidF3332njz/+WD/++KMWL14sm81229fPmjVLrVq1Up48ecy2PXv2aMqUKWrbtq3y5s1717FlhI0bN97Rdt6rjh07aty4cerfv79y5859V8v4+++/1bVrV+XJk0evvvqqYmJi9Omnn+rPP//UZ599Jm9v71u+ftCgQfr666/19NNPq0qVKjp37pwWLVqkjh07au3atSpevLgk6cUXX9RTTz3l9tqrV69q+PDhqlev3l3FDuDuka9Tlpp8vX37dr388suqU6eO/vvf/+rPP//UtGnTFBERoREjRtzxOq9cuaL33ntPOXPmTPH59957TzNnzlTz5s3VvXt3HT16VAsWLNCRI0c0a9asu95Wb29vjRo1StL13x6bNm3S2LFjtX//fn344Ye3fX18fLzmzZunnj17yuFwmO330761b98+t9jTg4+Pj5588knNmTNH3bp1u+vfBwcPHlTPnj1Vvnx5DRkyRH///bc+/fRTnThxQjNnzryjZdSrV09PPPGEW1vlypWTzTd58mRNnTpVzZs3V9u2bZWQkKA///xT586du6vYAdyb++l7NSNlxpw9bty4ZG0HDhzQvHnz7umYh5yddXK2y+XSs88+q6NHj6pz584qW7asTp48qUWLFunbb7/VF198kezYP6n+kiS936eMQLH6PuFyuRQfHy8fHx+rQ8FdmDRpkurWrevWFhAQoMGDB2vt2rV6+umnzfb3339fYWFhWrlypUqWLJmq9VSsWFHz5893a3vmmWf04osvav78+Ro4cKD5xbVw4UL5+flp3rx5ZkGyY8eOatGihVauXHlPxermzZurQIECkqTOnTurf//+2rx5s/bu3XvTXmNXr15Vjhw59Pvvv+uPP/6462E0MoPbFXjTSvPmzTVq1Cht3LgxWSH4Tn388ce6evWqVq5cqWLFikmSqlWrpmeffVarVq1Sx44db/rac+fOafPmzXruuec0ePBgs71WrVrq0aOHvvzyS3M/SunH2eeffy5JatOmzV3FDmRG5Ov7W2ry9bhx4+Tv769PP/1U2bJd/0mdK1cuTZ8+Xd27d1f58uXvaJ3Tpk1Trly5VLduXX311Vduz50/f15z5szRE0884XYAXKZMGb3zzjv6+uuv1aRJk7va1mzZsrkVLrt06aKnn35aX3zxhYYMGaIHHngg2WsMw1BsbKyyZ8+ubdu26eLFi2rRosVdrT8zyKjPaYsWLTRz5kz98MMPCg4OvqtlfPDBB8qbN6/mz59vHqSWKFFC//nPf/Ttt9+qfv36t11GmTJlkhWr/23v3r2aOnWqhgwZck+/BYH7ATn7/pbZcrakFL9jd+3aJZvNptatW6dm89yQs7NOzt67d6/279+vt956S127djXby5Ytq2HDhmnnzp165JFH3F5zY/3FUzAMyG1ERERo2LBhCgkJUdWqVfX4449r1apV5vPx8fGqU6eOhg4dmuy10dHRqlq1qsaOHWu2xcXFadKkSXrkkUcUEBCghg0baty4ccku30gaU2fNmjVq1aqVqlatqh07dkiS1q9fr3bt2ikoKEg1atRQmzZtNHfu3GTrj4uLU2hoqB566CEFBgbq5ZdfTvHS/oULF6pVq1YKCAhQ/fr1NWLEiBQvndmwYYPatWunatWqqW7duho0aJBbD4shQ4Zo4cKFZvxJ/24laXzeH3/80Vx2mzZt9OOPP0q6fmlPmzZtVLVqVbVr106///57smUcPXpUAwYMUJ06dcz5/p04Ll26pLFjx6pNmzbm+9a7d2/98ccfbvP9+OOP8vf31xdffKFp06apQYMGqlq1qnr06KGTJ0+6zXv16lUdPXr0joZL+HcSlaRmzZqZ8SeJjIzUypUr1aFDB5UsWVJxcXFpMl5S8eLFdfXqVcXHx5tt0dHR8vX1dSusZsuWTfnz53c7C50WHnroIUnSmTNnJF0fEqJ169Y6cOCAunbtqurVq+uDDz6QJG3ZskVeXl6qVauW+frJkyebB+lNmzY1962k5SUkJGjq1Klq1qyZAgIC1KRJE33wwQcpvnd3ur/fTFRUlIYMGaJatWqpZs2aGjp0aLLhWVIas/qPP/7QM888o2rVqqlBgwb66KOPtGLFCrftuNHPP/+sp556SlWrVlXTpk1THJ6lYMGC8vf3T7a/R0VF6ejRo4qKirrt9mzevFmNGjUyC9WSFBISojJlymjDhg23fG3S0C6FChVya/fz85N0+x8U69atU86cOdW0adPbxgncDvn6f8jX6Z+vjxw5oiNHjqhDhw7mQa90/eDRMAxt2rTptuuSpBMnTmjOnDkaOnSo23KS7N27VwkJCW6X7EpSy5YtJV3fx9KK3W5XnTp1JElnz56V9L+/+44dO8y/e9KlsVu2bFHx4sVVqlQpcxm327diYmI0ZswYNWzYUAEBAWrevLlmzZqVbKiy1OT1mzl37pxeeuklBQUF6aGHHtLYsWOVmJjoNk9K418m7eNVq1ZVs2bNtGTJEvMy25Rs2bJFrVu3VkBAgFq1aqVvvvkm2TwBAQHKly9fsv394sWLOnr06G2HeYuOjtb333+vxx9/3K031RNPPKGcOXPeNl/f6Nq1a4qNjb3p83PnzlWhQoXUvXt3GYZxx5csA6lBzv4fcrbn5OyUxMXFafPmzapdu7aKFClyR6+5E+Ts6zwxZycdYxcsWNCt/XbH2NHR0SkO/Xq/omf1LVy7dk3dunXTqVOn1LVrV5UoUUIbN27UkCFDFBkZqR49esjLy0vNmjXTl19+qREjRrgV/rZs2aK4uDjzgMLlcqlv377avXu3OnTooPLly+vPP//U3LlzdeLECX300Udu6//hhx+0YcMGde3aVfnz51fx4sX13Xff6bXXXlNwcLAGDRokSTp27Jh++eUX9ejRw+31o0aNUt68edWvXz+dPXtWc+fO1ciRIzVhwgRznsmTJ2vKlCkKCQlR586ddfz4cS1evFj79+/X4sWL5eXlJen6DSWGDh2qqlWr6rXXXlNERITmzZunX375RatXr1bevHnVsWNHnT9/Xt99912Kl7/czMmTJ/V///d/6tSpkzn20osvvqgRI0boww8/VOfOnSVJn3zyiV555RVt3LhRdvv18yyHDx9W586d9cADD6hPnz7mh//ll1/W5MmTzTNOp0+f1pYtW/TYY4+pRIkSunDhgpYuXapnnnlG69evT3YmcsaMGbLZbHruuecUHR2tmTNnatCgQfrss8/Mefbt26fu3burX79+d3XZzIULFyRJ+fPnN9t2796t2NhYlS5dWgMGDNCWLVvkcrkUGBio4cOHq1KlSne07GvXrikmJkYxMTH66aeftHLlSgUGBroVoevUqaMZM2ZowoQJatu2rWw2m9auXasDBw647SNp4dSpU5KkfPnymW2XLl1Snz591KpVKz3++OPml/GePXvkdDrNfU+SHnnkEZ04cULr1q3T0KFDzfcs6ezhf/7zH61atUrNmzfXs88+q3379mn69Ok6evSopk6dai7nTvf3W3nllVdUokQJvfbaa/r999/12WefqUCBAnr99ddv+ppz586Zn8/nn39eOXPmvOUQGydPntTAgQP11FNPqW3btlqxYoWGDBmiKlWq6MEHH3Sbt0qVKtqyZYtb25dffqmhQ4cqNDRU7dq1u2VcERERCggISPZctWrVUkzeNypVqpSKFCmi2bNnq2zZsqpcubLOnz+v9957TyVKlEhWXLnRxYsX9f3336tFixY3vYwOuFPka/J1RufrpAP7f48J+MADD6hIkSI6ePDgHS179OjRqlu3rho2bJjiwUvSgd6/D0yShgT77bff7nAr7szp06cluefr48eP6//+7//UsWNHdejQQWXLlpV0PV//+54Dt9q3DMNQ37599eOPP+qpp55SpUqVtGPHDo0bN07nzp3TsGHDzHnvNK/fTGJionr16qVq1arpjTfe0M6dO/Xpp5+qZMmS6tKly01f9/vvv6t3797y8/NT//795XK5NHXq1Jv2Vtq9e7c2b96sLl26KFeuXJo/f74GDBigrVu3uu0v0vXhNn755Re3toULF2rKlCmaN29eikWXJIcOHVJCQkKyfO3t7a1KlSrd8f62atUqLVq0SIZhqHz58urbt2+yq5t27typoKAgzZs3T9OmTdOlS5fk5+enF1980e1eIsDdImeTsz01Z6dk+/btioyM1OOPP35H86cGOdszc3ZAQIBy5sypiRMnytfXV+XKldPJkyf13nvvqWrVqgoJCUn2mqZNmyomJsbsCDZkyJBkHcruOwZuas6cOYbT6TQ+//xzsy0uLs7o2LGjERgYaERFRRmGYRg7duwwnE6n8fXXX7u9vk+fPkbTpk3Nx6tXrzYqVqxo/PTTT27zLV682HA6ncbu3bvNNqfTaVSsWNE4fPiw27yjRo0yatSoYSQkJNw07hUrVhhOp9Po2bOn4XK5zPbRo0cblSpVMiIjIw3DMIyIiAijSpUqxnPPPWckJiaa8y1YsMBwOp3G8uXLzW0ODg42WrdubVy7ds2cb+vWrYbT6TQmTpxoto0YMcJwOp03je3fGjdubDidTuOXX34x25Lez2rVqhlnz54125csWWI4nU7jhx9+MNt69OhhtG7d2oiNjTXbXC6X0bFjR+PRRx8122JjY9220TAM4/Tp00ZAQIAxZcoUs+2HH34wnE6n0aJFC7dlzp0713A6ncahQ4eSzTtp0qQ73t4bDRs2zKhUqZJx/Phxs2327NmG0+k06tSpYzz11FPGmjVrjIULFxohISFG7dq1jXPnzt3RsqdPn244nU7zX48ePYywsDC3ea5cuWIMHDjQ8Pf3N+erXr268eWXX97V9hiGYUyaNMlwOp3GsWPHjIiICOP06dPGkiVLjICAACMkJMSIiYkxDMMwnnnmGcPpdBqLFy9OtowGDRoY/fv3T9Y+c+ZMw+l0GqdPn3ZrP3jwoOF0Oo0333zTrX3MmDGG0+k0du7caRjGne/vt9u2oUOHurW//PLLRp06ddzaGjdubAwePNh8/M477xj+/v7G77//brb9888/Rp06dZJtU9Jn4sbviYiICCMgIMAYM2ZMsrg+/vhjw+l0GhcuXDDbkr4DVqxYcctt2rdvn+F0Oo1Vq1Yle27s2LGG0+l0+xyk5NdffzWaNWvmtr+1bdvWOH/+/C1fN3/+fMPpdBrbtm275XzAnSBfk6+TZFS+TspJ/86thmEY7du3Nzp06HDb5W7dutWoXLmyue8MHjzYCAwMdJvnwIEDhtPpNKZOnerW/s033xhOpzPZ/HcqaV0RERFGRESEcfLkSePjjz82/P39jTZt2pjzJf3dv/nmG7fXx8fHG/7+/inmpZvtW19++aXhdDqNjz76yK29f//+hr+/v3Hy5EnDMO48r99q25xOp9v+YhiG8eSTTxpt27Z1a/v3fvHCCy8Y1atXN/7++2+z7cSJE0blypWTbZPT6TSqVKlixn1j7PPnz08W13//+1+jWrVqbm1Jvy1u/KykZMOGDcl+GyQZMGCAUa9evVu+3jAMo2PHjsacOXOMLVu2GIsWLTJat25tOJ1OY+HCheY8ly5dMn+HBgYGGjNnzjTWr19v9OrV66a/24DUImeTs5N4Ws5OSf/+/Y2AgADj8uXLd74R/0LOvi4r5eytW7ca9erVczvGfu6554zo6Gi3+ebMmWOMHDnSWLNmjbFx40Zj1KhRRuXKlY1HH33U/C69XzEMyC1888038vPzcxtbyMvLS926dTN7rErXhzjInz+/vvjiC3O+y5cv6/vvvzfP+ErXb7pWvnx5lStXThcvXjT/JQ2RkHRZTpLatWurQoUKbm158+bV1atX9d133902/g4dOrgNCF+rVi0lJiaal4l8//33io+PV/fu3c2zqJL09NNPK3fu3Nq+fbuk6zcEiIiIUOfOnd169jRq1EjlypXTtm3bbhvLrVSoUMFtHOPq1atLuv6+3jg0QVJ70hnES5cu6YcfflCLFi0UHR1tvp///POP6tevrxMnTpiXUHl7e5vbmJiYqH/++Uc5c+ZU2bJlU7zsqV27dm5n8JOGpEhat3T9sqNDhw7d1RnftWvXavny5Xr22WdVpkwZsz3pUkubzaY5c+aoTZs26tKli6ZOnarLly+bl+ncTqtWrTR79my9//775v577do1t3m8vb1VpkwZNW/eXB988IHee+89BQQE6PXXX9fevXtTvU03euyxxxQcHKymTZvqrbfeUunSpTV9+nS3m0N6e3un2Ov30qVLqbqBYtJ++u+7Kz/33HNuz9/p/n47nTp1cntcq1YtXbp0ybxcJyU7duxQYGCgW8/4fPny3XSs5goVKrgNg1KgQAGVLVvWbf9LkvRe/fPPP2Zbu3btdOjQoVv2qpZkXgacUg/vpM/6v/eblNZfqVIlPf/885o6daoGDx6ss2fPauDAgbe8zHjdunUqUKAAN1dEmiBfk6+TZFS+TvpuvNn35+2+O5MuI+/UqVOyfedGVapUUfXq1TVjxgytWLFCZ86c0fbt2zV8+HB5eXnd8nv2dmJiYhQcHKzg4GA98sgj+uCDDxQYGJisF1SJEiX08MMPu7VdvnxZhmGkKl9/8803cjgc6tatm1v7c889J8MwzKt57jSv305Sr8EkNWvWTHHYrSSJiYnauXOnmjZt6tYbsHTp0sm2P0lISIjbJdUVK1ZU7ty5b5qvr1275nb5cP/+/XXo0KFb9tCS7n1/k6QlS5aoR48eatq0qTp37qwVK1bI6XTqww8/NF8fExMj6fpn9t1331WvXr3UsmVLffLJJ6pQoYKmTZt22/UAt0POJmcn8bSc/W/R0dHatm2bGjZsmKp8mRJytjtPz9kFChRQ5cqV9eqrr2rq1Knq37+/du/enWxopB49eui///2v2rRpo+bNm+vNN9/UmDFjdOLECS1atOi268nMGAbkFs6ePavSpUu7JRlJ5uD7YWFhkq6P8/voo49q3bp1iouLk7e3tzZv3qz4+Hi3RHry5EkdPXr0poO0R0REuD0uUaJEsnm6dOmiDRs2qE+fPnrggQdUr149tWjRQg0aNEg2741JSPpfUStprKyk+MuVK+c2n7e3t0qWLGkm3KT5ki4huVG5cuW0e/fuFLfnThUtWtTtcZ48eSQp2ZhOSeP9JMV/6tQpGYahiRMnauLEiSkuOyIiQg888IBcLpfmzZunRYsW6cyZM27jH9142UyS27139+Lnn3/Wm2++qfr16+vVV191ey5pmI7GjRsrV65cZntgYKBKlCihPXv23NE6ihcvruLFi0uSWrdurf/+97969tlntXHjRnMdI0eO1K+//qpVq1aZ+3iLFi3UunVrvfvuu26XY6XW5MmTlTt3bmXLlk1FihRxSwpJHnjggZsOg2GkYqyls2fPym63J1uHn5+f8ubNm2w/vt3+fjs32zcuX76c7K68N8YYGBiYrD2l90VK/pmQJF9fX12+fDlZe9J7dTd3Kk76YZzSeGJJBZBbjV8eFRWlrl27qlevXuYPEun6pUvdunXTihUrUrx06/Tp09qzZ4+eeeaZOx7vDbgV8jX5OklG5+ubfX/e7t4Pc+bM0T///HNHB+OTJ0/WK6+8Yl5y63A41LNnT/300086fvz4nW5OMj4+Pvr4448lXd+XSpQokeJ4mint30lSm68LFy6cLFcmfU6T9uM7zeu34uPjk+wy4Jvl0SQRERG6du2aSpcuney5lNqkm+frlPa/e8nX97q/pcTb21tdu3bV8OHDdeDAAdWqVcv8XeDl5aXmzZub89rtdrVo0UKTJ09WWFhYss8dkBrkbHJ2Ek/M2TfatGmTYmNj0+Rm8uRsd56cs0+fPq3u3btr7NixZi5u1qyZihcvriFDhmj79u1q2LDhTV/fpk0bjR07Vt9//72ef/75VMefWVAlSCOtWrXS0qVL9c0336hZs2bauHGjypUrp4oVK5rzuFwuOZ3OFG8UISVPHCntxAULFtTq1av17bff6ptvvtE333yjlStX6sknn3S7yYSkZD8AkqTmSyojOByOVLUnxe9yuSRdP2t3s7NnSV+aH3/8sSZOnKj27dtr4MCB8vX1ld1u1+jRo1N8P9Lrvfvjjz/Ut29fPfjgg5o0aVKyQl3hwoUlJb9hnXT9b3+3ibx58+ZatmyZfvrpJz388MOKi4vTihUr1Lt3b7dt9fLy0sMPP6yFCxeaPwrvRq1atW57N9qbfUnny5fvrrbzbhLJ3ciIz9XN9v2UJL1X/x5n604k7W/h4eHJngsPD1e+fPluuQ9s2rRJFy5cUJMmTdza69Spo9y5c+uXX35JsVi9du1aSUqTH25AapGv7x75+n+SbnITHh6e7OAnPDxc1apVu+myo6KiNG3aNHXp0kXR0dHmlTkxMTEyDENnzpxRjhw5zHs5PPDAA1q8eLFOnDihCxcuqHTp0vLz81P9+vXdeo6llsPhSHHcw39Laf/29fWVzWZLkwLDzdxLXk9NHr0Xt9v3bxQZGakcOXLcVWE5aX87f/58sufCw8PNfJ5aSftuUkEgX7588vHxUd68eZNtW9L+GBkZSbEaGYacfffI2f+TkTn7RmvXrlWePHnUuHHje9o+iZydFu6XnL1y5UrFxsYm22+Sjrl/+eWXWxarpevfe7cq9t8PKFbfQvHixXXo0CG5XC63L9Zjx45Jcj8zWLt2bfn5+emLL75QjRo19MMPP+jFF190W16pUqX0xx9/KDg4+J4+zN7e3mrSpImaNGkil8ult99+W0uXLtVLL71007NIKUmK/9ixYypZsqTZHhcXpzNnzphfhknzHT9+PNkZ6+PHj7u9DxlVMJRkxuzl5XXbL+5Nmzapbt26Gj16tFt7ZGTkXRX57sapU6fUu3dvFShQQDNmzHDrOZ0k6aYHN94BOsn58+eTnaG/U0mXmkRFRUm6fnlXQkJCsjvsStfv5utyucwfKhmtXLlyKV7yc7N9q3jx4nK5XDp58qR5ple6fnONyMhIs4f5ne7v6aF48eLJ7nQt/e/Gk/fizJkzyp8//21PDqTkgQceUIECBXTgwIFkz+3bt8/tQCAlST1V/r0fGYYhl8uV4v4lXR8CpFSpUin2NgfuBvmafJ2W7iRfJw3rtH//freD3HPnzunvv/9Whw4dbrr8y5cvKyYmRjNnztTMmTOTPd+0aVM1bdo02U3BypQpYxanjxw5ovDw8NsO95ResmXLplKlSqU6X+/cuVPR0dFuPbWSPqdJ+fpO83paK1iwoHx8fFLM1ym1pdaZM2fu+nec0+lUtmzZdODAAbcepXFxcTp48KBatGhxV8tNuvQ56TeE3W5XpUqVtH///mSdFpIOujPqcwjPRc4mZ6elzJqzz58/rx9//FFt27a96w5gaYWcnXpW5uyIiAgZhpHsWDohIUFS8mPvfzMMQ2fPnlXlypXvKv7MgjGrb6FBgwYKDw93GycrISFB8+fPV86cOVW7dm2z3W6367HHHtPWrVu1Zs0aJSQkuO2Y0vUhFs6dO6dly5YlW9e1a9fMceJu5cZxaZPW6+/vLynlywxuJSQkRF5eXpo/f77b2aTly5crKirKPFsTEBCgggULasmSJW7r2L59u44ePapGjRqZbUljEqfnWbskBQsWVJ06dbR06dIUz1pdvHjRnHY4HMnOmG3YsCHFovCdunr1qo4ePeq2npsJDw/Xc889J5vNplmzZt20sJjUU+Crr75yW+63336rv/76y+0HQ1RUlI4ePWoWoCXdNJbly5fLZrOZxfCCBQsqb968+vLLL93+pleuXNHWrVtVrly5uzqLmBYCAwN1+PDhZPtz0r514/ZKMvfTuXPnurXPnj3b7fk73d/TQ/369bV37163O/9eunTJ7GF8L3777bdkRd+U9o2befTRR7Vt2zb99ddfZtvOnTt14sQJPfbYY2ZbfHy8jh496vZZSyqa3PgdKUlfffWVYmJi3MboTvL777/r6NGjbuMUAveKfE2+vpX0yNcPPvigypUrp2XLlrkdNCxevFg2m83t+/Pf38kFCxbU1KlTk/2rW7eufHx8NHXqVL3wwgs3jdHlcum9995Tjhw5kt1HISMFBgameLLzZvtWgwYNlJiYmOz+G3PmzJHNZjMvt7/TvJ7WknqtffXVV27728mTJ7Vjx457Xv7vv/+uGjVquLVdvHhRR48edRsTMyV58uRRcHCw1qxZ43aPjM8//1wxMTFu+1tK+3tK+350dLTmzp2r/Pnzm78Ppevff4mJiVq9erXZFhsbq7Vr16pChQpuY4MCd4OcTc6+FU/J2V988YVcLlemuZKUnJ06VubsMmXKyDAMbdiwwW2569atkyS3InRKn5NFixbp4sWLN70y4n5Bz+pb6Nixo5YuXaohQ4bot99+U/HixbVp0yb98ssvGjZsWLLxe1q0aKH58+dr0qRJcjqdbmeWJOmJJ57Qhg0bNHz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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 3, figsize=(18, 6))\n", + "\n", + "factual_sufficiency_mean = factual_sufficiency[\"overshoot_int\"].mean().item()\n", + "axs[0].hist(factual_sufficiency[\"overshoot_int\"])\n", + "\n", + "axs[0].set_title((\n", + " f\"Factual\\n overshoot mean: {factual_sufficiency_mean:.2f}, Pr(too high): \"\n", + " f\"{factual_sufficiency['os_too_high_int'].mean().item():.2f}\"\n", + "))\n", + "axs[0].axvline(x=factual_sufficiency_mean, color=\"grey\", linestyle=\"--\")\n", + "\n", + "counterfactual_sufficiency_lockdown_mean = counterfactual_sufficiency_lockdown[\"overshoot_int\"].mean()\n", + "axs[1].hist(counterfactual_sufficiency_lockdown[\"overshoot_int\"])\n", + "axs[1].set_title((\n", + " f\"Counterfactual_lockdown\\n overshoot mean: {counterfactual_sufficiency_lockdown_mean:.2f}, \"\n", + " f\"Pr(too high): {counterfactual_lockdown['os_too_high_int'].mean():.2f}\"\n", + "))\n", + "axs[1].axvline(x=counterfactual_sufficiency_lockdown_mean, color=\"grey\", linestyle=\"--\")\n", + "\n", + "counterfactual_sufficiency_mask_mean = counterfactual_sufficiency_mask[\"overshoot_int\"].mean()\n", + "axs[2].hist(counterfactual_sufficiency_mask[\"overshoot_int\"])\n", + "axs[2].set_title((\n", + " f\"Counterfactual_mask\\n overshoot mean: {counterfactual_sufficiency_mask_mean:.2f}, \"\n", + " f\"Pr(too high): {counterfactual_mask['os_too_high_int'].mean():.2f}\"\n", + "))\n", + "axs[2].axvline(x=counterfactual_sufficiency_mask_mean, color=\"grey\", linestyle=\"--\")\n", + "\n", + "for i in range(3):\n", + " axs[i].set_xlim(5, 40)\n", + " axs[i].axvline(x=overshoot_threshold, color=\"red\", linestyle=\"-\")\n", + "\n", + "#plt.savefig(\"counterfactual_sir_search_sufficiency.png\")\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " lockdown_obs lockdown_int apr_lockdown mask_obs mask_int apr_mask \\\n", + "11 1.0 0.0 1 1.0 1.0 0 \n", + "38 1.0 0.0 1 1.0 1.0 0 \n", + "51 1.0 0.0 1 1.0 1.0 0 \n", + "104 1.0 0.0 1 1.0 1.0 0 \n", + "110 1.0 0.0 1 1.0 1.0 0 \n", + "\n", + " lockdown_efficiency_obs lockdown_efficiency_int \\\n", + "11 0.0 0.0 \n", + "38 0.0 0.0 \n", + "51 0.0 0.0 \n", + "104 0.0 0.0 \n", + "110 0.0 0.0 \n", + "\n", + " wpr_lockdown_efficiency mask_efficiency_obs mask_efficiency_int \\\n", + "11 0 0.00 0.00 \n", + "38 0 0.45 0.45 \n", + "51 0 0.45 0.45 \n", + "104 0 0.00 0.00 \n", + "110 0 0.00 0.00 \n", + "\n", + " wpr_mask_efficiency joint_efficiency_obs joint_efficiency_int \\\n", + "11 1 0.7 0.00 \n", + "38 0 0.7 0.45 \n", + "51 0 0.7 0.45 \n", + "104 1 0.7 0.00 \n", + "110 1 0.7 0.00 \n", + "\n", + " overshoot_obs overshoot_int os_too_high_obs os_too_high_int \n", + "11 15.616409 21.598530 0.0 1.0 \n", + "38 31.680214 25.210352 1.0 1.0 \n", + "51 32.041954 24.760708 1.0 1.0 \n", + "104 30.147129 17.757517 1.0 0.0 \n", + "110 15.187485 23.535439 0.0 1.0 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "counterfactual_sufficiency_lockdown.head()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "chirho", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 75ddfaf13acaa752c8e67b7a2f7e70941ea6aa06 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Thu, 15 Aug 2024 15:24:11 -0400 Subject: [PATCH 043/111] reveresed accidental commit --- .../explainable_categorical_alternate.ipynb | 112 ++++++++---------- 1 file changed, 52 insertions(+), 60 deletions(-) diff --git a/docs/source/explainable_categorical_alternate.ipynb b/docs/source/explainable_categorical_alternate.ipynb index a9e6938b..d4990530 100644 --- a/docs/source/explainable_categorical_alternate.ipynb +++ b/docs/source/explainable_categorical_alternate.ipynb @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -106,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 180, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -221,10 +221,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 180, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -295,14 +295,14 @@ }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2989)\n" + "tensor(0.3023)\n" ] } ], @@ -329,26 +329,20 @@ }, { "cell_type": "code", - "execution_count": 187, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(5.7317e-06)\n", - "tensor(2988.9890)\n", - "tensor(5026.)\n", - "tensor(9.6378e-06)\n" + "tensor(0.6017)\n" ] } ], "source": [ - "mask_intervened = trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 1\n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))\n", - "print(torch.sum(torch.exp(log_weights)))\n", - "print(mask_intervened.float().sum())\n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(torch.exp(log_weights)))" + "mask_intervened = trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" ] }, { @@ -369,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -403,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -428,14 +422,14 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(2.8435e-06)\n" + "tensor(2.8425e-06)\n" ] } ], @@ -459,14 +453,14 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(2.8377e-06)\n" + "tensor(2.8358e-06)\n" ] } ], @@ -484,14 +478,14 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.0754)\n" + "tensor(0.0718)\n" ] } ], @@ -522,14 +516,14 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2900)\n" + "tensor(0.2862)\n" ] } ], @@ -556,15 +550,14 @@ }, { "cell_type": "code", - "execution_count": 194, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(1.0000)\n", - "tensor(0.5258)\n" + "tensor(1.0000)\n" ] } ], @@ -581,8 +574,7 @@ "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", "\n", "mask_intervened = (trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_lightning\"][\"value\"] == 0)\n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))\n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(torch.exp(log_weights)))" + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" ] }, { @@ -603,7 +595,7 @@ }, { "cell_type": "code", - "execution_count": 160, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -668,7 +660,7 @@ "sally_hits\n", "\n", "\n", - "\n", + "\n", "prob_sally_hits->sally_hits\n", "\n", "\n", @@ -686,7 +678,7 @@ "bill_hits\n", "\n", "\n", - "\n", + "\n", "prob_bill_hits->bill_hits\n", "\n", "\n", @@ -704,7 +696,7 @@ "bottle_shatters\n", "\n", "\n", - "\n", + "\n", "prob_bottle_shatters_if_sally->bottle_shatters\n", "\n", "\n", @@ -722,19 +714,19 @@ "\n", "\n", "\n", - "\n", + "\n", "sally_throws->sally_hits\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "bill_throws->bill_hits\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "sally_hits->bill_hits\n", "\n", "\n", @@ -746,7 +738,7 @@ "\n", "\n", "\n", - "\n", + "\n", "bill_hits->bottle_shatters\n", "\n", "\n", @@ -755,10 +747,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 160, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -836,7 +828,7 @@ }, { "cell_type": "code", - "execution_count": 161, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -872,7 +864,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -907,14 +899,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2508)\n" + "tensor(0.2494)\n" ] } ], @@ -943,14 +935,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.5009)\n" + "tensor(0.5014)\n" ] } ], @@ -970,7 +962,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1006,7 +998,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1035,14 +1027,14 @@ }, { "cell_type": "code", - "execution_count": 173, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.1550)\n" + "tensor(0.1553)\n" ] } ], @@ -1086,14 +1078,14 @@ }, { "cell_type": "code", - "execution_count": 174, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2194)\n" + "tensor(0.2195)\n" ] } ], @@ -1111,14 +1103,14 @@ }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.0647)\n" + "tensor(0.0667)\n" ] } ], @@ -1136,14 +1128,14 @@ }, { "cell_type": "code", - "execution_count": 176, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2767)\n" + "tensor(0.2777)\n" ] } ], @@ -1154,14 +1146,14 @@ }, { "cell_type": "code", - "execution_count": 177, + "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.1999)\n" + "tensor(0.2014)\n" ] } ], From 2a4b3a97172b048de7185b28d82382c9866b6247 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Thu, 15 Aug 2024 19:04:23 -0400 Subject: [PATCH 044/111] modifed descriptions and arguments a bit --- .../explainable_categorical_alternate.ipynb | 119 +++++++++++------- 1 file changed, 74 insertions(+), 45 deletions(-) diff --git a/docs/source/explainable_categorical_alternate.ipynb b/docs/source/explainable_categorical_alternate.ipynb index d4990530..516cea41 100644 --- a/docs/source/explainable_categorical_alternate.ipynb +++ b/docs/source/explainable_categorical_alternate.ipynb @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -106,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -160,12 +160,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's start with a very simple model, in which a forest fire can be caused by any of the two things: a match being dropped (`match_dropped`), or a lightning strike (`lightning`), and either of these factors alone is already deterministically sufficient for the `forest_fire` to occur. A match being dropped is more likely than a lightning strike (we use fairly large probabilities for the sake of example transparency). For illustration, we also include a causally irrelevant site representing whether a ChiRho developer smiles, `smile`." + "Let's start with a very simple model, in which a forest fire can be caused by any of the two things: a match being dropped (`match_dropped`), or a lightning strike (`lightning`), and either of these factors alone is already deterministically sufficient for the `forest_fire` to occur. A match being dropped is more likely than a lightning strike (we use fairly large probabilities for the sake of example transparency). For illustration, we also include a causally irrelevant site representing whether a ChiRho developer smiles, `smile`. A but-for analysis assigns causal role to a node if it having a different value would result in a different outcome. We will implement this concept and reflect on it in this section." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -174,57 +174,88 @@ "\n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "%3\n", + "\n", "\n", "\n", "u_match_dropped\n", - "\n", - "u_match_dropped\n", + "\n", + "u_match_dropped\n", "\n", "\n", "\n", "match_dropped\n", - "\n", - "match_dropped\n", + "\n", + "match_dropped\n", + "\n", + "\n", + "\n", + "u_match_dropped->match_dropped\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "forest_fire\n", + "\n", + "forest_fire\n", + "\n", + "\n", + "\n", + "u_match_dropped->forest_fire\n", + "\n", + "\n", "\n", "\n", "\n", "u_lightning\n", - "\n", - "u_lightning\n", + "\n", + "u_lightning\n", "\n", "\n", "\n", "lightning\n", - "\n", - "lightning\n", + "\n", + "lightning\n", + "\n", + "\n", + "\n", + "u_lightning->lightning\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "u_lightning->forest_fire\n", + "\n", + "\n", "\n", "\n", "\n", "smile\n", - "\n", - "smile\n", + "\n", + "smile\n", "\n", - "\n", - "\n", - "forest_fire\n", - "\n", - "forest_fire\n", + "\n", + "\n", + "smile->forest_fire\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -288,21 +319,21 @@ "source": [ "**Causal Query 1** What is the probability that dropping a match has a causal impact on the forest fire?\n", "\n", - "To answer the above question, we compute the probability of both forest fire not occurring if we intervene on the match to not be dropped, and forest fire occurring if we intervene on the match to be dropped. i.e. $P(f'_{m'}, f_m)$. this computation can be carried out using `SearchForExplanation`.\n", + "To answer the above question, we compute the probability of both the forest fire not occurring if we intervene on the match to not be dropped (the \"but-for\" part), and the forest fire occurring if we intervene on the match to be dropped, that is, $P(f'_{m'}, f_m)$. This computation can be carried out using `SearchForExplanation`.\n", "\n", - "The potential cause (`antecedent`) we're considering is `match_dropped=1`, we contrast it with what would happen if we intervened on it to not happen (`alternatives`). We are interested in whether an outcome variable `forest_fire` (`consequent`) has value 1 under these two interventions. Note that these two inteventions correspond to `match_dropped=1` being a sufficient and necessary cause for `forest_fire=1`. In this simple case, the notion corresponds to Pearl's notion of probability of necessity and sufficiency - although what `SearchForExplanation` can do goes beyond it, for instance, by allowing for the estimands to be context-sensitive, as we will illustrate later in this notebook." + "The potential cause (`antecedent`) we're considering is `match_dropped=1`. We inspect what would happen if we intervened on it to not happen (`alternatives`), and what happens if we intervene on it to happen, do(`match_dropped=1`). We are interested in whether (and with what probability) an outcome variable `forest_fire` (`consequent`) has both value 0 under the first (this is the \"but-for\" part) and value 1 under the second intervention. Note that these two interventions correspond to `match_dropped=1` being a necessary and sufficient cause for `forest_fire=1`. In this simple case, the notion corresponds to Pearl's notion of probability of necessity and sufficiency - although what `SearchForExplanation` can do goes beyond it, for instance, by allowing for the estimands to be context-sensitive, as we will illustrate later in this notebook." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.3023)\n" + "tensor(0.3030)\n" ] } ], @@ -349,7 +380,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note that the result above matches our intuition. `match_dropped` has a causal effect on `forest_fire` only when `lightning` is not there and the corresponding probability is $0.6$." + "Note that the result above matches our intuition. `match_dropped` has a causal effect on `forest_fire` only when `lightning` is not there and the probability of that happening is $0.6$." ] }, { @@ -363,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -397,7 +428,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -422,14 +453,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(2.8425e-06)\n" + "tensor(2.8435e-06)\n" ] } ], @@ -453,14 +484,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(2.8358e-06)\n" + "tensor(2.8719e-06)\n" ] } ], @@ -478,14 +509,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.0718)\n" + "tensor(0.0701)\n" ] } ], @@ -536,21 +567,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This again matches our intuition, since the probability of `match_dropped=1` and `lightning=1` is $0.28$ where the set $\\{m, l\\}$ is a cause. Now, we move to context-sensitive causal explanations.\n", - "\n", - "TODO_R: this needs to be better explained, something's off with this argument." + "This again matches our intuition. Conditioning brings the factivity requirement into the picture, and so now what we are estimating is the probability of `m` and `l` in fact happening *and* having a causal impact on the forest fire. Since in general, the two-element set has deterministically complete control over the forest fire, the only non-trivial probability is the one brought in by the factivity requirement - the probability of both `match_dropped=1` and `lightning=1`, which is $0.28$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "One can also compute $P(f_{m, l}, f'_{m', l'} | m, l)$ as follows by subselecting the samples with `match_dropped=1` and `lightning=1`. Since {`match_dropped=1`, `lightning=1`} always leads to `forest_fire=1` and {`match_dropped=0`, `lightning=0`} always leads to `forest_fire=0`, we have $P(f_{m, l}, f'_{m', l'} | m, l) = 1$ that we get as a result of the following code snippet." + "One might also be interested in computing the causal impact of the set without factoring in the factivity requirement, so that the result mirrors the intuition that the two-element set has complete control over the outcome. To get to this point, one can compute $P(f_{m, l}, f'_{m', l'} | m, l)$ as follows by subselecting the samples with `match_dropped=1` and `lightning=1`. Since {`match_dropped=1`, `lightning=1`} always leads to `forest_fire=1` and {`match_dropped=0`, `lightning=0`} always leads to `forest_fire=0`, we have $P(f_{m, l}, f'_{m', l'} | m, l) = 1$, which we get as a result of the following code snippet." ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 10, "metadata": {}, "outputs": [ { From 78c9aaef135fa51514a7690f1af80457ec1bdb56 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Fri, 16 Aug 2024 10:07:20 -0400 Subject: [PATCH 045/111] trying out things --- docs/source/counterfactual_sir.png | Bin 126305 -> 127306 bytes .../explainable_categorical_alternate.ipynb | 64 ++-- docs/source/explainable_sir.ipynb | 310 +++++++++++++++--- 3 files changed, 307 insertions(+), 67 deletions(-) diff --git a/docs/source/counterfactual_sir.png b/docs/source/counterfactual_sir.png index 5644276c6017bf1d9aa7ebbaa87c283e6e76a5d1..43fa84b439d792c2ad4aebd28cb8b74e0afbad69 100644 GIT binary patch literal 127306 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z4HFjOL_1+6Qh0^;eU$A>TC4UCrL1G*Ka{fn#d`nyORxOb1N3`~|6ilR|H(-uq|Pe^ VmugilU1i{(`F`ttxu(Y>{sY^vuDAdI diff --git a/docs/source/explainable_categorical_alternate.ipynb b/docs/source/explainable_categorical_alternate.ipynb index ce601870..a5b59a1d 100644 --- a/docs/source/explainable_categorical_alternate.ipynb +++ b/docs/source/explainable_categorical_alternate.ipynb @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -106,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -221,10 +221,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -295,14 +295,16 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.3010)\n" + "tensor(1.)\n", + "tensor([-5.9605e-08, 0.0000e+00, -5.9605e-08, ..., 0.0000e+00,\n", + " 0.0000e+00, -5.9605e-08])\n" ] } ], @@ -316,8 +318,9 @@ " consequent_scale=1e-5,\n", ")(forest_fire_model)\n", "\n", - "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", - "print(torch.exp(logp))" + "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(forest_fire_model)()\n", + "print(torch.exp(logp))\n", + "print(log_weights)" ] }, { @@ -329,22 +332,22 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(5.7879e-06)\n", - "tensor(3009.9888)\n", - "tensor(5003.)\n", - "tensor(9.6203e-06)\n" + "tensor(0.6093)\n", + "tensor(3035.9878)\n", + "tensor(4983.)\n", + "tensor(1.0000)\n" ] } ], "source": [ - "mask_intervened = trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 1\n", + "mask_intervened = trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0\n", "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))\n", "print(torch.sum(torch.exp(log_weights)))\n", "print(mask_intervened.float().sum())\n", @@ -353,22 +356,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([-2.0027e-05, -2.0027e-05, -2.3024e+01, ..., -2.0027e-05,\n", + " -1.1512e+01, -2.0027e-05])\n", + "tensor(0.6093)\n", + "tensor([9.9998e-01, 9.9998e-01, 1.0014e-10, ..., 9.9998e-01, 1.0013e-05,\n", + " 9.9998e-01])\n", + "tensor(1.0000)\n" + ] + } + ], "source": [ "lw = log_weights\n", "print(lw.squeeze())\n", "\n", - "mask_intervened = (tr.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0) \n", + "mask_intervened = (trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0) \n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))\n", + "print(torch.exp(lw.squeeze()))\n", "\n", - "with mwc:\n", - " oth = gather(tr.nodes[\"os_too_high\"][\"value\"], IndexSet(**{\"lockdown\": {2}, \"mask\": {2}}))\n", - " print(oth.shape)\n", - " os = gather(tr.nodes[\"overshoot\"][\"value\"], IndexSet(**{\"lockdown\": {2}, \"mask\": {2}}))\n", + "# with mwc:\n", + "# oth = gather(tr.nodes[\"os_too_high\"][\"value\"], IndexSet(**{\"lockdown\": {2}, \"mask\": {2}}))\n", + "# print(oth.shape)\n", + "# os = gather(tr.nodes[\"overshoot\"][\"value\"], IndexSet(**{\"lockdown\": {2}, \"mask\": {2}}))\n", "\n", "denom = torch.sum(torch.exp(lw.squeeze()) * mask_intervened.squeeze().float()) / torch.sum(torch.exp(lw.squeeze()))\n", - "print(denom/0.25)" + "print(denom)" ] }, { diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 41fe28a9..35038d0f 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 19, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -86,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -127,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -150,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -169,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -223,12 +223,32 @@ "\n", "\n", "with ExtractSupports() as s:\n", - " one_run = policy_model()\n" + " one_run = policy_model()\n", + "\n", + "import pyro.distributions.constraints as constraints\n", + "s.supports[\"os_too_high\"] = constraints.independent(base_constraint=constraints.boolean, reinterpreted_batch_ndims=0)\n" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'lockdown': Boolean(), 'mask': Boolean(), 'lockdown_efficiency': IndependentConstraint(Real(), 0), 'mask_efficiency': IndependentConstraint(Real(), 0), 'joint_efficiency': IndependentConstraint(Real(), 0), 'beta': Interval(lower_bound=0.0, upper_bound=1.0), 'gamma': Interval(lower_bound=0.0, upper_bound=1.0), 'S': IndependentConstraint(Real(), 1), 'I': IndependentConstraint(Real(), 1), 'R': IndependentConstraint(Real(), 1), 'l': IndependentConstraint(Real(), 1), 'overshoot': IndependentConstraint(Real(), 0), 'os_too_high': IndependentConstraint(Boolean(), 0)}\n" + ] + } + ], + "source": [ + "print(s.supports)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -266,21 +286,21 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(1.)\n" + "tensor(997362.)\n" ] } ], @@ -290,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ @@ -332,12 +352,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 63, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -448,7 +468,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -471,25 +491,25 @@ "witnesses = {key: s.supports[key] for key in [\"lockdown_efficiency\", \"mask_efficiency\"]}\n", "consequents = {\"os_too_high\": torch.tensor(1.0)}\n", "\n", - "# with MultiWorldCounterfactual() as mwc:\n", - "# query = with SearchForExplanation(\n", - "# supports=supports,\n", - "# alternatives=alternatives,\n", - "# antecedents=antecedents,\n", - "# antecedent_bias=0.0,\n", - "# witnesses=witnesses,\n", - "# consequents=consequents,\n", - "# consequent_scale=1e-8,\n", - "# witness_bias=0.2,\n", - "# ):\n", - "# with pyro.plate(\"sample\", exp_plate_size):\n", - "# with pyro.poutine.trace() as tr:\n", - "# policy_model_all()" + "with MultiWorldCounterfactual() as mwc:\n", + " with SearchForExplanation(\n", + " supports=supports,\n", + " alternatives=alternatives,\n", + " antecedents=antecedents,\n", + " antecedent_bias=0.0,\n", + " witnesses=witnesses,\n", + " consequents=consequents,\n", + " consequent_scale=1e-8,\n", + " witness_bias=0.2,\n", + " ):\n", + " with pyro.plate(\"sample\", exp_plate_size):\n", + " with pyro.poutine.trace() as tr:\n", + " policy_model_all()" ] }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 73, "metadata": {}, "outputs": [], "source": [ @@ -497,43 +517,244 @@ " supports=supports,\n", " alternatives=alternatives,\n", " antecedents=antecedents,\n", - " antecedent_bias=0.0,\n", - " witnesses=witnesses,\n", + " antecedent_bias=-0.5,\n", + " # witnesses=witnesses,\n", " consequents=consequents,\n", " consequent_scale=1e-8,\n", - " witness_bias=0.2,\n", - " )(policy_model_all)" + " # witness_bias=0.2,\n", + " )(policy_model_all)\n", + "\n", + "# $P(…) [0.25 X 1(o | do(l, m)) 1(o’ | do (l’, m’)) + 0.25 X 1(o | do(l)) 1(o’ | do (l’)) + 0.25 X 1(o | do(m)) 1(o’ | do (m’)) + 0.25 X 1(o)1(o’)]" ] }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 74, "metadata": {}, "outputs": [], "source": [ - "logp, tr, mwc, lw = importance_infer(num_samples=10000)(query)()" + "logp, tr, mwc, lw = importance_infer(num_samples=1000)(query)()" ] }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 76, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor([ -inf, -inf, -inf, ..., 16.1154, -inf, -inf])\n", - "tensor(2432)\n", - "torch.Size([10000, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n", - "tensor(0.6594)\n" + "tensor([ 1.6115e+01, -inf, -inf, -inf, -inf,\n", + " -inf, 1.6115e+01, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " 1.6115e+01, 1.6115e+01, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, 1.6115e+01, -5.0000e+15,\n", + " -inf, -5.0000e+15, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -5.0000e+15, -inf, -inf, -inf,\n", + " -5.0000e+15, -inf, -5.0000e+15, -inf, -inf,\n", + " -inf, -inf, -5.0000e+15, -inf, 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1.6115e+01,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -5.0000e+15, -inf, -inf, -inf,\n", + " 1.6115e+01, -5.0000e+15, -inf, 1.6115e+01, -inf,\n", + " 1.6115e+01, -inf, -5.0000e+15, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -5.0000e+15,\n", + " -inf, -inf, -5.0000e+15, -5.0000e+15, -inf,\n", + " -inf, -inf, -inf, -inf, -5.0000e+15,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, 1.6115e+01, 1.6115e+01, -inf, 1.6115e+01,\n", + " 1.6115e+01, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, 1.6115e+01, -inf, -inf,\n", + " -inf, 1.6115e+01, -5.0000e+15, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " 1.6115e+01, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " 1.6115e+01, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, 1.6115e+01, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, 1.6115e+01,\n", + " -inf, -inf, -5.0000e+15, -inf, 1.6115e+01,\n", + 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-inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, 1.6115e+01, -inf, -inf,\n", + " 1.6115e+01, -5.0000e+15, -5.0000e+15, 1.6115e+01, -inf,\n", + " -inf, 1.6115e+01, -inf, -inf, -inf,\n", + " -5.0000e+15, -5.0000e+15, -inf, -inf, -inf,\n", + " 1.6115e+01, 1.6115e+01, -inf, -5.0000e+15, -inf,\n", + " -inf, -inf, -inf, -inf, -5.0000e+15,\n", + " -5.0000e+15, -5.0000e+15, -inf, -inf, -inf,\n", + " -inf, 1.6115e+01, -inf, -inf, -inf,\n", + " -inf, 1.6115e+01, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -5.0000e+15, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " 1.6115e+01, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, 1.6115e+01, -inf, -inf,\n", + " -inf, -inf, -inf, 1.6115e+01, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, 1.6115e+01,\n", + " -inf, -inf, -inf, -inf, -5.0000e+15,\n", + " -inf, -inf, -5.0000e+15, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -5.0000e+15, -inf, -inf, -inf, -inf,\n", + " -inf, -5.0000e+15, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " 1.6115e+01, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, 1.6115e+01, -inf, -inf, -inf,\n", + " -inf, -5.0000e+15, -inf, -5.0000e+15, -inf,\n", + " 1.6115e+01, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, 1.6115e+01, -inf, -inf,\n", + " -inf, -5.0000e+15, -5.0000e+15, -inf, -inf,\n", + " -inf, -5.0000e+15, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, 1.6115e+01, -inf,\n", + " 1.6115e+01, -inf, -inf, -inf, -5.0000e+15,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -5.0000e+15, -inf, -inf,\n", + " -inf, -inf, -inf, -5.0000e+15, -inf,\n", + " -inf, -inf, -5.0000e+15, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -5.0000e+15, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, 1.6115e+01, -inf,\n", + " -inf, 1.6115e+01, -inf, -5.0000e+15, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -5.0000e+15, -inf, -inf, -inf,\n", + " -5.0000e+15, -5.0000e+15, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -5.0000e+15, -inf, -inf, -inf,\n", + " -inf, -inf, -5.0000e+15, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -5.0000e+15, -inf,\n", + " -inf, 1.6115e+01, 1.6115e+01, -5.0000e+15, 1.6115e+01,\n", + " -inf, -inf, -inf, 1.6115e+01, -inf,\n", + " -inf, 1.6115e+01, -inf, 1.6115e+01, -inf,\n", + " -5.0000e+15, -inf, -inf, 1.6115e+01, -5.0000e+15,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, 1.6115e+01,\n", + " -5.0000e+15, -inf, -inf, -inf, -inf,\n", + " 1.6115e+01, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, 1.6115e+01, -inf, -inf,\n", + " -inf, -5.0000e+15, -inf, -inf, -5.0000e+15,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -5.0000e+15, -inf, -inf,\n", + " -inf, -5.0000e+15, 1.6115e+01, -inf, -inf,\n", + " -inf, -5.0000e+15, -inf, 1.6115e+01, -inf,\n", + " -inf, 1.6115e+01, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, 1.6115e+01,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, 1.6115e+01, -inf, -5.0000e+15,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, 1.6115e+01, -inf, -inf,\n", + " -inf, -inf, -inf, 1.6115e+01, -inf,\n", + " -inf, -inf, -inf, -inf, 1.6115e+01,\n", + " -inf, 1.6115e+01, -inf, 1.6115e+01, -inf,\n", + " -5.0000e+15, -inf, -5.0000e+15, -inf, -5.0000e+15,\n", + " -5.0000e+15, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -5.0000e+15,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf,\n", + " 1.6115e+01, -inf, -inf, -inf, -inf,\n", + " -inf, -5.0000e+15, -inf, -inf, -inf,\n", + " -5.0000e+15, -inf, -5.0000e+15, -inf, -inf,\n", + " -inf, -inf, -5.0000e+15, -inf, -inf,\n", + " 1.6115e+01, -inf, -inf, -inf, -inf,\n", + " -inf, -inf, -inf, -inf, -inf])\n", + "tensor(1000)\n", + "torch.Size([1000, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n", + "tensor(907599.)\n" ] } ], "source": [ "print(lw.squeeze())\n", "\n", - "mask_intervened = (tr.nodes[\"__cause____antecedent_lockdown\"][\"value\"] == 1) & (tr.nodes[\"__cause____antecedent_mask\"][\"value\"] == 0)\n", + "mask_intervened = (tr.nodes[\"__cause____antecedent_lockdown\"][\"value\"] == 0) & (tr.nodes[\"__cause____antecedent_mask\"][\"value\"] == 0)\n", "print(mask_intervened.sum())\n", "\n", "with mwc:\n", @@ -541,8 +762,9 @@ " print(oth.shape)\n", " os = gather(tr.nodes[\"overshoot\"][\"value\"], IndexSet(**{\"lockdown\": {2}, \"mask\": {2}}))\n", "\n", - "denom = torch.sum(torch.exp(lw.squeeze()) * mask_intervened.squeeze().float()) / torch.sum(torch.exp(lw.squeeze()))\n", - "print(denom/0.25)\n" + "denom = torch.sum(torch.exp(lw.squeeze()) * mask_intervened.squeeze().float())/torch.sum(mask_intervened.squeeze()).float()\n", + "print(denom)\n", + "# print(denom/torch.sum(torch.exp(lw.squeeze())))\n" ] }, { From de604b0565ec2cf564990a40f103eb37c8548e98 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Fri, 16 Aug 2024 10:42:01 -0400 Subject: [PATCH 046/111] clean up --- docs/source/explainable_categorical.ipynb | 691 ++++++--- .../explainable_categorical_alternate.ipynb | 1236 ----------------- tests/explainable/test_handlers_components.py | 4 - 3 files changed, 520 insertions(+), 1411 deletions(-) delete mode 100644 docs/source/explainable_categorical_alternate.ipynb diff --git a/docs/source/explainable_categorical.ipynb b/docs/source/explainable_categorical.ipynb index 2bae0454..b8cc7b4d 100644 --- a/docs/source/explainable_categorical.ipynb +++ b/docs/source/explainable_categorical.ipynb @@ -11,11 +11,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The **Explainable Reasoning with ChiRho** package aims to provide a systematic, unified approach to causal explanation computations in terms of different probabilistic queries over expanded causal models that are constructed from a single generic program transformation applied to an arbitrary causal model represented as a ChiRho program. The approach of reducing causal queries to probabilistic computations on transformed causal models is the foundational idea behind all of ChiRho. The key strategy underlying \"causal explanation\" queries is their use of auxiliary variables representing uncertainty about what the proposed interventions are and which interventions or preemptions to apply, implicitly inducing a search space over counterfactuals.\n", + "The **Explainable Reasoning with ChiRho** package aims to provide a systematic, unified approach to causal explanation computations. The package provides a single generic program transformation that can be applied to any arbitrary causal model representable as a Chirho program. This program transformation allows several causal explanation queries to be modeled in terms of probabilistic queries. This approach of reducing causal queries to probabilistic computations on transformed causal models is the foundational idea behind all of ChiRho and in this module, has been leveraged for causal explanations as well.\n", "\n", - "The goal of this notebook is to illustrate how the package can be used to provide an approximate method of answering a range of causal explanation queries with respect to models in which categorical variables play the key role. As the key tool will involve sampling-based posterior probability estimation, a lot of what will be said *mutatis mutandis* applies to more general settings where variables are continuous (to which we will devote another tutorial).\n", + "The goal of this notebook is to illustrate how the package can be used to provide an approximate method of answering a range of causal explanation queries in causal models with only categorical variables. As the key tool will involve sampling-based posterior probability estimation, a lot of what will be said *mutatis mutandis* applies to more general settings where variables are continuous (to which we will devote another tutorial).\n", "\n", - "In yet [another notebook](https://basisresearch.github.io/chirho/actual_causality.html) we illustrate how the module allows for a faithful reconstruction of a particular notion of local explanation (the so-called Halpern-Pearl modified definition of actual causality [(J. Halpern, MIT Press, 2016)](https://mitpress.mit.edu/9780262537131/actual-causality/)), which inspired some of the conceptual steps underlying the current implementation." + "In yet [another notebook](https://basisresearch.github.io/chirho/actual_causality.html) we illustrate how the module allows for a faithful reconstruction of a particular notion of local explanation (the so-called Halpern-Pearl modified definition of actual causality [(J. Halpern, MIT Press, 2016)](https://mitpress.mit.edu/9780262537131/actual-causality/)), which inspired some of the conceptual steps underlying the current implementation.\n", + "\n", + "Before proceeding, the readers should go through the introductory tutorials on [causal reasoning in Chirho](https://basisresearch.github.io/chirho/tutorial_i.html). They might also find a notebook on [actual causality](https://basisresearch.github.io/chirho/actual_causality.html) helpful." ] }, { @@ -24,25 +26,25 @@ "source": [ "**Outline**\n", "\n", - "[Causal explanation and counterfactual thinking](#causal-explanation-and-counterfactual-thinking) \n", + "[Motivation](#motivation)\n", "\n", + "[Setup](#setup)\n", "\n", - "[Witness nodes and context sensitivity](#witness-nodes-and-context-sensitivity)\n", + "[But-for Causal Explanations](#but-for-causal-explanations) \n", "\n", - "[Probability of causation and responsibility](#probability-of-causation-and-responsibility)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Causal explanation and counterfactual thinking" + "[Context-sensitive Causal Explanations](#context-sensitive-causal-explanations)\n", + "\n", + "[Probability of causation and responsibility](#probability-of-causation-and-responsibility)\n", + "\n", + "[Further Discussion](#further-discussion)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "# Motivation\n", + "\n", "Consider the following causality-related queries:\n", "\n", "- **Friendly Fire:** On March 24, 2002, A B-52 bomber fired a Joint Direct Attack Munition at a US battalion command post, killing three and injuring twenty special forces soldiers. Out of multiple potential contributing factors, which were actually responsible for the incident?\n", @@ -51,33 +53,21 @@ "\n", "- **Explainable AI:** Your pre-trial release has been refused based on your [COMPAS score](https://en.wikipedia.org/wiki/COMPAS_(software)). The decision was made using a proprietary predictive model. All you have access to is the questionnaire that was used, and perhaps some demographic information about a class of human beings subjected to this evaluation. But which of these factors resulted in your score being what it is, and what were their contributions?\n", "\n", + "Questions of this sort are more specific and local as they pertain to actual cases that come with their own contexts, unlike average treatment effects discussed in an earlier [tutorial](https://github.com/BasisResearch/chirho/blob/master/docs/source/tutorial_i.ipynb). Being able to answer such context-sensitive questions is useful for understanding how we can prevent undesirable outcomes and promote desirable ones in contexts similar to the ones in which they had been observed. Moreover, these context-sensitive causality questions are also an essential element of blame and responsibility assignments. \n", "\n", - "Questions of this sort are more local than those pertaining to average treatment effects, as they pertain to actual cases that come with their own contexts. Being able to answer them is useful for understanding how we can prevent undesirable outcomes similar to ones that we have observed, or promote the occurrence of desirable outcomes in contexts similar to the ones in which they had been observed. These context-sensitive causality questions are also an essential element of blame and responsibility assignments. If the phenomenon we're trying to explain is the behavior of a predictive model, we are dealing with a problem in explainable AI; but the underlying intuition behind the workings of **Explainable Reasoning with ChiRho** is that causally explaining the behavior of an opaque model is not that much different from providing a causal explanation of other real-world phenomena: we need to address such queries in a principled manner employing some approximate but hopefully reliable causal model of how things work (be that events outside of computers, or a predicitive model's behavior). **Explainable Reasoning with ChiRho** package aims to provide a unified general approach to the relevant causal explanation computations.\n", - "\n", - "At some level of generality, a useful point of departure is a general counterfactual one. On one hand, we can ask whether the event would have occurred had a given candidate cause not taken place. This is sometimes called the *but-for test*, has a tradition of being used as a tool for answering causality and attribution queries. \n", - "\n", - "- It is often used in [the law of torts](https://plato.stanford.edu/entries/causation-law/) to determine if a defendant's conduct was the cause of a particular harm. The test is often formulated as follows: \"But for the defendant's conduct, would the harm have occurred?\" \n", - "- A major philosophical position in the analysis of causality is that the definition of causal dependence should be formulated in terms of counterfactual conditionals (Lewis, 1973. “Causation”, Journal of Philosophy, 70: 556–67). On this approach, $e$ causally depends on $c$ if and only if, if $c$ were not to occur $e$ would not occur. (The view does not remain uncontested, see the [SEP entry on counterfactual theories of causation](https://plato.stanford.edu/entries/causation-counterfactual/)).\n", - "- At least a few major approaches to explainable AI (such as [LIME](https://arxiv.org/abs/1602.04938), or [Shapley values](https://papers.nips.cc/paper_files/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html)) are based on the idea that explanations can be obtained by perturbing or shifting the input values and observing the changes in the output. This to a large extent can be thought of as a way of evaluating the but-for condition: if the input value was different, would the output value change? \n", - " \n", - "More generally, we can ask about the probability with which an alterantive intervention would lead to a cahnge in the outcome (perhaps while conditioning on other items of information), in line with the ideas present in Pearl's *Probabilities of causation...* and Chapter 9 of Pearl's *Causality*. While immensely useful, the but-for condition is not fine-grained enough to answer all the questions we are interested in or to give us the intended answers in cases in which the underlying causal model is non-trivial. We will illustrate this observation in this tutorial. \n", - "\n", - "\n", - "On the other hand, we can ask whether given our model (and perhaps conditioning on other pieces of information we posses), intervening on a given candidate cause to have a given value results in the outcome being as observed (or, more generally, the probability of that outcome being as observed) - this is conceptually similar to Pearl's probability of sufficiency. \n", - "\n", - "We will start with these two approaches, but soon we will notice that often our explanatory questions are more local and a more fine-grained tool is needed. The general intuition (inspired by Halpern's *Actual Causality*) that we implemented is that when we ask local explanatory questions, we need to keep some part of the actual context fixed and consider alternative scenarios insofar as potential causes are involved. That is, we (i) search through possible alternative interventions that could be performed on the candidate cause nodes, (ii) search through possible context nodes that are to be intervened to be at their factual values even in the counterfactual worlds, (iii) see how these options play out in intervened worlds, and (iv) investigate and meaningfully summarize what happens with the outcome nodes of interest in all those counterfactual worlds. " + "In this notebook, we demonstrate the use of `SearchForExplanation`, a handler that provides a unified approach to answering such questions on a wide range of levels of granularity." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's start with a very simple model, in which a forest fire can be caused by exactly one of two things: a match being dropped (`match_dropped`), or a lightning strike (`lightning`), and either of these factors alone is already deterministically sufficient for the `forest_fire` to occur. A match being dropped is more likely than a lightning strike (we use fairly large probabilities for the sake of example transparency). For the sake of illustration, we also include a causally irrelevant site representing whether a ChiRho developer smiles, `smile`." + "## Setup" ] }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -102,13 +92,21 @@ "from chirho.explainable.handlers import ExtractSupports, SearchForExplanation\n", "from chirho.indexed.ops import IndexSet, gather\n", "from chirho.observational.handlers import condition\n", + "from chirho.observational.handlers.soft_conditioning import soft_eq, KernelSoftConditionReparam\n", "\n", "pyro.settings.set(module_local_params=True)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first setup the essentials for performing probabilistic inference on the transformed causal models. We define a function for performing importance sampling on a model and a few other utility functions." + ] + }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -139,28 +137,35 @@ " **kwargs\n", " )\n", "\n", - " # resample using importance weights to get posterior samples\n", - " idx = dist.Categorical(logits=log_weights).sample((num_samples,))\n", - " for name, node in importance_tr.nodes.items():\n", - " if node[\"type\"] != \"sample\" or pyro.poutine.util.site_is_subsample(node) or node[\"is_observed\"]:\n", - " continue\n", - " importance_tr.nodes[name][\"value\"] = torch.index_select(\n", - " importance_tr.nodes[name][\"value\"],\n", - " -max_plate_nesting - 1 - len(importance_tr.nodes[name][\"fn\"].event_shape),\n", - " idx,\n", - " )\n", + " return torch.logsumexp(log_weights, dim=0) - math.log(num_samples), importance_tr, mwc, log_weights\n", "\n", - " with pyro.poutine.replay(trace=importance_tr), mwc:\n", - " trace = pyro.poutine.trace(model).get_trace(*args, **kwargs)\n", + " return _wrapped_model\n", "\n", - " return torch.logsumexp(log_weights, dim=0) - math.log(num_samples), trace, mwc\n", + "# The following functions are needed for conditioning on random variables defined using `pyro.deterministic`\n", + "def _soft_eq(v1: torch.Tensor, v2: torch.Tensor) -> torch.Tensor:\n", + " return soft_eq(constraints.boolean, v1, v2, scale=0.001)\n", "\n", - " return _wrapped_model" + "def reparam_config(data):\n", + " return {i: KernelSoftConditionReparam(_soft_eq) for i in data}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## But-for Causal Explanations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start with a very simple model, in which a forest fire can be caused by any of the two things: a match being dropped (`match_dropped`), or a lightning strike (`lightning`), and either of these factors alone is already deterministically sufficient for the `forest_fire` to occur. A match being dropped is more likely than a lightning strike (we use fairly large probabilities for the sake of example transparency). For illustration, we also include a causally irrelevant site representing whether a ChiRho developer smiles, `smile`. A but-for analysis assigns causal role to a node if it having a different value would result in a different outcome. We will implement this concept and reflect on it in this section." ] }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -216,10 +221,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 113, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -258,31 +263,46 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Throughout this tutorial, we assume all nodes are binary and use $'$ as negation. Once we specify (i) the distributions for the nodes we use (`supports`), (ii) candidate causes $X_i = x_i$ (`antecedents`) (iii) their alternative values ($X_i = x_i'$), (iv) elements of the current context (`witnesses`), and (v) the `consequents` of interest $Y=y$. The `SearchForExplanation` handler transforms the original model into one in which interventions and alternative interventions on the antecedents are applied in parallel counterfactual worlds stochastically preempted and context elements are stochastically selected and preempted to be kept at the factual values in all counterfactual worlds.\n", + "Before we further go into causal queries, let us describe some notation. Let $F$ refer to the `forest_fire`, $f$ stand for $F=1$, $f'$ for $F=0$. The notation $M$ stands for `match_dropped`, with analogous conventions. We also place interventions conditioned on in subscripts. As an example, $f_{m'}$ stands for $F=1$ when $do(M=0)$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Throughout this tutorial, we consider different kinds of causal queries and compute them using a unified program transformation, which takes place using the handler `SearchForExplanation`. It takes the following inputs:\n", + "1. the distributions for the variables we use (`supports`),\n", + "2. the candidate causes $X_i = x_i$ (`antecedents`),\n", + "3. their alternative values ($X_i = x_i'$) (`alternatives`),\n", + "4. the candidate elements of the current context (`witnesses`), and \n", + "5. the `consequents` of interest $Y=y$. \n", "\n", - "First, let's go back to our original query. Let $F$ be the `forest_fire`, $f$ stand for $F=1$, $f'$ for $F=0$, $M$ stand for `match_dropped`, with analogous conventions. We also place interventions conditioned on in subscripts, so that, for example\n", - "$P(f_{m'})$ stands for $P(F=1\\vert do(M=0))$.\n", + "The `SearchForExplanation` handler then takes these arguments and transforms the original model into another model in which interventions on antecedents and witnesses are applied stochastically. Once the antecedents $A \\subseteq$ `antecedents` and witnesses $W \\subseteq$ `witnesses` are chosen from the candidates via sampling, parallel counterfactual worlds are created to condition on `A` being sufficient and necessary causes for the consequent with the context `W`. For more details on `SearchForExplanation`, please refer to the [documentation](https://basisresearch.github.io/chirho/explainable.html#chirho.explainable.handlers.explanation.SearchForExplanation).\n", "\n", - "We are currently interested in $P(f'_{m'}, f_m)$, that is the probability of both forest fire not occurring if we intervene on the match to not be dropped, and forest fire occurring if we intervene on the match to be dropped." + "Now we are ready to use `SearchForExplanation` for answering but-for causal questions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "First, suppose we are interested in asking the question of whether dropping a match has causal power over whether the forest fire occurs. We assume all relevant nodes are binary. The potential cause (`antecedent`) we're considering is `match_dropped=1`, we contrast it with what would happen if we intervened on it to not happen (`alternatives`). We are interested in whether an outcome variable (`consequent`) has value 1 under these two interventions. The counterfactual world in which we intervene with `alternatives` is world 1, and the counterfactual world in which we intervene with `antecedents` is world 2. We will be interested in cases in which none of these interventions have been preempted (more about this later), so we will sample with appropriate masks as well." + "**Causal Query 1** What is the probability that dropping a match has a causal impact on the forest fire?\n", + "\n", + "To answer the above question, we compute the probability of both the forest fire not occurring if we intervene on the match to not be dropped (the \"but-for\" part), and the forest fire occurring if we intervene on the match to be dropped, that is, $P(f'_{m'}, f_m)$. This computation can be carried out using `SearchForExplanation`.\n", + "\n", + "The potential cause (`antecedent`) we're considering is `match_dropped=1`. We inspect what would happen if we intervened on it to not happen (`alternatives`), and what happens if we intervene on it to happen, do(`match_dropped=1`). We are interested in whether (and with what probability) an outcome variable `forest_fire` (`consequent`) has both value 0 under the first (this is the \"but-for\" part) and value 1 under the second intervention. Note that these two interventions correspond to `match_dropped=1` being a necessary and sufficient cause for `forest_fire=1`. In this simple case, the notion corresponds to Pearl's notion of probability of necessity and sufficiency - although what `SearchForExplanation` can do goes beyond it, for instance, by allowing for the estimands to be context-sensitive, as we will illustrate later in this notebook." ] }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.3102)\n" + "tensor(0.2987)\n" ] } ], @@ -296,7 +316,75 @@ " consequent_scale=1e-5,\n", ")(forest_fire_model)\n", "\n", - "logp, trace, mwc = importance_infer(num_samples=10000)(query)()\n", + "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", + "print(torch.exp(logp))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above, strictly speaking, is not our answer yet. Remember that interventions on antecedents are chosen stochastically (with default probability $0.5$ for each candidate node). Thus the above is rather $P(f'_{m'}, f_m)P(m \\text{ was intervened on})$. To obtain $P(f'_{m'}, f_m)$ we therefore need to multiply the result by 2, obtaining $0.6$. In general, we don't need to keep track of the analytic solutions, and we can reach the similar conclusion by post-processing the samples to reject those where `match_dropped` was not intervened on. So, without knowing or using an analytic form (which in general will not be manageable), to compute $P(f'_{m'}, f_m)$, we can subselect the samples as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.6000)\n" + ] + } + ], + "source": [ + "mask_intervened = trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the result above matches our intuition. `match_dropped` has a causal effect on `forest_fire` only when `lightning` is not there and the probability of that happening is $0.6$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Causal Query 2** What is the probability that a Chirho developer has a causal impact on the forest fire?\n", + "\n", + "The intuitive answer is obviously zero, and we show that the same conclusion can be drawn using `SearchForExplanation`." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(1.0011e-05)\n" + ] + } + ], + "source": [ + "query = SearchForExplanation(\n", + " supports=forest_fire_supports,\n", + " antecedents={\"smile\": torch.tensor(1.0)},\n", + " consequents={\"forest_fire\": torch.tensor(1.0)},\n", + " witnesses={}, \n", + " alternatives={\"smile\": torch.tensor(0.0)},\n", + " consequent_scale=1e-5,\n", + ")(forest_fire_model)\n", + "\n", + "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", "print(torch.exp(logp))" ] }, @@ -304,19 +392,44 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "More interestingly, in cases of overdetermination, a similar estimation would lead us to assign no causal role to any of to co-contributing factors. This can be seen in the context in which both causes occurred. Trivially, if lightning occurred, then had no match been dropped, the forest fire, caused by lighning, would still occur (a symmetric reasoning goes through for the lightning as well), $P(f'_{m'}\\vert m, l) = P(f'_{l'}\\vert m, l)=0$. Intuitively, these quantities are not good guides to the causal role of `match_dropped` and `lightning`, as we think they did played a causal role. This is the first illustration of why the but-for analysis is not fine-grained enough." + "The above probability is already 0, and the following post-processing does not affect the result. We still provide the following code snippet for the sake of completeness." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(1.0011e-05)\n" + ] + } + ], + "source": [ + "mask_intervened = trace.nodes[\"__cause____antecedent_smile\"][\"value\"] == 0\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The examples above show how `SearchForExplanation` can be used for but-for analysis. Note, however, that such analysis would not work in a case of overdetermination, where each of the two factors can alone cause the outcome. Consider the case where both `match_dropped` and `lightning` did occur. In this case, if we try to determine the causal role of `match_dropped`, it would come out to be zero (a symmetric reasoning works for lightning as well). This results in $P(f'_{m'}, f_m, m, l) = P(f'_{l'}, f_l, m, l)=0$. This is a canonical example of the limitations of the but-for analysis." ] }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.4234)\n" + "tensor(2.8055e-06)\n" ] } ], @@ -325,55 +438,72 @@ " supports=forest_fire_supports,\n", " antecedents={\"match_dropped\": torch.tensor(1.0)},\n", " consequents={\"forest_fire\": torch.tensor(1.0)},\n", - " witnesses={}, # potential context elements, we leave them empty for now\n", + " witnesses={}, \n", " alternatives={\"match_dropped\": torch.tensor(0.0)},\n", - " antecedent_bias=-0.5,\n", " consequent_scale=1e-5,\n", - ")(condition(\n", - " data={\"u_match_dropped\": torch.tensor(1.0)}\n", - ")(forest_fire_model))\n", + ")( # We need to reparametrize as we are conditioning on deterministic nodes\n", + " pyro.poutine.reparam(config=reparam_config([\"match_dropped\", \"lightning\"]))(\n", + " condition(data={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)})\n", + " (forest_fire_model)\n", + " ))\n", "\n", - "logp, trace, mwc = importance_infer(num_samples=10000)(query)()\n", + "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", "print(torch.exp(logp))" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 29, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(2.7924e-06)\n" + ] + } + ], "source": [ - "## Witness nodes and context sensitivity" + "mask_intervened = trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Some of these intuitions in the forest fire example may be salvaged by considering a two-membered antecedent set, estimating $P(f'_{m',l'}, f_{m,l})$. " + "One thing we can do, is to consider the set containing both `match_dropped` and `lightning`. Then we can estimate $P(f'_{m',l'}, f_{m,l}, m, l)$ to determine their joint causal role, which comes out to be greater than 0, as follows." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.4375)\n" + "tensor(0.0670)\n" ] } ], "source": [ "query = SearchForExplanation(\n", " supports=forest_fire_supports,\n", - " antecedents={\"match_dropped\": 1.0, \"lightning\": 1.0},\n", + " antecedents={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)},\n", " consequents={\"forest_fire\": torch.tensor(1.0)},\n", " witnesses={},\n", - " alternatives={\"match_dropped\": 0.0, \"lightning\": 0.0},\n", - ")(forest_fire_model)\n", + " alternatives={\"match_dropped\": torch.tensor(0.0), \"lightning\": torch.tensor(0.0)},\n", + " consequent_scale=1e-5,\n", + ")(\n", + " pyro.poutine.reparam(config=reparam_config([\"match_dropped\", \"lightning\"]))(\n", + " condition(data={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)})\n", + " (forest_fire_model)\n", + " ))\n", "\n", - "logp, trace, mwc = importance_infer(num_samples=10000)(query)()\n", + "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", "print(torch.exp(logp))" ] }, @@ -381,16 +511,89 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This already suggests a more complicated picture, as it turns out that we need to pay attention to membership in larger antecedent sets that would make a difference (that is one reason why we need stochasticity in antecedent candidate preemption: to search for such sets).\n", + "Now to get our estimand of $P(f_{m, l}, f_{m', l'}, m, l)$, we would need to multiply our result by four, as we now have made two stochastic decisions about interventions, each with probability $0.5$. Or, we can post-process the sample:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.2772)\n" + ] + } + ], + "source": [ + "mask_intervened = (trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_lightning\"][\"value\"] == 0)\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This again matches our intuition. Conditioning brings the factivity requirement into the picture, and so now what we are estimating is the probability of `m` and `l` in fact happening *and* having a causal impact on the forest fire. Since in general, the two-element set has deterministically complete control over the forest fire, the only non-trivial probability is the one brought in by the factivity requirement - the probability of both `match_dropped=1` and `lightning=1`, which is $0.28$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One might also be interested in computing the causal impact of the set without factoring in the factivity requirement, so that the result mirrors the intuition that the two-element set has complete control over the outcome. To get to this point, one can compute $P(f_{m, l}, f'_{m', l'} | m, l)$ as follows by subselecting the samples with `match_dropped=1` and `lightning=1`. Since {`match_dropped=1`, `lightning=1`} always leads to `forest_fire=1` and {`match_dropped=0`, `lightning=0`} always leads to `forest_fire=0`, we have $P(f_{m, l}, f'_{m', l'} | m, l) = 1$, which we get as a result of the following code snippet." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(1.0000)\n" + ] + } + ], + "source": [ + "query = SearchForExplanation(\n", + " supports=forest_fire_supports,\n", + " antecedents={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)},\n", + " consequents={\"forest_fire\": torch.tensor(1.0)},\n", + " witnesses={},\n", + " alternatives={\"match_dropped\": torch.tensor(0.0), \"lightning\": torch.tensor(0.0)},\n", + " consequent_scale=1e-5,\n", + ")(forest_fire_model)\n", "\n", - "But even then, the but-for analysis does not pay sufficient attention to the granularity of a given problem and its causal structure. There are asymmetric cases where the efficiency of one cause prevents the efficiency of another, in which our causal attributions should also be asymmetric, but \"being a member of the same larger antecedent set\" isn't.\n", + "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", + "\n", + "mask_intervened = (trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_lightning\"][\"value\"] == 0) & (trace.nodes[\"match_dropped\"][\"value\"] == 1) & (trace.nodes[\"lightning\"][\"value\"] == 1)\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Context-sensitive Causal Explanations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As the previous example showed, the but-for analysis is not sufficient for identifying causal roles. This induces the need to pay attention to the membership of variables in larger antecedent sets that would make a difference (that is one reason why we need stochasticity in the antecedent candidate preemption: to search for such sets). But even then, the but-for analysis does not pay sufficient attention to the granularity of a given problem and its causal structure. There are asymmetric cases where the efficiency of one cause prevents the efficiency of another, in which our causal attributions should a be asymmetric, but \"being a member of the same larger antecedent set\" isn't. We illustrate using a simple example.\n", "\n", - "A simple example is breaking a bottle. Suppose Sally and Bob throw a rock at a bottle, and Sally does so a little earlier than Bob. Suppose both are perfectly accurate, and the bottle shatters when hit. Sally hits, and the bottle shatters, but Bob doesn't hit it because the bottle is no longer there. " + "Consider the example of breaking a bottle. Suppose Sally and Bob throw a rock at a bottle, and Sally does so a little earlier than Bob. Suppose both are perfectly accurate, and the bottle shatters when hit. Sally hits, and the bottle shatters, but Bob doesn't hit it because the bottle is no longer there." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -399,154 +602,153 @@ "\n", "\n", - "\n", - "\n", - "\n", + "\n", + "\n", "\n", - "%3\n", - "\n", + "\n", "\n", "\n", "prob_sally_throws\n", - "\n", - "prob_sally_throws\n", + "\n", + "prob_sally_throws\n", "\n", "\n", "\n", "sally_throws\n", - "\n", - "sally_throws\n", + "\n", + "sally_throws\n", "\n", "\n", "\n", "prob_sally_throws->sally_throws\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "prob_bill_throws\n", - "\n", - "prob_bill_throws\n", + "\n", + "prob_bill_throws\n", "\n", "\n", "\n", "bill_throws\n", - "\n", - "bill_throws\n", + "\n", + "bill_throws\n", "\n", "\n", "\n", "prob_bill_throws->bill_throws\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "prob_sally_hits\n", - "\n", - "prob_sally_hits\n", + "\n", + "prob_sally_hits\n", "\n", "\n", "\n", "sally_hits\n", - "\n", - "sally_hits\n", + "\n", + "sally_hits\n", "\n", "\n", - "\n", + "\n", "prob_sally_hits->sally_hits\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "prob_bill_hits\n", - "\n", - "prob_bill_hits\n", + "\n", + "prob_bill_hits\n", "\n", "\n", "\n", "bill_hits\n", - "\n", - "bill_hits\n", + "\n", + "bill_hits\n", "\n", "\n", "\n", "prob_bill_hits->bill_hits\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "prob_bottle_shatters_if_sally\n", - "\n", - "prob_bottle_shatters_if_sally\n", + "\n", + "prob_bottle_shatters_if_sally\n", "\n", "\n", "\n", "bottle_shatters\n", - "\n", - "bottle_shatters\n", + "\n", + "bottle_shatters\n", "\n", "\n", - "\n", + "\n", "prob_bottle_shatters_if_sally->bottle_shatters\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "prob_bottle_shatters_if_bill\n", - "\n", - "prob_bottle_shatters_if_bill\n", + "\n", + "prob_bottle_shatters_if_bill\n", "\n", "\n", - "\n", + "\n", "prob_bottle_shatters_if_bill->bottle_shatters\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "sally_throws->sally_hits\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "bill_throws->bill_hits\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "sally_hits->bill_hits\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "sally_hits->bottle_shatters\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "bill_hits->bottle_shatters\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -624,7 +826,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -654,10 +856,28 @@ " }\n", ")(stones_model))\n", "\n", - "logp, trace, mwc = importance_infer(num_samples=10000)(query)()\n", + "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", "print(torch.exp(logp))" ] }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(1.0013e-05)\n" + ] + } + ], + "source": [ + "mask_intervened = trace.nodes[\"__cause____antecedent_sally_throws\"][\"value\"] == 0\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -669,7 +889,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "An intuitive solution to the problem, inspired by the Pearl-Halpern definition of actual causality (which we discuss in [another notebook](https://basisresearch.github.io/chirho/actual_causality.html)) is to say that **in answering actual causality queries, we need to consider what happens when part of the actual context is kept fixed.** For instance, in the bottle shattering example, given the observed fact that Bob’s stone didn’t hit, in the counterfactual world in which we keep this observed fact fixed, if Sally nad not thrown the stone, the bottle in fact would not have shattered. \n", + "An intuitive solution to the problem, inspired by the Pearl-Halpern definition of actual causality (which we discuss in [another notebook](https://basisresearch.github.io/chirho/actual_causality.html)) is to say that **in answering actual causality queries, we need to consider what happens when part of the actual context is kept fixed.** For instance, in the bottle shattering example, given the observed fact that Bob’s stone didn’t hit, in the counterfactual world in which we keep this observed fact fixed, if Sally had not thrown the stone, the bottle in fact would not have shattered. \n", "\n", "\n", "For this reason, our handler allows not only stochastic preemption of interventions (to approximate the search through possible antecedent sets) but also stochastic witness preemption of those nodes that are considered part of the context (these needn't exclude each other). In a witness preemption, we ensure that the counterfactual value is identical to the factual one (and by applying it randomly to candidate witness nodes, we approximate a search through all possible context sets)." @@ -677,14 +897,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2521)\n" + "tensor(0.2513)\n" ] } ], @@ -695,6 +915,7 @@ " consequents={\"bottle_shatters\": torch.tensor(1.0)},\n", " witnesses={\"bill_hits\": None},\n", " alternatives={\"sally_throws\": torch.tensor(0.0)},\n", + " consequent_scale=1e-5\n", ")(condition(\n", " data={\n", " \"prob_sally_throws\": torch.tensor(1.0),\n", @@ -706,23 +927,40 @@ " }\n", ")(stones_model))\n", "\n", - "logp, trace, mwc = importance_infer(num_samples=10000)(query)()\n", + "logp, trace, mwc, log_weights = importance_infer(num_samples=100000)(query)()\n", "print(torch.exp(logp))" ] }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.5019)\n" + ] + } + ], + "source": [ + "mask_intervened = trace.nodes[\"__cause____antecedent_sally_throws\"][\"value\"] == 0\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Admittedly, our search through contexts is very simple and degenerate, as the only part of the actual context which stochastically is kept fixed at the factual value is `bill_hits`. But already with this search, sally throwing is diagnosed as having non-null probability. In fact, the definition of actual causality in Halpern's book (*Actual causality*) contains an existential quantifier: a variable is an actual cause if there is at least one context in which a change in the outcome variable would result from changing the antecedent to have an alternative value, so our search provides a correct diagnosis here.\n", + "Admittedly, our search through contexts is simple as the only part of the actual context which stochastically is kept fixed at the factual value is `bill_hits`. But already with this search, Sally's throw is diagnosed as having impact on the bottle shattering with non-null probability. In fact, the definition of actual causality in Halpern's book (*Actual causality*) contains an existential quantifier: a variable is an actual cause if there is at least one context in which a change in the outcome variable would result from changing the antecedent to have an alternative value, so our search provides a correct diagnosis here.\n", "\n", - "Crucally, as intended, an analogous inference for whether `bill_throws` is a cause of the bottle shattering, yields a different\n", - "result and assigns null causal role to bill." + "Crucially, as intended, an analogous inference for whether `bill_throws` is a cause of the bottle shattering, yields a different result and assigns null causal role to Bill's throw." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -752,48 +990,60 @@ " }\n", ")(stones_model))\n", "\n", - "logp, trace, mwc = importance_infer(num_samples=10000)(query)()\n", + "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", "print(torch.exp(logp))" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 39, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(1.0013e-05)\n" + ] + } + ], "source": [ - "## Probability of causation and responsibility" + "mask_intervened = trace.nodes[\"__cause____antecedent_bill_throws\"][\"value\"] == 0\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We might use non-trivial probabilities and be interested in more involved queries. Suppose we aren't sure what part of the context we want to hold fixed, allowing both `sally_hits` and `bill_hits` to be witness candidates, so we attach equal weights to all four possible context sets. \n", + "## Probability of Causation and Responsibility\n", "\n", - "Suppose also that beyond knowing the non-degenerate probabilities involved, we don't know who threw the stone, and we only observed the bottle has been shattered. We can use the handler to estimate the answer to a somewhat different question involving the probabilities that changing the value of `sally_throws` or changing the value of `billy_throws` (whatever these are in the factual world) would lead to a change in the outcome variables, and that fixing them to be at the factual values would result in the outcome variable having the same value. We also allow both `sally_hits` and `bill_hits` as potential witnesses.\n", + "In the examples above, we have shown how `SearchForExplanation` can be used to perform but-for analysis and context-sensitive analysis. In this section, we extend how we can combine these queries ina single model and perform more involved queries about probabilities of causation and responsibility.\n", "\n", - "For example, we can sample to estimate quantities such as the fraction of possible causes of the bottle shattering in which Sally and Billy are each responsibile:" + "We take the earlier defined `stones_model` with non-trivial probabilities and the single observation that the bottle was shattered. We do not know who threw the stone and thus it is not obvious what context to hold fixed. We can capture all these different possibilities using the single program transformation performed by `SearchForExplanation` and post-process the samples to answer different queries." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Degree of responsibility of Sally: tensor(0.7581)\n", - "Degree of responsibility of Billy: tensor(0.6069)\n" + "tensor(0.1543)\n" ] } ], "source": [ "query = SearchForExplanation(\n", " supports=stones_supports,\n", - " antecedents={\"sally_throws\": None, \"bill_throws\": None},\n", + " antecedents={\"sally_throws\": torch.tensor(1.0), \"bill_throws\": torch.tensor(1.0)},\n", " consequents={\"bottle_shatters\": torch.tensor(1.0)},\n", - " witnesses={\"sally_hits\": None, \"bill_hits\": None},\n", + " witnesses={\"bill_hits\": None, \"sally_hits\": None},\n", + " alternatives={\"sally_throws\": torch.tensor(0.0), \"bill_throws\": torch.tensor(0.0)},\n", + " consequent_scale=1e-5\n", ")(condition(\n", " data={\n", " \"prob_sally_throws\": torch.tensor(0.8),\n", @@ -806,30 +1056,129 @@ " }\n", ")(stones_model))\n", "\n", - "logp, trace, mwc = importance_infer(num_samples=20000)(query)()\n", - "\n", - "nodes = trace.nodes[\"_RETURN\"][\"value\"]\n", - "with mwc:\n", - " st_responsible = gather(nodes[\"sally_throws\"], IndexSet(sally_throws={1})) != \\\n", - " gather(nodes[\"sally_throws\"], IndexSet(sally_throws={2}))\n", - " bt_responsible = gather(nodes[\"bill_throws\"], IndexSet(bill_throws={1})) != \\\n", - " gather(nodes[\"bill_throws\"], IndexSet(bill_throws={2}))\n", - "\n", - "print(\"Degree of responsibility of Sally:\", st_responsible.sum() / st_responsible.numel())\n", - "print(\"Degree of responsibility of Billy:\", bt_responsible.sum() / bt_responsible.numel())" + "logp, trace, mwc, log_weights = importance_infer(num_samples=100000)(query)()\n", + "print(torch.exp(logp))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we show how our earlier analysis on the `stones_model` can be carried out through some analysis on the samples we get through this model where we have both `sally_throw` and `bill_throws` as candidate causes and both `bill_hits` and `sally_hits` as context nodes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first compute the probability of causation for `sally_throws`. We compute the probability that the set {`sally_throws=1`} is the cause of bottle shattering." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.2195)\n" + ] + } + ], + "source": [ + "mask_intervened = (trace.nodes[\"__cause____antecedent_sally_throws\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_bill_throws\"][\"value\"] == 1)\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.float().squeeze())/mask_intervened.float().sum())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Note that we assumed Sally to be more likely to throw, more likely to hit, and more likely to shatter the bottle if she hits. For this reason, we expect her to be more likely to be causally responsible for the outcome. Conceptually, these estimates are impacted by some hyperparameters, such as witness preemption probabilities, so perhaps a bit more clarity on can be gained if we think we have a complete list of potential causes and normalize. " + "We similarly compute this probability for `bill_throws`." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.0667)\n" + ] + } + ], + "source": [ + "mask_intervened = (trace.nodes[\"__cause____antecedent_sally_throws\"][\"value\"] == 1) & (trace.nodes[\"__cause____antecedent_bill_throws\"][\"value\"] == 0)\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.float().squeeze())/mask_intervened.float().sum())" ] }, { "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "We can also use the same model as above to compute the degree of responsibility for bill and sally as follows. We interpret the degree of responsibility assigned to sally for bottle shattering as the probability that `sally_throws=1` is part of the cause. Similarly, the degree of responsibility assigned to billy for shattering the bottle is the probability that `billy_throws=1` is a part of the cause." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.2777)\n" + ] + } + ], + "source": [ + "mask_intervened = (trace.nodes[\"__cause____antecedent_sally_throws\"][\"value\"] == 0)\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.float().squeeze())/mask_intervened.float().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.2014)\n" + ] + } + ], + "source": [ + "mask_intervened = (trace.nodes[\"__cause____antecedent_bill_throws\"][\"value\"] == 0)\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.float().squeeze())/mask_intervened.float().sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we assumed Sally to be more likely to throw, more likely to hit, and more likely to shatter the bottle if she hits. For this reason, we expect her to be more likely to be causally responsible for the outcome and that is the result we got. Conceptually, these estimates are impacted by some hyperparameters, such as witness preemption probabilities, so perhaps a bit more clarity on can be gained if we think we have a complete list of potential causes and normalize. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Further Discussion\n", + "\n", + "In this notebook, we have shown how `SearchForExplanation` can be used for fine-grained causal queries for discrete causal models. We further elaborate on its application in for different queries. \n", + "\n", + "**Explainable AI**: If the phenomenon we're trying to explain is the behavior of a predictive model, we are dealing with a problem in explainable AI; but the underlying intuition behind the workings of **Explainable Reasoning with ChiRho** is that causally explaining the behavior of an opaque model is not that much different from providing a causal explanation of other real-world phenomena: we need to address such queries in a principled manner employing some approximate but hopefully reliable causal model of how things work (be that events outside of computers, or a predicitive model's behavior). **Explainable Reasoning with ChiRho** package aims to provide a unified general approach to the relevant causal explanation computations. At least a few major approaches to explainable AI (such as [LIME](https://arxiv.org/abs/1602.04938), or [Shapley values](https://papers.nips.cc/paper_files/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html)) are based on the idea that explanations can be obtained by perturbing or shifting the input values and observing the changes in the output. This to a large extent can be thought of as a way of evaluating the but-for condition: if the input value was different, would the output value change? \n", + "\n", + "**Other Applications**: Causal queries, specifically but-for tests are often used in [the law of torts](https://plato.stanford.edu/entries/causation-law/) to determine if a defendant's conduct was the cause of a particular harm. The test is often formulated as follows: \"But for the defendant's conduct, would the harm have occurred?\". A major philosophical position in the analysis of causality is that the definition of causal dependence should be formulated in terms of counterfactual conditionals (Lewis, 1973. “Causation”, Journal of Philosophy, 70: 556–67). On this approach, $e$ causally depends on $c$ if and only if, if $c$ were not to occur $e$ would not occur. (The view does not remain uncontested, see the [SEP entry on counterfactual theories of causation](https://plato.stanford.edu/entries/causation-counterfactual/))." + ] } ], "metadata": { @@ -848,7 +1197,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.10.14" } }, "nbformat": 4, diff --git a/docs/source/explainable_categorical_alternate.ipynb b/docs/source/explainable_categorical_alternate.ipynb deleted file mode 100644 index 516cea41..00000000 --- a/docs/source/explainable_categorical_alternate.ipynb +++ /dev/null @@ -1,1236 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Explainable reasoning with ChiRho (categorical variables)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The **Explainable Reasoning with ChiRho** package aims to provide a systematic, unified approach to causal explanation computations. The package provides a single generic program transformation that can be applied to any arbitrary causal model representable as a Chirho program. This program transformation allows several causal explanation queries to be modeled in terms of probabilistic queries. This approach of reducing causal queries to probabilistic computations on transformed causal models is the foundational idea behind all of ChiRho and in this module, has been leveraged for causal explanations as well.\n", - "\n", - "The goal of this notebook is to illustrate how the package can be used to provide an approximate method of answering a range of causal explanation queries in causal models with only categorical variables. As the key tool will involve sampling-based posterior probability estimation, a lot of what will be said *mutatis mutandis* applies to more general settings where variables are continuous (to which we will devote another tutorial).\n", - "\n", - "In yet [another notebook](https://basisresearch.github.io/chirho/actual_causality.html) we illustrate how the module allows for a faithful reconstruction of a particular notion of local explanation (the so-called Halpern-Pearl modified definition of actual causality [(J. Halpern, MIT Press, 2016)](https://mitpress.mit.edu/9780262537131/actual-causality/)), which inspired some of the conceptual steps underlying the current implementation.\n", - "\n", - "Before proceeding, the readers should go through the introductory tutorials on [causal reasoning in Chirho](https://basisresearch.github.io/chirho/tutorial_i.html). They might also find a notebook on [actual causality](https://basisresearch.github.io/chirho/actual_causality.html) helpful." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Outline**\n", - "\n", - "[Motivation](#motivation)\n", - "\n", - "[Setup](#setup)\n", - "\n", - "[But-for Causal Explanations](#but-for-causal-explanations) \n", - "\n", - "[Context-sensitive Causal Explanations](#context-sensitive-causal-explanations)\n", - "\n", - "[Probability of causation and responsibility](#probability-of-causation-and-responsibility)\n", - "\n", - "[Further Discussion](#further-discussion)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Motivation\n", - "\n", - "Consider the following causality-related queries:\n", - "\n", - "- **Friendly Fire:** On March 24, 2002, A B-52 bomber fired a Joint Direct Attack Munition at a US battalion command post, killing three and injuring twenty special forces soldiers. Out of multiple potential contributing factors, which were actually responsible for the incident?\n", - "\n", - "- **Overshoot:** In dealing with an epidemic, multiple different policies were imposed, leading to the overshoot (the number of those who became infected after the peak of the epidemic) rising from around 15% in the unintervened model to around 25%. Which of the policies caused the overshoot and to what extent?\n", - "\n", - "- **Explainable AI:** Your pre-trial release has been refused based on your [COMPAS score](https://en.wikipedia.org/wiki/COMPAS_(software)). The decision was made using a proprietary predictive model. All you have access to is the questionnaire that was used, and perhaps some demographic information about a class of human beings subjected to this evaluation. But which of these factors resulted in your score being what it is, and what were their contributions?\n", - "\n", - "Questions of this sort are more specific and local as they pertain to actual cases that come with their own contexts, unlike average treatment effects discussed in an earlier [tutorial](https://github.com/BasisResearch/chirho/blob/master/docs/source/tutorial_i.ipynb). Being able to answer such context-sensitive questions is useful for understanding how we can prevent undesirable outcomes and promote desirable ones in contexts similar to the ones in which they had been observed. Moreover, these context-sensitive causality questions are also an essential element of blame and responsibility assignments. \n", - "\n", - "In this notebook, we demonstrate the use of `SearchForExplanation`, a handler that provides a unified approach to answering such questions on a wide range of levels of granularity." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "env: CUDA_VISIBLE_DEVICES=-1\n" - ] - } - ], - "source": [ - "%env CUDA_VISIBLE_DEVICES=-1\n", - "from typing import Callable, Dict, List, Optional\n", - "\n", - "import math\n", - "import pyro\n", - "import pyro.distributions as dist\n", - "import pyro.distributions.constraints as constraints\n", - "import torch\n", - "from chirho.counterfactual.handlers.counterfactual import \\\n", - " MultiWorldCounterfactual\n", - "from chirho.explainable.handlers import ExtractSupports, SearchForExplanation\n", - "from chirho.indexed.ops import IndexSet, gather\n", - "from chirho.observational.handlers import condition\n", - "from chirho.observational.handlers.soft_conditioning import soft_eq, KernelSoftConditionReparam\n", - "\n", - "pyro.settings.set(module_local_params=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We first setup the essentials for performing probabilistic inference on the transformed causal models. We define a function for performing importance sampling on a model and a few other utility functions." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def importance_infer(\n", - " model: Optional[Callable] = None, *, num_samples: int\n", - "):\n", - " \n", - " if model is None:\n", - " return lambda m: importance_infer(m, num_samples=num_samples)\n", - "\n", - " def _wrapped_model(\n", - " *args,\n", - " **kwargs\n", - " ):\n", - "\n", - " guide = pyro.poutine.block(hide_fn=lambda msg: msg[\"is_observed\"])(model)\n", - "\n", - " max_plate_nesting = 9 # TODO guess\n", - "\n", - " with pyro.poutine.block(), MultiWorldCounterfactual() as mwc:\n", - " log_weights, importance_tr, _ = pyro.infer.importance.vectorized_importance_weights(\n", - " model,\n", - " guide,\n", - " *args,\n", - " num_samples=num_samples,\n", - " max_plate_nesting=max_plate_nesting,\n", - " normalized=False,\n", - " **kwargs\n", - " )\n", - "\n", - " return torch.logsumexp(log_weights, dim=0) - math.log(num_samples), importance_tr, mwc, log_weights\n", - "\n", - " return _wrapped_model\n", - "\n", - "# The following functions are needed for conditioning on random variables defined using `pyro.deterministic`\n", - "def _soft_eq(v1: torch.Tensor, v2: torch.Tensor) -> torch.Tensor:\n", - " return soft_eq(constraints.boolean, v1, v2, scale=0.001)\n", - "\n", - "def reparam_config(data):\n", - " return {i: KernelSoftConditionReparam(_soft_eq) for i in data}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## But-for Causal Explanations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's start with a very simple model, in which a forest fire can be caused by any of the two things: a match being dropped (`match_dropped`), or a lightning strike (`lightning`), and either of these factors alone is already deterministically sufficient for the `forest_fire` to occur. A match being dropped is more likely than a lightning strike (we use fairly large probabilities for the sake of example transparency). For illustration, we also include a causally irrelevant site representing whether a ChiRho developer smiles, `smile`. A but-for analysis assigns causal role to a node if it having a different value would result in a different outcome. We will implement this concept and reflect on it in this section." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "u_match_dropped\n", - "\n", - "u_match_dropped\n", - "\n", - "\n", - "\n", - "match_dropped\n", - "\n", - "match_dropped\n", - "\n", - "\n", - "\n", - "u_match_dropped->match_dropped\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "forest_fire\n", - "\n", - "forest_fire\n", - "\n", - "\n", - "\n", - "u_match_dropped->forest_fire\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "u_lightning\n", - "\n", - "u_lightning\n", - "\n", - "\n", - "\n", - "lightning\n", - "\n", - "lightning\n", - "\n", - "\n", - "\n", - "u_lightning->lightning\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "u_lightning->forest_fire\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "smile\n", - "\n", - "smile\n", - "\n", - "\n", - "\n", - "smile->forest_fire\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def forest_fire_model():\n", - " u_match_dropped = pyro.sample(\"u_match_dropped\", dist.Bernoulli(0.7))\n", - " match_dropped = pyro.deterministic(\n", - " \"match_dropped\", u_match_dropped, event_dim=0\n", - " ) # notice uneven probs here\n", - "\n", - " u_lightning = pyro.sample(\"u_lightning\", dist.Bernoulli(0.4))\n", - " lightning = pyro.deterministic(\"lightning\", u_lightning, event_dim=0)\n", - "\n", - " # this is a causally irrelevant site\n", - " smile = pyro.sample(\"smile\", dist.Bernoulli(0.5))\n", - "\n", - " forest_fire = pyro.deterministic(\n", - " \"forest_fire\", torch.max(match_dropped, lightning) + (0 * smile), event_dim=0\n", - " )\n", - "\n", - " return {\n", - " \"match_dropped\": match_dropped,\n", - " \"lightning\": lightning,\n", - " \"forest_fire\": forest_fire,\n", - " }\n", - "\n", - "with ExtractSupports() as extract_supports:\n", - " forest_fire_model()\n", - " forest_fire_supports = {k: constraints.boolean for k in extract_supports.supports}\n", - "\n", - "pyro.render_model(forest_fire_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before we further go into causal queries, let us describe some notation. Let $F$ refer to the `forest_fire`, $f$ stand for $F=1$, $f'$ for $F=0$. The notation $M$ stands for `match_dropped`, with analogous conventions. We also place interventions conditioned on in subscripts. As an example, $f_{m'}$ stands for $F=1$ when $do(M=0)$." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Throughout this tutorial, we consider different kinds of causal queries and compute them using a unified program transformation, which takes place using the handler `SearchForExplanation`. It takes the following inputs:\n", - "1. the distributions for the variables we use (`supports`),\n", - "2. the candidate causes $X_i = x_i$ (`antecedents`),\n", - "3. their alternative values ($X_i = x_i'$) (`alternatives`),\n", - "4. the candidate elements of the current context (`witnesses`), and \n", - "5. the `consequents` of interest $Y=y$. \n", - "\n", - "The `SearchForExplanation` handler then takes these arguments and transforms the original model into another model in which interventions on antecedents and witnesses are applied stochastically. Once the antecedents $A \\subseteq$ `antecedents` and witnesses $W \\subseteq$ `witnesses` are chosen from the candidates via sampling, parallel counterfactual worlds are created to condition on `A` being sufficient and necessary causes for the consequent with the context `W`. For more details on `SearchForExplanation`, please refer to the [documentation](https://basisresearch.github.io/chirho/explainable.html#chirho.explainable.handlers.explanation.SearchForExplanation).\n", - "\n", - "Now we are ready to use `SearchForExplanation` for answering but-for causal questions." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Causal Query 1** What is the probability that dropping a match has a causal impact on the forest fire?\n", - "\n", - "To answer the above question, we compute the probability of both the forest fire not occurring if we intervene on the match to not be dropped (the \"but-for\" part), and the forest fire occurring if we intervene on the match to be dropped, that is, $P(f'_{m'}, f_m)$. This computation can be carried out using `SearchForExplanation`.\n", - "\n", - "The potential cause (`antecedent`) we're considering is `match_dropped=1`. We inspect what would happen if we intervened on it to not happen (`alternatives`), and what happens if we intervene on it to happen, do(`match_dropped=1`). We are interested in whether (and with what probability) an outcome variable `forest_fire` (`consequent`) has both value 0 under the first (this is the \"but-for\" part) and value 1 under the second intervention. Note that these two interventions correspond to `match_dropped=1` being a necessary and sufficient cause for `forest_fire=1`. In this simple case, the notion corresponds to Pearl's notion of probability of necessity and sufficiency - although what `SearchForExplanation` can do goes beyond it, for instance, by allowing for the estimands to be context-sensitive, as we will illustrate later in this notebook." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(0.3030)\n" - ] - } - ], - "source": [ - "query = SearchForExplanation(\n", - " supports=forest_fire_supports,\n", - " antecedents={\"match_dropped\": torch.tensor(1.0)},\n", - " consequents={\"forest_fire\": torch.tensor(1.0)},\n", - " witnesses={}, # potential context elements, we leave them empty for now\n", - " alternatives={\"match_dropped\": torch.tensor(0.0)},\n", - " consequent_scale=1e-5,\n", - ")(forest_fire_model)\n", - "\n", - "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", - "print(torch.exp(logp))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above, strictly speaking, is not our answer yet. Remember that interventions on antecedents are chosen stochastically (with default probability $0.5$ for each candidate node). Thus the above is rather $P(f'_{m'}, f_m)P(m \\text{ was intervened on})$. To obtain $P(f'_{m'}, f_m)$ we therefore need to multiply the result by 2, obtaining $0.6$. In general, we don't need to keep track of the analytic solutions, and we can reach the similar conclusion by post-processing the samples to reject those where `match_dropped` was not intervened on. So, without knowing or using an analytic form (which in general will not be manageable), to compute $P(f'_{m'}, f_m)$, we can subselect the samples as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(0.6017)\n" - ] - } - ], - "source": [ - "mask_intervened = trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0\n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that the result above matches our intuition. `match_dropped` has a causal effect on `forest_fire` only when `lightning` is not there and the probability of that happening is $0.6$." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Causal Query 2** What is the probability that a Chirho developer has a causal impact on the forest fire?\n", - "\n", - "The intuitive answer is obviously zero, and we show that the same conclusion can be drawn using `SearchForExplanation`." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(1.0011e-05)\n" - ] - } - ], - "source": [ - "query = SearchForExplanation(\n", - " supports=forest_fire_supports,\n", - " antecedents={\"smile\": torch.tensor(1.0)},\n", - " consequents={\"forest_fire\": torch.tensor(1.0)},\n", - " witnesses={}, \n", - " alternatives={\"smile\": torch.tensor(0.0)},\n", - " consequent_scale=1e-5,\n", - ")(forest_fire_model)\n", - "\n", - "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", - "print(torch.exp(logp))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above probability is already 0, and the following post-processing does not affect the result. We still provide the following code snippet for the sake of completeness." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(1.0011e-05)\n" - ] - } - ], - "source": [ - "mask_intervened = trace.nodes[\"__cause____antecedent_smile\"][\"value\"] == 0\n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The examples above show how `SearchForExplanation` can be used for but-for analysis. Note, however, that such analysis would not work in a case of overdetermination, where each of the two factors can alone cause the outcome. Consider the case where both `match_dropped` and `lightning` did occur. In this case, if we try to determine the causal role of `match_dropped`, it would come out to be zero (a symmetric reasoning works for lightning as well). This results in $P(f'_{m'}, f_m, m, l) = P(f'_{l'}, f_l, m, l)=0$. This is a canonical example of the limitations of the but-for analysis." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(2.8435e-06)\n" - ] - } - ], - "source": [ - "query = SearchForExplanation(\n", - " supports=forest_fire_supports,\n", - " antecedents={\"match_dropped\": torch.tensor(1.0)},\n", - " consequents={\"forest_fire\": torch.tensor(1.0)},\n", - " witnesses={}, \n", - " alternatives={\"match_dropped\": torch.tensor(0.0)},\n", - " consequent_scale=1e-5,\n", - ")( # We need to reparametrize as we are conditioning on deterministic nodes\n", - " pyro.poutine.reparam(config=reparam_config([\"match_dropped\", \"lightning\"]))(\n", - " condition(data={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)})\n", - " (forest_fire_model)\n", - " ))\n", - "\n", - "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", - "print(torch.exp(logp))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(2.8719e-06)\n" - ] - } - ], - "source": [ - "mask_intervened = trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0\n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One thing we can do, is to consider the set containing both `match_dropped` and `lightning`. Then we can estimate $P(f'_{m',l'}, f_{m,l}, m, l)$ to determine their joint causal role, which comes out to be greater than 0, as follows." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(0.0701)\n" - ] - } - ], - "source": [ - "query = SearchForExplanation(\n", - " supports=forest_fire_supports,\n", - " antecedents={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)},\n", - " consequents={\"forest_fire\": torch.tensor(1.0)},\n", - " witnesses={},\n", - " alternatives={\"match_dropped\": torch.tensor(0.0), \"lightning\": torch.tensor(0.0)},\n", - " consequent_scale=1e-5,\n", - ")(\n", - " pyro.poutine.reparam(config=reparam_config([\"match_dropped\", \"lightning\"]))(\n", - " condition(data={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)})\n", - " (forest_fire_model)\n", - " ))\n", - "\n", - "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", - "print(torch.exp(logp))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now to get our estimand of $P(f_{m, l}, f_{m', l'}, m, l)$, we would need to multiply our result by four, as we now have made two stochastic decisions about interventions, each with probability $0.5$. Or, we can post-process the sample:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(0.2862)\n" - ] - } - ], - "source": [ - "mask_intervened = (trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_lightning\"][\"value\"] == 0)\n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This again matches our intuition. Conditioning brings the factivity requirement into the picture, and so now what we are estimating is the probability of `m` and `l` in fact happening *and* having a causal impact on the forest fire. Since in general, the two-element set has deterministically complete control over the forest fire, the only non-trivial probability is the one brought in by the factivity requirement - the probability of both `match_dropped=1` and `lightning=1`, which is $0.28$." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One might also be interested in computing the causal impact of the set without factoring in the factivity requirement, so that the result mirrors the intuition that the two-element set has complete control over the outcome. To get to this point, one can compute $P(f_{m, l}, f'_{m', l'} | m, l)$ as follows by subselecting the samples with `match_dropped=1` and `lightning=1`. Since {`match_dropped=1`, `lightning=1`} always leads to `forest_fire=1` and {`match_dropped=0`, `lightning=0`} always leads to `forest_fire=0`, we have $P(f_{m, l}, f'_{m', l'} | m, l) = 1$, which we get as a result of the following code snippet." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(1.0000)\n" - ] - } - ], - "source": [ - "query = SearchForExplanation(\n", - " supports=forest_fire_supports,\n", - " antecedents={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)},\n", - " consequents={\"forest_fire\": torch.tensor(1.0)},\n", - " witnesses={},\n", - " alternatives={\"match_dropped\": torch.tensor(0.0), \"lightning\": torch.tensor(0.0)},\n", - " consequent_scale=1e-5,\n", - ")(forest_fire_model)\n", - "\n", - "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", - "\n", - "mask_intervened = (trace.nodes[\"__cause____antecedent_match_dropped\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_lightning\"][\"value\"] == 0)\n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Context-sensitive Causal Explanations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As the previous example showed, the but-for analysis is not sufficient for identifying causal roles. This induces the need to pay attention to the membership of variables in larger antecedent sets that would make a difference (that is one reason why we need stochasticity in the antecedent candidate preemption: to search for such sets). But even then, the but-for analysis does not pay sufficient attention to the granularity of a given problem and its causal structure. There are asymmetric cases where the efficiency of one cause prevents the efficiency of another, in which our causal attributions should a be asymmetric, but \"being a member of the same larger antecedent set\" isn't. We illustrate using a simple example.\n", - "\n", - "Consider the example of breaking a bottle. Suppose Sally and Bob throw a rock at a bottle, and Sally does so a little earlier than Bob. Suppose both are perfectly accurate, and the bottle shatters when hit. Sally hits, and the bottle shatters, but Bob doesn't hit it because the bottle is no longer there." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "prob_sally_throws\n", - "\n", - "prob_sally_throws\n", - "\n", - "\n", - "\n", - "sally_throws\n", - "\n", - "sally_throws\n", - "\n", - "\n", - "\n", - "prob_sally_throws->sally_throws\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "prob_bill_throws\n", - "\n", - "prob_bill_throws\n", - "\n", - "\n", - "\n", - "bill_throws\n", - "\n", - "bill_throws\n", - "\n", - "\n", - "\n", - "prob_bill_throws->bill_throws\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "prob_sally_hits\n", - "\n", - "prob_sally_hits\n", - "\n", - "\n", - "\n", - "sally_hits\n", - "\n", - "sally_hits\n", - "\n", - "\n", - "\n", - "prob_sally_hits->sally_hits\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "prob_bill_hits\n", - "\n", - "prob_bill_hits\n", - "\n", - "\n", - "\n", - "bill_hits\n", - "\n", - "bill_hits\n", - "\n", - "\n", - "\n", - "prob_bill_hits->bill_hits\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "prob_bottle_shatters_if_sally\n", - "\n", - "prob_bottle_shatters_if_sally\n", - "\n", - "\n", - "\n", - "bottle_shatters\n", - "\n", - "bottle_shatters\n", - "\n", - "\n", - "\n", - "prob_bottle_shatters_if_sally->bottle_shatters\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "prob_bottle_shatters_if_bill\n", - "\n", - "prob_bottle_shatters_if_bill\n", - "\n", - "\n", - "\n", - "prob_bottle_shatters_if_bill->bottle_shatters\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "sally_throws->sally_hits\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "bill_throws->bill_hits\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "sally_hits->bill_hits\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "sally_hits->bottle_shatters\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "bill_hits->bottle_shatters\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def stones_model():\n", - " prob_sally_throws = pyro.sample(\"prob_sally_throws\", dist.Beta(1, 1))\n", - " prob_bill_throws = pyro.sample(\"prob_bill_throws\", dist.Beta(1, 1))\n", - " prob_sally_hits = pyro.sample(\"prob_sally_hits\", dist.Beta(1, 1))\n", - " prob_bill_hits = pyro.sample(\"prob_bill_hits\", dist.Beta(1, 1))\n", - " prob_bottle_shatters_if_sally = pyro.sample(\n", - " \"prob_bottle_shatters_if_sally\", dist.Beta(1, 1)\n", - " )\n", - " prob_bottle_shatters_if_bill = pyro.sample(\n", - " \"prob_bottle_shatters_if_bill\", dist.Beta(1, 1)\n", - " )\n", - "\n", - " sally_throws = pyro.sample(\"sally_throws\", dist.Bernoulli(prob_sally_throws))\n", - " bill_throws = pyro.sample(\"bill_throws\", dist.Bernoulli(prob_bill_throws))\n", - "\n", - " # if Sally throws, she hits with probability prob_sally_hits\n", - " # hits with pr=0 otherwise\n", - " new_shp = torch.where(sally_throws == 1, prob_sally_hits, 0.0)\n", - "\n", - " sally_hits = pyro.sample(\"sally_hits\", dist.Bernoulli(new_shp))\n", - "\n", - " # if Bill throws, he hits with probability prob_bill_hits\n", - " # if sally doesn't hit sooner,\n", - " # misses otherwise\n", - " new_bhp = torch.where(\n", - " bill_throws.bool() & (~sally_hits.bool()),\n", - " prob_bill_hits,\n", - " torch.tensor(0.0),\n", - " )\n", - "\n", - " bill_hits = pyro.sample(\"bill_hits\", dist.Bernoulli(new_bhp))\n", - "\n", - " # you can use a analogous move to model the bottle shattering\n", - " # if being hit by a stone doesn't deterministically\n", - " # shatter the bottle\n", - " new_bsp = torch.where(\n", - " bill_hits.bool(),\n", - " prob_bottle_shatters_if_bill,\n", - " torch.where(\n", - " sally_hits.bool(),\n", - " prob_bottle_shatters_if_sally,\n", - " torch.tensor(0.0),\n", - " ),\n", - " )\n", - "\n", - " bottle_shatters = pyro.sample(\"bottle_shatters\", dist.Bernoulli(new_bsp))\n", - "\n", - " return {\n", - " \"sally_throws\": sally_throws,\n", - " \"bill_throws\": bill_throws,\n", - " \"sally_hits\": sally_hits,\n", - " \"bill_hits\": bill_hits,\n", - " \"bottle_shatters\": bottle_shatters,\n", - " }\n", - "\n", - "\n", - "with ExtractSupports() as extract_supports:\n", - " stones_model()\n", - " stones_supports = {k: constraints.boolean if not k.startswith(\"prob_\") else v for k, v in extract_supports.supports.items()}\n", - "\n", - "pyro.render_model(stones_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For now let us assume that the relevant probabilities are 1 (this forces both Sally and Bill to throw stones, makes them perfectly accurate and makes the bottle always shatter if hit). Let us start with the type of analysis we performed for the forest fire case. " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(1.0013e-05)\n" - ] - } - ], - "source": [ - "query = SearchForExplanation(\n", - " supports=stones_supports,\n", - " antecedents={\"sally_throws\": torch.tensor(1.0)},\n", - " consequents={\"bottle_shatters\": torch.tensor(1.0)},\n", - " witnesses={},\n", - " alternatives={\"sally_throws\": torch.tensor(0.0)},\n", - " consequent_scale=1e-5,\n", - ")(condition(\n", - " data={\n", - " \"prob_sally_throws\": torch.tensor(1.0),\n", - " \"prob_bill_throws\": torch.tensor(1.0),\n", - " \"prob_sally_hits\": torch.tensor(1.0),\n", - " \"prob_bill_hits\": torch.tensor(1.0),\n", - " \"prob_bottle_shatters_if_sally\": torch.tensor(1.0),\n", - " \"prob_bottle_shatters_if_bill\": torch.tensor(1.0),\n", - " }\n", - ")(stones_model))\n", - "\n", - "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", - "print(torch.exp(logp))" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(1.0013e-05)\n" - ] - } - ], - "source": [ - "mask_intervened = trace.nodes[\"__cause____antecedent_sally_throws\"][\"value\"] == 0\n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sally's throw does not satisfy the but-for condition: if she hadn't thrown the rock, the bottle would still have shattered. Of course, the combined event of Sally throwing a rock and Bob throwing a rock is a but-for cause of the bottle shattering. But that doesn't capture the clear asymmetry at work here. Intuitively, Sally's throw is the (actual) cause of the bottle breaking in a way that Bob's throw isn't. Sally's throw actually caused the bottle to shatter and Bob's throw didn't, in part because Bob's stone didn't actually hit the bottle." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "An intuitive solution to the problem, inspired by the Pearl-Halpern definition of actual causality (which we discuss in [another notebook](https://basisresearch.github.io/chirho/actual_causality.html)) is to say that **in answering actual causality queries, we need to consider what happens when part of the actual context is kept fixed.** For instance, in the bottle shattering example, given the observed fact that Bob’s stone didn’t hit, in the counterfactual world in which we keep this observed fact fixed, if Sally had not thrown the stone, the bottle in fact would not have shattered. \n", - "\n", - "\n", - "For this reason, our handler allows not only stochastic preemption of interventions (to approximate the search through possible antecedent sets) but also stochastic witness preemption of those nodes that are considered part of the context (these needn't exclude each other). In a witness preemption, we ensure that the counterfactual value is identical to the factual one (and by applying it randomly to candidate witness nodes, we approximate a search through all possible context sets)." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(0.2494)\n" - ] - } - ], - "source": [ - "query = SearchForExplanation(\n", - " supports=stones_supports,\n", - " antecedents={\"sally_throws\": torch.tensor(1.0)},\n", - " consequents={\"bottle_shatters\": torch.tensor(1.0)},\n", - " witnesses={\"bill_hits\": None},\n", - " alternatives={\"sally_throws\": torch.tensor(0.0)},\n", - " consequent_scale=1e-5\n", - ")(condition(\n", - " data={\n", - " \"prob_sally_throws\": torch.tensor(1.0),\n", - " \"prob_bill_throws\": torch.tensor(1.0),\n", - " \"prob_sally_hits\": torch.tensor(1.0),\n", - " \"prob_bill_hits\": torch.tensor(1.0),\n", - " \"prob_bottle_shatters_if_sally\": torch.tensor(1.0),\n", - " \"prob_bottle_shatters_if_bill\": torch.tensor(1.0),\n", - " }\n", - ")(stones_model))\n", - "\n", - "logp, trace, mwc, log_weights = importance_infer(num_samples=100000)(query)()\n", - "print(torch.exp(logp))" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(0.5014)\n" - ] - } - ], - "source": [ - "mask_intervened = trace.nodes[\"__cause____antecedent_sally_throws\"][\"value\"] == 0\n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Admittedly, our search through contexts is simple as the only part of the actual context which stochastically is kept fixed at the factual value is `bill_hits`. But already with this search, Sally's throw is diagnosed as having impact on the bottle shattering with non-null probability. In fact, the definition of actual causality in Halpern's book (*Actual causality*) contains an existential quantifier: a variable is an actual cause if there is at least one context in which a change in the outcome variable would result from changing the antecedent to have an alternative value, so our search provides a correct diagnosis here.\n", - "\n", - "Crucially, as intended, an analogous inference for whether `bill_throws` is a cause of the bottle shattering, yields a different result and assigns null causal role to Bill's throw." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(1.0013e-05)\n" - ] - } - ], - "source": [ - "query = SearchForExplanation(\n", - " supports=stones_supports,\n", - " antecedents={\"bill_throws\": torch.tensor(1.0)},\n", - " consequents={\"bottle_shatters\": torch.tensor(1.0)},\n", - " witnesses={\"sally_hits\": None},\n", - " alternatives={\"bill_throws\": torch.tensor(0.0)},\n", - " consequent_scale=1e-5,\n", - ")(condition(\n", - " data={\n", - " \"prob_sally_throws\": torch.tensor(1.0),\n", - " \"prob_bill_throws\": torch.tensor(1.0),\n", - " \"prob_sally_hits\": torch.tensor(1.0),\n", - " \"prob_bill_hits\": torch.tensor(1.0),\n", - " \"prob_bottle_shatters_if_sally\": torch.tensor(1.0),\n", - " \"prob_bottle_shatters_if_bill\": torch.tensor(1.0),\n", - " }\n", - ")(stones_model))\n", - "\n", - "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", - "print(torch.exp(logp))" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(1.0013e-05)\n" - ] - } - ], - "source": [ - "mask_intervened = trace.nodes[\"__cause____antecedent_bill_throws\"][\"value\"] == 0\n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())/torch.sum(mask_intervened.float()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Probability of Causation and Responsibility\n", - "\n", - "In the examples above, we have shown how `SearchForExplanation` can be used to perform but-for analysis and context-sensitive analysis. In this section, we extend how we can combine these queries ina single model and perform more involved queries about probabilities of causation and responsibility.\n", - "\n", - "We take the earlier defined `stones_model` with non-trivial probabilities and the single observation that the bottle was shattered. We do not know who threw the stone and thus it is not obvious what context to hold fixed. We can capture all these different possibilities using the single program transformation performed by `SearchForExplanation` and post-process the samples to answer different queries." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(0.1553)\n" - ] - } - ], - "source": [ - "query = SearchForExplanation(\n", - " supports=stones_supports,\n", - " antecedents={\"sally_throws\": torch.tensor(1.0), \"bill_throws\": torch.tensor(1.0)},\n", - " consequents={\"bottle_shatters\": torch.tensor(1.0)},\n", - " witnesses={\"bill_hits\": None, \"sally_hits\": None},\n", - " alternatives={\"sally_throws\": torch.tensor(0.0), \"bill_throws\": torch.tensor(0.0)},\n", - " consequent_scale=1e-5\n", - ")(condition(\n", - " data={\n", - " \"prob_sally_throws\": torch.tensor(0.8),\n", - " \"prob_bill_throws\": torch.tensor(0.7),\n", - " \"prob_sally_hits\": torch.tensor(0.9),\n", - " \"prob_bill_hits\": torch.tensor(0.8),\n", - " \"prob_bottle_shatters_if_sally\": torch.tensor(0.9),\n", - " \"prob_bottle_shatters_if_bill\": torch.tensor(0.8),\n", - " \"bottle_shatters\": torch.tensor(1.0),\n", - " }\n", - ")(stones_model))\n", - "\n", - "logp, trace, mwc, log_weights = importance_infer(num_samples=100000)(query)()\n", - "print(torch.exp(logp))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we show how our earlier analysis on the `stones_model` can be carried out through some analysis on the samples we get through this model where we have both `sally_throw` and `bill_throws` as candidate causes and both `bill_hits` and `sally_hits` as context nodes." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We first compute the probability of causation for `sally_throws`. We compute the probability that the set {`sally_throws=1`} is the cause of bottle shattering." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(0.2195)\n" - ] - } - ], - "source": [ - "mask_intervened = (trace.nodes[\"__cause____antecedent_sally_throws\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_bill_throws\"][\"value\"] == 1)\n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.float().squeeze())/mask_intervened.float().sum())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We similarly compute this probability for `bill_throws`." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(0.0667)\n" - ] - } - ], - "source": [ - "mask_intervened = (trace.nodes[\"__cause____antecedent_sally_throws\"][\"value\"] == 1) & (trace.nodes[\"__cause____antecedent_bill_throws\"][\"value\"] == 0)\n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.float().squeeze())/mask_intervened.float().sum())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also use the same model as above to compute the degree of responsibility for bill and sally as follows. We interpret the degree of responsibility assigned to sally for bottle shattering as the probability that `sally_throws=1` is part of the cause. Similarly, the degree of responsibility assigned to billy for shattering the bottle is the probability that `billy_throws=1` is a part of the cause." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(0.2777)\n" - ] - } - ], - "source": [ - "mask_intervened = (trace.nodes[\"__cause____antecedent_sally_throws\"][\"value\"] == 0)\n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.float().squeeze())/mask_intervened.float().sum())" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(0.2014)\n" - ] - } - ], - "source": [ - "mask_intervened = (trace.nodes[\"__cause____antecedent_bill_throws\"][\"value\"] == 0)\n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.float().squeeze())/mask_intervened.float().sum())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that we assumed Sally to be more likely to throw, more likely to hit, and more likely to shatter the bottle if she hits. For this reason, we expect her to be more likely to be causally responsible for the outcome and that is the result we got. Conceptually, these estimates are impacted by some hyperparameters, such as witness preemption probabilities, so perhaps a bit more clarity on can be gained if we think we have a complete list of potential causes and normalize. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Further Discussion\n", - "\n", - "In this notebook, we have shown how `SearchForExplanation` can be used for fine-grained causal queries for discrete causal models. We further elaborate on its application in for different queries. \n", - "\n", - "**Explainable AI**: If the phenomenon we're trying to explain is the behavior of a predictive model, we are dealing with a problem in explainable AI; but the underlying intuition behind the workings of **Explainable Reasoning with ChiRho** is that causally explaining the behavior of an opaque model is not that much different from providing a causal explanation of other real-world phenomena: we need to address such queries in a principled manner employing some approximate but hopefully reliable causal model of how things work (be that events outside of computers, or a predicitive model's behavior). **Explainable Reasoning with ChiRho** package aims to provide a unified general approach to the relevant causal explanation computations. At least a few major approaches to explainable AI (such as [LIME](https://arxiv.org/abs/1602.04938), or [Shapley values](https://papers.nips.cc/paper_files/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html)) are based on the idea that explanations can be obtained by perturbing or shifting the input values and observing the changes in the output. This to a large extent can be thought of as a way of evaluating the but-for condition: if the input value was different, would the output value change? \n", - "\n", - "**Other Applications**: Causal queries, specifically but-for tests are often used in [the law of torts](https://plato.stanford.edu/entries/causation-law/) to determine if a defendant's conduct was the cause of a particular harm. The test is often formulated as follows: \"But for the defendant's conduct, would the harm have occurred?\". A major philosophical position in the analysis of causality is that the definition of causal dependence should be formulated in terms of counterfactual conditionals (Lewis, 1973. “Causation”, Journal of Philosophy, 70: 556–67). On this approach, $e$ causally depends on $c$ if and only if, if $c$ were not to occur $e$ would not occur. (The view does not remain uncontested, see the [SEP entry on counterfactual theories of causation](https://plato.stanford.edu/entries/causation-counterfactual/))." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "pyro1.9", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tests/explainable/test_handlers_components.py b/tests/explainable/test_handlers_components.py index 0613176f..cc3d4ead 100644 --- a/tests/explainable/test_handlers_components.py +++ b/tests/explainable/test_handlers_components.py @@ -382,10 +382,6 @@ def test_consequent_eq_neq(plate_size, event_shape): ) } - w_initial = ( - dist.Normal(0, 0.1).expand(event_shape).to_event(len(event_shape)).sample() - ) - @Factors(factors=factors) @pyro.plate("data", size=plate_size, dim=-4) def model_ce(): From e40e6c1e03cfe1d6ebbf36f80c66d3f5aa5b8368 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Fri, 16 Aug 2024 13:22:44 -0400 Subject: [PATCH 047/111] restored tutorial_i --- docs/source/tutorial_i.ipynb | 44 +++++++++++++----------------------- 1 file changed, 16 insertions(+), 28 deletions(-) diff --git a/docs/source/tutorial_i.ipynb b/docs/source/tutorial_i.ipynb index 34dc10e0..ca704abb 100644 --- a/docs/source/tutorial_i.ipynb +++ b/docs/source/tutorial_i.ipynb @@ -1553,9 +1553,7 @@ " return bayesian_population_intervened_causal_model(n_individuals), context\n", "\n", "\n", - "results, counterfactual_context = bayesian_population_counterfactual_model(\n", - " n_individuals\n", - ")\n", + "results, counterfactual_context = bayesian_population_counterfactual_model(n_individuals)\n", "\n", "with counterfactual_context:\n", " # ChiRho's `MultiWorldCounterfactual` effect handler automatically constructs named index sites\n", @@ -1567,10 +1565,14 @@ " # world given by the specific `IndexSet`. Here, `smokes=0` refers to the counterfactual\n", " # world in which `smokes` was not intervened on.\n", " smokes_factual = gather(results[\"smokes\"], IndexSet(smokes={0})).squeeze()\n", - " smokes_counterfactual = gather(results[\"smokes\"], IndexSet(smokes={1})).squeeze()\n", + " smokes_counterfactual = gather(\n", + " results[\"smokes\"], IndexSet(smokes={1})\n", + " ).squeeze()\n", "\n", " cancer_factual = gather(results[\"cancer\"], IndexSet(smokes={0})).squeeze()\n", - " cancer_counterfactual = gather(results[\"cancer\"], IndexSet(smokes={1})).squeeze()\n", + " cancer_counterfactual = gather(\n", + " results[\"cancer\"], IndexSet(smokes={1})\n", + " ).squeeze()\n", "\n", "print(\"smokes_factual --- \", smokes_factual)\n", "print(\"smokes_counterfactual --- \", smokes_counterfactual)\n", @@ -1645,39 +1647,29 @@ }, "outputs": [], "source": [ - "counterfactual_model_conditioned = condition(\n", - " bayesian_population_counterfactual_model, data\n", - ")\n", + "counterfactual_model_conditioned = condition(bayesian_population_counterfactual_model, data)\n", "\n", - "counterfactual_conditioned_results, counterfactual_conditioned_context = (\n", - " counterfactual_model_conditioned(n_individuals)\n", + "counterfactual_conditioned_results, counterfactual_conditioned_context = counterfactual_model_conditioned(\n", + " n_individuals\n", ")\n", "\n", "with counterfactual_conditioned_context:\n", " # ChiRho's `condition` only conditions the model on the observational part\n", " # of the model, not the counterfactual part.\n", " assert torch.allclose(\n", - " gather(\n", - " counterfactual_conditioned_results[\"smokes\"], IndexSet(smokes={0})\n", - " ).squeeze(),\n", + " gather(counterfactual_conditioned_results[\"smokes\"], IndexSet(smokes={0})).squeeze(),\n", " data[\"smokes\"],\n", " )\n", " assert not torch.allclose(\n", - " gather(\n", - " counterfactual_conditioned_results[\"smokes\"], IndexSet(smokes={1})\n", - " ).squeeze(),\n", + " gather(counterfactual_conditioned_results[\"smokes\"], IndexSet(smokes={1})).squeeze(),\n", " data[\"smokes\"],\n", " )\n", " assert torch.allclose(\n", - " gather(\n", - " counterfactual_conditioned_results[\"cancer\"], IndexSet(smokes={0})\n", - " ).squeeze(),\n", + " gather(counterfactual_conditioned_results[\"cancer\"], IndexSet(smokes={0})).squeeze(),\n", " data[\"cancer\"],\n", " )\n", " assert not torch.allclose(\n", - " gather(\n", - " counterfactual_conditioned_results[\"cancer\"], IndexSet(smokes={1})\n", - " ).squeeze(),\n", + " gather(counterfactual_conditioned_results[\"cancer\"], IndexSet(smokes={1})).squeeze(),\n", " data[\"cancer\"],\n", " )" ] @@ -1779,15 +1771,11 @@ "predictive_counterfactual_posterior = pyro.infer.Predictive(\n", " bayesian_population_counterfactual_model, guide=guide, num_samples=num_samples\n", ")\n", - "predictions_counterfactual_posterior = predictive_counterfactual_posterior(\n", - " n_individuals\n", - ")\n", + "predictions_counterfactual_posterior = predictive_counterfactual_posterior(n_individuals)\n", "\n", "with counterfactual_conditioned_context:\n", " predictions_int_posterior = {\n", - " k: gather(\n", - " predictions_counterfactual_posterior[k], IndexSet(smokes={1})\n", - " ).squeeze()\n", + " k: gather(predictions_counterfactual_posterior[k], IndexSet(smokes={1})).squeeze()\n", " for k in predictions_counterfactual_posterior.keys()\n", " }\n", "\n", From 1c36d312a1d63f012eb77531cbfb6bba6fae31f9 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Mon, 19 Aug 2024 10:33:03 -0400 Subject: [PATCH 048/111] fix for undo_split --- chirho/explainable/handlers/components.py | 39 +- docs/source/counterfactual_sir.png | Bin 127306 -> 126734 bytes docs/source/explainable_sir.ipynb | 581 +++++++++------ docs/source/test_notebook.ipynb | 659 ++++++++++++++++++ tests/explainable/test_handlers_components.py | 2 +- 5 files changed, 1038 insertions(+), 243 deletions(-) create mode 100644 docs/source/test_notebook.ipynb diff --git a/chirho/explainable/handlers/components.py b/chirho/explainable/handlers/components.py index 4db8fb42..589bc1cc 100644 --- a/chirho/explainable/handlers/components.py +++ b/chirho/explainable/handlers/components.py @@ -1,4 +1,3 @@ -import itertools from typing import Callable, Iterable, MutableMapping, Optional, TypeVar import pyro @@ -113,28 +112,36 @@ def undo_split( """ def _undo_split(value: T) -> T: - antecedents_ = [ - a - for a in antecedents - if a in indices_of(value, event_dim=support.event_dim) - ] + antecedents_ = { + a: v + for a, v in indices_of(value, event_dim=support.event_dim).items() + if a in antecedents + } factual_value = gather( value, - IndexSet(**{antecedent: {0} for antecedent in antecedents_}), + IndexSet(**{antecedent: {0} for antecedent in antecedents_.keys()}), event_dim=support.event_dim, ) # TODO exponential in len(antecedents) - add an indexed.ops.expand to do this cheaply - return scatter_n( - { - IndexSet( - **{antecedent: {ind} for antecedent, ind in zip(antecedents_, inds)} - ): factual_value - for inds in itertools.product(*[[0, 1]] * len(antecedents_)) - }, - event_dim=support.event_dim, - ) + + index_keys = [] + for a, v in antecedents_.items(): + if index_keys == []: + for value in v: + index_keys.append({a: {value}}) + else: + temp_index_keys = [] + for i in index_keys: + for value in v: + t = dict(i) + t[a] = {value} + temp_index_keys.append(t) + index_keys = temp_index_keys + index_keys = index_keys if index_keys != [] else [{}] + + return scatter_n({IndexSet(**ind_key): factual_value for ind_key in index_keys}, event_dim=support.event_dim) return _undo_split diff --git a/docs/source/counterfactual_sir.png b/docs/source/counterfactual_sir.png index 43fa84b439d792c2ad4aebd28cb8b74e0afbad69..1fc7f4bb20d0428035a8e15788c1394f7df77b3e 100644 GIT binary patch literal 126734 zcmd431zT3#7Bx%>QVL3oA|(<65)u;9A%aMUfFL2=B^}Z!h=?=@lG31rlys*wNOvRs 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b/docs/source/explainable_sir.ipynb index 35038d0f..a4a49c28 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 42, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -86,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -127,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -150,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -169,9 +169,19 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mask tensor(0.)\n", + "lockdown tensor(0.)\n", + "mask_eff tensor(0.)\n" + ] + } + ], "source": [ "overshoot_threshold = 20\n", "lockdown_time = torch.tensor(1.0)\n", @@ -231,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -248,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -293,14 +303,14 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(997362.)\n" + "tensor(0.)\n" ] } ], @@ -310,9 +320,52 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mask tensor(0.)\n", + "lockdown tensor(0.)\n", + "mask_eff tensor(0.)\n", + "mask tensor(0.)\n", + "lockdown tensor(0.)\n", + "mask_eff tensor(0.)\n", + "mask tensor(0.)\n", + "lockdown tensor(0.)\n", + "mask_eff tensor(0.)\n", + "mask tensor(1.)\n", + "lockdown tensor(1.)\n", + "mask_eff tensor(0.1000)\n", + "mask tensor(1.)\n", + "lockdown tensor(1.)\n", + "mask_eff tensor(0.1000)\n", + "mask tensor(1.)\n", + "lockdown tensor(1.)\n", + "mask_eff tensor(0.1000)\n", + "mask tensor(1.)\n", + "lockdown tensor(0.)\n", + "mask_eff tensor(0.4500)\n", + "mask tensor(1.)\n", + "lockdown tensor(0.)\n", + "mask_eff tensor(0.4500)\n", + "mask tensor(1.)\n", + "lockdown tensor(0.)\n", + "mask_eff tensor(0.4500)\n", + "mask tensor(0.)\n", + "lockdown tensor(1.)\n", + "mask_eff tensor(0.)\n", + "mask tensor(0.)\n", + "lockdown tensor(1.)\n", + "mask_eff tensor(0.)\n", + "mask tensor(0.)\n", + "lockdown tensor(1.)\n", + "mask_eff tensor(0.)\n" + ] + } + ], "source": [ "# conditioning (as opposed to intervening) is sufficient for\n", "# propagating the changes, as the decisions are upstream from ds\n", @@ -352,12 +405,12 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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cLFmyxL/uxo0bOXToEO3atSMuLg44/WurpLK6hLVp0waADz/8kKeeeorvv/+ezMxMQL3uevfuXWb3rZYtW5aa5+s6mZubC0BqaiopKSlERUWRlJRUav3Q0FD/+fZdk2dzDVeE1+tl7dq16HS6Mrt89ejRo0L7OdPzVp6K/v35xMXFlfk36Kv/dKbn53SdzevVrFmzUvPi4+MBAmqNtW7dGlBrUb311lusXr0al8uF0Whk4MCBdO7c+ewOQgghxH+a1JQSQghx0YSHh5c5Pz8/n3nz5rF8+XL27t1LWloaiqIEZC8oJxni3ZfR9Pvvv5+0NozdbicnJwdQM7SioqKwWCxncihAcVZSefWPqlWrBhRnHpRU3rk4ka/O1OOPP37S9XyBp8qVKzN+/HhefPFF/vjjD/744w8A6tevzzXXXEO/fv38P0RPZefOncydO5dNmzZx4MABCgoKAPyvS1mvSVhYWJn70uvVryBerxcoPne+4M+JfOfuVLKzs3G5XERGRhISEnLSfZV8HXr37s2cOXP44YcfuO222wC1ODjgzx6B07+2SgZGTgzOATRv3pwRI0YwYcIEfv75Z37++Wc0Gg2NGzfmuuuu4/bbby/z2vDV+ynJFzA88ZyeLOh44rk4m2u4InyvT1RUFGazudTyitYOO9PzVp7TWfdk7fRlfh07duy09nemzvV7zonXEMCzzz5LSkoKf/31FzNmzGDGjBkEBQXRsWNHevfuXeFAohBCCFEWCUoJIYS4aMrqprZ7927uueceMjIyiIyMpGnTpvTs2ZOEhATatm1Lly5dTrlf3w+qevXq0bBhw1Ouf7Ksq9NxskBZyXYZjcZSy07WZa8kX1u7du1abtAF1GP3ueGGG+jUqRO//fYbf/75J6tWrWL37t3s3r2bWbNm8fHHH9O8efOTPu+HH37Im2++CUDdunXp2rUrdevWJTExkYMHD/LKK6+UuV1FukFVZL2SGVonc6rXAIrPYcnXoWnTptSpU4c1a9Zw/PhxYmJiWLRoEUFBQQEZV6d7bZVU3jEOHDiQXr168csvv/Dnn3+yZs0atmzZwpYtW/jkk0/4/PPPqVmzZsA2FblezuRcnM01fC5otdoKv9Znct5O9rynw2QynXS5L+h6Kmf73nM2r1dF/zZDQ0P56KOP2Lx5M7/++iv//PMPW7ZsYcmSJSxZsoTrrruOSZMmnX7jhRBCCCQoJYQQ4hLzyiuvkJGRwf3338/QoUMDfqD6sppOJTY2FoCGDRvy1ltvnXJ9l8uFwWAgJyeHwsLCUhkcDoeDr776ijp16tCuXbty9xMXF8f+/fs5fPgw9evXL7X80KFDAKdVH6ms5zhw4AADBgygffv2Fd4uNDSUm2++mZtvvhmArVu38vbbb7NixQreeecdf+2hshw6dIgJEyYQGhrK+++/7+/O43OybSvKl61VXt0uX0bIqURERPhfy/z8/DIDd+W9Dr1792bixIksWbKEunXrkpaWRu/evQO66Z3utVVR0dHR9OvXj379+uH1evn3338ZO3YsW7ZsYfr06YwZM+a09+nLOjt8+HC56/jOha820vm+hiMjIzGZTGRnZ1NQUFCq7lN6evppBWrOx3mriPKuR9+5Lpmd5gv+lHVceXl5Z9WOC/Ge45OYmEhiYiJDhgwhLy+Pn3/+mTFjxrB48WLWrl1b6n1BCCGEqAipKSWEEOKSsmHDBgAefvjhUhkTf/31l/9+ye4lJ/LVm1mzZk1AbRSfzZs3c+211zJ48GAURcFgMJCYmIjH42HFihWl1l+1ahWvvPIKn376KVB+hoHveRcvXlzm8p9//hmgzPo+FeV7Dl83vBONHz+em2++mS+//BJQC2537dqVb775JmC9xo0b8+yzzwLFXQLLs2nTJrxeL23bti3zh6fvnFUkM6c8vmDfb7/9VurHe3mvS1kMBgMtWrTA6/X6i72X5Ha7+fXXX4HSdbluuukmNBoNS5cu9b9WvXv3DljndK+tUxk7diwdO3b01wYCNWundevWPPLII0BxV8zTVaVKFapWrUpWVlaZNYXy8vL8f1O+4zqTa7iiGTe+da+66iq8Xi+//fZbqeXLli2r0H5O97ydThsrYv/+/WUW9vfVJCt5fnxBTV/Nq5J873clnU5bz/d7TlZWFn379qVXr14B80NDQ7ntttv8td7O9BoVQgghJCglhBDikuKrwXPiD9Y1a9bw6quv+h+XVVTbp3r16nTv3p2jR48ycuRI8vPz/csyMjIYOXIkycnJASN33XXXXYD6Y7fkj83MzEzGjx8PFNcW8nXdsdlsAcGx2267jaCgIL7++mu+/vrrgDYtWLCAb7/9lqCgoFKjZJ2O22+/naCgIObMmcOPP/4YsOz3339n9uzZ7Nixg8TEREAtNH7kyBHee++9gLoyiqL4C8eXHBGuLL7XZOPGjf7R8kDNMHvnnXdYvnw5oGaUnammTZvSqlUr9u/fz/jx4/3n1ev1Mm7cuFOOuFjSPffcA6gBuq1btwa09+WXX+bgwYM0aNCAVq1aBWxXpUoVkpKSWLVqFb/++itxcXGlMuPO5No6mcqVK5OWlsbbb78dsC+32+0PKPheyzPhOxcvvPCCP2sGoKCggGeffZb8/Hy6du3qr0l0Jtewr2tYyfZXpE3jx49n7969/vl79+5l4sSJFdrH6Z43XxvPNjPJR1EUhg8fHvDcixcvZsGCBYSGhnLrrbf65/uKqM+ePTsgUDlr1iy2bNlSat/lvb+U5Xy/50RGRuLxeNi1a1epjMiUlBT/oAIlR7kUQgghTod03xNCCHFJuffeexk7dizDhg1j3rx5xMbGcvDgQbZv305ERASxsbGkpaWRlpZ20ppKr776KsnJyfz444/89ddfJCYmotFoWLt2LTabjZYtWzJkyBD/+jfeeCP//PMP8+fP54YbbiApKQmdTse6devIy8vjf//7H9dffz2gBmnCwsLIzc2lX79+1KhRg7feeov4+HjGjRvH0KFDGT58OLNmzaJ27drs37+fHTt2YLFYGD9+fIWLOZel5HMMHTqUqVOnUqdOHVJTU/0/cJ9//nl/vaPu3btzzTXX8Msvv3DNNdfQsmVLgoOD2bVrFwcOHCAmJoYnn3zypM+ZlJREo0aN2LZtG9ddd50/W2rTpk1kZGRQv359du/eTXp6+hkfF6gBwQEDBjBr1iyWLVtGgwYN2LFjB8nJyTRr1oyNGzdWaD89evRg0KBBfPTRR9x66620atWKyMhINm7cyNGjR6latSoTJ04ss45Q7969WbVqFRkZGQwaNKjMdU732jqZO+64g59++ol///2Xbt260axZM4xGI9u2bePIkSPUqVOHe++9t0L7Ksvdd9/N+vXr+fnnn/3XtcViYe3atWRlZZGQkBDQxe1MruFatWoB+Ed27NKlS0BQ5kQdOnTgwQcfZPr06dx8881cddVVAKxcuZLGjRtX6Do63fPma+O0adNYv379WRforl27Nrt37+aaa66hdevWpKWlsX79egwGA+PHjw/oLjdgwAAWLVrE4sWLuf7660lISGD37t3s37+f3r178+233wbsu7z3l7JciPecl19+mbvvvpuxY8fy5ZdfUrduXfLz81m3bh0Oh4MHHnjAf36FEEKI0yWZUkIIIS4pAwcOZMKECSQmJrJr1y6WLl2K3W7n7rvv5rvvvvMHhpYuXXrS/URHR/Pll1/y5JNPEhcXx5o1a9iwYQO1a9fm+eef5+OPPy410t5rr73Gm2++SePGjVm3bh1///03VatWZdSoUbz22mv+9bRaLW+99RZ169Zl27Zt/PXXX/56V9deey1fffUVPXv2JCMjg19//ZXc3FxuueUWFixYEFA0+0xde+21LFiwgJtuuom8vDyWLVtGeno6Xbt2Zfbs2f5MFFC7Ar399ts8/fTT1KpVi3///Zdly5bh9Xq5++67+eabb045sp1Op2PWrFkMHDiQqKgoVqxYwdq1a6levTovv/wyX3/9NWFhYWzatOmsAlM1a9Zk/vz53HHHHRQWFrJ06VKCg4OZNm0anTp1Oq19DRs2jGnTptG2bVt27NjBsmXLCA4O5pFHHuHrr7+mTp06ZW533XXX+a+LE7vu+ZzJtVUek8nEzJkzefDBB4mOjmbVqlWsWLGCoKAgHn74YebPn3/aI8OVpNVqmThxImPHjqVJkyb8+++//PXXX1SqVIlnn32W+fPnl6o3dLrXcI8ePRg4cCBBQUH8+eefrFu37pTtevrpp3nnnXdo3Lgxa9euZcuWLfTp04cZM2ZU6LhO97zdcccd/npqf/75Z5kZSqejUqVKfP755zRp0oQVK1awe/duunbtyrx58+jWrVvAuomJicyZM4dOnTqRnp7O8uXLiYmJ4eOPP6Znz56l9n2y95eynO/3nObNm/P5559z3XXXkZuby++//87WrVtp2bIl7777Ls8888xZ7V8IIcR/m0Y5mwIQQgghxCWgU6dOHD9+nN9///2sMgKEEEIIIYQQF45kSgkhhLis5eTkkJmZiUaj8dc+EkIIIYQQQlz6pKaUEEKIy1JmZiaDBg0iMzMTt9tNy5YtK9xlSgghhBBCCHHxSaaUEEKIy1JycjLp6elkZWWRlJTEuHHjLnaThBBCCCGEEKdBakoJIYQQQgghhBBCiAtOMqWEEEIIIYQQQgghxAUnQSkhhBBCCCGEEEIIccFJUEoIIYQQQgghhBBCXHASlBJCCCGEEEIIIYQQF5wEpYQQQgghhBBCCCHEBSdBKSGEEEIIIYQQQghxwUlQSgghhBBCCCGEEEJccBKUEkIIIYQQQgghhBAXnASlhBBCCCGEEEIIIcQFJ0EpIYQQQgghhBBCCHHBSVBKCCGEEEIIIYQQQlxwEpQSQgghhBBCCCGEEBecBKWEEEIIIYQQQgghxAUnQSkhhBBCCCGEEEIIccFJUEoIIYQQQgghhBBCXHASlBJCCCGEEEIIIYQQF5wEpYQQQgghhBBCCCHEBSdBKSGEEEIIIYQQQghxwUlQSgghhBBCCCGEEEJccBKUEkIIIYQQQgghhBAXnASlhBBCCCGEEEIIIcQFJ0EpIYQQQgghhBBCCHHBSVBKCCGEEEIIIYQQQlxwEpQSQlyyFEW52E04I5dru4UQQogrgXwOi7LIdSHEpUmCUkL8h9199900atSIzZs3l7m8W7duDB8+/KyfJyEhgcmTJ5/WNvPnz2fcuHFn/dwX2u7du7njjjsC5p3J8QshhBAns27dOgYPHkyHDh1ITEyke/fuvPDCC+zdu/diNy3A5MmTSUhIuGDPt27dOh588MEL9nyXmieeeKLUd7fhw4eTkJBQ7nT48OFy95ecnMyTTz5Jx44dadWqFXfccQf//PNPwDo2m41x48bRrVs3WrRowe23315qnYrwXSslp0aNGtG2bVsee+wxdu/eXeF9ffTRRzzzzDMA5Obm8txzz7F27drTbtOZGD58ON26dTvpOgsXLiQhIYGUlJQK77ci22RlZdGlSxcOHTpU4f2WVFBQwMsvv0yHDh1o0aIFDzzwAPv27Tvldlu3buWBBx7gqquuom3btgwaNIitW7cGrHP06FGGDh3KVVddRcuWLXnsscc4cODAGbVTXFkkKCXEf5zH42HEiBE4nc7z9hzz5s3j1ltvPa1t3nvvPbKzs89Pg86jRYsWsX79+oB5Z3L8QgghRHmmT59O//79sdvtPP/888ycOZOHH36Ybdu20adPH3788ceL3cSLZv78+ZdcYO5C8Hq9jBkzhsWLF5da9uijjzJv3ryAafr06VgsFjp37kyVKlXK3GdWVhZ33XUX+/bt4/nnn2fixInExMQwaNAgVq9e7V9v1KhRzJ07l3vuuYcpU6ZQqVIl7r//fjZu3HhGx1KynZ9++ikvvPAC27dvp3///qSlpZ1y+7179/LBBx/w7LPPArB9+3a+/fZbvF7vGbXnfOjSpQvz5s0jLi7unO43MjKSgQMH8vzzz59RZtjTTz/NokWLePrppxk3bhzHjh1jwIAB5OTklLtNcnIyd911F4WFhYwZM4axY8fidDq58847/QEtm83GwIED2b59Oy+99BJvvfUWx44d46677rosv++Lc0t/sRsghLi4QkND2b17N1OnTmXIkCHn5TmaN29+XvZ7ufivH78QQohzZ+nSpUyYMIHBgwfz+OOP++cnJSVx88038/TTTzN8+HCsViv169e/iC0VF8qOHTt47bXX2Lx5M2azudTyGjVqUKNGjYB5gwcPJjw8nLfeeguNRlPmfr/55huysrL46quviI+PB6BDhw707t2bmTNnkpSURGFhIT/99BMPPvgg99xzDwBt27ale/fufPHFFzRr1uy0j+fE702tWrWicuXK9O/fn6+//vqUmXBvvvkmPXv29Lf5UhQVFUVUVNR52fedd97Je++9xy+//MK1115b4e3Wr1/P0qVLmT59Op07dwagdevWdO/enc8//5xHHnmkzO0+/fRTLBYLH3zwAUFBQQBcddVVdOvWjTlz5jBq1CgWL17M/v37+eGHH/zvS1arle7du7No0SL69et3lkctLmeSKSXEf1zDhg25+eab+fDDD9myZctJ1/V4PHz22Wf06tWLpk2b0qVLF9566y0cDsdJtyvZfW3VqlUkJCTwzz//MGjQIJo1a0aHDh1488038Xg8gNpt8PDhw3z99dcBacpHjhxh6NChJCUl0axZM+655x62bdvmf56UlBQSEhL4+OOPuf7662nWrBnvvfceCQkJLF26NKBN27dvJyEhgV9++QUAh8PB+PHj6dy5M02aNKFXr1789NNPAdt069aNSZMmMW7cONq3b0/Tpk257777/KnHkydPZsqUKaWO+cTue8ePH2fEiBF07tyZpk2bcsstt/Dbb7+VOmefffYZI0eOJCkpiRYtWvDkk0+Snp7uX+fgwYM8/PDDtG3blmbNmnH77bfzxx9/nPS1EEIIcXmbMmUKderU4bHHHiu1zGAw8Morr6DT6ZgxYwYAgwYNom/fvqXWffTRR7npppv8j9euXctdd91Fs2bNSEpKYtiwYWRmZvqXL1y4kEaNGjF//nw6dOhAUlISe/bsqfBn0bJly7jppptITEzkuuuu45tvvglYXpHPRofDwdSpU7n++utJTEzk2muvZfr06f4MmOHDh/P1119z+PBhEhISWLhwYZnncPLkyVx//fX88ssv9OzZk8TERHr37s369evZsGEDt956K02bNqVnz56luqHt2rWLhx56iJYtW/q7IJ3YVWrHjh08/vjjXHXVVTRu3JhOnTrx2muvUVhY6F+nIp/zvu5aq1atKvM4fIYNG4bH42HevHlER0efdF2AP/74gyVLljBixAjCwsLKXS8+Pp6BAwcGBHd0Oh01a9bk4MGDALhcLrxeLyEhIf519Ho9oaGhZGVlnbItFdWkSRMAf1fDyZMnc8011zBlyhSSkpLo2LEjOTk57Nq1i2XLltGzZ09A/d45YMAAAAYMGMDdd9/t3+dPP/1E3759adGiBR06dGDUqFGlMoI2b97MfffdR9u2bWnZsiUPP/xwhbsRLly4kOuuu47ExERuuummgL+Lsrriff3119xwww3+9f/55x8aNWpU6jreuHEj/fr1IzExkS5duvDhhx8GLDcajVx33XV88MEH/nm+79/l/U0ArFixgqCgIDp27OifFxUVRZs2bU76/bJOnToMGjTIH5ACCAoKolKlSv7rpEePHsydOzcgUG4wGABO+TtCXPkkKCWE4PnnnycyMvKU3fhGjRrF2LFj6dGjB++99x79+/dnzpw5PProo6edIvzMM8/QqlUr3n//fXr27MmHH37I/PnzAfULd2xsLJ07d/anNmdmZtKvXz+2bt3Kiy++yIQJE/B6vfTv379Umv7kyZN54IEHGD9+PH369KFGjRqlujL88MMPRERE0LlzZxRF4bHHHuOLL77g3nvv5b333qNFixYMGTKk1Jfm2bNns2/fPsaOHctrr73Gli1bGDZsGAC33nort9xyC1B+l7309HRuueUW1q5dy5AhQ5g8eTJVq1blscce47vvvgtYd+LEiXi9Xt5++22ee+45li5dyuuvvw6oafoPPfQQdrud8ePHM23aNCIiInjkkUdITk4+rddCCCHE5SEzM5MtW7bQtWvXcrNbIiIiaN++vT+gc9NNN7F169aAz4bc3Fz+/PNPevfuDcCaNWsYOHAgZrOZd955h+eff57Vq1czYMCAgECKx+Pho48+YsyYMYwYMYLatWtX+LNo1KhRDBw4kPfee49KlSoxfPhwduzYAVTss1FRFB5++GE+/PBDbr31Vt5//32uv/563nnnHUaPHg2ogbbOnTsTGxvLvHnz6NKlS7nn8ujRo7zxxhs8/PDDvPvuu+Tm5vLEE08wdOhQbr31VqZOnYqiKAwZMsR/Dvbv30+/fv3IyMhg3LhxjBkzhkOHDnHHHXeQkZEBqME1X9fKN954gxkzZnDjjTfy6aefMnv27IA2nOxzHoq7eDVu3Ljc4wAYP348c+fOpUGDBiddz3cex40bR1JSEtdff/1J173hhhv8dZl8cnJyWLNmjT+4EBoaSp8+fZg9ezbr168nNzeXjz76iN27dwcEPc/W/v37AQIyvo4cOcIff/zBxIkTGTFiBOHh4Xz//ffExsb6s60aN27MqFGjAPUa9F0r06ZNY+jQoTRv3pxJkybx2GOPsXjxYu6++27/671y5Up/ndDXX3+d1157jdTUVPr163fKLqKpqalMnz6dJ598ksmTJ6PRaHjiiSf818mJvvnmG4YPH07Lli2ZNm0a1113HY8++qj/H2xLeumll7jxxhuZPn06LVq04M033yz1j6/XX389W7Zs8Z+3xo0bn/JvYu/evVSrVg2dThcwv0aNGv79lOXOO+/k/vvvD5iXnJzM7t27A66Tli1bAuB0OtmxYwfDhw8nMjKS//u//yt33+I/QhFC/Gfdddddyl133aUoiqL89ttvitVqVd5++23/8q5duyrDhg1TFEVRdu/erVitVuWDDz4I2Mc333yjWK1WZdmyZeU+j9VqVSZNmqQoiqKsXLlSsVqtysSJEwPW6datm/LQQw+V+dyKoihvv/22kpiYqKSkpPjnORwOpXv37srgwYMVRVGUQ4cOKVarVXn++ecD9j1p0iSlefPmit1uVxRFUbxer9KlSxdl1KhRiqIoyooVKxSr1ar8+OOPAds988wzSocOHRSXy+VvU9euXRW32+1fZ/LkyYrValUyMzP9z2W1Wss9/vHjxyuNGzcOOA5FUZR77rlH6dChg+LxePzb3HHHHQHrDB8+XGnevLmiKIpy/PhxxWq1Kt99951/eW5urvL6668ru3btUoQQQlx5Nm3apFitVmXOnDknXe+NN95QrFarkp2drRQUFCjNmzdXpkyZ4l8+f/58pUGDBsrRo0cVRVGU22+/XenZs2fA59u+ffuUhg0b+p9rwYIFitVqVb755hv/OhX5LPJ9Lv7xxx/+dZKTkxWr1ap88skniqJU7LNx2bJlitVqVX744YeAdaZOnapYrVb/8w0bNkzp2rXrSc9PWW364IMPFKvVqsyfP98/b9GiRYrValW2bdumKIqiDB06VGnfvr2Sl5fnXycrK0tp1aqV8sYbbyiKoijLly9X+vfvH7COoihKz549lUGDBvkfn+pz/kyd+P3pRL/++qtitVqVv/7667T37fF4lMGDBysNGzZUNm7c6J9//PhxpU+fPorVavVPJa+3ivK9Li6Xyz/l5eUpa9asUfr06aO0atVKOX78eMC6a9asCdjHLbfcojzyyCMB83zfPVeuXKkoiqJkZ2crTZo0UV588cWA9dasWRPw93XLLbcoN9xwQ8DfRU5OjpKUlKQ88cQT5R7HsGHDFKvVquzZs8c/7++//1asVqvy66+/KopS/Pd06NAhRVEUpUuXLgHfgxWl+JpcsGBBwDaff/65fx2bzaY0btxYef311wO2zc3NVaxWq/LZZ5+V284TDRo0SOnXr1+p+W+//bbSuHHjCu/Hbrcrt99+u9K8efNSf9O+57FarUqDBg38xyb+2yRTSggBqF3TbrrpJj788MNSo2UA/oKWN954Y8D8G2+8EZ1Od8rU8hO1aNEi4HGlSpWw2Wzlrv/PP//QsGFD4uPjcbvduN1utFotV199NX///XfAug0bNgx4fNNNN2Gz2fz/ivTvv/9y5MgR/78Q//PPP2g0Gjp37uzft9vtplu3bqSlpQWkaScmJgb8C1KlSpUAsNvtFTru1atX06JFC6pWrVqqjWlpaQEjnJxYU6FSpUr+54mJiaFevXq8+OKLDBs2jO+//x6v18uIESOkhogQQlyhlKKsZF+3l/L4PqcURSEoKIgePXoEdEn/8ccfadeuHfHx8djtdjZu3OjPHPZ9BlavXp26devy119/Bey75Gfs6XwWtW7d2n+/WrVqgJqxBRX7bFy9ejV6vb5Udo8vG6dk4e2K8mVu+I4FCKiBFBEREdDOlStXkpSUhNls9p+nkJAQWrdu7f8u0rFjR+bMmYPJZGLPnj389ttvvPfee2RmZpbKRj/Z5/z58tlnn9GwYUPat29/Wtu5XC6effZZFi9ezMiRI2natCkAGRkZ3HrrreTm5jJ+/Hg++eQT7r//fqZNm8bHH398Rm1s3Lixf2rVqhX9+/fH6XT6M+lLOvE736FDh/zXV3k2bNiA0+n0d/Hzad26NVWrVmX16tXYbDY2b97M//3f/wV87wsLC6Nr166nvN4iIyOpW7eu/7GvTXl5eaXWTU5O5siRI6Wu7RO/c5dsp4/FYiEmJsZ/jfqEhoYSFhZ2WqP7KSfp9VBeZuaJ8vPzeeihh9i8eTNvvvlmqb9pgEceeYRZs2Zx8803M2LECH9PCfHfJYXOhRB+L7zwAv/88w8jRoxgwYIFAct8fexP/DKg1+uJjIws80P2ZE4sxKnVak/6YZidnU1ycnK5Kewlv8SV7NMOULNmTVq0aMGPP/7I//3f//Hjjz9So0YN/5fR7OxsFEUJ+HJa0vHjx/1feiwWS6l2AxUe0SUnJ4fq1auXmu/7MlzyS0VZz+U7RxqNho8++shfyPKbb77BYDDQo0cPXn75ZcLDwyvUHiGEEJcP3w88X12d8hw6dIjg4GB/UKV3795899137Nixg5iYGFatWuXvJpabm4vX62XGjBn+OlQlmUymgMclP2NP57Oo5Ha+z07fZ1pFPhtzcnKIjIws1bXI973kdL+HAAF1kHxO/OwtKTs7m59++qlUzUnAX7Ta1x3vs88+w2azUblyZZo2bVrqPJb1XKf6LnS2srOzWbVqFUOHDj2t7XJzc3n88cdZs2YNL774Iv379/cvmz9/PqmpqSxevJhatWoBapFrRVGYMGECN998M5GRkaf1fF999ZX/vsFgIDY2ttxaWcHBwQGP8/PzT/oaQvF3Wt/1VVJMTAx5eXnk5eWhKMpJ1zmZE7+L+oI6ZX1f9NVuO/EYy3puqPh1Y7FYyM/PP2k7SwoJCQmoaeZTUFBAaGjoKbdPTU3loYceYv/+/UycOJEePXqUuZ4vqNauXTsOHz7M+++/L6NU/8dJUEoI4RceHs5LL73EY489xrRp00otA0hLSwv4Vw+Xy0VWVtZpf+E4XaGhoSQlJfHcc8+VudxoNJ50+5tuuomxY8eSl5fHokWL/DUCfPsOCgoqVevBp2bNmmfe8BOEh4eXOZyxb97pnMf4+HheeuklRo8ezY4dO1i0aBEzZswgMjLSXzNBCCHElSM6OprmzZuzePFinnzySX9wp6T8/Hz++usvunXr5p/Xrl07YmNj+fnnn4mNjcVkMvlH5QoODkaj0TBw4MAyMzNO9QP/XHwWVeSzMTw8nKysLDweT0Bg6vjx4/51zrfQ0FDat2/PvffeW2qZXq/+rJo+fTqzZs3i5Zdf5tprr/X/mPfVnLyYli9fjtvtPmUtqZKOHj3KvffeS0pKCm+//Xap+j9HjhwhOjraH5DyadOmDTNnzuTgwYOn/dokJiae1volRUREnDJg5PtOm56eTp06dQKWpaWlUb16dUJDQ9FoNGUGadLS0vwB33PBl3V/Yr2p8upPVVRubu5pnfvatWuzYsUKvF5vwHtLcnJyQNZXWXbu3Ml9992Hw+Hgo48+ok2bNgHLN23aREpKCjfccEPA/MaNG7N+/foKt1FcmaT7nhAiQI8ePejZsyfTp08PGHUnKSkJoFTB8B9//BGPx0OrVq3OaTtO/KKdlJTE/v37qV27NomJif7p22+/5auvvir1L6cnuuGGG1AUhXfffZeMjIyA4ptJSUnYbDYURQnY965du5g6dSput/uM232iNm3asH79+lL/yv3dd98RGxtb4QDY+vXrad++PZs2bUKj0dCwYUOGDBmC1WrlyJEjFW6vEEKIy8vjjz/O/v37efvtt0st83g8jB49msLCwoDCwzqdjl69erF06VIWLVpEjx49/JkcISEhNGrUiH379gV8BtavX5/JkyeftHv+ufosqshnY1JSEm63m0WLFpVaB/B/DznV5/DZ8I042LBhQ/95atKkCbNmzfKP5rtu3Trq1avH//73P39A6tixY+zatavCWdXny8aNG6lUqVKZXarKkp+fzz333MPx48f5+OOPyyxIXadOHTIzMwPKD4BaKkGr1VKlSpVz0vaKqlq1KqmpqQHzTvyO2KxZM4xGIz/88EPA/LVr13LkyBFatmxJUFAQTZo04eeffw4oNp6Xl8eyZcvO6ffeSpUqUaNGDf815LNkyZIz3mdOTg52u/20zn/Hjh0pKChg+fLl/nmZmZmsXbuWDh06lLtdamoq9957LxqNhrlz55YKSAH8+eefPPvsswGvjcfjYeXKlSQkJFS4jeLKJJlSQohSXnzxRVauXBnwr0P16tWjT58+TJo0CbvdTps2bdi+fTtTpkyhbdu2dOrU6Zy2ISwsjG3btrF69WqaNm3KwIED+fbbbxk4cCCDBg0iMjKSn376iS+//JIRI0accn++kfY+//xzWrRoERD86dy5M23atOHRRx/l0UcfpW7dumzatIlJkybRqVMnf0p+RdsN6uh+zZo1K9Ud4d577+W7775j4MCBPP7440RERPDNN9+wcuVKXn/99Qp/mW7UqBFms5nnnnuOwYMHExMTw99//8327dv9Qx8LIYS48nTq1Inhw4czfvx4tm/fzv/+9z/i4uJISUlh7ty5bN++nTFjxpQaia1379589NFHaLXaUt30hg4dyoMPPsjTTz/NTTfd5B9lb+PGjTz66KPltuVcfRZV5LPx6quvpm3btrzwwgscO3aMBg0asHr1ambMmEGfPn2oV68eoH4Op6en88cff9CwYUPi4uJO4+ye3KOPPkq/fv146KGHuOOOOzCZTMybN49ff/2VSZMmAdC0aVOmTZvG9OnTad68OcnJyXzwwQc4nc7TrheVmZnJwYMHqVevXpldDU/Xzp07/eepLAcPHiQzM9Nf62rSpEkcOHCAwYMHo9fr2bBhg39do9FIo0aNuOWWW5gzZw4PPPAAgwcPJj4+nr///puPPvqI/v37+7tXHj16lKNHj9KoUaNTZrefjQ4dOvD555+jKIq/y5wvOLhs2TLCw8Np0KABDz74IFOnTsVgMNC1a1dSUlJ49913/d93AZ5++mnuu+8+HnzwQe68805cLhfTp0/H6XTy2GOPnbM2+0bme+aZZxg9ejTXXHMNO3bsYOrUqcCZBVrXrVsHqIEmUAOMe/bsoUaNGuV+r23Tpg1JSUk8++yzPPvss0RERDB58mRCQ0MDehjs2bMHp9NJo0aNAHjttdfIyMjg5ZdfJj8/P+A6CQkJoV69evTr148vvviChx56iMcffxyDwcDnn3/Orl27mDlz5mkfn7iySFBKCFFKREQEL730Eo8//njA/DFjxlCzZk0WLFjAjBkziIuLY8CAATz66KPn/F8mBw0axOuvv859993Hxx9/TOvWrfniiy+YMGECL730Eg6Hg1q1ajFmzJgKp8T37t2bX3/9lV69egXM12q1TJ8+nXfffZcPPviAjIwM4uPjuffee0/7S8e1117Lt99+y/Dhw7nlllt46aWXApbHxsYyd+5cJkyYwGuvvYbL5aJBgwZMmzaN7t27V/h5TCYTH330ERMmTGDMmDHk5uZSq1YtXnnlFfr27XtabRZCCHF5uffee2nRogWffPIJ48aNIzMzk9jYWDp06MCYMWPKDDw0aNAAq9VKVlYW7dq1C1jWsWNHZs6cyZQpU3jiiScwGAw0btyYjz/+uFQx7pLO1WdRRT4bNRoNH3zwAZMmTWLWrFlkZmZSrVo1hg4dGtCdrm/fvvzxxx889thjPPHEEzz44IMVbsepNGjQgM8++4yJEyfy3HPPoSgKVquVqVOn+tv50EMPkZWVxezZs5k6dSqVK1emd+/e/vbn5ub6/wHrVJYtW8aIESOYPXs2bdu2Pev2Z2RknDQrZdq0aXz99dfs3LkTKM7UmTx5MpMnTw5Yt2rVqvz++++EhITw+eefM2HCBN544w0KCwupWbMmo0ePDqgTNH/+fKZMmcJvv/12ykLkZ+Paa69l6tSpbNq0yV+0vn79+vTs2ZPPPvuM5cuX88MPP/iDqHPmzGHevHlERERw/fXX89RTT/mzCNu1a8fHH3/MpEmTGDp0KEajkdatWzNu3LhzPqhMr169sNlszJw5kwULFlC/fn1GjhzJyJEjS9Wnqog///yTpk2b+rPitm7dyoABAxg7duxJ/zanTJnCG2+8wfjx4/F6vbRs2ZJ33nknoD7cyy+/zOHDh/n9999xOp0sW7YMoMzuuklJSXz66afExMQwd+5c3nzzTUaPHo3NZqNp06Z88sknAYXbxX+TRjmf1fSEEEKIK5DH48Hlcl3sZohzxGAwnLILsBBCiLPTv39/3nnnnVKD5pxrDz/8MJGRkYwdO/a8Ps+59MMPP9CoUaOAGlfLli3joYce4ttvvy2V+XgyNpuNTp06MW7cuHKLjQtxKZFMKSGEEKKCFEXh6NGjZGdnX+ymiHMsIiKCSpUqVXjYayGEEBW3atUq7HZ7uSPKnUtDhgzhzjvvZPDgwRe8ptWZ+u6775g4cSJPPfUUlStXJjk5mUmTJpGUlHRaASmAL774gvr1659WBr4QF5NkSgkhhBAVlJqaSnZ2NnFxcQQFBUkA4wqgKAo2m43jx48TERFB5cqVL3aThBDiinP48GGCgoIuyCiJoI6CuGPHjjIHBLgUZWVlMWHCBP78808yMzOJiYnhuuuu44knniA4OLjC+8nMzOTmm2/m008/PaejRwtxPklQSgghhKgAj8fDrl27iIuLIzo6+mI3R5xjGRkZHD9+HKvVKl35hBBCCCEukPM3ZqoQQghxBfHVkDqTgqPi0ud7XaVWmBBCCCHEhSNBKSGEEOI0SJe9K5O8rkIIIYQQF94ZB6WcTic9e/Zk1apV/nmHDh1i4MCBNG/enBtuuIEVK1YEbPP333/Ts2dPmjVrxoABAzh06FCFn09RFPLz85HehkIIIYQQFSPfn4QQQghxKTujoJTD4WDo0KHs3r3bP09RFB577DFiYmJYsGABvXv35vHHH+fIkSMAHDlyhMcee4y+ffvy1VdfERUVxaOPPlrhL0kFBQW0atWKgoKCM2myEEIIIcR/jnx/EkIIIcSl7LSDUnv27OG2227j4MGDAfNXrlzJoUOHeOWVV6hbty4PPfQQzZs3Z8GCBQDMnz+fJk2aMGjQIOrXr8/YsWM5fPgwq1evPjdHIoQQQojL3s8//0xGRgYAkydP5u677wZg4cKFdOvWrdzthg8fzvDhwy9IG4UQQgghxLlx2kGp1atX07ZtW+bNmxcwf+PGjTRq1CigAGyrVq3YsGGDf3nr1q39yywWC40bN/YvvyTk7IC0vyBrE+Tvg8I08DgudquEEEKI/4TDhw/z1FNPYbfbARg0aBCTJ0++yK0SQgghhBDni/50N7jzzjvLnJ+WlkZcXFzAvOjoaI4ePVqh5Rdd1ib4uVnZy3QWMISDMRyMUWCKAVMsmGPBXAmCqqmTpSpYKoP2tE+rEEKIy1B5XdAVTtE1XangeifsT1EUFBT/8/ofl5xXdL+sbUrOK7lumfdRUP8P3I+/PScsC2jnKZaXOU+Bo5nqd4K9WXvJN+f795GalsqRvCM4PU62pW0rdd5jg2MrdB6FEEIIIcSl5ZxFT+x2O0ajMWCe0WjE6XRWaPlFF1wdKvVQs6U8BeC2g7dQXeaxq1NhRQJoWjDHQ3ANCKoOwbUg1AoRDcFcVQ1YabSg0ZW4PfF+iXlCCHGZ8ireUgGRsuadah3gpOuXdXviNl6v179vj9dDoaeQQnchTo8Tl8dFobsQl9eFw+XA6XXi8DhweVw4PU7/OkaMXB12NeY8MzqHrvj5vF7w2E4eMPIFeSgjsFQUwCnZZv/9E4I7/nOrNcNpjBa3aMEifpr/E7mZuVSrXY27H78bj8fDmCFj+GzpZ/713n/jfQAeHv4wBfkFzBg/g63/bgUNtLiqBQOfGkhQsJoR/dOXP7F44WLycvKwNrEyaOgg4irHoSgK33z6Db9+9yvOQicJTRMY+ORAYuJjAOjftT8PPPMA337+LblZubRs35L7nr4Ps8XMA7c8AMCDtzzIg8MeJP1oOts3bOeFd17A4XGgKAofT/uYJd8swRJkodcdvbiu73Vk2bNKHfMvv/zCxIkTOXz4MPXr1+e5554jKSmpwudMCHFxebwKOu3lPSrmlXAMQghxvp2zoJTJZCI7OztgntPpxGw2+5efGIByOp2EhYWdqyacHWMkdPsFFAUUD3hdalCqMAMcaeBMh8J0KDyuPnakgzMTHEXLHZngyla3LUxVp4xVgc+hC4LgmhBaD0ITICxBzbjSaIuDUhTdan23JtAa1UlnAo2+aL5eve+/NRQ/FkL8pymKGnzxKsWBGF+gx3e/vGUlg0InPvZ4PXgUdfIqXrxeb/F9peh+0bxSwaITAjQo4PK4cHgcFLoLcbgdONwOCj2F/nlOjxoccrqd/uDQiZPL61KDR16nP4hU8rFvucurLnN73bi97jM6rzWDa9K8Q3OMdiO4/CebBuvvJyR307l7ASsgL6wZO1vM8AemNGhQ/y/6r8T8/bv2M/eDuTzz2jPUqFWDn776iUkvTWLIqCEABBmC/OvqtXo0aAgxhvDF7C/Iy85j/Afj8bg9THh5Aj/P/ZlBjw1i0TeL+Hr21wwePph6CfX45P1PmPbKNCbPmsy3X37Lyt9X8vyrzxMZHcmCzxbw5rA3mTF3Bnq9+hm14OMFDB05lKjoKN585U0+f/dzRr42kmmzpvHowEd575P3qF23NnM/mYtRZ6RqaFWizFGkH0sn7WAaH8z6gB3bdvDWmLdo0bgF1vbWgPOzY8cOhg0bxssvv0zTpk35448/eOCBB/juu++oWbPmhXqZhBBnQafV8OQX69lzPP9iN+WM1IsL4d1+LS52M4QQ4pJ3ziIY8fHx7NmzJ2Beenq6v8tefHw86enppZY3bNjwXDXh3NBoSgR3LGqwinql1/N61KCV265mVjmz1UCV/QjkHwRnhhrIsh+F/L1QkAweG+RuVye+V/djioPI5hDTFqLagDECFC8obvC61f3jUZ9P8ZzYWDWYpdUBJQJVOjNozaC3FAW0DEVT0X1N0ePT+Fd2IcS54wv4BAR0vJ6AgFHJZSdOHq/HH1zx3fcoxbelglB4QSFgvoLin+/vCuV7S/Al5WjUZS6PC5vLpgaMXEWBI7cDu9vuDx4Vugv9ky+4ZHfZi4NOJYJPvsdOz6WRKavX6jHqjBi0Bv+tQadORq0RvU6PUWukWlA1THoTwYZgdEZdUfgHdFrDBW9zkMFCg5gGaCqQUXuw4CBajZaW9VtSr349mj3TjE3XbvK/zjXDi4M0IYYQAKqHVSc3LZeI0AhaWFtgsVio9k41FBSqhFbh1+9/5a4Bd3H7zbcDUOvFWsz+ZDZhujAWfraQ4S8Mp3OnzgA0f6U513a7lh3rdtC5izpv0P2DuOGaGwAY/vxwHn3wUUaNHkWNSjXU54+vTlx4HCa9Cb1WT5gpDLPejMlkYszYMURERJDYMJEt67fw09c/cXX7qwOOeebMmdx222306tULgAEDBrBmzRrmzp0rxdCFuIzsOZ7P1iO5F7sZQgghzqNzFpRq1qwZ06dPp7Cw0J8dtW7dOlq1auVfvm7dOv/6drudbdu28fjjj5+rJlxYWh1og0EfDMSoGVBeT1GAKgcKj6lZVG4b6AzqeoXHIHcX5GyB7M2Qtxscx+HoEnUCNXsqthNUvhZC6py6HYpHDV4pRUErxQXOQjWopbjVzC8fja5ERpVBzdzSB5cIXhVlY/mysyRoJUQAX0DIo3jKvS25jtujBo582Tq++yUzjbx4A4JFHsXjzyzSaDSlum1BcTaMVqNFo1FvtRptwDwNGpxuJwWuAuxuOzaXjXxnPjaXDZvLRoGrwH8/35lPvjPfP6/AWRCwjlfxnvdza9KZ1KCD3oRZV3SrN2PSmTDpiyadqfhx0a1RZ8SkK7rVmzBoDepjvTrfF1gy6tTJoDMU3y8KPmkr2lXaDfpcPfHB8ZjMJv9sb/dlFHhs5+fElEcX5M+GOpV27dtRr349but7Gw0aNqBz1870/V9fkpOTT7rdHXfdwdAnhtK9c3fatm1L92u78383/B8ABw4c4KFGD/nXjY6JZsjTQ7DZbBw7dowRz44IaJ/D4eBgcvGovc1bNPffb9S4ER6Ph+TkZCIjIwGKs+tKdulEoUq1KoSGh+Ip+keahAYJfPP1N6XavnfvXn7++eeAQVlcLhcdO3as0DkTQgghhBAXxjkLSiUlJVG5cmVGjBjBo48+ytKlS9m0aRNjx44F4H//+x8zZ85k+vTpdO3alalTp1KtWjXatm17rppw8Wl1oA0DQ5hao8ptUzOo7IfVbn9aM8RdDVVuUAM+bjvkbFW7+aX/Dbk7i6e9H0JIPah8nRqgCqpa9nNqdKDTVax9XndxBpbiUrO5Co8C3qLglaYo28oAOiPogsEYphZ615rUDCydWQJW4rJ2YmbRidlGvvu+7l5Ot9N/3989DfXWl9FUsoYQGkDB/4PcFzAqa/IFRHwBJZ1WFxBYUhQFm8tGriOXHEcOuY7cgPt5jjzynHnqfWce+Y58/+N8Z77/h/u5YtFbCDIEEWQIwmKwEKQvvm/RW8q9NevN/scmnQmLQZ3nm4w6Y8UDQ5cijaboHyguTRaLhdmfz2bd2nX8uexPvvvmO76a9xVvvPlGwHoKCm6PG51OrZfVOqk1Py75kT+W/sGK5SsY8/IY/l7xN6+MfQW9Xo/X68XtdQcETgsdai3GMePHUKNWDTSogVUNGkLDQ3F51H6PikbB6XGiQYPDpY5y68Xr71rp6yZ6Yh0unVb9vNMUpfUpioLRYPTP9/F4PDzwwAPcfPPNAfN9/2gmhBBCCCEuDecsKKXT6Zg2bRojR46kb9++1KxZk6lTp1KlShUAqlWrxuTJk3n99deZOnUqLVq0YOrUqRX+l97Lkj5InSyV1XpTtlSwpYDzIBijwRAC0a3VyfqYmlmV/g8c/U29zd8Du/fA7mkQ0w5q3Aqx7YtqT50BrR7QQ3mbK4oarPIWTc50KDxSnG3l6wKoM4E+HIyhxd0EfYGrK/n1FJeckgEkX4DpxMnXvczpVusTlezi5s9u8nr99Xh8gSUtWnRanT9opNPo/IEkk8YUMO/EH8Rl8QWYsgqzyC7MJsueRbYjm+zC4imnMIccR07xrSPnjOsf+eg0OkKMIf4p2Bis3hqCi+cZggk2BhNkCPLP9wWfgg3B/sDTZR04ugKUHKUuYJS7E+adOH/Txk2sXb2WQQ8Monmr5jw8+GH+r9v/sWq1WvcwOzeb4BA1qJaSkkKNmjXwKB4+//Rz6lvrc2PvG7mx940s+XkJr4x6Ba1GS40aNdi7ey9du3VFo9GQnZ1N3159+eyLz4iKjiIvO4+EugkAuJ1unn32WQYOGkiVWPU7wcG9B2mR2AINGjbv2ozBYKBR/UbkZOcA+K9Tk86kXsOmEMw6M4dTDqP36LFYLGg0GnZu20m9uvWwGCwB56p27dqkpKQE1I8aP348tWvX5tZbbz2Hr4oQQpQtNsR0xRQ6v1KOQwhxaTqroNTOnTsDHtesWZM5c+aUu37nzp3p3Lnz2Tzl5UmjUWtTGSMhpBbYDkH+fjWLyhyrBnkATNFQtac6uXLh2O+QugQy1qiZVOl/g6UKVO8L1W9Rg1rnup2aom58ZfG6wOsAjwNcKVDgQk0J0asBKb0ZDBFgDFeDVLqioJz8kBWnwe11BxSoLhl0crqd2D12f20il8dV3HWuKMB04lDxOq0OnUYXcGvQGgIen2mwxat4yS7MJsOWQYY9g0x7Jpn2TDLsGWTZs8gsVB9n2bPIKsw64xpKRp2RMFMYYaYwwk3h/vuhxlD11hTqvx9iDCHUGOqfZ9abr+zg/yXuxNHzSmb+lHxccvS9ktlFvltfvS9fhpD/vm9+UTdO33xfF04NGsKCw5j5wUwqxVWiXbt2rF27FrvdzvXXXs+cWXP47OPPuO3221iyeAm7duyibu26hBhDyE7PZsLCCbz++utERETwx29/0KhRI0JNodwz4B7Gjh1L44aNqVu3LhMnTqRatWrUrVWXewfey5RJU4iPjadOnTpMmzaNDes3kFAvAaNO/XyZOmUqNarXwGQyMfb1sfTp04fw0HA8LjW7b9fOXURHRavHoSnOHnQ4HAwfPpzBgwezbt06Fi9ezBdffFHqvA8cOJD+/fuTmJhIly5d+P3335k1axaffPLJuXtxhRDiJMIs+su+WDtIwXYhxPknQ7VdaPogtW6UOR7y9oP9kJrB5BuFz8cQBtVuVidbChxcAIe/Uwup75oC+2ZBzX5Q8w41CHQh+Aqmn3jVeN3FwSpnMuQXdRnSFXX5M0SoBdz1RTW4dEGSUfUfVDLY5BshzRd4srls2N127C67muGkuNV6TIobxVtchNs3OphOo1NvfQEmg84//1wEYDxeD5n2TNJsaRwvOE66Ld0/pdnSyLBnkG5LJ8ueddpd5Ew6E5GWSCLNkUSYI/xTuCm81P0wUxjh5nBMOpMEli4wX30vXwF6j+JBcSmEe8P9XT9PDDiVVFYgqeQIeVqN1p+R5x81T6MpXrfkqHqn8bgszROb8/rrrzNt2jTGjhlLlSpVePPNN0lsnMirr77KxIkT+WzOZ1xzzTX079+frKwsdFodTz31FPn5+Tz66KPYbDbatGnDm2++CUDv3r05duwYL7/8Mvn5+SQlJTFp0iQA7rvvPgoKChg1ahT5+fk0adKEmTNnEh5e/Fl18803M3z4cHJzc7nxxhsZOXIkAFFRUdx000089dRTPPPMM6WOpWHDhsTHx3PbbbcRGRnJ66+/TpMmTUofc/PmjB8/nsmTJzN+/Hhq1KjBhAkTaNOmTbnn6WL75ZdfStXZvO6665g0aRLbtm1j9OjR7Nq1i3r16vHyyy+XedxCiEuPFGsXQoiT0ygnphVcovLz82nVqhXr1q0jJOQcZwhdLIpXrTWVuxOcmWCpVH6WEoCnEFJ/gQOfQv4+dZ4uCGrcArUHqIGfS4WiFAWqCtXJWzR+us6sZlGZosEQrmZ76UPUYJe4bHm8HrWLXNHkCzwVugspcBb4R2HzB5uKftD7flD7gkwnTmeTxVQet9fN8YLjHMs/xrECdTpecDxgyrBnVLi4twYN4eZwoi3RRFuiibJElZoizBFEWaKINEeW6mYkzi9fcMlXO8zXddP32Bdw8q3rKy6v0WjQoUOrVbtn6tChV/SE28OpUbMGZrO5OBupRFaSL8AElBtk+q8HGBMSEpg9e/YlV1OysLCQ/fv3U7t27YtSe+q9995j48aNvPrqq/55JpMJvV7PtddeS69evbjllluYO3cuP//8M7/88gtBQUGn3O8V+f1J/GfcOGn5ZRvQualZZSbd0fKyPgaAxlXC+PGJThe7GUKIK5hkSl1MGq0aiDKEQc5OKDgApkgwhJa9vs4M1XpB1Rvh2FLYOxPydsH+2XBoIdS9D2rcVtwd8GLSaIoLo/v4AlVumxpUU7xFhdqD1ICaKVoNUBlCL41jEH6+oJPD41Bv3eptgauAfGc+he5CtYtdiYATqD/AfSOc6bV6gnXB6I1qwOl8/TAvcBaQmp9Kal4qR/KPcDT/aMCUbksvM7PlRDqNjuigaGKCYogNiiU2KJaYoBhigmKItkT770daItFr5a30Qik52mHJgvUlA05exRsQDPJ33yzqsqnX6gkyBPlH4fON0ucLhPqy7k6873a6ST6QTLAxGLNRCmaLc2vv3r1YrVZiY2MD5n/11VeYTCaee+45NBoNI0eO5M8//2TRokX07dv3IrVWCCGEEOLckF9SlwJ9EEQ2VUe6y9sJHhuY4srv4qbRQqXuEN8N0pbD7vcgbzfsfBcOzleLple69tLrIldWoMrrVo+38CjYDqrHpgtWs6jMsWqASh+qjgYoziuXx4XD48DhduDwOCh0F2Jz2ch35GNz29Sudm4XbkUtvO3rSqfX6jFoDQTpgzAYDec14ARqplNqXiqH8w6TkpvC4bzDHMk74p9yHDmn3IdRZyQuOI744HjiguOoFFKJ2KBY4oLj/FOkObJCBczF2fMFmPyjIZYYGdGreAOCnBqNpjh4pFFvgwxBmPQmzDozJr2pzKy7EwNOZ3KNejUVy54T4kzs3buX9u3bl5q/ceNGWrVqVZxtp9HQsmVLNmzYIEEpIYQQQlz2JCh1qdDqILSuGoTJ2Qb2w2CpevLAkkYDcVdDbAc4/JM6Sp/9CGwcCclfQuMREFrvwh3DmdDqQRumZosBKB5w28GZBvaUokyqYDWLyhStrmcIleLpZ8jj9VDoLvRPDo+DPEceeY48NSDlUYuH+2rl6LV6DDoDBq2BYH0wBpPhgmQFOT1OUnJTOJhzkJTcFA7lHuJQ7iEO5x4mNT/1lF3rwk3hVAqpRJXQKlQKqUSlkEpUDqlMpZBKxAfHE2WJ+s93oboQyhsR0e11+0c5RMGfxeQLHll0Fkx6ExaDBYve4r8OSwZBSwab5LW8vJw4SIpQu47u37+fFStW8MEHH+DxeLj++ut54oknSEtLo169wM/y6Ohodu/efZFaK4QQQghx7khQ6lJjjlPrSmVtBNthCKpy6gCMRqd266t8DRyYA/s+geyN8Hd/tRB6vQfVbKzLgUan1pnyjSzoy6Syp6hd/nQmtYufpVKJkf6kG82JfPWcCt2F2F12CpwF5Dpysblt/m54iqIGnnxBJ6POSIQpAoPOcM7rOJVFURSOFRwjOSeZ5Oxk/+3BnIMcLTh60sCTSWeialhVqoaqU5XQKlQJrULV0KpUDq1MiFHqppxPvjpNvmL1viL2bq8bL17/SHK+QJNBa0Cv0xNsCPYHmox6o79rZ8lbX0aTEP8lR44cwW63YzQaeeedd0hJSeG1116jsLDQP78ko9GI03lmo3kKIYQQQlxKJCh1KTJGQFQLyNpUIjBVgR9pOjPUvR+q9IQdE9S6UwfmwNFfoOGzEN/lfLf83Dsxk8rjAHcB5OxATeUJBmM0WOLULn/6kEuv2+J55Pa6sbvs/pHr8px5avDJaVMLixd1f9JpdJj0JgxaA6HGUKJ10Rck8ORrY0puCvuz9rMvex8Hsg/4J7vbXu52wYZgaoTXoFpYNaqHVad6eHWqhVajenh1oi3Rkh1zHnm8noBgk2+URC9qoFCDWqfJl71k1puJskQRbAzGpDNh1BkDgp2++/KaCVG2qlWrsmrVKsLDw9FoNDRs2BCv18uzzz5LUlJSqQCU0+m8KMXYhRBCCCHONQlKXaoMYRDZHLI3FwWmqlYsMAVqFlGLNyFtBWx7U+0KuP4ZqHSNGpwyRZ3Xpp9XOpM6maLUQuluGxQeAVuyGpQzRKjHb4xQg1RXSDc/RVFweBzYXDbsLjv5znyyHdnkO/IDutxp0aq1dfRmgo3BFzQQ4FW8HMk7wt6svezN3MuerD3sy9pHcnYyLt/oiyfQaXRUC6tGzYia1AwvnmqE15AudueRV/GqoySWCDi5vMXdNrVarZq9VJTBFG4O92c5+YqD+yZfYEoIcXYiIiICHtetWxeHw0FsbCzp6ekBy9LT04mLi7uArROXE49XQaeVz08hhBCXB/klcSkzhEBkM8jWgO1IUWDqNIIssR2hY2vY8yEc+FTNmMpYA42evTQLoZ8ujTawq5+nEFw5YD+qFkY3hIG5ctGIhhFq3a7LgKIo2N12bC4bNpeNXEcuWfYs7G47he5C/8hiJp0Jk95EhCkCo854QQM4+c58dmfsZndm8bQ3c2+5mU9mvZk6EXWoFVGL2pG1qR1Rm1oRtagWVk0CGueJ2+vG6XH6g09Oj1PtEqlRCyUbtUUjz+lNxATFEGwIxqRXr6mAoJNkOAlx3i1fvpxnnnmGZcuWYbFYANi+fTsRERG0atWKGTNmoCgKGo0GRVH4999/efjhhy9yq8WlSqfV8OQX69lzPP9iN+WMdUmI5dnrGlzsZgghhLgA5NfgpU4fBOFNwOsCe6oamDodOjMkPK6O1rflFXWUvo0jIXUJNH5eLR5+pSg5sp/XCa48yNkMGr0aoLJUUTOsLrEAVaG7kAJnATaXjRxHDpn2TOyu4gCUTqPDrDdj1psJN4Vf0CCOoiik29LZkb6DnRk72Zmxk10Zuzicd7jM9Y06I7UialEvsh51o+pSN7IudSLrUCmk0gXrLvhf4va6cbgduLwu/62vgLheo8egN2DUGokMiiTUEEqQMcgfzDTqjJh0Jgw6w8U+DCH+81q0aIHJZOKFF17gscce49ChQ4wfP57777+f66+/ngkTJjBmzBj69evHF198gd1u5//+7/8udrPFJWzP8Xy2Hsm92M04Y3Vjgy92E4QQQlwgEpS6HBhCICIRMtdB4TEwx5/+PsIbQrvZsG8W7J0Jx/9Qi6k3HqEGrK40WmPxiH1eV1GAamtRdlW4OrKhKUrt5ncBgyUer4cCVwH5znzyHHlk2DPId+b7A1BatFgMFoIMQURZoi54IOd4wXG2pW1je/p2tqdvZ2f6TjLsGWWuGx8cjzXaSr2oelij1Nvq4dUl8+kc89V3crgdxUXqiyqJ6zV6jHo1oyk+JJ5QYyhmgxmTTu3C6Qs+SUBQiEtbSEgIM2fO5PXXX+d///sfwcHB9OvXj/vvvx+NRsMHH3zA6NGj+fLLL0lISGD69OkEBV0mA5gIIYQQQpyE/Hq8XBgjigJT68GReWZ1obQGqPeAWvB802jI2wUbhkHl/1O79PmKiV9ptAb1fJmi1NH8XLlqBpXWUBSgqqIGrwzh57xLo8PtIN+Zr9aAKswmw56B3WXH6XGi1Wj9GVARpogLPuJYriOXbWnb2HJ8C1vTtrI9fTvptvRS62k1WmpH1CYhOoGEmAQSohOoH1WfcHP4BW3vlc6X9eQLPLk8ah0ujVbjLx4eGRRJuCkci97iv3ZMehMmnUm62IlypaSk0L17d3777TeqVat20nXnzZvHxIkTcTgczJ8/n3r16p3RczqdTr755htuu+22M9p+4cKFTJkyhd9///2Mtr8c1a9fn48//rjMZU2bNuXrr7++wC0SQgghhDj/JCh1OTHHQURjyNqgZv4YQs9sP6H1od0nsGc67PsEUn9Ws7ASR0HMVee0yZccrb5EgMql1qDK2qTWoDJGqSMdGqOL61SdpkJ3IXmOPPKceaTb0sktzMXmtuH1etFr9VgMFqIsURh1xlPv7Bxye93sydzD5uOb2XxsM1vStnAw52Cp9XQaHXUi69AwpiENYhrQMKYh9aPrY9bLKE/nitvrptBdqAaf3A7cilsdSFKrZj2ZdCZigmMIM4X5A09mvZr9dKEDl+LKULlyZVasWEFU1Kn/MePNN99kwIAB/O9//6NSpUpn/Jw//vgj77///hkHpYQQQgghxH+DBKUuN0HVwOOA7E1qFzWd6cz2ozWA9TGIu1rNmrIdhLWPQ81+YH28uDbTlUxrAFMMmCgqkp4NGalqHS9TLFgqq8Grk5yLkkGo4wXHyXXkYnfZ8SpeTDoTwcZg4k3xF7xLW05hDpuPb2bjsY1sPLaRrce34vA4Sq1XPaw6jWMb0ziuMY1iG5EQnSABqHPE4/Xg8DhwuB1q5lPRCIR6jd4/QqKvy53FYAkIPknWkziXdDodsbGxFVo3Ly+PpKQkqlY9zfqFJ1AU5ay2F0IIIYQQ/w0SlLochdQGdx7k7YPgaqA5i+yJiETo8DnsfBcOzofkLyB9JTR9Va1D5eNyg9MFHo86uT3qfK0WdFr1Vq8Hs1G9f7kpWSTdbYPCVCg4qGajWSqpdbyMkbgUhTxnHrmOXNIK0sguzMbmsuFVvJj1ZoIMQUSaIy94DZ/UvFTWH13PhqMb2HB0A/uy95VaJ8QYQmJcIk3impAYl0ij2EZEmCMuaDuvVE6Pk0J3oRqA8jrwer3otDo1+KQz+zOfLHoLFoMFi95ywUdMFOeRooDNdmGfMyiowt2NS3bf6969O+PHj2fGjBkcOHCApk2bMm7cOKpXr05CQgIA99xzD0lJSXz66afs2rWLV199lY0bN1K5cmUGDBhA//79/fv+9ttvee+990hNTaVhw4aMGjWKvLw8RowYAUBCQgK//fYbVatWZdq0acydO5fCwkJat27NqFGjqFKlCgDHjh1j5MiRrF27ltq1a9O5c+dzfMKEEEIIIcSlSIJSlyONFsIaqMETe6qaPXU2dGZoNAxiO8GWl6HgAKwcCJX6Q1hvyMqHvAI1KKUoalDK6y1qi0YNQmm1oNOByQBhIepkNqtBKlPRdLkEq/RB6qR4UVx55GduIde5mgwPpCl6CjQGPFoTJp2ZYGMw4abwC9qtSlEUDuUe4t/Uf1mXuo71R9dzNP9oqfVqhNegWXwzmsU3IzEukdqRtaXg9VnyKl5/AKrQXejPfjJoDZj1ZiKDIokwRRBsDMaiVwvWm/VmCT5dyRQFOnaEv/++sM/boQMsX35GdfAmT57Mq6++SnR0NE8++STvvPMOEyZMYMWKFXTs2JHJkyeTlJREYWEhDzzwAH369OHVV19l3759vPjiiwQHB3PzzTezfPlyRo4cyciRI2nfvj2ffvopDz30EL/99hvPP/88H330EV999RVRUVHMmTOH77//ngkTJhATE8NHH33EoEGD+P777zEYDDz55JMEBQUxf/58du/ezciRI4mMjDwPJ04IIYQQQlxKJCh1udKZIbwRZKwFR7raDe1sRV8FjabDjvFgXwWps+HIMtDdDUHV1UCTLyvKF2BSFPAqapDKXZRNdTQdDqUCGnV9o0GdQoLVfVjMYDGB2aQGqy6xH+xOj4tcZwHZjjyO2tLJc9oodNnRex0EayHeGILeFAV6M+j0cJ4DUoqikJKbwrrUdaw9spZ1qetIs6UFrKPT6GgQ04DmlZrTolILmsU3I9IiP+jOhlfx+oNPhe5C3F43Wo3W3/WualhVwk3hBBmC/NlPBp3hYjdbXAyX2HvYqdx77720a9cOgDvuuIPPPvsMwN/FLzw8nIiICObPn090dDRPPfUUALVq1eLw4cPMnj2bm2++mXnz5tGzZ0/uuOMOAJ577jkMBgM5OTmEhoYGdBv88MMPGT16NG3btgXglVdeoWPHjixfvpzq1auzfv16li5dSpUqVahfvz5btmxh0aJFF/K0CCGEEEKIi0CCUpczY4QamMpcB+580J9ZcW7cbjiWDinHIDMblFvA0gic84CDoLwJ2jvA2L30jy+NBnRFwSeDXg04leTxqIEqpwvSMuHIMVBQ1/dlUIWGQGjQRc2ssrkKyXbkkW7PJq0wi3yXDUVRsOjNhBmDiQsqUSDY41ALpDvSQBsE5hi19pQhDDTn5k8qrSCNNUfWsObIGlYfXs2xgmMBy406I01im9CicgtaVm5JYlwiQQYZHvxMeRUvDrcDu9seEIAy681YDBYqhVQizBRGkCHIH4SSrDMBqO+By5df0t33TlSzZk3//ZCQEFwuV5nr7du3jx07dtCiRQv/PI/Hg06nBuL3799Pv379/MuMRiPDhg0rtZ+CggKOHj3KkCFD0JZ4Xy8sLOTAgQM4HA4iIiL8XfkAEhMTJSglhBBCCPEfIEGpy52lMoRZIWcraM3q6HKnIzMb9h1Ss5tMRoiJUoNLVAJPM8iZDs5tkPsJFK6D8PtBF13x/et0YNGVHaxyONUp75j62BesKplZFRqsZlRZTMVBq3MQrFIUhTxXAdmOfI7a0skqzMPmKkSn1RJiCKJSUAz68jKgdCZ1UhTw2MGeArYUtf6UKQ6MkaAPPq0fjDaXjX9T/2XV4VWsSllVqiaUXqsnMS6R1lVa06pyKxLjEjHpz7DI/X+coig4PA4K3YXYXXZcXpc/AGXWm6kcWjkwAKW3SPc7cXIaDQQHX+xWVJjBULGMPrfbTbt27Rg1alSZy/X6in3eeDxqDcJ3332X2rVrBywLDw/nn3/+KVUYvaJtFEIIIYQQlzcJSl3uNBoIqQuuXLAdVutLVeQHdKEDDh2BA4fB44VKMWqh8pJ00RA5DGy/QN48cG6B9BEQehdYOp1dlxWdDoIs6lRSmZlVCmg1YDSpwarQYAgPLRGsqlg3QK/iJddZQFZhLqkF6WQ783B4XJh0RkIMFqLN4acXfNBoiutPeT1qtlr+HnVUP0MEmGPVbDZt6eCRV/GyK2MX/6T8wz8p/7Dp2CbcXnfxrtHQIKYBbaq0IalqEs0rNZdR8c6Qy+PC7rZjd9lxep0AmHQmLAYL1cOrE2GOIMgQ5K8DJQEoIVS1a9fmt99+o1q1av7sqG+//ZbNmzfzwgsvULNmTXbs2OFf3+PxcM011/Dmm28G/B2FhYURHR1NWloaXbp0AcDpdDJ06FDuu+8+rFYrOTk5JCcn+7O4tm/ffuEOVAghhBBCXDQSlLoSaPUQ1hBceWp9KfMphv7OyIad+yAzByLDIdhS/roaLQRfB6amataUaw/kzgDHGggbBLpzXLeovMwqr1fNqvIFqw4fBTSg16mZXSYjBAepAasSNau8JgO5LhtZhbkcLjhOjqMAp9eFRW8i3BiKWW88N+3W6sAYDoQXde/LVrv36SxqvS9TNNkehZWHV/P3ob9ZeXglmfbMgF1UDa1KUtUk2lZtS5sqbQg3h5+btv2H+OpA2V127B47Xq8Xo86IWW8mLiSOKEsUwYZggo3BBBmCpAueECdx0003MWXKFEaNGsWgQYNISUlhzJgx3HvvvQDcfffdDBo0iNatW9OyZUs+/fRTFEWhcePGpKWlkZOTw4EDB6hWrRoDBw7knXfeITo6mjp16jBt2jT+/fdfxowZQ3h4OO3ateP555/nxRdfJCUlhTlz5hB8GWWfiSuHx6ug08o/TgghhBAXigSlrhSGkKL6UmvLry+lKGrm0Y79ah2pKnEV7wqnrwxRL0LBT5C/ABwbIH04hN4Bls7nv9CvVlsUbDpFzarDx1AUhVwcZOpdHNbYyNF7cJr1BJmDibSEYbKEgd5YOjPsXCnq3qd4vezM3MmKwz/w9/EdbMlMxktxFxWL3kKbqm1oV60dV1W9iurh1c9Pe65gLo8Lm8uG3W3H6XGq3fAMZoINwdSIqEGoMZRgYzDBhmApQi7EaQoJCWHGjBm8/vrr3HzzzURERNC/f38eeughANq0acPo0aOZOnUqaWlpNGnShPfffx+z2cxVV11FzZo16dWrF59//jn33XcfBQUFjBo1ivz8fJo0acLMmTMJD1eD7xMnTuTFF1+kX79+VKlShbvvvpuFCxdezMMX/1E6rYYnv1jPnuP5F7spZ6RLQizPXtfgYjdDCCGEqDAJSl1JLJUgtH7Z9aU8HrV21N6Dane36DMYrU+jhZCeYGquZku59kHuTCj8B8LuA33cOTuUCiuRWZXvKSTTlc8RZzaZjjwKnXaCvVoiCw2Ycp2gOIBMNbNKrweDQc0SCzKD0Vhcy8poOOMgW6HbwepjW1l+ZAN/pW7guD0rYHm90Eq0r9SI9lXa0KxqOwzmGHUkRXFKvlpQNpcNm8uGgoJBa1DrQIVUJiqoOAtKuuEJUaxatWrs3LkTwH/r07dvX/r27et/fOLyxo0b+0fnK8stt9zCLbfcUmp+REREqaDSkCFDGDJkSJn7iYyMZMqUKQHzHn/88XKfV4jzac/xfLYeyb3YzTgjdWMlw1AIIcTlRYJSVxpffamCFAiurgZXHE7YdQAOpEB0ROk6TqfLUA2iRoNtEeQtUAuhZ4yAkP9B0LXnbAS6inB4XWS48jnqzCbNnYfN48SsMRBuDCbeHFHGFgq4PeByq+elwKYG7NCodasMejVAZTGp58loULsG6vXqfb2uVMAq3Z7N8iPr+fPIv6w+thWHp3gkK7POSFJ8EzpWaUb7ys2oFBStdu9z50P+brVIuuncj953JfAqXrUbXtGIeAoKZr2ZIEMQVUOrEmYOI8QYQogxBP3pFvgXQgghhBBCCHHRyS+5K42/vlS+WtOIcNi2Bw4fg/gYNbByLmi0EHwDmFqp2VLO7ZA3F+wrIGwgGK1ntt8CO6RlwPFMSM+E3HzIK1Cn/AIodKK4XLidDtxOB06vGzMKtbQa6mp1aAwGvCYDXqMBr9mI12zEHWLGE2LBE2zBHWrBFRGMOzwEV0QISliJbo5er9oN0O2GLIfaHRAFf+0qfVGGldnEAXJZlrOdZRlb2ZJ7IOAQKgfF0KlKCzpVaU7LuAaYdCfUrQoYvc9WYvS+kKLR+yLU7pf/sXpHbq8bu8uOzWXD6XGi0WiwGCyEm8KpE1mHUFMoIcYQgg3BkgUlhBBCCHEBxIaYrohaa1fCMQhxpZKg1JXIV1/qyN+wKxnS8qBy7PmpoaSPh8jhYP9THaHPfQgyXwVLFwi9DbShpbdRFDXgsycZko9A8mE4eARSjqqZS6egAQxF01nmfOEJMuGMCsMVHYYzJhxnTBjO2AiccRE44iNxRoeDXovX5WJbfgpL07ezLH83ye7AIuWNjZW4OtRK54gG1I2ogcZiBp0ecuxgdBV1F9Sr3Q39B6IBfbA6eT3gKYD8vWpgUR+mFqw3hIMu6PzX7LoI3F43Bc4C7G47Lo8LrVZLkD6IuJA4oi3R/iwoi+FsX2UhhBBCCHEmwiz6y77WWr24EN7t1+JiN0MIUQ4JSl2xwiDZCwf3Qe2E81fUG9SMnqAuYG4FeV+oASr7MihcC6H/A0Mn2HEAtuyC7Xthx151BMDyhARBbDTERuIND8UebCTXoiHbosFuAL3JjNloQWs0omg1aLwKKAoaRUHj8qB1uIomJzq7E12BHV1+Ifp8G/o8O/rsAgzZ+WhdbnQ2BxZbGpaUtFLNcGvhj1rwVQsj39XzcMTi8S8zoCUppA5XRzTk6tD6xGqC1G6BbrcacPN4wVfUXKstCkrpwGiCkKJugQZDUX2roiwsQ4jahc/rUjPdcjNAa1IDU/4A1eVbf8rlcVHgKsDusuPyujBoDVgMFqqEViHKEuXPhDKemFkmhBBCCCEuqsu51poQ4tImQakrkc0GmzZBFlC7CbiOga7SBRghLxTCHwDL1bB9Bqw/Bps/gR2zoVA5YV0t1KoKtapBzSpQoyrUqKx2MSwqWp7myuVgYSbZ7gI0Gg0RuiCCdSZcgKvMBpwGRUFrc2DIysOYkYsxPRdDRg5kZLOSVH6IzOSH6g7SgwGcAIQ4oOcu6LMDrt/jJdS1D0d8JoVVd1JYNYbCarEUVlNvPVFBxc/l9ao1rNxuNRMsO0fNFlNQ61jpdWoGlU6n1q+ymIrqWBlB5wFSQX8YTCFgilYnQ+glH6DyBaFsLhturxuD1kCQMYjq4dWJskT5M6FkVDwhhBBCCCGE+G+SoNSVxm5XA1KpqVC9OiguyCkEZ6YazDhfFAV2H4Dla2HFWjh4rORCCAMaR0BiB2jUAurXUkcBLMGreMlw5ZNacJxUZzYFHgchWjOVjOHoNTrOKY0Gb7AZR7CZ/CqRrMrby6/Z+/kjdwd5nkL/ahFaC92oxo3ZMXRN0RN+LAuTPQOzJgON14k5NRNzaias3RWwe1dECPbqsRRWj1Nva8Rhrx6HJ/zE7oxFhdd9U74NsnPVQJYGQFMUsNKCJh10e8GgheAwCI6GoFiwRII5RM280pUuxH6hnBiEMuqMBBmCqBlRk0hzpD8TSoqSCyGEEEIIIYQACUpdWRwO2LIFjhyBatWK6hfpIKQO5G4HV56aYXMupRyFX1bAr3/B0fTi+XodNGsILetDw1SIXQXabOAnsOSBIQZQg1IOr4vjrlwOOTJId+WhoCFCZyHGdI7bWoLL62ZV3l5+yd7KHzk7yPcWB6Ki9SF0DW9It4hGtAyp5Q+IZRZNACgKhqw8TIfTMR/OwJyShuVQGubD6RjTczBk52PIzids8/7A540IwV4jTp1qxqvBqhpxeC2BAbpiCri96giBbp0auLK7IeM4eA6qq+iNYIoAcwSYwyA4FCwWMJnAYFS7ChqN6v1z2I3zZEEoXyZUqDEUnfYcBxSFEEIIIYQQQlwRJCh1pXA61YDUoUNQtWpgQW1TpBqYytkBWsPZd/uy2WHpSli8HLbuLp5vNkJSM+jURr0NKdGFzX0z5M0Hx7qimlP/4LB0JUV3FQdcTnI9NswaI3H6MAznKZPGrXhYnbePX7K3sCxne0BGVIw+lG4RjegR0ZhmwTXQnWrkO40GV1QYrqgw8hPrBCzS2h2YU9IxHzqO5eBxLIeOYz6Uhul4dnGwatO+gG0ccRH+QJW9RjyFNeMorBqD4q85VdS1zy+y6NYLrkJw5ENhLtj1kG0GjQU0ZnWUPw1F9asMalDKEgRBlqIgVVG3QX1REXadvvj+CRlXvsLkNpfNXxMqyBhEjfAa/ppQEoQSQgghhBCXkitlBEGQUQTFlUmCUlcClwu2boUDB9SAVFnZMOZ48BRC/n4wR4PmDOr4HDgM3/2qZkbZigI6Wg20ToRrO0H7licETkrQV4XIp/A6duHJm4vBvQeTfQk1+R29riWZxk54tWGn36ZTcCse1uUf4JesLSzN2UaOx+5f5gtEXVMUiNKeKhBVQV6LCVv9qtjqVw2Yr7U7MB9KUwNVB49jPngMy8HjGDPzMB3PxnQ8m4gS3QAVnZbCKtFFgarigJUzPkKtyaXuFQxB6oQCXof6OntzQVdYNLpfKHh1oGjU4KXNBsfc/jrsgBq40hUFo4oCVW69jgK9gk3vxaUDg8FMkDmU6iFxRIXHEhocSUhQBHqpCSWEEEIIIS5RV8IIgiCjCIorlwSlLnduN2zbBvv3Q5UqajZMWTQaCKoG7kIoTFVHc6tInSavF1ZugAWLYMP24vnVKsENXaB7e4iJLG9rP5fXTZorj4MOLWn0JEZ3gCbeVYQqqdT0rKa6/V/S9K05ZmiPSxtRkSMvv8mKl40FB1mSvYXfsreS6S7wL4vWhxQFoppULCPqHPJaTNis1bBZqwXM1+XZ1EBV8nEsB49hST6GOfk4elshlkNqt8CSPCaDv9ufOqmZVa7IUNCa1QlFHcXPnQeOLNDqQWdRR/CzBIEuXJ3no3hxu13YnAXYXHk4C53ovRCk6KmmCSJKF0KoVkuoXkFvyAZdnhr81OvVroJmMwQFqd0EjUb1Oix5XyfZU0KI82vhwoVMmTKF33///WI3RQghxCVIRhAU4tIkQanLmccD27fD3r1QqZIaADgZrR5Ca4PihMJ0MMeVXxTb6YRf/oavfoaDR4q210C7ltC7B7RoVCJbp3yFXifHnLkcKEwny52PQaMnxhCGUduSXUoLwjx7qOxaSog3hXj3SuLcq8nSNeaYoQM2XZUKnwpFUdhmP8KSrM38kr2F467iD5xwnYVuEY25NqIJLUNqXdBAVEV4QoPIb1yL/Ma1imcqCoaMXCzJapDKn111KA2dw0Xw7sME7z4csB93iMUfqPIVVi+sEYc7PFwteO9xgK1oG50Zt96CXWugAA1OdOi0OoJNFioHVyfaGEGoPohQfXBgd0pFKapv5S6esrOL7ytF6VcaTXHQyhegCg4uDmCVDFyV7Fp4kYq0CyGEEEIIIYS48CQodbnyeGDHDtizB+Lj1aLWFaEzqfWlvE5wpKsZUyUV2NUuegsWQ1aOOi/YAj27qcGo+JgKPU2+p5BURzYHHRnkuG0E60xUMUYGBoQ0GnL19cnV1SPUu5dKzhWEefcR5dlMlGczedpaHDdcRbYuodysrj32YyzO3sySrM0cdmb55wdrTXQNb8g1kU1oG1r33I/ed75pNLhiwnHFhJPbylo83+PBdDSrOFBVdGtKzUCfbyd0WzKh25IDduUKD6aweiy2arHkVIsko2oYWZWDcYXoCNboqGQMI8YUS4gpnlBTNEZjBOjLqTtWMth0Moqidit1u9Vbmw1yc9X7Sol+g75glK+ulcmkBq5OzLo6cZLglRBCCCGEEEJc9iQodTnyemHXLnWKi1MzT06HIQRC60PuTnBkgCkacvNh4WL4egnk29T14qKh73VqN71gS4V2ne0u4LAjixRnJjaPkzCdhRqmaDQnCyJoNOTp6pFnqYfFk0q86y+iPFsI9R4g1HEApyaMdH0r0vWtcGnDOOjIYEnWZpZkb2Ff4XH/bsxaA53CErguMpF2ofUwaa/AWkc6HY6qMTiqxpDdvrF/tsbpwpySrhZV99esOo7peBaGnAIMOQWEbjlAfIldecOCoWZVtDUqQbVYqBoF1eKKsu5CwBgFumDQW9Rg5unQaIqDSuVRFDVoVTLzKjcXMjNLZ13pShRk1+vV/ZrNahDLZCqerzuhcLtvfkUCaUKcIUVRsLlsF/Q5gwxBJ39fLSElJYXu3bvzxBNPMGvWLHr16kW7du2YOHEihw8fpn79+jz33HMkJSUB4Ha7mTRpEgsXLsRut9OhQwdefvllIiMjcTgcTJo0iR9++IGcnByuuuoqRo8eTeXKlRkyZAhGo5Fx48b5n/vpp5/GbDYzZswYUlNTefnll/nnn3+Ijo6mb9++PPLII+h0OhYuXMiXX35JdHQ0K1euZPTo0fTq1Ytp06Yxd+5cCgsLad26NaNGjaJKFTWL9tixY4wcOZK1a9dSu3ZtOnfufO5PtBBCCCGEOK/kV9rlxheQ2rkTYmJOPyDlYwyHUCscWgNfzYIf/wJ7UfHyGlXgjl7Q7aoK/ZBXFIVMdz6HHJkccWbj9LqI1AcTYwo97WbZdZU5oLuFw95riHWvJsb1L0YlF7dtKYvzl/F5vokNhcWj5hk0OjqEWbk2ogmdwhKw6E7RhfGiUsDtVYMwJbOFNJqiEfL0cIZdCxWjgbzacRyrEYGtXS2cihstGkJdGiodtRF7JJeQlAxMB4+hTU6Fo2locwtg8y51KslsKgpSxapBqppVoVZtqJ0AQZFqbaryMqlOh0ZTnPl0KiW7C7rdUFCgBrA8ntLn07dvnU7tYloyOGU2q5lYFkvZmVi+7oaSiSUqSFEUOn7ckb8P/X1Bn7dD9Q4sv3d5hQNTAP/++y8LFizAZrNx55138vLLL9O0aVP++OMPHnjgAb777jtq1qzJu+++yzfffMPrr79OlSpVGD16NKNHj2bSpEmMHj2af//9l3HjxhEREcFbb73Fo48+yoIFC7jxxht5/vnncblcGAwGnE4nS5cuZcqUKSiKwuOPP06DBg34+uuvSUtLY9SoUWg0Gh577DEA1q9fz8MPP8zQoUOJjIxkzpw5fP/990yYMIGYmBg++ugjBg0axPfff4/BYODJJ58kKCiI+fPns3v3bkaOHElk5KlrHAohhBBCiEuHBKUuJx6PGozauROio9UuTmcqIwPmzIH5X0KhQ51Xtwb07w0dW4Pu1MERr+ItKl6ezjFnLh4UonRBBBnOfhQ9lzacTZqr+K0gmN+y1/CvLQN1uLhCdED3IC03hteibWQn9IY6FzGIoIDLrU5OV4kMn6L2aIqCJUpR4MkXKDlxKFePt3jbgHpMRVMZ9btcXjc2rxO714lT8aDTaAjWmqlsDCfKEEKozkyozoKxsh5OHKij0AGHUtV6YQePQPJhSD4Ch4+py/akqFNJGg3ERkDVeDVwWbO2OtWqC1VqguE8BgRPN9PJV/uq5ORygd0OaWmBgSzf+T5Z8OrE+ldGo9TAEn4aLo/r4J577qFGjRo8++yz3HbbbfTq1QuAAQMGsGbNGubOncuwYcP48ssvGTZsGFdffTUAL7/8Mj///DM5OTl8++23zJgxg6uuugqAt956iy5duvDXX39x9dVX4/V6WbVqFR07dmTFihWYzWbatm3LypUrOXLkCPPnz0er1VKnTh2GDRvGiBEj/EEpjUbDI488grnoH1s+/PBDRo8eTdu2bQF45ZVX6NixI8uXL6d69eqsX7+epUuXUqVKFerXr8+WLVtYtGjRhT6tQgghhBDiLEhQ6nLhdqtFzffsgdhY9cfymUhPh08/ha++AkdRMKpBfejXFZIagynq1E1RPBx35pLsSCfNmYcGiDaEnJPucpmufH7P2cYv2Vv4Nz8ZBTVwoEFDq+DK3BJq4a6go1TVFQD7wLUPhzuCLH0TsnRNsGkrn79AgeIFZ1GNJKdLDWxoNKDXqYGKsBAINqsBDa22OOihKEUBjxKTVhe43KuoxeUdTnXf+QVgd6gBorx8UMChuLArbmxaLx4N6I1GLMYgqlqiiNQHE6ozE6IzY9RW4M/abIL6tdSpJLcbUtOKg1WHUounvAI4nqVO63cEbqfXQeU4qF4NqteA6jXVgFX16mp3wAvdda6ita+g7G6EOTlq4LZkN0JQX0dfRpVv5MGSNbBKBq1KZl+JK5ZGo2H5vcsv6e57PlWrVgVg7969/Pzzz8ybN8+/zOVy0bFjR7KyssjOzqZx4+LuwfXq1WPw4MFs3LgRr9dLs2bN/MsiIiKoXbs2e/fupVOnTvTo0YMlS5bQsWNHlixZwnXXXYdOp2Pv3r1kZ2fTqlUr/7Zer5fCwkKystR6gNHR0f6AVEFBAUePHmXIkCFoSwTlCwsLOXDgAA6Hg4iICH9XPoDExEQJSgkhhLhixYaY8HgVdCf+4/Zl5ko4BnFuya+ly4HLBVu3wr59alHzM+myl5YGs2fDwoXFwajGjeGBB6BDB3BkQv4eKEwDU0yZgR2n180xVw4HCtPJcOVh0OiJM4QGjs52BjJd+SzN2c5v2VtZm78fL8VBgMSgalwbmUj38MbEGdUMrKOKB5tnH9HuDYR7dmJSsqnkWkEl1wocmgiydQ3I0SeQp61VboH0UyoZgHI41W6TvkCH2QjhoRASBKaiLmBmoxqwOBuW4rpNiqJgd9mx2fIptBeguFwYFQ1BHi21vEYiXDpC3VpC3Fr02R7QuEFfCAZPUZsMaobV6dLroXpldepQ/OMRRYGcPEg5qk6+QNXho2p2lctdPI81gfvU6aBSPFSrBtWqq7dVqxZPISFndr7OlbPpRpidXX7mVcn6V0FBpUceLBm8Mhol6+oyptFoCDYGX+xmnJKpaEAMj8fDAw88wM033xyw3Gw2oz9JENVUzoAaHo8Hr9cLwA033MCIESN44YUX+P3335k6dSqg1qmqU6cO06ZNK7V9aGhoqf17PB4A3n33XWrXrh2wfnh4OP/88w/KCd12DRX5GxZCCCEuU2EWPTqthie/WM+e4/kXuzlnpF5cCO/2O7ELh/ivk6DUpa6wUM2Q2rcPKleu+Ch7PkePqsGob75RM3EAEhPh/vuhffviH8LmaNAZIG8PFB4Hc4w/oGP3OP3BqEx3PkFaI5WNEWc1ol26K49lOdv5NXsr/+YfCAhENbJU4ZrIJvSIaEJlY0TpjTU6ddQ+fX00ipNwz24i3Vv9Aap490ri3StxYyZXV69oqotLG152Y8oLQBkMaoAnIqw4AGUygclQZne6s+H2urG7HdjcDpweJxo0mPUmgkPCqBlTgzBjCCFGC8F6Czpt0Xl3utRMKt+UV6AWrC90QIFNPR5Qg0JGQ/F0JjWTNBr1PESEQRNr4DKvF9Iy1WDVkWPq7eFUNViVmq4GrA4fUadVq0vvOyxMDU5VrqzeVqmi3vdNZ9NN9Vyr6MiDvqCVy6X+Defnq/eLfrgD6jV0sqyrsoJXknUlzoHatWuTkpJCzZo1/fPGjx9P7dq1ufXWW4mMjGTHjh0kJCQAsH37dh566CF++ukn9Ho9GzZsoFOnTgBkZWWRnJzsDxy1b98ej8fDxx9/jNlspnXr1v7nPHLkCFFRUf4g1F9//cXChQsZP358qTaGhYURHR1NWloaXbp0AcDpdDJ06FDuu+8+rFYrOTk5JCcn+49j+/bt5+eECSGEEJeQPcfz2Xok92I3Q4hzRn7hXMpyctQMqWPH1B/qJxvJ7ET798Mnn8DPP6tZHADNmqmZUW3blh2UMIRBWAPI3weFx8jXh5DqsnHQkUGO20aIzkx1YxTaMyzGfcSRxdKc7SzN2cbGgkP+rnkADS1V6B7RiB4RTahWgS6EPorGSLa+Mdn6xmgVJ6GevUR4dhLu3omBAqI8W4jybAHAroklV1eHfG918jxV8Dj1xV3wygpAmU3q7TnOYPEqXgrdTuweB3a3A6/ixaDVY9GbiLNEEmUOI8QQRIghCIveVH4XHV+QKeyETCNHUTdAe6F6W2CD3AI1WJWTpwaJKKp75atbZTQW16863ePVaiE+Rp1aNTnhYL2QkV2UUZUKR45C6nE4mg5HMyCnqGB5bq4afC1LeLjaBdAXpIqPVx/7pqiocx4kPCslM69O1s3W113Q5QrsMugLJvr2VTLrymCA4ODirKsTuwr6prPN2hNXtIEDB9K/f38SExPp0qULv//+O7NmzeKTTz4B4O677+bdd98lPj6e6OhoxowZQ/PmzQkJCeHWW2/l1Vdf5dVXXyU8PJy33nqLSpUq0aFDBwD0ej3XXnst77//Prfeeqv//atjx45UrVqVZ599liFDhpCXl8eLL75I+/bt0ZVzvQ4cOJB33nmH6Ohof5bVv//+y5gxYwgPD6ddu3Y8//zzvPjii6SkpDBnzhyCgy/9jDUhhBBCCFFMglKXqtRU2LZNzbCoWrViPzIVBTZtUguYL1tW3J0oKQnuvRdatz5lwEHRWcg2V+KI/RgpGespQEO4KZoapujTrl+iKAq77Ef5I3cHf+TsYKc9NWB546CqdI9oTPfwxlQ1nf2ISV6NkRx9Q3L0DcHoJdhziDDnLsK8ewkmFQtpWNxpxLMKNGC3xJNnqU9+cD0KQhNwhlQ+5z/mFUXB4XFRWBSAcns9aACz3kSQwUy14DjCTMEE6y2EGIIw6M7Bn6TJqE4nBqtc7uKAlS+7qsAG+TZ1ns1WHLBSStTKMuqLs3QqUAA/gFYLsVHq1LxR6eUFuUWBqmPqdDSzuG7V8UzIt6vBmpwctcB/WfR6iItTp/j44vu+KTZWHanyUssy0unU6WTZjydmXTkc6siDJ2Zd+QJhJbsM+jKvfMGrEyffutJt8D+nefPmjB8/nsmTJzN+/Hhq1KjBhAkTaNOmDQAPPvggeXl5PPXUU7jdbrp06cKLL74IwLBhwxg3bhxPPPEETqeT9u3bM2vWLIwl/tHkxhtvZN68edx4443+eTqdjvfee49XX32V2267jaCgIK6//nqGDRtWbjvvu+8+CgoKGDVqFPn5+TRp0oSZM2cSHq5mvU6cOJEXX3yRfv36UaVKFe6++24WLlx4Pk6ZEEIIIYQ4Ty6xX2kCjwcOHIAdO9QfjNWqnXobtxt+/RXmzlUzq3y6dIGBA6FJk/K2LH5axUOGM4dD9mMcdWTgQkNkSF1iXFngKQS9GSowwpTD6+Lf/AOsyN3Fnzk7SXVl+5dp0dAipCZdwxvRJbwhlYzldKc7Ex5P0Qh4RYXCFYUCTSgFxqtINV6NLlhDqD6FUPdeQh17sTiOYPEew1JwjLiCFXAcXPpw8oPrYQuugy2oFgVBtfHoQyvcBDUA5aTQ46TQ7cDldaPRaDBqDVj0JqqHxBNhCiXIYCZYbzl5FtT54MuECjmhO5yiqOfMV2TdX2zdphZcd7rAnq9eZ94SdZMM+qLAVYksntMNWgWHQf0wqH9Cl0DFDV4n5ObAsXQ4mgZp2WqwKj1HnY5nQWa22q4jR9SpPBqNmlEVG1scpIqJCbwfHa1Ol1LwqqJZV15vYK0rm03NPnO7SwevSmZe+UYa9I026BtVsGTgSgJYl71q1aqx84Sg7o033hgQNCrJYDAwfPhwhg8fXmqZxWLhpZde4qWXXir3+dq2bVvq+QCqV6/O9OnTy9ymb9++9O3bN2CeTqdjyJAhDBkypMxtIiMjmTJlSsC8xx9/vNx2CSGEEEKIS88l9OtLkJMD/8/eecdJUaT//9Nh4s5GdlmSgAqLZFZwEQER9JTzMOuZTkVU9ETxDCfinYoRxZ96KqCiYsJ0iHqmr4E7s2BAARNKEpBd2Jwm9XR3/f6o7p6e2dkEuzs7u8+bV1HVVdXd1T29Pd2feZ6nfv0V+P13IDsbSG9GENmzB3jzTR4vau9eXud0AtOnA+eeCxx8cLO7DGlhlClV2BXcizKl2phJLxNuybDe0HKAYAkPhC55AdmDeHFqj1KNNXVb8FnNr/iqfhuCumK1uQQHxqcfjCmZh2ByRgFyHPsb1JoZ4lOE5xGVV0siF0fcLqBnD8DjNlzweGweTRBQjSGoxtEAADlSA1/9Lzz5t8Ab2AGHWoPsmnXIrlln7S3s7IGgZwACnv4Ieg5A0HMAwq58aIyfu7CmIKxFLAsop+SAW3Khjy8PWc50pDk88MpueB3ufXZ7bHcEIWpdlQjzfCsKEI4YcZLCgD8IBIK8PRSIzmDHN2rMNCg3zFsiXAkyIMlAthfI7g0cYtQz3RCsVIBFgEgYKK8CyquNVAtUmKnGqKsy3AgreNq0qYn9CkBWFhewTJGqRw++HJ+ys1sWHL0jEMVoHKqmsFte2WNemcv2wNHxApYkcfHK5Yrm8aJVfCIRiyAIgiAIgiCIJiBRqjOgqsDOncDmzfwFsXfvxl92VRX49FMuRK1ZE7WC6NEDOP104LTT+AtzEzDGUKPWY2+4Ar+HSlEb8cMtOpHvzGk4k57kBdIGArKPz8wXrkBAcOC70F6srd2CNXVb8Fu4PGaVXDkdkzIKMCmzAIenHwy32IpYWNFRGi/NajQIuREGibsnyUBWOpDmjcZ+MmedawGqIxPV2UWozi4CAAi6Am/gN6T5tyAtsB3ewG9wh/fApVTApVQgq+Zba11NkFHvyEPA3Rthd18IaQfCkXEQHL4D4XFlwyO7osHIuwKmhVVaI5Y6lkgYseUqF6yCIS5gKQoQ0KKWOwL45ymKUcFDtiXTtS0eQQQEJ2BeUw4A/XsC/Y12phmClS2pClDt5yJVZS1QWQdU1Rt5rVFXC1RVA5oOVFXxtHVr8+fG5+N/b1lZXKTKzublxlJaWnKFmtbMNGhaX2laNP5VTU20jrFYEUuyfW7mZ2q6ETqdXMiyPusEApZZT0IWQRAEQRAEQXQbSJRKJowB5eV8Zr3du3lA50TueqoKfPMN8MEHwIcfcrcck7FjgZNPBo4+ulkriZAWRoVSg+JQGcqUKoT1CDLkNPRz92zSiieka/ghVIlvqn7E19U/4Af/TmiIugSJEDDC2w9HZAzGpIwCDPH0brlbmvnia1o9qSoXK8AMtzAHF558RmwclxHY2dW2wZxVyCh398duR28o6YdD0VWIqh/ZSjFylFJkKXuQoZTAG9oNSVeQqZQgUykBar+N3ZArF/D2B7x9AU9fwNsH8PQBPL15237MWNhpMUUrNCJaMZt1myU0GmVFAYJhnkzXQb8WFUJMqzwBXMCS4kQrSYwuCwI/v5IEwBanyQnAC6CPMRZogK4ZopWR6xqgqUBdgAtU1fVG8gNVdbxcUw9U1xnLtfzara/naefOlp0rSeLiVGYmTxkZPNnL8cnn41aTHR283LS+aimmcKXZPr+6OqC6mpd1vaElliTZPldDmDKDt7tcUesve3t8bi8TBEEQBEEQnZY8nwuaziCJqf8jZFc5js4AiVLJgDHuRrRjRzQOTt++sbFs6uuBL7/k1lAff8wtN0x69ABmzABOOgno3x9NoeoqqiJ1KFOqUBIuR23ED6cgI8uRHnXRi6MqUovva7fgu9pfsKH2V/xUvx0q02L69HZm4fC0/jjc2w9FGYOQ7soB0IiwZcW70WJzQYy1fMrwAWluwGWIT+aMeHLbvGxquo6IrkLRI1C0CCK6ClVXAQgQBQEuyQmn6ECuJwuZTh+8Djfc0hFwS86o9RPTuDtj/XbAv93IdwD+nUCkGgiX81T1bcMBCBLgzgfcvQB3T55cPQF3HuDK46KVqwfQyOeSsghCdKbAptD1WHHSLJvxwsKGeBVWooKWpkevL8aiFliCEBU7JDG2LEmAKAGSo+GsfWkAehllptmSHlvWFKA+FBWrauqB2nqgJshnOqzxA3V+Xq718/awwo/FdCNsLWlpXJwykylW+XwNy2lpseW0NMDrbV8rpMas2xqDsVgBy/wcFYUL7/bPFeBjZyz62ZpWdtZnK0WtwExBy5yJsKWphbM4Mru4RnQZ6HMlCIIgiPYlwyNDEgVc9dJ32FJan+zh7DODevrw4FmFyR5Gl4FEqY5EVYHKSmDXLi5GMcYDLLtc3G1v/XqevvoK2LDBFp8H3Lpi2jTg2GOBwsImX/5UXUW1Wo8KpRoloXLUqPwPPl1qaBVVrwbwi38Hfq7/DT/WbcWPdVtRHOeOBwC5ziyMyxyKcZnDcFjWMPR19wS0IBCpBYLlQH254dLjBJjExQJm+NuZwpND5rGePG7Aa05n74jO8Laflg4646JTRNeg6qolPOlMhyk8OUQZTsmBdIcX6U4vfA4vXJITbtkJt+SCW3Y2HftJkABvP54wObYtUsvFqcDvQHA3ENgNBIt5Hi41BK1inppCTufilDPHyLN52ZnFk8PMMwFnZtSVLdURxaZjW9nRdS5umqKVJXaarmaaEQPLiIOlGIJQWI0KWaYQYoodgGFtZRO0RDEqaokOfq4lEXCKQK4I9LRdK0wHn7lQNUQsI8HIQyGgto4LVrV+Hki+zpbqg0YKAHVGXh/k7o8An3XP7+ex5Pb1/Ho8UYEqzWvkaYDXJlyZyezr8UTr3O5om8ezf0Hh7TGrWoMpZuk2QdLMw+Fom+liaK5j368oxn3GUtRyyxS27EHfDUssBwCoKgJ1dfDYXQ2by4mUIBAIAOCB3onWQ78YEwRBEC1lS2k9fiyubb4j0S3oUFEqHA7j1ltvxfvvvw+3241Zs2Zh1qxZHTmEjocxHoelvJy76JmuLLW13FLql1+AjRt58GW7CAVwK6iJE3kaN67Jl7eAFkJNpB6VkRqUhqtQp/qhMx0+2YterlzoTMfO4B58Vf0jtgV2Y0tgF36t34Hd4bKE2xvo6YMxGYMxxjcYY9IORl85G4Jp0VCvATV7o52FHEBIAyQ/gBDg0HmAcW8G4PIALu9+C0+arkNlKlRdMyycuPCkMi1GT3CIspVyPVlIkz3wOtxwSU64JIeVy/Gxs9oCRwaQNYKneJjGLaiCJUBwDxepQmVAqJQn08KKRQC1jif/by3br+Th+3ZkcEHLkW6UfTw5fNGynGbLvTxmmOQB2uN8tCeiIQy1MIYYgKhYpdqsczRD3NIMsUrTjRkc1WiMLFP0UIzZB3UtNocACAzcUpAZsbJswoeVfEBmBpAlcGHLEkcEw2rQtPTSjO0YYpYS4WJUvT86I2JdAPAHeLD5+iAQCEXzQAgIhHlMr0AY8IeiAo4pbLUVsjG5gMecwc8IgO7xGLHebHXmLH8xwdLN5Ir2d9n6mP0czqgVkylm7SumqBUvbmlaNPC7WWcmYz0JQFZGBkpVFQiF4HXZZtBsSpAyxU57OZFg1dw2Gqsj9gvGGAKBAEpLS5GVlQWJXEH3ia7wy/dRQ/Lw9+MOab4jQRAEQRBtRoe+iS5atAg//PADnnnmGRQXF2PevHno06cPpk+f3pHDaH/CYW4RtWUL8OOPXHjauRMoKwNKSoDffuMvuvHk5QFjxnBLqCOOSBxfCsYDtBZCvRZAXSSAcqUa1YaLXkWkFnWqHxVKNYrD5dgdKsWu4F7sDpVCR2LXhN6OHAxx9cVw9wEY7joAw1z94JM8/AVZkgFd4i9msswtKjzGi6Mjbup42QGIOqDWA0oNEKkEtBCgGy/BmguAExBcYIIIjelQdQ0a06AxHZquQWWaldtdKURBhCxKcIgyZEFGhtMNr+yGR3ZxtzvJAadhBeWSnHCIcsvjWnUElutePpDdSB/GuBgVLuezHSoVQLiK50oVoFRzF0Glmi9H6gDo3GJNCwKhvY1suAWILkOk8sQld4LcxXPRxcui27AgcvE60QlIzmg5JjmSJ4CZLlqt9Y603MwM0cpuiaPb2sw6Tecil2JzP1QiUSFE1YAIM6wJdS5ixVj1WDu2WXHJgCsL8GQDPQ0xSxAMQSt+WYzWC+D7UI3A8yFDuPIHgUAgKmIFjfpQmAenDxp5IAyEFF4fMtwnQ4bbJMC3W69yoaw9EQQuapvWlTHJye9FMbmt3SEDTpdxr5K5wBXT7uD3LqdxHzPrzHqHzVrK4UAvrRoIAKVM4+c6eqHEfX5mdYL7rulqGj3A6HoNbltCw74x/cx2IXFdzGpNLAONCGUt7NcFyMrKQq9evZrvSDRKqv/yfXBeWrKHQBAEQaQAXSU2Vmc5hg57OwwEAli5ciUef/xxDB8+HMOHD8fmzZvx/PPPp4YoparRoL1VVVx0qqoCSkuB33/nYlNJCXetKSnhMWPMX9gTkZ4ODB4MDBkCDB3KxajevWMe9hU9gvJwNfYoFSgJlvEUKkNxuAyl4UqUK9WoitSjWq1DuVqDoK40eQg+0Y2DXL1woKsXDvL0wRBvfxT4+iPDk81fxEyhSZa5GJVodizb+HSmQzfEJJ3p0FgYuqpDZxI0KRO64IWm+qFFAtDVeiBcB+hhgEUg6DokUYQkOSEJDkiyE07JjXSHFx5HGtwyF5rslk8OUYZD4nmTLnapiiBELZ58BzXfn+mA6gciNUaq4y6EkVpeVuvjkr9hYoY4qocBJQygqsldtg2mK5zDJlQ5AMHMZS5c2cuCIWYJsq1O5mKfaOTWshTtJ0iGFZJZZ5btSYzLjTLEuDajDDMwt2DrYyQ4owHXIcRuBwIXsZhgCFJCVNzR9aiwZZaZblhkGbkZRFw1RC+71ZcZRFzTjdkHDYst3XAp1BmgA3C4gXQ3kJ5pCF4wrLxMUcSmjAiIteqxJ03j4pSiRAPUh41l010ybEtKhF9fIbNsb4/ErWdYqCmRWPe7cISnJCMA6A2gp9eLSM+egMMWdN+MWSZJgCTE1Yv82pQNl0E5PtaZzZ1QMKzpLIu7OOFRNK4ny9JOQIxIKcSJk/Z+dis9M5eNa1SMG4c1brPOOCZBBBwi/9uLWVfgx2jtS44dszku69oyxmWKXlYfu2UZbH9jxrbMBnt/TxaQceA+f64Oh4MspAiCIAiCaBFdITZWZ4qL1WGi1KZNm6CqKgoLowc+duxYPProo9B1HWILA8y2G34/gldfiZqdv0IPBqCFAtBDQeh+P7RAPTQlDF0ANBFQjaQJ0XJEMnIRiOQCSj6gOAQomekI52QilJ2OcJYPoXQPgple+J0iAnoIfn0n6rVfULfpBdT9FIRfD6NOD6JWCyDEWv8CluPIQL67B/p4e6FvWm/0S+uNPr7eOCDzAOR4cgBJgi6JYKIAXRSgCwKqwAUmBmYJTYxFoLMwdE0HNABhwAoiDV4WBAESJIiiCEmQIAoiREGEJErwODxwiA44JSdcsgsOkbvMyUyDDBWSrvCyFoQDCmQtDAm6MROaauxIM1KEx6nSJZ7Hiwjmy78lACRf7e0QBNFw1UsHkNiqrll0BVADPGmBqNWVGuBWblrQlgcBLcwFLC1kWMEp0WU9DOgRo49Rrys8KDjsAq1utIXb4CR0Fczr1vbyDcFWbwpciApcVp8EZfNvQBKi2+cr23IhQR2Mv3Hb35C5zGzLksCDwnvtfUyEJpbj/jaZCG665oprM9ZRGaAwnkeMsqIb9ToQARDRARVGvW05wriAFzHWVW3Lqi2Z/TRbWWV82epjtNuOSwoEIP32Gwjwy8sSn2BcuqYgZpaNPoJt2erfSLvVJ65diFtPFoBT/wT887mOP3aCIAiCILotqW4h3FnoMFGqrKwM2dnZcNqmGM/NzUU4HEZ1dTVycnI6aigJ2fr1+yjMfQp1fdtyqwxArZFs1LRuKx7JjQxnOtKd6Uh3+pDhykC2JxtZrmxke7OR5clBrjcXPbw94JbdfCUBECFCgABB4KleECEIQky9JSQJEmRRhiRKcIgOnhuxl+IFJ3PZLNvrZMOKqdWuc7pmCBkKFzZYhOdm0sKAbggfTOP9ETECSRuuT1ZQafsbsV1JM6sENHSVsb2kCwJiXpCtY0n0Ih+/jbi2mPUTtDVKE/1adG5bsR9BjlpotQWJdq2rcZ+pEldnK5vCJFONZc3WrhmBxM0+WlxSo9dHTJ05e54eF4jcED7tgcnN9RBXZ8V5iiszFu1r1jfiKts4hhsftNavmso0dZkKAJxG6gwYHzM0cNHLzBOV7SlRXVPJvg/dthyf2/s31i++Pj4lqmcJlptDN06QlsSL1/Em8M/k7b4j6JZxOQmCIAiC6PJ0mCgVDAZjBCkA1rKiNO121hG4Csch/Ysc1CuVEAXBkG1ESIYYI4qSlceKMiIkUbbEG9lwM5MlhyXwOCQHnJLLyJ1wy264JBfPZRd8Th8XnFzpSHOkwef0IcudhQxXBjJcGXBIjqi4FJeLghhTZwpCZr192V5nT50i9pIoAaIHgKf5vozFCRGmGBUnLiBeqLILBiy2zj5TmtlmttvXs9fxwdjq7MuI7dcgrkyil7dGYs80WteKF8B9fldsy5dM0y0v/jPugiqMJVTZBCuzDBjCFhoKWSyu3bqedJvwaq8z+sRfmzH7iq9nsfs292P1jd+mfdlWbnTZXmc/H4nqbPtOOAYkaGvkbynR30qj226kPX5s1j2CJa6P2USCbTPW8HhizlH8NhOs32C4ic5hM2NotD1ue41tUzcEJw2Gq6jpWgrDZdTYjw7eTzeuBcsVFbY61rBsrW/Ua8b51Y39m2KXbua6TUBjXKg/5swEx9a16DZxOQmCIAiC6FZ0mCjlcrkaiE/mstvt7qhhNEq/zAOw+8YK1IXrwMAsoQdAA+EnXgwCEFMm2hnBsO7p2Dj97UNLXhKb7d+K9dukf1ObSkWBKRXHTBBEDEIX+D5ogpSPy0kQBEEQBNEIHfYUl5+fj6qqKqiqCtmYzrusrAxutxsZGc27DZkzsdXXt28gMUN2iqnTY+LhEARBEATRueiYIPhpaWlJ+QFqf+JydtTzEwAMzBChK4523097ke/h5ymVj6MrHAPQNY6jKxwD0DWOoyscA9A1jqMrHAPQNY5jYIbYIc8GQPPPTx0mSg0dOhSyLGP9+vUYN24cAGDdunUYOXJki4Kc+/18yvEpU6a06zgJgiAIgiASsW7dOvh8vg7f7/7E5aTnp5azDcCzyR7EftIVjgHoGsfRFY4B6BrH0RWOAegax9EVjgHoGsexDcDYhR2zr+aenzpMlPJ4PDj55JOxYMEC3HXXXSgtLcXy5cuxcGHLzkTPnj3x8ccfJ+1XSoIgCIIgujdpaWlJ2e/+xOWk5yeCIAiCIJJJc89PHRqEYf78+ViwYAEuuOAC+Hw+XHnllTj22GNbtK4oiujVq1c7j5AgCIIgCKJzsT9xOen5iSAIgiCIzkyHilIejwf33HMP7rnnno7cLUEQBEEQRMqyv3E5CYIgCIIgOivNB3MiCIIgCIIgkoY9LqdJa+JyEgRBEARBdFboSYYgCIIgCKITY4/LuXHjRqxevRrLly/H+eefn+yhEQRBEARB7BcCM+cKJgiCIAiCIDolwWAQCxYswPvvvw+fz4eLLroIM2fOTPawCIIgCIIg9gsSpQiCIAiCIAiCIAiCIIgOh9z3CIIgCIIgCIIgCIIgiA6HRCmCIAiCIAiCIAiCIAiiwyFRiiAIgiAIgiAIgiAIguhwSJQCEA6HceONN2LcuHGYNGkSli9fnuwhpSx79+7F3LlzUVRUhMmTJ2PhwoUIh8PJHlbKM3v2bNxwww3JHkZKoygKbr31Vhx22GE44ogjcP/994NC6u0bJSUluPTSS3HooYdi2rRpePrpp5M9pJREURTMmDEDX375pVW3a9cuzJw5E2PGjMHxxx+Pzz77LIkjTB0Sncv169fjrLPOQmFhIY477jisXLkyiSPsWrTFtfvWW2/hmGOOwejRozFnzhxUVla297Dbnba4DseNG4chQ4bEJL/f395Db3cSnZs77rijwbGuWLGi0W08/fTTmDx5MgoLC3HjjTciGAx2xNDblfjzcsMNNzQ4J0OGDGl0ps2ampoGfcePH9+Rh9CmNPUe0Z3vMU2dl+58j2nqvHTn+0tj56VT318YwW677TZ2wgknsB9++IG9//77rLCwkP3f//1fsoeVcui6zv785z+ziy++mP3666/s66+/Zn/4wx/Y3XffneyhpTRvvfUWKygoYPPmzUv2UFKam266iR177LFsw4YN7IsvvmDjx49nL774YrKHlZL8+c9/Zn/729/Y9u3b2QcffMBGjx7N3n///WQPK6UIhUJszpw5rKCggK1du5Yxxu+hJ5xwArv22mvZli1b2KOPPspGjx7Ndu/eneTRdm4SncvS0lI2btw4dt9997Ht27ezt956i40cOZJ9+OGHyR1sF6Atrt0NGzawUaNGsddee439/PPP7C9/+QubPXt2Rx5Gm9MW1+GePXtYQUEB27lzJystLbWSrusdeCRtT6JzwxhjM2fOZI899ljMsQYCgYTbePfdd9nYsWPZ//73P7ZhwwZ2/PHHs1tvvbWjDqFdSHReamtrY87Hd999x0aMGME++OCDhNv45ptvWFFRUcw65eXlHXkYbUZT7xHd+R7T1HnpzveY5t47u+v9panz0pnvL91elPL7/WzkyJExX5JLlixhf/nLX5I4qtRky5YtrKCggJWVlVl1b775Jps0aVISR5XaVFVVsSOPPJKddtppJErtB1VVVWzYsGHsyy+/tOoee+wxdsMNNyRxVKlJdXU1KygoYL/88otVd8UVV6Tsl3cy2Lx5MzvxxBPZCSecEPMy8sUXX7AxY8Ywv99v9b3gggvYQw89lKyhdnoaO5cvvPACmz59ekzfm266iV1zzTXJGGaXoa2u3b///e8x32nFxcVsyJAhbOfOne17AO1EW12Hn3/+OZs4cWK7j7cjaezcMMbY5MmT2aefftqi7Zxzzjkx19PXX3/NRo0a1ehLZmenqfNiZ9asWey6665rdDv//ve/2Zlnntlew+xQmnqP6M73mKbOS3e+xzT33tld7y+teR/vTPeXbu++t2nTJqiqisLCQqtu7Nix2LBhA3RdT+LIUo+8vDw88cQTyM3Njamvr69P0ohSn3vuuQcnnXQSBg0alOyhpDTr1q2Dz+dDUVGRVTd79mwsXLgwiaNKTdxuNzweD1599VVEIhFs27YN3377LYYOHZrsoaUMX331FcaPH4+XX345pn7Dhg0YNmwYvF6vVTd27FisX7++g0eYOjR2Lk1z9Xjo+2j/aKtrd8OGDRg3bpy13Lt3b/Tp0wcbNmxol3G3N211HW7ZsgUHHnhgu4wxWTR2burr67F3714MHDiw2W1omobvv/8+5poZM2YMIpEINm3a1NZD7hAaOy921qxZg6+//hrXXHNNo322bNnSonOYCjT1HtGd7zFNnZfufI9p6rx05/tLS9/HO9v9Re6QvXRiysrKkJ2dDafTadXl5uYiHA6juroaOTk5SRxdapGRkYHJkydby7quY8WKFTj88MOTOKrUZc2aNfjmm2/w5ptvYsGCBckeTkqza9cu9O3bF6+//joeffRRRCIRnHrqqfjrX/8KUez22nyrcLlcuPnmm3H77bfj2WefhaZpOPXUU3HGGWcke2gpwznnnJOwvqysDD179oyp69GjB/bs2dMRw0pJGjuX/fr1Q79+/azliooKvP3227jyyis7amhdkra6dktLS7vUtd5W1+HWrVsRDAZx3nnnYfv27Rg6dChuvPHGlH6JbOzcbN26FYIg4NFHH8Unn3yCrKwsXHjhhTjllFMa9K2trUU4HI65ZmRZRlZWVpe7ZuwsW7YMp5xyCnr37t1on61bt0JVVZx++unYu3cvxo0bh/nz5zf4+0oFmnqP6M73mKbOS3e+xzR1Xrrz/aWl7+Od7f7S7d/GgsFgjCAFwFpWFCUZQ+oy3Hvvvfjpp59w9dVXJ3soKUc4HMYtt9yCm2++GW63O9nDSXkCgQB27NiBl156CQsXLsS8efPw3HPPUYDufWTr1q2YOnUqXn75ZSxcuBDvvvsu3njjjWQPK+Vp7PuIvov2j1AohCuvvBK5ubk488wzkz2cLklrr91QKNTtrvWWXIfbtm1DTU0N/vrXv2Lp0qVwu92YOXNml7Tw27ZtGwRBwEEHHYRly5bhjDPOwE033YQPPvigQd9QKAQA3eqa2bVrF9auXYvzzjuvyX7btm1DfX095s+fjwceeAClpaW47LLLoGlaB420/bC/R9A9Jkpj71fd/R5jPy90f4mS6HrpjPeXbm8p5XK5Glxw5jKJAfvOvffei2eeeQYPPPAACgoKkj2clGPx4sUYMWJEjNJN7DuyLKO+vh733Xcf+vbtCwAoLi7Giy++iFmzZiV5dKnFmjVr8Morr+Djjz+G2+3GyJEjsXfvXjzyyCM48cQTkz28lMblcqG6ujqmTlEU+i7aD/x+Py6//HL89ttveOGFF+DxeJI9pC5Ja6/dxp69uurn09Lr8Mknn0QkEkFaWhoA4P/9v/+HKVOm4MMPP8QJJ5zQkUNud04++WRMnToVWVlZAIBDDjkEv/32G1588UX84Q9/iOnrcrkANPyxuCtfM++99x6GDh3abPiGt99+G4IgWH9rDz30ECZNmoQNGzbg0EMP7Yihtgvx7xF0j+E09n7V3e8x8edl8ODBdH9B49dLZ7y/dHtLqfz8fFRVVUFVVauurKwMbrcbGRkZSRxZ6nL77bfjqaeewr333ovjjjsu2cNJSd5++22sXr0ahYWFKCwsxJtvvok333wzJvYZ0XLy8vLgcrksQQoADjzwQJSUlCRxVKnJDz/8gAEDBsQ8CA4bNgzFxcVJHFXXID8/H+Xl5TF15eXlKemG0Rmor6/HRRddhM2bN+OZZ57pMnFXOiOtvXYb65+Xl9duY0wWrbkOnU6n9bII8Jelfv36Ye/evR0w0o5FEATrhdHkoIMOSnisWVlZcLlcMdeMqqqorq7uktcMAHz66ac4+uijm+3n8Xhivo979OiBrKyslL5mEr1H0D2m8fer7n6PSXRe6P7S9Pt4Z7y/dHtRaujQoZBlOSZQ3rp16zBy5EiKNbMPLF68GC+99BLuv/9+/OlPf0r2cFKW5557Dm+++SZef/11vP7665g2bRqmTZuG119/PdlDS0lGjx6NcDiM7du3W3Xbtm2LEamIltGzZ0/s2LEj5helbdu2xcQ0IPaN0aNH48cff7RMyQH+fTR69Ogkjio10XUdV1xxBX7//Xc899xzGDx4cLKH1KVp7bU7evRorFu3zlouKSlBSUlJl7vWW3MdMsZwzDHH4NVXX7XqTNfzgw46qCOG26E8+OCDmDlzZkzdpk2bEh6rKIoYOXJkzDWzfv16yLKMQw45pL2H2uEwxvD99983a4lQX1+Pww47DGvXrrXq9u7di6qqqpS9Zhp7j+ju95jGzkt3v8c0dl66+/2lqffxznp/6faqi8fjwcknn4wFCxZg48aNWL16NZYvX47zzz8/2UNLObZu3YqlS5fikksuwdixY1FWVmYlonX07dsXAwYMsFJaWhrS0tIwYMCAZA8tJTnooINw1FFHYf78+di0aRM+/fRTLFu2DGeffXayh5ZyTJs2DQ6HA//85z+xfft2/O9//8Ojjz7arF860TxFRUXo3bs35s+fj82bN2PZsmXYuHEjTj/99GQPLeV45ZVX8OWXX+KOO+5ARkaG9V0U7/5BtA3NXbuKoqCsrMyKQ3H22WfjP//5D1auXIlNmzbh+uuvx1FHHYUDDjggmYfR5jR3HdrPiyAIOOqoo/Dwww/jyy+/xObNm3H99dejV69emDJlSnIPpB2YOnUqvv76azz55JPYuXMnXnjhBbz++uuWS30oFIp5fjznnHPw5JNPYvXq1di4cSMWLFiAP//5zynvXpOI3bt3w+/3J3StsZ8Xn8+HsWPHYuHChdi4cSN+/PFHXH311Zg8eTKGDBnS0cPeb5p6j+jO95imzkt3vsc0dV668/2luffxTnt/YQQLBALs+uuvZ2PGjGGTJk1iTz31VLKHlJI89thjrKCgIGEi9o958+axefPmJXsYKU1tbS37+9//zsaMGcMmTJjAHn74YabrerKHlZJs3ryZzZw5kx166KHsmGOOYU899RSdy32koKCArV271lr+7bff2LnnnstGjBjB/vSnP7HPP/88iaNLLeznctasWQm/i/7yl78keZRdh9Zcu2vXrmUFBQVs165dVt2qVavYlClT2JgxY9icOXNYZWVlh46/vWjNdRh/XkKhEFu4cCGbOHEiGz16NLv00ktZcXFx0o6lrYm/Zj744AN2wgknsJEjR7Lp06ez9957z2pbtWpVg+fHxx57jE2YMIGNHTuWzZ8/n4VCoQ4be3sSf17Wr1/PCgoKWDgcbtA3/rxUV1ezG264gY0fP54VFhay6667jlVXV3fIuNua5t4juus9pqnz0p3vMc1dL931/tLceems9xeBMcbaXuoiCIIgCIIgCIIgCIIgiMbp9u57BEEQBEEQBEEQBEEQRMdDohRBEARBEARBEARBEATR4ZAoRRAEQRAEQRAEQRAEQXQ4JEoRBEEQBEEQBEEQBEEQHQ6JUgRBEARBEARBEARBEESHQ6IUQRAEQRAEQRAEQRAE0eGQKEUQBEEQBEEQBEEQBEF0OCRKEQSRMgwZMgTXXnttg/pXX30V06ZNS8KICIIgCIIgCIIgiH2FRCmCIFKKt956C2vWrEn2MAiCIAiCIAiCIIj9hEQpgiBSir59++K2226DoijJHgpBEARBEARBEASxH5AoRRBESvG3v/0Ne/fuxZNPPtlonz179uCqq65CUVERxo8fjzvuuMMSsV599VWcd955eOihhzB+/HiMGzcOCxcuBGPMWv+ll17CtGnTUFhYiPPOOw+//PJLux8XQRAEQRAEQRBEd4NEKYIgUor8/HzMnTsXjz76KHbt2tWgXVEUXHDBBQgGg3juuefwr3/9Cx999BEWLVpk9fnuu++wfft2vPjii7jpppvw7LPP4osvvgAA/O9//8PixYtx00034bXXXsPYsWNx/vnno6ampsOOkSAIgiAIgiAIojtAohRBECnHeeedhwEDBuDOO+9s0Pbpp59i7969uPfeezFkyBBMmDABN998M1588UX4/X4AgKZpuP3223HQQQfhpJNOwiGHHILvv/8eAPDEE0/g0ksvxdSpUzFw4ED87W9/Q9++ffHGG2906DESBEEQBEEQBEF0deRkD4AgCKK1SJKEBQsW4JxzzsHq1atj2rZu3YqBAwciMzPTqjv00EOhqip27twJAOjRowd8Pp/V7vP5oKqqtf69996L+++/32oPh8P47bff2vGICIIgCIIgCIIguh8kShEEkZIceuihOO2003DnnXfi4osvtupdLleDvpqmxeROp7NBHzOmlKZpuPHGGzFhwoSYdruIRRAEQRAEQRAEQew/5L5HEETKct111yEQCMQEPT/wwAPx22+/obq62qpbv349ZFlG//79m93mgQceiD179mDAgAFWevTRR7F+/fp2OAKCIAiCIAiCIIjuC4lSBEGkLNnZ2bjuuuuwe/duq27ixIk44IADcP311+OXX37B2rVrcfvtt2PGjBnIyMhodpsXXnghnnnmGbz++uvYuXMn7r33Xvzf//0fDj744PY8FIIgCIIgCIIgiG4Hue8RBJHSnH766Vi1ahVKS0sB8HhTS5cuxe23344///nPSEtLwwknnIBrrrmmRds7/vjjUV5ejoceegjl5eUYNGgQHnnkEQwcOLAdj4IgCIIgCIIgCKL7ITAzkApBEARBEARBEARBEARBdBDkvkcQBEEQBEEQBEEQBEF0OCRKEQRBEARBEARBEARBEB0OiVIEQRAEQRAEQRAEQRBEh0OiFEEQBEEQBEEQBEEQBNHhkChFEARBEARBEARBEARBdDgkShEEQRAEQRAEQRAEQRAdDolSBEEQBEEQBEEQBEEQRIdDohRBEARBEARBEARBEATR4ZAoRRAEQRAEQRAEQRAEQXQ4JEoRBEEQBEEQBEEQBEEQHQ6JUgRBEARBEARBEARBEESHQ6IUQRAEQRAEQRAEQRAE0eGQKEUQBEEQBEEQBEEQBEF0OCRKEQRBEARBEARBEARBEB0OiVIEQRAEQRAEQRAEQRBEh0OiFEEQBEEQBEEQBEEQBNHhkChFEMQ+wxjrlvveH1J13ARBEASRCtD3LJEIui4IovNCohRBdAPOO+88DBkyJCaNGzcO559/Pr766qtWb2/Pnj2YPXs2du/ebdVNmzYNN9xwQ6u3NWTIEDz88MOtWmflypW45557Wr2vZLN582acffbZMXX7cvwEQRBE92bdunW48sorMXHiRIwcORJHH300/vnPf2Lr1q3JHloMDz/8MIYMGdJh+1u3bh1mz57dYfvrDPz444+45JJLcPjhh2P8+PGYNWsWfvzxx5g+jDE8+eSTOPbYYzFy5Egcd9xxeP7555vdtqIouP/++3HUUUdh1KhROPHEE/HOO+/E9AkEArjnnnswbdo0FBYW4swzz8SaNWtafRzmtWJPw4YNw/jx4zFnzhxs3ry5xdtavnw5rrvuOgBAbW0trr/+enzzzTetHtO+cMMNN2DatGlN9nn11VcxZMgQ/P777y3ebkvWqaqqwlFHHYVdu3a1eLt2/H4/br31VkycOBGFhYW45JJLsG3btmbX++WXX3DxxRejqKgIkyZNwrx581BeXh7Tp6ysDP/85z8xdepUFBYW4tRTT21wLRHdFznZAyAIomMYNmwYbrnlFgCApmmoqqrCiy++iIsuugivvvoqBg8e3OJtffHFF/j444/bZFwvv/wyevXq1ap1HnnkERQVFbXJ/juSd999F999911M3b4cP0EQBNF9WbZsGe6//35MmjQJN954I/Ly8rBjxw68+OKLOOWUU7Bw4UL86U9/SvYwk8LKlSs7nTDXnuzYsQN/+ctfMGLECNx5550QBAHLly/HOeecg9deew0HHXQQAGDRokV47rnnMHfuXIwcORKffPIJbrvtNsiyjDPPPLPR7V933XX4/PPPce2112LgwIH4z3/+g2uuuQY+nw9HHnkkAODmm2/G6tWrcfXVV2PQoEH497//jYsvvhgvvPACRo8e3epjevnll62ypmkoLi7GAw88gHPPPRdvv/028vLymlx/69ateOyxx/DGG28AAH7++Wf85z//wWmnndbqsbQXRx11FF5++WX07NmzTbebnZ2NmTNn4sYbb8Szzz4LQRBatf61116LDRs24O9//zt8Ph8WL16M888/H2+//TYyMzMTrlNeXo4LLrgAvXv3xsKFCxEOh/H//t//wyWXXIJ///vfcDgcUBQFF198Merq6jB37lz07NkT7733Hq6++mooioKTTz65DY6eSGVIlCKIboLP58OYMWNi6o444ghMmDABr776KubNm5eUccWPqbvR3Y+fIAiCaDkffvgh7rvvPlx55ZW44oorrPqioiKcfPLJuPbaa3HDDTegoKCgVT82EanJc889B4/Hg8ceewxerxcAcPjhh2PatGlYsWIFbr75Zvz+++94+umncdNNN+Gcc84BAEyYMAElJSX47LPPGhWlvvnmG7z33ntYtmwZpkyZYq23Y8cOfPLJJzjyyCMRCoXwzjvvYPbs2bjgggsAAOPHj8fRRx+Nl156aZ9EqfjnorFjx6J3794499xz8dprrzVrCXfvvfdixowZyM/Pb/W+O4qcnBzk5OS0y7bPOeccPPLII/jggw9w7LHHtni97777Dh9++GHM5z1u3DgcffTReOGFF/DXv/414Xr//e9/UVVVhX//+9/o378/ACA9PR0XX3wxvvvuOxQVFeGjjz7Cpk2bsHLlSowaNQoAMHHiRBQXF+OJJ54gUYog9z2C6M54PB64XK4Gv6S88847OPXUU1FYWIiJEyfi5ptvRk1NDQBuPjx//nwAwNFHHx3jsheJRLBo0SJMnDgRY8aMwaxZs7Bjx44mx2B3X/vyyy8xZMgQrFmzBrNmzcLo0aMxceJE3HvvvdA0DQB3E9y9ezdee+21GDPm4uJiXHPNNSgqKsLo0aNxwQUX4KeffrL28/vvv2PIkCF46qmnMH36dIwePRqPPPIIhgwZgg8//DBmTD///DOGDBmCDz74AAAQDoexaNEiTJkyBSNGjMAJJ5zQwOR42rRpeOihh3DPPffgiCOOwKhRo3DRRRfht99+A8DN0hcvXtzgmOPd90pLSzF//nxMmTIFo0aNwumnn47//ve/Dc7Z888/j3/84x8oKipCYWEhrrrqqhhT6Z07d+Kyyy7D+PHjMXr0aJx55pltZt1GEARBJIfFixfjoIMOwpw5cxq0ORwO3HbbbZAkCY8//jgAYNasWTj11FMb9L388stx4oknWsvffPMN/vKXv2D06NEoKirCvHnzUFlZabW/+uqrGDZsGFauXImJEyeiqKgIW7ZsafF3zUcffYQTTzzRch17/fXXY9pb8t0XDoexZMkSTJ8+HSNHjsSxxx6LZcuWQdd1ANxt6rXXXsPu3bsxZMgQvPrqqwnP4cMPP4zp06fjgw8+wIwZMzBy5EicdNJJ+O6777B+/XqcccYZGDVqFGbMmNHADe3XX3/FpZdeikMPPRSHHnoo5syZ08BVatOmTbjiiitw+OGHY/jw4Zg8eTLuuOMOhEIhq09LvsdNd60vv/wy4XEAwEEHHYRZs2ZZghQAeL1e9OrVCzt37gQArF69Gi6XC6effnrMuv/617+aDB/w7rvvon///pZAAQCCIOCll17CP//5TwD8uU/Xdfh8PquPLMtIT09HVVVVo9tuLSNGjAAAK2zEww8/jD/84Q9YvHix5TJWU1ODX3/9FR999BFmzJgBgD9Xnn/++QCA888/H+edd561zaaedU2+//57XHTRRRg/fjwOPfRQXHbZZS12I3z11Vdx3HHHYeTIkTjxxBNj/i4SueK99tprOP74463+a9aswbBhwxpcxxs2bMBZZ52FkSNH4qijjsITTzwR0+50OnHcccfhscces+rM5+vG/iYA4LPPPoPX68WkSZOsupycHBx22GFNPj+Gw2EAiLkGsrKyAADV1dVW25lnnomRI0fGrHvQQQdZ1ynRvSFRiiC6CYwxqKoKVVURiURQVlaG++67D4qixJg0L126FNdccw3GjBmDhx56CHPmzMF7772H8847D6FQCEcddZT1a8nixYtx+eWXW+u+88472Lx5M+6++27ccsst+OGHH3D11Ve3eqzXXXcdxo4di0cffRQzZszAE088gZUrV1r7zMvLw5QpUyzT58rKSpx11ln48ccfcdNNN+G+++6Drus499xzG5jxP/zww7jkkkuwaNEinHLKKejfvz/efvvtmD5vvfUWsrKyMGXKFDDGMGfOHLz00ku48MIL8cgjj6CwsBBXX311g4fqZ599Ftu2bcPChQtxxx134IcffrAs0M444wzrgfDll1/GGWec0eC4y8vLcfrpp+Obb77B1VdfjYcffhh9+/bFnDlzLDN0kwceeAC6ruP+++/H9ddfjw8//BB33XUXAEDXdVx66aUIBoNYtGgRli5diqysLPz1r39tViQkCIIgOieVlZX44YcfMHXq1EbdcrKysnDEEUdYgs6JJ56IH3/8MebeX1tbi08++QQnnXQSAODrr7/GzJkz4Xa78a9//Qs33ngjvvrqK5x//vkxQoqmaVi+fDnuvPNOzJ8/HwceeGCLv2tuvvlmzJw5E4888gh69eqFG264AZs2bQLQsu8+xhguu+wyPPHEEzjjjDPw6KOPYvr06fjXv/5lhSa4/PLLMWXKFOTl5eHll1/GUUcd1ei53LNnD+6++25cdtllePDBB1FbW4u5c+fimmuuwRlnnIElS5aAMYarr77aOgfbt2/HWWedhYqKCtxzzz248847sWvXLpx99tmoqKgAwMW1c889F8FgEHfffTcef/xx/OlPf8Jzzz2HZ599NmYMTX2PA1EXr+HDhzd6HOeccw4uvvjimLodO3Zg8+bNlqXczz//jAEDBuDrr7/GKaecguHDh2PatGkxbnKJ2LRpEwYPHow333wTf/zjHzFs2DD88Y9/xOrVq60+6enpOOWUU/Dss8/iu+++Q21tLZYvX47NmzfHiJ77y/bt2wHAssQB+I+RH3/8MR544AHMnz8fmZmZePPNN5GXl2dZWw0fPhw333wzAH4NmtdKc8+6ALB27VorDuhdd92FO+64AyUlJTjrrLOadREtKSnBsmXLcNVVV+Hhhx+GIAiYO3eudZ3E8/rrr+OGG27AoYceiqVLl+K4447D5Zdfbv0ga2fBggX405/+hGXLlqGwsBD33ntvgx9Xp0+fjh9++ME6b8OHD2/2b2Lr1q3o168fJEmKqe/fv7+1nUT88Y9/RF5eHm677TaUlpZi165dWLRoEfLy8nDEEUcA4J4Zt912W8x9KxKJ4OOPP8agQYMa3TbRjWAEQXR5/vKXv7CCgoKE6dFHH7X6VVdXsxEjRrCbbropZv2vv/6aFRQUsBUrVjDGGFu1ahUrKChgu3btsvpMnTqVTZkyhSmKYtU98MADrKCggNXV1TU6toKCAvbQQw8xxhhbu3YtKygoYA888EBMn2nTprFLL700Zl/z5s2zlu+//342cuRI9vvvv1t14XCYHX300ezKK69kjDG2a9cuVlBQwG688caYbT/00ENszJgxLBgMMsYY03WdHXXUUezmm29mjDH22WefsYKCAvb222/HrHfdddexiRMnskgkYo1p6tSpTFVVq8/DDz/MCgoKWGVlpbWvgoKCRo9/0aJFbPjw4THHwRhjF1xwAZs4cSLTNM1a5+yzz47pc8MNN7AxY8YwxhgrLS1lBQUF7I033rDaa2tr2V133cV+/fVXRhAEQaQeGzdujPkuboy7776bFRQUsOrqaub3+9mYMWPY4sWLrfaVK1eyQw45hO3Zs4cxxtiZZ57JZsyYEfP9tW3bNjZ06NAG3/uvv/661acl3zXm997HH39s9dmxYwcrKChgzzzzDGOsZd99H330ESsoKGBvvfVWTJ8lS5awgoICa3/z5s1jU6dObfL8JBrTY489xgoKCtjKlSutunfffZcVFBSwn376iTHG2DXXXMOOOOKImGeaqqoqNnbsWHb33Xczxhj79NNP2bnnntvguWfGjBls1qxZ1nJz3+P7SjAYZGeeeSYbM2aMdT4vvvhiNn78eHb44YezFStWsC+++IL985//ZAUFBeyll15qdFt//OMf2cSJE9mRRx7JXnvtNfbZZ5+xyy+/nA0ZMiTm3JWWlrJTTjkl5tnSfr21FPNziUQiVqqrq2Nff/01O+WUU9jYsWNZaWlpTN+vv/46Zhunn346++tf/xpTZz5brl27ljHW8mfd008/nR1//PExfxc1NTWsqKiIzZ07t9HjmDdvHisoKGBbtmyx6r744gtWUFDAVq9ezRhr+Bx91FFHxTznMha9JletWhWzzgsvvGD1CQQCbPjw4eyuu+6KWbe2tpYVFBSw559/vtFxxjNr1ix21llnNai///772fDhw5tcd/Xq1WzUqFHW53/YYYexn3/+ucl17rzzTlZQUMDee++9Fo+R6LqQpRRBdBOGDx+OV155Ba+88gpWrlyJJ598EhdccAEeeOABPPDAAwCA9evXQ1EUy+zZZNy4cejbt2+zM/WNGjUKDofDWu7Xrx8A/qtsaygsLIxZ7tWrFwKBQKP916xZg6FDhyI/P9+yBhNFEUceeSS++OKLmL5Dhw6NWT7xxBMRCASsX5m+/fZbFBcXW78gr1mzBoIgYMqUKda2VVXFtGnTUFZWFmPGPXLkyJhfmMwA5sFgsEXH/dVXX6GwsBB9+/ZtMMaysrKYGVDiYy706tXL2k9ubi4GDRqEm266CfPmzcObb74JXdcxf/58ijFCEASRojBjSnv792wizO8hxhi8Xi+OOeaYGJfzt99+GxMmTEB+fj6CwSA2bNhgWQab33EHHHAADj74YHz++ecx27Z/h7bmu2bcuHFWOf7ZoCXffV999RVkWcb06dMb9DG30VoOPfTQmGMBEBMDyXRBMse5du1aFBUVwe12W+fJ5/Nh3Lhx1rPGpEmTsGLFCrhcLmzZsgX//e9/8cgjj6CyshKKosTsv6nv8X2hvr4el156Kb7//nvce++91vmMRCKoqqrCrbfeinPPPRcTJkzA7bffjkmTJllhBRJhWtUvXboUJ598MiZOnIiHHnoIgwYNwtKlSwEAFRUVOOOMM1BbW4tFixbhmWeewcUXX4ylS5fiqaee2qfjGD58uJXGjh2Lc889F4qiWJbyduKf6Xbt2mVdX43RkmfdQCCA77//Hn/84x9jnusyMjIwderUZq+37OxsHHzwwdayOaa6uroGfXfs2IHi4uIG13ZjkxXY/5Y8Hg9yc3MbPGenp6cjIyOjVbP7mfeXRDQVMP3NN9/EFVdcgWnTpuHJJ5/E0qVLMXjwYMyaNSuhRRljzLpWLrroolbFvSK6LhTonCC6CWlpaQ18uSdNmoRAIIAnnngC559/vuVLbz6c2cnNzU34ZWrHHtMAAESR695mvIeW4na7G2ynqS/L6upq7Nixo1ETd/tDXvwYBwwYgMLCQrz99tv44x//iLfffhv9+/e3Hlarq6vBGIt5eLVTWlpqPRR5PJ4G4wZafvw1NTU44IADGtSbn4f9oSPRvsxzZM6+Ywa6fP311+FwOHDMMcfg1ltvbXQGFYIgCKLzYooMZlydxti1axfS0tIsUeWkk07CG2+8gU2bNiE3Nxdffvml5SZWW1sLXdfx+OOPW3Go7Lhcrphl+3doa75r7OuZ343md1ZLvvtqamqQnZ3dwLXIFCmaez5JhD0Gjkn8d6ud6upqvPPOOwmnsTeDVpvueM8//zwCgQB69+6NUaNGNTiPifbV3LNOU5SUlODSSy/F9u3b8cADD+CYY46x2tLS0qwf1+xMnjwZn332GcrLyxM+96WlpSEvLy/m2UqSJEyYMMFy/Vu5ciVKSkrw3nvvYeDAgQB4oHXGGO677z6cfPLJyM7ObtWxvPLKK1bZ4XAgLy8PPXr0SNg3LS0tZrm+vr7JzxBAi5516+rqwBhrs+dhU9RJ9Dxoxm6LP8ZE+wZaft14PB7U19c3OU47Pp8vJqaZid/vR3p6eqPrLV68GIWFhdYP3AAPYn788cfjwQcfxEMPPWTVK4qCG264AW+//TYuuugiXH/99S0eH9G1IVGKILo5I0aMwMqVK/H7779bD5Dl5eXWNMImZWVlCR8aOwPp6ekoKipq9MvN6XQ2uf6JJ56IhQsXoq6uDu+++64VQ8DcttfrbRALwmTAgAH7PvA4MjMzUVZW1qDerGvNg11+fj4WLFiAW265BZs2bcK7776Lxx9/HNnZ2VZMBYIgCCJ16NGjB8aMGYP33nsPV111lSXu2Kmvr8fnn3+OadOmWXUTJkxAXl4e/u///g95eXlwuVyWdYIpWMycOTOhZUZzL/ht8V3Tku++zMxMVFVVQdO0GGGqtLTU6tPepKen44gjjsCFF17YoE2W+SvVsmXL8PTTT+PWW2/Fsccea73MxwcZb0t++eUXXHTRRQiHw1i+fDkOO+ywmPYBAwaAMYZIJBIjjqmqCqDhD4H29UpKSsAYi7GUUVXVWqe4uBg9evSwBCmTww47DE8++SR27tzZ6s8m/gfU1pCVldWsYNSSZ9309HQIgpBQpCkrK7ME37bAtKqPjzfVWPypllJbW9uqc3/ggQfis88+g67rMfeWHTt2xFh9xbN79+4YERTg19SIESNivAnq6uowe/ZsrF+/HjfeeKM1WyNBABTonCC6PRs3boQkSTjggAMwevRoOJ1OvPXWWzF9vvnmGxQXF1vWQokehDuS+P0XFRVh+/btOPDAAzFy5Egr/ec//8Err7zS4JfVeI4//ngwxvDggw+ioqIiJjhnUVERAoEAGGMx2/7111+xZMkS66FuX8Ydz2GHHYbvvvuuwa/gb7zxBvLy8losgH333Xc44ogjsHHjRgiCgKFDh+Lqq69GQUEBiouLWzxegiAIonNxxRVXYPv27bj//vsbtGmahltuuQWhUCgm+LUkSTjhhBPw4Ycf4t1338UxxxxjWXL4fD4MGzYM27Zti/mOGzx4MB5++OEmZ35rq++alnz3FRUVQVVVvPvuuw36AMDYsWMBtO/ziTnj4NChQ63zNGLECDz99NPWbL3r1q3DoEGDcNppp1mC1N69e/Hrr7+22mq8JZSUlODCCy+EIAh48cUXGwhSACwLqfhJXf73v/9hyJAhCS3GzPWqq6tjXDgVRcGnn35qne+DDjoIlZWVMeEFAB4KQRRF9OnTZ7+Or7X07dsXJSUlMXXxz4Atedb1er0YMWIE/u///i8m2HhdXR0++ugj6/jbgl69eqF///7WNWTy/vvv7/M2a2pqEAwGW3X+J02aBL/fj08//dSqq6ysxDfffIOJEyc2ut5BBx2Eb7/9NsZaKxwO48cff7R+zFZVFZdddhm+//57PPDAAyRIEQ0gSymC6CbU19dj/fr11rKiKPjf//6HVatW4cwzz7RMz2fPno0lS5bA4XBg6tSp+P333/Hggw9i0KBBOOWUUwBwn3oA+OCDD3DkkUc2+QtKe5CRkYGffvoJX331FUaNGoWZM2fiP//5D2bOnIlZs2YhOzsb77zzDv79739j/vz5zW7PnGnvhRdeQGFhYYz4M2XKFBx22GG4/PLLcfnll+Pggw/Gxo0b8dBDD2Hy5MnWeWvpuAE+u9/o0aMbWJ5deOGFeOONNzBz5kxcccUVyMrKwuuvv461a9firrvuavHD9rBhw+B2u3H99dfjyiuvRG5uLr744gv8/PPP1tTIBEEQROoxefJk3HDDDVi0aBF+/vlnnHbaaejZsyd+//13vPjii/j5559x55134pBDDolZ76STTsLy5cshimIDN71rrrkGs2fPxrXXXosTTzzRmmVvw4YNMTPsxtNW3zUt+e478sgjMX78ePzzn//E3r17ccghh+Crr77C448/jlNOOcWawSsjIwPl5eX4+OOPMXToUPTs2bMVZ7dpLr/8cpx11lm49NJLcfbZZ8PlcuHll1/G6tWrLRelUaNGYenSpVi2bBnGjBmDHTt24LHHHoOiKK2OF1VZWYmdO3di0KBBjQpHd9xxByoqKnDrrbc2eM7z+XwYNGgQxo8fj6lTp2LhwoUIBoMYPHgwXn/9dXz77bdWbCgA2LlzJyorK61YVyeccAJWrFiB6667Dtdeey3y8/Px7LPPYs+ePXjwwQcBcAuwFStW4JJLLsGVV16J/Px8fPHFF1i+fDnOPfdcy71yz5492LNnD4YNG9as9fr+MHHiRLzwwgsx1l2mOPjRRx8hMzMThxxySIueda+99lpcdNFFmD17Ns455xxEIhEsW7YMiqJgzpw5bTZmc2a+6667Drfccgv+8Ic/YNOmTViyZAmAfRNa161bB4ALTQB/B9iyZQv69+/f6HPrYYcdhqKiIvz973/H3//+d2RlZeHhhx9Genp6jAfBli1boCgKhg0bBgC46qqrMGfOHFx11VU4/fTToSgKnnnmGezduxf33XcfAOD555/HN998gzPPPBO9evWKuU6BhvHViO4HiVIE0U346aefcOaZZ1rLLpcL/fv3x9VXX42LLrrIqjcfLFesWIGXX34ZWVlZmD59Ov72t79Zv6yOHz8eRxxxBO677z6sWbMGy5Yt69BjmTVrFu666y5cdNFFeOqppzBu3Di89NJLuO+++7BgwQKEw2EMHDgQd955Z4tN5k866SSsXr0aJ5xwQky9KIpYtmwZHnzwQTz22GOoqKhAfn4+LrzwwlY/lBx77LH4z3/+gxtuuAGnn346FixYENOel5eHF198Effddx/uuOMORCIRHHLIIVi6dCmOPvroFu/H5XJh+fLluO+++3DnnXeitrYWAwcOxG233YZTTz21VWMmCIIgOhcXXnghCgsL8cwzz+Cee+5BZWUl8vLyMHHiRNx5550Jp1g/5JBDUFBQgKqqKkyYMCGmbdKkSXjyySexePFizJ07Fw6HA8OHD8dTTz3V5MtiW33XtOS7TxAEPPbYY3jooYfw9NNPo7KyEv369cM111wT40536qmn4uOPP8acOXMwd+5czJ49u8XjaI5DDjkEzz//PB544AFcf/31YIyhoKAAS5YsscZ56aWXoqqqCs8++yyWLFmC3r1746STTrLGX1tba/1A1RwfffQR5s+fj2effRbjx49v0K4oCj766CMASOgqWVRUhOeeew4A8OCDD2Lx4sV46qmnUFlZiUGDBmHx4sUxbp5Lly7Fa6+9hl9++QUAj+f01FNP4f7778cDDzwAv9+PYcOG4emnn7biTPl8Przwwgu47777cPfddyMUCmHAgAG45ZZbcMYZZ1jbXrlyJRYvXoz//ve/zQYi3x+OPfZYLFmyBBs3brSC1g8ePBgzZszA888/j08//RRvvfVWi551J0yYgKeeegoPPfQQrrnmGjidTowbNw733HNPm08ac8IJJyAQCODJJ5/EqlWrMHjwYPzjH//AP/7xjwbxqVrCJ598glGjRllx6H788Uecf/75WLhwYZN/m4sXL8bdd9+NRYsWQdd1HHroofjXv/4VEx/u1ltvxe7du/G///0PAHD00Udj2bJlWLp0Ka644gqkpaVh1KhReOWVVyxx3LT6evnll614ZHbMa47ovghsXyPqEQRBEARBEARBEEQznHvuufjXv/7VYAa9tuayyy5DdnY2Fi5c2K77aUveeustDBs2LCbG1UcffYRLL70U//nPfxpYPjZFIBDA5MmTcc899zSI9UQQnRWKKUUQBEEQBEEQBEG0C19++SWCwWCjM8q1JVdffTXef//9lIqh+cYbb+CSSy7Bm2++iW+++QarVq3CLbfcgqKiolYJUgDw0ksvYfDgwa2ysCeIZEOWUgRBEARBEARBEES7sHv3bni93g6ZJRHgsyBu2rQp4YQAnZGqqircd999+OSTT1BZWYnc3Fwcd9xxmDt3LtLS0lq8ncrKSpx88sl47rnn2nR2aIJob0iUIgiCIAiCIAiCIAiCIDocct8jCIIgCIIgCIIgCIIgOhwSpQiCIAiCIAiCIAiCIIgOZ59FKUVRMGPGDHz55ZdW3a5duzBz5kyMGTMGxx9/PD777LOYdb744gvMmDEDo0ePxvnnn49du3bt+8gJgiAIgiAIgiAIgiCIlGWfRKlwOIxrrrkGmzdvtuoYY5gzZw5yc3OxatUqnHTSSbjiiiusmQ+Ki4sxZ84cnHrqqXjllVeQk5ODyy+/HC0NacUYQ319fYv7EwRBEARBdHfo+YkgCIIgiM5Mq0WpLVu24M9//jN27twZU7927Vrs2rULt912Gw4++GBceumlGDNmDFatWgUAWLlyJUaMGIFZs2Zh8ODBWLhwIXbv3o2vvvqqRfv1+/0YO3Ys/H5/a4dMEARBEATRLaHnJ4IgCIIgOjOtFqW++uorjB8/Hi+//HJM/YYNGzBs2DB4vV6rbuzYsVi/fr3VPm7cOKvN4/Fg+PDhVnvSYQwoXwvsfhso/QSo/Bao3QwE9wKakuzREQRBEARBEARBEARBdCnk1q5wzjnnJKwvKytDz549Y+p69OiBPXv2tKg96VR9B7w/ofF2yQM4MgFnNuDKA9y5Rt4L8PYFvP2N/ADAmdlx4yYIgiAIgiAIgiAIgkhBWi1KNUYwGITT6YypczqdUBSlRe1JJ30QcMBpQM1PgOoHtACgBngOAFqQp9AeAD83vS1HBhenvAcAaQOBjEOAzKGAbxAguQBBBAQZEKTYJJp1NCkiQRAEQRAEQRAEQXRGNJ1BEoVkD2O/6CzH0GailMvlQnV1dUydoihwu91We7wApSgKMjIy2moI+4cjA5j8Ci8zBjAV0COGEFUGhEqBcBkQLImWlQogbKRIDc/VOiBSC9T8yJMdQeZCVfogLlRlHMKXRQmAKUyZuQsQ3VzEkpyGiCUDoiMqXkEEBMHIjWSVk39xEQRBEARBEARBEERXQxIFXPXSd9hSWp/soewTg3r68OBZhckeBoA2FKXy8/OxZcuWmLry8nLLZS8/Px/l5eUN2ocOHdpWQ2g7BAEQHFwAkr2AqweQeUi0nTFAC0WtqcIVQLjcsLAKclEqUg0EiwH/TqBuC1C/lff3b+dpzwd8W45MIHsM0GM8kDOO70sP8+0yzUg6H5M5c45pTWUKUhBsghRsZcP6CnYrLDnWIssuZFnbicst4asFOVl5EQRBtBmMMTCwmHJjdQD2u5xon61pa2q5uT6MxY3LVmf1jx87GMCAdFc6Dso+aB/OMEEQBEEQxL6xpbQePxbXJnsYKU+biVKjR4/GsmXLEAqFLOuodevWYezYsVb7unXrrP7BYBA//fQTrrjiirYaQschCIDs4cnVA0g7gAdDj9QASjUXo5RqIGMoj0Ele7iwFCwB6n4Fqr8HqjdyV8FIDVD6MU8A4DsYyJsM9D4WSB+c2OLJFKqYDoAZYpVuy3UAmiFuMaOPZqyrG30A2F4UGjlQLnJZwpMQK4TZly0xzCaQibKxLBvilumaaLMIa5Hg1YhY1mROAhlBdHdMMUNneox4ozM9RtyJb2+sX2tynelWYoxBh61sb7MJLzp06LoOHbHjAWLFGPux2cUa85be3HpWfSPrChB4P/PrhwGCIDTaBmCfl63tge/DjoDostmWqC6khtDL1ytlRClFUXDqqafipptuwvjx4wEAu3btwk033YT169ejT58+uPHGGzFp0iRrnS+++AJ33XUXdu3ahdGjR+POO+/EAQcckKxDIAiCIAiCaDPaTJQqKipC7969MX/+fFx++eX48MMPsXHjRixcuBAAcNppp+HJJ5/EsmXLMHXqVCxZsgT9+vWzHshSHskJSHmAOw/wDeTWU4HdQLiUW1G5co1A6H2B/Kl8HT0C1PwMVHwJlH3B3f3qt/K0/Wkg7UCg93FAn+mAt190X2YMqvaGGYKWJXyxOCHMFMGAWFFM5f20+PUQXd/ahnVQRn0TeYxAhagAhXjhKq6vdb5aIpC1QPBq0oKsGaGMILoR8QKMXYhpINrE1TdWp+kadOhQdZXX6zo0pkFnutWm6VrMuqboYYlAhghjF4Ws8UK32s3cFD8sMaaZW5V9HQECBEGwcntdY2VTeIkXYkRBjOnf1LZa0ha/j3hRKFWoCFQkewgtJhwO49prr8XmzZutOsYY5syZg4KCAqxatQqrV6/GFVdcgXfeeQd9+vRBcXEx5syZgyuvvBKTJ0/GkiVLcPnll+ONN95I2c+MIAiCIAjCpM1EKUmSsHTpUvzjH//AqaeeigEDBmDJkiXo06cPAKBfv354+OGHcdddd2HJkiUoLCzEkiVLuuYDlegAPL0Adz535fPvAAK7uPWUO8+wIDL6ZY/iadAlvL18LbD3v0DZ59zNb8ujPPUYD/Q/A+g5uWMEKSAqsiCaJY14ccsSwWyWYjHiWZxA1kBYQwKBzHyrTIDlPmk/H41ZkCFOiIoXzOSoIGYXyKwA+GZdIyJXk5ZhLejXYNxEd8UUe0wRR2dRgcee7O3x9aquQmWqVbZy23YsiyTYxCabdZDdPcsUSxhjDaxz4gUU0RCoBfCyXfyx56IgJuzb2LbiRSSCaAu2bNmCa6+9NsZyDQDWrl2LXbt24aWXXoLX68XBBx+MNWvWYNWqVbjyyiuxcuVKjBgxArNmzQIALFy4EBMnTsRXX33VdX7YIwiCIAii27JfotQvv/wSszxgwACsWLGi0f5TpkzBlClT9meXqYUgAM5MwDES8PThFlDBEu7O5+zRUBBwZnGrqD7TgUg9sPdDoORdoOIrbk1V8SXg7gX0OxHIGgWkFwCunKQcWocTbyWVTFpkQRbXZrpP6jqAUJylmek30wKRzB5bLFppaGHx4lNcSuRqCSFWFGtSKEsQe6xJwauR+kbFMrIqaw5T0NGYBk3XrDy+zi4kaUxDRIsgokeg6irPNTVqaWQm03XMtEiC1uDl2e46ZWIKPnaxx27RIwoiRDG2vbEyQXRlTBHp6quvxpgxY6z6DRs2YNiwYfB6vVbd2LFjsX79eqt93LhxVpvH48Hw4cOxfv16EqUIgiAIgkh52sxSimgCQQDcuTy+VKgEqP2Fu/Z5ekWtpuJx+IB+J/AU2A3sehX4/XUgtAfYsizaz9UD8A0GMgq4SJUxBEjr33HWVN2RzmRBZtKkKGa3JANiXC3BAF1BbJyxpoQyxAljduEsvmxm8ZZZTYhlDay/7K6XZlkERLuA1ohlWVOiV7x1WbPiWduIZowxaCxqURRftucRLQJFV6K5GrEskkzRyBSRNKZFrY1sBn2mG5kAAZIoJc4FCQ7RESMo2RNBEG3DOeeck7C+rKzMmhTGpEePHtizZ0+L2gmCIAiCIFIZEqU6ElHisaEcGTzIeWA34MkHJHfT63n7AkOuBAbN5rP2lX4K1G3mLoHhCp4q1tr24wLSB3GBKn2IkQ9qfj9E6mLF2UruMGJIJJDFuE82IpYxDUbAn0bWb6wu3rKsMcHMWDZFMLsFniVIibZ+8aKZAMYEaNCh6gwqGFQGqLoODYDKGC8LgKKpCOsaFF2DoquI6Bo0pkNjPHKRpuvQLNc1AUwQjGEKEAQRoihBFEVIggRRkCAJMq8TJLgFCaLshChIEAURkiQjKqrZjo0szwiiUxMMBuF0OmPqnE4nFEVpUTtBEARBEEQqQ6JUMnBkANmHApKPu/Q5fNx1rzkkF9B3Bk8AoAb5+nW/cpGq9heea0EeNL3mx+i6ggSkDQQyDuEilZnLae1xhATROS3K7NhmrWSMGW5t3BopoquGixtfVjUNIU1BWA0jrCkIa4ph5aRBt1k4MXO7jAECgwgBksCTCBGSKEISRDgEbqEkGZZJkt0iya6faba6GCsue2e7gGbWGWUhgbiGODfM+ID9EAFRsFmo2bYZI3LFC17x9WhmHfPaaGRbJKgR3QSXy4Xq6uqYOkVRrJmMXS5XAwFKURRkZGR01BAJgiAIgiDaDRKlkoXkBLKGA850Lh6FK7grXmuQPUDWCJ5MmM4tqGp/NUSqX3iuVEZn9it+2+gscFe/jKFA+mAgbQBP3r6A6Ey4S4JIFbjQpELVNUR0NSapuoqQqiCsKwhGwghrEWjQoOk6VF2DytRooG3GA22bFkuSIEKWvZAEEU5BMoQmXt8ucZFiLMKAhtZjJnpcH8T20c1JAuK2Y20jzvrMtpkGhmYtIUYgSySm2evsLpL2ZXu5JbNgmmUx1j0zRiCLt4qLF8Pi2+LHGy/+kcBG7B/5+fnYsmVLTF15ebnlspefn4/y8vIG7UOHDu2wMRIEQRAEQbQXJEolE0HgIpAgA9UbgHDl/gcuF8SouNT7D7yOMSBcDtRuMtLPXKgK7eUzA/p38IDqFiLg6W1spz/g7c/ztAF8RkGKM0MkEcaYJTQpeoQLTRoXm8KagqAaRlANI6SFoTINqqZBZYYlky0slgABsihzkUmU4BBkeBwSF5jEThRPqbNbnDVGjJsl0LiYlkh0Q6xAptuFs7h17NtpTFTbH1olrhl18RZrrRbZ7H2A5oW2pkS11ghuicYfd3wJtx23HRLfWsXo0aOxbNkyhEIhyzpq3bp1GDt2rNW+bt06q38wGMRPP/2EK664IinjJQiCIAiCaEtIlOoMePvyvK2EqXgEAXDn8dRzcrQ+XBkVqeq3Af6dXKDSAkBwN0/lX8RuS3TyuFhpAwDvAbGiVaIZBQmiFZiCk6JHoGgRS3RStAgCaghBNYyAGuazyGkqF5t0w20O3LJJFiTIohwVmpwuyCIXm2iGtw4kxvIpRYkR0JhN5NoHgY3ZLNX0eDEtbt2YtrgxAG0jtpk0KbrZ2hoIX/b17PXx4ptdrEo00QDQYIKBBgKd2HCf9vGEqgCWujPRFhUVoXfv3pg/fz4uv/xyfPjhh9i4cSMWLlwIADjttNPw5JNPYtmyZZg6dSqWLFmCfv360cx7BEEQBEF0CUiU6ix4+wJgQNUGQKniM/W1N64cIO8InkwY466E/t+4G6B/JxDYYeS/85na6rfxFI+UBqT14yKVJVgZuSOLBCsCjDEoegRhTYGiqZbwFFLDqIsEEFDDRmwnDRFjpjkA1qxwDlGGLEhwiDLcTidkkQtQBNEuNLASStpI2ocGrqFWAxKLbra2eOHN3J4lvpnLTYh6iba9LyJcqBrQ+zbTqfMiSRKWLl2Kf/zjHzj11FMxYMAALFmyBH369AEA9OvXDw8//DDuuusuLFmyBIWFhViyZAmJ7ARBEARBdAnoba4z4e3H86oNQKSWB0TvaAQBcOfy1GNcbJuuAqE9hkXVTiCwMypcBUsAzc/dAmt/abhd2ccFKu8BQJqRe/vx3JlDglUXIqKpVjDwsBaxXOrqIgEE1RCfhU5TjbhNAMAswclMHqfbEJykZB8OQXRdUtU1NJ5I6s1C98svsd+TAwYMwIoVKxrtP2XKFEyZMqW9h0UQBEEQBNHhkCjV2fD2AzQFqN7IXeUkd7JHFEWUDSGpX6x1FcAtqALFXKgyBSv/Li5ahfYCar0Ry+rnhtuVvFGBKibvB7h7UgyrTobd2skSnSJh1KsB1EeCCGsKIhqP92QXnZySg1s4SU6kO7xwiDL90k8QBEEQBEEQBNGNIVGqM+IbCKh+oH4z4OnLxaDOjujk4/YNbNimhYDAbi5QWel3Ll6F9vIYVnW/8pRou54+UZHK09dW7t25RLsuRkRTEdL4zHQhLWwJT7VKAIrhfqfqKhj47HSyJMMpynCKDnhdbjhEufMECycIgiAIgiAIgiA6HSmgdnRDBBHIGGIEHN/D402lskWJ5AbSD+YpHsvC6nfDHfD3aAru5u3+33hKhKsnPz+evkbeJ7rs6kFWVs3AGENYUxDSFITUMEKagvpIALVhP4Ja2LJ6sgtPLtEBp+SEj6ydCIIgCIIgCIIgiP2ARKnOiuQEMocBWhAIlwLu/GSPqH1oysJKV7kllSVS/W5YXBm55ufnJlwKVH2XeNue3lyg8vQBvH147unL6x2ZqS32tQJTfAoawlNQDaFOCaBaqTeCjkeg6ioAQBIlOEUHXJIDWa50OEUHCU8EQRAEQRAEQRBEm0OiVGfGkc6Fqcp1yQt8nkxEmVs9efsCiJv6mjEgUsOtqQI2sSpYbKQ9hpXVDp4SIaUZolVvQ6yy56krWkU0FQE1hKAaQlANo1YJoEapQ8gIPq7pOgAGhyjDKTnglpzIdKbRLHYEQRAEQRAEQRBEh0JvoZ0dd08gvQCo2ghIHkB0JHtEnQNBAJxZPGUOb9huzhQYLObugcHd0XKoGAhXcEur+i08JULyRAUqdy9b3ovnrtykxvtijCGohhFUQwioIfgjQVSH6+FXgwipChRd4S53ogyXJT75aEY7giAIgiAIgiAIolNAolQqkDYQUKp5gHBvv5S03ulw7DMF9kjQroW4NVVwNxAsMZLNykqp4K6T9dt4SoQgAa48Q6TKtwlW+dHURtZWOtMRiHDxKaCGUBv2o0qpM+JAhaEzPsOdW3LCJTmQ486AUyIBkyCI5MEYg6prCOsRY3KEiFGOzt5pr+P1EWN2zwgiuq3O3s/II7rK29Qgzhp8LCaOSvYREwRBEARBEK2FRKlUQJSAjAIuTCkV3EKH2D8kd+OxrABACxuWViVcpAqV2Mp7eRvTeB7a0/R+XD25WOXqCXjyee7O51Zw7p4NhKt4Aao6XIfqcB1CKne/YwBkUYJbcsIru5HtyoAkUkB3giCaxhSJQlrYmNxAMSY6CCOkRRBS+WybZp1VtvoplpgUblCOik0hTTGEJQU6Yx1ybB8Vf4ebO2RPBEEQBEEQRFtColSq4EgHMg8BKr4B5BAXO4j2Q3IBaQN4SgTTgHBlVLgK7eUpaIhUob2AUsUtsgI7eWoEJjqhO3MRcWYjKGehXkxDvehDnehFSM6E6swBXHlId3qRK2VR0HGC6KLoTEdIVRDUwnxSApXnATWEoBZGSFUs68jopAXhmNkzzfaQasyqaZRNsUhjetKOzyk64JT4JAouyQGn6IRTcsApynDJTmNmTwccogy3ZLRJjpj1eFk21uVx8ZRwDQ7vfWjSjosgCIIgCILYd0iUSiU8fQDfQUDdZiDtAEAg65ikIUiAO4+nrJGJ+2hhIFQaFaxCpdCCJdCDJWChUojhMshqLQRdgRQqhhQqhhtAdiO7jMjpiDiyEXFkIeLIhuLIRsSRaavLQsSRCQj0Z00Q7Yk5m6VpzcgtG834btE4b/ZySA0jYAhMIVNoMgSkgCE+hTWlw45BEkS4JCfckhNu2Qm35ILLcP91yy5DOHIafaJls4+5rrlsTprgjFnPaYlJDlGG2E7fWRW1u+CWne2ybYIgCIIgCKJ9obfXVEIQgIzBfNa5wO88npHsSfaoiEZgohNBZx7qhTT45d6okGtQ46xHMC0MVdcgCAK8oogsFoRPr4crUg1HpArOSCUcSjWckSo4IpVwRKohMhUOtQ4OtQ4INm51BRjilcxFKtWRGRWs5ExDxMqEKmdCk7wUn4zoNqi6Cn8kBL8ahD8ShD8SnSDArDMFJr8aQiAS5LkaQtCoC6rRvD3d0gQIcMtOeCQXPLILbtkFj+TidbILbonXuSW+7DHKbqu/KTI5bP25UGRuh2bbJAiCIAiCIDoD9FSaakhuIHsMD74d2MldxNw9kzoLHMHRdA1+NYh6JYhapR7loWr4I/wFlgH8hVB2Ic+T1eCFMGCkhDAGSauHU6mCwxCueKqGI2KKV9VwRGogQIuKV6FdTY5XFxyIODKgypmIyBmGiJWBiJzJxSw5wxCwMqBJaSRgEUlB03X+dxUJoF4JoN4mItVHAqiPhOCP8Pr6SDBGZPLblsNapF3G55XdljDklT3wyi54ZTfcsgtpshuemHaz7DaWXTHLpgjlkpzkpksQBEEQBEF0C0jJSEUcPu4y5ukN1G/lMY1kL+DMIeGgA1F1FfWRIOoUP6rDdagM18IfCULRVAiCYL2E5rgz9s9tRRCgyekIyukIon/j/ZgOWa3nApVaDTlSY4lVZp0jUgtZrYGsBSCyCFxKBVxKRbNDYJAMASsdqpyBiJwB1WErG/WqnIGIIx266KFrkQAAhDUFdUoA9ZEAahU/6iK8XKeYQpLfKtdF/JbwVG8ITQE11KbjcUkOeGU30hweeGUPfA43vLLHWHYjzeGGV3bD6/AgTXbDay7LbqTJHngcLqTJHkN4crabSxpBEARBEARBdAdIlEpVBIHHM3Jmc1Gqbgu3nHLm8KDoRJuj6irqlADqIgFUhepQEapGQA0hoquQBBFpDg+yXOlwSUmKbSKIUB0ZUB0ZTYtXAARdgSNSA1mtMUSrGjjUGi5kqbWQ1VqjvRayFoAADc5IFZyRqhYNRRckQ6gyxSqfLU+HKpltPitnIsWE6YwwxhDSwqhR/KgN16M2EkCd4ucCk+JHrSEqmeV6JYBaJcAFpkigzSyUXJIDPocXabIHPocHPqet7PAizeFBmsNt5J6Yvl4HF5TSHG5yWyMIgiAIgiCITgQ9nac6osyDnrtyuShV/xugVHPBimbo2y80XUOd8QJeGSdCOUQZHtmFXHcWHFLq/Rkx0QnFlQfFlddsX0GPQFbrDNGqjgtVlmhVZ7TVGvV1kPQwRKbBGamGM1Ld4jHpgtMQqYwk+aDZyqqcDk1OM8o+aFIaVDmNB50nmoUxhvpIADWKHzXhetQq9VxoUupRq/hRY+S1Sj1qwoboFOH1qq7t174FCPA5PEh3euFzpCHd6UW6gyefwwuf08vbHWnwObmgZLUZAlMq/p0RBEEQBEEQBNE09JTfVZA9QMYQwN0L8G8H/Lu4NZUrj+JNtRCd6ZaLUVWoFuXBGvjVoCVCeWV3yopQ+wMTHYg4cxBx5rSov6ArXLQyBCue6g3Rqt62HG0ToEFkCpyRSjgjla0anyZ6oBpilSanQZO8UdFKSoMme3kupRmillGWPCk7g6Wqa6hV6lEdrkd1uA7VSh1qjHKNUs/LRl6j8FSn+KExfZ/3KYsSMhxpSHemIcOW0k2RyZmGDEcaMpxe+JxpXHRyepHhSIPX4SY3N4IgCIIgCIIgGtC93q67A85MwDEa8PThwdCDJdxiytUjZV/A25NAJGS83Ndhb7AKfsPdyHTH644i1P7CRCcizlxEnLktXIFB1EM2saoeslYHySrXQ1b9fFnjdZLmh6zx0PCSHoSkBOFCeavHagpamuS1kip5LdFKk7xc6BLNshea6LXaWBsIvowx+CNBVIXrUBWuRXW4DlXhOkts4sJTbVSACtehLtJoWPxmcUtOZDp9yHCmIdPlQ4bTh0ynD5kuLjBlOn3IcKUhw+lDhsOLDJcPGY40eGQXBd8mCIIgCIIgCKJNobftrogg8Bn5nD2A0B4j3tTvPNaUI6tbB6COaKrhtlSP0kAVapR6BNUQBAjwOtzJjQnVXREE6JIHiuSB4urZ8vWYxsUqjSdZ9dsEq2idpPkhqVzEMvtKugIgKmjtK7rgMMQsj5Wrohs1cGGvJmKvLqBMZSjTdJRHNJSrCioiCiqUECqVACqVAKrD9VBZ693jBAiWsJTl9Bl5OjJdpsjkQ6axnGUToej6JgiCIAiCIAiis0CiVFdGlABvX+7CFyzmM/UFdnJhypkJqCoQCAGhMBBWAE0DGABdB3SjzFjcNgVucSUIgCgay0ZZEABJAiSRL5u5LPOyLPH2DiQaR6ceFcFalIeqUB8JgDFmTNnuQQ93JlmApCKCZAV2b/WqugrJLlJpwahopQaMtoBVx1Q/qsJ+VCh+lCkhVEQi2KsBpVoEe9UalGo1KNVgpTBrfgzx+ESghyQiV5aR43Cgh+xEjtOFbIcHOU4vMp1pyHalI9OVjixXJnzuTAiSF5rkhi66oUku6CIvt4UFF0EQBEEQBEEQRHtDby7dAckJeA4AFA9QuxXY+QtQUQooMqDJgBIBIICrUAIvWrmBIEQFKmb9F60TRAA6wIztmEKVJPI2uyjlcgIuF+B2Ak4HF62cDsAhx5b3EUWLWHF19gTKUav4EdYikEUJPocXvby5kEUKjt2dYaKMMHyo0RgqIhFUhIIoD0ZQGQ6iIliLynANKkI8VYZqUB2uB0PrlKY0SUKu7ESuLCFXFtFTEtBTAvIkDT1FFflCBL1EDXkSkCsBHhEAdACKkfyxG1SNFFedCF2QLIEqKla5oIku6MayJrqgiy7okq3NniR7ndOqJzdggiAIgiAIgiDaChKluiqqCtTXA3V1QGUlT8EgoCiA6gGEHIBVAU4FyMoBHGltu3/GAJ1x6ytdBzSd50qEW2dpmmGZZQhhYIZFlcQFKYcMeD1AmpsLWA4H4HIYuZO3G9ZNZkyeGqUe5cEawxoqCMZ0eGQ3Mp3pcMvkstRdCKlhlIeqjWuhGuXBalSEeLkiVI2KIBebqsK1rQr8LUBAtisdOe5M5LgzkOPKsMrZrgz0cGci25VhLKfDLbua3SZjKiq0EKq1EEQ9CEkLQtJDELWQkQch2ct6CKIWhqQHo330MCQtBFEPQWQqAEBkGkTND2h+ILLPpzIhuuAwRCqnTcRyJsjNsiNaJzhtbTyxhPUOQKCvJ4IgCIIgCILo6tBTf1eBMcDvB2pquABVVgYEAlyckiTA6wWysrjAY6L6geBeILQXCO4B5DRA9gJCG1gRCQIgCdxCqqVoGhBR+ZjDCuAPAiUqF7NMyy3DmkpzyqhxMtQ4dexBADUIIyjokJ0u+NIy0NubC0kki46uRCASQlmoCuXBapQFqw3BiS+Xh6J1/kjLY0QJEJDl8qGHOws93JnIcWeihzvDVs60ylnO9La/pgQZmuyDJvvaZntMhaSFDaEqCFE3yobQxcu8LnaZi10x7VZSIOoKBMNSTGQRiFqEC17tCINoCVS66AQTooKVLji4mCU6LZGMWfUOq96s00UHmCWmmeX4XLb6kDUYQRAEQRAEQXQMJEqlMqrKRaiqKqC0lJdDIS5CpaUBubncsqgx5DQg/SDA0xMIlQOhUp4LIm+TPB0bFF0yY04ltjAJq2FUh2tRFazAnqpK1Cl+qKoKjyDDJziR53AbVla1gNfNLa1chougmUio6nQoWgTloWqUBqpQHqpCWbAapcFKQ3ziy+XBKvjVUIu36ZKcyDWEplxPFi97MhvUZbnSu5YrpyBDk2VoSGtbAynGILBIjFAl6WEuZDHFELQU3saUaFlXbIkvC7rChS09DFG35wpEpkQPBToknVuDdTRcELOLVrJN5JLBTAFLkG119n5GLkjRdqs+vmykuGW+XynaLkgklhEEQRAEQRBdDhKlUpGKCmDvXp7q6riFkdcLZGQAPVsxe5mJ7AN8PsDTF4jUAEolEK4EInU8WLrsBcQOFqgM/FoI1WoAZZE6lEfqUK+FIAgC0tLc6JmRBdlu1WW3tKqoBkorrDBZcDgApwx4PECaXaxy8voODsDeHWCMoVbxoyxYhb3BSpQFqlAarERZsAqlwSorrw7XtXibHtmFPE82ct1ZVp7rybSVudiU5vBQ8Pq2RBDABCc00QkN6e23H0v8MoWsCASmxIhWglEvGv3sy4K5HlNt4pdRz1QI1noRYz+2HFFXTi6IhQGEgdZPjNhuMEjQRbtQZQhYhmjFxTLJErGibbKtzd4eX5ai27KJYQ1TonqzTmzQDhg5/U0SBEEQBEEQcZAolWpUVADffstd9dLTgfx8HoupLZAcgJQLuHMBNQSotUCoEohUAUotIMqA5G1XCyrGGGq1IKrVAPYqNajS/AhoYciQkC650deZDbExa4HGLK10nYtVERWorgXKKnm9gGhgdbeLx69yG9ZWTgcPzO4g66pE6ExHVbgOpYFK7A1W8jxQidIgF55KjXJYU5rfGACHKCPPk4U8Tw56erKR64mKTnw5G3keLjYRXZgY8auDYZohUqlc7GJqnHilGsKXatQZuSF22dtFxtsaliMQmGaIYyoEpsb2YSoEplnbFuKC6wvQIOkaeCD81INBtIlVYkLhKr7eXAcxgpcIBr4MQUK+GkZF7tRkHx5BEARBEASxD5AolUrU1QE//ACEw8ABB7TvvmQ3T+6esQKVWg2E6rigI6UBsme/Y1BpTEeNGkC1GkCJUo0aLYiwrsAlOJAuudHD6ds/qxdRNGb8iwt2zmxiVW09UFkdnU1QkrgoJUuA05gp0OOKmymwa4pWpuC0N1CB0kAl9gQqURo0RadKXh+sgqq3TDbIdPrQ05ONPG82enpykOfJ5stG3tObg8z9/YwJYn8RJOiGxWSnMY5iuiVURUUyzagzxCxdtZatflYfI9e1mD6ivY1ptn5xy9Bsy1qCdZqoSzBbpQAdAtMB1sbR9wF4mALgnjbfLkEQBEEQBNG+kCiVKoRCXJCqqgL69evYfTcQqOoBpZq7+YXKo30kDyC2bJY7lWmoVgOoivhRrFSjTgsiwlR4RRcyJQ/cjoz2Ox4TQTTc9xKMWdMAVeOugP4AUGu4SQoAIBhWWWIzopUxi2AnE63qlQD2BCqstDfAhSazXBqsRERXm92OAMGyaMo3xKZ8bw7yvTno6clBTy8XnlwSzXxIEPuEIIIJTkve6TRiWUtgegOhCjBFNt0mZtn6QQdi1rH1Ay9HhTpmiWaBUDXqciagR7KPmSAIgiDaGU1nkMTU/iG3KxwD0baQKJUKqCrw889ASQkXpJJpUWIJVLmAFuECVaQOUCp4rkcMNz8PILljrKgiuooq1Y9K1Y+ScDVqNT5Lmld0Ilf2wSF2osvRdAWMt64CADBA1Q3hqjnRSuail8cQrkyhyiEDkhy1xmqjzzSiqSgNVsaITnv8FTbRqaJFAcMFCOjhzjREph7IN0SmfG8PXufJQa4nC3Jn+swIgug8CKLhZtfEZBttREXtLqS5stt9PwRBEASRbCRRwFUvfYctpfXJHso+cdSQPPz9uENS+hiA6HEQbQO9UXZ2dB3YvBn47Tegd+/OFZBbcgBSNuDKBlg/QA0YVlQ1PGB6uByKFkEVVJTrKvaoIdRrYQCAT3KhlzMzNlB5yiBExaTmRKtIAtGKgX+OomFpJYmAwwm4HHx7pnWVaW1luBIyWUKdGkSJv9wmOJXHCFDlwWqwBG4z8WQ6fcj39kAvU2Ty9kAvU4Dy5iDPnQ2HRLcHgiAIgiAIguhMbCmtx4/Ftckexj5xcF4agNQ+BiB6HETbQG+dnRnGgK1bgV9/BXJzE7uZdRYEEXD4AIcPYWcOqsKVKAuUoDRSgrpwGQQ1DB8E9JackGQXt6DqsjGEmhOtwAUqTQM0nSclAL1GRblah5JIDfbodShR67BHq0OJzvM9ai38evMBjp2ijHxvD/ROy40Rnnp7cy3xyS27mt0OQRAEQRAEQRAEQbQnJEp1VhgDtm8HfvoJyMoCvN5kj6hJQloYVZE6lIarUKZUol4NQBQkpLt7ok/aAEhMBbQQT2odoAZ5XCoGbhUkOLnlldD+rh7JQmUa9ig12KNUo1ipRolSjT2RGpQY5b2RWqis+agx2aIHvaR0nuR09JIy0EtKR29nFno5M5HtSIfgkIx4WTbrK1kGmASENSASbnPXQYIgCIIgCIIgCIJoDSRKdVZ27uSBzTMyAJ8v2aNJSGIhSkS6nIa+7p4QBVuAb8HJg6A7MgD05LGn9BCghQHVz5MW4NZDAI+3JDgAyQkgNVz8wnoEe5QalESqLaHJnsoiddCbca2TIKKnIwO9nZno5cyyhKbejiz0cmahlzMD7kTB5JkOaIyfP13j7oNhv7GsI2a3khh1HTTdBB0OwGnEv5LlaBB3SQJEIRpjy1yXIAiCIAiCIAiCIPYTEqU6I7//zgWptDQuSnUi7EJUqVIBvxqEKIjISCRENYXo4ElOB1y5ANMAPQxoCheqtHo+01+kHtBVWMHD4YgKVuhYC5+QrqBEqUGxUoUSpQYlSpVl8VSiVKNCbT5Yn1OQucjkzEJvBxedeOJ1uY70fYuzJYj8r1lubl1muAzaXAeDYcAfNAQsFttXMFwRBdEQpARAlKIzDDrNuFeGkGUGdxdFQBaj9WSNRRAEQRAEQRAEQcRBolRngjFg1y7g+++5xUpWVrJHBAAIaqEYi6h9FqKaQpAAycsTACCfC1VaGNAVLlipAZ60EBetAK5LiQ5AkPmsf4KMfRWrAlrYEptKIqb4xF3t9ijVqFT9zW7DIzotgamPMxu9HGaZWzrlyGltc772GZvVU0vQdZ40nV+fmgZEIkAoHG1jDNFznkjIEvn+nIZFlkOKClmiEGuBJQrGeob4JQqGeyeJWgRBxMIYA2vR1A4EQRBEd0fTGSSRnicJojNColRnQdd5UPOff+YWUkkWpBIJUZIgIUNOQ7Y7A0JHiASCBMhegHkATYMgZEMQIxAiYQAhCKoCqCEIagCI+CGwCKCqgM4g8GBVEJjEtwMR9bqKEr0Wu7U6FOu1KNbrsFuv4zmrQzULNTskn+BEbzEdfaUM9JUy0NtIfR2Z6CNnIkP0QJBEMEEARAFMFABBAIMIqAKYroFJLCq2dHZE09WvFevYhSwz1xQgGIq2ATaXQkPUEsAFKuPc8dxmneWQo3GwZKMsSrHCliQaVmNibBtBEFERh8GQcqJlXh/bzvhKVtlsj9mWUQZg2waiUpFt2WwDmPVnD2braxe3Y5btdYBg3i/MsqYgW6S/c4LoanQFEaErHAPQNY5DEgVc9dJ32FLavGdDZ+WoIXn4+3GHJHsYBNHmkCjVGYhEgF9+ATZvBnJykhZDKqiFUBWsRnndHlTVlSLsr4MjoiFHk3GAKkBUIjyFIxCUCERFgRDhy7ysQlQivC6iQjCSGIlAUFUIEc3IVQiaxvuoKgRVg6BpMTl0DYKm82W9Zb+DV7uBHZnAb1nRtMNWrvI0v42sIDCwOpoG1MQuZ4UUABVG2n+YKIKJApghqDBJ5HVGGbYyTxKYbPSRJdt6krXMZAm6rWzlcUlPUBdT7zCXZavMHHHLiQKl74uQBRiCFePxsXTGLbB0I1ZWxBC1zDpmtAuC8a7KYkWseHdDWYq6Fjpkm3WWFCuGma6GligmASJirb7Iaqvb0laiTosEHcagxwk5LEbINWko5AhmNYvLjVZBEGD+A/glbdUIZh1vNfua64oQeX+B9xUFXicKYuyyaPSFYJVFQYAoRPsK8eOJ22fMeOz1QnT0giBAUKrgcmbt9+dLEETnItVFhEE9fXjwrMJkD6NNSPXPwhRztpTW48fi2mQPZ585OC8t2UMgiHaBRKlkEwgAmzYBv/0G5OcDbnfbbj8UAsrLgcpKoKIiNlVWQqsoBysvB+pq4Qop6KOq6NO2I2gTGIAKb1R0MsUmK88Ealpw6nKCwIBaEQPqBAyoFdG/VsCAOhEDawX0rxWQqcQKDoL5nscAuBhUh7HA3yYhMGbFYRIMIUUwRJOWiGmCrkPQAajNz7rXWYmKWDIvO4yyw17HhSzdITdeZ60XWx/TxymDyUbuMAQ0p7muBCaK0aDvpruhqgKKEit0maIWBP4hM9ubu4CouAXBJlKJUQs3WeYuhlZQeCOOlmmplcjqyxK8DKGL3BMBxAs9epzIE18HMKY3sNrRbXVAVODRDXEn1lKnobgjCIJRF2uhIwjRbQKC9TGZwogI0eqXSNQR4wQdAUadTdCRBCkq1rRCzBEFsUkhp0kRJ0YIQtx6trqYbTYUkDoNggrIbfzdSRBEpyDVRYSuRCp/FiTmEETnhkSpZMEYsGcP8OuvXDDq04fH22kJmgZUVcUKTOXliZf9TcdBasrhQXc5o8nphO5ygDkdvOxwgLkc0J08MYcDustpiAoOQ0BwgMmmsCAbFjZcVOApam2jigJKxQB2w3Stq0axVo0StQrFahVKIpUI6UqzpyZLTkcfdx56u3qgjzsPvVy56OvOQy9XD/R25SJNjjOX0lWA8bRTV42A6xEey4qFjXYNgDGrXbyBgigZZ9G0ohGjca0M4Qo6M8QnLojYc8FwbxN0BsFwdxNU3eiv8zrTYkyztanmsmYs61FrM3ubmdvKomrrZ0tWfcRYjqix7Ua9YLrfGYiqxkW1UPOfT0egO41rzdmI0OWUGxe5zH6yDCaLfNm0HnOI0EXDWkwSovWiCF02LNQcknFtC+DKE4sKTqYwhTj3RDOXRJsbohSdHVEUY8UrezLr7SKaKFrWXUwQwAQGJohggt3lKoHg04RFj96M2MPbWdR4LV7IYQ3/dJghAiYSQABYgo4pflhijk3kkUxhRpQhGaINT1zMkUSR2/YIgtEuxPSJF1nEGBGmoYgjNrDqiRV1uGiUYoIOQRAEQRAEQSQREqWSQSjE40dt3coDmh9wQPOWEuXlwH//C3zwAbBxYzQuTwtgLhdYTjYiOZnwZ6XBn+FBfaYHak4WxJxcSD1yoWamQ3e7uADldoE5HW1qvRHSFOxVKlASKseecAX2hCtQEjbK/nLsUSqhseathXKdWejtyuXJnYverh7o7cpDH3cuerl6wCO18tdyUUbTfwZ6VJhimq2s8rIeAZhilFXen+kNPh8mgLvaySIMMxlDwHIafinmGFIgzpQpbpmilSliGS6aUdfN2D6NtkdULnhFVC6MRVSIimq5fIoRFYJitClqwnXsiApfH4EknR/wz9tuAabLXPDSHZK1rDm4K6ZmiFuazMUtvixCM5IuS1Ad0Tqrj0M21ud9NJcjum2nzIViw4JMFEQIoghBFABJ5suSCEGUeC5LEAQpWicatjqiCEmUIIoiJEmCIEqQJBmiKEIUJYiiDEkUIUoiJNEBUYzuRxTlGGGHu33FCj3c2iduuRmRJ5GYRBAEQRAEQRBEakKiVEeiacDevVyMKisDevYEPE0EOvr9d+Dzz4EPPwS+/TZW6BBFIDsb6NGDp9zcaLlHD2g9clCX6UZ1pgvFoh81qh9BPQyX6ESGnAaP6Gqzlzmd6aiM1GJvuNIQnKLC015DfKqK1DW7HQkierpy0NvVA70s0Sma57ty4BKdbTLmliMCLdqnOROdFpug2eojhqhl5owLWjDdyeIssUwE8GDtgjEeGFY4guFSZrqYfiyZ4QABAABJREFUCZJ9hf0/9MYw4ltprhZa9rUhMdY8ptWOrgOqBjESAcIRQySLQAgbwpZN4BKUaLwzUTFELyUSI4aJEW4VJqoqJIWLYpKqQVS4NZlk5GJEhWT0lRQVohb9+xQYICkqoKgdfo7iYeZsiIZIZsXVMgPG28tGAHnB4YBg9jWDyjvkuOQwkgw4HbyP0wm4HHwdp4Mnl5Mnp5nL3E3Z5YjOBGm3+LKswMQ4izI0tBRLZDmWsJwCYi9BEARBEARBdENIlOoINA0oLeVxo0pL+YvcAQc0fFGqqwM2bADWrgW++ALYuTO2fcQI4A9/AKZMAXr3bjCrWEgLo0atR5VSi71KJWojlVA1DV7mRoachp5STquHzhhDnRbA3nCFJTrtDVeiVKm0RKe94UpEWPMv3x7Rhd6GRVMvV67hUsfd6nq5c5HrzIIsNOVQ2JkxXqhb/CdlBPRmWlSQgm0Ztnq7hRbMsilkRfi2oBvugo0IWyamViWYcYzsybgezRd5iFz0Mdy/AAG6JQoJltuXbsX1sbmGAVE3r2bazP8FxA9dsNWxqNtWghg4ogQIsgB4nEY0HlsMHZt1jSSIkMyy4d4lgteZuSRI0MHdxTTEWecAUese2N25AFGHJX5JCrcQ48KXagT/1yFEInxZiU4EACXCUyQCRFSeK0bZrFciXOCytyXqb5Zt8cwExow2tT2lytZjimWyHJcnKsctOxLU2evt4pnZz2mIZS4nLzuMssMRrXPJgGwIaw4H4JCiwpsZI0wAorHGGhPLECuwoZE+LakjCIIgUpI8n6tLzFpHEATR3pAo1Z6EQjyu086dXIySZaBXL56rKhepfvmFu+N99x23oGK213JJAsaMASZNAo4+msedsqEzHXVqADVqPcrDVaiI1CCghgABSJM8yHNmwyE2/hEzxlCj1qM0XIlSpcoSm0rDldirVPLlcCWCerjZQxUgIM+ZhV6uHsh39bDy3i5ThOqBDDmNXG0sDMumBCJcNGizbshNejT+D2DE79HBmAZd14x+xjLj7oM6jHpb0qEBTAfTNcPFkItcAgMYM+oEWAHcDenJek8WeVAiboDC7OIQeMBnAeARfMx4P1z4EQUBoihBNmboEiFCkrh7mCTIMYGdBcEK8cxdweyikCDFzPzFy0bwZ4iGYGSsa7qKmdvrqOvO1TG7aRZNswlVNgErXrxS1Wi9qiXuF5MbSY3rY66rGG2RCBAx6lTbenZsYllKYMb9MoPbm1ZespmLgCRHZ3qMaTMD4tuWZXt7XJ2YoM7hiOYx1mpSQ8s1S5Az15N5PylOrJPElgtjCZdbWE/3fYIguiEZHjnlZ60DYM1cRxAE0V50qCgVDodx66234v3334fb7casWbMwa9asjhxC+xOJ8MDlpaXcVa+ykqe6OmD3bmDHDh7cfOtWIJxA7OnfHzj0UGDiROCwwwCfz2pijMGvBVGvBlAdqcNepQr1agCKHoFTkOGTvchyp0OAAL8WxO5QKcqVaiuVKdUoU6pQrlShVKlCmVKFsB5p0WFlyj6b2JTDy84cqy7PmQW5CQGsMxMb4LkpIcg+RXs0EHTUGiiBmGRs3xR7IAACRDCmw5rxy2wDokGdETuzljnTFhAbAFqQHJAM4SZq9cOFGgkiJEGCIAiQBQkiBMiiHGP5E2P1I4gQGIMIBoEhKj4BEAWzTjD6GO+dpkhl6xN1HrRZb4EZAeVNCzGdt5sWY2YsLuM8Wzn/hBCNlG2vMyzLLK85+3pxkbVNGppjtQ4h0YL9pVtI0D++TYjrGv/C3si2rPUSveDHbxOAUwCcEvht3o0GHRrVCex9hMT1rba54oImVGM2xIhmE6sigKbHCmiqFiuYaVpUDIuose0Nlu39tbh1jGXV1mYKajHtCeLb6YaLbqqIaC3BtPayB9kX4wLum3VmPzGuzSzb1zOFOdGol219TCFOihfg7EJfvIhnq7PEPlMglAAEgQlFQF6yTyhBEERDUnnWOoBmriMIov3pUBVh0aJF+OGHH/DMM8+guLgY8+bNQ58+fTB9+vSOHMb+wxgQDPIZ8EpKuNC0cydPu3cDxcXR2e8qKxsPSu71AoMHA8OGcYuoMWN4TCiDsKYgoNQioIVQGanF9rpd2BsuR2m4ErWqH0EtDL8WQo1aj8pILSoitaiI1KBcrWmx2AQAWZIPPR1ZyHdmId+RjZ7ObPR0ZKGXrexuKq6SCkANtnh/JpaIYxN3GswCFt9mzPClG0pErFsYM5a5fAQI3AoIsGYE40IQdwVjAgAwCEwwrHxMISjeNSsqDAEw7HC4gGQKP1wEchjCkDHNOwwxyLTWsSyCYJQFS2zi25eiM3w1JRwZY4jOBia23BIo/lJsCwuGxkQeIU4oaWxX8WOIF6bM3CbeWcJTvGAV34fZ+lrbERpuw26haC0Lcds32/To8VgimB4du7ktgdmOR4+uH9NmCnP2cSK2T4NxNYbtuIW4qph9Cw3rGrSzxF0a3ae5mGB78csiuEWZdUsxZ7Bs4xhl+yNAMsaFMtWYdTOi2UQrI2lmrjdfr9nbdVudYbEY31fTeZtma9P1uHYj12y5qjesM/s2epzGNtHy74xOyZHjgI9PSfYoCIIgCIIgiFbSYaJUIBDAypUr8fjjj2P48OEYPnw4Nm/ejOeff75ziFKaBqxYwS2Y/H6eAgFu4VRbC9TU8HJ1NU+KAoC/86giTxEJiBi5YqYeQNjrQqhvT4TzcxHKy0awVy4C+dkI+Nyo0wKoVQOoUT9H7S8foFYLoEbzo1KrR60WQL0eQp0ehF8Ptfr9Kk10I9eRiVxHBvIcWciTM9HDkYFcOQu5jkzkOfiyLPHLIEYkAiwhKMhUBPRIkwKSFR+oyZdmWC/7gmFBEnW5MmUg7gIGmzgjiNy6SDIEGQmSMcW7KfxwmUgURCNmkGi1JRJ37LOAxYg79r5our3Vs341d172ZRuWBtHymRhbvY+2XKet+1k0oXq1ZFstumb3cxv7s74ldJlCltVg62QTxZradiLRqME69v56w3rzutcT7MsSwxJs01wv/nqNqsbR7TM9cV38zhiL7WcJcnGuaHZrNqsONgEuXkAVAJEBLqPCgwR9bH1jhmUbF+L6JLK0SzQua7wsbj8Jxhm/j8YwhTHdsAbTdJ7ruk1IM+otQUuL7WNtI8GytZ0E29ATCGX2bVh9bOvpjeRmH92+jgZMmNj08RMEQRAEQRCdkg4TpTZt2gRVVVFYWGjVjR07Fo8++ih0XYeY5NmRKj9fjVM/moldGYAuAJoX0NMAvSegCYAmGvVCVITSjNQ8YQC7jGRQZqRWkuZIQ7rThyxXJjJdmchyZSLDlYFsdxayXdnIcmch052JHE8OXJIz5uWLv6uZcXi4hY3fjLsjmDGBbG5jQtSaRxT41PCWVZC9LPAp46OuYYaQY1gBtXS5JX1bHRuoLcSgjthHe4oabbVOa/q3hwDVVuJQRwhV7b2PfW1rr+22VVu8gGYuN1eOT6ZIYk9mf11PvE5T27P3sY+zsbyx40l4jE1ss7l9JrLws4tiDXIJEGSAGd8NAosaqrmEhuM3ERK0NRANWcO6RNtpTbml6weDQH5+4+t1EbpFCASCIAiCILodHSZKlZWVITs7G05n1A0sNzcX4XAY1dXVyMlp/cxwbcnug/Lw8cC2255TdEAWHZBFGU7JAYfogENywCE64Xa44JRccMtuuCQXPA4PPA4PvLIXXocXPqcPGa4MpDvTkeHKQIYrAzmeHGR5suCSXJYIZIpC8aJNonJrhJ9EfVOWjhh7Kp8fgiD2jeaEqZYIUmbelODUkvaOyJs77sbaEgl+zR1j/D7tLvCJxDrGgKwsdHW6TAgEgiAIgiAIGx0mSgWDwRhBCoC1rBiucMlkZL9DsfmKzVi/dz0YYzHWQLIoQxIka9lhiEyyJMMhOuCUnHDJLjhEB1yyC07RCVmSEZ3CPtbdyz6VfXwbQRAEkQLEuAASRPvS6UMgEARBEARB7CMdJkq5XK4G4pO57Ha7E60SgxmrqL6+/aZU7eXqhen99/PhzphITFfbMM4PQRAEQRBJJy0tLSk/Iu1PCISOeH4CAE1nkMTUF2m7wnF0hWMwGZghQlfaeCKMDiLfw//uUvkYgK5xHF3hGICucRxd4RiArnEcAzPEdn82MGnu+anDRKn8/HxUVVVBVVXIMt9tWVkZ3G43MjIyml3f7/cDAKZMmdKu4yQIgiAIgkjEunXr4PP5Ony/+xMCgZ6fCCI5bAPwbLIH0QZ0hePoCscAdI3j6ArHAHSN49gGYOzCjtlXc89PHSZKDR06FLIsY/369Rg3bpw1uJEjR7YoyHnPnj3x8ccfJ+1XSoIgCIIgujdpaWlJ2e/+hECg5yeCIAiCIJJJc89PHSZKeTwenHzyyViwYAHuuusulJaWYvny5Vi4sGXynCiK6NWrVzuPkiAIgiAIonOxPyEQ6PmJIAiCIIjOTIeJUgAwf/58LFiwABdccAF8Ph+uvPJKHHvssR05BIIgCIIgiJRif0MgEARBEARBdFYExuLnXiYIgiAIgiA6C8FgEOPHj8fy5cutEAhLlizBmjVrsGLFiiSPjiAIgiAIYt9pPpgTQRAEQRAEkTTsIRA2btyI1atXY/ny5Tj//POTPTSCIAiCIIj9giylCIIgCIIgOjnBYBALFizA+++/D5/Ph4suuggzZ85M9rAIgiAIgiD2CxKlCIIgCIIgCIIgCIIgiA6H3PcIgiAIgiAIgiAIgiCIDodEKYIgCIIgCIIgCIIgCKLDIVGKIAiCIAiCIAiCIAiC6HBIlAIQDodx4403Yty4cZg0aRKWL1+e7CGlLHv37sXcuXNRVFSEyZMnY+HChQiHw8keVsoze/Zs3HDDDckeRkqjKApuvfVWHHbYYTjiiCNw//33g0Lq7RslJSW49NJLceihh2LatGl4+umnkz2klERRFMyYMQNffvmlVbdr1y7MnDkTY8aMwfHHH4/PPvssiSNMHRKdy/Xr1+Oss85CYWEhjjvuOKxcuTKJI+z6fPDBBxgyZEhMmjt3brKHlXLQfaHtSXRO77jjjgbX64oVK5I4ys5PU8/4dI22nqbOJ12f+8aOHTtw0UUXobCwEEcddRSeeOIJq42u0dbT1Pls62tUbosBpzqLFi3CDz/8gGeeeQbFxcWYN28e+vTpg+nTpyd7aCkFYwxz585FRkYGnn/+edTU1ODGG2+EKIqYN29esoeXsrz99tv4+OOPccoppyR7KCnNHXfcgS+//BJPPvkk/H4/rr76avTp0wdnnXVWsoeWcvztb39Dnz598Oqrr2LLli247rrr0LdvX/zhD39I9tBShnA4jGuvvRabN2+26hhjmDNnDgoKCrBq1SqsXr0aV1xxBd555x306dMniaPt3CQ6l2VlZbjkkktw9tln4+6778aPP/6I+fPnIy8vD0cddVTyBtuF2bJlC6ZOnYrbb7/dqnO5XEkcUepB94W2J9E5BYCtW7fi2muvjXm28vl8HT28lKGpZ/zrr7+ertFW0tw7E12frUfXdcyePRsjR47Ea6+9hh07duCaa65Bfn4+ZsyYQddoK2nqfJ5wwgltfo12e1EqEAhg5cqVePzxxzF8+HAMHz4cmzdvxvPPP0+iVCvZtm0b1q9fj88//xy5ubkAgLlz5+Kee+4hUWofqa6uxqJFizBy5MhkDyWlqa6uxqpVq/DUU09h1KhRAIBZs2Zhw4YNJEq1kpqaGqxfvx633347Bg4ciIEDB2Ly5MlYs2YNiVItZMuWLbj22msbWOqtXbsWu3btwksvvQSv14uDDz4Ya9aswapVq3DllVcmabSdm8bO5erVq5Gbm4trrrkGADBw4EB8+eWXePPNN0mUaie2bt2KgoIC5OXlJXsoKQndF9qexs4pwK/Xiy66iK7XFtLUM/6RRx5J12grae6dia7P1lNeXo6hQ4diwYIF8Pl8GDhwICZMmIB169YhNzeXrtFW0tT5NEWptrxGu7373qZNm6CqKgoLC626sWPHYsOGDdB1PYkjSz3y8vLwxBNPWDdXk/r6+iSNKPW55557cNJJJ2HQoEHJHkpKs27dOvh8PhQVFVl1s2fPxsKFC5M4qtTE7XbD4/Hg1VdfRSQSwbZt2/Dtt99i6NChyR5ayvDVV19h/PjxePnll2PqN2zYgGHDhsHr9Vp1Y8eOxfr16zt4hKlDY+fSdIWIh76P2o+tW7di4MCByR5GykL3hbansXNaX1+PvXv30vXaCpp6xqdrtPU0dT7p+tw3evbsiX/961/w+XxgjGHdunX4+uuvUVRURNfoPtDU+WyPa7TbW0qVlZUhOzsbTqfTqsvNzUU4HEZ1dTVycnKSOLrUIiMjA5MnT7aWdV3HihUrcPjhhydxVKnLmjVr8M033+DNN9/EggULkj2clGbXrl3o27cvXn/9dTz66KOIRCI49dRT8de//hWi2O21+Vbhcrlw88034/bbb8ezzz4LTdNw6qmn4owzzkj20FKGc845J2F9WVkZevbsGVPXo0cP7NmzpyOGlZI0di779euHfv36WcsVFRV4++236RfRdoIxhu3bt+Ozzz7DY489Bk3TMH36dMydOzfm+YpoHLovtD2NndOtW7dCEAQ8+uij+OSTT5CVlYULL7yQwiQ0QVPP+HSNtp6mziddn/vPtGnTUFxcjKlTp+K4447DXXfdRdfofhB/Pn/44Yc2v0a7vSgVDAYbPDCZy4qiJGNIXYZ7770XP/30E1555ZVkDyXlCIfDuOWWW3DzzTfD7XYnezgpTyAQwI4dO/DSSy9h4f9n777joyjX//+/ZmZ7Ng0IHUKVDlIEET0K5xzxqMeCelQUj4qKH8HuUfHYUVH52RAbChasX4/I0aOnyLEroqKIcgDpLZSE1M1uts38/pidyW4aCSUFrqePcWZnZmfv3eySnXeu+56ZM8nPz+fOO+/E6/Vy6aWXNnXzWpz169czduxYLrnkEtauXcuMGTMYPXo0p512WlM3rUWr7feR/C7aPxUVFVx99dW0adOGc889t6mbc0jKy8uz37+PP/4427Zt47777qOiooLbb7+9qZvXosm/Cwfehg0bUBSFHj16cOGFF/Ldd99xxx134Pf7pRt6PSV/x3/ppZfkPbqfkl/PlStXyvtzP82ePZuCggLuvvtuZs6cKf+O7qeqr+eAAQMO+Hv0sA+l3G53tTekdVvCgH03a9YsXn75ZR577DGOOOKIpm5OizNnzhwGDhyY8lcUse8cDgeBQIBHHnmETp06AeZJ1BtvvCGhVAMtWbKEv/3tb3z22Wd4PB4GDRrErl27eOaZZySU2k9ut5vi4uKUdZFIRH4X7Yfy8nKuuuoqNm3axOuvv47X623qJh2SOnXqxNKlS8nMzERRFPr164eu6/zlL39h+vTpaJrW1E1sseTfhQPvjDPOYOzYsWRlZQHQt29fNm3axBtvvCEn/fVQ9Tu+vEf3T9XXs3fv3vL+3E/WWLzhcJibbrqJs846i1AolLKPvEfrr+rr+cMPPxzw9+hh32+lXbt2FBUVEYvF7HX5+fl4PB4yMjKasGUt14wZM3jxxReZNWsW48ePb+rmtEgffPABixcvZujQoQwdOpT333+f999/P2XsM1F/OTk5uN1uO5AC6N69Ozt27GjCVrVMv/zyC7m5uSm/yPv3709eXl4TturQ0K5dOwoKClLWFRQUVCs5F/UTCASYPHkya9eu5eWXX5bxOQ6yrKwsFEWxb/fs2ZNwOExJSUkTtqrlk38XDjxFUeyTKUuPHj3YtWtX0zSoBanpO768R/ddTa+nvD/3TUFBAYsXL05Z16tXL6LRKDk5OfIebaC6Xs9AIHDA36OHfSjVr18/HA5HykBny5YtY9CgQTLWzD6YM2cOb775Jo8++iinnHJKUzenxVqwYAHvv/8+ixYtYtGiRYwbN45x48axaNGipm5aizRkyBDC4TAbN260123YsCElpBL107ZtWzZv3pxSYbphw4aU8XvEvhkyZAgrV66koqLCXrds2TKGDBnShK1qmXRdZ9q0aWzbto0FCxbQu3fvpm7SIe2LL75g1KhRKX+JXrVqFVlZWTI2536SfxcOvCeeeIKLL744Zd3q1avp0aNH0zSohajtO768R/dNba+nvD/3zbZt25g2bVpKMPLLL7/QqlUrhg8fLu/RBqrr9VywYMEBf48e9qmL1+vljDPO4O6772bFihUsXryY+fPnc9FFFzV101qc9evX8/TTT3P55ZczfPhw8vPz7Uk0TKdOncjNzbWntLQ00tLSyM3NbeqmtUg9evTghBNOYPr06axevZovvviCuXPncv755zd101qccePG4XQ6uf3229m4cSMff/wxzz77LJMmTWrqprV4I0eOpEOHDkyfPp21a9cyd+5cVqxYwdlnn93UTWtx/va3v7F06VLuu+8+MjIy7N9FVbuYiANj6NChuN1ubr/9djZs2MBnn33Gww8/zGWXXdbUTWvx5N+FA2/s2LF89913zJs3jy1btvD666+zaNEi6c5fh7q+48t7tOHqej3l/blvBg0axIABA7jttttYt24dn332GbNmzeLKK6+U9+g+qOv1PBjvUcUwDOMAtr9FCoVC3H333fznP//B7/czefLkaumf2Lu5c+fyyCOP1LhtzZo1jdyaQ8utt94KwIMPPtjELWm5ysrKmDFjBh999BFer5eJEycyderUlO4mon7WrVvH/fffz4oVK2jVqhUXXHABf/7zn+W13Ad9+vThlVdeYdSoUQBs3ryZv/71r/z000/k5uZy2223ccwxxzRxK1uG5Ndy8uTJfPnll9X2GTlyJAsWLGiC1h361q5dywMPPMDy5ctJS0vjvPPOk39j95H8u3DgVX1NFy9ezOzZs9m0aROdOnXi+uuv58QTT2ziVjZfe/uOL+/Rhtnb6ynvz32za9cuZsyYwZIlS/B6vVx44YVMmTIFRVHkPboP6no9D/R7VEIpIYQQQgghhBBCCNHoDvvue0IIIYQQQgghhBCi8UkoJYQQQgghhBBCCCEanYRSQgghhBBCCCGEEKLRSSglhBBCCCGEEEIIIRqdhFJCCCGEEEIIIYQQotFJKCWEEEIIIYQQQgghGp2EUkIIIYQQQgghhBCi0UkoJYQQQgghhBBCCCEanYRSQogWo0+fPtx4443V1i9cuJBx48Y1QYuEEEIIIYQQQuwrCaWEEC3KP/7xD5YsWdLUzRBCCCGEEEIIsZ8klBJCtCidOnXi3nvvJRKJNHVThBBCCCGEEELsBwmlhBAtynXXXceuXbuYN29erfvs3LmTa6+9lpEjRzJq1Cjuu+8+O8RauHAhkyZNYvbs2YwaNYoRI0Ywc+ZMDMOw7//mm28ybtw4hg4dyqRJk1izZs1Bf15CCCGEEEIIcbiRUEoI0aK0a9eOa665hmeffZatW7dW2x6JRPjzn/9MKBRiwYIFPP7443z66ac8/PDD9j4//vgjGzdu5I033uCOO+7glVde4euvvwbg448/Zs6cOdxxxx28++67DB8+nIsuuoiSkpJGe45CCCGEEEIIcTiQUEoI0eJMmjSJ3Nxc7r///mrbvvjiC3bt2sWsWbPo06cPo0eP5s477+SNN96gvLwcgHg8zowZM+jRowenn346ffv25eeffwbghRdeYMqUKYwdO5Zu3bpx3XXX0alTJ957771GfY5CCCGEEEIIcahzNHUDhBCioTRN4+6772bixIksXrw4Zdv69evp1q0bmZmZ9rphw4YRi8XYsmULAK1bt8bv99vb/X4/sVjMvv+sWbN49NFH7e3hcJhNmzYdxGckhBBCCCGEEIcfCaWEEC3SsGHDOOuss7j//vu57LLL7PVut7vavvF4PGXucrmq7WONKRWPx7ntttsYPXp0yvbkEEsIIYQQQgghxP6T7ntCiBbrpptuIhgMpgx63r17dzZt2kRxcbG9bvny5TgcDrp27brXY3bv3p2dO3eSm5trT88++yzLly8/CM9ACCGEEEIIIQ5fEkoJIVqs7OxsbrrpJrZv326vGzNmDF26dOHmm29mzZo1fPPNN8yYMYNTTz2VjIyMvR7zkksu4eWXX2bRokVs2bKFWbNm8c9//pOePXsezKcihBBCCCGEEIcd6b4nhGjRzj77bN555x12794NmONNPf3008yYMYM//elPpKWl8cc//pEbbrihXsc7+eSTKSgoYPbs2RQUFNCrVy+eeeYZunXrdhCfhRBCCCGEEEIcfhTDGkhFCCGEEEIIIYQQQohGIt33hBBCCCGEEEIIIUSjk1BKCCGEEEIIIYQQQjQ6CaWEEEIIIYQQQgghRKOTUEoIIYQQQgghhBBCNDoJpYQQQgghhBBCCCFEo5NQSgghhBBCCCGEEEI0OgmlhBBCCCGEEEIIIUSjk1BKCCGEEEIIIYQQQjQ6CaWEEEIIIYQQQgghRKOTUEoIIYQQQgghhBBCNDoJpYQQQgghhBBCCCFEo5NQSgghhBBCCCGEEEI0OgmlhBBCCCGEEEIIIUSjk1BKCCGEEEIIIYQQQjQ6CaWEEEIIIYQQQgghRKOTUEoIIYQQQgghhBBCNDoJpYQQQgghhBCHBMMwmroJohmS94UQzZeEUkKIg2LSpEn06dOH8847r9Z9rr/+evr06cOtt956QB/7ySefpE+fPgf0mPW1bds2+vTpw8KFC5vk8YUQQhz6li1bxtVXX82YMWMYNGgQv/3tb7n99ttZv359UzctRWP/Pl62bBlXXHFFoz1ec7By5Uouv/xyjj76aEaNGsWll17KypUrU/YxDIN58+Zx4oknMmjQIMaPH89rr72212OXl5dzzz33MGbMGIYOHcrll1/Ohg0bUvYJBoM89NBDjBs3jqFDh3LuueeyZMmSBj8P672SPPXv359Ro0YxdepU1q5dW+9jzZ8/n5tuugmA0tJSbr75Zr7//vsGt2lf3HrrrYwbN67OfRYuXEifPn3Ytm1bvY9bn/sUFRVxwgknsHXr1nofN1l9ft41+fHHH5k0aRJDhgxh9OjRTJ8+nYKCgpR9vv7662o/3z59+jBlypR9aqs4tDiaugFCiEOXqqosX76cnTt30r59+5RtwWCQTz75pIlaJoQQQrRMc+fO5dFHH+XYY4/ltttuIycnh82bN/PGG29w5plnMnPmTE455ZSmbmaTePvtt5tdMHcwbd68mQsvvJCBAwdy//33oygK8+fPZ+LEibz77rv06NEDgIcffpgFCxZwzTXXMGjQID7//HPuvfdeHA4H5557bq3Hv/HGG/npp5/4y1/+gt/vZ86cOVx00UV88MEHZGZmAnDnnXeyePFirr/+enr16sX/+3//j8suu4zXX3+dIUOGNPg5vfXWW/ZyPB4nLy+Pxx57jAsuuIAPPviAnJycOu+/fv16nnvuOd577z0AVq1axd///nfOOuusBrflYDnhhBN46623aNu27QE9bnZ2NhdffDG33XYbr7zyCoqiNOj+9fl5V7VixQomTZpEz549efDBB/F4PMyfP59zzz2XRYsWkZ6eDpg/B7/fz7x581Lun5GRsW9PVhxSJJQSQhw0/fv3Z926dfzrX//i4osvTtn2ySef4PV65ZeREEIIUU+ffPIJjzzyCFdffTXTpk2z148cOZIzzjiDG2+8kVtvvZUjjjiC3r17N2FLRWNYsGABXq+X5557Dp/PB8DRRx/NuHHjePXVV7nzzjvZtm0bL730EnfccQcTJ04EYPTo0ezYsYMvv/yy1lDqxx9/5JNPPmHu3Lkcf/zxAIwYMYLf/va3vP766/zf//0fFRUVfPjhh1xxxRX8+c9/BmDUqFH89re/5c0339ynUOrII49MuT18+HA6dOjABRdcwLvvvrvXSrhZs2Zx6qmn0q5duwY/dmNp1aoVrVq1OijHnjhxIs888wwfffQRJ554Yr3vV5+fd02eeeYZ0tPTeeWVV+zg6uijj+YPf/gDL7zwAtdffz1ghlJ9+vSp9vMVAqT7nhDiIPL5fBx//PH861//qrbtww8/ZPz48Tgcqdl4YWEh99xzD2PHjmXgwIGMHDmSqVOnppQrb9myhSuvvJJRo0YxZMgQzj33XD777LNa25GXl8cJJ5zAhAkTKC0trXW/n3/+mcmTJzNq1CiGDRvGlVdemVIuvnTpUvr06cOSJUu49NJLGTJkCGPGjGHWrFnE4/FqxysuLmbQoEE8+uijKetDoRDDhw/nmWeeqbUtQgghRFVz5syhR48eTJ06tdo2p9PJvffei6ZpPP/88wBceumlTJgwodq+V111Faeddpp9+/vvv+fCCy9kyJAhjBw5kltuuYXCwkJ7+8KFC+nfvz9vv/02Y8aMYeTIkaxbt67ev48//fRTTjvtNLvr2KJFi1K27969m+nTp3P88cczePBgzj77bP773/+m7BMOh3nqqac46aSTGDRoECeeeCJz585F13XA7Db17rvvsn379jq70T/55JOcdNJJfPTRR5x66qkMGjSI008/nR9//JHly5dzzjnnMHjwYE499dRq3dB+/fVXpkyZwrBhwxg2bBhTp06t1lVq9erVTJs2jaOPPpoBAwZw3HHHcd9991FRUWHv06dPH1577TX++te/MnLkSIYOHcq1116b0uXJ6q61dOnSGp8HQI8ePbj00kvtQArM717t27dny5YtACxevBi3283ZZ5+dct/HH3+cJ598stZjf/nll/h8Po499lh7XatWrTjqqKPsn3E0GkXXdfx+v72Pw+EgPT2doqKiWo/dUAMHDgRg+/btgPkz/P3vf8+cOXMYOXIkxx57LCUlJfz66698+umnnHrqqYD5ve2iiy4C4KKLLmLSpEn2MT/88EMmTJjA0KFDGTNmDHfeeSclJSUpj7u374V1WbhwIePHj2fQoEGcdtppKZ+Lmrrivfvuu5x88sn2/kuWLKF///7V3sc//fQT5513HoMGDeKEE07ghRdeSNnucrkYP348zz33nL3O+v5a19AS9fl512TDhg0MHz48pZLK6/UyePBgPv30U3vd6tWr6devX63HEYc3CaWEEAfVySefbHfhswQCAT7//HP7S4PFMAymTJnCV199xU033cS8efOYNm0aS5Ys4a677gJA13WmTJlCKBTi4Ycf5umnnyYrK4v/+7//Y/PmzdUePz8/n4svvpisrCxefPHFWiuzvvnmG84//3wAHnjgAe677z527NjBeeedV60rwE033cTw4cN59tlnOfXUU3nhhRd4++23qx0zKyuL3/3ud7z//vspA2x+9NFHBINBzjjjjPq9iEIIIQ57hYWF/PLLL4wdO7bWbjlZWVkcc8wxdqBz2mmnsXLlypTfj6WlpXz++eecfvrpAHz33XdcfPHFeDweHn/8cW677Ta+/fZbLrroopQgJR6PM3/+fO6//36mT59O9+7d6/37+M477+Tiiy/mmWeeoX379tx6662sXr0agIKCAs4++2y+//57rr/+ep588kk6derE1KlT7S5YhmFw5ZVX8sILL3DOOefw7LPPctJJJ/H444/b3w+uuuoqjj/+eHJycnjrrbc44YQTan0td+7cyYMPPsiVV17JE088QWlpKddccw033HAD55xzDk899RSGYXD99dfbr8HGjRs577zz2LNnDw899BD3338/W7du5fzzz2fPnj2AGa5dcMEFhEIhHnzwQZ5//nlOOeUUFixYwCuvvJLShsceewxd13n00Ue5+eab+eSTT3jggQfs7VYXrwEDBtT6PCZOnMhll12Wsm7z5s2sXbvWrpRbtWoVubm5fPfdd5x55pkMGDCAcePGpXSTq8n69evp3LkzmqalrO/atSsbN24EID09nTPPPJNXXnmFH3/8kdLSUubPn8/atWtTQs/9ZT1e165d7XV5eXl89tlnPPbYY0yfPp3MzEzef/99cnJy7GqcAQMGcOeddwLme9B6rzz99NPccMMNHHnkkcyePZupU6fy73//m0mTJtk/74Z8L6xqx44dzJ07l2uvvZYnn3wSRVG45ppr7PdJVYsWLeLWW29l2LBhPP3004wfP56rrrqqxj943n333ZxyyinMnTuXoUOHMmvWrGrDYZx00kn88ssv9us2YMCAvX4m6vPzrkl2djZ5eXnV1m/dutUObMPhMBs3bmT79u2cfvrpDBw4kLFjxzJv3jwZgF4A0n1PCHGQnXDCCXi93pQufB999BGtW7dm+PDhKfvu3r0br9fLLbfcwogRIwCzDHzLli32l6c9e/awYcMG+8snwODBg5kzZw6RSCTleEVFRVxyySV4PB5efPHFWvvDAzzyyCPk5uYyd+5c+xfysccey+9//3tmz57NE088Ye97zjnn2H+lHj16NIsXL+bTTz+tcVD3s846iw8//JClS5dy9NFHA+aXj2OOOYYOHTrU+3UUQghxeLOqRDp16lTnfrm5ufz3v/+lpKSEE088kXvuuYd//OMf9u+t//znP8TjcfsPQ4888gjdu3fnueees3//DRkyhFNOOYV33nmHCy64wD72lVdeaZ/Y5ufn1/v38X333cdvfvMbwDzJ/f3vf8+3335L3759efHFFyksLOTf//63/dyOP/54Lr74Yh5++GFOPfVUvvjiC77++mseffRRe7ysMWPG4PF4eOKJJ7jooovo3bs3rVq1wuVy7bWLUCgU4q677rLbtG7dOh555BHuv/9+u6IoGAxyzTXXsHHjRvr168ecOXPwer289NJLdmXQ6NGj+d3vfscLL7zALbfcwq+//kq/fv144okn7H2OOeYYvvrqK5YuXZrS9eyII45g5syZ9u0VK1akVJbvSxeviooKbrnlFlwuFxdeeCFghpm7du3ipptuYtq0afTo0YMPP/zQDmtq675XVlaWUgFlSUtLo7y83L59ww03sGbNmpTvQNdccw0nn3xyg9puicViKc9n9erVPPDAA6Snp6cEXbFYLOX7IphB0qBBg+zQ1u/306tXLwB69epFr169KCkp4ZlnnuFPf/qT/RqA+fO44IIL7Pd8Q74XVqXrOk899RQ9e/YEwO12c/HFF7N8+XJ++9vfVtv/iSeeYOzYsdx3330AHHfccTidTh555JFq+95www12WHbkkUfy0Ucf8c033zB27Fh7n0GDBgGwZMkSunfvjt/v3+tnor4/76rOOussbr/9du6//34uu+wyVFXlpZdeYt26dfbP8tdffyUWi7Fx40auv/56MjMz+e9//8usWbMoLS21u/iJw5eEUkKIg8rj8TBu3LiUUOqDDz7gD3/4Q7W/9LZr145XXnkFwzDYtm0bmzdvZsOGDfzwww/2F9w2bdrQq1cv7rjjDr788kuOPfZYfvOb3zB9+vRqj33ZZZexdu1aXn75ZbKzs2ttYzAY5Oeff2batGkpfyHKyMhg7Nix1cqWhw4dmnK7ffv2BIPBGo99zDHH0LFjR/7+979z9NFHs3PnTpYsWcKsWbNqf9GEEEKIKqyKAqfTWed+1u8xwzDw+Xz87ne/48MPP7RDqQ8++IDRo0fTrl07QqEQP/30E5MnT8YwDPskskuXLvTs2ZOvvvoqJZRK7n7TkN/HycFB586dAezu9N9++y1Dhw6tFraddtppTJ8+nQ0bNvDtt9/icDg46aSTqu3zxBNP8O233zZ4DK1hw4alPBcgZQykrKyslHZ+8803jBw5Eo/HY79Ofr+fESNG8PXXXwNmaHHssccSjUZZt24dmzdv5tdff6WwsNA+nqVqSNC+fXtCoVCDnkOyQCDA1KlT+fnnn3niiSfs1zMajVJUVMSTTz5pjzE0evRo8vLymDNnTq2hVF0VLNb3tz179nDOOefgcDh4+OGHadeuHV988QVPP/00Pp+PSy65pMHPo6bKsN69ezNnzpxqg5xX7Q62devWat/Rqlq+fDmRSKRatf6IESPo1KkT3377LWeeeWaDvhdWlZ2dbQdSUPmeLysrq7bv5s2bycvL49prr01Zf8opp9QYSiV/lrxeL23atKk2NEV6ejoZGRkNurpffX7eNTnnnHMIBALMnj3bHlx9/PjxnHvuubzzzjsAdOvWjblz5zJo0CA7aB09ejQVFRXMmzePyy67zB4QXRyeJJQSQhx0f/jDH5g2bRo7d+7E7XazZMkSrrvuuhr3fe+993j00UfZsWMHWVlZ9OvXD4/HY2+3rixjDeK4aNEinE4nv/vd77jnnntSqqFCoRCdO3fmkUce4a233kJVa+6xXFZWhmEY9pfSZG3atKn2JSK5PWBeZbC2X+aqqjJhwgRefPFF7rrrLv7+97/j9/v5/e9/X+P+QgghRE2skMGqmKrN1q1bSUtLs0OQ008/nffee4/Vq1fTpk0bli5dancTKy0tRdd1nn/+eXscqmRutzvldvLYRQ35fZx8P+t3sfV7s6SkhC5dulR7bOt3cmlpKSUlJWRnZ1frWmSFFDWd7O9NTVUhXq+31v2Li4v58MMP+fDDD6tts060re54r732GsFgkA4dOjB48OBqr2NNj1XXd4m92bFjB1OmTGHjxo089thj/O53v7O3paWloSiKXc1mOe644/jyyy8pKCio8fuP3+9PGePKUl5ebgcIb7/9Njt27ODf//433bp1A8xBrg3D4JFHHuGMM86o84+CNfnb3/5mLzudTnJycmjdunWN+6alpaXcDgQCdf4MAXvcqLq+8zX0e2FVye93qAx1rPHPklljt1V9jjU9NtT/feP1egkEAnW2M1l9ft61ueSSS7jwwgvZsmUL2dnZtGrViptvvtn+Nyg9Pb3a+w/M3hTWFTNlAPTDm4RSQoiD7je/+Q1paWn861//wufz0blzZ3vQymTff/89t9xyC5MmTWLy5Mn2lVMefvhhli1bZu/Xrl077r77bu666y5Wr17Nv/71L55//nmys7Pt8QIAXn75ZVatWsXll1/OK6+8Uu0KgJb09HQURanxl3F+fn61v2421IQJE3jqqaf4/PPP+ec//8nJJ59c4xdUIYQQojatW7fmyCOP5N///jfXXnttjX9oCQQCfPXVV4wbN85eN3r0aHJycvjnP/9JTk4ObrfbrpixAouLL77Y7haXbG8n+PX9fVyXzMxM8vPzq6231mVnZ5OZmUlRURHxeDwlmNq9e7e9z8GWnp7OMcccU2P1j3XRlrlz5/LSSy9xzz33cOKJJ9on81UHGT+Q1qxZw+TJkwmHw8yfP5+jjjoqZXtubi6GYRCNRlO+e1jVXlX/0Gbp3r07X375Jbqup7zXNm/ebFcB5eXl0bp1azuQshx11FHMmzfPDikawup6ti+ysrL2GhhZYWlBQQE9evRI2Zafn0+XLl0O+vfCZO3btweoNt5UbeNP1VdpaWmDXvv6/Lxr8vPPP7Njxw5OPPHElP3+97//0b9/f3t5+fLlnHfeeSnHtsbvOlhXIhQthwx0LoQ46FwuF7/73e/497//zT//+c8av/iCeTlaXde5+uqr7UAqHo/bZfG6rvPjjz9yzDHHsGLFChRFoV+/flx//fUcccQR1QZazMnJ4Te/+Q1/+MMfeOKJJ2otY/b5fAwcOJB//vOfKYNKlpWV8emnn1Yb+6qhOnXqxOjRo3nllVdYtWpVjVdCEkIIIfZm2rRpbNy4sdpVXcH8fXnXXXdRUVGRMvi1pmn88Y9/5JNPPuFf//oXv/vd7+xKDr/fT//+/dmwYQODBg2yp969e/Pkk0/WeeW3hvw+rstRRx3Fjz/+WK0C7L333iMnJ4fc3FxGjhxJLBardjVfayB06/d0bRXRB4J1xcF+/frZr9PAgQN56aWX+OijjwBYtmwZvXr14qyzzrIDqV27dvHrr7/WWCWzv3bs2MEll1yCoii88cYb1QIpwK5Q+eCDD1LWf/zxx/Tp06fGijEwuyKWl5fzxRdf2OsKCwv5/vvvGTNmDGBe/a+wsJANGzak3PeHH35AVVU6duy4X8+voTp16sSOHTtS1lWtrhsyZAgul4t//OMfKeu///578vLyGDZs2EH/Xpisffv2dO3a1X4PWf7zn//s8zFLSkoIhUINev3r8/OuybfffstNN92U0oXwq6++Yu3atXbF3q+//so999xT7WqWH374IZ06dbK7N4rDl1RKCSEaxcknn8yUKVNQVZXbb7+9xn0GDx4MwL333stZZ51FSUkJr732mn2FnmAwSP/+/fF4PNx8881cffXVtGnThq+//ppVq1bZl/2t6rbbbuOLL77grrvuYt68eTXuc+ONNzJ58mSuuOIKJk6cSDQaZe7cuUQikRovvd1QZ599NjfccAM9e/ZMGbNCCCGEqK/jjjuOW2+9lYcffphVq1Zx1lln0bZtW7Zt28Ybb7zBqlWruP/+++nbt2/K/U4//XTmz5+PqqrVuundcMMNXHHFFdx4442cdtpp9lX2fvrpJ6666qpa27Ivv49rcskll/Dee+9x8cUXM23aNLKysli0aBHffPMNDzzwAKqq8pvf/IZRo0Zx++23s2vXLvr27cu3337L888/z5lnnmkPZp2RkUFBQQGfffYZ/fr1o23btg14det21VVXcd555zFlyhTOP/983G43b731FosXL2b27NmA+T3m6aefZu7cuRx55JFs3ryZ5557jkgk0uDxogoLC9myZQu9evWqNTi677772LNnD/fccw+BQIDly5fb26xBvkeNGsXYsWOZOXMmoVCI3r17s2jRIn744Qeefvppe/8tW7ZQWFhod6M66qijGDlyJH/5y1/4y1/+QlZWFk8++STp6en2QNtnn302r776Kpdffrn9B8Wvv/6a+fPnc8EFF9jdK3fu3MnOnTvp378/LperQa9DQ4wZM4bXX38dwzDsLnNWOPjpp5+SmZlJ3759ueKKK3jqqadwOp2MHTuWbdu28cQTT9CrVy/OPPNM4OB/L7RYV+a76aabuOuuu/j973/P6tWreeqpp4B9C1qt3gXHHnssYFZQrlu3jq5du9ZalVSfnzeYFwWIRCJ2FdRpp53G3Llzue6665g8eTJ5eXk8+OCDDBs2zB6Yfvz48fbFAK677jratm3LP/7xDz7++GNmz559UMNk0TJIKCWEaBTHHHMMGRkZdOjQodYy4FGjRnHnnXfy4osv8q9//Ys2bdowatQo5syZw9SpU1m2bBnHH3888+fPt6+SU1paSrdu3bj33ntrrUBq27YtN9xwA/feey+LFi3ijDPOqLbP6NGjefHFF5k9ezY33HADLpeLESNG8NBDDzV48NSaHH/88SiKIlVSQggh9ssll1zC0KFDefnll3nooYcoLCwkJyeHMWPGcP/999sBTbK+fftyxBFHUFRUxOjRo1O2HXvsscybN485c+ZwzTXX4HQ6GTBgAC+++GKd47y43e4G/z6uSU5ODm+88QaPPPII9913H9FolL59+/L000/bVypTFIXnnnuO2bNn89JLL1FYWEjnzp254YYbUrrTTZgwgc8++4ypU6dyzTXXpFztbn/17duX1157jccee4ybb74ZwzA44ogjeOqpp+x2TpkyhaKiIl555RWeeuopOnTowOmnn263v7S0lIyMjHo93qeffsr06dN55ZVXGDVqVLXtkUiETz/9FKDGrpIjR45kwYIFgHl1tzlz5thXOuzVqxdz5sxJ6eb59NNP8+6777JmzRp73Zw5c3jwwQd5+OGH0XWdYcOG8fjjj9td4Px+P6+//jqPPPIIDz74IBUVFeTm5nLXXXdxzjnn2Md5++23mTNnDv/9738PalXMiSeeyFNPPcWKFSvsPwD27t2bU089lddee40vvviCf/zjH3aI+uqrr/LWW2+RlZXFSSedxHXXXWdXER7s74XJ/vjHPxIMBpk3bx7vvPMOvXv35q9//St//etfq41PVR+ff/45gwcPtsehW7lyJRdddBEzZ86s87O5t583wD333MP27dv5+OOPAfPzO2/ePB588EGmTZtGRkYGEyZM4Nprr7Wr1LxeLy+++CKPPfYYs2fPpqioyB68Pnn8M3H4Uox9HVFPCCFEvX344YfcfPPNfPbZZ7UO2CmEEEIIcSi64IILePzxx6tdQe9Au/LKK8nOzmbmzJkH9XEOpH/84x/0798/ZYyrTz/9lClTpvD3v/+9WuVjXYLBIMcddxwPPfSQBD6ixZBKKSGEOIgWL17Mzz//zJtvvsmECRMkkBJCCCHEYWXp0qWEQqFaryh3IF1//fVMnDiRq6++utHHtNpX7733Ho899hjXXXcdHTp0YPPmzcyePZuRI0c2KJACePPNN+ndu7ddvSdESyCVUkIIcRC99NJLPP744wwfPpzHH398r5fVFUIIIYQ4lGzfvh2fz9coV0kE8yqIq1evrvGCAM1RUVERjzzyCJ9//jmFhYW0adOG8ePHc80115CWllbv4xQWFnLGGWewYMECcnNzD2KLhTiwJJQSQgghhBBCCCGEEI1OhroXQgghhBBCCCGEEI1un0OpSCTCqaeeytKlS+11W7du5eKLL+bII4/k5JNP5ssvv0y5z9dff82pp57KkCFDuOiii9i6deu+t1wIIYQQQgghhBBCtFj7FEqFw2FuuOEG1q5da68zDIOpU6fSpk0b3nnnHU4//XSmTZtGXl4eAHl5eUydOpUJEybwt7/9jVatWnHVVVdR396DhmEQCATqvb8QQgghxOFOvj8JIYQQojlrcCi1bt06/vSnP7Fly5aU9d988w1bt27l3nvvpWfPnkyZMoUjjzySd955B4C3336bgQMHcumll9K7d29mzpzJ9u3b+fbbb+v1uOXl5QwfPpzy8vKGNlkIIYQQ4rAk35+EEEII0Zw1OJT69ttvGTVqFG+99VbK+p9++on+/fvj8/nsdcOHD2f58uX29hEjRtjbvF4vAwYMsLcLIYQQQgghhBBCiMOHo6F3mDhxYo3r8/Pzadu2bcq61q1bs3Pnznptb3LBPFh8HIT3gCMdnNaUCa7sysndGtxtwWNN7cDTHlStqZ+BEEIIsV+Su3gZGA1a35Bt+7K9rn1cmgu3w11tfyGEEEII0bw1OJSqTSgUwuVypaxzuVxEIpF6bW9ysXII7YR4EKIlEGrAfRXNDKi8Hc0prQek94D03ubkbmPuo6iJuVZ5WwghRIMZhoGBkTIHqq2ra1vVdVWPW9v25PU1LScfp+p9qt7WdR0AncS8ym27PRhgpN42jCptq7Jf8jpr36rr7NezHmFSXWMS1RlOVQ2SqtyufrOGMGovAVVbX1tGdBpRbR8hhBBCiIMhrhtoqtLUzdgvzeU5HLBQyu12U1xcnLIuEong8Xjs7VUDqEgkQkZGxoFqwv7J6A1n5kHRTxAphEgxREshWgyREnMeLTHXRwohUpRYXwJGHEI7zIll1Y/tagX+nompF2QOAF87M5hSXUmTG1QHKI7qc8UBqtO8LYQQB4kVcuiGjmEk5jXcrms5ef+qy8nzuB5HR0fXdXOe9BjWtpQ2JParNXRKDmwS4QzUHEYl72/vU+U+GIACCop5vyrLiR1TlhVFwcCocT9rm/UYipL6JUBJ7Gytr3q76n712Xdv+9d2n5r2qem+9b3/3p5HXcep6z4AJeESwvHwXu/bXEQiESZMmMAdd9zBqFGjAPPqxXfccQfLly+nY8eO3HbbbRx77LH2fb7++mseeOABtm7dypAhQ7j//vvp0qVLUz0FIYQQ4rCnqQrXvvkj63YHmrop+6RXWz9PnDe0qZsBHMBQql27dqxbty5lXUFBgd1lr127dhQUFFTb3q9fvwPVhP3nyoR2v6l9u6GDHoF4CGJBs7oqnA+BzRDcZnb9ixZDeLd5O7gVKnaZIVZhIRR+V3ksTwfIGmgGVNlDwNUa0KHqX4MVNRFEaZUBleYBzZuYXKA4E4FVcsAl3QmFOFTohl4Z1CSWq05W+FPjtqSgJ6bHiBvmcexl3VyuGgzZj41uVt8kB0xVK3Yq05zq4U2V4MZaZ4UQqqKikBp6KIpS73WqoqYEGjXtW5/1VZdFy6BFWs7vu3A4zI033ljj1YuPOOII3nnnHRYvXsy0adP48MMP6dixo3314quvvprjjjuOp556iquuuor33ntP3qtCCCFEE1q3O8DKvNKmbkaLd8BCqSFDhjB37lwqKirs6qhly5YxfPhwe/uyZZVVRKFQiP/9739MmzbtQDXh4FPURCDkMceXAuAIyB5WWVUV2pmonjLMkAsFAuuhdA2U/Qqlq6B0LVTsgJ07YOdH5mHSe0POcdD2N5DZv7JrnxE3Jz2WWI5CpAKMAjBiqSGW6qysrlI94PCDw2e2V3WB5q5clq6DQhwUVnAUN+K1LlcNmOKGGRbF9BjReNQMixIBUsyIpQRFKdVDVdZVZQdD1u1EIGOFOHUta4qWEuAkz5NDoKqBkjg0GIZhvh/1aMr70rrdkCn5/W29t611ycvJ73vrM5N8fytAtbcZlfufN/A8xnQd09QvW53WrVvHjTfeWO2zal29+M0338Tn89GzZ0+WLFnCO++8w9VXX51y9WKAmTNnMmbMGPvCM0IIIYQQLdkBC6VGjhxJhw4dmD59OldddRWffPIJK1asYObMmQCcddZZzJs3j7lz5zJ27FieeuopOnfufGh8oXJ4zcnbzgyXwoVQsdMcPD0WAF8nyBpUGQTFAlD0MxT9AIU/QPHPULbWnDbMNwdP7/AH6HQK+LtXdvOri2GYIZUeM4OreLkZkukx7L4iqsOsqtLcoKWBKwM0X2XQpjoTVVct56/OQhxI1gly1RPjmuaxeIyIHiEajxLRI8Ti5gm7bpjd0eLE7S5qhmGYt3Wr35Y1qwyNVEW1wx5r2V6XCImcqjM1OFK1lGoh0fLE9ThRPUo4FiYSjxCOh4nGo4Tj5u1IvPI9Fo1HK9fp0cptVW5H9WjKfaxQKfl9au1jBU3J97Pe+y3Jxxs/5vbf3N7UzaiTFSJdf/31HHnkkfb6/bl68SHxHUoIIYQQh7UDFkppmsbTTz/NX//6VyZMmEBubi5PPfUUHTt2BKBz5848+eSTPPDAAzz11FMMHTqUp5566tD767qigqeNOfl7QLgAAhuhfCu4sszqKYcfckabE5jjVBV8Dbs/h/wlZpe/jS+ZU2Z/6HQadDzZrHqq9XGVym58eGveR4+Z3Q/1CEQKoCIvUWmlgOYErCorB6iJoE3zJroGOpO6CTorq7IOtZ+fOCSkVBrVVrERj1ERr0g50a9W0ZSoykgeXyi5e5qmaCnhkaaat52aE7fiTt2WqDwSzVs0HqUiVpEyhePhauus9eFYOGVurY/EIqnbk9ZZ77dwPExMjzX1U643p+rEoToaNGmKVvN6VbO3aaqWsr+qqLXuV227ohGKhTi689FN/fLs1SF79WIhhBBCiP2wX6HUmjVrUm7n5uby6quv1rr/8ccfz/HHH78/D9myOHzg6GpWPpVvhfKNUL4Z3DmpAZMrywydOp5sBka7v4Dt/zCDqpL/mdOvc6DzmZB7Lnjb71t7rMCJKuGWYZjVVXrU7CIYD5vjZVUkugxWPYaSqLhSk8a3cqSlhlf2WFcyvpXYP4ZhEDfitXYfisaj1YKBlConPW5XLZkHNGeKYoZKVpBkLbs0l33ia50ES5jUvMT1OKFYiFA0RHm0nFA0RDAWNOfRIMFokIpYBcFo0N4vFAvZy1aoFIqZy8nrmrJCSFVUXJoLt+Y2g83E3KW5zEl12bedmjPltktz4VTNZYfqSJk7NacdKFnrnVriduIYDtVh72Ptb623wp/m+jnYE9yDz1nHH22auRZ/9WIhhBBCiP0gl3JrDJobMnqZYVJgkxlOxYJmNVVVqgva/9acwoWw41+w5W8Q3AKbFsDm16HdWOhxKWQccWDapyiguOrZRTCe2k0wEgIjH/R4oookUUZiXz0wEUw50hIDsyfGtJKB2Q97uqFX625khU6RWMQODCpiFfbYSrF45bgyyRfhUlHtagsrXHKqTjSn1uxPqA8XuqETioYIRAKUR8vNeaS8cjlaTjAatNeVR8sJRoKVy4mwqTxS3ihXWlMVFa/Di8fhwa258Tg85rLDbd+uabnq3OPw2KGRtezW3Obc4U4JoRxyddXDUou/erEQQgghxH6Qb8CNyemHrAHgbgUlq6B8mxlU1XYi4m4F3SZC7nmQ/7UZSO35FnYuNqd2v4Vel0N6r8Zpv6KYQRMOqCtDsse3iprzeBBipYnbSX2g7IHZnYmKK18ivHJXCawSIZaECi1GTePcWN2VglGzosWqaLIm3dBTusZV7f7j1txoLs0OnUTjiutxyqPllIZLKQuXURZJTOHUeSASsKfk2+WR8sor9B0gVnCU5krD6/Dic/rwOr34HOY8eZ3H4cHr8NrrrZApeZs1dzvcOFWnBJmiURwSVy8WQgghhNhHEko1NkUBX0czoCr5HwS3g6etOX5TrfdRoe2x5lS2FtbPN0OpXf+FXR9D+99D7/+DtC6N9zzqkjK+VS2qDsweC0CkKPWKgoqSuFJg4liaL3FFQW/l1QRVF6huCa0akVXhZA3MbE1WV6pgNJgyeHJcT3SHUsyKpuRxZTyaB4ezcuwYcfDF9Bhl4TJKwiUUVxRTEi6hNFxKSUWJvVwaLqUkXEJZuMy+HYgEDkiopCkafpcfv8tPmjONNFdajXOf05ey7HP6SHOm4XV67XUuzSXBkWjxDourFwshhBAJcd1AU+X7m6gkoVRTcWZA9jBwpEPZOjCyzKBqb9J7w5EzoWwyrHveDKZ2/scMp3LPg56TwZl+0Ju/3+ozMLuhm9VVejTRVbAAKnYkxrlSsAdot8au0nzmc9c8icDKnRRc1RGQiRSGYdhVTdYYTZF4hEAkQDASJBgLmuM6xc0KJyuoUFFTxqLxODx2+CQODsMwKI+WUxgqpKiiiOKKYopC5jxlCpvzkooSyiJl+/WYHoeHDHcGfpefdFd6yrLf5SfdnW4v1zS5NbcESUIkOayvXiyEEOKwo6kK1775I+t2B5q6KfvshD45/GV836ZuxiFDzhabkuYyr66nuqFkpbmuPsEUmF32hj4EpWvMQdALlsCmV80B0ntfCZ3PqL1bYEuhqGaopLlr3m6FVtYg7SlXFEzcX3Wa42VpbjMAtLoHpoRW7sOuyko39JTBwa1Bn8vCZZRHy+0udzE9Zg8QnjwAss/hw+kyx8CRgOHAiukxiiuKKQgWUBgqpDBUyJ7QHopCRXb4ZM2LQkVE9eg+PU66K51MTyaZ7sTkySTDnUGGO4NMd+WyNVkBlFOTgFeIA0muXiyEEOJws253gJV5pU3djH3WMyetqZtwSGnhqcUhQFEgvae53NBgCiCjD4x4EvK/gtWPm4Oo/+9B2LoQBtwGWQMPeJObDSu0orbQKl5ZaRUPQrTE7C5odUFKvlKglmZWWTm8oFaptFLUxnpGB1wkHkm9jH20gpJwCeVRc7DoaCxKzDAvSa+gVA7IrHlId6XLuDoHUDAaJL88n4JQAQXBymlPcA8FIXNeGCqkuKK4wd3kvA4v2Z5ssrxZZHuyzWVPVo1TpjuTdHe6VLAJ0YTk6sVCCCGEECY5K2kOrGBKUaD4F8BoeBe8nDHQehRsfQfWPQdlv8I3l0CXCXDEVLO74OFG0UDTzO58VdljWkVqqLIyEgOwuxKBlc98/Ry+RFDlaXaBVXK1U0WswhwzqKKEiliFGT7FoxgY9iXnXZoLn8OHy+2ScGI/xfQYBcECdpfvZnf5bvKD+eSX59vz3cHdFAQLCEaD9T6mqqhke7Jp7W1NK2+ralO21wyeWnlbkeXJwuOo4T0uhBBCCCGEEM2cnI02F4oC/h7mcvEvZtjhaGBZoOqA3HOhw4mw+gnI+4cZUu36BPreAB3GH3bd1Gq1t8HYdSuwipgDsFfsTOoWqCUFVv5EhVVSYHUQuwTG9Jh99Tqru11JRQmhWIhwzLyaHZhd7axLzqe50qTiaR/F9Th7QnvYGdjJrsAudpUnTYFd7C7fzZ7QHvPKgfXgc/po42tTOXnb0NrXmtbe1rTxtaG1tzWtfa3JdGfKwO9CCCGEEEKIQ56EUs2JFUwZMfPKfGpiAO+GcmXD4Luh06lmV77yTbDidnNA9P7TwZNzoFt+6FEdiTG5fNW3pQRWiQorXU9cLdDqEug2rxTozEh0CbQCK0+9Bl03DINwPEwwGiQYDVIeKac4XEwgHKAiVmGOI2SApmq4NTduh9vsbifj/TRIOBZmZ2AnOwI7zKnMnO8K7DKDqPJdxI34Xo+jKRo5aTm0TWtLW19beznHl2MHUDm+HNJc0v9cCCGEEEIIISwSSjU3igL+nhANmmFSWiezMmdftB4BY96ADS/B+nmw+3Mo/MGsmur0R6ma2ld1BlbRysAqvAuC2zDHsLKuFJjo9ufMsK8UaCguKgwI6jrBWAWl4VKKQkUEY0EqYhXoho6iKLhVM3xq5W0llU/1FNNj7AzsJK8sj+1l2+35jrId5JXlsSe0Z6/HsAKndmntaOdvZ87T2tHe394ModLa0srbCrWZdOUUQgghhBBCiJZCQqnmSHVAVj/QQxDaAb7O+3EsJ/S6HNqNhV/uNSuwfrkXdn4EA28HT7sD126RqG5zAlUqYgzDHr8qHCmmvGwr5ZFyyqLl7IkECcZ1KjAwFDeqIw2PKwOPO51Mtx+HI61e1VWHq/JIOdvKtrG1ZCvbSrexvWw720u3s71sOzsDO/da6eR1eOmQ3oEOfnNq729Ph3Rz3j6tPW18baQrnRBNwDAMdENPmeJGHMMwiBtxe10wGpQqRCGEEEKIFkpCqeZK80DmANjzPVTk73+Xu/ReMGo+bH4d1j4HBUvgy3Oh31+g48lSNXWQxPU45bEQ5dEKApEgeypKKIuWE4qF0Q0dVVHxaGn4nBrZCmhGHIyQWSkX3WUGlHZ3wLTKsatUV+V0GFToBKNBtpRsYUvJFraWbmVryVZzXrqVwlBhnfd1a246pHegY3pHOqV3ooM/aTm9A5nuTKk6E+IAs0MkPW6HSXWtAzOEQsEuLlVQUBQFTdFQFRVVVVFRzWVFxak5cakuWntbk+3NbtLnK4QQQggh9o2EUs2ZKxOyBkDhMoiWNfyKfFWpDuh+EeT8Bn6+C0pWmvNdn8CA28Dd6sC0+zAWiUcJRIMEoiGKKsoorCghFK8gHIuiqgpuzYXX4SHD5cext+ob+wqBUYhXQKwM9ETVj6ImwionqN5EV8CksEpzm1cQbEFieoztpdvZXLKZLSVb2Fyymc3Fm9lSuoWCYEGd983yZNE5ozNdMrrQKb0TnTM60zmjM53SO9Ha11q61gnRQFZYFNNjKWGSFSQlB0tVwyQMUFQFDQ1N1VBVFQ3NDJhUDY/Dg1Nz4lSddrDk0BxoSmJ/RbWXrUDKWk7eLmGyEEIIIUTL17LOWg9H3vaQ3gdKfgbNmxjLaD/5u8GoebDxFVg3F3Z/CoXfmVfn6/RHyBwolVP1VBELE4iGKIuUs6eihJJIgGCsgrgeR1M1fA4PWe503L59GLC+risE6nEwomZgFS2GcH7lNtUBisustnOkJQZadyUGW3eZ25rw5xuMBtlUvImNxRvZVLyJTcWb7PDJunpgTbI92XTJ7ELXjK50zuhM18yudMnoQpfMLvhd/kZ8BkK0DFY3t+QAKTlYiukxe5uiKBgYZrCEGSo5FIddoWQtuzU3bs1thkmayw6UHKojJUiqba4qqoRJQgghhBDCJqFUS+DvBtEiCG6HtC4H5piqA3peCjnHwoq7ILAWti40J29XaPcH6HIqpHU4MI93iAjHI5RFgpRFysmvKKIkHCAYC6PrOm6HC5/DTVtvq71XQe0vVQM0M3hKZhhmUGVEIV5uBlaJrjHmfRLVVY408+qAqssMqqwugfs6qH4NApEAG4o22NPG4o1sKNrArvJdtd7HrbnJzcwlNyuX3MxcumZ2tacMd8YBa5sQLY1VsWSFSsmBknUbMIMlc6Gy65uaqDRKVC55NA8uhwu36saluXA73HZo5FAdaKqWEjIlL0vVoRBCCCGEOJAklGoJVAdk9IVIKYQLwN1m/49pGFBSBvkuiF0L8Z9B+Q6UnyG0BTY9B5vmgrM/ZP8W2vwGfJng84LXfdhUUkXjMcqi5ZRGyikIFVMcKaM8WoFh6Lg1Fz6nl0xXOpraTE7UFMUMmaihMkuPg2EOtk64AEI7E/fBrJ5SnWY1nsNvhl2aq7LCqo6B1itiFWws2si6onWsL1zPhqINrC9aX2f41Nrbmm5Z3eiW1Y3uWd3t5bZpbeWkVxzyrGCpasCUvM4OlxLsaqOkcMntNK/I6XK4cGtmwGQFSA7VUS1gsm7LZ0wIIYQQQjQXEkq1FM50yOxrDnzuqKheIdMQhcWwaTsUFEE0Cv406HAMqMdCrBxCS6HiS4ivhehK2L0Sdj0HDAbHKEgbBK2yID0N0nzg94Hj0Hgr6YZOIBqkNFLOnlApBRVFBGMVxPQ4Ls2J3+Glg69N8wmhGkLVAK8ZPCUz4qAnxq6KlkKkwO7CkzzQuq55yasIsK5kO2uLt7C2eBPrijawrXSbPVBxVTm+HHpk97Cn7lnd6Z7VnUxP5kF9qkI0puSucFbIZE1xPY5O4vOR+FypqloZEiXCJo/LY3aNc7jtMZeSw6Wqk4RLQgghhBDiUHBoJAmHC29H8PeAwDrwdW74VdciUdiSBxu3QTwO2ZngrlJR40iD9HHmFNsFoa8SAVU+8B3Ev4PSLCg5EvSh4OhqVk5lZZjHS/OaQVXV4zZj4XiE0kg5xRVl7ArtoTRSTjgexaFqpDm8tPVm4zgQY3k1V4oGmmYOjp5QEQuzrngra4o2srZ4M78Wb2Vd6XaCsXCNh8h0p9Mrqxu9snvSI7snPVv3pmerPqS793NwfiGaiBUo2eFSUkWTPfZZomDUGng7OTBKc6bhdrjtoMmpOasFS061cp2MsySEEEIIIQ5Hh/CZ9iFIUSCjN0RLoGK3OQh6fRUUwfotkL8HsjLN6qa9cbSD9AngPwOiaxMB1VIwikH5FLRPQe0E0RGQNxi2ZoCqgtcDGX5onWUGVGlec10zYRgG5dEQJZEA+aEiCiqK7S55XoeHTFc6HkfLCdX2V3G4jDVFm1lTvNmcF21mS2AHumFU29epOuiR0YnemZ3pldmJXunt6JXeltYuD4p9hu4AJQ7B9RBJA4cvMfB6UlfAAzh2lRD1ZVU0JVcxJYdOyeMxWV3lrNDIrbnJcmThcXjwODx2V7nksElCJiGEEEIIIRpGQqmWRvNARh/Y8y3EAub4P3UxDNi0DdZtBt2ADu1Aa2CFlaKCq485ZVwI4Z/MgCr8E8S3mxN/B09vcI8EfTDsicGO3WZI5XGbXQRbZZpd/vxpjT4ulW7olIQDFIfL2BUspDhcRjBegUt14ne24C55DVRYUcKqok2sKtzImqLNrC7axI5gQY37tnJncER2Lr2zutAnK5feWV3JTW9fd9WYYYCR6AoYr4BYmTmWFSSuJugyQyt77Cp30pUBnU1+ZUDRMiWHS1Un3dDNiiaj8opyDs2BQ3HgcrjIdGTicXjwOr04VSdOzWmHS1bg5FSdaAf74gVCCCGEEEIchiSUaok8OZDeC4pXmlVItVWdxOOwYQus3QzpfjMQ2l+KCzxHmZNeDhXfQcUSiKwyq6mia4HXwdUPskeBaxhEXFAagN0FZuDgdpuVWm2yzblVTXWAw4iYHqM4HKCoopS8YAFlkXKiegyvw43f5SXHkX1AH6+5KQ6XsapwIysLN7CqaCOrCzexK1RY476d/W3pk5VLn+xuHJHVlb7Z3WjjzWr4gyqKOQZVTQOj1zR2lW6Y90m+MqDmM7uRqq5EaOVMXBnQKYHVYaZqZVM0Hk0NmwAUs/tccrc4v9uP1+HF6/Ti1tx20JQcMlnLQgghhBBCiKYj38hbqrTuEN4DoV3g61h9ezQGv240x49qnXVwus+paeA7wZziRVDxLVR8A9F1EPmfOfGyGVB5R0LWcFAzoCIM5UHYU2RW1rjdZijVKtMMz9K85lX+nA1/e0bjMYojZewJFbMzuIeSSADDMPA5vbT2ZOLSar+KXEsWjFawumgTKwvX87/CjawsXE9eefUKKAWFrunt6Zfdjb7Z3ejbqjt9srqS7joAgeXe1DB2lS35yoCRPVCRdOU+1QGKw+z+p/kSk1Vh5TSrrBSHBFYtTFyPE9Wj1UKnuBHHMAxQQFEqK5ucqhOf04fX6bUDp+SwyaW57GUZAFwIIYQQQoiWQUKplkpzJbrxLYVomXl1PktFGFZvgG07IKd14ww6rmVD2nhziu02x56q+BZimyCy0px4CVx9wT0CMkdAq3ZmKBWOQKgC1m8FXTfDKE+imsoal8rvqzVYi+kxisJmEJVXXkBZNAgY+J0+2vva4DjEut3EdZ2Npdv5Zc96filczy971rOhdFuNY0B19benf6vu9G3Vnf7Z3emTnUua01vDUZtYrVcGTHQHNKIQj0AsaFZb2fezrg7orAys1MT4VYpLugQ2keTAyapuiupR4nrcHhzcGhjcqTpxaJXVTWnONDtscmmulMBJutAJIYQQQghxaJFQqiVztwZ/byj+2TyZVx3mFfZWrYftO6Fdzj5VG+03R1vw/9GcYrsSXfy+hdhGs5tfZBWULQBnT3APB88wyOoI2Ymz1VjMDNYKS2BnvhkoeD3mWFSts8DvI+7zUKRG2BMuIS9QQFm0HMMOolofUkFUYUUJv+xZz8971vPLnnWsLNxAMFZRbb923lb0b9WD/q262/NGqYA6mKzugDih6o80ZfyqWgIrHKA5QPWaXQKTuwNalVYy6HqD6IZONB5NCZ2iulnhZEkOnJyak0xPJl6nF5/TZwdMVQMnGRhcCCGEEEKIw4+EUi2dvxuEC8zuTq52sGYjbN8F7XPA0Qx+vI524D/VnGL5EP4eKr5PjD+13pwC/w+09mY45R4Gzl5mAOVPBCq6DhVh9OISSnZsYY8eZLsaosQZR0/3kZ6WRbv0LBxeX9OEcAdQTI+zrmQrKwrW8vOedawoWMf28t3V9vM5PPRv1Z2BrXsysFVPBrTuSY730B4jq5o6x68yzDGsjKgZVEWLIZKPdXE1FDVxX83s/ufwgcObGIjdeViPY5UcNCWHT9aV6RRFMcOmRODkd/tJc6bhc/rssMmarIonCZyEEEIIIYQQNWnZZ/DCPHnO6AO7C+F/v8CWImjbunkEUlU5csDxB0j7A8SLIfwDVCwzx56K74TyD81J8YN7MLiPBPdgAobGHrWc7e5CitRyonocX1ylXcyBIz8Ku/NB2wMuJ3g8kJFmdv/zuM2ui67mO45UIBJkxZ51rChYy08Fv/JL4XpCsXDKPgoK3TM6Mqh1Lwa17sXA1j3pntHpsLha4D5TFHOcKRzVK6wgMYZVzJziQYiVVl4lEKpXWWlecHgquwoqVmjVssayMgyjWtgUiUfMbnUJyVed8zl9pLnSSHOm4Xa4UwIna5LASQghhBBCCLGvmmFyIRrMlQ27XfDrr9CpR7MOYWxaFvjGmZMegvAKM6QK/wRGACq+hoqvMVCIKh0JKF0x1F5kO3Jxu2oYIyseN7suBsqhuMS8qpummqGUFVR5PeBxmQOrN0FFlWEY7AzuYXn+GpYX/MpPBWtZX7LNrkCxpDm9dgA1pE1vBrbqid/la/T2HtJUDTOtqmHQdXscq1iiyqrEvFKgNWaXQiKMciQqqjyJroGuysota2rkSisrdIrEI3bFUyQeQUdPNF2xQyenanar87v8dre65MmtuWUMJyGEEEIIIcRBJaHUoWDDBthcCh16AAGghY0jpHrBOwrdcxRF0VICFSsxwj/SKr6WDArJNraTbWwHfQnRuJ9SrSelWi9KtZ7EFL95DE0Dr5Y6GLoVVJUHK4MqpyMxOSE9MXi60wlupzl3HbgQQTd01pdsY3n+r/xYsIaf8n9lV6iw2n5d/O0Y3KY3Q9r0ZnDr3lIF1dTqGscKwNATY1fFkroGJoVWUHnFQNVhhlaap7LSygqrVGflfvVUtdIpJXQyElerUx1217l0dzp+px+v01tjpZNcpU4IIYQQQgjRlCSUauny8mDVKshuBb42ULzSrOxwZjZ1y+otGA9TEC1jW6SQPdEAccNNumMs+e4/4DFKyYivJTP+K+nxDTiNAK1jP9E69pN5X7U9ZWoPSrUeBLRcdCWp8qWmoCoWh2jUvOJfoNwcr8ra16GZgZXXC153ZUWVy1GvwCqmx1hVuIkf8lezvGANy/N/TVwJMKlJikaf7FyObHMER+YcweDWvWnjzTpQL6VoDIpqVkXhqiW0qlppVQaRQjPMSj6GVW2V3EVQcxLVIYpCRDeIYRAxdHRUDAy70qlq6ORz+VIqnKRrnRBCCCGEEKIlkFCqJSsshF9+MQOTjAxzXVoulKxOXI2vhm5uzUTc0CmMBtgZLWFHpJhArAKf5qKNIx2XWvm2jChZFKhHUeA8CsWIkaZvITO+joz4enz6Dnz6Tnz6TtrFvsZApVztTJnWnTKtGwG1C4ZS5TVwJMInb9UGxROBVQyKS6EgZg6KrQCqWlld5fOAzwtOB2GHwi+BLfxYvIEfCn9lRcFaKuKRlMN6HW4Gte7F0Jw+DG3ThwGte+B1eBCHsL1UWsV1nWisgki8gki8jFgsTFQPY+hxFMPAqTpwqBouh5sspzmeU5o7HZfDj9udhcvlx+3043KkoWhJVxFUnWbYJYQQQgghDklx3UBT5Y+O4tAioVRLFQjAzz9DOAwdO1au97Q1K6WCeeZyMztJDcUj5EdL2RLeQ2EsgAFkaT5auVvvtarDUBwEtB4EtB5sBxxGgPT4RtLjG8iIr8dtFOPXt+DXt9Ah+hk6GuVqJwJaNwJqLgGtC7pSSyCkaebkrmW8qmiMikiIFYVr+CG0lR/C2/glspMI8ZRdMx0+jszqwdBWvRnati99WnXH4XabwZY4LJhd7GJErDGd9BhRPYZhGICBpmo4FXNcp3SnnzSnF7/Lh1tz4lKduDWXuawoqOigRxNVV1GIF0Iwv/LBFDVpUHaneSVBzaq6clfpKpjcdVDGihJCCCGEaGk0VeHaN39k3e5AUzdln5zQJ4e/jO/b1M0QzYyEUi1ROAwrV5qVUl26pG5TVPDlQjRgdhlyt2maNiYxDIOiWDk7IyVsjxRSlqiKauvIwKnu+1swpvgpcgyiyDEIAJdemAipNpGub8RllJKubyFd32K2A4WQ2p6A2pWA1pWA2oWomlXr8Sv0CD+Vb2VZYCPLAptYGdxOzEgNoVo7/AzzdmGYuzPDnB3prmahApQDWyOwY51ZmeV2m1VWbpdZcWVVXrkcZhgmWpSYHk8MKB5LBFDRxBXsFBQFnKoDZ6KbXbY7A7/Li0dz49bM0MmlOXEnrnC3V1odlXVG3OwiaHUVjAUgUpToPpg0xlVyeKUmBmjXvOaxNW8irHIkBVeOpLm8P4UQQgghmot1uwOszCtt6mbsk545LWzsY9EoJJRqaeJxcwyp7duhU6eaxzhyeMDfE0r/Z45n40xv/HYCUT1GfrSMbeFCdkdLiekxWoc12pcrOAIlOMp2opVXoAXDaOUhcx6KoFZE0CrMuRqJokRjqOEYajSGEoubU1xHicchbqBgmIOY21dHM1+TuOIC1UBRdRRNR1ENfNoOfI4dtHUsBSfoDo2420PM5aPE62NJjsrnbcJ8lRVgua+MqJJ6Zbz2pHGUsxPDfF0ZmtmTThntURw1DiwEMR1iMbNbYGkACosTbVTMboGOxKDrbjf43GZg5XCYY1c5HYmuhhJaNQXDMIjoUaJ6PKnaKZp4i5nVTi7ViVN1kOny43d58Tk8iSonlx0+OVXHwR3XSUlU+NV0FcGUJ5QUXhlxiFeYAZaeuF15wKQAS6sMsFSPWXllBViqI1FxlTSgu31bqgKFEEIIIYQQ9SOhVEtiGLB2LWzcCO3bm4FFbdxZkNYNSn81B2VW93LSuj90HfYUQ0Eh5BcR3r2b4O6dhAsK0IpK6VMS4siSEM7SIIqu7/VwjaXCAUs6x/mkUzmfdivnm84QrZL/dCmBEzZVTt2LylH4FfgVWAxA3Osm5vcQT/MS93uI+b3E0n3E/V5i6V5znuEjlu6rnKe5zZ9nLG5eHbCk1HwdFQDFDBpUtTKY8rjMyelMDMqeCLSsAdodmnQRbKCq1U5RPUZMj5Fc7eTSnDg1J9nuDNJdPjyOymonq7ud1hIqieobXkFlUGVVX8UrIFZeGWilVGApiQHbtcpJTfx7o7nNSiy74io5wNIqB3qXIEsIIYQQQojDloRSLcmWLbBmDbRubVbX7I23vXkyGdyaGF9qP06eSwOQtxvydpnTjnzYmQ+79kD+HjNcSXBT96lv3OOqDGv8XuJpHuI+D3GfG93rJu51obtd6B4nutuJ7nSgu5wYLgeGQ8PQNAyHas5VBVQFQ1Eqq8YMAwzsCipF11HiOpFYlF+iO/kuup1v9DyWG7uJKKkhWecojC2DE4rhhALoXgxUKOhhJ3q2g5hHQQkZqMEIaiQGgBYKo4XCkF9S75fTUBQzwMpII5bhI5qZRiwzzZwngqtouhlqRdM8xD0u0BOBgEJiEHYlUUmlmoGD0wGeRMWVy5kaWDkclfseJldkq3lsp9qrndKcXtKcTVDt1NxY3fzqE2AZeiKoskKsOMRDZhWWtc6qDsSqZFSTQinN7B6oaGaQpXrMUEtzVQmvtOq37ekw+tkIIYQQQghxiJFQqqXYudMcR8rvh7R69sVVVPNqfPEQhAvA067u/eO6GTRt3m5OW3fAtp3mvLTuwfQMVaEi20+oVRrxNlnorbOItkonmuUnluU354nAxXA2ztsuZsRZHdzB94ENfB/YyPLgFir0aOUOCrRxpDMivTsj/N0ZnpZLd6eG38gjTd+OL74dXc9DI4xGBI3KK+sZqIRibQhVtCUSyiISzCAWSoNyBUegAi0QxBEIoZWFcJQFcVjzUnO9YhiJdSHYvvfnYmgq0cy0lNczmpVGLNNHND2NaLqXaIaXqN+L7rLCR6Xy6oGqmgilFHC6wO1MjG+VXG3V8iqv4rpuVjvpMSLxaFK1EygoOFQHLq32sZ2s8KlFVDs1R4qaqHJy1v8+Voilx4HEXA9DLJgUcCWFxYqSCGPVKmGUmjT2lbOyIlR1Vgmx9jZJqCWEEEIIIURTkVCqJSgqgl9+MUOCrKyG3Vdzgb87lFRAeA+4W6duLyyGd/4N3/8MW/IgEq3xMAC0zoIObaFTO2Id2lDSJo3tWSo7sx1EW2WQ5U7Hozbg5PQA0w2dtaFdfBfYwLLARn4IbKZcD6fsk+1IMwMovxlE5Va56l8MKCabYgaYKwwdt1GET9+BT8/DF8/Dq+/ESRCvowCvvwD8JN3fS0htS0htS0XSPKYkBYnxuBlIlQZxlJTjKC3HWRLEUVqOo6QcZ0k5jpIAzmLztiMQQonruArLcBWW7f11cDmJZieCq2wzvIpmmFVY0QyfOaV7iPk9iRPyRBWLmqi4Sq68sgZjd7kqgyqHVnm1QivAOkjjXiVXO8USwVOkxivZOUh3+vA7ffhdXjtwcmkOc646D69qp+bM7ubXwPtZYZUdXCUqsWLhKttq6CKsKJVBFlplmJbS5dDqZuiqDLZSgrAallGrbJP3mBBCCCGEEA0hoVRzV1oKK1ZAKAQdO+7bMZzpkN4bSldDpBhcWZBfCG99AB98khpEOZ2Q2xG6doQuHSqnTu3A6yEYD7MjUszmigJK4kG8qotsRxqO/ekauI8Mw2BjOJ/vyjbyfWAjPwQ2UhIPpeyTrnnsAGqEvzs9PW0bFk4oKmGlNWG1NUUMtB4Yp1GGV9+JT9+BV9+FV9+Fx9iDgxDp+mbS9c0ph4nho0JtQ0jNoUJtQ9jfhoqMNgS6dN1rt0olGjPDquJyHMVlOIvLcRaVmeuKAjiLyxLzAFowjBqJ4t5VhHtXUd2vn6qYXQaz0ytDrKzk8CotUX3lwXBoqd2wFKUypLICKmuAdmeiq6C13Q6yEkGAQwWtsithbWM7GYBapdopw5VGutOH1+HZtyvZiZbLCoQaUpVlMYzEgO56anhlxEGPmlVaWOv0xHvdqOFASlKgpWKGUmqVkCv5SoaOyuXkfah6n6rHqrpP869aFEIIIYQQYl/IWVxzFgiYgVRxsXmlvf3hzjaDqXVfwf97Cz76BqJmNyf69YIJJ8IR3c1KKK36CVBJLMiOYB5bw3soi1eQoXnp7GqF2ognS4ZhsDVSyPeJEGpZYCN7YqndCn2qi6H+XEb4u3OUvwe9ve3RDnQbFYWokkFUzaCUIypXG1E8egFeYzcefTfexOQyinEQxK9vwa9vSX1OqISVVlSorQgrralQWxNOLEeUDFA0DKeDaJtMom0y9960cMQOrZxFgSrzymVHSTmKbiRu1901EzDHtrLCq+z01AAr06q+8qG7HOYVIq38CgNdN4goOjF0ImpiWTFDMUPTcLhcuFxunC4P6S4PfncaflcWbpcHt9ONy+nG7fbicnlQnI4W0a1QNDPWgOz7yw63jKRB35PDrDgYoZr3s7ohVm9cUiWXgllCpiQFVdY2LfWqiNb4WvaymnofagnPkoO1qvva26TiSwghhBBCNA4JpZqr8nIzkCooMAOp/T1J2LIFXnwRPvzQDA0ABveBC8+AYQNqPL5hGBTFytkWKSQvXERIj5Kl+ejqat1oXaHywkV8H6gMoXZFS1O2uxUHQ9K6MiLdDKH6+To2SdUWgKE4CWkdCNEhZb1iRPDoe/AYBXj0fHMy9uDR96ASNdfHC6ofD5WwkkVEySasZhNWsomo2eY6NZsYvmo/N8PtItLORaRddt2NjcdxlARTw6rCshpDLDUWt8e/8m7ZXedhYx4X4ew0KjJ9hLJ9hLPSCGel4c42x8IyMjNwZWXizsjEozhxGwquqIo7ouLWFTQUUMJghLG7FDoSJ9xaUtdBt8vsUuhxmtV9VcfDsq5Q2ILGxxItQEq4dYC6KhsGZgVXcpClJ63TE4PIR8xx/1L2T9qv9kZjVzfa4ZTVbTc5ALPCMWu5ythdKctqIiBTUsOsGufW4yatq3W/GvaRgEwIIYQQ4pAmoVRzFAyagdSuXdC58/6dUK9bBy+9BP/5D+iJsVZGDIGzj4GhQ8BRfdB03dApiJaxNVzIzkgxMUMn25FGjjNj39tRTzsjJSxLCqHyIsUp252KxiBfZ4b7u3NUeg8G+jrjaubdtgzFVWNYhaHjNMrwGAW49T149ELcxh7c+h7cRjEqMTxGIR6jEGoYJieOk4iSSUTNJKJkmcuJ21Elk4iSgaHUcuKsacRapRNrlU6o5j0SbTRQS4MohSWoRSVohaU4ikpxFgZwFQfwFAXxFJfjKSrHEY7iqIjg2BEhbUfdXQdRFcjKgFZZkJ0JrRJTdmJqlQXZGZCVBl6P+d6N62agGo5AsMJcjicqVLBOXJOCLDVpfCxHIsjyus0rV1rdCVWtcllL6m6YHIDJSbE4mBQFc5wrOKi/kq1qLqCyG6PVTTFp2QrG9EiV9XrlcbDW1VUBBnYgBjWETqQGUCnBVNV5crWXRmVollivquDIAH/ugXq1hBBCCCFEI2neZ/OHo2AQfv7ZvNpep077HkitWGFWRn3xReW6Y4+FyZNh4EAIbofyTWBEwZkFQNzQyY+WsrliD7uixSiotHKkHdTBy3dFSlgW2MSywEaWBTaxLVKYsl1DZYCvE8MTV8gbktYFj+o6aO1pVIpKVMkkSiZlWs/UbYnAym0U4taLcBtFuPRic24U4TLK0IjiNQrw1lBlZYnhI6KmE1UyiCgZRBVrOT2xnE4EL1EMokacmBEnasSJ6HF0zLBH8YCjcxrOLhm4FA2v6sKveVA0F0pivBxDceCqiOIqKkcpKoHCEthTDEXF5ty+XQIlZaAb5rrCkr2/Tk6HGWBlZ5pBVXZm4nbycmKe6QeURIgVrwyyKsIQCJrrY4mTbitssk6srTDKqspKDqisKxW6nJWDvifvmzxAvLUsoZZoTpp6bCorzLKDsKTllHCMpO1WEBZLqhBL2m7dN14B3vYSSgkhhBBCtEASSjUnJSVmIJWfbw5q3tArmuk6fP01vPIK/PCDuU5RYNw4uOQS6Nu3ct+0zqC5oWwd0dBudisamyN7yI+U4VBUchwZB6UCKS9cxA/lm/khsIkfagmh+vo6MMLfnWH+bhyZlkua5j7g7Wj2kgKrgNa9+mYjhssowWUU49ITc6MksVyK0yhBI4qDIA49COyq9aEMFCJ4CSt+ooofXUlH0bJQ1ExUNRNVy0bTMnBoWbi1bJTa3hd+L/gzzIHx6xKPQ3GZeeXHwmIoKq0yL6lcDgTNsc/yC81pr6+bYgZTmRmVwVVWBmSmQ1Y6ZGWa88x0c70/zQyQDKOyGktPmuI6xGIQCpvtttZVDppVuawqldVWVUMtaxB4l9XdUEva1wq01NR11vGsYwnRklmB2MHIaMN7DsJBhRBCCCFEY5BQqrnYvdsMpAKBhnfZq6iADz6A11+HzYmrvmkanHwy/PnP0K1bjXcLOzPZ6WzFprJlFIX34Ha3or0r84CNyWQYBlvCe/gxKYTaGU2tjLFCqOF2CNUVv+Y5II9/KDMUB2GlNSEjm6gST1Q56cTUxLIex0kFHgKkEcRPOT6CeCnHSzkeI4DDKEMzAigYuAniNoJgJMaMitfx4Iof1PTElAGqdduf2OZPWpcGShopVxjUNGidZU57E4mYAVVRaSKsKqlcLk6aF5dCScAMl4rLzGnz9r0fX1Ug3W+GVNWmxPqMxHJGOmT4Ic1bc/VTcqCVHGBZoVbyNutKhooBhpLUg6lKtZaiVIZS1hhZVjWWw6rGcpjPwwrBFKUy1KoakKlq6r7WslRzCSGEEOIQFtcNNFW+7wjRHEko1dQMA7Ztg5UrzRPWhgxqvm0bvPsuLFpkVlkBpKXBhAlw7rnQvn2NdyuPhdgZLmBTaAcl0QBp3vZ0dPjQYgGIlIDDC5qHhv5JO27orA3tYnn5ZpaXb+bHwOZqV8fTUOnn68gwfzcJofZCN3Q7bIrqUWLxODE9RkyPYRhmtxVFN3Ci4kTFgUoWDrw4ScOLiwxcRgdcqLgUDZei4VQclUGI+SiglIERAEpBCQBl5jolAARAKTfXGQHM7jIBiAcgvqP+T0bxJkKrtERQ5TPn9rIvsc2XdNtnvhfbtoZ2bfb+GHHd7BpYXAoliSDLCqyKrfVl5vqS0kR3PsNcV1JW/+eiaWY4VXVKT6sMrjLSzLArPa1y8rjr/mzXWK2VWBeNQThauU/i51+5DHbFlv3ztSq4MMfOUpMGjlaVpHlSlZbDAU7NrOpyaOBwVlZxJXdrrBZ01bBNCCGEEKKZ0FSFa9/8kXW7937V6ebqhD45/GV8373vKEQLI6FUU6qogPXrzcnng+y9XDENzKqLL7+EhQthyZLKAWY7doTzzoPTTzeDqSoMw6A4WsaOcAHbKnYTiIXIcPjo4mmHqqjgaQuxIERLIFIM4ULz6kqaB1QXNQVUoXiElcFtLC/fwk/lW1hRvpVyPZyyj0txMNDXiSP9uQxL68bgtC74DofueFaggA5xo/JKWXFzHJRYPBEyGXGiiaApasSJG3HMFEFHQcWpqDjQcKoamZoLr+bGp6bj1py4NCdOzYFLdeFyOHE6XKhOZ5VKGoc52dUxVYIJPaltVgCiG4nxmOJmhU8wZIYikQjEy0AvAaM8EVYFgKC5rASBcvO2PU8Mo26EIB4C8vfhxVTNUEvxglplrnhB8YDqMZc9HujggY4eULNAaW9uVzyguBNTomorFoPSgBlYlSRPpeb6kkDlutLEcjhivi5W1VZDOB1md0ErpLKXfZXL/jTw+xJTWuU8PW3fq5ms4Mq6cpt1W08KtuJx0MOV7wFrf3u8H2tgalLH4FJVc52qpb7HrC6IToc5WYPNW+9Nq2uikvy+TKraqno7eb/kKjCp8BJCCCFEPa3bHWBlXmlTN2Of9cypfo4nxKFAQqmmYBhmd71ffzXHj8rJMUOpuvb/3//gn/80r6JXmDS2zujRZmXUcceZ4UMVMT1GQaSY7RX57AzvIapHyXKm09XTDiX5hE7RwJluTu4csxImUgqxMogGMFDYpUdYUbGbFeXbWFG+hTWhncSrXBYuTXUzJK0LR/pzOTItlwG+TrgP4kDpB07iJL1qkGSFNFZoY6+zBuCtrE4xdIgTJ4ZBDJ2YirmsGMQUHSNxcq2oGppDw+Fx4XT6cDqdpDu9pLl8eF0eXJoLp8OJy+FOzM3bqsOZOiB38kn6wT45j8YgEjWDnGjMHCw8FjMn3YC4tU5PDbjiUQiXmGFWrBTi5WagFQ9gBlaJSa0AKkAJmXPD2mZd8j5xvxquQthwzsqASvVAphuy3KC4zHW4QHUD2aC0S+zrMqeIBmVxKIuZUyACpREoCyemkDmVlpuVWGXlUBYwX5tobN/CLDB/zmmJsCrNmryJdd7U277k217zts8LPs+Bq2CyQi1drx5oWoPJh6NJAVjyHCrH5LKu3kZq4GV1ZbQCKqqGV4lw1QqnrK6NDkcd3RoTx7SDNLXmQKymoCx5WQghhBBCCHHASCjV2EIh2LgRNmwwT3C6dKn5RMcwYPVq+OwzM4jasqVyW3Y2/PGPZhjVuXOND1MWKyc/XMTWil0URUrRFI1Wzgw89alS0txUAKviBfxStomfS3/ll8BGdker/2WhnTODwWldGZLWlSPTcuntbYfWaFd4qiVIsiqSDL3KybNROdmXKk86MYak6o3Kk9C4ahBTVOKqQkzDnuKqQlwxKsMmzYGmqjgcTnPSHKQ53HhdXrxOL26XG6fDZU6qA5fqxKk6cKhaakDYHFkVL/vKuhKeFWZFrXArMYUjZlVWqMLcx756XgXoQdADoCdCKyMMShhITGrithIxb1vbDGtekVi2Eq2oedVJI7BvIZcTaJWY9raj4gL8EHZCUIOABuWKWUhWTmJZT5ri5hSIQTAKgagZ9OlGIuAq34cGJ/F6EqGV11y25lZo5fOAN7HsTUzJy8m3XU4z+DkYrPDKCrysz3ry51lPfM51w/wZJ3/G7SovqDEAS15WqR6AVavWIjUES77aYnL1lkOrDL6s+ycfwwrXrOAtJQirEnzVVDWWXCHW3P/NEEIIIYQQoh4klGosoRBs324ORF5SUnN1VCBgXjXvq6/g88/NKiqL2w0nnAB/+AMcfXSNVVHheIQ90RJ2hfewO1xEMF5BmualgyenzsHLY0acjcHtrCzbwMrABlaWbWB9+dZqVVAaKkekdWZwWlcGedpypCeH9prX3KhqiW5+VvezmtQRIlU94YTKE8+q4+WgVFZUQGWQZJ3A2SdvzqTBoVW7G5GhKsQV7GqmeKKaKa6YFU46mEGTZp5Iqg4HDtWcNEXFrTrIcrjxOtx4HR4cioZTdeDUHOY8aWr2YVNjsbptuepRNWckug/GEkFW3KrKstbFU7sZRqNmFVc4muhiqFdW61jhFoZ5WXkiidAqCmoU1BioEVBioEQrJ6KJkCuaCLQScyNS80TEDLowkp5IIvwCcCWmrH147SKk9oisOoVqWE6eh6gcuD5UYU57ivahIVWoCni0xORIzJ3mstsBXqd5252Ye5zgcZmT22mOseVxgduVWHYn1nvMf+9UDbPrpgpoleNakTQpiX1QQHEmra+6Xx2fw7oCsKrdHHVAj6aurzZR2a3aqv4iEUJhVA5sb++T1DUyJRyrcrum8EpRzH/jrHG/1ES33eSrOCZXhdVWLVY1IKvalbKmgEwIIYQQQogDQEKpgy0QgJ07YdMmKC2FzEzo2tX8Yl9QYHbL+/FH+P57WLOmMpABs2Jh9Gg4/ngzkKphrKhwPEJxrIz8cBE7w3sIxIJoikamw08bV1a1/YPxCjYEt/Nr+WbWBDazOrCJdcGthPVotX3bOLMY5O/BQH93Bvq6M8CXiwdn4uRNh1gEYiGzmiVSDrEi84RNT4QAipY4sdTMgEhNGkMmufJAUc1zR+tEKvkqYw41qRtO4gQreUwZVSWGThyIq2agFFMgrhjEDZ24oROz2pOgKOBQzIBJU1UcigOfpuHR3HgcLjya2w6VHKqWMneqDjT1wFydUNTCOtGuIXjdq+RAywqwkm/remWlViRaOcVildVcccN8D1vhVkplHdiVNCkDe1sn6wao8UTIFatcJoqZDEUTQZYVWEXNsMxeTtxOXu+JQbq1TyyxPXHbiCetS74dx06ijMTDh2qYKuoxr8AsSrNuRxIvg25AMGZOpI4ld0A4qQzzXIA7sS7RwzJl2VXD/s6kdU7ApYJTAZeSWE7cdmrgVs1/a9TkECsRdqXctgKw5GqlpHUk3y95XdX9SN0veTKsZRK3AUOtrOqzLlJgKKnLUPkWTd5mHSf5mPYfDpIeV0l6XtY+atJtxar2sgKqRPinJqrDrM+DXSmmgupICruqHi8pMEt+bCXpOCnrkvZJCdA0sztw5sDa3klCCCGEEKIZa9RQKhwOc8899/Cf//wHj8fDpZdeyqWXXtqYTWgcoZA57tPOnWbwVFwMZWWwZ4/ZdW/1ali1yhxXqqquXeGoo8wgavhws2IgiW7olMdDlEQDFESKKYgUE4gFURSVDEcanTxtUVDIjxSxKrCJrRU72RLaycbgdjYEt5MXLqixyWmqm37uzgzwdGWApyv9fV1p52yFYoU/KKCroOjmiYHLYYZmaqvEX+qtoChxwkzMHNjaCGKeHMcwr/SmJMImp1lZpTnB4cRQHcRVDd3QiRtx4oZBXI8nbuvEjMSyrmOeYYN1kqWpqhkwKRoOVUNTVLyqE7fmwu1w4lZdODUzWLKqmhxVAie10bocioNqXwMtqxImuTrLqrKy1llBVVyHWBQisdSuiNZ9opghQtxRwxhkVOZbyXO1ysl2bYN/Jw9Wn1w9U1MlkKED8URgVZ95vJb1ieNY+8SjUBGBijAEI2aFWihirgtFoSIK4Zg5D8UgHE/cjpvLFXGI6FChp87DBkSSKs0SOR372WOxUj36ampUhlnJk6OG5eT53pa1Kutqm7Q6lhtSdJmcQTVUcrFfvNa9mh/fGDjjy6ZuhRBCHJLiuoGm7usvFiGEqFujhlIPP/wwv/zyCy+//DJ5eXnccsstdOzYkZNOOqkxm3FgGQbs2mWOEbVpkzlftw62bTNDqPx82LEjtQLKoqrQrRsMGgQjRpghVNu2KbtE9RjBeAWBWJCSWIDd4SJ2hwvYESqgNFpGMFpBaayMneE97AwXkhfZw45oIWGjeuWTpbUjg56+TvTN6EnfVr3p0+oIumR2RXU4SBk3xfrrt1LlRNiqeEp5GQziieDIDI+sECmGrofRY2Hi8RDxeBg9XoERD5p/3Y6ZJ6mKETeLpRQVTVHQVA1VceLSXLgdLlyaB7fDg8vhwaG6cGhOHKoTLRE0OdTEpLSQMZpE82KFpdp+VMHZV7KzxsPSKwMpa701N6rctrsnVummaO9btXtrDWOlmU8ElKpdxBLLyZUlqjM10Kqp+5bdXcvalji+W4G0vQRi+0rXzfHFkqeKcPV1kai5PpIIw8IRczkSgXC4cp9IokundduqkAtHK2/rSSmMVWBWceCe0gGjKYnKUQUciUo9R9V1Su3LqpIIuKxtmNvUKuus/VQFNCN1faLwtfI+JKoDSdqmmOu0xD7Wdmuy76MkrTfMx1KonNvvX7v8y5yMKrch8TnIPXivvRBCHOY0VeHaN39k3e5AUzdln5zQJ4e/jO/b1M0QQtSi0UKpYDDI22+/zfPPP8+AAQMYMGAAa9eu5bXXXms+odTatbB+PZSXQzBozsvKzG53paVmxVNRkVn9VFhoLhcWmieSe2Gk+Yj0yCXUrTOhHl0J9uxCsHM7gg6Dskg5RdESigr+TdGOMopiZWYVVLSEoniA4niQkng5pXqQ0nio2lhPNdEUlQ7etnT1d6JLeme6ZebSs1UverTuRZa/NaiaHSQZhkEkMY+nzONm0KTr6DHdDpwMw0ic55onowYGCgqqoqIqaiJQMiuXNM2B2+HB6XPiUl24NHOyxmfSMHCgo6HjwDDnhoFGDIceRTUiZvdAI5boFqib1RpGHIxQZfGD9Rf9lK4hCmCNN6VWWZ/onpK8Lbm7jYRaoqFSgq0DdMXJ5CtAxuOpg3wnb0sef622K+LpVthlJC1bwVi8ythKJI3tljT2GyQ9RtIYSskBGCSCMZJuV92e+J+a9FlTqiw7EoPr+9NSQ7Xkfa3j2FfsS/rc1rSP9dj2vxOYzz0SNV+PSNQcoywcSVTAJXXxtAIta33yPJJYTq6cswbyj1RZbwWPkWhiTLSkirvkCwFUFU+8Bw4XyVdOTL7iqKamrrf+YPInDc5s6kYLIcSha93uACvzSpu6GfukZ071IVCEEM1Ho4VSq1evJhaLMXToUHvd8OHDefbZZ9F1HbWJB04N//g99950FFszQVcgrphzXYG4Wnk7ngGxLMwBshVzHtUg5lCJOhOTQyXsUIhoBhHVoII4YYLAqsQEFCamfZTu9NPa24pWnmxa+VrTJi2HtmntyPHl0MbXhja+NjjU6j/eMiNMWVkeAIqqoKLaYVLVSdM0XKoLTdVwaS6cqhOn5sShOuzwSVO0vc4PSNWSnuhOZCTGy9ETcyNmnixbQZUeSwxEHE3sk5j0RPdBI3FibySqTbCu0pW0zv7rvELKOEIp/VpqYT9X66Q3+XYN261l+yWqa/+UB6qyvYb7VrtbTfvWcrveP7Ma9qvxvnUd7wC8Pxr8HtuXx9zPdu7L58AOuti/qx/Wlx12JQ3uXfU2pG6rGlIlDwBeddDwlKthVg3OjMp18aTt8XhldUzyhQ8MK0QjtVosebDxlEqbWva1r9JH5bFdTnMA9uRKs+R/CpLvk/K+MMyfmZE0T75/1c+5/ZGt8nm2riBojYGmG6lVdFW7llpjpyVf5TJ5nX2fpCq+5P2rzu0LBSTd167qq3I7paKvhu16vPLnaHeNTXr/1PpeTISn9e1D+NOa+u3Xgh02QyAIIYQQ4rDSaKFUfn4+2dnZuFwue12bNm0Ih8MUFxfTqlWrxmpKjZZ7injgN/tzBJ16jVkCqKi4HS7cmhu35sbj9OJ1evE5fHidXtKcafjdftJd6fhdfjJcGWR6MsnyZJHtySbLm4XH4THDIVRU1RpPyWFXKDkSA3JbAZOiKGiKVj14StrHCpKs5WbVBc4aMB333vasnaFXTtZJLVVup2yzTl71ypPXatuM1GX7MaxtVW/XcGyzcanHgKTHTWyvfCKJWS0n3TXeSN6nlvU1v2h7uV3H+rpOOOv9eLXt1tB2H6DHPciHODgH3dvnuJ6PkSg8PDCsflt1PVgtarrSXfJk7lRlGzWHVva+ycHUXm4ndyGruk+t66p8/pKPmTzeGNZtxRzDDxUMR1Ib63oONbTDCtmq3S/lBa15OaXNtexedb8USvXnXVXcSOrialQGWFW7vFqhZcq+yYGpDsccU0s7Dh2H5BAIQgghhDjsNVooFQqFUgIpwL4diURqukujOqrvb3np9Jf4ceePKV3RksMbBXOsIyv8cSbGNHJqTtyqG4fmSIyB5MajefA4zMkKmrxOLx7Ng0tzoaoqCgqKotR7riqV9xH7wLqaU0tVU5hUY2hV0/Y69qm23172rfU+texX1/p6BVf1DWYOVKC1P21oyCEPSopV9UEa4TEaqjm26TBSW1B0oJYberumz0F9gq+q67zp1fc5hLSEIRAOlYGQD5XnIYQQQrQUjRZKud3uauGTddvj8ez1/kbiC2ggcPAG2Dur11mc1eusg3Z8Wy1FVUbiPyFartq+yMsXfCFapLp6/TY3B/H7gSUtLa1J/jC0P0MgNMb3J8uzn64nryR00B/nYOmY6eXKE3o2dTNEwqEQEB4Kz8HSLUNFjxygMTMbWTuv+W9gS34OcGg8j0PhOcCh8Ty6ZaiN8t0A9v79qdFCqXbt2lFUVEQsFsORuFx7fn4+Ho+HjIyMvd6/vNy8Lvjxxx9/UNsphBBCCFGTZcuW4ff7G/1x92cIBPn+1DDPN3UDhBAH3AbglaZuxAFwKDyPQ+E5wKHxPDYAw2c2zmPt7ftTo4VS/fr1w+FwsHz5ckaMGGE3btCgQfUa5Lxt27Z89tlnTfZXSiGEEEIc3tLSmuYKTvszBIJ8fxJCCCFEU9rb96dGC6W8Xi9nnHEGd999Nw888AC7d+9m/vz5zJxZv3hOVVXat29/kFsphBBCCNG87M8QCPL9SQghhBDNWaOFUgDTp0/n7rvv5s9//jN+v5+rr76aE088sTGbIIQQQgjRouzvEAhCCCGEEM2VYhiNcgkoIYQQQgixD0KhEKNGjWL+/Pn2EAhPPfUUS5Ys4dVXX23i1gkhhBBC7Lu9D+YkhBBCCCGaTPIQCCtWrGDx4sXMnz+fiy66qKmbJoQQQgixX6RSSgghhBCimQuFQtx999385z//we/3M3nyZC6++OKmbpYQQgghxH6RUEoIIYQQQgghhBBCNDrpvieEEEIIIYQQQgghGp2EUkIIIYQQQgghhBCi0UkoJYQQQgghhBBCCCEanYRSQDgc5rbbbmPEiBEce+yxzJ8/v6mb1GLt2rWLa665hpEjR3Lccccxc+ZMwuFwUzerxbviiiu49dZbm7oZLVokEuGee+7hqKOO4phjjuHRRx9FhtTbNzt27GDKlCkMGzaMcePG8dJLLzV1k1qkSCTCqaeeytKlS+11W7du5eKLL+bII4/k5JNP5ssvv2zCFrYcNb2Wy5cv57zzzmPo0KGMHz+et99+uwlbKA4W+RwdOmr6Wd5333306dMnZXr11VebsJWiLnWdB8jnsmWp62cpn8uWZ/PmzUyePJmhQ4dywgkn8MILL9jbmsNn09Hoj9gMPfzww/zyyy+8/PLL5OXlccstt9CxY0dOOumkpm5ai2IYBtdccw0ZGRm89tprlJSUcNttt6GqKrfccktTN6/F+uCDD/jss88488wzm7opLdp9993H0qVLmTdvHuXl5Vx//fV07NiR8847r6mb1uJcd911dOzYkYULF7Ju3TpuuukmOnXqxO9///umblqLEQ6HufHGG1m7dq29zjAMpk6dyhFHHME777zD4sWLmTZtGh9++CEdO3ZswtY2bzW9lvn5+Vx++eWcf/75PPjgg6xcuZLp06eTk5PDCSec0HSNFQeUfI4OHTX9LAHWr1/PjTfemPIdyO/3N3bzRD3UdR5w8803y+eyBdnbOZ18LlsWXde54oorGDRoEO+++y6bN2/mhhtuoF27dpx66qnN4rN52IdSwWCQt99+m+eff54BAwYwYMAA1q5dy2uvvSahVANt2LCB5cuX89VXX9GmTRsArrnmGh566CEJpfZRcXExDz/8MIMGDWrqprRoxcXFvPPOO7z44osMHjwYgEsvvZSffvpJQqkGKikpYfny5cyYMYNu3brRrVs3jjvuOJYsWSKhVD2tW7eOG2+8sVql3jfffMPWrVt588038fl89OzZkyVLlvDOO+9w9dVXN1Frm7faXsvFixfTpk0bbrjhBgC6devG0qVLef/99yWUOkTI5+jQUdvPEsxQavLkyeTk5DRBy0RD1HUe8Jvf/EY+ly3I3s7p5HPZshQUFNCvXz/uvvtu/H4/3bp1Y/To0Sxbtow2bdo0i8/mYd99b/Xq1cRiMYYOHWqvGz58OD/99BO6rjdhy1qenJwcXnjhBfsfL0sgEGiiFrV8Dz30EKeffjq9evVq6qa0aMuWLcPv9zNy5Eh73RVXXMHMmTObsFUtk8fjwev1snDhQqLRKBs2bOCHH36gX79+Td20FuPbb79l1KhRvPXWWynrf/rpJ/r374/P57PXDR8+nOXLlzdyC1uO2l5Lq6tBVfL76NAhn6NDR20/y0AgwK5du+jWrVvTNEw0SF3nAfK5bFnq+lnK57Lladu2LY8//jh+vx/DMFi2bBnfffcdI0eObDafzcO+Uio/P5/s7GxcLpe9rk2bNoTDYYqLi2nVqlUTtq5lycjI4LjjjrNv67rOq6++ytFHH92ErWq5lixZwvfff8/777/P3Xff3dTNadG2bt1Kp06dWLRoEc8++yzRaJQJEybwf//3f6jqYZ/NN4jb7ebOO+9kxowZvPLKK8TjcSZMmMA555zT1E1rMSZOnFjj+vz8fNq2bZuyrnXr1uzcubMxmtUi1fZadu7cmc6dO9u39+zZwwcffCB/kT+EyOfo0FHbz3L9+vUoisKzzz7L559/TlZWFpdccokMZ9BM1XUeIJ/LlqWun6V8Llu2cePGkZeXx9ixYxk/fjwPPPBAs/hsHvahVCgUSgmkAPt2JBJpiiYdMmbNmsX//vc//va3vzV1U1qccDjMXXfdxZ133onH42nq5rR4wWCQzZs38+abbzJz5kzy8/O588478Xq9XHrppU3dvBZn/fr1jB07lksuuYS1a9cyY8YMRo8ezWmnndbUTWvRavt9JL+L9k9FRQVXX301bdq04dxzz23q5oiDTD5Hh44NGzagKAo9evTgwgsv5LvvvuOOO+7A7/dLd/EWIPk84KWXXpLPZQuW/LNcuXKlfC5bsNmzZ1NQUMDdd9/NzJkzm83vzMM+lHK73dVedOu2hAH7btasWbz88ss89thjHHHEEU3dnBZnzpw5DBw4MOWvFGLfORwOAoEAjzzyCJ06dQIgLy+PN954Q0KpBlqyZAl/+9vf+Oyzz/B4PAwaNIhdu3bxzDPPSCi1n9xuN8XFxSnrIpGI/C7aD+Xl5Vx11VVs2rSJ119/Ha/X29RNEgeZfI4OHWeccQZjx44lKysLgL59+7Jp0ybeeOMNOflt5qqeB8jnsuWq+rPs3bu3fC5bMGuc4nA4zE033cRZZ51FKBRK2acpPpuHfb+Vdu3aUVRURCwWs9fl5+fj8XjIyMhowpa1XDNmzODFF19k1qxZjB8/vqmb0yJ98MEHLF68mKFDhzJ06FDef/993n///ZSxz0T95eTk4Ha77UAKoHv37uzYsaMJW9Uy/fLLL+Tm5qb8surfvz95eXlN2KpDQ7t27SgoKEhZV1BQUK2sWtRPIBBg8uTJrF27lpdfflnGvzhMyOfo0KEoin3ia+nRowe7du1qmgaJeqnpPEA+ly1TTT9L+Vy2PAUFBSxevDhlXa9evYhGo+Tk5DSLz+ZhH0r169cPh8ORMpjXsmXLGDRokIw1sw/mzJnDm2++yaOPPsopp5zS1M1psRYsWMD777/PokWLWLRoEePGjWPcuHEsWrSoqZvWIg0ZMoRwOMzGjRvtdRs2bEgJqUT9tG3bls2bN6dUmG7YsCFl/B6xb4YMGcLKlSupqKiw1y1btowhQ4Y0YataJl3XmTZtGtu2bWPBggX07t27qZskGol8jg4dTzzxBBdffHHKutWrV9OjR4+maZDYq9rOA+Rz2fLU9rOUz2XLs23bNqZNm5YSHP7yyy+0atWK4cOHN4vP5mGfuni9Xs444wzuvvtuVqxYweLFi5k/fz4XXXRRUzetxVm/fj1PP/00l19+OcOHDyc/P9+eRMN06tSJ3Nxce0pLSyMtLY3c3NymblqL1KNHD0444QSmT5/O6tWr+eKLL5g7dy7nn39+UzetxRk3bhxOp5Pbb7+djRs38vHHH/Pss88yadKkpm5aizdy5Eg6dOjA9OnTWbt2LXPnzmXFihWcffbZTd20Fudvf/sbS5cu5b777iMjI8P+XVS1+4g49Mjn6NAxduxYvvvuO+bNm8eWLVt4/fXXWbRokXS7b6bqOg+Qz2XLUtfPUj6XLc+gQYMYMGAAt912G+vWreOzzz5j1qxZXHnllc3ms6kYhmE06iM2Q6FQiLvvvpv//Oc/+P1+Jk+eXC0BFns3d+5cHnnkkRq3rVmzppFbc2i59dZbAXjwwQebuCUtV1lZGTNmzOCjjz7C6/UyceJEpk6diqIoTd20FmfdunXcf//9rFixglatWnHBBRfw5z//WV7LfdCnTx9eeeUVRo0aBcDmzZv561//yk8//URubi633XYbxxxzTBO3smVIfi0nT57Ml19+WW2fkSNHsmDBgiZonTiY5HN06Kj6s1y8eDGzZ89m06ZNdOrUieuvv54TTzyxiVsparK38wD5XLYce/tZyuey5dm1axczZsxgyZIleL1eLrzwQqZMmYKiKM3isymhlBBCCCGEEEIIIYRodId99z0hhBBCCCGEEEII0fgklBJCCCGEEEIIIYQQjU5CKSGEEEIIIYQQQgjR6CSUEkIIIYQQQgghhBCNTkIpIYQQQgghhBBCCNHoJJQSQgghhBBCCCGEEI1OQikhhBBCCCGEEEII0egklBJCCCGEEEIIIYQQjU5CKSFEi9GnTx9uvPHGausXLlzIuHHjmqBFQgghhBBCCCH2lYRSQogW5R//+AdLlixp6mYIIYQQQgghhNhPEkoJIVqUTp06ce+99xKJRJq6KUIIIYQQQggh9oOEUkKIFuW6665j165dzJs3r9Z9du7cybXXXsvIkSMZNWoU9913nx1iLVy4kEmTJjF79mxGjRrFiBEjmDlzJoZh2Pd/8803GTduHEOHDmXSpEmsWbPmoD8vIYQQQgghhDjcSCglhGhR2rVrxzXXXMOzzz7L1q1bq22PRCL8+c9/JhQKsWDBAh5//HE+/fRTHn74YXufH3/8kY0bN/LGG29wxx138Morr/D1118D8PHHHzNnzhzuuOMO3n33XYYPH85FF11ESUlJoz1HIYQQQgghhDgcSCglhGhxJk2aRG5uLvfff3+1bV988QW7du1i1qxZ9OnTh9GjR3PnnXfyxhtvUF5eDkA8HmfGjBn06NGD008/nb59+/Lzzz8D8MILLzBlyhTGjh1Lt27duO666+jUqRPvvfdeoz5HIYQQQgghhDjUOZq6AUII0VCapnH33XczceJEFi9enLJt/fr1dOvWjczMTHvdsGHDiMVibNmyBYDWrVvj9/vt7X6/n1gsZt9/1qxZPProo/b2cDjMpk2bDuIzEkIIIYQQQojDj4RSQogWadiwYZx11lncf//9XHbZZfZ6t9tdbd94PJ4yd7lc1faxxpSKx+PcdtttjB49OmV7coglhBBCCCGEEGL/Sfc9IUSLddNNNxEMBlMGPe/evTubNm2iuLjYXrd8+XIcDgddu3bd6zG7d+/Ozp07yc3Ntadnn32W5cuXH4RnIIQQQgghhBCHLwmlhBAtVnZ2NjfddBPbt2+3140ZM4YuXbpw8803s2bNGr755htmzJjBqaeeSkZGxl6Peckll/Dyyy+zaNEitmzZwqxZs/jnP/9Jz549D+ZTEUIIIYQQQojDjnTfE0K0aGeffTbvvPMOu3fvBszxpp5++mlmzJjBn/70J9LS0vjjH//IDTfcUK/jnXzyyRQUFDB79mwKCgro1asXzzzzDN26dTuIz0IIIYQQQgghDj+KYQ2kIoQQQgghhBBCCCFEI5Hue0IIIYQQQgghhBCi0UkoJYQQQgghhBBCCCEanYRSQgghhBBCCCGEEKLRSSglhBBCCCGEEEIIIRqdhFJCCCGEEEIIIYQQotFJKCWEEEIIIYQQQgghGp2EUkIIIYQQQgghhBCi0UkoJYQQQgghhBBCCCEanYRSQgghhBBCCCGEEKLRSSglhBBCCCGEEEIIIRqdhFJCCCGEEEIIIYQQotFJKCWEEEIIIYQQQgghGp2EUkIIIYQQQgghhBCi0UkoJYQQQgghhBBCCCEanYRSQgghhBBCCCGEEKLRSSglhBBCCCGEEEIIIRqdhFJCCCGEEEKIQ4ZhGE3dBNHMyHtCiOZLQikhRIpJkyYxadKkg/4427Zto0+fPixcuLBB91u6dCl9+vRh6dKlB6llzcO4ceO49dZbm7oZQgghmqlly5Zx9dVXM2bMGAYNGsRvf/tbbr/9dtavX9/UTUvx5JNP0qdPn0Z7vGXLlnHFFVc02uM1BytXruTyyy/n6KOPZtSoUVx66aWsXLkyZR/DMJg3bx4nnngigwYNYvz48bz22mt7PfZjjz1Gnz59qk3z5s2z93n77bdr3Ofee+9t0POYNGlStWMMHDiQE044gXvuuYeSkpJ6H+vKK6/k7bffBmDt2rWcf/75DWrL/qjPd7hbb72VcePGNei49bnPkiVLOP3004lGow06tmXz5s1ceeWVjBgxglGjRnHXXXcRCAT2er+FCxdy6qmn2v8WzZkzh1gsVuv+Dz74YKOcb4iWwdHUDRBCCCGEEELU39y5c3n00Uc59thjue2228jJyWHz5s288cYbnHnmmcycOZNTTjmlqZvZJN5+++1mF8wdTJs3b+bCCy9k4MCB3H///SiKwvz585k4cSLvvvsuPXr0AODhhx9mwYIFXHPNNQwaNIjPP/+ce++9F4fDwbnnnlvr8VevXs3IkSO58cYbU9Z37NjRXl61ahXdu3fnwQcfTNmnTZs2DX4+/fv356677rJvR6NRVq5cyaOPPsqqVat44403UBSlzmMsXLiQXbt2cdZZZwHwr3/9ix9//LHBbTmYrrrqKi666KIDftzRo0fTqVMnnn76aa699toG3be0tJQ///nPtGnThgcffJDCwkJmzZrFtm3bUkLIql5++WUeeOABxo8fz1/+8heKioqYPXs2a9as4cknn6y2//z583nxxRcZOXJkg5+fODRJKCWEEEIIIUQL8cknn/DII49w9dVXM23aNHv9yJEjOeOMM7jxxhu59dZbOeKII+jdu3cTtlQ0hgULFuD1ennuuefw+XwAHH300YwbN45XX32VO++8k23btvHSSy9xxx13MHHiRMAML3bs2MGXX35ZZyi1atUqJkyYwJFHHlnnPoMGDapzn/ry+/3VjnPUUUdRXl7O7Nmz+emnn+p8nIqKCv6//+//46677kJVm2+noK5dux60Y//f//0fEydO5Pzzz6dt27b1vt8bb7xBcXExCxcupFWrVgC0a9eOK664gmXLljF8+PBq94nH4zz99NOMGTOG2bNn2+v79+/PH//4R7766ivGjBkDwNatW3nooYf4+OOPSU9P389nKQ4lzfeTKoRo1r766ismTpzI8OHDGTVqFDfeeCM7duxI2WfDhg1MmzaNkSNHctRRRzFlypRa/3ppGAbTp09n8ODBfPnll/b6N998k/HjxzN48GAuvPBC8vLyqt1306ZNXHPNNYwZM4YjjzySSZMmsWzZMgCKi4vp378/L730kr3/jh076NOnD3/5y1/sdbquM2rUKJ577jm7a+E///lPrrnmGoYOHcrIkSO5/fbbCQaDdb4uu3fvZvr06Rx//PEMHjyYs88+m//+978p+/Tp04fXXnuNv/71r4wcOZKhQ4dy7bXXUlBQUOMxzzrrLM4777xq6y+++GIuueSSOtsjhBDi0DJnzhx69OjB1KlTq21zOp3ce++9aJrG888/D8Cll17KhAkTqu171VVXcdppp9m3v//+ey688EKGDBnCyJEjueWWWygsLLS3L1y4kP79+/P2228zZswYRo4cybp169iyZQtXXnklo0aNYsiQIZx77rl89tln1R7v008/5bTTTrO7ji1atChle31+f4bDYZ566ilOOukkBg0axIknnsjcuXPRdR0wuze9++67bN++vc4hAp588klOOukkPvroI7vL0emnn86PP/7I8uXLOeeccxg8eDCnnnoqS5YsSbnvr7/+ypQpUxg2bBjDhg1j6tSpbN26NWWf1atX///t3XeYFFW+PvC3UofpnsTMMOTsCAICorCKLuLdq97VNaBeXbPgyq5iQq+Ka8BVwLBmwZxx1Z9huaZ1V9a0rijKFbNIzkxOnaq6qs7vj1NVXdXTMwwDTE/PfD889VTVqaru6qZ7pvud7zmF2bNn4xe/+AVGjx6NI444ArfddhsSiYSzT3s+C7z++uu7HLJg2LBhmDFjhhNIAUBeXh769OmDzZs3AwCWLVsGv9+PU0891XPsfffdl7GSxVZXV4fKykqMGjWq1X0YY1i9enWb++wNY8aMAQDnc+A555yDq6++GpdddhnGjx/vfB567bXXoKoqpk2bBoD/Xz/00EMA+HNuP95dvZZs77zzDqZPn44JEyZgypQpuOmmm9rVjTCZTOLOO+90PpvOmDEDmzZtcrand8VLJpP485//jF/+8pc48MADMXPmTCxduhT7778/tm7d6rnt119/HccccwzGjh2LE044ocX7bezYsejXrx+efvppp83uRpt+W26ffPIJJk6c6ARSAHD44YcjFArh448/znhMTU0NGhoacOSRR3raKyoqUFxcjA8//NBpW7hwITZt2oRnn312n79eSG6hUIoQstuWLl2KGTNmoG/fvrjnnnswd+5cfPXVVzj99NNRW1sLAKisrMTpp5+OjRs3Yt68ebjrrrtQU1OD8847Dw0NDS1u87bbbsNbb72Fhx56CIcffjgAYMmSJbj55psxdepULF68GOPGjcONN97oOW7t2rWYPn06tm7dihtuuAF//vOfIQgCzjvvPKxYsQJFRUUYP348Pv30U+cY+wPml19+6bR9/fXXLX6p3nzzzU4J9MyZM/Hqq6/i4YcfbvV5qampwamnnoovv/wSV155JR588EH0798fl1xyCd544w3Pvvfeey9M08Q999yDa665Bh988AEWLFiQ8XZPPfVUfPXVV54PMzt27MDnn3+e8YsGIYSQ7qmurg7fffcdpk2b1moXpqKiIhx22GFOoHPCCSfg+++/9/wOaWpqwscff4wTTzwRAPDFF1/g/PPPRyAQwH333Yfrr78eK1aswLnnnusJUgzDwFNPPYX58+dj7ty5GDp0KGbNmoV4PI4777wTixcvRlFREf7whz947g8AbrrpJpx//vl4+OGH0adPH1x33XX46aefALTv9ydjDL///e/xxBNP4LTTTsMjjzyCY489Fvfdd5/T3eviiy/G1KlTUVZWhpdffrnFF2W3nTt34vbbb8fvf/973H///WhqasJll12GOXPm4LTTTsOiRYvAGMOVV17pPAcbNmzAGWecgdraWtxxxx2YP38+tmzZgt/+9rfO55+qqiqcddZZiMfjuP322/H444/juOOOw/PPP4/nnnvOcw67+ixw5JFH4uWXX8bo0aNbfRxnnnkmLrzwQk/bpk2bsGbNGqdS7scff8TgwYPxxRdf4OSTT8bo0aNx1FFH4eWXX271dgE4/z8ffvghpk2bhtGjR+Okk07yhCCbN29GNBrFt99+i2OOOQajR4/OGDruqQ0bNgAABg4c6LT97W9/QygUwsMPP+w8B2+88QaOPPJI+Hw+AMBpp53mhHEvv/wyTjvttHa9lgBg8eLFmDNnDsaPH48HHngAl1xyCf7+97/jnHPO8bwvMnnnnXewZs0a3H777bj55pvx3Xff4corr2x1/5tuugnPPvsszj77bCxatAilpaUtPvMC/PPfY489hssvvxwPPvggBEHAZZdd5rz+bMceeyzeeustZ/20007Dyy+/3Gbl1Lp16zB06FBPmyRJGDBggPP8pysoKIAsyy3+aNzY2IimpiZPYHvFFVfgjTfewCGHHNLqOZAeihFCiMvZZ5/Nzj777Fa3G4bBpkyZwmbMmOFp37RpExs9ejS74447GGOM3X777ezAAw9kVVVVzj47duxgRx55JPvwww/Zli1bWEVFBXvttdfYn//8ZzZ69Gj2wQcfOPuapskOPfRQdsUVV3ju56abbmIVFRXss88+Y4wxdvnll7PJkyez5uZmZ59kMsmOOeYYdsoppzDGGHv00UfZ+PHjmaZpjDHGrr76anbyySeziooKtmXLFsYYY/fffz+bNm0aY4w553b11Vd77vucc85hxx9/fKvPzZ133slGjx7Ntm7d6mk/77zz2JQpU5hhGIwxxioqKthvf/tbzz7XXXcdGz9+vLM+bdo0du211zLGGGtqamIHHnggu//++53tDz/8MJs4cSKLx+Otng8hhJDu5ZtvvmEVFRVsyZIlbe53++23s4qKCtbQ0MCi0SgbP348e+ihh5ztr7zyChs5ciTbuXMnY4yx008/nR1//PFM13Vnn/Xr17NRo0Y59/Xaa6+xiooKtnTpUmefqqoqVlFRwd544w2nrampiS1YsID9/PPPjDHGHnjgAVZRUcE++ugjZ59NmzaxiooK9uyzzzLG2vf788MPP2QVFRXsrbfe8uyzaNEiVlFR4dzftdde6/w+b02mc3r00UdZRUUFe+WVV5y2d999l1VUVLAffviBMcbYnDlz2GGHHeb5zFFfX88mTpzIbr/9dsYYY//617/YWWed5dmHMcaOP/54z2en9nwW6Ih4PM5OP/10Nn78eOf5vPDCC9nkyZPZL37xC7ZkyRL26aefshtuuIFVVFSwl156qdXbeuKJJ1hFRQWbOXMm++STT9j777/PZsyYwUaOHMk+/vhjxhhjf/vb31hFRQU79dRT2fvvv88+/vhjNmfOHFZRUcFefvnl3Tr3s88+m5111lksmUw6U01NDXvnnXfYpEmT2Omnn85M03T2HTduHFNV1Tm+ubmZjRo1ij399NOe27X/v23teS01NDSwMWPGsBtvvNGzzxdffLHL9+C0adPY1KlTnc+djDF27733soqKCud14X6dbtq0ie2///7sqaee8tzOjBkzPJ9Vr732WlZRUcHWrl3r7PPpp5+yiooKtmzZMs+x7733Xot9d2XMmDHsnnvuadF+xhlnsAsuuKDV46666io2evRo9sorr7CGhga2bt06NmPGDDZmzBh27rnnZjxmV983SM9ClVKEkN2yYcMGVFdX4/jjj/e0Dxo0CBMmTMCKFSsA8KvfjB8/HmVlZc4+ffr0wQcffICpU6c6bS+88AIee+wxHHfccZ6/aK5fvx61tbVO+bXtv/7rvzzrK1aswLRp0xAOh502WZZx3HHH4bvvvkM0GsXUqVMRi8Xw9ddfAwA+++wznHfeeQgGg/jiiy8AAB9//HGLv6imj1nQp0+fNrvvrVixAhMmTED//v097SeccAKqq6uxfv36Nm87Ho9nvN38/HwcffTRnmqrv/71r/j1r3+NQCDQ6vkQQgjpXph1WXtFUdrcT5IkZ/+8vDz86le/wjvvvONsf/vtt3HooYeivLwc8XgcX3/9NaZOnQrGGHRdh67rGDhwIIYPH45///vfntt2d7spLS3FiBEjcOONN+Laa6/Fm2++CdM0MXfu3BbjWR188MHO8oABAwDwii2gfb8/V6xYAVmWceyxx7bYx76N3XXQQQd5HgsAjBs3zmkrKirynOdnn32GSZMmIRAIOM9TOBzGwQcf7FRkH3744ViyZAn8fj/Wrl2Lf/7zn3j44YdRV1cHTdM89787nwXaIxKJYNasWfj2229x1113Oc9nMplEfX09brnlFpx11lk49NBDceutt+Lwww93urZl8l//9V945JFH8Oijj2LKlCmYNm0aHnnkEQwdOtQZP+iQQw7BI488gmeffRbTpk3DEUccgbvvvhuHHXYYHnjgAec1215ffPEFRo8e7UyHHXYY5syZgzFjxuDuu+/2VAgOGzbMqYgCeBWRYRjO66s17XktrVq1Cpqmtfi8e/DBB6N///67fL0deOCBnvdp+mve7fPPPwdjrMX5pN83ABQXF2P48OEtbre5udmzn93eVne9dG39X7U1uPwtt9yCE044ATfccAMmTZqEk08+GRMmTMDo0aMRDAbbff+k56KBzgkhu8XuepfpiiqlpaX44YcfnP129aEA4KXhhx9+ON566y2cd955OOCAAwDA6a9fXFzs2d8dctn7tXYujDFEIhHsv//+6Nu3Lz799FMUFxejqqoKhx12GA466CCsWLECU6dOxffff9/iKiXpv0hFUWzzF3ZjY6OnrNx9LoD3g8ju3vapp56KN954A19++SUkScLGjRtxxx13tLo/IYSQ7scOGbZt29bmflu2bEEoFHJClRNPPBFvvPEGfvrpJ5SWluLzzz93uok1NTXBNE08/vjjzjhUbn6/37PuHrvIvtLbww8/jPfeew9Lly6Foij41a9+hVtuuQWFhYUZj7MHoLZ/77Xn92djYyOKi4udwM1mfy5I/1LeHu4/aNna+hLd0NCAd955xxPw2exxeOzueC+88AJisRj69u2LAw88sMXzmOm+dvVZoC07duzArFmzsGHDBtx777341a9+5WwLhUIQBMHzR0EAOOKII/DJJ5+gpqYm42epfv36ea6yB/BAdMqUKXjppZcAACUlJS3+gAgAU6dOxaeffoqampoWn93aMnr0aNxyyy0A+OvL7/ejb9++Gf+vQqGQZ91+Dbhfa5m057Vkfw5t7TPmrl5v6edgv+bTx6wC4IzdVlJS4mlPX890u3ZYlH679mtrd94X4XAY0Wi0RXskEkF5eXmrx4VCISxYsAB//OMfsX37dvTr1w+hUAivvvoqBg8e3O77Jz0XhVKEkN1if8DNNCh3dXW1EyLl5+d7Bki1LV++HAMGDHB+iV5++eU499xzcdxxx+GGG27AK6+8AkmSnNtJ7yOfPh5VYWFhq+cCpEKtqVOnYvny5SgpKcHQoUNRVlaGyZMn4//9v/+HTz75BIFAAJMnT96NZ6KlwsJC537bOpeOmDRpEgYNGoR3330Xoihi2LBhe+UqN4QQQnJHSUkJxo8fj7///e+4/PLLM15dLBKJ4N///rdnEOVDDz0UZWVl+Nvf/oaysjL4/X4cffTRAFKBxfnnn4/jjjuuxe3tqtKhvLwc8+bNw80334yffvoJ7777Lh5//HEUFxd7xudpS3t+fxYWFqK+vh6GYXjChKqqKmeffS0/Px+HHXZYxouMyDL/WvXYY4/hmWeewS233IKjjz7aucpY+iDje9Pq1asxc+ZMqKqKp556qsWYPYMHDwZjDMlk0hOO6boOAK1WXX/00UdIJBI45phjPO2qqjoh3JdffoktW7bg5JNPbrGPJEmeYLI9QqEQxo4du1vH2OzXQKZqJLf2vJbs866pqcGwYcM8x1dXV2cMUTvKDnxqamo8IWCmz9Ht1dofd9sydOhQZ3B8m2EY2Lp1q/PzIpMPPvgABQUFmDhxolMhWVtbi507dzp/bCakLdR9jxCyW+xAxz14IsD/Krtq1SqnFP7ggw/G119/7fmFWltbiwsvvNAzQGZpaSkCgQBuuukmfP/9986VQoYMGYK+ffvi3Xff9dzPBx984Fk/5JBD8MEHHyASiThthmHg7bffxtixY52y7iOPPBLffvstPv74Y0yaNAkAv2Ty1q1b8dJLL2HKlCmeEvCOOOSQQ/DVV1+1+Av2G2+8gbKysj36a5EgCJg+fTqWLVuG999/v8WHP0IIIT3D7NmzsWHDBtxzzz0tthmGgZtvvhmJRMIz+LUkSfjNb36DDz74AO+++y5+9atfORUX4XAYBxxwANavX4+xY8c603777YcHH3ywzSu/ffXVVzjssMPwzTffQBAEjBo1CldeeSUqKioyXi23Ne35/Tlp0iTout7ic4Hdtd2+XH2moG5vsa84OGrUKOd5GjNmDJ555hm89957APjwBSNGjMApp5ziBFKVlZX4+eefM1bJ7KkdO3bgggsugCAIePHFFzMOIm1XSL399tue9vfffx/7779/xiokAHj33Xcxd+5czx8EY7EYPvzwQ+cPeZ999hmuu+46z0DYpmni73//OyZMmLDHn612R3l5OSRJws6dOz3t6a+J9ryWxo0bB5/P1+Lz7pdffont27d7un7uqYkTJ0KSJOc1ZPvHP/7R4dusrKwEgBaVbm2ZMmUKvvjiC89n908++QSxWAxTpkxp9biXXnoJd955p6ft2WefhSRJGavoCElHlVKEkBZ27tyJZ555pkV7RUWF07d/7ty5uOqqq3DCCSegvr4eDz30EAoLC52/Hp5//vlYunQpLrzwQsyaNQuKojhX3PnNb37Topx46tSpOPbYY/Hggw/imGOOwcCBA3H11Vfjqquuwg033IBjjz0Wq1atwosvvug5bvbs2fj4449x7rnn4qKLLoKiKFiyZAm2bNmCJ554wtnvF7/4BURRxIcffuh8kB89ejRCoRBWrlyJ+fPn7/HzdsEFF+CNN97A+eefj9mzZ6OoqAhLly7FZ599hgULFuzxB+Xp06c7lzK2r5hECCGkZzniiCNw3XXX4c4778SPP/6IU045Bb1798bWrVvx4osv4scff8T8+fMxcuRIz3EnnnginnrqKYii2KKb3pw5c3DRRRc5v9ftq+x9/fXXuPjii1s9lwMOOACBQADXXHMNLr30UpSWluLTTz/Fjz/+iHPPPbfdj6k9vz9/+ctfYvLkybjhhhtQWVmJkSNHYsWKFXj88cdx8sknY8SIEQD41cBqamrw0UcfYdSoUW1ebWx3XXzxxTjjjDMwa9Ys/Pa3v4Xf78fLL7+MZcuWOWMsHXjggVi8eDEee+wxjB8/Hps2bcKjjz4KTdN2e7youro6bN68GSNGjGg1OLrttttQW1uLW265BZFIBKtWrXK2hcNhjBgxApMnT8a0adOwcOFCxONx7Lfffli6dCn+7//+D4sXL3b237x5M+rq6pxK7AsvvBDvvvsufve732HWrFlON894PI5LL70UAHDGGWfgpZdewu9//3tcfvnlCAaD+Mtf/oKff/4ZL7zwgnPba9euhaZp+7RyJi8vDwcddBBWrlyJ888/32kvKCgAALz11lsYN25cu19LF110ERYtWgRFUTBt2jRs3boV999/P0aMGLFX/zg4cOBAnHLKKbjnnnuQTCYxcuRIvPfee84fYjvy+XHlypUYMGCAczW9nTt3OpVLrQWFZ555JpYsWYILLrgAs2fPRkNDA+666y788pe/9IRwq1atQq9evTBo0CAAwDnnnIOZM2diwYIFOOqoo7B8+XI8+uij+N3vfufsQ0hbKJQihLSwefNmLFy4sEX7qaeeisMOOwzTp09HKBTCo48+iksuuQThcBhHHHEE5syZ4/TH79u3L/7yl7/grrvuwnXXXQefz4fJkyfj3nvvRWFhYcY+7tdffz0++eQT3HjjjXjmmWdw/PHHQxRFLF68GP/7v/+LiooK/OlPf8KcOXOcY/bbbz/85S9/wT333IO5c+dCEAQceOCBeO655zyDqgaDQUyePNlTKSXLMg4++OCMg5x3RFlZGV588UXcfffduO2225wPFosXL8Z//Md/7PHtl5eXY+TIkSgtLW2zbz8hhJDu7YILLsCECRPw7LPP4o477kBdXR3KysowZcoUzJ8/3/lS7TZy5EhUVFSgvr4ehx56qGfb4YcfjieffBIPPfQQLrvsMiiKgtGjR+Ppp59us6u43+/HU089hbvvvhvz589HU1MThgwZgj/96U+YPn16ux9Pe35/CoKARx99FA888ACeeeYZ1NXVYcCAAZgzZ46nO9306dPx0Ucf4ZJLLsFll12Giy66qN3nsSsjR47ECy+8gHvvvRfXXHMNGGOoqKjAokWLnPOcNWsW6uvr8dxzz2HRokXo27cvTjzxROf8m5qanJBkVz788EPMnTsXzz33XMYhBjRNw4cffggAGbtKTpo0Cc8//zwA4P7778dDDz2Ep59+GnV1dRgxYgQeeughTzfPxYsX469//StWr14NABg+fDiWLFmCe+65B3/84x+haRoOOeQQzJ8/3+m+VlpaihdeeMH5v4tGoxg7diyeeeYZz6Dxt9xyC7Zt24b333+/XY+9o4455hg8+OCDUFXV6ap49NFH43//939x3XXX4dRTT8W8efPa9Vqyg9YlS5bg5ZdfRlFREY499lhcccUVuxy3anfdeOONyMvLw1NPPYVIJIJDDz0Uf/jDH7Bo0aIO3de//vUvz8Dpr7zyCh566CH885//bHXM1169euG5557DggULcPXVVyMUCuHYY4/FNddc49nv9NNPx8knn4zbb78dAP/5cffdd+Phhx/Gyy+/jH79+uGGG27AOeecs9vnTXomgXV0ND1CCCGdqrKyEtOmTcMDDzzgGcCUEEIIIaQr0zQN06dPb9Edbm+Lx+P41a9+hf/5n//BSSedtE/va29paGjAxx9/jCOOOMIzBtQdd9yB119/vc0utJl8+eWXmDFjBpYtW7ZXKwUJ2VeoUooQQrq4H3/8Ef/85z/x97//HUOGDPH8VZMQQgghpKt74okn9viCMu0RDAZx6aWX4sknn8RvfvObFlfY64qCwSDmz5+PUaNG4bzzzkNeXh5WrVqFJUuWYNasWbt9e0888QTOO+88CqRIzqBKKUII6eJWrVqFmTNnory8HPfcc0+LcUIIIYQQQrqy1atXY/jw4c5VCve13/3ud/iP//gPnHHGGZ1yf3vqxx9/xH333YdVq1YhHo9j0KBBOOOMM3DWWWc5V6xuj+XLl2PhwoV49dVXO3WQeUL2BIVShBBCCCGEEEIIIaTT7btrphJCCCGEEEIIIYQQ0ooOh1KapuH444/3DLy2ZcsWnH/++Rg/fjx+/etf45NPPvEc8+mnn+L444/HuHHjcO6552LLli0dP3NCCCGEEEIIIYQQkrM6FEqpqoo5c+ZgzZo1ThtjDJdccglKS0vx2muv4cQTT8Ts2bOxfft2AMD27dtxySWXYPr06Xj11VfRq1cvXHzxxWhv70HGGCKRSLv3J4QQQgjp6ejzEyGEEEK6st0OpdauXYv//u//xubNmz3tn332GbZs2YI//elPGD58OGbNmoXx48fjtddeAwC88sorGDNmDGbMmIH99tsPCxcuxLZt27BixYp23W80GsXEiRMRjUZ395QJIYQQQnok+vxECCGEkK5st0OpFStWYPLkyXj55Zc97V9//TUOOOAA5OXlOW0TJ07EqlWrnO0HH3ywsy0YDGL06NHOdkIIIYQQQgghhBDSc+z2NTnPPPPMjO3V1dXo3bu3p62kpAQ7d+5s1/asU2uBj0/mcyUfkMOAEgbkAsBXBCiFfO4rBvylqSnQm2/bjUt1EkIIIYQQQgghhPR0ux1KtSYej8Pn83nafD4fNE1r1/asi28Hqj8B0IExF0Q/EOjDA6pgXyBvEBAeAoSGAOGhQHgEIPkBQbImCrAIIYQQQgghhJBcZJgMkpjb3+u7ymPYa6GU3+9HQ0ODp03TNAQCAWd7egClaRoKCgr21insmaKxwPFrgNrlgFYPJJsAPQoYMUBrtNabAK0JSDYAyUYg2cy3myoQ28SnTAQJCPYHQoOA0FAgv4LfX94gHlZJPkCQAVG2Qit7WQZEJbVOCCGEEEIIIYSQrJJEAZe/9BXWVkWyfSodMqJ3GPefMSHbpwFgL4ZS5eXlWLt2raetpqbG6bJXXl6OmpqaFttHjRq1t05hzxUM51NrGAOYDhgqYCR4GKU1AM3rgOhGILEDSFQCiWpArQHUal6BZcSB2GY+VX+Suj2lkFdRFewPFI4B8vezwicBEERAlABIqYBKCgBSkE+izPeByOfpy8661MZ2QgghPYl9BTYGBsYYmFUdbC+nb29tOf24TLdtL7e1395aDypB9An32QvPECGEEEJI+6ytiuD77U3ZPo2ct9dCqXHjxuGxxx5DIpFwqqNWrlyJiRMnOttXrlzp7B+Px/HDDz9g9uzZe+sU9j1BAASFVy8pYd6W1x8oGg2YOq+a0mO8iipRySupdNWqsKoD4tuAyDqg6SegeS3fr34lnwAeNhWPB0omA2VTAH8/gBnWlAS0OMBqeDDmvrSzILRcdwIoaxlCquugO7CyK7EEmYdgTnWWfbwEb5glZQ68PPtlvwSQEEL2NndwYzIz43JbbQxWe1pbe+emacKEmZpbt5V+LiZMMJM5+9r3C2QOldyPzdnH7srO2g6WMh3rzJ2bSC3D/vXAAEEQwMAgQABjrMPruqmjX34/CqUIIYQQQnLQXgulJk2ahL59+2Lu3Lm4+OKL8cEHH+Cbb77BwoULAQCnnHIKnnzySTz22GOYNm0aFi1ahAEDBmDy5Ml76xSyS5QBsQBQCoBgH171pEd417/ETl45FRoK9D2GV0gxA2heAzR+B9R9BdSt5N0Ca5bzafV9QHgY0PtIoPwoXk3V3rCHMQCmVdllgH+rMF1tJoAkr/TS7X1N7z6CYH2JSB9jS7CCLckVeKVVYNlhFyRA9PGwywm/pDbCLfc8fT9CSE/nDmHckx26pG/P1O4OhuxtBjNgmAYMZsA0rXVr2YQJwzRaHOucjx3gMHjCIYCHNSZMZ7sTzghw5gK8P9d3tY8AAYIgOG2C9XvB3S4ImffP2G5vE1O3Y99upvtobVt7tu8rtbFaT0hGCCGEEEJyx14LpSRJwuLFi/HHP/4R06dPx+DBg7Fo0SL069cPADBgwAA8+OCDWLBgARYtWoQJEyZg0aJFnfKBNSsEkQdUSgGQNwDQm3m3vthWIL6Th1gFI4GiMcDgM3ggFFkP1K7gXfzqVvL1yHpg/VM8oOr/G6DvfwGB0l3ctwBAsv4ivZfHomot8HICLQMwk7xqjGUIu+zzY+nfutxBlwRvZZcdatkhl5yqWMsYYqUv21Vcdns3fc0RkmUmSwU47sDHs5623T3ppg7DNJBkSR4SWZNu6p7bcSqH4AqbGEuFQK7KHQBOtp6p0gZIBTSiFYCLgtgizHHPRUF09rXXWwuHWguFCCGEEEIIIXuYWKxevdqzPnjwYCxZsqTV/adOnYqpU6fuyV3mJkFIBVShIYBaBUQ2psIpfymf54/g05Az+cDq1f8GKj/g88h6YPX9wM8PAb0OAQpH8aAqPIzfpuTvvMeyLwIvZqQFXa51pvNukE64ZYVfdiWXp/ui9aUv49hadtBldVG0JyfgaiXQ8kzubfQFk+Q+u0LIPW9RPWSFSfaypmvQmY6kkYTOdOiG7gRHGSuZGIMBo0U1i11N4w6I7MDHHfa4QyJRECGK3u2tLRNCCCGEEEK6NrqkW2cTJSDYF/D3doVTOwA5BPiKU0GHUgD0+y8+JZuBne8B294CGr4Baj/jU+pGeTVW/nAgbE35I4C8gblz1T5hLwZdLcKttDDL1Kxui0bLgMt7Ui0ruDxja9nhllW9JfpcXRTTJrGNkIuQPeCuJEpfNpjhVB8ZzICma0iaSWiGBs3gy4ZpOKGRaXqrmwB4upDZ4ZEAAZIoZZ4LEhRR8QRK7okQQgghhBBCbDmSWHRD7nAqvh1o+hmIbQEC5S2rnpR8YOB0PkU2AjWfAdH1qe59yabU1f0qP0gdJyhAeCgPq/L3A/IreFjlK+neVT6CwMMhAICyZ7fF3JVZJjxBlpkE9Ki3LeO4JgJaDggvAaI996WqtUSfq6tiKwFXi6kb/1/2IO4QKX2yw6akkYRqqE6opBkadFP3dH9zVyfZPWTTq5AkQeJzUXIqjwJCAIIgeLYRQgghhBBCyL5EoVS2iRIQGsirpCJrgcgmQA60HhyFh/DJxhig1vKr+kXW8ZCqeS1fNuJA8898wt9Sx/iKXSHVfnwQ9dCQ3Kmq6kxOF8A9eG6ccbhc4ZazrPH/J9bOYEuUkLliy5eaJJ8rsHIFW2LaOoVa+5RhGkiaSSdYShquZataSdVVJIwEVF31VDS5B9222cGSJEhOmCSLsjO3l+1QibqvEUIIIYQQQro6SiG6CiUMFB0I+MuApp+A2DYgr69VWdMGQeADnwdKgVLXlQyZySuwmtfysKt5Lb/aX3QzoNXzAdVrV7huR7EqqiqAggogf3+gYD9ADu+bx9uTOF3/JHS4csvpkmgHWnpad8RIWlfFTF0R06q1RCkt1PLzKi0pQzdET9VWWtDVg7iDpqSRRNJMeuZxPY6EnkBCTyBpJD3d5+zucPa4SoIgQBZlJ2SSBAk+yedpo+5uhBBCCCGEkO6MQqmuRBCBvP68u17D90B0Kw+mRF8Hb2sAn8qPTLUbCSugWpuqompew7uhNf3Ep22u28kbYAVUI3lFVcFIwN9rTx8p2V2eLokd0KJay2g91GJGphOwQiwRTsBmB1ySP9X9UAq4Aiw5Q6iVXqnVNap5TGYiaSSdcZbsoEkzNCSSCcT1OOLJODRT83Src4+7ZHd9k0UZksjniqhAVlLrhBBCCCGEEEJS6FtSV6QUAL0mAE1BoHkDEOi19yqWpABQNIZPNsasqqqf+dhWzauBptVAohKIbeVT5T9T+/t7pwKqwpFWUNW7ywQMJIM9rdZyroSYPrZWEkgm0sIuoMWg8e6rFrqrtOzxtCRXpVamqqyMoVb7Xm/uwCl9iiVjiCfjiOtxp1tdetjk7h4nizICUgCyIjvhEyGEEEIIIYSQjqFQqquSAkDhWEAK8oDI1AFf0b65L0HgFVp5/YHyaal2rYHfd/NqoPEnPo9u5lcNrK4Cqv+V2tdXzCuqCkdZgdUoINiPgqruwqnU6uCPDM+YWa7l9AotZqYd6L4CYuZAy4AEFQI0BmjMhMZMqIaBqK4ipquIGxqSpoEkY9CZCWbdpgDBEzb5JT9CSojCJkL2IsM0nC6tqqHyua561u1lVeeD+LvX3XN7HDbN0JAwEjxc1jWohopzDjwHUwZNyfbDJYQQQgghu4lCqa5MlPgYT1IAaPgW0LDvgqlMfEV8nCr3WFV6zKqo+okHVk0/8cHVtXqg9jM+2ZQCq9ufPY3i3QEpqOp5hI5XaZlmEpquQtUTUPW49cU0gWgyiogWRUJXkTQ1awwnEwwMAgRIgghFUiBLPgQEBfmSAllSIIqKVXElA4IPMK11QwJMu1ui7K3ucndXJKSbYIxBNVSnWtCeJ/QE4klrrsc9Y6XZk73dmYxUmzts0k29Ux7L99Xfd8r9EEIIIYSQvYtCqa5OEIDQYAAC0PBN5wdT6eQ8oHg8n2yGyselskOqpp/4mFXJppYDqsthb1BVOArIG2iNVUR6KpOZSOgaVMOekojrCTQnY4gm49BMHUmDd6/jBCiiBFn0wxcIIyRKUKyKJw/7aoaeQeINwEzyeZJZg8ZnOCnnyouSq1pLTI2f5UwyWoyz1WJZotc42SMmMxFPxhFLxlKTHkNMs+ZWm71PXI972uzQKaZ711nGF/++4Zf8CMgBBOSAs+yX/fBLfj63lu3tftkPn+Rz2nySL7W/5IdP5ttUXcWY3mN2fQKEEEIIIaTLoVAqV4QG8XnDt7xbXTaDqXSSv+U4VWYSiKzj3f6afuRT81reXavuSz45x4dcY1SN4vPQIKpK6WZ4VYaGhBU8JXQN0WQczckYYskENDMJzUzCMA0AAgRBgF9SoIgyApIPBUoIsihB2J1KO0ECBGC3f9R5Boa3x9CyAixTA3QDAHONoZV+v+5Ay1oWpVSg5VzxUIITaGUaSF6UAYgUaOUouxIpokUQ0SKIJqOIanyKJCN82W5LRhFLxvhc43O7zZ72JTv4CSpBBGU+BRQeIDnrVqAUVIKecMk+xg6ZgnLQEzoF5SB8km/33ru7oTZWS11uCSGEEEJyFIVSucQdTMV38qvgdeTKfJ1BVFLVUDiJt5k67+rX9COvpmr8kVdYGVGg/v/4ZJOCvOuiHVIVjARCQ6wv6aQrM0wDCUNDQlcRN1QkdA1NWgTNyThUQ4NmXdlOgAhREOCzgqeQEkCRmA+5K3y59AwMv5sYc13FkAEwANPkQa2pwhkkHmbLQEuAdZ/2/YupsErwWQO++wBJ8QZYTqAlu0IwCrT2lKqraNaaEdEiaFabneWIFvEsu6eoFnUCqIgWgZHxapYdJwoi8pQ8hJQQgkoQeUoe8uS81LJrCsgB5Cl5CMpBZ27vZy/bYRKFOoQQQgghJBvoG36uCQ3iQVRkA5CoBsD4IONyKNtntmuiDBRU8Akn8jZTB6IbUyFV0498zCojDjR8zSfneD+Qv5/3qn/h4TwAI53OMA3EneBJRTQZR6MW5d3tjCRUU4PJGEQIkCUZflFBQPKh0Bdq2c2uO7G7+sH1Jb+93/c9gZarUsvUAOYKtFoMCA9XoCXySZAAUUwLtKwrHbaoyJLQahfEHMYYQzQZRZPahCa1Cc1qMxrVRjRrzU7I1Kw2o0lrQkSNoEnj+9ihk2Zoe+U87CAp7AsjpIQQ8oUQUkII+8JOwGS3hXwhp80Ol+zlkC8Ev+TfZxVHhBBCCCGEdLZu/M2wGwv2AQK9AbUWiG/jVVNqLR9YXCnMrYHERRnIH8Gn/sfzNmYA0U1WSGUPqL6aV1Q1fsenLdbxggzkD+dX/isYybsB5lcAcjBrD6m7scd7iusJxA0VsWQC9Wozoq7KJwZmdbfzwScqyPfloUQqhJjjoUanyxRotRczM4yflSHQam+FlrvLoSC7xtCS4KnIElpZtsfi2gsYY4hoETSqjWhINKBJbUKj2ojGRCMa1UYndHKWE01OwLSnlUoCBIR9YeT78hH2hxH2hVPrvrB3UsIt2kK+EIJykIIkQgghhBBCMqBQKlcJIhAo41O4iQdT0S1AdDOvmvIV5W5XN0ECwsP41P843sZMILYlNZC6Pah6sikVWm17w74BXlGWv781VpU1+Yqz9pByRdLQEdMTiOsJxPQEGrUImrQoErqGhKGCMQZBEOETZfglHwp8Yfglhb5wdwWCaI2ftRe6HDIrwHJ3OUwfQ0uAa9kO00RXQGWHVK5xtCQfGAREknHUqxE0aBE0aM1oSDSjQW1ypka1CY2JJjSoDWhM8KBpT8Iln+RDgb8ABb4C5PvzUeAvQL4v37Nc4C/gYZM/37Oep+RRuEoIIYQQQsg+kqOpBfFQCvgUGgQkqniVUXwnD6V8xXwg8lwniPwqhKHBQN9jeBtjQGKnN6RqWg2o1fw5iG4Cdv4jdRv+3rzrYL7VhTB/fyCvf853UeqohK4iZoVP0WQc9YlmRPU4ErrmXOVOEWUEZD9CSgDF/gJIYs98rrq9PajQMpmJJrUJ9fEm1CUaUK82oT7RiHq1GfWJJjRoEdSrEdSrzWjQomjQojAydT9sh4DkR6E/H4X+Amde4C9EYaAQhf5CFPgLURCw5v4iFAaKkO/PR0AOdOj+CCGEEEIIIfsWhVLdiRTgwVSwnxXMbAHUKsA0AH+OjDu1OwQBCPblU/m0VLtam6qeal4NNP0MxDbz56K6Cqj+JLWvlGd1H9wvFVaFR3Sr7n/2Ve+iyThiegLNWgx1aiNiOh8LymS8611A8sEvKSgJFEKR6EdDT6YZSdSpTahLNKIu0YTaRCNfVptQn2iytjWhXm1Cg9rcoZApTw6g2J+PQl8YRf58FPlDKPKFUejLQ6EvhCJfHoqUPBQqAd6mBOBvrfrT6TJoTSIDhEZAiwJJ2RpPS3Z1P8w0dpar26LTJuZWd2iSEzRNw/Tp03HjjTdi8uTJAIAtW7bgxhtvxKpVq9CvXz9cf/31OPzww51jPv30UyxYsABbtmzBuHHjMH/+fAwcODBbD4EQQgghZK+hb57dkSjzoCbQB9DqgNhWIL7DGneqkFdVdecvWv4SoOwwPtn0KNC81gqq1vCwqnkdYMSAhm/45BCAvAGusGo/HlTlSFWVO4BqUqOeAMpgJkRBREDyISD7UegLd42r3ZF9zjBNNGjNqI03oCbRiNpEA2oTjaiNN6LGXrbCp+ZkbLdvv8AXQpE/H738BSj2F6DIn4/iQD6K/QUo9ufzdau9yB+GX9pLVw61ux7a42h5xtNK8O1tjacFZBhTS0jrgrirYEvMEGyJ3i6NhABQVRVXXXUV1qxZ47QxxnDJJZegoqICr732GpYtW4bZs2fjnXfeQb9+/bB9+3ZccskluPTSS3HEEUdg0aJFuPjii/HGG29Q12lCCCGE5DwKpbozQeABjb8ECA/lwVR0C58Ua9ypjlzyPhfJIaB4HJ9sps4rqJp+toKqNfzKf2oNH78qtgWo/CC1vxTk41zlj+AhlT1AexbHqtJN3ep+l0BEi6E20YjmZBQJQ4NuGk4AFZT9KPLlU/e7bkg3ddQmmlAdr0dNvB41iQZUxxtQE29ATYLPaxONqFMbYbJMiUxmkiChJFCAXoFC9AoUoJffXi60lnnAVBIoRJEvP3vVdXsyOLzNM0i8HWIxV7CVHnq1di5pFVuCyK+ACMEaMF5yja+leAeE94RbmSq2xJ7z87qbWrt2La666iqwtPfhZ599hi1btuCll15CXl4ehg8fjuXLl+O1117DpZdeildeeQVjxozBjBkzAAALFy7ElClTsGLFCqfSihBCCCEkV1Eo1VPY407lDQISlXy8pdg2QPTx0EpUsn2GnU+UUwOq49hUu1afCqma1gCRtUBkPWDEgcbv+eTmK7HCquFAeLh1m8MBJbxXT5cxhoShIpKM8zGg1CbUJ5oR11UkTR0AEJB9CEp+FFAFVM5jjKFBbUZVvB7VLaYGHkIlGlCXaAJrNSXxEiCg2J+P0mARegUKURooREmgCCVW2FQSKEBJoAi9AgUo8IV6zgDfezJIvC1jxZbdZlhXQTTBB4zfnXBLaFl95VRuWVdHFOW08ErMHHDBDr9cFWGk09gh0pVXXonx48c77V9//TUOOOAA5OXlOW0TJ07EqlWrnO0HH3ywsy0YDGL06NFYtWoVhVKEEEIIyXkUSvU0chAID+Fd0RLVvFIoUcW3+Xp1q7GUOsxXDJRM4pPN1Hk3yOafgcg63hWweS0Q3wZotUBdLVD3hfd2/L1ToVd4GK9WCw8DlPx2nYZhGojqcUS0OJqTUdTGebeqhKHBNE34JBlBOYBegQL4pB4YKuYwwzRRm2hAVbwOlbF6VMZqUR2vR2W8DlWxOlTH61EVr3fCxl2RBAmlwSKUBgpRFixGaaDIWk/NS4KFKPYXUFi5r+yNii3AFWS5K7dclVxmMtUl0Q642qqAc8IpAc4YWZ7gSuHtolW5JSoZKrTSAq6M4Zf9+ElrzjzzzIzt1dXV6N27t6etpKQEO3fubNd2QgghhJBcRqFUTyUqQF4/INiHjzUV3QqoO/gA6b4iQM6nv6K7iTIP88JDvO16DIhu4ONTRdbxiqrmdXxQdXuq/cx7jL+MB1ShIdZ8KBAegqRUiIgeRyQZQ32iGbWJRsSNBDRDhwAgIPsRlP3oFSjoORUsOchkJuoSTaiM1WFnrBaVsVpUxupQGa9FVawOlbE61CQa2j04eLE/H72DvVAWLEZZsBi984pRFihGabAIva22Qn+YXhPdheAOd/bwV3SLgAvwdk3UAahtV3AJSFt3j5FlV1/ZYZeQGoPLGUReclV1uQaPF9zHugeVzxB2dfMB5+PxOHw+7xhrPp8Pmqa1azshhBBCSC6jUKqnE0QgUMYnbRgfdyq2BYhu5t3PetK4Ux0h5wGFo/nklmzmAZUzbeDhVaKSB39qNVC7wnuMGAR8ZTCVMij+cpQE+8PIGwQW6g/WE7tXdlEJXcXOWC2forXYEatx1u0Aqj0VTpIg8pAp2Au984pRHixBWV4xervaSgNFVAVHOm5vBlyAVZFlhVbpVVxwj8FltbenqyJgdZ1MC6FahF1Wt0WIfKB5QeKPSZKBRCPAeu3548sSv9+PhoYGT5umaQgEAs729ABK0zQUFBR01ikSQgghhOwzFEqRFF8hn0KDeJe+6CYgtp1XVfmKAcmf7TPMHUp+i4HVVUNDJFYNtXE11KY1EKIb4U9sQ75WhVCyFooZR3FiM4oTm4Hm1E0xCFD9ZVD9fZAI9EXC3weqvy8SgXIklV50Za+9rFmLYnu0BjtiNdgRtaZYjRNANajNu7wNAQLKgkUozytBeV4vlAetub2e1wu9/IU08DzJLc5YVMAed1N0Y2nBlqfLoumq6soUeDEgVg+EB+y98+lk5eXlWLt2raetpqbG6bJXXl6OmpqaFttHjRrVaedICCGEELKvUChFWpLzUuNOqTX8an1qFWAavHJqLw/g3V1pRhLNWhRNWhQ1iQY0qM2I6QmYpgB/3hiECg5GQPZDFiUIpga/WolAYgcCie18ru5AILEDkplAQK1CQK1CYdM3nvswBQWqv5wHVYFya7kcqr8PkkoRBVYZRJJxbI9UYXusBtsj1dgRrcH2mDWP1iCSjO3yNkJyAH1CpeibV4o+VtDUN1SK8rwS9MkrQVmwCLJIP14JaRdnoPkOyvFubOPGjcNjjz2GRCLhVEetXLkSEydOdLavXLnS2T8ej+OHH37A7Nmzs3K+hBBCCCF7E31rIq0TFSDYFwj0AbQ6XjUV384H9papa1+6pKGjORlFsxZDTbwB9VoTosk4TJPBL/sQkgMozMvPWB3DRB8SwYFIBAembWBQkg3wqzutkGon/IkdCKg74VerIbIkgomtCCa2Ao3eQ3lg1Ruqv9ya94bq43PNV9ptuwRqRhI7rMBpW7QK2yLV2BatxnZratKiu7yNYn8++uaVom+IT33s5bwS9A2VIt8X6oRHQgjpCSZNmoS+ffti7ty5uPjii/HBBx/gm2++wcKFCwEAp5xyCp588kk89thjmDZtGhYtWoQBAwbQlfcIIYQQ0i1QKEV2TRAAfwmf8odZXfs2A7EdfGwPXzEgBbJ9lp3OZCaatRiatAjqEs2oSdQjpidgmCYUSebVNHmle3a1M0FA0leMpK8Ykfy0rhrMgE+r4UGVuhMBtRJ+Z7IDq20IJra1uFkGAUmll9UtsDdUXxk0fxmf+8qQVAq7bJUVYwwNajO2RauwNcKnbdFqbLPmVbE6sDYHsAGK/Pnol1eKfuEy9A2Von+oDH1DZehnVT8FZOqqSgjpHJIkYfHixfjjH/+I6dOnY/DgwVi0aBH69esHABgwYAAefPBBLFiwAIsWLcKECROwaNEiCN148HdCCCGE9BwUSpHdI4f4FePyBvCufbHtgFrJgyqlgE9dNMzYU4wxxPQEmrQo6hNNqE7UI6LFoZlJKKKMPDmA3sHizuu2JUjQ/OXQ/OUAxnm3MR0+rRZ+tYp3C1Sr4FOr+LpWCcnU4EvWwpesRX7kpxY3bQoKNF+JFVaVQvOVQvXxueYr3eddAw3TRGW81gqdKrElUsnDJ2uK6ok2jw/KfvQLlaF/qDf6h8rQL1xmrfMQKqQE99m5E0LIrqxevdqzPnjwYCxZsqTV/adOnYqpU6fu69MihBBCCOl0FEqRjnF37Us28qvKxbYCsW3WwOhF3aJ6SjOSaNKiaFSbURWvR5MWRVxPQBBEhOQAegUKuubV0QTZCayaMda7jTHIeiP8ajV8WrUVVFU76z6tFiJLIqDuREDd6Rl03bkJSNB8vaD5SryTklo2pbaDH93UsT1agy2RSmxprnTCpy2RSmyPVkM3jVaPFSCgd7AY/cNl6B8uR/9QGQaEyzHACp+K/QVURUAIIYQQQgghXRyFUmTPCAIPoHxFQGgor56KbwPUaqt6Kt+qnsqNsafcXfJqE42oTTQikoyDMRNBOYCQEkBJoDC3Aw9BgK4UQVeKEMV+LbczHT6tDn4roPKpNTy80mrg02rg0+ohwOBBllbd6t0YYhBRpRhrWRirdT/W6gLWqjo2JGLYGGvC9ngDDGa2erwiyugXKsPAcDkGhHtbE1/uGyqFX/LtjWeDEEIIIYQQQkiWUChF9h7JB+T14xVUyUYgUc0DqtgOK7wqBKSQdVnx3cCYdw6kbmMvhEMJXUWjFkGD2oydsTpEtJjTJS+kBNE3rzTj4OTdliBD8/eG5u+deTszoSTrrYCqFrJag+rINmxursTGaC02xCNYq+pYk4xjQzKO1uudgKAADPfJGBoIYnAwH4NCvTAwvxwD8gegOL8/TF8JkkoRmEgBFCGkJZOZuxxDjhBCCCGEdF0USpG9z109FRwIRCqB5q1A9Q5AWw9AAYQ8gMmAYQLJJKAbQFIHDIO3GQYPoUyrksb5zsHgXDvczqNEkd+nKPJJEgFZAiSJz/0+QJEBWQYUGaYkokmPo8mMoyrRiHo9gqihQRAF5MkBFAfyqQonjT24+KbmHdjUvBObrWlT8w5sjVRBM5OtHhsQJQwNhjDM78MIRUCFrGOklMD+Uhz9JEAQdPA+gs0AtgPR74AogJ2p29ClPCSVIj7J1lwpRFIpgq4UWm2FMDoSehJCOhVjDAYzYTITJmPW3Ey1gXnXGQNjDPYvAgGCFUQJEAUBoqnDL9HHGUIIIYSQXESf4sieSSYBVU1NmgYkEkA0CsTjvM0wAF0HVBNQDUCrAvQIYCQB2Q8oIV5lZYdKAniwYE82Z5l5q6Z0K8BiSAVZjHmWE4aGRjOBehZHJYsjAhUaGHySjLAUQJEUgChJPLiSJSBgBVmStW4HXaKUWpdyo0vi7kjoKg+cIjuxsWmHEzxtbt6J5mSs1eNkUcKAUDkG5ffBoHxrHu6Dgfl9UBYsgphhUPRKU0W91gAlWccrr5L1ULR6KMkGa53PRZaEbMQgGzEEE9vbPH9TkKDLhUgqhdDlAh5cyQXOsu4sF0CX83OmWykhXYEdJnlDpVR4lB4qmda+/Ic6n9s/xkVBhAgRkiBCFEUeLkGEIsp8kvhcFiX4RAWyKEESJIiCAEmQIIliaj3ZiIC/KHtPDCGEEEII6TAKpciuMcbDpXicB06JBNDcDDQ1pYIoTUsFRYIAKAqfZBnw+fhcKuVBDmOAHgW0Bj72lB4BTB2Qg/zqfsKevyxNZqLZSKBRj6Em2YxaPYKYqQGmiTwhgF5CPnzMOhdmAgbjwZmmASbjQZqZ1iVEFPj5S1Y1liQDfgXwKfwxKnKqOsuqyuJBVtfq+mcyE1WxOmy0qp42Ne2wKqB2YGesttXjBAjoGyrBoPy+GBS2w6e+GJTfB33ySiCLuxfwMNEPNVAONVDexk4MkhGzgipr0hugJBshO+uNfN2IQmQGfMk6+JJ1u75/CDCkkBNQ8cAq3wqx8jNMYepGSHLWLoMk13bDCZPsn4E8VBIEoUWYJAkiBAhQJAV5ggRFkuETFSiiDJ+k8P0E0QmRJIEfI4lSapsgOkFTpgB7lwSd/2GDEEIIIYTkHAqliJdhALEYn+JxHjw1NPAgyq56AlJhk88HFBTweXvDF0EAlDCf8voByWZAawLUKkCtB5hhBVR5gND+K9tppo5GPYYGPYadyUY0GXGoZhJ+waqGUvI69oUHAMB4SGUagG51L9Q0IJ5oGWAJSHUflKzugwG/VX2l8LDKDqxkeZ9VXMX1BDY184onO3Ti1U87kDC0Vo8r8IUwOL8vBuf3weD8vhhkLQ8IlyMgd/IXP0GAIYdgyCEkgv3b3tVMQtYboSSboOiNkJM8rFL0Rsh6E+RkExS9iS/rEQhgkI0IZCPS7tMxRD8PqKQwDDnshFW6FIYuh2HIIeiS1SaHYEhhGFIQ6PDrjpCOBEom3NVJdqDkrjQSrYBJEkT4JQWKqMBnVSe5K5PcYZITIHkCpj0IkwghhBBCSI9HoVRP5g6golGgvh5obOQBlGaFFrIM+P1AIAAUFvL1vUkQ+QDovkIeUOkRHlBpNYDWCJhJQArwgEr0ew5ljCFqqk41VE0ygoiRAANDnuhHsZQHv9L+UGsXJ8orpURx1+8aZvJxsXSDT5EY0NjMn2/Bui3JqqCygym/Dwj6UtVlspTqPqjIvDIrw1hJjDFUx+udwGlj83ZrvgOVbVQ9SYKEgeFyDC7og0FW+DQkvy8G5/dFkT8/J68uyEQFSV8pkr7SduxsQtabrakJit4MOWkHVs3OpDjLEQgwIJkqJE2FHzXtPy+rIkuXwzCkPGsegi7lWSFWCIYUci3nWfuFYIoBCrS6ATtM0s22giUTejsqlGSxY4GSuzoptY0v5+L7nRBCCCG5yTAZJJE+e5AUCqV6kkQCiER4ANXQANTVpSqgAF7tFAgARUV8ubO/qIhSKqBi/XlAlWwG1FpAbwaMOuiChCYmoBEmdiYb0ajHETc1yJAQlgLo6yuClO0v8YIIyGIbAR5LVVu5K65qdN6dUBD43BVcaSLDFrMZG416bNTrsFGtwaZ4NTbGqhAz1FZPpcif71Q9DSnox4Ongr7oHyqDLPbgt78gQlcKoSuF7dufMYhm3AmqJD0CWY84gZVsRHibYbdHIBlRSKbaoYos524hwJCCVlBlB1ZBHmg5UzBtObVuigGYop8Gf99DhpkpQDLaXaUkCqK3OknkAZMiKQiJ7u5uMhSry5sdGMl2kESBEiGEEEK6AUkUcPlLX2Ft1e5/Nu4qjty/DP9zzMhsn0a30YO/lXZzpsnDp0iETzU1fByoeJxvUxQgGORd7/xd8EurIAJKAaAUIObrhcZENeriO1AV3YZmrRaGkUBQUBD2hVHmKwCEXBpPRLAqpFp222vQY9iYqOahU6zaWd6WbIDZymXPJQjoLxdhsK8XhvhLMSRQhsHhcgwp6IeivEJebeW+IqEoAkkDkIRWK7BIGkGAKeVBk/Kg+dsYAyv9MFODZMR4eGXEIOlRK8CKQjaiPLhylmOQ9JizLLKkFWjxQd6xG9VZbu5gyxQDqeBKDMCUgjCkAAwxCNOaG1LA2Y+32et2wJV7lVsmM2GY7QmVjLSBuQH7Cm9O1zd7LCSI8MkKfNbYSZmqlGRB8oZJgujZTl3eCCGEENITra2K4PvtTdk+jQ4bXhbK9il0KxRKdReGwcOn5mbeBa+mhnfLU1UeOgQCPIQqKuryV40zmYkmPYrGZARVah3qkk2IGXGIgoRwaADKw0MgM40Plp5sBow4YDTzxykG+BX90HUfo8FM7NAasFGtwcZENTapNdiYqMFGtQb1erTV40KiH0MCpRjiL8PgQAmG+sswJFCGAUohFCZaXQZ1Pk+aQGUzgObUd2swHkhJ1lUO7SsJ+qzB2v0+71hYma442MUGbe/qmOiDLvqgK0W7fawdaElWKCXpVnBlxJ12z7oZd63zZQEsLdjac4bot0IqP0wxCFP0W8sBa5sfphVgGValFm/zp7Z7Jh8Myd/mBQ7cXd7Sq5aMtOX0SiV+Mc/2h0qKKLuCJF6hJLfoCkehEiGEEEIIIXsDhVK5KplMVUHV1QG1tbwKStN4oJCX13WroDJQDQ2NegT1ySbsVOsQ0WN8kHJRQVjKQ4lSmNZNJQgohUDQBAyVB1PJKJBs4kGVaVohlR+QfcjGSz1mqNis1jrh00YrfNqi1kJleqvH9VEKMThQaoVOpRjiL8XgQClK5V2M9SSDB0ttMU0+GaY1aLsOJNRUG5jr+7w19pUgeK846JN5pZ0dYoli6qqDkhV2SWIq2MqB119X5A60Wu+g2dYNMIimCtFMuIKsBCQzDtGznuDr1n58/wREe19ThWTEIVjppmSqkEwVSusv4Q4xIcEQFeiCD4bogyEorrniBFim6AcTfWCiH0z0Q5ACEKQARDkPkhyEIPFJlAJ8kvmyJAf5PlIQkqhQqEQIIYQQQkgXQKFUrtE0YO1aYMcOHkIlk6mueL168bGgcgBjDM16FI16BLVaI2q0BkSNOAAgTwygl1IAn9ieQcpFQAryydeLX7nPSPBJjwDJCA+rTN0KqXzWpMAadXyPH0eN3uxUOrnDp8pkY6vH+QQZg/wlGBIoxWB/KYYGyjDEX4pB/hLkSf5Wj9tjoti+wdoBa8B264qDJvOOf2WHW/YYWJ7bF1KVWKIVbDlXG5T461VybbcHfLfPzR1oUWVWxwkCr1iSAh2q1PJgDMxI8OpEIwHBiEEw4nwyeYAlmipEg4dYsqlBZhoka1liGmQzCZlpzrpkas6YWwAgwoBoGlCQAIw9f/htEhRA8vHKStEHSH7r54Lfave72qx1Zz/F1Z42SZnalbS5tSx03WpOQgghhBBCOguFUrkkGgV++AHYsoV3wysr2/tXw9uHNDOJxmQEDclmVGq1aEpGoZpJKIKMsBxEP3/ZnlcuCBIgh/jkL7FCKhUwE4Ae5wOmmwkeVgE8GBEUQFL4HABME0LSgJjUIegGhKQOTbOqnrQabNTrsdFowHrWgI2sEVGh9ZKREtOHYXoYI5JhDNdDGJ4MYbiWh4F6EJL7IluwutphQ9rjAQ99BAHMmkMQwETXXBTBRAFMFAGJz5kV7jD3JEt8myyl1tOWTYXPIYrWgO1A+7tCMh5emYYVZlnVV4aWqsaygyxn3B7rOPuxSdb9Sna45Q60XFciFF2TfZwdiDnHu+ZUrZWh25vhGmOJucZYMl3jKgF2Nzh7cG5JCEHy5TtXf5NFCX7JB5+oQBRlyJKSGpzbNaaSKEoQBBEQJAjWsiCIAEtaQXKcv0eNBH+PGq7Js65m2K5aFZOuZWduHeOuTmRJQE8CaL277L4ntgyq3JNgt8muIEv2hlzOums5fe7ZLqdtl/nPPbvdOSZtP0Gi9xAhhBBCCNkncifR6Onq6oBvvwXq64H+/XMijDKZiYgeQ5MeRY3WgNpkIyJ6HGAMAcmPQjkfAamDlV2GASkahxSLQ4wlIMXikGIJiPE4pLgKMZ6AFE9ATKh8iquQEipEVYWYSPBJ5dsETYOo6YCWxPaggTVFJlaXAKtL4cw3FwIs03cyAZBMYHgdsH8tMLIG2L8GGGXNS+IagDpryh1MELxBlSJbyzKYYs9lvs2n8HWfDFORYfqsba656bf3UVLrPhlMUaxlfj+mKMGUrQoqw+CVgKrGQywn1AKcIMu9DHiDKffcrtayJ1lOhV4tqrXsdni3Z/lLefuvANf2YN08IBKdcZX8sgJFVPiV30QZftHHQyUxbWBu66pxsiDv3au/CVYooxTs4TO0C8zgAalph1daWoBlrZua1aal7aO1PjnHJtO2Jb1zz8UKTOuYDnXO7HyC5Aqu0uaC5A22nHXrGFFqY5u97J7S98u0zdVmJIDSQ7P9DBFCCCGEkA7o+slGT8cYsH078P33fNDyAQOy/uW4LQlDdQYpT6+GCslB9PWXQhJEwDR5qNRcD7kpCrk5Aqk5CikShRyJQYpEITXHIEVjkKN8LkXjkKIxiLE4pITW4XOsDwA/lgA/lwA/D+Lz1aXAml5ArI2MrDgBVNSL2K9RQkWjgv0iCiqaFQyNKVAE2alIgiiAlYpAbwENdtWSaFU6ie6qJwCw52ncg5MzQLCri0zGdzdNCCYPagTGAIOvC1Z1kmBtFwxr3TAg6CYEe9kweRWYzpdF3dtfSmAMQlIHkjqkeIef6g4zZYmHXH4Fps8KrtKXA760NhmGHXrZk2IHZRJMmbcZihV8ySLSq4EgIDWOlvV/5Qmq2t0NMS3wEgUYAEwwa2BuIy1gaj1UEgQBjKHVK8Apkgy/PVC3pLR7sG47bNrjUClXCBIgBwEEs3P/jPFgzBNYWWEVS6a12e16WlvS2te9rqe16959mG616d79W2yzj9MBmBnO3+AT1H3fvbIjeh8J9D0622dBCCGEEEJ2E4VSXZmq8vGj1q3jV8/r1y/bZ9SCbupoTjQjUrsDjTVbkajbCbO+Hr6mKMqbVQyLaPA3xyA3NUNu4sGT3MTDJ4GxXd/BLph+H4xgAEZeAGYwCCPoh5kXQGNIwdoihnWFSawLaVgfTGC9L4oNcjPqxNYrE2SIGOArw+BgOQYHyjEoj4/1NFgpQJEACM4XOf6tjAnAelEGIPNBwAUZvMQmxzDmBFROWJXUISbdyzrv1qhbc01PtWv2dj4XVR2CluTLmrVdS0K05unrosrnNlE3AN2AFNt3VSSmLPFAK+AKuPwKDL8Ppl92BWEyDEWG7pOg+2Q+KSJ0n4SkIiEZkPncJyHpl6D6ZZgBu8IDVigFHihJMiRFgSgrkGQZoizDJ/vhk338CnCyHz7JB8XaLkkSJEnhAZPEu8bxNplPigJRpLGJcoIgpCqDkJfts2kbMzMEVq65s2y0stzKNmakrVs/S5lu3aeR4Tj3dvdtuPY3NaDPf2b7WSOEEEIIIR1AoVRXVVMD/PQTUFUF9O7NBzLvLLoONDTwroJ1dZ45q6+HXl8Ds7YWaKiH2NCE4kgMxQAGduCujIAfRn4Yen4ejHAIen4IRjgEIz8EPZwHI5QHw56Hgnw5LwgjlIdmP7A1WYvNiUpsjVdiS6ISW+I7sTmxGTVaQ5v329tXjEHBPhgY7IPBwb4YEuyLQcE+6OcvhSy28bZghqubjm517YkBut3FJ+LtUmYHVqLVhQXu6pwuRBB4F732jC2/rzCWCqtUK7BKaN51VeMBlpqEqGkQE9Y2Ncm3JZJ8f/dxCXubBklN8goz8OBL1A0gltj7D0UUwHw+sIAC5vfzKxX6fYBfAfx+CH4fn3x8jtamgI8H0gE/n/KCvE2SvONnOVc/tMfespbdA8bbV1N0VW+lqsJcY3J55jkYsJI9J4ipbpW5QK3l4wgSQgghhJCcQ6FUV6NpwKZNwJo1fPycgQP3/IshY3yQ9Lo6b8iUvm7PG1u/apwAIFNuwQQBekEYemE+9IIwDNeyXmDPw6lt+SEY+WEwpfWXIGMMDXoztiWqsDVeha2JzdiSqMTW6ips3VyJ2jaubgcARXI+BgbLMSjYh0+BPs56nhRo55OX/gRIgJTXcuxv56/3dlhld8dJuMasMZ0KK35bVhcvyKmBxXO10mpvEAQwvwLDr8DI501OlzYw3s0NDCZjMGHCcM+tbQwtq+8ECLyrmyBCAiAnTciqAV9Sh0814VMN+JMmfKoOn2pAVnVIapJPiaQTZokJDYIVbgkJDUJChZCwBnGPq3ye5NVegskgJKw2RPb+c+VTeEjlBFh+Hlb5FD75fbx7oSsIg1/hV+d0rwf8VuhlhV95AX68M+aWfbVG12DzshWCiaIrHHONuyWkB1tpAZc7AHPvay8TQgghhBBCegwKpboKwwB27uTd9WprgZISIBxuff9kklczpYdM6cu1tXyu7d4YTEwUYRTmQysIIV4YQrwgD1pRGGZREYTiXkBxMfSifB48FeZDzw/ziozdpJoadiRqsV2txrZEFbYnqrE1UYVtCb4eNdoe0KhQDmNQsA8GBMoxMNjbCp54+FTQmX85F6yqFPgzbDStoMru1mIHV/YgyaoVVmlWVxZXsCJYty2IAKS08EpAV6y6YoxZ4VHbYZJp7Wcw04qS3IESf1yiAEjggZIoCHwZAmRBQlCSoECGIopQBBk+QYbsXCHOmqxAii+72q31vT6ekmEAdlCVUIFEwhtapU/p7Zn2U+1l13tYS/JpX7GDLb8vFWTZbT4f4JOtueKafK7j3OsK4LMqvQJplWDOFRThGpxeSFV+2VdQdFeCtRWE2d0l08cEc125MuM2CsUIIYQQQgjJCgqlso0x3lVv/XoeSvn9maujqquBjz/m07ffAk1Nu39fwSDQqxefiouBkhKw4mJohWHEC/PQXBhATUhEfb6M5iCviAiIfoSkAAKiv0Nf4DUziUq1DtvVauxI1GCHWoPtiWpst+bVWv0ub6Pc1wv9A73RP9AbA4PlGBgox4BgbwwI9EZ+TnTZEOFc9j0j5hpXJcNYK4YGMM0KsqzwillXonOzv9gLIqxLx6XWBYGHW60EWelBEmMMhis4ai1Y8t69AAYGwbrSmyjwAMkdJkmCiIAoQxZE+EQZiiBZk2xVMwne8MhZFpyBve1tXZIkAaEgn/Y20+RXImwr3HK2ay0DLU/I5V7XUnObHXo1R/f+43Bzgq60EEtxB12ytS4DsjVX7DZ7uw/wW1dVdB/nc4dgijXumyuYAryhmB1q2SGYIKQNZu/q8mjPBaSFXwDQSuDlDs0EoeVxrQVqhBBCCCGEdFMUSmWLrvOgaetWoLKSh1Pl5fyLFsC/OH77LfDll8CnnwI//NDyNkSRh0tFRbyyqrg4FTjZ4ZN7PRiEbuqIGglE9Bga9QhqtAbEjAQShgpBEBAQ/ciTAujfjhCKMYZGPYJKtQ6Vai12qrXYodZgp2t5V2M7AUCeFEA/fxn6BcrQP1CG/gEeOPUP8DZ/roxr0mGusafaZAVRzIBpJmEyHczUYSAJZugwmA5maDBYEoxpMAwdjJkwmA4T/GpvzOk+mIqQ+BXeRIiCxCdRgAgJEiQIgghJlOETRCiiD4ogQxZkPhi3IPGwyKpkclcmia2ESSKEnnO1t71NFIFggE/7gh16pQdbmsaX3QGXaoVYcdd2zRVuqa5j1GRq/4TGq8ls+7riK50opoVermUn+LJDL1fIld5mX4VRUfj4XbI7KLO7OvoAn1XZpVjBmU+yAq5MARZaVnPBFVY53SWt6jH7Co9S2hUiM91WprCstX3Sw7D0SrRMFWdUZUYIIYQQQjqIQqnOFo3yyqiNG3n3O0nioVEiAaxYAXz3HfDVV8A333i73AkCMGYMMHUq8ItfAH36AAUFbX4RYIwhbqqI6nFEjXrUN2xCvd6MuKEiaeoQBAF5oh/5Uh5KlSJPWMAYQ32yCVVqPaq1elSqdajS6lCl1vEQSuNBVMLcdbdAv+hDP38p+gZK0c9f5pn3D5ShSM7vtkGFXYHEmAkDpqsiye7CZqbGQ7Kqk5irexsPjAQwxgArQAIAQRAg2t3a4IeoBF3VSTwAUiBBFgQoEDxziTFe8MEYJAgQYUJiJkTTgAQTEuxl6/suAwSB8eosWJe1Z84DtCq7rAb7NPk3af66ZdaXVma1C6K1n+CaW+32bboHhHe6MzLPLHWfaeNIpbc7m1377eo2bPYXcftc7JepIHiP8RzeSrtn/1buLz0csKf0c8kYKqRts2/PHWy0ZV+HXjbDSAVU7kBLbWPSktZcs0Iu1zF2sOXsa+9vtSddoZdp8iAtvu+u6rhLTkjlCrKUDGGYO/hyBrGX0sb3klKT3bXR3W7fhiTzIEuRAEnxHufeX3JXVmZ6XbkqzDyv1fSuk6IrSHMHZkJqXRCRsUos0322VpFmb9MiQB59nCGEENI6w2SQ7N9hOay7PA5C3Dr1U5yqqrjlllvwj3/8A4FAADNmzMCMGTM68xQ6H2NAJMIDqB07eCC1bh2/ql5lJbBhA/Djj8C2bS2PLSkBDj4YOOQQ4PDDgdLSNu8qYaiIGQlEjTia9RhqtUZeBWWqYAAUSAiIfkgQETE11GgNqNEaUZNsQK3WgGqtHlVqPWq0elRrDUgyvV0PsZdSgHJ/Cfp4plL08ZegX6C0y4dO7lDIhMHnzIRpmjwcMhkY+Lq1BNM0eLhkmmAwIUAEY6Zz5T0BPJASBIEPtA0+FyFAAFzrIg+LwMdF4v+sLm2iDJG5ur8x8GojltYlzl5n/PadsZIY84YnrS1nIghW90HTCqOsOWN8GSZg2svMNbn3NXgb0+GEWfwZt+b2Ma21WXP3l2D7qoa8wXu+zpfYtHGGPF9uXWMYebo32lUgSIVmDPwxmtaKHQzaV1e0Aza4zs8dIrUIh6xzTm9zvzcM67kz7Ko4Zs2t+zSZdV7W88Xc62lhGGOpCSwtOPM+xRDs7a7wzQlC7VlauOWe72qcJmfdWs4L8qkzmGYquHImd9BlzZO6d3vStb+a9K5raevOsp5aTlrLSVdoC/BQzjCsQfC7IDsUyxSASWkhmCcMc13t0TP+l5gKvJyrQdrrrjZnLnu3ydaYevbtitY+sr0uAWICGD4cGHRUtp89QgghXZQkCrj8pa+wtmofXICmkxwypBg3Hj8626dByF7XqaHUnXfeie+++w7PPvsstm/fjmuvvRb9+vXDscce25mnsW8lEnyw8rVreeC0bh2vitq2jQdRVVW8614mgwcDo0cDY8fyIGrwYO8XVgtjDAmTB1AxI4H6ZDM2xXZgu1qNKrUODclmNOhRRPU4mvUoGvUI6pNNqNUaUZtshM6MDHeeWS85H2W+YvRWilDuK0ZvXzHKlWKUW/PeShH8opL6EszP0PV8MIA1taxMsfdzV6qkVZXwsMgEYyaYaVUcWRVEzKk2Si3zrfZy6mpszA5n7PtxAhsAVkgkwh7/iI9rJMCa2+MiiSJkgY+FJAsSZMEHSeThkSzKVtc3KygSJWc8JUmUIYrWbUCEJEp8OwS+LEg8QPJUALiWgQxXNbPCAHeXmUxXN7P3SQ9K3O27Wu7I9ra22QGJXXklIFWBlR5wsbT9PNvS2pwQzA7ErHV3oAa7zXWME9a4gyf369jdjpbLnuNtQtq6+7G3V1oAx1yBlBM4IXXf7vdfi4fAACZ4H2/6bbjfh6brcZppczsAs0Mz3QonDQMwdMCwQjQnzEMqXEsP+ezzc/+Ma/E8MesKlWhlcHP+7nW6ujndydLGiRIEPuZUQEmtu/d3nvPW/i8yNbczaGcM0I1UQOWEWK7QyrPNta7rqf103dqmu7Ylvbdtt7vn9rJu8P0Nw9uWzj4m10z/FXDM77J9FoQQQrqwtVURfL+9A+PydhHDy0I5H64duX8Z/ueYkdk+DdLFdFooFYvF8Morr+Dxxx/H6NGjMXr0aKxZswYvvPBC1wmlTBOIxbxTc7N3amryXuGupoaPDVVdzZcj7fgBEQwCQ4cCw4YBQ4eCDR8Odb+hiAZERJJRNGsRNCW3onbNd6hLNqE22YjqZANqko2o1ptQozeh3oiiwYiiyYwhYiZ2+6EWinkokQtQphSiVC5AqVyAMrkApUohypQip90n+sDswEeAHfPAtL7YGUYSEUMDIPAQKW0fHjUwMEEAExk89RyiYBVnpFWpWF8WBcgQJR7wCAIPewRBgGCNeyQIkhMYSaIMWZQhCRJkSebrggRZVCBJ1lhJgujsay+n2qVUmJS+bi0DaKUapo3l9gY7pHM5wVR68NSB5fQgq0PrSN2u+/yAVMhmL7cIy+xgzL1P+n1luv8M21sEy66AKD0sarFoB1ima2Jpy0YqlDJd+zPrvD3bXIGXafDAyzB4IGYYqdvVXSGY+/kwjbTwzbpNz7q9vx0kMjgVYZ6rYAqp45w2uN7nVoNnGZnXIQASgDx7mwwIcoYKO/fPCVd7eojmPo/dZQdm9pS0giojrc1+zpOGFWaZqbl7X3sfu83IsJ9hZDjO5HPTTO1jt2XaZh/vVBSCX92REEII6QFyOVwbXpYLF6kina3TQqmffvoJuq5jwoQJTtvEiRPxyCOPwDRNiFkeJJVt2oS/nTYeW8wGmAJgiIAp8MlwrRsCoIt83V7Wi4FkKV9OikBSFqDl+aEFFGgBBWpAhuqXkfCJUGUBcdFAnFUhbm5FnC1DrEGF+QXb9Um2QYSAAl8+ivyFKPQVOPNifyEK/YUo9hfytkAhCpR8yJIC+0ppzPkuk/pSZQCotL5ICRAgiJJVmMC7OomCYAU1ghPw8JAIEEXJCYkEUeAVRqIMSZQgCdbkDoAE3t3Mve60I3N7+rGE7Banu1+2T6SLazGeVyvBVHvCq3btk2G9RYVZG7fpDsScwMu9zLyTu81sZb9Mx7m7WHruzwpNnFDNHcwZacFZhrDMfkh2O9LPN+3/wQnKXPvYPFVnrhe6XSXqDhNtijW5gzlPEOa6bXdg5iywzNsyBWmedmTe7t6nrSC/OQKU9mnZ3s30yCEQCCGEENLtdVooVV1djeLiYvh8qSuplZaWQlVVNDQ0oFevXp11Khl9WfkVjjuuYS/dGgOQsKY0u+gV4RMVBOQg8nx5yFP4FFJCyPflI9/PpwJfAYoCRSgMFKLIX4SiQBEK/AWQJdkJcpzgR7Suo2YvC95gyA6D3OHP7ralB0gUEhHSTbQWJpA90yLkamPa1f7p29tab++2to7JGNLtxcfj3sf9fGWa20pKgeLsfoboDD1iCARCSJdEg2sTQvalTgul4vG4J5AC4Kxr2q6v4LavjZ5wNH63fQbWVP4IQZKsbmN2Fy7J6urFu3vJ1jhBktX1S7aqguxqIFmUoYgKn0sKFFGBX/YjKAcRkAMIKkGEfWGElTDyfDx0CvvCCCkhyJLsCXcECM7c3ZZp2Q6GKBQihJAurKPd7Xqi9Aqw1kIrSerc8+pkOTEEAiF7WXcIQrrDYwByf5BwGseIkK6t00Ipv9/fInyy1wOBfXwJ8nbIU/Lw2ElPZvs0CCGEEGKjAA9A1x8CgZB9obsEIbn8GIDU46BxjAgh+0qnhVLl5eWor6+HruuQZX631dXVCAQCKCgo2OXxzPqraKQ9A4kTQgghhOxloVAoK9XIezIEQmd9fuouFSHd4XF0h8dg0xMxmFo826fRIVo8hkgkktOPAUg9jiEFIkxNyfbpdEh5EDn/GIDu8Ti6w2MAusfjGFIgdlq2sqvPT50WSo0aNQqyLGPVqlU4+OCDAQArV67E2LFj2/UXvmg0CgCYOnXqPj1PQgghhJBMVq5ciXA43On3uydDINDnJ0KyYz2A57J9EntBd3gc3eExAN3jcXSHxwB0j8exHsDEhZ1zX7v6/NRpoVQwGMRJJ52EefPmYcGCBaiqqsJTTz2FhQvb90z07t0bH330Udb+SkkIIYSQni0Uyk4XkD0ZAoE+PxFCCCEkm3b1+anTQikAmDt3LubNm4fzzjsP4XAYl156KY4++uh2HSuKIvr06f6XfCaEEEIIcduTIRDo8xMhhBBCujKBsfRrKxNCCCGEkK4iHo9j8uTJeOqpp5whEBYtWoTly5djyZIlWT47QgghhJCOo8u1EEIIIYR0Ye4hEL755hssW7YMTz31FM4999xsnxohhBBCyB6hSilCCCGEkC4uHo9j3rx5+Mc//oFwOIyZM2fi/PPPz/ZpEUIIIYTsEQqlCCGEEEIIIYQQQkino+57hBBCCCGEEEIIIaTTUShFCCGEEEIIIYQQQjodhVKEEEIIIYQQQgghpNNRKAVAVVVcf/31OPjgg3H44YfjqaeeyvYp5azKykpcdtllmDRpEo444ggsXLgQqqpm+7Ry3kUXXYTrrrsu26eR0zRNwy233IJDDjkEhx12GO655x7QkHods2PHDsyaNQsHHXQQjjrqKDzzzDPZPqWcpGkajj/+eHz++edO25YtW3D++edj/Pjx+PWvf41PPvkki2eYOzI9l6tWrcIZZ5yBCRMm4JhjjsErr7ySxTPs/t577z3sv//+numyyy7L9mn1CPSzJPsy/R/cdtttLd4TS5YsyeJZdj9tfe+g90DnaOv/gN4DnWPTpk2YOXMmJkyYgCOPPBJPPPGEsy1X3gdytk+gK7jzzjvx3Xff4dlnn8X27dtx7bXXol+/fjj22GOzfWo5hTGGyy67DAUFBXjhhRfQ2NiI66+/HqIo4tprr8326eWst99+Gx999BFOPvnkbJ9KTrvtttvw+eef48knn0Q0GsWVV16Jfv364Ywzzsj2qeWcK664Av369cPrr7+OtWvX4uqrr0b//v3xn//5n9k+tZyhqiquuuoqrFmzxmljjOGSSy5BRUUFXnvtNSxbtgyzZ8/GO++8g379+mXxbLu2TM9ldXU1fve73+G3v/0tbr/9dnz//feYO3cuysrKcOSRR2bvZLuxtWvXYtq0abj11ludNr/fn8Uz6hnoZ0n2Zfo/AIB169bhqquu8nx+C4fDnX163VZb3zuuueYaeg90gl1996P3wL5nmiYuuugijB07Fn/961+xadMmzJkzB+Xl5Tj++ONz5n3Q40OpWCyGV155BY8//jhGjx6N0aNHY82aNXjhhRcolNpN69evx6pVq/Dvf/8bpaWlAIDLLrsMd9xxB4VSHdTQ0IA777wTY8eOzfap5LSGhga89tprePrpp3HggQcCAGbMmIGvv/6aQqnd1NjYiFWrVuHWW2/FkCFDMGTIEBxxxBFYvnw5hVLttHbtWlx11VUtKvU+++wzbNmyBS+99BLy8vIwfPhwLF++HK+99houvfTSLJ1t19bac7ls2TKUlpZizpw5AIAhQ4bg888/x8mwOn4AAA0LSURBVJtvvkmh1D6ybt06VFRUoKysLNun0mPQz5Lsa+3/AODviZkzZ9J7Yh9p63vHL3/5S3oPdIJdffej98C+V1NTg1GjRmHevHkIh8MYMmQIDj30UKxcuRKlpaU58z7o8d33fvrpJ+i6jgkTJjhtEydOxNdffw3TNLN4ZrmnrKwMTzzxhPNDyRaJRLJ0RrnvjjvuwIknnogRI0Zk+1Ry2sqVKxEOhzFp0iSn7aKLLsLChQuzeFa5KRAIIBgM4vXXX0cymcT69evxf//3fxg1alS2Ty1nrFixApMnT8bLL7/saf/6669xwAEHIC8vz2mbOHEiVq1a1clnmDtaey7tLgTp6PfRvrNu3ToMGTIk26fRo9DPkuxr7f8gEomgsrKS3hP7UFvfO+g90Dna+j+g90Dn6N27N+677z6Ew2EwxrBy5Up88cUXmDRpUk69D3p8pVR1dTWKi4vh8/mcttLSUqiqioaGBvTq1SuLZ5dbCgoKcMQRRzjrpmliyZIl+MUvfpHFs8pdy5cvx5dffok333wT8+bNy/bp5LQtW7agf//+WLp0KR555BEkk0lMnz4df/jDHyCKPT6b3y1+vx833XQTbr31Vjz33HMwDAPTp0/Haaedlu1Tyxlnnnlmxvbq6mr07t3b01ZSUoKdO3d2xmnlpNaeywEDBmDAgAHOem1tLd5+++0u95fB7oIxhg0bNuCTTz7Bo48+CsMwcOyxx+Kyyy7zfL4iexf9LMm+1v4P1q1bB0EQ8Mgjj+Djjz9GUVERLrjgAhqKYS9q63sHvQc6R1v/B/Qe6HxHHXUUtm/fjmnTpuGYY47BggULcuZ90ONDqXg83uIDk72uaVo2TqnbuOuuu/DDDz/g1Vdfzfap5BxVVXHzzTfjpptuQiAQyPbp5LxYLIZNmzbhpZdewsKFC1FdXY2bbroJwWAQM2bMyPbp5Zx169Zh2rRpuOCCC7BmzRrceuutOPTQQ3HCCSdk+9RyWmu/j+h30Z5JJBK49NJLUVpaitNPPz3bp9Mtbd++3Xn93nfffdi6dStuu+02JBIJ3HDDDdk+vR6HfpZk3/r16yEIAoYNG4azzz4bX3zxBW688UaEw2Hq6r6PuL93PPPMM/QeyAL3/8H3339P74FO9sADD6Cmpgbz5s3DwoULc+p3QY8Ppfx+f4v/GHudwoCOu+uuu/Dss8/i3nvvRUVFRbZPJ+c89NBDGDNmjOevD6TjZFlGJBLB3Xffjf79+wPgX6JefPFFCqV20/Lly/Hqq6/io48+QiAQwNixY1FZWYmHH36YQqk95Pf70dDQ4GnTNI1+F+2BaDSKiy++GBs3bsRf/vIXBIPBbJ9St9S/f398/vnnKCwshCAIGDVqFEzTxP/8z/9g7ty5kCQp26fYo9DPkuw76aSTMG3aNBQVFQEARo4ciY0bN+LFF1+kL+T7QPr3DnoPdL70/4P99tuP3gOdzB6DWFVVXH311TjllFMQj8c9+3TV90GP77dSXl6O+vp66LrutFVXVyMQCKCgoCCLZ5a7br31Vjz99NO46667cMwxx2T7dHLS22+/jWXLlmHChAmYMGEC3nzzTbz55puesc9I+5WVlcHv9zuBFAAMHToUO3bsyOJZ5abvvvsOgwcP9vxCO+CAA7B9+/YsnlX3UF5ejpqaGk9bTU1Ni9Jr0j6RSAQzZ87EmjVr8Oyzz9K4FvtYUVERBEFw1ocPHw5VVdHY2JjFs+qZ6GdJ9gmC4HwZtw0bNgyVlZXZOaFuLNP3DnoPdK5M/wf0HugcNTU1WLZsmadtxIgRSCaTKCsry5n3QY8PpUaNGgVZlj0Dfq1cuRJjx46lsWY64KGHHsJLL72Ee+65B8cdd1y2TydnPf/883jzzTexdOlSLF26FEcddRSOOuooLF26NNunlpPGjRsHVVWxYcMGp239+vWekIq0T+/evbFp0yZPhen69es94/eQjhk3bhy+//57JBIJp23lypUYN25cFs8qN5mmidmzZ2Pr1q14/vnnsd9++2X7lLq1f/3rX5g8ebLnL7I//vgjioqKaGzOLKCfJdl3//334/zzz/e0/fTTTxg2bFh2Tqibau17B70HOk9r/wf0HugcW7duxezZsz1h33fffYdevXph4sSJOfM+6PGpSzAYxEknnYR58+bhm2++wbJly/DUU0/h3HPPzfap5Zx169Zh8eLF+N3vfoeJEyeiurramcju6d+/PwYPHuxMoVAIoVAIgwcPzvap5aRhw4bhyCOPxNy5c/HTTz/hX//6Fx577DH89re/zfap5ZyjjjoKiqLghhtuwIYNG/D+++/jkUcewTnnnJPtU8t5kyZNQt++fTF37lysWbMGjz32GL755huceuqp2T61nPPqq6/i888/x2233YaCggLnd1F6dw6yd0yYMAF+vx833HAD1q9fj48++gh33nknLrzwwmyfWo9EP0uyb9q0afjiiy/w5JNPYvPmzfjLX/6CpUuX0pABe1Fb3zvoPdA52vo/oPdA5xg7dixGjx6N66+/HmvXrsVHH32Eu+66C7///e9z6n0gMMZYtk8i2+LxOObNm4d//OMfCIfDmDlzZotkl+zaY489hrvvvjvjttWrV3fy2XQv1113HQDg9ttvz/KZ5K7m5mbceuuteO+99xAMBnHmmWfikksu8XQ3Ie2zdu1azJ8/H9988w169eqFs846C+eddx49lx2w//7747nnnsPkyZMBAJs2bcIf//hHfP311xg8eDCuv/56HHbYYVk+y9zgfi5nzpyJTz75pMU+kyZNwvPPP5+Fs+v+1qxZgwULFmDVqlUIhUI444wz6GdsJ6KfJdmX/n+wbNkyPPDAA9i4cSP69++PK6+8EkcffXSWz7L72NX3DnoP7Hu7+j+g90DnqKysxK233orly5cjGAzi7LPPxqxZsyAIQs68DyiUIoQQQgghhBBCCCGdrsd33yOEEEIIIYQQQgghnY9CKUIIIYQQQgghhBDS6SiUIoQQQgghhBBCCCGdjkIpQgghhBBCCCGEENLpKJQihBBCCCGEEEIIIZ2OQilCCCGEEEIIIYQQ0ukolCKEEEIIIYQQQgghnY5CKUIIIYQQQgghhBDS6SiUIoTkjP333x9XXXVVi/bXX38dRx11VBbOiBBCCCGEEEJIR1EoRQjJKW+99RaWL1+e7dMghBBCCCGEELKHKJQihOSU/v37409/+hM0Tcv2qRBCCCGEEEII2QMUShFCcsoVV1yByspKPPnkk63us3PnTlx++eWYNGkSJk+ejNtuu80JsV5//XWcc845eOCBBzB58mQcfPDBWLhwIRhjzvEvvfQSjjrqKEyYMAHnnHMOVq9evc8fFyGEEEIIIYT0NBRKEUJySnl5OS677DI88sgj2LJlS4vtmqbhvPPOQzwex/PPP4/77rsPH374Ie68805nn6+++gobNmzAiy++iBtvvBHPPfccPv30UwDA+++/j4ceegg33ngj/vrXv2LixIk499xz0djY2GmPkRBCCCGEEEJ6AgqlCCE555xzzsHgwYMxf/78Ftv+9a9/obKyEnfddRf2339/HHroobjpppvw4osvIhqNAgAMw8Ctt96KYcOG4cQTT8TIkSPx7bffAgCeeOIJzJo1C9OmTcOQIUNwxRVXoH///njjjTc69TESQgghhBBCSHcnZ/sECCFkd0mShHnz5uHMM8/EsmXLPNvWrVuHIUOGoLCw0Gk76KCDoOs6Nm/eDAAoKSlBOBx2tofDYei67hx/11134Z577nG2q6qKjRs37sNHRAghhBBCCCE9D4VShJCcdNBBB+GUU07B/PnzceGFFzrtfr+/xb6GYXjmPp+vxT72mFKGYeD666/HoYce6tnuDrEIIYQQQgghhOw56r5HCMlZV199NWKxmGfQ86FDh2Ljxo1oaGhw2latWgVZljFo0KBd3ubQoUOxc+dODB482JkeeeQRrFq1ah88AkIIIYQQQgjpuSiUIoTkrOLiYlx99dXYtm2b0zZlyhQMHDgQ11xzDVavXo3PPvsMt956K44//ngUFBTs8jYvuOACPPvss1i6dCk2b96Mu+66C3/7298wfPjwfflQCCGEEEIIIaTHoe57hJCcduqpp+K1115DVVUVAD7e1OLFi3Hrrbfiv//7vxEKhfCb3/wGc+bMadft/frXv0ZNTQ0eeOAB1NTUYMSIEXj44YcxZMiQffgoCCGEEEIIIaTnEZg9kAohhBBCCCGEEEIIIZ2Euu8RQgghhBBCCCGEkE5HoRQhhBBCCCGEEEII6XQUShFCCCGEEEIIIYSQTkehFCGEEEIIIYQQQgjpdBRKEUIIIYQQQgghhJBOR6EUIYQQQgghhBBCCOl0FEoRQgghhBBCCCGEkE5HoRQhhBBCCCGEEEII6XQUShFCCCGEEEIIIYSQTkehFCGEEEIIIYQQQgjpdBRKEUIIIYQQQgghhJBO9/8BNt9YQxiOoXEAAAAASUVORK5CYII=", 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", 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    " ] @@ -468,13 +521,16 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "mask tensor(0.)\n", + "lockdown tensor(1.)\n", + "mask_eff tensor(0.)\n", "dict_keys(['lockdown', 'mask', 'lockdown_efficiency', 'mask_efficiency', 'joint_efficiency', 'beta', 'gamma', 'S', 'I', 'R', 'l', 'overshoot', 'os_too_high'])\n" ] } @@ -509,7 +565,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -517,8 +573,8 @@ " supports=supports,\n", " alternatives=alternatives,\n", " antecedents=antecedents,\n", - " antecedent_bias=-0.5,\n", - " # witnesses=witnesses,\n", + " antecedent_bias=0.5,\n", + " witnesses={},\n", " consequents=consequents,\n", " consequent_scale=1e-8,\n", " # witness_bias=0.2,\n", @@ -529,225 +585,298 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mask tensor([[1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.]])\n", + "lockdown tensor([[1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 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-4.0500e+15])\n", + "tensor(20)\n", + "torch.Size([])\n", + "tensor(0.)\n", + "tensor([0.0266, 0.0287, 0.0337, 0.0285, 0.0349, 0.0267, 0.0318, 0.0271, 0.0300,\n", + " 0.0428, 0.0509, 0.0490, 0.0360, 0.0190, 0.0333, 0.0356, 0.0369, 0.0274,\n", + " 0.0296, 0.0280])\n", + "tensor([0.0266, 0.0287, 0.0337, 0.0285, 0.0349, 0.0267, 0.0318, 0.0271, 0.0300,\n", + " 0.0428, 0.0509, 0.0490, 0.0360, 0.0190, 0.0333, 0.0356, 0.0369, 0.0274,\n", + " 0.0296, 0.0280])\n", + "tensor([0.0266, 0.0287, 0.0337, 0.0285, 0.0349, 0.0267, 0.0318, 0.0271, 0.0300,\n", + " 0.0428, 0.0509, 0.0490, 0.0360, 0.0190, 0.0333, 0.0356, 0.0369, 0.0274,\n", + " 0.0296, 0.0280])\n", + "tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1.,\n", + " 1., 1.])\n", + "tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1.,\n", + " 1., 1.])\n", + "tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1.,\n", + " 1., 1.])\n", + "tensor([0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000,\n", + " 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000,\n", + " 0.1000, 0.1000])\n", + "tensor([0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000,\n", + " 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000,\n", + " 0.1000, 0.1000])\n", + "tensor([0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000,\n", + " 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000,\n", + " 0.1000, 0.1000])\n" ] } ], diff --git a/docs/source/test_notebook.ipynb b/docs/source/test_notebook.ipynb new file mode 100644 index 00000000..69421fc0 --- /dev/null +++ b/docs/source/test_notebook.ipynb @@ -0,0 +1,659 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Callable, Dict, List, Optional\n", + "\n", + "import math\n", + "import pyro\n", + "import pyro.distributions as dist\n", + "import pyro.distributions.constraints as constraints\n", + "import torch\n", + "from chirho.counterfactual.handlers.counterfactual import \\\n", + " MultiWorldCounterfactual\n", + "from chirho.explainable.handlers import ExtractSupports, SearchForExplanation\n", + "from chirho.indexed.ops import IndexSet, gather, indices_of\n", + "from chirho.observational.handlers import condition\n", + "from chirho.observational.handlers.soft_conditioning import soft_eq, KernelSoftConditionReparam\n", + "\n", + "pyro.settings.set(module_local_params=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "match_dropped tensor(1.)\n", + "match_dropped Provenance:\n", + "frozenset({'u_match_dropped'})\n", + "Tensor:\n", + "0.0\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "u_match_dropped\n", + "\n", + "u_match_dropped\n", + "\n", + "\n", + "\n", + "match_dropped\n", + "\n", + "match_dropped\n", + "\n", + "\n", + "\n", + "u_lightning\n", + "\n", + "u_lightning\n", + "\n", + "\n", + "\n", + "lightning\n", + "\n", + "lightning\n", + "\n", + "\n", + "\n", + "smile\n", + "\n", + "smile\n", + "\n", + "\n", + "\n", + "forest_fire\n", + "\n", + "forest_fire\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def forest_fire_model():\n", + " u_match_dropped = pyro.sample(\"u_match_dropped\", dist.Bernoulli(0.7))\n", + " match_dropped = pyro.deterministic(\n", + " \"match_dropped\", u_match_dropped, event_dim=0\n", + " ) # notice uneven probs here\n", + "\n", + " print(\"match_dropped\", match_dropped.squeeze())\n", + "\n", + " u_lightning = pyro.sample(\"u_lightning\", dist.Bernoulli(0.4))\n", + " lightning = pyro.deterministic(\"lightning\", u_lightning, event_dim=0)\n", + "\n", + " # this is a causally irrelevant site\n", + " smile = pyro.sample(\"smile\", dist.Bernoulli(0.5))\n", + "\n", + " forest_fire = pyro.deterministic(\n", + " \"forest_fire\", torch.max(match_dropped, lightning) + (0 * smile), event_dim=0\n", + " )\n", + "\n", + " return {\n", + " \"match_dropped\": match_dropped,\n", + " \"lightning\": lightning,\n", + " \"forest_fire\": forest_fire,\n", + " }\n", + "\n", + "with ExtractSupports() as extract_supports:\n", + " forest_fire_model()\n", + " forest_fire_supports = {k: constraints.boolean for k in extract_supports.supports}\n", + "\n", + "pyro.render_model(forest_fire_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def importance_infer(\n", + " model: Optional[Callable] = None, *, num_samples: int\n", + "):\n", + " \n", + " if model is None:\n", + " return lambda m: importance_infer(m, num_samples=num_samples)\n", + "\n", + " def _wrapped_model(\n", + " *args,\n", + " **kwargs\n", + " ):\n", + "\n", + " guide = pyro.poutine.block(hide_fn=lambda msg: msg[\"is_observed\"])(model)\n", + "\n", + " max_plate_nesting = 9 # TODO guess\n", + "\n", + " with pyro.poutine.block(), MultiWorldCounterfactual() as mwc:\n", + " log_weights, importance_tr, _ = pyro.infer.importance.vectorized_importance_weights(\n", + " model,\n", + " guide,\n", + " *args,\n", + " num_samples=num_samples,\n", + " max_plate_nesting=max_plate_nesting,\n", + " normalized=False,\n", + " **kwargs\n", + " )\n", + "\n", + " return torch.logsumexp(log_weights, dim=0) - math.log(num_samples), importance_tr, mwc, log_weights\n", + "\n", + " return _wrapped_model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "query = SearchForExplanation(\n", + " supports=forest_fire_supports,\n", + " antecedents={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)},\n", + " consequents={\"forest_fire\": torch.tensor(1.0)},\n", + " witnesses={}, # potential context elements, we leave them empty for now\n", + " alternatives={\"match_dropped\": torch.tensor(0.0), \"lightning\": torch.tensor(0.0)},\n", + " consequent_scale=1e-5,\n", + " antecedent_bias=0.5\n", + ")(forest_fire_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "match_dropped tensor([[0., 0., 0.],\n", + " [1., 1., 0.],\n", + " [1., 1., 0.],\n", + " [1., 1., 0.],\n", + " [0., 0., 0.],\n", + " [1., 1., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [1., 1., 0.],\n", + " [0., 0., 0.]])\n", + "match_dropped tensor([[0., 0., 0.],\n", + " [1., 1., 0.],\n", + " [1., 1., 0.],\n", + " [1., 1., 0.],\n", + " [0., 0., 0.],\n", + " [1., 1., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [1., 1., 0.],\n", + " [0., 0., 0.]])\n" + ] + } + ], + "source": [ + "logp, trace, mwc, log_weights = importance_infer(num_samples=10)(query)()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IndexSet({})\n" + ] + } + ], + "source": [ + "with mwc:\n", + " print(indices_of(trace.nodes[\"match_dropped\"][\"value\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pyro\n", + "import pyro.distributions as dist\n", + "import pyro.distributions.constraints as constraints\n", + "import pytest\n", + "import torch\n", + "\n", + "from chirho.counterfactual.handlers.counterfactual import MultiWorldCounterfactual\n", + "from chirho.counterfactual.ops import split\n", + "from chirho.explainable.handlers import random_intervention, sufficiency_intervention\n", + "from chirho.explainable.handlers.components import ( # consequent_eq_neq,\n", + " ExtractSupports,\n", + " consequent_eq,\n", + " consequent_eq_neq,\n", + " consequent_neq,\n", + " undo_split,\n", + ")\n", + "from chirho.explainable.internals import uniform_proposal\n", + "from chirho.explainable.ops import preempt\n", + "from chirho.indexed.ops import IndexSet, gather, indices_of\n", + "from chirho.interventional.handlers import do\n", + "from chirho.interventional.ops import intervene\n", + "from chirho.observational.handlers.condition import Factors" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[[[[[[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]]]],\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[2., 2., 2., 2., 2., 2., 2., 2., 2., 2.]]]]]],\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[[6., 6., 6., 6., 6., 6., 6., 6., 6., 6.]]]]],\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[6., 6., 6., 6., 6., 6., 6., 6., 6., 6.]]]]]],\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[[2., 2., 2., 2., 2., 2., 2., 2., 2., 2.]]]]],\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[2., 2., 2., 2., 2., 2., 2., 2., 2., 2.]]]]]]])\n", + "IndexSet({'split1': {0, 1}, 'split2': {0, 1, 2}})\n", + "IndexSet({'split1': {0, 1}, 'split2': {0, 1, 2}})\n", + "tensor([[[[[[[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]]]],\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[2., 2., 2., 2., 2., 2., 2., 2., 2., 2.]]]]]]])\n", + "tensor([[[[[[[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]]]],\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[2., 2., 2., 2., 2., 2., 2., 2., 2., 2.]]]]]],\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]]]],\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[2., 2., 2., 2., 2., 2., 2., 2., 2., 2.]]]]]],\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]]]],\n", + "\n", + "\n", + "\n", + "\n", + " [[[[[2., 2., 2., 2., 2., 2., 2., 2., 2., 2.]]]]]]])\n" + ] + } + ], + "source": [ + "import pyro.distributions.constraints as constraints\n", + "import torch\n", + "from chirho.counterfactual.handlers.counterfactual import MultiWorldCounterfactual\n", + "from chirho.counterfactual.ops import split\n", + "from chirho.explainable.handlers.components import undo_split\n", + "from chirho.indexed.ops import IndexSet, gather, indices_of\n", + "\n", + "with MultiWorldCounterfactual():\n", + " x_obs = torch.ones(10)\n", + " x_cf_1 = 2 * x_obs\n", + " x_cf_2 = 3 * x_cf_1\n", + " x_split = split(x_obs, (x_cf_1,), name=\"split1\", event_dim=1)\n", + " x_split = split(x_split, (x_cf_2, x_cf_1), name=\"split2\", event_dim=1)\n", + "\n", + " print(x_split)\n", + "\n", + " undo_split2 = undo_split(\n", + " support=constraints.independent(constraints.real, 1), antecedents=[\"split2\"]\n", + " )\n", + " x_undone = undo_split2(x_split)\n", + "\n", + " print(indices_of(x_split, event_dim=1))\n", + " print(indices_of(x_undone, event_dim=1))\n", + "\n", + " print(gather(x_split, IndexSet(split2={0}), event_dim=1))\n", + " print(x_undone)\n", + "\n", + " assert indices_of(x_split, event_dim=1) == indices_of(x_undone, event_dim=1)\n", + " assert torch.all(gather(x_split, IndexSet(split2={0}), event_dim=1) == x_undone)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'split1': {0}}\n", + "{'split1': {0}}\n", + "{'split1': {1}}\n", + "{'split1': {1}}\n", + "{'split1': {2}}\n", + "{'split1': {2}}\n", + "{'split1': {0}, 'split2': {1}}\n", + "{'split1': {0}, 'split2': {1}}\n", + "{'split1': {0}, 'split2': {2}}\n", + "{'split1': {0}, 'split2': {2}}\n", + "{'split1': {1}, 'split2': {1}}\n", + "{'split1': {1}, 'split2': {1}}\n", + "{'split1': {1}, 'split2': {2}}\n", + "{'split1': {1}, 'split2': {2}}\n", + "{'split1': {2}, 'split2': {1}}\n", + "{'split1': {2}, 'split2': {1}}\n", + "{'split1': {2}, 'split2': {2}}\n", + "{'split1': {2}, 'split2': {2}}\n", + "[{'split1': {0}, 'split2': {1}, 'split3': {2}}, {'split1': {0}, 'split2': {1}, 'split3': {3}}, {'split1': {0}, 'split2': {2}, 'split3': {2}}, {'split1': {0}, 'split2': {2}, 'split3': {3}}, {'split1': {1}, 'split2': {1}, 'split3': {2}}, {'split1': {1}, 'split2': {1}, 'split3': {3}}, {'split1': {1}, 'split2': {2}, 'split3': {2}}, {'split1': {1}, 'split2': {2}, 'split3': {3}}, {'split1': {2}, 'split2': {1}, 'split3': {2}}, {'split1': {2}, 'split2': {1}, 'split3': {3}}, {'split1': {2}, 'split2': {2}, 'split3': {2}}, {'split1': {2}, 'split2': {2}, 'split3': {3}}]\n" + ] + } + ], + "source": [ + "index_keys = []\n", + "antecedents = {\"split1\": {0, 1, 2}, \"split2\": {1, 2}, \"split3\": {2, 3}}\n", + "for a, v in antecedents.items():\n", + " if index_keys == []:\n", + " for value in v:\n", + " index_keys.append({a: {value}})\n", + " else:\n", + " temp_index_keys = []\n", + " for i in index_keys:\n", + " for value in v:\n", + " print(i)\n", + " t = dict(i)\n", + " t[a] = {value}\n", + " temp_index_keys.append(t)\n", + " index_keys = temp_index_keys\n", + "\n", + "print(index_keys)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (448003560.py, line 4)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m Cell \u001b[0;32mIn[28], line 4\u001b[0;36m\u001b[0m\n\u001b[0;31m if a, v in indices_of(value, event_dim=0)\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "\n", + "antecedents_ = {\n", + " a\n", + " for a in antecedents\n", + " if a in indices_of(value, event_dim=0)\n", + "}\n", + "\n", + "factual_value = gather(\n", + " value,\n", + " IndexSet(**{antecedent: {0} for antecedent in antecedents_}),\n", + " event_dim=support.event_dim,\n", + ")\n", + "\n", + "# TODO exponential in len(antecedents) - add an indexed.ops.expand to do this cheaply\n", + "\n", + "\n", + "\n", + "scatter_n(\n", + " {\n", + " IndexSet(\n", + " **{antecedent: {ind} for antecedent, ind in zip(antecedents_, inds)}\n", + " ): factual_value\n", + " for inds in itertools.product(*[[0, 1]] * len(antecedents_))\n", + " },\n", + " event_dim=support.event_dim,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "with MultiWorldCounterfactual():\n", + " for a in indices_of(value, event_dim=0):\n", + " print(a)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "import pyro\n", + "import pyro.distributions as dist\n", + "import pyro.distributions.constraints as constraints\n", + "import pytest\n", + "import torch\n", + "\n", + "from chirho.counterfactual.handlers.counterfactual import MultiWorldCounterfactual\n", + "from chirho.counterfactual.ops import split\n", + "from chirho.explainable.handlers import random_intervention, sufficiency_intervention\n", + "from chirho.explainable.handlers.components import ( # consequent_eq_neq,\n", + " ExtractSupports,\n", + " consequent_eq,\n", + " consequent_eq_neq,\n", + " consequent_neq,\n", + " undo_split,\n", + ")\n", + "from chirho.explainable.internals import uniform_proposal\n", + "from chirho.explainable.ops import preempt\n", + "from chirho.indexed.ops import IndexSet, gather, indices_of\n", + "from chirho.interventional.handlers import do\n", + "from chirho.interventional.ops import intervene\n", + "from chirho.observational.handlers.condition import Factors\n", + "\n", + "SUPPORT_CASES = [\n", + " pyro.distributions.constraints.real,\n", + " pyro.distributions.constraints.boolean,\n", + " pyro.distributions.constraints.positive,\n", + " pyro.distributions.constraints.interval(0, 10),\n", + " pyro.distributions.constraints.interval(-5, 5),\n", + " pyro.distributions.constraints.integer_interval(0, 2),\n", + " pyro.distributions.constraints.integer_interval(0, 100),\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "import pyro\n", + "import pyro.distributions as dist\n", + "\n", + "def model():\n", + " x = pyro.sample(\"x\", dist.Delta(torch.tensor(1.0)))\n", + "\n", + " x_split = pyro.deterministic(\n", + " \"x_split\",\n", + " split(x, (torch.tensor(0.5),), name=\"x_split\", event_dim=0),\n", + " event_dim=0,\n", + " )\n", + "\n", + " x_undone = pyro.deterministic(\n", + " \"x_undone\",\n", + " undo_split(support=constraints.real, antecedents=[\"x_split\"])(x_split),\n", + " event_dim=0,\n", + " )\n", + "\n", + " x_case = torch.tensor(1)\n", + " x_preempted = pyro.deterministic(\n", + " \"x_preempted\",\n", + " preempt(\n", + " x_undone, (torch.tensor(5.0),), x_case, name=\"x_preempted\", event_dim=0\n", + " ),\n", + " event_dim=0,\n", + " )\n", + "\n", + " x_undone_2 = pyro.deterministic(\n", + " \"x_undone_2\",\n", + " undo_split(support=constraints.real, antecedents=[\"x\"])(x_preempted),\n", + " event_dim=0,\n", + " )\n", + "\n", + " x_split2 = pyro.deterministic(\n", + " \"x_split2\",\n", + " split(x_undone_2, (torch.tensor(2.0),), name=\"x_split2\", event_dim=0),\n", + " event_dim=0,\n", + " )\n", + "\n", + " x_undone_3 = pyro.deterministic(\n", + " \"x_undone_3\",\n", + " undo_split(support=constraints.real, antecedents=[\"x_split\", \"x_split2\"])(\n", + " x_split2\n", + " ),\n", + " event_dim=0,\n", + " )\n", + "\n", + " return x_undone_3\n", + "\n", + "with MultiWorldCounterfactual() as mwc:\n", + " with pyro.poutine.trace() as tr:\n", + " model()\n", + "\n", + "nd = tr.trace.nodes\n", + "\n", + "with mwc:\n", + " x_split_2 = nd[\"x_split2\"][\"value\"]\n", + " x_00 = gather(\n", + " x_split_2, IndexSet(x_split={0}, x_split2={0}), event_dim=0\n", + " ) # 5.0\n", + " x_10 = gather(\n", + " x_split_2, IndexSet(x_split={1}, x_split2={0}), event_dim=0\n", + " ) # 5.0\n", + " x_01 = gather(\n", + " x_split_2, IndexSet(x_split={0}, x_split2={1}), event_dim=0\n", + " ) # 2.0\n", + " x_11 = gather(\n", + " x_split_2, IndexSet(x_split={1}, x_split2={1}), event_dim=0\n", + " ) # 2.0\n", + "\n", + " assert (\n", + " nd[\"x_split\"][\"value\"][0].item() == 1.0\n", + " and nd[\"x_split\"][\"value\"][1].item() == 0.5\n", + " )\n", + "\n", + " assert (\n", + " nd[\"x_undone\"][\"value\"][0].item() == 1.0\n", + " and nd[\"x_undone\"][\"value\"][1].item() == 1.0\n", + " )\n", + "\n", + " assert (\n", + " nd[\"x_preempted\"][\"value\"][0].item() == 5.0\n", + " and nd[\"x_preempted\"][\"value\"][1].item() == 5.0\n", + " )\n", + "\n", + " assert (\n", + " nd[\"x_undone_2\"][\"value\"][0].item() == 5.0\n", + " and nd[\"x_undone_2\"][\"value\"][1].item() == 5.0\n", + " )\n", + "\n", + " assert torch.all(nd[\"x_undone_3\"][\"value\"] == 5.0)\n", + "\n", + " assert (x_00, x_10, x_01, x_11) == (5.0, 5.0, 2.0, 2.0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tests/explainable/test_handlers_components.py b/tests/explainable/test_handlers_components.py index 4b0916cf..66b1aefd 100644 --- a/tests/explainable/test_handlers_components.py +++ b/tests/explainable/test_handlers_components.py @@ -89,7 +89,7 @@ def test_undo_split(): x_cf_1 = torch.ones(10) x_cf_2 = 2 * x_cf_1 x_split = split(x_obs, (x_cf_1,), name="split1", event_dim=1) - x_split = split(x_split, (x_cf_2,), name="split2", event_dim=1) + x_split = split(x_split, (x_cf_2), name="split2", event_dim=1) undo_split2 = undo_split( support=constraints.independent(constraints.real, 1), antecedents=["split2"] From a5f20aa2de04f84978c65f887cc698f166787f6f Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Mon, 19 Aug 2024 11:24:45 -0400 Subject: [PATCH 049/111] tests for undo_split added --- tests/explainable/test_handlers_components.py | 34 +++++++++++++++---- 1 file changed, 27 insertions(+), 7 deletions(-) diff --git a/tests/explainable/test_handlers_components.py b/tests/explainable/test_handlers_components.py index 66b1aefd..883ce8ca 100644 --- a/tests/explainable/test_handlers_components.py +++ b/tests/explainable/test_handlers_components.py @@ -83,13 +83,14 @@ def test_random_intervention(support, event_shape): assert torch.all(support.check(samples)) -def test_undo_split(): +@pytest.mark.parametrize("num_splits", [1, 2, 5]) +def test_undo_split(num_splits): with MultiWorldCounterfactual(): x_obs = torch.zeros(10) x_cf_1 = torch.ones(10) x_cf_2 = 2 * x_cf_1 - x_split = split(x_obs, (x_cf_1,), name="split1", event_dim=1) - x_split = split(x_split, (x_cf_2), name="split2", event_dim=1) + x_split = split(x_obs, (x_cf_1,)*num_splits, name="split1", event_dim=1) + x_split = split(x_split, (x_cf_2,)*(num_splits+1), name="split2", event_dim=1) undo_split2 = undo_split( support=constraints.independent(constraints.real, 1), antecedents=["split2"] @@ -100,9 +101,28 @@ def test_undo_split(): assert torch.all(gather(x_split, IndexSet(split2={0}), event_dim=1) == x_undone) +def test_undo_split_multi_dim(): + with MultiWorldCounterfactual(): + x_obs = torch.ones(10) + x_cf_1 = 2 * x_obs + x_cf_2 = 3 * x_cf_1 + x_split = split(x_obs, (x_cf_1,), name="split1", event_dim=1) + x_split = split(x_split, (x_cf_2, x_cf_1, x_cf_2), name="split2", event_dim=1) + x_split = split(x_split, (x_cf_2, x_cf_1), name="split3", event_dim=1) + + undo_split23 = undo_split( + support=constraints.independent(constraints.real, 1), antecedents=["split2", "split3"] + ) + x_undone = undo_split23(x_split) + + assert indices_of(x_split, event_dim=1) == indices_of(x_undone, event_dim=1) + assert torch.all(gather(x_split, IndexSet(split2={0}, split3={0}), event_dim=1) == x_undone) + + @pytest.mark.parametrize("plate_size", [4, 50, 200]) @pytest.mark.parametrize("event_shape", [(), (3,), (3, 2)]) -def test_undo_split_parametrized(event_shape, plate_size): +@pytest.mark.parametrize("num_splits", [1, 2, 5]) +def test_undo_split_parametrized(event_shape, plate_size, num_splits): joint_dims = torch.Size([plate_size, *event_shape]) replace1 = torch.ones(joint_dims) @@ -114,7 +134,7 @@ def model(): w = pyro.sample( "w", dist.Normal(0, 1).expand(event_shape).to_event(len(event_shape)) ) - w = split(w, (replace1,), name="split1", event_dim=len(event_shape)) + w = split(w, (replace1,)*num_splits, name="split1", event_dim=len(event_shape)) w = pyro.deterministic( "w_preempted", @@ -146,11 +166,11 @@ def model(): with mwc: assert indices_of( nd["w_undone"]["value"], event_dim=len(event_shape) - ) == IndexSet(split1={0, 1}) + ) == IndexSet(split1=set(range(num_splits+1))) w_undone_shape = list(nd["w_undone"]["value"].shape) desired_shape = list( - (2,) + (num_splits+1,) + (1,) * (len(w_undone_shape) - len(event_shape) - 2) + (plate_size,) + event_shape From 97b5ffac38e4752e0589ce45da0591ed892919d9 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Mon, 19 Aug 2024 12:44:46 -0400 Subject: [PATCH 050/111] cleanup --- docs/source/counterfactual_sir.png | Bin 126734 -> 0 bytes docs/source/counterfactual_sir_search.png | Bin 42433 -> 0 bytes docs/source/explainable_sir.ipynb | 1875 --------------------- docs/source/test_notebook.ipynb | 659 -------- 4 files changed, 2534 deletions(-) delete mode 100644 docs/source/counterfactual_sir.png delete mode 100644 docs/source/counterfactual_sir_search.png delete mode 100644 docs/source/explainable_sir.ipynb delete mode 100644 docs/source/test_notebook.ipynb diff --git a/docs/source/counterfactual_sir.png b/docs/source/counterfactual_sir.png deleted file mode 100644 index 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a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb deleted file mode 100644 index a4a49c28..00000000 --- a/docs/source/explainable_sir.ipynb +++ /dev/null @@ -1,1875 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numbers\n", - "import os\n", - "from typing import Tuple, TypeVar, Union\n", - "from typing import Callable, Dict, List, Optional\n", - "import math\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import pyro.distributions as dist\n", - "import seaborn as sns\n", - "import torch\n", - "from pyro.infer import Predictive\n", - "\n", - "import pyro\n", - "from chirho.counterfactual.handlers.counterfactual import \\\n", - " MultiWorldCounterfactual\n", - "from chirho.dynamical.handlers.interruption import StaticEvent\n", - "from chirho.dynamical.handlers.solver import TorchDiffEq\n", - "from chirho.dynamical.handlers.trajectory import LogTrajectory\n", - "from chirho.dynamical.ops import Dynamics, State, on, simulate\n", - "from chirho.explainable.handlers import SearchForExplanation\n", - "from chirho.explainable.handlers.components import ExtractSupports\n", - "from chirho.indexed.ops import IndexSet, gather, indices_of\n", - "from chirho.interventional.ops import Intervention, intervene\n", - "from chirho.observational.handlers import condition\n", - "\n", - "R = Union[numbers.Real, torch.Tensor]\n", - "S = TypeVar(\"S\")\n", - "T = TypeVar(\"T\")\n", - "\n", - "\n", - "sns.set_style(\"white\")\n", - "\n", - "seed = 123\n", - "pyro.clear_param_store()\n", - "pyro.set_rng_seed(seed)\n", - "\n", - "smoke_test = \"CI\" in os.environ\n", - "num_samples = 10 if smoke_test else 300\n", - "exp_plate_size = 10 if smoke_test else 2000" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class SIRDynamics(pyro.nn.PyroModule):\n", - " def __init__(self, beta, gamma):\n", - " super().__init__()\n", - " self.beta = beta\n", - " self.gamma = gamma\n", - "\n", - " def forward(self, X: State[torch.Tensor]):\n", - " dX: State[torch.Tensor] = dict()\n", - " dX[\"S\"] = -self.beta * X[\"S\"] * X[\"I\"]\n", - " dX[\"I\"] = self.beta * X[\"S\"] * X[\"I\"] - self.gamma * X[\"I\"]\n", - " dX[\"R\"] = self.gamma * X[\"I\"]\n", - "\n", - " return dX\n", - "\n", - "\n", - "# TODO add running overshoot to states?\n", - "\n", - "\n", - "class SIRDynamicsLockdown(SIRDynamics):\n", - " def __init__(self, beta0, gamma):\n", - " super().__init__(beta0, gamma)\n", - " self.beta0 = beta0\n", - "\n", - " def forward(self, X: State[torch.Tensor]):\n", - " self.beta = (1 - X[\"l\"]) * self.beta0\n", - " dX = super().forward(X)\n", - " dX[\"l\"] = torch.zeros_like(X[\"l\"])\n", - " return dX" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.15116800367832184\n" - ] - } - ], - "source": [ - "init_state = dict(S=torch.tensor(99.0), I=torch.tensor(1.0), R=torch.tensor(0.0))\n", - "start_time = torch.tensor(0.0)\n", - "end_time = torch.tensor(12.0)\n", - "step_size = torch.tensor(0.1)\n", - "logging_times = torch.arange(start_time, end_time, step_size)\n", - "init_state_lockdown = dict(**init_state, l=torch.tensor(0.0))\n", - "\n", - "# We now simulate from the SIR model\n", - "beta_true = torch.tensor([0.03])\n", - "gamma_true = torch.tensor([0.5])\n", - "sir_true = SIRDynamics(beta_true, gamma_true)\n", - "with TorchDiffEq(), LogTrajectory(logging_times) as lt:\n", - " simulate(sir_true, init_state, start_time, end_time)\n", - "\n", - "sir_true_traj = lt.trajectory\n", - "\n", - "\n", - "def get_overshoot(trajectory):\n", - " t_max = torch.argmax(trajectory[\"I\"].squeeze())\n", - " S_peak = torch.max(trajectory[\"S\"].squeeze()[t_max]) / 100\n", - " S_final = trajectory[\"S\"].squeeze()[-1] / 100\n", - " return (S_peak - S_final).item()\n", - "\n", - "\n", - "print(get_overshoot(sir_true_traj))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def bayesian_sir(base_model=SIRDynamics) -> Dynamics[torch.Tensor]:\n", - " beta = pyro.sample(\"beta\", dist.Beta(18, 600))\n", - " gamma = pyro.sample(\"gamma\", dist.Beta(1600, 1600))\n", - " sir = base_model(beta, gamma)\n", - " return sir\n", - "\n", - "\n", - "def simulated_bayesian_sir(\n", - " init_state, start_time, logging_times, base_model=SIRDynamics\n", - ") -> State[torch.Tensor]:\n", - " sir = bayesian_sir(base_model)\n", - "\n", - " with TorchDiffEq(), LogTrajectory(logging_times, is_traced=True) as lt:\n", - " simulate(sir, init_state, start_time, logging_times[-1])\n", - " return lt.trajectory" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def MaskedStaticIntervention(time: R, intervention: Intervention[State[T]]):\n", - "\n", - " @on(StaticEvent(time))\n", - " def callback(\n", - " dynamics: Dynamics[T], state: State[T]\n", - " ) -> Tuple[Dynamics[T], State[T]]:\n", - "\n", - " with pyro.poutine.block():\n", - " return dynamics, intervene(state, intervention)\n", - "\n", - " return callback" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mask tensor(0.)\n", - "lockdown tensor(0.)\n", - "mask_eff tensor(0.)\n" - ] - } - ], - "source": [ - "overshoot_threshold = 20\n", - "lockdown_time = torch.tensor(1.0)\n", - "mask_time = torch.tensor(1.5)\n", - "\n", - "\n", - "def policy_model():\n", - "\n", - " lockdown = pyro.sample(\"lockdown\", dist.Bernoulli(torch.tensor(0.5)))\n", - " mask = pyro.sample(\"mask\", dist.Bernoulli(torch.tensor(0.5)))\n", - "\n", - " lockdown_efficiency = pyro.deterministic(\n", - " \"lockdown_efficiency\", torch.tensor(0.6) * lockdown, event_dim=0\n", - " )\n", - "\n", - " mask_efficiency = pyro.deterministic(\n", - " \"mask_efficiency\", (0.1 * lockdown + 0.45 * (1 - lockdown)) * mask, event_dim=0\n", - " )\n", - "\n", - " joint_efficiency = pyro.deterministic(\n", - " \"joint_efficiency\",\n", - " torch.clamp(lockdown_efficiency + mask_efficiency, 0, 0.95),\n", - " event_dim=0,\n", - " )\n", - "\n", - " lockdown_sir = bayesian_sir(SIRDynamicsLockdown)\n", - " with LogTrajectory(logging_times, is_traced=True) as lt:\n", - " with TorchDiffEq():\n", - " with MaskedStaticIntervention(lockdown_time, dict(l=lockdown_efficiency)):\n", - " with MaskedStaticIntervention(mask_time, dict(l=joint_efficiency)):\n", - " simulate(\n", - " lockdown_sir, init_state_lockdown, start_time, logging_times[-1]\n", - " )\n", - "\n", - " trajectory = lt.trajectory\n", - "\n", - " t_max = torch.max(trajectory[\"I\"], dim=-1).indices\n", - " S_peaks = pyro.ops.indexing.Vindex(trajectory[\"S\"])[..., t_max]\n", - " overshoot = pyro.deterministic(\n", - " \"overshoot\", S_peaks - trajectory[\"S\"][..., -1], event_dim=0\n", - " )\n", - " os_too_high = pyro.deterministic(\n", - " \"os_too_high\",\n", - " (overshoot > overshoot_threshold).clone().detach().float(),\n", - " event_dim=0,\n", - " )\n", - "\n", - " return overshoot, os_too_high\n", - "\n", - "\n", - "with ExtractSupports() as s:\n", - " one_run = policy_model()\n", - "\n", - "import pyro.distributions.constraints as constraints\n", - "s.supports[\"os_too_high\"] = constraints.independent(base_constraint=constraints.boolean, reinterpreted_batch_ndims=0)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'lockdown': Boolean(), 'mask': Boolean(), 'lockdown_efficiency': IndependentConstraint(Real(), 0), 'mask_efficiency': IndependentConstraint(Real(), 0), 'joint_efficiency': IndependentConstraint(Real(), 0), 'beta': Interval(lower_bound=0.0, upper_bound=1.0), 'gamma': Interval(lower_bound=0.0, upper_bound=1.0), 'S': IndependentConstraint(Real(), 1), 'I': IndependentConstraint(Real(), 1), 'R': IndependentConstraint(Real(), 1), 'l': IndependentConstraint(Real(), 1), 'overshoot': IndependentConstraint(Real(), 0), 'os_too_high': IndependentConstraint(Boolean(), 0)}\n" - ] - } - ], - "source": [ - "print(s.supports)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "def importance_infer(\n", - " model: Optional[Callable] = None, *, num_samples: int\n", - "):\n", - " \n", - " if model is None:\n", - " return lambda m: importance_infer(m, num_samples=num_samples)\n", - "\n", - " def _wrapped_model(\n", - " *args,\n", - " **kwargs\n", - " ):\n", - "\n", - " guide = pyro.poutine.block(hide_fn=lambda msg: msg[\"is_observed\"])(model)\n", - "\n", - " max_plate_nesting = 9 # TODO guess\n", - "\n", - " with pyro.poutine.block(), MultiWorldCounterfactual() as mwc:\n", - " log_weights, importance_tr, _ = pyro.infer.importance.vectorized_importance_weights(\n", - " model,\n", - " guide,\n", - " *args,\n", - " num_samples=num_samples,\n", - " max_plate_nesting=max_plate_nesting,\n", - " normalized=False,\n", - " **kwargs\n", - " )\n", - "\n", - " return torch.logsumexp(log_weights, dim=0) - math.log(num_samples), importance_tr, mwc, log_weights\n", - "\n", - " return _wrapped_model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(0.)\n" - ] - } - ], - "source": [ - "print(torch.exp(logp))" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mask tensor(0.)\n", - "lockdown tensor(0.)\n", - "mask_eff tensor(0.)\n", - "mask tensor(0.)\n", - "lockdown tensor(0.)\n", - "mask_eff tensor(0.)\n", - "mask tensor(0.)\n", - "lockdown tensor(0.)\n", - "mask_eff tensor(0.)\n", - "mask tensor(1.)\n", - "lockdown tensor(1.)\n", - "mask_eff tensor(0.1000)\n", - "mask tensor(1.)\n", - "lockdown tensor(1.)\n", - "mask_eff tensor(0.1000)\n", - "mask tensor(1.)\n", - "lockdown tensor(1.)\n", - "mask_eff tensor(0.1000)\n", - "mask tensor(1.)\n", - "lockdown tensor(0.)\n", - "mask_eff tensor(0.4500)\n", - "mask tensor(1.)\n", - "lockdown tensor(0.)\n", - "mask_eff tensor(0.4500)\n", - "mask tensor(1.)\n", - "lockdown tensor(0.)\n", - "mask_eff tensor(0.4500)\n", - "mask tensor(0.)\n", - "lockdown tensor(1.)\n", - "mask_eff tensor(0.)\n", - "mask tensor(0.)\n", - "lockdown tensor(1.)\n", - "mask_eff tensor(0.)\n", - "mask tensor(0.)\n", - "lockdown tensor(1.)\n", - "mask_eff tensor(0.)\n" - ] - } - ], - "source": [ - "# conditioning (as opposed to intervening) is sufficient for\n", - "# propagating the changes, as the decisions are upstream from ds\n", - "\n", - "# no interventions\n", - "policy_model_none = condition(\n", - " policy_model, {\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(0.0)}\n", - ")\n", - "unintervened_predictive = Predictive(\n", - " policy_model_none, num_samples=num_samples, parallel=True\n", - ")\n", - "unintervened_samples = unintervened_predictive()\n", - "\n", - "# both interventions\n", - "policy_model_all = condition(\n", - " policy_model, {\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(1.0)}\n", - ")\n", - "intervened_predictive = Predictive(\n", - " policy_model_all, num_samples=num_samples, parallel=True\n", - ")\n", - "intervened_samples = intervened_predictive()\n", - "\n", - "policy_model_mask = condition(\n", - " policy_model, {\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(1.0)}\n", - ")\n", - "mask_predictive = Predictive(policy_model_mask, num_samples=num_samples, parallel=True)\n", - "mask_samples = mask_predictive()\n", - "\n", - "policy_model_lockdown = condition(\n", - " policy_model, {\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(0.0)}\n", - ")\n", - "lockdown_predictive = Predictive(\n", - " policy_model_lockdown, num_samples=num_samples, parallel=True\n", - ")\n", - "lockdown_samples = lockdown_predictive()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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tsO6mTZtITEykXbt2VKxYESj/tVVcad3AWrduDcAHH3zAY489xo8//kh6ejqgXnf9+vUrtctWixYtSszzd5fMzs4G4OjRoyQlJREVFUVCQkKJ9UNDQwPn239Nns01XBY+n4+1a9ei0+lK7ebVs2fPMu3nTM/byZT178+vYsWKpf4N+us9nen5Ka+zeb2aNm1aYl5sbCxAUG2xVq1aAWrtqUmTJrF69WrcbjdGo5FBgwbRpUuXs3sSQggh/rOkhpQQQoiLJjw8vNT5ubm5zJ49m2XLlrF3715SUlJQFCUoa0E5xTDu/kymJUuWnLIWjMPhICsrC1Azs6KiorBYLGfyVICibKST1TuqVq0aUJRxUNzJzsWJ/HWlhg0bdsr1/EGnypUrM3HiRMaMGcOff/7Jn3/+CUC9evW4+uqrGTBgQOBH6On8+++/fPXVV2zevJkDBw6Ql5cHEHhdSntNwsLCSt2XXq9+BfEPce8/d/7Az4n85+50MjMzcbvdREZGEhIScsp9FX8d+vXrx+eff85PP/3ErbfeCqiFwIFA1giU/9oqHhQ5MTAH0KxZM0aPHs3kyZP59ddf+fXXX9FoNDRs2JBrrrmG2267rdRrw1/fpzh/sPDEc3qqgOOJ5+JsruGy8L8+UVFRmM3mEsvLWivsTM/byZRn3VO105/xdfz48XLt70yd6/ecE68hgKeeeoqkpCSWL1/OzJkzmTlzJlarlY4dO9KvX78yBxGFEEKIE0lASgghxEVTWte03bt3c/fdd5OWlkZkZCRNmjShd+/exMfH06ZNG7p27Xra/fp/TNWtW5cGDRqcdv1TZVuVx6mCZMXbZTQaSyw7VTe94vxt7dat20kDLqA+d7/rr7+eTp06sXjxYv766y9WrVrF7t272b17N7NmzeLjjz+mWbNmpzzuBx98wOuvvw5AnTp16NatG3Xq1KFx48YcOnSIl156qdTtytL1qSzrFc/MOpXTvQZQdA6Lvw5NmjShdu3arFmzhuTkZCpUqMCCBQuwWq1BmVblvbaKO9lzHDRoEH369GHRokX89ddfrFmzhq1bt7J161Y++eQTvvzyS2rWrBm0TVmulzM5F2dzDZ8LWq22zK/1mZy3Ux23PEwm0ymX+wOup3O27z1n83qV9W8zNDSUjz76iC1btvD777+zYsUKtm7dym+//cZvv/3GNddcw9SpU8vfeCGEEP95EpASQghxSXnppZdIS0vjnnvuYcSIEUE/Tv3ZTKcTExMDQIMGDZg0adJp13e73RgMBrKysigoKCiRueF0Ovn222+pXbs27dq1O+l+KlasyP79+zl8+DD16tUrsTwxMRGgXPWQSjvGgQMHGDhwIO3bty/zdqGhodx4443ceOONAGzbto033niDv//+mzfffDNQa6g0iYmJTJ48mdDQUN59991AFx6/U21bVv4srZPV6fJngpxORERE4LXMzc0tNWh3stehX79+TJkyhd9++406deqQkpJCv379grrmlffaKqvo6GgGDBjAgAED8Pl8rF+/ngkTJrB161bef/99xo8fX+59+rPNDh8+fNJ1/OfCXwvpfF/DkZGRmEwmMjMzycvLK1HnKTU1tVxBmvNx3sriZNej/1wXz0rzB35Ke145OTln1Y4L8Z7j17hxYxo3bszjjz9OTk4Ov/76K+PHj2fhwoWsXbu2xPuCEEIIcTpSQ0oIIcQlZePGjQA88MADJTIlli9fHrhfvEvJifz1ZdasWRNUC8Vvy5Yt9OrVi+HDh6MoCgaDgcaNG+P1evn7779LrL9q1SpeeuklPvvsM+DkmQX+4y5cuLDU5b/++itAqfV8ysp/DH/XuxNNnDiRG2+8kW+++QZQi2t369aN7777Lmi9hg0b8tRTTwFF3QBPZvPmzfh8Ptq0aVPqj07/OStLRs7J+AN9ixcvLvHD/WSvS2kMBgPNmzfH5/MFCrsX5/F4+P3334GSdbj69u2LRqNh6dKlgdeqX79+QeuU99o6nQkTJtCxY8dALSBQs3VatWrFgw8+CBR1vyyvKlWqULVqVTIyMkqtIZSTkxP4m/I/rzO5hsuaaeNft23btvh8PhYvXlxi+R9//FGm/ZT3vJWnjWWxf//+Uov4+2uQFT8//oCmv8ZVcf73u+LK09bz/Z6TkZHBTTfdRJ8+fYLmh4aGcuuttwZqu53pNSqEEOK/TQJSQgghLin+mjsn/lhds2YN48aNCzwurYC2X/Xq1enRowfHjh3j2WefJTc3N7AsLS2NZ599loMHDwaN0HXnnXcC6g/d4j8009PTmThxIlBUS8jfXSc/Pz8oMHbrrbditVqZP38+8+fPD2rT3Llz+f7777FarSVGwyqP2267DavVyueff87PP/8ctGzJkiV8+umn7Ny5k8aNGwNqUfEjR47wzjvvBNWRURQlUCS++MhvpfG/Jps2bQqMigdqZtmbb77JsmXLADWT7Ew1adKEli1bsn//fiZOnBg4rz6fj9dee+20IysWd/fddwNqcG7btm1B7X3xxRc5dOgQ9evXp2XLlkHbValShYSEBFatWsXvv/9OxYoVS2TEncm1dSqVK1cmJSWFN954I2hfHo8nEEzwv5Znwn8unnvuuUC2DEBeXh5PPfUUubm5dOvWLVCD6EyuYX93sOLtL0ubJk6cyN69ewPz9+7dy5QpU8q0j/KeN38bzzYjyU9RFEaNGhV07IULFzJ37lxCQ0O55ZZbAvP9BdM//fTToCDlrFmz2Lp1a4l9n+z9pTTn+z0nMjISr9fLrl27SmRCJiUlBQYQKD6apRBCCFFW0mVPCCHEJWXw4MFMmDCBkSNHMnv2bGJiYjh06BA7duwgIiKCmJgYUlJSSElJOWUNpXHjxnHw4EF+/vlnli9fTuPGjdFoNKxdu5b8/HxatGjB448/Hlj/hhtuYMWKFcyZM4frr7+ehIQEdDod69atIycnh//9739ce+21gBqgCQsLIzs7mwEDBlCjRg0mTZpEbGwsr732GiNGjGDUqFHMmjWLuLg49u/fz86dO7FYLEycOLHMhZtLU/wYI0aMYPr06dSuXZujR48Gftw+88wzgfpGPXr04Oqrr2bRokVcffXVtGjRApvNxq5duzhw4AAVKlTg0UcfPeUxExISuOqqq9i+fTvXXHNNIEtq8+bNpKWlUa9ePXbv3k1qauoZPy9Qg4EDBw5k1qxZ/PHHH9SvX5+dO3dy8OBBmjZtyqZNm8q0n549ezJkyBA++ugjbrnlFlq2bElkZCSbNm3i2LFjVK1alSlTppRaN6hfv36sWrWKtLQ0hgwZUuo65b22TuX222/nl19+Yf369XTv3p2mTZtiNBrZvn07R44coXbt2gwePLhM+yrNXXfdxYYNG/j1118D17XFYmHt2rVkZGQQHx8f1K3tTK7hWrVqAQRGcOzatWtQQOZEHTp04L777uP999/nxhtvpG3btgCsXLmShg0bluk6Ku9587dxxowZbNiw4ayLccfFxbF7926uvvpqWrVqRUpKChs2bMBgMDBx4sSgLnIDBw5kwYIFLFy4kGuvvZb4+Hh2797N/v376devH99//33Qvk/2/lKaC/Ge8+KLL3LXXXcxYcIEvvnmG+rUqUNubi7r1q3D6XRy7733Bs6vEEIIUR6SISWEEOKSMmjQICZPnkzjxo3ZtWsXS5cuxeFwcNddd/HDDz8EgkJLly495X6io6P55ptvePTRR6lYsSJr1qxh48aNxMXF8cwzz/Dxxx+XGFHv5Zdf5vXXX6dhw4asW7eOf/75h6pVq/L888/z8ssvB9bTarVMmjSJOnXqsH37dpYvXx6ob9WrVy++/fZbevfuTVpaGr///jvZ2dncfPPNzJ07N6hA9pnq1asXc+fOpW/fvuTk5PDHH3+QmppKt27d+PTTTwMZKKB2/3njjTd44oknqFWrFuvXr+ePP/7A5/Nx11138d133512BDudTsesWbMYNGgQUVFR/P3336xdu5bq1avz4osvMn/+fMLCwti8efNZBaVq1qzJnDlzuP322ykoKGDp0qXYbDZmzJhBp06dyrWvkSNHMmPGDNq0acPOnTv5448/sNlsPPjgg8yfP5/atWuXut0111wTuC5O7K7ndybX1smYTCY+/PBD7rvvPqKjo1m1ahV///03VquVBx54gDlz5pR7BLjitFotU6ZMYcKECTRq1Ij169ezfPlyKlWqxFNPPcWcOXNK1Bcq7zXcs2dPBg0ahNVq5a+//mLdunWnbdcTTzzBm2++ScOGDVm7di1bt26lf//+zJw5s0zPq7zn7fbbbw/UT/vrr79KzUwqj0qVKvHll1/SqFEj/v77b3bv3k23bt2YPXs23bt3D1q3cePGfP7553Tq1InU1FSWLVtGhQoV+Pjjj+ndu3eJfZ/q/aU05/s9p1mzZnz55Zdcc801ZGdns2TJErZt20aLFi146623ePLJJ89q/0IIIf67NMrZFHwQQgghLgGdOnUiOTmZJUuWnFUmgBBCCCGEEOLCkAwpIYQQl7WsrCzS09PRaDSBWkdCCCGEEEKIS5vUkBJCCHFZSk9PZ8iQIaSnp+PxeGjRokWZu0kJIYQQQgghLi7JkBJCCHFZOnjwIKmpqWRkZJCQkMBrr712sZskhBBCCCGEKCOpISWEEEIIIYQQQgghLijJkBJCCCGEEEIIIYQQF5QEpIQQQgghhBBCCCHEBSUBKSGEEEIIIYQQQghxQUlASgghhBBCCCGEEEJcUBKQEkIIIYQQQgghhBAXlASkhBBCCCGEEEIIIcQFJQEpIYQQQgghhBBCCHFBSUBKCCGEEEIIIYQQQlxQEpASQgghhBBCCCGEEBeUBKSEEEIIIYQQQgghxAUlASkhhBBCCCGEEEIIcUFJQEoIIYQQQgghhBBCXFASkBJCCCGEEEIIIYQQF5QEpIQQQgghhBBCCCHEBSUBKSGEEEIIIYQQQghxQUlASgghhBBCCCGEEEJcUBKQEkIIIYQQQgghhBAXlASkhBBCCCGEEEIIIcQFJQEpIYQQQgghhBBCCHFBSUBKCCGEEEIIIYQQQlxQEpASQgghhBBCCCGEEBeUBKSEEEIIIYQQQgghxAUlASkhhBBCCCGEEEIIcUFJQEoIcclSFOViN+GMXK7tFkIIIS5n8vkrSiPXhRCXLglICfEfdtddd3HVVVexZcuWUpd3796dUaNGnfVx4uPjmTZtWrm2mTNnDq+99tpZH/tC2717N7fffnvQvDN5/kIIIURp1q1bx/Dhw+nQoQONGzemR48ePPfcc+zdu/diNy3ItGnTiI+Pv2DHW7duHffdd98FO96l5pFHHinxnW3UqFHEx8efdDp8+PBJ97dnzx7uv/9+WrduTZs2bRg5ciQpKSknXf/o0aO0bNnyjL7vlNbOhg0b0rFjR5566imOHj1a5n2NGzeOKVOmAHDs2DHuu+++Uz7Pc+muu+7irrvuOuU6Z/J3UZZt9u3bR/fu3cnOzi7Xvv1SU1N54oknaNOmDS1btmTEiBEkJyefdrsVK1Zw55130rp1azp06MDw4cM5dOhQ0DoHDx7k0UcfpWPHjrRs2ZLbb7+dFStWnFE7xZVHf7EbIIS4uLxeL6NHj2bevHkYjcbzcozZs2dTqVKlcm3zzjvvkJCQcF7acz4tWLCADRs2BM07k+cvhBBCnOj999/njTfeoGPHjjzzzDPExMRw8OBBvvrqK/r378+ECRO44YYbLnYzL4o5c+ZcckG5C8Hn8zFhwgQWLlxI//79g5Y99NBDDBgwIGheVlYWjz76KAkJCVSpUqXUfR4/fpyBAwdSo0YNXn/9dRwOB1OmTGHw4MHMnz8fg8EQtL6iKDzzzDPk5uae8fOIiYnh7bffDjz2eDzs37+fSZMmsWHDBn766SfMZvMp97FixQoWLVrEwoULAfjnn3/4888/z7hN58Mtt9xCp06dzvl+a9euTY8ePXj55ZeZOHFiubb1eDzce++95Obm8sILL+DxeJg8eTJDhw5l3rx5JV5vv3Xr1jF06FB69OjBpEmTyM/PZ8aMGdx+++38+OOPREVFkZGRwZ133klERATPPPMMISEhzJkzhyFDhvDJJ59clt/1xbklASkh/uNCQ0PZvXs306dP5/HHHz8vx2jWrNl52e/l4r/+/IUQQpy9pUuXMnnyZIYPH86wYcMC8xMSErjxxht54oknGDVqFHa7nXr16l3ElooLZefOnbz88sts2bKl1GBNjRo1qFGjRtC84cOHEx4ezqRJk9BoNKXud86cOeTk5PDOO+8QGRkJQFRUFAMHDmTlypUlAipffvkl+/btO6vnYjQaS3xfatWqFQaDgZEjR7J48eLTBlsnTJjAoEGDsFgsZ9WW86lSpUrn7R8p77vvPrp27crdd99Nw4YNy7zdggUL2L59Oz///DN169YFoEGDBvTu3Ztff/2Vvn37lrrdzJkzqVOnDm+99RZardrxqkWLFnTt2pX58+czdOhQvvvuOzIyMvj222+JjY0FoEOHDvTr148PP/xQAlJCuuwJ8V/XoEEDbrzxRj744AO2bt16ynW9Xi9ffPEFffr0oUmTJnTt2pVJkybhdDpPuV3xLmurVq0iPj6eFStWMGTIEJo2bUqHDh14/fXX8Xq9gNpV8PDhw8yfP5/4+HiSkpIAOHLkCCNGjCAhIYGmTZty9913s3379sBxkpKSiI+P5+OPP+baa6+ladOmvPPOO8THx7N06dKgNu3YsYP4+HgWLVoEgNPpZOLEiXTp0oVGjRrRp08ffvnll6BtunfvztSpU3nttddo3749TZo0YejQoRw4cABQU6r9/7pX/Dmf2GUvOTmZ0aNH06VLF5o0acLNN9/M4sWLS5yzL774gmeffZaEhASaN2/Oo48+SmpqamCdQ4cO8cADD9CmTRuaNm3Kbbfddsn9S6AQQohz4+2336Z27do8/PDDJZYZDAZeeukldDodM2fOBGDIkCHcdNNNJdZ96KGHgn5grl27ljvvvJOmTZuSkJDAyJEjSU9PDyyfN28eV111FXPmzKFDhw4kJCSwZ8+eMn8G/fHHH/Tt25fGjRtzzTXX8N133wUtL8tnotPpZPr06Vx77bU0btyYXr168f777+Pz+QC1y9f8+fM5fPgw8fHxzJs3r9RzOG3aNK699loWLVpE7969ady4Mf369WPDhg1s3LiRW265hSZNmtC7d+8SXYp27drF/fffT4sWLWjRogUPP/wwiYmJQevs3LmTYcOG0bZtWxo2bEinTp14+eWXKSgoCKxTls/3efPmER8fz6pVq0p9Hn4jR47E6/Uye/ZsoqOjT7kuwJ9//slvv/3G6NGjCQsLO+l6//d//8eXX34ZCEYBgSyZE7/zJSYmMmnSJMaNG3fa45+Jxo0bAwS63Y0aNYq7776bsWPH0qJFC66//nq8Xi9//PEHu3btCgSt5s2bx+jRowHo0aNHoDtjWb/LLl++nP/7v/+jZcuWtGnThieeeKJMXQcVRWHmzJl07dqVJk2acNttt7F58+bA8tK633344Yf06NGDJk2aMGDAAJYsWVLq63+6v6WYmBjatm3Le++9F5hXlmvp77//Ji4uLhCMAqhbty516tQ55fdK/3dxfzAKIDY2ltDQ0EC3vdjYWAYNGhQIRgHodDpq1qxZomuf+G+SgJQQgmeeeYbIyEhGjx6Ny+U66XrPP/88EyZMoGfPnrzzzjvccccdfP755zz00EPlLhj55JNP0rJlS95991169+7NBx98wJw5cwD1S3dMTAxdunRh9uzZVKxYkfT0dAYMGMC2bdsYM2YMkydPxufzcccdd5RI0Z82bRr33nsvEydOpH///tSoUYOff/45aJ2ffvqJiIgIunTpgqIoPPzww3z99dcMHjyYd955h+bNm/P444+X+LD/9NNP2bdvHxMmTODll19m69atjBw5ElDTsG+++WZA7aZ3yy23lHjeqamp3Hzzzaxdu5bHH3+cadOmUbVqVR5++GF++OGHoHWnTJmCz+fjjTfe4Omnn2bp0qW88sorgJqif//99+NwOJg4cSIzZswgIiKCBx98kIMHD5brtRBCCHFpS09PZ+vWrXTr1u2kWS0RERG0b98+EMzp27cv27ZtC/pMyM7O5q+//qJfv34ArFmzhkGDBmE2m3nzzTd55plnWL16NQMHDgwKoni9Xj766CPGjx/P6NGjiYuLK/Nn0PPPP8+gQYN45513qFSpEqNGjWLnzp1A2T4TFUXhgQce4IMPPuCWW27h3Xff5dprr+XNN99k7NixgBpk69KlCzExMcyePZuuXbue9FweO3aMV199lQceeIC33nqL7OxsHnnkEUaMGMEtt9zC9OnTURSFxx9/PHAO9u/fz4ABA0hLS+O1115j/PjxJCYmcvvtt5OWlgaogbU77rgDh8PBq6++ysyZM7nhhhv47LPP+PTTT4PacKrPd4CuXbsye/bs02a5TJw4ka+++or69eufcj3/eXzttddISEjg2muvPeW6UVFRgUCQ0+lk48aNvPTSS9SoUYOOHTsG1vP5fIwaNYrrrruOzp07n7YNZ2L//v0AQZlea9eu5ejRo0yfPp0nnngCnU7HDz/8QLNmzQKBj65du/Lggw8C6vfKhx56CCjbd9nvvvuOIUOGULlyZd544w1Gjx7Nhg0buO222wKv98msW7eORYsWMWbMGF5//XWSk5N58MEH8Xg8pa7/9ttvM2nSJK677jpmzJhB06ZNeeyxx0pd91R/S37XXnstS5YsIS8vL3AeTnct7d27l1q1apWYX6NGjcD5L82DDz4Y+N7rt3r1arKysgJZmtdffz1PPvlk0DpZWVmsWbNGMjmFShFC/Gfdeeedyp133qkoiqIsXrxYsdvtyhtvvBFY3q1bN2XkyJGKoijK7t27Fbvdrrz33ntB+/juu+8Uu92u/PHHHyc9jt1uV6ZOnaooiqKsXLlSsdvtypQpU4LW6d69u3L//feXemxFUZQ33nhDady4sZKUlBSY53Q6lR49eijDhw9XFEVREhMTFbvdrjzzzDNB+546darSrFkzxeFwKIqiKD6fT+natavy/PPPK4qiKH///bdit9uVn3/+OWi7J598UunQoYPidrsDberWrZvi8XgC60ybNk2x2+1Kenp64Fh2u/2kz3/ixIlKw4YNg56HoijK3XffrXTo0EHxer2BbW6//fagdUaNGqU0a9ZMURRFSU5OVux2u/LDDz8ElmdnZyuvvPKKsmvXLkUIIcSVY/PmzYrdblc+//zzU6736quvKna7XcnMzFTy8vKUZs2aKW+//XZg+Zw5c5T69esrx44dUxRFUW677Tald+/eQZ9r+/btUxo0aBA41ty5cxW73a589913gXXK8hnk/zz8888/A+scPHhQsdvtyieffKIoStk+E//44w/FbrcrP/30U9A606dPV+x2e+B4I0eOVLp163bK81Nam9577z3Fbrcrc+bMCcxbsGCBYrfble3btyuKoigjRoxQ2rdvr+Tk5ATWycjIUFq2bKm8+uqriqIoyrJly5Q77rgjaB1FUZTevXsrQ4YMCTw+3ef7mTrxe9OJfv/9d8VutyvLly8v13579eql2O12pUmTJspff/0VtOyjjz5SOnfurGRnZyuKEvx9pzz8r53b7Q5MGRkZyl9//aV0795d6d69e+A73MiRIxW73a4cPXo0aB/t2rVTXn755aB5/ms3MTFRUZSyfZf1er1Khw4dgl4zRVGv3YYNGyqvvfbaSZ/HnXfeqTRp0kTJyMgIzPvmm28Uu92u7NixQ1GU4O+JeXl5SpMmTZRx48YF7WfMmDGK3W5XVq5cGbTNqf6W/Hbs2HHa7+Unuuaaa5QnnniixPwnnnhC6dWrV5n3k5aWpvTq1Uvp2LFjib8DP6/XqwwfPlxp0KCBsmnTpjLvW1y5JENKCAGo3dH69u3LBx98wLZt20osX716NUCJ/vs33HADOp3utGnlJ2revHnQ40qVKpGfn3/S9VesWEGDBg2IjY3F4/Hg8XjQarV07tyZf/75J2jdBg0aBD3u27cv+fn5gW5769ev58iRI4F/IV6xYgUajYYuXboE9u3xeOjevTspKSns3r07sK/GjRuj0+mC2g3gcDjK9LxXr15N8+bNqVq1aok2pqSkBNVgOLGWQqVKlQLHqVChAnXr1mXMmDGMHDmSH3/8EZ/Px+jRo+VfnIQQ4gqjFGZunKy4sJ//80lRFKxWKz179gzqfv7zzz/Trl07YmNjcTgcbNq0KZAp7P/sq169OnXq1GH58uVB+y7+2Vqez6BWrVoF7lerVg0gMBJYWT4TV69ejV6vL5HV4+926P9+Uh4tWrQIei6gdj/yi4iICGrnypUrSUhIwGw2B85TSEgIrVq1CnwH6dixI59//jkmk4k9e/awePFi3nnnHdLT00tkn5/q8/18+eKLL2jQoAHt27cv13Zjx47lww8/pF27djzwwAMsW7YMULNq3nzzTV566SVCQ0PPun2HDx+mYcOGgalNmzbcc889REdHM3369KAaWREREUF1mPLz80lLSwtcXydTlu+y+/fvJyUlhd69ewetU6NGDZo3b37a661u3bqB6weKrvmcnJwS627cuJGCgoIS1/aJx/Y71d+Sn/9vyV/uoiyUU/RyOFlG5omSk5O5++67SU5O5u233yYkJKTEOm63m6eeeoqFCxfy7LPP0qRJkzK3UVy5pKi5ECLgueeeY8WKFYwePZq5c+cGLcvKygLU/unF6fV6IiMjS/2gPZUTi29qtdpTfiBmZmZy8ODBk6YcF/8iZ7Vag5bVrFmT5s2b8/PPP3Pdddfx888/U6NGjcAX0szMTBRFCfqCWlxycnLgi/iJhTL9/eb9dSxOJysri+rVq5eY7/9CXPyLRWnH8p8jjUbDRx99xDvvvMOiRYv47rvvMBgM9OzZkxdffJHw8PAytUcIIcSlz/8j83TD1ycmJmKz2QI/iPv168cPP/zAzp07qVChAqtWrQp0DcvOzsbn8zFz5sxA3aniTCZT0OPin63l+Qwqvp3/M9P/WVaWz8SsrCwiIyOD/jEIir6PlPf7B1Dqj+VTFcLOzMzkl19+KVFbEtTubUCgC94XX3xBfn4+lStXpkmTJiXOY2nHOt13oLOVmZnJqlWrGDFiRLm39Qew2rZtyw033MDMmTNp3749o0eP5tprr6VDhw5B3dF8Ph8ejwe9vnw/M2NiYnjnnXcCj41GI5UqVSr1+4zNZgt67L8GTvz+d6KyfJfNzMwEiq7B4ipUqBBUu7Q0J7bhVN8T/bXa/NeQ38nqgZ3qb8nPf22VZ8TDkJCQQBe/4nJzc8sUbPz333954IEHyMvL44MPPggK7vplZ2czbNgw1qxZw5gxY7jjjjvK3D5xZZOAlBAiIDw8nBdeeIGHH36YGTNmlFgGkJKSEvQvmW63m4yMjKDCl+dDaGgoCQkJPP3006UuNxqNp9y+b9++TJgwgZycHBYsWMDtt98etG+r1VqixoNfzZo1z7zhJwgPDyclJaXEfP+88pzH2NhYXnjhBcaOHcvOnTtZsGABM2fOJDIyMlBXQwghxOUvOjqaZs2asXDhQh599NGgIsJ+ubm5LF++nO7duwfmtWvXjpiYGH799VdiYmIwmUz06tULUH/UazQaBg0aVOroZacbqexcfAaV5TMxPDycjIwMvF5vUFAqOTk5sM75FhoaSvv27Rk8eHCJZf7Ay/vvv8+sWbN48cUX6dWrV+CH/Ik1di6GZcuW4fF4Tls7ym/lypU4nU66dOkSmKfX64mPj2fXrl0cPXqUTZs2sWnTphK1NmfMmMGMGTNYvHjxaTOWijMajYG6VeXlvwZOzBY6UVm+y/qDucWLzPulpKSc0+vNn+WVlpZG7dq1A/OLDypQXv5zUJ52xsXFsWPHjhLzDx06dNosppUrV/Lwww8TGhrKF198UWqW/rFjxxg8eDBJSUm88cYbXHfddWVum7jySZc9IUSQnj170rt3b95///2gD0T/sKwnFgf/+eef8Xq9tGzZ8py248Qv2wkJCezfv5+4uDgaN24cmL7//nu+/fbbEv9yeqLrr78eRVF46623SEtLCxphKCEhgfz8fBRFCdr3rl27mD59+kkLUZal3Sdq3bo1GzZsKPGv3D/88AMxMTFlDn5t2LCB9u3bs3nzZjQaDQ0aNODxxx/Hbrdz5MiRMrdXCCHE5WHYsGHs37+fN954o8Qyr9fL2LFjKSgo4J577gnM1+l09OnTh6VLl7JgwQJ69uwZyLIICQnhqquuYt++fUGfffXq1WPatGmn7Ip/rj6DyvKZmJCQgMfjYcGCBSXWAQLfP073+Xs2/CMLNmjQIHCeGjVqxKxZswKj9a5bt466devyv//9LxCMOn78OLt27SpzFvX5smnTJipVqlSia+TJfP/99zz99NNBWTa5ubls2LCB+Ph4KlasyLfffltiArj11lv59ttvqVix4nl5LqUxGo3ExMSUGAWvtO+ScOrvsnFxccTExPDTTz8FrZOYmMjGjRtPmk1/JurXr09oaGjgGvL77bffznifx44dA6BKlSpl3qZjx47s3buXPXv2BObt2bOHvXv30qFDh5Nut337dh544AEqV67M7NmzSw1G5ebmBrryffzxxxKMEiVIhpQQooQxY8awcuXKoH8dqlu3Lv3792fq1Kk4HA5at27Njh07ePvtt2nTpg2dOnU6p20ICwtj+/btrF69miZNmjBo0CC+//57Bg0axJAhQ4iMjOSXX37hm2++CQzreyr+EfW+/PJLmjdvHhT46dKlC61bt+ahhx7ioYceok6dOmzevJmpU6fSqVOnEqnUp2s3qKP4NW3atERXhMGDB/PDDz8waNAghg0bRkREBN999x0rV67klVdeKfMX6quuugqz2czTTz/N8OHDqVChAv/88w87duxg4MCBZW6vEEKIy0OnTp0YNWoUEydOZMeOHfzvf/+jYsWKJCUl8dVXX7Fjxw7Gjx9fYsS1fv368dFHH6HVakt0zRsxYgT33XcfTzzxBH379g2Mprdp06bAqGSlOVefQWX5TOzcuTNt2rThueee4/jx49SvX5/Vq1czc+ZM+vfvHxiqPiwsjNTUVP78808aNGhwTgMiDz30EAMGDOD+++/n9ttvx2QyMXv2bH7//XemTp0KQJMmTZgxYwbvv/8+zZo14+DBg7z33nu4XK5y14dKT0/n0KFD1K1bt9TuheX177//Bs5TaQ4dOkR6enqgttU999zDggULePDBBxk6dCgul4uZM2eSl5fH8OHDT5nNVLFixaBlJ+77fOnQoQPr168Pmuf/TrZo0SI6d+5cpu+yWq2WESNGMHr06MDfRUZGBm+//Tbh4eGlZsmdqZCQEO655x6mTp2KxWIhISGB1atX89VXXwFnFmRdt24dFoslUG+qLNfS9ddfz7vvvsu9997LE088AcDkyZOx2+1BAaTt27djNBoD19Kzzz6Lx+Nh+PDhHD16NCggGBUVRY0aNZg6dSoHDhxg+PDh6PV6Nm7cGFjHaDRy1VVXlfs5iiuLBKSEECVERETwwgsvMGzYsKD548ePp2bNmsydO5eZM2dSsWJFBg4cyEMPPXTO/2VyyJAhvPLKKwwdOpSPP/6YVq1a8fXXXzN58mReeOEFnE4ntWrVYvz48WVOh+/Xrx+///47ffr0CZqv1Wp5//33eeutt3jvvfdIS0sjNjaWwYMH8/DDD5er3b169eL7779n1KhR3HzzzbzwwgtBy2NiYvjqq6+YPHkyL7/8Mm63m/r16zNjxgx69OhR5uOYTCY++ugjJk+ezPjx48nOzqZWrVq89NJL3HTTTeVqsxBCiMvD4MGDad68OZ988gmvvfYa6enpxMTE0KFDB8aPH19q0KF+/frY7XYyMjJo165d0LKOHTvy4Ycf8vbbb/PII49gMBho2LAhH3/88SkDCOfqM6gsn4kajYb33nuPqVOnMmvWLNLT06lWrRojRowICg7cdNNN/Pnnnzz88MM88sgj3HfffWVux+nUr1+fL774gilTpvD000+jKAp2u53p06cH2nn//feTkZHBp59+yvTp06lcuTL9+vULtD87OzsQIDmdP/74g9GjR/Ppp5/Spk2bs25/Wloa8fHxJ10+Y8YM5s+fz7///gtAnTp1+OKLL5g8eTJPP/00Ho+HhISEk15jp3Livs+Xa665hh9//JHjx48TGxsLQJs2bWjfvj2TJ09mxYoVvP/++2X6LnvTTTdhs9l47733ePjhhwkJCaFTp06MGDGiRP2ps3X//fejKAqzZ8/mww8/pGnTpjz55JNMmDDhtDWxSvPXX3/RtWvXQK3WslxLRqORjz/+mPHjxzNmzBgMBgMdOnRg9OjRQbXAhg0bRtWqVfnss89ITEwM1NN65JFHSuyzf//+vPrqq4Fsr2nTpjFt2rSgdapWrcqSJUvK/RzFlUWjnM8KekIIIcQVxuv14na7L3YzxDliMBhO2+VXCCHEmevVq9dZdUMrC0VR6Nu3L9dcc02Jf1C9VHk8Hn766SfatGlD5cqVA/O/+OILXn75ZVatWlXmICaogx5cffXVfPvtt5J5JC4bkiElhBBClIGiKBw7diwwAo+4cviHEC/r8NZCCCHK5vvvvw8q2H2+aDQannrqKZ555hkGDRp0Tro6nm96vZ6ZM2fyySef8OCDDxIZGcmuXbt48803ufHGG8sVjAL46KOPuPbaayUYJS4rkiElhBBClMHRo0fJzMykYsWKWK1WCV5cARRFIT8/n+TkZCIiIoL+hVoIIcTZ27dvH5UqVTqj7mdnYuzYsYSFhQVqIV3qEhMTeeONN1i1ahXZ2dlUqVKFvn37cv/992MwGMq8n71793LPPfcwf/78wEiBQlwOJCAlhBBCnIbX62XXrl1UrFiR6Ojoi90ccY6lpaWRnJyM3W6X7ntCCCGEEBfI+RsfVQghhLhC+GtGXah/4RUXlv91ldpgQgghhBAXjgSkhBBCiDKSbnpXJnldhRBCCCEuvDMOSLlcLnr37s2qVasC8xITExk0aBDNmjXj+uuv5++//w7a5p9//qF37940bdqUgQMHkpiYWObjKYpCbm4u0sNQCCGEEOL05LuTEEIIIS5lZxSQcjqdjBgxgt27dwfmKYrCww8/TIUKFZg7dy79+vVj2LBhHDlyBIAjR47w8MMPc9NNN/Htt98SFRXFQw89VOYvSXl5ebRs2ZK8vLwzabIQQgghxH+KfHcSQgghxKWs3AGpPXv2cOutt3Lo0KGg+StXriQxMZGXXnqJOnXqcP/999OsWTPmzp0LwJw5c2jUqBFDhgyhXr16TJgwgcOHD7N69epz80yEEEIIcVn79ddfSUtLA2DatGncddddAMybN4/u3bufdLtRo0YxatSoC9JGIYQQQghxbpQ7ILV69WratGnD7Nmzg+Zv2rSJq666Kqjga8uWLdm4cWNgeatWrQLLLBYLDRs2DCy/JGTvgrQ1kLUD8pPAlQk+78VulRBCCHHFO3z4MI899hgOhwOAIUOGMG3atIvcKiGEEEIIcb7oy7vB//3f/5U6PyUlhYoVKwbNi46O5tixY2VaftGlrIBF7Utfpg8DYzgYIsAYAaYYMMeot5ZKYKmiTtaqYK4E2nKfViGEEJcBRVFO2tVc4RRd0JUyrlfG/fnboCgKgf/88wrvKyio/59iebF9BO33hPVPu6zYsYqve+L2geeknLAtcCxD/T5wIPMA+Zb8wDbHU49zLOcYbq+bnak7S+w/2hpdhrMphBBCCCEuNecscuJwODAajUHzjEYjLperTMsvupA4qNABcveCJx+8eaAUZkd5stWJMhRh1+jUoJS1OthqgK0mhMZDeH2w1QKtQV2nxKQHre58PkMhhDivfIqvKEBSeFvavHN1C5x6HUXBhw+fz1fUlsJlPp8Pt89NgacAp9eJy+vC5XPh8rhwe904fU5cHhcurwu3z43BZyDBlkByXjI6t67oOD4fePNPGiAq0U5OHxQ6MbBzYpDHqzVBOUaFWzB3Ab/M+YXs9GyqxVXjrmF34fV6Gf/4eL5Y+kVgvXdffReAB0Y9QF5uHjMnzmTb+m2ggeZtmzPosUFYbWoW9C/f/MLCeQvJycrB3sjOkBFDqFi5Ioqi8N1n3/H7D7/jKnAR3ySeQY8OokJsBQDu6HYH9z55L99/+T3ZGdm0aN+CoU8MxWwxM/R/QwEY+r+h3DfyPlKPpbJj4w6ee/M5HF4HPsXHh9M/5LfvfsNitdDn9j5cc9M1kF/yOS9atIgpU6Zw+PBh6tWrx9NPP01CQkKZz5kQQpwtr09Bp728R/C8Ep6DEOLSds4CUiaTiczMzKB5LpcLs9kcWH5i8MnlchEWFnaumnB2LJWg19+gKOBzg88FnlxwpoEztWgqSFFvXengSgNneuHjDHBnqkEsx2F1SlsZfAytSQ1UhdSBsPrqZKupZlRptIVBKaM66Uzq+lpD4XJ9YTDrxFv5kBDiv86n+AKToijBj1FKLDtxXvHAUfHlXp8Xr8+LDx9exYvPp24TuI+6TvFAT/FgUPH//EEW/3FADbAUDwo5PU41OORxFQWJSpncPjdOjxO3z60+9roD8/2P3T530a3//gmPy5qpBFDTVpN3O7yLId8A/o8yRaH+hnsIyd58Hl7Vk8sJa8q/zWee9P1fgwb1fw0Hdh/gq/e+4olxT1C9VnV+mfsLU1+YyiNjHgHApDOhKdyPXqN+JbAarHz1yVfkZObw2juv4fF6mPLSFH758hcGPzyYBd8vYP6n8xk2chh14uvw6bufMv2l6bz18Vv8OOdHVi5eyeiXRhMZHcncL+by+sjXee/L99Dr1f3P/Xgujz/zOJHRkUwaN4kv3vqCZ8Y9w9uz3mbYoGFMnzWduDpxfP3p1xh1RiqHVCbCFEHq8VSSDyUz4+MZ7Nqxi8njJ9OsYTM6tO0Q9Px37tzJyJEjefHFF2nSpAl//vkn9957Lz/88AM1a9Y8Xy+LEEIE0Wk1PPr1BvYk517sppyRuhVDeGtA84vdDCHEFe6cBaRiY2PZs2dP0LzU1NRAN73Y2FhSU1NLLG/QoMG5asK5odGAzqhOhhA1UFUanxu8BerkyVMDU640cBwtrD+Vrs4rOAq5+yDvAPickLtHnY4tVPejD4GIRhDZEqJaqF3/8IHiU4NbQV1DNIXBKX9GlR50ZtBaQG9V2+wPaGkNhQEtowSthLjI/EGbQDDHf7/Y/BOXFZ88Pg8enwevz6veV4ruBwWWCjOCSmQnnTAf1GBFoNuT/y3C/3ZTGMzwr+dTfBR4CnB4HLi8LgrcBRR41cdOjxpIcngcFHgK1OCSx0mBt9h9T9G6Bd6ieQWeArzKpVGnT6vRYtAaMOgMGHXGovtaI3qtnhq2Ghh1Rix6C1qDFo1Gg0YB3UXoom0xWLBH29Fo1DKQxQNQJzq88TBajZaW9pbUrVeX5nHN2dxrc+C1rh1ZO7BuiDEEgJrhNclJzSEiNIIW9VtgsVio8WYNFBSqhVVjyY9LuHPgnQzoP0Ddx/O1+fSTT4nURzL/i/mMem4UXTp3QUGh2bhmXNP9Gnat20Xnrp0BGDx0MNddfR0Ao0aP4uH7H2bM82OoHlsdgGqx1YgJi8GkM6HT6ggxhmDWmzGZTLw0/iUiIiJoVL8RW9Zv4Zf5v9C5feeg5/zhhx9y66230qdPHwAGDhzImjVr+Oqrry7ZwueLFi1i2LBhQfOuueYapk6dyvbt2xk7diy7du2ibt26vPjiizRq1OgitVQIUR57knPZdiT7YjfjP+9KyPS6Ep6DEKU5Z9+kmzZtyvvvv09BQUEgK2rdunW0bNkysHzdunWB9R0OB9u3by/xBeyyoTWokyEUiIGQWuDzqFlV7iw1MOXKBK9DzXbSh0JBshqMytwGWVsha7u6fupKdQIwVYSY9lCpJ0S1Cq5HpSiFQSqPeizFA64sUNLU+0qxX5NafVFgSmdRj6+3FmVe6UygNUs3QSFOQlEUvIqaIXSq20AmkeJVM3V8LjxeNYDk9rrxKIVBo8KMIv9+i2cylVaTSFMskKxBg1ajBkG0Gi3awkCE/74GDTqNDo1WE1gXwO1zk+fKI8+dR747n3xXPrnuXHJdueS58wLLAreF9/Pd+YF5/iDU+aZBg0lvUgMPOpN6X2fGpDcFHgfuFz426oyB+watIXBr1psx6oxqYElnwKQzBYJLQY+LBZ70pwsseUCfradySGVMZlNgtq/Hn+R5S+kzdj7prOhK+YeG4l3+/I/btmtL3Xp1ufWmW4mvH0+Xbl248X83cuiAOlKux+cJbO/DB6jXza3/dytPPfYUPTr3oHXb1nTv2Z1e1/XC5XVxYP8Bhj4wNHBdhEWG8fBjD5Ofn8/x48cZ9dSowDUI4HQ6OXDgAB0UNZOpcbPG+BQfGjTUv6o+Xq+XgwcPEhkZCajXQvHrX6fVodFoqFqtKhWiKgTWadiwIfPnzseoCy4HsHfvXn799degwVfcbjcdO3Ys/7m+QPbs2UO3bt0YN25cYJ7JZCI/P5/77ruPPn368Oqrr/LVV19x//33s2jRoqBBZIQQQpycZKsJcek6ZwGphIQEKleuzOjRo3nooYdYunQpmzdvZsKECQD873//48MPP+T999+nW7duTJ8+nWrVqtGmTZtz1YSLT6tXi54bI8BaQ82ccmWo3fcKktXlFdqpwSZQg0o5eyB9NaSugoyN4EyGpO/UyRAOsV2hUi+Ibl3UrQ89nCqOpCiguAu7HrrVNhQkF9bEUtT9+LsD6m1q0XaDTQ1Q6S1qAEtrOK+nSogLQVGUouwixVsi08gfWPIHkvxdv1xeVyD7qHjQqXg3uOLZRChqAEmDRv3xXHjrDxhpNVoMOkPQ8uLLNCfJYlQUhTx3HtnObLKcWWQ7s4OmHFeOeuvMIcelTrnO3MD94sGGc8GgNWA1WLEarFj0FiwGi3rfYMGqV2/NenNgmUVf+Lhwvn+Z/75JZwosM2gNJz0PlzSNRn0fPYXSakOVdX7QYz+fWz00GhSUQIZUoEnFHhjNRj7+/GPWr1vPsj+X8eP3PzL3m7m88vorAIHrEUDxKuj0Okw6Ex3ad2Dh4oX8seQPlv21jFfHvcralWt59bVXMRjUoJ/NaAs6lq9ADWhNmTKFWnG1gtoTHh4eyMAKt4QTagpVt3Gq24QYQ7AZ1PNoNVgJNYVi0pvQa/WBDCm9To/NWHSu9Ro9JqMakCzO6/Vy7733cuONNwbN9/9j2aVo79692O12YmJiguZ/++23mEwmnn76aTQaDc8++yx//fUXCxYs4KabbrpIrRVCiMuPZKsJcWk6ZwEpnU7HjBkzePbZZ7npppuoWbMm06dPp0qVKgBUq1aNadOm8corrzB9+nSaN2/O9OnTL88fIGWh0ahd/gwh6uh7zvTC2lJH1bpUpgpqxlJ4fXWKG6h2/8vYCMeXwPGlaiAp6Xt1slSF6v2ham9129MdW1PYda80ireoTpYrqzBYpf4oUDO/zGrbjJGFASurGqTSWaT7n7iovD4vbp87EFhye4vu+x/7u4T5awx5fV68eIvqIfkzkop1U9No1AwjrUYbFEjSaXQYtIYS887kfUtRFHJduaTlpZFRkEFWQRaZBZlBU5Yzi6yCLPW28P7ZdmnToCHEGBI02Qw2bEZb0H3/rdVgxWYIvvVPBt2VH6g+WXdKxaMQ4YsIBDZPFzQKBItOyHQLuq8putUW637nD24CQff9j0/cX/GA1InH2LBxA6tWreLBBx6kW8dujHpqFB06dGDrhq3qSi6whahBnqNHjlKrVi0sBguzZs0iPj6eW2++lVtvvpWff/6Z0aNHY5xspGbNmuzZtYdePXsBkJGRwXXXXce3335LdHQ06Wnp9OjeQ929y8WIESMYOnQo0VHqaHj//vsvV111FQDbt23HYDBQu3btQB3Kk/19JSYm4nA4sFgsAGzevJnatWuXWC8uLo6kpKSgelETJ04kLi6OW265pdR9X2x79+6lffuSI/1u2rSJli1bBs6JRqOhRYsWbNy4UQJSQgghhLjsnVVA6t9//w16XLNmTT7//POTrt+lSxe6dOlyNoe8PGm0YK6gTiFxkHsQ8g+pXfrMFYu65enMUKGtOjV4GjI2wLFFcPQ3NZi1623Y/Q5U7Apxd6m1p86oPTrQ6dTjFf996c+s8jrBna1maym+woyqwuwpYxQYwgoDVTZ1H0KcBX+QyV9w2h9YcvvcuDyuoFpFbq87EBDwd5Pz8/8Q12l06LS6wK1BZ8CsMQeCTWcaUCqNx+ch3ZFOuiOd1PzUwP10RzoZBRnqrSOD9IJ0MgsyzzhjyagzEm5Ss0r8t2HGMPXWFEaoseg2xBhCqCk0EHyyGqxB3aeuZMW7Wfqz24rfLx5sKl5s3Z/l5g8M+a8Rf5BSq9GqBb8Ll+u1+uDAUfGaW5piQaLSHpcSPDqf/zBjtViZMX0GMRViaNeuHWvWrCE/P5+ePXsyc+ZM3n33XW677TYWLlzI9u3bqVWrFgDHjh1j9uzZTJgwgYiICBYuXBgIIt11111MmDABu91OnTp1mDJlCtWqVaNatWoMGjSIN998k+joaGrXrs2MGTNYv34948ePD7Rp6tSpVK1aFZPJxMsvv0z//v2x2Ww4nU5ALUru775XnNPpZOTIkQwfPpx169axcOFCvv766xLrDRo0iDvuuIPGjRvTtWtXlixZwqxZs/jkk0/Owxk+e4qisH//fv7++2/ee+89vF4v1157LY888ggpKSnUrVs3aP3o6Gh27959kVorhBBCCHHuXPhqrP91hjA1kGSpDLl71YwpvQWM0cHZR1q92k0vujXUHwFHF0HiPMjaAscXq1NUS4gbpAawzsUPmuKZVYbQovmKVw1SeQvUAu2KT11XawFjmNp2Q2hRkOo/8uNXnJp/BLXSRkBzep3kufJwuB1qzaViWU4+xRfIOtFoNOi1enQanXqr1WHUGQPBptPW/TlDLq+LlLwUUvJTSM1PDdz6pzRHGqn5qWQWZJZ731aDlQhTBBGWCCLNkUSYIwg3hau35vDA43BTOOHmcMJMYZj1/63Ab/G6XIGuk6XU7vJ3V/NnvGk0GnTo0GoLg4+F9w1aAzadDYPWgFFvDNSO8gcoiwcrT7z1L3M73Rw4cACb0YbZePm8Hg0aNGD8+PHMmDGDl156iSpVqvD6669Tv359xo0bx5QpU/jss8+4+uqrueOOO8jIyADg0UcfJScnhwcffJD8/Hxat27N66+/DkC/fv04fvw4L774Irm5uSQkJDB16lQAhg4dSl5eHs8//zy5ubk0atSIDz/8kPDw8ECbbrzxRkaNGkV2djY33HADzz77LABRUVH07duXxx57jCeffLLU5xIbG8utt95KZGQkr7zySqnFvZs1a8bEiROZNm0aEydOpEaNGkyePJnWrVuf8/N7Lhw5cgSHw4HRaOTNN98kKSmJl19+mYKCgsD84oxGY4lRi4W4kkjx5ktDTIhJXgshxHmnUUqrpnsJys3NpWXLlqxbt46QkJCL3Zxzw+cFxxHI3qUWN7dUCi5iXpqc3XDgSzjyS2FNKCDUDvUegJhOF65LneJVC7Z7CkcaRCks3m4DYwW1jpYhVB1FULr5XZG8Pi8urwun1xmou+TyutTi2e58HG5HUNaTV/EWZaGgZpmUNunOc6F9h9vB8bzjHMs9xvG84yTnJZeYspxZZd6fTqMj0hJJlCWKCpYKgftRligizZHqY3MUkZZIIs2RJerdXOn8WUvFs9pOvH/ix5BWqy01OFS8WLm/vlDxYGXxQOWJ25+tgoIC9u/fT1xc3CVdi+hSFx8fz6effnrJ1Y+82K9vZmYm4eHhgWy5hQsX8tRTT5GQkED9+vWDAnSvv/46e/fu5d133z3tfq/I707iP+FyLkAN0DU+hqeuqc8NU5ddtnWL+jatzNTbW8hrcQloWCWMnx/pdLGbIcR5IRlSF5NWB7bqYAyHrB2Qn6R24dOfYuSc0HrQeCzUvV8NTCXNh5xdsH4ERDQF+zCIugCjMGh0arBJX/gFV1HA5wRPPuTuVh/rTOpyc0UwRBR29bOc/7aJc8Kn+NSAk8eJ06vWZHJ5XeS6c8lz5VHgKVADToV1nPx0Wl1g5DKDzoDFYAkEm843RVHIKMjgSM4RjuUeC9weyzum3uYeI9tZti8jRp2RGGuMOtliqGCtQLQlmgrWCoEp2hJNuDn8P9Mlzq94kKm0qXiNLv8IgIFAkVYXGDGv+Ch6/uWlBinPUVBJiEtVRERE0OM6dergdDqJiYkhNTU1aFlqaioVK1a8gK0T4sK73AtQ14k59WAXlxN5LYQQ55MEpC4FhjCIagH6UMjdo2YemaJPvY2lEjQYAXWGwP7P4eBXkLkJVt8LMR3A/giE1rkw7Qc1C0pnLqwpFaXO8xaoIw1m7VQf6yxq5pQlVh1BUB+qBuXEReNTfDg9Tgo8BYGgk8PjIMeZQ547L9DFzu11BwIMBq0BvVavdoMy2DCYDRck2OSX787ncPZhDuccJik7iSM5Rzicc5gjOUc4knMEp9d52n3YDDZibbHEhsRS0VaRWJt6659irDGEmcKu3EEXTqL4qIQenyeogHzxDDcNmqBgkV6nx2awYdKro+aZdGr2kkFnCKznv24uRBacEJeTZcuW8eSTT/LHH38ECrbv2LGDiIgIWrZsycyZM1EUtUC+oiisX7+eBx544CK3WgghhBDi7ElA6lKhNUB4A7UmU+Y2KDgO5tjTb2eMgPhhUPM22PsBJH0HKcshdSXUuBXq3hdcD+pC8geoTKh1p7z54EpVC7RrjWr2lKWSWijdGKGeA3FeuL3uQGHwAk8B+e58sp3Z5LpycflcuDwuvD5vINhg0pnU7Ca9hTBj2AUfXS3XlcuhrEMcyjpEUnYSidmJJGYnkpSdRLoj/ZTbatAQY4uhckhldQqtTKwtlkohlQKTf/j5/5LiNb1ODDb5i3v7a3b5J5PeRIQ+AovBgkVvUbPe/NlvxW71Wv1/LngnzsyJg6EIaN68OSaTieeee46HH36YxMREJk6cyD333MO1117L5MmTGT9+PAMGDODrr7/G4XBw3XXXXexmCyGEEEKcNQlIXUo0GrBWA60JMjapBc8tlcu2rTkGGo6GWnfArmlwfKmaNXXkV7UbX7U+aje7i0WjDe7i53OBO1ftqogGDCFqAM5UAYyRanc/UW4ur4sCTwEOtwOHx0GuK5dsZzYOtyPQxU5BQavRBmrxWPVqke0LmeUE6gh1SdlJHMg8wMGsgxzMPKgGobIPnTboFG4Kp2pYVaqGqlOV0CpUCa1C1dCqVAqpdMEDaJeCE0cr9N/66zP5g03+IFKoKRSbwYbVYFWLfOsMgWLf/scX+poQ4r8oJCSEDz/8kFdeeYX//e9/2Gw2BgwYwD333INGo+G9995j7NixfPPNN8THx/P+++9jtZ6ia78QQgghxGVCfm1ciswxah2ojI2Qf0QNSpU1+8BWA5q/rmZI7ZgMefth28tqralGz6k1qC4FWiOYotTJ51G79uXuhZy9RXWnzDESnDoJr88byHRyeBzkOnPJdGaS78rH6XUGAhF6rV4tAK0zEWWJwqA1XPBMlgJPAQcyD7AvYx/7M/dzIPMA+zP3k5iVqHYDO4loSzTVw6pTPbw6NcJrUD2sOtXCqlE1tCqhpouU9XcR+Wt6eXyeQAH54gXBdVpdINhk1puJtkZjM9gChcD9ASf/9F+reyXEpaxevXp8/PHHpS5r0qQJ8+fPv8AtEkIIIYQ4/yQgdakyRat1pTI2qV3cLFXLN1pdhbbQ4Ss49A3seQ+ytsE/d6oZVHXvK6z1dInQ6tXC7sZwtWufJ1cNpOXuK+zWV7koOPUfzNjw+DzkufJweBzku/PJdGSS5cwK1HxSFDXjyaQ3YdabsRltGHXG0+/4PLTzYOZB9mTsYU/6HvZm7GVfxj4OZx9Wu4SVwqK3UDOiJjXD1alGeA1qRdSiWli1/2S3Oo/Pg9vrVmt3+dRbf+0mjVaDUatmMJn0JipYK6gBJ4M5EHT0B5v+ixliQgghhBBCiMvLf+/X/eXEGAmRhZlSjmNgLWP3PT+tHmr9H1S6Gna8DseXwP5P4dhitXtfhbbnpdlnRaNVi7wbwtTglDsHcnarkzEcLFXU4JQhXF33CuPxech355PnyiPPlUdGQQbZzuxAdzs0BDJgQo2hVLBUuCi1ezIcGfyb9i970vewK20Xe9L3sC9zX9Boe8WFm8KJi4gjLjKO2pG1iYuIo1ZELWJtsf+52kPFRy90+9w4PU58ig+Foow2g85AuCmcEFNIoFi4SWcK3ErASQghhBBCCHG5k4DUpc4YDhGNIX09FKSowZjyMsdA84mQ/Bdsf03NuFo7DKr2hfqPX7yi56ej0RZlTvk84MmBrO2Qo1cLoVurgjFarT91GVIURQ0+udXgU7ojnSxnFg63A5fXFajzZNabibJEXZSsJ0VROJp7lJ2pO/k37V/+Tf2Xf9P+JSU/pdT1bQYbdSLrUCeqjnobWYe4yDiiLdH/ucCT2+vG6XUGute5vW5AzXTyZzOFm8IJCwvDarRi1puDgk4yEp0QQgghhBDiSiYBqcuBKQoiGkHGBnBlqiPSnYmKnSGqJex+Bw7OhsM/qLWmGj4DFTueyxafe1q9mjFmjFQLoruyIO0Y6G1giQVzZbWb4yXcpc/tdZPryiXPnUdWQRap+ak4PA4K3AUoKBh1Rix6y0UNPh3PO872lO1sT9nOjtQd7EzdSZYzq8S6GjRUD6tOveh61IuqF7itHFL5Pxd48gecnB4nTq8zMFqhvzi4SW+ioq0ioaZQzHpzIPBk1psl6CSEEEIIIYT4z7p0f72LYJZK4Guo1pTS6M88K0hvgwZPQqWesOUlyD8E6x+DKter8w1h57TZ54XWWJQp5smDvETIPagG6ixVwVLxkngeBZ4Ccl255LpySctPI7MgE4fbgdvnRqfRYTFYCDGEXLRud9nObLYlb2Nryla2JW9je+r2Uke302v11ImsQ3x0PPEV4omPjqdeVD1sRtsFb/PF5PF5KPAUlBp4MulMhJhCqGKqQogxBLPejEVvCRQU/68F6cSlJSkpiR49erB48WKqVat2ynVnz57NlClTcDqdzJkzh7p1657RMV0uF9999x233nrrGW0/b9483n77bZYsWXJG2wshhBBCiEufBKQuJ9bq4HVB1lY1E+hsCpNHNoMOX8Lud+HAl3DkF0hbo47EF9PhnDX5vNPb1MnnAXc2ZG6BXDOYKqpd+kzRoL0w9XYcbgc5rhxyXbkk5yWT48wh352PT/Fh1BmxGqzE2GLQX4QsLq/Py96MvWw+vpktyVvYkryFQ1mHSqyn0+ioG1WXq2KuokGFBjSo0IA6UXUuSsbWxaIoCk6vMxB8cnldoAG9Ro9Jb8JqsFI5tDKhplAsegsWQ1HgSYhLUeXKlfn777+Jioo67bqvv/46AwcO5H//+x+VKlU642P+/PPPvPvuu2cckBJCCCGEEFc+CUhdTjQaCK0N3gLI2aUGXM4muKEzQ/3HILY7bHlBzZZa9yhU6wfxj19etZm0erVroykKPA5wHIH8JDVryloNzBXPea0sp8dJtjObHFcOyXnJZDuzcbgdKCiYdGrgItwUflG6ZeW58tiSvIVNxzex6dgmtqZsJd+dX2K96mHVaVSxEQ1jGtIwpiH1outh1l9CIzCeZ16fVw08FQagPD5PUO2u2JBYIswRWAwWLHoLVoNVMp7EZUen0xETU7b6gzk5OSQkJFC1atWzOqailD6yphBCCCGEEH4SkLrcaLQQZgdvbuHIe1XVQNXZiGyiZkvtmg4Hv4ak79XaUo2ehwptwOmCvHzweMGngNcLiqIe12gAgx4MBjAZQH8JXFJ6izopXrXWVMYm0FvVbo+WKmoh9DMIEnl8HrKd2WQ7s0nNTyXDkUG+Ox8FBbPOjMVgIdIcifYijP6X7khnw7ENbDy2kfVH17M7fTc+xRe0js1go2HFhjSp2ITGsY1pGNOQCHPEBW/rxeLvcuefFEVBq9WqWU/6wqwnYyhWgzUQgJIaT+K0FAXySwZ7zyurtVzv+8W77PXo0YOJEycyc+ZMDhw4QJMmTXjttdeoXr068fHxANx9990kJCTw2WefsWvXLsaNG8emTZuoXLkyAwcO5I477gjs+/vvv+edd97h6NGjNGjQgOeff56cnBxGjx4NQHx8PIsXL6Zq1arMmDGDr776ioKCAlq1asXzzz9PlSpVADh+/DjPPvssa9euJS4uji5dupzDEyaEEEIIIS5Fl0D0QJSbzgjhV6n1k5ypZzbyXol9mqHBExDbDba8WDgS38Ng7g6+G8CpqMEoNKBRQPHfAjqdOpkMEGKDqHCwmNXJbFIDVhcjo0SjK5Y1lQd5h9TJGAW2GmrW1Cm6PSqKEihAnu5IJyU/hTxXHh6fB6POSIgx5KJlQKXmp7Lu6DrWH13PuqPrOJB5oMQ6VUKq0LRSU5rGqlPtyNr/mQCLP/OpwFOAw+MIBJ8seguhplCqh1Un1KQGn6wGdYQ7yXoS5aYo0LEj/PPPhT1uhw6wbNkZv69OmzaNcePGER0dzaOPPsqbb77J5MmT+fvvv+nYsSPTpk0jISGBgoIC7r33Xvr378+4cePYt28fY8aMwWazceONN7Js2TKeffZZnn32Wdq3b89nn33G/fffz+LFi3nmmWf46KOP+Pbbb4mKiuLzzz/nxx9/ZPLkyVSoUIGPPvqIIUOG8OOPP2IwGHj00UexWq3MmTOH3bt38+yzzxIZGXmOT5wQQgghhLiUSEDqcmUIg/CGkL4O3DnnpjtafgHkVgL9SFC+Bs0/ULAENJsh4j6wNCh9O68PPB5wuSEtE46lqD/UjIUZUyYj2CxqsMpiBotJDVSZTRcuUFW81pQrQz1v+lC1O5+lEhjCQaPB7XWT7cwmsyCT43nHySrIosBTgE6jw2a0UdFW8aLUgMosyGTtkbWsPbKWNUfWcDDrYIl16kbVpXml5jSv1JxmlZpR0VbxgrfzYvDXfHK4HTg8DrXbXWHwyWqwUi2sGmHmsKDgkxDnzGUYyBw8eDDt2rUD4Pbbb+eLL74ACHTrCw8PJyIigjlz5hAdHc1jjz0GQK1atTh8+DCffvopN954I7Nnz6Z3797cfvvtADz99NMYDAaysrIIDQ0N6ir4wQcfMHbsWNq0aQPASy+9RMeOHVm2bBnVq1dnw4YNLF26lCpVqlCvXj22bt3KggULLuRpEeKK4PUp6LSX3/uSEEKI/yYJSF3OLJUgNF4t5K0zqaPPnYk8ByQdhSPJatc8qwUq3geeTpD1AfhSIWsCuHtB6C2gMQVvr9OqWVsmI4QWjrymKOD2qJPLrR7j8HE1o0qrUdc1myDECmEhYDaDuXCeyXj+fuRp9WpGmaKoRdCzd+LI3E6WxkYaZo67XeR6HPh8PiwGC2GmsIsS2CnwFLDh6AZWH1nNqsOr2JW2K2i5Bg32aDstKregZeWWNK/UnHBz+AVv58Xg8XnId+fjcDtw+Vxo0GDSm7DoLcSGxBJuDsdmsEnmkzj/NBo1U+kS77J3opo1awbuh4SE4Ha7S11v37597Ny5k+bNmwfmeb1edDo103L//v0MGDAgsMxoNDJy5MgS+8nLy+PYsWM8/vjjaLVFXZoLCgo4cOAATqeTiIiIQPc9gMaNG0tASogzoNNqePTrDexJzr3YTTkjXeNjeOqa+he7GUIIIS4QCUhd7kLiwJMDufvBVl2tMVVWXi8cTYG9hyAnFyLCoGqloh86ukZQ4RXI+QIcf0H+QnBugvB7wBh/6n3760sZDYDlhOP6wOVSa1MdS4XEo6jDmOnU9c0mCA+B0JCiwJXZVLivcyPX7SDTVUByfi6peSnkO1LRAjZrDLGhceitMWc3imE5+RQfu9J2sTJpJSuTVrLp+CbcvuAfiXUi69C6SmtaVWlFi8otCDOFXbD2XSz+7Cd/AMqreDFoDVgMFmJsMURbo7EZbNiMagDqYtTvEv9xGg3YbBe7FeViMJTtvdTj8dCuXTuef/75Upfry1gz0Ov1AvDWW28RFxcXtCw8PJwVK1aUKIJe1jYKIUrak5zLtiPZF7sZZ6ROzOX1fiqEEOLsSEDqcqfVQVh9NdunIFnNmiqLrBw1EHXkONiswYGooP1bIfxeMLeGrI/AewzSx4P1JNlSZaHTFtWYKs7jAacbHE61fV4fUNj1z1AYqAq1qRlc/mCXqTAzy2g4ZcaAoijkuvPJcOZwLD+VjIIc8j0FGLV6QowhRFqj0SqK2v0xZxc4ksBcCUzRoA85LxlbGY4MVh5eyT+J/7Dq8CrSHelBy2NtsbSp2oaEqgm0rtKaaGv0OW/Dpcbr8+LwOHC4HRR4CkADZr0Zi95CldAqhJvDCTGGYDPYMOjkB6sQ51NcXByLFy+mWrVqgayo77//ni1btvDcc89Rs2ZNdu7cGVjf6/Vy9dVX8/rrrwdlJoaFhREdHU1KSgpdu3YFwOVyMWLECIYOHYrdbicrK4uDBw8Gsrd27Nhx4Z6oEEIIIYS4KCQgdSXQW9Qi5+lr1cCU4RSZM4oCScdg1wFwOiE2Ri06fjqmZlBhAuR8WSxbagOEDQXTVefoeejVyVYso8rf9c/lhnxHYaDKCxQWVDcUjvJXSp0qxWQkV+clw5XLkdwUMpzZOLxOzDoToQYr0ebw4O5cGsAUAUo4ePMh74AamDJGq938jBGgOfM/GZ/iY3vKdpYnLmd54nJ2pOxAoSgrwKK30KpKK9pWa0vbqm2pEV7jiu9u5u9+l+/Ox+V1BWo/RZgjqGCtQIgphBBjiGQ/CXER9O3bl7fffpvnn3+eIUOGkJSUxPjx4xk8eDAAd911F0OGDKFVq1a0aNGCzz77DEVRaNiwISkpKWRlZXHgwAGqVavGoEGDePPNN4mOjqZ27drMmDGD9evXM378eMLDw2nXrh3PPPMMY8aMISkpic8//xzbZZZ5JoQQQgghykcCUlcKcwyE2gvrSZlLryfl8cC+RDUzymqB6NjyHUNrK5Yt9TF4kyFjAli6QegANZvqXAvq+lfK/t0ecLuL6lQlHSfP5yRdcXBU6yBN58Jh1GKxhRFmC6eiJRS0BrWW1MmCPRpNURF0rxOcKVBwHIzhataUMbLM3flyXbmsTFrJ34f+5p+kf0pkQdmj7LSv3p621drSNLbpFZ/14/a6AwEot8+NXqvHarBSKaQS0dZoQoxqAEoKjwtx8YWEhDBz5kxeeeUVbrzxRiIiIrjjjju4//77AWjdujVjx45l+vTppKSk0KhRI959913MZjNt27alZs2a9OnThy+//JKhQ4eSl5fH888/T25uLo0aNeLDDz8kPFytfTdlyhTGjBnDgAEDqFKlCnfddRfz5s27mE9fCCGEEEKcZxKQupLYaoErG/IPgLV6cMDFUQC79sOho1AhsmR3ufIwNYMKr0LObHAsBsdScG6EsLvB3PLsnkN5GfRg0FNg1pHuzuOYK58UTw757gJMXg1hPj0V87SQnQO+bLWgusEABp1aSN1mBpNJzcwyGgL7o7B7CjoT6GLA51VrdWXtVDPSTBXBXEEdqe+EwNbh7MMsO7SMvw79xboj6/Aq3sAym8FG22pt6VC9A+2qtSPGFnMhz9YFd2IAyl//qVp4NaIsUYQYQwg1hl7xgTghLqZq1arx77//AgRu/W666SZuuummwOMTlzds2DAwCl9pbr75Zm6++eYS8yMiIkoElB5//HEef/zxUvcTGRnJ22+/HTRv2LBhJz2uEEIIIYS4/ElA6kqi1UF4PHiy1Ywefz2p7FzYvgdS06FSGbvonfZYFggfBJY2kPUheI9D5ptgagFhA0F3/usdeRQvGe48kt3ZHHVlkuMtwKDREaazEG0OKb27m8+nZlV5POp5Sc9Uu/6hFGZGFQakTCawFhZTNxRmaBktYAoFpQAch6DgCBgjUUwV2ZF9jD8OLuPPg3+yN2Nv0CFrhtekU41OdKzRkWaVmqHXXrl/dh6fhzxXXlAAymq0Uj28elEAyhR6RZ8DIYQQQgghhBCnJ78KrzR6a2E9qTVqPal8DWzdpdZeqhIL2nNch8fYQB2JL/c7yPsFnOshdRuE/E8tfK7Rndl+8xxwLBnSstSgUVomZGSh5OXjyc3FnZuLJy8Po9tNdZ+POK+C1qegQYOi16HotCg6LT6jAZ/ZiNdiVG+tZjyhFjxhNvU23IY7IgR3ZChem0mtT+XxqsO4Z2WrASw0an0pf70qswmPSc86x37+SNvMn2k7SXblBJqu0+hoWqkpnWt0pnPNztQIr3H25/kS5fV5yXfnk+fOw+V1Bbrg+TOgQo2hEoASQgghhBBCCFGC/Eq8EpljILQ+HFgOe7Mgzw2VK56XkeIA0Bgh9FYwt4fsj8G9q7D4+TK1G58x/uTbZmbDnoPqdOCwOurf4WOQmVPq6hrAUDidaz6DHndkCK4K4bhiwnHFROCKCccZG4mzYgTZJoWV2f+y9PBOljn2ku1zBra1aPS0M1eja2g8HSq1JDy6LtiiwWuGnJyikQK1Zxigu0T4FB8Ot4M8dx5OjxONVoNNb6NySGWirdGEmkKlC54QQgghhBBCiNOSgNSVyhUGB5yQdgRqxp+/YFRxhmoQ9aw6Cl/O1+BJhPSXwdxOLXpOOOw+CFv+Vad/90Fqxkl3p0SE4o0KpyDCSna4iZxwE74QCwZbCFqbDZ/FhM/gz4bSqfWhAI3Xh8bjVW9dHnQFLrQOJ7oCF7q8AvQ5+ehzHOhy8jFk5WHIyEGf40Dr9mBKzsSUnBloQ64RfqkHcxvAz3bIK1YrPtprpLu2Bl2iGtI89ipMaMGZB6nJcOyI2q3RGAHmcDDa1O6AJiNYrGCxFAapjIXdAY2gN1yY16mcCjwF5LpycbgdaDQaLAYLkZZIYm2xhJpCCTOFYdSVUkRfCCGEEEIIIYQ4CQlIXYnS02HTZvBGQPU4cKWrWVMXgkYL1q5qcfOcb2H/Eti4AjathF06KPCU3KZqJahXE+KqQbXKuKtUICXGQqLOQao7G7fiJVRnIUxnRqs5x10O/c12ezBk5GJIz8aVmspfeXv41XCYJeGZFOiUwHrVs+CmHerU4ZALnbIH2IPP+DMFVSpQUD2GgmoVcFSPoaCKloLQbNC6wecAjxXcZrXAutej1q7SALrCulV6PZjMasF5s1l97J8MhRlWFyBo5fa6yXPnkevKxaf4MOvNhBpDqR1ZmzBTGGGmMCwGy3ltgxBCCCGEEEKIK5sEpK40mZmwaRPk5UG1WuCOhKwd4M4BQ+j5P77PB1t3w1+rYdVWOOJfoAAesGngqurQrC00tEOdGmBVgxvZHgfHXVkkOtPI9mZi8OmI0Nswa89/969crZdlhkMsMm5lRegeXCFFgbNqxih6hF/F1dpatDAYsdRLx2RLI7tyGuYjqZiOpKF1ebAeOIb1wLHg06HX4awShaNaJI7qFXDEVcVRtx6uqjXAYFPPi8dTNOXkQEaGWsvKT0NwYMpsBqtNzbbSG4q6AxqM6jrlDFj5FB/57nxyXbm4vC4MWgM2o406UXWIskQRZgrDZrCVXiReCCGEEEIIIYQ4AxKQupJkZ6vBqJwcqFJFDUwYIyCkFmT9C1oj6Ezn/riKAtt2wx+r4K81kFasG55eB43joUU42LdDlSzQHgK9FkJr4zWaSHNlcdiZwTF3Fg6vi3C9harGiPOWDeWX73Xyd/YuFmVuZXn2blxKURCqhimanhEN6RHeELulUiAYk18Z8hvWCt6R14cpOQNzUirmpBTMiSlYEpMxJ6agK3BhOZSC5VAKsKtoE7MBR41YHHHVcdSJw1GnFo646ngjIko2VPEVBay8XjXomJoKvsLMrRMDVharGuQzmooKsZ8QsDqxG57VYCU2JJYYa0wgC0p3mde7EkIIIYQQQghx6ZKA1JUiN1cNRmVmQtWqwVky5krgyYe8g2rXPc05etmT0+C3ZbBwGRxJLppvs0KHFtChJbRoGMiAQnFB3kLI+wE8ByDjVXK0cfyrSSBDW4VInZWKhrBz07aTKPC5+Sd7N79lbmFZ1i6cijuwzB+EujqiEXXNsWXPCNJpcVaOxlk5mqzWxQq4+3wYU7LUANWh41gOJmM5dBxzUiq6Ajchu5II2ZUErAhs4oqJwhFXnfza1XHUroGjTg0KqlVSg0mGk9RpKh6w8nggPQ2SC7sEAmjAo9OQq3GTp/XiNeoxW8MJC4mmTmglwq1RhFmjMFlD1VpWBilILoQQQgghhBDi/JKA1JUgPx82b4a0tJLBKFAf22qC1wUFx8BcUa31dCa8Xli+Hn5eCuu2qtlRoNY96tgSurSBlo3UbmQn0hjJtVxNMg0w5P9IVd9GInz76cR+MrFzRNcDB5XPrF2n4PZ5WJWzl4WZW/kzawf5PldgWVVjJL0iGtEzolFQJtQ5odXiio3EFRtJdit70XyvF/ORdCwHj2M5eAzL/iNYDiZjSsnGmJKOMSWd8NWbAqv7DAYctariqF1dzaiqXYP8OjXwhhd2wdRoSwSsfIqPfK+TPK8Dp7cAvQ9sipE4TQhRPjPh2UZs2To0mlQgFbRaNRBlNILJBKGhYLUWBaiK17HyP9ZJBpUQQgghhBBCiDMjAanLncOhBqOOH4dq1dTAQmm0egiJA58TnKlqUKo80rPgl6Xw45LgkfGa1odru0CnVmpQqhSKopDhyeOwK4MjrgzyvE7CDVeTrulMVc9fRHs2EuHdRYR3F5k6O8cMncnT1Shf+07gVXyszz3Ab5lbWJK5nSyvI7CskiGcqyMacXVkIxpYqlz42kg6nVr8vHoMGR0bBWZr8/Kx7D+Edd8RLInpWA6lYzmYjK7AhW33AWy7DwTtxhUdURigUgNVWbUqk1olnDydFxSw6M1UMIZT0RRHmN5GmN6GXnuSP3mvF9xudcrPh6wsNdsKioKOOl1RIEqvV4NVFgvYbGpdK6OxKKDlvy91p4QQF8i8efN4++23WbJkycVuihBCCHHOxISY8PoUdNrL+3v1lfAcxLknAanLWUEBbNkCR4+qmVEnC0b56c0QWlstcu7KVOtLnc6eg/Dtr7B0JXgKC21HhMJ1XeH6LlAl9qSb+hQfae5cEp1pHHVl4lF8ROitVDCpmT0ebBzU3cgxQycqu5YS5d0SCEzlaGtx1NCZHF2dMgc1FEVhS34iv2VsZVHmVtI8uYFl0fqQQBCqsbXaea9PdSZ8Nit5jeqT16g+KG7wFoC7AGNqPtakHCyJWVgOHMe6/zCmoykY0zIxpmUSvnZL0T50WtzVq6LUrYOunh1DvfpQLxoqhZ36POp06mQuPagIFNWw8ncNzM9X65a53UVBK42mKFhlNKr7s9nUwJU/A+vE29Ndt0IIIYQQQvxHhVn06LQaHv16A3uSc0+/wSWobsUQ3hrQ/GI3Q1yCJCB1uXI6Yds2SEpSg1Fl7T5lCIOQOpCz8+Qj7/l8sGYzzPkVNmwvmt+gLvTrCV0SSu+SV8ir+Eh2Z3OwIJVkdxYatETprZi1pddAcmqjOWC+maO+bsS6/ybas5FQ3wFCnQdwaCpy3NCOdH0TFE3JYyqKwu6C4yzM2MyizK0ccWUGloXpLHQPv4prIhvTIqQWunMVhPL5wO0BlwtcbvWxRqMGVvyTsXD0uzMNtmgM6gh6+lBc1Vy4KkeS0dyJAzt5Gi0up5aIo7nEJOVQ4VAaoQeOYtp3AG1OLqYDiXAgEX7/o2h/NhvUqVNyiooqe5v8hdNNpyiMryhqgMrlUoNWGRmQkqIGshSlKChWvBugxQIhIWoXQX92VfEsKwlYCSGEEEKI/7g9yblsO5J9sZshxDklAanLkcsF27fDwYNqMEpfzpfRXAGUupCzG9waMISo8z0eNRPq65/gwGF1nlarBqBuvg7q1z7lbt0+D8fd2RwoSCHNk4seLTH6MIwn6yZ2Aqc2mkOmfhw1dCXWvZwKnvVYlGRqub6nqmsRqYbWpOhb4daGc7Agld8yt7AwYwsHnKmBfVi1RrqEN+CaiEa0Ca2DoYzHPinFB67C4JPTVRRUMRZm90SGg8WkjnjnKuzy5iwMyOTmFVvfqG5jKF/tJZfPQ57PTZ7Pi6LRYdFAOBoqWo2Ex1cnrEk0RkusGmjU2dTulHv2BE8HDkBentq1c/Pm4ANEREDt2mpwqnZtiItTbyMjz6y7XeC5nqQAO6gBPI+nqItgVpY6aqDHU3RMf7DK3y0wNFS9NZmCg1YGg3QLFBeVoijku/Mv6DGtBmu5uhonJSXRo0cPHnnkEWbNmkWfPn1o164dU6ZM4fDhw9SrV4+nn36ahIQEADweD1OnTmXevHk4HA46dOjAiy++SGRkJE6nk6lTp/LTTz+RlZVF27ZtGTt2LJUrV+bxxx/HaDTy2muvBY79xBNPYDabGT9+PEePHuXFF19kxYoVREdHc9NNN/Hggw+i0+mYN28e33zzDdHR0axcuZKxY8fSp08fZsyYwVdffUVBQQGtWrXi+eefp0qVKgAcP36cZ599lrVr1xIXF0eXLl3O7YkWQgghhBDnlQSkLjdutxqM2r8fqlQpfzDKz1JJDbbk7FYDKL+vhW9+geOFwR2rGa7vBjf1gtgKp9yV0+fmuCuL/QWppHtyMWsMVDKEo9ecWdFrtzacJNP1HDV2I9qznorulZiULNz5f7Ig90++zDGxyekMrG/U6OkQZueayEZ0DLOfNBOrTDweKHAVZvh4QatRgx4mI0RHqCMImoxgPk2NJKcLCpzqbb4DsvPUfeY7wOsDlGKFwvVg1INGi0fxku91ke9z4lI8GDV6QnRm6pljiTTYCNOZsWpN6o9RxaOOnph7QD2m3gzmCGhZF9o0A71VLXju8ahBqX37YO/eoikpSR2Vcf16dSouPFwNTNWqpU5xceoUG3v2GUta7amDVoqittnlUq/3jAy1RprPpy7XaIqCVUajmv0VGqp2DywerDKZpPC6OK8URaHjxx35J/GfC3rcDtU7sGzwsnLXv1u/fj1z584lPz+f//u//+PFF1+kSZMm/Pnnn9x777388MMP1KxZk7feeovvvvuOV155hSpVqjB27FjGjh3L1KlTGTt2LOvXr+e1114jIiKCSZMm8dBDDzF37lxuuOEGnnnmGdxuNwaDAZfLxdKlS3n77bdRFIVhw4ZRv3595s+fT0pKCs8//zwajYaHH34YgA0bNvDAAw8wYsQIIiMj+fzzz/nxxx+ZPHkyFSpU4KOPPmLIkCH8+OOPGAwGHn30UaxWK3PmzGH37t08++yzREZGno9TLoQQQgghzgMJSF1OnM6iYFTlyuqP8rPhDYXvN8I38yGzsD9yRBj87xro2wNCbKfc3OF1cdSVyYGCFDI9+dh0JqoaI89Z1zivxsI2GvNWLizJWMsGR1rhEic6oIdVxw0RdWkd2RWjoWr5D+DPfnI6C7veKWpgyGiACpEQagOzSZ1M5SzQbTKqU3Guwswpp1s9Zp4DX24ejvwc8jLzKPC60Gp1hJhtVDVHEG2LItRoJVRnKf2cavRqZpQhTH0u3gJwpULBEdAaQWcBUzToQ6BWVTULqlevou0LCtRA1d69arBq3z712jp8WM1a2rBBnYozm6FmTTVIVbNm8GSxlP38nIo/4HSy69vnK8qucjohN1cNrhUvvm40qgE/f9F1fw0r/1S8W+CZBnWFADRcPhl6d999NzVq1OCpp57i1ltvpU+fPgAMHDiQNWvW8NVXXzFy5Ei++eYbRo4cSefOnQF48cUX+fXXX8nKyuL7779n5syZtG3bFoBJkybRtWtXli9fTufOnfH5fKxatYqOHTvy999/YzabadOmDStXruTIkSPMmTMHrVZL7dq1GTlyJKNHjw4EpDQaDQ8++CDmwlp2H3zwAWPHjqVNmzYAvPTSS3Ts2JFly5ZRvXp1NmzYwNKlS6lSpQr16tVj69atLFiw4EKfViGEEEIIcYbkl9jlwl/APDFRzYw6m2BUZiZ89RXMnq3+mAeIjYRbroXre5YMpJwgz1vAUWcmB51pZHnzCdWaqW6KOmeFwlPdOSzN2sGijK1syDuIghpo0KChla0Kt4SauMt6hEq6AuBfcP1LnqcqafqmZOgb4dGElL5jn0/NWHIWZt5AYeFtE8RGqzWMLIUBqPORWWM0oBj0FJid5LnBEaqAYsKq6In06YnFSliBQmiuB6PTA1kuUHKA3MKMH0NR178TabRqRpTeWvhcXeB1QN4BUACdSV1miAS9Tb1vMkP9+upUnD9QdeCAGqDyT4mJ6rJ//1WnE1WsCDVqBE/Vq6vX66nqTpWXVluUAVWa4t0Bc3LUDCuPpyhgBaUHrUJCSq9hJVlW4iQ0Gg3LBi+75Lvs+VWtqgbu9+7dy6+//srs2bMDy9xuNx07diQjI4PMzEwaNmwYWFa3bl2GDx/Opk2b8Pl8NG3aNLAsIiKCuLg49u7dS6dOnejZsye//fYbHTt25LfffuOaa65Bp9Oxd+9eMjMzadmyZWBbn89HQUEBGRnqyK3R0dGBYFReXh7Hjh3j8ccfR1ssK7OgoIADBw7gdDqJiIgIdN8DaNy4sQSkhBBCCCEuIxKQuhw4HGrtn8OHz6xmlN+xY/DFFzB/vhpYALVb1t13Q8dG4DwEWgdQekAqx+PgiCuDQ840cr1OwnRmahijz+iH0YnS3LkszdrOosytbMg9iI+i4EETW3V6RTSmR8RVxBjCADiiuMn37iTas5Ew715svsPYXIep7vqVHG0tMvUNySAej8ugBqC8PtCgBpvCQiA8BCxmdSpv9lM5KIqC0+siz1OAw12AgoJZZyLMaKN2WFXCTDbCjDYsenPxjdTufgVOyC9QR7PLzFW7+2Vlq5lWUDianaEoG6t44ERrVCdDuLo/nxO8+eBUf/ihM6kZVMaIwgCVBbQW0BaOtFdaoMrjUa/BgwfVANXBg0VTZiYkJ6vT2rXB22k0UKmSGpyqWhWqVQuebKfOxCs3f/H1U2VslRa08gcp/fvwZ1NZrepks5UMVkmG1X+eRqPBZjzH1/B5YioM4nq9Xu69915uvPHGoOVmsxn9Ka5n00mCwF6vF19hl9rrr7+e0aNH89xzz7FkyRKmT58OqHWpateuzYwZM0psHxoaWmL/Xq86qutbb71FXFxc0Prh4eGsWLECpXiQGTCcbdawEEIIIYS4oOSX1KUuJwe2blWDSWcajDpwAD75BH79Vf0hDtCgAQwZAl26FNUEKrBAzl5wpoExKhCkyfTkcdiZQZIrnXyvkwidjerGqLMORCW7slmStZ0lmdvYkHcokAkF0NBalZ4RDbk6ohGVjBEltlU0BjL0jcnQN0av5BLl2UKUeyM25Shhvv2EufZTXfmJPE01MsMakBXenIKQmmC1nHKEwLN1YgDKpyiY9UZCjFZqhlYi3BhCqNGGVW8++fnTaIqCZZHhRfPdHnAUqIEqRwHk5kNWjhpwy84Nrk1lLJZNZdCDzqxOBooCVD4H5GWqGVTawnX0oerIizpL4WQKLjTu755X2JUnICsLDh1Sp8RENUh16JDalS4vD44eVafSRESo13aVKkW3/vuVKp1919TSnC5o5Q9YuVzBRdeLb+8fKdBkUoNVNltwsMp/DP96MlqguETExcWRlJREzZo1A/MmTpxIXFwct9xyC5GRkezcuZP4+HgAduzYwf33388vv/yCXq9n48aNdOrUCYCMjAwOHjwYCBq1b98er9fLxx9/jNlsplWrVoFjHjlyhKioqEAAavny5cybN4+JEyeWaGNYWBjR0dGkpKTQtWtXAFwuFyNGjGDo0KHY7XaysrI4ePBg4Hns2LHj/JwwIYQQQghxXkhA6lKWnAzbtkF2tvrjvDxdhxQFNm2Czz+HP/8s6q7UsiUMHgxt2pTMCjJXBI0RcvagOI6TrreQ6MrkiCsTl89NpN5GBVPoWT2lQ840lmZu54+sHWzJTwpadpWlCj0jG9EzvCFVTKcpTFus/pPH5SbZZyfZeBVGk4MI034ivdsJcR0khCRCnElUS16EMzOa7LCmZIc2Iif0Krz6s89q8Ck+Cjwu8j0FODxOKMyAshkt1AiJJdwUSqjBis1gOftMMoNeHRExrFiXREUpKqBe4FJrKmXnQU6uOj8vvzDzR1NUoN3gD1iFgiGi8Im4wOsEZwo4CgNHOlNhV78w9bhac9G8E7tnhodD48bqVJyiqNlHiYnqlJQUPGVmFk3btpV8zhoNxMSoNdP8U6VKwdO5zrCCsges3G41gy0rK7hboEajbq/TqZM/cGWxFI0WaDQGF2f3H1MCV+I8GzRoEHfccQeNGzema9euLFmyhFmzZvHJJ58AcNddd/HWW28RGxtLdHQ048ePp1mzZoSEhHDLLbcwbtw4xo0bR3h4OJMmTaJSpUp06NABAL1eT69evXj33Xe55ZZbAu97HTt2pGrVqjz11FM8/vjj5OTkMGbMGNq3b4/uJJ9tgwYN4s033yQ6OjqQXbV+/XrGjx9PeHg47dq145lnnmHMmDEkJSXx+eefYzsf7wdCCCGEEOK8kIDUpUhR1OyS7dvVukdVq5a9S5nHowagPv9crTnl17mzGog6MWBwAq8xlFRLRZLyj3A0axtenYUoYxTWwq5y5eVTfGzPP8xf2f/yR9ZO9hUkBy1vYqtOj/CGdI+4isqlZEIVNcxbVBDc5S4KrphN6iiANiuYTbgsJpK17UgGDK40wrM2EJ61ibCcbZhcacSkLiEmdQkKGvKtcWSHXkVuSH1yQ+rh01lP+3w8Pg8Oj5N8jxOn14UWDWa9iVCDldphVQk1Wgk5VwGostBoigqvF6co6nk6cbS/3Hy1G6CjQM2u8noBTWEhcX1hkMQKeh1ovWqgynEE8gtHuNMVdgXUhxQLUhlBa1Lnn/icNRqIilKnYnVnAnJz4cgRtSugfzpyRM2mOnxYDbD5uwJu2lT6OQgJUUf/O3GqWFG9jYm58N0CFUU9tx6POnm96nMtrZ6Vf1/+AJbBoHabNJvVroLFC7EXL8wuta3EGWrWrBkTJ05k2rRpTJw4kRo1ajB58mRat24NwH333UdOTg6PPfYYHo+Hrl27MmbMGABGjhzJa6+9xiOPPILL5aJ9+/bMmjULY7FRM2+44QZmz57NDTfcEJin0+l45513GDduHLfeeitWq5Vrr72WkSNHnrSdQ4cOJS8vj+eff57c3FwaNWrEhx9+SHi4mjk6ZcoUxowZw4ABA6hSpQp33XUX8+bNOx+nTAghhBBnISbEhNenoNNePoPBnMyV8jwuFRrlxCIMl6jc3FxatmzJunXrCAk5SdHqK4HLBXv2wO7d6g/tiIiybZeRAd99B99+C8ePq/OMRrjhBrjjDnVUtFMd1ucm2ZnOIccxUlyZaH1eohQnZldGYbZHKFC2zI18r5PVuftYlvUvf2fvIs2TG1imQ0ur0Di6hTegS3j9QE2oYEph4KmwALmiFBWytpnVDKFy1n/S+JyE5WwnLHsLoTnbsBQcOeGIGhyWGuSG2Mm11SXPVgenIYYCnwuHx4nD48Sn+NBpdVj1ZsKMNqLN4YQY1ACURW+6MAGoc8Ff3N3lVoNVLrdaUywnXw1auQuDKG4PoKhd+vR60GlBr4DWVzgp6rnXaAtH9TOCLkQNaPkDVIHpDIIn/uwqf3c/f6Dq2DH1Gj92TM0eLAubTQ1MVawIFSqo92Ni1PvFJ7P59Ps61/xBq+LBK38wy+cLzrryd//TFwYPLRY1aGU2FwWqite/8j8WZ62goID9+/cTFxcXKLwtrhxX6uv7n/nuJILcMHUZ246U8fPxEtO3aWWm3t7isn4OcGU8jyvhOcCV8TyupOfw6Ncb2JOce/oNLlF1K4bw1oDmF7sZVxT5pXIpSU2FXbvUH9oVK566KDMUdcubNw8WLSoqyhweDrfcok7R0afcRa4nn+PONBIdx8lw52DWGqlkjEKv1QOKOiqb4wg408EQpgYXSjRD4aAzleXZu1mevYsNeQdxK97AcpvWRLuwunQOq0/HMDth+uLPS1EDH87C7k8eT9EPb5MRKkSqdZ8sZjULyHBml6yiNZEV3pyscPUNxOBKJzRnG6E5OwjJ+xezMxmr4yBWx0EqpiwCwKm1km2pRb61FpqwePQRDbGExGEzWjHqLuPiuVptUUAv/IQumMUzq1yF2WguV2HNKocawPJ4oMAfsAIUFyj5RUEqLWqGldEABn+3P3NRXSqtEbSGwowqg1q/qrRgXvHsqmIjfgXJy1ODUydO/qyq48fVdfzTgQOnPjc2mxqYio5WpwoVitoQHQ2RkUW352rkQH+A6XR8vqKugh6P2lUwO7socOXn7y5YfDKbi7oLnhisKh7k0p/ktRBCCCGEEOIs7UnOvWyDauL8kIDUpcDlgn37YO9eNSBQrdqpu+McOwY//QQ//6zW5fG76iq49Va4+upT/lj2KT7S3dkccaRw1JlKvreAEJ2FquaK6IJqA2nAGK4Wuy5IBlcauHPBGEK6x8Xq3H2sytnL6px9HHdnBR2jqjGSDmF2OofH09JWC4M/wOX2qN3G/D+qoairWHQEhFgLu6CZwXx+Rr/zKT5ytDZSQppRYG6AK9qDxZNFrPP/2bvv+Cjq/H/grynb0khCQmgKIoYuBDCI4FH01K+HXc+OCKfcieCJBeFEUVQUvlgoiqhYsP4Q5FT8espZzoKInICoKL0FUkjfPjOf3x+fmdnZzaaXzSbv5z3mZuYzZT87bra8+Hw+cxiZvkNI8x1CgvcgHJoHme5fAPcvQOFH/GA5CUjuDSSdCiT10uenhA0CH9cEIXTXvmg0LRRUBRU+V/S5xxe6O6Ci8DGtyryAVg5oQUBT+J0OJQmwSfq4SU4eWtkTAUeyHlbJelBlAwSZr4vVvFUlJvI7RfbqVf1zcruBwkI+FRTw4LeoKLR+4gRf9/tDwdXBg7Vfq8REHkylp/OWjGlpfEpNjT4lJjbuNSKKofGmamIEV0Zrq2CQ36mzsJCvM71lm9Hy0Bjjyugy6HTy9w+H/ncYGW5FTjTmFSGEEEIIIaQBKJCKJVXlP4j37uXzjh15N71oCgqAzz7j048/hrrxuFzAuecCV1wBDBxY48O5FS+KAqU46ivAiUAZNGhIlVOQUdPYTQAgOVBi74Ctlfvx35Id2FqxB3v9J8J2sQsyhib1wFnJp+GslGz0kFMhKCoPKkoqAI3xMMK4A1zHVCDRpf/4tfPwqRnGxAmoQfjVAPz6XGMMgiDAIdrglO3ompSJVHsyEmxOJMh8kkSJj51U/jtQ9jNQ/htQ8Ru/A6FSCZRs45OVnAQk9uBTwslAQncgoRuf21LbRlgF8PAh2phVVpoWHlYFlVA3wEAA8Pp510CfFwj4AU8pECwAlCAfrN64VjaHPlaSDbA7AJsLsCcBdicgO3hYZQRWgqQvS1UHXDfugFdTt1XGeBBlhFUnTvCpuDi0XFISWleUUHh15Ej157WSJN56sbopObnqPDmZvyfUJ/Spa3AFhMIro4ugqoYGaDfCK+P6GOc2AixjwHabLTRIu9F1MHJcrGjztvI3QQhpM2hcEEIIIaRlUSAVC5rGA6YDB3iXIlmu2ipK0/g4Ups2Af/5D7BjR/g5hg0DLroIGD+ejyFTjYAWRHGgDPn+Ehz3F8GtepEgOpFhT4VdjN7tTGMaDnqPYXv5buyo2I0d5XtwwJtXZb9sRyZGJHTHCNcpGGI/GU5N5D9iKwBIlbzlk8MOZKTybnd2/Yer096krSoYYwhoQT18CiKgBaFqGgAGm2iDQ7Yh2ZaAk5KykGRPQILshEt2wCU7IEaGFwbRDqQO5JN5YYJA5X6gci9QuY/PK/byLo1KJQ+vyqLcKU5KAFydAWdnwNWFT85OgKMT4MwCnJm8FVpbIYo1t7IyqGoorLIuK4o+GLs+ppXfw4MrbwUQPKIPCq7xsa0g6CGJHoJINj6Wlc0B2Jz6AO02fXLw4MoMrWQ+N8qSkvhUy3hrZnhVXMynkpLQVFoamhtTSQlvfaWqoWPqQxB4vYxwyhpUGfPIKTExfNnpjB4AGeFVXakqf2+yDtju9fIB242yyGEJBSEUXlknYxwshyMUalm3RTvG2CaKMQu04mTYRVJP9N+VAIAkCnE9vsnYPpm45/y+sa4GIYQQUmcUSLWkQIC3rjh0iHe7k2V+FzCbjf+IO3CAB09btgCbN1f94Xr66TyAOuccoEuX6h9GC6IkWI6iQCmO+0+gMuiBIIjoICeho61D2ODbGtOQ5y/CrsoD+LVyP36p2IdfKw+gUvVUOW9vZ1cMdZ2KYc5TMNTZE2kQAeYF4AZEH5DaAUjMBFxJgMO4nX3TjLWkMQ1BTUFQVXj4pClQVAUMvMWTTZThkOxItiUgxZGIZFsinLIdTokHT00y5pNoA1Ky+WSl+gHPYcB9kE+ew4DnKOA9CvjyAdWjB1j7qj+3nAQ4MgBHRz63p/PJkcbntlTArk9yUtVWQPHICBhqam1lMFpaKWoovFL0ACQYAAI+3uLK5wP8XiBYDvhOAKoCqBrf1/zBqQ/ELuitfUTj7nYOHmbJdn3cKz0gkW2AZG2JJQIOEejaEeiaqf+3EEPnjBaU+Hy85VG0qbw8NI+c/H5e74oKPjWUKPJgKiEh1GIsMZG3sLSWG4OkG5Oxbow/ZSwbdwGsC00LtcQywizjrplGkBWtNRYQCrOMllmiGN5Ky3r3Qbs9FGQZ+xiTdd16LmNeBzYbfw/xeDxw1Ta+H4k7Hg//zDP+O5P2K57HNzk1s4nvKEsIIYQ0sxYNpPx+Px566CF88skncDqdmDx5MiZPntySVWh5msZbSRQW8tvYl5fzH0GaBvz2G28F9dNPwM6d/EeplcsFDB8OjBwJjBvH7woWBWMMlaoHZcFKnAiUoyhYgkrFA0BAspSArs5MCBBwIliGXZX7sd+bhz3uw9jtPoy9nsNwq74q53QINgxwnoTBzp44PfFUDEo5FamuNMCVACQmhLro2O2AxADmBvyFQLAC0HyAqPDBrTWxTndYUzUeOCmawoMnfVKZBgECH+dclGGXbHBIdmS4UsNCJ4dkg1NywCbFIGOVHHxcqeTeUZ6YH/AeA3zH+dxY9hUC/gI9sPLxFlZKJeA+UPvjCRK/66EtBbB1AOwpgJwC2JL08mQeWslJgJwYPkkJgJzAw7V4UteBvw2aHkIZoZWxrqqAEggNDh7089DHH+DLwUrAqwJqUG8NxEKBSligJenBk9FCS29tJRkte4wQS2+dJer1z7IDXbIAqXvoDoUQEQrJhPDlQJDf/bCikoc3lW4+lVeEugwaYZXbzfexzt3uUP0bG2pFMu7yZ4RTRmhlrEdOxphU1mVjrCrrutFqyunky8Z4V0ZwZYRagUBo2dgeGWZZuxpawywj6DLmNltobh3o3RJcSaKIVElCwfHjgKoiweXi4b41gDSWI+ek1WKMwePxoKCgAKmpqZCaoes4IYQQQgiJrkV/vS9cuBA7d+7Eq6++iry8PMyaNQtdu3bFBRdc0JLVaH5Ga4j9+3nQtHs3H2fmxAl+2/q9e/kPxUgOB9CvH5CTA5x5Jm8RFeVfazWmwa16Ual4URasQFGwFBVBD8oVN0qDFXCrXhT7S5HnLcBRXyGO+Atw0JcPt1Y1eAIAmyChl6ML+iedggEpvdGvYx+c2rE3ZGcCYLOHBh2XbTX8wErm3c9UL6BUAP4SsEAp1EABFE2FCkARZCgQ+cQ0aCx0ZzBJlCALEmySDJtkQwdHEhJlFxJsTthFGxySzQyj4uoOd5IDSOrJp2gY40GU/wTgLwpNgRIgUMzn/mIgWAoEygDVDTCVrwdLG14vwcaDKePOd7ILEJ18LunLkpPXX3ICon63PNFYt4fWRVuUuT18LrTwmEGiCNjFhrXQU1Xeqso61zRLmTFguD5X9DsRBoN8OagAig8IMIAplhZAGg+4VCPcYgAEgAmAEBGkCJYARdBDXZcIJAmA4OD/fcTOfF9R4gGZpHc9FMVQay5R5HewdPv4oPPG+F1eLy/z6pPHq5cbZVHWvXoLNFX/uw0E+BQZpDc1QQiFVNZ5ZHc/o5WUMVlbT1lDJiN8ihzXygifjC6BkfvYbOgsSUBGBgrc7lCgFa2+1ZVFC6oi96/rOeu7L6lRamoqOnfuHOtqEEIIIYS0Ky0WSHk8HqxZswYvvPACBgwYgAEDBmD37t1444034iOQUlXeusAYE8aY8vOBvDweNB0/HpoXFfEfqNWRZX5nsFNP5be0HzQIyM4OBVCMQQn6UVRRhHzfCRzz5OO4vxhHfQU4FjiBAn8xioLlKFMrUaZ6cEIpR6lWtZudlQgB3VxZ6JHcHaem9MRpaafitI7Z6JF+CmS7s9rAiTEGjWnQNAUqU6FqKjSmmcvWOYP+w1pIBGQ7ZFmBpAUgaz5IihsJYEgQNbgkG5yyAzbZAZvkgt2WAJvsgF1OgE1yhHUrbNMEgbdosiVXH1pZaQEeTAXLeLe0YLm+XMGDrWAFDwSDFYDi5gGWYkweQPPz87CgflwzhwlWghFQ6S2HBL3lkCAj7I561rkgWZat65JlMsqliHJjEsOXYS0TI7rcWbaZLaGM7n0iIItVj4EdEJyWMoHPNcbDJmPOoLe0AqCCh1IaAzRBD6sEQFMBDXpLLj38MronakYApodbRlAGxvczzsc0fbv+WEwDoA8WLwFIZkAyADgAOEN33TP+5kSj5ZdlLkC/HuCPH1B40BXUWyqZ60HAp9+FMaDw5WCQt0Dz64PaB4J8X79ebty10SgPBPjcwJjeis3fzC/Q2gkAugDolJCAYKdOess9/bUn60GgrAeIkmgpt6yLgmVdCN8mRemaKAp6y7vIroaS/tiCHqLp64LI/xFB0tdlmR8vWwahN1uDGeezhc5v7Gd9TNno2iroXViNclvE60MMvZYEWF5XxjZLmVEedoFre9+vZrso84C8gWw2G7WMIoQQQkitMpMcbeIGGK3pObRYILVr1y4oioKcnByzbNiwYVixYgU0TYMY61uHl5XBc9cMlB/ZC9XnheZ1Q/N5oXrc0NyVUAN+qCKgCjDnihiagpI+F4FAKhDoyMv8yQnwp6fAn5YMf4ck+JNd8KS44HHJ8LAAKrUyuLXPUFGwAZXHfahUfajQvKhQvfCw+v8Ac0gOZLky0DWpC7okdUXXlK7onNIV3VNOQtfkrpBFmYdLTAMDD5qKmAear9JsscQY0397hl6koiBCFERIosTnggRJlOCyuWAX7bDLdjglJ2ySDbIoQxZlc9kmGnMJohbgrahUL6B49a5qFbyLFAvwVkFM/5EN6D9crAGCjKpj9kSst2WinQ+C7ozefbNWmsLHtFI8fG78d1Ctk0+f+3n3S9XPyzQ/D8TM9UDE5OcDv2sBQA2AJysWLKh3hWv0VWhnLF35jB/2QPiyJIZCgbDJUiZErEcuM+uHklEWuQweQDgAJFm2GcdYuzVaj6mybj3Wpk+WczEGKOCtzAIMCOpTAICqlynQyzRAYUAQfFKYvq6F1oMsVG5uZ/xcQf1cimVdjbK/pe6SxwPpwAEQnQi9tZ5lbgRV1nm0MiHyGCHK+aLtr2+XRODKPwH3vRqLZ050remLLSGEENJcUlxy3N8Ao3enJDxzTU7tO7aQFgukCgsLkZaWBrvljk4ZGRnw+/0oLS1Fenp6S1Ulqp++Woszs16D56SmPrNHn46Hirz6VEd20Y5kRzKS7clIdiQjxZGCVGcqUp2pSHOmIdWZioyEDGQkZiDZlgxRECEIghkiCYIAEXzOBwC3QRKkUHgkybAJoWVJkMzwyQierHNZlM3lerdkEmXeVSySFuRBBwvqoYZ18uvhiB54MJXvx1QAWijAMlqGWFnHkYkm7F/po/yrfbU/4q27RfkhH3ZstP2qrVA1dWzAcdUSQ2NMNVZND6spvMua8d+RBaOUKaEyc1mNKFd5yyFjH6ZZyhUAmr5dDb0mIteZFlo3Xyf6ftDHHmKavqyF78MiX2N6osZUS1nka5BFX28QLfzQ1nAzsLrWoSF/AgYJPPhqLTTowRV4qBo5V+u4HjkZ57XOrdsi99Us+7Io2yL3s5aziPOyava1TnW9NmB62ByDF2jw3TYfSLX2MTjj/cs5QHeoI4QQUnfxfAOM1qbFAimv1xsWRgEw1wOBQEtVo1rOEaOQtK0DvEoZRCPAgQBJD3VEUYIoSvq6ZGkpJELSWwTJomwGPZIoQdZbBtklO2ySDQ7JYc6dNieckhMO2YEkexJSHClItCUi0Z6IZHsyUp2pSHGkIMWRAofs0Af25iFT5HKVAAqh5chtxtTqiLa6D7RthA0wQgbr3AgR9F9a1qAAzFIeERgwyy8zxkLbqxxrDRasZdZ1hO/HaksTqvkBF+2uY7UdU89dmuigGhjjGlnvyNYaEpUYMF9zltemWWaEXFFea1VeiwjNra/PsNefEWJFOYe1LjVuQ8T2mtYRWq+2zLJsHovwdbMOkftar2FdtoVtqGZb5N8Xs/ztW84ZuR52jGYeGnZdwo6x1Le6v+lo7xHV7hN53aKco6bnG1anyNNE2cfoBqox3lJM05+HxnhLMo3pb50sNIC+ptfBWNZY6Bqp1jLr+SzbjUup6duBUHdX41zGHADGXIq2Lh7G4Iz3L+d0hzpCCCGk5bVYIOVwOKoET8a6s663D29Gp2X2Qf79pajwV4CBmaEPgLAQyBoAWcsAtJ9xj2JNlMCbT8SxalttVVNeUyuvmo5r8mOshzfy+BYTL/UkhDSIEIO7q7aguB+DkxBCCCGkGi32LS4rKwslJSVQFAWyfvv2wsJCOJ1OpKSk1Ho803/8VlY2b3NwPWYKK9Pq3G+BEEIIIS2rhhuINKHExMSY/MNTY8bgbKnvTgDQM0WEFoiju+BGyHLx6xTPz4OeQ+vRFp5HW3gOQNt4HvQcWo+28Dx6pogt8r0AqNt3pxYLpPr16wdZlrFt2zYMHz4cALB161YMGjSoTgOau91uAMCYMWOatZ6EEEIIIZG2bt2KpKQmGHuvnhozBid9d6q7fQBei3UlGomeQ+vRFp5HW3gOQNt4HvQcWo+28Dz2ARi2oGUeqy7fnVoskHK5XLj00ksxb948PPbYYygoKMCqVauwYEHdrkanTp3w5ZdfxuxfKAkhhBDSfiUmxmaMocaMwUnfnQghhBASK3X57tSiAy/Mnj0b8+bNw0033YSkpCRMnz4d5513Xp2OFUURnTt3buYaEkIIIYS0Ho0Zg5O+OxFCCCGkNWvRQMrlcuGJJ57AE0880ZIPSwghhBASlxo7BichhBBCSGtV++BNhBBCCCEkJqxjcBrqMwYnIYQQQkhrRd9kCCGEEEJaKesYnDt27MDGjRuxatUqTJw4MdZVI4QQQghpFIEZ9wQmhBBCCCGtjtfrxbx58/DJJ58gKSkJU6ZMwaRJk2JdLUIIIYSQRqFAihBCCCGEEEIIIYS0KOqyRwghhBBCCCGEEEJaFAVShBBCCCGEEEIIIaRFUSBFCCGEEEIIIYQQQloUBVIA/H4/5syZg+HDh2P06NFYtWpVrKsUt/Lz8zFjxgzk5ubi7LPPxoIFC+D3+2Ndrbh366234r777ot1NeJaIBDAQw89hDPOOANnnXUWnnzySdAQeg1z7NgxTJ06FUOHDsX48ePxyiuvxLpKcScQCGDChAnYvHmzWXb48GFMmjQJQ4YMwYUXXoivv/46hjWMH9Gu5bZt23DNNdcgJycH559/PtasWRPDGpLm1hR/Tx9++CHOPfdcDB48GNOmTUNxcXFzV7tZNcXfxfDhw9GnT5+wye12N3fVm020a/LII49UeY6vv/56ted45ZVXcPbZZyMnJwdz5syB1+ttiao3q8jrct9991W5Jn369Kn2zp5lZWVV9h0xYkRLPoUmU9PvmPb6nlLTNWmv7yk1XRN6T2kARtjDDz/MLrroIrZz5072ySefsJycHPZ///d/sa5W3NE0jf35z39mf/nLX9jvv//OtmzZwv74xz+yxx9/PNZVi2sffvghy87OZrNmzYp1VeLa3Llz2Xnnnce2b9/Ovv32WzZixAj21ltvxbpacenPf/4z+/vf/87279/PPv30UzZ48GD2ySefxLpaccPn87Fp06ax7Oxs9t133zHG+PvnRRddxO666y62Z88etmLFCjZ48GB29OjRGNe2dYt2LQsKCtjw4cPZ4sWL2f79+9mHH37IBg0axD7//PPYVpY0i6b4e9q+fTs7/fTT2Xvvvcd+/fVXdsMNN7Bbb721JZ9Gk2qKv4vjx4+z7OxsdujQIVZQUGBOmqa14DNpOtGuCWOMTZo0iT3//PNhz9Hj8UQ9x8cff8yGDRvGPvvsM7Z9+3Z24YUXsoceeqilnkKziHZdysvLw67Hjz/+yAYOHMg+/fTTqOf44YcfWG5ubtgxRUVFLfk0mkRNv2Pa63tKTdekvb6n1PZ7t72/pzREuw+k3G43GzRoUNiH0/Lly9kNN9wQw1rFpz179rDs7GxWWFholn3wwQds9OjRMaxVfCspKWF/+MMf2BVXXEGBVCOUlJSw/v37s82bN5tlzz//PLvvvvtiWKv4VFpayrKzs9lvv/1mlt1+++3t8gO0IXbv3s0uvvhidtFFF4X9APj222/ZkCFDmNvtNve96aab2JIlS2JV1Vavumv55ptvsgsuuCBs37lz57KZM2fGopqkGTXV39M999wT9hmbl5fH+vTpww4dOtS8T6AZNNXfxTfffMNGjRrV7PVtCdVdE8YYO/vss9lXX31Vp/Ncd911Ya+hLVu2sNNPP73aH5utXU3XxWry5Mns7rvvrvY8/+///T929dVXN1c1W0xNv2Pa63tKTdekvb6n1PZ7tz2/pzRUu++yt2vXLiiKgpycHLNs2LBh2L59OzRNi2HN4k9mZiZefPFFZGRkhJVXVlbGqEbx74knnsAll1yC3r17x7oqcW3r1q1ISkpCbm6uWXbrrbdiwYIFMaxVfHI6nXC5XFi3bh2CwSD27duH//73v+jXr1+sqxYXvv/+e4wYMQLvvPNOWPn27dvRv39/JCQkmGXDhg3Dtm3bWriG8aO6a2k0n49En0VtT1P9PW3fvh3Dhw8317t06YKuXbti+/btzVLv5tRUfxd79uzBKaec0ix1bGnVXZPKykrk5+ejZ8+etZ5DVVX89NNPYa+TIUOGIBgMYteuXU1d5RZR3XWx2rRpE7Zs2YKZM2dWu8+ePXvqdA1bu5p+x7TX95Sarkl7fU+p6Zq09/eUhpJjXYFYKywsRFpaGux2u1mWkZEBv9+P0tJSpKenx7B28SUlJQVnn322ua5pGl5//XWceeaZMaxV/Nq0aRN++OEHfPDBB5g3b16sqxPXDh8+jG7dumH9+vVYsWIFgsEgLr/8cvztb3+DKLb7XL5eHA4HHnjgAcyfPx+vvfYaVFXF5ZdfjquuuirWVYsL1113XdTywsJCdOrUKaysY8eOOH78eEtUKy5Vdy27d++O7t27m+snTpzAhg0bMH369JaqGmkhTfX3VFBQ0Gb+/prq72Lv3r3wer248cYbsX//fvTr1w9z5syJyx+U1V2TvXv3QhAErFixAv/5z3+QmpqKm2++GZdddlmVfcvLy+H3+8NeJ7IsIzU1NS5fJ0D118Vq5cqVuOyyy9ClS5dq99m7dy8URcGVV16J/Px8DB8+HLNnz67yN9Xa1fQ7pr2+p9R0Tdrre0pN16S9v6c0VLv/Jeb1esPCKADmeiAQiEWV2oxFixbhl19+wZ133hnrqsQdv9+PBx98EA888ACcTmesqxP3PB4PDh48iLfffhsLFizArFmzsHr1ahqMu4H27t2LcePG4Z133sGCBQvw8ccf4/333491teJadZ9F9DnUOD6fD9OnT0dGRgauvvrqWFeHtJD6/j35fL529fdXl7+Lffv2oaysDH/729/w7LPPwul0YtKkSW2qpeG+ffsgCAJ69eqFlStX4qqrrsLcuXPx6aefVtnX5/MBQLt6nRw+fBjfffcdbrzxxhr327dvHyorKzF79mw89dRTKCgowF//+leoqtpCNW0e1t8x9J7CVffbrj2/p1ivCb2nNEy7byHlcDiq/Ec31ikIaLhFixbh1VdfxVNPPYXs7OxYVyfuLFu2DAMHDgxL4EnDybKMyspKLF68GN26dQMA5OXl4a233sLkyZNjXLv4smnTJrz77rv48ssv4XQ6MWjQIOTn5+O5557DxRdfHOvqxS2Hw4HS0tKwskAgQJ9DjeB2u3HbbbfhwIEDePPNN+FyuWJdJdJC6vv3VN13wbb4mqnr38VLL72EYDCIxMREAMD//u//YsyYMfj8889x0UUXtWSVm82ll16KcePGITU1FQDQt29fHDhwAG+99Rb++Mc/hu3rcDgAVP3H6rb6OgGAf/3rX+jXr1+tw0Zs2LABgiCYf19LlizB6NGjsX37dgwdOrQlqtrkIn/H0HtK9b/t2vN7SuQ1Oe200+g9pQHafQuprKwslJSUQFEUs6ywsBBOpxMpKSkxrFn8mj9/Pl5++WUsWrQI559/fqyrE5c2bNiAjRs3IicnBzk5Ofjggw/wwQcfhI11RuouMzMTDofDDKMA4JRTTsGxY8diWKv4tHPnTvTo0SPsS1j//v2Rl5cXw1rFv6ysLBQVFYWVFRUVxV2Xh9aisrISU6ZMwe7du/Hqq6+2ifFNSN3V9++puv0zMzObrY6xUJ+/C7vdbv5wBPiPp+7duyM/P78FatoyBEEwfzgaevXqFfU5pqamwuFwhL1OFEVBaWlpm3udGL766iucc845te7ncrnCvhN07NgRqampcftaifY7pr2/p1T32649v6dEuyb0ntIw7T6Q6tevH2RZDhuUbuvWrRg0aBCNLdMAy5Ytw9tvv40nn3wSf/rTn2Jdnbi1evVqfPDBB1i/fj3Wr1+P8ePHY/z48Vi/fn2sqxaXBg8eDL/fj/3795tl+/btCwuoSN106tQJBw8eDPsXnX379oWNI0Dqb/Dgwfj555/NJtwA/ywaPHhwDGsVnzRNw+23344jR45g9erVOO2002JdJdLC6vv3NHjwYGzdutVcP3bsGI4dO9am/v7q83fBGMO5556LdevWmWVG1/devXq1RHVbxDPPPINJkyaFle3atSvqcxRFEYMGDQp7nWzbtg2yLKNv377NXdUWxxjDTz/9VGsLp8rKSpxxxhn47rvvzLL8/HyUlJTE5Wulut8x7fk9pbpr0p7fU6q7JvSe0jDtPnFxuVy49NJLMW/ePOzYsQMbN27EqlWrMHHixFhXLe7s3bsXzz77LG655RYMGzYMhYWF5kTqp1u3bujRo4c5JSYmIjExET169Ih11eJSr169MHbsWMyePRu7du3CV199hZUrV+Laa6+NddXizvjx42Gz2XD//fdj//79+Oyzz7BixYpax5ggNcvNzUWXLl0we/Zs7N69GytXrsSOHTtw5ZVXxrpqcefdd9/F5s2b8cgjjyAlJcX8HIrsbkHartr+ngKBAAoLC80xbq699lr885//xJo1a7Br1y7ce++9GDt2LE466aRYPo0mVdvfhfWaCIKAsWPHYunSpdi8eTN2796Ne++9F507d8aYMWNi+0Sa0Lhx47Blyxa89NJLOHToEN58802sX7/e7Mrv8/nCvsNed911eOmll7Bx40bs2LED8+bNw5///Oc22b3m6NGjcLvdUbvrWa9LUlIShg0bhgULFmDHjh34+eefceedd+Lss89Gnz59WrrajVLT75j2+p5S0zVpr+8pNV0Tek9pIEaYx+Nh9957LxsyZAgbPXo0e/nll2Ndpbj0/PPPs+zs7KgTaZxZs2axWbNmxboaca28vJzdc889bMiQIWzkyJFs6dKlTNO0WFcrLu3evZtNmjSJDR06lJ177rns5ZdfpmvZANnZ2ey7774z1w8cOMCuv/56NnDgQPanP/2JffPNNzGsXXyxXsvJkydH/Ry64YYbYlxL0pzq8/f03XffsezsbHb48GGzbO3atWzMmDFsyJAhbNq0aay4uLhF698c6vN3EXlNfD4fW7BgARs1ahQbPHgwmzp1KsvLy4vZc2kqka+TTz/9lF100UVs0KBB7IILLmD/+te/zG1r166t8h32+eefZyNHjmTDhg1js2fPZj6fr8Xq3pwir8u2bdtYdnY28/v9VfaNvC6lpaXsvvvuYyNGjGA5OTns7rvvZqWlpS1S76ZU2++Y9vieUtM1aa/vKbW9Tug9pf4ExhiLdShGCCGEEEIIIYQQQtqPdt9ljxBCCCGEEEIIIYS0LAqkCCGEEEIIIYQQQkiLokCKEEIIIYQQQgghhLQoCqQIIYQQQgghhBBCSIuiQIoQQgghhBBCCCGEtCgKpAghhBBCCCGEEEJIi6JAihBCCCGEEEIIIYS0KAqkCCFxoU+fPrjrrruqlK9btw7jx4+PQY0IIYQQQgghhDQUBVKEkLjx4YcfYtOmTbGuBiGEEEIIIYSQRqJAihASN7p164aHH34YgUAg1lUhhBBCCCGEENIIFEgRQuLG3//+d+Tn5+Oll16qdp/jx4/jjjvuQG5uLkaMGIFHHnnEDLDWrVuHG2+8EUuWLMGIESMwfPhwLFiwAIwx8/i3334b48ePR05ODm688Ub89ttvzf68CCGEEEIIIaS9oUCKEBI3srKyMGPGDKxYsQKHDx+usj0QCOCmm26C1+vF6tWr8fTTT+OLL77AwoULzX1+/PFH7N+/H2+99Rbmzp2L1157Dd9++y0A4LPPPsOyZcswd+5cvPfeexg2bBgmTpyIsrKyFnuOhBBCCCGEENIeUCBFCIkrN954I3r06IFHH320yravvvoK+fn5WLRoEfr06YORI0figQcewFtvvQW32w0AUFUV8+fPR69evXDJJZegb9+++OmnnwAAL774IqZOnYpx48ahZ8+e+Pvf/45u3brh/fffb9HnSAghhBBCCCFtnRzrChBCSH1IkoR58+bhuuuuw8aNG8O27d27Fz179kSHDh3MsqFDh0JRFBw6dAgA0LFjRyQlJZnbk5KSoCiKefyiRYvw5JNPmtv9fj8OHDjQjM+IEEIIIYQQQtofCqQIIXFn6NChuOKKK/Doo4/iL3/5i1nucDiq7KuqatjcbrdX2ccYQ0pVVcyZMwcjR44M224NsAghhBBCCCGENB512SOExKW7774bHo8nbIDzU045BQcOHEBpaalZtm3bNsiyjJNPPrnWc55yyik4fvw4evToYU4rVqzAtm3bmuEZEEIIIYQQQkj7RYEUISQupaWl4e6778bRo0fNslGjRuGkk07Cvffei99++w3fffcd5s+fjwkTJiAlJaXWc95888149dVXsX79ehw6dAiLFi3C//3f/+HUU09tzqdCCCGEEEIIIe0OddkjhMStK6+8EmvXrkVBQQEAPr7Us88+i/nz5+PPf/4zEhMTcdFFF2HmzJl1Ot+FF16IoqIiLFmyBEVFRejduzeee+459OzZsxmfBSGEEEIIIYS0PwIzBk8hhBBCCCGEEEIIIaQFUJc9QgghhBBCCCGEENKiKJAihBBCCCGEEEIIIS2KAilCCCGEEEIIIYQQ0qIokCKEEEIIIYQQQgghLYoCKUIIIYQQQgghhBDSoiiQIoQQQgghhBBCCCEtigIpQgghhBBCCCGEENKiKJAihBBCCCGEEEIIIS2KAilCCCGEEEIIIYQQ0qIokCKEEEIIIYQQQgghLYoCKUIIIYQQQgghhBDSoiiQIoQQQgghhBBCCCEtigIpQgghhBBCCCGEENKiKJAihBBCCCGEEEIIIS2KAilCCCGEEEIIIYQQ0qIokCKEEEIIIYQQQgghLYoCKUJIgzDG2uVjN0a81psQQgiJB/Q5S6Kh1wUhrRcFUoS0cTfeeCP69OkTNg0fPhwTJ07E999/X+/zHT9+HLfeeiuOHj1qlo0fPx733Xdfvc/Vp08fLF26tF7HrFmzBk888US9HyvWdu/ejWuvvTasrCHPnxBCSPu2detWTJ8+HaNGjcKgQYNwzjnn4P7778fevXtjXbUwS5cuRZ8+fVrs8bZu3Ypbb721xR6vNfj5559xyy234Mwzz8SIESMwefJk/Pzzz2H7MMbw0ksv4bzzzsOgQYNw/vnn44033qj13AcPHsQdd9yB0aNHY9iwYbj22muxadOmKvutW7cOF110EQYNGoTx48dj2bJlUFW1Xs/DeK1Yp/79+2PEiBGYNm0adu/eXedzrVq1CnfffTcAoLy8HPfeey9++OGHetWnoe677z6MHz++xn3WrVuHPn364MiRI3U+b12OKSkpwdixY3H48OE6n9fK7XbjoYcewqhRo5CTk4NbbrkF+/btq/GY8ePHV/nvZkzW65Cfn4+77roLubm5GDp0KKZMmYKffvqpQfUkbY8c6woQQppf//798eCDDwIAVFVFSUkJ3nrrLUyZMgXr1q3DaaedVudzffvtt/jyyy+bpF7vvPMOOnfuXK9jnnvuOeTm5jbJ47ekjz/+GD/++GNYWUOePyGEkPZr5cqVePLJJzF69GjMmTMHmZmZOHjwIN566y1cdtllWLBgAf70pz/FupoxsWbNmlYXyjWngwcP4oYbbsDAgQPx6KOPQhAErFq1Ctdddx3ee+899OrVCwCwcOFCrF69GjNmzMCgQYPwn//8Bw8//DBkWcbVV18d9dwlJSW44YYbkJqaijlz5iApKQlr1qzB5MmT8eqrr5rfw9544w3Mnz8fkydPxpw5c7Bt2zYsX74cgUAAM2fOrPdzeuedd8xlVVWRl5eHp556Ctdffz02bNiAzMzMGo/fu3cvnn/+ebz//vsAgF9//RX//Oc/ccUVV9S7Ls1l7NixeOedd9CpU6cmPW9aWhomTZqEOXPm4LXXXoMgCPU6/q677sL27dtxzz33ICkpCcuWLcPEiROxYcMGdOjQIeoxy5YtQyAQCCvbtm0bFixYgGuuuQYAUFFRgWuvvRZerxd33HEHevbsiX/961+44YYbsHr1apx++ukNe8KkzaBAipB2ICkpCUOGDAkrO+usszBy5EisW7cOs2bNikm9IuvU3rT3508IIaTuPv/8cyxevBjTp0/H7bffbpbn5ubi0ksvxV133YX77rsP2dnZ9fqHJhKfVq9eDZfLheeffx4JCQkAgDPPPBPjx4/H66+/jgceeABHjhzBK6+8grlz5+K6664DAIwcORLHjh3D119/XW0gtX79epSUlODdd99FVlYWAGDUqFG45JJL8NJLLyE3NxcejweLFy/GlClTcM8995jnLi8vx7ffftugQCrye9GwYcPQpUsXXH/99XjvvfdqbQG3aNEiTJgwwaxza5Seno709PRmOfd1112H5557Dp9++inOO++8Oh/3448/4vPPP8fKlSsxZswYAMDw4cNxzjnn4M0338Tf/va3qMf1798/bL2yshIzZ87E2LFjzf9Wa9euxdGjR/Hmm29i2LBhAPhrqbS0FI899hjefvvthjxV0oZQlz1C2imXywWHw1HlX1A++ugjXH755cjJycGoUaPwwAMPoKysDABvMjx79mwAwDnnnBPWTS8YDGLhwoUYNWoUhgwZgsmTJ+PgwYM11sHaZW3z5s3o06cPNm3ahMmTJ2Pw4MEYNWoUFi1aZDb9Hj9+PI4ePYr33nsvrOlyXl4eZs6cidzcXAwePBg33XQTfvnlF/Nxjhw5gj59+uDll1/GBRdcgMGDB+O5555Dnz598Pnnn4fV6ddff0WfPn3w6aefAgD8fj8WLlyIMWPGYODAgbjooovw0UcfhR0zfvx4LFmyBE888QTOOussnH766ZgyZQoOHDgAgDdFX7ZsWZXnHNllr6CgALNnz8aYMWNw+umn48orr8S///3vKtfsjTfewD/+8Q/k5uYiJycHd9xxB4qKisx9Dh06hL/+9a8YMWIEBg8ejKuvvrrJWrURQgiJjWXLlqFXr16YNm1alW02mw0PP/wwJEnCCy+8AACYPHkyLr/88ir73nbbbbj44ovN9R9++AE33HADBg8ejNzcXMyaNQvFxcXm9nXr1qF///5Ys2YNRo0ahdzcXOzZs6fOnzVffPEFLr74YrO72Pr168O21+Wzz+/3Y/ny5bjgggswaNAgnHfeeVi5ciU0TQPAu0q99957OHr0KPr06YN169ZFvYZLly7FBRdcgE8//RQTJkzAoEGDcMkll+DHH3/Etm3bcNVVV+H000/HhAkTqnRP+/333zF16lQMHToUQ4cOxbRp06p0j9q1axduv/12nHnmmRgwYADOPvtsPPLII/D5fOY+dfkcN7pobd68OerzAIBevXph8uTJZhgFAAkJCejcuTMOHToEANi4cSMcDgeuvPLKsGOffvrpGocMyMrKwqRJk8KCHUmS0KNHD/Pc33zzDdxuN2688cawY2fNmoV333232nPX18CBAwHAHCpi6dKl+OMf/4hly5YhNzcXo0ePRllZGX7//Xd88cUXmDBhAgD+vXLixIkAgIkTJ4bVs6bvuoaffvoJU6ZMwYgRIzB06FD89a9/rXPXwXXr1uH888/HoEGDcPHFF4f9XUTrfvfee+/hwgsvNPfftGkT+vfvX+V1vH37dlxzzTUYNGgQxo4dixdffDFsu91ux/nnn4/nn3/eLDO+X1f3NwEAX3/9NRISEjB69GizLD09HWeccUa9vj8+++yzKC4uxgMPPGCW7d27Fx06dDDDKMOIESPw448/VrnupP2hQIqQdoAxBkVRoCgKgsEgCgsLsXjxYgQCgbBmzM8++yxmzpyJIUOGYMmSJZg2bRr+9a9/4cYbb4TP58PYsWPNfyVZtmwZbrvtNvPYjz76CLt378bjjz+OBx98EDt37sSdd95Z77refffdGDZsGFasWIEJEybgxRdfxJo1a8zHzMzMxJgxY8zmzsXFxbjmmmvw888/Y+7cuVi8eDE0TcP1119fpen+0qVLccstt2DhwoW47LLLcPLJJ2PDhg1h+3z44YdITU3FmDFjwBjDtGnT8Pbbb+Pmm2/Gc889h5ycHNx5551VvlC/9tpr2LdvHxYsWIBHHnkEO3fuNFueXXXVVeaXwXfeeQdXXXVVleddVFSEK6+8Ej/88APuvPNOLF26FN26dcO0adPMpueGp556Cpqm4cknn8S9996Lzz//HI899hgAQNM0TJ06FV6vFwsXLsSzzz6L1NRU/O1vf6s1ICSEENI6FRcXY+fOnRg3bly1XXFSU1Nx1llnmWHOxRdfjJ9//jnsvb+8vBz/+c9/cMkllwAAtmzZgkmTJsHpdOLpp5/GnDlz8P3332PixIlhIYqqqli1ahUeffRRzJ49G6ecckqdP2seeOABTJo0Cc899xw6d+6M++67D7t27QJQt88+xhj++te/4sUXX8RVV12FFStW4IILLsDTTz9tDkdw2223YcyYMcjMzMQ777yDsWPHVnstjx8/jscffxx//etf8cwzz6C8vBwzZszAzJkzcdVVV2H58uVgjOHOO+80r8H+/ftxzTXX4MSJE3jiiSfw6KOP4vDhw7j22mtx4sQJADxYu/766+H1evH444/jhRdewJ/+9CesXr0ar732WlgdavocB0LdugYMGFDt87juuuvwl7/8Jazs4MGD2L17t9lC7tdff0WPHj2wZcsWXHbZZRgwYADGjx8f1jUumgsvvNAch8lQVlaGLVu2hJ07OTkZRUVFuP766zFw4ECMGjUKzz77bJMOIr5//34AwMknn2yW5eXl4csvv8RTTz2F2bNno0OHDvjggw+QmZlptrIaMGCAGYw88MAD5multu+6APDdd9+Z434+9thjeOSRR3Ds2DFcc801tXYLPXbsGFauXIk77rgDS5cuhSAImDFjhvk6ibR+/Xrcd999GDp0KJ599lmcf/75uO2226KOwzVv3jz86U9/wsqVK5GTk4NFixZV+YfVCy64ADt37jSv24ABA2r9m9i7dy+6d+8OSZLCyk8++WTzPLXJy8vDa6+9hilTpqBbt25meVpaGtxud5XgyQg26zOWFmmjGCGkTbvhhhtYdnZ21GnFihXmfqWlpWzgwIFs7ty5Ycdv2bKFZWdns9dff50xxtjatWtZdnY2O3z4sLnPuHHj2JgxY1ggEDDLnnrqKZadnc0qKiqqrVt2djZbsmQJY4yx7777jmVnZ7OnnnoqbJ/x48ezqVOnhj3WrFmzzPUnn3ySDRo0iB05csQs8/v97JxzzmHTp09njDF2+PBhlp2dzebMmRN27iVLlrAhQ4Ywr9fLGGNM0zQ2duxY9sADDzDGGPv6669ZdnY227BhQ9hxd999Nxs1ahQLBoNmncaNG8cURTH3Wbp0KcvOzmbFxcXmY2VnZ1f7/BcuXMgGDBgQ9jwYY+ymm25io0aNYqqqmsdce+21Yfvcd999bMiQIYwxxgoKClh2djZ7//33ze3l5eXsscceY7///jsjhBASf3bs2BH2WVydxx9/nGVnZ7PS0lLmdrvZkCFD2LJly8zta9asYX379mXHjx9njDF29dVXswkTJoR9fu3bt4/169evyuf++vXrzX3q8lljfO59+eWX5j4HDx5k2dnZ7NVXX2WM1e2z74svvmDZ2dnsww8/DNtn+fLlLDs723y8WbNmsXHjxtV4faLV6fnnn2fZ2dlszZo1ZtnHH3/MsrOz2S+//MIYY2zmzJnsrLPOCvtOU1JSwoYNG8Yef/xxxhhjX331Fbv++uurfO+ZMGECmzx5srle2+d4Q3m9Xnb11VezIUOGmNfzL3/5CxsxYgQ788wz2euvv86+/fZbdv/997Ps7Gz29ttv1/ncqqqy6dOns379+rHt27czxhh78MEH2ZAhQ9jIkSPZihUr2KZNm9j//u//sr59+7LFixfXq+7Gf5dgMGhOFRUVbMuWLeyyyy5jw4YNYwUFBWH7btmyJewcV155Jfvb3/4WVmZ8t/zuu+8YY3X/rnvllVeyCy+8MOzvoqysjOXm5rIZM2ZU+zxmzZrFsrOz2Z49e8yyb7/9lmVnZ7ONGzcyxqp+jx47dmzY91zGQq/JtWvXhh3z5ptvmvt4PB42YMAA9thjj4UdW15ezrKzs9kbb7xRbT0jTZ48mV1zzTVVyp988kk2YMCAOp3jscceYzk5Oay0tDSsfPfu3WzAgAFs4sSJ7Pfff2dlZWXsn//8Jxs+fHjU/46k/aEWUoS0AwMGDMC7776Ld999F2vWrMFLL72Em266CU899RSeeuopAHwQwkAgYDZ1NgwfPhzdunWr9Y58p59+Omw2m7nevXt3APxfY+sjJycnbL1z587weDzV7r9p0yb069cPWVlZZiswURTxhz/8Ad9++23Yvv369Qtbv/jii+HxeMx/Xfrvf/+LvLw881+ON23aBEEQMGbMGPPciqJg/PjxKCwsDGu6PWjQoLB/WTIGK/d6vXV63t9//z1ycnLC/lXJqGNhYWHYnU4ix1jo3Lmz+TgZGRno3bs35s6di1mzZuGDDz6ApmmYPXs2jSlCCCFxiuktTqyfs9EYn0OMMSQkJODcc88N62a+YcMGjBw5EllZWfB6vdi+fbvZItj4jDvppJNw6qmn4ptvvgk7t/UztD6fNcOHDzeXI78b1OWz7/vvv4csy7jggguq7GOco76GDh0a9lwAYPDgwWZZampqWD2/++475Obmwul0mtcpKSkJw4cPN79rjB49Gq+//jocDgf27NmDf//733juuedQXFxcZeDnmj7HG6KyshJTp07FTz/9hEWLFpnXMxgMoqSkBA899BCuv/56jBw5EvPnz8fo0aPNoQRqEwwGcc899+Bf//oX/vGPf5iDUAeDQXg8Htxyyy2YOnUqzjzzTNx111246qqr8PLLL6OysrLez2PAgAHmNGzYMFx//fUIBAJmC3mryO90hw8fNl9f1anLd12Px4OffvoJ//M//xP2vS4lJQXjxo2r9fWWlpaGU0891Vw36lRRUVFl34MHDyIvL6/Ka7u6GxNY/5ZcLhcyMjKqfM9OTk5GSkpKvVoesRpatNVlcHS/3493330XV155ZZUB0Hv37o0VK1bg8OHDmDBhAs444wy88sormDFjBgDA6XTWuZ6kbaJBzQlpBxITEzFo0KCwstGjR8Pj8eDFF1/ExIkTzaa0xhczq4yMjKgfpFbWMQwAQBR53m2M71BXkR9MoijW+EFZWlqKgwcPVtus3foFL7KOPXr0QE5ODjZs2ID/+Z//wYYNG3DyySebX1RLS0vBGAv74mpVUFBgfiFyuVxV6g3U/fmXlZXhpJNOqlJu/PewfuGI9ljGNTLusmMMarl+/XrYbDace+65eOihh6q9UwohhJDWywgYjHF0qnP48GEkJiaagcoll1yC999/H7t27UJGRgY2b95sdg0rLy+Hpml44YUXzHGnrBwOR9i69TO0Pp811uOMz0bjM6sun31lZWVIS0ur0p3ICChq+34STVJSUpWyyM9Wq9LSUnz00UdVxpAEYA5QbXTBe+ONN+DxeNClSxecfvrpVa5jtMeq7btOTY4dO4apU6di//79eOqpp3Duueea2xITE81/WLM6++yz8fXXX6OoqCjq9z5DeXk5br/9dmzZsgVz587F9ddfH3ZuAFW6gv3hD3/AO++8g71794aFfHVhHXvKZrMhMzMTHTt2jLqv8fiGysrKGv8bAqjTd92Kigowxprs+7AR6ET7PmiM1Rb5HKv7b1LX143L5apXIJiUlBQ2hpnB7XYjOTm51uO//vprVFZW4qKLLoq6ffTo0fj3v/9thmQnnXSS+d+avpcSCqQIaccGDhyINWvW4MiRI+YHQlFRkXmrYENhYWHUL4ytQXJyMnJzc3HvvfdG3W6322s8/uKLL8aCBQtQUVGBjz/+2BwzwDh3QkJClbEfDD169Gh4xSN06NABhYWFVcqNsrS0tDqfKysrC/PmzcODDz6IXbt24eOPP8YLL7yAtLQ0cwwFQggh8aNjx44YMmQI/vWvf+GOO+4wgx2ryspKfPPNNxg/frxZNnLkSGRmZuL//u//kJmZCYfDYd59ywgrJk2aFLVFRm0/7pvis6Yun30dOnRASUkJVFUNC6UKCgrMfZpbcnIyzjrrLNx8881Vtsky/zm1cuVKvPLKK3jooYdw3nnnmT/kIwcUb0q//fYbpkyZAr/fj1WrVuGMM84I296jRw8wxhAMBsOCMUVRANTcOuX48eO4+eabceTIETz55JP4n//5nyrnBlCl9VcwGARQNdCsi8h/PK2P1NTUWsOiunzXTU5OhiAIUQOawsJCM+xtCkZr+sjxpaobb6quysvL6/V3ccopp+Drr7+Gpmlh7y0HDx4Ma+1VnS+++ALdu3eP+t8vLy8P33zzDS655JKw3xK//PILUlNTa23VRto+6rJHSDu2Y8cOSJKEk046CYMHD4bdbseHH34Yts8PP/yAvLw8s5VQtC/BLSny8XNzc7F//36ccsopGDRokDn985//xLvvvlvlX1QjXXjhhWCM4ZlnnsGJEyfC7jxk3NaYMRZ27t9//x3Lly83v9A1pN6RzjjjDPz4449V/vX7/fffR2ZmZp3Drx9//BFnnXUWduzYAUEQ0K9fP9x5553Izs5GXl5enetLCCGkdbn99tuxf/9+PPnkk1W2qaqKBx98ED6fL2yga0mScNFFF+Hzzz/Hxx9/jHPPPddswZGUlIT+/ftj3759YZ9xp512GpYuXVrjHd6a6rOmLp99ubm5UBQFH3/8cZV9AJh372rO7yfGnQX79etnXqeBAwfilVdeMe/Ku3XrVvTu3RtXXHGFGUbl5+fj999/r3dr8bo4duwYbr75ZgiCgLfeeqtKGAXAbBkVeQOXzz77DH369InaUgzg4eZNN92EgoICvPzyy1XCKIC3hBIEIeq5U1NT6xRkNKVu3brh2LFjYWWR3wHr8l03ISEBAwcOxP/93/+FDSxeUVGBL774osrd4hqjc+fOOPnkk83XkOGTTz5p8DnLysrg9XrRtWvXOh8zevRouN1ufPXVV2ZZcXExfvjhB4waNarW47dt21Ztb4ITJ07g/vvvD3s/KSwsxIYNGzB+/Pg6dQkkbRu1kCKkHaisrMS2bdvM9UAggM8++wxr167F1VdfbTY3v/XWW7F8+XLYbDaMGzcOR44cwTPPPIPevXvjsssuA8D70APAp59+ij/84Q8t/oUjJSUFv/zyC77//nucfvrpmDRpEv75z39i0qRJmDx5MtLS0vDRRx/h//2//4fZs2fXej7jjnpvvvkmcnJywoKfMWPG4IwzzsBtt92G2267Daeeeip27NiBJUuW4OyzzzavW13rDfC7+A0ePLhKi7Obb74Z77//PiZNmoTbb78dqampWL9+Pb777js89thjdf6i3b9/fzidTtx7772YPn06MjIy8O233+LXX381b39MCCEk/px99tm47777sHDhQvz666+44oor0KlTJxw5cgRvvfUWfv31Vzz66KPo27dv2HGXXHIJVq1aBVEUq3TNmzlzJm699VbcdddduPjii8276W3fvj3sTrqRmuqzpi6ffX/4wx8wYsQI3H///cjPz0ffvn3x/fff44UXXsBll12G3r17A+Cfs0VFRfjyyy/Rr18/dOrUqR5Xt2a33XYbrrnmGkydOhXXXnstHA4H3nnnHWzcuBFLliwBwMfSfPbZZ7Fy5UoMGTIEBw8exPPPP49AIFDv8aGKi4tx6NAh9O7du9rQ6JFHHsGJEyfw0EMPVfmel5SUhN69e2PEiBEYN24cFixYAK/Xi9NOOw3r16/Hf//7Xzz77LPm/ocOHUJxcbE5ttWSJUtw4MABTJ8+HbIsh53bbrejf//+OOmkk3DDDTfgxRdfhCzLOOOMM/D555/j/fffx9y5c83xzo4fP47jx4+jf//+tbZab4xRo0bhzTffBGPMDDmMYPCLL75Ahw4d0Ldv3zp9173rrrswZcoU3HrrrbjuuusQDAaxcuVKBAIBTJs2rcnqbNyB7+6778aDDz6IP/7xj9i1axeWL18OoGEh69atWwHwkAngvwH27NmDk08+udrvrWeccQZyc3Nxzz334J577kFqaiqWLl2K5OTksJ4De/bsQSAQQP/+/c0yVVWxb9++KuNyGQYOHIihQ4di3rx5uPfeeyFJEp5++mlIkoTp06fX+/mRtocCKULagV9++QVXX321ue5wOHDyySfjzjvvxJQpU8xy40vl66+/jnfeeQepqam44IIL8Pe//938F9URI0bgrLPOwuLFi7Fp0yasXLmyRZ/L5MmT8dhjj2HKlCl4+eWXMXz4cLz99ttYvHgx5s2bB7/fj549e+LRRx+tczP5Sy65BBs3bqzS910URaxcuRLPPPMMnn/+eZw4cQJZWVm4+eab6/2F5LzzzsM///lP3Hfffbjyyisxb968sO2ZmZl46623sHjxYjzyyCMIBoPo27cvnn32WZxzzjl1fhyHw4FVq1Zh8eLFePTRR1FeXo6ePXvi4YcfxuWXX16vOhNCCGldbr75ZuTk5ODVV1/FE088geLiYmRmZmLUqFF49NFHzXDGqm/fvsjOzkZJSQlGjhwZtm306NF46aWXsGzZMsyYMQM2mw0DBgzAyy+/XGXgbaum+qypy2efIAh4/vnnsWTJErzyyisoLi5G9+7dMXPmzLAudJdffjm+/PJLTJs2DTNmzMCtt95a53rUpm/fvnjjjTfw1FNP4d577wVjDNnZ2Vi+fLlZz6lTp6KkpASvvfYali9fji5duuCSSy4x619eXm7+41RtvvjiC8yePRuvvfYaRowYUWV7IBDAF198AQBRu0fm5uZi9erVAIBnnnkGy5Ytw8svv4zi4mL07t0by5YtC+va+eyzz+K9997Db7/9BiDUQmfp0qVYunRp2Lm7deuGzz77DAAwZ84cdO7cGe+88w5WrlyJk046CY888giuuuoqc/81a9Zg2bJl+Pe//92s3bPOO+88LF++HDt27DDHrjrttNMwYcIEvPHGG/jqq6/w4Ycf1um77siRI/Hyyy9jyZIlmDlzJux2O4YPH44nnniiyW8Qc9FFF8Hj8eCll17C2rVrcdppp+Ef//gH/vGPf1QZj6ou/vOf/+D00083x537+eefMXHiRCxYsKDGv81ly5bh8ccfx8KFC6FpGoYOHYqnn346bIynhx56CEePHjX/+wN8fDVFUap9bQuCgKVLl2LBggV44IEHAPDfEkuXLq1XKy7SdgmsoSPoEUIIIYQQQgghNbj++uvx9NNPV7lTXlP761//irS0NCxYsKBZH6cpffjhh+jfv3/YmFZffPEFpk6din/+859VWjzWxOPx4Oyzz8YTTzwRNrg9Ia0ZjSFFCCGEEEIIIaTJbd68GV6vt8a7+TWVO++8E5988klcjZn5/vvv45ZbbsEHH3yAH374AWvXrsWDDz6I3NzceoVRAPD222/jtNNOq1fLekJijVpIEUIIIYQQQghpckePHkVCQkKL3A0R4Hc73LVrV9TB/1ujkpISLF68GP/5z39QXFyMjIwMnH/++ZgxYwYSExPrfJ7i4mJceumlWL16dZPeBZqQ5kaBFCGEEEIIIYQQQghpUdRljxBCCCGEEEIIIYS0KAqkCCGEEEIIIYQQQkiLanAgFQgEMGHCBGzevNksO3z4MCZNmoQhQ4bgwgsvxNdffx12zLfffosJEyZg8ODBmDhxIg4fPtzwmhNCCCGEEEIIIYSQuNSgQMrv92PmzJnYvXu3WcYYw7Rp05CRkYG1a9fikksuwe23327e5SAvLw/Tpk3D5ZdfjnfffRfp6em47bbbUNchrBhjqKysrPP+hBBCCCHtGX13IoQQQkhrVu9Aas+ePfjzn/+MQ4cOhZV/9913OHz4MB5++GGceuqpmDp1KoYMGYK1a9cCANasWYOBAwdi8uTJOO2007BgwQIcPXoU33//fZ0e1+12Y9iwYXC73fWtMiGEEEJIu0PfnQghhBDSmtU7kPr+++8xYsQIvPPOO2Hl27dvR//+/ZGQkGCWDRs2DNu2bTO3Dx8+3NzmcrkwYMAAc3vMMQYUfgMc/Qgo+A9Q/F+gfDfgzQdUf6xrRwghhBBCCCGEENJmyPU94LrrrotaXlhYiE6dOoWVdezYEcePH6/T9pgr/ArYOKb67aITsHcAbKmAMwOwZwCODMCZBSR0BVzd+JR4Ei8ThBarOiGEEEJav0AggMsvvxxz587FiBEjAPDxN+fOnYtt27aha9eumDNnDkaPHm0e8+233+Kxxx7D4cOHMXjwYDz66KM46aSTYvUUCCGEEEKaTL0Dqep4vV7Y7fawMrvdjkAgUKftMddhANDtEqDid0BxA6oHUDx8DgCaD/D5AF8+UPFbzecSHYCrK5DQHUg4CUjuDaT0AZL7Ao50QBABQbLMrZMICLJlnYItQgghJN75/X7cddddUcffzM7Oxtq1a7Fx40bcfvvt+Oijj9C1a1dz/M3p06fj7LPPxvLly3Hbbbfh/fffh0DfDwghhJAWp2oMkhjfn8Gt6Tk0WSDlcDhQWloaVhYIBOB0Os3tkeFTIBBASkpKU1WhcRwdgTHr+TJjAFMATdGDqBNAoAjwFQC+QsCbBwROAH5jKuTzYCkQKAM0P+Dez6dI9nQg6VQg+TQgOZvP5UQ9iNIDKuhzUQQEGyDaeAst0QZIDkCU9X2E6ueCWM02EYAQPqcvtYQQQkiz2bNnD+66664qg4sb42++/fbbSEhIwKmnnopNmzZh7dq1mD59etj4mwCwYMECjBo1yhw+gRBCCCEtSxIF3PH2j9hTUBnrqjRI705JeOaanFhXw9RkgVRWVhb27NkTVlZUVGR208vKykJRUVGV7f369WuqKjQdQQgFQXAB9jQAvcP3YRofW0r18hZVwTI9lCoDPEf4cqAU8OcDlfuByr28dVWgGCguBoq3GA/GQ6mOZwDpw4HU03ngxDSAqYDmBZRKvsxUHpaBhY5FlDvnhIVQQmgdxroYKjf3tbTYghBah8iDMRjbLROs5xfqURZRv7qeg4IzQgghccgIkO68804MGTLELG/M+JsUSBFCCCGxsaegEj/nlce6Gm1CkwVSgwcPxsqVK+Hz+cxWUVu3bsWwYcPM7Vu3bjX393q9+OWXX3D77bc3VRValiACsotPjnQAJ/EQSfHwllLeQt6qSnHzcMeRzltdVewByn4GSnfyuTePdxOs+B048AYg2oH0YUDWOUDWWP3c9cQ0ACwUXkWuQ4vYpgJaIGIfFnEOazn0dfNi6NlYLbeVjhZAGdcSlvWw0Cpy3dLSywyqjPBMCm8dJkqWgC3aY0eGXdG2VxOS1bocGQgSQggBeDc1pn+WGC2GGFjYck37Ra7bRBscsqPlnkADtNnxNwkhhBBCGqHJAqnc3Fx06dIFs2fPxm233YbPP/8cO3bswIIFCwAAV1xxBV566SWsXLkS48aNw/Lly9G9e/e29S98ggjYkviU0B1QvDyc8uQBvuOAFuTd9VIHAz31kMJfBJz4ATixGSjaDPgLgKJNfPplAQ+nOp8HdPkj79pX13oAoXyntWBRgq5qQy+mZ196aMY0mEFatfvXEp4JQu2hWeRFMwKxKiGasSyGdgwLpaKVGSFaxNzavTKyvC6BWXWtzaq0SmvkMgVrhDQLxhg0ppmhTFPPAdRrGwBeH8agQQurn1lPjYXW9WM1aPpbcOg467LxWMZzjgyXrMvmNoT2C83Cj0tzpeHM7mc2y3+b5hb3428SQgghhDRCkwVSkiTh2WefxT/+8Q9cfvnl6NGjB5YvX46uXbsCALp3746lS5fisccew/Lly5GTk4Ply5e37UE5jRZUzs56MHVM7853CHB2BOQkfqe+rhfwiTE+7lT+F0D+v4Hy34AT3/Np12Kg8x+B7pcCqYPiMxwIC3RiWpO6Y9YWZUDVQA3hAVjkMtNCx2lRwraw4/Qyo0Wbee7GsIRj1YZq0Vqn1RSsRWmlVmu4JkQsV/d4dV2u67F12Q8Ry6StigxWzNDFGrZEWY+2rbplTdOgMhUqU81jVKZC1VQezGg8oDH2ixoQRQY8NexjBjYRbxVmmQBzLljeeBljYdvM4/Vl47PZOEYQhLDlaNsiP8+tZZHbw461nluMvk+0dQCo8FfAr/hr+s/eqsX9+JuEEEIIIY3QqEDqt9/C7zbXo0cPvP7669XuP2bMGIwZM6YxDxmfBIGPQ2VPA5J6AO7DPHgKlALOTrybnrFfUi8+nTqZh1fHNwJHPwDcB4Gj7/MpoTuQ0hdI6g0kn8pbXSV0A++eRpqUIIB3BYx1RRqoSliGiPUooVpkYBY1WEP0fauEazV19axGrS3ZagjZjOOrDdqsxyHieGvYZqxXF7jBsj0ydGtIQFbd8xGinC/yHHU9ZzXrMQzijFCoPpMR/pjrYFA1HgApmsKDIE2DoinQmGaWW48zHtsa9JjhU0QYBPBwR4Bghjg1LQN8LgpilTBG1FuvGuuRgY0kSFWOiRbq1FbWnvgUX6yr0ChtavxNQgghhJB6arIWUqSO5ESgQ1/AlQVU7OOhk+TgLaUif0wkdAd6TQJOuQko3Q4cXg8c/5Qf4zkCYGNoX9Gh373PuIOfPtlTW+65kdYnHlul1SZqsBa5Hhm0WbdH7GuGbQjvEhq2b3WPB1QfuumhV5VwzfgPEVEeNYiz/rerSwCH8HnUEM66LXLfqt1EGROggUEDoDIGjQEqNGiMQQXPJ/myBg0CX9bDH5VpCGoqFL2FkMI0qJqGIFP0FkP8KWtMgx4P8Tlj0Bhf5pdJsFwty3MXBYAZYYwIQQBEPdQRIEIU+XMRBQmi/nxEUYQAEbIg6vuKZqBjPVYQRH5eUQQhzaXdjb9JCCGEEGJBgVSs2NOA9BzA1YUPaO45wpfFKP9JBAFIG8KnfncDpTv4Xfsq9vJ55T5A8wPlv/DJypEZCqdSsoHkbCDxZGpNReKXEBG8tJWgrTrVBl7VBG9mly4NKuMthlRV1buR6WWa0YUs1LpI1VQENcWcFJWvK0zh4ZDeBY0HTQzMaKWktyTieVp4HUJBD28dJFrXwYMgSRAgCwJ4TKS3JBIFCCx0XBhrqKYZr4GIkC5sx8htkSGfPq8S1kWcq0rLu2ihnmg51hjLz7pP5B1OEQoAo9Upav0jz2m9BpHPMdp+1n2iPVY19Yj8u4taFq0u1TxWO2zNFQ2Nv0kIIYSQ9owCqVgSRCChK2BLAcp/BTyHAYc+tlR1bElA5ll8MjCVB1oVe/RpN5+8RwF/IZ+Kvg3tLzqA5N48nErpw6ekXnUfNJ0Q0iJ4WKRBMbukGaGSZVnjy34tgKCmIKAqCGpBczsPkZg+phGf8+BIgDGAkLEkCgIkgbcOkgQJoiTrQZIIG0KhUvi8hVsQVQnowjYiakiHavYJG3vJsm+VY/X9WJSyaOeM9pjRgsQoD10t4z9SU+wXLZSKFjhVOSDaPtGCrsjymoIqozwytIoS+AEI3bRDX/dX8s/FOEXjbxJCCCGkPaNAqjWwJQFpOXow9Tug+ABnRt2PFyQgsQefOp8TKlfcloDqd6B8N1C5B1C9QNnPfLKyd+TdBBNP5vOEk4CEk4HE7jWHZISQGjHGoGgqFKbwud6NzRo0KZoKn+pHUFPgV4Lwa0Gz9ZKmH68hNI4SD5IEMGjm+ESSIEGyhEWSIMImynqZaJbF9Y/ZKl0WY1aT+BS1hV3YDuHzyC6qqGHfsPNFhm4sStCn7xct7As7RTWPAwDeEkBNi1K31ovG3ySEEEII4SiQai1EmbdYsqUApTsB73HA1blx55QTgbTBfDIwjbfEqtjN7+JX/htQ8RvgPwEE9Kl0e9Vz2dP0gMqYugOJ+rKN7vZD2hfGGO/OpodMQVUxAyaj3Kf6ebCkBhDQgvo4SprZdU7TNP1ntT5gNgBJDIVKkiBCEiXYRbs+3lEbCZRIbEXteheTmjSNYKD2fQghhBBCSKtEgVRrIgj6OFJ2oGQb4DnGQ6mm/PEpiJbWVOeGyoOVPKjyHAY8hwD3kdB6oBgIlPCpdEfVc9o6hEIqc64v29NorBASF4xWTGHjKFmWfUoAfi0Ab9APvxqEChWKqnepY0po/CQAgABZlMxQSRJE2AQZTpsROEmQaLBsQgghhBBCSDtGgVRr5OgIpA8FSrYD3jzA1bX5Qx1bEtChH58iKZU8oPIeAdx6YOU5zMet8hcBwTKgrAwo21n1WCkRSOgWEVTpYZUzkwZXJy0iqIaCpYAWNNcDahAexQ+f6oNX8ZvjNRktn4yMSRB4BzlZ7/4mixJsggyX3WGGSy0+lhIhhBBCCCGExDEKpForexq/q17JdsBzlA9+HqsfvHIS0KEvnyIpHj54uvsQD6g8lpZVvgJAdfPxqyp+r3qsYOPPK6E74OoWCqtc3XiIJTmb/7mRuGa0agpoQQTUIA+b9KDJq/jhUXzwKH4oeje6oMbvMGd2lRMEyIIEWZQhixJkQYLTZtdbN0nUNY4QQgghhBBCmgkFUq2ZPRVIzwGKtwHeYzykaW3kBCD5ND5FUv28hZcZUh0JTd48gAUB90E+RePICIVTxtwIrBwdYxfQkRajMc0MmgKqwsdj0sMmt+KFR/HzO8qpKoKMh018PCYGSQ+YbHrYREETIYQQQgghhLQeFEi1drYUIO104MRWwJcPOLNiXaO6kxxA0il8isRU3oLKGlJ5jvDWVp4jvJugv4hP0QZZF+28K6OrayiwcnXlLa5c3QBbcvM/P9JojDEEND7wt1/lrZz8agAexYfKgBdexY+gPmh4UFOMoyDqd48zJpfdyVs4idQFlBBCCCGEEELiAQVS8cCeCqQNAor/C/iLAUd6rGvUeILEB3B3dQE6nlF1e6CMj1nlyQuFVEbLKl8+oAUA9wE+RSMn83MndAOcXfSgypi68DsQkhahair8qhE68eDJq/hQEfTAE/SZrZ+sgRO/u5yNt2yS7EixJUIWqWUTIYQQQgghhLQVFEjFC2cnIHUgUPwjEKxo+y2A7B341GFA1W2aAviO87G1vHmhboHGcqAEUCqAioroY1cB/M6Arq78LobOLqFwzFi3pdDdAetBYxr8aoDfiU4NwKcG4Any0Mkd9JqDiauaCkCAIAiwizLskg12yY4kWwJsokyBEyGEEEIIIYS0ExRIxZOE7nxcptKdvMubrUP7DE1EOTQAejSKNxROVZmOAcFyfmfAYBlQ/mv0c0gJPJxydQGc1nlnPndk8Hq0MwE1CJ/qh08xQicvKoIeVAa8vKWTPp6TIACiIPLASbQh0eZEqphMXeoIIYQQQgghhACgQCr+GOMxVe4HPId4KNVeg6nqyC4g+VQ+RaNUAt7jlpDqOA+qvMcA3zHewkr1AJX7+BSVCDgz+Zhezs6AKyu0bKzbUuPyvwtjzGzl5FP88Cp+VAQ8KA9WwqcG4FeCCGpB8JZOgF20wS5R6EQIIYQQQgghpO4okIo3gsiDFldXHqZUHgDchwB7StwGIC1OTgKSe/MpGtXHQyqfHlQZc6PMl68Pyp7PJ+yIfh7RzrtaOrP43KHPrZM9PWZ3CzQGFPfqoZNP8aM84EZZgAdPATWIoKpAEABZ717nEG1IdLqoex0hhBBCCCGEkEahQCpeGa2AXF15WOLez4MpWzIfBD1GIUebIDmBpJ58ioapfHB5X74eVh23LOcD/nzAf4IPvG4Mxl4dQQIcmXpgpc+dmYCjE+8WaJTLrkY9JUVTzODJo/hQoQdPXsUPvxqEoqkAGGx68OSU7OhgT4TcDrslEkKaBmMMiqbCrwUR0ENuvhw076rJ77JpKdMC5nJQU8L2M9aDWmi7L+jBRT1GYczQWD9bQgghhBBSX/RrM97JLiC5F7+LnCdPv/PcYQqmmpMg6d31MgEMjL6PFgR8BfqUD/gty758wFfIQyum6q2ujtf8mHKSHlhl8qDKnMLXmeSEXw3Ao/jgVfxwB70o8VegMuiBXw0gqCnQNAZZkuCQ7HBIdqRQ8ERIu2C9+YBPv+tl2LJ5J0xj3XJ3TCVoKQ/o4ZFepgTg15eNMMm4oyYDa/bnZRNteLjZH4UQQgghhDQ1+hXaVkhOPZjqxltMVe7nLXPkRMCeRsFUSxNt/L9FQrfq99EUIHDCElwV6sFVIeAv5GX+QkD18nGvlEreEq4GquiEKqdAk5LApBRIcjJSbalIsaeD2dPB7B2huFKhyMn0miCkFTHGbvMqfvj0uVfxw6vy7rQ+1R/qXmuM72a5wYBPL/dblvm20HJAC8b0OTokG2yiDQ6JT3bRDrskwyHZzS7BNkmGXd/HJtpC281tMuyS3dxXDboxPLNfTJ8XIYQQQghpGAqk2hrJwbuaubrwVjeV+wHPYd7ChoKp1kWU9fGlsmreT6mE5iuE352HoPcYFO8xqN4CMH8h5EAJ7EopnEo5ZBaErPkgB3xIREGNp2QQoMgpCNpSoMgdELR1QFDuAMWWEj6XUxC0JQMCvVUQAoSCI4/ig0fxw6v44A764FN5d1hPkJd5FL0s6NdbLPrgtYRKoXCJb/cpgRZpTWQwQh+n7IBTby3JgyI7X5ftYeVGV15HxL7G/sYdNfmxNv38oWPtoq1Zxp0rrTwGWbQ3+XkJIYQQQkjzo1+ZbZXkABJ78Du++Y7zwc89RwApAXCk8W5npFVijJljPbmDXpT6K1EaqIA3KMCvZoDZMyA7JTglO5wy/0EoQoCo+WALloYmpQy2YJm5LitlsAXLISvlEMD4dqUMwOFa66RISQjKKVBsyTykklOgVFlP1qcken2RViWgBuFRfKgMeuEJekPLig+eoA9uxbrsgyfo5XPFB69e5lVCc401b3DkkGxwSg64ZD45JQecsh0uyQGnWWYP2+aQjO2hIKnKdtlhBkki/eMEIYQQQgiJMQqk2jozmLK0mHIf5WNP2dN4Kx0SU341ALf+47jc78YJfxk8QR98agAa02ATZTglO5LtCciQUqttZaBJLvglF/zOLjU/IFMhKxU8rFLKIAfL9QCrHLK1TCmHrFRAgAZZrYSsVgL+2p8PgwBVSjQDqqAZVIUCK2OuSsY8ge4QSaoIqgrciheVQQ8qA15U6svuoBeVQS/c+mSUuRVj3Re2HtSUZqmfS3YgQXbCKTmQaHPCJTuRIPPQKEFfdslOPVhywmUJmYwpQd9uBFBOyQFJpLCIEEIIIYS0fZRGtBeSHUg8mXfl8x7ng597j/Fye0cKplqIxjTLj2gvirylqFQ88Cp+qJoKURDNH7PpzpTmacUgSFBsqVBsqfDWti/TIKlu2IJlPMRSyiHrrax4YMVDKznIt0mqGwKYJcA6VqcqMYh6UJUERUqCallWZH1dSoIiJ0LVyxQ5EUywU5DVSjHG4FZ8qAy4URH0oCLgQUXQg0p9ubLKstfcboRMfrVpxzxySnYk2lx6WOREos2FRJsTCbJLX+flCTYXEmUeMBlBU6LsRILNiUTZxVspUXBECCGEEEJIo1AK0d6INiDxJH2MqXzAfZAHVKLM78onOWNdwzbFaP1UGfSi1FeBYn8ZPIoPATUIQRDMbjbJrkTIYivs5iaIUOVkqHJy3fZnKmSlkodUUadKyKo+V8ohK5WQND8EaLDpIVd9aIJND6kSoUiJenCVANVaJiXq+ySELTMad6ZWqqahIuhGeYBPFQE3yoP6POBBhR42lQfcqNTnPHji603Vtc0Ij5JsLiTZEszlRNkVWjYm2WUGTdbtLtnZOv/GCCGEEELaAVVjkET6h2QSjgKp9kqU+R3gnJ35nd3ch0N3fJNcgC2Fd/cjdcYY08em4S08inylKA+44Qn6oDINsighQXaigz0ZTrmNhiGCBMXWAYqtQ90P0QKhEEuthKS49eCqEpJSaS7Lipuv68sCVIgsCHuwFAiW1ruqmmCDKiXoQVaCHlgl6GU8vFIll6U8fFkTnXHTOiugBlEecKPUX6GHS5Uo0+flAXfYsnW7O1hrG7payaKEFFsikuwJSLIlIMWeiGRbghkuGeXGurlNL0+UXdQSiRBCCCEkzkmigDve/hF7CipjXZUGG9snE/ec3zfW1WhTKJBq70SJt5ZydgaUCsBfDHjzgEAJoAUAOYGHU9SapAqj+11l0IsyfwWKfKXm3bYYY3BIdiTITmQldKSWGTVgoh1BezqC9vR6HMQgaj7ewkp187BKdeshlicUbKluSKoHkuox95FUDwQwiCwI0RjYvQ5jY1WpAgRLSKXPRZe5rEn6slnm5GX6uiY69bmjzsEWY4wPdB+oRKm/AqX+CpTpy2WBSpT73SgLVOplfF4eqIRXacATtEiQnUi2JyLFloAURxJSbIlItvNwyQiZjGWjPNmWiCSbCw7J3ix3VyOEEEIIIfFlT0Elfs6rX4+I1uTUzMRYV6HNoUCKcILAgydbCh9rKlimh1PHAP8JQAu2+3BK1VRU6mPblJoBlBcBVYEgCHDJfGDjjs4O9AO8uQkCNMmFgOQCkFm/Y5mmh1lGQOXVwyweXEmKB5Kmz1VPqFz1muGWAE0fK4tvbyivBhSqQD5zoIDZUKDJKNQkFGkCilTghMpQpKgoVlQUK0EUB/1QGtgNThQEpNiTkGJPRAd9nmJPQgc9TDLLzcApER3sPHiSaYw5QgghhBBCSBOjXxmkKkHkd+CzpwFJp1jCqTw+V42WU0lteswpVVPNQZZLfOUo8urjP2lBSILY9rvftVWCCE1KQEBKQL3DLEBvneUPBVSaF6LqhaR6EQiUo9RXihJ/GYp95Sjxu1EccONE0IcTAT9OKAGcCCooUjUUKgxuM1vyoz7NtFwCkCGFpo4i0FEKTWmyjDTZjjSbAx1kJ1LtTiTZXGB6iyxNckATHVBFJ1/Xy1RRhiYyaGIQmuiFxjRoQT/fV3KACTb+/kAIIYQQQgghjUSBFKlZlXCqnHfn8x4LdeuTHICczMeeiuOWQUYLqIqgu0oAZYz/lO5MgV2yxbqqpIVoTEOZvxInfGUo8ZfjhK8cJf5yFPvKUewrQ7G/HCVGmb+8QV3jZEFCmj0B6Y4EpNtcSLfZkSbb0FG2IcMmoaMk6KETQ6akIUNQkQQ/RNUPSfOZ4Zio+SHASLgUffIADPXNu2q+JoIdquSAJtqhCfpctOvBVmiZibaIMn0fwRZWxkQ7NMEOzbI/E2xgghTX7yeEEEIIIYSQmlEgRepOEPmd+OypQGJPHk4FSwFvPg+n/EWAIPOWU3IiILTucZM0ppm3nTe64FUGvAhqCiRRpACqjbKGTMZU7CvDCX8ZTnh5yFSsl5f6K6AyrV7nt4ky0h0pSHOmIN2RglRHMtIcKUhzJoeWLfNEm6vOXTwDAPKq28gYBBaEpPogGkGV5oeor0uqH6Lm58uascyDLb6vTy8L6GGXZR8WNB9GZAGISqBe16QhGIRQaGUGVjZogh5i6cuaaOPhl2DTwy2bpUzfLtigibI+N/azrOtzvizrLcEoDCOEEEIIIaQ5USBFGkYQAHsHPiX2ABQ3ECjloZSvCPAcA8B41z45sVV07TPuglcecKPMX4F8bwncQQ/8ahCiICLJ5qIAKo55FR+KvGU44StFka8MRd5SPXAqrRI+1Tdk6mBPQrozBWmOFKQ7U5Du6MDneuiUppelOVOQKDtjM4aYIIAJdiiiHUBK056baTyoMgKrsOVq5sxYDoYdJxjHs4DlXEGzzHw6YJD0UC0WeGAlVwm0QmWyHoLJYcuavk/VZVlf5q2/om4zH7P6deoySQghhBBC2goKpEj1VLXqpGmhuXVijM+RCigu3nrKX8YDKvUwH3dKtAGyCxBdgKS/9ESRh1sCAAh8XRR4mbEsSaF1SeTrkhg6tgY+xW/eyr7AU4yKoAdexQcBAhJsNAZUa8cYQ2XQgyJfKQq9pSjylurLJXrgVIoibxmKfKXwKL56nbuDPQkdnR3MKT1iOV0Pn9IcKbBJ7fytUhChSU5ozR0s6628jIBKYNawKqiHWca2YCjQYkEIWpDfOVELQGAKRC1oCb8U87zGfqHHCfL9La3AAEBkCsAUSFr9XlfNjUHQwynJElZJYQFWaF3Swy4prIyJ1v0itlW7Xk0Zou0jRjmWT9D3pxZohBBCCCGknf/KaudUFXC7+eT1Aj4f4PeHJmsApao8dDLmxp2+BCG0HEkQAM0GqC5A0fQufnmA5uXHiDZAdgKCnf+rv8AAZvxIYaGgSrCET2ZIpZfZbIDDDjhsgN0ORRJQjgDKmQ9FqhslQQ88CAKyCKc9ge6C14q4g149ZCpBobcEhZagqdBbwrf5SuBXg7WfTOeU7MhwpaKjMxUZerDE1zsgw5lqCZxS6M5xrZHeyksV7VDRwrfVZQyCNbhiCkRN4aEYUyBElmmKGWQJmn6cvh8PwPRJ0/dhCgRNDS2b2/ix/NxqaBsLQmAqRKaGXyLw0A5GgKZGeS5xIDxYEyMCKzFUbgZYkSEX3xbQFOSnnhXrp0MIIYQQQhqAfpG1J34/UFnJp5ISPnm9QEDvJiPqrY9kWW+FJPHAx+kMbTNCoYYGOmoQUN16F79iIOgGVL0FguTkA6SLjlC3FKPllcYsLbL0ZVUD83tQWVKEcsWD4kAFClU33PBD0TQ4RBsS7S6kSk6IkgzI5YDdxieHHbDpz1PWn6so8mVjnTSIoqk44StDobcYBd4SFHh44FTgLUGhtxiFeuBUnxZNybYEZLrSQmGTiwdMGa7UsHlCrLrLkfgnCLxLHmzQWtOfP2MQmGoGVEZgFR5eqWFzUQ+7rPuHtqt60BW+XvU80bZFK9ei7gcYjxM9MasSrDWCrDX+HIQQQgghpOVRINWWeb1ARQVQXg4UFfFlj4eHOTYb4HIBqamAw9FydZJsgJTKB0ZP6AYoPkD18GAqWKIHVeWhfUU9pLJ0mfJpAZQrAZQpXuSjDBWCDz45AMkpIVFKQyfJAVmQAMZDK6gaoKmAogA+vxlmheitsYwgShIBux1wOvhkDbEc9nbd1cSn+PWQqRj53mIUekuQ7+HzAg8PoIr9ZdCqazUXIVF2ItOVjkxXKjJcachwdUAnY90MmtKoWyVpvwS9JVE8f1yHhVZGYKVZAqyI7dAAs9wSeoHPYVn3+EpQnjQAZ8b6ORJCCCGEkHqL42+4pAqfj4dPRgBVVsbLGOOhU0IC0KFD62r9Izv55EgHWHdA9fOASvXwO/cpbqi+MlQofpQzBQWqDyVaEB6mgYHBJdqRIjnRyRZlEGdBBGSxbq9yTQ+tVMaDK48XKK8MBVcC9O6Beosxl52HVrLMW1oZLauM1mVxyB30osBbjHxPMQo8Jcj3njCDpwJPCQq8xSgPuOt0LkmQkOlKRSdXGjJcaWFzYznTlYpEm6uZnxUhJOYEUe9i1/Q3jCitPAZZpMCaEEIIISQeUSAVz4LBUABVWAiUlvJWUYzx0CQhAUhP513R4oEgmAGVR01AuehEse8EChU/KlkAgUAF7FoQSaKAVCZCNMed4v/aDjTieYr6mFQyeCuoSJoGBBU+lZYDRYo+dpY+zpXR5c/oAmi0qrLbQkGVzQiv5BbvFuhVfMj3FOO45wQPnPTgKV9fz/cWwx301ulcLtmBLFdHdErg4VKmK53PE9KQ5UpHpisN6c4U/t+HEEIIIYQQ0iiqxiCJ7beXBmm7KJCKN4wBx47xAKqoiA9Irqq8BVRiIpCWFj8BlIWiKahQPChTKlHgL0ZJsAJe1QdBEJAouZBuPwX2ZBsPnzQ/v2uf6gPUSt7tL1jOWzgJAiDKgGADJDuAJgp9RDHUZa8KxgdtV9WqXQONOxBagytJH6tKkgGXg7e6stlC43ZZx7GSZb5/Dd0Eg6piBkw8cDoRvlyPlk1JtgRkJfCAKSuhI2/RlJCOzgkdkenigVOizUXjNBFCCCGEENJCJFHAHW//iD0FlbGuSoOM7ZOJe87vG+tqkFaIAql4omnAvn3AL7/wgCQhAcjK4qFFHHIrXpQrbhQHy1DgL0Gl4oHCVDhEO5IkFzraotwNT5AAKYFPBk3hIZXm10MqD6D4gWAlD4gAQJT0kEoGBBmNak1VhRAKkWpkCa4UBfAHAK+PL1vHXBIEs8UVEwWUwIfjzM0nrRLHlXLkB8pw3F+KfH8pTvjLwVD7mE2JshNZCR2RlZBuzju50vV1vkxd6AghhBBCCGl99hRU4ue88lhXo0FOzWzhuyeTuBGfSUZ7pGnA7t3Arl18IPKkpFjXqN4UTUG54kaZUol8fzHKgpXwqj6IgoREyYlMexpsYgNekqLMJ+tt4jUF0AL65AcUrz42lQ9QFb6PoB8LuZmCqkiW4EpvaeXTgjxcCpbheKAMxwOl+lSG/CAvD1RzlyorOyRk2ZKRJacgy9YBWc5UdHbpwVNiR3RO6IgkZ5KlFZYY1+NdEUIIIaRtaitdk9rK8yCEkOZEgVQ8UFXgt9/41LEj75oXJ6prBeUU7UiSE6K3gmoKZkhlaUkFDVCDPKRiAb3bn0e/05+Ph1hGQyNR4q2xRBkQbeBBVf3qyRhDqerBMT1gOh4oxTE9eMrXl0uU2rvSCRCQYUtCZ1sHZNk7VJ3LyUgTnBA06N0GjbsLqkCFBpQHABxH2N0EjfGuJH28K5vMQzLzToN610JjbCxZ5KFdLd0HCSGEEEIaI967JgFA705JeOaanFhXgxBCWj0KpFq7YJC3itq9G+jUCXC17i5VtbWC6mRPg9yQVlBNQgQkB5+smKqHUQFAC/JJ8QKajy+r/lDXP8AMq4JMQL7qxvFgBZ/04OlY0AigyuBnwVpr5RLt6GIGTKnIsqWgiz0Vne0d0NmWik625Ia1HIvGGNdK1aKPdxVJEHgIJYr8eUuCPkC7zTL2lWgJuCwhlrksUEssQgghhNRZPHdNIoQQUncUSLVmbjcfL+rwYT5WlNMZ6xpFFbNWUE1FMO54Fx5UVSoeHPfm47i/AMd9hTjmK8Ix/wkcDxTjeKAEhcGKOozcBHSUk9DFnspDJr1Vk7Hc2d4BKVILDhJuvZtgXUQGWCoDgj7A7dVbYzGYLa/AF3kAJQKCGFq23oHQZgNs+qDushQaM8touSUK4aGWSK2yCCGEEEIIIaStoUCqtTpxAti5EyguBrp1a1UDl7fuVlB1ozENxcFyHPcX4ZjvBI75i3Dcz+f5/hM45itCheqp9Tx2QUZnRzo629PQ2d4BXWwp6CwnoouciM5yEjrZEmAHEJZciXpYA0mfB8H/FAXUt1tgs6tvgAWEh1iM8WVFAwJKaJum6deE8afM9OdtBFiICLVEfcwrYwwuWb8DoSjwllvGPmZrLkG/46LEe1ta96FwixBCCCGk3aDxvAhpvVp3atAeMQYcPQr8/DMQCAAnnRTzH9CMMXhUH8qUShQHylAQKIFb8UKFBodgQ5LkQgZLgBhUIHqDEIJlEIMKhGAQQlCBqM8FRYEQVCEqSmjdWFZViEEFUDUIqgpBUflcVSGomrkOTePr1mU94OBzBkHT4IOKPIcfh50BHHUFcSghgCPOIA4nBHE4QcGRhCACdehFluYXcZJbxsluGSd5ZJzk1icPnzL9MgRBBEQNQCmYUKqHIaKZsUDU8xbBmLPQXBT07QKYCPPOehAFMEnik966iMl8meljOfHt1jmfYC7r5XK0uQQm6/vIfFkzy/l6g8ObhoRYBv2/ITS99RXTeKssTeF/Dxqr2r2Q8WvJL6reWkuwBFtmOCWGAixJqj3csoZcghgaP0uy7EMIIYQQQlq1eB+XbGyfTNxzft9YV4OQZkGBVGvi9QJ79gD79/Oxorp2bfrH0DSgvBwoKeGtr4zJ7QZ8PnPSvF4EfW4oPg+CPjeYz4eEQADJAQWnBhRIQQViIGhOLYkBKHYBhzoAB1P5/FAH4GCH0PLx5NrPI2pA1wqgRxlwchnQo1SfW5aTAxqAgD61L0wQ9HBKD69kiYdWxmSTw9f1SZMlMJsEJstgNssxNgmaXmYea9P3sclV95FlaMa++pyX8e3VBkJMb31lBFoaC7XUUhmgBMLLagu3jHAqrNUWQi3dbLJlzCzLYPHWFllGSGcNuyJDLgE01hZpcxjjzUMZGPRFMOhdfcH/DBmYpRz6ftGPY6ETAwB8agBJor2ZnwUhhJB4F8/jkp2aGT83tCKkviiQag0YA44dA37/nYdD9R28XFGA0lLeza+4mM+NwMlYNraVlPCxf2ohgo+o5Khtxyh4YCBDs9n0wMFmljFZDgUNUih44GGGDCZJ8NqAPJeCI84AjjoCOOLw4Yjdh6M2H47avMiTPPCKtT8HF5PRFUnoylLQFUnohg7oIiShi5CCbkhBlpQMW7oEdBTAjK5yAsxWQcf1CQAsv4j03ZjxS0rfxiBoxroGQZ9DYxAY0+ea2aoLjIW16DJaeAmqFt7yS1X1cpUvqyoEJWi2HguVKRBURW9JZsxDLcvMdVWDoGiWlmdaaJsWPiKWwBiEoAIElQa8CpqfZg3GrMGVEYTZ5YhQSw4Pt4wgzFb9Pvx8EjRJNFuUGcuaJINJAjRJD5gYC02APrcEW8a6gJpDLqN7ohFuGS24jOMEMfQ6NQOviFZgQKi7o2DZZj2ONBnGWNTAJTxICa0DVffnGQwLW7eGOYhYN04VbR9zm/4+ZV0P7R/6f7DQugDBPJcgCGYd+Us4SjmqOZa/S4Y1shQgmOPlCfr/zG0CqpRbx9YLHYewczglO5JsrftmH4QQQgghJDoKpGKtogLYtw84eBCw24GTT66+m9Thw8DnnwO//hoeOJWXw/JLp25SUqClpUJJTYE/NQlulwyvXUTQJkFz2iE7EyE7EwCnA5rdDuawQ7Pbodlt0Bw2MLsNmsPOQye7zZwzm1xjNy+VaTgRKEW+vxj5gRM47juB/EAxjvtPIN9fgHx/MU4Ey+r0FDraOqCzo6M+ZSDLkY4ujgx0cWags6MjOshJNQ4WXvsIUfFMbx3EVH2uAbAsm2Ua7w6nKYAW4GFXIMi7W1pCLjFg6V6ph1iiNfQy1hUW2h40tjG+LWiEYxrv0mnux7fxsshlRd+uQFTCQ0hRUQFFBXyxb73GRIGHX3Y51MrLbgm2ZElfl0MtyYywS5b1LpMiP4esd7m0LGvWbpVmyzTjsfTWazaj9ZkNkPXuioAllLIGUpYWWkYIJlkmUQrtZ4RedQjAmN4tlYkijyUExlva6X+HoRCm+kCmpjCmuiDGGrJYQ5iqIQ1/TAH88rCw903re0UoeAmFNbzcGroAQtTAxQxUzGDFst0SyhiPIYQtC+HnM4/RtxsTABEiREHUzy9A1I8X9X1EiGYtRNHYJvJ1QQzV11qHyOdQTf3Cw6XwkMi6bN0efs7Qtal6varZHiXMEoJlkKiFFCGEEEJIXKJAKlYqK4EjR3gQ5fUCmZlV76KnabzV1JdfAl98AezeXf35RBFISwPS06udAqkpqOzgRFmyDflahTkYuSAIcIlOJEouOKWGf7HXmIaSYDny/Sf0wKm4ynJhoBQqq711k1O0I8vREV0cPFzKcqTr67yskyMddtHW4Lq2fQK/e6BQvy5gZkcapuqtfYz/Vsa6HmIxppfp+0Lfl6l6FzglIhAzfsBroUdhxlyz/uKvsXY8AGMQA3q4FdT0wErTQy9NH6fMCLH0dUuoJQT1AC1gCbyMYCwQ1NcVHsIZ4VjQMv5ZQD/G0tVP0Bg/toW7r1ZHk0RoendJcy5LUG1iaFkW+X6yURZaV/VWYZpsnEffR7LsZzPOKUOzi2HdKY0AjreAtPHwzW6DIMsQ9DBMkERAkCBIAgRRgqAHY4IedgmiCEEQIULQ9xV5qCJIEMXQsiCKEEWJBzCSCFHkobgoSWH7GwGNNYCp0mLHKI8SvESGMuH7RA9cogVR1QUt4S2DoteNRKFKDRvrjhBCmlFmkqNNDKTdFp4DIaR1o0CqpVVUAHl5PIiqrORhUUYG32Z03fv+e2DzZmDLFt4VzyBJwLBhwJln8m591sCpQ4cq48/41QAqVQ8qFA+KAqUoDVbArZ4A8zI4BDsSJRfSbSkQhdq77xgtmwoCxSjwl6AgUIx8fzEKAiUo8BfzKVCCIKu9e5cEERmONGTZ082wyWjhZLR4qq11E2lmgj6mUZO8RRhBFiwtswAebFmXLSGVGXoZxzIwTYMGFYzxCYyBMUVvTaOCMU2fM76sPzaDpreiMco0c9wavn+oBU2oBU6olY5ltBsYnfAAmK3DxKACKcggBjWIqgpJD8lERYOoaJCCvLWYqKiQFA1iUIMUVCApDGJQ5dsVTd/GgzFJb3EmBvS5cXxAMcMz0Zjry1aiqkFUNfA7OLYeTBQBm94F0dIdkRmDy0sSBJsc6qYo8bkQOQC9MbfJVddtNr1c0uc2/ph2G9/msPNlhw1wOELLNjvgtIfOYbdFaR0Gveuk0eoM4ftE6x4ZVh6xTgghpE1KccltZiDteH4OAGhAcEJaOQqkWoLfz7vWHTsGFBbyFlGpqTxI2r0b2LkT+OknYMcOvp9VQgIwfDgwfjwwejQ/rhpe1YdKxYtK1YMTgTI9gPJCYwx20YZEyYkujkxIEQGUR/WhwF+CwgAPlQoDpSi0hE2FgRKcCJRBhVbNI4cIENDR3gGd7OlmwJRl562bjFZOGfbUKnUgLYuFdY3SLGFMqEuVpneFso5aw8KOZWFBjvW8WkS3qbAxcoRQHUJ9p/hjQAAEJoTvA5gtZgARAuwRLU4iuhkxBmNUMEEfvgmMQRQEiEa5IECEwIdtYhHr4N2ZjEcTRUBiot4dClXOLTBmKWfh60yfC8Yx+nbB2BY+GV3CeB0ZBCZYtgv6eYRQHfQuZWCMjyUXVIBAEAgG+XIwCARUfTlyUkPHBPV9FGOuAAHLujGFrVuOVyKP1+dq+HuGoGmAXwP84UFZq41mrON4SRJfty5X2SaH9jHLLWGaTQIkOXy7rZqAzWYJ2GQpFLTZjX2soZvMz22zbLPL+mOJ+jn0rpzRul0a4RoiwrLIQM3Yz9ynicK1eh0fsS/TWvELiBDS3rWFgbTj+TkANCA4Ia1diwZSfr8fDz30ED755BM4nU5MnjwZkydPbskqtAzGAI+Hj+1UXAz8/DMPngoLgaIi3jpq924gP7/qsZIE9O8PjBjBp0GD+I+LCCpT4TECKL0FVIXqgVf1Q4MGO2xQocKr+lEWrERRoJRPwVJzuTBQiqJACdyqr05PK9SyKQ2Z9jRkOTqikz0NnRzp6KSvZ9pTIYvtL+cMC3j0MEaLCHgYGLR6BDzRzsnbDmlmuMPH6YF+Yzi9/U5EuMP0QMZkDXEswY5+KnNsGb5r1cDHCG8ECLzrlN4VShQsyxD0deuyAEmQLIFL+Jg2kd2a6ruPNaAyu2ZVs0+rYrYEC7XFCis3t5kbLHPLMSxiG7MuRx6H8O1Rzx15noh9qhzPwlcBQDMCqiAQCPCATFH1wCwIBAN6qBUMhWlmyKWXK4rlGCV8u6Ifo6qhgExRwoMyJSJoM8YeUyK2q1ECd1XjUyvpitkkjHHBzDtCRowhZt0WObZYlTHHohwfFtRFrItieLkRyskRA/lLEeePPJd1bpOA4blAVqwvLCGEEEIIqa8WTQ4WLlyInTt34tVXX0VeXh5mzZqFrl274oILLmjJajQNvz80sPixY3zA8bw8Pu3fz+dGAOX3V3+erCweQA0aBJx+OtC3b5WxpBRNgUf1oShQimP+EzjsPY5D3uMo0AcAL1PcqFQ8KFfcKAtWH/BrwQABAABJREFUoiRYjhPBMih1GKvJkCg5kWlPQyc9bMq0p5pBk1Gebu8QvWVTlR/40QYEalgIUJegJ1pLHo2Fd74KW7ecw9qKx9qqp7qQx7y7lCXkCRugGKEAxXjWYes1BTx6eCMJohmoSEagA5GHP0a4Ax7umEFNHYKbsEGMLfVqM8FOvDJbmsS6Iu2c0cosELCEWkFLKKYAasT2KlMwYl0P0pRgKPxS1dC+qjUYi7ZPlOVo26xzVQ/cqrubqsZ4UKioaG1dOhts1Ejg64tjXQtCCCGEEFJPLRZIeTwerFmzBi+88AIGDBiAAQMGYPfu3XjjjTdaRyAVDAKvvQYcOMBbN3k8gNvNx3kqL+djP5WXA2VlPIjy8VZFDIAqAkERUEQgKAEBia8HJCCQDPjTBfg7dYSvc0f4O3WEp3M6vJ06wtsxBV4ZqFA9KFcKUB5cj/IfvShTK1GmuFGqVqJM8aBC86JM9UBB3QMmQ4roQrqcjAwpGR3lFGTIyciQUpAppyBDTkGG3AHpchISRAcAmMGNEeVoKgPzMjCPG15WoQc5oS5amqW7V9TjLeFR6M5UOmZZEKzroR/n5ng9CLXKMYYCFkUevvDDBbNLlXV/vo8AkQmQLC11RAiQIEWEPoIZCIWCJUtAwyzBD8JDGlEQ9S5YEWEOAEEQQyEQgx4+WY8XQ+N4RbtbohH8RL2TotknDead9Op1vL69pm3mQ9WzbjWdN/Lc9VGX46rbp6Zjm6vbUUMfs7XVpz77NOS4WFyn6o616eNNuVzh3deiHR/Zva0uj1WX5cZirPrAKlrIVd0+mhZ+Dk0LP95Ytx5vLTMm63kit1u3RW6vy/KwM5ruuhFCCCGEkBbTYoHUrl27oCgKcnJyzLJhw4ZhxYoV0DQNohjbMYWObXwPV337FxxL4gGT5gK0BEDtBGgCL1Mj5orI57VjAIr0SefWp3qSBRkp9mR00KcUewpS7MlItSejgz0FHRwdkOpIQZo9FR0cybBJdj2zCO+6JRi3gIeACkGAW28Jw0tCoQmMFjyCNQwSIFla5IgQIIk8zpFE3rpHEoywh7fikSxdt6ytbMLXrcFSKLQJb9kjWAIdRDlHRCuh6n7g1RSU1GV7U5yjsY/R3Mc39NiGnNcoq2lbY45tzGNG09yPWd/r25yPWd/y2rZVt09dn399z1vXbXWpQ+T2aNe2putd38eoq5qC45qCYWPg+Gj7Wdcjg+ea9o12bF2W6/r8IssCgdCNQdqwdjPkASGEEELalRYLpAoLC5GWlga73W6WZWRkwO/3o7S0FOnp6S1VlagOZmfhm5Ob7nw2UYYs2iALEuySHTbRBrtkg02ywS454JAcsMsOOGQHXDYXXLILCbYEuGQXEu2JSLIlIdmRjGR7MpKdyUhzpCHNlYZEWyIkUTLH55FEKdSdKyK4MVvuRIY7TbidEEIAtK6As7HH1hYwRQujqguoajpnbY9RUz2bMvSrab/G7FuXx6zuukZbr26fhAS0dW1qyANCCCGEEF2LBVJerzcsjAJgrgcCgZaqRrXOPHUMdk3bhR35OwCAt+wReUsfWZQhCRIkSYIsyJBFGTbJBlmUIQsy7JIdDtkBu2Q3wydZksPCHABRl61zABTyEELiU2O65BFCqtXqhzwAoGoMkhj/f+Nt4Xm0hedACCGk/WixQMrhcFQJnox1Z8Qg3tEYt3+vrKxs+srpujm7oVuPbo07CQMfxkfld0MjhBBCSPxLTEyMyT8aNWbIg5b47mRY8cVe5JV5m/1xmstpnZJw3YgebSLMief/FoO6d8BVw05CzxQRWsAW6+o0WJaL/93F8/NoC88BaBvPg55D69EWnkfPFLFFvhcAdfvu1GKBVFZWFkpKSqAoCmSZP2xhYSGcTidSUlJqPd7t5gMujRkzplnrSQghhBASaevWrUhKSmrxx23MkAf03al+no51BQg2AHg81pVoAvsAvBbrSjRSW3gOQNt4HvQcWo+28Dz2ARi2oGUeqy7fnVoskOrXrx9kWca2bdswfPhwALyCgwYNqtOA5p06dcKXX34Zs3+hJIQQQkj7lZiYGJPHbcyQB/TdiRBCCCGxUpfvTi0WSLlcLlx66aWYN28eHnvsMRQUFGDVqlVYsKBu8ZwoiujcuXMz15IQQgghpPVozJAH9N2JEEIIIa1ZiwVSADB79mzMmzcPN910E5KSkjB9+nScd955LVkFQgghhJC40dghDwghhBBCWiuBsbrcO5oQQgghhLQ0r9eLESNGYNWqVeaQB8uXL8emTZvw+uuvx7h2hBBCCCENV/vgTYQQQgghJCasQx7s2LEDGzduxKpVqzBx4sRYV40QQgghpFGohRQhhBBCSCvm9Xoxb948fPLJJ0hKSsKUKVMwadKkWFeLEEIIIaRRKJAihBBCCCGEEEIIIS2KuuwRQgghhBBCCCGEkBZFgRQhhBBCCCGEEEIIaVEUSBFCCCGEEEIIIYSQFkWBFAC/3485c+Zg+PDhGD16NFatWhXrKsWt/Px8zJgxA7m5uTj77LOxYMEC+P3+WFcr7t1666247777Yl2NuBYIBPDQQw/hjDPOwFlnnYUnn3wSNIRewxw7dgxTp07F0KFDMX78eLzyyiuxrlLcCQQCmDBhAjZv3myWHT58GJMmTcKQIUNw4YUX4uuvv45hDeNHtGu5bds2XHPNNcjJycH555+PNWvWxLCGpLE+/fRT9OnTJ2yaMWNGrKsVF+i9pvGiXcNHHnmkymvy9ddfj2EtW6eafhfQ67BuarqG9Dqsm4MHD2LKlCnIycnB2LFj8eKLL5rb6HVYNzVdw8a+DuXmqHC8WbhwIXbu3IlXX30VeXl5mDVrFrp27YoLLrgg1lWLK4wxzJgxAykpKXjjjTdQVlaGOXPmQBRFzJo1K9bVi1sbNmzAl19+icsuuyzWVYlrjzzyCDZv3oyXXnoJbrcbd955J7p27Yprrrkm1lWLO3//+9/RtWtXrFu3Dnv27MHdd9+Nbt264Y9//GOsqxYX/H4/7rrrLuzevdssY4xh2rRpyM7Oxtq1a7Fx40bcfvvt+Oijj9C1a9cY1rZ1i3YtCwsLccstt+Daa6/F448/jp9//hmzZ89GZmYmxo4dG7vKkgbbs2cPxo0bh/nz55tlDocjhjWKD/Re03jRriEA7N27F3fddVfYd7OkpKSWrl6rVtPvgnvvvZdeh3VQ228reh3WTtM03HrrrRg0aBDee+89HDx4EDNnzkRWVhYmTJhAr8M6qOkaXnTRRY1+Hbb7QMrj8WDNmjV44YUXMGDAAAwYMAC7d+/GG2+8QYFUPe3btw/btm3DN998g4yMDADAjBkz8MQTT1Ag1UClpaVYuHAhBg0aFOuqxLXS0lKsXbsWL7/8Mk4//XQAwOTJk7F9+3YKpOqprKwM27Ztw/z589GzZ0/07NkTZ599NjZt2kSBVB3s2bMHd911V5XWed999x0OHz6Mt99+GwkJCTj11FOxadMmrF27FtOnT49RbVu36q7lxo0bkZGRgZkzZwIAevbsic2bN+ODDz6gQCpO7d27F9nZ2cjMzIx1VeIGvdc0XnXXEOCvySlTptBrsgY1/S74wx/+QK/DOqjttxW9DmtXVFSEfv36Yd68eUhKSkLPnj0xcuRIbN26FRkZGfQ6rIOarqERSDXmddjuu+zt2rULiqIgJyfHLBs2bBi2b98OTdNiWLP4k5mZiRdffNF8wzRUVlbGqEbx74knnsAll1yC3r17x7oqcW3r1q1ISkpCbm6uWXbrrbdiwYIFMaxVfHI6nXC5XFi3bh2CwSD27duH//73v+jXr1+sqxYXvv/+e4wYMQLvvPNOWPn27dvRv39/JCQkmGXDhg3Dtm3bWriG8aO6a2l0aYhEn0Xxa+/evejZs2esqxFX6L2m8aq7hpWVlcjPz6fXZC1q+l1Ar8O6qeka0uuwbjp16oSnn34aSUlJYIxh69at2LJlC3Jzc+l1WEc1XcOmeB22+xZShYWFSEtLg91uN8syMjLg9/tRWlqK9PT0GNYuvqSkpODss8821zVNw+uvv44zzzwzhrWKX5s2bcIPP/yADz74APPmzYt1deLa4cOH0a1bN6xfvx4rVqxAMBjE5Zdfjr/97W8QxXafy9eLw+HAAw88gPnz5+O1116Dqqq4/PLLcdVVV8W6anHhuuuui1peWFiITp06hZV17NgRx48fb4lqxaXqrmX37t3RvXt3c/3EiRPYsGED/WtnnGKMYf/+/fj666/x/PPPQ1VVXHDBBZgxY0bYdzcSjt5rGq+6a7h3714IgoAVK1bgP//5D1JTU3HzzTfT0AoRavpdQK/DuqnpGtLrsP7Gjx+PvLw8jBs3Dueffz4ee+wxeh3WU+Q13LlzZ6Nfh+0+kPJ6vVW+0BjrgUAgFlVqMxYtWoRffvkF7777bqyrEnf8fj8efPBBPPDAA3A6nbGuTtzzeDw4ePAg3n77bSxYsACFhYV44IEH4HK5MHny5FhXL+7s3bsX48aNw80334zdu3dj/vz5GDlyJC6++OJYVy1uVfdZRJ9DjePz+TB9+nRkZGTg6quvjnV1SAPk5eWZfx9PP/00jhw5gkceeQQ+nw/3339/rKsXd+i9pvH27dsHQRDQq1cv3HDDDdiyZQvmzp2LpKQk6rpeA+vvgldeeYVehw1gvYY///wzvQ7racmSJSgqKsK8efOwYMECej9sgMhrOGDAgEa/Dtt9IOVwOKq86Ix1CgIabtGiRXj11Vfx1FNPITs7O9bViTvLli3DwIEDw/5VhDScLMuorKzE4sWL0a1bNwD8R85bb71FgVQ9bdq0Ce+++y6+/PJLOJ1ODBo0CPn5+XjuuecokGoEh8OB0tLSsLJAIECfQ43gdrtx22234cCBA3jzzTfhcrliXSXSAN26dcPmzZvRoUMHCIKAfv36QdM03HPPPZg9ezYkSYp1FeMKvdc03qWXXopx48YhNTUVANC3b18cOHAAb731FgUB1Yj8XUCvw/qLvIannXYavQ7ryRiT1+/34+6778YVV1wBr9cbtg+9DmsWeQ3/+9//Nvp12O77qmRlZaGkpASKophlhYWFcDqdSElJiWHN4tf8+fPx8ssvY9GiRTj//PNjXZ24tGHDBmzcuBE5OTnIycnBBx98gA8++CBsrDNSd5mZmXA4HGYYBQCnnHIKjh07FsNaxaedO3eiR48eYR/W/fv3R15eXgxrFf+ysrJQVFQUVlZUVFSlKTmpm8rKSkyZMgW7d+/Gq6++SmNsxLnU1FQIgmCun3rqqfD7/SgrK4threITvdc0niAI5o8vQ69evZCfnx+bCrVy0X4X0OuwfqJdQ3od1k1RURE2btwYVta7d28Eg0FkZmbS67AOarqGlZWVjX4dtvtAql+/fpBlOWzwsq1bt2LQoEE0tkwDLFu2DG+//TaefPJJ/OlPf4p1deLW6tWr8cEHH2D9+vVYv349xo8fj/Hjx2P9+vWxrlpcGjx4MPx+P/bv32+W7du3LyygInXTqVMnHDx4MKxl6b59+8LG7CH1N3jwYPz888/w+Xxm2datWzF48OAY1io+aZqG22+/HUeOHMHq1atx2mmnxbpKpBG++uorjBgxIuxfsX/99VekpqbSOJ8NQO81jffMM89g0qRJYWW7du1Cr169YlOhVqy63wX0Oqy76q4hvQ7r5siRI7j99tvDApKdO3ciPT0dw4YNo9dhHdR0DVevXt3o12G7T1xcLhcuvfRSzJs3Dzt27MDGjRuxatUqTJw4MdZVizt79+7Fs88+i1tuuQXDhg1DYWGhOZH66datG3r06GFOiYmJSExMRI8ePWJdtbjUq1cvjB07FrNnz8auXbvw1VdfYeXKlbj22mtjXbW4M378eNhsNtx///3Yv38/PvvsM6xYsQI33nhjrKsW13Jzc9GlSxfMnj0bu3fvxsqVK7Fjxw5ceeWVsa5a3Hn33XexefNmPPLII0hJSTE/hyK7h5D4kJOTA4fDgfvvvx/79u3Dl19+iYULF+Ivf/lLrKsWl+i9pvHGjRuHLVu24KWXXsKhQ4fw5ptvYv369TQEQISafhfQ67BuarqG9Dqsm0GDBmHAgAGYM2cO9uzZgy+//BKLFi3CX//6V3od1lFN17ApXocCY4w1Y/3jgtfrxbx58/DJJ58gKSkJU6ZMqZL0kdqtXLkSixcvjrrtt99+a+HatC333XcfAODxxx+PcU3iV0VFBebPn49PP/0ULpcL1113HaZNmxbWDYTUzZ49e/Doo49ix44dSE9Px/XXX4+bbrqJrmU99enTB6+99hpGjBgBADh48CD+8Y9/YPv27ejRowfmzJmDs846K8a1jA/WazllyhR8/fXXVfbJzc3F6tWrY1A70li7d+/GY489hm3btiExMRHXXHMNvX/XA73XNF7kNdy4cSOWLFmCAwcOoFu3brjzzjtx3nnnxbiWrUttvwvodVi72q4hvQ7rJj8/H/Pnz8emTZvgcrlwww03YOrUqRAEgV6HdVTTNWzs65ACKUIIIYQQQgghhBDSotp9lz1CCCGEEEIIIYQQ0rIokCKEEEIIIYQQQgghLYoCKUIIIYQQQgghhBDSoiiQIoQQQgghhBBCCCEtigIpQgghhBBCCCGEENKiKJAihBBCCCGEEEIIIS2KAilCCCGEEEIIIYQQ0qIokCKEEEIIIYQQQgghLYoCKUJIXOjTpw/uuuuuKuXr1q3D+PHjY1AjQgghhBBCCCENRYEUISRufPjhh9i0aVOsq0EIIYQQQgghpJEokCKExI1u3brh4YcfRiAQiHVVCCGEEEIIIYQ0AgVShJC48fe//x35+fl46aWXqt3n+PHjuOOOO5Cbm4sRI0bgkUceMQOsdevW4cYbb8SSJUswYsQIDB8+HAsWLABjzDz+7bffxvjx45GTk4Mbb7wRv/32W7M/L0IIIYQQQghpbyiQIoTEjaysLMyYMQMrVqzA4cOHq2wPBAK46aab4PV6sXr1ajz99NP44osvsHDhQnOfH3/8Efv378dbb72FuXPn4rXXXsO3334LAPjss8+wbNkyzJ07F++99x6GDRuGiRMnoqysrMWeIyGEEEIIIYS0BxRIEULiyo033ogePXrg0UcfrbLtq6++Qn5+PhYtWoQ+ffpg5MiReOCBB/DWW2/B7XYDAFRVxfz589GrVy9ccskl6Nu3L3766af/z959x0dR538cf83M9nQg9A6CgHRMROBA7F2xHpZDLKignuKp+Dt74c5+CqjYu5717L03VKTYkN5berLJ9pnfH7Mz2U0jAZKQ8Hn6mMfMTtvvJLtm9833+xkAHn30UaZNm8ZBBx1Ez549+fvf/06XLl148803m/QahRBCCCGEEKK1czR3A4QQoiE0TePGG29k8uTJfPzxx0nbVq1aRc+ePcnIyLDXjRgxgmg0yvr16wFo27Ytqamp9vbU1FSi0ah9/J133sk999xjbw+FQqxdu7YRr0gIIYQQQggh9j4SSAkhWpwRI0Zw0kkncdttt3HeeefZ691ud7V9Y7FY0tzlclXbx6ohFYvFuPbaaxk9enTS9sQASwghhBBCCCHErpMhe0KIFunKK6+koqIiqcB5r169WLt2LcXFxfa6xYsX43A46N69+w7P2atXL7Zu3UqPHj3s6aGHHmLx4sWNcAVCCCGEEEIIsfeSQEoI0SJlZWVx5ZVXsmnTJnvdmDFj6NatG1dddRV//vkn33//PbfccgvHHHMM6enpOzznOeecw1NPPcUbb7zB+vXrufPOO3nvvffo06dPY16KEEIIIYQQQux1ZMieEKLFOvnkk3n11VfZvn07YNaXmjdvHrfccgunnnoqKSkpHHvssVxxxRX1Ot9RRx1Ffn4+999/P/n5+fTt25cHH3yQnj17NuJVCCGEEEIIIcTeRzGs4ilCCCGEEEIIIYQQQjQBGbInhBBCCCGEEEIIIZqUBFJCCCGEEEIIIYQQoklJICWEEEIIIYQQQgghmpQEUkIIIYQQQgghhBCiSUkgJYQQQgghhBBCCCGalARSQgghhBBCCCGEEKJJSSAlhBBCCCGEEEIIIZqUBFJCCCGEEEIIIYQQoklJICWEEEIIIYQQQgghmpQEUkIIIYQQQgghhBCiSUkgJYQQQgghhBBCCCGalARSQgghhBBCCCGEEKJJSSAlhBBCCCGEEEIIIZqUBFJCCCGEEEIIIYQQoklJICWEEEIIIYQQQgghmpQEUkIIIYQQQgghhBCiSUkgJYQQQgghhGjxDMNo7iaIPZC8LoTYc0kgJYTY7c466yz69+/P6aefXus+l19+Of379+eaa67Zrc/9wAMP0L9//916zvrauHEj/fv357XXXmuW5xdCCNH6LVy4kEsuuYQxY8YwePBgDj74YP75z3+yatWq5m5akqb+e7xw4UIuuOCCJnu+PcFvv/3G+eefzwEHHEBubi5Tp07lt99+S9rHMAwee+wxDjvsMAYPHszhhx/Oc889t8Nzr1y5kmnTprH//vuTm5vL1VdfTV5eXq37b9myhZEjR/LAAw80+DquueYa+vfvnzQNGjSIsWPH8o9//IMtW7bU+1y33HIL9957LwBbt27lggsuYNOmTQ1u084466yzOOuss+rcZ2feF/U5ZvXq1UycOJHS0tIGnduSn5/PzJkzyc3NZeTIkVxxxRVs3759h8d99tlnnHzyyQwePJi//OUv3H777ZSXl9vba/rdJk5N9bsRey5HczdACNE6qarK4sWL2bp1Kx07dkzaVlFRwWeffdZMLRNCCCFapvnz53PPPfcwduxYrr32WrKzs1m3bh0vvPACJ554IrNnz+boo49u7mY2i5dffnmPC+Ua07p16zjzzDPZb7/9uO2221AUhccff5zJkyfz+uuv07t3bwDuuOMOnnnmGS699FIGDx7Ml19+yc0334zD4eC0006r8dzbtm3j7LPPpnv37tx5550EAgHuvfdezjnnHF5//XWcTmfS/oZhcO211+L3+3f6erKzs5kzZ479OBqNsmbNGu666y4WLVrE22+/jcfjqfMc3333HR999BEffPABAN9++y1ffPHFTrepMZxyyimMGzdut5+3d+/eHHzwwdx6663ccccdDTo2Go1y/vnn4/f7ufHGG4lGo9x9992ce+65vPbaa9V+35aPPvqISy65hJycHO677z4ikQjz5s1j0aJFvPDCCzgcDi6++OJq/0BdUlLCZZddRk5ODp07d97paxatgwRSQohGMXDgQFauXMn777/PlClTkrZ99tlneL1e0tPTm6dxQgghRAvz2Wefcffdd3PJJZcwY8YMe31OTg4nnHACM2fO5JprrqFfv37ss88+zdhS0RSeeeYZvF4vDz/8MD6fD4ADDjiAiRMn8uyzz3L99dezceNGnnzySa677jomT54MwOjRo9myZQtff/11rYHUyy+/TFlZGQ8++CBZWVkAtGnThrPPPpvvv/++WqDy/PPPs3r16l26HpfLxbBhw5LWjRo1CqfTydVXX80nn3yyw7B19uzZTJkyBa/Xu0ttaUwdO3as9g+1u8sFF1zAhAkT+Nvf/sagQYPqfdz777/P77//zjvvvEPfvn0BGDBgAMcccwzvvfcexx13XI3HPfDAA/Tp04dHH30Ul8sFmL+zQw89lNdee41TTz2V7t27071796TjLrnkEjIyMrjrrrtQFGUnr1a0FjJkTwjRKHw+H+PHj+f999+vtu3dd9/l8MMPx+FIzsQLCwu56aabOOigg9hvv/3Iyclh+vTpbNy40d5n/fr1XHjhheTm5jJ06FBOO+20Ov/1a/PmzUyYMIFJkybV2Y35l19+4dxzzyU3N5cRI0Zw4YUXsmLFCnv7ggUL6N+/P9999x1Tp05l6NChjBkzhjvvvJNYLFbtfMXFxQwePJh77rknaX0gEGDkyJE8+OCDtbZFCCGEqGrOnDn07t2b6dOnV9vmdDq5+eab0TSNRx55BICpU6cyadKkavtefPHFSV8wf/rpJ84880yGDh1KTk4OV199NYWFhfb21157jYEDB/Lyyy8zZswYcnJyWLlyZb3/Hn/++eccd9xx9nCxN954I2n79u3bmTVrFuPHj2fIkCGcfPLJfPLJJ0n7hEIh5s6dyxFHHMHgwYM57LDDmD9/PrquA+awoNdff51NmzbVOXT+gQce4IgjjuCjjz7imGOOYfDgwRx//PEsWrSIxYsXc8oppzBkyBCOOeYYvvvuu6Rjly9fzrRp0xgxYgQjRoxg+vTpbNiwIWmfZcuWMWPGDA444AAGDRrEuHHjuPXWWwkGg/Y+/fv357nnnuP//u//yMnJYfjw4Vx22WXk5+cn/cz79+/PggULarwOMHvETJ061Q6jwPzs1bFjR9avXw/Axx9/jNvt5uSTT0469r777qtzaN3kyZN5/vnn7TAKsHvJhEKhpH03bNjAXXfdxS233FLr+XbF4MGDAeyhXddccw1/+9vfuOGGGxgxYgRHHXUUsViMzz//nOXLl9uh1WuvvcasWbMAOPjgg+0SEbFYjOeee45jjz2WIUOGMGHCBO66665q1/XNN98wefJkRo4cSW5uLjNnzqzX0EHDMHjkkUeYMGECQ4YM4bTTTmPp0qX29pqG3z322GMcfPDBDBkyhNNPP51PP/20xt//jt5L2dnZHHDAATz88MP2uvq8lr7++mt69eplh1EAffv2pU+fPnV+xl69ejVjx461wyiAdu3a0bt3bz7//PMaj/niiy/48MMPmTVrlvzDtAAkkBJCNKKjjjrKHrZn8fv9fPnllxxzzDFJ+xqGwbRp0/jmm2+48soreeyxx5gxYwbfffcdN9xwAwC6rjNt2jQCgQB33HEH8+bNIzMzk4suuoh169ZVe/68vDymTJlCZmYmTzzxRK1/+L7//nv++te/AnD77bdz6623smXLFk4//fRq3f+vvPJKRo4cyUMPPcQxxxzDo48+yssvv1ztnJmZmRxyyCG89dZbScU0P/roIyoqKjjhhBPq90MUQgix1yssLOTXX3/loIMOqrVHQWZmJgceeKAd5hx33HH89ttvSX8fS0tL+fLLLzn++OMB+PHHH5kyZQoej4f77ruPa6+9lh9++IGzzz47KUSJxWI8/vjj3HbbbcyaNYtevXrV++/x9ddfz5QpU3jwwQfp2LEj11xzDcuWLQPMujUnn3wyP/30E5dffjkPPPAAXbp0Yfr06bz55puA+fngwgsv5NFHH+WUU07hoYce4ogjjuC+++6zPx9cfPHFjB8/nuzsbF566SUmTJhQ689y69at/Otf/+LCCy/kP//5D6WlpVx66aVcccUVnHLKKcydOxfDMLj88svtn8GaNWs4/fTTKSgo4N///je33XYbGzZs4K9//SsFBQWAGaydccYZBAIB/vWvf/HII49w9NFH88wzz/D0008nteHee+9F13XuuecerrrqKj777DNuv/12e/uECRN46aWX6uzlMnnyZM4777ykdevWrWPFihV2D7k//viDHj168OOPP3LiiScyaNAgJk6cyEsvvVTrecHsDWUFQaFQiMWLF3PzzTfTvXt3xo4da++n6zrXXHMNRx55JH/5y1/qPOfOWrNmDUBSL5uffvqJLVu2MHfuXGbOnImmabz55psMGzaMDh06AObP8KKLLgLMMPfiiy8GzNfj7NmzOeSQQ3jwwQc544wzePbZZ7n44ovtz2tvvPEGU6dOpVOnTtxzzz3MmjWLRYsWcdppp9m/79osXLiQjz76iOuuu44777yT7du3c9FFFxGNRmvcf86cOdx1110ceeSRzJs3j6FDh/L3v/+9xn3rei9ZjjjiCD799FO7jlN9XkurVq2iZ8+e1dZ3797d/vnXJDMzk82bNyeti0QibNmypVpYC+Z7+d///jc5OTkcccQRtZ5X7F1kyJ4QotFMmDABr9ebNGzvo48+om3btowcOTJp3+3bt+P1ern66qsZNWoUALm5uaxfv97+4FRQUMDq1avtD54AQ4YMYc6cOYTD4aTzFRUVcc455+DxeHjiiSfIyMiotZ133303PXr0YP78+WiaBsDYsWM59NBDuf/++/nPf/5j73vKKafY/zo9evRoPv74Yz7//PMaC7ifdNJJvPvuuyxYsIADDjgAMD/kHHjggXTq1KneP0chhBB7N6t3SJcuXercr0ePHnzyySeUlJRw2GGHcdNNN/H222/bf7c+/PBDYrGY/Y9Cd999N7169eLhhx+2//4NHTqUo48+mldffZUzzjjDPveFF15oBz15eXn1/nt866232mFF9+7dOfTQQ/nhhx/Yd999eeKJJygsLOSDDz6wr238+PFMmTKFO+64g2OOOYavvvqKb7/9lnvuucfu/TJmzBg8Hg//+c9/OPvss9lnn31o06ZNjcO+qgoEAtxwww12m1auXMndd9/NbbfdZvckqqio4NJLL2XNmjUMGDCAOXPm4PV6efLJJ0lNTQXMzwCHHHIIjz76KFdffTXLly9nwIAB/Oc//7H3OfDAA/nmm29YsGBBUsH1fv36MXv2bPvx0qVLk3qUt2nThjZt2tR5HVUFg0GuvvpqXC4XZ555JmAGmdu2bePKK69kxowZ9O7dm3fffZfrr78eoNYhe4mOO+441q5di8fjYc6cOUl1nJ566ik2btzIQw891KC21iYxtPH7/fzyyy/Mnj2brl27JoWM0WiUm2++OWno2/fff580pK9NmzZ2iDVgwAC6du3KypUreeWVV5g5c6b9+xgzZgzt27fnqquu4ssvv2TcuHHcddddjB07lrvvvts+n9Ub67HHHuOqq66q9RpcLhfz588nMzMTMEPgf/7zn6xcuZJ99903ad+KigoeeeQRzjjjDK688krA/PwZCARqDA3rei9ZBg8eTCQS4aeffmL8+PH1ei2VlZXRo0ePautTUlKSCpRXddJJJ/HQQw8xf/58Tj75ZILBIPfddx9lZWVJPfcsn376KatWreKf//xnne0RexfpISWEaDQej4eJEycmfch65513OPLII6v9C2+HDh14+umnGTlyJBs3buSbb77hmWee4eeff7Y/3LZr146+ffty3XXXcfXVV/PWW2+h6zqzZs2qVi/jvPPOY8WKFVx77bVJXc6rqqio4JdffuHII4+0P4wDpKenc9BBB/HDDz8k7T98+PCkxx07dqSioqLGcx944IF07tyZ//3vf4D5r7LfffcdJ554Yq3tEUIIIaqyem7UVlzYYv0dMwwDn8/HIYccwrvvvmtvf+eddxg9ejQdOnQgEAiwZMkSxo8fj2EYRKNRotEo3bp1o0+fPnzzzTdJ5x4wYIC93JC/x9Y/MgF07doVwB5C/8MPPzB8+PBqQdtxxx1nh14//PADDoejWo8Ka9hh1b/T9TFixIikawEziLMkhglghh05OTl4PB7755SamsqoUaP49ttvATNIePbZZ3G73axcuZJPPvmEBx98kMLCwmohXdXQrGPHjgQCgQZfh8Xv9zNt2jR++eUX7rzzTvvnGYlEKCoq4qabbuKMM85g9OjR3HLLLYwdOzapgHhdbrjhBh577DFGjx7NhRdeyFdffQWYvWruu+8+br75ZtLS0na67ZZNmzYxaNAge8rNzeW8886jbdu2zJ07NykIy8zMTAqjKioqKCgosF9ftbFeK1VrUR199NFomsaCBQtYs2YNeXl51Xryd+/eneHDh+/w9da3b1/79QOVr/mysrJq+y5evJhgMFjttV31uS11vZcs1u8+sdzFjiT25K+qrhpPl1xyCeeffz73338/o0eP5rDDDiMlJYWDDz64xjpezz33HAMGDODAAw+sd9tE6yc9pIQQjerII49kxowZbN26FbfbzXfffVdrV+Q333yTe+65hy1btpCZmcmAAQOSPoBYd5B58MEH+eijj3jjjTdwOp0ccsgh3HTTTUm9oAKBAF27duXuu+/mpZdeQlVrzt/LysowDMP+QJqoXbt21T5AVL3Di6qqtf4hV1WVSZMm8cQTT3DDDTfwv//9j9TUVA499NAa9xdCCCFqYn3J3NEt0jds2EBKSor9hfj444/nzTffZNmyZbRr144FCxbYQ8NKS0vRdZ1HHnnErjuVyO12Jz1O7PHQkL/HicdZf4utv5slJSV069at2nNbf5NLS0spKSkhKysr6R+NwKyXAzV/0d8RqwdToroKYRcXF/Puu+8mhXsWq/eJNQTvueeeo6Kigk6dOjFkyJBqP8eanquuzxI7smXLFqZNm8aaNWu49957OeSQQ+xtKSkpKIpi92KzjBs3jq+//pr8/PwaP/8kssKDAw44gKOPPppHHnmEAw88kFmzZnHEEUcwZsyYpJ5Nuq4TjUar1Qndkezs7KT6mi6Xi44dO9bYwz0lJSXpsfUaqKlXTqKSkhL7uRI5HA6ysrIoKyujuLgYoNbPhb///nudz1G1DdZr3qp3lsiq1Va1B1Pbtm13eO6q7yWL9dpqyB0PU1NTa+wJ5ff76wwbHQ4HV155JZdccgkbNmygffv2pKenc8YZZ1T7vRUXF7NgwQKuuOKKerdL7B0kkBJCNKq//OUvpKSk8P777+Pz+ejatSv77bdftf1++uknrr76as466yzOPfdcuwbAHXfcwcKFC+39OnTowI033sgNN9zAsmXLeP/993nkkUfIysqya0mA2Y38jz/+4Pzzz+fpp5+udqc/S1paGoqiJBUTteTl5SX9K9fOmDRpEnPnzuXLL7/kvffe46ijjqrxw6kQQghRm7Zt2zJs2DA++OADLrvsshr/kcXv9/PNN98wceJEe93o0aPJzs7mvffeIzs7G7fbzWGHHQZUhhVTpkyp8e5lO7pTWX3/HtclIyODvLy8auutdVlZWWRkZFBUVEQsFksKpbZv327v09jS0tI48MADOeecc6pts4KX+fPn8+STT3LTTTdx2GGH2V/kqxYU353+/PNPzj33XEKhEI8//jj7779/0vYePXpgGAaRSCTps4cVIFX9RzbL999/TygUSgqyHA4H/fv3Z/ny5WzZsoUlS5awZMmSaoW1582bx7x58/jkk0922GMpkcvlsutWNZT1Gqjr5jWAHZLk5eUl9cqzepJlZWXZn/tq+1y4O19vVi+vgoICevfuba9PvKlAQ1k/g4a0s1evXvzxxx/V1q9fv54hQ4bUetyCBQsIh8OMGzfOLogejUZZvnx5tdEAX331FdFoVGpHiWpkyJ4QolG5XC4OOeQQPvjgA957771ab9m7aNEidF3nkksuscOoWCxmd4XXdZ1FixZx4IEHsnTpUhRFYcCAAVx++eX069evWlHF7Oxs/vKXv3DkkUfyn//8p9auyz6fj/3224/33nsv6W55ZWVlfP7559VqXTVUly5dGD16NE8//TR//PFHjXc8EkIIIXZkxowZrFmzptrdW8H8e3nDDTcQDAaTCl1rmsaxxx7LZ599xvvvv88hhxxi97JITU1l4MCBrF69msGDB9vTPvvswwMPPFDnXbka8ve4Lvvvvz+LFi2q1vPrzTffJDs7mx49epCTk0M0Gq12116r6Ln1d7q2ntC7g3VnwQEDBtg/p/32248nn3ySjz76CDCLWfft25eTTjrJDqO2bdvG8uXLa+wds6u2bNnCOeecg6IovPDCC9XCKMAOlN55552k9dZd3GrqKQbwv//9j6uuuiqpl43f72fRokX079+f9u3b88orr1SbAE499VReeeUV2rdvv7sudYdcLhfZ2dnV7oJX9TWRk5MDVP95vPPOO8RiMUaOHEmvXr3Izs7m7bffTtpnw4YNLF68OGm4567ad999SUtLs19Dlg8//HCnz2ndSKhz5871Pmbs2LGsWrWKlStX2utWrlzJqlWrGDNmTK3HffDBB1x33XVEIhF73auvvkppaWlSTz2AJUuW0LFjxx3WwRN7H+khJYRodEcddRTTpk1DVdVaCxla/wJz8803c9JJJ1FSUsJzzz1n3z2koqKCgQMH4vF4uOqqq7jkkkto164d3377LX/88Qdnn312jee99tpr+eqrr+waCDWZOXMm5557LhdccAGTJ08mEokwf/58wuFwjbfXbqiTTz6ZK664gj59+iTVqBBCCCHqa9y4cVxzzTXccccd/PHHH5x00km0b9+ejRs38sILL/DHH39w2223VSucfPzxx/P444+jqmq1oXlXXHEFF1xwATNnzuS4446z76a3ZMkS+65kNdmZv8c1Oeecc3jzzTeZMmUKM2bMIDMzkzfeeIPvv/+e22+/HVVV+ctf/kJubi7//Oc/2bZtG/vuuy8//PADjzzyCCeeeKLdMyM9PZ38/Hy++OILBgwYsFsDkYsvvpjTTz+dadOm8de//hW3281LL73Exx9/zP333w+Yn2PmzZvH/PnzGTZsGOvWrePhhx8mHA43uD5UYWEh69evp2/fvrWGRrfeeisFBQXcdNNN+P1+Fi9ebG9LTU2lb9++5ObmctBBBzF79mwCgQD77LMPb7zxBj///DPz5s2z91+/fj2FhYV2bavzzjuP999/n4suuohzzz2XcDjMI488Qnl5OZdcckmdvZnat2+ftK3quRvLmDFj+Pnnn5PWWXdX/uijj/jLX/5C3759OfHEE7n//vsJBALsv//+/PHHH8yZM4fc3FzGjRuHqqpcccUVzJo1y35fFBUVMWfOHDIyMmrsJbezUlNTOe+887j//vvxer3k5OTwww8/8MILLwA7F7IuXLgQr9dr15uqz2vpqKOO4qGHHuL8889n5syZgHnDg379+nHkkUfa+/3++++4XC77PXf66afz3//+l2uuuYaTTz6ZZcuWcffdd3PUUUfZ4Z/lzz//tI8TIpEEUkKIRnfggQeSnp5Op06d6NOnT4375Obmcv311/PEE0/w/vvv065dO3Jzc5kzZw7Tp09n4cKFjB8/nscff9y+G05paSk9e/bk5ptvrrXnUfv27bniiiu4+eabeeONNzjhhBOq7TN69GieeOIJ7r//fq644gpcLhejRo3i3//+d7XirDtj/PjxKIoivaOEEELsknPOOYfhw4fz1FNP8e9//5vCwkKys7MZM2YMt912W41f+Pbdd1/69etHUVERo0ePTto2duxYHnvsMebMmcOll16K0+lk0KBBPPHEE3UGCG63u8F/j2uSnZ3NCy+8wN13382tt95KJBJh3333Zd68eRx88MGAWa/q4Ycf5v777+fJJ5+ksLCQrl27csUVVySFA5MmTeKLL75g+vTpXHrppUl3tdtV++67L8899xz33nsvV111FYZh0K9fP+bOnWu3c9q0aRQVFfH0008zd+5cOnXqxPHHH2+3v7S01A5IduTzzz9n1qxZPP300+Tm5lbbHg6H+fzzzwFqHB6Zk5PDM888A8B//vMf5syZY9/RsG/fvsyZMydpaOe8efN4/fXX+fPPPwHo06cPzz33HHfffTdXXXUV0WiUnJycWl9jdal67sZy+OGH89Zbb7Ft2za7p31ubi4HHnggd999N9999x3z58/ntttuo0ePHrz66qs88sgjtG/fnrPPPpuLL77YDoAmTZpESkoKDz/8MNOnTyc1NZVx48ZxxRVXVKs/taumTZuGYRi89NJLPPbYYwwdOpQrr7yS2bNn77AmVk2+/PJLJkyYYA/H3NFrCcweZk888QS33XYb1113HU6nkzFjxjBr1qykWmAzZsygS5cu9murX79+PPzww9x9991ceOGFtGvXjgsvvJBp06ZVe46CggL69+/f4OsRrZ9i7GwFPSGEEPXy7rvvctVVV/HFF1/UWqhSCCGEEKI1Ouyww3ZpGFp9GIbBcccdx+GHH86MGTMa9bl2l2g0yttvv01ubi6dOnWy1z/33HPceuutLFiwoN4hJpg3PTj00EN55ZVXGDhwYGM0WYjdTnpICSFEI/n444/55ZdfePHFF5k0aZKEUUIIIYTYq/zvf/9LKtjdWBRF4R//+AfXXnstU6ZMqXV42p7E4XDwyCOP8NRTT3HRRReRlZXF8uXLue+++zjhhBMaFEYBPP744xxxxBESRokWRXpICSFEI3nyySe57777GDlyJPfdd1+dt84VQgghhGhtVq9eTceOHXdq+NnOuOGGG0hPT7drIe3pNmzYwD333MOCBQsoLS2lc+fOHHfccUybNg2n01nv86xatYrzzjuP119/fZfvEC1EU5JASgghhBBCCCGEEEI0qca7P6oQQgghhBBCCCGEEDXY6UAqHA5zzDHHsGDBAnvdhg0bmDJlCsOGDeOoo47i66+/Tjrm22+/5ZhjjmHo0KGcffbZbNiwYedbLoQQQgghhBBCCCFapJ0KpEKhEFdccQUrVqyw1xmGwfTp02nXrh2vvvoqxx9/PDNmzGDz5s0AbN68menTpzNp0iReeeUV2rRpw8UXX0x9RwwahoHf76/3/kIIIYQQezP57CSEEEKIPVmDA6mVK1dy6qmnsn79+qT133//PRs2bODmm2+mT58+TJs2jWHDhvHqq68C8PLLL7PffvsxdepU9tlnH2bPns2mTZv44Ycf6vW85eXljBw5kvLy8oY2WQghhBBiryOfnYQQQgixJ2twIPXDDz+Qm5vLSy+9lLR+yZIlDBw4MOkOCiNHjmTx4sX29lGjRtnbvF4vgwYNsrfvEaIVEC4BPdbcLRFCCCGEEEIIIYRotRwNPWDy5Mk1rs/Ly6N9+/ZJ69q2bcvWrVvrtb3ZFf8CH+RALGg+1rzgSAVnOrgywZkJrixwtwF3dnxqB94O4O0M3i7mvorSnFchhBBC7HESh4wZGLVu25ntmqKhqdruaKYQQgghhGhCDQ6kahMIBHC5XEnrXC4X4XC4XtubneYFZ0ZlIBULmFMor2Hn8HSElG7g6wEpPSG1J6QPgNQ+oLlBUUHRQHGYc/kQLYQQTcYwDAwMO+SwlhuyLvE8dZ2zIcs1nbPqttr21XXdnKMnPTYwamx3tedKaItuJB+LkRwIWc9hGDW0v8pzJK6rur7qtpq2V1Xb8VneLEZ1HlXTIUIIIYQQu1VMN9DUlt0JZU+6ht0WSLndboqLi5PWhcNhPB6Pvb1q+BQOh0lPT99dTdg1aX3hxC0QzINwIYSLIVIM4SIIFUCkyFwXLjYfh4sSthdCrNwMsMrXmFNVigN8Xc2QKm0fSB8I6f3B4QPVZU6aG1Q3qE5QHfHQymEuq05QrPU7fXNEIYTYrXRDxzAMc45hP65rOXH/2rbXtk3XdXT0ynnC89sTOoZu2PvZ56gtQEoMiRKCGHtdDYFO0nkSA5mE4zAA62+9AYqi2OsTl8HcT0Exz1/DMda2mh4nUuIHW+urPq6qpu0KCcs1HJe4vabnquvYOo9v4HkA/GE/5WGpjySEEEKIpqGpCpe9uIiV2/3N3ZSd0rd9Kv85fXhzN8O22wKpDh06sHLlyqR1+fn59jC9Dh06kJ+fX237gAEDdlcTdp2igLe9OdVFj4EeMntTxYIQ9UPFJqhYDxUbILDVDK1C+RDYDOWrzf3K15rT9s/jz6dBam/I2A+yhkLGIDN4SvpXYMXsRWX1qlIdZmileeNhlhVguapM0vNKiL1NUihTx1QtwLHWU7k+pseI6TGiRhRd14kZ5rKhG0T1KDEjlhQuWc9fNVCye9wYZkCUFMLE16Nghy9V11WdJ4YWqqImPVYUxZ5balpvr7PWq9XDm9rOU3W56nNYx4umEdWjzd0EIYQQQuxlVm7389vm0uZuRquw2wKpoUOHMn/+fILBoN0rauHChYwcOdLevnDhQnv/QCDA77//zowZM3ZXE5qOqoHqMwMhS3o/iIXMcCpSaoZSkRKz15TiAD0IFRuhbAWU/AbFv0G4wHxctgI2vm7ulzkEssdAx4PNHlWGAUYMjKg56VGIlpk9s4xoQnilVPaqUp2geeI1sFLjoZXb7IGlecztQohmYwU1MSNmhz/1eawbOhE9YgZCeoxIzFy2AqKkwInk4ClxXbWhUVXzk3jwoyoqarxHpqqoScGLFQZZy7XtV9dc7J1iesx+3Vqv3aTH8e2J661lKyi1HvvDfkZ2GtnclySEEEIIIXbCbgukcnJy6NSpE7NmzeLiiy/ms88+Y+nSpcyePRuAk046iccee4z58+dz0EEHMXfuXLp27Upubu7uakLz0+Khj7utOTQvWm4O66vYbNaiSulpBk69zwEUCG6F4qVQ8CPkL4DgFij62ZyWPwDp+0LHQ8zJ17Xu5zaMeGAVMadomTn00P7XYwU0JyjxNjrTwZlm9rTSPPGgyiXDAYXYAcMwiBmVX4oTvzwnrrPmET1CJBYx53qESDRi9joyzOFkMWLmOfX4nFhl3Z7EzCaeISmKgqZoSYGRgoKmauZc0VDU5G1WeJQYFonWxXqthWNhwrEwkZi5bL3+wrq5LmnZ2hYLE9WjSa/VqB5N2sfanrg+cV1UjxKNVYZKSeurTFVrR+2q/TvvzwkDTtit5xRCCCGEEI1vtwVSmqYxb948/u///o9JkybRo0cP5s6dS+fOnQHo2rUrDzzwALfffjtz585l+PDhzJ07t/V+MVIUs3eSM9W8A1+kGILboXwjlG8AV7pZAL1TJ+h0uBkoVWyE/O9g22dQuBBKl5nT8jnQNge6nggdJtTcw0lR4jWmaun9ZBigh80pVmG2xwqrVAcoLjOwUl3xkMoXr2nlrDyv6orPnRJciRbP6nlUW08Na1tEjxCKhiq/6OsRO3Cq2nspqW5QfG6FRKqioqmaHRSpiopTc+JW3EnrEnsbiZYlpscIxUIEo0FC0VDlcixkPo6vC8XM15P9OP76stbby9Fw0vpQLGQHSFWnmBFr7svfJZqi4VAdSZOmavZ6TY3PEx8rDgwMjt7n6OZufr2Fw2EmTZrEddddZ/+D3IYNG7juuutYvHgxnTt35tprr2Xs2LH2Md9++y233347GzZsYOjQodx2221069atuS5BCCGEEGK32aVA6s8//0x63KNHD5599tla9x8/fjzjx4/fladsmRQFXFnmlNLTrDflXwPl68HdxuyppCjm3flSukGPU80heds+g60fmz2oCn4wJ1cWdDkWepwOnh3UuqraBqsHV1V61KyJpUfjvbpKzGVDp7JwixoPrpwJdaxSwOGN3z3QCrPiNawUp9SxEk1KN/RqPTms3hzWPBgL2sGAHSwRSxoGRNUSbqj2F2MrVNIUDafqTAqYrF5LYs8ViUWoiFQQiAYIRAJURCsIRAIEo0ECkYC5Pmo+tiZre9IUC1ZfFw3uMfWMFBRcmgun5sSpOu1ll+rCoTnMx6ozabtDdSTta4VCifvZ6xIe2/OEYxo67cp7pzhYjEPdbf+21qhCoRAzZ85kxYoV9jrDMJg+fTr9+vXj1Vdf5eOPP2bGjBm8++67dO7cmc2bNzN9+nQuueQSxo0bx9y5c7n44ot588035f83QgghhGjxWsanuNZEc0Nab/B2NAug+9ead+7zdjCDHIsrC7pNMqeKzbDpf7Dxf2ah9DVPw9rnofOR0OtsSO21a21S48XS62LE4iFVNGFIYD3qWGk+cKRUBlX2ZPW+kg/Uona6oVcbOpQ4hMgKFkLREGE9XDl8Lh4uJQ4NsnoqWYGSpmo4NScexWMHSy3li+3ewDAMwrEw5ZFyysPl5jxSTkW4onI5UlHrZAVO9nKkokl7Ebk0Fx6HB7fmxu1wm/P4sktzJa1LfGwtuzRX5WPNbfamq2Xu0lxJk4Sje56VK1cyc+bMavXbvv/+ezZs2MCLL76Iz+ejT58+fPfdd7z66qtccsklvPzyy+y3335MnToVgNmzZzNmzBh++OGH1lXyQAghhBB7Jfn21VwcPkjvD95OULoKKtaZPaVcmdX39XWGfS6CPudD3tdmGFX0M2x6y5zaT4B9pkHaPo3XXkUDTQNq6GFlqbGOVZXQSlGShwFqPrP4usNbGVRpVmDlksCqFYvq0Wq1bsKxMOFomEDUDBCsXieJw+gSb3tvBUtWLwu35sbn9O1yrwuxawzDIBgNUhYuoyxUhj/iN+dhf42TFTpZy9a8sXocuTQXXocXj8OD1+nF6zAnj9NTuRzfZoVK1rLX4cXtcONxeOzJrSU/dmkuGXYpklgB0uWXX86wYcPs9UuWLGHgwIH4fJU3SRk5ciSLFy+2t48aNcre5vV6GTRoEIsXL5ZASgghhBAtngRSzc2ZDllDzCCqbLk5nM/b0QyAqlIdZg2pDhOgaCmseQq2fwHbPzfnnY4wg6kdFUBvLDuqYwXmMEA9AkbE7HEVLjSLu8dvDQ9UBlZavJ6VIy0eWMWHHEoPqz2ebug11rkJRoN2b5fEoXR2b6b43d0Sh/O4NJcdMkkPpqZjhUoloRJKQ6WUhkopCZVQFiqz56WhUsrClfOyUJk93529kXxOHynOFHPuSrGXE6cUZwpep7faY6/Dm7Sfx+GR15FocpMnT65xfV5eHu3bJw+/b9u2LVu3bq3XdiGEEEKIlkw+le8JVA3SeoE7E0qWmYXPvR3MIW+1yRoCWXebtahWzoetH8GW92Drh+Ywvz7nm/Wp9jSKGq9jVUtPK8OIh1XxKVwEwW0JgZV1t8B4vSpHWnxIoCchsIrPpYdCozEMo8ZizP6wn4qwWaMnrIeJxsx6ThYVNakGjdvpxulxSm+mRqYbOv6wn+JgcbWpJFRCSbDEnheHis3wKViS9LvbGZqikepKJc2dRporjVRXatKU4kyp8bEVOqW4zOBJehuJ1ioQCOByuZLWuVwuwuFwvbYLIYQQQrRkEkjtSVxZ0GYklP4JZSvB09YMW+qS2guGzYaSv8GKueZd+ta/DJvfg77ToPspO64PtSdRFDNsUl01b7d6WOkR846BwS3xHlfxIYFW4XXNFR8OmGYOj9Q8lWGV6paC6/Vg3TEs8U5ggWiAslAZ5ZFye7hdVI/adVESixv7HD6cLnNZwqbdyzAMSkOlFAWLKAwUUhgopChYRFGgKHkeLDJDp2DJTvdYcqgOMtwZpLvT7SnNlZb0ONWVaq9Pc6fZyx6HR373QtTB7XZTXFyctC4cDuPxeOztVcOncDhMenp6UzVRCCGEEKLRtKCkYi+huSBjoDkvWWYOa3Nl7Pi4jH1h1ANQ8BP8eR+ULoNld8PG12HAP6Dt/o3e9CZh9bCq6W6BEL9jYNicEntXGUa8eLvLHOrnSKkhrPLsdT2rEns6WXcKKw+XUxYpozxcbtd3iunmkDpVUe3CyR7NQ7orXQKn3cQwDPxhP/kV+RQECsivyKcwUEhBoICCigJ72QqgdiZgSnGmkOnJJMOTQaY7PvdkkuGunGd4zPDJWvY6vPL7FaKRdOjQgZUrVyaty8/Pt4fpdejQgfz8/GrbBwwY0GRtFEIIIYRoLBJI7YlUDdL6mT2FSn+HUBTcbet3bNtRMPop8458y+eCfzX8eBF0PAwGzKz/eVoq+46Bvurb9Ghlz6pwcc1hleYCLdUsMO/wxYf/eeJDAltukXXd0Kvdyt6qCxSKhQhHw0QNs4C0SmXoZAUYUnNn51m9mfIq8sivyCevIo+88jz7sTUVBAoIxxo2DCfNlUaWN4ssTxZtvG3I8mTZj615pieTLE8WGZ4MXFotPQ+FEM1i6NChzJ8/n2AwaPeKWrhwISNHjrS3L1y40N4/EAjw+++/M2PGjGZprxBCCCHE7iTfMvdUimLWldJcUPwrhPLB3a6ex2pmHakOB8PKh2H9K2ZtqYLvof/focuxLTZY2SV2WOWtvs0Oq0IQLoDg5oRhgM54zyqXWYTemZ5Qs8oKq/aMt5LV4ykQCRCIBghEApQESygNl9pD73RdtwuHuzU3Hs1DhjtDQqedENNjFAQK2F6+nW3l29hevt1ezivPY3v5dvIr8gnFQvU+Z5orjTbeNrTztaOtry1tvebUxtuGNt42tPW2JctrBlASMAnRsuXk5NCpUydmzZrFxRdfzGeffcbSpUuZPXs2ACeddBKPPfYY8+fP56CDDmLu3Ll07dpV7rAnhBBCiFZBvoHu6XxdzCFkRYvNIWiurPof68qAgVeZAdRvt5q1qX69GTa/C4P+D1K6NVqzW5w6w6p4UKVHILAZytfFNyhmYKi64kMA08GZEh/61/i9qmJ6jEA0QEWkgopIhV2IOhANEIqGiOkxO3jyODykOFPI8mShSf2serF6Nm0t38pWf+W0rXwb2/zb7NCpvkPnMtwZZKdk097Xnra+tmT7smnna5c0tfG2weOo42YGQohWRdM05s2bx//93/8xadIkevTowdy5c+ncuTMAXbt25YEHHuD2229n7ty5DB8+nLlz58owWiGEEEK0ChJItQTeTmDE4qFUSf1qSiXKGAAHPAXrnocVD0PhT/DN6dBvOvQ4fa+qmbRTVKc5VWXoCfWqSiC4vfJugFavKs0NjgxwpSUHVZqnQT/3qB61g6eKSAVFgSJKQ6UEo0FC0ZCZjSkaHodHejzVk2EYFAYK2eLfwpayLWz2b7bnVvhUEanY4Xk0RaOdrx0dUjrQPqU92SnZdEjpUDmPB09uRy11z4QQddINvdapIlJBurtlFfj+888/kx736NGDZ599ttb9x48fz/jx4xu7WUIIIYQQTU6+sbYUvq7xUGqJGWQ40xp2vOqAXmdDh4nw2+1Q8AMsuwe2fQr73SC9pXaGolaGS1XzKqtXVSwEkY1QHqk8RnVX1qpyZcRrVVUGVbqiUh4upyJSQXmknKJAESWhEgKRAJFYBBRwqk48Dg/p7nTcPrf8a3ktKiIVbCrdxKayTWws3cjmss1sKtvE5rLNbC7bXK+hdG28beiY0pEOqR3omNqRDimV8w6pHWjrbSu9zoSowgqMrBsixPSY+diI1RouGYYB1v/KDFAUxb6ZgoqKqqrmcsLjNHcamZ7M5rxUIYQQQgixkySQakl83c1aR8W/msGGI2UnztEVRs2FDa/Cn/ebva6+OR36nAtdj6t/nSpRN6tXVdV3mBGDWDheqyofgpsJRkL4oyHK9RjFkTBFsQgBnIQMBVQXLqcPjyuDLE+W9LKpwjAMioPFbCjdwIbSDWws3cjG0o1sKt3ExrKNFAYK6zxeQSE7JZvOqZ3plNaJzmmd6ZTaiY6pHemU2okOqR1kCJ3Y6xiGQcyI1RgiJa6zlqsG4oZhoKgKGhqqqqIpWmWQpKg4NScu1YVDdeDUnDhVJ07NiUN1oCoqmmrubx2nqVrScuI2VVElkBdCCCGEaKEkkGpJFAVSe5u9b0r/qBwWtjPn6X4ytDsQfr0FCn+EFfPMAujtRkOXYyB7nDncTOxWOgrluo4/EsUfDpIfLKYsUkEgEsTQwzgw8Coq6Rq4FQeKoYDhgEh+fPhfSuXd/6yeVjvzGmhhSkOlrC9Zz7qSdawvWc/6kvVmCFWygfJIeZ3HZrgz6JLWhS7pXeic1pkuaZXzjqkdcWo1DMcUooWzwqOqwVLVZQOjsmdSvFcSCtXCJE3VcKgOvE4vTtWJS3OZc4cLTdHs0KjqPDFQsuYSIAkhhBBCCJBAquVRFEjrC9EAlK+BlK7mXfV2hq8z7D/XLHK+/lUo+QXyvjYnRxp0Ogw6Hw2Zg/fOu/LtBjE9hj8SoCxSTknIT36whIpIgFAsjKIoeDQ3XoebTFcamlpDTSnDACNq1qmKVUCkxOxlhWIOw1Qc5lC/moIqpfEKqjeGSCzCxtKNrCtZx9ritawrWWcHUMXB4jqP7ZDSgW7p3eia3jVp6pLWhTR3A4e3CrEHSQyWonq0WtBkza2hbgoKhmHYYVJijyJNNevMuTRX0mSFTYmhkUN11BgyCSGEEEIIsbtIINUSqQ7IHAB6AAJbzGF4O0tRzR5RXY4B/1rY/I4ZUAW3mcP6NrwKvm5mMNX5SPOuf6JWMT1GWaSCsnA5RcEyCoIlVESDhPUImqLic3hId6XicdSzV5OigFJLUXU9CkYYYkGIloEeMwMszQE4wRGvU+X0geI2e1ipLjOwasagqjxcztqStawpWsPa4rWsKTbnG0s31nnHuvYp7emW3o3uGd3pntGdbund6JbejS7pXWRYnWgRqoZLVYOlqB41eyslSAyWrLlTc5KqpeJ2uHFpLtwOtx0oOVSHHSYlrpPeSUIIIYQQYk8jgVRLpXkgY5B5x7zgdvC03/VzpvY077y3z4VQuBA2vWMWPa/YACsfMqfMoWYw1fHQht/trxXSDR1/pIKycAWFwRLyA5UBlEPV8Dk8tPGk42qMYWGqA3BA1U4LeiweVIUhuh0CUXO9oprhluaK96hKrbwToBoPq3bjHRf9YT+ri1YnTWuK17CtfFutx/icPnpk9DCnzMp5t/Ru+Jy+3dY2IXYH3dCTgqWoHk0OmqoErIqi4FCSex55NA9uhxu3w41HM3svJQZKiVNiuCSEEEIIIURLJ4FUS+bKgIz9oGghhEt2X0CkaNA2x5yiV5uh1Ob3oOBHKF5iTn/cBdkHQqcjof04MyDbS1REgpSG/RSHytgWKKI8UkEoZvaASnF6Gy+Aqi9VA7ygeZPX6zEwImYNslA+BLaa6xXiPaicoHrNOzgmhlSayxwaWItQNMSa4jWsLFzJqqJV5lS4qs7gqa23LT0ze9I7qzc9M3vSM7MnvTJ7ke3Llh4collZgZIdLiX0ZorqVrgbn8UDpsReSV6nF4/DYwdN1jaraHfVSd2NIbAQQgghhBAtiQRSLZ23A0T3heKlZoiwu4Mhh69ySF8wD7Z8YA7pK1sO2780J80HHSZAp8OhbW68507rEdWjlIbNGlDbKgopCfsJRIMoKPicHjJcafUfgtecVA3Qqr9GjJg5/E8PQ6QYQnkJx8SHC2peDC2VLcFSVpRsZEXxOlYWr2Vl0WrWl6xHN/QanzLbl02vrF70yepDr8xe9M7qTa/MXmR4pHedaDpWT6aqk9WTycCA+Eg5e6hbfO7RPGbAFJ+sHkyJAZNTrVyWQFUIIYQQQoj6aV3Jwd4qtSdE/VC2cteKnAPoOkRjEImay4ZROeGGNsdD2xOgYg3kfwr5H0NwixlSbX4XnBnQYaIZTrUZvmttaUYVkSAlYT8FwWK2VxRRHg0Q03XcmpMUp5e2nozW88VT0UDTku6qGIyGWVWynhVFa1hetJ7lJetZUbKZ8miwxlNkuNLok9WTvll96dO2L32y+tG7TR/S3elNdRViL5TYkymiR5LCJjtkUkBV1MqQSXHgdrjJdGTidXrxOrx2wGQFS4nLMjxOCCGEEEKIxiGBVGugqJDe3wylAlsbXni8qAQ2bgV/hRlGRaMQ08HQzS90diBFvMi2Eh+yMgrU/cG1DvSfIPazeRe4ja+bkyML2vwF2k+E7JHg3nOH9emGTlm4Ij4Mr4CiYBmBaBBV0UhxemjvzcLRynp+WUrD5SwvWseyorX8WbyeP4vWsq5sC7Eaej05VI3e6V3om9GFfdI70zetA33T2tPOlWIGdKoGOEHTIbQZoiXxnnvxYuqqs87hf0JAco+mSCySFDpVDZoSeyeludPwOrx4nV7cmtsOlqrOJWQSQgghhGhaMd1AU1vJP+iL3Ua+GbYWmhsyBpp1nkIF4G6742MiUdiwBVZvgHAEPG5waOBygaaCqlaGT3adE6vHFGYPKl2HWD+I9QFlEsT+hNhCYClEi2D7/8yJdHCNMAOq7P0hNRV8XnA230swqkcpCfkpCpWxpSKf0lA5YT2CW3OR2tp6QcUVBUv5o2gty+LTn0Vr2VSeV+O+me40+mV2p39mD/bJ7E6/zO70TO9UdzBnFVS36lTpW83Xi6IkDf9DSwGHN74uHlgprma9+59oGjE9Vq03UyQWMYfOGQYolbWZrGFxPqePFFeKHTS5NFeNYZPUYxJCCCGE2DNpqsJlLy5i5XZ/czdlp03on80/Dt+3uZvRqkgg1Zq4MiFjABT+DNEKs/5TbYpLYeU62JIHmenQLms3NWK0ORlRCP8GgQUQWghGKYQ/h62fwxYfKIPBPRIyRkCbdmY45fOC120OH2sk4ViE4lAZ+YFitlUUUhYpRzd0fE4vWZ403FoLqAVVT8WhMpYVreW3wtX8UbiGP4rWsq2ioMZ9u6Rk0y+rB/0ze7BvVk/6ZXYn25vV8ECutoLqhm6GVEYUIn4IF8Z73RnxmmNOs6i65jPvAKjFC6rbUzMWiRf1lhg2WT2bInqEmB6zC4Fbd4pzqk4cmoNUdypeh5cUZwpOzWmGTaozKXSSHk1CCCGEEC3fyu1+fttc2tzN2Gl9slOauwmtjgRSrY23C6SVQckf5tC9qr1ZDMMcnrd8rdkrqnP7xgmAFAe4h5qTEYXwrxD8CYILAT+wAEILYLsbtvUHYxA4hoA3C9JSID0VvB7weXa5J1UwGqI4VEZeoIjtgSL84QoAUl0+Ovja4mgFX3YrIkH+KFrD74Wr+b3QnNfW86l7WkcGZPViQFZPM3zK6kG6q5H/56qo8RpV7ur/10nsVRUugGDC3flUByjO+LA/Hzh9Zk8qq0fVDu4AKHYfwzCI6BEisUhy6GTE7zxngKqaQ+isHktpnjRSnCn4nL5qQZP1uLX1QhRCCCGEEELUj3yTa20UBdL6mvWkKjaBr2vlMKhYDFavh5XrzZCnbWYTtckB7mHmlH4OhJdB6EcI/gx6EShLzUl/CQJ9oGIQbO4PejtwOcHtMkOqNhnxkMprBlV1BGnBaIiiUBnbK4rICxbiD1egKhppLh+dUrLR1JY7tCeqx1hdspFfC1fxa8EqfitczZrSTehWna8E3VM7MqCNGT4NaNOL/lk9SXV6azhrM6q1V5VR2asqFozXSIsmHOeMh1Uus1eVw2uGVFavKgmrGsTq3WQFTtbciL+uFEVJKvid4c7A5/KR4kzBpbmSgiYJm4QQQgghdh+pvyRaK/m21hqpTkgfYA6NCuWBpz2EwmavqHWbzGDH10yhhKKBe5A5pZ0N0bVmr6nQQohuAn0FsAJUwNkZnIPBGAD53WDLdjOkcLvMelfpqeZww/hwv5BbpShUxrZ4CFUWLkdTNNJdKXRJbd9i68vkBYr4pWAlvxas4teClfxeuIZgLFxtvw7eNgxs05uBbXoxsE1vBrTp1fg9nxqTopihEjUMo0wcAhgLQrTM7GllUR2VPag0nzl8VXVWDv9TXWaYtRcFJlaPpqq9nKzASVVVO0hyO9y08bYhxZWCx+GxQyaX5rKLhbfU95MQQgghREvT0usvSe0lURsJpForZ6pZ5LzwJyjeDiu3wdY8aB/vdbQnUFRw9jantFMgug1CiyG0yOxFFdtsTnwAigd8A8E9BJR9IeKGbQVE1m+iyAiQp0XYqlVQ5tVQfV7SfRl09WWgehq3JtXuFo5F+LNoHUsLVvBrwSqWFqysse5TitPLoDa9GdSmD4Pa9ma/Nn1o581s+gY3l8QhgFUZOujROnpWOQAHaA5QvQk1q5zJoVUL611lFQgPx8LJgRNm4KQpGi7NZRcJT3On4XP6cDvcdqHwxJ5OQgghhBBiz9GS6y9J7SVRm5b1jUs0jLcD0AUWvQt+FTp32rPDGUcHcBwOKYeDXgHhXyC0FEJLQC+B0M/mBMTU9pRqfVnv7cRGI5toRCUtotGlwkAt8IPiB8c2cDnMHlSpKfGeVS5wOs1Qbg/oHVMQLGFp/gqW5q9gSf4KlhWtJaxHkvZRFYW+Gd0Y1KYPg9v2ZXC7PvRI6yQ9VGqjqHX0rDLMoMqImqFVpBjCeWCNdlSUeM8pR7x3lcfsYWX3rLICK0eTB1aJQ+rCsbC9bLY7OXBKcaWQ6kolxZmC22GGTdbc2kcIIYQQQgghmpN8K2nNSkthVQlUeKBNFFrSuGPVB55cczJ0jMg6QsGFEPoFV2wtmr6dLH07WcAQFModXSl196ZM60252hVDcUAkahZuLy6D/CLQDXBoZoF0pwM8HkjxmuGUy1kZVO1CAfW66IbOmtLNLM5bztKCFSzJX85G//Zq+2W4UhnSri+D2+7DkLZ9GdCmFyl7Wt2nlsoKnHBCTdmsEaveu0pP6F2lqObxqhavU5UYWDkq61pZPawaEHpaRcPDsbDd0ykcC5tD6hRQFdUuCu5z+uzQKbGHkxU6SeAkhBBCCCGE2NPJt5bWqrgYliwx5333B/9yCOWb9aRakKAepiDiZ3NYJz+6DwGlB+lO6KZsJUtfQ3psNR6jgFR9A6n6Boh8gY4Dv9qNMq0Xfk9Pyn3xgArMwu6RKERjUFwK+YVmrxlFMcMqh8MMplLihdNdrnhgFV/fgGLooViY3wvXsDjvTxbnL2dp/grKIhVJ+ygo9M7owtB2/RjSti9D2u1Dt9QOUgy6uShavBdhDUMBIX5HwLoCKyUeRDniAZXHLNbucIPiJIpK1ICwoRM2DCK6TjR+vFU03Boy187djjR3Gl6H1w6brOBJhtQJIYQQQgghWjoJpFqjoiIzjCothS5d4nfe6wMlv0O4GFyZzd3COsUMnaJoOdvCJWwJF1MWC+JSNDI0H+2d6QD4aYef/QBw6cWkxVaTHltFmr4Gp+EnXV9Dur4GIqDjoFztgl/rSZnag3J3V3SPr8qzGmZIFYmaBeDLK8zwyuyaEg+qHOB1m0GV222ucznjcwelsSBL8pezOG85i/L+5I+iNUQSwwrAo7kY3LYvQ9v1Y2i7fdivbR/SWnLh8b2NqmF2rao5sDL0KNFYiHA0RCTsJxwNEo6FMAwdAE1RcWounJqHNKeXVFc6qZ4s3O5M3M40XE4fbmcqLmcKiha/W2ADe1oJIYQQQgghREsggVRrY/WMKiuDzp0rv8g60yC1D5Qug2i5Wch5D+OPBcmPlLExVEhh1I8BpKteurna1NljKKxmUqCOoMA5AgwDt5FPWmyNOenrcBp+0vR1pOnr6AQYKATUDvjV7vi17pSr3QgrmWaw5HBA1dFxum4GVZEolPihoBgMyI+Vsyi8iUWRLSwKbWRlOM8uRWRp685gWLt9GNZ+X4a224d+md1lOFULpxs64VjUHF5n37kuhvnKMns5OTUHLlcG7VI6kub04XV4cGtO3JoLt6LiUgwcCvE7BUZAL4ZQEYTiT2LVqFLiwwA1j3mnQM1XWctKcZp1rqwhgvK6EkIIIYQQQrQg8g2mNSkuhsWLzZ5RiWGUxdMOYj2hbGV8OFEtw5KaUNSIURDxsyVczLZICRWxED7VTXtHOs6d+YKtKISUbEJqNvnOnHhAVUBqbB1p+lpSY+txG0X49K349K20j/4AQERJpVztil/tRrnWhQq1M7riMc+pquB2sZlyfg6vY1HFWhaVr2N9qPrd77o7Mhnm6sxwVxeGe7rQxd0WxeWEsBOKHBAsqOxZZdWscmjSA2YPE40XEDeDpyhhPUJMN3vMKQq4VCcuzYnP4SbN24ZUl68ycIrPXapz54de6gmF140IhAMQyjOHDFZWYK8SXDniNa28ZniVWM/KqmllL0tBfCGEEEIIIUTzkkCqtSgpqRymV1MYZfF1hlgAKtaDp4NZM6cZlEUD5EXK2BAqpDhajqqoZGhe2rnTKneK6aihMGowghYKo4YiKOEoaiSKEo2hhqMQ01F0HSVmTug6igGJX9oNBQxFo1TtS4naD1UN4VYL8Cj5eNR8PFohTqefTMcyMp3LwAm6C37TsvhET+HLoM73FaVsifiTrkFBYR9vB4an9GB4ak+GpXSnnTOh/YZeOQwwEISycrO3FQaQULPK4QCPG7wus2aVNTzQoVXusyffHbGFMguHm2FTRI8SikXQdR1FAU3VcKlOnKqDDFcqqS4vKQ5vUuDk1lw4tUb6X6jqABw1F163WHcMtMIrK7gyqgZXmO9zO7yyCrJ7QXObc9URD68cCQXZrWUJTIUQQgghhBC7nwRSrUFpqdkzqri4smZUbRQVUnqCHobgNrPIeWOHUroOpX5ihcWU5m2lpGArgcIClBI/vcoj+MojOP0BHOVBtPIgWiCEWhFCC0Uat10JDGBZO/iiJ3zRw5xvSSsCiux9HDEYlQ9j8xyMKfEysiKDFFcWUR/EfFuJpRQR83mIpniIpXqJpXiIpXiIpniJpaSApiY/YzQWD6wiEAxBftQMGVBAwQyhNNWcOxxm7Sqvp/IugU5HZXglgVU15l3rovawurBuDrMz4oGlQ3XgVB24VCdZ7nQyXCnJQ+vi4ZOm7qE/W+uOgWo9CpzboVX8LoKxCoiWxdcnhKQY5v8jVAegJfS8cps9KlU3ODzJvbIS5/ay9MASQgghhBBC1E0CqZautBQWLapfGGXRnJDWF9AhmA+e7J3/AmkYUOqHLXmwvQC258fnBZBfBAXFGIXFKNEYGpAVnxr0FIqC7naak8uJ4dTQXQ4MpwNDU81JVUFVzLkCdqhjxNtoGCiGYfaiiukQi/Fnapivs4N83T7EV50ibPclV4ByRSF3E4xfC+PXwegNkBIBiAJl8Wljva/DCqqiaV6iqV5iaT6iaT6i6QlTmo9oegrRDC9RjxdDxSyubhda10ExzOvStMrJoZm9rDyuykLrib2rrN5WDbhLYEtgGIbZwyne08kMn6Ik1nNyaead67Lc6aS5fHgcbjt08mguXJoTtbUHKFaPq/owYpXBlRG/q2A4WBloxQu0m+LvM6vnlTVZQ4JVTzzMciaHVnaPLa3K41b+exBCCCGEEELYJJBqyUpK6t8zqirNDal9zaE9wbx4T6lajjcMKCqBjVthw1bYtBU2bYMt282pIljnU1lnDad5iWammlNGih3AxFLjAU28R1HM5yHmcxPzutE9Tgznrt9lzDAMVgfzWOhfw0L/Wn4uX0tRtDxpH7fiYHBKN0ak9mSErzuDnR3x9QMtGEENhNgcLMZXvgVvYDvuikJcFUU4AwEIABXxKQCUg1EBRrmKUgFK0PwC7ygP4igP4t5WVLV5tYr53ETSU4hm+IhmpBKxfm4ZKUTSfUTTvERSzV5ZUZ/b7I1mJPR2UdUqPa00c1igxxXvWZUwNFBTzbvIORL23wPEdN0uIB6OmcPronoUq56TU3Xg0px4HG7aO9uQ4vTicbh2Xz2nvY0dKrnqt7+hV4ZYRtT8f0osBNFA9RBLURJ6AaqVdy1UNHNZ0eJBliseYrmThxomBl6Jj+1JfsdCCCGEEEK0FBJItVTW3fR2JoyyOLyQ3hdK/oRQPrjaQH4xrNkAazfB+s2wbrM5L6+o+1xts6BDW/T2bahol0ZBpou8TCf+LC+utu1wt21rBiBNpH4BlJOh8QBqZGpPBvm64qpSSD3mhVi6uRykA6X0T9quGQG8+rb4ZBZK9+h5aIRRiH8JjwLl5qSXa4T9aUTLU4iVu9H9LihTwG/gKA3iKKvAUVKOoyyAoutoFSG0ihBsLdzxNasK0TQfkXjgZ859RDJSiKT5iKZ7iaR6zWWf0wyrEjuFaWo8wFIrwylXvPC62xUPqrSE+lbx3leqssvhVVSPEY5F7HpO4VgE3dABBVVRzHpOmoM0p49Up49UlzdpWJ073tNJNANFjfdsasDP3zASQqyEQEtPDLJ0wApY7SdLfk47iLKWrcLtroR5lfAKNTkAQ60SaqkSbAkhhBBCCNEEJJBqiRLvprezYZR9riA8/wUsXGCGUOW19HZSFeiQDV07QtcO0KUjdG4PndpDx3aUazrbI6WsDxZQFC3HqWhkOlJIq099m92gagC10L+G4lhyiGYFUCNTe8UDqC47dye/BDHFi1/riV/rmdAYHZdRYgdVHi0PrysPT0YeKlE8FAPF1c4VUVIJKm0pV7sSNNoQCaQSK/Ogl7nQysJmUFVcjrOkHEdpOc5iP46ScpzF5Wj+AIpu4Cwxt+/w56UoRNN9RLJSzZ5XWalEMuO9rtLNeSTNSyTNS8zrsoc92kO01BrCK4cDXA5wx+8gaA8pNIdURtAJqzoRxSCMTsSI1lrPKc3pw+f0VCki7sSxi78vsYdQFDM82pk/QVaYhZ4QZsXnegSi5UBi0AXJySuYr2OlMoCyQyklIdhKKPRu362waoiVGIypCdvUGh5LyCWEEEIIIUQi+XbX0uTnwy+/QFlZ3XfT25GtW+Hpp+GNNyAcrlyvqdAlG3p1hR7doWcX6N7ZDKBcyeGSYRgURcvZEt7OpvIi/LEgqaqHzq4stEauBaMbejyAMsOnn/1rmySAqhdFJaxkEVazKGHfyvWGjssoxqPn4THy8eiVk5NynIYfp+EnTV9n7u8C2ppThBRCahYhpQ1hNYsKpRNhNZOQkklYyQAdHKUVOIvLcZT4cRb5cZb4zQCr2I+j2I/TWi4trxJebavzcnSHZve4sgMsq+dVRoo5bDDdRzDVTcip2L2cIkaMSLyXmKKoOBwOXKoLl8NFltNLqicVrzcVt9eL2+PDrXlxOz2oznhvLLVKjSz5Qi8gIczaBYZBcqBlkBRi6RGI6sn7YNQSboE9BNEOnrQq66yp6t0LHQk9uKyeZlVCrsTzJIZd1ZYVqcElhBBCCCFaFAmkWpItW8wwKhLZ+TBq40Z46il46y2IRs11Q4bApEnQv7/ZAypWDMHNZk8DRwo4UpO+6ESNGHmRMjaGCtgeLiVixMhypNDG1bbR6vTohs6q4PZ4ALWWRTUEUB7VyRBfMwRQ9aWohJU2hNU21Yb+qUYQj16A2yjAo+fj1otwGwW49UKcVJiBlV5OKhshlnxaA4WIkkbYk0moUybhLhmElUwCSjYRtS9hJYMYnsrXS0yP97AqjwdVfpxFZZWPi8oqA62yAGo0hqugFFdB6Q4vMeZyEM40wysjKx2yMlDaZKBkpKNlZuLI8ODMSEdJT4OIE4IxyCsFozjh56Qk9L6KB1KqaoZSbpfZ+8rlMGthWT20EmthqWp8OKFaOcxQiERWaKRoNGioYW2SAq74XQvt5YSQi0DCvkYtwxJra29i4BUPquywSqkSUjkS5lplrS47/Eo4n3WOpOBLacDcOpcEYkIIIYQQomH2oG/rolaGAevXw6+/ml+0O3Zs+DlWrYInn4QPPzTv2gYwYgScdx7sv3+VcCvNvPNeKB8CWyC4HTQ3AdXDtqifdcECimPlaKhkOVLwNMKwvJihsyKwjZ/9a/i5fB2L/GspiQWS9vGoToaldI/XgOrFQG/nPSuAagBd8VChdaGCLtW2qUYwHlAV2nOXUYxbL8ZlFKMSxWWU4jJKSWV9tcAKIIaLiJJOWEkjomYQTkkjkppOpFsaQaUdZUovIkoqUSCixwgbZg+naCSMVlyGp6gcT0kAX2kAb3EAX3EF3pIgnpIKnMV+tCI/aiCIFo7i3V6Cd3sJsKnui/a4ICsDMjMgHl6Zj9PMKT0V0lLNuccNkSiUB8zC7bFY5Zf4xKGEihLvTZUwnFDT4iGW06x/ZdXDcjgqg67Ewu+t+K6EopEkBVyNwB6mCJXBV0KwRUL4pSeGXEZyQIZRQ/hl3Y40fh32ditsSgialIQgLCmUokpQVdOQRUdCaGXV8Kop3Kp67sR1CeFX4jbVBQ7fbvphCyGEEEKIptIyv73vTWIxWL0a/vgDUlIgM7Nhx//6q9kj6rPPKteNHg3nnGMGUrVxeMDRFcOdTUnFBraWLmdD+Qb8RgSfM4OOrkwcu/HLV9SIsaxiMz+Xr+Nn/1oW+9fj15PrWXlVF8NSujMytScjUnsy0Ndlt7ZhT6UrHgJaJwJ0qr7R0HEY5biNYlxGCS49Prcfl+KgAo0wmpGPx8jHqrVe7VRAGC9hJZUwqcTUNHBkoHXIgs6ZaGoHHFoWTkcWLjUTRa3ysw+GzLsxFpbE58VQVFq5rrAEiuPbgmFz2pJnTjvi0CAz3Zyy0iuXq05pKeDxmMFTLP7lPBKFULjyccz68m6FsEZyryw1XqjdCqrc8TsSOh1miGX1vtLUhOArobi7olQGXIk9vYTYWbtjmGJDJQVakBxyxUMwexhjYjAWTVjG3CexF5gVjJEYKNdFScjMjOR1VlDlbgfZo3f1ioUQQgghRBOTQGpPFgjAsmWwbh1kZUFqav2O03X46it45hmz+LnloINg6lQYMGCHp4gZMfLDxWwKbGdrqICw6iEjfR+6xUIo0VIIF4HmNu/UtxMvo6Ae4dfyjSyOB1BLKzYQ1CNJ+6SobobGA6iRqT3Z19d5rwigGkRRiSppREmjnG5EjRgRPWbWb4pPhhHEbZThNcpJUfz4qMBnlOPFnFyGH4dRioKOmwBuIwDkmT2tYkCkxicGNR3UtPiUbk5pqZCRDr3TQO0Maj9QU81JcVUeHgiawVRRSWVolbQcnxeXmr2iojHILzKn+vC4zHZkpkFGWsJy4rr4lJkGPq/5xTgxtIrFzCCrImhus8Ms68u0dcc3zHWqUhlQKar52A651MqhhFbvLFdiuBXvjWX31FKTwy01YW6dS4jGVLV31J7Efg8aEC4GPbyDA4QQQuzNYrqBpkotUiH2RBJI7any8+G336Cw0Byi53Lt+Jjycnj3XXjxRTPEAvML75FHwllnQe/eOzxFMBYiL1zEusAWCsKlKEAbZzped9vKnWJBiMRDqYgf9Fg8nPJh1japriRawZLy9SwqX8cS/3p+D2wmaiSPLcvQvAxL6cGIeA+ofbwdJIBKYBhGUtAUtedWjwNwKBpOVcOBhk91kqam4VOcuBUHbkXFZWi40XCh4UABPd7bQY8B5aD4QS0DpRTwg1EKeolZV0wvNSfDbz6fXmJO9aW4QYmHU2oKeFMgJRW6p4CSAmoWqF3jy774PAUiGpSUm+FUUQkUlyUsl1Y+tqZINN4DKx+25devbaoaD6jiQwQz4kMG0625tT5hXaovuedT1dAqcTkaryGkB831esI+9pBDA4x4V5DEcAslHnIlhFFWYGUNLUwMuhJ7bKk19NSqGnJpEnCJFsYexkfjDZMUQgjRamiqwmUvLmLldn9zN2WnTOifzT8O33fHOwrRAkkgtaeJxcww6c8/zS+rXbvu+Mvi2rXw3//CO++YoRSYvalOOglOOw3at9/h05ZG/GwNFbAhuI2SiB+f6qGjqw2OmmoyaR5z8rSDaIUZSoWLzH+pNgwM1cmmWAWLK7aypHwdS8rXszpYfVhWtjON4Sk9GZHag2EpPejtyUbdE/81vtEZGDGdmB4jYkSIxKJEYzHzTnV6FMPQQTdQDAMHKk5DxYlChuHApzpJUdy4FCduRcOFA5eq4UI1f3d2iGHVWlHj9ZD1+Lb4OqcDcEM03QxRotF4iBIf2qYQD0Ac5g3EtACo5eZEeTysKqs+GX7Q/ZhDdkLmpBc0/EekeCAzBdr4QPHGAysvKD4zyFK88ckDQQ1KDSiNQWkESsJQEoTSAJSUQYkfSkor5xXxkMjqpdUQqb7KOlfpKZV1r9JS4utTIDUl/jg+ZaRVu2NlNXZgZVQGWHp8+JRuQDhi9gpJDLbscAtzrigJw5wSfteJYZf12O695QBnfKiiy2EWi0/slaUmHFPTMMXE55CeXEIIIYTYQ6zc7ue3zaXN3Yyd0ic7pbmbIESjkUBqT1JYCCtXwqZNkJFhTrUpL4dPPoG334aff65c3707nHIKHHvsDof4RfUoBZESNgfz2BosIGiESddS6ObpUM9gSAVHKiHVxbJwMUv961hauoKl/jUURMur7d3d3ZbhKT0YHg+guriyGu2ufI0vISyoGhwYVC4DRixGVI8SNXQiRpQoBhEjSsQwMIwYKAqKoqJpGk7FgUN14NWctNNS8bncuDUXLs2Fy+XG5fTgcrhwuVyoLnf1UKBqGGAtKzWsS9xm34EvZvYwisbMUCocMe/qGI5CRcAMb0IhiKoQdEMsI14kP96zxw4m1Mqi4IaBeXcxvzmnHIz4pASAivj2cnPZqAC9AvRyID4UxwiaU33DLE98SspilXgvLW98ngJKG4i5wK9BmQplCpQZ4DegNAr+GJSGoSwCZSEz1CoNQEXIPKW/wpy2bG/Yy8ftigdVPnOe6jPDqsTlFJ+5nJoCKV5zOSU+aTsR9NjBlp7wejUS6mxFag+4IJ5rJfTiqi3ksoJOpWrYZfXkSgi/agu0rLCsptetfX5FAi8hhBBCCCFaMAmk9gTBoNnLafVqMwTo1Mm8K1hV0Sj89BO8954ZRgXjRb9VFcaOhVNPhZycHX5J80cryA8XsyGwjcJICaqikulIo73WZodNNQyDzaE8fi1bxa9lq1hauoI/y9dVG37nUDQG+LoyzNeNod72DPFk00bzxG877gSHi8qi0k3IqNLTxFqOWbdpryFcsr9/J3wZj4c4URWihk5UNYgqlVNENcCpocTv5OZwpuBwuHFoGl6Hm7ZOLz6nB4/Tg9PhwuV0mUGTw43T4cThdCV/MW8q1l3m6mIYZoARjpivyUh8spaDIbP2UihceUdHUsFIDEgTevLEdDMAMwyIRSt79RgAMdAioAZBDcfnIVCCgDUPghEAPWCGWXZ4FTDXG0HsejPWtqp88alDPX9OUSDeOYwyoFyFcs0MtvwqlCvx7YYZcJXHKieDyp9PQT1rYlXldVeGU6k+M7BK8Zm1sFLiky++LvGxtd3rMaddDYSrBlyGkfz+iuoQi8R/H/Ht1u/cLo6dMFzReq+pJA9ZVFXsuyjay/G5VXvLvluilhx6JYZb9Qm9Ep+jamArhBBCCCGE2G0kkGpOoRBs22YGUYWF0LZt9V5NoRAsWACffgpffgmlCV1Nu3c3e0IdeaRZZ6oOYT1CQbiEraF8toeKKI8FSNG8dHS3q7NOU3GkjN/9a/i9bDW/lq3iN/8qiiJl1fZr40xnSNo+DEk3pwGpPXGr8bpXRgxigXjtqXKI+s25HsWsjxMPqTQX5niwGlhferHCo4QeHnbIlBAkQeWXXagsPJ3Yi0ipMled4HBgaCpRDaKaQkxTiCo6UYjPDQxFsb/MOjQHDs1pzh1OUpxufK4UvA43Ls2JS3PiVB24NIc5V51oVe9O19IoijnkbEfDzhIlDiVLZNVZisbM8MoaKmj10IpEIRw2e+8EQ+bjWMwMOvT4fnqVIuPmE5m/V4fVgywaD7Qi5qSEQInPjXA8qApVBlbW8MJqU9icO8OQEYIM63r0+FRjBfiE68XuJFZtqqhhXnXZqt0cCJlTfYu810QFPA7wxiefE7yJkzthHp987niY5Y2v81Y+9njiQbNmTkotc9QdBzw1hVuJjw0qH0dj5vDFqj27rGOoEnrZvbtIDr2q9ryyAjBFSQ6tHFaPLrWyRpdVn6vqsEWU5KCr6rkTA7HEIbRVe4QJIYQQQgjRCkkg1RwqKmDrVrNXVHExpKRAt26Vw5tWrYLvvzeDqJ9/NkMpS5s25t3yjj4aBg+u88tKRI9SFCmlMFzC5lA+pRE/mqKR4UilnSuz2v6l0XKW+deyzL+WP+Ih1KZQ9dpPDkWjX0p3Bqf1Naf0vnR2Z1cZfhcvlG1/oXSCroLqBTUTokGzwHMkaNagivrNos+xmPnFUdHML3uKA9DM0Moa2pP4xU1TQHVVFnfWtHgNHEfS3c50BWIqRDHMuaGbvZkwiKkGupVbxa/BoThwqJo5KRqpDjdezW0HTU7VgTMeMiVOLXcIYiNTlOS5RVXN/wu563keO8CKVoZTiT3arLvkRaPmUMNgxAy0wpHKmljhWMLd9GLJwaUVZFUtBF61dpI1NE2JxoOqSEJglTBR5bERgbT4nEjlOiMcfxxJeBxNeBzfPxKBCr0yoIqPdqx1njhVAMH4cjzbpSJqTruLC3OopDthqvrYDbgVcCngUcGdOGnxuSNhrplzlxbvYalipkla5bKSEA5Zj5PCr/iyvU2Nh1NKfJ74WIVYfNnanrRvlbmhxAOyhHVK/HxK/HzWuez18XYo1v/T4suqmrBNrbxeVQOHMyH0sup9OUBzVh5n/QwUq/cXtfT+SgzZalkWQgghhBCikUkg1VSiUTN8yssza0SVlkJ6OmRnwx9/mLWgli6FX36Boiq9Hjp0MEOoiRNh6NA6h1QFYkFKo+UUhEvYEsrHHzW/faZqPjp72qMpKrqhszG4nZXl61lRvoEV5Rv407+2xvAJoLu7PQO93dnP24NBnu70c3XGrWiVQ9pKDCChho713d7+klSlnozmAFc6ONpW1pVRFVBjmGOhovG7kQWAmPmlHz3heCcxzUFMVYmpDmKKShSIGWZh8KgRI6br5rGYQ8YURbFDJk1VcSgaKZobt8OJVzPrNDlUzQ6WHAkhk0PVJGjaU1hfrp0O8DbwWCuwisUqe2RZy1ZYZffKiiTM47WVwuF4+BWrnCd1+FIx0xdvzXfFsx471OQAoKHFv414TywjHljZwVW0yvpYwvb4RNQMfkMhKI/XwgqEzHlFCAIRCITj8wgEohCMQiAWX9YhGDPnAR1CBgSNyp9DPIOrx0VQmYo1gDM+uTHDr6rLriqTs455bescNTze0dtfqcc+tbF+FGD972rXWMFYUqPioZtSdVuVqep2RU1er1jnUBOWlfjTqMn72XOq7Ju4XGVd0mPr2KrtI7l9RhQ6HQ0dxu+GH54QQgghhGhKTRpIhUIhbrrpJj788EM8Hg9Tp05l6tSpTdmEphWJmMFTYSGsWQPLl5th1NatsGGDWcB8w4bKIWYWtxtGjoTcXDjgAOjdu9aeUGE9gj9aQWm0nO2hQoqjfgLRoPl53VAojfrZHNjOuopNrK3YzNrAVtaGthLQa/7W2MXZln293djX242BKT0Z6OtJmiulsoeSHSCpZp2rpN4j9Z00+3oMwyBmxIjpsepzPUosFiIWC2DoEfOLtB6GWAg1FkKLxTD7TxlogFfTcKtO3E4XHkcqTs1t1m1SnQnLLnNZQqa9T2KY1VCxWHKYZfWuiiWEXHrC9kg80IpYwxCjCYFXvIaSXccsFs9lrKFllYumhB6BiT1cNNXsQag6QUlJ6N1Sj94uqUDbnfsxVmPE7/pXETCHVQbjIVcgaE7BIAQD5jwQ3yeUsF8oHH8cNqdQpHIKhs2foSXeUYyK3dT2+nIq4FDMubXsstZROXcq5l9Va53DMB9b6zTAaSSvdxiVk5b4WCf+P7jKfbUa5hrJYZiSmHA1UNXDdvI0Ta4sHwbObO5WCCFEqxPTDTRVPi8LIRpPkwZSd9xxB7/++itPPfUUmzdv5uqrr6Zz584cccQRTdmM3S8YhO3bYeNGWLfODJ/WrDGXt2wxA6iCgur1cyzt25vD76xpwABwuartFtGjBKIBKsLlbKrYxgr/ejYFtrEllE9euIjiqJ+iSBn50VI2Rwop0Wv/1uZUHPRO684+Gb3om9mHfm37sW+7/qR7MiqHLCX2bqojvInpMXRDJ2bE5/FQSTei6IZONBZFj+roho5BQm8Kxey5pKGhqRqqquJQHGiqhsfhwaW5cDvceDQPTs2JI95zyZ4AjVi8E4OOYiSEVtF4ketYMN6TJQqEIVphBgKJjYD4v87H69xYw2dQEh4nDPVJ/Nd7sXewi703oG5WVYk9tHSjcrigFWxZNdGscMt6bAVaUSvwivfiSrpbnnUuvbLGUmIRcailgHhCCFa1XhIkDO+iyjqwi4pbx1iF1a1QjCrH7crPLRQfdplYMD9sBVjxbVawFYoP00xcH45UTomPI/HHkcTt8fWJIoY5BXbtUhpN4pBlR8KkqZXzxGV7riZsUytrrjm0+HBoa594AGrNVaX6ejU+aUrlsfayYo+YNKeE/RWjcruSsI+SOLdeo0ZlAKcAGBAKgHv/ZvihCyFE66epCpe9uIiV2/3N3ZSdNqF/Nv84fN/mboYQohZNFkhVVFTw8ssv88gjjzBo0CAGDRrEihUreO655/acQGrZMrOuU0WFOZWXg99v9nIqKzOnoiIzXCoqqpzKqhf5rkk01UeoR1cqenUl0Ksrga4dqOicTbnPiT8WwB8ppziylKLfvqYwWkZRtIziqJ/CmJ/8aCnFegUlsQpKYxVE6zm2o407k86pneiR0YOemT3p1aYPPTJ70iW9C2p8+F7iVBoPkawAKTFsqo2qqKiKaoZKioqmmHOXFr9znOoylzWXHSZpqmbOFS3psbVut/VeMgwzjNIThi3Zy7H4cgxiocowywq2jFh8bhWstr7s6wnFkmtht7/K0BbU6sNbqg6VqW2IStJwGmrZT+yxdqWHVm2q3uUu6Q6RNa2LH2MkrLcKgMfiBeVj8V5bVnhmh2XWjQWM+I0FYpWBV2IBcbvguJ78GMzXqZ0Fx8MwA5LfS9YQsCpBmXV84h0Ck7bVsK+SeK6E81F13yr7GPFi6ZEYRMLV7yRphVnhaGWYFYkm9IyLVPaOs/azgkXrmFiscn0kUjl0NFJ1XWJPu1jCnSsTWMfuTazgVFPhBANOau4GCSFE67Ryu5/fNpc2dzN2Wp/slOZughCiDk0WSC1btoxoNMrw4cPtdSNHjuShhx5C13XUZi6iWv7Vp9x888FsSTPr2erxKabG5/HlmAdi3SDaw1wXVSGimfOoUyPiUAk7VcIOiGgKIdUgpOqEiKJTASyPT5j/2r5q59uc6kwh05NJlrcNbb1taetra887+DrQPqU9XmdyoR1FUTAw2F6xHQ0z+NHiw9cSH7sVN06HE6dq9kxyaS6c8TvEaYpWr7m6J/QiUhRQnOawpoYy9PiX7ljCcsLcDqhimF+89eT1VthlRGs+l7WffSzYBWWSAi89YR2V6+19DGr8Ul+1F1id42+UHWy3dqsafCnJ86TttYRkte5T27lrOlUd+9b4PDtoU63qcw07+xw72K8hTbV6lST9PqwuJrvrSagMmhJDKGtu3/2OKuGXtZ3KMDdpGwn7JEyJPcYguVeZbiQHana7Eh+T3DvMqPLYfh8lPD81HJO4zoFZXNzrALz1e9soSu29YxO3W0FYbTcASHxs1T+LxSBqVA4JtQLEaGKgWCVcTFqXMOkJd6+sut3alth7z3oeu5efdXzCZA9vjf9u7PPVME/qQRhfrkvi824v3MEvoeXb60oeCCGEEGKv0GSBVF5eHllZWbgShqK1a9eOUChEcXExbdq0aaqm1Oh7XyF3jN3Vs1QW0d4Rp+rEHe815HF68TrMyePw4HP5SHGmkOpKJcWZQpo7jXR3OhnuDDI9mWR6MmnrbYvP6UNVzR5JiWGQ1ePI6rlU01S1N5O1znos9ZVIGJq3C8O06iOpx5WREFIlrtvBclJYVTXQqmE7VN+nznWJ57HW61WOqek5E77U17i96nLV/UhertYzreqxNR1PDftQfZ9a19VwfLX21qEeOV8Dd2zgvtQdiOzsc1lZV433WFCrzJtQ4nuixsCL5NdRrdup/piEfUg4LvE8Na2v+l5KbEPSfvH1up68vxXw2c+dsB4qa8Pb9QgT9rPPsYPnrunnktiGxHVV19f1eqzt+GrnqLrdSO7hlxhqWbXXYvH29htQ+/O3Eq225IEQQggh9mpNFkgFAoGkMAqwH4fD9botU6MaP/wEHlEe4dftv6IQ7zWEUmuAo6lmAORQHDg1Z+WQNMVh1j1KqIHkc/jwOX14reDJ6cWhmj96RVFQUKrNrVCopnWilbFq8YjmUWtYU89wqiHH1nt7Q56rgedstOPZxeCr3k/SBM+xI3tCG5pA4g03koKlBi7v7H41Pa5pndtXfZ9WpEWUPBBCVCMFwYUQYseaLJByu93Vgifrscfj2eHxRvwDqN/feEX1Tu93OvRrtNNXskoSVWHE/wOI7Zb7fwshhBA7qx5DcPeE0+qY9R4bWUpKSrP8o9CulDxois9OrUlrCBBawzW0Jg99vorNJXvqHTHqNrhrBqeM7EbPdBU93MijBRpRB6/5/8CWfB1yDXuO1nAdPdPVJvtcUJ/PTk0WSHXo0IGioiKi0SgOh/m0eXl5eDwe0tPTd3h8eXk5AOPHj2/UdgohhBBCVLVw4UJSU1Ob/Hl3peSBfHYSQuysd4B/NXcjdoPVwNPN3YhdJNew52gN17EaGDm7aZ6rPp+dmiyQGjBgAA6Hg8WLFzNq1CjAbODgwYPrVdC8ffv2fPHFF832L5RCCCGE2HulpDTPnZp2peSBfHYSQgghRHOpz2enJgukvF4vJ5xwAjfeeCO3334727dv5/HHH2f27PrFc6qq0rFjx0ZupRBCCCHEnmNXSh7IZychhBBC7MmaLJACmDVrFjfeeCN/+9vfSE1N5ZJLLuGwww5ryiYIIYQQQrQYu1ryQAghhBBiT6UYRpPcFkkIIYQQQjRQIBAgNzeXxx9/3C55MHfuXL777jueffbZZm6dEEIIIcTO23HxJiGEEEII0SwSSx4sXbqUjz/+mMcff5yzzz67uZsmhBBCCLFLpIeUEEIIIcQeLBAIcOONN/Lhhx+SmprKueeey5QpU5q7WUIIIYQQu0QCKSGEEEIIIYQQQgjRpGTInhBCCCGEEEIIIYRoUhJICSGEEEIIIYQQQogmJYGUEEIIIYQQQgghhGhSEkgBoVCIa6+9llGjRjF27Fgef/zx5m5Si7Vt2zYuvfRScnJyGDduHLNnzyYUCjV3s1q8Cy64gGuuuaa5m9GihcNhbrrpJvbff38OPPBA7rnnHqSE3s7ZsmUL06ZNY8SIEUycOJEnn3yyuZvU4oTDYY455hgWLFhgr9uwYQNTpkxh2LBhHHXUUXz99dfN2MKWo6af5eLFizn99NMZPnw4hx9+OC+//HIztrD12B2v27fffptDDjmEoUOHMn36dAoLCxu72btsd7zGRo0aRf/+/ZOm8vLyxm76Tqvpmm+99dZq1/Dss8/Weo4nn3yScePGMXz4cK699loCgUBTNH2XVL3ua665pto19+/fv9a7XJaUlFTbNzc3tykvod7q+szemt/XdV13a35f13XdrfW9Xds1t+b3NcC6des499xzGT58OBMmTODRRx+1t+2R721DGDfffLNx7LHHGr/++qvx4YcfGsOHDzfee++95m5Wi6PrunHqqaca5513nrF8+XLjxx9/NA499FDjX//6V3M3rUV7++23jX79+hlXX311czelRbvuuuuMww47zFiyZInx7bffGrm5ucYLL7zQ3M1qkU499VTj73//u7FmzRrjo48+MoYOHWp8+OGHzd2sFiMYDBrTp083+vXrZ3z//feGYZj//zz22GONmTNnGitXrjQeeughY+jQocamTZuaubV7tpp+ltu3bzdGjRpl3H333caaNWuMt99+2xg8eLDx2WefNW9jW7jd8bpdsmSJMWTIEOP11183/vjjD+PMM880Lrjggqa8jAbbHa+xrVu3Gv369TPWr19vbN++3Z50XW/CK6m/mq7ZMAxjypQpxsMPP5x0DRUVFTWe4/333zdGjhxpfPrpp8aSJUuMo446yrjpppua6hJ2Sk3XXVpamnS9ixYtMvbbbz/jo48+qvEcP/30k5GTk5N0TH5+flNeRr3U9Zm9Nb+v67ru1vy+3tF3tNb43q7rmlvr+9owDCMWixmHHXaYMXPmTGPNmjXG559/bowYMcJ4880399j39l4fSJWXlxuDBw9O+oM7d+5c48wzz2zGVrVMK1euNPr162fk5eXZ69566y1j7Nixzdiqlq2oqMj4y1/+Ypx00kkSSO2CoqIiY+DAgcaCBQvsdQ8//LBxzTXXNGOrWqbi4mKjX79+xp9//mmvmzFjxh75YWRPtGLFCuO4444zjj322KQvPd9++60xbNgwo7y83N73b3/7m3H//fc3V1P3eLX9LJ9//nnjiCOOSNr3uuuuM6644ormaGarsLtet//4xz+S/pZt3rzZ6N+/v7F+/frGvYCdtLteY998840xZsyYRm/v7lDbNRuGYYwbN8746quv6nWeyZMnJ70OfvzxR2PIkCG1fsltbnVdd6KpU6caV155Za3n+e9//2ucdtppjdXM3aauz+yt+X1d13W35vf1jr6jtcb3dkO+l7aW97VhGMa2bduMyy67zCgrK7PXTZ8+3bjhhhv22Pf2Xj9kb9myZUSjUYYPH26vGzlyJEuWLEHX9WZsWcuTnZ3No48+Srt27ZLW+/3+ZmpRy/fvf/+b448/nr59+zZ3U1q0hQsXkpqaSk5Ojr3uggsuYPbs2c3YqpbJ4/Hg9Xp57bXXiEQirF69mp9//pkBAwY0d9NahB9++IHc3FxeeumlpPVLlixh4MCB+Hw+e93IkSNZvHhxE7ew5ajtZ2l1y69K/hbtvN31ul2yZAmjRo2yH3fq1InOnTuzZMmSRmn3rtpdr7GVK1fSq1evRmnj7lbbNfv9frZt20bPnj13eI5YLMYvv/yS9LseNmwYkUiEZcuW7e4m7xa1XXei7777jh9//JErrrii1n1WrlxZr59Rc6vrM3trfl/Xdd2t+X1d13W31vd2fb+Xtqb3NUD79u257777SE1NxTAMFi5cyI8//khOTs4e+9527NaztUB5eXlkZWXhcrnsde3atSMUClFcXEybNm2asXUtS3p6OuPGjbMf67rOs88+ywEHHNCMrWq5vvvuO3766SfeeustbrzxxuZuTou2YcMGunTpwhtvvMFDDz1EJBJh0qRJXHTRRajqXp/LN4jb7eb666/nlltu4emnnyYWizFp0iROOeWU5m5aizB58uQa1+fl5dG+ffukdW3btmXr1q1N0awWqbafZdeuXenatav9uKCggHfeeYdLLrmkqZrW6uyu1+327dtb1Ot8d73GVq1aRSAQ4KyzzmLNmjUMGDCAa6+9do/8MlvbNa9atQpFUXjooYf48ssvyczM5JxzzuHEE0+stm9paSmhUCjpd+1wOMjMzGxxv+tE8+fP58QTT6RTp0617rNq1Sqi0Sgnn3wy27ZtY9SoUcyaNava67651fWZvTW/r+u67tb8vq7rulvre7u+30tb0/u6qokTJ7J582YOOuggDj/8cG6//fY98r29138TCwQCSWEUYD8Oh8PN0aRW48477+T333/n8ssvb+6mtDihUIgbbriB66+/Ho/H09zNafEqKipYt24dL774IrNnz+bqq6/mmWeekWLcO2nVqlUcdNBBvPTSS8yePZv333+fN998s7mb1aLV9rdI/g7tmmAwyCWXXEK7du047bTTmrs5rU5DX7fBYLDVvc7r8xpbvXo1JSUlXHTRRcybNw+Px8OUKVNaVK+91atXoygKvXv3Zv78+Zxyyilcd911fPTRR9X2DQaDAK3qd71hwwa+//57zjrrrDr3W716NX6/n1mzZnHvvfeyfft2LrzwQmKxWBO1dOckfmbfm97XtX1Xae3v68Tr3lve2zX9rlv7+/r+++/noYce4o8//mD27Nl77Ht7r+8h5Xa7q/1QrccSBOy8O++8k6eeeop7772Xfv36NXdzWpw5c+aw3377JSX7Yuc5HA78fj933303Xbp0AWDz5s288MILTJ06tZlb17J89913vPLKK3zxxRd4PB4GDx7Mtm3bePDBBznuuOOau3ktltvtpri4OGldOByWv0O7oLy8nIsvvpi1a9fy/PPP4/V6m7tJrU5DX7e1feZqqb+b+r7GHnvsMSKRCCkpKQDcddddjB8/ns8++4xjjz22KZu800444QQOOuggMjMzAdh3331Zu3YtL7zwAoceemjSvm63G6j+D7st+Xf9wQcfMGDAgB2WUHjnnXdQFMV+D9x///2MHTuWJUuWMGLEiKZoaoNV/cy+t7yva/uu0trf11Wve5999mn17+3aftet+X0NMHjwYMDs6HDllVdy0kknVbsj4p7w3t7re0h16NCBoqIiotGovS4vLw+Px0N6enoztqzluuWWW3jiiSe48847Ofzww5u7OS3SO++8w8cff8zw4cMZPnw4b731Fm+99VZSrTNRf9nZ2bjdbjuMAujVqxdbtmxpxla1TL/++is9evRI+uM1cOBANm/e3Iytavk6dOhAfn5+0rr8/Pw9vjv4nsrv93PuueeyYsUKnnrqqRZT+6Glaejrtrb9s7OzG62NjaUhrzGXy2V/aQXzQ37Xrl3Ztm1bE7R091AUxf7Caundu3eN15CZmYnb7U76XUejUYqLi1vk7xrgq6++4uCDD97hfl6vN+nvY9u2bcnMzNxjf9c1fWbfG97XtX1Xae3v65quu7W/t+v6Xtoa39f5+fl8/PHHSev69u1LJBIhOzt7j3xv7/WB1IABA3A4HEnFvBYuXMjgwYOltsxOmDNnDi+++CL33HMPRx99dHM3p8V65plneOutt3jjjTd44403mDhxIhMnTuSNN95o7qa1SEOHDiUUCrFmzRp73erVq5MCKlE/7du3Z926dUn/YrJ69eqkugui4YYOHcpvv/1md4cH82/R0KFDm7FVLZOu68yYMYONGzfyzDPPsM8++zR3k1qthr5uhw4dysKFC+3HW7ZsYcuWLS3udd6Q15hhGBxyyCG89tpr9jprGHnv3r2borm7xX/+8x+mTJmStG7ZsmU1XoOqqgwePDjpd7148WIcDgf77rtvYzd1tzMMg19++WWHPSH8fj/7778/33//vb1u27ZtFBUV7ZG/69o+s7f293Vt193a39e1XXdrfm/X9b20tb6vN27cyIwZM5LCsl9//ZU2bdowcuTIPfK9vdcnLl6vlxNOOIEbb7yRpUuX8vHHH/P4449z9tlnN3fTWpxVq1Yxb948zj//fEaOHEleXp49iYbp0qULPXr0sKeUlBRSUlLo0aNHczetRerduzcTJkxg1qxZLFu2jK+++or58+fz17/+tbmb1uJMnDgRp9PJP//5T9asWcOnn37KQw89tMPx96JuOTk5dOrUiVmzZrFixQrmz5/P0qVLOfnkk5u7aS3OK6+8woIFC7j11ltJT0+3/w5VHYIidt2OXrfhcJi8vDy7zsZf//pX/ve///Hyyy+zbNkyrrrqKiZMmEC3bt2a8zIabEevscTrVhSFCRMm8MADD7BgwQJWrFjBVVddRceOHRk/fnzzXkgDHHTQQfz444889thjrF+/nueff5433njDHvYeDAaTPu9NnjyZxx57jI8//pilS5dy4403cuqpp+7Rw3pqs2nTJsrLy2sc1pN43ampqYwcOZLZs2ezdOlSfvvtNy6//HLGjRtH//79m7rZdarrM3trfl/Xdd2t+X1d13W31vf2jr6Xtsb3NZjD9AYNGsS1117LypUr+eKLL7jzzju58MIL99z3tiGMiooK46qrrjKGDRtmjB071njiiSeau0kt0sMPP2z069evxknsmquvvtq4+uqrm7sZLVppaanxj3/8wxg2bJgxevRo44EHHjB0XW/uZrVIK1asMKZMmWKMGDHCOOSQQ4wnnnhCfpY7oV+/fsb3339vP167dq1xxhlnGPvtt59x9NFHG998800ztq5lSfxZTp06tca/Q2eeeWYzt7J1aMjr9vvvvzf69etnbNiwwV736quvGuPHjzeGDRtmTJ8+3SgsLGzS9u+shrzGql53MBg0Zs+ebYwZM8YYOnSoMW3aNGPz5s3Ndi31VfV3/dFHHxnHHnusMXjwYOOII44wPvjgA3vbq6++Wu3z3sMPP2yMHj3aGDlypDFr1iwjGAw2Wdt3RdXrXrx4sdGvXz8jFApV27fqdRcXFxvXXHONkZubawwfPty48sorjeLi4iZpd0Ps6DN7a31f13Xdrfl9vaPfd2t8b+/omlvj+9qydetWY/r06caIESOMMWPGGA8++KD9OX1PfG8rhmEYuzfiEkIIIYQQQgghhBCidnv9kD0hhBBCCCGEEEII0bQkkBJCCCGEEEIIIYQQTUoCKSGEEEIIIYQQQgjRpCSQEkIIIYQQQgghhBBNSgIpIYQQQgghhBBCCNGkJJASQgghhBBCCCGEEE1KAikhhBBCCCGEEEII0aQkkBJCCCGEEEIIIYQQTUoCKSFEi9C/f39mzpxZbf1rr73GxIkTm6FFQgghhBBCCCF2lgRSQogW4+233+a7775r7mYIIYQQQgghhNhFEkgJIVqMLl26cPPNNxMOh5u7KUIIIYQQQgghdoEEUkKIFuPvf/8727Zt47HHHqt1n61bt3LZZZeRk5NDbm4ut956qx1gvfbaa5x11lncf//95ObmMmrUKGbPno1hGPbxL774IhMnTmT48OGcddZZ/Pnnn41+XUIIIYQQQgixt5FASgjRYnTo0IFLL72Uhx56iA0bNlTbHg6H+dvf/kYgEOCZZ57hvvvu4/PPP+eOO+6w91m0aBFr1qzhhRde4LrrruPpp5/m22+/BeDTTz9lzpw5XHfddbz++uuMHDmSs88+m5KSkia7RiGEEEIIIYTYG0ggJYRoUc466yx69OjBbbfdVm3bV199xbZt27jzzjvp378/o0eP5vrrr+eFF16gvLwcgFgsxi233ELv3r05/vjj2Xffffnll18AePTRR5k2bRoHHXQQPXv25O9//ztdunThzTffbNJrFEIIIYQQQojWztHcDRBCiIbQNI0bb7yRyZMn8/HHHydtW7VqFT179iQjI8NeN2LECKLRKOvXrwegbdu2pKam2ttTU1OJRqP28XfeeSf33HOPvT0UCrF27dpGvCIhhBBCCCGE2PtIICWEaHFGjBjBSSedxG233cZ5551nr3e73dX2jcViSXOXy1VtH6uGVCwW49prr2X06NFJ2xMDLCGEEEIIIYQQu06G7AkhWqQrr7ySioqKpALnvXr1Yu3atRQXF9vrFi9ejMPhoHv37js8Z69evdi6dSs9evSwp4ceeojFixc3whUIIYQQQgghxN5LAikhRIuUlZXFlVdeyaZNm+x1Y8aMoVu3blx11VX8+eeffP/999xyyy0cc8wxpKen7/Cc55xzDk899RRvvPEG69ev58477+S9996jT58+jXkpQgghhBBCCLHXkSF7QogW6+STT+bVV19l+/btgFlfat68edxyyy2ceuqppKSkcOyxx3LFFVfU63xHHXUU+fn53H///eTn59O3b18efPBBevbs2YhXIYQQQgghhBB7H8WwiqcIIYQQQgghhBBCCNEEZMieEEIIIYQQQgghhGhSEkgJIYQQQgghhBBCiCYlgZQQQgghhBBCCCGEaFISSAkhhBBCCCGEEEKIJiWBlBBCCCGEEEIIIYRoUhJICSGEEEIIIYQQQogmJYGUEEIIIYQQQgghhGhSEkgJIYQQQgghhBBCiCYlgZQQQgghhBBCCCGEaFISSAkhhBBCCCGEEEKIJiWBlBBCCCGEEEIIIYRoUhJICSGEEEIIIYQQQogmJYGUEEIIIYQQQgghhGhSEkgJIYQQQgghhBBCiCYlgZQQQgghhBBCCCGEaFISSAkhhBBCCCGEEEKIJiWBlBBCCCGEEKLFMwyjuZsg9kDyuhBizyWBlBAiyVlnncVZZ53V6M+zceNG+vfvz2uvvdag4xYsWED//v1ZsGBBI7VszzBx4kSuueaa5m6GEEKIPczChQu55JJLGDNmDIMHD+bggw/mn//8J6tWrWrupiV54IEH6N+/f5M938KFC7ngggua7Pn2BL/99hvnn38+BxxwALm5uUydOpXffvstaR/DMHjsscc47LDDGDx4MIcffjjPPfdcg57nX//6V7XPhtbvt7bphx9+qPf5azrXwIEDyc3NZfr06axYsaLe53r88ce58sorASgtLeWqq67ip59+qvfxu+Kaa65h4sSJde7z2muv0b9/fzZu3Fjv89bnmKKiIiZMmMCGDRvqfd5E5eXl3HTTTYwZM4bhw4dz/vnns3r16h0e9+eff3LeeeeRk5PD2LFjufrqq8nPz0/aZ9u2bcycOZOcnBxGjBjBueeeyy+//LJT7RStj6O5GyCEEEIIIYTYsfnz53PPPfcwduxYrr32WrKzs1m3bh0vvPACJ554IrNnz+boo49u7mY2i5dffnmPC+Ua07p16zjzzDPZb7/9uO2221AUhccff5zJkyfz+uuv07t3bwDuuOMOnnnmGS699FIGDx7Ml19+yc0334zD4eC0007b4fM8/vjjPPHEE+Tk5CStP+WUUxg3blzSukgkwuWXX052djZDhgxp8DW99NJL9nIsFmPz5s3ce++9nHHGGbzzzjtkZ2fXefyqVat4+OGHefPNNwH4448/+N///sdJJ53U4LY0lgkTJvDSSy/Rvn373XrerKwspkyZwrXXXsvTTz+NoigNOn7mzJksWbKEf/zjH6SmpjJnzhzOPvts3nnnHTIyMmo8Jj8/n7/97W906tSJ2bNnEwqFuOuuuzj//PP573//i9PppKysjL/+9a8EAgEuu+wyevbsyQcffMCZZ57JM888s1OvE9G6SCAlhBBCCCHEHu6zzz7j7rvv5pJLLmHGjBn2+pycHE444QRmzpzJNddcQ79+/dhnn32asaWiKTzzzDN4vV4efvhhfD4fAAcccAATJ07k2Wef5frrr2fjxo08+eSTXHfddUyePBmA0aNHs2XLFr7++us6A6kNGzbw73//m08//ZS0tLRq2zt27EjHjh2T1s2ePZvy8nJefPFFPB5Pg69p2LBhSY9HjhxJp06dOOOMM3j99dd32APuzjvv5JhjjqFDhw4Nfu6m0qZNG9q0adMo5548eTIPPvggH330EYcddli9j1u0aBGfffYZ8+fPZ/z48QCMGjWKgw8+mOeff56LLrqoxuM++eQTioqK+O9//0v37t0BSEtL47zzzmPRokXk5OTw6quvsmnTJp5//nlGjhwJwJgxYyguLub222/nxRdf3MWrFi2dDNkTQuyUb775hsmTJzNy5Ehyc3OZOXMmW7ZsSdpn9erVzJgxg5ycHPbff3+mTZtW679eGobBrFmzGDJkCF9//bW9/sUXX+Twww9nyJAhnHnmmWzevLnasWvXruXSSy9lzJgxDBs2jLPOOouFCxcCUFxczMCBA3nyySft/bds2UL//v35xz/+Ya/TdZ3c3Fwefvhhezjhe++9x6WXXsrw4cPJycnhn//8JxUVFXX+XLZv386sWbMYP348Q4YM4eSTT+aTTz5J2qd///4899xz/N///R85OTkMHz6cyy67rFoXZ8tJJ53E6aefXm39lClTOOecc+psjxBCiNZhzpw59O7dm+nTp1fb5nQ6ufnmm9E0jUceeQSAqVOnMmnSpGr7XnzxxRx33HH2459++okzzzyToUOHkpOTw9VXX01hYaG9/bXXXmPgwIG8/PLLjBkzhpycHFauXMn69eu58MILyc3NZejQoZx22ml88cUX1Z7v888/57jjjrOHi73xxhtJ2+vzdzMUCjF37lyOOOIIBg8ezGGHHcb8+fPRdR0wh0q9/vrrbNq0qc5yAA888ABHHHEEH330EccccwyDBw/m+OOPZ9GiRSxevJhTTjmFIUOGcMwxx/Ddd98lHbt8+XKmTZvGiBEjGDFiBNOnT682PGrZsmXMmDGDAw44gEGDBjFu3DhuvfVWgsGgvU99PgNYQ7TqKk/Qu3dvpk6daodRAD6fj44dO7J+/XoAPv74Y9xuNyeffHLSsffddx8PPPBArecGM1xat24dTz31FAMGDKhzXzCHbj3zzDPMmDGDrl277nD/+tpvv/0A2LRpE2D+Dg899FDmzJljDxMrKSlh+fLlfP755xxzzDGAWeLh7LPPBuDss89OGnL47rvvMmnSJIYPH86YMWO4/vrrKSkpSXreX375hXPPPZfc3FxGjBjBhRdeWO+hg6+99hqHH344gwcP5rjjjkt6X9Q0/O7111/nqKOOsvf/7rvvGDhwYLXX8ZIlSzj99NMZPHgwEyZM4NFHH03a7nK5OPzww3n44YftdVapi7pKZHz99df4fD7Gjh1rr2vTpg37779/je9pSygUAiA1NdVel5mZCZifv8HstZaRkWGHUZbc3FwWLVpU7ecu9j4SSAkhGuyNN95g6tSpdOrUiXvuuYdZs2axaNEiTjvtNAoKCgBzvPhpp53G2rVrufHGG7nzzjvtrr3WH6lEt956K2+//TZz5syx/yA+++yz3HDDDYwfP5558+YxdOhQrrvuuqTjVq5cyaRJk9i4cSP//Oc/ueuuu1AUhb/97W/88MMPZGZmMmzYML799lv7GOtDZmJNgSVLllBcXMyECRPsdTfccANdunRh3rx5nHvuubzyyis8+OCDtf5c8vPzOfnkk/npp5+4/PLLeeCBB+jSpQvTp0+3u49b7r33XnRd55577uGqq67is88+4/bbb6/xvCeffDKLFi1i3bp19rotW7awYMGCGr9sCCGEaF0KCwv59ddfOeigg2odipOZmcmBBx5ohznHHXccv/32W9LfjtLSUr788kuOP/54AH788UemTJmCx+Phvvvu49prr+WHH37g7LPPTgpRYrEYjz/+OLfddhuzZs2iV69eTJs2jUAgwB133MG8efPIzMzkoosuSno+gOuvv54pU6bw4IMP0rFjR6655hqWLVsG1O/vpmEYXHjh/7d333FS1Pf/wF/Ttl9vdJByglhAEILli/DNV42SGNF8Y4ndaL6KqGiiGAvGguVnB6IYe81XSYglydcQe8RGAsaC0j3a9bJ92uf3x2dmdnZv77iDu9vb4/18PIaZ+czs7Ozu7bH7uvfnM7/A7373O/zkJz/BI488ghNOOAEPPPAAbr75ZgA8ZJs5cyYqKirw+9//Pu3/8ky7d+/GnXfeiV/84hd48MEH0dbWhvnz52PBggX4yU9+gqVLl4Ixhquuusp5DrZs2YLTTz8djY2NuOuuu3D77bejpqYGZ5xxhvO5p66uDmeddRbi8TjuvPNOPPbYYzjppJPw7LPP4plnnkk7hz19BrC7dU2cOLHDx3HmmWfioosuSmvbtm0bNmzY4FTIff311xg5ciQ+/fRTnHLKKZg4cSJmz56d1jWuI1deeSVeffVVHHHEEXvcF+BdA4cNG4Zzzz23S/t31ZYtWwDAqcABgJ07d+Ldd9/F/fffj4ULF6KoqAivvfYaKioqnCqriRMn4qabbgLAfwbtn5Vly5ZhwYIFmDRpEh566CFcdtll+L//+z+cffbZzuv90Ucf4YwzzgAA3HHHHbjtttuwa9cunH766XvsFrpr1y4sX74cV1xxBR5++GEIgoD58+c7PyeZVq5cieuuuw6HH344li1bhuOPPx6XXnopDMNot++iRYtw0kknYfny5Zg8eTLuuecevP3222n7nHDCCfjiiy+c523ixIl7fE9s2rQJw4YNgyRJae0jRoxwjpPND37wA1RUVOA3v/kN6urqUFNTg7vvvhsVFRU48sgjAfCuhNFotF3wZIem3RlLiwxQjBBCXH72s5+xn/3sZx1uNwyDHXXUUeyCCy5Ia9+2bRubOHEiu+uuuxhjjN15553s0EMPZXV1dc4+u3btYsceeyx75513WE1NDauurmYrVqxg/+///T82ceJE9vbbbzv7mqbJZsyYwa688sq0+7nppptYdXU1++ijjxhjjF1xxRVs+vTpLBwOO/tomsaOP/54duqppzLGGHv00UfZpEmTmKqqjDHGrrnmGnbKKaew6upqVlNTwxhj7MEHH2SzZs1ijDHn3K655pq0+z777LPZnDlzOnxu7r77bjZx4kS2ffv2tPZzzz2XHXXUUcwwDMYYY9XV1eyMM85I2+e6665jkyZNctZnzZrFrr32WsYYY21tbezQQw9lDz74oLP9t7/9LZsyZQqLx+Mdng8hhJCB4fPPP2fV1dXsueee63S/O++8k1VXV7OWlhYWjUbZpEmT2JIlS5ztL7/8Mhs/fjzbvXs3Y4yxn/70p2zOnDlM13Vnn82bN7MJEyY497VixQpWXV3NVq5c6exTV1fHqqur2auvvuq0tbW1sTvuuIN9++23jDHGHnroIVZdXc3effddZ59t27ax6upq9vTTTzPGuvb/5jvvvMOqq6vZ66+/nrbP0qVLWXV1tXN/1157rfP/eEeyndOjjz7Kqqur2csvv+y0/fWvf2XV1dXsq6++YowxtmDBAnbkkUemfdZobm5mU6ZMYXfeeSdjjLH333+fnXXWWWn7MMbYnDlz0j4zdeUzwN6Ix+Pspz/9KZs0aZLzfF500UVs+vTp7Hvf+x577rnn2IcffshuuOEGVl1dzV566aUuH3tPnw2//vprVl1dzf73f/93r87dfl00TXOmcDjMPv30U3bKKaewKVOmOJ8n7X0//fTTtGOcdtpp7H/+53/S2j766KO0z4wtLS3s4IMPZjfeeGPafp9++mna++u0005jJ554Ytr7orW1lU2bNo3Nnz+/w8dx7bXXsurqarZx40an7cMPP2TV1dVs1apVjLHU+8n+/HnssceySy65JO049s/kihUr0m7zwgsvOPvEYjE2ceJEdscdd6Tdtq2tjVVXV7Pnn3++w/PMdMEFF7DTTz+9Xft9993HJk6c2OltV61axQ499FBWXV3Nqqur2RFHHMG+/vprZ/uGDRvYxIkT2TnnnMO+/fZb1trayv70pz+xqVOnZn0dyf6HKqQIId2yZcsW1NfXOyXRthEjRmDy5MnOVVXWrFmDSZMmpQ1AOWjQILz99ttO/3QAeP7557F8+XKcdNJJaX+92bx5MxobGzFr1qy0+/nBD36Qtv7JJ59g1qxZaeXCsizjpJNOwhdffIFoNIqZM2ciFoth3bp1APhfvs4991z4/X58+umnAID33nuv3V+PMscyGDRoUKdd9j755BNMnjwZQ4cOTWv/0Y9+hPr6+rSrlWQ7djwez3rcgoICHHfccWlVVnZ5996M0UAIISS/MOuy9YqidLqfXeHAGEMgEMD3v/99/PnPf3a2v/HGG5gxYwaqqqoQj8exbt06zJw5E4wx6LoOXdcxfPhwjBkzBv/4xz/Sju3utlVeXo6xY8fixhtvxLXXXovXXnsNpmli4cKF7cavmjp1qrNsd+Vqa2sD0LX/Nz/55BPIsowTTjih3T72Mbrr8MMPT3ssAHDYYYc5bXa3I/s8P/roI0ybNg0+n895nkKhEKZOnepUYB999NF47rnn4PV6sXHjRvz973/Hb3/7WzQ1NUFV1bT7785ngK6IRCK45JJL8O9//xv33HOP83xqmobm5mbccsstOOusszBjxgzceuutOProo7FkyZK9vr9Mzz//PMrKypzKu701ceJEZ5oyZQrOOussqKqKJUuWtBvQPLMbYU1NzR67Cq5duxaqqrb7DDt16lQMHToUn3zyCWKxGP7973/jBz/4QVrFUGFhIWbNmrXHn7eSkhKMGTPGWbfPKRwOt9t327Zt2LlzZ7uf7Y4uTOB+L/n9fpSXlzs/o7aCggIUFhZ2q/LI/v2STWeDo7/22muYN28eZs+ejccffxzLli3DuHHjcMEFFziVZGPHjsUjjzyCmpoazJkzB0cccQSeeuopzJ8/HwDocyyhQc0JId1jd7ezP8C5lZeX46uvvnL268oYAuvXr8fRRx+N119/Heeeey4OOuggAHBKe0tKStL2z/xA0tra2uG5MMYQiURw4IEHYvDgwfjwww9RUlKCuro6HHnkkTj88MPxySefYObMmfjyyy9xxRVXpB3D7/enrYui2Ol/2q2trRg+fHjWcwGQ9qGhu8c+7bTT8Oqrr+Kzzz6DJEnYunUr7rrrrg73J4QQMnDYAYM9jk5HampqEAwGnUDl5JNPxquvvor169ejvLwcH3/8sdM1rK2tDaZp4rHHHnPGnXLzer1p6+6xiuwrutkDKK9cuRKKouD73/8+brnllrSrcrlvJ4r8b+H2/3dd+X+ztbUVJSUl7boT2Z8Hsn3R3xP3H7Fsmf8vu7W0tODPf/5zWrhnsweotrvgPf/884jFYhg8eDAOPfTQds9jtvva02eAzuzatQuXXHIJtmzZgvvvvx/f//73nW3BYBCCIKT9IRAAjjnmGHzwwQdoaGjI+hmqOwzDwN/+9jeceOKJ8Hg8+3SsV155xVlWFAUVFRUoKyvLum8wGExbj0Qinb6GQOqzZUefG8PhMMLhMBhjne7TGffPO5AKdOzxztzssdoyH2NHr0lXf278fj8ikUin5+kWCoWyjmMajUazDmhvW7JkCSZPnoz777/faTvqqKNw4okn4sEHH8RDDz0EgIe1f//7352QbPjw4c5r3dEV/Mj+gwIpQki32B9ys/3HVV9f7wRIBQUFaYOi2lavXo1hw4Y5/0FfccUVOOecc3DSSSfhhhtuwMsvvwxJkpzjZPa5zxx/qqioqMNzAVKB1syZM7F69WqUlZXhgAMOQEVFBaZPn47//d//xQcffACfz4fp06d345lor6ioyLnfzs5lb0ybNg0jRozAX//6V4iiiNGjR7f7CyshhJCBqaysDJMmTcL//d//4YorrnCCHbdIJIJ//OMfmD17ttM2Y8YMVFRU4C9/+QsqKirg9Xqdq2/ZYcV5552XtSJjT1/uq6qqsGjRItx8881Yv349/vrXv+Kxxx5DSUmJM17PnnTl/82ioiI0NzfDMIy0UKqurs7Zp7cVFBTgyCOPzHohEVnmX6eWL1+Op556CrfccguOO+4454t85oDiPembb77BhRdeiGQyiSeeeKLdeE8jR44EYwyapqUFY7quA+iZ6pR169ahubm5XQX73jjkkEP2+rbFxcV7DIvs8KOhoQGjR49O21ZfX4/hw4ejoKAAgiB0+NnS/hzcE+yrFGZ+1u1ovKmuamtr69b74oADDsAHH3wA0zTTfrds27Ytrdor044dO9ICUID/TB188MHOAPA7d+7EP/7xD5x88slp4fNXX32F4uLiHh0An+Qn6rJHCOkWO8x5/fXX09pramqwdu1apwx+6tSpWLduXVoo1djYiIsuuijtih3l5eXw+Xy46aab8OWXX+LJJ58EAIwaNQqDBw/GX//617T7yRy88YgjjsDbb7+d9pcgwzDwxhtv4JBDDnH+Wnfsscfi3//+N9577z1MmzYNAL888vbt2/HSSy/hqKOO2ue/7B1xxBH417/+1e4v2K+++ioqKiowcuTIvT62IAiYO3cuVq1ahbfeegunnHLKPp0rIYSQ/DJv3jxs2bIF9913X7tthmHg5ptvRiKRSBvoWpIk/PCHP8Tbb7+Nv/71r/j+97/vVHCEQiEcdNBB2Lx5Mw455BBnGjduHB5++OFOr/D2r3/9C0ceeSQ+//xzCIKACRMm4KqrrkJ1dXXWq+F2pCv/b06bNg26rrf7PGB3Y7ev3pUtpOsp9pUFJ0yY4DxPBx98MJ566in87W9/A8CHKhg7dixOPfVUJ4yqra3Ft99+m7U6Zl/t2rUL559/PgRBwIsvvph18HG7MuqNN95Ia3/rrbdw4IEHZq0U665169ZBlmUceuih+3ysfTF06NB2V3vOrKo77LDD4PF42n2G/eyzz7Bz504cfvjhCAQCOPjgg/GXv/wlbWDxcDiMd955p93V4vbFoEGDMGLECOdnyPbmm2/u9TFbW1sRj8cxZMiQLt/m6KOPRjQaxfvvv++0NTU14bPPPsNRRx3V4e1Gjx6Nf/7zn2lVWslkEl9++aUTPjU2NuKGG25I+31SX1+PN954A7Nnz+60SyDZP1CFFCGknd27d+Opp55q115dXY0jjzwSCxYswMKFC3H11VfjRz/6EZqbm7FkyRIUFRU5fz0877zzsHLlSlx00UW45JJLoCiKc4WdH/7wh+3+ijVz5kyccMIJePjhh3H88cdj+PDhuOaaa3D11VfjhhtuwAknnIC1a9fixRdfTLvdvHnz8N577+Gcc87BxRdfDEVR8Nxzz6Gmpibtcrjf+973IIoi3nnnHefD/MSJExEMBrFmzRrcfvvt+/y8nX/++Xj11Vdx3nnnYd68eSguLsbKlSvx0Ucf4Y477tjnD8tz5851LtO8r+M0EEIIyS/HHHMMrrvuOtx99934+uuvceqpp6KyshLbt2/Hiy++iK+//hq33347xo8fn3a7k08+GU888QREUWzXNW/BggW4+OKLnf/P7avprVu3DpdeemmH53LQQQfB5/PhV7/6FS6//HKUl5fjww8/xNdff41zzjmny4+pK/9v/sd//AemT5+OG264AbW1tRg/fjw++eQTPPbYYzjllFMwduxYAHyMn4aGBrz77ruYMGECKisru/Hsdu7SSy/F6aefjksuuQRnnHEGvF4vfv/732PVqlVOt6RDDz0Uy5Ytw/LlyzFp0iRs27YNjz76KFRV7fb4UE1NTfjuu+8wduzYDkOj2267DY2NjbjlllsQiUSwdu1aZ1soFMLYsWMxffp0zJo1C4sXL0Y8Hse4ceOwcuVK/POf/8SyZcuc/b/77js0NTXtVeX1t99+i2HDhmXtmgjwz5S7d+/GQQcdtM9/+OvMUUcdhRdeeAGMMSfksIPBd955B0VFRRg/fjwuvvhiLF26FIqiYNasWdi+fTsefPBBjB071vlj39VXX40LL7wQF198Mc4880xomobly5dDVVVcdtllPXbO9hX4rrnmGtx88834r//6L6xfvx5Lly4FsHch65o1awDAuWJ1JBLBxo0bMWLECKd7aaYjjjgC06ZNwy9/+Uv88pe/RHFxMR5++GEUFBQ4VxsE+JWtVVV1hte44oorcNlll+GKK67AaaedBlVV8fTTT6O2thb33nsvAODggw/G4YcfjkWLFuFXv/oVJEnCAw88AEmScPnll3f78ZGBhwIpQkg73333HRYvXtyu/bTTTsORRx6JuXPnIhgM4tFHH8Vll12GUCiEY445BgsWLHDGdBg8eDBeeOEF3HPPPbjuuuvg8Xgwffp03H///SgqKspaVn399dfjgw8+wI033oinnnoKc+bMgSiKWLZsGf70pz+huroav/nNb7BgwQLnNuPGjcMLL7yA++67DwsXLoQgCDj00EPxzDPPtBv8cfr06WkVUrIsY+rUqVkHNN8bFRUVePHFF3Hvvffitttug6ZpGD9+PJYtW4b//M//3OfjV1VVYfz48SgvL0dVVdU+H48QQkh+Of/88zF58mQ8/fTTuOuuu9DU1ISKigocddRRuP32251wxm38+PGorq5Gc3MzZsyYkbbt6KOPxuOPP44lS5Zg/vz5UBQFEydOxJNPPtlpOOH1evHEE0/g3nvvxe233462tjaMGjUKv/nNbzB37twuP56u/L8pCAIeffRRPPTQQ3jqqafQ1NSEYcOGYcGCBWld6ObOnYt3330Xl112GebPn4+LL764y+exJ+PHj8fzzz+P+++/H7/61a/AGEN1dTWWLl3qnOcll1yC5uZmPPPMM1i6dCkGDx6Mk08+2Tn/trY2FBYWdun+3nnnHSxcuBDPPPNM1uEEVFXFO++8AwBZu0dOmzYNzz77LADgwQcfxJIlS/Dkk0+iqakJY8eOxZIlS9K6di5btgx//OMf8c0333T3qUFDQ0On4wC9/PLLWLJkCf7+97/3aves4447DkuXLsXnn3/uDFA/btw4zJkzB88//zzef/99vP76606A+txzz+H3v/89iouLccIJJ+DKK690qgdnzJiBJ598Eg899BAWLFgAj8eDqVOn4q677mo3aP+++uEPf4hYLIbHH38cK1aswLhx4/DrX/8av/71r9uNR9UV7733Hg499FBn3Lkvv/wS55xzDhYvXtzpe3PJkiW48847cffdd8M0TRx++OF44IEH0l7bW265BTt27MBbb70FAPjP//xPLF++HMuWLcO8efMQDAZx6KGH4pVXXnGCcUEQ8PDDD2Px4sW46aabAADTp0/Hww8/3K0qLjJwCWxvR9AjhBDSp2prazFr1iw89NBD7frsE0IIIYT0R2eddRYeeOCBdhem6Wm/+MUvUFJSkvWPqv3V66+/joMOOihtTKt33nkHl1xyCf70pz+1q3jsTCwWwzHHHIO77rqLPieSvEFjSBFCSD/39ddfY8mSJbjoooswatSotL9qEkIIIYT0Vx9//DHi8fg+X82vK6666iq8+eab3RrHLNdeffVV/PznP8drr72Gzz77DCtWrMDNN9+MadOmdSuMAoCXXnoJ48aN65GqfEL6ClVIEUJIP7d27VpceOGFqKqqwn333dftDyiEEEIIIbmwY8cOBAKBPrkaIsCvdrh+/fqsg//3R83Nzbj33nvx3nvvoampCeXl5Tj++OMxf/58BIPBLh+nqakJP/7xj/Hss8/u00V0COlrFEgRQgghhBBCCCGEkD5FXfYIIYQQQgghhBBCSJ/a60BKVVXMmTMHH3/8sdNWU1OD8847D5MmTcKJJ56IDz74IO02H374IebMmYPDDjsM55xzDmpqavb+zAkhhBBCCCGEEEJIXtqrQCqZTGLBggXYsGGD08YYw2WXXYby8nKsWLECJ598MubNm+cMKrdz505cdtllmDt3Ll555RWUlpbi0ksvRVd7DDLGEIlEurw/IYQQQsj+jD47EUIIIaQ/63YgtXHjRvz3f/83vvvuu7T2jz76CDU1NfjNb36DMWPG4JJLLsGkSZOwYsUKAMDLL7+Mgw8+GBdccAHGjRuHxYsXY8eOHfjkk0+6dL/RaBRTpkxBNBrt7ikTQgghhOx36LMTIYQQQvqzbgdSn3zyCaZPn47f//73ae3r1q3DQQcdhEAg4LRNmTIFa9eudbZPnTrV2eb3+zFx4kRnOyGEEEIIIYQQQgjZP8jdvcGZZ56Ztb2+vh6VlZVpbWVlZdi9e3eXtudcfBfw/mmA1grIIT4pBYBSCCjFgMeeSgFvBeCrALzlgK8KkAN7ODghhBBCCCGEEEIIsXU7kOpIPB6Hx+NJa/N4PFBVtUvbcy66DWj4cO9uKxcA/kGAbxDgHwwERwHBA4DQAUBoNA+tRAkQJAAinwsiIAg9+QgIIYQQQgghhBDSSwyTQRLz+3t8f3oMPRZIeb1etLS0pLWpqgqfz+dszwyfVFVFYWFhT53Cvin/HnDSN0DTx4DaDGhtgB7lk9oK6G18rrUCWgvfrrUCpgboYSAcBsIbsh9bLgSCI3lQFToAKDwQKBgHSH5AVADRY829gChbgZUECLJr3V625oQQQkg/YA+YzcB6ZL27bR7JA7/i78mHRAghhBCSlSQKuOKlf2FjXSTXp7JXxlaG8ODpk3N9Go4eSzaqqqqwcePGtLaGhganm15VVRUaGhrabZ8wYUJPncK+K6rmU0dMAzBVwEwARgLQY0B8NxDZAMR2AIlaINkIqE1Aog6I7+RtehvQ+m8+2UQFCI3j4VTxRKDwYED2Ae2uhCO4qqska9kKqUQZqYorGRBFax85VYUFwarGEq19BWvumux90Flb/0hQCSGkv2CMgYHt0xxAt/fJdt+ZbfZtTNOE1QrTNGHCTO0HBjDAZKZzO5OZAAATJsD4MU1Yba5jdTVkcj9Xadsz5q6MqUtBlVupvxQzhs/o0mtGCCGEELKvNtZF8OXOtlyfxoDQY4HUYYcdhuXLlyORSDhVUWvWrMGUKVOc7WvWrHH2j8fj+OqrrzBv3ryeOoXeJ0qA6Afg+ktswRheXWVEAS3CK6cSu/mcmTwoUpuA6FYgsplXUbV+yaur2r7i0/Y/8gCp6GB+rIqjgMIJPARiDGBGxqTzYMwwrQDLnjN+n9aXDOcTvn2cNEJqmxNAWWFVWhjlCrQEdzCWWbkl7SHgygzBOttGCCEpdoBiMpOHJ1ZwYrd1tp5tW2Y7YwyGacCE6YQ2BjP4Mms/GczoODhyhT124NOd/cDg/Hp2ll1zQRDS9hOsnRlY2rK9r83eJggCBAhp2+ztdltXttvHytzHuQ/rfva0n3u9K/tm7t+abIVq9JOu/4QQQgghpFt6LJCaNm0aBg8ejIULF+LSSy/F22+/jc8//xyLFy8GAJx66ql4/PHHsXz5csyaNQtLly7FsGHDMH369J46hdwRJUAs5AOgYwhQMBZQW3jXv/hOHiKVTAYG/ScgWVVQsRqg5d9Ay+dA4yfW+jo+bXwU8A/l+w/6Pg+neqObXodhlmm1w5pbIRhzBWD8T+eufdv/1RqAFYa5ltPCL8lqt0Ip0d5mV4HJqeowQckSfHUUakl72E6hFyH7KltQ053JDoQM04Bu6jCYAcM0YDADOtNhmqaznhYmWRU7TsjjrupxBUx2QMMYax/wZFl2BzXu8EUUxKxhTeZcFMQO9+msDeg4JCJ7JlMXdkIIIYSQvNVjn+QkScKyZcvw61//GnPnzsXIkSOxdOlSDBkyBAAwbNgwPPzww7jjjjuwdOlSTJ48GUuXLh2YH8BFGfCV8yk0Gkg2ALHveDc+Uwe8JUBwBJ+GnsRvE9sBNH4MNKzmU3wHsOUZPgWGA8NOBob+CPCW9tx52qFQX7wEWUOvjEDLXja1VFvmNqfyy67wQnoYltYl0Q69hNS6E05JvNtkWvCVUe2VGXJlhlrt2uzbEdK/OJU9VsCTbdld/eNsNw1opgbd1PnEdOgGX04LlpAKmJzJeb+mZHa3cgczdqDjDoAyl0VBhCAKafu7twPtAyRCCCGEEEJI/7RPgdQ333yTtj5y5Eg899xzHe4/c+ZMzJw5c1/uMv+IEuCvAnyVvGIqtoOHU2orb7P/uhsYCgTmAsPnAnocaPgHsHsVUP8Br576dgmw4RGg6lhgyBygaALgLcvpQ+sWQUCqK18vYuYeQi8DgAGYSUC3Ay97PyOj0svuK+Nu6qBbYlr3RtcA9ILiCruUDsIsqfN2Gsdrv2Yyk1cQWQFSZ3Pd1KEZGjRTg2qo0E0dqqG262qWOXdXEzldwpAKityTOwySBAmCKGTdZrcRQgghhBBCSDZU695XBIFXN3lLgcAQILwRiO8C5ADgKU0PG2Q/76o36Ps8nNr9JlDzR6D1Cx5S7V7F9/OUAKEx/Ip9BWP5PDSadwvcX/V2lzwn8DLTuzDa7cywrrxoprfbgVjmeF7OunusrszKLldAJSp8m+ixuoraVV7uYCtb2NXRNgq5+gJjzOmWZlcbubuqORVIho6kkYRqqNAMDUkj6YxlZICHTvYyM1kqM3WNL5QZIEmC5MwVUYEkShAgpM0JIYQQQgghpK9RIJUL3jJAKebd8sIbeAWUfxAPGTLJft5db9jJQNu3fAD0ho/5bdRmoOkzPjlE3hWwYBxQUA0UjgMKDgS85RQ+9ITeCrwyg620ii1X0GXEXOFXZjdGIEtCYYVQWQaad0Ipu5JLTgVcTjVXZpCVJeBqF3QN/J8zJ0ByTZqhpS0njARUXUXSSEIzNR5AMT0VKjEDANpVJzkBkig5y4qkwCf4nHaqPiKEEEIIIYTkOwqkckWUeHDkLQdav+FX4fOWAkqo49sUVgMHXcuXjYR11b6NPNSKbOKBldbCjxXdCuz+W+q2nhIroKrmAVXhgfz+BaqO6Bec8bx6+PVoV9GVscwMgMVd3Rm70HUx82qJ7UIuu8uiK9wSXeNzdSnYklxtvR9w2RVMuqlDMzWn25vTBc7QENfjSBgJJLREWmWT3WXOTQAPluxQSRIlyKIMj+BJa6dxjgghhBBCCCH7Kwqkck0OACWH8iAq/A0Pmnzle76d5AOKDuKTjTFAbQTaNgDhb61pAxDZyqupGj/mk/sYoXFA0XgrpBoPFIzOXqlF8lNvVHRlDbbs9T10WeQnhfSAyz0QvWvQebu6K20AelcllyijfXjlnktgEKAzBs00oJoGNNOAxnjIpBoq4nqcB01WyOQevNvpEgceMMmiDFmUnUDJI3nSwiZCCCGEEEIIIV1H36L6A1HiXeuUENDyFRDbDviHdD9IEARecVVRDlTMSLUbiVQFVfhboO0bPjcSQOu/+eQcQwYKxvBwyp4Kxu3f41KRdD0dcjnVWdmquDruqshME5qpW0GTDtXUoRo6NGYgZuiIGUnEDQO6aUAzTehgMBgDswIrQZAhSx7Iohey7IMkyfCKHsiiAklSIDpXXLSr1wRX4OUe0J4QQgghhBBCSHdRINWf+AcDUgBo+Te/Gl9gaM984ZV8QNFEPtmYAURrgLb1vDKrzZq01tQy/sT3FSQgOMoKqCakKqpk/76fGyGCwIPQDCYzoRoakoYG1dSgGiZUw0BcTyKqxxHTk1b3Oh2aqcNkJj8ceEdCRRQhCwoUxQsfRMiiaA0Vz6xqLTsEiwB6GNA6Oz/ryoruweZF+2qLVnDlXFnR7p6YrTuju80+lvtqitSFjxBCCCGEELJ/oECqv/EUASWTgOZ1PRtKZRIkIDSKTziBtzEGJHYDrV/zkKr1ax5YqU28wiqyCdj5hnUAkd/WCakm8DGq5EDPnysZkBhjUE0NSUOFauhIGiqShoqYnkBEjSOuJ6GaGnTranS8kx+DJEpQRJlPggy/xwdFlCGJvVStZF9N0R5jK+0Ki1YFl13V5YzB5bp9Rg/FVDDl7qoopEIqO9Syl0XZCr+k7CEXVW+RPMMYg2ZqSOpJJI0kEnrCWVYNFQk9AdVQ09rc7aqhOu0xLYbjRh+HmaNm5vphEUIIIYSQbqJAqj9SQkDJYUDL50B0OxAc2jeDjwsCr9LyDwYGzeZtjAHJBqDt61RA1fY1b4ts5tPOP1sHyBZSUSXV/syuckpYYVPS0JDQkwirMUT0uPXlklc4cTxw8ogKZFFCQOZhkyzmcABwQbTGkuqB9yBj6VdRBAAYqXZTB1gCqXDLfSXFbOfWQfWWE3B1VL1ld0PMVrnVSehFBjSTmUjqPCCK63Ek9IQzZa5nTvbt3AFTwrCCJnfwZPB11uEPdffVRetw5Ywre+x4hBBCCCGkb1Ag1V/ZoRTWAbGdvVcptSeCAPgq+FT5H6n2hBVSuYOqZP0eQqqDqLvfAMVDpyQSOg+e4noSrWoEUS1uje3Er1gHCBAEQBEVeEQZHsmDkBKAIsr7xxXn7EomAD3y6zdr9ZYVZDG98+qtzMotIEv1lpAeTAkKbxcVq9uiwsfAy3q1xYzqLad7omtMLrJXNENDTIshrscR02J8WYsjpvN5XI/zdS2GhJ5w9nW2WQP52yGTvS1pJPv8sQgQ4JW98Epe+GQfvJIXXtkLj+Thc5HP7X0UUXGWvbIXJjMxcyRVRxFCCCGE5CMKpPozOchDqWZ3KNVPvsT5ygHfMUDlMam2ZAPQuh5o+6obIdUEa34gdffr5xhjTuiUMFSn0qlNi/AKKF2DZmqwQyePqMAjKfBJHhR5gnQlut7Q09Vb7kALVpdEJ+AyAJZ0VXjtqXoLSHUd7ErAZVVydRpwdVTB1b+7KOqmjpgWQ1SNIqpZkxrlbVrU2WaHS+7J3u4OoHSnorD32AGRT/bBr/idsMgv+512r8zXvXIqTLLb3evuoMme223yPobRLYkW+t1CCCGEEJKn6FNcfycHgeKDgcbPgEQt4B+U6zPqmLccqDyaT7YuV1IJQHBkatB0J6QK5eSh7M/s4Cmu8/ApbiTRpkbQpkaR0FWopgbD5KGELMrwSAq8ooKgz7//VDoNRE7lUg8dr133xIzAy9QBdCPgArJXcKErA8xnXCkxW7VWltCLAYjrcYSTYUTUCCJahM+tKapG+VyLOut22BTRUusJPdFDT2o6j+SBX/YjoAT43MPnfsXvtPtkn7PdDpf8st/Zzyf70kImu03sxwEfIYQQQggZGCiQygdKIVB8CNC0BlCbAU9Jrs+o67JVUiUaUmNRtX6VCqmiW/m06y/WjgIQGM7DKTukKjiQD/xOeoRqaIjrScT1BOJ6Em1qFC1qBAk9iaShwmQMAINihU4B2YdiqQCy2AdjmpH81uPdE/cw/hYYYKqpSi8wMJNflbFNjyOixtGmxRDWEgirMYT1BMJaHGEtgYie4O1aHBEtgYgWQ0RPIKLFYVhXb+wJHsmDoBJEQAkgqAQR9PBlez2gBNq1+RV/+twOoBQ/VQYRQgghhJC8Rp9m84WvAiiaCDT/i3dvyefKIV854MuopEpaIVXr+lRYlagFYt/xafebrtsPtgKqA4ECa+4t7z/dGfshu+oppiUQN5KIqDG0JCOI6DFXVztAEWV4JQ8PnrwUPJH+QTN0tGlRtKkRtCajTsVeqxpBWI1Z8yjfJ8nnYTWKNjUGwwmv9p4kiAgpfoRkH0KKD0HZm7HuQ0j2Iajw9qDiR0jm86A9VwJQZA9S1Vl2NZcEwOqqmK27Ylo1l5gK+pgOmGZedFkkhBBCCCEkGwqk8klgGGAkgNYveRWAUpDrM+o53nKg4mg+2dRmoO0bK6Baz7v8xXcAiV18qn07ta+n1AqoDuTzwgP587UffkkzmYm4nkRMTyCmJdCmRtCcDPMueEYSJmMQBRE+yQOf5EHIF4Ai0a8C0vsYY4jpCbSqEbQkw2hJRtCaDKNFjaA1GUGrarXZgVMygjY1gug+dnmTBAmFngAKPEEUKAEUeoIIKan1Ak/AWrfmGcteydP1rqj2QPNZK7mscbnSBptHF7srItXdMLPLotPlUHJNcupKi063RTvgyhx/KyPsQkYXRggU+PcQVVUxd+5c3HjjjZg+fToAoKamBjfeeCPWrl2LIUOG4Prrr8fRR6f+L/zwww9xxx13oKamBocddhhuv/12DB8+PFcPgRBCCCGkx9C30HwiCEDBGAAmENlqdd8r5VfkG4g8JUD59/hk08KukOobIPyN9Vw0AQ2r+WSTAkDBOKCwGiio5vPQGEDy9flD6S2MMcT1JKJ6HDEtgdZkBM1qGHEtwbvcgUEWJfisqqcSbyEkcf8L6Ujv0E3DCpbCaE62oSUZseZhNFvtrWoEzYk2tFghlLaXA3ILEFDg4WFSoSeEImte6Amg0BNCgSeIQiXorBd6grzNE4BP8vbd2GbOQPNAj/4XmzXoMtOvtGhqaV0W211ZscNzFlxjc2ULu+ygSkpVddnr9nLWcKsrgZf7/ga2ZDKJq6++Ghs2bHDaGGO47LLLUF1djRUrVmDVqlWYN28e/vznP2PIkCHYuXMnLrvsMlx++eU45phjsHTpUlx66aV49dVXabw+QgghhOQ9CqTyjSDy6h//ECBaA8RqALUR8JQN3GDKTSkAyqbyyWYkgPBGV0j1LV83YkDLOj45RCA4ggdVBdVAoTX3VuTFF6KkoSKqxRHV4mhTo2hMtFqVTypMZkIRZV715PGjTCqigYlJtzDGENZiaEq0oinRhqZkG5oz5i3JNjQleADVqkb26n48ooISbwGKvCEUewtQ5AnxyZsxdy2HlMD+Hab2VtAFuMIuK+ACkDYAvTMIPXMFXkgNVr83gZdddeXellbZlRF6iXa1VkbAlWzLiyu0bty4EVdffTUYS3+yPvroI9TU1OCll15CIBDAmDFjsHr1aqxYsQKXX345Xn75ZRx88MG44IILAACLFy/GUUcdhU8++cSpsCKEEEIIyVcUSOUrpQAoPggIDncFU02AtzS/x5faG5KPX4mw+OBUm6kD0W1AeAOvomr7lgdVanNq8PTdf0vtrxQBBWOtoGosEBrLq6lkf18/GofJTES1OCJaHBE1hsZEK8JaFHE9CYOZkAQRftmLoOJDqa+QwieSlR0yNSZa0BhvRUOiFU2JVjTa86QVPiVa0ZRsg252b8wlAQKKvCGUeAtQbE0l3kJn3Wn32AFUqG8rlsieOWFXL40Z12F1l91mD0qfrcLLHsi+g2MnI4CvrHfOuwfZAdJVV12FSZMmOe3r1q3DQQcdhEAgFapNmTIFa9eudbZPnZr6A4zf78fEiROxdu1aCqQIIYQQkvcokMp3TjA1zAqmtgPJ/TSYchNl3r2xYAyAE3gbY0Cy0aqg+tYKqTbwQdO1Vn4Vw6Y1roMIfByqAiucsueB4fz4PUw1NH51Ly2O5kQYzclWxPQEVEODIAjwSh74JS8KAyEabJxAN3U0JtrQEG9BQ6IFDfEWNCZa0JBo5fN4Cxqt4Km73eRCSgAl3gKU+opQ6itEqbcQJd5ClPr4vMTHA6dSbyEKPaH9u3KJ7FlvVncZJtDNEDUXzjzzzKzt9fX1qKysTGsrKyvD7t27u7SdEEIIISSfUSA1UCiFQPHE9IqpZDPgLdm/gyk3QbCu8FcOVByZajeSQHQL0LaBB1SRjbzLn9rEn8dYTfoA6oIChEbxcCo02prGAIEhrsvc71ncuqx8RI2hIdGCVjXCq59MA5IoISD7UOQpgE/29NxzQPo9wzTRnGxDfbzZmlpQH29GQ6LFWW+IN6M5GQbbY1+plAIlgDJfMcr8RSj1FqLMV4QyXxFKnXkhSn1FKPEWwCvRzxwhfSEej8PjSX+/eTweqKrape2EEEIIIfmMAqmBxg6mAsN4tZQTTJUCcjDXZ9c/SV6gcDyf3JKNPJiKbErNI5usMaus8MpN9FpB1WggOBoIHcAn/1AwQUJcTyKsRRFWY6iPNyOsxRDXEjDB4JU8CMheVPpLqfppAEvoSdTFm1EXa+LzeDPq43y5PtbsBE+GM45P5yRBRJmvCOW+YpT5i1HuK0a5vwhlvmKU+4pQ7i9Gma8Ypb5CCpkI6Ye8Xi9aWlrS2lRVhc/nc7Znhk+qqqKwsLCvTpEQQgghpNdQIDVQeYr4FBgGRL8D4jusrnxUMdVl3jI+lbvG6WAmEN9lhVObgbAVUkW3AmbSugLgN2mHMQUZCU8l2jyVaFXKEVYqwQLDEAgMR0mogsZ+GiASuoraeCNqY02oizVhd8xajjc5AVRXBwEXBQGl3iJU+ktQ7i9Bhb8YFf4SlNtzXzHK/cUo9hRQdzlC8lhVVRU2btyY1tbQ0OB006uqqkJDQ0O77RMmTOizcySEEEII6S0USA10niLAcwi/slxaxRQFU3tFEIHAUD5V/keqnRlIhDcj0bIeRngTjPBGeBM7EEzuhsw0BJI7EUjuxKCMwyU9ZUh6ByPhG4yEdzASvkFI+AZDU0qtK0+R/sAwTTQkWrA71oDdsUbsjjY6gROfN3Y5bPJJHlT6S1EZKHHmFf5SVPpLUOEvQaW/BKW+IqqUI2Q/cNhhh2H58uVIJBJOVdSaNWswZcoUZ/uaNamxDePxOL766ivMmzcvJ+dLCCGEENKTKJDaX7grppxgqgnwlAIKBVN7QzN0tKkRtKlR1Cea0ZKMIK4VgPkOgz80HQHZB7+kwKs1wZfYmT4ld0HRw/CqjfCqjSgMf5F2bFNQkPAOQtI3CAnvYCR9VUh4q5D0DoIuF1qXPSc9JaGrPGiKNWBXtAG7Yg1W6NRgBU7NMNieB072y15U+ctQFShFZaAUgwKlqPSXoipQhkp/CaoCpQgpAbrCHCEEADBt2jQMHjwYCxcuxKWXXoq3334bn3/+ORYvXgwAOPXUU/H4449j+fLlmDVrFpYuXYphw4bRFfYIIYQQMiBQILW/sYOp4HAgagdTjVbFVAEFHZ0wmYmwGkObGnGuXhbV4jBMEz7Zg4DiR4m3oF0XPNVbAdVbgbaiw9LaJT0MX2KXFVLtgi+5G77ELniTdRCZhkCiBoFETbvzMES/FU5Zk8+aeyqhKcX0GmaR0JPYFWvAjki9EzjtijZgZ5SvNyXb9ngMSZBQFShBVaAMgwLlGBQotZZ5AFUVKEMBhU2EkG6QJAnLli3Dr3/9a8ydOxcjR47E0qVLMWTIEADAsGHD8PDDD+OOO+7A0qVLMXnyZCxdupR+zxBCCCFkQKBAan+lFALFB/FgKraDjzOV/I6HVUoRhRqWuJ5AazKClmQYtfFmRNQYVFODIsoIKn5UBcr2umuVIRcgGipANFSdvoEZ8CYb4E26Q6paeJO18KiNkMw4gvGtCMa3tj+m6IHqqUTSa02eSiS9FUh6K6F6ysHEgTmwtW7q2B1rxI5IPXZG+bQjWo+dkXrsijWgMdG6x2MEZB8GBcowOFiOwYFyDAqWY3CQB06DA+Uo8xXTeE2EkH32zTfp4wyOHDkSzz33XIf7z5w5EzNnzuzt0yKEEEII6XMUSO3vlAKgaLwVTO0Cott4OKWEAE/JfjeOkWEaaFOjaFUjqIs3oTkRRlxPQICAgOJDia+g969WJki86slXhcy6HcFU4U3WOwGVN1kLX3I3vMk6eNQGSKYKf2I7/IntWQ+tKsVWYFWBpKcCqqccSS+fq55SQOifvxIYY2hVI9geqcOOaB12ROqxw7VcG2+EyVinxwjKPgwOVmBIsAJDguUYHCzHkGCFFT6VocgToqoDQgghhBBCCOkj/fPbJ+l7chAoHAsEhwHx3fyqcdHtgOzj40yJA/dHxa6Cakq0oTbWhKgeh2bq8EoehBQ/ynxF/SaoYKIHCf9QJPxDs2zU4U02wqvW8oAqWcfDK7UO3mQdJDMBj9YCj9aCUPTb9jeHAE0pRdJTxrsZesqsqRxJTzlUTxmY6O21x6abBnbHGrE9UsuDp0gdtjvhUy2ieqLT23slBYMDFRgSKsdQJ3iq5MuhCupORwghhBBCCCH9yMBNGcjekXxAaBTgHwIk64DINh5QiRKvmJJ8uT7DfWaPBdWSDPMqqGQYMS0OQRARUvwo9xVDkfLwrSHITmVVO4xBMiJWQFUPjz1X6+FNNsCjNkBkGjxaIzxaI5AlsAIAXQq5gqpSqAqfa54yqEopNKUErJPwUjU07IzWoyZSi5pwLWoitdgRqUNNpBY7ow17HDi80l+CYaFKDA1WYmioEkOCFRgaqsDQYCVKfYXtxu8ihBBCCCGEENI/5eG3btInJA+/Ip9vMJCs54OfJ+oAUwc8xelX5tN1QLMmwwB0azIMwDQBwwR0zWpjgGkAJuPtyOhmJQiAKAKiwLsLyiIgS4Ak8zZJ4tslCZBEPskyb5Mla1+p3RhYqqGhVY2gJRHG7ngj2pJRJA0NXllBSAmg1Fs4sKtnBAGGXICYXIBYcHT77cyErLfBqzbAk6yHR22AR23k62qj1R0wAdmIQI5HEIhvy3o3DAKiYgHWowDfGj5s0ERsUg1sSiaxLR7GzngYLPM1d/GICoYEKzC8oBLDglUYGqrAsFAVhoUqMThY3vvdJQkhhBBCCCGE9AkKpEjnRAnwVABGCFBLgch2YNc2INoCJCXAkAHNBDSDB026wYMmJ9xh1rLAwyOAr9uTTRAAewwge26aqTljqXZ+A35sUeDnaAdSohVSKQpiMkOLpKJR0FBnRhCBBiZJCPgCKAkUwBv099rTlncEEbpSDF0pRjQ4tv12xiAZMShaEzxqI4REPXaHa1AT3o2t0UZsjYexKZHARpXhO70NrN3oVykhARjnETDK68UofxAj/EUYEarA0NAglIWGwPCUQFOKoSnFMCV6jQghhBBCCCFkIKJAiqQwBsTjfIrF+Ly1FYhEgGQSUFUeDukeQPcCRguAOKB4AH8x4AnxMKgvr0Rmmk4VFjMMhLUoWuJR1La0oEmLIGYkIJlASPRisOCBJMmA3AYo9YDHCwR9gNfDq6w8ilVlJQOK3LePox8ymYm6eDO2te3Cd+HdfIrw+c5oPQxmdnjbkOzBKH8Io70+jPVIGKeYqJYSmCBEMUhIQBAYgIQ1NQLJzUCSL7oZoheaUgRdLoamFPFJLoJuzfm2ImhK4YC9giAhhBBCCCGEDEQUSO2v7PApFgOiUaClhU+JBJ9MkwcyHg+fCgoAReHd4WymDqgtvCuf2gToDQCCgBDss6vzGQLQyhJoNqOo1VvRYsSRFFR4/AoKQmUoEz3pXfFMk3ct1HUgGgNa26xKLIEXXdld/iQJ8HkBnycVWCnWZC+7n4s8Flaj2BbehW3h3dgW5uHTtrZd+C5Si6Shdng7n+TBiIJBGB6qsuaDMKKAL5d00AVyJ4DdRgKK1mxNLVCsgdYVrclab4WitUAyE5DMJKRkHR/PbA8M0WcFVIVWaFUITS6ELhdAlwuhKwXWeiF0OQQIA+P1I4QQQgghhJB8RIHU/sIweKVTJMKrnhobeRiVSPBwSpYBnw8IBoHS0q5VB4ky4CsHvGWAHgGSTTycStTzbnRyASD1/FXZNFNHix5Dkx7BbrUVbUYcOjMQEL0okvzwKYWdnLPIAyZvtmoalhr/SjeAtgjQbI2DZfUQ5F0DZR5ceRTA7wP83vaBlSzzarF+Mi6VburYHqm3gqdd2Na2ywmfmpIdd6+TBAnDQpUYUTAII0KDMLJwEEYUDMbIgkEo9xXv1bhbpuRDUhqMpG9wp/uJRgKK1gpZt0MqHlQpeitkrRWK3gpFa4Ost0JkOg+wkgkgWdul89ClkBVWFUCX+bImF8CQ3e0F1n4hGFKgz4JWQgghhBBCBhLDZJDE/vHdiPQfFEgNVLrOw6dwGGhq4gFUPA5oGg9VfD4gEOh6+NQZQQCUAj75hwBaKw+ltCZeOSX5+SDowt7/uCVMFS16DA1aGLVqGyJGAgwMQdGLCrkASidXduvGA0mFSR0xTf7c6gYQTwDhaGqMKyA14Lo7tPJZVWbu6ip7u9x+APZ90ZIMY1t4F7ZagZM93x6p6/QKdhX+EowsSIVNIwsGY2TBYAwOlkMWc1NJxIMrH5LIctVAN2t8K1lvswKsMGS9FYreBllr43M9bG1vg2REIYDxAdqNCJDc1aXzYRBgSEErvApBl0Iw5GD6sjMPwpBC0OUADKnvKgYJIYQQQgjpjyRRwBUv/Qsb6yK5PpW9duyBFfjl8eNzfRoDCgVSA4Vp8gCqrY2HT3YFlKbxrnZ+Pw+fPL08zo6kAJJVNWXEALWVV00lmwFmAHIAkINd6i4VM5Jo1qOo18Ko19oQNZIQBJGPB+UphpSLL/lON8YOttuBlWHNE0mgxUy/oqATWllXCvR6reDKm+oy6FwxsH1wpZsGdkUbsDW8E1vbdmJr2y5stSqfWpLhDk/dJ3l40FQ42AmceAg1CEEljwcPFwQYchCGHNxj1RUAgBmQ9Yg1tVnzsGu5jYdVTnsEkpnICLG6d4qG6IcuB3mgJQX4+UqB1Lp7ku02PwwpCFP0UqBFSBYmM2Ewkz7IEEIIIXliY10EX+7suHdGfzemIpjrUxhw6HNcPovFeADV0gLU1vJASlV59U0gAJSX8zAqFwSBB09yEPAPArQ2Pt5UsgFINPB95ACfrHCKMYaIkUCLEUOt2opGPYK4oUKCiALJh6GeEoj9/Yu5HVh1xjR5F0r7ioRtkdTg7K4LCUYFHduMFmw1W7BVa8JWvRlb1UbUJBuhdVLtVBUow8iCQRhVMASj7PCpcDCq/KV71cVuwBEk6AofGB0Y2rWbmBokI+oEVLIRhWTNZT3iLDtzIwpZj0IyEwAAyYxDUuMAGrp9urwyy88n0Q6q+Nx0LfPtfmfZFH1pyzzYotef9C3GGEzGwMDDI3vdZCZMWHNmWvu4tjETDAzMubqq+2eXQRAECBAgmjoKPDn6f44QQgghhOwTCqTyjWkC27cDO3fysaDicf4lMxAASkp4tU1/I4iAp5hPgWGAFgbUZiDZCBavR5uRQIvAsMtQ0WIkEDNUeEUZBZIfZZ7QwAtRROtKhIoCxhjqtTC2JhuxNdGArckGbE3UY2uyAXVax3898ELCSKUEI+VSjFJKMdJThlG+coz0lcEv+3nFlUfhY2UJMhATATWcGrDdrs4aIAOz9zYmKtDFYuhKcTdvqPNgyohZIRWfS0YUshGDZG3jIVYMkhF39pWMGERmWJVZMchGDO0uQ9idU7GCLSeoEn0wJV/nc9GbtmxIPifcMiTvPnXDJf2PHQylzcHAWCpMMqygqKPgSBAEpwezOziSBAmCAIgQIYoiRKddhCLKUEQZsijxSZChiBJkUYYo8NuKggBRECEJIkRnEiBpYXjlPK7wJIQQQgjZj9G3iXximsC33wLr1/PgKRgEysryq+pBlGF6itAqSGgRZOzSk2jRm5FQG+FnBgpEDyqUIB8MXejl7oV9SDN1bFebsSVRj63Jeid82pZoQNTsuP9XmRzCSG8ZRvkqMMpb7swHeYpS1WJ2xZVhVVlpGu8qaNpdBW0s1V1QEvkkWuNcKTLgsQdjl1Pb7X3t7oOisO9jju1PBNlVjdVNjEFgWkZQ5V6O88ora100Eq62GCQjAdHkbQKYE2zBiAFazzw8U5B5aCX6YIhemJIXpuix2ry8zT1lbOfLHtd+nrR2Crw6ZpgmGLIHSO6qo/Q2BliVSAJEMPArjDrXbLBCHzsoEgTBaRMhQpJEyAIPihRJggQJHkmGLMquoKjjAKmjffbpjw4io58TQgghhJA8RZ/i8oVhpMKo8nJeEZVHdFNHqx5Bk9qGXckGtGlRaExHQPKhqGAMqsRq/kVZj/GufUYcMMI8MBG91tX6+n8QEtbjVpVTA7Ym67HFmu9INsOAmfU2EkQM9ZZglLeCVzl5y3GArxyjvBUo7Mpf/p2Kqy6coGkCpgHoZmq8q6SaCrVszPpHEKzxq+wAywqpFNeg7fYVBdPCLsm6IqEVetnbSdcJApjggS56ul+Z5cYYRDPphFOSmYBoxCEZCUhmHKKRtOYJSGaSb3PPzURqHzMJyUhCAP9ZEZkO0dABI9ozjznz1CE64ZQzCR4w0QNTVJx1U1SsttS6KXrARCV9u+BuV3i7s78CJihggtzjIX9HlUep4Ch7eMSrjlLhkQABDAyAKygShKxVRx5JgSLwqiNFlCGJIjyiAkmUsgZDmfNsAdKAq1YlhBBCCCE5RYFUPjAMHkR9+21ehVGqqaFFC6NJbcXuZCPa9ChMZiIg+VHmKYJHzEhQ5AI++Sp5IKXHAS0C6BEeUjHGB00XvYCoIFcBlclM7FZbsS3ZkBY+bU00oFHv+KoRAdGDUd5yjPRVOIHTKF85hnlK4emRqwR2gR1edfXu7LGtDNfcUFMVWKYJmIwP78L4F2UI4F/oJTF7kLWnaizRXcEl5lcFYH8kCDAl3g1vn4It9yFNHaIroOKBF58ke7mjdlPd41ywBlMTYEIyE85YXH3FFBSr+suaO+syDFhzQYYJCYYgwxD43HTm9iRZt5UA0cPDLnsu8fBLED2A6IEkeiFIHsiSD6Lkhyz5IcpeiKIHouiBJEodVhllC5EoPCKEEEIIIf0dBVL9na4D33zDw6iKCn61vH4sYSTRrIXRoDajTm1GRI8BAIKSH1WeUshdCl4EQArwyVsGmLpVMRUD9DCvotKsigyJf8GD6EG3AirGIGg6RFWHoBvpy7oBUTcQ1xLYpjdji9mMLawVm1kbNgut2CJGkBA6GVRc92KcFsQYNYixaoBPyQAGGV6rc4yt3prAq2FEIbUsCIAogNmBjCCASda6KIJJIpgogFnBDbPbZCl9WZbSlxUJTJJgutY7rVzqboBlcwdZjPFQVVWBeCK1zXktrH8EgYdSdpgliryLoGyFWM5cSp2XPbm7IEoCdS/sZUyUYYgFMFDQU73/XAdnEJjuCqg0CMwdWqkQTY3PmQoYSQimCsG05yoEazu/nbU/01JzpkE0dUhMsyY97RTsfWDGe/rR7T1B5kG8qKSWM+fOstzJPnL6Pmn7d9ImyvwCFJnbBSnLNil9PW0fek8SQgghhBCOAqn+LJEAvv4a2LIFqKoCfL5cn1E7jDFEjThatDDq1WY0qC2I6nGIgoSQ5MdgbwWkffkCouuQonFIsTikWAJSzIAYSUKKtEGMRiHF2iDGopDiCYgJFWLSgJg0IKk6xIQOUdUgJjUIqg4xqUHUrDaVfwE1BWBHAbC+HPimHPimLLVcYw/7k+X0FQMY1wiMbwAObAQm1KeWC5NJAEkATXv/uPsQE0WYigSmyGCKFVYpMkxFBvNYc0Wy1hWYigzTI/N9PK5lr5Lax8tvZ3oVmB4Fplfh7R6rzc+X0wZVZyZgMGtupta1JBCNZ1yJkKUqp5i1bAdYaUGVq3uhLPGrTtrBlSi4qrJcwZZI1Vm9yR77KG2QbNdg2e513nVNgMk84H1SBT5mkMhHPRKsijxJkNLGPZLE1DhIkiildV1zz52KIgiQYEJiOiSmQ2Q6ZOgQTd0Kp3i7YPJlmKo1aa7lzHUty9y1zLQO2qzjuy+5CQBMBwwrnM9rQsehlSC52u1tYsa6az/7l/OQOUDVzJw+KkIIIYQQ0n0USPVXbW3AF18Au3cDQ4bwL9L9hMlMtOlRtGoR7Eo2oEULI2Yk4BEUFMgBFPsKUgNuMwYxGoccjkAKRyG3RSBFopDDUb4eiUGKWMvRGKRoDFIkxkOoaAyi2jP1F61e4Nsy4NthqeDpW2uKdTJ2enlcwLg2GdVhBeMiHoyLejAu6sWIpAeSwCt1mCiChQSgUIA2VkSjAN4uWN3X7G5sAG/LIDDGv3taAw4L9tx0zU0GwQpk+LoJwTAhWOuCzgc1FwwTgmFA0LPPRd2AYKSPZSWYJqSkCSR7vNZlj0xZcgKr7JOn43afex8ZhiLzIEyWYVoBmqlIvCqLMSvMYqkAyx1subsYiu5wKyPQsq9S6O6GmBaCCVY3RXue38FWd6665l7PHDjbHvso84pr9rhEkjWQtixI8EoKFFHhV1kTZCgSvwKb3RVNEq0BtsX0gbI7mudt1zVT5yGUqVtBlWqFVZrV7pq7wyz7Nmn7aa51I+P2evp8T23M4JN73b2fez0rxs/H6MHfN0YMmPirnjseIYQQQgjpExRI9Ud1dTyMCoeBYcPSq0hyRDN1tGphtLTWoal+G9TGeoitrQiGk6gKq/CF45BbI5DDEcitYT61RSCHoxCMjru3dZXh88IM+GAE/DACfph+Hwy/F6bfb819iPtlbA7p2OSPY5MnjC1KGJulNmwVWtGAjsegkSBiuLcUI73lGOkrt8Z54oOLF8vtx+syAWzd50eUQ6bpdE20uycKmtVt0Znz7ouiqqe6M9rtqu5Umgma4VScCWqq+kxUtfT1JK9Uc1enAYBo3T9ivTdGkOlRYPgygyw+N9zBllXZZdiVXNbcsKu6FIlPHsVp4+9NV6jl6mLpBFV2u5w54LvEuyFaAWbavu7bSiJ4ZVBmWJYedDHGXKGR2UmYZIdHphMmMcbDImaPA2aP4eS6wlraINquq65lVh3xubzHoKijgIlYRBmADOT+1//eYYyHUzCtgCpLkMWscIyZGWGW6dpmr5upYAzWxRmYAehRoPx7uX60hBBCCCFkL1Ag1Z/oOrBtGx8vijFg6NDer6zQdaC5GWhsTJ83NUFrrIfR1ACzqRFicwtKWsMoVzv6q3fnTEWBXhiCURCEXhBMzUNBGKEAdGtuhAIwggEYQb81D8AI+JxQTjd17EjWoyZei5p4Lb5L7MJ38VrUxHdjV7LRugJVdmVKIUb6KjHSW4YRniKMVAox0lOIoVIQsmB965OsL4GiNQ1EogjmEcE8vOpu3+PCbjJNHmTZAZUdViXV9PDK3W4vJ/iyZLcnrGMkrOVkarvzcFWtxyrt2j0URXYCLsOu3vIpVgjGK7dMV1dFwxVyGYoE3SvzSZGgeWQYPgmqvU0SwATAEAAm8rHjTZHXHUESAKSCKcEKu0RZhijJECUFoiTyZUGAIMq84kiWIUsyFMkLWVagiDIU2RowW5QgShIkiS9LssK7sonZr8JGV10jnRKsrnmANcZfL1FbUvdDCCGEEELyCn2K6y9aW/ng5Tt2AMXFQGHh3h9L04CmJh4u2XP3clNTampt7fAwijVlMj0K9KJCaMUF0AsLoBcXQC8MQS9KzY3CEF8uLIBeEATzebt8+klTxY5EPbbHa1DTVovtdbXYHq/Dd4la7E40wIDZ4W2Dkg8j/IMxwj8II3yDMMJfhZHWeqhdtZPJu42YKsBUvmxEASMJmEn+l3dmBVwCrCv7Sa6wiqo59ooognlFGF6l98IwxiCoGqREKsTic5W32WFWQnUCLckdaiU0vp5UU7ePW7dParzbJMArxTQdCPf8uD6mLML0eawuiR4wnwfwKGBeD5hXAbwewKsAHg9fVhQIXg8ERYHg8UDwKhC8PoheD0SPF6LP2s/n5ZOipLqUimLq6ohO10Mh1UUxrcuiPYi8a+B45/Zi9kqutEnIXglGCCGEEEII2a9QIJVrhgHU1PAwKpHg40XJnbwsDQ3A+vXpwVJDQ2q5sZGPP9UNTBKhFxUiURxCoiiAeFEIRnEhUFoKlJTAKCmCVlQAvagQekkhTJ93n75AMsbQood56JSoww5r2h7n81q188HAfaIHw31VGOavwkj/IAz3DcII/yAM91ehTCnqRtWGCEhePqWxgir3+CymCpiJVFhlxNKvFCdaA+/CHpTXXqfQqi8wxmB1PuPd02DClBnMkAQW9MGAN32bNcaRYc15vJSqrrPHPAIEnpnAGizbuk6irJmQkwaUhAZPUodHNaBYczmhQ07q8CR1HnQl7XDLruRSIVhBl2BNSCRTc+vnStRNiJEEEOml7oyyxIMprwfwenm45fVYAZcr6PIoqUlxL8t8H489V5xgDD5r7pWt4ModWLm7N7ranCs6Sq4rJ1pBmDM2l5Tq4mh3ixRcy2LGsn2VxY7CMHuZEEIIIYQQ0ucokMoV0+RjRW3ZAtTWAgUFfLyobHbvBt56i0/r1qWqdjojSUBZGQ+VSkv5clkZ9JIixIqCiBb5URcU0Fggoi0gQZBkhCQ/gpIfSg90VYsZCexM1PMp2eAs70jUYUeyHjGj8y/ZQcmPYb5KDPdX8fDJV4Vh/kqM8A1Cuae4l7sKWUEVslV1ucIq90C/RtKqsrKulgXDGuPEdVMB1pWhrKBKlFzdWgb+l+L2oVH6uEb2NjskareP66pq7rGOBGuAcsEKjvj4Rtb4RxB4GwSIggRFEiFB5N3XIEEWJMiCCNnphmbvK0KCPVaSu50fT7LGUpLQw4NmMwZoOpBIZplU13Iie3tSBeL2crY2NfX7QzeASIxPvUlxVXOlBVzZwi4r4JJlPles4EtR0tfTwjG73domSVaXRlfwBHQ/DBOt49hjfmWGYe7JPp67Wsy9HzLWRaH9MlWKEUIIIYSQ/QwFUn2NMV7RtGULsGsX/+I1eHB6VZSm8eDp44+B1at5RZTbmDFAVVXWwMlZLiwERBG6qSOsxxA2YmhINqNZDyOix8DA4BO8CMp+DBO93fpSzRhDmx7FrmQDdicbsCvRiF3JBj4l+LxVj+zxOBWeEgzzVWKYrxJDfZUY6qvAcD8Pn4rkUD8dn6azsApIDbbrHqDXSIVXziXhreorWIP1mhndEO0vy4LI7xP2l1nrS7EzZkrPBVlZq4zstixhUnpo1L7SKBUe8TYBAu8FBhFCB6GRJIjwiiIUQXKCI8kKjBQrNLIH2JbsgbadACm1bgdG7n37589TBkFIBTSFoZ4/PmP894sdYiXV9NArqVptairUSltX09tU99zervH7sGnW+p5/JfQMUWgfdrkDLMUddFnhliyn2u3B5hUZUCRe4aVIgKzwubOfu91etrdZA9G7wyogVc1ld490h1LIqOySxPRwTBRS1WKi1L7KS3AfP8t9ZLbZlWH2+yLbfmIH55sP7yVCCCGEENLvUSDVVxIJHkTt2MErowCgspJ/cYrFgH/9i4dQn3/Ol+OuMWkEAZg0CZg9G5g1Cxg0qMO70U0dESOOcLIOTWoYjVoLYnoCGtPhEWQEZD8GectTg3hnEdXjqFObUJvkk728O9mI2mQjdicbETeTe3zIhXIQQ7wVGOKrwFBfBQZ7y63gqRKDfWXw9uZAtzljdxPKNvqWm+sqUcxwXVXKNVmXa2eGBpPpYKYBxpIwwa+QZjIdpmkFQ0gFSqbAa4lM8GjIhAAT1hXU7C++gjV3gho+2YFOtsBIACAJIjyiBEUQIUGCLPIgKLPSyA6JRNcxJffV2rJUG9m3Ib1IsMMaT+8EXjbDTIVUqpYeYCU7mFQrzEq69rNv615WNb7d2ZYRgJksFbDliiSlAi93+GVPsmtZklJBmL0uy6lxu2S5/The7rmSuV1OhWWSaB3LOp4ipcIsd/gEwPm94A6ggIy2zLHGso0ZJrmCMnc3SvdYY1mCMuf3UuZ9dhKqCQKgRQCPvy9fXUIIIYQQ0kP6NJBKJpO45ZZb8Oabb8Ln8+GCCy7ABRdc0Jen0Lc0jQ8aXlfHq6FaW3n41NgIbN0KbNjAp40b21fIlJYC06cD3/sen8rKst6FamqI6DFEjDia1TY0aq1OACULEgKSD+WeYiiiDN3U0aC14rv4LtQnW1CvNqNebUG92oQ6tRn1yWbUqU2I7qE7na1MKUKVtwyDvWUY5C3HYF85hljzwd7yLIOIDxx2NzKkVQjZYxPBtZzqcua+HbMqkBhjYAL4INxW1zMmMPBgywPAA0EWrOgIEBiDCGYtm9YcEAUTdmdAyWSQBAYZDBJjUARANhkkQbBua80FBpFZyww8FBIE3pYWIgkQRavaSJSR+uIopr5IOpVaduAF8AfmWhasqhFrbCaY4MdhsMqoeHyW3iWVZV1M26ejLqysC7ft7D6y7dtZd9ls29rdPmO7UzkjtJ+7v6jbX/CB7Nszu4HluoJFEgG/j099wTT5FUOT7tBKSwVbmp5qs7drWioE0zTXbTRrf+t27ttrmfu42twMg0+5DMU6IgiuwModfskdhF4yr/iSMtrtge3tsb7S5mJ6V0e7TczY7owRZrXZgZrdfdKuEpNdx5Iz7lePAWUlwKjjcv3MEkII6acMk0ESc/zZaB8NhMdASDZ9Gkjdfffd+OKLL/D0009j586duPbaazFkyBCccMIJfXkavUfXgZ07gW+/5YOUb9zIK6J27eLjRO3cCUSj2W87aBBwyCHAYYfxaqjq6naD7RrMQMxIIGYkENXjaFBb0KyFUZdsQqPWiogeQ8xIImrE0ay1oUlrQ6Paiga1BQ1qC1r0cJcfSkgKoMpbyidPKSq9pRjkLbOmclR6S/ahwsn6Vp4tVOg0EMi4HeNhD88zXGEQM13thhMA2eGQex8eKllBETOte+FBEwQBArPWeVKUyl+Y3ZFOsLqiCc4yj12siiIIUAQRMhOsSiIRMiSIAl+WIFlBkSsIypw7x3VNrrGO3G0A+BdO0xpUibHUc2paNVOmgVTwY/JEyzBdbfbjde3DrAk6PzasLohgAAzr+WKp+xNSz6ETNNmvobPdanL/35otSHHarCArLbxxhTLICGjsICf9DlLr7ucrbQ6kB2xIjUNkt7ULfVxdtJybuyvRMsIkm+tnOTVZj5WZsErfMvZzt7neN4zx19i9PwTruRbQ7gnPrDaBazmzIiVz3KTM8EtAevVMXwdjopiq/CoI9s19ujHGx+TSMoMqPdWm66kgS3O12/vp7jY9/TbuY+s6X1e11LJ9HHtZd63rRvtztbtR5jtB4GHVj2cDJ12a67MhhBDST0migCte+hc21vXV+AE969gDK/DL48cPiMdASKY+C6RisRhefvllPPbYY5g4cSImTpyIDRs24Pnnn+8/gZRp8sAoFuNTNApEIvyqdeEwn1paUle2a2zk87o6oL6et+/pQ74gAMOHA2PHAuPGAePGwTywGtHSECJaDGEtgma1FfXb30Kj2oImK1DapTaiXm1Fsx5Gqx5BqxFDmxFDmxl3Ru7pCgkiyuUCVMhFqJAK+Vzm80qlCJVSESrlIgTEjDGS7O+zKoAkwNDkjB8EwDWGEAMTrIohK8zhtUPWftZSqj21zLuZWSMROSGD6+7tahqrQbC+8ItWly+4uocBgChI1vdjq0uYIEKBCFmUIQkiJJF3F5MgQbLHKhJlqyJIsup+RIii6FxlTRQl6/5ECFa7KIoQBcmaW+MjCSLfV5RSr3vml3QnvBA736ejL/iZx8nW1t39u3qMbNuZHTqZ6QEV3O1wtbsn9+3QcZu9zAwrYLMCNZZxn8xqt4MdZCzbQZlzbKTOzR18puUp7oAt23vOHZiyjLaMffgT1/44mcEUs55X5tru7JexzbDHI7Nub7jCKdMKq0zX7ex10zqG6bq9YW83+bJmWqGW6/j2+ZkZz1nauVvtdjjqJrgX7O12kIpU0GUPYN+uKszeR0xvB6x8MCMM6/DnWkibdVyJZt9vlvewE9wB8IqAVwKEjPf+PtnH2ztBlVW5ZQdehpEKrXQjFXA588x2I3UMd3u741jvz3b7u5YN07VsZCyb6fsYhvVzlsG+CEBD8749P4QQQga8jXURfLmzLdensVfGVPA/tg2Ex0BIpj4LpNavXw9d1zF58mSnbcqUKXjkkUdgmibEHF9621j/Nd44ezp2sTAMETAFPhn2XOTL7rkuAroE6EMBbThf1yRA9SlQ/R5oPg+SPhkJn4ykV0JSEZGQgBhUxM21SLBPEIuqSKxR9+ncBQgoVEIoUQpR4ilEiacIJd4ilHiKUJy2XogCJeh8OeJRgekqTuJfBpsgoNGpRuFzQRDsFuu7ogBBkKxiCSu6EfjYQ3yCNe6QYAU3AkSRb5Ot8EYSZUgCD4NEQeLLEp87gZAVANkhjxP2WPflBEMCD4UEO6ASJV5VZN/e2oc/YXsIdrIFMWRgYRkhCrqyDjghlr3cnbZs+3TWnnVbttvt6bYd3L6jfdIer9VuV1+5J6ciyx18WSGhkdFmurcjFRpm3qe9n3sfJwCzjmsaPOM0dVeg5grd7GDUDuucY2Yc23nIrhAQSAUfdnCX+bOQVsmG9Nu2q/R0/zwgI4NM+42avl9aMJdxOycAZq79kTqG4NonW5tg3UCxxpTKDNxcsw5/FwoZ+6Xt00nA125fez/Wwb6uddP9+jMr9GJAOAoMHdn+PgaY/W7IA0JIv0DdxAghva3PAqn6+nqUlJTA40l18yovL0cymURLSwtKS0v76lSyem/XRzh5Tte7tHVOs6aM7nmGNXVAhACv7IVfDiCoBBD0BBFQggh5gijwFqLQU4hCXyEKvYUo8ZWg2FeMEn8JirxFvLLHClzsUEawu3QJohXyWHO7TZT4oNIiD4Xs/d23zzxWV7d1tE5Iv+B0pUP6l2oy8LULj7LMs3WlzFzP1t6V42U7Tkf3m7avuef93F023WGgO1QE0ufMPq8sjy3tHDPbMp7PbEFn5nxP+2c0t9uWra0MQGn2MRYHkgE/5AEhpF/K965uAHUVI6S/67NAKh6Pp4VRAJx1Vd23CqGeMOXIU3Fe09vY2rARgixDEmUIosC7YlnVN5LdXUu0u3hZYwGJMmRRhiIpkAQJiqRAERWnnYdMfvgUH3ySD0EliAJvAQJKAH7Fj5ASQtAThF/2O5Vi7tAn29wOeDKDocxlQgghLlQB2XM6C5y6sq2ryx212evKnq5qmt/yYsgDQkg7A6W6KJ+7iQHUVYyQ/q7PAimv19sueLLXfb4+uhJTJwq9hXjy1GdyfRqEEEJIfqBwr0/09yEPgIHzxXsgPA56DP1HvlcXUWURIaQv9FkgVVVVhebmZui6Dlnmd1tfXw+fz4fCwsI93p5ZfwmNRPLzlzohhBBC8lcwGMxJ5fG+DHnQl5+dHnlnE3a2xnv9fnrLIcOK8JMpw/P6cYyrDOHM6SMHRJiTz68DkPp50hMxmGp+Pg41HkMkEsGoQhGmmr+VqFV+5P3joMfQfwyExzGqUOyzTKUrn536LJCaMGECZFnG2rVrMXXqVADAmjVrcMghh3Tpr3vRKB+PaebMmb16noQQQgghmdasWYNQKNTn97svQx7QZ6euewPAnbk+iR7wQK5PgAAYGD9PmwEMhL4jA+Fx0GPoPwbC49gMYMrivrmvrnx26rNAyu/348c//jEWLVqEO+64A3V1dXjiiSeweHHXno3Kykq8++67OfsLJSGEEEL2X8FgbsYh2ZchD+izEyGEEEJypSufnfoskAKAhQsXYtGiRTj33HMRCoVw+eWX47jjjuvSbUVRxKBBg3r5DAkhhBBC+o99GfKAPjsRQgghpD8TGMt2KRtCCCGEEJJr8Xgc06dPxxNPPOEMebB06VKsXr0azz33XI7PjhBCCCFk7+X+0iyEEEIIISQr95AHn3/+OVatWoUnnngC55xzTq5PjRBCCCFkn1CFFCGEEEJIPxaPx7Fo0SK8+eabCIVCuPDCC3Heeefl+rQIIYQQQvYJBVKEEEIIIYQQQgghpE9Rlz1CCCGEEEIIIYQQ0qcokCKEEEIIIYQQQgghfYoCKUIIIYQQQgghhBDSpyiQApBMJnH99ddj6tSpOProo/HEE0/k+pTyVm1tLebPn49p06bhmGOOweLFi5FMJnN9Wnnv4osvxnXXXZfr08hrqqrilltuwRFHHIEjjzwS9913H2gIvb2za9cuXHLJJTj88MMxe/ZsPPXUU7k+pbyjqirmzJmDjz/+2GmrqanBeeedh0mTJuHEE0/EBx98kMMzzB/Znsu1a9fi9NNPx+TJk3H88cfj5ZdfzuEZDkx/+9vfcOCBB6ZN8+fPz/Vp7Rfo90fuZXsNbrvttnbvieeeey6HZznwdPY9g94DfaOz14DeA71v27ZtuPDCCzF58mQce+yx+N3vfudsy9f3gJzrE+gP7r77bnzxxRd4+umnsXPnTlx77bUYMmQITjjhhFyfWl5hjGH+/PkoLCzE888/j9bWVlx//fUQRRHXXnttrk8vb73xxht49913ccopp+T6VPLabbfdho8//hiPP/44otEorrrqKgwZMgSnn356rk8t71x55ZUYMmQI/vCHP2Djxo245pprMHToUPzXf/1Xrk8tLySTSVx99dXYsGGD08YYw2WXXYbq6mqsWLECq1atwrx58/DnP/8ZQ4YMyeHZ9m/Znsv6+nr8/Oc/xxlnnIE777wTX375JRYuXIiKigoce+yxuTvZAWbjxo2YNWsWbr31VqfN6/Xm8Iz2D/T7I/eyvQYAsGnTJlx99dVpn9dCoVBfn96A1dn3jF/96lf0HugDe/quR++B3mWaJi6++GIccsgh+OMf/4ht27ZhwYIFqKqqwpw5c/L2PbDfB1KxWAwvv/wyHnvsMUycOBETJ07Ehg0b8Pzzz1Mg1U2bN2/G2rVr8Y9//APl5eUAgPnz5+Ouu+6iQGovtbS04O6778YhhxyS61PJay0tLVixYgWefPJJHHrooQCACy64AOvWraNAqptaW1uxdu1a3HrrrRg1ahRGjRqFY445BqtXr6ZAqgs2btyIq6++ul113kcffYSamhq89NJLCAQCGDNmDFavXo0VK1bg8ssvz9HZ9m8dPZerVq1CeXk5FixYAAAYNWoUPv74Y7z22msUSPWgTZs2obq6GhUVFbk+lf0G/f7IvY5eA4C/Jy688EJ6T/SSzr5n/Md//Ae9B/rAnr7r0XugdzU0NGDChAlYtGgRQqEQRo0ahRkzZmDNmjUoLy/P2/fAft9lb/369dB1HZMnT3bapkyZgnXr1sE0zRyeWf6pqKjA7373O+cXlC0SieTojPLfXXfdhZNPPhljx47N9anktTVr1iAUCmHatGlO28UXX4zFixfn8Kzyk8/ng9/vxx/+8AdomobNmzfjn//8JyZMmJDrU8sLn3zyCaZPn47f//73ae3r1q3DQQcdhEAg4LRNmTIFa9eu7eMzzB8dPZd2F4JM9H9Rz9q0aRNGjRqV69PYr9Dvj9zr6DWIRCKora2l90Qv6ux7Br0H+kZnrwG9B3pfZWUlHnjgAYRCITDGsGbNGnz66aeYNm1aXr8H9vsKqfr6epSUlMDj8Tht5eXlSCaTaGlpQWlpaQ7PLr8UFhbimGOOcdZN08Rzzz2H733vezk8q/y1evVqfPbZZ3jttdewaNGiXJ9OXqupqcHQoUOxcuVKPPLII9A0DXPnzsX//M//QBT3+1y+W7xeL2666SbceuuteOaZZ2AYBubOnYuf/OQnuT61vHDmmWdmba+vr0dlZWVaW1lZGXbv3t0Xp5WXOnouhw0bhmHDhjnrjY2NeOONN/r9XwjzCWMMW7ZswQcffIBHH30UhmHghBNOwPz589M+T5GeRb8/cq+j12DTpk0QBAGPPPII3nvvPRQXF+P888+n4RZ6UGffM+g90Dc6ew3oPdC3Zs+ejZ07d2LWrFk4/vjjcZ0/GjkAAAqqSURBVMcdd+Tte2C/D6Ti8Xi7D0/2uqqquTilAeOee+7BV199hVdeeSXXp5J3kskkbr75Ztx0003w+Xy5Pp28F4vFsG3bNrz00ktYvHgx6uvrcdNNN8Hv9+OCCy7I9enlnU2bNmHWrFk4//zzsWHDBtx6662YMWMGfvSjH+X61PJWR/8X0f9D+yaRSODyyy9HeXk5fvrTn+b6dAaMnTt3Oj+zDzzwALZv347bbrsNiUQCN9xwQ65Pb79Dvz9yb/PmzRAEAaNHj8bPfvYzfPrpp7jxxhsRCoWoO3svcX/PeOqpp+g9kAPu1+DLL7+k90Afeuihh9DQ0IBFixZh8eLFef3/wH4fSHm93nYvlL1OQcDeu+eee/D000/j/vvvR3V1da5PJ+8sWbIEBx98cNpfIcjek2UZkUgE9957L4YOHQqAf6F68cUXKZDqptWrV+OVV17Bu+++C5/Ph0MOOQS1tbX47W9/S4HUPvB6vWhpaUlrU1WV/h/aB9FoFJdeeim2bt2KF154AX6/P9enNGAMHToUH3/8MYqKiiAIAiZMmADTNPHLX/4SCxcuhCRJuT7F/Qr9/si9H//4x5g1axaKi4sBAOPHj8fWrVvx4osv0pfxXpD5PYPeA30v8zUYN24cvQf6kD2+cDKZxDXXXINTTz0V8Xg8bZ98eQ/s931Vqqqq0NzcDF3Xnbb6+nr4fD4UFhbm8Mzy16233oonn3wS99xzD44//vhcn05eeuONN7Bq1SpMnjwZkydPxmuvvYbXXnstbawz0nUVFRXwer1OGAUABxxwAHbt2pXDs8pPX3zxBUaOHJn2H9xBBx2EnTt35vCs8l9VVRUaGhrS2hoaGtqVX5OuiUQiuPDCC7FhwwY8/fTTNKZFLyguLoYgCM76mDFjkEwm0dramsOz2j/R74/cEwTB+SJuGz16NGpra3NzQgNYtu8Z9B7oW9leA3oP9L6GhgasWrUqrW3s2LHQNA0VFRV5+x7Y7wOpCRMmQJbltAG/1qxZg0MOOYTGltkLS5YswUsvvYT77rsPJ510Uq5PJ289++yzeO2117By5UqsXLkSs2fPxuzZs7Fy5cpcn1peOuyww5BMJrFlyxanbfPmzWkBFemayspKbNu2La2ydPPmzWlj9pDuO+yww/Dll18ikUg4bWvWrMFhhx2Ww7PKT6ZpYt68edi+fTueffZZjBs3LtenNOC8//77mD59etpfY7/++msUFxfT2Js5QL8/cu/BBx/Eeeedl9a2fv16jB49OjcnNEB19D2D3gN9p6PXgN4DvW/79u2YN29eWsj3xRdfoLS0FFOmTMnb98B+n7j4/X78+Mc/xqJFi/D5559j1apVeOKJJ3DOOefk+tTyzqZNm7Bs2TL8/Oc/x5QpU1BfX+9MpHuGDh2KkSNHOlMwGEQwGMTIkSNzfWp5afTo0Tj22GOxcOFCrF+/Hu+//z6WL1+OM844I9enlndmz54NRVFwww03YMuWLXjrrbfwyCOP4Oyzz871qeW1adOmYfDgwVi4cCE2bNiA5cuX4/PPP8dpp52W61PLO6+88go+/vhj3HbbbSgsLHT+H8rszkH23uTJk+H1enHDDTdg8+bNePfdd3H33XfjoosuyvWp7Zfo90fuzZo1C59++ikef/xxfPfdd3jhhRewcuVKGhagB3X2PYPeA32js9eA3gO975BDDsHEiRNx/fXXY+PGjXj33Xdxzz334Be/+EVevwcExhjL9UnkWjwex6JFi/Dmm28iFArhwgsvbJfwkj1bvnw57r333qzbvvnmmz4+m4HluuuuAwDceeedOT6T/BUOh3Hrrbfib3/7G/x+P84880xcdtllaV1OSNds3LgRt99+Oz7//HOUlpbirLPOwrnnnkvPZTcdeOCBeOaZZzB9+nQAwLZt2/DrX/8a69atw8iRI3H99dfjyCOPzPFZ5gf3c3nhhRfigw8+aLfPtGnT8Oyzz+bg7AamDRs24I477sDatWsRDAZx+umn0+/UPkS/P3Iv8zVYtWoVHnroIWzduhVDhw7FVVddheOOOy7HZzlw7Ol7Br0Het+eXgN6D/S+2tpa3HrrrVi9ejX8fj9+9rOf4ZJLLoEgCHn7HqBAihBCCCGEEEIIIYT0qf2+yx4hhBBCCCGEEEII6VsUSBFCCCGEEEIIIYSQPkWBFCGEEEIIIYQQQgjpUxRIEUIIIYQQQgghhJA+RYEUIYQQQgghhBBCCOlTFEgRQgghhBBCCCGEkD5FgRQhhBBCCCGEEEII6VMUSBFCCCGEEEIIIYSQPkWBFCEkLxx44IG4+uqr27X/4Q9/wOzZs3NwRoQQQgghhBBC9hYFUoSQvPH6669j9erVuT4NQgghhBBCCCH7iAIpQkjeGDp0KH7zm99AVdVcnwohhBBCCCGEkH1AgRQhJG9ceeWVqK2txeOPP97hPrt378YVV1yBadOmYfr06bjtttucAOsPf/gDzj77bDz00EOYPn06pk6disWLF4Mx5tz+pZdewuzZszF58mScffbZ+Oabb3r9cRFCCCGEEELI/oYCKUJI3qiqqsL8+fPxyCOPoKampt12VVVx7rnnIh6P49lnn8UDDzyAd955B3fffbezz7/+9S9s2bIFL774Im688UY888wz+PDDDwEAb731FpYsWYIbb7wRf/zjHzFlyhScc845aG1t7bPHSAghhBBCCCH7AwqkCCF55eyzz8bIkSNx++23t9v2/vvvo7a2Fvfccw8OPPBAzJgxAzfddBNefPFFRKNRAIBhGLj11lsxevRonHzyyRg/fjz+/e9/AwB+97vf4ZJLLsGsWbMwatQoXHnllRg6dCheffXVPn2MhBBCCCGEEDLQybk+AUII6Q5JkrBo0SKceeaZWLVqVdq2TZs2YdSoUSgqKnLaDj/8cOi6ju+++w4AUFZWhlAo5GwPhULQdd25/T333IP77rvP2Z5MJrF169ZefESEEEIIIYQQsv+hQIoQkncOP/xwnHrqqbj99ttx0UUXOe1er7fdvoZhpM09Hk+7fewxpAzDwPXXX48ZM2akbXcHWIQQQgghhBBC9h112SOE5KVrrrkGsVgsbYDzAw44AFu3bkVLS4vTtnbtWsiyjBEjRuzxmAcccAB2796NkSNHOtMjjzyCtWvX9sIjIIQQQgghhJD9FwVShJC8VFJSgmuuuQY7duxw2o466igMHz4cv/rVr/DNN9/go48+wq233oo5c+agsLBwj8c8//zz8fTTT2PlypX47rvvcM899+Avf/kLxowZ05sPhRBCCCGEEEL2O9RljxCSt0477TSsWLECdXV1APj4UsuWLcOtt96K//7v/0YwGMQPf/hDLFiwoEvHO/HEE9HQ0ICHHnoIDQ0NGDt2LH77299i1KhRvfgoCCGEEEIIIWT/IzB78BRCCCGEEEIIIYQQQvoAddkjhBBCCCGEEEIIIX2KAilCCCGEEEIIIYQQ0qcokCKEEEIIIYQQQgghfYoCKUIIIYQQQgghhBDSpyiQIoQQQgghhBBCCCF9igIpQgghhBBCCCGEENKnKJAihBBCCCGEEEIIIX2KAilCCCGEEEIIIYQQ0qcokCKEEEIIIYQQQgghfYoCKUIIIYQQQgghhBDSpyiQIoQQQgghhBBCCCF96v8DshXZgt7RcrsAAAAASUVORK5CYII=", - "text/plain": [ - "
      " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def add_pred_to_plot(preds, axs, coords, color, label):\n", - " sns.lineplot(\n", - " x=logging_times,\n", - " y=preds.mean(dim=0).squeeze().tolist(),\n", - " ax=axs[coords],\n", - " label=label,\n", - " color=color,\n", - " )\n", - " axs[coords].fill_between(\n", - " logging_times,\n", - " torch.quantile(preds, 0.025, dim=0).squeeze(),\n", - " torch.quantile(preds, 0.975, dim=0).squeeze(),\n", - " alpha=0.2,\n", - " color=color,\n", - " )\n", - "\n", - "\n", - "fig, axs = plt.subplots(4, 2, figsize=(12, 6))\n", - "\n", - "colors = [\"orange\", \"red\", \"green\"]\n", - "\n", - "add_pred_to_plot(\n", - " unintervened_samples[\"S\"], axs, coords=(0, 0), color=colors[0], label=\"susceptible\"\n", - ")\n", - "add_pred_to_plot(\n", - " unintervened_samples[\"I\"], axs, coords=(0, 0), color=colors[1], label=\"infected\"\n", - ")\n", - "add_pred_to_plot(\n", - " unintervened_samples[\"R\"], axs, coords=(0, 0), color=colors[2], label=\"recovered\"\n", - ")\n", - "\n", - "axs[0, 1].hist(unintervened_samples[\"overshoot\"].squeeze())\n", - "axs[0, 0].set_title(\"No interventions\")\n", - "axs[0, 1].set_title(\n", - " f\"Overshoot mean: {unintervened_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {unintervened_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", - ")\n", - "\n", - "\n", - "add_pred_to_plot(\n", - " intervened_samples[\"S\"], axs, coords=(1, 0), color=colors[0], label=\"susceptible\"\n", - ")\n", - "add_pred_to_plot(\n", - " intervened_samples[\"I\"], axs, coords=(1, 0), color=colors[1], label=\"infected\"\n", - ")\n", - "add_pred_to_plot(\n", - " intervened_samples[\"R\"], axs, coords=(1, 0), color=colors[2], label=\"recovered\"\n", - ")\n", - "axs[1, 0].set_title(\"Both interventions\")\n", - "axs[1, 0].legend_.remove()\n", - "\n", - "\n", - "axs[1, 1].hist(intervened_samples[\"overshoot\"].squeeze())\n", - "axs[1, 1].set_title(\n", - " f\"Overshoot mean: {intervened_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {intervened_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", - ")\n", - "\n", - "\n", - "add_pred_to_plot(\n", - " mask_samples[\"S\"], axs, coords=(2, 0), color=colors[0], label=\"susceptible\"\n", - ")\n", - "add_pred_to_plot(\n", - " mask_samples[\"I\"], axs, coords=(2, 0), color=colors[1], label=\"infected\"\n", - ")\n", - "add_pred_to_plot(\n", - " mask_samples[\"R\"], axs, coords=(2, 0), color=colors[2], label=\"recovered\"\n", - ")\n", - "axs[2, 0].set_title(\"Mask only\")\n", - "axs[2, 0].legend_.remove()\n", - "\n", - "axs[2, 1].hist(mask_samples[\"overshoot\"].squeeze())\n", - "axs[2, 1].set_title(\n", - " f\"Overshoot mean: {mask_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {mask_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", - ")\n", - "\n", - "add_pred_to_plot(\n", - " lockdown_samples[\"S\"], axs, coords=(3, 0), color=colors[0], label=\"susceptible\"\n", - ")\n", - "add_pred_to_plot(\n", - " lockdown_samples[\"I\"], axs, coords=(3, 0), color=colors[1], label=\"infected\"\n", - ")\n", - "add_pred_to_plot(\n", - " lockdown_samples[\"R\"], axs, coords=(3, 0), color=colors[2], label=\"recovered\"\n", - ")\n", - "axs[3, 0].set_title(\"Lockdown only\")\n", - "axs[3, 0].legend_.remove()\n", - "\n", - "axs[3, 1].hist(lockdown_samples[\"overshoot\"].squeeze())\n", - "axs[3, 1].set_title(\n", - " f\"Overshoot mean: {lockdown_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {lockdown_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", - ")\n", - "\n", - "\n", - "fig.tight_layout()\n", - "fig.suptitle(\"Trajectories and overshoot distributions\", fontsize=16, y=1.05)\n", - "sns.despine()\n", - "\n", - "plt.savefig(\"counterfactual_sir.png\")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mask tensor(0.)\n", - "lockdown tensor(1.)\n", - "mask_eff tensor(0.)\n", - "dict_keys(['lockdown', 'mask', 'lockdown_efficiency', 'mask_efficiency', 'joint_efficiency', 'beta', 'gamma', 'S', 'I', 'R', 'l', 'overshoot', 'os_too_high'])\n" - ] - } - ], - "source": [ - "with ExtractSupports() as s:\n", - " policy_model()\n", - "\n", - "supports = s.supports\n", - "print(supports.keys())\n", - "\n", - "antecedents = {\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(1.0)}\n", - "alternatives = {\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(0.0)}\n", - "witnesses = {key: s.supports[key] for key in [\"lockdown_efficiency\", \"mask_efficiency\"]}\n", - "consequents = {\"os_too_high\": torch.tensor(1.0)}\n", - "\n", - "with MultiWorldCounterfactual() as mwc:\n", - " with SearchForExplanation(\n", - " supports=supports,\n", - " alternatives=alternatives,\n", - " antecedents=antecedents,\n", - " antecedent_bias=0.0,\n", - " witnesses=witnesses,\n", - " consequents=consequents,\n", - " consequent_scale=1e-8,\n", - " witness_bias=0.2,\n", - " ):\n", - " with pyro.plate(\"sample\", exp_plate_size):\n", - " with pyro.poutine.trace() as tr:\n", - " policy_model_all()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "query = SearchForExplanation(\n", - " supports=supports,\n", - " alternatives=alternatives,\n", - " antecedents=antecedents,\n", - " antecedent_bias=0.5,\n", - " witnesses={},\n", - " consequents=consequents,\n", - " consequent_scale=1e-8,\n", - " # witness_bias=0.2,\n", - " )(policy_model_all)\n", - "\n", - "# $P(…) [0.25 X 1(o | do(l, m)) 1(o’ | do (l’, m’)) + 0.25 X 1(o | do(l)) 1(o’ | do (l’)) + 0.25 X 1(o | do(m)) 1(o’ | do (m’)) + 0.25 X 1(o)1(o’)]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mask tensor([[1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.]])\n", - "lockdown tensor([[1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.]])\n", - "mask_eff tensor([[[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]]])\n", - "mask tensor([[1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.]])\n", - "lockdown tensor([[1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.]])\n", - "mask_eff tensor([[[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]],\n", - "\n", - " [[0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000],\n", - " [0.1000, 0.1000, 0.1000]]])\n" - ] - } - ], - "source": [ - "logp, tr, mwc, lw = importance_infer(num_samples=20)(query)()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([-4.0500e+15, -4.0500e+15, -4.0500e+15, -4.0500e+15, -4.0500e+15,\n", - " -4.0500e+15, -4.0500e+15, -4.0500e+15, -4.0500e+15, -4.0500e+15,\n", - " -4.0500e+15, -4.0500e+15, -4.0500e+15, -4.0500e+15, -4.0500e+15,\n", - " -4.0500e+15, -4.0500e+15, -4.0500e+15, -4.0500e+15, -4.0500e+15])\n", - "tensor(20)\n", - "torch.Size([])\n", - "tensor(0.)\n", - "tensor([0.0266, 0.0287, 0.0337, 0.0285, 0.0349, 0.0267, 0.0318, 0.0271, 0.0300,\n", - " 0.0428, 0.0509, 0.0490, 0.0360, 0.0190, 0.0333, 0.0356, 0.0369, 0.0274,\n", - " 0.0296, 0.0280])\n", - "tensor([0.0266, 0.0287, 0.0337, 0.0285, 0.0349, 0.0267, 0.0318, 0.0271, 0.0300,\n", - " 0.0428, 0.0509, 0.0490, 0.0360, 0.0190, 0.0333, 0.0356, 0.0369, 0.0274,\n", - " 0.0296, 0.0280])\n", - "tensor([0.0266, 0.0287, 0.0337, 0.0285, 0.0349, 0.0267, 0.0318, 0.0271, 0.0300,\n", - " 0.0428, 0.0509, 0.0490, 0.0360, 0.0190, 0.0333, 0.0356, 0.0369, 0.0274,\n", - " 0.0296, 0.0280])\n", - "tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1.,\n", - " 1., 1.])\n", - "tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1.,\n", - " 1., 1.])\n", - "tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1.,\n", - " 1., 1.])\n", - "tensor([0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000,\n", - " 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000,\n", - " 0.1000, 0.1000])\n", - "tensor([0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000,\n", - " 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000,\n", - " 0.1000, 0.1000])\n", - "tensor([0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000,\n", - " 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000, 0.1000,\n", - " 0.1000, 0.1000])\n" - ] - } - ], - "source": [ - "print(lw.squeeze())\n", - "\n", - "mask_intervened = (tr.nodes[\"__cause____antecedent_lockdown\"][\"value\"] == 0) & (tr.nodes[\"__cause____antecedent_mask\"][\"value\"] == 0)\n", - "print(mask_intervened.sum())\n", - "\n", - "with mwc:\n", - " oth = gather(tr.nodes[\"os_too_high\"][\"value\"], IndexSet(**{\"lockdown\": {2}, \"mask\": {2}}))\n", - " print(oth.shape)\n", - " os = gather(tr.nodes[\"overshoot\"][\"value\"], IndexSet(**{\"lockdown\": {2}, \"mask\": {2}}))\n", - "\n", - "denom = torch.sum(torch.exp(lw.squeeze()) * mask_intervened.squeeze().float())/torch.sum(mask_intervened.squeeze()).float()\n", - "print(denom)\n", - "# print(denom/torch.sum(torch.exp(lw.squeeze())))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[ 0.0000e+00, 0.0000e+00, 0.0000e+00],\n", - " [ 0.0000e+00, -inf, 0.0000e+00],\n", - " [ 0.0000e+00, 0.0000e+00, -5.0000e+15]])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tr.trace.nodes[\"__cause____consequent_os_too_high\"][\"fn\"].log_factor[:, :, :, :, :, 6].squeeze()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "def get_table(\n", - " trace, mwc, antecedents, witnesses, consequents, others=None, world: int = 1\n", - "):\n", - "\n", - " values_table = {}\n", - " nodes = trace.trace.nodes\n", - " witnesses = [key for key, _ in witnesses.items()]\n", - "\n", - " with mwc:\n", - "\n", - " for antecedent_str in antecedents.keys():\n", - "\n", - " obs_indices = IndexSet(\n", - " **{\n", - " name: {0}\n", - " for name, ind in indices_of(nodes[antecedent_str][\"value\"]).items()\n", - " }\n", - " )\n", - " obs_ant = gather(\n", - " nodes[antecedent_str][\"value\"],\n", - " obs_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " int_indices = IndexSet(\n", - " **{\n", - " name: {world}\n", - " for name, ind in indices_of(nodes[antecedent_str][\"value\"]).items()\n", - " }\n", - " )\n", - " int_ant = gather(\n", - " nodes[antecedent_str][\"value\"],\n", - " int_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " values_table[f\"{antecedent_str}_obs\"] = obs_ant.squeeze().tolist()\n", - " values_table[f\"{antecedent_str}_int\"] = int_ant.squeeze().tolist()\n", - "\n", - " apr_ant = nodes[f\"__cause____antecedent_{antecedent_str}\"][\"value\"]\n", - " values_table[f\"apr_{antecedent_str}\"] = apr_ant.squeeze().tolist()\n", - "\n", - " if witnesses:\n", - " for candidate in witnesses:\n", - " obs_indices = IndexSet(\n", - " **{\n", - " name: {world}\n", - " for name, ind in indices_of(nodes[candidate][\"value\"]).items()\n", - " }\n", - " )\n", - " obs_candidate = gather(\n", - " nodes[candidate][\"value\"],\n", - " obs_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " int_indices = IndexSet(\n", - " **{\n", - " name: {world}\n", - " for name, ind in indices_of(nodes[candidate][\"value\"]).items()\n", - " }\n", - " )\n", - " int_candidate = gather(\n", - " nodes[candidate][\"value\"],\n", - " int_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " values_table[f\"{candidate}_obs\"] = obs_candidate.squeeze().tolist()\n", - " values_table[f\"{candidate}_int\"] = int_candidate.squeeze().tolist()\n", - "\n", - " wpr_con = nodes[f\"__cause____witness_{candidate}\"][\"value\"]\n", - " values_table[f\"wpr_{candidate}\"] = wpr_con.squeeze().tolist()\n", - "\n", - " if others:\n", - " for other in others:\n", - " obs_indices = IndexSet(\n", - " **{\n", - " name: {0}\n", - " for name, ind in indices_of(nodes[other][\"value\"]).items()\n", - " }\n", - " )\n", - "\n", - " obs_other = gather(\n", - " nodes[other][\"value\"],\n", - " obs_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " int_indices = IndexSet(\n", - " **{\n", - " name: {world}\n", - " for name, ind in indices_of(nodes[other][\"value\"]).items()\n", - " }\n", - " )\n", - "\n", - " int_other = gather(\n", - " nodes[other][\"value\"],\n", - " int_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " values_table[f\"{other}_obs\"] = obs_other.squeeze().tolist()\n", - " values_table[f\"{other}_int\"] = int_other.squeeze().tolist()\n", - "\n", - " for consequent in consequents.keys():\n", - "\n", - " obs_indices = IndexSet(\n", - " **{\n", - " name: {0}\n", - " for name, ind in indices_of(nodes[consequent][\"value\"]).items()\n", - " }\n", - " )\n", - " obs_consequent = gather(\n", - " nodes[consequent][\"value\"],\n", - " obs_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " int_indices = IndexSet(\n", - " **{\n", - " name: {world}\n", - " for name, ind in indices_of(nodes[consequent][\"value\"]).items()\n", - " }\n", - " )\n", - " int_consequent = gather(\n", - " nodes[consequent][\"value\"],\n", - " int_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " values_table[f\"{consequent}_obs\"] = obs_consequent.squeeze().tolist()\n", - " values_table[f\"{consequent}_int\"] = int_consequent.squeeze().tolist()\n", - "\n", - " values_df = pd.DataFrame(values_table)\n", - "\n", - " return values_df\n", - "\n", - "\n", - "table = get_table(\n", - " tr,\n", - " mwc,\n", - " antecedents,\n", - " witnesses,\n", - " consequents,\n", - " others=[\"joint_efficiency\", \"overshoot\"],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      .........................................................
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      153 rows × 18 columns

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      " - ], - "text/plain": [ - " lockdown_obs lockdown_int apr_lockdown mask_obs mask_int apr_mask \\\n", - "7 1.0 0.0 0 1.0 1.0 1 \n", - "12 1.0 0.0 0 1.0 1.0 1 \n", - "17 1.0 0.0 0 1.0 1.0 1 \n", - "44 1.0 0.0 0 1.0 1.0 1 \n", - "68 1.0 0.0 0 1.0 1.0 1 \n", - "... ... ... ... ... ... ... \n", - "1932 1.0 0.0 0 1.0 1.0 1 \n", - "1940 1.0 0.0 0 1.0 1.0 1 \n", - "1949 1.0 0.0 0 1.0 1.0 1 \n", - "1984 1.0 0.0 0 1.0 1.0 1 \n", - "1986 1.0 0.0 0 1.0 1.0 1 \n", - "\n", - " lockdown_efficiency_obs lockdown_efficiency_int \\\n", - "7 0.0 0.0 \n", - "12 0.0 0.0 \n", - "17 0.0 0.0 \n", - "44 0.0 0.0 \n", - "68 0.0 0.0 \n", - "... ... ... \n", - "1932 0.0 0.0 \n", - "1940 0.0 0.0 \n", - "1949 0.0 0.0 \n", - "1984 0.0 0.0 \n", - "1986 0.0 0.0 \n", - "\n", - " wpr_lockdown_efficiency mask_efficiency_obs mask_efficiency_int \\\n", - "7 0 0.10 0.10 \n", - "12 0 0.10 0.10 \n", - "17 0 0.45 0.45 \n", - "44 0 0.45 0.45 \n", - "68 0 0.10 0.10 \n", - "... ... ... ... \n", - "1932 0 0.10 0.10 \n", - "1940 0 0.10 0.10 \n", - "1949 0 0.45 0.45 \n", - "1984 0 0.45 0.45 \n", - "1986 0 0.10 0.10 \n", - "\n", - " wpr_mask_efficiency joint_efficiency_obs joint_efficiency_int \\\n", - "7 1 0.7 0.10 \n", - "12 1 0.7 0.10 \n", - "17 0 0.7 0.45 \n", - "44 0 0.7 0.45 \n", - "68 1 0.7 0.10 \n", - "... ... ... ... \n", - "1932 1 0.7 0.10 \n", - "1940 1 0.7 0.10 \n", - "1949 0 0.7 0.45 \n", - "1984 0 0.7 0.45 \n", - "1986 1 0.7 0.10 \n", - "\n", - " overshoot_obs overshoot_int os_too_high_obs os_too_high_int \n", - "7 27.413948 20.081240 1.0 1.0 \n", - "12 28.143654 18.176426 1.0 0.0 \n", - "17 23.878532 29.118336 1.0 1.0 \n", - "44 32.594929 25.202908 1.0 1.0 \n", - "68 30.271990 18.733744 1.0 0.0 \n", - "... ... ... ... ... \n", - "1932 33.913177 16.335846 1.0 0.0 \n", - "1940 18.856628 23.330811 0.0 1.0 \n", - "1949 32.299339 26.959858 1.0 1.0 \n", - "1984 21.073696 29.347139 1.0 1.0 \n", - "1986 32.659847 15.735228 1.0 0.0 \n", - "\n", - "[153 rows x 18 columns]" - ] - }, - "metadata": {}, - 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SNmzY4DADK/1U68jISH3wwQd64403JP1fUjo+Pt78cV36vwG/9H+zt65dJiRdXFyc2cYDDzygcuXKaf369Tp8+LDCwsKUJ08eVa9eXRs3bpS7u7tq1qyZ6YAcAJA5+vq7t6/PjgEDBujQoUP65JNPFBISIk9PTyUmJuqrr75yqNeiRQu1aNFC//zzj9atW6dZs2Zp4MCBCg0NNV/Dtm3bavjw4Wrfvr2GDx+usLAw+mb8a1wQEbDIoUOHdP78eT333HOqUKGCuX7ir7/+KulqpyZJNWrU0G+//eZw+s+ePXsUFRWl3bt3m6drXcvX1zfDhRbSO+Frb4eHh6tx48ZmYnrXrl06e/as+dwAgLvXiy++qPPnz2vChAkZ7ouLi9NHH32kChUqmKe7Llu2zKHOsmXLlJaWptDQUElZ63uyKr1PTBcWFqbY2FhVrlxZ1apVU7Vq1VS1alV98sknWrlyZbba3rZtm+x2u/r06WMOtNLS0sxThu12u8qVK6fChQubS22l+/bbbxUVFaWUlJQMMUpXX4PTp087lG3dutXcPnfunA4fPqzWrVurWrVq5mD7+r7/3woLC5N08/csX758qly5coZ9/PXXX83rYqQrWrSoKlasqM6dO2vevHnmxZNq1aolSVq+fLlDG9e2WbZsWfn5+en77793qHPs2DFt377d4dTkiIgIbdy40fyOIslcD3Pt2rVq2LDhbb0eAHCvoq+/e/v67NiyZYsef/xxhYeHy9PTM9N4XnnlFfXq1UvS1R8KmjVrpp49eyo1NdXhO4Gfn59sNpveeustxcfHO1xPArhdzJwGLFK2bFn5+vrqww8/lIeHhzw8PLRixQp98803kq5e6ECSevbsqbZt26p79+567rnndOXKFU2YMEGBgYGqU6eOmbRev369ypcvr6CgIDVs2FA///yzRo8erUaNGmnz5s1asmSJw/MHBgbqxx9/1Jdffqny5ctr3759mj59umw2m/ncAIC7V3BwsPr27asJEybo4MGDatmypQoXLqwDBw5ozpw5SkpK0oQJE1S+fHm1atVKkyZNUmJiomrWrKm9e/dqypQpCg8PNy88l5W+J6sKFCigrVu3atOmTapRo4Z69uypdu3aqXv37mrfvr28vLy0YMECrVq1SpMmTcpW24GBgZKkt99+W88884wuXLigefPmad++fZKuzn7y9fVVnz599Pbbb6tIkSJq1KiRDh8+rEmTJqljx44qWLCgOeNq5cqVql+/vsqXL6+IiAgtW7ZMQUFBKlOmjBYtWqQjR46Yz12kSBGVLl1a8+bNU4kSJVSgQAGtXbtWn332mSTlWP9boUKFLL1nL7/8sl566SX169dPLVu2VHx8vN5//301btxY/v7+2rVrl0O7vXv31o8//qg33nhDixYtUpkyZdS2bVt98MEHSk1NVeXKlfXtt99q//795mPc3NzUr18/RUdHq3///nrqqad07tw5TZkyRQULFnSYIdegQQN99NFHkv4vwV6rVi1z4EtyGgCyh77+7u3rsyMwMFDfffedAgICVKJECW3dulUzZ850GPvXqlVLw4YN03vvvaf69esrISFBU6ZM0UMPPWQuSXKtSpUq6fnnn9dHH32kJ5980uHHZiC7SE4DFsmfP7+mTZumMWPGqG/fvuYMprlz56pbt27avHmzGjVqpCpVqujzzz/X+PHj9corr8jX11cNGjTQgAED5OnpKU9PT73wwgtasGCB1qxZo99++03PPPOMjh49qsWLF2v+/PmqWbOmJk2apPbt25vPP3jwYKWkpGjChAlKTk7W/fffr5deekmxsbH6+eefs3ThDACAa3vppZdUpUoVzZs3T++8844uXLigkiVLKiIiQj169FDJkiUlXV0KqkyZMlq4cKFmzZqlYsWK6bnnnlPPnj3NWUVZ6XuyqkePHpo2bZq6deumH374QZUqVdK8efP0wQcfaNCgQTIMQ/7+/po6daoeffTRbLUdHh6uoUOH6uOPP9by5ctVtGhRhYeHa8qUKerVq5d5IeKOHTvKx8dHc+bM0YIFC1SiRAl169ZN3bp1M9t55JFHNH78eK1fv14zZ85UdHS0UlNT9d5778nDw0PNmzdX//79zWUwJGnatGkaNWqUBg8eLE9PT1WoUEHTp0/XO++8o82bN6tTp07Zfr0yk5X3rGHDhvrwww/Nfb/vvvv05JNPqk+fPpm2mTdvXg0dOlTdu3fXzJkz1atXLw0bNkxFixbV3LlzdeHCBdWrV089evRwmKUXGRmpfPnyacaMGerVq5d8fX1Vr1499evXz2F909DQUOXPn19FixY1ywMCAuTr66vixYs7XIwRAJA19PV3b1+fVe+++65GjBihESNGSJIeeughDR8+XEuXLjUvbtyuXTulpKRo/vz5+uKLL+Tt7a3atWtr4MCBypMnT6bt9unTR8uXL9cbb7yhJUuWmLOygeyyGVwxBAAAAAAAAADgZMycBgAAAO5Qqampt6zj5uaW6bqYAADgzkdfj3sdyWkAAADgDnT8+PEsncrcu3fvGy7FAQAA7lz09QDLegAAAAB3pOTkZIeLC95IsWLFVLx4cSdEBAAAchJ9PUByGgAAAAAAAABgARasgWX4XQSZ4bgAgDsLn8vIDMcFANx5+GxGZjgucKcjOQ1J0uTJk1WxYkWnPd+WLVsUFRXltOe7E+zevVvdunVTrVq1FB4erhdffFG7d+92qGMYhubMmaPHH39c1apVU5MmTTRv3rxbtp2cnKz3339fERERCgwM1FNPPaUffvghQ71FixbpySefVLVq1dSoUSNNmTJFaWlp2dqP9GPl2r8qVaooPDxcvXr10oEDB7Lc1kcffaQBAwZIkhISEjRo0CBt3rw5W/HcrsGDB6tRo0Y3rbNo0SJVrFhRx48fz3K7WXnMuXPnFBERoWPHjmW53WtdunRJw4cPV506dRQSEqJu3brp0KFDt3zc/v371bVrV4WFhalu3bp67bXXFB8ff8P6n3/++S1fIwDORX+d+7LSX18rO5+VH3zwQYY+tGLFipozZ45Z5+zZs3rjjTdUr1491ahRQ507d9aePXuyvR+DBw/O8DwBAQGqW7euBg4cqJMnT2a5rREjRuiDDz6QJJ06dUpRUVH666+/sh3T7ejUqZM6dep00zq383+RlcccOnRIjRo1UkJCQrbaThcfH6/+/fsrPDxcoaGh6tevn86cOXPLx50+fdp8XPXq1W95DLz77ru3fI0AOB99du67E8bYn376qR577DEFBgaqVatWWrNmTbb3gzH2zd3JY+xt27apU6dOCgoKUu3atRUdHZ3pGDsnjpO7GclpWOLrr7/WwYMHrQ7DaY4cOaJnn31WV65c0ahRozR69GglJyerQ4cODh94Y8aM0QcffKDWrVtr5syZatSokd5++20tWLDgpu0PGDBA8+bNU1RUlD788ENVrlxZ/fr106+//mrWmTdvnl5//XXVq1dPM2fOVJs2bfThhx9q4sSJt7VPCxYsMP8+//xzvfHGG9q7d686duyouLi4Wz7+4MGDmjFjhgYOHChJ2rt3r7799lvZ7fbbiic3REREaMGCBSpWrFiOtlu4cGF17txZr7/++m39it2/f38tX75c/fv313vvvafTp0/rueee04ULF274mPj4eD3//PP6+++/NXr0aL3++uvatGmTunXrppSUlAz1ly1bpnfffTfbsQG4u9BfZ95fp8vuZ+W+ffsUFhbm0IcuWLBATz75pKSrA+g+ffpo1apV6tu3r95//33Z7XY9++yztzXY8vPzc3ieTz/9VH379tWvv/6qTp066cqVK7dsY/369Vq5cqV69OghSfr999/vuAFVmzZtbvld6XaUK1dOjz76qEaOHJntx6ampqpbt27asWOH3nrrLb311lvaunWrunTpkmm/m+7ixYvq2LGj9u7dq+HDh2v8+PG6dOmSXnjhhUwT2x999JE+/vjjbMcH4O5Dn+38MfbHH3+s9957Ty1bttTkyZP1wAMP6KWXXrrtZDBj7NtnxRh7x44d6tSpkxISEvTuu+/qnXfe0fHjx9W2bVv9888/Zr2cPk7uRh5WBwDcCz7//HPlzZtXM2bMkI+PjySpVq1aatSokebOnauhQ4fq+PHj+uSTT/Tmm2+qQ4cOkqTatWvr5MmTWrdundq2bZtp25s3b9aKFSs0c+ZMNWjQwHzckSNH9Ouvv6p+/fq6fPmyxo8fry5dupgdVe3atZWQkKDff/9d/fr1y/Y+BQcHO9wODQ1VyZIl1bFjRy1evPiWv9qPHTtWLVq0uKMv6nDffffpvvvuy5W2O3TooOnTp2vlypV6/PHHs/y4bdu26ZdffnF4v2vUqKFHH31UX3zxhV566aVMH/fTTz/p3Llz+uqrr/Tggw9KkvLnz6+uXbtq27ZtCgsLkyT9/fffmjhxohYsWKBChQr9u50EABeTlf5auv3Pyr179yoyMjJDH5ruf//7nzZv3qyRI0eqdevWkqTq1aurVq1a+vbbb9W7d+9s7Y+np2eG56pRo4by5Mmj1157TT/99JOeeOKJm7YxevRode7cWXnz5s3WcztTiRIlVKJEiVxpOyoqShEREXr++ecVEBCQ5cctX75ce/bs0bJly1ShQgVJUuXKldWiRQv9+OOPeuqppzJ93Keffqrz58/rhx9+MAfuVatWVWRkpP744w+1aNFCknTs2DG99957+vnnn5U/f/5/uZcA4HqsHmNfuXJF06ZN0wsvvKBevXpJkurXr6927dpp6tSpt/XDIWPsf8fZY+zp06crf/78+uyzz1SwYEFJV4/BZs2aafbs2Xr11Vdz5Ti5GzFz2knOnDmj6OhoNWjQQIGBgWrdurV++ukn8/4XX3xRkZGRGR7Xs2dPhy+vmzdv1rPPPqugoCCFhYXptdde09mzZ837Fy1apCpVqujrr79WnTp1FBYWptjYWB09elQ9evRQeHi4goKC1LZt20xnvaxevVpPPfWUebrLkiVLsrUfkpSUlKSpU6eqadOmqlatmh5//HHNnDnT/LVu8ODBWrx4sf766y9VrFhRixYtyvQ1mzx5spo2baqVK1eqRYsWqlatmp5++mlt27ZN27dvV5s2bRQYGKgWLVpo/fr1Do/9888/1b17d1WvXl3Vq1dXr169Msw42rdvn3r37q1atWopICBA9erV08iRIx1mEVWsWFHz5s3TkCFDFBYWppCQEPXt29fhNI30U0w2btyY6X5IV2fevPjii2anKUk+Pj4qUaKEjh49KklatWqVvLy8zMFougkTJmjy5Mk3bHv58uV68MEHzQ9RSbLZbJo/f77eeOMNSdJvv/2mS5cuZTjl87XXXtM333xzw7azq2rVqpJknuo7efJkPfbYY5oyZYq5lMSFCxf0559/avXq1eYAa+PGjXruueckSc8995xDnD/88IMiIyMVEhKiOnXqaOjQoRl+vdy5c6e6dOlinv7ao0ePLJ/6tGjRIjVp0kTVqlXTU0895fB/kdnpQ4sXL1bz5s3N+uvXr1eVKlUyHMcxMTFq166dqlWrpoiICM2ePdvhfk9PTzVp0kQzZswwyzZu3HjT/wlJWrdunXx8fFS3bl2z7L777lPNmjVvOpMtKSlJkuTr62uWpSdUzp8/b5Z9+OGHWrdunSZPnqyGDRvesD3gbkV/TX99q/5aur3PyrNnz+r06dOqXLnyDetk9lnt4+MjLy8vh8/qf6tatWqS/q+/Hjx4sJ5//nkNGzZM1atXV/PmzZWWlqbVq1frzz//NBPYixYtUnR0tCTp0Ucf1eDBgyVJaWlpmjdvnp588kkFBgYqIiJC48aNM/cn3W+//aYOHTooNDRU4eHh6t+/f5aWFzEMQ7NmzTJPrW7btq127Nhh3p/ZqfNz5szRo48+qsDAQLVr104///xzpu//rf6X/Pz8VKtWLYf+OivH0rp161S2bFkzMS1JFSpUUPny5W/aX69YsUJNmjRxmFHm5+entWvXmt+bpKs/Ghw5ckSffvrpTY8p4G5Gn02fbeUYOyYmRgkJCXrssccc6jz22GPauHFjls5OygrG2P/nThtjHzp0SKGhoWZiWpLy5s2rwMBArV692ozZGceJqyM57QTx8fFq3bq1Nm/erFdffVWTJ09W6dKl1atXLy1dulSS9NRTT2n37t06cuSI+biEhAT9+uuvevrppyVJmzZtUufOneXt7a0JEybo9ddf1x9//KHnnnvO4YBOS0vTRx99pFGjRik6Olply5ZV9+7dlZiYqDFjxmjatGkqVKiQXnrpJYfnk6ShQ4eqc+fOmj59ukqUKKHBgwdr3759Wd4PwzDUo0cPzZ4921w2omnTppowYYKGDRsm6eqXgQYNGpinmkZERNzwtTt16pTeffdd9ejRQxMnTlRCQoJefvll9evXT23atNHUqVNlGIb5i5QkHT58WO3atdPff/+t9957T6NGjdKxY8fUvn17/f3335KufgHo2LGjEhMT9e6772rWrFl64okn9Pnnn+uzzz5ziOGDDz6Q3W7X+++/r0GDBumXX37RO++8Y96fflrKzWbTdOjQQV27dnUoO3LkiA4cOKCHH35Y0tXZVGXKlNGmTZvUqlUrBQQEqFGjRrc83Wjfvn16+OGH9d1336lZs2aqUqWKmjVrplWrVpl19u7dq/z58ys+Pl4dO3ZU1apVVadOHU2bNi1HL45w+PBhSTJn5krSiRMntGbNGn3wwQeKjo5WwYIF9d1338nPz8/8ZTggIMCcjTZ06FDzWJk2bZr69eun4OBgTZo0Sb169dKKFSscTkXesGGD2rdvL0l65513NHLkSJ08eVLt2rW75WltJ0+e1MyZM9W3b19NnjxZNptNL7/8snmcXG/JkiUaPHiwqlevrmnTpqlJkybq2bNnput2v/XWW3riiSc0c+ZMhYSEaOzYsfrll18c6jRt2lS7du0yX7eAgIBb/k8cPHhQ999/v9zd3R3KH3zwQbOdzDRr1kx+fn56++23debMGR07dkxjxoyRn5+fHnnkEbNeu3bttGLFimz90gzcLeiv6a+z0l9Lt/dZmf7+rF69Wg0bNlRAQIBatmzpMOipVKmSatWqpWnTpunPP//U+fPn9e677+rKlStq3rx5lp/rVjLrrzdv3qyTJ09q6tSp6t+/v9zd3bV06VIFBwebM7AiIiLM2UNTpkxRz549JV09HkePHq3GjRtr+vTp6tixo+bOnauePXua3zOWLFmiF198USVLltT777+v6Ohobdu2TW3btr1hv5tuy5YtWrlypd58802NHTtWZ86c0UsvvaTU1NRM60+ZMkXjxo1Ts2bNNG3aNAUFBemVV17JtO7N/pfSNW3aVD///LMuXbpkvg63OpYOHjyohx56KEP5zfrrlJQUHTx4UGXLltWECRNUt25dBQQEqFOnThkSAq+88oqWLl2qmjVr3jAG4G5Gn02fbfUYO32sef1nfZkyZZSWlubwo/a/wRj7/9xpY+zChQvrxIkTGcqPHTtm/nDjrOPE5RnIdWPGjDECAgKM48ePO5Q///zzRp06dYy0tDTj0qVLRnBwsDFlyhTz/q+//tqoVKmScerUKcMwDKNt27ZGixYtjNTUVLPOoUOHjMqVKxtz5841DMMwFi5caPj7+xtLliwx65w5c8bw9/c3li5dapYlJCQY77zzjvHnn38ahmEYkyZNMvz9/Y01a9aYdY4cOWL4+/sbn376aZb3Y/Xq1Ya/v7/x/fffO9SZOnWq4e/vbz7fa6+9ZjRs2PCmr1tmMc2YMcPw9/c3vv76a7Ns+fLlhr+/v7Fnzx7DMAyjX79+xiOPPGL8888/Zp1z584ZoaGhxrvvvmsYhmGsXbvW6Nixo0MdwzCMFi1aGC+++KJ529/f32jfvr1DncGDBxvBwcE3jf1WEhMTjbZt2xrBwcHm69m1a1cjPDzcqFWrljF37lzj999/N9544w3D39/fmD9//g3batasmVGnTh2jfv36xuLFi41169YZPXv2NCpWrGi+dsOGDTOCg4ON2rVrGx9++KGxfv16Y9y4cUalSpWM8ePHZyv29PclJSXF/Pvnn3+MTZs2Ga1atTJCQ0ONM2fOONTdtGmTQxutW7c2XnrpJYeyDRs2GP7+/saGDRsMwzCM8+fPG1WrVjXefPNNh3qbNm0y/P39zWO+devWRvPmzR3+Ly5cuGCEhYUZL7/88g3347XXXjP8/f2N2NhYs+z33383/P39jVWrVhmG8X//T8eOHTMMwzAiIiKM7t27O7STfkwuXLjQ4TFffPGFWefy5ctGQECA8c477zg8NiEhwfD39zfmzZt3wziv9+KLLxrt2rXLUP7+++8bAQEBN33sqlWrjMDAQMPf39/w9/c3atasaezdu/eG9bPyfwrcTeiv6a+vl1l/fb2sflbOnj3b8Pf3N7p06WKsW7fO+Pnnn40XX3zRqFSpkvHrr7+a9Q4dOmQ0atTI/KyuWLGisWjRomzHnh7Xtf31uXPnjF9//dVo1KiR0ahRIyMxMdGs6+/vb5w8edKhjdq1axsjR450KLu+bzxw4IDh7+9vzJgxw6HekiVLDH9/f2P16tVGWlqaUadOHYf3zDCuHrsBAQHGe++9d8P9ePbZZ43AwEDj3LlzZtlXX31l+Pv7m31Y+jFoGIZx6dIlIzAw0BgxYoRDO2+++abD94ys/C+l27t3r7kvWdWkSROjf//+Gcr79+9vPP7445k+Jj4+3vD39zfq1KljtGvXzvjll1+MFStWGE888YQRFhZmfsZc79lnnzWeffbZLMcG3A3os+mzr+fsMXb665aSkuLw2N9++83w9/c3tmzZkuXYGWO75hg7/fvIyJEjjVOnThlnzpwxxowZY1StWtWoVKmSw77kxHFyN2PmtBP88ccfCgkJUenSpR3Kn3rqKcXFxenQoUPy8fFR48aNHa7+umzZMtWuXVvFixdXYmKiYmJi1KBBAxmGodTUVKWmpuqBBx5Q+fLl9dtvvzm0fe3pfUWLFlWFChX05ptv6rXXXtN3330nu92u6Ohoh1lA0tV1ddLdf//9kmReoTwr+/HHH3/Iw8NDTZs2zVAnvY3sql69usO+SFJQUJBZlr40QXqcGzZsUFhYmLy9vc3XydfXVzVq1NDvv/8uSapbt67mzp0rLy8vxcbG6qefftL06dN19uxZJScnOzz/9es+lShRQomJidnej3QXL15U9+7dtXPnTo0dO9Z8PVNSUnTu3DkNHz5cHTt2VO3atTVixAjVrVtXU6ZMuWF7KSkpiouL07Rp09SyZUvVqVNHkyZNUoUKFTRt2jSzzuXLl9WtWzd1795dtWrVUv/+/dWmTRt9/PHHunjxYrb3IyAgwPwLDQ1Vx44dlZycrClTpsjPz8+h7vWnmx47dsw8vm5k+/btSk5OdjiFVbp6jJYuXVp//PGHLl++rJ07d6pZs2YOv3IWKFBADRs2vOXxVrhwYZUvX968nR7TtRcvSHfkyBGdOHEiw7F9o7U6r/1fyps3r4oWLWoeo+ny58+vAgUKZOtKxcZNZrrbbLYb3vfdd9+pd+/eatSokebMmaNp06bp4Ycf1osvvnhPXTgFuBn6a/rra92ov75dzZo104cffqgZM2aoTp06atiwoT788EOVLVtWkyZNknR1dk3btm1VoEABTZo0SR9//LHatGmjN954Qz/++GO2n/Ovv/5y6K/Dw8PVtWtXFSlSRFOnTpW3t7dZt1ChQg7rNl++fFl///33Lfvr9GPl+v7wiSeekLu7uzZu3KjDhw8rLi4uQ5/+4IMPKiQk5JbHW4UKFRzW9r5Zf719+3ZduXIlw7F9/XOnu9n/Urr09z63++trL5Q4e/ZsRUREmKfuX7p0SfPmzcvy8wN3O/ps+uxrWTHGvtVFBt3csp9uY4x9lauMsdu0aaPBgwfrm2++Uf369VWvXj3zgojp37Fy4zi5G3FBRCe4cOGCHnjggQzl6Z1A+j/T008/raVLl2rfvn0qWrSoNm7caJ7akpCQILvdrlmzZmnWrFkZ2vLy8nK4fe26SzabTR999JG5MPySJUuUJ08eNW7cWMOHD3dYH+fax6X/k6T/o2ZlPy5cuKDChQtnOB0i/YM0sw+kW7l23cV0N7soT/pFZK79EpIufeH99FOI5s2bp8uXL6tkyZIKDAzM8Dpm9lxubm63vRTGyZMn1b17dx0+fFgffPCBGjdubN6XL18+2Ww2h3WtJKlevXpat26d4uPjzdf6Wvny5ZOfn5/DKU/u7u6qXbu2ebpSvnz5JCnDqSz169fXggULdPDgQYcvI1lx7VrVefLkkZ+fn4oUKZJp3fTnT3fx4sVbXlgpfc2rzPa5aNGi+ueff/TPP//IMIyb1rmZa4936f86nsw6kPR1567fx8yeW8r6cZM3b95s/Tjg6+vrsB5bukuXLt30gkhTpkxRSEiIPvjgA7OsTp06at68uSZOnGgmRoB7Gf01/XW6m/XXt6tUqVIqVaqUQ1mePHlUp04dzZ8/X5L0ySefmKeOFy5cWJL0yCOPKCEhQW+//baaNm1600HS9fz8/DR9+nTztqenp0qUKOFwLKW7vq9OPwau7yuvl95fXz9o9vDwUOHChfXPP/+Y62XfqL/es2fPTZ/j+hjSj/mb9dfXX2zpRt9Rbva/lC792Mpuf52+DMi1Ll68eMP+Ov09CA8Pd3g/SpUqpfLly9/ydQLuJfTZ9NnprBpjp3+WX7p0yeH9Tu8rbuditYyx/+95MnOnjbEl6YUXXtCzzz6ro0ePqnDhwrrvvvs0aNAg8wee3DhO7kYkp52gYMGCiouLy1CeXpY++Khdu7b8/Pz0448/ys/PT15eXuZahukfqp07d870V6RbfQgVL15cb731loYNG6Z9+/Zp+fLlmjVrlgoXLmyuPZQT+1GwYEGdO3dOaWlpDp3nmTNnHPY1N+XPn1+PPPKIXnjhhQz3eXhcPeRnzpypTz75RMOHD9fjjz9ufiBcf6GEnLR//3516dJFSUlJ+uijjzKsEVimTBkZhqGUlBSHDjx9PcVrZzdd/7iTJ0/KMAyHAWtqaqr5mDJlykhShl+s02foZPaF4VbSL6Z0OwoVKnTLTi39gzs+Pl7lypVzuC8uLk4PPPCA8ufPL5vNlmlHEhcX5zDL6t9Kn012/VpZt1on81YSEhKy9X9RtmxZrVu3Tna73eFX1iNHjjj8Qn29v/76K0NyxdvbW1WrVs3yhS2Aux39Nf21dOv++natWbNGV65cUZMmTRzKk5KSzIH9iRMnVK5cuQyvf82aNbV8+XL9/fffNxywZcbT0/O2++v0GK6fkXS99P46Li7OYeZf+my1woULm/3xjfrrnDzeru2vr/3+cO3FzbIr/TXIbn+9d+/eDOVHjx5VYGBgpo/Jnz+/7rvvvgzf1yTH73UA6LMl+mzJ2jF22bJlJV0dh137uX7kyBHlyZMn0x8dboUxtjK9nV3OGmPv3LlTJ0+e1OOPP+5Qb8+ePapSpYrZdnpbOXWc3I2YP+4ENWvW1LZt28yrq6ZbunSp/Pz8zMShu7u7nnzySf3yyy9avny5GjdubP7q5OvrqypVqujQoUOqVq2a+ffwww9r8uTJN72K7bZt2/TII49ox44dstlsqly5sl599VX5+/tnunj7v9mPsLAwpaamavny5RnqSFJoaKik3D11If3qyZUrVzZfp6pVq+qTTz7RypUrJV29sE6FChX0zDPPmJ3m6dOn9eeff97ytIvbcfLkSb3wwguy2Wz68ssvMx3opv+au2zZMofy9KvLZ/brdvrjzp8/73DaWXJystauXWu+3vXr15fNZsu07UKFCt30Azc3lC5dWidPnnQou34mQFBQkDw9PfX99987lG/evFknTpxQ9erV5ePjo6pVq+rHH390uGDCP//8o9WrV5v7nxNKlCihBx980DyG0v33v/+97TYvXLigxMTEDDPpbqZu3bq6dOmS1q5da5adPXtWmzdvVp06dW74uHLlymnr1q0OvywnJSVp9+7ddIjA/0d/TX+dlf76di1fvlzR0dHmLGLp6tIZq1evVnh4uKSrA5jY2FiHOpK0detW5c+fP0cHhLfi6ekpPz+/DP319cdEWFiYpIzfX5YtW6a0tDSFhoaqbNmy8vPzy9CnHzt2TNu3b3c4vfzfqlSpkvLnz5+j/fWpU6ckKdv99cGDBxUbG2uWxcbG6uDBgzftrxs0aKDff//dIZl+6NAhHT582OF0ZuBeR59Nn231GDskJEQ+Pj5asWKFWccwDK1cuVJhYWHy9PT81/uYHYyxr3LmGPuPP/7QgAEDHH7I/+2333TgwAFzYtiddpzcqZg57QQvvPCCli5dqs6dO6t3794qVKiQlixZog0bNuidd95x6ESefvppffTRR3Jzc8twalG/fv0UFRWl/v3766mnnjJP+4yJiTGvlp6ZKlWqyNvbW4MGDVKfPn1UtGhR/f7779q7d6+ee+65HN2P+vXrKzw8XG+88YZOnz6tSpUq6Y8//tCsWbPUqlUrVahQQdLV9Yri4+O1Zs0aVa5cWcWKFcvmq3pjPXv2VLt27dS9e3e1b99eXl5eWrBggVatWmUuXRAYGKhp06Zp5syZCg4O1pEjRzRjxgwlJydne62rs2fP6ujRo6pQocINO7eRI0fq77//1vDhw3Xx4kVt377dvM/X11cVKlRQeHi4GjZsqNGjRysxMVEPP/ywlixZoq1bt5rrWklXZ9ycPXvWXKfrySef1Ny5czVgwAD1799fxYsX12effaZTp05p4sSJkqQHHnhAzz77rGbPni0PDw/VrFlTv/zyi5YuXao333xTefLkkXR18HXq1ClVqVIlVz8k69Spoy+++MLhl+j0LzCrV69WwYIFValSJUVFRWnq1KnKkyePGjZsqOPHj2vixImqUKGCWrVqJUnq37+/unTpoqioKHXo0EEpKSmaOXOmkpOT1atXrxyLOf0qwwMGDNCwYcP02GOPad++fZo6daqk2/syuGXLFklXO0Pp6qk9sbGxevDBBzOcjpyuZs2aCgsL08CBAzVw4EAVKlRIkydPVv78+c0rKktXB8DJycnmL7Z9+/ZVr1691LdvX7Vu3VrJycn69NNPdfr0aY0fPz7bsQN3I/pr+uus9NdZdX1/3bVrVy1fvty8/kP6qeSJiYnq06ePpKvv3XfffafOnTure/fuyp8/v/773/9q2bJlio6ONmenXd92bqlTp462bt3qUFagQAFJ0sqVK1W/fn2zT540aZISExNVs2ZN7d27V1OmTFF4eLjq1asnNzc39evXT9HR0eb/xblz5zRlyhQVLFgw05l4t8vX11ddu3bVpEmTlDdvXoWFhemPP/7Ql19+Ken2++u8efOayeGsHEvNmzfXhx9+qG7duql///6SpPHjx8vf31/NmjUz6+3Zs0eenp7msdWrVy+tWrVKXbp0Ua9evZScnKwJEyaoRIkSuTr7EHA19Nn02VaPsfPmzasXX3zRHK+GhIRo4cKF2r17tz777DOzbcbYN+bqY+ynnnpKM2fO1CuvvKIuXbroxIkTevfdd1W9enVzTfisHif3PGddefFed/ToUaNv375GjRo1jKCgIKNt27bm1Uqv16JFC6NOnToOV0ZN9/vvvxsdOnQwAgMDjdDQUOO5555zuErr9Vc+TXf48GGjd+/eRu3atY2AgADjiSeecLg67bVXOb+Wv7+/MWnSpGztx+XLl413333XqFevnhEQEGA0adLEmD17tpGWlmbW2b9/v9G0aVMjICAgw9XdbxZTZvt3/RVoDcMwdu3aZXTp0sUICQkxgoODjf/85z8OcSYlJRnDhw836tSpYwQGBhpNmjQxJk2aZEyePNmoWrWqceHChUz3P7O40mO69vmvlZSUZFSpUsXw9/fP9O/aq6tfuXLFGDdunFG/fn2jatWqRsuWLY2VK1c6tJd+BdxrnT9/3hg6dKhRu3ZtIzAw0GjXrl2Gq/empaUZs2bNMho3bmwEBAQYTZs2Nb766qtM9+364+dm+38zN6qbftX77du3O8TXr18/o1q1asYTTzxhln/xxRdG8+bNjYCAAKNOnTrGW2+9ZZw/f96hvQ0bNpj/FzVq1DB69OhhXrX6RjK7mvWxY8cyvSrwta/H/Pnzjccee8wICAgwWrZsaXz99deGv7+/sWLFihs+xjAMo2HDhsZrr73mUDZs2DCjdevWDvtx7fPfyPnz543BgwcbNWrUMKpXr2507drVOHjwoEOdZ599NsP+rVmzxmjbtq1RrVo1o1atWkZUVJSxd+/ebL1GwN2O/pr+Oiv99bVu9FmZWX+9a9cu48UXXzTCwsKM4OBgo1u3bsb+/fsd6hw8eNDo1auXERoaaoSEhBht2rQx+5ibtZ3VuLJT96effjIqV65snDp1yiy7ePGi0blzZyMgIMDo1q2bYRiGkZqaakybNs149NFHjYCAAKNhw4bG+++/b1y5csWhveXLlxutWrUyAgICjPDwcGPAgAHGiRMnbhrbs88+m+G1v/5Yuv69ttvtxrRp04wGDRoYAQEBRocOHYyPP/7Y8Pf3N3bt2pXpY9Jldix17drV6Nu3r3n7VsdSuhMnThi9evUygoODjZo1axqvvPKKcfr0aYc6DRs2zLB/Bw4cMLp3724EBwcboaGhRp8+fYyTJ0/e8Hkye42AewF9Nn32nTDGnjp1qtGgQQOjWrVqRqtWrYzVq1dnum+Mse/OMfbOnTuNjh07GsHBwUb9+vWNkSNHGv/8849DnawcJ/c6m2Hc5qrzAO5KHTt21IQJEzJc2Cin9ejRQ4ULF9bo0aNz9Xly0vfff68qVao4rM+1evVqde/eXd9++60qVaqU5bYuX76sevXq6b333suRC20BAO4tjz/++L867TUrDMPQU089pSZNmqh37965+lw5JTU1Vd9//73Cw8NVsmRJs3zevHkaOXKkNm7caM7+zoq//vpLjz32mL755htzphQAANnBGPvGGGNDYs1pANfYuHG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- "text/plain": [ - "
      " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "factual = table[\n", - " (table[\"lockdown_int\"] == 1)\n", - " & (table[\"mask_int\"] == 1)\n", - " & (table[\"wpr_lockdown_efficiency\"] == 0 & (table[\"wpr_mask_efficiency\"] == 0))\n", - "]\n", - "\n", - "\n", - "counterfactual_lockdown = table[\n", - " (table[\"lockdown_int\"] == 0)\n", - " & (table[\"mask_int\"] == 1)\n", - " & (table[\"wpr_lockdown_efficiency\"] == 0)\n", - "]\n", - "\n", - "display(counterfactual_lockdown)\n", - "\n", - "counterfactual_mask = table[\n", - " (table[\"lockdown_int\"] == 1)\n", - " & (table[\"mask_int\"] == 0)\n", - " & (table[\"wpr_mask_efficiency\"] == 0)\n", - "]\n", - "\n", - "\n", - "fig, axs = plt.subplots(1, 3, figsize=(18, 6))\n", - "\n", - "factual_mean = factual[\"overshoot_int\"].mean().item()\n", - "axs[0].hist(factual[\"overshoot_int\"])\n", - "axs[0].set_title(\n", - " f\"Factual\\n overshoot mean: {factual_mean:.2f}, Pr(too high): {factual['os_too_high_int'].mean().item():.2f}\"\n", - ")\n", - "axs[0].axvline(x=factual_mean, color=\"grey\", linestyle=\"--\")\n", - "\n", - "counterfactual_lockdown_mean = counterfactual_lockdown[\"overshoot_int\"].mean()\n", - "axs[1].hist(counterfactual_lockdown[\"overshoot_int\"])\n", - "axs[1].set_title(\n", - " f\"Counterfactual_lockdown\\n overshoot mean: {counterfactual_lockdown_mean:.2f}, Pr(too high): {counterfactual_lockdown['os_too_high_int'].mean():.2f}\"\n", - ")\n", - "axs[1].axvline(x=counterfactual_lockdown_mean, color=\"grey\", linestyle=\"--\")\n", - "\n", - "counterfactual_mask_mean = counterfactual_mask[\"overshoot_int\"].mean()\n", - "axs[2].hist(counterfactual_mask[\"overshoot_int\"])\n", - "axs[2].set_title(\n", - " f\"Counterfactual_mask\\n overshoot mean: {counterfactual_mask_mean:.2f}, Pr(too high): {counterfactual_mask['os_too_high_int'].mean():.2f}\"\n", - ")\n", - "axs[2].axvline(x=counterfactual_mask_mean, color=\"grey\", linestyle=\"--\")\n", - "\n", - "for i in range(3):\n", - " axs[i].set_xlim(5, 40)\n", - " axs[i].axvline(x=overshoot_threshold, color=\"red\", linestyle=\"-\")\n", - "\n", - "plt.savefig(\"counterfactual_sir_search.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
      " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_counterfactual_by_context(data, name, other):\n", - "\n", - " grouped_data = data.groupby([\"wpr_lockdown_efficiency\", \"wpr_mask_efficiency\"])\n", - "\n", - " fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n", - "\n", - " for (lockdown_efficiency, mask_efficiency), ax in zip(\n", - " grouped_data.groups.keys(), axs.flatten()\n", - " ):\n", - " data_subset = grouped_data.get_group((lockdown_efficiency, mask_efficiency))\n", - " mean_overshoot = data_subset[\"overshoot_int\"].mean().item()\n", - "\n", - " fixed = mask_efficiency if name == \"lockdown\" else lockdown_efficiency\n", - " ax.hist(data_subset[\"overshoot_int\"])\n", - " ax.set_title(\n", - " f\"{other} eff fixed: {fixed}\\nOvershoot mean: {mean_overshoot:.2f}, Pr(too high): {data_subset['os_too_high_int'].mean().item():.2f}\"\n", - " )\n", - " ax.set_xlim(5, 35)\n", - " ax.axvline(x=mean_overshoot, color=\"grey\", linestyle=\"--\")\n", - " ax.axvline(x=overshoot_threshold, color=\"red\", linestyle=\"-\")\n", - "\n", - " plt.suptitle(\n", - " f\"Counterfactual {name} by {other.lower()} efficiency contexts\",\n", - " fontsize=16,\n", - " y=1,\n", - " )\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "\n", - "plot_counterfactual_by_context(counterfactual_lockdown, \"lockdown\", \"Mask\")\n", - "\n", - "plot_counterfactual_by_context(counterfactual_mask, \"mask\", \"Lockdown\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "sufficiency_table = get_table(\n", - " tr,\n", - " mwc,\n", - " antecedents,\n", - " witnesses,\n", - " consequents,\n", - " world=2,\n", - " others=[\"joint_efficiency\", \"overshoot\"],\n", - ")\n", - "\n", - "\n", - "factual_sufficiency = sufficiency_table[\n", - " (sufficiency_table[\"lockdown_int\"] == 1)\n", - " & (sufficiency_table[\"mask_int\"] == 1)\n", - " & (\n", - " sufficiency_table[\"wpr_lockdown_efficiency\"]\n", - " == 0 & (sufficiency_table[\"wpr_mask_efficiency\"] == 0)\n", - " )\n", - "]\n", - "\n", - "counterfactual_sufficiency_lockdown = sufficiency_table[\n", - " (sufficiency_table[\"lockdown_int\"] == 0)\n", - " & (sufficiency_table[\"mask_int\"] == 1)\n", - " & (sufficiency_table[\"wpr_lockdown_efficiency\"] == 0)\n", - "]\n", - "\n", - "counterfactual_sufficiency_mask = sufficiency_table[\n", - " (sufficiency_table[\"lockdown_int\"] == 1)\n", - " & (sufficiency_table[\"mask_int\"] == 0)\n", - " & (sufficiency_table[\"wpr_mask_efficiency\"] == 0)\n", - "]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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      " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(1, 3, figsize=(18, 6))\n", - "\n", - "factual_sufficiency_mean = factual_sufficiency[\"overshoot_int\"].mean().item()\n", - "axs[0].hist(factual_sufficiency[\"overshoot_int\"])\n", - "\n", - "axs[0].set_title((\n", - " f\"Factual\\n overshoot mean: {factual_sufficiency_mean:.2f}, Pr(too high): \"\n", - " f\"{factual_sufficiency['os_too_high_int'].mean().item():.2f}\"\n", - "))\n", - "axs[0].axvline(x=factual_sufficiency_mean, color=\"grey\", linestyle=\"--\")\n", - "\n", - "counterfactual_sufficiency_lockdown_mean = counterfactual_sufficiency_lockdown[\"overshoot_int\"].mean()\n", - "axs[1].hist(counterfactual_sufficiency_lockdown[\"overshoot_int\"])\n", - "axs[1].set_title((\n", - " f\"Counterfactual_lockdown\\n overshoot mean: {counterfactual_sufficiency_lockdown_mean:.2f}, \"\n", - " f\"Pr(too high): {counterfactual_lockdown['os_too_high_int'].mean():.2f}\"\n", - "))\n", - "axs[1].axvline(x=counterfactual_sufficiency_lockdown_mean, color=\"grey\", linestyle=\"--\")\n", - "\n", - "counterfactual_sufficiency_mask_mean = counterfactual_sufficiency_mask[\"overshoot_int\"].mean()\n", - "axs[2].hist(counterfactual_sufficiency_mask[\"overshoot_int\"])\n", - "axs[2].set_title((\n", - " f\"Counterfactual_mask\\n overshoot mean: {counterfactual_sufficiency_mask_mean:.2f}, \"\n", - " f\"Pr(too high): {counterfactual_mask['os_too_high_int'].mean():.2f}\"\n", - "))\n", - "axs[2].axvline(x=counterfactual_sufficiency_mask_mean, color=\"grey\", linestyle=\"--\")\n", - "\n", - "for i in range(3):\n", - " axs[i].set_xlim(5, 40)\n", - " axs[i].axvline(x=overshoot_threshold, color=\"red\", linestyle=\"-\")\n", - "\n", - "#plt.savefig(\"counterfactual_sir_search_sufficiency.png\")\n", - "\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      \n", - "
      " - ], - "text/plain": [ - " lockdown_obs lockdown_int apr_lockdown mask_obs mask_int apr_mask \\\n", - "11 1.0 0.0 1 1.0 1.0 0 \n", - "38 1.0 0.0 1 1.0 1.0 0 \n", - "51 1.0 0.0 1 1.0 1.0 0 \n", - "104 1.0 0.0 1 1.0 1.0 0 \n", - "110 1.0 0.0 1 1.0 1.0 0 \n", - "\n", - " lockdown_efficiency_obs lockdown_efficiency_int \\\n", - "11 0.0 0.0 \n", - "38 0.0 0.0 \n", - "51 0.0 0.0 \n", - "104 0.0 0.0 \n", - "110 0.0 0.0 \n", - "\n", - " wpr_lockdown_efficiency mask_efficiency_obs mask_efficiency_int \\\n", - "11 0 0.00 0.00 \n", - "38 0 0.45 0.45 \n", - "51 0 0.45 0.45 \n", - "104 0 0.00 0.00 \n", - "110 0 0.00 0.00 \n", - "\n", - " wpr_mask_efficiency joint_efficiency_obs joint_efficiency_int \\\n", - "11 1 0.7 0.00 \n", - "38 0 0.7 0.45 \n", - "51 0 0.7 0.45 \n", - "104 1 0.7 0.00 \n", - "110 1 0.7 0.00 \n", - "\n", - " overshoot_obs overshoot_int os_too_high_obs os_too_high_int \n", - "11 15.616409 21.598530 0.0 1.0 \n", - "38 31.680214 25.210352 1.0 1.0 \n", - "51 32.041954 24.760708 1.0 1.0 \n", - "104 30.147129 17.757517 1.0 0.0 \n", - "110 15.187485 23.535439 0.0 1.0 " - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "counterfactual_sufficiency_lockdown.head()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "chirho", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/source/test_notebook.ipynb b/docs/source/test_notebook.ipynb deleted file mode 100644 index 69421fc0..00000000 --- a/docs/source/test_notebook.ipynb +++ /dev/null @@ -1,659 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Callable, Dict, List, Optional\n", - "\n", - "import math\n", - "import pyro\n", - "import pyro.distributions as dist\n", - "import pyro.distributions.constraints as constraints\n", - "import torch\n", - "from chirho.counterfactual.handlers.counterfactual import \\\n", - " MultiWorldCounterfactual\n", - "from chirho.explainable.handlers import ExtractSupports, SearchForExplanation\n", - "from chirho.indexed.ops import IndexSet, gather, indices_of\n", - "from chirho.observational.handlers import condition\n", - "from chirho.observational.handlers.soft_conditioning import soft_eq, KernelSoftConditionReparam\n", - "\n", - "pyro.settings.set(module_local_params=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "match_dropped tensor(1.)\n", - "match_dropped Provenance:\n", - "frozenset({'u_match_dropped'})\n", - "Tensor:\n", - "0.0\n" - ] - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "u_match_dropped\n", - "\n", - "u_match_dropped\n", - "\n", - "\n", - "\n", - "match_dropped\n", - "\n", - "match_dropped\n", - "\n", - "\n", - "\n", - "u_lightning\n", - "\n", - "u_lightning\n", - "\n", - "\n", - "\n", - "lightning\n", - "\n", - "lightning\n", - "\n", - "\n", - "\n", - "smile\n", - "\n", - "smile\n", - "\n", - "\n", - "\n", - "forest_fire\n", - "\n", - "forest_fire\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def forest_fire_model():\n", - " u_match_dropped = pyro.sample(\"u_match_dropped\", dist.Bernoulli(0.7))\n", - " match_dropped = pyro.deterministic(\n", - " \"match_dropped\", u_match_dropped, event_dim=0\n", - " ) # notice uneven probs here\n", - "\n", - " print(\"match_dropped\", match_dropped.squeeze())\n", - "\n", - " u_lightning = pyro.sample(\"u_lightning\", dist.Bernoulli(0.4))\n", - " lightning = pyro.deterministic(\"lightning\", u_lightning, event_dim=0)\n", - "\n", - " # this is a causally irrelevant site\n", - " smile = pyro.sample(\"smile\", dist.Bernoulli(0.5))\n", - "\n", - " forest_fire = pyro.deterministic(\n", - " \"forest_fire\", torch.max(match_dropped, lightning) + (0 * smile), event_dim=0\n", - " )\n", - "\n", - " return {\n", - " \"match_dropped\": match_dropped,\n", - " \"lightning\": lightning,\n", - " \"forest_fire\": forest_fire,\n", - " }\n", - "\n", - "with ExtractSupports() as extract_supports:\n", - " forest_fire_model()\n", - " forest_fire_supports = {k: constraints.boolean for k in extract_supports.supports}\n", - "\n", - "pyro.render_model(forest_fire_model)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def importance_infer(\n", - " model: Optional[Callable] = None, *, num_samples: int\n", - "):\n", - " \n", - " if model is None:\n", - " return lambda m: importance_infer(m, num_samples=num_samples)\n", - "\n", - " def _wrapped_model(\n", - " *args,\n", - " **kwargs\n", - " ):\n", - "\n", - " guide = pyro.poutine.block(hide_fn=lambda msg: msg[\"is_observed\"])(model)\n", - "\n", - " max_plate_nesting = 9 # TODO guess\n", - "\n", - " with pyro.poutine.block(), MultiWorldCounterfactual() as mwc:\n", - " log_weights, importance_tr, _ = pyro.infer.importance.vectorized_importance_weights(\n", - " model,\n", - " guide,\n", - " *args,\n", - " num_samples=num_samples,\n", - " max_plate_nesting=max_plate_nesting,\n", - " normalized=False,\n", - " **kwargs\n", - " )\n", - "\n", - " return torch.logsumexp(log_weights, dim=0) - math.log(num_samples), importance_tr, mwc, log_weights\n", - "\n", - " return _wrapped_model" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "query = SearchForExplanation(\n", - " supports=forest_fire_supports,\n", - " antecedents={\"match_dropped\": torch.tensor(1.0), \"lightning\": torch.tensor(1.0)},\n", - " consequents={\"forest_fire\": torch.tensor(1.0)},\n", - " witnesses={}, # potential context elements, we leave them empty for now\n", - " alternatives={\"match_dropped\": torch.tensor(0.0), \"lightning\": torch.tensor(0.0)},\n", - " consequent_scale=1e-5,\n", - " antecedent_bias=0.5\n", - ")(forest_fire_model)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "match_dropped tensor([[0., 0., 0.],\n", - " [1., 1., 0.],\n", - " [1., 1., 0.],\n", - " [1., 1., 0.],\n", - " [0., 0., 0.],\n", - " [1., 1., 0.],\n", - " [0., 0., 0.],\n", - " [0., 0., 0.],\n", - " [1., 1., 0.],\n", - " [0., 0., 0.]])\n", - "match_dropped tensor([[0., 0., 0.],\n", - " [1., 1., 0.],\n", - " [1., 1., 0.],\n", - " [1., 1., 0.],\n", - " [0., 0., 0.],\n", - " [1., 1., 0.],\n", - " [0., 0., 0.],\n", - " [0., 0., 0.],\n", - " [1., 1., 0.],\n", - " [0., 0., 0.]])\n" - ] - } - ], - "source": [ - "logp, trace, mwc, log_weights = importance_infer(num_samples=10)(query)()" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "IndexSet({})\n" - ] - } - ], - "source": [ - "with mwc:\n", - " print(indices_of(trace.nodes[\"match_dropped\"][\"value\"]))" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pyro\n", - "import pyro.distributions as dist\n", - "import pyro.distributions.constraints as constraints\n", - "import pytest\n", - "import torch\n", - "\n", - "from chirho.counterfactual.handlers.counterfactual import MultiWorldCounterfactual\n", - "from chirho.counterfactual.ops import split\n", - "from chirho.explainable.handlers import random_intervention, sufficiency_intervention\n", - "from chirho.explainable.handlers.components import ( # consequent_eq_neq,\n", - " ExtractSupports,\n", - " consequent_eq,\n", - " consequent_eq_neq,\n", - " consequent_neq,\n", - " undo_split,\n", - ")\n", - "from chirho.explainable.internals import uniform_proposal\n", - "from chirho.explainable.ops import preempt\n", - "from chirho.indexed.ops import IndexSet, gather, indices_of\n", - "from chirho.interventional.handlers import do\n", - "from chirho.interventional.ops import intervene\n", - "from chirho.observational.handlers.condition import Factors" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[[[[[[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]]]],\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[2., 2., 2., 2., 2., 2., 2., 2., 2., 2.]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[6., 6., 6., 6., 6., 6., 6., 6., 6., 6.]]]]],\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[6., 6., 6., 6., 6., 6., 6., 6., 6., 6.]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[2., 2., 2., 2., 2., 2., 2., 2., 2., 2.]]]]],\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[2., 2., 2., 2., 2., 2., 2., 2., 2., 2.]]]]]]])\n", - "IndexSet({'split1': {0, 1}, 'split2': {0, 1, 2}})\n", - "IndexSet({'split1': {0, 1}, 'split2': {0, 1, 2}})\n", - "tensor([[[[[[[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]]]],\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[2., 2., 2., 2., 2., 2., 2., 2., 2., 2.]]]]]]])\n", - "tensor([[[[[[[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]]]],\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[2., 2., 2., 2., 2., 2., 2., 2., 2., 2.]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]]]],\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[2., 2., 2., 2., 2., 2., 2., 2., 2., 2.]]]]]],\n", - "\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]]]],\n", - "\n", - "\n", - "\n", - "\n", - " [[[[[2., 2., 2., 2., 2., 2., 2., 2., 2., 2.]]]]]]])\n" - ] - } - ], - "source": [ - "import pyro.distributions.constraints as constraints\n", - "import torch\n", - "from chirho.counterfactual.handlers.counterfactual import MultiWorldCounterfactual\n", - "from chirho.counterfactual.ops import split\n", - "from chirho.explainable.handlers.components import undo_split\n", - "from chirho.indexed.ops import IndexSet, gather, indices_of\n", - "\n", - "with MultiWorldCounterfactual():\n", - " x_obs = torch.ones(10)\n", - " x_cf_1 = 2 * x_obs\n", - " x_cf_2 = 3 * x_cf_1\n", - " x_split = split(x_obs, (x_cf_1,), name=\"split1\", event_dim=1)\n", - " x_split = split(x_split, (x_cf_2, x_cf_1), name=\"split2\", event_dim=1)\n", - "\n", - " print(x_split)\n", - "\n", - " undo_split2 = undo_split(\n", - " support=constraints.independent(constraints.real, 1), antecedents=[\"split2\"]\n", - " )\n", - " x_undone = undo_split2(x_split)\n", - "\n", - " print(indices_of(x_split, event_dim=1))\n", - " print(indices_of(x_undone, event_dim=1))\n", - "\n", - " print(gather(x_split, IndexSet(split2={0}), event_dim=1))\n", - " print(x_undone)\n", - "\n", - " assert indices_of(x_split, event_dim=1) == indices_of(x_undone, event_dim=1)\n", - " assert torch.all(gather(x_split, IndexSet(split2={0}), event_dim=1) == x_undone)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'split1': {0}}\n", - "{'split1': {0}}\n", - "{'split1': {1}}\n", - "{'split1': {1}}\n", - "{'split1': {2}}\n", - "{'split1': {2}}\n", - "{'split1': {0}, 'split2': {1}}\n", - "{'split1': {0}, 'split2': {1}}\n", - "{'split1': {0}, 'split2': {2}}\n", - "{'split1': {0}, 'split2': {2}}\n", - "{'split1': {1}, 'split2': {1}}\n", - "{'split1': {1}, 'split2': {1}}\n", - "{'split1': {1}, 'split2': {2}}\n", - "{'split1': {1}, 'split2': {2}}\n", - "{'split1': {2}, 'split2': {1}}\n", - "{'split1': {2}, 'split2': {1}}\n", - "{'split1': {2}, 'split2': {2}}\n", - "{'split1': {2}, 'split2': {2}}\n", - "[{'split1': {0}, 'split2': {1}, 'split3': {2}}, {'split1': {0}, 'split2': {1}, 'split3': {3}}, {'split1': {0}, 'split2': {2}, 'split3': {2}}, {'split1': {0}, 'split2': {2}, 'split3': {3}}, {'split1': {1}, 'split2': {1}, 'split3': {2}}, {'split1': {1}, 'split2': {1}, 'split3': {3}}, {'split1': {1}, 'split2': {2}, 'split3': {2}}, {'split1': {1}, 'split2': {2}, 'split3': {3}}, {'split1': {2}, 'split2': {1}, 'split3': {2}}, {'split1': {2}, 'split2': {1}, 'split3': {3}}, {'split1': {2}, 'split2': {2}, 'split3': {2}}, {'split1': {2}, 'split2': {2}, 'split3': {3}}]\n" - ] - } - ], - "source": [ - "index_keys = []\n", - "antecedents = {\"split1\": {0, 1, 2}, \"split2\": {1, 2}, \"split3\": {2, 3}}\n", - "for a, v in antecedents.items():\n", - " if index_keys == []:\n", - " for value in v:\n", - " index_keys.append({a: {value}})\n", - " else:\n", - " temp_index_keys = []\n", - " for i in index_keys:\n", - " for value in v:\n", - " print(i)\n", - " t = dict(i)\n", - " t[a] = {value}\n", - " temp_index_keys.append(t)\n", - " index_keys = temp_index_keys\n", - "\n", - "print(index_keys)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "invalid syntax (448003560.py, line 4)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m Cell \u001b[0;32mIn[28], line 4\u001b[0;36m\u001b[0m\n\u001b[0;31m if a, v in indices_of(value, event_dim=0)\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" - ] - } - ], - "source": [ - "\n", - "antecedents_ = {\n", - " a\n", - " for a in antecedents\n", - " if a in indices_of(value, event_dim=0)\n", - "}\n", - "\n", - "factual_value = gather(\n", - " value,\n", - " IndexSet(**{antecedent: {0} for antecedent in antecedents_}),\n", - " event_dim=support.event_dim,\n", - ")\n", - "\n", - "# TODO exponential in len(antecedents) - add an indexed.ops.expand to do this cheaply\n", - "\n", - "\n", - "\n", - "scatter_n(\n", - " {\n", - " IndexSet(\n", - " **{antecedent: {ind} for antecedent, ind in zip(antecedents_, inds)}\n", - " ): factual_value\n", - " for inds in itertools.product(*[[0, 1]] * len(antecedents_))\n", - " },\n", - " event_dim=support.event_dim,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "with MultiWorldCounterfactual():\n", - " for a in indices_of(value, event_dim=0):\n", - " print(a)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "import pyro\n", - "import pyro.distributions as dist\n", - "import pyro.distributions.constraints as constraints\n", - "import pytest\n", - "import torch\n", - "\n", - "from chirho.counterfactual.handlers.counterfactual import MultiWorldCounterfactual\n", - "from chirho.counterfactual.ops import split\n", - "from chirho.explainable.handlers import random_intervention, sufficiency_intervention\n", - "from chirho.explainable.handlers.components import ( # consequent_eq_neq,\n", - " ExtractSupports,\n", - " consequent_eq,\n", - " consequent_eq_neq,\n", - " consequent_neq,\n", - " undo_split,\n", - ")\n", - "from chirho.explainable.internals import uniform_proposal\n", - "from chirho.explainable.ops import preempt\n", - "from chirho.indexed.ops import IndexSet, gather, indices_of\n", - "from chirho.interventional.handlers import do\n", - "from chirho.interventional.ops import intervene\n", - "from chirho.observational.handlers.condition import Factors\n", - "\n", - "SUPPORT_CASES = [\n", - " pyro.distributions.constraints.real,\n", - " pyro.distributions.constraints.boolean,\n", - " pyro.distributions.constraints.positive,\n", - " pyro.distributions.constraints.interval(0, 10),\n", - " pyro.distributions.constraints.interval(-5, 5),\n", - " pyro.distributions.constraints.integer_interval(0, 2),\n", - " pyro.distributions.constraints.integer_interval(0, 100),\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "import pyro\n", - "import pyro.distributions as dist\n", - "\n", - "def model():\n", - " x = pyro.sample(\"x\", dist.Delta(torch.tensor(1.0)))\n", - "\n", - " x_split = pyro.deterministic(\n", - " \"x_split\",\n", - " split(x, (torch.tensor(0.5),), name=\"x_split\", event_dim=0),\n", - " event_dim=0,\n", - " )\n", - "\n", - " x_undone = pyro.deterministic(\n", - " \"x_undone\",\n", - " undo_split(support=constraints.real, antecedents=[\"x_split\"])(x_split),\n", - " event_dim=0,\n", - " )\n", - "\n", - " x_case = torch.tensor(1)\n", - " x_preempted = pyro.deterministic(\n", - " \"x_preempted\",\n", - " preempt(\n", - " x_undone, (torch.tensor(5.0),), x_case, name=\"x_preempted\", event_dim=0\n", - " ),\n", - " event_dim=0,\n", - " )\n", - "\n", - " x_undone_2 = pyro.deterministic(\n", - " \"x_undone_2\",\n", - " undo_split(support=constraints.real, antecedents=[\"x\"])(x_preempted),\n", - " event_dim=0,\n", - " )\n", - "\n", - " x_split2 = pyro.deterministic(\n", - " \"x_split2\",\n", - " split(x_undone_2, (torch.tensor(2.0),), name=\"x_split2\", event_dim=0),\n", - " event_dim=0,\n", - " )\n", - "\n", - " x_undone_3 = pyro.deterministic(\n", - " \"x_undone_3\",\n", - " undo_split(support=constraints.real, antecedents=[\"x_split\", \"x_split2\"])(\n", - " x_split2\n", - " ),\n", - " event_dim=0,\n", - " )\n", - "\n", - " return x_undone_3\n", - "\n", - "with MultiWorldCounterfactual() as mwc:\n", - " with pyro.poutine.trace() as tr:\n", - " model()\n", - "\n", - "nd = tr.trace.nodes\n", - "\n", - "with mwc:\n", - " x_split_2 = nd[\"x_split2\"][\"value\"]\n", - " x_00 = gather(\n", - " x_split_2, IndexSet(x_split={0}, x_split2={0}), event_dim=0\n", - " ) # 5.0\n", - " x_10 = gather(\n", - " x_split_2, IndexSet(x_split={1}, x_split2={0}), event_dim=0\n", - " ) # 5.0\n", - " x_01 = gather(\n", - " x_split_2, IndexSet(x_split={0}, x_split2={1}), event_dim=0\n", - " ) # 2.0\n", - " x_11 = gather(\n", - " x_split_2, IndexSet(x_split={1}, x_split2={1}), event_dim=0\n", - " ) # 2.0\n", - "\n", - " assert (\n", - " nd[\"x_split\"][\"value\"][0].item() == 1.0\n", - " and nd[\"x_split\"][\"value\"][1].item() == 0.5\n", - " )\n", - "\n", - " assert (\n", - " nd[\"x_undone\"][\"value\"][0].item() == 1.0\n", - " and nd[\"x_undone\"][\"value\"][1].item() == 1.0\n", - " )\n", - "\n", - " assert (\n", - " nd[\"x_preempted\"][\"value\"][0].item() == 5.0\n", - " and nd[\"x_preempted\"][\"value\"][1].item() == 5.0\n", - " )\n", - "\n", - " assert (\n", - " nd[\"x_undone_2\"][\"value\"][0].item() == 5.0\n", - " and nd[\"x_undone_2\"][\"value\"][1].item() == 5.0\n", - " )\n", - "\n", - " assert torch.all(nd[\"x_undone_3\"][\"value\"] == 5.0)\n", - "\n", - " assert (x_00, x_10, x_01, x_11) == (5.0, 5.0, 2.0, 2.0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 7e1344c492ebcdfed0aef630f9f121e4478293c3 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Mon, 19 Aug 2024 13:22:17 -0400 Subject: [PATCH 051/111] lint --- chirho/explainable/handlers/components.py | 19 ++++++++++------- tests/explainable/test_handlers_components.py | 21 ++++++++++++------- 2 files changed, 25 insertions(+), 15 deletions(-) diff --git a/chirho/explainable/handlers/components.py b/chirho/explainable/handlers/components.py index 589bc1cc..cd23fe74 100644 --- a/chirho/explainable/handlers/components.py +++ b/chirho/explainable/handlers/components.py @@ -126,22 +126,25 @@ def _undo_split(value: T) -> T: # TODO exponential in len(antecedents) - add an indexed.ops.expand to do this cheaply - index_keys = [] + index_keys: list[dict[str, set[int]]] = list() for a, v in antecedents_.items(): if index_keys == []: - for value in v: - index_keys.append({a: {value}}) + index_keys = [dict({a: {value}}.items()) for value in v] else: temp_index_keys = [] for i in index_keys: - for value in v: - t = dict(i) - t[a] = {value} - temp_index_keys.append(t) + # for value in v: + # t = dict(i) + # t[a] = {value} + # temp_index_keys.append(set(t)) + temp_index_keys.extend([dict(tuple(dict(i).items()) + tuple({a: {value}}.items())) for value in v]) index_keys = temp_index_keys index_keys = index_keys if index_keys != [] else [{}] - return scatter_n({IndexSet(**ind_key): factual_value for ind_key in index_keys}, event_dim=support.event_dim) + return scatter_n( + {IndexSet(**ind_key): factual_value for ind_key in index_keys}, + event_dim=support.event_dim, + ) return _undo_split diff --git a/tests/explainable/test_handlers_components.py b/tests/explainable/test_handlers_components.py index 08ccf8c3..d3839d9a 100644 --- a/tests/explainable/test_handlers_components.py +++ b/tests/explainable/test_handlers_components.py @@ -89,8 +89,10 @@ def test_undo_split(num_splits): x_obs = torch.zeros(10) x_cf_1 = torch.ones(10) x_cf_2 = 2 * x_cf_1 - x_split = split(x_obs, (x_cf_1,)*num_splits, name="split1", event_dim=1) - x_split = split(x_split, (x_cf_2,)*(num_splits+1), name="split2", event_dim=1) + x_split = split(x_obs, (x_cf_1,) * num_splits, name="split1", event_dim=1) + x_split = split( + x_split, (x_cf_2,) * (num_splits + 1), name="split2", event_dim=1 + ) undo_split2 = undo_split( support=constraints.independent(constraints.real, 1), antecedents=["split2"] @@ -111,12 +113,15 @@ def test_undo_split_multi_dim(): x_split = split(x_split, (x_cf_2, x_cf_1), name="split3", event_dim=1) undo_split23 = undo_split( - support=constraints.independent(constraints.real, 1), antecedents=["split2", "split3"] + support=constraints.independent(constraints.real, 1), + antecedents=["split2", "split3"], ) x_undone = undo_split23(x_split) assert indices_of(x_split, event_dim=1) == indices_of(x_undone, event_dim=1) - assert torch.all(gather(x_split, IndexSet(split2={0}, split3={0}), event_dim=1) == x_undone) + assert torch.all( + gather(x_split, IndexSet(split2={0}, split3={0}), event_dim=1) == x_undone + ) @pytest.mark.parametrize("plate_size", [4, 50, 200]) @@ -134,7 +139,9 @@ def model(): w = pyro.sample( "w", dist.Normal(0, 1).expand(event_shape).to_event(len(event_shape)) ) - w = split(w, (replace1,)*num_splits, name="split1", event_dim=len(event_shape)) + w = split( + w, (replace1,) * num_splits, name="split1", event_dim=len(event_shape) + ) w = pyro.deterministic( "w_preempted", @@ -166,11 +173,11 @@ def model(): with mwc: assert indices_of( nd["w_undone"]["value"], event_dim=len(event_shape) - ) == IndexSet(split1=set(range(num_splits+1))) + ) == IndexSet(split1=set(range(num_splits + 1))) w_undone_shape = list(nd["w_undone"]["value"].shape) desired_shape = list( - (num_splits+1,) + (num_splits + 1,) + (1,) * (len(w_undone_shape) - len(event_shape) - 2) + (plate_size,) + event_shape From 05dcf16107a8a88e339dbe5c7cc4b4cd95c09f31 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Mon, 19 Aug 2024 13:29:10 -0400 Subject: [PATCH 052/111] lint and clean up --- chirho/explainable/handlers/components.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/chirho/explainable/handlers/components.py b/chirho/explainable/handlers/components.py index cd23fe74..93f83141 100644 --- a/chirho/explainable/handlers/components.py +++ b/chirho/explainable/handlers/components.py @@ -125,7 +125,6 @@ def _undo_split(value: T) -> T: ) # TODO exponential in len(antecedents) - add an indexed.ops.expand to do this cheaply - index_keys: list[dict[str, set[int]]] = list() for a, v in antecedents_.items(): if index_keys == []: @@ -133,11 +132,12 @@ def _undo_split(value: T) -> T: else: temp_index_keys = [] for i in index_keys: - # for value in v: - # t = dict(i) - # t[a] = {value} - # temp_index_keys.append(set(t)) - temp_index_keys.extend([dict(tuple(dict(i).items()) + tuple({a: {value}}.items())) for value in v]) + temp_index_keys.extend( + [ + dict(tuple(dict(i).items()) + tuple({a: {value}}.items())) + for value in v + ] + ) index_keys = temp_index_keys index_keys = index_keys if index_keys != [] else [{}] From c0f05e062e93d8261ffac44bd8d1122be0bcb488 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Mon, 19 Aug 2024 13:38:16 -0400 Subject: [PATCH 053/111] lint typing error --- chirho/explainable/handlers/components.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/chirho/explainable/handlers/components.py b/chirho/explainable/handlers/components.py index 93f83141..22042dbc 100644 --- a/chirho/explainable/handlers/components.py +++ b/chirho/explainable/handlers/components.py @@ -125,7 +125,7 @@ def _undo_split(value: T) -> T: ) # TODO exponential in len(antecedents) - add an indexed.ops.expand to do this cheaply - index_keys: list[dict[str, set[int]]] = list() + index_keys: Iterable[MutableMapping[str, Iterable[int]]] = list() for a, v in antecedents_.items(): if index_keys == []: index_keys = [dict({a: {value}}.items()) for value in v] From 9ad8017a72f52ef42e3aa8ad3a215fe6e4227bd8 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Tue, 20 Aug 2024 10:53:40 -0400 Subject: [PATCH 054/111] attempt to introduce inference --- docs/source/counterfactual_sir.png | Bin 0 -> 126305 bytes docs/source/counterfactual_sir_search.png | Bin 0 -> 42433 bytes docs/source/explainable_categorical.ipynb | 98 +- docs/source/explainable_sir.ipynb | 1525 +++++++++++++++++++++ 4 files changed, 1574 insertions(+), 49 deletions(-) create mode 100644 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a/docs/source/explainable_categorical.ipynb b/docs/source/explainable_categorical.ipynb index b8cc7b4d..05dd29d2 100644 --- a/docs/source/explainable_categorical.ipynb +++ b/docs/source/explainable_categorical.ipynb @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -106,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -221,10 +221,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 23, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -295,14 +295,14 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2987)\n" + "tensor(0.2967)\n" ] } ], @@ -329,14 +329,14 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.6000)\n" + "tensor(0.5978)\n" ] } ], @@ -363,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -397,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -422,14 +422,14 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(2.8055e-06)\n" + "tensor(2.7926e-06)\n" ] } ], @@ -453,14 +453,14 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(2.7924e-06)\n" + "tensor(2.8803e-06)\n" ] } ], @@ -478,14 +478,14 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.0670)\n" + "tensor(0.0692)\n" ] } ], @@ -516,14 +516,14 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2772)\n" + "tensor(0.2774)\n" ] } ], @@ -548,7 +548,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -593,7 +593,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -658,7 +658,7 @@ "sally_hits\n", "\n", "\n", - "\n", + "\n", "prob_sally_hits->sally_hits\n", "\n", "\n", @@ -676,7 +676,7 @@ "bill_hits\n", "\n", "\n", - "\n", + "\n", "prob_bill_hits->bill_hits\n", "\n", "\n", @@ -694,7 +694,7 @@ "bottle_shatters\n", "\n", "\n", - "\n", + "\n", "prob_bottle_shatters_if_sally->bottle_shatters\n", "\n", "\n", @@ -706,19 +706,19 @@ "prob_bottle_shatters_if_bill\n", "\n", "\n", - "\n", + "\n", "prob_bottle_shatters_if_bill->bottle_shatters\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "sally_throws->sally_hits\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "bill_throws->bill_hits\n", "\n", "\n", @@ -730,13 +730,13 @@ "\n", "\n", "\n", - "\n", + "\n", "sally_hits->bottle_shatters\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "bill_hits->bottle_shatters\n", "\n", "\n", @@ -745,10 +745,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 33, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -826,7 +826,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -862,7 +862,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -897,14 +897,14 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2513)\n" + "tensor(0.2504)\n" ] } ], @@ -933,14 +933,14 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.5019)\n" + "tensor(0.5005)\n" ] } ], @@ -960,7 +960,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -996,7 +996,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1025,14 +1025,14 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.1543)\n" + "tensor(0.1549)\n" ] } ], @@ -1076,14 +1076,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2195)\n" + "tensor(0.2179)\n" ] } ], @@ -1101,14 +1101,14 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.0667)\n" + "tensor(0.0674)\n" ] } ], @@ -1126,14 +1126,14 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2777)\n" + "tensor(0.2765)\n" ] } ], @@ -1144,14 +1144,14 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2014)\n" + "tensor(0.2008)\n" ] } ], diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb new file mode 100644 index 00000000..d3b73971 --- /dev/null +++ b/docs/source/explainable_sir.ipynb @@ -0,0 +1,1525 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "import numbers\n", + "import os\n", + "from typing import Tuple, TypeVar, Union, Optional, Callable\n", + "import math\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import pyro.distributions as dist\n", + "import seaborn as sns\n", + "import torch\n", + "from pyro.infer import Predictive\n", + "\n", + "import pyro\n", + "from chirho.counterfactual.handlers.counterfactual import \\\n", + " MultiWorldCounterfactual\n", + "from chirho.dynamical.handlers.interruption import StaticEvent\n", + "from chirho.dynamical.handlers.solver import TorchDiffEq\n", + "from chirho.dynamical.handlers.trajectory import LogTrajectory\n", + "from chirho.dynamical.ops import Dynamics, State, on, simulate\n", + "from chirho.explainable.handlers import SearchForExplanation\n", + "from chirho.explainable.handlers.components import ExtractSupports\n", + "from chirho.indexed.ops import IndexSet, gather, indices_of\n", + "from chirho.interventional.ops import Intervention, intervene\n", + "from chirho.observational.handlers import condition\n", + "\n", + "R = Union[numbers.Real, torch.Tensor]\n", + "S = TypeVar(\"S\")\n", + "T = TypeVar(\"T\")\n", + "\n", + "\n", + "sns.set_style(\"white\")\n", + "\n", + "seed = 123\n", + "pyro.clear_param_store()\n", + "pyro.set_rng_seed(seed)\n", + "\n", + "smoke_test = \"CI\" in os.environ\n", + "num_samples = 10 if smoke_test else 300\n", + "exp_plate_size = 10 if smoke_test else 2000" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "class SIRDynamics(pyro.nn.PyroModule):\n", + " def __init__(self, beta, gamma):\n", + " super().__init__()\n", + " self.beta = beta\n", + " self.gamma = gamma\n", + "\n", + " def forward(self, X: State[torch.Tensor]):\n", + " dX: State[torch.Tensor] = dict()\n", + " dX[\"S\"] = -self.beta * X[\"S\"] * X[\"I\"]\n", + " dX[\"I\"] = self.beta * X[\"S\"] * X[\"I\"] - self.gamma * X[\"I\"]\n", + " dX[\"R\"] = self.gamma * X[\"I\"]\n", + "\n", + " return dX\n", + "\n", + "\n", + "# TODO add running overshoot to states?\n", + "\n", + "\n", + "class SIRDynamicsLockdown(SIRDynamics):\n", + " def __init__(self, beta0, gamma):\n", + " super().__init__(beta0, gamma)\n", + " self.beta0 = beta0\n", + "\n", + " def forward(self, X: State[torch.Tensor]):\n", + " self.beta = (1 - X[\"l\"]) * self.beta0\n", + " dX = super().forward(X)\n", + " dX[\"l\"] = torch.zeros_like(X[\"l\"])\n", + " return dX" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.15116800367832184\n" + ] + } + ], + "source": [ + "init_state = dict(S=torch.tensor(99.0), I=torch.tensor(1.0), R=torch.tensor(0.0))\n", + "start_time = torch.tensor(0.0)\n", + "end_time = torch.tensor(12.0)\n", + "step_size = torch.tensor(0.1)\n", + "logging_times = torch.arange(start_time, end_time, step_size)\n", + "init_state_lockdown = dict(**init_state, l=torch.tensor(0.0))\n", + "\n", + "# We now simulate from the SIR model\n", + "beta_true = torch.tensor([0.03])\n", + "gamma_true = torch.tensor([0.5])\n", + "sir_true = SIRDynamics(beta_true, gamma_true)\n", + "with TorchDiffEq(), LogTrajectory(logging_times) as lt:\n", + " simulate(sir_true, init_state, start_time, end_time)\n", + "\n", + "sir_true_traj = lt.trajectory\n", + "\n", + "\n", + "def get_overshoot(trajectory):\n", + " t_max = torch.argmax(trajectory[\"I\"].squeeze())\n", + " S_peak = torch.max(trajectory[\"S\"].squeeze()[t_max]) / 100\n", + " S_final = trajectory[\"S\"].squeeze()[-1] / 100\n", + " return (S_peak - S_final).item()\n", + "\n", + "\n", + "print(get_overshoot(sir_true_traj))" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "def bayesian_sir(base_model=SIRDynamics) -> Dynamics[torch.Tensor]:\n", + " beta = pyro.sample(\"beta\", dist.Beta(18, 600))\n", + " gamma = pyro.sample(\"gamma\", dist.Beta(1600, 1600))\n", + " sir = base_model(beta, gamma)\n", + " return sir\n", + "\n", + "\n", + "def simulated_bayesian_sir(\n", + " init_state, start_time, logging_times, base_model=SIRDynamics\n", + ") -> State[torch.Tensor]:\n", + " sir = bayesian_sir(base_model)\n", + "\n", + " with TorchDiffEq(), LogTrajectory(logging_times, is_traced=True) as lt:\n", + " simulate(sir, init_state, start_time, logging_times[-1])\n", + " return lt.trajectory" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "def MaskedStaticIntervention(time: R, intervention: Intervention[State[T]]):\n", + "\n", + " @on(StaticEvent(time))\n", + " def callback(\n", + " dynamics: Dynamics[T], state: State[T]\n", + " ) -> Tuple[Dynamics[T], State[T]]:\n", + "\n", + " with pyro.poutine.block():\n", + " return dynamics, intervene(state, intervention)\n", + "\n", + " return callback" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "overshoot_threshold = 20\n", + "lockdown_time = torch.tensor(1.0)\n", + "mask_time = torch.tensor(1.5)\n", + "\n", + "\n", + "def policy_model():\n", + "\n", + " lockdown = pyro.sample(\"lockdown\", dist.Bernoulli(torch.tensor(0.5)))\n", + " mask = pyro.sample(\"mask\", dist.Bernoulli(torch.tensor(0.5)))\n", + "\n", + " lockdown_efficiency = pyro.deterministic(\n", + " \"lockdown_efficiency\", torch.tensor(0.6) * lockdown, event_dim=0\n", + " )\n", + "\n", + " mask_efficiency = pyro.deterministic(\n", + " \"mask_efficiency\", (0.1 * lockdown + 0.45 * (1 - lockdown)) * mask, event_dim=0\n", + " )\n", + "\n", + " joint_efficiency = pyro.deterministic(\n", + " \"joint_efficiency\",\n", + " torch.clamp(lockdown_efficiency + mask_efficiency, 0, 0.95),\n", + " event_dim=0,\n", + " )\n", + "\n", + " lockdown_sir = bayesian_sir(SIRDynamicsLockdown)\n", + " with LogTrajectory(logging_times, is_traced=True) as lt:\n", + " with TorchDiffEq():\n", + " with MaskedStaticIntervention(lockdown_time, dict(l=lockdown_efficiency)):\n", + " with MaskedStaticIntervention(mask_time, dict(l=joint_efficiency)):\n", + " simulate(\n", + " lockdown_sir, init_state_lockdown, start_time, logging_times[-1]\n", + " )\n", + "\n", + " trajectory = lt.trajectory\n", + "\n", + " t_max = torch.max(trajectory[\"I\"], dim=-1).indices\n", + " S_peaks = pyro.ops.indexing.Vindex(trajectory[\"S\"])[..., t_max]\n", + " overshoot = pyro.deterministic(\n", + " \"overshoot\", S_peaks - trajectory[\"S\"][..., -1], event_dim=0\n", + " )\n", + " os_too_high = pyro.deterministic(\n", + " \"os_too_high\",\n", + " (overshoot > overshoot_threshold).clone().detach().float(),\n", + " event_dim=0,\n", + " )\n", + "\n", + " return overshoot, os_too_high\n", + "\n", + "\n", + "with ExtractSupports() as s:\n", + " one_run = policy_model()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "# conditioning (as opposed to intervening) is sufficient for\n", + "# propagating the changes, as the decisions are upstream from ds\n", + "\n", + "# no interventions\n", + "policy_model_none = condition(\n", + " policy_model, {\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(0.0)}\n", + ")\n", + "unintervened_predictive = Predictive(\n", + " policy_model_none, num_samples=num_samples, parallel=True\n", + ")\n", + "unintervened_samples = unintervened_predictive()\n", + "\n", + "# both interventions\n", + "policy_model_all = condition(\n", + " policy_model, {\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(1.0)}\n", + ")\n", + "intervened_predictive = Predictive(\n", + " policy_model_all, num_samples=num_samples, parallel=True\n", + ")\n", + "intervened_samples = intervened_predictive()\n", + "\n", + "policy_model_mask = condition(\n", + " policy_model, {\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(1.0)}\n", + ")\n", + "mask_predictive = Predictive(policy_model_mask, num_samples=num_samples, parallel=True)\n", + "mask_samples = mask_predictive()\n", + "\n", + "policy_model_lockdown = condition(\n", + " policy_model, {\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(0.0)}\n", + ")\n", + "lockdown_predictive = Predictive(\n", + " policy_model_lockdown, num_samples=num_samples, parallel=True\n", + ")\n", + "lockdown_samples = lockdown_predictive()" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "def importance_infer(\n", + " model: Optional[Callable] = None, *, num_samples: int\n", + "):\n", + " \n", + " if model is None:\n", + " return lambda m: importance_infer(m, num_samples=num_samples)\n", + "\n", + " def _wrapped_model(\n", + " *args,\n", + " **kwargs\n", + " ):\n", + "\n", + " guide = pyro.poutine.block(hide_fn=lambda msg: msg[\"is_observed\"])(model)\n", + "\n", + " max_plate_nesting = 9 # TODO guess\n", + "\n", + " with pyro.poutine.block(), MultiWorldCounterfactual() as mwc:\n", + " log_weights, importance_tr, _ = pyro.infer.importance.vectorized_importance_weights(\n", + " model,\n", + " guide,\n", + " *args,\n", + " num_samples=num_samples,\n", + " max_plate_nesting=max_plate_nesting,\n", + " normalized=False,\n", + " **kwargs\n", + " )\n", + "\n", + " return torch.logsumexp(log_weights, dim=0) - math.log(num_samples), importance_tr, mwc, log_weights\n", + "\n", + " return _wrapped_model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def add_pred_to_plot(preds, axs, coords, color, label):\n", + " sns.lineplot(\n", + " x=logging_times,\n", + " y=preds.mean(dim=0).squeeze().tolist(),\n", + " ax=axs[coords],\n", + " label=label,\n", + " color=color,\n", + " )\n", + " axs[coords].fill_between(\n", + " logging_times,\n", + " torch.quantile(preds, 0.025, dim=0).squeeze(),\n", + " torch.quantile(preds, 0.975, dim=0).squeeze(),\n", + " alpha=0.2,\n", + " color=color,\n", + " )\n", + "\n", + "\n", + "fig, axs = plt.subplots(4, 2, figsize=(12, 6))\n", + "\n", + "colors = [\"orange\", \"red\", \"green\"]\n", + "\n", + "add_pred_to_plot(\n", + " unintervened_samples[\"S\"], axs, coords=(0, 0), color=colors[0], label=\"susceptible\"\n", + ")\n", + "add_pred_to_plot(\n", + " unintervened_samples[\"I\"], axs, coords=(0, 0), color=colors[1], label=\"infected\"\n", + ")\n", + "add_pred_to_plot(\n", + " unintervened_samples[\"R\"], axs, coords=(0, 0), color=colors[2], label=\"recovered\"\n", + ")\n", + "\n", + "axs[0, 1].hist(unintervened_samples[\"overshoot\"].squeeze())\n", + "axs[0, 0].set_title(\"No interventions\")\n", + "axs[0, 1].set_title(\n", + " f\"Overshoot mean: {unintervened_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {unintervened_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", + ")\n", + "\n", + "\n", + "add_pred_to_plot(\n", + " intervened_samples[\"S\"], axs, coords=(1, 0), color=colors[0], label=\"susceptible\"\n", + ")\n", + "add_pred_to_plot(\n", + " intervened_samples[\"I\"], axs, coords=(1, 0), color=colors[1], label=\"infected\"\n", + ")\n", + "add_pred_to_plot(\n", + " intervened_samples[\"R\"], axs, coords=(1, 0), color=colors[2], label=\"recovered\"\n", + ")\n", + "axs[1, 0].set_title(\"Both interventions\")\n", + "axs[1, 0].legend_.remove()\n", + "\n", + "\n", + "axs[1, 1].hist(intervened_samples[\"overshoot\"].squeeze())\n", + "axs[1, 1].set_title(\n", + " f\"Overshoot mean: {intervened_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {intervened_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", + ")\n", + "\n", + "\n", + "add_pred_to_plot(\n", + " mask_samples[\"S\"], axs, coords=(2, 0), color=colors[0], label=\"susceptible\"\n", + ")\n", + "add_pred_to_plot(\n", + " mask_samples[\"I\"], axs, coords=(2, 0), color=colors[1], label=\"infected\"\n", + ")\n", + "add_pred_to_plot(\n", + " mask_samples[\"R\"], axs, coords=(2, 0), color=colors[2], label=\"recovered\"\n", + ")\n", + "axs[2, 0].set_title(\"Mask only\")\n", + "axs[2, 0].legend_.remove()\n", + "\n", + "axs[2, 1].hist(mask_samples[\"overshoot\"].squeeze())\n", + "axs[2, 1].set_title(\n", + " f\"Overshoot mean: {mask_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {mask_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", + ")\n", + "\n", + "add_pred_to_plot(\n", + " lockdown_samples[\"S\"], axs, coords=(3, 0), color=colors[0], label=\"susceptible\"\n", + ")\n", + "add_pred_to_plot(\n", + " lockdown_samples[\"I\"], axs, coords=(3, 0), color=colors[1], label=\"infected\"\n", + ")\n", + "add_pred_to_plot(\n", + " lockdown_samples[\"R\"], axs, coords=(3, 0), color=colors[2], label=\"recovered\"\n", + ")\n", + "axs[3, 0].set_title(\"Lockdown only\")\n", + "axs[3, 0].legend_.remove()\n", + "\n", + "axs[3, 1].hist(lockdown_samples[\"overshoot\"].squeeze())\n", + "axs[3, 1].set_title(\n", + " f\"Overshoot mean: {lockdown_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {lockdown_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", + ")\n", + "\n", + "\n", + "fig.tight_layout()\n", + "fig.suptitle(\"Trajectories and overshoot distributions\", fontsize=16, y=1.05)\n", + "sns.despine()\n", + "\n", + "plt.savefig(\"counterfactual_sir.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "with ExtractSupports() as s:\n", + " policy_model()\n", + "\n", + "from pyro.distributions import constraints\n", + "supports = s.supports\n", + "supports[\"os_too_high\"] = constraints.independent(base_constraint=constraints.boolean, reinterpreted_batch_ndims=0)\n", + "\n", + "antecedents = {\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(1.0)}\n", + "alternatives = {\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(0.0)}\n", + "witnesses = {key: s.supports[key] for key in [\"lockdown_efficiency\", \"mask_efficiency\"]}\n", + "consequents = {\"os_too_high\": torch.tensor(1.0)}\n", + "\n", + "with MultiWorldCounterfactual() as mwc:\n", + " with SearchForExplanation(\n", + " supports=supports,\n", + " alternatives=alternatives,\n", + " antecedents=antecedents,\n", + " antecedent_bias=0.0,\n", + " witnesses=witnesses,\n", + " consequents=consequents,\n", + " consequent_scale=1e-8,\n", + " witness_bias=0.2,\n", + " ):\n", + " with pyro.plate(\"sample\", exp_plate_size):\n", + " with pyro.poutine.trace() as tr:\n", + " policy_model_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.0317)\n" + ] + } + ], + "source": [ + "query = SearchForExplanation(\n", + " supports=supports,\n", + " alternatives={\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(0.0)},\n", + " antecedents={\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(1.0)},\n", + " antecedent_bias=0.0,\n", + " witnesses={key: s.supports[key] for key in [\"lockdown_efficiency\", \"mask_efficiency\"]},\n", + " consequents={\"os_too_high\": torch.tensor(1.0)},\n", + " consequent_scale=1e-8,\n", + " witness_bias=0.2,\n", + " )(policy_model_all)\n", + "\n", + "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", + "print(torch.exp(logp))" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(4.6869e-10)\n", + "tensor(2427.)\n", + "tensor([ -inf, -inf, -inf, ..., -inf, -19.8070, -inf])\n", + "tensor([[[1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.]],\n", + "\n", + " [[1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.]],\n", + "\n", + " [[1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.]],\n", + "\n", + " ...,\n", + "\n", + " [[1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.]],\n", + "\n", + " [[0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.]],\n", + "\n", + " [[1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.]]])\n", + "tensor([[[ 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, -inf, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000]],\n", + "\n", + " [[ 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, -inf, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000]],\n", + "\n", + " [[ 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, -inf, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000]],\n", + "\n", + " ...,\n", + "\n", + " [[ 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, -inf, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000]],\n", + "\n", + " [[ 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, 0.0000, -18.4207]],\n", + "\n", + " [[ 0.0000, 0.0000, 0.0000],\n", + " [ 0.0000, -inf, 0.0000],\n", + " [ 0.0000, 0.0000, 0.0000]]])\n" + ] + } + ], + "source": [ + "mask_intervened = (trace.nodes[\"__cause____witness_mask\"][\"value\"] == 1) & (trace.nodes[\"__cause____antecedent_mask\"][\"value\"] == 1) \n", + "# print(((torch.exp(log_weights) * mask_intervened.squeeze()) > 0).float().sum() / mask_intervened.float().sum())\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze()) / mask_intervened.float().sum())\n", + "\n", + "print(mask_intervened.float().sum())\n", + "\n", + "print(log_weights)\n", + "print(trace.nodes[\"os_too_high\"][\"value\"].squeeze())\n", + "print(trace.nodes[\"__cause____consequent_os_too_high\"][\"log_prob\"].squeeze())" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "def get_table(\n", + " trace, mwc, antecedents, witnesses, consequents, others=None, world: int = 1\n", + "):\n", + "\n", + " values_table = {}\n", + " nodes = trace.trace.nodes\n", + " witnesses = [key for key, _ in witnesses.items()]\n", + "\n", + " with mwc:\n", + "\n", + " for antecedent_str in antecedents.keys():\n", + "\n", + " obs_indices = IndexSet(\n", + " **{\n", + " name: {0}\n", + " for name, ind in indices_of(nodes[antecedent_str][\"value\"]).items()\n", + " }\n", + " )\n", + " obs_ant = gather(\n", + " nodes[antecedent_str][\"value\"],\n", + " obs_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " int_indices = IndexSet(\n", + " **{\n", + " name: {world}\n", + " for name, ind in indices_of(nodes[antecedent_str][\"value\"]).items()\n", + " }\n", + " )\n", + " int_ant = gather(\n", + " nodes[antecedent_str][\"value\"],\n", + " int_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " values_table[f\"{antecedent_str}_obs\"] = obs_ant.squeeze().tolist()\n", + " values_table[f\"{antecedent_str}_int\"] = int_ant.squeeze().tolist()\n", + "\n", + " apr_ant = nodes[f\"__cause____antecedent_{antecedent_str}\"][\"value\"]\n", + " values_table[f\"apr_{antecedent_str}\"] = apr_ant.squeeze().tolist()\n", + "\n", + " if witnesses:\n", + " for candidate in witnesses:\n", + " obs_indices = IndexSet(\n", + " **{\n", + " name: {world}\n", + " for name, ind in indices_of(nodes[candidate][\"value\"]).items()\n", + " }\n", + " )\n", + " obs_candidate = gather(\n", + " nodes[candidate][\"value\"],\n", + " obs_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " int_indices = IndexSet(\n", + " **{\n", + " name: {world}\n", + " for name, ind in indices_of(nodes[candidate][\"value\"]).items()\n", + " }\n", + " )\n", + " int_candidate = gather(\n", + " nodes[candidate][\"value\"],\n", + " int_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " values_table[f\"{candidate}_obs\"] = obs_candidate.squeeze().tolist()\n", + " values_table[f\"{candidate}_int\"] = int_candidate.squeeze().tolist()\n", + "\n", + " wpr_con = nodes[f\"__cause____witness_{candidate}\"][\"value\"]\n", + " values_table[f\"wpr_{candidate}\"] = wpr_con.squeeze().tolist()\n", + "\n", + " if others:\n", + " for other in others:\n", + " obs_indices = IndexSet(\n", + " **{\n", + " name: {0}\n", + " for name, ind in indices_of(nodes[other][\"value\"]).items()\n", + " }\n", + " )\n", + "\n", + " obs_other = gather(\n", + " nodes[other][\"value\"],\n", + " obs_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " int_indices = IndexSet(\n", + " **{\n", + " name: {world}\n", + " for name, ind in indices_of(nodes[other][\"value\"]).items()\n", + " }\n", + " )\n", + "\n", + " int_other = gather(\n", + " nodes[other][\"value\"],\n", + " int_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " values_table[f\"{other}_obs\"] = obs_other.squeeze().tolist()\n", + " values_table[f\"{other}_int\"] = int_other.squeeze().tolist()\n", + "\n", + " for consequent in consequents.keys():\n", + "\n", + " obs_indices = IndexSet(\n", + " **{\n", + " name: {0}\n", + " for name, ind in indices_of(nodes[consequent][\"value\"]).items()\n", + " }\n", + " )\n", + " obs_consequent = gather(\n", + " nodes[consequent][\"value\"],\n", + " obs_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " int_indices = IndexSet(\n", + " **{\n", + " name: {world}\n", + " for name, ind in indices_of(nodes[consequent][\"value\"]).items()\n", + " }\n", + " )\n", + " int_consequent = gather(\n", + " nodes[consequent][\"value\"],\n", + " int_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " values_table[f\"{consequent}_obs\"] = obs_consequent.squeeze().tolist()\n", + " values_table[f\"{consequent}_int\"] = int_consequent.squeeze().tolist()\n", + "\n", + " values_df = pd.DataFrame(values_table)\n", + "\n", + " return values_df\n", + "\n", + "\n", + "table = get_table(\n", + " tr,\n", + " mwc,\n", + " antecedents,\n", + " witnesses,\n", + " consequents,\n", + " others=[\"joint_efficiency\", \"overshoot\"],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      153 rows × 18 columns

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      " + ], + "text/plain": [ + " lockdown_obs lockdown_int apr_lockdown mask_obs mask_int apr_mask \\\n", + "7 1.0 0.0 0 1.0 1.0 1 \n", + "12 1.0 0.0 0 1.0 1.0 1 \n", + "17 1.0 0.0 0 1.0 1.0 1 \n", + "44 1.0 0.0 0 1.0 1.0 1 \n", + "68 1.0 0.0 0 1.0 1.0 1 \n", + "... ... ... ... ... ... ... \n", + "1932 1.0 0.0 0 1.0 1.0 1 \n", + "1940 1.0 0.0 0 1.0 1.0 1 \n", + "1949 1.0 0.0 0 1.0 1.0 1 \n", + "1984 1.0 0.0 0 1.0 1.0 1 \n", + "1986 1.0 0.0 0 1.0 1.0 1 \n", + "\n", + " lockdown_efficiency_obs lockdown_efficiency_int \\\n", + "7 0.0 0.0 \n", + "12 0.0 0.0 \n", + "17 0.0 0.0 \n", + "44 0.0 0.0 \n", + "68 0.0 0.0 \n", + "... ... ... \n", + "1932 0.0 0.0 \n", + "1940 0.0 0.0 \n", + "1949 0.0 0.0 \n", + "1984 0.0 0.0 \n", + "1986 0.0 0.0 \n", + "\n", + " wpr_lockdown_efficiency mask_efficiency_obs mask_efficiency_int \\\n", + "7 0 0.10 0.10 \n", + "12 0 0.10 0.10 \n", + "17 0 0.45 0.45 \n", + "44 0 0.45 0.45 \n", + "68 0 0.10 0.10 \n", + "... ... ... ... \n", + "1932 0 0.10 0.10 \n", + "1940 0 0.10 0.10 \n", + "1949 0 0.45 0.45 \n", + "1984 0 0.45 0.45 \n", + "1986 0 0.10 0.10 \n", + "\n", + " wpr_mask_efficiency joint_efficiency_obs joint_efficiency_int \\\n", + "7 1 0.7 0.10 \n", + "12 1 0.7 0.10 \n", + "17 0 0.7 0.45 \n", + "44 0 0.7 0.45 \n", + "68 1 0.7 0.10 \n", + "... ... ... ... \n", + "1932 1 0.7 0.10 \n", + "1940 1 0.7 0.10 \n", + "1949 0 0.7 0.45 \n", + "1984 0 0.7 0.45 \n", + "1986 1 0.7 0.10 \n", + "\n", + " overshoot_obs overshoot_int os_too_high_obs os_too_high_int \n", + "7 27.414051 20.081270 1.0 1.0 \n", + "12 28.143715 18.176403 1.0 0.0 \n", + "17 23.878517 29.118385 1.0 1.0 \n", + "44 32.594925 25.202904 1.0 1.0 \n", + "68 30.271997 18.733753 1.0 0.0 \n", + "... ... ... ... ... \n", + "1932 33.913155 16.335825 1.0 0.0 \n", + "1940 18.856632 23.330830 0.0 1.0 \n", + "1949 32.299316 26.959848 1.0 1.0 \n", + "1984 21.073544 29.347124 1.0 1.0 \n", + "1986 32.659851 15.735229 1.0 0.0 \n", + "\n", + "[153 rows x 18 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "factual = table[\n", + " (table[\"lockdown_int\"] == 1)\n", + " & (table[\"mask_int\"] == 1)\n", + " & (table[\"wpr_lockdown_efficiency\"] == 0 & (table[\"wpr_mask_efficiency\"] == 0))\n", + "]\n", + "\n", + "\n", + "counterfactual_lockdown = table[\n", + " (table[\"lockdown_int\"] == 0)\n", + " & (table[\"mask_int\"] == 1)\n", + " & (table[\"wpr_lockdown_efficiency\"] == 0)\n", + "]\n", + "\n", + "display(counterfactual_lockdown)\n", + "\n", + "counterfactual_mask = table[\n", + " (table[\"lockdown_int\"] == 1)\n", + " & (table[\"mask_int\"] == 0)\n", + " & (table[\"wpr_mask_efficiency\"] == 0)\n", + "]\n", + "\n", + "\n", + "fig, axs = plt.subplots(1, 3, figsize=(18, 6))\n", + "\n", + "factual_mean = factual[\"overshoot_int\"].mean().item()\n", + "axs[0].hist(factual[\"overshoot_int\"])\n", + "axs[0].set_title(\n", + " f\"Factual\\n overshoot mean: {factual_mean:.2f}, Pr(too high): {factual['os_too_high_int'].mean().item():.2f}\"\n", + ")\n", + "axs[0].axvline(x=factual_mean, color=\"grey\", linestyle=\"--\")\n", + "\n", + "counterfactual_lockdown_mean = counterfactual_lockdown[\"overshoot_int\"].mean()\n", + "axs[1].hist(counterfactual_lockdown[\"overshoot_int\"])\n", + "axs[1].set_title(\n", + " f\"Counterfactual_lockdown\\n overshoot mean: {counterfactual_lockdown_mean:.2f}, Pr(too high): {counterfactual_lockdown['os_too_high_int'].mean():.2f}\"\n", + ")\n", + "axs[1].axvline(x=counterfactual_lockdown_mean, color=\"grey\", linestyle=\"--\")\n", + "\n", + "counterfactual_mask_mean = counterfactual_mask[\"overshoot_int\"].mean()\n", + "axs[2].hist(counterfactual_mask[\"overshoot_int\"])\n", + "axs[2].set_title(\n", + " f\"Counterfactual_mask\\n overshoot mean: {counterfactual_mask_mean:.2f}, Pr(too high): {counterfactual_mask['os_too_high_int'].mean():.2f}\"\n", + ")\n", + "axs[2].axvline(x=counterfactual_mask_mean, color=\"grey\", linestyle=\"--\")\n", + "\n", + "for i in range(3):\n", + " axs[i].set_xlim(5, 40)\n", + " axs[i].axvline(x=overshoot_threshold, color=\"red\", linestyle=\"-\")\n", + "\n", + "plt.savefig(\"counterfactual_sir_search.png\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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ixYttHqevr6+RP39+w2KxGOvXrzeXXb582fD09DQaNWpktiX08+VJrl+/bp43q1evtlm2YMECw2KxGO+8847x4MEDs/1J79m4XtdHY33082bQoEGGxWIxmjVrZty+fdts37dvn+Hr62t4eXkZV69eNQwj7vevNYYaNWoYoaGhZvudO3eMVq1axYrD+vljsViM//3vf8bdu3fNZdbvg2LFitl8vrZr186wWCxG165dbR7/b7/9ZuTLl88IDAw0IiIijMjISKNYsWKGxWKx+T6xPh6LxWI0b948jmff1po1a8zvgBMnTpjt165dM6pUqWJYLBbjl19+MQwj4e8Xw/jnvV6tWjXj3LlzZvvBgwcNb29vw2KxGH/++Wes5/jx1/nkyZNGgQIFDH9/f2Pbtm1me3R0tDF69GjDYrHYnKuGYRg//fSTGe+NGzeM8PBwo1KlSobFYjG+//57m3Xjer2t74/AwEDj0qVLZntkZKTRsWNHw2KxGOPHj3/mcwwAiI2eUgDwHKy/iFp7/CSUtbfD4MGDzV9lJSllypQaOXKkUqVKpTVr1rzw5eMl6f3331dQUJB5P23atKpZs6YkPbPni/RwKMeZM2dUrlw5tWjRwmYYYvny5dWwYUPdunVLixYtirVtjRo1lDZtWkmSs/PTv3KuXr2qihUrqn379rGumFW4cGHlzZtXkmx+AZ8/f74kaciQITbzyLz11ltq166dLBaLTp069czHmBBXr15VyZIl1bJlSxUuXNhmWe7cuVW0aNFYcT6PtGnTqkuXLubzbb1SoSRzSKNV3rx59fbbb8swDHN43/PEeeXKFbm6utpM6Ozk5KR27dqpf//+T+yBdPr0abVq1Up37tzRiBEjVLVq1QQ/3i5duujtt98273t5eZlDY62vs/RPr5BHezxID4fVREdHx6s3SFys78mePXuqQIECZru7u7s+++wz5cyZU4cOHTJ7E+3Zs0eHDx9W3rx51b17d5vzu3HjxgoMDFTKlCljDSWSHl7d64cffpCfn5+mT5+uFClSmMsiIiK0ZMkSubm5afDgwUqSJIm5rH79+ipXrlys/X3zzTeKiYnRRx99pFKlSpntzs7O6tixowIDA3Xx4kWtWLFCkszz6NHhbHv37lV4eLjeffddSTKH2ErS77//LsMw4jz2i36+LFy4UDdv3tQHH3xgHtuqQYMGKleunEJDQ/Xrr78+c1/xFRERocWLF8vV1VXDhw+3mSvKx8dHjRs3lsVieWr806ZNk/TwtcyWLZvZnjJlSg0dOlRubm6aN29erN5Srq6uGjhwoM1FLxo0aCB3d3ddu3ZN165dk/Swl9S6deuUNm1aDRkyRO7u7ub677zzjqpWrars2bPr9OnTcnV1NT8PHu8tZe09GJ8LSnz33XeSHg5XzJ07t9mePn16devWTbly5TKHZSb0/fKoLl26KGvWrOZ9b29v86qox48ff2ac33zzjSIiIvTJJ58oMDDQbHd2dtb//vc/eXp6ateuXTZDHKtUqaKqVavq+vXrGjlypEaNGqXTp0/rnXfe0fvvv//MY965c0fh4eFKliyZzfyRrq6u6tatmz777LM43x8AgGejKAUAz8HV9eHFS6OjoxO87YULF3Tu3DmlT5/eJqG2SpUqlUqXLi3J9g/D5+Xr6xurzVrAeXzuqrhY/6iwFjEeZ/0jeNu2bbGW5cuXL95xenl5acyYMWrRooXZFh0drdOnT2vFihW6deuWJJnzmly6dEmnT59WxowZ5ePjE2t/TZs21YoVK1765cSzZMmikSNHqlu3bmabYRg6d+6c1qxZYxZ54hq6kxBeXl42f4hKD/84lOJ+Xq1/WD948OC54yxcuLDu37+vOnXqKDg4WPv371dMTIwyZMigJk2aqEiRIrGOe+nSJbVo0UJXr17V+++/r2rVqiX4sTo5Oal69eqx2q3zmj36PqhevbqSJEmiX375xeb8XbZsmZydnVW7du0EHz8qKkq7d++Ws7OzKlasGGu5q6urORTRep5bYypbtmyc88V9++23mj9/vvmaWQUHB2v27NlycXHR+PHjlTJlSpvlBw4c0L1791SgQIFY20qyGVZrtWPHDkmKc54oSeZrYo05KChIyZIlsykYhISESJI+/PBDubq62jzn1uG5j88zJ73454v1+Xy0sPWop32+PC/rc+zt7R1rUnRJ6tGjh5YuXapixYrFuf2VK1d06tQppUqVSt7e3rGWZ8qUSfny5dOdO3d0+PBhm2U5cuQwC/VW7u7uZqHD+pxZH2/x4sXjHCI+atQoLVy40CzWx1WsjYiI0OrVq5UyZcpnDqU1DEM7duyQs7NznMWVChUq6KefflKLFi2e6/3yKD8/v1ht1kJ4XPPiPe5p54yTk5NKliwpKfb354ABA5QxY0YtWrRIc+fOlYeHhwYPHvzM40kPf4DKlSuXLly4oLp162ratGk6duyYJClnzpxq1KiRTXEOABB/rokdAAA4Ig8PDx09ejTOXhDPcvnyZUl66uXJrb+8v4w5KtKkSROrzcXFRVL8LrltnXdm6NChGjp06BPXu3jxYryO/TTR0dH6+eeftWrVKh0/flznz583Jxi3/uFv/P+cJtbn8c0330zQMV6WjRs3aunSpTp27JhCQ0PNYllcBYrnEddzZ93305a9SJxDhgxR+/btdejQIU2YMEETJkxQ2rRpVbp0adWtWzfOwuT27dvl5OQkZ2dnLV++XG3btk3wa+Lh4RGrACf989paX2vp4WOvWLGiVq5cqTVr1qhWrVo6ePCgjh8/rpIlSz7X+XDz5k1FRkYqXbp0sYpEVo+/J63/J/R4y5cvl6urq6KiojRjxgz17dvXZrn1scZVLHk0jri2ebT3ydNiT5IkiYoWLar169fr+PHjyps3r0JCQuTh4SEvLy8VLFhQ+/fvV1hYmJIlS6atW7cqe/bsNr1nrF7W50vHjh2ful5cny/P63lfOytrzHfu3JGnp+cz1320CJM6deo417P+0GF9zhIaY+7cueXv7689e/Zo586dKly4sNavX6+bN2+qQYMGz5z78MaNG4qMjFT69OmfeZXC53m/PCqu58D6+I045op7nPX5f1avyMfnTEuXLp369eunLl26mPM6xlX4fZKxY8fqk08+0bFjx3Ts2DGNHDlSHh4eKl++vN5//32KUgDwnChKAcBz8Pb21u+//659+/Y9c1LlsLAwTZ48WYGBgSpevHi8km5rD6y4/lB/2vpxedEiifWPpKCgIJthXY+LK7l/1pC9R927d0/NmzfX/v37lTRpUhUoUEAlSpRQ3rx5FRAQoMGDB5s9QqTn66WWUHEdIyYmRu3bt9f69evl5uYmLy8v1a5dW3ny5FGhQoX07bffavny5S98bOsfac/reeLMnDmzFi9erB07dmjdunXaunWr/vzzTy1fvlzLly9Xq1at1KtXL5ttnJycNGjQIB06dEjz58/X559/rq+//jpBsT46RC0ujz8X9erV08qVK7V8+XLVqlXL7B0SnyFKcXme9+TjV2OMr/z582vw4MFq0qSJvv32W/OqdVbPer/GdV48K/64Pk/KlSun9evXa+vWrXrzzTd14MABValSRdLD9/qePXu0a9cupUiRQrdv335iAeBFP1+ssZUrV+6JBQ7p4QTtL8uLfnZYt0+bNq3NcMm4eHh42NyP7/P1PDHWrVtXe/bs0fLly1W4cOEEvS8ScrwX/Q57WedMtWrVnvodE1eP0t9//928vXTpUtWsWTPe8Xh6emr16tXasmWL1q9fr5CQEJ0+fVrff/+9fvjhB/Xt29e8yisAIP4oSgHAc6hYsaK+/vprrV+/Xg8ePHjqH9Vr1qzR9OnTtWjRIm3ZssUs7Dx6qfPHWa9alDFjRkn/FHee1PPg9u3bz/U44sP6R1WNGjVUv379V3acmTNnav/+/SpWrJjGjx8f69f0xx+jNa4n9aC4evWq1q5dq3z58sU5xMjqac9tXM/rjz/+qPXr18vT01PTpk2L1aPl8auWJZbnjdPJyUmBgYHm0NJr165p8eLFGjNmjGbNmqWmTZva9PKrVKmS6tevrypVqui3337T+vXrtWrVqgQN47t69apiYmJi/YFpHWL4eG+RokWLKmvWrNq2bZtu3bqlNWvWKHXq1HEObYuPtGnTys3NTbdu3VJYWFicxRHre9I6j5z1fXzp0qU49xkSEqKrV68qMDDQ5rkfMWKE8ubNq/bt22vUqFHq16+fOYeU9E8PKevcPY97tNeYVaZMmXTu3Dn9/fff5nCuR1mfx0fnwLNeRS8kJEQ5c+ZUVFSU+ZoHBQXp66+/1rZt28y4XtV8OZkyZdLp06fVrFmzF7oiZkJYPzue9Nr99ddf2rVrlwoWLBhnTyjr9kmSJNHIkSMTJcYDBw7o5MmTKlSokHl1uKpVq+rLL7/Ub7/9pp49e2rz5s3KlStXnMPlHvfoe+D+/fuxelY9ePBAixYtUq5cuVS4cOEEv19epkyZMunvv/9W586dlSNHjnhvt27dOi1ZskTZsmVTunTpFBISou+++06NGzeO9z5cXV1VpkwZ8/1z/vx5zZkzR7NmzdKYMWPUsGHDeP+YBAB4iDmlAOA5FChQQIGBgbp8+bImT578xPVu3rxpLn///ffl6uqqLFmyKGvWrLpx40acc0bduXNHW7ZskSRzDh/rpLjWSXAfdfz48XjNw/EsT/q12BqDdV6Zx82ZM0c1atTQxIkTX+j4e/bskSQ1adIkVkHq0qVLOnnypKR/ikdZs2bVG2+8oStXrujIkSOx9vfrr79qwIABWrlypaQnP76nPbf79u17Ypx169aNVei5e/euuTw+Q5depYTGeeLECdWoUUOtW7e2WTdDhgxq06aNPD09ZRhGrD+SrX+ApUqVSn369JH0cBjgzZs34x1reHi4du3aFat9zZo1khRr7jUnJyfVqVNHkZGRmjBhgi5evKhq1ao9s8fVk7i5ucnf318xMTFxTqgdFRWl3377TdI/89hYJ2betGlTnPscM2aMunfvHmuIr/X5atmypTmZtnXSbOnhZ0vq1Kl16NChOAtTGzZsiNVmfY/+8ssvccby008/2cQuPewVly9fPm3fvj3WHD2FChWSm5ubtm/frk2bNilVqlSxJst/WZ71+TJ8+HDVrl1bP/zww0s7pre3t9zd3XXw4ME43/eLFy9Wv379zHm2HpctWzZlyZJFly5d0tGjR2MtDw8PV82aNdW4cePnvuCB9fwKCQmJc366mTNnqlevXjaTsadIkUJVqlTRtWvXNHbsWD148CDevQfd3NxUsGBBRUdHa/PmzbGWb9u2TV988YW+/fbb53q/PI/n/U7q1q2b6tatq7Vr15ptN2/e1IABAyRJX3zxhQYPHixXV1eNHDnSvDjE044bEhKid999V/3797dpz5Ili3r37q3UqVPr3r17CfrcAwA8RFEKAJ7TZ599pmTJkmny5Mn66quvzIm4rUJDQ9WuXTudPXtW2bNnV9u2bc1lzZs3lyR9+umn5i/K0sNiQY8ePRQWFqZy5cqZc8S8/fbbcnd3V2hoqNatW2euf/v2bX3++ecv5fFY/6C/d++eTUGlatWq8vDw0K+//qpZs2bZDN3Yv3+/xo8frz///POZc6s8i3X43/r1622Ocf78eXXs2NEcLmWdyFt6WMCSHj6Pj/7xHxoaqokTJ8rZ2dmc6Nz6+B7vIWQd4rFz504dOnTIbL948aJGjBjxxDg3bdpkM4Trxo0b6tKli27cuBErzsSQ0Dhz5sypy5cva/Pmzfr5559t9nXw4EGdPHlSyZMnf+owqmrVqqlkyZK6du3aU+cfi8vAgQNt5p/ZuXOnpkyZIjc3NzVt2jTW+nXq1JGzs7PmzZtn3n8R1vfk8OHDbc6DyMhIff755zp79qzy5cungIAASQ97a+XOnVtHjhxRcHCwzTn73Xffad++fbJYLMqfP3+cx3Nzc9Nnn30mJycnTZ482Sy6urm56YMPPlB0dLR69uxpc77+8ssv5hX0HtWkSRO5uLho2rRpNgUFwzAUHBysHTt26I033ojVk6xs2bK6e/euFi5cqDfeeEM5c+aUJCVNmlS+vr46fPiwjhw5olKlSpk9pl62Bg0aKHny5Jo7d65WrVpls2zdunWaM2eOjh49qoIFC760Y6ZIkULvvfeeIiMj1bdvX5sJ2Q8cOKC5c+cqadKkT5w4XvrnfOnZs6dNUSMiIkKfffaZjh07pnv37sU5B1h85MiRw3wvDRo0yOY9vH79ev3888/KkCGDSpQoYbOddTj5vHnz5OLiYnOVzmexfp4OHTrUpph2/fp1DR8+XJLMKysm9P3yPJ70md20aVO5uLho3LhxsQqH8+fP18qVK3X8+HGbHrJffPGFrly5otq1a6tEiRLKly+fWrVqpXv37ql3794233nWwvGdO3fMNk9PT509e1Y//vhjrAL6hg0bdPv2bWXJkiXWcE0AwLMxfA8AnlPu3Ln1zTffqG3btpo1a5bmz5+vggULKmPGjLp48aL279+v6Oho5cmTR1OmTLEZ4tC0aVPt2bNHP/30k6pWrarAwEAlS5ZMO3fu1I0bN+Tp6akhQ4aY6ydPnlyNGzfWrFmz1KFDB3P9HTt2KE2aNAoMDHzhK/WlT59eqVOn1u3bt9WwYUNlz55dI0eOVLJkyTR+/Hi1adNGX331lebOnStPT0/dvHlTu3fvlmEYat68+XMPnbJq0qSJfvrpJy1atEi7d+9W3rx5df36de3Zs0eGYejtt9/WX3/9patXr5rbtGrVSjt27NCmTZtUsWJFBQYGKiIiQjt37tT9+/fVqVMnc76ebNmyycXFRX/++aeaN28uT09P9e3bV9mzZ1elSpW0Zs0aNWjQwLzi1rZt25QnT55Yl4avV6+evv32W23evFmVKlWSt7e3wsLCtHv3bt2/f1958uTRiRMnbOJMDAmN09XVVYMGDVKnTp3UuXNneXt7K1u2bLpx44Z27dql6Oho9e3b17zK35MMHDhQ1atX17Jly1SzZs1YfzTHJWPGjHrw4IEqV66sokWL6u7du9qxY4diYmI0cOBAWSyWWNtkyZJFxYsX1+bNm5U3b944r8CYEBUqVFCrVq00c+ZM1a9fXwEBAUqXLp327dunixcvKmvWrBozZow5xNDZ2VmjR49WixYtNGHCBK1cuVIWi0Vnz57VkSNHlCJFCo0ZM+apxwwICFD9+vX1ww8/qH///po3b56cnJzUvn177d69W9u3b1eFChVUpEgRXb16Vbt37zYns35UgQIF1KdPHw0ZMkQffvih/Pz8lDlzZh09elSnT59W2rRpNWbMmFjDrMqWLauvv/5at2/fNocjWQUFBWnnzp2SXt3QPenhcMVhw4apa9eu6tq1qyZOnGhe5ezgwYOSpL59+z6xuPe8evbsqYMHD2rDhg0qX768ChcurFu3bmnnzp2Kjo7WsGHDnjrJeLNmzbRv3z6tXr1a1atXV8GCBZU2bVrt379fly9fVoYMGTR69OgXinHIkCFq3LixfvjhB23evFkFCxbU5cuXtWfPHrm6umr06NGxJiUvXLiw+VlZtmzZp84D+Lhq1aopJCRECxcuNL+XXFxctGvXLt25c0d169Y15x1L6PvleViH5m3YsEFt27aVv7+/2rVrpwIFCqhv374aPHiwWrRoIS8vL2XLlk1//fWXjh8/LhcXF40YMcIc/m69eEb69OnVu3dvc/8dO3bUL7/8ol27dmn27Nlq1aqVJJnF2UmTJmnPnj2qVauWKlSooB49emjo0KFq3Lix/Pz8lClTJl26dEl79+6Vi4uLBgwY8NIudAEA/yX0lAKAF+Dr66vVq1frk08+kaenp44dO6Y1a9aYc30MGDBAS5cujfVrubOzs8aMGaOhQ4eqQIEC2r17t7Zs2aLMmTOrR48eWrhwYay5OHr27Km+ffsqd+7c2r17tw4cOKB3331XCxcuNJPvF+Hs7KyRI0cqd+7cOnz4sLZs2WL2/ipUqJCWLVumhg0byjAMbdq0SWfPnlVQUJAmTpwY6wpiz8PX11ffffedSpUqpdu3b2vdunU6c+aMKlSooAULFqhr166SHvYSsHJ1ddXkyZPVv39/5ciRQyEhIdq5c6fy5cunUaNGqUOHDua6GTJk0JAhQ5QtWzbt2rXLZj8jR45Ux44dlSVLFoWEhOj48eNq3Lixvv3221h/9GXLlk0LFy5U5cqVFRUVpXXr1unYsWMKCgrSzJkzzR4Fj+4/MTxPnJUqVdKMGTNUunRpnT9/XmvXrtWJEydUunRpzZ49W40aNXrmcbNnz66PP/5Y0sNLsD/aC+VJkidPru+++06lS5fW9u3btX//fhUuXFizZs166jGtQ5xetJeUVa9evTRp0iQFBQXp6NGj2rBhg1KkSKGPP/5YS5cuVa5cuWzWz5cvn5YuXaqGDRvqwYMHWrdunS5duqTq1atr8eLF8Zqcu3v37sqQIYN27dql+fPnS3rYQ2TGjBnq1q2b0qVLp40bN+rKlSvq3r27OnfuHOd+mjZtqrlz56p8+fI6ffq01q1bp5iYGDVv3lw//vhjnD1WfH19lS5dOkmxh1lZ77u4uKh06dLPfvJeQKVKlbR48WLVrFlTd+7c0YYNG3T16lWVK1dOc+bMMXvlvEwpU6bUvHnz9L///U8ZMmTQhg0bdODAAQUGBmrGjBmqXbv2U7e3FiWHDRumggUL6ujRo9q8ebNSpUqlli1batmyZXr77bdfKEbrhQc+/PBDubm5ad26dTp58qTKlSun+fPnx3k1TOnF3heDBw/WiBEj5O3trV27dmnr1q3KmjWrBgwYoMGDB9usm9D3S0J5e3urW7du8vDw0JYtW7R161ZzWZMmTTRv3jxVrFhRFy9e1Pr163Xv3j1VrVpVixYtMnu5Xbt2TZ999pkkqV+/fub5Lj18n1l7Go8dO9bsrdioUSPz9d+0aZNZHG3RooXGjBmjIkWK6OTJk1q7dq3+/vtvVa1aVQsXLnylxVsA+DdzMuJzCQ0AAIDXTM2aNfXXX39p48aNCbq0O/BvFRERodKlS8vFxUUbNmx4ZcMuAQB4WegpBQAAHMb9+/dlGIZmzZqlY8eOqVq1ahSk8J8WExOjiIgIRUVFaeTIkbpx44YaNmxIQQoA4BDoKQUAABxG6dKldePGDUVERCh58uRasWLFc08mDfwbREREyN/fX05OToqMjNQbb7yhVatWPXP+NwAAXgf0lAIAAA7Dz89PhmHI09NTU6ZMoSCF/zx3d3fly5dPTk5O8vf31/Tp0ylIAQAcBj2lAAAAAAAAYHf0lAIAAAAAAIDdUZQCAAAAAACA3VGUAgAAAAAAgN1RlAIAAAAAAIDdUZQCAAAAAACA3VGUAgAAAAAAgN1RlAIAAAAAAIDdUZQCAAAAAACA3VGUAgAAAAAAgN1RlAIAAAAAAIDdUZQCAAAAAACA3VGUAgAAAAAAgN1RlAIAAAAAAIDdUZQCAAAAAACA3VGUAgAAAAAAgN1RlAIAAAAAAIDdUZQCAAAAAACA3VGUAgAAAAAAgN1RlAIAAAAAAIDdUZQCAAAAAACA3VGUAgAAAAAAgN1RlAIAAAAAAIDdUZQCAAAAAACA3VGUAgAAAAAAgN1RlAIAAAAAAIDdUZQCAAAAAACA3VGUAgAAAAAAgN1RlAIAAAAAAIDdUZQCAAAAAACA3VGUAl5DTZs2VdOmTV/5cc6dOydPT08tWbIkQdtt27ZNnp6e2rZt2yuK7NU7duyYateurQIFCqhq1aqKiopS79695e/vr0KFCumPP/6Ic7vZs2erRIkS8vHx0aRJk+z2WklS+fLl1bt37wRvd+DAATVt2lT+/v4qWbKkRo8erYiIiFcQIQAAiYsc6tX7L+VQVmFhYSpfvnyCX28Az+aa2AEAQGKYOHGizp8/r4kTJyp9+vT6/ffftXTpUrVv317FixeXl5dXrG3CwsI0bNgwlS1bVq1atVK2bNlUqVKlRIg+/kJDQ9WyZUv5+flp7NixOnnypMaMGaObN2/qiy++SOzwAACAg/mv5FBWt27dUvv27fX3338ndijAvxJFKQD/STdu3JDFYlGZMmUkSUuXLpUk1alTR2+99Vac29y6dUsxMTGqUKGCihQpYrdYX8S0adOUIkUKTZo0Se7u7ipTpoySJk2qQYMGqV27dsqSJUtihwgAABzIfyWHkqS1a9dqyJAhunv3bmKHAvxrMXwPcGBbtmzRBx98oICAAAUFBalbt266cOGCzTqnTp1Sx44dFRgYqCJFiqht27Y6efJknPszDEN9+vSRj4+PNm/ebLYvWLBAlStXlo+Pj5o0aaLz58/H2vb06dPq1KmTSpQoIT8/PzVt2lS7du2SJN28eVNeXl6aPXu2uf6FCxfk6empHj16mG0xMTEKCgrSlClTzG7xP/30kzp16iR/f38FBgbq008/1b179576vNy8eVMDBgxQ8eLFVbBgQb3//vsKCQkxl3t6emr79u3asWOHPD09bbp0V6hQIc6u5EuWLFH58uUlSX379pWnp6ck22ECc+bMidWV/48//lC+fPk0ceJEs23nzp1q0qSJfH19FRgYqF69eun69es2xzt69Khatmwpf39/lStXTsuXL48VU+/evc04nmTz5s0qU6aM3N3dzbYqVaooJibG5jUGAOC/hBwqbuRQ/7h9+7Y6duyoIkWKaPr06U9dF8DzoygFOKhly5apVatWevPNNzV69Gj16dNHe/bsUYMGDXTt2jVJ0qVLl9SgQQOdPn1an332mUaMGKGrV6+qefPmunnzZqx9Dh48WCtXrlRwcLBKliwpSZo7d64GDhyoMmXKaNKkSfL19VX//v1ttjtx4oTq1Kmjc+fO6dNPP9XIkSPl5OSk5s2ba/v27UqbNq38/Py0detWcxtrgrNz506zbd++fbp586bKli1rtg0cOFBZs2bVpEmT9OGHH2rRokWaPHnyE5+XBw8eqHnz5lq7dq26dOmi4OBgZc6cWa1btzaP+f3338vLy0teXl76/vvvNWbMGH388ceSpODgYA0cODDWfsuWLavg4GBJ0scff6zvv/8+1jpNmzZVkSJFNGzYMF2/fl1hYWHq27ev/Pz81K5dO0nSjh071KJFCyVNmlRjx45V3759tX37djVr1kz37983X7cmTZrozp07GjFihDp37qyRI0fq0qVLNsdr3759nHFY3b9/X3///bfefvttm/b06dMrZcqU+uuvv564LQAA/1bkUHEjh7KVNGlSrVq1SsOGDVO6dOmeui6A58fwPcABxcTEaOTIkSpZsqRGjRplthcqVEhVq1bVjBkz1LNnT82ePVsRERGaNWuWPDw8JEn58uVTo0aNtG/fPuXOndvcdtSoUfr+++8VHBys0qVLS3r4q9+kSZNUtWpV9e3bV5JUsmRJhYWFacGCBea2wcHBcnd315w5c5QyZUpJDxOQ6tWra/jw4Vq0aJHKli2ryZMnKzIyUm5ubgoJCZG3t7cOHTqkc+fOKVu2bPr999+VNWtWeXp66ty5c5KkMmXKqFevXpKkYsWKacuWLdqwYYO6desW53Pz448/6ujRo/rhhx/k6+srSSpdurSaNm2qkSNHavHixfLz8zPj9PPzkyTzl8/8+fMrW7ZssfabPn165c+fX5KUPXt2c7tHOTk5aejQoapZs6ZGjBghFxcX3bx5U998841cXFzM5/ntt9/WlClTzDZfX19Vq1ZNixcvVuPGjTV79mxFR0dr6tSpSp8+vSTp7bff1vvvv29zvOzZsyt79uxxPg+SdOfOHUkyH+ujUqRIobCwsCduCwDAvxE5FDmUNY6n5VCS5O7urly5cj11HQAvjp5SgAP666+/dOXKFVWvXt2mPXv27PL399f27dslSbt27ZKfn5+ZTElS5syZtX79enMeAEmaN2+epk6dqmrVqtn8wnbq1Cldu3ZN5cqVsznOu+++a3N/+/btKleunE3xw9XVVdWqVdPBgwd19+5dlSlTRvfu3dO+ffskPeyS3bx5cyVLlkw7duyQJG3atMnm+JJiJS6ZM2d+atfzkJAQeXh4yNvbW1FRUYqKilJ0dLTKlSungwcP6tatW0/c9mV466231L17dy1dulQLFy7Up59+as6vEB4ern379qlMmTIyDMOM76233lLu3Lm1ZcsWSf+8btZkSnqYdCV0/qeYmJinLndyckrgowMAwLGRQ5FDAXi90FMKcEDWbuMZM2aMtSxjxow6fPiwuV5cv1g97ujRoypZsqRWrlyp5s2bm1dNsSYfj3dZfjRBs673pFgMw1BYWJg8PT315ptvauvWrUqXLp0uX76s4sWLq1ChQtq+fbvKlCmjQ4cOqXPnzjb7SJYsmc19Z2dnGYbxxMdy8+ZNXblyRd7e3nEuv3LlitKkSfPE7V+GqlWr6quvvpIklShRwmy/ffu2YmJiNG3aNE2bNi3WdkmSJJH08PmM63V7/Hl/FmuCG9fknGFhYUqVKlWC9gcAgKMjhyKHAvB6oSgFOKC0adNKkq5evRpr2ZUrV8wEKFWqVLEmf5Qe/hKWLVs2s6dM586d1axZM1WrVk2ffvqpFi5cKBcXF3M/1vkVrB6fSyFNmjRPjEX6JyErU6aMQkJClCFDBr399tvy8PBQUFCQfvjhB23evFlJkyZVUFBQAp6J2FKlSqWcOXNq5MiRcS6PT4L5ogYPHqwUKVLI3d1dAwYM0JQpUyQ9HDLn5OSkFi1aqFq1arG2syaP6dKli/P5jGsOi6dJkSKF3njjDZ05c8am/dq1a7p7967N0AMAAP4LyKGejBwKQGJg+B7ggKzJyMqVK23aQ0NDtXfvXhUqVEiSVLhwYe3bt88mqbp27Zpat26tjRs3mm0ZM2ZU0qRJNWDAAB06dEizZs2SJOXMmVNvvvmmfv75Z5vjrF+/3uZ+kSJFtH79eps5iqKjo7Vq1SoVLFjQvPJb2bJldeDAAW3atEmBgYGSpKJFi+rcuXNasGCBSpQoYXOVuOcRGBioCxcuKEOGDCpYsKD5b8uWLZo+fbo5B8GrsmbNGq1cuVJ9+vTRgAEDtGHDBi1evFjSw55LXl5eOnXqlE1sefPm1YQJE7Rt2zZJD5+TPXv22EzKeeLECYWGhiY4nhIlSmjDhg2KiIgw23755Re5uLioaNGiL/hoAQBwLORQT0YOBSAxUJQCXlMXL17U7NmzY/3bunWrnJ2d1bVrV23evFndunXTxo0btWzZMrVs2VJp0qRRy5YtJUktWrSQu7u7WrdurV9++UXr1q1Tu3btlDlzZtWoUSPWMcuUKaMqVapowoQJCg0NlZOTk7p3767169fr008/1ebNmxUcHKz58+fbbNexY0c9ePBAzZo1088//6y1a9eqdevWCg0NVdeuXc31ihYtKmdnZ23YsMH8Nc/b21spUqTQrl27Ys2F8Dzq1KmjLFmyqGXLllq6dKn++OMPjR49WuPGjVOmTJnk5ub2wsd4kuvXr+uzzz5TyZIlVatWLVWoUEEVKlTQ0KFDdfHiRUmK9bqtW7fOvKqNtbt88+bNlSZNGn344Yf65ZdftHr1an388cexYj979qz27t371Jhat25tJtHr16/XrFmzNHToUL3//vvMrwAA+Fcih3o+5FAAEgPD94DX1NmzZzV06NBY7fXq1VPx4sVVp04dpUiRQlOmTFGHDh2UMmVKlSpVSl27djXHzb/55pv67rvvNGLECPXu3Vvu7u4KCgrSmDFjlCZNGvPqbI/q27evNm/erP79+2v27NmqXr26nJ2dNWnSJP3444+yWCz64osvbBKlvHnz6rvvvjMvq+zk5CQfHx/NmTNHhQsXNtdLliyZgoKCbH7lc3V1VeHCheOcoPN5JE+eXPPmzdOoUaM0YsQI3blzR1mzZlW3bt3UqlWrF97/03z++ecKDw/X559/brYNGDBAVatWVb9+/TRjxgyVLFlSM2bMUHBwsDp16iQ3Nzd5e3tr1qxZ5oSk6dKl0/z58zVkyBD17t1bKVKkUOvWrbV69Wqb402aNElLly7VsWPHnhhT7ty5NXPmTA0fPlydOnVSunTp1KJFC3Xq1OmVPAcAACQ2cqjnQw4FIDE4GU+b7Q4AAAAAAAB4BRi+BwAAAAAAALujKAUAAAAAAAC7oygFAAAAAAAAu6MoBQAAAAAAALujKAW8BrjeAOLCeQEAwJPxPYm4cF4AjoWiFOxm165d+uSTT1SiRAkVLFhQ77zzjj799FOdPHkysUOzMWHCBHl6etrteLt27VKbNm3sdrzXwaFDh/TRRx+paNGiCgoKUqtWrXTo0CGbdQzD0IwZM1SpUiUVLFhQlStX1rx5856577///ludO3dWsWLFFBQUpPbt2+vs2bM264SFhWnYsGGqUKGC/Pz8VKNGDc2bN08xMTEJehzWc+XRf15eXgoKClKHDh10/PjxeO9r5syZ6t69uyTp9u3b6tmzp3bu3JmgeJ5X7969Vb58+aeus2TJEnl6eurcuXPx3m98trlx44bKli2r0NDQeO/3UXfv3tXnn3+uEiVKyN/fXx999JFOnTr1zO327Nmjpk2bytfXV8WKFVOfPn109erVJ66/bt06u34uAIAV+VPcyJ9ebv40ZsyYWDmNp6enZsyYYa4TFRWlsWPHqkyZMvL19dUHH3ygffv2Jfhx9O7dO9ZxvL29VbJkSfXo0UMXLlyI974GDRqkMWPGSJIuXryoNm3a6O+//05wTM+jadOmatq06VPXeZ73RXy2OXXqlMqXL6/bt28naN9WV69eVbdu3RQUFKSAgAB17dpVly9ffuZ2O3fu1AcffKBChQqpbNmyGjx4sMLCwmzWOXnypNq1ayd/f38FBgaqU6dOOn369HPFif8G18QOAP8NU6dO1ejRo1WyZEn17dtXHh4eOnPmjObPn6/33ntPQ4cOVbVq1RI7zESxcOHC1y6xfJXOnDmjJk2aqECBAhoyZIicnJw0c+ZMffDBB1q6dKly5colSRo+fLi+/fZbderUSQULFtSmTZv0xRdfyNXVVQ0aNIhz3/fv31erVq0UFRWl/v37K0mSJBo/fryaNm2qFStWKHXq1DIMQ//73/904MABderUSbly5VJISIgGDx6smzdvqkOHDgl+TN9//715Ozo6WufPn9eYMWPUuHFjrVq1Sh4eHk/d/uTJk5oyZYqWL18uSTpy5Ih+/PFH1a1bN8GxvCply5bV999/r0yZMr3U/aZLl04tWrRQ3759NWfOHDk5OSVo+27dumnfvn3q0aOHUqZMqeDgYDVr1kyrVq1SmjRp4txm//79atq0qXLnzq2vvvpKSZMm1cyZM9WgQQMtW7ZMqVKlsll/27Zt6tat23M/RgB4XuRPT0b+9PLyJ0k6evSoAgMDY33fZcmSxbz91VdfadGiRerWrZuyZs2qWbNmqUWLFlq2bJly5MiRoMfj4eGh4OBg835UVJT++usvjRw5Unv27NHKlSuVNGnSp+4jJCREv/76q3755RdJ0tatW7Vx48YExfGq1a9fX6VKlXrp+82VK5feeecdDR48WMOHD0/QtlFRUfroo48UFhamzz77TFFRURo1apQ+/PBDLVmyRG5ubnFud/z4cbVs2VIBAQEaO3asLl26pJEjR+rcuXP6+uuvJUmhoaFq1KiRUqVKpQEDBihDhgxatGiRGjRooMWLFytbtmwv/NjxL2QAr9i6desMi8ViTJgwIdayiIgI45NPPjEKFChg/Pnnn4kQXWzjx483LBaL3Y7Xq1cvo1y5cnY7XmIbNGiQUaxYMePu3btm2927d42goCDj888/NwzDMEJDQ418+fIZ8+bNs9m2c+fORseOHZ+4799//92wWCzG1q1bzbaTJ08aFovFWLJkiWEYhnHw4EHDYrEYq1evttl2wIABhp+fnxETExPvx/K0c2XHjh2GxWIxpkyZ8sz9tG3b1vjiiy/M+3/88YdhsViMP/74I96xvIhXdQ4uXrzYsFgsRmho6FPXe/DggREYGGj88ssvCdr/7t27DYvFYmzYsMFsu3btmuHn52dMmjTpidu1a9fOKFq0qHHz5k2z7d69e0aZMmWM0aNHm2137twxRo8ebeTPn98IDAy06+cCAJA/PR3508vLnwzDMEqVKmWMGTPmicvPnz9veHl52ez7wYMHRtmyZY1+/fol6LE87bVbunSpYbFYjJUrVz5zPzVq1DBmzJhh3o9v3vGyNGnSxGjSpMlL329830uXL182vLy8jIMHDyZo/ytWrDAsFotx/Phxs+348eOGp6en8eOPPz5xu1GjRhkFCxY0wsLCzLb58+cbFovFOHfunGEYD8/TAgUKGGfPnjXXiY6ONurWrWt07do1QXHiv4Phe3jlgoODlStXrjh7oLi5uemLL76Qi4uLpk2bJklq1aqV6tSpE2vd9u3bq2bNmub9nTt3qkmTJvL19VVgYKB69eql69evm8uXLFkiLy8vLVy4UCVKlFBgYKBOnDihs2fPql27dgoKCpKvr68aNGgQ568qGzZsUM2aNc2uz8uWLbNZfvnyZfXp00dlypSRj4+P6tWrp7Vr19qs8+DBA02cOFFVqlRRwYIFValSJU2dOtUcJta7d28tXbpUf//9tzw9PbVkyZI4n8MJEyaoSpUq+vXXX1W9enUVLFhQtWrV0p49e7R3717Vr19fPj4+ql69ukJCQmy2/fPPP9W2bVsVKlRIhQoVUocOHWINlTp69Kg6duyookWLytvbW6VKldLgwYN1//59cx1PT0/NmzdP/fr1U2BgoPz9/dW5c2ebIU/W4Vrbtm2L83FID3/ZadWqlZInT262JU+eXJkzZzaH2f32229KkiSJ6tWrZ7Pt2LFjNWHChCfu+8GDB5KkFClSmG1p06aVJN28edNsa9CggYoVKxYrrnv37unatWtP3H9CFChQQJLMLuQTJkxQxYoVFRwcrMDAQJUsWVK3bt3Sn3/+qQ0bNqh69eqSHvbKadasmSSpWbNmNt3CV69erTp16sjf318lSpTQgAEDdOvWLZvjHjhwQB9++KGCgoJUqFAhtWvXLt7DCJcsWaLKlSurYMGCqlmzps37Iq6heEuXLlXVqlXN9UNCQuTl5RXrPN63b58aNmyoggULqmzZspo+fbrNcnd3d1WuXFlTpkwx27Zt2/bU94Qkbd68WcmTJ1fJkiXNtvTp06tIkSJP/aX01KlTCggIsOlJlSxZMvn4+GjDhg1m26JFi/TDDz9owIABatKkyRP3BwCvAvkT+dOjXmX+dP36dV26dEn58+d/4johISGKiopSxYoVzTZ3d3eVLVv2pfZOKliwoKR/8qfevXurefPmGjhwoAoVKqSqVasqOjpaGzZs0J9//mn2FFyyZIn69OkjSXrnnXfUu3dvSQ97sM+bN081atSQj4+PypYtq5EjR5o5o9WWLVv0wQcfKCAgQEFBQerWrVu8hhEahqFp06apbNmy8vHxUYMGDbR//35zeVxD8WbMmKF33nlHPj4+atiwoTlFwOOv/7PeSx4eHipatKhN/hSfc2nz5s16++23lSdPHrMtT548yp0791NfywcPHsjV1VXJkiUz2x7Ps0+dOqU8efLorbfeMtdxdnZ+Zm6G/zaKUnilrl+/roMHD6pcuXJPHJaTNm1aFS9e3ExIatasqUOHDunMmTPmOrdv39amTZtUq1YtSdKOHTvUokULJU2aVGPHjlXfvn21fft2NWvWzCYRiI6O1syZMzVkyBD16dNHb7/9ttq2bavw8HANHz5ckyZNUtq0afXxxx/bHE+SBgwYoBYtWmjy5MnKnDmzevfuraNHj0p6OA67Xr162rlzp7p06aIJEyYoa9as6tChgzkEyzAMtWvXTtOnT1f9+vX19ddfq0qVKho7dqwGDhwo6WGiWKZMGXl4eOj7779X2bJln/hcXrx4UV999ZXatWuncePG6fbt2+rUqZO6du2q+vXra+LEiTIMQ126dDGfg7/++ksNGzbUtWvXNGzYMA0ZMsTsVmstvly+fFmNGzdWeHi4vvrqK02bNk3VqlXTt99+qzlz5tjEMGbMGMXExGj06NHq2bOn1q9fry+//NJcbh3i5e3t/cTH8cEHH6h169Y2bWfOnNHx48eVN29eSQ+Hr+XIkUM7duzQe++9J29vb5UvX95mmFxcSpYsqdy5c2vEiBEKDQ3VlStXNGjQICVPnlwVKlSQJHl7e+uLL74wv0StfvvtN6VPn17p06d/6jHi66+//pIkZc+e3Ww7f/68Nm7cqDFjxqhPnz5KkyaNVqxYIQ8PD/n5+ZnxDRgwQNLDc9B6rkyaNEldu3aVn5+fxo8frw4dOuiXX35R06ZNzdf7jz/+UKNGjSRJX375pQYPHqwLFy6oYcOGzxzicOHCBU2dOlWdO3fWhAkT5OTkpE6dOj2xSLds2TL17t1bhQoV0qRJk1S5cmW1b99e0dHRsdb97LPPVK1aNU2dOlX+/v4aMWKE1q9fb7NOlSpVdPDgQfN58/b2fuZ74uTJk8qWLZtcXFxs2rNnz27uJy7p0qXT+fPnY7WHhoba/MFRvnx5rVu3Tg0bNnzivgDgVSB/In963KvMn6yvz4YNG1SuXDl5e3urdu3aNkWEkydPKkWKFLGmJMiRI4cuX76su3fvPvUY8RVX/rRz505duHBBEydOVLdu3eTi4qLly5fLz89Pb7zxhqSHz+HHH38s6WFBt3379pIeno9Dhw5VhQoVNHnyZDVu3Fhz585V+/btzQnRly1bplatWunNN9/U6NGj1adPH+3Zs0cNGjR45o+Vu3bt0q+//qr+/ftrxIgRunz5sj7++GNFRUXFuX5wcLBGjhypd999V5MmTZKvr6/+97//xbnu095LVlWqVNG6devM5z8+59LJkyeVM2fOWO3Pyp+s00oMHTpUN27c0PHjxzVx4kRZLBbly5dP0sMc68qVK4qMjLTZNjQ0VHfu3LH5kRgwJWY3Lfz77d+/37BYLMbcuXOfut5XX31lWCwW4+bNm8bdu3cNPz8/Izg42Fy+cOFCI1++fMbFixcNwzCMBg0aGNWrVzeioqLMdU6dOmXkz5/fPJa1C++yZcvMdS5fvmxYLBZj+fLlZtvt27eNL7/80uz+bu0yu3HjRnOdM2fOGBaLxfjmm28MwzCM4cOHG97e3mZXVavmzZsbJUqUMKKjo40NGzbE2f144sSJhsViMY8Xn+7nccU0ZcoUw2KxGAsXLjTbfv75Z8NisRiHDx82DMMwunbtahQvXty4c+eOuc6NGzeMgIAA46uvvjIM4+GQt8aNG9usYxiGUb16daNVq1bmfYvFYjRq1Mhmnd69ext+fn5Pjf1ZwsPDjQYNGhh+fn7m89m6dWsjKCjIKFq0qDF37lxj69atxqeffmpYLBZjwYIFT93f7t27zaFWFovFKFCggLF58+anbjN79mzDYrEYM2fOTFDs1tclMjLS/Hfnzh1jx44dxnvvvWcEBAQYly9ftll3x44dNvuoV6+e8fHHH9u0PT587+bNm0aBAgWM/v3726xnHSJoPefr1atnVK1a1eZ9cevWLSMwMNDo1KnTEx9Hr169DIvFYpw4ccJs27p1q2GxWIzffvvNMIzYXeLLli1rtG3b1mY/1nNy8eLFNtt899135jr37t0zvL29jS+//NJm29u3bxsWiyXWkIOnadWqldGwYcNY7aNHjza8vb2fuN0PP/xgWCwWY/DgwcbFixeNy5cvG8OHDzcKFChg5MuXL85t7D0sBcB/G/kT+dOzvMz8afr06YbFYjE+/PBDY/Pmzca6deuMVq1aGfny5TM2bdpkGIZh9O/f3yhVqlSsba3fqdZzLD6sr92j+dONGzeMTZs2GeXLlzfKly9vhIeHm+taLBbjwoULNvsoVqyYMXjwYJu2x3OV48ePxzmVwrJly8zh/9HR0UaJEiVsXjPDeHjuent7G8OGDXvi42jSpInh4+Nj3LhxI9bzceTIEcMwbPOHu3fvGj4+PsagQYNs9tO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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_counterfactual_by_context(data, name, other):\n", + "\n", + " grouped_data = data.groupby([\"wpr_lockdown_efficiency\", \"wpr_mask_efficiency\"])\n", + "\n", + " fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n", + "\n", + " for (lockdown_efficiency, mask_efficiency), ax in zip(\n", + " grouped_data.groups.keys(), axs.flatten()\n", + " ):\n", + " data_subset = grouped_data.get_group((lockdown_efficiency, mask_efficiency))\n", + " mean_overshoot = data_subset[\"overshoot_int\"].mean().item()\n", + "\n", + " fixed = mask_efficiency if name == \"lockdown\" else lockdown_efficiency\n", + " ax.hist(data_subset[\"overshoot_int\"])\n", + " ax.set_title(\n", + " f\"{other} eff fixed: {fixed}\\nOvershoot mean: {mean_overshoot:.2f}, Pr(too high): {data_subset['os_too_high_int'].mean().item():.2f}\"\n", + " )\n", + " ax.set_xlim(5, 35)\n", + " ax.axvline(x=mean_overshoot, color=\"grey\", linestyle=\"--\")\n", + " ax.axvline(x=overshoot_threshold, color=\"red\", linestyle=\"-\")\n", + "\n", + " plt.suptitle(\n", + " f\"Counterfactual {name} by {other.lower()} efficiency contexts\",\n", + " fontsize=16,\n", + " y=1,\n", + " )\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "plot_counterfactual_by_context(counterfactual_lockdown, \"lockdown\", \"Mask\")\n", + "\n", + "plot_counterfactual_by_context(counterfactual_mask, \"mask\", \"Lockdown\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "sufficiency_table = get_table(\n", + " tr,\n", + " mwc,\n", + " antecedents,\n", + " witnesses,\n", + " consequents,\n", + " world=2,\n", + " others=[\"joint_efficiency\", \"overshoot\"],\n", + ")\n", + "\n", + "\n", + "factual_sufficiency = sufficiency_table[\n", + " (sufficiency_table[\"lockdown_int\"] == 1)\n", + " & (sufficiency_table[\"mask_int\"] == 1)\n", + " & (\n", + " sufficiency_table[\"wpr_lockdown_efficiency\"]\n", + " == 0 & (sufficiency_table[\"wpr_mask_efficiency\"] == 0)\n", + " )\n", + "]\n", + "\n", + "counterfactual_sufficiency_lockdown = sufficiency_table[\n", + " (sufficiency_table[\"lockdown_int\"] == 0)\n", + " & (sufficiency_table[\"mask_int\"] == 1)\n", + " & (sufficiency_table[\"wpr_lockdown_efficiency\"] == 0)\n", + "]\n", + "\n", + "counterfactual_sufficiency_mask = sufficiency_table[\n", + " (sufficiency_table[\"lockdown_int\"] == 1)\n", + " & (sufficiency_table[\"mask_int\"] == 0)\n", + " & (sufficiency_table[\"wpr_mask_efficiency\"] == 0)\n", + "]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 3, figsize=(18, 6))\n", + "\n", + "factual_sufficiency_mean = factual_sufficiency[\"overshoot_int\"].mean().item()\n", + "axs[0].hist(factual_sufficiency[\"overshoot_int\"])\n", + "\n", + "axs[0].set_title((\n", + " f\"Factual\\n overshoot mean: {factual_sufficiency_mean:.2f}, Pr(too high): \"\n", + " f\"{factual_sufficiency['os_too_high_int'].mean().item():.2f}\"\n", + "))\n", + "axs[0].axvline(x=factual_sufficiency_mean, color=\"grey\", linestyle=\"--\")\n", + "\n", + "counterfactual_sufficiency_lockdown_mean = counterfactual_sufficiency_lockdown[\"overshoot_int\"].mean()\n", + "axs[1].hist(counterfactual_sufficiency_lockdown[\"overshoot_int\"])\n", + "axs[1].set_title((\n", + " f\"Counterfactual_lockdown\\n overshoot mean: {counterfactual_sufficiency_lockdown_mean:.2f}, \"\n", + " f\"Pr(too high): {counterfactual_lockdown['os_too_high_int'].mean():.2f}\"\n", + "))\n", + "axs[1].axvline(x=counterfactual_sufficiency_lockdown_mean, color=\"grey\", linestyle=\"--\")\n", + "\n", + "counterfactual_sufficiency_mask_mean = counterfactual_sufficiency_mask[\"overshoot_int\"].mean()\n", + "axs[2].hist(counterfactual_sufficiency_mask[\"overshoot_int\"])\n", + "axs[2].set_title((\n", + " f\"Counterfactual_mask\\n overshoot mean: {counterfactual_sufficiency_mask_mean:.2f}, \"\n", + " f\"Pr(too high): {counterfactual_mask['os_too_high_int'].mean():.2f}\"\n", + "))\n", + "axs[2].axvline(x=counterfactual_sufficiency_mask_mean, color=\"grey\", linestyle=\"--\")\n", + "\n", + "for i in range(3):\n", + " axs[i].set_xlim(5, 40)\n", + " axs[i].axvline(x=overshoot_threshold, color=\"red\", linestyle=\"-\")\n", + "\n", + "#plt.savefig(\"counterfactual_sir_search_sufficiency.png\")\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      " + ], + "text/plain": [ + " lockdown_obs lockdown_int apr_lockdown mask_obs mask_int apr_mask \\\n", + "11 1.0 0.0 1 1.0 1.0 0 \n", + "38 1.0 0.0 1 1.0 1.0 0 \n", + "51 1.0 0.0 1 1.0 1.0 0 \n", + "104 1.0 0.0 1 1.0 1.0 0 \n", + "110 1.0 0.0 1 1.0 1.0 0 \n", + "\n", + " lockdown_efficiency_obs lockdown_efficiency_int \\\n", + "11 0.0 0.0 \n", + "38 0.0 0.0 \n", + "51 0.0 0.0 \n", + "104 0.0 0.0 \n", + "110 0.0 0.0 \n", + "\n", + " wpr_lockdown_efficiency mask_efficiency_obs mask_efficiency_int \\\n", + "11 0 0.00 0.00 \n", + "38 0 0.45 0.45 \n", + "51 0 0.45 0.45 \n", + "104 0 0.00 0.00 \n", + "110 0 0.00 0.00 \n", + "\n", + " wpr_mask_efficiency joint_efficiency_obs joint_efficiency_int \\\n", + "11 1 0.7 0.00 \n", + "38 0 0.7 0.45 \n", + "51 0 0.7 0.45 \n", + "104 1 0.7 0.00 \n", + "110 1 0.7 0.00 \n", + "\n", + " overshoot_obs overshoot_int os_too_high_obs os_too_high_int \n", + "11 15.616409 21.598530 0.0 1.0 \n", + "38 31.680214 25.210352 1.0 1.0 \n", + "51 32.041954 24.760708 1.0 1.0 \n", + "104 30.147129 17.757517 1.0 0.0 \n", + "110 15.187485 23.535439 0.0 1.0 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "counterfactual_sufficiency_lockdown.head()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "chirho", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From cdb8aa4e5af2c985b687dbb5241a7ff322c36b16 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Tue, 20 Aug 2024 16:53:12 -0400 Subject: [PATCH 055/111] importance sampling and inference --- docs/source/counterfactual_sir.png | Bin 126305 -> 127591 bytes docs/source/counterfactual_sir_search.png | Bin 42433 -> 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zU2Pyd_N>coK{oJY-jOo0k>-X(vopzPkW=KZqrp}5`DUy0b>bgHmlqG9779Ol<4qkG zE!E7Y-WsYS;Hk1+ln6~}FzVJXMNc3{?xgNFk>$7h2y_X$M{wV!<2WmK+Pk^%OuA_-6w zP`G_{{kr&YU|qZ}ITDkxj<1pvCcK%3Q!gZH?8y02)~$PuH5hj4m-NSDy7#ci3IA}L zzst3JqC);o7xXF1@xPGT{QnOGe)r$>e>8?;Gi+PTm;8`q_k$G)3skphZB16O`2D{C D;2Cj6 diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index d3b73971..6f651125 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -2,10 +2,11 @@ "cells": [ { "cell_type": "code", - "execution_count": 72, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ + "# Setup\n", "import numbers\n", "import os\n", "from typing import Tuple, TypeVar, Union, Optional, Callable\n", @@ -14,6 +15,7 @@ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import pyro.distributions as dist\n", + "from pyro.distributions import constraints\n", "import seaborn as sns\n", "import torch\n", "from pyro.infer import Predictive\n", @@ -49,10 +51,14 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ + "# dS = - beta . SI\n", + "# dI = beta * SI - gamma * I\n", + "# dR = gamma * I\n", + "\n", "class SIRDynamics(pyro.nn.PyroModule):\n", " def __init__(self, beta, gamma):\n", " super().__init__()\n", @@ -69,7 +75,7 @@ "\n", "\n", "# TODO add running overshoot to states?\n", - "\n", + "# beta = (1 - l) beta0\n", "\n", "class SIRDynamicsLockdown(SIRDynamics):\n", " def __init__(self, beta0, gamma):\n", @@ -85,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -97,6 +103,8 @@ } ], "source": [ + "# Computing overshoot in a simple SIR model\n", + "\n", "init_state = dict(S=torch.tensor(99.0), I=torch.tensor(1.0), R=torch.tensor(0.0))\n", "start_time = torch.tensor(0.0)\n", "end_time = torch.tensor(12.0)\n", @@ -126,10 +134,12 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ + "# Defining a Bayesian SIR model where we have priors over beta and gamma distributions\n", + "\n", "def bayesian_sir(base_model=SIRDynamics) -> Dynamics[torch.Tensor]:\n", " beta = pyro.sample(\"beta\", dist.Beta(18, 600))\n", " gamma = pyro.sample(\"gamma\", dist.Beta(1600, 1600))\n", @@ -149,10 +159,12 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ + "# Defining intervention\n", + "\n", "def MaskedStaticIntervention(time: R, intervention: Intervention[State[T]]):\n", "\n", " @on(StaticEvent(time))\n", @@ -168,10 +180,12 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ + "# Defining the policy model and extracting supports\n", + "\n", "overshoot_threshold = 20\n", "lockdown_time = torch.tensor(1.0)\n", "mask_time = torch.tensor(1.5)\n", @@ -218,23 +232,22 @@ " event_dim=0,\n", " )\n", "\n", - " return overshoot, os_too_high\n", - "\n", - "\n", - "with ExtractSupports() as s:\n", - " one_run = policy_model()\n" + " return overshoot, os_too_high" ] }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# conditioning (as opposed to intervening) is sufficient for\n", "# propagating the changes, as the decisions are upstream from ds\n", "\n", + "# Doing but-for analysis\n", + "\n", "# no interventions\n", + "num_samples = 10000\n", "policy_model_none = condition(\n", " policy_model, {\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(0.0)}\n", ")\n", @@ -269,57 +282,12 @@ }, { "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [], - "source": [ - "def importance_infer(\n", - " model: Optional[Callable] = None, *, num_samples: int\n", - "):\n", - " \n", - " if model is None:\n", - " return lambda m: importance_infer(m, num_samples=num_samples)\n", - "\n", - " def _wrapped_model(\n", - " *args,\n", - " **kwargs\n", - " ):\n", - "\n", - " guide = pyro.poutine.block(hide_fn=lambda msg: msg[\"is_observed\"])(model)\n", - "\n", - " max_plate_nesting = 9 # TODO guess\n", - "\n", - " with pyro.poutine.block(), MultiWorldCounterfactual() as mwc:\n", - " log_weights, importance_tr, _ = pyro.infer.importance.vectorized_importance_weights(\n", - " model,\n", - " guide,\n", - " *args,\n", - " num_samples=num_samples,\n", - " max_plate_nesting=max_plate_nesting,\n", - " normalized=False,\n", - " **kwargs\n", - " )\n", - "\n", - " return torch.logsumexp(log_weights, dim=0) - math.log(num_samples), importance_tr, mwc, log_weights\n", - "\n", - " return _wrapped_model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 80, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
      " ] @@ -329,6 +297,9 @@ } ], "source": [ + "# Plotting them, if I do but-for analysis of mask or lockdown, they both come out to not be the cause. That is not true if I use both as the cause, so what exactly is their role and\n", + "# how can we get more fine-grained information\n", + "\n", "def add_pred_to_plot(preds, axs, coords, color, label):\n", " sns.lineplot(\n", " x=logging_times,\n", @@ -428,16 +399,22 @@ "plt.savefig(\"counterfactual_sir.png\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# analysis of os_too_high using SearchForExplanation" + ] + }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "with ExtractSupports() as s:\n", " policy_model()\n", "\n", - "from pyro.distributions import constraints\n", "supports = s.supports\n", "supports[\"os_too_high\"] = constraints.independent(base_constraint=constraints.boolean, reinterpreted_batch_ndims=0)\n", "\n", @@ -446,7 +423,7 @@ "witnesses = {key: s.supports[key] for key in [\"lockdown_efficiency\", \"mask_efficiency\"]}\n", "consequents = {\"os_too_high\": torch.tensor(1.0)}\n", "\n", - "with MultiWorldCounterfactual() as mwc:\n", + "with MultiWorldCounterfactual() as mwc_plate:\n", " with SearchForExplanation(\n", " supports=supports,\n", " alternatives=alternatives,\n", @@ -459,118 +436,297 @@ " ):\n", " with pyro.plate(\"sample\", exp_plate_size):\n", " with pyro.poutine.trace() as tr:\n", - " policy_model_all()" + " policy_model_all()\n", + "\n", + "tr = tr.trace" ] }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "def importance_infer(\n", + " model: Optional[Callable] = None, *, num_samples: int\n", + "):\n", + " \n", + " if model is None:\n", + " return lambda m: importance_infer(m, num_samples=num_samples)\n", + "\n", + " def _wrapped_model(\n", + " *args,\n", + " **kwargs\n", + " ):\n", + "\n", + " guide = pyro.poutine.block(hide_fn=lambda msg: msg[\"is_observed\"])(model)\n", + "\n", + " max_plate_nesting = 9 # TODO guess\n", + "\n", + " with pyro.poutine.block(), MultiWorldCounterfactual() as mwc_imp:\n", + " log_weights, importance_tr, _ = pyro.infer.importance.vectorized_importance_weights(\n", + " model,\n", + " guide,\n", + " *args,\n", + " num_samples=num_samples,\n", + " max_plate_nesting=max_plate_nesting,\n", + " normalized=False,\n", + " **kwargs\n", + " )\n", + "\n", + " return torch.logsumexp(log_weights, dim=0) - math.log(num_samples), importance_tr, mwc_imp, log_weights\n", + "\n", + " return _wrapped_model" + ] + }, + { + "cell_type": "code", + "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.0317)\n" + "tensor(0.0324)\n" ] } ], "source": [ "query = SearchForExplanation(\n", - " supports=supports,\n", - " alternatives={\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(0.0)},\n", - " antecedents={\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(1.0)},\n", - " antecedent_bias=0.0,\n", - " witnesses={key: s.supports[key] for key in [\"lockdown_efficiency\", \"mask_efficiency\"]},\n", - " consequents={\"os_too_high\": torch.tensor(1.0)},\n", - " consequent_scale=1e-8,\n", - " witness_bias=0.2,\n", - " )(policy_model_all)\n", - "\n", - "logp, trace, mwc, log_weights = importance_infer(num_samples=10000)(query)()\n", + " supports=supports,\n", + " alternatives={\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(0.0)},\n", + " antecedents={\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(1.0)},\n", + " antecedent_bias=0.0,\n", + " witnesses={key: s.supports[key] for key in [\"lockdown_efficiency\", \"mask_efficiency\"]},\n", + " consequents={\"os_too_high\": torch.tensor(1.0)},\n", + " consequent_scale=1e-8,\n", + " witness_bias=0.2,\n", + " )(policy_model_all)\n", + "\n", + "logp, importance_tr, mwc_imp, log_weights = importance_infer(num_samples=10000)(query)()\n", "print(torch.exp(logp))" ] }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(4.6869e-10)\n", - "tensor(2427.)\n", - "tensor([ -inf, -inf, -inf, ..., -inf, -19.8070, -inf])\n", - "tensor([[[1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.]],\n", - "\n", - " [[1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.]],\n", - "\n", - " [[1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.]],\n", - "\n", - " ...,\n", - "\n", - " [[1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.]],\n", - "\n", - " [[0., 0., 0.],\n", - " [0., 0., 0.],\n", - " [0., 0., 0.]],\n", - "\n", - " [[1., 1., 1.],\n", - " [1., 1., 1.],\n", - " [1., 1., 1.]]])\n", - "tensor([[[ 0.0000, 0.0000, 0.0000],\n", - " [ 0.0000, -inf, 0.0000],\n", - " [ 0.0000, 0.0000, 0.0000]],\n", - "\n", - " [[ 0.0000, 0.0000, 0.0000],\n", - " [ 0.0000, -inf, 0.0000],\n", - " [ 0.0000, 0.0000, 0.0000]],\n", - "\n", - " [[ 0.0000, 0.0000, 0.0000],\n", - " [ 0.0000, -inf, 0.0000],\n", - " [ 0.0000, 0.0000, 0.0000]],\n", - "\n", - " ...,\n", - "\n", - " [[ 0.0000, 0.0000, 0.0000],\n", - " [ 0.0000, -inf, 0.0000],\n", - " [ 0.0000, 0.0000, 0.0000]],\n", - "\n", - " [[ 0.0000, 0.0000, 0.0000],\n", - " [ 0.0000, 0.0000, 0.0000],\n", - " [ 0.0000, 0.0000, -18.4207]],\n", - "\n", - " [[ 0.0000, 0.0000, 0.0000],\n", - " [ 0.0000, -inf, 0.0000],\n", - " [ 0.0000, 0.0000, 0.0000]]])\n" + "tensor(0.0524)\n", + "tensor(0.0749)\n", + "tensor(4.1971e-10)\n", + "tensor(4.8991e-10)\n", + "tensor(0.0635)\n", + "tensor(0.0268)\n" ] } ], "source": [ - "mask_intervened = (trace.nodes[\"__cause____witness_mask\"][\"value\"] == 1) & (trace.nodes[\"__cause____antecedent_mask\"][\"value\"] == 1) \n", - "# print(((torch.exp(log_weights) * mask_intervened.squeeze()) > 0).float().sum() / mask_intervened.float().sum())\n", + "# P(m, l, )\n", + "trace = importance_tr\n", + "mask_intervened = (trace.nodes[\"__cause____antecedent_lockdown\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_mask\"][\"value\"] == 0) \n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze()) / mask_intervened.float().sum())\n", + "\n", + "mask_intervened = (trace.nodes[\"__cause____antecedent_lockdown\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_mask\"][\"value\"] == 1) \n", "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze()) / mask_intervened.float().sum())\n", "\n", - "print(mask_intervened.float().sum())\n", + "mask_intervened = (trace.nodes[\"__cause____antecedent_lockdown\"][\"value\"] == 1) & (trace.nodes[\"__cause____antecedent_mask\"][\"value\"] == 0) \n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze()) / mask_intervened.float().sum())\n", "\n", - "print(log_weights)\n", - "print(trace.nodes[\"os_too_high\"][\"value\"].squeeze())\n", - "print(trace.nodes[\"__cause____consequent_os_too_high\"][\"log_prob\"].squeeze())" + "mask_intervened = (trace.nodes[\"__cause____antecedent_lockdown\"][\"value\"] == 1) & (trace.nodes[\"__cause____antecedent_mask\"][\"value\"] == 1) \n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze()) / mask_intervened.float().sum())\n", + "\n", + "mask_intervened = (trace.nodes[\"__cause____antecedent_lockdown\"][\"value\"] == 0)\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze()) / mask_intervened.float().sum())\n", + "\n", + "mask_intervened = (trace.nodes[\"__cause____antecedent_mask\"][\"value\"] == 0)\n", + "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze()) / mask_intervened.float().sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fine grained analysis of overshoot variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Factual plot" ] }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(26.8324)\n", + "tensor(0.8090)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Works for both plate trace and importance trace\n", + "mwc = mwc_imp\n", + "trace = importance_tr\n", + "\n", + "with mwc:\n", + " data_to_plot = gather(trace.nodes[\"overshoot\"][\"value\"], IndexSet(**{\"lockdown\": {0}, \"mask\": {0}}))\n", + " print(data_to_plot.mean())\n", + " print(gather(trace.nodes[\"os_too_high\"][\"value\"], IndexSet(**{\"lockdown\": {0}, \"mask\": {0}})).mean())\n", + "\n", + "hist_fact, bin_edges = torch.histogram(data_to_plot, bins = 28, range=(5, 40), density=True)\n", + "plt.bar(bin_edges[:28].tolist(), hist_fact, align='center', width = 35/28)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.5622)\n", + "tensor(21.3728)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# slightly different for plate and importance\n", + "mwc = mwc_imp\n", + "trace = importance_tr\n", + "\n", + "with mwc:\n", + " data_to_plot = gather(trace.nodes[\"overshoot\"][\"value\"], IndexSet(**{\"lockdown\": {1}, \"mask\": {1}}))\n", + " lockdown_intervened = (trace.nodes[\"__cause____antecedent_lockdown\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_mask\"][\"value\"] == 1) & (trace.nodes[\"__cause____witness_lockdown_efficiency\"][\"value\"] == 0) \n", + " data_to_plot = data_to_plot.squeeze()[torch.nonzero(lockdown_intervened.squeeze())]\n", + "\n", + " os_too_high = (gather(trace.nodes[\"os_too_high\"][\"value\"], IndexSet(**{\"lockdown\": {1}, \"mask\": {1}})))\n", + " os_too_high = os_too_high.squeeze()[torch.nonzero(lockdown_intervened.squeeze())]\n", + "\n", + "print(os_too_high.squeeze().mean())\n", + "print(data_to_plot.mean())\n", + "\n", + "hist_lockdown, bin_edges = torch.histogram(data_to_plot, bins = 28, range=(5, 40), density=True)\n", + "\n", + "plt.bar(bin_edges[:28].tolist(), hist_lockdown, align='center', width = 35/28)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.9071)\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'overshoot')" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Same for plate and importance\n", + "mwc = mwc_imp\n", + "trace = importance_tr\n", + "\n", + "with mwc:\n", + " data_to_plot = gather(trace.nodes[\"overshoot\"][\"value\"], IndexSet(**{\"lockdown\": {1}, \"mask\": {1}}))\n", + " mask_intervened = (trace.nodes[\"__cause____antecedent_lockdown\"][\"value\"] == 1) & (trace.nodes[\"__cause____antecedent_mask\"][\"value\"] == 0) & (trace.nodes[\"__cause____witness_mask_efficiency\"][\"value\"] == 0) \n", + " data_to_plot = data_to_plot.squeeze()[torch.nonzero(mask_intervened.squeeze())]\n", + "\n", + " os_too_high = (gather(trace.nodes[\"os_too_high\"][\"value\"], IndexSet(**{\"lockdown\": {1}, \"mask\": {1}})))\n", + " os_too_high = os_too_high.squeeze()[torch.nonzero(mask_intervened.squeeze())]\n", + "\n", + "print(os_too_high.squeeze().mean())\n", + "pr_too_high = data_to_plot[data_to_plot > torch.tensor(overshoot_threshold)].mean()\n", + "\n", + "hist_mask, bin_edges = torch.histogram(data_to_plot, bins = 28, range=(5, 40), density = True)\n", + "\n", + "plt.bar(bin_edges[:28].tolist(), hist_fact, align='center', width = 35/28, alpha = 0.5, color='blue')\n", + "plt.bar(bin_edges[:28].tolist(), hist_lockdown, align='center', width = 35/28, alpha = 0.5, color='pink')\n", + "plt.bar(bin_edges[:28].tolist(), hist_mask, align='center', width = 35/28, alpha = 0.5, color='green')\n", + "plt.legend([\"factual\", \"counterfactual_lockdown\", \"counterfactual_mask\"])\n", + "plt.ylabel(\"pr\")\n", + "plt.xlabel(\"overshoot\")" + ] + }, + { + "cell_type": "code", + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -579,7 +735,7 @@ "):\n", "\n", " values_table = {}\n", - " nodes = trace.trace.nodes\n", + " nodes = trace.nodes\n", " witnesses = [key for key, _ in witnesses.items()]\n", "\n", " with mwc:\n", @@ -714,8 +870,8 @@ "\n", "\n", "table = get_table(\n", - " tr,\n", - " mwc,\n", + " importance_tr,\n", + " mwc_imp,\n", " antecedents,\n", " witnesses,\n", " consequents,\n", @@ -725,7 +881,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -770,309 +926,14 @@ " \n", " \n", " \n", - " \n", - " 7\n", - " 1.0\n", - " 0.0\n", - " 0\n", - " 1.0\n", - " 1.0\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0.10\n", - " 0.10\n", - " 1\n", - " 0.7\n", - " 0.10\n", - " 27.414051\n", - " 20.081270\n", - " 1.0\n", - " 1.0\n", - " \n", - " \n", - " 12\n", - " 1.0\n", - " 0.0\n", - " 0\n", - " 1.0\n", - " 1.0\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0.10\n", - " 0.10\n", - " 1\n", - " 0.7\n", - " 0.10\n", - " 28.143715\n", - " 18.176403\n", - " 1.0\n", - " 0.0\n", - " \n", - " \n", - " 17\n", - " 1.0\n", - " 0.0\n", - " 0\n", - " 1.0\n", - " 1.0\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0.45\n", - " 0.45\n", - " 0\n", - " 0.7\n", - " 0.45\n", - " 23.878517\n", - " 29.118385\n", - " 1.0\n", - " 1.0\n", - " \n", - " \n", - " 44\n", - " 1.0\n", - " 0.0\n", - " 0\n", - " 1.0\n", - " 1.0\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0.45\n", - " 0.45\n", - " 0\n", - " 0.7\n", - " 0.45\n", - " 32.594925\n", - " 25.202904\n", - " 1.0\n", - " 1.0\n", - " \n", - " \n", - " 68\n", - " 1.0\n", - " 0.0\n", - " 0\n", - " 1.0\n", - " 1.0\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0.10\n", - " 0.10\n", - " 1\n", - " 0.7\n", - " 0.10\n", - " 30.271997\n", - " 18.733753\n", - " 1.0\n", - " 0.0\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 1932\n", - " 1.0\n", - " 0.0\n", - " 0\n", - " 1.0\n", - " 1.0\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0.10\n", - " 0.10\n", - " 1\n", - " 0.7\n", - " 0.10\n", - " 33.913155\n", - " 16.335825\n", - " 1.0\n", - " 0.0\n", - " \n", - " \n", - " 1940\n", - " 1.0\n", - " 0.0\n", - " 0\n", - " 1.0\n", - " 1.0\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0.10\n", - " 0.10\n", - " 1\n", - " 0.7\n", - " 0.10\n", - " 18.856632\n", - " 23.330830\n", - " 0.0\n", - " 1.0\n", - " \n", - " \n", - " 1949\n", - " 1.0\n", - " 0.0\n", - " 0\n", - " 1.0\n", - " 1.0\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0.45\n", - " 0.45\n", - " 0\n", - " 0.7\n", - " 0.45\n", - " 32.299316\n", - " 26.959848\n", - " 1.0\n", - " 1.0\n", - " \n", - " \n", - " 1984\n", - " 1.0\n", - " 0.0\n", - " 0\n", - " 1.0\n", - " 1.0\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0.45\n", - " 0.45\n", - " 0\n", - " 0.7\n", - " 0.45\n", - " 21.073544\n", - " 29.347124\n", - " 1.0\n", - " 1.0\n", - " \n", - " \n", - " 1986\n", - " 1.0\n", - " 0.0\n", - " 0\n", - " 1.0\n", - " 1.0\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0.10\n", - " 0.10\n", - " 1\n", - " 0.7\n", - " 0.10\n", - " 32.659851\n", - " 15.735229\n", - " 1.0\n", - " 0.0\n", - " \n", " \n", "\n", - "

      153 rows × 18 columns

      \n", "" ], "text/plain": [ - " lockdown_obs lockdown_int apr_lockdown mask_obs mask_int apr_mask \\\n", - "7 1.0 0.0 0 1.0 1.0 1 \n", - "12 1.0 0.0 0 1.0 1.0 1 \n", - "17 1.0 0.0 0 1.0 1.0 1 \n", - "44 1.0 0.0 0 1.0 1.0 1 \n", - "68 1.0 0.0 0 1.0 1.0 1 \n", - "... ... ... ... ... ... ... \n", - "1932 1.0 0.0 0 1.0 1.0 1 \n", - "1940 1.0 0.0 0 1.0 1.0 1 \n", - "1949 1.0 0.0 0 1.0 1.0 1 \n", - "1984 1.0 0.0 0 1.0 1.0 1 \n", - "1986 1.0 0.0 0 1.0 1.0 1 \n", - "\n", - " lockdown_efficiency_obs lockdown_efficiency_int \\\n", - "7 0.0 0.0 \n", - "12 0.0 0.0 \n", - "17 0.0 0.0 \n", - "44 0.0 0.0 \n", - "68 0.0 0.0 \n", - "... ... ... \n", - "1932 0.0 0.0 \n", - "1940 0.0 0.0 \n", - "1949 0.0 0.0 \n", - "1984 0.0 0.0 \n", - "1986 0.0 0.0 \n", - "\n", - " wpr_lockdown_efficiency mask_efficiency_obs mask_efficiency_int \\\n", - "7 0 0.10 0.10 \n", - "12 0 0.10 0.10 \n", - "17 0 0.45 0.45 \n", - "44 0 0.45 0.45 \n", - "68 0 0.10 0.10 \n", - "... ... ... ... \n", - "1932 0 0.10 0.10 \n", - "1940 0 0.10 0.10 \n", - "1949 0 0.45 0.45 \n", - "1984 0 0.45 0.45 \n", - "1986 0 0.10 0.10 \n", - "\n", - " wpr_mask_efficiency joint_efficiency_obs joint_efficiency_int \\\n", - "7 1 0.7 0.10 \n", - "12 1 0.7 0.10 \n", - "17 0 0.7 0.45 \n", - "44 0 0.7 0.45 \n", - "68 1 0.7 0.10 \n", - "... ... ... ... \n", - "1932 1 0.7 0.10 \n", - "1940 1 0.7 0.10 \n", - "1949 0 0.7 0.45 \n", - "1984 0 0.7 0.45 \n", - "1986 1 0.7 0.10 \n", - "\n", - " overshoot_obs overshoot_int os_too_high_obs os_too_high_int \n", - "7 27.414051 20.081270 1.0 1.0 \n", - "12 28.143715 18.176403 1.0 0.0 \n", - "17 23.878517 29.118385 1.0 1.0 \n", - "44 32.594925 25.202904 1.0 1.0 \n", - "68 30.271997 18.733753 1.0 0.0 \n", - "... ... ... ... ... \n", - "1932 33.913155 16.335825 1.0 0.0 \n", - "1940 18.856632 23.330830 0.0 1.0 \n", - "1949 32.299316 26.959848 1.0 1.0 \n", - "1984 21.073544 29.347124 1.0 1.0 \n", - "1986 32.659851 15.735229 1.0 0.0 \n", - "\n", - "[153 rows x 18 columns]" + "Empty DataFrame\n", + "Columns: [lockdown_obs, lockdown_int, apr_lockdown, mask_obs, mask_int, apr_mask, lockdown_efficiency_obs, lockdown_efficiency_int, wpr_lockdown_efficiency, mask_efficiency_obs, mask_efficiency_int, wpr_mask_efficiency, joint_efficiency_obs, joint_efficiency_int, overshoot_obs, overshoot_int, os_too_high_obs, os_too_high_int]\n", + "Index: []" ] }, "metadata": {}, @@ -1080,7 +941,7 @@ }, { "data": { - "image/png": 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zhEn(8nhl4pN4*qkX#5KXvUo=c+2`Vt6gV#liULclpzgdIF60fG%5dbH1AGp15t%bvQt71j0(gFMDyEiNT@0D2gzZD(9@rIrEYz|^0NXB0-MQ#W3D#g8a2S)F zy!-FYWKxp|O-L|pyi8Hd-eteARj1c}M>&LUS$>-JgsHH9y<9|zVe{fSTmCy7TeQ|9nbs|Sc357 diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 6f651125..c18c1ccd 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 100, "metadata": {}, "outputs": [], "source": [ @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 101, "metadata": {}, "outputs": [], "source": [ @@ -91,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 102, "metadata": {}, "outputs": [ { @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 103, "metadata": {}, "outputs": [], "source": [ @@ -159,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 104, "metadata": {}, "outputs": [], "source": [ @@ -180,11 +180,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 105, "metadata": {}, "outputs": [], "source": [ - "# Defining the policy model and extracting supports\n", + "# Defining the policy model\n", "\n", "overshoot_threshold = 20\n", "lockdown_time = torch.tensor(1.0)\n", @@ -237,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 106, "metadata": {}, "outputs": [], "source": [ @@ -282,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 107, "metadata": {}, "outputs": [ { @@ -408,7 +408,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 151, "metadata": {}, "outputs": [], "source": [ @@ -436,14 +436,12 @@ " ):\n", " with pyro.plate(\"sample\", exp_plate_size):\n", " with pyro.poutine.trace() as tr:\n", - " policy_model_all()\n", - "\n", - "tr = tr.trace" + " policy_model_all()" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 109, "metadata": {}, "outputs": [], "source": [ @@ -481,14 +479,14 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 129, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.0324)\n" + "tensor(0.0308)\n" ] } ], @@ -510,24 +508,24 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 111, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.0524)\n", - "tensor(0.0749)\n", - "tensor(4.1971e-10)\n", - "tensor(4.8991e-10)\n", - "tensor(0.0635)\n", - "tensor(0.0268)\n" + "tensor(0.0488)\n", + "tensor(0.0760)\n", + "tensor(4.3182e-10)\n", + "tensor(4.8446e-10)\n", + "tensor(0.0626)\n", + "tensor(0.0241)\n" ] } ], "source": [ - "# P(m, l, )\n", + "# Computing probability of different sets of variables being the cause and degree of responsibility\n", "trace = importance_tr\n", "mask_intervened = (trace.nodes[\"__cause____antecedent_lockdown\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_mask\"][\"value\"] == 0) \n", "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze()) / mask_intervened.float().sum())\n", @@ -564,78 +562,58 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 130, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(26.8324)\n", - "tensor(0.8090)\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 94, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
      " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "# Works for both plate trace and importance trace\n", - "mwc = mwc_imp\n", - "trace = importance_tr\n", + "def histogram_data(trace, mwc, masks, world):\n", + " with mwc:\n", + " data_to_plot = gather(trace.nodes[\"overshoot\"][\"value\"], IndexSet(**{\"lockdown\": {world}, \"mask\": {world}}))\n", + "\n", + " mask_tensor = torch.ones(importance_tr.nodes[\"__cause____antecedent_mask\"][\"value\"].shape).bool()\n", + " for key, val in masks.items():\n", + " mask_tensor = mask_tensor & (trace.nodes[key][\"value\"] == val)\n", + " data_to_plot = data_to_plot.squeeze()[torch.nonzero(mask_tensor.squeeze())]\n", + "\n", + " os_too_high = (gather(trace.nodes[\"os_too_high\"][\"value\"], IndexSet(**{\"lockdown\": {world}, \"mask\": {world}})))\n", + " os_too_high = os_too_high.squeeze()[torch.nonzero(mask_tensor.squeeze())]\n", "\n", - "with mwc:\n", - " data_to_plot = gather(trace.nodes[\"overshoot\"][\"value\"], IndexSet(**{\"lockdown\": {0}, \"mask\": {0}}))\n", - " print(data_to_plot.mean())\n", - " print(gather(trace.nodes[\"os_too_high\"][\"value\"], IndexSet(**{\"lockdown\": {0}, \"mask\": {0}})).mean())\n", + " overshoot_mean = data_to_plot.mean()\n", + " os_too_high_mean = os_too_high.mean()\n", "\n", - "hist_fact, bin_edges = torch.histogram(data_to_plot, bins = 28, range=(5, 40), density=True)\n", - "plt.bar(bin_edges[:28].tolist(), hist_fact, align='center', width = 35/28)" + " hist, bin_edges = torch.histogram(data_to_plot, bins = 28, range=(5, 40), density=True)\n", + " return hist, bin_edges, overshoot_mean, os_too_high_mean\n" ] }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 131, + "metadata": {}, + "outputs": [], + "source": [ + "hist_fact, bin_edges, os_fact, oth_fact = histogram_data(importance_tr, mwc_imp, {}, 0)\n", + "hist_mask, bin_edges, os_mask, oth_mask = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 0, \"__cause____antecedent_lockdown\": 1, \"__cause____witness_mask_efficiency\": 0}, 1)\n", + "hist_lockdown, bin_edges, os_lockdown, oth_lockdown = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 1, \"__cause____antecedent_lockdown\": 0, \"__cause____witness_lockdown_efficiency\": 0}, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 137, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.5622)\n", - "tensor(21.3728)\n" + "Overshoot mean\n", + "factual: 26.768293380737305 counterfactual mask: 26.331018447875977 counterfactual lockdown: 20.962488174438477\n", + "Probability of overshoot being high\n", + "factual: 0.8101999759674072 counterfactual mask: 0.8904281854629517 counterfactual lockdown: 0.5546666383743286\n" ] }, { "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 95, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
      " ] @@ -645,51 +623,56 @@ } ], "source": [ - "# slightly different for plate and importance\n", - "mwc = mwc_imp\n", - "trace = importance_tr\n", - "\n", - "with mwc:\n", - " data_to_plot = gather(trace.nodes[\"overshoot\"][\"value\"], IndexSet(**{\"lockdown\": {1}, \"mask\": {1}}))\n", - " lockdown_intervened = (trace.nodes[\"__cause____antecedent_lockdown\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_mask\"][\"value\"] == 1) & (trace.nodes[\"__cause____witness_lockdown_efficiency\"][\"value\"] == 0) \n", - " data_to_plot = data_to_plot.squeeze()[torch.nonzero(lockdown_intervened.squeeze())]\n", - "\n", - " os_too_high = (gather(trace.nodes[\"os_too_high\"][\"value\"], IndexSet(**{\"lockdown\": {1}, \"mask\": {1}})))\n", - " os_too_high = os_too_high.squeeze()[torch.nonzero(lockdown_intervened.squeeze())]\n", - "\n", - "print(os_too_high.squeeze().mean())\n", - "print(data_to_plot.mean())\n", + "plt.bar(bin_edges[:28].tolist(), hist_fact, align='center', width = 35/28, alpha = 0.5, color='blue')\n", + "plt.bar(bin_edges[:28].tolist(), hist_lockdown, align='center', width = 35/28, alpha = 0.5, color='pink')\n", + "plt.bar(bin_edges[:28].tolist(), hist_mask, align='center', width = 35/28, alpha = 0.5, color='green')\n", + "plt.legend([\"factual\", \"counterfactual_lockdown\", \"counterfactual_mask\"])\n", + "plt.ylabel(\"pr\")\n", + "plt.xlabel(\"overshoot\")\n", "\n", - "hist_lockdown, bin_edges = torch.histogram(data_to_plot, bins = 28, range=(5, 40), density=True)\n", + "print(\"Overshoot mean\")\n", + "print(\"factual: \", os_fact.item(), \" counterfactual mask: \", os_mask.item(), \" counterfactual lockdown: \", os_lockdown.item())\n", "\n", - "plt.bar(bin_edges[:28].tolist(), hist_lockdown, align='center', width = 35/28)" + "print(\"Probability of overshoot being high\")\n", + "print(\"factual: \", oth_fact.item(), \" counterfactual mask: \", oth_mask.item(), \" counterfactual lockdown: \", oth_lockdown.item())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sufficiency worlds" ] }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 167, + "metadata": {}, + "outputs": [], + "source": [ + "hist_fact, bin_edges, os_fact, oth_fact = histogram_data(importance_tr, mwc_imp, {}, 0)\n", + "hist_mask, bin_edges, os_mask, oth_mask = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 0, \"__cause____antecedent_lockdown\": 1, \"__cause____witness_mask_efficiency\": 0}, 2)\n", + "hist_lockdown, bin_edges, os_lockdown, oth_lockdown = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 1, \"__cause____antecedent_lockdown\": 0, \"__cause____witness_lockdown_efficiency\": 0}, 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 168, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.9071)\n" + "Overshoot mean\n", + "factual: 26.768293380737305 counterfactual mask: 26.77214813232422 counterfactual lockdown: 26.866281509399414\n", + "Probability of overshoot being high\n", + "factual: 0.8101999759674072 counterfactual mask: 0.8060453534126282 counterfactual lockdown: 0.8146666884422302\n" ] }, { "data": { - "text/plain": [ - "Text(0.5, 0, 'overshoot')" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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      " ] @@ -699,331 +682,57 @@ } ], "source": [ - "# Same for plate and importance\n", - "mwc = mwc_imp\n", - "trace = importance_tr\n", - "\n", - "with mwc:\n", - " data_to_plot = gather(trace.nodes[\"overshoot\"][\"value\"], IndexSet(**{\"lockdown\": {1}, \"mask\": {1}}))\n", - " mask_intervened = (trace.nodes[\"__cause____antecedent_lockdown\"][\"value\"] == 1) & (trace.nodes[\"__cause____antecedent_mask\"][\"value\"] == 0) & (trace.nodes[\"__cause____witness_mask_efficiency\"][\"value\"] == 0) \n", - " data_to_plot = data_to_plot.squeeze()[torch.nonzero(mask_intervened.squeeze())]\n", - "\n", - " os_too_high = (gather(trace.nodes[\"os_too_high\"][\"value\"], IndexSet(**{\"lockdown\": {1}, \"mask\": {1}})))\n", - " os_too_high = os_too_high.squeeze()[torch.nonzero(mask_intervened.squeeze())]\n", - "\n", - "print(os_too_high.squeeze().mean())\n", - "pr_too_high = data_to_plot[data_to_plot > torch.tensor(overshoot_threshold)].mean()\n", - "\n", - "hist_mask, bin_edges = torch.histogram(data_to_plot, bins = 28, range=(5, 40), density = True)\n", - "\n", "plt.bar(bin_edges[:28].tolist(), hist_fact, align='center', width = 35/28, alpha = 0.5, color='blue')\n", "plt.bar(bin_edges[:28].tolist(), hist_lockdown, align='center', width = 35/28, alpha = 0.5, color='pink')\n", "plt.bar(bin_edges[:28].tolist(), hist_mask, align='center', width = 35/28, alpha = 0.5, color='green')\n", "plt.legend([\"factual\", \"counterfactual_lockdown\", \"counterfactual_mask\"])\n", "plt.ylabel(\"pr\")\n", - "plt.xlabel(\"overshoot\")" + "plt.xlabel(\"overshoot\")\n", + "\n", + "print(\"Overshoot mean\")\n", + "print(\"factual: \", os_fact.item(), \" counterfactual mask: \", os_mask.item(), \" counterfactual lockdown: \", os_lockdown.item())\n", + "\n", + "print(\"Probability of overshoot being high\")\n", + "print(\"factual: \", oth_fact.item(), \" counterfactual mask: \", oth_mask.item(), \" counterfactual lockdown: \", oth_lockdown.item())" ] }, { - "cell_type": "code", - "execution_count": 69, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "def get_table(\n", - " trace, mwc, antecedents, witnesses, consequents, others=None, world: int = 1\n", - "):\n", - "\n", - " values_table = {}\n", - " nodes = trace.nodes\n", - " witnesses = [key for key, _ in witnesses.items()]\n", - "\n", - " with mwc:\n", - "\n", - " for antecedent_str in antecedents.keys():\n", - "\n", - " obs_indices = IndexSet(\n", - " **{\n", - " name: {0}\n", - " for name, ind in indices_of(nodes[antecedent_str][\"value\"]).items()\n", - " }\n", - " )\n", - " obs_ant = gather(\n", - " nodes[antecedent_str][\"value\"],\n", - " obs_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " int_indices = IndexSet(\n", - " **{\n", - " name: {world}\n", - " for name, ind in indices_of(nodes[antecedent_str][\"value\"]).items()\n", - " }\n", - " )\n", - " int_ant = gather(\n", - " nodes[antecedent_str][\"value\"],\n", - " int_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " values_table[f\"{antecedent_str}_obs\"] = obs_ant.squeeze().tolist()\n", - " values_table[f\"{antecedent_str}_int\"] = int_ant.squeeze().tolist()\n", - "\n", - " apr_ant = nodes[f\"__cause____antecedent_{antecedent_str}\"][\"value\"]\n", - " values_table[f\"apr_{antecedent_str}\"] = apr_ant.squeeze().tolist()\n", - "\n", - " if witnesses:\n", - " for candidate in witnesses:\n", - " obs_indices = IndexSet(\n", - " **{\n", - " name: {world}\n", - " for name, ind in indices_of(nodes[candidate][\"value\"]).items()\n", - " }\n", - " )\n", - " obs_candidate = gather(\n", - " nodes[candidate][\"value\"],\n", - " obs_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " int_indices = IndexSet(\n", - " **{\n", - " name: {world}\n", - " for name, ind in indices_of(nodes[candidate][\"value\"]).items()\n", - " }\n", - " )\n", - " int_candidate = gather(\n", - " nodes[candidate][\"value\"],\n", - " int_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " values_table[f\"{candidate}_obs\"] = obs_candidate.squeeze().tolist()\n", - " values_table[f\"{candidate}_int\"] = int_candidate.squeeze().tolist()\n", - "\n", - " wpr_con = nodes[f\"__cause____witness_{candidate}\"][\"value\"]\n", - " values_table[f\"wpr_{candidate}\"] = wpr_con.squeeze().tolist()\n", - "\n", - " if others:\n", - " for other in others:\n", - " obs_indices = IndexSet(\n", - " **{\n", - " name: {0}\n", - " for name, ind in indices_of(nodes[other][\"value\"]).items()\n", - " }\n", - " )\n", - "\n", - " obs_other = gather(\n", - " nodes[other][\"value\"],\n", - " obs_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " int_indices = IndexSet(\n", - " **{\n", - " name: {world}\n", - " for name, ind in indices_of(nodes[other][\"value\"]).items()\n", - " }\n", - " )\n", - "\n", - " int_other = gather(\n", - " nodes[other][\"value\"],\n", - " int_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " values_table[f\"{other}_obs\"] = obs_other.squeeze().tolist()\n", - " values_table[f\"{other}_int\"] = int_other.squeeze().tolist()\n", - "\n", - " for consequent in consequents.keys():\n", - "\n", - " obs_indices = IndexSet(\n", - " **{\n", - " name: {0}\n", - " for name, ind in indices_of(nodes[consequent][\"value\"]).items()\n", - " }\n", - " )\n", - " obs_consequent = gather(\n", - " nodes[consequent][\"value\"],\n", - " obs_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " int_indices = IndexSet(\n", - " **{\n", - " name: {world}\n", - " for name, ind in indices_of(nodes[consequent][\"value\"]).items()\n", - " }\n", - " )\n", - " int_consequent = gather(\n", - " nodes[consequent][\"value\"],\n", - " int_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " values_table[f\"{consequent}_obs\"] = obs_consequent.squeeze().tolist()\n", - " values_table[f\"{consequent}_int\"] = int_consequent.squeeze().tolist()\n", - "\n", - " values_df = pd.DataFrame(values_table)\n", - "\n", - " return values_df\n", - "\n", - "\n", - "table = get_table(\n", - " importance_tr,\n", - " mwc_imp,\n", - " antecedents,\n", - " witnesses,\n", - " consequents,\n", - " others=[\"joint_efficiency\", \"overshoot\"],\n", - ")" + "# Counterfactual lockdown by mask efficiency contexts" ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 169, "metadata": {}, - 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      " - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [lockdown_obs, lockdown_int, apr_lockdown, mask_obs, mask_int, apr_mask, lockdown_efficiency_obs, lockdown_efficiency_int, wpr_lockdown_efficiency, mask_efficiency_obs, mask_efficiency_int, wpr_mask_efficiency, joint_efficiency_obs, joint_efficiency_int, overshoot_obs, overshoot_int, os_too_high_obs, os_too_high_int]\n", - "Index: []" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
      " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "factual = table[\n", - " (table[\"lockdown_int\"] == 1)\n", - " & (table[\"mask_int\"] == 1)\n", - " & (table[\"wpr_lockdown_efficiency\"] == 0 & (table[\"wpr_mask_efficiency\"] == 0))\n", - "]\n", - "\n", - "\n", - "counterfactual_lockdown = table[\n", - " (table[\"lockdown_int\"] == 0)\n", - " & (table[\"mask_int\"] == 1)\n", - " & (table[\"wpr_lockdown_efficiency\"] == 0)\n", - "]\n", - "\n", - "display(counterfactual_lockdown)\n", - "\n", - "counterfactual_mask = table[\n", - " (table[\"lockdown_int\"] == 1)\n", - " & (table[\"mask_int\"] == 0)\n", - " & (table[\"wpr_mask_efficiency\"] == 0)\n", - "]\n", - "\n", - "\n", - "fig, axs = plt.subplots(1, 3, figsize=(18, 6))\n", - "\n", - "factual_mean = factual[\"overshoot_int\"].mean().item()\n", - "axs[0].hist(factual[\"overshoot_int\"])\n", - "axs[0].set_title(\n", - " f\"Factual\\n overshoot mean: {factual_mean:.2f}, Pr(too high): {factual['os_too_high_int'].mean().item():.2f}\"\n", - ")\n", - "axs[0].axvline(x=factual_mean, color=\"grey\", linestyle=\"--\")\n", - "\n", - "counterfactual_lockdown_mean = counterfactual_lockdown[\"overshoot_int\"].mean()\n", - "axs[1].hist(counterfactual_lockdown[\"overshoot_int\"])\n", - "axs[1].set_title(\n", - " f\"Counterfactual_lockdown\\n overshoot mean: {counterfactual_lockdown_mean:.2f}, Pr(too high): {counterfactual_lockdown['os_too_high_int'].mean():.2f}\"\n", - ")\n", - "axs[1].axvline(x=counterfactual_lockdown_mean, color=\"grey\", linestyle=\"--\")\n", - "\n", - "counterfactual_mask_mean = counterfactual_mask[\"overshoot_int\"].mean()\n", - "axs[2].hist(counterfactual_mask[\"overshoot_int\"])\n", - "axs[2].set_title(\n", - " f\"Counterfactual_mask\\n overshoot mean: {counterfactual_mask_mean:.2f}, Pr(too high): {counterfactual_mask['os_too_high_int'].mean():.2f}\"\n", - ")\n", - "axs[2].axvline(x=counterfactual_mask_mean, color=\"grey\", linestyle=\"--\")\n", - "\n", - "for i in range(3):\n", - " axs[i].set_xlim(5, 40)\n", - " axs[i].axvline(x=overshoot_threshold, color=\"red\", linestyle=\"-\")\n", - "\n", - "plt.savefig(\"counterfactual_sir_search.png\")\n", - "plt.show()" + "hist_lockdown_fix, bin_edges, os_lockdown_fix, oth_lockdown_fix = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 1, \"__cause____antecedent_lockdown\": 0, \"__cause____witness_lockdown_efficiency\": 0, \"__cause____witness_mask_efficiency\": 1}, 1)\n", + "hist_lockdown_notfix, bin_edges, os_lockdown_notfix, oth_lockdown_notfix = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 1, \"__cause____antecedent_lockdown\": 0, \"__cause____witness_lockdown_efficiency\": 0, \"__cause____witness_mask_efficiency\": 0}, 1)" ] }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 178, "metadata": {}, "outputs": [ { - "data": { - "image/png": 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Gj4+PUahQoRjHbcWKFYbFYjH69Onz1O1azxdvb29j/fr1Zvnhw4cNb29vw2KxGMWKFTOOHz9uLps/f75hsViMOnXqmGWHDh0yLBaLUblyZSMkJMRmG7/++qthsViMQoUKGeHh4YZhGMb58+cNi8ViVK9e3WZfw8LCjPfeey/G7+Xx83rZsmWGt7e3UapUKSMoKOip+2kYhnH9+nXD39/fyJ8/v7Fy5UqbZT/++KNhsViMKlWqGA8ePDDL4/qbje1vxzBiv94MHjzYsFgsRqtWrYzbt2+b5QcOHDAKFSpkFChQwLh69Wqs+/loDPXq1TOCg4PN8jt37hht27aNEcej59Jnn31m3L1711xmvR+UKlXK5vraoUMHw2KxGN26dbPZ//Xr1xv58uUz/P39jfDwcCMiIsIoVaqUYbFYbO4n1v2xWCzG+++/H8vRt7V27VrzHvDo7+/atWtGzZo1DYvFYqxZs8YwjGe/JhnG/66lderUMc6dO2eWHz582PDx8TEsFovNOR3X7/nkyZNGwYIFjSJFihg7d+40y6OioozRo0cbFovF5npgGIaxatUqM94bN24YYWFhRvXq1Q2LxWIsXLjQpm5sv2/rNcjf39+4fPmyWR4REWF07tzZsFgsxvjx4596jAEAMdFTCgCeg/UbUWuPn2dl7VEyZMgQ81tZSUqVKpVGjhyp1KlTa+3atTbD8Z7Xe++9p4CAAPNnT09PvfXWW5IU69C7x61bt05nzpxRpUqV1Lp1a5thiJUrV1aTJk1069YtLV68OMZ769WrJ09PT0mSs/OTbzlXr15VtWrV1LFjxxhPzCpevLjy5s0rSTbfgC9YsECSNHToUJt5ZF5//XV16NBBFotFp06deuo+PourV6+qbNmyatOmjYoXL26zLHfu3CpZsmSMOF+U9Xzp1auXChYsaJa7u7trwIABypkzp/7++2+zN9G+ffv0zz//KG/evOrRo4fNsW/evLn8/f2VKlWqGMNcpIdPnvrpp59UuHBh/fDDD0qZMqW5LDw8XEuXLpWbm5uGDBmiZMmSmcsaNWqkSpUqxVjfrFmzFB0drY8++kjlypUzy52dndW5c2f5+/vr0qVL+vXXXyVJFStWlCSboVb79+9XWFiYatWqJUnm8E9J+uOPP2QYRqzbftFz38rV1VWDBw9WqlSpzLJ3331XlSpV0o0bN7R69WpJD3vhVa5cWWFhYVqzZo3NOqy9WJ5lYv+KFSuaw2olycfHx/zbaN68ufk3Ickc8vjoNeP27duqUaOGunXrpowZM9qsu27dukqTJo3CwsJ07do1SQ/PbUlKly6dzb5ae8INGTJEhQoVijXWtWvXqk+fPkqXLp1mzZql3Llzx2sfFy1apJs3b6pZs2bm79eqcePGqlSpkoKDg7Vu3bp4rS8+wsPDtWTJErm6umr48OE2c0X5+fmpefPmslgsTzxHpk6dKunh30v27NnN8lSpUmnYsGFyc3PTvHnzYvSWcnV11Zdffmnz0IvGjRvL3d1d165dM38Xly9f1saNG+Xp6amhQ4fK3d3drF+lShXVrl1bb7zxhk6fPi1XV1e9/fbbkmL2lrIORYvPeTd//nxJD4crPvr7S58+vbp3765cuXKZQ1+f9Zr0qK5duypbtmzmzz4+PuZTUU+cOPHUOGfNmqXw8HB16dJF/v7+Zrmzs7M+++wzeXt7a8+ePTZDHGvWrKnatWvr+vXrGjlypEaNGqXTp0+rSpUqeu+99566zTt37igsLEweHh4280e6urqqe/fuGjBgQKzXIADA05GUAoDn4Or68OGlUVFRz/zeixcv6ty5c0qfPr1Ng9oqderUKl++vCTbD9/PK7YPkdYEzuNzV8XG+qHCmmx5nDXRsHPnzhjL8uXLF+84CxQooDFjxqh169ZmWVRUlE6fPq1ff/1Vt27dkiRzXpPLly/r9OnTypgxo/z8/GKsr2XLlvr1118T/HHiWbNm1ciRI9W9e3ezzDAMnTt3TmvXrjWTUbEN3XkekZGR2rt3r5ydnVWtWrUYy11dXVW9enVJ//sdWM+bihUrxjqX2Zw5c7RgwQKlT5/epnzixImaOXOmXFxcNH78eJvEhCQdOnRI9+7dU8GCBWO8V5LNkE+r3bt3S1Ks80RJUp06dWxiDggIkIeHh82H2e3bt0uSPvjgA7m6utr8XViHjj4+B5r04ue+la+vr3LkyBGj3JowejSe2IbwhYSEaOvWrcqZM6eKFSsW7+3GFr/1uHt7e9uUWxMrj553pUuX1vjx481jbF1+4sQJLV682Bxian1P3rx55enpqX379qlp06aaM2eOTp8+LenhMWjUqFGsyaZt27apW7duioqKUr9+/WySZU9jPWcfTR4+6knXl+dlPY99fHxiTIouST179tSyZctUqlSpWN8fEhKiU6dOKXXq1PLx8YmxPFOmTMqXL5/u3Lmjf/75x2ZZjhw5zES9lbu7u5nosJ6X1v0tXbp0rEPER40apUWLFpnH2jqE79HhaeHh4Vq5cqVSpUplXiPiYhiGdu/eLWdn51iTK1WrVtWqVavUunXr57omPapw4cIxyqxPNnx07rq4POmccXJyUtmyZSXFvH/2799fGTNm1OLFizV37lx5eXlpyJAhT92e9PALqFy5cunixYt69913NXXqVB07dkySlDNnTjVt2tQmOQcAiD9XRwcAAK8iLy8vHT16NNaeJk9z5coVSXri48mt37wnxBwVadOmjVHm4uIiKX6P3LbO7TNs2DANGzYsznqXLl2K17afJCoqSqtXr9aKFSt04sQJXbhwwZxg3JpcMf5/ThPrcXzttdeeaRsJ5ffff9eyZct07NgxBQcHm8my2JJAL+LmzZuKiIiI0XvlUY+fL9b/n/XY/PLLL3J1dVVkZKSmTZumPn362Cy3HvPYPsg/Gkds73m0Z8STYk+WLJlKliypTZs26cSJE8qbN6+2b98uLy8vFShQQL6+vjp48KBCQ0Pl4eGhP//8U2+88UasyZIXPfet4ord+vTIy5cvm2XlypVTlixZtGvXLl28eFGvvfaafv31V0VFRT1TLykp9vit59fjiY24zrsHDx5o+fLlWr9+vYKCgnTp0iVz3x//m/Lw8NC4cePUo0cP7d27V3v37pX0sOdhlSpV1KRJk1jniFq1apWZqJ82bZpq1KhhHuensV5fOnfu/MR6sV1fntfz/n1YWWO+c+dOjORgbHUfTcKkSZMm1nrW42f93TxrjLlz51aRIkW0b98+/fXXXypevLg2bdqkmzdvqnHjxk+d+/DGjRuKiIhQ+vTpn/qUwue5Jj0qtmNg3X8jlon6H2c9/k+bI+vxeenSpUunL774Ql27djXndYwtuR6XsWPHqkuXLjp27JiOHTumkSNHysvLS5UrV9Z7771HUgoAnhNJKQB4Dj4+Pvrjjz904MCBWJ8G9qjQ0FBNmTJF/v7+Kl26dLwa3dYeWI8O2YhP/di8aJLE+iEpICDA/DY7NrE17p82ZO9R9+7d0/vvv6+DBw8qefLkKliwoMqUKaO8efOqWLFiGjJkiNnrRnq+XmrPKrZtREdHq2PHjtq0aZPc3NxUoEABvfPOO8qTJ4+KFi2qOXPm6JdffkmwGJ7nfHn8SYHxlT9/fg0ZMkQtWrTQnDlzzKfWWT3tXLJ+sHzU0+KP7VyvVKmSNm3apD///FOvvfaaDh06ZE4OHhAQoH379mnPnj1KmTKlbt++HeeH04RKED46TDE2j+63s7Oz6tevrylTpujXX39Vu3bt9PPPP8vZ2VnvvPPOM23Xzc3tecI1XblyRS1atNCZM2eUKlUq+fn5qUqVKvL29pa/v79at24d40mEJUuW1Pr167V582b9/vvv2rFjh4KDgzVz5kzNmzdPY8aMidE7Jm3atJo6dar69u2rw4cPa+bMmfrggw/iFaP191+pUqU4ExzSw0nwE8qLXjus7/f09LQZkhobLy8vm5/je04+T4zvvvuu9u3bp19++UXFixc3e03FJxn6LNt70XvYi/5dWtddp06dJ95jYuup+8cff5ivly1bprfeeive8Xh7e2vlypXatm2bNm3apO3bt+v06dNauHChfvrpJ/Xp08d8yisAIP5ISgHAc6hWrZq+/fZbbdq0SQ8ePHjih9a1a9fqhx9+0OLFi7Vt2zYzsfPoo84fZ31qkXUeGGvDO67eHbdv336u/YgP64eqevXqqVGjRom2nenTp+vgwYMqVaqUxo8fH+Pb9Mf30RpXXD0orl69qg0bNihfvnxxzoMjPfnYxnZcf/75Z23atEne3t6aOnVqjF5Djz8Z7kV5enrKzc1Nt27dUmhoaKwf3K3ni3WOM+s59mgPnkdt375dV69elb+/v038I0aMUN68edWxY0eNGjVKX3zxhTmHlPS/HlKPJzKsrL2iHpUpUyadO3dO58+fj3VYl3W446Pzs1mford9+3blzJlTkZGR5lDXgIAAffvtt9q5c6cZV2LP5RLbfkn/+xt+vNfju+++q2+//VZr1qxRjRo1dPToUZUrVy7OHmaJZcyYMTpz5ozq1aunoUOHxrhOxXXdSJ48uWrWrGkmAk+ePKlvv/1Wv/zyi4YPHx4jKfXpp5+qUKFCGjRokJo2baoJEyaoevXqNvPlxSVTpkw6ffq0WrVq9UJPxHwW1mtHXH8f//77r/bs2SNfX99Ye0JZ358sWTKNHDnSITEeOnRIJ0+eVNGiRc2nw9WuXVtfffWV1q9fr169emnr1q3KlStXrMPlHvfodeb+/fsxelY9ePBAixcvVq5cuVS8ePFnviYlpEyZMun8+fP69NNPYx1WG5eNGzdq6dKlyp49u9KlS6ft27dr/vz5at68ebzX4erqqgoVKpjXqAsXLmj27NmaMWOGxowZoyZNmsT7yyQAwEPMKQUAz6FgwYLy9/fXlStXNGXKlDjr3bx501z+3nvvydXVVVmzZlW2bNl048aNWOeMunPnjrZt2yZJKlGihCSZk+JaJ8F91IkTJ+I1D8fTxPVtsTUG69w9j5s9e7bq1aunSZMmvdD29+3bJ0lq0aJFjITU5cuXdfLkSUn/Sx5ly5ZNmTNnVkhIiI4cORJjfevWrVP//v3122+/SYp7/550bA8cOBBnnO+++26MJMPdu3fN5c8yPOxJ3NzcVKRIEUVHR8c62XNkZKTWr18v6X9zrFgnDd6yZUus6xwzZox69OgRY/ip9cNUmzZtzImerRM6Sw/P+zRp0ujvv/+ONTG1efPmGGXW8+fxib+tVq1aZRO79HBYXL58+bRr164Y88cULVpUbm5u2rVrl7Zs2aLUqVPHmHA+oe3bty/WZOPatWslKcbccK+//rr8/f11+PBhzZkzR9KzTXCeUKzn4ocffhgjIXXgwAFzn6zn6i+//KJq1app8uTJNnVz586t/v37S4o5JEr633lTpEgRvffeewoLCzPrP83Tri/Dhw/XO++8o59++ile64sPHx8fubu76/Dhw7H+3S9ZskRffPGFOZfZ47Jnz66sWbPq8uXLOnr0aIzlYWFheuutt9S8efPnfuCB9W94+/btsc5PN336dH3++ec2k7GnTJlSNWvW1LVr1zR27Fg9ePAg3uedm5ubfH19FRUVpa1bt8ZYvnPnTg0aNEhz5sx5rmvS83jee1L37t317rvvasOGDWbZzZs3zXNy0KBBGjJkiFxdXTVy5EidPXv2qdvdvn27atWqpX79+tmUZ82aVb1791aaNGl079493bx5M977BwB4iKQUADynAQMGyMPDQ1OmTNHXX39tTsRtFRwcrA4dOujs2bN644031L59e3PZ+++/L0nq27ev+Y2y9DCp0bNnT4WGhqpSpUrmXDZvvvmm3N3dFRwcrI0bN5r1b9++rYEDBybI/lg/tN67d88moVK7dm15eXlp3bp1mjFjhs3QjYMHD2r8+PE6fvz4U+dWeRrr8L9NmzbZbOPChQvq3LmzOSTtwYMH5rIWLVpIengcH02wBAcHa9KkSXJ2djYnOrfu3+PJBesQj7/++kt///23WX7p0iWNGDEizji3bNliM0zuxo0b6tq1q27cuBEjzhdlPV+GDx9uE2NERIQGDhyos2fPKl++fOYk2iVLllTu3Ll15MgRTZw40eZ4zp8/XwcOHJDFYlH+/Plj3Z6bm5sGDBggJycnTZkyxUwIurm5qVmzZoqKilKvXr1sjuWaNWvMJ+g9qkWLFnJxcdHUqVNtPuwahqGJEydq9+7dypw5c4xJ0itWrKi7d+9q0aJFypw5s3LmzCnpYS+eQoUK6Z9//tGRI0dUrly5Fx7m9jShoaHq16+fOW+Y9PAJYNu2bVO2bNlincTdOqx33rx5Sps2bayTwCc267n66Idz6eGTB3v27Gn+bD1X8+bNq7Nnz2r27NkxnlppHZIa20MFHtWjRw9lzJhRf/75p5YsWfLUGBs3bqwUKVJo7ty5WrFihc2yjRs3avbs2Tp69Kh8fX2fuq74SpkyperXr6+IiAj16dPHZtL7Q4cOae7cuUqePHmck/NL//ub7NWrl01SIzw8XAMGDNCxY8d07969WOdZi48cOXKobNmyunbtmgYPHmxzrdm0aZNWr16tDBkyqEyZMjbve/S8c3FxMZ/KFx/W6+mwYcNskmnXr1/X8OHDJcl8euWzXpOeR1zX7JYtW8rFxUXjxo2LkThcsGCBfvvtN504ccKmh+ygQYMUEhKid955R2XKlFG+fPnUtm1b3bt3T71797a551mTrHfu3DHLvL29dfbsWf3888/as2ePzTY3b96s27dvK2vWrDGGawIAno7hewDwnHLnzq1Zs2apffv2mjFjhhYsWCBfX19lzJhRly5d0sGDBxUVFaU8efLou+++sxni0LJlS+3bt0+rVq1S7dq15e/vLw8PD/3111+6ceOGvL29NXToULN+ihQp1Lx5c82YMUOdOnUy6+/evVtp06aVv7//Cz+pL3369EqTJo1u376tJk2a6I033tDIkSPl4eGh8ePHq127dvr66681d+5ceXt76+bNm9q7d68Mw9D777//wh+6W7RooVWrVmnx4sXau3ev8ubNq+vXr2vfvn0yDENvvvmm/v33X/Ox9ZLUtm1b7d69W1u2bFG1atXk7++v8PBw/fXXX7p//74++eQT80N09uzZ5eLiouPHj+v999+Xt7e3+vTpozfeeEPVq1fX2rVr1bhxY/OJWzt37lSePHliPBq+YcOGmjNnjrZu3arq1avLx8dHoaGh2rt3r+7fv688efIoKCjIJs4XVbVqVbVt21bTp09Xo0aNVKxYMaVLl04HDhzQpUuXlC1bNo0ZM8Yciujs7KzRo0erdevWmjBhgn777TdZLBadPXtWR44cUcqUKTVmzJgnbrNYsWJq1KiRfvrpJ/Xr10/z5s2Tk5OTOnbsqL1792rXrl2qWrWqSpQooatXr2rv3r3mRMuPKliwoAIDAzV06FB98MEHKly4sLJkyaKjR4/q9OnT8vT01JgxY2IMAapYsaK+/fZb3b592xwqYxUQEKC//vpLUuIP3ZMePhly/fr1ql69unx9fc3jmDp1ao0dOzbW4To1atTQ4MGDdefOHdWtW9chQ3pat26tPXv2aPz48dqwYYOyZ8+uy5cv68CBA0qePLmyZ8+uc+fOmedq/vz51apVK7P3Y9GiRZUuXTqdOXNGR48eVYoUKRQYGPjEbaZJk0a9e/dWjx49NHz4cFWsWPGJQ7gyZ86sb775Rt26dVO3bt00adIk8ylnhw8fliT16dMnzgTq8+rVq5cOHz6szZs3q3LlyipevLhu3bqlv/76S1FRUfrmm2+eOMl4q1atdODAAa1cuVJ169aVr6+vPD09dfDgQV25ckUZMmTQ6NGjXyjGoUOHqnnz5vrpp5+0detW+fr66sqVK9q3b59cXV01evToGJOSFy9e3LxWVqxY8YnzAD6uTp062r59uxYtWmTel1xcXLRnzx7duXNH7777rjmk81mvSc/DOjRv8+bNat++vYoUKaIOHTqoYMGC6tOnj4YMGaLWrVurQIECyp49u/7991+dOHFCLi4uGjFihDn83frwjPTp06t3797m+jt37qw1a9Zoz549mjlzptq2bStJZgJ88uTJ2rdvn95++21VrVpVPXv21LBhw9S8eXMVLlxYmTJl0uXLl7V//365uLiof//+Cf6gCwD4L6CnFAC8gEKFCmnlypXq0qWLvL29dezYMa1du9ac66N///5atmxZjG/LnZ2dNWbMGA0bNkwFCxbU3r17tW3bNmXJkkU9e/bUokWLYnyQ69Wrl/r06aPcuXNr7969OnTokGrVqqVFixaZje8X4ezsrJEjRyp37tz6559/tG3bNrP3V9GiRbV8+XI1adJEhmFoy5YtOnv2rAICAjRp0qQYT2l7HoUKFdL8+fNVrlw53b59Wxs3btSZM2dUtWpV/fjjj+rWrZukh70ErFxdXTVlyhT169dPOXLk0Pbt2/XXX38pX758GjVqlDp16mTWzZAhg4YOHars2bNrz549NusZOXKkOnfurKxZs2r79u06ceKEmjdvrjlz5sT40Jc9e3YtWrRINWrUUGRkpDZu3Khjx44pICBA06dPN3sUPLr+hPD5559r8uTJCggI0NGjR7V582alTJlSH3/8sZYtW6ZcuXLZ1M+XL5+WLVumJk2a6MGDB9q4caMuX76sunXrasmSJfGaOLpHjx7KkCGD9uzZowULFkh62Hth2rRp6t69u9KlS6fff/9dISEh6tGjhz799NNY19OyZUvNnTtXlStX1unTp7Vx40ZFR0fr/fff188//xxrb4pChQopXbp0kmIOAbL+7OLiovLlyz/94L2gAgUKaNasWXrttdf0+++/6+LFi+ZxjKvnUPLkyeXj4yPJMUP3JKl69eqaNm2aSpQoofPnz2vjxo0KCQlR/fr1tXTpUrNnzKPnamBgoAYMGCAfHx8dPnxYGzdu1K1bt/Tuu+/q559/jlePpXr16ql06dK6efOmBg8eHK84lyxZorfeekt37tzR5s2bdfXqVVWqVEmzZ882e+UkpFSpUmnevHn67LPPlCFDBm3evFmHDh2Sv7+/pk2b9tRJ6a2J32+++Ua+vr46evSotm7dqtSpU6tNmzZavnx5rE8qfBZZsmTRkiVL9MEHH8jNzU0bN27UyZMnValSJS1YsEAlS5aM9X3WoX/Pc94NGTJEI0aMkI+Pj/bs2aM///xT2bJlU//+/TVkyBCbus96TXpWPj4+6t69u7y8vLRt2zb9+eef5rIWLVpo3rx5qlatmi5duqRNmzbp3r17ql27thYvXmz2crt27ZoGDBggSfriiy/Ma4r08Fpm7Wk8duxYs0do06ZNzd//li1bzORo69atNWbMGJUoUUInT57Uhg0bdP78edWuXVuLFi2yS4IcAJIiJyM+j9AAAABAvF27dk0VKlSQxWLR0qVLHR0O/iPCw8NVvnx5ubi4aPPmzYk+tBUAgBdFTykAAIAEEBkZqcjISN2/f18DBw5URESE2RsJSCzR0dEKDw9XZGSkRo4cqRs3bqhJkyYkpAAArwTmlAIAAEgAwcHBqlu3rqSHCar8+fObE0MDiSUyMlJFihSRk5OTIiIilDlzZrVu3drRYQEAEC/0lAIAAEgAWbNmVaZMmeTq6qqyZcvq22+/lasr3/8hcbm7uytfvnxycnJSkSJF9MMPPyh16tSODgsAgHhhTikAAAAAAADYHT2lAAAAAAAAYHckpQAAAAAAAGB3JKUAAAAAAABgdySlAAAAAAAAYHckpQAAAAAAAGB3JKUAAAAAAABgdySlAAAAAAAAYHckpQAAAAAAAGB3JKUAAAAAAABgdySlAAAAAAAAYHckpQAAAAAAAGB3JKUAAAAAAABgdySlAAAAAAAAYHckpQAAAAAAAGB3JKUAAAAAAABgdySlAAAAAAAAYHckpQAAAAAAAGB3JKUAAAAAAABgdySlAAAAAAAAYHckpQAAAAAAAGB3JKUAAAA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", - "text/plain": [ - "
      " - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "Overshoot mean\n", + "mask_efficiency fixed: 18.921985626220703 mask_efficiency not fixed: 26.140743255615234\n", + "Probability of overshoot being high\n", + "mask_efficiency fixed: 0.38847583532333374 mask_efficiency not fixed: 0.9764150977134705\n" + ] }, { "data": { - "image/png": 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", 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v8cwzz9CvXz98fHzo3r17BVcoIlL52OzbMyUlBS8vL5ycnCzT6tatS25uLmlpadSpU8eq/bJlywCK/J/6pUuXSE9PJz8/n+HDh5OUlERAQADR0dHUr1+//DtSUFC6Z9PILV2+fJm6devSunVroPCZKa1ateKOO+4o0Xqu3ZeM7vLly9x33300btwYAB8fHxtXJCJiezY7pZWdnW0VdgDL67y8vGKvJysrC4DZs2cTFhbG8uXLycvLY9SoURRUxJGXSurMmTP4+vry1Vdf0aVLF4KCgpg9ezbHjh0jIiKCtm3bMmrUKDIyMoDC93zevHl07NgRf39/unTpwoYNGyzr++677yynk7p27cr69euvu91du3YREBDA7t27i1Xnvn37iIiIICAggLCwMHbu3AkUBtspU6Zw9uxZfH19LaeltmzZgq+vL2fOnLE6VWUymVi4cCGhoaEEBwfz/PPPk5qaClif0jKbzbz11luEhoYSEhLC6NGjOXv2rKUeX19fPv74Y/r06UPr1q0ZNGgQp0+ftsw/dOgQAwcOJDAwkEcffdTyeIzu3bvzz3/+06pvYWFhbNy4sUiflyxZwsSJE4mKiqJdu3a0b9/ecvQKoKCggFWrVtG1a1fL6byjR48CMGXKFPbu3cvSpUt5+umni6z76umrwYMH8/TTT/PDDz9Ypm3cuJHWrVvzyy+/AJCcnEybNm3YtWsXAL/99hujR48mMDCQLl26sHTpUvLz8y3r/uyzzywXBrzyyitW80REKjubBR5nZ+ciwebq65I8XdnBwQGA/v37Ex4eTkBAAAsWLODYsWMcPHiwzOqtqlauXMmyZcuYNWsWa9asYcyYMUycOJF3332XgwcP8tFHH1naffXVVyxZsoQdO3YQHh7OrFmzuHDhAvn5+YwfP54ePXqwfft2xo0bx8yZMzl+/LjVtvbv389LL73Eq6++SseOHW9ZW0pKCqNGjSIiIoKtW7cyYsQIpkyZwr59++jVqxfTpk2jQYMG7Nmzh48//piePXvSs2dP9uzZQ8OGDa3W9cYbbxAXF8fcuXPZsGEDf/zxB1FRUUW2uXbtWrZu3crrr7/Ohg0b8Pb2ZtiwYVy5csXSZsmSJURGRrJ582ZSU1NZvHgxAH/88QfDhg3Dz8+PuLg4Ro0axeTJk0lKSqJ3796WsAaFYeLkyZM3PJ20c+dOnJ2diYuLY/jw4SxYsICTJ08C8NZbbxEbG8u0adOIi4ujcePGjBgxgqysLCIjIwkKCmLYsGHXHT+zZ88eSx+unf/4448TFBTEvHnzMJvNzJgxg+7du/PII49gNpsZM2YM3t7exMXFMW/ePLZu3crbb78NwPHjxxk/fjwDBw5k06ZNmEwm4uPjb/URi4hUGjYLPPXr1yc1NRWT6f8G4qakpODi4oKHh0ex1+Pl5UWNGjVo0aKF1TRPT0/OnTtXpjVXRf/4xz9o1aoVffr0wdvbm969e9OhQweCg4Np3749J06cAAqffTVnzhzatm1L06ZNGT16NFeuXOHUqVNcvnyZtLQ06tatS5MmTejbty///Oc/rU6VnDhxgmeffZbJkyfTq1evYtW2bt06HnzwQZ566imaNWvGY489xt/+9jdWr16Ni4sLtWrVwsHBAR8fH2rVqoWLiwsuLi74+PhYgi4UHrX58MMPeeGFF+jUqRN33XUXM2fO5O677y6yzVWrVjFp0iTuv/9+WrZsySuvvEJ6errVEamhQ4fSvn177rnnHgYOHEhiYiLwf2OHXn75ZVq0aEFERAQTJ04kJyeHPn36cPDgQcs+t337dkJDQ6ldu/Z1++7p6cnkyZNp1qwZI0aMwNPTk8TERMxmM2vXrmXcuHF07dqVli1bMmvWLBwcHPif//kfatWqRY0aNahZsyaenp5F1nv1M6ldu3aR+XZ2drzyyiv8v//3/3jxxRc5efIkkZGRAHz//fecPXuWWbNm0aJFC+6//34mT57Me++9B8CmTZsICQlhyJAhtGzZkunTp1OvXr1ifc4iIpWBzcbw+Pn54ejoyMGDBy1Pl46Pj6dNmzZFBizfjKOjI/7+/iQlJVm+aC9evEhqaqplDEN11rRpU8u/XVxcrN4TFxcXy1G1Rx55hG+//ZZXX32VEydOcPjwYQDy8/Px9PRk4MCBvPzyyyxbtoyHH36Yfv36WX2Zz5kzB5PJVOTIy82cOHGCL7/80mrg+pUrV2jevHmJ+piamkpaWhr+/v6WaXfddRdjx461apeZmcm5c+d44YUXrPaxnJwcTp06ZXn952eAubu7W47+nDx5knvvvddq2aFDh1r+7evry44dOxgyZAjbt29n1KhRN6y5SZMmVqHNzc0Nk8nEH3/8QVpaGoGBgZZ5NWrUoHXr1iQnJxfn7bip5s2bM3LkSJYsWUJMTIxlfFNycjJpaWkEBwdb2hYUFJCTk0NqairJycn4+flZ1fTn1yIilZ3NAo+rqyvh4eFER0czd+5czp8/T2xsLPPmzQMKj/Zc/b/6Wxk6dChTp07Fz8+Pe+65h9deew0/Pz8CAgLKuxuV3p+/VIEbhslFixaxceNGIiIiCA8PJyoqii5duljmR0dH8+STT7Jr1y527drFhg0bWLZsmeVKoQEDBlCjRg1mz55N+/bti4zPuh6TyURYWBijR4+2ml7SJ6kXt/3VMSdvvPFGkVD15/BWo0aNUm2nd+/efPrpp3Ts2JEzZ85c9waZN9uG2WzG2dn5hrWX1Zi0pKQkHBwc+OGHHwgPDwcKP4sWLVpYLg74s1q1alnq+7MbvU8iIpWRTW88OHXqVPz9/Rk8eDAzZ85k7NixljEPoaGhbNu2rVjr6dGjB1OnTuW1114jIiKC/Px8li1bhp2dXXmWbyjr169n+vTpvPjii/Tq1Yvs7Gyg8EsuJSWFmTNn0qxZM5599lk2bdrEAw88YHXpf7du3XjuuefIzs5m5cqVxdpm8+bN+eWXX2jWrJnl5/PPP2fr1q0lqt3DwwMvLy+SkpIs044cOUKnTp3Iycmxauft7U1KSoplew0bNuS1116zjJ+5mTvvvJOjR49affGPHz+eVatWAdCnTx8SEhLYsmULDz30EG5ubiXqBxSGi7p161qNP7ty5Qo//fRTiY98Xc+uXbvYs2cPb7/9Nlu3buW7774DCj+Ls2fPUqdOHct7c+bMGd58803s7Oy4++67+c9//mNZT0FBgdX7LSJS2dk08Li6uhITE8OBAwfYvXs3Q4YMscw7evQoERERRZaJiIi47j12nnjiCb744gsSEhJ45513aNCgQXmW/n/s7cGhAn5KcJqvNDw9Pfnyyy85ffo0+/btY9KkSUDhQPLatWvz2WefMXfuXP773//y448/kpSUxL333mu1Dnd3dyZMmMA777zDmTNnbrnNQYMGkZiYyKJFizh16hRbt25l4cKFNGrUqMT1P/3007zxxht8//33/Pzzz5bxSNceIRwyZAiLFy/miy++4NSpU7z88svs37/fagzYjYSFhZGWlsb8+fM5deoUmzdv5vPPP6dDhw4ANGrUiICAAFavXk3v3r1L3Ic/1/jmm2/yxRdfkJyczPTp08nNzbWcsq1ZsyanTp3ijz/+KNF6MzIymDVrFs8++yydOnXiqaeeIioqitzcXEJDQ2ncuDEvvfQSR48eZd++fUyfPh1XV1ccHBx44oknSExMZPny5Zw4cYKYmBirq9tERCq76nMXu/Lg4FB4M8CK3F45mTt3LtHR0fTu3Zv69evTv39/HBwcLEdKli1bxty5c+nbty9ubm48/vjj9O/fv8h6/vrXv/LBBx8we/ZsyxU+N9K4cWPefvttFixYwLvvvkv9+vWZMmUKffv2LXH9I0eO5PLly4wfPx6TyUTnzp2ZPn16kXbDhw8nMzOTGTNmkJGRQevWrXn33XdvOLj4zzw8PFixYgVz585lzZo1NG3alNdff91qLEuvXr04evQonTt3LnEfrho2bBgZGRlMnz6djIwMgoKCWLNmjWW8Tf/+/Zk2bRojRowgLi6u2OtdtGgRLi4ulnFHY8aM4d///jdvvfUWEyZMYPny5cyaNYsnnniCmjVr0qNHDyZPngwUjmtavnw58+bNY/ny5TzyyCM3vPGhiEhlZGe+9sR8NZSRkUFwcDDx8fG4u7tbzcvJyeHkyZM0b968RJfLS/W0aNEizp07R0xMjK1LsSn93pSDnFxIuVi6G53WcATPWnAxvXTLO9iDTx1wuf4YMxFbudn397V0hEekDCQlJXHkyBHef/99li9fbutyRETkGgo8Ui4OHTrE4MGDbzi/UaNGlrsUG0FiYiKzZ89m0KBBltssiIhI5aHAI+WiVatWbNmy5YbzS3rpeWX3+OOP8/jjj9u6DBERuQFjfetIpeHk5GR1Az8RERFbsull6VVJdX4QqUhJ6fdFRCobHeG5BScnJ+zt7Tl79iw+Pj44OTnphoYiN2A2m8nLyyMlJQV7e/ti3XFbRKQiKPDcgr29Pc2bN+e3337TjdZEiqlmzZrccccdJXounohIeVLgKQYnJyfuuOMOTCaT5XlMInJ9Dg4OODo66kioiFQqCjzFZGdnR40aNfTARBERkSpIx5tFRETE8BR4RERExPAUeERERMTwFHhERETE8BR4RERExPAUeERERMTwFHhERETE8BR4RERExPAUeERERMTwFHhERETE8BR4RERExPAUeERERMTwFHhERETE8BR4RERExPAUeERERMTwFHhERETE8BR4RERExPAUeERERMTwFHhERETE8GwaeHJzc5k2bRohISGEhoYSGxt7y2X27dtH165dbzh/+/bt+Pr6lmWZIiIiUsU52nLj8+fPJzExkdWrV3P27FkmT55Mo0aN6NGjx3XbHz16lHHjxuHs7Hzd+ZcuXWLOnDnlWbKIiIhUQTY7wpOVlcXGjRuJjIzE39+fbt26MWLECNatW3fd9uvXr2fAgAF4e3vfcJ3z58+nadOm5VWyiIiIVFE2CzxJSUmYTCaCgoIs04KDg0lISKCgoKBI+2+++YaYmBiGDBly3fXt3buXvXv3Mnr06PIqWURERKoomwWelJQUvLy8cHJyskyrW7cuubm5pKWlFWm/bNkyunfvft115eXlMX36dGbMmIGLi0t5lSwiIiJVlM0CT3Z2tlXYASyv8/LySrSut956C39/f0JDQ8usPhERETEOmw1adnZ2LhJsrr4uyVGaY8eO8eGHH7J169YyrU9ERESMw2aBp379+qSmpmIymXB0LCwjJSUFFxcXPDw8ir2eTz/9lPT0dLp16wZAfn4+AEFBQcycOZO+ffuWffEiIiJSpdgs8Pj5+eHo6MjBgwcJCQkBID4+njZt2mBvX/wzbU899RRhYWGW1wkJCbz00kts2bLlpld0iYiISPVhs8Dj6upKeHg40dHRzJ07l/PnzxMbG8u8efOAwqM9tWrVuuXpLU9PTzw9PS2vz507B0CzZs3KrXYRERGpWmx6p+WpU6fi7+/P4MGDmTlzJmPHjrVciRUaGsq2bdtsWZ6IiIgYhJ3ZbDbbughby8jIIDg4mPj4eNzd3W1djohIUTm5kHIR8ovep+yWajiCZy24mF665R3swacOuFz/LvcitlKS7289PFREREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDM+mgSc3N5dp06YREhJCaGgosbGxt1xm3759dO3a1Wqa2Wxm5cqVdOnShXbt2jF48GCOHz9eXmWLiIhIFWPTwDN//nwSExNZvXo1UVFRLF26lB07dtyw/dGjRxk3bhxms9lq+vr164mNjWX69Ols2rSJJk2a8Pe//53s7Ozy7oKIiIhUATYLPFlZWWzcuJHIyEj8/f3p1q0bI0aMYN26dddtv379egYMGIC3t3eReXFxcQwbNoyHH36Y5s2bEx0dTVpaGvv37y/vboiIiEgVYLPAk5SUhMlkIigoyDItODiYhIQECgoKirT/5ptviImJYciQIUXmTZo0ib59+1pe29nZYTabuXz5crnULiIiIlWLzQJPSkoKXl5eODk5WabVrVuX3Nxc0tLSirRftmwZ3bt3v+66QkJCaNCggeX1xo0bMZlMBAcHl3ndIiIiUvXYLPBkZ2dbhR3A8jovL6/U601ISCAmJobhw4fj4+NzWzWKiIiIMdgs8Dg7OxcJNldfu7i4lGqdBw4cYPjw4XTq1Ilx48bddo0iIiJiDDYLPPXr1yc1NRWTyWSZlpKSgouLCx4eHiVe3w8//MCwYcN44IEHeP3117G31y2GREREpJDNUoGfnx+Ojo4cPHjQMi0+Pp42bdqUOKwcO3aMZ599lo4dO7J48WJq1KhRxtWKiIhIVWazwOPq6kp4eDjR0dEcOnSIXbt2ERsbyzPPPAMUHu3Jyckp1rpmzJhBw4YNmTp1KqmpqaSkpJRoeRERETE2m573mTp1Kv7+/gwePJiZM2cyduxYy5VYoaGhbNu27ZbrSElJ4cCBAxw/fpzOnTsTGhpq+SnO8iIiImJ8duZrb1tcDWVkZBAcHEx8fDzu7u62LkdEpKicXEi5CPlF71N2SzUcwbMWXEwv3fKODlDXq+TL/ZmDQ2EdImWoJN/f2vtEROTm7OwgPx/SLsN1bgx7S/b24O2pwCM2pb1PRESKp6CgdEeIRCoBXbstIiIihqfAIyIiIoanwCMiIiKGp8AjIiIihqfAIyIiIoanwCMiIiKGp8AjIiIihqfAIyIiIoanwCMiIiKGp8AjIiIihqfAIyIiIoanwCMiIiKGp8AjIiIihqenpYuIFMcVE+Tnl355BweooT+5Irai3z4RkeLIz4c/0qCgoOTL2tuDt6cCj4gN6bdPRKS4CgogvxSBR0RsTmN4RERExPAUeERERMTwFHhERETE8BR4RERExPA0aFmkGkhNhfT00i9fuzZ4eZVdPSIiFU2BR6QaSE+H7dshM7Pky7q5Qc+eCjy3xc6u8L85uaVfR2kuhxcRCwUekWoiMxMyMmxdRTVlZ1d4H5+0y6ULLo6O4OFW9nWJVCMKPCIiFaW09/Gx19EdkdulwCNSBdzOGBwHB8i9jTMpIiJGoMAjUgXczhgcHx8IDi77mkREqhIFHpEqorRjcNw09ENERPfhEREREeOzaeDJzc1l2rRphISEEBoaSmxs7C2X2bdvH127di0y/d///jePPPIIgYGBPPfcc1y8eLE8ShYREZEqyKaBZ/78+SQmJrJ69WqioqJYunQpO3bsuGH7o0ePMm7cOMxms9X0Q4cOERkZyZgxY9iwYQOXLl1i6tSp5V2+iIiIVBE2CzxZWVls3LiRyMhI/P396datGyNGjGDdunXXbb9+/XoGDBiAt7d3kXlr166lZ8+ehIeH06pVK+bPn8/XX3/N6dOny7sbIiIiUgXYLPAkJSVhMpkICgqyTAsODiYhIYGC69yY65tvviEmJoYhQ4YUmZeQkEBISIjldcOGDWnUqBEJCQnlUruIiIhULTYLPCkpKXh5eeHk5GSZVrduXXJzc0lLSyvSftmyZXTv3v266zp//jz16tWzmubt7c25c+fKtGYRERGpmmwWeLKzs63CDmB5nZeXV6J15eTkXHddJV2PiIiIGJPNAo+zs3ORQHL1tYuLS5msy9XV9faKFBEREUOwWeCpX78+qampmEwmy7SUlBRcXFzw8PAo8bouXLhgNe3ChQv4+PiUSa0iIiJStdks8Pj5+eHo6MjBgwct0+Lj42nTpg329iUrKzAwkPj4eMvr3377jd9++43AwMCyKldERESqMJsFHldXV8LDw4mOjubQoUPs2rWL2NhYnnnmGaDwaE9OTk6x1jVw4EA+/vhjNm7cSFJSEpMmTaJz5840bdq0PLsgIiIiVYRNbzw4depU/P39GTx4MDNnzmTs2LGWK7FCQ0PZtm1bsdYTFBTEK6+8wltvvcXAgQOpXbs28+bNK8/SRUREpAqx6cNDXV1diYmJISYmpsi8o0ePXneZiIgIIiIiij1dpDJITS184nlpODhAbm7Z1iMiUt3oaekiFSA9HbZvL3zieUn5+EBwcNnXJCJSnSjwiFSQzEzIyCj5cm5uZV+LiEh1Y9MxPCIiIiIVQYFHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAyvVIFn37595OXllXUtIiIiIuWiVIHnueee48SJE2Vdi4iIiEi5KFXgufvuuzl06FBZ1yIiIiJSLkr18NDatWszY8YM3nzzTZo0aYKTk5PV/Pfee69MihMREREpC6UKPH5+fvj5+WE2m0lLS8POzg5PT88yLk1ERESkbJQq8Dz77LO8+eabbNy4kYsXLwJQv359nnzySUaOHFmmBYqIiIjcrlIFnpiYGHbu3MmLL75I69atKSgo4D//+Q9vvvkmeXl5jBkzpqzrFBERESm1UgWeuLg43nrrLe677z7LtFatWtG4cWNefPFFBR4RERGpVEp1lZarqys1atQoMt3DwwM7O7vbLkpERESkLJUq8EyaNIlp06bx5ZdfkpaWRkZGBvv27WP69OkMHjyYs2fPWn5EREREbK1Up7RefPFFoHDw8tUjOmazGYAjR46waNEizGYzdnZ2HDlypIxKFRERESmdUgWezz//vKzrEBERESk3pQo8jRs3Lus6RERERMqNnpYuIiIihqfAIyIiIoanwCMiIiKGV6oxPCIiJZGaCunppV++dm3w8iq7ekSk+lHgEZFyl54O27dDZmbJl3Vzg549FXhE5PbY9JRWbm4u06ZNIyQkhNDQUGJjY2/Y9vDhw/Tv35/AwED69etHYmKiZZ7ZbGbJkiV06tSJv/zlL4wfP97yUFMRqRwyMyEjo+Q/pQlJIiLXsmngmT9/PomJiaxevZqoqCiWLl3Kjh07irTLyspi5MiRhISEsHnzZoKCghg1ahRZWVkAbNiwgY8++ogFCxawbt06zp8/T2RkZEV3R0RERCopmwWerKwsNm7cSGRkJP7+/nTr1o0RI0awbt26Im23bduGs7MzkyZNomXLlkRGRuLm5mYJR19//TW9evXivvvu45577mHEiBF8//33Fd0lERERqaRsFniSkpIwmUwEBQVZpgUHB5OQkEBBQYFV24SEBIKDgy2PsbCzs6Ndu3YcPHgQAE9PT7766it+//13cnJy+OSTT/Dz86uwvoiIiEjlZrPAk5KSgpeXF05OTpZpdevWJTc3l7S0tCJt69WrZzXN29ubc+fOAfDcc8/h6OhIp06daNeuHfv27WPhwoXl3gcRERGpGmwWeLKzs63CDmB5nZeXV6y2V9v9+uuvuLi48Pbbb7NmzRoaNGjAtGnTyrF6ERERqUpsFnicnZ2LBJurr11cXIrV1sXFBbPZzOTJkxk6dCgPP/wwwcHBLF68mP/3//4fCQkJ5dsJEakQ/3s2W0Sk1Gx2H5769euTmpqKyWTC0bGwjJSUFFxcXPDw8CjS9sKFC1bTLly4QL169bh48SK//fYbvr6+lnkNGzbEy8uLX3/9lcDAwPLvjIiUGycnMJvh1KnSr0M3LhQRmwUePz8/HB0dOXjwICEhIQDEx8fTpk0b7O2tDzwFBgbyzjvvYDabsbOzw2w2s3//fkaPHk3t2rVxcnIiOTmZli1bAnDx4kXS0tJo0qRJhfdLRMpWjRqF9+PZvVs3LhSR0rPZKS1XV1fCw8OJjo7m0KFD7Nq1i9jYWJ555hmg8GhPTk4OAD169ODSpUvMmTOH48ePM2fOHLKzs+nZsyeOjo5EREQQExPDjz/+yLFjx3jppZcIDAykTZs2tuqeiJQx3bhQRG6HTW88OHXqVPz9/Rk8eDAzZ85k7NixdO/eHYDQ0FC2bdsGgLu7OytWrCA+Pp6IiAgSEhJYuXIlNWvWBGDatGl0796diRMn8vTTT+Ph4cGyZcssl7GLiIhI9WbTZ2m5uroSExNDTExMkXlHjx61eh0QEEBcXNx11+Ps7MzkyZOZPHlyudQpIiIiVZtNj/CIiIiIVAQFHhERETE8BR4RERExPAUeERERMTwFHhERETE8BR4RERExPAUeEbkl3dJKRKo6m96HR0Qqv9t9lpWDA+TmlmlJIiIlpsAjIjd1u8+y8vGB4OCyr0tEpCQUeESkWK4+y6qk3NzKvhYRkZLSGB4RERExPAUeERERMTwFHhERETE8BR4RERExPAUeERERMTwFHhERETE8BR4RERExPAUeERERMTwFHhERETE8BR4RERExPAUeERERMTwFHhERETE8BR4RMTw7O1tXICK2pqeli4ihOTmB2QynTpV+HQ4OUM8DnMusKhGpaAo8ImJoNWpARgbs3g2ZmaVbh48P9OutwCNSlSnwiEi1kJlZGHxKw82tbGsRkYqnMTwiIiJieAo8IiIiYngKPCIiImJ4CjwiIiJieDYNPLm5uUybNo2QkBBCQ0OJjY29YdvDhw/Tv39/AgMD6devH4mJiVbzd+zYwaOPPkrbtm0ZNmwYv/76a3mXLyIiIlWETQPP/PnzSUxMZPXq1URFRbF06VJ27NhRpF1WVhYjR44kJCSEzZs3ExQUxKhRo8jKygJg//79TJw4kaFDh7J582acnJyYMGFCRXdHREREKimbBZ6srCw2btxIZGQk/v7+dOvWjREjRrBu3boibbdt24azszOTJk2iZcuWREZG4ubmZglHsbGx9O3blwEDBtCiRQsiIyNJSUnh4sWLFd0tERERqYRsFniSkpIwmUwEBQVZpgUHB5OQkEBBQYFV24SEBIKDg7H73/vD29nZ0a5dOw4ePAjA3r176datm6V906ZN+eKLL6hTp075d0REREQqPZsFnpSUFLy8vHBycrJMq1u3Lrm5uaSlpRVpW69ePatp3t7enDt3jkuXLpGenk5+fj7Dhw+nQ4cOPPvss/z+++8V0Q0RERGpAmwWeLKzs63CDmB5nZeXV6y2eXl5lnE8s2fPJiwsjOXLl5OXl8eoUaOKHCkSERGR6slmgcfZ2blIsLn62sXFpVhtXVxccHBwAKB///6Eh4cTEBDAggULOHbsmOWUl4iIiFRvNgs89evXJzU1FZPJZJmWkpKCi4sLHh4eRdpeuHDBatqFCxeoV68eXl5e1KhRgxYtWljmeXl54enpyblz58q3EyIiIlIl2Czw+Pn54ejoaHUUJj4+njZt2mBvb11WYGAgBw4cwGw2A2A2m9m/fz+BgYE4Ojri7+9PUlKSpf3FixdJTU2lcePGFdIXERERqdxsFnhcXV0JDw8nOjqaQ4cOsWvXLmJjY3nmmWeAwqM9OTk5APTo0YNLly4xZ84cjh8/zpw5c8jOzqZnz54ADB06lDVr1rB9+3aSk5OZNm0afn5+BAQE2Kp7IiIiUonY9MaDU6dOxd/fn8GDBzNz5kzGjh1L9+7dAQgNDWXbtm0AuLu7s2LFCuLj44mIiCAhIYGVK1dSs2ZNoDAQTZ06lddee42IiAjy8/NZtmyZ5TJ2ERERqd4cbblxV1dXYmJiiImJKTLv6NGjVq8DAgKIi4u74bqeeOIJnnjiiTKvUURERKo+PTxUREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMz6YPDxWpKlJTIT29dMs6OEBubtnWIyIiJaPAI1IM6emwfTtkZpZ8WR8fCA4u+5pERKT4FHhEiikzEzIySr6cm1vZ1yIlY29/e59DzZplV4uI2IYCj4gYmpMT+HiZ6PiXfEym0q3DxRWcHAvKtjARqVAKPCJiaI6O4Eg+pvNpZF0uXWhxrOuIXTMdqhOpyhR4RKRauJJTQF526QKPKVdHd0SqOl2WLiIiIoanwCMiIiKGp8AjIiIihqfAIyIiIoanwCMiIiKGp8AjIiIihqfAIyIiIoanwCMiIiKGp8AjIiIihqfAIyIiIoanwCMiIiKGZ9PAk5uby7Rp0wgJCSE0NJTY2Ngbtj18+DD9+/cnMDCQfv36kZiYeN1227dvx9fXt7xKFhERkSrIpoFn/vz5JCYmsnr1aqKioli6dCk7duwo0i4rK4uRI0cSEhLC5s2bCQoKYtSoUWRlZVm1u3TpEnPmzKmo8kVERKSKsFngycrKYuPGjURGRuLv70+3bt0YMWIE69atK9J227ZtODs7M2nSJFq2bElkZCRubm5FwtH8+fNp2rRpRXVBREREqgibBZ6kpCRMJhNBQUGWacHBwSQkJFBQUGDVNiEhgeDgYOzs7ACws7OjXbt2HDx40NJm79697N27l9GjR1dI/SIiIlJ12CzwpKSk4OXlhZOTk2Va3bp1yc3NJS0trUjbevXqWU3z9vbm3LlzAOTl5TF9+nRmzJiBi4tLudcuIiIiVYvNAk92drZV2AEsr/Py8orV9mq7t956C39/f0JDQ8uxYhEREamqHG21YWdn5yLB5urra4/S3Kiti4sLx44d48MPP2Tr1q3lW7CIiIhUWTYLPPXr1yc1NRWTyYSjY2EZKSkpuLi44OHhUaTthQsXrKZduHCBevXq8emnn5Kenk63bt0AyM/PByAoKIiZM2fSt2/fCuiNiIiIVGY2Czx+fn44Ojpy8OBBQkJCAIiPj6dNmzbY21ufaQsMDOSdd97BbDZjZ2eH2Wxm//79jB49mq5duxIWFmZpm5CQwEsvvcSWLVvw9vau0D6JiIhI5WSzwOPq6kp4eDjR0dHMnTuX8+fPExsby7x584DCoz21atXCxcWFHj168PrrrzNnzhwGDBjA+vXryc7OpmfPntSsWRNPT0/Leq8OZG7WrJktuiUi5cDeHtzcSresqyv87wWeIlKN2SzwAEydOpXo6GgGDx6Mu7s7Y8eOpXv37gCEhoYyb948IiIicHd3Z8WKFURFRfHhhx/i6+vLypUrqVmzpi3LF5EK4OQEPl4mOv4lH5Op5Mu7uYOLUwEODmVfm4hUHTYNPK6ursTExBATE1Nk3tGjR61eBwQEEBcXd8t13n///UWWFZGqy9ERHMnHdD6NrMsFt17gGi4NHLFr5Ia9Ao9ItWbTwCMiUlxXcgrIyy554DHllXwZETEePS1dREREDE9HeKRaSE2F9PTSLevgALm5ZVuPiIhULAUeqRbS02H7dsjMLPmyPj4QHFz2NYmISMVR4JFqIzMTMjJKvlxpL4cWEZHKQ4FHROQWrt4L9dIlKLhSiuVdwK0W6EIxEdtR4BERuQV7BygogP+ehksXS768hw/4NlDgEbElBR4RkWIymeCa5xgXb7lSHBUSkbKly9JFRETE8BR4RERExPAUeERERMTwNIZHRKSc6SovEdtT4BERKWe6ykvE9hR4REQqiK7yErEdjeERERERw1PgEREREcNT4BERERHDU+ARERERw1PgEREREcNT4BERERHDU+ARERERw1PgEREREcNT4BERERHDU+ARERERw1PgEREREcNT4BERERHDU+ARERERw1PgEREREcNT4BERERHDU+ARERERw7Np4MnNzWXatGmEhIQQGhpKbGzsDdsePnyY/v37ExgYSL9+/UhMTLTMM5vNrFy5ki5dutCuXTsGDx7M8ePHK6ILIiIiUgXYNPDMnz+fxMREVq9eTVRUFEuXLmXHjh1F2mVlZTFy5EhCQkLYvHkzQUFBjBo1iqysLADWr19PbGws06dPZ9OmTTRp0oS///3vZGdnV3SXREREpBKyWeDJyspi48aNREZG4u/vT7du3RgxYgTr1q0r0nbbtm04OzszadIkWrZsSWRkJG5ubpZwFBcXx7Bhw3j44Ydp3rw50dHRpKWlsX///oruloiIiFRCNgs8SUlJmEwmgoKCLNOCg4NJSEigoKDAqm1CQgLBwcHY2dkBYGdnR7t27Th48CAAkyZNom/fvpb2dnZ2mM1mLl++XP4dERERkUrPZoEnJSUFLy8vnJycLNPq1q1Lbm4uaWlpRdrWq1fPapq3tzfnzp0DICQkhAYNGljmbdy4EZPJRHBwcPl1QERERKoMmwWe7Oxsq7ADWF7n5eUVq+217aDwaFBMTAzDhw/Hx8enjKsWERGRqsjRVht2dnYuEliuvnZxcSlW22vbHThwgL///e906tSJcePGlUPVIlIa9vbg5la6ZV1d4X/PZouIlJrNAk/9+vVJTU3FZDLh6FhYRkpKCi4uLnh4eBRpe+HCBatpFy5csDrN9cMPPzB69Gg6dOjA66+/jr29bjEkUhk4OYGPl4mOf8nHZCr58m7u4OJUgIND2dcmItWHzQKPn58fjo6OHDx4kJCQEADi4+Np06ZNkbASGBjIO++8g9lstgxI3r9/P6NHjwbg2LFjPPvss3Ts2JGFCxdaApSI2J6jIziSj+l8GlmXC269wDVcGjhi18gNewUeEbkNNjsM4urqSnh4ONHR0Rw6dIhdu3YRGxvLM888AxQe7cnJyQGgR48eXLp0iTlz5nD8+HHmzJlDdnY2PXv2BGDGjBk0bNiQqVOnkpqaSkpKitXyImJ7V3IKyMsu+Y8pr+QhSUTkWjY97zN16lT8/f0ZPHgwM2fOZOzYsXTv3h2A0NBQtm3bBoC7uzsrVqwgPj6eiIgIEhISWLlyJTVr1iQlJYUDBw5w/PhxOnfuTGhoqOXn6vIiIiJSvdn03I+rqysxMTHExMQUmXf06FGr1wEBAcTFxRVp5+PjU6StiIiIyJ9pZK+IiIgYngKPiIiIGJ4Cj4iIiBiert+WSi81FdLTS7+8gwPk5pZdPSIV7eqdOi5dgoIrpVjeBdxqga7sl+pMgUcqvfR02L4dMjNLt7yPD+ixalKV2TtAQQH89zRculjy5T18wLeBAo9Ubwo8UiVkZkJGRumWLe0jDUQqG5MJrvMIwVsvV4qjQiJGozE8IiIiYngKPCIiImJ4CjwiIiJieAo8IiIiYngKPCIiImJ4CjwiIiJieAo8IiIiYngKPCIiImJ4CjwiIiJieAo8IiIiYngKPCIiImJ4epaWiNySvX3pn0nm6gp2dmVbj4hISSnwSLlLTS184nlpODhAbm7Z1iMl4+QEPl4mOv4lH5Op5Mu7uYOLUwEOelS3iNiQAo+Uu/R02L698InnJeXjA8HBZV+TFJ+jIziSj+l8GlmXC0q8vEsDR+wauWGvwCMiNqTAIxUiMxMyMkq+XGlPo0jZu5JTQF52yQOPKa/ky4iIlDUNWhYRERHDU+ARERERw1PgEREREcNT4BERERHD06BlqRZu5z4yNWsWLm/L7Ts62rZ+EZGqToFHDO927yPj4gLeXg44OZXu1+V2t1+zJjRpYMdD95vJyyv58rdbv8htu3rnyZzbuKmWgwPU0D4spae9Rwzvdu8j41jHHsf6ntQo5R/bsriPjUMTNwr+uEzWxYqvX+S22dlBfj6kXYaCUtymwN4evD0VeOS2aO+RaqPU95Epozs93+59bEy5tq1fqq6rpzQvXYKCK6VY3gXcasFt3zuyoADydV8msQ2bBp7c3FxmzpzJp59+iouLC8OGDWPYsGHXbXv48GGioqI4duwYd911FzNnzqR169aW+f/+979ZvHgxKSkphIaGMmvWLOrUqVNRXRERqbTsHQqzxn9Pw6WLJV/ewwd8G5RB4BGxIZsOZZw/fz6JiYmsXr2aqKgoli5dyo4dO4q0y8rKYuTIkYSEhLB582aCgoIYNWoUWVlZABw6dIjIyEjGjBnDhg0buHTpElOnTq3o7oiIVGomE+TllfzHVIqjQiKVjc0CT1ZWFhs3biQyMhJ/f3+6devGiBEjWLduXZG227Ztw9nZmUmTJtGyZUsiIyNxc3OzhKO1a9fSs2dPwsPDadWqFfPnz+frr7/m9OnTFd0tERERqYRsFniSkpIwmUwEBQVZpgUHB5OQkEDBNYPaEhISCA4Oxu5/R/rb2dnRrl07Dh48aJkfEhJiad+wYUMaNWpEQkJC+XdEREREKj2bjeFJSUnBy8sLJycny7S6deuSm5tLWlqa1fiblJQU7rrrLqvlvb29+fnnnwE4f/489erVKzL/3LlzxarFbDYDkFGap1vKLWVng4dH4dVKJeXmVnhIvbTLA7i6QmZWLmbHbOycSj5gssDensysDNzcruDpWfHbz7dzJCPTjgL76lm/rZevDDXYevkCe0cyMuyokZ1duqusTI7gaFf4x6C0V2llZOjcmhRx9Xv76vf4zdgs8GRnZ1uFHcDyOu+am43cqO3Vdjk5OTedfyuZmZkAPPTQQ8XvgIiIiFQKmZmZ1KpV66ZtbBZ4nJ2diwSSq69dXFyK1fZquxvNd3V1LVYt9erV4+uvv8bNzc1y2kxEREQqN7PZTGZmZpGzPNdjs8BTv359UlNTMZlMOP7vuYqUlBRcXFzw8PAo0vbChQtW0y5cuGDp4I3m+/j4FKsWe3t7GjRoUNquiIiIiI3c6sjOVTYbtOzn54ejo6Nl4DFAfHw8bdq0wf6aB/8EBgZy4MAByzk6s9nM/v37CQwMtMyPj4+3tP/tt9/47bffLPNFRESkerNZ4HF1dSU8PJzo6GgOHTrErl27iI2N5ZlnngEKj/bk5OQA0KNHDy5dusScOXM4fvw4c+bMITs7m549ewIwcOBAPv74YzZu3EhSUhKTJk2ic+fONG3a1FbdExERkUrEzlycoc3lJDs7m+joaD799FPc3d0ZPnw4Q4YMAcDX15d58+YREREBFN5cMCoqiuTkZHx9fZk5cyb33nuvZV2bN2/mzTffJD09nQ4dOjBr1iy8vLxs0S0RERGpZGwaeEREREQqgk0fLSEiIiJSERR4RERExPAUeERERMTwFHhs6LPPPsPX19fq5/nnn7d1WeUuLy+PPn368MMPP1imnT59miFDhtC2bVt69erFnj17bFhh+bveezB79uwi+8PatWttWGXZ+/3333n++ee577776NixI/PmzSM3NxeoHvvAzfpfHT5/gF9++YXhw4cTFBRE586dWbVqlWVeddgHbtb/6rIPXDVy5EimTJlieX348GH69+9PYGAg/fr1IzExsUy3Z7MbDwocP36chx9+mFmzZlmmOTs727Ci8pebm8vEiRMtz0GDwvsqPffcc9xzzz1s2rSJXbt2MWbMGLZt20ajRo1sWG35uN57AJCcnMzEiRP561//apnm7u5e0eWVG7PZzPPPP4+Hhwfr1q0jPT2dadOmYW9vz6RJkwy/D9ys/5MnTzb85w9QUFDAyJEjadOmDXFxcfzyyy9MmDCB+vXr06dPH8PvAzfrf1hYWLXYB6765JNP+Prrry19zcrKYuTIkYSFhfHqq6/ywQcfMGrUKD777DNq1qxZJttU4LGh5ORk7rnnnmLfEbqqO378OBMnTizykLfvv/+e06dPs379emrWrEnLli357rvv2LRpE2PHjrVRteXjRu8BFO4Pw4cPN+z+cOLECQ4ePMi3335L3bp1AXj++eeJiYmhU6dOht8Hbtb/q4HHyJ8/FN4B38/Pj+joaNzd3bnzzjtp37498fHx1K1b1/D7wM36fzXwGH0fAEhLS2P+/Pm0adPGMm3btm04OzszadIk7OzsiIyM5JtvvmHHjh2W29PcLp3SsqHk5GTuvPNOW5dRYfbu3cv999/Phg0brKYnJCRw7733WqX44OBgq7twG8WN3oOMjAx+//13Q+8PPj4+rFq1yvJlf1VGRka12Adu1v/q8PlD4XMLFy9ejLu7O2azmfj4eH788Ufuu+++arEP3Kz/1WUfAIiJieGxxx7jrrvuskxLSEggODjY8jxLOzs72rVrV6afvwKPjZjNZk6ePMmePXt49NFHeeSRR1iwYEGxn/BeFQ0aNIhp06YVeahrSkpKkQe/eXt7c+7cuYosr0Lc6D1ITk7Gzs6Ot99+m06dOtG3b1/i4uJsVGX58PDwoGPHjpbXBQUFrF27lgceeKBa7AM36391+Pyv1aVLFwYNGkRQUBCPPvpotdgH/uza/leXfeC7775j3759/OMf/7CaXhGfv05p2cjZs2fJzs7GycmJxYsXc+bMGWbPnk1OTg4vv/yyrcurUFffhz9zcnIydPi71okTJ7Czs6NFixY89dRT/Pjjj0yfPh13d3e6detm6/LKxWuvvcbhw4f56KOP+Ne//lXt9oE/9/+nn36qdp//m2++yYULF4iOjmbevHnV7u/Atf339/c3/D6Qm5tLVFQUM2bMwMXFxWpeRXz+Cjw20rhxY3744Qdq166NnZ0dfn5+FBQU8NJLLzF16lQcHBxsXWKFcXZ2Ji0tzWpaXl5ekV8IIwsPD+fhhx/G09MTgFatWnHq1Ck++OADw/yx+7PXXnuN1atXs2jRIu65555qtw9c2/+77767Wn3+gGX8Rm5uLi+++CL9+vUjOzvbqo2R94Fr+79//37D7wNLly6ldevWVkc6r3J2di4Sbsr689cpLRvy9PS0nK8EaNmyJbm5uaSnp9uwqopXv359Lly4YDXtwoULRQ5vGpmdnZ3lD91VLVq04Pfff7dNQeVo1qxZ/POf/+S1117j0UcfBarXPnC9/leXz//ChQvs2rXLatpdd93FlStX8PHxMfw+cLP+Z2RkGH4f+OSTT9i1axdBQUEEBQWxdetWtm7dSlBQUIX8DVDgsZHdu3dz//33W/0fzZEjR/D09KROnTo2rKziBQYG8tNPP5GTk2OZFh8fT2BgoA2rqlhvvPGG5cG5VyUlJdGiRQvbFFROli5dyvr161m4cCG9e/e2TK8u+8CN+l9dPv8zZ84wZswYqy/xxMRE6tSpQ3BwsOH3gZv1f82aNYbfB9asWcPWrVvZsmULW7ZsoUuXLnTp0oUtW7YQGBjIgQMHLFewms1m9u/fX7afv1ls4vLly+aOHTuaJ0yYYE5OTjZ/9dVX5tDQUPPKlSttXVqFuOeee8zff/+92Ww2m00mk7lXr17m8ePHm48dO2ZesWKFuW3btuZff/3VxlWWrz+/BwkJCeZ7773XvGrVKvMvv/xiXrdunbl169bm/fv327jKsnP8+HGzn5+fedGiRebz589b/VSHfeBm/a8On7/ZXPi7HhERYR42bJj5559/Nn/11VfmBx980Pyvf/2rWuwDN+t/ddkH/mzy5MnmyZMnm83mwu/EBx54wDxr1izzzz//bJ41a5a5Q4cO5szMzDLbngKPDR07dsw8ZMgQc9u2bc0dOnQwL1myxFxQUGDrsirEn7/szWaz+dSpU+Ynn3zS3Lp1a3Pv3r3N3377rQ2rqxjXvgefffaZOSwszNymTRtzjx49zDt37rRhdWVvxYoV5nvuuee6P2az8feBW/Xf6J//VefOnTM/99xz5nbt2pk7dOhgXr58ueXvntH3AbP55v2vLvvAVX8OPGZz4f/4hYeHm9u0aWN+/PHHzT/99FOZbs/ObL7OHdBEREREDERjeERERMTwFHhERETE8BR4RERExPAUeERERMTwFHhERETE8BR4RERExPAUeERERMTwFHhERETE8BR4RKRaOXPmDL6+vpw5c6Zc1v/HH3+wffv2clm3iJSeAo+ISBlasGABX3/9ta3LEJFrKPCIiJQhPa1HpHJS4BGRCnXu3DnGjRvHfffdx/3338/s2bPJy8ujY8eObNq0ydLObDbTqVMnPv74YwD27dtHREQEAQEBhIWFsXPnTkvbKVOmMGXKFPr27Uv79u05deoU27Zt49FHH6VNmzb06tWLXbt2WdWxa9cuHnnkEQIDAxk9ejTp6emWeQcOHGDgwIG0bduWLl268MEHH1gtu3nzZnr27ElAQAARERH8+OOPACxZsoS4uDji4uLo0qVLmb93IlJ6CjwiUmHy8vIYPHgw2dnZrFmzhsWLF/PVV18xf/58evTowWeffWZpe/DgQdLS0ujatSspKSmMGjWKiIgItm7dyogRI5gyZQr79u2ztP/4448ZP348K1asoFatWkyaNIlRo0axY8cO+vXrx4QJE0hLS7O0j4uLY+HChbz33nv89NNPvPPOOwAkJyczePBg/vKXv7B582bGjh1LTEyMpbbNmzcza9YsRo0axZYtW3jwwQcZOXIkv//+O8OGDaNnz5707NmTjz76qGLeVBEpFkdbFyAi1cfu3bv5/fff+fDDD6lduzYAM2bM4Nlnn2X16tUMHTqUjIwM3N3d2blzJw899BDu7u6sWrWKBx98kKeeegqAZs2aceTIEVavXk1ISAgAbdq0sRxVOXz4MFeuXKFBgwY0btyYYcOG4evri7OzMxkZGQC89NJLBAQEANCzZ0+SkpIA+PDDD7n33nuZMGECAC1atCA5OZlVq1bRrVs31qxZw9NPP014eDgAL774Ij/++CNr165l4sSJuLi4AFCnTp0KeEdFpLh0hEdEKkxycjJ33nmnJewAtGvXDpPJhJubGz4+PpYBv59++im9evUC4MSJE3z55ZcEBQVZftauXcupU6cs62ncuLHl335+fnTu3JmhQ4fSo0cPFixYQJMmTXB1dbW0ueOOOyz/rlWrFrm5uZYarwahq4KCgkhOTr7h/LZt21rmi0jlpCM8IlJhnJ2di0zLz8+3/LdXr17s3LmTZs2akZqaSufOnQEwmUyEhYUxevRoq2UdHf/vT9if121nZ8eKFSs4dOgQn3/+OZ999hnvv/8+77//PrVq1QLA3v76/793vRoLCgosdd6oDwUFBTfruojYmI7wiEiFad68OadOnbIaS3Pw4EEcHR2544476N27N99++y07d+6kS5culiMyzZs355dffqFZs2aWn88//5ytW7dedzvJycnExMQQEBDACy+8wCeffELDhg3ZvXt3sWpMSEiwmnbgwAGaN29+w/kJCQmW+XZ2dsV+P0Sk4ijwiEiF6dChA02bNmXSpEkcPXqU77//nlmzZtGnTx88PDzw8/OjXr16rF27lp49e1qWGzRoEImJiSxatIhTp06xdetWFi5cSKNGja67HQ8PDz744AOWLVvG6dOn+eqrr/j111+59957b1njoEGDOHLkCAsXLuTkyZPExcXx/vvv8+STTwIwZMgQ1q5dy5YtWzh58iQLFiwgKSmJxx9/HABXV1d+/fVXfv/99zJ4x0SkrCjwiEiFcXBwYNmyZQA88cQTTJgwga5du/LKK69Y2vTq1QsHBwc6depkmda4cWPefvttdu/eTZ8+fVi8eLHlMvTr8fHxYcmSJezcuZPevXvzyiuvMGHCBEJDQ29ZY6NGjVixYgW7d+8mLCyM5cuXM2XKFPr162ep74UXXuDNN9+kb9++7N27l9jYWFq2bAnAY489xsmTJ+nbt6/uySNSidiZ9RspIiIiBqcjPCIiImJ4CjwiIiJieAo8IiIiYngKPCIiImJ4CjwiIiJieAo8IiIiYngKPCIiImJ4CjwiIiJieAo8IiIiYngKPCIiImJ4CjwiIiJieP8fDZ0ODYbVKGUAAAAASUVORK5CYII=", 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      " + "
      " ] }, "metadata": {}, @@ -1031,334 +740,74 @@ } ], "source": [ - "def plot_counterfactual_by_context(data, name, other):\n", - "\n", - " grouped_data = data.groupby([\"wpr_lockdown_efficiency\", \"wpr_mask_efficiency\"])\n", - "\n", - " fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n", - "\n", - " for (lockdown_efficiency, mask_efficiency), ax in zip(\n", - " grouped_data.groups.keys(), axs.flatten()\n", - " ):\n", - " data_subset = grouped_data.get_group((lockdown_efficiency, mask_efficiency))\n", - " mean_overshoot = data_subset[\"overshoot_int\"].mean().item()\n", - "\n", - " fixed = mask_efficiency if name == \"lockdown\" else lockdown_efficiency\n", - " ax.hist(data_subset[\"overshoot_int\"])\n", - " ax.set_title(\n", - " f\"{other} eff fixed: {fixed}\\nOvershoot mean: {mean_overshoot:.2f}, Pr(too high): {data_subset['os_too_high_int'].mean().item():.2f}\"\n", - " )\n", - " ax.set_xlim(5, 35)\n", - " ax.axvline(x=mean_overshoot, color=\"grey\", linestyle=\"--\")\n", - " ax.axvline(x=overshoot_threshold, color=\"red\", linestyle=\"-\")\n", - "\n", - " plt.suptitle(\n", - " f\"Counterfactual {name} by {other.lower()} efficiency contexts\",\n", - " fontsize=16,\n", - " y=1,\n", - " )\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", + "plt.bar(bin_edges[:28].tolist(), hist_lockdown_fix, align='center', width = 35/28, alpha = 0.5, color='blue')\n", + "plt.bar(bin_edges[:28].tolist(), hist_lockdown_notfix, align='center', width = 35/28, alpha = 0.5, color='pink')\n", + "plt.legend([\"mask_efficiency fixed\", \"mask_efficiency not fixed\"])\n", + "plt.ylabel(\"pr\")\n", + "plt.xlabel(\"overshoot\")\n", "\n", - "plot_counterfactual_by_context(counterfactual_lockdown, \"lockdown\", \"Mask\")\n", + "print(\"Overshoot mean\")\n", + "print(\"mask_efficiency fixed: \", os_lockdown_fix.item(), \" mask_efficiency not fixed: \", os_lockdown_notfix.item())\n", "\n", - "plot_counterfactual_by_context(counterfactual_mask, \"mask\", \"Lockdown\")" + "print(\"Probability of overshoot being high\")\n", + "print(\"mask_efficiency fixed: \", oth_lockdown_fix.item(), \" mask_efficiency not fixed: \", oth_lockdown_notfix.item())" ] }, { - "cell_type": "code", - "execution_count": 13, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "sufficiency_table = get_table(\n", - " tr,\n", - " mwc,\n", - " antecedents,\n", - " witnesses,\n", - " consequents,\n", - " world=2,\n", - " others=[\"joint_efficiency\", \"overshoot\"],\n", - ")\n", - "\n", - "\n", - "factual_sufficiency = sufficiency_table[\n", - " (sufficiency_table[\"lockdown_int\"] == 1)\n", - " & (sufficiency_table[\"mask_int\"] == 1)\n", - " & (\n", - " sufficiency_table[\"wpr_lockdown_efficiency\"]\n", - " == 0 & (sufficiency_table[\"wpr_mask_efficiency\"] == 0)\n", - " )\n", - "]\n", - "\n", - "counterfactual_sufficiency_lockdown = sufficiency_table[\n", - " (sufficiency_table[\"lockdown_int\"] == 0)\n", - " & (sufficiency_table[\"mask_int\"] == 1)\n", - " & (sufficiency_table[\"wpr_lockdown_efficiency\"] == 0)\n", - "]\n", - "\n", - "counterfactual_sufficiency_mask = sufficiency_table[\n", - " (sufficiency_table[\"lockdown_int\"] == 1)\n", - " & (sufficiency_table[\"mask_int\"] == 0)\n", - " & (sufficiency_table[\"wpr_mask_efficiency\"] == 0)\n", - "]\n" + "# Counterfactual mask by lockdown efficiency contexts" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 176, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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      " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "fig, axs = plt.subplots(1, 3, figsize=(18, 6))\n", - "\n", - "factual_sufficiency_mean = factual_sufficiency[\"overshoot_int\"].mean().item()\n", - "axs[0].hist(factual_sufficiency[\"overshoot_int\"])\n", - "\n", - "axs[0].set_title((\n", - " f\"Factual\\n overshoot mean: {factual_sufficiency_mean:.2f}, Pr(too high): \"\n", - " f\"{factual_sufficiency['os_too_high_int'].mean().item():.2f}\"\n", - "))\n", - "axs[0].axvline(x=factual_sufficiency_mean, color=\"grey\", linestyle=\"--\")\n", - "\n", - "counterfactual_sufficiency_lockdown_mean = counterfactual_sufficiency_lockdown[\"overshoot_int\"].mean()\n", - "axs[1].hist(counterfactual_sufficiency_lockdown[\"overshoot_int\"])\n", - "axs[1].set_title((\n", - " f\"Counterfactual_lockdown\\n overshoot mean: {counterfactual_sufficiency_lockdown_mean:.2f}, \"\n", - " f\"Pr(too high): {counterfactual_lockdown['os_too_high_int'].mean():.2f}\"\n", - "))\n", - "axs[1].axvline(x=counterfactual_sufficiency_lockdown_mean, color=\"grey\", linestyle=\"--\")\n", - "\n", - "counterfactual_sufficiency_mask_mean = counterfactual_sufficiency_mask[\"overshoot_int\"].mean()\n", - "axs[2].hist(counterfactual_sufficiency_mask[\"overshoot_int\"])\n", - "axs[2].set_title((\n", - " f\"Counterfactual_mask\\n overshoot mean: {counterfactual_sufficiency_mask_mean:.2f}, \"\n", - " f\"Pr(too high): {counterfactual_mask['os_too_high_int'].mean():.2f}\"\n", - "))\n", - "axs[2].axvline(x=counterfactual_sufficiency_mask_mean, color=\"grey\", linestyle=\"--\")\n", - "\n", - "for i in range(3):\n", - " axs[i].set_xlim(5, 40)\n", - " axs[i].axvline(x=overshoot_threshold, color=\"red\", linestyle=\"-\")\n", - "\n", - "#plt.savefig(\"counterfactual_sir_search_sufficiency.png\")\n", - "\n", - "plt.show()\n" + "hist_mask_fix, bin_edges, os_mask_fix, oth_mask_fix = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 0, \"__cause____antecedent_lockdown\": 1, \"__cause____witness_mask_efficiency\": 0, \"__cause____witness_lockdown_efficiency\": 1}, 1)\n", + "hist_mask_notfix, bin_edges, os_mask_notfix, oth_mask_notfix = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 0, \"__cause____antecedent_lockdown\": 1, \"__cause____witness_mask_efficiency\": 0, \"__cause____witness_lockdown_efficiency\": 0}, 1)" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 179, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overshoot mean\n", + "lockdown_efficiency fixed: 26.283220291137695 lockdown_efficiency not fixed: 26.437496185302734\n", + "Probability of overshoot being high\n", + "lockdown_efficiency fixed: 0.8850364685058594 lockdown_efficiency not fixed: 0.9024389982223511\n" + ] + }, { "data": { - "text/html": [ - "
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", "text/plain": [ - " lockdown_obs lockdown_int apr_lockdown mask_obs mask_int apr_mask \\\n", - "11 1.0 0.0 1 1.0 1.0 0 \n", - "38 1.0 0.0 1 1.0 1.0 0 \n", - "51 1.0 0.0 1 1.0 1.0 0 \n", - "104 1.0 0.0 1 1.0 1.0 0 \n", - "110 1.0 0.0 1 1.0 1.0 0 \n", - "\n", - " lockdown_efficiency_obs lockdown_efficiency_int \\\n", - "11 0.0 0.0 \n", - "38 0.0 0.0 \n", - "51 0.0 0.0 \n", - "104 0.0 0.0 \n", - "110 0.0 0.0 \n", - "\n", - " wpr_lockdown_efficiency mask_efficiency_obs mask_efficiency_int \\\n", - "11 0 0.00 0.00 \n", - "38 0 0.45 0.45 \n", - "51 0 0.45 0.45 \n", - "104 0 0.00 0.00 \n", - "110 0 0.00 0.00 \n", - "\n", - " wpr_mask_efficiency joint_efficiency_obs joint_efficiency_int \\\n", - "11 1 0.7 0.00 \n", - "38 0 0.7 0.45 \n", - "51 0 0.7 0.45 \n", - "104 1 0.7 0.00 \n", - "110 1 0.7 0.00 \n", - "\n", - " overshoot_obs overshoot_int os_too_high_obs os_too_high_int \n", - "11 15.616409 21.598530 0.0 1.0 \n", - "38 31.680214 25.210352 1.0 1.0 \n", - "51 32.041954 24.760708 1.0 1.0 \n", - "104 30.147129 17.757517 1.0 0.0 \n", - "110 15.187485 23.535439 0.0 1.0 " + "
      " ] }, - "execution_count": 25, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ + "plt.bar(bin_edges[:28].tolist(), hist_mask_fix, align='center', width = 35/28, alpha = 0.5, color='blue')\n", + "plt.bar(bin_edges[:28].tolist(), hist_mask_notfix, align='center', width = 35/28, alpha = 0.5, color='pink')\n", + "plt.legend([\"lockdown_efficiency fixed\", \"lockdown_efficiency not fixed\"])\n", + "plt.ylabel(\"pr\")\n", + "plt.xlabel(\"overshoot\")\n", + "\n", + "print(\"Overshoot mean\")\n", + "print(\"lockdown_efficiency fixed: \", os_mask_fix.item(), \" lockdown_efficiency not fixed: \", os_mask_notfix.item())\n", "\n", - "counterfactual_sufficiency_lockdown.head()" + "print(\"Probability of overshoot being high\")\n", + "print(\"lockdown_efficiency fixed: \", oth_mask_fix.item(), \" lockdown_efficiency not fixed: \", oth_mask_notfix.item())" ] } ], diff --git a/docs/source/explainable_sir_original.ipynb b/docs/source/explainable_sir_original.ipynb new file mode 100644 index 00000000..f386bfe2 --- /dev/null +++ b/docs/source/explainable_sir_original.ipynb @@ -0,0 +1,1265 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "import numbers\n", + "import os\n", + "from typing import Tuple, TypeVar, Union\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import pyro.distributions as dist\n", + "import seaborn as sns\n", + "import torch\n", + "from pyro.infer import Predictive\n", + "\n", + "import pyro\n", + "from chirho.counterfactual.handlers.counterfactual import \\\n", + " MultiWorldCounterfactual\n", + "from chirho.dynamical.handlers.interruption import StaticEvent\n", + "from chirho.dynamical.handlers.solver import TorchDiffEq\n", + "from chirho.dynamical.handlers.trajectory import LogTrajectory\n", + "from chirho.dynamical.ops import Dynamics, State, on, simulate\n", + "from chirho.explainable.handlers import SearchForExplanation\n", + "from chirho.explainable.handlers.components import ExtractSupports\n", + "from chirho.indexed.ops import IndexSet, gather, indices_of\n", + "from chirho.interventional.ops import Intervention, intervene\n", + "from chirho.observational.handlers import condition\n", + "\n", + "R = Union[numbers.Real, torch.Tensor]\n", + "S = TypeVar(\"S\")\n", + "T = TypeVar(\"T\")\n", + "\n", + "\n", + "sns.set_style(\"white\")\n", + "\n", + "seed = 123\n", + "pyro.clear_param_store()\n", + "pyro.set_rng_seed(seed)\n", + "\n", + "smoke_test = \"CI\" in os.environ\n", + "num_samples = 10 if smoke_test else 300\n", + "exp_plate_size = 10 if smoke_test else 10000" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "class SIRDynamics(pyro.nn.PyroModule):\n", + " def __init__(self, beta, gamma):\n", + " super().__init__()\n", + " self.beta = beta\n", + " self.gamma = gamma\n", + "\n", + " def forward(self, X: State[torch.Tensor]):\n", + " dX: State[torch.Tensor] = dict()\n", + " dX[\"S\"] = -self.beta * X[\"S\"] * X[\"I\"]\n", + " dX[\"I\"] = self.beta * X[\"S\"] * X[\"I\"] - self.gamma * X[\"I\"]\n", + " dX[\"R\"] = self.gamma * X[\"I\"]\n", + "\n", + " return dX\n", + "\n", + "\n", + "# TODO add running overshoot to states?\n", + "\n", + "\n", + "class SIRDynamicsLockdown(SIRDynamics):\n", + " def __init__(self, beta0, gamma):\n", + " super().__init__(beta0, gamma)\n", + " self.beta0 = beta0\n", + "\n", + " def forward(self, X: State[torch.Tensor]):\n", + " self.beta = (1 - X[\"l\"]) * self.beta0\n", + " dX = super().forward(X)\n", + " dX[\"l\"] = torch.zeros_like(X[\"l\"])\n", + " return dX" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.15116800367832184\n" + ] + } + ], + "source": [ + "init_state = dict(S=torch.tensor(99.0), I=torch.tensor(1.0), R=torch.tensor(0.0))\n", + "start_time = torch.tensor(0.0)\n", + "end_time = torch.tensor(12.0)\n", + "step_size = torch.tensor(0.1)\n", + "logging_times = torch.arange(start_time, end_time, step_size)\n", + "init_state_lockdown = dict(**init_state, l=torch.tensor(0.0))\n", + "\n", + "# We now simulate from the SIR model\n", + "beta_true = torch.tensor([0.03])\n", + "gamma_true = torch.tensor([0.5])\n", + "sir_true = SIRDynamics(beta_true, gamma_true)\n", + "with TorchDiffEq(), LogTrajectory(logging_times) as lt:\n", + " simulate(sir_true, init_state, start_time, end_time)\n", + "\n", + "sir_true_traj = lt.trajectory\n", + "\n", + "\n", + "def get_overshoot(trajectory):\n", + " t_max = torch.argmax(trajectory[\"I\"].squeeze())\n", + " S_peak = torch.max(trajectory[\"S\"].squeeze()[t_max]) / 100\n", + " S_final = trajectory[\"S\"].squeeze()[-1] / 100\n", + " return (S_peak - S_final).item()\n", + "\n", + "\n", + "print(get_overshoot(sir_true_traj))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def bayesian_sir(base_model=SIRDynamics) -> Dynamics[torch.Tensor]:\n", + " beta = pyro.sample(\"beta\", dist.Beta(18, 600))\n", + " gamma = pyro.sample(\"gamma\", dist.Beta(1600, 1600))\n", + " sir = base_model(beta, gamma)\n", + " return sir\n", + "\n", + "\n", + "def simulated_bayesian_sir(\n", + " init_state, start_time, logging_times, base_model=SIRDynamics\n", + ") -> State[torch.Tensor]:\n", + " sir = bayesian_sir(base_model)\n", + "\n", + " with TorchDiffEq(), LogTrajectory(logging_times, is_traced=True) as lt:\n", + " simulate(sir, init_state, start_time, logging_times[-1])\n", + " return lt.trajectory" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def MaskedStaticIntervention(time: R, intervention: Intervention[State[T]]):\n", + "\n", + " @on(StaticEvent(time))\n", + " def callback(\n", + " dynamics: Dynamics[T], state: State[T]\n", + " ) -> Tuple[Dynamics[T], State[T]]:\n", + "\n", + " with pyro.poutine.block():\n", + " return dynamics, intervene(state, intervention)\n", + "\n", + " return callback" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "overshoot_threshold = 20\n", + "lockdown_time = torch.tensor(1.0)\n", + "mask_time = torch.tensor(1.5)\n", + "\n", + "\n", + "def policy_model():\n", + "\n", + " lockdown = pyro.sample(\"lockdown\", dist.Bernoulli(torch.tensor(0.5)))\n", + " mask = pyro.sample(\"mask\", dist.Bernoulli(torch.tensor(0.5)))\n", + "\n", + " lockdown_efficiency = pyro.deterministic(\n", + " \"lockdown_efficiency\", torch.tensor(0.6) * lockdown, event_dim=0\n", + " )\n", + "\n", + " mask_efficiency = pyro.deterministic(\n", + " \"mask_efficiency\", (0.1 * lockdown + 0.45 * (1 - lockdown)) * mask, event_dim=0\n", + " )\n", + "\n", + " joint_efficiency = pyro.deterministic(\n", + " \"joint_efficiency\",\n", + " torch.clamp(lockdown_efficiency + mask_efficiency, 0, 0.95),\n", + " event_dim=0,\n", + " )\n", + "\n", + " lockdown_sir = bayesian_sir(SIRDynamicsLockdown)\n", + " with LogTrajectory(logging_times, is_traced=True) as lt:\n", + " with TorchDiffEq():\n", + " with MaskedStaticIntervention(lockdown_time, dict(l=lockdown_efficiency)):\n", + " with MaskedStaticIntervention(mask_time, dict(l=joint_efficiency)):\n", + " simulate(\n", + " lockdown_sir, init_state_lockdown, start_time, logging_times[-1]\n", + " )\n", + "\n", + " trajectory = lt.trajectory\n", + "\n", + " t_max = torch.max(trajectory[\"I\"], dim=-1).indices\n", + " S_peaks = pyro.ops.indexing.Vindex(trajectory[\"S\"])[..., t_max]\n", + " overshoot = pyro.deterministic(\n", + " \"overshoot\", S_peaks - trajectory[\"S\"][..., -1], event_dim=0\n", + " )\n", + " os_too_high = pyro.deterministic(\n", + " \"os_too_high\",\n", + " (overshoot > overshoot_threshold).clone().detach().float(),\n", + " event_dim=0,\n", + " )\n", + "\n", + " return overshoot, os_too_high\n", + "\n", + "\n", + "with ExtractSupports() as s:\n", + " one_run = policy_model()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# conditioning (as opposed to intervening) is sufficient for\n", + "# propagating the changes, as the decisions are upstream from ds\n", + "\n", + "# no interventions\n", + "policy_model_none = condition(\n", + " policy_model, {\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(0.0)}\n", + ")\n", + "unintervened_predictive = Predictive(\n", + " policy_model_none, num_samples=num_samples, parallel=True\n", + ")\n", + "unintervened_samples = unintervened_predictive()\n", + "\n", + "# both interventions\n", + "policy_model_all = condition(\n", + " policy_model, {\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(1.0)}\n", + ")\n", + "intervened_predictive = Predictive(\n", + " policy_model_all, num_samples=num_samples, parallel=True\n", + ")\n", + "intervened_samples = intervened_predictive()\n", + "\n", + "policy_model_mask = condition(\n", + " policy_model, {\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(1.0)}\n", + ")\n", + "mask_predictive = Predictive(policy_model_mask, num_samples=num_samples, parallel=True)\n", + "mask_samples = mask_predictive()\n", + "\n", + "policy_model_lockdown = condition(\n", + " policy_model, {\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(0.0)}\n", + ")\n", + "lockdown_predictive = Predictive(\n", + " policy_model_lockdown, num_samples=num_samples, parallel=True\n", + ")\n", + "lockdown_samples = lockdown_predictive()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def add_pred_to_plot(preds, axs, coords, color, label):\n", + " sns.lineplot(\n", + " x=logging_times,\n", + " y=preds.mean(dim=0).squeeze().tolist(),\n", + " ax=axs[coords],\n", + " label=label,\n", + " color=color,\n", + " )\n", + " axs[coords].fill_between(\n", + " logging_times,\n", + " torch.quantile(preds, 0.025, dim=0).squeeze(),\n", + " torch.quantile(preds, 0.975, dim=0).squeeze(),\n", + " alpha=0.2,\n", + " color=color,\n", + " )\n", + "\n", + "\n", + "fig, axs = plt.subplots(4, 2, figsize=(12, 6))\n", + "\n", + "colors = [\"orange\", \"red\", \"green\"]\n", + "\n", + "add_pred_to_plot(\n", + " unintervened_samples[\"S\"], axs, coords=(0, 0), color=colors[0], label=\"susceptible\"\n", + ")\n", + "add_pred_to_plot(\n", + " unintervened_samples[\"I\"], axs, coords=(0, 0), color=colors[1], label=\"infected\"\n", + ")\n", + "add_pred_to_plot(\n", + " unintervened_samples[\"R\"], axs, coords=(0, 0), color=colors[2], label=\"recovered\"\n", + ")\n", + "\n", + "axs[0, 1].hist(unintervened_samples[\"overshoot\"].squeeze())\n", + "axs[0, 0].set_title(\"No interventions\")\n", + "axs[0, 1].set_title(\n", + " f\"Overshoot mean: {unintervened_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {unintervened_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", + ")\n", + "\n", + "\n", + "add_pred_to_plot(\n", + " intervened_samples[\"S\"], axs, coords=(1, 0), color=colors[0], label=\"susceptible\"\n", + ")\n", + "add_pred_to_plot(\n", + " intervened_samples[\"I\"], axs, coords=(1, 0), color=colors[1], label=\"infected\"\n", + ")\n", + "add_pred_to_plot(\n", + " intervened_samples[\"R\"], axs, coords=(1, 0), color=colors[2], label=\"recovered\"\n", + ")\n", + "axs[1, 0].set_title(\"Both interventions\")\n", + "axs[1, 0].legend_.remove()\n", + "\n", + "\n", + "axs[1, 1].hist(intervened_samples[\"overshoot\"].squeeze())\n", + "axs[1, 1].set_title(\n", + " f\"Overshoot mean: {intervened_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {intervened_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", + ")\n", + "\n", + "\n", + "add_pred_to_plot(\n", + " mask_samples[\"S\"], axs, coords=(2, 0), color=colors[0], label=\"susceptible\"\n", + ")\n", + "add_pred_to_plot(\n", + " mask_samples[\"I\"], axs, coords=(2, 0), color=colors[1], label=\"infected\"\n", + ")\n", + "add_pred_to_plot(\n", + " mask_samples[\"R\"], axs, coords=(2, 0), color=colors[2], label=\"recovered\"\n", + ")\n", + "axs[2, 0].set_title(\"Mask only\")\n", + "axs[2, 0].legend_.remove()\n", + "\n", + "axs[2, 1].hist(mask_samples[\"overshoot\"].squeeze())\n", + "axs[2, 1].set_title(\n", + " f\"Overshoot mean: {mask_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {mask_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", + ")\n", + "\n", + "add_pred_to_plot(\n", + " lockdown_samples[\"S\"], axs, coords=(3, 0), color=colors[0], label=\"susceptible\"\n", + ")\n", + "add_pred_to_plot(\n", + " lockdown_samples[\"I\"], axs, coords=(3, 0), color=colors[1], label=\"infected\"\n", + ")\n", + "add_pred_to_plot(\n", + " lockdown_samples[\"R\"], axs, coords=(3, 0), color=colors[2], label=\"recovered\"\n", + ")\n", + "axs[3, 0].set_title(\"Lockdown only\")\n", + "axs[3, 0].legend_.remove()\n", + "\n", + "axs[3, 1].hist(lockdown_samples[\"overshoot\"].squeeze())\n", + "axs[3, 1].set_title(\n", + " f\"Overshoot mean: {lockdown_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {lockdown_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", + ")\n", + "\n", + "\n", + "fig.tight_layout()\n", + "fig.suptitle(\"Trajectories and overshoot distributions\", fontsize=16, y=1.05)\n", + "sns.despine()\n", + "\n", + "plt.savefig(\"counterfactual_sir.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['lockdown', 'mask', 'lockdown_efficiency', 'mask_efficiency', 'joint_efficiency', 'beta', 'gamma', 'S', 'I', 'R', 'l', 'overshoot', 'os_too_high'])\n" + ] + } + ], + "source": [ + "with ExtractSupports() as s:\n", + " policy_model()\n", + "\n", + "supports = s.supports\n", + "print(supports.keys())\n", + "\n", + "antecedents = {\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(1.0)}\n", + "alternatives = {\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(0.0)}\n", + "witnesses = {key: s.supports[key] for key in [\"lockdown_efficiency\", \"mask_efficiency\"]}\n", + "consequents = {\"os_too_high\": torch.tensor(1.0)}\n", + "\n", + "with MultiWorldCounterfactual() as mwc:\n", + " with SearchForExplanation(\n", + " supports=supports,\n", + " alternatives=alternatives,\n", + " antecedents=antecedents,\n", + " antecedent_bias=0.0,\n", + " witnesses=witnesses,\n", + " consequents=consequents,\n", + " consequent_scale=1e-8,\n", + " witness_bias=0.2,\n", + " ):\n", + " with pyro.plate(\"sample\", exp_plate_size):\n", + " with pyro.poutine.trace() as tr:\n", + " policy_model_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def get_table(\n", + " trace, mwc, antecedents, witnesses, consequents, others=None, world: int = 1\n", + "):\n", + "\n", + " values_table = {}\n", + " nodes = trace.trace.nodes\n", + " witnesses = [key for key, _ in witnesses.items()]\n", + "\n", + " with mwc:\n", + "\n", + " for antecedent_str in antecedents.keys():\n", + "\n", + " obs_indices = IndexSet(\n", + " **{\n", + " name: {0}\n", + " for name, ind in indices_of(nodes[antecedent_str][\"value\"]).items()\n", + " }\n", + " )\n", + " obs_ant = gather(\n", + " nodes[antecedent_str][\"value\"],\n", + " obs_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " int_indices = IndexSet(\n", + " **{\n", + " name: {world}\n", + " for name, ind in indices_of(nodes[antecedent_str][\"value\"]).items()\n", + " }\n", + " )\n", + " int_ant = gather(\n", + " nodes[antecedent_str][\"value\"],\n", + " int_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " values_table[f\"{antecedent_str}_obs\"] = obs_ant.squeeze().tolist()\n", + " values_table[f\"{antecedent_str}_int\"] = int_ant.squeeze().tolist()\n", + "\n", + " apr_ant = nodes[f\"__cause____antecedent_{antecedent_str}\"][\"value\"]\n", + " values_table[f\"apr_{antecedent_str}\"] = apr_ant.squeeze().tolist()\n", + "\n", + " if witnesses:\n", + " for candidate in witnesses:\n", + " obs_indices = IndexSet(\n", + " **{\n", + " name: {world}\n", + " for name, ind in indices_of(nodes[candidate][\"value\"]).items()\n", + " }\n", + " )\n", + " obs_candidate = gather(\n", + " nodes[candidate][\"value\"],\n", + " obs_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " int_indices = IndexSet(\n", + " **{\n", + " name: {world}\n", + " for name, ind in indices_of(nodes[candidate][\"value\"]).items()\n", + " }\n", + " )\n", + " int_candidate = gather(\n", + " nodes[candidate][\"value\"],\n", + " int_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " values_table[f\"{candidate}_obs\"] = obs_candidate.squeeze().tolist()\n", + " values_table[f\"{candidate}_int\"] = int_candidate.squeeze().tolist()\n", + "\n", + " wpr_con = nodes[f\"__cause____witness_{candidate}\"][\"value\"]\n", + " values_table[f\"wpr_{candidate}\"] = wpr_con.squeeze().tolist()\n", + "\n", + " if others:\n", + " for other in others:\n", + " obs_indices = IndexSet(\n", + " **{\n", + " name: {0}\n", + " for name, ind in indices_of(nodes[other][\"value\"]).items()\n", + " }\n", + " )\n", + "\n", + " obs_other = gather(\n", + " nodes[other][\"value\"],\n", + " obs_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " int_indices = IndexSet(\n", + " **{\n", + " name: {world}\n", + " for name, ind in indices_of(nodes[other][\"value\"]).items()\n", + " }\n", + " )\n", + "\n", + " int_other = gather(\n", + " nodes[other][\"value\"],\n", + " int_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " values_table[f\"{other}_obs\"] = obs_other.squeeze().tolist()\n", + " values_table[f\"{other}_int\"] = int_other.squeeze().tolist()\n", + "\n", + " for consequent in consequents.keys():\n", + "\n", + " obs_indices = IndexSet(\n", + " **{\n", + " name: {0}\n", + " for name, ind in indices_of(nodes[consequent][\"value\"]).items()\n", + " }\n", + " )\n", + " obs_consequent = gather(\n", + " nodes[consequent][\"value\"],\n", + " obs_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " int_indices = IndexSet(\n", + " **{\n", + " name: {world}\n", + " for name, ind in indices_of(nodes[consequent][\"value\"]).items()\n", + " }\n", + " )\n", + " int_consequent = gather(\n", + " nodes[consequent][\"value\"],\n", + " int_indices,\n", + " event_dim=0,\n", + " )\n", + "\n", + " values_table[f\"{consequent}_obs\"] = obs_consequent.squeeze().tolist()\n", + " values_table[f\"{consequent}_int\"] = int_consequent.squeeze().tolist()\n", + "\n", + " values_df = pd.DataFrame(values_table)\n", + "\n", + " return values_df\n", + "\n", + "\n", + "table = get_table(\n", + " tr,\n", + " mwc,\n", + " antecedents,\n", + " witnesses,\n", + " consequents,\n", + " others=[\"joint_efficiency\", \"overshoot\"],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      " + ], + "text/plain": [ + " lockdown_obs lockdown_int apr_lockdown mask_obs mask_int apr_mask \\\n", + "10 1.0 0.0 0 1.0 1.0 1 \n", + "11 1.0 0.0 0 1.0 1.0 1 \n", + "13 1.0 0.0 0 1.0 1.0 1 \n", + "29 1.0 0.0 0 1.0 1.0 1 \n", + "36 1.0 0.0 0 1.0 1.0 1 \n", + "... ... ... ... ... ... ... \n", + "9928 1.0 0.0 0 1.0 1.0 1 \n", + "9941 1.0 0.0 0 1.0 1.0 1 \n", + "9964 1.0 0.0 0 1.0 1.0 1 \n", + "9983 1.0 0.0 0 1.0 1.0 1 \n", + "9992 1.0 0.0 0 1.0 1.0 1 \n", + "\n", + " lockdown_efficiency_obs lockdown_efficiency_int \\\n", + "10 0.0 0.0 \n", + "11 0.0 0.0 \n", + "13 0.0 0.0 \n", + "29 0.0 0.0 \n", + "36 0.0 0.0 \n", + "... ... ... \n", + "9928 0.0 0.0 \n", + "9941 0.0 0.0 \n", + "9964 0.0 0.0 \n", + "9983 0.0 0.0 \n", + "9992 0.0 0.0 \n", + "\n", + " wpr_lockdown_efficiency mask_efficiency_obs mask_efficiency_int \\\n", + "10 0 0.10 0.10 \n", + "11 0 0.45 0.45 \n", + "13 0 0.10 0.10 \n", + "29 0 0.10 0.10 \n", + "36 0 0.10 0.10 \n", + "... ... ... ... \n", + "9928 0 0.10 0.10 \n", + "9941 0 0.45 0.45 \n", + "9964 0 0.10 0.10 \n", + "9983 0 0.45 0.45 \n", + "9992 0 0.10 0.10 \n", + "\n", + " wpr_mask_efficiency joint_efficiency_obs joint_efficiency_int \\\n", + "10 1 0.7 0.10 \n", + "11 0 0.7 0.45 \n", + "13 1 0.7 0.10 \n", + "29 1 0.7 0.10 \n", + "36 1 0.7 0.10 \n", + "... ... ... ... \n", + "9928 1 0.7 0.10 \n", + "9941 0 0.7 0.45 \n", + "9964 1 0.7 0.10 \n", + "9983 0 0.7 0.45 \n", + "9992 1 0.7 0.10 \n", + "\n", + " overshoot_obs overshoot_int os_too_high_obs os_too_high_int \n", + "10 29.467434 18.516724 1.0 0.0 \n", + "11 28.244480 27.621283 1.0 1.0 \n", + "13 15.683098 23.994499 0.0 1.0 \n", + "29 14.286774 23.541285 0.0 1.0 \n", + "36 26.499218 20.113390 1.0 1.0 \n", + "... ... ... ... ... \n", + "9928 31.073330 18.907593 1.0 0.0 \n", + "9941 28.345650 27.761520 1.0 1.0 \n", + "9964 20.732349 22.889946 1.0 1.0 \n", + "9983 26.055958 28.706375 1.0 1.0 \n", + "9992 25.797340 21.365763 1.0 1.0 \n", + "\n", + "[739 rows x 18 columns]" + ] + }, + "metadata": {}, 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66SebHlzZt3JHRERo+vTp+u9//yvp/4rWycnJ5gV46f8KAtL/9f765zAk2ZKSksx93H333apevbo2b96sI0eOKCQkRO7u7qpfv762bNkiV1dX3X///bk22AEAuSPX37m5viCGDx+uw4cP691331VwcLA8PDyUmpqqjz76yGa9du3aqV27dvrrr7+0adMmLVy4UCNGjFCDBg3M32HXrl01fvx4de/eXePHj1dISAi5GTeNBzYCDujw4cM6f/68evfurZo1a5pjN3733XeSriQ8SWrYsKF++OEHm1uL9u/fr/79+2vfvn3mrWD/5O3tneMhENkJ+p+vQ0NDFR4ebhau9+7dq7Nnz5rvDQC4s/Xt21fnz5/XjBkzcixLSkrS22+/rZo1a5q3065bt85mnXXr1ikrK0sNGjSQlL/8k1/ZeTFbSEiIEhISVLt2bQUEBCggIEB169bVu+++qy+//LJA+961a5esVqsGDx5sNsSysrLMW5KtVquqV6+uu+66yxzOK9vHH3+s/v37KyMjI0eM0pXfwenTp23m7dy505w+d+6cjhw5os6dOysgIMBsjF+d/29WSEiIpGv/zYoVK6batWvnOMbvvvvOfDZHtrJly6pWrVrq06ePFi9ebD7c6YEHHpAkrV+/3mYf/9znvffeKx8fH61du9ZmncTERO3evdvm1uewsDBt2bLFPE+RZI7H+f3336t58+Y39PsAgH8rcv2dm+sLYseOHXrkkUcUGhoqDw+PXON54YUXFBkZKenKhYQ2bdpo4MCByszMtDkn8PHxkcVi0SuvvKLk5GSb51kAN4qe14ADuvfee+Xt7a358+fLzc1Nbm5u+uKLL7RixQpJVx7CIEkDBw5U165dNWDAAPXu3VuXL1/WjBkzFBgYqMaNG5tF7c2bN6tGjRoKCgpS8+bN9c033yg6OlotWrTQ9u3btWbNGpv3DwwM1Oeff64lS5aoRo0aOnDggObNmyeLxWK+NwDgzlavXj09//zzmjFjhg4dOqQOHTrorrvu0sGDB7Vo0SKlpaVpxowZqlGjhjp27KhZs2YpNTVV999/v3755RfFxMQoNDTUfDBefvJPfpUoUUI7d+7Utm3b1LBhQw0cOFDdunXTgAED1L17d3l6emrZsmX66quvNGvWrALtOzAwUJI0YcIEderUSRcuXNDixYt14MABSVd6T3l7e2vw4MGaMGGCypQpoxYtWujIkSOaNWuWevbsqZIlS5o9tr788ks99NBDqlGjhsLCwrRu3ToFBQWpatWqWrVqlY4ePWq+d5kyZVS5cmUtXrxYFSpUUIkSJfT999/r/fffl6RCy8E1a9bM199syJAheu655zR06FB16NBBycnJevPNNxUeHi5fX1/t3bvXZr+DBg3S559/rv/+979atWqVqlatqq5du2r69OnKzMxU7dq19fHHH+vXX381t3FxcdHQoUMVFRWlYcOG6fHHH9e5c+cUExOjkiVL2vSwa9asmd5++21J/1eAf+CBB8yGMcVrACgYcv2dm+sLIjAwUJ9++qn8/f1VoUIF7dy5U7GxsTbt/wceeEDjxo3T66+/roceekgXL15UTEyMqlWrZg558k9+fn568skn9fbbb+uxxx6zuRgNFBTFa8ABFS9eXHPnztWUKVP0/PPPm72f4uLi1K9fP23fvl0tWrRQnTp19MEHH2jatGl64YUX5O3trWbNmmn48OHy8PCQh4eHnnrqKS1btkwbN27UDz/8oE6dOunYsWNavXq1li5dqvvvv1+zZs1S9+7dzfcfNWqUMjIyNGPGDKWnp6tKlSp67rnnlJCQoG+++SZfD/UAADi/5557TnXq1NHixYv16quv6sKFC6pYsaLCwsL07LPPqmLFipKuDDdVtWpVrVy5UgsXLlS5cuXUu3dvDRw40OyVlJ/8k1/PPvus5s6dq379+umzzz6Tn5+fFi9erOnTp2vkyJEyDEO+vr6aM2eOHn744QLtOzQ0VGPHjtU777yj9evXq2zZsgoNDVVMTIwiIyPNhyX37NlTRYsW1aJFi7Rs2TJVqFBB/fr1U79+/cz9PPjgg5o2bZo2b96s2NhYRUVFKTMzU6+//rrc3NzUtm1bDRs2zBxmQ5Lmzp2ryZMna9SoUfLw8FDNmjU1b948vfrqq9q+fbt69epV4N9XbvLzN2vevLnmz59vHnvp0qX12GOPafDgwbnu08vLS2PHjtWAAQMUGxuryMhIjRs3TmXLllVcXJwuXLigpk2b6tlnn7Xp5RcREaFixYppwYIFioyMlLe3t5o2baqhQ4fajK/aoEEDFS9eXGXLljXn+/v7y9vbW+XLl7d5WCQAIH/I9Xdurs+v1157TRMnTtTEiRMlSdWqVdP48eP1ySefmA9f7tatmzIyMrR06VJ9+OGHKlKkiBo1aqQRI0bI3d091/0OHjxY69ev13//+1+tWbPG7NUNFJTF4IklAAAAAAAAAAAHQ89rAAAAwEllZmZedx0XF5dcx+UEAACOj1yPfzuK1wAAAIAT+uOPP/J1q/SgQYPyHOoDAAA4LnI9wLAhAAAAgFNKT0+3efhhXsqVK6fy5cvfhogAAEBhItcDFK8BAAAAAAAAAA6IAXHgsLiugtzwuQAAx8L3MnLD5wIAHAvfy8gNnws4A4rXyJfZs2erVq1at+39duzYof79+9+293ME+/btU79+/fTAAw8oNDRUffv21b59+2zWMQxDixYt0iOPPKKAgAC1atVKixcvvuZ+e/XqpVq1auX5k5uUlBS1aNFCo0aNKvBxZH9W/vlTp04dhYaGKjIyUgcPHsz3vt5++20NHz5cknTx4kWNHDlS27dvL3BMN2LUqFFq0aLFNddZtWqVatWqpT/++CPf+83PNufOnVNYWJgSExPzvd9/+vvvvzV+/Hg1btxYwcHB6tevnw4fPnzd7X799Vc988wzCgkJUZMmTfTSSy8pOTnZZp3MzEzNmDFDzZo1U1BQkHr06KH4+PgbihNA4SNf33r5ydf/9MEHH1w3n+Rm79698vf316pVq/Jc55tvvrnhvzf5+tocOV8PHz4813O69evXm+scP35czz//vBo1aqTQ0FANHDhQx44du6E4ARQ+8vWtd6va15I0ffr0XL+HFy1adNP7vtqoUaNyvI+/v7+aNGmiESNG6OTJk/ne18SJEzV9+nRJ0qlTp9S/f38dP368wDHdiF69eqlXr17XXOdG/i/ys83hw4fVokULXbx4sUD7zpacnKxhw4YpNDRUDRo00NChQ3XmzJnrbvftt9+qc+fOCggI0EMPPaRXX31Vf//9d57r3+g5478BD2yEQ1q+fLkOHTpk7zBum6NHj+qJJ55Q3bp1NXnyZFksFr399tvq0aOHVq9ererVq0uSpkyZog8++EBDhgxRQECAvvvuO02YMEFubm7q2rVrrvseN26cUlJSbOYdO3ZML730kv7zn//kuk10dPRNJ7Fly5aZ01lZWTpx4oSmT5+unj17at26dfLx8bnm9ocOHdKCBQv0ySefSJJ++eUXffzxx+rUqdNNxVWYwsLCtGzZMpUrV65Q93vXXXepT58+evnll/X+++/LYrEUaPthw4YpPj5eI0aMkLe3t2JiYtS7d2+tW7dOJUuWzHWb5ORkPfnkk6pYsaKio6OVlpamN954Q/369dNHH30kd3d3SdJrr72mFStWaNiwYapcubLeeecd9enTR2vWrFHVqlVv+tgBOBfyde75Otu6dev02muvFXgMyvT0dI0aNUqZmZl5rrNlyxYNGzbsho7jn8jXN84e+VqSDhw4oHbt2uUoAlSrVk2SdPnyZfXt21eZmZkaM2aMPD09NWvWLPXq1UuffvqpSpQoUeBjBeDcyNeF176WrnwPh4SE5MjDlSpVMqdvdN+58fHxUUxMjPk6MzNTR44c0RtvvKFdu3Zp7dq1KlKkyDX3sXnzZn355Zf64osvJEk//vijNm7cWKA4brUuXbqoadOmhb7f6tWr6+GHH9akSZM0ZcqUAm2bmZmpfv36KSUlRa+88ooyMzM1bdo0Pf3001q1apXZTr7al19+qcGDByskJEQzZsxQRkaG5s6dq127dmnJkiVyc7Mtx97oOeO/BcVrwAF88MEH8vLy0oIFC1S0aFFJ0gMPPKAWLVooLi5OY8eO1R9//KF3331XY8aMUY8ePSRJjRo10smTJ7Vp06Y8E2DNmjVtXmdlZWnSpEny8/PT6NGjc6y/ceNGff755ypevPhNHVO9evVsXjdo0EAVK1ZUz549tXr16ute+Z86daratWvn0F/epUuXVunSpW/Jvnv06KF58+bpyy+/1COPPJLv7Xbt2qVvv/1WsbGxatasmSSpYcOGevjhh/Xhhx/queeey3W7r7/+WufOndNHH32ke+65R5JUvHhxPfPMM9q1a5dCQkJ08uRJLVmyRKNHjzY/g02aNFGrVq20cOFCTZo06SaPGgAcW37ytST9+eefmjlzppYtW6ZSpUoV+H1mzJihv/76K9dlKSkpWrhwoRYuXKjixYvr0qVLN3w8Evn6Zt3ufJ2WlqYjR47oySefzPG3y7Z9+3b9/vvvevfdd9WoUSNJ0r333qs2bdro66+/VseOHQt2kADgZG5l+1q6cqE2IiIiz+/hm9l3bjw8PHK8V8OGDeXu7q6XXnpJX3/9tR599NFr7iM6Olp9+vSRl5dXgd77dqpQoYIqVKhwS/bdv39/hYWF6cknn5S/v3++t1u/fr3279+vdevWmbWV2rVrq127dvr888/1+OOP57rd7NmzVaNGDb311lvy8PCQdOVv1rJlS61atcrsSHiz54z/Fgwb4iDOnDmjqKgoNWvWTIGBgercubO+/vprc3nfvn0VERGRY7uBAwfa/LNs375dTzzxhIKCghQSEqKXXnpJZ8+eNZevWrVKderU0fLly9W4cWOFhIQoISFBx44d07PPPqvQ0FAFBQWpa9euuV6F27Bhgx5//HHztpc1a9YU6DikKyfdc+bMUevWrRUQEKBHHnlEsbGxslqtkq7cFrN69WodP35ctWrVyvN22dmzZ6t169b68ssv1a5dOwUEBKh9+/batWuXdu/erS5duigwMFDt2rXT5s2bbbb97bffNGDAANWvX1/169dXZGRkjls+Dxw4oEGDBumBBx6Qv7+/mjZtqkmTJuny5cvmOrVq1dLixYs1evRohYSEKDg4WM8//7zNUAvZt51u2bIl1+OQrlwJ7Nu3r5lYJalo0aKqUKGCeYvnV199JU9PT3Xu3Nlm2xkzZmj27Nl57vtqS5cu1b59+zR+/HjzSzTbhQsX9N///lcjRoy4Jb1y6tatK0lmr+7Zs2erZcuWiomJMYequHDhgn777Tdt2LBB7dq1k3Sld1nv3r0lSb1797bpafTZZ58pIiJCwcHBaty4scaOHasLFy7YvO/PP/+sp59+WqGhoapfv76effbZfN8OvWrVKrVq1UoBAQF6/PHHbf4vcrulePXq1Wrbtq25/ubNm1WnTp0cn+P4+Hh169ZNAQEBCgsL01tvvWWz3MPDQ61atdKCBQvMeVu2bLnm/4Qkbdq0SUWLFlWTJk3MeaVLl9b9999/zSvraWlpkiRvb29zXnbyPH/+vKQrV+szMzPVsmVLmzjDwsIc7qo9cKuQr8nX18vXkjR//nxt2rRJs2fPVvPmzfPcX2527txpUwi/2ooVK/TRRx9p7NixeuKJJwq07/wiX/8fR8vXv/32mzIzM1W7du0818nO6cWKFTPnXZ3TgTsd+Zp8fava12fPntXp06ev+T1cWG336wkICJD0f/l61KhRevLJJzVu3DjVr19fbdu2VVZWljZs2KDffvvNLHCvWrVKUVFRkqSHH37YHC40KytLixcv1mOPPabAwECFhYXpjTfeMPNKth9++EE9evRQgwYNFBoaqmHDhuVr+BLDMLRw4UKFhYUpMDBQXbt21Z49e8zluQ0BsmjRIj388MMKDAxUt27dzCHTrv77X+9/ycfHRw888IBNvs7PZ2nTpk269957bToF1qxZUzVq1Lhmvj58+LCaNGliU3MpW7asqlevrg0bNpjzbuac8d+E4rUDSE5OVufOnbV9+3a9+OKLmj17tipXrqzIyEjzFszHH39c+/bt09GjR83tLl68qO+++07t27eXJG3btk19+vRRkSJFNGPGDL388svaunWrevfubZMQsrKy9Pbbb2vy5MmKiorSvffeqwEDBig1NVVTpkzR3LlzVapUKT333HM27ydJY8eOVZ8+fTRv3jxVqFBBo0aN0oEDB/J9HIZh6Nlnn9Vbb72lLl26aP78+WrdurVmzJihcePGSbpywtCsWTP5+Pho2bJlCgsLy/N3d+rUKb322mt69tlnNXPmTF28eFFDhgzR0KFD1aVLF82ZM0eGYejFF180fwdHjhxRt27d9Oeff+r111/X5MmTlZiYqO7du+vPP/+UdOUkoWfPnkpNTdVrr72mhQsX6tFHH9UHH3yg999/3yaG6dOny2q16s0339TIkSP17bff6tVXXzWXZ9+qeq2rez169NAzzzxjM+/o0aM6ePCg7rvvPklXru5WrVpV27ZtU8eOHeXv768WLVrY3O57PX///bdmzZql9u3bKzAwMMfyiRMnqkaNGurWrVu+91kQR44ckSSzZ68knThxQhs3btT06dMVFRWlkiVL6tNPP5WPj495ddnf399sxI8dO9b8rMydO1dDhw5VvXr1NGvWLEVGRuqLL75Qr169zL/3Tz/9pO7du0uSXn31VU2aNEknT55Ut27drnvr3MmTJxUbG6vnn39es2fPlsVi0ZAhQ8zPydXWrFmjUaNGqX79+po7d65atWqlgQMHKisrK8e6r7zyih599FHFxsYqODhYU6dO1bfffmuzTuvWrbV3717z9+bv73/d/4lDhw6pSpUqcnV1tZl/zz33mPvJTZs2beTj46MJEybozJkzSkxM1JQpU+Tj46MHH3zQ3HexYsVy3EJetWpVnTlz5prjdwF3AvI1+To/+VqSunXrpi+++KJAPXElKTU1VVFRURowYECe4ze2aNFC33zzzS3L1RL5+p8cLV9n/x8vX75cTZo0Ud26dXM8f6JJkyaqUaOGpk6dqsTERCUlJWnixIkqWrSowsPD89w3cKcgX5Ovb2X7Ovvvs2HDBjVv3lz+/v7q0KGDTSGzMNru+ZFbvt6+fbtOnjypOXPmaNiwYXJ1ddUnn3yievXqmXdJhYWFmXf4xMTEaODAgZKufB6jo6MVHh6uefPmqWfPnoqLi9PAgQPNBzuuWbNGffv2VcWKFfXmm28qKipKu3btUteuXfPMu9l27NihL7/8UmPGjNHUqVN15swZPffcc3kOkxYTE6M33nhDbdq00dy5cxUUFKQXXngh13Wv9b+UrXXr1vrmm2/Mdmt+PkuHDh0yh+X6p+vl61KlSunEiRM28zIyMnTy5EmbCzs3es74r2PA7qZMmWL4+/sbf/zxh838J5980mjcuLGRlZVl/P3330a9evWMmJgYc/ny5csNPz8/49SpU4ZhGEbXrl2Ndu3aGZmZmeY6hw8fNmrXrm3ExcUZhmEYK1euNHx9fY01a9aY65w5c8bw9fU1PvnkE3PexYsXjVdffdX47bffDMMwjFmzZhm+vr7Gxo0bzXWOHj1q+Pr6Gu+9916+j2PDhg2Gr6+vsXbtWpt15syZY/j6+prv99JLLxnNmze/5u8tt5gWLFhg+Pr6GsuXLzfnrV+/3vD19TX2799vGIZhDB061HjwwQeNv/76y1zn3LlzRoMGDYzXXnvNMAzD+P77742ePXvarGMYhtGuXTujb9++5mtfX1+je/fuNuuMGjXKqFev3jVjv57U1FSja9euRr169czf5zPPPGOEhoYaDzzwgBEXF2f8+OOPxn//+1/D19fXWLp0ab72+9577xl+fn7G4cOHcyz73//+Z/N+zZs3N1566aUCx579d8nIyDB//vrrL2Pbtm1Gx44djQYNGhhnzpyxWXfbtm02++jcubPx3HPP2cz76aefDF9fX+Onn34yDMMwzp8/b9StW9cYM2aMzXrbtm0zfH19zc98586djbZt29r8X1y4cMEICQkxhgwZkudxvPTSS4avr6+RkJBgzvvxxx8NX19f46uvvjIM4//+nxITEw3DMIywsDBjwIABNvvJ/kyuXLnSZpsPP/zQXOfSpUuGv7+/8eqrr9pse/HiRcPX19dYvHhxnnFerW/fvka3bt1yzH/zzTcNf3//a2771VdfGYGBgYavr6/h6+tr3H///cYvv/xiLh8zZozRtGnTHNt99NFHhq+vr/ldBNypyNfk66vllq+vlp/fUbaJEycaHTp0MDIyMozExESb/JGb7N/tjSBfO2e+Hj9+vOHr62uMGDHC+Omnn4z169cbERERRmBgoE3O3rlzpxESEmLm9Lp16xqbNm3Kd3yAMyNfk6+vVpjt67feesvw9fU1nn76aWPTpk3GN998Y/Tt29fw8/Mzvvvuu5vad26y/3b/zNfnzp0zvvvuO6NFixZGixYtjNTUVHNdX19f4+TJkzb7aNSokTFp0iSbeVfnxoMHDxq+vr7GggULbNZbs2aN4evra2zYsMHIysoyGjdubPM3M4wrn11/f3/j9ddfz/M4nnjiCSMwMNA4d+6cOS+7HZmdv/55XvP3338bgYGBxsSJE232M2bMGJvzjPz8L2X75ZdfzGPJr1atWhnDhg3LMX/YsGHGI488kud2b775pvn7/PPPP43jx48bI0aMMOrWrWs8/PDDuW5TkHPGfxt6XjuArVu3Kjg4WJUrV7aZ//jjjyspKUmHDx82e0p89tln5vJ169apUaNGKl++vFJTUxUfH69mzZrJMAxlZmYqMzNTd999t2rUqKEffvjBZt//vMWlbNmyqlmzpsaMGaOXXnpJn376qaxWq6Kiomx6EUlXxujJVqVKFUkyn9ian+PYunWr3Nzc1Lp16xzrZO+joOrXr29zLJIUFBRkzsu+TTI7zp9++kkhISEqUqSI+Xvy9vZWw4YN9eOPP0q60mMlLi5Onp6eSkhI0Ndff6158+bp7NmzSk9Pt3n/q8eeqlChglJTUwt8HNlSUlI0YMAA/fzzz5o6dar5+8zIyNC5c+c0fvx49ezZU40aNdLEiRPVpEkTm4c3XMvixYvVokUL3XvvvTbzz549q7Fjx2rkyJE5/n43yt/f3/xp0KCBevbsqfT0dMXExOTouXv1LVeJiYnm5ysvu3fvVnp6unmrcraGDRuqcuXK2rp1qy5duqSff/5Zbdq0senZVKJECTVv3vy6n7e77rpLNWrUMF9nx5TbOKRHjx7ViRMncny28xp77J//S15eXipbtmyOpx8XL15cJUqUsLnN+XqM/39FPDfXepDUp59+qkGDBqlFixZatGiR5s6dq/vuu099+/Y1e7xda9+S5OJCSsGdjXxNvv6nvPL1jdqyZYuWLVum6OjoHA/xuZXI11c4S75+4okn9NZbb2nKlCkKDQ1Vq1at9M4778jLy0vz58+XJLNnqJ+fnxYsWKCFCxfqoYce0qBBg7R9+/Z8xwg4K/I1+fqfCrt93aZNG82fP18LFixQ48aN1bx5c82fP1/33nuvZs2adVP7zsvx48dt8nVoaKieeeYZlSlTRnPmzLF5WGOpUqVsxo2+dOmS/vzzz+vm6+zPytX58NFHH5Wrq6u2bNmiI0eOKCkpKUdOv+eeexQcHHzdz1vNmjVtxnW+Vr7evXu3Ll++nOOzffV7Z7vW/1K27L/97cjXgwcPVr9+/TRr1iw1atRIjzzyiIoVK6aHH37Yoccdd1Q8sNEBXLhwQXfffXeO+dmJIvsfrn379vrkk0904MABlS1bVlu2bDFvn7l48aKsVqv5AJ+reXp62rz+59hP2U/ezX7YzJo1a+Tu7q7w8HCNHz/e5mnn/9wuu1CV/c+cn+O4cOGC7rrrrhy3SGY3jvJ6ONG1/HOM3mzX+jI4f/68PvvsM5s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+ "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "factual = table[\n", + " (table[\"lockdown_int\"] == 1)\n", + " & (table[\"mask_int\"] == 1)\n", + " & (table[\"wpr_lockdown_efficiency\"] == 0 & (table[\"wpr_mask_efficiency\"] == 0))\n", + "]\n", + "\n", + "\n", + "counterfactual_lockdown = table[\n", + " (table[\"lockdown_int\"] == 0)\n", + " & (table[\"mask_int\"] == 1)\n", + " & (table[\"wpr_lockdown_efficiency\"] == 0)\n", + "]\n", + "\n", + "display(counterfactual_lockdown)\n", + "\n", + "counterfactual_mask = table[\n", + " (table[\"lockdown_int\"] == 1)\n", + " & (table[\"mask_int\"] == 0)\n", + " & (table[\"wpr_mask_efficiency\"] == 0)\n", + "]\n", + "\n", + "\n", + "fig, axs = plt.subplots(1, 3, figsize=(18, 6))\n", + "\n", + "factual_mean = factual[\"overshoot_int\"].mean().item()\n", + "axs[0].hist(factual[\"overshoot_int\"])\n", + "axs[0].set_title(\n", + " f\"Factual\\n overshoot mean: {factual_mean:.2f}, Pr(too high): {factual['os_too_high_int'].mean().item():.2f}\"\n", + ")\n", + "axs[0].axvline(x=factual_mean, color=\"grey\", linestyle=\"--\")\n", + "\n", + "counterfactual_lockdown_mean = counterfactual_lockdown[\"overshoot_int\"].mean()\n", + "axs[1].hist(counterfactual_lockdown[\"overshoot_int\"])\n", + "axs[1].set_title(\n", + " f\"Counterfactual_lockdown\\n overshoot mean: {counterfactual_lockdown_mean:.2f}, Pr(too high): {counterfactual_lockdown['os_too_high_int'].mean():.2f}\"\n", + ")\n", + "axs[1].axvline(x=counterfactual_lockdown_mean, color=\"grey\", linestyle=\"--\")\n", + "\n", + "counterfactual_mask_mean = counterfactual_mask[\"overshoot_int\"].mean()\n", + "axs[2].hist(counterfactual_mask[\"overshoot_int\"])\n", + "axs[2].set_title(\n", + " f\"Counterfactual_mask\\n overshoot mean: {counterfactual_mask_mean:.2f}, Pr(too high): {counterfactual_mask['os_too_high_int'].mean():.2f}\"\n", + ")\n", + "axs[2].axvline(x=counterfactual_mask_mean, color=\"grey\", linestyle=\"--\")\n", + "\n", + "for i in range(3):\n", + " axs[i].set_xlim(5, 40)\n", + " axs[i].axvline(x=overshoot_threshold, color=\"red\", linestyle=\"-\")\n", + "\n", + "plt.savefig(\"counterfactual_sir_search.png\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_counterfactual_by_context(data, name, other):\n", + "\n", + " grouped_data = data.groupby([\"wpr_lockdown_efficiency\", \"wpr_mask_efficiency\"])\n", + "\n", + " fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n", + "\n", + " for (lockdown_efficiency, mask_efficiency), ax in zip(\n", + " grouped_data.groups.keys(), axs.flatten()\n", + " ):\n", + " data_subset = grouped_data.get_group((lockdown_efficiency, mask_efficiency))\n", + " mean_overshoot = data_subset[\"overshoot_int\"].mean().item()\n", + "\n", + " fixed = mask_efficiency if name == \"lockdown\" else lockdown_efficiency\n", + " ax.hist(data_subset[\"overshoot_int\"])\n", + " ax.set_title(\n", + " f\"{other} eff fixed: {fixed}\\nOvershoot mean: {mean_overshoot:.2f}, Pr(too high): {data_subset['os_too_high_int'].mean().item():.2f}\"\n", + " )\n", + " ax.set_xlim(5, 35)\n", + " ax.axvline(x=mean_overshoot, color=\"grey\", linestyle=\"--\")\n", + " ax.axvline(x=overshoot_threshold, color=\"red\", linestyle=\"-\")\n", + "\n", + " plt.suptitle(\n", + " f\"Counterfactual {name} by {other.lower()} efficiency contexts\",\n", + " fontsize=16,\n", + " y=1,\n", + " )\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "plot_counterfactual_by_context(counterfactual_lockdown, \"lockdown\", \"Mask\")\n", + "\n", + "plot_counterfactual_by_context(counterfactual_mask, \"mask\", \"Lockdown\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "sufficiency_table = get_table(\n", + " tr,\n", + " mwc,\n", + " antecedents,\n", + " witnesses,\n", + " consequents,\n", + " world=2,\n", + " others=[\"joint_efficiency\", \"overshoot\"],\n", + ")\n", + "\n", + "\n", + "factual_sufficiency = sufficiency_table[\n", + " (sufficiency_table[\"lockdown_int\"] == 1)\n", + " & (sufficiency_table[\"mask_int\"] == 1)\n", + " & (\n", + " sufficiency_table[\"wpr_lockdown_efficiency\"]\n", + " == 0 & (sufficiency_table[\"wpr_mask_efficiency\"] == 0)\n", + " )\n", + "]\n", + "\n", + "counterfactual_sufficiency_lockdown = sufficiency_table[\n", + " (sufficiency_table[\"lockdown_int\"] == 0)\n", + " & (sufficiency_table[\"mask_int\"] == 1)\n", + " & (sufficiency_table[\"wpr_lockdown_efficiency\"] == 0)\n", + "]\n", + "\n", + "counterfactual_sufficiency_mask = sufficiency_table[\n", + " (sufficiency_table[\"lockdown_int\"] == 1)\n", + " & (sufficiency_table[\"mask_int\"] == 0)\n", + " & (sufficiency_table[\"wpr_mask_efficiency\"] == 0)\n", + "]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 3, figsize=(18, 6))\n", + "\n", + "factual_sufficiency_mean = factual_sufficiency[\"overshoot_int\"].mean().item()\n", + "axs[0].hist(factual_sufficiency[\"overshoot_int\"])\n", + "\n", + "axs[0].set_title((\n", + " f\"Factual\\n overshoot mean: {factual_sufficiency_mean:.2f}, Pr(too high): \"\n", + " f\"{factual_sufficiency['os_too_high_int'].mean().item():.2f}\"\n", + "))\n", + "axs[0].axvline(x=factual_sufficiency_mean, color=\"grey\", linestyle=\"--\")\n", + "\n", + "counterfactual_sufficiency_lockdown_mean = counterfactual_sufficiency_lockdown[\"overshoot_int\"].mean()\n", + "axs[1].hist(counterfactual_sufficiency_lockdown[\"overshoot_int\"])\n", + "axs[1].set_title((\n", + " f\"Counterfactual_lockdown\\n overshoot mean: {counterfactual_sufficiency_lockdown_mean:.2f}, \"\n", + " f\"Pr(too high): {counterfactual_lockdown['os_too_high_int'].mean():.2f}\"\n", + "))\n", + "axs[1].axvline(x=counterfactual_sufficiency_lockdown_mean, color=\"grey\", linestyle=\"--\")\n", + "\n", + "counterfactual_sufficiency_mask_mean = counterfactual_sufficiency_mask[\"overshoot_int\"].mean()\n", + "axs[2].hist(counterfactual_sufficiency_mask[\"overshoot_int\"])\n", + "axs[2].set_title((\n", + " f\"Counterfactual_mask\\n overshoot mean: {counterfactual_sufficiency_mask_mean:.2f}, \"\n", + " f\"Pr(too high): {counterfactual_mask['os_too_high_int'].mean():.2f}\"\n", + "))\n", + "axs[2].axvline(x=counterfactual_sufficiency_mask_mean, color=\"grey\", linestyle=\"--\")\n", + "\n", + "for i in range(3):\n", + " axs[i].set_xlim(5, 40)\n", + " axs[i].axvline(x=overshoot_threshold, color=\"red\", linestyle=\"-\")\n", + "\n", + "#plt.savefig(\"counterfactual_sir_search_sufficiency.png\")\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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      " + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [lockdown_obs, lockdown_int, apr_lockdown, mask_obs, mask_int, apr_mask, lockdown_efficiency_obs, lockdown_efficiency_int, wpr_lockdown_efficiency, mask_efficiency_obs, mask_efficiency_int, wpr_mask_efficiency, joint_efficiency_obs, joint_efficiency_int, overshoot_obs, overshoot_int, os_too_high_obs, os_too_high_int]\n", + "Index: []" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "counterfactual_sufficiency_lockdown.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "chirho", + "language": "python", + 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z4HFjOL_1+6Qh0^;eU$A>TC4UCrL1G*Ka{fn#d`nyORxOb1N3`~|6ilR|H(-uq|Pe^ VmugilU1i{(`F`ttxu(Y>{sY^vuDAdI diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index c18c1ccd..081623aa 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -1,12 +1,52 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Causal Explanations in Models with Continuous Random Variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The **Explainable Reasoning with Chirho** package aims to provide a unified, principled approach to computations of causal explanations. We showed in an earlier [tutorial](https://basisresearch.github.io/chirho/explainable_categorical.html) how Chirho provides a handler `SearchForExplanation` to carry out the program transformations needed to compute causal queries and explanations. We specifically used discrete models and in this tutorial, we extend the usage of `SearchForExplanation` to causal models with continuous random variables.\n", + "\n", + "We take an epidemiological dynamical system model (described in more detail in this [tutorial](https://basisresearch.github.io/chirho/dynamical_intro.html)) and show how but-for analysis is not enough to derive conclusions about effects of different policies during a pandemic. We, then, show how various causal explanation queries can be computed using `SearchForExplanation` and in-built inference algorithms. We also demonstrate how more fine grained analysis can be done by post-processing the samples. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Outline\n", + "\n", + "- Setup\n", + "- Bayesian epidemiological SIR model with Policies\n", + " - SIR model\n", + " - Bayesian SIR model\n", + " - Baysian SIR model with Policies\n", + "- But-for analysis for Bayesian SIR model\n", + "- Causal Explanations using `SearchForExplanation`\n", + "- Fine-grained analysis for `overshoot` using sample traces\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "We first install the required dependencies for this example: PyTorch, Pyro, Chirho and some auxiliary variables.\n" + ] + }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 209, "metadata": {}, "outputs": [], "source": [ - "# Setup\n", "import numbers\n", "import os\n", "from typing import Tuple, TypeVar, Union, Optional, Callable\n", @@ -49,9 +89,25 @@ "exp_plate_size = 10 if smoke_test else 2000" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bayesian Epidemiological SIR model with Policies\n", + "\n", + "Now, we build the epidemiological SIR model, one step at a time. We first encode deterministic SIR (Susceptible, Infected, Recovered) dynamics. Then we add uncertainty to the parameters that govern these dynamics, namely $\\beta$ and $\\gamma$. These parameters have been described in much detail in the [dynamical systems tutorial](https://basisresearch.github.io/chirho/dynamical_intro.html). We then integrate the resulting model into another model that describes the policy mechanisms such as imposing lockdown and masking restrictions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### SIR Model and Simulation" + ] + }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 210, "metadata": {}, "outputs": [], "source": [ @@ -91,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 211, "metadata": {}, "outputs": [ { @@ -132,9 +188,25 @@ "print(get_overshoot(sir_true_traj))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The number $0.15$ is the overshoot you get if $\\beta = 0.03, \\gamma = 0.5$. This value is observed by simulating the SIR dynamics model with these values and calculate overshoot directly. Now one can add uncertainty to the parameters of $\\beta, \\gamma$ and get a Bayesian SIR model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bayesian SIR model\n", + "\n", + "In Bayesian SIR model, we specifically add uncertainty to $\\beta$ and $\\gamma$ by inducing $\\beta$ to be drawn from the distribution Beta(18, 600), and $\\gamma$ to be drawn from the distribution Beta(1600, 1600). " + ] + }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 212, "metadata": {}, "outputs": [], "source": [ @@ -157,9 +229,19 @@ " return lt.trajectory" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bayesian SIR model with Policies\n", + "\n", + "Now we integrate the Bayesian SIR model with the effect of different policies. We consider two possible policies, lockdown and masking, where each can be implemented with $50\\%$ probability. We encode their efficiencies which further affect the Bayesian SIR model via a utility function (`MaskedStaticIntervention`) defiend below. The model also computes `overshoot` and `os_too_high` indicating the overshoot and if it was too high for further analysis.\n", + "\n" + ] + }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 213, "metadata": {}, "outputs": [], "source": [ @@ -180,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 214, "metadata": {}, "outputs": [], "source": [ @@ -235,9 +317,24 @@ " return overshoot, os_too_high" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## But-for Analysis with Bayesian SIR model with Policies\n", + "\n", + "Now that we have the Bayesian SIR model with Policies, we can do but-for analysis to idenitfy which one of the policies cause overshoot to be too high. To perform but-for analysis, we investigate the following four scenarios:\n", + "1. Model where non of the policies were applied\n", + "2. Model where both lockdown and masking were enforced\n", + "3. Model where only masking was imposed\n", + "4. Model where only lockdown was imposed\n", + "\n", + "We create these four models by conditioning on the policies being imposed as required. The models obtained are similar to intervened models since the variables `lockdown` and `mask` do not have any variables upstream to them." + ] + }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 312, "metadata": {}, "outputs": [], "source": [ @@ -277,19 +374,24 @@ "lockdown_predictive = Predictive(\n", " policy_model_lockdown, num_samples=num_samples, parallel=True\n", ")\n", - "lockdown_samples = lockdown_predictive()" + "lockdown_samples = lockdown_predictive()\n", + "\n", + "predictive = Predictive(\n", + " policy_model, num_samples=num_samples, parallel=True\n", + ")\n", + "samples = predictive()" ] }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 336, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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      " + "
      " ] }, "metadata": {}, @@ -297,9 +399,6 @@ } ], "source": [ - "# Plotting them, if I do but-for analysis of mask or lockdown, they both come out to not be the cause. That is not true if I use both as the cause, so what exactly is their role and\n", - "# how can we get more fine-grained information\n", - "\n", "def add_pred_to_plot(preds, axs, coords, color, label):\n", " sns.lineplot(\n", " x=logging_times,\n", @@ -317,7 +416,7 @@ " )\n", "\n", "\n", - "fig, axs = plt.subplots(4, 2, figsize=(12, 6))\n", + "fig, axs = plt.subplots(5, 2, figsize=(12, 7.5))\n", "\n", "colors = [\"orange\", \"red\", \"green\"]\n", "\n", @@ -391,6 +490,23 @@ " f\"Overshoot mean: {lockdown_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {lockdown_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", ")\n", "\n", + "add_pred_to_plot(\n", + " samples[\"S\"], axs, coords=(4, 0), color=colors[0], label=\"susceptible\"\n", + ")\n", + "add_pred_to_plot(\n", + " samples[\"I\"], axs, coords=(4, 0), color=colors[1], label=\"infected\"\n", + ")\n", + "add_pred_to_plot(\n", + " samples[\"R\"], axs, coords=(4, 0), color=colors[2], label=\"recovered\"\n", + ")\n", + "axs[4, 0].set_title(\"All interventions with equal probabilities\")\n", + "axs[4, 0].legend_.remove()\n", + "\n", + "axs[4, 1].hist(samples[\"overshoot\"].squeeze())\n", + "axs[4, 1].set_title(\n", + " f\"Overshoot mean: {samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", + ")\n", + "\n", "\n", "fig.tight_layout()\n", "fig.suptitle(\"Trajectories and overshoot distributions\", fontsize=16, y=1.05)\n", @@ -403,45 +519,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# analysis of os_too_high using SearchForExplanation" + "The plots above show what happens in the four different scenarios. We observe that in the model where none of the policies were imposed, there was very low probability of high overshoot $0.24$. On the other hand, when both policies were imposed, the probability of high overshoot was relatively higher $0.81$. \n", + "\n", + "To identify, which one of `lockdown` and `mask` is the cause, we analyze the models where only one of the policies were imposed. It seems that when only one of the policies were imposed, the probability of high overshoot seems to be even higher $0.96$ and $0.9$. This indicates that both `lockdown` and `mask` were essentially helping in keeping the overshoot low but we know that is not true (as evident from the first two plots).\n", + "\n", + "So, what these plots show is that there is a need of a more fine grained analysis where we not only control the variables being intervened on (that is, policies), we also control on keeping part of the context (that is, other variables in the model) fixed. In the next section, we show how this analysis can be carried out with the help of `SearchForExplanation`." ] }, { - "cell_type": "code", - "execution_count": 151, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "with ExtractSupports() as s:\n", - " policy_model()\n", + "## Causal Explanations using `SearchForExplanation`\n", "\n", - "supports = s.supports\n", - "supports[\"os_too_high\"] = constraints.independent(base_constraint=constraints.boolean, reinterpreted_batch_ndims=0)\n", - "\n", - "antecedents = {\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(1.0)}\n", - "alternatives = {\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(0.0)}\n", - "witnesses = {key: s.supports[key] for key in [\"lockdown_efficiency\", \"mask_efficiency\"]}\n", - "consequents = {\"os_too_high\": torch.tensor(1.0)}\n", - "\n", - "with MultiWorldCounterfactual() as mwc_plate:\n", - " with SearchForExplanation(\n", - " supports=supports,\n", - " alternatives=alternatives,\n", - " antecedents=antecedents,\n", - " antecedent_bias=0.0,\n", - " witnesses=witnesses,\n", - " consequents=consequents,\n", - " consequent_scale=1e-8,\n", - " witness_bias=0.2,\n", - " ):\n", - " with pyro.plate(\"sample\", exp_plate_size):\n", - " with pyro.poutine.trace() as tr:\n", - " policy_model_all()" + "We first setup a function for performing importance sampling through the model that returns cumulative log probabilities of the samples, sample traces, handler for multiworld counterfactual reasoning and log probabilities. We use these objects later in the code to subselect the samples." ] }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 245, "metadata": {}, "outputs": [], "source": [ @@ -477,20 +573,39 @@ " return _wrapped_model" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, we setup the query as follows:\n", + "1. `supports`: We extract supports of the model using `ExtractSupports` and enrich it with additional information of `os_too_high` being a Boolean.\n", + "2. `antecedents`: We have put `lockdown=1` and `mask=1` as possible causes.\n", + "3. `alternatives`: We provide `lockdown=0` and `mask=0` as alternative values.\n", + "4. `witnesses`: We include `mask_efficiency` and `lockdown_efficiency` as candidates to be included in the context to be kept fixed.\n", + "5. `consequents`: We put `os_too_high=1` as the outcome we wish to analyze the causes for.\n", + "6. `antecedent_bias`, `witness_bias`, `consequent_scale`: We set these parameters to have equal probabilities of choosing causes and preferring minimal witness sets. Please refer to the documentation of `SearchForExplanation` for more details." + ] + }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 354, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.0308)\n" + "tensor(0.1309)\n" ] } ], "source": [ + "with ExtractSupports() as s:\n", + " policy_model()\n", + "\n", + "supports = s.supports\n", + "supports[\"os_too_high\"] = constraints.independent(base_constraint=constraints.boolean, reinterpreted_batch_ndims=0)\n", + "\n", "query = SearchForExplanation(\n", " supports=supports,\n", " alternatives={\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(0.0)},\n", @@ -500,50 +615,105 @@ " consequents={\"os_too_high\": torch.tensor(1.0)},\n", " consequent_scale=1e-8,\n", " witness_bias=0.2,\n", - " )(policy_model_all)\n", + " )(policy_model #it was policy_model_all earlier)\n", + " )\n", "\n", "logp, importance_tr, mwc_imp, log_weights = importance_infer(num_samples=10000)(query)()\n", "print(torch.exp(logp))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have setup the query and drawn 10000 samples from it, we can analyze the samples and their log probabilities to compute queries of interest. We first compute the probabilities that different sets of antecedent candidates have causal effect over `os_too_high`." + ] + }, + { + "cell_type": "code", + "execution_count": 357, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_prob(trace, log_weights, mask):\n", + " mask_intervened = torch.ones(trace.nodes[\"__cause____antecedent_lockdown\"][\"value\"].shape).bool()\n", + " for i, v in mask.items():\n", + " mask_intervened &= (trace.nodes[i][\"value\"] == v)\n", + " print(mask, (torch.sum(torch.exp(log_weights) * mask_intervened.squeeze()) / mask_intervened.float().sum()).item())" + ] + }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 358, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.0488)\n", - "tensor(0.0760)\n", - "tensor(4.3182e-10)\n", - "tensor(4.8446e-10)\n", - "tensor(0.0626)\n", - "tensor(0.0241)\n" + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0} 0.20220299065113068\n", + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1} 0.2057761698961258\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0} 0.11375554651021957\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1} 2.610623717202998e-09\n" ] } ], "source": [ - "# Computing probability of different sets of variables being the cause and degree of responsibility\n", - "trace = importance_tr\n", - "mask_intervened = (trace.nodes[\"__cause____antecedent_lockdown\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_mask\"][\"value\"] == 0) \n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze()) / mask_intervened.float().sum())\n", - "\n", - "mask_intervened = (trace.nodes[\"__cause____antecedent_lockdown\"][\"value\"] == 0) & (trace.nodes[\"__cause____antecedent_mask\"][\"value\"] == 1) \n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze()) / mask_intervened.float().sum())\n", - "\n", - "mask_intervened = (trace.nodes[\"__cause____antecedent_lockdown\"][\"value\"] == 1) & (trace.nodes[\"__cause____antecedent_mask\"][\"value\"] == 0) \n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze()) / mask_intervened.float().sum())\n", - "\n", - "mask_intervened = (trace.nodes[\"__cause____antecedent_lockdown\"][\"value\"] == 1) & (trace.nodes[\"__cause____antecedent_mask\"][\"value\"] == 1) \n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze()) / mask_intervened.float().sum())\n", + "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_lockdown\": 0, \"__cause____antecedent_mask\": 0})\n", + "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_lockdown\": 0, \"__cause____antecedent_mask\": 1})\n", + "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_lockdown\": 1, \"__cause____antecedent_mask\": 0})\n", + "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_lockdown\": 1, \"__cause____antecedent_mask\": 1})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that one can also compute above queries by giving specific parameters to `SearchForExplanation` instead of subselecting the samples as we did in the tutorial for explainable module for models with categorical variables.\n", "\n", - "mask_intervened = (trace.nodes[\"__cause____antecedent_lockdown\"][\"value\"] == 0)\n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze()) / mask_intervened.float().sum())\n", + "Also, we use the log probabilities above to identify whether a particular combination of intervening nodes and context nodes have causal power or not. One can also obatin these results by explictly analyzing the sample trace as we do in the next section." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also compute degree of responsibilities assigned to both lockdown and mask as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 361, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Degree of responsibility for lockdown: \n", + "{'__cause____antecedent_lockdown': 0} 0.20397219061851501\n", + "\n", + "Degree of responsibility for mask: \n", + "{'__cause____antecedent_mask': 0} 0.15853415429592133\n" + ] + } + ], + "source": [ + "print(\"Degree of responsibility for lockdown: \")\n", + "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_lockdown\": 0})\n", + "print()\n", "\n", - "mask_intervened = (trace.nodes[\"__cause____antecedent_mask\"][\"value\"] == 0)\n", - "print(torch.sum(torch.exp(log_weights) * mask_intervened.squeeze()) / mask_intervened.float().sum())" + "print(\"Degree of responsibility for mask: \")\n", + "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_mask\": 0})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Further notebook is still work in progress. Some questions I had:\n", + "1. Normalization of degree of responsibility is not super clear to me that why we would want to do that.\n", + "2. The plots below are updated with `policy_model` instead of `policy_model_all`." ] }, { @@ -562,7 +732,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 301, "metadata": {}, "outputs": [], "source": [ @@ -587,7 +757,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 302, "metadata": {}, "outputs": [], "source": [ @@ -598,7 +768,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 366, "metadata": {}, "outputs": [ { @@ -606,14 +776,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "factual: 26.768293380737305 counterfactual mask: 26.331018447875977 counterfactual lockdown: 20.962488174438477\n", + "factual: 24.13526153564453 counterfactual mask: 21.76302719116211 counterfactual lockdown: 20.221193313598633\n", "Probability of overshoot being high\n", - "factual: 0.8101999759674072 counterfactual mask: 0.8904281854629517 counterfactual lockdown: 0.5546666383743286\n" + "factual: 0.722599983215332 counterfactual mask: 0.5667539238929749 counterfactual lockdown: 0.47914034128189087\n" ] }, { "data": { - "image/png": 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", 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", 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      " ] @@ -629,6 +799,7 @@ "plt.legend([\"factual\", \"counterfactual_lockdown\", \"counterfactual_mask\"])\n", "plt.ylabel(\"pr\")\n", "plt.xlabel(\"overshoot\")\n", + "sns.despine()\n", "\n", "print(\"Overshoot mean\")\n", "print(\"factual: \", os_fact.item(), \" counterfactual mask: \", os_mask.item(), \" counterfactual lockdown: \", os_lockdown.item())\n", @@ -646,7 +817,7 @@ }, { "cell_type": "code", - "execution_count": 167, + "execution_count": 292, "metadata": {}, "outputs": [], "source": [ @@ -657,7 +828,7 @@ }, { "cell_type": "code", - "execution_count": 168, + "execution_count": 293, "metadata": {}, "outputs": [ { @@ -665,14 +836,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "factual: 26.768293380737305 counterfactual mask: 26.77214813232422 counterfactual lockdown: 26.866281509399414\n", + "factual: 24.143041610717773 counterfactual mask: 26.505809783935547 counterfactual lockdown: 22.47197723388672\n", "Probability of overshoot being high\n", - "factual: 0.8101999759674072 counterfactual mask: 0.8060453534126282 counterfactual lockdown: 0.8146666884422302\n" + "factual: 0.7235000133514404 counterfactual mask: 0.8883248567581177 counterfactual lockdown: 0.7112860679626465\n" ] }, { "data": { - "image/png": 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", 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", 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      " ] @@ -688,6 +859,7 @@ "plt.legend([\"factual\", \"counterfactual_lockdown\", \"counterfactual_mask\"])\n", "plt.ylabel(\"pr\")\n", "plt.xlabel(\"overshoot\")\n", + "sns.despine()\n", "\n", "print(\"Overshoot mean\")\n", "print(\"factual: \", os_fact.item(), \" counterfactual mask: \", os_mask.item(), \" counterfactual lockdown: \", os_lockdown.item())\n", @@ -705,7 +877,7 @@ }, { "cell_type": "code", - "execution_count": 169, + "execution_count": 294, "metadata": {}, "outputs": [], "source": [ @@ -715,7 +887,7 @@ }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 295, "metadata": {}, "outputs": [ { @@ -723,14 +895,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "mask_efficiency fixed: 18.921985626220703 mask_efficiency not fixed: 26.140743255615234\n", + "mask_efficiency fixed: 19.993305206298828 mask_efficiency not fixed: 21.670305252075195\n", "Probability of overshoot being high\n", - "mask_efficiency fixed: 0.38847583532333374 mask_efficiency not fixed: 0.9764150977134705\n" + "mask_efficiency fixed: 0.4733840227127075 mask_efficiency not fixed: 0.5932203531265259\n" ] }, { "data": { - "image/png": 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v8cwzz9CvXz98fHzo3r17BVcoIlL52OzbMyUlBS8vL5ycnCzT6tatS25uLmlpadSpU8eq/bJlywCK/J/6pUuXSE9PJz8/n+HDh5OUlERAQADR0dHUr1+//DtSUFC6Z9PILV2+fJm6devSunVroPCZKa1ateKOO+4o0Xqu3ZeM7vLly9x33300btwYAB8fHxtXJCJiezY7pZWdnW0VdgDL67y8vGKvJysrC4DZs2cTFhbG8uXLycvLY9SoURRUxJGXSurMmTP4+vry1Vdf0aVLF4KCgpg9ezbHjh0jIiKCtm3bMmrUKDIyMoDC93zevHl07NgRf39/unTpwoYNGyzr++677yynk7p27cr69euvu91du3YREBDA7t27i1Xnvn37iIiIICAggLCwMHbu3AkUBtspU6Zw9uxZfH19LaeltmzZgq+vL2fOnLE6VWUymVi4cCGhoaEEBwfz/PPPk5qaClif0jKbzbz11luEhoYSEhLC6NGjOXv2rKUeX19fPv74Y/r06UPr1q0ZNGgQp0+ftsw/dOgQAwcOJDAwkEcffdTyeIzu3bvzz3/+06pvYWFhbNy4sUiflyxZwsSJE4mKiqJdu3a0b9/ecvQKoKCggFWrVtG1a1fL6byjR48CMGXKFPbu3cvSpUt5+umni6z76umrwYMH8/TTT/PDDz9Ypm3cuJHWrVvzyy+/AJCcnEybNm3YtWsXAL/99hujR48mMDCQLl26sHTpUvLz8y3r/uyzzywXBrzyyitW80REKjubBR5nZ+ciwebq65I8XdnBwQGA/v37Ex4eTkBAAAsWLODYsWMcPHiwzOqtqlauXMmyZcuYNWsWa9asYcyYMUycOJF3332XgwcP8tFHH1naffXVVyxZsoQdO3YQHh7OrFmzuHDhAvn5+YwfP54ePXqwfft2xo0bx8yZMzl+/LjVtvbv389LL73Eq6++SseOHW9ZW0pKCqNGjSIiIoKtW7cyYsQIpkyZwr59++jVqxfTpk2jQYMG7Nmzh48//piePXvSs2dP9uzZQ8OGDa3W9cYbbxAXF8fcuXPZsGEDf/zxB1FRUUW2uXbtWrZu3crrr7/Ohg0b8Pb2ZtiwYVy5csXSZsmSJURGRrJ582ZSU1NZvHgxAH/88QfDhg3Dz8+PuLg4Ro0axeTJk0lKSqJ3796WsAaFYeLkyZM3PJ20c+dOnJ2diYuLY/jw4SxYsICTJ08C8NZbbxEbG8u0adOIi4ujcePGjBgxgqysLCIjIwkKCmLYsGHXHT+zZ88eSx+unf/4448TFBTEvHnzMJvNzJgxg+7du/PII49gNpsZM2YM3t7exMXFMW/ePLZu3crbb78NwPHjxxk/fjwDBw5k06ZNmEwm4uPjb/URi4hUGjYLPPXr1yc1NRWT6f8G4qakpODi4oKHh0ex1+Pl5UWNGjVo0aKF1TRPT0/OnTtXpjVXRf/4xz9o1aoVffr0wdvbm969e9OhQweCg4Np3749J06cAAqffTVnzhzatm1L06ZNGT16NFeuXOHUqVNcvnyZtLQ06tatS5MmTejbty///Oc/rU6VnDhxgmeffZbJkyfTq1evYtW2bt06HnzwQZ566imaNWvGY489xt/+9jdWr16Ni4sLtWrVwsHBAR8fH2rVqoWLiwsuLi74+PhYgi4UHrX58MMPeeGFF+jUqRN33XUXM2fO5O677y6yzVWrVjFp0iTuv/9+WrZsySuvvEJ6errVEamhQ4fSvn177rnnHgYOHEhiYiLwf2OHXn75ZVq0aEFERAQTJ04kJyeHPn36cPDgQcs+t337dkJDQ6ldu/Z1++7p6cnkyZNp1qwZI0aMwNPTk8TERMxmM2vXrmXcuHF07dqVli1bMmvWLBwcHPif//kfatWqRY0aNahZsyaenp5F1nv1M6ldu3aR+XZ2drzyyiv8v//3/3jxxRc5efIkkZGRAHz//fecPXuWWbNm0aJFC+6//34mT57Me++9B8CmTZsICQlhyJAhtGzZkunTp1OvXr1ifc4iIpWBzcbw+Pn54ejoyMGDBy1Pl46Pj6dNmzZFBizfjKOjI/7+/iQlJVm+aC9evEhqaqplDEN11rRpU8u/XVxcrN4TFxcXy1G1Rx55hG+//ZZXX32VEydOcPjwYQDy8/Px9PRk4MCBvPzyyyxbtoyHH36Yfv36WX2Zz5kzB5PJVOTIy82cOHGCL7/80mrg+pUrV2jevHmJ+piamkpaWhr+/v6WaXfddRdjx461apeZmcm5c+d44YUXrPaxnJwcTp06ZXn952eAubu7W47+nDx5knvvvddq2aFDh1r+7evry44dOxgyZAjbt29n1KhRN6y5SZMmVqHNzc0Nk8nEH3/8QVpaGoGBgZZ5NWrUoHXr1iQnJxfn7bip5s2bM3LkSJYsWUJMTIxlfFNycjJpaWkEBwdb2hYUFJCTk0NqairJycn4+flZ1fTn1yIilZ3NAo+rqyvh4eFER0czd+5czp8/T2xsLPPmzQMKj/Zc/b/6Wxk6dChTp07Fz8+Pe+65h9deew0/Pz8CAgLKuxuV3p+/VIEbhslFixaxceNGIiIiCA8PJyoqii5duljmR0dH8+STT7Jr1y527drFhg0bWLZsmeVKoQEDBlCjRg1mz55N+/bti4zPuh6TyURYWBijR4+2ml7SJ6kXt/3VMSdvvPFGkVD15/BWo0aNUm2nd+/efPrpp3Ts2JEzZ85c9waZN9uG2WzG2dn5hrWX1Zi0pKQkHBwc+OGHHwgPDwcKP4sWLVpYLg74s1q1alnq+7MbvU8iIpWRTW88OHXqVPz9/Rk8eDAzZ85k7NixljEPoaGhbNu2rVjr6dGjB1OnTuW1114jIiKC/Px8li1bhp2dXXmWbyjr169n+vTpvPjii/Tq1Yvs7Gyg8EsuJSWFmTNn0qxZM5599lk2bdrEAw88YHXpf7du3XjuuefIzs5m5cqVxdpm8+bN+eWXX2jWrJnl5/PPP2fr1q0lqt3DwwMvLy+SkpIs044cOUKnTp3Iycmxauft7U1KSoplew0bNuS1116zjJ+5mTvvvJOjR49affGPHz+eVatWAdCnTx8SEhLYsmULDz30EG5ubiXqBxSGi7p161qNP7ty5Qo//fRTiY98Xc+uXbvYs2cPb7/9Nlu3buW7774DCj+Ls2fPUqdOHct7c+bMGd58803s7Oy4++67+c9//mNZT0FBgdX7LSJS2dk08Li6uhITE8OBAwfYvXs3Q4YMscw7evQoERERRZaJiIi47j12nnjiCb744gsSEhJ45513aNCgQXmW/n/s7cGhAn5KcJqvNDw9Pfnyyy85ffo0+/btY9KkSUDhQPLatWvz2WefMXfuXP773//y448/kpSUxL333mu1Dnd3dyZMmMA777zDmTNnbrnNQYMGkZiYyKJFizh16hRbt25l4cKFNGrUqMT1P/3007zxxht8//33/Pzzz5bxSNceIRwyZAiLFy/miy++4NSpU7z88svs37/fagzYjYSFhZGWlsb8+fM5deoUmzdv5vPPP6dDhw4ANGrUiICAAFavXk3v3r1L3Ic/1/jmm2/yxRdfkJyczPTp08nNzbWcsq1ZsyanTp3ijz/+KNF6MzIymDVrFs8++yydOnXiqaeeIioqitzcXEJDQ2ncuDEvvfQSR48eZd++fUyfPh1XV1ccHBx44oknSExMZPny5Zw4cYKYmBirq9tERCq76nMXu/Lg4FB4M8CK3F45mTt3LtHR0fTu3Zv69evTv39/HBwcLEdKli1bxty5c+nbty9ubm48/vjj9O/fv8h6/vrXv/LBBx8we/ZsyxU+N9K4cWPefvttFixYwLvvvkv9+vWZMmUKffv2LXH9I0eO5PLly4wfPx6TyUTnzp2ZPn16kXbDhw8nMzOTGTNmkJGRQevWrXn33XdvOLj4zzw8PFixYgVz585lzZo1NG3alNdff91qLEuvXr04evQonTt3LnEfrho2bBgZGRlMnz6djIwMgoKCWLNmjWW8Tf/+/Zk2bRojRowgLi6u2OtdtGgRLi4ulnFHY8aM4d///jdvvfUWEyZMYPny5cyaNYsnnniCmjVr0qNHDyZPngwUjmtavnw58+bNY/ny5TzyyCM3vPGhiEhlZGe+9sR8NZSRkUFwcDDx8fG4u7tbzcvJyeHkyZM0b968RJfLS/W0aNEizp07R0xMjK1LsSn93pSDnFxIuVi6G53WcATPWnAxvXTLO9iDTx1wuf4YMxFbudn397V0hEekDCQlJXHkyBHef/99li9fbutyRETkGgo8Ui4OHTrE4MGDbzi/UaNGlrsUG0FiYiKzZ89m0KBBltssiIhI5aHAI+WiVatWbNmy5YbzS3rpeWX3+OOP8/jjj9u6DBERuQFjfetIpeHk5GR1Az8RERFbsull6VVJdX4QqUhJ6fdFRCobHeG5BScnJ+zt7Tl79iw+Pj44OTnphoYiN2A2m8nLyyMlJQV7e/ti3XFbRKQiKPDcgr29Pc2bN+e3337TjdZEiqlmzZrccccdJXounohIeVLgKQYnJyfuuOMOTCaT5XlMInJ9Dg4OODo66kioiFQqCjzFZGdnR40aNfTARBERkSpIx5tFRETE8BR4RERExPAUeERERMTwFHhERETE8BR4RERExPAUeERERMTwFHhERETE8BR4RERExPAUeERERMTwFHhERETE8BR4RERExPAUeERERMTwFHhERETE8BR4RERExPAUeERERMTwFHhERETE8BR4RERExPAUeERERMTwFHhERETE8GwaeHJzc5k2bRohISGEhoYSGxt7y2X27dtH165dbzh/+/bt+Pr6lmWZIiIiUsU52nLj8+fPJzExkdWrV3P27FkmT55Mo0aN6NGjx3XbHz16lHHjxuHs7Hzd+ZcuXWLOnDnlWbKIiIhUQTY7wpOVlcXGjRuJjIzE39+fbt26MWLECNatW3fd9uvXr2fAgAF4e3vfcJ3z58+nadOm5VWyiIiIVFE2CzxJSUmYTCaCgoIs04KDg0lISKCgoKBI+2+++YaYmBiGDBly3fXt3buXvXv3Mnr06PIqWURERKoomwWelJQUvLy8cHJyskyrW7cuubm5pKWlFWm/bNkyunfvft115eXlMX36dGbMmIGLi0t5lSwiIiJVlM0CT3Z2tlXYASyv8/LySrSut956C39/f0JDQ8usPhERETEOmw1adnZ2LhJsrr4uyVGaY8eO8eGHH7J169YyrU9ERESMw2aBp379+qSmpmIymXB0LCwjJSUFFxcXPDw8ir2eTz/9lPT0dLp16wZAfn4+AEFBQcycOZO+ffuWffEiIiJSpdgs8Pj5+eHo6MjBgwcJCQkBID4+njZt2mBvX/wzbU899RRhYWGW1wkJCbz00kts2bLlpld0iYiISPVhs8Dj6upKeHg40dHRzJ07l/PnzxMbG8u8efOAwqM9tWrVuuXpLU9PTzw9PS2vz507B0CzZs3KrXYRERGpWmx6p+WpU6fi7+/P4MGDmTlzJmPHjrVciRUaGsq2bdtsWZ6IiIgYhJ3ZbDbbughby8jIIDg4mPj4eNzd3W1djohIUTm5kHIR8ovep+yWajiCZy24mF665R3swacOuFz/LvcitlKS7289PFREREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDM+mgSc3N5dp06YREhJCaGgosbGxt1xm3759dO3a1Wqa2Wxm5cqVdOnShXbt2jF48GCOHz9eXmWLiIhIFWPTwDN//nwSExNZvXo1UVFRLF26lB07dtyw/dGjRxk3bhxms9lq+vr164mNjWX69Ols2rSJJk2a8Pe//53s7Ozy7oKIiIhUATYLPFlZWWzcuJHIyEj8/f3p1q0bI0aMYN26dddtv379egYMGIC3t3eReXFxcQwbNoyHH36Y5s2bEx0dTVpaGvv37y/vboiIiEgVYLPAk5SUhMlkIigoyDItODiYhIQECgoKirT/5ptviImJYciQIUXmTZo0ib59+1pe29nZYTabuXz5crnULiIiIlWLzQJPSkoKXl5eODk5WabVrVuX3Nxc0tLSirRftmwZ3bt3v+66QkJCaNCggeX1xo0bMZlMBAcHl3ndIiIiUvXYLPBkZ2dbhR3A8jovL6/U601ISCAmJobhw4fj4+NzWzWKiIiIMdgs8Dg7OxcJNldfu7i4lGqdBw4cYPjw4XTq1Ilx48bddo0iIiJiDDYLPPXr1yc1NRWTyWSZlpKSgouLCx4eHiVe3w8//MCwYcN44IEHeP3117G31y2GREREpJDNUoGfnx+Ojo4cPHjQMi0+Pp42bdqUOKwcO3aMZ599lo4dO7J48WJq1KhRxtWKiIhIVWazwOPq6kp4eDjR0dEcOnSIXbt2ERsbyzPPPAMUHu3Jyckp1rpmzJhBw4YNmTp1KqmpqaSkpJRoeRERETE2m573mTp1Kv7+/gwePJiZM2cyduxYy5VYoaGhbNu27ZbrSElJ4cCBAxw/fpzOnTsTGhpq+SnO8iIiImJ8duZrb1tcDWVkZBAcHEx8fDzu7u62LkdEpKicXEi5CPlF71N2SzUcwbMWXEwv3fKODlDXq+TL/ZmDQ2EdImWoJN/f2vtEROTm7OwgPx/SLsN1bgx7S/b24O2pwCM2pb1PRESKp6CgdEeIRCoBXbstIiIihqfAIyIiIoanwCMiIiKGp8AjIiIihqfAIyIiIoanwCMiIiKGp8AjIiIihqfAIyIiIoanwCMiIiKGp8AjIiIihqfAIyIiIoanwCMiIiKGp8AjIiIihqenpYuIFMcVE+Tnl355BweooT+5Irai3z4RkeLIz4c/0qCgoOTL2tuDt6cCj4gN6bdPRKS4CgogvxSBR0RsTmN4RERExPAUeERERMTwFHhERETE8BR4RERExPA0aFmkGkhNhfT00i9fuzZ4eZVdPSIiFU2BR6QaSE+H7dshM7Pky7q5Qc+eCjy3xc6u8L85uaVfR2kuhxcRCwUekWoiMxMyMmxdRTVlZ1d4H5+0y6ULLo6O4OFW9nWJVCMKPCIiFaW09/Gx19EdkdulwCNSBdzOGBwHB8i9jTMpIiJGoMAjUgXczhgcHx8IDi77mkREqhIFHpEqorRjcNw09ENERPfhEREREeOzaeDJzc1l2rRphISEEBoaSmxs7C2X2bdvH127di0y/d///jePPPIIgYGBPPfcc1y8eLE8ShYREZEqyKaBZ/78+SQmJrJ69WqioqJYunQpO3bsuGH7o0ePMm7cOMxms9X0Q4cOERkZyZgxY9iwYQOXLl1i6tSp5V2+iIiIVBE2CzxZWVls3LiRyMhI/P396datGyNGjGDdunXXbb9+/XoGDBiAt7d3kXlr166lZ8+ehIeH06pVK+bPn8/XX3/N6dOny7sbIiIiUgXYLPAkJSVhMpkICgqyTAsODiYhIYGC69yY65tvviEmJoYhQ4YUmZeQkEBISIjldcOGDWnUqBEJCQnlUruIiIhULTYLPCkpKXh5eeHk5GSZVrduXXJzc0lLSyvSftmyZXTv3v266zp//jz16tWzmubt7c25c+fKtGYRERGpmmwWeLKzs63CDmB5nZeXV6J15eTkXHddJV2PiIiIGJPNAo+zs3ORQHL1tYuLS5msy9XV9faKFBEREUOwWeCpX78+qampmEwmy7SUlBRcXFzw8PAo8bouXLhgNe3ChQv4+PiUSa0iIiJStdks8Pj5+eHo6MjBgwct0+Lj42nTpg329iUrKzAwkPj4eMvr3377jd9++43AwMCyKldERESqMJsFHldXV8LDw4mOjubQoUPs2rWL2NhYnnnmGaDwaE9OTk6x1jVw4EA+/vhjNm7cSFJSEpMmTaJz5840bdq0PLsgIiIiVYRNbzw4depU/P39GTx4MDNnzmTs2LGWK7FCQ0PZtm1bsdYTFBTEK6+8wltvvcXAgQOpXbs28+bNK8/SRUREpAqx6cNDXV1diYmJISYmpsi8o0ePXneZiIgIIiIiij1dpDJITS184nlpODhAbm7Z1iMiUt3oaekiFSA9HbZvL3zieUn5+EBwcNnXJCJSnSjwiFSQzEzIyCj5cm5uZV+LiEh1Y9MxPCIiIiIVQYFHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAyvVIFn37595OXllXUtIiIiIuWiVIHnueee48SJE2Vdi4iIiEi5KFXgufvuuzl06FBZ1yIiIiJSLkr18NDatWszY8YM3nzzTZo0aYKTk5PV/Pfee69MihMREREpC6UKPH5+fvj5+WE2m0lLS8POzg5PT88yLk1ERESkbJQq8Dz77LO8+eabbNy4kYsXLwJQv359nnzySUaOHFmmBYqIiIjcrlIFnpiYGHbu3MmLL75I69atKSgo4D//+Q9vvvkmeXl5jBkzpqzrFBERESm1UgWeuLg43nrrLe677z7LtFatWtG4cWNefPFFBR4RERGpVEp1lZarqys1atQoMt3DwwM7O7vbLkpERESkLJUq8EyaNIlp06bx5ZdfkpaWRkZGBvv27WP69OkMHjyYs2fPWn5EREREbK1Up7RefPFFoHDw8tUjOmazGYAjR46waNEizGYzdnZ2HDlypIxKFRERESmdUgWezz//vKzrEBERESk3pQo8jRs3Lus6RERERMqNnpYuIiIihqfAIyIiIoanwCMiIiKGV6oxPCIiJZGaCunppV++dm3w8iq7ekSk+lHgEZFyl54O27dDZmbJl3Vzg549FXhE5PbY9JRWbm4u06ZNIyQkhNDQUGJjY2/Y9vDhw/Tv35/AwED69etHYmKiZZ7ZbGbJkiV06tSJv/zlL4wfP97yUFMRqRwyMyEjo+Q/pQlJIiLXsmngmT9/PomJiaxevZqoqCiWLl3Kjh07irTLyspi5MiRhISEsHnzZoKCghg1ahRZWVkAbNiwgY8++ogFCxawbt06zp8/T2RkZEV3R0RERCopmwWerKwsNm7cSGRkJP7+/nTr1o0RI0awbt26Im23bduGs7MzkyZNomXLlkRGRuLm5mYJR19//TW9evXivvvu45577mHEiBF8//33Fd0lERERqaRsFniSkpIwmUwEBQVZpgUHB5OQkEBBQYFV24SEBIKDgy2PsbCzs6Ndu3YcPHgQAE9PT7766it+//13cnJy+OSTT/Dz86uwvoiIiEjlZrPAk5KSgpeXF05OTpZpdevWJTc3l7S0tCJt69WrZzXN29ubc+fOAfDcc8/h6OhIp06daNeuHfv27WPhwoXl3gcRERGpGmwWeLKzs63CDmB5nZeXV6y2V9v9+uuvuLi48Pbbb7NmzRoaNGjAtGnTyrF6ERERqUpsFnicnZ2LBJurr11cXIrV1sXFBbPZzOTJkxk6dCgPP/wwwcHBLF68mP/3//4fCQkJ5dsJEakQ/3s2W0Sk1Gx2H5769euTmpqKyWTC0bGwjJSUFFxcXPDw8CjS9sKFC1bTLly4QL169bh48SK//fYbvr6+lnkNGzbEy8uLX3/9lcDAwPLvjIiUGycnMJvh1KnSr0M3LhQRmwUePz8/HB0dOXjwICEhIQDEx8fTpk0b7O2tDzwFBgbyzjvvYDabsbOzw2w2s3//fkaPHk3t2rVxcnIiOTmZli1bAnDx4kXS0tJo0qRJhfdLRMpWjRqF9+PZvVs3LhSR0rPZKS1XV1fCw8OJjo7m0KFD7Nq1i9jYWJ555hmg8GhPTk4OAD169ODSpUvMmTOH48ePM2fOHLKzs+nZsyeOjo5EREQQExPDjz/+yLFjx3jppZcIDAykTZs2tuqeiJQx3bhQRG6HTW88OHXqVPz9/Rk8eDAzZ85k7NixdO/eHYDQ0FC2bdsGgLu7OytWrCA+Pp6IiAgSEhJYuXIlNWvWBGDatGl0796diRMn8vTTT+Ph4cGyZcssl7GLiIhI9WbTZ2m5uroSExNDTExMkXlHjx61eh0QEEBcXNx11+Ps7MzkyZOZPHlyudQpIiIiVZtNj/CIiIiIVAQFHhERETE8BR4RERExPAUeERERMTwFHhERETE8BR4RERExPAUeEbkl3dJKRKo6m96HR0Qqv9t9lpWDA+TmlmlJIiIlpsAjIjd1u8+y8vGB4OCyr0tEpCQUeESkWK4+y6qk3NzKvhYRkZLSGB4RERExPAUeERERMTwFHhERETE8BR4RERExPAUeERERMTwFHhERETE8BR4RERExPAUeERERMTwFHhERETE8BR4RERExPAUeERERMTwFHhERETE8BR4RMTw7O1tXICK2pqeli4ihOTmB2QynTpV+HQ4OUM8DnMusKhGpaAo8ImJoNWpARgbs3g2ZmaVbh48P9OutwCNSlSnwiEi1kJlZGHxKw82tbGsRkYqnMTwiIiJieAo8IiIiYngKPCIiImJ4CjwiIiJieDYNPLm5uUybNo2QkBBCQ0OJjY29YdvDhw/Tv39/AgMD6devH4mJiVbzd+zYwaOPPkrbtm0ZNmwYv/76a3mXLyIiIlWETQPP/PnzSUxMZPXq1URFRbF06VJ27NhRpF1WVhYjR44kJCSEzZs3ExQUxKhRo8jKygJg//79TJw4kaFDh7J582acnJyYMGFCRXdHREREKimbBZ6srCw2btxIZGQk/v7+dOvWjREjRrBu3boibbdt24azszOTJk2iZcuWREZG4ubmZglHsbGx9O3blwEDBtCiRQsiIyNJSUnh4sWLFd0tERERqYRsFniSkpIwmUwEBQVZpgUHB5OQkEBBQYFV24SEBIKDg7H73/vD29nZ0a5dOw4ePAjA3r176datm6V906ZN+eKLL6hTp075d0REREQqPZsFnpSUFLy8vHBycrJMq1u3Lrm5uaSlpRVpW69ePatp3t7enDt3jkuXLpGenk5+fj7Dhw+nQ4cOPPvss/z+++8V0Q0RERGpAmwWeLKzs63CDmB5nZeXV6y2eXl5lnE8s2fPJiwsjOXLl5OXl8eoUaOKHCkSERGR6slmgcfZ2blIsLn62sXFpVhtXVxccHBwAKB///6Eh4cTEBDAggULOHbsmOWUl4iIiFRvNgs89evXJzU1FZPJZJmWkpKCi4sLHh4eRdpeuHDBatqFCxeoV68eXl5e1KhRgxYtWljmeXl54enpyblz58q3EyIiIlIl2Czw+Pn54ejoaHUUJj4+njZt2mBvb11WYGAgBw4cwGw2A2A2m9m/fz+BgYE4Ojri7+9PUlKSpf3FixdJTU2lcePGFdIXERERqdxsFnhcXV0JDw8nOjqaQ4cOsWvXLmJjY3nmmWeAwqM9OTk5APTo0YNLly4xZ84cjh8/zpw5c8jOzqZnz54ADB06lDVr1rB9+3aSk5OZNm0afn5+BAQE2Kp7IiIiUonY9MaDU6dOxd/fn8GDBzNz5kzGjh1L9+7dAQgNDWXbtm0AuLu7s2LFCuLj44mIiCAhIYGVK1dSs2ZNoDAQTZ06lddee42IiAjy8/NZtmyZ5TJ2ERERqd4cbblxV1dXYmJiiImJKTLv6NGjVq8DAgKIi4u74bqeeOIJnnjiiTKvUURERKo+PTxUREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMT4FHREREDE+BR0RERAxPgUdEREQMz6YPDxWpKlJTIT29dMs6OEBubtnWIyIiJaPAI1IM6emwfTtkZpZ8WR8fCA4u+5pERKT4FHhEiikzEzIySr6cm1vZ1yIlY29/e59DzZplV4uI2IYCj4gYmpMT+HiZ6PiXfEym0q3DxRWcHAvKtjARqVAKPCJiaI6O4Eg+pvNpZF0uXWhxrOuIXTMdqhOpyhR4RKRauJJTQF526QKPKVdHd0SqOl2WLiIiIoanwCMiIiKGp8AjIiIihqfAIyIiIoanwCMiIiKGp8AjIiIihqfAIyIiIoanwCMiIiKGp8AjIiIihqfAIyIiIoanwCMiIiKGZ9PAk5uby7Rp0wgJCSE0NJTY2Ngbtj18+DD9+/cnMDCQfv36kZiYeN1227dvx9fXt7xKFhERkSrIpoFn/vz5JCYmsnr1aqKioli6dCk7duwo0i4rK4uRI0cSEhLC5s2bCQoKYtSoUWRlZVm1u3TpEnPmzKmo8kVERKSKsFngycrKYuPGjURGRuLv70+3bt0YMWIE69atK9J227ZtODs7M2nSJFq2bElkZCRubm5FwtH8+fNp2rRpRXVBREREqgibBZ6kpCRMJhNBQUGWacHBwSQkJFBQUGDVNiEhgeDgYOzs7ACws7OjXbt2HDx40NJm79697N27l9GjR1dI/SIiIlJ12CzwpKSk4OXlhZOTk2Va3bp1yc3NJS0trUjbevXqWU3z9vbm3LlzAOTl5TF9+nRmzJiBi4tLudcuIiIiVYvNAk92drZV2AEsr/Py8orV9mq7t956C39/f0JDQ8uxYhEREamqHG21YWdn5yLB5urra4/S3Kiti4sLx44d48MPP2Tr1q3lW7CIiIhUWTYLPPXr1yc1NRWTyYSjY2EZKSkpuLi44OHhUaTthQsXrKZduHCBevXq8emnn5Kenk63bt0AyM/PByAoKIiZM2fSt2/fCuiNiIiIVGY2Czx+fn44Ojpy8OBBQkJCAIiPj6dNmzbY21ufaQsMDOSdd97BbDZjZ2eH2Wxm//79jB49mq5duxIWFmZpm5CQwEsvvcSWLVvw9vau0D6JiIhI5WSzwOPq6kp4eDjR0dHMnTuX8+fPExsby7x584DCoz21atXCxcWFHj168PrrrzNnzhwGDBjA+vXryc7OpmfPntSsWRNPT0/Leq8OZG7WrJktuiUi5cDeHtzcSresqyv87wWeIlKN2SzwAEydOpXo6GgGDx6Mu7s7Y8eOpXv37gCEhoYyb948IiIicHd3Z8WKFURFRfHhhx/i6+vLypUrqVmzpi3LF5EK4OQEPl4mOv4lH5Op5Mu7uYOLUwEODmVfm4hUHTYNPK6ursTExBATE1Nk3tGjR61eBwQEEBcXd8t13n///UWWFZGqy9ERHMnHdD6NrMsFt17gGi4NHLFr5Ia9Ao9ItWbTwCMiUlxXcgrIyy554DHllXwZETEePS1dREREDE9HeKRaSE2F9PTSLevgALm5ZVuPiIhULAUeqRbS02H7dsjMLPmyPj4QHFz2NYmISMVR4JFqIzMTMjJKvlxpL4cWEZHKQ4FHROQWrt4L9dIlKLhSiuVdwK0W6EIxEdtR4BERuQV7BygogP+ehksXS768hw/4NlDgEbElBR4RkWIymeCa5xgXb7lSHBUSkbKly9JFRETE8BR4RERExPAUeERERMTwNIZHRKSc6SovEdtT4BERKWe6ykvE9hR4REQqiK7yErEdjeERERERw1PgEREREcNT4BERERHDU+ARERERw1PgEREREcNT4BERERHDU+ARERERw1PgEREREcNT4BERERHDU+ARERERw1PgEREREcNT4BERERHDU+ARERERw1PgEREREcNT4BERERHDU+ARERERw7Np4MnNzWXatGmEhIQQGhpKbGzsDdsePnyY/v37ExgYSL9+/UhMTLTMM5vNrFy5ki5dutCuXTsGDx7M8ePHK6ILIiIiUgXYNPDMnz+fxMREVq9eTVRUFEuXLmXHjh1F2mVlZTFy5EhCQkLYvHkzQUFBjBo1iqysLADWr19PbGws06dPZ9OmTTRp0oS///3vZGdnV3SXREREpBKyWeDJyspi48aNREZG4u/vT7du3RgxYgTr1q0r0nbbtm04OzszadIkWrZsSWRkJG5ubpZwFBcXx7Bhw3j44Ydp3rw50dHRpKWlsX///oruloiIiFRCNgs8SUlJmEwmgoKCLNOCg4NJSEigoKDAqm1CQgLBwcHY2dkBYGdnR7t27Th48CAAkyZNom/fvpb2dnZ2mM1mLl++XP4dERERkUrPZoEnJSUFLy8vnJycLNPq1q1Lbm4uaWlpRdrWq1fPapq3tzfnzp0DICQkhAYNGljmbdy4EZPJRHBwcPl1QERERKoMmwWe7Oxsq7ADWF7n5eUVq+217aDwaFBMTAzDhw/Hx8enjKsWERGRqsjRVht2dnYuEliuvnZxcSlW22vbHThwgL///e906tSJcePGlUPVIlIa9vbg5la6ZV1d4X/PZouIlJrNAk/9+vVJTU3FZDLh6FhYRkpKCi4uLnh4eBRpe+HCBatpFy5csDrN9cMPPzB69Gg6dOjA66+/jr29bjEkUhk4OYGPl4mOf8nHZCr58m7u4OJUgIND2dcmItWHzQKPn58fjo6OHDx4kJCQEADi4+Np06ZNkbASGBjIO++8g9lstgxI3r9/P6NHjwbg2LFjPPvss3Ts2JGFCxdaApSI2J6jIziSj+l8GlmXC269wDVcGjhi18gNewUeEbkNNjsM4urqSnh4ONHR0Rw6dIhdu3YRGxvLM888AxQe7cnJyQGgR48eXLp0iTlz5nD8+HHmzJlDdnY2PXv2BGDGjBk0bNiQqVOnkpqaSkpKitXyImJ7V3IKyMsu+Y8pr+QhSUTkWjY97zN16lT8/f0ZPHgwM2fOZOzYsXTv3h2A0NBQtm3bBoC7uzsrVqwgPj6eiIgIEhISWLlyJTVr1iQlJYUDBw5w/PhxOnfuTGhoqOXn6vIiIiJSvdn03I+rqysxMTHExMQUmXf06FGr1wEBAcTFxRVp5+PjU6StiIiIyJ9pZK+IiIgYngKPiIiIGJ4Cj4iIiBiert+WSi81FdLTS7+8gwPk5pZdPSIV7eqdOi5dgoIrpVjeBdxqga7sl+pMgUcqvfR02L4dMjNLt7yPD+ixalKV2TtAQQH89zRculjy5T18wLeBAo9Ubwo8UiVkZkJGRumWLe0jDUQqG5MJrvMIwVsvV4qjQiJGozE8IiIiYngKPCIiImJ4CjwiIiJieAo8IiIiYngKPCIiImJ4CjwiIiJieAo8IiIiYngKPCIiImJ4CjwiIiJieAo8IiIiYngKPCIiImJ4epaWiNySvX3pn0nm6gp2dmVbj4hISSnwSLlLTS184nlpODhAbm7Z1iMl4+QEPl4mOv4lH5Op5Mu7uYOLUwEOelS3iNiQAo+Uu/R02L698InnJeXjA8HBZV+TFJ+jIziSj+l8GlmXC0q8vEsDR+wauWGvwCMiNqTAIxUiMxMyMkq+XGlPo0jZu5JTQF52yQOPKa/ky4iIlDUNWhYRERHDU+ARERERw1PgEREREcNT4BERERHD06BlqRZu5z4yNWsWLm/L7Ts62rZ+EZGqToFHDO927yPj4gLeXg44OZXu1+V2t1+zJjRpYMdD95vJyyv58rdbv8htu3rnyZzbuKmWgwPU0D4spae9Rwzvdu8j41jHHsf6ntQo5R/bsriPjUMTNwr+uEzWxYqvX+S22dlBfj6kXYaCUtymwN4evD0VeOS2aO+RaqPU95Epozs93+59bEy5tq1fqq6rpzQvXYKCK6VY3gXcasFt3zuyoADydV8msQ2bBp7c3FxmzpzJp59+iouLC8OGDWPYsGHXbXv48GGioqI4duwYd911FzNnzqR169aW+f/+979ZvHgxKSkphIaGMmvWLOrUqVNRXRERqbTsHQqzxn9Pw6WLJV/ewwd8G5RB4BGxIZsOZZw/fz6JiYmsXr2aqKgoli5dyo4dO4q0y8rKYuTIkYSEhLB582aCgoIYNWoUWVlZABw6dIjIyEjGjBnDhg0buHTpElOnTq3o7oiIVGomE+TllfzHVIqjQiKVjc0CT1ZWFhs3biQyMhJ/f3+6devGiBEjWLduXZG227Ztw9nZmUmTJtGyZUsiIyNxc3OzhKO1a9fSs2dPwsPDadWqFfPnz+frr7/m9OnTFd0tERERqYRsFniSkpIwmUwEBQVZpgUHB5OQkEDBNYPaEhISCA4Oxu5/R/rb2dnRrl07Dh48aJkfEhJiad+wYUMaNWpEQkJC+XdEREREKj2bjeFJSUnBy8sLJycny7S6deuSm5tLWlqa1fiblJQU7rrrLqvlvb29+fnnnwE4f/489erVKzL/3LlzxarFbDYDkFGap1vKLWVng4dH4dVKJeXmVnhIvbTLA7i6QmZWLmbHbOycSj5gssDensysDNzcruDpWfHbz7dzJCPTjgL76lm/rZevDDXYevkCe0cyMuyokZ1duqusTI7gaFf4x6C0V2llZOjcmhRx9Xv76vf4zdgs8GRnZ1uFHcDyOu+am43cqO3Vdjk5OTedfyuZmZkAPPTQQ8XvgIiIiFQKmZmZ1KpV66ZtbBZ4nJ2diwSSq69dXFyK1fZquxvNd3V1LVYt9erV4+uvv8bNzc1y2kxEREQqN7PZTGZmZpGzPNdjs8BTv359UlNTMZlMOP7vuYqUlBRcXFzw8PAo0vbChQtW0y5cuGDp4I3m+/j4FKsWe3t7GjRoUNquiIiIiI3c6sjOVTYbtOzn54ejo6Nl4DFAfHw8bdq0wf6aB/8EBgZy4MAByzk6s9nM/v37CQwMtMyPj4+3tP/tt9/47bffLPNFRESkerNZ4HF1dSU8PJzo6GgOHTrErl27iI2N5ZlnngEKj/bk5OQA0KNHDy5dusScOXM4fvw4c+bMITs7m549ewIwcOBAPv74YzZu3EhSUhKTJk2ic+fONG3a1FbdExERkUrEzlycoc3lJDs7m+joaD799FPc3d0ZPnw4Q4YMAcDX15d58+YREREBFN5cMCoqiuTkZHx9fZk5cyb33nuvZV2bN2/mzTffJD09nQ4dOjBr1iy8vLxs0S0RERGpZGwaeEREREQqgk0fLSEiIiJSERR4RERExPAUeERERMTwFHhs6LPPPsPX19fq5/nnn7d1WeUuLy+PPn368MMPP1imnT59miFDhtC2bVt69erFnj17bFhh+bveezB79uwi+8PatWttWGXZ+/3333n++ee577776NixI/PmzSM3NxeoHvvAzfpfHT5/gF9++YXhw4cTFBRE586dWbVqlWVeddgHbtb/6rIPXDVy5EimTJlieX348GH69+9PYGAg/fr1IzExsUy3Z7MbDwocP36chx9+mFmzZlmmOTs727Ci8pebm8vEiRMtz0GDwvsqPffcc9xzzz1s2rSJXbt2MWbMGLZt20ajRo1sWG35uN57AJCcnMzEiRP561//apnm7u5e0eWVG7PZzPPPP4+Hhwfr1q0jPT2dadOmYW9vz6RJkwy/D9ys/5MnTzb85w9QUFDAyJEjadOmDXFxcfzyyy9MmDCB+vXr06dPH8PvAzfrf1hYWLXYB6765JNP+Prrry19zcrKYuTIkYSFhfHqq6/ywQcfMGrUKD777DNq1qxZJttU4LGh5ORk7rnnnmLfEbqqO378OBMnTizykLfvv/+e06dPs379emrWrEnLli357rvv2LRpE2PHjrVRteXjRu8BFO4Pw4cPN+z+cOLECQ4ePMi3335L3bp1AXj++eeJiYmhU6dOht8Hbtb/q4HHyJ8/FN4B38/Pj+joaNzd3bnzzjtp37498fHx1K1b1/D7wM36fzXwGH0fAEhLS2P+/Pm0adPGMm3btm04OzszadIk7OzsiIyM5JtvvmHHjh2W29PcLp3SsqHk5GTuvPNOW5dRYfbu3cv999/Phg0brKYnJCRw7733WqX44OBgq7twG8WN3oOMjAx+//13Q+8PPj4+rFq1yvJlf1VGRka12Adu1v/q8PlD4XMLFy9ejLu7O2azmfj4eH788Ufuu+++arEP3Kz/1WUfAIiJieGxxx7jrrvuskxLSEggODjY8jxLOzs72rVrV6afvwKPjZjNZk6ePMmePXt49NFHeeSRR1iwYEGxn/BeFQ0aNIhp06YVeahrSkpKkQe/eXt7c+7cuYosr0Lc6D1ITk7Gzs6Ot99+m06dOtG3b1/i4uJsVGX58PDwoGPHjpbXBQUFrF27lgceeKBa7AM36391+Pyv1aVLFwYNGkRQUBCPPvpotdgH/uza/leXfeC7775j3759/OMf/7CaXhGfv05p2cjZs2fJzs7GycmJxYsXc+bMGWbPnk1OTg4vv/yyrcurUFffhz9zcnIydPi71okTJ7Czs6NFixY89dRT/Pjjj0yfPh13d3e6detm6/LKxWuvvcbhw4f56KOP+Ne//lXt9oE/9/+nn36qdp//m2++yYULF4iOjmbevHnV7u/Atf339/c3/D6Qm5tLVFQUM2bMwMXFxWpeRXz+Cjw20rhxY3744Qdq166NnZ0dfn5+FBQU8NJLLzF16lQcHBxsXWKFcXZ2Ji0tzWpaXl5ekV8IIwsPD+fhhx/G09MTgFatWnHq1Ck++OADw/yx+7PXXnuN1atXs2jRIu65555qtw9c2/+77767Wn3+gGX8Rm5uLi+++CL9+vUjOzvbqo2R94Fr+79//37D7wNLly6ldevWVkc6r3J2di4Sbsr689cpLRvy9PS0nK8EaNmyJbm5uaSnp9uwqopXv359Lly4YDXtwoULRQ5vGpmdnZ3lD91VLVq04Pfff7dNQeVo1qxZ/POf/+S1117j0UcfBarXPnC9/leXz//ChQvs2rXLatpdd93FlStX8PHxMfw+cLP+Z2RkGH4f+OSTT9i1axdBQUEEBQWxdetWtm7dSlBQUIX8DVDgsZHdu3dz//33W/0fzZEjR/D09KROnTo2rKziBQYG8tNPP5GTk2OZFh8fT2BgoA2rqlhvvPGG5cG5VyUlJdGiRQvbFFROli5dyvr161m4cCG9e/e2TK8u+8CN+l9dPv8zZ84wZswYqy/xxMRE6tSpQ3BwsOH3gZv1f82aNYbfB9asWcPWrVvZsmULW7ZsoUuXLnTp0oUtW7YQGBjIgQMHLFewms1m9u/fX7afv1ls4vLly+aOHTuaJ0yYYE5OTjZ/9dVX5tDQUPPKlSttXVqFuOeee8zff/+92Ww2m00mk7lXr17m8ePHm48dO2ZesWKFuW3btuZff/3VxlWWrz+/BwkJCeZ7773XvGrVKvMvv/xiXrdunbl169bm/fv327jKsnP8+HGzn5+fedGiRebz589b/VSHfeBm/a8On7/ZXPi7HhERYR42bJj5559/Nn/11VfmBx980Pyvf/2rWuwDN+t/ddkH/mzy5MnmyZMnm83mwu/EBx54wDxr1izzzz//bJ41a5a5Q4cO5szMzDLbngKPDR07dsw8ZMgQc9u2bc0dOnQwL1myxFxQUGDrsirEn7/szWaz+dSpU+Ynn3zS3Lp1a3Pv3r3N3377rQ2rqxjXvgefffaZOSwszNymTRtzjx49zDt37rRhdWVvxYoV5nvuuee6P2az8feBW/Xf6J//VefOnTM/99xz5nbt2pk7dOhgXr58ueXvntH3AbP55v2vLvvAVX8OPGZz4f/4hYeHm9u0aWN+/PHHzT/99FOZbs/ObL7OHdBEREREDERjeERERMTwFHhERETE8BR4RERExPAUeERERMTwFHhERETE8BR4RERExPAUeERERMTwFHhERETE8BR4RKRaOXPmDL6+vpw5c6Zc1v/HH3+wffv2clm3iJSeAo+ISBlasGABX3/9ta3LEJFrKPCIiJQhPa1HpHJS4BGRCnXu3DnGjRvHfffdx/3338/s2bPJy8ujY8eObNq0ydLObDbTqVMnPv74YwD27dtHREQEAQEBhIWFsXPnTkvbKVOmMGXKFPr27Uv79u05deoU27Zt49FHH6VNmzb06tWLXbt2WdWxa9cuHnnkEQIDAxk9ejTp6emWeQcOHGDgwIG0bduWLl268MEHH1gtu3nzZnr27ElAQAARERH8+OOPACxZsoS4uDji4uLo0qVLmb93IlJ6CjwiUmHy8vIYPHgw2dnZrFmzhsWLF/PVV18xf/58evTowWeffWZpe/DgQdLS0ujatSspKSmMGjWKiIgItm7dyogRI5gyZQr79u2ztP/4448ZP348K1asoFatWkyaNIlRo0axY8cO+vXrx4QJE0hLS7O0j4uLY+HChbz33nv89NNPvPPOOwAkJyczePBg/vKXv7B582bGjh1LTEyMpbbNmzcza9YsRo0axZYtW3jwwQcZOXIkv//+O8OGDaNnz5707NmTjz76qGLeVBEpFkdbFyAi1cfu3bv5/fff+fDDD6lduzYAM2bM4Nlnn2X16tUMHTqUjIwM3N3d2blzJw899BDu7u6sWrWKBx98kKeeegqAZs2aceTIEVavXk1ISAgAbdq0sRxVOXz4MFeuXKFBgwY0btyYYcOG4evri7OzMxkZGQC89NJLBAQEANCzZ0+SkpIA+PDDD7n33nuZMGECAC1atCA5OZlVq1bRrVs31qxZw9NPP014eDgAL774Ij/++CNr165l4sSJuLi4AFCnTp0KeEdFpLh0hEdEKkxycjJ33nmnJewAtGvXDpPJhJubGz4+PpYBv59++im9evUC4MSJE3z55ZcEBQVZftauXcupU6cs62ncuLHl335+fnTu3JmhQ4fSo0cPFixYQJMmTXB1dbW0ueOOOyz/rlWrFrm5uZYarwahq4KCgkhOTr7h/LZt21rmi0jlpCM8IlJhnJ2di0zLz8+3/LdXr17s3LmTZs2akZqaSufOnQEwmUyEhYUxevRoq2UdHf/vT9if121nZ8eKFSs4dOgQn3/+OZ999hnvv/8+77//PrVq1QLA3v76/793vRoLCgosdd6oDwUFBTfruojYmI7wiEiFad68OadOnbIaS3Pw4EEcHR2544476N27N99++y07d+6kS5culiMyzZs355dffqFZs2aWn88//5ytW7dedzvJycnExMQQEBDACy+8wCeffELDhg3ZvXt3sWpMSEiwmnbgwAGaN29+w/kJCQmW+XZ2dsV+P0Sk4ijwiEiF6dChA02bNmXSpEkcPXqU77//nlmzZtGnTx88PDzw8/OjXr16rF27lp49e1qWGzRoEImJiSxatIhTp06xdetWFi5cSKNGja67HQ8PDz744AOWLVvG6dOn+eqrr/j111+59957b1njoEGDOHLkCAsXLuTkyZPExcXx/vvv8+STTwIwZMgQ1q5dy5YtWzh58iQLFiwgKSmJxx9/HABXV1d+/fVXfv/99zJ4x0SkrCjwiEiFcXBwYNmyZQA88cQTTJgwga5du/LKK69Y2vTq1QsHBwc6depkmda4cWPefvttdu/eTZ8+fVi8eLHlMvTr8fHxYcmSJezcuZPevXvzyiuvMGHCBEJDQ29ZY6NGjVixYgW7d+8mLCyM5cuXM2XKFPr162ep74UXXuDNN9+kb9++7N27l9jYWFq2bAnAY489xsmTJ+nbt6/uySNSidiZ9RspIiIiBqcjPCIiImJ4CjwiIiJieAo8IiIiYngKPCIiImJ4CjwiIiJieAo8IiIiYngKPCIiImJ4CjwiIiJieAo8IiIiYngKPCIiImJ4CjwiIiJieP8fDZ0ODYbVKGUAAAAASUVORK5CYII=", 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", 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      " ] @@ -745,6 +917,7 @@ "plt.legend([\"mask_efficiency fixed\", \"mask_efficiency not fixed\"])\n", "plt.ylabel(\"pr\")\n", "plt.xlabel(\"overshoot\")\n", + "sns.despine()\n", "\n", "print(\"Overshoot mean\")\n", "print(\"mask_efficiency fixed: \", os_lockdown_fix.item(), \" mask_efficiency not fixed: \", os_lockdown_notfix.item())\n", @@ -762,7 +935,7 @@ }, { "cell_type": "code", - "execution_count": 176, + "execution_count": 296, "metadata": {}, "outputs": [], "source": [ @@ -772,7 +945,7 @@ }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 297, "metadata": {}, "outputs": [ { @@ -780,14 +953,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "lockdown_efficiency fixed: 26.283220291137695 lockdown_efficiency not fixed: 26.437496185302734\n", + "lockdown_efficiency fixed: 21.956771850585938 lockdown_efficiency not fixed: 21.55644989013672\n", "Probability of overshoot being high\n", - "lockdown_efficiency fixed: 0.8850364685058594 lockdown_efficiency not fixed: 0.9024389982223511\n" + "lockdown_efficiency fixed: 0.5478423833847046 lockdown_efficiency not fixed: 0.5529412031173706\n" ] }, { "data": { - "image/png": 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", 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      " ] @@ -802,6 +975,7 @@ "plt.legend([\"lockdown_efficiency fixed\", \"lockdown_efficiency not fixed\"])\n", "plt.ylabel(\"pr\")\n", "plt.xlabel(\"overshoot\")\n", + "sns.despine\n", "\n", "print(\"Overshoot mean\")\n", "print(\"lockdown_efficiency fixed: \", os_mask_fix.item(), \" lockdown_efficiency not fixed: \", os_mask_notfix.item())\n", From 81bb6e7247ac1f1d3b711a0b6a5b40ddcd523217 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Fri, 23 Aug 2024 14:34:32 -0400 Subject: [PATCH 058/111] explainable_sir first draft completed --- docs/source/counterfactual_sir.png | Bin 160256 -> 161649 bytes docs/source/explainable_sir.ipynb | 236 +++++++++++++++++++---------- 2 files changed, 159 insertions(+), 77 deletions(-) diff --git a/docs/source/counterfactual_sir.png b/docs/source/counterfactual_sir.png index be057369de25a17cc7c79bb0a8270b7639e2c5d9..98399c554005cfc8167372cb29cae1a49528250c 100644 GIT binary patch literal 161649 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Policies\n", - "- But-for analysis for Bayesian SIR model\n", - "- Causal Explanations using `SearchForExplanation`\n", - "- Fine-grained analysis for `overshoot` using sample traces\n" + "- [Setup](#Setup)\n", + "- [Bayesian epidemiological SIR model with Policies](#Bayesian-epidemiological-SIR-model-with-Policies)\n", + " - [SIR Model and Simulation](#SIR-model-and-simulation)\n", + " - [Bayesian SIR model](#Bayesian-SIR-model)\n", + " - [Baysian SIR model with Policies](#Bayesian-SIR-model-with-policies)\n", + "- [But-for analysis for Bayesian SIR model with Policies](#But-for-Analysis-with-Bayesian-SIR-model-with-Policies)\n", + "- [Causal Explanations using `SearchForExplanation`](#Causal-Explanations-using-SearchForExplanation)\n", + "- [Fine-grained analysis for `overshoot` using sample traces](#Fine-grained-analysis-for-`overshoot`-using-sample-traces)\n", + "- [For advanced readers: Looking into different contexts](#for-advanced-readers-looking-into-different-contexts)\n" ] }, { @@ -43,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 209, + "execution_count": 503, "metadata": {}, "outputs": [], "source": [ @@ -107,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 210, + "execution_count": 504, "metadata": {}, "outputs": [], "source": [ @@ -147,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 211, + "execution_count": 505, "metadata": {}, "outputs": [ { @@ -206,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 506, "metadata": {}, "outputs": [], "source": [ @@ -241,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 213, + "execution_count": 507, "metadata": {}, "outputs": [], "source": [ @@ -262,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": 214, + "execution_count": 508, "metadata": {}, "outputs": [], "source": [ @@ -334,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": 312, + "execution_count": 509, "metadata": {}, "outputs": [], "source": [ @@ -384,12 +385,12 @@ }, { "cell_type": "code", - "execution_count": 336, + "execution_count": 510, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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++uknbd0tW7Zw5MgRWrduTUxMDFD6a6ugorqEtWjRAoD33nuPp59+mm+//ZbU1FRAve769OlTZPetpk2bFprn6zqZmZkJwIkTJzh69CgREREkJiYWWj84OFg7375r8lKu4ZLwer2sX78eg8FQZJevLl26lGg7F3veilPS3z+fmJiYIn8HffWfLvb8lNalPF+NGjUqNC82NhbAr9ZY8+bNAbUW1VtvvcXatWtxuVyYzWYGDRpEhw4dLu0ghBBC/KNJTSkhhBBXTWhoaJHzs7OzWbBgAStWrGD//v0kJyejKIpf9oJyniHefRlNv/7663lrw+Tl5ZGRkQGoGVoRERFYrdaLORTgbFZScfWPKlWqBJzNPCiouHNxLl+dqSeeeOK86/kCT+XLl2fChAm8/PLL/P777/z+++8A1K5dm65du9K/f3/ti+iF7N69m88++4ytW7dy6NAhcnJyALTnpajnJCQkpMhtGY3qRxCv1wucPXe+4M+5fOfuQtLT03G5XISHhxMUFHTebRV8Hvr06cP8+fP57rvvuOuuuwC1ODigZY9A6a+tgoGRc4NzAI0bN2bkyJFMnDiRJUuWsGTJEnQ6HfXr16d79+78+9//LvLa8NX7KcgXMDz3nJ4v6HjuubiUa7gkfM9PREQEAQEBhZaXtHbYxZ634pRm3fO105f5derUqVJt72Jd7tecc68hgOeee46jR4+ycuVK5syZw5w5c7DZbLRt25Y+ffqUOJAohBBCFEWCUkIIIa6aorqp7d27l//85z+kpKQQHh5Ow4YN6dmzJ/Hx8bRs2ZKOHTtecLu+L1S1atWibt26F1z/fFlXpXG+QFnBdpnN5kLLztdlryBfWzt16lRs0AXUY/e59dZbadeuHcuWLeOPP/5gzZo17N27l7179zJ37lw+/PBDGjdufN79vvfee7z55psA1KxZk06dOlGzZk0SEhI4fPgwr732WpGPK0k3qJKsVzBD63wu9BzA2XNY8Hlo2LAhNWrUYN26dZw+fZqoqCh+/PFHbDabX8ZVaa+tgoo7xkGDBtGrVy9+/vln/vjjD9atW8f27dvZvn07H330EZ9++ilVq1b1e0xJrpeLOReXcg1fDnq9vsTP9cWct/PttzQsFst5l/uCrhdyqa89l/J8lfR3Mzg4mA8++IBt27bxyy+/sGrVKrZv385PP/3ETz/9RPfu3Zk6dWrpGy+EEEIgQSkhhBDXmNdee42UlBT++9//MmzYML8vqL6spguJjo4GoG7durz11lsXXN/lcmEymcjIyMButxfK4HA4HHzxxRfUqFGD1q1bF7udmJgYDh48yLFjx6hdu3ah5UeOHAEoVX2kovZx6NAhBg4cyE033VTixwUHB3Pbbbdx2223AbBjxw7efvtt/vzzTyZPnqzVHirKkSNHmDhxIsHBwbzzzjtadx6f8z22pHzZWsXV7fJlhFxIWFiY9lxmZ2cXGbgr7nno06cPkyZN4qeffqJmzZokJyfTp08fv256pb22SioyMpL+/fvTv39/vF4vGzduZNy4cWzfvp3Zs2czduzYUm/Tl3V27NixYtfxnQtfbaQrfQ2Hh4djsVhIT08nJyenUN2nM2fOlCpQcyXOW0kUdz36znXB7DRf8Keo48rKyrqkdpTFa45PQkICCQkJDB06lKysLJYsWcLYsWNZunQp69evL/S6IIQQQpSE1JQSQghxTdm8eTMAjzzySKGMiZUrV2o/F+xeci5fvZl169b51Ubx2bZtG926dePJJ59EURRMJhMJCQl4PB7+/PPPQuuvWbOG1157jY8//hgoPsPAt9+lS5cWuXzJkiUARdb3KSnfPnzd8M41YcIEbrvtNv73v/8BasHtTp068dVXX/mtV79+fZ577jngbJfA4mzduhWv10vLli2L/OLpO2clycwpji/Yt2zZskJf3ot7XopiMplo0qQJXq9XK/ZekNvt5pdffgEK1+Xq3bs3Op2O5cuXa89Vnz59/NYp7bV1IePGjaNt27ZabSBQs3aaN2/Oo48+CpztillaFSpUoGLFiqSlpRVZUygrK0v7nfId18VcwyXNuPGt26pVK7xeL8uWLSu0/LfffivRdkp73krTxpI4ePBgkYX9fTXJCp4fX1DTV/OqIN/rXUGlaeuVfs1JS0ujX79+9OrVy29+cHAwd911l1br7WKvUSGEEEKCUkIIIa4pvho8535hXbduHWPGjNHuF1VU26dy5crcfPPNnDx5khdffJHs7GxtWUpKCi+++CJJSUl+I3fde++9gPplt+CXzdTUVCZMmACcrS3k67qTm5vrFxy76667sNlsfPnll3z55Zd+bVq0aBFff/01Nput0ChZpfHvf/8bm83G/Pnz+f777/2W/frrr8ybN49du3aRkJAAqIXGjx8/zqxZs/zqyiiKohWOLzgiXFF8z8mWLVu00fJAzTCbPHkyK1asANSMsovVsGFDmjVrxsGDB5kwYYJ2Xr1eL+PHj7/giIsF/ec//wHUAN2OHTv82jt69GgOHz5MnTp1aNasmd/jKlSoQGJiImvWrOGXX34hJiamUGbcxVxb51O+fHmSk5N5++23/bbldru1gILvubwYvnPx0ksvaVkzADk5OTz33HNkZ2fTqVMnrSbRxVzDvq5hBdtfkjZNmDCB/fv3a/P379/PpEmTSrSN0p43XxsvNTPJR1EURowY4bfvpUuXsmjRIoKDg7nzzju1+b4i6vPmzfMLVM6dO5ft27cX2nZxry9FudKvOeHh4Xg8Hvbs2VMoI/Lo0aPaoAIFR7kUQgghSkO67wkhhLim3H///YwbN47hw4ezYMECoqOjOXz4MDt37iQsLIzo6GiSk5NJTk4+b02lMWPGkJSUxPfff8/KlStJSEhAp9Oxfv16cnNzadq0KUOHDtXW79GjB6tWrWLhwoXceuutJCYmYjAY2LBhA1lZWdx+++3ccsstgBqkCQkJITMzk/79+1OlShXeeustYmNjGT9+PMOGDWPEiBHMnTuX6tWrc/DgQXbt2oXVamXChAklLuZclIL7GDZsGDNmzKBGjRqcOHFC+4L7wgsvaPWObr75Zrp27crPP/9M165dadq0KYGBgezZs4dDhw4RFRXFU089dd59JiYmUq9ePf7++2+6d++uZUtt3bqVlJQUateuzd69ezlz5sxFHxeoAcGBAwcyd+5cfvvtN+rUqcOuXbtISkqiUaNGbNmypUTb6dKlC4MHD+aDDz7gzjvvpFmzZoSHh7NlyxZOnjxJxYoVmTRpUpF1hPr06cOaNWtISUlh8ODBRa5T2mvrfO6++25++OEHNm7cSOfOnWnUqBFms5m///6b48ePU6NGDe6///4Sbaso9913H5s2bWLJkiXadW21Wlm/fj1paWnEx8f7dXG7mGu4WrVqANrIjh07dvQLypyrTZs2PPTQQ8yePZvbbruNVq1aAbB69Wrq169fouuotOfN18aZM2eyadOmSy7QXb16dfbu3UvXrl1p3rw5ycnJbNq0CZPJxIQJE/y6yw0cOJAff/yRpUuXcssttxAfH8/evXs5ePAgffr04euvv/bbdnGvL0Upi9ec0aNHc9999zFu3Dj+97//UbNmTbKzs9mwYQMOh4MHH3xQO79CCCFEaUmmlBBCiGvKoEGDmDhxIgkJCezZs4fly5eTl5fHfffdxzfffKMFhpYvX37e7URGRvK///2Pp556ipiYGNatW8fmzZupXr06L7zwAh9++GGhkfZef/113nzzTerXr8+GDRv466+/qFixIqNGjeL111/X1tPr9bz11lvUrFmTv//+m5UrV2r1rrp168YXX3xBz549SUlJ4ZdffiEzM5M77riDRYsW+RXNvljdunVj0aJF9O7dm6ysLH777TfOnDlDp06dmDdvnpaJAmpXoLfffptnnnmGatWqsXHjRn777Te8Xi/33XcfX3311QVHtjMYDMydO5dBgwYRERHBn3/+yfr166lcuTKjR4/myy+/JCQkhK1bt15SYKpq1aosXLiQu+++G7vdzvLlywkMDGTmzJm0a9euVNsaPnw4M2fOpGXLluzatYvffvuNwMBAHn30Ub788ktq1KhR5OO6d++uXRfndt3zuZhrqzgWi4X333+fhx56iMjISNasWcOff/6JzWbjkUceYeHChaUeGa4gvV7PpEmTGDduHA0aNGDjxo2sXLmScuXK8dxzz7Fw4cJC9YZKew136dKFQYMGYbPZ+OOPP9iwYcMF2/XMM88wefJk6tevz/r169m+fTt9+/Zlzpw5JTqu0p63u+++W6un9scffxSZoVQa5cqV49NPP6VBgwb8+eef7N27l06dOrFgwQI6d+7st25CQgLz58+nXbt2nDlzhhUrVhAVFcWHH35Iz549C237fK8vRbnSrzmNGzfm008/pXv37mRmZvLrr7+yY8cOmjZtypQpU3j22WcvaftCCCH+2XTKpRSAEEIIIa4B7dq14/Tp0/z666+XlBEghBBCCCGEKDuSKSWEEOK6lpGRQWpqKjqdTqt9JIQQQgghhLj2SU0pIYQQ16XU1FQGDx5Mamoqbrebpk2blrjLlBBCCCGEEOLqk0wpIYQQ16WkpCTOnDlDWloaiYmJjB8//mo3SQghhBBCCFEKUlNKCCGEEEIIIYQQQpQ5yZQSQgghhBBCCCGEEGVOglJCCCGEEEIIIYQQosxJUEoIIYQQQgghhBBClDkJSgkhhBBCCCGEEEKIMidBKSGEEEIIIYQQQghR5iQoJYQQQgghhBBCCCHKnASlhBBCCCGEEEIIIUSZk6CUEEIIIYQQQgghhChzEpQSQgghhBBCCCGEEGVOglJCCCGEEEIIIYQQosxJUEoIIYQQQgghhBBClDkJSgkhhBBCCCGEEEKIMidBKSGEEEIIIYQQQghR5iQoJYQQQgghhBBCCCHKnASlhBBCCCGEEEIIIUSZk6CUEEIIIYQQQgghhChzEpQSQgghhBBCCCGEEGVOglJCCCGEEEIIIYQQosxJUEoIIYQQQgghhBBClDkJSgkhhBBCCCGEEEKIMidBKSGEEEIIIYQQQghR5iQoJYQQQgghhBBCCCHKnASlhBBCCCGEEEIIIUSZk6CUEEIIIYQQQgghhChzEpQSQgghhBBCCCGEEGVOglJCCCGEEEIIIYQQosxJUEoIIYQQQgghhBBClDkJSgkhhBBCCCGEEEKIMidBKSGEEEIIIYQQQghR5iQoJYQQQgghhBBCCCHKnASlhBBCCCGEEEIIIUSZk6CUEOKaoyjK1W7CRble2y2EEELcaOQ9WRRFrgshrj0SlBLiH+i+++6jXr16bNu2rcjlnTt3ZsSIEZe8n/j4eKZNm1aqxyxcuJDx48df8r7L2t69e7n77rv95l3M8QshhBAltWHDBp588knatGlDQkICN998My+99BL79++/2k3zM23aNOLj48tsfxs2bOChhx4qs/1da4YMGVLoc9yIESOIj48vdjp27FiJtr19+3bq16/P4sWLi10nOzv7oj9L+q6VglO9evVo2bIljz/+OHv37i3xtj744AOeffZZADIzM3n++edZv359qdt0MUaMGEHnzp3Pu87ixYuJj4/n6NGjJd5uSR6TlpZGx44dOXLkSIm3W1BOTg6jR4+mTZs2NGnShAcffJADBw5c8HE7duzgwQcfpFWrVrRs2ZLBgwezY8eOYtc/ceIEzZo1k8/KAuPVboAQ4urweDyMHDmSxYsXYzabr8g+FixYQLly5Ur1mFmzZpGYmHhF2nMl/fjjj2zatMlv3sUcvxBCCFESs2fP5u2336Zt27a88MILREdHk5SUxGeffUbfvn0ZN24cPXr0uNrNvCoWLlx4zQXmyoLX62XcuHEsXbqUvn37+i177LHH6N+/v9+8jIwMnnrqKRITE6lQocIFt+90OhkxYgRut/u8640bN67EQa7iLFiwQPvZ4/Fw/PhxJk2axIABA/j++++Jjo4+7+P379/Pu+++yzfffAPAzp07+frrr7n99tsvqV2XU8eOHVmwYAExMTGXdbvh4eEMGjSIF154gXnz5qHT6Ur1+GeeeYYtW7bw3HPPERQUxPTp0xk4cCDff/89oaGhRT4mKSmJe++9lwYNGjB27Fh0Oh0ffPAB99xzD19++SU1atTwW19RFF544QWys7Mv+jjFjUOCUkL8QwUHB7N3715mzJjB0KFDr8g+GjdufEW2e734px+/EEKIK2P58uVMnDiRJ598kieeeEKbn5iYyG233cYzzzzDiBEjiIuLo3bt2lexpaKs7Nq1i9dff51t27YREBBQaHmVKlWoUqWK37wnn3yS0NBQ3nrrrRIFLiZPnkxWVtZ51/n9999ZsmQJwcHBpTuAc5z7GapZs2aUL1+eAQMG8OWXX14wE+7NN9+kZ8+exMbGXlI7rqSIiAgiIiKuyLbvueceZs2axc8//0y3bt1K/LhNmzaxfPlyZs+eTYcOHQBo3rw5N998M59++imPPvpokY/7+OOPsVqtvPvuu9hsNgBatWpF586dmT9/PqNGjfJb/9NPPy1R9pX4Z5Due0L8Q9WtW5fbbruN9957j+3bt593XY/HwyeffEKvXr1o2LAhHTt25K233sLhcJz3cQW7r61Zs4b4+HhWrVrF4MGDadSoEW3atOHNN9/E4/EAarfBY8eO8eWXX/qlJh8/fpxhw4aRmJhIo0aN+M9//sPff/+t7efo0aPEx8fz4Ycfcsstt9CoUSNmzZpFfHw8y5cv92vTzp07iY+P5+effwbA4XAwYcIEOnToQIMGDejVqxc//PCD32M6d+7M1KlTGT9+PDfddBMNGzbkgQce4NChQ4Caaj59+vRCx3xu973Tp08zcuRIOnToQMOGDbnjjjtYtmxZoXP2ySef8OKLL5KYmEiTJk146qmnOHPmjLbO4cOHeeSRR2jZsiWNGjXi3//+N7///vt5nwshhBA3junTp1OjRg0ef/zxQstMJhOvvfYaBoOBOXPmADB48GD69etXaN3HHnuM3r17a/fXr1/PvffeS6NGjUhMTGT48OGkpqZqyxcvXky9evVYuHAhbdq0ITExkX379pX4fem3336jd+/eJCQk0L17d7766iu/5SV5n3Q4HMyYMYNbbrmFhIQEunXrxuzZs/F6vYDaberLL7/k2LFjxMfHF9vNbNq0adxyyy38/PPP9OzZk4SEBPr06cOmTZvYvHkzd955Jw0bNqRnz56sWrXK77F79uzh4YcfpmnTpjRt2pTHH3+8UFepXbt28cQTT9CqVSvq169Pu3bteP3117Hb7do6JXnP93XXWrNmTZHH4TN8+HA8Hg8LFiwgMjLyvOuCGjz66aefGDlyJCEhIRdcf+PGjUUGFwrKyMjgpZde4rnnnivRNkurQYMGAFoW1rRp0+jatSvTp08nMTGRtm3bkpGRwZ49e/jtt9/o2bMnoH4GHThwIAADBw7kvvvu07b5ww8/0K9fP5o0aUKbNm0YNWoUGRkZfvvdtm0bDzzwAC1btqRp06Y88sgjJe5GuHjxYrp3705CQgK9e/f2+70oqivel19+ya233qqtv2rVKurVq1foOt6yZQv9+/cnISGBjh078t577/ktN5vNdO/enXfffVeb5/ssfr6ul3/++Sc2m422bdtq8yIiImjRosV5P2vWqFGDwYMHawEpAJvNRrly5Th8+LDfukeOHOGtt95izJgxxW5P/LNIUEqIf7AXXniB8PBwRo4cidPpLHa9UaNGMW7cOLp06cKsWbMYMGAA8+fP57HHHit1wchnn32WZs2a8c4779CzZ0/ee+89Fi5cCKgfsqOjo+nQoYOWzpyamkr//v3ZsWMHL7/8MhMnTsTr9TJgwIBCqfnTpk3jwQcfZMKECfTt25cqVarw/fff+63z3XffERYWRocOHVAUhccff5zPP/+c+++/n1mzZtGkSROGDh1a6IPyvHnzOHDgAOPGjeP1119n+/btDB8+HIA777yTO+64A1DTze+8885Cx33mzBnuuOMO1q9fz9ChQ5k2bRoVK1bk8ccf11LLfSZNmoTX6+Xtt9/m+eefZ/ny5bzxxhuAmpr/8MMPk5eXx4QJE5g5cyZhYWE8+uijJCUlleq5EEIIcf1JTU1l+/btdOrUqdjslrCwMG666SYtoNO7d2927Njh9z6RmZnJH3/8QZ8+fQBYt24dgwYNIiAggMmTJ/PCCy+wdu1aBg4c6BdI8Xg8fPDBB4wdO5aRI0dSvXr1Er8vjRo1ikGDBjFr1izKlSvHiBEj2LVrF1Cy90lFUXjkkUd47733uPPOO3nnnXe45ZZbmDx5Mq+88gqgBto6dOhAdHQ0CxYsoGPHjsWey5MnT/J///d/PPLII0yZMoXMzEyGDBnCsGHDuPPOO5kxYwaKojB06FDtHBw8eJD+/fuTkpLC+PHjGTt2LEeOHOHuu+8mJSUFUINrAwYMIC8vj//7v/9jzpw59OjRg48//ph58+b5teF87/lwtotX/fr1iz0OgAkTJvDZZ59Rp06d867nO4/jx48nMTGRW2655YLr5+XlMXLkSB5++OHz1gYbM2YMNWvWLNRN8HI5ePAggF/G1/Hjx/n999+ZNGkSI0eOJDQ0lG+//Zbo6Ggt26p+/fpaMG3UqFHatTJz5kyGDRtG48aNmTp1Ko8//jhLly7lvvvu057v1atXazVD33jjDV5//XVOnDhB//79L9hF9MSJE8yePZunnnqKadOmodPpGDJkiHadnOurr75ixIgRNG3alJkzZ9K9e3cee+wx7Y+3Bb366qv06NGD2bNn06RJE958881Cf4i95ZZb2L59u3be6tevf8Hfif3791OpUiUMBoPf/CpVqmjbKco999zDf//7X795SUlJ7N271y9b0+v1MmLECP71r3/Rvn37Yrcn/lmk+54Q/2ChoaG89tprPProo8V249u3bx9ffPEFzzzzjJYq3aZNG2JiYnj++ef5448/tPTekrjzzju1v+y2bt2aX375hd9++43+/ftTr149zGYzERER2geJjz76iPT0dD777DMqVqwIQPv27bn11luZMmUKU6dO1bb9r3/9y69WQO/evfnggw+w2+0EBASgKAo//PADt9xyC2azmZUrV7JixQomTZrErbfeCkC7du3Iy8vjrbfeomfPnhiN6stkSEgIM2fO1N6kDx8+zLRp00hLS6NcuXJa7ajiuux9+OGHpKamsnTpUu04OnTowKBBg5gwYQI9e/ZEr1f/ThAXF8e4ceO0x27dupUff/wRgJSUFA4cOKB96AZo2LAh06dPP29gUQghxI3BlyXiey8pTtWqVVm2bBkZGRl069aN0aNH891332nvwT/99BMej0fLJpk4cSLVq1fn3Xff1d7rGjVqRI8ePVi0aBEDBgzQtv3II49oX2yTk5NL/L70+uuva19Eq1SpQteuXVm7di116tQp0fvkihUr+Ouvv3j77be1ellt2rQhICCAKVOmMHDgQGrXrk1ERARms/mC3ejz8vJ45ZVXtDbt27ePiRMnMnbsWO2PTbm5uQwZMoSDBw9St25dpk+fjtVqZe7cuQQFBQHq55kuXbrw3nvvMXz4cPbs2UPdunWZMmWKts5NN93EypUrWbNmjV/Xs/O950PJu3iVppD8r7/+yv79+3nppZdKtP7EiROx2Ww8/PDDnDx5ssh1fv75Z5YtW8Z3331X6hpGRSlYt8put7Nr1y7eeOMNgoOD/bL73G43w4cPp3nz5tq81atXk5CQoLUjKCiIWrVqAVCrVi1q1apFRkYGs2bN4q677vLL/oqLi2PAgAHaNT9x4kSqVq3K7Nmztd+Ltm3b0rVrV6ZOncqUKVOKPQav18uMGTOoWbMmABaLhUGDBrF582ZuvvnmQutPmTKFTp068frrrwPqZ1KTycTEiRMLrTts2DAtWNa4cWN+/vlnVq9eTadOnbR1EhISAFi1ahXVq1cnKCjogr8TWVlZ2jVbUGBgIDk5Oed9bEF2u53hw4djNpu59957tfkfffQRR48e5Z133inxtsSNTzKlhPiH69y5M7179+a9994rcoSMtWvXAhQqltqjRw8MBsMF08nP1aRJE7/75cqVIzc3t9j1V61aRd26dYmNjcXtduN2u9Hr9bRv356//vrLb926dev63e/duze5ubnaX442btzI8ePHtb8Kr1q1Cp1OR4cOHbRtu91uOnfuTHJysl9qdkJCgt9fjXxBqLy8vBId99q1a2nSpEmhLxG9e/fWPtD7nPuBoVy5ctp+oqKiqFWrFi+//DLDhw/n22+/xev1MnLkSKkbIoQQ/wC+DGWTyXTe9XzvWYqiYLPZ6NKli1/39O+//57WrVsTGxtLXl4eW7Zs0bKIfe+HlStXpmbNmqxcudJv2wXfb0vzvlQwcFCpUiVAzdiCkr1Prl27FqPRWCi7xxek8H1mKY2mTZv6HQuowTifsLAwv3auXr2axMREAgICtPMUFBRE8+bNtc8lbdu2Zf78+VgsFvbt28eyZcuYNWsWqamphQJ153vPv1I++eQT6taty0033XTBddesWcOCBQsYN26c9oe6c6WmpjJq1Cief/75CwZLS6p+/fra1KxZMwYMGIDT6dSy6gs69/PfkSNHtOurOJs3b8bpdGpBWZ/mzZtTsWJF1q5dS25uLtu2beNf//qX32fAkJAQOnXqdMHrLTw8XAtIwdlrvqi6XElJSRw/frzQtV3cYAUFf5esVitRUVHaNeoTHBxMSEhIqUb3O18PiJIGG7Ozs3n44YfZtm0bb775pnZN7N+/n8mTJ/Paa69dcs0xcWORTCkhBC+99BKrVq1i5MiRLFq0yG+Zr1/9uR8AjEYj4eHhFyx4ea5zi2/q9frzvgGmp6eTlJRUbNp6wQ9uBfuxg/pX4iZNmvD999/zr3/9i++//54qVapoH0DT09NRFMXvA2lBp0+f1j7oWK3WQu0GtBoWF5KRkUHlypULzfd9AC74QaKoffnOkW80E1/xyq+++gqTyUSXLl0YPXp0saOiCCGEuDH4vuBdaHSzI0eOEBgYqAVV+vTpwzfffMOuXbuIiopizZo1WjexzMxMvF4vc+bM0epQFWSxWPzuF3y/Lc37UsHH+d5Hfe9vJXmfzMjIIDw8vFDXIt9nlNJ+JgGKzAo59324oPT0dH744YdC9ScBLaPJ1x3vk08+ITc3l/Lly9OwYcNC57GofV3oc9GlSk9PZ82aNQwbNuyC6+bk5DBy5EgefPBBatWqhdvt1j73eL1e3G43RqORV199lVq1anHHHXf4ZTj5ApwGg6HU2VNffPGF9rPJZCI6OrrYWlmBgYF+97Ozs8/7HMLZz7e+66ugqKgosrKyyMrKQlGU865zPud+LvWdg6I+O/pqt517jEXtG0p+3Vit1lKNcBcUFORX08wnJyenRIGkEydO8PDDD3Pw4EEmTZpEly5dgLOjft9yyy20adPG7zopeC2JfyZ55oUQhIaG8uqrr/L4448zc+bMQstATc8v+Ncvl8tFWloa4eHhV7RtwcHBJCYm8vzzzxe53Gw2n/fxvXv3Zty4cWRlZfHjjz9qqc6+bdtstkL1HXyqVq168Q0/R2hoKMnJyYXm++aV5jzGxsby6quv8sorr7Br1y5+/PFH5syZQ3h4uFYnQQghxI0pMjKSxo0bs3TpUp566iktuFNQdnY2K1eupHPnztq81q1bEx0dzZIlS4iOjsZisWijcgUGBqLT6Rg0aFCRmRkX+oJ/Od6XSvI+GRoaSlpaGh6Pxy8wdfr0aW2dKy04OJibbrqJ+++/v9Ay35fq2bNnM3fuXEaPHk23bt20L/O+LoFX04oVK3C73SWqJbV9+3aOHTvGjBkzmDFjht+yF198kRdffJHdu3ezdOlS4Gwhcp9jx47x1VdfMW/ePFq2bFmqdvq6nl2MsLCwCwaMfJ9vz5w5Q40aNfyWJScnU7lyZYKDg9HpdEUGaZKTk7WA7+Xgy8A/t95UcfWnSiozM7NUvxfVq1fnzz//xOv1+r22JCUl+WV9FWX37t088MADOBwOPvjgA1q0aKEtO3HiBFu2bGHLli2F6rbOnDmTmTNnsmzZsgtmuIkbk3TfE0IA0KVLF3r27Mns2bP9RtpJTEwEKFQw/Pvvv8fj8dCsWbPL2o5zP1wnJiZy8OBBqlevTkJCgjZ9/fXXfPHFF4X+WnquW2+9FUVRmDJlCikpKX51CBITE8nNzUVRFL9t79mzhxkzZvj9Fae07T5XixYt2LRpU6G/bH/zzTdER0eXOAC2adMmbrrpJrZu3YpOp6Nu3boMHTqUuLg4jh8/XuL2CiGEuH498cQTHDx4kLfffrvQMo/HwyuvvILdbvcrPGwwGOjVqxfLly/nxx9/pEuXLlomR1BQEPXq1ePAgQN+74e1a9dm2rRp5+2qf7nel0ryPpmYmIjb7farueRbB9A+k1zoPflS+EYcrFu3rnaeGjRowNy5c7WRfTds2ECtWrW4/fbbtYDUqVOn2LNnT4kzrK+ULVu2UK5cuRJ1s6tfvz5ffPGF3zRr1ixAvQZ92UznrvPFF18QHR1Np06d+OKLLy5YpP1yq1ixIidOnPCbd+7nxUaNGmE2m/nuu+/85q9fv57jx4/TtGlTbDYbDRo0YMmSJX7FxrOysvjtt98u62fgcuXKUaVKFe0a8vnpp58uepsZGRnk5eVRoUKFEj+mbdu25OTksGLFCm1eamoq69evp02bNsU+7sSJE9x///3odDo+++wzv4AUQExMTJHXCcBdd93FF198QUxMTCmPUNwoJFNKCKF5+eWXWb16td9fhGrVqkXfvn2ZOnUqeXl5tGjRgp07dzJ9+nRatmxJu3btLmsbQkJC+Pvvv1m7di0NGzZk0KBBfP311wwaNIjBgwcTHh7ODz/8wP/+9z9Gjhx5we35Rtr79NNPadKkiV/wp0OHDrRo0YLHHnuMxx57jJo1a7J161amTp1Ku3btSlRYtGC7QR3dr1GjRoW6INx///188803DBo0iCeeeIKwsDC++uorVq9ezRtvvFHiD9D16tUjICCA559/nieffJKoqCj++usvdu7cqQ13LIQQ4sbWrl07RowYwYQJE9i5cye33347MTExHD16lM8++4ydO3cyduzYQiOx9enThw8++AC9Xl+om96wYcN46KGHeOaZZ+jdu7c2yt6WLVt47LHHim3L5XpfKsn7ZPv27WnZsiUvvfQSp06dok6dOqxdu5Y5c+bQt29frZh1SEgIZ86c4ffff6du3bqX9cvuY489Rv/+/Xn44Ye5++67sVgsLFiwgF9++UUbfKVhw4bMnDmT2bNn07hxY5KSknj33XdxOp2lrheVmprK4cOHqVWrVpFdDUtr9+7d2nkqyuHDh0lNTaVx48YEBQUVyljy1SeqWLGitqyorCaz2UxYWJjfspMnT3Ly5EltYJsrpU2bNnz66acoiqJ1mfMFB3/77TdCQ0OpU6cODz30EDNmzMBkMtGpUyeOHj3KlClTtM++AM888wwPPPAADz30EPfccw8ul4vZs2fjdDq1QQMuB9/IfM8++yyvvPIKXbt2ZdeuXVqG2sUEWjds2ACogSZQMyj37dtHlSpViv2M26JFCxITE3nuued47rnnCAsLY9q0aQQHB/v1Nti3bx9Op5N69eoB6iAGKSkpjB49muzsbDZv3qyt6ys0X1z2W0xMzCVlxonrnwSlhBCasLAwXn31VZ544gm/+WPHjqVq1aosWrSIOXPmEBMTw8CBA3nssccu+18jBw8ezBtvvMEDDzzAhx9+SPPmzfn888+ZOHEir776Kg6Hg2rVqvmNjHMhffr04ZdffqFXr15+8/V6PbNnz2bKlCm8++67pKSkEBsby/3331/qDxrdunXj66+/ZsSIEdxxxx28+uqrfsujo6P57LPPmDhxIq+//joul4s6deowc+bMIkdgKY7FYuGDDz7QRgfKzMykWrVqvPbaa/Tr169UbRZCCHH9uv/++2nSpAkfffQR48ePJzU1lejoaNq0acPYsWOLDDzUqVOHuLg40tLSaN26td+ytm3b8v777zN9+nSGDBmCyWSifv36fPjhh+cdsetyvS+V5H1Sp9Px7rvvMnXqVObOnUtqaiqVKlVi2LBhft3p+vXrx++//87jjz/OkCFD/Ea7u1R16tThk08+YdKkSTz//PMoikJcXBwzZszQ2vnwww+TlpbGvHnzmDFjBuXLl6dPnz5a+zMzM7U/Zl3Ib7/9xsiRIy+qC1xRUlJSzjtS38yZM/nyyy/ZvXv3Je/rXAsXLmT69OlXvJtWt27dmDFjBlu3btWK1teuXZuePXvyySefsGLFCr777jstiDp//nwWLFhAWFgYt9xyC08//bSWRdi6dWs+/PBDpk6dyrBhwzCbzTRv3pzx48df9gFmevXqRW5uLu+//z6LFi2idu3aWjfJc+tTlcQff/xBw4YNtay4HTt2MHDgQMaNG3fe383p06fzf//3f0yYMAGv10vTpk2ZPHmyX3240aNHc+zYMX799VecTie//fYbQJHddRMTE/n4449L3X7xz6FTrmQlPSGEEEIIIYQQAhgwYACTJ08uNIDO5fbII48QHh7OuHHjruh+LqfvvvuOevXq+dW4+u2333j44Yf5+uuvC2U+nk9ubi7t2rVj/PjxWrFxIa5VkiklhBBCXIDH48Hlcl3tZojLxGQyXbAenRBCiMtrzZo15OXlFTui3OU0dOhQ7rnnHp588slS1VS6mr755hsmTZrE008/Tfny5UlKSmLq1KkkJiaWKiAF8Pnnn1O7du1SZeMLcbVIppQQQghRDEVROHnyJOnp6Ve7KeIyCwsLo1y5cqUeplwIIcTFOXbsGDabrUxGSQR1FMRdu3YVOSDAtSgtLY2JEyfyxx9/kJqaSlRUFN27d2fIkCEEBgaWeDupqancdtttfPzxx5d1JGkhrhQJSgkhhBDFOHHiBOnp6cTExGCz2SSAcQNQFIXc3FxOnz5NWFgY5cuXv9pNEkIIIYT4x5Lue0IIIUQRPB6PFpCKjIy82s0Rl5HVagXg9OnTxMTESFc+IYQQQoir5KKHzXI6nfTs2ZM1a9Zo844cOcKgQYNo3Lgxt956K3/++affY/766y969uxJo0aNGDhwIEeOHLn4lgshhBBXkK+G1MWMeCOufb7nVWqFCSGEEEJcPRcVlHI4HAwbNoy9e/dq8xRF4fHHHycqKopFixbRp08fnnjiCY4fPw7A8ePHefzxx+nXrx9ffPEFERERPPbYY5S096CiKGRnZ5d4fSGEEOJykC57N6Z/6vMqn6eEEEIIcS0pdVBq37593HXXXRw+fNhv/urVqzly5AivvfYaNWvW5OGHH6Zx48YsWrQIgIULF9KgQQMGDx5M7dq1GTduHMeOHWPt2rUl2m9OTg7NmjUjJyentE0WQgghhBDI5ykhhBBCXFtKHZRau3YtLVu2ZMGCBX7zt2zZQr169fy6OTRr1ozNmzdry5s3b64ts1qt1K9fX1suhBBCiH+eJUuWkJKSAsC0adO47777AFi8eDGdO3cu9nEjRoxgxIgRZdLGi3Hq1CmGDBlCYmIi7dq1Y9y4cTgcDuDSyx3MnTuXdu3a0aRJE1544QXy8vLK7LiEEEIIIS6nUgel7rnnHl544QWtSKhPcnIyMTExfvMiIyM5efJkiZZfdYoCmXsgfTtkHwJHCnicV7tVQgghxA3r2LFjPP3001pQZfDgwUybNu0qt+rSKYrCkCFDyMvL45NPPmHSpEksX76cyZMnX3K5g6VLlzJ9+nRee+01PvroI7Zs2cKbb755NQ9XCCGEEOKiXbbR9/Ly8jCbzX7zzGYzTqezRMuvusP/g5X9C883WMEUCuYw9dYSCZZoCIgBSwxYy4OtIlgrqrdGKYgrhBA3uvPV41G4QK0epYTrnW+bijpPQdHaoihK4Xn5PyvqA7Tlfj8XeGxRPxfcTsG2lPp+gf36lp9KPwVAUkYSdptde0xySjIns0/i8rjYc2aP/zaAQFMgiqJcs3WhDhw4wObNm1m5ciVRUVEADBkyhPHjx9O+fXuOHDnC559/js1mo2bNmqxatYpFixbx5JNP+pU7ABg3bhxt2rTRMtXnzZvHf/7zHzp16gTA6NGjeeCBB3juuecK/cFQCCGEEOJad9mCUhaLhfT0dL95TqeTgIAAbfm5ASin00lISMjlasKlCakDIfXAfgLcueBVU+zx5KmTvYQZXaYwsFWCwKrqFFQdQupCcBwYA0GnB52hwOS7b1Rv9TIstRDi+qUFRvJvvYq30DxFyZ9/zryS3Ba1j/Mt83q9WvDF6/Xixavt3+Vx4fA4cHgcON1O9dbjxOl14nQ70Xv01DHWITknGaPLeHa7ihfcuUW34ZxgUaF2UUSwCP9A0LmBGwCP3qKFpUoSzPpx0Y/8sPAHMlMzqVS9Evc9cR8ej4exQ8fyyfJPtPXe+b93AHhkxCPkZOcwZ8IcdmzcATpo0qoJg54ehC1Q/WPLD//7gaWLl5KVkUVcgzgGDxtMTPkYFEXhq4+/4pdvfsFpdxLfMJ5BTw0iKlYNxgzoNIAHn32Qrz/9msy0TJre1JQHnnmAAGsA9/e7H4DB/Qbz0PCHOHPyDDs37+SlyS+R587Dq3iZM2MOP331E1ablV5396J7v+7kOHNQUNBxNij1888/M2nSJI4dO0bt2rV5/vnnSUxMLMXVe/lER0fz3nvvaQEpn+zs7Esqd9C8eXO2bdvGE088oS1v3LgxLpeLXbt20aRJkyt7YEKIi+bxKhj012YgvaRuhGMQQlx7LltQKjY2ln379vnNO3PmjNZlLzY2ljNnzhRaXrdu3cvVhEsT3gh67gCvS53cueBMU7vxOZLBcSb/NgWcKWA/oy53puTPS1MDWa50yEiHjO3n7ECvZlUFVoXgeAiNVwNVBgvgC07lB6j0ZtCZ1GUGC+hN+UErI+gL3prO3r9G/1oshCgbBQM9XsWrTb755y4rav1z5ymKgsfrwYsXj9eDR/Go8xSPNl/x5t/PX1Yw2ORrl4KCF+/ZgE2BQI7H68HutmN323F4HNg9dhwux9kAUf7k8DhweVxn53mduNwuNYDkceLyuHB51eUur0u971GXuz1udX2PC7fXjdPjxO11XzC4UzWwKu+0eQdDjgEc2ommzqb/EpS59Qo/o/6yQhqxu8mcEr3WJ+1N4rN3P2Poa0OpXK0ySxYtYeqrUxny8hAAzAY1a1mHDoNO/UNIgDGATz/6lMy0TMbNGofH42Hya5P5/tPvuf/x+/nxqx/5ct6XPP7849SMr8nH737MjNdmMPmDyXy78FtWL1vN8NeGExERwaJPF/Hm828y69NZGI3qx4wvPvyCoS8MJTwynIljJjJ/ynxGvjaS6XOn88SgJ5g+dzrVa1Tn83mfYzKYKBdUjlBLKGdOneH04dPM+GAGe3bu4e033qZx/ca0SGzhF5DatWsXw4cPZ/To0TRs2JDff/+dBx98kG+++YaqVate7qfjgkJCQmjXrp123+v1Mn/+fFq1anVJ5Q4yMzNxOBx+y41GI2FhYddOOQQhRJEMeh1Pfb6Jfaezr3ZTLkqtmCCm9JfAtxDi8rtsQalGjRoxe/Zs7Ha7lh21YcMGmjVrpi3fsGGDtn5eXh5///2331/7rgl6kzoZbRAQBdQuvI7izc+gsucHr9LPBq5yj+YHqVLBfhpyDkH2fnBlQN4xdTrzl7odnQlC4iGiGUQ2h+A6gAfc2aB41P0obrXelbrj/McZCmRXGdXsKkMA6K1gtKpBLd9xaD/n3wohroqCwSGP4jn7s9dT5PxzJ4/Xg9vrxuV14fV6cXvdWjDI7XUXGVgquE9A/dkXHPK9rvi+1/vu6nRaxo/vvtfrVQNGbrsWNLJ77DjcDvLceWcDSvnBJbtbXeZ3/9zl+ffdXnfZPhHFMOgMmAwmTHoTZoMZo95I+aDyGPVGzAYzBqMBnU6HTgG9/rK9dZaY1RhArfBa6PRqKUidTqcFZXToUP+r909sOYFep6dFfAtq1a5FsxrN2Np9q/ac1gyvqW032BwMQPWw6mSfySY8JJzmdZpjtVqpOrkqCgqVQyqz/Lvl3DvwXu7ud7e6jVE1mffRPCJMEXz16VeMeGkEHdp3AKDpmKZ069yNfZv20aGjOu+B/z5Az249ATC/YOaxhx7j1VdepVq5agBUK1eN8uHlsZqsmPQmwgPCCTQFYrFYeGPcG4SFhdGkfhP+3vw3P371I21bt/U7P++//z533XUXvXr1AmDgwIGsW7eOzz777Joohv7mm2/y999/88UXXzB37tyLLndgt9u1+8U9Xghx7dp3OpsdxzOvdjOEEOKactk+WScmJlK+fHlGjhzJY489xvLly9m6dSvjxo0D4Pbbb+f9999n9uzZdOrUiRkzZlCpUiVatmx5uZpQdnR6tSueMVCtMRVYGbz5wSR3FuSdUgNUnlw1GGQKU5dl74PMXZC+DdK3qtlVGdvV6eBHYAyCyESI7QQx7dXtF8UXrPJ68oNXbnBmgJKSH8QC7duHlkllAoMJDPntNgTkZ2FZ1Db6MrKEEH58ASFfdtC5t75gUsF5vqwdt9etZee4lfzAUYEuZAWzi3wBJR8tOOS7n/9Pr9Oj06m3ep2+yHlGvRG9To9X8ZLlzCLPlUeuK5dsZzY5rhxyXbnkOHO0+znOHP/b/HV86zk8jqJOzWWlQ4fFaCHAGIDFYFF/NgRgMVq0+9rPBgtmg1lb32wwY9KbCDAGYDKYsBgs2q3ZYFaX++bnB51MBtPZZXoThqK6TrvBmGmkUnAlLAEWbbZy8+/keHKv+DnxY7BhKmFGbOubWlOrdi3u6ncXderWoUOnDvS7vR9JSUnnfdzd997NsCHDuLnDzbRs2ZKbu93Mv279FwCHDh3i4XoPa+tGRkUy9Jmh5ObmcurUKUY8NwK97uzYKQ6Hg6RDSVpAtGHjhngUDwDx9eLxeDwcPHiQsPAwAPV3JD+4qqDg8rrwKB4qVKxAYHAgLo8LgLg6cXz95deF2r5//36WLFniNzKwy+Wibdu2hdYta2+++SYfffQRkyZNIi4u7pLKHVgsFu3+uculnpQQQgghrkeXLShlMBiYOXMmL774Iv369aNq1arMmDGDChUqAFCpUiWmTZvGG2+8wYwZM2jSpAkzZsy4ZouUlpreAOZQdbJVAncOOFIh7wQ4TqvBo7BGENVKXV9R1Kyp1E1wZhWkrFGzqU79qk56C0S3gfLdILpdfje/fDo96MwlGzvR684PYLnA41CDY3mus9lXOn1+90CzGqgyBYMpCPQB+YGr/ElX6oEahbhm+LKMfFlFRd33KB6ta1jBbmIexYPX68WD52x3tgLd4VDQslR8hZd16DDoDVqQqOBkMpj8AkoGvcEvsHQuh9tBpiOTDEcGmY7MQlOWM4ssR5bfbbYzmyxnFna3/bKeR4POQKA5EKvRis1kw2qyYjOqtwV/DjAGYDWq86zG/Pv5831BJ9/8AOPZwNN1836g0xX/R4OryBfEDAgI4KNPPmLD+g388fsffPPVNyxcsJA33nwDwC87zeV2YTAacHldNGnehG9+/IY/fvuDlStWMnb0WFb+uZLRb4zGaDBqQdaCtZzsTvUae+PNN6harapfl7qQ0BAt0Gowng36KZ78eQaDFgw06o0Y9UYMOvX3wZetZjQYCTAGaBl9Bp0Bi0kNRhbk8Xh48MEHue222/zm+wI9V8uYMWP47LPPePPNN+nevTtwaeUOwsLCsFgsnDlzhpo11Yw3t9tNeno60dHRZXBEQgghhBCX1yUFpXbv3u13v2rVqsyfP7/Y9Tt06ECHDh0uZZfXD18mla2S2p0v5wjYj6sZVJYotZudrZI6VeqlBq0ydkLyn3DiJ8g9fDZAZQqFij2h8u0QWKV07dAbAaMaWCqK4jlbR8uTA640NZAF+QErCxjMYAhSRyA02tQRCQ3W/GDVdfIlUtwwvIrXPwOpiMnhVmsS+W6LynIqWJga0AJLviwjvU6PQWfQAkkWncVvXnFBpPNxuB2k29NJs6eRbk/3mzIcGeqtPcPv/uXIUrKZbASZgwgyBam35iACzYHqrSmQQHMggaZAbCabdt/3s81k0wJRZoP5+gkcXaOKKmauzS9qXhHrFqRDzagr+Lxs27KN9WvXM/jBwTRr0YzHn3qc7p26s3HdRgDycvMICgpCh47jx45TtVpVLAYLH8/7mPi4eG6//XbuuP0OlvywhFEvjSLQFEjValVJ2p9EYFc1GJeenk6vHr1YsHABkZGR5KTnUK9WPUDN2nn2mWcZ/MBgKsVUAuDwvsM0S1C782/fux2TyUT9uPpaxpDVZCXQHIjJoGat2Uw2zAYzR48eRXErWhbQ39v/pmbNmliM/kHM6tWrc/ToUb/6URMmTKB69erceeedpXuSLpPp06fz+eef8/bbb3PLLbdo8y+l3IFerychIYENGzZomeabN2/GaDRSp06dMjw6IcqOFNcWQogbW9kXxvin0enU2lSWSHBVU4NTOUlqVlRAzNkMJJ0BwhqoU62HIWuPGpw68SPYT8GhT9QpMhGq9ofotpcne0lnAIOh6KCV4gGPUy3g7jwDeccBpUCwKgDM4WAKUYNVxkA1WCVZVaKUfCOhaQWq8299NZTsbjt5rjwcbgdOr9MvwOTO77JaMHujYDc2g07NxvBlXviCSkV21yolRVHIceVwJvcMKXkppOamqrd5qaTZ09TbvDRS7eptjivnovaj1+kJsYQQYg4hJCD/1hJCsCVYvTUHqz+bQwgyBxFsCdbmBZoCL8ux/lP5jZpXcFS8ou6rPxTqdqldmwW+U51bDwrQumD65vuy7s6tGeULxhTMSio4LzwonPfefY8KsRW4qfVNrFu3jrzcPG7tfivzPpzH/A/m8+9//5ulS5eye9duataoidVkJTU5lf/74v8YN24cYWFh/PrLr9SrVw+z0czA+wYybtw46sbXpWbNmkyaNIlKlSpRtXJVBg0axNQpU4mOiqZGjRrMnDmTTZs2UatmLe14pk2bRqVKlbBYLLz++uv07duXwMBAHA416Lpr1y7Cw8MLnX+Hw8Hw4cN58skn2bBhA0uXLuXzzz8vtN6gQYMYMGAACQkJdOzYkV9//ZW5c+fy0UcfleLZvnz279/PzJkzeeihh2jWrBnJycnaskstd3DPPfcwatQo4uLiiImJ4dVXX+Wuu+6S7nvihnW9FwgH6BgfzXPdr+/AcXSQ5YYIEN4IxyDEjUaCUmVFp8sP4IRBQKwadMo5AgGRai2pc9cNiVenuMcg+S848oV6m7JWnYJqQY1BUL6rGli6Im02qBldnPNBV/GoXQE9djWjy5dZZQhQg1LmMHUyBKpdAYvL0hI3PEVRtNHQzh0hze6yk+fOI8+Vp46Qlp/l5PGqwaaCX8J9gSVfUEnr2lMgq+lytzvDkUFyTjJncs+QnJtMcq76s29KyU0hJS+l1JlMBp2BsIAwwq3hhFnybwPCCAsII9QSSmhAqN9tWEAYNpPtsh/jP0lRtb8K1grz/XxuwMmoGCmvlNe6eRYKCuUHkfwCSOcpQH5uMKm4+5eqQf0GvDH2DWbOnMnrY16nQoUKvPnmm9StW5cxY8YwadIkPv74Y7p27cqAAQNIS0sD4KmnniIrK4tHH32U3NxcWrRowZtvvglAnz59OHXqFKNHjyY7O5vExESmTp0KwAMPPEBOTg6jRo0iOzubBg0a8P777xMaGqq16bbbbmPEiBFkZmbSo0cPXnzxRQAiIiLo3bs3Tz/9NM8++2yhY6lbty6xsbHcddddhIeH88Ybb9CgQYNC6zVu3JgJEyYwbdo0JkyYQJUqVZg4cSItWrS4LOe0tJYtW4bH42HWrFnMmjXLb9nu3bsvqdxBjx49OHbsGKNGjcLpdNKtWzeee+65Mj9GIcrS9V4gvGb0tdflu7RCrMbrPkAoIwgKcW3SKQUr617DsrOzadasGRs2bCAoKOjCD7jWeRxnR+ZTvBBQ7sJd4XKPqcGpw4vVrnYAtspqcKpCj/yueleJoqgZVb5RCT0u0OvVEQFNwWqXRVOwGoAzBkq3vxuEy+PC4XEUqsWU48oh15lLnjtPy3byFTEGQEHLZCpqupKZPV7FS2peKqdyTnE6+zSnck6pP+ec1qbk3GScnpKPZBVoCiTSFkmkNZIIa4Q2hQeEE2mNJNwaTnhAOOHWcILNwdIF7iL5isMXrAPm685ZMHvOr0i8Anq9XgtsFsyU8xU5NxvNWhF03zoGvQGv00v6yXSqVa+GNUANzhcMJMnzWDLx8fHMmzfvmhvYxG63c/DgQapXr37Va0+VpRvu85T4R+gxdcV1HZTq3ag8U+9uel0fx41wDPUrhPD9kHZXuxlCiHNIptTVYrComVDmSMjYAblHwVr+/IElW0WIfwpq3A+H/weHPoPcI7B9jNq1L+5xiG5/dQI+Ot3Zoug+ikcNUrky1S6IoC43BoIlWu32ZwqRINU1zBd0crjPBp7y3HnaKG1Ot5oF5fK68HrVgJNOp/MLMJkNZgJNgVc82FSwzadyTnE86zgnsk9wMvskJ7LU25M5JzmVfQqX11WibYUFhBFtiybaFk2ULYroQPU20hqp3UbaIgsVXRYlUzDIVDCwVPDnc0ck1IJL+cEjo95IoCkQi/HsiHwFi2YXDHQW7M5p0BkuGFSy2+1k6bPK7NoVQgghhBDin0aCUldbQBQYmqmBqbxjate+C3V3M4VAzf9C1QFwZBEc+BCyD8DGZyC8McQNgfCGZdL889IZ8jOj8v8S68umcudC1m7wetXugcYgtb6WKTQ/SCV1McqKr3udL/Bkd9uxu+1kO7PJdmZrgSin16kGBxQ1MGDSm9QsE4MJq8mqZZiUVZtT8lI4mnmUY1nHOJZ5jGNZxziedZzjWcdJzk0+m5FVDB06omxRxAbFEhsYS0xgjHYbExijBqICozEbzGVyTDca3+iG504eRQ00+UYsBDDqCgSM9AasBisWo0Ubmc9kMGmBJZPeVCjIZLyaGaJCCCGEEEKISyKf5q8FpiAIb6LWY8o+AJbwwnWmimK0QvV7oVIfOPARJH0GaZthzWCocCvED1G7zV0r/LKpIgoEqbLVkQdBPQeWCAjIz6QyhoBkKFwyl0ctFu7wOLSi4dnObDIdmX5d7wqOQGc2mDEbzAQYAzAbzGVa08ireDmdc5qjmUc5nHGYI5lHOJp5VLu1u+3nfbzFYKF8cHnKB5WnXFA5ygeVp3xwecoFlqNcUDmiA6MlmHERihr50Nc1UxvJUFe4a6bNZMNqsmI1WgkwBWgBJl/AyRdsMhlMUjvrBnTuSL1CCCGEEEL4yLeya4XBDKH11aBMxt/qlztTCWs9mIIh/gmoehfsfQeOfQvHf4DTv6sj+VW56+rWmypOwSCVBbW2lidX7eqXewT0JjAGg7VcfpH4ULXboyiWL9spz52nZTxlOjLVkes8DlweF4qiaF3sLAa1y1OwORij3ljmNXJynDkcyjjEofRDJKUncTjjMEkZ6u35CojrdXpiA2OpFFKJCsEVqBhc0e82whoh9X5KSVEUv9EPC2Y4KYqiBix1Oow6oxZMCrIEYTVasZqs2rVkMpi0gJPvVgJNQgghhBBCiKJcg5GKfzC9AYJrATq1O59Op9ZbKqmAGEgYBZX7wd8TIPNv2PU2HP0a6r94bXTpOx+d3r+7n9cJrmzI2KXeNwaqGVSWaDVI9Q/u5uf2usl15ZLnyiPPnUeWI4sMewZ57jwt8wnQMp4sBgthljDMBvNVCdak29M5kHZAmw6lH+JQxiFO55wu9jEGnYEKwRWoHFqZKiFVqBRSicqhlakUrAaiTAZTGR7B9c+reHF61FEOC46E6KvZ5AtU+oJJoZZQbGYbVqNVq9dUsNtmWXbZFEIIIYQQQtyYJCh1rdHpILgG4Mnv0qYDo6102whrAK0/hKPfwJ7p6gh/ax5QM6biHitdoOtq0pvVrnyWCLVoujsHcpIg+yAYbGrXRGtsfoCqlOfoOqEoipb5lOvKJdeZS5ojjWxHNna3XS3YrYBBb8BisGAxWgg2B1+1gE22M5v9afvZn7pfuz2QfoDUvNRiHxNpjaRqWFWqhqpTtbBqVAmtQoXgCtLFrhR8mU4FA09Oj9Mvy8msVwNLFqOFCGsEgeZArXvmuZNkNwkhhBBCCCGuNPnGdy3S6SG4ttqdLWMXWGMuXPy80DYMULkvxHaC3VPULn2HF8Dp36DeSIhpe0WafsXoDGdH61MU8OSA/TjkJuUHqKKv+wCVLwCV68ol15VLpiOTtLw0ct252N12vIoXHTot+BQeEH7VMp+8ipdjmcfYk7qHPSl72Ju6l30p+ziefbzYx1QMrkj1sOpUD6+u3oZVp2pYVUIsIWXY8uubrzC9y+vC4XZoIx/6CtAXrAUWYgkh2BKM1WjVRqXzZc1JlpkQQgghhBDiWiBBqWuVTg/BcWpgKms3WCuoNZZKyxwGCa9A+e6wY5w6wt/Gp6HCv6DOs2AOvdwtv/J0urPd/AoFqALVboy+AFVpg3llyOF2kOPKUQNQ9kxS81LJcedgd9tRFAU9em0EsjBL2FXrKuXyuDiQfoBdZ3ax+8xudqfsZm/qXnJduUWuHxMYQ83wmtSKqEWN8BrUDK9J9bDqWE3/3O6WpVEw40krQp/fzU6v02MymDDrzdhMNoItwQSZg7AYLVqw0lfbSWpqCSGEEEIIIa51EpS6lukNEBIPHifkHITAymqw6mJEtYK2C2Dvu3DoEzi+BFLWQf0XIKb95W13WTo3QOXOUYuk5xxUi6QHxOYHqCKuarF3r+Ilx5lDtjObbGc2KXkpZDuyyXPn4fF60Ol0asFoo5VwS/hVC0C5vW72pe5j15ld/J38N7vO7GJv6l41G+ccZoOZmuE1iYuMo3ZEbWpH1qZWeC1CA67DQOdV4Kvx5HA71FuPQ6vvZDKYtOym6MBogs3BBBgDtKBTgDFAsp2EEEIIIYQQ1z0JSl3r9EYIraOOSpd3AmwVL35bhgCo8xSU6wzbXlXrM20cBhV6QN1n1VH8rmc6nTpioSlIzTBzZ0POAcg+oGaEWStCQJQ6it8Vrpfj8ri0AFS6PV3NgnLl4HQ70enULnhWk5VgS/BVq5ukKArHso6x/fR2diTvYEfyDnaf2V3kqHdB5iDiI+OpE1WH+Mh44iPjqRpWVWo+lYBX8eJwO3B4HFqXO0VR0Ol1WlZTuC1cLSxusmnZcZLxJC7F0aNHufnmm1m2bBmVKlU677oLFixg0qRJOBwOFi5cSK1atS5qn06nk6+++oq77rrroh6/ePFipk+fzq+//npRjxdCCCGEENcf+UZ5PTBaIaw+pKwH+xk1sHIpwhLgpk/ys6bmw/Hv1ayphFFqRtWNQKc/W4PK6wZ3FmRshyyzmjVlqwiWyMtW9N3pcZLlyFKzoHJTSLOnkefKw+11Y9AbsBqthFpCCQi8et0Jc5w57EjewdZTW9l+ejvbk7eTbk8vtF6QOYi6UXXPTtF1qRhcUYIjF6AoihZ4cngcWpFxnV5HgEEtJh4bFEuIJQSrSc2K8wWgZBQ7cbmVL1+eP//8k4iIiAuu++abbzJw4EBuv/12ypUrd9H7/P7773nnnXcuOiglhBBCCCH+eSQodb0wh6mBqdSN4Mq69KwmX9ZUbEfYNhpyD8P6J6DKnRA3RA2E3Sj0RrW+lDkcPA5wpUPKCTUgZY2FgPLqCH+lqNnl8rjIcmaR5cjiTO4Z0u3p5Lhy8Hg9mA1qvZ/owOirmkl0Mvskm09uZsupLWw5tYV9qfvwKl6/dUx6E3GRcTSIaUD96PrUj65P5dDKMvLaBbi9buxuO3a3HYfboRah1+m0LndRtig188lsU7tlmtQAlJxXUVYMBgPR0dElWjcrK4vExEQqVryETFzQup8KIYQQ16LoIAser4JBf33/ofVGOAYhCpKg1PXEWh6C4yFjGxgsoDdf+jbDG6lZU3umwuGF6nRmNSSMVjOq7A5wOMHpUrvHgXqr14HJBBYzmIxnl13rDBYwxJwtkJ5zGLKT1KCftSIERKvZVeccj1fxkuXIIsuZRUpuCmdyz5DrysXtdWM2mAk0BRIbGHvVglBexcvBtINsOrmJTSc3sfnkZk7lnCq0Xvmg8iTEJpAQo05xkXGYDZfhOrqB+TKf7G672rVRAYPeQIAxgEBTIJVCKmmj3PkyoCTz6QanKJBbdKH/K8ZmK9XrbMHuezfffDMTJkxgzpw5HDp0iIYNGzJ+/HgqV65MfHw8AP/5z39ITEzk448/Zs+ePYwZM4YtW7ZQvnx5Bg4cyIABA7Rtf/3118yaNYsTJ05Qt25dRo0aRVZWFiNHjgQgPj6eZcuWUbFiRWbOnMlnn32G3W6nefPmjBo1igoVKgBw6tQpXnzxRdavX0/16tXp0KHDZTxhQgghhL8QqxGDXsdTn29i3+nsq92ci1IrJogp/Ztc7WYIcVlJUOp6E1RdzZTKPQS2ypcnGGS0Qr3hENMRtr6qFgpf8wCYugHdwQV4PGfX9+3TZFQDU2YTBNkgKBACLGC1gMUCAWbQX6OZIQULpHvd4MpUg33ZFrBEg7UieXobGW476fZ0TmWfItuZjdPjxKg3EmgKJCYw5qoGofal7mPDiQ1sPLGRjSc2kuHI8FvHoDNQJ6oOjWIb0TC2IQ1jGxITGHNV2ns98HW/82VA+Yq7m/QmAowBRNmiCAsII9AciNVoxWayYTFarnKrRZlTFGjbFv76q2z326YNrFhx0a/506ZNY8yYMURGRvLUU08xefJkJk6cyJ9//knbtm2ZNm0aiYmJ2O12HnzwQfr27cuYMWM4cOAAL7/8MoGBgdx2222sWLGCF198kRdffJGbbrqJjz/+mIcffphly5bxwgsv8MEHH/DFF18QERHB/Pnz+fbbb5k4cSJRUVF88MEHDB48mG+//RaTycRTTz2FzWZj4cKF7N27lxdffJHw8PDLfOKEEEIIf/tOZ7PjeObVboYQIp8Epa43egOExoM7E+ynwHrx9T80DiekpMPpEPA8A97/gX4juJaCfgeEPwyWav6PURRwucDpVrOoTp4B18n8NurAbAaLSQ1UhQRd28EqvREsEXhMoWTmpZCZvJ1TuctJ9yjkGgLRmcOxBUQTHhB+1YIQiqJwIO0A646v0wJR5wahLAYLjWIb0bhcY5qUa0KDmAZYTTdQN8zLzOF2kOfO8wtA+Ua2Kx9UnnBrOFaTGnyymWxS1F2cdb1khhZw//3307p1awDuvvtuPvnkEwCti19oaChhYWEsXLiQyMhInn76aQCqVavGsWPHmDdvHrfddhsLFiygZ8+e3H333QA8//zzmEwmMjIyCA4O9us2+N577/HKK6/QsmVLAF577TXatm3LihUrqFy5Mps2bWL58uVUqFCB2rVrs337dn788ceyPC1CCCGEEOIqk29Z1yOjDULrqsXJL6W+lMejBpMOHYX0LDAaICQSrEPBvh4yPgDvUUh7FYL6QWAP0OV3S9LlB57MRXT98njBmd/lLzkVjuV3IysYrAoJguACwaoAi9oVsIy/7Dk9LtIdWaQ5MjmZm0KWMxeX141FbybQ4CWcPPQejxp800eDPgz0ZROYOpl9kjXH1rDu2DrWHV9HSl6K33KbyUbjco1pWq4pzco3o05UHUyGktfF+idxeVxaAMrhVkcXNBvNfgEoX/DJZrJJ9ztRPJ1OzVi6xrvvnatq1araz0FBQbhcriLXO3DgALt27aJJk7NdAzweDwaD+jtx8OBB+vfvry0zm80MHz680HZycnI4efIkQ4cORV/gjxB2u51Dhw7hcDgICwvTuvIBJCQkSFBKCCGEEOIfRoJS16uAmAL1pQJKVaQbRYHUDDh0RA1KBQRA+Wj/7KWA5mCqDZkfgGMjZC8ExwYIfQiMFyiGa9CDNUCdCioYrDp5Bo6cAAU1GGYxq1NwEIQEns2ougLBqjy3nXRHNmfy0jidl0a2Kw9QCDRaiQoIw2Q459fCVxzdnqwWRw+IAUs4GAvXnroUOc4c1p9Yz5qja1hzbA1JGUl+yy0GC43LNaZ5heY0L9+cutF1JXunCF7Fi91tJ9eVi91jx+v1YtKbsJqsRNoiiQiIIMgSRKApUAJQ4uLodBB4eUbuLCsmU8neI9xuN61bt2bUqFFFLjcaS/aa48nv8j1lyhSqV6/utyw0NJRVq1YVKoxe0jYKIYQQQogbh3yjvZ4FVQNXhloDylapZAESpwsOHIbDJ9TgVGwUFPclwxAKYU+DfSVkfgyuA3DmZQi+A2y3QGlHEis2WOVRuxA6nJB1Cg67AZ0arDKbig5WWfIDViUMCuW67KQ5MknOSyM5L41sVy56nYFgk43ytigM5+tOaLCok+IFdy7kHIK8o2AKVQNU5rCLyp7yKl72pOxh1dFVrDq6ii0nt+BRztbuMugM1I+uT4uKLWhRoQUNYxtKUfIiOD1O8lx55LnzcHld6NARYFKLkFcNq0qwOZhAcyCBpkDJJBPiAqpXr86yZcuoVKmSlh319ddfs23bNl566SWqVq3Krl27tPU9Hg9du3blzTffRFfg9TgkJITIyEiSk5Pp2LEjAE6nk2HDhvHAAw8QFxdHRkYGSUlJWhbXzp07y+5AhRBCCCHENUGCUtczvRFC4tXAlCMFAqLOv352LuzeDyeSITK8cHCoKDodWNuCuR5kvAfObZD1mdq9L/RBMJa/9OMwGMBmVaeC3Pn1qs4NVhn0arAqwOJfYD3AfDZoZTBogahTuSkk56WT53Zg0OsJMQVSKSgWfWmDajo9mILUyePMP+9nwFAweyr4vMG6TEcma46u4a+jf/HXkb8KdcmrFFKJVhVb0apSK5pXaE6QOah0bbzBKYqiZUHlefJQvAomg5oFVTGkImEBYQSZ1SyoAGOA35dkIcSF9e7dm+nTpzNq1CgGDx7M0aNHGTt2LPfffz8A9913H4MHD6Z58+Y0bdqUjz/+GEVRqF+/PsnJyWRkZHDo0CEqVarEoEGDmDx5MpGRkdSoUYOZM2eyceNGxo4dS2hoKK1bt+aFF17g5Zdf5ujRo8yfP5/A6ywDTQghhBBCXBoJSl3vTEEQUgdS16tZPEZb0eslp8LuA5CRBeVj1EBQaRgiIPw5yPsdsj4B11448yIE9YXAW8/WmrqcjEZ1OjdY5fGcDVb5FViHPAOkGV2cMjg5bXaSqweTxUKwLYRIWwQ682XKlDGYwRB5TvbUETCF+WVPKYrCofRDrDi8gj8P/8mWU/7ZUFajlRYVW9C6UmtaV2pNpZBKl6d9NwiP10OeO49cVy4OjwMUsJqsBJoCqRJWhRBLCIGmQALNgdKVUYjLICgoiDlz5vDGG29w2223ERYWxoABA3j44YcBaNGiBa+88gozZswgOTmZBg0a8M477xAQEECrVq2oWrUqvXr14tNPP+WBBx4gJyeHUaNGkZ2dTYMGDXj//fcJDQ0FYNKkSbz88sv079+fChUqcN9997F48eKrefhCCCGEEKKMybe4G4G1PATVgsxdEFjJP0CkKGrtpt0H1Z8rxF58HSSdDmwdwdIAMj4E51bI/h/Y10Lof8FU9YKbuCwMBrAatEwvp9dNqjubU/Z0Tuelkp2di8njJdhrJBIzOl02mDLUAJfJBIFWdTKZ1KLrpvz5FzMiYBHZU+68U2xMO8YfyftYcWIzx7JO+D2kelh12lRuw02Vb6JxucbSJa8At9dNriuXXFcuTo8TvV6PzWgj3BpOTGCMlgVlM9kkC0qI86hUqRK7d+8G0G59+vXrR79+/bT75y6vX7++NjpfUe644w7uuOOOQvPDwsIKBZWGDh3K0KFDi9xOeHg406dP95v3xBNPFLtfIYQQQghx45Gg1I1Ap4Pgmmox7rxTYMsfzUhR1JH1dh1Qs41CL3KUvnMZoiD8WbD/CZmfgPsQpIyCwH9B4G2gL0G3wEvkVbykuXNIdmVx3JFGlteODh0h5gAqW8POCVgo4PaAy61mV+XkqtlWoAa4jAY1MBVggUBb/qiCRrWLoCk/aHWBAEi2M5c/T2zhj+Mb+evEVrJdZ0fmMumNtCjXiDZV2tO2akcqhlygUPw/iMvj0oJQLq8Lo96IzWSjfFB5ImwRBJmDCDYHYzGWzYiHQgghhBBCCCHKjgSlbhQGi9qNL2UtODPAFAIHj6pd9oID1bpLl5NOB9Z2YE6AzHngWAc534N9DYQMAkujy7s/RQGXm+zsdNKz0jidcYpsRzZej4dgTJTTmdHr9SjGXBSDAcVowGs24rGa8VotKKb8roDn9ATE61WDVS43ZGRDSro6IiBKfvfBAgErq0WtWWUygdlIsjub35O38dvxjaw//Tdu79lueeGWYNpVaEL78g1JjKiKTaeAwQa6PHCmgSkYdP+8Xz+Xx0WOK4dcVy5urxuT3oTNbKNyaGXCreEEm4MJMgdJQXIhhBBCCCGE+Af4530rvpFZIiA4DlI3Q9IZ2HsEQoLVYuBXiiEMwoeAfaManPKcgbS3ICARggeotahKIisHjp+C46fhxGm1GHtKOqRnoKRmQHomOpebICAIKG3lJa/RgMdmwR1iwx1swx0SiDvEhis8CFd4MK6IYFwRITijQnCHBqpd+dwetdi62wPpWXAmjaOudJbn7WO5fT9bnf7d8qrZYugQ3ZAO5ZtQP7o2Bov5bO0uRQFPnjpqX94xMAZBQKxae8oQePFdKq9xxQWhqoRWIcIaQbBFDUJJPSghhBBCCCGE+OeRb4I3GmtlOLkFtm2EmBpXNiBVUEBTdYS+7MWQu1StM+XYDIF91G59uvzMF0WBk8mwcz8cOAz7D8P+I5CSVuymzw3XeM0mPFYzismIotOBXodi0INXQefxoHd70Lk96B1u9E4XAHq3B31mLqbM3MI7OHf7RgPOqFCc0aE4Y8LZXtnM1+UyWWI9xW7Ff7S8BpYKdLTVpKO5OtWM4eDWwVEXnNyTX6vKqNa+sgaoP5utYNCpxdEdu8BoBXM4WKLAHAr667ub2rk1oSQIJYQQQgghhBCiOPLN8EaiKHAoCY46IaoCmPOAMgpKgVpLKuQesLZRs6ZceyBrIexZBvsaws5c2LG3+ABUZBhKuWjs5cLJiA4kJdREZogFfXgo5shICArEG2AGQykKkns8GPKc6PMcGHIcGLNy1SkzF2NGDqa0LExp2ZhTMzGlZmFKy0Ln9rDXm8rCqFQWxR/k75j8bSlg8EKHI3p6ng6hq6sC4VHlsFeKxl4xGEdUqNpN0OtVM6w8HrA7ITsXPF7QKYDubB0rowFMWWA6DkYdWIMhMBYCo8EWAeaAaz6DyuP1kOvKJceVg9PjxKA3EGgKpGJIRTUIZQ4m2BIsQSghhBBCCCGEEIXIN8UbyeHDsHMnRJUDUwRk/A3uHDBe5npSF+KMge3dYbUe1u+G1FTgt7PLjQaoVRVqV4MalaFmFexVYzltdHHYkUKqKwd0EGawEWhQM4e8F9sWgwFPkBVPkBVXdPGrKYrCfvtpfkndxrK07Rx0p2rLTF4dnU8GcPsOhX4b7UTmeYH0/Onvs9vQ63DEhmOvHENe5Wg1WJV/6w3wjbCngNt7tlugw6H+7PWCNx08e/MzqoLAGgnB+VNAgFrLyphffN1XhF13ESMGXgKv4iXPlUeuK5c8dx56nZ5AUyCxQbFE26IJtgQTbA6WmlBCCCHENcDjVTDor+0/cAkhhPhnk6DUjeLYMdi+HWw2CApS59mqQvZetQj6lS6qnZMHf22EP9bCum3gcp1dZtZDHUWd4oB6iRD1bzBEkuHO5aQznSOOI2Tl2bEaTMSYgjGVUWbNQXsyP6Vt45f0HRx0JGvzTToDrYNrc3NYPdqHxBPc1Aq3QpLDyakTqViOpxBwPAXLsTMEHDtDwNEzGHPtBJxIJeBEKmFrd2nbUnQ6nDFh5FWNJa9KTP4Ui6NiJEqh7pWKeu4cWZB9BLKOAhYwBKmF0g0B+QXY86eA/K6BVqt/0MpkVG/1hks+R3a3nWxnNna3HYAAUwBh1jDiAuMIsYTI6HhCCCHENcqg1/HU55vYdzr7ajflonSMj+a57nWudjOEEEJcQRKUuhGcOqUGpEwmCAs7O99WAdzZYD+pFtW+3F3B3G5YuxWWroA1W/wDURXLQatG0KIhNKwDhnTIXgj21aCsQklez2ljItt1jchUTIQarFS2RKArg+5qRxwp/JS2nZ/St7Hfflqb7wtEdQmrT/vQeIIMAYUeq1jM5FUrR161cucsUDCmZRNwLBnrkWQCjiRjPXKagCPJmDJysJxKw3IqzT9YZdBjrxilBqsKTM7oUDBFqhXdFZdaIN2TDQanWhRdHwyKARQvZGVCWqraPdDHYDg7cqDFogasAgIKB6xMJnW9c7KtnB6n1iXPq3ixGCwEm4OpEV6DEEsIIZYQrKZzhzEUQgghxLVo3+lsdhzPvNrNuCg1o8s4218IIUSZk6DU9S4lBbZtU7t/xcT4L9MbIKgaeHLAmQqWyMuzz0PH4Mff4eeVkF7gQ07l8tChJXRIhOqVzgmCxeAMeZh0000E5HxBiPcwse6VRLGW06bWnDLchOcKBqROOjP4JX07P6Vt4++849p8o85Aq+CadA1rQIfQOkUGokpEp8MdEUx2RDDZCTX8FhnTswk4chpr0mmsh09jPXwK6+HTGHId+fdPw4pt2voem4W8qrHk+gJV1WLJqxKL16RXg4zeNDCY1RH8rGFgDFaz4Xwl4T2+UQPdkJsHmVngcYPiaytgOJtt5TYbyTUq5JgU3HowmQMIsoVRK6g8YcFRhARFEmgJLpOAoRBCCCGEEEKIfw4JSl3PMjLUgJTdDuXLF72O0QpBNSFzJ7iywBR8cfvyeNTueV/9DJt3np0fFgJd2kC3tmp9qCICF7keByedGRx2pJDm9mDR30EN4wkqu38n0Huc8q4/iHGt4bSpJadNrXHrLs9fxdLcOfySvoOlaVvZnHNYm29AT4vg6nQNS6BTaF1CjFc268cdFkR2WJB/sEpRMJ3JwJp0Kn86jTXpFAHHzmDIdRC08zBBOw/7bccRHUZe1Rg1U6tKJHmVQrDHBoHFqtYNM4WpXfyMVjVIZSm6S53X6ybXmUuOMxtHXh6GHAhUjFTS2Yg0BBNs0BNscGEwJ4MxDUyH1G3ZbOpkNp/NtDp30pdtjSshxD/b4sWLmT59Or/++uvVbooQQgghhLgIEpS6XuXkqAGpjAyoWPH861rCIagGZO4GvREMpQjCZGTBd8vh22WQnF/8W6+DVk3glvbQspGacVOETHceJ5xpHHGkkunJI1gfQEVzOAadnhzC2GWsQ6hnFxWcv2JTTlHe9QexrlWcMTblpKkNLn1YyduZL9tjZ3n6Tpamb2Vd1kE8BUqkNwmsSvfwBDqH1iPCFFTqbV9WOh2u6DBc0WFkNo8/O9vlxnIsBWvSKWxJJ7EeUoNW5pRMLMnpWJLTCVu/R1vfazRgrxRFXpUI8ipFqFlV1SvjKlcJTIFgsKGgx+F1ku3JI8+j1oWyGa1EBccSbQknxBhIsDEQs75AcXKvV+2O6Xart1lZkJam3leUs8FHvV7tLujrChgQUHTwquDPRuM1P6qgEEIIIYQQ15roIMsNMYDBjXAM4vKRoNT1yG5XA1JnzqgBqZJ8wQ+IBY8dsg9CgBF0Fxgd7fgp+OJH+PEPcDjVeWHBcGsn6NkJYqOKfJiiKKS5czjmTOOYI5U8r5NQg40q5sjC3b90OjKMdckwxBPm2UU51x8Eeo8T415DtHsdqYYGnDa1Jtdw/qCb3eviz8w9LE3bxsrMPTgVt7asnrUC3cIT6BrWgFhz6AVP09WmmIzYq8VirxZLGg21+YbsPDWj6tCpAtlVpzDYndgOncJ26JTfdtyBFrIqhZNROYL0quXxVK9GaM14apSvRagpiGCjDev5uirq9Wp2VDHZVpqCXQVdLsjMVEdb9AWvQL0+tTpX+fWsCgavzg1aFQxeCSGEEEIIIQAIsRqv+wEMasUEMaV/k6vdDHENkW991xunE3bsgBMn1IBUSbtL6XRgqwRuO9iPQ0AM6IoYmW33AfjsO/hz/dmgQq2qcMctar0oc9HBLK/iJcWVzVFHKsedabgVL+HGQKJNISVom550Yz3SDXUJ9h6gnPMPQrwHifRsJdKzlWx9ZU6bWpFmqKe12a14WJt1gKVp2/gtYyc5Xoe2ueqWaLqHJ9AtPIEql6uO1lXmCbKSXb8a2fWrnZ3p9WI+nY718Gksh05gPnQS2+FTBB5Pw5jjIHz3ScJ3nwT+PvuY8BCoXg1q1oSacVCrNtSoASEleJ6KYjCo0/mCV4pSOHiVlwfJyer8gsGrgiMLmkxqkfbAQDWI5QtcnZuBJVlXoowpikKuK7dM92kz2UpV1+3o0aPcfPPNDBkyhLlz59KrVy9at27NpEmTOHbsGLVr1+b5558nMTERALfbzdSpU1m8eDF5eXm0adOG0aNHEx4ejsPhYOrUqXz33XdkZGTQqlUrXnnlFcqXL8/QoUMxm82MHz9e2/czzzxDQEAAY8eO5cSJE4wePZpVq1YRGRlJv379ePTRRzEYDCxevJj//e9/REZGsnr1al555RV69erFzJkz+eyzz7Db7TRv3pxRo0ZRoUIFAE6dOsWLL77I+vXrqV69Oh06dLi8J1oIIYS4DlzPAxgIcS4JSl1PXC41IJWUpAakDEUElc5Hb4Tg6qA4wX4GAqLVkdcUBbbugk+/hfVnC27TshHceSs0rlvsF3+v4iXZlcVhxxlOOjNQgEhjIAF6c+mPT6cjy1CTLGtNbJ5jxLhXE+7eTpD3CEGOIzgIZImzFp9lK/yUvo90z9kvheVMoXQLT+CW8IbUDoi9OkW5vV41yJJ/LNqk16EVIb9MPIqXXK+DnAgDrvBoDI1jsektRJmCiCCAsBOZBCadRn/oGBw8ohanP5kMaZmQthU2bvXfYGQEVK+hBqiqV1enatUgMvLSgz4Fg03noyhnA1duNzgcajfVkyfV86rTqevo9WczqXxZV1br2S6D5wavzObS/64IUQxFUWj7YVv+OvJXme63TeU2rLh/Ralf2zZu3MiiRYvIzc3lnnvuYfTo0TRs2JDff/+dBx98kG+++YaqVasyZcoUvvrqK9544w0qVKjAK6+8wiuvvMLUqVN55ZVX2LhxI+PHjycsLIy33nqLxx57jEWLFtGjRw9eeOEFXC4XJpMJp9PJ8uXLmT59Ooqi8MQTT1CnTh2+/PJLkpOTGTVqFDqdjscffxyATZs28cgjjzBs2DDCw8OZP38+3377LRMnTiQqKooPPviAwYMH8+2332IymXjqqaew2WwsXLiQvXv38uKLLxIeHn4lTrkQQgghhCgDEpS6XvgCUgcPqkXNL7Zrk8ECwbVA2QN5p2HrCTUYtWOvulyvh5tvgn/fCtUrF7sZj+LltCuTJPsZTjkzMOj0RBqDsOgv0C2whHINFTlkuJ0jxq6czF7Oz+lbWZiVwxH3Fm2dCIOFLmEJdAtvRMPAyuh1ZVRk2+0Gp0ud3L5R7ZSz9ZXIv6+gBqoUJT8bSIc2BJ7BAMb8Lm0mo/rzedrvVbzkep3kepzYFSd69AQaLJQzhxBlCiHYEECwIQCTPv+6qB0LtWv7byTPDknH4dBROHRYDVYlHYfkdEhJVaf16/0fExx8NkBVtap6W62aGhS93N3rdLqzGVDn4/GcrXfldqt11VJS1HkFt1Uw66pgofaAgMJBq5LsV4h8usscZL6S/vOf/1ClShWee+457rrrLnr16gXAwIEDWbduHZ999hnDhw/nf//7H8OHD6d9+/YAjB49miVLlpCRkcHXX3/NnDlzaNWqFQBvvfUWHTt2ZOXKlbRv3x6v18uaNWto27Ytf/75JwEBAbRs2ZLVq1dz/PhxFi5ciF6vp0aNGgwfPpyRI0dqQSmdTsejjz5KQIDanfi9997jlVdeoWXLlgC89tprtG3blhUrVlC5cmU2bdrE8uXLqVChArVr12b79u38+OOPZX1ahRBCCCHEZSJBqeuB2w07d8KBA2pAynwRWUgF6QNgwwl4fw7syx/hzWSCf7WHf/eActHFN0XxcNqZSZIjmdPOLIw6PbGmkLPBkMtkf95pfkrfxk9p2zjiTNXmh+j19A30cncw3GxzoNNtI83oIdXrJktf/byBnYvi9qhdJn0BKK+iBpFMJggJguBAtUujFlzKPw9eL6Co63u9+VlU+bduN+Q51CCR0wU5ef4FxI1GvAYDuQYvuQYPdjzo0WEzmIk0BRFtDibYYCXYEFC6IKA1AOrUUKeCcvIg6RAcPAxJR+HwKThyGk6lqgXOt25Vp4IMBqhUSQ1U+aYqVdTpcmRXnY+vy+D5+LKufMGr3Fy13pXLVbjWlS/rymhUg1aBgWrm1blZV2az1LkS6HQ6Vty/4prvvudTMX8gjP3797NkyRIWLFigLXO5XLRt25a0tDTS09OpX7++tqxWrVo8+eSTbNmyBa/XS6NGjbRlYWFhVK9enf3799OuXTu6dOnCTz/9RNu2bfnpp5/o3r07BoOB/fv3k56eTrNmzbTHer1e7HY7aWlpAERGRmoBqZycHE6ePMnQoUPRF+iabrfbOXToEA6Hg7CwMK0rH0BCQoIEpYQQQgghrmPyDeta5wtI7dsH5cpdWkDK7Yaff4YPP1QDXAABFri1FfS/HSKL7wLh8ro55crkkD2ZM64szDoj5cyhGIuqS3WRDtnP8HP6Nn5O38EB+2ltvkVnon1oPN3CGnBTSG2CySbKvRGPewsWJZ0o9yai3Jtw6YJIM9Qj3ViPLH3VomtmnY/XqxZ1d7rU4MW5AaiQIPV8BZjVzBvjZTh2jwccLjxOB3m52eTmZOLIyUbncGJ16Qn3GInR2Qg2WAg2BRJgsandMPVm0F2mX99AK9Srq04+XgfkZsGRo3D4JBxLgaNn4FgyHD0JdofajTQpqfD2bDaoXFkNUFWqpP5cubL6c1RU2dSAKmnWVcE6Vy6XOnjAiRMFsts4203QaFR//3yBK1+dq3ODV9JV8Ian0+kINAde7WaUiCW/3pvH4+HBBx/ktttu81seEBCA8TzBVksx9eI8Hg9erzq66a233srIkSN56aWX+PXXX5kxYwag1qmqUaMGM2fOLPT44ODgQtv35Hd/njJlCtWrV/dbPzQ0lFWrVqH4fi/zmSTDUQghhBDiuiZBqWuZ0wl//6122YuNvfBIaOfbzvffw0cfwdGj6rygIOjfH+7sBZwEVxYonkKBHKfXzUlnOgftZ0h1Z2PVmyhvDrtswahD9jMsS9/BL+nb2Ws/O4KcUWegdXAtuocn0D4kHpvh7LE7Cee4+WaOmzoR6D1CpHsL4e4dmJRsYtxriXGvxYWNDGMd0g3xZBlq4NWdc+4ULzjd6rlxOFG71enAYoZAG4QEqplFVsvlC0AV4PZ6yHPbyXU7cHqc6Ix6bBFBRJQrR7Q1jGCjlWCPkQAPYHeq7czOhcxsNWiWkanekl+3ymxSJ1N+4MRwiRljegsEWaBuFNQFFLdaJN/rALcTUjLhRBocT4XjafnBqhNw4qSalbR7tzqdKyBA7fpXqZI6Vax4dipf/uKv8Yvly5AKOM9IhAUzrux2NXusYMYVFK5x5esqaLEU7ioogStxFVSvXp2jR49StWpVbd6ECROoXr06d955J+Hh4ezatYv4+HgAdu7cycMPP8wPP/yA0Whk8+bNtGvXDoC0tDSSkpK0wNFNN92Ex+Phww8/JCAggObNm2v7PH78OBEREVoQauXKlSxevJgJEyYUamNISAiRkZEkJyfTsWNHAJxOJ8OGDeOBBx4gLi6OjIwMkpKStOPYuXPnlTlhQgghhBCiTEhQ6lqVlwfbt8ORI2qG1MV8Wc/Jga++gk8+gdP5mUdhYXD33XDXXWq9IAB3KGQdAPtpCIgCnQm718lJZwaH7GdIc2dj01uoaA7HcInd4xRF4YA9mV8zdvBL+g72F8iIMqCnZXBNuoU3oENIHYKN1vNvTKcnx1CVHENVjphvJdhzgHDP34S5d2Eilyj3RqLcG/FiIEtXlQxvDbJcFbG7w85m0gRYIDpCDUAFWNSpmBEGL4XT4yLXbSfP7cDldWPQ6bGZAoixhhMZEEqwOZAgk5UAYwmeZ5dbzVSyO9SAWl4eZOZAbh7k2iE9U83yQjnbrdCcnzVkMpZ8xMaCdEYwBQFBYAFssVDRBV47eJxqQBMdeA1wOhNOZsLJdDh6Co6fhCPH4NQpNaizf786FSUqCipUOBukqlBBvS1f/tIzBS/WhYq0F+wq6HJBdjakpZ3tkulTsJug1LgSZWzQoEEMGDCAhIQEOnbsyK+//srcuXP56KOPALjvvvuYMmUKsbGxREZGMnbsWBo3bkxQUBB33nknY8aMYcyYMYSGhvLWW29Rrlw52rRpA4DRaKRbt26888473HnnnVo3w7Zt21KxYkWee+45hg4dSlZWFi+//DI33XQThmICs4MGDWLy5MlERkZqWVYbN25k7NixhIaG0rp1a1544QVefvlljh49yvz58wkMvD6y1oQQQgghRGESlLoWZWXBtm3ql/iLKSh95gwsWABffKFuCyA6Gu67D/r2VevlFGQMgpB4yDKTm3OIE8AhVzYZ7lyCDAFUMkdcUhFxRVH4O+84y9P/5teMvznsSNGWqYGoGnQOq0/H0LqEGW0Xtw+dkUxjHJnGOJL0LoJd+wh17yZMtx+LPoNQ5QChugNgBmdACFm2umSGNCA7vD5OS/E1tC6GV/FidzvJ8zjIcztQFAWT3ojVaKFyUCzhASEEmawEmWyYDRcReDDlB5qCz/kilt8VEIfj7G1OHmTlqAGsvOz8DB84G7AygdlY+oCVTq8WzTdYwHcIihe8Lqhkhgoh4C0P1D3b3VAxQYodTqbCsdPqaIDHT8Hx43DsmJphdeaMOp1bw8onMtI/SBUbe/Y2NhbCw8ume2BBJekqWHBkQZcrP5CYWTjjqmBXQV+NK9/Igr59+CZf8EqyrkQJNG7cmAkTJjBt2jQmTJhAlSpVmDhxIi1atADgoYceIisri6effhq3203Hjh15+eWXARg+fDjjx49nyJAhOJ1ObrrpJubOnYu5QJC4R48eLFiwgB49emjzDAYDs2bNYsyYMdx1113YbDZuueUWhg8fXmw7H3jgAXJychg1ahTZ2dk0aNCA999/n9DQUAAmTZrEyy+/TP/+/alQoQL33XcfixcvvhKnTAghhBBClAEJSl1rzpxRR9lLT1cDUqX5wrl3rxqM+uEHtbsXqHV97r0XevY8b5ZJptfFcb2JIx4HWTmHCTUFU8USfVGFdUHt9rc++yB/ZOzij8zdnHZlastMOgMtg2tyc2h9OoTWIeRCGVHnc24dKAUw6MkyVyUrKJ6jwYFYjOmEuXYTkreDoJx9mL2ZRGavITJ7DRwHhzmS7MB4soPiyQmsRZ61UqkKpjs8TvLcDuxuB06PG71OR4DRQqApgCpBsYSYgwg0WQkyWTHor2AAwWAAmwFsRXRFc7nzz5OzQHfAPMjK7w6Ylw1uF3gBFPX4tQwr49mMq/NdDwUDVT6KArjzs6nsEO6AMBvUqQ6G2qAz5z/GBtkeOJWiTieS4eRptb7TiRNq4MrhUEfZS0lRswiLYjJBTIwaoIqJKTxFRalTWRcsLxi4OjcoXFBRNa7cbjXg6NsOnA1a+YJTVqs6+bKuipqkSPs/SqVKldh9ThfaHj16+AWNCjKZTIwYMYIRI0YUWma1Wnn11Vd59dVXi91fy5YtC+0PoHLlysyePbvIx/Tr149+/fr5zTMYDAwdOpShQ4cW+Zjw8HCmT5/uN++JJ54otl1CCCGEEOLaJt9SrhUej1o0es8e9UtoxYoly/hwu+GPP+Dzz2HjxrPzExJg4EBo377YwJaiKKS5MjlmP81x+xlyvXbCbBWoYo5AZz8JzjQwB4OuZNk8Z1xZrMzcy8rMPazO2keu16kts+rNtA2Jo1NoXW4KqU2Q4Tw1fIrj8agBlIIBKH2BOlDBNrBZ1ULkAQFaHSgH5ThFHU7RB53XSVD2XoKzthOS9Te23ENYnClYnH8RmfaXuhu9hVxbdXICa5Jjq06urRpOcwzodDg9Luz5QSinRz0+s8GE1RBAxaAYwizBBJqsBBqtWI2Wiw7qXXa+7CqKyETzBaxcLjXDyulUA0BZuWqXQIcTcnLVGlwAKOo1VXDEQZMBDMbCtax0OsAExnOuIUUBxaVmVnlywZkBei+UByqEQrMo0DUEg1kNWP0/e/cdIEV5/w/8PWXr9eOOoykocPRygocISIlRYlBji0ajIkZMBDFWxESDFZWfGqWIqNgw6hdRY4sFY41gQQEbShNODq63rbMz8/z+eGZmZ/f2Ghy3t8fnlYwz80zZZ/Z2ud33Pc8zkhuoDwHl1UB5FVBWAZRXAmXlwP79fKqu5tewdy+fmiIIQG5uNKCKn7p14/Pc3ObHmjoUWjPGlXkHR3My7yyoqsZdHw3GnRxjJjO4Mu8uaAZb9jGxWuquSAghhBBCCCHthL55dAY+Hx8Ues8eICuLd7VryS+/AK++Crz+enS8KEkCpk7lA5iPGtVkqKXqKiqVWpQEy1AWroYGDdlyJvKc2XwHOR1wpAHBMiBcBUgOXobY86lMwzf+Emxo2IH/1f+ErcF9Mdvz5AyckDUIk7MGY2z6UXCJre2qZnR1Ckd4YBIxwhBJ5C13zADK7eJjQbmcrR4HiolONGQOQ0PmMJQCELUQ0vzbke77Een+n5Dm3wlJDyHDtxUZvq3WcRHRjTpXLzS4+iDk6QOW0R/unKHwunLgdbiRJh/iVlCHkhVYJcAYDwHDSmx4FQgCgTDvhqYoQECzhSICf6mYwZUs8WWHbVkQeCsp0Wi9Z//xMY0PrK6rPLCK1AO6xs9ZAKBnJiDkAuIw3spKdAOSB9AYUFUHVNbwqawSqKwCKiqBigreHbaykoebZourRIOx26Wl8ZCqWzceUsVPOTnRKSOjY7oOimJ0wPTm2MMrTYt2GzTL4s9phlGSxMMplysaYNmDq6YmQgghhBBCWpCf7oKmM0hiJ/nj/QHqCtfQWdA3iWTSdd416ccfeUuHnj2bH5fG5wM++AB47TVg48ZoeXY2cOaZwFln8W5LTfCrQVQoNSgJlqFKqYUsyMhxZMItJfhyK3mA9CMBZyYQ2A+Eq8AkL3ZGfPjStwufNezARt8u+PVwzGFDvb0xIWMgJmYNwhBPz5bHolKNMMNs/WQO0O0wWm1kpQPpacYg5E7jbmbt97LVJTdq0gdjn6c/wrknIqyG4A3tR05oN/KUvcgO7UV6uBQOPYS84E7kBXcCtQDM/M3dA0jrB6T34/O0voD3CMDdvU1dADstwWiJ5moiAGGMh1Xmz0+xBYmBoDGWVYj/jIMhHo5oxqDoYPz8MeGVaFt3Ng55GAOg8cBKjwCqD9Dr+DrAG4IdCaBvLiB053eTFB38boKSiw/YXh+IhlfVtUBVDVBVbZuqeHilKPxmAX4/D4xbIsv8vZidzUMqczk7m4fN9mVz8noPXZDV2vAK4D8TM7xSjbtSBgLRdfu4V+bPzD6Zg7fbp0TBlfWzlaPhJCGEEEIIOWxkemRIooCrnv8a28t9ya7OARnQPR0PnleU7Gp0GRRKJQNj/Ivvrl18rBy3G+jTJ/EXNJ8P+PBDYN06YMMG/sUf4PuOHw+cdhrvotfEF0+NaahW6rE/XInSUCX8WhBpkgc93fmQheZb9ugM2BnxY5N/DzbWfoON9dtRrQVi9smWvBiX0R/HZQzA8ZkD0c2RnuiCefgUMcfKsXUzMsMIlxPolsVbQTmNcXLcznYdxDmiqQjrEYQ1BYoWgaqrYAxwSDJckgNe2YVeaXnIyBsEj+yCV3bDI7sgggH+XUD9j0DDdj75dgDhCiC0n09VG2IfTHQCnt6Atw/g6QV4ehrzXoCnAHBkd40v5IJg/LxaGOTbDKpUNRpimWFkSOF35Qsr/HUSjhjhlcpfhILAzyGIttDKDETMlj1iNORgOqLBlWbcIdAHhDV+HhFAPoB8NyD2BoQjjQDRDLBk3mU1oAF1DUB1PVBTD9TU8Tsb1tTyqboGqK3jd9rz+/n1mAO1t5bDAWRmNj1lZETn8csuV/u9hsxwqTUYiw2xzFZYoVC0zHx/Wz+7BEGWJEVDM6fZ5TYuvLLP45cJIeQQo7+CE0LIobO93IfvSutb3pF0eRRKdbS6OuDnn4GSEv5lraAgtnUUY8COHcCnn/Jp06bYrjZHHQWcfDIfuLxHj4QPwRhDnepDZbgWe8MVqDUGGc+WM6Jd9BKoV/34vmEnvvPtxOb6n/BN/XY0xIVQLkHGKG9vHOvthfGZA1GY3g+iIMMKnkJho3uQrYWFIPAQQZZ5i6du2XzsJ4eDB0+OFkKNNlB1FYqmQtEjULQIFF0FY4xXQZDhknn41MPTDRlOL9yyCx5jciVqMWbKGMgnO6UW8P/MJ99uPg/sAQJ7AV3hQZZ/V+LziS7emspdALjzAZcxufMBZzfAZUzSIWxJ01FaE1wBxh3qtGh4ZQZY5mspovIAKxw2AiwVCBvbdZ2HIYwZvUyF6GObIYZoBBqiEW6JAiAJgKAD0Pk59BCg6TzYElUgG0C2A0A3ALlGt0NzwHeJh1mCCER0oD4I1AX4WFx1AaDex0Oteh8Ps+obgLp6PtXXR1uXmV0J20qWeTiVnt54SkuLXbZP6em8hZbXy9eba52ZiH2sqtayB1nmz0rTeOhuLps/v/jHEkXbz1CMdjW034XQnBIFX/GT/Xytrj5reSeScujnSlqS6n/JnzIoH9efPDjZ1SCEEEKaRaFUR1BV/qWztJQPyBwO84GUPR6+betWHj5t3sznFRWxx/frB/z618CJJwL9+yd8CJ3pqFf9qI00YF+oEtWReigsgjTRgx7ObpDF2B91tVKHn/x7sM2/Bz/6d+P7hl3YE9rf6Lwe0YXhGf1RlDUIY9MLMdx9JJyRCBCs5lP9Hj7guOwCnB7AYYRM6WlAmicaRjhk4xb2B/eSU3WNB0+6iogxqZoKBv7lQhZlOETe8inPk40MhxdehwduyQmX5IRHdsEptU8ABmc24BwN5IyOLWcaH48rUAIEfgGC+4BgqTHtA5QqQA8b20uafwzRBThzAWeO8XjG3JEVnTuyAEdmdJI8qRlkCULz41vF0zQjsDJb66jRMnOuKNHB8cNKNBgJ67wVlWaEUYwhOmaasSwK0RBEMuYCjHLdCKh0PkkMyHEC2RLA0gDoQFPfdwWRn1+J8CDLHwQazMkP+EL8zog+Y90f5EGX329s90fHi6qp4dPBcDiiAZUZVtkn885+Xm90jKlE6/ZB1M1WT9Y1H0CQBUTDLHuQZV57KGQEibZAMr6bIWPREMr6WdqWzXGy4gd9N/ZzCAKgqgg0NMAj2+4+2dKcpIRAgP/RxdHWYJYcVlL5L/n989OSXQVCCCGkRR0aSoXDYdx6661455134Ha7MWvWLMyaNasjq9BxNI23hqit5ePR1NbyL1F1dXz9p5+iUzAYe6zLBRx7LO+ed/zxwBFHJHyIkBZGvepHfcSPfeFK1Kt+KHoEbtGJbDkDTlFGpVKLTfU/YVewFLsCpfg5UIodgV9QFalLeM7ezjwM9RyJke6+GO3qi4HOnpDNAbyZDEQYILuBvKMA9xBAjgAI8HF9xIjRGsoDyF7eha2V4yoxxnjgxFQjeNJ44MQ0aLoGABAEAZIgwSHKkEUJXtmFDEcuvLIbbtkJp+SAW3LBJTnglBwtj2d1qAgS4O3FJ4xrvF2PAKFyIFTGp3AFEKqIzpUqIFzNB/nWw0BoH5/a8vhyBuBI53M5nS9LaYCcxn82chpvhWUte4x1j7Hs4Xe8k9z8fJ2R2drF1YZjdJ0HUWpcCyvVDKiMuaYbgZaty6nVykfn7wMzCNHjgi1juCxrbg+3BAEQjdZcggvIcAOZWUBvM9RgfH+AB18wuyPaQi7GeIsxvxFc+UPGwPMhwB821kNA0Fj2h/h4XsGwsY+xrhhdgSMR/u9SXeJ/Ew6Y2TLS7Y4Omu52RZft40+53dEufC4X4DJuYOByAU77WFXGdqczOjePNYOlePZQy/xZmeuK0rjMfI4FARJjyM7MRLmqAuEwvE4nv6OmPXxKFEjZ94lftmvNeZrbTg4IYwyBQADl5eXIzs6GRN1BCSGEEEKSpkNDqXvvvRfffvstnnrqKZSWlmL+/Pno1asXpk+f3pHVODTMcaK2b+cDl2/dyu+QV1rK7/q1bx+/ZX0i6enAyJF8Gj0aGDGCf+Gy0ZmOgBZCQAvBrwVRHq5BhVKDfaEKVCl1qFcDqFd9qAzXoDRcib2hcuwNVSDMIgkfUoCAIxzdMNDVCwPdvTEsrR+GpPdDtiuLfwk0v0zK9tvF21oSxAc+egTQ/Hzg6XANmNIATauGynRoAHTBAVUQoTERqsBbPDEWvX29IAiQBR42yaIEp+RAlisdabLHCpycosM2lxu1/koZogPw9uZTc9QgD6iUmripDojUApE63oUw0sDvUBepB1iEt9SK1PKpXerr4uGUObeWXTx4tO6AZ9xNT3RFBxePmTuNedyyYJYZYzmJMu8il3D9IL88Wi1kDvC1o2l8nCvNDK+0aFBlzu3hh6bzsbEUNdqay+yOqDNbSMb4enzIZXZ/ZUA06QIA4/nPzgFyRVtgYYRaAqITwMfRYsYcAt9P03irzVAICChAOMS73wbC0RArFOLrIcU2GetBhbc+M8vDxqQb6ZmqAj6Vt+zqKKJg/Bslx86dtn+7HLLRetNsJWX/N848xmn9m9fDIQNFxSgfOIR3QxbsTyzigiNEtwkxhbH7CnHlTe0ff/5G52nNuYVGp2zyvAn3jb+eBHVMhkTPYRtlZ2ejRxPd4AkhhBBCSMfosG/1gUAAa9aswaOPPophw4Zh2LBh2LZtG5599tnOE0oFg7x1k9/P7zzl9wMNDXyqr+fzmhoePlVURO/SVVbGl+NbPCXSrRswcCCfBg2CNqA/fL3z0aAF4Iv4UafUo7LsE1QqtahUalEdqUNZuBplSjVqVB9qVR/qND9qNT98eqjFh5MgoqerG/p5euGojD7ol3EkjsrqiwE5/eF1ZSS8KxYDHyCdMRYz15kOXQ9CC/qgMz1axvTo2BwCACELgsMDSVYhMRWSFoSk+eFgKjIEAW5RgsvhhEt2QZZccMhuOCS3MXfBIbkgt1cXu1QmewC5Dx8svTUYA7QQoNYDER+gNvCQMOLjc9VvTD5ADfCWWOZcCxrLQWPd9trSw3zqFATjjnpGUCVIsYFVo8koF21lEPk6RKNMtO0vGuX2bWL0OMEIJQTJqIsYd4wYraM1HpIAOO3bxGjQxAAwIXYOADps220TBCO8MtZ1HdAYn6yQywy3EA22NDEaejEh2s2NAXAywAGAiQDzgt/C0HgsCLZQzNYKzAovjC6N5rUBgKobLcxUPmi9ORZYRI22PAsbrdCUiDG3LUfiyiJx+1l3ebSFfCadRcOxdiIA6Ikn0N3rRSQvr31bKpldQ83JXDcH9bda2dnm5nYB0WXR6G5qLgtx3U/NdclYFkQeUNq7p0KI1kEU+HtEFGIfU7Kdz15nUQQcxntIMl/35g0JBGM8N1t9RPA/eJityOyPa+9eaT2+FN3PfF9l5wB9ink4ewAcDge1kCKEEELIAclPd3WJm2F0lmvosFBq69atUFUVRUVFVtmYMWOwYsUK6LoOUUxSVytD8J03seZvv0O1HIEuwJo0wfg+Z1tWjUkTALU7EOkJRIyyiEOE4nHyySVDcckIuiSEHQKCMhASVAT1rQjqmxHSIlC2qsDWA6+3LEjo5s5FgScP+Z485HnzUJDWHT3Se6FHZi/kp+dDlBxgYGCM8QAJDFVgqIIRohm9g2B8jxNEASJEiIIxiaK1LokSXJILDtkBp+iEU+KTLMqQRInPBT63l8mCCJmpPNzQQkZ4EuQhiRbgLa2YCmgNRgufBAMexwwwHR8MmK0XbMuCsW4eb/+S3RUJghFkefgA6geDMdvPKhj9mdl/fnoY0MJ8UHctxOe6YiuP2MoijedMNeYRo1w1ltXo9kYDMzHjdaImqjU5UHGNfw6KjI5tg6sDiNgmNcGyfR6/bC/T4rZr0e2SGoCk7WlUbi3b5/bJLCPt6wgX8NV2IC8r2TVJisNqOARCCCGkk8n0yCl/M4wB3dPx4HlFLe/YATrsq0NFRQVycnLgdEbvcJaXl4dwOIza2lrk5uZ2VFUSerbyPVw2I3FXt7bRAYSMyYaBf9FpgiiI8MhuuCU30pxpSHN4keZMR5ojDRmuTGS6MpHhykCWKws5nhzkenKR68lFpjMToiBCEAQIgsCXIVjBkCiK1rIkSjHLVuhkmyQhWm7uH78sHHCo40S0FYb9uWG2cEK1hRUq74rGNEAzwg37Nl3lzzczulQxe/MS3dYaxFZudYOyMQdE5iuNtx+wVpzroAOyAz2+jceJLj41e8pDEPYxzXhNaNHXg257XcRMKv9ZM82Ym+t67H7Q4/Yzx2yyrVtl8cv215a5jcXtZ9sn5rUXtz/MgblZG8taswxYza2s17x9H9jOD9s2xJ0j/pz245HgfPZz2OYxdzmLO8fBEsHHFTuwBjMdw7xkM6DSERtg6YgGWLptXbOtq3HrifZpajlRGWtmPz3BfonKWFxZ/L72dSTYxuLOFb8cf347hxFmH6a69HAIhBBCSIpI5ZthdCYdFkoFg8GYQAqAta4o7dfV4kCdcuq1uPjdCuyr+wWSKEMQJR7yGOGNJEg84BFliIIIWeCBj0N0NAp7nJITDtEBWZThklzwODxwy254HB54HB5kODLgdXrhdXjhkT3IcGbAKTkhijxQEgSh0dwMm8wyK4iyLdv3SSmCwMcjgrPFXRtpFCLYv8zb1u1fyBN+kUfidaDpL9ox2+LK479BJbz1eFP7tLBvc7cxb83jtFpzx7VjqNAh5030UB34WKRp9vdjzHr8PojdL2Hglai8ifdViwFaE+dtzTmaPCTBvzUs7nFZ3PMQ88+K3vi8VhmLXW/0kE1tj/t3q6n3hb282XPY6t7s9mbW46/bZI61pjMgvS+Q2ytxXbu4lBgOgRBCCCGklToslHK5XI3CJ3Pd7XZ3VDWa1CujF5488+lkV4O0lTWODyGEENL1dfbhEEydZZwKQgghhHRuHRZKFRQUoKamBqqqQjZu211RUQG3243MzMwWjzcH0vb5UrPPJiGEEEJSS1paWqdrfXywwyF05OepFR/sQGldK24C0wmN6JOFc8YcgX6ZInQlNW++UuDhP2e6huTrCtdB19A50DV0Dl3hGvplih2WrbT0earDQqkhQ4ZAlmVs2rQJY8eOBQBs3LgRI0aMaNVf9fx+flvxyZMnH9J6EkIIIYQA/HNKenp6sqsR42CHQ6DPU63zBoC7k12Jg7QTQKr3AegK1wB0jeuga+gc6Bo6h65yDWMWdcxjtfR5qsNCKY/Hg9/97ndYuHAh7rrrLpSXl2PVqlVYtKh1z0T37t3x4Ycfdsq/WhJCCCGk60lLS0t2FRo52OEQ6PMUIYQQQjpSS5+nOvLG3ViwYAEWLlyIiy++GOnp6bjyyitx0kkntepYURTRo0ePQ1xDQgghhJDO62CHQ6DPU4QQQgjpTATG6BZUhBBCCCGpIBgMYty4cVi1apU1HMKyZcuwfv16rF69Osm1I4QQQghpm85xixZCCCGEENIi+3AIW7Zswbp167Bq1SpcdNFFya4aIYQQQkibUUspQgghhJAUEgwGsXDhQrzzzjtIT0/HpZdeipkzZya7WoQQQgghbUahFCGEEEIIIYQQQgjpcNR9jxBCCCGEEEIIIYR0OAqlCCGEEEIIIYQQQkiHo1CKEEIIIYQQQgghhHQ4CqUAhMNh3HTTTRg7diwmTpyIVatWJbtKKausrAzz5s1DcXExJk2ahEWLFiEcDie7Wilt9uzZuPHGG5NdjZSlKApuvfVWHHvssTj++ONx//33g4bSOzD79u3D5ZdfjmOOOQbTpk3Dk08+mewqpRRFUTBjxgx89tlnVllJSQlmzpyJ0aNH45RTTsEnn3ySxBqmjkTP5aZNm3DeeeehqKgIJ598MtasWZPEGnZtL730EgYNGtRoGjx4cML9TzvttEb7/vTTTx1c6wPXHu/d119/HSeeeCJGjRqFOXPmoLq6+lBX+6C1x/ts7NixjX72fr//UFf9gCW65jvuuKPRNaxevbrJczz55JOYNGkSioqKcNNNNyEYDHZE1Q9K/HXfeOONCd/jTd3ls66urtG+48aN68hLaLXmvqt01fd1c9fcld/TzV13V31fN3XNnf49zQi77bbb2Kmnnsq+/fZb9s4777CioiL2n//8J9nVSjm6rrPf//737E9/+hP76aef2BdffMF+/etfs7vvvjvZVUtZr7/+OissLGTz589PdlVS1s0338xOOukktnnzZvbpp5+ycePGseeeey7Z1UpJv//979lf//pXtmvXLvbuu++yUaNGsXfeeSfZ1UoJoVCIzZkzhxUWFrINGzYwxvi/maeeeiq79tpr2fbt29mKFSvYqFGj2N69e5Nc284t0XNZXl7Oxo4dy+677z62a9cu9vrrr7MRI0aw999/P7mV7aKCwSArLy+3ptLSUvbrX/+a3XnnnY32VVWVjRgxgn3++ecxx0QikSTUvO3a4727efNmNnLkSPbyyy+zH374gf3xj39ks2fP7sjLaLP2eJ/t37+fFRYWsj179sT87HVd78Arab1E18wYYzNnzmSPPPJIzDUEAoGE53jrrbfYmDFj2H//+1+2efNmdsopp7Bbb721oy7hgCS67vr6+pjr/frrr9nw4cPZu+++m/AcX375JSsuLo45prKysiMvo1Wa+67SVd/XzV1zV35Pt/S9tCu+r5u75s7+nj7sQym/389GjBgR88tn2bJl7I9//GMSa5Watm/fzgoLC1lFRYVV9tprr7GJEycmsVapq6amhp1wwgnsrLPOolDqANXU1LChQ4eyzz77zCp75JFH2I033pjEWqWm2tpaVlhYyH788UerbO7cuZ3yl3Jns23bNnbaaaexU089NeZD/6effspGjx7N/H6/te/FF1/MHnrooWRVtdNr6rn817/+xaZPnx6z780338yuueaaZFTzsLNixQp24oknsnA43Gjbzz//zAYPHsxCoVASanZw2uu9e/3118f8Hi8tLWWDBg1ie/bsObQXcIDa6332v//9j02YMOGQ17c9NHXNjDE2adIk9vHHH7fqPOeff37M6+CLL75gI0eObPLLbrI1d912s2bNYtddd12T5/m///s/du655x6qarab5r6rdNX3dXPX3JXf0y19L+2K7+u2fBfvbO/pw7773tatW6GqKoqKiqyyMWPGYPPmzdB1PYk1Sz35+fl47LHHkJeXF1Pu8/mSVKPUds899+D000/HgAEDkl2VlLVx40akp6ejuLjYKps9ezYWLVqUxFqlJrfbDY/Hg5deegmRSAQ7d+7EV199hSFDhiS7ap3e559/jnHjxuGFF16IKd+8eTOGDh0Kr9drlY0ZMwabNm3q4BqmjqaeS7OJejz6/XPo1dbW4tFHH8W1114Lp9PZaPv27dvRs2dPuFyuJNTu4LTXe3fz5s0YO3astd6zZ0/06tULmzdvPiT1Pljt9T7bvn07jjrqqENSx/bW1DX7fD6UlZWhX79+LZ5D0zR88803MT/r0aNHIxKJYOvWre1d5XbR1HXbrV+/Hl988QWuueaaJvfZvn17q56jZGvuu0pXfV83d81d+T3d3HV31fd1a7+Ld8b3tNxhj9RJVVRUICcnJ+aDVF5eHsLhMGpra5Gbm5vE2qWWzMxMTJo0yVrXdR2rV6/Gcccdl8Rapab169fjyy+/xGuvvYaFCxcmuzopq6SkBL1798Yrr7yCFStWIBKJ4Mwzz8Rf/vIXiOJhn8m3icvlwi233ILbb78dTz/9NDRNw5lnnolzzjkn2VXr9M4///yE5RUVFejevXtMWbdu3bB///6OqFZKauq57NOnD/r06WOtV1VV4Y033sCVV17ZUVU7bD333HPo3r07pk+fnnD7jh074HA4cPnll+Pbb7/FUUcdhRtuuAEjR47s4Jq2XXu9d8vLy1Pqvd5e77MdO3YgGAziwgsvxK5duzBkyBDcdNNNnfJLbVPXvGPHDgiCgBUrVuCjjz5CdnY2LrnkEpxxxhmN9q2vr0c4HI75WcuyjOzs7JT7WdutXLkSZ5xxBnr27NnkPjt27ICqqjj77LNRVlaGsWPHYsGCBY1e98nW3HeVrvq+bu6au/J7urnr7qrv69Z+F++M7+nD/ltZMBhs9Jc9c11RlGRUqctYvHgxvv/+e1x99dXJrkpKCYfD+Mc//oFbbrkFbrc72dVJaYFAALt378bzzz+PRYsWYf78+XjmmWdogO4DtGPHDkydOhUvvPACFi1ahLfeeguvvvpqsquVspr6/UO/ew5OKBTClVdeiby8PJx77rnJrk6XxhjDmjVr8Mc//rHJfXbt2oW6ujqcc845WLlyJfr374+LL74Y+/bt68Catq+2vndDoVCXe6+35n22c+dO1NXV4S9/+QuWL18Ot9uNmTNnplQLxp07d0IQBBx99NFYuXIlzjnnHNx888149913G+0bCoUAoEv9rEtKSrBhwwZceOGFze63c+dO+Hw+LFiwAA888ADKy8vx5z//GZqmdVBND4z9u8rh8r5u6vtZV39P26/7cHlfJ/pZd9b39GHfUsrlcjV6QZnrFAgcuMWLF+Opp57CAw88gMLCwmRXJ6UsXboUw4cPj0m6yYGRZRk+nw/33XcfevfuDQAoLS3Fc889h1mzZiW5dqll/fr1ePHFF/Hhhx/C7XZjxIgRKCsrw8MPP4zTTjst2dVLSS6XC7W1tTFliqLQ756D4Pf7ccUVV+Dnn3/Gv/71L3g8nmRXqUv75ptvUFZWht/+9rdN7nP77bcjFAohPT0dALBw4UJ89dVX+Pe//40///nPHVXVdtXW925TnzVT9fXZ2vfZ448/jkgkgrS0NADA//t//w+TJ0/G+++/j1NPPbUjq3zAfve732Hq1KnIzs4GAAwePBg///wznnvuOfz617+O2dfsotqVftZvv/02hgwZ0uJQEm+88QYEQbDeAw899BAmTpyIzZs345hjjumIqrZZ/HeVw+F93dT3s67+no6/7oEDB3b593VTP+vO+p4+7FtKFRQUoKamBqqqWmUVFRVwu93IzMxMYs1S1+23344nnngCixcvxsknn5zs6qScN954A+vWrUNRURGKiorw2muv4bXXXosZ94y0Tn5+PlwulxVIAcBRRx2V0n+hT5Zvv/0Wffv2jflwNnToUJSWliaxVqmtoKAAlZWVMWWVlZWdrrtDqvD5fLj00kuxbds2PPXUUykxvkmq+/jjjzF27FhkZWU1uY8sy1YgBcD663RZWVlHVPGQaOt7t6n98/PzD1kdD5W2vM+cTqf15RXgX+769OmTUj97QRCsL66mpl6/2dnZcLlcMT9rVVVRW1ubkj9rgL/Hf/WrX7W4n8fjifl80K1bN2RnZ3fan3Wi7ypd/X3d1Pezrv6eTnTdXf193dx38c76nj7sQ6khQ4ZAluWYQew2btyIESNG0JgzB2Dp0qV4/vnncf/99zf7l1PStGeeeQavvfYaXnnlFbzyyiuYNm0apk2bhldeeSXZVUs5o0aNQjgcxq5du6yynTt3xoRUpHW6d++O3bt3x/ylaOfOnTFjEZC2GTVqFL777juraTjAf/+MGjUqibVKTbquY+7cufjll1/wzDPPYODAgcmu0mFhy5YtLf7F9MILL8TSpUutdV3X8eOPP+Loo48+1NU7ZNr63h01ahQ2btxore/btw/79u1Lufd6W95njDGceOKJeOmll6wys0t9Kv3sH3zwQcycOTOmbOvWrQmvQRRFjBgxIuZnvWnTJsiyjMGDBx/qqrY7xhi++eabFt/jPp8Pxx57LDZs2GCVlZWVoaamplP+rJv6rtKV39dNXXNXf083dd1d+X3d3HfxzvyePuxTF4/Hg9/97ndYuHAhtmzZgnXr1mHVqlW46KKLkl21lLNjxw4sX74cl112GcaMGYOKigprIq3Xu3dv9O3b15rS0tKQlpaGvn37JrtqKefoo4/GlClTsGDBAmzduhUff/wxVq5ciT/84Q/JrlrKmTZtGhwOB/7+979j165d+O9//4sVK1a02CedNK24uBg9e/bEggULsG3bNqxcuRJbtmzB2WefneyqpZwXX3wRn332Ge644w5kZmZav3viu2KQ9rVt27ZGXQA0TUNFRYUVYE+bNg1PPvkk3nvvPezcuRO33XYbGhoaEg4omypaeu8qioKKigpr7I0//OEP+Pe//401a9Zg69atuOGGGzBlyhQcccQRybyMNmvpfWa/bkEQMGXKFCxZsgSfffYZtm3bhhtuuAE9evTA5MmTk3shbTB16lR88cUXePzxx7Fnzx7861//wiuvvGINARAKhWI+555//vl4/PHHsW7dOmzZsgULFy7E73//+07dzacpe/fuhd/vT9jNx37d6enpGDNmDBYtWoQtW7bgu+++w9VXX41JkyZh0KBBHV3tZjX3XaWrvq+bu+au/J5u7rq76vu6pe/info9zQgLBALshhtuYKNHj2YTJ05kTzzxRLKrlJIeeeQRVlhYmHAiB27+/Pls/vz5ya5Gyqqvr2fXX389Gz16NBs/fjxbsmQJ03U92dVKSdu2bWMzZ85kxxxzDDvxxBPZE088Qc9lGxUWFrINGzZY6z///DO74IIL2PDhw9lvf/tb9r///S+JtUst9udy1qxZCX/3/PGPf0xyLbu2ESNGsI8++iimrKSkJOZno+s6e/jhh9mUKVPY8OHD2QUXXMB+/PHHZFT3oLTlvbthwwZWWFjISkpKrLK1a9eyyZMns9GjR7M5c+aw6urqDq3/gWrL+yz+ukOhEFu0aBGbMGECGzVqFLv88stZaWlp0q6lteJ/1u+++y479dRT2YgRI9j06dPZ22+/bW1bu3Zto8+5jzzyCBs/fjwbM2YMW7BgAQuFQh1W94MRf92bNm1ihYWFLBwON9o3/rpra2vZjTfeyMaNG8eKiorYddddx2prazuk3m3R0neVrvi+bu6au/J7uqWfdVd8X7d0zZ35PS0wxtihibsIIYQQQgghhBBCCEnssO++RwghhBBCCCGEEEI6HoVShBBCCCGEEEIIIaTDUShFCCGEEEIIIYQQQjochVKEEEIIIYQQQgghpMNRKEUIIYQQQgghhBBCOhyFUoQQQgghhBBCCCGkw1EoRQghhBBCCCGEEEI6HIVShBBCCCGEEEIIIaTDUShFCOn0Bg0ahGuvvbZR+UsvvYRp06YloUaEEEIIIYQQQg4WhVKEkJTw+uuvY/369cmuBiGEEEIIIYSQdkKhFCEkJfTu3Ru33XYbFEVJdlUIIYQQQgghhLQDCqUIISnhr3/9K8rKyvD44483uc/+/ftx1VVXobi4GOPGjcMdd9xhhVgvvfQSLrzwQjz00EMYN24cxo4di0WLFoExZh3//PPPY9q0aSgqKsKFF16IH3/88ZBfFyGEEEIIIYQcriiUIoSkhIKCAsybNw8rVqxASUlJo+2KouDiiy9GMBjEM888g3/+85/44IMPcO+991r7fP3119i1axeee+453HzzzXj66afx6aefAgD++9//YunSpbj55pvx8ssvY8yYMbjoootQV1fXYddICCGEEEIIIYcTCqUIISnjwgsvRN++fXHnnXc22vbxxx+jrKwMixcvxqBBgzB+/HjccssteO655+D3+wEAmqbh9ttvx9FHH43TTz8dgwcPxjfffAMAeOyxx3D55Zdj6tSp6NevH/7617+id+/eePXVVzv0GgkhhBBCCCHkcCEnuwKEENJakiRh4cKFOP/887Fu3bqYbTt27EC/fv2QlZVllR1zzDFQVRV79uwBAHTr1g3p6enW9vT0dKiqah2/ePFi3H///db2cDiMn3/++RBeESGEEEIIIYQcviiUIoSklGOOOQZnnXUW7rzzTvzpT3+yyl0uV6N9NU2LmTudzkb7mGNKaZqGm266CePHj4/Zbg+xCCGEEEIIIYS0H+q+RwhJOddddx0CgUDMoOdHHXUUfv75Z9TW1lplmzZtgizLOPLII1s851FHHYX9+/ejb9++1rRixQps2rTpEFwBIYQQQgghhBAKpQghKScnJwfXXXcd9u7da5VNmDABRxxxBG644Qb8+OOP2LBhA26//XbMmDEDmZmZLZ7zkksuwVNPPYVXXnkFe/bsweLFi/Gf//wH/fv3P5SXQgghhBBCCCGHLeq+RwhJSWeffTbWrl2L8vJyAHy8qeXLl+P222/H73//e6SlpeHUU0/FNddc06rznXLKKaisrMRDDz2EyspKDBgwAA8//DD69et3CK+CEEIIIYQQQg5fAjMHVCGEEEIIIYQQQgghpINQ9z1CCCGEEEIIIYQQ0uEolCKEEEIIIYQQQgghHY5CKUIIIYQQQgghhBDS4SiUIoQQQgghhBBCCCEdjkIpQgghhBBCCCGEENLhKJQihBBCCCGEEEIIIR2OQilCCCGEEEIIIYQQ0uEolCKEEEIIIYQQQgghHY5CKUIIIYQQQgghhBDS4SiUIoQQQgghhBBCCCEdjkIpQgghhBBCCCGEENLhKJQihBBCCCGEEEIIIR2OQilCCCGEEEIIIYQQ0uEolCKEEEIIIYQQQgghHY5CKUIIIYQQQgghhBDS4SiUIoQQQgghhBBCCCEdjkIpQkibMcYOy8c+GKlab0IIISQV0e9dkgi9LgjpfCiUIqQLu/DCCzFo0KCYaezYsbjooovw+eeft/l8+/fvx+zZs7F3716rbNq0abjxxhvbfK5BgwZhyZIlbTpmzZo1uOeee9r8WMm2bds2/OEPf4gpO5DrJ4QQQuw2btyIK6+8EhMmTMCIESPwq1/9Cn//+9+xY8eOZFctxpIlSzBo0KAOe7yNGzdi9uzZHfZ4ncF3332Hyy67DMcddxzGjRuHWbNm4bvvvovZhzGGxx9/HCeddBJGjBiBk08+Gc8++2yL5967dy+uuuoqjB8/HuPGjcMVV1yBPXv2NLm/z+c74M+H5mvFPg0dOhTjxo3DnDlzsG3btlafa9WqVbjuuusAAPX19bjhhhvw5ZdftrlOB+LGG2/EtGnTmt3npZdewqBBg/DLL7+0+rytOaampgZTpkxBSUlJq89r5/f7ceutt2LChAkoKirCZZddhp07d7Z43I8//og//elPKC4uxsSJEzF//nxUVlY2uf8zzzzT4nNEDg9ysitACDm0hg4din/84x8AAE3TUFNTg+eeew6XXnopXnrpJQwcOLDV5/r000/x4Ycftku9XnjhBfTo0aNNxzz88MMoLi5ul8fvSG+99Ra+/vrrmLIDuX5CCCHEtHLlStx///2YOHEibrrpJuTn52P37t147rnncMYZZ2DRokX47W9/m+xqJsWaNWs6XTB3KO3evRt//OMfMXz4cNx5550QBAGrVq3C+eefj5dffhlHH300AODee+/FM888g3nz5mHEiBH46KOPcNttt0GWZZx77rkJzx0KhTBr1iyoqoqbb74ZLpcLDz30EC688EK89tpryMzMbHTMokWLYv6AeSBeeOEFa1nTNJSWluKBBx7ABRdcgDfeeAP5+fnNHr9jxw488sgjePXVVwEAP/zwA/7973/jrLPOOqh6tacpU6bghRdeQPfu3dv1vDk5OZg5cyZuuukmPP300xAEoU3HX3vttdi8eTOuv/56pKenY+nSpbjooovwxhtvICsrK+ExlZWVuPjii9GzZ08sWrQI4XAY/+///T9cdtll+L//+z84HI6Y/d944w3cfffdKCgoOODrJF0HhVKEdHHp6ekYPXp0TNnxxx+P8ePH46WXXsL8+fOTUq/4Oh1uDvfrJ4QQcuDef/993Hfffbjyyisxd+5cq7y4uBi/+93vcO211+LGG29EYWFhm/74RFLTM888A4/Hg0ceeQRerxcAcNxxx2HatGlYvXo1brnlFvzyyy948skncfPNN+P8888HAIwfPx779u3DJ5980mQo9eWXX+Lnn3/Gk08+ifHjxwMAjjrqKPzmN7/Be++9hzPOOCNm/w8//BD/+c9/kJGRcVDXFP85acyYMejZsycuuOACvPzyyy22hFu8eDFmzJjRqUOP3Nxc5ObmHpJzn3/++Xj44Yfx7rvv4qSTTmr1cV9//TXef/99rFy5EpMnTwYAjB07Fr/61a/wr3/9C3/5y18SHvfee++hpqYG//d//4cjjzwSAJCRkYE//elP+Prrr60/KldVVeHBBx/ECy+8gOzs7IO7SNJlUPc9Qg5DHo8HLper0V9O3nzzTZx55pkoKirChAkTcMstt6Curg4Aby68YMECAMCvfvWrmCbZkUgE9957LyZMmIDRo0dj1qxZ2L17d7N1sHdf++yzzzBo0CCsX78es2bNwqhRozBhwgQsXrwYmqYB4N0E9+7di5dffjmm2XJpaSmuueYaFBcXY9SoUbj44ovx/fffW4/zyy+/YNCgQXjiiScwffp0jBo1Cg8//DAGDRqE999/P6ZOP/zwAwYNGoR3330XABAOh3Hvvfdi8uTJGD58OE499VS8+eabMcdMmzYNDz30EO655x4cf/zxGDlyJC699FL8/PPPAHgz9KVLlza65vjue+Xl5ViwYAEmT56MkSNH4uyzz8Z7773X6Dl79tln8be//Q3FxcUoKirCVVddFdM0es+ePfjzn/+McePGYdSoUTj33HPbrXUbIYSQzmHp0qU4+uijMWfOnEbbHA4HbrvtNkiShEcffRQAMGvWLJx55pmN9r3iiitw2mmnWetffvkl/vjHP2LUqFEoLi7G/PnzUV1dbW1/6aWXMHToUKxZswYTJkxAcXExtm/f3urfPR988AFOO+00q+vYK6+8ErO9Nb8Lw+Ewli1bhunTp2PEiBE46aSTsHLlSui6DoB3m3r55Zexd+9eDBo0CC+99FLC53DJkiWYPn063n33XcyYMQMjRozA6aefjq+//hqbNm3COeecg5EjR2LGjBlYv359zLE//fQTLr/8chxzzDE45phjMGfOnEZdpbZu3Yq5c+fiuOOOw7BhwzBp0iTccccdCIVC1j6t+b1udtf67LPPEl4HABx99NGYNWuWFUgBgNfrRY8ePaxuduvWrYPL5cLZZ58dc+w///nPZocTCIfDAIC0tDSrzAwTamtrY/atq6vD3//+d1x//fUJW1AdrOHDhwOA1QpryZIl+PWvf42lS5daXcbq6urw008/4YMPPsCMGTMA8M+ZF110EQDgoosuwoUXXmids7nPvqZvvvkGl156KcaNG4djjjkGf/7zn1vdjfCll17CySefjBEjRuC0006LeV8k6or38ssv45RTTrH2X79+PYYOHdrodbx582acd955GDFiBKZMmYLHHnssZrvT6cTJJ5+MRx55xCozP2839Z4AgE8++QRerxcTJ060ynJzc3Hsscc2+3nSfJ2kp6dbZYleJytWrMAnn3yCJUuWYOrUqU2ejxxeKJQipItjjEFVVaiqikgkgoqKCtx3331QFCWmCfPy5ctxzTXXYPTo0XjooYcwZ84cvP3227jwwgsRCoUwZcoU668jS5cuxRVXXGEd++abb2Lbtm24++678Y9//APffvstrr766jbX9brrrsOYMWOwYsUKzJgxA4899hjWrFljPWZ+fj4mT55sNXWurq7Geeedh++++w4333wz7rvvPui6jgsuuKBRs/0lS5bgsssuw7333oszzjgDRx55JN54442YfV5//XVkZ2dj8uTJYIxhzpw5eP7553HJJZfg4YcfRlFREa6++upGH6Kffvpp7Ny5E4sWLcIdd9yBb7/91mqBds4551gfAF944QWcc845ja67srISZ599Nr788ktcffXVWLJkCXr37o05c+ZYzc5NDzzwAHRdx/33348bbrgB77//Pu666y4AgK7ruPzyyxEMBnHvvfdi+fLlyM7Oxl/+8pcWQ0JCCCGpobq6Gt9++y2mTp3aZLec7OxsHH/88Vagc9ppp+G7776L+V1QX1+Pjz76CKeffjoA4IsvvsDMmTPhdrvxz3/+EzfddBM+//xzXHTRRTFBiqZpWLVqFe68804sWLAARx11VKt/99xyyy2YOXMmHn74YfTo0QM33ngjtm7dCqB1vwsZY/jzn/+Mxx57DOeccw5WrFiB6dOn45///Kc1VMEVV1yByZMnIz8/Hy+88AKmTJnS5HO5f/9+3H333fjzn/+MBx98EPX19Zg3bx6uueYanHPOOVi2bBkYY7j66qut52DXrl0477zzUFVVhXvuuQd33nknSkpK8Ic//AFVVVUAeLh2wQUXIBgM4u6778ajjz6K3/72t3jmmWfw9NNPx9Shud/rQLSL17Bhw5q8jvPPPx9/+tOfYsp2796Nbdu2WS3lfvjhB/Tt2xdffPEFzjjjDAwbNgzTpk2L6SaXyMSJE9G/f38sXrwYJSUlqKiowO233w6v14sTTzwxZt/bb78d/fv3x3nnndfsOQ/Url27AMBqiQPwP05++OGHeOCBB7BgwQJkZWXhtddeQ35+vtXaatiwYbjlllsA8Neg+Vpp6bMvAGzYsMEaF/Suu+7CHXfcgX379uG8885rsYvovn37sHLlSlx11VVYsmQJBEHAvHnzrNdJvFdeeQU33ngjjjnmGCxfvhwnn3wyrrjiCusPtHYLFy7Eb3/7W6xcuRJFRUVYvHhxoz+2Tp8+Hd9++631vA0bNqzF98SOHTvQp08fSJIUU37kkUda50nkN7/5DfLz83HbbbehvLwcJSUluPfee5Gfn4/jjz/e2u+8887D22+/3abWW+QwwAghXdYf//hHVlhYmHBasWKFtV9tbS0bPnw4u/nmm2OO/+KLL1hhYSFbvXo1Y4yxtWvXssLCQlZSUmLtM3XqVDZ58mSmKIpV9sADD7DCwkLW0NDQZN0KCwvZQw89xBhjbMOGDaywsJA98MADMftMmzaNXX755TGPNX/+fGv9/vvvZyNGjGC//PKLVRYOh9mvfvUrduWVVzLGGCspKWGFhYXspptuijn3Qw89xEaPHs2CwSBjjDFd19mUKVPYLbfcwhhj7JNPPmGFhYXsjTfeiDnuuuuuYxMmTGCRSMSq09SpU5mqqtY+S5YsYYWFhay6utp6rMLCwiav/95772XDhg2LuQ7GGLv44ovZhAkTmKZp1jF/+MMfYva58cYb2ejRoxljjJWXl7PCwkL26quvWtvr6+vZXXfdxX766SdGCCEk9W3ZsiXmd3NT7r77blZYWMhqa2uZ3+9no0ePZkuXLrW2r1mzhg0ePJjt37+fMcbYueeey2bMmBHz+2znzp1syJAhjT4HvPLKK9Y+rfndY/4e/PDDD619du/ezQoLC9lTTz3FGGvd78IPPviAFRYWstdffz1mn2XLlrHCwkLr8ebPn8+mTp3a7POTqE6PPPIIKywsZGvWrLHK3nrrLVZYWMi+//57xhhj11xzDTv++ONjPuPU1NSwMWPGsLvvvpsxxtjHH3/MLrjggkafg2bMmMFmzZplrbf0e/1ABYNBdu6557LRo0dbz+ef/vQnNm7cOHbcccex1atXs08//ZT9/e9/Z4WFhez5559v9nxfffUVKy4utj5DDh8+nH3yyScx+7zzzjsxjxf/ma21zJ9LJBKxpoaGBvbFF1+wM844g40ZM4aVl5fH7PvFF1/EnOPss89mf/nLX2LKzM+aGzZsYIy1/rPv2WefzU455ZSY90VdXR0rLi5m8+bNa/I65s+fzwoLC9n27dutsk8//ZQVFhaydevWMcYaf66eMmVKzOdexqKvybVr18Yc869//cvaJxAIsGHDhrG77ror5tj6+npWWFjInn322SbrGW/WrFnsvPPOa1R+//33s2HDhjV77Lp169jIkSOt18mxxx7Lfvjhhyb3b837lBweqKUUIV3csGHD8OKLL+LFF1/EmjVr8Pjjj+Piiy/GAw88gAceeAAAsGnTJiiKYjVzNo0dOxa9e/du8U59I0eOjBnAsE+fPgD4X2HboqioKGa9R48eCAQCTe6/fv16DBkyBAUFBVZrMFEUccIJJ+DTTz+N2XfIkCEx66eddhoCgYD1V6WvvvoKpaWl1l+M169fD0EQMHnyZOvcqqpi2rRpqKioiGm2PWLEiJi/KJkDmAeDwVZd9+eff46ioiL07t27UR0rKipi7ngSP8ZCjx49rMfJy8vDgAEDcPPNN2P+/Pl47bXXoOs6FixYQGOKEEJIF8GMW9rHDxwcz/y9xBizWrXYu6C/8cYbGD9+PAoKChAMBrF582arpbD5O++II45A//798b///S/m3PbfqW353TN27FhrOf6zQmt+F37++eeQZRnTp09vtI95jrY65phjYq4FAEaNGmWVmV2QzHpu2LABxcXFcLvd1vOUnp6OsWPHWp89Jk6ciNWrV8PlcmH79u1477338PDDD6O6uhqKosQ8fnO/1w+Ez+fD5Zdfjm+++QaLFy+2ns9IJIKamhrceuutuOCCCzB+/HjcfvvtmDhxojXMQCJma7nBgwfjkUcewaOPPooTTjgBc+fOte5kV11djVtuuQU33HBDo5/fgRo2bJg1jRkzBhdccAEURbFaztvFf8YrKSmxXl9Nac1n30AggG+++Qa/+c1vYj7nZWZmYurUqS2+3nJyctC/f39r3axTQ0NDo313796N0tLSRq/tpm5WYH8veTwe5OXlNfrcnZGRgczMzDbd3c/89yWR5gZMf+211zB37lxMmzYNjz/+OJYvX46BAwdi1qxZh9VNB8iBoYHOCeni0tLSMGLEiJiyiRMnIhAI4LHHHsNFF11k9Z03P4zZ5eXlJfzlaWcfwwAARJHn3eb4Dq3ldrsbnae5X461tbXYvXt3k03a7R/q4uvYt29fFBUV4Y033sBvfvMbvPHGGzjyyCOtD6e1tbVgjMV8WLUrLy+3PgR5PJ5G9QZaf/11dXU44ogjGpWbPw/7h4xEj2U+R+bddsyBLV955RU4HA6ceOKJuPXWW5u8YwohhJDUYX7pb+nuZiUlJUhLS7NCldNPPx2vvvoqtm7diry8PHz22WdWN7H6+nrouo5HH33UGofKzuVyxazbf6e25XeP/Tjzd6X5O6w1vwvr6uqQk5PTqGuRGVK09HklEfsYOKb437V2tbW1ePPNNxuNMQnAGrTa7I737LPPIhAIoGfPnhg5cmSj5zHRY7X02ac5+/btw+WXX45du3bhgQceiOlel5aWZv2xzW7SpEn45JNPUFlZmfBz4IoVK1BQUIBHH30UTqcTAP8ced555+Guu+7CSy+9hIULF2LAgAE4++yzoaqqdawZcEqS1OY7wL344ovWssPhQH5+Prp165ZwX/t4VwAP5pr7GQJo1WffhoYGMMba7fOx+Rwk+nxojt0Wf42JHhto/evG4/HA5/M1W0+79PT0mDHNTH6/v9nB65cuXYqioiLrD94AMGHCBJxyyil48MEH8dBDD7W6DuTwQ6EUIYep4cOHY82aNfjll1+sD4yVlZXWbYNNFRUVCT8kdgYZGRkoLi7GDTfckHC7+eGpKaeddhoWLVqEhoYGvPXWW9aYAea5vV5vo7EfTH379j3wisfJyspCRUVFo3KzLCcnp9XnKigowMKFC/GPf/wDW7duxVtvvYVHH30UOTk51hgKhBBCUle3bt0wevRovP3227jqqquscMfO5/Phf//7H6ZNm2aVjR8/Hvn5+fjPf/6D/Px8uFwua1wXM7CYOXNmwpYZLX3Bb4/fPa35XZiVlYWamhpomhYTTJWXl1v7HGoZGRk4/vjjcckllzTaJsv8q9XKlSvx5JNP4tZbb8VJJ51kfZmPH2S8Pf3444+49NJLEQ6HsWrVKhx77LEx2/v27QvGGCKRSEw4ZoZI8X8YNO3duxfDhw+P+UwliiLGjBmDZ599FgDw9ttvA4gORG4/9pVXXsHTTz+NcePGtel64v+g2hbZ2dktBkat+eybkZEBQRAShjQVFRXtevc4s5V9/HhTTY0/1Vr19fVtel8cddRR+OSTT6Dresy/Lbt3745p9RVv7969jcYYc7vdGD58eKsHhSeHL+q+R8hhasuWLZAkCUcccQRGjRoFp9OJ119/PWafL7/8EqWlpVZroUQffDtS/OMXFxdj165dOOqoozBixAhr+ve//40XX3yx0V9S451yyilgjOHBBx9EVVVVzB2IiouLEQgEwBiLOfdPP/2EZcuWxfwlsK31jnfsscfi66+/bvRX71dffRX5+fmtDsC+/vprHH/88diyZQsEQcCQIUNw9dVXo7CwEKWlpa2uLyGEkM5t7ty52LVrF+6///5G2zRNwz/+8Q+EQqGYwa8lScKpp56K999/H2+99RZOPPFEqyVHeno6hg4dip07d8b8zhs4cCCWLFnS7J3f2ut3T2t+FxYXF0NVVbz11luN9gGAMWPGADi0n1fMOw4OGTLEep6GDx+OJ5980rp778aNGzFgwACcddZZViBVVlaGn376qc2tyFtj3759uOSSSyAIAp577rlGgRQAq4VU/E1e/vvf/2LQoEEJW4wB/M5+W7Zsiel2yBjD119/bf3R0hwmwj7l5+dj6tSpePHFF5sdpP1Q6N27N/bt2xdTFv+ZsDWffb1eL4YPH47//Oc/MYONNzQ04IMPPrBeb+2hR48eOPLII63XkOmdd9454HPW1dUhGAyiV69erT5m4sSJ8Pv9+Pjjj62y6upqfPnll5gwYUKTxx199NH46quvYlprhcNhfPfdd532j9uk86CWUoR0cT6fD5s2bbLWFUXBf//7X6xduxbnnnuu1dR89uzZWLZsGRwOB6ZOnYpffvkFDz74IAYMGIAzzjgDAKzb+7777rs44YQTmv2LyaGQmZmJ77//Hp9//jlGjhyJmTNn4t///jdmzpyJWbNmIScnB2+++Sb+7//+DwsWLGjxfOad9v71r3+hqKgoJvyZPHkyjj32WFxxxRW44oor0L9/f2zZsgUPPfQQJk2aZD1vra03wO/uN2rUqEa/nC+55BK8+uqrmDlzJubOnYvs7Gy88sor2LBhA+66665Wf7geOnQo3G43brjhBlx55ZXIy8vDp59+ih9++MG6FTIhhJDUN2nSJNx4442499578cMPP+Css85C9+7d8csvv+C5557DDz/8gDvvvBODBw+OOe7000/HqlWrIIpio25611xzDWbPno1rr70Wp512mnWXvc2bN8fccTdee/3uac3vwhNOOAHjxo3D3//+d5SVlWHw4MH4/PPP8eijj+KMM87AgAEDAPDfu5WVlfjwww8xZMgQdO/evQ3PbvOuuOIKnHfeebj88svxhz/8AS6XCy+88ALWrVtndVEaOXIkli9fjpUrV2L06NHYvXs3HnnkESiK0ubxoqqrq7Fnzx4MGDCgyeDojjvuQFVVFW699dZGn/vS09MxYMAAjBs3DlOnTsWiRYsQDAYxcOBAvPLKK/jqq6+wfPlya/89e/agurraGuvqiiuusO7ud/HFF0OWZaxduxabNm2yrjdRqyan04ns7OyYbfv378f+/fsxdOjQFluzH4wJEybgX//6FxhjVpc5Mxz84IMPkJWVhcGDB7fqs++1116LSy+9FLNnz8b555+PSCSClStXQlEUzJkzp93qbN6Z77rrrsM//vEP/PrXv8bWrVuxbNkyAAcWtG7cuBEAD5oA/p1g+/btOPLII5v8HHvssceiuLgY119/Pa6//npkZ2djyZIlyMjIiOlRsH37diiKgqFDhwIArrrqKsyZMwdXXXUVzj77bCiKgqeeegplZWW477772lx3cnihUIqQLu7777/Hueeea627XC4ceeSRuPrqq3HppZda5eYHydWrV+OFF15AdnY2pk+fjr/+9a/WX1LHjRuH448/Hvfddx/Wr1+PlStXdui1zJo1C3fddRcuvfRSPPHEExg7diyef/553HfffVi4cCHC4TD69euHO++8s9VN5E8//XSsW7cOp556aky5KIpYuXIlHnzwQTzyyCOoqqpCQUEBLrnkkjZ/CDnppJPw73//GzfeeCPOPvtsLFy4MGZ7fn4+nnvuOdx333244447EIlEMHjwYCxfvhy/+tWvWv04LpcLq1atwn333Yc777wT9fX16NevH2677TaceeaZbaozIYSQzu2SSy5BUVERnnrqKdxzzz2orq5Gfn4+JkyYgDvvvNMKaOwGDx6MwsJC1NTUYPz48THbJk6ciMcffxxLly7FvHnz4HA4MGzYMDzxxBONBuO2a6/fPa35XSgIAh555BE89NBDePLJJ1FdXY0+ffrgmmuuielOd+aZZ+LDDz/EnDlzMG/ePMyePbvV9WjJ4MGD8eyzz+KBBx7ADTfcAMYYCgsLsWzZMquel19+OWpqavD0009j2bJl6NmzJ04//XSr/vX19dYfrFrywQcfYMGCBU12gVMUBR988AEAJOwqWVxcjGeeeQYA8OCDD2Lp0qV44oknUF1djQEDBmDp0qUx3TyXL1+Ol19+GT/++CMAHjitXr0aDz74IK677jo4HA4MGjQITz/9NIqLi9v03K1ZswZLly7Fe++91+JA5AfjpJNOwrJly7BlyxZr0PqBAwdixowZePbZZ/Hxxx/j9ddfb9Vn3/Hjx+OJJ57AQw89hGuuuQZOpxNjx47FPffc0+43kTn11FMRCATw+OOPY+3atRg4cCD+9re/4W9/+1uj8ala46OPPsLIkSOtcei+++47XHTRRVi0aFGz782lS5fi7rvvxr333gtd13HMMcfgn//8Z8z4cLfeeiv27t2L//73vwCAX/3qV1i5ciWWL1+OuXPnIi0tDSNHjsSLL77YKBwnJJ7ADnQkPUIIIYQQQgghpJUuuOAC/POf/2x0B7329uc//xk5OTlYtGjRIX2c9vT6669j6NChMWNcffDBB7j88svx73//u03hTiAQwKRJk3DPPfc0GuuJkM6GxpQihBBCCCGEEHJIffbZZwgGg03eUa49XX311XjnnXdSakzNV199FZdddhlee+01fPnll1i7di3+8Y9/oLi4uM2tjZ5//nkMHDiwTS3uCUkWailFCCGEEEIIIeSQ2rt3L7xeb4fcJRHgd0HcunVrwhsCdEY1NTW477778NFHH6G6uhp5eXk4+eSTMW/ePKSlpbX6PNXV1fjd736HZ555pl3vFk3IoUKhFCGEEEIIIYQQQgjpcNR9jxBCCCGEEEIIIYR0uAMOpRRFwYwZM/DZZ59ZZSUlJZg5cyZGjx6NU045BZ988knMMZ9++ilmzJiBUaNG4aKLLkJJScmB15wQQgghhBBCCCGEpKwDCqXC4TCuueYabNu2zSpjjGHOnDnIy8vD2rVrcfrpp2Pu3LnW4HKlpaWYM2cOzjzzTLz44ovIzc3FFVdcgdb2HmSMwefztXp/QgghhBASiz5PEUIIIaQzaXMotX37dvz+97/Hnj17Yso3bNiAkpIS3Hbbbejfvz8uv/xyjB49GmvXrgUArFmzBsOHD8esWbMwcOBALFq0CHv37sXnn3/eqsf1+/0YM2YM/H5/W6tMCCGEEEJAn6cIIYQQ0rm0OZT6/PPPMW7cOLzwwgsx5Zs3b8bQoUPh9XqtsjFjxmDTpk3W9rFjx1rbPB4Phg0bZm0nhBBCCCGEEEIIIYcPua0HnH/++QnLKyoq0L1795iybt26Yf/+/a3annRKHbB9JaArgJwOODKMKRtwZgPOHGM5BxClJFeWEEIIIYQQQgghJLW1OZRqSjAYhNPpjClzOp1QFKVV25Nuz/8Bm25oxY4CD6Zc3QBXHuAuADy9AG8fwNMbSDsSyBgIuHtQeEUIIYQQQgghhBDShHYLpVwuF2pra2PKFEWB2+22tscHUIqiIDMzs72qcHCOOAto2AbU/wRofkANAKofUH1ApIHPtQAABijVfGrY1vT5JE80rEofAGQOAjIHA2lHAbIbEKTGkyjb1mU+UbBFCCGEEEIIIYR0GprOIIlCsqtxUDrLNbRbKFVQUIDt27fHlFVWVlpd9goKClBZWdlo+5AhQ9qrCgfHlQsU3du4XFd5lz5d4SFVsAwI/gL49wChciBcxsvClUCkFghVAOEKQAsCvh18Kv8wej7RBWQMADIGAVmD+dyZBTAGCGJcUCXyYEpy8eNEl7EsAxABQTDmxgTBttyasuS/AAkhhBBCCCGEkFQiiQKuev5rbC/3JbsqB2RA93Q8eF5RsqsBoB1DqVGjRmHlypUIhUJW66iNGzdizJgx1vaNGzda+weDQXz//feYO3due1Xh0BBlIwTy8rGlvL0BHMO36RpvPaX6gEg9D6TMZaUGiNQBwX08mGrYDvh2AnoYqPuOT78Yj5FRCOQdB3QbB2QP54EU04xJBSIhvqxrANMB2G/jLMSuCwKs0MmaG+PZW8GUfbvRQgu2EMzcLsi2oEyIHms/f6PHEmLP39T2ROeikIwQQgghhBBCSArYXu7Dd6X1ya5Gymu3UKq4uBg9e/bEggULcMUVV+D999/Hli1bsGjRIgDAWWedhccffxwrV67E1KlTsWzZMvTp0wfjxo1rryp0PFECRGNAdE9PHi6pfh5IhfYD4SogfSDQczrgyuHH+H4G6r4BarYAtd8A/l1Aw0982vU07/aXPxHoeRKQdzxvGdUWjAHQ4+bMCLPs6xqf62FANcpitptl5nJc+AXwEInFlZnlMcEVYkMqK4CKn5uBmWwLqsxgzAjHIPLnXZDQONBqKTRrptx+bMJjCCGkc2OMgYGBGf8um8v2eWv2s5e1x7L9Mc3l+G1NrTNmq5ttXRIlHJ1zNBySo52fRUIIIYQQ0pHaLZSSJAnLly/H3/72N5x55pno27cvli1bhl69egEA+vTpgyVLluCuu+7CsmXLUFRUhGXLlkHoSq1jBAFwpPMp7Qgg4gOUKh5EBfbygCmtL5DRH+jzO36MUgNUfgZUfsrnShWw/10+yWlA9ylA7xlA7tjWtSQSBPBWT4fuMlsUH4jZQy6rTDcyLiMwM0MzXQcQits3/njd9jhAbGBmW44PzRqFTogGUonCM0GIO8ZsTWaEY4J9MgMzGa0LypprLdbCvgmP70LvI0KSSGe6FYLYlxkz1uMCnERl9nn8OZrdV9ehQ4/OjWN1pkcn6GA6s/azHqOpUMgeBhn/4/9PHFA1eQxfAQODAIHvb/6zY/7dwlgWBMF6HIBvS3hM3DbzOPu6nWAczMDgkBzoldGLQilCCCGEkBR3UKHUjz/+GLPet29frF69usn9J0+ejMmTJx/MQ6YWM6Dy9OLjT/l3A8H9gOQAXPk8eHDmAL2m84npQP0PwD4jlAqVAaVv8CmtLx+MvfcMwNFJBodvSmcIxhKJCcuAJluGJQzBGB9XzB6mmYEaYFuOfnlr1LLMEt/qTLDN4luYxYVmMQFW/LoZnBmtyMxWZZBsLb4ko6VZK1uJJQzIWrlPwv0624uCdDb2oCcmjIkLaOzb44Ob+G2arkFjxqRH5zp0Ptd1aEyLCZqsukCPCXLMc5rbY1rwmEGM+RZPMBfQ+D1gD2AECBAEwZo3VwagyX1FI3gXxNjyRMuJzpNom7mcbIqmoCZUk+xqEEIIIYSQdtBuLaVIM0QHH4vK3QMIl/PxpfwlgLsbIKdH9xNEIGsYnwbNA2q3AKX/4ZN/N7D1fuCnZUC3YiBjIJBZyO/sl3aEEUSQZnXWsAxI0CoMaD40s21nmrG73ni7/XzMth3mrKngzNBsa7MEIVTCFmfx62YQZg/HzFZo9kH5bWWJAq7WhmKJ9m+2BZptmbpuNmKGPZquRZeZ1ihAamrSdA2qrkLVVWtZg2YtxwdN9qDIDIusFkOMNX4JWxmvYIVHZqgiCmKjZQB82Rb8WOuiEFPemjkhhBBCCCGk9SiU6kiixMeecuYC/p/5AOhKPeDubgw2biOIQM5oPg2ax4OpPS8Cvu1Axcd8MkluHk5lFvJxrTIH8dBKcnfgxZGDYoUh0VmnFDNGGdCoxVhz4Zm926UZkDXZ6sx+HsQGYy2KaZaCVoVoMSEVGu8X003T3M8eoAnRZdHevbO5QKyJ1mRN1fVAziXwYMbeQsgMhqzWQkaoFL8c0SKIsAhUTYXKVETUiNXayAqJzK5mZoiEaHDU9E8nNhQSBdEKgeLLZFG2lgE0uUwIIYQQQghJTRRKJYPk4sGRKw9o2MbHm/IUNB0iyWnAkWfz7nv13/MB0hu2GwOk7wC0EFD3LZ8sIu/ylzkIyBxsTIP4oOyEHKiYlkmdXMLWZ02NcWYP0RJ13WxinybPhQRBmhDNy1pqoWYFaiJ0MGiMQdN1qNCNZQaN6VCZxpeNfSK6BkVXoWgqVGhQdA2arkMDg84Efi6AB0mCcVkQjbF8+DIgQBAliGYgJIoQIUIQREiCBEGQIAkCHKIEERIEcx9BgijIEEUzuIsLBWNa0tnKYwLZ+LAt/njYygkhhBBCCCGpjkKpZHJ14+NDyRk8ZHJmNj9elCBEu/eZmMa7Ajb8BNQbd/Gr/5EPmO7fxad9b0X39/SOhlRZQ/jcmX3ILpGQpOlkrc94CyUdKlOh6hoPlczua+Yy06BoESiqgpCmQNHDUDUVOmNQdQ06NOi6BlXXjPGLYsMyQQAkQYQIAZLV4giQIMAhiJCMbmaSLEEUADEm4DG7gjIAilFpWA3cDlhTARRs5fFlMccAjUIq+1hrjW5OYAam9pZqtpZr8WOy2VvLNRuO2R87UeiWoM6NwrUWzp2wnBBCCCGEkK6LQqlkEx08HJI8fJBzLQy481t/vCAB6f341POkaHm4kodT9T8C9Vv5PLg3OpW9F93X3RNIPwpIO5K3rvIac3f31GgRQ0gHM7vERXTVCJZ4oKRa4yXxbSFVQVhXENYURDTNGDvJ6CJnGzspSoAoCJBECZIg8oBJECGLEpyyuSymTtc1Ft/CDGjcmsx+/YlapMHW5dN2Xnvrt/jWa/bzxbeSQ9wuB6vZ0M22rTXBG9BE+GautzKAs7bHh3CJ6hMfltkfG3Hr9u32c8VfS/xz0sT1NRnMJaoDIYQQQgjpiiiU6gwEEcg4GpC9QO13QKCUjz11MB/IXXlAfh6QPyFaFqk3QqofgLofeFgVKAFC+/hU+WnsOSS3EVDFhVVpfakbIOlyGGNWyBTRVWNZtcrCGm+9FFJ5yGSGUGbApOkaol+oGQRBhCyIkEQJohEwOQQZbgfvBmeWdenBsTtZa7V2FxOqJQjeGoVutm2JbjjQ6vANaBzAmfvE1QlxuwktrLdFcy3CYnZqKpSzH2/fFh9UITaQ01QgEga0cQdYcUIIIYQQ0llQKNWZeHrw8aZqNgPBfQcfTMVzZALdjuWTKdLAu/z59/A7/JlTcC8fq6rB6BIYz5mTILA6EvD2oQHWSaeiM52HTJpqhU2KHkFE4/OgGkZIVRDSwjxo0oyudUw18gBjtCUjYJIFCbLRkskpO4xlCZKYAi2XSPuKbxnVFYO3piRsBQfEhnRNhXLmPD7Iiz+3bZtuW1dDQMQH6OGDugRCCCGEEJJ8FEp1Ns4cIGcUUP01ENrPg6lDyZEB5I7hk52uAsFSI6z6GQjsMZb3AOFyQKnhU+3muBMKgLvACKiOjJ17ejW+yyAhB0EzWjUpegSKpkLRIojoKsKagqAahj8SRFiL8LvHGS2e7HeHE4yucWbQ5BBkeJwuK2jq0q2YCDkYyWwFJzgAxdfBD0oIIYQQQg4FSgg6I2cOkDOaB1PB/bwFVUcTZaMV1JEAJsZuUwO8259/DxAwW1eV8LnawMO00H6g6vPY4wSJB1PeI4yw6ojosrsHBVakkYjGAyYeOkWg6CrCqoKAGoI/EkRIU6LjOlktm/j3ZR40yZBFCS7JiTSHB7IgU4smQgghhBBCCOkkKAXorFy5vMVUzSYgVMZbH3UWshfIHMQnO8aASJ0RUu2Jtq4KlPBlLWQslzQev0qQeWCVdkQ0rDInT08KrLooVVcR1iI8eNIiCGsRhLQwfEoQfjXIW0EZrZ94CycBAsBbN4kyHKIMr+yGwwifqGUTIYQQQgghhKQO+qbfmbnzgJyRvMWUUsNbUHVmggA4s/mUMyp2G2NAuCIaUtnDqsAvgK4Yy3sSnFfiLam8R/DQytPbmPcBvL1pDKtOjDEGReehkxk+hbUIfEoADRE/wlp0bCcrdBIEOEQZDlGCU3TA7XTBIVILJ0IIIYQQQgjpaiiU6uzc3YGsIbzFlOgE5LRk1+jACAK/Fnd3oNvY2G1MB0Ll0VZUgRLeHTDwC1/Ww3zg9eBeoGpD43O78vkA657efO7tHV135tBtxQ8xe/Bk3pkuqIbRoATgU4NQNAWKxu9kx8AACHCIEhySA05RhtvphUN0UOhECCGEEEIIIYcZCqVSgfcIPo5T/fc8mBIdya5R+xJEPm6Wp0fsnQEBo4VVZbRFlTUZoZXq4y2wwhVAzdeNzy15jLCqtzHvZVvvRa2s2kDTNYQ0BSE1zOeaggbFj3rFb3S/UxHRIwD4AOIOUYZTlOGUnEh3eOEQZepeRwghhBBCCCHEQqFUKhAEIGMAoAYB/y7edU04TFqVCALgzudT/B0CGQMi9UDQHlbt5fPgXt76SgsCvu18SsTZzRh8vRefe3rzMaw8vXhI1tUCwFZQdRVBM3gy7mBXp/jhjwT5uE+6Asaig4k7JQcFT4QQQgghhBBC2oxCqVQhykDWYEALAMFSHpwIUrJrlVyCADiz+JQ1rPF2XQGC+3hQFTTDqtLouuoHlCo+1X2T6AF410B7SOXpxdfdRsuuFG5ppekagmoYQS2MoBpGIBJEbdgHvxpEWFMQ0Xh3O0EQ4ZIccIoOZDi96CZlQTxcQlFCCCGEEEIIIYcMhVKpRPYA2cOB2m8A/17A4QWcuYdPq6m2Ep1AWl8+xbNaWZUa41WVAoFSY30fn+thIFzOp9rNiR/DmcvDKXcPW1hlC60cWUkf04oxhpARPAWNlk+1SgN8StC6650OBlEQ4ZaccEoOZLsy4BQd1OqJENImmq7zf1f0CELWXTX5DQ4ULRJTbt5xk49JF+Hjz1nLxnY9epxZFlLDGJjZA5OGX5bsyyWEEEIIIQeJQqlU48wC8sYBwf2Abydv/SOn04DebRXTympI4+2M8RZUwf3RkCq4DwjtM9b381ZrSjWf6r5P/DiiC3AXGMFVAQ+r3AVGmTFvx8HrNV1DQA3xlk9qCHVhP2qVBoTUMMKaAo3pECHAKTngkpzIdKbDJVH4REhXxRhDRFdjx4Mz5mFjbDjzBgUhLYyQqhjbIghpYdtdM5WYmxmEbOVm8BTSItCY1iHXtddfjogW6ZDHIoQQQgghhw6FUqlIdPBxpdwFPCzx7QT8e3jA0gla5nQJggC48viUPbzxdrOlVcgIqIL7o8shYwpX8dZWgT18aoqcFg2q3N0BV/fonQrdBbwLoSOz0c81oqkIqCFrqgnVo17xI6SFEdFVMMYgizJckgNe2Y0cVybd4Y6QTsYMjQJqCCF7d1o1zNeNyWztaM1VBUEtbB0TUu03IYiGSyEtDJ2xpFybQ5R5AC7yENwMw52iDJfstModkgyXxNf5GHV83SE64JIccBj/jjmNbsQi05HncMAhHX5j/hFCCCGEdDUUSqUyyQmk9+OtcAJ7Ad/P/K50zmweYpBDx97SKnNw4n10BQiWAaGyaFBlrRuT6uNjW/l28qkJTHRBd3WD6ugGxZENn5SGBiENPjEdPjENIUcmdGc3OB3pyHJmUOsnQg4BxhjCmgK/GkJQDcEfiYbCAWM5qIYQUMPGPHbZDJjsy0E1BI3pHVJ/yeii65ZdcEtOuCRn7LpsrBvb+MSDI7fsgktyJN5mrPPQyWGMQec8ZCG4Egmgxr/vkJybEEIIIYR0LAqlugLJDWT052MZBX7h4VR4N+DqBjjSk127w5fo5C3a0o5oeh/VD4QqjJCqHAiVQQ+VQQ/uBwuVQQxXQFIbIOhhSMFSSMFSuABkAOiZ6HRSGiKObNuUE7suZyPiyIKewgO0E9IWOtOtsdT8ahD+SMi2bKyrQQSs8hACxrZAJBQNoIz5oWx15BBleGQXPLIbHsllLPNAyFx3m3MjSIrZZoRK5n583dzPCVmkX/kdqaysDHfeeSc2bNgAl8uFU045Bddccw1cLhdKSkpw8803Y9OmTejVqxduuukmTJw40Tr2008/xV133YWSkhKMGjUKd955J444Ivq75Mknn8Tjjz8On8+H3/zmN7j55pvh8XiScZmEEEIIIQeFPqF2JbIXyCzkd4gL/AL4d/Pxjly5fNwp0ukwyYuQuwf8UjZ8ziNQ62pAjasBwfQQwpoCAQLcgoAsBJGh+eHV6uGI1MAZqYEjUgOHUgNnpBaOSA1EFoGs+SFrfnhCe5t9XE10G0FVFiJyFlRHlhFaZSLiyIZqmzP6IkuSJKKp8EUC8EWCxty+zIMjXyRgzKPr1rIRNjG0f5Dkld18chhzI0zy2ia37EKaw22Uu6zAyWuETm7ZBa/ssvaVxcP8jqpdCGMM8+bNQ2ZmJp599lnU1dXhpptugiiKuOGGGzBnzhwUFhZi7dq1WLduHebOnYs333wTvXr1QmlpKebMmYMrr7wSkyZNwrJly3DFFVfg1VdfhSAIePvtt7F06VIsXrwY3bp1w4IFC7B48WLccsstyb5sQgghhJA2o2+bXZEjHcgaDHh7Af4S3qUvXMNbTsneZNfusKbqKvyREHyRABoUPypDtfBHQghpChhjcIgS3LIr4d3vwsaUEGOQtAAckVo4ItVwROqM5RpjXguHysskPQxJD0EK74c7vL/lOktpRnCVYcwz+VzORMSRCVXOhCpnIeLIgC56aEwzYoloKuojfjQoAfiMeXQ9gAYlgAbjvWCGSbzMD1+E3x2yvUiCiHSHF2kON7yyB17ZjXSHB2kOjxUupZvLssfYzw2vw4M0Y3uacZxbdkKku56SZuzcuRObNm3C//73P+Tl5QEA5s2bh3vuuQcnnHACSkpK8Pzzz8Pr9aJ///5Yv3491q5diyuvvBJr1qzB8OHDMWvWLADAokWLMGHCBHz++ecYN24cnn76aVx88cWYOnUqAODWW2/FpZdeiuuvv55aSxFCCCEk5VAo1ZU5MoHsYYC3Dx8IPVgChKuNcIo+uHaEkBq2Wm1Uh+tQE2pAUOUDkQuCALfkhFd2I9edeXBfcgUBmpwGTU5DyNO72V1FLchDK7XOCqxka73OtlwPAZrV+qrpRCxKF2QjpMpARM6E6uDL1rqcbm1X5QxokhegL/edmqqrqFf8qFcCqFd8xrI5+aygqV7xo8EoN4OmUDuFSh7ZhXSHF+kOT8J5msPTaNkre5Du9CBN5uU0zhrpSPn5+XjsscesQMrk8/mwefNmDB06FF5v9I9EY8aMwaZNmwAAmzdvxtixY61tHo8Hw4YNw6ZNmzB27Fh88803mDt3rrV99OjRiEQi2Lp1K4qKig7thRFCCCGEtDMKpQ4HzizAOYKPbeTfzbv2hasonGpnjDEE1JDVAqQyWIuGiB8BNQTGAIcowWMEUM4k3jVKlzwISx6E0aP5HZkOSfPDEamHbAZWar2xXg+HaswjdZDVBkh6CCJT4YxUwxmpblVdGAQjqOIhlSplGOu8TJPToUrptn3SoUlpFGQdAFXX0KD4Uaf4UBv2oV7xoU7xoS7sQ50RMNUZoVNd2GeFTn41dNCPne7wItPpRbojDRlOLzIcXmQ404y5F+kOXpbu8CDdmYZ0h8dY5yETdWsjqSYzMxOTJk2y1nVdx+rVq3HcccehoqIC3bt3j9m/W7du2L+ft1xtbnt9fT3C4XDMdlmWkZ2dbR1PCCGEEJJKKJQ6nDiz+ZR2pBFO7aVw6iDoTLfGsqkLN6AyVAufEkRI402K3Mb4MVnOjEN2F6pDShChyRnQ5AwAzbe+AgBBD8MRaYBshFWy2mAEVw18iphlfB9JD0EAg8Moay0GAZrktQIqVU6HKqUZAVZ0zrfb90sDhK7xT56qa0a41GBMfNleZi6bwVNDJHBQj5nu8CLLmYZMZzoynF5kOtOQ4UhDlovPM51pyHAacyN0ynSmIU32pObrn5B2tHjxYnz//fd48cUX8eSTT8LpdMZsdzqdUBTesjAYDDa5PRQKWetNHU8IIYQQkkq6xjc00jbOHD6l9bWFU5WAI4NPArVKSERnOh/3RgmiNtyAilAtApEgQqoCURDgcbiR7vQgT8o+LLsJMdEFxeWC4spreWcAgq5C0nxWeCWpPiPA8kXXNR9kNTpJehACWLRLYRtpoosHVFIaNNmbeFny8klOg2ouS17oovuQjZcVUsOoCTegJlzP56EG1JrLxrwu3IAaI3A6mICJh0vpyHKlI9uZjkxnGrJc6ch0piPLtm4GUGb4RMESIQdm8eLFeOqpp/DAAw+gsLAQLpcLtbW1MfsoigK3m98V1eVyNQqYFEVBZmYmXC6XtR6/ncaTIoQQkio0nUESD7/vSyQxCqUOZ/ZwKlTOw6nAPv7F25HJ79h3GIYrJrMlVIMSsEIofySAsBaBJEjwyE5kOtPR3ets+WSkESbKUMVsqI5stLqDGFMhq34eUGl8Lms+SPYyzQdZ9Rvb/Tz40niIwwd5D7e6e2HMQxsttDTJA01KM+behPMQXKjQBFSoGirUCCoiEVRFQqgKB1Cj+FATqjfCJh44BdVWDNgVR4BgBUg5rkxkOdOR7TLDpgw+d2Ug2wigeOCUTl3hCOlAt99+O5577jksXrwYJ598MgCgoKAA27dvj9mvsrLS6pJXUFCAysrKRtuHDBmC7OxsuFwuVFZWon///gAAVVVRW1uL/Pz8DrgiQggh5OBJooCrnv8a28t9ya7KAZkyKB/Xnzw42dXoMiiUItFwKv1oPhB6qBwI7edjTwkS4MwEpLQuH1CZY0LxMXUaUB6sgT8SRFiLQBAEeGUXspwZcMsUQiWNIEN1ZEF1ZLXtOGN8LEkLRAMrLQBJNco0XiapAdt6wJj8EJmGsM5QFvFjv+ZHmVqJMg18Uvm83Fgu14Bqve2X5hQEdHM40c3hRo7TjVynFznOdOS4MpDtzkS2KwtZnlxkurKR5c1DuisXkkT/hBPSWS1duhTPP/887r//fkyfPt0qHzVqFFauXIlQKGS1jtq4cSPGjBljbd+4caO1fzAYxPfff4+5c+dCFEWMGDECGzduxLhx4wAAmzZtgizLGDyYPhwTQghJHdvLffiutD7Z1Tgg/fPTkl2FLoW+0ZAo0QF4CvikDeQBVbAMCJfzZdHBu/d1oYAqpIatO4mVB6rREAkgqIYgQIDX4UamM51CqK7ANj6W4ooWa7qOmnA9KkO1qArVoUqtQ1XEWA7VoSpUi6pQPapCtfBFgm16SBFAniSguyygQNRRIAP5EtDdNuXb5hkigyCEwW9zWBd7MrM47ve2Jrqgi25okpvPRRd0yQ1NdEOX3NBFl7Vs7quLLmN/V6NlXXSBCXKXeX8Tkiw7duzA8uXLMXv2bIwZMwYVFRXWtuLiYvTs2RMLFizAFVdcgffffx9btmzBokWLAABnnXUWHn/8caxcuRJTp07FsmXL0KdPHyuEOv/883HLLbegsLAQ3bt3x8KFC/H73/+euu8RQgghJCVRKEUSk9yAtxef1ACgmAFVZTSgkjMAObUCqoimosG4fX15sBq1YR+CkRAYGDyyG2kON7q5sw7LMaG6krCmoDJYi8pQLSqDdcacr1eZ81AdasL10Blr9Xkdooxcdxa6ubPQzZ2JXFeWsZ6JHHcmcl2Z1vZMZxpE8y6BjEFgEaPlVQiSHoSoBSFpAYhaCLV6CPVaEJIW5Hcx1ILWfpIWhKiHIWkhiDovF8CbYpndER1qXTO1bhsGCbrohCa5YsIqXXQaIVfjsujcWJZsy4LT2B7dl+6eSLq69957D5qm4eGHH8bDDz8cs+3HH3/E8uXL8be//Q1nnnkm+vbti2XLlqFXr14AgD59+mDJkiW46667sGzZMhQVFWHZsmXW76Xf/va32Lt3L2655RYoioKTTjoJ119/fYdfIyGEEEJIe6BQirRM8gDOHoDcHXD7gVAV4C8HgpVApBSAAEheI6CKG6/GHu6IojEJvNxcFsVDFmyZg5PXK35UBetRGeJd8jSmwyU5kebwIMeVEQ0PSKcW0VRUhmpREaxBRZDPK0N8udJar0W90vpB0EVBQI4rE93cWchzZ/PAyZOFXFcW8jzZyHVnIc/Nw6cMh/fAAktBABOcUEUnVEfbD49hBVxmWGXM9RBELWTMwxD1kK2Mr4uasZ8eNpaNcj0Mkam8qtB4GKa3rWVYW+iCbAVVLCa0clhBFotb10UnmLXuMNbNZWPdKNdFh7HssMooCCMdafbs2Zg9e3aT2/v27YvVq1c3uX3y5MmYPHnyAZ+fEEIIISRVUCh1OFNVwO8HAgEgHAY0DVAUPkUifLui8HJdBxiLzhkDIpqxTz0Q3gFoQQAMkFw8yBId0bBJMP4jCLGhlAD+ZdEMp5wOwCEDsgw4JEB2AJJoTDIgS8ayxPeRJT7Z7gxmdsmrCdejPFiDBsWPsBaBLEpIkz0o8OZCFuml35kwxlBrjONVEaxBebDaCp7Kg9WoDNaiPFiD2nBDq8/pFB3I82Qhz52DPE828tzZyPPw0Mmau7OR48pMrTvL2QKudsVUSFqYB1S2iQdXihF02SfFmPg+grEu2bYLzCjTwhBZ9G5hIlMhaiqgHfhdBNtKFyRbUGUEXoIDTJSNucM2l5teF2Qr7LKOtcrk6P6CbDyeHLtdkCkgI4QQQgghxEDfzA8XjAHBIODz8am6GqirA0IhHkABPCgSBB74WK2ajEmSotvNlk1CJiD04Mu6Cqg+IOIDIpW8y58W5sGU5AYEl7GfEWiZczPo0gFoESAQMtZ1QDMCMJjdqwS+LIpGEMUDKk0U0CBEUC9rKBeCqBEj8CMCQZLgcXmR5U6D25keDbFIh1J1DZWhWpQHqlEerDHm1SgLmMFTDcqDNYjoaqvO5xBl5BshU74nB/meaOhkLud7cg68VdPhSpChyTI0HKKBG40WXmaQFQ21jMkIsISYskhsGYtA1CN83TqXAkGPGMerRhBmHIfoiPMi0wCmQdJbfa/HQ4ZBNMKqaFBlhld8kmwhlsS3G4FXzLptfyYax9mOYTHLTe3T3JxPFKIRQgghhJBDhUKproox3gKqoYGHTxUVvFVU0OiS43QCHg+Qm8uXD5oDcHkA5AOsL6D6+RSuBiL1gFbLd5PdPKQS3Qf+UJqOYCSIOsWHWsWPMoUPQh1WFbiYiHTBgWw4IQoCgPpoSyrJaFXlcgAeN2+VJUm8ZZbVOkvmZaRVVF1FRbAWZYFqlAWreOAUqEFZsBplgSqUB2tQFapt9bhNua5MK2jK9+Sgu9e2bMyznOkUNqUio4WXJjqhIb1jHpNpPMQywiy+rNjK1Og2pkDQVYgsYoRckei6GYYx1To2uk21tiUsY6rVNdJ6KqBD0hUASuJ6dzIMQqOgKjo1VS6BITbY4vuKtuPERvtF95XAEHsMBBEMEiK6CoUdxO8QQgghhBDSaVAo1ZUoCg+g6uqA8nIeSAWDvIWS2w14vTyEOtRdlQSR36XPkQF4egBqyAipGgClhreo0mr4fpKHB1VC8wPt6ExHvRZEvRpEeaQe1aofAS0MQRCQ5nEhNy0DzoRd8hhvcaVqgG50NwyFgara6HYGHkSZoZUsA1434HYBDkdsaOU8fEIrnemoCtWhLFCF/YEq7A/woKnMNq8K1YGh5cBJEiR09+SguzeXzz256O6Nnee5s+GQ6J8k0o4ECbokAXBDS2Y9GLMCKh5Y8TAsumyGVxEITIOgx63HBF7Gum0/gWkQbeePbjPLbce0MBdZ42dKAG/lBhZJwpOX2NGCE8HR1yW7GoQQQggh5CDRN8BUpuu8K15dHVBVBVRW8tZQjPFWUF4v0K1b8u+OJ7v5hG4AO5KPI6P6AaUBiNTyMan0CB8kXXLzMalEJ8J6BHVqADWqH/uVevi0IBSmwSnISJdcyHF6WzFAuRANnJqjaXxSNSCsAIEgX2eA1WXQHMfKYYRWHndsCytHarW08ikBI2yqwv5AJcoC1dgfqLJCqPJgDVS95a/ysiihwJOLAm8uuntyUeDthnxPDnp4c40QKhe57kwaTJ4cvgSBd9UDD9+TGpC1hDEAmhFU2ScjtNJVAHpcoNWKKe6cYHHnsM6pG2U6EHN8dDvTI6hwdEeBeLB3DSCEEEIIIclGoVSq0XUeQNXWAvv389ZQ4TBv0ZOeDvTs2blDEUEE5HQ+uQt46yUtCGh+sHAdfOFy1IXKURGuRZUWgB8ABAe8she5cnoTraHagRlcNdWTUdd5KyvNCK38geig70A0tDJDKo+Lt7RyOmIHZDeXD/HPyBzHab+/0gqe9tmW9weq4I+0fHc1SRCR58mxQqce3m4o8PLgyZzT3QsJ6UIEAYAxHlWy69IEJRJAjX8fCpJdEUIIIYQQctAolEolug789BOwbRsPR7xeICuLd81LUSoY6piKWk1BmRZEnc4QEiQ4nFlIZ1noxVSILMwHQdcj/A58goO3pkIHBiGiGB17y5Nguz20UuJbWoGP0S4Zdw6UJR4iupwHHFyF1LAVNO0LVBrLvMXTfn8VyoPV0Jje5PGmLGc6Crzd0MOc0rrZwqduyHNnQxY7cchJCCGEEEIIISRlUSiVKnSdh1Fbt/IueV5vsmt0wIJaCLURH6ojdSgLV8OnBqAxHV7JjUxHBrq782x7M0BTAC0E6CE+HpUa4oOn6zoPe0SHMTnRoUGVnT20SogBqm50EVSBYBjwBRoHV0YY5YOCfbofpYIf+/QG7FfrsS9ci33hGuwL1aBGaWixSpIgoYfRmqlnWl40eDLWC7y58MipG2gSQgghhBByuNF0Bkmkm/6QroNCqVRgBlI//MAHKk+xQEpnOhrUAOpUH8rD1aiO1COgBiEIItIlDwqcuZCb7JYn8FZRkgtAFoACgGlGSBXmc9UHaGFADfCAxwyqBBmQnAA6Q0sfwWoJxZwONGghlCoN2KfUYJ9Si31KLUqt5To0tOK29V7BgR6OLPR0ZKOnOycaOHnz0DMtD93SciDJMm9dZrbQOtSD3BNCCCGEEEIOGUkUcNXzX2N7uS/ZVTkgUwbl4/qTBye7GqQToVCqs2MM2LGDt5DKzQXS0pJdo1aJ6CrqVB9qlQbsVypRF/EjrCtwiU6kS17kujMhHOgA7IIEyGkAbM+FHuEhlR7mAZUWMFpU+fi4VQAgSjyoEmUeWh2iVlWMMdRrQSNoioZNpUb4tE+phV8Pt3ieLMmLns4s9HRmRydHNno4MtFTykAmc0LQdX53Qd1oheVjgA8AqvgkicZ4V+YkAy4H7zLodEaDKrO7oCTG3okw2YPkE0IIIYQQQmJsL/fhu9L6ZFfjgPTPT43vs6TjUCjVmWkasH07D6Syszt9IBXQQqiL+FCl1KJcqUGD6gdjDB7JjWw5A26pue5tB8nswof0aJmuAroSndQgD6v0iNGqyjZIuSDzsEuU+TKaDmPaK3TqJqejhzMLvZw51rynIws9nTno6cyCV3Id3HMCBujMuLOgHh3vKhjiIZY1ULvA9xWM1lyCGA2zZImHV06Zj4NlhlhmaCWa+xmBlyhQayxCCCGEEEIIIa1CoVRnpSg8jNqxg48h1QkDqaa65YmChDTJjR6uPMhCErvOiUarKNi7OzKjVZU5GeNVaUEeYml8nKd6LYTSSD32qT6URhqwL9KAvZE67FP41NrQqaczG72cOehla+3Uy5mNHs4suMVDGNIBAIRoSNSaO6czHdCYcUdEowVWSAEC8SGWcW6An18SY4MsSYqGWA5j4HYzsLJaY4m85ZokGHORwixCCCGEEEIIOcxQKNUZBYPAt98CJSVAQUGnurteom55ih6BU3QcfLe8DiEAohM+XUWpUofScCVKQxXYF65Eaagc+0IVKA1XwacFWzxTN8mLHs5M9HJmo7czBz1d2ejpzEUvZzcjdGpNEtSJCKLxL0IbgkQzrNJsc7Wp1liA1SJLFKOBmbVsBFgOCXDIRpgV1wLL3h1RlKJBF7XQIoQQQgghhJCUQ6FUZ1NXB3z3HVBWBvTuzb+YJ5nZLa9SqUG5UgOfGrC65eU4MuASHICmQYyoEPw+iEoEohKBEIlAiKgQIxEIijFXVQgRPonmsqpFyzUtuq5pEDTdWNatdZjlmsbHVNJ1Y2wlBoHx9XpRRYlHwR5PBHvSFOz2RLAnLYLdaSr2pKmodektXndeUEQ/n4x+PglH+mUc6ZPQ1yejr0/CEQERHk0EH1m9DhDqwATw7EUQwEQhOhcFMJGPz8SMAIWJPFRhogBmBC3MLJdEMFEEk3gZJBFMkvh6zMTLrO1yE3NJhO6QjGUJzCGBycb5ZL6sG2W8K2MbQkWr+15bXlFGt0JdM1pmGeGVqgJhhYdY8YGWYA+2wIMoQYgLtkT+fjEGlIdDsgVaUuNgSxSiLbwECrcIaQkzWkoyMDDG57Atm+WIWTbXADBmLTNmlVrntm+zysGMbcbjMQZNV+jDCyGEkANCd64jpPOhz3Wdha4De/cCP/4I+P1Anz6H7stxJAJUVwNVVXxeXQ0EAkAoBIRCYIEAlKAPkaAP4UADtGAAcjiE3mEV/SIa5HAEkqJCUBSIYQWiokDQWcuP245q3cDP2cDuLD7/OQfYnW0sZwM1npbPkecHjqoF+tmmo2r4vG8d4I3oAJRDdAWdl24EVcwh25aN8Mpalm1hlgzmkKA7ZGtf3WGUGduYQ25UpjtlMId5HhnMKVvn0B0ymNtlnR+S7b1gdjNkuhFu2UItRYktM74gx44RFtdaC7YuiCKiXRDNcbNkKa7Fli28im/tJSRqAUYh1+HICllaGd60FNwkCm1it0VDG3PZLIf1eHw5KtqCUYAACLYeugn2NfNqAQIEQYD5P2sPAVaZ2WKW7wdrf9vZIEKMHiPyY0TjeFEQ+bpgrhtlECAwFU49ALfceVoRE0IISQ105zpCOh8KpTqDYBDYtg3YtQvwenkg1VaMAfX1PGiqrIydm5O5XlfX7KkEAC5jSm92z8R4cOGA7nTw0MHhMAIJcy4bQYYRQMiyFYIwiQcdlU4dJZ4wfnGFUeIKY68jiBJHECWOAPZKftSLkRbrkcPc6CNkojey0FvINKZs9BGy0EvIQlquE6ybYMsrol+Yfok5k/Xnfgj82yNfMZYFc1lnxnY+CUY4IhjhiaCpvJWXrgG6CkFV+VzT+DY1AkFT+aRq/FjNbCXGu8aZy4JuLjNj0htPqjnXonNzMtZFVWv0vImqBqgaH0+qk2CiaIRYkvUaskIup31djlmPLktx2yTrtcZkCbok8nOZZaIIXRaN16fRik02ujUKxsDwTIjNuszWW2YLLjPkEsyuibY7HJrL9q6LAqIt1eKDLZjrMM5rhmBCl7xDYnygEw1kDjzM0WNa+RxciCOY/2H2PZoOcMwwBUBssNNCeBMT3AiCFdKIEGNCGxEiRNHYF4K1LJrHQIQoivz8sD+e0OhxE5XF7N/oWKFRGBVTJghx1xsbapnnbhNdAZQaQEqxLtKEkMNaV2ih0xWuAaA71xHS2VAolUyMAeXlfEDz6mqge/fmx4+qrwc+/hj45ZfYwKmykh8faTmosR5aksByc6DmZiOUlYaAW4LiFKG6nBDdXkjeNAhuD3S3E7rTGZ27nGAuJ3SnA7oxZ04nDwBcTjCHI7ZVSwIa01Gp1GJfuBL7Q5XYF67EvnAV9ocrsc9YD+ktByI5jgz0dOWjlzsPPV156OHKQ293Pnq68tDTnQev1PRzqQHo1L+KmGZMeuwcmtECSANYhA/OzlRjrgMwJsaMfRDz5RlA9Bu1wH9OgorYYCvCIGgaRA0QIhoPt1Sdd89UNQgRHmbxZV4mxs2FiLEc0YyumsY8ovKunOax1n4qRHNfYy7YmmwIug4ppAChjvoBNMYEoXEo5rSFY/bwy2wxZu8eaQ/BzMDLIRvdK42A1hltnWaFcE4HmMvBw1uXbIRftsBLEGPvhChFJyZKYAKDLgoARDCB8W6bDGBGqzAGQBeNl4gg8OsUmDEXAYFBB3gw10ywozcR6tgzXzNQitsC+ws0JsSIa43TVEsc0R5wWC1tBKsFTvNBjmS0wuHhUXRbNMRpS4BzMOFNU8FN5x6njxBCSGukegudY/vl4OYZw5JdDUJIF0ShVLJUVwO7d/PBzCWp6e56DQ3Ahx8C774LfPYZ76LUnMxMfre+vLzoPDcXyMuDkpOJhmwP6jKcKHUrqNeCCOsROAQZ6bIHaZIHonDwXY2CWghl4WrsD1dhf7iKh0/hKuwLVaIsXIUypRoqa9xCJ143RxZ6ufPRw9UNPV15RvjEQ6germ7wNBM6pTxB4lNbWOGVDkCzBVP2oMoIr5jKx3SCBiapYMzYn0cUiLYEs99xL1E97fU1C8zJHJ9KsM2NfQTRdnCC1xxjgKZbwVaj4EqJxJUbYVckGn6Z+1jrYd46KQkk6AABAABJREFUTYxoEJRI43MrtsdQ+PH2lmQCY0Z5C+/BQ0yXeCsuzSFDl0XosgTNCMB0h7FulOtGAGaFYfbuk7IIJvMgTXc4wByi1YJRd8qA2arRKYM5HYDDAcHlBJxOMI8LgtMJ0e2CKMkQZAmiKEGSHBBFEaIoQ5RECKIIQTAnAYIkGXMjHBJlo9uWCEG0bzOOFQWjnC/zVkMU5hBCCElNqd5CJ9WDNer6RkjnRKFUR2toAPbs4YFUJALk5wMuV3Q7Y8BPPwHr1wOffgps3gxotgBnwABg5Mho6GQGT2b45HRau6q6inrVjwY1gAqlBjWRegS0Bj5Iue5GlpwBt+REW6hMQ6VSizIjcDLDJ3N9f7gKdWrLv6gkiChw5aKHqxt6GC2beGunbujlzkeBKxcusW11O+wdSJBlsQVQVmBoBlW6LZzSo2FVTIsszRaG2Vt3sei5waITQ7TMlnnpjPFWOvaxdxzgk2B22RLBBDdfFgRbtyyBR2pmlyoB0XUh+ui62ZbHbGnDBKOVED9IgGBs48GcpOoQIiokRYMU0SBGNMiqBtlcVzXIER6gmduliAZJia5Ht6mQ4vYVjSBMNIIxe7AmGEGZvdWYqOkQNR1yOLnhmEWWogPMm3dNlGVjWbKtm2N0OWL3ddr2d5jbHMa6MRmhGBwy//fS5eBlLicvd/OwDC4n4Da2m/tLRmAqiHHdK+OW7YP829ft3SrN9fj9CSGEHDJdpctYV5HqwRohpPOhUKojaBrvZldayu+qFwjwMCktjbd8+v57Hj5t2sSnqqrY448+GjjpJODEE4F+/Zp8GFVX4Ys0oEH1o1ppQFWkFn41CJVpcIoOpEseZLsymmwNpTEdVUotypVq7A9XoyxchfJwDcoUHj6Vh6tRodRYX+qb45XcVgsnHjzZlt15yHfmQGqHVlmkbWIHS9at7lc6s0bV4cGQsb8OgDEBzOz6xQQe9ghGVy0hwbnMVlYAb4EFYywuYysYIBjH8+AKEITonfYEgUFk5tBNjHfDYuBjc/Ga8FZLYEZ7LGPOGESBQWQMkiBCYAIkwRi7XBAgMtFal8wuX+aY5yy2jZcAY8BlARBcgvE4Is81mLlu67qF6Nwcx4evi9FuZEaLIf6EmD8RwbaSeHwzMPCB3SMRQFGBiAYoEWNgd5UvRzS+XbWvq3xSVL7NWrctR1TbeW3rkWaO0eJaOZpjkHVW9tAsfgB7cxB9+10bzRDNflx8uRl2yRIPx8ywzSlHwzCno3GoZg/XzHJZjg3grO2O6J0h7QGZFY7ZxhOLL0u0X1PHEkJIJ5fqrXMAaqFDCCHN6dBQKhwO49Zbb8U777wDt9uNWbNmYdasWR1ZhY6j63wMqNpaPgZUdTXg8/FBxktKeGuobdv4PBQ3UI7HA4wdCxx/PDB+fJMDn0d0FT41AJ8WQLXSgOpIHQJaCIoegSxISJM86O7MgSzKCGghVCg12OrbjUqlBuVKDcrD1ShXalBhLFcqtdCgt3hpkiChuzPHCpsKXLkosIVOBa5cpEvew7rrTEvhj338HSPGiRvMmUVb9Jjj9djH7rEN6AwjvLG6xlllQnQbAAiCFXWYAyLHhCpx4+aY4aUgCJCsAZRFSIJojbkjGWcSBaNc4OuSIBmDOyN6xywhemctazlubB779phxgRotizFj9jQKWu2ttMx1W1hmdVG0WmrZWnTFrNvOEdPSy1bG4rabx1uPqcceZ7Y4A2zL9vPZW5AZ1yGChxRe2I6D7TjEnSO+LG577AjdjfdpdH7bjqpuBFFmiGULwFT7chPb1Kb21/j+5nGqGg28InFlEds2++MkCsc6e2jWHEEwxgeTbGOFxS9LgCwag+kb44qZd4w0xxqz72eGcObx1r62AE4ygzfRFt5JttZucrSlm2ye19hmDupvTY7Yx7VvE41zmtfkkGMDOPM5sAdrAH+PiC3/cYQQ0nWkcuscgFroEEJIczo0lLr33nvx7bff4qmnnkJpaSnmz5+PXr16Yfr06R1ZjUND1/mA4zt2AD/+yAcv/+WXaOuoffuAmprEx2Zk8C55o0bxacSImG54AKAzHUEtjIAWgl8LolqpQ4VSi7JQFSojtahX/QhqYfjUAGrUBlQqtahS6lAZqUWVUgu/1roRoiWIyHPloMCZa4RNueju5KGT2d0ux5HZqVo5NRnasGjg07glEP9Co9sCI+suXcZ5eFxgREOMgRkBjyAI0dxCYLzLlwBrEGh7GJco/LGHPWYwA8AKdARB5C15IBrhDl+WBck6RjZCn+gxsWGNfeyd1gQ70f2bPjalQkZBjB1HuyuID4piAjMkKIsPmMyyJgKnRkFWU8c3s2+iesZWsIltiY5vzfa4ZWMsMqvFmBmcqRG+rpmtzCLRVl/x262wTQU0ewgWP2lxy/HrtnLNvmwL0OzleoI/CDCW2qHagTBbhknmnSnNZdtA/oIIDB8IvDIRoBvwEUIIIYSktA4LpQKBANasWYNHH30Uw4YNw7Bhw7Bt2zY8++yznSeUCgZ5Sya/n3ex8/v5GFANDbzVU0MDD5YqK4GKCt76qaKC30GvshIIh1t+jLw8oLAQGDgQKCyEOuBo+Hp2g08Lwhfxoy7SgMp9H6FSqUWlUouaSD3KlGqUKTWojtSjVvOjTvOjXgugXg+26fLSRBfypEzky5nIl7PQXc6y5t3lLBTIWciVMhIHThqAAMD8ITAEoYIZ4/REu3tZLX5srYCssAiArtu2G6UxYZG99Q/M8IeHPoDZEkhE/Bdas5uU2aUKEPgwMVaXKnurmmjXLN7Nirf2kYwgRhZEiEyItvYBbwEkCVJMYGO16AEgilJsly+jHgmDIAh8AGfzXECj/ZsNf+K3WSGE+ZxoifdvbrDyps59MFpzrgN5vJaOaY9raK+6N7XPwdbxYJ+Ddn0O7V0PD+K8B7ot0XZZ4L/Z3LZtLf0s4rvBtWa/1tStLXQ9GorZp0Rl8dvi99G05ssSzeO3248369bUceY+9rL469H12H2awlj0HM3Z+AOgdrXkmRBCCCHk8NNhodTWrVuhqiqKioqssjFjxmDFihXQdR1iojvPdaDg229gzd/PQLUcgS7AmjQB0MTYZdWYNAFQewCR3kDEKIs4RCheFxS3g09OCUGXhLBTQFACQoKKoP49gvrXCKoKIj9owA8HXm8RArKcmch1ZiHHmYUcVyafO7OQ68pGtjMLua5M5Diz4ZE9RvQjRFsAGQESBAFhCCiF0eML0VY/jDGjzByWxAxY+B3U+FjBtgjIPoYOBGtMHwEiZFGK7foliJDE2PBHFBO1/rF394rvEmYLiuKPsbcGEqLRj1l+0FoT9hzM/ofyMVraz769uX1bs198eVseu7VlrT2mrfVqy2McyPbWHNua57WlfVr7s2nttrYe29rnobWP19z52vJcJipvbVmidUGIltmX4zW1X6JjBCHaBc+8MYa5X1P7J3q8RPNE+yfa1lwQ19z+TR1jD5/MwMqc7CGZuW7fpij8JiGOw7eZ1GE1HAIhhBBCurQOC6UqKiqQk5MDp61bWl5eHsLhMGpra5Gbm9tRVUno2ar/4rIZkXY4kw4gaExxxc0M1yQJEjyyG27ZDa/DizRHGtKc6UhzpiHDmYEsdxYyXZnIdGYi25ONHHcOcjw5yHRmQhZlHsbYAxmBt/SRBAmCKECCxMMfo8uXGf7IohxzjH05JthpYltb9iOEdAHtHW4daFCXaL2l5dZut8b+amZ7c/PWPkZTx7TlcZrbxz61piz+8c0uhbreeFtz57Tv31Q9Ad4dzwyW2hp8u918jKrDVJceDoEQQgghh5UO+0QXDAZjAikA1rqiKB1VjSadcuq1uPjdCuyr+wWSKEMQjfF7jCCHhzjRYEcWZIiiCIfogCRKkEUZksDnTskJWZQhizJckgsehwdu2Q2PwwOPw4MMRwbSXGnwyl6+7syAS3YZLY3QKMyJnycKhCgAIoR0iEPVLZF0fW0JEJtbBvjr7TBtKZUSwyEQQgghhLRSh4VSLperUfhkrrvd7o6qRpN6ZfTCk2c+nexqEEIIIV1TS136SKt09uEQSOei6QySSO83QgghnVeHhVIFBQWoqamBqqqQjSb3FRUVcLvdyMzMbPF4885mPp/vkNaTEEIIIQQA0tLSOl3r44MdDqGjPk91hTCkK1wDAKz4YAdK69p2c5zOYkSfLJwz5gj0yxShK6nbOrLAw99zqXwddA2dA11D59AVrqFfpthh2UpLn6c6LJQaMmQIZFnGpk2bMHbsWADAxo0bMWLEiFb9Vc/v9wMAJk+efEjrSQghhBAC8M8p6enpya5GjIMdDoE+T5FU8gaAu5NdiXawE0Cq98ega+gc6Bo6h65yDWMWdcxjtfR5qsNCKY/Hg9/97ndYuHAh7rrrLpSXl2PVqlVYtKh1z0T37t3x4Ycfdsq/WhJCCCGk60lLS0t2FRo52OEQ6PMUIYQQQjpSS5+nOvTWNQsWLMDChQtx8cUXIz09HVdeeSVOOumkVh0riiJ69OhxiGtICCGEENJ5HexwCPR5ihBCCCGdicBYc/dhJoQQQgghnUUwGMS4ceOwatUqaziEZcuWYf369Vi9enWSa0cIIYQQ0jZ0ixZCCCGEkBRhHw5hy5YtWLduHVatWoWLLroo2VUjhBBCCGkzailFCCGEEJJCgsEgFi5ciHfeeQfp6em49NJLMXPmzGRXixBCCCGkzSiUIoQQQgghhBBCCCEdjrrvEUIIIYQQQgghhJAOR6EUIYQQQgghhBBCCOlwFEoRQgghhBBCCCGEkA5HoRSAcDiMm266CWPHjsXEiROxatWqZFcpZZWVlWHevHkoLi7GpEmTsGjRIoTD4WRXK6XNnj0bN954Y7KrkbIURcGtt96KY489Fscffzzuv/9+0FB6B2bfvn24/PLLccwxx2DatGl48sknk12llKIoCmbMmIHPPvvMKispKcHMmTMxevRonHLKKfjkk0+SWMPUkei53LRpE8477zwUFRXh5JNPxpo1a5JYQ9IZvfvuuxg0aFDMNG/evGRXq9Oif7PaLtFzdscddzR63a1evTqJtewcmvvOQK+zxJp7zuh11rTdu3fj0ksvRVFREaZMmYLHHnvM2kavtcSae84OxWtNPtgKdwX33nsvvv32Wzz11FMoLS3F/Pnz0atXL0yfPj3ZVUspjDHMmzcPmZmZePbZZ1FXV4ebbroJoihi/vz5ya5eSnrjjTfw4Ycf4owzzkh2VVLWHXfcgc8++wyPP/44/H4/rr76avTq1QvnnXdesquWcv7617+iV69eeOmll7B9+3Zcd9116N27N379618nu2qdXjgcxrXXXott27ZZZYwxzJkzB4WFhVi7di3WrVuHuXPn4s0330SvXr2SWNvOLdFzWVFRgcsuuwx/+MMfcPfdd+O7777DggULkJ+fjylTpiSvsqRT2b59O6ZOnYrbb7/dKnO5XEmsUedF/2a1XaLnDAB27NiBa6+9NuazXHp6ekdXr1Np7jvDDTfcQK+zBFr6nkWvs8R0Xcfs2bMxYsQIvPzyy9i9ezeuueYaFBQUYMaMGfRaS6C55+zUU089JK+1wz6UCgQCWLNmDR599FEMGzYMw4YNw7Zt2/Dss89SKNVGO3fuxKZNm/C///0PeXl5AIB58+bhnnvuoVDqANTW1uLee+/FiBEjkl2VlFVbW4u1a9fiiSeewMiRIwEAs2bNwubNmymUaqO6ujps2rQJt99+O/r164d+/fph0qRJWL9+PYVSLdi+fTuuvfbaRi30NmzYgJKSEjz//PPwer3o378/1q9fj7Vr1+LKK69MUm07t6aey3Xr1iEvLw/XXHMNAKBfv3747LPP8Nprr1EoRSw7duxAYWEh8vPzk12VTo3+zWq7pp4zgL/uLr30Unrd2TT3neGEE06g11kCLX3PotdZYpWVlRgyZAgWLlyI9PR09OvXD+PHj8fGjRuRl5dHr7UEmnvOzFCqvV9rh333va1bt0JVVRQVFVllY8aMwebNm6HrehJrlnry8/Px2GOPWf9Qmnw+X5JqlNruuecenH766RgwYECyq5KyNm7ciPT0dBQXF1tls2fPxqJFi5JYq9Tkdrvh8Xjw0ksvIRKJYOfOnfjqq68wZMiQZFet0/v8888xbtw4vPDCCzHlmzdvxtChQ+H1eq2yMWPGYNOmTR1cw9TR1HNpdmOIR79/iN2OHTvQr1+/ZFej06N/s9quqefM5/OhrKyMXndxmvvOQK+zxJp7zuh11rTu3bvjn//8J9LT08EYw8aNG/HFF1+guLiYXmtNaO45O1SvtcO+pVRFRQVycnLgdDqtsry8PITDYdTW1iI3NzeJtUstmZmZmDRpkrWu6zpWr16N4447Lom1Sk3r16/Hl19+iddeew0LFy5MdnVSVklJCXr37o1XXnkFK1asQCQSwZlnnom//OUvEMXDPpNvE5fLhVtuuQW33347nn76aWiahjPPPBPnnHNOsqvW6Z1//vkJyysqKtC9e/eYsm7dumH//v0dUa2U1NRz2adPH/Tp08dar6qqwhtvvHFY/6WTxGKMYdeuXfjkk0/wyCOPQNM0TJ8+HfPmzYv5DEjo36wD0dRztmPHDgiCgBUrVuCjjz5CdnY2LrnkksN+WIbmvjPQ6yyx5p4zep21zrRp01BaWoqpU6fi5JNPxl133UWvtRb8f/buOz6qMv3//+uUacmkQUKH0KuIgAsiNti1rOva3XVVXBQVP4qsBQusBQVE5YsFsbFiQ1f92VjbFlkVG6KyAoqC9E5ISJ3J9HN+f5yZk5kUSCgpcD0fnsepM3PPeEjmvHPd96n+mf3444+H5Fw74kOpQCBQ48tIYj0cDjdFkw4bs2bN4qeffuLNN99s6qa0KKFQiHvuuYe7774bt9vd1M1p0SorK9m8eTOvvfYaM2fOpLCwkLvvvhuPx8OVV17Z1M1rcdavX8+oUaO44oorWLt2LdOmTWPEiBGcffbZTd20Fqmu3z/yu+fABINBbrjhBnJzc/njH//Y1M0RzcSOHTvsf3OPPvoo27ZtY/r06QSDQe68886mbl6LID+zGm7Dhg0oikL37t257LLL+Pbbb7nrrrvwer3S9T1J8jXDCy+8IOdZPSR/ZqtWrZLzrB7mzJlDUVERU6dOZebMmfIzrR6qf2YDBgw4JOfaER9KuVyuGideYl0Cgf03a9YsXnzxRR555BF69+7d1M1pUebOnctRRx2V8tcQsX90Xcfn8zF79mw6duwIWBcmr776qoRSDbRkyRLefPNNFi9ejNvtZuDAgRQUFPDUU09JKLWfXC4XpaWlKdvC4bD87jkAfr+f6667jk2bNvH3v/8dj8fT1E0SzUTHjh1ZunQpWVlZKIpCv379MAyDW2+9lcmTJ6NpWlM3sdmTn1kNd+655zJq1Ciys7MB6Nu3L5s2beLVV1+VsCCu+jWDnGf7Vv0z69Wrl5xn9ZAYpzcUCjFp0iQuuOACAoFAyjFyrqWq/pn973//OyTn2hHff6Vt27aUlJQQjUbtbYWFhbjdbjIzM5uwZS3XtGnTeP7555k1axann356Uzenxfnggw9YtGgRgwcPZvDgwbz33nu89957KeOeifrJy8vD5XLZgRRAt27d2LlzZxO2qmX68ccfyc/PT/lF3b9/f3bs2NGErWrZ2rZtS1FRUcq2oqKiGqXkon58Ph/jxo1j7dq1vPjiizK2hqghOzsbRVHs9R49ehAKhSgrK2vCVrUc8jOr4RRFsS/eErp3705BQUHTNKiZqe2aQc6zvavtM5PzrG5FRUUsWrQoZVvPnj2JRCLk5eXJuVaLvX1mPp/vkJxrR3wo1a9fP3RdTxnQbNmyZQwcOFDGnNkPc+fO5bXXXuPhhx/md7/7XVM3p0VasGAB7733HgsXLmThwoWMHj2a0aNHs3DhwqZuWoszaNAgQqEQGzdutLdt2LAhJaQS9dOmTRs2b96cUlm6YcOGlHF8RMMMGjSIVatWEQwG7W3Lli1j0KBBTdiqlskwDCZMmMC2bdtYsGABvXr1auomiWbm888/Z/jw4Sl/Ff/555/Jzs6W8UPrSX5mNdxjjz3G2LFjU7atXr2a7t27N02DmpG6rhnkPKtbXZ+ZnGd127ZtGxMmTEgJTX788UdatWrF0KFD5Vyrxd4+swULFhySc+2IT108Hg/nnnsuU6dOZeXKlSxatIjnnnuOyy+/vKmb1uKsX7+eJ598kquvvpqhQ4dSWFhoT6L+OnbsSH5+vj2lp6eTnp5Ofn5+UzetxenevTunnHIKkydPZvXq1Xz++efMmzePP/3pT03dtBZn9OjROBwO7rzzTjZu3MjHH3/M008/zZgxY5q6aS3WsGHDaN++PZMnT2bt2rXMmzePlStXcuGFFzZ101qcN998k6VLlzJ9+nQyMzPt3z3Vu4CII9fgwYNxuVzceeedbNiwgcWLF/PQQw9x1VVXNXXTWgz5mdVwo0aN4ttvv2X+/Pls2bKFv//97yxcuPCIH0Jgb9cMcp7Vbm+fmZxndRs4cCADBgxgypQprFu3jsWLFzNr1iyuvfZaOdfqsLfP7FCda4ppmuZBan+LFQgEmDp1Kv/5z3/wer2MGzeuRgIo9m3evHnMnj271n1r1qxp5NYcPu644w4AHnjggSZuSctUUVHBtGnT+Oijj/B4PFxyySVcf/31KV04RP2sW7eOGTNmsHLlSlq1asWll17Kn//8Z/ksG6BPnz689NJLDB8+HIDNmzfz17/+lRUrVpCfn8+UKVM4/vjjm7iVLUPyZzlu3Di++OKLGscMGzaMBQsWNEHrRHO0du1a7r//fpYvX056ejoXX3yx/D7YB/mZ1XDVP7NFixYxZ84cNm3aRMeOHbnppps47bTTmriVTWtf1wxyntW0r89MzrO6FRQUMG3aNJYsWYLH4+Gyyy5j/PjxKIoi51od9vaZHYpzTUIpIYQQQgghhBBCCNHojvjue0IIIYQQQgghhBCi8UkoJYQQQgghhBBCCCEanYRSQgghhBBCCCGEEKLRSSglhBBCCCGEEEIIIRqdhFJCCCGEEEIIIYQQotFJKCWEEEIIIYQQQgghGp2EUkIIIYQQQgghhBCi0UkoJYRo9vr06cMtt9xSY/vbb7/N6NGjm6BFQgghhBBCCCEOlIRSQogW4f3332fJkiVN3QwhhBBCCCGEEAeJhFJCiBahY8eO3HfffYTD4aZuihBCCCGEEEKIg0BCKSFEi3DjjTdSUFDA/Pnz6zxm165d/OUvf2HYsGEMHz6c6dOn2yHW22+/zZgxY5gzZw7Dhw/n2GOPZebMmZimaT/+tddeY/To0QwePJgxY8awZs2aQ/6+hBBCCCGEEOJIJaGUEKJFaNu2LRMnTuTpp59m69atNfaHw2H+/Oc/EwgEWLBgAY8++iiffvopDz30kH3M999/z8aNG3n11Ve56667eOmll/jqq68A+Pjjj5k7dy533XUX77zzDkOHDuXyyy+nrKys0d6jEEIIIYQQQhxJJJQSQrQYY8aMIT8/nxkzZtTY9/nnn1NQUMCsWbPo06cPI0aM4O677+bVV1/F7/cDEIvFmDZtGt27d+ecc86hb9++/PDDDwA8++yzjB8/nlGjRtG1a1duvPFGOnbsyLvvvtuo71EIIYQQQgghjhR6UzdACCHqS9M0pk6dyiWXXMKiRYtS9q1fv56uXbuSlZVlbxsyZAjRaJQtW7YA0Lp1a7xer73f6/USjUbtx8+aNYuHH37Y3h8Khdi0adMhfEdCCCGEEEIIceSSUEoI0aIMGTKECy64gBkzZnDVVVfZ210uV41jY7FYytzpdNY4JjGmVCwWY8qUKYwYMSJlf3KIJYQQQgghhBDi4JHue0KIFmfSpElUVlamDHrerVs3Nm3aRGlpqb1t+fLl6LpOly5d9vmc3bp1Y9euXeTn59vT008/zfLlyw/BOxBCCCGEEEIIIaGUEKLFycnJYdKkSWzfvt3eNnLkSDp37sxtt93GmjVr+Prrr5k2bRpnnXUWmZmZ+3zOK664ghdffJGFCxeyZcsWZs2axT//+U969OhxKN+KEEIIIYQQQhyxpPueEKJFuvDCC3nrrbfYvXs3YI039eSTTzJt2jT+8Ic/kJ6ezu9//3tuvvnmej3fmWeeSVFREXPmzKGoqIiePXvy1FNP0bVr10P4LoQQQgghhBDiyKWYiQFVhBBCCCGEEEIIIYRoJNJ9TwghhBBCCCGEEEI0OgmlhBBCCCGEEEIIIUSjk1BKCCGEEEIIIYQQQjQ6CaWEEEIIIYQQQgghRKOTUEoIIYQQQgghhBBCNDoJpYQQQgghhBBCCCFEo5NQSgghhBBCCCGEEEI0OgmlhBBCCCGEEEIIIUSjk1BKCCGEEEIIIYQQQjQ6CaWEEEIIIYQQQgghRKOTUEoIIYQQQgghhBBCNDoJpYQQQgghhBBCCCFEo5NQSgghhBBCCCGEEEI0OgmlhBBCCCGEEEIIIUSjk1BKCCGEEEIIIYQQQjQ6CaWEEEIIIYQQQgghRKOTUEoIIYQQQgghhBBCNDoJpYQQQgghhBCHFdM0m7oJohmS80KI5kdCKSHEQTVmzBj69OnDxRdfXOcxN910E3369OGOO+44qK/9+OOP06dPn4P6nPW1bds2+vTpw9tvv90kry+EEOLIs2zZMm644QZGjhzJwIED+fWvf82dd97J+vXrm7ppKRr79/OyZcu45pprGu31moNVq1Zx9dVXc9xxxzF8+HCuvPJKVq1alXKMaZrMnz+f0047jYEDB3L66afzyiuvNOh1HnjgAcaMGbPXY3w+H6NHj96v73mJcyV56t+/P8OHD+f6669n7dq19X6u5557jkmTJgFQXl7ObbfdxnfffdfgNu2PO+64g9GjR+/1mLfffps+ffqwbdu2ej9vfR5TUlLCKaecwtatW+v9vMn8fj/33nsvI0eOZPDgwVx99dVs2LBhn4/7/vvvGTNmDIMGDWLEiBFMnjyZoqKiWt/D73//ewYOHMjo0aOZO3cusVhsv9oqDg8SSgkhDjpVVVm+fDm7du2qsa+yspJPPvmkCVolhBBCHD7mzZvHpZdeSiAQYMqUKcyfP59rr72Wn376ifPOO48PPvigqZvYZN54441mF8wdSps3b+ayyy4jGAwyY8YMZs6cSTgc5pJLLkkJEx566CEeeeQRLrzwQubNm8fo0aO57777eP311+v1Os899xzPP//8Po+bOXMm27dv3+/3A/D666/b04IFC7jzzjv5+eefufTSSyksLNzn49evX88zzzzDrbfeCsDPP//MP/7xDwzDOKB2HUynnHIKr7/+Om3atDmoz5uTk8PYsWOZMmXKflWG3XLLLfzrX//illtu4cEHH6SgoIDLL7+csrKyOh+zcuVKxowZQ3l5OQ888AD3338/27Zt449//CMVFRX2ca+88gpTpkzhxBNPZN68eVx00UU8/fTTPPbYY/v1XsXhQW/qBgghDj/9+/dn3bp1/Otf/2Ls2LEp+z755BM8Hg+ZmZlN0zghhBCihfvkk0+YPXs2N9xwAxMmTLC3Dxs2jHPPPZdbbrmFO+64g969e9OrV68mbKloDAsWLMDj8fDMM8+QlpYGwHHHHcfo0aN5+eWXufvuu9m2bRsvvPACd911F5dccgkAI0aMYOfOnXzxxRf88Y9/rPP5t27dyoMPPsjHH39MRkbGXtuyePFi/vnPf+7zuH055phjUtaHDh1K+/btufTSS3nnnXf2WQk3a9YszjrrLNq2bXtA7TiUWrVqRatWrQ7Jc19yySU89dRTfPTRR5x22mn1ftz333/PJ598wrx58zj55JMBOPbYY/n1r3/N3//+d/7v//6v1sc99dRTZGRk8NJLL5GVlQVY5+Bvf/tbnn32WW666SYqKyuZPXs248aNs8PCESNGUF5ezldffcXNN998gO9atFRSKSWEOOjS0tI4+eST+de//lVj34cffsjpp5+Orqdm4sXFxdx7772MGjWKo446imHDhnH99denlCdv2bKFa6+9luHDhzNo0CD++Mc/snjx4jrbsWPHDk455RTOP/98ysvL6zzuhx9+YNy4cQwfPpwhQ4Zw7bXXppSHL126lD59+rBkyRKuvPJKBg0axMiRI5k1a1at5calpaUMHDiQhx9+OGV7IBBg6NChPPXUU3W2RQghhNiXuXPn0r17d66//voa+xwOB/fddx+apvG3v/0NgCuvvJLzzz+/xrHXXXcdZ599tr3+3XffcdlllzFo0CCGDRvG7bffTnFxsb3/7bffpn///rzxxhuMHDmSYcOGsW7dunr/fv700085++yz7a5jCxcuTNm/e/duJk+ezMknn8zRRx/NhRdeyH//+9+UY0KhEE888QRnnHEGAwcO5LTTTmPevHl2Bcwdd9zBO++8w/bt2/farf7xxx/njDPO4KOPPuKss85i4MCBnHPOOXz//fcsX76ciy66iKOPPpqzzjqLJUuWpDz2l19+Yfz48QwZMoQhQ4Zw/fXX1+gqtXr1aiZMmMBxxx3HgAEDOPHEE5k+fTrBYNA+pk+fPrzyyiv89a9/ZdiwYQwePJi//OUvKV2eEt21li5dWuv7AOjevTtXXnmlHUiB9V2sXbt2bNmyBYBFixbhcrm48MILUx776KOP8vjjj9f53GBVPm3evJkXX3yRfv361XlcWVkZd955J7feeush+ePjUUcdBWBXYT3++OOceuqpzJ07l2HDhnHCCSdQVlbGL7/8wqeffspZZ50FWN/jLr/8cgAuv/zylO6HH374Ieeffz6DBw9m5MiR3H333TUqgvb1PXFv3n77bU4//XQGDhzI2WefnfLvoraueO+88w5nnnmmffySJUvo379/jfN4xYoVXHzxxQwcOJBTTjmFZ599NmW/0+nk9NNP55lnnrG3Jb7P7m2oiS+++IK0tDROOOEEe1urVq341a9+tdfv3Bs2bGDo0KF2IAXg8Xg4+uij+fTTTwH48ssv8fv9Nbp/3n777bz55pt1Prc4/EkoJYQ4JM4888waXfh8Ph+fffaZ/SUhwTRNxo8fz5dffsmkSZOYP38+EyZMYMmSJdxzzz0AGIbB+PHjCQQCPPTQQzz55JNkZ2fzf//3f2zevLnG6xcWFjJ27Fiys7N5/vnn6/xy9PXXX/OnP/0JgPvvv5/p06ezc+dOLr744hql/5MmTWLo0KE8/fTTnHXWWTz77LO88cYbNZ4zOzub3/zmN7z33nspZdMfffQRlZWVnHvuufX7EIUQQohqiouL+fHHHxk1ahSKotR6THZ2Nscff7wd6Jx99tmsWrUq5fdleXk5n332Geeccw4A3377LWPHjsXtdvPoo48yZcoUvvnmGy6//PKUICUWi/Hcc88xY8YMJk+eTLdu3er9+/nuu+9m7NixPPXUU7Rr14477riD1atXA1BUVMSFF17Id999x0033cTjjz9Ox44duf7663n33XcB6/vCtddey7PPPmt3+znjjDN49NFH7e8L1113HSeffDJ5eXm8/vrrnHLKKXV+lrt27eKBBx7g2muv5bHHHqO8vJyJEydy8803c9FFF/HEE09gmiY33XST/Rls3LiRiy++mD179vDggw8yY8YMtm7dyp/+9Cf27NkDWOFaomvlAw88wN/+9jd+97vfsWDBAl566aWUNjzyyCMYhsHDDz/MbbfdxieffML9999v70908RowYECd7+OSSy7hqquuStm2efNm1q5da1fK/fzzz+Tn5/Ptt99y3nnnMWDAAEaPHl2vrns33ngj7777Lr/61a/2ety0adPo0aPHXscVPRAbN24EoEuXLva2HTt2sHjxYh555BEmT55MVlYW7733Hnl5eXa11YABA7j77rsB6xxMnCtPPvkkN998M8cccwxz5szh+uuv59///jdjxoyx/3835HtidTt37mTevHn85S9/4fHHH0dRFCZOnGifJ9UtXLiQO+64gyFDhvDkk09y+umnc91119X6B9CpU6fyu9/9jnnz5jF48GBmzZpVY3iMM844gx9//NH+3AYMGLDPfxPr16+nU6dOaJqWsr1Lly7289QmJyeHHTt21Ni+detWO7D9+eefycjIoKioiEsvvZSjjjqKkSNH8uSTT8oA9Ec46b4nhDgkTjnlFDweT0oXvo8++ojWrVszdOjQlGN3796Nx+Ph9ttv59hjjwVg+PDhbNmyxf6ytGfPHjZs2GB/2QQ4+uijmTt3LuFwOOX5SkpKuOKKK3C73Tz//PMpf7Wpbvbs2eTn5zNv3jz7F/AJJ5zAqaeeypw5c1L6uF900UX2X6VHjBjBokWL+PTTT2v98nXBBRfw4YcfsnTpUo477jjA+rJx/PHH0759+3p/jkIIIUSyRJVIx44d93pcfn4+//3vfykrK+O0007j3nvv5f3337d/j/3nP/8hFovZfyiaPXs23bp145lnnrF/Hw4aNIjf/e53vPXWW1x66aX2c1977bX2hW1hYWG9fz9Pnz6dk046CbAuck899VS++eYb+vbty/PPP09xcTH//ve/7fd28sknM3bsWB566CHOOussPv/8c7766isefvhhfve73wEwcuRI3G43jz32GJdffjm9evWiVatWOJ3OGl3AqgsEAtxzzz12m9atW8fs2bOZMWOGXVFUWVnJxIkT2bhxI/369WPu3Ll4PB5eeOEFvF4vYH0n+M1vfsOzzz7L7bffzi+//EK/fv147LHH7GOOP/54vvzyS5YuXZrS9ax3797MnDnTXl+5cmVKpfn+dPEKBoPcfvvtOJ1OLrvsMsAKMwsKCpg0aRITJkyge/fufPjhh3ZYs7fue717997na3700Uf897//5f33368zLG2IaDRqLweDQVavXs39999PRkZGSnVfNBpN+f4IVpA0cOBAux1er5eePXsC0LNnT3r27ElZWRlPPfUUf/jDH+zPIPFeL730Uvucb8j3xOoMw+CJJ56gR48eALhcLsaOHcvy5cv59a9/XeP4xx57jFGjRjF9+nQATjzxRBwOB7Nnz65x7M0332yHZccccwwfffQRX3/9NaNGjbKPGThwIABLliyhW7dueL3eff6bqKiosM/ZZOnp6fj9/jofd8EFF3DnnXcyY8YMrrrqKlRV5YUXXmDdunX2/8vi4mJisRjXXHMNf/7zn7nhhhv48ssvefzxxwkGg9J97wgmlVJCiEPC7XYzevTolC9WH3zwAb/97W9rfFlp27YtL730EkOHDmXbtm18+eWXLFiwgP/973/2F9rc3Fx69uzJXXfdxe233857772HYRhMnjy5xngZV111FWvXrmXKlCnk5OTU2cbKykp++OEHfvvb36b8RSgzM5NRo0bxzTffpBw/ePDglPV27dpRWVlZ63Mff/zxdOjQgX/84x+A9dfYJUuWcN5559XZHiGEEGJfEhUFDodjr8clfq+ZpklaWhq/+c1v+PDDD+39H3zwASNGjKBt27YEAgFWrFjBySefjGmaRKNRotEonTt3pkePHnz55Zcpz53chashv5+Tg4NOnToB2N3rv/nmGwYPHlwjbDv77LPt4Oubb75B13XOOOOMGscknqOhhgwZkvJewArjErKzs1Pa+fXXXzNs2DDcbrf9OXm9Xo499li++uorwAotXn75ZVwuF+vWreO///0vTz31FMXFxTWCuuohQbt27QgEAg1+Hwk+n4/x48fzww8/MGvWLPvzjEQilJSUcO+993LppZcyYsQIpk2bxgknnMDcuXP3+/XAChvuvvtubrvttn2GpfU1YMAAexo6dCiXXnop4XCYuXPnkpeXl3Js9S6FW7dutc+vuixfvpxwOFyjev/YY4+lY8eOfPPNNw3+nlhdTk6OHUhB1TmfPPB3wubNm9mxY0eNczsRvlaX/G/J4/GQm5tbY6iKjIwMMjMzG3R3v71VLO0tbLzooou44447ePPNNznppJM48cQT7YHO3W43YJ2DlZWVXH311YwfP57jjjuOW265hYsuuojnn38en89X73aKw4tUSgkhDpnf/va3TJgwgV27duFyuViyZAk33nhjrce+++67PPzww+zcuZPs7Gz69etn/xID6xfhc889Zw/auHDhQhwOB7/5zW+49957U6qhAoEAnTp1Yvbs2bz++uuoau35e0VFBaZp2l9Ck+Xm5tb40pDcHrDuMljXL29VVTn//PN5/vnnueeee/jHP/6B1+vl1FNPrfV4IYQQoj4SF/37urvZ1q1bSU9Pt0OVc845h3fffZfVq1eTm5vL0qVL7W5i5eXlGIbB3/72N3scqmQulytlPXnsoob8fk5+XOJ3c+L3aFlZGZ07d67x2onf0eXl5ZSVlZGTk1Oja1EipKjtYn9faqsK8Xg8dR5fWlrKhx9+mBLwJSQqmhLd8V555RUqKytp3749Rx99dI3PsbbX2tt3i33ZuXMn48ePZ+PGjTzyyCP85je/sfelp6ejKIpdzZZw4okn8sUXX1BUVFTr96H6mDp1Kj179uTCCy9MqXBKBJyapjW4eip5jCGHw0FeXh6tW7eu9dj09PSUdZ/Pt9f/h4A9btTevgM29HtidcnnO1SFOrXdATAxdlv191jX/5P6njcej6dBYY/X600Z0yzB7/fvc/D6K664gssuu4wtW7aQk5NDq1atuO222+yfQYn/T9W7D5500km8/vrrrF+/PiUQFkcOCaWEEIfMSSedRHp6Ov/6179IS0ujU6dO9iCVyb777jtuv/12xowZw7hx4+w7pTz00EMsW7bMPq5t27ZMnTqVe+65h9WrV/Ovf/2Lv/3tb+Tk5NjjAwC8+OKL/Pzzz1x99dW89NJLNe4AmJCRkYGiKLX+8i0sLLR/ie6v888/nyeeeILPPvuMf/7zn5x55pm1fiEVQggh6qt169Ycc8wx/Pvf/+Yvf/lLrX948fl8fPnll4wePdreNmLECPLy8vjnP/9JXl4eLpfLvitXIrAYO3ZsrZUZ+7rAr+/v573JysqisLCwxvbEtpycHLKysigpKSEWi6UEU7t377aPOdQyMjI4/vjjueKKK2rsS9zEZd68ebzwwgvce++9nHbaafbFfPVBxg+mNWvWMG7cOEKhEM8991yN8Z/y8/MxTZNIJJLyXSQRIlX/w1tD/Pvf/wao8R1v+/btLFy4kJdeeonhw4c36DkTXc/2R3Z29j4Do0RYWlRURPfu3VP2FRYW0rlz50P+PTFZu3btAGqMN1XX+FP1VV5e3qB/F926deOLL77AMIyUny2bN29Oqfqq7ocffmDnzp2cdtppKcf99NNP9O/fH7DOQaBGtWAkEgFqht/iyCHd94QQh4zT6eQ3v/kN//73v/nnP/9ZZwny999/j2EY3HDDDXYgFYvF7DJ4wzD4/vvvOf7441m5ciWKotCvXz9uuukmevfuXWNgxby8PE466SR++9vf8thjj9VZtpyWlsZRRx3FP//5z5RBJCsqKvj0009rjH3VUB07dmTEiBG89NJL/Pzzz7Xe+UgIIYRoqAkTJrBx48Yad3kF6/fnPffcQzAYTBn8WtM0fv/73/PJJ5/wr3/9i9/85jd2JYfX66V///5s2LCBgQMH2lOvXr14/PHH93rnt4b8ft6bX/3qV3z//fc1KsDeffdd8vLyyM/PZ9iwYUSj0Rp3900MhJ74vV1XhfTBkLjjYL9+/ezP6aijjuKFF17go48+AmDZsmX07NmTCy64wA6kCgoK+OWXX2qtkjlQO3fu5IorrkBRFF599dVaByRPVEh98MEHKds//vhj+vTpU2vFWH29+eabNaa8vDxGjRrFm2++uddB2g+Fjh07snPnzpRt1avrBg0ahNPp5P3330/Z/t1337Fjxw6GDBlyyL8nJmvXrh1dunSxz6GE//znP/v9nGVlZQQCATp06FDvx5xwwgn4/X4+//xze1txcTHfffcdI0eOrPNx33zzDZMmTUrpQvjll1+ydu1au2LvpJNOQlGUWs/B7OzsvYZe4vAmlVJCiEPqzDPPZPz48aiqyp133lnrMUcffTQA9913HxdccAFlZWW88sor9h15Kisr6d+/P263m9tuu40bbriB3NxcvvrqK37++Wf7Nr/VTZkyhc8//5x77rmH+fPn13rMLbfcwrhx47jmmmu45JJLiEQizJs3j3A4XOutthvqwgsv5Oabb6ZHjx5SkiyEEOKgOPHEE7njjjt46KGH+Pnnn7ngggto06YN27Zt49VXX+Xnn39mxowZ9O3bN+Vx55xzDs899xyqqtbopnfzzTdzzTXXcMstt3D22Wfbd9lbsWIF1113XZ1t2Z/fz7W54oorePfddxk7diwTJkwgOzubhQsX8vXXX3P//fejqionnXQSw4cP584776SgoIC+ffvyzTff8Le//Y3zzjvPHsw6MzOToqIiFi9eTL9+/WjTpk0DPt29u+6667j44osZP348f/rTn3C5XLz++ussWrSIOXPmANb3mieffJJ58+ZxzDHHsHnzZp555hnC4XCDx4sqLi5my5Yt9OzZs87gaPr06ezZs4d7770Xn8/H8uXL7X2JQb6HDx/OqFGjmDlzJoFAgF69erFw4UL+97//8eSTT9rHb9myheLi4n0OiJ2stqomp9NJdnZ2yr5du3axa9cu+vfvj9PprPfzN9TIkSP5+9//jmmadpe5RDj46aefkpWVRd++fbnmmmt44okncDgcjBo1im3btvHYY4/Rs2dPewzQQ/09MSFxZ75JkyZxzz33cOqpp7J69WqeeOIJYP+C1kRvgxNOOAGwKijXrVtHly5d6hw8/1e/+hXDhg3j1ltv5dZbbyU7O5vHH3+cjIwMe2B1sG4KEA6H7Sqos88+m3nz5nHjjTcybtw4duzYwQMPPMCQIUPsMd86d+7MZZddxrPPPouu6/zqV7/ik08+4d133+Wuu+7a5zh54vAloZQQ4pA6/vjjyczMpH379nX+BWT48OHcfffdPP/88/zrX/8iNzeX4cOHM3fuXK6//nqWLVvGySefzHPPPWffFae8vJyuXbty33331VmB1KZNG26++Wbuu+8+Fi5cyLnnnlvjmBEjRvD8888zZ84cbr75ZpxOJ8ceeywPPvhgjQFa98fJJ5+MoihSJSWEEOKguuKKKxg8eDAvvvgiDz74IMXFxeTl5TFy5EhmzJhhBzTJ+vbtS+/evSkpKWHEiBEp+0444QTmz5/P3LlzmThxIg6HgwEDBvD888/vNaBwuVwN/v1cm7y8PF599VVmz57N9OnTiUQi9O3blyeffNK+U5miKDzzzDPMmTOHF154geLiYjp16sTNN9+c0p3u/PPPZ/HixVx//fVMnDgx5W53B6pv37688sorPPLII9x2222Ypknv3r154okn7HaOHz+ekpISXnrpJZ544gnat2/POeecY7e/vLyczMzMer3ep59+yuTJk+vsAhcOh/n0008Bau0qOWzYMBYsWABYd3ebO3eufafDnj17Mnfu3JRunk8++STvvPMOa9asaehHs09vvPEGc+fO5b///e8+ByI/EKeddhpPPPEEK1eutP8g2KtXL8466yxeeeUVPv/8c95//307RH355Zd5/fXXyc7O5owzzuDGG2+0qwgP9ffEZL///e+prKxk/vz5vPXWW/Tq1Yu//vWv/PWvf60xPlV9fPbZZxx99NH2OHSrVq3i8ssvZ+bMmXv9tzl37lweeOABHnroIQzDYMiQITz66KMp48Pde++9bN++nY8//hiw/v3Onz+fBx54gAkTJpCZmcn555/PX/7yl5QqtSlTptCuXTtef/115s2bR+fOnZk+fToXXXRRg9+fOHwo5v6OpCeEEGKfPvzwQ2677TYWL15c5wCdQgghhBBHgksvvZRHH320xh30DrZrr72WnJwcZs6ceUhf52B6//336d+/f8oYV59++injx4/nH//4R43Kx72prKzkxBNP5MEHH0wZ8F6I5kgqpYQQ4hBYtGgRP/zwA6+99hrnn3++BFJCCCGEOKItXbqUQCCw33f5a4ibbrqJSy65hBtuuKFBYyo1pXfffZdHHnmEG2+8kfbt27N582bmzJnDsGHDGhRIAbz22mv06tXLrt4TojmTSikhhDgEXnjhBR599FGGDh3Ko48+us/b6AohhBBCHM62b99OWlpao9wlEay7IK5evbrWGwI0RyUlJcyePZvPPvuM4uJicnNzOf3005k4cSLp6en1fp7i4mLOPfdcFixYYN/xTojmTEIpIYQQQgghhBBCCNHoDt39UoUQQgghhBBCCCGEqMN+h1LhcJizzjqLpUuX2tu2bt3K2LFjOeaYYzjzzDP54osvUh7z1VdfcdZZZzFo0CAuv/xytm7duv8tF0IIIYQQQgghhBAt1n6FUqFQiJtvvpm1a9fa20zT5Prrryc3N5e33nqLc845hwkTJrBjxw4AduzYwfXXX8/555/Pm2++SatWrbjuuuuob+9B0zTx+Xz1Pl4IIYQQQqSS71NCCCGEaE4aHEqtW7eOP/zhD2zZsiVl+9dff83WrVu577776NGjB+PHj+eYY47hrbfeAuCNN97gqKOO4sorr6RXr17MnDmT7du3880339Trdf1+P0OHDsXv9ze0yUIIIYQQAvk+JYQQQojmpcGh1DfffMPw4cN5/fXXU7avWLGC/v37k5aWZm8bOnQoy5cvt/cfe+yx9j6Px8OAAQPs/UIIIYQQQgghhBDiyKE39AGXXHJJrdsLCwtp06ZNyrbWrVuza9eueu1vcuEyWP8sGGHQveDIAD0DnNnxKQcc8WVFxocXQgghhBBCCCGEOBANDqXqEggEcDqdKducTifhcLhe+5vclv8Pvp9UjwNVcLWOT3ngbgueDpDWCdI6Qlo+ZPQCdx4oyiFvthBCiOYlMVaPiWmvJy83ZF/yen2OSR4nqKHb9vWa9d3ekH17e87aHps4RlVUOmd1RlcP2tcYIYQQQgjRBA7atzmXy0VpaWnKtnA4jNvttvdXD6DC4TCZmZkHqwkHpvP5UP4LVPwCUX/S5INohTWPBQEDQoXWxOq6n0/3gqcjpHcBb0/I7AtZ/cDTCTQnKFrqpOpJ63rSNqnKEkIcPhIhjGEa9nL1+d72NWQO1LovpR2GgUH89UwTA8N+fQDDNFLaY2CAiX1s8uOqv2ZiOfF6yZ9Byj6q2lU123cwtbfns/dVe966tqEkZkrV8yf/XSWxKf7HlsRzJB+vKEpqiJS0WH1fyus0cL9hGrh1N7lpuWS4MhBCCCGEaGwxw0RTW3YRSnN5DwctlGrbti3r1q1L2VZUVGR32Wvbti1FRUU19vfr1+9gNeHAuFrDkFmp20wTzKjVpc8IQ8QHwV1QuQ38WyBYUDWF9kCkBIKFEC62QqyKNdbER1XPqadDRm8rpMroa1VV6R5AsQIoVQMS4ZRqBVOqC1Q3aC5rUvR4YOUA1ZG6LNVZQohamGY87ImHPslhi72ctK+245O3JT8uZsYwDIOoGcU0TWJmjJgRswIeo+qY6o+324W1zfqvlmCpeiAUPxbFCi9qe68o2MdgJoUpSfsS2xLPoSgKCoq9vSH7Eut1PraWx9fnOZKPqX5c8vq+nrOuY1uicCxMSbCkqZshhBBCiCOYpir85bXvWbfb19RN2S8923h57OLBTd0M4CCGUoMGDWLevHkEg0G7OmrZsmUMHTrU3r9s2TL7+EAgwE8//cSECRMOVhMOPkWpCntIt8aVSu8MrX9l7TeiEKu0wqpwqVU9Fau0xqcKl0CkFAI7oGIdVKwF/yar+qrke2sCK3zKOgpyj4fc4ZDR0wrDiIERg1gIopVgxqyAzEy6ykKxQixFrwqqNI816WmgOuOTIx5sOSW4EqKZSgQ2MTNWFfYYsdTgJylMqj7FjBhRI0rUiNrLMTNGzLSW9xYspYRMSZVAieqURNVKbetQFaRUn6vxSk8Fazn5eFVRU/bX9vjkgKa2bUIIIYQQQjSVdbt9rNpR3tTNaPEOWig1bNgw2rdvz+TJk7nuuuv45JNPWLlyJTNnzgTgggsuYP78+cybN49Ro0bxxBNP0KlTJ4YPH36wmtD4VB3UTHBkQloHMA2rQipcApU7rMqpjD7Q6RxwZFnBUsU6KF1pTSUrIbgTSldY07qnwNka2p4M7U+HnMF7775nmlVhlRG15uFSMIus9ar+FvEwKh5K6V6rYkv3WGGV5o7vd0iXQSEaIDlIihmxWueJwMjeZsQIG2FrHgunrCdCoxhVVUXVu5fV6CIVF49r7PAnEfokb1NQ0BQNXdXtfcnHAinbhdgXwzSIxCJEjSgRI2IHo/szJQLURLCaWE9ejhpRwrEw+Vn5nNjlxKZ++0IIIYQQ4gAdtFBK0zSefPJJ/vrXv3L++eeTn5/PE088QYcOHQDo1KkTjz/+OPfffz9PPPEEgwcP5oknnji8LnwU1QqoHJmQ1gUi5RAqAv9mqNxqBUGZva2xpfL/aD0msBMKv4Kir2DPtxDeA1vftiZXG2h/KnT8vVVBVeP1FKtCCh20vbTLNMCIWJMZgdBuCITjVVfEx69yJI1v5YyHVS6r6iqli6AePzZpfjj9PxRHnOQqo+SLYLvSKGk5HA0TjoUJG2HC0TBRM1qzmskwiBGzq43swkawu5slQiBN0VAUxZ6rioqqqrgUl31M9WBJHHmiRpRILGKde0lTKBaytsfPx7ARto+LGJGUxyTWk+fhWLjquY0w0Vg09TgjQjQWtQOnxL5EOBQxIhim0SSfiUN1cPvI25vktYUQQgghxMFzQKHUmjVrUtbz8/N5+eWX6zz+5JNP5uSTTz6Ql2w5FAWcWdaU1skai8q3CfzbwZEOzlbWMZ720OUCazIiULwMdv4bCj6xwqNNr1hTzhDochG0HWUFQw1qi1o1HlVtElVWZjTeZTBgDe5uxuJTymi1Vd0FE4Oxa+54iOWxXiM5tLIrsBwNb7cQDWSYxj6rMcKxMKFoiGAsaAVLiUoM4uMgGfHxj5IHg64lTFIVFU215rqq19imSsXhYSlqRAlGgwSjQes8igYJxqqWE+eWvR4LEYqGCMVChGNhgtGgfQ6GYvHt0XDVciJwilYtx8xYU7/telNQ0FW9xuRQHbVu11QtdV3RUrbVtq4oCvnZ+Tg0R1O/XSGEEEIIcYAkJWgMmgvS88Hd3gqnKtZC5RZwt7UCnQTVAbnHWdOAyVYF1Y4PYPdnUPI/a3LlWuFUlz+Cw3tw2qfGq63qwzSsoMqIVnUdjJRVBVuQVIGVFGAlKqxUj9Vt0K7ASg6tHPEqLTktRRW7KiNevZFYTlRqBKNBghErJIgYkdSuPvGBt5OpWMFR4mJXUzQcmgO34rZDpcSFr2iZwrEwlZFKApGANY8GrOWotS2xHogGCEaD9nHBmLWcCJ0S+xPHBKPBJg+INEXDqTlTJofmwKk6cepOnKq17lAd1vb4MbqqVx0f35cIihLPkbyeHCQ5tNTjE/scmqNG8KSpeyvbPThkoHMhhBBCiMOHXP03Js0J6V2sO/1VrLO69alOK2iqfgGsOqHtKdYULICtC2Hb21Z3wLVPwcYFVhfA/D+BM7vx3oOixu8KWI+/UKcEWPG7GEYrIRSvyEru06TqqQGWlmaFV3paUmjlTJ1LJUqLltwlqXrXouTQIBKL2N3kEgN2A/bpk6i2SIyV5NAceFSPHTpJxVLzZ5omwWgQf8SPL+zDF/bhj/ipDFfa2/wRP5WRSiojlfjDScsRP4FIwJpHrXApakQPeZsVFNy6G7fuxqW7cGkuXLrL2qalbkve59JcODVnynan5rSWq607NId9fCI40iW0F0IIIYQQhxH5dtsU9HTIPhrcbaD8F2u8KU+HuiuE3G2h13jocSXsWgQbngffBlg/Hzb93aqa6v5ncGQ07vvYl/oGWKaZOli7GYFwEQSjVrBlD9iupo5tpXnilVdpVd0G7TsOSnDVlBLd5JKnSCxiB03+iD9lbJpEiJC4s1siYEoETmlaWkpXHtG8RGIRKsIVVIQqUua+sM+eJ6aKUIUdMiXm/rD/kFQgOTUnHt1DmiMNj+7B47CW3bobj8NjbYtvT8zduhuP7rEDp8Q2t5a0rLtxqA6pphNCCCGEEOIASSjVVBLjSTmyoOxnq2rKU607X3WqAzr81rozX8GnsP5ZqPgFNr4A296BHldbY1PVp4qpOVGUqmqovUkM2G5GrXm4DMw98TDLiD+PktolUHPHq67Sq8IqrVpwJRosEovUGP8mFA3ZAUMoFkrpZgfYd39LVHs4VAcuh8sOn+QCv2mZpokv7KMsVEZ5qJzyUDmlwVJ7uTxUTkW4grJQGRWhCnu9PFROMBo8KG1QFRWv00uaI61q7rDm6c50a+5IJ92Zjkf32NvS9DQ8Dg/pjvi6w1qXqiIhhBBCCCGaN/nG3tT0NMg52gpPKtZaXfH2VfGkqNButDXoeeFnsGYu+DfC6v8HW16H3hOg7ejD7654iQHbqWPAdkgKriJWWBUuA7PIWrafR6sKrjQHaOnW+FyaOymsciVVWx1mn2M9GKaROhhzfNDmRLVL8h29Et3pEgMcJ8aa8Tq9duAkGlciYCoJllAaLK11KguVURYsozRUSlnQCqIOtFop3ZFOhiuDDKc1eV1ea+6smtc2pTvS8Tq9uHW3hJNCCCGEEEIcQeRqsTlQHZDVzwpFyn+2xl5ytd734xQF2pwMuSNh+7uw9hmrK+Dy26H1cOh/mzXA+pGkPsGVPcZVBGIhiPogsC3eS9BMqraKh1JamhUUau54N8HDI7QyTTMlcArFQlRGKqkIVeCP+K3gKRomGh/APhE6JQZKznRlNtrAxgKC0SB7KvdQEixhT2APJYESigPFlASr5iWBEjuI2t9xlVyai2x3NlmuLDJdmWS4Msh2Z5PhzCDTlWlPGa4MMp2ZdgjldXrlXBBCCCGEEEI0iIRSzYWiQkYPK/goXQmhYnC1qt9jVR06nw/tz4CNL1nTnqXwxcXWWFPdx+69W+CRJnG3wbo+k5Rqq8T4Vjvj41tRM7TSvUmVVvGwSnPFl5v+n1jMiNl3EAtGg3bwVBGusG9Hn7hLnaqo9qDKHt1DlitLKp0OoagRpThQTFFlEUWVRewJ7GFP5R5rHl8uDhSzJ7CHykhlg5/fo3vIceeQ7ckm251NtiubLHeWHTplu7Pt5Sx3FlmuLFz6XgJdIYQQQgghhDiI5GqzuUnraM1LlkO4tGF31tPToNe10PEs+OkhKPrKGndqxz9hwGTIPe4QNPgwtK9qq0RolQiuQgXxSqvkAdkTdwp0gZ4RD62SqqwSodVBrLIyTCP1dveRgDX+T7jC6ooXC2HG2+hQrbt6uTW3BE+HQMyIURIsodBfyO7K3RT6CymsLKTQX0hRoIgifxFFgSJKAiWY9l0o982pOclx59Da05pWnlbkeHLIcefYy63c1jwRNrl1CaOFEEIIIYQQzZdciTZHaR3BjEHJCkABZ1YDH98Jhj4GBZ/A6tkQ2A7fTYCOZ0Pfm5rfXfpamkRopdUVWsWqQqtYJUTKwB+p2m+PZ+VMGs/KUxVU1aPKKhS1utoFogEqw5X24NSJ7niGaaCqKk7VurV8hjOD1lprVLkb4QGLGlGKKoso8Bew27fbmvur5rv9uymqLKr3+EyaotHK04rctFxae1pb8zQrdGrtaW0HUK3TWpPuSJcxl4QQQgghhBCHDQmlmqv0LmDEoOwHKwRpaJCkKNZg6LnHwS9zYcv/Z407VfQV9L8D2p5ySJotsAZS17TauweaZnw8q3AdXQOTqqw0D6aWTkDRCRgmlbEYFdEwJWE/lbEIoWiIiBFBVVSr8kmPh08eCZ8OREWogp2+nez07WSXb5c9FfgKKPAXUFhZaA/uvjeqotLK04o2aW3ITc+15mm55KblkpeeZy17csnx5Mj/LyGEEEIIccSIGSaaKn9oFRYJpZozb1fAgNIf4yHFfnTF0dOsAc/bnwY/3AeVW+D7SdD+dOh3W8OrsMSBURSrSkp11NhlmibBaCX+UAWV4VLKQ2UUB0oIRAIEY2FMQFUduHQPbqeXTGcmuiPLuoOg6rQqq1QVkB/we1MeKmdHxQ572unbac93VuzEH/Hv8zk0RaNtelvapLehrTc+T29LXnqeNU/Lo3Vaa+kWKYQQQgghRDWaqvCX175n3W5fUzdlv5zSJ49bT+/b1M04bMgVU3OmKODtBrEglK+xuvXt70VuzjEw8u+w7m+wcQHs/DcUL4Oj7oS8Ew5qs0X9RGJRfJFK/NEA5SE/xaEy/JEgwVgI0wRNVXHrGaS5cmmlO1Eh9c6BkVIIFVU9oaqDolfdMVBPjw+67rQGZdfig7Mf5t2/IrEIO3072V6+ne0V1rStfBvbK7azo2IHvvC+f/llu7Np721PO287e2qb3tZebuVpJdVNQgghhBBC7Kd1u32s2lHe1M3YLz3y0pu6CYcVCaWaO0WFzN7W2ESV263xovY3VNDc0OcGaDsKfrgH/Jth2Y3Q6RzoeS248w5q00UV0zSpjAatECoSYE+wjPKwn8pokKgRQ1VUPLqLNN1NK3dm3YGH5gSctb2AFVaZESu4Cu+xBmA3TUBJuuOgbo1jpafFx62KD8bewgKrYDTItvJtbCvfxtbyrWwt22qv7/Lv2mf3ulaeVnTI6EB7b/sa83bedngcnkZ6J0IIIYQQQghx5JJQqiVQHZDZD6KVECwAT7sDe77so+D4V+CXJ2Hzq7DtH7DtPcgbAR1/D21OsipsxH6LGTH80QC+cIDysI+iYGlSFZSJU3OSprto48k5OF28Et0CcYBWy36jtsAqvk/V4o/TQfVYFVaaOx5YJSqtHFZA2oiiRpQdFTvYXLqZzWWb2VK2ha3lW9lStoUCf8FeH+vSXHTM7EinzE50zOhoTx0yOtAho4OETkIIIYQQQgjRDEgo1VI4vJA1wOpyFy4FZ/aBPZ/mhn43WwOe/zIXSldC4ZfW5Mi0xpzq8DvrNVtI9UxTihkxfJEAFRE/JcFyikPl+MIBwvGByD26i3SHm9burKa5e5pdKVXLPiMGZtgKrCKlEC6sFljp8XGrEoGVKz6GlbMquDqAwKosWMbG0o1sLtvMptJN9nx7+fa93sHO6/TSJbMLnbI60TmzM50zO9MpsxOdMjvR2tNa7lInRDNlmiYxM4ZhGhimQcywluvaZmJaP5MUUFAwMclwZsi/cSGEEEKIw4CEUi2JOxey+kHx91aotD8Dn1fXaggc95zVlW/7+7D9Awjthi1vWFN6Vyuc6vDbA6/QOowYpoEvUkl52AqhigJlVEaDhI0ImqKS7vCQ487ApbWAijNVAzx7CawiVWNY1SuwciSFVlZgZZomu/272Vi6kY2lG9lQsoHNpZvZWLqRkmBJnU1zaS7ys/LpktWlxpTlaqKAT4gjXHKoFDNitS4bpkHUiKYESsnBkqqoaKqGpmioimqvuxQXDt2BU3Xi0Bw4VAcOzWHtVzT7MQ7NQbqjeY/nUFBQwIwZM/j6669xuVyceeaZ3HzzzbhcLqZPn86CBQtSjr/rrru47LLLAHj//fd59NFHKSws5IQTTmDatGm0atUKsD7/2bNn8+abb2IYBhdeeCGTJk1CVWWcOyGEEEK0PBJKtTRpnSFcDr5f4uNL1ZYk7If0fOh9PfS6FvZ8awVUBZ+AfxOsfQLWPgmthkKHM6HdaNC9B+d1WxB/JEB52E9ZqIKCQAn+SCWhWFUI1cqdiVOreVe9Fk3VAK32ALSWwMo0TAqD5az3FbC+opANvgI2lO9iY/lO/NFAnS/TNr0tXbO70jW7K/lZ+eRn59M1qyt56XkyoLgQh0hykJRcmZQcLkWNKKZpVt3U0wRFqRkqaWo8KNLScagOnLoTp+bEoTrQVA1d1VNCpbrmh0vQbJomEydOJDMzk1deeYWysjKmTJmCqqrcfvvtrF+/nltuuYXzzjvPfozXa/1eXblyJX/961+599576du3LzNmzGDy5Mk888wzADz//PO8//77zJ07l2g0yq233krr1q0ZN25ck7xXIYQQQogDIaFUS6MokNkLImUQKIC0Dgf+nCVlUBmExF+06QqtJ0Crq6D8KyhZBL6VUPydNf30oDXuVIczIPd4qzLmMBSJRSkP+ygP+ymoLKYs7CMQDaKgkOZwk+1qIZVQh0hFNMi6sm2sLd3C+rJt9lQRqaz1eE1R6ZyeS7eMtnTL6EC3zA50ze5CflY30jw5qXcI1JzWnQSFEPuUqFxKDphiZoyoEU0JnuyqpThFVdAVPSVg0lQNj8ODU3Xi1J24NBcOzWGHSrqq28cmlpMDJ2HZsGEDy5cv58svvyQ3NxeAiRMn8uCDD9qh1Lhx48jLq3mDkZdffpnf/va3nHvuuQA89NBDjBo1iq1bt9K5c2deeuklJk6cyLHHHgvApEmTeOyxxySUEkIIIUSLJFd9LZHmsrrx7fn2wMaX8gdg6w7YuhPCESvwSrpgQTHB7ACMAbUE1BXAd2AUwK6PrEn1Qs6J0PY0aDsMXK4DfntNxTRN/JEAZWEfxcEydgdK8EcCxAwDj8NFuu5pujGhmlDUiLGlYhdry7awtnQLa0u3sq50KwWB4lqP1xSVzt529MjqSPesTnTP7Ej3rI508bbDoelgxqzxq4z4wOvRYigvsh6sEB9U3WEFU1pa/E6BjmpjWDlkrDNx2Ioa0VoDpuQKJtOs+mGtKIodCqmqiq7odrjk0ly4dJfdHS45SEoOmpLDpiPtZ9yhkJeXx7PPPmsHUgk+nw+fz0dBQQFdu3at9bErVqzg6quvttfbt29Phw4dWLFiBU6nk507d/KrX/3K3j906FC2b9/O7t27adOmzSF5P0IIIYQQh4qEUi2VqxVk9oGS5aB5rKCqviJR2L4LNm0HXyW0zgbPPsanMtpAtCdEz4HwBggvBWMZGOWw55/W9FMWuIdD3mjIORq8aZDuAa35/vU8ZsQoD/spDVVQUFlMaaiCQCyEpqh4HWm0TWuNfgT99d8fCbC2dAtrSq0A6peSzawr20bYiNR6fLu01vTK7kyPrE70zLLm+Rnt996NUdGsc6K2c9Y0rLDKjEIsCFEfBKJV+1XdqqBSHUmBlataaOU4eN1ahTgIEoFS1IjaQVNiOdE9TlGsAbxTxltSNTtgSnOk4dJduDQXbt2dEiQllpMnCZeaVmZmJieeeKK9bhgGL7/8Mscddxzr169HURSefvppPvvsM7Kzs7niiivsrny1hUutW7dm165dFBYWAqTsTwRfu3btklBKCCGEEC2OhFItWXoXaywf30ZrrKn6XICU+2DNRthVCJle6Ni2fo9TVXCq4HRA2gBgAJhjIfwzBL+C4LdglkHwP7D1P7ClFajHgGsYtDoKcrKsgCrNA+6mraYKxyJ2NdSuyj1UhCuJGFE8uguv00Ouln1EXMyVBMtZU7qZ1SWbWFOymTUlm9nqK7C6+FTj0V30yupMz+wu9M7uTM8sa/I60w5uoxQ1HlbVFliZVlhlxiuswnsgVFBz4HVVj98IwAO6x6qqSqmy0qXKShwQ0zSJGtGUYMleN6xucomgCUBV1JRKJKfuJEPPwK25cetunJozJVBKVDRJwHT4mDVrFj/99BNvvvkmq1atQlEUunfvzmWXXca3337LXXfdhdfr5dRTTyUYDOJ0pnYNdzqdhMNhgsGgvZ68DyAcDjfeGxJCCCGEOEgklGrJFBUyekO4zLpjnrvt3o8vKII1G8BfCR3agnaAA0grKrgGWFPmWAj9AMGvIfQ/oBjMjyH4MexoBdsGgnI0uHpBZga0yo5XUqVBmvuQhwTBaIjSUAWFgRJ2B0rwRQKASZruJtedbXUrO4ztCZbxc/FGfi7ZyOqSTawu2UxB5Z5aj23jyaFPTj69s/Ppld2F3tld6ORt0/QDjiuKFTDh2MudAuPdAiM+CJdYlVcJ1ausEhWGieqqRHAlY1kdkRLjMiUHTMlToorJNE17LCa7+5umk+ZIw61bIZNLd+FQHbWGTImBv8WRY9asWbz44os88sgj9O7dm169ejFq1Ciys7MB6Nu3L5s2beLVV1/l1FNPxeVy1QiYwuEwHo8nJYByxbvLJ471eDyN96aEEEIIIQ4Sufpq6fQ0yOxrDUAe9dV+VzzDgM3bYd1mQIF2bQ5+CKQ4wD3EmswQhFZA8BsIfQ9mMaiLgcUQyYE9A6GgP9AVXPEufrnZ4PVaywepy18wGqIkVMHuyhIKg8X4wpWoiobX4aF9Wi7aYXr77NJQBT8Xb+Snko38VLyBn4s3sjtQUuuxXTLa0TenK32y8+mTk0+f7Hxy3JmN3OKDxL5TYB1VVkYEiFZVWZlRq8rKNK1KQEWPj2XliAdWaaldAu2ugfJjsyVJdJWrPqWMzaSAgpI6zpKmk+5Ix+Pw2NVMiXDJoTpSlnVVl0omUatp06bx6quvMmvWLE4//XTAGgMsEUgldO/ena+//hqAtm3bUlRUlLK/qKiIvLw82ra1/vhUWFhIp06d7GWg1kHThRBCCCGaO7m6Ohx42kJGTyhdZVUdqUn/W6NR+GUjbNxmVShlpB/69igucA+zppSAagWYJcBnoH0GiteqnvIPgJLuYDrAqYPbbXX3y/I2uMtfKBamOFjO7soSdgf24I8EUBWVTGc6HZtDtc9BFoyGWF2yiR+LN/BT8QZW7dnAdv/uGscpKHTNbE/fnG70y+lKv1bd6JXdBa/jCPnLuqJYA6fjrL3KKjH4uhmFWAii/qrQKvF4JR5Iac6q7oGqy/r3piYNzi6hVaOoK2yKGJGqu8wpVV3nElOaIw2Pw4NH9+B2uGsETA7NIUGTOCjmzp3La6+9xsMPP8wZZ5xhb3/sscf4/vvveeGFF+xtq1evpnv37gAMGjSIZcuWcf755wOwc+dOdu7cyaBBg2jbti0dOnRg2bJldii1bNkyOnToIONJCSGEEKJFkqunw4W3u9VdKbgL0qwvqkSisHoDbN4Gea3B5dz7cxwKKQFVGEI/Qug7CP4PTB9EvgK+sipSXP1BOxpifWF7ADbHQMUKqdI80CrL6u6XCKoc1ukbiUUpDpWxu7KYgkCiIkoly+klx5t52FxYGqbBxvId/LhnPT/uWc+q4vWsL9tGLLmLWlwXbzv6tepG/1bd6JfTjT45+aQfKQHU/kgMvl7bWFZQLbSKD8BeI7RKdA/Uk7oHxu8UqDri4VViTKvDKxw9mJLHa4oYkarlWATDNKw7NFIzbPK6vHh0j33HuUS4VH0uXedEY1i/fj1PPvkk11xzDUOHDrWrmQBGjRrFvHnzmD9/PqeeeipffPEFCxcu5KWXXgLgT3/6E2PGjOGYY45h4MCBzJgxg1NOOYXOnTvb+//f//t/tGvXDoDZs2dz5ZVXNv6bFEIIIYQ4CCSUOlyoDqsbX7gMwqVWFVIikGrTGpxNEEhVpziruvhlxiD8C4SWWVOsCELLgeXWsXpXSBsEjqMh5rXGwdpTYoUAukbM5aDEbVLkgR2Knwo1guJykeHJPGwqokpDFfywZx0/7FkXD6E24I8EahyX58lhQKvuDGjVnf6tutO/VTcynI1QEXckqXdoFR/XKrl7YIKqAzpoulVhlai2SoxzpepVAdZhOhh7oropOWxKBE4ogGl1bdJVHV3T0RUdl+6ilaOVXdnk1JwpIVOiW93h8G9eHD7++9//EovFeOqpp3jqqadS9q1Zs4bHHnuMOXPm8Nhjj9GxY0dmz57N4MGDARg8eDD33Xcfc+bMoaysjJEjRzJt2jT78ePGjWPPnj1MmDABTdO48MILGTt2bGO+PSGEEEKIg0YxTbPmrbaaIZ/Px9ChQ1m2bBleby3jJgmLfzMUfAsbK2B7EbTJte6Y15yZJkS3WqFU6HuIrCflal7xgusoTOdAKvReFAVNtlfupqSyDCMSIUN1keFKR3O5rAoqb7rV3c/ttMI4R/PPXmOGwcby7awoWsvKPWv5oWgdW3y7ahzn0V30y+nGUa17WFOrHrRJa9UELRYNYt85MD6mlRHDGt8qljjAqp6yq600UN3x4MpdVYGVCKzs4Kr5BDExI1ajsilqRImaUesAE1RVTRmHyaN78Dg9pOlpODWnHTAlB0+62vz//QrRksj3KSGEEM3B7+Z8zqod5U3djP1y9qD2zPnTkBb9HgZ0yOSDiSc2dTMAqZQ6/DjawZYobPgZOvVp/oEUWBUhji7W5D0bjHJr/KnQCuuOfqYPgl+jBL8mEzBpjaJ2Jze7NwG9G6bihGgEQhEorYDCYkABXbMCKbcLMtKsboAupzU5Hdbg1k3EHwnww551rCxay8p4NVRtVVD5Ge05unVPjmrdk4Gte9A9qxO6dD9qefZ150CoVm0Vg0g5REqSgiusaiIlqeJKSYxv5arqGmhXXMWX0Q646sowDSKxiB061QicsLrTOdT4eEya1Z0u3ZGOR/fUGThJdZMQQgghhBBHNgmlDiexGKxeA4UmtM8HfNTZ3ag5UzOJuUdSrA1it16CP/QTWdFfaMtWsswCsthDlrEHwt9ihDX8amfKtR5UuLvhT+todbUCiMYgEgF/AErLrWoVRQFHfED19DSrssoOqnSrsuoQdJsqqCxmedEalhf+woqitawr24JRrUgxTXdzVOseHN26FwNze3JUqx5kueSv2EcMu4vgXpiGFVwRD7AMP0TLa+kqGL8ToaLFwylXPLhyJVVbWVVZpqIRNSFimkSqda1LFNIqqmIHTg7NUWvgVD10OlzGchNCCCGEEEIcOhJKHS4MA9asgXXroEMXUPKgfJV1FzG95Ywv5IsFKYpUsDVUTHHEBwpkqvmonj74FAXd9JMR20BmbB2ZsQ04zTIyjE1kGJsgAjGc+LQuVKjd8Gld8bs7gMdd9QKmAeGoNQj8nlIoKCJ+iy6rqsrptO5gmOaxqqmcDiuwcuj7DgziEgOSf1+4hhVFv7C88Bd2VhbVOK5Dei5Ht+7FoNzeHJ3bi55ZndGasHpLtACKGr+LIHupuDKt89y0qq5iRphIuIKoESYSDRMxYkSMqBWKqiqg4dBd6JoDh+Yhw5FBujuLNHcWLmc6Ts2D05GG05GGQ/PgdKSjqI7DcswrIYQQQgghROOSUOpwYJqwYQP88gu0aWN1U8MNaV2hYm3VHcCaqagZY0/Ex85wKQWRMvyxEOmqi3bOLHQl9co7qqRTog+kRB8IponL3BMPqTaQEduIToCs2DqyYuviIZUDv9oZn5ZPhZZPpdoJI1EZlcwwrKAqErHCqt177FvKo+tWFZXbDelucLmtkMrlAF0nqiusLt3M94Vr+F/hGlYWraUs7Et5elVR6JPdlUG5vTgmrzeDcnuT58k5pJ+rOPxZd6qLxSucrMmqcorFC6dMu1ud05GG06WTo3tId7hxa06cqopTUXCqCk4UHIqJRmL8q/J4F0IglHR3wcSYV5o7XnnlBt1V1WUwecwruyuhdDkVQgghhBBC1CSh1OFg82b46SfIyQGPp2q7pz1EfBDcCe62za6ywR8LUhipYEtoDyURP4qikKV5yHVl1O8JFIWQkktIzaXIMQxMA4+5m4zYRjJiG/HGNqMTINPYQKaxASJgolKptsOndsGvdcGndiaiZFoVI3WFVdF4ZVW5D4pLCRlRfgzv5PvITv4X3s7K4A6CZiTlYW7NycCc7hzTpg/H5PXlqNY9SHd4EKIhzHiXunBi8PD4FIuPM6UQv1OdquFQdTIcaaQ7PHidaThVHafmwKk67LlD288f+aZR8w6DkbKkdSP1+ES3QTQrkFL0eIgVH/9KcyYFXNXm9rJUDQohhBBCCHG4k1Cqpdu+HVatAq/XmpKpGnjzIeaHcDG4WjdNG5MYpkFx1M+OcAk7w6V7rYpqMEUloLQjoLZjt2MEmAZuswhvbBMZsc14jc04zXLSjR2kGzsg+jUAYSUDv9oZv9oRv9aJSrUDhhIfi0tVCWiwIrSD/wU28b1vMz9WbiNixlJeOlN1c4yzA4OdHRns6kBfdzt0hxOiOpS4IFRsdQV0OKwqK1235g692YWFovEYpmEFTbGqwClsRDFNA1BQFNAVHaem41B1vK5M0h1u0nWPFTTZoZOOU3WgHaqKJEW1JrWeFZfJ4ZUZAzMC4WDVHQir3/RVUa0gS9HjgZYWr7ByVo2HZQdZWlJwlfyYxLqEWUIIIYQQQrQUEkq1ZLt3w48/WkFHVlbtx+ge8PaA8p+srjiOzMZtY1zIiFAYqWBrqIjCSAUmkK2l1V4VZRiowTBqKIIajsbnEZRIFCUaQ4kaqNEYxAwUw0AxzPg4OkkXuooCCpiqSlT1UqwOZI86CE0L4NaKcCuFeLTduLU9OJ0VOB0/keP4CZxQocEnoSz+G3TzRWWIlYEyoqReRLfWvQzxdmVwej5DvF3p7s5LvZNYLBYfaL2qwspqnxLvEhgf1NrhsLoBeuJ3BkyEVbpmLev1GPxaNFsxw7AHD7fmVlc7ewBxBbuCyaE5yHZl4HV4cOsunKoDVyJ4iodPLWbwcDV+h8D63mghccdBI4Y1iHsMjABEfUnhFlSN5h7/HNRECJUIqtR4mOWwqrJUhxVoKVq14CopzErZJ4GWEEIIIYQQjUlCqZaquBh++MEKP9q23fuxrmzwdofyNfGLtEbqRhYzqCgupLhgG8WFO4iVlpJdEaGzL4LLF0TzBdF9AbTKIJo/iFYZQg2E0EKRfT/3QeRzwped4dOu1vRtR4ipZUCZfUxnP5xcpHJ8hYsRlZl0VlsR9mYTTYsRS99OLH0P0XQ3sXQPsXQ3Ua8HI81VszsgWF2dYjGIxrsGBkPWOFamaV1zq4oVRGmqFUo5HOB2WVOiyio5tHLEL8ZFo7NCp0hKlZN11zqoGs/J6kbn0py0cnnwOj24NVd8W1X3Ooeqt5zQ6WCzg6EGPMY0q8KsRBdCM1otzIolhdVJgZaS9JqoVRVWihavzkoEW4m7FWo1J7XaeuJ5ZPwsIYQQQhxCMcNEU4/Q74zisCShVEtUXm4FUpWV0KFD/R7jbguxIPg2gls/8IHPTRNKymBXEewqhII9UFQMu/dgFhVjFBajllWQYZhkAPn78xKKguHUMVwOTKcDw6Fh6kmTpmIq1p3zzFpCGcU0wTBRDMOaR2MoMQO/EmVp6xCftQ/zWYco37UziFV7eNcSOHkznLIJTt4EXUtBwQAC8alg3+1XFWLpHqIZiSmNmNdDNDONaEZa6jwr3VrOSANNsQKrWMwKrQJBqPBb41slF4okqqg0tSq4cjmtboKaVhVYaVqD7h4oqiSHTuGkgcSTQ6dEpZNbd5HryCZdT1Q6VYVRBzSek6idEh98fX9+jZlG1ThZpoFdnWVGIBqstt+s/uD46yeFWSnBlpo00LszabB3R2r4lbKs1h6QJZ5PCCGEECJOUxX+8tr3rNvt2/fBzdApffK49fS+Td0M0YzIVVJLU1lpBVKlpdCxY/0fpyiQ1tm64AruAHeb+EXQXpgmFJXA1p2wfRdsK4AdBbC9AHbuhnDtFU0KVXerNxWIZnmJZKcTzUyvCl8y04h6raqimNdDNN2NkeYi5nZieKy56Tw44y0FjTDL/VtY5tvEsoqNrKosJEbqwMwdnTkMScvnWFcnjtU60KWtB7VrBPW4MJFAiM2VZXgqd+MK7MHlL8VRWY7uD1n5VGXS5AfTD0oEFMNEr6hEr6isd1tNRSHm9RDJSrM/r0hW/HNLfI5Z6db+DCcxh8MKrwJB8FVay4lugmAFU6paFVI5HdbkdiVVXGlJAVd8+Qip2KnevS5R6WSYJgqkhE5puot0RzZePQ2X7sSp6lbgFK940lX5cdpi2GHPAfw/s6uzEsFWUpAVDdfcltz9UFGqwi5FwQqj1KSAKz5XE/N4oJUIuxJ3OFSrB1lJj691W/X9R8a/cyGEEOJws263j1U7ypu6GfulR156UzdBNDNyFdWSBAKwcqU1llSnTg2/oFA1yOgGZhiCReDOsy5OTBN274FN22DjNmu+ZQds2WmFHXU+nwK5rYi1bU1lbgbFOS5KclxEc7Nw5uZhts4mmpXW6BU6QSPCj/6tfOfbyHe+jfxYuZ1otYHJ2zuyGertyrEZ3RiS3pUOrpyU/aF6vI5mBnAbu0kzduExCvAYu/AYu9EIQxjwA7745AfTpxD1pRHzuTF8OmaFglJholZE0CsC6BUBFDM5yCraZxsMXbNCqmwvkRyvHVxFcjKIZqcTyUwj4vUQzfQQczmsroKGUbPqSotfoOqJyisnOHVrvCuHIzWwakEBVl2hU30rnSR0EnWyu+0dhKpTO7wyqoVZBhCBaChpe7zbItXG0avRvlrCLpT4upJa4WXfKbG2geRrC7aUurfZ26u/vtKsf1YIIYQQQoimIVdZLUUwaAVSO3daFVL7O4aQ5oKMnmD+AjvWwz+Wwr8/t6psaqOq0KENdGoHHdtBx7bW1KENpTludho+toWL8cWCeFU3WXoamqLSmKNChY0oP1Ru5buKjSzzbeLHym2EzWjKMW0dWVYI5e3Gsd5uNUKo/RFTPPi1fPxaUudE08BpluN2FeBJ340nrxC3YU0aYRz4ceCv8VxRPPjMjoQrs4hWeIlWuDHKdShXUcsiOMr96GV+HKU+a17mt8bgisZw7inHuWfffykxHLpVaZXtJZKTYQVZifV49VUkI41IhgczEoUKI6nyKk5RUgMsVbW6BjodVQO1J3crTB4bS9Ot5YN4YRo1YvHAKWaP6xSNJYall+51ogVQ4rWlB3r30ersUMtMndvLiUHlQxBNCsZSjgfYS/CVGJ/LDp8UaxvJIVRyCKbUPj6XosYrwNR4QJZ8fB1zVQdnKwm6hBBCCCFaOLkKawlCIavL3vbtViB1oJVHeyrg+Q/gH+9Zd4cD6zk7t4OuneJTR+jcwQqgHFWniWEaFEf9bAsVsyOwi5ARIVtLo7OzdaMN0hwxoqyq3M53vo0s821kpX8roWohVJ4jg6HxAOpYbzc6OnMap32KSljJJqxmU06fqu2micMsw2MU4jL34DaKrLDK3IPTLEcngFfZDunbIR1oV/XQKG7CajYhpRU+tQthJYeQkk0k4sUo19HKI1ZYVerDUerDURJfLrOWHaU+K8CKRHEVluEqLKvR7OpiaS67+iqSnWFVYSXWM9PsKZrmtu4y6A9UBVj2NaxZFV4pajykUuN3GHRYlVhOp7UtuYuhpoKqYaoKUQ0ipkHETAqdjHiVCKCpGk7Vga5qe+1eJ6GTOOI01nhUyUGXHXzFl+3KLqOqussIV223AzCqKsHA2pfcxRFqrusZkDscHN5D/x6FEEIIIcQhI1dpzV04DD/+CFu3WoGUfgD/y3buhBdegHffhUi8lql/D/jjKBh2vFXtUoeoGaMwUsGW4B52R8owgFZaGm0dmfvfnnqKmjF+skOoTazwbyFopNZitda9DEmqhOriaryQrF4UhYiSTUTNBnql7FLNMC6j2A6rXGYJLmMPLrMkHlgF0Y1dpLELYtWe1wsRbzrhTllWGKZkEVJzqFC6ElYyiShZRBQvSjgWr7Ly4SiuCqz00qrgygq0KlDDUbTKEFplCPeOPXt9W6aiEM1Mq+o6mOONV2B5ieZ4reqrzHRroPc0N8RMiATBHyAWtSqcomaUCPHgCQPDGswJFBWH7sChOXA4nGS50khzpZPu9uJ0uXA6XLg0D06HE6fTje5wVqvMat5dC4U4LDTFYOxGGMIl7L2KSwghhBBCtAQSSjVngcDBCaS2boXnn4cPPrCqWQCGDIGrr4ZBfaHiF4jsgVgmaGkpDw0ZEXZHytkULKI46kNHJVfPwHkIx9dJhFDLfJtY5tvI8lpCqGwtzaqEyrBCqK6u3OYVQjWAoTgJaO0IJJdHxSlmBJdZitMojodVxda6WYrLKEEjZHUJNPyks6PW5zdRCSsZRDIzCWdlEsnPJKJmEFRaE1G6ElEyiCgZGIoLTBPNH7S6CJZUWEFVcUV83YejtMIOtfQyP4ph4oh3J9zXHQkNVSGclUYwK41QdhqhrDSi2VYFViw7EzUnC0erVuhZmegZXpyKjtNUcBoqTkNBDZkQMCAWALMyXjlhvUNUxeoemNytMFGV5XRYFVkuHXRH6uDvyQFW9S6H+9tFVgghhBBCCCFEvUgo1VxVVFiB1K5d+x9IrV0LL74I//mPNbA1wLBhcNVVViiVkNUXKndCqBDC5eDIoBKNnZEyNgeLKItV4lGdtHNkoR/scU+wuuP9FNjBMt9G/ufbxAr/VgJGOOWYLC2NId58u0ted3ce6hFwq3RTcRBU8giqebXu18wATqMUp1mG0yxNWi7HaZbhMCtQMHCZZbjMvXfbi+EkoniJaBlEWnuJ5nqtdcVLSGlNREknqngJk0ZU0YhEIlDhQy0uRy+tQC+pwFnqx11Wiau0Ek9ZJa6yAK5SP86KAKph4i7x4y6pOaZWDaoCmRmQkwnZmZCTZc0TU07ycpYVPBmmdZ7HYvG5AeEgVPir1u1uRWCNfWNWBVTJgZaqVt2t0KHHJ0dVgKWqNcfOSgRhyaFWCw1KhRBCCCGEEKIxSCjVHJWUWIOal5Y2fAwp04Tly61uel9+WbV95EgrjBo4sOZj9HTI7AnRdpT7trCj7Be2BndRoUCmM4dOzlYHNQAKGRFWVW7nf75NdggVMlMrobI0D0O8XY+4EKqhYoqHgOYhQPvaDzBjOEwfTrMch1luzx1GRXy5AodZgUbYmsxi3GbxPl83ioMwaUQ86cQ6pWN08aKomShqDoraFVXLRFcz0bVMNC0bDDeU+qCkHEpK4/Oy+FRtudxnBUyl5dZUH05HVUiVlRFfzoCsTMjypm7PyoD0tKrAKHE3wljSPBaDYBQqg6n7TQNrsGUTzESopVh3tkyeJ4+TtbdgS0scnxRkJcKt5JBLwi0hhBBCCCHEYUhCqeZm1y5YtcrqutexY/0vRqNR+PhjePVVa1B0sC5qf/1r+POfoW/fOh9qmiYlkXK2BXazI1xKQPOQnZ5Pl2glSiwARinoXlCd+/WW/LEQP/i38j//Jr73bWZV5fYad8ezKqG62nfIkxDqIFE0a1wpsjBNk6gZI2oaRNQYUTNGJL6umEHcVOLGTxoB0gjgIYCbSjxU4jQrcZh+NLMChRg6EXTKwCyzxzPeR0NASYccL7ROB9VrravpoLQFtXt8OR1MD5SbUBaDsgiUVlphVVmFFVKVlENpGZTG10NhCEdg9x5rqg9ds8KpzAwrtMrKgExv1bbMpG2JKd1T+7/HRGhlB1tmUqVW1BoEPvmYlEGc48EWSlU4pSjxcEpJDakc8XBL06xB4nU9qUorORBLntcSckkFlxBCCCGEEKKZkFCquYhEYMMGq8udrkP7Oipfqisrg3fegTfegIL4mD4OB/z+9zBmDHTuXOdDY2aMonAp2wK72RXaQ9SMkqNnkufNsQ4wYxCpgHApRMsgUg6aEzQ3KHUHVMURH8v9W/jev5nlvs38EthFrFpqkRiYfIi3K0PSu9LNnSsh1AFIhE2pc2vZTOqu5lBUdFVDR8OtOWmlOkjX3LhU3RrDSdFxqJq1rOo1u2uapjWek1EBRnnS5EuaV1iTWWGtm0Gsu2z5IOarOVh7XTLjU2cVlDRQ00DxxOc5oHQE1QMhB5SrUAGUG1AWhfIolIWgPARlASj1Q5nPCraCIeuOgXtKram+NA0y0lODqkyvtS3DC5nxfd6k9QwvpLnrFwIlKrGSuxomlqNRq92xpGArMbfFAy5FsRYTIZUSD7gUpVo1lmaFXLoWvyOiZo3LlRxsKdXCrERQpiQdUz34EkIIIYQQQoh6klCqOSgrg9WrYft2yM2F9PS9H2+asGwZ/OMf8N//WnfoA2jVCi64wJpyc+t8eCgWZne4mM2BnewJl6Gi0sqRhVurFjQpGjizrSkWgKgfIqUQiQcPmgNTcbE5UsZy/1ZW+Lewwr+FLaGa1SrtHdkM8XZlsDefId6udHa2arEDkzem2sMmaw4mZjyI0BQVXdHQTSt4SseFR9VIw4lT0XCYqjVHxWkqOFDRDdW6G17IrAo3EmN5mfFtiYHEE0U9YC0oajwYahUPQZLDChV0JSmoMECpBCWQFGj5wfRbc8NvBVZGZXxbZXxbJVaCZVQFWnVJj081x4pPooLihkg2+Fzg08GnQYUSn0yoMKAiZoVaFRGoCENFCEJRq0tfQ7oU2i+rgjfNCq+86fF5Leve5O1pVhdDb1rDuu8mJAKr5HAruYIrGgMjZG2z9xtV/9+BGt0UUUClKuyiekWXUhVWJaq4El0VHfHKruQQq7aKsOTQq3plV/JxQgghhBBCiMOChFJNKRqFHTtgzRqorNz3gObbt1uDlr/7rnVHvYTeveGSS+C006y7jNXCNE3Ko34KQnvYGiygPOLHo7po52yNXp876Wke0DxU6un8FFrNyvK1rKxYyw/+rZTFgjUO7+Fuw+D0fI7x5nNMej7tnFn7fo3DnWmACbFYjJgZJWrEiMaixIx4VVPMmieCAcU0UVHQzcSkkqXqeBQdt+LApeo40HEoKg5Fw6FoOFUdh+pA0eIX7slVMollki7wdT3p4r/6mEhJlTKJ7mbJwUUslhp0xGJWN7pEwBGNQjjRpQ0wnGA4QMmOfx6kdjPTahkoXIuCEqoKqcxAPLwKpC4b8cDLDFqTEd+fWAescKsS9ErIxprqK4xVieWrZfIDPgX8atK6aU2R+OdT7rOm/eF2gtcNaZ54WOWxgmtvujVPT2zzWMekp8WPja+necDj3r/Xrq62sMs0q84Dw4BINP7/IHl/4ryBqoQzOelM6sKoJp+nSedvctVX4txNDr4Sg9TrerxSrI7wq3rAlRyqqkrtxwshhBBCCCEOCQmlmoJpwu7dVne9ggLrwrJTp9qP3bULFi2Cjz6yxppKSEuD00+H886Dfv3qvHAKGxGKw2VsDxZSENpDyIyQqaXTyd1mn93lDNNgc2AnP1as54eK9fxYsY51/q0Ydncwi0txMCC9I4Pc7RmU1paj3W3JdKRbXfw0ByiOBn08TStxgR0fKCmxnAhiDDMeLiWOATMWs4IlDGKmQcyMETVNYkqMqGESJWZVm6hW1yo13i1OUzV0VcWp6GTpabgdLjy6C4fuwOFw4tCcOHQnDmd8XXdbYVNDKk2a6kI7udtZLCmkisasKRafRyIQilhhVjgc3x+FkAFGLB52GfFzKBPMTEBJDa80DfTqYVbS4OCmAWaoKqAyg2AE46FVqNq+ULVtYWvZEYK0ELQJxbfFt9v/Fkxq7ZcYxgqp/FQFVtWnyvhUfT2RpQXD1kQDK7SS6QqkqeDRwKNXmxyQ5gCPE9Kc4HFZyx4XpLnA7bKCLXvuBt0JxMMgNKuqEg0UHaucKrGsW0GSPU8cV8f5Vz3kSv53l1iPGmBEqoKv6uFY9TssJudeyRVfyaEXycFX4t8IqSFY4o6LdoirgabEt2k1/30lVw9Wf97kY+oKxJRG+rcqhBBCHEFihommyu9WIZoTCaUak2lad9bbvBm2bbMuONq3T62OikatO+999ZU1/fJL1T5VhaFD4Ywz4NRTrWCqFoZpUBIppzBUwvZQIb5IJaqikuPIwKPVXjFhmiYF4WJ+rtjIT76NrKpYzyrfBvyxQI1j27paMzCjB4MyenN0Zi96p3fBoerWGFSxoDVFfBDzQ6zSCiDAGq8G3ZorDpL6gzX0g6wWFFkVSNa6YXVJq/WYal3SEhes9jqgKFa8oEIUk5gCMcUkpphW6KSYRAFTU1GcGqZmVW/ojjQ0TUfTdXRVx6XpeHQ3Lt0KmpwOJ7qu49Cc6JpuhU2aA13X0R2uw68qI3ER72jgj5jkMCsRXlVfj8YgFEoNs2KGtWzfQS+WNJA42GMtqTpoWaDm1Lzbnd1VLL68L6YJRFJDKns5PhEGM1Jte6Ta9kh8W2I9aq3HQuALQ2UE/JH4PAaVRlVwFYhPyeuJQKsSCMXbGjWhPGZNhBv2/6Q2DsAdn1zxqfp69SllnwJOFdwauFRwaVWTHg+u7LBLrZpXX9YSy1o89KrlmOTtyetmPIwy1fg8aT2mVNuWdJyB9RwGSfuw9ivxdUWLnx8a8XSLqm6vSrwdelUFI2pVsEW8YlGJvw81sS8x5le8e6SaqBTTredKdI+sEapVC8KqB15KtcfUdZwQQghxGNBUhb+89j3rdu9nBXsTO6VPHreeXvcNrIRoiSSUagyRCBQVWUHU7t3WBXZeHrhcEAxaIdTy5da0YgX4/VWPVRQ45hgrhPr1r6F161pfImbGKI1UUBKuYGeoiNJIBTEzRoaeTnt3HlpSVZRhGuwIFrLGv4Vf/JtZ49vMz76N7ImU1Xhel+qkn7crAzN6clRGTwZm9KCNq1Xt71PRQE+3uvrp2WBErYAqGoRIECJ+iAasKodo1AqQMLFOw6SLSJR9BkcpF1FUu4BKqmyIaSqGAjFNIaqCoSnxoCkeOmFgKICqoKiafVGmqzqqqqHrDjRVw6k7yNBduB3W5FQd1oDhqo5D1a1lRcOhVVVBif2wP2FWoithogoruSor0a0wsT8ctrqXhSLWv8tYLLWbYXK3xL12L6tWmaa5QPVUbUsOvA5E29rerwHEgyszihVmxddTtkchGoZKP1QGoTIAgSD4AhAIVU3+MAQiEEjMoxCMWvNAzJpCMQgYVXdZjGdoVOzvG0tUltVSXaZhBVfOapMLKwxL7HNU2+6stj15OXlefXl/8vHq48sfQL5uP9c+72C5r+dKCr9SGqYmLSf2JYKmvUxKbctJj1OS1hW1apuSCP5IPYbqx9W2bS/Pn7LdhIyekHfiAX5oQgghjkTrdvtYteMAKtCbUI+8fYw9LEQL1KihVCgU4t577+U///kPbrebK6+8kiuvvLIxm9B4YjFrAPOSEiuMKi62xo0qLYVNm6xxpNassbrwRaOpj83OhhEj4Pjj4bjjICenxtObpknACFEe8VEaqaAgXEx5xE/UjJGmuclz5qAAO0JF/Fixng2V29lYuYONge1srNyOv5ZxoDRUerjb08/ThQGefAZ4utDD2RYdDfuuYKURoCApKKqF/df2xF/enaC4wJVtXUAmLprV+MU1ISBidW8jZnXFSgyYrTkwUDE0naiiYqgahqoSVcBQTGJmvJoJA1NRrOuyxNgzmKiKiqZoaIqKplrLTlXDqTpw6Q5cqhOX7rQGCVc1O1zSE0FTfF1CpmZMUawqkb2Nx1aX5PGwEoFW8rZYtW6EkYgVatlT0hhakWi8m1ksnrnEw62UUFWhRsBVa/etpGoVe1/yMU72dgdMmwtrAPiDwTSt9xgIWncCDATjUyh1WzAedgVDSdsSU9L2YBhC8SkYwR5gPUZV5VdjcSjgUK3qLV2x5g7FmnSlatlBfJ2k9fiyDjhMa64DenzZAWhm/Fijap9mxo+JH6cZ8f2mNW9onqkkJ1wHgVnHcnNR8Cn0uQuyMpq6JUIIIYQQ4gA0aij10EMP8eOPP/Liiy+yY8cObr/9djp06MAZZ5zRmM04NGIxqwpq3Tqry93q1VYYtXOnNW7U9u1QUUdZQevWMHiwVRF1zDHQq1eNO27FzBiVsSD+aABfNMDucDGFoRJ2BAvZEy2nIuKnLFzO7rC1bVtwN7vCxcTq+PO7U9Hp6WpPb3cneqV1on96V3qldcatu+LdRJLG60mM0aMlxk5RqkKn6oMPV9tmKgoxTAzFxABiimHNMTFMA8M0iBkxDCNCLBbCNMJgRFDMKGYsBEYYxQijE0MlikoEHWsAcJem41A13Jo1/pJL96BpDnTViabq6JoTTXWgaw50xYmmScAkqtnfbobJEtVVieDKDrZiVevJ2xN3wotGIBJLHWsrGk16fC3jKRlGPCCGWiu5Et0UgRoVhIn3m9w1K3kAcYXUbVQ7TlHA6bCmgx0EmKYV8CWCqmDIqmpLWY/EQ6x4oBUOWxVvoXDVsZHEenyesh7v7hmJP85MSloipvX/orKWyq2moipWdz1H8h0M490b9eRltWqg98TA77qaup7onqrHJ/umAkrSeGxKfEocEz9v7DtpKtWmxHMk2pqoEDSrHqspVfvV+LKmxIu34uermTR+X6J0zDDqWI4fG4tAOLvWQjshhBBCCNGyNFooVVlZyRtvvMHf/vY3BgwYwIABA1i7di2vvPJK8wmlAgGrusnvr5oqKqypvNyal5RAYaHVHa+42Aqidu+GPXusC6O9URRo1866W17v3tCnD5EeXfG1zqAiWokv7KM0Uk7R1kUUhUvZEy5lT6ScglAxuyMlFEcrKI36KYv5KY35CZj7HhvGpTrp7G5Lt4zOdM/Ip1tWPt2yupKf3RXd6aqqMNGsQMkEOywyTAMTk5gRqzFPPsYwoximEe9pp2AmLvaioKgKGhqqqqIqKipWtZKqqDg0B07VGlvJoTlwak6cmtOqbFI1tHjFkqZqaKaJRgwdE8000BQD1YxZFVexsDX+jhEAI2x1GzRj8Sm+36ywLjyt/xGkdAVMjDOTGNsleZyXlO4n1bqqVO9iIo5MdqXWQXq+RPiUfGfDRFBVfV/yslltWyzRhTFevRVNDsli1R5TddfHlMHCTVLHY0u0z3rjVbOUf1pJYZmipIZddret5G3V9jsd4HJW7aPacdUfW2O/Yr98yutV/4wTA+2HI0khVrRqPdHVMxJNWk4EW4nlpIq5lHm15Wi09uVI1Pr/FIlVjb2XYJhWV+dIZL9Oo2YvUQ2b+KNH9fHdtKQ/gFTfjwk9OsDvmvpNCCGEEEKIA9VoodTq1auJRqMMHjzY3jZ06FCefvppDMNAPdCxVw5Q4N8f8Mad51GsRzAU7CmmWINeJy9H41NMhWgHiHSGSHxbxKkRTnMRdjsIu3TCLp2ASyPoUAjqJgEzQtBcTqXxDQFfmOiKA/tTr1tzketuRdu0NuSm5dImLY823ra0z+hA+8yOZHtyMDExTdMOmQB2EgAzUDU2DMSLLBQrPFJUFEVBU7SUua7ouHU3uqbjVJ3WeEuaE4fqsLrKxcOkxLy2bYm5cqiCHCMeRiUHU0Ysab3aZETiQVZ8PJ5EqEW8KsWMJY1/Ff9LvX3RHt9u1hJ41UdyVUvK2C3J+6sdk1hM3p7yPCkvUMvrJG+v5diUjdXbUuubqLZaRxvqXK/raev5evV53pYUGipKvHLlEL9O8k0AUu5iR7UbAySHXnUcV+PGAsnPbVR1ZUzcvTJmVFuudqe9xL8tI6mddpuTtyX9W0venhyq2V0n459tygD4iUXTCkBcTusug8nPnfyYxD/v5H9/1cMy+3nrsc3epVSFg9FEd9Lkwf7j2yPRqvXksdJiRuqNAGK1LCcfU9u+WufJ47QlzROhZqI6MBbvupr82MSxezv/oolxxfYjeNu2a99/CDqMHVHDIQghhBDisNZooVRhYSE5OTk4nVXjoOTm5hIKhSgtLaVVqzoGz24kr+z5mKvPOhh/kU4MiFKNwV5veqWrOm7NjUd3k+5MJ92RTrrTS7oznQxnBlnuLLJcWWS6Msnx5NDK04rWntakO9JRFMWaUOwwya4wIh4CqRq6otvbE8FT8nJi/KXq+5LDJVVp2vCwXtTE3bsOkGkkhU6J5cRFePV91UKqGoGVWfuyGSO1i4qR+nyJ10sJxcw6Xi9xgV5tXv0C3r6Or348tTxHjQ+ljuVa1s26nqOux9f1mPq0pR6H73OnUo9jDsVxtT20Woi2z8+yrte2n6DhD6+R4+0r2KvtNRI3L2jgwPXJd8pMuWsm1QKq6sckrVPHc1QPuKi2nhKC1fJ6yctGfN0wqo6vHuil7K/lNRL7FKwfW7qR8k/dvhNh1QdU7XOo/rnU8ZmmLNf1s6COx9f6HHUcn3KumlVhlV35Z6aO3WaaScFkosov/hkkwq1EoGlXBRrQsxu4ar+b7JHgsB4OQQghhBBHlEYLpQKBQEogBdjr4Wbw184zf38Lf/6okJ1l29BUHUW1KnlUNTmQ0exgRld0VFXFoVp3Z9NV3e5u5tSc8YGydVyaC4/Dg8fhIU1Pw+1wk+HMIN2ZTpqehsfhwev04tJdKFSFS7XNVUVN2ZYIoBLLiXVxkCTfRaolqX5RWH09eV5rCFWfkKqOC9R9HVvr8bU9po5tdYYzewld9icc269j6vNaB+E1Dtnj9/bUh/C5637RJnjNQ20/3lNtYVBd83rvq+fz7+2Y+i5Xf72GPP/ejld1yMqreewRoEUMhyCEEEIIUU+NFkq5XK4a4VNi3e1u+r92dsjowAvnv9TUzRDiwKUEk8q+C1yEEEK0GM19OAQhxOErZphoqnyxFEIcXI0WSrVt25aSkhKi0Sh6/NbthYWFuN1uMjMz9/n4xODZPp/vkLZTCCGEEAIgPT292VUgH+hwCPJ9qv4OhwtweQ/Nx+HyPp7+dD07ygJN3Yz9MrBTFhcN7UzXTBUj7Gjq5uyXth7r57e8h6Z1OLyHrplqo30X2Nf3qUYLpfr164eu6yxfvpxjjz0WgGXLljFw4MB6/VXP7/cDcPLJJx/SdgohhBBCgPU9xev1NnUzUhzocAjyfUoIcaT6AHigqRtxgDYALb1vj7yH5mEDMHRm47zWvr5PNVoo5fF4OPfcc5k6dSr3338/u3fv5rnnnmPmzPp9Em3atGHx4sXN8q+WQgghhDj8pKenN3UTajjQ4RDk+5QQQgghGtO+vk81WigFMHnyZKZOncqf//xnvF4vN9xwA6eddlq9HquqKu3atTvELRRCCCGEaL4OdDgE+T4lhBBCiOZEMc0mua2SEEIIIYRooEAgwPDhw3nuuefs4RCeeOIJlixZwssvv9zErRNCCCGEaBi5RYsQQgghRAuRPBzCypUrWbRoEc899xyXX355UzdNCCGEEKLBpFJKCCGEEKIFCQQCTJ06lf/85z94vV7GjRvH2LFjm7pZQgghhBANJqGUEEIIIYQQQgghhGh00n1PCCGEEEIIIYQQQjQ6CaWEEEIIIYQQQgghRKOTUEoIIYQQQgghhBBCNDoJpYBQKMSUKVM49thjOeGEE3juueeaukktVkFBARMnTmTYsGGceOKJzJw5k1Ao1NTNatGuueYa7rjjjqZuRosVDoe59957+dWvfsXxxx/Pww8/jAylt3927tzJ+PHjGTJkCKNHj+aFF15o6ia1KOFwmLPOOoulS5fa27Zu3crYsWM55phjOPPMM/niiy+asIUtR22f5fLly7n44osZPHgwp59+Om+88UYTtlA0Nx999BF9+vRJmSZOnNjUzTpsyM+3xlHb5zx9+vQa5/bLL7/chK1smfZ2DSPn8sGzt89ZzuWDZ/PmzYwbN47Bgwdzyimn8Oyzz9r7muP5rDd1A5qDhx56iB9//JEXX3yRHTt2cPvtt9OhQwfOOOOMpm5ai2KaJhMnTiQzM5NXXnmFsrIypkyZgqqq3H777U3dvBbpgw8+YPHixZx33nlN3ZQWa/r06SxdupT58+fj9/u56aab6NChAxdffHFTN63FufHGG+nQoQNvv/0269atY9KkSXTs2JFTTz21qZvW7IVCIW655RbWrl1rbzNNk+uvv57evXvz1ltvsWjRIiZMmMCHH35Ihw4dmrC1zVttn2VhYSFXX301f/rTn3jggQdYtWoVkydPJi8vj1NOOaXpGiuajXXr1jFq1CimTZtmb3O5XE3YosOH/HxrHLV9zgDr16/nlltuSfmu6PV6G7t5LdrermFuu+02OZcPkn1dK8q5fHAYhsE111zDwIEDeeedd9i8eTM333wzbdu25ayzzmqW5/MRH0pVVlbyxhtv8Le//Y0BAwYwYMAA1q5dyyuvvCKhVANt2LCB5cuX8+WXX5KbmwvAxIkTefDBByWU2g+lpaU89NBDDBw4sKmb0mKVlpby1ltv8fzzz3P00UcDcOWVV7JixQoJpRqorKyM5cuXM23aNLp27UrXrl058cQTWbJkiYRS+7Bu3TpuueWWGhV6X3/9NVu3buW1114jLS2NHj16sGTJEt566y1uuOGGJmpt81bXZ7lo0SJyc3O5+eabAejatStLly7lvffek1BKANaFe+/evcnLy2vqphxW5Odb46jrcwbr3B43bpyc2wdgb9cwJ510kpzLB8m+rhXlXD44ioqK6NevH1OnTsXr9dK1a1dGjBjBsmXLyM3NbZbn8xHffW/16tVEo1EGDx5sbxs6dCgrVqzAMIwmbFnLk5eXx7PPPmv/kEnw+XxN1KKW7cEHH+Scc86hZ8+eTd2UFmvZsmV4vV6GDRtmb7vmmmuYOXNmE7aqZXK73Xg8Ht5++20ikQgbNmzgf//7H/369WvqpjV733zzDcOHD+f1119P2b5ixQr69+9PWlqavW3o0KEsX768kVvYctT1WSa6AFQnv39Ewvr16+natWtTN+OwIz/fGkddn7PP56OgoEDO7QO0t2sYOZcPnr19znIuHzxt2rTh0Ucfxev1Ypomy5Yt49tvv2XYsGHN9nw+4iulCgsLycnJwel02ttyc3MJhUKUlpbSqlWrJmxdy5KZmcmJJ55orxuGwcsvv8xxxx3XhK1qmZYsWcJ3333He++9x9SpU5u6OS3W1q1b6dixIwsXLuTpp58mEolw/vnn83//93+o6hGfyTeIy+Xi7rvvZtq0abz00kvEYjHOP/98LrrooqZuWrN3ySWX1Lq9sLCQNm3apGxr3bo1u3btaoxmtUh1fZadOnWiU6dO9vqePXv44IMP5K/YArC6jGzcuJEvvviCZ555hlgsxhlnnMHEiRNTvv+JhpOfb42jrs95/fr1KIrC008/zWeffUZ2djZXXHGFDPvQQHu7hpFz+eDZ2+cs5/KhMXr0aHbs2MGoUaM4/fTTuf/++5vl+XzEh1KBQKDGF5LEejgcboomHTZmzZrFTz/9xJtvvtnUTWlRQqEQ99xzD3fffTdut7upm9OiVVZWsnnzZl577TVmzpxJYWEhd999Nx6PhyuvvLKpm9firF+/nlGjRnHFFVewdu1apk2bxogRIzj77LObumktUl2/f+R3z4EJBoPccMMN5Obm8sc//rGpmyOagR07dtj/3h599FG2bdvG9OnTCQaD3HnnnU3dvMOS/HxrHBs2bEBRFLp3785ll13Gt99+y1133YXX65Wu9Qcg+RrmhRdekHP5EEn+nFetWiXn8iEwZ84cioqKmDp1KjNnzmy2P5uP+FDK5XLV+J+QWJdAYP/NmjWLF198kUceeYTevXs3dXNalLlz53LUUUel/CVB7B9d1/H5fMyePZuOHTsC1sXJq6++KqFUAy1ZsoQ333yTxYsX43a7GThwIAUFBTz11FMSSu0nl8tFaWlpyrZwOCy/ew6A3+/nuuuuY9OmTfz973/H4/E0dZNEM9CxY0eWLl1KVlYWiqLQr18/DMPg1ltvZfLkyWia1tRNPOzIz7fGce655zJq1Ciys7MB6Nu3L5s2beLVV1+VC/n9VP0aRs7lQ6P659yrVy85lw+BxNjEoVCISZMmccEFFxAIBFKOaQ7n8xHff6Vt27aUlJQQjUbtbYWFhbjdbjIzM5uwZS3XtGnTeP7555k1axann356Uzenxfnggw9YtGgRgwcPZvDgwbz33nu89957KeOeifrJy8vD5XLZgRRAt27d2LlzZxO2qmX68ccfyc/PT/ml1b9/f3bs2NGErWrZ2rZtS1FRUcq2oqKiGmXVon58Ph/jxo1j7dq1vPjiizIuhUiRnZ2Noij2eo8ePQiFQpSVlTVhqw5f8vOtcSiKYl/EJ3Tv3p2CgoKmaVALV9s1jJzLB19tn7OcywdPUVERixYtStnWs2dPIpEIeXl5zfJ8PuJDqX79+qHresrgXsuWLWPgwIEy5sx+mDt3Lq+99hoPP/wwv/vd75q6OS3SggULeO+991i4cCELFy5k9OjRjB49moULFzZ101qcQYMGEQqF2Lhxo71tw4YNKSGVqJ82bdqwefPmlMrSDRs2pIzjIxpm0KBBrFq1imAwaG9btmwZgwYNasJWtUyGYTBhwgS2bdvGggUL6NWrV1M3STQjn3/+OcOHD0/56/DPP/9Mdna2jB16iMjPt8bx2GOPMXbs2JRtq1evpnv37k3ToBasrmsYOZcPrro+ZzmXD55t27YxYcKElEDvxx9/pFWrVgwdOrRZns9HfOri8Xg499xzmTp1KitXrmTRokU899xzXH755U3dtBZn/fr1PPnkk1x99dUMHTqUwsJCexL117FjR/Lz8+0pPT2d9PR08vPzm7ppLU737t055ZRTmDx5MqtXr+bzzz9n3rx5/OlPf2rqprU4o0ePxuFwcOedd7Jx40Y+/vhjnn76acaMGdPUTWuxhg0bRvv27Zk8eTJr165l3rx5rFy5kgsvvLCpm9bivPnmmyxdupTp06eTmZlp/+6p3uVCHJkGDx6My+XizjvvZMOGDSxevJiHHnqIq666qqmbdtiSn2+NY9SoUXz77bfMnz+fLVu28Pe//52FCxfKEAUNtLdrGDmXD569fc5yLh88AwcOZMCAAUyZMoV169axePFiZs2axbXXXttsz2fFNE2zSVvQDAQCAaZOncp//vMfvF4v48aNq5HUin2bN28es2fPrnXfmjVrGrk1h4877rgDgAceeKCJW9IyVVRUMG3aND766CM8Hg+XXHIJ119/fUo3DlE/69atY8aMGaxcuZJWrVpx6aWX8uc//1k+ywbo06cPL730EsOHDwdg8+bN/PWvf2XFihXk5+czZcoUjj/++CZuZcuQ/FmOGzeOL774osYxw4YNY8GCBU3QOtHcrF27lvvvv5/ly5eTnp7OxRdfLL8LDjL5+dY4qn/OixYtYs6cOWzatImOHTty0003cdpppzVxK1uWfV3DyLl8cOzrc5Zz+eApKChg2rRpLFmyBI/Hw2WXXcb48eNRFKVZns8SSgkhhBBCCCGEEEKIRnfEd98TQgghhBBCCCGEEI1PQikhhBBCCCGEEEII0egklBJCCCGEEEIIIYQQjU5CKSGEEEIIIYQQQgjR6CSUEkIIIYQQQgghhBCNTkIpIYQQQgghhBBCCNHoJJQSQgghhBBCCCGEEI1OQikhRLPXp08fbrnllhrb3377bUaPHt0ELRJCCCGEEEIIcaAklBJCtAjvv/8+S5YsaepmCCGEEEIIIYQ4SCSUEkK0CB07duS+++4jHA43dVOEEEIIIYQQQhwEEkoJIVqEG2+8kYKCAubPn1/nMbt27eIvf/kLw4YNY/jw4UyfPt0Osd5++23GjBnDnDlzGD58OMceeywzZ87ENE378a+99hqjR49m8ODBjBkzhjVr1hzy9yWEEEIIIYQQRyoJpYQQLULbtm2ZOHEiTz/9NFu3bq2xPxwO8+c//5lAIMCCBQt49NFH+fTTT3nooYfsY77//ns2btzIq6++yl133cVLL73EV199BcDHH3/M3Llzueuuu3jnnXcYOnQol19+OWVlZY32HoUQQgghhBDiSCKhlBCixRgzZgz5+fnMmDGjxr7PP/+cgoICZs2aRZ8+fRgxYgR33303r776Kn6/H4BYLMa0adPo3r0755xzDn379uWHH34A4Nlnn2X8+PGMGjWKrl27cuONN9KxY0fefffdRn2PQgghhBBCCHGk0Ju6AUIIUV+apjF16lQuueQSFi1alLJv/fr1dO3alaysLHvbkCFDiEajbNmyBYDWrVvj9Xrt/V6vl2g0aj9+1qxZPPzww/b+UCjEpk2bDuE7EkIIIYQQQogjl4RSQogWZciQIVxwwQXMmDGDq666yt7ucrlqHBuLxVLmTqezxjGJMaVisRhTpkxhxIgRKfuTQywhhBBCCCGEEAePdN8TQrQ4kyZNorKyMmXQ827durFp0yZKS0vtbcuXL0fXdbp06bLP5+zWrRu7du0iPz/fnp5++mmWL19+CN6BEEIIIYQQQggJpYQQLU5OTg6TJk1i+/bt9raRI0fSuXNnbrvtNtasWcPXX3/NtGnTOOuss8jMzNznc15xxRW8+OKLLFy4kC1btjBr1iz++c9/0qNHj0P5VoQQQgghhBDiiCXd94QQLdKFF17IW2+9xe7duwFrvKknn3ySadOm8Yc//IH09HR+//vfc/PNN9fr+c4880yKioqYM2cORUVF9OzZk6eeeoquXbsewnchhBBCCCGEEEcuxUwMqCKEEEIIIYQQQgghRCOR7ntCCCGEEEIIIYQQotFJKCWEEEIIIYQQQgghGp2EUkIIIYQQQgghhBCi0UkoJYQQQgghhBBCCCEanYRSQgghhBBCCCGEEKLRSSglhBBCCCGEEEIIIRqdhFJCCCGEEEIIIYQQotFJKCWEEEIIIYQQQgghGp2EUkIIIYQQQgghhBCi0UkoJYQQQgghhBBCCCEanYRSQgghhBBCCCGEEKLRSSglhBBCCCGEEEIIIRqdhFJCCCGEEEIIIYQQotFJKCWEEEIIIYQQQgghGp2EUkIIIYQQQgghhBCi0UkoJYQQQgghhBBCCCEanYRSQgghhBBCCCGEEKLRSSglhBBCCCGEOKyYptnUTRDNkJwXQjQ/EkoJIQAYM2YMY8aMOeSvs23bNvr06cPbb7/doMctXbqUPn36sHTp0kPUsuZh9OjR3HHHHU3dDCGEEC3AsmXLuOGGGxg5ciQDBw7k17/+NXfeeSfr169v6qalePzxx+nTp0+jvd6yZcu45pprGu31moNVq1Zx9dVXc9xxxzF8+HCuvPJKVq1alXKMaZrMnz+f0047jYEDB3L66afzyiuvNOh1fvzxRwYMGLDX73Eff/zxfv//TpwryVP//v0ZPnw4119/PWvXrq33cz333HNMmjQJgPLycm677Ta+++67/WpXQ91xxx2MHj16r8e8/fbb9OnTh23bttX7eevzmJKSEk455RS2bt1a7+dN5vf7uffeexk5ciSDBw/m6quvZsOGDft83Pfff8+YMWMYNGgQI0aMYPLkyRQVFdU47sUXX+TUU0/l6KOP5rzzzmPx4sX71U5x+JBQSgghhBBCiBZm3rx5XHrppQQCAaZMmcL8+fO59tpr+emnnzjvvPP44IMPmrqJTeaNN95odsHcobR582Yuu+wygsEgM2bMYObMmYTDYS655JKUMOGhhx7ikUce4cILL2TevHmMHj2a++67j9dff71erxMOh7njjjuIRqN1HrN06VJuueWWA35Pr7/+uj0tWPD/s3ff8VHU+R/HXzOzNT1A6F0MTToGEZVigTs5C+JPbIigooJ4ggpYEKXZC03B3guinOXOO/HErignWJEqPZBet8/8/pjdyW4KBAgJgc/Txzgz3yn73WWTnX3n+/3Oy9x11138/vvvXH755WRlZR3w+M2bN7NkyRJuu+02AH7//Xf+8Y9/oOv6YdetpgwaNIg333yTxo0b1+h5U1NTGTNmDHfcccchtQybMmUKH330EVOmTOGBBx5g7969jB49moKCgiqP+emnn7jyyispLCzk/vvvZ+7cuezcuZNLLrmEoqIia7/nn3+eBx54gAsuuIAFCxbQqlUrbrjhhloLC8XRyVbXFRBCCCGEEEJU36effsojjzzCTTfdxMSJE63yjIwMLrjgAqZMmcK0adNIT0/nxBNPrMOaitrw8ssv43a7WbJkCXFxcQCccsopDBkyhFdeeYUZM2awc+dOXnjhBe6++24uu+wyAPr378+ePXv48ssvueSSSw74OI8//nhMwBCtuLiYp59+mqeffprExERKS0sP6zn17NkzZr1Pnz40a9aMyy+/nHffffeALeEeeughhg8fTpMmTQ6rHkdSgwYNaNCgwRE592WXXcaTTz7Jxx9/zDnnnFPt43788Uc+/fRTli5dysCBAwHo27cvZ555Jq+99ho33HBDpcc9+eSTJCYm8tJLL5GcnAyY78G//OUvPPPMM9xyyy14vV4WL17M1VdfzYQJEwA444wzGDVqFIsWLeL5558/zGct6itpKSWEOChfffUVl112GX369KFfv35MmTKFPXv2xOyzZcsWJk6cSEZGBieffDLjx4+v8i+WhmEwffp0unfvzpdffmmVv/HGGwwdOpTu3btzxRVXsHv37grH/vnnn0yaNIkBAwbQs2dPrrzyStasWQNAfn4+Xbp04YUXXrD237NnDx07drT+agag6zr9+vVjyZIlVtfCf/3rX0yaNIlevXqRkZHBXXfddcCLq3379jF9+nQGDhxI9+7dGTlyJJ988knMPh07duTVV1/lzjvvJCMjg169enHzzTdX2rQZ4KKLLmLUqFEVyseMGcPVV1+93/oIIYQ4di1cuJD27dtbX+yi2e127rvvPjRN4+mnnwZg7NixjBgxosK+N954I+edd561/sMPP3DFFVfQo0cPMjIymDp1Krm5udb2d955hy5durBs2TIGDBhARkYGmzZtYvv27Vx//fX069ePHj16cMkll1TaJWfVqlWcd955VtexFStWxGyvzmepz+dj0aJFDBs2jG7dunHOOeewdOlSqwXMtGnTePfdd9m1a9d+hwtYsGABw4YN4+OPP2b48OF069aN888/nx9//JG1a9dy8cUX0717d4YPH84333wTc+yGDRsYP348vXv3pnfv3kyYMKFCV6n169czceJETjnlFLp27crpp5/O7Nmz8Xq91j7VuS6IdNfa3/AF7du3Z+zYsVYgBRAXF0fTpk3Zvn07ACtXrsTpdDJy5MiYYx9//HEWLFhQ5bkj/ve//1kBV2Xefvtt3nrrLWbMmMEVV1xxwPMdipNOOgmAXbt2Aea/4dlnn83ChQvJyMjgtNNOo6CggA0bNrBq1SqGDx8OmK23Ro8eDcDo0aNjhqv45z//yYgRI+jVqxcDBgxgxowZFVoE/fzzz4wbN45+/frRu3dvrr/++mp3I3znnXcYOnQo3bp147zzzov5uaisK967777LX//6V2v/b775hi5dulR4H69bt45Ro0bRrVs3Bg0axDPPPBOz3eFwMHToUJYsWWKVRYbC2F/Xyy+//JK4uDhOO+00q6xBgwacfPLJ++1mt2XLFvr06WMFUgBut5vu3buzatUqq86FhYWcffbZ1j6KonD22Wfz3XffxfxsiOOLhFJCiGpbsWIFY8eOpVmzZjz66KNMnz6dH3/8kUsuuYScnBwA9u7dyyWXXMKff/7JzJkzeeihh8jOzuaqq64iPz+/wjlnz57NBx98wMKFC60PwFdeeYV77rmHgQMHsnjxYnr06MHdd98dc9ymTZsYMWIEO3fu5K677uLhhx9GURSuuuoqVq9eTUpKCj179uTrr7+2jolcVEY3EV63bh35+fkMGjTIKrvnnnto0aIFixcvZty4cbz99ts8+eSTVb4u2dnZjBw5kh9++IFbbrmFBQsW0KJFCyZMmMB7770Xs+9jjz2Grus8+uij3H777Xz66afMnTu30vOOHDmSH3/8kW3btllle/bs4bvvvqv0y4UQQohjX25uLr/88guDBw9GUZRK90lJSeHUU0+1Ap3zzjuPX3/9NebzpLCwkM8//5zzzz8fgO+//54xY8bgcrl4/PHHueOOO1i9ejWjR4+O+bIYCoV47rnnmDNnDtOnT6ddu3aMHz8ej8fDgw8+yOLFi0lJSeGGG26IeTyAGTNmMGbMGJ588kmaNm3KtGnTWL9+PVC9z1LDMLj++ut55plnuPjii3nqqacYNmwYjz/+OPfccw9gBm0DBw4kLS2NN998M+bzvbzMzEzuv/9+rr/+ep544gkKCwuZNGkSkydP5uKLL2bRokUYhmG18gDYunUro0aNIicnhwceeIA5c+awY8cOLr30UutaaN++fVbXyvvvv5+nn36ac889l5dffpmXXnoppg4Hui6IdPHq2rVrlc/jsssu45prrokp27ZtGxs3brRayv3++++0adOG77//ngsvvJCuXbsyZMiQanXd83g8TJ8+nfHjx1c5VtSQIUP473//W+kf02rK1q1bAWjdurVVtnv3bj777DMee+wxpk+fTnJyMu+//z5paWlWa6uuXbtaYdqMGTOs98rixYuZPHkyPXv2ZP78+UyYMIF///vfXHnllda/97fffsull14KwNy5c5k9ezZ79uxh1KhRB+wiumfPHpYuXcrNN9/MggULUBSFSZMmWe+T8lasWMG0adPo3bs3ixcvZujQodx4442EQqEK+86cOZNzzz2XpUuX0qtXLx566CE+/fTTmH2GDRvGL7/8Yr1uXbt2PeDPxObNm2nZsiWapsWUt27d2jpPZVJTUyv9A/KOHTuswDbyerVt2zZmnzZt2hAKhawAVRyHDCGEMAzjiiuuMK644ooqt4dCIWPAgAHG2LFjY8q3bdtmdO3a1XjggQcMwzCM+++/3+jevbuxb98+a589e/YYgwYNMlatWmXs2LHDSE9PN5YvX248/PDDRteuXY1PP/3U2lfXdaN///7G3//+95jHmTFjhpGenm58++23hmEYxs0332z069fPKCoqsvYJBALG0KFDjYsuusgwDMNYsmSJ0bNnT8Pv9xuGYRi33nqrceGFFxrp6enGjh07DMMwjCeeeMIYPHiwYRiGVbdbb7015rGvvPJKY/jw4VW+Ng8++KDRtWtXY+fOnTHlV111lTFgwAAjFAoZhmEY6enpxqWXXhqzz7Rp04yePXta64MHDzamTp1qGIZhFBYWGt27dzeeeOIJa/uTTz5p9OnTx/B4PFXWRwghxLHrp59+MtLT041XXnllv/vdf//9Rnp6upGfn2+UlJQYPXv2NBYuXGhtX7ZsmdGpUycjMzPTMAzDuOSSS4zhw4cbwWDQ2mfLli1G586drcdavny5kZ6ebqxYscLaZ9++fUZ6errx3nvvWWWFhYXG3LlzjQ0bNhiGYRjz58830tPTjc8++8zaZ9u2bUZ6errx4osvGoZRvc/SVatWGenp6cYHH3wQs8+iRYuM9PR06/GmTp1qfbZXpbI6LVmyxEhPTzeWLVtmlX300UdGenq68dtvvxmGYRiTJ082Tj311Jjrj7y8PKNPnz7G/fffbxiGYXzxxRfG5ZdfHrOPYRjG8OHDY66jqnNdcCg8Ho9xySWXGD179rRez2uuucbo16+fccoppxivvPKK8fXXXxt33XWXkZ6ebrzxxhv7Pd+sWbOMCy64wAgEAjHXcVWJvLaHInJsIBCwpqKiIuP77783LrzwQqNPnz7WNWZk3++//z7mHCNHjjRuuOGGmLJvv/025joyPz/fOOmkk4y77747Zr/vv/8+5udr5MiRxl//+teYn4uCggIjIyPDmDRpUpXPY+rUqUZ6erqxadMmq+zrr7820tPTjZUrVxqGUfbzFLkmHTRokDF+/PiY80Tek5HXO3LMa6+9Zu1TWlpqdO3a1Zg7d27MsYWFhUZ6errx6quvVlnP8saOHWuMGjWqQvmjjz5qdO3atcrj3nrrLSM9Pd2YPXu2kZmZaezbt8948MEHjZNOOsno1KlTzHMJBAIxx3711VdGenq6sWbNmmrXUxxbpKWUEKJatm7dSlZWltUUOqJ169b06tWL1atXA+Ydb3r27ElaWpq1T9OmTfn000+tvukAr776KkuXLuXcc8+N+YvNli1byMnJYfDgwTGP85e//CVmffXq1QwePJiEhASrzGazce655/LLL79QUlLCwIEDKS0tZd26dYD5166rrroKt9vN999/D8Dnn39e4S9G5ccxaNq06X67761evZpevXrRokWLmPLzzjuPrKysmEFGKzu3x+Op9LyJiYmcc845Ma2tIs26XS5XlfURQghx7DLCAxfb7fb97hdp6WAYBnFxcZx11ln885//tLZ/+OGH9O/fnyZNmuDxeFi3bh0DBw7EMAyCwSDBYJBWrVpxwgkn8NVXX8Wcu3PnztZyo0aN6NChA3fffTdTp07l/fffR9d1pk+fXmE8q759+1rLLVu2BMwWW1C9z9LVq1djs9kYNmxYhX0i5zhYvXv3jnkuAD169LDKUlJSYur57bffkpGRgcvlsl6nhIQE+vbta7XOPu2003jllVdwOp1s2rSJTz75hCeffJLc3Fz8fn/M4x/MdUF1FBcXM378eH7++Wceeugh6/UMBALk5eVx7733cvnll9O/f39mzZrFaaedxsKFC6s833fffcebb77JvHnzsNlqbzjirl27WlOfPn24/PLL8fv9LFy4MOYaE2Lfj2C2zom8v6qydu1a/H5/hevavn370qJFC1avXk1paSk///wzf/nLX2JaDiUlJTF48OADvt9SU1M54YQTrPVInSobl2vbtm3s3r27wnv73HPPrfTc0T9LbrebRo0aWe/RiMTERJKSkg7q7n7GfgZGr6plJsDFF1/MtGnTePvttznjjDM4/fTTrYHOI9esBxpkXlUlmjheyUDnQohqiXS9i1ywRWvUqBG//fabtd+BLgTAHGvhtNNO44MPPuCqq66iS5cuAFY//tTU1Jj9y1+AFBQUVFkXwzAoLi6mY8eONGvWjK+//prU1FT27dvHqaeeSu/evVm9ejUDBw7k119/5eabb445h9vtjllXVXW/H9IFBQW0atWq0roAMRcJB3vukSNH8t577/HDDz+gaRp//vknDzzwQJX7CyGEOLZFQobIuDpV2bFjB/Hx8Vaocv755/Pee++xfv16GjVqxHfffWd1EyssLETXdWug6vKcTmfMevTYRYqi8Nxzz1mDKq9YsQK73c5ZZ53FvffeGzPGTPRxkS+gkc/A6nyWFhQUkJqaWqFrUeQaoapBuPcn+o9bEeU/q6Pl5+fzz3/+Mybgi4gMWh3pjvfqq69SWlpKs2bN6N69e4XXsbLHOtB1wf7s2bOH8ePHs3XrVh577DHOOussa1t8fDyKosT8gRDg9NNP58svvyQ7O7vCdVVJSQnTp0/n2muvpUOHDgSDQStY0HWdYDB4xIKqt99+21q22+2kpaXRsGHDSveNj4+PWS8uLt7vvyGUXW9WdS1ZVFREUVERhmHsd5/9iX6/Q1moU1k4Exm7rfxzrOyxofrvG7fbTXFx8X7rGS0hIaHSsU5LSkpITEzc77FXX301V1xxBdu3byc1NZUGDRpw++23W7+DIseXlJTE/F6I1O9A5xfHLgmlhBDVEvlAqeyDKisrywqREhMTYwZFjfjmm29o2bKl9YF88803M3r0aM4991zuuusuli1bhqZp1nnK97cvPx5VcnJylXWBslBr4MCBfPPNNzRs2JB27dqRlpZGv379eOutt/jyyy9xuVz069fvIF6JipKTkyu9PXH5uhyKjIwMWrduzUcffYSqqrRv377CX1WFEEIcPxo2bEjPnj3597//zc0331xp64Li4mK++uorhgwZYpX179+ftLQ0/vWvf5GWlobT6bTuyhUJLMaMGVNpy4wDfcFv0qQJM2fO5J577mH9+vV89NFHPP3006Smplrj9xxIdT5Lk5OTycvLIxQKxQRT+/bts/Y50hITEzn11FMrveFIJKBZunQpL7zwAvfeey/nnHOO9WW7/CDjNemPP/5g3Lhx+Hw+nnvuOU4++eSY7W3atMEwDAKBQEw4FgwGASptgf3LL7+wa9cuFi1axKJFi2K23Xnnndx555388ccfR+DZQLdu3Q752JSUlAMGRpFQJDs7m/bt28dsy8rKolWrViQmJqIoSpXXm5Fr45rQtGlToOL1b1XjT1VXYWHhQf1ctGvXji+//BJd12N+t2zbti2m1Vd5P//8M3v27OGcc86J2e+3336z/vDcrl0761zdu3ePObfdbq80lBbHB2kjJ4Solkig88EHH8SU79ixg7Vr11rN3/v27cu6detigqmcnByuueaamLt2NGrUCJfLxYwZM/j111+t28C2bduWZs2a8dFHH8U8TvnBG08++WQ+/fTTmL/+hEIhPvzwQ7p164bD4QDMAUJ//vlnPv/8czIyMgDzFrU7d+7kjTfeYMCAAda+h+rkk0/mxx9/rPBX6/fee4+0tDTatGlzyOdWFIURI0awcuVK/vvf/3LhhRceVl2FEELUfxMnTmTr1q08+uijFbaFQiHuuecevF5vzODXmqbxt7/9jU8//ZSPPvqIs846y2rJkZCQQJcuXdiyZQvdunWzphNPPJEFCxbs985vP/74I6eeeio//fQTiqLQuXNnbrnlFtLT0ysd+Lgq1fkszcjIIBgMVrhGiHRz79OnD3BkuwFF7jjYuXNn63U66aSTeOGFF/j4448BcyiDDh06cNFFF1mB1N69e9mwYcMBuzAdij179nD11VejKAqvv/56hUAKsFpIffjhhzHl//3vf+nYsWOlLca6du3K22+/HTNFbvwyceLEmNZMR5MWLVpUuDN0+dZ1PXr0wOFwVLiu/eGHH9i9eze9e/cmLi6Ok046iX/9618xg40XFRWxatUq6/1WE5o2bUrr1q2t91DEf/7zn0M+Z0FBAR6Ph+bNm1f7mNNOO42SkhK++OILqyw3N5cffviBAQMGVHnc6tWrufXWW2N6B3z11Vds3LjRarHXq1cv4uLi+Pe//23tYxgGH3/8MRkZGYd9PS7qL2kpJYSwZGZm8sILL1QoT09P59RTT2Xy5MlMnz6dKVOmcN5555GXl8fChQtJTk62/mI4ZswYVqxYwTXXXMP48eOx2+3WXXb+9re/VfjL1cCBAxk2bBgLFixg6NChtGrViltvvZUpU6Zw1113MWzYMNauXcvrr78ec9zEiRP5/PPPGT16NNdddx12u51XXnmFHTt2xNwW95RTTkFVVVatWmVdvHft2pX4+HjWrFnDnDlzDvt1u/rqq3nvvfcYM2YMEydOJCUlhRUrVvDtt98yd+7cw744HjFihHW75shdkoQQQhy/Tj/9dKZNm8aDDz7I77//zkUXXUTjxo3ZuXMnr7/+Or///jtz5syhU6dOMcedf/75PPfcc6iqWqGb3uTJk7nuuuusz/jIXfbWrVvHjTfeWGVdunTpgsvl4vbbb+emm26iUaNGfP311/z++++MHj262s+pOp+lZ5xxBv369eOuu+5i7969dOrUidWrV/P0009z4YUX0qFDB8Ac8yc7O5vPPvuMzp0707hx44N4dffvxhtvZNSoUYwfP55LL70Up9PJm2++ycqVK5k/fz4A3bt3Z/HixSxdupSePXuybds2lixZgt/vP+jxonJzc9m+fTsdOnSoNDgC807GOTk53HvvvRQXF7N27VprW0JCAh06dKBfv34MHjyYefPm4fF4OPHEE1mxYgX/+9//WLx4sbX/9u3byc3NpWfPniQkJFRosRQZn6hFixYH3ZopMzOTzMxMunTpckQDiAEDBvDaa69hGIbVQj8SDq5atYrk5GQ6derEddddx6JFi7Db7QwePJidO3fyxBNP0KFDB+uPgFOmTGHcuHFcd911XHbZZQQCAZYuXYrf72fChAk1VufInfluvfVW7rnnHs4++2zWr19vtVA7lGvJNWvWAFh3ty4uLmbTpk20bt3a6mpa3sknn0xGRga33XYbt912GykpKSxYsIDExETrLoRg3gXb7/dbraDOO+88li5dyt///nfGjRvH7t27uf/+++ndu7c15pvb7Wbs2LHWa96rVy+WL1/Or7/+WuGulOL4IqGUEMKyfft25s2bV6F85MiRnHrqqYwYMYL4+HiWLFnChAkTSEhI4PTTT2fy5MnWeA7NmjXjtdde46GHHmLatGk4HA769evHY489RnJycqXNqe+44w6+/PJL7r77bl544QWGDx+OqqosXryYf/zjH6Snp3PfffcxefJk65gTTzyR1157jUcffZTp06ejKArdu3fnpZdeqjD4Y79+/WJaStlsNvr27VvpIOeHIi0tjddff51HHnmE2bNnEwgE6NSpE4sXL+bMM8887PM3adKETp060ahRI5o0aXLY5xNCCFH/XX311fTq1YsXX3yRBx54gNzcXNLS0hgwYABz5syxApponTp1Ij09nby8PPr37x+z7bTTTuPZZ59l4cKFTJo0CbvdTteuXXn++ef3223c6XTy3HPP8cgjjzBnzhwKCwtp27Yt9913HyNGjKj286nOZ6miKCxZsoT58+fzwgsvkJubS8uWLZk8eXJMd7oRI0bw2WefMWHCBCZNmsR1111X7XocSKdOnXj11Vd57LHHuP322zEMg/T0dBYtWmTVc/z48eTl5fHSSy+xaNEimjVrxvnnn2/Vv7CwkKSkpGo93qpVq5g+fTovvfRSpcMN+P1+Vq1aBVBpV8mMjAxefvllAJ544gkWLlzI888/T25uLh06dGDhwoUx3TwXL17Mu+++e0S65S1btoyFCxfyySefVGv80UN1zjnnsGjRIn766Sdr0PoTTzyR4cOH8+qrr/LFF1/wwQcfWCHqK6+8wptvvklKSgrDhg3j73//u9WKsH///jz//PPMnz+fyZMn43A46Nu3Lw888ECFgfwP19/+9jdKS0t59tlnWb58OSeeeKLVTbL8+FTV8fnnn9O9e3drHLpff/2V0aNHM2/evP3+bC5cuJD777+fBx98EF3X6d27N48//njMOFD33nsvu3bt4r///S9g/vw+++yz3H///UycOJGkpCRGjBjBzTffHNNKbcKECWiaxltvvcVzzz1Hhw4dWLx4cY22OhP1j2Ic6kh6QgghasXevXsZPHgw8+fPjxm0VAghhBCiPrn88st5/PHHK9zApqZdf/31pKamVvrH1qPVBx98QJcuXWLGuFq1ahXjx4/nH//4R4WWj/tTWlrK6aefzgMPPCDXjuKoJ2NKCSHEUer3339n4cKFXHPNNbRt2zbmL5lCCCGEEPXJd999h8fjqfKOcjXplltu4T//+c9BjWtW19577z2uvfZa3n//fX744QeWL1/OPffcQ0ZGxkEFUgBvvPEGJ554Yo202BfiSJOWUkIIcZRau3Yt48aNo0mTJjz66KMHfUEihBBCCHG02LVrF3FxcbVyl0Qw74K4fv36Sm8IcDTKy8vjkUce4fPPPyc3N5dGjRoxdOhQJk2aRHx8fLXPk5ubywUXXMDLL798WDfbEaK2SCglhBBCCCGEEEIIIWqddN8TQgghhBBCCCGEELXukEMpv9/P8OHD+e6776yyHTt2MGbMGHr27Mlf//pXvvzyy5hjvv76a4YPH06PHj0YPXo0O3bsOPSaCyGEEEIIIYQQQoh665BCKZ/Px+TJk9m4caNVZhgGEyZMoFGjRixfvpzzzz+fiRMnWoPL7d69mwkTJjBixAjefvttGjRowI033kh1ew8ahkFxcXG19xdCCCGEELHkekoIIYQQR5ODDqU2bdrE//3f/7F9+/aY8m+//ZYdO3Zw3333ccIJJzB+/Hh69uzJ8uXLAVi2bBknnXQSY8eO5cQTT2TevHns2rWL1atXV+txS0pK6NOnDyUlJQdbZSGEEEIIgVxPCSGEEOLoctCh1OrVq+nXrx9vvvlmTPm6devo0qULcXFxVlmfPn1Yu3attb1v377WNrfbTdeuXa3tQgghhBBCCCGEEOL4YTvYAy677LJKy7OysmjcuHFMWcOGDcnMzKzW9jrnz4dNT4PuB1sC2BPDUwo4UsCRGl5OBVWr27oKIYQQQgghhBBC1HMHHUpVxePx4HA4YsocDgd+v79a2+vc9mWw9vZq7KiYwZSzkTm5mkBcC3C3gLiWENcaEjuY5RJeCSGEEEIIIYQQQlSqxkIpp9NJfn5+TJnf78flclnbywdQfr+fpKSkmqrC4Wl1ERRthMINECqBYAkESyFYDIEicx4qBQzw55pT0Yaqz6fFQ1xziGtlhlSJ6ZDUCeLagM0Filb1pNrCy5G5UmsvgxBCCHE0iwzQrchnoxBCCCHqSEg30NT6fS1ytDyHGgulmjRpwqZNm2LKsrOzrS57TZo0ITs7u8L2zp0711QVDo+zAfR6sGK5HjS79Ol+M6jy7AXPLijZBt694N0H3kzwZYM/D3xZ5nKoxAy5ijbC3v+WnU9zhwOqjpDY0Zzb4s1tihoOoVQgElBpoDpAdYLmBM0VDqtUQAkfE7VMeF1RopYr2W4dW/dvQiGEONYZhoGBETMHKpQZGNXev/x++9te2XL0/pHlA+1nGAY6OgC6rpdtw0A39JhjIuvR5dH/YZQ7f9RjANbjRN8lLlIvh+agd7PeuGyuQ/sHEUIIIYQ4DJqqcPMbP7JpX3FdV+WQdGicwBOjetV1NYAaDKV69OjB0qVL8Xq9VuuoNWvW0KdPH2v7mjVrrP09Hg+//fYbEydOrKkqHBmqzZyIM8eWimsB9Da36SGz9VSwGAKF4M0yg6tAPvjyzLl3DxRtMqfirRDyQP46cwJAgeQu0OhUaHSK2ZoKAwwdjJA5BYvAKChbr/I2zop5bHQgBWVhlBVMKeY+kcDKCrnKtdJSI621ygdc5ZfLB2JVLUsAJoSoOZGQQzd0Mywx9Jj1yrZFhzjRZZVtr3AuXSdkhNAN3ZzQMXQzpAnpIWs9clxkqjJAigppgLKgppJgKXr/yL7R+yso5n4K1VoOnwRFUazzRJar2i+aEt4YKS+/frD7VbVNIWo5XB7Ug5QGSgmEAhJKCSGEEKLObNpXzK+7C+u6GvVejYVSGRkZNGvWjOnTp3PjjTfy6aef8tNPPzFv3jwALrroIp599lmWLl3K4MGDWbRoES1btqRfv341VYXap2qghgdEdzczW0CFSs0WU55M8OVAMB2a/dUMtACKt0D+T2VT6U4o+NWcNj8N9mRofAY0GwoN+oYDsYNk6IT/BA3o4YDLMMsIL1uhVxAMb+y+1jn0SgKwcPAVUxQdWoWXUYhpjRUJpyKhV4UALLJcPsjSqBBy7S8AiwRxQohaFQl2DmaqEN6EzxHSQ4SM8KSbIVBQD5rbospigqRIcIRuBTVWoBTdQscoC3OiQ5eYX21KbFn5kERRFGt+SGWRcrXivsB+zxFdXn75eOEP+cnz5tV1NQ7o448/rvCHt6FDhzJ//nx+++037rnnHjZs2ECHDh249957Oemkk6z9PvjgAx5//HGysrI47bTTmDVrFg0aNADMn7VHHnmEt99+G13XGTlyJLfeeiuqKp9/QgghhKh/aiyU0jSNxYsXc+eddzJixAjatGnDokWLaN68OQAtW7ZkwYIFzJ07l0WLFtGrVy8WLVp0bF1MK4rZFc8Wbw56Hiwxg6mSbeDZA6odEtpBUjq0Hmke490H2d+Ep28hUAC73jcnRwNoeha0+BskH0Q3R6uFVM0/xUoZUWFXTAhWrowQBH3lyoxy++5HVeFXhbAqHHhFgi/VBthAs1cSYmlUHnhVFZAdQ+9XcVyygp+ocCfS+ic67IluEWTtp4cI6AGCetCcjKBZFgrEBEvlwyagrGVRuZ9zq1VOZD0qbFEVNSaUURW1QlCjoKApGoqqVLotcg4g5nghjrRNmzYxePBgZs2aZZU5nU5KS0u57rrr+Nvf/sb999/P66+/zvjx4/n444+Ji4vjp59+4s477+Tee++lU6dOzJkzh+nTp7NkyRIAnn/+eT744AMWLlxIMBjktttuo2HDhowbN66unqoQQgghxCE7rFDqjz/+iFlv06YNr7zySpX7Dxw4kIEDBx7OQ9YvkYDK3dwMn0q2mWNSqTZwpZkhh6sxtDzfnPQg5K2FzI8hc6U5mPr2t8wp+SRofbEZUmnOun5msRQFcwysI/w4RnSQFT2Pav1FeAywkF7F/uWbRJR/HpWFXlq51l72shZekVZeVtl+WntVCMHKtwKTL8qiatFhUlAPlrUm0kMVAqZImT/oJ2AECIaCBEIBAnogtqUROroe1Xop3P3MokQvlgU70VN08KMqKqpaSVm5/YQ4HmzevJn09HTS0tJiyt9++22cTie33347iqJw55138vnnn/PRRx8xYsQIXnnlFf7yl79wwQUXAPDggw8yePBgduzYQatWrXjppZeYNGkSffv2BeDWW2/liSeekFBKCCGEEPVSjbWUEvuh2sw78bmbmuFU0SYo2WkOrm5PjN2vYV9z6nwb5HwHu/8JmZ9AwS/w8y+w/jFofHp4kPQTIfFEsB8ldzA80o5097yYll6VhV4hKm3tVWVXx+i6lw+8NCoEXopmhlvRLbwUe2zXxvKtuKzlKsIuVTtyr5c4aIZhxIRKkRZHkaAoetkX9BHQA/hDfvwhvxkolW/JpOuECJlBUiVdzhQUNFWrECSpiopNtVVarkoXWCFqxObNmzn11FMrlK9bt44+ffqUtQpUFHr37s3atWsZMWIE69at49prr7X2b9asGc2bN2fdunU4HA727NnDySefbG3v06cPu3btYt++fdbNZYQQQggh6gsJpWqToprBlKMBlPxpji8VKARXk4pjR6k2SBtgTp2mwM4VsGO5ece/Xe8D75ft62oavptf+K5+SR3NMmmRcHCsFl9HIMixQq3oVluhqHArBHrAHJOsfCgWGecruoWXopS1+lIqC7uiuyRGAq6okEsNzyu05gqHW2ok5NIqbpfQAjADJqsbWxVTIBTAG/LiD/rx62a4FNPCSQ+VhUpAZDBqc9EMlDTFDJUi4ZJNtVUokyBJiKOLYRhs3bqVL7/8kiVLlhAKhRg2bBiTJk0iKyuLDh06xOzfsGFDNm7cCFBpuNSwYUMyMzPJysoCiNneqFEjADIzMyWUEkIIIUS9I6FUXdAc5rhSrjQo3AClu8xufDZ35fs7G8AJY6HdaLP1VP4vULQBijaCZzd4M81p32dlx9iTywKqxI7mmFRxrSRQqCuKYoZCNa3S1l2VhF1Bneq37IrcnVGj8lZdWll3RdUeDr0c5QarP0CoFenqeJR1XdQN3ermFgiZYydFj6HkDXjxhXx4gh4rYAoaQYKhoNVyKSLSCkJTNCtciswdmqNCayUhxLFj9+7deDweHA4Hjz/+ODt37mT27Nl4vV6rPJrD4cDv9wPg9Xqr3O71eq316G2AdbwQQgghRH0ioVRdcqRCgz5gSzQDJj2h7C59lYluPRURKDKPLfzDDKoK/4DizeaA6TmrzSlCiw8HVZ3MkCqpM8S3lqCqPjsSrbuiB563WneFytaNYLgLY7mgy9CrPmckzIqEW+UHl1fKd1e0h1tzadULuCpsjw25Iq2WImFT9Nwb8OIJevAEPPh1f0zXusjA3YqiYGCgEm6ppGrYVBs21YZTcaI5ykInIYRo0aIF3333HcnJySiKQufOndF1ndtuu42MjIwKAZLf78flcgHmYOiVbXe73TEBlNPptJYB3O4q/rAlhBBCCHEUk1Cqrqk2MyCyx0PBb2b3PGfj6rccsSdCg97mFKH7oWgLFK6Hoj+g4HczuAqVQN7/zClCi4fkTpDUxaxHchdwtzhqWq6IOhAzcH0N/YqIaaFVvsXW/lpzwf4GpTdQCBg6Ad3Ab+j49RABw8BvhPCGDEpDfkpDZlkQCBoQNHSMSGswVFRVw6Y5sKl2bJoDl+rAZnOiaYnYtPAYX/LzIIQ4SCkpKTHrJ5xwAj6fj7S0NLKzs2O2ZWdnW13vmjRpUun2tLQ0mjRpAkBWVhYtW7a0loEKA6oLIYQQQtQHEkodDRQF4tuA5ob8n8Gz6/CCIdVhBk3JncrK9KA5jlXhejP8KlxvtqoKlUDuGnOKsCeZraiSu0ByV3PuknEqxGE4xEHqQ3oIvx7AHwqG5+bkCXgpCZRQGvQSCPkIhIIEdT8hq0WXjqqAXVGxqSo2FOJUBbuioUW6y0WyrvBNG8vqGBmUvtzYXIS7J0a6KVp3XIy+g2L0/pXdhTES+EnIJcSx7IsvvuDWW29l1apVVgum33//nZSUFPr06cPTTz9d1grTMPjf//7H9ddfD0CPHj1Ys2YNI0aMAGDPnj3s2bOHHj160KRJE5o3b86aNWusUGrNmjU0b95cxpMSQgghRL0kodTRxNUYGvSFvLWHH0yVp9ogsYM5tRhulkWCqoLfoPD38HyDOfh6znfmFOFsVBZQRebHy13/xBGjGzr+UABfKIAv5Mevm8slfg8lQQ+eoI+AHiQY7k5nYKCgoKoqNkXDrtqw2+JwO2zYVA3boXafq9BlEaAs4DK7LUa6LBrlWnKFRY1Db64f6ZArsi4hlzj6GIaBP+THF/LhC/rwhXx4g15r3R/y4w16K+zjD/ljliPrkf0ix3Vo0IHTW59e10+zSr169cLpdHLXXXcxYcIEduzYwYMPPsg111zDsGHDeOSRR5gzZw6jRo3ijTfewOPx8Je//AWASy+9lCuvvJKePXvSrVs35syZw6BBg2jVqpW1/eGHH6Zp06YAPPLII4wdO7bOnqsQQgghxOGQUOpo40iG1J7hYGoPuJsduS+c0UEV55llegCKNoUDqt/MedFm8GWbA6lHD6Ye1yocUIVDqqSOoLmOTF1FvRXSQ/hCAbwhnxU+lQa8FAVKKQ14CRhBAiFzQHEwBwjXIoGTaiPO5sKumqGTcqR+Fmq6y2JthVyooEaHU7bwOFzRd1ysLMiqIuCK2SaORYZhENADeAIevEFzPDVv0HvQUyRkstYrCZ6Myrre1pANORvwh47egb0TEhJ49tlnmTt3LhdddBHx8fGMGjWKa665BkVRWLJkCffccw9vvfUWHTt2ZOnSpcTFxQFmoHXfffcxf/58CgoKGDBgALNmzbLOPW7cOHJycpg4cSKapjFy5EjGjBlTR89UCCGEEOLwKIZR5S24jirFxcX06dOHNWvWkJCQUNfVOfL8+ZD7IwRLIK5Z3dYl5A13+/vVDKkKfoXSnRX3UzQz4LKCqq6Q0K5mB+EWRyXd0PGF/HiDfrwhP76Qn+JAKcV+D6VBr9ntTg+gGwZgoCoqDs2OXbXhUO1HPnSq72JCrnCQFR1yRYKtyH7W4PT7OWf04PPlQy7Cd4u0WnLZooKuqC6I0YPXS8hVoyItjTxBDyX+EjxBD6WBUkoDpXgCHkqD4Xmg1BqoP3rZEzQnayD/cLk36DW7udYiVVFx2Vw4NScOzYHT5sSluXDawuuaE6fNGbM9shyZostURaVpQlP+r+v/kehMrNXnciw47q6nhBBCiCPk3Plf8OvuwrquxiHp2jyJDycdHa3OpaXU0cqRAqk9IO/HshZTdUVzma23UnuWlfnzzQHUC3+F/HBY5c8xx6kq/AN2vBM+1h0enyqq69+RbP0ljqigHsQT9OEN+fEGfXiCPgr8xZQEPPjCwVMwFEJRQFM1K3BKdMRhV+1oqoQThySmJVcNqDTk0qOCrfDg85EWXDGh137qaAXQ2qGHXPW4JZdu6JQGSinxl1ASKCmbh5dLA6UVliNhUkmgxAqdItORDo9sqg2XzYXb5o6Zu+wucx4OkiLlTpvTKotsj94ventk2WVzYVNr9lLDH/KT582r0XMKIYQQQoi6IaHU0czZwAyCctaALwecDeu6RmUcKZDW35zA/LLq3RtuTfVLuEXV7xAqrXjHP0dq7NhUyV3NMnHUiIRPkQCq2F9Kob+EkqAHX8hPIBQe30lRcag2HJqdOJuLFC3x0Md1ErXniIVchLsrVifkqm5LLsy6EhmTKzKP7qaoATbQbPsPtfbTjTFohCj2F8dMRb4iigNl6yX+kpjt0cFTsb+Y0kDpEemy5tScxDvicdvcuO1u4uxxxNnicNvduG3memS5wjy87LK5iLPHWeGT2+6u8bBICCGEEEKIgyVXpEc7Z0NI6Qq5/4NgMdiO0qb2igLupubU9EyzzAhBybZwS6pwUFW0Efx5kPWVOUW4m4cDqi6Q1AWSO4Mtvm6ey3FEN3S8QT+lQS+eoJeSgId8X3G58Mkc58mlOXBodlKciThUu3S1E2ViQq4aCCWrDLmixuQqF3IZeoiS8FhlhQEPReGpMOChyF9KUdBLUcAbLvda24sDHoqDHkqDvsOvd5imaCQ4Eoizx1nzeEc88fZ4c9keT7zDXI6sR5bLT26bG02CXiGEEEIIcYySUKo+cDeHpFLI/wXcdtCcdV2j6lE0SGhvTi3/ZpaFfGYwZbWm+tUMrjy7zSlzZeRgiG9rhlORoCopXQZSPwyBUNAKn0qDXgr8xRT4iq1ByHXDQFUUnJoDp+aQ8EnUCd3QKfKXUuAvptBfTL6vmEJ/CYX+Ygoic6ushMKAOS/y10x3N7fmIMHuJsHuItHmIsHuIt5mrifYXMSH5wk2J/F2NwmOOOJtLhLsccTZ3CQ43Dg1J4oa6YpYbgB6azncUssakytqHt3SywiYd0qtMP6XEEIIIYQQ9Z+EUvWBopjBTqgEirdCXIv6O3i45oSUk8wpIlAEhb+XhVQFv5ldAUu2mtPuf5r7KRoknGAGVUnhsCqxA6iOunkuRzF/KEBp0EtpwGz9lOsrpDhQijfot+5yZ9fsuDQH8XYXDVxJqEfpOD2i/grqIStYyvcVke8vIt9XRIHPLCvwF4XnZtBkBlElh9UFzq7aSHLEk2iPJ8kRR6IjnkR7HInh5SR7PAmOOGt7Qnhbgj2OhIPt0hY9uHzM2Fzl77IYPQB9FWNzRd9tUYkeNyvSdTG6+2I44EI9QNhVvqti9Phd5QKxo3isLiGEEEIIceySUKq+UDUziAl6oHS3OVj4sTIeiD0RGmaYU4QvxxyTquDXssDKnwtFG8yJf5j7KTZIPCHckqqTGVglnFB/WpPVgEAoSEnQQ2nAS3GglFxvIcVBM4AK6iEUwGkzWz81cCXh0Ox1XWVRTwVCQfJ8heT6Csn3FZHrLSQvvJznKwovF5PnK7RaMx1qwBRvc5HkSCDZmUCSI55kRwLJjgSSnPEkORJIsseby/Z4khxlk1Nz1F7rPkWtuS6LEVUOQm/euRJDN1tO4a1e2BVT36gwygquolpfWWN1lZ/2E3jFLKuxAVf5Vl/SwksIIYQQQpRzjKQax4lIK6N8wJMJmh0cDY+dcCqasyE0Ps2coGwgdatF1W9QuB4CBWV3/IuIaVHVyZwSTzwmuv6F9BCl4bGfigMecn0FFPlL8QR9VgDlsjlxaQ4S3XEykLHYL8MwKAl4yPEVkOstDE8F5Eav+wrJC8+LA6WH9DhJjnhSHImkOBNIdiaS4kggxZloLSc7zcDJmjsSsGvH6Xu3pgehj4gep8tq2QUQCgdgRtRYXVFhV1WD0ke37Iqso1YeelVomWWLCrzUqNArclz5cEuN3aYHzK7gQgghhBCi3jtOr/rrsUirIu9esyufZ48Ztjgb1N8ufdURPZB6k8FmmWGYz7/wd7NVVeF6czlQULFFFSoktIXEjpDUMRxWpYM9qY6eUPV4gz6KA57wAORmyxRPyIs/ZHbBc2oO3DYnjVwpx++XeFGBL+Qnx1tAtiefHG8BOd788HqBtZ7rLSTHW4A/3J2zujRFJcWZSKoziVRnIqmu8NyZRIozgRRnUni7OSU5EuSOjEeDmh6MvjyjXGuuSKBVvtVXpaFXNVp5RZ6DokIoBMEgBE8FZ2LNPxchhBBCCFFr5FtsfaRqENccXI3BmwlFm6F0l3m3Okfq8TMuiKKYr0Nc86g7/hnma2KFVOvNVlT+HCjeYk57/lV2DlczM5xKTDfDqsQTzYHl66CbiW7oVguoYn8p2d58igKleIJedMPArtpw25ykOBNxajKO1vHIE/SS5ckny5NHtjefLE8+2Z48K3zK9uaT7THfNwcjzmaOK9bAmUxDV7K5HF5PdSXSwGmWpTgTSXLEy/hjoiKrK+MREt3SSykBf1b4zoxCCCGEEKI+k1CqPlNtENcSnOFwqngLlOwARxLYU47P8TsUxRxvy90Mmg4pK/dmmwFVUTikKtwAnl3g3WNO+z4r29eWYIZTiSeaYVViB3Oq4e5/QT1oBlCBUvJ9ReR4CykNePCF/CiKgktz4rI5SXEkoqkSAhzLgnqIHG8BWZ5c9nny2FeaR5Ynj32ePLI9eWYQ5c2jJOCp9jkdqp1G7mQaOJNp5E6hoSsSOCXTyJVMQ1cKDVxJNHQl47IdP2OwiXoquqWXIpcuQgghhBDHCrmyOxZoDohvDa4mZoupkq1Quh3syeZ0PIZT5bkagStqjCow7/pXtMEMqIo2mGFV8RYIFkPej+ZkUSCuVTigOtGcJ3QI3wmxeoFRIBSkOFBKUaCUPG8ROd58PEEffj2ApqjEhQd2dtmkFdSxJBAKsi8cNu0tzTWXS3PZW5rLXk8uWZ48crz56NXpvgS4bU7SXKk0cqeYkyuFtPA8uizBHld7A34LIYQQQgghxCGQUOpYojkhsb3ZSqh0F5T8CSXbwZl61I+dVCfsidCgjzlF6AHzdYsEVUUbzcmfZwZ9pdth73/L9tdckNDeHFg9sYM5TzgBnI0IGiGK/GYIles1x/LxBH0E9KDVFa+hK1nGgqrHQrpOrq+AzJIcMktzyCzNNsOm0lz2luaw15NLjregWufSFDUcMKWSFpdKY3cqae4G4Xm43J1KvN19hJ+VEEIIIYQQQtQO+TZ8LLK5IakDxDWD0p1QvA3828HRAOwJdV27o5tqL+u6x7ll5b6ccEC1yZyKN5mtqkLesrsBRglq8RQ7m1Job0yBPQ2vqznOuDYkuJtg0+y1+5zEIfMGfWSW5rCnJJs9pdlm8BQTQOURqsa4Ng7VTuO4VBq7G9AkriFNwsuN3ak0jmtAY3cDUp1J0k1TCCGEEEIIcVyRUOpYZos3B+92t4DSHWarKX+ueac+m4RTB8XZ0JwanWIVGaEApYWb8RX8TrBwA0rxZtzencT5s7GFSkgp3UwKm2NOE9Ti8Lqax0weV3P8jrTjZ4D6o0hpwMvukiz2lGazuySbzJLs8HoOe0qyyPMVHfAcmqKS5k6laVzDcODUwJo3dZvzFGeidKUTQgghhBBCiHIklDoe2BMgubM5/lHJDjOg8uWZ3foknDoovpCfQn8JBb5i9pXmUugvwRNMQUvoT1zKIOLtbpwKuLx7cHl34fbsxOXdjdu7E6dvL7ZQKQklm0go2RRzXl2x43U2xetqhs/VDK+zGV6XOemadNc6VL6Qnz0lOewuyWJX8T52l2axuziL3eHwqcBffMBzxNtcNI1vRLO4RjSLb0jTuEY0jW9ohVBprlRp4SSEEEIIIYQQh0BCqeOJPQlSupp37CvdKeFUNeiGTpG/lAJ/MdmePHJ9hZT4PRgYuG0uEhxuGrlTYlrBGIAnrjWeuNbkRZ1L0QM4fZm4vbtxeXbh8u42J18mqhEgzruDOO+OCnUI2JLxupridTbD52qK19kEn7MpPmdjDPX4HhTdMAxyvAXsLN7HrpJ9ZvBUksWucAi1z5N3wHMkOeLDgVMjmsen0SzeXI6UJcqA4UIIIYQQQghxREgodTxyJJtTTDiVK2NOhXmDPgr8xeR5C9nryaPYX4pfD+BQbcTb3TSLTzukljGGasfrboXX3QpSozfoOPzZ4ZBqDy7fnvByJvZggTkVF5BY/Efs+VDwOxqEA6om4bCqCT5n43Bg5TzMV+LoEAgF2V2axc7ifeFpL7uKs8x5SRa+kH+/x8fZXDSPT6N5fBot4tNonmAGTy3i02gW14gER1wtPRMhhBBCCCGEENEklDqeRcKp+FZQEgmncsItpxLhOGkdEt0aKsuTR663gNKgFwyIs7to4ErCcSQHJ1dU/M7G+J2NKUzuGbNJC5bg9O3F5duD05uJy5dprnsz0XQPTn8OTn8OFP1a4bR+e6oZUDnMkMrvTLOWg7ako+rf1xv0s6tkHzuK97KzaC87is1pZ/FeMktz0A2jymNVRaFpXEOax6fRMqGJGT4lmAFUi/jGMp6TEEIIIYQQQhylJJQS4W59XcxwyrPbHBC9dIdZbk86JgfgDoSC5PuLyPcWkenJodBXgk/341TtJNjjSIlPRD0KnnfIFk+prT2l8e1jNxgGtmAhTt/ecGi111qOjF3lCOThCOSRyB8Vz6s68DvS8DnSwmFVGn5HI3xOcx7S4ms8tPKF/OwqzmJ7cSY7ijLZXrSXHcWZ7Cjex97SnP0e69QctExobE7xTWiV2JgW8Y1pmdCEpnENsWvyq0wIIYQQQggh6hv5JifK2BPB3tHs1ufJhJI/ze59tjhwpIKiHfgcoRCEdAiGQNdjJwMwjPByJS1fFAVUFVQlalkFTTPLNA20cNkhKA14yfcVkePNZ58nj5KAB93QibO7SXUl4tTq0fhMikLQnkzQnkxJQnqFzVqwGKdvX1lQ5c/C6cvC4d+Hw5+Lpvtxe3fh9u6q9PQh1YXf0TAcVjXE72gUnpvLAXtKpWFlUA+RWZrNtqJMthdlsr1oD9uL9rK9KJPM0hwMqm7xFG930zqhKS0TGtMqsQmtEprQMsGcN3QlS2snIYQQQgghhDjGSCglKrLFQ+IJ5t36vHuh+E8o2QUhBZQECCjgD0AwaM5LveD1QSBghlLBEOgG6CEziNJ1MHTQgZhQQgGFigGVopQLqNSyMEoNL9vt4LSD02FOdhtoNnMengybRpHhI99XzN7SHHK9hXiCXlRFJcEeR5O4htjUagRt9VDIlkCpLaFiCyvMAdcd/hwc4aDK6c/C4c/C4cvG6c/GHixA071VhlaGAXtDKr8aifwecrEhoLHBH2KT18t2TxEBQ6+yXvE2F60Sm9IqoQmtE5vSKqEprcMBlHSzE0IIIYQQQojji4RSIpaug9cLHo859wYhPw6ys6F4F5Rkm+GTlgCa0wyPNA1sNjMs0jRwOMIhUjhQim79VB2GUTaF9LKWVZFlfwA8PnM5GAzvax4aQqdADZCv+MnUi8lX/HgdGs64eBJdiTR0xaM47GDY2E+jnWOaodrxuZriczWlqJLtiu7D4c8BTya7C7ayvWAHW4uz2FKSz2ZvKRt8QQp0HSgIT7FcCnSwQ7oD2rvctHcn0SahIa0TmpIU14SgowEBRyp+eyoBe8pxfwdBIYQQQgghhDheSSh1PNN1KC2FkhJzXlgI+fnhMMprhj2KYgZOzmRo2AAaeMHIBX8OhHxmqypbfM2OO6VEBVjagVsyBfQg+cFScoPFZPrzKfSVEgz5iTNsJIdsNAmoUFgCRglgmCGZzQZ2zWxx5XaBy1nWysphB1skaDs2W1JFGIZBvq+IP4v28Gfhbv4s3MOfReZ8T2lWlQOMKyi0cCfTLi6JE10uOjhUOtl1Omte2lKAM1iIggF4zCm4F/J/g/yK5wpq8QTsKRUnW2Q5mYA9BV11HVWDswshhBBCCCGEODwSSh1P/H4oLjan/HzIyzPDKL/fDKDsdnA6IT4eGjSoYuymJKAxBIvBmwfeTPDuA9VujklVS61efHqA/GAp2YEi9voLKAp5MTCIV52kuZKxq/t5a+s6BIJmKyuPF4pKzDLCLa40LRxKhUMrl9MMruy2cJgVnjvqT2gV0nV2l2TxZ9FuthbutgKobUV7KPAXV3lcgj2ONolNaZPYjLZJzWmT2Iw2iU1pldik0jG4SoBfAYwg9kA+Dn8u9kBe2TyQjz0QKctHNfzYQiXYQiVVjm9lPQfVQdCWbIZUthSC9iQCtmQC9iSrPGhLImBPQlfdEmAJIYQQQgghxFFOQqljmc8HRUXmlJ0NBQVmCBUKmaGK2w1JSWYQdbBf4G0JkJAA7qYQyDPHnvIXgB40B0av6dZTgCfkJz9Uyj5/AVmBIkp0H6CQoDpp6kjGVp2B2MEM2yJjUVUmMi5W+dAq0mooMvh6JKByO83gymE3y2xR22zaIQ/Mfii8QT/bi/awtcgMnraGw6ftRZn49UClxygoNItvSNvE5rRJakabxGa0CwdQhzzAuGIj4GhEwNGo6n0MAy1Uit0KqgrCy+EpmG+VaboXTfej+c0xsA5EV2xmQGVLImhPCi8nErQlEbQlRk1JBG0JhLQ4CbGEEEIIIYQQopZJKHUsCQbNLniFhWYIlZ9vds3TdTN4iouDJk3MwKSmaHbQGoMzDYJF4Ms1W05FWk/ZwmNPHaKSkJf8YCn7/IVkBYsoDflRUUjSXDS3p6DWcPAFhO/yp1UdWkXGsgqFzFZmHm94bKvwdgUzjIqcx+EAV2RAdntZK6xIgGW3HXRwVRzw8GfhLrYU7GZr4S62hgOo3SVZVd7hzqnZwy2dzNCpbVJz2iY2o3ViU1y2Q/83OmSKQsgWT8gWj9fdYr+7qiEvtmBBOKQqwB4swBYowB4sDM8LsAcKsQUL0XQvqhHEEcjFEcg1exAegIFG0JYQnhLL5ppZFrIlWMtBWyIhLZ6gLb56d6QUQgghhBBCCFEpCaXqM8MwQ6eCAsjNhawscz0YNIOQhARo1qx2upgpCtiTzMndHAL54M025/5c0NxgjwfFfsBTFYe85AVL2OsvICdYTGnIh12xkai5aOCIr/s7tKmq+fpWxdDNQdmD4RZXpaVQWBRubRXeJya4itxN0GHeUdDhsLoQ5htetpTuY2tJJluL97C1aA9bC3exz5NX5cMnOeJpm9icdknNy8KnpOY0i2uEVouttmqSrrnway78ziYH3FfRfdgDRdiCBdiCReHgygyszPUibMGi8Hoxmu5FIWQGW8GKA7fvT1CLCwdUCeZcM0O2oBYXDrLiCVn7lC2HNHeNtyQUQgghhBCiPgjpBpoqvRSESUKp+kbXzQAqPx/27TMDKa/XDDHi46Fx45ptCXUoNIfZesrVGIIlZrc+776o7n1uswVVuJWJYRhWEJXpLyA3WIwnFMChaiRqbho6Euo+iDoYigo2df//DlHBlREMklOUw5asfWzxZ7M1kMPWQC5bg7nk6VU380lzJNEuvilt45vSLqkZ7ZJa0C6lBQ3iGqDYtOO2O5qhOvE7nfid++k6GEXR/diCxeGgKjwPFVnrWrAYW6g4vK0ELVSMLVQKgC1Uii1UWq0uheWZgVZkcofDqsiyOQ+GAyw9UqaWbdPVQ+h2K0Qd0Q0d3TAwDIOQoWNgrlvlhMutMh0dw7zZQni72dkYDD1IvGqrX58LQgghhLBoqsLNb/zIpn1Vj217NBvUMY3bhnaq62ocMySUqk90HTZuhA0bzK5jkTGhGjeu65pVLXJ3PndTCBSBvxB8+zC82RSFvOShsyfkJy/kxasHcIZbRKXZk+q65jXOMAz2BgrZ6t3HFm8WW71ZbPHtY6s3i6KQt8rjmttTaOdoSHt7Q9raGtDOlko7NZlEJdzlzgAKFShRYG8maFlmKGa3m+NcOR1lrbJsNrNlVnT3wuM4wAIwVAcBRwMCjgYHcVAoHFCZg7RrMYFVCVqwJGpeihYK7xssQTXMsb0igdYh1xulLMBS3eiay1y3ll1ly6qbkOZCV13hkMtFSI2su8IBl7TcOt6ZgZBRITAywyEzKCoLlcL7Grq1HjmH2RTUQEEpC5MU0BQNBQVVUVAV1ZorKNgUDadmx67asCk2bKqGTdWwqzY0VUNFMeeKgmoEsQWLibfH1+XLJYQQQojDsGlfMb/uLqzrahySE9LkGqQmSShVXxgGbNoE69dDaqrZKqo+UVQMexKFikYesCfoJS+Qh8+fg4sQiYqdxvZ4c/wppXbu4Hek6IbOHn+BGT75wuGT1wyfSnV/pceoKLR0NqCtM40TXGm0czWmvSuNNs5GuCu5y10Fhg4hwwwr9RAEdfCFB7WPDNKuKGVzLRxERQZit9vCIZbN7D4YKY90L7RpoIYDLE2t1cHbj0qKZg6gbk/Cd7CH6gG0UClaqNQMrkKlaMFSND08D0UmjznXPbHrIQ8KBgrGYQdb0UKqA111E9Kc6FZgFVl2ElKd6FrUcrhc1yLrTnTVEZ6XTcb+7oQpDpnVmqia4ZFVjhEOjkBRFOv+DZFcWkExwyMFVFQURUFTzeBIRUXTVGyKhk21Ydc0NKLDIzUcNqloimoFT5qixQRR0etl+x3E7xTdD37bcR2mCyGEEEIcK+TbQn0QCaR+/73eBVK6oVMYLCEvUMgebzZ5gSL8egC36iQ5sR0u9UQIeSFYDIECCHkgVGQGIKozPEj60RmABI0Qu3x5bLWCpyy2evex1ZuNz6j8TncaKm1cDWnnNEOntq402rvSaO1siFM98HhbVVJU86fZVo3xwyIBlh4yuxCGdPD4oMRjhlhGJQOlR0IsTQ1PNjPAirTGim55pYXDLDXcjVGzgapIkBVmqHaCajJBe/JBB1rmCQxU3Yeqe6PCKi+a7kENecx13YsWCq9HlnUvWsiLGtlX91kBF2De3VD3Yw/W6NPFQENXHeGAy4GuRMIrR1SQ5cCw1u3oiiNqn/A2xV62PVKuOKLW7aAcfR9pkdZHZnc0vZJubFV3WzMMI9zmCMq3PIpuZaQp1QuP7JoNu2qLCYZiA6Oqw6RIuXSZE0IIIYQQNenou4IXsQwDNm+G336DlJR6EUiFjBAFgWLyAoXs9mZTGCzBrweI01yk2hNxquVa/kS6+LkamwFVqBT8RWZQ5cs399HsZkil2jG7htQevx5kuy+HP31ZVre7rd4stvmyCRihSo+xKxptnY1o60rjBFdj2rnSaO9qTCtnA2x1fce2SIBFNesRHWLpRuxdB3W9XEsszH+eSIClqGUtq7TwnQbt4UDLZisLr7SoFliqGhVsKbUzUH99oihmiyXNRdCecnjnMgwUw2+GVbovPPeadzAM+cwy3RO17AsHYr5wuGWGY2aZP3wOc7uCblaXkNnaaz/jo9UUAxVdtWMoZUGVuWxHV+wY0fNw0GWotqhtNkKKDR0bIdVGCI2QohFSbAStZY0QNgKKSgiNIGq4TEVXbOZjooYbJhookcAnXGa2QgoHSVV0W7OrZd3XyodFBwqNIuUSHgkhhBBCiPpAQqmjWShU1mUvJcW8m95RKqgHyQ8Wk+cvZLcvm6JACQGCxKkuGtiTcFSrFZBi3qVPc4OjIegBs+VU0APBcCuqQHgwPM1hTthQgjqqx4/m9aP6/Kie8NwXqDgFgqj+IIo/ajkQQgkE8RgBNrlK2eD28UeCjz8S/WxICrA5MUSoikY+7gB0ylHokq3QORs6Z0PXvdAu38CuZ4GRBfxW6VM1Il8aVcVcDk+G+c0VQ1XNdU01l9XwsjVpGOGWS4amots0DJtmltvDyzatrNxuM9ft5rJut2HYNXPusKHbwnN72Vx32DAcdnRHeNluw3A6DtxtJhJWhaLmofJBVtlrYYVa0WGWqpQLtMItsuxaWaAV6YqoRYVZalSYJS209k9RMBQnQdVZ86fWg2UBVlRoFTv3oxp+M/Qy/Kh6wNqm6OF1I7JvwCw3/DHralSrRAUdTfcBPqg8L64VBiqodgzFZgbpkUmxg+qw1hXVgVJhuy1qOVJuiyqvbB61HFkvv73CtnJzGVNMCCGEEELUAQmljlaBgBlGbd5sdtk7CgMpvx4gP1BErr+ATF8OhcESdEMnXnPTyJGC/UBjyRgGis+PragEragEW0kpWnEJWlEpWkkJWokHraQUrTg8LylBKy1FKylFLfWgef1oHj9KSK92nQ0gOw5+T4P1jeD3RvB7U3O+PaXq45K8ZuDUOQu6ZkGXLHO5TQGohkFZwlJ99bUdg6Eq6A47ujMcVjntZcGV0162zZoclS6HXOXKXHZ0u4au2dBtmvkC6bo5PpY/YHYvjA60FAADjHAHp+ixslSlLJxSw3dCtGlloZZmKxvgPTq8Cod/EmwdPkONtDYyW3dW1o0tFD1QdiXd2SLzsn/wsi5siqKYXddQ0AjiQEczgtjQ0fQgdoLYCWHHMOdGCJsRxK7oqHoAzQiiGMHwPIBqBFH1AIoeQDXMuRKZ6wHQ/SiGOUePzKOWoyjooPtQ6jgcOzhquaBKC4dVWlRwpZXbVlmZFi4rt0+Fcq2SKRyORfYjsn+5ydDBmVbXL5gQQgghhKgBEkodjTwes7ven39C06bgctV1jSzekI/8QBHZ/nz2+nMpDnoAg3jFSXOfE1dRKbb8vdgKirAVFmErLA4vF2MrLEYrLMZWVIKtqBitqAQ1UHMD2Oh2Dd1lJ+S0E3DZ2NpAZX2ayh8NdP5ICrEx0c+GeB959qq/JTbUHXTQkzjBSKa9ksIJaionqCmkxSdAig0j3WyVhKpSqir8pqlWCGKEgwtDLWv1VGnyFMmvDCO8bKDoRrgrlQF6eF3Xy8pDOkooZAZwumEtKyEdJRgqm0em8LoaNFuBRcrVcKuwSLnqD6IEzdZiqj+IGgiilJur/gCqv+zfSdENMxD0Vj5oe03RHeHgyuUwJ6edUGTZ5TCXnWXbQ05bOOCyEYpq3RWKhGd2GyF7OGQyov8hokKtSEstoltsETvIuy3cBTG6hVZ0IKZFhVuKClq5gKyedKuqMIh2eKyjgwmRosfWB6xubOXvvhY9BlJk3KOybmy2Cl3Xqpyr4XOpBzlw9uEyDDBCFYMqPQBGMKqsqvVA2TEx6+HtVpkf9Kh1I1jJevnycnMjaNa14r94OFzz148gTYuDFufWdS2EEEIIIcRhklDqaFNYCL/+Cnv2QIsWZpeluuL1YuTk4MnaQ2n2bkr37cKXsw81N4+UghKaF5biKijBXlCELb8IRa9+i6VohqYRTIwjlBBPKCGOYLw5D8W7CcXHmctx4eV4F7rbTSjORSjOTb5DZ6uSzxYjl22e3fzp2cU2TyY7vFlVjvekAM0cKdaYT+1cabRzmoOOp9jiKj2m5JCe2THEMKwQS/UHzMDKZ4ZVVlfJ8LZIV0klvKx5A+Y+kWN8flRvwNzXG14On0PzlXXFMsOwABTWzN3lInSbhu52VAy4XI5wqOkoa8kVbvUVctjQnTYzKAuHXGVlDnSHLdyaKtyiRyG21ZaixLbC0rSylluRQeLVqGBLLQs7Y1pwRUKuyFhd4X2rCpAquxNb+QApeiDt6NZIRMY/ihpUu3yIFD32kV217TdEqio4MrfV8zGQFKWshRGV/w45qhh6OESLCqmiQ6sKy6HwVNV6dFkoap+oc6Ob49JFl0fvTyhqe/kpWFbnyJTYwewKKYQQQggh6jUJpY4WhgG7dsEff0BxMbRseWQGeA4GITcXcnLMKTvbnHJyrHIjvE0pLUXB/IpV3a9ZoTg3weREgskJBJMSzeWkhJgplJRAMDGBYGI8ocR4dLdrv61H/HqAXd59bPNkst2TyTbPHnNesIfcQGGVxzlVO61dTWjrakJbVyPaOFJoZ0+kjT0Rl6KY3cCgrHuIqgE6R+vd/uqUomA47IQcdkK4j9zj6LoZXnnDQVd4nDDNU7auef0x27VIsBW9b/R+4eVIN081GEIt8mArqtmBt0OVtOiygq2ollwhh0bQYSPoNFtuBZ02Ag4bQadmzh0aAacdv8tG0KZgqAohxSgbbywyqQpKuEzVNFTNhmKzoak2FE1Fs2koNjuqoqFpZgskm2bHFp7bNTt2ux1Vs6GqKppmQ1U1c66Zx6iaDc0abDv2jm31OkQ63imRVoB1+EePw6H7wZ9X17UQQgghhBA1QEKpo4HXCxs3wpYt4HabgdTBMAwoKSkLl6qa5+RAfr65/35Ef9UM2W0EUpMIpaYQSEkimJpkzqOXk5MIpiQSTErEcBzal5ygHmS3L5sdnr3s8Gay3bOXHeEQao8vG30/YzalOVJp425KG3cz2rqb0yauGW3dzWjqbFh5F57orjW6D4Je0D3hgdW95l/rIy+EagNs4e5XNqp9xzpxaFTVar1U05RA0GqZpXn9qB5fTOCleXyxYVip11yOlEeCrvCg+prXj81b1rLrSHRpNBTFHLPL5cBwOtDdDnA6MFxOc+5wgMsBDgeKy4HiMLerDgeKw47idKK6nFa5OTnNY+y22FZYVpfFqFZdkeXIwPI21RyPy66VlSuUnSemRZgau7y/FmD1qFujEEIIIYQQouZIKFWXDAP27YMNGyArC5o02f/4UZmZsGoVbN9eMXTyeqv/uJqG0aABesNU/KnJlCbHUZjipijZhS81EVIbYktrDA0aose7a+zLok/3s9ubxU7vPnZ69rHDu5ed3r3s8Oxlty+bUBXd7QDiNBetXU1pE9fMmrdxN6W1qynxtoNsuRO5o1UMw+xCEjPOi8+841/IVzGwgvAX6ajBeFWN8ABEB1cfcVjKuqoZ4TGPDEKYXdPMDmuGOaA2BrozPCXZ0NEwiCPSeS0ygLa5ZCrrrhbuwoaCuYbZ7QwFZ8DA4Q3g8IVweEPY/UHs4eBL8/jRolt0efwoXj+q12fOPT4Unx88PhSPD7xeFK8PwuGWYpjjd1HDYZd5csUMqVzhkCo6sIqEWA57WZhlt4PTbs4d4SlS5rCbx7js4HCCw2Yeo2pl3Q1V1QywlHKBV3RZJOhSVTMAUyNBmFZ2F0Y1KviikgAt5txK5WFY+W6VQgghhBBCiDohoVRdycszBzLfudP8UtSqVeVfjrKzYeVK+M9/4Kef9n/O+Hho2BAaNao4b9QIX0oihSlu8uNU9gbzKAyU4NMD2BUbCTY3cZobLdyy6FBGhzIMg5xAAbu9WVb4tMubxS7vXnZ5s9jnzwt/6a+cU3XQytWEVu4mtHI1obW7qTU1tCcf4e5CShVhFZhjoUTGTokaRDjkA8MPoUirq3BoFf0UFaUsrLLuPhUJro6PL8Ox4ZC5FB0gVQiOKtkfMP/9DYPoUZCU8GDaKiqKYt6JTYkKj1QUNEXFqarYFQ2bYg6mbVM07IoWHudIQUO1wictKoTSIuMnKYo1QHfZ9iP476fr4PODxwteX9TcFzX3lptXNvnLliP7BAKRf5iybUeKqsaGXNFBVyTIctgrTnabuc1uM4Mvu1a2HL3daQObwwzBIi2/ygdRCljhlULFMCy6pZcVfqllA9lramwgVj7wij5fzOOXC73K16l8OCYBmRBCCCGEOA5JKFXbiorMlk7bt4PfbwZG0a2jgkH45Rf4+mtzWr++bJuiQK9e0KNHTNhkLbtjWwwF9SCFwRKKgqVk+fPICxRSGsrCKDFway6SbYm4tOp3kzIMg7xAIXt8OezxZbPHm8VuXzZ7vNns9plBlFfff4uOeM1FC1cTWroa09rdhJbhEKqlqwlpjpTavWNWtanmgLpVDqobGcA3MjBw9CDA0XfjCoHhMwOH8uEVhL8ka+YXZcJ3gIsEWGpUWaW39Dt0ZXdVKwuDKguNImV6uTLDal9UPnIsGzRbUUBDRVFU81lUERzZFBUbGnbVDIwiAVJ0KBQbHEUFSNZ21Qqbjnh4dKSoKrhd5lTTQiEzrPJFhVU+f8VAK6Yssn+4zO8vV+4DX6BsWQ+/E3QdSr3mVBsiwZWzqrArEmhFLVvBl63szorWslbWddERtd0WDsVsqnmszWYOWK+GfzYjwZMSHV5BhXAsOsCiXFgVCcWsOzpGBsOPDHIfDsqizxU5N+XOrSgVHy96PVLn6NCs/DFKuX2EEEIIIYSoARJK1YZQyBxEfPduswteaakZJDVubH65W7vWnNatM+dFRbHHd+8OZ58NZ55pHlOFoB6kOOShKFhCrr+I7EAepUEvQSOEQ7WToLlJcSZW+SXdG/Kzz5/LXl8Oe325ZPpyrGmPL5tMXw6+A4ROCgqNnQ1o4WxEC1cTWrjSaOlqTAtXY1q6G5NiSzwGB0iOhEYHGE8r5m5SetQdpfSy1ld6EDC7ERqhIHrIj4GOHgihh/eNBEOGYqATaYkEOgqGEp4bmPtZXzjNf3OD8JdWI/Jl1OyqFglwokMjzSyxQiAtHBppaNhUMzyyqRq2SkKg8i2OogOl6BZI9To4qq80DeLd5nQkGAYEguHgyl8WbkXWI4GWL2Bu84WXy2/3R7ZHTdFlgfByMKpLbSBoTqU1O4h9tdm02NZc0YFXdAgWHXhZQZdWFm5VmIfv0KiVW9a0qPOU2z+yHN3Cy/p9EP5fTJAVVVZpMIX1O6NCQFXZOGJWl0wltqx8kFY+tKvsMcuXGUEIlUCj/Y+PKIQQQhyLQrqBFvlDmBDHgFoNpXw+H/feey//+c9/cLlcjB07lrFjx9ZmFWqPrpvhUn4+7NhhhlIFBWXrGzaY06ZNZd1pIpKSoF8/GDAATjnFbAVViYAepDhYSnGolFx/ETmBfDwhH349gE3RiNfcNHakoikahcESMn05ZPnzyPLns8+Xyz5/njXf68uhIFh8wKeloNDIkUIzZ0Oau9Jo5kyjuasRzZyNaOFqTFNnQ+zqsZt1RloVEdVqyAgPwx5Z1q1uaGZ5ZLms5ZE53pGhgGI2MTLHnlc1UGyAARoodlAMAxUDBXOuGqAYuvn9LryuoaMZYENHMwxsihGeq0SGZlcBVTFQDcU6jxY9XlLMmEkKqhoOklTNmptdECMfgOFWW9aX1OgWXOF1I+rD0og+1ihrJaabrxDoZWUxA/EblS7G7LO/gfuNKo6v9LiD2LeqxzzATQRiVBXOli+vsF5uISZoKHdM+ZYz0S1yokOKmqQoZS2TEuJr/vzlhXQzyIoOrfzhkMsfKAu8/IGK5YFAufJgWVkk+PJHTYFgufVyv7uDIXPy1FLLsOpSlLIgTNPKgrHyIVYk9LJVEoBZy5GB79Vyy1pZKGUr1w3SWo6ME6bFdouMnF9VwnMt6tioVmOR1mJGEJw6ND0dHEl1/eoKIYQQtUpTFW5+40c27Tvwd7ej0aCOadw2tFNdV0McRWo1PXjwwQf55ZdfePHFF9m9ezdTp06lefPmDBs2rDarcWToujlo+ebNZti0fr0ZPu3eDXv3mvPCwsqPbdDA7JLXs6c579TJvNCPPr2h4wn5KA15KQl5yPEXsM+fS6Y3hxx/AYXBEkpDHopDXvIChWT788n254XnBfiNQOWPXY5LddDE2ZCmUVMTZ0OaORvSzNmIJoccOoW/rBvl5lGbYnc3YjeWCyGswMeIhECR5Uh5yAqBYgKi8D56OBjRw+vmI5lhkzl2UeSRdTCUcEc0I9xyCLNljxHVwihcrkQGwUbBrqjYDLPFUaSFkdklLbIcDoCMcCBkRLUqgthBtcNztXy3tehWRopS9nopRLXCioQ+BhAy94mU6Xr4GMPcFyOqJZdu7q8bZrfDyDbrXOFjDMrOgRF+7Oh6GGX1iogJUCKtpIzKQxIr8IqEKpWUW0FL+OSKWsm5lLJtEWpUeWSfmOMq6eIUXX/rXOWOO9DYQJX9HEC5l6mKn4Hol7PK84TfwIYR9W8VtW7osY9l/XuZ7/2ytDTq8a1t4edbWRewmBAs6nWICcSiW92UPyayrVwXt/3RjmBXxwOJtAoLRIVZ1nIQApGgK2p7MFgWfgWC5rq1f/n1gBlyRfa1tpdbDgYhECo7NhiqpJ6VhGj1UST0Sm8NZ46u69oIIYQQdWLTvmJ+3V3Fd8uj3AlptfBHS1Gv1FooVVpayrJly3j66afp2rUrXbt2ZePGjbz66qtHTyjl8ZitmUpKyqaiInMqLDTneXnm4ONZWWbrp337zCknx/xL+4E0aQLp6dYUPPEEitOSKQqWUuwvpiBQRPauVWT588jxF5AbyGevL4+9gTxyA4UUhEooCJaQr5dSoh/cX+OT1TjSbMmk2ZJIsyXTxJZMY1syabZkmtpSaGxPJlFxx3avi+QcfsAHhpFtthAKhw+6HhlTKBz+KIb5fVcpG48IhZhWQ4ZC2VhE4VZCeviLr6EYmG15or4xK0pZtqGYrbUIt/Ixv8Oa3cAUqytaOMJRFOvuaXZUbKot3DrI7H6moaFFxi5SbWXhkhruWqaGu68p4RZDijkYthK1XVW0sv1i9tHM17H8FH4+1nx/+1RWXv74qsoOdv8DlVW2HAmuIgFY+XDL/BeNKg8HYeXLI9sUYo+LPrd1zsgylIVjengyosorqYP1GESFK1GhTkz2YYSDGaKOLf9zERW8WftFLVdXdKCkKOGgMLYqMeczwg8aeR0i2ys0MAsHT7peLpCKPGa5oEqP3ieyLVym6+HHDC/ruhlW6obZUikU/rfQA+F/jnBZ5Jx6VDhm/kIoewyiHj/6eVjHlXsxVBWrctGt8axfVkT93ITLywdhVXUXI3qfyHJ1tgN2xZywg+IoF2BW8vhlG6uvui3bjPC/SyQAC4XC4VU44ArqZWWB8PZgqCzcil4PhtcD5eZWefTx5bZFl4X0ctv12H30qH2CoYo/BxG6Dn4dMnPM+gkhhBBCiHqt1kKp9evXEwwG6dWrl1XWp08fnnrqKXRdR63juw55/v0hy+66kFxbAF3BmkIKhNTY5WB4CqkQbA6BVhAIlwUcGn63w5xcNvwOGx6nis+u4rEZeI0gHuNnPPoPeDx+Aj+FDly5/dAUjVR7EqnOZBo4kkkNTw2cyWVlzmRSHSnYNbsZBBlmOGS1DzJ0UBUKDYUCpaxVEOGgxwyKFPPrpBIeZUgNtwkKt9hRFAVFVcNtTsxghnAXMEVRzIGrUc0QKNwlzKbZUMJzMxzSUFUt3MBCiwl6lHDYoyqKtayokTIzAIqER2X7lu1zUEHOkejSJOpOdKs7KxgxYstjWiOV3/8A+5QvK9/Kr8ry/W2LDqH2s+1QtldouRb1PCvbVtW+MeekXFm5+hvhACImHNPLwhMrJIua9HL763rZshVklSuP3lcPxW7XjbIyIxyY6aGyuujlzxf1fA3KArLK3kPRIV706xwd8EW/NuYv00pey3DIZUQFMtH7VTim/LHllq3wDaxgzGkHV3RgRuz+lZZTbnu535GRVprl96vsHJUdX74sEvoaelmAphtlYZXPD+4EcByhcdHqgeNqOAQhhKhBMh6TEEefWgulsrKySE1NxeEou4NZo0aN8Pl85Ofn06BBg9qqSqVezfkv1w6via4NIcATnqLomK2NqmBTbbg0F26bi3h7PHGOOOLtCcQ74klyJpHkTCLZlUySM4kUVwqprlRS3akkOhLRwqGMEg6RzBBHQVPMoEdRFatVUCSs0cItfGyqLeaY6GVVUau97UD7ClFnolulyDWIqK7yQZdRPoSqpLyygKz8/hXWq9h2oOOq2j8yt+qix94NsXz4ZwV50ftQFoxZx+yn3gYV62CulKtX1KaYllCVHBtzGqPicnw8OBM4Xh3TwyEIIcQRJOMxCXH0qbVQyuPxxARSgLXur063tyPsr3+bwlUfZ7GnYCeaarO6X0Va6kRa8USCHZtiQ1VV7KodTdXCXcPMuUNzYFNt2FQbTs2J2+7GZXPhtrtx290kOZKId8QTZ4vDbXeT4EjAaXOGO58RE/JEAp7yQU+kLHq/6GUhhBCHoTrjWYlYlQVT+ys7nGNUFdzHZ0upejEcghCigmOhhc6x8BxAxmMS4mhTa6GU0+msED5F1l2uOhigtpzmic15YcRLdV0NIYQQon6SLtC14mgfDuFYcix8AZfncPQ4VlroHAvPQQhxdKm1UKpJkybk5eURDAaxhe8sl5WVhcvlIinpwLd0NsJ/JS0urp+/BIUQQghRv8THxx91rY8PdziE2rqeOlaChKdWbWZ3gefAOx6FurVM5uI+reQ5HAUizyPoLUX318/n4feUUlxcfEw8h7ZJKrrfXtfVOSRN3MhzOAocC8+hbZJaa9nKga6nai2U6ty5MzabjbVr19K3b18A1qxZQ7du3ar1V72SkhIABg4ceETrKYQQQggB5nVKQsLRNXbV4Q6HINdTx48PgfvruhKH6Vh4DnBsPI8tQH3vUyLP4eggz+HosAXoM692HutA11O1Fkq53W4uuOACZs6cydy5c9m3bx/PPfcc8+ZV75Vo3Lgxn3322VH5V0shhBBCHHvi44++sTsOdzgEuZ4SQgghRG060PVUrYVSANOnT2fmzJlcddVVJG1K7wAAAJNYSURBVCQkcNNNN3HOOedU61hVVWnatOkRrqEQQgghxNHrcIdDkOspIYQQQhxNFMOIuX+zEEIIIYQ4Snk8Hvr168dzzz1nDYewaNEivvnmG1555ZU6rp0QQgghxMGRW7QIIYQQQtQT0cMh/PTTT6xcuZLnnnuO0aNH13XVhBBCCCEOmrSUEkIIIYSoRzweDzNnzuQ///kPCQkJjBs3jjFjxtR1tYQQQgghDpqEUkIIIYQQQgghhBCi1kn3PSGEEEIIIYQQQghR6ySUEkIIIYQQQgghhBC1TkIpIYQQQgghhBBCCFHrJJQCfD4fd9xxB3379uW0007jueeeq+sq1Vt79+5l0qRJZGRkcPrppzNv3jx8Pl9dV6teu+6665g2bVpdV6Pe8vv93HvvvZx88smceuqpPProo8hQeodmz549jB8/nt69ezNkyBBeeOGFuq5SveL3+xk+fDjfffedVbZjxw7GjBlDz549+etf/8qXX35ZhzWsPyp7LdeuXcuoUaPo1asXQ4cOZdmyZXVYQ3E4Pv74Yzp27BgzTZo0qa6rdUyS30u1q7LXe/bs2RXe76+88kod1rL+29/3EXl/Hxn7e83lPV7ztm3bxrhx4+jVqxeDBg3imWeesbbVx/e4ra4rcDR48MEH+eWXX3jxxRfZvXs3U6dOpXnz5gwbNqyuq1avGIbBpEmTSEpK4tVXX6WgoIA77rgDVVWZOnVqXVevXvrwww/57LPPuPDCC+u6KvXW7Nmz+e6773j22WcpKSnhlltuoXnz5owaNaquq1bv/P3vf6d58+a88847bNq0iVtvvZUWLVpw9tln13XVjno+n48pU6awceNGq8wwDCZMmEB6ejrLly9n5cqVTJw4kX/+8580b968Dmt7dKvstczKyuLaa6/l0ksv5f777+fXX39l+vTppKWlMWjQoLqrrDgkmzZtYvDgwcyaNcsqczqddVijY5P8Xqpdlb3eAJs3b2bKlCkx13oJCQm1Xb1jxv6+j9x+++3y/j4CDvQdUN7jNUvXda677jq6devGu+++y7Zt25g8eTJNmjRh+PDh9fI9ftyHUqWlpSxbtoynn36arl270rVrVzZu3Mirr74qodRB2rJlC2vXruWrr76iUaNGAEyaNIkHHnhAQqlDkJ+fz4MPPki3bt3quir1Vn5+PsuXL+f555+ne/fuAIwdO5Z169ZJKHWQCgoKWLt2LbNmzaJt27a0bduW008/nW+++UZCqQPYtGkTU6ZMqdBC79tvv2XHjh288cYbxMXFccIJJ/DNN9+wfPlybrrppjqq7dGtqtdy5cqVNGrUiMmTJwPQtm1bvvvuO95//30JpeqhzZs3k56eTlpaWl1X5Zglv5dqV1WvN5jv93Hjxsn7vYbs7/vIGWecIe/vI+BA3wHlPV6zsrOz6dy5MzNnziQhIYG2bdvSv39/1qxZQ6NGjerle/y47763fv16gsEgvXr1ssr69OnDunXr0HW9DmtW/6SlpfHMM89Yv4wiiouL66hG9dsDDzzA+eefT4cOHeq6KvXWmjVrSEhIICMjwyq77rrrmDdvXh3Wqn5yuVy43W7eeecdAoEAW7Zs4X//+x+dO3eu66od9VavXk2/fv148803Y8rXrVtHly5diIuLs8r69OnD2rVra7mG9UdVr2Wkq0B58vlTP23evJm2bdvWdTWOafJ7qXZV9XoXFxezd+9eeb/XoP19H5H395Gxv9dc3uM1r3Hjxjz++OMkJCRgGAZr1qzh+++/JyMjo96+x4/7llJZWVmkpqbicDisskaNGuHz+cjPz6dBgwZ1WLv6JSkpidNPP91a13WdV155hVNOOaUOa1U/ffPNN/zwww+8//77zJw5s66rU2/t2LGDFi1asGLFCp566ikCgQAjRozghhtuQFWP+0z+oDidTmbMmMGsWbN46aWXCIVCjBgxgosvvriuq3bUu+yyyyotz8rKonHjxjFlDRs2JDMzszaqVS9V9Vq2bNmSli1bWus5OTl8+OGHR/VfBUXlDMNg69atfPnllyxZsoRQKMSwYcOYNGlSzLWaODzye6l2VfV6b968GUVReOqpp/j8889JSUnh6quvlmEbDsP+vo/I+/vI2N9rLu/xI2vIkCHs3r2bwYMHM3ToUObOnVsv3+PHfSjl8XgqXORE1v1+f11U6Zjx0EMP8dtvv/H222/XdVXqFZ/Pxz333MOMGTNwuVx1XZ16rbS0lG3btvHGG28wb948srKymDFjBm63m7Fjx9Z19eqdzZs3M3jwYK6++mo2btzIrFmz6N+/P+edd15dV61equrzRz57Do/X6+Wmm26iUaNGXHLJJXVdHXGQdu/ebf1sPP744+zcuZPZs2fj9Xq566676rp6xzz5vVS7tmzZgqIotG/fniuuuILvv/+eu+++m4SEBOkaX0Oiv4+88MIL8v6uBdGv+a+//irv8SNo/vz5ZGdnM3PmTObNm1dvf4cf96GU0+ms8I8UWZdA4NA99NBDvPjiizz22GOkp6fXdXXqlYULF3LSSSfF/MVBHBqbzUZxcTGPPPIILVq0AMwvPK+//rqEUgfpm2++4e233+azzz7D5XLRrVs39u7dy5NPPimh1CFyOp3k5+fHlPn9fvnsOQwlJSXceOON/Pnnn7z22mu43e66rpI4SC1atOC7774jOTkZRVHo3Lkzuq5z2223MX36dDRNq+sqHtPk91LtuuCCCxg8eDApKSkAdOrUiT///JPXX39dvrDXgPLfR+T9feSVf81PPPFEeY8fQZGxh30+H7feeisXXXQRHo8nZp/68B4/7vuvNGnShLy8PILBoFWWlZWFy+UiKSmpDmtWf82aNYvnn3+ehx56iKFDh9Z1deqdDz/8kJUrV9KrVy969erF+++/z/vvvx8z7pmonrS0NJxOpxVIAbRr1449e/bUYa3qp19++YU2bdrEfKh16dKF3bt312Gt6rcmTZqQnZ0dU5adnV2h2bWonuLiYsaNG8fGjRt58cUXZfyKeiwlJQVFUaz1E044AZ/PR0FBQR3W6vggv5dql6Io1pf1iPbt27N37966qdAxpLLvI/L+PrIqe83lPV7zsrOzWblyZUxZhw4dCAQCpKWl1cv3+HEfSnXu3BmbzRYz+NeaNWvo1q2bjDlzCBYuXMgbb7zBo48+yrnnnlvX1amXXn75Zd5//31WrFjBihUrGDJkCEOGDGHFihV1XbV6p0ePHvh8PrZu3WqVbdmyJSakEtXTuHFjtm3bFtOydMuWLTHj+IiD06NHD3799Ve8Xq9VtmbNGnr06FGHtaqfdF1n4sSJ7Ny5k5dffpkTTzyxrqskDtEXX3xBv379Yv7S+/vvv5OSkiLjfNYC+b1Uu5544gnGjBkTU7Z+/Xrat29fNxU6RlT1fUTe30dOVa+5vMdr3s6dO5k4cWJMsPfLL7/QoEED+vTpUy/f48d96uJ2u7nggguYOXMmP/30EytXruS5555j9OjRdV21emfz5s0sXryYa6+9lj59+pCVlWVNovpatGhBmzZtrCk+Pp74+HjatGlT11Wrd9q3b8+gQYOYPn0669ev54svvmDp0qVceumldV21emfIkCHY7Xbuuusutm7dyn//+1+eeuoprrzyyrquWr2VkZFBs2bNmD59Ohs3bmTp0qX89NNPjBw5sq6rVu+8/fbbfPfdd8yePZukpCTrs6d8Nw1x9OvVqxdOp5O77rqLLVu28Nlnn/Hggw9yzTXX1HXVjgvye6l2DR48mO+//55nn32W7du389prr7FixQoZYuAw7O/7iLy/j4z9vebyHq953bp1o2vXrtxxxx1s2rSJzz77jIceeojrr7++3r7HFcMwjLquRF3zeDzMnDmT//znPyQkJDBu3LgKia44sKVLl/LII49Uuu2PP/6o5docO6ZNmwbA/fffX8c1qZ+KioqYNWsWH3/8MW63m8suu4wJEybEdA0R1bNp0ybmzJnDTz/9RIMGDbj88su56qqr5LU8CB07duSll16iX79+AGzbto0777yTdevW0aZNG+644w5OPfXUOq5l/RD9Wo4bN44vv/yywj4ZGRm8/PLLdVA7cTg2btzI3LlzWbt2LfHx8YwaNUp+bx9B8nupdpV/vVeuXMn8+fP5888/adGiBbfccgvnnHNOHdey/jrQ9xF5f9e8A73m8h6veXv37mXWrFl88803uN1urrjiCsaPH4+iKPXyPS6hlBBCCCGEEEIIIYSodcd99z0hhBBCCCGEEEIIUfsklBJCCCGEEEIIIYQQtU5CKSGEEEIIIYQQQghR6ySUEkIIIYQQQgghhBC1TkIpIYQQQgghhBBCCFHrJJQSQgghhBBCCCGEELVOQikhhBBCCCGEEEIIUesklBJCHPU6duzIlClTKpS/8847DBkypA5qJIQQQgghhBDicEkoJYSoFz744AO++eabuq6GEEIIIYQQQogaIqGUEKJeaNGiBffddx9+v7+uqyKEEEIIIYQQogZIKCWEqBf+/ve/s3fvXp599tkq98nMzOTmm28mIyODfv36MXv2bCvEeuedd7jyyiuZP38+/fr1o2/fvsybNw/DMKzj33jjDYYMGUKvXr248sor+eOPP4748xJCCCGEEEKI45WEUkKIeqFJkyZMmjSJp556ih07dlTY7vf7ueqqq/B4PLz88ss8/vjjrFq1igcffNDa58cff2Tr1q28/vrr3H333bz00kt8/fXXAPz3v/9l4cKF3H333bz77rv06dOH0aNHU1BQUGvPUQghhBBCCCGOJxJKCSHqjSuvvJI2bdowZ86cCtu++OIL9u7dy0MPPUTHjh3p378/M2bM4PXXX6ekpASAUCjErFmzaN++Peeffz6dOnXi559/BuCZZ55h/PjxDB48mLZt2/L3v/+dFi1a8N5779XqcxRCCCGEEEKI44WtrisghBDVpWkaM2fO5LLLLmPlypUx2zZv3kzbtm1JTk62ynr37k0wGGT79u0ANGzYkISEBGt7QkICwWDQOv6hhx7i0Ucftbb7fD7+/PPPI/iMhBBCCCGEEOL4JaGUEKJe6d27NxdddBFz5szhmmuuscqdTmeFfUOhUMzc4XBU2CcyplQoFOKOO+6gf//+MdujQywhhBBCCCGEEDVHuu8JIeqdW2+9ldLS0phBz9u1a8eff/5Jfn6+VbZ27VpsNhutW7c+4DnbtWtHZmYmbdq0saannnqKtWvXHoFnIIQQQgghhBBCQikhRL2TmprKrbfeyq5du6yyAQMG0KpVK26//Xb++OMPvv32W2bNmsXw4cNJSko64DmvvvpqXnzxRVasWMH27dt56KGH+Ne//sUJJ5xwJJ+KEEIIIYQQQhy3pPueEKJeGjlyJMuXL2ffvn2AOd7U4sWLmTVrFv/3f/9HfHw8f/vb35g8eXK1zvfXv/6V7Oxs5s+fT3Z2Nh06dODJJ5+kbdu2R/BZCCGEEEIIIcTxSzEiA6oIIYQQQgghhBBCCFFLpPueEEIIIYQQQgghhKh1EkoJIYQQQgghhBBCiFonoZQQQgghhBBCCCGEqHUSSgkhhBBCCCGEEEKIWiehlBBCCCGEEEIIIYSodRJKCSGEEEIIIYQQQohaJ6GUEEIIIYQQQgghhKh1EkoJIYQQQgghhBBCiFonoZQQQgghhBBCCCGEqHUSSgkhhBBCCCGEEEKIWiehlBBCCCGEEEIIIYSodRJKCSGEEEIIIYQQQohaJ6GUEEIIIYQQQgghhKh1EkoJIYQQQgghhBBCiFonoZQQQgghhBBCCCGEqHUSSgkhhBBCCCGEEEKIWiehlBBCCCGEEEIIIYSodRJKCXEIDMOo6yocsvpc9wM5lp+b2D/5txdCCBFNPhdEZeR9IcTRR0IpUe9MmTKFjh078txzz1XYNmTIEKZNmwbAd999R8eOHfnuu++qPFf0/tX1ySefMHXq1IOr9FEgMzOT6667jl27dlllh/L8jxZXXnklV155pbW+bNkyHnjgAWv9nXfeoWPHjuzcubMuqlejFixYQMeOHeu0Dh07dmTBggWHfZ7qPJfyP7vTpk1jyJAh1vby79vFixfz7LPPHtRjCCHEsWDNmjXcdNNNDBgwgG7dunHmmWdy1113sXnz5rquWoza/r28Zs0arrvuulp7vKPBr7/+yrXXXsspp5xCv379GDt2LL/++muV++/Zs4c+ffpU67N927Zt3HzzzZx22mn06dOHSy+9lG+++SZmn+LiYh544AHOOussevbsyd/+9jdeffVVdF0/qOcRea9ET126dKFfv35MmDCBjRs3Vvtczz33HLfeeisAhYWF3H777fzwww8HVZ9DVf7apTKHcq1anWPy8vIYNGgQO3bsqPZ5o5WUlHDvvfcyYMAAevXqxbXXXsuWLVv2e8yQIUMq/LtFpujXITMzk8mTJ3PKKafQu3dvJkyYwJ9//nlI9RTHDltdV0CIg1FUVMTKlStJT0/nzTff5Oqrr0ZRlEM+38KFC0lISDioY1544YVDfry69PXXX/PZZ5/FlB3K8z9a3HPPPTHrTz75JBkZGXVUG1GTunbtyptvvkmHDh0q3V7+ffvEE08wceJEa/3iiy/m9NNPP+L1FEKIurR06VIeffRRTjvtNO644w7S0tLYtm0br7/+OhdeeCHz5s3j3HPPretq1olly5YddcHckbRt2zauuOIKTjrpJObMmYOiKDz33HNcdtllvPvuu7Rv3z5mf8MwuOOOOyguLj7gufPy8rjiiitISUnhjjvuICEhgWXLljF27FhefPFFMjIyMAyDv//97/z8889MmjSJ9u3b88033zB79mzy8/OZMGHCQT+nN99801oOhULs3r2bxx57jMsvv5wPP/yQtLS0/R6/efNmlixZwnvvvQfA77//zj/+8Q8uuuiig67LkTJo0CDefPNNGjduXKPnTU1NZcyYMdxxxx289NJLB/1dacqUKaxbt47bbruNhIQEFi5cyOjRo/nwww9JTk6u9JiFCxfi9/tjytauXcu8efMYNWoUAKWlpYwZMwZFUZg5cyYOh4PFixdzxRVX8MEHH5CSknJIz1fUfxJKiXrlgw8+AODOO+/kqquu4ttvv6V///6HfL4uXbrUVNXqpfr8/KsKLET9l5CQQM+ePavcfqD3bdOmTWnatGkN10oIIY4en376KY888gg33XRTTCifkZHBBRdcwJQpU5g2bRrp6emceOKJdVhTURtefvll3G43S5YsIS4uDoBTTjmFIUOG8MorrzBjxoyY/V977bUDtnyJWLFiBXl5ebz99ts0adIEgAEDBnD++efz7LPPkpGRwW+//cYXX3zB448/zl/+8hcA+vfvT0FBAc888ww33njjQQcj5a8D+vTpQ7Nmzbj88st59913D9gS7qGHHmL48OFWnY9GDRo0oEGDBkfk3JdddhlPPvkkH3/8Meecc061j/vxxx/59NNPWbp0KQMHDgSgb9++nHnmmbz22mvccMMNlR5X/tqsuLiYyZMnM2jQIOvf6t///jdbt27lgw8+sH4vpaenc+aZZ/LRRx9Z4ZU4/kj3PVGvLF++nP79+3PKKafQpk0b3njjjcM6X3Q3oJ07d9KxY0f+9a9/MWnSJHr16kVGRgZ33XUXpaWlgNllbPXq1axevTqme1F+fj4zZszg1FNPpVu3bvzf//1fhWbNHTt2ZOHChYwYMYLu3buzcOFCOnfuzCuvvBKzX25uLl27drVaZOm6ztKlSzn77LM56aSTGDp0KC+//HLMMVdeeSV33nknS5cuZdCgQXTr1o1Ro0bx008/AWZT3+nTpwNw5plnWs+5fDeooqIi5s2bx1lnnUW3bt0YPnw4b7/9doXXbP78+TzwwAOceuqpdO/enXHjxsU0vc3NzWXKlClWd4Lzzz+fFStWVPnvMHHiRM4777yYsquuuoqTTjoJr9drlc2ZM4ehQ4dazznSfW/IkCHs2rWLd999t0KT5nXr1jFq1Ci6devGoEGDeOaZZ6qsR8SGDRsYP348vXv3tpoWl28CvXPnTm688UZ69+7NgAEDePLJJ7nzzjtjuhRW1uWtsi4My5YtY8SIEfTs2ZPu3btz/vnn869//euA9YyuS8eOHfnwww+5/vrr6dGjB4MGDWLRokUxzeaHDBnC3Llzueqqq+jevTt33nknAPv27WP69OkMHDiQ7t27M3LkSD755JMKj1NcXMytt95Kr1696N+/P7Nnz8bj8VjbQ6EQS5cuZfjw4XTv3p2ePXsyatQovv322wrnWrlyJUOHDqVbt25cfPHFMT8vB+p6G/2+jbyWCxcutJYre41XrlzJiBEj6NatGwMGDGD27NnWzzWA1+tl5syZnHHGGZx00kkMGzYspkugEEIcTRYuXEj79u0rbYFit9u577770DSNp59+GoCxY8cyYsSICvveeOONMZ+/P/zwA1dccQU9evQgIyODqVOnkpuba21/55136NKlC8uWLWPAgAFkZGSwadMmtm/fzvXXX0+/fv3o0aMHl1xySYXW2QCrVq3ivPPOo1u3bgwdOrTCtUF1Po98Ph+LFi1i2LBhdOvWjXPOOYelS5dan3fTpk3j3XffZdeuXXTs2JF33nmn0tdwwYIFDBs2jI8//pjhw4db1ys//vgja9eu5eKLL6Z79+4MHz68wjVdda4T1q9fz8SJEznllFPo2rUrp59+OrNnz465runYsSOvvvoqd955JxkZGfTq1Yubb76Z7OzsmNf8QMNRtG/fnrFjx1qBFEBcXBxNmzZl+/btMfvu2LGDhx9+mFmzZlV5vmhNmjRhzJgxMeGOpmm0adMm5tyXXHJJhT8Ut2/fntLSUnJycqr1WAdy0kknAVhDUSxYsICzzz6bhQsXkpGRwWmnnUZBQQEbNmxg1apVDB8+HDCvK0aPHg3A6NGjY67V/vnPfzJixAh69erFgAEDmDFjBgUFBTGP+/PPPzNu3Dj69etH7969uf7666vdjfCdd96xrnfOO++8mJ+Lyrrivfvuu/z1r3+19v/mm2/o0qVLhffxga5vHQ4HQ4cOZcmSJVZZ5Pqqqp8JgC+//JK4uDhOO+00q6xBgwacfPLJlf5MV2Xx4sXk5ubGBKJnnXUWr7/+ekxQbrfbAfPnWhy/JJQS9cbGjRv5+eefueCCCwC44IIL+OSTT2I+uGvCPffcQ4sWLVi8eDHjxo3j7bff5sknn7S2denShS5duvDmm2/StWtXfD4fV111FZ988gm33HILCxcupGnTplxzzTUVLmKeeuop/va3vzF//nyGDh1KRkYGH374Ycw+H330EYZhWE3uZ86cyfz58znvvPN46qmnGDZsGHPnzmXRokUxx/373//mk08+4a677uLRRx8lOzubm266iVAoxKBBg6y/bCxcuJAbb7yxwvP2er1cdtllvP/++1xzzTUsXryYPn36cOedd/LUU0/F7PvSSy+xZcsW5s2bx+zZs/nll19ixtm67bbb2Lx5M/feey9PP/00Xbp0YerUqZWGEwADBw5kw4YN1kWLz+fjxx9/JBAIsHbtWmu/zz//nMGDB1c4fuHChaSlpTFw4MAKzaBnzpzJueeey9KlS+nVqxcPPfQQn376aaX1ANi6dSujRo0iJyeHBx54gDlz5rBjxw4uvfRSq34lJSVceeWVbNiwgdmzZ3PnnXeyYsUKPv744yrPW5VXX32VGTNmcNZZZ7FkyRIefvhhHA4Ht956K5mZmQd1rpkzZ5KQkMCCBQs4//zzWbhwIY888kiFx+vWrRuLFy9m5MiRZGdnM3LkSH744QduueUWFixYQIsWLZgwYYLV5D3i5ZdfpqSkhMcff5zx48ezbNkya6wGgIcffpjFixdzySWX8MwzzzBr1izy8/O5+eabY8IrMFs7jh49mgULFhAfH8+1117Lzz//fJCvXlnz/pEjR8Y09Y/2/vvvM2HCBNq3b8+iRYuYOHEi7733HjfeeKM14OncuXP5/PPPmTp1Ks8++yxnnnkmDz74IMuXLz/oOgkhxJGUm5vLL7/8wuDBg6tsfZKSksKpp55qBTrnnXcev/76K9u2bbP2KSws5PPPP+f8888H4Pvvv2fMmDG4XC4ef/xx7rjjDlavXs3o0aNjgpRQKMRzzz3HnDlzmD59Ou3atWP8+PF4PB4efPBBFi9eTEpKCjfccEPM4wHMmDGDMWPG8OSTT9K0aVOmTZvG+vXrAar1eWQYBtdffz3PPPMMF198sXVd9Pjjj1vd+m+88UYGDhxIWloab775JoMGDarytczMzOT+++/n+uuv54knnqCwsJBJkyYxefJkLr74YhYtWoRhGNxyyy3Wa1Cd64R9+/Zx+eWX4/F4uP/++3n66ac599xzefnll3nppZdi6vDYY4+h6zqPPvoot99+O59++ilz5861tke6eHXt2rXK53HZZZdxzTXXxJRt27aNjRs3xgQAuq4zbdo0/vKXv3DGGWdUeb5of/3rX2M+6wEKCgr4/vvvrXN37dqV++67r0L3q5UrV9Zoa6CtW7cC0Lp1a6ts9+7dfPbZZzz22GNMnz6d5ORk3n//fdLS0qzWVl27drXCkRkzZljvlcWLFzN58mR69uzJ/PnzmTBhAv/+97+58sorrX/vb7/9lksvvRQwrxVmz57Nnj17GDVq1AG7iO7Zs4elS5dy8803s2DBAhRFYdKkSVWGdCtWrGDatGn07t2bxYsXM3ToUG688UZCoVCFfatzfTts2DB++eUX63WLDI+wv5+JzZs307JlSzRNiylv3bq1dZ4D2b17Ny+99BLjxo2jRYsWVnliYiK9e/cGwO/3s379eqZNm0ZqaqrVwk4cn6T7nqg3li9fTkpKijVY3oUXXsiCBQt4++23uf7662vscQYOHGgFLP379+err75i1apVTJkyhQ4dOlhj2UQ+6N566y3Wr1/PW2+9RY8ePQA444wzuPLKK3n44YdjvtT27duXq6++2lo///zzueOOO9i9ezfNmzcH4MMPP+TUU08lLS2NrVu38tZbbzF58mSr6etpp52GoigsWbKEyy67jNTUVACCwSDPPvusVb+SkhKmTp3K77//zkknnWR9gHfu3JmWLVtWeN7vvPMOGzZs4I033qBXr14AnH766QSDQRYvXsyoUaOsi42kpCQWL15sfWBt376dBQsWkJeXR2pqKqtXr2bChAmcddZZgNmdICUlBYfDUeVrDvDNN98wfPhw/ve//6FpGu3ateP777/nlFNOYceOHfz555+VhlJdunTB4XDQoEGDCs29J0+ebF1M9OzZk48//phvv/220vOAGXC53W5eeOEF67Xs378/Z511Fs888wxTp07l3XffZc+ePfzjH/+wWuR0796dYcOGVXrO/dmxYwfjxo2LCQpbtGjBiBEjWLNmzUGNB9K1a1cefvhhwHwPlpaW8uKLL3LDDTdYz6V58+YxF5cPPfQQubm5/Pvf/7YuHAYOHMiYMWN48MEHGT58OKpq/v3ihBNOYNGiRaiqysCBA1EUhblz57JhwwbS09PZt28ft9xyS8xfIJ1OJzfddBN//PFHzL/Nvffea71e/fv358wzz+Tpp59m/vz5B/X6Rc7ZtGnTSrv8GYbBww8/zOmnn269NgBt27ZlzJgxfPbZZwwaNIjVq1czYMAA6/Xu168fcXFxNGzY8KDqI4QQR1qklUj0l73KtGnThk8++YSCggLOOecc7r33Xj744AOrddV//vMfQqGQ1ZrkkUceoV27dixZssT6fO/Rowfnnnsuy5cv5/LLL7fOff3111tfbLOystiyZYsVBgFWi/DyY8zMnj3bCkNat27N2WefzerVq+nUqRPPP//8AT+PvvjiC77++mseffRR6/f1gAEDcLlcPPHEE4wePZoTTzyRBg0a4HA49tsVHMDj8XDPPfdYddq0aROPPPIIc+bMYeTIkYA5Ds6kSZPYunUrnTt3rtZ1woYNG+jcuTNPPPGEtc+pp57KV199xXfffRfT9Sw9PZ158+ZZ6z/99BMfffSRtX4ooY7X62Xq1Kk4HA6uuOIKq/zFF19k586dFf7YeDB0Xefuu++muLi4QhAW7cUXX2T16tVMmzbNuo44GMFg0Fr2er2sX7+euXPnkpiYGNO6LxgMMnXqVPr27WuVffvtt3Tr1s0KbRMSEqxhHzp06ECHDh0oKCjgySef5P/+7/9iWvOkp6dz+eWXW+/5Rx55hDZt2rB06VLr5+K0007j7LPPZv78+TzxxBNVPgdd11m0aBEnnHACYF4TjRkzhrVr13LmmWdW2P+JJ55g8ODBzJ49GzCvw+12e4U/MEL1rm+7desGmNfX7dq1O+DwCGD2mqhsvNn4+HhKSkr2e2zEiy++iMPhsFqnVeaGG27gyy+/RFVV5syZU+Pjaon6RVpKiXohEAjw3nvvcdZZZ+H1eiksLCQ+Pp4+ffrw1ltvHfSdPfan/C/rpk2bxnTzKe+bb74hLS2Nrl27EgwGCQaDhEIhBg8ezC+//BLTBLhz584xx55zzjk4nU7++c9/AuZfVNasWWP91fLbb7/FMAyGDBlinTsYDDJkyBB8Ph9r1qyxzhUdmAFWM+vyLVSqsnr1alq0aGEFUhHnnXcePp+PdevWWWXdunWL+QtKZPyeyGP169ePBQsWMGnSJJYtW0Z2djZTp061/jpSXuPGjenSpQtff/01YL6mvXv35uSTT2b16tWA2UoqKSmJPn36VOv5RERfpLjdbho1akRhYWGV+3/77bdkZGTgcrms1zshIYG+ffta9fvhhx9o1apVTBexli1bVnjtqmPatGnceuutFBYWsnbtWv7xj3/w6quvAlS4mD+QSCvCiKFDhxIIBPjxxx+tsvLvwdWrV9OrV68KX27OO+8864tGxLBhw2IuLCNjFHz//feA+YXmqquuIjc3lx9++IHly5dbf92Ofi52uz1mfAOn08kZZ5xhnacmbdmyhczMzAo/QyeffDIJCQl89dVXgPmefeutt7j22mt55ZVX2LFjBxMmTNjvXxOFEKIuRFp4Rrq9VCXyOW0YBnFxcZx11lnW9QaYfwTr378/TZo0wePxsG7dOgYOHIhhGNbvylatWnHCCSdYvysjoj9LGjVqRIcOHbj77ruZOnUq77//PrquM3369ArjWUV/Jkf+QBb5TK7O59Hq1aux2WwV/ggUCSki1wwH4//bu/MwOcpy7+PfWnqdfcskM9kI2UOAEEhkk+WoIKIooMflqCwCHgmoIGhAEAwQIQdEIBFBdhB4AeWg6AGjgqAsEglLgJCNbJNMZjJr713L+0d11XT39Exmksksyf25rrqq6qnq7qc7nZnu3zzPXdmfTaqrqwG8PzIC3h/k3H725XPCMcccw8MPP0wgEGDt2rX85S9/4Ze//CUtLS3dfrcX+tzZ189uhUQiES644ALeeecdlixZ4r2e69at49Zbb+WnP/0pJSUlu3Xf6XSayy67jOeee44rr7ySgw8+uOB5Dz/8MIsXL+bTn/40Z5111m491qxZs7xl7ty5fO1rXyOVSnmj47Plf7bZvHlzwT/AZlu5ciWpVMoLZV2HH3449fX1vP7668RiMd555x0+/elP53zuLS0t5YQTTtjl+62iosILpKDrPd/Z2dnt3I0bN9LQ0NDtvd3THyf78vm2pKSE0tLSfl3dz/35Ukhf6oIlk0mefPJJzjzzzB6LooMTSt1///18/vOfZ+HChTzxxBN97qPY98hIKTEivPDCC+zcuZMnn3yyW40jgJdeesn769yeCoVCOfuqqvb6A7qtrY2mpqYeh1U3NTV5P5Sz5/qD85ebT3ziEzz77LN861vf4o9//COhUMgbYdTW1gb0/AupsbGx134DfQ7s2tvbC17JxP2Alv2LbleP9fOf/5w777yTP/3pTzz33HOoqspRRx3FT3/60x7/snvcccfxv//7v4ATSn3yk59kzJgx/O///i+pVIqXXnqJY489Fl3v34+t3fn3/OMf/5jzwd3l/qWyvb294F8ta2trc/5N+mLTpk1cffXVvPLKK/h8PiZNmsT06dOB3j8YFJJfzDO7v67892B7ezvjxo3rdl+F/t3z3x/uKCL3nHfeeYdrr72Wd955h1AoxOTJk70RgNnPpaKiottfTauqqnoNC3eX+3/o2muv5dprr+12fMeOHYAznXD06NE888wzLFq0iEWLFjFnzhyuueYa799DCCGGA/f3qDtiqiebN2+mqKjIC1VOO+00nnnmGT744AOqq6t57bXXvGliHR0dWJbF3Xff7dWhyhYIBHL2s3+XuFd6c4sqP/300/h8Pj7xiU9w7bXX5nwxzb6d+3vA/f3Ql99H7e3tVFRUdJta5P5+KvRlf1cKjQrJ/+yQrS+fE9zpeI888gixWIwxY8Zw8MEHd3sdCz3Wrj6n9Gbbtm1ccMEFbNiwgZ///Ofe50nTNFm4cCEnn3wyRx99dM4oJMuyMAxjl5+vOjo6WLBgAf/617+46qqrckbOZd/XTTfdxH333cepp57KjTfeuNtXyc7+vO/z+aipqelx9HJRUVHOfiQS6fXfELo+G7nvr2zV1dV0dnbS2dmJbdu9ntOb/M9c7mtR6LO5W7st/zkWemzo+/smFAr16SqLruLi4oKlUaLRaJ/CzJdffplIJMJnP/vZXs9zQ7UjjzySrVu3cuedd/LFL36xz/0U+xYJpcSI8NRTTzFu3Diuv/76nHbbtlmwYAGPPfbYgIVS/VVSUsLEiRNzpgZl29Vfaj73uc9x/vnns3HjRp599llOOukk7xdNaWkp4AyDzf+FC3hf+AdCWVlZt9oP4IRqgDdNsC9KSkq47LLLuOyyy1i/fj1/+ctfWLZsGddeey133XVXwdscf/zxLFu2jFWrVrFq1SquvPJK6urqSCaTvPHGG7z22msFQ4WBVlJSwlFHHZUzzdLlfmCrqKjoVjgUugKQbPl1ALJH3VmWxfnnn4/P5+PJJ59kxowZ6LrO2rVrvYCuP1pbW3P23ZoFvU1BKysr8/6NsxX6d89/fu45VVVV3jB+t+D6pEmTUFWVF198keeeey7ndu6HvOwPqs3NzXvlCjTu/6HLL7+cefPmdTvuflny+/3893//N//93/9NQ0MDf/vb31i2bBmXXnppt7pvQggxlKqqqjj00EN57rnn+O53v1twalQkEuEf//iHV/IAnC9/NTU1/OlPf6KmpoZAIOCNWi0qKkJRFM4666yCfwjb1Rf82tparrnmGn7yk5/wwQcf8H//93/cfffdVFRUePV7dqUvv4/KyspobW3FNM2cYMr9A0N/Pqvsrr58Trjrrru4//77ufbaa/nUpz7lfZl3pwTuDatXr+bcc88lmUxy7733csQRR3jHtm3bxltvvcVbb73Vrbj8smXLWLZsGX/5y196/My6fft2zj77bLZs2cItt9xSsP5PKpXi0ksv5fnnn+ecc87h8ssv3+1ACrqmnu2O8vLyXQZG7u//5uZmJk2alHOsqamJcePGUVJSgqIoBUOapqambjW09oQ76yC/3tSeFonv6Ojo1/+LAw44gJdffhnLsnJ+tmzcuDFn1FdPXnjhBcaOHVvw3+/tt99my5YtnHLKKTnts2bNyhnVL/Y/Mn1PDHtNTU289NJLfOYzn2H+/Pk5y8c+9jFOPvlkXnzxxX6PUNld+R/+5s2bx7Zt26iqqmL27Nne8o9//INf//rX3f6al++YY46hurqaBx98kFWrVnlT96Drrwitra05993S0sIvfvGLgiFIX/ud74gjjmDr1q3dfik888wz+Hy+Hodo59u6dSvHHXecVw9h0qRJnHfeeRx11FE0NDT0eLvZs2dTWVnJsmXLCAQCHHTQQYwaNYpJkyZxxx13kEwmey3KuTv1CgpxryQ0Y8YM7/U+6KCDuP/++71C5kceeSRbtmzJKczd1tbW7bUrLi7u9r7897//7W23trayYcMGzjzzTGbPnu19mP373/8O9H2Um2v58uU5+8899xyhUChnGkK+I444gjfffLPbX9yfeeYZampqmDBhgtfm9sv17LPPoigK8+bNY/369bS1tfGNb3yDyZMne/8ehZ5LPB7PKXofjUZ54YUXmD9/fr+er6u3f/tJkyZRVVXFli1bcv4P1dbWcvPNN/Pee++RSCQ46aSTuPfeewEn7P3a177GZz7zmV7fs0IIMVQWLFjAhg0buOWWW7odM02Tn/zkJyQSiZyaP5qm8dnPfpa//e1v/N///R+f+MQnvJEcxcXFzJw5k/Xr1+f8rJwyZQq33357r1d+e/PNNznqqKN4++23URSFGTNm8P3vf5+pU6f262doX34fzZs3D8MwcmouuecA3hT/gfpMUEhfPiesWLGCyZMnc8YZZ3iBVGNjIx9++OGAlpxwbdu2jbPPPhtFUXj00UdzAilwyiS4sw2yF4AvfelLPPnkkz3W9IlEInzzm99kx44d3HfffT0WpF64cCF//vOfWbhwIT/84Q/3KJDaU/X19Wzbti2nLf/z+CGHHILf7+cPf/hDTvsbb7xBQ0MDhx12GOFwmIMOOog//elPOX9k7Ozs5IUXXuh3SYnejB49mvHjx3e7aM7zzz+/2/fZ3t5OPB7v1x+xjznmGKLRKC+99JLX5pZlOProo3d5+5UrV/ZYruPvf/87l112Wc6/jWmavPrqq92umiz2LzJSSgx7Tz/9NIZh9DiF7fOf/zxPPPEE/+///b9B6U9paSlvvvmmd4nW008/nYcffpizzz6bb3/724wZM4Z//vOf3H333fzXf/1Xn2o+fOYzn+Hhhx+mtrY254v5tGnT+NznPsdVV13F1q1bOeigg7wh2WPHjmXixIn96jfAn//8Zz7+8Y93+2vH6aefzm9+8xsuvPBCLr74YsaOHctf//pXnnrqKRYsWODdflfq6+sZPXo01113HZFIhPHjx/Puu+/y4osvcsEFF/R4O1VV+fjHP87TTz/NMccc4wU08+fP59FHH+Xwww/v9S9SpaWlvPfee7z++ut9DtAK+c53vsOXv/xlLrjgAr7yla8QCAR4/PHHWb58uVeE+7TTTvNeq0suuYSSkhLuvPPObgUgjz/+eJ599lkOOeQQJkyYwG9/+9uc0WhVVVXU19fzyCOPMHr0aEpLS3nppZe8K/P0t6bEn/70J6qqqjjuuON4/fXXeeSRR/j+97/fbfh4trPPPptnnnmGs846iwULFlBeXs7TTz/Nq6++yg033JDzwf6dd97hyiuv5NRTT+Wdd97htttu48wzz2TixIleYcw777wTXdfRdZ3nnnvO+9Cb/Vx8Ph9XXHEFl1xyCcXFxdx1110kEomCV4Xsi9LSUv7973/zr3/9K6fGAjj/v77//e9z9dVXo2kaJ5xwAh0dHSxbtozGxkZmzZpFMBhk1qxZ3HHHHfh8PqZNm8aGDRv43e9+x0knnbRbfRJCiL3p2GOP5Uc/+hE33XQT77//PmeccQajRo1iy5YtPProo7z//vtcf/313aYfn3baadx7772oqtptmp57UZVLL72Uz33uc95V9t56661efz7PnDmTYDDI5ZdfzkUXXUR1dTX//Oc/ef/993stdJyvL7+PPv7xjzN//nx+/OMf09jYyPTp03n99de5++67+cIXvuAVsy4tLaW5uZkXX3yRGTNmDGgR5b58Tjj44INZtmwZd911F4ceeigbN27kV7/6FalUqt+/21taWti0aVO32qHZrrvuOnbu3Mm1115LJBLJuXKxW+S7p5FHo0aNyjm2adMmWlpavFpXt912Gx999BEXXXQRuq7n3Lff72fmzJksX76cP/zhD5x44okceuihOedA1wVptm/fzvbt2739veXoo4/mN7/5Tc6obDccfOGFFygrK2P69Omcf/75LF26FJ/PxwknnMCWLVv4xS9+weTJk/nCF74AwKWXXsq5557L+eefz1e/+lXS6TR33XUXqVTKu2jAQHCvzPeDH/yAn/zkJ3zyk5/kgw8+8K62vTtBq1t79phjjgGcgHHt2rWMHz++x9HpRxxxBPPmzfNmPJSXl3P77bdTUlLiFVYH56IAqVSKmTNnem2mabJ+/fpudbpcX/7yl3nssce44IILWLBgAT6fj9/85jd8+OGH3HPPPf1+fmLfIaGUGPZ++9vfMmXKFKZOnVrw+Ny5cxk7dixPPPHEoPxV5mtf+xrvvvsu5513HosXL+azn/0sjzzyCDfffDNLliyhs7OT+vp6Lr30Us4555w+3edpp53GAw88kHOlM9fixYv51a9+xWOPPcb27dupqqrilFNO4Xvf+94uR2Flmz9/PkcddRQ333wzr7zySrdpdKFQiIceeoibb76ZX/ziF0QiESZNmpRzBZq+uuOOO7jlllv4xS9+QWtrK2PGjGHBggU5V5sp5LjjjuPpp5/OCebcUGpXBafPOeccbrjhBs4991zuu+++fvU32/Tp03nkkUf4+c9/zuWXX45t20ydOpWlS5d6V0rx+/3ce++93HjjjSxatAhd1/niF7/Y7QPWwoULMQyDG2+8EV3XOeWUU7j00kv58Y9/7J2zbNkyrr/+en70ox/h9/uZPHkyv/zlL7nhhht44403cq5ktyvf/e53ef3113n88ccZM2YMV199dc4HiEJqamp49NFHufnmm7nuuutIp9NMnz6dZcuWdbsyzIUXXsi7777Lt7/9bUpKSvjWt77FggULAOfD3rJly7jpppv47ne/S1FRETNmzODhhx/mvPPO44033vCmkVRWVnLppZdyyy230NTUxCGHHMLDDz/cbfh8X337299m2bJlnHfeeQVrfHzxi1+kqKiIX//61zz++OOEw2EOO+ww/ud//serX/LTn/6UW2+9lXvvvZempiaqqqo488wz+e53v7tbfRJCiL3t7LPPZs6cOTzwwAPceOONtLS0UFNTw9FHH83111/vBTTZpk+fztSpU2ltbeXII4/MOXbMMcdwzz33cMcdd3DxxRfj8/mYNWsW9913X69X7AoEAtx7773eVes6OjqYOHEiP/3pTzn99NP7/Hz68vvIvfrwbbfdxv33309LSwtjx47lkksuyZlOd/rpp/Piiy96f2jb1eeP/ujL54QLLriA1tZWHnzwQZYuXcqYMWM47bTTvP53dHT0+Y99L7zwAgsXLuTBBx8sOKI4lUrxwgsvABScKjlv3jweeuihPj+/ZcuW8bvf/Y7Vq1cDXSN1br/9dm6//facc+vr6/nrX//qnfPXv/6Vv/71r93u050a+MQTT3DHHXf0OlVwIHzqU59i6dKlvP32295o8SlTpnDqqafyyCOP8NJLL/GHP/zBC1EffvhhHn/8ccrLyzn55JP53ve+5/1B78gjj+S+++7jtttu45JLLsHv93P44Ydz4403divkv6c++9nPEovFuOeee3jqqaeYMmUKV155JVdeeWWvf2Dsyd///ncOPvhgrw7dqlWr+MY3vsHixYt7/b95xx138LOf/YybbroJy7I47LDDuPXWW3Pqw1177bVs3bo159+7ra0NwzB6fG9XV1fz6KOPsmTJEn7yk58Qi8U4+OCDeeCBB7r9UVHsXxR7dyvpCSGEyOEGSP358DcQtmzZwn/8x3/s8kOGEEIIIcRQ+trXvsatt95a8OI6A+nb3/42FRUVLF68eK8+zkD6wx/+wMyZM3P+SPfCCy9wwQUX8L//+7/9uvBKLBbj2GOP5cYbb/QK3gsxXElNKSGEEEIIIYQQe9Vrr71GPB7v8YpyA+n73/8+zz///IiqDfnMM89w3nnn8fvf/5433niDp556ip/85CfMmzev31cCfuyxx5gyZUq3Ue9CDEcyfU8IIYQQQgghxF41duxY7rnnnkEptzFt2jQuuOAC/ud//qfgBQGGoxtvvNErB9LS0kJ1dTUnn3wyF198cb/up6Wlhfvvv5+HHnpoSAvOC9FXMn1PCCGEEEIIIYQQQgw6mb4nhBBCCCGEEEIIIQbdbodSqVSKU089lddee81r27x5M2eddRaHHnoop5xyCi+//HLObf75z39y6qmncsghh/CNb3yDzZs3737PhRBCCCGEEEIIIcSItVuhVDKZ5JJLLmHNmjVem23bXHjhhVRXV/PUU09x2mmnsWDBAq+4XENDAxdeeCGnn346Tz75JJWVlXznO9+hr7MHbdsmEon0+XwhhBBCCJFLPk8JIYQQYjjpdyi1du1avvSlL7Fp06ac9ldffZXNmzfz05/+lAMPPJALLriAQw89lKeeegqAJ554goMOOohzzjmHKVOmsHjxYrZu3crrr7/ep8eNRqPMnTuXaDTa3y4LIYQQQgjk85QQQgghhpd+h1Kvv/468+fP5/HHH89pf+utt5g5cybhcNhrmzt3LitXrvSOH3744d6xUCjErFmzvONCCCGEEEIIIYQQYv+h9/cGX/3qVwu2NzU1MWrUqJy2qqoqtm/f3qfjQ862oPk1sFLgKwa9BHwl4CsHLQhyOU0hhBBCCCGEEEKIAdPvUKon8Xgcv9+f0+b3+0mlUn06PuQ+egRe+UbhY6offGXOEqiCQA0EqiFYA8ExEB6bWeohVAfqgL2sQgghRiDbtrGxvW0AGztnO//Yrs7d1fnZ+wPVtjvtfb3Nrm5X6Lh7jqqojCsbhy6/b4UQQgghRrQB+zQXCARoa2vLaUulUgSDQe94fgCVSqUoLS0dqC7smap5UHM0xLaCEQUzBkYcsJzRU8kmZ4ms7f1+FA2CozMh1QQoPgDKpkPpDCfAUjXnnJ4WVQdlty+KKIQQw54b2Ni2jWVb3nb22rKtbm17sgZ6POb2wbIsLKwe+5fTV9vuOjeznf3cCm1nP3+3P4XW2N2DnEL3s6v7y7/P3tpQ6Lktq11RlNygKGsz+5iCktPX/Nv1dlzJPHBPxy3bIqgHqQ5XUxIoQQghhBAji2nZaOrInom0LzyH4WLAQqna2lrWrs0NbJqbm70pe7W1tTQ3N3c7PmPGjIHqwp4pnQaffNnZtkyw02CmINmSCaR2QLIZ4tsz+zshtdNpSzZDqhVSbWAbEN/qLDtfy30MvRiKJkDJVCiZBmXTnBFXigq4wZTqBFOqH9SAs9YCoOiZwCqzqBqgFgi1tEF+4YQQI4UbrHghTGbbDYDyj/XWlr1vWiYWFqZlYtqm02abXrtt2RiW4YU42eEO0LXvpDGFAyU3BMo6BxsnOMlbe4FHgWP55yiZqdluEKIoCgqK1+4eyz6vr8ey2wrdb/7j9nYf+ed4xwpMLe/p/ELnjkQpM0VronWouyGEEEKI3aSpCt997E3W7ogMdVd2y+RRxfziy3OGuhv7jAELpQ455BDuuusuEomENzpqxYoVzJ071zu+YsUK7/x4PM57773HggULBqoLA0fVAM2pJeUvhZKJ3c8xU2AlwIhBujMTULVBfBsktkG6zQmropsgss5ZGxFoX+UsruAYqJzrjNSqnANa2BmZZcQBMxOQmXkPrjg1rtwQC9VZK2omsPKB4ssEW77MkhmB5YZXXqBVYJ1zfzJqS4jB4gY2pm16YZBpZW1ntRdaDNPAsAwM28CyLAzLwLRNp80yuodNWGCTE0zZ2F67N1IlfxRN3n524JK9VhU1JxjJ39cUrVsY09O60DlCCCGEEGJkWrsjwqqGjqHuhhgGBiyUmjdvHmPGjGHhwoV85zvf4W9/+xtvv/02ixcvBuCMM87gnnvu4a677uKEE05g6dKljB07lvnz5w9UFwaX5ncWXymERgNTwEw4AVVyZyaY6gTbdoItxQ+xjdCxGtrecZbIOue8hj84CyqUzYLa42H0JyFcV/ixbRuwnOLs7oKZ2TbASGa2M21YmdtA11ABd9cNoxTn8VG69t2AyhuhpWXCLi1r5FahIEuje7BVaNt9XCFGNneEUP7aDZW8UUOZYMkwDdJWGsMySJuZtZX2zrNtGxOza1RRZuSRO1Ws2xSqjExc44U/qqI623ltWmZEpXs8e9sNfFQJpIUQQgghhBB72YCFUpqmsWzZMq688kpOP/10JkyYwNKlS6mrc4KVsWPHcvvtt3PDDTewdOlS5syZw9KlS/etv3ZrQWcJ1kDJZGdKX7IZYlvAaIXgKCiZAmM/55xvRKD1bWh+BZpfhegGaH/HWT683QmoRn8S6k52pvm5FAVvut+esk0nsLJNnKERboiVFXYZydwgDLvrdk6H3DvLvW836PLCqOz97NDLlwmo3KDLlxt49RhqFdrPDsQk9BJ944Y+hmVgWqY3wih72z2WttIkzSRpI03KSpE2nTDJsixMnHX2qCbsrHo4WVPH3BCo4KKqBJRATlv+SCMhhBBCCCGEGOn2KJRavXp1zv6ECRN4+OGHezz/uOOO47jjjtuThxw5VD1zdb4ap45UfBtENzoBlV4E/kqnxlTNUc4CTr2qppdh+3JoWdE11e/D22HU8TD+TKg8fGCDFkXLZEp74QpGdnaIlb3uKfRyA7Ks0V2KkpV1uUVh3L5DTsjVp5FeWbW5vKLyuxrR1ZdjYjhxwyR3cUclZS8JI0HSSJI0k6TMVMHRTZZleSGSW6NIxRlp5IZFmuJs+zSfFyS5bZrUeBNCCCGEEEKIHsm1lAeDHoaSA50r8iUaoXONU2MqOAr0UNd5odFO8DT+TGeE1fa/QMP/OSOnGv/iLOHxzkir0Z9w7m8429uBjTd6K286o9tmm/Q80svdLtTvrCBMUXBSifzgyw2m3BBMy4z4yipE79X3yg6yehj9VWjaY07gJtypbmkrnTPlzbAMUkaKhJkgbsRJGkkveDItM6f4djZN1dAUDU3V0FUdTdHwqb6cdpnCJoQQQgghhBB7j4RSg0kLQNF4Zype5zqnxlS6zQmn8qfiBaphwn86S+ca2PQUNPwRYpvgwzucpXQa1P6HM8WvaNyQPKUh5U1j3IuP0etoLzfcMjLF6aMUDMd6fQ5ZUxvdICp/tJeiOlMaFS1rWqNbwF7rHmZ1m8JYqJaX+zhDH3hlh00pM5UTPMXSMeJGnHg6njvSyTawra4i3AqKFyy5IVNQC6L5ugInmfYmhBBCCCGEEMOLhFJDQQ9D+UHOyKjONRDdAqFapx5VISVTYNaPYNpFsO152P5nZ3pfx2pnWbMMymZD/SlOQOUvH9Sns0/b66O9CtXpyh79ZYKVBuK5x7zQC7IKFeWGYPlTD7uN8Mq6YmO3kV7ZNb0KFLMvOLWxewBm2hYpM5WzpK00CSNBNBUlno6TNJMYtuFdOc4t4O1enU1XdW8J+AJOyCSjmIQQQgghhBBixJNQaqgoilNvylfmBEuR9eArAX9Zz7fRi2DcF5wl1QaNLzj1p3a+3lUg/f2boeYYGPt5qDmy+wgsMbzkhF6+gb3vnBFbBaY5eoFXovvorz6N8lIwbEhZBknLJGVapCyDhGkQM5NEzRQJwyCNjWHbGJaNrWigqiiqD58WwKf50JUARZoPXfWh+wMoqjvSS8lbZ09ldAO54THaS4j9lTt6MX+UoxtAuyMh3YsCeOfmTcPNn5JbaN+brmummFw5mWPHHzvUT18IIYQQQuwhCaWGmuaH8llO4NT5ASSSEKjZ9RdtfzmM+7yzJJth23Ow9Vno/BB2vOAswVqo/5xTgyo0Zq8/FTHMDMAoL9u2SVsGSTNF0kxn1ikiqTiRVIS4mcQw06TMJKZlAlZmKp2Krij4VI2wouFTNXRdcd7WtgXEwYxDZpBY4f6DF0aR2fZCKHe0lzv6S8sb2aU5bZreNc2zUBF8bwpo1jHUvONCDD9uOJMyUzkF+5NmkpSR8vbdtrSZ7jrHSHrTZZNGMmcUY86oxkyY5G1nhUnuttVTbb697I2GN7jlU7cMyWMLIYQQQoiBI6HUcKCoUDLJCabaV0FsK4Tr+h4oBKph4tecpXMtbHkGGp51iqqvuxvW/doZPTX+S1A9f+9ORxMjUto0SJhJEpnQKZ5OEjFidKZipKw0KTNNynSm1imArur4VB2/5qNYD6KrFegDfaU5b3RXVj0vsqcw2l0jvuxU10gw8m7T24AvL6Byi9jnhV05gVd28OUWs8+7nbfOL46v5j5WoVFgYkSzbdu7qmPciJMwEt2WpJEkYWZtGwmSZjLneNJM5qwTZuF20zaH+ikX5Nf8+FQfPs2HT/Xh1/xoqpbTrqs6ftXvbTs/T7puo6u6d6zQPkBdaR0+bYBHlwohhBBCiEEnodRwEqp1rsbX+hbEGiBc3/+RGiWTYcYlMG0BNP4NNj8NLf+CppecJTwOxp0BY09zpguK/YZt26SsNHEjScJwAqhIOkZ7MuKFUWnTwMJGRUHXdPyqjl/1EQ4E8au+wS0Wrqh7t4g9dJ/WSF7dLsvInLeLwEuhcPjljfbKBFVe+KXkhl/QNbore7SXV/NLzQu58gIupVAwlncbCcAAZ4RRPB3PKaIfM2JeWywdI2EkvOP524l0V+jk3t4NlYYqKHLDn4AewKf6COgBAloAv+Z32rUAft3vBUFBPegdy7+t1675vPN7bcuESX7NP2gXFEiZKVoTrXv9cfZUY2Mj119/Pa+++iqBQIBTTjmFSy65hEAgwObNm7nqqqtYuXIldXV1XHHFFRxzzDHebf/5z39yww03sHnzZg455BCuv/56xo3ruqDJ/fffzz333EMkEuHTn/40V111FaFQqFA3hBBCCCGGNQmlhhtfKVQckgmmtu5eMAWg+mHMSc4S+Qg2Pwlbfw+xzbD6Vlj7K6g/Fcb/JxRPHOAnIYZa2jSImwniRpK4kaQjGaU9HSFhJElmarUAaKpGQPPjV3XKAyWDHzwNNS/42ku117zAC7qCL+g24gs7E4DZWQFY5ry+jPhydZvy6E5bLBCAKbpzrurLG/2VVxhfyd92R31p3c/ZC1MebdsmbsSJpqJE01EiqQixdMzbj6ajXpgUTTnb0XS0K2QyYsRSMS94SprJAe1fIZqiEdSD3ZaAHnDWWiC3XQt4QZJ7nrftHsu0uft+zU9QD+JTfWgDPUpR7DHbtrn44ospLS3lkUceob29nSuuuAJVVbn88su58MILmTp1Kk899RTLly9nwYIF/PGPf6Suro6GhgYuvPBCLrroIo499liWLl3Kd77zHZ555hkUReG5557jjjvuYMmSJVRVVbFw4UKWLFnC1VdfPdRPWwghhBCi3ySUGo7cYKrlzT0LplzFE2HGD2DKd6DhT7Dpcaew+qYnnKX6KKfuVM0xPV8BUAxLlm15wVPcSBBNx2lLRogacRKGU/MFnOl2fs1HQPNT6i9CV+W//qDwalbBXgu+svVlyqMXgCWyAq/sqzn2QqErjCJ/BJY7LdGtz6VhoRI1UnSmk0TMJJ3pOJF0zFmnYkQNZz+SihHJhEnOfpRoOkY0EzbtjbpFmqJR5C8ipIcI+ULeOqyHc/d9YYJ60Nt3t91AyT3X29dDMq1MsH79elauXMk//vEPqqurAbj44ou58cYb+fjHP87mzZt57LHHCIfDHHjggbzyyis89dRTXHTRRTzxxBMcdNBBnHPOOQAsXryYo48+mtdff5358+fz4IMP8s1vfpMTTjgBgGuvvZZzzz2Xyy67TEZLCSGEEGLEkW+mw5WvFCrnOMFUfCuE9jCYAtDDMP4MGHe6M6Xvo0eh6WVo/qezaEVQewLUnQyVh4MEF8OKaZnEjISzpBN0pKK0pTqJp5MkrRS2baMoKkHNT0DzURksxS9fjvcve2HKo2EZtKeidKaidCQjdKQidKYidLht6SidqRid3toJnTpTcaJGArtPQ7x2TUWhyBekSA9l1kHCvhBFvhBhPbP2hZ02PUzIF6bIX0Q4czzkC1Okhwn7iwj7wk5w1Odpj3tnFJjYd9XU1PDrX//aC6RckUiEt956i5kzZxIOh732uXPnsnLlSgDeeustDj/8cO9YKBRi1qxZrFy5ksMPP5x33nmHBQsWeMcPPfRQ0uk0H3zwAXPmzNm7T0wIIYQQYoBJ6jCc+Uqh4lBo+TckdzhX0xsIigJV85wluhm2PO1cvS+xHRr+4Cz+Shj9CWf6X/nB8mVskFm2RSydIGrEM6OfOmlLRkiYSVKmM/rJp+oE9QDF/hBVWhnqfl4rSPTOtCw601HakhHaU520JyO0pyKZdTSz3UlHKkp7KkpHJniKGYk9fmy/6qPEH6bIF6LEF6bYF6bYF8qswxT7M9tZgVOxu9YDhH0BQprPydtyRoJl7e9yymMCrAQkWiBBbh2wgld6zC5anzcKzAuueip+n1c7LP++stc5NcbUvHMKXXlSjASlpaUce+yx3r5lWTz88MN87GMfo6mpiVGjRuWcX1VVxfbt2wF6Pd7R0UEymcw5rus65eXl3u2FEEIIIUYSCaWGO38ZlM9ygqlUG/jLB/b+i8bBtItg6oXQ9rYTTm37M6RaYNP/c5bgGBjzKSegKpkiX4z2AmfqXcILoFoSHSTMJEkzBeAUJ9b8+2fdJ1FQ3EjQkuigNdmZCS07aU120JaMeOu2ZCdtmQCqIxXdo1FLxb4wZf4iSvxFlPqLKPGFnXVmO7u9xB+m2FeUWYcIaP4BfOZ7wa6mPbrnAFhpeqz9Zffz9c25+iN0hVT0HGJ5tbwyazUrJEMD1Q3LwAvXoHDgVTBAy649lj9aLLsvoj+WLFnCe++9x5NPPsn999+P35/7f8Lv95NKOT/v4/F4j8cTiYS339PthRBCCCFGEgmlRoLgKCibCa0rnaLEetHAP4aiOqOyKg6F6ZfCztdh2/9B4wuQ2AYbHnCWoomZAuqfgqIJA9+P/YBlW0TTcSLpOB2pCDvj7USMOHEjgWXb+FSdkB6g1F9MQJMAan9hWCZtyU52JtppTXY460QHLcmO7utkpxdY9leRL0S5v5gyfzFlgdx1qb+YMn8RZYESSv1FlPmLKfGHKfEVoan78Ei8wbjSY76cIvh5o7+8/ay1G5Z5oZh7Xj+L4udfKbJgMJYdQIETTGWO9RaOeaPItKxRY7sIxrzHyA7H3MdUuvqVfVvb6H8AOISWLFnCAw88wM9//nOmTp1KIBCgra0t55xUKkUw6NR0DAQC3QKmVCpFaWkpgUDA288/LvWkhBBCCDESSSg1UoTHgRmHtlUQ0kEL7L3HUnWoOcpZzIRTd2rbc9D0D4h+5Fy5b+2voGQqjP4kjPkkhMfuvf6McKZl0pl2Cjm7oUI0HSdppFEVhaAeIKQHKPeX7Ntf/PdDtm3TnoqwM9FOc7yNnYl2dibcde7Snoz0eyRTQPNRHiilIlBCRaCE8kAJ5f4SyoOZdaCE8kBxZl1CmRS5Hz5yiuAPkezQC+gaGebu5wVn3cIx9z6srtv0VjQ/PxDLOZY/rdG9gdq17YZVVhqMFJjzgJL+PutBtWjRIh599FGWLFnCSSedBEBtbS1r167NOa+5udmbkldbW0tzc3O34zNmzKC8vJxAIEBzczMHHnggAIZh0NbWRk1NzSA8IyGEEEPNtGw0Vf5oLfYd8u1kpFAUKJ7sBFOd65wQaDC+XGpBp7bU6E9AOgI7XoBtz8PO16DzQ2dZsxRKZ8Do/4Da/3CmBO7HTMv0RkG1JDrZmWgjbiRJWWk0RaXIF6LMX0IwPMynNIkeuWFTc7yNpngrTfE2mhKtNMfbnCXhtO9MtJO2jD7fr6ooVARKqQiUUhkspTJQSmWwjMpgV5sTQDn7IT0gI+nE7vOCILdhEK4QWUjOqDHInUbpyhoFZqXAiHYFY8PUHXfcwWOPPcYtt9zCySef7LUfcsgh3HXXXSQSCW901IoVK5g7d653fMWKFd758Xic9957jwULFqCqKrNnz2bFihXMnz8fgJUrV6LrOtOnTx/EZyeEEGKoaKrCdx97k7U7IkPdld1y/LQaLjtJfmeJLhJKjSSq5oQ/VtopUB6uH9wr5PmKof5UZ0m1OVP7tj8PO9+Ajved5cM7MiOoToTaE6HogH2+/og7Ha8jFaU12UFTrI2YkSBlpdFVjbAepCpYhk+T/24jgWGZ7Ey00xjbyY54KztiLeyIt7Aj3kpTvJUdsVaaE60kzb5/IS7zF1MVLKMqWEZ1qNzbrnTbguVUBkspk9FyYn/U31FjNkDbXuvOQFi3bh3Lli3j/PPPZ+7cuTQ1NXnH5s2bx5gxY1i4cCHf+c53+Nvf/sbbb7/N4sWLATjjjDO45557uOuuuzjhhBNYunQpY8eO9UKor371q1x99dVMnTqVUaNGcc011/ClL31Jpu8JIcR+ZO2OCKsaOoa6G7vlwJq9UIpGjGjyLXmk0QLO1fBgaIIpl78cxn3eWZItzgiq7X+BljeyRlDdCeHxUHuCs5TNzLqa1MgWSyfoSDmFpHfEW4mkYyTNrpFQlcFS/JpvqLsp8ti2TWuyg+2xnc4S3UljrIXGuLPeEWuhOdGG6dX26V2Zv5iaUAWjQhVUh8qpDpVTE8xsB8u9AEreC0LsX/7yl79gmia//OUv+eUvf5lzbPXq1Sxbtowrr7yS008/nQkTJrB06VLq6uoAGDt2LLfffjs33HADS5cuZc6cOSxdutQbFfmZz3yGrVu3cvXVV5NKpfjUpz7FZZddNujPUQghhBBiICi2PTKqhUYiEebOncuKFSsoLi4e6u4MPTMJbe86NZ4Gaypfb+xMvZFUG+x40Qmpdr4OdtZokkAV1HwcRn0cqo5wpgaOEIZl0JGK0pGK0hhroS3ZSdxIoKAQ9gUJ6yGCukzHG2qGZdAYa2FbbCfbok1sizbnBFDbYztJ9WHKj6aoVIcqqA1VMipcwahQJaNCFYwKV2YCqApqQuXD/6pyQuyDUukYrdFtHHvQeZQU1w11d0Yc+TwlhBAj32due2nEjpT63CFjuO0rh43o5zCrrpRnLz52qLuxz5CRUiOVFoDyg5zt2EYI7cURU8kUxBPO2t2OxsG0wDDAssDKzjbHAV+Hoi+CuQqstyD1NiR3wpbfOYsagLLDYNSxMPrjEBq9d/q+B2LpBO2pCC2JdnbEWokYMQzLJKj5KfKFqAqWST2fQWZaFk3xFrZGm2jwlma2ZdY74i1Yu8jZFRSqQ+XUhioZXVRFbaiK2nAltWF3XUlloEym0QkhhBBCCCHEXiah1EjmBVM2RDdB0djMJbn3UCwBkShEYtDaDp1RSCTBNJ3jqgq6Dlrmkt3ul/f8gMbyg3kI2AeDkgJrNdjvgvIeWG3Q+oqzrL4JfOOhZC5UzoOqwyBUDAH/oNajsmyLzlSMtmQnjfGdtCac0VCqolHkCzIqVCFXLhsE0XScLZEdbI3ucNaRHWyNNrE1soNtsWYMy+z19n7Vx+hwFaOLqhgTrmZMUTWjw9WZ/SpGhSqlvpcQQgghhBBCDAPyzWykc4Mp24BYg1Njqr91mwwDOiJO+NTU4mwnks50PL8fQkGoKQJtTwOvUcCxzv2mN0H835BcCdYGZ79lE7T8Dtb6QZ0CvoOgbC5UToVgEEIBJ6gawLDKtEzaUxFaEx1sizXTnoySstIEND/FMhpqr2lLdrIl0sjmzkY2RxrZHNnB1oiz3Zrs7PW2uqoxJlxNXVFNZqlmTGZdV1RDZbAUdR+pXSaEEEIIIYQQ+zIJpfYFWhDKZ4OVHUz1MUhp64C1G6FppzMFz++HohBUlO29UUqKAv4JzsIXwOqE5LuQfBtS74DVDtYqSK6CHY9DYwnYk0GdBv7pEBgDpSVQEoZAoCusCgb61GfDMmhLOkFUQ6yZjlQE0zIJ6UEqgiVSJ2iARNJxNnduZ2PndjZ1bmNzpJFNndvZHGmkIxXt9bblgRLGFo2ivngU9cU11BeNYmzxKOqLaqgJVcrUOiGEEEIIIYTYB0gota/Qi6DiYGh5E+LbITym9/NNE7Zsh3WbIJGCUVXOlLyhoJZA6EhnsW0wNkHyHUi9B6kPQOkE5U3gTUgBqQroPBCsA8A+ENRqJ5zy+6C4CIrDmaCqK6wyNGhNdrIz3sb22E46UlFs2ybsC1ITrJDpXLvJsEwaok1s7NzGxo5tzrpzGxs7t7Mz0d7rbUeFKhhbXMu44lrGFo9iXEktY4uc7WJ/eJCegRBCCCGEEEKIoSLfxPclvlJnxFTLm5DYAcFRhc+LxJzRUVu3OyFOXQ/nDQVFAd8EZ+FUsFOQWgupVZB6H9LrgVbgDVDfcG6jljkjqZKTIDYBGkaBrWDaFm26QbOSZJsap8NvYwf8FIWKqQ2XoQcD4PN11cQSPYqlE2zs3MaGjgY2dDTwUWcDGzu2sSmyvdcaT1XBMsaXjGZccW1mPZrxJU4QFdQDg/gMhBBCCCGEEEIMNxJK7WsClU6NqdYVkGoDf3nu8eZW+GAdtHdCTZUzumg4U/wQmOksAFYC0mucgCq12gmprHawVgArALBVPwltHE3Ust2sYGe6Cp9VQm1ER7fTYEdBawKf7owOCwUgHHKm//l05zXx+53t/UwkFWN9x1bWd2xlQ3sDGzLb22M7e7xNQPMzoWQ0E0rGZJbRjM+sZcSTEEIIIYQQQoie7H/fuvcHoVqwZkHLSlB9ztQ+24atjfDBerAsqKsd1CvbDRg1CIHZzgLOSKr0elKJVZip1fiMDegkCJnrGM86xgPokFTKiKpjial1xLR6oozGMnRIG9AegZ1tzmsEXWGVzwfhoFPo3ac7UwH1TGg1wgOraDrOho6trG3fwvp2J3ha376FHfHWHm9TESjhgNI6JpbWM7FkTGa7jtpwpRQWF0IIIYQQQgjRbyP7m7XoWXg8mAloWwU+YNMOWLvJGRFUVjLUvRsQKctgpxFjezLIDnMKMSYQ1HVqtSiV9jaKrc0UmVsI2k0E7HYCZjuV5ipIO7dPKFXE9DHE/GOIaWOIqWMwlTAYphNWpVIQjYFpgWIDCuiaE0zpeldg5fd1BVW+zHqYBH5p02Bj5zbWtm9mXfsWb90Qbe7xNqNCFUwqG8sBpXWZpZ4DSusoD+wb7xshhBBCCCGEEMODhFL7KkWB4skQbYOVL8EOE6oqnVBqBLNsizYjxo50B1uTrXSacTRUyvQw1ZnQxKaMndSxk7kAqHaSsLWVInMrRVYDYWsrAbuNoL2ToLmTSvNdL6hKKaXE1Vpi2mjivtHEi2pJKFWgaIANhgFpE9Jp2JlwCsbbtvN66xpoWu4IKzes8vv2amBl2zY7E+2sadvEmvbNrGnbzNq2TWzobOix5lNVsIwDy8ZyYOlYDiwby6SyeiaV1suUOyGEEEIIIYQQg0JCqX1ZZwTWJZxAqsx2aieNUHEzRbPRyZbkTnamoxi2SYkWot5fscupY5YSIKJNIqJN8to0O0qR2UDI2kY4swTtFvx2B36zgzJzTdft0Uio1cSVWhLqKOK+GhKBUSSVCvAe23ZGWBmmE1ztbOsKrCAzuioTWAUDEPI7VwfU9dwRVrq2y9fCsAw+6tjGh22b+LBtIx+2bWJN2yZak50Fzy/SgxxYNo7J5eM4sGwsk8ucEEpGPgkhhBBCCCGEGEoSSu2rtm+HVasgEoEpH4PoWkg2QWDUsJlatiumbdGSjrA93c72VBsRI0lQ9VGpFxFQ96xAu6kU0aFPoYMpXptqJwhZjYStRkLW9syyA40UYauRMI2QNejIQiehVpNQqp21WkPCV00yUIWlFGU9mg2G5YRVhgFtSWg2wM4cU5SuKYG+TOH1zCirKAZrEttZHW1gdcdmPsxMv0tbRrfnpCoK44pHM6V8HFPKxzOlzFmPDlehjJB/cyGEEEIIIYQQ+w8JpfY1lgUffQTvv+9MJRs71mlXJ0P7+5BqgUDVkHZxV6JmgqZ0J5uTLbSkI6BAmRpiXKByr4YrlhIkqk0gqk3oarQt/HY7IauRkNVI0GoiZDcRtJpQMQhb2wmzPSesAmcaYEKpIqlWkVCrSCqVJP1VJAMV2EpeoGZZYJq0JTtY3bqND7ZtY3WykQ/SO9hstGEX6GuRFmBq8Vimlo1zQqjKiRxYOZ6gLzjgr4sQQgghhBBCCLE3SCi1LzEMWL0a1q6FkhIoK+s65iuGkinQ8T6k28BXPlS9LMiyLVqMKNtSbTSkWomaSYrUAKP9ZejKrqe07TWKSkqpIKVW0M70rnbbwm+3ZYVUzQStJoLWTnRizjRAuwOsDTl3Z6OQVkrYapbyeirAvxM2byWTvBNvpyEdKdiFUXoJU4O1TPOPYppew1Stkjq1FBUFLKBDg2gStq3PTA0M5tawcq8YqGsjZpScEEIIIYQQQoh9n4RS+4p4HN57DzZuhFGjIFSgoHmgHEomQ8dqSHeCb+hrCsXNFE3pDrakWmhOd2ID5VqYKn/x8J5ypqiklEpSaiUdTMs5pNkxgtZOAtZOgnYz0fQO3ovv4O14O/9OmLyR7GCz0VHwbg/0wSGBALNDxcwIVjEtNJpS/yhSShkppZy0Upwpup5hO6OsnOmBJrRHnHpWduZqgQq5Vwz0pgb6M7WsshZtCMM/IYQQQggh9jLTstHUYfwdQ4j9kIRS+4KODnj3XaeOVF2dM0KmJ8EasA3oWOMU6daLej53L7Fsi1YjyvZUOw2pVjqNBGHNzyi9FJ86st+SUTPJ+7FG3otv5b3YVt6PNbA11Vrw3AP9YQ4NBpgbgMMDKY7wRynXAJKZZSfYHzqbGTYqaaWElFLqBFVqGWmllJReSspXSlopJa2UdQVXbmiVzhRgb01CU0tXPStVzVwxMC+0ckdZ+bMKsau9F5QXQgghhBCF7QthyL7wHDRV4buPvcnaHYVnKAx3x0+r4bKTpu/6RCFGkJGdAIjcguZjx/ZttEtwNFgmdK51pnNp4b3fTyBtGTSlO9mU3ElzuhPTtijTQowPjMxC3CnLYE2ikVXRLbwX28qq2FY+SjZjF6gCNc5fycxwPTPCdcwI1zEtNIZiLbf+03rbwG+347fb8Ftt+O02AnYbfqs9096OguVtw+ZutazAnSJYTFopyYRUJaS1YtJ6CalgCWmlBEMpJq0Uga1kCrCbkExBLJ65aqBzT2ha10irYADCQQj4u0Irn88JrmSUlRBCCCFEj0Z6GDJ5VDG/+PKcoe7GgFi7I8KqhsKzFoa7A2sGf0CBEHubhFIjlWHAunWwZo0TGNTX971ekKJAuN4ZRRNZD34VtL1XIDtqJmhMdbApuZNWI4pf0QfkCnqDybItNiVbWBXbwqrYVlbFtvBhfDtpu3sqVOsrY2a4nlnhOmaE65kRqqNULzCdMo+t6CSVKpJUQaGMx7bw2RH8djs+u92pW2W148vUr3LWnZngqhO/3Qk09Px4KBiESavFTkgVKCatFGMoRaTJrO0QhhnEMMDuSENLW97UwMxIqkAAwgEnuHKnBvp9Mi1QCCGEECJjJIchNcWBfWKklBBi+JFQaiTq7IQPPoDNm6GqCoqL+38figJFYwELIh+BXwEtMGBdtG2bdjNGQ7KNLakWImaCEjVIvb8CTenDNDDbRk2mUeNJtHgKNZFZUmnUlIGaSqOkTRQjs5gWimmi2DZYNtg2SmbAkq1knq+iYGsqtqpiaypoKpauYWctll/H9uk0+wxWKjt5mybeMXfwbmo7nVaiWzfLtDCzwnXMCo9lZriemeF6qny78e/RF4rqjHyiFBjXw+tmodtRfHZnJqjq7Lb47U50O4qCjY8oPisKNDpF03t8bDB8QQx/EYYSzgRXYQw7iGEFMWIBjE6/s20FMZUQpl7kBFPBIBQFneDKHVnl8znHRuAIOSGEEEKI/U1pSB/xo71k6psYKPtKSDtcnoOEUiNNczO8/bZTR6q+3hmpsrsUFYrGOyOmoptAD4GvdI+6Z9oWO9OdbE220pBuI20ZlGthxvsq0SNx/C078LV04mvtRG+P4muLordH0Tui6JE4WiSOHkmgRRMoVm8pycBJavDmGHitHl4bC6+OhQ0V3c8LpuGw7XBEo8bcnX7mtocYnw5ihS3MUCNWqA0ztBazKIAZCmAWBTHDQcxwZrsohFEUxCwKYvv1vRPIKCqGUoJBCXHqej7PttCJ4bMi+DIhlRNaRdHtiLMmis+OoNsxFCx0Euh2Auyd3e9Po9voLhsFww5hxIKYkaATYBHAJIyhhjG0IsxACUawHNNfghksxQiUYYbKQB85o+iEEEIIsfcMly9NwjGSR3vJ1DcxUPaFkHY4TcmVUGokaW6GlSshmXTqRw1EqKGoUDwR9LAzYiqxAwJVuVd464OkmWLnjq3s3LiO9NZtFO3o4PCdMULNnQR2tOHb2YFqFCiAtAu2omAF/ZhBP1bQh+X3YQV82H4dy6c7I590HVt3RkChKtiKAqqCM7/Mduoj2TaKbWNbFpv8SVaURllREuON8hjvlCZJFXi603cqzN+qMH+Txce2wEE7wGeBU8gpnll2j6VrmMUhzKIgRnEQsziEURzy1kZJuKutJKutKAjaABQcV1QMijG0YuKM7v1c20IjkQmuouh2FN2OZa1j6GRt23E0UpmRWDF8SqyH6YhAIrPkMfFjqGFMNYyphTH0Iky9CNNXjKk7bblLKGsdwlb9e/4aCSF2ybItLNsGbCzbxsbGzqwt29m2sLFsy9u27cw+YNuW12Znfl7b2Cgo2Di/5mx3yjDgnGBQoukjshahEKL/RvoXP5AROkLsq0ZySDucSCg1UmQHUqN3ESL0l6JCaHRuMOWvLDydL5mCTQ2weRtsaiC9aQvWpq1o25qpS6R7G5sDQLqsiHRFCenKYtJlxRhlRc5SWoRR0hXMmMVBzFAAK7BnV32Lmknei23lndhm3olu4d3YFlqNaLfzKvQiDgrXc1B4HAcVjWVWuL6rELltYxsmqxIp1EQazZ1KmHCmFmrxJGpmrcUSaLGks0QTXfvudjSBYtmohonaFsHX1r8PWLaiOEFWSSakyqydxW0LY5Rm2kqddtu/ByOPFBWTMKYSJklN325ip9HtODoxtExQpdtxtEybs51At2NodB3TSAOgkUKzUmC1gUHOFQj7wlJ0TC2EpYa8oMpUg05bZtvSgl675R0PYuWtbcUv0wzFsJcdBLkBUc42thMAZQVHVn6AlBMY2U75ONxAyL2Ag7OtKAq27fzXUBUVFefntKooKCgoirO4+yoqqqqgqxq6qqEqKrqqoaGhqSqaoqIpGqqqouLeVkWBzDpzX4qCahto6U6KfPIXbyH2FyP9i5+M0BFCiJ5JKDUSNDXBW2/tnUAqm68USqdDbDNENkNjK2xqg/Vb4aMtztLQ6NRscm+SdXNbVUhVl5EcXUmytoLUqHJSNeWkaspI1ZSTrijG9u29t5xlW2xINPNObDOrYlt4J7qFdYkd3a6Gpysa00NjOCg8loOKxnJQeCz1/oqe/+quKNg+HdOnY5aQiU12k22jxpPeFEUtEkePxtE6M+tIpi0SR++Mo3XGurbjSRTbdvYjcdjW0ueHNYN+L7QySgsEV+52SVebHdj9IMtWfKQVX6b+Vd8ptoFGAs1OZEKruLNvxdDMOJoZQ7MyIZaSQiPprJUUmpJEIwWAahuoRifQudvPwXsuKFlBVSATYgWw1EAP+13blhrwjnW1+zNLAFuRulr7MtOysgKgzEihrIDIhm4jiHYdEIEXDKF4P9+UrDAoOyBSldxtBQVNUVFUFU1R0BUdVXFCITcscrfd/exwKH/bC52ytt0wyg2rvMceqPe6lXJGX8r/HSGEEEKIEU9CqeFuxw6nhtTeDKTSaedKfh98AKtXO8uaNRAvPD0tXRyio76CzvpKzHG1WONGkxxTRWpU+V4NnfK1GlHejW7hndhm3o06V8WLWt2H1IzxlXvh08FF45gaGj10V/5TFKxwkFR4N652aJiZgCrmhFidMXRvnWnriDn1ubKOK5aFlkihJVIEmtr7/HBmwJcVXoW6RrNlhVdmaQijJNNeGsYK7tmoIlvRnWmFSj+KxZsmpA0wTDDSqEbMGWlFAk1No2kmqpJ2ttU0mmagutukUEmh2Sk0O4lqJ521lUDLvJcUbDQrjmbt/nTNHp8vSlZI5cdSnNDKzm5TfU6Ypfi8UMtWfZnz/V37ii9zrh9b8Xm3tRX3PnyZEGwApn/uI9yQyMbCtLNCo8yUsvzAyDnHPWZnppVljyTqmmKmZIU13iifvMBmVwGRpjqhjrOt5Ywg0rJCISf8yR5dpHQLi7LXbj+EEEIIIYQYahJKDVe27Vxdb9UqZ38gAynbho0b4eWX4Z//hDffdIKpfH4/TBxHekIV7fUlbB1fSUNdOXZFCRW+Yvzq4L19UpbBh/HtvBtzpuC9E93M1lRrt/NCqp+Z4TpnFFR4LLOLxlHtKxm0fu5VuoZRXoxR3o/AxradEVluSNUR80Isb7+ja9s9TzVMtGQaLdmOv7nvQZala3lTCsNeYJU/1dAsDntTNtH7V8Msh6Y5i9sHyrDIjGizLTBtJ7iyTDAtSFuZfcv5v+DlCYpzP4ri9EdRUHUL1WehaRaqbqJqBppioJJGU1KodjoTaqVRSTr7dgrVSqJZSVQriWqlMuusfdsA3MAr6QVgg8FSdGxFzwms3H1L8XkBl63qTril6Jk2veuYomOrunee1+bdr551jp51rGvb3QetYJDZFQIVHmVkeaOK3JpGeGFSds0i3FpFWcGRW6dIyRrNoyigKVrOlDNNUVFVFV1xQiFd0bwRRD5V94Ki3OCnQGCUGb2UHwwN+AgiIYQQQgghRhgJpYYj0+wauVRUBOXle36fiQS88Qb84x9OELV1a+7x0lKYNg2mT4dp07CnTKF1TAXbzFa2RjYRiW6hxEpQpYfR/KXA3httYds2W1ItvJupAfVubAsfxreTtrsXSj8gUMNBRWOZnakFNSlYg97PIu37NEXJ1OgKkRpT2bfbuFMM80deZY3A0jrygq3OOGracGpltUbwtfavVpYZCnTVFCsJZ227dcYyQVZR0Am1MnXHdlkrS1Gdn3J9Cb3cAMsNr0wLKwlWHAwLsBSwdVB8YIcAOzfI0lTn8TQFVA18urMEdecqmZrq1EfTNFBtVMUZwaViopJGzYRdqp3OBFkpVDuJaqVR3H0rlQm9Ul3tOftuKJZGsdOolnNfCl1XslRtA2wDzSpQYX6ImIqGhY6taFje0rXfFWRp3deqDooOqg9F0UF1/o0U1V30THvWPj4UzTnfPU9VfVnbGqrq99pQdOfiD2pmrWiZx9S6jimajEITQgghhBCinySUGm5SKSeMWr8eKiqguB+jYrK5o6FefdUJoVascKYAunw+OOwwOPpoOOoomDABFIW0ZdCcamNLvJEdne+RttKU+0oYX3UYSroTko2QbAEt6BRGH4Bwamc6wnuxrayKOVPwVsW20mF2nypVroW9aXgHFY1lVqieEj20x48v8mRNMUzVVvTtNraNmkw7NbCyphS6o7C07LbOTA0ttzYWOEXi40kCO9r61VXL7+t+9cKioFcs3yhyr2gYxHTbi5x226/njtBxA6yClwrsqQOWU2PNdkIsL9hKpyCecP4fmmbWDK+uETuWomDlB1qqAmoA9CInSNM18Gmg6c4xn5Y5R80KuVRnGqDmPIylKliKgq24U9MAKw1mCsVOg5VEyQRaWCkUKw2ZIEu1DTQrjWqbqHYaLbNWbQMNE8020WwD1TYzxwznmG2iYmRuZ2QWs2ttpVFsE8U2UO3uozI120TD7CqZNGKpmfAqK7jyAqtMqIWaF27ln6sWOKb2cLy3bRXI9IVC52WtKdDm3j6/TdEAJe8895xMO2rW+Wrv+9m3FUIIIYQQ+x0JpYaT1lYnkNq2DWprIdjPukPt7c5oqFdfdZZt23KPjx7tBFBHHQXz5kE47B3qSEfYkWxlS6KRtnQnuqJT4SslqPm7bu8vA18xpFoh3gjJVtB8oBfT13Cqw4jzQbwhE0I568Z09+lhfkVnWmg0B4XHMqsvxcjF0FIUrKAfK+gnXVPe99uZFlo04YRVkXhOeOW0x53C7+7xSDxTJD7uXMUwlcbfkoaW/hc0t3QtK6TKhFbZSzjTHg5iFgWcdda2FfI7oZAKoOVW/c/iFa926xJ508tMLNPCskxsy8CyDKyUhW2bWFZmipplYlnO1DVsG0VRsbGcAte27Ty2oqGoCoqioqrOtqpoqJqGouuoup67VjV0PYiu6fg0H7rPh67p6D4fmqo7Vz/TdFRNy6xVFM25XW/T0fpcr8i2wTbAMsBOO4GZlc60ueuUc7xbe95x736y1+ncfXfbSoNt5rZ5a7P3bdvMvb8CozYz76pMMLlHl0PYf+UEXgpOYOWus4MsBUpnwnHHDG1/hRBCCCHEHpNQajgwTWdU05o1zmim+npnus+uRCLOVfneeANefx0+/DAzEiPD54M5c+BjH3NGRE2alPPX6JSVZmeqnYZEEzuSLSSsFCV6mLrgKLSepqEoGgSqwVcG6Q5INkOyzflyrodB6QqxOow4q+PbeD/ewPuxBj6INbA51f2KcQoKEwPVzuincD2zwmOZHByFbxBrVokhoqmYpWHM0jD9qqpkWWixZFdwFc0NrNwwS4smvKsa6pE4Wsy56qFi2c5Uw7YIvrb+TTV02QoYQT/pcIB02I8R9pMO+THCAdLhAEbI77SHAphhP2Y4gBEOoAQD2EUBCAUgFISAH83vw6+E0RUVDRVdVdHQMsWvnVpGqluDiK7C1qpto9qgWaBaNiqgutuWjWKBmrZRk86+amcV5FbMTLgSd/ZVnFE1qtI1aitnWmJm6qE7gkvXu9buqK2cRelauyPCso9pPlD9oKsjb5SMbQNWVnCVH2RltdlWXrBlFTiedXs32LINZyopVmaddXvMrtu5x+ys2+U8Rta5tpW5/0LnW1mPvavbZLfZmfeRnXs7bz/zWvXpdc2EfZmaa71qe7tv5wkhhBBCiGFNvvUPtY4OJ4zatAnKyqC6uudzEwlnBNQbb8C//w1r1zrTh7JNmgRHHAFHHglz50Iod3qbZVu0pyPsSLWwNdFERzqCruiU+4oZpfWx5hCA6oNAFfjLsVMdNMc+YnXbe6yObWN1cierE00FC5ED1PsrmBmuZ2a4jpnheqaH6ijSAn1/bLFf8K56RnaB66y2IFjBAHaVH8suzbTaWQWwnXPJFLWGzLXRbNASKXzRBL5oCn80SSCexhdN4o+l8MeS6NEk/mgSPZbEF02ix502PZZEiyVRDRPFBl88hS+egp178DwVBSUUcP6vhoPOEgpCOJRZZ+272+4SDjojKt2AKxyEooAT/PSXW/jdsjPTEvOmJppmZoSSe57Vte38gzmvsGI7iZ2bf6lkQi41K5jKBF5uAOYGXtnBl5YVfqlZgVa3+1Bzj0PWueQGYe75e0pRgNwC+6IXbjBlZwVX5Adc7nvJzNoucA42mEnnjyBDdRVVIYQQQggxYCSUGiqxmBNEbdwI8TiMGeOMbMqXSDjFyZcvd66WF8+rtVRf79SGmjfPCaMKhFq2bRMxY96oqNZ0JykrTale1PuoqAJSVpqPYg2siW1mTXQTH0Y3sTa6mZZ0R8Hz633lTA/XMT1cx4xwHTNCdZTp4YLnipHFDYjcK55ZBUIk72poBcIiu8cCQk6aoWSNDFIU1ck23JFCmemi7qghTVUzo4y0zCgjNTPCyL0SmjvdLHP7ssxII/cKa4qCRoHzMiOScqaj2Tak0hCNQTSetc5sxzLbBdcxiCWcelPROFgWim07bbHEHoVbOQJ+J6gKZsKq7HX2Egp0b3OXgL/7fijQ/yDGC7AyAZeVvZ/ZdgMvd9+287bBG+HlrbPa3ODLDbrIDr3yArDsAMsNv7xATO3azw60FLKCNbLCsaygS8l+rF2ct79xQzwFBuRjh5VyppELIYQQQogRT0KpwZZIOFe+27DBGSVVWZkbJNm2E1a98oqzvPFGboHyMWPgmGOcaXlz5kBNTcGHsW2bTiNKmxFhe6KZnal24laKsBqgwldCQPUXvJ3LsE22JnawPrqV9fGtrI1uZl1sCxvj2zEL1FNRUZgYrmNa0QSmFY1jarCW6YEKSq0UGJkgTQuA3vvjioGxy1FG3batPQqN3JpCCkpW2KPgU3z4VA09KyzSFCdA0hXNC4EUcMIlpSss6h4Q9SEwGgyK4gQ0AT9Ulu/+/dg2JFOZQCqetY47oVUs4YTQ8WRXkBXPnBNPZu0nIJF0zrUy/2bJlLPQ/1pbu6RrEAhAMPMa5GxnLwEI+LrW/gLn+N12n7Md8Dv7wUDmmK9vIY47wsu2nJzKzgq9vGOZxbAy9aUyYVe3ECxzf+7VFd1ATCFrBJg7KozuQVShQCw7LMsOxbTsdVYw5l6lsVCwRt5+/uPnh1+Fjvd2vhBCCCGEEINIQqnBYNvQ1gY7dsCWLU5B8rIyGD/eObZ2rVMb6q23YOVKaGjIvX1dHfzHf8AnPgEzZ/b45SFtGXQYETqMKNsSzbSlO0laaQKqn1I9XHB6XsxMsDneyEfxBj6KbcusG9gY306qwFWyAIq1MFOKxjGlaDxTi8YzpWg8B4brCRaagmebYEQh3enUoEpHnbooqu7Uk9ECDMQV/EYKd+SQs73rsKinUUcuBSWz744gcVvxQiIlK9TJD41UNHTNHVmkois6vt5GGBUIjbrOU7JGHA1RYDSSKErXKKTKsj2/P9uGdDorpMqs3f3stuwlnuzelkxltjPrZLIr8DJMMGLOqK/B4PN1hVZuiOX3Oe3+Akt2u0/PbfPpmW29e1vOWu++X2jan23nLlYmzMoOwdyQzA3ALDKhGF1BWP59uffjjRAjLyBTsv+7Fw7JvCVzUk6wBd2DLvKmR+bVFStUK0xRu0aVFQrg8vtRqG/5bfThPNtwRtcJIYQQQogRb1BDqWQyybXXXsvzzz9PMBjknHPO4ZxzzhnMLgwe24Zo1Amjtm6F5mbo7HSusNfQ4ARRH37oLJG8QsvZBcqPOgoOPLBgEGXaJhEjTqcRZWeqg+Z0K1EjjolFWA1SppcQUH20pjtYF9vC1kRTZtnB5vh2NiUaaU619fgUAqqfA0J1HBCu58CisUwOj2Vy0Thq/ZV9DxsUDXylzmKPBjPhLOnOTFjV4XwpU3BCKtWXqRMyeEHVrkYVeVdMywqK+jsdTVGct4QbFuVPSXPDIsULjPLDIi0zwqiXotfZodNwHWUk9h5FyQQ2e2E0om1D2sgNrZIpJ6zygquUM60x53jeknK3013bqbSzZJ9jZf1fSqedZaipqhNm6Xlhla5333ZrYeW0ZbdroOWts4/rmnMbLWtf07ruO7veVnZ7odFXkBt4QVdo5gZfbjBmmF1BWv6x7NvnhHHuC1QoPMMZXUbeOc6JmbDJ3S4QpLn3lx1MOR0FLQUnzgdfyR78owohhBBCiKE2qKHUTTfdxLvvvssDDzxAQ0MDP/zhD6mrq+Pkk08ezG7sPa2tXWHT6tVOvaiGBti+3VmamwvfLhSC2bPh4IPhkEOcJZxbd8m2bZJWipiZIGrGaU13siPZwrZEM42pFjrSESJmnA4jSlOqle3JnTQmW9ie3EnSSvXa7TK9mInB0UwMjWZicAwTg6M5IFRHXaDKq92T82Ukmcht6+pk1naBNq/RD1SBWg5WEkhAOu6EVFYMbAPLysRAioKl6li2iq2oWIqCjYKdV6vICYickUi5oRF4ha5tQMmMKMgaWACKc+ExRUGxc0cZqTZZdYyc+kM6zmggPRPy6GjomRpGKnjnKoqCanfd1guGMuObvIDIzgqL3HApeySEyxsR4S5ue4FgKf/fxk3Fcva732yP5D9Gt+M99C3/Pgo98UI3GYg8bXdeA+957OJ4b4+l5DXY5P2b5H3Bd7+kuyNX3GOFRpS4I3ryv+AXvC3OaJeeRqx4/VW6Rh+VFvfwBAeQYXQFVW5olcoOsDJBVSpvyW9z99NG137+dtrInGd0tRmZ9myW5QRw9P7zdNhx62XpmQDM23bb9dxaWjnHs+ttFVoXaHMDsb5sZ1+d0au7ldnOLoLvjr5SM1eCtFJQojn/TkKIXpmWjaYquz5RCCGEGCKDFkrFYjGeeOIJ7r77bmbNmsWsWbNYs2YNjzzyyPAJpeJxp85TNNq1RCJOW2ens25rg507oanJWTc3O9Pympu7FyEvpLwcpkxxlqlTMQ+cRGRcLRGSRFIROtJRmne+SnNDG82pVlpSnexIt9CYaqUl3UGbEaHNjNJuRum0Er2MzumioFCjl1Lnq6TOV0m9r4rx/lGMC9Qw3l9DqR7O/RKqKM4Uk7hTy8qbcqYoXVPIFNuZZaJkAiDoNoLICYky20pmW7EzV0LzEiFQQfGHsP1B3MuiK7aBapuoVgqsFKqdQsVCsa3Md5XM6CBVxa/o6KofVdHQNR+6oqOpOrqqoys6qqpn1T3K1C9Sta4QSM2ERVn3qaJ5dY5URXXOcQspK1mvU6FAKPuczL9AzrGc83o4VuCm3R5jV3rq2+7cV3/t8i77OtKuQDgynLj96S1oy1boPK/Nzg1zs4t8WxZYpjPCxcxcDc+tm2S6V8rLWrKnjVl0H+WSPVIm89BdV9KzndtkB2PuruKGu2qmv9lzyOg+1Yqs7W7TyXo6zz2udBVb73Y86xxvBE2B+9wT7ugwN6BKpZ1tw+zadkOtQtve2swsmdDL3TbMrvOy2/LXptl1P6bZdX9m1n277YXeX2bmeGoYjDYbSBUl8LkLhroXQgx7mqrw3cfeZO2OyK5PHoaOn1bDZSdNH+puCCGE2IsGLZT64IMPMAyDOXPmeG1z587lzjvvxLIs1IG4TPceiD//R5648vO06GksBW8xFTDV3G0js5gKGKMgPQbSmba0TyUV8pMK+UgFfKQCGvGARtKnktAhoZgkrLXErFXE7RSpdQas2/1+qyiU+8uoDlZQFaigKlhJdbCC6lA1NaFqakJVVIWq0DUdWyETGpHZtmhXFNq75lDkBi44gRZKJqhRFBS0zB+t1UwwpKIoKoqioKkaCgq66kNTVTRVR1M1NEVDU53ASFO0TMjjrBWlKyzKX5SsIEm1DFQ7jWqbTmCFmQmt0s7lwa3MtEDbyNRvMbMWK+uJZdVf8r7IqllrN3jKtLlffLPXOe0UOEbWPnnnCjEI8usT5dc+srPOyZ6elRNoFdi3bbqKiRd4DCsrLLMsMDPnm1lhmWU7IUl+cXHswiGa93yy27PCtJypadntdO1nB2suxX3MzI430q/QEMWsTU11rmTYWzi9q7bsYC17Pzugy79tzrpA/xQl8zqbXUGUaTlhlZW1dgMst83MajfzzjNtJ/xy/81y7sfquS07KM1+vPzbmVnnZAesBY9nh7AmTKpzRnntp/arcghij63dEWFVQ+GrJA93B9YUDXUXhBBC7GWD9omuqamJiooK/Fn1Tqqrq0kmk7S1tVFZ2b0I92B6pPkvnHfqQPwl2QISmSWLDfRy96qiEtKDBLUgRb4iwv4wRb5iivxFlAZKKQmUUBoopTRQSkWwgopgBZXhSkr9peiq7tQlygQ8bpjjBkLZ25qqoaKiZwKj/FAoOwgqFBj1dtwtqj2kLDMTTBldoZSVHVCZgBtaWWCmASMzCiUNZNrdcMsdDeJuu1+coWs/Z8RI3nb2l+TsQMxb92RXx/PO6TZ1Lv/22aFcdnP+7frahx7uL/++h9xw6EO+YdInNzfd478HZAew2u7dRU+BWU4QVeA8CpzbU2BV6Hyy75fuj5N9PxQ4bmX3MfNzIactc77l/iyhh2N5t8t5LHL3u1607pv5t1Xo+i3vzz5fw/m38vVw/3mBYE8KTdnO395V3/On5yp5xwuNRrQtKC4uXHx+P7HPl0MQQgghxH5j0EKpeDyeE0gB3n4qNfQ1Ok757KV8889NbGvfgqbqKG5gkxnRoykaamb0j5q5SpmqqvhUnxf4uEGPX/Ojqzo+1Ydf8xP0BQnpIUK+ECEtRHGgmGJ/MWFfmJAeothfTEALoKpdwU52yJO/zj7W0/Z+S3W/bBW4EuDusLOmNOUEU/mBVIFtyLptdlBF3vG+tOdt9/QFstB+wS+VPX3R3EUQ1tdpar3dT4/30Zf73p377aeBuh/nzgbwvvbEcOnH3rQPPsdCYVj2OqeNXo718LMj/7Z9ObfbDfLP6+E59Hp/BfZ7bMusNR+UVnU/vh8YEeUQhBBCCCH6aNBCqUAg0C18cveDweBgdaNHdSV13H/6g0PdDTHcuEWghRBCiGFguJdD2JdIkXAhhBBi7xu0UKq2tpbW1lYMw0DP1IFoamoiGAxSWlq6y9vbmb+YRiIjs1CjEEIIIUaWoqKiYTf6eE/LIQzW56l9JdC584V1NLT34UI2w9DssWV8ce44JpaqWCnfUHdnt9SGnPfqSH4OsG88D3kOw4M8h+FhX3gOE0vVQctWdvV5atBCqRkzZqDrOitXruTwww8HYMWKFcyePbtPf9WLRqMAHHfccXu1n0IIIYQQ4HxOKS4uHupu5NjTcgjyeWr/8Szws6HuxB5aD+wL8xj2hechz2F4kOcwPOwrz2Hu4sF5rF19nhq0UCoUCvH5z3+ea665hhtuuIEdO3Zw7733snhx316JUaNG8eKLLw7Lv1oKIYQQYt9TVDT8rvy1p+UQ5POUEEIIIQbTrj5PDer1lBcuXMg111zDN7/5TYqLi7nooov41Kc+1afbqqrK6NGj93IPhRBCCCGGrz0thyCfp4QQQggxnCi2PaCXmhJCCCGEEHtJPB5n/vz53HvvvV45hKVLl/LKK6/w8MMPD3HvhBBCCCH6Ry7RIoQQQggxQmSXQ3j77bdZvnw59957L9/4xjeGumtCCCGEEP0mI6WEEEIIIUaQeDzONddcw/PPP09xcTHnnnsuZ5111lB3SwghhBCi3ySUEkIIIYQQQgghhBCDTqbvCSGEEEIIIYQQQohBJ6GUEEIIIYQQQgghhBh0EkoJIYQQQgghhBBCiEEnoRSQTCa54oorOPzwwznmmGO49957h7pLI1ZjYyMXX3wx8+bN49hjj2Xx4sUkk8mh7taIdv755/OjH/1oqLsxYqVSKa699lqOOOIIjjrqKG655RaklN7u2bZtGxdccAGHHXYYJ554Ivfff/9Qd2lESaVSnHrqqbz22mte2+bNmznrrLM49NBDOeWUU3j55ZeHsIcjR6HXcuXKlXz5y19mzpw5nHTSSTzxxBND2EMxkP785z8zbdq0nOXiiy8e6m6NCPJzZ88Uev2uu+66bu/Hhx9+eAh7Ofz09n1A3n9909trKO/Bvtm4cSPnnnsuc+bM4fjjj+fXv/61d0zeh7vW2+s30O9BfSA6PNLddNNNvPvuuzzwwAM0NDTwwx/+kLq6Ok4++eSh7tqIYts2F198MaWlpTzyyCO0t7dzxRVXoKoqP/zhD4e6eyPSs88+y4svvsgXvvCFoe7KiHXdddfx2muvcc899xCNRvn+979PXV0dX/7yl4e6ayPO9773Perq6vjtb3/L2rVr+cEPfkB9fT2f/OQnh7prw14ymeTSSy9lzZo1Xptt21x44YVMnTqVp556iuXLl7NgwQL++Mc/UldXN4S9Hd4KvZZNTU2cd955fOUrX+FnP/sZq1atYuHChdTU1HD88ccPXWfFgFi7di0nnHACixYt8toCgcAQ9mhkkJ87e6bQ6wewbt06Lr300pzPZsXFxYPdvWGrt+8Dl19+ubz/+mBX36nkPbhrlmVx/vnnM3v2bH73u9+xceNGLrnkEmprazn11FPlfbgLvb1+n/3sZwf8Pbjfh1KxWIwnnniCu+++m1mzZjFr1izWrFnDI488IqFUP61fv56VK1fyj3/8g+rqagAuvvhibrzxRgmldkNbWxs33XQTs2fPHuqujFhtbW089dRT3HfffRx88MEAnHPOObz11lsSSvVTe3s7K1euZNGiRUycOJGJEydy7LHH8sorr0gotQtr167l0ksv7TZC79VXX2Xz5s089thjhMNhDjzwQF555RWeeuopLrrooiHq7fDW02u5fPlyqqurueSSSwCYOHEir732Gr///e8llNoHrFu3jqlTp1JTUzPUXRkx5OfOnunp9QPn/XjuuefK+7EHvX0f+PjHPy7vvz7Y1XcqeQ/uWnNzMzNmzOCaa66huLiYiRMncuSRR7JixQqqq6vlfbgLvb1+big1kO/B/X763gcffIBhGMyZM8drmzt3Lm+99RaWZQ1hz0aempoafv3rX3s/PF2RSGSIejSy3XjjjZx22mlMnjx5qLsyYq1YsYLi4mLmzZvntZ1//vksXrx4CHs1MgWDQUKhEL/97W9Jp9OsX7+ef//738yYMWOouzbsvf7668yfP5/HH388p/2tt95i5syZhMNhr23u3LmsXLlykHs4cvT0WrpTG/LJ7599w7p165g4ceJQd2NEkZ87e6an1y8SidDY2Cjvx1709n1A3n9909trKO/Bvhk1ahS33norxcXF2LbNihUr+Ne//sW8efPkfdgHvb1+e+M9uN+PlGpqaqKiogK/3++1VVdXk0wmaWtro7Kycgh7N7KUlpZy7LHHevuWZfHwww/zsY99bAh7NTK98sorvPHGG/z+97/nmmuuGerujFibN2+mvr6ep59+mjvvvJN0Os3pp5/Of//3f6Oq+30m3y+BQICrr76aRYsW8eCDD2KaJqeffjpf/OIXh7prw95Xv/rVgu1NTU2MGjUqp62qqort27cPRrdGpJ5ey7FjxzJ27Fhvf+fOnTz77LPyF899gG3bbNiwgZdffplf/epXmKbJySefzMUXX5zz2U3kkp87e6an12/dunUoisKdd97J3//+d8rLyzn77LOlzEKW3r4PyPuvb3p7DeU92H8nnngiDQ0NnHDCCZx00knccMMN8j7sh/zX79133x3w9+B+H0rF4/FuH2rc/VQqNRRd2mcsWbKE9957jyeffHKouzKiJJNJfvKTn3D11VcTDAaHujsjWiwWY+PGjTz22GMsXryYpqYmrr76akKhEOecc85Qd2/EWbduHSeccAJnn302a9asYdGiRRx55JF87nOfG+qujUg9/f6R3z17JpFIcNFFF1FdXc1//ud/DnV3xB5qaGjw/q/ceuutbNmyheuuu45EIsGPf/zjoe7eiCM/d/bM+vXrURSFSZMm8V//9V/861//4qqrrqK4uFimsvcg+/vA/fffL++/3ZD9Gq5atUreg/1022230dzczDXXXMPixYvl52A/5b9+s2bNGvD34H4fSgUCgW5vQHdfAoHdt2TJEh544AF+/vOfM3Xq1KHuzohyxx13cNBBB+X8hUTsHl3XiUQi3HzzzdTX1wPOF5xHH31UQql+euWVV3jyySd58cUXCQaDzJ49m8bGRn75y19KKLWbAoEAbW1tOW2pVEp+9+yBaDTKd77zHT766CN+85vfEAqFhrpLYg/V19fz2muvUVZWhqIozJgxA8uyuOyyy1i4cCGapg11F0cU+bmzZz7/+c9zwgknUF5eDsD06dP56KOPePTRRyUQKCD/+4C8//ov/zWcMmWKvAf7ya3Pm0wm+cEPfsAZZ5xBPB7POUfehz3Lf/3+/e9/D/h7cL+fv1JbW0trayuGYXhtTU1NBINBSktLh7BnI9eiRYu47777WLJkCSeddNJQd2fEefbZZ1m+fDlz5sxhzpw5/P73v+f3v/99Tt0z0Tc1NTUEAgEvkAI44IAD2LZt2xD2amR69913mTBhQs4v7JkzZ9LQ0DCEvRrZamtraW5uzmlrbm7uNqRc9E0kEuHcc89lzZo1PPDAA1JvYx9SXl6Ooije/oEHHkgymaS9vX0IezUyyc+dPaMoivdFzDVp0iQaGxuHpkPDWKHvA/L+659Cr6G8B/umubmZ5cuX57RNnjyZdDpNTU2NvA93obfXLxKJDPh7cL8PpWbMmIGu6zmFzVasWMHs2bOl5sxuuOOOO3jssce45ZZb+MxnPjPU3RmRHnroIX7/+9/z9NNP8/TTT3PiiSdy4okn8vTTTw9110acQw45hGQyyYYNG7y29evX54RUom9GjRrFxo0bc0aWrl+/PqeOj+ifQw45hFWrVpFIJLy2FStWcMghhwxhr0Ymy7JYsGABW7Zs4aGHHmLKlClD3SUxQF566SXmz5+f81ft999/n/Lycqn7uRvk586e+cUvfsFZZ52V0/bBBx8wadKkoenQMNXT9wF5//VdT6+hvAf7ZsuWLSxYsCAnKHn33XeprKxk7ty58j7chd5ev4ceemjA34P7feoSCoX4/Oc/zzXXXMPbb7/N8uXLuffee/nGN74x1F0bcdatW8eyZcs477zzmDt3Lk1NTd4i+q6+vp4JEyZ4S1FREUVFRUyYMGGouzbiTJo0ieOPP56FCxfywQcf8NJLL3HXXXfxla98Zai7NuKceOKJ+Hw+fvzjH7Nhwwb++te/cuedd/L1r399qLs2Ys2bN48xY8awcOFC1qxZw1133cXbb7/NmWeeOdRdG3GefPJJXnvtNa677jpKS0u93z3500TEyDNnzhwCgQA//vGPWb9+PS+++CI33XQT3/rWt4a6ayOS/NzZMyeccAL/+te/uOeee9i0aRO/+c1vePrpp6UkQJbevg/I+69vensN5T3YN7Nnz2bWrFlcccUVrF27lhdffJElS5bw7W9/W96HfdDb67c33oOKbdv2APZ/RIrH41xzzTU8//zzFBcXc+6553ZL/8Su3XXXXdx8880Fj61evXqQe7Pv+NGPfgTAz372syHuycjU2dnJokWL+POf/0woFOKrX/0qF154Yc5UENE3a9eu5frrr+ftt9+msrKSr33ta3zzm9+U17Ifpk2bxoMPPsj8+fMB2LhxI1deeSVvvfUWEyZM4IorruCoo44a4l6ODNmv5bnnnsvLL7/c7Zx58+bx0EMPDUHvxEBas2YNN9xwAytXrqSoqIgvf/nL8nO8H+Tnzp7Jf/2WL1/ObbfdxkcffUR9fT3f//73+dSnPjXEvRw+dvV9QN5/u7ar11Deg33T2NjIokWLeOWVVwiFQvzXf/0XF1xwAYqiyPuwD3p7/Qb6PSihlBBCCCGEEEIIIYQYdPv99D0hhBBCCCGEEEIIMfgklBJCCCGEEEIIIYQQg05CKSGEEEIIIYQQQggx6CSUEkIIIYQQQgghhBCDTkIpIYQQQgghhBBCCDHoJJQSQgghhBBCCCGEEINOQikhhBBCCCGEEEIIMegklBJCCCGEEEIIIYQQg05CKSHEsDdt2jQuvfTSbu2//e1vOfHEE4egR0IIIYQQQggh9pSEUkKIEeEPf/gDr7zyylB3QwghhBBCCCHEAJFQSggxItTX1/PTn/6UVCo11F0RQgghhBBCCDEAJJQSQowI3/ve92hsbOSee+7p8Zzt27fz3e9+l3nz5jF//nyuu+46L8T67W9/y9e//nVuu+025s+fz+GHH87ixYuxbdu7/WOPPcaJJ57InDlz+PrXv87q1av3+vMSQgghhBBCiP2VhFJCiBGhtraWiy++mDvvvJPNmzd3O55KpfjmN79JPB7noYce4tZbb+WFF17gpptu8s5588032bBhA48++ihXXXUVDz74IP/85z8B+Otf/8odd9zBVVddxe9+9zvmzp3LN77xDdrb2wftOQohhBBCCCHE/kRCKSHEiPH1r3+dCRMmcP3113c79tJLL9HY2MiSJUuYNm0aRx55JFdffTWPPvoo0WgUANM0WbRoEZMmTeK0005j+vTpvPPOOwD8+te/5oILLuCEE05g4sSJfO9736O+vp5nnnlmUJ+jEEIIIYQQQuwv9KHugBBC9JWmaVxzzTV89atfZfny5TnH1q1bx8SJEykrK/PaDjvsMAzDYNOmTQBUVVVRXFzsHS8uLsYwDO/2S5Ys4ZZbbvGOJ5NJPvroo734jIQQQgghhBBi/yWhlBBiRDnssMM444wzuP766/nWt77ltQcCgW7nmqaZs/b7/d3OcWtKmabJFVdcwZFHHplzPDvEEkIIIYQQQggxcGT6nhBixPnBD35ALBbLKXp+wAEH8NFHH9HW1ua1rVy5El3XGT9+/C7v84ADDmD79u1MmDDBW+68805Wrly5F56BEEIIIYQQQggJpYQQI05FRQU/+MEP2Lp1q9d29NFHM27cOC6//HJWr17Nq6++yqJFizj11FMpLS3d5X2effbZPPDAAzz99NNs2rSJJUuW8Kc//YkDDzxwbz4VIYQQQgghhNhvyfQ9IcSIdOaZZ/LUU0+xY8cOwKk3tWzZMhYtWsSXvvQlioqK+OxnP8sll1zSp/s75ZRTaG5u5rbbbqO5uZnJkyfzy1/+kokTJ+7FZyGEEEIIIYQQ+y/FdguqCCGEEEIIIYQQQggxSGT6nhBCCCGEEEIIIYQYdBJKCSGEEEIIIYQQQohBJ6GUEEIIIYQQQgghhBh0EkoJIYQQQgghhBBCiEEnoZQQQgghhBBCCCGEGHQSSgkhhBBCCCGEEEKIQSehlBBCCCGEEEIIIYQYdBJKCSGEEEIIIYQQQohBJ6GUEEIIIYQQQgghhBh0EkoJIYQQQgghhBBCiEEnoZQQQgghhBBCCCGEGHT/HzGCI3zS8L7fAAAAAElFTkSuQmCC", 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      " ] @@ -537,7 +538,7 @@ }, { "cell_type": "code", - "execution_count": 245, + "execution_count": 511, "metadata": {}, "outputs": [], "source": [ @@ -588,14 +589,14 @@ }, { "cell_type": "code", - "execution_count": 354, + "execution_count": 512, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.1309)\n" + "tensor(0.1215)\n" ] } ], @@ -631,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": 357, + "execution_count": 513, "metadata": {}, "outputs": [], "source": [ @@ -644,17 +645,17 @@ }, { "cell_type": "code", - "execution_count": 358, + "execution_count": 514, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0} 0.20220299065113068\n", - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1} 0.2057761698961258\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0} 0.11375554651021957\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1} 2.610623717202998e-09\n" + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0} 0.19823434948921204\n", + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1} 0.1833265870809555\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0} 0.10898739099502563\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1} 2.7269246860583962e-09\n" ] } ], @@ -683,7 +684,7 @@ }, { "cell_type": "code", - "execution_count": 361, + "execution_count": 515, "metadata": {}, "outputs": [ { @@ -691,10 +692,10 @@ "output_type": "stream", "text": [ "Degree of responsibility for lockdown: \n", - "{'__cause____antecedent_lockdown': 0} 0.20397219061851501\n", + "{'__cause____antecedent_lockdown': 0} 0.19081200659275055\n", "\n", "Degree of responsibility for mask: \n", - "{'__cause____antecedent_mask': 0} 0.15853415429592133\n" + "{'__cause____antecedent_mask': 0} 0.15390829741954803\n" ] } ], @@ -720,19 +721,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Fine grained analysis of overshoot variables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Factual plot" + "## Fine grained analysis of `overshoot` using sample traces\n", + "\n", + "In this section, we use the samples we obtained earlier to analyze the distribution of `overshoot` variable in different counterfactual worlds. We first define a function to obtain histogram data from the samples in a particular world and then we demonstrate the plots for `overshoot` distribution in different settings." ] }, { "cell_type": "code", - "execution_count": 301, + "execution_count": 516, "metadata": {}, "outputs": [], "source": [ @@ -755,20 +751,27 @@ " return hist, bin_edges, overshoot_mean, os_too_high_mean\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first plot the distribution of `overshoot` in the factual world (indicated by 0) and necessity counterfactual worlds (indicated by 1) where intervened variables are set to their alternative value. One can see how the distribution changes in the counterfactual worlds. When `mask` is set to 0, the probability of high overshoot is lower than that when `lockdown` is set to 0. This provides us the intuition that `lockdown` has a higher role in inducing high overshoot." + ] + }, { "cell_type": "code", - "execution_count": 302, + "execution_count": 517, "metadata": {}, "outputs": [], "source": [ - "hist_fact, bin_edges, os_fact, oth_fact = histogram_data(importance_tr, mwc_imp, {}, 0)\n", - "hist_mask, bin_edges, os_mask, oth_mask = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 0, \"__cause____antecedent_lockdown\": 1, \"__cause____witness_mask_efficiency\": 0}, 1)\n", - "hist_lockdown, bin_edges, os_lockdown, oth_lockdown = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 1, \"__cause____antecedent_lockdown\": 0, \"__cause____witness_lockdown_efficiency\": 0}, 1)" + "hist_fact_nec, bin_edges, os_fact_nec, oth_fact_nec = histogram_data(importance_tr, mwc_imp, {}, 0)\n", + "hist_mask_nec, bin_edges, os_mask_nec, oth_mask_nec = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 0, \"__cause____antecedent_lockdown\": 1, \"__cause____witness_mask_efficiency\": 0}, 1)\n", + "hist_lockdown_nec, bin_edges, os_lockdown_nec, oth_lockdown_nec = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 1, \"__cause____antecedent_lockdown\": 0, \"__cause____witness_lockdown_efficiency\": 0}, 1)" ] }, { "cell_type": "code", - "execution_count": 366, + "execution_count": 518, "metadata": {}, "outputs": [ { @@ -776,14 +779,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "factual: 24.13526153564453 counterfactual mask: 21.76302719116211 counterfactual lockdown: 20.221193313598633\n", + "factual: 24.31097984313965 counterfactual mask: 21.902610778808594 counterfactual lockdown: 20.758800506591797\n", "Probability of overshoot being high\n", - "factual: 0.722599983215332 counterfactual mask: 0.5667539238929749 counterfactual lockdown: 0.47914034128189087\n" + "factual: 0.7299000024795532 counterfactual mask: 0.5736842155456543 counterfactual lockdown: 0.5078909397125244\n" ] }, { "data": { - "image/png": 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", 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", 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      " ] @@ -793,42 +796,43 @@ } ], "source": [ - "plt.bar(bin_edges[:28].tolist(), hist_fact, align='center', width = 35/28, alpha = 0.5, color='blue')\n", - "plt.bar(bin_edges[:28].tolist(), hist_lockdown, align='center', width = 35/28, alpha = 0.5, color='pink')\n", - "plt.bar(bin_edges[:28].tolist(), hist_mask, align='center', width = 35/28, alpha = 0.5, color='green')\n", + "plt.bar(bin_edges[:28].tolist(), hist_fact_nec, align='center', width = 35/28, alpha = 0.5, color='blue')\n", + "plt.bar(bin_edges[:28].tolist(), hist_lockdown_nec, align='center', width = 35/28, alpha = 0.5, color='pink')\n", + "plt.bar(bin_edges[:28].tolist(), hist_mask_nec, align='center', width = 35/28, alpha = 0.5, color='green')\n", "plt.legend([\"factual\", \"counterfactual_lockdown\", \"counterfactual_mask\"])\n", "plt.ylabel(\"pr\")\n", "plt.xlabel(\"overshoot\")\n", + "plt.title(\"Counterfactual - Necessity World\")\n", "sns.despine()\n", "\n", "print(\"Overshoot mean\")\n", - "print(\"factual: \", os_fact.item(), \" counterfactual mask: \", os_mask.item(), \" counterfactual lockdown: \", os_lockdown.item())\n", + "print(\"factual: \", os_fact_nec.item(), \" counterfactual mask: \", os_mask_nec.item(), \" counterfactual lockdown: \", os_lockdown_nec.item())\n", "\n", "print(\"Probability of overshoot being high\")\n", - "print(\"factual: \", oth_fact.item(), \" counterfactual mask: \", oth_mask.item(), \" counterfactual lockdown: \", oth_lockdown.item())" + "print(\"factual: \", oth_fact_nec.item(), \" counterfactual mask: \", oth_mask_nec.item(), \" counterfactual lockdown: \", oth_lockdown_nec.item())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Sufficiency worlds" + "We can have similar plots for sufficiency worlds (indicated by 2) where variables are intervened on to be their antecedent value. The resulting plots show that when `mask` is set to be 1, there is a higher probability of high overshoot but the distribution is more flat as compared to the distribution when `lockdown` is set to `, that has higher peaks." ] }, { "cell_type": "code", - "execution_count": 292, + "execution_count": 519, "metadata": {}, "outputs": [], "source": [ - "hist_fact, bin_edges, os_fact, oth_fact = histogram_data(importance_tr, mwc_imp, {}, 0)\n", - "hist_mask, bin_edges, os_mask, oth_mask = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 0, \"__cause____antecedent_lockdown\": 1, \"__cause____witness_mask_efficiency\": 0}, 2)\n", - "hist_lockdown, bin_edges, os_lockdown, oth_lockdown = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 1, \"__cause____antecedent_lockdown\": 0, \"__cause____witness_lockdown_efficiency\": 0}, 2)" + "hist_fact_suff, bin_edges, os_fact_suff, oth_fact_suff = histogram_data(importance_tr, mwc_imp, {}, 0)\n", + "hist_mask_suff, bin_edges, os_mask_suff, oth_mask_suff = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 0, \"__cause____antecedent_lockdown\": 1, \"__cause____witness_mask_efficiency\": 0}, 2)\n", + "hist_lockdown_suff, bin_edges, os_lockdown_suff, oth_lockdown_suff = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 1, \"__cause____antecedent_lockdown\": 0, \"__cause____witness_lockdown_efficiency\": 0}, 2)" ] }, { "cell_type": "code", - "execution_count": 293, + "execution_count": 520, "metadata": {}, "outputs": [ { @@ -836,14 +840,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "factual: 24.143041610717773 counterfactual mask: 26.505809783935547 counterfactual lockdown: 22.47197723388672\n", + "factual: 24.31097984313965 counterfactual mask: 26.651079177856445 counterfactual lockdown: 22.560808181762695\n", "Probability of overshoot being high\n", - "factual: 0.7235000133514404 counterfactual mask: 0.8883248567581177 counterfactual lockdown: 0.7112860679626465\n" + "factual: 0.7299000024795532 counterfactual mask: 0.8868421316146851 counterfactual lockdown: 0.7044476270675659\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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      " ] @@ -853,31 +857,107 @@ } ], "source": [ - "plt.bar(bin_edges[:28].tolist(), hist_fact, align='center', width = 35/28, alpha = 0.5, color='blue')\n", - "plt.bar(bin_edges[:28].tolist(), hist_lockdown, align='center', width = 35/28, alpha = 0.5, color='pink')\n", - "plt.bar(bin_edges[:28].tolist(), hist_mask, align='center', width = 35/28, alpha = 0.5, color='green')\n", + "plt.bar(bin_edges[:28].tolist(), hist_fact_suff, align='center', width = 35/28, alpha = 0.5, color='blue')\n", + "plt.bar(bin_edges[:28].tolist(), hist_lockdown_suff, align='center', width = 35/28, alpha = 0.5, color='pink')\n", + "plt.bar(bin_edges[:28].tolist(), hist_mask_suff, align='center', width = 35/28, alpha = 0.5, color='green')\n", "plt.legend([\"factual\", \"counterfactual_lockdown\", \"counterfactual_mask\"])\n", "plt.ylabel(\"pr\")\n", "plt.xlabel(\"overshoot\")\n", + "plt.title(\"Counterfactual - Sufficiency World\")\n", "sns.despine()\n", "\n", "print(\"Overshoot mean\")\n", - "print(\"factual: \", os_fact.item(), \" counterfactual mask: \", os_mask.item(), \" counterfactual lockdown: \", os_lockdown.item())\n", + "print(\"factual: \", os_fact_suff.item(), \" counterfactual mask: \", os_mask_suff.item(), \" counterfactual lockdown: \", os_lockdown_suff.item())\n", "\n", "print(\"Probability of overshoot being high\")\n", - "print(\"factual: \", oth_fact.item(), \" counterfactual mask: \", oth_mask.item(), \" counterfactual lockdown: \", oth_lockdown.item())" + "print(\"factual: \", oth_fact_suff.item(), \" counterfactual mask: \", oth_mask_suff.item(), \" counterfactual lockdown: \", oth_lockdown_suff.item())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also try to combine samples from both sufficiency and necessity worlds to draw conclusions. We first visualize samples where only lockdown was intervened on and then we analyze samples where masking was intervened on." + ] + }, + { + "cell_type": "code", + "execution_count": 521, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 2, figsize=(16, 8))\n", + "\n", + "ax = axs[0]\n", + "hist_lockdown = hist_lockdown_nec.unsqueeze(1) * hist_lockdown_suff.unsqueeze(0)\n", + "ax.imshow(hist_lockdown, cmap = \"viridis\")\n", + "ax.set(xticks = range(0, 28, 2), xticklabels = bin_edges[0:28:2].tolist())\n", + "ax.set(yticks = range(0, 28, 2), yticklabels = bin_edges[0:28:2].tolist())\n", + "ax.set(xlabel = \"Overshoot in Sufficiency Intervention\", ylabel = \"Overshoot in Necessity Intervention\", title=\"Overshoot in counterfactual lockdown\")\n", + "ax.axvline(x=15.8, color=\"grey\", linestyle=\"--\", label = \"Overshoot too high\")\n", + "ax.axhline(y=15.8, color=\"grey\", linestyle=\"--\")\n", + "\n", + "ax.axvline(x=(os_lockdown_suff-5)*28/35, color=\"white\", linestyle=\"--\", label = \"Mean Overshoot\")\n", + "ax.axhline(y=(os_lockdown_nec-5)*28/35, color=\"white\", linestyle=\"--\")\n", + "\n", + "ax.legend(loc = \"upper left\")\n", + "\n", + "ax = axs[1]\n", + "hist_mask = hist_mask_nec.unsqueeze(1) * hist_mask_suff.unsqueeze(0)\n", + "ax.imshow(hist_mask, cmap = \"viridis\")\n", + "ax.set(xticks = range(0, 28, 2), xticklabels = bin_edges[0:28:2].tolist())\n", + "ax.set(yticks = range(0, 28, 2), yticklabels = bin_edges[0:28:2].tolist())\n", + "ax.set(xlabel = \"Overshoot in Sufficiency Intervention\", ylabel = \"Overshoot in Necessity Intervention\", title=\"Overshoot in counterfactual mask\")\n", + "ax.axvline(x=16.8, color=\"grey\", linestyle=\"--\", label = \"Overshoot too high\")\n", + "ax.axhline(y=16.8, color=\"grey\", linestyle=\"--\")\n", + "\n", + "ax.axvline(x=(os_mask_suff-5)*28/35, color=\"white\", linestyle=\"--\", label = \"Mean Overshoot\")\n", + "ax.axhline(y=(os_mask_nec-5)*28/35, color=\"white\", linestyle=\"--\")\n", + "\n", + "ax.legend(loc = \"upper left\")\n", + "\n", + "sns.despine()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is evident from the plot above that counterfactual for lockdown has more probability mask in the top right quadrant (low overshoot in necessity world and high overshoot in sufficient world). This gives us a more clear picture into why lockdown has more causal role in overshoot being too high as compared to masking." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## For advanced readers: Looking into different contexts\n", + "\n", + "`SearchForExplanation` allows the users to perform an even finer grained analysis by visualizing distributions of random variables when different contexts are kept fixed in the model. To illustrate this, we consider the following two scenarios:\n", + "1. Intervene on `lockdown=1` while keeping `mask_efficiency` fixed or not.\n", + "2. Intervene on `mask=1` while keeping `lockdown_efficiency` fixed or not." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Counterfactual lockdown by mask efficiency contexts" + "We first intervene on `lockdown` being 1 and analyze how the distribution of `overshoot` change as we keep the `mask_efficiency` fixed or not." ] }, { "cell_type": "code", - "execution_count": 294, + "execution_count": 522, "metadata": {}, "outputs": [], "source": [ @@ -887,7 +967,7 @@ }, { "cell_type": "code", - "execution_count": 295, + "execution_count": 523, "metadata": {}, "outputs": [ { @@ -895,14 +975,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "mask_efficiency fixed: 19.993305206298828 mask_efficiency not fixed: 21.670305252075195\n", + "mask_efficiency fixed: 20.179100036621094 mask_efficiency not fixed: 22.04108238220215\n", "Probability of overshoot being high\n", - "mask_efficiency fixed: 0.4733840227127075 mask_efficiency not fixed: 0.5932203531265259\n" + "mask_efficiency fixed: 0.4541666805744171 mask_efficiency not fixed: 0.6267281174659729\n" ] }, { "data": { - "image/png": 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", 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", 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      " ] @@ -917,6 +997,7 @@ "plt.legend([\"mask_efficiency fixed\", \"mask_efficiency not fixed\"])\n", "plt.ylabel(\"pr\")\n", "plt.xlabel(\"overshoot\")\n", + "plt.title(\"Counterfactual Lockdown\")\n", "sns.despine()\n", "\n", "print(\"Overshoot mean\")\n", @@ -930,12 +1011,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Counterfactual mask by lockdown efficiency contexts" + "We then intervene on `mask` being 1 and analyze how the distribution of `overshoot` change as we keep the `lockdown_efficiency` fixed or not." ] }, { "cell_type": "code", - "execution_count": 296, + "execution_count": 524, "metadata": {}, "outputs": [], "source": [ @@ -945,7 +1026,7 @@ }, { "cell_type": "code", - "execution_count": 297, + "execution_count": 525, "metadata": {}, "outputs": [ { @@ -953,14 +1034,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "lockdown_efficiency fixed: 21.956771850585938 lockdown_efficiency not fixed: 21.55644989013672\n", + "lockdown_efficiency fixed: 21.957277297973633 lockdown_efficiency not fixed: 21.78769874572754\n", "Probability of overshoot being high\n", - "lockdown_efficiency fixed: 0.5478423833847046 lockdown_efficiency not fixed: 0.5529412031173706\n" + "lockdown_efficiency fixed: 0.5786407589912415 lockdown_efficiency not fixed: 0.563265323638916\n" ] }, { "data": { - "image/png": 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", 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      " ] @@ -975,6 +1056,7 @@ "plt.legend([\"lockdown_efficiency fixed\", \"lockdown_efficiency not fixed\"])\n", "plt.ylabel(\"pr\")\n", "plt.xlabel(\"overshoot\")\n", + "plt.title(\"Counterfactual Mask\")\n", "sns.despine\n", "\n", "print(\"Overshoot mean\")\n", From dc3957f61cc7ff48f97c99943fca651f5a37da15 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Fri, 23 Aug 2024 14:41:05 -0400 Subject: [PATCH 059/111] cleanup --- docs/source/counterfactual_sir_search.png | Bin 41638 -> 0 bytes docs/source/explainable_sir_original.ipynb | 1265 -------------------- 2 files changed, 1265 deletions(-) delete mode 100644 docs/source/counterfactual_sir_search.png delete mode 100644 docs/source/explainable_sir_original.ipynb diff --git a/docs/source/counterfactual_sir_search.png b/docs/source/counterfactual_sir_search.png deleted file mode 100644 index 0b924255aaacc60f5155bcb367a30bd465791340..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 41638 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os\n", - "from typing import Tuple, TypeVar, Union\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import pyro.distributions as dist\n", - "import seaborn as sns\n", - "import torch\n", - "from pyro.infer import Predictive\n", - "\n", - "import pyro\n", - "from chirho.counterfactual.handlers.counterfactual import \\\n", - " MultiWorldCounterfactual\n", - "from chirho.dynamical.handlers.interruption import StaticEvent\n", - "from chirho.dynamical.handlers.solver import TorchDiffEq\n", - "from chirho.dynamical.handlers.trajectory import LogTrajectory\n", - "from chirho.dynamical.ops import Dynamics, State, on, simulate\n", - "from chirho.explainable.handlers import SearchForExplanation\n", - "from chirho.explainable.handlers.components import ExtractSupports\n", - "from chirho.indexed.ops import IndexSet, gather, indices_of\n", - "from chirho.interventional.ops import Intervention, intervene\n", - "from chirho.observational.handlers import condition\n", - "\n", - "R = Union[numbers.Real, torch.Tensor]\n", - "S = TypeVar(\"S\")\n", - "T = TypeVar(\"T\")\n", - "\n", - "\n", - "sns.set_style(\"white\")\n", - "\n", - "seed = 123\n", - "pyro.clear_param_store()\n", - "pyro.set_rng_seed(seed)\n", - "\n", - "smoke_test = \"CI\" in os.environ\n", - "num_samples = 10 if smoke_test else 300\n", - "exp_plate_size = 10 if smoke_test else 10000" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "class SIRDynamics(pyro.nn.PyroModule):\n", - " def __init__(self, beta, gamma):\n", - " super().__init__()\n", - " self.beta = beta\n", - " self.gamma = gamma\n", - "\n", - " def forward(self, X: State[torch.Tensor]):\n", - " dX: State[torch.Tensor] = dict()\n", - " dX[\"S\"] = -self.beta * X[\"S\"] * X[\"I\"]\n", - " dX[\"I\"] = self.beta * X[\"S\"] * X[\"I\"] - self.gamma * X[\"I\"]\n", - " dX[\"R\"] = self.gamma * X[\"I\"]\n", - "\n", - " return dX\n", - "\n", - "\n", - "# TODO add running overshoot to states?\n", - "\n", - "\n", - "class SIRDynamicsLockdown(SIRDynamics):\n", - " def __init__(self, beta0, gamma):\n", - " super().__init__(beta0, gamma)\n", - " self.beta0 = beta0\n", - "\n", - " def forward(self, X: State[torch.Tensor]):\n", - " self.beta = (1 - X[\"l\"]) * self.beta0\n", - " dX = super().forward(X)\n", - " dX[\"l\"] = torch.zeros_like(X[\"l\"])\n", - " return dX" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.15116800367832184\n" - ] - } - ], - "source": [ - "init_state = dict(S=torch.tensor(99.0), I=torch.tensor(1.0), R=torch.tensor(0.0))\n", - "start_time = torch.tensor(0.0)\n", - "end_time = torch.tensor(12.0)\n", - "step_size = torch.tensor(0.1)\n", - "logging_times = torch.arange(start_time, end_time, step_size)\n", - "init_state_lockdown = dict(**init_state, l=torch.tensor(0.0))\n", - "\n", - "# We now simulate from the SIR model\n", - "beta_true = torch.tensor([0.03])\n", - "gamma_true = torch.tensor([0.5])\n", - "sir_true = SIRDynamics(beta_true, gamma_true)\n", - "with TorchDiffEq(), LogTrajectory(logging_times) as lt:\n", - " simulate(sir_true, init_state, start_time, end_time)\n", - "\n", - "sir_true_traj = lt.trajectory\n", - "\n", - "\n", - "def get_overshoot(trajectory):\n", - " t_max = torch.argmax(trajectory[\"I\"].squeeze())\n", - " S_peak = torch.max(trajectory[\"S\"].squeeze()[t_max]) / 100\n", - " S_final = trajectory[\"S\"].squeeze()[-1] / 100\n", - " return (S_peak - S_final).item()\n", - "\n", - "\n", - "print(get_overshoot(sir_true_traj))" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "def bayesian_sir(base_model=SIRDynamics) -> Dynamics[torch.Tensor]:\n", - " beta = pyro.sample(\"beta\", dist.Beta(18, 600))\n", - " gamma = pyro.sample(\"gamma\", dist.Beta(1600, 1600))\n", - " sir = base_model(beta, gamma)\n", - " return sir\n", - "\n", - "\n", - "def simulated_bayesian_sir(\n", - " init_state, start_time, logging_times, base_model=SIRDynamics\n", - ") -> State[torch.Tensor]:\n", - " sir = bayesian_sir(base_model)\n", - "\n", - " with TorchDiffEq(), LogTrajectory(logging_times, is_traced=True) as lt:\n", - " simulate(sir, init_state, start_time, logging_times[-1])\n", - " return lt.trajectory" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "def MaskedStaticIntervention(time: R, intervention: Intervention[State[T]]):\n", - "\n", - " @on(StaticEvent(time))\n", - " def callback(\n", - " dynamics: Dynamics[T], state: State[T]\n", - " ) -> Tuple[Dynamics[T], State[T]]:\n", - "\n", - " with pyro.poutine.block():\n", - " return dynamics, intervene(state, intervention)\n", - "\n", - " return callback" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "overshoot_threshold = 20\n", - "lockdown_time = torch.tensor(1.0)\n", - "mask_time = torch.tensor(1.5)\n", - "\n", - "\n", - "def policy_model():\n", - "\n", - " lockdown = pyro.sample(\"lockdown\", dist.Bernoulli(torch.tensor(0.5)))\n", - " mask = pyro.sample(\"mask\", dist.Bernoulli(torch.tensor(0.5)))\n", - "\n", - " lockdown_efficiency = pyro.deterministic(\n", - " \"lockdown_efficiency\", torch.tensor(0.6) * lockdown, event_dim=0\n", - " )\n", - "\n", - " mask_efficiency = pyro.deterministic(\n", - " \"mask_efficiency\", (0.1 * lockdown + 0.45 * (1 - lockdown)) * mask, event_dim=0\n", - " )\n", - "\n", - " joint_efficiency = pyro.deterministic(\n", - " \"joint_efficiency\",\n", - " torch.clamp(lockdown_efficiency + mask_efficiency, 0, 0.95),\n", - " event_dim=0,\n", - " )\n", - "\n", - " lockdown_sir = bayesian_sir(SIRDynamicsLockdown)\n", - " with LogTrajectory(logging_times, is_traced=True) as lt:\n", - " with TorchDiffEq():\n", - " with MaskedStaticIntervention(lockdown_time, dict(l=lockdown_efficiency)):\n", - " with MaskedStaticIntervention(mask_time, dict(l=joint_efficiency)):\n", - " simulate(\n", - " lockdown_sir, init_state_lockdown, start_time, logging_times[-1]\n", - " )\n", - "\n", - " trajectory = lt.trajectory\n", - "\n", - " t_max = torch.max(trajectory[\"I\"], dim=-1).indices\n", - " S_peaks = pyro.ops.indexing.Vindex(trajectory[\"S\"])[..., t_max]\n", - " overshoot = pyro.deterministic(\n", - " \"overshoot\", S_peaks - trajectory[\"S\"][..., -1], event_dim=0\n", - " )\n", - " os_too_high = pyro.deterministic(\n", - " \"os_too_high\",\n", - " (overshoot > overshoot_threshold).clone().detach().float(),\n", - " event_dim=0,\n", - " )\n", - "\n", - " return overshoot, os_too_high\n", - "\n", - "\n", - "with ExtractSupports() as s:\n", - " one_run = policy_model()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# conditioning (as opposed to intervening) is sufficient for\n", - "# propagating the changes, as the decisions are upstream from ds\n", - "\n", - "# no interventions\n", - "policy_model_none = condition(\n", - " policy_model, {\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(0.0)}\n", - ")\n", - "unintervened_predictive = Predictive(\n", - " policy_model_none, num_samples=num_samples, parallel=True\n", - ")\n", - "unintervened_samples = unintervened_predictive()\n", - "\n", - "# both interventions\n", - "policy_model_all = condition(\n", - " policy_model, {\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(1.0)}\n", - ")\n", - "intervened_predictive = Predictive(\n", - " policy_model_all, num_samples=num_samples, parallel=True\n", - ")\n", - "intervened_samples = intervened_predictive()\n", - "\n", - "policy_model_mask = condition(\n", - " policy_model, {\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(1.0)}\n", - ")\n", - "mask_predictive = Predictive(policy_model_mask, num_samples=num_samples, parallel=True)\n", - "mask_samples = mask_predictive()\n", - "\n", - "policy_model_lockdown = condition(\n", - " policy_model, {\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(0.0)}\n", - ")\n", - "lockdown_predictive = Predictive(\n", - " policy_model_lockdown, num_samples=num_samples, parallel=True\n", - ")\n", - "lockdown_samples = lockdown_predictive()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
      " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def add_pred_to_plot(preds, axs, coords, color, label):\n", - " sns.lineplot(\n", - " x=logging_times,\n", - " y=preds.mean(dim=0).squeeze().tolist(),\n", - " ax=axs[coords],\n", - " label=label,\n", - " color=color,\n", - " )\n", - " axs[coords].fill_between(\n", - " logging_times,\n", - " torch.quantile(preds, 0.025, dim=0).squeeze(),\n", - " torch.quantile(preds, 0.975, dim=0).squeeze(),\n", - " alpha=0.2,\n", - " color=color,\n", - " )\n", - "\n", - "\n", - "fig, axs = plt.subplots(4, 2, figsize=(12, 6))\n", - "\n", - "colors = [\"orange\", \"red\", \"green\"]\n", - "\n", - "add_pred_to_plot(\n", - " unintervened_samples[\"S\"], axs, coords=(0, 0), color=colors[0], label=\"susceptible\"\n", - ")\n", - "add_pred_to_plot(\n", - " unintervened_samples[\"I\"], axs, coords=(0, 0), color=colors[1], label=\"infected\"\n", - ")\n", - "add_pred_to_plot(\n", - " unintervened_samples[\"R\"], axs, coords=(0, 0), color=colors[2], label=\"recovered\"\n", - ")\n", - "\n", - "axs[0, 1].hist(unintervened_samples[\"overshoot\"].squeeze())\n", - "axs[0, 0].set_title(\"No interventions\")\n", - "axs[0, 1].set_title(\n", - " f\"Overshoot mean: {unintervened_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {unintervened_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", - ")\n", - "\n", - "\n", - "add_pred_to_plot(\n", - " intervened_samples[\"S\"], axs, coords=(1, 0), color=colors[0], label=\"susceptible\"\n", - ")\n", - "add_pred_to_plot(\n", - " intervened_samples[\"I\"], axs, coords=(1, 0), color=colors[1], label=\"infected\"\n", - ")\n", - "add_pred_to_plot(\n", - " intervened_samples[\"R\"], axs, coords=(1, 0), color=colors[2], label=\"recovered\"\n", - ")\n", - "axs[1, 0].set_title(\"Both interventions\")\n", - "axs[1, 0].legend_.remove()\n", - "\n", - "\n", - "axs[1, 1].hist(intervened_samples[\"overshoot\"].squeeze())\n", - "axs[1, 1].set_title(\n", - " f\"Overshoot mean: {intervened_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {intervened_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", - ")\n", - "\n", - "\n", - "add_pred_to_plot(\n", - " mask_samples[\"S\"], axs, coords=(2, 0), color=colors[0], label=\"susceptible\"\n", - ")\n", - "add_pred_to_plot(\n", - " mask_samples[\"I\"], axs, coords=(2, 0), color=colors[1], label=\"infected\"\n", - ")\n", - "add_pred_to_plot(\n", - " mask_samples[\"R\"], axs, coords=(2, 0), color=colors[2], label=\"recovered\"\n", - ")\n", - "axs[2, 0].set_title(\"Mask only\")\n", - "axs[2, 0].legend_.remove()\n", - "\n", - "axs[2, 1].hist(mask_samples[\"overshoot\"].squeeze())\n", - "axs[2, 1].set_title(\n", - " f\"Overshoot mean: {mask_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {mask_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", - ")\n", - "\n", - "add_pred_to_plot(\n", - " lockdown_samples[\"S\"], axs, coords=(3, 0), color=colors[0], label=\"susceptible\"\n", - ")\n", - "add_pred_to_plot(\n", - " lockdown_samples[\"I\"], axs, coords=(3, 0), color=colors[1], label=\"infected\"\n", - ")\n", - "add_pred_to_plot(\n", - " lockdown_samples[\"R\"], axs, coords=(3, 0), color=colors[2], label=\"recovered\"\n", - ")\n", - "axs[3, 0].set_title(\"Lockdown only\")\n", - "axs[3, 0].legend_.remove()\n", - "\n", - "axs[3, 1].hist(lockdown_samples[\"overshoot\"].squeeze())\n", - "axs[3, 1].set_title(\n", - " f\"Overshoot mean: {lockdown_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {lockdown_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", - ")\n", - "\n", - "\n", - "fig.tight_layout()\n", - "fig.suptitle(\"Trajectories and overshoot distributions\", fontsize=16, y=1.05)\n", - "sns.despine()\n", - "\n", - "plt.savefig(\"counterfactual_sir.png\")" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dict_keys(['lockdown', 'mask', 'lockdown_efficiency', 'mask_efficiency', 'joint_efficiency', 'beta', 'gamma', 'S', 'I', 'R', 'l', 'overshoot', 'os_too_high'])\n" - ] - } - ], - "source": [ - "with ExtractSupports() as s:\n", - " policy_model()\n", - "\n", - "supports = s.supports\n", - "print(supports.keys())\n", - "\n", - "antecedents = {\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(1.0)}\n", - "alternatives = {\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(0.0)}\n", - "witnesses = {key: s.supports[key] for key in [\"lockdown_efficiency\", \"mask_efficiency\"]}\n", - "consequents = {\"os_too_high\": torch.tensor(1.0)}\n", - "\n", - "with MultiWorldCounterfactual() as mwc:\n", - " with SearchForExplanation(\n", - " supports=supports,\n", - " alternatives=alternatives,\n", - " antecedents=antecedents,\n", - " antecedent_bias=0.0,\n", - " witnesses=witnesses,\n", - " consequents=consequents,\n", - " consequent_scale=1e-8,\n", - " witness_bias=0.2,\n", - " ):\n", - " with pyro.plate(\"sample\", exp_plate_size):\n", - " with pyro.poutine.trace() as tr:\n", - " policy_model_all()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "def get_table(\n", - " trace, mwc, antecedents, witnesses, consequents, others=None, world: int = 1\n", - "):\n", - "\n", - " values_table = {}\n", - " nodes = trace.trace.nodes\n", - " witnesses = [key for key, _ in witnesses.items()]\n", - "\n", - " with mwc:\n", - "\n", - " for antecedent_str in antecedents.keys():\n", - "\n", - " obs_indices = IndexSet(\n", - " **{\n", - " name: {0}\n", - " for name, ind in indices_of(nodes[antecedent_str][\"value\"]).items()\n", - " }\n", - " )\n", - " obs_ant = gather(\n", - " nodes[antecedent_str][\"value\"],\n", - " obs_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " int_indices = IndexSet(\n", - " **{\n", - " name: {world}\n", - " for name, ind in indices_of(nodes[antecedent_str][\"value\"]).items()\n", - " }\n", - " )\n", - " int_ant = gather(\n", - " nodes[antecedent_str][\"value\"],\n", - " int_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " values_table[f\"{antecedent_str}_obs\"] = obs_ant.squeeze().tolist()\n", - " values_table[f\"{antecedent_str}_int\"] = int_ant.squeeze().tolist()\n", - "\n", - " apr_ant = nodes[f\"__cause____antecedent_{antecedent_str}\"][\"value\"]\n", - " values_table[f\"apr_{antecedent_str}\"] = apr_ant.squeeze().tolist()\n", - "\n", - " if witnesses:\n", - " for candidate in witnesses:\n", - " obs_indices = IndexSet(\n", - " **{\n", - " name: {world}\n", - " for name, ind in indices_of(nodes[candidate][\"value\"]).items()\n", - " }\n", - " )\n", - " obs_candidate = gather(\n", - " nodes[candidate][\"value\"],\n", - " obs_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " int_indices = IndexSet(\n", - " **{\n", - " name: {world}\n", - " for name, ind in indices_of(nodes[candidate][\"value\"]).items()\n", - " }\n", - " )\n", - " int_candidate = gather(\n", - " nodes[candidate][\"value\"],\n", - " int_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " values_table[f\"{candidate}_obs\"] = obs_candidate.squeeze().tolist()\n", - " values_table[f\"{candidate}_int\"] = int_candidate.squeeze().tolist()\n", - "\n", - " wpr_con = nodes[f\"__cause____witness_{candidate}\"][\"value\"]\n", - " values_table[f\"wpr_{candidate}\"] = wpr_con.squeeze().tolist()\n", - "\n", - " if others:\n", - " for other in others:\n", - " obs_indices = IndexSet(\n", - " **{\n", - " name: {0}\n", - " for name, ind in indices_of(nodes[other][\"value\"]).items()\n", - " }\n", - " )\n", - "\n", - " obs_other = gather(\n", - " nodes[other][\"value\"],\n", - " obs_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " int_indices = IndexSet(\n", - " **{\n", - " name: {world}\n", - " for name, ind in indices_of(nodes[other][\"value\"]).items()\n", - " }\n", - " )\n", - "\n", - " int_other = gather(\n", - " nodes[other][\"value\"],\n", - " int_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " values_table[f\"{other}_obs\"] = obs_other.squeeze().tolist()\n", - " values_table[f\"{other}_int\"] = int_other.squeeze().tolist()\n", - "\n", - " for consequent in consequents.keys():\n", - "\n", - " obs_indices = IndexSet(\n", - " **{\n", - " name: {0}\n", - " for name, ind in indices_of(nodes[consequent][\"value\"]).items()\n", - " }\n", - " )\n", - " obs_consequent = gather(\n", - " nodes[consequent][\"value\"],\n", - " obs_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " int_indices = IndexSet(\n", - " **{\n", - " name: {world}\n", - " for name, ind in indices_of(nodes[consequent][\"value\"]).items()\n", - " }\n", - " )\n", - " int_consequent = gather(\n", - " nodes[consequent][\"value\"],\n", - " int_indices,\n", - " event_dim=0,\n", - " )\n", - "\n", - " values_table[f\"{consequent}_obs\"] = obs_consequent.squeeze().tolist()\n", - " values_table[f\"{consequent}_int\"] = int_consequent.squeeze().tolist()\n", - "\n", - " values_df = pd.DataFrame(values_table)\n", - "\n", - " return values_df\n", - "\n", - "\n", - "table = get_table(\n", - " tr,\n", - " mwc,\n", - " antecedents,\n", - " witnesses,\n", - " consequents,\n", - " others=[\"joint_efficiency\", \"overshoot\"],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      " - ], - "text/plain": [ - " lockdown_obs lockdown_int apr_lockdown mask_obs mask_int apr_mask \\\n", - "10 1.0 0.0 0 1.0 1.0 1 \n", - "11 1.0 0.0 0 1.0 1.0 1 \n", - "13 1.0 0.0 0 1.0 1.0 1 \n", - "29 1.0 0.0 0 1.0 1.0 1 \n", - "36 1.0 0.0 0 1.0 1.0 1 \n", - "... ... ... ... ... ... ... \n", - "9928 1.0 0.0 0 1.0 1.0 1 \n", - "9941 1.0 0.0 0 1.0 1.0 1 \n", - "9964 1.0 0.0 0 1.0 1.0 1 \n", - "9983 1.0 0.0 0 1.0 1.0 1 \n", - "9992 1.0 0.0 0 1.0 1.0 1 \n", - "\n", - " lockdown_efficiency_obs lockdown_efficiency_int \\\n", - "10 0.0 0.0 \n", - "11 0.0 0.0 \n", - "13 0.0 0.0 \n", - "29 0.0 0.0 \n", - "36 0.0 0.0 \n", - "... ... ... \n", - "9928 0.0 0.0 \n", - "9941 0.0 0.0 \n", - "9964 0.0 0.0 \n", - "9983 0.0 0.0 \n", - "9992 0.0 0.0 \n", - "\n", - " wpr_lockdown_efficiency mask_efficiency_obs mask_efficiency_int \\\n", - "10 0 0.10 0.10 \n", - "11 0 0.45 0.45 \n", - "13 0 0.10 0.10 \n", - "29 0 0.10 0.10 \n", - "36 0 0.10 0.10 \n", - "... ... ... ... \n", - "9928 0 0.10 0.10 \n", - "9941 0 0.45 0.45 \n", - "9964 0 0.10 0.10 \n", - "9983 0 0.45 0.45 \n", - "9992 0 0.10 0.10 \n", - "\n", - " wpr_mask_efficiency joint_efficiency_obs joint_efficiency_int \\\n", - "10 1 0.7 0.10 \n", - "11 0 0.7 0.45 \n", - "13 1 0.7 0.10 \n", - "29 1 0.7 0.10 \n", - "36 1 0.7 0.10 \n", - "... ... ... ... \n", - "9928 1 0.7 0.10 \n", - "9941 0 0.7 0.45 \n", - "9964 1 0.7 0.10 \n", - "9983 0 0.7 0.45 \n", - "9992 1 0.7 0.10 \n", - "\n", - " overshoot_obs overshoot_int os_too_high_obs os_too_high_int \n", - "10 29.467434 18.516724 1.0 0.0 \n", - "11 28.244480 27.621283 1.0 1.0 \n", - "13 15.683098 23.994499 0.0 1.0 \n", - "29 14.286774 23.541285 0.0 1.0 \n", - "36 26.499218 20.113390 1.0 1.0 \n", - "... ... ... ... ... \n", - "9928 31.073330 18.907593 1.0 0.0 \n", - "9941 28.345650 27.761520 1.0 1.0 \n", - "9964 20.732349 22.889946 1.0 1.0 \n", - "9983 26.055958 28.706375 1.0 1.0 \n", - "9992 25.797340 21.365763 1.0 1.0 \n", - "\n", - "[739 rows x 18 columns]" - ] - }, - "metadata": {}, 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- "text/plain": [ - "
      " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "factual = table[\n", - " (table[\"lockdown_int\"] == 1)\n", - " & (table[\"mask_int\"] == 1)\n", - " & (table[\"wpr_lockdown_efficiency\"] == 0 & (table[\"wpr_mask_efficiency\"] == 0))\n", - "]\n", - "\n", - "\n", - "counterfactual_lockdown = table[\n", - " (table[\"lockdown_int\"] == 0)\n", - " & (table[\"mask_int\"] == 1)\n", - " & (table[\"wpr_lockdown_efficiency\"] == 0)\n", - "]\n", - "\n", - "display(counterfactual_lockdown)\n", - "\n", - "counterfactual_mask = table[\n", - " (table[\"lockdown_int\"] == 1)\n", - " & (table[\"mask_int\"] == 0)\n", - " & (table[\"wpr_mask_efficiency\"] == 0)\n", - "]\n", - "\n", - "\n", - "fig, axs = plt.subplots(1, 3, figsize=(18, 6))\n", - "\n", - "factual_mean = factual[\"overshoot_int\"].mean().item()\n", - "axs[0].hist(factual[\"overshoot_int\"])\n", - "axs[0].set_title(\n", - " f\"Factual\\n overshoot mean: {factual_mean:.2f}, Pr(too high): {factual['os_too_high_int'].mean().item():.2f}\"\n", - ")\n", - "axs[0].axvline(x=factual_mean, color=\"grey\", linestyle=\"--\")\n", - "\n", - "counterfactual_lockdown_mean = counterfactual_lockdown[\"overshoot_int\"].mean()\n", - "axs[1].hist(counterfactual_lockdown[\"overshoot_int\"])\n", - "axs[1].set_title(\n", - " f\"Counterfactual_lockdown\\n overshoot mean: {counterfactual_lockdown_mean:.2f}, Pr(too high): {counterfactual_lockdown['os_too_high_int'].mean():.2f}\"\n", - ")\n", - "axs[1].axvline(x=counterfactual_lockdown_mean, color=\"grey\", linestyle=\"--\")\n", - "\n", - "counterfactual_mask_mean = counterfactual_mask[\"overshoot_int\"].mean()\n", - "axs[2].hist(counterfactual_mask[\"overshoot_int\"])\n", - "axs[2].set_title(\n", - " f\"Counterfactual_mask\\n overshoot mean: {counterfactual_mask_mean:.2f}, Pr(too high): {counterfactual_mask['os_too_high_int'].mean():.2f}\"\n", - ")\n", - "axs[2].axvline(x=counterfactual_mask_mean, color=\"grey\", linestyle=\"--\")\n", - "\n", - "for i in range(3):\n", - " axs[i].set_xlim(5, 40)\n", - " axs[i].axvline(x=overshoot_threshold, color=\"red\", linestyle=\"-\")\n", - "\n", - "plt.savefig(\"counterfactual_sir_search.png\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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zZ1eqbgCqSDEKsXVgbGkbABie/pmOrt29sWXHm1mXAVXpkEOpJEmira0tmpubY926dbFnz55YsmRJ1NTUxLXXXhuXXnpptLa2xvr162PDhg2xePHiePDBB2PixImVrB+AKlCMmnh8/3/LugwAyBX9E8i7Qw6lXnnllejs7IyNGzfGuHHjIiKira0tbr311jj99NNj+/bt0dHREaNHj47JkyfHE088EevXr4/LLrusYsUDAAAAkE+HHEq1tLTE6tWrS4HUO3p7e2Pz5s0xderUGD16dGn/zJkzo7Ozs2yFAlDNkqiLYkREDERNhFsQAOAQ6J9Avh3yp+81NzfHnDlzSq+LxWKsXbs2Tj311Oju7o7x48cPef/YsWNj586d5asUgKpVF8X491HPxr+PerY0XAMAB6d/Anl3yKHUu7W3t8cLL7wQV1xxRezduzfq6+uHfL++vj76+/s/dIEAAAAAVJ//VCjV3t4ea9asifb29mhtbY2GhoYDAqj+/v5obGwsS5EAAAAAVJcPHEotXbo07rnnnmhvb4+5c+dGRMSECROip6dnyPt6enoOuKUPAAAAACI+YCi1bNmy6OjoiNtuuy3mz59f2j99+vTYsmVL7Nu3r7Rv06ZNMX369PJVCgAAAEDVOORQatu2bbF8+fK45JJLYubMmdHd3V36mjVrVhx11FFx/fXXx9atW2PVqlXx3HPPxYUXXljJ2gEAAADIqUMOpR555JEYHByMFStWxOzZs4d81dbWxvLly6O7uzsWLFgQv/71r+POO++MiRMnVrJ2AIBc2rVrV7S1tcWsWbNizpw5ccstt0RfX19ERGzfvj0WLlwYM2bMiHnz5sXjjz+ecbUAAJVRd6hvXLRoUSxatOh9vz9p0qRYu3ZtWYoC4PCSRCH+Ovjx0jZUsyRJoq2tLZqbm2PdunWxZ8+eWLJkSdTU1MS1114bl156abS2tsb69etjw4YNsXjx4njwwQf9sg84gP4J5N0hh1IAUCmDUROP9k/OugxIxSuvvBKdnZ2xcePGGDduXEREtLW1xa233hqnn356bN++PTo6OmL06NExefLkeOKJJ2L9+vVx2WWXZVw5MNLon0DefeBP3wMA4D+vpaUlVq9eXQqk3tHb2xubN2+OqVOnxujRo0v7Z86cGZ2dnSlXCQBQeUIpAIAUNTc3x5w5c0qvi8VirF27Nk499dTo7u6O8ePHD3n/2LFjY+fOnWmXCQBQcUIpADJXF4PxjVF/im+M+lPUxWDW5UCq2tvb44UXXogrrrgi9u7dG/X19UO+X19fH/39/RlVx0gwWEyyLoERSv8E8s4zpQAAMtLe3h5r1qyJ22+/PVpbW6OhoSHeeOONIe/p7++PxsbGbApkRKitKcTlHc9G1+7e1I99xvEtcc3cE1I/LgCHB6EUAEAGli5dGvfff3+0t7fH3LlzIyJiwoQJ0dXVNeR9PT09B9zSx+Gna3dvbNnxZurHndxyROrHBODw4fY9AICULVu2LDo6OuK2226L+fPnl/ZPnz49tmzZEvv27Svt27RpU0yfPj2LMgEAKkooBQCQom3btsXy5cvjkksuiZkzZ0Z3d3fpa9asWXHUUUfF9ddfH1u3bo1Vq1bFc889FxdeeGHWZQMAlJ3b9wAAUvTII4/E4OBgrFixIlasWDHke3/5y19i+fLl8Z3vfCcWLFgQkyZNijvvvDMmTpyYUbUAAJUjlAIASNGiRYti0aJF7/v9SZMmxdq1a1OsCAAgG0IpADKXRCG2D36stA0ADE//BPJOKAVA5gajJjb0fzLrMgAgV/RPIO886BwAAABGmJamhhgsJpnWkPXxqX6ulAIAAIARpnlUXdTWFOLyjmeja3dv6sc/bnxT3HHRyakfl8OLUAqAzNXFYFzUuDkiIjr2TY+BqM24IgAY+fTPw0PX7t7YsuPNrMuAihBKATAifKRQzLoEAMgd/RPIM8+UAgAAACB1QikAAAAAUieUAgAAACB1QikAAAAAUieUAgAAACB1Pn0PgMwlUYj/GGwqbQMAw9M/gbwTSgGkYLCYRG2NYfH9DEZN/O/+E7IuAwByRf8E8k4oBZCC2ppCXN7xbHTt7s2shjOOb4lr5hpcAQCAkUEoBZCSrt29sWXHm5kdf3LLEZkdGwAA4N2EUgBkri4G48uNf46IiF/smxYDUZtxRQAw8umfQN4JpQAYERoLA1mXAAC5o38CeVaTdQEAlTZYTLIuAQAAgHdxpRRQ9bJ+yLgHjAMAABxIKAUcFrJ8yLgHjAMAABzI7XsAAAAApE4oBQAAAEDq3L4HQOaSKER3cXRpGwAYnv4J5J1QCoDMDUZN/M++qVmXAQC5on8Ceef2PQAAAABSJ5QCAAAAIHVu3wMgc7UxGBc0bImIiAf6TozBqM24IgAY+fRPIO+EUgBkrhARH63pL20DAMPTP4G8c/seAAAAAKkTSgEAAACQOqEUAAAAAKkTSgEAAACQOqEUAAAAAKnz6XsAZC6JiL8XG0vbAMDw9E8g74RSAGRuMGrjV30nZV0GAOSK/gnkndv3AAAAAEidUAoAAACA1Ll9D4DM1cZgnNfwYkRE/I++KTEYtRlXBAAjn/4J5J1QCoDMFSLi4zX7StsAwPD0TyDv3L4HAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOp8+h4AmUsi4q1ifWkbABie/gnknVAKgMwNRm38su9TWZcBALmifwJ55/Y9AAAAAFInlAIAAAAgdW7fAyBztVGMcxpeioiI/9V3Qgz6nQkADEv/BPJOKAVA5gqRREvNP0vbAMDw9E8g70TpAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKTOp+8BMCLsS7QkAPig9E8gz/wEAyBzA1Eb9++bkXUZAJAr+ieQd27fAwCAgxgsJlmXAABVyZVSAABwELU1hbi849no2t2b+rHPOL4lrpl7QurHBYA0CKWAihksJlFbU8i6DHKgNopxVv3LERHxcH9rDLqQFxhhunb3xpYdb6Z+3MktR6R+TPJD/wTyTigFVEylf7Pc0Lc3/vv/216wfGP0NYw64D1+w5wPhUjiqNre0jYAMDz9E8g7oRRQUZX8zfKo/n2l7Rf/463YW7//gPf4DTMAAMDI5PpOAAAAAFInlAIAAAAgdUIpKLOR8rHRI6UOAAAAeC+eKQVlluXHRr/juPFNccdFJ2d2fAAAABiOUAoqIKuPjYY825+4eBcAPij9E8gzoRQAmRuI2li779NZlwEAuaJ/AnknVgcAAAAgdUIpymKkPFR7pNSRtZamBucCAACAEc3te5SFh3uPLM2j6jL/f3LG8S1xzdwTMjk2+VMbxTizfltERPyuf3IM+p0JAAxL/wTyTihF2Xi498iT5f+TyS1HZHJc8qkQSfxb7Z7SNgAwPP0TyDtROgAAAACpK2so1dfXF0uWLInPfOYzMXv27Lj77rvL+dcDABwWzFQAwOGgrLfv/eAHP4jnn38+1qxZEzt27IjrrrsuJk6cGGeffXY5DwMAUNXMVADA4aBsodQ///nP+MUvfhF33XVXnHjiiXHiiSfG1q1bY926dQYoAIBDZKYCAA4XZbt976WXXoqBgYE4+eT//+lnM2fOjM2bN0exWCzXYQAAqpqZCgA4XJTtSqnu7u74+Mc/HvX19aV948aNi76+vnjjjTfiyCOPPOifT5K3Py2itzebj6/nwzumuSaK/R/J9PgjZf1kfS4mjHr731KWdaRRQ33fQPTWvJ2tt46ti/6GA49zuJyLvNTxfjUUkprYv2d/RES0jv1IJIXaTOpI00j6mXW4eee8vzN7jDRmqpEpq58XWf+8cvyRffxy9M+DzVMj/b/f8SvLrMLBlGueKiRlmsh+9atfxR133BG/+93vSvu2b98eX/ziF+Oxxx6LT3ziEwf98zt37ozPf/7z5SgFAGBYhzKfZMFMBQDkxYedp8p2pVRDQ0P09/cP2ffO68bGxmH//Pjx4+Oxxx6LI444IgqFQrnKAgAYIkmS+Mc//hHjx4/PupT3ZKYCAEa6cs1TZQulJkyYEH//+99jYGAg6ure/mu7u7ujsbExmpubh/3zNTU1I/K3lQBA9fnoRz+adQnvy0wFAORBOeapsj3ofMqUKVFXVxednZ2lfZs2bYpp06ZFTU3ZDgMAUNXMVADA4aJsk82oUaPi/PPPjxtvvDGee+652LBhQ9x9993x9a9/vVyHAACoemYqAOBwUbYHnUdE7N27N2688cb47W9/G01NTXHxxRfHwoULy/XXAwAcFsxUAMDhoKyhFAAAAAAcCg8mAAAAACB1QikAAAAAUieUAgAAACB1IyKUevjhh+P4448f8tXW1pZ1WVWjv78/zj333HjqqadK+7Zv3x4LFy6MGTNmxLx58+Lxxx/PsMLq8F7n+aabbjpgba9duzbDKvNr165d0dbWFrNmzYo5c+bELbfcEn19fRFhPZfTwc6z9Vxer732Wlx88cVx8sknxxlnnBGrV68ufc+aLp+DnedqW9PmqcozU6XDTFVZZqp0mKnSYZ5KRyXnqbpKFPxBdXV1xZlnnhlLly4t7WtoaMiwourR19cXV111VWzdurW0L0mSuPTSS6O1tTXWr18fGzZsiMWLF8eDDz4YEydOzLDa/Hqv8xwRsW3btrjqqqviggsuKO1rampKu7zcS5Ik2traorm5OdatWxd79uyJJUuWRE1NTVx77bXWc5kc7Dxfd9111nMZFYvFWLRoUUybNi0eeOCBeO211+LKK6+MCRMmxLnnnmtNl8nBzvN5551XdWvaPFVZZqp0mKkqy0yVDjNVOsxT6aj0PDUiQqlt27ZFa2trtLS0ZF1KVenq6oqrrroq3v0Bi08++WRs3749Ojo6YvTo0TF58uR44oknYv369XHZZZdlVG1+vd95jnh7bV988cXW9of0yiuvRGdnZ2zcuDHGjRsXERFtbW1x6623xumnn249l8nBzvM7A5T1XB49PT0xZcqUuPHGG6OpqSmOOeaY+OxnPxubNm2KcePGWdNlcrDz/M4QVU1r2jxVOWaqdJipKs9MlQ4zVTrMU+mo9Dw1Im7f27ZtWxxzzDFZl1F1/vjHP8Ypp5wSP//5z4fs37x5c0ydOjVGjx5d2jdz5szo7OxMucLq8H7nube3N3bt2mVtl0FLS0usXr261NTf0dvbaz2X0cHOs/VcXuPHj48f//jH0dTUFEmSxKZNm+Lpp5+OWbNmWdNldLDzXI1r2jxVOWaqdJipKs9MlQ4zVTrMU+mo9DyV+ZVSSZLEX//613j88cdj5cqVMTg4GGeffXa0tbVFfX191uXl2le+8pX33N/d3R3jx48fsm/s2LGxc+fONMqqOu93nrdt2xaFQiF+9rOfxe9///sYM2ZMfOMb3xhyWSOHprm5OebMmVN6XSwWY+3atXHqqadaz2V0sPNsPVfOF77whdixY0eceeaZMXfu3Pje975nTVfAu8/z888/X1Vr2jxVWWaqdJipKs9MlQ4zVfrMU+moxDyVeSi1Y8eO2Lt3b9TX18ePf/zjeP311+Omm26Kffv2xXe/+92sy6tK75zvf1VfXx/9/f0ZVVSdXnnllSgUCnHsscfG1772tXj66afjhhtuiKampjjrrLOyLi/X2tvb44UXXohf/vKXce+991rPFfKv53nLli3Wc4X85Cc/iZ6enrjxxhvjlltu8TO6Qt59nk888cSqWtPmqWz495oOM1XlmKnSYaaqPPNUOioxT2UeSh199NHx1FNPxcc+9rEoFAoxZcqUKBaLcc0118T1118ftbW1WZdYdRoaGuKNN94Ysq+/vz8aGxuzKahKnX/++XHmmWfGmDFjIiLihBNOiFdffTXuv/9+DedDaG9vjzVr1sTtt98era2t1nOFvPs8f/KTn7SeK2TatGkR8fbDfa+++ur40pe+FHv37h3yHmv6w3v3eX7mmWeqak2bp7KhB6XDTFUZZqp0mKnSYZ5KRyXmqRHxTKkxY8ZEoVAovZ48eXL09fXFnj17Mqyqek2YMCF6enqG7Ovp6Tng8kY+nEKhUPrH+Y5jjz02du3alU1BVWDp0qVxzz33RHt7e8ydOzcirOdKeK/zbD2XV09PT2zYsGHIvuOOOy72798fLS0t1nSZHOw89/b2Vt2aNk+lTw9Khx5UfmaqdJipKss8lY5Kz1OZh1J/+MMf4pRTThmSYr744osxZsyYOPLIIzOsrHpNnz49tmzZEvv27Svt27RpU0yfPj3DqqrPHXfcEQsXLhyy76WXXopjjz02m4JybtmyZdHR0RG33XZbzJ8/v7Tfei6v9zvP1nN5vf7667F48eIhDfv555+PI488MmbOnGlNl8nBzvN9991XVWvaPJUNPSgdelB5manSYaaqPPNUOio+TyUZe+utt5I5c+YkV155ZbJt27bk0UcfTWbPnp2sWrUq69KqSmtra/Lkk08mSZIkAwMDybx585Jvf/vbycsvv5ysXLkymTFjRvK3v/0t4yrz71/P8+bNm5OpU6cmq1evTl577bVk3bp1yUknnZQ888wzGVeZP11dXcmUKVOS22+/Pdm9e/eQL+u5fA52nq3n8hoYGEgWLFiQfPOb30y2bt2aPProo8nnPve55N5777Wmy+hg57na1rR5Kj1mqnSYqSrDTJUOM1U6zFPpqPQ8lXkolSRJ8vLLLycLFy5MZsyYkZx22mnJT3/606RYLGZdVlX518aeJEny6quvJl/96leTk046KZk/f36ycePGDKurHu8+zw8//HBy3nnnJdOmTUvOPvvs5KGHHsqwuvxauXJl0tra+p5fSWI9l8tw59l6Lq+dO3cml156afLpT386Oe2005IVK1aUep81XT4HO8/VtqbNU+kwU6XDTFUZZqp0mKnSY55KRyXnqUKSJEmZruoCAAAAgEOS+TOlAAAAADj8CKUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASN3/BY2Ybns8AVAAAAAAAElFTkSuQmCC", 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", - "text/plain": [ - "
      " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_counterfactual_by_context(data, name, other):\n", - "\n", - " grouped_data = data.groupby([\"wpr_lockdown_efficiency\", \"wpr_mask_efficiency\"])\n", - "\n", - " fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n", - "\n", - " for (lockdown_efficiency, mask_efficiency), ax in zip(\n", - " grouped_data.groups.keys(), axs.flatten()\n", - " ):\n", - " data_subset = grouped_data.get_group((lockdown_efficiency, mask_efficiency))\n", - " mean_overshoot = data_subset[\"overshoot_int\"].mean().item()\n", - "\n", - " fixed = mask_efficiency if name == \"lockdown\" else lockdown_efficiency\n", - " ax.hist(data_subset[\"overshoot_int\"])\n", - " ax.set_title(\n", - " f\"{other} eff fixed: {fixed}\\nOvershoot mean: {mean_overshoot:.2f}, Pr(too high): {data_subset['os_too_high_int'].mean().item():.2f}\"\n", - " )\n", - " ax.set_xlim(5, 35)\n", - " ax.axvline(x=mean_overshoot, color=\"grey\", linestyle=\"--\")\n", - " ax.axvline(x=overshoot_threshold, color=\"red\", linestyle=\"-\")\n", - "\n", - " plt.suptitle(\n", - " f\"Counterfactual {name} by {other.lower()} efficiency contexts\",\n", - " fontsize=16,\n", - " y=1,\n", - " )\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "\n", - "plot_counterfactual_by_context(counterfactual_lockdown, \"lockdown\", \"Mask\")\n", - "\n", - "plot_counterfactual_by_context(counterfactual_mask, \"mask\", \"Lockdown\")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "sufficiency_table = get_table(\n", - " tr,\n", - " mwc,\n", - " antecedents,\n", - " witnesses,\n", - " consequents,\n", - " world=2,\n", - " others=[\"joint_efficiency\", \"overshoot\"],\n", - ")\n", - "\n", - "\n", - "factual_sufficiency = sufficiency_table[\n", - " (sufficiency_table[\"lockdown_int\"] == 1)\n", - " & (sufficiency_table[\"mask_int\"] == 1)\n", - " & (\n", - " sufficiency_table[\"wpr_lockdown_efficiency\"]\n", - " == 0 & (sufficiency_table[\"wpr_mask_efficiency\"] == 0)\n", - " )\n", - "]\n", - "\n", - "counterfactual_sufficiency_lockdown = sufficiency_table[\n", - " (sufficiency_table[\"lockdown_int\"] == 0)\n", - " & (sufficiency_table[\"mask_int\"] == 1)\n", - " & (sufficiency_table[\"wpr_lockdown_efficiency\"] == 0)\n", - "]\n", - "\n", - "counterfactual_sufficiency_mask = sufficiency_table[\n", - " (sufficiency_table[\"lockdown_int\"] == 1)\n", - " & (sufficiency_table[\"mask_int\"] == 0)\n", - " & (sufficiency_table[\"wpr_mask_efficiency\"] == 0)\n", - "]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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      " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(1, 3, figsize=(18, 6))\n", - "\n", - "factual_sufficiency_mean = factual_sufficiency[\"overshoot_int\"].mean().item()\n", - "axs[0].hist(factual_sufficiency[\"overshoot_int\"])\n", - "\n", - "axs[0].set_title((\n", - " f\"Factual\\n overshoot mean: {factual_sufficiency_mean:.2f}, Pr(too high): \"\n", - " f\"{factual_sufficiency['os_too_high_int'].mean().item():.2f}\"\n", - "))\n", - "axs[0].axvline(x=factual_sufficiency_mean, color=\"grey\", linestyle=\"--\")\n", - "\n", - "counterfactual_sufficiency_lockdown_mean = counterfactual_sufficiency_lockdown[\"overshoot_int\"].mean()\n", - "axs[1].hist(counterfactual_sufficiency_lockdown[\"overshoot_int\"])\n", - "axs[1].set_title((\n", - " f\"Counterfactual_lockdown\\n overshoot mean: {counterfactual_sufficiency_lockdown_mean:.2f}, \"\n", - " f\"Pr(too high): {counterfactual_lockdown['os_too_high_int'].mean():.2f}\"\n", - "))\n", - "axs[1].axvline(x=counterfactual_sufficiency_lockdown_mean, color=\"grey\", linestyle=\"--\")\n", - "\n", - "counterfactual_sufficiency_mask_mean = counterfactual_sufficiency_mask[\"overshoot_int\"].mean()\n", - "axs[2].hist(counterfactual_sufficiency_mask[\"overshoot_int\"])\n", - "axs[2].set_title((\n", - " f\"Counterfactual_mask\\n overshoot mean: {counterfactual_sufficiency_mask_mean:.2f}, \"\n", - " f\"Pr(too high): {counterfactual_mask['os_too_high_int'].mean():.2f}\"\n", - "))\n", - "axs[2].axvline(x=counterfactual_sufficiency_mask_mean, color=\"grey\", linestyle=\"--\")\n", - "\n", - "for i in range(3):\n", - " axs[i].set_xlim(5, 40)\n", - " axs[i].axvline(x=overshoot_threshold, color=\"red\", linestyle=\"-\")\n", - "\n", - "#plt.savefig(\"counterfactual_sir_search_sufficiency.png\")\n", - "\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      lockdown_obslockdown_intapr_lockdownmask_obsmask_intapr_masklockdown_efficiency_obslockdown_efficiency_intwpr_lockdown_efficiencymask_efficiency_obsmask_efficiency_intwpr_mask_efficiencyjoint_efficiency_obsjoint_efficiency_intovershoot_obsovershoot_intos_too_high_obsos_too_high_int
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      " - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [lockdown_obs, lockdown_int, apr_lockdown, mask_obs, mask_int, apr_mask, lockdown_efficiency_obs, lockdown_efficiency_int, wpr_lockdown_efficiency, mask_efficiency_obs, mask_efficiency_int, wpr_mask_efficiency, joint_efficiency_obs, joint_efficiency_int, overshoot_obs, overshoot_int, os_too_high_obs, os_too_high_int]\n", - "Index: []" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "counterfactual_sufficiency_lockdown.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "chirho", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From a3942bc2ae0769954c2ae1320a95608515aadf6e Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Fri, 23 Aug 2024 15:46:40 -0400 Subject: [PATCH 060/111] removed wip --- docs/source/explainable_sir.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 76962902..da60d558 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -712,7 +712,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Further notebook is still work in progress. Some questions I had:\n", + "Some questions I had: [This markdown text should be removed in the final draft]\n", "1. Normalization of degree of responsibility is not super clear to me that why we would want to do that.\n", "2. The plots below are updated with `policy_model` instead of `policy_model_all`." ] From d00598aa4596a6b12719171efd6f3386246c3874 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Mon, 26 Aug 2024 09:47:22 -0400 Subject: [PATCH 061/111] revised intro --- docs/source/explainable_sir.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 76962902..e6bb864a 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -11,9 +11,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The **Explainable Reasoning with Chirho** package aims to provide a unified, principled approach to computations of causal explanations. We showed in an earlier [tutorial](https://basisresearch.github.io/chirho/explainable_categorical.html) how Chirho provides a handler `SearchForExplanation` to carry out the program transformations needed to compute causal queries and explanations. We specifically used discrete models and in this tutorial, we extend the usage of `SearchForExplanation` to causal models with continuous random variables.\n", + "The **Explainable Reasoning with Chirho** package aims to provide a unified, principled approach to computations of causal explanations. We showed in an earlier [tutorial](https://basisresearch.github.io/chirho/explainable_categorical.html) how Chirho provides a handler `SearchForExplanation` to carry out the program transformations needed to compute causal queries and explanations, focusing on on discrete variables (we assume the reader is familar with it). In this notebook we illustrate the usage of `SearchForExplanation` for causal models with continuous random variables in the context of a dynamical system.\n", "\n", - "We take an epidemiological dynamical system model (described in more detail in this [tutorial](https://basisresearch.github.io/chirho/dynamical_intro.html)) and show how but-for analysis is not enough to derive conclusions about effects of different policies during a pandemic. We, then, show how various causal explanation queries can be computed using `SearchForExplanation` and in-built inference algorithms. We also demonstrate how more fine grained analysis can be done by post-processing the samples. " + "We take an epidemiological dynamical system model (described in more detail in this [tutorial](https://basisresearch.github.io/chirho/dynamical_intro.html)) and show how the but-for analysis is not sufficiently fine-grained to allow us to derive the right conclusions about effects of different policies during a pandemic. Next, we illustrate how various causal explanation queries can be computed using `SearchForExplanation` and inference algorithms. We also demonstrate how more detailed causal queries can be answered by post-processing the samples obtained using the handler. " ] }, { From aed343fde7dcd35a6b98e31b79ad4eb4b538b294 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Mon, 26 Aug 2024 10:10:45 -0400 Subject: [PATCH 062/111] added intro to the policy model --- docs/source/explainable_sir.ipynb | 24 ++++++++++++++++-------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index e6bb864a..80e357ba 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -39,12 +39,12 @@ "source": [ "## Setup\n", "\n", - "We first install the required dependencies for this example: PyTorch, Pyro, Chirho and some auxiliary variables.\n" + "The main dependencies for this example are PyTorch, Pyro, and ChiRho.\n" ] }, { "cell_type": "code", - "execution_count": 503, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -96,7 +96,18 @@ "source": [ "## Bayesian Epidemiological SIR model with Policies\n", "\n", - "Now, we build the epidemiological SIR model, one step at a time. We first encode deterministic SIR (Susceptible, Infected, Recovered) dynamics. Then we add uncertainty to the parameters that govern these dynamics, namely $\\beta$ and $\\gamma$. These parameters have been described in much detail in the [dynamical systems tutorial](https://basisresearch.github.io/chirho/dynamical_intro.html). We then integrate the resulting model into another model that describes the policy mechanisms such as imposing lockdown and masking restrictions." + "Now, we build the epidemiological SIR (Susceptible, Infected, Recovered/Removed) model, one step at a time. We first encode the deterministic SIR dynamics. Then we add uncertainty about the parameters that govern these dynamics - $\\beta$ and $\\gamma$. These parameters have been described in much detail in the [dynamical systems tutorial](https://basisresearch.github.io/chirho/dynamical_intro.html). We then incorporate the resulting model into a more complex causal model that describes the policy mechanisms such as imposing lockdown and masking restrictions.\n", + "\n", + "Our outcome of interest is overshoot, the proportion of the population that remains susceptible after the epidemic peak but eventually becomes infected as the epidemic continues. One way to compute it is to:\n", + "\n", + "1. Find the time at which the number of infected individuals is at its peak, `t_max`.\n", + "2. Determine the proportion of susceptibles at `t_max` in the whole population, `S_peak`.\n", + "3. Find the proportion of susceptibles (those not infected) at the end of the logging period, `S_final`.\n", + "4. Calculate the additional ratio of infected individuals since the peak as `S_peak - S_final`.\n", + "\n", + "This quantity is of interest because epidemic mitigation policies often have multiple goals that need to be balanced. One goal is to increase `S_final`, i.e., to limit the total number of infected individuals. Another goal is to limit the number of infected individuals at the peak of the epidemic to avoid overwhelming the healthcare system. A further goal is to minimize the proportion of the population that becomes infected after the peak, that is, the overshoot, to reduce healthcare and economic burdens. Balancing these objectives involves making trade-offs.\n", + "\n", + " Suppose we are working under constraint that the overshoot show be lower than 20% of the population, and we implement two policies, lockdown and masking, which together seem to lead to the overshoot being too high. In fact, only one of them is responsible, and we are interested in being able to identify which one. " ] }, { @@ -108,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 504, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -131,9 +142,6 @@ " return dX\n", "\n", "\n", - "# TODO add running overshoot to states?\n", - "# beta = (1 - l) beta0\n", - "\n", "class SIRDynamicsLockdown(SIRDynamics):\n", " def __init__(self, beta0, gamma):\n", " super().__init__(beta0, gamma)\n", @@ -148,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 505, + "execution_count": 4, "metadata": {}, "outputs": [ { From 13f29da158ece1e89f11117131db5a7dad613711 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Mon, 26 Aug 2024 10:20:14 -0400 Subject: [PATCH 063/111] added policies description --- docs/source/explainable_sir.ipynb | 25 +++++++++++++++---------- 1 file changed, 15 insertions(+), 10 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 80e357ba..33a03227 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -119,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -142,7 +142,9 @@ " return dX\n", "\n", "\n", - "class SIRDynamicsLockdown(SIRDynamics):\n", + "# l is a parameter describing the strenght of the intervening policies \n", + "# it is a value between 0 and 1, and (1-l) is the fraction of the original unintervened beta\n", + "class SIRDynamicsPolicies(SIRDynamics):\n", " def __init__(self, beta0, gamma):\n", " super().__init__(beta0, gamma)\n", " self.beta0 = beta0\n", @@ -156,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -168,7 +170,8 @@ } ], "source": [ - "# Computing overshoot in a simple SIR model\n", + "# Computing overshoot in a simple SIR model without interventions\n", + "# note it's below the desired threshold\n", "\n", "init_state = dict(S=torch.tensor(99.0), I=torch.tensor(1.0), R=torch.tensor(0.0))\n", "start_time = torch.tensor(0.0)\n", @@ -201,7 +204,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The number $0.15$ is the overshoot you get if $\\beta = 0.03, \\gamma = 0.5$. This value is observed by simulating the SIR dynamics model with these values and calculate overshoot directly. Now one can add uncertainty to the parameters of $\\beta, \\gamma$ and get a Bayesian SIR model." + "The number $0.15$ is the overshoot you get if $\\beta = 0.03, \\gamma = 0.5$. This value is observed by simulating the SIR dynamics model with these values and calculate overshoot directly." ] }, { @@ -210,12 +213,12 @@ "source": [ "### Bayesian SIR model\n", "\n", - "In Bayesian SIR model, we specifically add uncertainty to $\\beta$ and $\\gamma$ by inducing $\\beta$ to be drawn from the distribution Beta(18, 600), and $\\gamma$ to be drawn from the distribution Beta(1600, 1600). " + "Now suppose we are uncertain about $\\beta, \\gamma$, and want to construct a Bayesian SIR model that incorporates this uncertainty. Say we inducing $\\beta$ to be drawn from `Beta(18, 600)`, and $\\gamma$ to be drawn from distribution `Beta(1600, 1600)`. " ] }, { "cell_type": "code", - "execution_count": 506, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -244,7 +247,7 @@ "source": [ "### Bayesian SIR model with Policies\n", "\n", - "Now we integrate the Bayesian SIR model with the effect of different policies. We consider two possible policies, lockdown and masking, where each can be implemented with $50\\%$ probability. We encode their efficiencies which further affect the Bayesian SIR model via a utility function (`MaskedStaticIntervention`) defiend below. The model also computes `overshoot` and `os_too_high` indicating the overshoot and if it was too high for further analysis.\n", + "Now we incorporate the Bayesian SIR model into a larger model that includes the effect of two different policies, lockdown and masking, where each can be implemented with $50\\%$ probability (these probabilities won't really matter, as we will be intervening on these, the sampling is mainly used to register the parameters with Pyro). We encode their efficiencies which further affect the model. Crucially, these efficiencies interact in a fashion resembling the structure of the stone-throwing example we discussed in the tutorial on categorical variables. If lockdown is present, this limits the impact of masking as agents interact less and so masks have fewer opportunities to block anything. The model also computes `overshoot` and `os_too_high` for further analysis.\n", "\n" ] }, @@ -254,7 +257,9 @@ "metadata": {}, "outputs": [], "source": [ - "# Defining intervention\n", + "# a utility function \n", + "# allowing for interventions on a dynamical system\n", + "# within another model\n", "\n", "def MaskedStaticIntervention(time: R, intervention: Intervention[State[T]]):\n", "\n", @@ -301,7 +306,7 @@ " event_dim=0,\n", " )\n", "\n", - " lockdown_sir = bayesian_sir(SIRDynamicsLockdown)\n", + " lockdown_sir = bayesian_sir(SIRDynamicsPolicies)\n", " with LogTrajectory(logging_times, is_traced=True) as lt:\n", " with TorchDiffEq():\n", " with MaskedStaticIntervention(lockdown_time, dict(l=lockdown_efficiency)):\n", From b6164e4f9ffafc4a3a8a9370aabfa6a115eaaec3 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Mon, 26 Aug 2024 10:36:08 -0400 Subject: [PATCH 064/111] added explanation of the but-for analysis --- docs/source/counterfactual_sir.png | Bin 161649 -> 161069 bytes 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zv`IL>o6D`gLY)Xi%-SVap8fyYoZTig5pFNs2LSmvZ5B0!!D}$Q83JrhZ+{QFa?auZMfSg_X?p}dkx})m;Z_#A;Yai->c-*O}sWUJ^>j940($+Q~ zmWd(jH~@BDpZwv;5BY*Z!2vLRX#4v5Bt3hhJhzQzyl=0eh_yXk69qUP2T*tct`ghI zKVbj06cuBzb8I+v7AJ??>jZ^Y%qibeGO{ugw<1nd9+ diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 33a03227..58a545ba 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -247,13 +247,13 @@ "source": [ "### Bayesian SIR model with Policies\n", "\n", - "Now we incorporate the Bayesian SIR model into a larger model that includes the effect of two different policies, lockdown and masking, where each can be implemented with $50\\%$ probability (these probabilities won't really matter, as we will be intervening on these, the sampling is mainly used to register the parameters with Pyro). We encode their efficiencies which further affect the model. Crucially, these efficiencies interact in a fashion resembling the structure of the stone-throwing example we discussed in the tutorial on categorical variables. If lockdown is present, this limits the impact of masking as agents interact less and so masks have fewer opportunities to block anything. The model also computes `overshoot` and `os_too_high` for further analysis.\n", + "Now we incorporate the Bayesian SIR model into a larger model that includes the effect of two different policies, lockdown and masking, where each can be implemented with $50\\%$ probability (these probabilities won't really matter, as we will be intervening on these, the sampling is mainly used to register the parameters with Pyro). We encode their efficiencies which further affect the model. Crucially, these efficiencies interact in a fashion resembling the structure of the stone-throwing example we discussed in the tutorial on categorical variables. If lockdown is present, this limits the impact of masking as agents interact less and so masks have fewer opportunities to block anything. We assume the situation is assymetric: masking has no impact on the efficiency of lockdown. The model also computes `overshoot` and `os_too_high` for further analysis.\n", "\n" ] }, { "cell_type": "code", - "execution_count": 507, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -276,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": 508, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -337,25 +337,27 @@ "source": [ "## But-for Analysis with Bayesian SIR model with Policies\n", "\n", - "Now that we have the Bayesian SIR model with Policies, we can do but-for analysis to idenitfy which one of the policies cause overshoot to be too high. To perform but-for analysis, we investigate the following four scenarios:\n", - "1. Model where non of the policies were applied\n", - "2. Model where both lockdown and masking were enforced\n", - "3. Model where only masking was imposed\n", - "4. Model where only lockdown was imposed\n", + "Suppose now we introduced both policies, and this resulted in an overshoot. What intuitively is the case is that lockdown limited the efficiency of masking, and it was in fact the lockdown that in this particular context caused the overshoot (this is consistent with saying that in the context where only masking has been implemented, masking would be responsible for the resulting overshoot being too high).\n", "\n", - "We create these four models by conditioning on the policies being imposed as required. The models obtained are similar to intervened models since the variables `lockdown` and `mask` do not have any variables upstream to them." + "We might try to use the but-for analysis to idenitfy which of the policies causes overshoot to be too high. To do so, we investigate the following four scenarios:\n", + "\n", + "1. None of the policies were applied\n", + "2. Both lockdown and masking were enforced\n", + "3. Only masking was imposed\n", + "4. Only lockdown was imposed\n", + "\n", + "The hope is that by looking at these we will be able to indentify the culprit. We create these four models by conditioning on the policies being imposed as required (in fact, this has the same effect as intervening here, as the sites are upstream from the model). The models obtained are similar to the intervened models since the variables `lockdown` and `mask` do not have any variables upstream to them. In principle we could emulate 1-4 using `do` with the same estimates. For the sake of completeness, we also illustrate the consequences of deciding randomly about the policies." ] }, { "cell_type": "code", - "execution_count": 509, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# conditioning (as opposed to intervening) is sufficient for\n", "# propagating the changes, as the decisions are upstream from ds\n", "\n", - "# Doing but-for analysis\n", "\n", "# no interventions\n", "num_samples = 10000\n", @@ -398,12 +400,12 @@ }, { "cell_type": "code", - "execution_count": 510, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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eGyVdRyVdm2VxvnP83//+V7FarUr37t2Vw4cP++Y7HA7l6aefVqxWq9KtW7cin3FK81w+l7179xa7XWZmpnLPPfcoVqtVee+994osL+9r+dq1a5VDhw4Vmb9v3z7fe9HmzZv9lpV07n7//XfFarUqN9xwQ7H3zfta/MEHH/jmTZ8+XbFarcpzzz1X5DOCw+FQVq9eXapjV/RnGiHE5Ue67wkhiggLC2PcuHEl/prXp08fLBaL3zyNRsPgwYNJSkpiz5497Nu3r1THmjVrFg6Hg0ceeYSePXv6LYuLi2Py5MkAfPTRR7756enpzJs3D1B/WWzYsGGRtiQnJ5epLkpqaio//PADGo2GCRMm+P1qGxgYyKRJkzCZTGzcuJENGzYUu49HH32UunXrlvqYHo/H1/3mtddeK3I/2rRpw//93//hdDpZuHChb356ejoAnTt3LrJPs9lMmzZtSt0GuLDHs0mTJjzyyCPodDrfPKvVyg033ADA6tWr/db/3//+B8Dw4cP9akvo9XrGjh1LdHR0mdoO6rXhcrno0aMHN910k9+yzp07M3DgQAC/rgRBQUH07NkTj8fDl19+WWSf3iypG264Ab3+TFLx9OnTURSFF154och5LtxVxXt9nu2mm27yde8p7OTJk4CaaVFcjZSmTZuWmEkwcuRImjdv7jfvgQceIDg4mAMHDnD06FHf/PXr17N582YCAgKYOHGi3+MeERHBhAkTAFiyZAnHjh3zLSvPOb5QR48e5aeffkKj0fDCCy/4PZ/DwsJ44YUXyrS/3NxcCgoKqFmzZrGjdMXFxV1QvZN77rmHFi1alGtbs9nMCy+8gNls9s2rVq2a73r6+OOPS+z6U1ku9Booy2uF9/nQuHHjYl8T6tevX6F1+qpVq1bkPa969erccccdRdpWkcp63PK8X5ZWdHQ0Y8aM8Xu9u+qqq3zdl+fMmVPmfVYFu93O/PnzAbXbbeFsOoPBwLPPPktUVBRHjhzhxx9/LHYf5X0u169fv9jtQkNDefbZZwH44YcfSty+LK/lAG3btqVmzZpF9lOvXj3+7//+77zHK6xDhw5cddVV7Ny5k/Xr1/stO3bsGMuXLycgIMAvY8n7WaRTp05FMskMBgPJycmlOnZFf6YRQlx+JCglhCiiNAGdgwcPMm/ePCZPnszTTz/NmDFjGDNmjO/LxP79+897HI/Hw2+//QaogZHiNG3aFIvFwo4dO3xfytauXYvT6aRJkyYkJiaW5a6V6M8///R1STk7OATqqDEdO3b0Hb84Ze0Os337dtLS0qhVq1aJ96Nt27YAfrWlmjVrBsD48eNZuXJlhXxZLe/j2bVr12K7NXi/3Beu3XH8+HEOHjwI4PsiWpjJZOK6664rc9vXrVsHqN06inPLLbcAakCmcG2UAQMGAPjqBnk5nU6++eYbv21BrReyZcsWzGazr6bJ2dq1awdQYuCypGukSZMm6HQ6vvjiC+bPn09aWlqx6xWnuLYYjUbfl5XCj4H3XHXq1MnX1aWwxMREGjZsiMfj8a1beLuynuMLUfg5WVw3vUaNGpVYp6c4ERERxMXFsWvXLqZMmcLevXsrpJ1e5bl2va6++upigy9du3YlLCyM3Nxctm3bdiHNu2AXeg2U5bWiXr16BAYG8ttvv/H2229z+PDhC27/uSQnJxMQEFCqtlXVccv7fllavXv3LraLqDcAeeDAgYt2HirS33//jc1mIywsrNjaRQEBAb7zV9J7+YU8l91uN2vWrGHmzJmMHz+esWPHMmbMGGbNmgWc+7NRWV7LvfLy8vj+++957bXXeO6553zv3UuXLj3v8c7mrUHnDep5LViwAJfLRb9+/QgJCfHN934WmTp1KkuXLiUvL6/UxyrsYnymEUJcXqSmlBCiiLi4uBKXud1uJkyYwMKFC1EUpcT1cnNzz3uczMxM33rF/UJW3PqxsbGkpKQAFJvtUF7eD3vnqlFSq1Ytv3ULi4yMLPbLxbl4v2gdOnTovF+uMzIyfH/fe++9/PXXX6xevZr77rsPg8FAQkICbdq0oU+fPr4PeKVxoY9n9erVi53vzfYpXMDYm3kTHh5OYGBgsduVp0bM+R477wd6u91OZmYmkZGRgBpAqlmzJvv372fDhg2+ulm//PILGRkZNG/e3C9z5siRIyiKQkFBAU2bNj1nm06dOlXs/JLaWKtWLcaOHcsrr7zChAkTmDBhAnFxcbRo0YIuXbpw3XXXlZi5WFLGiPcxKPwBv7TX+c6dO4sEFM+1XUnn+EJ4r5dztTU+Pr5MBfJfeeUVRo4cyZw5c5gzZw5hYWE0a9aMq6++mhtuuOGCRuw71+vm+ZzrPsbFxZGZmemXuVYVLvQaKMtrRVBQEC+99BJjx47ljTfe4I033iA6OpoWLVrQqVMnrr/++hJfQ8qjLG2rSGU5bnnfL0urpMc1KCiIsLAwMjMzOX78eJn2WR7Lli1j2bJlRebfcssttG7d+rzbewP653o+nuu9/HzbnsuBAwcYPnz4OetJnuuzUVleywFWrFjB2LFjzznoQ2k+i3ndcMMNvPrqq/z000+kpaURExODw+Hgs88+A2Dw4MF+69944438/vvvfPPNN4wYMQKdTkf9+vVp1aoVvXr1KnWmVEV+phFCXJ4kKCWEKKJwF5KzffTRRyxYsMCX6p+UlERUVJTvF9bHH3+cb7/99pwBDi+Px+P7u6Rf3wsrTaHZqnKuc1YS7zmKjo72ZWGVpHDXrYCAAObMmcOWLVtYuXIlGzduZOPGjWzdupU5c+bwn//8h+eff75UbbjQx1OrvXwTbjUaDf3792fatGksXrzYF5TyjvTjzaTy8p6D8haJBs5ZrHrIkCH07t2bFStW8Ndff/HXX3/x3Xff8d133zF9+nTmz59f7LD2l/NjUNlat27NihUr+OWXX/jzzz/ZuHEjq1at4rfffmPatGnMnDmz1F+kzlae14CLpfBr66WirNdpr1696NChA8uXL+evv/5iw4YN/PTTT/z0009MmzaNDz74oEyZchXZtopSluNeCu+XpXlf9yrvNbhjx44i2augZg2XJihVEcr7XB45ciR79uyha9eu3HfffdSvX5+goCAMBgMOh+O8P2aU5Xo4fvw4jz76KAUFBdx3333069eP+Ph4LBYLWq2WVatWlarIeGEBAQHceuutvP/++3z66acMHz6cpUuXcvLkSVq3bl0ki1yr1TJ16lQefPBBfvnlFzZs2MCGDRv45JNP+OSTT+jatSszZ87067Jb0nEr6jONEOLyJEEpIUSZfP/99wC88MILxdbGOXDgQKn3FR4ejtlspqCggKeeeqrUWQreXxP/+eefUh/rfLy//p6rm4h3WUX9UlytWjVArY8zZcqUMm/frFkz3y+ILpeLZcuWMXr0aD7++GN69epF+/btz7uPinw8z8d73k6dOkVeXl6xmQ7eLLiy7vfQoUMcPnwYq9VaZLl3KG+TyVRkRLn+/fszY8YMlixZwjPPPENubi4rV67EbDYXGZXP+3hpNBpefPHFi/JFNioqittuu803+tG+fft45pln2LhxI6+++iovv/zyBe2/vNf5hZzjC23rua6J8lwvZrOZ6667ztdFJyMjgzfeeIOFCxfy9NNP8/PPP5evwRfgXMPNe+9j4cfDG3AoqbtMampqBbYO3/Er+xoIDg7mpptu8nUhO3r0KBMnTmT58uVMnDixxNptV6Lyvl+WVknXYG5uri8Tx/saCBfvGhwxYgQjRowo17aAL3B/rteGin4vB/W1eteuXURGRjJjxgy/2lyAr+t6RVmxYgUFBQX06NGDJ598ssjy8h5v8ODBzJkzh4ULF/LAAw/4nmNnZ0kV1qBBA18Xa0VR+OOPP3j88cf5+eef+fLLL0s9cl5FfKYRQlye5OdVIUSZZGVlAcWnt+/Zs4edO3eWel86nY4OHToAZ4IjpdG+fXsMBgPbtm0rdZ0V7wdol8tV7PI2bdqg1Wp9Q9ifLS0tjZUrVwJn6gZdKG/x6r17954z3b809Ho91113nS/jqrSPQ0U+nudTrVo1Xxefb7/9tshyh8NR6qKshXnrbhX36zrA559/DqhZMmd/UahRowbJycnk5uaydOlSvv76a18x57PrqsXGxpKQkEBeXp7vWrjY6tevz3333QeoGQQXynuuVq5c6asXVtj27dvZsWMHWq3Wr7hsec/x+Z5359KmTRs0Gg3bt28vttD+zp07y9R1ryQRERG+L3Wpqam+5wScaX9F1ckqye+//+4r9lvYr7/+SmZmJoGBgX5157xfpksagODXX38tdv6FPB4X8jyrKNWrV2fkyJFAxTwfLoYLOcfnUt73y9L64Ycfiu2m+NVXXwFQu3btIoFqKP4aVBTFV//qbBfr/Hh562plZmayfPnyIssLCgpYsmQJUHHv5XDmvTQmJqbY6//rr7+usGMVPl5xXf4URfHVRSyrGjVq0L17d9LS0pg2bRobN24kJiamSGH9kngHmrn++uuB8j9Py/uZRghxeZKglBCiTLx1nObPn++Xnp+Wlsbo0aPL/EFz+PDhGAwG/vvf/7J48eJiU/53797tK9oJav2m22+/HYBRo0axe/duv/UVRWHNmjXk5OT45nl/4S3pS1yNGjW47rrrUBSFcePG+dUEstlsjBs3DrvdTlJSkq+b14UyGAwMHz4cRVEYPnx4kRFv4EzR1E2bNvnmzZ8/v9gssRMnTrB161bf/SmNin48z2fo0KGAOopd4cfC7Xbz8ssvl6nAt9edd96JXq9n2bJlvi9QXqtWrfKNXHjPPfcUu733V9xFixb5Rt0r6ZfdRx55BFBHdVqxYkWR5YqisHnzZlatWlWm+7BmzRp+/fVXnE5nkf398ssvQOkf03Np3bo1zZs3p6CggHHjxpGfn+9blpGRwbhx4wC1kHLhejflPcfe5115iorXqFGDHj164PF4GD9+vF9tlKysLMaPH1+m7kQpKSl89tlnxdZY8T6WoaGhfqMfer94X2jQ+HwKCgoYP348BQUFvnnHjx/3ZVAOGjTIr+tn+/btfV10ChekVxSFjz76qMRRxSIiIjAYDJw8efKcdWiKc6HPs7LYvn07S5Ys8TsfXt7HqiJH36tIF3KOz6c875ellZaWxssvv+wXgN23bx9vvfUWcOa128vbzfWrr77ye347nU7++9//8vfffxd7HO9rwsGDB4u83lUEk8nky+p5+eWX/TKmnE4nkydP5sSJE8THx5e7G3Zx6tSpg06nY/fu3UUKqK9YsYIPP/ywwo4FZ4rh//jjj37vm263mzfffNNvcJSyuvPOOwF49913ARg4cGCxgbYvv/zS95mjsNzcXN/rUmnqc1XkZxohxOVJuu8JIcrkwQcfZOXKlXz66aesXbuWxo0bk5uby59//knNmjXp0aMHP/30U6n316RJE/773//6Rqh54403aNCgAeHh4WRlZbF7926OHTtGnz59/H6pe/LJJzly5AgrVqzgxhtvpHnz5r6CwHv27OH48eMsX77cl+3SvHlzYmJi2L59O/3798dqtaLX66lbt64vE2XcuHH8888/bN68mR49etCuXTt0Oh1//vknGRkZxMfHM3Xq1Ao9n3fccQepqanMnj2bwYMHc9VVV1GrVi3MZjMnTpxg586dZGdnM378eN9Q059++ikTJkwgPj6eq666iqCgIE6dOsX69espKCigffv2xY46VJyKfjzPZ/Dgwfz+++/8/PPP3HjjjbRr147Q0FA2b97MiRMnuP322/nkk0/KtM+EhATGjRvH+PHjeeqpp/jf//5H3bp1SU1NZePGjSiKwogRI0qs29W9e3dCQ0NZs2YNoH6ILqmbQLdu3XjmmWd4+eWXeeihh6hduzZ169b1PQY7d+4kPT2dYcOGnbdOWGG7du3ipZdeIigoiMaNGxMTE4Pdbmf79u2kpKQQHBzMqFGjynReSvLqq68ydOhQli9fzrXXXkvr1q1xuVysXbuW3NxcmjRp4gtOeZX3HHfs2BGLxcKyZcu4/fbbqVOnDlqtlpYtW5aqS8e4cePYuXMn69at49prr6Vt27YoisLatWt9o2sVFxwsTnZ2Ns8++ywvvPACDRs29BV2PnjwINu3b0ej0fDkk0/61T/p3r07M2fOZO7cuezZs4dq1aqh1Wrp1q1bsd1dy+umm27il19+oXv37rRq1Qq73c7atWux2WwkJSX5soO8qlevzh133MFHH33EXXfdRatWrQgLC2Pnzp0cPXqU+++/3/eFsjCDwUC3bt348ccfuemmm2jVqpWvfs7kyZPP2cYLfZ6VRWpqKo8++ihms5nGjRtTvXp1XC4Xu3fvZv/+/RgMhmK7LF0KLuQcn0953y9LY9CgQXz22Wf88ssvNG/enKysLN9Itz169OA///mP3/qtWrXi2muvZfny5dx88820atUKk8nE9u3byc3N5c477+Sjjz4qcpwaNWqQmJjI1q1b6devH4mJiZhMJsLDw3niiScu6Px4jRw5kq1bt7JmzRr69OlDu3btCAwMZNOmTaSmphIWFsabb75Z4uAR5REREcHgwYN9z8nWrVsTExPD/v372bZtGw899BBvv/12hR2va9euNGnShG3bttGrVy/atm1LQEAAW7ZsIS0tjWHDhvHee++Va9+tW7emcePGbN++HYPBwMCBA4tdb+nSpYwePZqYmBgaNWpESEgI2dnZbNiwgZycHKxWK7feeut5j1eRn2mEEJcnCUoJIcqkefPmfPHFF7zxxhv8/fffrFixwvcF6aGHHmLSpEnn3L64IcF79+5N06ZNmTt3LqtXr2bDhg243W6ioqKoVasWgwcPLjJEs9Fo5K233uK7775j8eLFbN26la1btxIWFkbt2rUZOnSo3xDrRqOR2bNn8/rrr7Np0yZ27tyJx+Ohbdu2vqBUeHg4CxYsYO7cuSxZsoTff/8dj8dDfHw8t912G/fcc0+F1Uop7KmnnqJ79+58/PHHbNiwgZUrV2IwGIiOjqZt27Z06dLF7wvGo48+yi+//MLmzZvZvHkzOTk5REZG0qxZM26++Wb69u1b6u4zF/p4lpVWq2XGjBnMnTuXzz//nHXr1mGxWGjVqhUzZ85k+/btZQ5KgfpLbsOGDZk9ezYbNmxg165dBAUF0blzZ+68806uvvrqErc1mUz07duXjz/+GFDrTBV3nXrdeeedtG/fnnnz5rF27VrWrFmDVqslKiqKRo0aFXm8SqNbt27k5uayfv16Dh48yObNmzGbzVSrVo3777+fwYMH+9VzuRA1a9Zk0aJFfPDBByxbtoxffvkFrVZL3bp16d27N3feeWexhX7Lc46joqJ47733mDlzJtu2bWPTpk14PB7cbnepglLR0dF8+umnzJw5k59++omff/6ZyMhI+vTpw6hRo3jllVfKdL+ffvpp/vzzT/bs2ePr4hYTE8NNN93EkCFD/LrIATRs2JDp06cze/ZsNm/ezJo1a1AUhWrVqlVoUCo+Pp7PP/+cN954gz/++IOsrCxq1KjB9ddfz7Bhw4p9PJ5++mlq1KjBZ599xsaNGwkMDCQpKYk33niD3NzcYoNSABMmTCAsLIyVK1fy448/+rJVShMwuZDnWVk0b96cxx9/nPXr17Nv3z527NiBTqejWrVqDB48mDvuuKNCR1+taBdyjs+nPO+XpdG8eXMGDhzItGnT+P3337HZbNSpU4dbbrmFO+64o9jXxDfeeIO33nqLb7/9lnXr1hESEkJycjKjRo0qNvPXa/r06bz66qusXbuW77//HpfLRVxcXIUFpYxGo69Y91dffcX69etxOBxUr16dIUOGMGzYsIsyiuDTTz9NQkICH3/8MVu3bkWn02G1Wnn99dfp06dPhQal9Ho9c+fO5d133+XHH39kzZo1BAUFkZSUxLRp08jLyyt3UArg6quvZvv27fTq1cvvs1Rh99xzD/Hx8WzcuJHt27eTmZlJWFgYDRo04Prrr2fAgAFYLJbzHqsiP9MIIS5PGqUsue9CCFFOr7zyCrNnz+bJJ5/0BYGEEEIIIcSlw+1206NHD1JSUliwYAFJSUlV3SQhxBVOakoJIS46RVF89SXq1q1bxa0RQgghhBDFWbhwISkpKSQlJUlASghRKSQXUghxUT355JPs3r2bnTt3EhsbW2HdO4QQQgghxIX7559/mD17NidPnmTlypVotVqeeuqpqm6WEOJfQoJSQoiL6uuvvyYwMJBrrrmG0aNHF1sbRQghhBBCVI0TJ07w+eefYzAYaNCgASNGjKiwkYaFEOJ8pKaUEEIIIYQQQgghhKh0UlNKCCGEEEIIIYQQQlQ6CUoJIYQQQgghhBBCiEonQSkhhBBCCCGEEEIIUekkKCWEEEIIIYQQQgghKp0EpYQQQgghhBBCCCFEpZOglBBCCCGEEEIIIYSodBKUEkIIIYQQQgghhBCVToJSQgghhBBCCCGEEKLSSVBKCCGEEEIIIYQQQlQ6CUoJIYQQQgghhBBCiEonQSkhhBBCCCGEEEIIUekkKCWEEEIIIYQQQgghKp0EpYQQQgghhBBCCCFEpZOglBBCCCGEEEIIIYSodBKUEkIIIYQQQgghhBCVToJSQgghhBBCCCGEEKLSSVBKCCGEEEIIIYQQQlQ6CUoJIYQQQgghhBBCiEonQSkhhBBCCCGEEEIIUekkKCWEEEIIIYQQQgghKp0EpYQQQgghhBBCCCFEpZOglBBCCCGEEEIIIYSodBKUEkIIIYQQQgghhBCVToJSQgghhBBCCCGEEKLSSVBKCCGEEEIIIYQQQlQ6CUoJIYQQQgghhBBCiEonQSkhhBBCCCGEEEIIUekkKCWEEEIIIYQQQgghKp0EpYQQQgghhBBCCCFEpZOglBBCCCGEEEIIIYSodBKUEkIIIYQQQgghhBCVToJSQogKMX36dBISEqq6GZeNMWPG0K1bt6puhhBCCFFqQ4YM4frrr6/qZghRblu2bCExMZGUlJSqbopPt27deOCBB8673tq1a0lISGDt2rVlPoZ32x9++OG86z766KOMGjWqzMcQorwkKCXEFWrRokUkJCTQtGlTjh8/XmT5pfTBcsOGDUyfPp3s7OyqbkqFOn78ONOnT2fHjh1V3RQhhBCXiD179vDEE0/QqVMnEhMT6dixI48//jh79uyp6qZd0mbNmsWyZcuquhmXnCVLlvDEE0/Qs2dPEhISGDJkSLHrjRkzhoSEhBKn4j4rnsvdd99NQkICEyZM8JtfUFDA008/zfXXX0+rVq1ISkrihhtu4H//+x9Op7Pc97Nbt25+7U1OTuY///kPP/30U5n28/rrr9O3b1/i4uJ88+bPn8+iRYvK3bYrzbBhw1i6dCk7d+684H199tln9O7dm6ZNm9KzZ0/mzp1bqu22bNnChAkT6Nu3Ly1atKBLly6MGjWK/fv3n3M7p9NJnz59SEhIYPbs2RfcflE59FXdACHExeVwOHj33Xd57rnnLupxHnroIe6///5ybbtx40ZmzJhB//79CQkJqeCWVZ20tDRmzJhBXFwcjRo18ls2ceJEFEWpopYJIYSoCkuXLuWxxx4jLCyMm2++mfj4eFJSUvj888/58ccfef311+nRo0dVN/OS9M4779CrVy+6d+9e1U25pHzyySds3bqVpk2bkpmZWeJ6AwcOJDk52W+eoiiMHz+euLg4YmNjS33MpUuXsmnTpmKXFRQUsHfvXq655hri4uLQarVs3LiRl156iS1btvDqq6+W+jhna9SoEXfffTegfsZauHAhw4cPZ/z48dx+++3n3X7Hjh2sXr2aBQsW+M3/5JNPCA8PZ8CAAeVuW2Vo06YNW7ZswWAwXNTjNG7cmMTERD744ANeeeWVcu9nwYIFPP/88/Tq1Yu7776b9evXM2nSJPLz88/7neH9999nw4YNXHfddSQkJHDixAnmz5/PgAEDWLhwIVartdjt5s2bx9GjR8vdZlE1JCglxBWuUaNGfPrpp9x///1l+sBRVnq9Hr3+0npJyc/PJyAgoKqbUayL/YFCCCHEpeXQoUM89dRT1KxZk/nz5xMREeFbdueddzJ48GCeeuopvv76a2rWrFlp7bLZbFgslko7nqhYr7zyCrGxsWi12nNmwCclJZGUlOQ3b/369eTn59OvX79SH89utzNlyhTuu+8+pk2bVmR5WFgYn376qd+822+/neDgYObNm8eYMWOIjo4u9fEKi42N5cYbb/Tdvummm+jZsycffvhhiUEpl8uFx+PBaDTyxRdfUKNGDVq0aFGu41c1rVaLyWSqlGP17t2b6dOnk5eXR2BgYJm3Lygo4PXXX6dLly6+6+S2227D4/Hw9ttvM3DgQEJDQ0vc/q677mLq1KkYjUbfvD59+tCvXz/effddpk6dWmSb9PR0Zs6cWeK1KS5d0n1PiCvcAw88gMfj4b333jvvui6Xi5kzZ9K9e3cSExPp1q0br732Gg6H47zbFldTypvWvWzZMq6//noSExPp27cvv/32m9923l9hrr32Wl9a9pEjR3zrfPXVVwwYMIBmzZrRtm1bHn300SK/gni7I27dupXBgwfTvHlzXnvtNR544AGuvfbaYts8cODAIr+KleVYe/fuZciQITRv3pxOnTr5neO1a9dyyy23ADB27Fjf/fKmhxdXU8pmszFlyhQ6d+5MYmIivXr1Yvbs2UUyqkpzXgFyc3OZPHky3bp1IzExkeTkZO6++262bdtW7PkQQghx8bz//vvk5+czceJEv4AUQEREBBMmTMBms/neS3744QcSEhJYt25dkX0tWLCAhIQEdu/e7Zu3b98+Ro4cSdu2bWnatCkDBgxg+fLlftt5u/avW7eO8ePHk5ycTOfOnYGyvWec6/3PKz09naeffpoOHTrQtGlTbrjhBhYvXlxkvdK89yUkJGCz2Vi8eLHv/XTMmDElnmtv/ZwlS5YwY8YMOnXqRFJSEiNHjiQnJweHw8HkyZNJTk4mKSmJsWPHFvtZpzSfCdavX8/IkSPp0qULiYmJdO7cmRdffJGCggK/9caMGUNSUhLHjx/n//7v/0hKSqJ9+/a8/PLLuN1uv3XT0tLYt29fqbq7Va9eHa22fF/pvv32WzQaTZnKObz33nsoisK9995bpmN5u8tVZKmG6Oho6tWr56sPdeTIEV+3rQ8//JDu3bvTtGlT9u3bB8Dy5ctp3749Go3Gt49u3bqxZ88e1q1b57u2CneBPHz4sO951bx5c2677TZ++eWXIm0p7fV+LuvXr+eWW26hadOmXHvttXz55Zd+y0uqKTV//nyuvfZamjVrxi233ML69esZMmRIsV05vUGha665hqZNmzJ06FAOHjxYZL0OHTpgs9lYvXq13/zU1FTf+TyXtWvXkpmZyX/+8x+/+YMHD8ZmsxV7Dgtr2bKlX0AKoE6dOlx11VX8888/xW4zdepU6tatyw033HDe9olLy6WV1iCEqHDx8fHceOONfPrppwwbNuyc2VLPPvssixcv9qXZbtmyhXfeeYd9+/Yxc+bMch3/r7/+YunSpfznP/8hMDCQuXPnMnLkSH7++WfCw8Pp0aMHBw4c4Ntvv2Xs2LGEh4cD+D6wv/3227z55pv07t2bW265hYyMDObNm8fgwYP58ssv/br7ZWZmMmzYMPr27csNN9xAZGQkTZo0YfTo0WzZsoVmzZr51k1JSWHTpk089dRTvnllOVZWVhb33XcfPXr0oHfv3vz4449MnToVq9VK586dqV+/PiNHjmTatGkMHDiQVq1aAeqbbHEUReGhhx7yBbMaNWrEypUreeWVVzh+/DhPP/10mc4rwPPPP8+PP/7IHXfcQf369cnMzOSvv/5i3759NGnSpFyPpxBCiPL5+eefiYuLo3Xr1sUub9OmDXFxcfz6668AdOnSBYvFwvfff0/btm391l2yZAlXXXWVrwvLnj17uP3224mNjWXYsGG+7R5++GGmT59epEvgCy+8QEREBA8//DA2mw0o/XvG+d7/QM2SGDJkCIcOHWLw4MHEx8fzww8/MGbMGLKzsxk6dChQ+ve+V155hWeffZZmzZpx2223AVCrVq3znvN3330Xs9nM/fffz8GDB5k3bx56vR6NRkN2djbDhw9n8+bNLFq0iLi4OIYPH+7btrSfCX744QcKCgq4/fbbCQsLY8uWLcybN49jx44VydZwu93ce++9NGvWjKeeeoo1a9bwwQcfULNmTb8v76+99hqLFy9m+fLlxMfHn/d+lofT6eT7778nKSmp1MdITU3lvffe48UXX8RsNp9zXYfDQW5uLna7na1bt/LBBx8QFxdH7dq1K6L5gHofjh07RlhYmN/8RYsWYbfbue222zAajYSGhnL8+HFSU1Np3Lix37pPP/00EydOxGKx8OCDDwIQFRUFwMmTJxk0aBD5+fkMGTKE8PBwFi9ezEMPPcS0adN8z6vSXu/ncvDgQUaNGsUtt9xC//79+eKLLxgzZgxNmjThqquuKnG7jz/+mAkTJtC6dWvuuusuUlJSePjhhwkJCaFatWpF1n/vvffQaDTcc8895Obm8v777/PEE0/w2Wef+a3XoEEDzGYzGzZs8Hv9GD16NOvWrWPXrl3nvD/bt28HIDEx0W9+kyZN0Gq17Nixwy/rrTQUReHkyZPFno8tW7bw5Zdf8vHHH/sFHcVlQhFCXJG++OILxWq1Klu2bFEOHTqkNG7cWJk4caJv+R133KH07dvXd3vHjh2K1WpVnnnmGb/9TJkyRbFarcqaNWvOebxp06YpVqvVb57ValWaNGmiHDx4sMhx5s6d65v3/vvvK1arVTl8+LDf9keOHFEaNWqkvP32237zd+3apTRu3Nhv/h133KFYrVblk08+8Vs3JydHSUxMVKZMmeI3/7333lMSEhKUlJSUch9r8eLFvnl2u125+uqrlREjRvjmbdmyRbFarcoXX3xR5HyNHj1a6dq1q+/2Tz/9pFitVuWtt97yW2/EiBFKQkKC3zks7Xlt1aqV8sILLxQ5thBCiMqVnZ2tWK1W5aGHHjrneg8++KBitVqVnJwcRVEU5bHHHlOSk5MVl8vlWyctLU1p2LChMmPGDN+8oUOHKtdff71it9t98zwejzJw4EClZ8+evnnezwa333673z4VpXTvGaV9//vwww8Vq9WqfPXVV755DodDGThwoNKiRQvf/SvLe1+LFi2U0aNHn7N9Xn/88YditVqV66+/XnE4HL75jz32mJKQkKDcd999fusPHDjQ7z25LJ8J8vPzixz/nXfe8fuMoSjq+77VavV73BRFUW666Salf//+fvO86579ueh8+vbtq9xxxx2lWnfFihWK1WpV5s+fX+r9jxgxQhk4cKDvttVqLfGa+fbbbxWr1eqbBgwYoOzcubPUxzpb165dlXvuuUdJT09X0tPTlR07diiPPvqoYrVafZ9vDx8+rFitVqVly5ZKenq63/arV69WrFarsmLFiiL7Lum8TZ48WbFarcqff/7pm5ebm6t069ZN6dq1q+J2uxVFKf31fq77dvZx0tPTi3x+9V7Xf/zxh6Io6nOvbdu2ys0336w4nU7feosWLVKsVqvfffJu27t3b7/Xif/973+K1WpVdu3aVaRdPXv2LPJc8b4GnM8LL7ygNGrUqNhl7du3Vx599NHz7uNsX375pWK1WpXPPvvMb77H41FuueUW5bHHHlMU5cx18P7775f5GKJqSPc9If4FatasyQ033MCnn35KWlpaset4f5n1FpD0uueee/yWl1WHDh38fs1s2LAhQUFBHD58+Lzb/vTTT3g8Hnr37k1GRoZvioqKonbt2kXSl41GY5HueEFBQVxzzTV8//33fl0BlixZQosWLahRo0a5jmWxWPx+4TEajTRt2rRU96s4v/32Gzqdrkiq9T333IOiKEW65pXmvIaEhLB58+Yyj6gjhBCiYuXl5QGctzaLd7l3/d69e5Oenu7Xhe/HH3/E4/HQp08fQM0S/uOPP+jduze5ubm+969Tp07RsWNHDhw4UOR94LbbbkOn0/nNK+17Rmne/3777Teio6P9uoUZDAaGDBmCzWbjzz//9K1Xlve+srrxxhv9ajg2a9YMRVG4+eab/dZr1qwZR48exeVyAWX7TFA4Y8hms5GRkUFSUhKKoviyRQo7u/ZRq1at/EoWAEyZMoVdu3ZdtCwpULvuGQwGevfuXar1//jjD5YuXVokc7sk7dq1Y86cObz55psMGjQIg8FAfn7+hTSZVatWkZycTHJyMjfeeCM//PADN954I0888YTfej179izSRfbUqVMAZRpQ59dff6VZs2Z+2Y2BgYEMHDiQlJQU9u7dC5T+ej+XBg0a+B0nIiKCunXrnvNz5datW8nMzOS2227zq+var1+/Eus1DRgwwK9bnPeYxR0nNDTUd9685s6de94sKVCzx0qqn2oymYp0bz2fffv2MWHCBJKSkujfv7/fskWLFrF79+4i14G4fEj3PSH+Jf7v//6Pr7/+mnfffZdnn322yPKUlBS0Wm2RdPjo6GhCQkJ8/fXLqnr16kXmhYaGlqqmwIEDB1AUhZ49exa7/OzC6rGxsUX6n4NaGHHZsmVs3LiRli1bcujQIbZt2+b3waqsx6pWrVqR9ODQ0NBSvVEXJyUlhZiYGIKCgvzm169f37e8sNKc1yeeeIIxY8bQpUsXmjRpQufOnbnpppsqtYCuEEKIosGmkpwdvLrmmmsIDg5myZIlvpHTlixZQqNGjahbty6gFlBXFIU333yTN998s9j9pqen+3XfLy7YUdr3jNK8/6WkpFC7du0itY6872mpqam+9cry3ldW3h+evIKDg4Gi76HBwcF4PB5ycnIIDw8v02eC1NRUpk2bxooVK8jKyvJbLzc31++2yWQqEiwJDQ0tst3FlpeXx/Lly+nYsaOvy/+5uFwuJk+ezI033uhXCuFcoqKifN3grrvuOmbNmsXdd9/N0qVLy13ovHnz5jzyyCNoNBrMZjP169cvNsh0rmCeUoaRj1NTU2nevHmR+fXq1fMtt1qtpb7ez6Wkz3Xnuja8+z37s7ter/fV8Drb2c8J7/kr7nO5oijl7gpnNptLrIlmt9vP2/2zsBMnTvDAAw8QHBzMm2++6RdQz83N5bXXXuPee+8t9hyKy4MEpYT4lyicLXWuYVgruh/22b/EepXmQ4HH40Gj0fDee+8Vu5+zRwsq6Q2ua9euBAQE8P3339OyZUu+//57tFot1113XbmPVdL9qiylOa99+vShdevW/PTTT/z+++/Mnj2b9957j+nTp/vqfgghhLj4goODiY6OPu8PF7t27SI2NtYXpDEajXTv3p2ffvqJ559/nvT0dDZs2MBjjz3m28bj8QBqdlGnTp2K3e/ZX1qLG8GrtO8ZVf3+VxYlFQAvab73PbS0nwncbjd33323r85WvXr1sFgsHD9+nDFjxvgeG69L5dwtW7asTKPuffnll+zfv58XXnihSFZXXl4eR44cITIy8pwjHvfq1YvXX3+d5cuXM2jQoHK1Ozw8nA4dOpx3veI+D3qDbxVZaL0iVda1cb5rv7Ds7Oxy1wCLjo7G7XaTnp5OZGSkb77D4SAzM5OYmJhS7ScnJ4dhw4aRk5PD/Pnzi9TGnT17Nk6nkz59+viuzWPHjvnaf+TIEWJiYor90VpcOiQoJcS/yEMPPcTXX39d7Cg5cXFxeDweDh486PtlB9Qij9nZ2SX+4lIRSgqE1apVC0VRiI+P9/0iXB4Wi4UuXbrwww8/MHbsWJYsWULr1q393tgq6liFlSXAFxcXx5o1a8jNzfX7xdg7wkh5z39MTAyDBw9m8ODBpKen079/f2bNmiVBKSGEqGRdu3bl008/Zf369cUWO1+/fj0pKSkMHDjQb37v3r1ZvHgxa9asYd++fSiK4tflypvJZDAYSvWF/Vwq6j0jLi6OXbt24fF4/L4Ee9/TvNkaF+u970KV9jPB7t27OXDgAC+//DI33XSTb/7vv/9eCa0sv2+++QaLxVJkFOCSHD16FKfTWaTrIagBqy+//NI3enNJ7HY7oAYZqoI3u+nsoBqU/HmtRo0a7N+/v8j84q7j0lzvFc2730OHDtG+fXvffJfLRUpKSpFRscvC5XJx9OjRUl8jZ2vUqBGgdjEs/PqxdetWPB4PDRs2PO8+7HY7Dz74IAcOHGDOnDk0aNCgyDpHjx4lKyuLvn37Flk2a9YsZs2axZdffulrj7g0SU0pIf5FatWqxQ033MDChQs5ceKE3zLvG8b//vc/v/lz5szxW34xeH9ZO/uDSs+ePdHpdMyYMaPILziKohTp534uffr0IS0tjc8++4ydO3cWqaFQkcfy8t6v0vwqd8011+B2u5k/f77f/A8//BCNRsM111xTpmO73e4i5zMyMpKYmJhih70WQghxcd17772YzWaef/75Iu8pmZmZPP/88wQEBHDffff5LevQoQNhYWEsWbKE77//nmbNmvl1qYuMjKRt27YsXLiw2LqRGRkZ521bRb9nXHPNNZw4cYIlS5b45rlcLubOnYvFYqFNmza+9Ur73mexWCoty6W0nwm8AYjC6yiKwkcffXRBx09LS2Pfvn0ldn+6EBkZGaxZs4YePXqUmNmUmprKvn37fLf79OnDzJkzi0ygfj6cOXOmr1tfRkZGsVk33tHdzh6NrbLExsZSvXp1tm7dWmRZQEBAsddW586d2bJlCxs3bvTNs9lsfPrpp8TFxfmCJKW93itaYmIiYWFhfPrpp756aKAGHS+0S+jevXux2+0kJSX5zT/72ihJ+/btCQsL45NPPvGb/8knnxAQEECXLl188zIyMti3b59fzTG3280jjzzCpk2bePPNN4u0w2vIkCFFrssJEyYAav2smTNnXtTabKJiSKaUEP8yDz74IF999RX79+/3G1K1YcOG9O/fn4ULF5KdnU2bNm34+++/Wbx4Md27d/f7BaaieYeafv311+nTpw8Gg4GuXbtSq1YtHnnkEV599VVSUlLo3r07gYGBHDlyhGXLlnHbbbdx7733luoYnTt3JjAwkJdffhmdTkevXr38llfksQrvMyQkhAULFhAYGIjFYinyZcKrW7dutGvXjtdff93369bvv//O8uXLGTp0aKmGvi4sLy+Pzp0706tXLxo2bIjFYmH16tX8/fffjBkzpkz7EkIIceHq1KnDlClTePLJJ+nXrx+33HIL8fHxpKSk8Pnnn3Pq1Clee+21Iq/3BoOBHj168N1335Gfn8/o0aOL7Pv555/nP//5D/369eO2226jZs2anDx5kk2bNnHs2DG+/vrrc7atot8zBg4cyMKFCxkzZgzbtm0jLi6OH3/8kQ0bNvD000/7sqLK8t7XpEkT1qxZw5w5c4iJiSE+Pr7Yej8VobSfCerVq0etWrV4+eWXOX78OEFBQfz4448XHDx77bXXWLx4McuXLz/vF+o///zTV0g7IyMDm83GW2+9BUCbNm2KBESWLFmCy+U6Z9e90aNHs27dOl930/r16/tl0RcWHx/vlyH19ddfs2DBArp3707NmjXJy8tj1apV/P7773Tt2tVXGw3UrKVrr72W/v37M2XKlHPez4pw7bXX8tNPPxWpldSkSRM++eQT3nrrLWrXrk1ERATJycncf//9fPfddwwbNowhQ4YQGhrKl19+yZEjR5g+fbovKFna672iGY1GRowYwcSJExk6dCi9e/cmJSWFRYsWlflz49lWr15NQEBAkezLs6+NkpjNZkaOHMmECRMYOXIknTp1Yv369Xz99dc8+uijhIWF+dadP38+M2bM4KOPPqJdu3aAWux/xYoVdO3alczMTL766iu//XsHW2jSpInve4SXNxuuQYMG58zeE5cOCUoJ8S9Tu3ZtbrjhBhYvXlxk2aRJk4iPj2fx4sUsW7aMqKgoHnjgAYYPH35R29SsWTNGjRrFggULWLlyJR6Ph+XLl2OxWLj//vupU6cOH374oe9XuWrVqnH11VeXKaXYZDLRrVs3vvnmGzp06ODXv92roo7lZTAYmDJlCq+99hrjx4/H5XLx0ksvFRuU0mq1vP3220ybNo0lS5awaNEi4uLieOqpp3wjIJaF2Wzm9ttv5/fff2fp0qUoikKtWrV8X1yEEEJUvt69e1OvXj3effddPv/8czIzMwkLC6Ndu3Y88MADWK3WYrfr06cPn332GRqNptjR0ho0aMAXX3zBjBkzWLx4MZmZmURERNC4cWMefvjh87arot8zzGYzc+fOZerUqSxevJjc3Fzq1q3LSy+95DdKblne+8aMGcO4ceN44403KCgooH///hctKAWl+0xgMBiYNWsWkyZN4p133sFkMtGjRw8GDx7sN0LhxfTHH38wY8YMv3negvfDhw8vEpT65ptviIyMvOCuniVp1aoVGzdu5LvvvuPkyZPo9Xrq1q3L2LFjueOOO/zWtdlsAOUufF5WN998M/PmzeOvv/7y60L78MMPk5qayvvvv09eXh5t27YlOTmZqKgoFixYwH//+1/mzZuH3W4nISGBWbNm+WX6lPZ6vxjuuOMOFEVhzpw5vPzyyzRs2JC3336bSZMmFVs7rrR++OEHevTocUEBtcGDB2MwGPjggw9YsWIF1atXZ+zYsQwdOvS82+7cuROAn3/+mZ9//rnI8sp6fonKoVHKMgSBEEIIcQXxeDzSnfFfxmAwXDLFhoUQ4t9s/vz5TJ06lZ9++sk3Ut/FNnToUGJiYvjvf/9bKcerCh6Ph+TkZHr06MGkSZPKvP2OHTvo378/ixcvllpMolJIppQQQoh/JYfDwf79+4uMjiSufGFhYcUOay+EEKLyrF27liFDhlRaQArgscceY/DgwTzyyCNVVki/ItntdoxGo9/72ZdffklmZiZt27Yt1z7fffddevXqJQEpUWkkU0oIIcS/jqIoHDp0CKfTSY0aNUocIllcWRRFwWazkZaWRlhYGNWrV6/qJgkhhBDltnbtWl566SWuu+46wsLC2L59O59//jn169fniy++wGg0VnUThTgvyZQSQgjxr+NyubDZbNSoUQOLxVLVzRGVyDvaVFpaGjExMdKVTwghxGUrLi6OatWqMXfuXLKysggNDeXGG2/kiSeekICUuGxIUEoIIcS/jtvtBpAPbP9S3kCk0+mUoJQQQojLVnx8PLNmzarqZghxQaS/ghBCiH8tqSn07ySPuxBCCCHEpUGCUkIIIYQQQgghhBCi0pU5KPXnn3/y4IMP0rFjRxISEli2bJnfckVRePPNN+nYsSPNmjXjrrvu4sCBA37rZGZm8vjjj9OyZUtat27N008/TV5eXqnboCgKubm5SI12IYQQ4vK2du1aEhISyM7OBmDRokW0bt36nNtMnz6dG2+8sTKad8WTz1RCCCGEqEplDkrZbDYSEhJ4/vnni13+3nvvMXfuXMaPH8+nn35KQEAA9957L3a73bfOE088wd69e5kzZw6zZs1i/fr1jBs3rtRtyMvLo1WrVmUKZAkhhBCiag0ZMoTJkyf7zUtKSmLVqlUEBwdXUav+3eQzlRBCCCGqUpmDUp07d+bRRx+lR48eRZYpisJHH33EQw89RPfu3WnYsCGvvPIKaWlpvoyqffv2sXLlSiZNmkTz5s1p3bo1zz77LN999x3Hjx+/8HskhBBCiMuG0WgkOjpa6jwJIYQQQvwLVWhNqSNHjnDixAk6dOjgmxccHEzz5s3ZuHEjABs3biQkJISmTZv61unQoQNarZYtW7ZUZHPKz5UPhz6HAwsg5Vs4/itk/AU5+6DgJHicVd1CIYQQFU1RwJVXuVM5ukz98MMP9OvXj2bNmtGuXTvuuusubDZbsVlI//d//8eYMWN8t+fPn0/Pnj1p2rQpHTp0YOTIkb5lHo+H9957jx49epCYmEiXLl14++23fcuPHj3KqFGjaN26NW3btuWhhx7iyJEjvuVjxozh//7v/5gxYwbt27enZcuWjBs3DofD4Vu+bt06PvroIxISEkhISODIkSNFuu95LVu2zNfWe++9l6NHj57zvHz22Wf07t2bpk2bct111zF//vwyn1shhBBCCFG59BW5sxMnTgAQGRnpNz8yMpKTJ08CcPLkSSIiIvwbodcTGhrq277K7ZsNf4049zq6QDCFgykKjFFgjgJzNQiIA0scWOLBUhsC40Ej9eSFEOJSpCiKWktHUWBZJzQnV1fq8T1RHXB1W4ECKKhtUVBQ/z9z2/v3yRMneezxxxg+ajidu3UmLy+PTRs3cdJ2EofbQb4zn7S8NN92dpcdm9NGak4qu7bvYtLkSYx9YSxNmjUhKyuLrZu2cijrEIqi8P6M91ny1RIefORBGjdrTMbJDA4fPMy+jH24XC4evuthGiY25KW3XkKn07HgwwXcefedTPtoGgaDgayCLFavXo0dOy+8+QJpx9KY9uI0XEYXg+8fzID7B7B9z3Zq1q3JoHsHqfdHe5J/Tv0DwLa0bQTmB3I46zC2fBuvTXuN+566D71Bz+zXZ3P/8PuZMGMCADn2HL/z+PXXX/Pmm28ybtw4GjVqxI4dO3juueewWCz079+/Uh9TIYQ4F7dHQae9fDNDL/f2CyEuPRUalLpixPWB4z+D7RC4bODOU/915YI7X13HnQe2PLAdOfe+NAYIqK4GqQJrQ4gVghtCSEMwhoJGd3rSFvrbO+nP/K3VXfz7LYQQ5+ELkigKHsXjFzjxKB6/5SWtV5p1zj6W33E8Hjx4fP8qioIHDyjgUTy4PW4KXAXY3XbsbjsOl4MCdwEOl0O97Xagc+toYW7BCdsJdE4diuIh2u3AVMnnM8+Rx67jW6CUXdf279uP2+Wmbuu65Fvy0Vq0tOzZkjRHGnaXnRxHDoeyDvnWz3flo3FqSM1JZef+nZjMJuom1UWxKIQEh9AhvgNpeWnk2/JZvGAxQ0cNJalrEgDVI6tTPaE6pwpOseqnVbjcLoY+NtTXze6eJ+5hWL9hrFu3jmZtmuH0ONEb9NzzxD2YzCYia0Zy890388msT7jprpvQmrVodVr0Rj0BoQEAuHDhPJ197PA4MLgNuBQXbpebO0feSb3G9QB4YMwDPDn0SXZv3039RvV923hNnz6dMWPG0LNnTwBq1qzJ3r17WbhwoQSlhBCXFJ1Ww6gFG9mbllvVTSmzBjFBvDkoqaqbIYS4wlRoUCo6OhqA9PR0YmJifPPT09Np2LAhAFFRUWRkZPht53K5yMrK8m1f5YLqwTVfnLmteMDjAsWpBqfsJ8F+AmypYEtR//bOs58ARwY4MtV5ilMNbtkOwdm/wJtjIfgqCLaqU1A90BnPBKk4K1ilM4HWBFoj6Myg1Z8OXOnVv7WG03+f/lfqcwhxRfIoHl8AyPt34ckbICpuvcLLCgeDXB4Xbo8bt+JGUdTb3gCPh9P/FtreO1KXL5hUKLvHb7nHg9PjpMBdQIFLnexOO3aP3RcksrvsvmCRdzr7ttPtxO624/Q4cbqdZ+Z7nL7l3r9dHtd5z2HtwNrMunoW+jy9753wWLO30HoKLtrjVhyP1lzsa7UGjS/44/1bg4YGVzWgaaumjL13LM3bNqdFmxYkd00mODgYrUaLXqsn0BCobqfRoNfqMWgNBBuDSe6QzJfVvuSxwY/Rql0rWiW3IrlzMgHmAE7sO4HT6aRjh45EBkT6HRvg5KGTHE89zn197/Nrp9PhpCC9gOpB1bHoLVxlvYq60XV9213d5mrmvD4HY76R6tWrY9KbCDIGUSu0lm//6YHpANQKrUVwcDDbLNvQ6XVc2/5atFo107heeD2Cg4NxnnRSJ6wOQYYgXxtsNhuHDh3imWee4bnnnvPNd7lcVVY8/Z133mHp0qX8888/mM1mkpKSeOKJJ6hXr55vHbvdzpQpU1iyZAkOh4OOHTvy/PPPExUV5VsnNTWV8ePHs3btWiwWCzfddBOPP/44ev2Zj29r165lypQp7Nmzh+rVq/PQQw8xYMCASr2/Qoiy2ZuWy7bU7POvKIQQ/wIVGpSKj48nOjqaNWvW0KhRIwByc3PZvHkzt99+O6COspOdnc3WrVtJTEwE4I8//sDj8dCsWbOKbE7F0WjVYBFG0AeCORpodGa54gG3Hdyns6nsmeBIB1cO2I6CMwscpyA/BXL3qbWp7GlQcFydTqxS96M1QXgLiGwLEa0hsC5oPOBxq8EtRwHgPn3bAxSqRaLRnAlQaXRqkEpnBm0A6C2ng12G0wEto/qv1ihdC4W4SBRFwa24zwR2FE+R297JO9932+PG6XaqGSMety9g5PK4/Nb1ZgidnWnkUTy+zCG/Yd413n80Z+ZrTgc90OBRPNjddvKd+b4AUoG7gAJnAfmuM/OK/O0s8Lud78rH7rL7/nUr7sp/AAox6owYtAaMOqNvqhlUE4POgElnQmfQFQr8hPgCQGX9Fyh2GfgHljTqSS9x3vl8OOdDNm/azJrVa1j25TIWzl7IR/M/IsAYQLAx2BfwATBgINAQSHxIPITAp59/yl9//sWaNWtY8P4CFn6wkHmfzKN6eHUAIgMiiQmMKXJMxaHQuHFjJk2ZVGRZeHg4weZgjDojOq2OUFOob1mQUQ0eBRoCCTIGodPoMOgMvsAZgFlvBiBAF0CAPgCj1ogGDWa92ReU8tJpdRi0BgqfJpvNBsDEiRNp3ry53/pnb19Z1q1bx+DBg2natClut5vXXnuNe++9l++++w6LxQLAiy++yK+//sobb7xBcHAwEydOZPjw4SxYsAAAt9vNAw88QFRUFAsWLCAtLY3Ro0djMBh47LHHADh8+DAPPPAAgwYNYurUqaxZs4Znn32W6OhoOnXqVCX3XQghhBCiLMoclMrLy+PQoTNdA44cOcKOHTsIDQ2lRo0a3Hnnnbz99tvUrl2b+Ph43nzzTWJiYujevTsA9evXp1OnTjz33HO88MILOJ1OJk6cSN++fYmNja24e1aZNFrQB6iTKVLtpudxqUEp+ynIT1UDU24HGEPAEKIGr7J3QuZWyPwbsraqgav0teoE6r5iu0G17mqwSnOOLnyKBxT36YwuNygucGSBkq7+7ftiqjmdYWUAnQF0AaCzgCHodKDK5J+RJdlW4l/GG0zyZg2d61+P4sHlcfmydJxuJy6PC4fH4R948nY1K3S7pICR+qf6n1ajRavRotGc+Vt7OpCs0+jQaM+s53A7sDlt5DnzyHXkkufMI8+h/u2dfMsceX7L85zq7QLXxc0S0ml0mPVmAgwBmHQmzHqzbzLpTb55Jp3Jd9tvvt6EUWfEpDv97+ll3kCTSWfyBZoMOoMvGFXsqG4u0GfriQuOw2Su7E57F0aj0dAiqQUtklpw/4P307dnX35e/jPh4eGcPHHSt57b7Wbv3r20adPGN0+v19MuuR3tktvxwIMP0Pnqzqxbt46OnTpiNptZt3YdN8XfpK6seP9RSGiYwNIflhIWHkZg0JmAkncdl8eFBw+7d+0m15brO6cbN23EYrEQGR2J0+1Ep9fhdKkZbd4AnNN9uvve6Yw3l8eFy+Vi85bNNGnaBA0aDh48SE5ODrXq1PJ17/SKiooiJiaGw4cPc8MNN1TgmS6/2bNn+92eMmUKycnJbNu2jTZt2pCTk8MXX3zB1KlTSU5OBtQgVZ8+fdi0aRMtWrRg1apV7N27lzlz5hAVFUWjRo0YNWoUU6dOZfjw4RiNRhYsWEB8fLyvmH39+vX566+/+PDDDyUoJYQQQojLQpmDUlu3buXOO+/03X7ppZcA6N+/P1OmTGHYsGHk5+czbtw4srOzadWqFe+//z4m05kP/VOnTmXixIkMHToUrVZLz549efbZZyvg7lxCtHowhqtTUB1wZqsj99kOgu2wWig9vKWaFQVq0CjvAJz8A9L/UEf7s6fDoc/UyRQJsddC3A0Q2rDo8TRaddIazt0uRVGDVB6nOjmyQDmpBrPUHZ3uCmhUg1P64NNTgJp5pTOD1iw1rsQlr3Bmkcvj8ss2Kjzf4XL4upc5XWeyk/yymDwe3Lj9khO93dXODhgVnnQaHXqtXg0gaTS+fwsHl85uc44jh6yCLLLt2WTZs8iyq3+fPeXYc8h2qP/mOHJwuB0Vdu68XcAsBgsWg8X3d4AhQP1XH+C7HaAPOPOv9+/Tt816s+9fs96MQXee1ydRhHImMgTA31v+Zt3adbTv0J7wiHC2/r2VU6dOUatuLYxmI29MfYOff/mZ+JrxfPzRx+Tk5OBW3DjcDlb9torUI6m0aNmC4JBg1qxag8fjIa5mHBq9hiF3D+HN195Eq9fSrEUzMk9l8s/ef7hxwI306tOLuR/O5fGRj/PAww8QExvDsaPH+Hn5z9x5151Uq14NDRpcThcvjn+R+x64j9TUVN5/+30G3T4Ii9ECGqgZX5OdW3dyKu0UFouF0NBQAvRqfSmL0YLFaMGsN6PX63njlTcY+/RY9Ho9kydNpnnz5rRr1Q5AzZYqZOTIkUyaNIng4GA6deqEw+Fg69atZGdnc/fdd1fa41WSnBy1MHtoqJpFtnXrVpxOp99oxfXr16dGjRq+oNSmTZuwWq1+3fk6duzI+PHj2bt3L40bN2bTpk2+oFbhdV588cVKuFdCCCGEEBeuzEGpdu3asWvXrhKXazQaRo0axahRo0pcJywsjFdffbWsh758abRgDFOnwJpql728g5B/VM1UMkWqGUlBddWpzu3gcUD6n3BsGaT9ejpA9ak6hTSGmgOgek+1a16Z2qI53Y2vhC+HiqJ2FfQ41C6Jzhw1eOXd1ptBpQ9W74/eot4HvUVdJplV4iLw1jhyedSiyN6/C09215naRHa33a9Gkvdfj8eDRqNBQfF1YdNpdb4AklajRafVqV2MtAbfMu/yYjNuzqHAVUBGfganCk5xKv+U79/Mgkx1smf6/vYGos7OAikLDRqCTcEEGYIIMgURZDw9GQr9bQzCYrD4/g40BGIxWnzdqywGC0adsdxt+LcqHDwq/BgWrrV19jy/7TjdrbLw7ULXmzerKCAwgA1/beCTeZ+Ql5dHterVeOSJR+h0TSecTif79uxjwrMT0Ol03HHnHbRt2xa9Vo/FYCEqLIoFcxfw/qz3cTgc1Kpdi1f++wrNGqtd50cOH0mAMYD33nqPtBNpREdFM3DQQIKNwQSbgpk3fx6vTn2V0Y+NJi8vj9jYWJKTk6kWWY0gYxAGnYHk5GTq16vPfXfdh8Ph4Prrr+fRRx7FaFCvqWH3DWPMmDH0v6E/BQUFLF++HL1O/SjizXjTaXUEBARw//33M/qp0Rw/fpzWrVszefJk9Fp9kXMDcOutt2I2m5k9ezavvPIKFosFq9XK0KFDL+hxrQgej4cXX3yRli1bYrVaAXUkYoPBQEhIiN+6kZGRvpGIT5486ReQAny3z7dObm4uBQUFmM3mi3KfhBBCCCEqioy+V9l0JgisBQE1oOAY5OyFvENgjlLrVXlpjRB9tTp5nJC+DlK+g+MrIHs7bNsOO1+Hmv2hzn/AXLQGSLloNKA5XW/q7KtD8ZwJVjlOqt0SUU5naJnVbCpjuNo9UWdR74+u+CK+QgBFur4V7gJXuLaR3W0vkuXkUTx++9KiBpS8mUk6rQ6jzuh3u7jspLLKd+aTnp/OSdtJTtpOkp6fTkZ+Bhn5Gb6/T+WfIiM/g3xXfrmOEWgIJNQUSog5hBBTCKGmUEJNoQSbggkxqfNCjCG+296ggcVgqZD7+G9zduDofLeLBI9O14Py/X32vNPZcSXVkypxH2cVGwdo1qgZcz6YU2QdAIwwacIkJk0oWvcJILldMsntkotd5vXw/z3Mw//3cLHLYmNieeWVV865PahZSyNHjix2Wd26dVm4cKHfvPj4eL8fuwYMGOAr1O0dTe9sI0aMYMSIEX7z+vXrR79+/c7bvsr2wgsvsGfPHj7++OOqbooQQgghxCVHglJVRasHSzwYIyF3P+TtV7vSmWPUZX7rGs4EqBynIOUbOLxY7QZ4YB4cXAhxfaHuELWe1cWi0Z7pwleY4gZ3AbjzITdT7R7oC1QFgjFCraWlD1Jvn6+LobjseRSPb4S0wqOjOdwOtRC2Mx+b0+af7aS4UDyK+sVcwZe1pNfqfZNJZ/LNq+jgi91l54TtBCfyTpBmS+NE3gn1tu2EGoDKO8nJ/JPYnLYy7deoMxJuDic8IJxwczhh5jDf7TBzGKGmUMLMYb6/Q82hvmwQUTre4vB+WXGn630VXqYoii8gpHPrqO6pjktxofVoiwaCCgeSSihoDhQJNHnn+d2WwPy/1oQJE/jll1+YN28e1apV882PiorC6XSSnZ3tly2Vnp7uG4k4KiqKLVu2+O3v5Em1ZljhdbzzCq8TFBQkWVJCCCGEuCzIN5+qpg+AsMZqMCpnjzpCnzm2aODHyxgOde+EOnfAydXwz//g1EY48iUc+QqqXwdXPaAGvCqLRqcGmwpneinuMyMS5qar3QK1erWrnyFM7bKoDwJDsJo9Ji4r3gDT2VOeMw+bw0a+K9/X1c7pcarBJgANvq5xvkCTweT7+2J9eXe6naTlpXEs7xjHco9xPPc4x/OOk5aX5vs3syCz1Psz681EWaKIDIgkIiCCKEsUEQER6mSOIMISQWRAJOHmcCwGiwQlysAbTPIGkorrhunlLRSv1Wp92XDebpjeLmuFi6DrdXpf5pzH6SHrWJZaG8uo1jQ6e6Q8IcpLURQmTpzITz/9xNy5c6lZs6bf8sTERAwGA2vWrKFXr14A/PPPP6SmptKiRQsAWrRowaxZs0hPTycyMhKA1atXExQURIMGDXzr/Pbbb377Xr16tW8fQgghhBCXOglKXSrMUWq3t+ydkPsPGEPV2yXRaCG6ozqd2qQGp06shKPfw7Gf1JpT9e9Vgz9VQaNT60wVrnnlcarZVAXH1Cwvjeb0yH+hYI5W61QZQkDq2VQ5l8eFw+04U6PJZafAVeAbqa1wVzu3Rw0SeAt5G3QGX1ZToCEQg85w0buU2V12juYeJTUnldScVI7lHiM1N5WjOUc5lnuMk7aTparVZNKZiA6MJsYSQ1RglPqvJYooSxTRgdFEBah/BxoDz7svofKOZlg4K65wEfrCQSZvMKhwgXidVofJcHoUPr0Js87su8b0Wr1fNp13O+/8cykoKCBXm1uqdUXpTJkypaqbcMl44YUX+Pbbb3nrrbcIDAz01YAKDg7GbDYTHBzMzTffzJQpUwgNDSUoKIhJkyaRlJTkCyh17NiRBg0a8NRTT/Hkk09y4sQJ3njjDQYPHozRqL5PDho0iPnz5/PKK69w880388cff/D999/zzjvvVNVdF+KicnsUdFr50aCqRAeZrojH4Eq4D0JcSSQodSnRGSEsUc04yt6pZhqZo8+/XXgLaNUCsnbC7hnq6H2HPlW7+dW9U+3WV1LmVWXSni6w7g22KW5w5YMjXc0Q02jVUQmNEWeCdPpgGenvInF5XGq9ptOBJ2/QKdeRS74z3xeYcnvcvq5IBq0Bg86AUWdUR1M7XQz8YlMUhYz8DI7kHOFI9hFSslPUf3NSSM1J5YTtxHn3YdQZiQ2MpVpQNWIDY4kNiiU2MJaYwBjfvyGmEMmQKSVvRlNxk1s5PVLh6VOp1+jR684EjQJMAZh0JgL0AZj0aqZc4UCTdypcbF6Iy8knn3wCwJAhQ/zmv/TSS756WU8//TRarZaRI0ficDjo2LEjzz//vG9dnU7HrFmzGD9+PAMHDiQgIID+/fv71euqWbMm77zzDi+99BIfffQR1apVY9KkSXTq1KkS7qUQlU+n1TBqwUb2puVWdVPKpUtCNE/2KmYU7ctESID+sn8MGsQE8eagpKpuhhCiEAlKXWo0WgiurwamMreBLRUCqpeuWHhoQ2gzQx21b/cMyNoGe99Rg1MNH4WYLpdW0XGNDgxB6gSng1Q2KDgKtoOni60HgSkaTBFqRtXpocNF6SiKQoGrwG/Kc+aRbc8m35mvdrvzONRR6dD4ggNGnVEdTauSgk7etmYWZHIo+xCHss5Mh7MPcyT7yHlrOQXoA6gRXIMawTWoHlSdGsE1qBZUjerB1akWWI2IgAgJOJXS2UEmb/F5l+JSxzbQqDWTvMEmvUaPSW8iTB9GgCGAAH0ABp3BF8T0dtf0Bp4kyCSudOcapdjLZDLx/PPP+wWizhYXF8d77713zv20a9eOL7/8sqxNFOKytTctl22p2VXdjHKpH31lZFpfzo+BEOLSI0GpS1VANbXW0qnNkH+09IEpgMg20P5DOLYMdr2hjpK38UmIbAuNnoKgOhex4RdAo1NrTBmC1dseB7jyIHcP5ChqVz9ThFp/yximZlFJkAFQs1a8Qad8p1pIPNueTbYjG7vbjsPl8HWT0ml0vjo7IaYQjDpjpQYJXB4Xh7MOcyDzAAeyDnAw8yAHsw5yIPMAOY6cErfToCE2KJb44HjiQ+KJC4kjLjiOGsE1iAuOI8wcJkGnUvAWoXd6nH6jH3oz4gBfRpM3qBRkCiLQEOgLNhl1Rr+Ak1FnlC5wQgghhBBCiDKToNSlzBgO4c0hYxPkH1MDVaX90q3RQPUeas2p/R/C/rmQvg5+HwT17oZ6d136Bca1RjAa1fOgeNSi6QXHIe8Q6M1qwfSA6qcDVCH/mm5+dpfdN4JdviufrIIssuxZvm54HsUDgEFrwKw3Y9FbCDOFVfqIbg63g0NZh9h3ah//nPqHf079w/7M/RzOOuxXR6gwb+CpVmgtaoXUomZoTd+/NYJrYJR6Y+dVOOhUeAREAAUFjeZ0N8yzAk7eouDeIJNRZ/QFnSTYJ4QQQgghhLgYJCh1qTOGQ0QLyNhY9owpULu7XfUQxPWDHVPhxCrY9x4cWwpNnoaIVhet6RVKo1W78ulPd/VzF4AzRw1SaQ1q1lRA3JlufldAgEpRFPJd+dicNmxOG7n2XDLtmeQ58rC77L6uVAadGnwKNAQSbg6v9IwVj+IhNSeVvRl72Zuxl32n9rEvYx8Hsw6WGHwK0AdQJ6wOdcLqUDu0tu/v+JB4zPpLoP7ZJc5b78vhdvgCT27FrRYK12owas9kMoUHhBNoVLOcvMGmwpMEnIQQQgghhBBVRYJSlwNjuFrM/NRGNQgTUK3s+7DEQ8vX4fhy2PFfyDsI6x6A+Jsg4ZEzdZ0uFzrzmeLtHocaoMr6GzR6tUB6QA115EFjmBrQusQpiuILPtmcNrLsWZzKP+XrkudRPOg0OnX0Mb2ZEFNIpWc+ARS4CtibsZdd6bvYnb6bPRl72JexjzxnXrHrBxoCqR9en3rh9agbXpd6Yeq/MYExUlfoPFweF3aXHafH6ftXURRfLSeD3oBRayTcEk6wIRiLUc10MunUUeq8WU8SdBJCCCGEEEJcqiQodbkwRUBYc8j4C+wZ6u2y0migWneIbAe7p8PhRXDkSzixGhKfVrv6XY60RjUAZYoEj/N0gGqrGqAyhp0JUBlCL5kaVHaXnTxnHnmOPLLsWaTb0v0CUN6ud4GGQCICIqokgJPryGVX+i52ntzpmw5mHfR1DyzMoDVQN7wuDcIb0CCiAfUj6lM/vD6xgbESFDkHj+LB4XZgd9l9mU8e1POr1+gx6tVsppigGIKNwQQYAnxBJ++/Etz7dxkyZAgNGzbkmWeeKdX6+/btY+zYsezYsYN69erx1VdfXeQWlk1CQgIzZ86ke/fuVd0UIYQQQghRBSQodTkxR0FYE7UrnzO3/NlNhmC161713rB1AtgOw1+PQI2+0OhxNdPocqU1qAE7U8TpDKpstVi8zgTGCLDUAGNkpWaGeRSP2v3OkUuOPYd0Wzo5jhzyXfm+DKiqDkDlO/PZlb6L7Se2q9PJ7RzKOlTsuuHmcBIiE7BGWrFGWrkq4ipqh9Wuksyty4W3u5237pe3xpNGo8GkU7OavBlPgcZAzHqzLyvOpDNJYE/4TJ8+Hb2+9M+16dOnExAQwA8//IDFYqmQNkggSQghhBBCVBT5Fnm5CYiDkAI1E0irP9OFrTwikuDqT2DP23DgY0j9DtL/gCbPQkynimtzVdEawRSlTm47ODMh/SjoLWCKVutzmSIu7BwWw+1xqwEoRw5ZBVmctJ3E5rRhd9l9QYgAQ0CVdcFze9zsz9zP1rStbDuxja1pW9l3al+xGVDVgqrRMLIhDaPUKSEygShLlARJSuD2uLG77WeCT24naE6PeKg3YtaZibREEmIKwaw3+00yep0ojbCwsDKtf+jQIbp06UJcXNzFaZAQQgghhBAXQIJSlxuNBoLqqiPR5exRa0VdSGBDZ4aGj0LstbD1BbXW1IZH1cLoDR+//GpNlURnAl2M+rfLBgVH1QwxfZAanDLHqJlU5QgMuDwuXxZURn4GGfkZ2Jw2HG4HWo2WQEMgIaYQTJaqyXjJKsji77S/2XJ8iy8QVVwNqGhLNI2jG9MoqpHv3/CA8Epv7+VAURTsbjsFrgK1653HgaIo6LSn637pzEQFRhFiCiFAH+AXfJKA3iVMUcBmq9xjWixl6lZcuPtet27duO222zh48CA//PADoaGhPPTQQwwcOBBQM5oAtm3bxsyZMxk+fDgjRozg6NGjTJkyhd9//x2tVkurVq145plniI+P9x3n888/Z86cORw8eJCwsDB69uzJuHHj6NatGwAPP/wwAHFxcaxYsQKAZcuWMXPmTPbu3UtMTAz9+/fnwQcf9GV2HThwgGeeeYYtW7ZQs2bNUndBFEIIIYQQVy4JSl2OtDoIaQjufHVEPkv8hddKCm8GHebDnllwYD6kfAPp6yDxOYhqf2Y9j0edFNQvcIoCOi2UoTtJldNb1EnxgCsXcvZC7t7T3fviT9efKrkLY+FMqHRbOun56dgcNpweJ3qtHovBQkRABEadsRLvlEpRFFJyUth0bBObjm1i8/HN7M/cX2Q9i8FC46jGJMYk0iS6CU1imhATGFPp7b0cuDwuX/DJW/NLo9Fg1Bkx683EBMUQZgojwBCgTqeDUBJ8uswoCnTsCKtXV+5xr74aVq4s92v4nDlzGDlyJA8++CA//vgj48ePp02bNtSrV49Vq1Zx991306lTJ+655x4sFgtOp5N7772XFi1aMH/+fPR6PW+99Rb33XcfX3/9NUajkY8//pgpU6bw+OOPc80115CTk8OGDRsANViVnJzMSy+9RKdOndDp1ED++vXrGT16NM8++yytW7fm0KFDPPfccwAMHz4cj8fDiBEjiIyM5LPPPiMnJ4cXX3yxYs6hEEIIIYS4bF1GkQThR2eE0CZq1o/9hJrpc8H7NEPDRyC2C/w9HmxHYP1wiOgN4XdAngdy8k4HpU4HpEANSplMYAkAixnMRvW2yQhmExgu0ctMo1WDT4YQ8LjU7n2nNqnnwRRzuntfFIrWgM1pI9uezan8U5ywnSDXkYvD7fAFoaIsURh0hkq/Cx7Fwz+n/uGvo3+x6dgmNh7byEnbySLr1QqpRbPYZjSNbUqzmGbUC68n3cWK4XQ7fQXn7W47HsWDXqvHrDdjMViIC4kj2BRMgP5MAErO4xXkMgwkXnPNNQwePBiAYcOG8eGHH7J27Vrq1atHdHQ0Op0Oi8VCdHQ0AF999RUej4fJkyf7AqcvvfQSbdq0Yd26dXTs2JG3336bu+++m6FDh/qO06xZMwAiItRBNkJCQnz7BJgxYwb3338//fv3B6BmzZqMGjWK//73vwwfPpzVq1fzzz//8P777xMbGwvAo48+yrBhwy7yGRJCCCGEEJeySzRaIErFEAShjdQR+S6k8HlhigK6BlDzVdg/Cxw/Q8b3kL4WdIPB1EgNQqFRv8BpALcH8vIhMxvcbnUfGo0ajDKeDkyFBatBK7PpzHQpBau0+tP1pwCXDaftMNkZ28hSNBz3GMhGT76iQ6PVEqgPrLJMKI/iYV/GPtYfXc9fqX+x8dhGsuxZfuvotXoaRzWmebXmNI9VJ+mGV5TT7STflU+BqwCH24GC4hv1MNwSTrgpnEBjIBaDBYvBgklvquomi4tJo1Ezli7x7ntn83bRA7VwflRUFOnp6SWuv3PnTg4dOkTLli395tvtdg4dOkR6ejppaWkkJyeXqR07d+5kw4YNzJo1yzfP7XZjt9vJz89n3759VKtWzReQAkhKSirTMYQQQgghxJXnEooKiHIJqAbBVsj8W62bpC1nto6iQHomHDwCGVngcELAAAhpDblzwHMSPNNB0wsCbwXNeb6gKwo4nep+8myQkQkeBVDAaFCDVZYACAmEQMuZrKoqDFbZnAVkOXI5mX+KtPxT5DlteJx5mHERZAwi0hyNxhQLhgA1U60SKIrCwayDrEtZ5wtEnR2EMuvNNI9tTlK1JJKqJ9EkuglmfcUWb7/cuTwu8p1qAKrAXQDgC0BFWaIIN/sHoKoi601cAjQaCAys6laUydkj8Wk0GhRvFmsxbDYbTZo0YerUqUWWRURElLvbqc1mY8SIEfTs2bPIMpNJArpCCCGEEKJ4EpS6EgTVBWc22A6BpWbZf3XPzoWDqZB6XA0mhYeqQSIAwsD8IuR8DPm/gO1HsG+C0GFgTCh5nxqNGngynhW8KRysys6FkxlFg1UBZggNuujBKkVRyHHmkWnP5bgtnYyCbPJdBWg1WoIMFmItUei1p3/VdzvAlQ1Z6aALUEfv89ae0mgrtF0nbSdZm7KWdSnr+DP1T9Ly0vyWB+gDSKqWRMvqLWlVvRWNohtVySh+lyqP4iHfme/LglJQ0GvULnhhAWFEBkQSZAySAJT4V2rSpAnff/89kZGRBAUVn10bFxfHmjVraN++fbHLDQYDbrfbb17jxo3Zv38/tWvXLnab+vXrc+zYMdLS0oiJUbubb9q0qfx3RAghhBBCXBHkm+yVQKuH0IZq0e6C42r2VGk4XXAoFQ6mQH4BRIarwZ8i+w+A0HvB3AayZoP7OGRMBksPCLoVtGXIyikcrCr8fahwsConD9JPFQ1WFe4G6A1WmYzq8lIG4jyKhyx7Lpn2HI7mnSTTkUOB24FJZyTYYCHSHFp8poDOCLpItZ1umzpyX/4RMISBORaMYWotqnIocBWw6dgm/jjyB3+k/MHejL1+y406I81jm9O6Rmva1GhD4+jGEoQqxO6y+3XD02q0mA1mAg2B1A6rTbAxGIvBQqAxsEq6XApxKenXrx+zZ8/moYceYtSoUcTGxpKamspPP/3EfffdR7Vq1RgxYgTPP/88kZGRXHPNNeTl5bFhwwaGDBkCnAlatWzZEqPRSGhoKA8//DAPPvggNWrUoFevXmi1Wnbu3Mnu3bt59NFH6dChA3Xq1GHMmDE89dRT5Obm8vrrr1fx2RBCCPFvEx1kwu1R0GkvvzqShV0J90EIL/lme6XQB6r1pdL/LF19qZw82P0PHD0BoSEQEXb+Y5iaQdRLkPPJ6ayppWrWVMi9YGp8Ye0vTbDq7G6ABoOaPWUyQnAQBFvUAuveQutmI2i1uD1ushy5ZBRkc9R2kix7Lk6PC4veTKgxmFh9GQIVGo16rvWBp4uj50DWdtBZwBxVKHuq5OLX3i55a46sYc3hNfx19C/sbvuZQ6ChYVRD2sa1pW1cW5rHNpfueKcVlwVl0Bp8Rci93fACDYEyAp4QxQgICGDevHlMnTqV4cOHk5eXR2xsLMnJyb7Mqf79+2O32/nwww955ZVXCAsL47rrrvPtY/To0UyZMoXPPvuM2NhYVqxYQadOnZg1axYzZ87kvffeQ6/XU69ePW699VYAtFotM2bM4JlnnuGWW24hLi6OZ599lvvuu69KzoMQQoh/p5AAPTqthlELNrI3Lbeqm1MuDWKCeHOQ1GUUVw4JSl1JzDFqfamsreeuL5WWDrv+UbvPVY8BXRlGD9NaCmVNfQDuNDj1EgR0geBBoK3geiwldQMENdPL4QS7A3KOg8sFaECrwWPQk6VzkW50k6LLJ1vnwqXTEmgJItISijEw4MLbptWDKfx09lS+mjmVn6IGpcyxahaV3gKo2VDrU9fz++HfWX14NSk5KX67ig2MpV1cO9rHt6dtXFvCzGEX3r4rgMvjwua0YXPa/LKggk3B1AmrQ4gpxBeEklHwxL/B3LlzfX+vWLGiyPKvvvrqnLcBoqOjefnll895nEGDBjFo0KBil3Xr1o1u3boVmd+pUyc6depU4j7r1q3Lxx9/7Ddv165d52yHEEIIcTHsTctlW2p2VTdDCIEEpa48QXXBmQV5hyHwrPpSHo/aVW/vQUADNWLLP+qTL2tqIeQvVzOn7BshZKgasKoMBr06nQ4wKYpCjruADHs2R/JSyMzPxulwEqhoidaYMGh0YMgF/UnQn94uwHwm20rv/beMwQ2NRg0+6S3gcYMrB7J3cqyggFXp+1l5bCvrj23xy4YyaA0kVU+iQ3wHkuOTqRdeT7J6ONMVz+a04fK4MGgNBBgCqBZUjUhLJIGGQIKMQZIFJYQQQgghhBBXAAlKXWm0eghpqAam7OlqlzIAtxv2HIB9hyA0GIIqIKNJGwChd0FA8ulaU0chcxqYkiDkTtBFXfgxSsHmtpPuyuWoPZOTrlzsHicWvZFIUwxGv9pLCrjcaoaV0wknC9TbGgXQqMEovV7tFmgxq5PBoNasMhjAZDhnVplH8bDj1EF+S93AytSN7M485Le8miWaq2smc3WtzrSJa0uAoQKytS5jiqJgd9t9mVAexYNRZ8RisFA7rDbh5nCCjEEEGYOkGLkQQgghhBBCXIEkKHUlMgRBaGNIXw+uPMAEu/fDP0cgqoRi5hfCmABRkyD3K8j7Ts2YOrENgvpD4HWgqYDLzONR62Cdyoa8PFx5NnLzssnKO0Vufg4Ol5MAtCRo9Bg0ejwGHYpeh2LQ4THo8QSYcFtMuE//6wq2nJURpajd/5xutTtgnk0N5KEBDWqwSq8Ho14ttB5gAoMBuw7+zNrHrye2sPLoJk4WZPr2qNVoaBZ5FR2rN6djjJX6llA0OqParc+dDTotaP89Q6UrikKBqwCb00a+Kx9FUTDpTWo9qOA4Qs2hBJuCpSueEEIIIYQQQvxLSFDqSmWuBsFXwcktcNgOh45DdITaPe1i0Bgh+FYI6ABZc8C5C3IXQsEqCL7z/IXQFQXSM2H/YUg5DsdOqEXYj52Ak6cgO+d0gXOVHgg7PZWXy2LGFWLBFWrBGRaMMyIYZ3gQzogQHFEhOKLDcEQFoxj1akaVywUFDnKzT7Eqby+/FPzD6oID2BSnb58WrZEO4Q3pFNOUq2OaEhYUpnYP1OvVLCu3HZyZYD+hFkc3RYEp4rzF0S9H3iBUnjNPLUquKJgNZoIMQdQKq0WoKZQgYxCBxkC0Gm1VN1cIIYQQQgghRCWToNSVSqMBU004uA72bYf4qy5eQKowfRxEPAMFv0P2x+BKUQuhm9qohdD1MWoAKi0dtu9Vp30H1SyunPOPgOEINOMIMqEEmMBsQjEZ8Rj1KFqtmtGk0YACGpcLjcuN1uVG43Chy7ejy3egtdnR5dvRKAp6WwF6WwEcyzjnMZ2hgRypGcxXjbV8XTOfVUGZODVnAmQx+mCuCWpA54AGtDLUwOjRQLYC2UdBl6YGoww6MJrAYlIfB4MBtPmQs+d0favwQsXRA8tf66sKebvj5TnyfJlQ3iBUnbA6hJpPB6EMgVIPSgghhBBCCCGEBKWuWE4n7NgNJ7RQPQ40NqCSuoppNBDQEUwtIPcLsC2HlD9hywbYGQu7bHAys+h2Wg3EV4ea1VGqRWGLCSUjykJqiIb0YB2EBBFqDsZc0qiCpeXxoMstQJ9jQ5+dhyErD0NGLoZTORgycjBkZGNMzyYt/xRf1XPyRaM8VtXKw1MomafRCei/A27Yp6MJAThr2CmIP05BnIeCuCgK4iLxWMxqF0BvllWeTc348nYLBDVYpQG0KaD/GwKDwBIFwTEQEAHmIDWAZTDAJZhN5O2OZ3PZ8Hg8BBgCCDQEUjustgShhBBCiEuI26Og08r7sRBCiEuLBKWuRC4X7NgB+/dDrfrgyYbsHeC2qV3GKoPbA9uOwCo9/BkFh04AbiBVXa7VQIPa0PgqsNaFejWhdg3seg0nnTkctmeQ7szBqbgJ0QUQrTNXXBcvrRZ3iAV3iAV7nH8x9lT7KVZkbWd55jb+tjn9ljW3h3B9Wgg37daSuDcX0/FMNB43kAYH04ocxhEZQkF8NPk1oymoGU1+zRgKakbjjvA+BqcLr7vc4DKA3QV5ueBIA88W0JkgIBTM4WAOgYBgdbRAs/lMoEp/+l+DHnQX/+nscDuwOW3kOnLxKB5MehOBhkBqhtYk1HSmJpQEoYQQQohLi06rYdSCjexNO39m+qWoS0I0T/ZqWNXNEEIIUcEkKHWlcbth1y7YuxeqVQOjEYgCVy3I3QdmY8UUHi/22B7YvAN+Wwcr10Nm9pllWg1Yq0GTLGhog3oKBBZAcCMwtSbbXcBxRwaH8tLJdudj1OgJ1wdiutCsqFJItZ9iWdY2lmVuY7stxTdfg4bmgTXpFtqYbmGNqWYM8y3bBuByY0rLxJR6EnNqOuaUk5iPnMScchJDZi7GdDXjKmTzPr/jOcODyK8ZQ36tGPJrx1JQS/3bE2QBQk6vpYCrABy5YD8CdgPkBIImACj0GOpOjxio14HBeCZoZTT6B6zKmW3l8rjIc+SR58zD5XFh1BkJMgbRIKIB4QHhBBuDpSaUEEIIcZnYm5bLttTs8694CaofXQEjRwshhLjkSFDqSuLxwJ49sHs3xMaCqVB3PUscuGxQcEytXVSRmSwHUmDpSlj2u1qs3Cs4EDq0hHYtoGUT9bbiAtsKyP0S3Mcgcxp52jh2a9tyWIknRG8h3hh+0YMcRx2ZLMvcyk9nBaK0aEgKqs21oU3oGtaIaENIyTvR67DXiMReI5KzP97pcvMxHzmB+fAJzEdOEHD4BObDaZhOZGE4lYvhVC4hW/7x28YeHUZ+7UKBqtqxFMRFoVi0aoDKnQ/kg84MpnDQB4HGpCaguVxQUAC5ueB2gbfklQY1g8o7eqDJqAatzGY1iOUNWOnVf91aDbbTxckdbgd6rZ5AYyC1QmsRaYkk2BhMkDFIRscTQgghhBBCCHHBJCh1pVAU2LcPdu6EqCg16FCYVg9BddQufPaTYI6+sOPlF8CKNfDdL7CrUHAlJAg6tYZr2kKLRmqwozCNHntAV07omqDkfkd11xoCPSm09iymkTaOVLqSzVUX1rYSHHdk8VPmVpZlbmOr7YhvvhYNLYPq0D2sCV1DGxNpCLrgY7mDAshrWIu8hrX85mvz7ZgPpxFwMI2Aw2mYD6URcCgNY0YOphOZmE5kErZ+t299RaeloEYk+bVjT0/R5MeF4YjIB50GtGYwhoExCCxBxYzgp5ypaeVyQW4eZGX7AlcexYNNcWDTurBrFTQ6PYGWEKoHxhAZVo0QSwTBhjD0egsoBlCM6iiIkhglhKgi3bp148477+Suu+6q6qYIIYQQQogLJEGpK4GiqPWjtm+H8HCwlFA3Sh8AwQ3U+lKOTDWYUVaHUuHr5WpmVF6+Ok+ng3bNoVcnNSvKUPxllesu4Jg9k4P2dLLdNgK07UgLaEecaw3RznUEelK4yj6PPG0Njhk6kqlrfMHFvU86c1iWuY2fMreyOe+Qb74GDS2DatMjLLHCAlGl4QkwYbPWxGat6Tdfl2Mj4NDpYNWh4wQcPI75YBp6WwEBh9VMK1Zt9a3vNhvVOlW1IsmPCyO/ZhT5deNwRVcDQwjoLaA1ApozWVKoI+QVeOzkuQvIdxcAGizaEMIxE6sNJhgjIYoRY54GsrJAyTx9wgrtR69XuwdaLBAQ4F/j6uzp7KCkEEIIIYQQotyig0xXxMAFV8J9EBVDvjFeCQ4fhm3bICQEgs4TXDGGQlADyN4JrjzQl6J/vqLA+r/hs+/hrzOBEeJi4fpu0ONqCA8tYVOFTJeNVMcpjjgyyHPbCdUFEG+M8HXRSzH24rjhamKdvxPjXEegJ5X69k8p0ERw3NCBdH0Siqb0taUyXXmsyNzB0sy/2ZB7AI+vLxu0CKxF97BEuoc1IcoQXOp9XmzuYAu5TeqQ26TOmZmKguFkti9I5Q1YmQ+fQFfgIHBPCoF7Uvz24wwOoKBmpNoNsE48+fXqkFkrnpxgCzaPHUXxYNaZCNEHUs8SR6ghiGC9hQDdWZl1Z1OUM9lWTqfaVTAnR73t8ZxZ7+zglcGgBq68U+GAldEowSshLpDD4cBoNFZ1M8rscm23EEIIUdVCAvSX/cAFDWKCeHNQUlU3Q1wi5Jvg5S4lBf7+W81aCTlH/aPCzFHgrgM5e9WC2TpT8es5nLB8NXz+vVo3CtSC5e2T4IZroVUiaIvPZPIoHtKduRyxZ5DqzMTpcRGuDyTKVHwgyKUJOh2c6ki0cy0xzrWYlQxqO74lzrGCE4ZWnNS3waENK3b7XHcBv2TtYOmpv1mb8w9uzgRKEi3x9DgdiIo1Fh88uyRpNDijQ3FGh5LdynpmvtuNOTUD86Hjp7Or1KCV6dgpDDn5GLYfIXj7EWCDbxN7RBCu2vEo9ephuKoxxgYN0TSIAFMpM8Q0mjMBpICAktc7O3hlt0NenlqA3+1Wl3v3d3bwymxW922xlJx5Zbj4he/Fv5eiKNictko9psVgKdNolUOGDOGqq65Cp9Px9ddfY7Vaee6553jllVf466+/CAgI4Oqrr2bs2LFEREQA4PF4mD17Np9++ilHjx4lKiqKgQMH8tBDDwGwa9cuJk+ezKZNmwgICKBnz56MGTOGwMBAVq1axUMPPcTvv/9OSKH3mEmTJrF7924++ugjANavX89rr73G1q1bCQ8Pp0ePHjz22GNYTmfuduvWjZtvvpmDBw+ybNkyevbsyZQpU867XXp6Os888wyrV68mKiqKRx55pCJOuxBCCHHZu5wHLhCiMAlKXc6OHVMDUgYDhIWVbVtLHHgckHcATJGnu3qdlpcP3yyHL36AjCx1XoAZ+nSG/j2hekyJu3Upbk44czhUkM5xZyagIVIfiNlQul/EXZpAjhq7cdzQkUjXBmKdqzEpmVR3rqSacxWZuoacMLQlR1sXm8fJyuzdLD31N6tz9uBU3L79JARUp2dYIj3CEqlhCi/bubnU6XQU1IymoGY0mVer5zzPbacgP4+AIycIP3yKiMOnCE/JJOBwGvoTmZgycjFl7ISNO4ElZ/YVHQV160G9elCnDtStq07h4eUrhl/W4JXTqQarHA6w2dR55wpe6fVq0CowUN1/4WyrwlMJwVIhSqIoCh3ndGT14dWVetyra17NyrtXlikwtXjxYm6//XY++eQTcnJyGDp0KLfeeitjx47FbrczdepUHnnkEV/A6NVXX+Wzzz5j7NixtGrVirS0NPbv3w+AzWbj3nvvJSkpic8//5z09HSeffZZJk6cyJQpU0hOTiYkJIQff/yRW2+9FQC3283333/vCxAdOnSIYcOGMWrUKF588UUyMjKYOHEiEydO5KWXXvK1+4MPPuDhhx9m+PDhpd5uzJgxpKWl8dFHH6HX65k0aRLp6ekXfN6FEEIIIcSlQYJSl6tjx2DzZvXvyMiyb6/RQGAtUJxgS1EDU1n5sGgpfLUM8k5nC0RHqIGovl0gqOSufk6Pi+PObA4UnCDdlYseLdH6EIza8l1iHo2RE4b2nNC3IdS9mxjnWkI8/2B27mBr1g4+zjHwXZ6bAuVMRlQ9czQ9w5rSIyyR2uaoch23zBQPOF3q5DqdDeT2+K+jQQ2yaDRqsESrBZ0W9Dq1/pZef3ql0nEpbmxuBzaPHYfiRq/RYtGaqBESS2Tz+gS3NBOsC0DnrceVa4ODKWq224HD6nQwBdKz4cRJdVq3zv8goaFnglR16pyZqldXa4hdqMLBq3NRFPWcegNYTiecPAlHj/p3G9Tp/ANXJtOZLoPemldGo/9UEfdDXFE0ZXgeVqU6derw1FNPAfDWW2/RuHFjHnvsMd/yF198kc6dO7N//36io6P56KOPGDduHP379wegVq1atG7dGoBvv/0Wh8PByy+/7MtOGjduHA8++CBPPPEEUVFR9OnTh2+//dYXlFqzZg3Z2dn06tULgHfeeYd+/fr5Co/XqVOHZ555hiFDhjB+/HhMp0eCbd++Pffcc4+vnc8888w5t0tNTeW3337js88+o1mzZgBMnjyZPn36XIzTKoQQQgghqoAEpS5H3oCUokD0BYyip9VDUH1IS4d3PoAf16ld9gBq1YBB10O35BILlwMUeBwcd6jBqAxXHgFaA9UMoeiLjAJXThodadoGfOWAn0/p+SV7L3keD6C2s4EBbgoO49rwVsQGtcOjOU9tpAvhdqvnx3E6OIKCr5C4UQ+BFjAbwWw6c84U1MCVcnoUPO+2jtOBLLsNnG7QnN6XrlCgSq8HvQ6X4ibf7SDP48ChuNBpNARqzVQ3hhFpCCZYZyZYZ8ZQUgAwyAJNrlKnwnLz4MAhOHAQDhyBQ8fhSBqknVKLnG/efCbw6WUwQM2aULu2OtWqpU61a6vZeuXJrjqXwplSZ48oWZi70AiDLpda7+rUKfVvOBMUPDtwFRioTibTmayrwoErybj619BoNKy8e+Ul330PoEmTJr6/d+7cydq1a0lKKlqX4dChQ+Tk5OBwOGjfvn2x+9q3bx8JCQm+gBRAy5Yt8Xg87N+/n6ioKPr168fAgQM5fvw4sbGxfPPNN3Tp0sXXnW/nzp3s2rWLb775xrcPRVHweDwcOXKE+vXrA5CYmOh37PNtt3//fvR6vd929evX9+tGKIQQQgghLm8SlLrcVFRACuDQIfjwQ1iy5MyX94Z14fYboEPLc34ht7ntHHVkcqDgJNmufCw6I3HG8DPZORfI4XHxR85elmVu49esneR57L5l1Qyh3BAay38Cc7nGkIpGkwksx2P7lSydlQx9U7J0V6FoLqCIrsdzOgDlUP/1KGp2k8moBp+CLWrwyeQNYOjLnnnjcoPdoZ57u0MNUtnyceXZyC/IIc+Zj8NpR6fRYtEaiTUGExUQRbApiGBzCMZSdoksUVAgJDZSJ1Cvqf9n77zj7KjK//+emVu31/TQEjZAQkLogdAixVCkSkQIHTSCIEUpIhCKAUGkKSBNSgQVAj+RAFK+ilIFhEgnQHrZXm+d8vvjzNy2d1uy2RKe977O65Q5c+bMnXv3znzu8zzHSUBHKyxfCSvXw5oGWFUPq+pg1Tr1enz1lUq5FBcrwWr8eCVUjRuXrm8KwSoTw1Ap2EV8NEhbXXkWV5GIEt9Ms7O7oBd8PRRSolVBQVq4yhWvNuV5CQOKpmkUBnqx+MMgE85wjY1EIhxwwAFcfPHFnfpVV1ezcuXKjT7e1KlT2WKLLVi8eDEnnHACL774IjfccEPWHL73ve8xd+7cTvuOHj0677x7s5/nYigIgiAIgiBsvogoNZxYuxaWLFHljRGkPvtMiVEvv5x2gdplZ/jeIbBdOYQquhSk2swoaxJNrIg30GbFKDHCjAuWp1bS2xjidpK32r7MK0SN8JdwYNlkDiqbwpSCcSnLgg/tRirMJVSYSwg79ZRbH1NufYyNnxZjIs3G9rT4arC0gq4OCzgQdwWoeCJtVRMMqFhaIytdN7BgthXUxuIzwBcmaZlEQhpR0yFREsLQwhQyilFaiErCFDs+ik2dQHtcxftKJKG9WQllOK6llieS+Dd8fpoGWhCKq2FKNUwB7DhYcbBiYJlQ3wZrmmFtE6xugNW1sHI1rK9V1kkff6xSLoWFSqQaNw7Gjk3nY8fCqFEDs/pepqVUV/GuMmNdJZPZFlfe+wKy41eFw2nhKlOskhhXwgAwefJkXnjhBcaOHYsvz+doq622IhQK8eabbzJ+/PhO2ydMmMBTTz1FJBJJWUu999576LrO1ltvnep3xBFH8MwzzzBy5Eh0XWf//fdPbdthhx1YunQpW265ZZ/m3tN+22yzDaZp8uGHH6bc97766itaWyWoqyAIgiAIwuaCiFLDAceB5cvho4/UA3XVBsRLchxlYfXgg/Daa+n2vfeG00+HadPANqH9a4isAn8x+NIWA81mB6vjTaxKNNJhxin3FbJFoLLPbie5RK0Er7V9zsvNH/Na6+dE7ERqW7W/mG+VTubAsslMLRyfV/hK6BWsC+zPOv9+hO11VFhLKDc/Iug0U259Qrn1CU5Cp10fR6uxLS1GDVG7EuKu8GCaabEiFITKMmUJFQykLaH6EcdxiFtJomaMiBnDcmz8uo+wL8jYohFUhEop8ocp8hcQMPLEW7JtiCXUqnZe3hZRrnixuIoFlkwCmlop0e93BSs39VUc0YMq+V13mQILxsZcscp19dR9kNSgtgPWt8KaOli9HlatgVWrYf16tQLfZ5+plIthwMiRSqAaM0bFrRozJl2uqhq4+E+9iXVl22nRyjSVaFVXlx2gXdfT1laeCOatLBgMpsWrTBFLhCthA/j+97/Pn//8Zy688ELOPPNMysrKWL58OYsXL+a6664jGAxy1llncdNNN+H3+9l5551pbGzkiy++4Lvf/S5HHHEEt99+O5deeinnnntuKtj4kUceSVXGd80RRxzBHXfcwd13380hhxxCIJD+33jWWWcxZ84crrnmGr773e8SDodZunQpr7/+OldeeWWXc+9pv2222YZ99tmHq666iquvvhrDMPjlL39JqDtXXkEQBEEQBGFYIaLUUMey4Isv1MN8UVHfV9mzLPjHP+DRR9VKfaAefg88EE49FWpq0n11HxRPAD0EkeU4VoIG3ceqeCNrEs0k7CTlvkKqQsUbdUrNZoR/tXzG/7V8wlttS4k7ZmrbSH8Js0p34FvdCFF50TSixmhWG6NZ7T+YsL2OMusTysxPKHDWU2yvoNhewdjkyyScQtq0rWgr2Za2kikkCkcpN61Q/7tiWbZN1IoTNWPErQSO4xA0AhT4w2xTWElZsJgifwFF/jC+3gSF13UoCKmUS9y19IrFVR6JQlsHRGMq2HkyATYqxpXftajyRCu/r3eiiGa4YmWGi5OdAD0B4xwYHYCdqkDfUa3o6AuDFYD1LbCmHtbVwZr1sHo1rFqlrP8SCVizRqV8GIaypho1SolUXnnkyHRe0J0lXD+j60pY6s5VMDM4u2kqN8GGhu5dBYNBdR7hcOcYV5lWV+IuKGQwcuRIHnvsMW6++WbOOOMMEokEY8aMYZ999kF3P9M/+tGPMAyD22+/ndraWqqrq/ne974HKJe6+++/n+uvv57jjjuOcDjMwQcfzKWXXpp1nC233JKpU6eyZMkSLr/88qxt2223HY888gi33nor3//+9wEYP358jwHJe7PfggULuOKKKzjppJOoqqri/PPP5/bbb9+4F00QBEEQBEEYMogoNZSJx+GTT+Drr5W1SF8evCMRePZZWLhQPfyDeqA9/HA4+WQV5ycfmo5VOIY6K8rKpvdZH6vH8hdR6Ssh7N/w4LIr4w282vIZr7Z+yvvtK7BIr5w2NlDOrLId+FbpDuxQMHbDXQEz4kBFE0Gizk6s1acTCEYp8a+kVP+a4uRSAnRQyUdUxj6C2NPEA1W0F21He9G2tBdOIhYaDRswB2UFlSBqxolacSzbQtd0wr4gZYFiKkOlFAcKKPIXUOAP9YvLYxbBgEolRdntlpW2qvLcFNsjKsXjSrjyBCuctJWP35cWrnxG12KIHlCJjOM6pnL7S7Yrq6oKoKIApm0L2g5KrPIVgRaApg4lVq2tV26Aq1crsWrtWhVDzbJU2+rVXZ97cbESp0aOhBEjVF5drcpeXlw8cIJOb2Jc2XY6MHsyqazJvBhXuSsLZlpdhUJpy6tMwSqz7OulyCgMOx555JFObVtttRV33nlnl/vous68efOYN29e3u2TJk3i4Ycf7vHYf/nLX7rcNnXqVB544IEut7/yyisbtF91dTX33HNPVttRRx3V/UQFQRAEQRCEYYOIUkOVpib49FP1YD56tHrg7A3LlsETT8Azz6iHXICSEjjuODj++G5d/+JWgtpEI8uja2lItGD4S6lAJ2hFIENE6g2mY/FBxwpea/2Cf7V8xtfxuqztNeFR7F+6PfuXbs+2oZF9dwNMCVCuyJIZByoUVHGgQiEIh0iEgtT796Ye0OwkhR1fUNL2McVtH1PY8RXBRD3Bxn9T2fhvNXejkI7CCXQUbEOkcBs6CrbB9JdmHd5zw4tZcWJWgqTryhYygoT9IUaEKygLFVPoC1EUKCBo9K8bYJ8wDCgMq5SLaWbE03Jzz7IqGlMWVx0RFYTds/AxDNfCx5exUqCRLYJovs5xohwbnKRy+0u2QrxerU7oA7bwwVYjQR8Pxj5ghMEIgOODxlZYX6/SunXKHXDdunTq6FCxn9raYOnSrl+HYDAtUlVVqeSVKyvTbQMlXul62nWvOzJXFezK6grS1yVzdUFPvAoG01ZZmcKVl4v1lSAIgiAIgiAIg4CIUkMNy1Lxo774QlmxjB3bcxDoRAJefRWefhrefDPdvsUWSog68siuAzsDrcl21scbWRFbR2uyg7AeZFSwCp9mqDhT8VqI10EiAoEyIL8FxvpEC2+0LeX11i94q+3LrEDlBjq7FG3FPqWT2LdkO8YGy3v/mpjuimkJN44PDqClLYNGVKiA5L2IA+XoftqLd6C9eAcAdCtGUfvnFHV8TmH75xR1fInP6qC0dQmlrUtS+8X95bQFx9IUGE1jYBRtwVEkQ6MI+QoZU1hFWaCYQn+YQn+YsC/Y/1ZQmwpPwMgnWDmOEqVS4l+GaBWJ5RGt3P000kJVbu7FqMo9DqYSq+w4mB3qfedd54DPFa1GgLEFGCGVdD9ofogmoLYR6hqgth5qa1WMp9paJWDV1UFrq/o8rVypUnf4/UqkqqhQuVfOTOXlKi8p2fTxrnx5BL585IpXXpB2L9ZVrtugz5cWsoJBJeJ6eaZglVseiKD0giAIgiAIgiB8I5Cni6FEa6sSo1asgNLS7gOaO46ypPrrX+GFF9S+oB44Z85UYtQee3TpwmM5Fg2JFtbE6lkXrydixSj1FTEuNCJbUNF9EB6jAl3H1kG8UQkCvgLarQTvtS/jrbYveavtS5bF67OOUWYUMKNkW/YpqWGvkm0pMnoITmvbStxIZsTicVBiRsCvhJNi1wLKE58CGxdjxzZCtJZOpbVUrexkmjF8HV8Rbl9KUXQZZbEVFCdqCSabCCabqOLD1L6OHoDCLdGKJkDRVlC4FRRuCQXj1Ws03NG09IqD+fCulycWJtzrFs8QrrwVDTsirluaKzQBGLorimSIVkaBm7vvQccBLCVS2Z6FVaOyuvLQDSg3oCII228Jeg0YQRUbTfcpq62kBfXNUN8EjU1Q3wj19dmpoUF9jpLJtBVWT+i6+qyWl6t4b+Xl6XJmKi1Np4KCTWOZ1FvByFth0It7ZZrqvDNXGcwUsHQ97Y6YaYWVmTIFq8zk7WN04/4pCIIgCIIgfKOoLgpi2Q6GPrzvDzeHcxgKiCg1FIhGVdynr79WsaBGj86/+pcnRL38skqZFh8jRsBhhymrqHHjujxUuxmhPtHMyuh6GpNKyCr3FVMd6MFyyVdEW3AEH3Ss5t26t3i3/Ws+jdVhk3Yf0tHYoWAse5Vsy17F27JDwZj8FkOZ4pP3UOxpFX6/sowpKVMiVDAIQb+7Yln/vV2TlkncShC3kySsJEnbVIfX/QRD44gXToRgMQQKSGBRGFtNMLIMve0LaP0MOr5Cs2LQ9oVKuYRGQsE4CI9VecFYJe6FRkOwYoNiVg05dD1trdYVnpulJzR61zxpqoDskZjKLUvlpqks4xzHtbxy3xiee6DPcF37XOHKZ7iGexnClRWFuJUtXAEU+qDIgK0rQatOB2PXA8pVUPNBwoLmVmhsgaYWaGxWqakJmpqhsVGlpiblRmfb7ram3r9uPl+2SFVSolJpqXId9PLiYtXu5UVF3a8K2Ft6s8JgJp6A5SXThFgs3W7nvM6eJZZhpAUtT8jy+zsLWZnCVW7ujSEIgiAIgiBsNpSEfRi6xvmP/5elte2DPZ0NYuKIIm773vTBnsZmgYhSg4m36tiXX6oH3LKyzgHIOzrg3XeVW95rr2UHew4GYb/94Dvfgd1269KNKGbFaUy2sj7eQG28iYgVo8AIMSpQ0eWKb7ZjsyK6ng/blvJB2xd80Po5X0fW4GSIUADj/aXsXrwVexRvy67F21Lic93APOHJtNIihG0DGuhaOh5RUYkrPrlWT17eDw+ipm2RsJIkbCU6xa0kjuMADn7dT8DwUeALMipcSXGggLAvSNgXIuwLEjByHtiLxwK7p+uODdE10P6Vm5ZBx3KILFfWPLH1KvFu54npASVahUZCaEQ6D1ZBsNrNK5V72nBH17u3tvKwrLRQmVtOJLNXFTRN9dmxbPWeslwLLA1Xw9JUMnxp8UrTwAB0B7BBt9SKgY4NjpUdmykIjAHGFoJWDNqWgK5WHvQsr3Q/2Dq0RqGlFZrboLVdCVrNbaqtpVV9rptb0uWEO/+GBpX6SiiUFqyKilTKLBcWdi4XFqZTQYH6v9EXq6W+uuw5Tlq8yhWyPBHLynnNwb1GGUKUF6zdCxifuRphpqDVXfL27+Z8ndx5CN8I5LoLfUV+DRcEQeh/lta289Ga1sGehjDIiCg1GLS1qXg3q1apB9PiYhX/SdPUtg8/hCVLlBi1ZIl6oPMIhZR73re+BXvv3eWKfDErTnOyjbpEE+vjjbSbEQzNoNRXRFWgLKuv6VisjK7j844VfNq+jE/av+aT9mV0WNFO424RGsUupduxc8kkdg5vzUgbiNZDrBVq17qWJ0HlUuUFww6HoCqshImAKzoF3Dg1GyE+WbZN0jYxbZOkbZKwTZJ20n3WdTB0g4DuJ6D7KQ+WUBoopMAfJmj4VUDyfOJTb9F01wJqHIzYN93uOJBsho6VEF0FkdUQWQXR1RBdC7E6sBMQWalSd/hLIFChUrBSxfMKlIPfzQOl4M9IRmj4ukh5IkJP4hW4q9ZZadHKs64yPQHESrsNerGwLEu58NkO2FZa0HIy3Alx0sKIhhKwNFNZYmlu0gHNSecBoBqoDgEhwHW51TQlYmnuTpoOjqbm0h6Dthi0Rd0UgfYotLWrlRAz83Y3iHtHRI0bi6lUl71wQJ9fa0+gKijILmcmL0h6ZtmrZ64A6AVS9z7LmTGr+oJtpwWrTPEqHlfWpN0JWt5xM4UoXc+21MoM8B4IYBgGJJMkOjoIe4K+9/npLh+unzEhi0hEfab8/WF9KHwjGO6/6O8/qZqfHrLdYE9DEARBEDohotRAEYtBc7NaTW/9euWmZ1lKnPr3v1UsqU8+ga++6vzANW4c7LlnOoU6xyuyHIsOM0qL2U5dvImGZAsdZhRN0ynxFTI2NAIbh9WxWv7T8jHLomtZFlnN0o5VfBVZTcJJdhozqPnZrmALpoa3YlpoK6YGxlFhFKuNGmC6D56lZVBlg98Epw20BPg1CBZCqAgChX1+kLNsG9MxSdoWpm1iZuSg4WCjazp+3Yff8OE3lPBUFAgTMoKEfAGCRoCg4SdoBAYu8LimuYJROZRP7bzdNiFWC7G1br7ezWvVanTxOog3gGMqi6tkK3Qs6+Wx/eAvVsnn5UUq+d3cVwhGocp9BW49nM6NsBIWh/KDt65DQFfiZm/xhCxPuLJcYcoTsTyxI9PN0IuX5W23bUjayrrKcgUtL0aW5ijRCdftUHdAt1W7pmWLWkENgiEYGQLK09u8+E1dzT8Sh444dETdPKbKkZgqR9y6V06lqGqPxtRYlqViSLX2869SXpD0cMiN+xZOt3lx4DLbPDe+zHJmCgQ6l8Ph9IqFua9VpqiVmyeTyurUSl9Ln+NQUF5Ona7jN030XAGqq3JuW6ZQ1ZVo1ZXI1ZtjCP2K4zhEIhFqa2spKytT4qQg9JLh/Iv+hOrCwZ6CIAiCIORl0ESphQsXcv/991NXV8d2223HL37xC6ZOzfMQP1yxLBV75tNPVVq6VMWAWrtWWTmsXq1EqnyMGwdTp6q05555Y0QlbZMOK0qHGaUp2UpDsoWmRBvr4g20JNtot6I0JdtYG69jVbSW1bFa1sUbsLDzHBDCWoCJwdFsGxzNDuEtmVy0FVsXjMXnC0AgmH6o9Adc17vMJeUzgo07JpjtagW1eCMk2yFWi+PYWJqBqRlY+DDRsHCwHBvTtrAcy3WnUA/2hm7g0wx8uoFP81EaCBP2hSjwBQn6AgR0HwFDWUEFDD9+3Yc2HB7kdB8UjFGpKxzbDejdAInGdJ5oVlZYiSZItKhyskWVHROcpNuvcWMn6QpVIVekcle7M8IqgLgRUtZwhruSnhFy4zIFMyzl/Bm5G7cpVfanV87TA+o1SbX5Nk28rQ0Rsjw8dzTLTgtZKYseO211lRJCHDDdwO+e0OW5sdpOuq/pqH1xMtqVe2kn6y1HAyegPn/lxVAJKTEMLb0gZqZVFyhXWc1JjxVLQCwK0bhatTCacMtx5R4ZzUixRDpFYyqPJ92660oZzxCz43GVWlr6/hpvCN7/oYD7P8mzwvQsogI5ud+ftY/m8zG6vJyvDz+a5c1NncUlVUi/llllcoSjLvp1+T9Jy8ryt3njZLbntGW2dxoqt60bsaynts2QsrIyRo0aNdjTEARBEARB+MYzKKLU4sWLWbBgAfPnz2fatGk89NBDnHHGGTz//PNUVlYOxpTy4zjpX9gjEeVG09qazpublfudt3JXbW061df3zhJh/HiYOBG23RZqamDHHTHLS+kwo7Ql2qiPN1O3+l80JFuojzexPt7E2ngd9ckWmpJtNJqttJgdNFsdtNmd3e1yCekBtgyOZOuCsWxVNJatS7akpnxbxpaMRfcHslfY8pKmYTt23mRZUWyzA8uxsGxLtbkCk6aX4/jCYMXQ7AQ+K4ZhRzAcCwOLIBohX5BQIEzYV4LfF8RvhPD7Qio3AsoKSvcNnKXTUEDTXVe9MmBCz/0dB6wIJNvAbHMtrNrcentG6uicrGg6t+PugDZYHSoNBprhxm3yZeed2ox0u2ZkxHzSM7a57ZqeUddz2t2cjHJWu5Hhjuf1y0h4Mat08HltWue+aEpvclAik62Bo2e0oUQpN+xVKti7o7lWWaSFK9PNLRss0kKXt783lqW5gpcrTDkaBMLgD0GxluG+6CYvLhe6Kmh6Ok6Xl+uQJZolkspdMmGm438l4m45md6eWfbcKjNTPJm2UvP6etZq3tiZVqSeNVuk5/97XREAtr3nQRKjRnUZk2+j0LUMF0L3feDTlXuzoWds19O5ZzGXSqj9dK+/llPPzb3tblk30mMbRkbfjOTNxXDfs0Zm8mWslKmrz5iRMZ7fSLdpmhL+UvHBMufpuVQaKr6b5m43NHd/b97efN15am57yuJQV3m4HIq32qDL4vf7xUJKEARBEARhiDAootSDDz7I8ccfz7HHHgvA/Pnz+cc//sGTTz7J2WefPRhTyiL5j1d48ZLjaIk1YzsOtgaWrp4hrYyyqafrppuSITC3guQESOqQCOgkC0IkCoLEwwHiBQFiIR+xgE40oBHFJGJ/StRZQiQap+PNOPE8rnS9JaD7qQ5VUB2qorqgilGFIxlVOIrRJaMZVTya8oJKNMOHjYPjODg42I7NGscGYiqQuQMkgYQaU3MfBgwMNE3D0A10TcfQVB4wAgT0AH7DT8AIEDSC+A2/snbSfRiayn26D5+m43OS+BwTw0kqISTRBnZUCSNWUln8OB2usJJxcpqWYUnThTBAxkNM6kE7o31zRNNcd7xCYCN++bdNsGLudfBSLKMtpq5XZm4lVNmOgxVX8bK8lFVPqtwxM+puW07wfBV43MoQyYR+J9OaamMJAIEcq6G8eU9tGWUnd5uhBDxLU/8TcpOZW3bSZW+b6WSU3boFehJC5vp0Xyunr+W4bRntmW1e2erNiyX0mQzNVAl0mrpzOWU/+N3/DerUBEEQBEH45lJdFNwsFsEYCucw4KJUIpHgo48+4gc/+EGqTdd19tprL/773/8O9HTy8vtPHuHcQ/uwxHu32EDETTnNse73NDSDAl+YQn8BBf4CCvyFFAeLKQmWUBwqoSRQQnmonPKwShWhCspCZWi6ho6OhoamqaRrukroSijyBCPXPc4r65qeyjOFp0whKl+5X3DsbMHCMdO5Y7kCiLvd8fpYGcl1hcLN3ZX2Uu1uAPSu0brY7rZrmZYlTnb/ntx0uqx3tW8//WPYUCHOcN3z+k256AbHUoKYY3bOU2XvGpvpa53qY+dst9N9st4blgpyjt25T6rNTZl9vPdQVruTvU/W+ywzz9zuvR9d9aJTPXM/MvbJfP/a6f0yx8wqkz1Gt+/5/sLporyBdPWR0IGhGpfaIS1OeckTueyMspXTZudpz1e3c8rdbeuq3tvk5OT5tuW25dsnX3tf3x6Zb2HLbUgAKz7r40CbN5t9SARBEARBGGKUhH3DfhGMiSOKuO170wd7GgMvSjU1NWFZVic3vcrKSr766quBnk5eDvzupXz7/y2jqa0OzedDM1QydANN09H1DMFGN9K5nrYIMnQDv+5PJZ/hw68rS6KAESDkCxH2hynyF1EYKKQoUKTqgaJUW8AIqGN64lKeXNf0LsteXdfSAtWQRtPd2EWdA7l3iZPz8N9JCMjIs0Qq6PrBPmObd4zch24nz0N4Vr+cPvnG6PR0ljlOpxPNW+y+sTdPf9302eAl0zdClBi2y7QPg3mnhLSc97iT773ubaObOvnf17mfg6zjZ+6bM1be93++z1POa92pPXe8PGPlG6e7vl0du9P+dk7udJHn9u/is+/kvl5AZlzA3PEyx+xq3E6fsdz/LXn+v3Wq0w25/XM3O+5CAW7ZdtwVFTPqKVfUjLpDut0wYNrR3U3iG8WwCYkgCIIgCJshw3kRjKGCrL6Xh0lVk3jujP8b7GkIvcGL9YPEBxEEQRC+eQz1kAgwNFwDBEEQBEEYmgy4KFVeXo5hGDQ0NGS1NzQ0UFVV1asxHPdX2Pb24WkmJwiCIAjC0KawsHDIWxj3R0iEgbqnuvsfX7KmZcMXJhhMdhxXynd3Gc9WJTp2Yqj6EHfPyLC6xnIOg8twP4fhPn+QcxgqyDkMDbYq0QdEU+npnmrARalAIMDkyZN54403OPDAAwGwbZs33niDk046qVdjdHSoVcH222+/TTZPQRAEQRC+ubz77rsUFRUN9jS6pT9CIsg9Vc88C9ww2JPYSL4CHh7sSWwkcg6Dz3CfP8g5DBXkHIYGXwG7LNj0x+npnmpQ3PdOO+00LrnkEqZMmcLUqVN56KGHiEajHHPMMb3af8SIEfzzn/8cFr9iCoIgCIIw/CgsLBzsKQwIck8lCIIgCMKmpKd7qkERpQ499FAaGxu5/fbbqaurY/vtt+e+++7rtfueruuMGjVqE89SEARBEARh6NIfIRHknkoQBEEQhMFk0AKdn3TSSb121xMEQRAEQRCy6Y+QCIIgCIIgCIOJrL4nCIIgCIIwTNnYkAiCIAiCIAiDiYhSgiAIgiAIw5SNDYkgCIIgCIIwmGiOtxawIAiCIAiCIAiCIAiCIAwQ+mBPQBAEQRAEQRAEQRAEQfjmIaKUIAiCIAiCIAiCIAiCMOCIKCUIgiAIgiAIgiAIgiAMOCJKCYIgCIIgCIIgCIIgCAOOiFJ5WLhwIbNmzWLHHXfku9/9LkuWLBnsKQ0r7rnnHo499limT5/OjBkz+NGPfsRXX3012NMa9vz+979n0qRJXH/99YM9lWHH+vXrufjii9ljjz2YOnUqRxxxBP/73/8Ge1rDCsuyuPXWW5k1axZTp07lwAMP5Le//S2yVkbP/Oc//+GHP/whM2fOZNKkSbz00ktZ2x3H4bbbbmPmzJlMnTqVU089lWXLlg3OZIcw3b2OyWSSm266iSOOOIKddtqJmTNn8rOf/Yz169cP4ow3X2bNmsWkSZM6pfnz5+ftv2jRok59d9xxxwGedd/ZVJ/doXyfuSk+Z3fccUen6//tb397U59Kr+npOl966aWd5n/GGWf0OO5Qvs7Q83nn+4xPmjSJ++67r8sxh/q17s0zSjweZ/78+eyxxx5Mnz6dH//4x9TX13c77lD+Hu/pnJubm7n22ms55JBDmDp1Kvvvvz/XXXcdbW1t3Y67oZ+LgaA313nu3Lmd5n/llVd2O+5Qvs7Q83mvWrWqy8/1c8891+W4A3Gtff062mbA4sWLWbBgAfPnz2fatGk89NBDnHHGGTz//PNUVlYO9vSGBW+//TYnnngiO+64I5Zlccstt3DGGWfw7LPPUlBQMNjTG5YsWbKExx9/nEmTJg32VIYdLS0tnHDCCeyxxx7ce++9lJeXs3z5ckpLSwd7asOKe++9l8cee4wbb7yRiRMn8uGHH3LZZZdRXFzMySefPNjTG9JEIhEmTZrEsccey7nnnttp+7333ssjjzzCDTfcwLhx47jttts444wzWLx4McFgcBBmPDTp7nWMxWJ8/PHHzJs3j+22247W1lauv/565s2bx6JFiwZpxpsvTzzxBJZlpepffPEFp512WrcPn0VFRTz//POpuqZpm3SO/cGm+OwO9fvMTfU523bbbXnwwQdTdcMwNsn8N4SerjPAPvvsw4IFC1L1QCDQ7ZhD/TpDz+f973//O6v+6quv8vOf/5xDDjmk23GH8rXuzTPKL3/5S/75z39y6623UlxczLXXXsu5557L448/3uW4Q/l7vKdzrq2tpba2lksuuYSJEyeyevVqrr76ampra7n99tu7Hbuvn4uBorfPoscffzznnXdeqh4Oh7sddyhfZ+j5vEePHt3pc/2nP/2J+++/n3333bfbsTf5tXaELI477jhn/vz5qbplWc7MmTOde+65ZxBnNbxpaGhwampqnLfffnuwpzIsaW9vdw4++GDntddec0466STnuuuuG+wpDStuuukm54QTThjsaQx7zj77bOeyyy7Lajv33HOdiy66aJBmNDypqalxXnzxxVTdtm1n7733du67775UW2trqzNlyhTnb3/722BMcViQ+zrm44MPPnBqamqc1atXD9Csvrlcd911zoEHHujYtp13+5NPPunssssuAzyr/qW/PrvD6T6zvz5nt99+u/Od73ynv6e3Sch3zpdccokzb968Po0znK6z4/TuWs+bN885+eSTu+0znK6143R+RmltbXUmT57sPPfcc6k+S5cudWpqapz//ve/eccYbt/jvXkuW7x4sTN58mQnmUx22WdDPheDRb5z7usz1XC7zo7Tu2t95JFHdrq/z2UgrrW472WQSCT46KOP2GuvvVJtuq6z11578d///ncQZza88cw/xTJlw7jmmmvYb7/9st6XQu955ZVXmDJlCueddx4zZszgqKOO4s9//vNgT2vYMX36dN58802+/vprAD799FPefffdHn9ZEbpn1apV1NXVZX2+i4uLmTZtmnzvbCTt7e1omkZJSclgT2WzJpFI8Ne//pVjjz22W+unSCTCAQccwH777ce8efP44osvBnCW/c+GfHY3x/vM3n7Oli9fzsyZM/nWt77FRRddxJo1awZohv3D22+/zYwZMzjkkEO46qqraGpq6rLv5nid6+vr+ec//8lxxx3XY9/hdK1zn1E+/PBDkslk1rWbMGECY8aM4f333887xnD7Hu/Nc1l7eztFRUX4fN07VfXlczGYdHXOzzzzDHvssQeHH344v/71r4lGo12OMdyuM/R8rT/88EM++eSTXn2uN/W1Fve9DJqamrAsq5NZbWVlpcRE2kBs2+aXv/wlO++8MzU1NYM9nWHHs88+y8cff8wTTzwx2FMZtqxcuZLHHnuM0047jR/+8If873//47rrrsPv93P00UcP9vSGDWeffTbt7e3Mnj0bwzCwLIsLLriA73znO4M9tWFNXV0dQN7vnZ5iWAhdE4/HufnmmznssMMoKioa7Ols1rz00ku0tbV1+/9066235pe//CWTJk2ira2NBx54gO9973s8++yzjBo1agBn239syGd3c7vP7O3nbOrUqSxYsICtt96auro6fvvb33LiiSfyzDPPDIvP5z777MNBBx3EuHHjWLlyJbfccgtnnXUWf/rTn/K6pm1u1xngqaeeorCwkIMPPrjbfsPpWud7Rqmvr8fv93cSWSsrK1Of+VyG0/d4b57LGhsb+d3vfsecOXO6Hauvn4vBoqtzPvzwwxkzZgwjRozgs88+4+abb+brr7/mzjvvzDvOcLrO0Ltr/cQTTzBhwgR23nnnbscaiGstopSwSZk/fz5ffPEFf/zjHwd7KsOOtWvXcv311/PAAw8MCT/l4YrjOEyZMoULL7wQgB122IEvvviCxx9/XESpPvDcc8/xzDPP8Otf/5qJEyfyySefsGDBAkaMGCGvozCkSCaTnH/++TiO02XgbaH/ePLJJ9l3330ZOXJkl32mT5/O9OnTs+qHHnoojz/+OD/5yU8GYJZCf9OXz9l+++2XKm+33XZMmzaNAw44gOeee47vfve7m3qqG81hhx2WKntBfg888MCU5cA3gSeffJIjjjiix/vR4XStv4nPKD2dc3t7Oz/4wQ+YMGFCl/HVPIbL56Krc84U3SZNmkR1dTWnnnoqK1asYIstthjoafY7PV3rWCzG3/72N370ox/1ONZAXGtx38ugvLwcwzBoaGjIam9oaKCqqmqQZjV8ueaaa/jHP/7BQw89NGx/CR1MPvroIxoaGjjmmGPYYYcd2GGHHXj77bd55JFH2GGHHbKCzApdU11dzYQJE7LattlmmyFtTj4U+dWvfsXZZ5/NYYcdxqRJkzjqqKM45ZRTuOeeewZ7asOa6upqAPne6SeSySQ/+clPWLNmDQ888MCQ+2V+c2P16tW8/vrrvTL9z8Tv97P99tuzYsWKTTSzTc+GfHY3l/vMjf2clZSUsNVWWw3b6z9+/PjUoin52Fyus8c777zD119/vUGi0lC91l09o1RVVZFMJmltbc3q39DQkPrM5zJcvsd7ei5rb2/nzDPPpLCwkN/+9rf4/f4+jd/T52Iw6Muz6LRp0wC6nP9wuc7Qu/N+/vnnicViHHXUUX0ef1NcaxGlMggEAkyePJk33ngj1WbbNm+88UbWL3xC9ziOwzXXXMOLL77IQw89xPjx4wd7SsOSPffck2eeeYann346laZMmcIRRxzB008/PaRMY4cyO++8cyoOkseyZcsYO3bsIM1oeBKLxTrFizEMA8dxBmlGmwfjxo2juro663unvb2dDz74QL53+oj3oLx8+XL+8Ic/UF5ePthT2uxZtGgRlZWV7L///n3az7IsPv/88y4f8oYDG/LZ3RzuM/vjc9bR0cHKlSuH7fVft24dzc3NXc5/c7jOmTzxxBNMnjyZ7bbbrs/7DrVr3dMzypQpU/D7/VnX7quvvmLNmjXstNNOeccc6t/jvXkua29v54wzzsDv93PXXXdtkIdGT5+LgWRDnkU/+eQTgC7nP9SvM/TtvJ988klmzZpFRUVFn4+zKa61uO/lcNppp3HJJZcwZcoUpk6dykMPPUQ0GuWYY44Z7KkNG+bPn8/f/vY3fve731FYWJjywS0uLiYUCg3y7IYPRUVFnXyACwoKKCsrk/hcfeCUU07hhBNO4O6772b27NksWbKEP//5z1xzzTWDPbVhxQEHHMDdd9/NmDFjUu57Dz74IMcee+xgT23I09HRkfUr8apVq/jkk08oLS1lzJgxnHzyydx1111sueWWqSWGR4wYwYEHHjiIsx56dPc6VldXc9555/Hxxx9zzz33YFlW6runtLR0yCxTvTlh2zaLFi3iqKOO6hQM92c/+xkjR47koosuAuDOO+9kp512Ysstt6S1tZX777+fNWvWDDl3nlz647N7yimncNBBB3HSSScBQ/8+sz8+Z7nnfOONN3LAAQcwZswYamtrueOOO9B1ncMPP3zgTzAP3Z1zaWkpd955J4cccghVVVWsXLmSm266iS233JJ99tkntc9wu87Q8/sb1EP3888/zyWXXJJ3jOF2rXt6RikuLubYY4/lhhtuoLS0lKKiIq677jqmT5+eJUp9+9vf5qKLLuKggw5C07Qh/T3e0zm3t7dz+umnE41Guemmm2hvb6e9vR2AioqK1I/gmefc0dHRq8/FYNHTOa9YsYJnnnmG/fbbj7KyMj777DMWLFjAbrvtliW+DqfrDL1/Bl++fDn/+c9/+P3vf593nMG41iJK5XDooYfS2NjI7bffTl1dHdtvvz333XffkDPLG8o89thjAMydOzerfcGCBUPqy1j4ZjB16lTuvPNObrnlFn77298ybtw4Lr/8cgnQ3UeuuOIKbrvtNubPn09DQwMjRoxgzpw5nHPOOYM9tSHPhx9+yMknn5yqL1iwAICjjz6aG264gbPOOotoNMqVV15Ja2sru+yyC/fdd5/Eksuhu9fx3HPP5ZVXXgHgyCOPzNrv4YcfZo899hi4iX5DeP3111mzZk1eYXrt2rXoetoYv7W1lV/84hfU1dVRWlrK5MmTefzxx5k4ceJATrnP9Mdnd+XKlVmrFA31+8z++JzlnvO6deu48MILaW5upqKigl122YU///nPG/QL/aagu3O++uqr+fzzz3n66adpa2tjxIgR7L333px//vlZYvdwu87Q8/sb1II7juN0KSoNt2vdm2eUyy+/HF3XOe+880gkEsycOZOrrroqq//XX3+dWtkMGNLf4z2d80cffcQHH3wAwEEHHZTV5+WXX2bcuHFA9jkbhtGrz8Vg0dM5e9ZwDz/8MJFIhNGjR3PwwQd3iq80nK4z9P4Z/Mknn2TUqFHMnDkz7ziDca01R3wvBEEQBEEQBEEQBEEQhAFGYkoJgiAIgiAIgiAIgiAIA46IUoIgCIIgCIIgCIIgCMKAI6KUIAiCIAiCIAiCIAiCMOCIKCUIgiAIgiAIgiAIgiAMOCJKCYIgCIIgCIIgCIIgCAOOiFKCIAiCIAiCIAiCIAjCgCOilCAIgiAIgiAIgiAIgjDgiCglCIIgCIIgCIIgCIIgDDgiSgmCMOS59NJLmTRpEr///e+z2l966SUmTZo0SLMSBEEQBEEQBEEQNgYRpQRBGBYEg0HuvfdeWlpaBnsqgiAIgiAIgiAIQj8gopQgCMOCvfbai6qqKu65554u+7zwwgscdthhTJkyhVmzZvHAAw9kbZ81axZ33303l112GdOnT2f//ffnT3/6U1aftWvXcv7557Prrruy++67M2/ePFatWrVJzkkQBEEQBEEQBOGbjIhSgiAMC3Rd58ILL+TRRx9l3bp1nbZ/+OGH/OQnP+HQQw/lmWee4dxzz+W2225j0aJFWf0efPBBpkyZwtNPP833v/99rr76ar766isAkskkZ5xxBoWFhSxcuJDHHnuMgoICzjzzTBKJxICcpyAIgiAIgiAIwjcFEaUEQRg2HHTQQWy//fbcfvvtnbY9+OCDzJgxg3POOYett96aY445hhNPPJH7778/q9++++7LiSeeyJZbbslZZ51FeXk5b731FgCLFy/Gtm2uv/56Jk2axIQJE1iwYAFr167l7bffHpBzFARBEARBEARB+KYgopQgCMOKiy++mKeffpovv/wyq/2rr75i5513zmrbeeedWb58OZZlpdoyA6NrmkZVVRUNDQ0AfPrpp6xYsYKdd96Z6dOnM336dPbYYw/i8TgrVqzYhGclCIIgCIIgCILwzcM32BMQBEHoC7vtthszZ87k17/+Ncccc0yf9/f5sv/taZqG4zgARCIRJk+ezM0339xpv4qKig2bsCAIgiAIgiAIgpAXEaUEQRh2XHTRRRx11FFsvfXWqbZtttmG9957L6vfe++9x1ZbbYVhGL0ad/LkyTz33HNUVlZSVFTUr3MWBEEQBEEQBEEQshH3PUEQhh2TJk3iiCOO4JFHHkm1nX766bzxxhv89re/5euvv+app55i4cKFnH766b0e94gjjqC8vJx58+bxzjvvsHLlSt566y2uu+66vMHVBUEQBEEQBEEQhA1HRClBEIYl5513HrZtp+qTJ0/m1ltvZfHixRxxxBHcfvvtnHfeeX1y8QuHwzz66KOMGTOGc889l0MPPZSf//znxONxsZwSBEEQBEEQBEHoZzTHC6YiCIIgCIIgCIIgCIIgCAOEWEoJgiAIgiAIgiAIgiAIA46IUoIgCIIgCIIgCIIgCMKAI6KUIAiCIAiCIAiCIAiCMOCIKCUIgiAIgiAIgiAIgiAMOCJKCYIgCIIgCIIgCIIgCAOOiFKCIAiCIAiCIAiCIAjCgCOilCAIgiAIgiAIgiAIgjDgiCglCIIgCIIgCIIgCIIgDDgiSgmCIAiCIAiCIAiCIAgDjohSgiAIgiAIgiAIgiAIwoAjopQgCIIgCIIgCIIgCIIw4IgoJQiCIAiCIAiCIAiCIAw4IkoJgiAIgiAIgiAIgiAIA46IUoIgCIIgCIIgCIIgCMKAI6KUIAiCIAiCIAiCIAiCMOCIKCUIgiAIgiAIgiAIgiAMOCJKCYIgCIIgCIIgCIIgCAOOiFKCIAiCIAiCIAiCIAjCgCOilCAIveKOO+5g0qRJNDY2btD+l156KbNmzernWW2+zJ07l7lz5w72NARBEAQhxdy5czn88MMHexqCsMEsWbKEKVOmsHr16sGeSopZs2bxgx/8oMd+b731FpMmTeKtt97q8zG8fZ9//vke+15wwQWcf/75fT6GIGwovsGegCAIG8aiRYu47LLLstoqKiqYOHEiZ555Jvvtt98GjXv33XczceJEDjzwwP6YZr/wz3/+kyVLlvDjH/94sKfSryxdupTnnnuOo48+mnHjxg32dARBEIQB4IsvvuCee+7hrbfeoqmpibKyMvbYYw9++MMfsu222w729IYsQ/H+ZLBpamriySef5P/+7//48ssvMU2TbbbZhlNPPZVDDz007z4fffQRd9xxB++99x7xeJzx48dz/PHHc/LJJ/d4vNdff5277rqLzz//HMuy2GqrrTjppJM46qijsvr98Y9/5M0332TJkiWsXbuWo48+mhtuuGGjznXWrFlZQlJFRQVbb701p512GgcddFCvx/nNb37DYYcdxtixY1NtCxcuJBwOc8wxx2zUHDcXzjrrLI499lg+/fRTtttuu40a6y9/+QsPPPAAq1atYvTo0X360XXZsmXcdtttvPvuu7S0tDB69GgOP/xwzjjjDMLhcKrfv//9bxYvXsySJUv48ssvGT16NK+88spGzVsYWESUEoRhznnnnce4ceNwHIeGhgaeeuopzj77bO6++24OOOCAPo93zz33cMghh/T7Td+1116L4zgbtO8///lPFi5cuFmKUnfeeSe77757J1Hq/vvvH6RZCYIgCJuKv//971x44YWUlZVx7LHHMm7cOFavXs0TTzzBCy+8wG9+85s+PWB/k9hU9yfDmffff59bb72Vfffdl3nz5uHz+XjhhRe44IILWLp0Keedd15W/3//+9/88Ic/ZIcdduBHP/oRBQUFrFixgnXr1vV4rJdffplzzjmHnXbaiR//+MdomsZzzz3HJZdcQnNzM6eeemqq73333UdHRwc77rgjdXV1/Xa+22+/PaeddhoAtbW1/OlPf+Lcc8/l6quv5oQTTuhx/08++YTXX3+dxx9/PKv9scceo7y8fMiLUrvtthtLlizB7/dv0uPssMMOTJkyhQceeIBf/epXGzzO448/zlVXXcUhhxzCaaedxjvvvMN1111HNBrl7LPP7nbftWvX8t3vfpfi4mJOOukkSktLef/997njjjv46KOPuOuuu1J9//a3v7F48WJ22GEHRowYscHzFQYPEaUEYZiz7777suOOO6bqxx13HHvvvTd/+9vfNkiU2lRs6i/QvuI4DvF4nFAoNNhTyUsgEBjsKQiCIAj9yIoVK/jZz37G+PHjWbhwIRUVFaltJ598MieeeCI/+9nP+Otf/8r48eMHbF6RSISCgoIBO57Qf0ycOJEXXnghy+rn+9//Pqeeeir33nsvZ555Zuratre3c8kll7D//vtz++23o+t9i+KycOFCqqurefjhh1P3KHPmzGH27NksWrQoS5R65JFHGDNmDJqmMX369I0/UZeRI0dy5JFHpupHHXUUBx98MH/4wx+6FKVM08S2bQKBAE8++SRjxoxhp5126rc5DSS6rhMMBgfkWLNnz+aOO+6go6ODwsLCPu8fi8X4zW9+k3q/ARx//PHYts1dd93FnDlzKC0t7XL///f//h+tra388Y9/TFmQzpkzB9u2efrpp2lpaUntf8EFF3Dttdfi9/v5wQ9+wBdffLEBZywMJhJTShA2M0pKSggGg/h82ZpzJBLhhhtuYL/99mPKlCkccsgh3H///VnWS5MmTSISifDUU08xadIkJk2axKWXXpo1TltbG5deeim77roru+yyC5dddhnRaLTHeeXGlFq1ahWTJk3i/vvv509/+hMHHnggU6ZM4dhjj2XJkiVZ+y1cuDA1Py952LbNH/7wBw477DB23HFH9tprL6688kpaWlqyju/56//rX//imGOOYerUqTz++OMcfvjhec2Ibdtmn332yfqVsa/HeueddzjuuOPYcccd+da3vsXTTz+d6rNo0aKUv/7JJ5+cOi8vTkA+8+aGhgYuv/xy9tprL3bccUe+853v8NRTT2X16e3rClBXV8dll13Gvvvuy5QpU5g5cybz5s1j1apVnV4PQRAEYeO47777iEajXHvttVmCFChXpGuuuYZIJMK9994LwPPPP8+kSZN4++23O431+OOPM2nSJD7//PNU25dffsl5553H7rvvzo477sgxxxzDyy+/nLXfokWLUmNeffXVzJgxI+Xu397ezvXXX8+sWbOYMmUKM2bM4LTTTuOjjz7qdPylS5cyd+5cpk2bxj777JOacya9+c6C/rs/ycSLn7N48WLuvPNO9tlnH6ZPn855551HW1sbiUSC66+/nhkzZjB9+nQuu+wyEolEp3H+3//7f6l7ht13350LLriAtWvXZvV55513OO+889h///2ZMmUK++23H7/85S+JxWJZ/S699FKmT5/O+vXr+dGPfsT06dPZc889ufHGG7EsK6tvbW0tX375JclksstzBBg/fnyWIAWgaRoHHnggiUSClStXptqfeeYZ6uvrueCCC9B1nUgkgm3b3Y6fSXt7O6WlpVk/mvl8PsrLyzv9wDd27Fg0Tev12BtKdXU122yzTcqtL/Me6A9/+AMHHnggO+64I19++SWgrL323HPPrLnNmjWLL774grfffjv13sq8/1q5cmXqczVt2jSOP/54/vGPf3SaS2/f793R3X0jdB1TauHChXzrW99i6tSpHHfccbzzzjtdusl5opD3o/Ypp5zC8uXLO/Xba6+9iEQivP7661nta9asSb2e3fHWW2/R3NzM97///az2E088kUgkkvc1zKS9vR2AysrKrPbq6mp0Xc/6sXvkyJFD7sdvoW+IpZQgDHPa29tTwccbGhp45JFHiEQifOc730n1cRyHefPm8dZbb3Hcccex/fbb869//Ytf/epXrF+/nssvvxyAX/3qV1xxxRVMnTqV448/HoAtttgi63g/+clPGDduHBdeeCEff/wxf/nLX6ioqOCnP/3pBs3/b3/7Gx0dHcyZMwdN07jvvvv48Y9/zEsvvYTf72fOnDnU1tby2muv5TUhvvLKK3nqqac45phjmDt3LqtWrWLhwoV8/PHHPPbYY1lfUl9//TUXXXQRc+bM4fjjj2frrbdm9uzZ3HnnndTV1VFdXZ3q++6771JbW5sVk6Evx1q+fDnnn38+xx13HEcffTRPPvkkl156KZMnT2bbbbdlt912Y+7cuTzyyCP88Ic/ZJtttgFgwoQJeV+nWCzG3LlzWbFiBSeeeCLjxo3j+eef59JLL6W1tZVTTjmlT68rwI9//GOWLl3KSSedxNixY2lsbOS1115j7dq1EuNKEAShn/m///s/xo4dy6677pp3+2677cbYsWP55z//CcD+++9PQUEBzz33HLvvvntW38WLF7PttttSU1MDqDhVJ5xwAiNHjuSss85K7XfOOedwxx13dHIJnD9/PhUVFZxzzjlEIhEArrrqKl544QVOOukkJkyYQHNzM++++y5ffvklkydPTu3b0tLCmWeeyUEHHcTs2bN54YUXuPnmm6mpqUkJXL39zurP+5N8/P73vycUCnH22WezfPlyHn30UXw+H5qm0drayrnnnssHH3zAokWLGDt2LOeee25q37vuuovbbruN2bNnc9xxx9HY2Mijjz7KiSeeyNNPP01JSQmgxMNYLMYJJ5xAWVkZS5Ys4dFHH2XdunUpCxEPy7I444wzmDp1Kj/72c944403eOCBBxg/fnzWw/stt9zCU089xcsvv7xB38f19fUAlJeXp9reeOMNioqKUqLYsmXLKCgo4Dvf+Q6XX355jxY4u+++O/feey+33norRx99NJqm8cwzz/Dhhx9y66239nmO/UEymWTdunWUlZVltS9atIh4PM7xxx9PIBCgtLSU9evXs2bNGnbYYYesvpdffjnXXnstBQUF/PCHPwSgqqoKUK/j9773PaLRKHPnzqW8vJynnnqKefPmcfvtt6c+V329R8tHT/eNXfHHP/6Ra665hl133ZVTTz2V1atXc84551BSUsKoUaM69b/33nvRNI3TTz+d9vZ27rvvPi6++GL+8pe/ZPWbOHEioVCI9957L+v/xyWXXMLbb7/NZ5991u35fPzxxwBMmTIlq33y5Mnous4nn3ySZfWWi/d++/nPf855551HWVkZ//3vf3nssceYO3euWHdubjiCIAxLnnzySaempqZTmjJlirNo0aKsvi+++KJTU1Pj/O53v8tq//GPf+xMmjTJWb58eaptp512ci655JJOx7v99tudmpoa57LLLstqP+ecc5zdd9+9x/lecsklzgEHHJCqr1y50qmpqXF23313p7m5OdX+0ksvOTU1Nc4rr7ySaps/f75TU1PTacz//Oc/Tk1NjfPXv/41q/3VV1/t1H7AAQc4NTU1zquvvprV96uvvnJqamqcRx55JKv96quvdnbaaScnGo1u8LH+85//pNoaGhqcKVOmODfccEOq7bnnnnNqamqcN998s9O5nXTSSc5JJ52Uqv/hD39wampqnP/3//5fqi2RSDhz5sxxdtppJ6etrc1xnN6/ri0tLU5NTY1z3333dTq2IAiC0L+0trY6NTU1zrx587rt98Mf/tCpqalJ/U+/8MILnRkzZjimaab61NbWOtttt51z5513ptpOOeUU5/DDD3fi8XiqzbZtZ86cOc7BBx+cavPuHU444YSsMR3HcXbZZRdn/vz53c7vpJNOcmpqapynnnoq1RaPx529997b+fGPf5xq6+13Vn/cn+TjzTffdGpqapzDDz/cSSQSqfYLL7zQmTRpknPmmWdm9Z8zZ07WPcqqVauc7bff3rnrrruy+n322WfODjvskNXu3Sdkcs899ziTJk1yVq9enWq75JJLnJqamqzr5jiOc9RRRzlHH310VpvXd+XKlb0630yampqcGTNmON///vez2o844ghn2rRpzrRp05xrr73WeeGFF5xrr73WqampcS644IIex+3o6HDOP/98Z9KkSal7zmnTpjkvvvhit/v15bp1xwEHHOCcfvrpTkNDg9PQ0OB88sknzgUXXODU1NQ41157reM46XugnXfe2WloaMja//XXX+90f+lx2GGHZd1zeVx//fWd7ufa29udWbNmOQcccIBjWZbjOL1/v3d3br25b/Te1959Yzwed3bffXfn2GOPdZLJZKrfokWLnJqamqxz8vadPXt21v+Jhx56yKmpqXE+++yzTvM6+OCDO31WvP8BPTF//nxn++23z7ttzz337NV77re//a0zderUrOecW265pdt9zj777KzPsjA8EPc9QRjmXHnllTz44IM8+OCD3HTTTeyxxx5cccUV/P3vf0/1efXVVzEMo5MZ7+mnn47jOLz66qu9Pt73vve9rPquu+5Kc3Nzysy2rxx66KFZPuXeL8iZJudd8fzzz1NcXMzee+9NY2NjKk2ePJmCgoJO5s3jxo1jn332yWrbeuut2X777Vm8eHGqzbIsXnjhBWbNmpUySe/rsSZOnJj1a7i3Skxvzisfr776KtXV1VlLcfv9fubOnUskEuE///lPVv+eXtdQKITf7+ftt9/u5H4oCIIg9C8dHR0APcZm8bZ7/WfPnk1DQ0OWC98LL7yAbdspS97m5mbefPNNZs+enbKebmxspKmpiZkzZ7Js2TLWr1+fdZzjjz8ewzCy2kpKSvjggw869c2loKAgy8IhEAiw4447Zn2/9fY7qz/vT/Jx5JFHZlkxT506FcdxOPbYY7P6TZ06lbVr12KaJgAvvvgitm0ze/bsrO/8qqoqttxyy6zv/EzXtUgkQmNjI9OnT8dxnJS1SCa5sY922WWXTm7zN9xwA5999lmfraRs2+biiy+mtbWVX/ziF1nbIpEI0WiUI488kiuuuIKDDz6YK664gjlz5vDss8+ybNmybscOBAJstdVWHHLIIdxyyy3cdNNNTJkyhZ/+9Ke8//77fZrnhvLvf/+bGTNmMGPGDI488kief/55jjzySC6++OKsfgcffHAnF9mmpiaAlIVbb/jnP//J1KlTs+7nCgsLmTNnDqtXr2bp0qVA3+/R8rEh940ffvghzc3NHH/88VlhO4444ogu4zUdc8wxWS6Y3d13l5aWpl43j0ceeaRHKylQ1mNdudQFg8FO7q358CxLr732Wu644w6OPfZY7rnnHh599NEe9xWGF+K+JwjDnKlTp2YFOj/88MM56qijuOaaa9h///0JBAKsXr2aESNGUFRUlLWv5yqWucRuT4wZMyar7n25t7S0dBq/N4wePTqr7n2Jtra29rjv8uXLaWtrY8aMGXm3NzQ0ZNW7urk79NBDueWWW1i/fj0jR47k7bffpqGhgdmzZ2/wsXLPC9S5bagAtHr1arbccstOgUm9a7hmzZpuj5/7ugYCAS6++GJuvPFG9t57b6ZNm8b+++/PUUcdleXGKAiCIGw8uWJTV+SKV/vuuy/FxcUsXrw49f2zePFitt9+e7beemtABVB3HIfbbruN2267Le+4DQ0NjBw5MlXP93148cUXc+mll7L//vszefJk9ttvP4466qhOQddHjRrVKV5QaWlp1oNqb7+z+vP+JB+59yzFxcVA5+/I4uJibNumra2N8vJyli1bhuM4HHzwwXnHzRQA1qxZw+23384rr7zS6Ts+9we7YDDYSSzZmHuDXK699lr+9a9/ceONN7LddttlbfPEs0zhBJSA8ac//Yn333+frbbaqsuxr7nmGj744AOeeuqp1HWdPXs2hx9+ONdff30n969NwbRp0/jJT36CpmmEQiEmTJiQV2TqTsxz+rAS9Jo1a5g2bVqndi/kwpo1a6ipqenzPVo+NuS+0Rs315XV5/N1ijXm0dV9fL77bsdxNjg2WCgU6jImWm8WGnr22We58soreeGFF1JuiAcffDCO43DzzTdz2GGHZbmnCsMbEaUEYTND13X22GMPHn74YZYvX96tH/qGjp+PvnzJZ5L7S21fxrNtm8rKSm6++ea823Nv/Lr6Apw9eza//vWvee655zj11FN57rnnKC4uZt99993gY3V1XgNFb17XU089lVmzZvHSSy/x73//m9tuu43f//73PPTQQ51iLgiCIAgbTnFxMdXV1T1aGHz22WeMHDkyJdIEAgEOPPBAXnzxRa666ioaGhp47733uPDCC1P7eMGqTz/99E7WwB65D6354gcdeuih7Lrrrrz44ou89tpr3H///dx7773ccccdqVhRMPjfb32hq3uWnu5lbNtG0zTuvffevOfrxbOxLIvTTjstFWdrm222oaCggPXr13PppZd2CiS+KV+7O++8kz/+8Y9cdNFFHHXUUZ22jxgxgi+++KJT4Gjv/qU78SORSPDkk09y5plnZr12fr+fffbZh4ULF5JIJDb5ysHl5eXstddePfbLd7/nCRi9+dFzMBioz1Vf7uNbW1vZcsstN+g41dXVWJZFQ0ND1nsukUjQ3NzMiBEjut3/j3/8I9tvv32nuFizZs1i0aJFfPLJJ716LwjDAxGlBGEzxFvFxQteOnbsWN544w3a29uzfo386quvUtuHMl39SrPFFlvwxhtvsPPOO/f4i0t3jB8/nqlTp/Lcc89x0kkn8fe//50DDzww6+aqv46VSV9+fRo7diyfffYZtm1n3VB41zD3l6/essUWW3D66adz+umns2zZMo466igeeOCBLsU3QRAEYcM44IAD+POf/8w777yTN9j5O++8w+rVq5kzZ05W++zZs3nqqad44403+PLLL3EcJ8uS17Nk8vv9G/2QNmLECE488UROPPFEGhoaOProo7n77ruzRKne0NvvrKF6f7LFFlvgOA7jxo1LWaTl4/PPP2fZsmXceOONWULQa6+9NgCzTLNw4ULuuOMOTjnlFM4+++y8fSZPnsxrr73G+vXrU5Y+oFb6g84/rmXS3NyMaZqdVgkEME0T27b7tJLfYOCdc74Vhru6HxszZgxff/11p/Z87+NNcY/WE964K1asYM8990y1m6bJ6tWrs1ar7iumabJ27dqslbP7wvbbbw8oF8PM/x8ffvghtm13suTLpb6+Pq8Lomd95bnaCpsHElNKEDYzkskkr732Gn6/P2U2vO+++2JZFgsXLszq+4c//AFN07IsggoKCobcr0jhcBjo/OvW7NmzsSyL3/3ud532MU2zT+dx6KGH8v777/Pkk0/S1NSUdcPf38fy8M6rra2tx7777rsvdXV1WbGvTNPkkUceoaCggN12261Px45Go8Tj8ay2LbbYgsLCwrzLYguCIAgbxxlnnEEoFOKqq67qFKelubmZq666inA4zJlnnpm1ba+99qKsrIzFixfz3HPPMXXq1CyXusrKSnbffXf+9Kc/pQSGTLwVervDsqxO30WVlZWMGDFig74TevudNVTvTw4++GAMw+DOO+/sZEHiOE7q+nkCRGYfx3F4+OGHN+r4tbW1fPnll126P2WyePFirrvuOo444gguu+yyLvt59zVPPPFEVvsTTzyBz+fLWuFxzZo1fPnll6l6ZWUlJSUlvPjii1nvh46ODv7v//6PbbbZpt9+sNtUjBw5ktGjR/Phhx922hYOh/O+t/bbbz+WLFnCf//731RbJBLhz3/+M2PHjmXixIlA/9+j9ZYpU6ZQVlbGn//85yyR5plnntlol9ClS5cSj8eZPn16Vnvue6Mr9txzT8rKynjsscey2h977DHC4TD7779/qq2xsZEvv/ySaDSaatt66635+OOPO4mCzz77LLqub5TgJgw9xFJKEIY5r776auqXmMbGRp555hmWLVvG2WefnfrVcdasWeyxxx785je/Sf1y8tprr/Hyyy9zyimnZJn1T548mTfeeIMHH3yQESNGMG7cuLz+9AOJtxT1ddddx8yZMzEMg8MOO4zdd9+dOXPmcM899/DJJ5+w99574/f7WbZsGc8//zw///nP+fa3v92rY8yePZsbb7yRG2+8kbKysk6/NvfnsTy23357DMPg3nvvpa2tjUAgwJ577tnJtB5gzpw5/OlPf+LSSy/lo48+YuzYsbzwwgu89957XH755X2O57Vs2TJOPfVUvv3tbzNx4kQMw+Cll16ivr6eww47rE9jCYIgCD2z1VZbccMNN/DTn/6UI444guOOO45x48axevVqnnjiCZqamrjllls6udr5/X4OOuggnn32WaLRKJdcckmnsa+66iq+//3vc8QRR3D88cczfvx46uvref/991m3bh1//etfu51bR0cH++23H4cccgjbbbcdBQUFvP766/zvf//j0ksv7fO59vY7a6jen2yxxRb85Cc/4de//jWrV6/mwAMPpLCwkFWrVvHSSy9x/PHHc8YZZ7DNNtuwxRZbcOONN7J+/XqKiop44YUXNlo8u+WWW3jqqad4+eWXu42PtGTJEn72s59RVlbGjBkzOl3nnXfeOSVg7rDDDhx77LE8+eSTWJbFbrvtxttvv83zzz/PD37wg6yYY5dccglvv/12yt3UMAxOP/10br31VubMmcORRx6Jbds88cQTrFu3jptuuinruK+88gqffvopoH4s/eyzz1I/6s2aNStlJbNq1Sq+9a1vcfTRR3PDDTds1GvWG771rW/x4osvdoqVNHnyZB577DF+97vfseWWW1JRUcGMGTM4++yzefbZZznrrLOYO3cupaWlPP3006xatYo77rgjJUr29z1abwkEAvz4xz/m2muv5ZRTTmH27NmsXr2aRYsWdfo/0ldef/11wuFwp/vh3PdGV4RCIc477zyuueYazjvvPPbZZx/eeecd/vrXv3LBBRdQVlaW6rtw4ULuvPNOHn74YfbYYw9AifivvvpqynKzrKyMf/zjH7z66qt897vfzXq/fvrpp7zyyitAOgas937bbrvtNtjaSxg4RJQShGHO7bffnioHg0G22WYbrr766qxV8nRd56677uL2229n8eLFLFq0iLFjx/Kzn/2M008/PWu8Sy+9lCuvvJJbb72VWCzG0UcfPeii1MEHH8zcuXN59tln+etf/4rjOCnh5JprrmHKlCk8/vjj/OY3v8EwDMaOHct3vvMddt55514fY9SoUUyfPp333nuP7373u3lXDOmvY3lUV1czf/587rnnHn7+859jWRYPP/xwXlEqFArxyCOPcPPNN/PUU0/R3t7O1ltvzYIFCzjmmGP6fOxRo0Zx2GGH8cYbb/DXv/4VwzDYZpttuPXWWznkkEP6PJ4gCILQM7Nnz2abbbbh97//PU888QTNzc2UlZWxxx578IMf/ICampq8+x166KH85S9/QdO0Tpa8oFbuevLJJ7nzzjt56qmnaG5upqKigh122IFzzjmnx3mFQiFOOOEEXnvtNf7+97/jOA5bbLFFSuzqK739zhrK9ydnn302W221FX/4wx/47W9/C6jvzr333jv1kOv3+7n77ru57rrruOeeewgGgxx00EGceOKJWSsUbiqWLl1KMpmksbGRyy+/vNP2BQsWZFnVzZ8/nzFjxrBo0SJeeuklxowZw2WXXcapp57a47HmzZvHuHHjePjhh/ntb39LIpFg0qRJ3H777Z3uG/7+97/z1FNPpeoff/xxaiXCUaNGpUQpL8zEQC2wcuyxx/Loo4/y7rvvZrnQnnPOOaxZs4b77ruPjo4Odt99d2bMmEFVVRWPP/44N910E48++ijxeJxJkyZx9913Z1n69Pc9Wl846aSTcByHBx98MBXg/q677uK6667LGzuutzz//PMcdNBBGyWonXjiifj9fh544AFeeeUVRo8ezWWXXcYpp5zS47677bYbjz/+OHfccQePPfYYzc3NjB07lgsuuKCTNenHH3/caZEHr3700UeLKDUM0JwNjU4sCIIgCIIgCIIgCBvAwoULufnmm3nxxRepqqoakGOecsopjBgxopN11+aEbdvMmDGDgw46iOuuu67P+3/yySccffTRPPXUU6nYUIKwKZGYUoIgCIIgCIIgCMKA8tZbbzF37twBE6QALrzwQp577jlWr149YMfclMTj8U5xz55++mmam5uz4oT1hd///vcccsghIkgJA4ZYSgmCIAiCIAiCIAjCMOOtt95iwYIFfPvb36asrIyPP/6YJ554ggkTJvDkk09mrSQtCEMViSklCIIgCIIgCIIgCMOMsWPHMmrUKB555BFaWlooLS3lyCOP5OKLLxZBShg2iKWUIAiCIAiCIAiCIAiCMOBITClBEARBEARBEARBEARhwOmzKPWf//yHH/7wh8ycOZNJkybx0ksvZW13HIfbbruNmTNnMnXqVE499VSWLVuW1ae5uZmLLrqInXfemV133ZXLL7+cjo6OXs/BcRza29s7BXUTBEEQBEEQeo/cUwmCIAiCMJj0WZSKRCJMmjSJq666Ku/2e++9l0ceeYSrr76aP//5z4TDYc444wzi8Xiqz8UXX8zSpUt58MEHufvuu3nnnXe48sorez2Hjo4Odtlllz4JWYIgCIIgCEI2ck8lCIIgCMJg0mdRar/99uOCCy7goIMO6rTNcRwefvhh5s2bx4EHHsh2223Hr371K2pra1MWVV9++SX/+te/uO6665g2bRq77rorV1xxBc8++yzr16/f+DMSBEEQBEEQBEEQBEEQhjz9uvreqlWrqKurY6+99kq1FRcXM23aNP773/9y2GGH8d///peSkhJ23HHHVJ+99toLXddZsmRJXrFrwGn/Cv43H2wT/EXgKwZfEQTKVPK7ebASglUq6f7BnbMgCIIgCIIgCIIgCMIwol9Fqbq6OgAqKyuz2isrK6mvrwegvr6eioqK7En4fJSWlqb2H3RWPwtfP9y3ffylEBwB4VEQHg3hMVAwHoq2hqJtoGAr8IVB00EzQNM2ydQFQRAEQRAEQRAEQdi0WLaDoQ/v5/qhcA79KkptNkw8S1lGtfwPzCiY7WB2QLINzLaMvEWVcdxyC7R/0cWgOoRGQtFWSqQq2hqKt4OiCWCElaWVEXAFKwM0X7qs+9Jtus/dNrzf/IIgCIIgCIIgCIIwXDF0jfMf/y9La9sHeyobxMQRRdz2vemDPY3+FaWqq6sBaGhoYMSIEan2hoYGtttuOwCqqqpobGzM2s80TVpaWlL7DzpGCCacln+bY4OdBDsBVhysDoiug/blEF0J0bUQq4VEA8QbIbYeomvAjkNsrUr1b2QcKwzFNVC8LZRMgpLJyqIqhaasq3QD8EQqA/QA6EElZmUKVl5/TQd0V7zSM9pyt+dr0zJyTQQwQRAEQRAEQRAEQchhaW07H61pHexpDGv6VZQaN24c1dXVvPHGG2y//fYAtLe388EHH3DCCScAMH36dFpbW/nwww+ZMmUKAG+++Sa2bTN16tT+nM6mQdPBCKrkLwaqoHBLqNpDxaAyO5RlVaIF4nWQbHVFrDgkmqBjGbR/CW1Loe0LsKLQ/IFKoASmsmlqvKo9oXh7wAbHUoKYY4HjHsdpddtswFEptaSz5ra5ZY30tk6ikytGpcpuf6+M5gpfruthSuQyMsQsVyxLuSZqOft74+Y7djft3fYRsUwQBOGbhuM4aPL/XxAEQRAEYbOgz6JUR0cHK1asSNVXrVrFJ598QmlpKWPGjOHkk0/mrrvuYsstt2TcuHHcdtttjBgxggMPPBCACRMmsM8++/CLX/yC+fPnk0wmufbaaznssMMYOXJk/53ZYKD7IFCqUsFYsC3l0hdvhNga5RIYHgtjDwdfoRKxOr6G5v+p1PgeRFdD03sqfXEXhEbBqANVKp3cP0KM46CELi+33XbbrZO9HUdZhqVEr8z96UIUy0eOUIZDWlzKk6cWh+xOLPPEMYNs8SxDQNN92RZhfRLButlHBDNBEPoJx3FwcFJ5vrbMHMi7rav98vXPbOuqnDumV/b62Lb6/rCxVR/HwcZtc2y1j5MxJ2/czHpGn9S+GePmHhOguqCaySMm9/NVEARBEARBEAaaPotSH374ISeffHKqvmDBAgCOPvpobrjhBs466yyi0ShXXnklra2t7LLLLtx3330Eg8HUPjfffDPXXnstp5xyCrquc/DBB3PFFVf0w+kMMXQDghUqFW0NyWaIrFLufPEGtYJf8bYqjT9G7RNZBfVvQsNbUP8WxNbBskdVCo1Wgtb4o1R8qg1F01CugBt/ihtNSuRycsp2xnZX9MorlnWzb6rsZIwF6RPPsBzr0sIsU3yiC8HME6QgW6jKsCZLCWaepVlGPSv4fQ/CWI+iWW7fLtq88xGEbwCO46QEksyy7dh5692Vu2pLjWHb2NhYtoXtqLJt26rs1h3bSbfTeeyUAJMr3ID7r7Bz37z9nezXwPv3pmlaantuObOvhpa37M0j11pJQ0u1aW7HzD65bfn69KZfe6KdkBHqwztAEARBEARBGKpoTuYd7TChvb2dXXbZhXfffZeioqLBnk7fSbSo2FMdK1RMqtAIFccqFyum4k+tewlqX1WufgDoUL23ErIqdgFfwYBO/xuD05PIlWkhBmnBLJ9QlqfcqY0cgQyyRLLcbVkiVq6VWZ5tKUEqR/xKCWc5FmaecIYb0ywzLlmncfX8x+pkPdbFth5FtaGgoAr9gSfgdJc80adTe559LcfCtE0sx8K2lRhkYSlRyFbbs0QkT1RyrUIzxSrPUidT0EmV84g0uds9ocYTZ3JFGl3Ts+r5+gB523PbNqR9c6E+Uk9psJQ9x+852FPZLBj291SCIAiCMIgcdvu/hm1MqcljSnj2vH0Gexqy+t6gkHLxG6fiS3WscK2qqlwRwMUIwcgDVLJisP4fsHKRcu2r+5dKoFwCiyekra6KJkLh+OyxhL6TayU11MgnmnWyMutKAMuxNOuLcJZJPhEtpaM52W1ZIhS9ENDIaMsXtD+zbGRYgGW0d7JA66Uw1kkQ60W/LoW44YntpK19LMfKqme2peoZ20zbJGknsWwlGJmOiWVbJK1ktsBEZ2uklDWR46REGki7bwEp6x6NtPCiu59TXdO7LWt6/n1yhZzNVdARBEEQBEEQhKGEiFKDib8IyqYqV7y2pdCxCkJVKt5ULkYIxnxbpfZlSpxa9xLEa1UcquhqZU3loQddoWoSlNSkV/gTq6rNh6EumnnkFbbyuVZ2IYqlYp11IZZ1Grc7IS2faJaLlpHlCk/0UUzLtEjLXPEyV1zLJ6j1wi0zp+wAlmNj2TYWXu6o3FE2QJnbbcchaVskrCSmbZF0TJKW6VobeQKRsiCyHCvLUskLNu1kvI6ZIpInAqXEoAxLIV3T0fU8bXnEJEEQBEEQBEEQNl9ElBpsNA3CoyBQoYKet36mrKKClV3vU7QVbH+hSolmdyW/pdD+hVrRr+1Ltdpfy8cqpQ8GBeOhZJISqUomqdTdsQRhY8kSdAZ1Jn2jS0s0T/QijzgGaQEtYz87t09+Qc1x3c0sx3KtjCwsx07lXjLdbQnbImEnSdomCbfNxsZy3dIsx8ZxwIIM8UhzxSTA0dA1DV0z0HUNQzPQ0DF0HU3T8esGGm67bmBoOprmQ9cM14ooVzzzhDNPaMsQ0zyxznFTPpEvZZmUI7xBev8sMZCcfTPeYF2NS+4+giAIgiAIgiAMFiJKDRWMgBKKfEXQ/BFE1iixqidrgUAZVO6qkodjQcdKV6D6XKXWzyFeB5EVKq17Md0/WA0l27li1UQo3Eq5FkogWeGbzEZaolm2hekooch0hSZPTDJd0Slpm8TMBEnbJG6p3Ey5ynkilIXtOK6nZDp4kabpGJqOrvkx9CC6oayNDE3Hr+kYeIKThoEXyyhXaIPOolqu4EZ6H8cGJ9lZWMscy8lp78ogLZOU9VoPfboSoMjYlik8aTnbsqzYSPfNt5CA1566/pkunJ41W66rZ+48MoW33PmTvW/WnPON0cX+qfFzxyS7TUQ4QRAEQRAEYQgiotRQQtOgYCwYYWj5SK3EFx4Nur+P4xjKmqpoKxh9ULo93ugKVJ+p1PaZimcVr4O6unSMKo/QSCjcUllXFW4BBVuoPDwWdHnrCN8sMkWmpG26ApOZKidtk6iZIGbFiVsJ17rJSolLpmMCmR6Fyv3N0AxXXNLx6Qa6puPXfRg+HUNTdUP/hruydRLISJdzrdfI2ZYrtuUbz84op7J8opt3/G6Et94IbN3RSUjrjQDntqWEq5y6ltsv1+IsR4jLtHzLEsncbXrOfrnHzZ1bXqu1fHMh53jk32ZGwC+u6IIgCIIgCJsDoiwMRYIValW9lk8gshzCY/ouTHU1bnBPqMpYsciMuELVp0qoav8aOpaD2Qax9So1vJ09jmYoscwTqQrGu+Xxql0CrAvDBNt1hUu64lLSVjGVPIulqBkjaiqRKWGZKWsnz4VOoRQITdPxaTqG6+ZmaAYBI4DPFZcMXeIkbTCdXPMGbSabHidXTEttoFP8tNxtXjnX4i2fqOfk9s8cJ5/AR2cxLncafaE78S5XmMq9/rEWsLYEBn+1GEEQBEEQBGHjEFFqqOIrgLIdAQ0iy/pPmMp3nPKdVPJwHEi2KHEqslJZU0VWpHMrpqy4Iqug/vXs8TSfsvYqcMWqwvFp0So8UgQrYUBwHEfFWbKS2YKTbRIz40StONFknLiVxHTMlEudbduZawamBCafbmBoBkEjQIHPcOueS5wg9CPDNQZbf9GlFVqGiBZrUW6kgiAIgiAIwrBHRKmhjBGAsimA078WUz2haSpWVaAMyqdlb3MciNd3FqwiK5VIZSfUto7lecb1u4LV+BzBarxyFRSXQKEXOI5Dwk6mBKeElSThCk4diSgRM0bMiqdd7Nz4TR6aKzL5NCUuBTKEJp8uoqkgDCq9soqTz6kgCIIgCMLmgqgAQx0j4FpMMbDCVFdoGoSqVcoMrg4qwHqs1hWqVmaIVishslr9st2xTKVO4/rUuRWOh/A4V6wa56YxoAcG4uyEIYDlriiXsFTwb0+AipgxOpIxomYsZfVk2iooOGgpyya/7hOxSRAEQRAEQRAEYRggotRwIFOY6lgBhWOHphucF2sqPBrYI3ubY0F0XdqiKiVarYToamVh5a0M2HlgZUmVEqncFB6rcn/xQJyd0E94lk5xK0HcSrrxmpK0u1ZOkSzRyUzt5wlOft1HwAhQ6A/j130Sp0kQBEEQBEEQBGGYIqLUcMEIQNlkV7xZq9zghlM8G81wXffGdt7m2MrCKrLKFaZWpa2rIqvAikBsnUqN73Te31/qClRj07lXFrfAQcF2bOJWgpiZSIlPUTNGWzJCRzKq3O0sJTo5GavQeaJToT/kWjzJtRMEQRAEQRAEQdhckSe+4YQRUsJUQ1StihceNdgz6h80XZ1LeFQel0AHEo3pwOqRVRBdlRasEo0qKHuyBVo/zjO2AaFRyjWwYIzKw2PT9UDl8BL3hhCO4yjhyUqkBKiOZJS2ZIRIMqbc7uykG89JQ9M0ArqPgOEnZAQo8Rfi0w0JFi4IgiAIgiAIgvANRUSp4Ya/RAU/b3wPEs0qGPnmjKZBsFKl3KDrAGZEuf95IpVXjq5RyU6otuhqaMwzvh50XQ49wWp0Rj4aAhXfeNHKtE1iphKfYu6qda2JDtqSHSTcQOPKzU5D1zQChj9l7VSmF0s8J0EQBEEQBEEQBCEvIkoNR0IjoHQHaHpfBQj3Fw32jAYPXwEUb6tSLo6tVgqMrnGFqtUQWZMWrGK1YMe7Dr4Ormg1CkKuSBUepXKvHqzabNwDk5ZJ1IqlBKj2RITWRAcdZtRd4S6J4zhoaPgNP0HX4qk0UChudoIgCIIgCIIgCEKfkSfJ4UrBeLCi0LZUWUyFqpR7n5BG05WAFxoB5Tt13m4nlRtkZDXE1kJ0rStarVZB2eN1rmi1XKW86GolwvBo5SYYct0QQyPTua94SFlbmbZJ1IynUkcySnO8jagVJ2YmSNpJHMDQdIJGgIDhpyRQRMCQoOKCIGw4lm2TsFWMOU/o9spxt+6VkxnbvPaEbZK0k3TEWjhywrfYc7BPSBAEQRAEQdhoRJQarmgaFNdAsNoNDr4aHFNZ7og41Tt0f3olv3x4olV0jRKsoutUHnPLsfXqNY+tV6krjLASp/KmEa5wVdjvwpXjOMrdzoyrVe2SMZribbQnI6kV7zLFp6DhpyJUgl/3SZwnQdjMMW3LXYQgHRcubiZTbrreypgxM0HCThCzkqn+cTOZ1ZawOq+mGbeSxO0kCbces5JYjtVv829Kxjhx76v6bTxBEARBEARhcBBRajijaRCsgEC5spxqXwGx1cptLVgp4tTG0pNo5dgQb3CFqvVqdcDoOiVaxdZDdD0km5VFW3cuggBGQdqqKzgiXQ5Vu/VqN75Vfksly7aImLGUANUa76Ap0UbMjBO3EliOja7pBA0/QSNAWbCYgO4X8UkQhiCmbREz40SttEWjJzDHrAQxM+662ap6NKce8/plls20ABWz4ph2/wlEG4KhGQQNPwHDT0D3ETQC+DPykC9AQPfjN3wEdNUv6NYdM8ZREw8c1Pn3xD333MPf//53vvrqK0KhENOnT+fiiy9mm222SfWJx+PccMMNLF68mEQiwcyZM7nqqquoqqpK9VmzZg1XX301b731FgUFBRx11FFcdNFF+Hzp27e33nqLG264gS+++ILRo0czb948jjnmmAE9X0EQBEEQhA1FRKnNAS8YeKAC4uOVq1l0jdoWrAQjOLjz21zRXNe9UHXXfaxY2pIquh7ibh5br2JaxWsh2QpWpGfhSjMgWIUTrMIMVJDwlRH3ldCqFdCkhWnXw7RqYRJaAX6fivlU4AtRHizB0MXtThD6m6RlumJwjA43V8JwnKgZSwnFnlgcddtT7rNWnEgyRtSKEzcTKREqaZsDeh4qPlwwJVoHjQAhI0DQF0i1hdz2gO4n6PNn191y0I01562wqQQnf2pbwNuuBzbqf1J9y3JKC0f24yvQ/7z99tuceOKJ7LjjjliWxS233MIZZ5zBs88+S0FBAQC//OUv+ec//8mtt95KcXEx1157Leeeey6PP/44AJZl8YMf/ICqqioef/xxamtrueSSS/D7/Vx44YUArFy5kh/84Ad873vf4+abb+aNN97giiuuoLq6mn322WfQzl8QBEEQBKG3iCi1OaFpKrZUsBLiWyiBI7oWcNv1wGDP8JuHEYLCLVXqCjOqRKp4nRKqPLEqVovjJi3RhOZYEFuPFluPH/ADhUAFsFXGcLbmI+kvU8lXRtJfmqqb/lKSPq9eogLlC8I3BNO1KOxIRlUyo0SSSlCKJGNEzGh2OamEpUiqLS00dZjRTW5tpGsaYSNIyBck7AtmlT2hKGQEUm2eUKTKQUK+zHogJR6p/ZUQJbHiNg33339/Vv2GG25gxowZfPTRR+y22260tbXx5JNPcvPNNzNjxgxAiVSHHnoo77//PjvttBP//ve/Wbp0KQ8++CBVVVVsv/32nH/++dx8882ce+65BAIBHn/8ccaNG8ell14KwIQJE3j33Xf5wx/+IKKUIAiCIAjDAnki3RzRNNftq1KtPtexXIlTmu6uFucf7BkKmfjCULQVduEWqQffjmSUpngrzfF2YlacpBkjaLVTbEcotjsostsJm234k034zWYCiSb8yWZ8Vju6YxJM1BNM1Pd46KSvGNNX4gpYJRmiVSmmr5Skv8TNi0XAEgYN07ZoT0boSEZpT0ZTZa/eYXrlCO3JWEpw6kh6opPqF7cSm2R+Ad2vRCNfkAJfSCV/iJARpMAXJOwLpbaHfSG3LV0PuwJSrvAkLrabD21tbQCUlpYC8OGHH5JMJtlrr71SfSZMmMCYMWNSotT7779PTU1NljvfzJkzufrqq1m6dCk77LAD77//fkrUyuzzy1/+cgDOShAEQRA2HMt2MHS5zxFElNq88VafC1ZBrM61nFoHuiHi1BAg5q5812HGaIm30RhvJZpUsWMcIGD4CRtByoLFBAsqU/slgSY35aLZSfzJFvxmM/5kdvIlW/CbLW69BQ0bv9mG32wjHFvd43xNo8gVqUpcAasE01esyr5SVXbbLKOwy/hXwjcLx3GImnHakhHaEh20JyNuOZIqtycjtCcitCejqu6W291tsX4WkwK6nwJ/iEJfmEJ/WkQq9IWzy942t80rF/pDrrikBCafLl+lQtfYts0vf/lLdt55Z2pqagCor6/H7/dTUlKS1beyspK6urpUn0xBCkjVe+rT3t5OLBYjFJLYkoIgCMLQxNA1zn/8vyytbR/sqWwQ+0+q5qeHbDfY09gskDvpbwKaDuGRynrKE6di68VyagCxHTtt5ZGI0BBroS3ZQdSMYzk2Pt0gbAQpDhRQZZRtsHWEo/tJBKtIBKt66GjjM9vxmS1KxHKFLF+y1RWuWtxtrfjMVjQcfFY7PqsdWNPzPNAxfUWYniWWr9gtd5WKcMS9dMhiOzbtySitiXZaEx20JjpoS0RUnkzX25JunuigLam2tycjWI7dL/MIGQGK/AUU+sMU+sMU+cOpepEvTFEgnCEmedtVXyUmqe1+Q776hIFj/vz5fPHFF/zxj38c7KkIgiAIwpBiaW07H61pHexpbBATqgsHewqbDXJn/k2iW3GqUmJO9SMJK5lyMWqKtdGUaKUjGSVhJdE0zY3rEqI0UDw4Qcg1XVk6+UuIhcd33zdDwPKZba5QlRasfGYbfjNd9lkR1wqrFb/ZCvRshQVg6QEso9gVs9xkKGHLSpULMX1FWG5ZLLL6hmmbtCY6aEm00xLvSIlMzYl2VY6rbUpk6qDFFZ3aEhEcnI06tqEZFAcKKParVOSWi/wFFAdU7olMRbl1V1wSiyRhuHHNNdfwj3/8g0cffZRRo0al2quqqkgmk7S2tmZZSzU0NFBdXZ3qs2TJkqzx6uuVW3ZmH68ts09RUZFYSQmCIAiCMCyQO/xvIrniVGRFOiC6rNa3QUTNWJYVVEuinYgZw7QtfLpBgStAhXzDUPjLELB61d02lTjliVRu2Z8qt7kiV6ubt6NhYdgJDLuBQLKh11Nz0LCMAlegSgtVplGI5XNzozDVnlm29aCKvzYMcRyHmBWnOd5OS7yd5kQbzfF2muNtruDUrvJUuY2WRAcdyehGHTdkBCgJFFIcKFS5v5CSQEFOvdAVnwrTIlSggJARlPhIwjcGx3G49tprefHFF3nkkUcYPz5b/J8yZQp+v5833niDQw45BICvvvqKNWvWsNNOOwGw0047cffdd9PQ0EBlpXLhfv311ykqKmLixImpPq+++mrW2K+//npqDEEQBEEQhKGOiFLfZDLFqXg9dKyE2FqwTfAXg68IxDKhE+kYOcqCpD7aTKvriuc4NgEjQIEvyIhwBT7dGOzpDjiO7iMZKCcZKO/lDg66HcsQq9zcUrlhKrdBT8AyrA6V2zHXrbADn9UB1PZtnhiYvgIso8AVrAowDa9egOUrdMthV9wKp7cZBa6o1T9WWqZt0RxvS6Umr5zIqcfbaXHb41Zyg49X7C+gNFhESaCI0kChmxdRGlTlYn+hW06LTaWBInF7E4ReMn/+fP72t7/xu9/9jsLCwlQMqOLiYkKhEMXFxRx77LHccMMNlJaWUlRUxHXXXcf06dNTgtLMmTOZOHEiP/vZz/jpT39KXV0dt956KyeeeCKBgPqB43vf+x4LFy7kV7/6Fcceeyxvvvkmzz33HPfcc89gnbogCIIgCEKfkCcMISMgejUkGpVAFV2jgqJrgK9YiVTfUDcpx3GImDHa3Fg6ddEm2pNRomYMgJC74lZ5sFiWVt8QNA3bCJMwwiSCI3q/n2PiMztSIlVKsLI6MEwvb1e5FUn1NawOdMdCw0oFet8QlJVWWCW9IEO0CpPQg9TbPtZbOrWmQ51lU2+a1CeTNCQTNCZjNCQiNMU7aHLd5TYEv+6jLFhMaaCIsqAnLBW7eRFlbl6akRf7CwfHZVQQvkE89thjAMydOzerfcGCBRxzzDEAXH755ei6znnnnUcikWDmzJlcddVVqb6GYXD33Xdz9dVXM2fOHMLhMEcffTTnnXdeqs/48eO55557WLBgAQ8//DCjRo3iuuuuY5999hmAsxQEQRAEQdh4RJQS0miu+16wEoq2gUQTxGohuh4ia9R2z4JqMxZfckWo2mgj7YkoMSuOpmnuMu8hKkOl4o40mGg+TH8ppr+UeF/2cxw0J5EhUkXwWREMM5Kqp9qsDgwrimFFiCc7qI930BCPUmfZrLcc1lsRas0ItVYD6y2odVODRZ8jMGlApaFT4TOo8vup8Aeo8Ico94epCBRQFix0RaYSykJllITKCPmLcIwwlhHC1kNYRghH8w9bt0RB2Fz47LPPeuwTDAa56qqrsoSoXMaOHcu9997b7Th77LEHTz/9dF+nKAiCIAiCMCQQUUrIj+5X1lOhEVC8rStQ1anA6JHVSpTyFYG/CLTh76IWNWOp1cRqI420JSNEzVhKhCoKhDdqVTxhCKFpOFqQZCBIu1VEY6yVBtOgPmbTEI3TELNpjCVoiHXQEGuhMd5CY6yViGsZ1+vDABU+P5U+gxE+g2qfxgjdYYRhM0I3GaklGWFAtZsqDDA0G7CBJBDJHtACom7qBgctJVDZehBLD2EbQWw96NaD2O62VF336gFsw2vrnBxx5xUEQRAEQRAEoR+RJwyhZ4wghEepZNVkCFS1EFmr+viLhlUMqoSVTK0wtj7SSGuyg4gbBDrsC4kl1DDGcRxaEu3UR5upjzXTEGvJKjfEWmiIttAQa6YtGel5wAyChp/yYAkVoVIqQyVUBEupCKl6ebCEylCJu72k55UVHQfdjmNaUdbbMeqtKIYdRbdiGFYM3Y5iWDG3LZ7eZsfcPJ7qo9txDFvZi2k4GLYaq79xMLD1AJbhClhaWsiytUBa2NJzywEcr5zq58/IAzia19cv1l6CIAiCIAiC8A1heCgIwuDgOGBZKtl2OrdD4IwFowqSjRBvhJZaSKwC2wJfyBWoMlbx01APmV7SvVxP57qu2r3cMPrtwdSyLdqSyhKqPtpEY7yVjmQUx3EIGgEK/GGJCTXEsR2blng7ddEm6mMt1EWbqIs20RBrpj7aQn2s2a23kLTNXo/r131UhkqpDJVSESqlKlRKZaiMilAJlaEyJT652wp9of4TKjVNWSwZ/bRsu2Oj23ElUFnRlFClWzF02xWxLHe7HUv1VYJXDN1OpNtS46hcw1ZTxtpkglfWqaDhaP6USKVEq4y6FsDR/TnlALbmV2VNbXMyt3tteXNfRt23WbsnC4IgCIIgCMJQQkSpbzqOA9EoRCLQ0QFtbaqeTEIikSFE5STHUckbwwZsHZIRiK8DK6JW8dN9YIRU8vAe6rVMYQr1IOiJVYau6oYOfj/4fSoFfODzg89Q+/kM8PlUbhhu3cAxDDqSUVoTHTTFW6mNNNFhRknaJgHdR6E/zOiCagn4PARwHIcOM0ZdtJG6aDO1kSbqY02u6NScEp/qY82YttXrcUsDRVSGSqkKl1EZKqMqrMSmqlAZVW57RaiUYn/B5mERp+nYRhjbCGP6y/p3aNvMEKsS3ZQTGW0JdMetW/F0OSNpqbakK36p/ykaKu6XbiWU2+IAY2s+HM2XEqkyBatUrvlxdF9WuVOb5nPbfW7Zny6ntvlxNCPVZusZ23PqIpYJgiAIgiAImxsiSn3TME0lPrW3Q2srNDSoeiymxCafTyXDSCev7lkzZVo45XuYd2wwI2C2KysqsxW8eDy+EBhh0PxpYct21D52Tt2y1XxjcVXOFcQ0AE1FlDZ04ppDq56gxYlT60RoNUxiBmgBP0WhQqoChfgDYXU+pgFY6hMgwtQmw7JtGmLN1EabqI02UhtRAlNttIm6aKObNxE1ex+qvCJYQlW4TKVQGdXhciU0uXUlQpUSMPyb8My+WTi6D0v3YVG4CQ/ioDlWhlClxCrNSabrThLNE7GcBJqdTJV1O6nqWX28/ZOuCGZmjJmZmylBDEB3THBMDLtvccQ2NcqCzBOpjBwBy8jK7Zy6oxmuYJbZnlvewDb0jHq+cjpB/1nACoIgCIIgCMMfEaU2dyxLCVBtbUqAamhIW0JpGoTDUFAAFRX9J85ouoox5S9y41DFIdkOZhskGlVuJVWAdCMEvgLQAn0+jO3YtFkxWpId1CdaaUy0KZc8yyaMQZFtUOUYaE4ciAMNSsDStLRllWEoC6xgAEJBCPjTllc+X9pCSx6iOpGwkimBaX2k0RWdGlkfbaQuokSo+lgzttO7deiK/QVUh8vd5IpN4XJGuPWqcDmVwVL8hvzb2izRXMEFH7ZRMLDHTgliaZFK5Ql0x0RLtZmqT2abk1SWZG6utquxVLvpli1XJDMzxjczjuvuk9puoeWYiSkLsiQ4SdUwCFZk/UFaxDKyhCvIrLs5BuS0bWGZ1I08fLBPQxAEQRAEQegH5Oluc8NxlOVTays0NUF9vRKlEgklsngCVKDvItAGYwRVohKcLZRrn9kBiVZINKvA6bYFugF6CHzhLlf0i1oJWq0oTWYH6xOttNsx4naSoOaj0BdkdLAYoycXF9tWxzPdOFnROLRHVNnTTzSyXQKDQSgIKdHKcyfMFK42M2sr0zZTYtP6SIObN7I+2kBtpIn1kQYa4629GsvQ9JRF04iCCldkcsWmVL2MsK+fYisJQl/JEsTCgz2bNI7tilZmSsBSwlhmW7quxLLM7Z1zPVW3crbn26erfrnJ7rQdLHQnv2qmYaM5dlpc2xCaXtvwfQVBEARBEIQhg4hSmwPxuBKhmpth/XplFRWPK+ueoqKBF6G6Q9NVEHRfEYRGqrhTVkS5+yVaINmiXP4cCzQDSw/QhkOLbVKXbKXR7CBqJwAo0AJUGIUE/H18G3tuiN3t5riClWmDaUFrOzS1KEELLVu0Mgz1+oYCEA5mW1h57pC+/CLbYGA7Ng2xFtZHGlgXaWBdlvCk8oZYCw49WzgFdD8jCsoZEa5gZEEFI8IVVIfLGVmQziuCpRK7SxA2BE1X1kEMU1dUxwHsDOEqW7TSskQ3t+z2J0vwUu1eW3uknkTVXlQP9vkJgiAIgiAIG42IUsOZujpYuRIaG5V1lOMod7ySEggNE6sT3Qd6CfhLlKufbRJNNNEaq6cpup71HV/RnmwjYSUI6D6K/IWU+4rQjQCwCYUOTQdfd8KVo8QqL3VEoLUtHfMKlPDlxeTyGco9MBRQ1lae26AXr8uzvOoHN8H2ZJR133wRdgABAABJREFUHfWu4NTgik+NrI+ottpoU68Chvt1X0psGllQyciCckaGK5X45Fo4lQdLNo8g4YIg9D+ahnLJM3ohcfeeemM5pb6ifhxREARBEIYXlu1g6HIPLmweiCg1XFm3Dj74QFlElZTA6NFK3BiG2I5NmxmhxWynNt5IY7KVqBkDDQqLtqJC0wk4NlhRFTzdikMi4gY710EPgO5XAtemFKqy0NJWUF2emBuo3bJUDK9YXNUBN7iVu9Kg6yJo6OB3La6CgQxLq/SqgpahU59oY22W6FTP2o6GVL09Gelx9rqmUR0qd8WmCkZl5aosgpMgCIIgCIIgDD0MXeP8x//L0tr2wZ7KBrH/pGp+esh2gz0NYYggotRwxBOkHAfGjh3s2WwQcStBi9lOU7KVdfFG2s0ICTtJQPdTZBRQHipG7xQbqlxlVhycBFiJ9Cp/thtMHdzVAf3pNGBCVQ663rPbpO26CVrKTTAWa2FdQzPrki2sNVtZZ7Wx1mpjnZvWm21Y2D0eujRQyMiCSkYVVKUEJ1WvZFRhJVWhMnz68BQxBUEQBEEQBOGbztLadj5a07sYr0ONCdWbcEVnYdghotRwI1OQqh4+ETUcx6HN7KDFbKch0UJ9opkOKwpAgR6iwl9CQO9l3BQjCATVuzdYCdhqNT87DnYCzChYHWAnVUB1z2/E8AE+lWt+VGCogcNxHNqsGGsTzaxNNrMu0aLKiWbWJZpZm2yhyezocRwDnRFGEaN9xYzSSxjlK2akUcRoXwmj/aWM9JdSEAgryyufu7JgwA3ObvsgakCiQ1lf6a6FlhcfSxAEQRAEQRAEQRAGCBGlhhNr18KSJao8DASppG3SYrbTnGhjXaKe1mQHMTtBQPNT5AszJlidxxpqQ9AzVvgDggCOEqXshEpWQglVZgysGFieVRWu258nVm24C6DjODSaHUpkSjazJlNwcgWoDjve4zgFeoBRgTJGB0oZ5XfzQBmjA2WM8pdS5e9qhUFHWVxZFthuzKtkDDqiabfBzNUFNU0JUoaRHf8q4EuLWIbP7eNuT+VuWQKYC4IgCIIgCIIgCBuIiFLDAceBFSvgo4+UGFBVNdgzyovjOESsmGsN1Uxtool2M4LjOISNEKW+YkYaA7UKoObGmso5nmNliFWudZUVTYtVtpkWbnQdNEMJVbqBjU59MsLaZHPKwmmNKzqpvIV4L5Y4LzMKlMDkCk+jA2WM9qfrJUZ4A2M5aWnBqDfYNtgWWI7KTRPiCWhzha2syMROOv5VSszSlaWVJ2BlxsBKrXBopPsZmhK5dE3ELEEQBEEQBEEQBEFEqSGPZcEXX8Bnn0FREZSVDfaMsjBtk1bXLW99rJEWs52oHUdHo8goYFSwCp82hNzCNM/KJ2d1QsfCtOLUxWtZE61lbaxOpUQDa+NNrE22sC7RitlDPCcNjWp/MaMCpYz2l2WJT2MC5YzylxIeMGGuB1LCUS/7O3ZawLKddAD3eCIdG8uN3w6OW9bSIpaekbwVBzPFLF1LW23pelr40vT0/l4uAdgFQRAEQRCEDUBWrhOEoYWIUkOZeBw++QS+/lpZRxUUDPaMAIhYMVqT7TQmW1nvBim3sAlpAQp9YSr9pX239HEcNNNCM810blmqbNmpOpat2i3Vju3VHVV2kyq7bY4NtkPcMVnrtLFaa2WV1s4arY3VejurtHZW6+2s0yNYWvcLlxuOxmg7zFgrzDgrzFgzyDgrzDgryBgryGg7TAAdx7MG0g233I6jR8BYj6MZoGs4uu7mqq9jpPdzUnUdDC1dd4WazLpKrkXSphRrNE/A6ovI6CgBK2WRpa4NiQREY8oK0LZV7qQULVKWWZ6gpelpCytdU5ZXKUHLdTv0LLKy+nUhbGlirSUIA43jODg46uOOA1llUtvcUs42wN0/bvVskSoIgiAIXSEr1wnC0EJEqaFKUxN8+qmKIzV6dM+ruPUHjqOEsGgUYrFUsqIdRCOtRDqaaW1vJBZpxYp14EtYbGFCKOngS1ro8QRaMomeSKIl3NyrJ030pMq1pIlumqmyZproprXR028LwPIyWF6aP19b3PMYARO2aIGtmmFLL29O18e0OfjsCBDZ6PluChxdx/G5opXPUIJVZp6vzTCw/UZ6u9vm+A1sr+5uT9V9vlRf22/g+H3psk/VbTdPjZPq48fxG70QhTxByxWyvLLlQLIbUUtzwNFICVueQEUegctzL/TcDjPjZnn7poSxzP1zRTKx3hL6h+6Em7RoA7nCTaZo42SMkzWmW84awUkdxd0/u52cY6ZR73fN/ag5aTPJvH29j4eW2k/D+/PGya5n9suoo1HoD1Poz7F2FQRBGCYMdyud4T5/D1m5ThCGDiJKDTUsC5YvVy578TiMHasemDcUx4GODqivh4YGaGxUuZcaG9NtjY3KHSsHAyhy04gNn0nfp+4JKz4ftqFTV6SxvFxjeYnDilJYUWyrVGSxssiiMdi9ax1AgakzPuZnXDTAFtEg4+NBxscCjI8FGR8LMjLhR88VF8rc5EAbkHpCcxw0x/F+wneFEdw2J709w3pLlV0rL0dZeXltygos29IrVbcy6yrPh2bbaImeX4ehgGPo2K5opQQvV8AK+DqLWn63rVPdyGjP2RbwpUUyQ8d2RTjb0NzcwPFp2J4AlRK3ICVoeWXoLHBlilG65lqSZYhcqQDxvpy+Gfvoes6Yuoqz71l8iUVXr+idkLNxIo7t2FnijRoxU8DJFm88cQUNJZJqnksrGf28UTyyxZvuhJt8ok3qmO7+OrprcKj665pq0zU9u667fdFSZV3TVD80dF1Xx8g8bmp+2QJTpzYt8wzytGeeb057p+N57bEG9GB5n94jgiBsHmwOgshwttLxLHSG6/xBrIwEYSgiotRQorVViVErVkBpafcBzR1HWVHV1mYLS7mCU0ODErf6iBXwYwVVsoNBCAZxQkHsYAA7EMAJ+rEDAVUPBnD8fmy3zQn4lTAQ8LtWMT7sgF/18ftwXEsZ2+9XFjd+HwndYT3trLWaWWM2s9ZsYl28gXXxBtbG61kXbyBuJ3qcd7FRwOhQFaOCVYwOVjI6VM3oVLmKMl9xl66FUWBZn1+p/sRRcZtsE7DcoOy2W7YzgrSbKlC7mXTdGt1kWmhmEs10xS3TTrk5KjELNMtRybTdsp3u77WbFrppKys2y3bHtdCTZmp7um651m4WemY5mVN2+2eiWTaGlYDYoLzYWTi67ophOQJYIFP4yrYAs10LMiVueVZiesqazPbEMJ8rtGVamvl92D5dje8z1GfL71pqeWJUyqLLLRtusHifL+2G6PO5K0i6FlypcoboBfmFL8/iy9XeHF3LK+rYTkp6ySvo2D2IObnWOGkBp3eWONlLRubvlylgbIiI4/XRvf31DRNwvL6ZAs6GiDf5hJusueYRbrJEm5xjb3bohlgmCsI3lOEs6EBaEBmuVjqehc5wnT+IlZEgDEVElBoKRKOwapWKHRWJKHc9v79zP8dRLn2vvAIvv6zEq95SWAgVFUroqqjAqawkWV5KpKyA1pIgtUU6zcUBIkUBfOFCCv2FFBhBdG3jrTQcx6HZbGN9vDElNK2PN7AuovK18QbqE82pB9vuqAqUMSpYyehgVSofHapy61UU+cIbPd/BQ0sHYu8VDo5j4ziWa+FjAa545dUdW7XZpit2ma645W1z1PaUpVfuqnu4lh4ZZVyRRHNFDqWEuNsz2jW37m1zSItaSTMlXGULWGbONhMts+z1Sbh9TLVdT5gZ/TP2T7juoQm3XzKjn50+Uc22MWKDL5DZupa2BvMZWG5uu2KX7Vdly5eb625/NzcMbL+O7fNhB3Qsn0/VXTHN9vuwAwaWX213fH6sgBLhnIAfXLFZMwyVdA3NMEBXZV03lNihaWi64epoOpqmoxsGuq4EGt0w0DVDiTZuu6YbbruGbvgwNFXfVEKOiDiCIAibFyKICIIgbF6IKDWYJBKwZg18+SW0tKiV9caPz+7T0QHvvgtvvgmvvQarV6e3+XwwciRUVirBqaJClSsrU+JTqh4KEbPitJodtCY7qE000mp2ELPi6JpBoRGiyAhTrvf9LdFuRlkfb2B9olHl8XS+Lt7A+kQDcbvnwLQBzc/IYAWjg1WMDFYyOlTJqAzxaWSwgoCeR6z7xuKKWBu6umGWmOWkRSpPyHKcdJ5qy7HgwnYFLivdLyV2eW5O6dxxwNGBIFhBbyIGWcHTU34+Oilxi7QApixsdGyclCuUo+Gui6ilLXU0zbXYyXCzQjlWOY6jXCITrrVZwlLxzzyBLKGEK0/EMpLK4ktPmviSlitwWRiuAJa53UgqqzEjoWKlGam+SjzTvb6uwKZniGO67aDHkxAfGoGcHcNwA8mrwPKOzw0u766eqHlB5n0+tMyg85mrKvp9SmT3+SCQ0R7wp/sFAuk8FFDbggG37odgUJWDAQj61XgBf8rSKyWGpqzMtIxtOTG/vHKn/hllLwmCIAiCIAiCsEkRUWowaG2FujpYuVK52JWUwBZbqIegtjb43/9gyRJ47z344AMVZ8ojGISZM+Fb34K991YWUF0Qs+K0W1HazAZqm5poMduJWjFwIGQEKTLCVPnLurQMcByHdivC+ngjtYkmauONbrlRlROq3mFFe3Xalf5SRgYrUkLTqGClEp/ccrm/RKwUBhLNcAWf7H8DubFz7CzXLTsj/k5uXVltObabAw6Wa83l5JRtt5whJbllHDtd72TNZaNhozkaGk6GVJUlW7nWL3Tqp6NC+xiacsDS/RpaQMco9GEQVtvRMHQdA0310XTXrSvD9YtMV610vB1d9+oqV3UAXZ2BpmOTEesHXcUBc0UqTxzTkhaaaUMiCUkTEpaK95Y0021JE0wTEqbaljDT7fn6JjPKiWTGPsn09pwFB9SKlxa4HsBD7tOZEsVcC8Pcut9QMb1SYpnXz5fulyGyZYtq/vR2vyeEuWKaJ6oF/entgYAa35/Z5gOfW88U4Xy+DPGL/GJYZj2fYNbVfoIgCIIgCIIwjBBRaqCIxaC5WcWBWr9euemZphKn/vUvFUvqk0/gq6867zt2LMyYAXvuCXvsAeHOLmqO4xC147SbEdrNCHWJZlrNdiJWHAeboBagwAhTHixG13SStklDopmliVXUJpqoSzRSl2imNt5EbaKROjeP9SKOE0CJr5ARgQpGBisYGax0xadKRgYqGRWqZESgXKycMnAyxJ7uhZ/OgZi9eDx2RhyfvGO529BwF6RTUZZTZUe5LmWuxpUZVFgJJ56kknZvAtKxd7ztmo6Ogab50A3dFX3U/oamY7giju6KQYZbNjQjJfZ4/VMCj+aJO11sR0NzPMHJcaUgx93HUc/xqHPXNDL6ev6IGZZcQJYbo3plM7Z75lauxRgZ5dTKf5ntmfvZ2WNlHRPQHfBl+kxmHC9Vzy1njJEvaHbWMfL16woHklaGkJUhfmW1W+k2M6Mt1d8tW1ZaLPPazJx+XsrXx7LS4+ZbodPbd7ihaelVHn1GRoywzBUgvThiRsa2zLYMK7bMffMKdUZ23RPgjEyrNiNbpPNlrkrpA39GPTPPPKauK2EuV/DrJLRBSuLsJLLpGe1kC25e2cmOTycIgiAIgiAMXwZNlFq4cCH3338/dXV1bLfddvziF79g6tSpgzWd/sey1Ip3n3+uxKYvv1SWUWvXKiFq1SrlspePceNg6lSV9txT1XNI2iYRK0aHFaXN7KAu0URbMkJ9opmmZCsdVoyIFaPdjNCYbKU+0UxDsoX6RDP1iWZazN4HiCz1FTEyWEF1oJwRgfKU6DQiWMHIgMoLjKG5PLcn5NiuGKCkA7tL8SczaHOWxVBmPaPdG5cMcUfTtNRqX0r/cB+uPLEjMxCyK76Aa8njCjGQLfx4eabljoGRUXYFHrePTzOyRCBvDDV+Z9EnVdbSdj7pfbSMuWbH98kcW+Lx9JGuRConn/CUR7Tq1CfP9qyxcvvQdT+66Jd3PnSxLZ+YlnsIJ0//3NfCG9e12kqJZJmiWVJZf5muSGZmWH6l+uZuN9PjWVbGdq9Pbu4Kb5bZuZ+VIa55QprXL99KmY6T7t/3dSiGFxrpVSYNPaesdd6WmXuul0bOvn4fnHo8nLPnYJ+dIAiCIAiCsJEMiii1ePFiFixYwPz585k2bRoPPfQQZ5xxBs8//zyVlZWDMaX8OI6K+9TRoSyb2tqU652XNzer1e3q61VeW5tO9fWqX3domoohNXEibLst1NTAjjtilpXQkYzQlmynPtZI/eqvlZgUb2J9vFEFBk8202y20Wx20GJ10GJFaLUiWOR5AOoCv2ZQZZRQ7Sul2lfCCF8pVUYJI32lVPtKGeHmoa4snOJuam8BWrIsdrJX6kpb7ngredmeJQ8Zq3GR7m9nrMxl54wHpGxdvFy1aentmoob5LlaecKLt68nznhbM4Mi6xnii4Gy8lEWP54QpAQdA8/qx0j1zxKAUkINGVY+aWFI99y+OvXPKecIST2+p3qFg4oJ1Uv6Ijb1tm9/9+uPsXpzrP4YY8DmkKffppxfb7bldtnY90GmFU1X/TLbcrd3t19/4DiuaGVueLLMbIHMsjLactpTeTK77qXU9oy67dUzRLbM/radccwutttdfPc4KGHOsqE/Q6WVvwbn9ON4giAIgiAIwqAwKKLUgw8+yPHHH8+xxx4LwPz58/nHP/7Bk08+ydlnnz0YU8oi+X8v8+Ilx9ESa8HGwdbA0lF5RtnUVd3UM9L/Z+++46Oo8z+Ov2ZmezYNkkgvggklVEVFURRRDwX7ySl2zoIFT+XEu5+/U2x4tjvbqWcX+6n4OwT19NQriiA2FBDpvaQnm022zff3x+xudpMQEgjZBD7Px2MeMzttv7ssyc473+9n3BA6GML9IaRD0KET8rgIepwE3A4CHgc1LhsBp4HfDjWEqDFX4FffUeMPUr0wQEDt3Tf3DFsa2fYMsh0ZdLJn0smZGX2cSbYjk07OLLIdGaTb00DTMDWFUla8YyqF0up6AxXHegBpVn0ehUoKgoB4OlS/90w88AHrzlzx7UR7C1nDuGIhkYGOoRvx27DHhnjpmo6NhOFeWr2ePqouZEoc5pXcw6d+T6DkczTYt/4FaqM9Q9j9Pk0dt7tztvTYvXmuxvZryfmau29r7Le7523NtjWnTXvz77in2/fkvdqT59/Xr605++/Jv3HscVPv0+7+Pa2xrY3v09i22LrEbU2dIyY2FM/prFvX2DmaCttaEsztKqDb22OUqgunEoOqSKThcuI8NsX2aeyYSKQu3ItErDvWjh/fsL1CCCGEEKLDafNQKhgMsmzZMq688sr4Ol3XOeqoo/j222/bujmN+utPr3DtqeWtdDYT8EenBBF221HF0AzS7B48Ng8eu4c0h4d0RwbprgwynNaU5cwi05VJljuLbFc2Wc4s7DY7DWoCaXU9dQzNujW7kTDEK7ZO13Rsug09GhAl9e7R6oU7jSwnPk9zj5EhX+KAsqcBZ3O378tj9+b4vQ1ZmxvOtSSQauo5mnPupvZp7XPEapfFHsd6JsXmsW2Jj3d1fOJ5Epeb2r+xNuyqrQ5H49ubOqap5cbWHXRQw+0HsP2+JIIQQggh9lttHkqVlZURiUQaDNPr3Lkzaxsr8p0CJ/zyZn7xf+soqypCs9nQDGuyQho9Ht5YQY6Bodusx7qBTbdh06197bo9PtkMG3bdjsNw4DAcuGwu3HY3XruXNEcaXofXeuzwxien4UyqJ9RYmFM/CGrOdiFECrXG8Dwh2pPmBnG7W1d/uan9E4OvA1yHKYkghBBCCNEIufteIwbkDOD9qZ+muhlCCCFE+7e7YYBin2rvJRFE+xExFYYu/0eFEEK0L20eSmVnZ2MYBiUlJUnrS0pKyMnJadY5Ync58/mafwc5IYQQQojmSktLa/e9i1ujJEJbfKfaH8KQ/eE1ADz52Rq2VtSkuhl7ZEiPTH55aE/6ZOiYwV3chKedO8ht/V/rqK+ho7cf5DW0F/Ia2oc+GXqbZCq7+07V5qGUw+Fg8ODBLFy4kPHRQqWmabJw4UIuuOCCZp2juroagLFjx+6zdgohhBDiwPX111/j9XpT3YwmtUZJBPlOJTqK+cC9qW7EXloLvJTqRuyFjt5+kNfQXshraB/WAofO3vfPs7vvVCkZvnfppZcyc+ZMCgsLGTp0KC+++CI1NTWcddZZzTo+Ly+Pf/3rXx3ir5hCCCGE6HjS0tJS3YQ2Id+phBBCCLEv7e47VUpCqVNOOYXS0lIeeeQRioqKGDhwIM8880yzh+/puk6XLl32cSuFEEIIIdqv1iiJIN+phBBCCJFKKSt0fsEFFzR7uJ4QQgghhEjWGiURhBBCCCFSSe6+J4QQQgjRQe1tSQQhhBBCiFSSUEoIIYQQooPa25IIQgghhBCppKnYvYCFEEIIIYQQQgghhGgjeqobIIQQQgghhBBCCCEOPBJKCSGEEEIIIYQQQog2J6GUEEIIIYQQQgghhGhzEkoJIYQQQgghhBBCiDYnoVQjXnnlFcaNG8eQIUP45S9/ydKlS1PdpA7lqaee4uyzz2bEiBGMHj2aq6++mrVr16a6WR3eX//6VwoKCrj77rtT3ZQOZ8eOHcyYMYMjjjiCoUOHMmnSJH744YdUN6tDiUQi/PnPf2bcuHEMHTqU8ePH8/jjjyP3yti9r776iquuuooxY8ZQUFDAxx9/nLRdKcXDDz/MmDFjGDp0KJdccgnr169PTWPbsabex1AoxP3338+kSZMYPnw4Y8aM4eabb2bHjh0pbLFoDx599FEKCgqSpl/84hepbla7Ij+jWm5379ktt9zS4HM3derUFLU29ZpzbRAIBJg1axZHHHEEI0aM4LrrrqO4uDhFLU695rxnF154YYPP2R/+8IcUtTj1Xn31VSZNmsTIkSMZOXIkkydP5l//+ld8u3zGku3u/WrLz5eEUvUsWLCA2bNnc8011zB37lwGDBjA1KlTKSkpSXXTOozFixczZcoU3nzzTZ5//nnC4TBTp07F7/enumkd1tKlS3n99dcpKChIdVM6nIqKCs477zzsdjtPP/008+fPZ+bMmWRmZqa6aR3K008/zWuvvcYf/vAHFixYwIwZM3jmmWeYM2dOqpvW7vn9fgoKCrjtttsa3f70008zZ84cbr/9dt58803cbjdTp04lEAi0cUvbt6bex9raWpYvX860adN45513eOyxx1i3bh3Tpk1LQUtFe3PIIYfw3//+Nz69+uqrqW5SuyI/o1pud+8ZwDHHHJP0uXvooYfasIXtS3OuDe655x4+/fRT/vznPzNnzhx27tzJtddem8JWp1Zzr6fOPffcpM/ZzTffnKIWp16XLl2YMWMG77zzDm+//TZHHnkk11xzDatWrQLkM1bf7t4vaMPPlxJJzjnnHDVr1qz440gkosaMGaOeeuqpFLaqYyspKVH5+flq8eLFqW5Kh+Tz+dRJJ52kPv/8c3XBBReou+66K9VN6lDuv/9+dd5556W6GR3eFVdcoX73u98lrbv22mvVTTfdlKIWdUz5+fnqo48+ij82TVMdffTR6plnnomvq6ysVIWFheq9995LRRM7hPrvY2O+//57lZ+fr7Zs2dJGrRLt0SOPPKJOO+20VDejw5CfUS3X2M+jmTNnqmnTpqWoRe1f/WuDyspKNXjwYPX+++/H91m9erXKz89X3377bYpa2b40dj0l1wW7N2rUKPXmm2/KZ6yZYu+XUm37+ZKeUgmCwSDLli3jqKOOiq/TdZ2jjjqKb7/9NoUt69iqqqoApGfKHrrjjjsYO3Zs0udSNN8nn3xCYWEh06dPZ/To0Zxxxhm8+eabqW5WhzNixAi+/PJL1q1bB8BPP/3E119/zbHHHpvilnVsmzdvpqioKOn/d3p6OsOGDZPfO3vJ5/OhaRoZGRmpbopIsQ0bNjBmzBhOOOEEbrrpJrZu3ZrqJnUY8jNqzy1evJjRo0dz8sknc9ttt1FWVpbqJrUb9a8NfvzxR0KhUNLnrF+/fnTr1o3vvvsuFU1sd3Z1PTVv3jyOOOIIJk6cyIMPPkhNTU0qmtfuRCIR5s+fj9/vZ8SIEfIZ243671dMW32+bPvkrB1UWVkZkUiEzp07J63v3Lmz1ETaQ6Zpcs899zBy5Ejy8/NT3ZwOZ/78+Sxfvpy33nor1U3psDZt2sRrr73GpZdeylVXXcUPP/zAXXfdhd1u58wzz0x18zqMK664Ap/Px4QJEzAMg0gkwg033MBpp52W6qZ1aEVFRQCN/t45kOsc7K1AIMADDzzAqaeeitfrTXVzRAoNHTqU2bNn07dvX4qKinj88ceZMmUK8+bNk89GM8jPqD1zzDHHcOKJJ9KjRw82bdrEQw89xOWXX84bb7yBYRipbl5KNXZtUFxcjN1ub/BHhM6dO8c/gweyXV1PTZw4kW7dupGXl8fKlSt54IEHWLduHY899lgKW5taK1eu5Fe/+hWBQACPx8Pjjz9O//79WbFihXzGGrGr9wva9vMloZTYp2bNmsWqVaukfsMe2LZtG3fffTfPPfccTqcz1c3psJRSFBYWcuONNwIwaNAgVq1axeuvvy6hVAu8//77zJs3jwcffDD+y3327Nnk5eXJ+yjalVAoxPXXX49SilmzZqW6OSLFxo4dG18eMGAAw4YN4/jjj+f999/nl7/8ZQpbJvZnp556anw5ViB4/Pjx8d5TBzK5Nmi5Xb1nkydPji8XFBSQm5vLJZdcwsaNG+nVq1dbN7Nd6Nu3L++++y5VVVV8+OGHzJw5k5dffjnVzWq3dvV+9e/fv00/XzJ8L0F2djaGYTQoal5SUkJOTk6KWtVx3XHHHXz22We8+OKLdOnSJdXN6XCWLVtGSUkJZ511FoMGDWLQoEEsXryYOXPmMGjQICKRSKqb2CHk5ubSr1+/pHUHH3ywDN9oofvuu48rrriCU089lYKCAs444wwuvvhinnrqqVQ3rUPLzc0FkN87rSQUCvGb3/yGrVu38txzz0lPGNFARkYGffr0YePGjaluSocgP6NaR8+ePcnOzmbDhg2pbkpK7eraICcnh1AoRGVlZdL+JSUl8c/ggaol11PDhg0DOKA/Zw6Hg969e1NYWMhNN93EgAEDeOmll+Qztgu7er8asy8/XxJKJXA4HAwePJiFCxfG15mmycKFC5PGVoqmKaW44447+Oijj3jxxRfp2bNnqpvUIR155JHMmzePd999Nz4VFhYyadIk3n333QO++3dzjRw5Ml4HKWb9+vV07949RS3qmGpra9E0LWmdYRgopVLUov1Djx49yM3NTfq94/P5+P777+X3TgvFAqkNGzbwwgsvkJ2dneomiXaourqaTZs2HdAXIS0hP6Nax/bt2ykvLz9gP3e7uzYoLCzEbrcnfc7Wrl3L1q1bGT58eBu3tn3Yk+upFStWABywn7PGmKZJMBiUz1gzxd6vxuzLz5cM36vn0ksvZebMmRQWFjJ06FBefPFFampqOOuss1LdtA5j1qxZvPfee/zlL38hLS0tPk43PT0dl8uV4tZ1HF6vt0EdLo/HQ1ZWltTnaoGLL76Y8847jyeffJIJEyawdOlS3nzzTe64445UN61DOf7443nyySfp1q1bfPje888/z9lnn53qprV71dXVSb0yNm/ezIoVK8jMzKRbt25cdNFFPPHEE/Tu3ZsePXrw8MMPk5eXx/jx41PY6vanqfcxNzeX6dOns3z5cp566ikikUj8d09mZiYOhyNVzRYp9sc//pHjjz+ebt26sXPnTh599FF0XWfixImpblq7IT+jWq6p9ywzM5PHHnuMk08+mZycHDZt2sT9999P7969OeaYY1LY6tTZ3bVBeno6Z599Nvfeey+ZmZl4vV7uuusuRowYccAGBrt7zzZu3Mi8efMYO3YsWVlZrFy5ktmzZzNq1CgGDBiQ4tanxoMPPsixxx5L165dqa6u5r333mPx4sU8++yz8hlrRFPvV1t/vjQlf+Zu4OWXX+bZZ5+lqKiIgQMHcuutt8a7q4ndKygoaHT97NmzJdzbSxdeeCEDBgzgf/7nf1LdlA7l008/5aGHHmL9+vX06NGDSy+9lHPPPTfVzepQfD4fDz/8MB9//DElJSXk5eVx6qmncs0118gF/24sWrSIiy66qMH6M888k3vvvRelFI888ghvvvkmlZWVHHroodx222307ds3Ba1tv5p6H6+99lpOOOGERo976aWXOOKII/Z180Q7dcMNN/DVV19RXl5Op06dOPTQQ7nhhhsO2HorjZGfUS3X1Ht2++23c80117B8+XKqqqrIy8vj6KOP5vrrrz9ghzw259ogEAhw7733Mn/+fILBIGPGjOG22247YHv97O4927ZtG7/97W9ZtWoVfr+frl27Mn78eK6++uoDduj673//e7788kt27txJeno6BQUFXH755Rx99NGAfMbqa+r9auvPl4RSQgghhBBCCCGEEKLNSU0pIYQQQgghhBBCCNHmJJQSQgghhBBCCCGEEG1OQikhhBBCCCGEEEII0eYklBJCCCGEEEIIIYQQbU5CKSGEEEIIIYQQQgjR5iSUEkIIIYQQQgghhBBtTkIpIYQQQgghhBBCCNHmJJQSQgghhBBCCCGEEG1OQikhRLt3yy23UFBQwF//+tek9R9//DEFBQUpapUQQgghhBBCiL0hoZQQokNwOp08/fTTVFRUpLopQgghhBBCCCFagYRSQogO4aijjiInJ4ennnpql/t8+OGHnHrqqRQWFjJu3Diee+65pO3jxo3jySef5He/+x0jRozguOOO44033kjaZ9u2bVx//fUcdthhHH744UybNo3Nmzfvk9ckhBBCCCGEEAcyCaWEEB2CruvceOONvPzyy2zfvr3B9h9//JHf/OY3nHLKKcybN49rr72Whx9+mHfeeSdpv+eff57CwkLeffddzj//fG6//XbWrl0LQCgUYurUqaSlpfHKK6/w2muv4fF4+PWvf00wGGyT1ymEEEIIIYQQBwoJpYQQHcaJJ57IwIEDeeSRRxpse/755xk9ejTXXHMNffv25ayzzmLKlCk8++yzSfsde+yxTJkyhd69e3P55ZeTnZ3NokWLAFiwYAGmaXL33XdTUFBAv379mD17Ntu2bWPx4sVt8hqFEEIIIYQQ4kAhoZQQokOZMWMG7777LmvWrElav3btWkaOHJm0buTIkWzYsIFIJBJfl1gYXdM0cnJyKCkpAeCnn35i48aNjBw5khEjRjBixAiOOOIIAoEAGzdu3IevSgghhBBCCCEOPLZUN0AIIVpi1KhRjBkzhgcffJCzzjqrxcfbbMk/9jRNQykFgN/vZ/DgwTzwwAMNjuvUqdOeNVgIIYQQQgghRKMklBJCdDg33XQTZ5xxBn379o2vO/jgg/nmm2+S9vvmm2/o06cPhmE067yDBw/m/fffp3Pnzni93lZtsxBCCCGEEEKIZDJ8TwjR4RQUFDBp0iTmzJkTX3fZZZexcOFCHn/8cdatW8fcuXN55ZVXuOyyy5p93kmTJpGdnc20adNYsmQJmzZtYtGiRdx1112NFlcXQgghhBBCCLHnJJQSQnRI06dPxzTN+OPBgwfz5z//mQULFjBp0iQeeeQRpk+f3qIhfm63m5dffplu3bpx7bXXcsopp/A///M/BAIB6TklhBBCCCGEEK1MU7FiKkIIIYQQQgghhBBCtBHpKSWEEEIIIYQQQggh2pyEUkIIIYQQQgghhBCizUkoJYQQQgghhBBCCCHanIRSQgghhBBCCCGEEKLNSSglhBBCCCGEEEIIIdqchFJCCCGEEEIIIYQQos1JKCWEEEIIIYQQQggh2pyEUkIIIYQQQgghhBCizUkoJYQQQgghhBBCCCHanIRSQgghhBBCCCGEEKLNSSglhBBCCCGEEEIIIdqchFJCCCGEEEIIIYQQos1JKCWEEEIIIYQQQggh2pyEUkIIIYQQQgghhBCizUkoJYQQQgghhBBCCCHanIRSQgghhBBCCCGEEKLNSSglhBBCCCGEEEIIIdqchFJCCCGEEEIIIYQQos1JKCWE2C8sWrSIgoICPvjgg1Q3BYBHH32UgoKCVDdDCCGEaDUXXnghEydOTHUzhNhjS5cupbCwkC1btqS6KXHjxo3jyiuv3O1+se+6ixYtavFztOR78g033MD111/f4ucQYk9JKCWE2GvvvPMOBQUFFBQUsGTJkgbblVKMHTuWgoKCZv3SFUIIIfZXq1atYsaMGRxzzDEUFhYyZswYbrrpJlatWpXqprVrTz75JB9//HGqm9GulJWV8cwzzzBlyhSOPPJIDjvsMM4991wWLFiwy2OWLVvGVVddxeGHH86wYcOYOHEiL7300m6fa+3atdxzzz386le/YsiQIRQUFLB58+bdHrdx48b4/j/88EOLXl+icePGxb9rFhQUMHr0aM4//3w++uijFp3nT3/6E6eeeirdu3ePr3vllVd455139rht+5vLL7+cf/zjH/z00097fa6//e1vTJgwgSFDhnDSSScxZ86cZh/7448/MnXqVEaOHMmIESO47LLLWLFiRdI+mzdvTvpc1J9uvfXWvX4NYt+zpboBQoj9h9Pp5L333uOwww5LWr948WK2b9+Ow+FIUcuEEEKI1PvHP/7BjTfeSFZWFmeffTY9evRgy5YtvPXWW3z44Yf86U9/4sQTT0x1M9ulp556ipNPPpnx48enuintxnfffcef//xnjj32WKZNm4bNZuPDDz/khhtuYPXq1UyfPj1p///+979cddVVDBo0iKuvvhqPx8PGjRvZvn17s55rzpw59O/fn379+jUIB3blnnvuwWazEQwG9+g1Jho4cCCXXnopADt37uSNN97g2muv5fbbb+e8887b7fErVqzgiy++4PXXX09a/9prr5Gdnc1ZZ521123cl0aNGsXSpUux2+379HkGDRpEYWEhzz33HPfdd98en+f111/ntttu4+STT+bSSy9lyZIl3HXXXdTU1HDFFVc0eeyyZcs4//zz6dq1K9deey2mafLqq69ywQUX8Le//Y2DDz4YgE6dOjXaxv/85z/MmzePo48+eo/bL9qOhFJCiFYzduxYPvjgA2699VZstrofL++99x6DBw+mvLw8dY0TQgghUmjjxo3cfPPN9OzZk1deeYVOnTrFt1100UVMmTKFm2++mb///e/07Nmzzdrl9/vxeDxt9nyi9fTv358PP/wwqdfP+eefzyWXXMLTTz/Nr3/96/i/rc/nY+bMmRx33HE88sgj6HrLBsyMGzeOr776Cq/Xy7PPPtusUOo///kP//3vf/n1r3/NE0880bIX14iDDjqI008/Pf74jDPO4KSTTuKFF17YZSgVDocxTROHw8Hbb79Nt27dGD58+F63JRV0XcfpdLbJc02YMIFHH32U6upq0tLSWnx8bW0tf/rTn+KfN4Bzzz0X0zR54oknmDx5MpmZmbs8/uGHH8blcvH666+TnZ0NwGmnncbJJ5/Mn/70Jx599FEAPB5P0mciZu7cuXi9XsaNG9fitou2J8P3hBCt5tRTT6W8vJzPP/88vi4YDPLhhx8yadKkRo959tln+dWvfsURRxzB0KFDOeussxod7/75559z3nnncdhhhzFixAhOPvlkHnrooSbbEwwGufLKKzn00EP55ptvmty3pKSE3//+9xx11FEMGTKE0047jblz5ybtE+si/Oyzz/LGG28wfvx4CgsLOfvss1m6dGmT57/gggs47bTTGt128sknM3Xq1CaPF0II0bE988wz1NTUcOeddyYFUmD9tf+OO+7A7/fz9NNPA/DBBx9QUFDA4sWLG5zr9ddfp6CggJ9//jm+bs2aNUyfPp3DDz+cIUOGcNZZZ/HPf/4z6bjYcPvFixdz++23M3r0aMaOHQtYocXdd9/NuHHjKCwsZPTo0Vx66aUsW7aswfOvXr2aCy+8kGHDhnHMMcfE25yoOb9XwQrF7r33XsaOHUthYSEnn3wyzz77LEqp+D4FBQX4/X7mzp0bH5Zzyy237PK9jtXPWbBgAY899hjHHHMMI0aMYPr06VRVVREMBrn77rsZPXo0I0aM4He/+12jPXn+7//+j7POOouhQ4dy+OGHc8MNN7Bt27akfZYsWcL06dM57rjjKCwsZOzYsdxzzz3U1tYm7XfLLbcwYsQIduzYwdVXX82IESM48sgj+eMf/0gkEknad+fOnaxZs4ZQKLTL1wjQs2fPpEAKQNM0xo8fTzAYZNOmTfH18+bNo7i4mBtuuAFd1/H7/Zim2eT5E2VlZeH1epu9fygU4u677+aiiy6iV69ezT6uJXJzczn44IPj9aESv6e98MILjB8/niFDhrBmzRoA/vnPf3LkkUeiaVr8HOPGjWPVqlUsXrw4/tm68MIL49s3bdoU/381bNgwzj33XD777LMGbWnu570pS5Ys4ZxzzmHIkCGccMIJvPvuu0nbd1VT6pVXXuGEE05g6NChnHPOOSxZsoQLL7ww6XXExEKhY489liFDhnDxxRezYcOGBvsdddRR+P1+vvjii6T1W7dujb+fTVm0aBHl5eWcf/75SeunTJmC3+9v9D1MtGTJEkaPHh0PpADy8vI4/PDD+fTTT6murt7lsTt37mTRokWcdNJJbRbiib0jPaWEEK2me/fuDB8+nPnz58e/5P773/+mqqqKU045pdFx5C+99BLjxo1j0qRJhEIh5s+fz/XXX89TTz3FcccdB1j1N6688koKCgqYPn06DoeDDRs2NBk01dbWcvXVV/Pjjz/y/PPPM3To0Cb3vfDCC9m4cSNTpkyhR48efPDBB9xyyy1UVlZy8cUXJ+3/3nvvUV1dzeTJk9E0jWeeeYbrrruOjz/+eJddqk8//XRuvfVWfv75Z/Lz8+Prly5dyvr165k2bdou2yeEEKLj+/TTT+nevXuDIe4xo0aNonv37vzrX/8C4LjjjsPj8fD+++9z+OGHJ+27YMECDjnkkPjvk1WrVnHeeedx0EEHcfnll8ePu+aaa3j00UcbDAmcNWsWnTp14pprrsHv9wNw22238eGHH3LBBRfQr18/ysvL+frrr1mzZg2DBw+OH1tRUcGvf/1rTjzxRCZMmMCHH37IAw88QH5+fvx3f3N/ryqlmDZtGosWLeKcc85h4MCB/Oc//+G+++5jx44d/P73vwfgvvvu49Zbb2Xo0KGce+65AM0KOv7617/icrm44oor2LBhAy+//DI2mw1N06isrOTaa6/l+++/55133qF79+5ce+218WOfeOIJHn74YSZMmMA555xDaWkpL7/8MlOmTOHdd98lIyMDsMLD2tpazjvvPLKysli6dCkvv/wy27dvj/cQiYlEIkydOpWhQ4dy8803s3DhQp577jl69uyZdPH+0EMPMXfuXP75z3/So0eP3b7O+oqLiwGSLugXLlyI1+uNh2Lr16/H4/Fw2mmn8fvf/77VL95ffPFFKisrufrqq/nHP/7RqueOCYVCbN++naysrKT177zzDoFAgHPPPReHw0FmZiY7duxg69atDBo0KGnf3//+99x55514PB6uuuoqAHJycgDrffzVr35FTU0NF154IdnZ2cydO5dp06bxyCOPxP9ftfR7ZGM2bNjA9ddfzznnnMOZZ57J22+/zS233MLgwYM55JBDdnncq6++yh133MFhhx3GJZdcwpYtW7jmmmvIyMigS5cuDfZ/+umn0TSNyy67DJ/PxzPPPMOMGTP429/+lrRf//79cblcfPPNN0k/P2bOnMnixYtZuXJlk69n+fLlABQWFiatHzx4MLqus2LFikZ7OMUEg0FcLleD9S6Xi1AoxKpVq3bZ423BggWYprnLP4iLdkgJIcReevvtt1V+fr5aunSpevnll9WIESNUTU2NUkqp6dOnqwsvvFAppdTxxx+vrrjiiqRjY/vFBINBNXHiRHXRRRfF1z3//PMqPz9flZSU7LINX375pcrPz1fvv/++8vl86oILLlBHHHGEWr58+W7b/8ILL6j8/Hz1f//3f0ntmDx5sho+fLiqqqpSSim1adMmlZ+frw4//HBVXl4e3/fjjz9W+fn56pNPPomve+SRR1R+fn78cWVlpRoyZIi6//77k577zjvvVMOHD1fV1dW7bacQQoiOqbKyUuXn56tp06Y1ud9VV12l8vPz4793brzxRjV69GgVDofj++zcuVMNGDBAPfbYY/F1F198sZo4caIKBALxdaZpqsmTJ6uTTjopvi72+/q8885LOqdSSh166KFq1qxZTbbvggsuUPn5+Wru3LnxdYFAQB199NHquuuui69r7u/Vjz76SOXn56u//OUvSc9z3XXXqYKCArVhw4b4uuHDh6uZM2c22b6Y2HeCiRMnqmAwGF9/4403qoKCAvXrX/86af/Jkyer448/Pv548+bNauDAgeqJJ55I2m/lypVq0KBBSevrf49RSqmnnnpKFRQUqC1btsTXzZw5U+Xn5yf9uyml1BlnnKHOPPPMpHWxfTdt2tSs15uorKxMjR49Wp1//vlJ6ydNmqSGDRumhg0bpu6880714YcfqjvvvFPl5+erG264oUXP8cwzzzTZvp07d6oRI0ao119/XSmV/D1xTx1//PHqsssuUyUlJaqkpEStWLFC3XDDDSo/P1/deeedSqm672kjR45s8J3xiy++aPBdLebUU09VF1xwQYP1d999t8rPz1dfffVVfJ3P51Pjxo1Txx9/vIpEIkqp5n/em3pt9Z+npKREFRYWqnvvvTe+Lva5/vLLL5VS1v+9ww8/XJ199tkqFArF93vnnXdUfn5+0muKHTthwoSknxMvvviiys/PVytXrmzQrpNOOqnB/5XYz4DdmTVrlho4cGCj24488sjdfuYmTpyoTjrppKSfU4FAQB133HEqPz9fffDBB7s89swzz1RHH310/N9HtH8yfE8I0aomTJhAIBDg008/xefz8dlnnzX5l4rEv4JUVFRQVVXFoYceGv8LCxD/a+Q///nP3XY1r6qqYurUqaxdu5Y5c+YwcODA3bb53//+N7m5uUm3ubbb7Vx44YX4/X6++uqrpP1POeWUpHHwsb96J3aTry89PZ0TTjiB+fPnx4ckRCIR3n//fU444QSp5yGEEPux2FCT3dVmiW2P7T9hwgRKSkqShvB9+OGHmKbJKaecAkB5eTlffvklEyZMwOfzUVpaSmlpKWVlZYwZM4b169ezY8eOpOc599xzMQwjaV1GRgbff/99g33rq1/DxeFwMGTIkKTfgc39vfrvf/8bwzAaDDO67LLLUErx73//u8m27M7pp5+e1IN56NChKKU4++yzk/YbOnQo27ZtIxwOA/DRRx9hmiYTJkyIv5+lpaXk5OTQu3fvpOFTid9j/H4/paWljBgxAqVU0neZmPq1jw499NAGd7G79957WblyZYt7SZmmyYwZM6isrOR///d/k7b5/X5qamriPbdPOukkbr31ViZPnsz8+fNZv359i56rKQ888AA9e/bkl7/8ZaudE6xC7aNHj2b06NGcfvrpfPDBB5x++unMmDEjab+TTjqpwRDZsrIyoO47ZXP861//YujQoUm9G9PS0pg8eTJbtmxh9erVQMu/Rzamf//+Sc/TqVMn+vbt2+R3yx9//JHy8nLOPffcpFqukyZN2mW9prPOOivpxkNNfYfNzMyMv28xc+bM2W0vKbB6j+1q9IDT6WwwvLW+888/n/Xr1/M///M/rF69mp9//pmZM2dSVFQUP39j1q1bx7Jlyzj11FNbXDdNpI4M3xNCtKpOnToxevRo3nvvPWpra4lEIpx88sm73P/TTz/liSeeYMWKFUn1HBLH+59yyin87W9/49Zbb+XBBx9k9OjRnHjiifziF79o8AvnnnvuIRgMMnfu3Ca7OyfasmULvXv3bnCufv36Adb4+URdu3ZNehz7xV9ZWdnk85xxxhksWLCAJUuWMGrUKL744guKi4ub7L4shBCi46sfNu1K/fDq2GOPJT09nQULFjB69GjAGpoycOBA+vbtC1gF1JVSPPzwwzz88MONnrekpISDDjoo/rixsGPGjBnccsstHHfccQwePJixY8dyxhlnNCi63qVLl6Tf0WD9Hky8UG3u79UtW7aQl5fXoFZRbL9YraA91a1bt6TH6enpQMPf4+np6ZimSVVVFdnZ2axfvx6lFCeddFKj500MALZu3cojjzzCJ598QkVFRdJ+Pp8v6bHT6WwQlmRmZjY4bk/deeed/Oc//+GPf/wjAwYMSNoWC88SgxOwAow33niD7777jj59+ux1G7777jv+7//+jxdeeKHVQ4Fhw4bxm9/8Bk3TcLlc9OvXr9GQqakwTyXUKtudrVu3MmzYsAbrY3d+27p1K/n5+S3+HtmY+p9J2P1nI3be+kNZbTZbg1pjMfX/T8Tev8a+wyqlGvxfb67YMLvGBAKBRofmJTrvvPPYvn07zz77bLw2V2FhIVOnTuXJJ5/cZcA/b948ABm618FIKCWEaHUTJ07kf//3fykuLubYY4/d5V+llixZwrRp0xg1ahS33XYbubm52O123n77bd577734fi6Xi1deeYVFixbx2Wef8Z///IcFCxbwxhtv8NxzzyX9tfeEE05gwYIF/PWvf+W+++7bJ38lqf/X5ZjdfdEZM2YMOTk5/P3vf2fUqFH8/e9/Jzc3l6OOOqrV2yiEEKL9SE9PJzc3d7c9DFauXMlBBx0UD2kcDgfjx4/no48+4rbbbqOkpIRvvvmGG2+8MX5MrAfxZZddxjHHHNPoeetftDZWP+iUU07hsMMO46OPPuLzzz/n2Wef5emnn+bRRx+N14qCXf8ObI929R1gV+tjv8dN00TTNJ5++ulGX2+sd3MkEuHSSy+N19k6+OCD8Xg87Nixg1tuuaVB7+59+d499thjvPrqq9x0002cccYZDbbn5eWxatUqOnfunLQ+FpK1VjB2//33c9hhh9GjR494D7BYb5uioiK2bt3aIBhpruzs7GZ9Z2os8IjV19rdHxBTpa3+X+3us5+osrKS3r1779Hz5ObmEolEKCkpSfrMBYNBysvLycvL2+05brjhBi677DJWrVpFeno6BQUF8Zsc7SpAfe+99+jbt2+DWlaifZNQSgjR6k488URuu+02vvvuO/70pz/tcr8PP/wQp9PJs88+m9SV+O23326wr67r8S7bv/vd73jyySf505/+xKJFi5K+oIwfP54xY8Zwyy23kJaWxqxZs3bb3u7du7Ny5UpM00z6Zb127Vqg4V+V9pRhGEycOJG5c+cyY8YMPv7440aHUAghhNj/HH/88bz55pssWbKk0WLnS5YsYcuWLUyePDlp/YQJE5g7dy4LFy5kzZo1KKWYMGFCfHusJ5Pdbt/rP3Lk5eUxZcoUpkyZQklJCWeeeSZPPvlkUijVHM39vdq9e3cWLlyIz+dL6i0V229XvT32tV69eqGUokePHvEeaY35+eefWb9+PX/84x+TgqDEuxC3hVdeeYVHH32Uiy++mCuuuKLRfQYPHsznn3/Ojh074j19wLpTGdCgB9ee2rZtG1u2bOGEE05osG3atGmkp6ezZMmSVnmuloi95vpDJYFd9gbq1q0b69ata7C+sc9xW3yPbKx9YPWWPPLII+Prw+EwW7ZsoaCgYI/PHQ6H2bZtG+PGjduj42PlM3788ceknx8//vgjpmk26Mm3K5mZmUk/L7/44gu6dOmS9BmO+f7779mwYQPTp0/fozaL1JGBlkKIVpeWlsbtt9/Odddd1+QvM8Mw0DQt6VbImzdvbnAL6/Ly8gbHxn7ZNXYL5zPOOINbb72V119/nfvvv3+37T322GMpKipiwYIF8XXhcJg5c+bg8XgYNWrUbs/RXKeffjoVFRX84Q9/wO/3c9ppp7XauYUQQrRfU6dOxeVycdtttzWo01JeXs5tt92G2+3m17/+ddK2o446iqysLBYsWMD777/P0KFDk4bUde7cmcMPP5w33ngjHjAkKi0t3W3bIpEIVVVVSes6d+5MXl5eo79nd6e5v1ePPfZYIpEIr7zyStLxL7zwApqmceyxx8bXeTyeNuvlctJJJ2EYBo899liDHiRKqfi/XyyASNxHKcVLL720V8+/c+dO1qxZs8vhT4kWLFjAXXfdxaRJk/jd7363y/1iQeZbb72VtP6tt97CZrMl3eFx69atrFmzZo/afscdd/D4448nTbGaYTNnzuSBBx7Yo/PurYMOOoiuXbvy448/Ntjmdrsb/WyNHTuWpUuX8u2338bX+f1+3nzzTbp3707//v2Btv0emaiwsJCsrCzefPPNeD00sIaw7W3Pt9WrVxMIBBgxYkTS+uZ+No488kiysrJ47bXXkta/9tpruN3u+B22wfoZtWbNGmpqapo854IFC/jhhx+4+OKLG+3xJUP3Oi7pKSWE2CfOPPPM3e4zduxYnn/+eX79618zceJESkpKePXVV+nVq1fSEIfHH3+cJUuWMHbsWLp37x7fr0uXLhx66KGNnvuCCy7A5/Pxpz/9ifT09PhtfhszefJk3njjDW655RaWLVtG9+7d+fDDD/nmm2/4/e9/36DWxd4YNGgQ+fn5fPDBB/Tr1y/pNttCCCH2X3369OHee+/lt7/9LZMmTeKcc86hR48ebNmyhbfeeouysjIeeuihBkPt7HY7J554IvPnz6empoaZM2c2OPdtt93G+eefz6RJkzj33HPp2bMnxcXFfPfdd2zfvp2///3vTbaturqasWPHcvLJJzNgwAA8Hg9ffPEFP/zwA7fcckuLX2tzf6+OGzeOI444gj/96U/xnh2ff/45//znP7n44ouT3ovBgwezcOFCnn/+efLy8ujRo0ej9X5aQ69evfjNb37Dgw8+yJYtWxg/fjxpaWls3rw53st56tSpHHzwwfTq1Ys//vGP7NixA6/Xy4cffrjX4dlDDz3E3Llz+ec//9lkfaSlS5dy8803k5WVxejRoxv8O48cOTIeYA4aNIizzz6bt99+m0gkwqhRo1i8eDEffPABV155ZVLNsZkzZ7J48eKk72JVVVXMmTMHgG+++Qawemilp6eTkZHBBRdcAFilCuqLvR+jRo1iyJAh8fWbN2/mhBNO4Mwzz+Tee+9t0Xu0J0444QQ++uijBrWSBg8ezGuvvcZf/vIXevfuHa+PesUVVzB//nwuv/xyLrzwQjIzM3n33XfZvHkzjz76aDwYacvvkYkcDgfXXXcdd955JxdffDETJkxgy5YtvPPOOw1+jrTUF198gdvtbtD7srHPRmNcLhfTp0/njjvuYPr06RxzzDEsWbKEv//979xwww1kZWXF933llVd47LHHeOmllzjiiCMA+Oqrr3j88cc5+uijycrK4vvvv+edd97hmGOO4aKLLmrwfLGbBw0fPnyvX7toexJKCSFSZvTo0dx99908/fTT3HPPPfTo0YMZM2awZcuWpF9248aNY8uWLbz99tuUlZWRnZ3N4YcfznXXXRcvWtqYq666iqqqqngwNWXKlEb3c7lczJkzhwceeIC5c+fi8/no27cvs2fP5qyzzmr113366adz//33S4FzIYQ4wEyYMIGDDz6Yv/71r7z11luUl5eTlZXFEUccwZVXXkl+fn6jx8Vu+KFpWtLQvZj+/fvz9ttv89hjjzF37lzKy8vp1KkTgwYN4pprrtltu1wuF+eddx6ff/45//jHP1BK0atXr3jY1VLN/b2q6zpPPPEEjzzyCAsWLOCdd96he/fu3HzzzVx22WVJ57zlllv4wx/+wJ///Gdqa2s588wz91koBXDFFVfQp08fXnjhBR5//HHAKvJ+9NFHx3uB2+12nnzySe666y6eeuopnE4nJ554IlOmTGmT3/GrV68mFApRWlrK73//+wbbZ8+endSrbtasWXTr1o133nmHjz/+mG7duvG73/2OSy65ZLfPVVFR0aCQ/nPPPQdYw9dioVRL+P1+wKo/1BbOPvtsXn75Zb7++uukIWHXXHMNW7du5ZlnnqG6uprDDz+c0aNHk5OTE+91//LLLxMIBCgoKODJJ59M6unT1t8jE11wwQUopXj++efjBe6feOIJ7rrrrkZrxzXXBx98wIknnrhXgdqUKVOw2+0899xzfPLJJ3Tt2pXf/e53XHzxxbs99qCDDsIwDJ599lmqq6vp0aMHv/nNb7jkkkuSbjQQE7t5UFN/hBbtl6ZacgsCIYQQe+3FF19k9uzZfPLJJ/uszoAQQgghRHv2yiuv8MADD/DRRx+Rk5PTJs958cUXk5eX16zyDh2VaZrxO1XfddddLT5+xYoVnHnmmcydOzdeLkOIfUlqSgkhRBtSSvHWW28xatQoCaSEEEIIccBatGgRF154YZsFUgA33ngj77//Plu2bGmz59yXAoFAg7pn7777LuXl5Ul1wlrir3/9KyeffLIEUqLNSE8pIYRoA36/n08++YRFixbx5ptv8pe//KXRO9MIIYQQQgjRHIsWLWL27Nn84he/ICsri+XLl/PWW2/Rr18/3n777aS7WwvRXklNKSGEaAOlpaXcdNNNZGRkcNVVV0kgJYQQQggh9kr37t3p0qULc+bMoaKigszMTE4//XRmzJghgZToMKSnlBBCCCGEEEIIIYRoc1JTSgghhBBCCCGEEEK0uRaHUl999RVXXXUVY8aMoaCggI8//jhpu1KKhx9+mDFjxjB06FAuueQS1q9fn7RPeXk5N910EyNHjuSwww7j97//PdXV1c1ug1IKn8/XoKibEEIIIYRoPvlOJYQQQohUanEo5ff7KSgo4Lbbbmt0+9NPP82cOXO4/fbbefPNN3G73UydOpVAIBDfZ8aMGaxevZrnn3+eJ598kiVLlvCHP/yh2W2orq7m0EMPbVGQJYQQQgghksl3KiGEEEKkUotDqbFjx3LDDTdw4oknNtimlOKll15i2rRpjB8/ngEDBnDfffexc+fOeI+qNWvW8J///Ie77rqLYcOGcdhhh3Hrrbcyf/58duzYsfevSAghhBBCCCGEEEK0e616973NmzdTVFTEUUcdFV+Xnp7OsGHD+Pbbbzn11FP59ttvycjIYMiQIfF9jjrqKHRdZ+nSpY2GXW3OtxZ+mAVmGOxesKWDzQuOLGuyR+fOzuDMBWcO6HIjQyGEEEIIIYQQQojmatUkpaioCIDOnTsnre/cuTPFxcUAFBcX06lTp+RG2GxkZmbGj0+5LfNh3UstO8aeBa5ccHcFV1dwd4O0XuDtB96DwdMLDAdoBmi6NQkhhGj3lFIo1B4vAyhU0nJLtiU+bs4+sce7W7e787d0vVLJr6P+/vWPSdxuKpNECkXC5qT3FCDLlUXf7L4IIYQQQqRKxFQYupbqZuyV9vAapHtPY/pfbvWMqvgBwjUQ9kG4GkJVEK6EkA/CVRCqsNahIFRuTVWrGj+nZliBVVpf8Pa1wqqMAeDpY4VVuh10h9XjSjOSp/g6mzXpNgm1hBDtVixAUEphKjO+3NQ6RXR9vXV7OjdNExMzPq///LEpFo4oU8X3a6x9kBwexV5nfD0J4U90l6bCp92ew1po9DFabKahlELTNBQq/jhxn6aOA+LHJu3XjH2Tniu6bVe0hMbU30+j3uMmzhPbvyZcQ/f07hJKCSGEECKlDF3j+te/ZfVOX6qbskf653l5+FcjUt2M1g2lcnNzASgpKSEvLy++vqSkhAEDBgCQk5NDaWlp0nHhcJiKior48SlnuKDfpY1vUyaYITCDEAlApBr826B6A/g3Qc1WCOyEQAkESqF2uzWZQfBvtqai/9Sdz5YBGQV1U3qBFVABaBooFe1ZFQulosu6HXSn1VbDlRBo2a25bo8u22VooRAHoMTgJR7A1AtkYuFLY9sTQ6LYuoiKEDEjRFQE07QeJ26LrUsMf6AupDIxsTKbxrfHg5nEMEZhBSQtmCcGGxoamqbF57F1QIP19feP7ZN4jK7pddv0hsfVX97V9sRzJ+7T2HPuLqg50BT7i1PdBCGEEEIIAFbv9LFsa2Wqm9GhtWpa0aNHD3Jzc1m4cCEDBw4EwOfz8f3333PeeecBMGLECCorK/nxxx8pLCwE4Msvv8Q0TYYOHdqazdk3NB0MpzXZ04EcSOsNuUdaNajCPmsKllvBVKjCWh+pgWAJ+NaBbw1UrbF6VYUrofQrawIrYOp0KHQeDbmjrXOrSHQyQYXBjECk1uq9pSLWusS/vmtacq8q3QmGG2xpVrt1R90Ueyw9r4RoU/XDHlOZRMxIg3X118emsBm2JhUmYkYImwnz6LGxIVOJPYASeyPF1jfWGyYWiMR64kBdsKJHf15oWMuJ4U7iPBbgaHrj2+uHQvH9JYQRQgghhBDigNDiUKq6upqNGzfGH2/evJkVK1aQmZlJt27duOiii3jiiSfo3bs3PXr04OGHHyYvL4/x48cD0K9fP4455hj+93//l1mzZhEKhbjzzjs59dRTOeigg1rvlaWCbqsrhu7pYYVRoQornKrZZhVNT+sDvc6xwiczCJU/Q/lSKP8BSr+xgquiz63pJ6xhfl1Pgi4nQVrP5rUjHl6FrXmkxgq/asLWNgC0aM8qmzV80PBEC7q7o72sEocKJsw1wwq9hDhAKaWSegzVn8dCpMR1oUiIkBkiFAkRNsPWXIWtkCg2zCwWONUbbpYocbgWWCFOLMiJhUOJ6wzNQNPr1tU/JjFgEgemiBkhZNZ9LoORoLUcXRf/vEbXJe4b356wb2NTLCzd5XaVEKqqcIOANWLW/V8Km9b/m1+P/DVH9jwy1W+fEEIIIYTYSy0OpX788Ucuuuii+OPZs2cDcOaZZ3Lvvfdy+eWXU1NTwx/+8AcqKys59NBDeeaZZ3A6nfFjHnjgAe68804uvvhidF3npJNO4tZbb22Fl9PO6LboHfo6W8XOg2VQvRlqt1pDAJ3ZkFVoTWD1dqpaBcULoWSRFVL51sCqJ6wpYyD0OB26TbB6Pe2KpoMW7Qm1K0pFg6voUMRgGdTutHpeJZ3LAN0AjLpwynBava90J9hcCQGWPSHEstftL0Q7VP/Ct/4FdOxiOBAOEDKti/VgJEjIDDXs0WSaRIigTJU0lCwWIumajqEbVlCkG/FgSNd07IY96XH9kEnsX5RS8c9Tbbg2/rkKRAIEw9F5dF3itlAkFJ/HPof19wtFQgTNIOFI2FpnBpMC0cbm9QuMdxQri1emuglCCCGEEKIVaKp+1dUOwOfzceihh/L111/j9XpT3ZyWUdGi6NWboWaLVZfKndd4gBSqhB2fwraPrOF9scDI8EC3X0DPcyAjf9+2NT50MGGK9cCKDSmM1b6C5BAr3hPLZQ0fjNe+sieEWPaE+lfGvnstYr9mKjOp50ZjvTcC4UDShX/iMLdYDw1l1hWJjtfziQZJhmbEw6VYeGTTbQ0CJdHxxIKimlANteFaasLWvKkpEAnULUc/W4nLgXCA2ogVOiWuC0QCqX65TTI0A7thx67bsRt2bLoNu27NY1Nse+K6+LZG1hu60ei+htZwfeI6Qzfi//cSt1eHqhmSN4Sjeh2V6rdrv9Chv1MJIYQQKXbqI//psDWlBnfLYP70Y1LdDLn7XpvTNHBkW1NaL6habRU/t7nA0Tl5aJw9w+oZ1eN0qyfT1vdh0ztQvd6ab3oHOo2yirJ3GtX6w+pitala8jGJh1axWlchCNaAKm6k9pUe7Ull1BVn193WEELDXS+wqjeJ/V79YUVJw9/MkBUehGoIhAMEzWDd8J6IFTbVz9tjF7eJc7tuT3osoVL7F4qEqA5V4w/5k6aaUA3+cHQe8lMTrolvqw3XxvepCUenUF3wVBOuSVmPIafhxGlz4jAcOHRHfNlpOLEbdpyG9Ti2bDfs1r6GA7tuj29LfBxbZ9frwqXYeptui+8bC51i62Mha3tX7C/uEO0UQgghhBC7J6FUKjkyodMIcB8ElaugepPVa8pwNbJvNvQ5H3qfB6Vfw6a3YccndUXSMwutcCr3mNQWLdcMMJrZ46l+z6tIwCreXhtOGEYYDdriNa1iQwhj4ZUnIaxy1M2l51W7FevVFBt+lBg4xUODUI01DCmhp5NpmnVD4yCpB4ahGUkX1oZmyNC3dkQpRU24Bl/QF5+qg9VUh6qt5VB1/HGDeagaf9AfD6JCZmifttWu23HZXPHJbXPHl502Z9K2WKCUuOw06j1OWBcLm2LrbLpNPqdCCCGEEOKAJqFUqmm6VRTd0dnqNeVbY4VV9oxd7K9B58OsqWY7rJsDm9+Fih/hm5usulMF10Hnw9v0ZewRzYgO92ui9hUkDCOM1sCK9b4KRIu5x88Xu+ugva72leEBmydaAyshtIovy3+B1pRYL6f+5A/58QetHiyJw+viPVQU8SFxsclluLDZ64byiNRQShGIBKgMVFIZqKQqUEVl0Jr7gj5rXdBargpUURW0pupgdTyEitSvV7eXnIYTj90Tn9w2tzW3uxs8dtvcSetdNlfd+ui2WNBkk58JQgghhBBCtBn59t1e2NyQNdgqYF65wuo15Mpt+hh3Fxj0W+h3GWx4HTa8YR371dWQMxryr9u3NafaSuIwwsZ6kcUk3nXQDFm9roLl9YYNJtx1ULdFi7V7rPAqXvPKYd2RMLYsw0TiYqFTvD5TLHBK6MkSK8IcMkPxGk2apiWFTQ7DQZo9TcKmFKgN11JeW05FoIKK2ugUsKbKQGX8cSx8ii23Rg8lXdPxOrx47V7SHGnWssNLmj2NNEdao3OP3VM3d6TFQygJj4QQQgghhOj45Ft9e6LpkH6wFZJULLOKoXu6Wr2JmuLsDPnXWMP7Vj9jDe0rXgjFX0L3iVY45ezUNq8hlZp918FIXY+riB/CldHHjQVX9mih9rRoeOVqGFrtZzWuYr1ikgo4h2rjPWFid/gKRUJW3SYNdPR4DZtY4BS7q5zYdyJmhMpAJWW1ZdZUU0Z5bTnlteWU1dYtJ057U2jb0Ay8Di8ZzgzSnelkOKx5fJ0jPR40xdYnrnPb3DJcTQghhBBCCBEnoVR75O5i1UyqWAb+LeDpvvtgCqy6U4N+C33Og58fh+0fwZZ51h38DrnKulvfgd67INbrSrcB7sb3UapuqKAZgnCVVWjeDBMvaKTpdbWrDHs0tEqzerzpiYGVM1qsvf1diMduSR8Ln2pCNVQEKqgOVVs9oMJBwioMyurplFhU2WP3SOi0jwQjQUprSimpKaHUH53XlFpTbSmlfmseC5n2pEC3oRlkubLIdGaS6cqMzzOcGWQ6rXlsOd2ZHn+cZk+TUEkIIYQQQgjRag7whKIdc2RC9nAo+75lwRRYNaqGz4ay82DFfVD5E6x4ADb/HwyaaZ1X7Jqm1d3xb1diva3MWIF2H9Qk9LbStIQ7Bzqs4YH29GivK2ebhVZKqQa3sq8MVlJZWxnvBRWKhFAodE2P36HLZbjIdGbKEKlWopTCF/RR7C+myF9Esb+YYn8xJTUl1txfQkmNNVUGWn5L2QxnBlmuLLJd2XRydyLLlRWfsl3ZZLoyrcfOLDJdmRIuCSGEEEIIIdoFueJsz2weyB4KZQqqt0Baj5bVN8oeCqNfhE1zYdVfoGoVLPq11WOq4Fqwefdd2/d3sbsM7ionVGbdEEEzBMFiqN1mrYeE0Co6/M/mTehpFQ2tDGezhwfGhtzF7l5XG66lotaqBVQbqbV6PZlhq7aTZtV0ctqcpDnSsOt2CSj2QigSoshfxM7qnfEp9jgWQhVVF7Vo2JxNt9HZ3ZlO7k7xqbO7M9nu7PjjxABKwkMhhBBCCNGRREyFocs1iJBQqv2zpUH2MFDfJfSYakEwpRnQ6xzoMh5WPgJb/g6b3oKd/4bBMyFv7D5r+gFN061QCWfj22OhVSy4Cuyo19MqcXigsy60MlyENYOaSAR/JEKNMqkM+iivLY8Pw4uY1l3O7IbdukOZzUOWU4KLPRE2wxRVF7GjegfbfdvZ7tvOjuod8fBpR/UOSmtKm30+r8NLjieHXE8uOZ4ccjw5dHZ3js87ezrT2d2ZDGeGBIVCCCGEEGK/Zega17/+Lat3+lLdlD1yXEEuvz15QKqbsV+Qq9SOIBZMlX0H/q3RYKqFF6yOLBjyB+h2Ciy7G/yb4JuboMuJMOhmqx6VaDux0MrYVWhlDQ8MhWrw1xThD66jOuCjLOijKlRLrYoQjCjQbdhsHpyODFzOdDJt6dhcHqsHluGI3rVQ7Io/5Gdb1Ta2+bax3bc9ab7Dt4Mif1GzajbZdTt5aXnkpeWRm5ZLniePHE+O9diTS26aFUK5bE3cPVIIIYQQQogDyOqdPpZtbXnpivagX25aqpuw35Ar1o7C7rWG45UsgcBOcB20Z+fpfBgc/Zp1l771c6xi6KVLrFpTXca3bptFi0TMCNXhGqpDtVSHaigNVFAV9FMbDhKMhNA0cBhOXK50snQDhwaaClt1rZQPaqI/0DWsHlbxIuxuq6ZV7K6BmsNarznaZQH21hQIB9jm28aWqi1srdoan7ZVbWOrbyvlteW7PUcscOri7cJBaQdxkPcga552EHnePPI8eWS5sqRnkxBCCCGEEEK0kIRSHYk9A7IKofRrCJZbvZ/2hOGyakp1GQ8/3A6+1fDdLdFeUzP3/Lyi2ZRS+MNW+FQdqqEsUEl5wEdNOEDIDKFpGg7djsvmpJMrA4ex+7pSdSc3o0FVrAh7dfTOgVG6DbCBYQPdHR0WGC24Hi/Abm9+Yf0UUkpREahgc+Xm+LSlakt8vrN6527Pke5Ip6u3K13Tu9LV25Uu3i7xqau3K9nubLnLoBBCCCGEEELsAxJKdTSuPMgcBKXfWeGBzbPn58ocAEfNgTXPwNoXrF5TRV9AlxOg+0TrLn1yMd4qQpEw1eEafCE/lYFqimvLqQkHqA0HUCjshh234STblY7TcOzdk2m61SOKXZzHDEcLsIchVA7BIlCxY2N3HrRZBddtHqv4upYQVjWz+HprUUpRXlvOxsqNbKrYxMaKjWyq3GRNFZuoDlU3ebzH7qFbeje6pXeje3p3uqV3o6u3K93Tu9M1vStehxT8F0IIIYQQQohUkFCqI/L0gnANVC4HvasVEuwp3Q6HTIO84+DHO6w79G35uzW5u1s1qLqdAmk9W635B4LacABfyAqhSmsrKAtURXtBhdE1HY/NSZrdRWdXZtsP+4r3lGpkm4ok9LLyQ7gSooXT48dqdiv0auVhgTWhGjZWbmRD+QY2VGxgQ/kGNlZsZGPlRnzBpgsg5qXl0SO9B90zutMjowfd0+vmMrROiI5NKUVERTCVialMQpFQqpskhBBCCCFaiYRSHZGmQXp/K5iqXmcFRnvboylzIBz1ilVMfct7sP2fULMF1jxtTVlDrXCqy4ngyGyVl7E/qQ0HqAr58QX9FNWWURHw4Q/XohTYDRsem4tcdwe4A55mgGHQ6F0DW2FYoNJslNSWs65sHesr1rO+vG7aUb2jyaZ18XahZ0ZPemb0pFdmL3pk9KBnRk+6Z3SXAuJCtFOxQCliWqFSYrjU2DoAhbJ6b2qAAk3XMDDQdR1d03HYHHgce9FLuA089dRT/OMf/2Dt2rW4XC5GjBjBjBkzOPjgg+P7XHjhhSxevDjpuMmTJ3PHHXfEH2/dupXbb7+dRYsW4fF4OOOMM7jpppuw2ep+lyxatIh7772XVatW0bVrV6ZNm8ZZZ52171+kEEIIIUQraOdXyGKXdJs1/C7ig9od4O669+fUdOg00poG3Qw7PoWtC6B4EZQvtaYVD0Du0dBtAuQes+u7x+3ngpEQVcFqKoPVCSFUAKVMnIYDj91NpiMdQ9+Phj82NSxQKauXVXRYoAqWsb18JWsrd7CuagdrfTtZV7WTdb4d+EI1u3yKTGcmvbN60zvTmnpl9ooHUBI8CZEaETMSD46aWlYolFLxMAkNNK0uUDI0A12zgiVDN3DZXDgMBw7DgV2347A5MDQDQzeS5rH9E9fZW1JnLwUWL17MlClTGDJkCJFIhIceeoipU6cyf/58PJ66QO3cc89l+vTp8cdutzu+HIlEuPLKK8nJyeH1119n586dzJw5E7vdzo033gjApk2buPLKK/nVr37FAw88wMKFC7n11lvJzc3lmGOOabsXLIQQQgixhySU6shsbsgYCKVfQbCidXow1QYgYv21moyx1hQqgaKPYec/wLcKdv7LmmxpcNAJ0PVk665+HaAw9p6KmBGqQn4qg9UU15RRFqjCF/JjmgqXzQqhspzpB1xBbKUUxbXlrKnYzOqKzayp2Mzais2sq9yKP1zb6DE6Gt3TOtPHm0sfbx590g+iT3o3eqd3JcvTqfFhgbpm9dQ6wN5fIVpLYo+lWJAUNsNJvZUiZgQTEw0NpRSapqFQVihULySy6Tbcdjd23Y7T5owHS7F9bLqtQZCUuE7X9P16WO2zzz6b9Pjee+9l9OjRLFu2jFGjRsXXu1wucnNzGz3Hf//7X1avXs3zzz9PTk4OAwcO5Prrr+eBBx7g2muvxeFw8Prrr9OjRw9uueUWAPr168fXX3/NCy+8IKGUEEIIIToECaU6OlcOpBdYvZgM1573XKoNwKZtsHk7hBNqCFljJ0AfANpAcG4H8yuIfAXh0rr6U7ZsyDkOupwMuSOiQ8A6Nn+olsqgj9LaSopqy/AFawiaIRy6jTS7my6eHGx6x3+dzVUdqomGT5tYVb6JNRWbWFOxhYpd1Huy6Qa9vF04OLM7fTO6c3BGd/pmdKNn+kHJxdybNSzQsOpV6S4rsLK5orWtojWsdHubFl8XIpUSeynVD5bij7H+uJAYMBlatMeSbmDTbOiajtPujIdKTpsTu26Ph0c23WYtR4Ol2LJNt+33oVJrq6qqAiAzM/mPR/PmzePvf/87ubm5HH/88Vx99dXx3lLfffcd+fn55OTkxPcfM2YMt99+O6tXr2bQoEF89913jB49OumcY8aM4Z577tnHr0gIIYQQonVIKLU/8PaxClJXrW15falIBLYXwbrNUF4FmengiQ4fiF1vKMA0wVSgekCkG6hTwFwFagmwFMJlsH2uNWmdwHMk5I6D7ELrfGluaOdD2SJmhMpgNRVBHztrSimrraImXIum6aTZXHRyZeBo50NGWoNSii3VRawq38jP5RtZVb6RVeWb2FK9s9H9dU2jp7cL/TJ7RKfuHJzRg17pBzWvhlaz7hYYBjMEoUoIllhBFpr1GdVsdQGV7rbuGKg7o3cQjN010GbtJ0Q7E+vBFAuTEnsz1Q+YYjRNw6Yl90SKhUouo25IXCxUaixkii1LsLTvmabJPffcw8iRI8nPz4+vnzhxIt26dSMvL4+VK1fywAMPsG7dOh577DEAiouLkwIpIP64qKioyX18Ph+1tbW4XDLsWQghhBDtm1yl7Q803eotFapqWX2pSh+sWgfbiqzgqFteC4OjI6xJhSHwI9R8AYFvQJVC9QJrWtcJ9BHgOhw6DYGsdCug8rjBlfp6VMFIiPJAFWWBSrb7S6kKVhMywzgNB167OzV3x2tDoUiYNZWbWVm2gZ/LN/BzNIiq3kXdp1x3Nv0ze9A/qyf9M62pT0bX5J5PrS1eQL2Ri6ukXlZBCPshsMMKUDXN+r+h2UE3rKDKiIZWmi06NNBe18tqPx5+KtpWLEwKm+EGgVPYDFuFvLF6MQF1Q9wS6iV5DS9OmxOn4cRldyUFTElhU8KwONE+zZo1i1WrVvHqq68mrZ88eXJ8uaCggNzcXC655BI2btxIr1692rqZQgghhBApIaHU/iKxvlSoEuwZu95XKdheDCvXgr8WuuSCbS8+CpoNXMOtSQUh8D3ULoLAt0ApqH9CzT9hSzZsGgLaEHAWQHo6dMoErwfSPOBxtUlvKn+olvJAFUU15RTXluEL+kEDj81FZ1fmftsbqjYcYFX5JlaUreOnsvX8VLaBtZWbCZuRBvvadRsHZ3QnP6sXh2T14pCsnhyS1YssZ3oKWt6E3fayiliBlQpDxA/hKqhJHBpoADYr+DJc1mRz14VVWrSXlYRWB7TE3kyJUyx4MjGJ5kxomhavuRTrneS0OXHZXNZkuLAbduyGfZdBU7u/S6dotjvuuIPPPvuMl19+mS5dujS577BhwwDYsGEDvXr1Iicnh6VLlybtU1xcDBCvQ5WTkxNfl7iP1+uVXlJCCCGE6BDkm+/+xJUD6flQFq0vpTdyoR4OW0P11mwEh93qHdWaNAe4RlmTWWsFVIGvrIBKlYH+b+DfEMqAsiFQNBBUP3B4rB5UnTIh3WsFVR432Pf+I6qUojpUQ3mwih3+UopryvGHajF0nXRHGl3Tcvevu+RhBVA/l29keek6fipbx4qy9ayr3IKpVIN90+0eCrJ7k5/Vm4Ls3hRk9aZPRtf948JYNwAD2EWvPLPujoGEfRAqt9YlHW+rGwqY2NNKtydPMjyww2ksYIpNpooOmdOsHk2GZmAzbPFhc+nOdNw2d1JPplg9pljglFifSRxYlFLceeedfPTRR8yZM4eePXvu9pgVK1YAdYHT8OHDefLJJykpKaFz584AfPHFF3i9Xvr37x/f59///nfSeb744guGDx/eiq9GCCGEEGLfkauo/U1aH+tOfP4N4OlpDWGKqamFleusYubZmVYItC/pLnAfYU0qAIEfoHYxBL4DVQnqc9A/B80FeiHUDIZ1/cF0g82whvdleCE7wwqoPO5m96ZSSuEL+SkLVLHdX0xpTSU1kQAO3Ua6I22/GpYXioRZVbGR5aVrWV66jhWl61hbuYWIMhvs29mVyYDsPgzI7kNBdm8GZPehqydnv3kvWiwWWu0qM0gMrRrraaXp0ZpWNqumleGOBsLOaFhlS+h1ZZO7B7YBpVSDHk3x4ElFUEpZtcgUSXWWbLqNNEcabrsbl+HCZXclhUz1A6cD7U6bomVmzZrFe++9x1/+8hfS0tLiNaDS09NxuVxs3LiRefPmMXbsWLKysli5ciWzZ89m1KhRDBgwALAKlvfv35+bb76Z3/72txQVFfHnP/+ZKVOm4HBYf3T61a9+xSuvvMJ9993H2WefzZdffsn777/PU089lbLXLoQQQgjREhJK7W90AzILrMLngSJwRXtC+Wth2SrYUQQH5bZKD6QW0ZzgOsyaVBiCy6H2awh8DWYFhJYAS6yLdtchYB8KaiAUdYKtO6xzOBxWKJWZAZleK6Ryu8DtjIdvvqCf0kAlW31FlAUqqY0EcRoO0u0ecj3Zbfua9wFTmWzy7eDHkjUsK1nL8tK1rCzfQCjxbnVRnV2ZDOrUlwHZfRmY3YeBnfqS6+7470GbalZoVe/OgSocH8plnSNWE8sWrWvlssIrzZYwNDAxuDpAA8LdqB82hcxQUtiEIt6rKTFosht20l1Wrya3zY3DcDQImey6NZxOgibRWl577TUALrzwwqT1s2fP5qyzzsJut7Nw4UJeeukl/H4/Xbt25aSTTuLqq6+O72sYBk8++SS33347kydPxu12c+aZZzJ9+vT4Pj179uSpp55i9uzZvPTSS3Tp0oW77rqLY445pm1eqBBCCCHEXpJQan9kS4OMAVC6xLpIrtWsQKqoFLoeBEaKL7w0GziHWpO6GELrrALpgW8hvAlCK60JwMiB9GHgGGoN8wtGYMt22BC2ekw5HPidUJqms90WpJga/LqJy51GhstLnq1Tal/rXioPVPFjyRprKl3DspI1VIX8DfbLcKQxqNPBDOrU15pn9yXP07Ffe4ewu+GBygQVDa7MMIQaGSKoadEwyoiGVC4wnPWCq1iPq+h8PwtP6vdoCkVCDXo21Q+bnDYnWbYs3HYrbIqFS/VDJ7nDnEiFlStXNrm9a9euvPzyy7s9T/fu3Xn66aeb3OeII47g3XffbUnzhBBCCCHaDQml9lfuLuDtD1u+hnU+KK2CrnmpD6Tq03Rw9LOm9F9CeGe0DtV3EFwBkWLw/9OaMMBRAN6hhGwDKTEz2F69k51lpVRv9+MwNTIMF7nONHD6Ia3aGqLodILTbs1t7be2S9iMsLpiEz8Ur2ZpyWqWlaxho297g/2chp2CrN4M7tyPwZ36MbjTwfTw5smFd3uk6dEAyb7r3lYqEg2pwtY8VAWhsnrBFdGaVdEeV1psqKAzoaeVLaHWlQ0wUt7rylRmg6Ap1sspdgc6FFZ4FK3XZDfseJ1e3DY3afa0pJ5MiaHTflHzTAghhBBCiAOcfKvfn6lcWF0FRRuhV0H7C6QaY8sD24mQdqJVKD24HIJLIbAUIkXRx8uxA9m4CWu9cLn7E0zPJ2RkWz1TgmEIha2eYdujF/Z2G9jt1nA/rwecjrrJYW+Tu/7VVx6o4oeS1SwtXsXSktUsL11LTTjQYL/e6V0p7NyPIdEQ6pCsnnJBvj/RDDAMwNFEcJXY4yoCZrS+VYOhgtGeW5rNWtad0eDKWTc8MB5e2ep6aO1Bz6tYYfDEYXSxKRY4xXs3GVbRb4/dU1e3yeaKh00OwxEPnKRnkxBCCCGEEAcOubLdX/l88MNyCGVC9x4QqQSjg9UT0l3gGkmVbSAl9lMorl2DK7ScHLWeHLUFJzX0UCshvBLC86nVOlFl9KXK6IvP3oeQN6vuXOEwBENQ6YPScuIFaOx2K7DyuKO9qhKCqlYMq5RSrK/ayvfFq/i+eBVLi1exoWpbg/28dk80gOrPkM79GdzpYDKd3lZpg+jAmtPjChJqXEWsKVQVHS5Yr+aYbgB63ZBBzR4Nr1xg2IkojZBShIGwUoSUNQ8rZfW+UqDrevwOc3bDTpo9DY/DY92RzuZM6tUUC53kLnRCCCGEEEKIRBJK7Y/8fvjhBygpgV79IVgKlSusu4cZnlS3rlmCZpiSsI+tgTKKQpX4zSBpehqZzmMJ6OPYqsKkmZvJiKwhPbKWNHMLLlWKK1xKbvhrAGq1TviMPlTpffAZvQm6s6zwKSaxV1V5pdWzCqyhUrGwyu2EtGjPKrstGlY5rGGATfTmCEZCLC9dx/fFP/Nd8c8sLV5FRdDXYL8+6d0YmtOfoZ0PYUhOf/pmdJNiy2LPxWtc7ZqpTELhAKFIgHAkQDhcRcgMEo4Erf8TmOiajl0zsBs2bLoDr91Dmt3q4eSwe3HY07HbPTjsXuyGC4c9DS02bDDeI2v/q30lhBBCCCGEaF0SSu1vamutQGr7duje3erp48qBcE/wrQWX0+od0Q4ppSgP+9kZqmBzoIyqSA02zSDTcJNrz0jeV7PhM/rgM/oAJ6CrAN7IBtIja0k31+Ext8dDqhy+ASCoZeDTe+MzeuHTe1Gj59X1jEpkmlZQFQpDhQ9KykFFx0jZbdZQK7vduhOgxw12G1V6mO8rN/B9xVq+LV3F8tJ1BM1Q0mmdhoPBnQ5mWM4hDM05hCGd+5PlTN83b6Y4ICmlCJlhwmaEkBmOLluPrU+wQtd0bLqBXbdht6XhdXXGY3Phsbtw6DYchh2Hbseu23DoBnZNQ4v1vIoVbFc1Vi+sYGLNM80KxTQjoXC7PaFwuyv6OHaXwV0NJZShe0IIIYQQQhwoJJTanwSDsGwZbN0K3bpF69REeXpE78RXBK68dnXhFzBDFIeq2BwoozhUSVBFyDBcdHNkYzSzp4WpOam05VNpywdAV7VWSGWuJz2yHo+5DYeqpFPkBzpFfgAggoNqvTvVRk+q9R5UGz0Ia14ryGssrEJBOALhCMU1ZXxbvJFvazbxXXALq0LFSaV9ADrZvQzLOpjhnfozLCefgs59sDvdKalfJfYP4Xgdp9hkhU9KmYCGphEtFm7DrtnIcKRZgZPNhdPmSA6dDBsO3d569ZuUqguuVMQKryK11s+d+PrE/yWa1ZMqVpRdj4ZZhhN0RzTMctQLrWx1+8XX71lNLCGEEEIIIUTqSSi1vwiFrEBqwwarh5St3j+tbgNvX+siMVgCzpzUtDMq1itqR6iCLYEyKiM1ODQbWbY0XLp9r89vai4qbQVUUgCAroJ4zM14IxtJj2wgzdyMQYAMcx0Z5rr4cQEtKxpQdcevd8OvdyWCky3BMr71beCb6vV859vApmBpg+fs5ejEMFcPRji7M8zehV5aBhpAtQY1Qdi2xvp3cTjA44wOCYwOE7TbrG12m4RWByjrTnWJPZys5YiKxEugWcPqrNDJZXPSORo4uWxOHLrdCpyiYZPDsLftUFBNq7tDYHPEQ6zEKQyh2ro7EiqTeCV3TbOO0YyEgu4JYVYsyNKj9bHi9bISe27FHhvSM0sIIYQQHVbEVBi6fIcR+wcJpfYHkQj89BOsWwdduzYMpGJsHkjvBxUrIFQB9sy2bSdWrajiUBVbgqXsDFURCofIDuocXAv2aj9GdQmGP4BRG0SvCWLUBtBrQ+jBEFowjB4IoYcjaIlTJAKmQlOqbh6lYhecuo4yNJSuEzEcVBj90G1hbEYNhuHHbvNj2Gtw2stx2MvZmfYj/0qHf7vhXw6NLXpyPygNOMTVhRHe3ozw9mF4Wi9y7E0MxYtEor2swlDth4pKa5igFj2bYVh1qgzDCq1cDmuoZWJYZRjRx3Ih3REl9nIKJgytUwmZi123xSev00Oa3QqdYr2b6ua2jn8HxpaGWFAXZGFGQ6voZCb2yIpEe2RFk7zYXNPqAql4kXc9IdByRIcXRufx8KqpSZdgSwghhBBtztA1rn/9W1bvbFiztiM4riCX3548INXNEO1EB7+qEZgm/PwzrF4NBx1kBRpNcWRZwVTlSusizpbW+m1SKlo4vAyKS6G0nEBxCbUlRQRLSnBU+BhQVcvwqlpsvho0s/7At7ZlarAsF/7VB/7VG/7dE3Ym3fBOYYvAqK1w7AZrOmoTZNVux3QVYXqWEna7CKelEUlzxaew120te92EvW7C6W4iXg/hdDfhdC/KaY+fn7BpBVehaGhVWQWRaC8RpayLZ5sNDN0KpZzOuiGGiYFVYnAlPa7aTKyWU2O9nFQ0dbJFwyabbuCxOUmzZ5Fmc+M0HNGwKTq0Lho8tdqwuv1JPMgCWvrxVmZCaGXWC7T80eGFVqH35GGGic8dGyoYC6RioZQtuV5WUrCl7yLISlhP4jr5dxdCCCHE7q3e6WPZ1spUN2OP9MvdB9egosOSUKojUwrWrIGVKyE3F1yu5h3nyoNwAHxronfLcrb8ecsqYesO2LoTthfBjuLovMQKokLJt6B3RqddMR02K8hJc2G6nUSik+l2YDrtmA57dG5D2W0om4FpM1DR8EXpVi8o9GiPiNiQH0W895QWMcE0UWaEn7RyFtmKWeQsZZGzjDJbclFyV0TjsDIXR++0c9wWGL0ljNcXQq8No9UqiL48vTaCXluDjRqgrEVvo+mwE86IhlQZHsIZaYTTPdFl63Eo00M400vY6yDsir6D4QhU+qxAMmLG/lGs1x3rbWXo1tBApwOcdmvZMKxgK7aPLaF3lmhSxDSjgVOoWbWcspxuPDY3afb9tJdTRxQLkdjD4cHKTAizTCAh3DJDVsgfX2cm9NaibuhhbJmEQItY2KUlBFyxGlrRoCtp2GHisXryY01PPnfSYwm7hBBCCCFE+yNXRh2VUtZwveXLITsb3O6WHZ/WHcwA+DeCK7eu90GiQBA2bYONW+vmm7dbYZS/drdPEcz24s/2UJPthewMVKdMwlleQplp0TDGmke8LpRj7+tI7UpEmayq2c4S3zq+8W3g2+r1VEWS2+/S7QxL68XItD6M9PZmsKcHjoTgYHXCvoaqwRPYQlr1FtzV23D7duKsLkX3m+DHmqrr5ma1jqq2oXw6ms9E94XQIgo9GMJRXIGjuKJZr0NpmvW+ZaZZ72F0CmV5o+usgCvk9RDyulDhCPj8Vg8spRJGMtULrwybFV65Yr2uYsFVtFdW4uP9sPeVUiqpllPiHetMFBpg6Ea8lpPTcNDJaQVO9Ws5OQ0Hdt3WtrWcRNuIh1p7+WszcQhiLMDCTA64Ignbmuq9hZYwSwio0IkXkk+aEmpsNbjzoZ4cYiUeT73zNLmPhF9CCCGEEKL5JJTqiGKB1LJlkJEBXu/uj6lP08Hbx7pDVu1Wq/D5dz/Dd8th3WbYsMXqBdXohRDWhUdeZ+iWB11yoUsONbmZlHRysiFDUZRp4LS7ybZ5sLdxr5CwivBzzXa+8a3na986vvVtxGcmh1Ae3cHwtF6M9PblUG8fBnq6YdOa12MoormpcvWnytUfOkdXqghOVYrb3Inb3IHb3InL3IlLlaJjAsG6EyigBkyfTtCXTrg6nbDPjelzoKoMqAS9Moit0o+90o+tshpbVQ2aUtgrqrFXVNOcCDLicliBVVY0uMryEoqGgqFMN+H0NEJeFyGvG2Wvtepdxf65YwFWLIgy9LqhgXa7FWDZbMlDCo2EoKsd9cCK9XJKvmtdGLORXk42zSDDkUaazU2aw510t7rEu9YJsccShyC2lnh4pRLmsdpasd5dYVC1ySFXk4FXUqOj7dbrlhODL3TrZ0Z8OVpUnnp3V4zfZTGhx1fSORJ6kcUDL63etug81oNMCCGEEEJ0aPKNrqNJDKS8XiuU2lO6DbwHwxdfw5xHYdWmhvuke6F3N+jZFXp1hR5doUcXK4hy2DGVSWm4mq3BMrYFy/FHgmQYLrob7jbrLRJWEVbWbOdr3zq+8a3nW98Gqs1A0j5pupPh3l4c6u3LyLQ+DPB0bXYI1SyaQUDLJaDnUs7gutUqjFOVREOqYmtSRbg8xeieMK68CqBhTykTGwEtm1q9EwGtPwEzi4jPQ6TKCq6M8gD2ymps5T7sFfXm5dXooTBGbRBjeylsb3inwPoibiehbG9yeJWdRijTSzjDTSjdQyhaF0vpmtX7ykzofYVKDq/0hBDLYQdHLMCKhlix/eLzPeuJ1VQvJ+syW1l3rNOt0Mlh2Ml2ZjTayykld6wTojXEA559JDYUsUHwlVD3LtbbiwiYwWhvr9j6+scnhGCaVvczpO4HirUc7wGm0SAMc+VBpxH77jULIYQQQog2IaFUR9KagVQkAp98As89B6tWWeucdhh7OBzSF/r0sKbsjEaHY4TMMEWBMjYFStkZqsBEkW2kkeNs4g50rSSsIvzk38bXvnV87VvP99UbGw2hRnp7M9Lbh5HevhS4u7RuCNVMSrNRqx1ErX5QvQ0mdlWJS5XgMotxmiW4VAlOsxSnKkMnjFsV4Y4U1R3jiU4HQYg0gnoWAS2LoJ6FX+tKUMskqGUR1DJQfs3qVVXuwxabl/uwlyWEV2U+7BU+9GAYoyaAURPAtbVkt68pnO62QqvEACsrOpQww0MoI41wuotQmssaAmqadUMIY8ONEmtgaXpCj6xYbywbOGyEdY2wrhHSTEKais9V9BjN0LDZHNgNO3bNRqbDi8fmkl5OQrSmeCjUxoFtUoiVEGoFy63h50IIIYQQosOTK7SOorUCqXAY3n8fXngBNmyw1nk8cM7ZMOkIcPutO/QZjQ8Qq4kE2RGqYENtCWVhH3bNRo4tPan+UmsLmWGW12zlG996vomGUH4zmLRPuuFieFpvDvX24VBvX/LdXTDac48XTSekZREiiyqjX/I2FcGhKnCqUpxmWXRuhVUOsxwbtdipxm5Wk8YWiDQ8fURzEMzOINQpk6CeQVDLIKRl4Nd6WMt6OmE8gIbuD2Av9yUHV+U+7GVV0XXV8e1axMRWVYOtqgb3pqKGT5z4MjTNKuIeqyOWGGBleanNcFGb7qbWa6c6zUFQV4Rrw0TMsNXLwlTYNA07BjYMnJpOJ8NBms2NS3fgsDtw2Bw4DHA4bDicLnSn0+qZZQcMBUbEmttMMEINhxjqUgNHiHYr9jO8/n/R1h7+KIQQQgghUka+2XUEkYjVm2nlSiuM2pNAqrYW5s2Dl16CbdusdRkZMHky/OpXkJkJkSBUb4TATghWgD0dbNbtOivCfrYHy9kUKKUqUotHd9DNkb1Pgp+AGeJH/2a+9W2IhlCbCKjku+NlGG5GREOokd6+HOI+qH2HUC2hGQS1TgTpRFUjnbsMVYPDLLdCKlWOw6yw5qoCh1mBnWoMgrhVMW5VDGbDcwAodEKal5CeTqhzOqGcdOuxlk611oOQ5iWseQlpaSjNDqaJ4autF1hZwwVt0dAqHm5VVKOZCnu0LlZzamCF01xEsryorAzMrHTIzkDLzsTIysTolI2RnQWZ6eBNA7fL6oEVuwNhTRh8FQl3JFTR0UDROVrdsEHdsO7SGOudZbcn1Mqyg82evK/NaDgsMXEIooRaQgghhBBCCLFHJJRq7wIBWLHC6iWVk2P1amqJ8nJ46y144w0oK7PWdeoEU6bAOedAWlrdvoYDMvpDuAvUFmP6t1Lq38bmSIBtZpBaFSbT8NDT0QmtFS/EqyMBfqjexDfVVj2oH/2bCank7j9ZhocR3j7REKoP/V15B2ztn4jmpsZwU0PXRrdrKoRDVWJXFTjMSiusUpXYo5NDVWFT1WiYOFQlDlW5++fEaQVW9jRCeR6CeR5qceHHTQ2Z1Kg8ajQXQdwEcaFhx+ML4KkI4KmowVtRi6eyFndFTXz4oFHuQy+rRCuvQjNNbNW12KprYUvx7t8Em2EFVJnpkJWRMCWsy0ive5yeZoVTkUhymBUxIViTvD5W70Yl1LjRtLqaWYnDDfVoj6tY7axYuBUr9B4LsXQtuW6WnnAOCbeEEEIIIYQQBygJpdqz6mr48UfYsgW6dAGns/nHbtpkBVHvvmv1kgLo2hUuuABOPx1crl0eGtZdFNm8bNSd7IyEMIMVdNI08gxn9Pbhe3fxXBau5jvfBr6t3sB3vg2srNlOpF53ns42b7QeVB9GpvWhryvngA2hWkppdgJaZwJ0hl2V0VIR7MqHXVXFJ5tZiaGqouutyYEfgwgGAQwVwKV2X3MKQOFEpXkh3YvWKx1NTwPdC1oW6N1BTwMtzZorN/h1qAhDeQ2UV0JZRcK8ylqOPfbXQjgCJeXW1By6Bhne5KAqIzrP9EYfJ8wzvJDmqfusK1UXZJlmcrBVG7balLguVgMHSAq2NK0ugNL15B5bhmEVg4/V1LIZVsAVC7R0zeq5ZdQLtRoLuWRYohBCCCGEEKIDkFCqvdqxA376CUpLoXt362J1d5SCRYusMOq//63r8ZGfDxddBOPHN3me2kiAHYFSNtZspzRYgU0zyMnoj0PTIVQFwTIIV1rLhgMMF2j23TRJsTlYyne+jXxXvYHvqjeyIdCwJ0w3RxYj0vowwtubEWm96eXs3Kq9sQ5kpjIJK5OwiiTNQ8rExA240TjIqjuu27BpOnYMnIYNt2YnXVe48eNUfuz4cagaDOXDpnzoygdmFZiV0bkPMNEIoJkBMJsXYgHgArrYoZsnGlh5QPOA7gatR93jkAN8GlSYUBmxwqzKIFQGoNwPFdVWkFVRCRVVUF1j3SmwvMqaNjazPbpeF1DVn9LTrDtTZnghI7qcnmZNblfjgZBSVjvM6F3KonWz4suhMJi1yaGXihZ51rTkOZp1E7LYUEQtGnDpiXPd+v8e68lliw5FtCUEXUnhmJF8bHyqt03+XwohhBBCCCFaiYRS7U0wCGvXwurV1gVgz567vwgsLYUFC6xeUevX160/6ig4/3w44ogmz1ER8rE9UMymmh1Uhf14DBddXDnJd6tzZIEjEyI1EKqGUBmE/WCGowGVEzQHQTPMypptfF+9ke+rN7K0ehMlYV+D5+znymN4Wm9GeHszPK03XRyZLXqbBEQaCZpiczPe80xDA2y6gU3TsWHgNhy4dTtu3YlLt+PQbdg1w5qUhkOzYUdDV9EePqaqC0fqS/xcxZb1AFBtTaYPlM+ax5f9CY+rrceq2nouQmBWABWNFnCPswM50akBAzQ36C7QciDiBJ8NKg0rzPJpUAlURQMtXxiqQlaoVVkLlTUQCFmhUKyHVksYBqR7rNpX6V5rOT0t+jhxHtsnOk/zgMdl/b9vjlhoFeuZZZp1oVcs5KoNJARhql4vroQeXLF/23jApScHVlq0R1d8GGO0V5ctIfiKFZFvEGxpDZe1RtYlbpPgSwghhBBCiAOChFLtSWmpVcx861bIzU2u91RfOAwLF8Lf/w7//rdVEwesYyZNgl/+Enr33vXhZpiSUAVba4vYXltCQAXJMLz0dB3URA8lDQyPNbk6Q7iGIv8WllYs5wffepb6t/JT7U6C9epB2TWDQZ7uDE/rxbC0XgxN60WWrYW1sQ4QsV5N8cCJCJFIhLAZIWyGUcQCBtCVhg0dmwIbOh4M3JoNNw5cGNjRrUnp8WWHZmBoBkmhBNE7GWqatSoeSmDto2l1wUR98fpLsRcQCz8MUOmgvNTdOithCFvikDOHEe31EwQtAHotUGNNph9UdDJrkueqJrpcay2r6DBVIlb4FUkIQ9OiU3MFAR9QFZ37gGob+Azw6VCtRdcr8JlQbUJVGMLK+r8Y65XVUpoGae66wCrNbYVVaR7wxpYT1nncCY/ddY+bG2zVV783l5kQdsVCrVC4bj+VEHiZ0bArsRZX4r99rHdX4udLSwi99HrLhpEcdtmi9bsMW3LQ1dixjW2PfY4lABNCCCGEEKLdkFCqPfD5YMMG2LgRQiGrd5TRSDGgcBgWL4aPP4bPPoPKhB4cgwfDaafBySeD17vLp6oKV1MUKGNT7Q7KQ1Xomk62LZ08o9Num1kbCfCTbz0/Vq3hR98afqhaw45Aw+FZWYaboe6uDPN0Y5inOwPTeuG0p2Fdke5vYhfxCohdvBO/WFcRkwgRwpGIFTSZYSLKJBINmSLKRCkVvYbX0HSwYWDDCpzsmk667sBlOHAbHqsXk2HHbtiwaQZ2w4Zdt2O32TH0aO8VQ6+7mLfVK7at1w8EdnExH7tYj1/Q0zDoSCoIbr0VdXWVItFC4tF5bIhaxIRwCIJhK9wIhqzPfCQCQRuENVBOiKRF39N6gVa8eLie8NoSXpMetCZVGw2sakAFEh7X1j2OTwFrMhOWnQFwBKBT4l0fw9Fp1x+FeJhVnTDVf1wN+KNT4uNQ9L30+a2JZhR8b4wGuG3W5LFbk9sOHmfd3OO0hhm6nVaQ5XFZjz1u62YKbhd40sDtAacDq/eZLTp3WnP2YChfYoClEsKuxMemadUMMwN1nylVLwBT0Tdc063jdhd+US+4SgxaE++oGCtcr0dDMEOvC8G0ev83Ggu6Gtun/vrEx0IIIYRocxFTYejyRykh2gsJpVKpthY2b7burOfzWXfFqx8oFRfDl19avaIWLkwOojp1gl/8wgqj+vff5dPURGopDVayPVBCUbAMf6QWr+Ghi7PeEL0EITPMGv9mlvvWsrxqHct9a1lTvblBQXIdjf5pPRmS3t+aMvrTy5GDpmohXAvhKgjXQKjSuqDUAN0enRzs+6Cq3vCzxB4eKjFEMutti14AQ8KwNY0IEUwFERUhrCkiKMKYRDSIaIqIUqjoRacWvTA1dAPDZmCzOzAMN06bHbfdhdPuxG13Y7fZsBsObLG5EQ2ebA4Mw0CLBTGN9QLZX+r8hMNWEBEPsiLW43AEzITlYNAaWhcKW8uRCATCDYMvzQZ4gXTr31dPCB/id7zbRaHwxPdSRUAF68Kq+HLQmghY4QlBUCFrW6foNhWK7lPvsQoC4Xrroq+nhoahVWy5ponl2GMT6/X6w9ZUUstes2PV+3IBznpzlwZODVx6dG5Yk1OvW3YY4IyGZA5bdLvdWqfbsMKtaMiFYYVJWjQUwqhbFw/C9OR9G1tGA6VF57q1rIiu063lsF63j6nV7YNO/MecqdWdS9Prjtei540vR7fH2q5FQ+DEMAySA+FY0GvUC1djAXL9QLl+4FU/MK4feCXul7gt8bjEfYUQQogDhKFrXP/6t6ze2bDESEdwXEEuvz15QKqbIUSrkVCqrSkFFRVWIfPNm63lrCzo1cvaXlwM330HS5fCkiXw88/Jx3fuDMcfbxUtHzGi8R5VWEFUecjHzkAZO4MlVEdqsWOQYfeS68hO2rc2EmC1fxOrqjfxk289K3zrWVW9kZBq2Cskx5FFobcfhen9KEzvz0BvH9Js7kZaYAdbOpBr1Z0yoxfv4RqIVEMkthyx3hMteiEZ65FhRi+SYkOC6gdLiaFR7H1FA01FLxyjQVL9izCsizMF0SAJIroiAkR0jYgOEV235oaB0o26iz5dQ9cMDMOGTdcxdBuGYcNjs+M0nNGQyYXdZsdm2LHZbNG5Hbtui9Z1MqweTSKZzda8Yv71hcMJPbDCdfWU4utjgVbICq+CQQhEwywzOhQtMdCK9cTRYgFFrOeNZvWe0dNAT08IuRKCLFu9kKullMIaehgNqwhZYVV8Ofo4cb0KYQVc0ZArELQKu/trwR9ImIJQE7ImfwhqwlATSZibUBudahTUqrqaXtGnp9HRiLH6VGbCzi3giE7O6OSot86xm8m+i8eNzW3Udahqrj3NzGM/YIj+kLG6biVMer15/fWxMIx62xs7ppHtWr11scBOq7+u3rKWGK5Fl3UjYb0WDd0SArek4/V6+ybupyevjx2jJzy3Xu/cseWkdmsQDkC3k/bwH0cIIYSA1Tt9LNtaufsd26F+uS2pSSFE+yehVFupqYGyMtiyBXbutC6Oa2th+3ZrON6qVbBihbW9voEDYfRoaxo6tNEgKqIi+MI1VIZ97AiUUhqqxB+uQdcMMmweejjzCJhBNtRsZ13NVtb7t7LOv4VV/k1sqtlh1SqqJ93wMCCtF4M9fRmc1odBnt7k2TLRYhfrpgm+ABBIHooT63VU18HIosD6yGVaQVX8IjuSPHwK05riQ3CigYXNbhVVtznAsKFsBhFDJ4KJqUEk1pNJg4imEdFMTKzAScWvaaxwSdN1DMMKlXTdwKYbGJqOS7fhMOw4dQcumyMeJhmatU980mzxZV2TYTgpY7Pt2U8xpeqGFoYjdSFV4pBDM1IXeIWivbNiU6xnVyzYMpW1f2INpsaKidevqdXoEDAddCdornrrYhfnTfACnffg/WhMKAw1tdbkr4kuV1s/y+JTrTWvrbWKqtcErHltAGqDUBuygrLakNXDrTYEwYSac9GOZLTFHyo1wK5ZkyO6bAMcmhVaxYIrR3RuA+wquk3VPbapun3tifsmLCfuawNskXrbiHfo2m2bW4OqN98fbP8Wuk9MdSuEEEIIIcReSlko9corr/Dss89SVFTEgAED+N///V+GDh2aqua0vnDY6g21cqUVNq1dC5s2WSHUjh1W+FRd3fA4TbOG4g0bBsOHw+GHW8P06gmaIfyRWnxhP2WhSoqDFdEhesWUh31UhaopC1WxtXYHm2t3srl2J8Whil02t5Ph5RBnN/Kd3Rjk7sVAdy+6OzpbQ8cSi10rVffXbps92lMkoX6Rpicv179dfXTZxOoMZWoKE2vom4lJRIUwzQCmChNRQUwzSCTsR0VqwQxGA68QqFqrX5Wmo2s6hq5jaA503Rr65jUc2A0HDsOF0+ayhsLp9mhPJR2bZmDTbRianhQ67brIu9ivaFpdoOXcw3MkBluxXlaJ9bRiIVcsrIqHYGEIRSASDbZC4WgoFg20IomFxM1or5tIXQ9B6wU07BVYvyh9YuBF/fXU/b+EenWQovvbDMjwWlNrMk0rqKoJRMP5oBViBQJWcBWIBlqJj+PzoFWHrDZgzWOPYz3ggtHwK7YuGKp7zxQQVNbUyI/elLBFf1bGirjXf2yP/jw1NLBH617Z9Lp6V7b6jzVr3/iyHn0c7clnaNERkXrCPLrOpteNfowvRz8P0VGUyY+1uv1j+8b/XqKSJ5U4N+t6ISqzke2NLZt151UKQkHgqH36TyOEEEIIIdpGSkKpBQsWMHv2bGbNmsWwYcN48cUXmTp1Kh988AGdO7fWn/lbgVLWhVB1Nfj9UFVlTZWV1ryszLpjXnGxNe3YYU07d0JJSeOhUyLDgD594JBDrKmgAAoLCXlc+EI+qoI+igNbKNr8A8XBcooDZewIlrItUEJxqJyyUBVl4SrKI9WUR6rxm4HdvqRMI42+7m70SetGH29P+mX14ZDsQ8jx5tbd5j0xRIpezJparFyNIqIpFBqmMhudIiqCUoqIikSPCEc7i2hWjywFmq5hYKDrVqikazqGZkfX3Nh1A4fuiBb0tuMwHNh1O4amYcPEwMRQ0TkKAxObimCYYQwVxIj1wFJmtCZQbIoNfaKuvkw8ZIsNG0kYqhIfjhKrVZM47CRxaIn0lDogJQZbrSEWasXDqYThhInbEgOsxrYlFpkPJwRhiYXCI8rqrQh15yJWXy3hcayGeOKQxngYlrANjbq7NVI3fLF+XaPYPno0MIkVV0/cFr8LZGJgtottJDze1d0hYwX1g8Hk5UC0yH6w3hTrFVf/cShWoD+x11zCvrHHsaAxHNseiS6Hk4ccQ12ttP2JrtWr3VavZtauarvFamfFtu2q9puuW3f3PEv+gCCEEEIIsT9ISSj1/PPPc+6553L22WcDMGvWLD777DPefvttrrjiilQ0KUnos0/4aOY5VNSUY6Ks4WA60WFhdcth3Xoc1hMmL4QyIVwAIR2CToNgmoug20nA4yDgdlDrslHr1Kmxa9SoIH7zO2rUYqrLAvj/FSDYSC2n5nIbLvLcOeS5c8h159DVexBdvF3pmt6Nrhnd8bozUFpdoKSUIqBMNhOMD+HTlIYK143z0KIXgjp1AVL9ya5bAZJNtzVYNqI9kaweTdayER32FltOnLdKbyUzGkKpsBVImeF6AVXCFEmo12PG5mGsv+hHe7rE/qIfWwfRecJf/evesbrHsWFb9dWvkZI4TqdBPZjE82r19kncRvLFenxdvX3qz3f5fje2fVfL9Q9tar9GjpMeantei6ol4iFWQl22WK+sxLvcxbdR7455iYFV4r6qrudYvMdYtIdLYriW2KMsqUacGU29E3qDxYYAx9cl9HhKPDYeplG3D5D0fycepkXfZ7cbXK66/eP/Txv5PxP/71Iv+GosCEtc19g5YncWjPeYiwZV4TCEY3XRokFVLMyKB4zhuqGmiTcCiISTh6HW3ydxmGr8+esdkxRiJu6XuN2sa1t8iGojzITAc1+yu+Cmff80QgghhBBi32rzUCoYDLJs2TKuvPLK+Dpd1znqqKP49ttv27o5jfrripe59pSyVjpbhLr7vifYzd3lAWy6jTSbB4/dQ5o9jTSHh3RHOumuTDKcGWQ4M8h2ZdPJ3YlO7k5ku7LxOrxouoaOjoZ1BzhN0xJ6I8WGrUVDoGh9JLthjwdFifvGlxO27Wp9u6LHxpvs6dgs6i6WVQQrfEoIpeKBlEpeThqSEguyVOPbks6TcP7E7UnHm/Xm1G1PbC91q+oWEvZJ3iHxBTeyLWG5sXCtyX3q79/I8Q1W1V+hNbbTHqxn1wFhc45N2ofd71c/rGjyeeufO35QM45pxeMb+y+8u7CwweuK/b9rpqRgioTAKXbuxPCrkf0Th3YlbafhfrCLbfXOG5MYvMQCuviyqqsbtqvXkHhc7LGmwFCgK6uuVPweEdH3TdnqXkvi+9vY56f+tgb/55MXGxzX6LH1n6uJc8V79CWEVPGefAmPY73+EkPLSDSEjG+Lvk/h2B8CYv/uiUNjo+9xLNQ8bUIjL+7Atd+XRBBCCCHEfqvNQ6mysjIikUiDYXqdO3dm7dq1bd2cRp3wy5v5xf+to6yqCM1mQzOsydANNE2PDzkzNANdNzB0Wzygsek2K/SJDkFL7DkUG4rmMBy4bC7cNjdpjjS8Di9ehxe3zR1fTnOk4TScVqgUC5fqzXVNT1qna3rS+vqPpV5SC8XuNNWSi+y2lhgyNQicdhVENXah20gg1WiA1ZwAqt7Va4ML6iYusJvap6n1TYY+uwtndrO9WYFSS56vtY5JPHwvj2/8pPvgnG15/n2gNd/nBqFSc+fNOEeL1jVxvvrrm1quf7JdPcfuzt/cfbzZDY85QHWYkghCCCGEEI2Qu+81YkDOAN6f+mmqmyHE7tWvrSOEEOKA0t5LIggh9i8RU2Ho8qVTCNF62jyUys7OxjAMSkpKktaXlJSQk5PTrHOo6F9Mfb62uI+4EEIIIQ40aWlp7b6HcWuURJDvVM2zP1yIy2toH/aH1/DkZ2vYWlGT6mbskSE9MvnloT3pk6FjBu2pbs4eOcht/cyW15Ba+8Nr6JOht8nv/919p2rzUMrhcDB48GAWLlzI+PHjATBNk4ULF3LBBRc06xzV0bvajR07dp+1UwghhBAHrq+//hqv15vqZjSpNUoiyHcqIcSBZD5wb6obsZfWAi+luhF7SV5D+7AWOHT2vn+e3X2nSsnwvUsvvZSZM2dSWFjI0KFDefHFF6mpqeGss85q1vF5eXn861//6hB/xRRCCCFEx5OWlpbqJrQJ+U4lhBBCiH1pd9+pUhJKnXLKKZSWlvLII49QVFTEwIEDeeaZZ5o9fE/Xdbp06bKPWymEEEII0X61RkkE+U4lhBBCiFRKWaHzCy64oNnD9YQQQgghRLLWKIkghBBCCJFKcvc9IYQQQogOam9LIgghhBBCpJKEUkIIIYQQHdTelkQQQgghhEglTcXuBSyEEEIIIYQQQgghRBvRU90AIYQQQgghhBBCCHHgkVBKCCGEEEIIIYQQQrQ5CaWEEEIIIYQQQgghRJuTUEoIIYQQQgghhBBCtDkJpRrxyiuvMG7cOIYMGcIvf/lLli5dmuomdShPPfUUZ599NiNGjGD06NFcffXVrF27NtXN6vD++te/UlBQwN13353qpnQ4O3bsYMaMGRxxxBEMHTqUSZMm8cMPP6S6WR1KJBLhz3/+M+PGjWPo0KGMHz+exx9/HLlXxu599dVXXHXVVYwZM4aCggI+/vjjpO1KKR5++GHGjBnD0KFDueSSS1i/fn1qGtuONfU+hkIh7r//fiZNmsTw4cMZM2YMN998Mzt27Ehhi0VbevTRRykoKEiafvGLX6S6WfsF+RnWtnb3ft9yyy0NPutTp05NUWs7ruZcrwQCAWbNmsURRxzBiBEjuO666yguLk5Rizuu5rzXF154YYPP9R/+8IcUtbjjevXVV5k0aRIjR45k5MiRTJ48mX/961/x7e31My2hVD0LFixg9uzZXHPNNcydO5cBAwYwdepUSkpKUt20DmPx4sVMmTKFN998k+eff55wOMzUqVPx+/2pblqHtXTpUl5//XUKCgpS3ZQOp6KigvPOOw+73c7TTz/N/PnzmTlzJpmZmaluWofy9NNP89prr/GHP/yBBQsWMGPGDJ555hnmzJmT6qa1e36/n4KCAm677bZGtz/99NPMmTOH22+/nTfffBO3283UqVMJBAJt3NL2ran3sba2luXLlzNt2jTeeecdHnvsMdatW8e0adNS0FKRKocccgj//e9/49Orr76a6ibtF+RnWNva3fsNcMwxxyR91h966KE2bOH+oTnXK/fccw+ffvopf/7zn5kzZw47d+7k2muvTWGrO6bmXhuee+65SZ/rm2++OUUt7ri6dOnCjBkzeOedd3j77bc58sgjueaaa1i1ahXQjj/TSiQ555xz1KxZs+KPI5GIGjNmjHrqqadS2KqOraSkROXn56vFixenuikdks/nUyeddJL6/PPP1QUXXKDuuuuuVDepQ7n//vvVeeedl+pmdHhXXHGF+t3vfpe07tprr1U33XRTilrUMeXn56uPPvoo/tg0TXX00UerZ555Jr6usrJSFRYWqvfeey8VTewQ6r+Pjfn+++9Vfn6+2rJlSxu1SqTSI488ok477bRUN2O/Jz/D2lZjP+tmzpyppk2blqIW7b/qX69UVlaqwYMHq/fffz++z+rVq1V+fr769ttvU9TK/UNj14ZyjbPvjBo1Sr355pvt+jMtPaUSBINBli1bxlFHHRVfp+s6Rx11FN9++20KW9axVVVVAUjPlD10xx13MHbs2KTPpWi+Tz75hMLCQqZPn87o0aM544wzePPNN1PdrA5nxIgRfPnll6xbtw6An376ia+//ppjjz02xS3r2DZv3kxRUVHS/+/09HSGDRsmv3f2ks/nQ9M0MjIyUt0U0UY2bNjAmDFjOOGEE7jpppvYunVrqpu035OfYamxePFiRo8ezcknn8xtt91GWVlZqpvU4dW/Xvnxxx8JhUJJn+1+/frRrVs3vvvuu1Q0cb+xq2vDefPmccQRRzBx4kQefPBBampqUtG8/UYkEmH+/Pn4/X5GjBjRrj/TtpQ+eztTVlZGJBKhc+fOSes7d+4sNZH2kGma3HPPPYwcOZL8/PxUN6fDmT9/PsuXL+ett95KdVM6rE2bNvHaa69x6aWXctVVV/HDDz9w1113YbfbOfPMM1PdvA7jiiuuwOfzMWHCBAzDIBKJcMMNN3DaaaelumkdWlFREUCjv3fawxj/jioQCPDAAw9w6qmn4vV6U90c0QaGDh3K7Nmz6du3L0VFRTz++ONMmTKFefPmyWdgH5KfYW3vmGOO4cQTT6RHjx5s2rSJhx56iMsvv5w33ngDwzBS3bwOqbHrleLiYux2e4M/bHTu3Dn+uRctt6trw4kTJ9KtWzfy8vJYuXIlDzzwAOvWreOxxx5LYWs7ppUrV/KrX/2KQCCAx+Ph8ccfp3///qxYsaLdfqYllBL71KxZs1i1apXUddgD27Zt4+677+a5557D6XSmujkdllKKwsJCbrzxRgAGDRrEqlWreP311yWUaoH333+fefPm8eCDD8Z/sc2ePZu8vDx5H0W7EgqFuP7661FKMWvWrFQ3R7SRsWPHxpcHDBjAsGHDOP7443n//ff55S9/mcKWCdG6Tj311PhyrCD0+PHj472nRMvJ9Urb2dV7PXny5PhyQUEBubm5XHLJJWzcuJFevXq1dTM7tL59+/Luu+9SVVXFhx9+yMyZM3n55ZdT3awmyfC9BNnZ2RiG0aCoeUlJCTk5OSlqVcd1xx138Nlnn/Hiiy/SpUuXVDenw1m2bBklJSWcddZZDBo0iEGDBrF48WLmzJnDoEGDiEQiqW5ih5Cbm0u/fv2S1h188MEyrKOF7rvvPq644gpOPfVUCgoKOOOMM7j44ot56qmnUt20Di03NxdAfu+0klAoxG9+8xu2bt3Kc889Jz1kDmAZGRn06dOHjRs3prop+zX5GZZ6PXv2JDs7mw0bNqS6KR3Srq5XcnJyCIVCVFZWJu1fUlIS/9yLlmnJteGwYcMA5HO9BxwOB71796awsJCbbrqJAQMG8NJLL7Xrz7SEUgkcDgeDBw9m4cKF8XWmabJw4UJGjBiRwpZ1LEop7rjjDj766CNefPFFevbsmeomdUhHHnkk8+bN4913341PhYWFTJo0iXfffVe6aDfTyJEj43WQYtavX0/37t1T1KKOqba2Fk3TktYZhoFSKkUt2j/06NGD3NzcpN87Pp+P77//Xn7vtFAskNqwYQMvvPAC2dnZqW6SSKHq6mo2bdqU8i/a+zv5GZZ627dvp7y8XD7rLbS765XCwkLsdnvSZ3vt2rVs3bqV4cOHt3FrO7Y9uTZcsWIFgHyuW4FpmgSDwXb9mZbhe/VceumlzJw5k8LCQoYOHcqLL75ITU0NZ511Vqqb1mHMmjWL9957j7/85S+kpaXFx6imp6fjcrlS3LqOw+v1NqjD5fF4yMrKkvpcLXDxxRdz3nnn8eSTTzJhwgSWLl3Km2++yR133JHqpnUoxx9/PE8++STdunWLD997/vnnOfvss1PdtHavuro6qbfG5s2bWbFiBZmZmXTr1o2LLrqIJ554gt69e9OjRw8efvhh8vLyGD9+fApb3f409T7m5uYyffp0li9fzlNPPUUkEon/7snMzMThcKSq2aKN/PGPf+T444+nW7du7Ny5k0cffRRd15k4cWKqm9bhyc+wttXU+52Zmcljjz3GySefTE5ODps2beL++++nd+/eHHPMMSlsdcezu+uV9PR0zj77bO69914yMzPxer3cddddjBgxIuUX8B3N7t7rjRs3Mm/ePMaOHUtWVhYrV65k9uzZjBo1igEDBqS49R3Lgw8+yLHHHkvXrl2prq7mvffeY/HixTz77LPt+jOtKfkzdwMvv/wyzz77LEVFRQwcOJBbb7013oVQ7F5BQUGj62fPni3h3l668MILGTBgAP/zP/+T6qZ0KJ9++ikPPfQQ69evp0ePHlx66aWce+65qW5Wh+Lz+Xj44Yf5+OOP/5+9+46Pos7/OP6amW3pPSBFQDShVxsIoojyQ8SCHpwiNmwoYj1FzzvEBudZQU8RsXOWU/EOQbGc53mKKDasCCiEnl42m60zvz9md7KbbCCBkAKfJ495zMx3yn53s2R33vl+v0NJSQm5ubmMHz+eq6++Wi7492D16tVccMEF9crPOuss5s2bh2EYzJ8/n1dffZXKykqGDh3K7Nmz6dGjRyvUtu3a3es4Y8YMTjrppLjHPf/88xxzzDH7u3qilV1//fV88cUXlJeXk5mZydChQ7n++utlLJJmIL/DWtbuXu877riDq6++mh9//JGqqipyc3M57rjjuPbaa6W7ZBM15nrF5/Mxb948li9fjt/vZ8SIEcyePVta7zTRnl7rHTt28Ic//IH169fj8Xg45JBDGDNmDFdddZV0w2+i2267jc8++4zCwkJSUlLIz8/nsssu47jjjgPa7ntaQikhhBBCCCGEEEII0eJkTCkhhBBCCCGEEEII0eIklBJCCCGEEEIIIYQQLU5CKSGEEEIIIYQQQgjR4iSUEkIIIYQQQgghhBAtTkIpIYQQQgghhBBCCNHiJJQSQgghhBBCCCGEEC1OQikhhBBCCCGEEEII0eIklBJCCCGEEEIIIYQQLU5CKSFEmzdr1izy8/N58sknY8rff/998vPzW6lWQgghhBBCCCH2hYRSQoh2wel0smjRIioqKlq7KkIIIYQQQgghmoGEUkKIdmH48OFkZ2ezcOHCBvdZuXIl48ePp1+/fowePZqnn346Zvvo0aN54oknuPXWWxk8eDAnnHACr7zySsw+O3bs4Nprr+XII4/k6KOPZvr06WzdunW/PCchhBBCCCGEOJhJKCWEaBdUVeWGG27gxRdfZOfOnfW2f//991x33XWceuqpLFu2jBkzZvDII4/wxhtvxOz3zDPP0K9fP958803OO+887rjjDn799VcAAoEA06ZNIykpiSVLlvDSSy+RmJjIpZdeit/vb5HnKYQQQgghhBAHCwmlhBDtxsknn0zv3r2ZP39+vW3PPPMMw4YN4+qrr6ZHjx5MnDiRKVOmsHjx4pj9jj/+eKZMmUK3bt247LLLyMjIYPXq1QCsWLECXde55557yM/Pp2fPnsydO5cdO3bw+eeft8hzFEIIIYQQQoiDhYRSQoh25aabbuLNN99k48aNMeW//vorQ4YMiSkbMmQImzdvJhQKWWXRA6MrikJ2djYlJSUA/PzzzxQUFDBkyBAGDx7M4MGDOeaYY/D5fBQUFOzHZyWEEEIIIYQQBx9ba1dACCGa4qijjmLEiBE88MADTJw4scnH22yxv/YURcEwDAA8Hg99+/bl/vvvr3dcZmbm3lVYCCGEEEIIIURcEkoJIdqdG2+8kTPPPJMePXpYZYcddhhfffVVzH5fffUV3bt3R9O0Rp23b9++vP3222RlZZGcnNysdRZCCCGEEEIIEUu67wkh2p38/HwmTJjACy+8YJVdcsklrFq1iscee4zffvuNpUuXsmTJEi655JJGn3fChAlkZGQwffp01qxZw5YtW1i9ejV333133MHVhRBCCCGEEELsPQmlhBDt0syZM9F13Vrv27cvDz/8MCtWrGDChAnMnz+fmTNnNqmLX0JCAi+++CKdOnVixowZnHrqqfzxj3/E5/NJyykhhBBCCCGEaGaKERlMRQghhBBCCCGEEEKIFiItpYQQQgghhBBCCCFEi5NQSgghhBBCCCGEEEK0OAmlhBBCCCGEEEIIIUSLk1BKCCGEEEIIIYQQQrQ4CaWEEEIIIYQQQgghRIuTUEoIIYQQQgghhBBCtDgJpYQQQgghhBBCCCFEi5NQSgghhBBCCCGEEEK0OAmlhBBCCCGEEEIIIUSLk1BKCCGEEEIIIYQQQrQ4CaWEEEIIIYQQQgghRIuTUEoIIYQQQgghhBBCtDgJpYQQQgghhBBCCCFEi5NQSgghhBBCCCGEEEK0OAmlhBBCCCGEEEIIIUSLk1BKCCGEEEIIIYQQQrQ4CaWEEEIIIYQQQgghRIuTUEoIIYQQQgghhBBCtDgJpYQQ+93q1avJz8/nnXfe2avjFyxYQH5+fjPXqn2ZOnUqU6dObe1qCCGEOIhNnTqV0047rbWrIcReW7FiBUcffTTV1dWtXRVLfn4+d9555x73e+ONN8jPz2fr1q1NfozIsd99990e9500aRL33Xdfkx9DiL0loZQQB6GmfDAJIYQQovmsX7+em266iZEjR9KvXz9GjBjBjTfeyPr161u7am3aE088wfvvv9/a1WhTysrKeOqpp5gyZQrHHnssRx55JJMmTWLFihUNHvPDDz9w5ZVXcvTRRzNw4EBOO+00nn/++SY/9sUXXxw3TIl8x2xo+te//tXkxwJiztGrVy9GjBjBJZdcwurVqxt9jlAoxIIFCzj//PNJSkqyyuW9Feuyyy7j73//O0VFRft0Hl3XWbRoEaNHj6Z///5MmDCBt956q9HHf/LJJ5x77rkMHDiQo446ipkzZzYYyH3wwQecddZZ9O/fnxNOOIH58+cTDAb3qf6i5dhauwJCCCGEEEIcDN59911uuOEG0tPTOfvss+nSpQvbtm3jtddeY+XKlTz00EOcfPLJrV3NNmnhwoWMHTuWMWPGtHZV2oxvvvmGhx9+mOOPP57p06djs9lYuXIl119/PRs2bGDmzJkx+//vf//jyiuvpE+fPlx11VUkJiZSUFDAzp07m/S47777Lt98803cbUcddVTcVjbPPfccP//8M8OGDWvSY0U77rjjOOOMMzAMg61bt/LSSy9x4YUXsnDhQkaNGrXH4z/88EN+++03Jk+eHFPeXt5bZ5xxBuPHj8fhcOzXxznppJNITk7m73//O9dee+1en+ehhx7iySefZNKkSfTv358PPviAG2+8EUVRGD9+/G6P/fDDD7nqqqvo06cPN954I263m+eff57zzjuPN998k8zMTGvfjz76iKuvvpqjjz6aP/3pT/zyyy88/vjjlJSUMGfOnL2uv2g5EkoJIYQQQgixnxUUFHDzzTfTtWtXlixZEnNRdcEFFzBlyhRuvvlm/vWvf9G1a9cWq5fH4yExMbHFHk80n8MPP5yVK1fSuXNnq+y8887joosuYtGiRVx66aXWz9btdnPLLbdYrUhUde86zPh8PubNm8ell17K/Pnz623v2rVrvfev1+tlzpw5HHvsseTk5OzV4wJ0796dM844w1o/+eSTOf3003n++ecbDKV8Ph92ux1VVXn99dcZMmQIHTp02Os6tCZN09A0bb8/jqqqjB07ln/+85/MnDkTRVGafI5du3bxzDPPMGXKFP785z8D8Lvf/Y7zzz+f++67j//7v//b7XO5//776dq1Ky+99JIVwo0ePZqzzjqLJ598klmzZln73nfffeTn5/P0009js5nxRlJSEgsXLuSCCy6gZ8+eTa6/aFnSfU8I0aAff/yRSy+9lCFDhjB48GAuvPDCuH8Zq6ys5N5772X06NH069eP448/nptvvpnS0tIGz+33+7niiisYOnQoX331lVW+Zs0azj77bPr378+YMWN4+eWX4x4fDAZ57LHHGDNmDP369WP06NE8+OCD+P1+a5+5c+dyzDHHYBiGVXbXXXeRn58f01S9uLiY/Px8/v73vwO1Y2CtWLGCxx9/nOOPP57+/ftz4YUXsnnz5mZ77SJN3L/88kvmzp3Lsccey6BBg7j66qt3+9pVV1czaNAg7r777nrbdu7cSe/evVm4cGGj6imEEKJlPPXUU9TU1HDXXXfFBFIAmZmZ3HnnnXg8HhYtWgTAO++8Q35+Pp9//nm9c7388svk5+fzyy+/WGUbN25k5syZHH300fTv35+JEyfywQcfxBwX+dz5/PPPueOOOxg2bJh1Me92u7nnnnusz/Jhw4Zx8cUX88MPP9R7/A0bNjB16lQGDhzIyJEjrTpHKykp4bbbbmP48OH079+f008/naVLl9bbz+PxMG/ePEaNGkW/fv0YO3Ysixcvjvnszs/Px+PxsHTpUqsLV/RFaV3Rn+OPPvooI0eOZPDgwcycOZOqqir8fj/33HMPw4YNY/Dgwdx6660x3x8i/vnPfzJx4kQGDBjA0UcfzfXXX8+OHTti9lmzZg0zZ87khBNOoF+/fowaNYp7770Xr9cbs9+sWbMYPHgwu3bt4qqrrmLw4MEce+yx/OUvfyEUCsXsW1hYyMaNGwkEAg0+RzADoOhACkBRFMaMGYPf72fLli1W+bJlyyguLub6669HVVU8Hg+6ru/2/PEsWrQIwzCYNm1ao4/597//TXV1NRMmTGjy4+1Ofn4+GRkZVpeuyM99+fLlPPTQQ4wcOZKBAwfidrvx+Xx8/PHHDB8+vN45dvfeaux34S1btlj//wYOHMikSZP4z3/+06Tn8/7773PaaafRr18/xo8fz3//+9+Y7fHGlNJ1nQULFjBixAgGDhzI1KlT2bBhA6NHj477f8Tv9zfqO+fw4cPZtm0bP/30U0x5QUEBBQUFjXougUCA8847zypTFIVzzz2XnTt38vXXXzd4bHl5ORs2bGDMmDExrcJ69epFz549Wb58uVW2YcMGNmzYwKRJk6xACsxw1jAMVq5cuce6itYnLaWEEHGtX7+eKVOmkJSUxKWXXorNZuOVV15h6tSpvPjiiwwcOBAwA5IpU6awceNGzj77bPr06UNZWRn//ve/2bVrV70v3mD+xeyqq67i+++/55lnnmHAgAEArFu3jmnTppGZmck111xDMBhkwYIFZGVl1TvH7bffztKlSxk7diwXX3wxa9euZeHChWzcuJHHHnsMgCOPPJJnn32W9evXk5eXB5hfHlVVZc2aNVxwwQVWGZhNzqMtWrQIRVG45JJLcLvdPPXUU9x000384x//aJbXLuLuu+8mNTWVGTNmsG3bNp577jnuvPNOHn744bjnT0pKYsyYMbz99tvceuutMX9peuuttzAMo9m/+AkhhNg3H374IZ07d+bII4+Mu/2oo46ic+fOfPTRRwCccMIJJCYm8vbbb3P00UfH7LtixQqOOOII67Nt/fr1nHvuuXTo0IHLLrvMOu7qq69mwYIF9boEzpkzh8zMTK6++mo8Hg8As2fPZuXKlZx//vn07NmT8vJyvvzySzZu3Ejfvn2tYysqKrj00ks5+eSTGTduHCtXruT+++8nLy/PCri8Xi9Tp06loKCAKVOm0KVLF9555x1mzZpFZWUlF154IQCGYTB9+nRWr17NOeecQ+/evfn444+577772LVrF7fddhtgtoS4/fbbGTBgAJMmTQLg0EMP3eNr/uSTT+Jyubj88svZvHkzL774IjabDUVRqKysZMaMGXz77be88cYbdO7cmRkzZljHPv744zzyyCOMGzeOc845h9LSUl588UWmTJnCm2++SWpqKmCGh16vl3PPPZf09HTWrl3Liy++yM6dO+u1JAqFQkybNo0BAwZw8803s2rVKp5++mm6du0ac/H+4IMPsnTpUj744AO6dOmyx+dZV3FxMQAZGRlW2apVq0hOTrZCsU2bNpGYmMjpp5/ObbfdhtPp3ON5t2/fzqJFi7j33ntxuVyNrs+yZctwuVzN3jW1oqKCyspKunXrFlP+t7/9DbvdzrRp0/D7/djtdr7//nsCgQB9+vSJ2Xd3763Gfp8rLi7m97//PTU1NUydOpWMjAyWLl3K9OnTmT9/fqOe95dffsm7777LeeedR1JSEi+88AIzZ87kww8/jPk51vXAAw/w1FNPceKJJzJy5Eh+/vlnpk2bhs/ni7t/Y79z9uvXD4Cvvvoq5jW76KKLADNo3J2ffvqJxMTEeq2UIt/5f/rppwZ/F0YC4njvMZfLxfr16ykqKiInJ4cff/wRgP79+8fs16FDBzp27FgvVBNtlCGEOOi8/vrrRl5enrF27doG97nqqquMvn37GgUFBVbZrl27jMGDBxtTpkyxyh555BEjLy/PePfdd+udQ9d1wzAM47PPPjPy8vKMt99+23C73cb5559vHHPMMcaPP/5Y7zH79+9vbNu2zSrbsGGD0bt3byMvL88q++mnn4y8vDzjj3/8Y8zx8+bNM/Ly8oxVq1YZhmEYJSUlRl5enrFkyRLDMAyjsrLS6NWrlzFz5kxj+PDh1nF33XWXcfTRR9er77hx4wyfz2ft99xzzxl5eXnGunXrGnzdmvLaRX4OF110kfXYhmEY9957r9G7d2+jsrLSKjv//PON888/31r/+OOPjby8POOjjz6KeewJEybE7CeEEKL1VVZWGnl5ecb06dN3u9+VV15p5OXlGVVVVYZhGMYNN9xgDBs2zAgGg9Y+hYWFRq9evYxHH33UKrvwwguN0047LeYzS9d1Y/LkycYpp5xilUU+d84999yYcxqGYQwdOtSYM2fObut3/vnnG3l5ecbSpUutMp/PZxx33HHGNddcY5U9++yzRl5envHPf/7TKvP7/cbkyZONQYMGWc/vvffeM/Ly8oy//e1vMY9zzTXXGPn5+cbmzZutskGDBhm33HLLbusXEfkcP+200wy/32+V33DDDUZ+fr5x6aWXxuw/efJk48QTT7TWt27davTu3dt4/PHHY/Zbt26d0adPn5jympqaeo+/cOFCIz8/P+b7zC233GLk5eXF/NwMwzDOPPNM46yzzoopi+y7ZcuWRj3faGVlZcawYcOM8847L6Z8woQJxsCBA42BAwcad911l7Fy5UrjrrvuMvLy8ozrr7++Uee+5pprjMmTJ1vreXl5e3zPlJWVGX379jWuvfbaJj+XaHl5ecZtt91mlJSUGCUlJca3335rXHjhhUZeXp7x9NNPG4ZR+3M/6aST6v1cXn311Qa/wzX03mrs97l77rnHyMvLM7744gurzO12G6NHjzZOPPFEIxQK7fG59e3bN+b9Hvmu+8ILL1hlkf+/kfdFUVGR0adPH+Oqq66KOd+CBQuMvLy8mOfUlO+cEX379jVmz54dU3biiSfG/F9pyOWXX26cdNJJ9co9Ho+Rl5dn3H///Q0eGwqFjCOPPNK48MILY8pLS0uNQYMGGXl5ecZ3331nGIZhPPXUU0ZeXp6xffv2euc5++yzjUmTJu2xrqL1Sfc9IUQ9oVCITz75hDFjxsSMC5Cbm8tpp53Gl19+idvtBszBLnv16hX3r0B1+6BXVVUxbdo0fv31V1544QV69+4d85j/+9//GDNmDJ06dbLKe/bsyYgRI2LOE/kr8sUXXxxTfskll8Rsz8zM5LDDDrNaQn311Vdomsa0adMoLi5m06ZNgPnXqSFDhtSr78SJE2OaDUf+ohPdHL6uprx2EZMmTYp57COPPJJQKMS2bdsafJzhw4eTm5vLsmXLrLJffvmFdevWcfrppzd4nBBCiJYXuf189B2/4olsj+w/btw4SkpKYrrwrVy5El3XOfXUUwGzq8tnn33GuHHjcLvdlJaWUlpaSllZGSNGjGDTpk3s2rUr5nEmTZpUbzyX1NRUvv3223r71pWYmBgzro/D4aB///4xn43//e9/ycnJ4bTTTrPK7HY7U6dOxePx8MUXX1j7aZrG1KlTYx7jkksuwTCMet2XmuqMM87Abrdb6wMGDMAwDM4+++yY/QYMGMCOHTusu3W999576LrOuHHjrNeztLSU7OxsunXrFnPHt+jWHB6Ph9LSUgYPHoxhGFYrjmjnnntuzPrQoUPr3VFs3rx5rFu3rsmtpHRd56abbqKyspI//elPMds8Hg81NTWcccYZ3H777ZxyyincfvvtTJ48meXLl1vfiRry2Wef8e6771qt1xpr5cqVBAKBZmnB/dprrzFs2DCGDRvG7373O7766isuvvhiq+VdxJlnnlmvlU15eTkAaWlpjXqspnyf++ijjxgwYEBMy5+kpCQmT57Mtm3b2LBhwx4fb/jw4TGt/3r16kVycvJuv3OuWrWKYDAY08oO4Pzzz2/wmKZ850xLS6OsrCym7N///vceW0mB2Voy3oDskRZ5dbu3RlNVlcmTJ7Nq1SoeeOABNm3axPfff891111ndWmNHB+ZN/RYu3sc0XZI9z0hRD2lpaXU1NTQo0ePett69uyJruvs2LGDI444goKCAk455ZRGnffee+/F7/ezdOlSjjjiiHqP6fV66zXBBujRo4cVNAFs27YNVVXrNd3PyckhNTU15oP1yCOPtI5ds2YN/fr1o3///qSnp7NmzRqys7P5+eefY744R0SHY4DVVL+ysrLB59iU125fHkdVVSZMmMBLL71ETU0NCQkJLFu2DKfTyf/93/81eJwQQoiWVzdsakjd8Or4448nJSWFFStWWHctW7FiBb1797Y+ZwoKCjAMg0ceeYRHHnkk7nlLSkpiBneOF3bcdNNNzJo1ixNOOIG+ffsyatQozjzzzHqDVnfs2LHeH3HS0tJYt26dtb5t2za6detWbzDtSFee7du3W/vl5uaSnJwcd7/d/XGmMep+vqakpABwyCGH1CvXdZ2qqioyMjLYtGkThmE0+P0meuya7du3M3/+fP79739TUVERs1/dP0I5nc56wxqkpaXVO25v3XXXXXz88cf85S9/oVevXjHbIiFN3e87EyZM4JVXXuGbb76he/fucc8bDAa55557OOOMM6zuV421bNky0tPTOf7445t0XDwnnXQS559/PoqikJSUxOGHHx53kP7dhXlG1Fhlu9OU73Pbt2+vNzQDwGGHHQaY75FIV9uG1H1Pgvne2N13wcj/o7rfh9PT0xsM35ryndMwjL0a5BzM91u8cdoi3Qr31P1z5syZlJWV8dRTT/Hkk08CMGLECM4++2xefvll63dk5DwNPVZTupmK1iOhlBCixZx00kmsWLGCJ598kvvuu2+v7/wS0ZgPyqFDh/Lqq6+yZcsW1qxZw9ChQ1EUhSFDhvDll1+Sm5uLrutx+7U3VL/GfqFprL19nDPPPJPFixdbA2O+9dZbnHDCCdaXbiGEEG1DSkoKOTk5McFNPOvWraNDhw5WSONwOBgzZgzvvfces2fPpqSkhK+++oobbrjBOiYyWPUll1zCyJEj45637kVrvPGDTj31VI488kjee+89PvnkExYvXsyiRYtYsGBBzJ3NWuLuX82loc/XPX3u6rqOoigsWrQo7vONBCGhUIiLL77YGmfrsMMOIzExkV27djFr1qx6A4nvz9fu0Ucf5e9//zs33ngjZ555Zr3tubm5rF+/vt44nZGQbHfB2Jtvvslvv/3GnDlz6rXqqq6uZuvWrWRlZZGQkBCzbfv27axZs4ZJkybFtFjbWx07dqw3UHk88YKI9PR0wHyeHTt23Oe6NLeG3hut+Z2zsrJyt+NZ7U5OTg6rV6+uF2wVFRUB5vtxdxwOB/fccw/XX389mzZtIisrix49enDjjTfG/GE6cjfHoqKiesFeUVFRk0NU0ToklBJC1JOZmUlCQgK//fZbvW2//vorqqpav/gPPfRQ1q9f36jzjhkzhhEjRjBr1iySkpKYM2dOzGO6XK64d7erW4/OnTuj6zqbN2+OGUCxuLiYysrKmDvRDB06FIBPPvmE7777jssvvxwwB5R96aWXyM3NJTExMWYQ133RlNduX+Xl5dGnTx+WLVtGx44d2b59O7fffnuznFsIIUTzOvHEE3n11VdZs2ZN3D+ErFmzhm3btjF58uSY8nHjxrF06VJWrVrFxo0bMQyDcePGWdsjLZnsdnujLth3Jzc3lylTpjBlyhRKSko466yzeOKJJ2JCqcbo3Lkz69atQ9f1mIvgX3/9FahtrdG5c2dWrVqF2+2OaS0V2a/uneVayqGHHophGHTp0iVuS5mIX375hU2bNvGXv/wlJgj65JNPWqCWtZYsWcKCBQu48MILre85dfXt25dPPvmEXbt2WS14wLzTHxD3xjQRO3bsIBAI1Ot6CGZg9eabb1p3RI4WuflKWxhWIPKct27dSn5+/h73b8r3uU6dOjW4X2T7/hA5b0FBQUyLxrKysn1ufbdr1y4CgUC9gcobq3fv3vzjH/9g48aNHH744Vb5t99+a21vjOzsbLKzswEzBF69ejUDBw60WkpFzvPdd9/FBFC7du1i586d1uD1om2TMaWEEPVomsZxxx3HBx98EPMXseLiYt566y2GDh1qfXk85ZRT+Pnnn3nvvffqnSfeX13OPPNMbr/9dl5++WX++te/xjzmiBEjeP/9963myGDe4vp///tfzDkiX46fe+65mPJnnnkmZjuYX9Y7dOjAs88+SzAYZMiQIYDZra+goIB33nmHgQMHxjTF3xdNee2awxlnnMEnn3zCc88912zN44UQQjS/adOm4XK5mD17dr1xWsrLy5k9ezYJCQlceumlMduGDx9Oeno6K1as4O2332bAgAExF6BZWVkcffTRvPLKK1bAEC3e7d7rCoVCVFVVxZRlZWWRm5sbt1vMnhx//PEUFRWxYsUKqywYDPLCCy+QmJho3e32+OOPJxQKsWTJkpjjn332WRRFiflMS0xM3G1XpuZ0yimnoGkajz76aL3vMoZhWD+/SOAWvY9hGDz//PP79PiFhYVs3LjRGj9nd1asWMHdd9/NhAkTuPXWWxvcLxJkvvbaazHlr732GjabLeYOj9u3b2fjxo3W+qmnnspjjz1WbwLzO9djjz0Wt0XKW2+9RadOnaw/ELamfv36WXfhqyvee6sp3+dGjRrF2rVr+frrr639PB4Pr776Kp07d44JZZrTsGHDsNlsvPTSSzHldf8/7Y3I6zR48OCY8oKCAgoKCvZ4/EknnYTdbufvf/+7VWYYBi+//DIdOnSIOW9j3++LFy+mqKgoZkzZI444gsMOO4xXX32VUChklb/00ksoiiJDWrQT0lJKiIPY66+/zscff1yv/IILLuC6667j008/5bzzzuO8885D0zReeeUV/H4/f/jDH6x9p02bxsqVK7n22ms5++yz6du3LxUVFfz73/9mzpw59cY0AHMARrfbzUMPPURKSgpXXnklANdccw0ff/wxU6ZM4dxzzyUUCvHiiy9y+OGHx3R56NWrF2eddRavvPIKlZWVHHXUUXz33XcsXbqUMWPGcOyxx8Y83pFHHsny5cvJy8uz+tj36dOHxMRENm3a1CyDb0Zr7GvXHE477TT++te/8t5773Huuec2S/N4IYQQza979+7MmzePP/zhD0yYMIFzzjmHLl26sG3bNl577TXKysp48MEH63W1s9vtnHzyySxfvpyamhpuueWWeueePXs25513HhMmTGDSpEl07dqV4uJivvnmG3bu3Mm//vWv3daturqaUaNGMXbsWHr16kViYiKffvop3333HbNmzWryc508eTKvvPIKs2bN4ocffqBz586sXLmSr776ittuu826mB89ejTHHHMMDz30ENu2bSM/P59PPvmEDz74gAsvvDDmtejbty+rVq3imWeeITc3ly5dusQdx6c5HHrooVx33XU88MADbNu2jTFjxpCUlMTWrVt5//33mTRpEtOmTeOwww7j0EMP5S9/+Qu7du0iOTmZlStX7nN49uCDD7J06VI++OCD3Y6PtHbtWm6++WbS09MZNmxYvZ/zkCFDrACzT58+nH322bz++uuEQiGOOuooPv/8c9555x2uuOKKmDHHbrnlFj7//HPru1fPnj0bbDHTpUuXei2koPbmK5dffnmDwy2sXr2aCy64gBkzZnDNNdfs/kXZR06nkxEjRrBq1SquvfbamG0Nvbca+33u8ssvZ/ny5Vx22WVMnTqVtLQ03nzzTbZu3cqCBQv2ebiKhmRnZ3PBBRfw9NNPc+WVVzJy5EjWrVvHf//7XzIyMvZ6PCiATz/9lE6dOtGnT5+Y8osuughgj4Odd+zYkQsuuIDFixcTDAbp378/77//PmvWrOH++++P6a4Y7/3+z3/+k3fffZejjjrK+n309ttv87vf/Y6xY8fGPNbNN9/M9OnTueSSSxg/fjy//PILS5Ys4Xe/+91et/QSLUtCKSEOYnX/shIxceJEjjjiCJYsWcIDDzzAwoULMQyDAQMG8Ne//jXmS2BSUpLVbPy9995j6dKlZGVlMWzYsJgvOHVdeeWVVFVVWcHUlClT6NWrF4sXL2bu3LnMnz+fjh07cs0111BUVFRvHI67776bLl26sHTpUt5//32ys7O54oormDFjRr3HGjp0KMuXL4/5S53NZmPQoEF8+umnzf4XvMa+ds0hOzub4447jo8++ijmbkhCCCHannHjxnHYYYfx5JNP8tprr1FeXk56ejrHHHMMV1xxRYODIZ966qn84x//QFGUmK57EYcffjivv/46jz76KEuXLqW8vJzMzEz69OnD1Vdfvcd6uVwuzj33XD755BPeffddDMPg0EMPtcKupnK5XLzwwgvcf//9LF26FLfbTY8ePZg7dy4TJ0609lNVlccff5z58+ezYsUK3njjDTp37szNN99s3VE3YtasWfz5z3/m4Ycfxuv1ctZZZ+23UArMoKF79+48++yzVqugjh07ctxxxzF69GjADAyfeOIJ7r77bhYuXIjT6eTkk09mypQpLfKZvGHDBgKBAKWlpXHvijd37tyYVnVz5syhU6dOvPHGG7z//vt06tSJW2+91QoamlPk7sDxbiQT4fF4gNpxgfa3s88+m2uuuYYdO3bEDKXQ0Hursd/nsrOzrR4AL774Ij6fj/z8fJ544glOOOGE/fqcbrrpJlwuF//4xz9YtWoVgwYNYvHixZx33nlx70jXGLqus3LlSs4555x9CrZuuukm0tLSeOWVV3jjjTfo3r07f/3rXxv1x+AePXpQUVHB3/72N7xeLz169GDOnDn1ujeD2TX60Ucf5dFHH+Wuu+4iMzOTK664olG/+0TboBjNPXqaEEKIFnX11Vfzyy+/xO1CKYQQQgjRFt13330sX76c9957b68DlKYIhUKceuqpjBs3juuuu26/P15rifQiuO6665g+fXqTj3///fe58cYbee+99/Y4ILkQzUHGlBJCiHassLBQWkkJIYQQot1ZvXo1V111VYsEUmCOE3Xttdfy97//nerq6hZ5zP3N6/XWK4uMuRo9TlhTLFq0iClTpkggJVqMtJQSQoh2aMuWLXz11Ve89tprfPfdd7z33nst1vxdCCGEEEK0vjfeeIOlS5dy/PHHk5iYyFdffcVbb73FiBEjWLx4cWtXT4hGkTGlhBCiHfriiy+49dZb6dSpE/PmzZNASgghhBDiIJOfn4+maTz11FNUV1eTlZVl3bBIiPZCWkoJIYQQQgghhBBCiBYnY0oJIYQQQgghhBBCiBbX5FDqiy++4Morr2TEiBHk5+fz/vvvx2w3DINHHnmEESNGMGDAAC666CI2bdoUs095eTk33ngjQ4YM4cgjj+S2225r0mBzhmHgdruRRl5CCCGEEHtPvlMJIYQQojU1OZTyeDzk5+cze/bsuNsXLVrECy+8wB133MGrr75KQkIC06ZNw+fzWfvcdNNNbNiwgWeeeYYnnniCNWvW8Oc//7nRdaiurmbo0KEHzF0ThBBCCCFag3ynEkIIIURranIoNWrUKK6//npOPvnketsMw+D5559n+vTpjBkzhl69enHfffdRWFhotajauHEjH3/8MXfffTcDBw7kyCOP5Pbbb2f58uXs2rVr35+REEIIIYQQQgghhGjzmvXue1u3bqWoqIjhw4dbZSkpKQwcOJCvv/6a8ePH8/XXX5Oamkr//v2tfYYPH46qqqxduzZu2NXi3L/Cd3NAD4I9GWwpYEsGR7o52cNzZxY4c8y5am/dOgshhBBCCCGEEEK0I80aShUVFQGQlZUVU56VlUVxcTEAxcXFZGZmxlbCZiMtLc06vtVtWw6/Pd+0Y+xp4MqFhEMgoZM5JR4KyYeZU+KhoDlBUUHRQFH2T92FEEIIIYQQQgixX4V0A01t39f1beE5NGsodcA4/DKzZVTFdxCsgaAbgtUQqIJgJQTcEKyCQIVZhhFeroCq9fHPqWhmYJXUIxxU9YTUXpDYzQyrVDtojnBgFZls5ly11a6rtnB5+37zCyGEaLsig14bGA2u1922u/0bWxbvXPHKXTYXifbEvX+CQgghhBD7SFMVrn35azYUulu7Knvl8NxkHvn94NauRvOGUjk5OQCUlJSQm5trlZeUlNCrVy8AsrOzKS0tjTkuGAxSUVFhHd/qNBf0vDj+NkMH3W9OIZ8ZVnm2g2czVG+Bmu3gLwZfKfhKwLsTanaAEQDPVnMq+rj2fPZUSMmH1HwzpErNN0Mni2K2rlI1IBxWqRqoDlDDYVZ0YBXZX1EBNRxeqVFle9pedz8Jv4QQB6dI8GIYBrqhW8u7m9c9bm+P2VOZrofrE7Vs1REDQzesMiDuNuvchoGO3qggqu7rE9keEyBFlqN2b0xY1dC5650X6JzSmaGdhjb4sxNCCCGEaAkbCt38sL2ytavRrjVrKNWlSxdycnJYtWoVvXv3BsDtdvPtt99y7rnnAjB48GAqKyv5/vvv6devHwCfffYZuq4zYMCA5qzO/qGoZmilucAOkAPJ3YHhoAfMVlWBKvCXmaFUoAqMEOg+8BWB+zdwb4SqDeYUqITSL8wJQEuAzCMh+xjIHg6JXczjjZAZiBkhMIJmGGZUhst0zG//BhiGGSTFfMlXiLk6UBSs0MmaR5YjIVVUeBUJvVDjtNrSdhNoRZXFW6+3TQhxsNIN3Qp/IgFKdFlT1uMt67qOjk5IDxEyQtZxIT1EiBCGbgYzuq6b2+uERxAVEkVCGIMm7YcBSlTQHymzfkUroKDU26agmOcO7xd9DgXFWo9ejqwDTdoevV63TFGUuPs09lwN7bun/etuL/GUEAgF4u4nhBBCCCHalyaHUtXV1RQUFFjrW7du5aeffiItLY1OnTpxwQUX8Pjjj9OtWze6dOnCI488Qm5uLmPGjAGgZ8+ejBw5kj/96U/MmTOHQCDAXXfdxfjx4+nQoUPzPbPWoNrBkWFOSYeaIZW/DLzF4N0BWqLZbc85yWzppPuhch2Uf2dOpV+Bv8RsSRVpTZWSBx1PhkNONgOq5mAYgF5nboTDrej1cABmRO+rh88Rve/uKLUBV0MBmBpZj3RZtJmtwazwK9IarDHBVnSQJsGXEPsqEhJFApzo9cZOIT1EUA8SNIJW6BM0gmYgVOec1mOigxHVwie6RU94PSasASuwiQ6AopcjoUxj52CGIaqimmVq/W11l/dUJvZde3gtFyxYwKOPPhpT1qNHD9555x0AfD4f8+bNY8WKFfj9fkaMGMHs2bPJzs629t++fTt33HEHq1evJjExkTPPPJMbb7wRm632q9vq1auZN28e69ev55BDDmH69OlMnDixZZ6kEEIIIUQzaHIo9f3333PBBRdY63PnzgXgrLPOYt68eVx22WXU1NTw5z//mcrKSoYOHcpTTz2F0+m0jrn//vu56667uPDCC1FVlVNOOYXbb7+9GZ5OG6PazcHPXbkQOtwMnKo3g7fQ3O7MgvT+5gRm0FO1HopXQfFnUPY1VP1iTusfg7Q+0PkM6PR/YEva+3opCmZXwH1+hnsWE3jVnYdDrciyHqBeWBY3/Ipu+WU1HaA2gAovR4dgaiSgCo/RhWb+fCJjdKm22jArJsiKsx4v/JLAS7QBhmEQMkK1LYCilnXDDIOiQ6DI9mAoSEAPmMFReAqEAtZ2ayKqNZNeux6v+1VEvXBHMedq+P9MZDmy3QqAwvtF9okOeCLnEaItO+KII3jmmWesdU3TrOV7772Xjz76iIcffpiUlBTuuusuZsyYwcsvvwxAKBTiiiuuIDs7m5dffpnCwkJuueUW7HY7N9xwAwBbtmzhiiuu4Pe//z33338/q1at4vbbbycnJ4eRI0e27JMVQgghhNhLTQ6ljjnmGNatW9fgdkVRuPbaa7n22msb3Cc9PZ0HHnigqQ/dvmkOc6BzVwfwFZvjT3l3mNtcubXhRmp4fKnDLgJ/Oez6D+x8F0rWQMWP5rRuPnQaB10nQsoRbXvcp5iugvtRdEuuBoOvoNk6LajX7hvZL9ICLLre0RfadcfksroqRro6Rsb7ske19opu5aURP+hqRLk4KEQCpUiroshyQ/OAHsAf8hMIBcxJD9QLnepOkTF5Ii2MIi2JogOh6GBIUzVzrmjYVBuaosWEShIQCdEwTdPijpVZVVXF66+/zv3338+wYcMAM6Q69dRT+eabbxg0aBD/+9//2LBhA8888wzZ2dn07t2ba6+9lvvvv58ZM2bgcDh4+eWX6dKlC7NmzQLMluhffvklzz77rIRSQgghhGg35O57LU1RzRDKmWOGU1XroXorODPBnhK7ryMdup5pTr4S2P42bHkDPAWw5TVzsqeZwVTKEZByuNndL/kw845+BxOr9Ze2x133ihVcRYKvOiGYETTHDQvWDcf0cKOuOi27Yuodb6D5qNZdVmuu6MDLXtvFsV6QpdUJueqWS9C1P8V0VwtPIaN2PbItoAfwBX34Q358QV9td7Y4rZvijTsUExqpmhUURQdI9YImCY+EaDGbN29mxIgROJ1OBg0axI033kinTp34/vvvCQQCDB8+3Nq3Z8+edOrUyQqlvvnmG/Ly8mK6840YMYI77riDDRs20KdPH7755hsr1Ire5957722x5yiEEEIIsa8klGotigKuHDNUqt5sDn4eqABXx3D3sjqcWdDjfOg+BUrXQMFrUPgf85jSNeZknVuDpG5mQJWaF573MkMusXf2V5gTN+wKRbXuCkDQF6e8bthlVTS2vg0GXeEWXKojHHrZw2WNCbXqbFf3UxDYRkRaJdXt2ha97A158QV9+II+s8VSpEVTVOsmICabVFHRVM0KjyLLdtUeUy5hkhDtz4ABA5g7dy49evSgqKiIxx57jClTprBs2TKKi4ux2+2kpqbGHJOVlUVRUREAxcXFMYEUYK3vaR+3243X68Xlcu2vpyeEEEII0WwklGptmgNSjwBXNlT+Ap5tkNDBvLtfPIoCWUeZU8hn3s2van3U9IsZVLl/Nacd79Qe68wNdw/sVTt3dWjb3f8OdPsj7KrXikuPXbeCLr3+fla3xbotuuqEXHUHkI9uyaXazZBLszcQcO0m5LLG7Nq/ImFSQA9YAVMkePIH/dSEavAGzaApMrZSdLe6aAoKNtVmtVLSVA27ZseluKxwSTvAgzshRKxRo0ZZy7169WLgwIGceOKJvP322xIWCSGEEEJEkVCqrXBkQMZg8w597g3gyAR78u6P0ZyQ1sucIgwDfEVmOFUZHiS9ch14toCvEIoKa+/sB2BPD4dU4fOk9oKEzhJUtWf7oytjvHDLWg+Fx+qKbs0Vwhyvq4EBsOOGXFrt+FxWl8WGWnLFaa2laAR1nYCuEzB0/HowvGwGUJ6Ah5pgDTWBGutOcMGQOTf0cD2V2pDJ6ganmq2XbHZz2RavJaMQQuxGamoq3bt3p6CggOHDhxMIBKisrIxpLVVSUmKNQZWdnc3atWtjzlFcXAwQs0+kLHqf5ORkCb6EEEII0W7I1VVbojkgvS9oCVD1szlGkTOraedQlNo7/uWMqC0PVpstqSp/NkOqynXhLoPlUPKZOUXYU6OCqj6Q2hsSOklQdTBr7hZd9UKuqDDLCMZtyRUKBfHrAfxGCH8wgF8P4teD+EJBqoN+akIBfEaIoK4TxCCo6xiooCooih1NtWPTnNhUJzabE6fNjqY4sGk2VLs9asD6eC3BNMzESlo8CSGarrq6mi1btpCTk0O/fv2w2+2sWrWKsWPHAvDrr7+yfft2Bg0aBMCgQYN44oknKCkpISvL/B7w6aefkpyczOGHH27t89///jfmcT799FPrHEIIIYQQ7YGEUm2NokLq4WBLgPIfoGYnJHTc9/PakiBjkDlFhHxmMFX5M1T8ZM6rNkCgEko+N6cIe1psSJXWR7r+ib0XJ+QKREKnUABfKIhfD+EL+qkO1lAdqMEb8hPUQwTCg4WbbZsMVFRsqoJddWFTIEm1YVPBhopi3YUxaowuwwcBHfwN1Y3aEMpqxRW+66Ki1A42b7Xoigw6r2K2UFNiQ6y4IVf0WF9CHNyCerB20P+QL2bZH/LXLgf9+HU/Fd4KTupxUmtXe7f+8pe/cOKJJ9KpUycKCwtZsGABqqpy2mmnkZKSwtlnn828efNIS0sjOTmZu+++m8GDB1uB0ogRIzj88MO5+eab+cMf/kBRUREPP/wwU6ZMweFwAPD73/+eJUuWcN9993H22Wfz2Wef8fbbb7Nw4cJWfOZCCCGEEE0joVRbldjZ7LpU9g14C82WT81Nc5rhUlof6Bou0wNQtREqfzKnip9rx6kqWW1OEY6M2oAqrQ+k9jHHxhIiDt3Q8YX8+EKR4MmPN+jHHfRQHajBFwpYwZRu6EQGbbdrNuyqOSXZ7djDXev22+Dfde+uGAm0IpPurR14HmpbedUZhsuiRLewCodW1qDzUSGXqoXnkdBrD2FWTPClRJ1biH0T1IN4g964ky/ks8Zbq7seWY6U+4O1IZM35LXuNBkdPPlD/nrjtDXGD0U/cGreqfvh2TePnTt3csMNN1BeXk5mZiZDhw7l1VdfJTMzE4DbbrsNVVWZOXMmfr+fESNGMHv2bOt4TdN44oknuOOOO5g8eTIJCQmcddZZzJw509qna9euLFy4kLlz5/L888/TsWNH7r77bkaOHNniz1cIIYQQYm8phtHQoC9tl9vtZujQoXz55ZckJ+9h3KX2zlsIpV+by60V+Oh+M6iq+LE2rKraEB43qA5nDqT1rg2p0vrIXf8OIpHgyRv0WwFUdaCGqoAHT8CLXw8QCN+1zgBUFGzh0MmhRsInO5p6ALUgMkJmWBUZZyvmTot1ljFq94+oG3ZZ4VQ43LJacUUmO7V3WAyHXWp47K26Y3dFh1l1W3VFzi3aHN3QqQnUWGOk1QRr8AQ8eIPemLHTvEEvNcHwPLxfdMAUvR7Z3xv07lVI1FwcmgO7asdlc+HQHNbktDnNueZEURSm9JvCVUdf1Wr1PJAcVN+phBBCiGY2fv7H/LC9srWrsVf6dkpl+czW/2OWtJRq61y5kDHQbDHlK2n6GFPNQXWEg6betWUhnzlGVSSoqvjRvBOgrwgKi6AwapyLhM7msam9Ia2v2Q1wT4O4izbNHwrgDfnwBv14Q3484eDJ7a8xu+DpAbOLnQGaquLQzBZOiTYXDs12cA0WrmjhRl/NMB6VEd0dMRIchGrL9SDgi23hhR6/BRfE6aoY3U0xEmBFdVdsasglLbkwDAN/yI8n4KE6UI0n4Kld9pvzmmAN1f5qK1CK7FMTqMETDM+jtnmD3hapu6qoODUnCfYEXJoLp82J0+bEpblw2WrXnZoTly1cptWWWfPIcp11h+bAZXNhV+3WutqILq3FnmLSnGkt8AoIIYQQQoj97SC6MmzHEjqC0R/KvwV/mdltrrVpTkjvZ04RQU94EPVwSFXxI3gKoGabOe18v3bfpG61LanSekNKvjmOlmgzDMPAF/JTE/ThDfnxBn1U+T1UBNx4gz78kTvXGaAoCg7VhkOzk2R3ka6mYFNlUPBmF9MVsBl+fRvRwVU4yEKPDbkML7WtuBoIuSKtuRSobWnVmJZc4XG5YkKuhsbeatkxucz3vw+3321N1YFqqv3VcZejyzwBD9X+anM9UE1QD+6XOiooJNgTSLAlkGBPINGWiMvuMtfDZS6bue6yueqtR6bIOSKhUmQ/u2rff91khRBCCCGEQEKp9iOxs9kyouwbs+WSLam1a1SfLREyB5tTRKAqPJD6D7VBlXcnVG82px1vh3dUIblH7EDqKYeDJre1bgmR8MmcvFT5PZT73eEueH5CuhlK2MLBk1O1k2JPwq7Jr5B2zQq5milAbO6WXLDn7opEteCyxueyYQDVAXPMskq/hyp/tdmaL1BNVXjdHfBQFQmdAtVU+dy4A+Z6la+q2buxuWwukuxJJNoTSbQn1i47Ekm0JZJgT4jZHgmLIuuJ9kRcNpe1HOnKJoQQQgghRHslV5TtSWLXcGukn8xWBqqjtWu0Z/YUyDrKnCL8Zebd/ip+NMOqyp/Nbn/ujea0bZm5n6JBcs/arn+pvSDlCLOVltgrIT1ETdCHJ+ilJuijKlBNua+KmqAPXyhAUDfHPbKrNpyaA5fmIM2RLK2eROM0c0suQ9fxhrxU+Kqo9FVR6a+mMlBFpa/aXA5PVQEPFeF5VaCGKn8NVYEa9N2mXY2joJBkTyDZkUSSPZFke5K1nORIItmeTJIjyQqTkp2p5jZnKon2JJLsySQ7U0iwJaDJ/yMhhBBCCCFiSCjVniiK2XooWAPVv0FS1/Z5S3lHBuQMN6cIb1Ftt7/I3F9m3vmv6hfgn+Z+kaAqtVdtWCUtquLyhwJW+FQdqKE8fFEfaf0ECpqq4tQcODUHqY6kg2usJ9GignqISr+bcp+bCr+bcl8VFT435X43FX4zaKrwu619IoGTXw/s0+PaVRupjiSS7YmkOpJIsSeQbE8k2Z5Ass1FqiORZJuLZEcCyTZneHKZc7uLRM2G2tCdFSMirbessblUUHxg+M07lwbDrbhQw3dXjL7TYqQFWN0uitHjcNXtuhjVakwIIYQQQoh2TK5A2xvVBmm9IOSBmh1mt74DgSvHnHKPN9cNA7y7ou7497PZuipQXhtUbfuXua+iQVIPM6hK7WW+PilHtM0ujvuJP3yXO0/QS3WghlJvJe6gB1/QvNudomDe0UpzkOpIxqnJWDFi7xmGQXXQS7mvkjJvFWW+Ssp9bnPur6LcW2XOfWb4VO6roirg2evHs6kaqfYk0pzJpNiTSHMmmXNHMimOcNjkSAqHTubcLEvEqe3nFqXWXRSJusOiEdtFUQ8C3qhxu6LusthQ4BVvfC6UqO6LSm24hVqn+2LU4PLU7e6o1g+6ovfZz2N1CSGEEEIIEU1CqfbIlgDpfaFkDdTsNMMc5QDrFqIo5gDvCR2h42izLBJUVf5kBlSVP5vL/jJwbzCn7W9FTgBJh5oDqKdGpl7gSG+tZ9RsgnqQ6oAZPlUHaijxVuAOevAG/QT1EArgtJld75JdiTLuk2gUX8hPqbeSUm8lZT5zXuqrpCwyDy+X+Sop81UR2IvBuxUUUhyJpDtSSHMmk+ZIrj8PL6c6kkh3JJPqSCbB1obHTrJaNEGzjc0FdcbnqjMIfb2wK7wtJgzbQ52tsCu61VUk7IoXeGmADbSo1l1xg614LbyiH+PgvAujEEIIIYSIT65W2ytHOmQMMO92V70V7EngyDyw/8IdHVR1ONEsMwzwFYZbUv0cDqrCY1RFBlPf+W7tOVwdICXPDKlS8iA1DxI6tdnXzTAMq/WTO+Ch1FtJhd+NN+jHrwdQUXDaHCRoTlISEqX7nYjhDfop9VVQUlNBibecEm9leF5hhU6l3gpKvRVUB71NPn+CzUmGM4V0Z2p4nmLN0x0ppDuTzWVnCmnhgElT2+b/tTYnZnyuZmboUS289HChXlu+T4EXxLa2irTWUmLDsEjopYQDL0Wt08prN6FXoApsMragEEIIIcSBQK5g2zNXrjk+U80OcP8G1VvAkQr29IPnL9GKYgZNrg6QO6q23FdiBnaV68yQquoX8GwxW1p5d0HRx7X7aknmuFSpeWa3v5Q8c9wqW0KLP51AKIg74KE6WEOFz02xt4KagBef7scwDByagwSbkyxXmrSAOkgZhkGlv5pibznFNeXWvMRbQXE4cIqsu5vYZc6u2sh0pZHhTCHTlUqmM5UMVyoZzujlFDKcZpnL1g5utiDqU9Tmb9kVEbeFV1Qrr0iZHogNvKK7NO6Jrwr07s1fdyGEEEII0eLkqra9U+1mNzVXh3A4tRGqC8CRBva0gyecqsuZVX8w9aAbqjaEW1OFx6Wq2gihaij/1pwsinm3w5QjwtPh5tTMrapqgl7cgRrcfg8l3goq/G5qgj4CehBNUUmwOUlxJJKtpbfd7kui2VQHaiiqKQtP5VHLZRR7KygOlzdl8G+HaifTlUqWK40sVzpZrlQyXWlkudKi5qlkOdNIsifI+0zsm/3ZwgvMIMtfA0bTu48KIYQQQoi2R0KpA4XmhOTuZtc2z7Zwy6kCcGaAPbW1a9c22JIhY5A5RehBqN5kBlSV68NB1Xrwl4KnwJx2fVC7v5YIyYeZAVXy4ZDS02xV5czc48NHuuK5Ax4qfWZLl6qAh5qgD8PQsWt2Em0uchLSpRveAUY3dMp9VRR6ythVU0qhp5TCmjIKa0opqimj0FNGUU1pk7rQpTqSyHalk52QTpYrnewEM3TKdqWT7UoLl6eRbE+UoEkcOJTIOFhCCCGEEOJAIFe+BxrNZQYlCYeY3dXcm8FfYI43ZU9u7dq1PaqtthVUp6hyX4kZTlVtCAdVG8ygL+SBiu/NKZojwwynkg8z5yk9MRK741GdVPnNW90X1ZRRHfBSEw4eEmwua0wetY2OaSX2zDAMyn1V7PKUstNTzK6aMnZ5Sij0lMYEUI0dGDzJnkBuQgY5CRlW6JSbkEF2QgY5CelW2X6/q5wQQgghhBBC7GcSSh2obInmYN4Jnc0WU54tZusfZxbYklq7dm2fM8ucso+tLdOD5utYFb7TX9VGc+7ZZt4BsHSNOYUpgKalojpysTs6kOnqRFpSN0KJ3QgdzF0r2xlv0M9OTwk7PcXs9JSwy1PCzuoSdtWUhucl+EJ77k6noJDlSiM3IYPcxExyEjLoEJ6bIVQmuQkZJNpdLfCshBBCCCGEEKL1SSh1oLMnQ3ofSOpitpqq2Qq+SDiV2Nq1a19UGyT3MCdOBsAb9FHlLcFbvg5fxU9o1ZtJ9G4lxb+LxGA5rlAlrppKcms2QEXtqYJaMl7XIeGpkzk5D8HnzNm/47GIeqr81eyoLma7p5id1cXsqC5hR2TZU0yZr6pR58lypdEhMYsOiZl0TMgiNzGDDolZ5CZk0jExk2zplimEEEIIIYQQMeQK6WBhT4WM/pDU1Ww5ZYVTmRJONUFQD1LprzbvflZTTpmvEk/AiwEkJA8mMX0YCTYniqKghmpwebeT4N2Gq2ZbeHk7Dn8RtpCb5Or1JFevjzm/rmj4nB3wOjvhc3U0QytnR3zOjgRtKdK6ai+4AzVsdxey3VPMdndROIAKz6uLG3WHugSbk0MSs+mYmEWHxCw6JmXRMTHLWs9NyMCh2Vvg2QghhBBCCCHEgUNCqYONI92ckg6F6s1Qs03Cqd0wDIPqQA0Vfjel3gqKasqpDtYQ1EM4NQdJNhfpyfHHhNK1BDxJPfEk9YwpV3Q/Lu8Oc/Jtx+XdHl7fiWr4SQiHV9EtqwCCWiI+Z0e8zg5mYOXsiM/ZAZ+zI6GDuEtmIBRkh6eYbe5CtlUXsq26iO3uIrZXF7O9uogKv3uP50h3ppihU1IWnRJzOCQpi45J2VYQlepIksHChRBCCCGEEKKZSSh1sIoJp7aEW06VhMOpgzfgAPCHAlT6q8N3Syulwu/GG/KjKao1CPW+dMMyVAc1id2oSexWZ4OOw1+K07fDDKp8O3F5d+L07cDpL8EW8mDz/EqS59d65wxqyficuWZg5eyAz5kbnjoQtLXv8asMw6DC72ZrJHRyF7HVvYtt1UVscxeyy1OKgbHbc6Q5kumclMMhSdl0Ss4JB0/Z5npSNgk2GcdJCCGEEEIIIVqahFIHO0eGOSUdGu7WF2455cg4aO7WZxgG7oAn3CWvgmJvGe6AB8MwSLC5SHUkkWvL3P8VUVT8zmz8zmyqUvvHbtJ9OH2F4ZBqFy7fDpy+XTh9u3AEyrGF3Ng87riBla448DlzzJDKkYPfmYPPkYvPmYPfkY2uJez/57YHuqFTVFPOFvcutlpToTXtqYudS3PQOTmXTkk5dE7KMefJuVYQlWRv/ecohBBCCCGEECKWhFLCZLWc6gaerebkKzHL7KnN09LGMEDXzbkRVQbm+ZXIXAG1fne45hTUg1T43FT43eysLqHSX222hlJVku2JHJKYg7af69AUhurEm9AVb0LXetvUkDccUBXi9BfWLvt24fCXhLsEbiPBuy3uuc1WVmZA5XOYc78zG58ju1lDq5CuU1hTSkHVTra4d7GlapcVQm2rLtzjHexyEjLonJRDl+RcuiR3oHMkeErOJdOZKt3rhBBCCCGEEKKdkVBKxHKkmVPSoVCz3Ww95SkAW4oZUEXGTtJ1CATBH4Bg0FwOhszlkA5+P/j8tfvoOugGYITn1AZSEWo4kEIxl1XVnGwa2G1gs8XOtUi5Bna7uWy3NRigeYM+yn1VlHor2eUppSpQTUjXSbS7SHEkkmPL2F+v6n6la6743QEBjCAOf0k4pCoKh1aFOHxFOP3FZgsrq5XVb3HPH9QS8TuyzLDKkYXfnhVeN6eAPd26Y2CkxVNB1Q4Kqnaxxb3Tmm9zF+HXGw6eNEWjU1K2FTpFzzsn5eCyOZvj5RJCCCGEEEII0UZIKCXis6eALQ+UbCgrgKJfoXID+BXw2cAfDqAik26YLZ0MAAW0cKCkabVhU6QFlBYOnqA2QDLCgZVBOMAC9EC4dZVR28oqpNcJswzzMSIBld0GLickujBcTtxKkHJ8FBnVlAQ9VCsBFFUj2Z5Ah8TMfRobql1QbPidHfA7O1AVZ7MaqsHpK8LhL8bpN+cOX3F4PRJaebDVeEis2QKYL3+JDr/44ZcArPPDL0E7vwQUNvqD1Oh6g9WxqRqdk3I5NKUDXZM70iU5l0NTOtIluQMdE7Owqdp+eiGEEEIIIYQQQrQ1B/gVuWi0UAg8ntqpshLKy8HrNVs9+XUI2CFQAnjMlkmJGZCUarZUas2ubqGQ2UorECTk91PpLqPcX8VOXznlhhevHsRhd5BsTyDdnoia4AKXD1w1tS2sbDZw2M3nchB1A9O1BGoSD6Um8dB627xBP9sqC9hWtoEtFZvZXLWDzdWlbPRUUR4K1tm7tgWUDehhhyPskOeAw8PL3ROSOSQxk5Ajk4Ajg4A9gYDdRsAexK96CIQcBJXU2tZ4QgghhBBCCCEOaBJKHaxqasDthupqKCszA6iaGjOAMgxwOMDphMRESE83QxsAPQT+cvAWgr8EQqWgJoOS2GphTkAxqFB9lKnVbFfKcdtq8GshEpISSFEzyNUcMcEVlW4oC4XHt8JsyWXTzOdos0Giy2xt5XCY5Q57VNfBA6slj2EYFNWUsalqB5srd5jBU9UONlXuYKenZLd3teuQkEm3lI50S8qkZ0IyhzsdHOFQ6KH6SApVYPeX4AiUYQ+UoRpBwA1eN3gLGq4PCgF7GkFbGgF7OgF7eG6LLEe2paGrroMqQBRCCCGEEEKIA42EUgcDwzBbP1VVmVNRkRlI1dSY2+x2cLkgLc0MonZ3oa9q4MoCZyYE3eAtAl8ReHeB5jS7/Sn7/23l0wOUBz2UBNzs9JdTFfICkKA6yLQl46jbLS/Sxc/pqH8yQzdDq0AIAgEo9ZldEg0Aw3w9bFFjWSU4w6FVOKyKBFp7GNOqNflCfrZU7WJT1XY2Ve5gU+V2NlXtoKBqJ56gt8HjUuyJdEs5hENTOnJoSke6px5Ct5RD6JrcocExngrrFhgGWsiNw1+KPVCGPVBuhVV2fxn2YEW4vAIFA0egHEegHGo27/Y56YqDgD2VgC2NYGRuSw0vpxK0pRKwpxK0pRC0pVjjXgkhhBBCCCGEaBsklDpQeb1mF7yKCjOEqqoyQyhFMQOohATIyNj7bneKYgZQ9hRI6ASBcjOY8pWGg65k0Jq39VRNyE95yEOhv4LigJuqUA2KopCsuujoSMO2t6GDooJNBZs9/nZdDw/gHjJbknlqzOVIIyIFM5jStHDLKocZfrmctV0DrZZY2n4Nrsp9VfxWud0MnSp3WCHU9uqiBls9aYpK5+RcuqUcQvcUM3TqlnoI3VI6ktEcd7VTFEK2FGpsKdQQZzD2CCOELViFPVAeOwUrsAfMyRYsxx6oRNO9qIYfZ3jsq8YIaskEbSkEwiFVw1MyQVsyuprQJgNGIYQQQgghhDhQSCh1oAgGzeCpogIKC2u74xmGGUAlJkJW1v65yLa5wNYRXLngrwR/MfiKwbsTNId55z41TgulRqgOeSkPeij0V1IUrMIT8qOikKK56OzIQG2J8YdU1QyaGhJpaRXUzS6Cbg9UVJmDskeCoEhrKy1yN0EHuMLhlT08llXMFL67YBwhXWeHpzgcPJktniJBVIXf3WA1k+2J9EjtZAZPqZ3ontKR7qmd6JyUi11rA78KFI2gPZ2gPZ2aPeyqhrzYgpXhoKoCe6DSXA9WYAtUYQ+a6+bkRsGw7jTo8u1oVHUMNCugCmpJhKxlcx6yJRHUIvMkQloyQVuSdCsUQgghhBBiD0K6gabKd2YhoVT75vNBaak5FRaaXfKCQbMLXlLSvrWE2huKCs50c0roYrae8hWBv8K8k54tAWzJu+1GZRgG7pCX8pCHXf4KSoJuPCEfdsVGiuYi05G07y13mpvV0mo3++i6OR5XJLjye6CyqnZcKzBbXGmaGVxpGn4VCqjkt1AZv/lL+M1bxCbPLgo8Rfj0QIMP1Skpm+4pncyWT6md6J5qzjObo9VTG6FrLvyaC78zd887Gzq2oDscUFVZc3uwKrzuDs9r11XDj0LIbKUVrGhS3cwwK5GQlkRISyRoS6pdDs9D4e1BLdFcj5oMtYEWe0K0U7qhhycD3TAw0AkZBka4zMAgZOgYhmHuh2HtbxhG7Y1dMQAFI+gjU26IIIQQQrRrmqpw7ctfs6Gw4T+qt2Un5Ofwh7G9WrsaBwQJpdqrmhpYuxa2bzeDjKQkyM2tHZC8tVmtpzqYY0/5K8Ld+4rN6wpbojkpGoZhUBXyUhasZoe/nPJgNZ6QH6dqJ0VzkeVIbv9hiqqaU5wfjzvkZZO3iN+8RfxWXWiGT75itgXK0RvocmdHo5sjg+6ObHo4s+mW2IEeKR3pltoJlyuxNtyKtLhSVDMAa6D11QFNUQnazbGmGn2I7q8Nq0JutKA7vO7GFqo216PnoepwmBUMh1lm6LU3dMVOSEsIh1RRczW6LAE9psxllqkJ1rKMoSWawogKjHSjNhSqDYjM0Chk6BgYUfuY64ZRm64ritlINxIiqYqCqqgoUcvRZTZFw6nZsas2NEUz56qKTdGwa3ZUao/RFA3VX0ZiYiMCaSGEEEK0aRsK3fywvbK1q7FXeuYktXYVDhhtJMEQTeL1wvffw7Zt0Llz2wmi4okZe+oQCFRBoBzdW0hl9RbKgzXs0H2UGSF8ho5TsZOqJZDThAChPTAMg9JgtRk8+cwAalN4uSjQcHiRrLro4cqmuyuHHq4cujuz6eHKppOahqYbZuurUKTboA6VReGrwXDbAlW1Wl6Zy+G7CUamyFhYkfGwNBW0cDfDyHEHIUN1EHBkEnBkNuEgA8XwYwuGQ6qQBy3ucjVaqAYtVI0W8mALetBCHjTd7LSoGgHUYAB7cN8+oM1wy1UbVKkudM1FSE0w55oLXY0ud6GrznC5M2o5su4ww03R6mpbHen1Wh5ZLY5iWhvpVpgEhAOk2PBIURQUzNBHUUBFRVEUNDUcJqGiaWZQZFNt2DUNDQ27ZsOmaGiqagZGVuikooXnkbLo9br7NolXAYd8ERRCCCGEOBC04TRDxOXzwXffwZYtbT+QqkNXFCoUlTLFxnZUKgwFvx4gIeQnDQOXooGqQDu+7tUNnR3+Cn7zFdW2fvIVsclbTGWo4ZGSsm0pteGT0wygerhyyLLtYyuxSLfBkBEe9yoIPr+5rOuRK9LaMZAiLboioZQaDrHsNnCEB23XbFEBVjjssoXDLFVp2S6jbYmiYChOAg4nAZoQZkUYejisqrFCKi3kQQvWLqvW9ppwmTmpurd22TC7dkbCLdi7FlvxhFSHFVhZwVVkWYtX7ghPrqjl6PLadUNxYCjaATceV92ua7Hd0/SoYKm2C1tkXrfbGhgoKKBghkdxWh6pqKiqEm5tpGFXNWyKDZta2wIpOiCqDYoaDo0i5e2+xaoQQgghhGhz2k+iIcw7v33/fbsKpEJGiPJAFWX+Knb4iqkIuAkQJFF1kZF4CE61GxghCHkhWA2BCgh5IFRlXpyqLrMrYBtLqvx6kAJfCZvCgVMkfNrsLcFnxB/vSUGhkyPdDJyigqfuzmxSbAn7p6K76TZYn2G2utL12nkwCF5fONzSo1phhfdXFDOgUiIhVjiostvqBFlRLbUigVdkLC5Vqz32YKaohGxJhGz72ALECKKFvGghL6oVXHnN4Er3ooYamOtetJAvPPei6uFl3WedWtP9aLqf5gy6YqqOGg6q7FZQFb2uKw6MyHJ4XVftZpm17EBX7OZyZB6vLGoeCcOMmOBIj2mRFKrTfa2hAKnePCpA0pT6rY/smg27Whsa2VStXrc1TTXDprqBUnR4tE8tj4QQQgghhGglbT/VECavF374ATZvbvOBVEAPUh6oojRQwU5fCZWBaoJGiCQtgSxHGo66AzkrGtiSzMmVYwZUoRoIuM3ufv5y8+pO00Bxmnf029eQStdRAkFUfxA1EETxB1EDIZRAECUYRAnqqMEQVcEafg2V8ZtezkYq2KhUsFGrokCtRm+g0YBDV+gRSOQIXyJH+JM4wpfA4d5EDvMnkGBEd4crA6UcWG9euSpgKEp4WcFQFVAVDFUNr6sYmmqWaaq1boRDHkNVMWyaWWbTMGwauk3D0DQMu7lu2MNlNg3DbsOwRbdMUWq78jV2rG29TogV0iHkhxpvVJBVe3oMw3p+VjAVfk6oUWGWTTPvShjdGiu6BVfkOC3cuu4g7WYYl2IjZEsmZEtunvMZOqruD4dU5qTpPtSQr35ZzOSvc1z0emQeQNV9KOE3iYKOFg7PWlpIsaErNkKKHV2xYSg2dDU8V8zgywjPUR2g2lFUhzlpDlAdKOFtSmS75kQJ76eqThTNjqo6UFQnquYwlzUHKDZQ7bVTZF2xmb8fpYWSEEIIIYQ4QLXdZEPUcrvNLns7dkCnTm0ykPKF/JQHqyjxm0FUVdCDYegk2xLJdWRgUxtbZwW0BHNyZIIeRKmpQKssR6ssRqsqRHNXo9X4UX1BNK+O6guheQOoNX40rx81MvkC5rovgOoLmMGTL4DqD6AGQ9Yj6gpsTYWfs83pp+za5Z0pNJh/pXqhdzH0KobeReZy7yLoUW5g06uBaqBoH1/Z/S86pNIdZlClO2zodhtGzNxubneY++kOu3WM7rSjO+zWsuGwh8ts6E6Huey0o7vs6E6bGbQZ4ZZZRp2uhZF1w6htmdVQoBXpLhgJpuzhllmReSTsiuynRYVbigpa1HYJtuJTVHTNHHdqX0W3RDJbHOmEdB10P+g+lJAPwqEV4TBL0QOo4RZamhFEMwKoRgDNCKAZQWxGEE0PmtsIz/VAeD9zXQ2XqUYQxTDPpxjBmLpp4X3ttHwgtkeKHdRIUBVeVmzhed31qEArsh6zLU6ZosVZ1qL20eLvZ5Vp9cus8jplMZOEbUIIIYQQB7u2l26IWOXl5l32SkuhS5c2deHsCXkpD1RR5CunyF+KO+hBURSStUQ6OrOwRd39S/H5sVW6sVVWoVVVY6usxlblNpcjc3c1WpUHrTo8uc15dIC0L6rt8EsWrMuGdVm1wdMvWeBxNHxcR49KfpWdPLeDPLeTvGonR9S46BBwWC11DJuK0UmBLgplkW5oarjFE1gtoeqJDOkUCWAMzAGzdSM812vXQzqKbpYpoRDoBkpID08hcx6MMw+GUAIhlFAINVweTQ2GzIHSvf5meJUbx1AVM8Ry1p3CAZbLQcgKshxRczMMC0UCsPAUctjR7Zo1GapCbEcqapcVwq3ulNhQy+rqGA61rLsXqlH7q7HhlqrUhlvRXRgPoIvtkB4Oj+p0VwtFxkOifvc2wmMkxRtM2xr3KGYsJA1VS0azp9bryuZQ7WiqFjMGUt3xkOJ1bzMH7N7Nz8HQQQ+EA7HwFPKBESkLmOt199H9oAfB8NfuFzMPmstGY9YD4XMFao834vy+MwIQCpgtSA8oapzwSo0KraLX1dp1FDjsYsg+trWfgBBCCCGE2EcSSrVlhYVmC6nqajOQauULXcMwcIc8lPsqKC7eQnXRdvTSEhIqPXR1B0iu9GKvdGOrcGOrqDTnlVXYKt2ovn0LPAxFIZSYQCgpAT0xgVCii1BCAnqik5DLhu6yE3KqBBwGBUkBNrq8bHTV8KvDy6+2Gn5TqtiJp8Hz2xSNQ52ZdHPm0N2ZRXdXDt1dOXRzZpEcp4WIB/htn55RK9J1K6hSg+Eui4GQ2Y0xEKyd+2vLVX8QxR8wl32B2q6P/kB4W2Q5EG6NFrRaqFllvoAViCm6geY1W7btD4aqEnI5zJZZLkdtyJXgMEMtV1TrLYeNkNMRbtVlI2S31ZY7opbDLcDMjKtOuBUdcEXCK5vNDKtstqi7HKq1wVZkX63OXNXM1nmRFlyR8zXmeccZE6nuXdkaGhOpgRGRYsKjSNCjhYMluxoZD8m8G5tdtVnjIdULjvY1RGpuigqa05zaEsMAIxgVUgXDwVWkLFC/zIjeHoxaD8auG0HzxgdGsP5+RtAMxKKXrW2h+NusslDspAeBqP0bpJvhYCj+OHy7tetD6POHvX2VhRBCCCFEGyGhVFsUDMKmTfDLL+Z6584t85ilpVBSYk6lpVBail5STKCkCL2kCL20FEdZBZ0rq+mq63s+Zx2GphFMSSKYmkwoJZlgahKh5CSCKUmEUpIIJpvzUHIiwaREQsmJhJKTzCAqwWWFcoZhUBqopKBmpzl5zfnmmh1srSnE38BA4wBpmotujgy6O8073XVPyKWbM5fOzoyYll0HNFXFcKhmF7uWfuxgqDak8kZCq9oullqkzBcu89bONWvdbx4bUxaeh1vVKbqOzeMFT/N2xTIUpbYlV3Tg5Yh0TQy34nKa3RRD0d0YIy26nDbrmKBDI+i0EXTa0FUVXQMds0uproSX1fCyCrqmoofHFFM0FUPVUFQVQ1FQwq22VE0zQyRNQ1VVVM1mBkuaDU21YdNs2G0ObDY7dps9HCiZg2prauyg2dHzhgIl0cwUJdwlzw4ktnZtmocRMsOnmNBKrw3IjGB4ux4Veumx4Vf08f4qyBne2s9KCCGEEEI0Awml2pqqKjOM2rwZMjMhJWXvz6XrUFFhhkzFxeYUCZwi4VNkqqiIewoVaKgdQTAliWBaCsG0VALpKeZyemq4LIVgagrB1GRr0pMSGt3ayzAMyoNVbKnZRUHVLrYUmcHTVm8hBTU7qd5NNxa7YqNrQge6JRzCoQkd6ebqQDdXNt0cmaRrDrMLTNAT1eIgBP4Ks3GIagc00MLjprSxu/61e+HB1/XEfR+fKK5gqHYcsfDYYlr0GGM1kVArepsZgmk1/pjQK3p8Ms1nBp2KUdvCq7FjwTe66k6b1WUx5LKjOxyEnDYMpwPD5QCHA8NpR3Gayzgd4AivuxwoDieqywEOJ2qCE9UZmRQ0ux1NMVA0A9QQKF5QfbEDxlutuTTzroi28P+D6K6NVhfGqLK6LcRitkeViYNXpDtec/2v8RbD/rpjqRBCCCGEaFESSrUVum4OZP7zz2Yw1amTefexhgQC5t34iopqg6W6oVNpqTlgdCMZmkogPRVvWjI16Un40pPRMzNQMrPQM9IJZKQRTE8152nJ+zzgesjQKfKVstVbGJ52mfOaQrZ4d+02eFJQOMSZRdeEjhxqBVCH0C2hIx2d2WiNacGhB2vHgjEi48fUQNBn3gFQD8beOU61AbbwRXYksJKL7TbFphFIcqInOdCNRHQMs4saZhc1c3Qks4ubEZ6b3dzMcvPHbdQ5qWJ2N/QHcPiC2L1B7L4gdl8Am9dct4WXHf4gtho/dn8ImzeAzWu2/tJ8fpRIIFbjQ/H5UbzhyRxwCZsviM0XBPbDuEFOR+3kcsauO+zheWTZbs7t9nD4FbXdaQOHExw2c93lAKfT3F8Lj/VjjbUV1a0xEljFhF+RMbyiwrDogEupc666AVh0CKZQfzmyjxBCCCGEEKLNklCqLSgpgV9/he3bweVqePyoQABWr4YPPoD//McMrxojIwOyssyWV9nZkJVFIDMNT3oSlalOdiUrlKbYcSfZcGpOkm2JJGquZuma4w7WsM1byHZvEdu8hWzzmfOt3kJ2eIsJ7Ga8EQWFXGcmXV25Zvjk6kDXhI50dXWgS0IuTnU3o5M3hhq+s1Rd1jgpgdpBiXU/6N5wcBUMz6MCP4XawXiJDNwbHqBXgqt6IgNkG1YYZJhjH1lBkVG7T0yZQeRfhIJirSvhgbVVVHPsIxRzcG0Uc3BtFOzhLmh2RcOmqNgUzRwHKdxFzRwvSakdDwnzeC0yKDcqmjWPlCl79//FMMDrq51qfFDjjS2ztnlj94u3T8wUNV6Xz29O+4uq1IZTdQMvRzjcigRZVuBlCy/bzPXoMkfUsfbwPg4HuMLrkQALau/CWDcEU6ICLC0qCIu+A6OmhltF1gnPlPB565XXWYf42+IdJ4QQQgghhKhHQqnWVFVljh1VUGC2aMrNNS+8ohUXw6pV8Omn8NlnsUFUZiZ062YGTnVCJ7KzzfXMTLDZCOhBqoLVVAarKfSVUh504wnWoCgKCaqLZFsCWXsR8riDNezwFbHDW8x2XzE7fcVs80bWi6gMVu/2eE3R6OTMposrly4JHcy5qwOHJnSgkytn34OnvaGEL2Djdlw06gwQHBkMOAAhPxi+2iCLUHg7seNHK2o4vApPqhoVYCm0hRCrMaGRQd0WR7Gtj+pSwnfBi7wM1gDa4cG0FZSoAEjBrtixqSp2bNhUFS0ysLai1QmJwgEStefTos7bLOHR/qIokOAyp+am62YQFQmpIss1ddbrbo9M/kCd7QHw1Qm9ImPL6YYZmtU07xheDaoXdIXndltU6BUdeNnqL0cGoLfKtdpwzK7VnsdmM8+vabUtwqIDK8BqIaZQG0pZ2+O0Fou0+IqcywrLIgPdR53LavUV/ThK/RZh8VqI1T0+um51QzQJzoQQQgghRCuQUKql6brZrW77drO7nsdjBkiJ4QFtCwth7Vr49ltYswbWr489PisLTjoJxoyBgQPD4Ul9ISOEO1hDZaCY0uoqigNleIJegkYIp+ogSUsgw5Wy2wv0gB6k0F/KLl8pu3wl5txfwk5vCTt9JezwleAONXxHu4h0WwqdXTl0cuXQ2ZVDZ1cuXVy5dHblkuvMbGcDjCvhcad2NzZKZADfUOxAvZEy3V/bCitSjj98161Q/d5jEL5oNC8udUNBR8HAHADbQAkPjq2EQyRlj+FRdEhm3nktUmA+Rys0ovbOa5HQyAqPFBWbGmltZLY40qJaHkVCoeiWR6p1bO0d3GofR0FDtfZr0TuxHYhUdf8FXhHBoBlOeX3g99e2yKqJrEcFWb6odWvuN8Mvnz/+ZJ0jKgAD8xj/XtyxrTlEwimHLSq0stcGXdFzeyT4Cm/XtNq5rc5y9F0ao9ftNrM1l10zJyS4NLoAAJtVSURBVDW8bLOZQVZk7C8tHIDFBGLEltULpuqu1+kWGW9ssOgyRY0fekVCNeqeO97jxSljD/v5A2Br8ds0CCGEEG1GSDfQrM96Ido3CaVagmGA2w3l5bBli9n6SdfNC65t2+Ddd83w6ccfzaAqmqJA794wfDgceyz07x83iArqQdyhGtxBD2WBSor9FXhCXvx6ALtiI1FzkevIwKbaMAyD6pCXgpqdFPrLKPSVUewvCy+XWkFUaaAypptUQ9JsyXRy5dDRmcUhzmw6uXLoFJ4f4swm6SAYkDa6ZVFtCAQ6KoZiR1ds6Ioe3jchqhVSCEMPoetBdIIYRshsdYJ5pynFADDDLCPcQkvBQDVCmCP4GOGgyDDbXRkGCgoa4FBUbNQGR5qhYNM0bJh3YTPDpvCd2pTIHdU0VLSobUrsfpjLmqLtXWhkGA0shyeMcLlRf3u842jMPnHWG6rDbo9pyr5R6r5OSgMr1sX47o6NukCP7Bx9wU6di/f9zWaDZBskt8Bd4oJBM6DyRwVZ0fO65ZEyX6C2PBCo3VZ3PTLFK4v++YZCUBPaL0N/7ZNI+GWLDrVscQIuNRxoabX71h3o3tpHNVtu2aK6PlrjgUWVWftGh2RR69a+4UH0tej18BRpfdbgezzqvR2ohEO6QK7cgU8IIcTBSVMVrn35azYUulu7KnvlhPwc/jC2V2tXQ7QRrRZKLVmyhMWLF1NUVESvXr3405/+xIABA1qrOs0vEICdO2HdOnNA8k2bYOtW2LXLnLZuNVtJ1aWqcPjhZiuoQYPg6KPNMaGiGIaBV/fhCXlxB2soC1RSGqikKuCh2F9GVchDTchHdaiGioCbYn85JYEKiv3lFPvLKfKX4dUbN76MQ7GT68yggzOLDo5MOjizOMSVRUdnNh2dWXR0ZpGo7a+WGHECDGseb/dI0KNjGLUDWxt6KBwQGRjR4ZFhjltkAIYRssYxgtqQCcDQdfNCSDdQFAUjvI/Z0shAMcyLpciYRQpYc8WI7pqG1ZXMpmiohoJNtaEZdjRFxY7ZwihyvIqCqka1LtJqu7epejiEwkA1wsu6EbUeCXV0cx7SzQoHI621wnPCt1pHN1tv4Q+HYuFjMZ8jerh1lR4+nxI9Any8n1mk5ZUaeaHMi04rNMHcpqj1Lz6JClcix0WWo/er26LCelwlzvmIajnC7gObeEFQzHqc/eMGTUbsvO57N16IFi/cij4uZr8654v8bDBiQz5FCZdHhV6RdSXqnNGvuRr1mkbGTopp/RJVHt1NbXetbxpqEdNYkZAlqYVDbsMwg6hAsE5wFYwNsQJBMwQLBMNToPaY6HVrHoyzHjD/j0aXBYPmYwXrLNd9rwRD5tReqUqdsb/qLkfNFQOmnAGjprZ2rYUQQohWs6HQzQ/bK1u7GnulZ05Sa1dBtCGtEkqtWLGCuXPnMmfOHAYOHMhzzz3HtGnTeOedd8jKymqNKsVnGGYXk+pqM0CqqjKnykpzXlZmdsUrLjanSOBUWGgOXl69+/GU0DTo3h3y8swpPx/69iWQ4MQdcFPld1Pk3ULR1m8p8pVR4i9np8/sOlccqKAs6KYi6KZS91AR8lDZiK500ZJVFzm2VHJsaeTa0sjR0sixp9JBS6eDLZ0OtjTStaT6LWIUwA8EgKoKoAKgNuDBwFAMdL32bmaRbmSGYWAomC2JFANDj9oWbpelh0OPyMDWRNprKYp5ja0q9e+PpphlihIJhTSzo5ka1eVMVcORhWb2QEHBHm5JpClOszuaoqIpGpqqoaFiU23WuEQqCkokJAq3IjLPG+mqZg6ubbUwUm3hUClcrmqoqhY/TIm+wN9dN5Y97Vt3UOXoc8Sb1102dPOCz9DDy9SGUNZ6JJgitsxKQqICLcLlug6EzGVDDwdiRu32SHgWOV/0uaw8xah93PD7Kra8gTAoZh79Xq7TGgOjzmalthzCoZoRc3iMeK9t9MNHh2XRJ6kXSClxtkWdzIgqjzx36+WKrEf2qRNoGUb4JY8sh/fR9dpzBMNBZUg3txs6BKOW9fCyHgj/6AwIRp2bqMeNPq8RXdfoukWFZdHPNW5oFT4+OjSLaVkTFX7FtBoDMwSN3l7n2Ej4GX1s9D4qkGCDRHtU/eKcL64mBnCNEdJrw6tgyAyqIkFWJESLlEW2RwKvSIAVjD42VHtcvHnkmLrLkfNHykPBhveNTHqc8FU3zC7NgYZvfhFj1TfN+nIKIYQQQojW0Sqh1DPPPMOkSZM4++yzAZgzZw7/+c9/eP3117n88stbo0oxAv/5N+/dcg4VNeVmMKJASDXH7wlFLQdVcz2oRk3JEEiDYD4EVPA7NfxJLvyJTnwJDnwJDrwuG16nSo0dPEYAj/41Xn011SU+PP/x4d/NHen2REUh3Z5KpiOVDHsqmY50MhwpZDrTybCnkeVMI9OZQYY9FafdGRUSAUo4WFLMGMgNRDcIjQQ/4ZXoLeHrs3BIo2jh67hwGBNu7WKGNma5Nb6QqqGGgyFVNbuFqaqKptjMYEgNj0+kalYApChRAVCkXFGj9ok8llonKFJrj1fN9XrhTFMCnYaCHbFvYrrvNWaZONuovx59XMw68c8Vbx+rLN5+8R5Tj9peZ4o5pu759Drnq7M9OhxrzPOIqWOd4+Ot767rYnRRdLgVCSutdQNzfLU6YVQoVPt8o8v1qOPrTVGPBVHnjArJIucIReoSCc8Ms2WgHg7bDKLCsujXPOp513vsqCddr9Vm9D7h5UhQVfd1jC5Tos5RL5GLKjbqFMSEZXHKbYr5ye4EFBtg202ASvzfXXFDNiP2uLr7xltX6i2Y2/Vw6BnSw0FVJAQNz4Oh+vtY6wZUe2DYiPr1FkIIIRpBxmMSom1p8VDK7/fzww8/cMUVV1hlqqoyfPhwvv7665auTlxP/vQiM04ta6azhYDq8BQlGJ52w6baSLIlkmhPJMmRRJI9kRRHKimuVFKd5pTuSifdmU56QjoZrgzSnGloWjicCbfQAaxwJhLgaGrtuECaUhsGqZGWQlHrkWOij7eCoUasR5brrkeWhYhRt6WK2H/2FD41JpyKG1g1cr+4ZQ0dv4djmnK+eqFXnEBsd/vV29bQeY3aAdobOnejl/Woux1GhWpWt029ti4NBXzU2QZRreXihG3xXsN42+vtq9ffFr2PSv1vHw2dr6HlQw5F1Drgh0QQQohmJOMxCdG2tHgoVVZWRigUqtdNLysri19//bWlqxPXSb+7mf/752+UVRWh2Gwomjlpqma2vokKb1RVswaN1lQNm2ozxwlSNRyqA5tqw67asWvm5NAcODQHLpuLBFsCyY5kkh3JJDmS6q07NacVBNWdR4dEdUOehkIkCYGEEDHq/T6o2+qlxWoi2pKGAqjdBVK7K2vs/k3Z7nTu+XkcJNrNkAhCiAPCgdLKSMZjEqLtkLvvxdEruxdvT/uwtashhBBCtLw9jpEl2pK2PiTCgeJAuBCX59A2tPfnIK2MhBDNrcVDqYyMDDRNo6SkJKa8pKSE7OzsRp3DCP+l1O1un78MhRBCCNG2JSXFudFHG9McQyK0xHeq9n4RHvHEfzayvaKmtauxV/p3SeN3Q7vKc2hl7f05ROof9HrQ/e2v/gD+Gg9ut5vuqSq6397a1dkrHRKQ59AGHAjPoXuq2iKZyp6+U7V4KOVwOOjbty+rVq1izJgxAOi6zqpVqzj//PMbdY7q8F3tRo0atd/qKYQQQoiD15dffklycnJrV2O3mmNIBPlOdXBYDsxr7UrsI3kOra+91x/gV+D51q7EPpLn0DYcKM9h6Nz9/zh7+k7VKt33Lr74Ym655Rb69evHgAEDeO6556ipqWHixImNOj43N5ePPvqoXfwVUwghhBDtT1LSwTFmh3ynEkIIIcT+tKfvVK0SSp166qmUlpYyf/58ioqK6N27N0899VSju++pqkrHjh33cy2FEEIIIdqu5hgSQb5TCSGEEKI1tdpA5+eff36ju+sJIYQQQohYzTEkghBCCCFEa5K77wkhhBBCtFP7OiSCEEIIIURrklBKCCGEEKKd2tchEYQQQgghWpNiRO4FLIQQQgghhBBCCCFEC1FbuwJCCCGEEEIIIYQQ4uAjoZQQQgghhBBCCCGEaHESSgkhhBBCCCGEEEKIFiehlBBCCCGEEEIIIYRocRJKxbFkyRJGjx5N//79+d3vfsfatWtbu0rtysKFCzn77LMZPHgww4YN46qrruLXX39t7Wq1e08++ST5+fncc889rV2VdmfXrl3cdNNNHHPMMQwYMIAJEybw3XfftXa12pVQKMTDDz/M6NGjGTBgAGPGjOGxxx5D7pWxZ1988QVXXnklI0aMID8/n/fffz9mu2EYPPLII4wYMYIBAwZw0UUXsWnTptapbBu2u9cxEAjw17/+lQkTJjBo0CBGjBjBzTffzK5du1qxxqKlLViwgPz8/Jjp//7v/1q7Wu2a/P7a//b0Gs+aNave+3ratGmtVNv2qTHXJj6fjzlz5nDMMccwePBgrrnmGoqLi1upxu1PY17jqVOn1nsv//nPf26lGrc/f//735kwYQJDhgxhyJAhTJ48mY8++sja3p7fwxJK1bFixQrmzp3L1VdfzdKlS+nVqxfTpk2jpKSktavWbnz++edMmTKFV199lWeeeYZgMMi0adPweDytXbV2a+3atbz88svk5+e3dlXanYqKCs4991zsdjuLFi1i+fLl3HLLLaSlpbV21dqVRYsW8dJLL/HnP/+ZFStWcNNNN/HUU0/xwgsvtHbV2jyPx0N+fj6zZ8+Ou33RokW88MIL3HHHHbz66qskJCQwbdo0fD5fC9e0bdvd6+j1evnxxx+ZPn06b7zxBo8++ii//fYb06dPb4WaitZ0xBFH8L///c+a/v73v7d2ldo1+f21/+3pNQYYOXJkzPv6wQcfbMEatn+NuTa59957+fDDD3n44Yd54YUXKCwsZMaMGa1Y6/alsdd/kyZNinkv33zzza1U4/anY8eO3HTTTbzxxhu8/vrrHHvssVx99dWsX78eaOfvYUPEOOecc4w5c+ZY66FQyBgxYoSxcOHCVqxV+1ZSUmLk5eUZn3/+eWtXpV1yu93GKaecYnzyySfG+eefb9x9992tXaV25a9//atx7rnntnY12r3LL7/cuPXWW2PKZsyYYdx4442tVKP2KS8vz3jvvfesdV3XjeOOO8546qmnrLLKykqjX79+xltvvdUaVWwX6r6O8Xz77bdGXl6esW3bthaqlWht8+fPN04//fTWrsYBS35/7X/xfrfdcsstxvTp01upRgemutcmlZWVRt++fY23337b2mfDhg1GXl6e8fXXX7dSLdu3eNd/ch3T/I466ijj1VdfbffvYWkpFcXv9/PDDz8wfPhwq0xVVYYPH87XX3/dijVr36qqqgCkZcpeuvPOOxk1alTM+1I03r///W/69evHzJkzGTZsGGeeeSavvvpqa1er3Rk8eDCfffYZv/32GwA///wzX375Jccff3wr16x927p1K0VFRTH/v1NSUhg4cKB87uwjt9uNoiikpqa2dlVEC9q8eTMjRozgpJNO4sYbb2T79u2tXaUDlvz+ajmff/45w4YNY+zYscyePZuysrLWrlK7Vvfa5PvvvycQCMS8l3v27EmnTp345ptvWqOK7V5D13/Lli3jmGOO4bTTTuOBBx6gpqamNarX7oVCIZYvX47H42Hw4MHt/j1sa+0KtCVlZWWEQiGysrJiyrOysmRMpL2k6zr33nsvQ4YMIS8vr7Wr0+4sX76cH3/8kddee621q9JubdmyhZdeeomLL76YK6+8ku+++467774bu93OWWed1drVazcuv/xy3G4348aNQ9M0QqEQ119/PaeffnprV61dKyoqAoj7udNexgFoi3w+H/fffz/jx48nOTm5tasjWsiAAQOYO3cuPXr0oKioiMcee4wpU6awbNkyeR/sB/L7q2WMHDmSk08+mS5durBlyxYefPBBLrvsMl555RU0TWvt6rU78a5NiouLsdvt9f6IkZWVZb3PReM1dP132mmn0alTJ3Jzc1m3bh33338/v/32G48++mgr1rZ9WbduHb///e/x+XwkJiby2GOPcfjhh/PTTz+16/ewhFJiv5ozZw7r16+XMR32wo4dO7jnnnt4+umncTqdrV2ddsswDPr168cNN9wAQJ8+fVi/fj0vv/yyhFJN8Pbbb7Ns2TIeeOAB68Nv7ty55Obmyuso2pRAIMC1116LYRjMmTOntasjWtCoUaOs5V69ejFw4EBOPPFE3n77bX73u9+1Ys2E2Hvjx4+3liODQ48ZM8ZqPSWaRq5N9r+GXuPJkydby/n5+eTk5HDRRRdRUFDAoYce2tLVbJd69OjBm2++SVVVFStXruSWW27hxRdfbO1q7TPpvhclIyMDTdPqDWpeUlJCdnZ2K9Wq/brzzjv5z3/+w3PPPUfHjh1buzrtzg8//EBJSQkTJ06kT58+9OnTh88//5wXXniBPn36EAqFWruK7UJOTg49e/aMKTvssMOkS0cT3XfffVx++eWMHz+e/Px8zjzzTC688EIWLlzY2lVr13JycgDkc6eZBAIBrrvuOrZv387TTz8trWMOcqmpqXTv3p2CgoLWrsoBSX5/tY6uXbuSkZHB5s2bW7sq7U5D1ybZ2dkEAgEqKytj9i8pKbHe56JxmnL9N3DgQAB5LzeBw+GgW7du9OvXjxtvvJFevXrx/PPPt/v3sIRSURwOB3379mXVqlVWma7rrFq1isGDB7dizdoXwzC48847ee+993juuefo2rVra1epXTr22GNZtmwZb775pjX169ePCRMm8Oabb0qT7UYaMmSINQ5SxKZNm+jcuXMr1ah98nq9KIoSU6ZpGoZhtFKNDgxdunQhJycn5nPH7Xbz7bffyudOE0UCqc2bN/Pss8+SkZHR2lUSray6upotW7a0iy/k7ZH8/modO3fupLy8XN7XTbCna5N+/fpht9tj3su//vor27dvZ9CgQS1c2/Zpb67/fvrpJwB5L+8DXdfx+/3t/j0s3ffquPjii7nlllvo168fAwYM4LnnnqOmpoaJEye2dtXajTlz5vDWW2/xt7/9jaSkJKsfa0pKCi6Xq5Vr134kJyfXG4crMTGR9PR0GZ+rCS688ELOPfdcnnjiCcaNG8fatWt59dVXufPOO1u7au3KiSeeyBNPPEGnTp2s7nvPPPMMZ599dmtXrc2rrq6OaamxdetWfvrpJ9LS0ujUqRMXXHABjz/+ON26daNLly488sgj5ObmMmbMmFasdduzu9cxJyeHmTNn8uOPP7Jw4UJCoZD12ZOWlobD4WitaosW9Je//IUTTzyRTp06UVhYyIIFC1BVldNOO621q9Zuye+v/W93r3FaWhqPPvooY8eOJTs7my1btvDXv/6Vbt26MXLkyFasdfuyp2uTlJQUzj77bObNm0daWhrJycncfffdDB48uF1c0LcFe3qNCwoKWLZsGaNGjSI9PZ1169Yxd+5cjjrqKHr16tXKtW8fHnjgAY4//ngOOeQQqqureeutt/j8889ZvHhxu38PK4b8mbueF198kcWLF1NUVETv3r25/fbbreaFYs/y8/Pjls+dO1fCvX00depUevXqxR//+MfWrkq78uGHH/Lggw+yadMmunTpwsUXX8ykSZNau1rtitvt5pFHHuH999+npKSE3Nxcxo8fz9VXXy0X/HuwevVqLrjggnrlZ511FvPmzcMwDObPn8+rr75KZWUlQ4cOZfbs2fTo0aMVatt27e51nDFjBieddFLc455//nmOOeaY/V090QZcf/31fPHFF5SXl5OZmcnQoUO5/vrrZaySfSC/v/a/3b3Gd9xxB1dffTU//vgjVVVV5Obmctxxx3HttddKF8kmaMy1ic/nY968eSxfvhy/38+IESOYPXu2tOJppD29xjt27OAPf/gD69evx+PxcMghhzBmzBiuuuoq6WrfSLfddhufffYZhYWFpKSkkJ+fz2WXXcZxxx0HtO/3sIRSQgghhBBCCCGEEKLFyZhSQgghhBBCCCGEEKLFSSglhBBCCCGEEEIIIVqchFJCCCGEEEIIIYQQosVJKCWEEEIIIYQQQgghWpyEUkIIIYQQQgghhBCixUkoJYQQQgghhBBCCCFanIRSQgghhBBCCCGEEKLFSSglhBBCCCGEEEIIIVqchFJCiDZv1qxZ5Ofn8+STT8aUv//+++Tn57dSrYQQQgghhBBC7AsJpYQQ7YLT6WTRokVUVFS0dlWEEEIIIYQQQjQDCaWEEO3C8OHDyc7OZuHChQ3us3LlSsaPH0+/fv0YPXo0Tz/9dMz20aNH88QTT3DrrbcyePBgTjjhBF555ZWYfXbs2MG1117LkUceydFHH8306dPZunXrfnlOQgghhBBCCHEwk1BKCNEuqKrKDTfcwIsvvsjOnTvrbf/++++57rrrOPXUU1m2bBkzZszgkUce4Y033ojZ75lnnqFfv368+eabnHfeedxxxx38+uuvAAQCAaZNm0ZSUhJLlizhpZdeIjExkUsvvRS/398iz1MIIYQQQgghDhYSSgkh2o2TTz6Z3r17M3/+/HrbnnnmGYYNG8bVV19Njx49mDhxIlOmTGHx4sUx+x1//PFMmTKFbt26cdlll5GRkcHq1asBWLFiBbquc88995Cfn0/Pnj2ZO3cuO3bs4PPPP2+R5yiEEEIIIYQQBwsJpYQQ7cpNN93Em2++ycaNG2PKf/31V4YMGRJTNmTIEDZv3kwoFLLKogdGVxSF7OxsSkpKAPj5558pKChgyJAhDB48mMGDB3PMMcfg8/koKCjYj89KCCGEEEIIIQ4+ttaugBBCNMVRRx3FiBEjeOCBB5g4cWKTj7fZYn/tKYqCYRgAeDwe+vbty/3331/vuMzMzL2rsBBCCCGEEEKIuCSUEkK0OzfeeCNnnnkmPXr0sMoOO+wwvvrqq5j9vvrqK7p3746maY06b9++fXn77bfJysoiOTm5WesshBBCCCGEECKWdN8TQrQ7+fn5TJgwgRdeeMEqu+SSS1i1ahWPPfYYv/32G0uXLmXJkiVccskljT7vhAkTyMjIYPr06axZs4YtW7awevVq7r777riDqwshhBBCCCGE2HsSSgkh2qWZM2ei67q13rdvXx5++GFWrFjBhAkTmD9/PjNnzmxSF7+EhARefPFFOnXqxIwZMzj11FP54x//iM/nk5ZTQgghhBBCCNHMFCMymIoQQgghhBBCCCGEEC1EWkoJIYQQQgghhBBCiBYnoZQQQgghhBBCCCGEaHESSgkhhBBCCCGEEEKIFiehlBBCCCGEEEIIIYRocRJKCSGEEEIIIYQQQogWJ6GUEEIIIYQQQgghhGhxEkoJIYQQQgghhBBCiBYnoZQQQgghhBBCCCGEaHESSgkhhBBCCCGEEEKIFiehlBBCCCGEEEIIIYRocRJKCSGEEEIIIYQQQogWJ6GUEEIIIYQQQgghhGhxEkoJIYQQQgghhBBCiBYnoZQQQgghhBBCCCGEaHESSgkhhBBCCCGEEEKIFiehlBBCCCGEEEIIIYRocRJKCSGEEEIIIYQQQogWJ6GUaFX5+fksWLDAWn/jjTfIz89n69atuz1uwYIF5Ofn7+/qHTBmzZrF6NGjW7sazWrq1KlMnTq10fuedtpp+7lGrast/p+YNWsWgwcPbtZz5ufnc+edd+5xv3i/S+q+Z7Zu3Up+fj5vvPFGox87+veVEEIcbA6Gz1NxYFu7di39+vVj27ZtrV0Vy+jRo7niiiv2uN/q1avJz89n9erVTX6MyLHvvPPOHve9/vrrufbaa5v8GELsLQmlxH6zZMkS8vPz+d3vftfaVYnx1VdfsWDBAiorK1u7Ks1q165dLFiwgJ9++qm1q9IqDvbnL5rHRx99JMGTEGK/Wr9+PTfddBMjR46kX79+jBgxghtvvJH169e3dtXatCeeeIL333+/tavRppSVlfHUU08xZcoUjj32WI488kgmTZrEihUr9njs448/Tn5+fqNDxnfffZfrrruOk046iYEDBzJ27FjmzZu3x+/TBQUF9O/fn/z8fL777rtGPVY8o0ePJj8/35qGDRvGeeedx3vvvdek8zz00EOMHz+ezp07W2VLlixp9B+oDgaXXXYZ7777Lj///PM+n+sf//gH48aNo3///pxyyim88MILjTpu1qxZMT/vutOuXbusfZ944gkmTZrEscceaz3OPffcQ2lp6T7XX7QMW2tXQBy4li1bRufOnVm7di2bN2+mW7duzXbu6dOnc/nll+/VsV9//TWPPvooZ511Fqmpqc1Wp9ZWWFjIo48+SufOnendu3fMtrvuugvDMFqpZvvH4sWLY9Z39/zFweeMM85g/PjxOByOBveJ/H6y2Wo/Cj/66COWLFnCNddcU2//tWvXomnafqmvEOLg8O6773LDDTeQnp7O2WefTZcuXdi2bRuvvfYaK1eu5KGHHuLkk09u7Wq2SQsXLmTs2LGMGTOmtavSZnzzzTc8/PDDHH/88UyfPh2bzcbKlSu5/vrr2bBhAzNnzox73M6dO1m4cCGJiYmNfqw//elP5Obmcvrpp9OpUyfWrVvHiy++yEcffcTSpUtxuVxxj7v33nux2Wz4/f69eo7RevfuzcUXXwyY3/teeeUVZsyYwR133MG55567x+N/+uknPv30U15++eWY8pdeeomMjAwmTpy4z3Xcn4466ijWrl2L3W7fr4/Tp08f+vXrx9NPP81999231+d5+eWXmT17NmPHjuXiiy9mzZo13H333dTU1OzxOm7y5MkMGzYspswwDO644w46d+5Mhw4drPIffviBXr16ceqpp5KUlMSvv/7Kq6++ykcffcSbb77ZpPe5aB0SSon9YsuWLVb48+c//5lly5YxY8aMZju/zWaLuZBsC2pqakhISGjtasS1vz+8WsPuwgaxf/h8Pux2O6ra9hvZapq2xwBJURScTmejz9mUfYUQoq6CggJuvvlmunbtypIlS8jMzLS2XXDBBUyZMoWbb76Zf/3rX3Tt2rXF6uXxeOSirZ06/PDDWblyZUyrn/POO4+LLrqIRYsWcemll8b92f7lL39h4MCB6LpOWVlZox5r/vz5HHPMMTFl/fr145ZbbmHZsmVxe0Z8/PHH/O9//+PSSy/l8ccfb+Kzq69Dhw6cccYZ1vqZZ57JKaecwrPPPttgKBUMBtF1HYfDweuvv06nTp0YNGjQPtelNaiq2mLfRcaNG8eCBQuorq4mKSmpycd7vV4eeughTjjhBObPnw/ApEmT0HWdxx9/nMmTJ5OWltbg8YMHD643BMSaNWuoqalhwoQJMeXxWrgPGjSImTNn8uGHHzJ+/Pgm11+0rLZ/ZSHapWXLlpGWlsaoUaMYO3Ysy5Yta9bzxxs/JzLWzPvvv89pp51Gv379GD9+PP/9739jjosk/ieddJLVBDR63Jl//vOfTJw4kQEDBnD00Udz/fXXs2PHjpjHioyp8P333zNlyhQGDhzIgw8+yBVXXMFJJ50Ut86TJ0+u9xeYpjzWhg0bmDp1KgMHDmTkyJEsWrTI2mf16tWcc845ANx6663W84o0RY43ppTH42HevHmMGjWKfv36MXbsWBYvXlyvRVVjXlcAt9vNPffcw+jRo+nXrx/Dhg3j4osv5ocffoj7egD8/PPP5Ofn88EHH1hl33//Pfn5+Zx11lkx+1566aUxX3iixwfa0/OP2N1ruCeN+VkBvPLKK4wZM4YBAwZwzjnnsGbNmnpjGTU0dlq8sQLWrFnDzJkzOeGEE+jXrx+jRo3i3nvvxev1Nrru0aLfu7///e8ZMGAAo0eP5qWXXopbl+XLl/PQQw8xcuRIBg4ciNvtBuDtt9+2Xo9jjjmGm266KaYpdbQtW7Ywbdo0Bg0axIgRI3j00Ufrvc8WL17M73//e4455hgGDBjAxIkTdzvuwb/+9S/Gjh1L//79mThxIl988UXM9saMT1d3TKlZs2axZMkSgJgm4hHxxpTatWsXt956K8OHD7f+b7z22mv1HuuFF15g/PjxDBw4kKOOOoqJEyc2++9FIUTb9tRTT1FTU8Ndd90VE0gBZGZmcuedd+LxeKzPpnfeeYf8/Hw+//zzeud6+eWXyc/P55dffrHKNm7cyMyZMzn66KOt343Rn69Q+7vx888/54477mDYsGGMGjUKaNrneGM+T0tKSrjtttsYPnw4/fv35/TTT2fp0qX19mvM95H8/Hw8Hg9Lly61fjfPmjWrwdc68hm2YsUKHn30UUaOHMngwYOZOXMmVVVV+P1+7rnnHoYNG8bgwYO59dZb47bkacxnf2M/pyPjLO7atYurrrqKwYMHc+yxx/KXv/yFUCgUs29hYSEbN24kEAg0+BwBunbtGhNIgfkHlzFjxuD3+9myZUu9Y7744gtWrlzJbbfdtttz11U3kAKsVmsbN26sty0QCHDPPfdwwQUXcOihhzbpsRorJyeHww47zBofKvK5vnjxYp599lnGjBlD//79rfp98MEHHHvssSiKYp1j9OjRrF+/ns8//9x6b0V/Z9uyZYv1/2rgwIFMmjSJ//znP/Xq0tj3++6sWbOGc845h/79+3PSSSfx5ptvxmxvaEypJUuWcNJJJ+32u2dEJBQ6/vjj6d+/PxdeeCGbN2+ut9/w4cPxeDx8+umnMeXbt2+P+/Oua/Xq1ZSXl3PeeefFlE+ZMgWPxxP3NdyTt956C0VRGtXlNPL/4kAbruVA1baamogDxrJlyzj55JNxOBycdtppvPTSS6xdu5YBAwbs18f98ssveffddznvvPNISkrihRdesFLyjIwMTj75ZDZt2sRbb73FrbfeSkZGBoD15fDxxx/nkUceYdy4cZxzzjmUlpby4osvMmXKFN58882Y7n7l5eVcdtlljB8/ntNPP52srCz69u3LLbfcUu+5btu2jW+++Yabb77ZKmvKY1VUVHDppZdy8sknM27cOFauXMn9999PXl4eo0aNomfPnsycOZP58+czefJkhg4dCsCQIUPivk6GYTB9+nQrzOnduzcff/wx9913H7t27ar3RWVPryvA7NmzWblyJeeffz49e/akvLycL7/8ko0bN9K3b9+49cjLyyM1NZU1a9ZYYd6aNWtQVZWff/4Zt9tNcnIyuq7z9ddfM2nSpLjnaczz39NruDuN/Vn94x//4M9//jODBw/mwgsvZMuWLUyfPp20tDQOOeSQ3T5GQ9555x28Xi/nnnsu6enprF27lhdffJGdO3daf3lqqoqKCi6//HLGjRvH+PHjefvtt7njjjuw2+1WuBfxt7/9DbvdzrRp0/D7/djtdt544w1uvfVW+vfvzw033EBJSQnPP/88X331Vb33bigU4tJLL2XgwIH84Q9/4OOPP2bBggWEQqGYQTSff/55Ro8ezYQJEwgEAixfvpxrr72WhQsXcsIJJ8TU6YsvvmDFihVMnToVh8PBSy+9xKWXXso//vEP8vLy9uo1ATM4Liws5JNPPmlUc/Xi4mImTZqEoihMmTKFzMxM/vvf//LHP/4Rt9vNRRddBMCrr77K3XffzdixY7ngggvw+XysW7eOb7/9tt5f+4QQB64PP/yQzp07c+SRR8bdftRRR9G5c2c++ugjAE444QQSExN5++23Ofroo2P2XbFiBUcccYT1O2/9+vWce+65dOjQgcsuu8w67uqrr2bBggX1ugTOmTOHzMxMrr76ajweD9D4z/HGfJ56vV6mTp1KQUEBU6ZMoUuXLrzzzjvMmjWLyspKLrzwQqDx30fuu+8+br/9dgYMGGB9F2hM0PHkk0/icrm4/PLL2bx5My+++CI2mw1FUaisrGTGjBl8++23vPHGG3Tu3DmmVX9jP/ub8jkdCoWYNm0aAwYM4Oabb2bVqlU8/fTTdO3aNebi/cEHH2Tp0qV88MEHdOnSZY/P8//bu/M4Kap77+Ofqup99hUYQFRwAEFk0SiK+xZRr2s0Xre4xGg0GqMJxuSqKAkkEq8ajRo0xn1JXB6JGKMxyTURNW5RFFF2mAFm33rvqvP8Ud09vQ3MwDA9A7/361VW1anT1aebtpfvnHMqU1NTE0DyO1rq/d9+++2cddZZ/XJxlJ7uB+DRRx+lo6OD7373u/zlL3/Z4fvKJRqNsnnzZkpLS9PKX3jhBcLhMGeffTYul4uSkhK2bNlCfX09++67b1rdm266idtvvx2fz8cVV1wBQGVlJWA/vm9+85sEg0EuuOACysrKePHFF7nyyiu55557kv9f9fb1vjXr1q3j2muv5ayzzuL000/n+eef58Ybb2TSpEnss88+Pd7uqaee4rbbbuOAAw7gW9/6FnV1dVx11VUUFxczfPjwrPqLFi1C0zQuueQSurq6eOihh7jhhhv4wx/+kFZv3LhxeDwePvzww7T3jzlz5vDee++xYsWKrT6ezz//HLB706WaNGkSuq6zfPnytF5v2xKNRnn11VeZNm1azv8nlFK0trZimibr1q1j4cKFGIaR9d4pBiklRD/79NNPVW1trfrXv/6llFLKsix1+OGHq3nz5mXVra2tVffcc09y//nnn1e1tbVqw4YNW72Pe+65R9XW1mada9KkSWrdunXJsuXLl6va2lr1+OOPJ8seeuihnPexceNGNXHiRHX//fenla9YsULtu+++aeXnn3++qq2tVU8//XRa3c7OTjV58mS1YMGCtPJFixap8ePHq7q6uu2+rxdffDFZFg6H1aGHHqq+973vJcs++eQTVVtbq55//vms52vOnDnqqKOOSu6//vrrqra2Vv3mN79Jq/e9731PjR8/Pu057O3zOmPGDDV37tys+96Wyy+/XJ111lnJ/auvvlpdffXVauLEieof//iHUkqpzz77TNXW1qo33ngjWe/8889X559/fq8ef2+fw1x6+28ViUTUzJkz1amnnqrC4XCy3rPPPqtqa2vT2trT6/ydd95RtbW16p133kmWBYPBrDY9+OCDaa8npXL/P5FL4rn43e9+lywLh8Pq1FNPVTNnzlSRSCStLcccc0xaGxKP8+STT1ahUChZ/re//U3V1taqu+++O1k2Z84cVVtbq26//fZkmWVZ6vLLL1eTJk1Szc3NPT7OSCSiTj75ZHXhhRemldfW1qra2lr16aefJsvq6urUfvvtp6666qpkWa7nOPM1s2HDhqzXzNy5c3t8HjPfr2666SZ16KGHqpaWlrR61113nZoxY0byMV155ZXqpJNOynlOIcTuoaOjQ9XW1qorr7xyq/WuuOIKVVtbqzo7O5VSSv3gBz9QM2fOVLFYLFmnoaFBTZgwQd17773JsosuukidfPLJaZ8/lmWpc845Rx1//PHJssR747nnnpt2TqV69zne28/T3//+96q2tlb9v//3/5JlkUhEnXPOOWrq1KnJx9eX7yNTp05Vc+bM2Wr7EhKfYSeffHLyc00p+/kcP368uuyyy9Lqn3POOWnfk/ryPa23n9OJz8TUfzellDrttNPU6aefnlaWqLut78O5tLa2qpkzZ6r//u//zjr2xBNPqBkzZiQ/f88///wd+ny66aab1MSJE9WaNWvSyhsaGtS0adPUM888o5Tqft198skn231fRx11lLrkkktUc3Ozam5uVsuXL1fXXXdd2veMxOf69OnT075jKKXU22+/rWpra9Wbb76Zde6TTjop7ftBws9+9jNVW1ur/v3vfyfLurq61NFHH62OOuooZZqmUqr3r/etPbbM+2lubs76TZH5PTEcDquvfe1r6swzz1TRaDRZ74UXXsj67pm47Yknnpj2PvHoo4+q2tpatWLFiqx2HX/88Vn/ryTeA7Zl7ty5auLEiTmPHXzwweq6667b5jlSvfnmm6q2tlY9+eSTOY83NDQkvyPW1taqww8/XL3yyit9ug+RPzJ8T/S7xYsXU1lZmezmq2kas2fPZsmSJVndk/vbIYcckvaXswkTJlBYWJiz+3Km119/HcuyOPHEE2lpaUkulZWVjBkzJqurrMvlyhqOV1hYyOGHH86rr76a1u18yZIlTJ06lZqamu26L5/Pl/bXBJfLxX777derx5XL//3f/2EYRla33ksuuQSlVNbQvN48r8XFxfznP//pcQhXT2bMmMHnn3+e/EvtBx98wOGHH86ECRP44IMPALv3lKZpyR5Q22N7n8Pe/lstW7aM5uZmvvnNb6bNd3X66adTVFS03e1OnTg0EAjQ0tLCtGnTUEol/wrVVw6Hg3POOSe573K5OOecc2hubs4apnHaaaeltSHxOM8999y0eQ2OPPJI9t5775zdsc8777zkdqJXUTQaZenSpTkfZ3t7O52dncnXRqZp06al/eWtpqaGY445hn/+8587/T0mQSnFX/7yF44++miUUmmvjVmzZtHZ2Zl8LouLi9m8eTOffPLJgLRNCDH4+P1+gG3OzZI4nqh/4okn0tzcnDaE77XXXsOyLGbPng3YPbffeecdTjzxRLq6upLvRa2trcyaNYu1a9dmfTafffbZWfPu9fZzvDefp//3f/9HVVVV2jAbp9PJBRdcQCAQSA657uv3kb469dRT0+bVnDJlCkopzjzzzLR6U6ZMYdOmTcRiMaBv39P6+jmdOffRjBkzsoaaL1iwgBUrVvS5l5RlWdxwww10dHTwP//zP2nHWltbueeee/jud7+bNXx0eyxevJg//vGPXHzxxey5555pxxYuXMjo0aP7/Qrc//znP5k5cyYzZ87k1FNP5c9//jOnnnoqN9xwQ1q9448/PusxJubO6stFjv7xj38wZcqUtN6NBQUFnHPOOdTV1bFy5Uqg96/3rRk3blza/ZSXl7PXXntt9XvqsmXLaGtr4+yzz06ba/eUU07pcb6mM844I+17auI+c91PSUlJ1pxjjz/++DZ7SYHde6ynOW3dbnefp6H405/+hNPp5MQTT8x5vKSkhEceeYQHHniAa665hrKysuRvCzH4yfA90a9M0+SVV17hoIMOSvuAnTJlCr/73e9YunQps2bN2mn3n2uIVElJSa/GE69duxalFMcff3zO45kTqw8bNiznZNuzZ8/mjTfe4KOPPmL69OmsX7+ezz77LG1IXF/va/jw4Wnj3xOPqzcfCrnU1dVRXV1NYWFhWvnYsWOTx1P15nm94YYbuPHGGznyyCOZNGkSRxxxBKeddto2J2s94IADiMVifPzxxwwfPpzm5mYOOOAAVq5cyfvvvw/YodS4ceOyumf3xfY+h739t6qvrwfIusqk0+ncoQlr6+vrueeee3jzzTdpb29PO5aY36mvqqursyY+TXyhrKurS5sANPMLceJx7rXXXlnn3XvvvZNBYoKu61mPP3Hb1NfZ3/72N+6//36WL1+eNq9H5r8ZZD/HifYHg0FaWlqoqqrKOt7fWlpa6Ojo4Nlnn+XZZ5/tsQ7Yl1Z+++23+cY3vsGYMWM49NBDOfnkk3coZBVCDC2ZYVNPMsOrww8/nKKiIpYsWZK8EtWSJUuYOHFi8r10/fr1KKW4++67ufvuu3Oet7m5Oe1qVbnCjt5+jvfm87Suro4xY8ZkXRgj8T0j8VnS1+8jfZX4Y2BC4o9Emd9rioqKsCyLzs5OysrK+vQ9rS+f0263OyssKSkpybrd9rr99tt56623+MUvfsGECRPSjt11112UlJRw/vnn7/D9vP/++/zkJz9h1qxZXHfddWnHPv74Y/7f//t//P73v+/3C6Psv//+fP/730fTNDweD2PHjs0ZMm0tzFN9uBp1fX09+++/f1b53nvvnTxeW1vb69f71vT0XXtrr43EeTOHsjocjqy5xhIy/59IPH+5fisppXJ+D+sNj8fT45xo4XC4x6s15uL3+/nrX//KrFmzcg4VBTscP+SQQwA46qijmDlzJueeey4VFRUcddRRfX8AYkBJKCX61TvvvENjYyOvvPIKr7zyStbxxYsX79RQqqerbfXmA8iyLDRNY9GiRTnPk/kjvqc306OOOgqv18urr77K9OnTefXVV9F1na9//evbfV/5vgx9b57X2bNnc8ABB/D666/zr3/9i4cffphFixbx61//eqtzNk2ePBm3282///1vampqqKioYK+99uKAAw7gqaeeIhKJ8MEHH+zwJaC39zns679Vb/T0AW9ZVtq+aZpcfPHFyfk79t57b3w+H1u2bOHGG2/Mqr8z9OVLw/Z6//33ufLKKznwwAO55ZZbqKqqwul08vzzz/OnP/1pp9//9kg89//1X/+VNSl/QmK+jrFjx/LnP/+Zv//977z11lv85S9/4amnnuKqq67q8XLdQohdS1FREVVVVdv8Q8iKFSsYNmxYMqRxuVwce+yxvP7669xyyy00Nzfz4Ycf8oMf/CB5m8T70SWXXMJhhx2W87yZP1pzXcGrt5/j+f5O0hc9hSI9lSe+1/T2s7+vn9M787m79957eeqpp7j++us57bTT0o6tXbuW5557jptuuomGhoZkeTgcJhqNsnHjRgoLC3v1x78vvviCK6+8kn322Yd77rkn6w+pd9xxBwcccACjRo1K/oE60dumsbGR+vr6rGCkt8rKypLBw9bk+u6SCDMG68TXA/X/1bZe+6k6Ojpy/iGwN6qqqjBNk+bmZioqKpLlkUiEtrY2qqure32uN954I+dV97Zm+vTpVFVVsXjxYgmlhgAJpUS/Wrx4MRUVFdx8881Zx15//XVef/115s6dOyA/dHvSUyCwxx57oJRi1KhROXuB9JbP5+PII4/kz3/+Mz/+8Y9ZsmQJBxxwQNpfKPvrvlL15S8ZI0eOZOnSpcmJxBNWr16dPL49qqurOe+88zjvvPNobm7m9NNP54EHHthqKOVyuZgyZQrvv/8+NTU1yW7EM2bMIBKJ8PLLL9PU1MSBBx641fve3r/kbEtv/60SX7DWrVuX/Gs2kPyyl/oXy8RfpTo7O9POkfkX4S+//JK1a9fyi1/8Iu0L5r/+9a/tfjxgX9Un8xLga9euBbb9b594nGvWrEl7nImyzC+almWxYcOGtOduzZo1aff12muv4Xa7efjhh9N6Hz7//PM525DrKjFr167F6/Xu8JCE3r6OysvLKSgowLKsXn1B9vl8zJ49m9mzZxOJRPje977HAw88wHe+850Bu7yzECK/jjrqKJ577jnef//9nJOdv//++9TV1aUNrwZ7CN+LL77I0qVLWbVqFUqptCEsiZ5MTqezV+9HW7M9n+O5jBw5khUrVmBZVtqP4MT3jMRnxc76PrKjevvZv7M+p/vqySef5Ne//jUXXXQRl19+edbxLVu2YFkW8+bNY968eVnHjznmGC688EJ+8pOfbPV+1q9fz2WXXUZ5eTmLFi3KORx106ZN1NXV5bwa9ZVXXklRUVGyJ/xASvRuynVV3p4++2tqapLfWVLleh335vXe3xLnXb9+PQcffHCyPBaLUVdXt0OT2cdiMTZt2pR19e7emjhxImAPMUx9/1i2bBmWZWX15NuaxYsX4/P5+tyWSCSS9V1bDE4yp5ToN6FQiL/85S8ceeSRfP3rX89azjvvPPx+P2+++WZe2+n1eoHsQOD444/HMIycl6tX8Ss69Nbs2bNpaGjgD3/4A1988UXW+Of+vK+ExOPqzV+ADj/8cEzT5Mknn0wr//3vf4+maRx++OF9um/TNLOez4qKCqqrq3NeYjnTjBkz+OSTT3j33XeTQ5rKy8sZO3Zs8jLTPV2tKKEvj78vevtvNXnyZMrLy3nmmWfSHvOLL76Y1abEX6xT5xgwTZPnnnsurV7ii03q/SqleOyxx3boMcVisbQhZ5FIhGeffZby8vIer5SYMHnyZCoqKrIe5z/+8Q9WrVqVdaU8IO11ppTiySefxOl0JkMtwzDQNC1tPqiNGzdmXco84aOPPkqb+2rTpk389a9/5dBDD93hvzT29nVkGAYnnHACr732Wtol2RMSQ/eArP+fXS4XY8eORSm1zct9CyF2HZdeeikej4dbbrkl632hra2NW265Ba/Xy2WXXZZ27JBDDqG0tJQlS5bw6quvMmXKlLQhdRUVFXzta1/j2WefTesFk5D6ftSTHf0cz3T44YfT2NjIkiVLkmWxWIzHH38cn8+X/ENTX76P+Hy+Aevl0tvP/p31Od3Q0MCqVat69RmxZMkS5s2bxymnnMKPf/zjnHX22Wcf7rvvvqxln332oaamhvvuuy/t6rv19fWsWrUq7RyNjY1ccsklaJrGww8/3OMfgW677bas+0nMGTZnzhwWLlzY26ehXw0bNowRI0awbNmyrGNerzfna+uII47gk08+4aOPPkqWBQIBnnvuOUaOHMm4ceOA3r/e+9vkyZMpLS3lueeeS86HBnaIs6NDQleuXEk4HGbatGlp5bleG7kcfPDBlJaW8vTTT6eVP/3003i93rTviy0tLaxatYpgMJh1npaWFpYuXcpxxx2X/I6WKhAI5Lzda6+9Rnt7e9bV/8TgJD2lRL9588038fv9PabYU6dOpby8nJdffjk5OWc+JH50/+///i+zZ8/G6XRy1FFHsccee/D973+fX/3qV9TV1XHsscdSUFDAxo0beeONNzj77LO59NJLe3UfRxxxBAUFBfziF79I/nhN1Z/3lXrO4uJinnnmGQoKCvD5fFlfXBOOPvpoDjroIP73f/83+ZeUf/3rX/z1r3/loosu6tVlllP5/X6OOOIITjjhBCZMmIDP5+Ptt9/m008/5cYbb9zm7Q844AAeeOABNm3alBY+HXDAATz77LOMHDky52Vtt/fx90Vv/62cTiff//73ufnmm7nooouYPXs2Gzdu5IUXXshqwz777MPUqVO58847aW9vp6SkhCVLlqR9oQD7r3p77LEHv/jFL9iyZQuFhYW89tprO/ylvLq6mkWLFlFXV8eee+7JkiVLWL58ObfffnuPk1ImOJ1ObrjhBn784x9z/vnnc9JJJ9Hc3Mxjjz3GyJEj+da3vpVW3+1289ZbbzFnzhymTJnCW2+9xd///neuuOKK5BfaI444gkceeYTLLruMk08+mebmZp566in22GOPnENdamtrufTSS7ngggtwuVzJLzzf+973duh5ge73h3nz5jFr1iwMw+Ckk07KWff666/n3Xff5eyzz+Yb3/gG48aNo729nc8++4ylS5cmJya+9NJLqaysZPr06VRUVLB69WqeeOIJjjjiiKx5VIQQu64999yTBQsW8MMf/pBTTjmFs846i1GjRlFXV8cf//hHWltbufPOO7M+g51OJ8cddxyvvPIKwWCQOXPmZJ37lltu4b//+7855ZRTOPvssxk9ejRNTU18/PHHbN68mZdffnmrbdvRz/FM55xzDs8++yw33ngjn332GSNHjuS1117jww8/5Kabbkq+9/Xl+8ikSZNYunQpjzzyCNXV1YwaNSrnfD/9obef/Tvrc/rOO+/kxRdf5K9//etW50f65JNP+NGPfkRpaSkzZ87M+neePn06o0ePpry8POc0CI8++ihA1rE5c+bw3nvvpX0GX3bZZWzYsIHLLruMDz74IG0OycrKSg499FCAnNN0JJ6PAw88kP322y9ZvnHjRo455hhOP/10FixY0OPj7C/HHHMMr7/+etZcSZMmTeLpp5/mN7/5DWPGjKG8vJyZM2dy+eWX88orr/Dtb3+bCy64gJKSEl566SU2btzIr3/962Qo2dvXe39zuVx873vf4/bbb+eiiy7ixBNPpK6ujhdeeKHP3+Uzvf3223i93qzel7leG7l4PB6uueYabrvtNq655hoOO+ww3n//fV5++WWuu+66tKGiTz75JPfeey+PPfZY8kJZCYnvxz0N3Vu3bh3f+ta3mD17NnvvvTe6rrNs2TJefvllRo4cyYUXXrh9T4AYUBJKiX7z8ssv43a7kx9KmXRd58gjj2Tx4sW0trb2OFHdzjZlyhSuvfZannnmGd566y0sy+Kvf/0rPp+Pyy+/nD333JPf//733HfffYA9oeehhx7apy6jbrebo48+msWLF3PIIYekjaVO6K/7SnA6nSxYsIA777yTW2+9lVgsxvz583OGMrquc//993PPPfewZMkSXnjhBUaOHMmPfvQjLrnkkj7ft8fj4dxzz+Vf//oXf/nLX1BKscceeyS/JG/LtGnTMAwDj8eT1p03EUptq5cU9O3x91Vv/63OOeccTNPk4Ycf5pe//CW1tbXcf//9OSeeXbhwITfffDO//e1vKS4u5qyzzuKggw7i4osvTntMDzzwAPPmzePBBx/E7XZz3HHHcd5556Vd+aivSkpKWLBgAfPmzeO5556jsrKSm2++mbPPPrtXtz/jjDPweDwsWrSIhQsX4vP5OPbYY/nhD3+YNeGoYRg89NBD3Hrrrdxxxx0UFBRw9dVXc9VVVyXrzJw5k5/97GcsWrSIn//854waNYobbriBurq6nF96DjzwQKZOncp9991HfX0948aNY/78+X3qCt6T448/ngsuuIBXXnmFl19+GaVUj6FUZWUlf/jDH7jvvvt4/fXXefrppyktLWXcuHFpVwI655xzWLx4MY888giBQIDhw4dzwQUX8N3vfneH2yuEGFpOPPFE9t57b37729/yxz/+kba2NkpLSznooIP4zne+Q21tbc7bzZ49mz/84Q9ompbz6lPjxo3j+eef59577+XFF1+kra2N8vJy9t1337T3257s6Od4rvM9/vjjLFy4kBdffJGuri722msv5s+fn3bl4r58H7nxxhu5+eabueuuuwiFQpx++uk7LZSC3n3276zP6d5auXIl0WiUlpaWtAvqJPTX9yCw55ICeOihh7KOfe1rX+vx+//WJK6ONhAXKAE488wzeeKJJ/jggw/SvlteddVV1NfX89BDD+H3+/na177GzJkzqays5JlnnuGOO+7giSeeIBwOM378eB544IG0nj69fb3vDOeffz5KKR555JHkBPf3338/8+bN26HpAf785z9z3HHH7VCgdt555+F0Ovnd737Hm2++yYgRI/jxj3/MRRdd1OtzJKaG6Wlo8rBhwzjhhBN45513eOmll4hGo4wcOZLzzjuPK664Im+/N0XfaKovlyAQQgjRZ4lu648//nieW2K3pbW1ddBOIC6EEEKI3cOTTz7JwoULef3116msrByQ+7zooouorq7mjjvuGJD7ywfLspg5cybHHXdczjnEtmX58uWcfvrpvPjii8m5oYTYmWROKSGEEEIIIYQQA+rdd9/lggsuGLBACuAHP/gBr776atbFZYaqcDicNe/ZSy+9RFtbG1/72te265y//e1vOeGEEySQEgNGhu8JIYQQQgghhBhQ99xzz4Df5/77759zsvOh6uOPP2b+/Pl8/etfp7S0lM8//5w//vGP1NbW8vWvf327zvm///u//dxKIbZOQikhhBBCCCGEEGKISVwM6PHHH09ePOfUU0/lhhtuwOVy5bt5QvSKzCklhBBCCCGEEEIIIQaczCklhBBCCCGEEEIIIQachFJCCCGEEEIIIYQQYsD1OZT697//zRVXXMGsWbMYP348b7zxRtpxpRR33303s2bNYsqUKXzrW99i7dq1aXXa2tq4/vrrmT59OgcccAA33XQTfr+/121QStHV1ZV1pQEhhBBCCNF78p1KCCGEEPnU51AqEAgwfvx4brnllpzHFy1axOOPP86tt97Kc889h9fr5dJLLyUcDifr3HDDDaxcuZJHHnmEBx54gPfff5+bb765123w+/3MmDGjT0GWEEIIIYRIJ9+phBBCCJFPfQ6ljjjiCK677jqOO+64rGNKKR577DGuvPJKjj32WCZMmMAvf/lLGhoakj2qVq1axVtvvcW8efPYf//9OeCAA/jpT3/KK6+8wpYtW3b8EQkhhBBCCCGEEEKIQc/RnyfbuHEjjY2NHHLIIcmyoqIi9t9/fz766CNOOukkPvroI4qLi9lvv/2SdQ455BB0XeeTTz7JGXYNuGgHrHsGrBg4CsFZZK9dpeAsia9LwZDLbAohhBBCCCGEEEJsj34NpRobGwGoqKhIK6+oqKCpqQmApqYmysvL0xvhcFBSUpK8fd6t/j18cO226zkKwF0Jrgp77RkG3hrwjbTXBWOgaJwdYgkhhBhylFIoVNp2Yu6dzO1c9bdWr6fb9XS8L/vbKst1297cfmvnyGpTjmPJ5yPjftL2UaTeZeZxgBJPCXuX7Y0QQgghhibTUhi6lu9m7JBd4TEMBv0aSu0yRp8JrZ+Afw3EAhDzgxmAWBdEu8CMz7sQ89uLf93Wz+csBu9IKNgDCsdBUS0UT7D3DRdoxjYWh73WjZ3/2IUQYgcppbCUlQxjFPH9lFAn83jmOrN+X9aWZWFhJe/HUvY+iuS+UipZJ3U7s12Jx5MaLCXKID1gSQ1TthZMbe0cPZ0nsdI0LSu0QSOtTmJfQ0Mp1X2blLK02+U6f0bdrZX3RCP9WGbdbR3v6XzBWJCRRSMllBJCCCGGMEPXuPaZj1jZ0JXvpmyXcdWF3P3Nafluxi6hX0OpqqoqAJqbm6murk6WNzc3M2HCBAAqKytpaWlJu10sFqO9vT15+7zzjYSDH0ovs0xQMbAiYAYh3AzBTRDYCME6CDVBuBFCWyDSApE2CDfYQwETS8dy4LXucxpeKNonvtTai6sU0EDT44sjvo4HVIYb9PhiuNPDK92xlWArfj4hxC4vLYxJWRJhT1ow08Px1IDGUhamZWJaJhYWpjKxrHgZJspSxKwYFlZ2mJTYJkeZSg99EsFRInTZVkiR9bgzbqehoWlacp0oA7LKe6qfehtd07tvr2ffLnO7p+OJ+8+sk1qe6zzC1hRoyncThBBCCNEPVjZ08Vl9R76bIfKsX0OpUaNGUVVVxdKlS5k4cSIAXV1d/Oc//+Hcc88FYNq0aXR0dLBs2TImT54MwDvvvINlWUyZMqU/m9O/dAOIh0LOIvBUQ8nE7uNWNN6rqgsi7RBpsntRhZoh1maXBTdB16r4stYOt9o+sZeEolqo+BqUHwhlU0B32mGYskBFIRICzHhIZpH+53JIBlqJ9mo6oIOeWDtBc4LuskMs3WXX1Qz7eDLASg2ycu3rEnQJsQMSoY+pzGTok7qfWpZZbimLqBklZsUwLdNeK3sds2LJOkCyl1AyZMroGdRTT5rUskTQo6HZwUxKcJO6ThxLnMfQjGR4k+t45n6uQEgIIYQQQgix6+pzKOX3+1m/fn1yf+PGjSxfvpySkhJqamq48MILuf/++xkzZgyjRo3i7rvvprq6mmOPPRaAsWPHcthhh/E///M/zJ07l2g0yu23385JJ53EsGHD+u+RDTTdCa4Se/GNtAOjmB8irXYYFWm1Q6tRp4CrDJSCrtXQvgzaPoW2ZfZwwc4v7WXtE3ZvqKpDYPixUHWYPQxwW5QCZQJWPMiKh1fKAqIQC3XXUVa8XmawlZDaY0vDvlhjyn5qYKU7gNTeWo6U7ZRQKxlwaWSHWz1ta/H6QuRfapiUCIVMZaatE8cTZZFYhKiKEjNjRM0oUSuaPK6UsnsbKZXcT/Q4gozhVpAz1NE1PdmLZ2tlmcclABJCCCGEEELkU59DqWXLlnHhhRcm9+fPnw/A6aefzoIFC/j2t79NMBjk5ptvpqOjgxkzZvDQQw/hdruTt1m4cCG33347F110Ebquc/zxx/PTn/60Hx7OIKLpdo8qZxH4Rts9qMLN9vxTgXp7LqnCvaC4FkafYd8m3ALN70Hzu9D0rj38b8vf7CURUI38L3ut9TC/lKbZgVB/UIrucCveKys1yFIWkBjSaKXUT60H6ROdZEyEoml0h1+J4Cs18EocS4Rczu5t3ZljeGIPwVYiGMt5XH6U744SPYwyexml9jwyLZNwLEzUihI2w92BUmYPJstKDmNLvsxTXu66pmPoRjIoSlt0HbfmznlMCCGEEEIIIXZlmsqceXUI6OrqYsaMGXzwwQcUFhbmuzl9Y0Uh1AD+9fbacIK7KnsYnFLQuQI2/xU2vwGBDd3HPMNg1Kl2QOUdPrDt709K0R10Za5Twy2VEoxZGds5Xr6a1l2e1tsqJYhKC7wSc3fFg65k+JWYXF7bSuDV07DG1ABMQq+dzVJWMlRKDGvLXMKxMGEznFwnhrml9miyLCs5bC05xw8ahm5gaEZWuJRaZmiG9DoSYgA0BZoocZdw8OiD892UXcKQ/k4lhBBiSDvpnreG7JxSk2qKeeWaw/LdjF2CXH1voOlOe3ifZ7jdE6pzJfg3gLsCnClfBjXNvkJf8QTY57v2kL76JVD3J3sy9ZW/hZUPQdlUGHY0DDty6AVUqb2kdpa0Xl5mPKwyuwMvZdpBIcEeeoZtI7NNG4aoASkhVtYQR2dK+JW634teXpkhWNr+rhmEJOZNilrRtHXMihG1ooRiIYLRYLInk2mZxFQsuc7M2w3NDo4SAZKhGzh1Z9q+9E4SQgghhBBCiIEjoVS+6AZ4R4CrHPxroWsNRNvssErP+GfRNCgeby/7fNcezrfxRWj5AFo/tJcvFkLxvjDiWBhxgt2bSmRMxu7s//NnDW1MDb8Sx0ywwhDLDL22Np9X8gHkmNdL6w6jksFUvHeXnjLMUU+5cmOPvbhyBV07d2ijUoqoFSViRoia8XU8cApGgwRjQYLRIBErkj7EzjLThsQ5dEcyUHLoDpyGE6/ulYBJCCGEEEIIIYYICaXyzXDbYZOnurvXlKcaHN6e69d83V4C9dDwN9jyd2j9GDo+t5cVv4by6TDi6/Yk6c6igXxEu5edfQXC1Hm6sgKt1Mnsc/T0ik+UndFgkqlOrqGNuXp56fHeXckJ7A0gMa9X+lUbFRpRZRExY0Qsk4hlr6PKxB8NE4gGCZkRoipGzLKSczelzr3k0B3JxW24KXAWJAMoIcSuTymVNs9b5rDclmALxZW9uPCHEEIIIYQY9CSUGixcZfZQPKMAOr8CqxBcpVu/ja8G9jzPXsLNdg+qTa9B60d2L6qWD2D5Qhh2FIw8BSoO3LkBiuh/iSGBO2OEXlbAlSMAw4RYOK0sakWJxCKErQjhWISIZRKMhemKhfBHQ0Qtk6gVI6oUVnzGby0+dM5pOHHoDlyGE59u4DQ8GLoTO/yKh17KiC86mDqYqZPhZwRnySAtcxhlruNCiARLWcneiolJ/CNmxF6sSNp+YvhsxIwQs2Ld5Sllqb0eE/tRK2X4bcp+zIx1b6cETqlDdbflzIln8sc9/jgAz5QQQgghhNiZJJQaTHQnlEwEZwG0L4fgZnsYXm9+ULsrYI+z7CW42Q6n6pdA1yrY9Gd78YyAUafAqNPs3lhi99ZDLy97eF2MkBkmbEYJmzHCZpSuSIDOqJ+wGSViRolY0fjoQ4Wm6bh0F06XD6fuwKs7cGo6hqbRHXClXp0xPsm9FbGHNiaPpw6DzGwvOcq07qCKRAhFejCVuCJl8kqN8e3Unl455/BKOWfq/aSGYzt7TjSxS1NKJSffD8VCaRPxJ7dT9iNmJG3C/ogZSZZHzAihWCgtaEo9nlqWCJKGksQw3cRQ3XFl4/LdJCGEEEII0Q8klBpsNA0KxoDhg/bPIFBnT4zel54e3uGw90Ww14XQsRzqFkP9nyG0yZ4gfdXDUHUYjD4DKg+WH9W7qZgVIxSLEDIjhMwwoViEzqifzkiAiBUlHLN7L6h4byenbvd0cukOil2FOHUHht6b185OGnaX7L0FaWFWZvilLLBC3fXTrvCYcr7M0EuD7qs1xreTQZSWEVTFA6/U7cRVHfXEZPQZYVfOXl9bC8DkSo4DyVJWcjL9YCyY3A7FQt378XVvltTgKVlmhvP9MAHQ0HAZLvv/b8OFU3em7bt0e9upO7vLDFe816MrrdypO7vL47dJBElOvfscqWUO3ZFWnthP1HHojrQ54hJX3xNCCCGEEEOfhFKDlacKjOnQ8hEE68Dbx2AK7Pol+9rL+Gvtuac2vGBPjN7wd3vxjrR7V438L3DJl/xdUdSMETRDhGIRgrEw/miQjogffyyY7PFkKYWGhtNw4Ir/oCzweHHqDrTBGoTszKGNkDKkMbUHF3QPcVTdQZcVTd9P3D4z+NoaLUcApqWGYBkBWGrvLz2xnTpksZfDHXPd9xALqmNWjEA0sM0lGAt2b0eDBGIBQtEQgZhdltgORoMDHhgZmoHH4cHtcOM23N3r+LbLcOUuy9h2Gk7chjsZHKXeLhE2JcoS+47B/P+5EEIIIYTYpUkoNZg5i+15plo/3v5gKsHwdE+Q3rUaNrwIdX+yz7vibvjqAfvY6DOheKL0yBiCzPjcTkEzbM/xFAnQFu4kGO8FFbWiKMDQdNyGC5fhpNhViMtwyJXqctH0nRd4ZUrt9aVMupOs1AAs3svLim6711dP0oY7Qs/hF/G1kRKCpfb+cmSEXEb6udN6fsXvJxmM2fWUglAsgj8WoCvSlVz8Ub+9jvjxR/0EooFkuT9i76ceC0QDOz1A8jg8eB1evA4vHqcnuZ+2dtprj8OD23Ant1OXRLnb4c7ad2RedVUIIYQQQojdgHwLHuxcJSnBVD14a3Y8MCrcGyZeD7VX2XNNrXsOOr+Ejf/PXnx7wIjjYPjxUDS2Px6F6GcRM0ogFiIYCxOIhWgLddIR8RMyw0TMKBYKXdPxGC7chpNyT/Hg7vW0u0vr9TXQwx0hu/eXgsScQz0EYJay8MfCdEaDdESDdEWDdCaWWIiuqL3YZSG6YiG64vW6oiG6YkHMnFeI3H5O3YnP6aPA6cPr9MbXPnyO+L6rEK/Di8/pw+Pw4HP68MXrJkKn5HZ87Xa4JbQVQgghhBBiJ5FQaihIC6Y22Vfd6w+Gx570fOSp0PYfWP8He4hfYL0979Sqh6FwbHdAVbBH/9yv6JOIGcUfDRKIhfBHg7SEOuiKBQjHokStGKDiQ3ecFLsKcBlO+REtsvUw3DEUi9AeCdIR6aI94qcj0kVH2E9HNL4dCcTX9nxjHRE/nVE/XdEAlurt2MSe6WgUOD0UOr0UOjwUOD0UODz4HC4KHR4KHe74vjt+LL7vdFNguPE53PicHgqcXpy6k+5J73sa/ph4HoyUHl7xCe81Iz75vQKC9pUnYxrp84CRca7U+b4y7ye1Z5rMCSaEEEIIIUQmCaWGClcJlE2B5vch1ASeyv47t6bZoVfZVIj5oeEt2PwXaHzbvnrfV6vs4X3F4+1wavix9uTrot/FrBj+qB0++aNBmkPtdMUC8eF3MXQ03A4XHsNFoceH05D/hYVNKYU/FqIt3JmydNEeSay7aAt30h7poj3cFQ+hugib0R26X7fhpNDpo9hVQJHTR5GrgEKnL77to9AZX1zeZLldZu97He4d68GX1gMsYx6wtJ5hifm/Uq/umNJbLNELDHIPhcx59cfEf1Kvwri1q0CmDIlEi0+CH9/GAD0ejCWGPGaFa1sJxdBShkqm3i7HlSTFTvfggw/yl7/8hdWrV+PxeJg2bRo33HADe++9d7JOOBxmwYIFLFmyhEgkwqxZs7jllluorOz+fK+vr+fWW2/l3Xffxefzcdppp3H99dfjcHS/97/77rssWLCAr776ihEjRnDllVdyxhlnDOjjFUIIIYTYXvKLdihxlUHpZGj5EKId9pxT/c1R0D33VLTT7jm1+XVofhc6VtjLl7+Gkkkw/Dg7oPIO7/927AaUUsneT4keUG2RzmQApQFuhwuv4abI65M5Z3YzSim6ogFawh20hjri605awx3xxQ6eWkMdtMUDp2hiyF0fGZpBiauAYlchxa6C5FLiKqTI5Yvv28cS+0VOe9ttuPr5kffRQAx97EnmkMi0CfEzhkMqy54vTEXSh0Qm6qrUsKwPtIzgCkgPtOL7Wmq4lQjD9HgwFp//KxmM6TkCr9SALUdgljl/WFrbUgO71Nvtut577z3OO+889ttvP0zT5M477+TSSy/llVdewefzAfDzn/+cf/zjH9x1110UFRVx++23c/XVV/PMM88AYJom3/nOd6isrOSZZ56hoaGBOXPm4HQ6+cEPfgDAhg0b+M53vsM3v/lNFi5cyNKlS/npT39KVVUVhx12WN4evxBCCCFEb8mv3KHGO8KeiLztU9Bd9hC8ncVZBKNOsZdIG2z+K2x+A1o+gPbP7GXFXVAy2Q6nhh9jt0/klOgF1Rn10x7uoinUTjAaIhSfpNlpOPEabso9xbgMZ55bK3YGS1l0RPw0BdtoDrXTEmqnOdROc7jdDp7i4VNLqJ2WcAcxy+zzfXgMFyXuIkpdhZS6iyh1F1HiKqTUXUhJ6rarkBJ3IcWuQgocHplvbHvs7CtAbkvaPGCQPVdYIjRL7Q2mcl8tcnuDsczeWcT300Kx1GGUWvcNE2FY2pUlE8MpNewrTKaGZPF6odZkZ7TB6uGHH07bX7BgATNnzuSzzz7jwAMPpLOzk+eff56FCxcyc+ZMwA6pZs+ezccff8zUqVP55z//ycqVK3nkkUeorKxk4sSJXHvttSxcuJCrr74al8vFM888w6hRo7jxxhsBGDt2LB988AG///3vJZQSQgghxJAgodRQVLgnmEG715JvpH0lrJ3NVQp7nGkv4SbY8jfY9Dq0fgTty+xlxV1Qsi8MOwaGHQ0Fo3d+uwaxqBmjKxqgMxqgNdRJc6iNYCxMxIqiazo+h5tCl5dKo1QCgSEuasZoDrXRFGqjMdhGU9DeToRP9tJGc6gDU/UtaCpweCj3lFDmLqLUXUyZu4hyTzGl7iLK4vv2tr32ONw76VGKQUdLDX7yIHWoZM4J9DN6i2XexspRRkaAljh36vDJYCuwc6+42N86OzsBKCkpAWDZsmVEo1EOOeSQZJ2xY8dSU1OTDKU+/vhjamtr04bzzZo1i1tvvZWVK1ey77778vHHHydDrdQ6P//5zwfgUQkhhBBC7DgJpYYiTYfiWjBD4F9vT3w+kEO73JWwxzfsJdQEW960e1C1fgTtn9vLl7+Gon1g2FH2Ujhulx+uETVjdEbtyaCbQ220hjsJxELELBOn7sDrkF5QQ42lLFrDnTQGWmkIttAQbKUp2EZDsIWmYBuNwVYaQ220hTv7dN4SVyEVnhIqPCWUJ9fFlLvja09xMojK+/A4IXqS2jtqIN/eIyH6Ps4xfyzL4uc//znTp0+ntrYWgKamJpxOJ8XF6cPwKyoqaGxsTNZJDaSA5P626nR1dREKhfB4dmJvaiGEEEKIfiCh1FClO6F0kr0dWGcPm9Pz8OPVUwljzraXcJM9B9WWv0HL+9D5lb2s/C14a6D6CHspmzqwIdpOYikrfjWyrmRvmEQI5dId+Jxeqr1lMhfUIGUpi+ZQO1sCLfGlmYagvd0QbKUh0EJjqLXXQ+gcukGVp4xKbymVnlIqPCVUetPXFZ5SKjzF8poQYjcyd+5cvvrqK5566ql8N0UIIYQQYtCRX0ZDmeGB0v0AHfxrwDts584xtS3uStjjLHuJtEPjW/Y8VM3vQbAe1j1tL85iqDwEqmZB1SE7Z8L2nSQQDdER6aI13ElDsJWuSICIFcWpO/A5PBJCDSL+aJDNgWY2+ZvYHGhmS6CZzfHFDqB6FzhpaJR7iqn2llPlLaPKWxpfl1HttUOoKm8ZJa5CGYYphEhz22238fe//50nnniC4cO7LwpSWVlJNBqlo6MjrbdUc3MzVVVVyTqffPJJ2vmampoA0uokylLrFBYWSi8pIYTYhZmWwtDle6fYNciv56HOcEHZZPsKSl2rwF0FDm//308sBoGQvUSjEI1BOAzBcPwS6ykU9hwhajRYF0LxuaC+gOjHEPrIvnLgpj/bCwaUToHqQ+2gqmifQTXMz7RMOiJ+2iNdNARbaA11EoyF0DSdAoeHMo8Mr8qXzoifen8Tm/yN8XUT9YFGNvmb2RxooiPi3+Y5dE2jylNGta+cYb5yqr32epjPDqCGecup9JZK0CiE6BOlFLfffjuvv/46jz/+OKNHp8+xOHnyZJxOJ0uXLuWEE04AYPXq1dTX1zN16lQApk6dygMPPEBzczMVFRUAvP322xQWFjJu3Lhknf/7v/9LO/fbb7+dPIcQQohdk6FrXPvMR6xs6Mp3U7bLkeOr+OEJE/LdDDFIyC+tXYHuhJJJ9hWLOr4Eb/WO95hSCjr90NYBre3Q0QWhMERiJGeddcQvH65nXAZJy9xQYI4Fay+wTgFrDWjLQfsctC3Q9pG9fHkvOMqhaAaUfQ2qDoKianAM7Ms0GAvRHu6iJdRBY6iVzkiAqBXDbbgodHqp8JRIj5gBEDYjbPI3sbGrgXp/I3Vdjfbab6+7ooFtnqPI6WNEQSXDfBWM8FUyzFfO8IIKhnkrGF5QQaWnFIduDMCjEULsTubOncuf/vQnfvOb31BQUJCcA6qoqAiPx0NRURFnnnkmCxYsoKSkhMLCQubNm8e0adOSgdKsWbMYN24cP/rRj/jhD39IY2Mjd911F+eddx4ul/3HkG9+85s8+eST/PKXv+TMM8/knXfe4dVXX+XBBx/M10MXQggxQFY2dPFZfUe+m7FdxlYV5LsJYhCRUGpXoRtQPAGUCZ0rt/+qfP4gtLXDpkY7jIpEwekEjxtKisHVH5N0Dwdm2j2swpsg9B+IfArqS4i1QOvr9rJaA300uCZD6XQo2x+8xXZb3C67Lf0QDiml8EeDtEU6aQi00BRqJxANJntDVXpKcRryv8rO0B7uYmNXAxv9W9jQuYU6fyN1XVvY2NVAQ7B1m7cvcxdRU1DFiIJKe/F1r4cXVFLo3Am9BoUQYhuefvppAC644IK08vnz53PGGWcAcNNNN6HrOtdccw2RSIRZs2Zxyy23JOsahsEDDzzArbfeyjnnnIPX6+X000/nmmuuSdYZPXo0Dz74IPPnz+exxx5j+PDhzJs3j8MOO2wAHqUQQgghxI6TX9q7Et2AkolgRcG/DgpG2b2nesM0oW4LrFoP/gC43VBUaAdAO629OnhH2guzQUUgsgLCy+yQKrYBrPUQWg+bl8AmAxgDei04asEzHorLoMhnt9fj6l5n9t7KkJikvC3cySZ/E23hToJmGKfuoMjpo6ywCF3b+jlE77SHu9jQtZl1nZvZ2GWHTxu67GVbQ+x8Dg8jC6oYWVhNTUEVNQVVjCqsZkRBJTUFlXgdMmeKEGLwWbFixTbruN1ubrnllrQgKtPIkSNZtGjRVs9z0EEH8dJLL/W1iUIIIYQQg4KEUrsa3Qkl+4IVgcAmu8fUtnoTdfrtMKpuMxQWwMjh+ZnXSXOBez974Vww2yCyDMKfQeRzsFqA1aBWQ/TPEHVA5x72sEC1N+h7givem6vAC0UF4PXYvarcLiyXg3YVpiXcwaZAE+1hP2Ezitfhosjlo8pRNvCPeRcRioVZ37mZ9fHwaX3K0h7Z+lj3Sk8po4uGMaqgmpGF1YyKLyMLqil1F8lQSSGEEEIIIYTYRUkotStyeKF0MrR8CMHN4BuRu55l2cP0Vq6Dzi4YVgXOQfSSMErBO8telAKzASKfQWQ5RL4Aqw1YDfpq4K+ABuYoCO0NgT1g8ygsq5R2K0SLHmETAdr0KFG3ga+wxJ6k3FcAygH0x7DEXZtSisZgK2s7N7G2o561HZtY12kvmwPNW71ttbeM0UXD2aNwGKMKh7FH0XBGFw5jZGGV9HYSQgghhBBCiN3UIEogRL9yFkHpfnYwFWoAT3X68UAIVm+A9XXg8cCoHoKrwULTwDHMXnxHx0OqzXY4FVkB0S/BbARrA7DBvo0OMb2AsDackFWFmxFUWzUYfge0d4DWDkoDpwEOpx3IFXjB57Hn0XI542sHGLvPZNgxy6TO38Ca9jrWdNSzpqOetZ2bWNdRjz8W6vF2xa4CxhSNYEzRcPYoGsEeRcMZUzScUYXVEjwJIYQQQgghhMgiodSuzF0OZVPsYCrcDO4KO8xpaLZ7R7W2Q1WFPbxtqNE0cIywF99RAAQjWwiElxENL8djrqNYNeDCz3C1iuHaKvt2BoQclfi9NQSMEQT0GgJWFVZMg3DEnk/LNAHNvnigw2GHVYkhgR43uFzdZc6hG1jFrBgbuxpY1V7H6o6NrI6HUOs6NxG1YjlvY2g6Iwur2bOohjHFw+110Qj2LB5BqbtogB+BEEIIIYQQQoihTEKpXZ2n2u4x1foxdDVBXQes2WgHKfmaO6ofRa0YLTE/myNtbIl24DeLcWmzKHEfh0/X8FmbKLA2UGDWUWDV4VateFQTHrOJCvMTABQaYa2cgGs4Qc8IAvpwgvpwoqrADqhiJgRD0NFlD3lMDaxcDnC5wee2AytnPKhK9LRy5D+wspRFvb+JVe0b05a1nfXELDPnbdyGiz2LRrBXcQ17FtewV/FI9iquYXThMLkSoRBCCCGEEEKIfiG/LncHvpHQ0QYfLIHmEFSPsCcAH6KUUrTFAjRFO9kYbqHDDKJpGiWGl3JXRXJibAX4jT3wG3skp4wylJ8Csw6fVR8PrOpxqXY8qhmP2QzmZ8n7ieEjoA8jaAwj6BxGqKCaoF6FpXnscCoWswMrf8AeDpgZWCV6Wfk89vOd2rsqEV71cyjYFu5kZdsGvmrfwMq2Daxs38DqjjqCsXDO+l6Hm72KRzK2eCR7lYxk73j4NKKgUq4+KIQQQgghhBBip5JQanfQ2AjLG6CrACoUuIdm76iAGaY51kVduJXmaCcRZVJkeBjhKsXoZYBiagV0OGrpoDZZ5lB+vNYmfOZmvNZmfNYmPKoJBwGKrTUUW2vSzhHWSgjpVYS0aoLOKkLuKkJ6FabmtSsoyw6rYrGUIYEWdkxGPLAy7MXj7r5CoDPesyoRWDmMHkOrmBVjbccmVrZv4Mu29ckgqjHYmrO+U3ewV3ENY0tGM7ZkFGPjAZSET0IIIYQQQggh8kVCqV2ZUrB+PSxfbg9D2+cA8K8F/zrwVII2+K84ZyqLlmgXm6PtbIq04Y+FcesOyhwFuPX+aX9MK6DTGEenMS5ZpqkoHqsRn7UFr7UZr9WARzXgUp24VTtus50SVqadJ0oBIb2CkF5FWK8g5Kwg7K4krJWhtMT/agpiKb2s2ruguc3+t0r2sjK6e1p53HQ5Lb6MNvFlYBMr/PV81VXHqs76Hud9GllQzT6loxlXMppxpaMYWzKK0YXDcej5H0oohBBCCCGEEEIkSCi1q4rFYOVKWLECCguhqsouLxgDKgaBOnu+KW1wBhWdsSBNsU7Wh1pojwVQGpToHsrd5cnheTuT0pwEjRqCRk1auaGCeKwGvFYjHqsBj2rEazXiUh048eO0/BRZ69PPhUZEKyWsldlhlV5O2FFOxFVGWCvD0ortekrRGG1nRddGVrRuYkVoM1+Gt1AXa8/ZxgLdxTjvcPYpGsk+xaPYp3g0Y8v3oMBTuNtdMVAIIYQQQgghxNAjodSuKBy2e0etWQOVleDzdR/THVCwF1gxCDXEg6nBMXwrpkyao13Uh1vZEm0nYEUo0N0McxXjGCThmal58Rtj8Btj0sp1FcZjNeNWTXisxvh2Mx6rGYMIbtWKW7WCtRqwO0aticHnIfh32MmHYZ2PwzGazNwTjw93llDrHcF4zzBq3dXs46ighiJ0y7J7wfkBvwlb1tlhVGI+K6/bHhrodtnliWGBieP64Pi3F0IIIYQQQgix+5FQalfT1QWffQZ1dTBiBLhc2XUMFxSOtXtMhRrjwVT+5pkKmGEaoh1sCLfQEu1CQ6PM4aPKWZy3NvWVpbkJGDUESO9ZhVLoVicN4TV8GVjP8uAmlgVb+TQUoM2KzzFFNFldBya6YJrbXqa6YZLbR6GzkIimE9EiRPTO+LZJVCshqpV2B4uJCdhNEyIR+6qBsVj28EAjvrhc9pUDE3NauZx2YOV2DYorBwohhBBCCCGE2HVJKLUraW6GZcugtRVGjrTDhZ44PFA0FqwV8R5TVQPaY0opRWvMz6ZIG/WRVjrNEAW6m+GukkHTK2p7KKWoi7SyPFDP58E6vgjUszywiS4rlFXXqRmM81QxyVPGfh4fUz0OprhMSrUO3FYbLtWGQQQIgBWggLrc94lOVCsiohUR1UrstVFMxFlMVCtKLpbmxp7Tyuxecl450ACHs/vKgR539+TrqYsMDxRCCCGE6BPTUhj60LzoEAz99sOu8RiE2JVIKLUrME1Ytw6+/NLuFTNqVO96PjkKoWQ8dHwV7zG184OpqBWjMdrJxnALDdEOYsqk1PCxh6tiQOaK6k9KKRqiHXweqOPzYD3LA3UsD9TTbgaz6ro0B/t4hzHRW8MEXw0TvDWM9VTh1NP/F2yOL/E7wCCIy2rHpdpwqXZ7seJr1YFTdaJhJY/Bxh7ba+IiqhXGQ6pCos4iYq7CeJm9xLQCoqYbYqr7yoGWZV84UMN+XSWuHuhy2lcO9LrtHldOh72W+ayEEEIIIXIydI1rn/mIlQ1d+W5Kn42rLuTub07LdzN22FD+NwA4cnwVPzxhQr6bIUS/kVBqqOvqsiczX78eSkq6JzTvLUchFNemBFOVO2Xy8y4zxJZIOxvCLbTG/Lg0B+X9eAW9gdAWC7A8UMdn8eXzQB3NsewPM6dmsI9nGBN9I5noq2Ff30j29lT1vQeYpmHiI2j4CDIidx1l4VRduFQ7znhI5VIdOC17297vxCCMQQRDteBRLdu86xheoo4CYs4CYlohUa2AmFZgl1seYqaHWNBNrNNFzHKD0uO9rOK9qBKBlc/TPSzQ6ZTASgghhBC7vZUNXXxW35HvZuzWhvK/wdiqgnw3QYh+JaHUULZ5sz1/VEeHPX+UczsDHkeBHUx1rkzpMbXjwYGlLFpifurCLWyOtuM3wxTpHka6yjBy9cgyLRxdQYyuYHJtdIUwAiGMUAQ9GMYIRtBDEfRIDD0SRY9E0SIxNNNCi5n2YlpoSoGl0Cwr626UroGuo3Td3jZ0LIeBSlm63DqflEf5oCzER8VBPir0s9adPQTPUBr7aGVMclazr6eGiQUj2bt4JA6vd2Dm6dJ0oloxUbY+/5auwjhVVzyo6koGVg7lj2/7cagunMqPhoWDIA4VBNXU80mN+ALE8BDTfMTwElMeYiEPZsBDrNGVLDONQmKOAmLOIkxvKZa3KN6zSuaxEkIIIYQY7KoK3TL0TQjR7ySUGqrq6+GTT+zt0aN3PABx+KCoFrSV9hxT7nLQ3dt1qsQQvfXhZhqjHViWorrTZExLCFfjZpwtHThbOnG2dtrrti4cHQEcnQG05OTfA8fUYHkVvDsS3osvnw4DM0dutk8zfK0ODqyDA+th6maFL9oCtABfJOspXcP0uDEL3FheN6bPjenzYBbYS6ygezu9zItZ6CVW4OnXgMbS3IQ1N2Eqtl5RWRiEcKqueEgVwJESWDmS+36cKoBBEA2FgxAOlRLa6fEll6i9qHadGB5M5bbXmpeY7sN0FmA6i4g5CzFdRZguex1zFWEaPkzDi2n4dkqPPiHEzmUpC0spQGEphUKh4mt7H1S8TuYxAIXCHw1RktdHIYQQu6dir0OGvgkRt6uEtIPhMUgoNRQlAilNg8rK/juvwwNF48Hwgn+DHVQ5i3p320iE4PqNdK5bQ+eG9eibGhi7pYOpDZ24mjvQY2avm2H63MQKvJiFHmKFXkyfB8vrwvS67bXbheV2olxOLLcDy+FAOVN6OhnxHlCavShdA4XdewrYbHbxqWrkP1YDn9LEMprxa7GsdlTFXEwLFTPNX8i0Dh/T29yUBRRGOIoei6AXR1CuKKGQ3XvLCIbRgxE0pdAshSMQwhHI7l3V6+fB4yJWFA+pClPWRfY6VuTr3i70YRbb28q1A0MiNR0TH6bmA6q3XV9ZGARxqABO5cdQwXhwFcBBAEd8P1lOEEMF0THRNAsnAZxaIOV8QCS+bIOpuZMBlemIh1W6vbYMb/yYF1Pv3rZ0D6bhiW97MQ2PhFtit6NUIgCyUAqs+Fph9RgMWVnhUWZgpFDKHsWrkpPQpa5Jzhuoazp6PLXWNQ0NDU2zl+Q+Gjo6uq6h6zoGOoauo2s61bpOua984J84IYQQgAx9EwJ2jZB2sMwTJ6HUUFNfD//5D+h6/wZSCYYTCveyA6muNRBuAld59wTo/iCsq4O1G2FdPWpdHdaGevQtzXiVwkvuKEPpOpGKIqKVJUQqiomWFxEtSyyFxEoKiBYXECv29WsPIb8ZZnmgjmWBjXwWXzdanfbBlEDYp7uY6Kthkm8Uk3wjmewbRbWzOG3y9Y74slVKoYej6IEwRjCMEQjbww/9ofTtxL4/hMMfH6aY2A6EATBCEYxQBBrb+/SYTbcTs8hHLDW8Ko7vF/niizdeJ75d4LFfU32l6ZgUYGoFhOnlfGZKoRPFUAEcKhQPtYIYye0QRnzfLo8fUyEMQhjxANFQYYxYGGJtEO570xMszYlpeJIhlal7sAwPlu6Oh1fuZJmpu7F0+5hlxMt1d7yuO7mtNOfADN8Uu4xEDyI7ALKSwZEdBlmoeJ2tBUSWsuxASCWCIUiEQlr8DU8Rz+vR0DU9HgiBjp4zGDI03Q6FNB1DM9C1+LZuB0tG/JhDdyRvp2s6upZYd2/bx9LrJNuhkbNOzgtghJrAJX2lhBBCCJF/QzmkHSwklBpK6ursHlI7K5BK0HRwVUNrEyx7G1atgnXNsKYOtqTPMaSRnFaImM9NuKaS8IhywsMTSxnh6lKi5UU7fYLrmDJZFWxIC6DWhBpTfpzZDHTGequZ5BvF5HgItZenKvc8V32laVgeF5bHRYxe9jLLZJoY/rAdUHUG7XVHsHu/K4ijM2DPvRXfNzoDOLpCaJaFEY5ihNtxNfU+zFK6Fu+F1UN4Vdy9nzhmFnlRzu14C9E0LFxYmoto328NyrQnbjcDGKYfIxaMbwe6J3TXIhh6FEOPoGtRe1uLYhBGV2EMK4yOHW7pKooeiwKd29Oa3E1ESwZUdqjlxtJdWIYbS3PFy13xxZ2ydtqhVnxt13Wm1HWhtMS2U8KvnSi1N5GVsZ0Ihixlz1lnpfQqso+nhEg9hERb60GUGhIZup4WEGm6jqFpODRHMiBy6HZY5NCNeGBkoG8lHOo+ln5fmeFRd2glrzEhhBBCCLFzSCg1FCgFa9bA8uX2ZOYV25gXaHs0NcG778Knn8KXX9pLKPfQs1h5MZ2jKmgZVYJ/ZCXaHiOwRo8gVlIwYD+QlVLUR9r4LCWA+iKwibDKjjmGO0uYXGAHUJN9o5jgG4FHdw1IO7eLYWAW272berroXk6WZfe+6rRDq2R4ldjvDNghVkfADrHi5UYogmYpnB0BnB2Bbd9PCnuIYXwYYWpgVZi6Hw+2Er22Cjxg7EAAqBn2EEOHDxyVkHPqMwUxs3sJx8A0wbKSWYCmWegOE0OPoesxDCOGYZjohtldjh1m6UTtMEtF0ta6FcYw7bVuhdHjrz8NhWGFMKztH77ZW5bmTAmsnMkQK7mtOeMhl6t7W3OidEfKvitjP+W45ogHYI74OR0ZZY7unpQ7WW+DolwhkZUxzCwRDGmafd7UkChRnhbQpAQ3mpYyvEzTMAwnDs2R7DVkaAaGruNI9CxKCYmS4Q899yYyMkOkbfUcEkIIIYQQYoiSUGqwi8XsgOirr6C42F76QzRq97pautReVqzIruN2w7hxqH32IbBHBZurTdZVxWgv8lHgLKHE4cMxQPPxtMb8fBao4/NAnR1E+etoM7MDlELdw76+mmQINck3igpn4YC0Me90HTM+91RkRO/nW9GiMTu46gikh1ed6eFV9/Eghj+IZqmUIYZtvb4/pWn2xO6J+bKK4nNixbcTc2flmk+r90M7NfuKfo6e3+KUsjBNe8EywVQQNcG07ABLpfZsAXTN7u1n6HYIk9h3OroXQ0PXY+haDIMYuha1F+yAyw63ot3bViQeaEXsRUXQTTvcSpZZETSV2I6iW2G0lN5/uoqim1Ew/b3+N+hvlmbYgVXa2oGlObB0BxYGlpa6OLA0AzOl3N7W7WPE15oeP4+OhQG6A10zQHOiaw57PjDdDtCIl2u6A013YegudMOJbrjQNC+64cDQXRiGB10z0HUjq0dQakikQXYPo2RPIz0ZTukDFMgJIYQQQgixK5JQajALh+Hzz+1eUtXV4PXu2Pk2buwOod5/HwIZoc7EiXDAATB+PIwfT3jkcBrNDjaGGmgMt2KZUUpVhNHRDjQrDJbDnhS9n9nzQNXzebDOXgfqqIu0ZtVzaAa13uHJOaAm+Uaxh7tcfiT2kXI6iJUXESvvw3BDy7LnwEoMHezo7o2VHFaYCLCSvbaCGMEwmlL2flewz21NTv6eOhF+gZdYoccO5ArsKxea8X37uH11w6yhhpoODr2X74IKLNUdXiXWsQgEQ3aAZdqT+dvzAEGsu0uWfV+GF/B1h1qGBnpGqOV02Mf1lCW5Hz+PbvfE0nTTDrtUFC0ZYkXRVQTNiia3sSJoZgRNRdEs+5hmRdBULLmvq2jK2j6nrmLZixXDUDF00i9coCszPqxyCNEckAyzEtuJxUjZTxw3urc1I6WOkbI4eijPvJ2B/Q+Zel49pY6echs95ZjefQ70+HlSz2dk3D7ztrpdP/O4EEIMEYPhKlFCCCH6l4RSg1VLi917adMmGDECXNsx3Ky9Hf79b3tY3nvv2XNSpSovh4MOgkMOsdfl5Sil6Ij52RJuZkPHp3RE/bh1F5WuUlx6/KpuZhgizfYk6OFmcBSA4dmuhxk0I6wIbmJ5sJ7PA/V8Eahnbbgpax4ogD3dlezrG8m+8RCq1jscly4v4bzQdcwiH2aRD0b0YThpzOwOqFLDqq54gNUV7C7zB7vL/PYwuO2d/B3Acjnt8Kqge4kVxEMrnzte5sUsiG/7PHbA5fNgFrjtqxo6nNvxrpkItKz4okBZqJiFZUVRAdMebmZaKMuyJ7mG5FA0BViaQuk6lgaWBkoDS9dQmn0RARxGvBeXAQ4dzXChdDfo8UFmDgNdd6AZGppTt3sJ6TqG4UAzDHTDwGE4cegOHLqBU3ckh58ZmoGu6+lzFAE6lh1QKTMluDLjvbxMe5sYWrxMix/HioAVs9cqBlY0vh/t3lfRlP3Uslj6bVSsu05yHc3Yj0GO9xNUDMzsq27ulhIhl5a6pAZdifAq5Zim5b4dKcfSbpN5jpRttIxzpuyjp6zjixWFUadD5cH5eb6EEHkz1K90deT4Kn54woR8N0MIIQYV+UU/2MRisG6dPVwvGoVRo3o/QXhnJ3z4IXzwgd0T6quvUoYgYZ9n//1h5kx7qa1NXnEtYkVpDjVRH2pkS7iZsIpSbBQwylOd3fPIcIO3BlxldigVaYFwlx1MOXxA7r+8d5khvgxu5otAPcuDm1gRrGdtqCn+EzzdcGdJPICqSQZRhdsZfIlBxGEQKy0kVtrHIZVmSs+srmDyioWJ8Kr7WMgOs/whjPi+I2AHWnokiqslCi3bN6G5ZeiYPjcxn5uY103M5yLmcxP1uYn6XES98SW+HfE67HoFLqIeFzGvi5jXieV0gKahuYgHPBqa5kTHnvBaj89VpAFGfO4hh9IwFDjQMCww0HAoDYcFhtLQYwo9BroFuqXQVeLcevz8doika3p3JyxNQ3eAoSl0B6Cb9lULHInFAS6d+B3He2wlen3l6Mmlad09ujKP5Zsys4MqFbN7vCW2U+ukbSf2zYxjKftp5Va8LFFudW9bMSA+XDTreOZipZw/s46Vvk28fYlzk1GnN88PZs7sbtCKtMKe38x3K4QQeTCUr3Q1tqog300QQohBR0KpwaSjw+4dtWEDlJVBVdXW62/aBB9/DP/5j72sXJkeQgHsvbfdC+qgg2D6dPD5koeUUrRHO2kMt7IhtIWOaBeGZlDmLMLbmwDI8IJvFLirINpuB1ThNhTQjMWX4RZWBDezIriJFYFNbIi05DxNlbOIiV47gJroq2Git4by3WUeKNE7ht49+Tv2a9ee4lrFh8t1T3StUsrsHkgmRiiM3hUPrvxhHIEgTn/YXgJhnIFIyrZd7ghEksc0pdBNC70ziLOz78MOUymHA+Vzo3welNcDPg94PVDgtYfo+jxoXi9acu0FrxsSa29iHb/tVubNyr7zlF5byrLnz0rsmxZEY2CFunt1qURd6L5iXPJk8Z4tKWFUYq6ttLL43FuO+NDERK8uh2HXcTjs0+pbCbdSz5l57lzHM2nx+8w9K/6uT1kpIVU8xMosSx6z0o+RWidRpjJupzLOmXGe5HZGvdTzoHLcXmWcJ17HDMGoU/PyVAohhBBCiP4lodRgEAzaQdTatfb2yJG5f2gqBatWwZtv2svKldl19tgDZsyw54aaMQMqK7OqdMUCtEQ7qA820hxtJ6qiFBkF1HiqMfo4v0jEirImuJmv/BtY6V/HV11r+SqwgZZY7kmXhzmLmeCtYYJvBBO9NUzw1VDp7MNcRmJIyBUQZZdZ3ccSV0lLrR/vtqFpGkqp+BpAxS9Tn3Ip+/gk1RoaRkovI6fmxOnw4HAX4yjTcWDg1A37qmjJq6olJq+2J6620IhqOiYaUU2zeyKFIuj+EEYghO4PogVC6P4QWiAI/iBsdR2AQAhCYfvxxGJoHTHo6KeJyZ0O8HjsoMoTD6s87uz9ZFnG8cTidtll7ngA5nFtu5dmIuRSiWGJKj3MSgRe0RioUI468f1EFx1N795OnB/N7nypJUIr4mXxMApyB1OJEMyIz73kSARijni9lLppgVfqPt3BWOJ+supk7A9GyeFyu8hHfqgJXCX5boUQQgghhOgHu8g31CEqEoH6ejtoam+3e0dVZMzPEwrBRx/BO+/AP/9pD+1LMAyYMMEekjdlCkydmjOEUkrhN4O0RTtpCLfQGGkjYIbw6C5KHUV4jG3PVxWzYmwIbWF1oJ5VgQ2sDtSxyr+R9cHNmFhZ9XU0xniq2cdTxXh3OeNd5UzwDqPUXW4P/+thiJ/YuXoXFvUcEiXK7NjADooSwYFK9qTpbWik49R0nLqBgY5D1+21ZuDQ7HViCJqh6clzGOjJck0jbT9XyNQvnD7Y0ezUtOzQ2R+0J0fPXAdDdngVDNrrQKIsCMGwXR4Mdwdc0ah93mgMol3QuRPm13A67LDKEw+rPK6UfVeOxQ1uZ8raZc+H53bZ+66Uui4n+Nzd21sLwBIhVmqQZcXfd1JDMcuCmGlv00MIhuru9JUIveJz0ttSrrqYCMMSwRNaegiVXMjuJWYY3UMYUyerT5TnCtJSl9T7SARkmb3B0m5HdkiWugghhBBCCDEISSiVDx0d0Nho945qbYWiIruHk6aB3w+ffQaffGKHUR99ZIdXCU4nHHwwHHMMHH44FBfnvAtTmXTGArRHu9gUbqI92kXADOHUHBQ7Cqh0lea8XWfMz7rgJtYGNtvrYD1rA/WsD23B7GFukmJHAeN8o9mnYDTjCkZTWzCGsb6ReIz4UBkzDGYAoh32j+dwG6DAcILuBt1F+rCg3U9vwyK2ERyh2efKtu2wSKd7rWt6fKJrHQMjKzAytJ6DoO4eSKnHdnJoNBQYOhQW2Et/iMXscCoRUgVD8fAqHmaFwvZ+4lgo3L1k7ofCEIpAOGJvJ15D0Zi9dAW23pb+YBjx4CoeXrmc3YFVYnGmbCfqOB256zgddp3EVQ1T6zozjieOGUZ6gJMYuqhS1omgS8XDLZWyH7NSbqO2spAy1Dqll5iy7PvPFZAlQqnU0CpxLDW0SgvKMoOuRFiWGFYZ70Wmp/QcS9TJPEdqL7WewrOe7heyA7TMOj3dTgghhBBC7NIklBoooRC0tdnzQG3ZAoGA/aOkoQH+9S97UvIvvrCH5FkZPY+GDbODqIMPticoL8yeb0kpRcAM0Rnz0xrtpCHSQlcsSMSK4tZdFDl8VDjt4Q7N0XZWdWykPtTIxlADG0Jb2BDcwsbQFlqiPU8c6TM87OkdwVjfaMb6RjK2YBRjfaOocpVtPVww3PbiKrMn+jUDYAYh1gmxAETjw5g0DXRn9zIIelNZykoGRIkroqWGRX3pZZTbzgmLUutnDlMzJCzaNTgcUOjov5ArQSmIRO2AKhFSBcN2OJ4aYoWj8bJ4eSSSfpvUcyS2k3Xit42mXP3ONCFg2iFbvmhad4jldNjPcSLASivL2E4ri8+flXYsPmzQmXLMYXQHYal1krdPPW7Ee1yl9r4yukOiXIFXsmdZokdZytxguUKzXHXip0/2KCN+LPH+kJxrjO7KenydGTiRWU5GYJXYzxFQJYO0eO+yaAdUjYDsjsFCCCGEEGKIyVso9eSTT/Lwww/T2NjIhAkT+J//+R+mTJmSr+b0P9OE5mb48svusGnDBti82Q6i6uuhJffE34wYAfvtZw/JO/hgGDMm66/GUStGwAzhN4N0RLtoirbREQ2wJdxEa7SLgBmkMz531JZwM1vCLWyONNtX1rOiW216pauUMZ7hjPGOYE/vcPby1rCXt4ZhrvLusCK1N07isuqJstRjqb9XkuUue3EUAxHQQ6CiEA1ANAhWF8krYwFohv3XfM1AKR2lGVia1h3+KCs56bVSyh7Fkys0SguXrOTxlFFn3dvxH2XdV0NLDYjiI2XiJboGugJXyhA0Aw2H5rCHoSktew4jlevqaImhat33Z6CjJ+8L+0qIOXtCZT7fiQeTPbSyFzfuP305ZeLfYJt1clTK9bocrBI9YRKPNxEspKzSeopk9TaJV9R6OKb10GuGXg7r0rTuIXY7m2WlBFbR+JISWiXL4nWi0fSyaKz7Nmn70e660Vj2drIslv5HgEQgF9n6e+SgkjpxfGKdnE/LyD6etq13l2XWTx5LXTuyy1NDssR2shdWyjDF5LHMSewT9VNCp8zeXYlAKlEWagW1m05aL4QQQgixi8lLKLVkyRLmz5/P3Llz2X///Xn00Ue59NJL+fOf/0xF5pxK+aSU/WPH77d7NnV22ktHh71ua7ODpaYmO4BqaOhemprseaK2ZeRIGDcO9tkHamthv/0wK8vxRwN0RrtoCrfStPltmsJtNIVb2RJpYVOoiaZoG62xLtqinbSZfjrMAO2mPz7Ea+t0NKodJdQ4yxnpKGe0q4rRzgpGOysZ5aqkMHHlveQPfQ0CQKAtrdxSiQFlKT2HNOzeQZrdDgvivYasZESS3E5eQc2K9yTSUboP++pMMdDil1C3wmixIErF0JTCnoraRNeSsZAd3xgGuuaIB0X2voEDh+ZA07S03kQOzZ7HSNcTvYPs3kKGZtj7ifBI17t7L2nxuqm9kRJ1U38wJZ/olO1keQ8hQOahnspSg4pkeU//0hkHtqf3k5brMWynvty+N1X7szfXzu4ZlhbUJsJbcvRWiYekyUnCE/MlpcyZZCk79E6dKyk5tAySV0PL7DWTeu7U42ldYrpXaQFnas+Y1FAsMbF35mszOf9SjmOpIVliOzH0rqfjuXrQ9BfTSg+qEkMWU7djse7AK2Z270fj+8k6qfvxemnH42szR53EOtcx07TXuULXWPxYuP+ekiHhglPhxMvy3QohhBBCCLGD8hJKPfLII5x99tmceeaZAMydO5e///3vPP/881x++eX5aFKa6N/f5PU5Z9EebLMvK6+BqWOvU7Zjevd+LL5E3RAbA9G9IapDxKUT9XmI+NyEvU7CPjchj4OQSyfo0ggSI6C+ImgtIxiO4H8vREht/1/pdTRKXSVUukspd5dR5SmjylNJpaecak851b5qKjzlGIZh/6bFQun2Vc0sTdGGojV+Lg0Nleg1lBmqaMTDHx1N15Lbevwv3XryWHfAY+hODN0OjBy6gUN3YugGhhZfdB1dN7rDpsS2ptkhExaaFUPHRFcxdGWiEUO3ouhW2F5UDF1Z6Cg0zO4f6JnhjlIkr0il2X2R7MeY2NdS9u1ntrssZZ18PlL2ZeibGEhpQ7Uyhm31NLdRapiVGVIl9lOHeWXWT1vi92uZKdspx7OGliXCs5RhY7kCtITUx5QoT25mDB1L7W2nxRO0XEPPEveb6LVGokq8nqGDwwNesoPZXEFtWoicEe7lCtFy3i71tjnOAXaAlgiorMTasssT4VXiuBl//mOx7tslF6t7nTxHyr6yMu5ra/Vj6WGpmXLfptldblnp95tax7TSXzPJ+qp7P9NQ6BUphBBCCCG2acBDqUgkwmeffcZ3vvOdZJmu6xxyyCF89NFHA92cnH67/HGunt267Yq9YhHvZpRdvI2pUwzNwOfw4nP68Dl9FDh9FLmLKHKXUOwupshdRKm7lDJvGaXeUso95ZR6SnEYju4eRPEfM93hjo5Dd9jDxOKhj0N32NukbKfUT4ZEPZQl97WM/ZTjAzZHkVJgRe2eVire48pKbKcuVrxeNN4bKwaY3XWx7DrKim8nfgUnfqwnhvyo9GNZv5NSf/ym/vrtzQ+qHPW2ep6M/V4/5z3V6+Xtd/h+BoFdIkjMDD6yi/p2rtQbb+XKeL3V47xHKdtZ8x31cLvUemTUz9xPnVC8pzqJfcvqPnciaEmEX8m5mRK3TQ3vVHfdtLL4uZNzNaW2OWM/8//1HuulVlXxvFx1f5orDXCk1E8Z5tZTkJOrJ19mk1LLs4K9HCEfkBwKnet2PdXPamP8fS0Rdiog7Ifa/XI/lt3ULj8lgugXpqUw9F3h804IIcSuZMBDqdbWVkzTzBqmV1FRwerVqwe6OTkd+40b+fr/W0trZyOaw4Fm2IuhG2iabg/p0nR7qFe8p4+uGxi6kQx1HJoDp+HEqTtx6N3bLsOFy3DhcXjwOr0UOgspcBVQ4CrA5/BR4CqgyFVEgasAt+FG1+OzC8WHi2WuE4FPb7Z1Lf8Th+90mgZGfM6qHZH8wZm5TgRSKT88M7fTAiy2sk9GWcr+No9l7Gf9kMs8luN4rh+oPd5Xatm2ArWttWsb9ftwaPtvtF0nzcM5U0+/k88/aO1ijztz3r2s9Tbqba1uT/XZSv1e3Z70f4aeAqxtHuuhXtbBnupklBUPzz6+mxoyUyKIvDN0jWuf+YiVDV35bsp2OXJ8FT88YUK+myGEEKKfydX3chhfOZ5XL/1bvpsh8ikxWXR/9BARQgghdpLBPiXCrmJX6WW0sqGLz+p7vtLyYDa2qp+vNiuEEGJQGPBQqqysDMMwaG5uTitvbm6msrJ313dW8b+WdnUNzb/0CCGEEGJwKygoGLih59upP6ZEGIjvVLtKoPPA31dR3x7MdzO2y36jSvjGjNHsWaxjRZz5bs52Gea1X6fyGPJnqLcf5DEMFvIYBoc9i/UByVS29Z1qwEMpl8vFpEmTWLp0KcceeywAlmWxdOlSzj///F6dw+/3A3DEEUfstHYKIYQQYvf1wQcfUFhYmO9mbFV/TIkg36l2D68AC/LdiB20Gngs343YQUP9MQz19oM8hsFCHsPgsBqYMX/n38+2vlPlZfjexRdfzJw5c5g8eTJTpkzh0UcfJRgMcsYZZ/Tq9tXV1fzjH/8YEn/FFEIIIcTQU1CwewwVku9UQgghhNiZtvWdKi+h1OzZs2lpaeGee+6hsbGRiRMn8tBDD/V6+J6u6wwfLpOcCiGEEGL31R9TIsh3KiGEEELkU94mOj///PN7PVxPCCGEEEKk648pEYQQQggh8kmuvieEEEIIMUTt6JQIQgghhBD5JKGUEEIIIcQQtaNTIgghhBBC5JOmEtcCFkIIIYQQQgghhBBigOj5boAQQgghhBBCCCGE2P1IKCWEEEIIIYQQQgghBpyEUkIIIYQQQgghhBBiwEkoJYQQQgghhBBCCCEGnIRSOTz55JMcffTR7LfffnzjG9/gk08+yXeThpQHH3yQM888k2nTpjFz5ky++93vsnr16nw3a8j77W9/y/jx4/nZz36W76YMOVu2bOGGG27goIMOYsqUKZxyyil8+umn+W7WkGKaJnfddRdHH300U6ZM4dhjj+W+++5DrpWxbf/+97+54oormDVrFuPHj+eNN95IO66U4u6772bWrFlMmTKFb33rW6xduzY/jR3EtvY8RqNR7rjjDk455RSmTp3KrFmz+NGPfsSWLVvy2GIx1Pz6179m/PjxacvXv/71fDdr0JD3sr7Z1vN14403Zr3eLr300jy1Nr9689shHA4zd+5cDjroIKZNm8b3vvc9mpqa8tTi/OrN83XBBRdkvb5uvvnmPLU4v5566ilOOeUUpk+fzvTp0znnnHP4xz/+kTwur61023q+dsZrS0KpDEuWLGH+/PlcddVVvPjii0yYMIFLL72U5ubmfDdtyHjvvfc477zzeO6553jkkUeIxWJceumlBAKBfDdtyPrkk0945plnGD9+fL6bMuS0t7dz7rnn4nQ6WbRoEa+88gpz5syhpKQk300bUhYtWsTTTz/NzTffzJIlS7jhhht46KGHePzxx/PdtEEvEAgwfvx4brnllpzHFy1axOOPP86tt97Kc889h9fr5dJLLyUcDg9wSwe3rT2PoVCIzz//nCuvvJIXXniBe++9lzVr1nDllVfmoaViKNtnn3345z//mVyeeuqpfDdp0JD3sr7Z1vMFcNhhh6W93u68884BbOHg0ZvfDj//+c/529/+xl133cXjjz9OQ0MDV199dR5bnT+9/a119tlnp72+fvSjH+Wpxfk1fPhwbrjhBl544QWef/55Dj74YK666iq++uorQF5bmbb1fMFOeG0pkeass85Sc+fOTe6bpqlmzZqlHnzwwTy2amhrbm5WtbW16r333st3U4akrq4udfzxx6t//etf6vzzz1fz5s3Ld5OGlDvuuEOde+65+W7GkHf55ZerH//4x2llV199tbr++uvz1KKhqba2Vr3++uvJfcuy1KGHHqoeeuihZFlHR4eaPHmy+tOf/pSPJg4Jmc9jLv/5z39UbW2tqqurG6BWiaHunnvuUf/1X/+V72YMCfJe1je53rPmzJmjrrzyyjy1aHDL/O3Q0dGhJk2apF599dVknZUrV6ra2lr10Ucf5amVg0eu31rym2HrDjzwQPXcc8/Ja6uXEs+XUjvntSU9pVJEIhE+++wzDjnkkGSZrusccsghfPTRR3ls2dDW2dkJID1TttNtt93GEUcckfa6FL335ptvMnnyZK655hpmzpzJaaedxnPPPZfvZg0506ZN45133mHNmjUAfPHFF3zwwQccfvjheW7Z0LZx40YaGxvT/v8uKipi//33l8+dHdTV1YWmaRQXF+e7KWIIWbduHbNmzeKYY47h+uuvp76+Pt9NGhLkvWz7vPfee8ycOZMTTjiBW265hdbW1nw3aVDI/O2wbNkyotFo2utr7Nix1NTU8PHHH+ejiYNKT7+1Fi9ezEEHHcTJJ5/Mr371K4LBYD6aN6iYpskrr7xCIBBg2rRp8trahsznK6G/X1uOHW3orqS1tRXTNKmoqEgrr6iokDmRtpNlWfz85z9n+vTp1NbW5rs5Q84rr7zC559/zh//+Md8N2XI2rBhA08//TQXX3wxV1xxBZ9++inz5s3D6XRy+umn57t5Q8bll19OV1cXJ554IoZhYJom1113Hf/1X/+V76YNaY2NjQA5P3d25/kMdlQ4HGbhwoWcdNJJFBYW5rs5YoiYMmUK8+fPZ6+99qKxsZH77ruP8847j8WLF8vraBvkvazvDjvsMI477jhGjRrFhg0buPPOO/n2t7/Ns88+i2EY+W5e3uT67dDU1ITT6cz6I0NFRUXytbe76um31sknn0xNTQ3V1dWsWLGChQsXsmbNGu699948tjZ/VqxYwTe/+U3C4TA+n4/77ruPcePGsXz5cnlt5dDT8wU757UloZTYqebOnctXX30lczJsh02bNvGzn/2M3/3ud7jd7nw3Z8hSSjF58mR+8IMfALDvvvvy1Vdf8cwzz0go1Qevvvoqixcv5le/+lXyQ3z+/PlUV1fL8ygGlWg0yrXXXotSirlz5+a7OWIIOeKII5LbEyZMYP/99+eoo47i1Vdf5Rvf+EYeWyZ2RSeddFJyOzFZ8LHHHpvsPbW7kt8OfdPT83XOOeckt8ePH09VVRXf+ta3WL9+PXvsscdANzPv9tprL1566SU6Ozt57bXXmDNnDk888US+mzVo9fR8jRs3bqe8tmT4XoqysjIMw8ia1Ly5uZnKyso8tWrouu222/j73//Oo48+yvDhw/PdnCHns88+o7m5mTPOOIN9992Xfffdl/fee4/HH3+cfffdF9M0893EIaGqqoqxY8emle29994yJKOPfvnLX3L55Zdz0kknMX78eE477TQuuugiHnzwwXw3bUirqqoCkM+dfhKNRvn+979PfX09v/vd76R3i9ghxcXF7Lnnnqxfvz7fTRn05L1sx40ePZqysjLWrVuX76bkTU+/HSorK4lGo3R0dKTVb25uTr72dkd9+a21//77A+y2ry+Xy8WYMWOYPHky119/PRMmTOCxxx6T11YPenq+cumP15aEUilcLheTJk1i6dKlyTLLsli6dGnaGEqxdUopbrvtNl5//XUeffRRRo8ene8mDUkHH3wwixcv5qWXXkoukydP5pRTTuGll17arbt298X06dOT8yAlrF27lpEjR+apRUNTKBRC07S0MsMwUErlqUW7hlGjRlFVVZX2udPV1cV//vMf+dzpo0QgtW7dOn7/+99TVlaW7yaJIc7v97Nhw4bd+odJb8l72Y7bvHkzbW1tu+XrbVu/HSZPnozT6Ux7fa1evZr6+nqmTp06wK3Nv+35rbV8+XKA3fL1lYtlWUQiEXlt9VLi+cqlP15bMnwvw8UXX8ycOXOYPHkyU6ZM4dFHHyUYDHLGGWfku2lDxty5c/nTn/7Eb37zGwoKCpLjcYuKivB4PHlu3dBRWFiYNQ+Xz+ejtLRU5ufqg4suuohzzz2XBx54gBNPPJFPPvmE5557jttuuy3fTRtSjjrqKB544AFqamqSw/ceeeQRzjzzzHw3bdDz+/1pPS02btzI8uXLKSkpoaamhgsvvJD777+fMWPGMGrUKO6++26qq6s59thj89jqwWdrz2NVVRXXXHMNn3/+OQ8++CCmaSY/e0pKSnC5XPlqthhCfvGLX3DUUUdRU1NDQ0MDv/71r9F1nZNPPjnfTRsU5L2sb7b2fJWUlHDvvfdywgknUFlZyYYNG7jjjjsYM2YMhx12WB5bnR/b+u1QVFTEmWeeyYIFCygpKaGwsJB58+Yxbdq03TI42NbztX79ehYvXswRRxxBaWkpK1asYP78+Rx44IFMmDAhz60feL/61a84/PDDGTFiBH6/nz/96U+89957PPzww/LaymFrz9fOem1pSv7MneWJJ57g4YcfprGxkYkTJ/LTn/402S1NbNv48eNzls+fP1/CvR10wQUXMGHCBH7yk5/kuylDyt/+9jfuvPNO1q5dy6hRo7j44os5++yz892sIaWrq4u7776bN954g+bmZqqrqznppJO46qqr5Af/Nrz77rtceOGFWeWnn346CxYsQCnFPffcw3PPPUdHRwczZszglltuYa+99spDawevrT2PV199Ncccc0zO2z322GMcdNBBO7t5Yhdw3XXX8e9//5u2tjbKy8uZMWMG11133W45/0ou8l7WN1t7vm699VauuuoqPv/8czo7O6murubQQw/l2muv3S2HO/bmt0M4HGbBggW88sorRCIRZs2axS233LJb9vzZ1vO1adMmfvjDH/LVV18RCAQYMWIExx57LN/97nd3y2HtN910E++88w4NDQ0UFRUxfvx4vv3tb3PooYcC8trKtLXna2e9tiSUEkIIIYQQQgghhBADTuaUEkIIIYQQQgghhBADTkIpIYQQQgghhBBCCDHgJJQSQgghhBBCCCGEEANOQikhhBBCCCGEEEIIMeAklBJCCCGEEEIIIYQQA05CKSGEEEIIIYQQQggx4CSUEkIIIYQQQgghhBADTkIpIYQQQgghhBBCCDHgJJQSQgx6N954I+PHj+e3v/1tWvkbb7zB+PHj89QqIYQQQgghhBA7QkIpIcSQ4Ha7WbRoEe3t7fluihBCCCGEEEKIfiChlBBiSDjkkEOorKzkwQcf7LHOa6+9xkknncTkyZM5+uij+d3vfpd2/Oijj+aBBx7gxz/+MdOmTePII4/k2WefTauzadMmrr32Wg444AC+9rWvceWVV7Jx48ad8piEEEIIIYQQYncmoZQQYkjQdZ0f/OAHPPHEE2zevDnr+LJly/j+97/P7NmzWbx4MVdffTV33303L7zwQlq9Rx55hMmTJ/PSSy/x3//939x6662sXr0agGg0yqWXXkpBQQFPPvkkTz/9ND6fj8suu4xIJDIgj1MIIYQQQgghdhcSSgkhhozjjjuOiRMncs8992Qde+SRR5g5cyZXXXUVe+21F2eccQbnnXceDz/8cFq9ww8/nPPOO48xY8bw7W9/m7KyMt59910AlixZgmVZ/OxnP2P8+PGMHTuW+fPns2nTJt57770BeYxCCCGEEEIIsbuQUEoIMaTccMMNvPTSS6xatSqtfPXq1UyfPj2tbPr06axbtw7TNJNlqROja5pGZWUlzc3NAHzxxResX7+e6dOnM23aNKZNm8ZBBx1EOBxm/fr1O/FRCSGEEEIIIcTux5HvBgghRF8ceOCBzJo1i1/96lecccYZfb69w5H+tqdpGkopAAKBAJMmTWLhwoVZtysvL9++BgshhBBCCCGEyElCKSHEkHP99ddz2mmnsddeeyXL9t57bz788MO0eh9++CF77rknhmH06ryTJk3i1VdfpaKigsLCwn5tsxBCCCGEEEKIdDJ8Twgx5IwfP55TTjmFxx9/PFl2ySWXsHTpUu677z7WrFnDiy++yJNPPskll1zS6/OecsoplJWVceWVV/L++++zYcMG3n33XebNm5dzcnUhhBBCCCGEENtPQikhxJB0zTXXYFlWcn/SpEncddddLFmyhFNOOYV77rmHa665pk9D/LxeL0888QQ1NTVcffXVzJ49m5/85CeEw2HpOSWEEEIIIYQQ/UxTiclUhBBCCCGEEEIIIYQYINJTSgghhBBCCCGEEEIMOAmlhBBCCCGEEEIIIcSAk1BKCCGEEEIIIYQQQgw4CaWEEEIIIYQQQgghxICTUEoIIYQQQgghhBBCDDgJpYQQQgghhBBCCCHEgJNQSgghhBBCCCGEEEIMOAmlhBBCCCGEEEIIIcSAk1BKCCGEEEIIIYQQQgw4CaWEEEIIIYQQQgghxICTUEoIIYQQQgghhBBCDDgJpYQQQgghhBBCCCHEgPv/Xt5bEhNuo0cAAAAASUVORK5CYII=", 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      " ] @@ -523,7 +525,7 @@ "\n", "\n", "fig.tight_layout()\n", - "fig.suptitle(\"Trajectories and overshoot distributions\", fontsize=16, y=1.05)\n", + "fig.suptitle(\"Trajectories and overshoot distributions in the but-for analysis\", fontsize=16, y=1.05)\n", "sns.despine()\n", "\n", "plt.savefig(\"counterfactual_sir.png\")" @@ -533,11 +535,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The plots above show what happens in the four different scenarios. We observe that in the model where none of the policies were imposed, there was very low probability of high overshoot $0.24$. On the other hand, when both policies were imposed, the probability of high overshoot was relatively higher $0.81$. \n", + "The plots above show what happens in the four different scenarios. We observe that in the model where none of the policies were imposed, ther probability of the overshoot being too high is relatively low, $0.24$. On the other hand, when both policies were imposed, the probability of the overshoot being to high was relatively higher $0.81$. \n", "\n", - "To identify, which one of `lockdown` and `mask` is the cause, we analyze the models where only one of the policies were imposed. It seems that when only one of the policies were imposed, the probability of high overshoot seems to be even higher $0.96$ and $0.9$. This indicates that both `lockdown` and `mask` were essentially helping in keeping the overshoot low but we know that is not true (as evident from the first two plots).\n", + "To identify which of `lockdown` and `mask` is the cause, we analyze the models where only one of the policies were imposed. In both cases, the probability of too high overshoot seems to be even higher - $0.96$ and $0.9$. Interestingly, the effect of the interventions is somewhat nuanced. Implementing both increases the risk of overshoot as compared to the no intervention model. But individual intereventions would have even worse consequences, which means that the two interventions while jointly increasing the risk to some extent mitigate each other's contribution to that risk as well.\n", "\n", - "So, what these plots show is that there is a need of a more fine grained analysis where we not only control the variables being intervened on (that is, policies), we also control on keeping part of the context (that is, other variables in the model) fixed. In the next section, we show how this analysis can be carried out with the help of `SearchForExplanation`." + "Crucially, the analysis does not allow us to distinghuish the intuitive role that the lockdown played, as opposed to masking (whose impact has been limited by the presence of lockdown). So, we need of a more fine-grained analysis where we not only control the variables being intervened on (that is, the policies), but also pay attention to what context we are in. We achieve that level of sensitivity by stochastically keeping part of the context (that is, other variables in the model) fixed (see the tutorial for categorical variables for a more extensive explanation of this method and simpler examples). The key idea is that starting with the scenario in which both interventions have been implemented, there is a context such that if we keep it fixed, removing lockdown would significantly lower the overshoot, but there is no context that we could keep fixed such that if in that context we remove the masking policy, the overshoot would decrease. In the next section, we show how this analysis can be carried out with the help of `SearchForExplanation`." ] }, { From 17be7f5699adef2043eb15ed1a0f1c070321916c Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Mon, 26 Aug 2024 11:16:28 -0400 Subject: [PATCH 065/111] revised parap description for SearchForExplanation --- docs/source/explainable_sir.ipynb | 63 +++++++++++++++++++++++++------ 1 file changed, 51 insertions(+), 12 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 58a545ba..73ddf6e3 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -37,7 +37,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Setup\n", + "## Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "\n", "The main dependencies for this example are PyTorch, Pyro, and ChiRho.\n" ] @@ -94,7 +100,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Bayesian Epidemiological SIR model with Policies\n", + "## Bayesian Epidemiological SIR model with Policies" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "\n", "Now, we build the epidemiological SIR (Susceptible, Infected, Recovered/Removed) model, one step at a time. We first encode the deterministic SIR dynamics. Then we add uncertainty about the parameters that govern these dynamics - $\\beta$ and $\\gamma$. These parameters have been described in much detail in the [dynamical systems tutorial](https://basisresearch.github.io/chirho/dynamical_intro.html). We then incorporate the resulting model into a more complex causal model that describes the policy mechanisms such as imposing lockdown and masking restrictions.\n", "\n", @@ -211,7 +223,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Bayesian SIR model\n", + "### Bayesian SIR model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", "\n", "Now suppose we are uncertain about $\\beta, \\gamma$, and want to construct a Bayesian SIR model that incorporates this uncertainty. Say we inducing $\\beta$ to be drawn from `Beta(18, 600)`, and $\\gamma$ to be drawn from distribution `Beta(1600, 1600)`. " ] @@ -245,7 +264,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Bayesian SIR model with Policies\n", + "### Bayesian SIR model with Policies\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "\n", "Now we incorporate the Bayesian SIR model into a larger model that includes the effect of two different policies, lockdown and masking, where each can be implemented with $50\\%$ probability (these probabilities won't really matter, as we will be intervening on these, the sampling is mainly used to register the parameters with Pyro). We encode their efficiencies which further affect the model. Crucially, these efficiencies interact in a fashion resembling the structure of the stone-throwing example we discussed in the tutorial on categorical variables. If lockdown is present, this limits the impact of masking as agents interact less and so masks have fewer opportunities to block anything. We assume the situation is assymetric: masking has no impact on the efficiency of lockdown. The model also computes `overshoot` and `os_too_high` for further analysis.\n", "\n" @@ -335,7 +360,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## But-for Analysis with Bayesian SIR model with Policies\n", + "## But-for Analysis with Bayesian SIR model with Policies" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "\n", "Suppose now we introduced both policies, and this resulted in an overshoot. What intuitively is the case is that lockdown limited the efficiency of masking, and it was in fact the lockdown that in this particular context caused the overshoot (this is consistent with saying that in the context where only masking has been implemented, masking would be responsible for the resulting overshoot being too high).\n", "\n", @@ -546,9 +577,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Causal Explanations using `SearchForExplanation`\n", + "## Causal Explanations using `SearchForExplanation`\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", "\n", - "We first setup a function for performing importance sampling through the model that returns cumulative log probabilities of the samples, sample traces, handler for multiworld counterfactual reasoning and log probabilities. We use these objects later in the code to subselect the samples." + "We first setup a function for performing importance sampling through the model that returns cumulative log probabilities of the samples, sample traces, a handler object for multiworld counterfactual reasoning, and log probabilities. We use these objects later in the code to subselect the samples." ] }, { @@ -594,12 +632,13 @@ "metadata": {}, "source": [ "Then, we setup the query as follows:\n", - "1. `supports`: We extract supports of the model using `ExtractSupports` and enrich it with additional information of `os_too_high` being a Boolean.\n", - "2. `antecedents`: We have put `lockdown=1` and `mask=1` as possible causes.\n", + "1. `supports`: We extract supports of the model using `ExtractSupports` and enrich it with additional information of `os_too_high` being a Boolean (constraints for deterministic nodes currently need to be specified manually).\n", + "2. `antecedents`: We postulate `lockdown=1` and `mask=1` as possible causes.\n", "3. `alternatives`: We provide `lockdown=0` and `mask=0` as alternative values.\n", - "4. `witnesses`: We include `mask_efficiency` and `lockdown_efficiency` as candidates to be included in the context to be kept fixed.\n", - "5. `consequents`: We put `os_too_high=1` as the outcome we wish to analyze the causes for.\n", - "6. `antecedent_bias`, `witness_bias`, `consequent_scale`: We set these parameters to have equal probabilities of choosing causes and preferring minimal witness sets. Please refer to the documentation of `SearchForExplanation` for more details." + "4. `witnesses`: We include `mask_efficiency` and `lockdown_efficiency` as candidates to be included in the contexts potentially to be kept fixed.\n", + "5. `consequents`: We put `os_too_high=1` as the outcome whose causes we wish to analyze.\n", + "6. `antecedent_bias`, `witness_bias`,: We set these parameters to have equal probabilities of intervening on cause candidates, and to slightly prefer smaller witness sets. Please refer to the documentation of `SearchForExplanation` for more details.\n", + "7. `consequent_scale` is set to effectively include values near 0 and 1 depending on whether the binary outocomes differ across counterfactual worlds." ] }, { From 89864d37d0ee5bd1c4a370c7806cfc6e9caada01 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Mon, 26 Aug 2024 11:28:40 -0400 Subject: [PATCH 066/111] finished up to the stopping point --- docs/source/explainable_sir.ipynb | 33 +++++++++++++++++++------------ 1 file changed, 20 insertions(+), 13 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 73ddf6e3..1f7d62ad 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -591,7 +591,7 @@ }, { "cell_type": "code", - "execution_count": 511, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -643,14 +643,14 @@ }, { "cell_type": "code", - "execution_count": 512, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.1215)\n" + "tensor(0.1257)\n" ] } ], @@ -670,7 +670,7 @@ " consequents={\"os_too_high\": torch.tensor(1.0)},\n", " consequent_scale=1e-8,\n", " witness_bias=0.2,\n", - " )(policy_model #it was policy_model_all earlier)\n", + " )(policy_model \n", " )\n", "\n", "logp, importance_tr, mwc_imp, log_weights = importance_infer(num_samples=10000)(query)()\n", @@ -681,12 +681,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now that we have setup the query and drawn 10000 samples from it, we can analyze the samples and their log probabilities to compute queries of interest. We first compute the probabilities that different sets of antecedent candidates have causal effect over `os_too_high`." + "The above probability itself is not directly related to our query. It is the probability that the overshoot is too high in the antecedents-intervened workd and not too high in the alterantives-intervened world, where antecedent interventions are preempted with probabilities $0.5$ at each site, and witnesses are kept fixed at the observed values with probability $0.5+0.2$ at each site. But more fine-grained queries can be answered using the 10000 samples we have drawn in the process. We first compute the probabilities that different sets of antecedent candidates have causal effect over `os_too_high`." ] }, { "cell_type": "code", - "execution_count": 513, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -699,24 +699,31 @@ }, { "cell_type": "code", - "execution_count": 514, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0} 0.19823434948921204\n", - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1} 0.1833265870809555\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0} 0.10898739099502563\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1} 2.7269246860583962e-09\n" + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0} 0.2081967145204544\n", + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1} 0.192521870136261\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0} 0.1043718010187149\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1} 2.6645385897694496e-09\n" ] } ], "source": [ + "# no preemptions on lockdown and masking, i.e. both interventions executed\n", "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_lockdown\": 0, \"__cause____antecedent_mask\": 0})\n", + "\n", + "# only lockdown executed, masking preempted\n", "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_lockdown\": 0, \"__cause____antecedent_mask\": 1})\n", + "\n", + "# only masking executed, lockdown preempted\n", "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_lockdown\": 1, \"__cause____antecedent_mask\": 0})\n", + "\n", + "# no interventions executed\n", "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_lockdown\": 1, \"__cause____antecedent_mask\": 1})" ] }, @@ -724,9 +731,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note that one can also compute above queries by giving specific parameters to `SearchForExplanation` instead of subselecting the samples as we did in the tutorial for explainable module for models with categorical variables.\n", + "Note that one could also compute above queries by giving specific parameters to `SearchForExplanation` instead of subselecting the samples, as we did in the tutorial for explainable module for models with categorical variables. Here, however, we illustrate that running a sufficiently general query ones produces samples that can be used to answer multiple different questions.\n", "\n", - "Also, we use the log probabilities above to identify whether a particular combination of intervening nodes and context nodes have causal power or not. One can also obatin these results by explictly analyzing the sample trace as we do in the next section." + "Also, we use the log probabilities above to identify whether a particular combination of intervening nodes and context nodes have causal power or not, which is made possible by the fact that our handler adds appropriate log probabilities to the trace (see the previous tutorial and documentation for more explanation). One can also obtain these results by explictly analyzing the sample trace as we do in the next section." ] }, { From 653e8f89737f381110de1ab9b5ea7a9e4004a8f8 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Mon, 26 Aug 2024 11:33:49 -0400 Subject: [PATCH 067/111] small revisions --- docs/source/explainable_sir.ipynb | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 1f7d62ad..e648d50a 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -789,7 +789,7 @@ }, { "cell_type": "code", - "execution_count": 516, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -821,7 +821,7 @@ }, { "cell_type": "code", - "execution_count": 517, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -832,7 +832,7 @@ }, { "cell_type": "code", - "execution_count": 518, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -840,14 +840,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "factual: 24.31097984313965 counterfactual mask: 21.902610778808594 counterfactual lockdown: 20.758800506591797\n", + "factual: 24.23866081237793 counterfactual mask: 21.874435424804688 counterfactual lockdown: 20.744991302490234\n", "Probability of overshoot being high\n", - "factual: 0.7299000024795532 counterfactual mask: 0.5736842155456543 counterfactual lockdown: 0.5078909397125244\n" + "factual: 0.7303000092506409 counterfactual mask: 0.5687074661254883 counterfactual lockdown: 0.5103896260261536\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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      " ] @@ -882,7 +882,7 @@ }, { "cell_type": "code", - "execution_count": 519, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -893,7 +893,7 @@ }, { "cell_type": "code", - "execution_count": 520, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -901,14 +901,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "factual: 24.31097984313965 counterfactual mask: 26.651079177856445 counterfactual lockdown: 22.560808181762695\n", + "factual: 24.23866081237793 counterfactual mask: 26.39071273803711 counterfactual lockdown: 22.133150100708008\n", "Probability of overshoot being high\n", - "factual: 0.7299000024795532 counterfactual mask: 0.8868421316146851 counterfactual lockdown: 0.7044476270675659\n" + "factual: 0.7303000092506409 counterfactual mask: 0.8816326260566711 counterfactual lockdown: 0.6857143044471741\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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      " ] @@ -943,12 +943,12 @@ }, { "cell_type": "code", - "execution_count": 521, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { - "image/png": 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      " ] From 13b9f42eacc962e0196aebdc0ecd0e3098061f4b Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Mon, 26 Aug 2024 11:34:16 -0400 Subject: [PATCH 068/111] black notebook --- docs/source/explainable_sir.ipynb | 457 +++++++++++++++++++++++------- 1 file changed, 356 insertions(+), 101 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index e648d50a..310ea5d3 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -68,8 +68,7 @@ "from pyro.infer import Predictive\n", "\n", "import pyro\n", - "from chirho.counterfactual.handlers.counterfactual import \\\n", - " MultiWorldCounterfactual\n", + "from chirho.counterfactual.handlers.counterfactual import MultiWorldCounterfactual\n", "from chirho.dynamical.handlers.interruption import StaticEvent\n", "from chirho.dynamical.handlers.solver import TorchDiffEq\n", "from chirho.dynamical.handlers.trajectory import LogTrajectory\n", @@ -139,6 +138,7 @@ "# dI = beta * SI - gamma * I\n", "# dR = gamma * I\n", "\n", + "\n", "class SIRDynamics(pyro.nn.PyroModule):\n", " def __init__(self, beta, gamma):\n", " super().__init__()\n", @@ -154,7 +154,7 @@ " return dX\n", "\n", "\n", - "# l is a parameter describing the strenght of the intervening policies \n", + "# l is a parameter describing the strenght of the intervening policies\n", "# it is a value between 0 and 1, and (1-l) is the fraction of the original unintervened beta\n", "class SIRDynamicsPolicies(SIRDynamics):\n", " def __init__(self, beta0, gamma):\n", @@ -243,6 +243,7 @@ "source": [ "# Defining a Bayesian SIR model where we have priors over beta and gamma distributions\n", "\n", + "\n", "def bayesian_sir(base_model=SIRDynamics) -> Dynamics[torch.Tensor]:\n", " beta = pyro.sample(\"beta\", dist.Beta(18, 600))\n", " gamma = pyro.sample(\"gamma\", dist.Beta(1600, 1600))\n", @@ -282,10 +283,11 @@ "metadata": {}, "outputs": [], "source": [ - "# a utility function \n", + "# a utility function\n", "# allowing for interventions on a dynamical system\n", "# within another model\n", "\n", + "\n", "def MaskedStaticIntervention(time: R, intervention: Intervention[State[T]]):\n", "\n", " @on(StaticEvent(time))\n", @@ -423,9 +425,7 @@ ")\n", "lockdown_samples = lockdown_predictive()\n", "\n", - "predictive = Predictive(\n", - " policy_model, num_samples=num_samples, parallel=True\n", - ")\n", + "predictive = Predictive(policy_model, num_samples=num_samples, parallel=True)\n", "samples = predictive()" ] }, @@ -537,15 +537,9 @@ " f\"Overshoot mean: {lockdown_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {lockdown_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", ")\n", "\n", - "add_pred_to_plot(\n", - " samples[\"S\"], axs, coords=(4, 0), color=colors[0], label=\"susceptible\"\n", - ")\n", - "add_pred_to_plot(\n", - " samples[\"I\"], axs, coords=(4, 0), color=colors[1], label=\"infected\"\n", - ")\n", - "add_pred_to_plot(\n", - " samples[\"R\"], axs, coords=(4, 0), color=colors[2], label=\"recovered\"\n", - ")\n", + "add_pred_to_plot(samples[\"S\"], axs, coords=(4, 0), color=colors[0], label=\"susceptible\")\n", + "add_pred_to_plot(samples[\"I\"], axs, coords=(4, 0), color=colors[1], label=\"infected\")\n", + "add_pred_to_plot(samples[\"R\"], axs, coords=(4, 0), color=colors[2], label=\"recovered\")\n", "axs[4, 0].set_title(\"All interventions with equal probabilities\")\n", "axs[4, 0].legend_.remove()\n", "\n", @@ -556,7 +550,11 @@ "\n", "\n", "fig.tight_layout()\n", - "fig.suptitle(\"Trajectories and overshoot distributions in the but-for analysis\", fontsize=16, y=1.05)\n", + "fig.suptitle(\n", + " \"Trajectories and overshoot distributions in the but-for analysis\",\n", + " fontsize=16,\n", + " y=1.05,\n", + ")\n", "sns.despine()\n", "\n", "plt.savefig(\"counterfactual_sir.png\")" @@ -595,34 +593,36 @@ "metadata": {}, "outputs": [], "source": [ - "def importance_infer(\n", - " model: Optional[Callable] = None, *, num_samples: int\n", - "):\n", - " \n", + "def importance_infer(model: Optional[Callable] = None, *, num_samples: int):\n", + "\n", " if model is None:\n", " return lambda m: importance_infer(m, num_samples=num_samples)\n", "\n", - " def _wrapped_model(\n", - " *args,\n", - " **kwargs\n", - " ):\n", + " def _wrapped_model(*args, **kwargs):\n", "\n", " guide = pyro.poutine.block(hide_fn=lambda msg: msg[\"is_observed\"])(model)\n", "\n", " max_plate_nesting = 9 # TODO guess\n", "\n", " with pyro.poutine.block(), MultiWorldCounterfactual() as mwc_imp:\n", - " log_weights, importance_tr, _ = pyro.infer.importance.vectorized_importance_weights(\n", - " model,\n", - " guide,\n", - " *args,\n", - " num_samples=num_samples,\n", - " max_plate_nesting=max_plate_nesting,\n", - " normalized=False,\n", - " **kwargs\n", + " log_weights, importance_tr, _ = (\n", + " pyro.infer.importance.vectorized_importance_weights(\n", + " model,\n", + " guide,\n", + " *args,\n", + " num_samples=num_samples,\n", + " max_plate_nesting=max_plate_nesting,\n", + " normalized=False,\n", + " **kwargs\n", + " )\n", " )\n", "\n", - " return torch.logsumexp(log_weights, dim=0) - math.log(num_samples), importance_tr, mwc_imp, log_weights\n", + " return (\n", + " torch.logsumexp(log_weights, dim=0) - math.log(num_samples),\n", + " importance_tr,\n", + " mwc_imp,\n", + " log_weights,\n", + " )\n", "\n", " return _wrapped_model" ] @@ -659,19 +659,22 @@ " policy_model()\n", "\n", "supports = s.supports\n", - "supports[\"os_too_high\"] = constraints.independent(base_constraint=constraints.boolean, reinterpreted_batch_ndims=0)\n", + "supports[\"os_too_high\"] = constraints.independent(\n", + " base_constraint=constraints.boolean, reinterpreted_batch_ndims=0\n", + ")\n", "\n", "query = SearchForExplanation(\n", - " supports=supports,\n", - " alternatives={\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(0.0)},\n", - " antecedents={\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(1.0)},\n", - " antecedent_bias=0.0,\n", - " witnesses={key: s.supports[key] for key in [\"lockdown_efficiency\", \"mask_efficiency\"]},\n", - " consequents={\"os_too_high\": torch.tensor(1.0)},\n", - " consequent_scale=1e-8,\n", - " witness_bias=0.2,\n", - " )(policy_model \n", - " )\n", + " supports=supports,\n", + " alternatives={\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(0.0)},\n", + " antecedents={\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(1.0)},\n", + " antecedent_bias=0.0,\n", + " witnesses={\n", + " key: s.supports[key] for key in [\"lockdown_efficiency\", \"mask_efficiency\"]\n", + " },\n", + " consequents={\"os_too_high\": torch.tensor(1.0)},\n", + " consequent_scale=1e-8,\n", + " witness_bias=0.2,\n", + ")(policy_model)\n", "\n", "logp, importance_tr, mwc_imp, log_weights = importance_infer(num_samples=10000)(query)()\n", "print(torch.exp(logp))" @@ -691,10 +694,18 @@ "outputs": [], "source": [ "def compute_prob(trace, log_weights, mask):\n", - " mask_intervened = torch.ones(trace.nodes[\"__cause____antecedent_lockdown\"][\"value\"].shape).bool()\n", + " mask_intervened = torch.ones(\n", + " trace.nodes[\"__cause____antecedent_lockdown\"][\"value\"].shape\n", + " ).bool()\n", " for i, v in mask.items():\n", - " mask_intervened &= (trace.nodes[i][\"value\"] == v)\n", - " print(mask, (torch.sum(torch.exp(log_weights) * mask_intervened.squeeze()) / mask_intervened.float().sum()).item())" + " mask_intervened &= trace.nodes[i][\"value\"] == v\n", + " print(\n", + " mask,\n", + " (\n", + " torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())\n", + " / mask_intervened.float().sum()\n", + " ).item(),\n", + " )" ] }, { @@ -715,16 +726,32 @@ ], "source": [ "# no preemptions on lockdown and masking, i.e. both interventions executed\n", - "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_lockdown\": 0, \"__cause____antecedent_mask\": 0})\n", + "compute_prob(\n", + " importance_tr,\n", + " log_weights,\n", + " {\"__cause____antecedent_lockdown\": 0, \"__cause____antecedent_mask\": 0},\n", + ")\n", "\n", "# only lockdown executed, masking preempted\n", - "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_lockdown\": 0, \"__cause____antecedent_mask\": 1})\n", + "compute_prob(\n", + " importance_tr,\n", + " log_weights,\n", + " {\"__cause____antecedent_lockdown\": 0, \"__cause____antecedent_mask\": 1},\n", + ")\n", "\n", "# only masking executed, lockdown preempted\n", - "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_lockdown\": 1, \"__cause____antecedent_mask\": 0})\n", + "compute_prob(\n", + " importance_tr,\n", + " log_weights,\n", + " {\"__cause____antecedent_lockdown\": 1, \"__cause____antecedent_mask\": 0},\n", + ")\n", "\n", "# no interventions executed\n", - "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_lockdown\": 1, \"__cause____antecedent_mask\": 1})" + "compute_prob(\n", + " importance_tr,\n", + " log_weights,\n", + " {\"__cause____antecedent_lockdown\": 1, \"__cause____antecedent_mask\": 1},\n", + ")" ] }, { @@ -795,21 +822,31 @@ "source": [ "def histogram_data(trace, mwc, masks, world):\n", " with mwc:\n", - " data_to_plot = gather(trace.nodes[\"overshoot\"][\"value\"], IndexSet(**{\"lockdown\": {world}, \"mask\": {world}}))\n", + " data_to_plot = gather(\n", + " trace.nodes[\"overshoot\"][\"value\"],\n", + " IndexSet(**{\"lockdown\": {world}, \"mask\": {world}}),\n", + " )\n", "\n", - " mask_tensor = torch.ones(importance_tr.nodes[\"__cause____antecedent_mask\"][\"value\"].shape).bool()\n", + " mask_tensor = torch.ones(\n", + " importance_tr.nodes[\"__cause____antecedent_mask\"][\"value\"].shape\n", + " ).bool()\n", " for key, val in masks.items():\n", " mask_tensor = mask_tensor & (trace.nodes[key][\"value\"] == val)\n", " data_to_plot = data_to_plot.squeeze()[torch.nonzero(mask_tensor.squeeze())]\n", "\n", - " os_too_high = (gather(trace.nodes[\"os_too_high\"][\"value\"], IndexSet(**{\"lockdown\": {world}, \"mask\": {world}})))\n", + " os_too_high = gather(\n", + " trace.nodes[\"os_too_high\"][\"value\"],\n", + " IndexSet(**{\"lockdown\": {world}, \"mask\": {world}}),\n", + " )\n", " os_too_high = os_too_high.squeeze()[torch.nonzero(mask_tensor.squeeze())]\n", "\n", " overshoot_mean = data_to_plot.mean()\n", " os_too_high_mean = os_too_high.mean()\n", "\n", - " hist, bin_edges = torch.histogram(data_to_plot, bins = 28, range=(5, 40), density=True)\n", - " return hist, bin_edges, overshoot_mean, os_too_high_mean\n" + " hist, bin_edges = torch.histogram(\n", + " data_to_plot, bins=28, range=(5, 40), density=True\n", + " )\n", + " return hist, bin_edges, overshoot_mean, os_too_high_mean" ] }, { @@ -825,9 +862,29 @@ "metadata": {}, "outputs": [], "source": [ - "hist_fact_nec, bin_edges, os_fact_nec, oth_fact_nec = histogram_data(importance_tr, mwc_imp, {}, 0)\n", - "hist_mask_nec, bin_edges, os_mask_nec, oth_mask_nec = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 0, \"__cause____antecedent_lockdown\": 1, \"__cause____witness_mask_efficiency\": 0}, 1)\n", - "hist_lockdown_nec, bin_edges, os_lockdown_nec, oth_lockdown_nec = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 1, \"__cause____antecedent_lockdown\": 0, \"__cause____witness_lockdown_efficiency\": 0}, 1)" + "hist_fact_nec, bin_edges, os_fact_nec, oth_fact_nec = histogram_data(\n", + " importance_tr, mwc_imp, {}, 0\n", + ")\n", + "hist_mask_nec, bin_edges, os_mask_nec, oth_mask_nec = histogram_data(\n", + " importance_tr,\n", + " mwc_imp,\n", + " {\n", + " \"__cause____antecedent_mask\": 0,\n", + " \"__cause____antecedent_lockdown\": 1,\n", + " \"__cause____witness_mask_efficiency\": 0,\n", + " },\n", + " 1,\n", + ")\n", + "hist_lockdown_nec, bin_edges, os_lockdown_nec, oth_lockdown_nec = histogram_data(\n", + " importance_tr,\n", + " mwc_imp,\n", + " {\n", + " \"__cause____antecedent_mask\": 1,\n", + " \"__cause____antecedent_lockdown\": 0,\n", + " \"__cause____witness_lockdown_efficiency\": 0,\n", + " },\n", + " 1,\n", + ")" ] }, { @@ -857,9 +914,30 @@ } ], "source": [ - "plt.bar(bin_edges[:28].tolist(), hist_fact_nec, align='center', width = 35/28, alpha = 0.5, color='blue')\n", - "plt.bar(bin_edges[:28].tolist(), hist_lockdown_nec, align='center', width = 35/28, alpha = 0.5, color='pink')\n", - "plt.bar(bin_edges[:28].tolist(), hist_mask_nec, align='center', width = 35/28, alpha = 0.5, color='green')\n", + "plt.bar(\n", + " bin_edges[:28].tolist(),\n", + " hist_fact_nec,\n", + " align=\"center\",\n", + " width=35 / 28,\n", + " alpha=0.5,\n", + " color=\"blue\",\n", + ")\n", + "plt.bar(\n", + " bin_edges[:28].tolist(),\n", + " hist_lockdown_nec,\n", + " align=\"center\",\n", + " width=35 / 28,\n", + " alpha=0.5,\n", + " color=\"pink\",\n", + ")\n", + "plt.bar(\n", + " bin_edges[:28].tolist(),\n", + " hist_mask_nec,\n", + " align=\"center\",\n", + " width=35 / 28,\n", + " alpha=0.5,\n", + " color=\"green\",\n", + ")\n", "plt.legend([\"factual\", \"counterfactual_lockdown\", \"counterfactual_mask\"])\n", "plt.ylabel(\"pr\")\n", "plt.xlabel(\"overshoot\")\n", @@ -867,10 +945,24 @@ "sns.despine()\n", "\n", "print(\"Overshoot mean\")\n", - "print(\"factual: \", os_fact_nec.item(), \" counterfactual mask: \", os_mask_nec.item(), \" counterfactual lockdown: \", os_lockdown_nec.item())\n", + "print(\n", + " \"factual: \",\n", + " os_fact_nec.item(),\n", + " \" counterfactual mask: \",\n", + " os_mask_nec.item(),\n", + " \" counterfactual lockdown: \",\n", + " os_lockdown_nec.item(),\n", + ")\n", "\n", "print(\"Probability of overshoot being high\")\n", - "print(\"factual: \", oth_fact_nec.item(), \" counterfactual mask: \", oth_mask_nec.item(), \" counterfactual lockdown: \", oth_lockdown_nec.item())" + "print(\n", + " \"factual: \",\n", + " oth_fact_nec.item(),\n", + " \" counterfactual mask: \",\n", + " oth_mask_nec.item(),\n", + " \" counterfactual lockdown: \",\n", + " oth_lockdown_nec.item(),\n", + ")" ] }, { @@ -886,9 +978,29 @@ "metadata": {}, "outputs": [], "source": [ - "hist_fact_suff, bin_edges, os_fact_suff, oth_fact_suff = histogram_data(importance_tr, mwc_imp, {}, 0)\n", - "hist_mask_suff, bin_edges, os_mask_suff, oth_mask_suff = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 0, \"__cause____antecedent_lockdown\": 1, \"__cause____witness_mask_efficiency\": 0}, 2)\n", - "hist_lockdown_suff, bin_edges, os_lockdown_suff, oth_lockdown_suff = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 1, \"__cause____antecedent_lockdown\": 0, \"__cause____witness_lockdown_efficiency\": 0}, 2)" + "hist_fact_suff, bin_edges, os_fact_suff, oth_fact_suff = histogram_data(\n", + " importance_tr, mwc_imp, {}, 0\n", + ")\n", + "hist_mask_suff, bin_edges, os_mask_suff, oth_mask_suff = histogram_data(\n", + " importance_tr,\n", + " mwc_imp,\n", + " {\n", + " \"__cause____antecedent_mask\": 0,\n", + " \"__cause____antecedent_lockdown\": 1,\n", + " \"__cause____witness_mask_efficiency\": 0,\n", + " },\n", + " 2,\n", + ")\n", + "hist_lockdown_suff, bin_edges, os_lockdown_suff, oth_lockdown_suff = histogram_data(\n", + " importance_tr,\n", + " mwc_imp,\n", + " {\n", + " \"__cause____antecedent_mask\": 1,\n", + " \"__cause____antecedent_lockdown\": 0,\n", + " \"__cause____witness_lockdown_efficiency\": 0,\n", + " },\n", + " 2,\n", + ")" ] }, { @@ -918,9 +1030,30 @@ } ], "source": [ - "plt.bar(bin_edges[:28].tolist(), hist_fact_suff, align='center', width = 35/28, alpha = 0.5, color='blue')\n", - "plt.bar(bin_edges[:28].tolist(), hist_lockdown_suff, align='center', width = 35/28, alpha = 0.5, color='pink')\n", - "plt.bar(bin_edges[:28].tolist(), hist_mask_suff, align='center', width = 35/28, alpha = 0.5, color='green')\n", + "plt.bar(\n", + " bin_edges[:28].tolist(),\n", + " hist_fact_suff,\n", + " align=\"center\",\n", + " width=35 / 28,\n", + " alpha=0.5,\n", + " color=\"blue\",\n", + ")\n", + "plt.bar(\n", + " bin_edges[:28].tolist(),\n", + " hist_lockdown_suff,\n", + " align=\"center\",\n", + " width=35 / 28,\n", + " alpha=0.5,\n", + " color=\"pink\",\n", + ")\n", + "plt.bar(\n", + " bin_edges[:28].tolist(),\n", + " hist_mask_suff,\n", + " align=\"center\",\n", + " width=35 / 28,\n", + " alpha=0.5,\n", + " color=\"green\",\n", + ")\n", "plt.legend([\"factual\", \"counterfactual_lockdown\", \"counterfactual_mask\"])\n", "plt.ylabel(\"pr\")\n", "plt.xlabel(\"overshoot\")\n", @@ -928,10 +1061,24 @@ "sns.despine()\n", "\n", "print(\"Overshoot mean\")\n", - "print(\"factual: \", os_fact_suff.item(), \" counterfactual mask: \", os_mask_suff.item(), \" counterfactual lockdown: \", os_lockdown_suff.item())\n", + "print(\n", + " \"factual: \",\n", + " os_fact_suff.item(),\n", + " \" counterfactual mask: \",\n", + " os_mask_suff.item(),\n", + " \" counterfactual lockdown: \",\n", + " os_lockdown_suff.item(),\n", + ")\n", "\n", "print(\"Probability of overshoot being high\")\n", - "print(\"factual: \", oth_fact_suff.item(), \" counterfactual mask: \", oth_mask_suff.item(), \" counterfactual lockdown: \", oth_lockdown_suff.item())" + "print(\n", + " \"factual: \",\n", + " oth_fact_suff.item(),\n", + " \" counterfactual mask: \",\n", + " oth_mask_suff.item(),\n", + " \" counterfactual lockdown: \",\n", + " oth_lockdown_suff.item(),\n", + ")" ] }, { @@ -962,31 +1109,49 @@ "\n", "ax = axs[0]\n", "hist_lockdown = hist_lockdown_nec.unsqueeze(1) * hist_lockdown_suff.unsqueeze(0)\n", - "ax.imshow(hist_lockdown, cmap = \"viridis\")\n", - "ax.set(xticks = range(0, 28, 2), xticklabels = bin_edges[0:28:2].tolist())\n", - "ax.set(yticks = range(0, 28, 2), yticklabels = bin_edges[0:28:2].tolist())\n", - "ax.set(xlabel = \"Overshoot in Sufficiency Intervention\", ylabel = \"Overshoot in Necessity Intervention\", title=\"Overshoot in counterfactual lockdown\")\n", - "ax.axvline(x=15.8, color=\"grey\", linestyle=\"--\", label = \"Overshoot too high\")\n", + "ax.imshow(hist_lockdown, cmap=\"viridis\")\n", + "ax.set(xticks=range(0, 28, 2), xticklabels=bin_edges[0:28:2].tolist())\n", + "ax.set(yticks=range(0, 28, 2), yticklabels=bin_edges[0:28:2].tolist())\n", + "ax.set(\n", + " xlabel=\"Overshoot in Sufficiency Intervention\",\n", + " ylabel=\"Overshoot in Necessity Intervention\",\n", + " title=\"Overshoot in counterfactual lockdown\",\n", + ")\n", + "ax.axvline(x=15.8, color=\"grey\", linestyle=\"--\", label=\"Overshoot too high\")\n", "ax.axhline(y=15.8, color=\"grey\", linestyle=\"--\")\n", "\n", - "ax.axvline(x=(os_lockdown_suff-5)*28/35, color=\"white\", linestyle=\"--\", label = \"Mean Overshoot\")\n", - "ax.axhline(y=(os_lockdown_nec-5)*28/35, color=\"white\", linestyle=\"--\")\n", + "ax.axvline(\n", + " x=(os_lockdown_suff - 5) * 28 / 35,\n", + " color=\"white\",\n", + " linestyle=\"--\",\n", + " label=\"Mean Overshoot\",\n", + ")\n", + "ax.axhline(y=(os_lockdown_nec - 5) * 28 / 35, color=\"white\", linestyle=\"--\")\n", "\n", - "ax.legend(loc = \"upper left\")\n", + "ax.legend(loc=\"upper left\")\n", "\n", "ax = axs[1]\n", "hist_mask = hist_mask_nec.unsqueeze(1) * hist_mask_suff.unsqueeze(0)\n", - "ax.imshow(hist_mask, cmap = \"viridis\")\n", - "ax.set(xticks = range(0, 28, 2), xticklabels = bin_edges[0:28:2].tolist())\n", - "ax.set(yticks = range(0, 28, 2), yticklabels = bin_edges[0:28:2].tolist())\n", - "ax.set(xlabel = \"Overshoot in Sufficiency Intervention\", ylabel = \"Overshoot in Necessity Intervention\", title=\"Overshoot in counterfactual mask\")\n", - "ax.axvline(x=16.8, color=\"grey\", linestyle=\"--\", label = \"Overshoot too high\")\n", + "ax.imshow(hist_mask, cmap=\"viridis\")\n", + "ax.set(xticks=range(0, 28, 2), xticklabels=bin_edges[0:28:2].tolist())\n", + "ax.set(yticks=range(0, 28, 2), yticklabels=bin_edges[0:28:2].tolist())\n", + "ax.set(\n", + " xlabel=\"Overshoot in Sufficiency Intervention\",\n", + " ylabel=\"Overshoot in Necessity Intervention\",\n", + " title=\"Overshoot in counterfactual mask\",\n", + ")\n", + "ax.axvline(x=16.8, color=\"grey\", linestyle=\"--\", label=\"Overshoot too high\")\n", "ax.axhline(y=16.8, color=\"grey\", linestyle=\"--\")\n", "\n", - "ax.axvline(x=(os_mask_suff-5)*28/35, color=\"white\", linestyle=\"--\", label = \"Mean Overshoot\")\n", - "ax.axhline(y=(os_mask_nec-5)*28/35, color=\"white\", linestyle=\"--\")\n", + "ax.axvline(\n", + " x=(os_mask_suff - 5) * 28 / 35,\n", + " color=\"white\",\n", + " linestyle=\"--\",\n", + " label=\"Mean Overshoot\",\n", + ")\n", + "ax.axhline(y=(os_mask_nec - 5) * 28 / 35, color=\"white\", linestyle=\"--\")\n", "\n", - "ax.legend(loc = \"upper left\")\n", + "ax.legend(loc=\"upper left\")\n", "\n", "sns.despine()" ] @@ -1022,8 +1187,30 @@ "metadata": {}, "outputs": [], "source": [ - "hist_lockdown_fix, bin_edges, os_lockdown_fix, oth_lockdown_fix = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 1, \"__cause____antecedent_lockdown\": 0, \"__cause____witness_lockdown_efficiency\": 0, \"__cause____witness_mask_efficiency\": 1}, 1)\n", - "hist_lockdown_notfix, bin_edges, os_lockdown_notfix, oth_lockdown_notfix = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 1, \"__cause____antecedent_lockdown\": 0, \"__cause____witness_lockdown_efficiency\": 0, \"__cause____witness_mask_efficiency\": 0}, 1)" + "hist_lockdown_fix, bin_edges, os_lockdown_fix, oth_lockdown_fix = histogram_data(\n", + " importance_tr,\n", + " mwc_imp,\n", + " {\n", + " \"__cause____antecedent_mask\": 1,\n", + " \"__cause____antecedent_lockdown\": 0,\n", + " \"__cause____witness_lockdown_efficiency\": 0,\n", + " \"__cause____witness_mask_efficiency\": 1,\n", + " },\n", + " 1,\n", + ")\n", + "hist_lockdown_notfix, bin_edges, os_lockdown_notfix, oth_lockdown_notfix = (\n", + " histogram_data(\n", + " importance_tr,\n", + " mwc_imp,\n", + " {\n", + " \"__cause____antecedent_mask\": 1,\n", + " \"__cause____antecedent_lockdown\": 0,\n", + " \"__cause____witness_lockdown_efficiency\": 0,\n", + " \"__cause____witness_mask_efficiency\": 0,\n", + " },\n", + " 1,\n", + " )\n", + ")" ] }, { @@ -1053,8 +1240,22 @@ } ], "source": [ - "plt.bar(bin_edges[:28].tolist(), hist_lockdown_fix, align='center', width = 35/28, alpha = 0.5, color='blue')\n", - "plt.bar(bin_edges[:28].tolist(), hist_lockdown_notfix, align='center', width = 35/28, alpha = 0.5, color='pink')\n", + "plt.bar(\n", + " bin_edges[:28].tolist(),\n", + " hist_lockdown_fix,\n", + " align=\"center\",\n", + " width=35 / 28,\n", + " alpha=0.5,\n", + " color=\"blue\",\n", + ")\n", + "plt.bar(\n", + " bin_edges[:28].tolist(),\n", + " hist_lockdown_notfix,\n", + " align=\"center\",\n", + " width=35 / 28,\n", + " alpha=0.5,\n", + " color=\"pink\",\n", + ")\n", "plt.legend([\"mask_efficiency fixed\", \"mask_efficiency not fixed\"])\n", "plt.ylabel(\"pr\")\n", "plt.xlabel(\"overshoot\")\n", @@ -1062,10 +1263,20 @@ "sns.despine()\n", "\n", "print(\"Overshoot mean\")\n", - "print(\"mask_efficiency fixed: \", os_lockdown_fix.item(), \" mask_efficiency not fixed: \", os_lockdown_notfix.item())\n", + "print(\n", + " \"mask_efficiency fixed: \",\n", + " os_lockdown_fix.item(),\n", + " \" mask_efficiency not fixed: \",\n", + " os_lockdown_notfix.item(),\n", + ")\n", "\n", "print(\"Probability of overshoot being high\")\n", - "print(\"mask_efficiency fixed: \", oth_lockdown_fix.item(), \" mask_efficiency not fixed: \", oth_lockdown_notfix.item())" + "print(\n", + " \"mask_efficiency fixed: \",\n", + " oth_lockdown_fix.item(),\n", + " \" mask_efficiency not fixed: \",\n", + " oth_lockdown_notfix.item(),\n", + ")" ] }, { @@ -1081,8 +1292,28 @@ "metadata": {}, "outputs": [], "source": [ - "hist_mask_fix, bin_edges, os_mask_fix, oth_mask_fix = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 0, \"__cause____antecedent_lockdown\": 1, \"__cause____witness_mask_efficiency\": 0, \"__cause____witness_lockdown_efficiency\": 1}, 1)\n", - "hist_mask_notfix, bin_edges, os_mask_notfix, oth_mask_notfix = histogram_data(importance_tr, mwc_imp, {\"__cause____antecedent_mask\": 0, \"__cause____antecedent_lockdown\": 1, \"__cause____witness_mask_efficiency\": 0, \"__cause____witness_lockdown_efficiency\": 0}, 1)" + "hist_mask_fix, bin_edges, os_mask_fix, oth_mask_fix = histogram_data(\n", + " importance_tr,\n", + " mwc_imp,\n", + " {\n", + " \"__cause____antecedent_mask\": 0,\n", + " \"__cause____antecedent_lockdown\": 1,\n", + " \"__cause____witness_mask_efficiency\": 0,\n", + " \"__cause____witness_lockdown_efficiency\": 1,\n", + " },\n", + " 1,\n", + ")\n", + "hist_mask_notfix, bin_edges, os_mask_notfix, oth_mask_notfix = histogram_data(\n", + " importance_tr,\n", + " mwc_imp,\n", + " {\n", + " \"__cause____antecedent_mask\": 0,\n", + " \"__cause____antecedent_lockdown\": 1,\n", + " \"__cause____witness_mask_efficiency\": 0,\n", + " \"__cause____witness_lockdown_efficiency\": 0,\n", + " },\n", + " 1,\n", + ")" ] }, { @@ -1112,8 +1343,22 @@ } ], "source": [ - "plt.bar(bin_edges[:28].tolist(), hist_mask_fix, align='center', width = 35/28, alpha = 0.5, color='blue')\n", - "plt.bar(bin_edges[:28].tolist(), hist_mask_notfix, align='center', width = 35/28, alpha = 0.5, color='pink')\n", + "plt.bar(\n", + " bin_edges[:28].tolist(),\n", + " hist_mask_fix,\n", + " align=\"center\",\n", + " width=35 / 28,\n", + " alpha=0.5,\n", + " color=\"blue\",\n", + ")\n", + "plt.bar(\n", + " bin_edges[:28].tolist(),\n", + " hist_mask_notfix,\n", + " align=\"center\",\n", + " width=35 / 28,\n", + " alpha=0.5,\n", + " color=\"pink\",\n", + ")\n", "plt.legend([\"lockdown_efficiency fixed\", \"lockdown_efficiency not fixed\"])\n", "plt.ylabel(\"pr\")\n", "plt.xlabel(\"overshoot\")\n", @@ -1121,10 +1366,20 @@ "sns.despine\n", "\n", "print(\"Overshoot mean\")\n", - "print(\"lockdown_efficiency fixed: \", os_mask_fix.item(), \" lockdown_efficiency not fixed: \", os_mask_notfix.item())\n", + "print(\n", + " \"lockdown_efficiency fixed: \",\n", + " os_mask_fix.item(),\n", + " \" lockdown_efficiency not fixed: \",\n", + " os_mask_notfix.item(),\n", + ")\n", "\n", "print(\"Probability of overshoot being high\")\n", - "print(\"lockdown_efficiency fixed: \", oth_mask_fix.item(), \" lockdown_efficiency not fixed: \", oth_mask_notfix.item())" + "print(\n", + " \"lockdown_efficiency fixed: \",\n", + " oth_mask_fix.item(),\n", + " \" lockdown_efficiency not fixed: \",\n", + " oth_mask_notfix.item(),\n", + ")" ] } ], From 4823be384b870ea8bca300d3e2febfc9e6876cfb Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Mon, 26 Aug 2024 13:45:52 -0400 Subject: [PATCH 069/111] tweaks --- docs/source/explainable_sir.ipynb | 116 +++++++++++++++++------------- 1 file changed, 67 insertions(+), 49 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 5be38d90..d7063349 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -22,15 +22,15 @@ "source": [ "## Outline\n", "\n", - "- [Setup](#Setup)\n", - "- [Bayesian epidemiological SIR model with Policies](#Bayesian-epidemiological-SIR-model-with-Policies)\n", - " - [SIR Model and Simulation](#SIR-model-and-simulation)\n", - " - [Bayesian SIR model](#Bayesian-SIR-model)\n", - " - [Baysian SIR model with Policies](#Bayesian-SIR-model-with-policies)\n", - "- [But-for analysis for Bayesian SIR model with Policies](#But-for-Analysis-with-Bayesian-SIR-model-with-Policies)\n", - "- [Causal Explanations using `SearchForExplanation`](#Causal-Explanations-using-SearchForExplanation)\n", - "- [Fine-grained analysis for `overshoot` using sample traces](#Fine-grained-analysis-for-`overshoot`-using-sample-traces)\n", - "- [For advanced readers: Looking into different contexts](#for-advanced-readers-looking-into-different-contexts)\n" + "- [Setup](#setup)\n", + "- [Bayesian Epidemiological SIR Model with Policies](#bayesian-epidemiological-sir-model-with-policies)\n", + " - [SIR Model and Simulation](#sir-model-and-simulation)\n", + " - [Bayesian SIR Model](#bayesian-sir-model)\n", + " - [Bayesian SIR Model with Policies](#bayesian-sir-model-with-policies)\n", + "- [But for Analysis with Bayesian SIR Model with Policies](#but-for-analysis-with-bayesian-sir-model-with-policies)\n", + "- [Causal Explanations using `SearchForExplanation`](#causal-explanations-using-searchforexplanation)\n", + "- [Fine-grained Analysis of `overshoot` using Sample traces](#fine-grained-analysis-of-overshoot-using-sample-traces)\n", + "- [For Advanced Readers: Looking into Different Contexts](#for-advanced-readers-looking-into-different-contexts)" ] }, { @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 113, "metadata": {}, "outputs": [], "source": [ @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 114, "metadata": {}, "outputs": [], "source": [ @@ -170,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 115, "metadata": {}, "outputs": [ { @@ -237,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 116, "metadata": {}, "outputs": [], "source": [ @@ -279,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 117, "metadata": {}, "outputs": [], "source": [ @@ -303,7 +303,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 118, "metadata": {}, "outputs": [], "source": [ @@ -362,7 +362,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## But-for Analysis with Bayesian SIR model with Policies" + "## But for Analysis with Bayesian SIR model with Policies" ] }, { @@ -384,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 119, "metadata": {}, "outputs": [], "source": [ @@ -431,12 +431,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 120, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
      " ] @@ -557,7 +557,7 @@ ")\n", "sns.despine()\n", "\n", - "plt.savefig(\"counterfactual_sir.png\")" + "# plt.savefig(\"counterfactual_sir.png\")" ] }, { @@ -589,7 +589,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 121, "metadata": {}, "outputs": [], "source": [ @@ -624,7 +624,15 @@ " log_weights,\n", " )\n", "\n", - " return _wrapped_model" + " return _wrapped_model\n", + "\n", + "from chirho.observational.handlers.soft_conditioning import soft_eq, KernelSoftConditionReparam\n", + "\n", + "def _soft_eq(v1: torch.Tensor, v2: torch.Tensor) -> torch.Tensor:\n", + " return soft_eq(constraints.boolean, v1, v2, scale=0.001)\n", + "\n", + "def reparam_config(data):\n", + " return {i: KernelSoftConditionReparam(_soft_eq) for i in data}" ] }, { @@ -643,7 +651,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 122, "metadata": {}, "outputs": [ { @@ -689,7 +697,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 123, "metadata": {}, "outputs": [], "source": [ @@ -699,6 +707,11 @@ " ).bool()\n", " for i, v in mask.items():\n", " mask_intervened &= trace.nodes[i][\"value\"] == v\n", + "\n", + " with mwc_imp:\n", + " mask_os_too_high = (gather(trace.nodes[\"os_too_high\"][\"value\"], IndexSet(**{\"mask\": {0}, \"lockdown\": {0}})) == 1)\n", + " mask_intervened &= mask_os_too_high\n", + "\n", " print(\n", " mask,\n", " (\n", @@ -710,17 +723,17 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 124, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0} 0.2081967145204544\n", - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1} 0.192521870136261\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0} 0.1043718010187149\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1} 2.6645385897694496e-09\n" + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0} 0.188730850815773\n", + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1} 0.1764705926179886\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0} 0.07399226725101471\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1} 0.0\n" ] } ], @@ -772,7 +785,7 @@ }, { "cell_type": "code", - "execution_count": 515, + "execution_count": 125, "metadata": {}, "outputs": [ { @@ -780,10 +793,10 @@ "output_type": "stream", "text": [ "Degree of responsibility for lockdown: \n", - "{'__cause____antecedent_lockdown': 0} 0.19081200659275055\n", + "{'__cause____antecedent_lockdown': 0} 0.18267367780208588\n", "\n", "Degree of responsibility for mask: \n", - "{'__cause____antecedent_mask': 0} 0.15390829741954803\n" + "{'__cause____antecedent_mask': 0} 0.1316295713186264\n" ] } ], @@ -809,14 +822,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Fine grained analysis of `overshoot` using sample traces\n", - "\n", + "## Fine grained analysis of `overshoot` using sample traces" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "In this section, we use the samples we obtained earlier to analyze the distribution of `overshoot` variable in different counterfactual worlds. We first define a function to obtain histogram data from the samples in a particular world and then we demonstrate the plots for `overshoot` distribution in different settings." ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 126, "metadata": {}, "outputs": [], "source": [ @@ -858,7 +876,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 127, "metadata": {}, "outputs": [], "source": [ @@ -889,7 +907,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 128, "metadata": {}, "outputs": [ { @@ -974,7 +992,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 129, "metadata": {}, "outputs": [], "source": [ @@ -1005,7 +1023,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 130, "metadata": {}, "outputs": [ { @@ -1090,7 +1108,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 131, "metadata": {}, "outputs": [ { @@ -1167,23 +1185,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## For advanced readers: Looking into different contexts\n", - "\n", - "`SearchForExplanation` allows the users to perform an even finer grained analysis by visualizing distributions of random variables when different contexts are kept fixed in the model. To illustrate this, we consider the following two scenarios:\n", - "1. Intervene on `lockdown=1` while keeping `mask_efficiency` fixed or not.\n", - "2. Intervene on `mask=1` while keeping `lockdown_efficiency` fixed or not." + "## For advanced readers: Looking into different contexts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "`SearchForExplanation` allows the users to perform an even finer grained analysis by visualizing distributions of random variables when different contexts are kept fixed in the model. To illustrate this, we consider the following two scenarios:\n", + "1. Intervene on `lockdown=1` while keeping `mask_efficiency` fixed or not.\n", + "2. Intervene on `mask=1` while keeping `lockdown_efficiency` fixed or not.\n", + "\n", "We first intervene on `lockdown` being 1 and analyze how the distribution of `overshoot` change as we keep the `mask_efficiency` fixed or not." ] }, { "cell_type": "code", - "execution_count": 522, + "execution_count": 132, "metadata": {}, "outputs": [], "source": [ @@ -1215,7 +1233,7 @@ }, { "cell_type": "code", - "execution_count": 523, + "execution_count": 133, "metadata": {}, "outputs": [ { @@ -1288,7 +1306,7 @@ }, { "cell_type": "code", - "execution_count": 524, + "execution_count": 134, "metadata": {}, "outputs": [], "source": [ @@ -1318,7 +1336,7 @@ }, { "cell_type": "code", - "execution_count": 525, + "execution_count": 135, "metadata": {}, "outputs": [ { From 3eecc4c2300e0ba36f1df0386a32d42e4b26382c Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Mon, 26 Aug 2024 14:27:23 -0400 Subject: [PATCH 070/111] small edits --- docs/source/explainable_sir.ipynb | 66 ++++++++-------- docs/source/inference.ipynb | 120 ++++++++++++++++++++++++++++++ 2 files changed, 153 insertions(+), 33 deletions(-) create mode 100644 docs/source/inference.ipynb diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index d7063349..e94c6102 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The **Explainable Reasoning with Chirho** package aims to provide a unified, principled approach to computations of causal explanations. We showed in an earlier [tutorial](https://basisresearch.github.io/chirho/explainable_categorical.html) how Chirho provides a handler `SearchForExplanation` to carry out the program transformations needed to compute causal queries and explanations, focusing on on discrete variables (we assume the reader is familar with it). In this notebook we illustrate the usage of `SearchForExplanation` for causal models with continuous random variables in the context of a dynamical system.\n", + "The **Explainable Reasoning with Chirho** package aims to provide a unified, principled approach to computations of causal explanations. We showed in an earlier [tutorial](https://basisresearch.github.io/chirho/explainable_categorical.html) how Chirho provides a handler `SearchForExplanation` to carry out the program transformations needed to compute causal queries and explanations, focusing on discrete variables (we assume the reader is familar with it). In this notebook we illustrate the usage of `SearchForExplanation` for causal models with continuous random variables in the context of a dynamical system.\n", "\n", "We take an epidemiological dynamical system model (described in more detail in this [tutorial](https://basisresearch.github.io/chirho/dynamical_intro.html)) and show how the but-for analysis is not sufficiently fine-grained to allow us to derive the right conclusions about effects of different policies during a pandemic. Next, we illustrate how various causal explanation queries can be computed using `SearchForExplanation` and inference algorithms. We also demonstrate how more detailed causal queries can be answered by post-processing the samples obtained using the handler. " ] @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -118,7 +118,7 @@ "\n", "This quantity is of interest because epidemic mitigation policies often have multiple goals that need to be balanced. One goal is to increase `S_final`, i.e., to limit the total number of infected individuals. Another goal is to limit the number of infected individuals at the peak of the epidemic to avoid overwhelming the healthcare system. A further goal is to minimize the proportion of the population that becomes infected after the peak, that is, the overshoot, to reduce healthcare and economic burdens. Balancing these objectives involves making trade-offs.\n", "\n", - " Suppose we are working under constraint that the overshoot show be lower than 20% of the population, and we implement two policies, lockdown and masking, which together seem to lead to the overshoot being too high. In fact, only one of them is responsible, and we are interested in being able to identify which one. " + " Suppose we are working under constraint that the overshoot should be lower than 20% of the population, and we implement two policies, lockdown and masking, which together seem to lead to the overshoot being too high. In fact, only one of them is responsible, and we are interested in being able to identify which one. " ] }, { @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -170,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -232,12 +232,12 @@ "source": [ "\n", "\n", - "Now suppose we are uncertain about $\\beta, \\gamma$, and want to construct a Bayesian SIR model that incorporates this uncertainty. Say we inducing $\\beta$ to be drawn from `Beta(18, 600)`, and $\\gamma$ to be drawn from distribution `Beta(1600, 1600)`. " + "Now suppose we are uncertain about $\\beta, \\gamma$, and want to construct a Bayesian SIR model that incorporates this uncertainty. Say we induce $\\beta$ to be drawn from the distribution `Beta(18, 600)`, and $\\gamma$ to be drawn from distribution `Beta(1600, 1600)`. " ] }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -273,13 +273,13 @@ "metadata": {}, "source": [ "\n", - "Now we incorporate the Bayesian SIR model into a larger model that includes the effect of two different policies, lockdown and masking, where each can be implemented with $50\\%$ probability (these probabilities won't really matter, as we will be intervening on these, the sampling is mainly used to register the parameters with Pyro). We encode their efficiencies which further affect the model. Crucially, these efficiencies interact in a fashion resembling the structure of the stone-throwing example we discussed in the tutorial on categorical variables. If lockdown is present, this limits the impact of masking as agents interact less and so masks have fewer opportunities to block anything. We assume the situation is assymetric: masking has no impact on the efficiency of lockdown. The model also computes `overshoot` and `os_too_high` for further analysis.\n", + "Now we incorporate the Bayesian SIR model into a larger model that includes the effect of two different policies, lockdown and masking, where each can be implemented with $50\\%$ probability (these probabilities won't really matter, as we will be intervening on these, the sampling is mainly used to register the parameters with Pyro). We encode their efficiencies which further affect the model. Crucially, these efficiencies interact in a fashion resembling the structure of the stone-throwing example we discussed in the tutorial on categorical variables. If lockdown is present, this limits the impact of masking as agents interact less and so masks have fewer opportunities to block anything. We assume the situation is asymmetric: masking has no impact on the efficiency of lockdown. The model also computes `overshoot` and `os_too_high` for further analysis.\n", "\n" ] }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -303,7 +303,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -379,12 +379,12 @@ "3. Only masking was imposed\n", "4. Only lockdown was imposed\n", "\n", - "The hope is that by looking at these we will be able to indentify the culprit. We create these four models by conditioning on the policies being imposed as required (in fact, this has the same effect as intervening here, as the sites are upstream from the model). The models obtained are similar to the intervened models since the variables `lockdown` and `mask` do not have any variables upstream to them. In principle we could emulate 1-4 using `do` with the same estimates. For the sake of completeness, we also illustrate the consequences of deciding randomly about the policies." + "The hope is that by looking at these we will be able to indentify the culprit. We create these four models by conditioning on the policies being imposed as required (in fact, this has the same effect as intervening here, as the sites are upstream from the model). In principle we could emulate 1-4 using `do` with the same estimates. For the sake of completeness, we also illustrate the consequences of deciding randomly about the policies." ] }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -431,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -564,11 +564,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The plots above show what happens in the four different scenarios. We observe that in the model where none of the policies were imposed, ther probability of the overshoot being too high is relatively low, $0.24$. On the other hand, when both policies were imposed, the probability of the overshoot being to high was relatively higher $0.81$. \n", + "The plots above show what happens in the four different scenarios. We observe that in the model where none of the policies were imposed, the probability of the overshoot being too high is relatively low, $0.24$. On the other hand, when both policies were imposed, the probability of the overshoot being to high was relatively higher $0.81$. \n", "\n", - "To identify which of `lockdown` and `mask` is the cause, we analyze the models where only one of the policies were imposed. In both cases, the probability of too high overshoot seems to be even higher - $0.96$ and $0.9$. Interestingly, the effect of the interventions is somewhat nuanced. Implementing both increases the risk of overshoot as compared to the no intervention model. But individual intereventions would have even worse consequences, which means that the two interventions while jointly increasing the risk to some extent mitigate each other's contribution to that risk as well.\n", + "To identify which of `lockdown` and `mask` is the cause, we analyze the models where only one of the policies were imposed. In both cases, the probability of too high overshoot seems to be even higher - $0.96$ and $0.9$. Interestingly, the effect of the interventions is somewhat nuanced. Implementing both increases the risk of overshoot as compared to the no intervention model. But individual interventions would have even worse consequences, which means that the two interventions while jointly increasing the risk to some extent mitigate each other's contribution to that risk as well.\n", "\n", - "Crucially, the analysis does not allow us to distinghuish the intuitive role that the lockdown played, as opposed to masking (whose impact has been limited by the presence of lockdown). So, we need of a more fine-grained analysis where we not only control the variables being intervened on (that is, the policies), but also pay attention to what context we are in. We achieve that level of sensitivity by stochastically keeping part of the context (that is, other variables in the model) fixed (see the tutorial for categorical variables for a more extensive explanation of this method and simpler examples). The key idea is that starting with the scenario in which both interventions have been implemented, there is a context such that if we keep it fixed, removing lockdown would significantly lower the overshoot, but there is no context that we could keep fixed such that if in that context we remove the masking policy, the overshoot would decrease. In the next section, we show how this analysis can be carried out with the help of `SearchForExplanation`." + "Crucially, the analysis does not allow us to distinghuish the intuitive role that the lockdown played, as opposed to masking (whose impact has been limited by the presence of lockdown). So, we need a more fine-grained analysis where we not only control the variables being intervened on (that is, the policies), but also pay attention to what context we are in. We achieve that level of sensitivity by stochastically keeping part of the context (that is, other variables in the model) fixed (see the tutorial for categorical variables for a more extensive explanation of this method and simpler examples). The key idea is that starting with the scenario in which both interventions have been implemented, there is a context such that if we keep it fixed, removing lockdown would significantly lower the overshoot, but there is no context that we could keep fixed such that if in that context we remove the masking policy, the overshoot would decrease. In the next section, we show how this analysis can be carried out with the help of `SearchForExplanation`." ] }, { @@ -589,7 +589,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -651,7 +651,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -692,12 +692,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The above probability itself is not directly related to our query. It is the probability that the overshoot is too high in the antecedents-intervened workd and not too high in the alterantives-intervened world, where antecedent interventions are preempted with probabilities $0.5$ at each site, and witnesses are kept fixed at the observed values with probability $0.5+0.2$ at each site. But more fine-grained queries can be answered using the 10000 samples we have drawn in the process. We first compute the probabilities that different sets of antecedent candidates have causal effect over `os_too_high`." + "The above probability itself is not directly related to our query. It is the probability that the overshoot is too high in the antecedents-intervened workd and not too high in the alternatives-intervened world, where antecedent interventions are preempted with probabilities $0.5$ at each site, and witnesses are kept fixed at the observed values with probability $0.5+0.2$ at each site. But more fine-grained queries can be answered using the 10000 samples we have drawn in the process. We first compute the probabilities that different sets of antecedent candidates have causal effect over `os_too_high`." ] }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -723,7 +723,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -771,7 +771,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note that one could also compute above queries by giving specific parameters to `SearchForExplanation` instead of subselecting the samples, as we did in the tutorial for explainable module for models with categorical variables. Here, however, we illustrate that running a sufficiently general query ones produces samples that can be used to answer multiple different questions.\n", + "Note that one could also compute above queries by giving specific parameters to `SearchForExplanation` instead of subselecting the samples, as we did in the tutorial for explainable module for models with categorical variables. Here, however, we illustrate that running a sufficiently general query once produces samples that can be used to answer multiple different questions.\n", "\n", "Also, we use the log probabilities above to identify whether a particular combination of intervening nodes and context nodes have causal power or not, which is made possible by the fact that our handler adds appropriate log probabilities to the trace (see the previous tutorial and documentation for more explanation). One can also obtain these results by explictly analyzing the sample trace as we do in the next section." ] @@ -785,7 +785,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -834,7 +834,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -876,7 +876,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -907,7 +907,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -992,7 +992,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -1023,7 +1023,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1108,7 +1108,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1201,7 +1201,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -1233,7 +1233,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -1306,7 +1306,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -1336,7 +1336,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 50, "metadata": {}, "outputs": [ { diff --git a/docs/source/inference.ipynb b/docs/source/inference.ipynb new file mode 100644 index 00000000..d4b99e30 --- /dev/null +++ b/docs/source/inference.ipynb @@ -0,0 +1,120 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Optional, Callable\n", + "import math\n", + "\n", + "import pyro.distributions as dist\n", + "import torch\n", + "\n", + "import pyro\n", + "from chirho.counterfactual.handlers.counterfactual import \\\n", + " MultiWorldCounterfactual\n", + "from chirho.explainable.handlers import SearchForExplanation\n", + "from chirho.explainable.handlers.components import ExtractSupports\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "def model():\n", + " a = pyro.sample(\"a\", dist.Normal(loc=torch.tensor(0.0), scale=torch.tensor(1.0)))\n", + " b = pyro.sample(\"b\", dist.Normal(loc=torch.tensor(0.0), scale=torch.tensor(1.0)))" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "with ExtractSupports() as s:\n", + " model()\n", + "\n", + "query = SearchForExplanation(\n", + " supports=s.supports,\n", + " alternatives={\"a\": torch.tensor(0.5)},\n", + " antecedents={\"a\": torch.tensor(-0.5)},\n", + " antecedent_bias=0.0,\n", + " witnesses={},\n", + " consequents={\"b\": torch.tensor(0.0)},\n", + " consequent_scale=1e-8,\n", + " )(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How can I compute the probability that `a=-0.5` is a sufficienct and necessary cause of `b=0` using `SearchForExplanation`?" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "def importance_infer(\n", + " model: Optional[Callable] = None, *, num_samples: int\n", + "):\n", + " \n", + " if model is None:\n", + " return lambda m: importance_infer(m, num_samples=num_samples)\n", + "\n", + " def _wrapped_model(\n", + " *args,\n", + " **kwargs\n", + " ):\n", + "\n", + " guide = pyro.poutine.block(hide_fn=lambda msg: msg[\"is_observed\"])(model)\n", + "\n", + " max_plate_nesting = 9 # TODO guess\n", + "\n", + " with pyro.poutine.block(), MultiWorldCounterfactual() as mwc_imp:\n", + " log_weights, importance_tr, _ = pyro.infer.importance.vectorized_importance_weights(\n", + " model,\n", + " guide,\n", + " *args,\n", + " num_samples=num_samples,\n", + " max_plate_nesting=max_plate_nesting,\n", + " normalized=False,\n", + " **kwargs\n", + " )\n", + "\n", + " return torch.logsumexp(log_weights, dim=0) - math.log(num_samples), importance_tr, mwc_imp, log_weights\n", + "\n", + " return _wrapped_model" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 5f19b3fc3b93fc171ad67acdd504add38a421603 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Mon, 26 Aug 2024 15:32:16 -0400 Subject: [PATCH 071/111] tweaks other than the heatmap --- docs/source/explainable_sir.ipynb | 84 +++++++++++++++---------------- 1 file changed, 42 insertions(+), 42 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index e94c6102..c3a17e23 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 76, "metadata": {}, "outputs": [], "source": [ @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 77, "metadata": {}, "outputs": [], "source": [ @@ -170,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -237,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ @@ -279,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 80, "metadata": {}, "outputs": [], "source": [ @@ -303,7 +303,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ @@ -384,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 82, "metadata": {}, "outputs": [], "source": [ @@ -431,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 83, "metadata": {}, "outputs": [ { @@ -589,7 +589,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 84, "metadata": {}, "outputs": [], "source": [ @@ -651,14 +651,14 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.1257)\n" + "tensor(0.1215)\n" ] } ], @@ -697,7 +697,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 86, "metadata": {}, "outputs": [], "source": [ @@ -709,7 +709,7 @@ " mask_intervened &= trace.nodes[i][\"value\"] == v\n", "\n", " with mwc_imp:\n", - " mask_os_too_high = (gather(trace.nodes[\"os_too_high\"][\"value\"], IndexSet(**{\"mask\": {0}, \"lockdown\": {0}})) == 1)\n", + " mask_os_too_high = (gather(trace.nodes[\"mask\"][\"value\"], IndexSet(**{\"mask\": {0}, \"lockdown\": {0}})) == 1) & (gather(trace.nodes[\"lockdown\"][\"value\"], IndexSet(**{\"mask\": {0}, \"lockdown\": {0}})) == 1)\n", " mask_intervened &= mask_os_too_high\n", "\n", " print(\n", @@ -723,17 +723,17 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 87, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0} 0.188730850815773\n", - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1} 0.1764705926179886\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0} 0.07399226725101471\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1} 0.0\n" + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0} 0.18636363744735718\n", + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1} 0.3100775182247162\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0} 1.5630010619105406e-09\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1} 1.9900530112693104e-09\n" ] } ], @@ -785,7 +785,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 88, "metadata": {}, "outputs": [ { @@ -793,10 +793,10 @@ "output_type": "stream", "text": [ "Degree of responsibility for lockdown: \n", - "{'__cause____antecedent_lockdown': 0} 0.18267367780208588\n", + "{'__cause____antecedent_lockdown': 0} 0.24750958383083344\n", "\n", "Degree of responsibility for mask: \n", - "{'__cause____antecedent_mask': 0} 0.1316295713186264\n" + "{'__cause____antecedent_mask': 0} 0.09557109326124191\n" ] } ], @@ -834,7 +834,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ @@ -876,7 +876,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 90, "metadata": {}, "outputs": [], "source": [ @@ -907,7 +907,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 91, "metadata": {}, "outputs": [ { @@ -915,14 +915,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "factual: 24.23866081237793 counterfactual mask: 21.874435424804688 counterfactual lockdown: 20.744991302490234\n", + "factual: 24.31097984313965 counterfactual mask: 21.902610778808594 counterfactual lockdown: 20.758800506591797\n", "Probability of overshoot being high\n", - "factual: 0.7303000092506409 counterfactual mask: 0.5687074661254883 counterfactual lockdown: 0.5103896260261536\n" + "factual: 0.7299000024795532 counterfactual mask: 0.5736842155456543 counterfactual lockdown: 0.5078909397125244\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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      " ] @@ -992,7 +992,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ @@ -1023,7 +1023,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 93, "metadata": {}, "outputs": [ { @@ -1031,14 +1031,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "factual: 24.23866081237793 counterfactual mask: 26.39071273803711 counterfactual lockdown: 22.133150100708008\n", + "factual: 24.31097984313965 counterfactual mask: 26.651079177856445 counterfactual lockdown: 22.560808181762695\n", "Probability of overshoot being high\n", - "factual: 0.7303000092506409 counterfactual mask: 0.8816326260566711 counterfactual lockdown: 0.6857143044471741\n" + "factual: 0.7299000024795532 counterfactual mask: 0.8868421316146851 counterfactual lockdown: 0.7044476270675659\n" ] }, { "data": { - "image/png": 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", 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", 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      " ] @@ -1108,12 +1108,12 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 94, "metadata": {}, "outputs": [ { "data": { - "image/png": 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YmBjkyJEjXc/HHomJiRg7diw2bdoEo9GI4OBgVKxYESqVKtXXIDXp2e5evo9pPadbt26hZs2aOHLkCCIjIzFs2LBkj/Pyh0tSL7fdlzHp2b7r1KmDTZs2AbAU6e+++y7y5s2LTZs2Qa/XY//+/ahVq1ayItXebZuIiMhRWMuyln09R9ay9teySTueX0rr+e/fvx/fffcdrl69Cl9fX5QsWVL6m5ePP23aNMybNw+//fYbduzYAYVCgXfeeQdjxoxJ1tkeHR2NMmXKSPOh+vr62pU/kbOxQ5JIBjly5ECFChWwY8cOfPnll8nOwnopPj4eBw4ckCZZBoC3334bOXPmxG+//YacOXNCq9VKR0N9fX0hCAK6du2KZs2apWjv9cLhdblz58a3336LUaNG4eLFi9i+fTsWLFiAt956S5oPJy3+/v548OBBivUv17311lvw9/fHkydPYDKZkhVy9+/fl2KcKXv27AAscyEVLVpUWn/nzh3cuHFDyufhw4cp/vbBgwfS/S/nT0r6vJ49eyZLjtmyZUPhwoUxefJkq/db6ySzJT4+Ht27d0doaCi2bduGokWLQqFQYO/evdixY0eyx0w6gfxLe/fuTfWo8uvzG71+tHz8+PHYsWMHpk+fjnfeeUcqoN5+++10P4fXpWe7S/o+J72a5ZUrVxATEyOdiRgSEoIff/wR06dPx+LFi9GsWTOUK1dOasfadvCy0H+ZS3q277p162LOnDn4559/8M8//2DEiBHIly8fdDodjh8/jiNHjmD06NEZfk2IiIgcibUsa1l7sJaVx40bN9CnTx80bNgQP/74IwoUKABBELBy5Urs379fisuWLRsGDx6MwYMH4+rVq9i1axfmzJmD0aNHS/OKApYLU3l7e6NNmzaYNm0aIiIiHP4ciN4Eh2wTyaRv3764du0apk6dmuI+k8mEUaNGITExEd27d5fWK5VKtGjRAnv27MH27dvRsGFD6UvQz88PpUuXxtWrV1GuXDnpX4kSJTBr1qxkV4Z73alTp/DOO+/g7NmzEAQBpUqVwldffYWQkBC7rrRWtWpVnDp1KsXR8s2bNyNnzpwoVKgQqlWrBqPRiO3bt6eIASB1DFkrbB2hfPnyUKvV0lCHlxYvXowBAwagRIkSyJkzZ4oLn9y8eROnT5+Whj68PMJ57949KeblMGF7vf7cq1Wrhrt37yJHjhzJ3tsDBw5g4cKFKY7Qp+bq1auIiYlB586dUbx4cemx9u3bB+DV0fcqVargwIED0tAPwDI5eM+ePfHPP/9YfUw/P79kzx9I+RqcOHEC4eHhybbdv//+G48fP87wmX7p2e5eble7d+9OFjN58mSMHz9euh0QEACVSoU+ffogT548iIiIgNFoBABUr14dp06dSnYRm6ioKNy8eVO6nd7tu1y5cggMDMScOXOg1WpRtmxZ5MqVC0WLFkVkZCR0Oh1q166dodeDiIjIGVjLspa1hbWsY/z999/Q6XTo2bMnChYsKHUiv+yMFEURt2/fRp06daTts2jRoujRowfeeeedFJ+FoKAghIaGomvXrli5ciXOnDnj0PyJ3hTPkCSSSa1atTBs2DBMnDgRFy5cQNu2bZErVy7cunULq1atwoULFzB+/HiULFky2d+1atUKixcvhkKhSDGcZcCAAejZsycGDhyIli1bSlcgPHPmDL744gubuZQuXRpeXl4YMmQI+vXrh6CgIBw8eBAXLlxA586d0/2cunXrhs2bN6Nr167o27cvAgICsHHjRhw+fBjfffcdFAoFateujfDwcERERCA6OholS5bE0aNHsWDBArRu3RrFixcHYDna+/DhQ+koZq5cuex4ddMvMDAQnTt3xpIlS6DRaFCtWjWcOXMGq1atwpAhQ6BQKDBgwAAMHz5cel2fPHmCyMhI+Pv7o1u3bgAsQ3AnTJiAkSNH4rPPPsPdu3cxe/bsDA19yJ49O06dOoVDhw6hdOnSaNOmDVasWIFu3bqhd+/eyJs3Lw4ePIgFCxagY8eOac7flFSRIkXg5+eHefPmQaVSQaVSYceOHVi3bh2AV/MTffHFF2jfvj169eolXdly+vTpKF++PGrUqCEVd4cOHUKxYsUQFhaGevXqYffu3ZgwYQLq16+P48ePY+PGjckev3z58vjtt9+watUqFCtWDBcvXsTcuXMhCEKG50JMz3ZXsmRJNG7cGJMmTUJiYiJKlSqFffv2Yc+ePVavrOjt7Y1Ro0ahZ8+eWLRoEXr16oUuXbpg3bp1+Oyzz6SrXU6bNi3Z65/e7fvlZ2Hjxo2oWbOmNDQ7PDwcq1atQpUqVaThbURERO6ItSxrWVtYyzpGmTJloFKpMGnSJHz66afQ6/X45Zdf8OeffwKwnM0ZGhqKPHnyYNy4cYiPj0fBggXx999/Y+/evejVq5fVdvv27YvffvsNERER+OWXX+x6P4iciR2SRDLq1q0bKlasiKVLl+KHH37A48ePkTNnTtSoUQPjx4+XCpqkSpYsiZCQEDx58iTF0ICaNWti0aJFiIyMRP/+/aFWq1GmTBn89NNPKSafTkqr1WLx4sWYMmUKxo8fj6dPn6Jw4cIYM2YM2rRpk+7nkzNnTqxatQpTpkzBuHHjYDAYULJkScyZMwcNGjQAYBkO8uOPP2LmzJlYsmQJHj9+jODgYAwYMEAqiACgTZs22Lt3L/r06YP+/fujZ8+e6c7DXoMHD0aOHDmwevVqLFy4EMHBwfjmm2/w0UcfSbn4+vrixx9/RJ8+feDn54datWphwIAB0nxBRYoUwQ8//IC5c+eiZ8+eKFasGMaOHZvsginp9cknn+Dvv/9Gjx49MGHCBLRo0QIrV67ElClTMGnSJMTFxSF//vwYOHAgPv30U7vazpYtG+bMmYOJEyfiyy+/hK+vL0qVKoUVK1agR48eOH78OOrXr4/SpUtj+fLlmDJlCv7v//4Pfn5+qFOnDgYNGgSNRgONRoNu3bphzZo12Lt3Lw4cOIC2bdvixo0b2LBhA1avXo2qVati5syZ6NChg/T4w4YNg8FgwPTp06HX6xEcHIzPP/8cUVFR2L17d4phMumRnu0OACZNmoTIyEgsXboUT548QbFixTBz5kw0bNjQart16tTBe++9h9mzZ+O9995D4cKFsWrVKowfPx7Dhg2Dr68vunfvnmxy/vRu3y/b37hxI8LDw6V1Lzsk69ata/frQERE5GysZVnLWsNa1jEKFSqEKVOmIDIyEp9//jn8/f1RoUIFLF++HJ06dcLx48cRGhqKyMhITJ06FTNmzMCTJ0+QN29e9O3b1+Y26O3tjZEjR6JXr16YP38++vTp47DnQPQmBDGjVx0gIiIiIiIiIiIishPnkCQiIiIiIiIiIiKnYYckEREREREREREROQ07JImIiIiIiIiIiMhp2CFJRERERERERERETsMOSSIiIiIiIiIiInIadkgSERERERERERGR07BDEoAoioiPj4coiq5OhYiIiIiyKNakRERElFWoXJ2AO3j27BkqV66M4CtloDArXZ0OETmIVjRiIX4HAHRHI+gE99sFar01WPjPNABA9zJfQZegd3FGRORJdprXujoFegOsSYkyP0+oRzMdQcjQn2m9NVj491QAQPeyAzyrLnf3A1sZfE9scvfnmwWlpybl3o+IsgydoEInNHV1GqnSJejRqWgfV6dBRERERA7gCfUoWegS9OhUrK+r0yDKtDhkm4iIiIiIiIiIiJyGHZJERERERERERETkNOyQJKIsQyOaECnuQqS4CxrR5Op0rNJ4aRB5ZAIij0yAxkvj6nSIiIiISEaeUI+ShcZLjcjD3yHy8HfQeKldnQ5RpuPWHZI7d+5EaGhosn/9+/e3Gnvw4EE0b94cYWFh6Ny5M27evOnkbInI3SkgIhRPEIonUMA9Jz5WKASEVi2O0KrFoVDIPNkzERHZjfUoEcnJE+pRslAoFEnqcrfuOiHySG59UZuoqCjUq1cPY8eOldZptdoUcXfu3EGfPn3Qr18/1KpVC7Nnz8YXX3yBzZs3Q5Dx6k0aHw2yB/nK2iZRRoiiiKcPn0H/3IOu9EZEROSB3K0eVaoUCMjrz4NW5BbMZhExd2NhMppdnQoREXkYt+6QvHLlCkJCQpAzZ85U49auXYuyZcvi008/BQBMmDABNWrUwNGjRxEeHv7GeQgCULfbO6jaIgwqjZIdkuRyoijCqDfh2JYz+POngxB5cJWIiMgh3KUeBYDsubKh86S2yB7kx3qU3ILlIHk8lg1ah6cP4l2dDhEReRC375B855130ow7c+YMqlSpIt329vZGmTJlcPr0aVkKwLrd3kGtj6ohMCAQCijfuD0iOZhhQq2PLGdo7Fl80MXZEBERZU7uUo8KAtCoVy3kK5Ibfl7ZALBDktyBCL9scWjUuzbWj/uVB8mJiCjd3LZDUhRFXLt2DX/99Rd+/PFHmEwmNG7cGP3794dGk/xCDw8ePECuXLmSrcuRIwfu3bv3xnlofTWo2iIMgQGBUIMXmCD3oYQSgQGBqNoiDAdWH+fwbSIiIpm5Sz0KAD4BPihetTB8vfygdN8SnrIgXy8/FK9SGD7+3ngWk+DqdIiIyEO4bTVz584dJCQkQKPRYPr06bh16xbGjRuHxMREREREJIt9GZeURqOBXv/mHTTZcvhCpVHyzEhySwooodIokT3IFw9vsEOSiIhITu5SjwKAdzYtlEolBPe+JiVlQQIUUKqU8M7uxQ5JIiJKN7ftkMyfPz+OHDkCf39/CIKAUqVKwWw2Y/DgwRg+fDiUylcdhFqtNkWxp9frkT179jfOQxAEztFDbo3bqH1iPOBM55gHT12dAhERwX3qUcDyfc9R2uS2BLAetYMn1KNkwbqcyHHctkMSAAICApLdLlasGHQ6HWJjYxEYGCitz507Nx4+fJgs9uHDhyhVqpQz0iQiD5EoqNAOLV2dRqoSn+vQLvdnrk6DiIheYD1KRHLyhHqULBKf69AuT3dXp0GUabntmI/9+/cjPDwcCQmvTvu/cOECAgICkhV/ABAWFoYTJ05ItxMSEnD+/HmEhYU5LV939fjJY0QunInOfT5B267v44shvfHLtvUwmUxOzyX6QTRafNIU0Q+iHdJ+TGwM/jqy3+b9V/+7ggv/nnfIY3/2ZVf8sXen1fvsed7nzp9Fi0+ayp0eERERZQDrUXmwHn2F9SgREZGF23ZIVqxYEVqtFhEREbh69Sr27t2LiRMnonv37jCZTHjw4IE0LKZt27Y4efIk5s+fj8uXL2P48OEIDg6W5YqGnuzBowcYOPL/EP3gHob2G47ZE+ehQ+sO2Pb7FoydMhpms9nVKcpqyerFOHbqmM37x08fh9t3bzsxI4ugHEFYNnsFgnIEOf2xiYiIKONYj7451qPJsR4lIiKycNsOST8/PyxatAiPHz9G27ZtMWLECLRv3x7du3fH3bt3UbNmTZw6dQoAEBwcjFmzZmH9+vX44IMPEBMTg9mzZ2f5eUx+XDoXuXPlwbdDx6JMybLIkysPar1dBxO+mYjzl/7Bb39sc3WKshLFNw1wDKVCibcCAqFU8MJIrqYRTZgs/onJ4p/QiM4/KyM9NF4aTN79LSbv/hYaL84vRETkSqxH3xzrUXsDHIP1qPvwhHqULDReakzeNQqTd42Cxkvt6nSIMh23nkOyRIkS+Omnn1KsDw4OxqVLl5Ktq1OnDurUqeOs1Nzek9gnOHryCEYO+jZF4ZErKBca1G6IHXt2oEnDZujWvws6teuEhnUaAQBEUUS3fp3R5aNuqFezPv65+DcWrpiPG7duIG/uvOjQ9hPUqFYTADBt3lQAwNXrV/Ak5jEmjpqMK9eisHL9Ctx/eB95cuZBp/Zd8HaVd6THP3T8ILb9vgWPY56gQtkK+Kr3APj5ZgMAXLx8AYt/XoSr16/AP3sAPmj+AZo0bCb97R97d2L91nW4/yAaBYML4rNPeqBsqXL4ef0K7N7/BwDg7wtnsWjGkmTPefi4obj/8D5mzJ+GcxfO4aveA3Dz9g0sXLEAF/49D29vHzSu3wTt3/8ICoWln/7oySNYuX4Fbt2+idw5c6Pjh53xTtUaNl/zG7dvYPC3A3HlvygUyFcAX/b8CkULF0P0g2h0/79uWDj9J+TOmRtP454icuFMnDp3Ev7ZA9C2eVvM+Wk2tqz8VWrrtz+2YfXG1Xj+/BlqhNdEn0/7Qa3ml+CbUkBEGB5Ky+5IoRAQVreMtExERK7FejTjWI8uSfacWY8S4Bn1KFkoFIokdbnbnstF5LH4qXoDQsJz2/90uvTHJiamK9YeV65FQRRFlCgaYvX+0iGlce3GVZhMJtQMr4mDxw5K912Kuoi4+DiEV66OJzGPMWbyt2hQ+13M+n4O2rZohxk/TsM/F/+W4v/8azc6teuMkYNGw8fbF1PnTsEHLT7EvEnz0bBuI0yOnIi4+Dgpfve+XRjcdxi+GzEBUdeisG7LOgDAzds3MGL8cJQtWRbTx83Cx20+waKfF+LQi9z+2LsTPy6di3YtP8SM7yIRVrYiRk8ahUePH6J1s7aoGV4LNcNrYerYGSme79f/F4GgwCD06NQTPTv3QmxcLIaOGYLAtwIxZcw0fN71C2z9fTM279gEADjzz2lMmD4e9Ws2wMwJkWhU7z1MnPU9oq5dtvma/75nB9o2/wCzJsyGn182zFkcaTVuUuT3iI2LxcRRk9G76+dYteHnFDEHjh7AmKFj8fVXEThw5C/8sfd3m49LREREWRfrUQvWoxasR4mIyFO49RmS7q5E7fI274uvURd3pi+UbhdrFA5FYoLV2OeVquHWj6+KgCIt60AV8yRF3L/HotKdW/yzeACAn6+f1ft9XxwBjouPQ63qdfD1+KF4nvAcPt4+OHDkL1SuUAU+3j74Zes6hJWtgOaNWgAA8uXJh6v/XcGm7RtRpmRZAECJoiVQrZJlfqQr/12B0WREUI4g5MqZG62btkHhAkUsR1NfPP2uHT5FSDFLYVozvBauXb8KANixZzuKFi6Gzu27AgCC8wXj5p2bWL91Hd6u+g62/L4ZLd5rifq1Glja+agb/r5wDlt/34IuH3WDRqMFAPhn90/xfLP5ZYNCoYCPty98fXyxefsmaLVa9P2sP5RKJQrkL4gnMY+xasPPeL9Ja2z7fSveqVYDrZq8DwDInzcY/175Fxu2/YLBfYdafU2bNmyK6lXeBgC0aNQSkyJ/SBFz++4tnP77NBZMW4Q8ufKiSKGi6NDmkxTF4ufdvkD+vMEoVKAwKpSriGs3rll9TCIiIsraWI+yHk2K9SgREXkKdkhmUi8LvycxT6xOXv34ySMAlsIo8K1AvBUQiOOnj6H223Vw8PhBdOvwKQDg5u2bOHbyKNp92kb6W6PJiPx58ku3c+XMLS0XLVQUVSpUxTcTRiB/3mBUr1wdjeq9By+tF2IRCwDImzuvFO/r4wODwSA9Vkix0GR5lipRCtt3WYaO3Lp9Ex1af5zs/pIlSuLmnZt2vjrArTs3UbxIcSiVr4YPlQwpjScxTxD/LB4379xEkwZNXnusUjavXAgAeZI9L1/oDfoUMf/d+A/Z/LIhT65XsSVLlEq1LR9v620RERERuTPWo6ljPUpERFkZOyTfwOV9Z23f+do8OVd+P2I7Vkg+cv7a5r1vkhYAoETRECgUCkRdu2y1AIy6dhmFCxaR5oGpVb02Dh49gHx58uHp01hUqVAVAGAym1C3Zj20a9k+2d+rkhROavWrC28IgoBRg0fj3yuXcOTEYRw6dhC//rEN338zEb4vitLX598QX8ydotGkvICH2WyWrr6Y9HGs3W8PW229/L/Gyvw4ZtEMs9n2xNMKIe0ZEBRKBcTXJzO3Mrn56/MsuWj+cyIiInJzrEctWI9asB7NpLLaxbHSsR3b11zGXj8hyT5GUCqT3YZS5gtEmeS9wJEoc3vu8p7YIhqNsrZHzsE5JN+A6O1j+59Wm/5YL690xdrDP7s/3q7yDtZsXAXTa0XLg0cPsPPP3/FevcbSutrVa+PUuZM4cOQvVKsUDi+tJafgvMG4c+8O8uXJJ/07cuIw/jz4p9XHvXnnJhatXIiQYqHo9GEXzJ44D0GBQTh57mSaOefPG4xLUReTrbt4+QLy583/Ipf8Ke6/FHUR+fMGA0jH93SSgOC8+RF1LQrGJDuui5cvwD+7P7L5ZXuRS/KJ6i25BKf5PFJTMH9BxD+Lx73796R1UdfSP/SJiIiIKCnWoymxHk0d61EiInIH7JDMxHp07oW4+Hh8+8NI/HPpH9x/eB+Hjh3EiPHDULZUOTRNcrXAooWLIfCtHNi2cytqVa8trW/asBmirl7G8v8txZ17t/HngT1Y9r8lyBWUy+pj+vn44rdd27Bmwyrcu38Px04dRfTD+yhaqFia+TZt2AzXrl/FsjVLcPvuLeza9we2/bEVzd5tDgBo1bQ1tv6+Bbv378Ltu7ewZPVPuHbjGhrVew8A4KX1wv2H0Xj0+KHV9r20Xrh19xbi4uNQp0Y9GA0GzF48Czdv38Dh44fw8/oVaNKgGQRBQKsm7+PA0b+weftG3Ll3Gxt/24BDxw6i6bvNrLadXvnzBqNS+cqYOX86rt24hlPnTmLluuVv1CbZJwFKJEDmI5oyS3iWiIRniWkHEhERuTnWo8mxHiXAM+pRskh8lohE1uVEDsEh25lYjrdyYPLoqVizcRUmz56Ip09jkTtXHjRu0BStGr+fYqhKreq1sHn7JlQOqyKty5UzN74ZNApLVv2EX7atR463gvDZJz1Qt0Y9q4/5VkAgvv6/CCxZ9RP+t2kN/LP7o0v7LqhUvhKiH0Snmm+uoFwYOehbLF61CBt+/QU5c+TCZ5/0QMM6jV7kVxtPYp5g5brleBL7BEULFsWYoeNQIF8BAEC9mvUxftpY9BveFyvnrYLw2iHqpg2bYcmqxbhz9za+/ioC3w4diwXL5uHLEf3gn80fLRu/j3YtPwQAhBYviQGfD8LP61fip1WLEZw3GEP7D0dYmQp2vQfWfNnzK8xaOAODRn6FHIE50LDOu1i/dd0bt0tpSxRUaInWrk4jVYnPdWiZrZOr0yAiIpIF61HWo5ScJ9SjZJH4XIeWb3VzdRpEmZYgpphAJOuJj49H5cqVEXylDBTm5EeqchYKRM+5nyB3UB4oeRSL3lCiLhFn/j6NymFVoFJZjgf8dWQ/fvp5ERbNWGJ3eyaYEP3wHuZ/vhIPrj+WOVsiIvI0O81rXZ0CvQHWpOQMrEc9EOeQfMPmHPD6cQ7JN2yOc0hmdumpSTlkm8iJNGoNZsyfjtUbfsa9+/dw8fIFrPrlZ9QIr+Xq1IiIiIgoC2A9SkRE7oBDtomcSKFQYMSAb/DTyoXY8OsG+Hj7oG6NeujUrrOrU8sS1KIJo3AIADAab8MguN8ZJmqtGqPWDQIAjP5gMgw6g4szIiIiosyE9ahreUI9ShZqrRoj13wFABjTfhrrciKZsUOSyMnKhJbB5DHTXJ1GlqSEiHDck5bdsaRQKhUIb1ZJWnbHHImIiMizsR51HU+oR8lCqVQgvGlFaZnvFZG8OGSbiIiIiIiIiIiInIYdkkREREREREREROQ07JAkIiIiIiIiIiIip2GHJBERERERERERETkNOySJiIiIiIiIiIjIadghSURERERERERERE6jcnUC5DgtPmkKAFg0YwlyBeVKdt9vf2zDnJ9mo0Obj/Fx246uSA+nzp3E2k3/w+Wr/0KlUqFE0RC0a9Ue5UqVc3ouP69fgXMXzmFCxA8Oaf/MP6cRGBCIAvkLOqR9Sp9EQYV38YGr00hV4nMd3lW0c3UaREREsmA9mn6sR7MGT6hHySLxuQ6NNB1cnQZRpsUzJDM5lVKFoyePpFh/6PghCILggows/tj7O8ZM/hZlS5XF1HEz8MPIyShetARGThiB3ft3uSwvR4n47mvExMa4Og0iIiIip2M96h5YjxIRkTvhGZKZXJmSZXHkxGE0b9RCWvf8+XNcvHwBRQsVc0lOj548wtwlc9G76xd4r15jaX3nD7sgu182zFsyBxXLVcRbAYEuyY+IiIiI5MN6lIiIiF7HDslMLrxydSz+eSGeP38OHx8fAMCx00dRpmRZJOoSk8X+tutXrNuyFk+fxqJ40RLo1bk3ChcsAgB49Pgh5i/7EWf+OQOdPhEFgwuhV+feKB1aBtEPotH9/7ph+P+NwE8/L8KjJ48QVqYCBnw+CNn8sqXIae+BPfD18cG7dRqluK/Fe62wZuNq7Du0D8H5gvHd9PFYOW8VvLReAICTZ0/i+xnjsXzuz9CoNVizcRV+++NX6PQ6lA4tg95dv5CGA7X4pCnat+6AX3duQ6mQUhj+5QjM/Wk2Dh0/BINBj/JlwvBFtz7IERgEADAZjZj702zs+Ws3tBot2rb4AO83bQMAMJvN2PjrL/j1j1/xJOYxQouXRM/OvaTXJ/5ZHJas+glHThyG3qBHtUrh6NWlN/x8s+GzL7sCAL4eP8ylQ5IIUIsmDMNRAMD3qAaDoHRxRimptWoMW9YPAPB951kw6AwuzoiIiOjNsB5lPUqveEI9ShZqrRpDl3wBAPih6xzW5UQy45DtNyAoBNv/Xht+IkdsRhQuUBg53grCibPHpXWHjh9E9cpvJ4s7evIIVv2yEr0698aM72ahTGgZfD1+OOKfxQEApsyZDLNoxqTRUzBjfCSCAoMw96fZydpYu2kNBvcdigkRPyDq6mVs+PUXqzldvnoZxQoXh0KRcvNTKpUIKRaKf69cQoWyFeGl1eLEmVe5Hzx2AOGVq0Or0WLr71vw54E/MajPEEwaPRUB/gEY+X0EjEajFH/s5BFMHDUZXdp3w9adW/D3xXMYM2wcpo6bgYSEBCxYMV+KvXD5AlQqNWZ8F4m2Ldth0cqFuHn7BgBg9YafsWHbL+jRqSemj5+JnEG5MGriSCQmWoro8dPG4er1q/hm0LcYO3w8bt25ienzpgEApo6dAQAY/n8j0LpZ2zTeMXIkJUTUxm3Uxm0oIbo6HauUSgVqt3sbtdu9DaWSu2giIkod61HWo6xHPYsn1KNkoVQqULttddRuW511OZED8AzJN1CiUlGb98XHPMOdqHvS7WJhhaGwsRN7HpeAW5fuSLeLlCsElTrlkbJ/j1/JUJ7hlavj6MkjqFW9NgwGA06fO4XeXb7Anwf3SDHrt65Du5btUa1SOACgY7vOOH76OPb8tQfNG7VA9SrV8U7VmgjKYTl62+zd5hg9cVSyx/m4bUeEFAsFANSpUReXr/xrNZ/4Z3EI8H/LZr5+vn6Ii4+DUqnEO1Vr4ODRA6hRrSZMZhOOHD+Efj2+BAD8snUdPu/WB+VKlwcA9PmsH7r06YiTZ09Iz6Nx/SYIzhcMANi++1doNFrkzpkb2fyy4f96fYWn8XHS4+Z4Kwe6d+wBQRDwfpPWWP3LKvx34xqC8xXA1t+3oHP7rgivXB0A0K97f/QY8Bn2HNiNkiVK4e8L5zBv8nzkz2t5rAFfDMYXg3vh1p1b0uNn880Gby/vNN8vIiIiovRiPcp6lPUoERF5InZIZgHVK1fHhBnjYTKZcOaf0yhUoDAC/AOSxdy8fRNLVi3GsjVLpHV6gx537t2GIAho0rAZ9h/ahwv/nsetu7dw5VoUzKI5WRv58uSTln28fWAymazm4+ebDU9intjM93HMYwS+mK+n9tt1MG7qGBiMBlz89wIMRiMqlq+EhMQEPHz8ED/M+h6KJEf09Xo9bt+9Ld3OlTO3tPxe/SbYd2gvOn/xCcqWKoe3q76DBrUbSvfnzpUn2dkBvj4+0BsMiHkag7j4OIS+KG4BWK7CWKQEbt25CT8fX/j6+EnFHwAUyFcAfr5+uHnnhlQAEhEREWVVrEctWI9ShohZ7ExK0frnNsPNmdOOsfp3RmWSZSPEJGc+I+lyVuAm7wllLuyQfAOXT161fedr3xlXzvyX7thr565nOCdrSoeWAQCcv/QPDh8/hLervJ0ixmw2oXunnggrUyHZeh9vH5jNZnwzYQSePX+GWtVro1qlcBiNRnw3fVyyWJVKney2aGMIQmjxUKzfuh4GowHq1/5Gr9fjxq3r0pHfMiXLwsvLG6fPncLJsyfwdtW3oVapodPpAADD+g9PVngBSDZPkEatkZYLBRfCwuk/4fjpYzh26iiWrVmCvQf/xPffTAQAq0N2RFFM1kZSZrMZZrMZ6jTuJyIiInIU1qOsR1mPEhGRJ+JECG9ANIu2/712FEuO2IxSKpWoUqEqjpw8jKOnjqB6lXdSxOTPmx+PHj9Evjz5pH//27Qal6Iu4ubtG/jn4t8YN/w7fNiqPapWrIbHMY8tuWbgaF3tt+tCp0vErzu3pbhv684tMBgMqF29DgBLUVYzvCaOnT6GwycOo9aL9X6+fgjIHoAnsU+kfHMG5cSSVYtx++4tq4+7e/8uHDt5BDXDa+Gr3gPx7ZCxOH/pH8Q8jUk1X18fXwT4v4WLUReldUajEVHXLiN/3mDkzxeMZ8/jcevOq8e9cesGnic8R3BeHo0mIiIix2E9ynqU9SgREXkiniGZRYRXro4ZP05Dnlx5kSdXnhT3v9+kNWYtnIl8efKjVEhp7Nj9G/46sh8ftmoPjVoLhaDAvkN7EV4pHJevXsbP61cAAAwG+680FvhWID7v1gezFs7A84TnqFW9NgBg/6G9WLvlf+j7WX8EvhUoxdeqXgcjvx8BjUaDsDJh0vpWTVtj+f+WISB7AILzBWP1hlW48O95BOf70urjPnv+DP/btAbZs/kjd6482HtwD4ICg5A9W/Y0c36/yfv4ed0K5HgrB/Lmzot1W9ZBbzCgVvXa8M/uj8phVTBt3mT06vIFABFzl8xBmZJlUahAYQCAl9YL129dR9HCxeDr42v3a0ZERETk6ViPsh4lIiJ6iR2SWUSl8pVhMplQvUp1q/fXersOnsTGYOW65YiJjUHB4IL4ZuAo5MuTHwDw+ad9sPqXn7FszRLkzxuMnp17Y9q8Kbh6/QreCgi02mZq6tWsj6DAIPxv0xps+m0jAMvQmdFDx6FcqXLJYkuWKIls2bKjcvnKUCpfzePRulkbJCQ8R+SimXie8BzFi5TA6KFj4eebDdY0e7c5Hj1+iKlzJyPuWRyKFymBiIGjoFSknLD9de83a4PnCc8xa6HlsUqVKIUJEd/DP7s/AOCrzwdi/tJ5iPhuOBQKJcIrV0ePTj2kv2/xXkv89PMi3I2+ix6detr7chERERF5PNajrEeJiIheEsSMjHFwgl9++QXDhw9PsV4QBFy8eDHF+pYtW+LSpUvJ1m3ZsgUhISFpPlZ8fDwqV66M4CtloDAnLwZyFgpEz7mfIHdQHiiRdqFA5EwmmBD98B7mf74SD64/dnU67k8U4QXLhMyJUAJJJo13J14+WgBA4nOdizMhIk+z07zW1SlkOqxJiVLHetROHlKPkgXrcqKMSU9N6rZnSDZt2hS1atWSbhuNRnTp0gV169ZNEWsymfDff/9hxYoVKFy4sLT+rbfeckKmROQxBAGJ7rvbk7DgISJyH6xJiUhWHlKPkgXrciLHcds9oZeXF7y8vKTbP/74I0RRxKBBg1LE3rp1CwaDAeXLl4dWq3VmmkRERESUibEmJSIiIpKfR1xlOyYmBgsWLMDAgQOh0WhS3B8VFYW8efOy8COiVKlFEwaLxzBYPAa1aHJ1OlapNSoMXtwHgxf3gVrjtseMiIiyJNakRPSmPKEeJQvW5USO5REdkqtWrUKuXLnQuHFjq/dfuXIFarUavXr1Qo0aNdCxY0ecPXvWyVkSkbtTQkQjXEcjXIcSbjl9LpQqJRp1rYtGXetCqeIcYURE7oQ1KRG9KU+oR8mCdTmRY7l9h6Qoili7di06duxoM+batWuIjY1Fu3btMH/+fBQrVgxdunTB3bt3ZXl8N73uDxEAbqNERETO4A41KfsuyG2JYD1KRER2cfvzjs+dO4fo6Gg0a9bMZszYsWORmJgIPz8/AMC3336LkydPYtOmTejdu/cbPX7co2cw6k0ww8QrGpLbMcMEo96Epw+fuToVIiKiTM3VNWlCnA4mkwkizABrUnIjIswwGU1IeJro6lSIiMiDuH2H5P79+1GlShX4+/vbjFGpVFLhBwCCIKBo0aKIjo5+48fXPdPj2JYzqPWRFoEBgVCwACQ3YYYJj2Me49iWM9A/17s6HSIiokzN1TXp85jniDr2H7K/mw1+XtkACG/cJtGbE/EsMR6Xj/2H57EJrk6GiIg8iNt3SJ49exaVKlVKNaZTp04IDw9H3759AQBmsxmXLl3CJ598IksOf/50EABQtUUYVBolBIEFILmWKIow6k04tuWMtH0SERGR47i6JhVF4Pd5+5CneC5kD3rGepTcgiiKePowHjt/3AeO2CYiInu4fYfk5cuX0bJly2TrTCYTHj9+DH9/f2g0GtSvXx+zZ89GqVKlUKRIESxbtgxxcXFo3bq1LDmIIrBn8UEcWH0c2YN8WQCSy1mKv2c8M5KIiMhJ3KEmffogHnM/W4aAPNmhULr9VPCUBZhNZsTcewqT0ezqVIiIyMO4fYfkw4cPkT179mTr7t69iwYNGmDZsmUIDw9H165dodPpMG7cODx8+BBhYWH46aefkg2ZkYP+uR4Pb7ADiIiIiCircZea1GQ049GtGNnaIyIiInIFQeTl0BAfH4/KlSsj+EoZKMycI5Io0xJF+MNyUCEWGsBNz3b2D7L84I19+NTFmRCRp9lpXuvqFOgNsCYlygI8pB4lC9blRBmTnprU7c+QJCKSjSAgFlpXZ5EmFjxEREREmZSH1KNkwbqcyHE4+QwRERERERERERE5Dc+QJKIsQy2a0BtnAQDzUB4Gwf2Gw6k1KvSe2gUAMG/AUhj0RhdnRERERERy8YR6lCxYlxM5FjskkxKUgCKLfCGYTa7OwPNxvhePowTQUrwCAFgglIfBDd9DpVqFll80BgAsGLoSBoPtz6qglHl/Jch70rzggCvACl7yDnES3gqQtT3Trbuytieo3ftr2vz8uatTICIimX+/CAp56yPRJPPvDpnrFWdTiiJaml/Uo4oK6eqQlLvmk71GU8hcQ6pkrn806gz9mdpHI9Xliyb+CvPzVxe4Fby9ZUntJVHmmkpMSJS1PdnfE5m3aVNMjKztgZdacQrP3psTERERERERERGRR2GHJBERERERERERETkNOySJiIiIiIiIiIjIadghSURERERERERERE7DDkkiIiIiIiIiIiJyGnZIEhERERERERERkdPIfO12IiL3pYMSHYWm0rI70iXo0bFoH2mZiIiIiDIPHZToqGguLZP70icY0KXyCGmZiOTFDkkiyjJEQUA0fF2dRqpEUUT09QeuToOIiIiIHMAT6lGyEEUR0TcfuToNokyLQ7aJiIiIiIiIiIjIaXiGJBFlGSrRjG7iOQDAT0I5GAX3OyajUivRbVwHAMBPEatgNJhcnBERERERyUUlml6rRzls212p1Ep0/boVAGDJd5tYlxPJzP1+jRMROYgKZnyIf/Eh/oUKZlenY5VKrcKHg1riw0EtoVLzmBERERFRZqKCiA/FS/hQvAQVRFenQ6lQqpX4oE8jfNCnEZRqdhwTyY0dkkREREREREREROQ07JAkIiIiIiIiIiIip2GHJBERERERERERETkNOySJiIiIiIiIiIjIadghSURERERERERERE7DDkkiIiIiIiIiIiJyGpWrEyAichYdlOguNJKW3ZEuQY/u5QZIy0RERESUeeigRHdFY2mZ3Jc+wYBetUZLy0QkL3ZIElGWIQoCrsPf1WmkShRFXD9/y9VpEBEREZEDeEI9ShaiKOL6pbuuToMo0+KQbSIiIiIiIiIiInIaniGZhKAQIIiCq9OwSjSLrk6BHE2Q+fiAaJa3vUxAJZrRQbwAAFgllIIxrdfcBe+JSq1Eh+FtAACrJvwCo8FkO1ju/BQy7/8UDjjmpZR3aJOolvlrUO7XUHDP7yQiInIjMtd8otnNz1nx8BrX7noUAGT+jSqmUl5miEnm98Qsc3tixn5Lq9RKfDSwOQBg9ZStqdflb0jUyzskXDQYZW1P9vdE5po+o+8xuRY7JIkoy1DBjM44DwBYi1AY3fAkcZVahc6j2gEA1k7e7NDCh4iIiIicyxPqUbJQqpXoNLwVAGDtzN9YlxPJjHs/IiIiIiIiIiIichp2SBIREREREREREZHTuEWHpF6vR/PmzXHkyBFp3c2bN9G1a1dUqFABTZs2xV9//ZVqG1u3bkXDhg0RFhaGPn364PHjx45Om4iIiIgyCdajRERERM7j8g5JnU6HAQMG4PLly9I6URTRp08fBAUFYf369WjVqhX69u2LO3fuWG3j7NmzGDFiBPr27Ys1a9bg6dOnGD58uLOeAhERERF5MNajRERERM7l0ovaREVFYeDAgRBfuyLS4cOHcfPmTaxevRo+Pj4oVqwYDh06hPXr16Nfv34p2lmxYgWaNGmC999/HwAwceJE1KtXDzdv3kSBAgWc8VSIiIiIyAOxHiUiIiJyPpeeIXn06FGEh4djzZo1ydafOXMGpUuXho+Pj7SucuXKOH36tNV2zpw5gypVqki38+bNi3z58uHMmTMOyZuIiIiIMgfWo0RERETO59IzJD/++GOr6x88eIBcuXIlW5cjRw7cu3fPavz9+/ftiieirEkPJfoIDaRld6RP1KNP+HBpmYiIHIv1KBE5kyfUo2RhSDSgX70x0jIRyculHZK2JCQkQKPRJFun0Wig11v/cZ6YmGhXPBFlTWZBwL8IdHUaqTKbRfx7/Iqr0yAiyvJYjxKRI3hCPUoWZrOIf0/+5+o0iDItl1/UxhqtVpuieNPr9fDy8rIr3tvb22E5EhEREVHmxXqUiIiIyHHcskMyd+7cePjwYbJ1Dx8+TDEMJq34nDlzOixHIvI8KtGMduIltBMvQSWaXZ2OVSq1Eu0GtkC7gS2gUnMYDxGRq7AeJSJH8IR6lCxUaiU+6N8YH/RvzLqcyAHcskMyLCwM//zzDxITE6V1J06cQFhYmM34EydOSLfv3r2Lu3fv2ownoqxJBTN6imfRUzwLFdyzAFSpVeg5sRN6TuwEldotZ9UgIsoSWI8SkSN4Qj1KFkq1Ej3GfogeYz+Ekh2SRLJzyw7JatWqIW/evBg+fDguX76M+fPn4+zZs/jggw8AWIa/PHjwACaTCQDQoUMHbNq0CWvXrsXFixcxZMgQ1K1bFwUKFHDl0yAiIiIiD8V6lIiIiMhx3LJDUqlUYs6cOXjw4AHatGmDzZs3Y/bs2ciXLx8A4NSpU6hZsybu3r0LAKhYsSLGjBmD2bNno0OHDvD398eECRNc+RSIiIiIyIOxHiUiIiJyHLcZD3jp0qVktwsVKoQVK1ZYjQ0PD08R36ZNG7Rp08Zh+RERERFR5sZ6lIiIiMg53PIMSSIiIiIiIiIiIsqc2CFJRERERERERERETsMOSSIiIiIiIiIiInIat5lDkojI0fRQYqBQR1p2R/pEPQbW/1ZaJiIiIqLMwxPqUbIwJBowuNkP0jIRyYsdkkkIGjUE0U1fEpNJ1uZEvVnW9jyCIO8JwYLS3QsId8/P+UQA55Bfui04PYO03xMRwLkDly03FKpUN1vBSytPWi/b06hlbk8ja3sAIAZkk7W9p6UDZW0v+/2HsrYn+PrK2p7czM+euToFIqIszxHft3ISDUZZ2xMUDqjgZP6dkJqM1KOy12hqmX/zyv07S+YaF95eGf7Tvy+9qO1yBCYbXmp6S94aTfkoTtb2xFh525P9PZF5mzY/fy5rexBFedsjqzhkm4iIiIiIiIiIiJzGTU8HJCKSn1I0o6k5CgDwq6I4TE48Gp5eSpUSTbvXBwD8unA3TEZ5z44mIiIiItfxhHqULJQqBZp8VB0A8NvqwzAZs+AoQyIHYockEWUZapjRz3QcALBTURQmNzxJXK1Rod+MrgCAncv2sUOSiIiIKBPxhHqULFRqJfp82xoAsHP9MXZIEsmMez8iIiIiIiIiIiJyGnZIEhERERERERERkdOwQ5KIiIiIiIiIiIichh2SRERERERERERE5DTskCQiIiIiIiIiIiKnYYckEREREREREREROY3K1QkQETmLHgpEqOpIy+5IrzMg4v1J0jIRERERZR6eUI+ShUFvwsjui6RlIpIXOySJKMswCwocFfK7Oo1UmU1mHP3ttKvTICIiIiIH8IR6lCzMJjOO/XnR1WkQZVo8JENEREREREREREROwzMkiSjLUIpm1Df/BwDYrSgMk+B+x2SUKiXqd6gBANi96gBMRg4PISIiIsosPKEeJQulSoF6LSsBAPZsPgmT0ezijIgyF3ZIElGWoYYZg02HAQD7FQVhcsOTxNUaFQYv7AUA2L/+CDskiYiIiDIRT6hHyUKlVmLgxPYAgP2/nWGHJJHMuPcjIiIiIiIiIiIip2GHJBERERERERERETkNOySJiIiIiIiIiIjIadghSURERERERERERE7DDkkiIiIiIiIiIiJyGl5lOwnBxxeCXC+JKO8VuASjUdb2zCb5r9wrmkVZ2xMUgqztQZC3/13QqGVtD4oseHzALPOV6tJ6DUUFYHix7KWBIMj8HqYlPc9Xo0q2LKRyNT9F9mwyJPWK6K2VtT2zn7es7QHA8wJ+srZ3p66szcH/hL+s7ZmCssvaHkR599O4fUfe9ogo6xFkrvcA+fd1bk5QKmVuUOb3RObfCJD7NwIAwRHboS1J61GlAkI6fqMIGo28Ocj8O0aQ+3eMt5eszZmz+2Ts77xevU7mbD4wqw3SbX0OeetcrUHe3+cKnV7W9uR+T+T+3SH3b32I8veXUErskCSiLMMAJcZ515WW3ZFBZ8S4TrOlZSIiIiLKPAxQYJymlrRM7stgMGHckDXSMhHJix2SRJRlmAUF9quLuDqNVJlNZuzfeMzVaRARERGRA5gFBfYrC7k6DUoHs8mM/X+cd3UaRJkWD8kQERERERERERGR0/AMSSLKMhSiGTWM1wEAB1SFYJZ7rhEZKJQK1GhRGQBwYMsJmE0yz7NJRERERC6jEM2oYb4JADigKOCW9ShZKJQK1KhXEgBwYM9F1uVEMnOLvZ9er0fz5s1x5MgRad3p06fx0UcfoWLFinjvvfewdu3aVNuoUqUKQkNDk/179uyZo1MnIg+ihgkRCX8iIuFPqOGe88CotSpELO+DiOV9oNbymBERkTOxJiUiR1PDjAj9fkTo90MNdnC5M7VaiYiJ7RExsT3Uavecf57Ik7n8165Op8PAgQNx+fJlad2DBw/Qo0cPdOjQAd9//z3++ecfDB8+HDlz5kTdunVTtBEdHY24uDj88ccf8PJ6dfUnH5+MXU2LiIiIiLIW1qREREREzuPSDsmoqCgMHDgQoigmW//HH38gKCgIAwYMAAAULlwYR44cwZYtW6wWf1euXEHOnDlRoEABZ6RNRERERJkIa1IiIiIi53Jph+TRo0cRHh6Or776ChUqVJDW16pVC6VKlUoRHx8fb7WdqKgoFCni3lfOJSIiIiL3xJqUiIiIyLlc2iH58ccfW10fHByM4OBg6fajR4+wbds29OvXz2r8lStXkJCQgE6dOuHatWsoVaoUvv76axaERERERJQm1qREREREzuUWF7VJTWJiIvr164egoCC0b9/easzVq1cRGxuLzz//HHPmzIGXlxe6du1q8+g1EREREZE9WJMSERERycflF7VJzbNnz/DFF1/gv//+w88//wxvb2+rcYsWLYLBYICvry8AYPLkyahTpw727NmDFi1aODNlIiIiIspkWJMSERERycttOyTj4+PRvXt33LhxA0uXLkXhwoVtxmo0Gmg0Gum2VqtFcHAwoqOjnZApEXkKI5SY7FVTWnZHRr0Jk3svlJaJiMi1WJMSkZyMUGCy+m1pmdyX0WjC5FEbpGUikpdbdkiazWb07dsXt27dwvLly1GsWDGbsaIo4t1338UXX3yBNm3aAACeP3+O69evo2jRos5KmYg8gElQYKemhKvTSJXJaMLOlX+5Og0iIgJrUiKSn0lQYKfK9r6E3IfJaMbOLaddnQZRpuWWHZLr1q3DkSNHMHfuXGTPnh0PHjwAAKjVagQEBECv1yM2NhaBgYFQKpWoW7cuZs2ahfz58yMwMBAzZsxAnjx5UKdOHRc/EyIiIiLyVKxJiYiIiBzDLTskd+zYAbPZjF69eiVbX61aNSxfvhynTp1C586dsWvXLgQHB2Pw4MFQqVQYOHAg4uPjUb16dcyfPx9KpXsOySQi11CIZlQx3gYAHFflh1lwv2EyCqUCVRqWAwAc/+MczCazizMiIsq6WJMSkdwUohlVzHcAAMcV+dyyHiULhVKBKm9bzmY9fugK63IimQmiKIquTsLV4uPjUblyZRSKqQOFXH20osw7K6NR1ubM8c9kbQ8ARLO8m5KgEGRtDzJ/2QsataztQZEFixGzzJ+TNF5DrWjA5rgVAICW2TpCJ8j8HqYlHc9X66PB5uj5AICWuXtC91xvM1YR4C9bagAgemvlbc/P+kUf3sTzAn6ytnezkbz7mVJT7sranikou6ztQeavfPHEP7K2R29up3mtq1OgN/CyJg2+UgYKcxbpxBRkrvcA2fd17k7h4yNvgzK/J6LeIGt7kPs3AgDBEduhDVrRiM2JqwEALb0+gk5I+/enYONCWhkm8+8YQe7fMd5esjZnzp6xz4jWS43NByMAAC3fGQdd4qttWZdL3s+d9l68rO0pHsTI2p7c74ncvztMF6JkbQ9mzhn6ptJTk2bBHhAiIiIiIiIiIiJyFXZIEhERERERERERkdOwQ5KIiIiIiIiIiIicxi0vauMqQjYfCJBpPg25566Re+6VhER52wMgCDI/Z5nnh5F7bhjBS955L6CS+eMo85yeDiH33ByK1OfbEkQlEPdi2csLQlpzSMo9R1E65oIVvDRJlrUQzLZzEAOyyZLWS8bs8s4No8sh82cEwONS8n5ORr67Ttb2Vi95V9b2nhX0lbU9yLxb8Dkp82cki837RkQewonzC2aEIHcNKfdFmExy13sOOKfGmReeEgG8/CmmVgHpmNNc0GrSjLGL3PMBquR9/cwyz0Ouz5Gx5yt4vXpv9EHe0CW++qzF5Zd3Hk6FQd45KTXPdbK2Z84mb34mP3m3aaXMv9vkviQIWcczJImIiIiIiIiIiMhp2CFJRERERERERERETsMh20SUZRihwGy/WtKyOzLqjZg9dJW0TERERESZhxFKRHpVl5bJfRkMJkyf9bu0TETyYockEWUZJkGJLT5lXZ1GqkxGM7Ys/tPVaRARERGRA5gEBbZoSrk6DUoHk8mMjVtOujoNokzLPU8RIiIiIiIiIiIiokyJZ0gSUZahEM0oa7gLAPhbnRdmwf2OySgUAspWLwEA+PvwZZg94WrpRERERJQuCtGMsqZoAMDfytxuWY+ShUIhoHzZAgCAs3/fZF1OJDN2SBJRlqGGCRNjNgMAWuXsDp0bniSu9lJj4qaBAIBWhfpB91zv4oyIiIiISC5qmDDp+XYAQMtsHd2yHiULjUaF6ZM/BgA0bjkFiYkGF2dElLlw70dEREREREREREROww5JIiIiIiIiIiIichp2SBIREREREREREZHTsEOSiIiIiIiIiIiInIYdkkREREREREREROQ07JAkIiIiIiIiIiIip1G5OgEiImcxQYGFvtWlZXdkMpiw8Nv10jIRERERZR4mKLBAW0VaJvdlNJowd8FuaZmI5MUOSSLKMoyCEut8K7o6jVQZDSasm/27q9MgIiIiIgcwCkqs05ZzdRqUDkajGWvWHnV1GkSZFg/JEBERERERERERkdPwDEkiyjIUohnFjQ8BAFGqIJgF9zsmo1AIKF6+IAAg6uwNmM2iizMiIiIiIrkoRDOKmx8BAKIUOdyyHiULhUJAieK5AQCXo6JZlxPJjB2SRJRlqGHCzCeW+Rlb5ewOnRueJK72UmPmzq8BAK0K9YPuud7FGRERERGRXNQwYdazrQCAltk6umU9ShYajQo/RnYFADRuOQWJiQbXJkSUyXDvR0RERERERERERE7DMySTEBUCRAiytCWYZWmG5KSQuf9dkGdbedWcvO2J8IAhBTI/Z7nJ/p6kp72kMYKQ6mskKmV+/RQyP1+lrM0BAMwyt5lHFStvgzJvM3I/X343EZHbccRwVTGLXQ1Xo5a1OUEl709EUS/vaA9B6YACQ+28n8VCkhJd0KghCOl4/3y8Zc3B7Ocla3uiWt73RP+WvPk9z5Oxz4hZ82q7eJ5LhcQkm3JcYXlrPvVzjaztqWJ8ZG1Pn0Pe90QfIO9nzk/u/YLRKG97ZBXPkCQiIiIiIiIiIiKnYYckEREREREREREROQ07JImIiIiIiIiIiMhp2CFJRERERERERERETuMWHZJ6vR7NmzfHkSNHpHXjxo1DaGhosn8rVqyw2caSJUtQq1YtVKxYEV9//TUSEhKckToReRATFFjhUwUrfKrA5B67vxRMBhNWTNqKFZO2wmTIYpPyExG5GGtSInI0ExRY4V0JK7wruW09ShZGkxkLNhzEgg0HYTTxyoBEcnP5VbZ1Oh0GDhyIy5cvJ1t/5coVDBw4EK1bt5bW+fn5WW1jx44diIyMxKRJk5AjRw4MHz4ckyZNwsiRIx2aOxF5FqOgxAq/qq5OI1XGFx2SRETkXKxJicgZjIISK3yruDoNSgdLh+QhV6dBlGm59JBMVFQUPvzwQ9y4cSPFfVeuXEHp0qWRM2dO6Z+3t7fVdpYtW4YuXbqgXr16KF++PEaPHo3169fziDQRERERpYk1KREREZFzubRD8ujRowgPD8eaNWuSrY+Pj0d0dDQKFy6cZhsmkwnnzp1DlSqvjjJVqFABBoMBFy9elDtlIvJggiiikPExChkfQxBFV6djlSAIKBSaF4VC80IQBFenQ0SUJbAmJSJn8YR6lCwEASiaPweK5s8BluVE8nPpkO2PP/7Y6vorV65AEATMmzcP+/btQ0BAALp165ZsqMxLT58+hU6nQ65cuaR1KpUKAQEBuHfvnsNyJyLPo4ERPz62/NhslbM7dFC7OKOUNN5q/Lh/FACgVeH+0D3XuzgjIqLMjzUpETmLBkb8GLMOANAqRze3rEfJQqtWYfWErgCA2t1nIFFvdG1CRJmMy+eQtObq1asQBAFFixZFx44dcezYMXzzzTfw8/PDu+++myw2MTERAKDRaJKt12g00Ov5Q56IiIiIMoY1KREREZFjuGWH5Pvvv4969eohICAAAFCyZEn8999/WLVqVYriT6vVAkCKQk+v19uc34eIiIiIKC2sSYmIiIgcw6VzSNoiCIJU+L1UtGhRREdHp4gNCAiAVqvFw4cPpXVGoxExMTHImTOno1MlIiIiokyKNSkRERGRY2ToDMlDhw7h3LlzMBgMEF+biLdv375vnNSMGTNw6tQpLFmyRFp38eJFFC1aNEWsQqFAuXLlcOLECYSHhwMATp8+DZVKhZIlS75xLkRERETkfhxdjwKsSYmIiIgcxe4Oye+//x7Lli1DyZIl4evrm+w+ua4IW69ePcyfPx+LFi3Cu+++i7/++gsbN27EsmXLAFjm6ImLi5OONn/88ccYOXIkQkJCkCtXLnz77bf48MMPOTyGiIiIKBNyRj0KsCYlIiIichS7OyTXr1+P77//Hi1btnREPgCA8uXLY8aMGZg5cyZmzJiB/PnzY8qUKahYsSIA4Ndff8Xw4cNx6dIlAECzZs1w+/ZtjBw5Enq9Ho0aNcLgwYMdlh8RERERuY4z6lGANSkRERGRo9jdIalUKlG+fHnZE3lZyL3UsGFDNGzY0GpsmzZt0KZNm2TrevbsiZ49e8qeFxFlHiYosM4nTFp2RyaDCetm/y4tExFRSo6qRwHWpETkWCYosM67vLRM7stoMmP5r8ekZSKSl90dkp988glmzZqFsWPHwsfHxxE5ERE5hFFQYqHfO65OI1VGgwkLR//i6jSIiNwa61Ei8lRGQYmFvtVdnQalg9FkxqzV+1ydBlGmZXeH5NGjR3Hq1Cls374dOXLkgFqtTnb/rl27ZEuOiIiIiOh1rEeJiIiIPJvdHZLWhqYQEXkCQRSRyxwHALivyAZRxgsfyEUQBOQKDgQA3L/1OMWVY4mIiPUoEXkuSz0aDwC4r/Bzy3qULAQByJMjOwDg3qOnYFlOJC+7OyRbt24NAEhISMD169dhNptRsGBB+Pn5yZ4cEZGcNDBi6aOVAIBWObtDB3Uaf+F8Gm81lp4YDwBoVbg/dM/1Ls6IiMj9sB7NRETOy/bG9AZZmxPlnivPJO+c2I44WCt3l2BqOWpEA5bGrQIAtMzWEToh7XpUSEiULTcAss9cKaqUsrankbmTVlRl7Bl7adXYtLQHAOC91tOQqHv1WTN6y5uj90N5P8eK+ARZ29Mo5X2+CqPMn2OZ9zPkHHZ3SBoMBkyaNAk///wzTCYTRFGESqVCixYtMHr0aGg0GkfkSUREREQEgPUoERERkaez+1DBDz/8gD179mDu3Lk4duwYjh49itmzZ+P48eOYNm2aI3IkIiIiIpKwHiUiIiLybHafIbl161bMmDED4eHh0ro6depAq9Vi0KBBGDp0qKwJEhERERElxXqUiIiIyLPZfYakKIrIkSNHivWBgYF49uyZLEkREREREdnCepSIiIjIs9ndIVm9enVMnjwZ8fHx0rqnT59i6tSpyY5SExERERE5AutRIiIiIs9m95Dtr7/+Gp07d0atWrVQpEgRAMC1a9dQoEABzJ07V/YEiYiIiIiSYj1KRERE5Nns7pDMnTs3tm7din379uHq1avQarUoUqQIatSoAYXC7hMuiYicxgwFtniXkZbdkdloxpbFf0rLRESUEutRIvJUZiiwWV1SWib3ZTKZsWHrSWmZiORld4ckAKjVajRo0AANGjSQOx8iIocxCErMzlbb1WmkyqA3Yvaw1a5Og4jI7bEeJSJPZBCUmO39tqvToHQwGE2YPucPV6dBlGmlq0OyVKlS+Ouvv5AjRw6ULFkSgiDYjL1w4YJsyRERERERAaxHiYiIiDKTdHVILl26FP7+/gCAZcuWOTQhIiKHEUX4i4kAgFjBC0jlx6wr+efwAwDEPopPI5KIKOtgPUpEmYIowl/UAQBiBa3b1qNk4Z/dGwAQ+zTBxZkQZT7p6pCsVq2atLxhwwaMGDECfn5+yWJiY2PxzTffJIslInInWhix5uESAECrnN2hg9q1CVmh9dFgzYXJAIBWhftD91zv4oyIiNwD61Eiygy0MOJ/8asAAC2zdXTLepQsvLRqbF7dDwDwXutpSNQZXJwRUeaSrg7JU6dO4fr16wCAjRs3okyZMikKwKtXr+Kvv/6SP0MiIiIiyvJYjxIRERFlHunqkPT29sasWbMgiiJEUcTChQuTXcFQEAT4+Phg0KBBDkuUiIiIiLIu1qNEREREmUe6OiRLliyJXbt2AQA6deqEyMhIaQ4fIiIiIiJHYz1KRERElHmkq0MyqeXLlzsiDyIiIiKidGE9SkREROTZ7O6QPH/+PMaNG4dz587BaDSmuP/ChQuyJEZEREREZA3rUSIiIiLPZneH5Ndff41s2bJhxowZKSYSJyIiIiJyNNajRERERJ7N7g7Jq1evYsuWLShUqJAj8iEichgzFNjpFSotuyOz0Yydqw9Jy0RElBLrUSLyVGYo8Lu6uLRM7stkMuO3neekZSKSl90dkqVKlcKVK1dYABKRxzEISkzJXt/VaaTKoDdiSv+lrk6DiMitsR4lIk9lEJSY4l3L1WlQOhiMJnw/7TdXp0GUadndIdmqVStERESgTZs2KFSoENRqdbL733//fblycz5BACDI0pQo88EuQZAnL8pCFB6wzZhkbs8TnrOcZN4viDK/fnK3BwCiUt72cirjZG3P7V/DLPYRocwrU9ejWY3ggDPERLkLDDenlPnLUWX3T8TUKWR+j+V+vg5oUxBlPptOo047xg6iVub2ZH79TF7yboMGX/n3M/rs8hZVRh95X0OtzNuM3O+J3M9X5YjvEnI4u7eqhQsXwsvLC7/++muK+wRBYAFIRO5LFKEVLRc/0Akq2Tv05KL10QAAdM/1Ls6EiMg9sR4lIo8litDiRT0K961HycLrRedtos7g4kyIMh+7OyR3797tiDyIiBxOKxqx6f4CAECrXD2gE+Q9cigHrY8Gm/6bCQBoVbg/OyWJiKxgPUpEnkoLIzbFLAMAtAroDB3crx4lCy+tGrtXfAkAqN9xBjsliWSWofNa4+LisHLlSowfPx6PHz/Gnj17cPPmTblzIyIiIiKyivUoERERkeeyu0Py33//RaNGjbB+/XqsWrUKz549w++//46WLVvi6NGjjsiRiIiIiEjCepSIiIjIs9ndITlu3Dh06NABv/zyizSB+IQJE/Dxxx9j4sSJGUpCr9ejefPmOHLkCABg2LBhCA0NTfGvc+fOVv8+NjY2RWx4eHiGciEiIiIi9+aIehRgTUpERETkLHbPIXnu3DmMGzcuxfqPPvoIK1eutDsBnU6HgQMH4vLly9K6ESNGYODAgdLt27dvo1OnTjaLv6ioKAQEBGDr1q3SOoXcV3MjIiIiIrcgdz0KsCYlIiIicia7OyQDAwNx7do1FCxYMNn6kydPIkeOHHa1FRUVhYEDB0IUxWTrs2XLhmzZskm3hw0bhsaNG6Nhw4ZW27l69SqKFCmCnDlz2vX4REREROR55KxHAdakRERERM5m9yHbHj16ICIiAitXroQoijh8+DBmzpyJMWPGoFu3bna1dfToUYSHh2PNmjU2Yw4dOoRjx45hwIABNmOioqJQuHBhux6biIiIiDyTnPUowJqUiIiIyNnsPkPyo48+Qq5cubBo0SJ4eXlh4sSJKFKkCMaOHYumTZva1dbHH3+cZsz8+fPRunVr5M2b12bMlStXYDQa8cEHHyA6OhpVqlTB8OHDkStXLrvyIaLMzSwI2K8tJi27I7PJjP2bT0jLRESUkpz1KMCalIicxwwB+9WFpWVyX2azGbsPXZKWiUhedndIRkdHo379+qhfv74j8knm5s2bOHz4MEaMGJFq3NWrVxEYGIjhw4dDFEVMmzYNvXv3xtq1a6FUKh2eJxF5BoOgwvi33nN1Gqky6IwY332Bq9MgInJrzqxHAdakRCQfg6DCeL8Grk6D0kFvMCFi6hZXp0GUadndIVm3bl1UqlQJTZs2RZMmTRAYGOiIvAAAO3bsQKlSpVC8ePFU47Zt2wZBEODl5QUAmDlzJmrWrIkzZ86gUqVKDsuPiIiIiJzPmfUowJqUiIiISG52zyH522+/oU6dOli/fj1q166Nrl27Yu3atYiNjZU9uf3796NBg7SPHnl7e0uFHwDkyJEDAQEBiI6Olj0nIiIiInItZ9ajAGtSIiIiIrnZ3SFZuHBh9OzZE7/88gu2b9+OOnXqYOPGjahTpw569eolW2KiKOLcuXNpHk2Oj49H1apVcfjwYWlddHQ0njx5gqJFi8qWDxF5Pq3ZgO335mD7vTnQmg2uTscqrY8G2+/Pw/b786D10bg6HSIit+SsehRgTUpE8tKKBmx/sgjbnyyCVnTPepQsvLRqHFw7CAfXDoKXVu3qdIgyHbs7JJPSarXQarXw9fWFIAhISEiQKy/cvn0bz549szo0JjExEQ8ePAAA+Pn5oXLlypgwYQLOnj2Lf/75B1999RVq1aqF0NBQ2fIhIiIiIvfjyHoUYE1KRERE5Ah2d0jevn0bS5YsQYcOHVC3bl1s3rwZNWrUwI4dO7Bs2TLZEnv06BEAwN/fP8V9v/76K2rWrCnd/uGHH1C6dGn07NkTnTp1Qv78+TF58mTZciEiIiIi9+GsehRgTUpERETkCHZf1KZBgwYoVaoUmjRpgsmTJyN//vyyJHLp0qVkt8PCwlKse6lNmzZo06aNdNvf3x8TJkyQJQ8iIiIicm+OqkcB1qREREREzmB3h2Tfvn3Rtm1b5M2b1xH5EBERERGlivUoERERkWeze8j20qVLYTQaHZELEREREVGaWI8SEREReTa7OySbNWuGuXPn4r///oNer3dETkRERERENrEeJSIiIvJsdg/Z3rdvH+7cuYMNGzZYvf/ChQtvnBQRkSOYBQFHtQWlZXdkNplxdOc5aZmIiFJiPUpEnsoMAUdVwdIyuS+z2YyDJ69Ky0QkL7s7JL///ntH5OEelEpAUMrTltwdCUq7T2ZNleCAzhgRoqztOSJHWSll2lYc1J5gMsnaniOIcj/nNLYZo6DGqMAWr+LTalDu91hM+zNiMIoY2XleunIQVfLuF8xqedszaeT/DBt95N3PVNZqZG1P7vdE7tdQSMc2SOQJMnU9mtWI/JH/xty95vOAjhxnfjvqocA3Pu9abpil/6RK0Ml7JrigkLdekfu3qipe3va02ozW9EaMGPo/AJbfDdok93g9lDdHzVODrO1B5m1GFS/z7yK58bvEI9ndIVmtWjUAQHx8PG7cuIHixYtDr9fDz89P9uSIiIiIiF7HepSIiIjIs9ndra/X6xEREYFq1arhgw8+QHR0NIYNG4bPPvsMsbGxjsiRiIiIiEjCepSIiIjIs9ndITlx4kRERUVhw4YN0GotJy3369cPT548wbhx42RPkIhILlqzARvu/YgN936E1izzsAiZaL012HhlKjZemQqtt7zDiYmIMgvWo0TkqbSiAZueLsemp8uhFd2zHiULLy81ft08AL9uHgAvL7Wr0yHKdOzukPz9998xYsQIhIaGSutCQ0MxduxY7Nu3T9bkiIjk5iUa4SUaXZ1Gqrx8tPDy0aYdSESURbEeJSJP5gUjvODe9ShZeHtr4M2TBIgcwu4OyWfPnsHb2zvFerPZDJO7T6hMRERERB6P9SgRERGRZ7O7Q7J+/fqYNm0a4uPjpXU3b97EuHHjUKdOHVmTIyIiIiJ6HetRIiIiIs9md4fkyJEjoVAoUK1aNSQkJKBt27Zo1KgRsmfPjoiICEfkSEREREQkYT1KRERE5NlU9v5BtmzZMGvWLNy4cQNXr16F0WhEkSJFUKxYMUfkR0RERESUDOtRIiIiIs9m9xmSDRo0QExMDAoWLIi6deuiYcOGKFasGKKjo/H22287IkciIiIiIgnrUSIiIiLPlq4zJLdv3469e/cCAG7fvo0xY8ZAq01+Bdjbt29DqVTKnyERkUxEQcBZTT5p2R2JooizB/+VlomIyIL1KBFlBiIEnFHmkZbJfZnNIk6fuSEtE5G80tUhWa1aNakABKz/SC5RogQGDRokX2ZERDLTCyoMzdHa1WmkSp9owJC2M1ydBhGR22E9SkSZgV5QYYhvE1enQemg1xvx1aCfXZ0GUaaVrg7JwMBATJgwAQCQP39+fPrpp/Dx8XFoYkREREREL7EeJSIiIso87L6oTd++fREfH4/Tp0/DaDSmODpdtWpV2ZIjIiIiInod61EiIiIiz2Z3h+TmzZsxatQoJCQkpLhPEARcuHBBlsSIiOSmNRuw9MEyAECXnJ2hU6hdnFFKWm8Nlh4bAwDoUnUkdAl6F2dEROR+WI8SkafSigYsi18LAOjs1w46wf3qUbLw8lJj1fLPAQAdOs1FYqLBxRkRZS52d0hOnToV7dq1Q//+/eHn5+eInIiIHMbfnOjqFNIUkCObq1MgInJrrEeJyJMFiDpXp0DpFBDAqUGIHEVh7x/ExMSgc+fOLP6IiIiIyCVYjxIRERF5Nrs7JOvVq4fff//dEbkQEREREaWJ9SgRERGRZ7N7yHbu3Lkxbdo0/PbbbyhUqBDU6uRzXry8+iERERERkSOwHiUiIiLybHZ3SMbGxqJ58+aOyIWIiIiIKE2sR4mIiIg8m90dkpn5iLMQEwfB/pfEKlEUZWlHak8v7xW9RKNR1vYAQDTL+5whmuVtT7B7hoLUPU95Zc83IgiyNif3NugJ0nrGZvHV58j8/DnMaVzVUHDBeyLC9Go5IQHic9tX2Vbej5Ulr5cUcfJe8Ef91FvW9gBAYfSVtb0iebrL2l7pew9lbc9f5o+xIPd+IQvuZ8g9ZOZ6lIiIiCgrSFfv27Fjx9LVmCAIqFKlyhslRETkKCIEXFIEScvuSDSLuHTymrRMREQWrEeJKDPwhHqULMxmERcv3ZWWiUhe6eqQ7NSpU7oaEwQBFy5ceKOEiIgcRS+o0N+vhavTSJU+0YD+9ca6Og0iIrfDepSIMgNPqEfJQq834vO+S12dBlGmla4OyYsXLzo6DyIiIiIim1iPEhEREWUeMk+qZ5/o6Gj0798f1apVQ61atTBhwgTodDoAwM2bN9G1a1dUqFABTZs2xV9//ZVqW1u3bkXDhg0RFhaGPn364PHjx854CkRERETkwViPEhERETmfyzokRVFE//79kZCQgJUrV2LatGnYs2cPpk+fDlEU0adPHwQFBWH9+vVo1aoV+vbtizt37lht6+zZsxgxYgT69u2LNWvW4OnTpxg+fLiTnxERuTutaMTSuLVYGrcWWlH+CzvJQeutwdKzE7H07ERovTWuToeIKFNjPUpEzuYJ9ShZaLUqrFr+OVYt/xxarTwXvyWiV1z2qbp69SpOnz6NAwcOICjIMqlv//798cMPP6B27dq4efMmVq9eDR8fHxQrVgyHDh3C+vXr0a9fvxRtrVixAk2aNMH7778PAJg4cSLq1auHmzdvokCBAs58WkTk1kTkEeOlZbckAHkKBUnLRETkOKxHicj5PKAeJQCWOYnz5PGXlolIXi47QzJnzpxYuHChVPy9FB8fjzNnzqB06dLw8fGR1leuXBmnT5+22taZM2eSXU0xb968yJcvH86cOeOQ3ImIiIjI87EeJSIiInINu8+QnDlzJpo1a4ZixYq90QNnz54dtWrVkm6bzWasWLEC1atXx4MHD5ArV65k8Tly5MC9e/estnX//n274m3RmA1QWjlKZRYEGIRXL5XWbLDZhigI0Gc0VjTYPkgmGqFLFmuE7WAhWaxGNEJ4LVZMMjwg8bXY1Hqpk8aqRVOy10sUxXTHpmgXSuDFUaeXsYJo/SiUtVhbdFBCfBGrEk1QyRSrh0K6VyWaoITZZqwBSpgFy6uqFM1QwWQ1ToCQ7tjX21WIZqhfi036fhihhCmV2KTsiTVBAaOgtDtWEEVoYHuIij2xZihgeBELUYQ2lViVmCQ/UYQWtj+fZggwCmrptlZMPdbw+mfZClEUIcLK5z4JrfjqE6gRjdAluy/5517x2v5Fp0iSbyr7ntdjNWbLPkI0W//M6RSaJLGGFPsTW7FqswFKMZXPvdKOWIX61efeZIRStP2ZS1SmP1YwCxAVltdcbTRCZUqlXbUqzVitWQ8AMAgq6fOpEk1Qiql8lpPEKkVTsu1UadIni9UrlDC/2N6VZhPUqbSbNFYhmqAxmyDYeI0NghImRfJY2/kmjRWhSXV/ooBR2p+kP1YQRWhlijVBSLaP8HJELACvVIbd2RNrhgB9BmOz0tC/zFyPApb30to+0Znbkq1zgUQgRU2a3liNaEpSQb306q8T04yF1dgU9aDMNamtIQsZr0nNUKVSO9oTq8er902umhSiWeaa9NW2Z4TitTrTdr42Y638RnjjmtRGreCImjTFvjqN+tUMIdmzSa3GS/fvVrNla9Wns3ZMK1ZMdvagYKUmTeV3a7Ka1FJniial1Wid8rWaNJXaMWms2mT9d/5LiTZitaZXf6M16QGTIWVNmkrNlKjSpDtWEM0QX9aOZiNUqdSvOoUqzViVzDXp6++JQfFabGq1Y5JYhWiG2myEykZ4xmtSMzSp7k+EZPuItGJfbuGsSTMWm152d0ieP38eCxYsQJEiRdCsWTM0bdpUlmEokyZNwvnz57Fu3TosWbIEGk3yudM0Gg30er3Vv01MTLQr3pZVD5bAz5xywzyqLYRRgc2l26vvL7b5ZpzV5MPQHK2l20sfLEeAmGg19l9VTvQPaifdnv9gNXKb46zGXlcGoJf/q9iZTzegkCnGamy0wg9dAjpItyc/3YIQ00OrsTHQ4kP1q3zHm/YiTHxgNTYRSrRUv8phpOkvhIt3rcYCwLvK9tLyMPNh1MYtm7EtFG2R+GJz/D/xOBqJ/8HWPqKd6n3EwgsA0Mt8Ci3NUTbb7aRqjmj4AQC6mc6infmCzdgeqqa4LgQAADqY/kEn8982Y/uq3sNleAMA3tefRw/dcZuxg30a46wqLwCgqeES+iYethk70uddHFVbPk/1DVcwMGG/zdjxPvWwX10EAFDDeB0jnu+xGTvZqyZ2akoAAKoYb2Nswh82YyO9qmOLphQAoKwpGpOeb7cZu0BbBeu05QAAxc2PMOvZVpuxyzUVsMKrIgCgoDkG859ttBm7VlMWC72qAgByifFYFr/OZuxmdUnM9n4bAOAv6vC/+FU2Y3epikrLWpiwNm61zdh9qsL4zre+dHvT0+U2Y4+qgjHSt5F0e83TVfCyUVieUebBEN8m0u1l8WsRICbpdjT5AFgIAPju2U70UzSQ7pofvyHJEB8ASXYX19WB6J23s3R7RvQqFDJYv5hCtDIbuub/TLo96f5ahOijrcbGKL3xUfH+0u1xt9aifMJNq7GJghrvhwyQbo+M+h/CYy9bjQWARlW/lZaHXt2A2k/O24xtWelrqVgccnIdml0/YTO2aYtRiNFaPvf9z25G2yuHbMbWqDEEt4ICAQCD1/+OXtv32YxtOO4rXM6fGwDQZ+sefLVpl83YL/N/gn+9LJ/7VjEn0P2x7XaH5PsQ57wLAgCaPD2LPg+TtHsteeyIkh1xJDAUANDg4RkMidpgs90xIe2xL6gsAKDmowsY+e8am7GTirXG77ksn88qMVEYf3GlzdhZRZphc55wAEBZPMAU2H5u81EOa2HJtzieYDZ224xdhlJYjjIAgIJ4ioXYaTP2fwjBApQHAOTCc6zAbzZjN6MYZsHy3PyhxzpssRn7OwphEiz7Hi+YsAUbbcbuQ36MxdvS7dRijyAPIlAzSf5b4G2jsDyDIAxCXen2cvyKAFivZy7hLfTFq33EQvxuM4fMJjPXowDwP2yDn5VCyJnbUh48txr7H7KjB15950ViNwrjqdXYe/BBJzSVbk/FnwjFk+RBL37vx0CDdkIrafV34n6EwXpNmgAlWgptpNujxIMIh+2O33fxgbQ8DEdRG7dtxrbA+69qUpxEI1y32ZfygdASsdACAHqLZ9ASV2y221Foimj4AgC6iefwIf61GdtdaITrsAwT7SBeQGfY/n7sIzTAFWQHYKlJuycesxk7xLfJq5pUfxF9UqtJ/d7DUY3lu6m+PgoDn+21GTverwH2ayw1Vg3DfxgRb/v7cbL6bexUWQ4kVDHfwVj9nzZjI9VVsUVl+Q4pa76PSXrb9esCdSWs01i+84qbH2NW4q82Y5ery2OFpgIAoKAYi/kJm23GOqomTUoLIzbHrbB5/z5VYUzwaybd3hg932asXb9bvYIxNN+r32tLbyyAvznBauy/2tz4Mn9H6faPt5Ygt9H65/66Jgd6FfpUuj3z5nIU0j+yGhutyo4uRXpJtyffWo0QnfXPcozKB+3LDZJuj7vyM8Lir1uNTVSo0Srs1Ry+Y86uRPVHl6zGAkC9hhOk5a//+R/q3n/xO9DHB4ClnY17xwHPn6NJvdFSTfr1wbVoccX278CGH41GjJelJv3q2CZ8ePGgzdiPwwch2vstAMCn13ai/U3bF1H7tGp/XPe11KQfX9+LLtdt11f9C3SUatL3Y06g+0Pbn+Uh+dvjrI/lc9809gz6PLD9Wf6m6Ec46h8CAKj/+BwG3bD9ORpX+APsf6s0AKBGzEVE/Gf7czQxpA125K4EAKj6JArf/WP7N9jMYs2xKV91AEBZ8QEmm2z/Hl6gCMNapeU3bnHxCSJNtuvM5YoyWAZLLGvSutJte+qI9LK7Q3LevHmIj4/Hzp07sX37dkRGRqJkyZJo1qwZmjRpgty5c9udxKRJk7B06VJMmzYNISEh0Gq1iImJSRaj1+vh5eVl9e+1Wm2KYk+v18Pb29vuXIiIiIjIvbEeJSIiIvJsgvj6OFs7xcXFYdGiRfjpp59gMBhQuXJltG/fHs2bN0/7jwGMHTsWq1atwqRJk9CsmeXIz7x583DgwAEsX/6qN3zmzJk4c+YMFi1alKKN9957D7169UKbNq+OlNarVw8DBw5MVx7x8fGoXLkyQmJrQmmljzYjQ7ZfvqyyDdk2yDxkO+7VWVayDdk2yzxkWyHz8BiI8g7ZfvEDQ7Yh2wKHbGck1t4h27+8OFrd0u8TaTuy3q4Ao8IFQ7Z9tFh7z3KG5Ad5uiMuwZwk9rUh2/7+yf72jYds27iqd0aHbCv9VLIP2Y4v5CvrkO2rLeQdsl3yB8vZ6HINjzHmSv4eu92Q7UNnOGT7BXcYHqMVjdgq2j5rNjPLDPUo8KomLRYVCqU55dCnTDlkO8l3sdsO2bZRL7jLkG0he/YXsTIN2VYq5a1JE16NFpNlyLaV3whvXJMqrQ81dNSQ7bXxllE6LbN1hA6qtIds+2V/9fdyDNn29pJ3yLZS5iHbvtYP5GR0yLYYoMrYkG0vNTb+ajlD8v2mE6BLfDVkOy5YI+uQba9bJnmHbN+JASDjkO3X3pM3HbJt8rP+uyOjNan60Fl5h2y/GK7PmjTjQ7Z3mtfajH8pw1fZPnXqFLZv347ff/8dsbGxaNSoEZo2bYoHDx5g6tSp2LdvHyZOnJhqG5GRkVi9ejWmTp2Kxo0bS+vDwsIwf/58JCYmSkehT5w4gcqVK1ttJywsDCdOnJAKwLt37+Lu3bsICwuz6znpFWoo0vGSJN1pyhorqG1fVfe19UmLu7TorcSabfy9tVhbDIIy2Qx8omB7J/96bHraTc+VzOxp1ygoU/mqtz/2ZXaW2PTNl2ASFDDZ6PJ9/fmmFvs6s6CA7rVY0caXrrVYe9qVI1YUBOiQvs+GPbFIM1bAdUXAi0VFmp+jpO+ITrDzs2yFtfckZawK1y9YhpLpoQKSnBb/er6KVPYv9ux79ApLu6LCemGQPDb97RoU6vR/7u2JVapkixUVr77cDSoVDOncBdqK1Vl5DY2CUiqA0mISlDAliTUobb8nJoUSpnTue8yCEolKZaqF++ux6WtXkIY2yhkrOigWjopF8g4PV8XaUxtkFpmxHgUs76UiHfsNd9iW7KtJrTwnG/WePXNRpawH5a1JUzuAmZF2jYICqZ8CYF+sUoqVpybFa6/9G9ekgvWqOsN1ppD632SoJk3H9iZfTZqkHoWQjvo1+ZVoZfktamX9m7T78oCtXTlY8bLOFFOpf16PTQ+D0p4681WsqFDhv/8sU0ckKtTQKYXXYlUwKNO3D0wrVisk6bhXqNL/u9VGrFrmmjS198QkKGFKd+2ogE6pgTEd77F9NakCiXbsT9KOtbyqrEkzFptedrc4btw4/PHHH3j06BFq166NwYMHo0GDBtBqtVKMr68vIiIiUm3nypUrmDNnDnr27InKlSvjwYNXc8RUq1YNefPmxfDhw/HFF19gz549OHv2LCZMsMztoNfrERsbi8DAQCiVSnTo0AGdOnVChQoVUK5cOYwfPx5169aVZS4hIso8dIIKPf1apx3oQroEPXpW/8bVaRARuTXWo0TkqTyhHiULnc6Ibj1SnhFPRPKwu0Py6tWr6NevHxo1aoRs2bJZjSlXrhxmz56daju7du2CyWTC3LlzMXfu3GT3Xbp0CXPmzMGIESPQpk0bFCpUCLNnz0a+fPkAWI6Gd+7cGbt27UJwcDAqVqyIMWPGYObMmYiNjUWNGjUwduxYe58aEREREXkA1qNEREREns3uOSSHDx+OESNGwM/PL9n62NhYfPPNN5g5c6asCTrDy/l6CsfVS9eQ7fR4w6k5U9Kn9yTz9DHHWb+a95t4fQ7JN2VrDsk3aFDe5ry0aQfZ0146hgPZQ/ZtMAty9/dEEeCfdpAdRG95t2lzdvkv5BBfyFfW9m43sz0fS0aUHvdQ1vYMueV9j9MzZNsuh8/K2x69sfTM15MZZMZ6FHhVkwZfKQOFlTkkMyWZv2sBpJhD8o05IkcZKW10yme8QXm3PTHB+pWbMyyV4cEZJvNzlpvC10feBr2tX5wro0SlvO+J6CdvDanPIX9NGhec9pBje/j/l5h2kB3Ut2NkbU/u98RoYw7JjFIe/lvW9kRjegfNky2yzSF56tQpXL9+HQCwceNGlClTJkUBePXqVfz1l+1L0xMRuZpWNGLWsy0AgH6+LdxyvjWttwaz9liGbPerNxa6BH0af0FElDWwHiWizMAT6lGy0GpVmBfZBQDQu+9S6HTspCKSU7r2ft7e3pg1a5blCrGiiIULF0KR5MiUIAjw8fHBoEGDHJYoEdGbE1HIHCMtuyUBKFQqv7RMREQWrEeJKHPwgHqUAFi+VwoXziktE5G80tUhWbJkSezatQsA0KlTJ0RGRsLfX95hZEREREREtrAeJSIiIso87D4/fPny5Y7Ig4iIiIgoXViPEhEREXm2dHVIlipVCn/99Rdy5MiBkiVLWj1dWRRFCIKACxcuyJ6ks2iy+UApqFOsN5vNMCSZL0LrbXsCVtEsQq8zQDCb044VRegTX12sRuultjlptqjXQ5eQJNbbdixEMVmsxkud4gIxovnV80l8rksWq0hlouiksWqtGsqkExiL5vTHptauRgWlSmnzIjRWY23QJeilC4motCqo1LY3+WSxamWqsfpEPfDi+ajUSijVtnMwJBpgfnHBH6VKCZXGRqxSCYPOCLPJ/CJWAZXGdg5JYxVKBdTa12KTXGTIqDfBZDTZjk0iWaxCgNor5WfiJZPBBKPB/lhBEKDxVqfYZlKNtcFsNMOgf7E9CwpofWx/5lQGHfDiek6CSgWvVCYIN5vMMBheXfAktXbNptf2EbZiTSbLPiLp5/612KT7DK2vFvrX9z1JP8oBSfIXAV2K/YmNhF+L1WhVEBQCRB/rE5yniE1lyErSWEWAV+qf+ySxarUyXbGJAQqoVUooU7noVaL+1WuWVmyxgjcgvhgupRKUUAmp7E/MhjRjNW9ZLrpj0Bmlz71KpYRSbfu5JY1VqhRQJdmfiK9Nwm7QG2F6GatUQJ3avidprEKAWqOCYGNkmMFggunl/kQhQJPavidJrEapgMbL9mfDaDAm20ekGfvivRMEIdXvT6PBBKMhfbEmo+nVPgKAl4/tizfZFWsyw6AzyB5rNouW75gMxKb2OmQGWaUeJTfk5hcKFE3yXqBN7ucrmqzXexlvUP73w6mDccUk75fJlK4Lb4oGeS9wKsh8ER9B7gsNyZyfKi5j7akMr7Y1VZweqiS1q1eMvDkq4+WdN15IlHkeernfE1lbg1RLk2dJ13awdOlSaUjM0qVLM+38CatOjEkxOToAHN19HqO6LZBurz45xuYPhLOHojD0o9nS7aWHRsE/R8o2AeDfMzfwZfMp0u0fdw9H7gI5rMZev3gHveqMk27P3D4UhUrmsxobfeMRulT9Rro9eeMAhFQsZDU25sFTfBj8uXR7/OYhCKtT2mps4rNEtAz8TLo9cs2XCG9S0WosADTSdJCWhy75ArXbVrcZ2zKgq9TR+OWc7mjUuY7N2Hb5eyP2oaVXqdekjmjZ+12bsZ1CvkT0dctVb7uN/hDtvmpmM7ZHpaG4fuE2AKDDkFboFNHGZmzfmiMRdf4OAOD93g3RfXQ7m7FDWkzC2QOXAABNu9RGn0mf2Iwd+fEsHN1puUJY/Q/CMXBWV5ux4z/7Efs3nwQA1GhWASMW9bIZO6XvT9i56hAAoEr9Mhizup/N2NlDfsaWRX8CAMq+XQITN9uei2vhqHVYF/k7AKB4WEHM/GOEzdgVP2zBiomWCbwLhuTBjwdH24xdN2s7Fo5aBwDIFRyIpWd+sBm7ZeFuzB7yMwDAP4cf1vw71Wbsrp//Aj5ZCMDSEfi/m5E2Y/dvOoHx3edLtzddn2Uz9ujOcxj58au21pyfDC9fG/uIvy5hSMtJ0u2lp39AQJD1q2N+t24A+tUfK92ef2gMchcMshp7/fI99Gr26rnPXN8PhUrksRobfesxutT/Xro9+efPEVKugNXYmCfP0L7+q9d/XGQnhFUpYjU2MUGPVu+82k99O6YtqlcvbjUWABrU+05aHv51S9SpW8pmbLMmk6ROyRGdGqLlO2Vsxtb/ai5i4i1X9Bz4YR18WK+CzdiPDo5DdOITAMBnRZvgo0L1bMZ2OzIR/z2LBgB8UrgBuhZ5L2XQMcv/+n80B//+bdmfvN/pbXQf2MRmu0O6LcTZY9cAAE0/qIo+ES1txg6LWIvDR68AAN6tXwbDBtvep40auwF791v2PTVrhGD0N61txn4/aRt2/H4OAFCtSlFMGG97nzZj1u/Y+GLfU7ZWKUzZ/a3N2PlDlmPtFMvnvniloph9ZILN2GWj12L56P8BAAqWyo+Ff0+zGfu/yZuxYIjlDLlcBYOw4tocm7Gb52zHrL6LAAD+Qdmx7v4im7G/L/kTkz61fId7+WixJX6Fzdh9aw9hbPtXn7nUYo9sO4mIFq+e+/+iF8Lb1/oBgDN//oNB9b+Vbi+/NgcBObNbjb10LAp9w4dLtxf+Y/s1ywyySj1KRERElBWkq0OyWrVq0nJ4eDgAy1mDCoUC9+/fx4kTJxAaGoqiRYs6JksiIiIiytJYjxIRERFlHoIo2nfO+4kTJ/B///d/mDRpEooWLYo2bdpAp9MhISEBkyZNQpMmts8CcVfx8fGoXLkyQtBMtiHbcPch2zFPpWUO2bYSm54h21qtFMsh254yZFuPOQ9WAQB6+rcD3HHItpcGs/ZEAAD61h2LuCfPXt33+pDtHG+9WnbDIdtCTl/Zh2w/Ke4l65Btr7YyD9kebNkvyDVkW5c3+dmzbjdk+89THLItc+ybDtne+mylzfjMJDPWo8CrmjT4ShkozPIOj3NbjjjT1c2HWMtN4esrc4PyDr8VE3VpB9kjle/1jJJ7CHNqtKIR8xM2AQB6ereCTkj7HCHB23qNllGCl7ztyb7N2KhJM8rk7512kBVarQoLV38BAOj+0RzoktT7CXkz1qYtPreepR1kB2V0jKztib7yPl9R5mlmzGcvydoezDJPhZEF7TSvTTPG7qH73333HZo2bYqwsDAsWrQIWq0Wu3fvxrZt2zBz5kyPLQABSyeTAmkXMLqE9M/HYFdsou25QUR98vuSdjimRW+lXfNz64WBtVhbDDoDkkXb6FyyGptau3qj5QdhOuZTkWLTwZikk0uOWIUdsS+ZjK86+1J4rRAyGc0wGdO3/ZhNZuievxZrYx4Nq7G22jWLDokVxRexqWwzKWLTKbVYHYAuAa+mEkBa7SYpeGXJwcocT6/H6p7r0SVsmPV2X9+f+Nj+VKW2P3ndy3kqRUXaxXjSOS3TYjCYknXqyhZrNKV/f5JGrDbJPt8ommAU07mPsBErJqTcbxmNJhhtfe5fY/ncv/pcJKbyPppMZqljMM12zSJMiQabHZJJmc1iqo+bItbG98mbxIqiY2IBZOpYe2oOT5eZ61Eiytx0ggpdfNq6Og1KB53OiE6tZ7o6DaJMy+5DGZcvX0aXLl3g7e2N3bt3o1GjRtBoNKhWrRru3LnjiByJiIiIiCSsR4mIiIg8m90dkkFBQYiKikJUVBTOnz+PevUsFwA4ePAg8ubNK3uCRERERERJsR4lIiIi8mx2D9nu2rUr+vTpA4VCgXLlyqFatWqYN28eIiMjMWGC7atnEhG5mkY0YvJTyxV/B2VvAX065uxxNo2XGpO3DgEADGo+0a5pFIiIsgrWo0TkqTSiEZMTdwAABnm955b1KFlotCpMndsFADDg86V2TV1ERGmze+/XuXNnVKlSBXfu3EHNmjUBANWrV0fdunVRsmRJ2RMkIpKLABEhpofSsjsSFAJCKhWRlomIKCXWo0TkqQSICDU/kpbJfSkEAaGl80vLRCSvDB2OCQ4ORrFixaDVanHx4kUcP34cZcqUkTs3IiIiIiKrWI8SEREReS6755D8448/ULt2bZw4cQLXr1/HJ598gg0bNuCLL77AihUrHJEjEREREZGE9SgRERGRZ7O7Q3L69Ono378/3nnnHaxduxZ58+bFtm3bMHXqVCxevNgRORIRERERSViPEhEREXk2uzskb9y4gSZNmgAAdu3ahXfffRcAUKJECTx+/Fje7IiIiIiIXsN6lIiIiMiz2T2HZL58+XDkyBHkzp0b165dQ/369QEAW7ZsQeHCheXOj4iIiIgoGdajRERERJ7N7g7J/v37Y8iQITCZTKhbty7KlSuHH374AatXr0ZkZKQjciQikk2M4OXqFNIU8zDO1SkQEbk11qNE5MlioHV1CpROMU+euToFokxLEEVRtPePHj9+jOjoaJQqVQoAcPXqVWTPnh1BQUGyJ+gM8fHxqFy5MgonvgsF1PI0ajbL084Lot4ga3vmmFhZ2wMAiPI+Zwh2zyiQOoUgb3PeMndsKZXytme2+6PtfFlsm4HJJG97QYGyNif6yrtNGwPk7/x9UlzeNv073Ja1PW1/efNLzJdN1vYEmXcLqt0n5W3Q/pKEXrPTvNbVKThNZqtHgVc1afCVMlCYZa4L3JUg83ctkOX2JQpfX5kblLeeEhN1srYne30GQJC7DpeZIPPvDsFL5hpN7m3GR978TP7esrYHAAl55W3T55a8HZ/K6BhZ2xN95X2+ordG1vbMZy/J2h7MMv9uy4LSU5NmaM/h7++P6OhoLFmyBE+fPkVcXBy0Wh7lISIiIiLnYD1KRERE5LnsHrJ99+5dfPrpp4iNjUVsbCwaNGiAhQsX4tSpU1i0aBFCQ0MdkScREREREQDWo0RERESezu4OyTFjxqBKlSr49ttvUaVKFQDA1KlTMWLECIwbNw7Lly+XPUkiIjloRCPGxW0HAERkawy9YPcu0OE0XmqM+9//AQAiPpz+/+3deZyNdf/H8fc5Z1YNJgxZKiT7DBqRIkvKFm2khKRCSHdkGW5R6laNJMmWfgmpO0WlTbSqO0tkjcpYsjWWOzIxM2f5/v6Y28mYnets4/V8PObxuOY63/mez7nOd655z/dcizLTrb1cAwAUB8U9j0ZGR8phcp5C6nZ75Mz4++9CVIm8jwj1eIwy0zPPqW1kdIRseZxKbYxRxqlzaxsRFSH72afbnvGz6SczzmgbLns+p4Ge2TY8MlwOxxltzzplO9+2+fUbESZHWN6n8halbcapTJ2+UlZYeJjCwq1pe2ZOCAt3yJFPW2e6U57/XdLHEeZQWEQebe12OTNc8rg9BbeVsrW1O+wKjzwrX52xuZ2ZLrldbm/biMi8L5eVra3dpoio/53imcsp2y6nSy5nLm1zcWZbm82myOgIKY8x4Xa6s7WNiM67Xo/LI2emy/t9ZInca4gwbj2etkImM1P/jLpBmbawPNtKksdt5Drj+/zbeuTMKLgGW2SEjDHZxk9kPqfQ5mgbFZ79cgv27L9/Gfm1zd5xtrYRkeGy2W0yeWznjFNntg2TLZ/T989sGx7hkCO//Ul67m3DI8P0xLN3SpLGjXxbzgxX9rZhjvz3JxmFb2uz2f7+vQ9zKCws77YZGa4C29r/9346M/7+vQ8Ld+S7nzqzrSPMrrDwv3+Xz35PnJnuM/YR9nz3U2e2tTvsCo9wyETl/h67XG65XWe0zaffbG2L8HtfqLYZZ+0j8mzrlsvpKlRbt8udbR+R39/lIrUtQjbwV44orCL/N/7DDz/o7bffluOM62yEh4dr4MCBuu2224pcAAD4i01GCa6D3uVgZLPblNC8lncZAJBTcc+jbx98RTExMTnWr/5ovf7ZeeLf7VLnKDqP6/9u/GqrHmsz3vv9/F3TFRtXKte2P6/docFNk7zfz9n6gi6pWj7Xtru37tWD8UO9309b84yq1rs017a/7z6kXtUHeb+f/PUTqnV1jVzbHjv8p7pd8oD3+399NFoNWtXLte2pv9LVpVRv7/fj3hmmph2vyrWtJN1o7+ZdHjXvYV3frVmebTvH9PRONP5jZn/d1KdVnm27lr9fx4/8KUkaMPledRnYPs+2PasNVOqew5Kk+56+W3c+1iXPtg/Uf1R7ftonSbp79G3qPe7OPNsOajJKO7b9Lkm6deBNevCp7nm2Hd7hGW36drskqeN9rTR4cq88247tNkVrPtskSWpz5zV6bMb9ebZ96t7pWvneD5Kk6zpfpX++PjDPtskPzNLy+d9IkhrflKCn3hueZ9uXHpmrpTOXS5LqN6+tScv/mWfbV0a/qUUvfCRJqtGomqZ9+2Sebec/tVjzn14sSbqsdiW9sv7ZPNsumvKx5vzz35Kk8peW1byfns+z7QezV+jloVkfhpQuV1Jv787nBltz50r33SebjCJLROiDQ6/k2fSbJWv0r36ver9/f89LebZds3yzHu/x9/P++6dJiroo90mETd//qpF3/t329e/HqXTZnPsdSfpl42965Oa/X/usL5JU4dKyubbd88vvGnDj39v0xaVDdXnNS3Jtm7r3v+rTfIL3++RFg1WzwWW5tj323zTddd3T3u+fmn2fEppUz7Vt+slM3Zo4zvv9uIl3qul1V+baVpJuvObv8TJq3G26/oa6OdosXpY1Vju3muidlBz+0E3q2KZ+nv3efO80HfvzlCTp4b6tdXuHRnm27dVlilIPHpMk3Tewjbr1ui7Ptg92f1l7dmbtT+6+r4V69WuVZ9tHbn5ev2zaK0m6pe/1emDMLXm2HXHnNG1etUOS1KHHtRr0VNc82z4+YK7WfJ113cY2NzfUsInd8mz79D/e0MplWyRJ17WtqzFT7smz7aRxS7R86QZJUuNmV2jC1J55tp32zEda+vYaSVL9FnX0/OeP59l29sgFWvT8h5KkGldV08ur/pVn23lPvqP547N+7y+rU1lztryQZ9u3J32gV0Zk/d6Xv6ycFuyanmfbD6Z/qpcGZ/0uly5XSu8cejXPtp/N/UrJfV+WlDURuDRtQZ5tv1n0vSZ0n+z9Pr+2/soRhVXkCcmoqCgdPXpU1apVy7Z+165duQanUGIynTJWTVJYfUMRl6vgNkVh9c1EJBmLX7PNbnGNHosvtuy09j2xWbz9zuF+Vf5n8c2fCrqgtjF/v2fG5ZIp4EL6eR3xca5MIW5qY5z2M5Zd+Y4ze4bFR09afEFyRz6f7J6r6P9aewHsnTsrWNpf3VOHLO0vPM3ii85b/bcpFPYzKJaKcx4Fiuz0vrjAfbI5ow37b58qbMb1mIL/NvvqrTI6q858nsiYwr+ms9vmOy6L0K9UhH6L1jb7/2EFt7V5jOxOU+D/bzZnVjtJsrkLaOs2f7cp4KVlb1uIAWIK169MEfYRnjPeu4JqML5q6znjfQ7uG1Mhd0W+y/bLL7+sjz76SCNGjNCjjz6qF198UYcPH9YLL7ygbt266ZFHHvFVrT5z+o6Gl//ZSvaiz9HmLsgnJD0nTljan+SLCUmLjw6z+A7MtgiL7sh+uj+L7+7HhGROkcapD05kfWLUpWRPZdjyfw8DMSEZWSJCH6TOliR1qdBPGSfzPvTdXuZiy2qTJBNt7c0gPCWtv8v2X5dZO9Gwr4O1Y7DuRGsnJF0VSlvan+V/m9ZstrY/nLcL5S7bxTGPSn9n0isOXMUp25yyXai2melO6X93TLbslG2Hw9pTts8YWxfMKdsejyLz+J2LNC4tSntTysxUl6i7lGELy7OtlHUativs7+e15JTt8CA9ZTsqPOvU5Ty2x7mesu2IK3lOp2xHRoVr0WdZR0Z2uylZGelOb9v08tGWnrJt330822nY+X2wn3nWKdu5tbUfOS7JwlO2z3pPzv+U7dzH27mesq2fdlh8ynbWfotTts/9lO3CZNIiz74NGjRIpUqV0vjx43Xq1Cn169dPZcuWVZ8+fXT//Xkfyg8AAABYobjn0YxTGbJ7Cv6g8swJMSvbnjmJaGXbXK8vlcdERVGuoezMcCpb63w+lM3RNr9+M13Z/iG0qm3WP8bWtbV72/49eVYQt8vtnezL4awPyfNtexaP25Pjw1STnvvY87g9hR6XHo/5u20BBy1ka1sAY7LaFubAAGNMvh8Uny0jrxqMS8rMLFzb/7GdMSFZtBpyb2vLZc6kSL/3Z/9+5jPZl6NtPk7/3ptCHEiSmVH4A3acmW45VbgxnFfbMycjvW1dbjkL+btRUNvoM/ZbLpdbrkL2m1dbey7vZ9H2ER65XX/3kd97ktW2cB/ye9weZZzyyKjgAz88bo8y3IXstwi/9+eyj7C6reS7v+HB0LawzulwwF69eunOO++U2+2W2+3WiRMnVKlSJatrAwAAAHJFHgUAAAhdRT6Hdd++ferataumTp2qEiVKqGTJkrrjjjvUvXt3/f77776oEQAAAPAijwIAAIS2Ik9Ijh8/XpUrV1bfvn296z7++GNVqFBBTzzxRJH6Sk1N1ZAhQ9SkSRO1aNFCEydOVEZG1mGgGzZs0F133aVGjRqpXbt2WrQo//PPGzdurFq1amX7+uuvv4r68gAUc+kKU7pV14r1kfS/MpT+l/WHxANAcUEeBRDK0uVQOjfhCAnppzKVXoTT2QEUXpH/K1+3bp3ef/99lS1b1rvu4osv1qOPPqo77rij0P0YYzRkyBCVKlVKb7zxho4fP67Ro0fLbrerb9++evDBB3X33XfrmWee0datW5WUlKS4uDi1atUqR1+pqak6ceKEVqxYoaiov2+iUKJEiaK+PADFWIYtXLeU6hXoMvKVcTJTt1zSP9BlAEBQI48CCFUZtjDdEn13oMtAIaSnO9Wl5cRAlwEUW0WekLz44ov1008/6bLLLsu2fufOnYqJKfzdT3fu3KkNGzbou+++U7ly5SRJQ4YM0bPPPqvLLrtM5cqV09ChQyVJVatW1erVq7V06dJcA2BKSori4uJ06aWXFvXlAAAAIMSQRwEAAEJbkScke/XqpbFjxyolJUX16tWTJG3fvl1z587NdtpMQeLi4jRnzhxv+DstLS1NLVq0UJ06dXL8TFpaWq597dixQ9WqVSvCqwAAAECoIo8CAACEtiJPSN53332Kjo7W22+/rTlz5igsLEyXX365kpKSdMsttxS6n1KlSqlFixbe7z0ejxYsWKBrrrlGVapUUZUqVbyPHT16VB999JEefvjhXPtKSUnRqVOn1KtXL+3atUt16tTR6NGjCYUAsgk3Lo099aUkaUJ0azltwXctyfDIcI19Y7AkacI90+TMcAa4IgAIPuRRAKEq3Lg1NvNrSdKEiJZy2riWZLAKj3Do8WfulCQ9OeptOTPdAa4IKF7O6b/xu+66S3fddZelhSQnJ+unn37SO++8k219enq6Hn74YZUrV07du3fP9Wd37typ48ePa+jQoYqJidErr7yiPn366KOPPirSaTsAije7jJq69nmXg5HdYVPTdg28ywCA3JFHAYQiu4yaeg54lxG8HHa7mjav6V12iglJwErnNCG5bt06vf7669qzZ49mzpyppUuXqnLlyurUqdM5FZGcnKzXX39dL7zwgmrWrOld/9dff2ngwIHavXu3Fi5cqOjo6Fx//tVXX5XT6dRFF10kSZo0aZJatmypL7/8Up07dz6nmgAAABC8yKMAAAChy17UH/jss8/Ur18/Va5cWbt27ZLL5VJYWJhGjRqlhQsXFrmACRMm6LXXXlNycrLatWvnXZ+Wlqb7779fv/76q15//XVVrVo1zz4iIiK84U+SIiMjVaVKFaWmpha5HgAAAAQ38igAAEBoK/KE5LRp0zR+/HiNHDlSDkfW9S769u2rf/3rX3rttdeK3Ndbb72lyZMnZ/s02+PxaPDgwdq3b5/mz5+vK6+8Ms8+jDFq27atFi9e7F138uRJ7dmzR9WrVy/iqwMAAECwI48CAACEtiKfsr1nzx41bNgwx/qEhIQifQKckpKi6dOnq1+/fkpMTNThw4e9j3355ZdavXq1ZsyYoVKlSnkfCw8PV2xsrDIzM3X8+HGVKVNGDodDrVq10ksvvaTKlSurTJkyevHFF3XJJZeoZcuWRX15AAAACHLkUQAAgNBW5AnJGjVqaOXKlerRo0e29UuWLFGNGjUK3c/nn38ut9utGTNmaMaMGdkea968uTwej/r3759tfZMmTTR//nz9+OOP6t27tz7//HNVqVJFw4cPV1hYmIYNG6a0tDRdc801mj17tvcTcwAAABQf5FEAAIDQVuQJyaSkJA0YMECrVq2S0+nUzJkztWfPHm3ZsiVHkMtPv3791K9fv6I+vSSpadOm+vnnn73fR0ZGatSoURo1atQ59QcAAIDQQR4FAAAIbUWekGzcuLE++eQT7wXDjx07poYNG+q5555TpUqVLC8QAKySYQtXu1L3BbqMfGWczFS7kn0CXQYABDXyKIBQlWELU7vonoEuA4WQnu7UTU2eCHQZQLFV5AlJSYqLi9MjjzxidS0AAABAoZBHAQAAQlehJiSTkpIK1ZnNZtO//vWv8yoIAAAAOBt5FAAAoPg4pyMkz7ZmzRrt379fpUuXtqK7gLFFRcimcGs6c3us6ed/jN1maX+y2a3tT5LNbu1rtrxGi7ehLdySX5+/WXzRe5vHWNqfTxj/jplw49KItK8kSc/FtJLTVsB7aPWYKUR/4ZFhGjHzAUnScwPmyJnhyrOtiYqwrDZJ8lwUZWl/ztLW9idJJ+Os3S80qr3T0v7SS5SxtL/M0ta+xzaLdwvhNov/NpkQ2G8haBWXPAoUlXG7Le3P4j279XnPY/3/MUbWbsP8hBu3Rjr/I0l6NvxaOW0F/w9gc+adB8+Fsfrvt8X/t9ns1vZnDzu3/7PCI8I04plukqTnRi2SM/Pv9yHiuLX/C9pOZljan8l0Wtqf1e8JIBVyQnLixIm5rk9NTdXTTz+t/fv3q0uXLho5cqSlxQGAlewyauHcJUmapJYBriZ3doddLW5pLEmaNOj/AlwNAAQP8iiA4sAho+s9v0mSJqmZrJ02gpXsDptatIuXJE0a806AqwGKn3Oa1vd4PHr99dc1bdo0VaxYUfPmzVOTJk2srg0AAADIFXkUAAAgdBV5QvLHH3/U+PHj9dtvv+mhhx5S3759FRZm8amrAAAAQB7IowAAAKGt0Mnt2LFjeu6557RkyRK1bt1aM2bMUKVKlXxZGwAAAOBFHgUAACgeCjUhuWjRIj3//POKiYnR9OnT1bp1a1/XBQAAAHiRRwEAAIqPQk1Ijh07VlLWp9IDBw7Mt+22bdvOvyoAAADgDORRAACA4qNQE5Lz5s3zdR0AAABAnsijAAAAxUehJiS5YyGA4iBDYbrl4j7e5WCUcTJTt1QZ6F0GAGQhjwIoDtLlUJfI7t5lBK+MU07d0nicdxmAtYLzP3IA8AWbTRkKD3QVBWIiEgAAoJiy2ZTOv+Ehg4lIwHfsgS4AAAAAAAAAwIWDj2YAXDDCjVtD/lopSZp6UQs5bcF3mkx4RJiGTO4lSZo6dL6cma4AVwQAAACrhBu3HnGukSS9GN4kKPMosoSHOzRk/K2SpKnj35PT6Q5sQUAxwxGSAC4Ydnl0Y+avujHzV9nlCXQ5ubKH2XVjj+t0Y4/rZA9jFw0AAFCcOGR0k2enbvLslEMm0OUgH/Ywu268NVE33ppILgd8oMhHSO7fv19TpkzR5s2b5XK5ZEz2nejnn39uWXEAAADA2cijAAAAoa3IE5IjRozQH3/8oXvuuUcxMTG+qAkAAADIE3kUAAAgtBV5QnLTpk1asmSJatSo4Yt6AAAAgHyRRwEAAEJbkS+EULVqVf33v//1RS0AAABAgcijAAAAoa3IR0g++OCD+uc//6n77rtPl19+ucLDw7M9fvXVV1tWHAAAAHA28igAAEBoO6drSErSE088keMxm82mbdu2nX9VAAAAQB7IowAAAKGtyBOS27dv90UdAOBzGQpT99ie3uVglHEyU92v/Id3GQCQE3kUQKhKl0PdIu/wLiN4ZZxyqnvzp73LAKxVqP/IDxw4oIoVK8pms+nAgQP5tq1UqZIlhQWEwyFZ9UfBZrOmn9Pdud2W9ie7tfVJkiwu0eoabRa/J1njxUJ2i/uzeaztzxeMxe9JId7j47qoCP0V+TK7ljh+7FTWQkFjLNzaSVUTae0YdEdav/2cMdaOmWZldlra3xfhcZb25462dhvaLN5PhxfcBLDMBZNHgSKyPONegKzehsaY/J5MxxVVtA6t/t/N6oxrdX0Oa+sz5/F/27E/07MWzurDE27xNrT4NQf7eyJ7YP7PQnAp1H+zbdq00XfffaeyZcuqTZs2stls2Xayp7/nFBkAAAD4AnkUAACg+CjUhOTnn3+uMmXKeJcBIBSFG7f6pf1HkjQ75lo5bcF3mkx4RJj6TegmSZo9dpGcma4AVwQAwYE8CqA4CDdu9XetkyTNCksMyjyKLOHhDvUb3kGSNDv5EzmdVp8SCFzYCjUhWbly5VyXASCU2OVR5/SfJElzYq6RZZdosJA9zK7OfVtJkuY88a7EZSQBQBJ5FEDx4JBRF/evkqQ5YVeJKxMGL3uYXV3uaipJmvPCMokJScBSnLgPAAAAAAAAwG8COiGZmpqqIUOGqEmTJmrRooUmTpyojIwMSdJTTz2lWrVqZftasGBBnn3NnTtXLVq0UKNGjTR69GidOnXKXy8DAAAAIYo8CgAA4H/W3qK1CIwxGjJkiEqVKqU33nhDx48f1+jRo2W32zVy5EilpKRo2LBhuu2227w/ExMTk2tfy5Yt07Rp05ScnKyyZcsqKSlJycnJevzxx/31cgAAABBiyKMAAACBUeQjJG+44QYdO3Ysx/rU1FQ1a9as0P3s3LlTGzZs0MSJE3XllVeqcePGGjJkiD788ENJUkpKiurWrau4uDjvV3R0dK59zZs3T/fee69at26thIQEPfHEE3r33Xf5VBoAAKAYIo8CAACEtkIdIfnpp5/q66+/liTt379fTz75pCIjI7O12b9/vxyOwt8gIi4uTnPmzFG5cuWyrU9LS1NaWppSU1NVtWrVAvtxu93avHmzBg8e7F3XsGFDOZ1Obd++XY0aNSp0TQAAAAhO5FEAAIDio1BHSDZp0iTb98aYHG2uvPJKTZ8+vdBPXKpUKbVo0cL7vcfj0YIFC3TNNdcoJSVFNptNM2fO1PXXX68uXbpoyZIlufbz559/KiMjQ+XLl/euCwsLU2xsrH7//fdC1wMAAIDgRR4FAAAoPgp1hGSZMmU0ceJESVLlypXVt29flShRwtJCkpOT9dNPP+mdd97R1q1bZbPZVL16dfXs2VNr167V2LFjFRMToxtvvDHbz6Wnp0uSIiIisq2PiIhQZmampTUCCG2ZCtO9ZXp4l4NR5imn7r1qtHcZAJCFPAqgOMiQQ70ibvEuI3hlprvUu/3z3mUA1iryf+SDBw/WoUOHNGvWLKWkpMjtdqt69erq1q1boU5pyU1ycrJef/11vfDCC6pZs6auvPJKtW7dWrGxsZKk2rVra/fu3XrzzTdzBMDTp+qcHfYyMzPzvMYPgAuTsdmU6igZ6DLyZYxR6t6jgS4DAIIaeRRAqDI2m1Jtud8cC8HFGKPUA8cCXQZQbBX5pjY//PCD2rVrp9WrV6tKlSqqUqWK1q5dq1tuuUXr1q0rcgETJkzQa6+9puTkZLVr106SZLPZvOHvtOrVqys1NTXHz8fGxioyMlJHjhzxrnO5XDp27Jji4uKKXA8AAACCG3kUAAAgtBX5CMlnnnlGPXv21LBhw7KtnzRpkpKTk/XWW28Vuq9p06bprbfe0uTJk9W+fXvv+hdffFE//vij5s6d6123fft2Va9ePUcfdrtd8fHxWrdunZo2bSpJ2rBhg8LCwlS7du0ivjoAxVmYcavPX2skSXMvaiKXLfhOkwkLd6jP6FslSXP/9Z5cTndgCwKAIEQeBRCqwoxb97k2SpJeC2sQlHkUWcLCHOozpK0kae7UFXK5yOWAlYp8hOSvv/6qO+64I8f6rl27atu2bYXuJyUlRdOnT9eDDz6oxMREHT582PvVunVrrV27Vq+++qp+++03LVy4UO+995769u0rKes6PYcPH/b21aNHD7366qtasWKFNm3apPHjx+vOO+/kFBkA2TjkUddTm9T11CY55Al0OblyhDvUdfBN6jr4JjnCCagAkBvyKIBQFSajbu5t6ubepjDlvDkXgocj3K5ufZqrW5/mcoQXeeoEQAGKfIRk5cqVtWnTphzX59m4caPKlStX6H4+//xzud1uzZgxQzNmzMj22M8//6wXX3xRU6dO1YsvvqjKlSvr+eefV6NGjSRJH3/8sZKSkvTzzz9Lkjp16qT9+/fr8ccfV2Zmpm666SYNHz68qC8NAAAAIYA8CgAAENqKPCH5wAMPaNy4cdq5c6cSEhIkZYW/+fPna+jQoYXup1+/furXr1+ej7dt21Zt27bN9bHbb79dt99+e5H6AwAAQPFAHgUAAAhtRZ6QPB28FixYoNdee02RkZGqVq2ann76aXXo0MHyAgEAAIAzkUcBAABCW5EnJKXcPxEGAAAA/IU8CgAAELrOaUJyxYoVmjNnjnbu3Cm3261q1aqpZ8+euvXWWy0uDwAAAMiJPAoAABC6ijwh+dZbb+nZZ59Vz5491a9fP3k8Hq1fv15PPPGEnE6nunXr5os6/cPjkay6866x+I5pVvfnCYE7ulldo8NmbX9W12ez+K7PVo8ZX7B6GxZ087szt4kxUoF3NrT4PSnM6z2zjcfk/zNua+uzuaztz+62fgw6Mq3tb296GWs79Fi8DZ1W/y2xtjsgUIp1HgWKyuGwtj+7xXcTtlncn93iTC9Z/przrdCckRUcdtlshXj/LH6PbRHhlvZn9fYzkRGW9ucpcW6v10T9/XMmOlyeM97YzNLndGxXnsJOWPuaHSesfY+tfk9MtLX9ITQV+bdozpw5GjduXLZPn9u2basrr7xSM2fOJAACCFqZClP/2K7e5WCUecqp/teO8y4DAHIijwIIVZkKU7/oLt5lBK+MDKceuHuGdxmAtYq8Bzx69KgaNmyYY32jRo108OBBK2oCAJ8wNpv2hFl8RJzFjDHa8zP7UgDID3kUQKgyNpv22GIDXQYKwRhpz67DgS4DKLaKfGx1nTp19N577+VYv2TJEtWoUcOKmgAAAIA8kUcBAABCW5GPkBw+fLj69Omj1atXq0GDBpKkDRs2aPv27Zo5c6blBQKAVcKMW3ed/FGS9FaJRnIV5po9fhYW7tBdj3aUJL31wsdyOd0BrggAgg95FECoCjNu3eXcLEl6Kzw+KPMosoSF2XV3nxaSpDfnrpTL4uutAxe6Ih8h2ahRIy1evFgJCQlKSUnRvn37dPXVV+uTTz7RNddc44saAcASDnnU89R69Ty1Xg6rb1hjEUe4Qz1HdlbPkZ3lCCegAkBuyKMAQpVDHvVyblIv56agzaPIEhbmUO8HWqr3Ay0VFkYuB6x2TlfRveKKK5SUlGR1LQAAAEChkEcBAABCV5EnJE+cOKFXXnlF27dvV0ZGhowx2R6fN2+eZcUBAAAAZyOPAgAAhLYiT0iOGDFCW7duVYcOHVSyZElf1AQAAADkiTwKAAAQ2oo8Ifn9999r3rx5SkhI8EU9AAAAQL7IowAAAKGtyDe1iYuLk8PBBV0BAAAQGORRAACA0FaoIyQPHDjgXb7nnnv0z3/+UyNGjFCVKlVyhMFKlSpZWyEAAAAueORRAACA4qNQE5Jt2rSRzWaTJO9Fw++77z7ZbLZsFxG32Wzatm2bD8oEgPPnlENDSt/qXQ5GznSnhrR92rsMAMhCHgVQHDjl0MNRHb3LCF6ZmS4Num+OdxmAtQo1Ifn555/7ug4A8DmPza5fwssHuox8eTxGv/y4J9BlAEDQIY8CKA48Nrt+cZQLdBkoBI/H6JdtBwpuCOCcFGpCsnLlytm+//PPPxUZGanIyEht375d3377rerVq6dmzZr5pEgAAABc2MijAAAAxUeRb2qzYsUKXX/99Vq3bp327Nmje+65R0uWLNHAgQO1YMECX9QIAJYIM251PblRXU9uVJhxB7qcXIWFO9R18E3qOvgmhYVzGg8A5IY8CiBUhRm3umZuUdfMLUGbR5ElLMyubvc0U7d7miksrMhTJwAKUOTfqilTpmjIkCG69tprtWjRIlWsWFEfffSRJk+erP/7v//zRY0AYAmHPHrg5Go9cHK1HPIEupxcOcIdeuCJrnrgia5yMCEJALkijwIIVQ559KBzvR50rg/aPIosYWEO9Xv4RvV7+EaFhZHLAasVeULyt99+U4cOHSRlXcvnxhtvlCRdeeWV+u9//2ttdQAAAMBZyKMAAAChrVDXkDxTpUqVtHr1alWoUEG7du1SmzZtJElLly5V1apVra4PAAAAyIY8CgAAENqKPCE5ZMgQjRgxQm63W61atVJ8fLyeffZZvfXWW5o2bZovagQAAAC8yKMAAAChrcgTkvXr19c333yj1NRU1alTR5LUrVs33X///SpXrpzlBQIAAABnIo8CAACEtiJPSN59992aNWuW6tev711XvXp1S4sKFBMeJlP0TZJHZ8aafv7H5rb4gsd2m7X9SZLH4juPWV2j3eL6HNb2ZwuzaOz9j7F6zPiCzeIabQW8J+aM57M7JFv+F6e2WfweG7kKbnTmczrs+Y4zE2ntmHFHWdufq4T1dyPMLGVtf13LrLW0v39F1ra0P+dF1l5A3eax9m9TpKW9AYVXnPNoFptk80FWs4LVGddh/Y0ijNviOxcXlC8CzB5b2toOwy3OpMeOW9qfLM7MkmQLD7e2w3x+T+wep3Tyf8slS8puL/i5PeUutqoySZIzNsrS/jwR1v4enypv7ftx4rJz+x2OPuN34WCLGJ1y/p3lTzU4dd51nemiNdb+HsdtsjalnawQYWl/p8pYu1+95BdrX6/n5ElL+0PuijwKypUrp6NHj/qiFgAAAKBA5FEAAIDQVuSPl+rWrauBAwcqPj5elStXVkRE9pnyiRMnWlYcAFjJKYdGxHbxLgcjZ7pTI26d7F0GAOREHgUQqpw2h0aUudW7jOCV4XKrz+xF3mUA1jqn4927dOlidR0A4HMem12bIioHuox8eTxGm/7zS6DLAICgRx4FEIo8Nrs2RwZ3HkUWjzFau3NfoMsAiq0iT0ha+Ylzamqqnn76aa1atUqRkZHq2LGjhg4dqnHjxmnJkiU52jdt2lTz5s3Lsf748eNq0qRJtnWxsbFavXq1ZbUCAAAgOFh9BCSZFAAAwL/O6QjJdevW6fXXX9eePXs0c+ZMLV26VJUrV1anTp0K3YcxRkOGDFGpUqX0xhtv6Pjx4xo9erTsdrvGjBmjYcOGedvu379fvXr1Uu/evXPta8eOHYqNjdWHH37oXWe3+gYmAEKew7jV8dQ2SdLH0XXkDsLTZBxhdnXs3UKS9PG8lXK7QuDmRAAQAFbkUYlMCsC/HMatDid/kiR9UqJuUOZRZAmz29WtSbwkadGazXJ5yOWAlYo8IfnZZ58pKSlJd955p7766iu5XC6FhYVp1KhROn78uHr06FGofnbu3KkNGzbou+++U7ly5SRJQ4YM0bPPPquRI0eqZMmS3rajRo1S+/bt1bZt2zz7qlatmuLi4or6cgBcQMLk0aC0lZKkz6JryR2E15EMiwjToGfuliR99tb3crsyA1wRAAQfq/KoRCYF4F9hxqNBf34jSVoeXZsJySAW7rDrn7e2kSS9t24rE5KAxYr8ke20adM0fvx4jRw5Ug5H1s6zb9+++te//qXXXnut0P3ExcVpzpw53uB3WlpaWrbvv//+e61du1ZDhw7Ns68dO3aoatWqhX8RAAAACFlW5VGJTAoAABAIRZ6Q3LNnjxo2bJhjfUJCglJTUwvdT6lSpdSiRQvv9x6PRwsWLNA111yTrd3s2bN12223qWLFinn2lZKSot9//11du3ZVixYt9Oijj+rQoUOFrgUAAAChw6o8KpFJAQAAAqHIE5I1atTQypUrc6xfsmSJatSocc6FJCcn66efftKjjz7qXbd3716tWrVKvXr1yvdnd+7cqbS0NCUlJemFF17QoUOHNGDAALnd7nOuBwAAAMHJV3lUIpMCAAD4Q5GvIZmUlKQBAwZo1apVcjqdmjlzpvbs2aMtW7ZoxowZ51REcnKyXn/9db3wwguqWbOmd/2yZctUp06dAoPlRx99JJvNpqioKEnS1KlT1bx5c23cuFFXXXXVOdUEAACA4OSLPCqRSQEAAPylyBOSjRs31ieffKKFCxdKko4dO6aGDRvqueeeU6VKlYpcwIQJE/Tmm28qOTlZ7dq1y/bYypUrdcMNNxTYR3R0dLbvy5Ytq9jY2CKfsgMAAIDgZ3UelcikAAAA/lTkCcmlS5eqbdu2euSRR877yadNm6a33npLkydPVvv27bM9ZozR5s2bNWDAgHz7SEtLU+vWrfXSSy95r/WTmpqqP/74Q9WrVz/vGgEAABBcrMyjEpkUAADA34o8ITlp0iSNHTtW119/vW6++Wa1bNlSkZGRRX7ilJQUTZ8+Xf369VNiYqIOHz7sfSwuLk779+/XX3/9leupMenp6Tpx4oTi4uIUExOjxMRETZw4URMmTJDD4dDTTz+tFi1aqFatWkWuC0Dx5ZRDj5fu6F0ORs4Mlx7vMc27DADIyao8KpFJAfiX0+bQ4xd38i4jeGW63Xrotfe8ywCsVeSb2nz99dd67bXXVLlyZT377LNq1qyZHnvsMX3xxRdyOp2F7ufzzz+X2+3WjBkz1Lx582xfknT06FFJUunSpXP87Mcff+xtJ0nPPvus6tatq379+qlXr16qXLmyJk2aVNSXBqCY89jsWhN5udZEXi6Prci7P7/wuD1as2KL1qzYIo/bE+hyACAoWZVHJTIpAP/y2OxaG1VVa6OqBm0eRRa3x+ibn3fpm593ye0xgS4HKHZsxpjz+s3aunWrli1bpjfeeENhYWFavXq1VbX5TVpamhITE3W5OsmucGs6Pb/NmoMtPdPS/tyHj1janyTJ6p203WZpdzaHtZ9A2kpEF9yoKP2FFfmA5Xy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"text/plain": [ "
      " ] @@ -1135,8 +1135,8 @@ " ylabel=\"Overshoot in Necessity Intervention\",\n", " title=\"Overshoot in counterfactual lockdown\",\n", ")\n", - "ax.axvline(x=15.8, color=\"grey\", linestyle=\"--\", label=\"Overshoot too high\")\n", - "ax.axhline(y=15.8, color=\"grey\", linestyle=\"--\")\n", + "ax.axvline(x=(overshoot_threshold - 5) * 28 / 35, color=\"red\", linestyle=\"--\", label=\"Overshoot too high\")\n", + "ax.axhline(y=(overshoot_threshold - 5) * 28 / 35, color=\"red\", linestyle=\"--\")\n", "\n", "ax.axvline(\n", " x=(os_lockdown_suff - 5) * 28 / 35,\n", @@ -1158,8 +1158,8 @@ " ylabel=\"Overshoot in Necessity Intervention\",\n", " title=\"Overshoot in counterfactual mask\",\n", ")\n", - "ax.axvline(x=16.8, color=\"grey\", linestyle=\"--\", label=\"Overshoot too high\")\n", - "ax.axhline(y=16.8, color=\"grey\", linestyle=\"--\")\n", + "ax.axvline(x=(overshoot_threshold - 5) * 28 / 35, color=\"red\", linestyle=\"--\", label=\"Overshoot too high\")\n", + "ax.axhline(y=(overshoot_threshold - 5) * 28 / 35, color=\"red\", linestyle=\"--\")\n", "\n", "ax.axvline(\n", " x=(os_mask_suff - 5) * 28 / 35,\n", @@ -1201,7 +1201,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 95, "metadata": {}, "outputs": [], "source": [ @@ -1233,7 +1233,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 96, "metadata": {}, "outputs": [ { @@ -1306,7 +1306,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 97, "metadata": {}, "outputs": [], "source": [ @@ -1336,7 +1336,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 98, "metadata": {}, "outputs": [ { From 0522d5ebff32671a4744af080430a944b6bcb914 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Tue, 27 Aug 2024 13:26:13 -0400 Subject: [PATCH 072/111] tweaks --- docs/source/explainable_sir.ipynb | 50 +++++++++++++++---------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index c3a17e23..b0f6f5eb 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -170,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -237,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -279,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -303,7 +303,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -384,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -431,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -589,7 +589,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -651,7 +651,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -697,7 +697,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -709,8 +709,8 @@ " mask_intervened &= trace.nodes[i][\"value\"] == v\n", "\n", " with mwc_imp:\n", - " mask_os_too_high = (gather(trace.nodes[\"mask\"][\"value\"], IndexSet(**{\"mask\": {0}, \"lockdown\": {0}})) == 1) & (gather(trace.nodes[\"lockdown\"][\"value\"], IndexSet(**{\"mask\": {0}, \"lockdown\": {0}})) == 1)\n", - " mask_intervened &= mask_os_too_high\n", + " mask_tensor = (gather(trace.nodes[\"mask\"][\"value\"], IndexSet(**{\"mask\": {0}, \"lockdown\": {0}})) == 1) & (gather(trace.nodes[\"lockdown\"][\"value\"], IndexSet(**{\"mask\": {0}, \"lockdown\": {0}})) == 1)\n", + " mask_intervened &= mask_tensor\n", "\n", " print(\n", " mask,\n", @@ -723,7 +723,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -785,7 +785,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -834,7 +834,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -876,7 +876,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -907,7 +907,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -992,7 +992,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -1023,7 +1023,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1108,7 +1108,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1201,7 +1201,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -1233,7 +1233,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1306,7 +1306,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -1336,7 +1336,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 23, "metadata": {}, "outputs": [ { From a85ae29df70627ef3b79f0c490030e064f91895d Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Tue, 27 Aug 2024 13:30:20 -0400 Subject: [PATCH 073/111] changes for correct rendering --- docs/source/explainable_categorical.ipynb | 189 ++++++++++++---------- 1 file changed, 107 insertions(+), 82 deletions(-) diff --git a/docs/source/explainable_categorical.ipynb b/docs/source/explainable_categorical.ipynb index 02e2423c..2c949678 100644 --- a/docs/source/explainable_categorical.ipynb +++ b/docs/source/explainable_categorical.ipynb @@ -24,26 +24,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Outline**\n", - "\n", - "[Motivation](#motivation)\n", - "\n", - "[Setup](#setup)\n", - "\n", - "[But-for Causal Explanations](#but-for-causal-explanations) \n", - "\n", - "[Context-sensitive Causal Explanations](#context-sensitive-causal-explanations)\n", - "\n", - "[Probability of causation and responsibility](#probability-of-causation-and-responsibility)\n", - "\n", - "[Further Discussion](#further-discussion)" + "## Outline\n", + "\n", + "- [Motivation](#motivation)\n", + "- [Setup](#setup)\n", + "- [But-for Causal Explanations](#but-for-causal-explanations) \n", + "- [Context-sensitive Causal Explanations](#context-sensitive-causal-explanations)\n", + "- [Probability of causation and responsibility](#probability-of-causation-and-responsibility)\n", + "- [Further Discussion](#further-discussion)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Motivation\n", + "## Motivation\n", "\n", "Consider the following causality-related queries:\n", "\n", @@ -67,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -106,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -165,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -177,54 +172,84 @@ "\n", "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "u_match_dropped\n", - "\n", - "u_match_dropped\n", + "\n", + "u_match_dropped\n", "\n", "\n", "\n", "match_dropped\n", - "\n", - "match_dropped\n", + "\n", + "match_dropped\n", + "\n", + "\n", + "\n", + "u_match_dropped->match_dropped\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "forest_fire\n", + "\n", + "forest_fire\n", + "\n", + "\n", + "\n", + "u_match_dropped->forest_fire\n", + "\n", + "\n", "\n", "\n", "\n", "u_lightning\n", - "\n", - "u_lightning\n", + "\n", + "u_lightning\n", "\n", "\n", "\n", "lightning\n", - "\n", - "lightning\n", + "\n", + "lightning\n", + "\n", + "\n", + "\n", + "u_lightning->lightning\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "u_lightning->forest_fire\n", + "\n", + "\n", "\n", "\n", "\n", "smile\n", - "\n", - "smile\n", + "\n", + "smile\n", "\n", - "\n", - "\n", - "forest_fire\n", - "\n", - "forest_fire\n", + "\n", + "\n", + "smile->forest_fire\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 23, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -295,14 +320,14 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2987)\n" + "tensor(0.2973)\n" ] } ], @@ -329,14 +354,14 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.6000)\n" + "tensor(0.5895)\n" ] } ], @@ -363,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -397,7 +422,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -422,14 +447,14 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(2.8055e-06)\n" + "tensor(2.7697e-06)\n" ] } ], @@ -453,14 +478,14 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(2.7924e-06)\n" + "tensor(2.7853e-06)\n" ] } ], @@ -478,14 +503,14 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.0670)\n" + "tensor(0.0661)\n" ] } ], @@ -516,14 +541,14 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2772)\n" + "tensor(0.2690)\n" ] } ], @@ -550,7 +575,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -595,7 +620,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -660,7 +685,7 @@ "sally_hits\n", "\n", "\n", - "\n", + "\n", "prob_sally_hits->sally_hits\n", "\n", "\n", @@ -696,7 +721,7 @@ "bottle_shatters\n", "\n", "\n", - "\n", + "\n", "prob_bottle_shatters_if_sally->bottle_shatters\n", "\n", "\n", @@ -708,31 +733,31 @@ "prob_bottle_shatters_if_bill\n", "\n", "\n", - "\n", + "\n", "prob_bottle_shatters_if_bill->bottle_shatters\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "sally_throws->sally_hits\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "bill_throws->bill_hits\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "sally_hits->bill_hits\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "sally_hits->bottle_shatters\n", "\n", "\n", @@ -747,10 +772,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 33, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -828,7 +853,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -864,7 +889,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -899,14 +924,14 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2513)\n" + "tensor(0.2514)\n" ] } ], @@ -935,14 +960,14 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.5019)\n" + "tensor(0.5031)\n" ] } ], @@ -962,7 +987,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -998,7 +1023,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1027,14 +1052,14 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.1543)\n" + "tensor(0.1537)\n" ] } ], @@ -1078,14 +1103,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2195)\n" + "tensor(0.2126)\n" ] } ], @@ -1103,14 +1128,14 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.0667)\n" + "tensor(0.0662)\n" ] } ], @@ -1128,14 +1153,14 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2777)\n" + "tensor(0.2745)\n" ] } ], @@ -1146,14 +1171,14 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.2014)\n" + "tensor(0.2013)\n" ] } ], @@ -1173,7 +1198,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Further Discussion\n", + "## Further Discussion\n", "\n", "In this notebook, we have shown how `SearchForExplanation` can be used for fine-grained causal queries for discrete causal models. We further elaborate on its application in for different queries. \n", "\n", From 716a24557db1c9d98280f3993cec93e06d0f8af2 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Tue, 27 Aug 2024 15:16:43 -0400 Subject: [PATCH 074/111] heatmap corrected --- docs/source/explainable_sir.ipynb | 139 +++++++++++++++++++++++------- 1 file changed, 106 insertions(+), 33 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index b0f6f5eb..24254d33 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 78, "metadata": {}, "outputs": [], "source": [ @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ @@ -170,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 80, "metadata": {}, "outputs": [ { @@ -237,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ @@ -279,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 82, "metadata": {}, "outputs": [], "source": [ @@ -303,7 +303,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ @@ -384,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 84, "metadata": {}, "outputs": [], "source": [ @@ -431,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -589,7 +589,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 86, "metadata": {}, "outputs": [], "source": [ @@ -651,7 +651,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 87, "metadata": {}, "outputs": [ { @@ -692,23 +692,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The above probability itself is not directly related to our query. It is the probability that the overshoot is too high in the antecedents-intervened workd and not too high in the alternatives-intervened world, where antecedent interventions are preempted with probabilities $0.5$ at each site, and witnesses are kept fixed at the observed values with probability $0.5+0.2$ at each site. But more fine-grained queries can be answered using the 10000 samples we have drawn in the process. We first compute the probabilities that different sets of antecedent candidates have causal effect over `os_too_high`." + "The above probability itself is not directly related to our query. It is the probability that the overshoot is too high in the antecedents-intervened workd and not too high in the alternatives-intervened world, where antecedent interventions are preempted with probabilities $0.5$ at each site, and witnesses are kept fixed at the observed values with probability $0.5+0.2$ at each site. But more fine-grained queries can be answered using the 10000 samples we have drawn in the process. \n", + "\n", + "We first compute the probabilities that different sets of antecedent candidates have causal effect over `os_too_high` conditioned on the fact that lockdown and masking were actually imposed in the factual world." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 88, "metadata": {}, "outputs": [], "source": [ - "def compute_prob(trace, log_weights, mask):\n", + "def compute_prob(trace, log_weights, mask, mwc):\n", " mask_intervened = torch.ones(\n", " trace.nodes[\"__cause____antecedent_lockdown\"][\"value\"].shape\n", " ).bool()\n", " for i, v in mask.items():\n", " mask_intervened &= trace.nodes[i][\"value\"] == v\n", "\n", - " with mwc_imp:\n", + " # Conditioning on masking and lockdown being actually imposed\n", + " with mwc:\n", " mask_tensor = (gather(trace.nodes[\"mask\"][\"value\"], IndexSet(**{\"mask\": {0}, \"lockdown\": {0}})) == 1) & (gather(trace.nodes[\"lockdown\"][\"value\"], IndexSet(**{\"mask\": {0}, \"lockdown\": {0}})) == 1)\n", " mask_intervened &= mask_tensor\n", "\n", @@ -723,7 +726,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 89, "metadata": {}, "outputs": [ { @@ -732,8 +735,8 @@ "text": [ "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0} 0.18636363744735718\n", "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1} 0.3100775182247162\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0} 1.5630010619105406e-09\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1} 1.9900530112693104e-09\n" + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0} 1.5629978422637691e-09\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1} 1.990049014466422e-09\n" ] } ], @@ -743,6 +746,7 @@ " importance_tr,\n", " log_weights,\n", " {\"__cause____antecedent_lockdown\": 0, \"__cause____antecedent_mask\": 0},\n", + " mwc_imp\n", ")\n", "\n", "# only lockdown executed, masking preempted\n", @@ -750,6 +754,7 @@ " importance_tr,\n", " log_weights,\n", " {\"__cause____antecedent_lockdown\": 0, \"__cause____antecedent_mask\": 1},\n", + " mwc_imp\n", ")\n", "\n", "# only masking executed, lockdown preempted\n", @@ -757,6 +762,7 @@ " importance_tr,\n", " log_weights,\n", " {\"__cause____antecedent_lockdown\": 1, \"__cause____antecedent_mask\": 0},\n", + " mwc_imp\n", ")\n", "\n", "# no interventions executed\n", @@ -764,6 +770,7 @@ " importance_tr,\n", " log_weights,\n", " {\"__cause____antecedent_lockdown\": 1, \"__cause____antecedent_mask\": 1},\n", + " mwc_imp\n", ")" ] }, @@ -785,7 +792,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 90, "metadata": {}, "outputs": [ { @@ -802,11 +809,11 @@ ], "source": [ "print(\"Degree of responsibility for lockdown: \")\n", - "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_lockdown\": 0})\n", + "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_lockdown\": 0}, mwc_imp)\n", "print()\n", "\n", "print(\"Degree of responsibility for mask: \")\n", - "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_mask\": 0})" + "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_mask\": 0}, mwc_imp)" ] }, { @@ -834,7 +841,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 91, "metadata": {}, "outputs": [], "source": [ @@ -876,7 +883,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ @@ -907,7 +914,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 93, "metadata": {}, "outputs": [ { @@ -992,7 +999,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ @@ -1023,7 +1030,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 95, "metadata": {}, "outputs": [ { @@ -1108,12 +1115,76 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 122, + "metadata": {}, + "outputs": [], + "source": [ + "masks = {\n", + " \"__cause____antecedent_mask\": 1,\n", + " \"__cause____antecedent_lockdown\": 0,\n", + " \"__cause____witness_lockdown_efficiency\": 0,\n", + " }\n", + "with mwc_imp:\n", + " data_nec = gather(\n", + " importance_tr.nodes[\"overshoot\"][\"value\"],\n", + " IndexSet(**{\"lockdown\": {1}, \"mask\": {1}}),\n", + " )\n", + "\n", + " data_suff = gather(\n", + " importance_tr.nodes[\"overshoot\"][\"value\"],\n", + " IndexSet(**{\"lockdown\": {2}, \"mask\": {2}}),\n", + " )\n", + "\n", + " mask_tensor = torch.ones(\n", + " importance_tr.nodes[\"__cause____antecedent_mask\"][\"value\"].shape\n", + " ).bool()\n", + " for key, val in masks.items():\n", + " mask_tensor = mask_tensor & (importance_tr.nodes[key][\"value\"] == val)\n", + " data_suff = data_suff.squeeze()[torch.nonzero(mask_tensor.squeeze())]\n", + " data_nec = data_nec.squeeze()[torch.nonzero(mask_tensor.squeeze())]\n", + "\n", + "a = torch.transpose(torch.vstack((data_nec.squeeze(), data_suff.squeeze())), 0, 1)\n", + "hist_lockdown_2d, _ = torch.histogramdd(a, bins = [28, 28], density=True, range=[5.0, 40.0, 5.0, 40.0])\n", + "pr_lockdown = (hist_lockdown_2d[:12, 12:].sum())\n", + "\n", + "\n", + "masks = {\n", + " \"__cause____antecedent_mask\": 0,\n", + " \"__cause____antecedent_lockdown\": 1,\n", + " \"__cause____witness_mask_efficiency\": 0,\n", + " }\n", + "with mwc_imp:\n", + " data_nec = gather(\n", + " importance_tr.nodes[\"overshoot\"][\"value\"],\n", + " IndexSet(**{\"lockdown\": {1}, \"mask\": {1}}),\n", + " )\n", + "\n", + " data_suff = gather(\n", + " importance_tr.nodes[\"overshoot\"][\"value\"],\n", + " IndexSet(**{\"lockdown\": {2}, \"mask\": {2}}),\n", + " )\n", + "\n", + " mask_tensor = torch.ones(\n", + " importance_tr.nodes[\"__cause____antecedent_mask\"][\"value\"].shape\n", + " ).bool()\n", + " for key, val in masks.items():\n", + " mask_tensor = mask_tensor & (importance_tr.nodes[key][\"value\"] == val)\n", + " data_suff = data_suff.squeeze()[torch.nonzero(mask_tensor.squeeze())]\n", + " data_nec = data_nec.squeeze()[torch.nonzero(mask_tensor.squeeze())]\n", + "\n", + "a = torch.transpose(torch.vstack((data_nec.squeeze(), data_suff.squeeze())), 0, 1)\n", + "hist_mask_2d, _ = torch.histogramdd(a, bins = [28, 28], density=True, range=[5.0, 40.0, 5.0, 40.0])\n", + "pr_mask = (hist_mask_2d[:12, 12:].sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 132, "metadata": {}, "outputs": [ { "data": { - "image/png": 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      " ] @@ -1127,7 +1198,7 @@ "\n", "ax = axs[0]\n", "hist_lockdown = hist_lockdown_nec.unsqueeze(1) * hist_lockdown_suff.unsqueeze(0)\n", - "ax.imshow(hist_lockdown, cmap=\"viridis\")\n", + "ax.imshow(hist_lockdown_2d, cmap=\"viridis\")\n", "ax.set(xticks=range(0, 28, 2), xticklabels=bin_edges[0:28:2].tolist())\n", "ax.set(yticks=range(0, 28, 2), yticklabels=bin_edges[0:28:2].tolist())\n", "ax.set(\n", @@ -1147,10 +1218,11 @@ "ax.axhline(y=(os_lockdown_nec - 5) * 28 / 35, color=\"white\", linestyle=\"--\")\n", "\n", "ax.legend(loc=\"upper left\")\n", + "ax.text(13, 2, 'pr(lockdown caused overshoot): %.4f' % pr_lockdown.item(), color=\"white\")\n", "\n", "ax = axs[1]\n", "hist_mask = hist_mask_nec.unsqueeze(1) * hist_mask_suff.unsqueeze(0)\n", - "ax.imshow(hist_mask, cmap=\"viridis\")\n", + "ax.imshow(hist_mask_2d, cmap=\"viridis\")\n", "ax.set(xticks=range(0, 28, 2), xticklabels=bin_edges[0:28:2].tolist())\n", "ax.set(yticks=range(0, 28, 2), yticklabels=bin_edges[0:28:2].tolist())\n", "ax.set(\n", @@ -1168,6 +1240,7 @@ " label=\"Mean Overshoot\",\n", ")\n", "ax.axhline(y=(os_mask_nec - 5) * 28 / 35, color=\"white\", linestyle=\"--\")\n", + "ax.text(13, 2, 'pr(masking caused overshoot): %.4f' % pr_mask.item(), color=\"white\")\n", "\n", "ax.legend(loc=\"upper left\")\n", "\n", @@ -1201,7 +1274,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 98, "metadata": {}, "outputs": [], "source": [ @@ -1233,7 +1306,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 99, "metadata": {}, "outputs": [ { @@ -1306,7 +1379,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 100, "metadata": {}, "outputs": [], "source": [ @@ -1336,7 +1409,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 101, "metadata": {}, "outputs": [ { From 2f0d0b7286cb2379c93cc570109442a429de62a2 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Tue, 27 Aug 2024 16:53:51 -0400 Subject: [PATCH 075/111] small edits --- docs/source/explainable_sir.ipynb | 91 +++++++++++++------------------ 1 file changed, 39 insertions(+), 52 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 24254d33..839d4306 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -107,7 +107,7 @@ "metadata": {}, "source": [ "\n", - "Now, we build the epidemiological SIR (Susceptible, Infected, Recovered/Removed) model, one step at a time. We first encode the deterministic SIR dynamics. Then we add uncertainty about the parameters that govern these dynamics - $\\beta$ and $\\gamma$. These parameters have been described in much detail in the [dynamical systems tutorial](https://basisresearch.github.io/chirho/dynamical_intro.html). We then incorporate the resulting model into a more complex causal model that describes the policy mechanisms such as imposing lockdown and masking restrictions.\n", + "We start with building the epidemiological SIR (Susceptible, Infected, Recovered/Removed) model, one step at a time. We first encode the deterministic SIR dynamics. Then we add uncertainty about the parameters that govern these dynamics - $\\beta$ and $\\gamma$. These parameters have been described in much detail in the [dynamical systems tutorial](https://basisresearch.github.io/chirho/dynamical_intro.html). We then incorporate the resulting model into a more complex causal model that involves two policy mechanisms: imposing lockdown and masking restrictions.\n", "\n", "Our outcome of interest is overshoot, the proportion of the population that remains susceptible after the epidemic peak but eventually becomes infected as the epidemic continues. One way to compute it is to:\n", "\n", @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -170,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -216,7 +216,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The number $0.15$ is the overshoot you get if $\\beta = 0.03, \\gamma = 0.5$. This value is observed by simulating the SIR dynamics model with these values and calculate overshoot directly." + "The number $0.15$ is the overshoot you get if $\\beta = 0.03, \\gamma = 0.5$. This value is observed by simulating the SIR dynamics model with these values and calculating the overshoot directly." ] }, { @@ -232,18 +232,17 @@ "source": [ "\n", "\n", - "Now suppose we are uncertain about $\\beta, \\gamma$, and want to construct a Bayesian SIR model that incorporates this uncertainty. Say we induce $\\beta$ to be drawn from the distribution `Beta(18, 600)`, and $\\gamma$ to be drawn from distribution `Beta(1600, 1600)`. " + "Now suppose we are uncertain about $\\beta$ and $\\gamma$, and want to construct a Bayesian SIR model that incorporates this uncertainty. Say we induce $\\beta$ to be drawn from the distribution `Beta(18, 600)`, and $\\gamma$ to be drawn from distribution `Beta(1600, 1600)`. " ] }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Defining a Bayesian SIR model where we have priors over beta and gamma distributions\n", "\n", - "\n", "def bayesian_sir(base_model=SIRDynamics) -> Dynamics[torch.Tensor]:\n", " beta = pyro.sample(\"beta\", dist.Beta(18, 600))\n", " gamma = pyro.sample(\"gamma\", dist.Beta(1600, 1600))\n", @@ -279,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -287,7 +286,6 @@ "# allowing for interventions on a dynamical system\n", "# within another model\n", "\n", - "\n", "def MaskedStaticIntervention(time: R, intervention: Intervention[State[T]]):\n", "\n", " @on(StaticEvent(time))\n", @@ -303,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -313,7 +311,6 @@ "lockdown_time = torch.tensor(1.0)\n", "mask_time = torch.tensor(1.5)\n", "\n", - "\n", "def policy_model():\n", "\n", " lockdown = pyro.sample(\"lockdown\", dist.Bernoulli(torch.tensor(0.5)))\n", @@ -379,19 +376,18 @@ "3. Only masking was imposed\n", "4. Only lockdown was imposed\n", "\n", - "The hope is that by looking at these we will be able to indentify the culprit. We create these four models by conditioning on the policies being imposed as required (in fact, this has the same effect as intervening here, as the sites are upstream from the model). In principle we could emulate 1-4 using `do` with the same estimates. For the sake of completeness, we also illustrate the consequences of deciding randomly about the policies." + "The hope is that by looking at these we will be able to indentify the culprit. We create these four models by conditioning on the policies being imposed as required (in fact, this has the same effect as intervening here, as the sites are upstream from the dynamical system model; we could emulate 1-4 using `do` with the same estimates). For the sake of completeness, we also illustrate the consequences of following a stochastic policy and deciding randomly about the interventions." ] }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# conditioning (as opposed to intervening) is sufficient for\n", "# propagating the changes, as the decisions are upstream from ds\n", "\n", - "\n", "# no interventions\n", "num_samples = 10000\n", "policy_model_none = condition(\n", @@ -431,12 +427,12 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
      " ] @@ -566,7 +562,7 @@ "source": [ "The plots above show what happens in the four different scenarios. We observe that in the model where none of the policies were imposed, the probability of the overshoot being too high is relatively low, $0.24$. On the other hand, when both policies were imposed, the probability of the overshoot being to high was relatively higher $0.81$. \n", "\n", - "To identify which of `lockdown` and `mask` is the cause, we analyze the models where only one of the policies were imposed. In both cases, the probability of too high overshoot seems to be even higher - $0.96$ and $0.9$. Interestingly, the effect of the interventions is somewhat nuanced. Implementing both increases the risk of overshoot as compared to the no intervention model. But individual interventions would have even worse consequences, which means that the two interventions while jointly increasing the risk to some extent mitigate each other's contribution to that risk as well.\n", + "To identify which of `lockdown` and `mask` is the cause, we analyze the models where only one of the policies were imposed. Interestingly, the effect of the interventions is somewhat nuanced. Implementing both interventions increases the risk of overshoot as compared to the no intervention model, but individual interventions would have even worse consequences, which means that the two interventions while jointly increasing the risk to some extent mitigate each other's contribution to that risk as well.\n", "\n", "Crucially, the analysis does not allow us to distinghuish the intuitive role that the lockdown played, as opposed to masking (whose impact has been limited by the presence of lockdown). So, we need a more fine-grained analysis where we not only control the variables being intervened on (that is, the policies), but also pay attention to what context we are in. We achieve that level of sensitivity by stochastically keeping part of the context (that is, other variables in the model) fixed (see the tutorial for categorical variables for a more extensive explanation of this method and simpler examples). The key idea is that starting with the scenario in which both interventions have been implemented, there is a context such that if we keep it fixed, removing lockdown would significantly lower the overshoot, but there is no context that we could keep fixed such that if in that context we remove the masking policy, the overshoot would decrease. In the next section, we show how this analysis can be carried out with the help of `SearchForExplanation`." ] @@ -589,7 +585,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -651,14 +647,14 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.1215)\n" + "tensor(0.1260)\n" ] } ], @@ -699,7 +695,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -726,17 +722,17 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0} 0.18636363744735718\n", - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1} 0.3100775182247162\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0} 1.5629978422637691e-09\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1} 1.990049014466422e-09\n" + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0} 0.23271501064300537\n", + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1} 0.2796352505683899\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0} 1.4128752612307949e-09\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1} 1.6559513760228128e-09\n" ] } ], @@ -816,15 +812,6 @@ "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_mask\": 0}, mwc_imp)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Some questions I had: [This markdown text should be removed in the final draft]\n", - "1. Normalization of degree of responsibility is not super clear to me that why we would want to do that.\n", - "2. The plots below are updated with `policy_model` instead of `policy_model_all`." - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -841,7 +828,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -878,12 +865,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We first plot the distribution of `overshoot` in the factual world (indicated by 0) and necessity counterfactual worlds (indicated by 1) where intervened variables are set to their alternative value. One can see how the distribution changes in the counterfactual worlds. When `mask` is set to 0, the probability of high overshoot is lower than that when `lockdown` is set to 0. This provides us the intuition that `lockdown` has a higher role in inducing high overshoot." + "We first plot the distribution of `overshoot` in the factual world (indicated by 0) and necessity counterfactual worlds (indicated by 1) where intervened variables are set to their alternative value. One can see how the distribution changes in the counterfactual worlds. When `mask` is set to 0, the probability of high overshoot is lower than that when `lockdown` is set to 0. This agrees with the intuition that `lockdown` has a higher role in inducing high overshoot." ] }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -914,7 +901,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -922,14 +909,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "factual: 24.31097984313965 counterfactual mask: 21.902610778808594 counterfactual lockdown: 20.758800506591797\n", + "factual: 24.302181243896484 counterfactual mask: 22.20435333251953 counterfactual lockdown: 20.830657958984375\n", "Probability of overshoot being high\n", - "factual: 0.7299000024795532 counterfactual mask: 0.5736842155456543 counterfactual lockdown: 0.5078909397125244\n" + "factual: 0.7376999855041504 counterfactual mask: 0.5904392600059509 counterfactual lockdown: 0.5146276354789734\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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      " ] @@ -999,7 +986,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -1030,7 +1017,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1038,14 +1025,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "factual: 24.31097984313965 counterfactual mask: 26.651079177856445 counterfactual lockdown: 22.560808181762695\n", + "factual: 24.302181243896484 counterfactual mask: 26.644834518432617 counterfactual lockdown: 22.424802780151367\n", "Probability of overshoot being high\n", - "factual: 0.7299000024795532 counterfactual mask: 0.8868421316146851 counterfactual lockdown: 0.7044476270675659\n" + "factual: 0.7376999855041504 counterfactual mask: 0.8966408371925354 counterfactual lockdown: 0.7127659320831299\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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      " ] @@ -1110,12 +1097,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can also try to combine samples from both sufficiency and necessity worlds to draw conclusions. We first visualize samples where only lockdown was intervened on and then we analyze samples where masking was intervened on." + "We can also combine samples from both sufficiency and necessity worlds to draw conclusions. We first visualize samples where only lockdown was intervened on and then we analyze samples where masking was intervened on." ] }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -1179,12 +1166,12 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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      " ] From dabb4c2ce965186c893a314e5610aad0a159b5c1 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Tue, 27 Aug 2024 18:11:20 -0400 Subject: [PATCH 076/111] fixed heatmap prob --- docs/source/explainable_sir.ipynb | 188 ++++++++++++++++-------------- 1 file changed, 101 insertions(+), 87 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 839d4306..bb8c9fd0 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 111, "metadata": {}, "outputs": [], "source": [ @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 112, "metadata": {}, "outputs": [], "source": [ @@ -170,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 113, "metadata": {}, "outputs": [ { @@ -237,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 114, "metadata": {}, "outputs": [], "source": [ @@ -278,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 115, "metadata": {}, "outputs": [], "source": [ @@ -301,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 116, "metadata": {}, "outputs": [], "source": [ @@ -381,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 117, "metadata": {}, "outputs": [], "source": [ @@ -427,12 +427,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 118, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
      " ] @@ -585,7 +585,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 119, "metadata": {}, "outputs": [], "source": [ @@ -620,15 +620,7 @@ " log_weights,\n", " )\n", "\n", - " return _wrapped_model\n", - "\n", - "from chirho.observational.handlers.soft_conditioning import soft_eq, KernelSoftConditionReparam\n", - "\n", - "def _soft_eq(v1: torch.Tensor, v2: torch.Tensor) -> torch.Tensor:\n", - " return soft_eq(constraints.boolean, v1, v2, scale=0.001)\n", - "\n", - "def reparam_config(data):\n", - " return {i: KernelSoftConditionReparam(_soft_eq) for i in data}" + " return _wrapped_model" ] }, { @@ -647,14 +639,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 142, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.1260)\n" + "tensor(0.1267)\n" ] } ], @@ -695,7 +687,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 147, "metadata": {}, "outputs": [], "source": [ @@ -722,17 +714,17 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 148, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0} 0.23271501064300537\n", - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1} 0.2796352505683899\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0} 1.4128752612307949e-09\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1} 1.6559513760228128e-09\n" + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0} 0.19745628535747528\n", + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1} 0.3155815303325653\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0} 1.559821827257224e-09\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1} 1.8681491908978387e-09\n" ] } ], @@ -788,7 +780,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 123, "metadata": {}, "outputs": [ { @@ -828,7 +820,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 124, "metadata": {}, "outputs": [], "source": [ @@ -856,7 +848,7 @@ " os_too_high_mean = os_too_high.mean()\n", "\n", " hist, bin_edges = torch.histogram(\n", - " data_to_plot, bins=28, range=(5, 40), density=True\n", + " data_to_plot, bins=36, range=(0, 45), density=True\n", " )\n", " return hist, bin_edges, overshoot_mean, os_too_high_mean" ] @@ -870,7 +862,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 125, "metadata": {}, "outputs": [], "source": [ @@ -901,7 +893,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 126, "metadata": {}, "outputs": [ { @@ -909,14 +901,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "factual: 24.302181243896484 counterfactual mask: 22.20435333251953 counterfactual lockdown: 20.830657958984375\n", + "factual: 24.31097984313965 counterfactual mask: 21.902610778808594 counterfactual lockdown: 20.758800506591797\n", "Probability of overshoot being high\n", - "factual: 0.7376999855041504 counterfactual mask: 0.5904392600059509 counterfactual lockdown: 0.5146276354789734\n" + "factual: 0.7299000024795532 counterfactual mask: 0.5736842155456543 counterfactual lockdown: 0.5078909397125244\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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      " ] @@ -926,27 +918,28 @@ } ], "source": [ + "width = 45/36\n", "plt.bar(\n", - " bin_edges[:28].tolist(),\n", + " bin_edges[:36].tolist(),\n", " hist_fact_nec,\n", " align=\"center\",\n", - " width=35 / 28,\n", + " width=width,\n", " alpha=0.5,\n", " color=\"blue\",\n", ")\n", "plt.bar(\n", - " bin_edges[:28].tolist(),\n", + " bin_edges[:36].tolist(),\n", " hist_lockdown_nec,\n", " align=\"center\",\n", - " width=35 / 28,\n", + " width=width,\n", " alpha=0.5,\n", " color=\"pink\",\n", ")\n", "plt.bar(\n", - " bin_edges[:28].tolist(),\n", + " bin_edges[:36].tolist(),\n", " hist_mask_nec,\n", " align=\"center\",\n", - " width=35 / 28,\n", + " width=width,\n", " alpha=0.5,\n", " color=\"green\",\n", ")\n", @@ -986,7 +979,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 127, "metadata": {}, "outputs": [], "source": [ @@ -1017,7 +1010,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 128, "metadata": {}, "outputs": [ { @@ -1025,14 +1018,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "factual: 24.302181243896484 counterfactual mask: 26.644834518432617 counterfactual lockdown: 22.424802780151367\n", + "factual: 24.31097984313965 counterfactual mask: 26.651079177856445 counterfactual lockdown: 22.560808181762695\n", "Probability of overshoot being high\n", - "factual: 0.7376999855041504 counterfactual mask: 0.8966408371925354 counterfactual lockdown: 0.7127659320831299\n" + "factual: 0.7299000024795532 counterfactual mask: 0.8868421316146851 counterfactual lockdown: 0.7044476270675659\n" ] }, { "data": { - "image/png": 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", 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", 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      " ] @@ -1042,27 +1035,28 @@ } ], "source": [ + "width = 45/36\n", "plt.bar(\n", - " bin_edges[:28].tolist(),\n", + " bin_edges[:36].tolist(),\n", " hist_fact_suff,\n", " align=\"center\",\n", - " width=35 / 28,\n", + " width=width,\n", " alpha=0.5,\n", " color=\"blue\",\n", ")\n", "plt.bar(\n", - " bin_edges[:28].tolist(),\n", + " bin_edges[:36].tolist(),\n", " hist_lockdown_suff,\n", " align=\"center\",\n", - " width=35 / 28,\n", + " width=width,\n", " alpha=0.5,\n", " color=\"pink\",\n", ")\n", "plt.bar(\n", - " bin_edges[:28].tolist(),\n", + " bin_edges[:36].tolist(),\n", " hist_mask_suff,\n", " align=\"center\",\n", - " width=35 / 28,\n", + " width=width,\n", " alpha=0.5,\n", " color=\"green\",\n", ")\n", @@ -1102,9 +1096,17 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 129, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.3526315789473684\n" + ] + } + ], "source": [ "masks = {\n", " \"__cause____antecedent_mask\": 1,\n", @@ -1122,6 +1124,7 @@ " IndexSet(**{\"lockdown\": {2}, \"mask\": {2}}),\n", " )\n", "\n", + "\n", " mask_tensor = torch.ones(\n", " importance_tr.nodes[\"__cause____antecedent_mask\"][\"value\"].shape\n", " ).bool()\n", @@ -1130,10 +1133,10 @@ " data_suff = data_suff.squeeze()[torch.nonzero(mask_tensor.squeeze())]\n", " data_nec = data_nec.squeeze()[torch.nonzero(mask_tensor.squeeze())]\n", "\n", - "a = torch.transpose(torch.vstack((data_nec.squeeze(), data_suff.squeeze())), 0, 1)\n", - "hist_lockdown_2d, _ = torch.histogramdd(a, bins = [28, 28], density=True, range=[5.0, 40.0, 5.0, 40.0])\n", - "pr_lockdown = (hist_lockdown_2d[:12, 12:].sum())\n", "\n", + "a = torch.transpose(torch.vstack((data_nec.squeeze(), data_suff.squeeze())), 0, 1)\n", + "hist_lockdown_2d, rough = torch.histogramdd(a, bins=[36, 36], density=True, range=[0.0, 45.0, 0.0, 45.0])\n", + "pr_lockdown = (hist_lockdown_2d[:16, 16:].sum()/hist_lockdown_2d.sum())\n", "\n", "masks = {\n", " \"__cause____antecedent_mask\": 0,\n", @@ -1159,19 +1162,28 @@ " data_suff = data_suff.squeeze()[torch.nonzero(mask_tensor.squeeze())]\n", " data_nec = data_nec.squeeze()[torch.nonzero(mask_tensor.squeeze())]\n", "\n", + " data_nec = data_nec.squeeze()\n", + " data_suff = data_suff.squeeze()\n", + "\n", + " sum = 0\n", + " for i in range(len(data_nec)):\n", + " if (data_nec[i] < overshoot_threshold) & (data_suff[i] > overshoot_threshold):\n", + " sum += 1\n", + " print(sum / len(data_nec))\n", + "\n", "a = torch.transpose(torch.vstack((data_nec.squeeze(), data_suff.squeeze())), 0, 1)\n", - "hist_mask_2d, _ = torch.histogramdd(a, bins = [28, 28], density=True, range=[5.0, 40.0, 5.0, 40.0])\n", - "pr_mask = (hist_mask_2d[:12, 12:].sum())" + "hist_mask_2d, _ = torch.histogramdd(a, bins = [36, 36], density=True, range=[0.0, 45.0, 0.0, 45.0])\n", + "pr_mask = (hist_mask_2d[:16, 16:].sum()/hist_mask_2d.sum())" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 130, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
      " ] @@ -1186,23 +1198,23 @@ "ax = axs[0]\n", "hist_lockdown = hist_lockdown_nec.unsqueeze(1) * hist_lockdown_suff.unsqueeze(0)\n", "ax.imshow(hist_lockdown_2d, cmap=\"viridis\")\n", - "ax.set(xticks=range(0, 28, 2), xticklabels=bin_edges[0:28:2].tolist())\n", - "ax.set(yticks=range(0, 28, 2), yticklabels=bin_edges[0:28:2].tolist())\n", + "ax.set(xticks=range(0, 36, 2), xticklabels=bin_edges[0:36:2].tolist())\n", + "ax.set(yticks=range(0, 36, 2), yticklabels=bin_edges[0:36:2].tolist())\n", "ax.set(\n", " xlabel=\"Overshoot in Sufficiency Intervention\",\n", " ylabel=\"Overshoot in Necessity Intervention\",\n", " title=\"Overshoot in counterfactual lockdown\",\n", ")\n", - "ax.axvline(x=(overshoot_threshold - 5) * 28 / 35, color=\"red\", linestyle=\"--\", label=\"Overshoot too high\")\n", - "ax.axhline(y=(overshoot_threshold - 5) * 28 / 35, color=\"red\", linestyle=\"--\")\n", + "ax.axvline(x=(overshoot_threshold) * 36 / 45, color=\"red\", linestyle=\"--\", label=\"Overshoot too high\")\n", + "ax.axhline(y=(overshoot_threshold) * 36 / 45, color=\"red\", linestyle=\"--\")\n", "\n", "ax.axvline(\n", - " x=(os_lockdown_suff - 5) * 28 / 35,\n", + " x=(os_lockdown_suff) * 36 / 45,\n", " color=\"white\",\n", " linestyle=\"--\",\n", " label=\"Mean Overshoot\",\n", ")\n", - "ax.axhline(y=(os_lockdown_nec - 5) * 28 / 35, color=\"white\", linestyle=\"--\")\n", + "ax.axhline(y=(os_lockdown_nec) * 36 / 45, color=\"white\", linestyle=\"--\")\n", "\n", "ax.legend(loc=\"upper left\")\n", "ax.text(13, 2, 'pr(lockdown caused overshoot): %.4f' % pr_lockdown.item(), color=\"white\")\n", @@ -1210,23 +1222,23 @@ "ax = axs[1]\n", "hist_mask = hist_mask_nec.unsqueeze(1) * hist_mask_suff.unsqueeze(0)\n", "ax.imshow(hist_mask_2d, cmap=\"viridis\")\n", - "ax.set(xticks=range(0, 28, 2), xticklabels=bin_edges[0:28:2].tolist())\n", - "ax.set(yticks=range(0, 28, 2), yticklabels=bin_edges[0:28:2].tolist())\n", + "ax.set(xticks=range(0, 36, 2), xticklabels=bin_edges[0:36:2].tolist())\n", + "ax.set(yticks=range(0, 36, 2), yticklabels=bin_edges[0:36:2].tolist())\n", "ax.set(\n", " xlabel=\"Overshoot in Sufficiency Intervention\",\n", " ylabel=\"Overshoot in Necessity Intervention\",\n", " title=\"Overshoot in counterfactual mask\",\n", ")\n", - "ax.axvline(x=(overshoot_threshold - 5) * 28 / 35, color=\"red\", linestyle=\"--\", label=\"Overshoot too high\")\n", - "ax.axhline(y=(overshoot_threshold - 5) * 28 / 35, color=\"red\", linestyle=\"--\")\n", + "ax.axvline(x=(overshoot_threshold) * 36 / 45, color=\"red\", linestyle=\"--\", label=\"Overshoot too high\")\n", + "ax.axhline(y=(overshoot_threshold) * 36 / 45, color=\"red\", linestyle=\"--\")\n", "\n", "ax.axvline(\n", - " x=(os_mask_suff - 5) * 28 / 35,\n", + " x=(os_mask_suff) * 36 / 45,\n", " color=\"white\",\n", " linestyle=\"--\",\n", " label=\"Mean Overshoot\",\n", ")\n", - "ax.axhline(y=(os_mask_nec - 5) * 28 / 35, color=\"white\", linestyle=\"--\")\n", + "ax.axhline(y=(os_mask_nec) * 36 / 45, color=\"white\", linestyle=\"--\")\n", "ax.text(13, 2, 'pr(masking caused overshoot): %.4f' % pr_mask.item(), color=\"white\")\n", "\n", "ax.legend(loc=\"upper left\")\n", @@ -1261,7 +1273,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 131, "metadata": {}, "outputs": [], "source": [ @@ -1293,7 +1305,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 132, "metadata": {}, "outputs": [ { @@ -1308,7 +1320,7 @@ }, { "data": { - "image/png": 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", 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", 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      " ] @@ -1318,19 +1330,20 @@ } ], "source": [ + "width = 45/36\n", "plt.bar(\n", - " bin_edges[:28].tolist(),\n", + " bin_edges[:36].tolist(),\n", " hist_lockdown_fix,\n", " align=\"center\",\n", - " width=35 / 28,\n", + " width=width,\n", " alpha=0.5,\n", " color=\"blue\",\n", ")\n", "plt.bar(\n", - " bin_edges[:28].tolist(),\n", + " bin_edges[:36].tolist(),\n", " hist_lockdown_notfix,\n", " align=\"center\",\n", - " width=35 / 28,\n", + " width=width,\n", " alpha=0.5,\n", " color=\"pink\",\n", ")\n", @@ -1366,7 +1379,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 133, "metadata": {}, "outputs": [], "source": [ @@ -1396,7 +1409,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 134, "metadata": {}, "outputs": [ { @@ -1411,7 +1424,7 @@ }, { "data": { - "image/png": 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", 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", 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      " ] @@ -1421,19 +1434,20 @@ } ], "source": [ + "width = 45/36\n", "plt.bar(\n", - " bin_edges[:28].tolist(),\n", + " bin_edges[:36].tolist(),\n", " hist_mask_fix,\n", " align=\"center\",\n", - " width=35 / 28,\n", + " width=width,\n", " alpha=0.5,\n", " color=\"blue\",\n", ")\n", "plt.bar(\n", - " bin_edges[:28].tolist(),\n", + " bin_edges[:36].tolist(),\n", " hist_mask_notfix,\n", " align=\"center\",\n", - " width=35 / 28,\n", + " width=width,\n", " alpha=0.5,\n", " color=\"pink\",\n", ")\n", From 8b6a39157154ff3ed1c5b1b6303c73104c7e5419 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Wed, 28 Aug 2024 15:01:52 -0400 Subject: [PATCH 077/111] math formulae and other tweaks --- docs/source/explainable_sir.ipynb | 253 +++++++++++++++++++++--------- 1 file changed, 179 insertions(+), 74 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index bb8c9fd0..ecdb5469 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -30,7 +30,7 @@ "- [But for Analysis with Bayesian SIR Model with Policies](#but-for-analysis-with-bayesian-sir-model-with-policies)\n", "- [Causal Explanations using `SearchForExplanation`](#causal-explanations-using-searchforexplanation)\n", "- [Fine-grained Analysis of `overshoot` using Sample traces](#fine-grained-analysis-of-overshoot-using-sample-traces)\n", - "- [For Advanced Readers: Looking into Different Contexts](#for-advanced-readers-looking-into-different-contexts)" + "- [For Advanced Readers: Looking into Different Contexts](#looking-into-different-contexts-for-advanced-readers)" ] }, { @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 200, "metadata": {}, "outputs": [], "source": [ @@ -75,7 +75,7 @@ "from chirho.dynamical.ops import Dynamics, State, on, simulate\n", "from chirho.explainable.handlers import SearchForExplanation\n", "from chirho.explainable.handlers.components import ExtractSupports\n", - "from chirho.indexed.ops import IndexSet, gather, indices_of\n", + "from chirho.indexed.ops import IndexSet, gather\n", "from chirho.interventional.ops import Intervention, intervene\n", "from chirho.observational.handlers import condition\n", "\n", @@ -91,8 +91,7 @@ "pyro.set_rng_seed(seed)\n", "\n", "smoke_test = \"CI\" in os.environ\n", - "num_samples = 10 if smoke_test else 300\n", - "exp_plate_size = 10 if smoke_test else 2000" + "num_samples = 10 if smoke_test else 300" ] }, { @@ -118,7 +117,7 @@ "\n", "This quantity is of interest because epidemic mitigation policies often have multiple goals that need to be balanced. One goal is to increase `S_final`, i.e., to limit the total number of infected individuals. Another goal is to limit the number of infected individuals at the peak of the epidemic to avoid overwhelming the healthcare system. A further goal is to minimize the proportion of the population that becomes infected after the peak, that is, the overshoot, to reduce healthcare and economic burdens. Balancing these objectives involves making trade-offs.\n", "\n", - " Suppose we are working under constraint that the overshoot should be lower than 20% of the population, and we implement two policies, lockdown and masking, which together seem to lead to the overshoot being too high. In fact, only one of them is responsible, and we are interested in being able to identify which one. " + " Suppose we are working under constraint that the overshoot should be lower than 24% of the population, and we implement two policies, lockdown and masking, which together seem to lead to the overshoot being too high. In fact, only one of them is responsible, and we are interested in being able to identify which one. " ] }, { @@ -130,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 201, "metadata": {}, "outputs": [], "source": [ @@ -170,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 202, "metadata": {}, "outputs": [ { @@ -216,7 +215,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The number $0.15$ is the overshoot you get if $\\beta = 0.03, \\gamma = 0.5$. This value is observed by simulating the SIR dynamics model with these values and calculating the overshoot directly." + "The number $0.15$ is the overshoot you get if $\\beta = 0.03, \\gamma = 0.5$. This value is observed by simulating the SIR dynamics model with these values and calculating the overshoot directly.\n", + "\n", + "Also, note that the above dynamical system introduces the variables: `S` - susceptible, `I` - infected, `R` - recovered and `l` - effect of intervention. These variables evolve over time and their dynamics are captured by the model. As we add features to our model, we also add new variables to this list. And even further on in the notebook, we will describe the probabilities we compute in terms of these variables." ] }, { @@ -232,12 +233,12 @@ "source": [ "\n", "\n", - "Now suppose we are uncertain about $\\beta$ and $\\gamma$, and want to construct a Bayesian SIR model that incorporates this uncertainty. Say we induce $\\beta$ to be drawn from the distribution `Beta(18, 600)`, and $\\gamma$ to be drawn from distribution `Beta(1600, 1600)`. " + "Now suppose we are uncertain about $\\beta$ and $\\gamma$, and want to construct a Bayesian SIR model that incorporates this uncertainty. Say we induce $\\beta$ to be drawn from the distribution `Beta(18, 600)`, and $\\gamma$ to be drawn from distribution `Beta(1600, 1600)`. This adds the random variables `beta` and `gamma` to the list of random variables that our model captures." ] }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 203, "metadata": {}, "outputs": [], "source": [ @@ -278,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 204, "metadata": {}, "outputs": [], "source": [ @@ -301,13 +302,13 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 249, "metadata": {}, "outputs": [], "source": [ "# Defining the policy model\n", "\n", - "overshoot_threshold = 20\n", + "overshoot_threshold = 24\n", "lockdown_time = torch.tensor(1.0)\n", "mask_time = torch.tensor(1.5)\n", "\n", @@ -355,6 +356,13 @@ " return overshoot, os_too_high" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have our full-fledged model of SIR dynamics along with interventions, we have a complete list of random variables in question: `S` - susceptible, `I` - infected, `R` - recovered, `l` - effec of intervention, `ld` - lockdown, `m` - masking, `le` - lockdown efficiency, `me` - mask efficiency, `je` - joint efficiency, `os` - overshoot, and `oth` - overshoot is too high. We use these notations in the rest of the notebook to describe the probabilities we are computing." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -381,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 250, "metadata": {}, "outputs": [], "source": [ @@ -427,12 +435,32 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 251, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['lockdown', 'mask', 'beta', 'gamma', 'lockdown_efficiency', 'mask_efficiency', 'joint_efficiency', 'S', 'I', 'R', 'l', 'overshoot', 'os_too_high'])" + ] + }, + "execution_count": 251, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "samples.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 252, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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ilHHjxikOh0NRFPU5Hj16dL7td5+f//3vfx7vmTt37lSaNGmi1K1bVzl06NBl2+R+biMiIpTJkydr16ndbldee+01JSIiQhkwYIC2fn7v97nldx0V97V3ue0GDx6sREREKM8884zH5+/x48e1z4sFCxZo83Of39yfR+7PsIJ069YtzzaKol7P7sf5/fff53uct956y+N5mTRpkhIREaH07t1bmxcbG6vUrVtXadq0qXLkyBGPY2zZskWpU6eOEhER4fH+437ec79333vvvUpERISyfv16j33ExsYqtWvX9vgu0qFDB6VOnToe14bL5VImTJigREREKMOHD8/zeHJfr9fidSaEECVFMqWEELeN7t27ExQUBIBer7692e122rdvzyuvvIKvr6/H+u5uDadPny50v7GxsSxfvpzw8HDefPNNjxH/qlSponVV+OSTT7T5X375JaB258ld+yQkJISXX36ZatWq5dutJbdFixaRmZlJr1696NWrl8ey+++/n169epGRkcFXX31VpHORn8WLF5OcnMwjjzzC3Xff7bHsoYceol27dpw6dUob3jsuLg6A8PBwjyLDISEhvPnmm0yaNEnLIilM2bJl6dixI0888YTHfH9/f7p16wbk/7wYjUbGjh3r8Rw89NBDmM1mEhMTSUxMBGD79u3s37+fOnXqMHDgQG1dk8nEG2+8QWho6GXb6DZ//nwAXn31VY+MM7PZzBtvvEGVKlXYu3cvGzduBKBNmzYEBwezbt06j659oHbRMBgM2mO8kmvLrUGDBtSvX1+7r9friY+PB9Tzq9PptGWVKlXirbfeYsqUKXleBwADBw6kVq1a2v3KlStr3b5ydzN0n4sJEyZ4PM9+fn688847+Pv78+uvv2pdgq7mGi4q97YjRoygTJky2vx69eoxaNCgIu3jSs9bQdq1a6edH51O57HPgowePZqwsDDtfqtWrejTpw82m63A7qnX2tU8Xw8++CDNmjXT7gcFBdGjRw8Aj6wO97m+dCCJRx55hJEjR/J///d/V/04Ro0aRUhIiHa/ffv2VKhQAYfDwbFjx656/yEhIYwcORKDwQCoz/GAAQMAz8f6zz//sHnzZmrVqsWwYcM83jMbNGjAoEGDsNvtRcrWdatcuTJDhw7Vrimj0ciYMWMIDQ1l7dq1nDx58qof37Vw5swZfv31V/z9/Xn77bc9Xj+VK1fWstY+/vjjPNuGh4d7fB4V9hmWkZFBvXr1uO+++/J8hoWHh2uZgfl9noSGhuZ5Xvr16wfAoUOHtHkJCQl06tSJQYMG5RlVsEmTJtSsWbPAY+Tmzpb6/vvvPeYvX74cp9Pp8ZqLj4/HaDRSqlQpbZ5Op+OZZ55h9OjRWkZVQUridSaEENeKBKWEELeN3D+q3bp27cqcOXM8fixlZ2ezb98+raaF0+kstNbI1q1bcTqdREVFeQQN3Fq2bIler2fbtm04nU4URWHLli3o9XratWuXZ/2OHTvy888/079//0Ifz5YtWwDo0qVLvsvvueceADZv3pxnWX7nIj+bNm0C8Dg/ubVu3dpjvejoaEwmEytWrGDgwIEsXryY8+fPA9C8eXN69+5dpFELx44dy6xZs7QfdQCJiYmsX7+ebdu2AeRbo6Ny5cpasM3NbDYTHBwMoNU0cp+7/OrlWCwWWrVqddk2glqPbPv27ej1ejp16pRnudFo1EaMdJ8jk8lE165dsdvt/Prrr9q6//zzD6dOnaJ58+baD43iXlu55de1pmnTpoD6Q+/FF1/khx9+ICkpCVCvu549e3r8WHdr1KhRnnlly5YF1Ho4AOfOneP06dOEhIQQExOTZ31/f3/tfLuvyau5hovC5XKxdetWDAYDLVu2zLP8cl3V3K70vBWkqK8/t1KlSuX7GnTXf7rS81NcV/N8NWjQIM88d+Hr3LXGmjRpAqi1m9555x02b96M3W7HbDbTv39/2rZte1WPwdvbO9/Xhvs1l5mZeVX7BzXgeenIf5e+XuDie0LTpk3zDay431+L8/zefffdeUaNtFgstGjRotj7up7c11KLFi3w8/PLszwmJobw8HDOnz+fJ5BWlC67br6+vkyaNClP18zY2Fj+/PNP9u/fD+T/eVK3bt0859J9nVitVm0Uxjp16jBt2jSPz2yn08nx48f54YcfSElJAdBqRRakW7duWCwWVq5c6fGaWLZsGXq9nnvvvVeb16RJE7Kzs+nduzczZ85k165duFwuQkNDefTRR7X3rIJc79eZEEJcSzL6nhDithEYGJjv/PT0dBYtWsSaNWs4cuQI8fHxKIrikb2gFFIHyZ3RtHr16kK/LGdlZXl8OQ0JCcHb2/tKHgpwMSupfPny+S6vUKECcPEvorkVdC4u5a7bNHjw4ELXcweeypYty5QpUxg9ejR//fWXVti3Zs2adOrUiT59+hR5BKYDBw7w1VdfsWvXLo4fP05GRgaA9rzk95wEBATkuy/3Dwv3jwj3ucv9V+bc3OfucpKTk7Hb7QQHB+f7wyr3vnI/Dz179uSLL77gxx9/5MEHHwTUWiKAlj0Cxb+2cgdGLg3OgVrjaOTIkUydOpWff/6Zn3/+GZ1OR926denSpQsPPfRQvtdGfiM3uQOGl57TwoKOl56Lq7mGi8L9/ISEhODl5ZVneUHHvdSVnreCFGfdwtrpzvy6tFDz9XKt33MuvYYAhg0bxunTp1m3bh1z585l7ty5+Pj40KpVK3r27FnkQGJB/P39881Mc79HFPZeX1T5vQ/l91jdr+/PP/+czz//vMD9ud9fi6Kg58YdFLvW18qRI0eYM2dOnvnVq1fn2WefLXC7y11LoF5P8fHxxMXFedRly++97XK2b9/ON998w969ezl58qQ2yEFxP09yB6lcLpcWTHQ6nfzyyy+sWLGCQ4cOcfbsWW0AhMKOkVtgYCCdOnXixx9/5Ndff6Vnz57s2bOHQ4cO0apVK+05BJg4cSKDBg1i7969zJgxgxkzZhAUFESbNm247777uOOOOwo91vV+nQkhxLUkQSkhxG0jv79EHzp0iMcff5zExESCg4OpX78+3bp1IzIykmbNmnHnnXdedr/uHxk1atSgdu3al12/KCM8FcXlvuC622U2m/MsK6y7Q27utrZr167AoAuoj93tnnvuoXXr1qxatYq///6bTZs2cejQIQ4dOsT8+fOZN28eDRs2LPS4H3/8MW+//Tag/rhp164d1atXJyoqipMnTzJ+/Ph8tytKN6iirJc7Q6swRfkB6z6HuZ+H+vXrU61aNbZs2UJcXBxhYWH88ssv+Pj4eGRcFffayq2gx9i/f3+6d+/Ob7/9xt9//82WLVvYs2cPe/bs4bPPPuPLL7+kcuXKHtsU5Xq5knNxNdfwtaDX64v8XF/JeSvsuMVhsVgKXX5pNkdBrva952qer6K+Nv39/fn000/ZvXs3v//+Oxs2bGDPnj38+uuv/Prrr3Tp0oXp06cXv/HFbMfVKOox3OcrKiqq0GLdxWnz5V4rl2ZwFaSo10pCQoIWUM8tJiam0KBUcdpw6WMq7nM4btw4vvzyS/R6PbVq1eLuu++mevXqNGjQgHXr1vHBBx/ku11Rj5OZmcnjjz/Orl278PLyol69erRs2ZKaNWvSuHFjJkyYoGWGXc7999/Pjz/+yPLly+nZs6fWla93794e65UpU4YlS5awZcsWVq9ezfr16zl48CDLly9n+fLlDBgwgOHDhxd4nOv9OhNCiGtJglJCiNva+PHjSUxM5P/+7/8YOnSoxw9Ud1bT5YSHhwNQu3Zt3nnnncuub7fbMZlMpKSkkJ2dnSeDw2q18u2331KtWjWaN29e4H5KlSrFsWPHOHPmjFazIjf3SEvFqY+U3zGOHz9Ov379tO4fReHv78+9996rdTfYu3cv7777LmvXruW9997Tag/l59SpU0ydOhV/f38++OADrZuBW2HbFpU7W6ugul3uv+JfTlBQkPZcpqen5xu4K+h56NmzJ9OmTePXX3+levXqxMfH07NnT49uesW9tooqNDSUPn360KdPH1wuF9u3b2fSpEns2bOHjz76iIkTJxZ7n+6sszNnzhS4jvtcuGsjXe9rODg4GIvFQnJyMhkZGXnqPiUkJBQrUHM9zltRFHQ9us917uw09w/p/B5X7lHurkRJvOe4RUVFERUVxUsvvURaWho///wzEydOZOXKlWzdujXP+8KtyP36btmyJS+99NI12eflrhV3to07MJo7cyu33N0MC9OsWTOPunJFVZT3C3cNpty11Ipr8+bNfPnll5QtW5aPP/7Y4w8ogEcX6iv16aefsmvXLpo3b8706dPzZFgV9VwC3HHHHZQvX55NmzaRkpLCr7/+SkBAQL6ZSzqdjpiYGK27dGJiIkuWLGHatGnMmzePxx577LLd5f8LrzMhxK1PakoJIW5rO3fuBOCZZ57JkzGRe0jkgr64w8V6M1u2bPGoA+G2e/duOnfuzPPPP4+iKJhMJqKionA6naxduzbP+ps2bWL8+PFad46C/lrrPu6lQ1O7/fzzzwD51vcpKvcx3N3wLjVlyhTuvfdevvnmG0AtuN2uXTuWLVvmsV7dunUZNmwYcLFLYEHctTGaNWuW7xdi9zm7mm427mDfqlWr8vx4L+h5yY/JZCI6OhqXy6UVe8/N4XDw+++/A3nrcvXo0QOdTscff/yhPVc9e/b0WKe419blTJo0iVatWnn81V6v19OkSRMtq6E4XYVyK1euHOXLl+fChQv51q1JS0vTXlPux3Ul13BxsiR0Oh133HEHLpeLVatW5Vn+559/Fmk/xT1v1zob59ixY/kWSXb/oM59ftxBTXfNq9zc73e5Faet1/s958KFC/Tu3Zvu3bt7zPf39+fBBx/Uar1d6TV6rVyr59d9PtesWZPvZ8xvv/3G3XffzRtvvFHkfeb33pWRkcHatWu1axYuXifuwR9yO3ToUL61ta7ldd24cWN0Oh3r1q0jPT09z/KNGzeSlJREhQoVilSHsCDua75z5855AlJOp1MbgOJqPk927NgBwKOPPponIBUbG8uRI0eAwr9HuOl0Onr37o3dbmfGjBmcP3+erl27emRLHj58mO7du+cpRh4aGsrAgQOJjIxEUZQCu2reKq8zIYRwk6CUEOK25q7Bc+kP1i1btvDmm29q9/MrgupWsWJFOnTowPnz5xk1apTHF+zExERGjRrFiRMnPEbuevTRRwH1x27uH5tJSUlMmTIFuFhbyP1lNDMz0+NL7YMPPoiPjw/fffcd3333nUeblixZwvfff4+Pj0+eUbKK46GHHsLHx4cvvviCFStWeCxbvXo1CxYsYP/+/URFRQFqofGzZ88yZ84cj7oyiqJoheNzjwiXH/dz8s8//3j8YLLb7bz33nusWbMGUDPKrlT9+vVp3Lgxx44dY8qUKdp5dblcTJ48+bKjJOX2+OOPA2qAbu/evR7tHTduHCdPnqRWrVo0btzYY7ty5coRExPDpk2b+P333ylVqlSezLgrubYKU7ZsWeLj43n33Xc99uVwOLSAgvu5vBLuc/H6669rWTOg/igeNmwY6enptGvXTqsjcyXXsLsrT34/ZAtr05QpU7Qfh6DWwpk2bVqR9lHc8+Zu49VmJrkpisKIESM8jr1y5UqWLFmCv78/DzzwgDbfXUR9wYIFHj+058+fz549e/Lsu6D3l/xc7/ec4OBgnE4nBw8ezJMRefr0aW1QgdyjXN4I7nNW1GuwIM2aNaN27drs3buXKVOmeHzOnDhxggkTJnD06FGqVq1a5H1u2rTJY7Q+m83G66+/TkpKCnfffbeWKVW1alXMZjOnTp1i9erV2vqpqamMGzcu330X97VXGPd7W3p6OsOGDdNqBoKacff6668DFz8rr5R7kIsNGzZ4BPazsrIYPXq0Nore1XyeuD+z/vjjD4/X3NmzZxk8eLBWW6qox+jduzd6vZ6FCxdq93OrUqUKcXFxrF27ll9++cVj2Z49ezhy5Ag+Pj55gnBut8rrTAgh3KT7nhDitvbEE08wadIkhg8fzqJFiwgPD+fkyZP8+++/BAUFER4eTnx8PPHx8YXWVHrzzTc5ceIEK1asYN26dURFRaHT6di6dSuZmZk0atTIo3tG165d2bBhA4sXL+aee+4hJiYGg8HAtm3bSEtL47777uOuu+4C1C+8AQEBpKam0qdPHypVqsQ777xD6dKlmTx5MkOHDmXEiBHMnz+fqlWrcuzYMfbv34+3tzdTpkwpcjHn/OQ+xtChQ5k1axbVqlXj3Llz2g/c1157Tat31KFDBzp16sRvv/1Gp06daNSoEb6+vhw8eJDjx48TFhbGCy+8UOgxY2JiqFOnDvv27aNLly7aX/Z37dpFYmIiNWvW5NChQyQkJFzx4wI1INivXz/mz5/Pn3/+Sa1atdi/fz8nTpygQYMG/PPPP0XaT8eOHRkwYACffvopDzzwAI0bNyY4OJh//vmH8+fPU758eaZNm5ZvHaGePXuyadMmEhMTGTBgQL7rFPfaKszDDz/MTz/9xPbt22nfvj0NGjTAbDazb98+zp49S7Vq1XjiiSeKtK/8PPbYY+zYsYOff/5Zu669vb3ZunUrFy5cIDIy0qOL25Vcw+7aO+6RHe+8806PoMylWrZsycCBA/noo4+49957tQLAGzdupG7dukW6jop73txtnD17Njt27LjqwsFVq1bl0KFDdOrUiSZNmhAfH8+OHTswmUxMmTLFo7tcv379+OWXX1i5ciV33XUXkZGRHDp0iGPHjnnUqHEr6P0lPyXxnjNu3Dgee+wxJk2axDfffEP16tVJT09n27ZtWK1WnnrqqULrL5UEd+2wP//8k6effpro6GieeeaZYu9Hp9Mxbdo0Hn/8cebNm8eKFSuoW7cu2dnZbN26FbvdTpcuXYoVmImOjmbixIl89913VKxYUXsfioiIYPTo0dp6Pj4+9O3bl3nz5vHcc89pr9UtW7YQGBhITExMnozH4r72Lmf8+PEcP36c1atX06FDB5o0aUJWVhabN2/GZrPRrVs3Lah8pe6++25mzpzJwYMH6dixIw0bNsRms7Fjxw7S0tKuyefJo48+ys8//8y3337L9u3bqVmzJklJSezYsQNFUbTXSFGPUa5cOVq0aMHatWupWbNmnj/kGI1G3nzzTYYMGcILL7xA3bp1qVChAhcuXNBGYn3ttdfyHaDC7VZ4nQkhhJtkSgkhbmv9+/dn6tSpREVFcfDgQf744w+ysrJ47LHHWL58uRYY+uOPPwrdT2hoKN988w0vvPACpUqVYsuWLezcuZOqVavy2muvMW/evDwj7U2YMIG3336bunXrsm3bNtavX0/58uUZM2YMEyZM0NbT6/W88847VK9enX379rFu3Tqt3lXnzp359ttv6datG4mJifz++++kpqZy//33s2TJEo+i2Veqc+fOLFmyhB49epCWlsaff/5JQkIC7dq1Y8GCBR4/GnQ6He+++y4vv/wyVapUYfv27fz555+4XC4ee+wxli1bdtmR7QwGA/Pnz6d///6EhISwdu1atm7dSsWKFRk3bhzfffcdAQEB7Nq166p+SFSuXJnFixfz8MMPk52dzR9//IGvry+zZ8/WhmIvquHDhzN79myaNWvG/v37+fPPP/H19eXZZ5/lu+++o1q1avlu16VLF+26uLTrntuVXFsFsVgsfPLJJwwcOJDQ0FA2bdrE2rVr8fHx4ZlnnmHx4sXFHhkuN71ez7Rp05g0aRL16tVj+/btrFu3jjJlyjBs2DAWL16cp95Qca/hjh070r9/f3x8fPj777/Ztm3bZdv18ssv895771G3bl22bt3Knj176NWrF3Pnzi3S4yrueXv44Ye1emp///13vhlKxVGmTBm+/PJL6tWrx9q1azl06BDt2rVj0aJFtG/f3mPdqKgovvjiC1q3bk1CQgJr1qwhLCyMefPm0a1btzz7Luz9JT/X+z2nYcOGfPnll3Tp0oXU1FRWr17N3r17adSoEe+//z6vvPLKVe3/Wqhbty4vv/wy4eHhrFu3jvXr11/xvqpWrcqyZct48skn8fHxYd26dezfv5969eoxadIk3n333SIX4wf12ps4cSJWq5XVq1ej1+t58skn+eqrr7SsIbdXX32V1157jerVq7N9+3Z2797N3XffzeLFi/Ot43Qlr73ChIaGsmjRIp5//nlCQ0O110p0dDTTpk1j6tSpxR4U4FJ+fn5888033HfffVgsFv766y927dpFnTp1mDZtGgsWLECn07F27VrsdvsVHaNBgwZ8+eWXtG7dWrtmT5w4QceOHfn6668ZOnQocPnvEbk1atQIyJsl5da5c2c++eQT2rRpw9mzZ1m1ahWHDx+mTZs2zJ8/n4cffrjQ/d8KrzMhhHDTKddibFwhhLiNtG7dmri4OFavXn1VGQFCCCGEEJfq0aMHx44d46+//tK6BwohxH+VZEoJIUQuKSkpJCUlodPp5IuiEEIIIa6J7OxsFEVh3rx5HDhwgK5du8r3DCGEQGpKCSEEoBYgHzBgAElJSTgcDho1alTkLlNCCCGEEIXp3LkzFy5cwGaz4ePjw+DBg290k4QQ4qYgmVJCCIE6ElJCQgIXLlwgJiaGyZMn3+gmCSGEEOI20bBhQxRFITIykg8//PCy9ReFEOK/QmpKCSGEEEIIIYQQQogSJ5lSQgghhBBCCCGEEKLESVBKCCGEEEIIIYQQQpQ4CUoJIYQQQgghhBBCiBInQSkhhBBCCCGEEEIIUeIkKCWEEEIIIYQQQgghSpwEpYQQQgghhBBCCCFEiZOglBBCCCGEEEIIIYQocRKUEkIIIYQQQgghhBAlToJSQgghhBBCCCGEEKLESVBKCCGEEEIIIYQQQpQ4CUoJIYQQQgghhBBCiBInQSkhhBBCCCGEEEIIUeIkKCWEEEIIIYQQQgghSpwEpYQQQgghhBBCCCFEiZOglBBCCCGEEEIIIYQocRKUEkIIIYQQQgghhBAlToJSQgghhBBCCCGEEKLESVBKCCGEEEIIIYQQQpQ4CUoJIYQQQgghhBBCiBInQSkhhBBCCCGEEEIIUeIkKCWEEEIIIYQQQgghSpwEpYQQQgghhBBCCCFEiZOglBBCCCGEEEIIIYQocRKUEkIIIYQQQgghhBAlToJSQgghhBBCCCGEEKLESVBKCCGEEEIIIYQQQpQ4CUoJIYQQQgghhBBCiBInQSkhhBBCCCGEEEIIUeIkKCWEEEIIIYQQQgghSpwEpYQQQgghhBBCCCFEiZOglBBCCCGEEEIIIYQocRKUEkLcdBRFudFNuCK3aruFEEKI2418Jov8yHUhxM1HglJC/Ac99thj1KlTh927d+e7vH379owYMeKqjxMZGcmMGTOKtc3ixYuZPHnyVR+7pB06dIiHH37YY96VPH4hhBCiqLZt28bzzz9Py5YtiYqKokOHDrz++uscOXLkRjfNw4wZM4iMjCyx423bto2BAweW2PFuNkOGDMnzPW7EiBFERkYWOJ05c6ZI+96zZw9169Zl6dKlHvPT09OZPHkyHTt2pGHDhnTv3p2FCxficrmK1Xb3tZJ7qlOnDs2aNeO5557j0KFDRd7Xp59+yiuvvAJAamoqr776Klu3bi1We67UiBEjaN++faHrLF26lMjISE6fPl3k/RZlmwsXLnDnnXdy6tSpIu83t4yMDMaNG0fLli2Jjo7mqaee4ujRo5fdLiEhgZdffplmzZrRuHFjhg4dSlxcnMc6ixcvzvf6Gz9+/BW1VdwejDe6AUKIG8PpdDJy5EiWLl2K2Wy+LsdYtGgRZcqUKdY2c+bMISYm5rq053r65Zdf2LFjh8e8K3n8QgghRFF89NFHvPvuu7Rq1YrXXnuN8PBwTpw4wVdffUWvXr2YNGkSXbt2vdHNvCEWL1580wXmSoLL5WLSpEmsXLmSXr16eSwbNGgQffr08ZiXkpLCCy+8QExMDOXKlbvs/m02GyNGjMDhcHjMVxSFF198kd27dzNkyBCqVavGhg0bmDBhAsnJyTz33HPFfiyLFi3S/u90Ojl79izTpk2jb9++rFixgvDw8EK3P3LkCB9++CHLly8H4N9//+X777/nvvvuK3Zbrpc777yTRYsWUapUqWu63+DgYPr3789rr73GggUL0Ol0xdr+5Zdf5p9//mHYsGH4+fkxc+ZM+vXrx4oVKwgMDMx3G4fDwVNPPUV6ejpvvPEGDoeDqVOn8uSTT7J06VJMJhOgPg9Vq1blf//7n8f2YWFhV/ZgxW1BglJC/Ef5+/tz6NAhZs2axUsvvXRdjtGwYcPrst9bxX/98QshhLg+/vjjD6ZOncrzzz/P4MGDtfkxMTHce++9vPzyy4wYMYKIiAhq1qx5A1sqSsr+/fuZMGECu3fvxsvLK8/ySpUqUalSJY95zz//PIGBgbzzzjtFCly89957pKWl5Zm/b98+1qxZw3vvvcfdd98NQPPmzUlJSeHjjz9m0KBBxQ6MXPodqnHjxpQtW5a+ffvy3XffXTYT7u2336Zbt26ULl26WMctSSEhIYSEhFyXfT/yyCPMmTOH3377jc6dOxd5ux07dvDHH3/w0Ucf0bZtWwCaNGlChw4d+PLLL3n22Wfz3e6XX35h3759rFixgho1agBQu3ZtunXrxs8//0yPHj0ANSgVFRUl35GFB+m+J8R/VO3atbn33nv5+OOP2bNnT6HrOp1OFi5cSPfu3alfvz533nkn77zzDlartdDtcndf27RpE5GRkWzYsIEBAwbQoEEDWrZsydtvv43T6QTUboNnzpzhu+++80hNPnv2LEOHDiUmJoYGDRrw+OOPs2/fPu04p0+fJjIyknnz5nHXXXfRoEED5syZQ2RkJH/88YdHm/79918iIyP57bffALBarUyZMoW2bdtSr149unfvzk8//eSxTfv27Zk+fTqTJ0+mRYsW1K9fnyeffJLjx48Daqr5zJkz8zzmS7vvxcXFMXLkSNq2bUv9+vW5//77WbVqVZ5ztnDhQkaNGkVMTAzR0dG88MILJCQkaOucPHmSZ555hmbNmtGgQQMeeugh/vrrr0KfCyGEELePmTNnUq1atXwzUEwmE+PHj8dgMDB37lwABgwYQO/evfOsO2jQIO3HIsDWrVt59NFHadCgATExMQwfPpykpCRt+dKlS6lTpw6LFy+mZcuWxMTEcPjw4SJ/Lv3555/06NGDqKgounTpwrJlyzyWF+Vz0mq1MmvWLO666y6ioqLo3LkzH330kdZNbMSIEXz33XecOXOGyMjIPN3M3GbMmMFdd93Fb7/9Rrdu3YiKiqJnz57s2LGDnTt38sADD1C/fn26devGhg0bPLY9ePAgTz/9NI0aNaJRo0Y899xzebpK7d+/n8GDB3PHHXdQt25dWrduzYQJE8jOztbWKcpnvru71qZNm/J9HG7Dhw/H6XSyaNEiQkNDC10X4K+//uLXX39l5MiRBAQEXHb97du388UXXzBmzJh8lz/00EM0b97cY161atXIzMwkMTHxsvsvinr16gFoXQ1nzJhBp06dmDlzJjExMbRq1YqUlBQOHjzIn3/+Sbdu3QD1O2i/fv0A6NevH4899pi2z59++onevXsTHR1Ny5YtGTNmDCkpKR7H3b17N08++STNmjWjUaNGPPPMM0XuRrh06VK6dOlCVFQUPXr08Hhd5NcV77vvvuOee+7R1t+wYQN16tTJcx3/888/9OnTh6ioKO68804+/vhjj+Vms5kuXbrw4YcfavPc38ULek0ArF27Fh8fH1q1aqXNCwkJoWnTpoV+11y7di1Vq1bVAlIANWrUoHr16tp2iqJw4MABateuXeB+xH+TBKWE+A977bXXCA4OZuTIkdhstgLXGzNmDJMmTaJjx47MmTOHvn378sUXXzBo0KBiF4x85ZVXaNy4MR988AHdunXj448/ZvHixYD6JTs8PJy2bdtq6cxJSUn06dOHvXv3Mnr0aKZOnYrL5aJv3755UvNnzJjBU089xZQpU+jVqxeVKlVixYoVHuv8+OOPBAUF0bZtWxRF4bnnnuPrr7/miSeeYM6cOURHR/PSSy/l+aK8YMECjh49yqRJk5gwYQJ79uxh+PDhADzwwAPcf//9gJpu/sADD+R53AkJCdx///1s3bqVl156iRkzZlC+fHmee+45LbXcbdq0abhcLt59911effVV/vjjD9566y1ATc1/+umnycrKYsqUKcyePZugoCCeffZZTpw4UaznQgghxK0nKSmJPXv20K5duwKzT4KCgmjRooUW0OnRowd79+71+JxITU3l77//pmfPngBs2bKF/v374+XlxXvvvcdrr73G5s2b6devn0cgxel08umnnzJx4kRGjhxJ1apVi/y5NGbMGPr378+cOXMoU6YMI0aMYP/+/UDRPicVReGZZ57h448/5oEHHuCDDz7grrvu4r333mPs2LGAGmhr27Yt4eHhLFq0iDvvvLPAc3n+/Hn+97//8cwzz/D++++TmprKkCFDGDp0KA888ACzZs1CURReeukl7RwcO3aMPn36kJiYyOTJk5k4cSKnTp3i4Ycf1oIvcXFx9O3bl6ysLP73v/8xd+5cunbtyueff86CBQs82lDYZz5c7OJVt27dAh8HwJQpU/jqq6+oVatWoeu5z+PkyZOJiYnhrrvuuuz6WVlZjBw5kqeffjrf2mB169Zl/PjxBAUFecz//fffr2k20LFjxwA8Mr7Onj3LX3/9xbRp0xg5ciSBgYH88MMPhIeHa9k4devW1YJpY8aM0a6V2bNnM3ToUBo2bMj06dN57rnnWLlyJY899pj2fG/cuFGrGfrWW28xYcIEzp07R58+fS7bRfTcuXN89NFHvPDCC8yYMQOdTseQIUMKDNItW7aMESNG0KhRI2bPnk2XLl0YNGiQ9sfb3N544w26du3KRx99RHR0NG+//XaeP8Tedddd7NmzRztvdevWvexr4siRI1SoUAGDweAxv1KlStp+CtquSpUqeebn3u7kyZNkZGSwe/duunTpQt26dfMNTov/Hum+J8R/WGBgIOPHj+fZZ58tsBvf4cOH+fbbb3n55Ze1VOmWLVtSqlQpXn31Vf7++28tvbcoHnjgAe0vu82bN+f333/nzz//pE+fPtSpUwez2UxISIj2ReKzzz4jOTmZr776ivLlywPQpk0b7rnnHt5//32mT5+u7fvuu+/2qBXQo0cPPv30U7Kzs/Hy8kJRFH766SfuuusuzGYz69atY82aNUybNo177rkHgNatW5OVlcU777xDt27dMBrVt8mAgABmz56tfUifPHmSGTNmcOHCBcqUKaPVjiooHXnevHkkJSWxcuVK7XG0bduW/v37M2XKFLp164Zer/6dICIigkmTJmnb7tq1i19++QWAxMREjh49qn3pBqhfvz4zZ84sNLAohBDi9uDOEnF/lhSkcuXKrFq1ipSUFDp37sy4ceP48ccftc/gX3/9FafTqWWTTJ06lapVq/Lhhx9qn3UNGjSga9euLFmyhL59+2r7fuaZZ7QftvHx8UX+XJowYQJt2rQB1B+rnTp1YvPmzdSqVatIn5Nr1qxh/fr1vPvuu1q9rJYtW+Ll5cX7779Pv379qFmzJiEhIZjN5st2EcrKymLs2LFamw4fPszUqVOZOHGi9semzMxMhgwZwrFjx6hduzYzZ87E29ub+fPn4+fnB6jfZzp27MjHH3/M8OHDOXjwILVr1+b999/X1mnRogXr1q1j06ZNHl3PCvvMh6J38SpOIfnVq1dz5MgRXn/99SKtP3XqVHx8fHj66ac5f/58kbb57LPP2Lx5MyNGjNC+3xRH7rpV2dnZ7N+/n7feegt/f3+P7D6Hw8Hw4cNp0qSJNm/jxo1ERUVpQVs/Pz8tg6dGjRrUqFGDlJQU5syZw4MPPuiR/RUREUHfvn21a37q1KlUrlyZjz76SHtdtGrVik6dOjF9+nTef//9Ah+Dy+Vi1qxZVK9eHQCLxUL//v3ZuXMnHTp0yLP++++/T7t27ZgwYQKgfic1mUxMnTo1z7pDhw7VgmUNGzbkt99+Y+PGjbRr105bJyoqCoANGzZQtWpV/Pz8LvuaSEtL067Z3Hx9fcnIyCh0u8qVKxe63b///guoPRxGjBiB0Whk2bJlDB8+HJvNxoMPPlho28TtSzKlhPiPa9++PT169ODjjz9m7969eZZv3rwZIE+x1K5du2IwGC6bTn6p6Ohoj/tlypQhMzOzwPU3bNhA7dq1KV26NA6HA4fDgV6vp02bNqxfv95j3UvTgXv06EFmZqb2l6Pt27dz9uxZ7a/CGzZsQKfT0bZtW23fDoeD9u3bEx8f75GaHRUV5fFXI3cQKisrq0iPe/PmzURHR+f5EdGjRw/tC73bpV8YypQpox0nLCyMGjVqMHr0aIYPH84PP/yAy+Vi5MiRUjdECCH+A9wZyu7CwQVxf2YpioKPjw8dO3b06J6+YsUKmjdvTunSpcnKyuKff/7Rsojdn4cVK1akevXqrFu3zmPfuT9vi/O5lDtwUKFCBUDN2IKifU5u3rwZo9GYJ7vHHaRwf2cpjkaNGnk8FlCDcW7u7B93Ozdu3EhMTAxeXl7aefLz86NJkyba95JWrVrxxRdfYLFYOHz4MKtWrWLOnDkkJSXlCdQV9pl/vSxcuJDatWvTokWLy667adMmFi1axKRJk7Q/1F3OF198waRJk7j77rvp37//FbWxbt262tS4cWP69u2LzWbTsupzu/T736lTp7TrqyA7d+7EZrNpQVm3Jk2aUL58eTZv3kxmZia7d+/m7rvv9vgOGBAQQLt27S57vQUHB2sBKbh4zedXl+vEiROcPXs2z7Vd0GAFuV9L3t7ehIWFadeom7+/PwEBAcUa3a+wHhCF1QUrynZNmzblgw8+4LPPPqNdu3a0bt2aqVOn0qJFC6ZPn17s3hfi9iGZUkIIXn/9dTZs2MDIkSNZsmSJxzJ3v/pLvwAYjUaCg4Pz/WAtzKXFN/V6faEfQsnJyZw4caLAtPXcX9x8fHw8llWuXJno6GhWrFjB3XffzYoVK6hUqZL2BTQ5ORlFUTy+kOYWFxenfdHx9vbO026gyEMdp6SkULFixTzz3V+Ac3+RyO9Y7nOk0+n49NNPteKVy5Ytw2Qy0bFjR8aNG1fgqChCCCFuD+6gjTtjqiCnTp3C19dXC6r07NmT5cuXs3//fsLCwti0aZPWTSw1NRWXy8XcuXO1OlS5WSwWj/u5P2+L87mUezv356j7860on5MpKSkEBwfn6Vrk/o5S3O8kQL5ZIZd+DueWnJzMTz/9lKf+JKBlNLm74y1cuJDMzEzKli1L/fr185zH/I51ue9FVys5OZlNmzYxdOjQy66bkZHByJEjeeqpp6hRowYOh0P73uNyuXA4HB6BKpfLxZQpU5g3bx7dunVj8uTJxS5w7vbtt99q/zeZTISHhxdYK8vX19fjfnp6eqHPIVz8fpvfqG9hYWGkpaWRlpaGoiiFrlOYS7+Xus9Fft8d3bXbLn2MBY1KV9Trxtvbm/T09ELbmZufn59HTTO3jIwM/P39C90uv0yq9PR0bbvQ0FCPTC63tm3bsn79ehISEi47qqK4PUlQSghBYGAgb7zxBs899xyzZ8/OswzU9Pzcf7202+1cuHCB4ODg69o2f39/YmJiePXVV/NdbjabC92+R48eTJo0ibS0NH755Rct1dm9bx8fnzz1HdzyS0O+UoGBgcTHx+eZ755XnPNYunRp3njjDcaOHcv+/fv55ZdfmDt3LsHBwVqdBCGEELen0NBQGjZsyMqVK3nhhRfy7RqVnp7OunXraN++vTavefPmhIeH8/PPPxMeHo7FYtFG5fL19UWn09G/f/98MzMu9wP/WnwuFeVzMjAwkAsXLuB0Oj0CU3Fxcdo615u/vz8tWrTgiSeeyLPMHaD56KOPmD9/PuPGjaNz587aj3J3l8Abac2aNTgcjiLVktqzZw9nzpxh1qxZzJo1y2PZqFGjGDVqFAcOHADAZrPx8ssv8+uvvzJgwABeffXVKw5IwcWuZ1ciKCjosgEj9/fbhIQEqlWr5rEsPj6eihUr4u/vj06nyzdIEx8fn6eG1tVwZ+BfWm/qaovEp6amFut1UbVqVdauXYvL5fJ4bzlx4oRH1ld+27m75+V28uRJ6tevD6gDKZw6dYpevXp5rGO1WjEYDPKH1f8w6b4nhACgY8eOdOvWjY8++shjpJ2YmBiAPAXDV6xYgdPppHHjxte0HZd+uY6JieHYsWNUrVqVqKgobfr+++/59ttv8/y19FL33HMPiqLw/vvvk5iY6FGHICYmhszMTBRF8dj3wYMHmTVrlkc9g+K2+1JNmzZlx44def6yvXz5csLDw4scANuxYwctWrRg165d6HQ6ateuzUsvvURERARnz54tcnuFEELcugYPHsyxY8d499138yxzOp2MHTuW7Oxs/u///k+bbzAY6N69O3/88Qe//PILHTt21DI5/Pz8qFOnDkePHvX4PKxZsyYzZswotKv+tfpcKsrnZExMDA6Hw6PmknsdQPtOciU1jIrKPeJg7dq1tfNUr1495s+fr43su23bNmrUqMF9992nBaRiY2M5ePBgkTOsr5d//vmHMmXKXLYmGahd6L799luPac6cOYB6DebOZho5ciS//fYbI0eOZPjw4VcVkLpa5cuX59y5cx7zLv2+2KBBA8xmMz/++KPH/K1bt3L27FkaNWqEj48P9erV4+eff/YoNp6Wlsaff/55Tb8DlylThkqVKmnXkNuvv/56xftMSUkhKyuLcuXKFXmbVq1akZGRwZo1a7R5SUlJbN26lZYtWxa63ZEjRzh8+LA27/Dhwxw5ckTbbuPGjYwYMcKjYLrL5WLlypVER0df9g/N4vYlmVJCCM3o0aPZuHGjx1+EatSoQa9evZg+fTpZWVk0bdqUf//9l5kzZ9KsWTNat259TdsQEBDAvn372Lx5M/Xr16d///58//339O/fnwEDBhAcHMxPP/3EN998w8iRIy+7P/dIe19++SXR0dEewZ+2bdvStGlTBg0axKBBg6hevTq7du1i+vTptG7dulijxbiHU/7xxx9p0KBBni4ITzzxBMuXL6d///4MHjyYoKAgli1bxsaNG3nrrbeK/AW6Tp06eHl58eqrr/L8888TFhbG+vXr+ffff7XhjoUQQtzeWrduzYgRI5gyZQr//vsv9913H6VKleL06dN89dVX/Pvvv0ycODHPSGw9e/bk008/Ra/X5+mmN3ToUAYOHMjLL79Mjx49tFH2/vnnHwYNGlRgW67V51JRPifbtGlDs2bNeP3114mNjaVWrVps3ryZuXPn0qtXL62YdUBAAAkJCfz111/Url2bUqVKFePsFm7QoEH06dOHp59+mocffhiLxcKiRYv4/ffftcFX6tevz+zZs/noo49o2LAhJ06c4MMPP8RmsxW7XlRSUhInT56kRo0a+XY1LK4DBw5o5yk/J0+eJCkpiYYNG+Ln55cnY8ldn6h8+fLast9//50ff/yR9u3b07BhQ3bu3OmxjXsgm/Pnz3P+/Hnt/vXSsmVLvvzySxRF0YJj7uDgn3/+SWBgILVq1WLgwIHMmjULk8lEu3btOH36NO+//7723Rfg5Zdf5sknn2TgwIE88sgj2O12PvroI2w2mzZowLXgHpnvlVdeYezYsXTq1In9+/drGWpXEmjdtm0boAaMQM2gPHz4MJUqVSrwO27Tpk2JiYlh2LBhDBs2jKCgIGbMmIG/v79Hb4PDhw9js9moU6cOoP4R+IMPPuCpp57i5ZdfBtQC+REREdx9990A9OnTh6+//ppnnnmGF154AW9vb7788ksOHjzIwoULi/34xO1DglJCCE1QUBBvvPEGgwcP9pg/ceJEKleuzJIlS5g7dy6lSpWiX79+DBo06Jr/NXLAgAG89dZbPPnkk8ybN48mTZrw9ddfM3XqVN544w2sVitVqlTxGBnncnr27Mnvv/9O9+7dPebr9Xo++ugj3n//fT788EMSExMpXbo0TzzxRLG/aHTu3Jnvv/+eESNGcP/99/PGG294LA8PD+err75i6tSpTJgwAbvdTq1atZg9e3a+I7AUxGKx8Omnn2qjA6WmplKlShXGjx9P7969i9VmIYQQt64nnniC6OhoPvvsMyZPnkxSUhLh4eG0bNmSiRMn5ht4qFWrFhEREVy4cIHmzZt7LGvVqhWffPIJM2fOZMiQIZhMJurWrcu8efMKHbHrWn0uFeVzUqfT8eGHHzJ9+nTmz59PUlISFSpUYOjQoR7d6Xr37s1ff/3Fc889x5AhQzxGu7tatWrVYuHChUybNo1XX30VRVGIiIhg1qxZWjuffvppLly4wIIFC5g1axZly5alZ8+eWvtTU1O1P2Zdzp9//snIkSNZsGABzZo1u+r2JyYmFjpS3+zZs/nuu++0bnlF4c7mWb16NatXr86zfNWqVVSoUIHFixczc+ZM7f710rlzZ2bNmsWuXbu0ovU1a9akW7duLFy4kDVr1vDjjz9qQdQvvviCRYsWERQUxF133cWLL76oZRE2b96cefPmMX36dIYOHYrZbKZJkyZMnjz5mg8w0717dzIzM/nkk09YsmQJNWvW1LpJXlqfqij+/vtv6tevr2XF7d27l379+jFp0qRCX5szZ87kf//7H1OmTMHlctGoUSPee+89j+5148aN48yZM9rzbTabmTdvHhMnTmT06NGYTCZatmzJyJEjtW6tYWFhLFy4UHuNZ2RkEBUVxfz58z0GFxD/PTpFytwLIYQQQgghhLjO+vbty3vvvXfdC1o/88wzBAcHM2nSpOt6nGvpxx9/pE6dOh41rv7880+efvppvv/++zyZj4XJzMykdevWTJ48mY4dO16P5gpxzUimlBBCCHEZTqcTu91+o5shrhGTyXTZenRCCCGurU2bNpGVlVXgiHLX0ksvvcQjjzzC888/X6yaSjfS8uXLmTZtGi+++CJly5blxIkTTJ8+nZiYmGIFpAC+/vpratasWaxsfCFuFMmUEkIIIQqgKArnz58nOTn5RjdFXGNBQUGUKVPmhhbjFUKI/5IzZ87g4+NTIqMkgjoK4v79+/MdEOBmdOHCBaZOncrff/9NUlISYWFhdOnShSFDhuDr61vk/SQlJXHvvffy+eefX9ORpIW4XiQoJYQQQhTg3LlzJCcnU6pUKXx8fCSAcRtQFIXMzEzi4uIICgqibNmyN7pJQgghhBD/WdJ9TwghhMiH0+nUAlKhoaE3ujniGvL29gYgLi6OUqVKSVc+IYQQQogb5IqHzbLZbHTr1o1NmzZp806dOkX//v1p2LAh99xzD2vXrvXYZv369XTr1o0GDRrQr18/Tp06deUtF0IIIa4jdw2pKxnxRtz83M+r1AoTQgghhLhxrigoZbVaGTp0KIcOHdLmKYrCc889R1hYGEuWLKFnz54MHjyYs2fPAnD27Fmee+45evfuzbfffktISAiDBg2iqL0HFUUhPT29yOsLIYQQ14J02bs9/VefV/k+JYQQQoibSbGDUocPH+bBBx/k5MmTHvM3btzIqVOnGD9+PNWrV+fpp5+mYcOGLFmyBIDFixdTr149BgwYQM2aNZk0aRJnzpxh8+bNRTpuRkYGjRs3JiMjo7hNFkIIIYQQyPcpIYQQQtxcih2U2rx5M82aNWPRokUe8//55x/q1Knj0c2hcePG7Ny5U1vepEkTbZm3tzd169bVlgshhBDiv+fnn38mMTERgBkzZvDYY48BsHTpUtq3b1/gdiNGjGDEiBEl0kYhhBBCCHF9FLvQ+SOPPJLv/Pj4eEqVKuUxLzQ0lPPnzxdp+U0h9RC4bGDyA6M/GP3AYL7RrRJCCCFuS2fOnOHFF19k1apVAAwYMEALSgkhhBBCiNvfNRt9LysrC7PZM4BjNpux2WxFWn7DnVgE6/rknW/wBlMgmIPVW0soWMLBKxwspcC7LPiUB+/y6q1RCuIKIcTtrrB6PAqXqdWjFGGdQvbnPraiKGj/8pnnPo57uXs/hf1fO16ubS9dr9Dt8rmfZ/tcj/98svqHqRPJJ8jyztK2i0uM43zaeexOOwcSDuTZl6/ZF0VRbuq6ULGxsUycOJGNGzdisVi45557GDp0KBaLhVOnTjF69Gh27txJuXLleO2112jVqpW27fr163nrrbc4deoUDRo0YOLEiVSsWFFbPn/+fD755BPS09O5++67GT16tDaioBBCCCHEreSaBaUsFgvJycke82w2G15eXtrySwNQNpuNgICAa9WEqxNQCwLqQNZZcGaqGVMAzix1yi5iRpcpCHwqgm8l8K0CflVz9h0JBh/QGdRJbwD0F+/rDKA3gu6KB0QUQogbTguKKAouxeURECnqssvdAkVe5nKpx3EpLvW4uHC6nNhddmwOG1anFZvTRrYjG7vTjtVp1W4NLgNR5ijiMuIw2o0Xj+NygTMzT1CooLZp8woI8mj/12I2eddx6iwoxYi//LLkF35a/BOpSalUqFqBxwY/htPpZOJLE1n4x0JtvQ/+9wEAz4x4hoz0DOZOmcve7XtBB9F3RNP/xf74+Kp/bPnpm59YuXQlaSlpRNSLYMDQAZQqWwpFUVj2+TJ+X/47tmwbkfUj6f9Cf8JKhwHQt11fnnrlKb7/8ntSL6TSqEUjnnz5Sby8vRjQewAAA+4bwMDhA0k4n8C/O//l9fdeJ8uZhUtx8fGsj/l12a94+3jT/eHudOndhUxHJgoKOi6elN9++41p06Zx5swZatasyauvvkpMTEzRT9o1pCgKQ4YMISAggIULF5KSksJrr72GXq/n1Vdf5bnnniMiIoIlS5bw+++/M3jwYH766SfKlSunDQzz/PPP07p1a2bNmsWgQYNYvnw5Op2OlStXMnPmTN5++21CQ0MZOXIkb7/9NmPGjLkhj1UIUTROl4JBf/MG0ovidngMQoibzzULSpUuXZrDhw97zEtISNC67JUuXZqEhIQ8y2vXrn2tmnB1ghtAt73gsquTIwNsF8CacHHKTgBbYs79JLAl5dxPVNd1WcGeDCnJkLL7kgPo1awq38rgH5ETqIoAgxcXg1M5twYL6M05kwX0ppyAVc7k/r/elHNrKPnzJYS4qeQO9LgUl8fkXpZ7eWHzct93upxaIMepONV5ilOb73K5tP9fGmxyt0tBDQZp2Tc58xxOB1anlWxHNlaHlWxnNlanFatDDRRpk8OGzWXD6rBqwSSby4bdab9467x4655nd9kLvC2Kyr6V+aDlBxgyDGDVTjS1dvwffqm7rtMzmb+0gAYciJ4LhWQGuQM0xw8f56sPv2Lo+KFUrFqRn5f8zPQ3pjNk9BAALAaLtr5BZ0CHDm+jN19+9iVpyWn8b87/cDgdvDf+PX768ieeeO4Jfln2C98t+I7BwwdTPbI6Cz5YwKzxs3j/0/dZ/u1yNq7ayIjxIwgOCWbpl0t5e/jbfLDwA4xG9WvGknlLePG1FwkJDeGdN99h4fsLGfnmSGbOn8ng/oOZOX8mVatVZdHnizAbzJTxK0OQJYiE2ATiTsYxe95sDvx7gHcnvkt03WiaxDTxCEjt37+f4cOHM27cOOrXr89ff/3FU089xfLly6lcufL1eloKdPToUXbu3Mm6desIC1ODc0OGDGHy5Mm0adOGU6dO8fXXX+Pj40P16tXZsGEDS5Ys4fnnn/cYGAZg0qRJtGzZUqvpuWDBAh5//HHatWsHwLhx43jyyScZNmyYZEsJcRMz6HW88PUODsel3+imXJEapfx4v0/0jW6GEOI2dM2CUg0aNOCjjz4iOztby47atm0bjRs31pZv27ZNWz8rK4t9+/YxePDga9WEa0NvUiejj9pFj4i86ygucGZfzKKyJasBK2scZJ5Rb23JkB0HGcch/QjYUyDrjDolrFf3ozOpgamQJhDSGPxrgd4FtmzACS4n4IJLu4m4s6rIudUbQe8NRm+1u6HelBPQMuUKbpkkC0uIGyh3EMipOC/+3+X0CCDlXnbpZHfacSpOHE4HDpdDDQ4pThwuR94AVE4QyD0f8JjvDhrpdDot48TdHSp31g6A1WHVgkfuKc99h5UsR5ZHgOnS5bnnuQNMNwODzoDJYMJsMGPSmzAZTJj0Jir6VcRkMGExWDCY1OCNDtDrr9lHZ5F5m7yoGVITnU4POnLaotP+n9vZf86i1+lpEtmEGjVr0KhqI3Z13qU9p9WCq2nr+pv9AagSVIX0hHSC/INoXKsx3t7eVH6vMgoKFQIqsPrH1Tza71H69FK7uVcbU40Fny0g2BTMsoXLGPH6CNq2aQtAozcb0bl9Zw7tOETbO9V5A/5vAN06dwNg5GsjGTRwEGPHjqVKmSrq8ctUoWxwWbyMXhj1RoK9gvEx+WCxWHhr0lsEBQXRsE5D9u3Yx8/LfqZl85Yej/mTTz7hwQcfpHv37gD069ePLVu28NVXX92QYujh4eF8/PHHWkDKLT09/aoGhmnSpAm7d+/2+O7UsGFD7HY7+/fvJzpafjAKcTM7HJfO3rOpN7oZQghxU7lm36xjYmIoW7YsI0eOZNCgQfzxxx/s2rWLSZMmAXDffffxySef8NFHH9GuXTtmzZpFhQoVaNas2bVqQsnR6dWglbt+lE8FNVDlyAB7mhqMyo5T7xtMYAwEZwakHYbU/WoWVfJuNbsqZa86HfsMjL4QGgOl20Gptur9SykKKM6cyaHeOq3qsbJz7mvt1KnBKIxqOwzeavF2o8/FLCyDJScby1zoX+CF+K9yB47c2UFallBOACm/eTaHDYfiwO6043Dl3Co5gSOXS8sqcmce5Q4ouWnBIff9nH96nR6dTr3V6/T5zjPmBE2yHdlk2jO1KcOWQYY9g0x7Jum2dDLsGeptrvm5/++eilP/6EpZDBa8jF54Gb2wGCxYjDmT4eKtl9ELs8GMxWBRb3Pmmw3mfO8XNLkDT+7/GwrKNnWAMdVIef/yWLws2mylw19kODOv+znxYPDBWMT36OYtmlOjZg0e7P0gtWrXom27tvS+rzcnTpwodLuHH32YoUOG0qFtB5o1a0aHzh24+567ATh+/DhP13kaULPdQsJCePHlF8nMyCQ2NpYRw0agz/WHD6vVyvHjx2mlqHWSohpE4XA5AIioHYHT6eTYsWMEBgcCaFlsLpf6OrA77ThdTsqVL4evvy92px0FhZq1arL8u+V52n7kyBF+/vlnj5GB7Xa7R52mkhQQEEDr1q21+y6Xiy+++II77rjjqgaGSU1NxWq1eiw3Go0EBQXdXAPHCCFuO+F+ltui+97t8BiEuN1cs6CUwWBg9uzZjBo1it69e1O5cmVmzZpFuXLlAKhQoQIzZszgrbfeYtasWURHRzNr1qybukhpsej0YPJXJ59y4MhSg05ZsWA9B04bBEVBeHN1fUVRs6aSdkDCBkjcpGZTxf6hTnoLhLeEsp0hvLUaPAI1cKQzoj51loJak3MMlxq4cndJtKWoXQ9zfhiALifLKiebyugHpgA140pvUYNYBq+crCwhbl0uxYXDlZNd5HJqWUbu++7/u7uT5e4iljvYdGlmkzujKHd2kU6nBo8MeoMWJMo9mQwmbbk7oKQFmC55P3QpLtKsaaRaU0m2JpNqTc0zpdnSSLOmedym29JJt6Vf02CSDh0+Jh98TD54G73xMfvgY/TB2+SNt9E7z62X0evivJz57qDTpZPFYLm1Pgt0uvz/aFDCchcOz33f4mVh3sJ5bN+6nTV/rmH5suUsXrSYt6a8BXAxQ00Bu8OO3qDH7rTTsElDvv/le9b8uYZ1a9YxcdxE1q1dx7i3xmE0GLVaXLnZ7Gr9xUlvT6JylcoXs7Z0EBB4sWakyWTCoDOomV05xbGMBiPmnBFu3UFFo96IXq/Hy6QGII1GI96mi13STDoTFrPFYx6A0+nkqaee4t577/WY787cvtHefvtt9u3bx7fffsv8+fOveGCY7Oxs7X5B2wshxPUQ4G2ULohCiOviqqINBw4c8LhfuXJlvvjiiwLXb9u2LW3btr2aQ946jDnd6bzLgr0KZJ6FzNNq/SlLKJj81AwrnwpQobua4ZTyL8SvhXO/QuZJiF2tTqZAKN8dKt4HvhUve2iNTg+6nIBTfhQFFLta1N1lB2us2kYUQKdmV+kt6o8vY4DaZoNPTtDKS2pZiRsid4CpoMnmtF2sUeSwqt3bXC6tu5s7sHRpdzWdTq2xo9fpPYJKBp1By6jJPa84gRR3gCkhK4Hk7OQCpxRrCinZKaRYU0i1pl51YMlsMONn9sPP5Kfemv3wNfuqtyZffM2+nrc5/3cHoHxMPviafPEyet1agaObSL5FzHMFlAqap0NX4POf+7nI3X1Ph45du3axddNWnhz4JE1jmvL8S8/T6c5O7Ni2AwB7th0/Xz8Azp09R+XKlfE2efP5Z59TM7ImD9z/AA/c/wA///Qzo0eNxs/sR+UqlTlx5AR3d1Yzp1IupNC1a1e+WfwNoaGhZCRnUKdmHUAdxOSVl19hwIABVAivAMDJIydpXF/tzr/r4C5MJhN1IupoA6S4g5hGgxG9Tq924zMYOX3qNC67S6uVtHfPXqpXq47ZYPY4B1WrVuX06dMe9aOmTJlC1apVeeCBB4r6VF0Xb7/9Np999hnTpk0jIiLiqgaGsVgs2v1Ll0s9KSFESZAuiEKIa01SYK43nQ7MwerkW0mtOZVxTM2K8ip9MQtJZ4CgeupU42lIO6gGp879AtmxcPwLdQptBpX7QHirq+9up9MVHLRSlJxglVXNsMqOVTOv0KtZWwaLOtKgOSgnUOWj3kqgSlwBd70ku+tid7fc/89dm8jmtHlmNykOj25vAHrUoJJRb7wYVDKY8NJ5aYGl4gaV8uNwOUjKSiIxM5HELHW6kHVBu03KTlJvs5JIzk7Gmbt7bTH4mHwIsAQQYAkg0BKIv8WfAHOAemsJwN/sr83zM/vhb/HH3+yPn9kPi/EyGZUiX06XE5fThUExXCz8nhNcKkoQyf1/9zV2aeBIqwWlQ+uG6c6yAzz/n0/tqHz3mzMvxC+Ejz/8mHKly9GiRQu2bNlCVmYW93S5h88+/YzPPv6Mhx56iJUrV7L/3/1Uq1oNi9FCQnwCS75dwqRJkwgKCmLVb6uoU6cOJoOJfo/1Y9KkSdSKrEX16tWZNm0aFSpUoFLFSvTv35/333+fsLAwqlWrxuzZs9m+fTsTJ07U2jR9+nTKly+PxWJhwoQJ9OrVC19fX6xWtYL8/v37CQ4OzvM8WK1Whg8fzvPPP8+2bdtYuXIlX3/9dZ71+vfvT9++fYmKiuLOO+9k9erVzJ8/n88++6y4T/019eabb/LVV1/x9ttv06VLF+DqBoYJCgrCYrGQkJBA9erVAXA4HCQnJxMeHl4Cj0gIIYQQ4tqSoFRJMvlDYC3wKqXWl8o6q2YfmYI8A0w6HQREqlPEIIhfD6e+VW8TN6mTf02o9gSU6aAGtK41nS5X8CnXfMWlBquc2Wr7M3JqlBjMavaUOwBn9FG7Axq8pVbVf5iiKGpXOOfF0dDc/7c6rWTZs8hyZGnZTO5Jq6+Uc+kYdAYMOjXIZNCrASZvvbc6PyfIdC3ZnXbiM+OJz4wnITOB+Iyc25z7CZkJJGYlkpydXOx9+5p8CfYOJtgrmCCvIG0KtARqt4FegR63JoPp8jsWHjxGCbyk/pc72y53EXjtestJFNXr9JhdZkoppTyuR3ftrtxBpEuDSUCx719LderUYeLEicyePZs333yTcuXK8fbbb1OrVi3efPNNpk2bxueff06nTp3o27cvFy5cAOCFF14gLS2NZ599lszMTJo2bcrbb78NQM+ePYmNjWXcuHGkp6cTExPD9OnTAXjyySfJyMhgzJgxpKenU69ePT755BMCAwO1Nt17772MGDGC1NRUunbtyqhRowAICQmhR48evPjii7zyyit5Hkvt2rUpXbo0Dz74IMHBwbz11lvUq1cvz3oNGzZkypQpzJgxgylTplCpUiWmTp1K06ZNr/n5LaqZM2fy9ddf8+6773LXXXdp869mYBi9Xk9UVBTbtm3TanLu3LkTo9FIrVq1SvDRCSGEEEJcGzrl0hSDm1R6ejqNGzdm27Zt+Pn53ejmXD2XE7JOQ+ohcKSr3fwuV7sp8wyc/BZOLQF3kV2fSlB9AJS7+/oEp4rKac0ZjTBb7QqITq1HZfQFS5haq8rkpwaqZBTA24bdqQaZLp2yHFlk2DPIsmfhcDq0rCen4tR+9OvQYdQbMelNWrDJqDdq0/VidViJzYglNj1Wvc2IJS4jjriMOGIzYonPiOdC9oUi78+gMxDiHUKoT6h66x1KsFcwId4hBHsHE+odSpBXECHeIQR5BWk1dETR5VdoPvetu4B27swlrSumXs2aM6AGMA16AxajBbPerI2s577+3EFOo96IQWfAaXcSezqWqlWr4uXlpQWgRPFERkayYMGCm25gk+zsbI4dO6Y9v9fSkSNH6N69OwMHDqRv374ey9yBuIiICG1gmDlz5rBixQrKlSvH6dOnueeeexg8eLA2MMzRo0f5/vvv0el0rFixgjFjxjB58mRKlSrFa6+9xh133MHrr79epLbddt+nhLiFdJ2+5pbt+tajQVmmP9zoln4MdcsFsGJI68uvKIQoUZIpdaPoDeBbGcwhkLJPreXkXUYN5BTEpzzUegGq9YeTi+DE12rtqd1vwLEvIPJ5CGtxYzKT3FlVbopLDVA5MyAtUe0OaDCDwRcs4WAOzCmq7itBqpuYw+XA6rBidVq1Ok1Wh5U0WxoZ9gwt+8nutHt0TTPqjJgMarDJZDDhbfIufJSzayjNmsbZ9LOcSzvHufRz2m1seiznM86TlJVUpP2Y9CZK+ZYizCeMcJ9wwnzC8kyh3qEEegVe80yt252iKB7F5nMXoXfX/XJzB4K0TLmcwJGXwctjdD6zwawtdwc2cweY3PeL81xl67KJ18VrdcSEKKpVq1bhdDqZM2cOc+bM8Vh24MCBqxoYpmvXrpw5c4YxY8Zgs9no3Lkzw4YNK/HHKIQQQghxLUhQ6kYz+UNwtNrNLf2oWqPJ5F/4NuZAqDEQqvSFk4vh6GeQfhi2vQAhTSFyCATWLpHmF0inz+nC53NxnjNbzaZKP6QGrQwWMPqr3RlN7iCVT8H7FNeFFmxyWsl2qIXB023ppFnTLo5E57Rp3Zx06DDpTZgMJkx6E15mr+ue3XRpe8+mneVM2hnOpJ65+P+0M5xLO0eaLe2y+/AyelHatzRl/MpQyrcUpX1LU8q3lMcUaAmUrJhiKqz4PFws+K3T6zDqjBcDRgbjxdH5coJN7qBmQZMEicTNbODAgQwcOLDA5Vc7MMzl9i+EEEIIcauQoNTNwGBWC5wbfSF1v1pc3BJ2+e2MvmrWVIVecHQenFgESVtgQz+ocC9EPKcGuW4WBi91MucUs3VmgyMDUvYDihqYMweDd+mcAFWAFE6/Rpwup0ex8GxHNum2dFKtqVgdVmwutdudu2udSa8GnMwGM15mrxLLcnKzOqycTj3NqdRTnEw5yanUU5xOPc3p1NOcTz9/2RHpQrxDKONXhrJ+ZSnrV1b9v796W8a3DAGWAAk4FYO7EL3DdbErpnvKXYvJXe/LqFOzkgIsAdqoahbDxUCTO6iZu/umPB+3r0tH6hVCiOJwuhQMevmMEEKI25UEpW4WOj34V1cDM8m7wZpQtMAUqJlTtV6ESg/CoTlw7mc4/R3ErlYDUxXuvTm7yLmDVBbU7n3OTLAlQNYZ0JvULCrvMmqgyhSkBu9EoRwuh1Y8PNuRTaY9kxRrChm2DDUjymlFURStnpPZYMZsMONr9sWkN5VoYEBRFOIz4zmefJzjycc5kXKCkyknOZF8gnPp5woNPHkbvSkfUJ4K/hUo51+O8gHlKe9fnnL+5SjrVxZvkwyNXlSXjnzoHvHQoThwPwV6vd4jgORv8cfX5Iu30Ruz0awFMN3Zc+5bCTQJIYS4Wga9jhe+3sHhuPQb3ZQrdmdkOMO6yGAEQgiRHwlK3Wx8yqk1oS78A9ZEsIQWb9sGb0LF3rBvstqlb+9bcPp7qDsKAiKuX7uvlk6nZn4ZfdX7LrtaAD5lv3rf6AdeYWpXP3Nw4bW3/gNciksLPmXZ1aLiyVnJZNgz1PpPDhugFnu2GNR6O/5mf0INoSXe7cnpcnI27SxHk49y7MIxjl44qgaiUo6Tac8scDtfky+VAitRMbAilQIqUSGggjaFeodKwKOInC6nVvfL3RXT4XJoQT+dTqdlxpkMJvwsfngbvfE1+WqBJrPBM/AkXeeEEEKUpMNx6bdscW2A6uG+N7oJQghx05Kg1M3Iu6yaOZT8D1iTwBJSvO1DoqHFF2q9qcMfQMpe2PAYVO0H1f/PsyD5zUpvUoNP5mBQnGo3v4wTkH4sZ0S/8IsBKuPtnRVjc9rIsmeRac8ky5HFhawLarc7p1p0XEHNfPIyemE2mAmyBGH2MZd40EZRFOIy4jiUdIgjF45wJOkIRy4c4XjycaxOa77bGHQGKgRUoHJQZaoEVqFSYCWqBKm3wV7BEngqAkVRsDltWuDJ/X93/Sa9Xq8FlbyN3oT5hOFr8tWKg186yTkXQgghhBBClBQJSt2sfMoBOYEp24WLdZiKSm+EKg9DmU7w7xS1K9/Reept3dfVwNWtQmdQa0yZAtQC6Y5MyDx1MUDlVUqdLCG3fAaV1WEl055Jpj2TDFsGSVlJZNgzyHZkY3Pa1CLjBhNeRi8188m75DOfALId2Ry9cJSDiQc5mHiQw0mHOZR0qMAi4xaDhcpBlakWVI2qwVWpGqROFQIqYDKYSrj1tx6X4lKDTU67Wnw+p74TOrRrwqxXs5iCvYPxN/vjZVKDlO5MOYvRUmLF6IUQQgghhBCiKOQXys3MpzygwIWdYE8Hk1/x9+EVBtFT4Pxq+Heymm20+Smo9BBEDL71sox0evU8mPzUbDJHhhqgyjie08Wv9MUAlf7mDnY4XA4ybBlk2DNIt6aTlJVEui2dLEcWDpcDvV6vjkRmsBDqHXrDgjdp1jQOJB5gf8J+DiQe4EDCAY6nHNdG48vNoDNQOagy1YOrUyOkBtWDq1M9uDrl/MuVaKH0W5GiKFr3OqvDqmU+KShqtpNezWTyt/jjZ/bDz+yHxWDBYrRot1LHSQghhBBCCHErkaDUzc6nAjiyIWW3mv10pZlAZdpDaBM48L5aY+rkIkhYD1FjIbjhNW1yidHpcgWoXDld/I5B+lEw+YN3OfAKV4uk3+CAiKIoZDmySLelaxlQydnJWgaUXqfHy+ilZkBZ/G9YRku6LZ1/4//l34SL0+nU0/muG+QVRERoBBEhEdQIqUFEaARVgqpgloL0hXJnPbmfe5vTpnW1MxlMWAwWfEw++Fv88bf4YzFY8DJ6YTGqt5LtJIQQQgghhLhdyK+bW4F/NXVkurRDapDqSn+UmgKg3mgo3RH2vKlmGG16Cqr0hZrP3hq1pgqi06uBKJO/WoPKngap+yHtoNr10aeCWjTe6K8Gs64zp8upZkDZ0km1ppKQkaB1w3MpLiwGC94m7xuaAWV32jmYdJA9cXvYG7eXfQn7OJF8It9R78r5lSMyLJLI0EhqhdUiMjSSMJ8wycophEtxYXVYtdpfNpdn8XmLwUKYTxgBlgB8TD5a0MldG0yIq3H69Gk6dOjAqlWrqFChQqHrLlq0iGnTpmG1Wlm8eDE1atS4omPabDaWLVvGgw8+eEXbL126lJkzZ7J69eor2l4IIYQQQtx6JCh1K9DpIaAWOLMg+zx4l7+6wEp4c2i1CPa/C2d+gONf5GRNjYfA22C4Wp0BzEHq5LKDPUXtAmnwAkuYmkFlCbumQTiny0m6LZ00WxrJ2ckkZiaSbktXs6D0erwN6mhmN3LUuNj0WHbH7WZX7C72xO1hf+J+bE5bnvXK+pWldnht6oTVoVZYLWqF1SLIK6jkG3yLUBRFCzxZnVY180lR0Ol1eBnUAFNpv9Ja8MkdePIyekmXRnHdlC1blrVr1xIScvmBMt5++2369evHfffdR5kyZa74mCtWrOCDDz644qCUEEIIIYT475Gg1K3CYIbAuuDMzglMlb26/Zn81a57pdvBnolql7eNj0ONp6Bq/yvPxrrZ6E1qAMoCOLIgOw4yTqtZY95lwbu0mklVzGLhLsVFmjWNNFsaF7IukJCZQKY9E7vLjh49vmZfgr2CsRhvTPaZ0+XkUNIhdp7fyT+x/7ArdhexGbF51gu0BFI3vC51S9Wlbnhd6oTXIcS7mKM9/oc4XA6yHdlaAMrhcqDX6bVC4mE+YQR5BeFt8sbb6I23yRsvo9cNKUYv/tsMBgPh4eFFWjctLY2YmBjKly9/VcdUlLxZlkIIIYQQQhTmNok8/EeY/CCoHiRuBVuymgl0tUq1gVb1Ye8kiF0Fhz6AuLVQfxz4VgaXC7KsYLd7bqfTgckEFhMYbpFsD6O3OikutXtf2iFIP6J26/OpoAavjD75bqooChn2DFKtqSRnJROfGa9lQhl0BnzNvoR4h9ywbldWh5W98XvZfm47O8/vZHfcbjLsGR7r6HV6aobUpH7p+tQrVY+oUlFUDKgoXfAK4K775A5AuRQXRr0Ri1Gt+VQuoBz+Zn98TD5aEEoyn/4jFAUyM0v2mD4+xcqQzd19r0OHDkyZMoW5c+dy/Phx6tevz+TJk6lYsSKRkZEAPP7448TExPD5559z8OBB3nzzTf755x/Kli1Lv3796Nu3r7bv77//njlz5nDu3Dlq167NmDFjSEtLY+TIkQBERkayatUqypcvz+zZs/nqq6/Izs6mSZMmjBkzhnLlygEQGxvLqFGj2Lp1K1WrVqVt27bX8IQJIYQQQohbgQSlbjWWULWLXdJOMHhfmy5o5iBo+D849zPsmwIpe2Dtw+DXB5zNwWYHh0P9Ieam14PRCCYDWCzg5wO+3uDlBV5msJjBy1Ii9ZuKTacHc6A6uWxgS1EDfUbfnOypsmAOwaY4tSBUXGYcqdZUsuxZ6PV6fI03NhMq25HNrthdbDu3je3ntrMnbg92l2fg0NfkS4PSDahfuj4NSjegbqm6+JjyD7r919mcNrLsWVrxcQUFs8GMl9GLYJ9gQrxC8DH5aAEoi8Eiwbz/KkWBVq1g/fqSPW7LlrBmzRW/p86YMYM333yT0NBQXnjhBd577z2mTp3K2rVradWqFTNmzCAmJobs7GyeeuopevXqxZtvvsnRo0cZPXo0vr6+3HvvvaxZs4ZRo0YxatQoWrRoweeff87TTz/NqlWreO211/j000/59ttvCQkJ4YsvvuCHH35g6tSphIWF8emnnzJgwAB++OEHTCYTL7zwAj4+PixevJhDhw4xatQogoODr/GJE0IIIVThfhacLgWD/tb+Dnc7PAYhcpOg1K3Ip6KaKZV2FHwrFLvrWR6KAmkZkB0FllFg/xQ4BGkLQL8Z/J4ESziQ681PcYHdAQ4nZGRCcqoauEIHBj2YTWpgyt8X/P3U/3tb1ACWl1kNat0M9GZ1hD5FQbGnk5G8j5S4bSQ4IUExk44RxWDB2+iNn8mPMO8bU9zb5rSxO243W89uZevZrfkGoUK9Q2lUthENyzQkukw01YOrS+ZOPuxOO1mOLC0LCtRR77yMXoT7hhPsFYyv2VcLQt2oQvTiJnYLBiSfeOIJmjdvDsDDDz/MwoULAbQufoGBgQQFBbF48WJCQ0N58cUXAahSpQpnzpxhwYIF3HvvvSxatIhu3brx8MMPA/Dqq69iMplISUnB39/fo9vgxx9/zNixY2nWrBkA48ePp1WrVqxZs4aKFSuyY8cO/vjjD8qVK0fNmjXZs2cPv/zyS0meFiGEEP8hAd5GDHodL3y9g8Nx6Te6OVekRik/3u8TfaObIcQ1JUGpW5FODwGRahe07Nirqy+Vmg4nzsD5eLDawNcfwkeC9Q9I+xpc+yFtLOgeB6/muX6M5WRK5cfpVLOrbHY4nwCnz4MC6HVgNqtd/vx8IcBPzaa6gcEqp8tJqi2DZGsa5zMTSbGmk2XPxOjKxk8HZb2CMRjD1Swqo7nEfoy6FBcHEw+y6cwmtpzZwo7zO7A6rR7rlPItReOyjWlctjGNyjaSrnj5cLqcWgDKPfKhOwMqzCeMYK9g/Cx+WgDKeLvUUhPXj06nZizd5N33LlW5cmXt/35+ftgv7ZKd4+jRo+zfv5/o6ItfeJ1OJ4acbtrHjh2jT58+2jKz2czw4cPz7CcjI4Pz58/z0ksvoc/1vp6dnc3x48exWq0EBQVpXfkAoqKiJCglhBDiujscl87es6k3uhlCiBzyC+xWZfCCwNqQuEXtfmYOLN72VpsaLDpxBrKyISQIwkMvLjd2Bks9SPkQ7EchZQ5kb4XA/qAPuEzbDOBtAG8vz/lOF9hsarAqPgnOxuYNVvn7QoC/GqzyyukCeI27ATpcDpKt6SRmJ3M+M4k0WwZ2lwNvowU/szfhPrm6jzgzIeucOpkCwCunMLrB+5q1xy02PZaNZzay6cwmNp/ZTHJ2ssfyEO8QmpRrQtNyTWlStgkVAipIECoX9yh4WfYsshxZOBUnep0eb6M3AZYAqgRVwd/ij6/JVzKgxNXR6cDX90a3olhMpqJd7w6Hg+bNmzNmzJh8lxsL+mPEJZxOJwDvv/8+VatW9VgWGBjIhg0b8hRGL2obhRBCCCHE7UOCUrcySygE1IILO9UgVVHqSykKxCbC0ZOQlAyBAWpAKj/GchAyGjJ+gPTvwboFEvZDwADwalL89hr0aqCqsGBVbCKcyRWsctem8vPJyazyysmsyqlbVcSgjN3p4II1laTsFM5lJJJqT0dRFHxN3oR6BWIuKEBh8FEnlxMcaZCyXw1IeYWphdFNAaC7si5yVoeVHed3sOH0Bjac3sDRC0c9lvuYfGhctjEx5WNoWq4p1YOrSxAqF/dIeO5aUOjAYlCLkJf2K02gVyC+Jl98zb54Gb0uv0MhBFWrVmXVqlVUqFBBy476/vvv2b17N6+//jqVK1dm//792vpOp5NOnTrx9ttve7w/BQQEEBoaSnx8PHfeeScANpuNoUOH8uSTTxIREUFKSgonTpzQsrj+/fffknugQgghhBDipiBBqVudbyWwJ0P6MbXWVGFBC6cTjp2GwyfUbKZypS/fXU5nBL9eYIlWs6YcpyH5ffBqAQGPgt7/6h9DgcGqnG6AVhuci4dT59BqVrmDUv5+4O+jdv/LHazS67VAVEJWMuczE0mzZwIKfiYfyviEYSxOvSW9QS0Ibw7KyZ46A1lnwRSYK3vq8oGPs2lnWXdqHetPrWfL2S1qMMV9CJ2eOuF1uKP8HTQr34yo0lHSnSwXm9NGpj2TLHsWdpcdg96At9GbIO8gwrzD8LP44WdWu+Lpr7bOmhD/UT169GDmzJmMGTOGAQMGcPr0aSZOnMgTTzwBwGOPPcaAAQNo0qQJjRo14vPPP0dRFOrWrUt8fDwpKSkcP36cChUq0L9/f9577z1CQ0OpVq0as2fPZvv27UycOJHAwECaN2/Oa6+9xujRozl9+jRffPEFvrdYBpoQQgghhLg68ov3VqfTg3+E2oXPGqcGSPKTbYWDx+DkOQgOVEfKKw5TFQgdD+lLIWMFZK8H2x4I6A9eTa/2UeSvoG6ADsfFYFVabM7IgIDRgMOkJ1nvIMFk56zRSprBAUYjfj4BlPULxnAtuofkzp6yp0HKv+p9LXvKX8uecrgc7IrdxZqTa1h3ch1Hkz2zocJ9wmleoTnNKzQnpnwMgV7F7IZ5m3J3xcu0Z5Jpz0RRFEwGE94mbyoGViTYOxhfky9+Zr8bNgKiELcjPz8/5s6dy1tvvcW9995LUFAQffv25emnnwagadOmjB07llmzZhEfH0+9evX44IMP8PLy4o477qBy5cp0796dL7/8kieffJKMjAzGjBlDeno69erV45NPPiEwUH2fmzZtGqNHj6ZPnz6UK1eOxx57jKVLl97Ihy+EEEIIIUqYBKVuB0YftRtf0lZwpIPRz3N5ShrsPwoJSVAqVB0Z70roTOD/EFgaQ+rH4DgDydPB0hQCHgdDCQVUjEZ18lEDay7FRYoji4TsZM5mJpBsTQeHAz+XgbI6szoCnTENjHFq3Spfb7VLoNmsnguzEUym4hdZ1xvAEgRKIDizIPM0ZJ0hTTGxPukUf5/dwfrTm0izpWmbGHQG6peuT8uKLWlRsQU1Q2pKlzzUIFSWI4tMeybZjmwURcFitOBr9qW8f3kCvQLxM6uZUDKioBCXV6FCBQ4cOACg3br17t2b3r17a/cvXV63bl1tdL783H///dx///155gcFBeUJKr300ku89NJL+e4nODiYmTNneswbPHhwgccVQgghhBC3HwlK3S68S4N/DUjeCz5e4O72lZQMew9BWiaUKaV2fbta5hoQ+qZaZyrjh5xaU/vAvw94t1Gzt0pAmiOLJEcGZ2xJXLBnYFOc+JkslPEujdGjzpOiZlPZHWpR99R0cLkAnVq3ypQT5PK2qIEui0UNVJlNarDKZCy8W6ROx3lrJn+e+Ye/z2xjW/x+nIpLWxxo8adFxZa0rtSGOyrcQYDlMoXi/wNciksrSJ5lz0Kn0+Fl9MLP7EfVoKoEeAVIVzwhhBBCCCGEuM1JUOp24lcNbMmQdR58K6gBqT0HIcsK5Upd22PpTOB/v9p1L2UuOE5A6ieQtRYCnwBj+Wt3LEWBjCzIyMSWmUlq+gWS0hJJsaZhc9qwYKSazoxRb0AxGVGMBhSjAZfZhNPHgtPbgtPHAt7ecGmvRZdLDVbZHZCSDonJaldAlJyMLIMamPKyqME+sxlMRhSTgSNZsfwZt4s/zmzjQPIJj91WDShHmzJRtA6vQVRoFQzmYLVrpfG/ObpU7kyoLHsWAN5mdVS8asHVCLBcDEJJ5pgQQgghhBBC/DdIUOp2ojep3fjsqXD2KBxKUGtJlQ67fsc0VYbQcZC5EtKXgP0AJIwC3+7g1x105svvw+6A0+fUYubn4uBsnPr/CymQnIpyIRWd3Q6AGQjLmYrLZTbhCPDBEeCDPcAHR6Av9mB/7CH+2EMCsIX4YwsPxB7ir9azcjjVDCuHA5KtuOIT2Wc7z+qsw/yRfYRTjmRt33p0NAisSttS9WlTNppKQeXVbCuDAVwOsKfn1J7yBq9QMIeCOVAtJH8bUhSFbEc2GfaMi5lQJi8CLAFUD66uZUJ5G70lCCWEEEIIIYQQ/1G35y/i/zJzINhLw671oHhD2XLX/5g6A/jeA14xkPoZWHdCxjI1ayrgEbA0udj9zWqDA0fh3yNw9BQcOQknz6oj7RW0+5xbp9GAy8uEy8uMy8usZkXpcrrg6XSgKOgcTvR2p3prtaPPsmKwqgEtvc2OOSEFc0JKoQ9H0euxhfpjCw8iq3QQa6vqWV46lV8s54glU1vPrDPQzLsKd3rXoLW5MiF6b8jUwdELag0rU04XQG8vtWugyQx6qzpSovEk+ASBdxkwBYHRt/AugreAbEc2GbYMshxZKCh4G73xt/hrmVD+Fn8JQgkhhBBCCCGE0EhQ6naTlAQH48AQDn7poDi1keCuO0MYBA0F61ZIXQiuBIidDocqw8FKsO8cHDqmZiBdytcbKpSBsqWwlg4mOcyX8/46LvgbcQX54R0Shtnb58ra5XRiyLJhSM/CmJaFMSUDY1omppR0TElp2mROTMWUmIridLLOL4XFVVNYWvsEcbnqxvtZoetRA10TAmmjlMNYphTZFYLIrmDGWjYQxWRUuwQ6HOrjzMqGtIxcQTcdmAw5gbRzYNoLPr7gGwZ+pcAnFLz81C6DRtNNHaiyOW1k2DLIdGTiUlxYDBb8zH5UDqpMoFcg/mZ/6Y4nhBBCCCGEEKJAEpS6naSkwK5dkJ0NVRtB6r9gTVBrGZUUnQ4SK8H6DrDxD9gbD/YTQK6aS6FBUKcm1KwMVStC9Uo4w4NJdKRzxnqB8/YUsl02AgzeBBi8r77QtcGA088bp583tjL5r+JUXOxIP87vyXtYfWEfSa6LGVGBDgNdT3tx3x6Fe3Zm4uVwAkk500WKXo+1TDDZFcLJqlSK7IrhZFUsRXb5MBSLu5aUktMt0AkOI2Q7IC0dbHGg7AajBbyCwDsYLAHg66fWwrJYLgaqTKaLRdhLsAi4w+Ugw5ZBhj0Dh8uB2WDGz+xHDf8aBHkH4W/2x8/sJ0EoIYQQ4ibhdCkY9PK5LIQQ4uYlQanbRXq6GpBKSYHy5dXgkF9VSM4EewqYAq/v8c/Ewl+b4K/NcNiz6DdhJqhnh1pApAWqdga/LqAzk+2yEWdL5WTaYRLt6eh1eoIM3pQyXf8R6lyKi50ZJ/kteQ+rk/eR6EjXlgUavLkzsDYdgurS1K8qpiZGuBf2WW1Yzl3A62wCXmcTsZxJwOtMAt6n4zFkWvE6m4jX2USCNu/X9qXodVjLhJBVqTRZlUupAatKpckuFwIGH8D9WBVwZIE1HbLTINsCqT6g8wKdRc140wGGnFpVJpMarPLyUgNX7kCVMdeogYYrf4m7FBeZ9kwybBlYnVaMeiM+Jh8qBVYixDtEK05u0JdQJp4QQgghisWg1/HC1zs4HJd++ZVvQndGhjOsS60b3QwhhBDXkQSlbgdZWbB7NyQmXgxIAZj8wa8KpOwHgxfoLdf2uKnpsHoD/LoGDhy7OF+vh4a1IaYBxNSHSuXAtg/SvlJH6cv4BlfW78Sa2/OvqxrJzmx8DGbKmAMxXueuhoqisDfzNCuT9/B78h7i7WnasgCDN+0Ca9MxqC5N/avl2xbFYia7Smmyq1ySfaYomC6k4XU6Aa+TcXifjldvT8VhTMvSglXBG/dpm7iMBjWrqkppsiqX1m7tweGgU8CRDc5M0GWpBdLNIWDyBczgyOkimJ4OycngcqqjBuoAveHiyIFGI3h552RbmT0DViaTujwnqORRnNyRhQ4dPiYfSvmVIswnTK0LZfbHZPhvjiAohBBC3IoOx6Wz92zqjW7GFake7nujmyCEEOI6k6DUrc5qhT174Px5NSClv6Q7l1dpcGRAxgnwKnX19aVcLti+F37+C9Zth5xR8dDrIboOtG0GrRpDoL/ndpa6uExvkJH5J5aMZZhdSZTN/pZAXTDnTe24YKx/3bqiKYrCoexYfr2wm1+Td3PWlqwt89N7cWdgLToF16OZf/UrD4rpdNhDArCHBJBWv1rug2NMTsf7ZBzeJ2K1W69T8RiybfgcP4/P8fMeu3L4+5BVuRSZVcqowarK4WSXU3A5MtQAksEHzEFq7SljAHDJeXM6wZkzaqDNBpmZ6v8Vd1vRsq1seoUMo4sMgwvFYsbLy48A7yCqB5Qj0DcUf99gvHwC1OCVEEIIIYQQQghxDckvzVuZ3Q779sGpU2pAypBPQEWnA9/K4MyG7LicwNQVBH8ysmDl3/D973A6VxClWkW4qy10aA5B+Xe5cyhO4u1pnMxOJM4eAoZ+1OVfKjo24KNcoJptKVn2NZw3tyHJUO+aFWY/aU3k1wu7WXlhN8es8dp8b72ZtoG16BRUj+b+NTDrr+PLQKfDEexPWrA/aQ2qX5zvcmGOS1YDVSdi8T4ei/fJWLzOJmJMy8R/z3H89xzXVld0OqxlQ8iqFE5WpRCyKoSQVaUU1nJlwStYHb3P4K2eO0POZM6bGedQnGQ6s8iwpmG32zA7dfjZTdTU+xKc6U2AzoKP3oVOfw6M8TkZVzlZVd45GVc+Pjl1rcwXb93/l3pSQogStHTpUmbOnMnq1atvdFOEEEKI6y7cz3Jb1Iq7HR6DuHYkKHWrcjph/344dgzKli08k0VvBL9q4LKBNQm8wop+nDOxsOQX+HWtOpIcgI8XdGqlBqNqVi4wEGFzOYi1p3A8O4EkRzpG9IQa/bDoTVwgnBRzM8LtmyhjX4e3Ek9V6xLK6VZz3tSSRGM0iq743cTibKn8mrybXy/sZl/WWW2+WWekZUBNugRH0SogAi+9udj7vqb0emxlQrCVCSGlWW1tts5qx+t0PN4nYvE5Hov3ifN4n4jDlJyeqwvgxd04LSayK4aQVTGMrCplyapejazq1XGElQadQa0L5bSS6cwiW7FhQI+v0ZsKfuUJNQcSYPTDz+iN4dJAoKLkjCCYK+MqI0P9v8ulLs95HGp3wJyugO7Ala/vxfnuyR28yi94KoQQQgghhChUgLfxlq8VV6OUH+/3ib7RzRA3EQlK3YpcLjh0CA4fhtKl1R/6l2P0VgNTKf8WrfD5vsPwzQpYu+1iAKJyebi3I3RsCT7eBW6a5bRxzpbMSWsiFxzpeOvNlDHlrRfl0lmINbch3hRDKfsmStk3YFEuUNn2I+XsfxJnjCHB1BSHrvB6AimOTFYl72Nl8i62p59AyemnZkBPjH81ugRHcWdgbfwMXpc/TzeYYjGRVb0cWdXLeYztZ0xOz8moUoNU3idi8T4Vh8Fqx/dwLL6HY4G92vrWAG9SK4WTWrksPtUqQ40IfCPq4h8YToDRD9PlssN0uovBpMI4nWrGnjt4lZwM8fHqfPd1o9NdzLgyGtXi7L6+F0cVdGdZ5c6+kqwrIYQQQggh8nUr14oT4lISlLrVKAocPQoHDkBYmDryWlGZA8G/GqQeUOtMGS8J9igKbP4HvvwB9hy8OL9ZQ3jgLmhYp9BAQYYzm3PWZE5YE0lxZuKv96KCOQT9ZboLunRenDe3JdbUnDDHDkrb12JRUihvX01Z+98kGaOIM95BlqGstk2W08ZfqftZeWEXG9KO4FCc2rIGvpXoEhRFx6C6hJj8in5+bmKOID/Sgvw8uwA6nVjOJWE6fg7T8bN4n4jF/2QCvnEpWFKzCN9zkvA9J4FNF7cpUwqqVYfqNaBaNXWqWlXtkncl3F0FC5M768puV2tcpaaq/3cHrtz7MuYqwO4eVdDb27OLYO4AltEowStR4hRFIdOeWaLH9DH5oCvGtX769Gk6dOjAkCFDmD9/Pt27d6d58+ZMmzaNM2fOULNmTV599VViYmIAcDgcTJ8+naVLl5KVlUXLli0ZN24cwcHBWK1Wpk+fzo8//khKSgp33HEHY8eOpWzZsrz00kuYzWYmT56sHfvll1/Gy8uLiRMncu7cOcaNG8eGDRsIDQ2ld+/ePPvssxgMBpYuXco333xDaGgoGzduZOzYsXTv3p3Zs2fz1VdfkZ2dTZMmTRgzZgzlypUDIDY2llGjRrF161aqVq1K27Ztr+2JFkIIIYQQJUqCUrcSRYHjx9U6UkFBVxZI8CoFDiukH1F/zBt8wOmCtVvUYNThE+p6RoOaEfXA3VClQqG7THNkccZ2gZPWRNKd2QQafKhkDi3WDygARWcm3tSMeGMTQpx7KGXfiK/rDGGOHYQ5dpCkq8CSrAosSUvlr9RDZLvs2rYRXmXoHBxFl+AoypqDintWroJycSQ8h1O91elAr1Nrd+l1FwM3Rj1qlfGr41CcZDitZDitOML1mEtVwq95BOGmABSjD4pdh9+pRHQnzsCxU3D0BJw4C4kpcD5OndZv8Nxp2bJqcOrSKSD/OmHFkjvryrvgDDuP7oIOB6SkqCNKOhyewavcWVcFdRnMXe/KZJIug+KaURSFVvNasf7U+hI9bsuKLVnzxJpiv69u376dJUuWkJmZySOPPMK4ceOoX78+f/31F0899RTLly+ncuXKvP/++yxbtoy33nqLcuXKMXbsWMaOHcv06dMZO3Ys27dvZ/LkyQQFBfHOO+8waNAglixZQteuXXnttdew2+2YTCZsNht//PEHM2fORFEUBg8eTK1atfjuu++Ij49nzJgx6HQ6nnvuOQB27NjBM888w9ChQwkODuaLL77ghx9+YOrUqYSFhfHpp58yYMAAfvjhB0wmEy+88AI+Pj4sXryYQ4cOMWrUKIKDg6/HKRdCCCGEECVAglK3khMnYPdu8PcHv6vIAPKtALgg5TCs3QiLVsLJnPpLXhbo0QHuuwvCCv+in+LI5LQ1idO2JDKdVoIMvlS2FKNeVUF0BpKMDUgy1MfLeZKDKatZkXKMpemnueA6ra1W0RxAl+BougTXp6pX+NUf93JcLrDZ1Qwfmx1cCqDkBEcM6uTjp853OsGV04XNalUDf05nzgh4ihokMbmzgnK2LSBg5VCcZDltZLis2BQnRp0eH72FSl6hhJj8CDB442fwwuDOSLMAtYOgdnXPHaVlwLETcPSYGqw6eR5OxkJyOpw7p07rL/mhHRoKVaqoAarKldX/V6midhu9dKTHq+UOMl3OpcGry3UZNJnUboI+Purk7jJ4aeBKRhgURaS7BsHlkvL4449TqVIlhg0bxoMPPkj37t0B6NevH1u2bOGrr75i+PDhfPPNNwwfPpw2bdoAMG7cOH7++WdSUlL4/vvvmTt3LnfccQcA77zzDnfeeSfr1q2jTZs2uFwuNm3aRKtWrVi7di1eXl40a9aMjRs3cvbsWRYvXoxer6datWoMHz6ckSNHakEpnU7Hs88+i1dO1u/HH3/M2LFjadasGQDjx4+nVatWrFmzhooVK7Jjxw7++OMPypUrR82aNdmzZw+//PJLSZ9WIYQQQghxjcivsFuFOyDl53f12SsOB/y2DeZ9Amdj1Xl+PtCrszoF+he4qaIoJDsyOWVL5Iz1AlaXnWCjL2GWgrcpLpfiYnfmaX69sJvfk/eS6LhYxK+sQcdD/gqP+EMTSyrZ+r0k6Q0kuRpg01/Lv5YrYLWrBb5tdjUgpdOBOSeIERyonjOzSZ2MRjXIlDtQoyjqdu4MKrt7skNmtjqiod0O2dnqOooCOh1OvY5Mg4tMvQubHnRGA34GL8qagwg1+eNv8MLf4HX5ulCX8veF+nXUyc1lhaQEOHYSTsXC6QQ4GQen4yA+Uc1USkyEbds892WxQKVK6lS58sXbihXVLL7rqSjBK0XxrHWVkaEGr3JnXV1a68pkUrOtfH3VroOXBq6kSLtADaKseWLNTd99z618+fIAHDlyhJ9//plFixZpy+x2O61atfp/9s47Tooqe/tPhc6TA0NGEIacBEEElrCmNevqGlGENayBXTCBu5iRFX8GEAyYs76Yw645r2JAARMZYWByno6V7vvHraoO05NgmMT58rncWFW3urumq54+51xUVVWhuroaw4cPt/sGDhyIq666Chs2bIBhGBg9erTdl5GRgf79+2P79u2YOnUqjjrqKLz//vuYMmUK3n//fRx77LGQJAnbt29HdXU1xo0bZ29rGAbC4TCqqqoAANnZ2bYgFQgEUFxcjPnz50OM+VsaDofx+++/IxKJICMjw3blA4CRI0eSKEUQBEEQBNGJIVGqM7BrF7BxI39YTm8iQHljRCLAW28BTz4JFBfztvRU4NQpwKnHA2kZDW7KGEOl5kdBpBJ7lSpoho4s2YdujlZw7zL3/0twLz6o/hkfVv+CErXG7kuXPJiZMQzHZozEWF9fZLBdyFHXgemb4GHl6KV+jF7qx/CLfVAlD0O1NKzlApWmAmFThNL1qMuZywlkZ3ALG7eTJ6ezeXGMhBjXPVeSYPSMAaoGXYkgGPIjGAogEglCjKjwRQR0M9zI0d1IVSSkim44JQfglAGnADgNwGHsv7WS6AJyevE0ngFMAfQwT4EQUFgF7Knk+d5S7ga4Zy//LG3dylMi6elcnEpMvXvzvraIASUIUSGpIRJjXUUigN9fP9aVZUUVG6Td5+PlWMHKSq1tQUZ0OARBgM/Z+AIMHQWXywUA0HUdF198MU499dS4frfbDbkRkdfaPhFd12EYBgDg+OOPx6JFi/Cvf/0LH3/8MVatWgWAx6kaMGAAHnjggXrbp6am1tu/rvPYgMuXL0f//v3jxqenp+Prr78Gi702ATiaWoyBIAiCIAiC6NCQKNWRsWJI/fILF0X2VZAKhYBXXgGefRYoL+dt2dnArFnAaacCRgkQKAC0ICDHx6kymIEK1Y/dkQoUKdXQwZAt+eBxNGPFvyZgjOHXUCE+rP4ZH1f/ir1Kld3nFZ2Ylj4Ux2aOxMSUAXFWQXU4FHXSoRBZGJnab8jSNiDV2IkUowApSgH64D0ExB6oloahRhqMkJgXL4QYBhBReNI07m7nlLnFU04mtyhyObm1jLuZAlQL0AwNQS2CoBaGoqsQBQG+NC+65eQhx5OBFIcXqQ4PXBq4lVY4wucaDAE1fm5ZVWuJJ+ZOHaalltPJz8OxD5e2IACCiwtVjnTADSCrFzAkzC2qDA3cxVAGygJAUR1QWA7sKQYKTLGqpITHgqqpAX7+uf4xUlK4OGWlXr2iKS+vbV3omhPryhKuLKurYJCfm6ZFrecsi6vYAO0+X7yrYKx45XCQcEW0Of3798eePXvQr18/u23ZsmXo378/zjzzTGRmZmLTpk0YPHgwAOC3337DpZdeiv/85z+QZRnr16/H1KlTAQBVVVXYtWuXLRwdeeSR0HUdTzzxBNxuN8aPH28fs7CwEFlZWbYI9b///Q+vvvoqli1bVm+OaWlpyM7ORllZGaZPnw4AUBQFCxYswNy5c5Gfn4+amhrs2rXLPo/ffvvtwLxgBEEQBEEQRJtAolRHRde5Fcrmzdxdb19c9mprgTVrgBde4K5LAH/wnzULOPXU6Mp9zAsITiD4O8BUwJEOnRkoV+uwO1KOIqUagIBsOQVucf9+lTaYgV+De/FR9a/4qOYXFCrVdp9bdGBq2mAckzECk9IGNXksQ3CjwjEWFY6xcBi1yNB/Q6b2C1KMXfAZRfAZReilfgQFqahh/VGj9EOd3guG6OGiU6oPSEsBPKb45HYdEPesiK4gZIpQmqFDFiV4ZTd6eLOR7c5AisODVKePW0LFIoPPKS0hfpimAWElKqxFFKDOD/iDvBwIchEFpmDikKNuho5mxm2yEEzRBqZ7JjMAQwF6yEA3FzAqBxCGApILEN2A5gRKaoC9Zdw1dG8RULAHKCjgcZ/8fmDTJp4SkSSgWzegZ89o6tEjmufmtn3cp1jhqiFiXQVVFairA6qq6rsKWvuxgrN7vdHg7MlWFiRXQaIVmT17Ns477zyMHDkS06dPx8cff4wnn3wSTz31FABg1qxZWL58OfLy8pCdnY0lS5ZgzJgxSElJwZlnnonbbrsNt912G9LT0/F///d/6N69OyZPngwAkGUZxxxzDB566CGceeaZtpvhlClT0KtXL1x77bWYP38+6urqsHjxYhx55JGQGvh8z549G/fddx+ys7NtK6sffvgBS5YsQXp6OiZNmoQbbrgBixcvxp49e/Dss8/C5+scVmsEQRAEQRBEfUiU6ogoCl9hb8cO/iDe0lX2iouB558HXnuNW0kB3BLloouAE06o/4AtiEBKH0B2QavditK67dhl6ChV6yAJInLlNDhbGr8oBo3pWO/fjY9rfsWnNb+hVK21+ywh6o8ZwzA5NR8ead8ssFQxDWXiRJQJh0EOVyND+w3pwnakSQVwCnXIFTYi17URDCICrn6oSxuO2oxRCPiywMT9t/qyMJiBkBZBSIsgrEXAADglGV7ZjX4pPZDpTkWKw4sUhxcOaR9fU1kGUmQe0yoWxrhlVaxYFQoBdUHuihcKA7Wmu9q+ClaCCEhunhym5R7TTUuqMMBqgFwG5KYB43IAcSwg+QA5BdAYUFwFFJYBhUU87d3LU2Eh/9xbAdcTY1gBXKTJzeUCVffuPOXlRfO8PL4IQFu4B8a9Js1wFTSMeOGqpoZbLTYWnN3p5KKVxxNvcZUoYrX1+RKdkjFjxmDZsmW4//77sWzZMvTt2xd33303Dj/8cADAJZdcgrq6OvzjH/+ApmmYPn06Fi9eDAC4/vrrceedd2LevHlQFAVHHnkknnzySThjPvMnnHACXnrpJZxwwgl2myRJePDBB3HbbbfhL3/5C7xeL4477jhcf/31Dc5z7ty5CAQCuPHGG+H3+zFixAg89thjSDcthe+9914sXrwYZ599Nnr27IlZs2bh1VdfPRAvGUEQBEEQBNEGkCjV0QgEuCBVUMAfthuI55GU337jVlHvvccfdgFg0CDggguAo49uVHCI6ApKmIFdzEBFsAQOQ0GetzscYguOH0NQj2Bt3XZ8VrMJX9ZuQY0eDQrsFZ2YnJaPozKGY3LaILj3VRTS9GggclXlbQ4ZmjMF5VnTUJ56PASngFRjF9KDPyOt7ie4IyVIiexEStlO9Ch7G4YgI+jtD39KPvwpg+H3DYQuNy9oO2MMEV1FWOcilGpoEAUBHtmNVIcXh6T1QKrDhxSnBz7ZA0k8wJYvgsAtwJLFr7JcFi13QMUMsF4X5G6B4Qhfnc+y7rFd0qxkWvhISdzOBAmQvIAEwNI7mQEYKreqUiuAcAkABmSKQLYPGD0UkA4DZB8XuCADVX6gpBwoKuUilSVQWUnTuOBqxUNLhsfDra3y8niemHJygKystnefE0V+LTd2PRtG/KqCwSC3drRiXAkCzyUpXrxyu/l5ezz1La5iE7kMHlT07t0bmzdvjms74YQT4kSjWBwOBxYuXIiFCxfW6/N4PLj55ptx8803N3i8iRMn1jseAPTp0werV69Ous3pp5+O008/Pa5NkiTMnz8f8+fPT7pNZmYmVq5cGdd25ZVXNjgvgiAIgiAIomNDolRHgTEej2fTJu7606tX81yVNA34+GPgxRd5MHSLceOACy8EJk1q1JIioIVQHCnHrlAxalQ/vJIbPbJGQw6XAZEKQFK5cNCMJdALI1X4snYLvqzdgu/9O6Ewze5Llzz4Q/oQzEwfhgmpA+BqqRugpgOqwlfEs+JAWWJJegqQmsJd3dwu7opnuoYwALXIQS346k8OpRxpdb8itfYXpPp/hVOtRkpgK1ICW4GSdwAAEWc3BHyHIuAbgIC3P0KevjAkDxRd5RZQugJFVwAATskBj+RGn5Q8ZLhS4XN44HO44ZHdLTu/A40ocjdFj7v+6oqGwUUqy8pKUbh7YMC0sFJUIOSPvu5A1MpKlqIWVg456nImiNylT0oQYZjOY1MZCqBWA5GyaFwsWQZ6y0DfboB0CI9vJrm4CyGkqGhVWsZFquJifs2UlPByTQ23DNu1i6eGkCQuTjWUsrOj4lVbuguKYtMWV0C8cKVp/LwrKuLdBYF48Uo24415PGasNHe8O2FiTgIWQRAEQRAEcQDITXFBNxgksXNb+3eFc+gokCjVEVAUYNs2YPt2/kDYp0/TLjm7dwNvvgm8/XY0eLksc4uos88GYpb2ToQxhiq1FkWRcuwJlSKgh5Eme9Hb3Q2iYD6MpngARyoQKgYilYAzzRQHokQMFesDu7G2bhv+V7sVO8Klcf29nJmYlj4E09KHYLSvL2ShOZZCfEU6bv1kPnQzmOKHg8dXSvVFxSe3m/c1E9WZg4rsP6Ai+w8AY3AqpUjxb0GqfzNS/FvgjhTBpZTCpZQiq+prc0YCAo5s1Lp7I+jpC6QcCmf6YLhS+sHr8MIruw+8FdSBRBSjgl4yVI2LVaoaFa/CEW5hFQyb9SB/r3Sd65fM3K8cI1xJVu6sL1YBANNM6yoNUKuASKkpWDEucskOU7TKAcQ+gOwBRCcgOgBBBhQDKKsEyiqAsnKgtJTHsSotjaaqKj5HS8xqivR0LlJlZfGUnQ1kZkbrWVlARgZv83rbxpXOEpmaIla40nVuhRkbpN3CsoqzRCxJilp1Wa6DsYJVYlmWyYWQIAiCIAiCaBZpHhmSKODvL/6IbaX+9p7OPjGwWwqWnz22vafRZSBRqj1hjFs4bN3KLT+aih/l9wOffAK89Rbwww/R9qws4M9/5iknp8HNFUNFuVKNvaFSlCpVUA0VmY405DgzkowWAVc2t5IKlwCRchgQsEUL4Hv/Lnxbtx3r/LsQYaq9hQQRo3x9MDktH1PS8nGou5sd8DYpms4fkC33O4MBYGbMHDnBAspauawVP7KCgKAjG5Vp4xHxjUIkR4Gg1CEzsge5yl5kRgqQHt4Dp1qFFLUcKWo5ULcesLQ3yQ34+gO+Q4CUfjz39QM8vblg0lWwXPgawhIRrffSKocjXLQKhU1hS+XiiBq1oIMALoJIEhevZAmQHPy1dUpRix2mm6KVaWWlB4GIFrWyAgBRBlIlIE0CBvUExP7RAOyizIUrXQCqa4CKaqC8AqioAioqubBbXs6vRyvpenQlwR07mn6dXC4uUFkiVWYmL6enR9vT0+OT+wBa1DVXvGKMn2usiOX388URdD3qCmy5D4pivBWWJPFr0xKy3O54wSp2HAlZBEEQBEEQBIBtpX78Uljb9ECiy0OiVHtRU8NdjAoK+INe797JV9vy+4HPPwc++ABYuzYaO0kUgSOOAE45BfjDHxpcHYwxhlotgLJIFXaHi1Gr+uEQZGQ40uBuIqi4xnRsCxVjfe0mrKv6GT/UbkGNHoobkyOn4ojUQ3FE2kBMSh2IdDlRVGOm650WtXyyHnItyxmXE8jJALweHnDb5QJcjlZdfUwzNER0FYquIqIrUA0e7FsSRbglJ7yyCz282Uh1DoRXngSv7IZHdnELKKUKqN0M1G4C6rYD/u2Afyegh4Ha33hKxJUNePtwgcrTA/D0NPMegLsbt+7pKjQlWgGmYGW+/7EilmqKV6EIEIlEhS1LJDFY1PJKEPhnwrLAkszVEiXRFEpMUZNZwlUIiOg8vlXcfCWghwT0TAWETEDI5++H6DSTDDABqA0C1bVAZQ1QVc3z6mqgssqsV3LLq6oqPvdIpPkWWBYuFxenrBU209N5sHarnpbG61ZbSkq03pSbX3OJDbDeHCyhykqaxlccTBSxYrGER0uYEkX+N8sK4G7lsdZaseXYNkkiQYsgCIIgCIIguggkSrU1gQAXonbt4vFvcnK4i4wFY9yN76uvgK+/Bn780VwtzWTAAOCYY4CTTuLBnBs6jBZCpVqDveEyVCo1iDAVqZIXPd3dIAnJ48VUq3X41b8TP9dtw4barfipbhuCejhujFd0YYyvDyZ4e+AIXz8c6u0NQXIDOjOtY4JRawsrvo0Vc8jlBLLSAZ/HfCA1VxFzto5Aoxk6VEOFomtQDQ0RXYFuGPyZW5ThlBxwSU7kejKR6vTCI7vhlpzwyC64JGfDVl3OTCDnCJ4sDA0IFgCB3wH/70BgFy8HCwC1hsfjilQAVeuT7FAAXFmAO48nVzfAnQu4cqLJmQU4M7jbWlegJS5nqha1okssRxQuYkUUU8BSAN3g7mi6FfMq5n0UBUCUouKVIACSAIiMJ0EDRBXQA1y8YnrU+koAkAkgSwSEXEDIM98PkQd3F2Xu0ipIQMQwRaw6oLYOqKnj5ZpaXq6p5UHLq2u5IF1by8WbSCTqWthSXC4uUsWm1FSe+3zRNp8vmlJSuDWm18vrXm/LxV9LGGouliWWrpvvkx4N5O73x/clYlllWcmqWysQWoHdLUGruUkUWyRssdhYXUSXgd5XgiAIgiCI9odEqbbAMLhVhbWKmN8fDaasaTy4+YYNwPr1PJWVxW9/yCE8VtRRRwGHHpr0EIwxBPQQqtU6lClVKI1UIaiH4RKdSJdT4E6I4VOuVGNrYDe2BHZjs38XfvXvwJ5w/Qdjn+TBqNSBGJuaj8N9gzDU1RuyrgPhWiBcBQQrAF0BHE7A6QOcHiAjDfC6uQjlkLno1Aqud7phQDU0aAYXnRQz5w8WDJIowSk64BBlZLhSkOrwwefwwCU54JZdcEvOxsWnliDKQEp/nhK1QbUWCO6JplARECo08yKAqVHRqubXho8hSFyYcmZxYcyRznNnhlk2c0c6j//lSOfulp1ZyGqJxY5hmIJVjOuZbrZZwoeqAmErFpYS7VNNccQwBRNmilkComVR4OKFqUNB0E0RSwAES9QCFzcEA0gBT0g1k4kg8PdSAADTyocJQDAC+ENAIAzUhYDaEOAP8ra6IOAP8BURA0Ggzm/WzRyIWmdVVOzfa+5yRQUqS6zyeKK51xtd3c9Kbnd8u7UCYGxuuV621BIrllghK7asKNw6y2ozjPgg7xaWiCWK8cmyvooN8O50xltkiSIcggBoGoJ1dfBY1qjW34+GcqLTEAzyVWEdDVgaEwRBEARBEAeeNhWlIpEIbrnlFrz//vtwu92YM2cO5syZ05ZTaDsMIxqXZc8eHq8mEIjWt24FtmzhuXljbONyAePH85XzjjwS6Ns36SFUQ0OdFkCtFkBxpALVah1CegQOQUaq7EOmnIpytQY/1W3DzmAhdoYK8XuwEDuCe1Gh1iTdZ19nNwz39MEoV1+Mdh2CQ109IIkiIJkPa4LELZ1ycgGPF5AMACHAqAEQBiTdfOjzAKKLB7VuBpqh88R0aIZmWj1p0A3dNFxhEAURDlGGQ+JWT5muNKQ4PXBLLrhMKyieHO0beNyRBqQP4ykRxrg7YLgkmiLlPIXL+Gp0kQpubcX0qHjVbETAkQLIMcmu+3guefnKdrKPv0927uW5nVz8/e6oiCLgFFtmaWcYpnBlCli2G5opZhlmWTe48KGYwpblemptbyXGTFEECcIWzP9MwUoEzwWNl8G4uOVzASlOoEe62S+afaxhcVE3uLtjMAT4w1zcCoa5uBUwy1YKhM2A9JHoNoEQj/OlmW52lrhVWbmv70RyXE7A5eYx4axYU253tGzFoIod4zLHOJ3xY2Jd/GLbElPiqoGWYBUraMUKW5YVlzUuwVpLApCRmopSTQMiEXidTgixx4gVomLFqcRyMsGqoW2b20/sM4wxBINBlJaWIiMjA1IruooTXQtaWYkgCIIgDjxtKkotW7YMP//8M5566ikUFhbi+uuvR8+ePXHccce15TQODIzxh7pt27jYtGkTd9MrLOTL1RcVNfzQ5/MBo0YBo0fzNGoUf+iK2z1DyIggqIcR0EKoUKpRolRib6gUZUo1alQ/arUAKpQqFIXLsSdcir2RMihMS3pIAQL6OnKQ7+6FQe5eGOY7BMNS+iPNlxl9OHSZlgOyuXS8w7QoaEjw0UOAFgCUGuhKBfRIFXQtAh0GNEGGLkjQIUCDCIOxOC8pURQhCxIckgxZkOBzeuCV3fA5PXCIMpym+51TdMApcWuoVrF4amsE03XPlQWkD214nKEBSjWglJt5VUyq5qKVWmOW63jZiAAwuKWW2kpBA0UXDzouuZOUXTwGkx1M3MlFSNGVEKPJWT9mk1UWZLPPES3H5WaAcqGV4ghZljL7arVnCVi2OKVHy4ltsQKYJWpZ8dUsKy1rnMb4WLBou2X5YxsAWa6JAgAPILuATAHINoUwmKKWEGvBZZ23ub1obi+YIlw4wgWqUAQIh7lVWTAMhBWzLSYPx9QjChBSTHdKM0UUHszeImK2tWX8SkniIqVloemI+dsVV4/5e2aVZTM2msMZ7ZNldHc4gDHjUTpwcP0A7Ymfybg++78kfYnbJYxNNqaxfTQoWgkJu02sxxw3cVySYpKNDzwNHnL/55KRkYHu3bvv936IrktnXx1q+uBcXHvskPaeBkEQBEE0SpuJUsFgEGvWrMEjjzyC4cOHY/jw4di6dSuee+65jiNKhUI81ksgwK2XAgHuIlJXx9vr6nhQ44oK7mJXUcEtoEpLeR4KNX2MrCxg0CAgPx/Iz4d+6AD4e+WijoXhV/2oVfwoL/kS5ZEqlKs1qFRqUKpWoThSiSqtDlWaHzV6ANV6AH4j3OThJIjo6crBIb5e6J/aB/3T+qF/Wj8cmnkoPG5f/RWyBBEGM5Im3YjAiISgMz2mTQezbZmsh2gnRLkHJFmFbKiQDBWS7oeLqfCIIlwAPLIDsijBIbngkN1wSB44ZBcckhsOyQVBOsjdKUQZcOfw1FwMxRSk6gDND6h+QDPLWpALhpo/WrdERN0qh6Jle58RnhqwrGs7hJg4TlISwcrKJS6axrWJMXUxpi7Bjg8V22a52yF2bExutwsx5Zj22DZZABwi4I0ZC4HnjHE3PjM+e/1k9hnWOFOUMlh0jKHzeG66JWaZ4pZluWWAD2TmdlrMPsCnBC8DPDLAUnibFVSexVp9mdY+tpJsWQsJUaELgimwGVyMshY3UFQuVqkqtzyLJNQVFVD0qGWaYsYKU/Xoao5WkHzLci1iWq8pMbHrAC72hXSgGX+KW/DJQw88gm5eL9ScnNa1VpKtOFliNLfKohjjQmqVhWg8LMn8HFl1e6xZFpJsY28bU5fE+H5rH1LM8YXYNqssA7K5nX0eZuw2WeafLcu6Vo49Byl+33Gx3iTEnbcQ2x/rigkgLQPodTgXxfcBh8NBFlJEs+jMq0Mdmutr7ykQBEF0SXJTXF3CmrajnEObiVKbNm2CpmkYO3as3TZu3Dg89NBDMAwDYqLbRRsTev8/WPPPU1EpqzAE2EkXAF2ML2tm0gVA6waoPQDVbFMdIhSvC4rbAcUlQ3HJCLkkRBwCQjIQhoYw24ygsRFhQ4WyRQO27Pu8ZUFCtjsLeZ5c5HqzkePNRZ6vG7qn9kD31F7ITekGUZTAwGAwA4wxMDBwh7AgmMEABTwB5rOnAAkSz0UJoiBChAhRFOEQHfBKXjhEB5yyE07RCafkhCRKkEUZkmDmZj02iczgK9bpYcAIA3okXhTRVcAIAlqtuWJaQoyYpMJAzEO+JRJYD9BWu20JEPPw3BmtrJpCdEYDpe8PjHEhSg+Z75eVR8z3LjaPcDHMCAOGyuOLWUKWofC2pLnCrcGYFm1jWrSNJa7gZq6qpye3/CP2E+vS2F9EAE4ztQUMgA5AA6AmSVqSXEvSrif0W3VdMOsMkhaEpO2OHk+LObbeRDlJDHdiP+njBn7YCuSkt/dM2oWDKhwCQRAEQXQw0jxyp7emHdgtBcvPHtv0wDagzUSpsrIyZGZmwhmzjHlOTg4ikQiqq6uRlZXVVlNJynPlH+HiE9WmBzaJGWMp8ad6Bv6Q0wCiIJqrwbnhc/rgc/h47vQh1ZmGNFcaUl2pSHelI9OTiSxPFrI8WUhzpkEURAiCAEEQeBmCLQ6JomiLRbFCkSiIdpIEKVoWpbh2qx5b3mcEERDNGEeJMMMUJ1QeCNwWLEyBgmlc8LD7rLG6ua3OX2RbzGL8vbBdoGLa7Xg/9sSaqMd2CUgaULnhk256360ikLV0Hy0YL7p5asp4rTWFPnslvAShytDMPutzkZhitrPrRnw/YtsScuszY5eN6GcnblsWLVt9dj9LaEPMfmM/h9a2CfWkbUje32AZMcdONgYx9ZhyvX3ElOvtK7ENMe0tuUb2AwH8W0wG4D4QB2il87DEs4aSgaiIZX6M4voSxyaOa6xs/mms15+YWCN9DY1ljWzPkvSzJNs2lCNJPRaHKXIfpHTpcAgEQRAE0UnozNa0HYk2E6VCoVCcIAXAriuKkmyTNuX4k67GhR+UoahmDyRRhiBySyExRpQRRRGSKejIgmxbDkmiFGch5JSccIgOyKIMl+SCx+GBW3bD4/DA4/AgxZECn9MHr8MLj+xBqjMVTskJUeSCUqy4JAhC0twWomLKsdt0OgSRu2HsiyuGLTzEigKJeeLDfgMP4cnqQJKH7mR9if1J6vUErcb6kz0Qx7Q1Jo4l7WvpA3ZLxh8oEaKNxI0GD9/Ox+/sJApgdlvi5zjZNRi3oyTbNnTtNOfabUk/Guhv7FiNbQPE/R2KPT5LmE/i/owk6ozdlmQOiftLOkejgfEJc63XFrvbZO9n/GEbHJ90TknOnbGoiyoApPYDsnriYKRThEMgCIIgCIJoJm0mSrlcrnrik1V3uw/IT9wtomdqTzx5+tPtPQ1iX7Dd+AiCIAiia9PRwyEQBEEQBEG0hDYTpfLy8lBVVQVN0yDL/LBlZWVwu91IS0trcntm/mrq93dOn02CIAiCIDoXPp+vw1kf7284hLa6n+oowVP3l0PSRBhK51x8Jc/D32c6h/anK5wHnUPHgM6hY9AVzuGQNLHNtJWm7qfaTJQaOnQoZFnG+vXrMX78eADAunXrMHLkyGb9qhcIBAAA06ZNO6DzJAiCIAiCAPh9SkpKkjiI7cj+hkOg+6mDhx0AOrsPQFc4B6BrnAedQ8eAzqFj0FXOYdzStjlWU/dTbSZKeTwenHrqqbj55ptxxx13oLS0FI8//jiWLm3eK9GtWzd89tlnHfJXS4IgCIIguh4+n6+9p1CP/Q2HQPdTBEEQBEG0JU3dT7WZKAUAixYtws0334wLL7wQKSkpuOqqq3DMMcc0a1tRFNG9e/cDPEOCIAiCIIiOy/6GQ6D7KYIgCIIgOhICY7S8FEEQBEEQRGcgFAph4sSJePzxx+1wCKtWrcLXX3+NZ599tp1nRxAEQRAE0TJoiRaCIAiCIIhOQmw4hI0bN+LDDz/E448/jgsuuKC9p0YQBEEQBNFiyFKKIAiCIAiiExEKhXDzzTfj/fffR0pKCubOnYvZs2e397QIgiAIgiBaDIlSBEEQBEEQBEEQBEEQRJtD7nsEQRAEQRAEQRAEQRBEm0OiFEEQBEEQBEEQBEEQBNHmkChFEARBEARBEARBEARBtDkkSgGIRCK44YYbMH78eEyZMgWPP/54e0+p01JSUoJ58+ZhwoQJmDp1KpYuXYpIJNLe0+rUXHLJJVi4cGF7T6PToigKbrnlFhx++OE48sgjcc8994BC6e0bRUVFuPTSS3HYYYdh5syZePLJJ9t7Sp0KRVFw4okn4ptvvrHbCgoKMHv2bIwZMwbHH388vvzyy3acYech2Wu5fv16nH322Rg7diyOPfZYrFmzph1nePDy6quvYvDgwfXSkCFDko4/+eST643dsmVLG89632mN6/rtt9/GUUcdhdGjR+OKK65AZWXlgZ72PtMa19748ePrveeBQOBAT32fSXbOt99+e71zePbZZxvcx5NPPompU6di7NixuOGGGxAKhdpi6vtM4jkvXLgw6XXd0KqfNTU19cZOnDixLU+hWTT23NJVr+PGzrmrXsuNnXNXvJYbOt8OfR0zgt16663spJNOYj///DN7//332dixY9l///vf9p5Wp8MwDPaXv/yF/fWvf2Vbtmxh3333HTv66KPZv//97/aeWqfl7bffZvn5+ez6669v76l0WhYvXsyOOeYYtmHDBvbVV1+xiRMnshdeeKG9p9Up+ctf/sL+8Y9/sJ07d7IPPviAjR49mr3//vvtPa1OQTgcZldccQXLz89na9euZYzxv5knnXQSu/rqq9m2bdvYQw89xEaPHs327t3bzrPt2CR7LUtLS9n48ePZ3XffzXbu3MnefvttNnLkSPbJJ5+072QPQkKhECstLbVTYWEhO/roo9mSJUvqjdU0jY0cOZJ9++23cduoqtoOM285rXFdb9iwgY0aNYq99tpr7LfffmPnn38+u+SSS9ryNJpNa1x7xcXFLD8/n+3evTvuPTcMow3PpPkkO2fGGJs9ezZ7+OGH484hGAwm3ce7777Lxo0bxz7++GO2YcMGdvzxx7NbbrmlrU6hxSQ759ra2rhz/fHHH9mIESPYBx98kHQf33//PZswYULcNuXl5W15Gk3S2HNLV72OGzvnrnotN/V82tWu5cbOtyNfxwe9KBUIBNjIkSPjvmhWrVrFzj///HacVedk27ZtLD8/n5WVldltb731FpsyZUo7zqrzUlVVxf7whz+wP//5zyRK7SNVVVVs2LBh7JtvvrHbHn74YbZw4cJ2nFXnpLq6muXn57PNmzfbbVdeeWWH+zLuiGzdupWdfPLJ7KSTToq7yf/qq6/YmDFjWCAQsMdeeOGFbMWKFe011Q5PQ6/l888/z4477ri4sYsXL2YLFixoj2kSMTz00EPsqKOOYpFIpF7f77//zoYMGcLC4XA7zGz/aK3r+tprr437ji8sLGSDBw9mu3fvPrAn0EJa69r73//+xyZPnnzA59saNHTOjDE2depU9sUXXzRrP+eee27c+//dd9+xUaNGNfjg2540ds6xzJkzh11zzTUN7uf//b//x84666wDNc1WobHnlq56HTd2zl31Wm7q+bSrXcsteR7vSNfxQe++t2nTJmiahrFjx9pt48aNw4YNG2AYRjvOrPORm5uLRx99FDk5OXHtfr+/nWbUubnzzjtxyimnYODAge09lU7LunXrkJKSggkTJthtl1xyCZYuXdqOs+qcuN1ueDwevPrqq1BVFTt27MAPP/yAoUOHtvfUOjzffvstJk6ciJdeeimufcOGDRg2bBi8Xq/dNm7cOKxfv76NZ9h5aOi1tMzTE6Hvn/aluroajzzyCK6++mo4nc56/du2bUOPHj3gcrnaYXb7R2td1xs2bMD48ePteo8ePdCzZ09s2LDhgMx7X2mta2/btm3o37//AZlja9PQOfv9fpSUlOCQQw5pch+6ruOnn36Ke4/HjBkDVVWxadOm1p7yftPQOcfy9ddf47vvvsOCBQsaHLNt27ZmvT7tSWPPLV31Om7snLvqtdzYOXfFa7m5z+Md7TqW2+QoHZiysjJkZmbG3Szl5OQgEomguroaWVlZ7Ti7zkVaWhqmTp1q1w3DwLPPPosjjjiiHWfVOfn666/x/fff46233sLNN9/c3tPptBQUFKBXr154/fXX8dBDD0FVVZx++un429/+BlE86DX5FuFyuXDjjTfitttuw9NPPw1d13H66afjzDPPbO+pdXjOPffcpO1lZWXo1q1bXFt2djaKi4vbYlqdkoZey969e6N37952vaKiAu+88w6uuuqqtpoakYQXXngB3bp1w3HHHZe0f/v27XA4HLj00kvx888/o3///rjuuuswatSoNp5py2mt67q0tLRT/B1orWtv+/btCIVCmDVrFnbu3ImhQ4fihhtu6JAPtw2d8/bt2yEIAh566CF8/vnnyMjIwEUXXYTTTjut3tja2lpEIpG491iWZWRkZHS49xho+JxjWb16NU477TT06NGjwTHbt2+Hpmk444wzUFJSgvHjx2PRokX1PuvtSWPPLV31Om7snLvqtdzYOXfFa7m5z+Md7To+6J/KQqFQvV/vrLqiKO0xpS7DXXfdhV9//RXz589v76l0KiKRCG666SbceOONcLvd7T2dTk0wGMSuXbvw4osvYunSpbj++uvxzDPPUIDufWT79u2YMWMGXnrpJSxduhTvvvsu3nzzzfaeVqeloe8f+u7ZP8LhMK666irk5OTgrLPOau/pHLQwxrBmzRqcf/75DY7ZuXMnampqcOaZZ2L16tU49NBDceGFF6KoqKgNZ9q6tPS6DofDXebvQHOuvR07dqCmpgZ/+9vf8MADD8DtdmP27Nmdyqpxx44dEAQBAwYMwOrVq3HmmWdi8eLF+OCDD+qNDYfDANBl3uOCggKsXbsWs2bNanTcjh074Pf7sWjRItx7770oLS3FZZddBl3X22imLSf2ueVguY4belbrytdy7DkfDNdysve4I17HB72llMvlqvdBsuokCOw7d911F5566ince++9yM/Pb+/pdCpWrlyJESNGxKncxL4hyzL8fj/uvvtu9OrVCwBQWFiIF154AXPmzGnn2XUuvv76a7z88sv47LPP4Ha7MXLkSJSUlODBBx/EySef3N7T65S4XC5UV1fHtSmKQt89+0EgEMDll1+O33//Hc8//zw8Hk97T+mg5aeffkJJSQlOOOGEBsfcdtttCIfDSElJAQDcfPPN+OGHH/DGG2/gsssua6uptiotva4bug/tbJ/d5l57jz32GFRVhc/nAwD83//9H6ZNm4ZPPvkEJ510UltOeZ859dRTMWPGDGRkZAAAhgwZgt9//x0vvPACjj766LixlmtqV3iPAeC9997D0KFDmwwt8c4770AQBPtzv2LFCkyZMgUbNmzAYYcd1hZTbRGJzy0Hw3Xc0LNaV76WE8950KBBXfpabug97ojX8UFvKZWXl4eqqipomma3lZWVwe12Iy0trR1n1nm57bbb8MQTT+Cuu+7Cscce297T6XS88847+PDDDzF27FiMHTsWb731Ft566624uGdE88jNzYXL5bIFKQDo379/p/4Vvr34+eef0a9fv7gbsmHDhqGwsLAdZ9W5ycvLQ3l5eVxbeXl5h3Jv6Ez4/X7MnTsXW7duxVNPPdXh45l0db744guMHz8e6enpDY6RZdkWpADYv1iXlJS0xRQPCC29rhsan5ube8Dm2Nq05NpzOp32QyzAH/R69+7dqd5zQRDsh1iLhj63GRkZcLlcce+xpmmorq7uVO+xxRdffIE//vGPTY7zeDxx9wvZ2dnIyMjokO9zsueWrn4dN/Ss1pWv5WTn3JWv5caexzvidXzQi1JDhw6FLMtxgevWrVuHkSNHUsyZfWDlypV48cUXcc899zT66yjRMM888wzeeustvP7663j99dcxc+ZMzJw5E6+//np7T63TMXr0aEQiEezcudNu27FjR5xIRTSPbt26YdeuXXG/EO3YsSMu/gDRMkaPHo1ffvnFNgkH+PfP6NGj23FWnRPDMHDllVdiz549eOaZZzBo0KD2ntJBz8aNG5v8JXXWrFlYuXKlXTcMA5s3b8aAAQMO9PQOGC29rkePHo1169bZ9aKiIhQVFXWavwMtufYYYzjqqKPw6quv2m2Wm31nes+XL1+O2bNnx7Vt2rQp6TmIooiRI0fGvcfr16+HLMsYMmTIgZ5qq8IYw08//dTkde33+3H44Ydj7dq1dltJSQmqqqo63Pvc0HNLV76OGzrnrnwtN3TOXfVabux5vKNexwe96uLxeHDqqafi5ptvxsaNG/Hhhx/i8ccfxwUXXNDeU+t0bN++HQ888AAuvvhijBs3DmVlZXYimk+vXr3Qr18/O/l8Pvh8PvTr16+9p9bpGDBgAKZPn45FixZh06ZN+OKLL7B69Wqcc8457T21TsfMmTPhcDjwr3/9Czt37sTHH3+Mhx56qEl/dKJhJkyYgB49emDRokXYunUrVq9ejY0bN+KMM85o76l1Ol5++WV88803uP3225GWlmZ/9yS6XxBtx9atW+u5Bui6jrKyMlvcnjlzJp588kl89NFH2LFjB2699VbU1dUlDTLbWWjqulYUBWVlZXZMjnPOOQdvvPEG1qxZg02bNuG6667D9OnT0adPn/Y8jWbT1LUXe76CIGD69Om4//778c0332Dr1q247rrr0L17d0ybNq19T6QFzJgxA9999x0ee+wx7N69G88//zxef/11OyxAOByOu/c999xz8dhjj+HDDz/Exo0bcfPNN+Mvf/lLh3X5aYi9e/ciEAgkdfmJPeeUlBSMGzcOS5cuxcaNG/HLL79g/vz5mDp1KgYPHtzW026Qxp5buup13Ng5d9VrubFz7orXclPP4x32OmYECwaD7LrrrmNjxoxhU6ZMYU888UR7T6lT8vDDD7P8/Pykidh3rr/+enb99de39zQ6LbW1tezaa69lY8aMYZMmTWL3338/MwyjvafVKdm6dSubPXs2O+yww9hRRx3FnnjiCXotW0h+fj5bu3atXf/999/Zeeedx0aMGMFOOOEE9r///a8dZ9e5iH0t58yZk/S75/zzz2/nWR68jBw5kn3++edxbQUFBXHvm2EY7MEHH2TTp09nI0aMYOeddx7bvHlze0x3v2jJdb127VqWn5/PCgoK7LZXXnmFTZs2jY0ZM4ZdccUVrLKysk3n31Jacu0lnm84HGZLly5lkydPZqNHj2aXXnopKywsbLdzaS6J7/EHH3zATjrpJDZy5Eh23HHHsffee8/ue+WVV+rd+z788MNs0qRJbNy4cWzRokUsHA632dz3lcRzXr9+PcvPz2eRSKTe2MRzrq6uZgsXLmQTJ05kY8eOZddccw2rrq5uk3k3l6aeW7riddzYOXfVa7mp97mrXctNnW9HvY4FxhhrfamLIAiCIAiCIAiCIAiCIBrmoHffIwiCIAiCIAiCIAiCINoeEqUIgiAIgiAIgiAIgiCINodEKYIgCIIgCIIgCIIgCKLNIVGKIAiCIAiCIAiCIAiCaHNIlCIIgiAIgiAIgiAIgiDaHBKlCIIgCIIgCIIgCIIgiDaHRCmCIAiCIAiCIAiCIAiizSFRiiAIgiAIgiAIgiAIgmhzSJQiCKLDM3jwYFx99dX12l999VXMnDmzHWZEEARBEARBEARB7C8kShEE0Sl4++238fXXX7f3NAiCIAiCIAiCIIhWgkQpgiA6Bb169cKtt94KRVHaeyoEQRAEQRAEQRBEK0CiFEEQnYJ//OMfKCkpwWOPPdbgmOLiYvz973/HhAkTMHHiRNx+++22iPXqq69i1qxZWLFiBSZOnIjx48dj6dKlYIzZ27/44ouYOXMmxo4di1mzZmHz5s0H/LwIgiAIgiAIgiAOVkiUIgiiU5CXl4d58+bhoYceQkFBQb1+RVFw4YUXIhQK4ZlnnsF9992HTz/9FMuWLbPH/Pjjj9i5cydeeOEFLF68GE8//TS++uorAMDHH3+MlStXYvHixXjttdcwbtw4XHDBBaipqWmzcyQIgiAIgiAIgjiYIFGKIIhOw6xZs9CvXz8sWbKkXt8XX3yBkpIS3HXXXRg8eDAmTZqEG2+8ES+88AICgQAAQNd13HbbbRgwYABOOeUUDBkyBD/99BMA4NFHH8Wll16KGTNm4JBDDsE//vEP9OrVC2+++WabniNBEARBEARBEMTBgtzeEyAIgmgukiTh5ptvxrnnnosPP/wwrm/79u045JBDkJ6ebrcddthh0DQNu3fvBgBkZ2cjJSXF7k9JSYGmafb2d911F+655x67PxKJ4Pfffz+AZ0QQBEEQBEEQBHHwQqIUQRCdisMOOwx//vOfsWTJEvz1r3+1210uV72xuq7H5U6ns94YK6aUruu44YYbMGnSpLj+WBGLIAiCIAiCIAiCaD3IfY8giE7HNddcg2AwGBf0vH///vj9999RXV1tt61fvx6yLKNv375N7rN///4oLi5Gv3797PTQQw9h/fr1B+AMCIIgCIIgCIIgCBKlCILodGRmZuKaa67B3r177bbJkyejT58+uO6667B582asXbsWt912G0488USkpaU1uc+LLroITz31FF5//XXs3r0bd911F/773//i0EMPPZCnQhAEQRAEQRAEcdBC7nsEQXRKzjjjDLzyyisoLS0FwONNPfDAA7jtttvwl7/8BT6fDyeddBIWLFjQrP0df/zxKC8vx4oVK1BeXo6BAwfiwQcfxCGHHHIAz4IgCIIgCIIgCOLgRWBWQBWCIAiCIAiCIAiCIAiCaCPIfY8gCIIgCIIgCIIgCIJoc0iUIgiCIAiCIAiCIAiCINocEqUIgiAIgiAIgiAIgiCINodEKYIgCIIgCIIgCIIgCKLNIVGKIAiCIAiCIAiCIAiCaHNIlCIIgiAIgiAIgiAIgiDaHBKlCIIgCIIgCIIgCIIgiDaHRCmCIAiCIAiCIAiCIAiizSFRiiAIgiAIgiAIgiAIgmhzSJQiCIIgCIIgCIIgCIIg2hwSpQiCIAiCIAiCIAiCIIg2h0QpgiAIgiAIgiAIgiAIos0hUYogCIIgCIIgCIIgCIJoc0iUIgiCIAiCIAiCIAiCINocEqUIgiAIgiAIgiAIgiCINodEKYIgCIIgCIIgCIIgCKLNIVGKIIgWwxg7KI+9P3TWeRMEQRBEZ4S+d4lk0OeCIDoeJEoRRBdm1qxZGDx4cFwaP348LrjgAnz77bct3l9xcTEuueQS7N27126bOXMmFi5c2OJ9DR48GPfff3+LtlmzZg3uvPPOFh+rvdm6dSvOOeecuLZ9OX+CIAiCiGXdunW46qqrMHnyZIwcORJ//OMf8a9//Qvbt29v76nFcf/992Pw4MFtdrx169bhkksuabPjdQR++eUXXHzxxTjiiCMwceJEzJkzB7/88kvcGMYYHnvsMRxzzDEYOXIkjj32WDz33HNN7nvv3r34+9//jkmTJmHixIm4/PLLsXv3brvfen8bSi2550y2r2HDhmHixIm44oorsHXr1mbv6/HHH8c111wDAKitrcV1112H77//vtnb7w8LFy7EzJkzGx3z6quvYvDgwdizZ0+z99ucbaqqqjB9+nQUFBQ0e7+xBAIB3HLLLZg8eTLGjh2Liy++GDt27Gh0m5kzZzb4/se+DmVlZfjXv/6FGTNmYOzYsTj99NPxn//8Z5/mSXQd5PaeAEEQB5Zhw4bhpptuAgDouo6qqiq88MILmDt3Ll599VUMGjSo2fv66quv8Nlnn7XKvF566SV07969Rds8+OCDmDBhQqscvy1599138eOPP8a17cv5EwRBEITF6tWrcc8992DKlCm44YYbkJubi127duGFF17AaaedhqVLl+KEE05o72m2C2vWrOlwwtyBZNeuXTj//PMxYsQILFmyBIIg4PHHH8e5556L1157DQMGDAAALFu2DM888wzmzZuHkSNH4vPPP8ett94KWZZx1llnJd13OBzGnDlzoGkaFi9eDJfLhRUrVmDWrFl46623kJaWhjPPPBNTp06N205VVcyfPx+5ubkYNWpUi8/ppZdessu6rqOwsBD33nsvzjvvPLzzzjvIzc1tdPvt27fj4YcfxptvvgkA+O233/DGG2/gz3/+c4vncqCYPn06XnrpJXTr1q1V95uZmYnZs2fjhhtuwNNPPw1BEFq0/dVXX40NGzbg2muvRUpKClauXIkLLrgA77zzDtLT05Nus3LlSiiKEte2fv16LF26FGeffTYAQFEU/PWvf0VdXR3mzZuHbt264b333sP8+fOhKApOPfXUfTpfovNDohRBdHFSUlIwZsyYuLYjjzwSkyZNwquvvorrr7++XeaVOKeDjYP9/AmCIIh955NPPsHdd9+Nq666CldeeaXdPmHCBJx66qm4+uqrsXDhQuTn57foxyeic/LMM8/A4/Hg4YcfhtfrBQAcccQRmDlzJp599lnceOON2LNnD5588kksXrwY5557LgBg0qRJKCoqwpdfftmgKPX999/j999/x5NPPolJkyYBAPr3748//elP+Oijj3Daaaehe/fu9X5oW7p0KQKBAF588UW43e4Wn1PifdK4cePQo0cPnHfeeXjttdeatIS76667cOKJJyIvL6/Fx24rsrKykJWVdUD2fe655+LBBx/EBx98gGOOOabZ2/3444/45JNPsHr1akybNg0AMH78ePzxj3/E888/j7/97W9Jtxs2bFhc3e/3Y8GCBZg+fbr9Xn366afYtGkT1qxZYwuVkydPRmFhIR599FESpQ5iyH2PIA5CPB4PXC5XvV9O/vOf/+D000/H2LFjMXnyZNx4442oqakBwM2FFy1aBAD44x//GOeyp6oqli1bhsmTJ2PMmDGYM2cOdu3a1egcYt3XvvnmGwwePBhff/015syZg9GjR2Py5Mm46667oOs6AG4WvHfvXrz22mtxZsuFhYVYsGABJkyYgNGjR+PCCy/Er7/+ah9nz549GDx4MJ544gkcd9xxGD16NB588EEMHjwYn3zySdycfvvtNwwePBgffPABACASiWDZsmWYNm0aRowYgZNOOqmeifHMmTOxYsUK3HnnnTjyyCMxatQozJ07F7///jsAboa+cuXKeuec6L5XWlqKRYsWYdq0aRg1ahTOOOMMfPTRR/Ves+eeew7//Oc/MWHCBIwdOxZ///vfUV5ebo/ZvXs3LrvsMkycOBGjR4/GWWed1WrWbQRBEETHYOXKlRgwYACuuOKKen0OhwO33norJEnCI488AgCYM2cOTj/99HpjL7/8cpx88sl2/fvvv8f555+P0aNHY8KECbj++utRWVlp97/66qsYNmwY1qxZg8mTJ2PChAnYtm1bs797Pv30U5x88sm269jrr78e19+c78JIJIJVq1bhuOOOw8iRI3HMMcdg9erVMAwDAHebeu2117B3714MHjwYr776atLX8P7778dxxx2HDz74ACeeeCJGjhyJU045BT/++CPWr1+PM888E6NGjcKJJ56Ir7/+Om7bLVu24NJLL8Vhhx2Gww47DFdccUU9V6lNmzbhyiuvxBFHHIHhw4dj6tSpuP322xEOh+0xzflet9y1vvnmm6TnAQADBgzAnDlzbEEKALxeL7p372672X344YdwuVw444wz4ra97777Gg0nEIlEAAA+n89uy8jIAABUV1cn3Wbz5s145plncOWVV6J3794N7ruljBgxAgDsMBL3338/jj76aKxcuRITJkzAlClTUFNTgy1btuDTTz/FiSeeCIDfZ15wwQUAgAsuuACzZs2y99nYva/FTz/9hLlz52LixIk47LDDcNlllzXbjfDVV1/Fsccei5EjR+Lkk0+Ouy6SueK99tprOP744+3xX3/9NYYNG1bvc7xhwwacffbZGDlyJKZPn45HH300rt/pdOLYY4/Fww8/bLdZ99sNXRMA8OWXX8Lr9WLKlCl2W1ZWFg4//PAW3U8+8MADqKysxI033mi3paSk4KyzzsLIkSPjxg4YMCDOHZQ4+CBRiiC6OIwxaJoGTdOgqirKyspw9913Q1GUOBPmBx54AAsWLMCYMWOwYsUKXHHFFXjvvfcwa9YshMNhTJ8+3f51ZOXKlbj88svtbf/zn/9g69at+Pe//42bbroJP//8M+bPn9/iuV5zzTUYN24cHnroIZx44ol49NFHsWbNGvuYubm5mDZtmm3qXFlZibPPPhu//PILFi9ejLvvvhuGYeC8886rZ7Z///334+KLL8ayZctw2mmnoW/fvnjnnXfixrz99tvIyMjAtGnTwBjDFVdcgRdffBEXXXQRHnzwQYwdOxbz58+vdxP99NNPY8eOHVi6dCluv/12/Pzzz7YF2plnnmnfAL700ks488wz6513eXk5zjjjDHz//feYP38+7r//fvTq1QtXXHGFbXZuce+998IwDNxzzz247rrr8Mknn+COO+4AABiGgUsvvRShUAjLli3DAw88gIyMDPztb39rUiQkCIIgOgeVlZX4+eefMWPGjAbdcjIyMnDkkUfags7JJ5+MX375Je67oLa2Fp9//jlOOeUUAMB3332H2bNnw+1247777sMNN9yAb7/9FhdccEGckKLrOh5//HEsWbIEixYtQv/+/Zv93XPjjTdi9uzZePDBB9G9e3csXLgQmzZtAtC870LGGC677DI8+uijOPPMM/HQQw/huOOOw3333WeHKrj88ssxbdo05Obm4qWXXsL06dMbfC2Li4vx73//G5dddhmWL1+O2tpazJs3DwsWLMCZZ56JVatWgTGG+fPn26/Bzp07cfbZZ6OiogJ33nknlixZgoKCApxzzjmoqKgAwMW18847D6FQCP/+97/xyCOP4IQTTsAzzzyDp59+Om4OjX2vA1EXr+HDhzd4Hueeey7++te/xrXt2rULW7dutS3lfvvtN/Tr1w/fffcdTjvtNAwfPhwzZ86Mc5NLxpQpU3DooYfirrvuQkFBAcrKynDbbbfB6/XiqKOOSrrNsmXL0Lt3b1x44YWN7rul7Ny5EwDQt29fu62wsBCfffYZ7r33XixatAjp6el46623kJuba1tbDR8+3BZHbrzxRvuz0tS9LwCsXbvWjgt6xx134Pbbb0dRURHOPvvsJl1Ei4qKsHr1avz973/H/fffD0EQMG/ePPtzksjrr7+OhQsX4rDDDsMDDzyAY489Fpdffrn9A20sN998M0444QSsXr0aY8eOxV133VXvx9bjjjsOP//8s/26DR8+vMlrYvv27ejduzckSYpr79u3r72fpigsLMTTTz+NuXPnolevXnb7kUceiVtvvTXu75aqqvjss88wcODAZu2b6KIwgiC6LOeffz7Lz89Pmh566CF7XHV1NRsxYgRbvHhx3Pbfffcdy8/PZ88++yxjjLFXXnmF5efns4KCAnvMjBkz2LRp05iiKHbbvffey/Lz81ldXV2Dc8vPz2crVqxgjDG2du1alp+fz+699964MTNnzmSXXnpp3LGuv/56u37PPfewkSNHsj179thtkUiE/fGPf2RXXXUVY4yxgoIClp+fz2644Ya4fa9YsYKNGTOGhUIhxhhjhmGw6dOnsxtvvJExxtiXX37J8vPz2TvvvBO33TXXXMMmT57MVFW15zRjxgymaZo95v7772f5+fmssrLSPlZ+fn6D579s2TI2fPjwuPNgjLELL7yQTZ48mem6bm9zzjnnxI1ZuHAhGzNmDGOMsdLSUpafn8/efPNNu7+2tpbdcccdbMuWLYwgCILo/GzcuDHuu7kh/v3vf7P8/HxWXV3NAoEAGzNmDFu5cqXdv2bNGjZkyBBWXFzMGGPsrLPOYieeeGLc99mOHTvY0KFD690HvP766/aY5nz3WN+Dn332mT1m165dLD8/nz311FOMseZ9F3766acsPz+fvf3223FjVq1axfLz8+3jXX/99WzGjBmNvj7J5vTwww+z/Px8tmbNGrvt3XffZfn5+ezXX39ljDG2YMECduSRR8bd41RVVbFx48axf//734wxxr744gt23nnn1bsPOvHEE9mcOXPselPf6/tKKBRiZ511FhszZoz9ev71r39lEydOZEcccQR79tln2VdffcX+9a9/sfz8fPbiiy82ur8ffviBTZgwwb6HHDFiBPvyyy+Tjv3tt99Yfn4++3//7//t09yt90VVVTvV1dWx7777jp122mls3LhxrLS0NG7sd999F7ePM844g/3tb3+La7PuNdeuXcsYa/697xlnnMGOP/74uOuipqaGTZgwgc2bN6/B87j++utZfn4+27Ztm9321Vdfsfz8fPbhhx8yxurfV0+fPj3uvpex6GfylVdeidvm+eeft8cEg0E2fPhwdscdd8RtW1tby/Lz89lzzz3X4DwTmTNnDjv77LPrtd9zzz1s+PDhzdrHHXfcwcaOHcuqq6ubHLtkyRKWn5/P3nvvvWbPkeh6kKUUQXRxhg8fjpdffhkvv/wy1qxZg8ceewwXXngh7r33Xtx7770AeCBCRVFsM2eL8ePHo1evXk2umjJq1Cg4HA67bplq19bWtmiuY8eOjat3794dwWCwwfFff/01hg4diry8PNsaTBRF/OEPf8BXX30VN3bo0KFx9ZNPPhnBYND+VemHH35AYWGh/Yvx119/DUEQMG3aNHvfmqZh5syZKCsrizPbHjlyZNwvSlZchVAo1Kzz/vbbbzF27Ni4X5OsOZaVlcWteJIYY6F79+72cXJycjBw4EAsXrwY119/Pd566y0YhoFFixZRTBGCIIguAjOXtI/93k2G9b3EGLOtWmJd0N955x1MmjQJeXl5CIVC2LBhg20pbH3n9enTB4ceeij+97//xe079ju1Jd8948ePt8uJ9wrN+S789ttvIcsyjjvuuHpjrH20lMMOOyzuXABg9OjRdpvlqmbNc+3atZgwYQLcbrf9OqWkpGD8+PH2vceUKVPw7LPPwuVyYdu2bfjoo4/w4IMPorKysl4w6Ma+1/cFv9+PSy+9FD/99BPuuusu+/VUVRVVVVW45ZZbcN5552HSpEm47bbbMGXKFDvMQDIsa7khQ4bg4YcfxiOPPII//OEPuPLKK5OuZPfcc88hOzvbvp/aV4YPH26ncePG4bzzzoOiKLblfCyJ93gFBQVNug025943GAzip59+wp/+9Ke4+7y0tDTMmDGjyc9bZmYmDj30ULtuzamurq7e2F27dqGwsLDeZ7uhxQpiryWPx4OcnJx6992pqalIS0tr0ep+1t+XZDQnYHokEsHLL7+MM844o8Gg6NZxli1bhqeeegpz585tUdwroutBgc4Joovj8/nq+W5PmTIFwWAQjz76KC644ALbd966GYslJycn6ZdnLLExDABAFLnebcV3aC6JgTBFUWz0y7G6uhq7du1q0KQ99qYucY79+vXD2LFj8c477+BPf/oT3nnnHfTt29e+Oa2urgZjLO5mNZbS0lL7Jsjj8dSbN9D886+pqUGfPn3qtVvvR+xNRrJjWa+RtdqOFdjy9ddfh8PhwFFHHYVbbrml0ZsDgiAIonNgiQxWXJ2GKCgogM/ns0WVU045BW+++SY2bdqEnJwcfPPNN7abWG1tLQzDwCOPPGLHoYrF5XLF1WO/U1vy3RO7nfVdaX2HNee7sKamBpmZmfVciyyRoqn7lWSkpKTUa0v8ro2luroa//nPf5IuY28Frbbc8Z577jkEg0H06NEDo0aNqvc6JjtWU/c+jVFUVIRLL70UO3fuxL333hvnXufz+ewf22KZOnUqvvzyS5SXlye9D3zooYeQl5eHRx55BE6nEwC/jzz77LNxxx13xMUn0nUdH3zwAY4//nh77L7y8ssv22WHw4Hc3FxkZ2cnHRsb7wrgwlxj7yGAZt371tXVgTHWavfHlqiT7P7Qit2WeI7Jjg00/3Pj8Xjg9/sbnWcsKSkpcTHNLAKBAFJTU5vc/ssvv4Tf78dJJ53U4BhFUbBw4UK88847mDt3Lq677rpmz4/ompAoRRAHKSNGjMCaNWuwZ88e+4axvLzcXjbYoqysLOlNYkcgNTUVEyZMaPDLrKkbopNPPhlLly5FXV0d3n33XTtmgLVvr9dbL/aDRb9+/fZ94gmkp6ejrKysXrvVlpmZ2ex95eXl4eabb8ZNN92ETZs24d1338UjjzyCzMxMO4YCQRAE0XnJzs7GmDFj8N577+Hvf/+7Le7E4vf78b///Q8zZ8602yZNmoTc3Fz897//RW5uLlwul22dYAkWs2fPTmqZ0dQDfmt89zTnuzA9PR1VVVXQdT1OmCotLbXHHGhSU1Nx5JFH4qKLLqrXJ8v80Wr16tV48sknccstt+CYY46xH+YTg4y3Jps3b8bcuXMRiUTw+OOP4/DDD4/r79evHxhjUFU1ThzTNA1A/R8GLfbu3YsRI0bE3VOJoohx48bhueeeixu7YcMGVFVV4U9/+tN+n0/iD6otISMjo0nBqDn3vqmpqRAEIalIU1ZWZgu+rYFlZZ8Yb6qh+FPNpba2tkXXRf/+/fHll1/CMIy4vy27du2Ks/pqiE8//RS9e/du8P2rq6vDJZdcgvXr1+OGG25o9bhjROeE3PcI4iBl48aNkCQJffr0wejRo+F0OvH222/Hjfn+++9RWFhoWwslu/FtSxKPP2HCBOzcuRP9+/fHyJEj7fTGG2/g5ZdfrvdLaiLHH388GGNYvnw5Kioq4lYgmjBhAoLBIBhjcfvesmULVq1aZd/E7cu8Ezn88MPx448/1vvV+80330Rubm6zBbAff/wRRx55JDZu3AhBEDB06FDMnz8f+fn5KCwsbPZ8CYIgiI7NlVdeiZ07d+Kee+6p16frOm666SaEw+G44NeSJOGkk07CJ598gnfffRdHHXWUbcmRkpKCYcOGYceOHXHfeYMGDcL999/f6MpvrfXd05zvwgkTJkDTNLz77rv1xgDAuHHjABzY+xVrxcGhQ4far9OIESPw5JNP2qv3rlu3DgMHDsSf//xnW5AqKSnBli1bWmxF3hyKiopw0UUXQRAEvPDCC/UEKQC2hVTiIi8ff/wxBg8enNRiDOAro23cuDHO7ZAxhh9//LHej5YbNmyALMsYNWrU/p7SftGrVy8UFRXFtSXeEzbn3tfr9WLEiBH473//GxdsvK6uDp9++qn9eWsNunfvjr59+9qfIYv3339/n/dZU1ODUCiEnj17NnubKVOmIBAI4IsvvrDbKisr8f3332Py5MlNbr9+/foGvQw0TcNll12Gn376Cffeey8JUoQNWUoRRBfH7/dj/fr1dl1RFHz88cd45ZVXcNZZZ9mm5pdccglWrVoFh8OBGTNmYM+ePVi+fDkGDhyI0047DQD3oQeADz74AH/4wx+a9YtJa5KWloZff/0V3377LUaNGoXZs2fjjTfewOzZszFnzhxkZmbiP//5D/7f//t/WLRoUZP7s1bae/755zF27Ng48WfatGk4/PDDcfnll+Pyyy/HoYceio0bN2LFihWYOnWq/bo1d94AX91v9OjR9W7iLrroIrz55puYPXs2rrzySmRkZOD111/H2rVrcccddzT75nrYsGFwu9247rrrcNVVVyEnJwdfffUVfvvtN3spZIIgCKLzM3XqVCxcuBDLli3Db7/9hj//+c/o1q0b9uzZgxdeeAG//fYblixZgiFDhsRtd8opp+Dxxx+HKIr13PQWLFiASy65BFdffTVOPvlke5W9DRs2xK24m0hrffc057vwD3/4AyZOnIh//etfKCkpwZAhQ/Dtt9/ikUcewWmnnWav4JWWloby8nJ89tlnGDp0KLp169aCV7dxLr/8cpx99tm49NJLcc4558DlcuGll17Chx9+iBUrVgDgsTYfeOABrF69GmPGjMGuXbvw8MMPQ1GUFseLqqysxO7duzFw4MAGhaPbb78dFRUVuOWWW+rd96WkpGDgwIGYOHEiZsyYgaVLlyIUCmHQoEF4/fXX8cMPP+CBBx6wx+/evRuVlZV2rKvLL7/cXt3vwgsvhCzLeOWVV7B+/Xr7fC22bNmC3r17J3VTBPhqh8XFxRg2bNh+u/c1xuTJk/H888+DMWa7zFni4Keffor09HQMGTKkWfe+V199NebOnYtLLrkE5557LlRVxerVq6EoCq644opWm7O1Mt8111yDm266CUcffTQ2bdqEVatWAdg3oXXdunUAuNAE8GeCbdu2oW/fvg3exx5++OGYMGECrr32Wlx77bXIyMjA/fffj9TU1DiPgm3btkFRFAwbNsxu03UdO3bsqBeny+K5557D999/j7POOgvdu3eP+5wC9eOrEQcPJEoRRBfn119/xVlnnWXXXS4X+vbti/nz52Pu3Ll2u3Uj+eyzz+Kll15CRkYGjjvuOPzjH/+wf0mdOHEijjzySNx99934+uuvsXr16jY9lzlz5uCOO+7A3Llz8cQTT2D8+PF48cUXcffdd+Pmm29GJBLBIYccgiVLljTbRP6UU07Bhx9+WM/3XRRFrF69GsuXL8fDDz+MiooK5OXl4aKLLmrxTcgxxxyDN954AwsXLsQZZ5yBm2++Oa4/NzcXL7zwAu6++27cfvvtUFUVQ4YMwQMPPIA//vGPzT6Oy+XC448/jrvvvhtLlixBbW0tDjnkENx66604/fTTWzRngiAIomNz0UUXYezYsXjqqadw5513orKyErm5uZg8eTKWLFmSdIn1IUOGID8/H1VVVZg0aVJc35QpU/DYY49h5cqVmDdvHhwOB4YPH44nnnii0YfF1vruac53oSAIePjhh7FixQo8+eSTqKysRO/evbFgwYI4d7rTTz8dn332Ga644grMmzcPl1xySbPn0RRDhgzBc889h3vvvRfXXXcdGGPIz8/HqlWr7HleeumlqKqqwtNPP41Vq1ahR48eOOWUU+z519bW2j9YNcWnn36KRYsW4emnn8bEiRPr9SuKgk8//RQAkrpKTpgwAc888wwAYPny5Vi5ciWeeOIJVFZWYuDAgVi5cmWcm+cDDzyA1157DZs3bwbA3eieffZZLF++HNdccw0cDgcGDx6Mp59+GhMmTIg7Vnl5eaPxK9esWYOVK1fio48+ajIQ+f5wzDHHYNWqVdi4caMdtH7QoEE48cQT8dxzz+GLL77A22+/3ax730mTJuGJJ57AihUrsGDBAjidTowfPx533nlnqy8ic9JJJyEYDOKxxx7DK6+8gkGDBuGf//wn/vnPf9aLT9UcPv/8c4waNcqOQ/fLL7/gggsuwNKlSxu9NleuXIl///vfWLZsGQzDwGGHHYb77rsv7r295ZZbsHfvXnz88cd2W3V1NTRNa/CzbVl9vfTSS3jppZfq9VufOeLgQ2D7GkmPIAiCIAiCIAiCIJrJeeedh/vuu6/eCnqtzWWXXYbMzEwsXbr0gB6nNXn77bcxbNiwuBhXn376KS699FK88cYb9SwfGyMYDGLq1Km488474wLeE0RHhGJKEQRBEARBEARBEAeUb775BqFQqMEV5VqT+fPn4/333+9UMTXffPNNXHzxxXjrrbfw/fff45VXXsFNN92ECRMmtEiQAoAXX3wRgwYNapHFPUG0F2QpRRAEQRAEQRAEQRxQ9u7dC6/X2yarJAJ8FcRNmzYlXRCgI1JVVYW7774bn3/+OSorK5GTk4Njjz0W8+bNg8/na/Z+Kisrceqpp+KZZ55p1dWiCeJAQaIUQRAEQRAEQRAEQRAE0eaQ+x5BEARBEARBEARBEATR5uyzKKUoCk488UR88803dltBQQFmz56NMWPG4Pjjj8eXX34Zt81XX32FE088EaNHj8YFF1yAgoKCfZ85QRAEQRAEQRAEQRAE0WnZJ1EqEolgwYIF2Lp1q93GGMMVV1yBnJwcvPLKKzjllFNw5ZVX2sHlCgsLccUVV+D000/Hyy+/jKysLFx++eVorvcgYwx+v7/Z4wmCIAiCIIh46H6KIAiCIIiORItFqW3btuEvf/kLdu/eHde+du1aFBQU4NZbb8Whhx6KSy+9FGPGjMErr7wCAFizZg1GjBiBOXPmYNCgQVi6dCn27t2Lb7/9tlnHDQQCGDduHAKBQEunTBAEQRAEQYDupwiCIAiC6Fi0WJT69ttvMXHiRLz00ktx7Rs2bMCwYcPg9XrttnHjxmH9+vV2//jx4+0+j8eD4cOH2/0EQRAEQRAEQRAEQRDEwYPc0g3OPffcpO1lZWXo1q1bXFt2djaKi4ub1d/uKNXAtkcAQwHkFMCRaqYMwJkBODPNciYgSu07V4IgCIIgCIIgCIIgiE5Oi0WphgiFQnA6nXFtTqcTiqI0q7/d2b0GWH9dMwYKXJhy5fDkzgM8PQFvb8DTC/D1BVIH8XYSrwiCIAiCIAiCIAiCIJLSaqKUy+VCdXV1XJuiKHC73XZ/ogClKArS0tJaawr7R58/A3VbgdotgB4AtCCgBQDND6h1PNeDABigVPJUt6Xh/UleU6zqw0WqtHwgbSjg7QvIbkCQkidRNstyTLvQZi8DQRAEQRAEQRAEQRANoxsMkti5n9M7yjm0miiVl5eHbdu2xbWVl5fbLnt5eXkoLy+v1z906NDWmsL+4coCxi6r325o3KXPULhIFSoBQnuAwC4gXAKES4FwMRCpAJQqIFIGRMq5gOXfxlPpJ9H9SR4gNR9IG8xT6mBA9gEQuPhkCVEQuaWVIAGiExBdgOTmSZAAQeQJoilaWXUh2pesP3EMQRAEQRAEQRAEQRDNRhIF/P3FH7Gt1N/eU9knBnZLwfKzx7b3NAC0oig1evRorF69GuFw2LaOWrduHcaNG2f3r1u3zh4fCoXw66+/4sorr2ytKRwYRJkneHlsKW8vAIfxPkPj4pPqB5QaQCnjwpVSA6hmChUCddu4FZZ/J6CHgOoNPAEABCB9KJBzJJA9EUgbwkUkpgPM4LlWB7BqwDDbwMwxscs5C7zdFrfEaBlm2RajhBjBSkwQwmTYQpYgx/THilsx+0JCOXZMU/3J9kFWYQRBEARBEARBEEQHZ1upH78U1rb3NDo9rSZKTZgwAT169MCiRYtw+eWX45NPPsHGjRuxdOlSAMCf//xnPPbYY1i9ejVmzJiBVatWoXfv3pg4cWJrTaHtEWVATAMcaYC3J8CGcDc/pYqLUUold93reQIXtJgB+HcA1RuB6p94HiwAan7lafujfF+5fwB6HAtkH26KRC2AMQAsKl7ZOQNgJOQaYBhmHTy3x8ZsA5j9lggmRMv1xDFE2+MEKcQLULZAlZhbgpYllCVaj4kALJGsOWJZA337vA1BEETbwxgDA4vLAdRra6wvsS1xv431N1RO3M4qJ443DP5dYsDg7db2iC/zr56Evti6OcYpOTG823A4pfhYlQRBEARBEETnotVEKUmS8MADD+Cf//wnTj/9dPTr1w+rVq1Cz549AQC9e/fG/fffjzvuuAOrVq3C2LFjsWrVKghd6UFfEKKr9nn7cAuncDl39QsWcDe91EN5fKm+Z/BtwmVA+Vqg/Cug4htArQUK3+bJkQF0PwrodSKQMaL5c4gVgtqLOPELMeUY0csWw2KFNMMUyhLHNiSWmdZhtpUYYo4ZI54lWpIly1simNnukqabZZx7pGS6Xsa4SiYTu+r1NVJvcPtEAa0LXU8EcYBhjMFgBhjMPKbeWDl2fEvaDMOAASOaxxzTTlZ/TFuDQlCiWAPwP5FJhKXE8XwoQ3QzFvNnVODbJPkTKgiCPbahsjXe2k/i97wAXhcEIa6ciAAhrt0aqzMdgiBgYNZAEqUIgiAIgiA6OfslSm3evDmu3q9fPzz77LMNjp82bRqmTZu2P4fsPAgCt3pypHGXv3AJd98L7AUcPsCZxce4c4HeJ/FkaNx6qvgDoPhDbnFV8DJPacO4kNXjGB5XqqMjCOAWTe09kQSSiVy2IBbbHyOYMSMqmLFY6zMg3roM0T77SS+Zi2WyeoKwFidIISo+2S9orICVWE8inNmWZQl9se0tEb8S3UQbFdIaautoHw6iPYgTZJKINJaw09CY2H7GGHRDh854MpgB3eC5xjQwg9l99YQmxrgVj3ltJ/YlWvVYlyuAaDlRnBHq91lCS6zg0tI2URDriTXJxgKo1x47PrHcWVB0BVXhqvaeBkEQBEEQBNEKtJqlFNEIkgvw9QU8PYBQMV+1L1gAuLvFC0yiDGQdxtOQq4GK74DC/3CBqvZX4Odbgc33AbmTebD01EE8d2W126l1OmxRJZp1KBoUzWKFrlhLMiAqlum830jsTxDeku7bIlE4a6TNEsPiXDQbsTqztkkUqBoV0GLdNRMC9dfbd3OEtca2acY+29sCsR2IFYMsgcdghi36JOuLTZqh8cQ06IYOzYjm9YQmJLdWMkyRmMV8Vm3xB6hnkRMr3gDgIk6McBMr6oiCyOuiUG+7ZGM7o4hDEARBEARBEB0VEqXaEtEB+PpwEaluOxD4nbe5cuo/7IoykDuJp6ELgD1vAgWv8FhVhf8F8N/oWFc2X8UvbXB0ZT9v74PyAbrT09FFs1jquWg2ZHWWTAQzhTMjtt3aR8I+Y7e195tIzGtm7yfJGAGIF6uQREizxiZaoiUIWJZ4BksAS1jx0nbljBXbGhPRmlluhvjGGKAzI5oMAzoMGIyZbbotIlllzdCgGipUXY0rN+RaZolIhi12Wq+cYL5NpmBkijyWoGOJPLFtkiDZolBiP4C4MkEQBEEQBEEQXQcSpdoD2QdkjOSue7VbgOAebkUlOpKPd2YCAy4E+p8PVHwP1PwE1G7lK/oFC4BIBRD5iselspB8QNogvppf6mC+wp/vkJYHTieIhuioLpoNkdQ6rBEhrTFLs6RiWoIA1ywxLRH+YhowoOm6LSpphg4dBnTGeAKDZvA+lelQDB2qrkM1dCiGBp1xRzTd4LGC7DoYmGFZGXEBS4AIJgACREiiDEGUIAkSRFGCCAmiwOuyJHLxSOBtoiiaYpElzCUR+WyLoiQWc/b5xnyABEuwFBJecwFgifuM3T6xnSAIgiAIgiCIzgApFO2FIHAhypHBV94L7AI8eY3HixIkIGciTxZaEKjbxl0CazcDdZu5FZYeAKrW82QhuoDUgUDaUCB9CM9TDiWhijg4aAcrNMMUlDRDh8Y0Li7ZbZpdjugKFENFRFOhGCo0Q4cB08KJ6dzCydAB072NwwUvgQmQBAGiIEAyLYpEUYIkCHBIot0nQoAowLQ4ShDmEoU1aGaKEeB0Fj1sU9gWa02MqWcRFtuZpC1uGwCNCV/JFhVIZllmW7YlsY5LtJ4DGhDdkhy/nvCWMO9621hNCceNPb962xEEQRAEQRBE54bUiPZG9gCZo7gYVbcNcJrB0Zu9vZdvnzkq2mZoXOSq3cSFKivXA0DNLzwVmGNFJxeqfAMAX7+Y1If3EQRhoxs6NKZD1TWeG5otMGmGDsVQEdZURHQFEUPh1k7QoRumtZMZh0lANB4SIEASuRWSJIi2sOQQZLgdvMzbpa4Ty6ieFZndgXox1GLb4wS0xH2Z5aRiW+w+YvbZ4DxaiThxC42Ib6if19vW/C+pEGa5Nja0GEGSvlihLm5RhWTzakAgSzqX2P7E84wV1RLOuUFxLsl5M72ZlocEQRAEQRBER4dEqY6A6ODudZIHqP0NMBQeZ2qf9ycDqYfy1OsE3sYM7iZYu4kfo8bMNT+31Kr5NXEn3JLL148Haff1A7xm7u4GildFdBUMZkA1NKi6xnMzaYYORVcR0iKIGApCWsS0eNJtcUo3uHuZIMAMtC1CFkRIomQLSQ7RAY9kiUsSJJGunfoCRLvN5MARJ6yxBOHNaksUyqz+RKEsVnQz4scaiUJbA/uJG5NEqGtM40m0fGuqnrhtY+JT3MCGrOMQ32eogKYA+gQAqY1MnCAIgiAIgujokCjVURBEIHUAt5iq3sjjRLmyW3f/vr489TiGt9lC1WZuWRXcxfPALkALAKG9PMXGqgL4HL3mviyhyhKvWmLlRRAHkESxSTFUux7RFYT0CIJqGBFdTRCadESfsgXIogTZFJlkUYZbdEJ2mHGWRKmdz5LosBwMwltT1IvdFteJ+HhrycS5ZNsyQDf4d5ShHoBJEwRBEARBEG0JiVIdDW9PAIzHglKqAWfGgTtWrFAVC2OAUgEEdkdFKiuF9gJ6mMewqttSf5+OjBjLqj5R4crbh7sqEkQrYDADis7jL6m6BsXQeF1XEdTCCGkRbtlkxnFSDc1eJU6AAEEQIIsyF5wECW6JC02yIJMlE0G0FnHufGg9YY4JQKSmlXZGEARBEARBtCckSnVEvL24FVP1BkARAGd62x5fELj7oCsHyDosvs/QuDBlCVZBS7jaDUTKALUaqK7m1l6JuLrxWFW2lZUpWnl6AZKrLc6M6CRohgbFtGhSDNUUoDQElJAtOqlMM2M7abZBhiAAsiDZgpNTcsJHYhNBEARBEARBEESHhESpjoqvDw/mWv0Tt2hydJC4GaIcddfD1Pg+LQgEC7hAFdwFBKzybkCtASKlPFWuS9ipALi7m4JVnxgLqz6ApycJVl0Qgxk8GLhp3RTRFYQ1BUEtjDo1gIjOLaBUppnudAAgwCFywckhynBLTqQ6vHCIctcJAE4QBEEQBEEQBHEQQaJUR8bXLypMiQ4ey6kjI3uBtME8JaJUxwhWBaaVVQEXrvQAEC7iqeLbhA0FwJ1nilW9Y5JZl71tcWbEPqAZGiIxglNEVxDQQvArIYS0iB3jSTM0MACiGb/JITngEGWkOr22xRNBEARBEARBEATR9SBRqiMjCEBKfx7DqXYzd+sTO+lb5szgKWNkfDtjgFIZFaiCu3nw9eBuILDHFKyKear8Lsl+s0yRqhfgiRWtegHO7IQly4nWRjd0RHQVYT1iC091ShB+NYiQHrHjPjFzBTFJlOAUuejkld1wSjLkzvqZJgiCIAiCIAiCIPYLehrs6AgikJYP6EEguJcLLl1JaBEEvsqgKxvIHBPfxxigVHHBKlhgilV7omW1hgtaSmXyGFaSm8ersgWrXma9p+kW2MEtzzoQSozwFNYVBNUQapQAAmoIqsFjPxnmClqWe51TciDNmQKHSPGcCII4+CgpKcGSJUuwdu1auFwuHH/88ViwYAFcLhduv/12PPPMM3HjFy9ejPPPPx8A8Pbbb+O+++5DWVkZpkyZgttuuw1ZWVkAAMYY7r77brz88sswDANnnHEGrrnmGoj0d5YgCIIgiE4IiVKdAdEBpA3lMZvCJYCne3vPqG0QBMCVxVPm6Pr9al2MULUHCO3hwl1wD3+d9DDg385TMlzZXKTymCKVt6dZ78FjXB1kFjyMMSiGipAWQdhcva5OCaJW9SOsK4hoKlRD5a52ggiX5IBTdCDF4UGmK42EJ4IgCBPGGObNm4e0tDQ899xzqKmpwQ033ABRFHH99ddj+/btuPrqq3HaaafZ26SkpAAANm7ciH/+85+45ZZbMGTIECxZsgSLFi3Cww8/DAB44okn8Pbbb2PlypXQNA3XXnstsrOzMXfu3HY5V4IgCIIgiP3h4Hrq7sw4UoD04TxIuFLNXeEOdhypQPpQnhIxFCBUBAQL+WqBwb1R0Sq0F9ACQKSCp2RWVhABd64pWPWIyXsA7h6AJw8QnQf8FA8Uis7Fp5C5kl2tEkSNUmcHH9cMDQAgmxZPLtGBFLcXDon+ZBAEQTTFjh07sH79evzvf/9DTk4OAGDevHm48847bVFq7ty5yM3Nrbfts88+iz/96U849dRTAQDLli3DjBkzUFBQgD59+uDpp5/GvHnzMH78eADANddcg+XLl5MoRRAEQRBEp4SeMDsT7hwuwFT/xMUWd06nFkYOKKIzZpXABBgD1FouToUKTaGqMCYVcVErXMJT1Y9JDmC6Hbp7cMs1W7DqHs0dKQf8NJtCN3QuPunc8smvBFEdqUNQD9uWTwAXn1ySA07JiTSnj+I8EQTRbDRDNwVtxXT1VaGYAnfEUO322NU2IwYvK7qKiKFEy2YcOmusVVbNfak6dxfOT++B94b/tb1PvUFyc3Px6KOP2oKUhd/vh9/vR0lJCQ455JCk227YsAEXX3yxXe/Rowd69uyJDRs2wOl0oqioCIcffrjdP27cOOzduxelpaXo1q3bATkfgiAIgiCIAwU9eXY2vH0AyQMEfufClCgDrpyDztVsvxAEwJnOU/qw+v3MACKVpmhVxIOsW2KVlYwIECnnqean5MeRfXzlQHd3nntiyu48wN2tVeNaKbqKoGn5FFBDqIn4UaP47QdC7nYnwCU54ZacZPlEEF0YzdDsGHDReHCRhDYeKy6i87bYlTIT67xNtcuWVWVEV6Azo83Pb2sNsy06OyJpaWmYOnWqXTcMA88++yyOOOIIbN++HYIg4KGHHsLnn3+OjIwMXHTRRbYrXzJxKTs7G8XFxSgrKwOAuH5L+CouLiZRiiAIgiCITgc9kXY2BIG7lbmyuUDl38lFEsnNYy8JUnvPsPMjiNwKzZ2TPJaVFYA9bIlUxdyiyhawingQdi0A+Hfw1BCODC5OxQpVdr0b4OoGyJ56m4W1CIJaGEEtbFo/+eHXgghrCjRDhyAADtEBlxls3CU5IHSlAPkE0cnRDQNh04IxZF7PIS0S12ZZOYaTtId1xWxX4rYLm0KSzvR2OS+n6OAuv5IDbskJpxl7ziU54ZJ47pBk3me2OyXZzLmrsMPc3tqX03IjlpxwiDJEpsMLDQ7J0S7nuC/cdddd+PXXX/Hyyy/jl19+gSAIGDBgAM4//3x89913WLx4MVJSUnD00UcjHA7D6Yy3gnY6nVAUBeFw2K7H9gGAoihtd0IEQRAEQRCtBIlSnRVB5G5jrlwuhPh3cDc02Qc4M3k/cWCIDcCePjz5GC1kuv8Vm0KVVS6JJj0MqNU81W1p8HBMToHuzIbmzEJITodfTEGd6EOd6EVQSkPEkQHBmQW37Eaqx0uudwTRyuiGgZAWRkAL81wNJ61bwhIXjCP12kJ2mVsntQWiIMAtueCWnHDLTrgkJzySCy6ZW0y6JCc8ssu2oHTFjLOEJLcUX48da9UtcUlsg+8eRQ2iKlB0wI/TWtx111146qmncO+99yI/Px+DBg3CjBkzkJGRAQAYMmQIfv/9d7zwwgs4+uij4XK56glMiqLA4/HECVAul8suA4DHU/8HDIIgCIIgiI4OPb12dkQJ8PbiVjWhIi5OBQq4a5ojnQsoRNsje4CUQ3hKBmOAVhcVqEIlQKQURrAIergECJdCipRBNMIQND9kzQ85uAtuAJnJdgcBqiMjJmVCdaSbudkmZ0B1pJE1HXFQYAlJfi2EgBqTtHBMOYSgGoZfDSFotge1EAJq2LZEDKghhA+ggCRAgFd2wS274JFd8EgueGQ3L5uJC0pWOTrObQpLsX1u2Wn28e0cokxWku3IbbfdhhdeeAF33XUXjj32WACAIAi2IGUxYMAArF27FgCQl5eH8vLyuP7y8nLk5uYiLy8PAFBWVobevXvbZQBJg6YTBEEQBEF0dEiU6iqIDsDXl7t9Bfdyt77gbu4e5kxv79kRiQgCDDkFQZeMgJSFgPMQVIRrUOsOIKRFoBoaREFECnSksSBSDD88Wg0cShUcahWcahUcajUcZi6AwWm2NwaDAE1ONQWrDKhyOjRHBq/L6VAd6dDMXJd8JGoS7YJmaPCrIdQpQfhVK4XicyVk9wVMN9aAFi8+tTaSIMIru+F1eOAzhSOfwwOP7IJXdsMju+12r+yGW3bxusPN+01ByeuIll2Sk0SjLsrKlSvx4osv4p577sFxxx1nty9fvhw//vgjnnzySbtt06ZNGDBgAABg9OjRWLduHU4//XQAQFFREYqKijB69Gjk5eWhZ8+eWLdunS1KrVu3Dj179qR4UgRBEARBdEpIlOpqSC4gdQBfAS64hwdE9+/irmaO1Pae3UGNoqv8AVoNoTpSh8pILYJq2HbjsdxoctwZ9QKQB82UFGZA1mq5UGWKVlywMpNWY5ZrIMCAQ6uFQ6sFQgWNztcQJFug0uS0aB5T5u1p0ORUssAibDRDh18NolYJoFYJwK8GUacEUacGUKsE4VcDZj0Iv5WbY/xqsFUtk2RRQorDC5/shs/h4Un2wOtwwyd74HPwdq/Vb/e54wQor8MNp0ix2YjmsX37djzwwAO45JJLMG7cONuaCQBmzJiB1atX47HHHsPRRx+NL7/8Eq+//jqefvppAMA555yDWbNmYcyYMRg5ciSWLFmC6dOno0+fPnb///3f/6F79+4AgLvvvhtz5sxp+5MkCIIgCIJoBUiU6qrIHiBtEODtyd35ArsApZIHSJdT2nt2XR7GGEJaxLbkKA9Xo1bhVlCaoUMWJXgkV+sEIRdEaI4MaI4MwNu/kUkZkLW6OKFKVmti6rW8rlVD1oMQmQ6nWgmnWtmsaWhSClQ5DZoj1RSsUnndLGuOVGhSKlRHGnQpBYxiX3VoDGbAr4ZQq/hREwmgTg2gRvGjNhJArRpAnRJAjcLzWitXg6hTAgi2kpWSV3YjxeFBisNr5z6r7rTavfDJHqQ43NG6g4tNKQ4vnJ0oGDbRdfjoo4+g6zoefPBBPPjgg3F9mzdvxvLly7FixQosX74cvXr1wt13342xY8cCAMaOHYtbb70VK1asQE1NDSZPnozbbrvN3n7u3LmoqKjAlVdeCUmScMYZZ2D27NlteXoEQRAEQRCthsAYY+09iebg9/sxbtw4rFu3DikpJKq0GLWOi1PB3TzAtiuLB0UnWgWDGQiY7ka1ih/l4Wo7+DHAraAsdx5Z7PgWRYKhQNZq4VBr4dBqTLGqFrJVN/tkrRayVgcBLf8zokleaHIKNCmVi1ZyCnQzj7abZTmVhKx9hDGGgBZGTaQONYofNRE/z5WA3VarBOw+y7qpVgmA7cP7GotPdiPF6UOqw4s0pxcpDh9SnV6kOrxINdtTnVxISnV4kWL2WcJSZ7hWiLbHCnQ+dcTFSE3p2d7T6XTQ/RRBEARBtA4nrPgCvxTWtvc09onhPdPwzryp7T0NAGQpdfDgSAUyhvGg6IHdQGgPEKkkcWof0Q3djm1THalDebgGATWIiKby1a5kF3wON7Ld6Z3S3YeJTqjOHKjOnGYMNnggdo2LVA6tDnKMYOUw89gkgEHWg5D1IIDSZs9LF92mYJUCXU6BJvvMcnyuyT7odu4FE51N77wTYAlM1ZG6mORHtVKHmiRlLj75oRn6Ph/TLTmR7kpFmsOLNFcK0pw+pDl8SHP6kOrkuZVS7XYuLJGoRBAEQRAEQRBEY5AodbDhTAecI3lQ9FhxypnJxalOKKC0BZYIVacGUBWuRXmoBkEtDMVQIQoifLIb6c5UuL1dQ/xoEYIIzZEGzZHWvPHMgKQHTCErVqwy67qfi1q6ueqg5oekByCAQTLCkJQwXChv+jgxGILTFqg0yQdd9kGXfGbZGy1LXlPY8vKy5IUhug/YdWEwA7VKAJXhWlRFalEVqUO1mVeF61CtcOGpKlyLasWP6kgdVEPbp2O5JAfSnSk8uaw8FelOH9KdKUgz29PsOheYyP2NIAiCIAiCIIgDBYlSByv1xKkiQKkARBfgzAAkd3vPsF3h8XR44OWqSC0qwjXwKyFzVTwBPocHWe40emDfFwQRupwKXU5FBD2atw0zIOlBLlLpfkiaH7IWiJZNkUuKywO2mCUyBU5VAZpYnTDpoSFAl3zQJY8tVHHRKlq3yproRi1klGkGylUN5aqKcjWCcvNzVBmutYPcc6GpDsY+eFC7JScyXKnR5ExBhivVFpsyXKnR3JWCDGcK3LKrxcchCIIgCIIgiNZGNxgkkYwhCA6JUgc7ljiVeigPhB4sBJRyIFwKSF7u9id1/YdZxhiPCaUGURWuRVm4Gn4lCMVQIQkiiVDtjSBCl7nLXqQl2zEDkh7iIpUegKQFY8oBs8zbJC3I2/WgmQIQmQ4wBr/iR4nuR4kOFGtAiQ6UWHlMuVQHQvsQhildkpHldCHL4UG2y4dMpw+ZrlRkutOQ4UpHmjsTGe4spHqzkeHKhNtxcIvGBEEQBEEQROdFEgX8/cUfsa3U395T2SemD87FtccOae9pdBlIlCI4spcnTy9Aq+MufaFCQKkCDIW79jnSALHriDIhLYw6JYiaiB8loQr4lRBCWgSiKMIru5DpToVLOgjd8boSgshd9WQflISuiK6gIlyDilANysPVqNCrUaHVoDxUw9vD1agIV6MyXAulhS5zXgHIkwXkigx5MtBNAvIkIFfi5VwJ6CbzPEcCnIIGQAMQAGJdE1UzxXxfM4jQJTcM0V0/F10wJA/PRTcMyQVdtMaYbaLLHO+KSU5AEPfpJSYIgiAIgiCIlrKt1N9pg4QfmksxmVsTEqWIeASBi0+ONO7ap9ZEBapIOWCoXLxypAGdLHi0qmuoU/mqYqWhSlRH/AiqIQCAR3Yj1elFjiejUwYmJ6IE1BDKQtUoD1ejPFSN8lCVXa4I19jlOjXYov36HB5ku9KR5U5DtjsdWe50M09DljsdWa40sy0NHtm0ZGIMohGBaIRjLLDC3HrLCEHXQyjTQzxWlh6CaJfD5jYh3m6EIRncRkyAwQPE60EuWLUSuuhMEKpcprjlgiG56olYuplzoctppmh/tO40RS8Kek4QBEEQBEEQRDwkShFRDAPQdUDTeK7rgAFATwcML6BVA+FKIFwCKIWApnLXPsmXXKAShJgEAAIgioBo5TFlwcwlEZBlnov7Z7nBGINfDaJWCaAiVIOycBUCagg6M+CSnPA5PMh0pUIkC5FOQVhTUB6uQmmwCmXhKi48hXheFiM8BbVws/fpEGXkuDOQ7U5Htic9WnZnINudhhxPhik4pcMt74MIKwgwJDcMyQ3NkdHy7WNhBkQjYopUSXIjDFEPQzJCEPVIQhsXxkTdbLOEMiNqPyYZilmv2795NoAhSDBEF5jgMMWqhFxwwhAdYJagJThsQYu3OcwxTjC7zMfwfcaXmeCghRsIgiAIgiAIooNDotTBCGNAOAwEAjxVVwN1dYCqRsUoncfSsfPYYMwMgOEAVAVQigAtwC2oRAcXqUTTSsR+IBTMjZhZNucAARDB3YYsYcoSoySJl52OmOQEZIn3yRIXrxwyrzt4OWKoqFUCqA7XoSRUgToliLCuQBYl+GQP8rxZkEX62HckDGagMlyLslAVSkNVKAtV8jxYhbJwNUqDlSgLVbXIssknu5HtyUCOOwO5nkzkuDOQY4lOZnu2Ox1pTl/nsYwTRBiSB4bkab19MgOioZgilSVkRWxRq167nZT47fQIRKZGx+oRCEyBaKgQwP92iEyHqLfMOm1/MWIEKkvUYmJ8GxNks89hil1Wm9Puix3Lx8hmm9xAmyNmW5lcIwmCIAiCIAiiAejp/GDAMLj45PcDtbVAWRmvh8NcHHKYgo8o8rLbHWPJFCMYJYMxQA8Aqp+7+ak1gB4xBSYPd/VrzG3HMPg+DMbLhgHoZh5RgFDEtNgy28GiVlcADFFAnaChVlBRxgKoFBQEZB3M6YDHzZe07+bycNHKkAHDFMKINkEzNJSFqlESrESpKTaVWuUgr5eHq6EZerP255Kc6ObJRK4nA9nuDHTzZCHXk4EcTyZyPBnoZgpQXgoE3jwE0bbkOiAwBsEWq9QYQcusMy5uCbbIZSZL0KpXj24jGqotfPGkQGBREQwAF8r0VvRx3EcYxKiAZQtWVl3mglZT9Zg2uy5IZptUvz02N8cYdltsf/xYEtAIgiAIgiCItoREqa6IJULV1XErKEuEikS4wOR2A14vkJW13y5yEARATuHJ0x3QwoDmB9RqIFLFhSqmA5KTi1SiO17gso7fgnAzEUNFjRZEtRpASbgatUoAiq7CaQA+yOjBZEiGCrAqANUAWNT6Spa5lZXbCXhcpvWVaXEVa321v6/LQUBUcKpAsSk0lQQrUBqsQolZrgzXgqHp5egECMh2p3PByZtpCk9ZZs5TN08mfA5P57FsIgBBABOc0EUnmic77ieMQWC6KYRZwpVZZpaoZeaGCoFpEJkSX7e2YSoEQ0vYzqzb4zRz/7E5HxP3MsAwXSMTw+13PBiEBMFKakDIStIOMWFMtA67LJpjE/chArF1iPX7wMuqriFg0N9ogiAIgiCIrgCJUl2FUIhbQVVXA6Wl3CrKEqG8XiA9nYtRBxrZzZM7B/Bp3LVPCwBKBbemUmqj4yQ3ILqa3CVjDH49jBo9hHK1DuVqHQJ6BAwMXtGFTG86XE2tCmjHyzJjZlVHgPKqqBshADhM10BJ4mKVxwm4Xdx6zHQPtMtdXBixXOqKg+UoDlaiOFiBkmAFSoKVdl4RrmmW4OQQZeR6MpDnzUY3Txa6eTN5Iv6cNQABAABJREFUbopO3bxZyHanQxYpEDaxnwimoAK5dd0cW0qMOMYFLC2hHBWvBCNhHHQItmCm8f3YYpe1rc5zWyTT49vtbfToPhrKE65hAdy6Daz9LcwaY5DoQWTkFe09DYIgCIIgCGI/IVGqs6LrXISqqeEiVHU1F6YAwOMB0tLaRoRqDFEGnOk8eXuaVlQBQKsDlCpe1qu5JiR5zHhUXKRSDQ21eghVagClWi1qtBDCugqHKCFFdKGHMwNSS9xMLDdER0PiFQM0gwtWmg4EgkBtHX+dBQBMiIpWDhlwOQGvm4tXDkfUyspplju4aBVQQygOVvAUqLDLJVYKVTbLpc4hyujmyUKeNxN53mzkeU2xyZuF7p5sdPNmIoOCyRMHGzHiGIC2sRLbV5huilTJhKv6fSLTAGY0Mq6BBKtsQIjbh9UfXwczYraxtuPtYBoqnN2RIdAtDEEQBEEQRGeH7ug6G6EQsGsXUFISDU7ucgE+H5CZ2bHdziwrKmQDrC+ghwAtCKhcpApGKlGj+FGhB1BqKPAzBh0ivJILaZIb3RxpB3BygiksNWStw0wrKzOF/UBFddTSSkB8EHaXi7sHupxxgdjjgrMfIAxmoCJcg+JABYqC5SgOVqAoUG4KUDxvTtBwURCQ685EnjcLed5sdDdFp7yYnFYvJIhOju0u13lQ1CCqAkWY2sHFf4IgCIIgCKJpSJTqTITDwMaNwJ493BIqO5tb6nRGzJXE6hhDtaGhRAygCgJCYBAFGT6BIU8EZMPgK/sJzIxN5UL7RCoXzHhUDVwyzHIPNLhoVesHqmq426C1+KCUsHKgy3QRdCYIV7Gxr5Kg6hpKQpUoDpSjMFDOhSczLwpUoCRU0SwrpzSnzxaaeJ6D7t5sdDfrOZ5McqkjCIIgCIIgCIIgDhgkSnUWIhHgp5+AvXuB3r0bFkc6OKqhoUbzo0qpRXGkArVaAIqhwiU6kSKnI9uVZwayZoCuAHoYMMzg6VoIUGsBQ+fucaLDTE60+5J6ggjIYuNXlBXTytABRQFCYaBc431WaCtRRFgwUMwCKDLqUAg/ivU6FGm1KFKqURSuQlmk6VhOoiCgm4eLSz18OehuCU6+bFN4yobP0Y4xdwiCIAiCIAiCaDG6wSCJZC1MdB06p7JxsBGJAD//DBQUAL16dTpBKqiHUaP6Ua5UoVSpgl8LgjEGr+RBpiMVLjGZtZfAraIkF4B0AHmAoQFGhCfdFKr0CBerGDNFHRkQHIDkQIf7eJuWUiFdQZFQh0JWjSJUc7FJqUahUoUipRqVWqDJXbkEGd3lNPRwpKOHMwPd3Zno4c1Gd18OevhykevNgux0ApIISKbVlSR2bPdOgiAIgiAIgiAaRRIF/P3FH7Gt1N/eU9knpg/OxbXHDmnvaRAdiA721E7UQ1G4ILVrV6cRpAxmwK8FUa35URqpRIVSg5AehiCISJW86O7KgSzsg1uYKPMEX8zBVMBQokKVHgS0CKAGuEUSAIgSIEhRy6oDbFUVNhQUKTW2yFRoCk6FpvhU1QzRySs60cOZYaeeZt7dkYEejjRkCR4IBuPWV7oO6Aag6kAlAyprAaHWdBk0hShJjLoPupyAU+Zug1YcLLtfNNtkXqaYLQRBEARBEATRodhW6scvhbXtPY194tBcX9ODiIOKjq9wHMyEQsAvvwC7dwM9e3ZoQUqz3fLqUKyUo1YNIGwo3C1P8iLbkW665bUyttAU88eN6dyCylB40kKAEeIClhaMWlUJllhliV0SeEfjhA0VxUoNipQq7FWqUWQKTi2xdPKJLvRyZqJ7jODUM0aESpM8rfB6MS5WGQbPdR2ImG6Dus5fB8sL0DqULHFXRMlMssxXF7RELFvkkqLWV5brIglZBEEQBEEQBEEQRAvouCrHwU5dHbeQKi7usBZSYT2CarUOlWotSiKVqNMC0JkBr+RGupyKPKmdgrALEiB7AXhjGplpVWWlCKCFTbFKM0UsblmlGBqK9SAKNT+K1DoUqrUoVGpta6cKrWlT2VjRqZctOmXa4lOq3BbxnISodZSjGcOZJV7FCFlqBAiEuIhlMAAsKjoxAKIQFarEGMHKaYpZDjNouyRF+yzBS5QASeBiliiQayFBEARBEARBEMRBRsdTOgigqoqvsldVxQUpqWOsgMYYg18PolqtQ1mkChVqDQJaCIIgIkXyIM+ZBVnsqB8pgQdEF53QDA3FioIizY+94TIUhUtRGC5BYbgcRZEKlKm1TQYS94oO9HRwsamXKxM9nJmm6JSJXm0mOrUyzQnWHgfjQpWhAzqLClmaaY1lmOKWOZSvQmiKWqIYFaJik7UCoSxxUcsSr6yxlpuhKETbJYlELYIgCIIgOjRdITh1VzgHgiA6Hh1VQTh4KS7mLnvBIF9lr51doTRDQ60WQI3mR1G4HDWqH2FDgVOQkSJ5kClnQVJ0iCEFglINUVUhKioERYWoahAUFYKqQVR5Lmga79d0CJrG+zQtWtd0nnQzaTxmkqAbZhvPoRsQDF4HYxBMQUQwDGgwUORW8btXxe4UFb/7NOxO0bErRcPvqToKvQaMJvQLrwr0rZNwSJ2IQ2pF9PNL6Fcn4pBaCX3rBGQpAgRBB1ABJlSY7m8CmCgAAsBsoUUAM8UTJnLXNiaKfJyZM9N6iJdFPk4S7TIzy3FtsmS3M8ksy1K03e6XwOSYPjMZsmT2SWCO+Pbmu+AJUTGoRX9JkohZhgGoKncvZCzGvdBUswTL1ZC/vpAkPkfLfdCehyloyRK30rJdCsV4YcvKk7WT+yFBtBjGGBiY6RVsifrRutVn+QyzuD4AzC6BxezP2re9DQBNC7fhmREEQbQOnT049cBuKVh+9tj2ngZBEF0QEqU6CqoK7NgBbN3KH7h79jwwxzEMoKYGqKgAysuBykqeQiGewmFowQC0kB9qKAA15AfCYWRGFHSLaJAVLiqJEQWiovBg222MJgJ70oDfM5KnPWmA3oTo5FaBfjVA/yrgkGqgf7WZm/WcICBAB6AfwDPpuBiyBOaQowKWKVwZZlv9ugzDIYHJZu6QwRzRfsMhx7c5om3W+Gjd7HdG63FCmWFwUYuZuSVqaRpfGCC2zX4INrdNZq0lJOSiGBW2JKl+MHhrW1sUi8lFicfRt6y4rLEEgWTCTXLRJirG1Bdtkgk2se32HliMwANmbh/ts0faf8J5QbCEXybYc4TZE3MmvEUQ4i4va3tBEGD9s9sF2G1WvDw7N8vWeCsXIfLtBL6NKAi8TXLA5ZDhkl0tfg8IgiDak84cnLqrQNZeBNHxIFGqI1BTA2zaBOzdC2RnAykpLd9HOMyFJktsSpZbSW9caJHN5G7hFAyHA4bTAeZ0mMKCg4sKTkeM2OAAky1RwmFa6MhxeUQWUehWUOAMo8AZQoEjhL1yAAVSAHukAIqFAHShcTHMAQk9hVT0FjLQU8xAb4nnvcQM9JYykS36IOaJAAQw63vJfEAqN5ON+dQmWAKHGSBcsCx5zCRYQgmDXRZ0DTB0nusaBDPBzlUIugpB07hFmGn9JWgaYDBeNgwzB7cMMxgE3eyzcwOCZuUxlmWaEbU80wyIsZZoKi+LWv3Pg6jpQJL29oKJQlTwcsowZJnnlqDllHm/M0bccsbmpgjmlM3PnQhDlkwhTYIhSWAO0cwlGKYVmiHxcZa1GQD+OUl0R7SEKct6TEgQuUQhasVliV6xboeWyJUodiXuSzDdFQUcVFZdlvBisMaFnP0VcVoq4HCEhLb64o3Vbr1dyYSbhkSbRMHG2r4hwUYUxPi6aI6FEC3H9Yv8GIg9plDv2Mna4sY31J5wXvXON0GMSmxPuuCDoQBKFdBecQsJgiAOQnJTXF1C0OnsFmvTB+fi2mOHtPc0CKJVIVGqPdE0oLAQ2LyZu+s1FdC8rAz49FO+Gl+syFReDvhb9oeVZWRAz8qAkpkOf5oLYbcMxSmBud2QPD5IXh+Y2w3D5QRzOWEkJifPmdMBw8WFpuZahIT1CIoiPH5TUbgcxZFyu14cLkepUtVkTCeHIKO7Kxs93bno6cpBD3cuetj1XGQ70yEKDc8n1KJXqy2wLHt0nqCbgdf1mHYzSDvTeHB2xng/LDHMQNKXzRI1BAGmGU9USIFobi9CVE0RS42KWaKmmXXN7NdNl8sYUUs1x6gaL2vx7XH9mm66dsbsU9UgKlp034q5H92InoLB+HaKCjS9uOEBI070skUuS/SKsf4yrcksizPDdKk0HDIMWTRFMTl+jEOMbivLMJwSmNMZL645HVHByhatrADyVnwtMSp+xcTuYgCYADBB4GXRFPsggIngrqXMbLPGCYhJ/Ca0SVGHGQ0KOoIA+zMa/ajWF3ME6784I7eWCTnW9W/1i9a2YrQsCiIXZwTRFnAES/KxhBtrjCnmCILAJZ4WiDfJhJuGRJvoudY/52YJNgRBEARxAEjzyF1G0OnMFmuH5vqaHkQQnQwSpdoDxoDSUu6uV1IC+Hw8flQyKiqAjz8GPvgA+PFHxPxUXx+XC8jJ4dZW2dlxZZadjWCmDzXpbpT5RJSzOvi1EBgz4JHcSJG8cLfCr86MMVRrdSiOVKA4XIHiSIUpOJXzcrgc1Vpdk/txCg70cOeghyvHznua5eaITp0PU0Bo9iVpCVF6NLeELRhRkQoGF7AMDYBmClpWvCYDgArL+ssQdb5KX9xKfZbdnMv0sTGtgQRuW2Ga7PChlvgFIWas9R5ZY1uAYURFLSUqeImKBkE1hS2rPUbY4v2mm2msWGaP5fsUI6oZ14xvIypq/PEUtZ4VGR+ntew8WhlDEqGbIphuWnsZpuWXblp5WW26bPaZYpdhWoMZTlP4sqzFHBKYwxF123TGuFs6LatHCczlAnM5AJcLgtMB0emAIDsgyjK3whEdECUBgiBBFCVIkghRkCBJsinoiBAkkeeiCEEUIIgSt+4RzSTwNt4XO1a0haRk1jck4hAEQRDEgYcEHYIgWhsSpdoSxviKert2AQUF3KqhR4946yhdB379FfjqK+B//wN++y1eiBo1Chg9Oio4xeYpKXHuPCE9jDotiBrVjxKlArVqABGjFrIqwSd50MOVA6mFwk5YV1CiVKAkUoniSAVKIlx4ik0RQ2lyPz7Jje6ueNGJl7PRw5WLLEcaPUA2igAIEk8txhK0mClmAUCsUGVErbAYzDEsasVlGAA03maYbbYboylyWYnB3KcVqLzp02KMgQkiDAAQGZhTAHOC1yGBQQZjbm69A8Ytehi4xY9guXEJUYugOLcuIcadK+oCFnVeirfRExgg6lz8khRTzFJ1yKoOSeGWX5JmQFa4G6Sk6JA03iepOkRVj8k1iOY+4trU6H5j81gRTYj5GyDqBkTdAMLqPrz3rYxguiVaQeUdklmXY4LOx6yoaNWtNkfMWCt3Osx2R8wYB+C0cis5AbeT5y6z7HIALjfvdzmilmOWSBpnYZaknJjsbZPUDyL3SYIgCIIgCII4UJAo1RaoKnex27OHW0hpGheR3G4emHnDBp7Wr+d5TU389sOHA0cfDRx1FNC9e4OHiegK6vQg6rQASiNVqNH8CGrcUc0juZEupzZqDaUYKkqVKpRGKlES4cJTiVLJc7PeHCsnAMh2pKOHOwfdXdno4cpBnpl3d2WjhzsHqZKXRKd2gjHAgBVLSwKDWQcAgcFIcLuK1i3BJzHeDgNjOs8N3XTfYmDQTfcuS+QCj7Nl7tWajMAYIBh8LGMQBD5GYODxtMAgCIzXYZg5j+kVa69l2WwJAs8lJprOigJEAZAgQGSMu2OJIq9DgGTG4LHi6kRdsyxrHED0JnO1MmPzmPF6oq5d8fV49zExwVVLSBA2hBiDMrOfgcf30jRAUfnfE0UDVDMpGm9TdbNPjW+PrdfbR0y/qkb3GduvJuSxixswZvZ1AIEsGYIQI4TFBq2X6rdbZUmKF9RkKSqqSVJUKJPMNqcjOsbpAGRLQJOjZacjXlCzRDiXVXaYgl6sICdFhS87ob5whiRticJZQ9sRBEEQBEEQRDvTpqJUJBLBLbfcgvfffx9utxtz5szBnDlz2nIKbYeuc3GpqoqLUVVVPG5UdTXw++98lb0tW4Bt27gwFUtKCnDEEcCRRwKTJgG5uUkPEdYj8Osh+LUgypVqVKt1COghGGBwC054JQ8y3akQIMCvB1EYKUNppBLlSjVKlEqURapsEapUqUSl2jxTXI/oMkWmbOS5stHdlY08V5YtOnVzZcEpOpreURfHqBdbhyWUEQ3cbAZxbkz4MWICMBu8xusCosHYTauhhGA8UVEoTmwRYuLuIL6eGJdHECDZwZJFSGYMHkHgoo5kBlgWBatPMMuSHUfHjskTc6zEODu8LX5+SfsZF6lEQeBlwRKpBPN52xLDrHI0CH20HruyWMwYq273s+h2ttWXkWS/lvhmRPdrj0+yf/P9qzcPFpubZRGAE4BTAq+4EsbEjGWx5xR7rMSxSYiJ+dRgu65zgUqzcg1QEtqsFFc3hTVVj+/XYscm9sVsY+d6/Lax2xlG/LwZi+6vM2Kt+CiJMeWYlSBFEZBjxiQG0o8dL8dsF1eWo3XLik2WEqzfYsqSxIW2egKflCDq/X/27jtOqur+//jrlumzlV2WKr2LiigESxQTS4yx+42xBSVqvook9hKjKCoKPzs2VOxRv9ZoNEUSexAVBRRF6W0pu7Btdqff+/vjzr07M1vYhWULfJ4+7uPOLXPnzHjZnfvecz5XJaNnXKOhYHYgmPpKkh6iQX2wZoe4RhyUThqECiGEEEKIVmnXUGrmzJl8++23PPPMM5SWlnLttdfSq1cvjjvuuPZsxu6RTFqFyFessAqX//CDNURv82arbtTGjVDdROhTUGANyTvgAGs+YkSDgudJM0ldMkJdMkJtIkxZrJLyWAWbo9uoiFv1oezhetvjVZTHKim357HKFg2pA/CoLrq7CynxFKbm3SjxFKSFT906XS+n7NDG7qdjDdky0oZrZYY/0FRwREaxZvu49tAyJTUsjFTh5rS8xyn8nF6w2A5jgMYLJKf1qmkq+NEVa4uqalYPn1T4Y4U+aUWcGzxuItTJCoWcx0paXx6l4fE60//3Li09OMoOkTKW07en7ZMdpqWvahBGtXSfjB2aeT2y2tTI/s22Oftl0gO8RnZoLDzMeHratqSRFmTFIRHPDL7sEM2eJ9KW43Hr53h62JZIpoVwaSFbMi0os8Mze79kMjMwc5aTDbelr2tM0rCmvYVCfYF+Le2ulFr6ulTtutFD4LVDIC+no1sthBBCCCF2QbuFUnV1dbzyyis8/vjjjBo1ilGjRrF8+XJeeOGFzhNKhcNW76ba2vqppsaaqquteUWFFT6Vl8P27dbjrVut5eweT40pKYEhQ2DoUBg6lMSQQYS651OTqCMUC1EZr6F84/uUxyrYFqtiW7zKGj4Xr6AiXkNVsjY11REyIq16e3mqnyI9l2I9l2I9j+72pOXR3ZVHiZ5PntpE4BS3JrOmBpNqkk6okxbkpAIgp/ePghMQWfumBUCp0Mje1wmSsIMdO/wxUUxrTmrolLXd6hFj9weyohRIv7m56oQ+6cFM/RAtNVV4WcOarPBHRTXtYV2aNQwsrcdPem8fNe24qj2cC8UaQkZmuFM/aIvMYAilvoBzevDTXEH7dM7/q8YCjJY8rw3t6Jitec22PNauHqelr9WWx9rZY7T6c1Oy5i04xs68dmu2NVhuxf6pmvwZ65rbP70nTmvbmv3cXf1/m0wP0xINp2QyFbIl6vdN35YetBnJzP1aMren9GU7QDOytidSteWSSSugS6TuFppI28fenr7e3tYUk/ogbkcdoT7/DpI7U1NPCCGEEEJ0Ju0WSi1btoxEIsGYMWOcdWPHjuXRRx/FMAxUtWPvpBb+5zu8cuMpbNfjGArOlFQgqWY+TqSmpAqJnhDvA/HUurhbI+ZzW5NHJ+bRCXtUoi6VsG4SMROEzW8JGwsJR2LEv2niL+QtpCoqBa5cCjx5FLrzKEhNhZ48Cj0FFLhzKfDkUuguRNesL/BGasiX3ZMIAAVqTIUa57rKTAVAOGVunADI7kFjxy9KqgcQOMGMNdoiNbzLCWM0q/ePPcwLFS11563GljN68NihTVbvnQY9fLLWp/cUyjhWav0ua2kI1FbP252vmb69uX3bar/sdW15rJ09zs681s4eZ0fHTV+3o3Y014aWfq6t3d6abW35WbR0ubFtjbWjta/VksfpGnan3PE8mz3kze1u+jiNvW5zj5t7zo6eu6vPMVJDXZNp4VZTc3u/9HWxGHTvbtXe2kvtVeUQxF4vaZhoqvTUFkKIPVW7hVJlZWUUFBTgdtcX2i4qKiIajVJZWUlhYWF7NaVRL2z7Dxee0BY1KpJAODWlMYBmOlLpqo5X8+LTvQRcAfxuPwFXkIA7QK4n15q8ueS6cynwFZDvzafQV0iOOwddtf43pgcvqpIKeEgVdVa0+gLPirVeV3VUVc0Mdhp5rNoFnVu4X1PPEUJ0cS0J4Fq7vbUBVmvDqZYGUs2ta65t6cFfR86zp/R2ZrdxR/u1dt/mPovGHtt/hEofpt7aUNrrbTDMfW+yR5dDECKLpir84aWvWbE11NFN2WlHDivm6mOHd3QzhBCiU2q3b3ThcDgjkAKc5VhLhr3tZsf/6kp++14Zm6o2oKk6iqpZoUqq546maFY9n9SyrliBjkt1oakauqqjKdbcrbnRVR1d1fFoHnwuH17di9/lx+vykuvOJeAO4Nf9+Fw+gu4gHt2TUcenqXl68NNcUCSEEG2uqZ8t8jNH7Ehb9D5LX1aUvbanVJcohyBEG1uxNcTS0pbdkKczGlQc6OgmCCFEp9VuoZTH42kQPtnLXq+3vZrRpF45vXj61Gc7uhlCCCHEnmdHtcNEi3X2cgiic5Ghb0IIITq7dgulSkpKqKioIJFIoKe63JeVleH1esnNzd3h8+27moVCXbfrrhBCCCG6jkAg0Ol6H+9qOQT5PtVye0qg8+gHKymtCu94x05odJ88zhjbl/65Kkas6/aOLPFZ/+a68vuQ99A5yHvoHPaE99A/V2237wI7+j7VbqHUiBEj0HWdRYsWcdBBBwGwcOFCRo8e3aK/6tXW1gJwxBFH7NZ2CiGEEEKA9T0lGAx2dDMy7Go5BPk+JbqSd4A7O7oRbWAV0NXHY8h76BzkPXQOe8p7GDujfV5rR9+n2i2U8vl8nHzyyUybNo077riDrVu3MnfuXGbMaNkn0b17dz788MNO+VdLIYQQQux5AoHOVwdmV8shyPcpIYQQQrSnHX2fatdb11x//fVMmzaN3/72twSDQS677DKOOeaYFj1XVVV69Oixm1sohBBCCNF57Wo5BPk+JYQQQojORDHN5u7DLIQQQgghOotwOMz48eOZO3euUw7hoYceYv78+Tz//PMd3DohhBBCiNaRW7QIIYQQQnQR6eUQlixZwrx585g7dy7nnXdeRzdNCCGEEKLVpKeUEEIIIUQXEg6HmTZtGv/6178IBoNMnjyZSZMmdXSzhBBCCCFaTUIpIYQQQgghhBBCCNHuZPieEEIIIYQQQgghhGh3EkoJIYQQQgghhBBCiHYnoZQQQgghhBBCCCGEaHcSSgHRaJQbbriBgw46iMMOO4y5c+d2dJO6rC1btjB16lTGjRvH4YcfzowZM4hGox3drC7toosu4rrrruvoZnRZsViMW265hYMPPphDDjmEe+65Bymlt3M2bdrExRdfzIEHHshRRx3F008/3dFN6lJisRgnnHACCxYscNatX7+eSZMmccABB3D88cfzySefdGALu47GPstFixZx5plnMmbMGI499lheeeWVDmxh1yXfiVrmvffeY9iwYRnT1KlTO7pZnYb8vGuZxj6n2267rcG59fzzz3dgKztOc9cVcj7Va+5zkvOp3tq1a5k8eTJjxozhyCOP5IknnnC2yflUr7nPaXedT/ouH2EPMHPmTL799lueeeYZSktLufbaa+nVqxfHHXdcRzetSzFNk6lTp5Kbm8sLL7xAVVUVN9xwA6qqcu2113Z087qkd955hw8//JBTTjmlo5vSZd12220sWLCAJ598ktraWi6//HJ69erFmWee2dFN63L++Mc/0qtXL15//XVWrFjBVVddRe/evTn66KM7ummdXjQa5corr2T58uXOOtM0ufTSSxk6dCivvfYa8+bNY8qUKbz77rv06tWrA1vbuTX2WZaVlXHhhRfym9/8hjvvvJOlS5dy/fXXU1xczJFHHtlxje2C5DtRy6xYsYKJEycyffp0Z53H4+nAFnUe8vOuZRr7nABWrlzJlVdemfHdLxgMtnfzOlxz1xXXXHONnE8pO7r+kvPJYhgGF110EaNHj+aNN95g7dq1XHHFFZSUlHDCCSfI+ZTS3Of0q1/9aredT3t9KFVXV8crr7zC448/zqhRoxg1ahTLly/nhRdekC9grbRq1SoWLVrEp59+SlFREQBTp07lrrvuklBqJ1RWVjJz5kxGjx7d0U3psiorK3nttdd46qmn2G+//QC44IILWLx4sYRSrVRVVcWiRYuYPn06/fv3p3///hx++OHMnz9fQqkdWLFiBVdeeWWDHnqfffYZ69ev56WXXsLv9zNo0CDmz5/Pa6+9xmWXXdZBre3cmvos582bR1FREVdccQUA/fv3Z8GCBbz99tsSSrWCfCdquZUrVzJ06FCKi4s7uimdivy8a5mmPiewzq3Jkyfv9edWc9cVP/3pT+V8StnR9ZecT5by8nJGjBjBtGnTCAaD9O/fnwkTJrBw4UKKiorkfEpp7nOyQ6ndcT7t9cP3li1bRiKRYMyYMc66sWPHsnjxYgzD6MCWdT3FxcU88cQTzg9EWygU6qAWdW133XUXJ510EoMHD+7opnRZCxcuJBgMMm7cOGfdRRddxIwZMzqwVV2T1+vF5/Px+uuvE4/HWbVqFV999RUjRozo6KZ1ep9//jnjx4/n5Zdfzli/ePFiRo4cid/vd9aNHTuWRYsWtXMLu46mPkt7uEI2+f3TOvKdqOVWrlxJ//79O7oZnY78vGuZpj6nUCjEli1b5Nyi+esKOZ/qNfc5yflUr3v37tx3330Eg0FM02ThwoV88cUXjBs3Ts6nNM19TrvzfNrre0qVlZVRUFCA2+121hUVFRGNRqmsrKSwsLADW9e15ObmcvjhhzvLhmHw/PPP85Of/KQDW9U1zZ8/ny+//JK3336badOmdXRzuqz169fTu3dv3nzzTR599FHi8Tinnnoq//u//4uq7vWZfKt4PB5uuukmpk+fzrPPPksymeTUU0/ljDPO6OimdXpnnXVWo+vLysro3r17xrpu3bqxefPm9mhWl9TUZ9mnTx/69OnjLG/bto133nlnr/sL566S70QtY5omq1ev5pNPPuGxxx4jmUxy3HHHMXXq1IzPbm8kP+9apqnPaeXKlSiKwqOPPspHH31Efn4+559//l5ZxqG56wo5n+o19znJ+dS4o446itLSUiZOnMixxx7LHXfcIedTI7I/p2+//Xa3nU97fSgVDocbfIGwl2OxWEc0aY8xa9YsvvvuO1599dWObkqXEo1Gufnmm7npppvwer0d3Zwura6ujrVr1/LSSy8xY8YMysrKuOmmm/D5fFxwwQUd3bwuZ+XKlUycOJHzzz+f5cuXM336dCZMmMCJJ57Y0U3rkpr6/SO/e3ZNJBLhsssuo6ioiF//+tcd3ZwuRb4TtUxpaanzWd13331s2LCB2267jUgkwo033tjRzeuU5Oddy6xatQpFURg4cCDnnHMOX3zxBX/+858JBoN7/VD59OuKp59+Ws6nJqR/TkuXLpXzqREPPPAA5eXlTJs2jRkzZsjPpyZkf06jRo3abefTXh9KeTyeBiecvSyBwM6bNWsWzzzzDPfeey9Dhw7t6OZ0KbNnz2bffffN+KuH2Dm6rhMKhbj77rvp3bs3YF1MvPjiixJKtdL8+fN59dVX+fDDD/F6vYwePZotW7bwyCOPSCi1kzweD5WVlRnrYrGY/O7ZBbW1tVxyySWsWbOGv/zlL/h8vo5uUpci34lapnfv3ixYsIC8vDwURWHEiBEYhsHVV1/N9ddfj6ZpHd3ETkd+3rXMySefzMSJE8nPzwdg+PDhrFmzhhdffHGvDhGyryvkfGpc9uc0ZMgQOZ8aYdfrjUajXHXVVZx22mmEw+GMfeR8avg5ffXVV7vtfNrrx6+UlJRQUVFBIpFw1pWVleH1esnNze3AlnVd06dP56mnnmLWrFkce+yxHd2cLuedd95h3rx5jBkzhjFjxvD222/z9ttvZ9T4EC1TXFyMx+NxAimAAQMGsGnTpg5sVdf07bff0q9fv4xf0CNHjqS0tLQDW9W1lZSUUF5enrGuvLy8QRdy0TKhUIjJkyezfPlynnnmGamhsRPkO1HL5efnoyiKszxo0CCi0ShVVVUd2KrOS37etYyiKM4Fn23gwIFs2bKlYxrUCTR2XSHnU0ONfU5yPtUrLy9n3rx5GesGDx5MPB6nuLhYzqeU5j6nUCi0286nvT6UGjFiBLquZxQyW7hwIaNHj5aaMzth9uzZvPTSS9xzzz388pe/7OjmdEnPPfccb7/9Nm+++SZvvvkmRx11FEcddRRvvvlmRzety9l///2JRqOsXr3aWbdq1aqMkEq0TPfu3Vm7dm1GL4pVq1Zl1PERrbP//vuzdOlSIpGIs27hwoXsv//+HdiqrskwDKZMmcKGDRt47rnnGDJkSEc3qUuS70Qt8/HHHzN+/PiMv6x///335OfnS92tJsjPu5a5//77mTRpUsa6ZcuWMXDgwI5pUAdr6rpCzqdMTX1Ocj7V27BhA1OmTMkIUL799lsKCwsZO3asnE8pzX1Ozz333G47n/b6bxg+n4+TTz6ZadOmsWTJEubNm8fcuXM577zzOrppXc7KlSt5+OGHufDCCxk7dixlZWXOJFqud+/e9OvXz5kCgQCBQIB+/fp1dNO6nIEDB3LkkUdy/fXXs2zZMj7++GPmzJnDb37zm45uWpdz1FFH4XK5uPHGG1m9ejX/+c9/ePTRRzn33HM7umld1rhx4+jZsyfXX389y5cvZ86cOSxZsoTTTz+9o5vW5bz66qssWLCA2267jdzcXOd3T/bwDtE8+U7UMmPGjMHj8XDjjTeyatUqPvzwQ2bOnMnvfve7jm5apyU/71pm4sSJfPHFFzz55JOsW7eOv/zlL7z55pt7ZcmB5q4r5Hyq19znJOdTvdGjRzNq1ChuuOEGVqxYwYcffsisWbP4/e9/L+dTmuY+p915PimmaZpt0P4uLRwOM23aNP71r38RDAaZPHlygxRQ7NicOXO4++67G932ww8/tHNr9hzXXXcdAHfeeWcHt6RrqqmpYfr06bz33nv4fD7OOussLr300oxhF6JlVqxYwe23386SJUsoLCzk7LPP5re//a18lq0wbNgwnn32WcaPHw/A2rVr+dOf/sTixYvp168fN9xwA4ccckgHt7JrSP8sJ0+ezCeffNJgn3HjxvHcc891QOu6LvlO1DLLly/njjvuYNGiRQQCAc4880z53ZJFft61TPbnNG/ePB544AHWrFlD7969ufzyyznmmGM6uJXtb0fXFXI+WXb0Ocn5VG/Lli1Mnz6d+fPn4/P5OOecc7j44otRFEXOpzTNfU6763ySUEoIIYQQQgghhBBCtLu9fvieEEIIIYQQQgghhGh/EkoJIYQQQgghhBBCiHYnoZQQQgghhBBCCCGEaHcSSgkhhBBCCCGEEEKIdiehlBBCCCGEEEIIIYRodxJKCSGEEEIIIYQQQoh2J6GUEEIIIYQQQgghhGh3EkoJITq9YcOGceWVVzZY//rrr3PUUUd1QIuEEEIIIYQQQuwqCaWEEF3C3/72N+bPn9/RzRBCCCGEEEII0UYklBJCdAm9e/fm1ltvJRaLdXRThBBCCCGEEEK0AQmlhBBdwh//+Ee2bNnCk08+2eQ+mzdv5g9/+APjxo1j/Pjx3HbbbU6I9frrr3PuuefywAMPMH78eA466CBmzJiBaZrO81966SWOOuooxowZw7nnnssPP/yw29+XEEIIIYQQQuytJJQSQnQJJSUlTJ06lUcffZT169c32B6Lxfjtb39LOBzmueee47777uODDz5g5syZzj5ff/01q1ev5sUXX+TPf/4zzz77LP/9738B+M9//sPs2bP585//zBtvvMHYsWM577zzqKqqarf3KIQQQgghhBB7EwmlhBBdxrnnnku/fv24/fbbG2z7+OOP2bJlC7NmzWLYsGFMmDCBm266iRdffJHa2loAkskk06dPZ+DAgZx00kkMHz6cb775BoAnnniCiy++mIkTJ9K/f3/++Mc/0rt3b9566612fY9CCCGEEEIIsbfQO7oBQgjRUpqmMW3aNM466yzmzZuXsW3lypX079+fvLw8Z92BBx5IIpFg3bp1AHTr1o1gMOhsDwaDJBIJ5/mzZs3innvucbZHo1HWrFmzG9+REEIIIYQQQuy9JJQSQnQpBx54IKeddhq33347v/vd75z1Ho+nwb7JZDJj7na7G+xj15RKJpPccMMNTJgwIWN7eoglhBBCCCGEEKLtyPA9IUSXc9VVV1FXV5dR9HzAgAGsWbOGyspKZ92iRYvQdZ199tlnh8ccMGAAmzdvpl+/fs706KOPsmjRot3wDoQQQgghhBBCSCglhOhyCgoKuOqqq9i4caOz7tBDD6Vv375cc801/PDDD3z22WdMnz6dE044gdzc3B0e8/zzz+eZZ57hzTffZN26dcyaNYu///3vDBo0aHe+FSGEEEIIIYTYa8nwPSFEl3T66afz2muvsXXrVsCqN/Xwww8zffp0/ud//odAIMCvfvUrrrjiihYd7/jjj6e8vJwHHniA8vJyBg8ezCOPPEL//v1347sQQgghhBBCiL2XYtoFVYQQQgghhBBCCCGEaCcyfE8IIYQQQgghhBBCtDsJpYQQQgghhBBCCCFEu5NQSgghhBBCCCGEEEK0OwmlhBBCCCGEEEIIIUS7k1BKCCGEEEIIIYQQQrQ7CaWEEEIIIYQQQgghRLuTUEoIIYQQQgghhBBCtDsJpYQQQgghhBBCCCFEu5NQSgghhBBCCCGEEEK0OwmlhBBCCCGEEEIIIUS7k1BKCCGEEEIIIYQQQrQ7CaWEEEIIIYQQQgghRLuTUEoIIYQQQgghhBBCtDsJpYQQQgghhBBCCCFEu5NQSgghhBBCCCGEEEK0OwmlhBBCCCGEEEIIIUS7k1BKCCGEEEIIIYQQQrQ7CaWEEEIIIYQQexTTNDu6CaITkvNCiM5HQikhRJs699xzGTZsGGeeeWaT+1x++eUMGzaM6667rk1f+8EHH2TYsGFtesyW2rBhA8OGDeP111/vkNcXQgix91m4cCGXXXYZhx56KKNHj+ZnP/sZN954IytXruzopmVo79/PCxcu5KKLLmq31+sMli5dyoUXXshPfvITxo8fzwUXXMDSpUsz9jFNkyeffJJjjjmG0aNHc+yxx/LCCy+06nXuvPNOzj333Ix19v/fpqbPP/+8xcdv7FgjR45k/PjxXHrppSxfvrzFx5o7dy5XXXUVANXV1VxzzTV8+eWXLX7+rrjuuus46qijmt3n9ddfZ9iwYWzYsKHFx23JcyoqKjjyyCNZv359i4+brra2lltuuYVDDz2UMWPGcOGFF7Jq1aodPu+HH37gd7/7HePGjeOwww7j2muvpby8vMn9n3vuuR1+RmLvoHd0A4QQex5VVVm0aBGbN2+mR48eGdvq6up4//33O6hlQgghxJ5hzpw53HPPPRx22GHccMMNFBcXs3btWl588UVOOeUUZsyYwS9/+cuObmaHeOWVVzpdMLc7rV27lnPOOYd9992X22+/HUVRmDt3LmeddRZvvPEGAwcOBGDmzJk899xzTJ06ldGjR/PRRx9x6623ous6v/71r3f4OnPnzuWpp55i3LhxGevPOOMMDj/88Ix18Xicyy+/nOLiYvbbb79Wv6eXX37ZeZxMJiktLeXee+/l7LPP5p133qG4uLjZ569cuZLHHnuMt956C4Dvv/+ev/71r5x22mmtbsvucuSRR/Lyyy/TvXv3Nj1uQUEBkyZN4oYbbuDZZ59FUZRWPf/KK69k8eLFXH311QSDQWbPns15553HO++8Q15eXqPPKS8v57e//S09e/ZkxowZRKNR/t//+39ceOGF/N///R8ulytj/3feeYc777yTkpKSnX6fYs8hoZQQos2NHDmSFStW8I9//INJkyZlbHv//ffx+Xzk5uZ2TOOEEEKILu7999/n7rvv5rLLLmPKlCnO+nHjxnHyySdz5ZVXct111zF06FCGDBnSgS0V7eG5557D5/Px2GOP4ff7AfjJT37CUUcdxfPPP89NN93Ehg0bePrpp/nzn//MWWedBcCECRPYtGkTn3zySbOh1Pr167nrrrv4z3/+Q05OToPtPXr0aPBHyBkzZlBbW8tLL72E1+tt9Xs64IADMpbHjh1Lz549Ofvss3njjTd22BNu1qxZnHDCCZ069CgsLKSwsHC3HPuss87ikUce4b333uOYY45p8fO+/vpr3n//febMmcMRRxwBwEEHHcTPfvYz/vKXv/C///u/jT7v3//+NxUVFfzf//0f++yzDwA5OTn87ne/4+uvv3aCzG3btnH//ffz8ssvk5+fv2tvUuwxZPieEKLN+f1+jjjiCP7xj3802Pbuu+9y7LHHouuZmfj27du55ZZbmDhxIvvuuy/jxo3j0ksvzeievG7dOn7/+98zfvx49t9/f37961/z4YcfNtmO0tJSjjzySE499VSqq6ub3O+bb75h8uTJjB8/ngMPPJDf//73Gd3DFyxYwLBhw5g/fz4XXHAB+++/P4ceeiizZs0imUw2OF5lZSWjR4/mnnvuyVgfDocZO3YsjzzySJNtEUIIIXZk9uzZDBw4kEsvvbTBNpfLxa233oqmaTz++OMAXHDBBZx66qkN9r3kkks48cQTneUvv/ySc845h/33359x48Zx7bXXsn37dmf766+/zsiRI3nllVc49NBDGTduHCtWrGjx7+cPPviAE0880Rk69uabb2Zs37p1K9dffz1HHHEE++23H6effjr//ve/M/aJRqM89NBDHHfccYwePZpjjjmGOXPmYBgGYA2beuONN9i4cWOzw+offPBBjjvuON577z1OOOEERo8ezUknncTXX3/NokWLOOOMM9hvv/044YQTmD9/fsZzf/zxRy6++GIOPPBADjzwQC699NIGQ6WWLVvGlClT+MlPfsKoUaM4/PDDue2224hEIs4+w4YN44UXXuBPf/oT48aNY8yYMfzhD3/IGPJkD9dasGBBo+8DYODAgVxwwQVOIAXWd7EePXqwbt06AObNm4fH4+H000/PeO59993Hgw8+2OSxwQqY1q5dyzPPPMOIESOa3ResYVzPPfccU6ZMoU+fPjvcv6X23XdfADZu3AhY/w+PPvpoZs+e7QwZq6qq4scff+SDDz7ghBNOAKzvceeddx4A5513Xsbww3fffZdTTz2VMWPGcOihh3LTTTdRVVWV8bo7+p7YnNdff51jjz2W0aNHc+KJJ2b8u2hsKN4bb7zB8ccf7+w/f/58Ro4c2eA8Xrx4MWeeeSajR4/myCOP5IknnsjY7na7OfbYY3nsscecdfb32eZKTXzyySf4/X4OO+wwZ11hYSEHH3xws9+5o9EoAMFg0Flnh06VlZXOukcffZRPPvmEBx98kIkTJzZ5PLF3kVBKCLFbHH/88c4QPlsoFOKjjz5yviTYTNPk4osv5tNPP+Wqq67iySefZMqUKcyfP5+bb74ZAMMwuPjiiwmHw8ycOZOHH36Y/Px8/vd//5e1a9c2eP2ysjImTZpEfn4+Tz31VJM9sz777DN+85vfAHDHHXdw2223sWnTJs4888wGXf+vuuoqxo4dy6OPPsoJJ5zAE088wSuvvNLgmPn5+fz85z/n7bffziio+d5771FXV8fJJ5/csg9RCCGEyLJ9+3a+/fZbJk6c2OSwnPz8fA455BAn0DnxxBNZunRpxu/L6upqPvroI0466SQAvvjiCyZNmoTX6+W+++7jhhtu4PPPP+e8887LCFKSySRz587l9ttv5/rrr2fAgAEt/v180003MWnSJB555BF69OjBddddx7JlywBr+M/pp5/Ol19+yeWXX86DDz5I7969ufTSS50hWKZp8vvf/54nnniCM844g0cffZTjjjuO++67z/m+cMkll3DEEUdQXFzMyy+/zJFHHtnkZ7l582buvPNOfv/733P//fdTXV3N1KlTueKKKzjjjDN46KGHME2Tyy+/3PkMVq9ezZlnnsm2bdu46667uP3221m/fj2/+c1v2LZtG2CFa2effTbhcJg777yTxx9/nF/+8pc899xzPPvssxltuPfeezEMg3vuuYdrrrmG999/nzvuuMPZbg/xGjVqVJPv46yzzuJ3v/tdxrq1a9eyfPlyp6fc999/T79+/fjiiy845ZRTGDVqFEcddVTGMLmm/PGPf+Stt97i4IMP3uG+YA0T7NOnD7/97W9btH9LrV69GsDpiQPWHyA//PBD7r33Xq6//nry8vJ4++23KS4udnpbjRo1iptuugmwzkH7XHn44Ye54oorOOCAA3jggQe49NJL+ec//8m5557r/P9uzffEbJs2bWLOnDn84Q9/4MEHH0RRFKZOneqcJ9nefPNNrrvuOg488EAefvhhjj32WC655JJG/wA6bdo0fvnLXzJnzhzGjBnDrFmzGpTHOO644/j222+dz23UqFE7/DexcuVK+vTpg6ZpGev32Wcf5ziN+cUvfkFxcTG33norW7duZf369cycOZPi4mIOOeQQZ78zzzyTf/7zn63qvSX2fDJ8TwixWxx55JH4fL6MIXzvvfce3bp1Y+zYsRn7bt26FZ/Px7XXXstBBx0EwPjx41m3bp3zZWnbtm2sWrXK+bIJsN9++zF79mxisVjG8SoqKjj//PPxer089dRTTY5/B7j77rvp168fc+bMcX4BH3bYYRx99NE88MAD3H///c6+Z5xxhvNX6QkTJjBv3jw++OCDRou6n3baabz77rssWLCAn/zkJ4D1ZeOQQw6hZ8+eLf4chRBCiHR2L5HevXs3u1+/fv3497//TVVVFccccwy33HILf/vb35zfY//6179IJpPOH4ruvvtuBgwYwGOPPeb8Ptx///355S9/yWuvvcbZZ5/tHPv3v/+9c2FbVlbW4t/Pt912Gz/96U8B6yL36KOP5vPPP2f48OE89dRTbN++nX/+85/OezviiCOYNGkSM2fO5IQTTuDjjz/mv//9L/fcc49TL+vQQw/F6/Vy//33c9555zFkyBAKCwtxu90NhoBlC4fD3HzzzU6bVqxYwd13383tt9/u9Ciqq6tj6tSprF69mhEjRjB79mx8Ph9PP/200ytkwoQJ/PznP+eJJ57g2muv5ccff2TEiBHcf//9zj6HHHIIn376KQsWLMgYejZ06FBmzJjhLC9ZsiSjp/nODPGKRCJce+21uN1uzjnnHMAKM7ds2cJVV13FlClTGDhwIO+++64T1jQ3fG/o0KEtfu1ly5bxySefcNtttzXoFd8aiUTCeRyJRFi2bBl33HEHOTk5Gb37EolExvdHsIKk0aNHO6FtMBhk8ODBAAwePJjBgwdTVVXFI488wv/8z/84n4H9Xs8++2znnG/N98RshmHw0EMPMWjQIAA8Hg+TJk1i0aJF/OxnP2uw//3338/EiRO57bbbADj88MNxuVzcfffdDfa94oornLDsgAMO4L333uOzzz7L6H00evRoAObPn8+AAQMIBoM7/DdRU1OT0dvJFggEqK2tbfJ5xcXF3HLLLVxxxRX8/e9/ByAvL49nn30243j2ZyFEOukpJYTYLbxeL0cddVTGF6t33nmHX/ziFw3+sltSUsKzzz7L2LFj2bBhA59++inPPfccX331lfOFtqioiMGDB/PnP/+Za6+9lrfffhvDMLj++usb1Mv43e9+x/Lly7nhhhsoKChoso11dXV88803/OIXv8j4i1Bubi4TJ05scLeYMWPGZCz36NGDurq6Ro99yCGH0KtXL/76178C1l9j58+fzymnnNJke4QQQogdsXvgZhcOzmb/XjNNE7/fz89//nPeffddZ/s777zDhAkTKCkpIRwOs3jxYo444ghM0ySRSJBIJOjbty+DBg3i008/zTh2+hCu1vx+Tg8O7GFd9vD6zz//nDFjxjQI20488UQn+Pr888/RdZ3jjjuuwT72MVrrwAMPzHgvYIVxNnsIkt3Ozz77jHHjxuH1ep3PKRgMctBBB/Hf//4XsEKL559/Ho/Hw4oVK/j3v//NI488wvbt2xsEddkhQY8ePQiHw61+H7ZQKMTFF1/MN998w6xZs5zPMx6PU1FRwS233MLZZ5/NhAkTmD59OocddhizZ8/e6dfL9sILL9CtWzenB97OGjVqlDONHTuWs88+m1gsxuzZsxsUOc8eUrh+/fodDhtctGgRsVisQe/9gw46iN69e/P555+3+ntitoKCgowQxm5TTU1Ng33Xrl1LaWlpg3O7qZsVpP9b8vl8FBUVNShVkZOTQ25ubqvu7pfewz9bcwXT3377baZMmcJRRx3Fk08+ycMPP8yQIUO44IIL9qqbDoidIz2lhBC7zS9+8QumTJnC5s2b8Xg8zJ8/nz/+8Y+N7vvWW29xzz33sGnTJvLz8xkxYkRGYUz7TjJ20cY333wTl8vFz3/+c2655ZaM3lDhcJg+ffpw99138/LLL6OqjefvNTU1mKbpfAlNV1RU1OBLQ3ahTlVVm/zlraoqp556Kk899RQ333wzf/3rXwkGgxx99NGN7i+EEEK0hB0y2D2mmrJ+/XoCgYATqpx00km89dZbLFu2jKKiIhYsWOAME6uursYwDB5//HGnDlU6j8eTsZxeu6g1v5/Tn2f/brZ/j1ZVVdG3b98Gr23/jq6urqaqqoqCgoIGQ4vskKKxi/0daaxXiM/na3L/yspK3n333YyAz2b3aLKH473wwgvU1dXRs2dP9ttvvwafY2Ov1dx3ix3ZtGkTF198MatXr+bee+/l5z//ubMtEAigKIrTm812+OGH88knn1BeXt7o96HWSCaTvPfeexx//PG43e5dOtarr77qPHa5XBQXF9OtW7dG9w0EAhnLoVCo2f+HgFM3qrnvgK39npgt/XyH+lDHrn+Wzq7dlv0em/p/0tLzxufzEQqFmm1numAwmFHTzFZbW9tokXvb7NmzGTNmDPfee6+z7tBDD+X444/n/vvv54EHHmhxG8TeR0IpIcRu89Of/pRAIMA//vEP/H4/ffr0cYpUpvvyyy+59tprOffcc5k8ebJzp5SZM2eycOFCZ7+SkhKmTZvGzTffzLJly/jHP/7B448/TkFBgVMfAOCZZ57h+++/58ILL+TZZ59tcAdAW05ODoqiNPrLt6ysbJfvCnLqqafy0EMP8dFHH/H3v/+d448/vtEvpEIIIURLdevWjQMOOIB//vOf/OEPf2j0Dy+hUIhPP/2Uo446ylk3YcIEiouL+fvf/05xcTEej8ep62IHFpMmTWq0Z8aOLvBb+vu5OXl5eZSVlTVYb68rKCggLy+PiooKkslkRjC1detWZ5/dLScnh0MOOYTzzz+/wTZ7uNqcOXN4+umnueWWWzjmmGOci/nsIuNt6YcffmDy5MlEo1Hmzp3boP5Tv379ME2TeDye8V3EHia3M3fIy7Z48WIqKir4xS9+scvHsoee7Yz8/PwdBkZ2WFpeXs7AgQMztpWVldG3b9/d/j0xnX33wux6U03Vn2qp6urqVv27GDBgAJ988gmGYWT8bFm7dm2zQ+82btyYEYKCdU7tu+++LS4KL/ZeMnxPCLHbuN1ufv7zn/PPf/6Tv//97012Qf76668xDIPLLrvMCaSSyaTTDd4wDL7++msOOeQQlixZgqIojBgxgssvv5yhQ4dSWlqacbzi4mJ++tOf8otf/IL777+/yW7Lfr+ffffdl7///e8ZRSRramr44IMPGtS+aq3evXszYcIEnn32Wb7//vtG73wkhBBCtNaUKVNYvXp1g7u8gvX78+abbyYSiWQUv9Y0jV/96le8//77/OMf/+DnP/+505MjGAwycuRIVq1axejRo51pyJAhPPjgg83e+a01v5+bc/DBB/P111836AH21ltvUVxcTL9+/Rg3bhyJRKLB3X3tQuj27+2meki3BfuOgyNGjHA+p3333Zenn36a9957D4CFCxcyePBgTjvtNCeQ2rJlCz/++GOjvWR21aZNmzj//PNRFIUXX3yx0YLkdg+pd955J2P9f/7zH4YNG9Zoj7HWWrx4Mbqus99+++3ysXZF79692bRpU8a67N51+++/P263m7/97W8Z67/88ktKS0s58MADd/v3xHQ9evRgn332cc4h27/+9a+dPmZVVRXhcJhevXq1+DmHHXYYtbW1fPzxx8667du38+WXX3LooYc2+byBAwfy1VdfZfTWikajLF26tNEekEKkk55SQojd6vjjj+fiiy9GVVVuvPHGRvexv7zceuutnHbaaVRVVfHCCy84d+Spq6tj5MiReL1errnmGi677DKKior473//y/fff+/c5jfbDTfcwMcff8zNN9/Mk08+2eg+V155JZMnT+aiiy7irLPOIh6PM2fOHGKxWKO32m6t008/nSuuuIJBgwZl1KgQQgghdtbhhx/Oddddx8yZM/n+++857bTT6N69Oxs2bODFF1/k+++/5/bbb2f48OEZzzvppJOYO3cuqqo2GKZ3xRVXcNFFF3HllVdy4oknOnfZW7x4MZdcckmTbdmZ38+NOf/883nrrbeYNGkSU6ZMIT8/nzfffJPPPvuMO+64A1VV+elPf8r48eO58cYb2bJlC8OHD+fzzz/n8ccf55RTTnGKWefm5lJeXs6HH37IiBEj6N69eys+3eZdcsklnHnmmVx88cX85je/wePx8PLLLzNv3jxniNJ+++3Hww8/zJw5czjggANYu3Ytjz32GLFYrNX1orZv3866desYPHhwk8HRbbfdxrZt27jlllsIhUIsWrTI2WYX+R4/fjwTJ05kxowZhMNhhgwZwptvvslXX33Fww8/7Oy/bt06tm/fvsOC2I358ccf6dOnT5O9wjdv3szmzZsZOXLkLg/va86hhx7KX/7yF0zTdIbM2eHgBx98QF5eHsOHD+eiiy7ioYcewuVyMXHiRDZs2MD999/P4MGDnRqgu/t7os2+M99VV13FzTffzNFHH82yZct46KGHgJ0LWu3RBocddhhg9aBcsWIF++yzT5PF8w8++GDGjRvH1VdfzdVXX01+fj4PPvggOTk5TmF1sG4KEIvFGDlyJAB/+MMfuPTSS/nDH/7A6aefTiwW45lnnmHLli2NFmoXIp2EUkKI3eqQQw4hNzeXnj17Ntntd/z48dx000089dRT/OMf/6CoqIjx48cze/ZsLr30UhYuXMgRRxzB3LlznbviVFdX079/f2699dYmeyB1796dK664gltvvZU333yTk08+ucE+EyZM4KmnnuKBBx7giiuuwO12c9BBB3HXXXc1KNC6M4444ggURZFeUkIIIdrU+eefz5gxY3jmmWe466672L59O8XFxRx66KHcfvvtTkCTbvjw4QwdOpSKigomTJiQse2www7jySefZPbs2UydOhWXy8WoUaN46qmnmg0oPB5Pq38/N6a4uJgXX3yRu+++m9tuu414PM7w4cN5+OGHnTuVKYrCY489xgMPPMDTTz/N9u3b6dOnD1dccUXGcLpTTz2VDz/8kEsvvZSpU6dm3O1uVw0fPpwXXniBe++9l2uuuQbTNBk6dCgPPfSQ086LL76YiooKnn32WR566CF69uzJSSed5LS/urqa3NzcFr3eBx98wPXXX8+zzz7L+PHjG2yPxWJ88MEHAI0OlRw3bhzPPfccYN3dbfbs2c6dDgcPHszs2bMzhnk+/PDDvPHGG/zwww+t/WgoLy9v9o7Hr7zyCrNnz+bf//73DguR74pjjjmGhx56iCVLljh/EBwyZAgnnHACL7zwAh9//DF/+9vfnBD1+eef5+WXXyY/P5/jjjuOP/7xj04vwt39PTHdr371K+rq6njyySd57bXXGDJkCH/605/405/+1KA+VUt89NFH7Lfffk4duqVLl3LeeecxY8aMZv9tzp49mzvvvJOZM2diGAYHHngg9913X8b/21tuuYWNGzfyn//8B4Cf/exnzJkzh4cffpgpU6YQCATYb7/9ePXVVxuE40JkU8ydraQnhBBih959912uueYaPvzwwyYLdAohhBBC7A3OPvts7rvvvgZ30Gtrv//97ykoKGDGjBm79XXa0t/+9jdGjhyZUePqgw8+4OKLL+avf/1rq8Kduro6Dj/8cO66664GtZ6E6Gykp5QQQuwG8+bN45tvvuGll17i1FNPlUBKCCGEEHu1BQsWEA6Hd/kufy1x+eWXc9ZZZ3HZZZe1qqZSR3rrrbe49957+eMf/0jPnj1Zu3YtDzzwAOPGjWt1b6OXXnqJIUOGOL33hOjMpKeUEELsBk8//TT33XcfY8eO5b777mv2NrpCCCGEEHu6jRs34vf72+UuiWDdBXHZsmWN3hCgM6qoqODuu+/mo48+Yvv27RQVFXHssccydepUAoFAi4+zfft2Tj75ZJ577jn69eu3G1ssRNuQUEoIIYQQQgghhBBCtLvdd79UIYQQQgghhBBCCCGasNOhVCwW44QTTmDBggXOuvXr1zNp0iQOOOAAjj/+eD755JOM5/z3v//lhBNOYP/99+e8885j/fr1O99yIYQQQgghhBBCCNFl7VQoFY1GueKKK1i+fLmzzjRNLr30UoqKinjttdc46aSTmDJlCqWlpQCUlpZy6aWXcuqpp/Lqq69SWFjIJZdcQktHD5qmSSgUavH+QgghhBAik3yfEkIIIURn0upQasWKFfzP//wP69aty1j/2WefsX79em699VYGDRrExRdfzAEHHMBrr70GwCuvvMK+++7LBRdcwJAhQ5gxYwYbN27k888/b9Hr1tbWMnbsWGpra1vbZCGEEEIIgXyfEkIIIUTn0upQ6vPPP2f8+PG8/PLLGesXL17MyJEj8fv9zrqxY8eyaNEiZ/tBBx3kbPP5fIwaNcrZLoQQQgghhBBCCCH2Hnprn3DWWWc1ur6srIzu3btnrOvWrRubN29u0fYOF6uClY+DEQM9CHoOuHLAnQ+ufHAX1D9WtY5tqxBCCCGEEEIIIUQX1+pQqinhcBi3252xzu12E4vFWrS9w637P/j66hbsqIKnENzdwFsM3hLw9QJ/H/D3Bn8/yBkMnu6gys0NhRBib2TX6zExneX0x63Z1txya56zo+ftaF1L17f0OTt6XmPb7X1URaVvXl90tc2+xgghhBBCiA7QZt/mPB4PlZWVGetisRher9fZnh1AxWIxcnNz26oJu6bvaVD9I9Qsh0QIEnWQrLUex2useTIMGBAtt6aaH5o+nh4AX2/w94WcIZA7DHJHQKAfqC5QtPpJ1dOW9ax1EmwJIfYsdghjzw3TaLDONFPrs9a1Zt7YazW2zTAMDIyM13TalNVGA2s7Jpnts7elhTqNPU7/DJxtZAVBzmzHIVNjx2tsnp7tNLYOxZ4p9cdX0l8gtUpRnOdn7Ju1f/a29Odlb29uW2PPN0wDr+6lyF9EjienwWchhBBCCLG7JQ0TTVV2vGMn1lneQ5uFUiUlJaxYsSJjXXl5uTNkr6SkhPLy8gbbR4wY0VZN2DWeQjhwVsP1Rjw1xSBRC+HNULcRatdAdCtEtlhTtBxilRAtsx4naqHmR2va8u/642l+yBkKucOtoCpniBVgoVjDAhUVsMMqDRQXqB7QPKB5rUBLdVnhVWOPhRCiCXboYk922OIsZ4Uyja1rbDlpJDEwSBpJkmbSWmcmnfWmkVpObUt/rt0u579GQqQG+6RCIed92csKO5w7gUdqnaJYv4iVVKKiKAoKirPe3pa+X/Y2+3nNbVcVtcG+TR2/qX2yX8fZtgv7dkWxZIyKSEVHN0MIIYQQezFNVfjDS1+zYmuoo5uyUwZ3D3L/mWM6uhlAG4ZS+++/P3PmzCESiTi9oxYuXMjYsWOd7QsXLnT2D4fDfPfdd0yZMqWtmrB7OGGP36op5e8N3az3hJGAZB3EQ1ZNqliZFUZFKyFRZa0Lb4TQCqhZAaHV1v6Vi6zJegHIGwVFE6BovBVSoYCZBDMBRtjqpWUmUuvS/yqtpHpWpSZVt0Iv3Qe6PxVouUFzW3PVY+0jhOiU7CAoaSTrH5vJjOAofVv2lDAS1mQmSBpJEkYCw7DWpx/HCYao7w3U2Ho7wLF7qWQvp/eesYOV7Lma6u3Z2LKqqA22Zwc0qqI2CHyygx8hhBBCCCHa24qtIZaWVnd0M7q8Nksoxo0bR8+ePbn++uu55JJLeP/991myZAkzZswA4LTTTuPJJ59kzpw5TJw4kYceeog+ffowfvz4tmpC+1N1UHPBlQv+XmAOtwKkWAWESyG2HXKHQu9fWYGWaVgBVeU3ULEEKpdYoVXVN9a0co5VSL3kCOh5LBSOtXpMNcU0U8FVIhVaxSG2DSJx67WcK0atPlxTPVYRdz0Aure+F5bqrg+2hBAtZgdFduhjP06f2+GS/ThuxIklYySNJDHDmseT8cyQCcMaVmb3RiKZORyqiSFOdtCTHujYy852NXO5qcdCNMXuDZcwEsSTcWtuxOuD0bSpsfV2aGoHpk1tt7elL8eSMfrn9+fwfQ7v6I9BCCGEEELsojZLIDRN4+GHH+ZPf/oTp556Kv369eOhhx6iV69eAPTp04cHH3yQO+64g4ceeogxY8bw0EMP7Vl/7VYU6459rhyrllSiBiLlULsW6tZbQVDOEGvo3j5nWM+JbIGy+VD+X9i2AOKVsOGv1uTpBj2Oht4nWM9p7PWUFgzbs0MrI57q2VVlrbPHs6h66jipmlaaB1RfasigO7VNr5+nDxmUC1exB2juAjhjWzJBNBkllowRT8adQCm9R1N6T6bsYWJ27yI7OGps0hQNl+pyAiVN0TLCJrF3SRpWCBNNRokn4xnzWDKWcS7Gk/H6dangM56MO48TRsLZbj+OG/H6UCm1b9yIk0gmnMfpoZO9T8JIdOjn4lJd3Hj4jR3aBiGEEEIIset2KZT64YfMQt/9+vXj+eefb3L/I444giOOOGJXXrLrUBSrB5Ur1xryF9liDd+r3Qguv3X3PkWx7t7X92RrMhJQ8TVs+hds+Q9Et8Hal6wpbzT0OwNKfmaFRa2h6oBu1aTKZppWDyvDHh4Yh1gEzG2p3ldmw2NlFGR3g2YHWL60OleutN5ZqRBrTwogRaeU3nujqSn9gj6aiDoX6Om9meyAyTooTi0iAE3RrPBI1RoESdnr9qjQXZA0kkSTUSKJCNGENbcne336cjQRbXQeS8aIJCIZ52B6yJR+fibNZEe/7RbTFA1d1Vs1aWrmcxo7hr1OUzW0VO/hfvn9cGlSR1EIIYQQoquTsVrtQfNAYB/w9bQKpdcst3pOebtnBkWqDt0OtqaR10D5Aih9B7a8bw3vW/INuO6BvqdC/7OsIYG7SlFASdWc2hHTrK91ZSbra2olaupDLeugqfdjh1d2vSuf9X41f1oPrFTdq/QAS4g0pmk22lvDfhxLxojE64OA9JpKdsCUzh7Wpimac0GsKqoTKqWHTqLrSRpJwokw4XiYungd4YQ1r4vXEY6HneVIIuLsZ8+ddYlwfbiUiDrLsWRsxw3YjTRFw625G0wuzWU9VusfuzRX5rLqwqW5cKnWsq7qzn7p2+z1uqrj0lLLqeOkB0XZz9FVvd1CWCl0LoQQQgix55BQqj2pLgj0te70V7PSGtan6uApajgMTnVB98OsKVpuDedb/7rV42rVXFj7IvQ9HQacbT2/PdiF1Vty2qQHWPbwwVhlKtBK74Fl33XQRbPF2tWsufRA6fLssCl9iJE9jyQiTkgQSUSsIXRmgkTSGkaXXmTbDpDSe1W4NXdG6CQ6t4SRIBQLURurJRS35rWxWmrj1lQXr3OW7cd18TpnOX2KJqPt0maP5sGre/HqXjy6B6+WmutePJoHj+5x5tnr3Jobr+7Frbkz1qWvT588mscJhYQQQgghhNiTyDfcjqAHIH80eIuh+keoXW8VSm+ql5CnCAZNhgG/ha0fwKqnoPoHWPMcrPs/q+fUoMlt03OqraQHWM11OMkIr+LNFGtX0+pauep7XOmBtMAqfXJJcNWB0gOn7KkuXkddrI5IMuL0eLKHz9k1mBSUzF4Zmguf6nNCJxkW13mYpkldvI6aWA010RprnnocioUIxULUxBp/XBurJRQL7ZYgSVM0fC4ffpcfn+6zHut+fC6fs+zVvfh0ax+v7s1YlzF3+fBoHme7W3NLfS8hhBBCCCHagIRSHUVRrOF8rnyo+s7qNeXtbvUQaoqqQ4+fW3Wlyj+FFU9aw/rWvggb34KB50O/MxuvHdVZZYRXzbTbHi5o17+KVYFZDkaSzODKrmdl97iyg6vs0Mpt9dASO8Uwjfr6N4loRuAUiocIx8MZRZNNw/p/pCiZYZNbc+N3+Z2hc6LjJIwEVZEqqqPVVEXr5/a6mliNtT5STXWsmppoDdXRakKxUJvVPfLqXgKuAAFXgKA7SMBtPfa7/ATcqXnWsl/343en5qn1Pt2HW3NLeCmEEEIIIUQnJ6FUR9N9ULC/VW+pZjmYqeLozVEUKD4Mig617tj3wwNQ8yP8OBvWvQJD/hd6Hb9n3RlP0UDTgGaKvDcIrrZbwx3Ta12pdo2rVG8rPQB6MFWw3ZMWWHn2+tAqYSQaFGiui9VRE68hHA87w+4SRgITE9M0M2rNuDU3AVcAl+aSXiXtzA6YKiIVVEYqM6aqaJU1j1RRGU3NI5XUxmt36TV1VSfXk0vQHSTHnWNNnhyC7qCzzn6cPQVcAQLugAxPE0IIIYQQYi8jVwCdgapD3nArJKn+HoxYy+pEKQoU/QS6jYPSv8Pyh60Q5ptpsP4NGHUd5AzZ7c3vNHYUXNlDBY2YNVQwUQOxCuuxzRkimCrMrget4MoJrDz1AdYeELQkjWSDu4fZQ6vC8bAzBM8wDTCt+k12cWSv5iXXnduuBY73VqZpEoqF2BbexvbwdmeqiFQ4jysjlc68Olpt1d1qJQWFoDtInjePPI815XhyyPPkkevJdR7neHLIdVvLuZ5cctw5eHWvnAdCCCGEEEKIVpFQqrNQVMgZaAVTlUsgug083Vr+3N6/hB4/g7UvwconoXIx/Pcc2Od/YMjFVriyt7OHCjbXG8NIq23VaGiVKsiuuetrWumBzLBK9XS6mlb2LejtqS5eR1W0itpYrXMLenuInV3Dya25yXHnyNC63SiSiFBeV+5M28Lb2FZnBU/2YzuIiqefhy2U58kj35tPgbeAfG8++d588rx5zuN8T359AOXNI8edI/+vhRBCCCGEEO1GQqnOxt/LmlcusgIRd0HLn6t5YeAk6HkcLLsHtvzHqje1+T0YeS2UTNwdLd6zqHZo1URtLyOR6mkVs+4mGNmaOTxQc4HiToVWAXDlWP9fVE99YLUbe1nFkrGMW9vXRGuoilQ5PaHiSSvYUBTFueNXjjuHblo3GWLXhuLJOOV15ZTVlbG1divldeVsrd1KWV0Z2+q2UVZXRlldGaFYqFXHDbgCFPoKM6YCbwEFvgIKvYUU+Aqc5VxPrgyHE0IIIYQQQnRqcsXSGfl7WUFH5RIr+GjtXfV8PWDMTCibD9/PhLr18PXVVpH0EVe3vAeWaMgJrfwNt5mG1avKiFuhVSIE4Q3WsEH7uYorrZdVjlWIPT2sUj3N9+RKMUyDcDxMXbyOcCJMKBqiMlpJXazOCp+MOJigqZpzK/qAO4BLdckQq10US8bYWruVzaHNbKndwtbarc6yHTxtD29v8fE8mocifxHd/N3o5utmPfZ1o5u/G4W+QuuxrxsFvgK8ehe6iYEQQgghhBBC7ICEUp1VoC9gQMViq1fNjoqfN6Z4AhS+BCsehzXPweZ5sO0LGHEl9PxFpxpetkdQVCtY0pqoaeX0sopbveAiW6wgy36u6k4VYE/VsnIFQfWQQKPOMKgzktQl405harsAuWEaaIqGR/fg1b0E3UFcmqv93vcexDRNKiIVbAptYlPNJid42hza7DxuaeDkUl0U+4spDhQ78yJ/kfXYbz0u8hcRdAclKBRCCCGEEELslSSU6sz8+4CRhMpvrB42ehNDypqjeWDYFKuX1Le3WnfpW3KTFVCNuqFlBdVF21CbqWdlJsGIk0iEqavbTF2sltpYiO3RENXxMBHTJG4AmguXKxevO48cd4BunhxUpwi7BFE7Ypom28PbKa0ppTRUyqaaTZTWlLIpZM03hzYTTUZ3eByP5qEkWEJJwJq6B7pnTCWBEvK9+RI2CSGEEEIIIUQzJJTqzBQFggMgGYHqZeDv3aKhXY3KGw4TnoXVz1g9p7Z+ZPXCGnW9FViJdmeYBnXxCHWJCLXxMNsj1VTFQoQTUaeotVvz4PMGKVRU3KqSKsKegPhWsOteZwwLDFhDAlVXKqhy73WBVSQRobSmlI3VG9lYs5EN1RvYWLORjdUbKQ2VEklEmn2+gkKRv4iewZ70yOlBj0APegR7OCFUj2AP8jx5EjgJIYQQQgghxC6SUKqzUxTIHQLJWqjbAP6+Oz/sTtVh0GTofoTVW6rmR1h0HfQ4GoZeCv4+bdt2kSGeTBCK1xGK11EVC7EtUk04HiFqxDBME7fmwqd5KPDm4NHcLTuoadYHVckoJGohnKjf3mRg5UkLrrpeYFUbq2V99Xo2VG9gffV61ldZjzfUbGBr7dZmn6ug0D3QnV45vegZ7GnNc3rSK2jNSwIlMvxRCCGEEEKI3SRpmGiq/IFXWCSU6gpUF+SNhEQdRDaDr+euHS9nMEx4BlY+Cauesu7Ot/k9KDgQep9g9ZzSGynkLVolkohSE68jFKtjW6QqrRdUAk1R8ekectx+irRdGOalKFbgRCMhVosDK0+q8Lo/s3eV5gal435ExJIx1letZ13VOtZWrWVd1TrWV1vL28Lbmn1uwBWgd25v+uT2oXdOb3rnWI975fSiR7AH7paGfkKITsEwDQzTIGkkiSVjHd0cIYQQQuwCTVX4w0tfs2Jr6+5E3VkcOayYq48d3tHN2GNIKNVV6AHI3xe2fWkVyXYX7NrxVBcM+T10PxyWPwrln0HFV9b0/SwoOcoKqArHWkW4xQ7VxSPUxGupidVRHq6kOl5LOBHFNA1cmgu/7qXYl4++s0MwW6vFgVUkdafA7MDKnXanQH/mUMA26mFl13haXbmatVVrWVO5hrVVa1lbuZZNoU0YdiH4RuR789kndx/65Pahb15f+ub2pU9uH/rk9pHhdUJ0IkkjaQVKZjIjXGpqHQpg4swVRUFRFFRUVFVFUzSC7mD7/SwVQgghRJtbsTXE0tLqjm7GThlUHOjoJuxR5BtdV+LpZvWYqvgaVO/OFT7PljcKDnrQuhPcxndh49tQtw5K37Embwn0Oh56/RKC/Xf99fYgkUSU6lgt1bFaysIVVMdqCafqFXl1Dz7dS4EnB7UzhnrNBlaGdadAM95MYOVKC6wCO6xhZZgGm0ObWVWxitWVq1lVsYq1lWtZXbmamlhNk80MuAL0y+vHPnn7NJiC7mAbfRhCiKaYppkRHCWMRKPBUlOBEgqoihUkqYqKpmrOY7fmxq26cWkuXJoLt+bGpbrQVM15jr1/+lxVVFyqS4bZCiGEEELsASSU6mr8fSBeDTU/gNYHFK1tjustgUHnw8BJ1t3+Sv8Gm/5lhVWrnrKmvFFWQNXzWHDnt83rdiHxZILqWIiaeB1bw9upjIYIxyOYmHh1D37dS6E3t3OGUK2hqK0cEripfrOisTlSw8rQVlbWlLOqZgurqtazunpDkwXGFRR65fSif35/+uf3p19eP/rl96N/Xn8KfYXS40mIXZQeLCWNZEaIZD+2t9v7o1j/NsEKlVRFRVd157GmavhcPlxqfZjk1t1OcKSrepOhUnq4tCfbsmULt99+O5999hkej4fjjz+eK664Ao/Hw2233cZzzz2Xsf+f//xnzjnnHAD+9re/cd9991FWVsZhhx3G9OnTKSwsBKz/P3fffTevvvoqhmFw+umnc9VVV6Gqe/bnKYQQQog9k4RSXY2iWDWh4tUQ3gL+Xrt+zMpqqA1j/VkbMLtD3gWQdx6EPoeKeVD1JVQttaZl90DRIdDrOKtouubd9TZ0QqZp1hclD1dTHqmgNh52ipIHXL7O2xNqd0nrYVUZrWFF5RZWVK1nRdUGVlatZ1XVRmqbCJ90RaNfTgkDc3vRP28fBuQPZEBBf/bJH4jHFeiyRdeFaE/pPZbSA6akmXTWmZhWsGRTyAyEFA1Vre+p5NE9uDQXHs1jhUlZoVJ2wGSHUxIYN800TaZOnUpubi4vvPACVVVV3HDDDaiqyrXXXsvKlSu58sorOeWUU5znBINW788lS5bwpz/9iVtuuYXhw4dz++23c/311/PYY48B8NRTT/G3v/2N2bNnk0gkuPrqq+nWrRuTJ0/ukPcqhBBCCLErJJTqijQP5A2H8s8hVrnzvZZqw7C+FNZvgljcChzSrmOs4ReFwBmgHg/qYuBLMNdD2cfWpHoh71AoORpKDgFf1y6QHkvGqYqFqIqG2FxXTnWslmgyjq5qBHQfJf5u6Gob9U7rIhJGgjXVm1heuY4fq9axonI9yyvXUx6pbHR/TdHol9ODQXl9GJTXh4F5vRmY05s+/gJ0Ja2nlZEAIlD9XdqQQFfqLoGBzNpVqscqui4XwWIPkx0opT9OGAkMMuuqKYqCrugZPY5cmoscPQe35sajefDoVriUHjDZwVL6ur0qUG9nq1atYtGiRXz66acUFRUBMHXqVO666y4nlJo8eTLFxcUNnvv888/zi1/8gpNPPhmAmTNnMnHiRNavX0/fvn159tlnmTp1KgcddBAAV111Fffff7+EUkIIIYTokiSU6qrcBVYwtf1r0HxWUNVS8QRs3AxrNkKoDgrzoHgH9akMAxL7QPJ4iK2H6AIwvgBjO1T825qWBcFzMBRNhG5jIRiAgA+0zh3i1MbDVEZrKA9XUR6pIBQPY5oGPt1LnjsHr7733KktFKvjh8q1LK9cxw+V6/ixYi2rqjcSNxKN7t87UMzg/L4MyuvD4Dxrvk+wBy6tFT9aMoYExqy7TKYNCbQCK72+hpXmT7s7oNsKshSXFOQXnYJpmlaolAqX7KlByJT6A4CqqvVBUSpsCrgCeHRPg4CpqUnby4LyrqC4uJgnnnjCCaRsoVCIUCjEli1b6N+/f6PPXbx4MRdeeKGz3LNnT3r16sXixYtxu91s2rSJgw8+2Nk+duxYNm7cyNatW+nevftueT9CCCGEELuLhFJdmb+vdSe+mlUQ6Nuyi/LqEPywGjaXQW4Qepe0rPeJqoJbBVzgGwIMAfNsiC+HyH8h/DmYNRB9Hza+DxtyQT0APOOg2xgoyLMCqoAf3B07RMswDWpidVRGa9hUW05VLERdIoKuagRdfnr6i9D2gtoc2yJVLKtY40w/VqxjY+3WRvcNuHwMyevLkPy+DMnfxwmgAq42KLa/o7sEmqmi60YcEuWpHlYpqg7ooOlWrz3ND7q34V0CFflRJ3aeHTQ5AVNW4GRioqBgmiaKavVkcnomaTo+lw+v7sWreZ2hcrqq41JdGeGSS3OhKZoMi9sD5ObmcvjhhzvLhmHw/PPP85Of/ISVK1eiKAqPPvooH330Efn5+Zx//vnOUL7GwqVu3bqxefNmysrKADK228HX5s2bJZQSQgghRJcjV2pdmaJCzjCIVUG0zCpW3pwt5fDDKqt3VK8S0HYxeFEUcA+1ppxzIfYdRD6DyJdANZgfQeQj2JgH60dbIZV3OOTlWr2zAn4rqPJ6dvuwrKSRpCoWoiJSzaa6cqqitcSMOB7NTY7LTzdv3h59IVgeruT7itV8t321E0KVhSsa3beHvxvDCvoxNL8fQ/P3YUj+PvQOFHfM56MoVi8oXNBYZxD7LoFGAuJVENtmBVnpz03vZaX7U2GVS3pZ7eXs4t/p4VJG0JQq9o1JRtBkTwFXwAmb3Jo7I2iyH9vLe/LPFtEys2bN4rvvvuPVV19l6dKlKIrCwIEDOeecc/jiiy/485//TDAY5OijjyYSieB2Z4b0brebWCxGJBJxltO3AcRisfZ7Q0IIIYQQbURCqa5O90HeCNj2BSRCoAcb7pNMwrpSWL7W6vHUawfh1c5QNPCMtqbcSRD9FiILIPoVmFWgfgJ8AtFc2DoaSkeCMhi8fiucKsyrH+7XRkP+kkaSymgN2yPVlNaVUx0LkTSS+HQvBd4cPNqeOSyvMlrDd9tX8932VSzdvorvt69utP6TgkK/nJ4ML+jHsIJ+DCvoz9D8fcj35LR/o3eW01OqkW1mMhVaJSAZsf59hBPU36c+u5eVz+plpbjrgyvVDq0kVOgq7GFyjQZNdgHw1J3l0ofN6ZruDJvzuXxO0W+X5soImuzeTRI0iZaaNWsWzzzzDPfeey9Dhw5lyJAhTJw4kfz8fACGDx/OmjVrePHFFzn66KPxeDwNAqZYLIbP58sIoDwej/MYwOdrg56rQgghhBDtTEKpPYG3u3VHvsql4PemLrZT4gn4cTWs2QB5OVbws7spLvCOsSYzDtFvIPJFKqCqBj4F7VNQ/GCOhppRsG0gmF5w6+D1Qn4u5OdYAZU/1ZuqBTKDqDKqoiEM0yDg8lHsLWhdraMuIJyIsKxiLUu3rWTp9lV8t30VG2vLGuynKgr9c3oxonAAIwr6M7xgAEPz98Hv2jPvnAhYQammAY2cO6ZphVZOL6tqiG0HM62otF3LSk0fGuixzm/VnVovPa3ai33HuYSRIJ6MZ4RNBkZ91qioGT2a/C4/PpcPn+7D6/I2CJjs0EmCJrE7TJ8+nRdffJFZs2Zx7LHHAlaxejuQsg0cOJDPPvsMgJKSEsrLyzO2l5eXU1xcTEmJ9UelsrIy+vTp4zwGGi2aLoQQQgjR2e1ZV+h7s+BAq75UZDP4rS+qxBOwbBWs3QDdu4G7A3oGKS7wHmhNZiI1xO9LiC4EoxriC4AF1gW+ewTo+0FiBJSGrd5dKlZI5fdl9qbye0G3Tl/DNKiKhtgWqWJjbZnTIyrg8lHiL0RX94zTPGkYrK7eyLfbV7J020q+3baSldUbMNJv/Z6yT04PRhUOZETBAEYWDmBYQT98+h4cQLWWoqTqTDXVyyq9llUyFVqlDQ2ErJ5WHtBSva0UPbOnleoCNOlt1Yz0gMkOnOJGnKSZdAqC20Po7EDJ7/Ljd/vxu/x4NE9Gjya35nYeyx3mREeZPXs2L730Evfccw/HHXecs/7+++/n66+/5umnn3bWLVu2jIEDBwKw//77s3DhQk499VQANm3axKZNm9h///0pKSmhV69eLFy40AmlFi5cSK9evaSelBBCCCG6pD3jal1YF765wyFWDbFKIADLVlrBTveiDi8uDlgX6579rMmcBPEVVjgVWQjJLRD7xpoA9L7gPwBc+0Gyj1UHa1sFGCa4XZgeFzV+ne1+2GDUUKnGSLg0At4A3X0Fe0QQVRmt4ZttK/hm2wq+3WYFUbWJSIP9uvsKGFU4iFHdBjKycCAjCvqT426HHnF7sh3VsmrQ0yoE8UorwHKOoVrnvKKl9bbypfW20lNzV32vrD1MeoHwuBF3Qqe4Ece00yYTq1eTVt9zKc+b5/Rusus1uVRXxmO545zozFauXMnDDz/MRRddxNixY53eTAATJ05kzpw5PPnkkxx99NF88sknvPnmmzz77LMA/OY3v+Hcc8/lgAMOYPTo0dx+++0ceeSR9O3b19n+//7f/6NHjx4A3H333VxwwQXt/yaFEEIIIdrAnncVtDdz50PeMNj8OaxZBxvKoaSTBFLZFLW+SHrwTEiWQuRriH5t3dEvsd6aeNsa5ucZDfn7EdaHsS2WpDS0ifLtlUTjYfyKm26eAG63D3xhyAlbw/28bvB4wNX5T3O7F9Ti8uUs2bacb8pXsC60ucF+Pt3DiIIBjO42mH27DWLfboMo9hV0QIv3cjvqaQVpNa2SVq+reCXEyrN6W2lYvahSPa4UTyq4ctcXaVez5p2kx5VdKNwOmdJ7OJmmad2RTlEy7jDn031083fD7/Lj1b0Ngia35pZhdGKP8O9//5tkMskjjzzCI488krHthx9+4P777+eBBx7g/vvvp3fv3tx9992MGTMGgDFjxnDrrbfywAMPUFVVxaGHHsr06dOd50+ePJlt27YxZcoUNE3j9NNPZ9KkSe359oQQQggh2oximo2M/emEQqEQY8eOZeHChQSDjRTzFpZoBP77OixfBPsM75ghe7vKqIHoEogutupRmaGMzVV0Y5van7A+lKg+CFNxQyIBsbg1JRJWjypdswI5jxtyAuDzWo/dbvC4rKLvHaQ2HuabbStYUr6cJaneULXxcIP9+uX0ZL9ug9m322BGdxvEwLw+6NJDZM9hpAIrM5FZmD39x7KiWr2tnB5XqaGCqietGHt6/au26XWVPpwuvZeTgVV3yy4Ubk8ezUPAHbCG0+ke3JrbGUaXfnc6IUTHku9TQgghOoNfPvAxS0urO7oZO+XE/XvywG8O7NLvYVSvXN6ZenhHNwOQnlJ7lkQCvl8GZQr07g9mDdCto1vVemoOpvcQqlwHUO6qoia6FH98Gd1ZS765hTy2kWdsg9hCjJhGrdqXam0QNZ4B1Pp6WxfvAIkkxOMQjkBVjXWhryhWLSq3K3WnP38qqHJZQZXbvVt6omyt287X5T+wqOxHFpcvZ0XVuga1oPy6l327DWK/bkMYXTSYfQsHkeeRC4Y9mt1TqrFi7DY7rCKZqm8VgnhVal36sXTrWGoqvFI8oKUCLCUtrFJ0TEUjjkk8aRA302o5mfXH1BTN6cHk0T0UugqdHk524OQETxI4CSGEEEIIIXaCXEXsKQwDfvgBVq2CPv2AYqheCola0LtOfaGoEac8XsPGaAVliRpiRpyg2oNc3wBCioZm1pGbXElucgW5yVW4zSpyjDXkGGsgDknchLR9qFEHUKP1p87by+ohZTMNqwB8LAHbq6Bsu3PXLtypUMqfKqxu97Jyuay7Amot66Fkmiarq0v5uuwHFpX/yOLyHyitLW+wX09/EfsXDWH/oqHsVzSEwXl90Tqw95bopJy7CNLMUEG7xpXV88pIRoknaognI6meTkniRgLDNDFVFVXR0TU3Ls2DS/eR587F78kn4CnArftxu3y49QBu3YfbFcCl+1MBmhBCCCGEEEK0HQml9gSmCcuXw48/QkmJVUcJD/j7Q81y0FL1aTop0zSpSNSyNV7NxmgF1ckwbkUnX/fjdWW2O6n4qdBHU6GPBtPEY24nJ7mS3OQqcpKr0QmTl1xBXnKFE1LVqn2p0foR0vpRq/bGdLsbDms0UmFVPAHbKmHrtvqwStetUMrrtQIrr8da53GBSyehqfxQtZ6vy3/g67JlLCr7kapY5pBDVVEYmt+P/YuGcEDRMPYvGkJ3f+Fu/VzFnitpGFYdJyORmpKpWk7WdkUBt+rC5c7HpbrI0z0EdB9+3YNbVXCjWHPFmlSM1LDBCohXQBxr2KB9h0FVSw0V9NbfZTB96KCaXrhd71S1r4QQQgghhBCdl4RSe4LVq2HZMigstIITm6+nNdQnUgreHp3uItHuFbU+up3yeA1J0yBH89LHXdCy27grClGlG1G1G+WucWAa+Iyt5BiryUmuJphci06YXGMlucZKiIOJSp3ak5C6DyFtH2rVvsTVXKu+lMdtTekMwxoWGU9AdQi2VxIzEiyNbear+Ca+jm1kSaSUOjOW8TSP6mJ04SAOKB7KAd2HM7rbYAIuXxt+emJPljCSJIwEMSd0ShBP2kPrTFRFtUInTcejuenm9RFw+fBqHtyaC4/mwq26cGsuXDtbODy7UHsyCom6+vpXjda9Sr/boLu+/pXmzRg+mHHHQWdoofQSFEIIIYQQYm8joVRXt2EDfPcd5OZCdsFSVYNgf0jWQmw7eDq+vpRpmlQl69gSq2JDWq+oQj2AR93F3lyKSljrQVjrwVbXBDANvGYZOck1BJNrCRprcZs1BIyNBIyNlCTmAxBT8gipfajV+lCr9qFO7WkVTwdQVSK6wjfRzXwVXsNXtWv4tnYDUTOznk+O6uEAd2/GuHsxxt2L4Z4SXC4PJHQod0FoqzWM0KVnTnrLhwWKPUcirYdTLHXXuoSRxMQKejRVw6XouDQdv+4h4MonqPvx6G7cqhVEuVPBk0vbTT/G04cN7oiZ6mll1A8hJFEDZmX9uoxjK/UBlpLqiaW40npjeVI9PLWs4Cor+JIeWUIIIYQQQnRpEkp1ZVu2wLffWsP1cnMb30f3QnAQVH8P8WpwNbHfbpYwk06tqC3xamJGnBzN13ivqGQSLRxDjcZRY3HUaBwlFkeNJ1ESSRR7nkyiGCYYJophNPq6pqqS0PxUqKPYru6LrkXwqOV41TJ86lY8egVuVxWFrioKXUvBAyEdPkjm85+wl0/CMb4OV5IwM49fqAcYE+jPmGA/xgb7M8jbPfN92D2sEkmr0HpNLSQNSIUOaJp1d0BdA7cHvG5rWGB2YOWS0KoratDTKVXXyf7/r6s6LlVHVzVyXH4CLqunk0dzpXo6uVM9nfSuUUBcUUFxQ0s7O5lGRg0sjCQYYUiE6teZ4Px7sV4kFV6pgB1mqakwK9UrS3VbYZYTYKWFWOl3MHSWpXeWEEIIIYQQHakLXO2IRm3bBkuWWENoCndQm8iTD8EBUP2D1RNBa6dhZIZB3fZtVGzdyLatpcQqtuOtjjC6NoE3FEWrCaPXRtBqw2i1EbS6KFo4ihpL7PjYbajWBf/tCx/0t6bPe0NCq8zYp1cdHFGmcEiVh0Nqc+hPIbFALolAjKR/HcnAVpIBL8mAl0TQRzLoIxnwYvrdjb0kJJNWYJVIQF0dVNfUh1aKYk2aVh9O+TxWaOV2Wct2oGUHV1IgvV1ZNZ0STl2nmGHdwc5MBSmaquFWXeiqRlD3EfAVEnT7G/Ry8mgutL2xgLiipgKhVvSOzCjmnkzrnZUeZqXWN3g9u2eWCqj1gZQ9zNDppeVO66HV0kl6agkhhBCi/SQNE02V7x9izyGhVFdUXQ3ffAORCPTq1bLneEsgGYHQKvDqu1743DStGkuby6xpyzbrTnZl2zC3bscs34ZSUYPfMPADvXfmJVQVw+OyJreO6dIxXBqmrmFqGqauYqoqKAqmqkD2D2fDRLF7URkmStJASSYJmwk+7xbho5IYH/eM80VJknhWLtCnCo5cY01HrIVB20HBBCKpqaxF7yHpdZMM+kjk+Ejk+EkEfSRy/SRy/SRz/M7jeG4g9TiA6XHV97RKJiEWs3pbJRKpOj5KqgB7aniVHV550npbZQdXug6aKhfQLWSYhlPHKZ7W48lM1VFSFQWXquPWXHh1D0WufAK6D6/uceo5WeFTF+np1BXYwdLO/NrK6Jll1C8bMUiGs7abDZ+vKGk9q9KCLdWepwVbigs0V1ZwpTb+OD0gk4BLCCGEEC2gqQp/eOlrVmwN7XjnTujIYcVcfezwjm6G6ETkaqmrqa21ekhVVUHvVkQ9igL+vmBEoW4jeLunLop2oKoG1pXChs2wcTNs2AKlW2DTVqiLNP5SqQnAVCCREyBeELRCl7wAidyAE9IkA14rtAl6Sfq8GD43SZ8Hw+fGdLXN6Rk14nxTu54vQ6tZGFrDt3VbiGfVuClx5TE22J+DfPtwsKs3fRN+tMEJ1EiMZDjK2roKfHVb8IS34amtwF1XjVYXhTBQS/28FszUsmKCFomhRWK4y6ta3N6k1536nPzE8wIk8oLE8wPWuryAtS4/QDzgI+HWrIvocARCdVaIlR5cqao1OUMF3VZvK68nLbBKbVPThhTu4RfHhmkQSyac2k71oZMBKPV3r9Os4KnAk0vQbRUSzx5it9tqOom2szM9s9LZvbRIC7ScYCsBiXDDbWD9O3JCLiWzLU6vrfQpVVsrvRi8fYdDJS3Aygiz1MbXZR9bCCGEEHuMFVtDLC2t7uhm7JRBxYGOboLoZORqqiupq7MCqfJyK5BqbXCgahDob91FK1IO3uL6i5WKKlizAVZvsOZrS60wqnoHCXy3fIySIsLFuVQUeNhe4CHaLQd39+4o3QqI5wWskKMdxYwE39StZ2FoDV/WrObbug3EsgqTl7hyOTA4gIOCAxgb7E9vd0HGHcoSqckWYkCD19HMWnzGFvzGZnzGFnzGZnxGGSoJMLCCqhqssCoERkglURsgGfJi1LgwQypKjYlaE0erCqPX1KEmkk6Q5dlSscP3aiqKE/bFC4LE84NWiFVgBVnxVAAYz/GRCHghErN6YRlGZrkeLS28UlUruHK7wOMCl6u+R5YdWqUvd8IAK3t4XdxIZvR0Sg+dXKnQKeDypno6uTPuXufWdrFXoej6nF5au8gJt8y0XlnJrKGJ4Sb2cxpDxj9eRaE+gEo9RkkLppS0QMsOt+zC8WnzxsKsBsGW0vw+KJ3y54EQQgghhOi8JJTqKsJhK5DatAn69Nn5GkKaB3IGg/kjbFkD73wJ//gItjfTk6ekCPr2hF7doU8P6N0DenWnpluAzUqYddFtVCXqCGge8nU/uqLRnlWh4kaCpXUbUz2hVrOkdn2Du+MV6TkclFMfQvVxF2aEUDsjqQQIaQMJaQPrV5oGHrMCr7EVn2cLvtwyvGYZXqMclQRuarCSqkwxJYdaehONFhKvCZCs9mLUuKFaQauOolfV4qqqRa8MWfOqWvTqOhTTxJXa5lu3tdn2mqpCIjdAPD9YH2Cl5vG8APFcnxViBX0kvXGoMet7Xtk9PuxaV4pSH2C59FSA5a7veaVpVtCVPm/DIYR2IfH0Xk7pNZ1URXWG13k0N4Uen9PTyVpXHzq5VH2XzwUhWiQj3GqjsNM0sXpppQdZRto6u0dXvH5d+jZnXbMNrw+30oMpJ4RKD7/U+pCLVA9MZ7hi+nL6sXYwT38N57V06/eZEEIIIYTo0iSU6goiEesue6WlVg+pXb0bW3UEnv03vPo6RKLWOkWxQqf+faB/b2vet6c1eeu/+JumSUWilo2xCkpja6lLxsjVvPT1FDa8i95ukjCTLKsr5cvQar4MrWZR7ToiRjxjn256kAOD/Tko1RtqH0+39gkeFJWo0o2o2o0qRtSvd8KqMrxmOV6jHI9hzV3U4TZrcFNDjnsddMOaUuIEiKoFRJVCatV9iCoFxNR8okYuRsiFqypshVUVIVxVIfSKEK7KWlyVIWt9ZcgKsAwTV2qZNc2/DcOlW2FVYZB4fk59eFVg9b6Kp+pfxQNezEQSasNWgGWk9+BIzfXUBajdG8ulW72v3Lo1nNBeb/e80lQSpkFMNYgrEMcgYSZJGEnMVA8RTdVwKTouTcevewik1XRyp4VRMrxO7PEUBetuhLDbfqWnB1lO6NVE+GUmwIzU79vo80htU7LmGW8sbZY2BNEOpnQfFB4Mun/3vGchhBBCCNEu5Gqts4tGYelSWL/eCqT0XfhfVl4Ozz0Hr71mBV0Ag/eB/5kIEw4Dv7fJpyZNg/J4DRui29kcqyRhGuTrfoo8OTvfnhZKmEl+CG9mYWg1X9asZlHtWuqMWMY++ZqfscEBTm+o/p6iztX7JSOsyqSZYTzGNrzmNjzGNjzGdjzmdjzGdlzU4aIWl1FLkA2QWQoL060Q755DrCSfmJJHVM0nqhQSUgYQU/KIqbkk8YFhoFfVWaFURQ2uylr0yhoryKoIOWGVXhFCr4ugxhN4yirxlFXu8K0lAl6rZlh+kHhBjhVe5QdI5OcQz/NbAVaOn3iODwwToy5CwqghloiRMJLESRI3kiRN0yrEpai4dB1ddeHSXORoLgIuPwFfEI/Xj9vjxe22Jo/qRdft4YWp3hmdfGihEF1ORvDVjpxgy8wMtow4JGpTPcOEEEIIIURXJqFUZ1ZX1zaB1ObN8Mwz8Ne/WndyAxg5Ei66CMbvD9U/QrwSkrmgZf7VOW4k2BqvZl20nK2xGlQUCl1BvOruq7OTMJP8mAqhFobW8HVoLbVGNGOfXM2X0RNqoLe43XpqtbWk4qNO60MdfRpsU80IHqMCj1mRCqoqcJsVuI1KPGalNSTQrMZtpgodNnKNlsRFXMkl5sslHsgl1ieXuJJLVClKhVc5JJQgZmpYkRKNZ4RUVoiVFl5V1Dg9s9REEr02gl4bgQ3lzb5PU4FY0Es0P0As3y7YHsQsyEUtzEcrKEAtyEPPz0PPzbXqORkqmmFC0oC6JITiVk00s4K0cvrWnRfV1HBCe7igXSPLro/ldtX3zNJUK8TS0vbPGGqo7fwQWSFE21DSbpuhZK1P1nZEi4QQQgghRBuTUKqzqqmBb76xAqWdDaTWroVnn4V33oFEqsbSfvvB734HEybU9yLJGw51myBaBrFqcOUQUV1sjlWxNlJORaIOt6JR4srFtRtub5/eE+qrJkKooOplTLCfE0IN8ZV02RCqNQzFS1jrSZieDTeaBrpZi9usxGNW4TYrcRtVuE1rchlVuKhDI45mWj2xMJp+rQR+4krQmvJzrLpTA4MklCAhpRsJJUAEPxHTQxzTquFUW4e2rRpXVS2eylq8lXX4qsL4KuvwVtXhqarDU2nVwFIME09NBE9NBNZv2/GbzwlCQS7k50BBHuTnWlNBXmp9LuTlWI/9vvoC7kl7SkIkYQ0tNIz6+lgZd0NLD7SU+jDLDqdcrvqaWS7dmpx9tMxQq7F10lNLCCGEEEIIIZokoVRntH27FUhVVkLfvq3vsfHdd/D00/D++/UX4AcdZIVRY8c2vFDWA5A7GBI9CIXWs6l6OevCm6jCJOjOp5c7H60NA6C4keC7cClfhdbwVWgNi2vXNRiOZ4dQY4P9OSg4kCG+kjZtwx5BUUkoOSTIoY6+je9ixnGb1bhSvalcRmpu1mRMKkl06tDNOnzm1mbDKxOI4yOuBEh6Aph9cmCfXBQ1B0Xtjqrloal56FoeupYLag4YLqiugYpq2F5pzSuq0qa05coaK0SqCVnTuhZ8FroGeakAyw6q0pftACs/F3KDkBOo/3dlh1npgZZhQCwO4WjmNmc4EVhFmK2ZE2o54VZanaz0QMvlqi8Eb+/vhFppoZgTjKn1y0IIIYQQQgixh5FQqrPZtMkasheJWD2kWtrTIpmEjz+Gl16CL7+sX//Tn8Jvfwv779/kU03TpDJeQ2m0jA2xSuo0N7mBPuyTiKAkwxCvsoIr1b1TbylixPi2dgNf1a7l69AaltRuIGpmFibP0byMCfRnbLA/Y1M9oSSE2nWm4iKqdCNsFhA3kyQUw5qnpnjqdvNuInipI6CE8RPGb4bxKWF8hPGYtehmCM0MoZq1KJi4CeM2w2CWNxtg1XOBGrQCobwADAqCGrAmpReoQ0BJLeODkAJVCaiKQUXICq2qqqGyOhVgpS3XRSCRhG0V1tQSqppqSw7kpea5QcjNqX+cPQ/4Gg+HTDMt0EoVe7eXY6meWun7pAdb9r9vk/pQSlFSQZSSFVBp9eGWrtUHXPb29GLy6WGWmrVeenAJIYQQQgghOgkJpTqLeBxWroQVK6yhej0bGa7VmFAI3noLXn4ZNm601mkaHHccnHceDBrU5FMN06A8VsnGyFZKI+XEjTgFrlyKAv2sHcwkxGsgVgmJKuux5gLNC0rTAVVVoo5FtetYFFrLotq1fB/eRCKrIG2+5mdM0AqhDgz2Z7C3+14xHK+tmaZJ0r47nVkfOMWNJEm7QDAKqgK6ouNSVHQ08nQfPtWNX/XgUXVcqo5b0XArOi7Vmjf6/8NMglGTmqrTplBqXgNmKG1dCKvQVRyMCmtqKX9q6uW2AivFB6ofFD+o3UDpa61LuKFagWoTqg2oTkJVHKqjUBWBqjBU1UJVDVSFoLbOCogqU6FWS6mq1cMqN1jf2yo3aA0zzA2k5qn1OYH69f4mwqxs2T22GoRd0frAyzCsx2b9/+O0/0lpwZZqPdbS51khl542112Nh2NOz66ssCt7m4RdQgghhBBCiFaQUKozqKqCZcusUKmoCAKB5vc3TViyBN58E957r/5Oerm5cMopcMYZ0KNHk0+PGXHKohWsC2+mLFaBoigU6Dn4tKy77ykauPOtKRm27nYUq7DmhhVQmYqHDYkQi2vXsbh2HYtC61gdLWvwmsWuHA4M9GdMsD8HBvsxwFPcue6O18lkh03ZcxRrHwUFDQUXGjrgQiNP8eJTdXyKjtvUcKHiNlVcporbUHEpKmpSqQ89TBPMWOomV/bQtOzbs6dTADeoxaCW1IcRDXroKNakxIA6UOrqgyqzFozUZNam1telHqfmZuq8NmPWREWjhdwB8Kam7s01222FWMliCLmhRoeQBjUK1AA1JoRSoVZNHKrjUBO1pnDc+qyqaqypNVQFAn4IpsKqoD81D9TPnXX++uWg33qeayd+TJtmVoBlh1hNhFymWR+EpUptOeGWmdarKz3osv8fK0pWry6tfoiipoFLs4L2jGBLscKxpoKtjHMoq9eYEEIIIYQQYo8hoVRHSiSgtBR++MG6096OCppv3myFUG+9BatX168fOBB+/Ws4/njw+Zp8enU8xNZoBRsiW6iM1+BW3XR3F7aseLnmA81HRM/h+6rvWVK9giU1K1hSu46KZLjB7v08RYwJ9OOAYD/GBPrRy52/94ZQpmFd5BsGSSNJIpkkYSRImgnrsWmkllNDv7D21U0FHdWZgqoLr6LjVzy4FQ2XouKyezapLlyaC11RUVR7eJaSFgylXdjraXedUzXQU3epU+yhXdT3eEn/f2YXCbfDq2QC4knrPI4nrd5+yaQVcsTjmT1+kgqYfmtSilOde5T6EENLtSP9DniqCUoUzHBaWBWunzf6OGJNRmqdGcFJsuxwSwFyUlNLxYFQ1lQD1KYe12pQq9Svq00FXNFUOFRTa02bduL88bisoYMBX31QFfBb4bUdXvl91jq/N7UtfdkHXs+u92JKD7pMIy30MutDrUQSjFh90OXM03t1kTls0e7ppVIfVNl1uuxzMT0A09ICL+duialzWNMzAywlPehqJODa0XYhhBBCCCHEbiWhVEcwTdi6FVatgi1brIvLPn0a37esDP79b/jXv6zeUTavF44+Gk46yaoX1cQFZ9xIsD1eRWmkjC3R7YSTUXJ0P7283XdYs8k0TdZHtvBtzUq+qVnBtzUr+bF2nRWepHEpGiP8vdnf15MDfCXs7+1BvjsHFBfobrrGaWZfcJuAfaFN1kV11gV26qI7YRokUz2YkpgkSFuHaXU2UVRQQUVF13R0VUNXdLyaC6/qxufy4nP5cOmu1OTG5fLgcrnRU8tKY/WGsnupNFaPqL2HViWT9b1x7KLh6esSqeAqkbR6+UViEIlay4kERA0wkvVFxzGtHk6m3zq+PQwt/U556YGWHXQ5vXziqaAqUh9aOVM0tT5av+zM0yY9Ct4odIumwq0omcW0mujCFScVUpEKr7Kmuqy5/bgOsLPeaNyatrdiqGE2XQGfljbp4HeBL31yg98Nfg/47MmbmvtS633WpLlSRd01UHRAs3pWZsxT6zPWNfMzJ/vfmv3vLH3ZMCCeyPx3mP48zMygK2NoY1r4Rdq/E7JCL3ueEZqmzis9PQxT60MwpbnjqZnHtcMu598sje+zt4b4QgghhBBir9IV0oI9h2lCRQWsXQsbNlgXHj17ZvaOSiTg22/hv/+1pmXL6rcpCowZA8cea03BYKMvY5gGlfGaVL2oMqoTIVRFJV/Podhd0GTzymOVLK1Zxfeh1SytWcnS0CqqE7UN9uvmymO/3CHsnzOE/XKHMDzYH7fqsuoNJSPWUL94yBrmF6+1AgawLuAUF6h66oK1tRddLQiOSOu9Qdo2M+1iVUldrJrOOCXnItRUFJKKSRJIqiYJTJKYqceQ1EwMt3UxqWgqaC5U3YWuamiqhq678Kgu8l0efC4fXpcHl2YFTbqqW0GTrqNrVtCkuVx7Xo8MuxcLrtY9z+5ZlUhYAZUdZCUSaSFXqkdWNAGxGERj9fvF4vUhmGH3Okv7f4wCihc0fyM1kexgK6tmUnPMRCq0itUHVc5Qw1j9424xMONp2+zH8fr1xNOW49ZyMga1UahNQF3cmtca9YFVenjV2HIYSI2AJGFCTcKa2oKL+iGTntTU1OP0dW7Ao9ZPXg08WZOup8Kr9EBLrZ9j18XS6h/T1OPUsqLUPxcVTBVrDKySNleteTK1bG8zsuYmacvUH0tJPd9+LXu9vU1R6sM5Ne19qKn3pqQtY5+Deqo3Y2qu6dY6OxBz7taYFWY1FYal9zyz90vflv2c9BBNCCGEEEKI3UBCqfYQj0N5uRVEbd1qXWQXF4PHA9GoFUItWgSLF1vzmqyaNfvtZ/WK+tnPoHvjRXOSZpKqeIiKeDWbo9uoiNWQMBMEdT89PcUZvaJM06Q0WsaPoXX8WLuOH2rX8n1oNWWxhkWo3YqL4cF+7JszmNE5gxmdM4gST7fGh+IpKqje1IV/HiTiVt2aRBRiYYikgiozZg39sjuamHYPCvtiTLM3NBoc1V9cqVkXT0rqws26YDVUSKoKSV21gqbU9WYSSGI46+znKop18ajpOpqqo6kamu7CpWkEXR58uheP5sKtuXCpOrpq9XhyqTq6ouHSrLnmtF+0ih0EtbaGkmnWh1hOL6xEfU+rZHrAFbfuiBe3p3h98BVLH26YOg4KKGnnYWMFxFUN606COZk91LTdMAzMNIBEWnhlP05gBVuJ1OPU3IhZQ4PrIlAXtu4EWBeBuhiEo9ZUF7NqZtXFIRKHugREEhBOQCQJ4SREDGuyO4SlcjNaWV7LYtDsLRs1UuFVau5OW3alrXdlrXdnrU9/nD7Pfuyi9fk4pHpc7aQWdLJrETs4a+2kNLY+9XMVtfF9FbV+2Xmc9hw7VIP6EI6sfZ3nK40cKzvASz+WmvkcTMgZDMWH78KHJ4QQYm+UNEw0VXojC9GZtGsoFY1GueWWW/jXv/6F1+vlggsu4IILLmjPJrSfZNIqYF5RYYVR27dbF4dVVVY9qB9/tKaVK60L43S5uTB+PBx6KPzkJ1bx80bUJSPUJGqpjNewObqN6ngtCZL4VS/d3HmoisrmSDnf1axmdd1GVodLWV23kZV1G6ltpA6UisIATw9G+vZhlG8fRnn2YYinJ7pVVdu6kKpKAlvrn2T/TLc7pahq/YWKmrrYUH3gDQDF1o6KFQuhJa0LZyUKpGr9KEb9X/M1FVPRSKo6hqaTVBSSqoqhqiSxezQZGIpCEgPTHjqTejpYd5zTFBVVVdEUFZeiEdTcuDUdt+bCq3msoXSqhq5oTtBkPbYnyW47NUWxgqydKQgOmXe4s0MtJ8RK1gdU9vZkMhVsxa1gK31oYtwOtrJqLjUYTmYPJcsKuZoq9u30XlHrh8xpO7ghgs2/cx9Lo+IJK9yKpAKtSATCESvoikRT6yOpwCuSGp6ZmqL2c1LDNaOpnm7RuLXO7t2YpL6nV3txKeBSwa1YPebcirVOT81dihVe6am5s0x9sKWnLeuAbqam1GMXoBmZ2zR7HyNrf1oWeilpdbraQmOHasPDt6lNOgy5DvJaUxxOCCHE3k5TFf7w0tes2Brq6KbslCOHFXP1scM7uhlCtKl2vdqeOXMm3377Lc888wylpaVce+219OrVi+OOO649m7F7JJNWL6gVK6ywadkyK4zatMmqG1VaCtVN1ITp1s2qC3XAAdZ82LAGBc+TZpK6ZIS6ZIRQoo6yaCVbotvZFC1je7yKqlgtVfFqymMVlEbKWR/ZyqbYNpJN9EjQ0Rjk6clQXx+GBvowItCfYYF++Nz+VG8jLbP4tdMjxL4wVuoDqGbWWaERJDExFBMDSJoGRqrmkmE/NmIYySgYcRQzgZmMgxFDMaKoRgyNJBomKiYaJi5FIahaPZY8uhuP7kFXPei6C011oatudM1tPdZc1mNFlYBJNLSzPbTSpQdbRqqnld3jyqmrlX4nvLShinawZReNt59jGtY6p8B4Mm24qpHqbdNIzSR7boe7jQ3nsgOxxnoe2p+JvS597tKtEKCtgwDTtD6HaKw+3IrFrLDKXheLp0KsVLAVSwVa0Vj9vnF7OTXPWI5b83jqeWZa2hI3rc+6rm3f1i6xh5a69My7GOpa1qSm3fFQrV92pS+rmY/T12lK2rq0x6qSWlbS9lPS1qetcyZSf1Cw9yO1H6l/Z2bqd4l9Z8dUzTgzVTcM0tYZ9f+PnMf2TSNiECvctV5mQggh9lortoZYWtrEdVknN6i4hX+UFKILabcr9Lq6Ol555RUef/xxRo0axahRo1i+fDkvvPBC5wmlwmGrJ1Ntbf1UU2NN1dXWvKLCKj5eXm71ftq61Zq2bbMujHakRw8YMsQKnoYOJTF4IDVFOYQSddTEQlTGqinf8G+2xSopj1WyPV7Nluh2tsQrqEiEqEyEqEzWUpmsJWzu+PU8ios+/h4MCPZhQO4+DMjrz4C8fvTPH4DL40td5KTqlSiKExKZpknSTDaY29vrpwRG6mLCtOs2GaAYdo8lBU3RUO3eSmioqopLdeHX/LhUF27djVt149bc1pC51BA4LdVzSVM1NNNEI4mOiWYaaIoBRmq4UiJVrNqIQjKaGsJkWDWuzCgkwtYFZ4a04VgZQ0/Sh46oadvT500MORF7r7YIttLZBbydIMusD7PsbenrnLviZa1LpupzJbKKxycSmfXWkiYYqXpTRlZIkL5skhbmKPUzMy0Is7c7Q3yVtJ6TpA3foj78Sg/R3C5rcgK11HY17XnZ25x1pP2bzHqt7M83kapPFounhViJ+uVYrH6Yp/PYDrbsx2nDQDPmWY/t8DH7cTxRfxfLZNbPKCP1/yTeRnXAOhtn+KuaVhsrPSzT6v/Qkb0dEwb1gl929JsQQgghhBC7qt1CqWXLlpFIJBgzZoyzbuzYsTz66KMYhoHawcVUw/98h1duPIXtehxDwZmSCiTVzMeJ1JRUIdEL4n0hnloXd2vEfG5r8ujEPDphj0rUpRLWTSJmgrD5DWHjS8J1MeJLdu1PvR7VTZGvG939RXT3F1Ps705xsDs9c3vTM6cXBb4CwAqMDNPATP23mYhVlDzrekdRFVRUK0RKBUnpyy7NhUu1ina7NbdVvFt1oWu6EyapipoRLDW1brcxUsMCzbS5kUwtNzIZceviz0zV5zFSz8FIC7dSf723Uzf79vbOX/Wh8XEuShPr7c1pF+4ZtV7St2ftYz9MX59xnOzXz36d9PWN7JuxMrstjb6JrMUm2tDkclOHbWy/5p7bzLaudiczRUkrGL8bZd+9Lj3wsh9D4/s4IXT6zQTSt5NZn8sJz9LWOb3AsgI3+9+T3SPMPq7dZjN9nb2ezDZkrKd+GagP0OxF0wpAvG7wuJ1dnNeDVM8eMrfZx7G3Z59nO1qXfhxn2GhafbRE2nI8azl9P3t7xp0vs/c3MmuvJbO2ZT+3sbtlGkbWkFZ7StT/f00amcNgm2IHgySxCpS10sYtDYe+70X2qnIIQgghhNijtVsoVVZWRkFBAW6321lXVFRENBqlsrKSwsLC9mpKo17Y9h8uPKEtvuA2URDFAJrp2KSrOl7Ni0/3EnAH8LsCBNwBAq4AuZ5ccj255HnzyPXkUugtpMBfQDdfNwJ6AFVVURQFBcUJj3RVR1VVdMUqyG2vs3seOaFT2mT3aEoPkbKXGy1w3tmoqTtc7SrTyBpKkirQ7AwjyR5mkhZSZQRWZtq6rMfpx3Qepx0vY1sjx8l4PfuKOWve2EV6Y/vZ+2avy/xQmnjcyLLZ1DGaen5Tz2lJW1qwe4t3UnawT/a/gR294I6O19hTsl5jh59lU6/pHKCVz08dwv5npDV2zGxNvcZO/HvMDracu2eSFU5lB1RkrTMa2d7IMck6VvpxMl4r7T1mB3P2sZ0gL+3fnbM9/TXS20nqRoFpr2HfPdLM/vzS2pe2mNG2Bv8004ZxN7lf9s+H7GM09nOisdfK+hnhhJCpzyFpZg5hbarXX6PrqB++OngAuD1NNHbPt0eXQxBCCCHEXqXdQqlwOJwRSAHOcqwlw952s+N/dSW/fa+MTVUb0FQdRbUCGFVND2bqe/3oiu4MQ9NSxbDt4WZ2DyJd1fFoHnwuH17di9/lx+vykuvOtYIn3Y/P5SPoDuLRPSgoTrjU2FxV1GYfq4raNUKjriL9blJdTXa41FxglbE+63GTIVUTF6g72rfR/Rt7ThPrmgxndhC67ExAtiv7tTpE2onX2G3Pb+yQu+GYO37RDnjNttYG76GxMKipeUu3ZTetuec0e4wWPM5+rR0dv6XLqg55xeyNukQ5BCGEEEKIFmq3UMrj8TQIn+xlr9fbXs1oUq+cXjx96rMd3Qwh2kb2cD3JKoUQYo/Q2cshCCH2XEnDRFPlS6UQom21WyhVUlJCRUUFiUQCPXVnubKyMrxeL7m5uTt8vpn6S2ko1DVv3ymEEEKIriUQCHS6Hsi7Wg5Bvk+13J5wAS7vofPYU97Hox+spLQqvOMdO6HRffI4Y2xf+ueqGDFXRzdnp5T4rJ/f8h461p7wHvrnqu32XWBH36faLZQaMWIEuq6zaNEiDjroIAAWLlzI6NGjW/RXvdraWgCOOOKI3dpOIYQQQgiwvqcEg8GObkaGXS2HIN+nhBB7q3eAOzu6EbtoFdDVx/bIe+gcVgFjZ7TPa+3o+1S7hVI+n4+TTz6ZadOmcccdd7B161bmzp3LjBkt+yS6d+/Ohx9+2Cn/aimEEEKIPU8gEOjoJjSwq+UQ5PuUEEIIIdrTjr5PtVsoBXD99dczbdo0fvvb3xIMBrnssss45phjWvRcVVXp0aPHbm6hEEIIIUTntavlEOT7lBBCCCE6E8U0O+S2SkIIIYQQopXC4TDjx49n7ty5TjmEhx56iPnz5/P88893cOuEEEIIIVpHbtEihBBCCNFFpJdDWLJkCfPmzWPu3Lmcd955Hd00IYQQQohWk55SQgghhBBdSDgcZtq0afzrX/8iGAwyefJkJk2a1NHNEkIIIYRoNQmlhBBCCCGEEEIIIUS7k+F7QgghhBBCCCGEEKLdSSglhBBCCCGEEEIIIdqdhFJCCCGEEEIIIYQQot1JKAVEo1FuuOEGDjroIA477DDmzp3b0U3qsrZs2cLUqVMZN24chx9+ODNmzCAajXZ0s7q0iy66iOuuu66jm9FlxWIxbrnlFg4++GAOOeQQ7rnnHqSU3s7ZtGkTF198MQceeCBHHXUUTz/9dEc3qUuJxWKccMIJLFiwwFm3fv16Jk2axAEHHMDxxx/PJ5980oEt7Doa+ywXLVrEmWeeyZgxYzj22GN55ZVXOrCFoqO89957DBs2LGOaOnVqRzdrjyM/z9pPY5/1bbfd1uA8f/755zuwlV1bc9cvcl63reY+azmv297atWuZPHkyY8aM4cgjj+SJJ55wtnWmc1vvsFfuRGbOnMm3337LM888Q2lpKddeey29evXiuOOO6+imdSmmaTJ16lRyc3N54YUXqKqq4oYbbkBVVa699tqObl6X9M477/Dhhx9yyimndHRTuqzbbruNBQsW8OSTT1JbW8vll19Or169OPPMMzu6aV3OH//4R3r16sXrr7/OihUruOqqq+jduzdHH310Rzet04tGo1x55ZUsX77cWWeaJpdeeilDhw7ltddeY968eUyZMoV3332XXr16dWBrO7fGPsuysjIuvPBCfvOb33DnnXeydOlSrr/+eoqLiznyyCM7rrGi3a1YsYKJEycyffp0Z53H4+nAFu155OdZ+2nsswZYuXIlV155Zcb3w2Aw2N7N2yM0d/1yzTXXyHndhnZ0rSjnddsyDIOLLrqI0aNH88Ybb7B27VquuOIKSkpKOOGEEzrVub3Xh1J1dXW88sorPP7444waNYpRo0axfPlyXnjhBQmlWmnVqlUsWrSITz/9lKKiIgCmTp3KXXfdJaHUTqisrGTmzJmMHj26o5vSZVVWVvLaa6/x1FNPsd9++wFwwQUXsHjxYgmlWqmqqopFixYxffp0+vfvT//+/Tn88MOZP3++hFI7sGLFCq688soGPfQ+++wz1q9fz0svvYTf72fQoEHMnz+f1157jcsuu6yDWtu5NfVZzps3j6KiIq644goA+vfvz4IFC3j77bcllNrLrFy5kqFDh1JcXNzRTdkjyc+z9tPUZw3WeT558mQ5z9tAc9cvP/3pT+W8bkM7ulaU87ptlZeXM2LECKZNm0YwGKR///5MmDCBhQsXUlRU1KnO7b1++N6yZctIJBKMGTPGWTd27FgWL16MYRgd2LKup7i4mCeeeML5IWMLhUId1KKu7a677uKkk05i8ODBHd2ULmvhwoUEg0HGjRvnrLvooouYMWNGB7aqa/J6vfh8Pl5//XXi8TirVq3iq6++YsSIER3dtE7v888/Z/z48bz88ssZ6xcvXszIkSPx+/3OurFjx7Jo0aJ2bmHX0dRnaQ8ByCa/f/Y+K1eupH///h3djD2W/DxrP0191qFQiC1btsh53kaau36R87ptNfdZy3nd9rp37859991HMBjENE0WLlzIF198wbhx4zrdub3X95QqKyujoKAAt9vtrCsqKiIajVJZWUlhYWEHtq5ryc3N5fDDD3eWDcPg+eef5yc/+UkHtqprmj9/Pl9++SVvv/0206ZN6+jmdFnr16+nd+/evPnmmzz66KPE43FOPfVU/vd//xdV3esz+VbxeDzcdNNNTJ8+nWeffZZkMsmpp57KGWec0dFN6/TOOuusRteXlZXRvXv3jHXdunVj8+bN7dGsLqmpz7JPnz706dPHWd62bRvvvPOO/CV7L2OaJqtXr+aTTz7hscceI5lMctxxxzF16tSM73li58nPs/bT1Ge9cuVKFEXh0Ucf5aOPPiI/P5/zzz9fSj3spOauX+S8blvNfdZyXu9eRx11FKWlpUycOJFjjz2WO+64o1Od23t9KBUOhxt8UbGXY7FYRzRpjzFr1iy+++47Xn311Y5uSpcSjUa5+eabuemmm/B6vR3dnC6trq6OtWvX8tJLLzFjxgzKysq46aab8Pl8XHDBBR3dvC5n5cqVTJw4kfPPP5/ly5czffp0JkyYwIknntjRTeuSmvr9I797dk0kEuGyyy6jqKiIX//61x3dHNGOSktLnX9X9913Hxs2bOC2224jEolw4403dnTz9mjy86z9rFq1CkVRGDhwIOeccw5ffPEFf/7znwkGgzKcvg2kX788/fTTcl7vRumf9dKlS+W83o0eeOABysvLmTZtGjNmzOh0P7P3+lDK4/E0+PDtZQkEdt6sWbN45plnuPfeexk6dGhHN6dLmT17Nvvuu2/GXxLEztF1nVAoxN13303v3r0B66LlxRdflFCqlebPn8+rr77Khx9+iNfrZfTo0WzZsoVHHnlEQqmd5PF4qKyszFgXi8Xkd88uqK2t5ZJLLmHNmjX85S9/wefzdXSTRDvq3bs3CxYsIC8vD0VRGDFiBIZhcPXVV3P99dejaVpHN3GPJT/P2s/JJ5/MxIkTyc/PB2D48OGsWbOGF198US7ed1H29Yuc17tP9mc9ZMgQOa93I7tGcTQa5aqrruK0004jHA5n7NOR5/ZeP36lpKSEiooKEomEs66srAyv10tubm4Htqzrmj59Ok899RSzZs3i2GOP7ejmdDnvvPMO8+bNY8yYMYwZM4a3336bt99+O6PumWiZ4uJiPB6PE0gBDBgwgE2bNnVgq7qmb7/9ln79+mX8sho5ciSlpaUd2KquraSkhPLy8ox15eXlDbpTi5YJhUJMnjyZ5cuX88wzz0hdir1Ufn4+iqL8f/buO76KKv//+Gtmbk0PJISmIGJCka6JiK6Au8IqNtRVUYqC4iqiKzYQFQVEZW0ICLjY609FvpZdd8XuiriygquC0ntJSL3JrXPn98fcO7k3BQKEFPg89zE7M2fKPfc6JHfeOeeMtX7iiSfi9/spKSlpxFod/eTnWcNRFMW6cY/q1KkTe/bsaZwKHSVqun+R6/rIqOmzluu6/hUUFLBs2bK4ss6dOxMMBsnMzGxS1/YxH0p17doVm80WN6jXypUr6dGjh4w5cwjmzp3LG2+8weOPP855553X2NVpll5++WXef/99li5dytKlSxk8eDCDBw9m6dKljV21ZqdXr174/X42bdpklW3cuDEupBJ106pVK7Zs2RLXsnTjxo1x4/iIg9OrVy9+/vlnfD6fVbZy5Up69erViLVqnsLhMBMmTGD79u28/PLLnHTSSY1dJdEIvvrqK/Ly8uL++rtmzRrS0tJkjNAjTH6eNZynnnqKMWPGxJWtXbuWTp06NU6FjgK13b/IdV3/avus5bquf9u3b2fChAlxwd5PP/1EixYt6NevX5O6to/51MXtdnPRRRcxbdo0fvzxR5YtW8Zzzz3HqFGjGrtqzc6GDRuYP38+1113Hf369SM/P9+aRN21a9eODh06WFNiYiKJiYl06NChsavW7HTq1ImBAwcyefJk1q5dy1dffcWiRYu48sorG7tqzc7gwYOx2+1MnTqVTZs28emnn7JgwQJGjhzZ2FVrtnJzc2nTpg2TJ09m3bp1LFq0iB9//JFLL720savW7Lz99tusWLGCGTNmkJKSYv3uqdrtQhzd+vTpg9PpZOrUqWzcuJEvvviCRx99lHHjxjV21Y568vOs4QwaNIj//Oc/LF68mK1bt/Laa6+xdOlSGZbgEO3v/kWu6/q1v89aruv616NHD7p3786UKVNYv349X3zxBbNnz+aGG25octe2YhiG0Siv3IR4vV6mTZvGv/71L5KSkhg7dmy1pFYc2KJFi3jsscdq3Pbrr782cG2OHnfffTcADz/8cCPXpHkqKytj+vTpfPzxx7jdbkaMGMFNN90U171D1M369euZOXMmP/74Iy1atOCqq65i9OjR8lkehJycHF566SXy8vIA2LJlC/fccw+rV6+mQ4cOTJkyhdNPP72Ra9k8xH6WY8eO5euvv662T25uLi+//HIj1E40lnXr1vHQQw+xatUqEhMTueKKK+Rn/hEiP88aTtXPetmyZcyZM4fNmzfTrl07/vKXv3DOOec0ci2bpwPdv8h1XX8O9FnLdV3/9uzZw/Tp01m+fDlut5urr76a8ePHoyhKk7q2JZQSQgghhBBCCCGEEA3umO++J4QQQgghhBBCCCEanoRSQgghhBBCCCGEEKLBSSglhBBCCCGEEEIIIRqchFJCCCGEEEIIIYQQosFJKCWEEEIIIYQQQgghGpyEUkIIIYQQQgghhBCiwUkoJYQQQgghhBBCCCEanIRSQogmLycnh0mTJlUrX7JkCYMHD26EGgkhhBBCCCGEOFwSSgkhmoUPPviA5cuXN3Y1hBBCCCGEEELUEwmlhBDNQrt27XjwwQcJBAKNXRUhhBBCCCGEEPVAQikhRLNw6623smfPHhYvXlzrPrt37+aWW24hNzeXvLw8ZsyYYYVYS5YsYeTIkcyZM4e8vDxOOeUUZs2ahWEY1vFvvPEGgwcPpk+fPowcOZJff/31iL8vIYQQQgghhDhWSSglhGgWsrKymDhxIgsWLGDbtm3VtgcCAUaPHo3X6+Xll1/mySef5PPPP+fRRx+19vnhhx/YtGkTr7/+Ovfeey8vvfQS33zzDQCffvopc+fO5d577+Xdd9+lX79+jBo1ipKSkgZ7j0IIIYQQQghxLJFQSgjRbIwcOZIOHTowc+bMatu++uor9uzZw+zZs8nJyaF///7cd999vP7665SXlwOg6zrTp0+nU6dOXHjhhXTp0oX//e9/APztb39j/PjxDBo0iI4dO3LrrbfSrl073nvvvQZ9j0IIIYQQQghxrLA1dgWEEKKuNE1j2rRpjBgxgmXLlsVt27BhAx07diQ1NdUq69u3L6FQiK1btwLQsmVLkpKSrO1JSUmEQiHr+NmzZ/P4449b2/1+P5s3bz6C70gIIYQQQgghjl0SSgkhmpW+fftyySWXMHPmTMaNG2eVO53Oavvquh43dzgc1faJjiml6zpTpkyhf//+cdtjQywhhBBCCCGEEPVHuu8JIZqd22+/nYqKirhBz0844QQ2b95McXGxVbZq1SpsNhvHH3/8Ac95wgknsHv3bjp06GBNCxYsYNWqVUfgHQghhBBCCCGEkFBKCNHspKenc/vtt7Njxw6rbMCAARx33HHceeed/Prrr3z77bdMnz6dYcOGkZKScsBzXnPNNbz44ossXbqUrVu3Mnv2bP7xj39w4oknHsm3IoQQQgghhBDHLOm+J4Roli699FLeeecd9u7dC5jjTc2fP5/p06fzpz/9icTERM4//3xuu+22Op3v3HPPpaCggDlz5lBQUEDnzp155pln6Nix4xF8F0IIIYQQQghx7FKM6IAqQgghhBBCCCGEEEI0EOm+J4QQQgghhBBCCCEanIRSQgghhBBCCCGEEKLBSSglhBBCCCGEEEIIIRqchFJCCCGEEEIIIYQQosFJKCWEEEIIIYQQQgghGpyEUkIIIYQQQgghhBCiwUkoJYQQQgghhBBCCCEanIRSQgghhBBCCCGEEKLBSSglhBBCCCGEEEIIIRqchFJCCCGEEEIIIYQQosFJKCWEEEIIIYQQQgghGpyEUkIIIYQQQgghhBCiwUkoJYQQQgghhBBCCCEanIRSQgghhBBCCCGEEKLBSSglhBBCCCGEEEIIIRqchFJCCCGEEEIIIYQQosFJKCWEEEIIIYQQQgghGpyEUkIIIYQQQoijjmEYjV0F0QTJdSFE0yKhlBACgJEjRzJy5Mgj/jrbt28nJyeHJUuWHNRxK1asICcnhxUrVhyhmjUNgwcP5u67727sagghhGgmVq5cyc0338yAAQPo0aMHZ599NlOnTmXDhg2NXbU4Tz/9NDk5OQ32eitXruT6669vsNdrCn7++Weuu+46TjvtNPLy8rj22mv5+eef4/YxDIPFixdzzjnn0KNHD4YMGcKrr756UK/z008/0b1792rf5b755htycnKqTePHjz+o8999993VztG9e3fOOOMM7rjjDnbt2lXnc02fPp0nnngCgN27d3P99dezY8eOg6rPoarLd+tD+XdRl2M2btzI4MGDKS0tPahzRxUUFDBp0iTy8vLo168ft912G3v37j3gcd9//z0jRoygb9++DBw4kBkzZuDxeOL2Wb9+PePHj+fUU08lLy+Pu+66i/z8/EOqpzg62Bq7AkIIIYQQQoiDt2jRIh5//HHOOOMMpkyZQmZmJlu2bOH111/n4osvZtasWZx33nmNXc1G8dZbbzW5YO5I2rJlC1dffTUnn3wyM2fORFEUnnvuOUaMGMG7775Lp06dAHj00Ud5+eWXmThxIj169ODLL7/kwQcfxGazcfnllx/wdQKBAHfffTehUKjatjVr1pCUlMTixYvjylNSUg76/WRmZjJ37lxrPRQKsWnTJv7617/yww8/8MEHH+ByufZ7juXLl/Pxxx/zz3/+EzBDsy+++OKg63IkXXbZZZx55pn1ft5OnTpx9tlnM2PGDB599NGDOjYUCnHdddfh8XiYNm0aoVCIxx57jLFjx7JkyRLsdnuNx61bt45rrrmGfv368eSTT7Jnzx7++te/sn37dhYsWADAnj17GDVqFMcffzyzZ8/G6/XyxBNPcM011/Duu+/Wem5xdJNQSgghhBBCiGbms88+47HHHuPmm29mwoQJVnlubi4XXXQRkyZN4u677yY7O5uTTjqpEWsqGsLLL7+M2+1m4cKFJCQkAHDaaacxePBgXnnlFe677z62b9/OCy+8wL333suIESMA6N+/P7t27eLrr7+uUyj15JNPUlZWVuO2NWvWkJOTQ+/evQ/7/TgcjmrnOeWUU7Db7dx111188sknBwxcZ82axZgxY3C73YddnyOldevWtG7d+oic+/rrr2fgwIGMHj2a7t271/m4jz76iF9++YUPP/yQzp07A9C1a1eGDRvGP/7xDy644IIaj3v//fdRFIV58+aRmJgIgK7r3H///ezYsYN27drx1ltvUVZWxjPPPEN6ejoALVq0YNSoUXz77bdHJKATTZ903xNCHJR///vfjBgxgn79+pGXl8ekSZOqNaPeuHEjEyZMIDc3l1NPPZXx48fX+tdKwzCYPHkyPXv25Ouvv7bK33jjDYYMGULPnj25+uqr2blzZ7VjN2/ezMSJExkwYAC9e/dm5MiRrFy5EoDi4mK6devGCy+8YO2/a9cucnJyuOOOO6yycDhMXl4eCxcutLoW/uMf/2DixIn06dOH3Nxcpk6dSkVFxX4/l7179zJ58mTOOussevbsyaWXXsonn3wSt09OTg6vvvoq99xzD7m5ufTp04dbbrmFgoKCGs95ySWXcMUVV1QrHzNmDNdcc81+6yOEEOLoNnfuXDp16sRNN91UbZvdbufBBx9E0zSeffZZAK699lqGDx9ebd8bb7wx7ibz+++/5+qrr6ZXr17k5uZy1113UVhYaG1fsmQJ3bp146233mLAgAHk5uayfv16tm7dyg033EBeXh69evXi8ssvr7FVyueff84FF1xgdR1bunRp3Pa6/D71+/3MmzePoUOH0qNHD8455xwWLVpEOBwGzO5f7777Ljt27NjvkAFPP/00Q4cO5eOPP2bYsGH06NGDCy+8kB9++IFVq1Zx2WWX0bNnT4YNG8by5cvjjv3tt98YP348ffv2pW/fvtx0001s27Ytbp+1a9cyYcIETjvtNLp3786ZZ57JjBkz8Pl81j51+W6wZMmSAw5h0KlTJ6699lorkAJISEigdevWbN26FYBly5bhdDq59NJL44598sknefrpp2s9d9R///tfK+Cqydq1a+natesBz3M4evToAWB1wbv77rsZPXo0999/P3379uXcc89F13U+//xzfvvtNyu4WrJkCZMnTwbg7LPPtoZK0HWdV199lfPPP5+ePXsycOBA/vrXv+L3++Nety7ff2tiGAbPPvssAwcOpGfPnlx++eX8+OOP1vaauuItXryYs88+m549e3LFFVfw6aef1vjf/0D/ljIzMznttNNYuHChVVaXa+nrr7/mhBNOsAIpgM6dO3PiiSfut6WZ3+/HZrPFhYBpaWmA+b0cYMSIEbz22mtWIAVYraOqfubi2CGhlBCizpYuXcq1115LmzZtePzxx5k8eTI//PADl19+Ofv27QPMZrmXX345mzdvZtq0acyePZuCggJGjx5t/UKKNWPGDD744APmzp3LGWecAcArr7zC/fffz1lnncX8+fPp1asX9957b9xx69evZ/jw4Wzfvp2pU6fy17/+FUVRGD16NN999x1paWn07t2bb775xjom+oXy+++/t8pWr15NcXExAwcOtMruv/9+2rVrx/z58xk7dixvv/02zzzzTK2fS0FBAZdeeinff/89f/nLX3j66adp164dN910E++9917cvk888QThcJjHH3+cO++8k88++4yHHnqoxvNeeuml/PDDD2zZssUq27VrFytWrKjxxkIIIcSxobCwkJ9++olBgwahKEqN+6SlpXH66adbgc4FF1zAzz//HPc7pbS0lC+//JILL7wQgP/85z+MGTMGl8vFk08+yZQpU/juu+8YNWpUXJCi6zrPPfccM2fOZPLkyZxwwgmMHz8er9fLo48+yvz580lLS+PPf/5z3OsB3HfffYwZM4ZnnnmG1q1bc/fdd7N27Vqgbr9PDcPghhtu4G9/+xuXXXYZCxYsYOjQoTz55JPcf//9gBm0nXXWWWRmZvLmm2/G/Y6vavfu3Tz88MPccMMNPPXUU5SWljJx4kRuu+02LrvsMubNm4dhGPzlL3+xPoNNmzZxxRVXsG/fPh555BFmzpzJtm3buPLKK63vQ3v37uWqq67C6/Xy8MMP8+yzz3Leeefx8ssv89JLL8XV4UDfDQYOHMibb76539YuI0aMYNy4cXFlW7ZsYd26dVZLuTVr1tChQwf+85//cPHFF9O9e3cGDx7Mm2++Wet5o7xeL5MnT2b8+PE1jmfk9/vZtGkTO3bs4MILL+Tkk09m0KBBLF68uF4HFt+0aRMAxx9/vFX2/fffs2vXLubNm8ekSZPQNI333nuP3r17k5WVBZif4Z///GfADHRvvPFGwLweZ82axe9//3ueeeYZrrrqKl555RVuvPFGq951+f5bm5UrV/Lxxx9z7733Mnv2bPbu3cuf//znGrs/Ruv217/+lT/+8Y/Wd+Bbb721xn33928paujQoXz66aeUl5dbn8OBrqUNGzbQsWPHauXHH3+89fnX5JJLLgHMFmpFRUWsW7eOefPmkZ2dTZcuXQCzVVQ0WPT7/axatYoHH3yQ448/3roPEMcgQwghDMO4+uqrjauvvrrW7bquGwMGDDCuvfbauPItW7YY3bt3Nx555BHDMAzj4YcfNnr27Gns3bvX2mfXrl3GwIEDjc8//9zYtm2bkZ2dbbzzzjvGX//6V6N79+7GZ599Zu0bDoeN/v37G7feemvc69x3331Gdna28e233xqGYRi33HKLkZeXZ5SVlVn7BINBY8iQIcYll1xiGIZhLFy40Ojdu7cRCAQMwzCM22+/3bj44ouN7OxsY9u2bYZhGMZTTz1lDBo0yDAMw6rb7bffHvfaI0eONIYNG1brZ/Poo48a3bt3N7Zv3x5XPnr0aGPAgAGGruuGYRhGdna2ceWVV8btc/fddxu9e/e21gcNGmTcddddhmEYRmlpqdGzZ0/jqaeesrY/88wzRr9+/Qyv11trfYQQQhzdfvzxRyM7O9t45ZVX9rvfww8/bGRnZxvFxcVGeXm50bt3b2Pu3LnW9rfeesvo0qWLsXv3bsMwDOPyyy83hg0bZoRCIWufjRs3Gl27drVe65133jGys7ONpUuXWvvs3bvXyM7ONt577z2rrLS01HjooYeM3377zTAMw5gzZ46RnZ1tfPHFF9Y+W7ZsMbKzs40XX3zRMIy6/T79/PPPjezsbOODDz6I22fevHlGdna29Xp33XWX9fu9NjXVaeHChUZ2drbx1ltvWWUfffSRkZ2dbfzyyy+GYRjGbbfdZpx++ulx30GKioqMfv36GQ8//LBhGIbx1VdfGVdddVXcPoZhGMOGDYv7LlWX7waHwuv1GpdffrnRu3dv6/McN26ckZeXZ5x22mnGK6+8YnzzzTfG1KlTjezsbOONN97Y7/mmT59uXHTRRUYwGIz7LhcVvSbPOecc4x//+IfxzTffGNOnTzdycnKMxx9//KDqHv1vFwwGramoqMj48ssvjcGDBxuDBw+2vgfdddddRnZ2trFr1664c/Tv39+YMWNGXFn02o1+B1y3bp2RnZ1tLFy4MG6/pUuXGtnZ2cbnn39e5++/Nbn66quNnj17GkVFRVbZ//t//8/Izs421qxZYxhG5TVoGIZRXl5u9OzZ05g+fXrcee69996478B1+bcUtWbNGuu91NWQIUOMSZMmVSufNGmScc455+z32Ndee83o0qWLkZ2dbWRnZxuDBg0ydu7cWeO+55xzjpGdnW307NnT+PLLL+tcP3H0kZZSQog62bRpE/n5+QwbNiyu/Pjjj6dPnz589913gPkXod69e5OZmWnt07p1az777DPOOussq+zVV19l0aJFnHfeeXF/wdy4cSP79u1j0KBBca/zxz/+MW79u+++Y9CgQSQlJVllNpuN8847j59++ony8nLOOussKioqWL16NQDffvsto0ePxu1285///AeAL7/8stpfUKuOYdC6dev9dt/77rvv6NOnD+3atYsrv+CCC8jPz2fjxo37PbfX663xvMnJyZxzzjlxra3effddzj333AMO7imEEOLoZURacBxoUGBN06z9ExIS+P3vf8/f//53a/uHH35I//79ycrKwuv1snr1as466ywMwyAUChEKhTjuuOM48cQT+fe//x137thuWhkZGXTu3Jl7772Xu+66i/fff59wOMzkyZOrjWd1yimnWMvt27cHsJ4QVpffp9999x02m42hQ4dW2yd6joPVt2/fuPcC0KtXL6ss2gUpWs9vv/2W3NxcXC6X9TklJSVxyimnWC20zzjjDF555RWcTifr16/nk08+4ZlnnqGwsJBAIBD3+gfz3aAuPB4P48eP53//+x+zZ8+2Ps9gMEhRUREPPPAAV111Ff3792f69OmcccYZcYOKV7VixQrefPNNZs2ahc1W85DEHTt2ZNGiRbz++usMHTqU/v37M3XqVC699FIWL15c6zhUtdmxYwfdu3e3pry8PMaNG0fLli2ZN29e3PegtLS0uHGZKioq2Ldvn3V91SZ6rVQdm+q8885D0zRWrFhR5++/tencubN1/UDlNV/T57Fq1Sp8Pl+1a7vqa0ft799SVPS//fbt2/dbz1jGflq21dYyE8wHL0ybNo0rr7ySF154gSeeeILExETGjBlT41AV999/P4sXL6Z///7ccMMNfPXVV3Wuozi6yEDnQog6iXa9i35Zi5WRkcEvv/xi7XegLwFgjjtwxhln8MEHHzB69Gi6desGQElJCUBcX3MgLuSK7ldbXQzDwOPxkJOTQ5s2bfjmm29IT09n7969nH766fTt25fvvvuOs846i59//plbbrkl7hxVB8RUVXW/v6BLSko47rjjaqwLxH9BONhzX3rppbz33nt8//33aJrG5s2beeSRR2rdXwghxNEveqN5oEfbb9u2jcTEROum+MILL+S9995j7dq1ZGRksGLFCqubWGlpKeFwmGeffdYahyqW0+mMW48duyj6pLdnnnmGjz/+mKVLl2K32/n973/PAw88QGpqao3Hqar59/Ho78G6/D4tKSkhPT3dCtyiot8TDjb8AOL+wBW1v8Gxi4uL+fvf/x4X8EW1aNECwOqO9+qrr1JRUUGbNm3o2bNntc+xptc60HeD/dm1axfjx49n06ZNPPHEE/z+97+3tiUmJqIoStwfCQHOPPNMvv76awoKCqp9tyovL2fy5Mlcd911dO7cmVAoZI3dFQ6HCYVC2Gw2kpOTq50XzO5i0SchHswA6JmZmXFDJzgcDlq3bh13LcW+r1jRayD2WqtJ9Dtn1e+YNpuN9PR0ysrK6vz9tzZV6xC95qOfYazo2G3RayiqZcuWBzx31X9LUdFry+Px7LeesZKSkqzufrE8Hg/Jyck1HhMKhZg/fz7nn39+3JhjeXl5/P73v2fx4sXcddddccecfvrpgDkg/3nnncezzz4rA50foySUEkLUSfQLbU1/6cjPz7dCpOTk5LgBUaOWL19O+/btrb+w3HLLLYwaNYrzzjuPqVOn8tZbb6FpmnWeqn30q45HlZqaWmtdoDLUOuuss1i+fDktW7bkhBNOIDMzk7y8PP7f//t/fP3117hcLvLy8g7ik6guNTXVet391eVQ5Obmcvzxx/PRRx+hqiqdOnWql6faCCGEaL5atmxJ7969+ec//8ktt9xi3ZDG8ng8/Pvf/2bw4MFWWf/+/cnMzOQf//gHmZmZOJ1OzjnnHKAysBgzZkyNTzU70BPMsrKymDZtGvfffz9r167lo48+4tlnnyU9Pd0a6+lA6vL7NDU1laKiInRdjwum9u7da+1zpCUnJ3P66afX+NCRaEuiRYsW8cILL/DAAw9wzjnnWDfzVQcZr0+//vorY8eOxe/389xzz3HqqafGbe/QoQOGYRAMBuPCsej4RjW1wv7pp5/YsWMH8+bNY968eXHb7rnnHu655x5+/fVXfvnlF1atWsUVV1wRdz1Gx+GqGrQciMPhsMYeOljRa6Bqq6GqogFXfn5+XOu8aIuy9PT0On//rQ/R1l779u2jU6dOVnlN36vrKvoZHEw9TzjhBNasWVOtfOvWrfTs2bPGYwoLC/F6vXGtDgHr+/e6desAs5Wh3++PCzBtNhs5OTn89ttvda6jOLpI9z0hRJ1EA50PPvggrnzbtm2sWrXK+iV0yimnsHr16rhfoPv27WPcuHFxT+zIyMjA5XJx33338fPPP/P8888DZvPvNm3a8NFHH8W9zmeffRa3fuqpp/LZZ5/F/eVH13U+/PBDevTogcPhAMy/0P3vf//jyy+/JDc3FzD/IrN9+3beeOMNBgwYYO17qE499VR++OGHan+xfu+998jMzKRDhw6HfG5FURg+fDjLli3j008/5eKLLz6sugohhDg6TJgwgU2bNvH4449X2xZ9DLvP54sb/FrTNM4//3w+++wzPvroI37/+99brS2SkpLo1q0bGzdupEePHtZ00kkn8fTTT+/3aV0//PADp59+Oj/++COKotC1a1f+8pe/kJ2dXePTc2tTl9+nubm5hEKhat8Tol3d+/XrB1BjUFdfok8c7Nq1q/U5nXzyybzwwgt8/PHHgDmcQefOnbnkkkusQGrPnj389ttvNbaSOVy7du3immuuQVEUXn/99WqBFGAFAR9++GFcefTpbjW1GOvevTtvv/123BRtwTRhwgTefvttwHwa4QMPPFDtKYV///vfadeuXZ1a0dcXh8NBZmZmtafjVb0mot8Lq34eH374Ibqu069fvzp//60PXbp0ITk52bqGov71r38d8jl3794NQNu2bet8zBlnnMGGDRtYv369VbZ+/Xo2bNjAgAEDajymZcuWpKWlWU/BjiosLGTz5s1WC8j/+7//484774z7/u7xePjhhx9qHEBfHBukpZQQwrJ7925eeOGFauXZ2dmcfvrp3HbbbUyePJlJkyZxwQUXUFRUxNy5c0lNTbX+WjhmzBiWLl3KuHHjGD9+PHa73XoqyPnnn1+tWf1ZZ53F0KFDefrppxkyZAjHHXcct99+O5MmTWLq1KkMHTqUVatW8frrr8cdN2HCBL788ktGjRrF9ddfj91u55VXXmHbtm387W9/s/Y77bTTUFWVzz//3Pri3r17dxITE1m5ciUzZ8487M/tmmuu4b333mPMmDFMmDCBtLQ0li5dyrfffstDDz102F+Mhw8fbj2qOfqEJCGEEMe2M888k7vvvptHH32UNWvWcMkll9CqVSu2b9/O66+/zpo1a5g5c6b11KuoCy+8kOeeew5VVat107vtttu4/vrrrd/z0afsrV692npaWU26deuGy+Xizjvv5OabbyYjI4NvvvmGNWvWMGrUqDq/p7r8Pv3d735HXl4eU6dOZc+ePXTp0oXvvvuOZ599losvvth6jH1KSgoFBQV88cUXdO3alVatWh3Ep7t/N954I1dccQXjx4/nyiuvxOl08uabb7Js2TLmzJkDQM+ePZk/fz6LFi2id+/ebNmyhYULFxIIBA56vKjCwkK2bt1K586dawyOwHya8b59+3jggQfweDysWrXK2paUlETnzp3Jy8tj0KBBzJo1C6/Xy0knncTSpUv573//y/z58639t27dSmFhIb179yYpKalai6Xo+ETt2rWztg0ZMoS//e1v3HXXXdx66620atWKDz74gE8//ZQ5c+ZY34Viz30kDRgwgP/+979xZSkpKQB8/PHH/O53v6Nz585cfPHFzJkzB6/Xy6mnnsqaNWuYO3cueXl5nHnmmaiqWqfvv/UhKSmJcePGMWfOHNxuN7m5uXz33XfWd+BD+T65cuVK3G63Nf5UXa6lc889lwULFnDdddcxadIkAB577DGys7Pjxnj95ZdfcDgcdO7cGU3TuPnmm5k+fTqJiYn88Y9/pKioiIULF6JpGtdeey0A48aN46OPPuLPf/4zY8eOJRAI8Oyzz1JeXs7NN9980O9PHB0klBJCWLZu3cqsWbOqlV966aWcfvrpDB8+nMTERBYuXMhNN91EUlISZ555JrfddpvVH79Nmza89tprzJ49m7vvvhuHw0FeXh5PPPEEqampNY71MGXKFL7++mvuvfdeXnjhBYYNG4aqqsyfP5//+7//Izs7mwcffJDbbrvNOuakk07itddesx7NqygKPXv25KWXXoob+NHtdpOXlxfXUspms3HKKafUOMj5ocjMzOT111/nscceY8aMGQSDQbp06cL8+fM5++yzD/v8WVlZdOnShYyMDOvRxkIIIcQ111xDnz59ePHFF3nkkUcoLCwkMzOTAQMGMHPmTCugidWlSxeys7MpKiqif//+cdvOOOMMFi9ezNy5c5k4cSJ2u53u3bvz/PPP7zdEcDqdPPfcczz22GPMnDmT0tJSOnbsyIMPPsjw4cPr/H7q8vtUURQWLlzInDlzeOGFFygsLKR9+/bcdtttcQHB8OHD+eKLL7jpppuYOHEi119/fZ3rcSBdunTh1Vdf5YknnuDOO+/EMAyys7OZN2+eVc/x48dTVFTESy+9xLx582jTpg0XXnihVf/S0lIrJDmQzz//nMmTJ/PSSy/VOORAIBDg888/B6ixq2Rubi4vv/wyAE899RRz587l+eefp7CwkM6dOzN37ty4bp7z58/n3Xff5ddff63zZ+J2u3n++ed54oknmDNnDkVFRZx00knMnTs3blyrQzn3oRgyZAjvv/8+e/bssb475eXlcfrpp/PYY4+xfPlyFi1axMyZM+nQoQPvvPMOzz77LK1atWLUqFHceOONVghUl++/9WX8+PEYhsGbb77J4sWL6dWrF7fffjuzZs064BhZNYl+1412zTzQtQRmS7Pnn3+emTNncu+992K32xkwYACTJ0+OG+h+woQJtGvXzrq2rr76apKTk3n++edZsmQJ6enpnHLKKcydO9dqKXXiiSfy6quv8thjj3HnnXcSCoXIzc2t9eeVODYoxqGOoieEEKJB7Nmzh0GDBjFnzpy4L3ZCCCGEEM3NOeecc1hd0urCMAwuuOAChgwZwoQJE47oa9WXUCjEBx98QF5eHm3atLHKX331VWbMmMGKFSvqHGSC+SCEP/zhD7z99tvWA4WEaIqkpZQQQjRRa9as4ZNPPuGf//wnHTt2jPsrphBCCCFEc/N///d/cYN4HymKonDHHXcwZcoUxowZU2tXtabEZrPx7LPP8uKLL/LnP/+Z9PR0fvvtN5588kkuuuiigwqkAJ577jmGDh0qgZRo8qSllBBCNFGrVq1i7NixZGVl8fjjj1cbF0QIIYQQojnZuHEjrVu3PqSuaIfi/vvvJyUlxRobqanbtm0bjz/+OCtWrKC0tJS2bdtywQUXWOO01tWGDRsYN24c7777rvUEQSGaKgmlhBBCCCGEEEIIIUSDO3LPShVCCCGEEEIIIYQQohaHHEoFAgGGDRvGihUrrLJt27YxZswYevfuzbnnnsvXX38dd8w333zDsGHD6NWrF6NGjWLbtm2HXnMhhBBCCCGEEEII0WwdUijl9/u57bbbWLdunVVmGAY33XQTGRkZvPPOO1x44YVMmDCBnTt3ArBz505uuukmhg8fzttvv02LFi248cYbqWvvQcMw8Hg8dd5fCCGEEELEk+9TQgghhGhKDjqUWr9+PX/605/YunVrXPm3337Ltm3bePDBBznxxBMZP348vXv35p133gHgrbfe4uSTT+baa6/lpJNOYtasWezYsYPvvvuuTq9bXl5Ov379KC8vP9gqCyGEEEII5PuUEEIIIZqWgw6lvvvuO/Ly8njzzTfjylevXk23bt3inqTQr18/Vq1aZW0/5ZRTrG1ut5vu3btb24UQQgghhBBCCCHEscN2sAeMGDGixvL8/HxatWoVV9ayZUt2795dp+2NLlAC6xdBOAC2JLAnR6Y0cKSBIz2ynA6q1siVFUIIIYQQQgghhGjeDjqUqo3X68XhcMSVORwOAoFAnbY3uq3/D1bdWYcdFTOYcmaYkysL3G0hoT2420Hi8ZB8klku4ZUQQgghhBBCCCFEjeotlHI6nRQXF8eVBQIBXC6Xtb1qABUIBEhJSamvKhye4y6BsnVQ+hvo5RCqgFA5hDwQLDPnegVgQKDQnMp+q/18WkIkrDrODKmSsyGlCyR2BJsTFK3KZDNDrNj1aJkQQghxjKs6MLeiKI1UEyGEEEIc6/SwgaY27+8iTeU91FsolZWVxfr16+PKCgoKrC57WVlZFBQUVNvetWvX+qrC4XG2gD6PVi8Ph8wufeGAGVJ594B3O5RvAd8e8O015/4CCBSDP99c1ivAs96c9n5WeT7NZQZUyTmQmmPO7clgGKCoVYIq1QymNCeokUlzgmoDVFCUyDwyocQs16Ws8S9AIYQ4mhmGgYFR4xyocVvV46rudzDb97dvdLmm/aPbwuEwAGEi86rrRrjyPEbMa0XrE7NedT+rLpFt0XPHHm99jlXq5rQ56dumLy6b69D+wwghhBBCHAZNVbjljR9Yv9fT2FU5JJ1bJfHUFX0auxpAPYZSvXr1YtGiRfh8Pqt11MqVK+nXr5+1feXKldb+Xq+XX375hQkTJtRXFY4M1RYJgRLMsaUS2gF9zW3hEOjeSGuqUvDlR5ZLIFBkjlPl2wVlkXDKswl0HxT/aE7bABRI6QqZ/aFlnrmsKGDokSkEQZ+5HA5B5AagVlZQpWCFT0SDqJht0aAq2ioL1XyfVqstW0wwdoCQq6b1amGZBGBCiPpjGAZhI2wGJ0a4xvX9LcfuX9v26FwP64QJEw6HzXnM60W3RddjJ6geOkXrHvu/SN5S475V94/uF3sMBuaPWRTzOAUwzJZE0e21LUP1Y6PLVVsiKZjr0fKq67Xtt799D/Y8AHpYxxvyEtSDEkoJIYQQotGs3+vh552ljV2NZq/eQqnc3FzatGnD5MmTufHGG/nss8/48ccfmTVrFgCXXHIJixcvZtGiRQwaNIh58+bRvn178vLy6qsKDU+1gRoZEN3dxmwBFSo3AynvLrOLX+gkaPtHcxwqw4DyTVD0IxT/zwymKrZC6S/mtGGxOch6q99BmyFmSKUe5H8iwwDCVeZGJMyKrBvhysDLiN03HNk39tgDiGutFVmODcPiArCY0MvqrhgNwPYXaqmRbowHCsAk+BKiMdQUyNQ2RcOfuLKY4w0MQuEQoXAI3dAJh83gR0c352E9bt9oa5ww8es1batLkEP0x0h0ORLiKChxc+Cgy1RFrRbU1LQvUK08dv+qy8eagB6gyFfU2NUQQgghhBD1oN5CKU3TmD9/Pvfccw/Dhw+nQ4cOzJs3j7Zt2wLQvn17nn76aR566CHmzZtHnz59mDdv3tH1pVpRwJ5kTgntzVZTgULwbIaKnWbXu8QTzDGmjr/EPMZXAPu+hfxvYN8Ks5XVzr+bkz0VWp8N7c6H1JPrFrooCqBV3lgdaUZsmBU7rxKAhYOAr4YALDqHyj/bV6VUhltxgVdNwZfdDL5Uew2tvqq2/IpZr6ksbv0ouk7FMc1q3WOEzdCnhvXYstjyoB4kGA6ih3UzNArr5npkuxUuxbYcCkeCqEhZnJjVaOsdBSU+xIlZBqqVqYqKoir73a+m8EcIIYQQQgjR+A4rlPr111/j1jt06MArr7xS6/5nnXUWZ5111uG8ZPOhKGYLKnuyOeC5bw94tpgtqDSn+eQ+RQVXBrQbZk6Gbrag2vUv2L3MDLS2LTGnlBw47jKzBZXN3djvrpIV4BxB1Vp/VW0JFht8eeO3GTqVrb9qfRMx4VQk1Isddyu2m6MVeNkqAy9rjK+YEKta6FVb+RH+7MRRIxoU6YZep3kwHCSoBwmFQwTCAUJ6ZesjwzBqnUe7iFktiCKioU60tY+maPHBEAo21Ra3T+y2qq2EhDjaffzxx9WGKBgyZAhz5szhl19+4f777+e3336jc+fOPPDAA5x88snWfh988AFPPvkk+fn5nHHGGUyfPp0WLVoAZsvExx57jLfffptwOMyll17K7bffjqrK7xMhhBBCND/11lJK7IdqN1tOudqY4VTZOqjYZgZTtsTK/RQN0nubU5fboOi/sOPvsPtjKP0Vfp4Bvz4JmQMig6VnQ8pJ5nmOZke69Vdc2FW1tVe4srWXUTXwimkRZvX9qVr3KmNxxbbwUqNze5Wwyw6avTLMiguytP2EXJoEXU1cNFiKhkNWN7VImGS1PtKDBMIBAnqAoB7Er/utrms6Zpe22BZMVbuaRWmKZgVDqqKiqZoVHtmxW+vRuQRHQtSf9evXM2jQIKZPn26VOZ1OKioquP766zn//PN5+OGHef311xk/fjwff/wxCQkJ/Pjjj9xzzz088MADdOnShZkzZzJ58mQWLlwIwPPPP88HH3zA3LlzCYVC3HHHHbRs2ZKxY8c21lsVQgghhDhkEko1JFWDhLbmk/48m8GzwRwM3dWq+thRqg1a5ppTl1thx/uw9W3w7oBd/zSnKEeLSECVAymRJ/slHhcJNMQBWaHXEfi8rKArtoWXHhNqBSHkr9LlsZbWXYoSeUpj1acu7i/oclTOq3VjPEDgFd2uynVUk7ARtkKl2qaAHsAf8uPX/QRCAavrW9XxkSB+8OpoKyNN0dBUzVq2a3acitNajwZNQoimZ8OGDWRnZ5OZmRlX/vbbb+N0OrnzzjtRFIV77rmHL7/8ko8++ojhw4fzyiuv8Mc//pGLLroIgEcffZRBgwaxbds2jjvuOF566SUmTpzIKaecAsDtt9/OU089JaGUEEIIIZolCaUag+aC1C5mC6eydVCxA9xZZnlNHGlwwkjoeBUUrjS7+JWtg7LfoHyb2c1v37fmFPsaVlCVYz7VL6mTGVaIhnMkWi5VbdlVtVVXtaArdtsB6mk9rTH2yYpafEuu6FxzxG+vS8DVxMfoCoVD1rhJ0eW4gEn34w168Yf8lWMrGSFrHtfdLdK0z6ba4sIlu2bHpbisQCnaDU4IcXTZsGEDp59+erXy1atX069fv8pB6xWFvn37smrVKoYPH87q1au57rrrrP3btGlD27ZtWb16NQ6Hg127dnHqqada2/v168eOHTvYu3cvrVq1OvJvTAghhBCiHkko1ZhcGWBPMbvmedabT+izJ9e+v6JCy1PNKUr3Qdl6M6Aq/c08V9lvZnnxj+ZkHW83B1lPyYHUbpDaFZJOlKCquTkSLbtqCres9RCE/RCKbeVVU2uumC6MihKpX5V5tDw23FIjXRZVe+SJjLWFXIfWksswDGt8parzgB7AG/LiDXnxBX1mAGWEzPGXoiFTTOulaLikKZrZBU6z41bdcaGTEEIYhsGmTZv4+uuvWbhwIbquM3ToUCZOnEh+fj6dO3eO279ly5asW7cOoMZwqWXLluzevZv8/HyAuO0ZGWYX/t27d0soJYQQQohmR0KpxqY5IK07aG4oW2ve/B/MGFGaC9JONqcoQ4fyrVC61gypSteaU8gDpb+Y0/Z3zX0VuzkuVUokpErtZj4hsGp3QnF0q+8WXfsNuSKD0ofKY7bHhlzRcKuGkKtKS64wEAiHCRoQAAIGBA2DgAEVegiv7scbChIyDEKGTsgw0I0wRky3R5tqx6Y6sGkObKoDpy0RzWGGThIyCSEOxc6dO/F6vTgcDp588km2b9/OjBkz8Pl8Vnksh8NBIBAAwOfz1brd5/NZ67HbAOt4IYQQQojmRJKHpkBRIaUz2BKg+CfzCX3uNodxPg2STjCntn80ywzDHI+qdC2UrIHSNeY8VAYlv5jTtsjxqrOyNVVKV0jtDonHywDaou7qKeQK6iEC4SCBkJ+A7iegm4N/lwfKqQiaLZyC4RDBcICQrmOgo0TG69IUBbtqw6Zo2FUVt6JhU80pbgB6Yp+yGPukxeiTFbXKgedrfMqiWsO2aGs26ZYnxLGoXbt2rFixgtTUVBRFoWvXroTDYe644w5yc3OrBUiBQACXy+zC73Q6a9zudrvjAiin02ktA7jdTejJvEIIIYQQdSShVFOS0BY0JxStOvxgqipFMZ8AmNAeWv/eLIsGVSVrzNZT0bAqVF69658tsTKgSu1mzl1ZctMtDose1vHrQfx6gEA4iF8P4gv5KQ95KQ968etBgnqIYGTcpuhITWa4ZMNmS8Cl2khWbdjq2n0u7gmKerQmlYPPh0Ng+Mx9iOxTddD5WPsNuGyVIVe1gCu2O+P+Ai618rxCNANhI0xAD+AL+ayB/q15dLnKenT/aPDsjwmhow8KiC77Q346t+jMmcef2dhvdb/S0tLi1k888UT8fj+ZmZkUFBTEbSsoKLC63mVlZdW4PTMzk6ysLADy8/Np3769tQxUG1BdCCGEEKI5kFCqqXG2hPTeUPgDeHeDu/WRe63YoKrNH8wyIxzp+vdLZQuq0l/NoKrwe3OKcrQ0A6q07pASCascqUeuvqJZCuoh/HoAvx7AF5l7Al7KQ168IT/BsNkaygqdFAWbquFQbdhVGy5HghlA1ddT5qxWTHDYPwIPGHD5Kvchdiyu2up2MAGXDTTbQQZc0oLrWGYYBqFwyBxHLejFF/KZ46mFfHFTTWVVJ3/Ij0+PX48Nn460baXbCOrBI/46h+qrr77i9ttv5/PPP7daMK1Zs4a0tDT69evHs88+i2EYKIqCYRj897//5YYbbgCgV69erFy5kuHDhwOwa9cudu3aRa9evcjKyqJt27asXLnSCqVWrlxJ27ZtZTwpIYQQQjRLEko1Rc6WkN7LbDHl2wuuBvyiqaiQ1NGc2p5rloVD4NkYCal+NifPBgjsg/yvzCkqoX2kNVUkpErpUvtTBcVRQw/rVuDkCwXw6X48AS+lQQ9+PUhADxIIByMNjgxzkHDVhkOzk2h3kaYm11/o1JAaJeCKbDusFlyxXRQ1s+41dVGklqDLOrcEXEdKUA9SEazAG/JSHijHG/JSEaywyiqCFXiDXmu96nLcPCaE0q1rq2HYVBtOzYnT5rTmDs2BS3NZZQ6bA6fmxGVz4dAc5vaYZYdmbo/OVUUlIzEDu9Z0H9LRp08fnE4nU6dO5aabbmLbtm08+uijjBs3jqFDh/LYY48xc+ZMrrjiCt544w28Xi9//KPZ3f7KK69k5MiR9O7dmx49ejBz5kwGDhzIcccdZ23/61//SuvW5h+tHnvsMa699tpGe69CCCGEEIdDQqmmypUJaT0jwVSB+aS+xqLaICXbnI67yCzTfWYLqpJfoOQnc16xDSq2m9Ouf5r7KZr5hL9oUJXWXQZSb8YCehCf7o8ETwEqgl5KAuWUB70E9CD+cCAueHJodhyqjRRHEg5NBg7frwYNuGroorjfFlwxT1GsFnCp5gMTqo7FpUZDqxoCrbiWXdFz1/Ng+w3MMAz8uh9PwENFsILyQDnlwfLK9WC5VRYNlzwBjxUmxZZXBCsIho9sKyBN0XDb3bhsLty2+LnL7jLnkeDIKo9MTpszfj0SKFnzmADKdgR+1gf0AEW+ono/b31KSkpi8eLFPPTQQ1xyySUkJiZyxRVXMG7cOBRFYeHChdx///38v//3/8jJyWHRokUkJCQAZqD14IMPMmfOHEpKShgwYADTp0+3zj127Fj27dvHhAkT0DSNSy+9lDFjxjTSOxVCCCGEODyKYezvT+1Nh8fjoV+/fqxcuZKkpKTGrk7D8e6Coh9AdTX9rnGBksgA6j9Vtqry76u+n+YyW1DFjk/lbictLpqQgB7EG/Lj0/14Q348wQqK/WVma6hQkJARImwYqIqKU7PjUO04NTt21Y6mNt9gQdQgLuCKhFixAVe0LG4e3k/ARXzAZQVW0aAr0k1RtcWHXLFdD+NacVUprylAO+BbNKgIVlAWKMMT8BxwioZN5YHyuPUj0QrJqTlx290k2BJw293msj2hct1mrkeX48oi69HAKXa5KbcyOpBoKHXm8WeS7Exu7Oo0O8fs9ykhhBCinp035yt+3lna2NU4JN3bpvDhxKYxPqc0V2nq3G0g5IXi/5mDoDflrnCOVMg4zZzAvGn174Xin2OCqjWgl5stwIpWVR5rT40EVJEppXvjtg47RuhhHW/IjzcaPgXM8KlC9+EPBa3WGpqq4dQcOFU7iS43dtWGIiHisSGuBVc9MMIxQVc0wIoGXDqEg5Xl0ZBrv90UzTArGNYpDfgoC/ooDXopDfnwBL2URtY9QR9lQS+lwQo8QS9lwXLKAhV4AuWUBcsJG+F6eXsKCgn2BJIcSXHzREciifZEczk6d8SvR5fddrc1PxItjYQQQgghhGgq5Ntuc5DUEXSv2V0uoV3z6fqmKOYT+lpnQevBZpkRhvItlWNTlfwMpesgWAIFy80pytkqElJFnvqX0rXptxZrwvx6gIqgD2/IT3nQS3GgjLJAhfnkOz2IAaiKglNz4NIcJLkSsGvN5FoTzYeiRgKumscQ8+sBSvweSgMVFAc8lAY8lPg9lATKKQ14KA2UUxKZl0WX/eV462FwbU1RSba7SbK7SLK5I8vRKYEku5tEewJJjgSS7EmR5WQSHYkkOZNJcCSTYHejKvZImBfbbbGmll5VBqMXQgghhBDiGCN3nM2BoprjOekVULHDHEy8ubZSUVRIOsGc2g0zy8IBKFtX+bS/kp/Bs8lsZbV3L+z9vPJ4d7vKoColMpC6XbofxDLHtglQHvRSEWktUuQrpTzkxa8HCYXNLkYOzY5Lc5DiSMKp2aXlk6h3vlCAYn8pxX4PxYEyiv3mVBLwRJY9kWVzXhLpIno4kuwJpDgSSLYnkuJIJMmRQIo9kWRHAsmORGvZ3C+RJHtCZN2NS3NGGoRFuh9Gx9qq2j2xapdFDDDKwFcGvloqpkBl+BTbtVCNjL8V7X4Y030RLWZ8rprG5qoadtVSLoQQQgghRBMloVRzodrNMCZUYY4z5W5z9NxsqI7KgdCjQhWRgdR/joxTFRlI3bvDnHZ/XLlvwnFmK6rUrub8GAuqfCE/FSEf5UEvZYEKCv0lVIT8+EJ+wkYYRVFxRVo/pTgSpTuQOCSGYeAN+Sn0l1LkK6XIX0qhv5RifxlFvjKKIsvF/jKK/Ob6oQZMmqKS4kgkxZFEqiOJVGciqY4kUhxJpDjM5WRHIqnRfZyJJNvNgKl+xjTT9tua65DEjslljT0VjumeGO26GPuExRrG51Kosq7EB11WC6yY8rhxujTzfamRiZgQK64FV2yoFTvu18GN1yWEEEIIIcT+yN1pc2JLhLSTofhHKN9qdmWzpx6dNwa2BGjRx5yigqVQurayRVXpWvDujDz1bxvs/lflvgnHmeGUNeWAI63B30Z9C+ohykPeSABVTqGvFE/Iiy/kRzfCaIqKy+bEpTlIcyTLoONiv8JGmNJAOft8JRT6StjnK6XQV0Khv4RCX6k5RUKoQn8p/kMImWyqRpojmTRnMmnOJNKcyaQ6kiLryZHQKSlSZs4T7e6j70mNVqAD9fqr19AjIVVMC66qA9PHjdPFgQejh5pbdqFE3kdsqyyt5sCr6iD0sU9tjG0lpsS8hlLl9Y7G321CCCGEECKOhFLNjbMlZPQ3u/F5NpnhlDMd7CmNXbMjz54CLXPNKSpQXBlUla4xW1fFBVUxLapcrc1wypq6mONWNdEbH8MwrBZQnmAF+3wllAbK8Yb8BMMh1GgLKJtTAigRpzzoZZ+vhAJvMft8JezzmfOC6LK3hEK/GTod7BPjnJqDdGcyLZwppLnMebozhTRnMunOFNJdyaQ5ks25M4VEm0u6hh5JyhFo1QVVWnZVCbuIGZje8FcOXA91C7ygSguvOoRe1qRC2IBgOYRD9fuehRBCCCFEg5NQqjnSXJB8otmFr2IbeLZAYCs4W4Dt2Om2Bpitn2Kf+AeVQVXpWjOkKl1rfk6+3ea094vKfe1p5nhdyTmReTYkdmiUweRD4RCeSABV6i+nwFdMRdCHPxzAMAwcmgO3zUmGK00GID8GGYZBedBLvq+IfG8xBd5i8r1FFHiLKfBFpsiyN3Rwg36nOpJo4UqhhSuVFs4UWrpSreV0VwotXWbwlO5MwW1zSsh0LIhr2VXPrGAr9imMUHPoFYgJvSLbgn4I+cwHgJB+ZOoohBBCCCEahNzZNme2BLPFj7stlG+Diq3gKwRXhrntWFVTUBX0QNmvkZAqElSVb4ZgMez7zpyiVAcknQjJJ5khVcpJ5nI9t0bz6wE8AfPx9NHuUrGtoBJsTpIcbjK0NAkBjnJBPUS+r4i9FUXke4vY6y2MzIso8JohVL636KDGaEqwuchwpdHClUKGO42WrlRaRtdd0fVU0p0pEnKKhqVEWkcdagsvtQJCu+q5UkIIIYQQojHIncjRwJ4Mad0goR2UbwHvdvBHwinN1di1axrsSdCinzlF6X7wbIDS3yoDq7L15lMOS9eYUyxXFiR3hqSTzHlyZ0jsWOdWVX49QFmggrKA2QqqJOChIuhDN8I4VBvuSIggAcHRxa8H2FtRxB5vIXsrCtnrLWRPhTlFw6d9vpI6ny/ZnkCmO50MdxoZrjQy3GnmemQ5Gjgl2OXfvhBCCCGEEKJpk7vfo4kjFRw9IfE4M5yq2G52gXBmgOZs7No1PZrTfKJharfKMiNsjtdV9ltkWmcGVd6d4NtjTvn/rtxfsZnd/ZI7R1pXdYakTuBuiz8cskKofF8RJX4PFSE/4XAYp81Bos1FVkJLbOoR6iIjjjg9HKbAV8zuigJ2V+xjd/k+M3DyRpa9hRT7y+p0LrtqI9OdRqa7Ba3c6WS60815QjqZrnRaJZjBk8sm/5aFEEIIIYQQRwcJpY5GjnRzrCR3+0jLqR2AYracUh2NXbumTVHNUC/xOGh9dmV50GMGVJ71ZkhVtg7KNoBebra28myIO42uOvA5WlPhaEW5IwvV1Y6UhA6kutuiSUuoZsMX8rOrooCd5QXsKi9gT8U+M3yq2Meu8gLyvUXo1ng4tXNqDrISWpDlbkFWQgtaJbSgVSR8ykpoSSt3OmnOZOmmKYQQQgghhDimyN3x0UqJhFDOluCPtJzy7jJDF2cGqPbGrmHzYk+CFn3MKULXQ5R7NuMv/oVg6a+ono24vdtJCu5FCwdI9W0l1bc17jS66sDnbIvP3Q6fqy0+V1u8rrb4na3MVleiQZUHvewqL2BneT47ywvYHQmgdpcXsLOioE6tnDRFIyshndYJLclKaElWQovKZXcLshJakuJIlMBJCCGEEEIIIaqQu+CjnaKAq5UZRPnyzcG9vbvNcZCcLSWcOgiGYVAR8lEaKKfIV0q+rwhPwEsgnIw96TQS0weRYHNhU8Dp34vbux23bzsu3w7c3h04/bvQwgESvZtJ9G6OPzcaPmdWJKhqjd/Vxlx2tkG3JTbOGz4K+PUAu8r3RUKnvewoz2enxwygdpbnUxLwHPAciXY3bRIyaJPYktYJGbRJzKB1QksreMpwpaGpagO8GyGEEEIIIYQ4ukgodaxQVHBngSsTfHsj4dQeCacOIKiHKA14KA2Us6eikJKAB2/Ih6KoJNpctHCl4NCqf3Z+Vxv8rjYUc2ploaHj9O/B7duJKzp5d+Dy70QLB3D7d+L274QqY14Hbcn4nW3wuVrjc7bB78wygytnFsYx3h3TMAwK/aVs9+xhhyefHeV72eExw6cdnr3ke4sxMPZ7jlRHEm0SM2iTkEHbxEzaJLakTWImbRJa0iYxg2SHhIJCCCGEEEIIcSRIKHWsUVRwtwZnJvjzJZyqItoaqsTvYZ+vmHxvMZ5gBXo4jNvuJNHmpqUr9dC6Yikafldb/K62VV40jD1YVBlU+Xbh8u/C5duFI1iEPVSGPVRGUvlv1U4ZsLfA58zCHwmp/M4sfM4sAo5MwkfJkxdD4RC7yvex3bOH7Z695lRuLu/w7MWnB/Z7vNvmpG1iJm0TM2kXmbdNMpfbJGaSZHc30DsRQgghhBBCCBFLQqljlapVD6d8eyNjTrU8pgZED4VDlAbKKfF72FNhPi3NpwdQFYVEu/vIPyFPUQk6WhJ0tKQspUfcJlX34fTvxuXbHZnvwuU3l216BY5gIY5gIXjWVDttwJ6G39EKv7MVfmcWAWemtR6ypZhdO5uIgB5kZ3k+2zx72Fa2h22ePWz3mPNd5QX7HUxcVRSy3C1pm5RJ+8RWtEtqRbtI6NQusZUMIC6EEEIIIYQQTZSEUse6aDjlamWOOVWx1RwQHQWcLeAoaW1TlS/kp9hfRqGvlD0VhXhCFYR0HbfdSZLDTYaW1iSCjLDmwpvQEW9Cx/gNhoGmeyJh1R6c/j24IvPKwKoYR7CY5BpaWOmqg4AjE78jMxJWxcwdGUdkHKugHmJHeT7bPLvZWrabbWV72Oox57sr9u23m51Ts9MuEji1T2rFcUlZ1nLbhEzs8kRDIYQQQgghhGh25E5OmGLHnPIXQPk28O2GcMgMp2wJ+z/eMEDXQQ9DSDeXw2FzPVpuGOZkHhB9YbPFTjQAUhRQInNVBVUBTYssq2DTzEk7uJZLhmHgCVZQ7C8j31vEPl8J5UEviqKSZHfTyp2OTW1G/xwUBd2WTHlSMuVJJ1XbrIU8OP17rcDKGcjH6c/HEdiLI1BojmHl24Hbt6PG0+uqG78zg4DDnPyODAKOlpEpI9LSqvrg3mEjzJ6KQraU7WJrmRk+bY2EUAdq8ZRgc9E+KYvjklpxXHJWZNmcZ7rTUGt4PSGEEEIIIYQQzVczugsXDUJRI0/ry4RAEVRsg/LtULIDSISwC4IhCAYhEABfEHx+czkaPulhMCLhVDSEMjDDJgziGsQoSqQsplVSdNEwzFBK1cy5ooIWnTRwOMAVmRwOsNnAYTcnmw3dplJqmC2idlfso9hfhjfkx6nZSbInkJaUfNQGHbotiQpbEhWJnaptU8JBHIF9OAL5ZnAVyMfhLzDngQLsoVK0sJcE7zYSvNtqPH9BWOPncAprdBe/hjTW+XXW+7xs9nrwh0O11sulOTg+uTXHJWVF5q05PhJAHfJYXUIIIYQQQgghmiUJpUQlwwCfDyoqwOs1p5IgFBrg8YBnHfg9YHOBlmSGRdFWS9GgyGkzWzRZrZuUwxu7yDAqW1yFw5XLwRD4AlCkR8KvMCgQNMIUG34KFT+7qaBMCRJ0argTkkhxp9DKmQCKHXQNNAOO4FBRTZWh2s2B0V2tKathu6r7cAT2ofj2sqd0E1tLtrHJs5eN5cVs8FbwWyBEga4DRTWe3w50skO2Q+FEt5tO7hSOT8rg+KQsWiS2IeRoQdCRTsCeTtCeCor8GBJCCCGEEEKIY5HcDR7LfD4zbCovh9JSKCoygyifzwyDFAXsdnC6ITMHsk4CvRh8eyBYZoZS9uQjOyi6Eum+t5/uev5wkKJQOfuCHvYESvCEKggHdRLRyDCc2CsUKC0Ho7yy9ZXNBvZIyyqXE9wucz1aZtPM964enS2pYpUFytlStovNpbvYXLbTnJfuYptnD7qh13pcliuZTgkpnOh0ke1U6WIP01X1cqJSilv3YDaJqzCn0G4o/gmK489hoBCyJRO0pxO0pxG0p1lhVXQ9ZEsjaE/FOMafDCmEEEIIIYQQRxsJpY4Vum6GT9EAat8+M5Dyes2gRtPA5YKEBEhP308IlGQOjB4oMsOpQBGEdbAngZbQYE90q9D9FIcqyA+Wkh8sozzsBxSSVCdZznRsrv00gQqHIRQyW1h5/VBWbra+ivYrtNnMll82m9kt0O00uwhGuwfabZGxrWwHPbZVYzEMgz3eQjaX7oxMu9hUZi7v85XUepzZ3a4NHVPa0CHZnDomt+H45NYk2KsPgu8HfgGUcAB7sBhHoBB7sDAyL8IRLDLngSLswWIUdOyhUuyhUvBu2e97CGmJBG2phOypZmhlSyVkTyFoM9dDthSC9hRCthSMY+jpkUIIIYQQQgjRXEkodbTSdTN0KiszW0Dt22d2ywsEzODI5QK3G9LSDj5YUW3mgOjODAiWmgOj+/PNgdE1p9l6Sqn/Vi3luo+iUAV7AyXkh8qo0APYUEnWXLS1H8RA2Kpqhk215RZ6pEtgKATlFVBaZpZFhr+qDK0i41o5HWZLK1skrLLbKlti2bQGC+oAQuEQ2z172VS605o2l+5kc9lOvCF/rcdlutPpGBM+dUxpS8eUtrRypx/SuFuG6iDgbEXA2Wo/O4WxhcqwB4uxB4si8ypTqAR7sBjVCGHTy7Hp5eDfecDX11W3FVAFbSmE7CmEbMmR9eTIsrkesiVJiCWEEEIIIYQQjUBCqaOFYcR3wysoMEOpQMAMnRITzQDK6ay/11QUcKSak7sdBIvN1lP+YnOMJ1ui+dS+QxxM3DAMPLqPolA5uwMlFOnlVIQCOFSNJM1FS0fSkRkYO9pd0FlTUGFAKPJEwVAIKrxQ6jFbXxmR7YoSH1w5nZXdBO2R1lXRllbREOsguwn6QgG2lO2KBE87rPBpq2c3oXDNXe40ReO4pCw6prThhJS2dEhua85T2pBkdx/0x3TYFJWQ3Wz55KVD7fsZBppejj1Ygi1Ugj1YEgmsSrEFS7CHSrAFS815qBTV0NHCXjS/F/x76lQVXXURsiXFhFXJ5rqWRMiWhB6ZRyddSyKsOhs0cBRCCCGEEEKIo42EUs2Z32+GUMXFsHev2SrK5zMDjoQEaNHCbMnTEGwusLU2n9wXLAV/odmCypcfGRA9CbTq3b2qMgyDskgQtStQTFGoHF84iEOxkaK5aOk8QkFUnSmRQKm20IrIgOy6GV6F9Ej3wMiTCYHK4CpmkHi73Tyf027+N4sEV+WE2FSxl00Vu9lUvptNZTvZWLKTneX5GHGPMazktjnpmNw20trJDKBOSGlL+8Qs7Foz/CevKOi2JHRbEtBu//saBppegS1UGgmxyszlUCm2UCm2YHS9LLKtDIUwWtiHFvDhDBTUuVphxRYJqBIr51oiuq3KPG45AV1LxFCb4X8HIRpA2AgTNgxrbhBGNwyMmPKg7mvsagohhBBCiHoid0bNTTAIu3eb3fEKCszWUYZhdsVLToaMjMZtvaGo4Egzp4T2ECwBXwEEi8zxpzSHGVCplS22DMOgVPdGgqgiikJe/OEALsVBsuailT2l0d7OIVFVc9rfvy4j8hTBkA66TqmnhE0Fe9kUKGBjYB+bQoVsDO1jj+6p9RSptgROSGzNCUmt6Zhshk+dUtvTKjkT1WY7NlvxKAq6zQyB/K42B97fCrE8Zmile6ywyhbyoIU85ja9LLKPB033oBo6qhHCESw2WwgepLDiIGSLhlQJ6Jo7EmpFlyvLzHlsWQJh1XXILRCFqC96OBwJjcIYhlEZJhENlcI1lBkYhoHZtFSx5taSYrbqVFBQFQVVUSvnqNhVG24tgQS7DaetHlv+CiGEEEKIRiGhVHMSCsHPP8OmTWbLmqQkaNOm6Q62rTlAyzTHnwqVm0/s8+dDsJRwqIBSDIoM2KVXUBSqIBAO4VbtpGpuXM0tiKqj4lA5G335bPTtZaMvn02R+b5Q7eFTS1sSnRwZnGBvwQm2FpxgS6eTmk664qpsNeZRwKtBfgFoRWBTwWaPtLyymddLtFtitKWXFtNS6xh4ymCNYkMssup2jGGghv1WQGXTy9FC5dh0T2ReXjnXy7GFzLmmV2DTKwBQjQCOYMAMaw+Rrrriwqqwte5GV12EI3NdcxPWXOiqG11zWfuFVac1l4Dr6FctJDqMAAkMFEVBQTEDJAVUVFQ1EiChoikqTs2OTbGhqSoO1Y6mmqGSTbWhKaoVOGmKFjdXFbXG7WqkjHCg8o8cQgghhGh29LCBph6Df0AXNZJQqrkIheCXX2DjRmjdun7HhmoItkTCmpsSzU2hdze7gtso8e4hECojwYB0exJOe2KkBVXz/gFlGAb7Qh4reNrsyzcDKH8+RaHyWo/LsqdygiuDTq5WdHK14gRXJic4M0mx1WW8p5ixrqJdB/0VUKpHxruK6eqnKGYYFe1CqMYM2u6IPHEwWh4NrSTEqqQohDUXAc0FZBzcsUYYTffGhFTlkfUKcwpVWdcrzPVwhVWuGuaYYVrYhxb2HVawFaWrjkhYZYZW5rLTnKtOwqqTsOYyg6zouuoirMWuV07msQ5Q5FfMoYhvdRQfGFmtkqgSLkWWo9FR1SAprtXRfgIkm6pFgqPKeXxIVLl8oGCpcbtaCyGEEKKp0lSFW974gfV7a//DfFM2MCeTO4Z0aexqHDXkjqE50HVYswbWr292gZRu6JQEPRQGStnlL6Ak6CFIiARbIunpJ+M0whCqMLv5hcohWGHeR6kusDmBJtoKDPPGcU+wNBI67WWTPxI++fIp1b21HtfOkW4GTpHQqZOrFR1dGSTVYcyt2sWMdXXgmpuhVViHsGF2IfQFoMJnXmuGYU6KUjmPhliaGpkiTxd02M2pamilaaAqEmRVpahWy6xDPkU4EAmofGjhClTdG1n3mgO8615U3QysKpdjy3yoYfN4BXOcMy0cQAsHsIdK6+udAhBWtEhQ5agyjywrjkiZI24fI7pubbdXliv2uGOMyHpjtvYyDMMKi3QjHDcOUmz5/lohxf5zA6zuajV1YVNVxWpxZNc0bIoNu6ph1+yoKGiqVmNQVNtcAiQhhBBCNLT1ez38vLN+v3s2lBMzD/27vKhOQqmmTtdh7VozkMrKahaBVCgcojjkoShQyk5/AaVBDyFDJ1Fz09KRikO1xx+gucHZEnQ/6N5IOFVidvcL66DazBZUmgNo+BtP3QizK1DMpmj4FGn1tNlXQHnYX+MxKgrtnS3o6MzkRFcmJ7ha0cmVSQdnBu5G73ISCbDqGvgZYdCNyhBL182nOnp9kUHdoy2xoje1MQO5K2plIKWqZoBlt5ktsjRbZYssVa0eZsWWCYuhOgipDkL21MM8kYFiBM3gKuxHDfsjwZYPVfdHyqIhlh8tso9qrfus48xjK5eVSFsd1dBR9QqIdFs8ksKKZgVUYdWOoUTCLMUeCbmiy3Z0xUZYic5t6NakmXPMeQgVXbURQiOEhq5oGJF9w4oWOVbDUO2gOlCwRVogxYdImqZarY6i3djMuYZWQ8sja65GjlertkySgFcIIYQQQhwdJJRqyoLBykCqVStwHU5LmiMrGA5RHCxjX6CY3f59lIbKCRthEjU3mY507HV52pjmNCdHGhitQfeZIVWw1Ayq/BWAAZp5A4gaCan0MKo/gOYLoPqCqL6AOfmD1qT5zXUlGDLLAiFzOToP6QRDQTY5KvgtwcdviX5+TQ7wa3KQdSkhfLVU36bDSUUKXQugW4FCl3zong85BQauUCEYhcCvNR8czXAUBUNRzCBHUTBUBVQFQ1XNdU2NlKnmsqZiRJbRYspsGoamYdhUwjabuR6d7Bpha9lmLtsjyw4bYbvNXLbbMBzx87DTXrmfw0bY6Txwi6xoWKVHAitdN69nn79y3cD876kAhlL5mdg0c0GLCatsNnNyRObR1lqRz6Uy3FLMpz1qMWGYtACpmaJgKGbAVa8MA8UIoYYDcaFV5XrsPBC/bsSuB63tSmRdsbYHUY0gqhGyXlY1dDB0s0tjIzFQQLWDEv0ZZTPDKjV+3dweMyn2yDZ7/DbFVr08WqZEylVb5fHRsqr7KLYqZZG5osm/DyGEEEII0agklGqqfD5zUPMtW8wWUk0wkPLrAYpDZRQEitnjL6QsZIZGSVoCWY4W2OoSROk6mqcCW1k5mqfcXPZUmMvlFWjlXnPu8aCVm2VqeQVahRfNa4ZPmj904NeJUeqEtRnmtCYD1mSZy+tbgF5LAwRnCLoUQNd86BaZuhZA50Jw6NHxmoyaD66NUbnQ3G4LDVWNBFR2wg47YactMrdjROY1TbrTUbnuqmHZbkNXVcJ2M2TDiHQ1DHjNVlpVW2YpxC/HdjNU1MpWVzbNbKEVXY7tUhjbUis22LJabCnS9fBgKAo6GkHVia7YMdTE/XZhq2k8pKrzWsdDMsBGCAc6TsLYDQO7EsZu6NjRsRFCM3RUI4gWCbI0I4QSNgMt1QhGwq4gSjiAEg6ZcyNoDqYdnfTocpXycIDYf/cKRqQsAHrt48c1KYpWJaiKLlctj65rVbZp8dsVLbJPtEyrcg61sjx2n+hxaDGvodV8nBEGR3pjf3JCCCGEEKIeSCjVFHk88L//wa5d0Lat+eS0JqJC91EcLCPfX0x+oJDykBcUhSTNTVtScJZ4sJXuw1ZSZk6lZdhKPOa81IOt1INWVo6tzIOttBytovaxlw6WoSiEXXZ0lx3daWNHuo21mQprWxj8mqbzW3KQ35IC7HYGaz1HUtjGieFkTiSVTqTSWUnnRC2dto4U1A42jE5aZQslVeU3Ta1s6aQqVZYjcVNMiyizopGbWCNyE2tgBi4YKGFzPCclEsCYcwMlHDYn3QxllFBkPaSj6OHIpJvlIT1mOYQa1M2ykG62CgvqVuswJRhCCeqowcqWY2owhBI7DwRRI9uilHAYzWe2TjtSDEWJC6x0l8NcdtkJOx3obgdhpyPy39xhhWK6KxKUOTT0mMBMj7b8skdbh0TDhMhytLWapmK11IoGW9HWWNFWW/ZIi61ogBUNxKL7Vg231JjzNMGWKXV9MpsZLkXGSSI6JlJlhFRTiGQ+nU1BU1QUzLGQ7KoNTdVwaDY0zHn0iWwHOx6SpjZy985wyAyrrCArGBNeBWO2RctC5twIVa5b24OR8qrroZjzh8DQ488b3Sdu/1D8cjgEkTHE4kRamFFLV+Qmy5YMF25u7FoIIYQQQojDJKFUU1NYaAZSRUXQvn3jjqej6xiFhVQU7KQ8fyee/B0E8vegFhWTXuKhfakPZ2k59uJSbMVlaL5Dv6nRE1yEkhLRkxLQo/PEBPREN6GkRMIJLnM9wY2e6EZ3uwgnuvE4VDZrpWw09rElsIvN3p1s8e5miy+finDtgUlLWxIdXRmc4MykY8yg45n25BoH/K2/6KwZC4fNACsaUvmDKP5IaOUPxsxDqP6Y7pOBoNmtMjr3B+O3+wNoseWR8EsxjCMWfIUddjPUcjniwi7dGQ29KoMu3Wkzwy+nzQy5HDazzGE3j7HbCbttGDZbZeAUG27FhlFW90KtMuSy26q01IrpdqhFj4uEWooKmjk8eViJmWoJkaLL8SFSpIooGBhmlKRghkeY4VFNT2azq7bIgNqRsZE0e41jH9UUHlUdG+mookZaDFGXp2Q2MiNcPagyguZ4cXEhll4l3KppXa+yrx6/r7VPlXVre7jKceEatlddj6l/So483VEIIYQQ4igg3+iaCl2Hbdvg11/NsXfatz8yLSoMA0pKYN8+cyooMOeFheZyYSHGvn0Y+wpQiktQDINEIBFoVYfTh20aodQUQqlJhFKTCaUkE0pJQk9JIhQz6clJhFIS0ZMSCSUlmK1OaqEbYXb5C9jq3c1W7y62eNeawdO+3ezx76v1OA2Vdq4MTnC1oqMzgw6OFE6wp9LRkUKyYsdsHaNWdi3Rmk6LtCZJVTGcKrrTjn4kX0cPRwIsc4wwzR+oHCfMFzTHDouUWeOI+QOo3gCaP4jq85vL0XHFvAHrHEo4MgB3wAzJKKm/LlaGqppBljs24HKgO23oTnMectrQHeY85LARtGvoTo2A04Zu1wi6bATtNgJOjaDTRsihYTg0IBJKRUIuRTUDHiXaskjVUG02VM2Gptlw2mzY7U5sNjs2zYbd5sKu2tE0G5qmRfbTrP2j59A0G6qmRfYzl1VVkyezHS0UFZToeHzNWDgAgaIm2epQCCGEEEIcHAmlmoLycvjtN9i8GVJSICPj4M/h91cPmqrOo1No/2MwRW59AbMLVTA1iVB6GqG0FEJpKQTTI/PIeigtmVBqCsHUZMKJ7kO6UQgbYfb6C9nq28M27x62+Xaz1buHrd7d7PDtJWjUXudUWxId3K3p4G5Dh4S2dIwst3dlVR9gPdpNRY92hYkMpq4HIFhu/tU++iGoNiAyhopqw3zyn9wEHXGaStjtJOyu5ydNGgZKIBQJsgJxYZXqDaB5/Sg+P0ok4FK9/pgwLGAdp0UCL5vPbOGlBc1rRgmHsVX4oKJ+B9o2bBqGy4HhcppzpwNcTnA6wOlAcTnBYc4VhwPFYUdxOsDhAKfd3M/hAJcjshwpczkjg8ZHuh9GuyrGtcyKdl2MjscVHXQ+Oi5Xle6JSmyLsNhujDEtvva3XQghhBBCCHFMkVCqMYXDsHu3GUgVFUHr1ubNY20KCuDzz80WVbEhU0EBlJYe1EsbqSmEW7YgmJ5KRWoCZWluPKlu/GnJ0LIltpatMFq2IJSaXG9dCEPhELv8+9ju28N2395I+LSHHb69bPfuJWDUPtaTQ7HT3t3KDJ7crTne3Ybj3a3p6G5Dmj257pVQNNASoOpbCofAiI4BExnrJfr0v3DQXA7HtA9SI4PuRgflVSOtWTjKuiY1UZVd1QzCkW5pkQ5qVle16LZw7DbCGC4DxaVgpEVCRxKIjoukoFSOhxRdJjK4Nma5XVWxoWFXNTRU7IaC069j8wXRfEFs/mAkuAqi+fxma61I8KX6zKBLiQZgXj+KL2A+2MDrB6/PfEKh1wehSNgV0lE8XvAcgU6kdhs4nZWBVTTsckTWY0Mthz1+skfmTnv8/vaY4+y2mLG51AOHX4oS83RF1fx3ZYuO52Wr4bgqIVfV5apBWG2vKYQQQgghhGgUEko1BsMww6RNm2DnTvOm8Ljjar45KiyETz+Ff/0LfvihcpDsmtjtZiurli3j5y1aQEYGvhYplKW6KEqyscsowxOqIBAO4lBsJNkSSNBc1ngvtcdD+3tbBiUhDzt9+ez057Pdl88O397IlM9uXwF6TQPtRmiKRjtXJse7WnOcO4vj3a05zpVFB3cbWjlboB3JsWiiraKq5W9GzLgrMfNwINLiKhpkRUKr2P88scGVosQ8XerYaHEVDY6MGsIhczlcY3AUNsw5YM2VyOdlxAyorSkqSjQ0igmPotucqoZNiQmQFBWbomGPjHUUHS9JjY6jFDmPuayixe5jvU4t1+BB5KJ1FgyZAZWvSlhllVXd5jfDrdhttU2RbowEQ+bkOYJPiottnRUbeEUDLXtMgBUNv+y2yrk9Oo8sO2yR/SPLDkflPjZbJPiKDaaID6+gMpiKHfMrOpi9okTmqhmIaVpkzK9Ii7LYkIsqr1NTABYNx6yQrOo24vcTQgghhBDiGCKhVEMrKYEtW8zWTuEwtGoV3zpK12HNGvj3v+Gbb+CXX+KDqB49oFevytApOmVkmF3/Ym5qguEQpSEPZaEK9voLKQ558IZKIABu1UW6PRnnQYwtYhgGpaFydvkL2O0vYKevgF3+AjOEigRR5fr+uy45VTvtXK1o52oVCZ9a0d6VRXt3Fq2dLbEpjfwkrWoUUO2AvYbACiBcw2C/kWXdb4ZYeiAyQK8f9HB8i6vIS5g3zdGWVmrlejTMiutUWT+MaiGRGSDphGvcdqDgyHwr0fAocq9dpdWRgoIWExw5VA27oqKhYYtpgWRTtCohUeV5tLhWTJXhUTQ4ig6+3exFg5jkxPo9r2GY49bFBlf+QGQ5Mvf7a1gPVN/fX6XMHzD3C8bE2tHysiMYfEWpamUAVlOrrrjAq2roFQ22NHPZppmhl6bFPHFRqx6U2exgj5RrWjyBO8EAAKEpSURBVOXFH9dSC7MwNpiK1jd2X6slV8ykxQ5yH7OuavHHx4ZcVHntmkKx/U51OEYIIYQQQoh6IKFUQwiFzJZRO3ea3fX8fjNEcrvNm7cff4TVq83phx/M4CpWt27whz/A738PbdrU/jLhEGXBCsr0CgoDJewLllAe8qIbYZyqg0TNTborudYb9kA4yN5AEXv8+9jjL2S3f19kKrCWKw4QOgG0tKfSzpVJO1cW7V2ZVgjV3tWKlo7UoyMwsKh1GDTYMEOpaHhF5IlU0Xnc493NfYywThi/2eEsbD5BzTAqw6BoSGQoBmFFwQDCKIQNsxNaWDGzB0NRzEGqFRUMcz8z/DK7qimKOSC8oqhm+639tDjaX3AUGwaZxysxLYzMuUZ8iyMVRQbQbgyKEmmR5IDUI9HECzN8DQQqAymv31z3BSLzmBArOsVtj8z9QfPnpT+ybJ0zZjkQE4CFw5UhWWNQVTPIsseEXo7YAMtWfdkWXY4JtqwxvKKTrfJJjZqtsgWXplWGZVXDM+sJjzbQIgGVFZhBtVZeRMpiQyioPaiqMURTYgKz2FZoSkwXztrOFfP6+31NIqF/OWTsp+WwEEIIIYRoFho0lPL7/TzwwAP861//wuVyce2113Lttdc2ZBUajq5DWZk5VtS2bebc46lsKbVunfmkvQ0bzJuuWImJkJcHp59uTq1qfu5dMBzCEzJDqKJAKQXBYrwhP0EjhF2xkWhzk+VsiYaKR69gb6CQ/EAR+f7iyHIhe/xF5EeCqMJg3calSrcn08aZQVtXZty8nSuTNs5MXFozf7JTLWK7o1W2FgrHBUWV2+K7rhlGOBIcmeGSoWCmRkq0C5ATFKc5GDcABqphoGCgGAYq5qQYmHOMSJsqHS1sYFfMhlw2w0DDwA7YVBXViNwXWnPzOCsgUhRUQ4kLizRFQVU0c4reJBpKZV2NyDz2JtKIWTfbSEW6iMXua74vs+VfOL4FYG3dUuP2qaWcWvapdnwdXmN/r1lTQa11qoO4MCBuQ2V5ba1SaryRp+Ygodr2Kjf7R4KmgttlTkeaYZjBVDSgCgTi163yYJXtQQjGbPMHzT8SxO0XMpej5db2UOWx4Zj/7uFwpEVZ4Mi/74NlBWBa5TxapsUMYB87iL2tylzTKvfV1Pht0VZd0X1i59Ggyjom2gqsyrF2zWwZah0be06tspWYoYNDh9ZngiOlsT9ZIYQQokHpYQNNPULf4YRoBA0aSj366KP89NNPvPjii+zcuZO77rqLtm3bMnTo0IasxpERDkN+Pqxfb4ZNa9fC9u2wa5fZOmrXruotoKLS0swueb17m/Nu3cwv/bGnN8J4dT8Vuo9y3cu+QAn5gSJ2+wooCJbgCVZQrvvw6BUUBUspCBRTECwx54Fi/OG63SQ5VTutHC1o7WxJlrMFrZ0ZZDlb0MaZQWtnBq2dLesQOkVu0owq85hNletG/Iaajql62nAk8Il2M4sMbB0XAMV0R4vbJyZEsoKlSGBkBkIKhhGOf1FDidy7R1sRmd3UVBQUo7J1kRnFqGiRcMcWnRtqpKWRig07NtVmhkSR7eZxMQNqx7RWUlXVDI1iuqtVBkiamTmoSuXnFQ27MCAUBsWIdCeMtNaKhkFGdFs0HApHtlPZiouYY9Hj91Oin40Rec1wzOuHI0FVuPp/cOv3p1q5zQqzjPhQJTaxUWPG4VJjt0Vb3VXpVhQbvsTuo8ZUwgoEqx6jVpZBpGVZ7Hpkn9jXjftiUFvYFGFE/s8wKj8ewyDu2q9pvdrxYQjXdGzVYyLzcOx5o/vFvDUjdgXrP4l1DkWpXK/aigUi/41iympq5RL9b6dU+dyrtY5Rq5+/NopSOV5VY9D1+NArGDLDq2BMoBW7HIjZHgxCUI9fD4XMfUKhyjG/gkFz4PvofrHLwZh9Y4+pqrby5iY69lf28XD2qMaujRBCCNHgNFXhljd+YP1eT2NX5ZAMzMnkjiFdGrsaoglpsFCqoqKCt956i2effZbu3bvTvXt31q1bx6uvvtp0Qimv1wyOyssrp7IycyotrWz5VFBgTvv2mUHU3r3mctUWTzXJyoKTToLsbMjOJtS5E56sdMqC5XiC5ZQEyyjY8Rn5gWL2BYopDJawx1/EnmAhRcEyikPllIQrKNHL8YQP7tHzKaqbDFsKmbZUWmmRuT2VVloqWfY0smxppGoJ1rhAFgUIAEEwSgvRlcoWQYYB4cjdbRizO5m5jcg6la2HIqeNjk2EokTCInNcIkOJBECqErmfrgwpFEWx7pmVSLcvc4wiMw5SFQUlOvB1ZOwic/wiBVWxoamqOaGhRZ6apkW6oGmKWaZYLYQUVNVmhkWqZnY1U7XKEEmN7KNoZmikaCiqWrmfNRZUDTfs1W7cleo33nHBSg3zw93nUMqqLluJRjTYCscEUlXm1r5EQqyYYCRuH+LLouWGTmXLqphyYubRwC2ubrF1VSrLFaXy9au9n8hytVA0NiytGqRW2edgWHWJK6zhXEpMvZTKMDE2gDJigkAjZh+U+KAr7hhqn8d+FuGYECscOW84bHbTi24PR8qMsLmsh831cCQ8iwZohGPOVTWIo3I70deK+ayIqReY77PWzzbyf1YQRpXgMnaspyrboq8XLY/dh5h9rf0Vs6liggLYQXHE/Nuv5fUPywHOEf3vEw28dN1c1kOVoVZIjwmz9Mp9QqHKbdZ+NazrB1iuWqaHaymvYZ+Qbta/JuEwBMKwe59ZJyGEEOIYtH6vh593HtzT15uKEzPrebxU0ew1WCi1du1aQqEQffr0scr69evHggULCIfDqNGb9Ubi/eeHvDX1YgptQcIK1qQroKvxy6HIpKsQagPB9hCMlAUdGgG3w5ycNgJOG16nit+u4rUZ+AjhDf+EN7wSrz9A8Ccdfjr0equKSro9hXRnKi0cqaQ7Ukl3mvMWzrTKZUc6ds38z23eFkbCoGirFqBMUSgziLS8iQRBioFiKNa9n6JGW/aoVgikRMYOwgptImFRpCuYomhoqoKGhqpGQiGlctmm2tBiwh8FpXKumK2FomXm+c0ASFHUasfEbo/uo1RrQUM93RiKJstqDVRT2BS7jRq2U7mPda79lVH9dWrap1p5Ddv2d1yt22s7vsq2Gj8Lat5Wa0BnVKlODeFetf2jwVYksDKIjKVWNRgLV9k3GoYZlUFXbKhVbR+j8vzEhmWx4Vh0/5hlq15VtkXrZQVzYLUIjP28ou8hHLstprzqZxzbii229VlcYKrEHxdbHs0slarbASVcucH6WUdMGWBXwaHF7xOzS+VL13Qeqv/srPEcse/rAMdXPU90Hv3vGQqbgVXYqAyrAkFwJ4DDXfO5jgHH1HAIQghRj6TrmxBNT4OFUvn5+aSnp+OIedJcRkYGfr+f4uJiWrRo0VBVqdGr+z7lumHBA+94QDrgjUwxwpitjWphUzRcNjdum4sEewKJjkQS7YkkOBJJcaaQ4kwh1ZVKiiOFdHc6aa40WrhbkOxIxqaa/xnNlj6qFeZoqmZ1AdMUzQp4NMUs1yKhUPSYqsfHru9v24HWZTBr0eDiuu8JcZBq6kpZbQrvfz+oDM5q65IZVxaueZ+almP3r23fqoFdNDSzAsGa6lFl3+g+Vqul/XwelR9e3Kza51m1LC5Qjd22v2MAdyI4kzhWHdXDIQghxBEkXd+EaHoaLJTyer1xgRRgrQcCjT8o7LnnT2L0x/nsKtmOptpQot25IqFNbMseVVGxKTZUVcWu2itb+yjm3KE5sKk2bKoNp+bEbXfjsrlw29247W6S7ckkOhNJsCWY645knDan1W2uaqATO48GPlWDIgmBhBCinkiLykNTW/hUW7BU03Jd91VV8wm2x6BmMRyCEOKodLS0MpKub0I0LQ0WSjmdzmrhU3Td5WqAJzQdQNvktrww/KXGroYQQgjRPEmY1yCa+nAIR5Oj4QZc3kPTcTS8D2llJIQ4EhoslMrKyqKoqIhQKIQt8mS5/Px8XC4XKSkHfqSzEfkrqcfTPH8ICiGEEKJ5SUxMbHKtjw93OISG+j51NNyAAyz4fAM7S7wH3rEJ6tE+lcv6HSfvoQk4Gt5H9D2EfBWEA83zPQS8FXg8HjqmqIQD9sauziHJciPvoQk4Gt5DxxS1wbKVA32farBQqmvXrthsNlatWsUpp5wCwMqVK+nRo0ed/qpXXl4OwFlnnXVE6ymEEEIIAeb3lKSkpjV21eEOhyDfp44dHwIPN3YlDtPR8B7g6HgfR8N72Ag0934x8h6ahqPlPfSb1TCvdaDvUw0WSrndbi666CKmTZvGQw89xN69e3nuueeYNatun0SrVq344osvmuRfLYUQQghx9ElMbHpjdxzucAjyfUoIIYQQDelA36caLJQCmDx5MtOmTWP06NEkJSVx8803c84559TpWFVVad269RGuoRBCCCFE03W4wyHI9ykhhBBCNCWKYcQ9y1kIIYQQQjRRXq+XvLw8nnvuOWs4hHnz5rF8+XJeeeWVRq6dEEIIIcTBkUe0CCGEEEI0E7HDIfz4448sW7aM5557jlGjRjV21YQQQgghDpq0lBJCCCGEaEa8Xi/Tpk3jX//6F0lJSYwdO5YxY8Y0drWEEEIIIQ6ahFJCCCGEEEIIIYQQosFJ9z0hhBBCCCGEEEII0eAklBJCCCGEEEIIIYQQDU5CKSGEEEIIIYQQQgjR4CSUAvx+P1OmTOGUU07hjDPO4LnnnmvsKjVbe/bsYeLEieTm5nLmmWcya9Ys/H5/Y1erWbv++uu5++67G7sazVYgEOCBBx7g1FNP5fTTT+fxxx9HhtI7NLt27WL8+PH07duXwYMH88ILLzR2lZqVQCDAsGHDWLFihVW2bds2xowZQ+/evTn33HP5+uuvG7GGzUdNn+WqVau44oor6NOnD0OGDOGtt95qxBqKhvTxxx+Tk5MTN02cOLGxq3XUkJ9dDaemz3rGjBnVru9XXnmlEWvZfO3vPkWu6fqzv89Zruf6s2XLFsaOHUufPn0YOHAgf/vb36xtze16tjV2BZqCRx99lJ9++okXX3yRnTt3ctddd9G2bVuGDh3a2FVrVgzDYOLEiaSkpPDqq69SUlLClClTUFWVu+66q7Gr1yx9+OGHfPHFF1x88cWNXZVma8aMGaxYsYLFixdTXl7OX/7yF9q2bcsVV1zR2FVrdm699Vbatm3LkiVLWL9+Pbfffjvt2rXjD3/4Q2NXrcnz+/1MmjSJdevWWWWGYXDTTTeRnZ3NO++8w7Jly5gwYQJ///vfadu2bSPWtmmr6bPMz8/nuuuu48orr+Thhx/m559/ZvLkyWRmZjJw4MDGq6xoEOvXr2fQoEFMnz7dKnM6nY1Yo6OH/OxqODV91gAbNmxg0qRJcd8Fk5KSGrp6zd7+7lPuvPNOuabryYHuB+V6rh/hcJjrr7+eHj168O6777JlyxZuu+02srKyGDZsWLO7no/5UKqiooK33nqLZ599lu7du9O9e3fWrVvHq6++KqHUQdq4cSOrVq3i3//+NxkZGQBMnDiRRx55REKpQ1BcXMyjjz5Kjx49GrsqzVZxcTHvvPMOzz//PD179gTg2muvZfXq1RJKHaSSkhJWrVrF9OnT6dixIx07duTMM89k+fLlEkodwPr165k0aVK1Fnrffvst27Zt44033iAhIYETTzyR5cuX884773DzzTc3Um2btto+y2XLlpGRkcFtt90GQMeOHVmxYgXvv/++hFLHgA0bNpCdnU1mZmZjV+WoIj+7Gk5tnzWY1/fYsWPl+j5M+7tP+d3vfifXdD050P2gXM/1o6CggK5duzJt2jSSkpLo2LEj/fv3Z+XKlWRkZDS76/mY7763du1aQqEQffr0scr69evH6tWrCYfDjViz5iczM5O//e1v1g+gKI/H00g1at4eeeQRLrzwQjp37tzYVWm2Vq5cSVJSErm5uVbZ9ddfz6xZsxqxVs2Ty+XC7XazZMkSgsEgGzdu5L///S9du3Zt7Ko1ed999x15eXm8+eabceWrV6+mW7duJCQkWGX9+vVj1apVDVzD5qO2zzLaPaAq+f1zbNiwYQMdO3Zs7GocdeRnV8Op7bP2eDzs2bNHru96sL/7FLmm68/+Pme5nutPq1atePLJJ0lKSsIwDFauXMl//vMfcnNzm+X1fMy3lMrPzyc9PR2Hw2GVZWRk4Pf7KS4upkWLFo1Yu+YlJSWFM88801oPh8O88sornHbaaY1Yq+Zp+fLlfP/997z//vtMmzatsavTbG3bto127dqxdOlSFixYQDAYZPjw4fz5z39GVY/5TP6gOJ1O7rvvPqZPn85LL72ErusMHz6cyy67rLGr1uSNGDGixvL8/HxatWoVV9ayZUt2797dENVqlmr7LNu3b0/79u2t9X379vHhhx822b8IivpjGAabNm3i66+/ZuHChei6ztChQ5k4cWLcdztx8ORnV8Op7bPesGEDiqKwYMECvvzyS9LS0rjmmmtkWIdDsL/7FLmm68/+Pme5no+MwYMHs3PnTgYNGsSQIUN46KGHmt31fMyHUl6vt9qXluh6IBBojCodNWbPns0vv/zC22+/3dhVaVb8fj/3338/9913Hy6Xq7Gr06xVVFSwZcsW3njjDWbNmkV+fj733Xcfbreba6+9trGr1+xs2LCBQYMGcc0117Bu3TqmT59O//79ueCCCxq7as1Sbb9/5HfP4fH5fNx8881kZGRw+eWXN3Z1xBG2c+dO69/Sk08+yfbt25kxYwY+n4+pU6c2dvWOSvKzq+Fs3LgRRVHo1KkTV199Nf/5z3+49957SUpKkq7zhyn2PuWFF16Qa/oIif2cf/75Z7mej4A5c+ZQUFDAtGnTmDVrVrP8GX3Mh1JOp7Paf6DougQCh2727Nm8+OKLPPHEE2RnZzd2dZqVuXPncvLJJ8f9lUEcGpvNhsfj4bHHHqNdu3aAeQPz+uuvSyh1kJYvX87bb7/NF198gcvlokePHuzZs4dnnnlGQqlD5HQ6KS4ujisLBALyu+cwlJeXc+ONN7J582Zee+013G53Y1dJHGHt2rVjxYoVpKamoigKXbt2JRwOc8cddzB58mQ0TWvsKh515GdXw7nooosYNGgQaWlpAHTp0oXNmzfz+uuvy038Yah6nyLX9JFR9XM+6aST5Ho+AqLjD/v9fm6//XYuueQSvF5v3D5N/Xo+5vuvZGVlUVRURCgUssry8/NxuVykpKQ0Ys2ar+nTp/P8888ze/ZshgwZ0tjVaXY+/PBDli1bRp8+fejTpw/vv/8+77//fty4Z6JuMjMzcTqdViAFcMIJJ7Br165GrFXz9NNPP9GhQ4e4X2jdunVj586djVir5i0rK4uCgoK4soKCgmpNrkXdeDwexo4dy7p163jxxRdlzIpjSFpaGoqiWOsnnngifr+fkpKSRqzV0Ut+djUcRVGsG/ioTp06sWfPnsap0FGgpvsUuabrX02fs1zP9aegoIBly5bFlXXu3JlgMEhmZmazu56P+VCqa9eu2Gy2uIG/Vq5cSY8ePWTMmUMwd+5c3njjDR5//HHOO++8xq5Os/Tyyy/z/vvvs3TpUpYuXcrgwYMZPHgwS5cubeyqNTu9evXC7/ezadMmq2zjxo1xIZWom1atWrFly5a4lqUbN26MG8dHHJxevXrx888/4/P5rLKVK1fSq1evRqxV8xQOh5kwYQLbt2/n5Zdf5qSTTmrsKokG8tVXX5GXlxf3V+E1a9aQlpYm44IeIfKzq+E89dRTjBkzJq5s7dq1dOrUqXEq1MzVdp8i13T9qu1zluu5/mzfvp0JEybEBXo//fQTLVq0oF+/fs3uej7mUxe3281FF13EtGnT+PHHH1m2bBnPPfcco0aNauyqNTsbNmxg/vz5XHfddfTr14/8/HxrEnXXrl07OnToYE2JiYkkJibSoUOHxq5as9OpUycGDhzI5MmTWbt2LV999RWLFi3iyiuvbOyqNTuDBw/GbrczdepUNm3axKeffsqCBQsYOXJkY1et2crNzaVNmzZMnjyZdevWsWjRIn788UcuvfTSxq5as/P222+zYsUKZsyYQUpKivW7p2p3DHH06dOnD06nk6lTp7Jx40a++OILHn30UcaNG9fYVTtqyc+uhjNo0CD+85//sHjxYrZu3cprr73G0qVLZQiCQ7C/+xS5puvP/j5nuZ7rT48ePejevTtTpkxh/fr1fPHFF8yePZsbbrihWV7PimEYRmNXorF5vV6mTZvGv/71L5KSkhg7dmy1FFcc2KJFi3jsscdq3Pbrr782cG2OHnfffTcADz/8cCPXpHkqKytj+vTpfPzxx7jdbkaMGMFNN90U19VD1M369euZOXMmP/74Iy1atOCqq65i9OjR8lkehJycHF566SXy8vIA2LJlC/fccw+rV6+mQ4cOTJkyhdNPP72Ra9k8xH6WY8eO5euvv662T25uLi+//HIj1E40pHXr1vHQQw+xatUqEhMTueKKK+TnfD2Tn10Np+pnvWzZMubMmcPmzZtp164df/nLXzjnnHMauZbNz4HuU+Sarh8H+pzleq4/e/bsYfr06Sxfvhy3283VV1/N+PHjURSl2V3PEkoJIYQQQgghhBBCiAZ3zHffE0IIIYQQQgghhBANT0IpIYQQQgghhBBCCNHgJJQSQgghhBBCCCGEEA1OQikhhBBCCCGEEEII0eAklBJCCCGEEEIIIYQQDU5CKSGEEEIIIYQQQgjR4CSUEkIIIYQQQgghhBANTkIpIUSTl5OTw6RJk6qVL1myhMGDBzdCjYQQQgghhBBCHC4JpYQQzcIHH3zA8uXLG7saQgghhBBCCCHqiYRSQohmoV27djz44IMEAoHGrooQQgghhBBCiHogoZQQolm49dZb2bNnD4sXL651n927d3PLLbeQm5tLXl4eM2bMsEKsJUuWMHLkSObMmUNeXh6nnHIKs2bNwjAM6/g33niDwYMH06dPH0aOHMmvv/56xN+XEEIIIYQQQhyrJJQSQjQLWVlZTJw4kQULFrBt27Zq2wOBAKNHj8br9fLyyy/z5JNP8vnnn/Poo49a+/zwww9s2rSJ119/nXvvvZeXXnqJb775BoBPP/2UuXPncu+99/Luu+/Sr18/Ro0aRUlJSYO9RyGEEEIIIYQ4lkgoJYRoNkaOHEmHDh2YOXNmtW1fffUVe/bsYfbs2eTk5NC/f3/uu+8+Xn/9dcrLywHQdZ3p06fTqVMnLrzwQrp06cL//vc/AP72t78xfvx4Bg0aRMeOHbn11ltp164d7733XoO+RyGEEEIIIYQ4VtgauwJCCFFXmqYxbdo0RowYwbJly+K2bdiwgY4dO5KammqV9e3bl1AoxNatWwFo2bIlSUlJ1vakpCRCoZB1/OzZs3n88cet7X6/n82bNx/BdySEEEIIIYQQxy4JpYQQzUrfvn255JJLmDlzJuPGjbPKnU5ntX11XY+bOxyOavtEx5TSdZ0pU6bQv3//uO2xIZYQQgghhBBCiPoj3feEEM3O7bffTkVFRdyg5yeccAKbN2+muLjYKlu1ahU2m43jjz/+gOc84YQT2L17Nx06dLCmBQsWsGrVqiPwDoQQQgghhBBCSCglhGh20tPTuf3229mxY4dVNmDAAI477jjuvPNOfv31V7799lumT5/OsGHDSElJOeA5r7nmGl588UWWLl3K1q1bmT17Nv/4xz848cQTj+RbEUIIIYQQQohjlnTfE0I0S5deeinvvPMOe/fuBczxpubPn8/06dP505/+RGJiIueffz633XZbnc537rnnUlBQwJw5cygoKKBz584888wzdOzY8Qi+CyGEEEIIIYQ4dilGdEAVIYQQQgghhBBCCCEaiHTfE0IIIYQQQgghhBANTkIpIYQQQgghhBBCCNHgJJQSQgghhBBCCCGEEA1OQikhhBBCCCGEEEII0eAklBJCCCGEEEIIIYQQDU5CKSGEEEIIIYQQQgjR4CSUEkIIIYQQQgghhBANTkIpIYQQQgghhBBCCNHgJJQSQgghhBBCCCGEEA1OQikhhBBCCCGEEEII0eAklBJCCCGEEEIIIYQQDU5CKSGEEEIIIYQQQgjR4CSUEkIIIYQQQgghhBANTkIpIYQQQgghhBBCCNHgJJQSQgghhBBCCCGEEA1OQikhhBBCCCGEEEII0eAklBJCCCGEEEIIIYQQDU5CKSEOgWEYjV2FQ9ac634gR/N7E/sn/+2FEELEkt8LoiZyXQjR9EgoJZqdSZMmkZOTw3PPPVdt2+DBg7n77rsBWLFiBTk5OaxYsaLWc8XuX1effPIJd91118FVugnYvXs3119/PTt27LDKDuX9NxUjR45k5MiR1vpbb73FI488Yq0vWbKEnJwctm/f3hjVq1dPP/00OTk5jVqHnJwcnn766cM+T13eS9V/u3fffTeDBw+2tle9bufPn8/ixYsP6jWEEOJosHLlSm6++WYGDBhAjx49OPvss5k6dSobNmxo7KrFaeifyytXruT6669vsNdrCn7++Weuu+46TjvtNPLy8rj22mv5+eefa91/165d9OvXr06/27ds2cItt9zCGWecQb9+/bjyyitZvnx5tf2WLFnC+eefT48ePRg8eDBz585F1/WDeh/RayV26tatG3l5edx0002sW7euzud67rnnuP322wEoLS3lzjvv5Pvvvz+o+hyqqt9danIo31XrckxRUREDBw5k27ZtdT5vrPLych544AEGDBhAnz59uO6669i4ceMBjystLWXatGnWcZdffnm16yQUCvHkk09y1lln0atXL0aMGMHq1asPqZ7i6CGhlGhWysrKWLZsGdnZ2bz55puH/deOuXPncuONNx7UMS+88AK7du06rNdtDN988w1ffPFFXNmhvP+m4v777+f++++31p955hmKi4sbr0Ki3nTv3p0333yT7t2717i96nX71FNP4fV6rfXLLruMN99884jXUwghGtOiRYu46qqr8Hq9TJkyhcWLF3PDDTfwyy+/cPHFF/Phhx82dhUbzVtvvdXkgrkjacuWLVx99dX4fD5mzpzJrFmzCAQCjBgxosYwwTAMpkyZgsfjOeC5i4qKuPrqq9m4cSNTpkzhiSeeICMjg2uvvZbvvvvO2u/VV19lypQpnHnmmSxatIjLLruMBQsW8NRTTx3Se3rzzTet6eWXX2bq1KmsWbOGq666ivz8/AMev2HDBhYuXMgdd9wBwJo1a/i///s/wuHwIdXnSBg4cCBvvvkmrVq1qtfzpqenM2bMGKZMmXJI90qTJk3io48+YtKkSTzyyCPs2bOHUaNGUVJSUusxuq5z3XXX8cknn3DHHXcwZ84cUlJSuP7661m7dq2138MPP8wLL7zAuHHjeOKJJ9A0jTFjxrBly5ZDeq/i6GBr7AoIcTA++OADAO655x5Gjx7Nt99+S//+/Q/5fN26dauvqjVLzfn9d+7cubGrII6QpKQkevfuXev2A123rVu3pnXr1vVcKyGEaDo+++wzHnvsMW6++WYmTJhglefm5nLRRRcxadIk7r77brKzsznppJMasaaiIbz88su43W4WLlxIQkICAKeddhqDBw/mlVde4b777ovb/7XXXqtTyxeApUuXUlRUxNtvv01WVhYAAwYM4MILL2Tx4sXk5uZSUVHBY489xtixY60QqH///pSWlvLNN99w2223HfR7qvo9oF+/frRp04arrrqKd99994At4WbPns2wYcOsOjdFLVq0oEWLFkfk3CNGjOCZZ57h448/5pxzzqnzcT/88AOfffYZixYt4qyzzgLglFNO4eyzz+a1117jz3/+c43Hvf/++/z0009WSy4wfx5dcMEF/Pvf/6ZLly7s2rWL119/nXvuuYcRI0YAcMYZZzBkyBCeffZZZsyYcZjvWjRX0lJKNCvvvPMO/fv357TTTqNDhw688cYbh3W+2G5A27dvJycnh3/84x9MnDiRPn36kJuby9SpU6moqADMLmPfffcd3333XVz3ouLiYu677z5OP/10evTowZ/+9KdqzVVzcnKYO3cuw4cPp2fPnsydO5euXbvyyiuvxO1XWFhI9+7deeGFFwAIh8MsWrSIP/zhD5x88skMGTKEl19+Oe6YkSNHcs8997Bo0SIGDhxIjx49uOKKK/jxxx8Bs6nv5MmTATj77LOt91y1G1RZWRmzZs3i97//PT169GDYsGG8/fbb1T6zOXPm8Mgjj3D66afTs2dPxo4dy+bNm+Pew6RJk6zuBBdeeCFLly6t9b/DhAkTuOCCC+LKRo8ezcknn4zP57PKZs6cyZAhQ6z3HO2+N3jwYHbs2MG7775brUnz6tWrueKKK+jRowcDBw7kb3/7W631iPrtt98YP348ffv2pW/fvtx0003VmkBv376dG2+8kb59+zJgwACeeeYZ7rnnnrguhTV1eaupC8Nbb73F8OHD6d27Nz179uTCCy/kH//4xwHrGVuXnJwcPvzwQ2644QZ69erFwIEDmTdvXtxfBAcPHsxDDz3E6NGj6dmzJ/fccw8Ae/fuZfLkyZx11ln07NmTSy+9lE8++aTa63g8Hm6//Xb69OlD//79mTFjRlwLJV3XWbRoEcOGDaNnz5707t2bK664gm+//bbauZYtW8aQIUPo0aMHl112Wdy/lwN1vY29bqOf5dy5c63lmj7jZcuWMXz4cHr06MGAAQOYMWOG9e8awOfzMW3aNH73u99x8sknM3To0LgugUII0ZTMnTuXTp06cdNNN1XbZrfbefDBB9E0jWeffRaAa6+9luHDh1fb98Ybb4z7/fv9999z9dVX06tXL3Jzc7nrrrsoLCy0ti9ZsoRu3brx1ltvMWDAAHJzc1m/fj1bt27lhhtuIC8vj169enH55ZdXa50N8Pnnn3PBBRfQo0cPhgwZUu27QV1+H/n9fubNm8fQoUPp0aMH55xzDosWLbJ+39199928++677Nixg5ycHJYsWVLjZ/j0008zdOhQPv74Y4YNG2Z9X/nhhx9YtWoVl112GT179mTYsGHVvtPV5XvC2rVrmTBhAqeddhrdu3fnzDPPZMaMGXHfa3Jycnj11Ve55557yM3NpU+fPtxyyy0UFBTEfeYHGo6iU6dOXHvttVYgBZCQkEDr1q3ZunVr3L7btm3jr3/9K9OnT6/1fLGysrIYM2ZMXLijaRodOnSwzv3vf/+b8vLyuO9AAHfddVe175GH4+STTwawhqJ4+umn+cMf/sDcuXPJzc3ljDPOoKSkhN9++43PP/+cYcOGAeb3ilGjRgEwatSouHr+/e9/Z/jw4fTp04cBAwZw3333VWsR9L///Y+xY8eSl5dH3759ueGGG+rcjXDJkiXW950LLrgg7t9FTV3x3n33Xc4991xr/+XLl9OtW7dq1/GBvt86HA6GDBnCwoULrbLo96va/k0AfP311yQkJHDGGWdYZS1atODUU0+t8d901D//+U9OPfXUuO9fTqeTf/7zn4wdOxaA5cuXEwqF+MMf/hBXz4EDB+733OLoJ6GUaDbWrVvH//73Py666CIALrroIj755JO4X9z14f7776ddu3bMnz+fsWPH8vbbb/PMM89Y27p160a3bt2s7kV+v5/Ro0fzySef8Je//IW5c+fSunVrxo0bV+1LzIIFCzj//POZM2cOQ4YMITc3t1rz+o8++gjDMDjvvPMAmDZtGnPmzOGCCy5gwYIFDB06lIceeoh58+bFHffPf/6TTz75hKlTp/L4449TUFDAzTffjK7rDBw40PrLRm1d9nw+HyNGjOD9999n3LhxzJ8/n379+nHPPfewYMGCuH1feuklNm7cyKxZs5gxYwY//fRT3Dhbd9xxBxs2/P/27jtAivL+4/h7ZrbvXoG7ox0dRIqoCEKsiDFq1GhiTH4mJtZYoohRY49GRSWKXUHF3mLFGI1JTExssaEoKihIk3ZwXL/bvjszvz9mZ273Gnd4XIHvKxln5pmyz+4td7OffZ5nVnPdddfx4IMPMn78eC677LIWwwmA6dOn880331BVVQVYF5yfffYZqVSKJUuWOPu98847zJgxo9nx9957LyUlJUyfPr1ZM+hrr72Wo48+mgULFjBp0iTmzp3Lm2++2WI9ANauXcuJJ55IVVUVN998MzfeeCMbNmzgF7/4hVM/+8Lrm2++4YYbbuCqq67i5Zdf5t///ner523N008/zTXXXMNhhx3GAw88wK233orH4+H3v/89W7Zs6dC5rr32WkKhEPfccw/HHXcc9957L7fddluzx5s4cSLz58/nhBNOoLKykhNOOIFPPvmECy+8kHvuuYfS0lLOO+88XnnllZxjn3zySSKRCHfeeSdnn302L7zwgjNWA8Ctt97K/Pnz+b//+z8eeughZs+eTW1tLRdccEFOeAVWa8eTTz6Ze+65h2AwyJlnnsmXX37ZwVcPp5veCSec0GqXvVdffZXzzjuPkSNHMm/ePGbOnMkrr7zCueee6zRrv+mmm3jnnXe47LLLePjhh/n+97/PLbfcwsKFCztcJyGE2JGqq6tZunQpM2bMQFGUFvcpLCxk//33dwKdY489lmXLluV0kamvr+edd97huOOOA+Djjz/m1FNPxefzceedd3LllVeyaNEiTj755JwgRdd1HnnkEW688UauuOIKRowYwdlnn00sFuOWW25h/vz5FBYW8tvf/rZZl5xrrrmGU089lfvuu48BAwZw+eWXO1172vP3yDRNzjnnHB566CGne9iRRx7JnXfe6XTpP/fcc5k+fTolJSU899xzHHLIIa2+llu2bOFPf/oT55xzDnfddRf19fXMmjWLiy66iJ/97GfMmzcP0zS58MILndegPdcJW7dudbpW/ulPf+LBBx/k6KOP5sknn+SJJ57IqcMdd9yBYRjcfvvtXHrppbz55pvcdNNNzna7i1drXdrBahXzm9/8Jqds3bp1rFy5MqelnGEYXH755fzwhz/k4IMPbvV82Y466qicv/UAdXV1fPzxx865v/76a/Ly8qisrOSkk05ijz324IADDmD+/PmdOrD42rVrARg6dKhTVlZWxttvv80dd9zBFVdcQUFBAa+++iolJSVOa6sJEyY4rcWuueYa570yf/58LrroIvbee2/uvvtuzjvvPF5//XV+/etfOz/vDz/8kF/84heAda1www03sHnzZk488cRtdhHdvHkzCxYs4IILLuCee+5BURRmzZrlvE+aevnll7n88svZZ599mD9/PkcccQTnnntui+Nytef69sgjj2Tp0qXO62YPj9DWv4nVq1czePBgNE3LKR86dKhznpYsX76c0aNH89hjj3HooYcyYcIEjj/++JwxvFavXk0wGKSkpCTn2GHDhrF161YikUir5xc7N+m+J3qNhQsXUlhY6Awa+JOf/IR77rmHF198kXPOOafTHmf69OlOwLLffvvx3nvv8dZbb3HxxRczevRoQqEQ0Nis+Pnnn2f58uU8//zz7LXXXgAcfPDB/PrXv+bWW2/N+VA7ZcoUTjvtNGf9uOOO48orr6SsrIxBgwYB8Nprr7H//vtTUlLC2rVref7557noooucZsoHHnggiqLwwAMP8Mtf/pI+ffoA1sCBDz/8sFO/SCTCZZddxtdff80ee+zh/AEfN24cgwcPbva8X3rpJb755hueffZZJk2aBMBBBx1EOp1m/vz5nHjiiRQWFgKQn5/P/PnznT9Y69ev55577qGmpoY+ffqwaNEizjvvPA477DDAar5bWFiIx+Np9TUH6xuUY445hk8//RRN0xgxYgQff/wx3/ve99iwYQPffvtti6HU+PHj8Xg89O3bt1lz74suusi5mNh7773597//zYcfftjiecAKuPx+P4899pjzWu63334cdthhPPTQQ1x22WX85S9/YfPmzfz1r391vhHac889OfLII1s8Z1s2bNjAGWeckRMUlpaWcvzxx7N48WInnGyPCRMmcOuttwLWezAajfL444/z29/+1nkugwYNyrm4nDt3LtXV1bz++uuUlpYC1s/j1FNP5ZZbbuGYY45BVa3vL0aNGsW8efNQVZXp06ejKAo33XQT33zzDWPGjGHr1q1ceOGFOd9Aer1ezj//fFasWJHzs7nuuuuc12u//fbj+9//Pg8++CB33313h14/+5wDBgxoscufaZrceuutHHTQQc5rAzB8+HBOPfVU3n77bQ455BAWLVrEAQcc4Lze06ZNIxAIUFRU1KH6CCHEjma3ErF/Z7dm2LBh/Oc//6Guro7DDz+c6667jr/97W9O66p//etf6LrutCa57bbbGDFiBA888IDz932vvfbi6KOPZuHChZx00knOuc855xzng21FRQVr1qxxwiDAaRGeTCZz6nTDDTc4YcjQoUP5wQ9+wKJFixg7diyPPvroNv8evfvuu7z//vvcfvvtzu/rAw44AJ/Px1133cXJJ5/MbrvtRt++ffF4PG12BQeIxWL88Y9/dOq0atUqbrvtNm688UZOOOEEAKLRKLNmzWLt2rWMGzeuXdcJ33zzDePGjeOuu+5y9tl///157733+Oijj3K6no0ZM4Y5c+Y461988QX//Oc/nfXt6eIVj8e57LLL8Hg8/OpXv3LKH3/8cTZu3Njsy8aOMAyDq6++mnA47ARh1dXV6LrOWWedxSmnnML555/Pe++9xz333EM8Ht+u7nvpdDrn+SxfvpybbrqJvLy8nNZ96XSayy67jClTpjhlH374IRMnTnRC21Ao5Az7MHr0aEaPHk1dXR333XcfP//5z3O6N44ZM4aTTjrJec/fdtttDBs2jAULFjj/Lg488EB+8IMfcPfdd7c5ZpZhGMybN49Ro0YB1jXRqaeeypIlS/j+97/fbP+77rqLGTNmON3YDjroINxud7MvGKF917cTJ04ErOvrESNGbHN4BLB6Tdjv2WzBYLDN0Ki6upp//vOfFBQUcOmll+L3+1mwYAGnn346zz//PGPHjm3z3GC1yLeXxa5FWkqJXiGVSvHKK69w2GGHEY/Hqa+vJxgMMnnyZJ5//vlOHbSw6S/rAQMG5HTzaeqDDz6gpKSECRMmkE6nSafT6LrOjBkzWLp0aU4T4HHjxuUce/jhh+P1evn73/8OWN+oLF682PnW8sMPP8Q0TQ499FDn3Ol0mkMPPZREIsHixYudc2UHZoDTzLppC5XWLFq0iNLSUieQsh177LEkEomcO2NMnDgx5xsUe/we+7GmTZvGPffcw6xZs3jhhReorKzksssuY5999mnxsfv168f48eN5//33Aes13Weffdh3332dQTTfeecd8vPzmTx5cruejy37IsXv91NcXEx9fX2r+3/44YdMnToVn8/nvN6hUIgpU6Y49fvkk08YMmRIThPlwYMHN3vt2uPyyy/n97//PfX19SxZsoS//vWvPP300wDNLua3xW5FaDviiCNIpVJ89tlnTlnT9+CiRYuYNGlSsw83xx57rPNBw3bkkUc6ARXgjFHw8ccfA9YHmlNOOYXq6mo++eQTFi5c6Hy7nf1c3G53zvgGXq+Xgw8+2DlPZ1qzZg1btmxp9m9o3333JRQK8d577wHWe/b555/nzDPP5KmnnmLDhg2cd955bX6bKIQQ3cFueeJ2u9vcz/47bZomgUCAww47zLneAOtLsP3224/+/fsTi8X4/PPPmT59OqZpOr8rhwwZwqhRo5zflbbsvyXFxcWMHj2aq6++mssuu4xXX30VwzC44oormo1nlf032f6CzP6b3J6/R4sWLcLlcjX7EsgOKbIH3m6v7GuT4uJiAOdLRsD5Qs6uZ3uuEw488ECeeuopvF4vq1at4j//+Q/33Xcf1dXVzf62t3Td2d5rt5aEw2HOPvtsvvzyS+bOneu8nqtXr+bOO+/k+uuvJy8vb7vOnUqluOSSS3j99de56qqr2HPPPZ3yaDTKmWeeydlnn833vvc9Lr74Yn72s5/x6KOPtmtA9aYmTJjgTJMnT+akk04imUw6reOzNb222bBhQ4tfwGZbsmQJyWTSCWVtU6ZMobS0lEWLFhGNRvnyyy/54Q9/mHPdm5+fz4wZM7b5fuvTp48TSEHje76hoaHZvuvWraOsrKzZe7u1Lyfbc32bl5dHfn5+h+7u11bLttZaZoL1HmhoaODhhx/myCOPZPr06TzwwAMEg0GnG/G2Ws1lX2OKXYu0lBK9wltvvUVVVRUvvvhii33T3333Xefbue/K7/fnrKuq2uYv0draWioqKlptVl1RUUFBQQFATl9/sL65Oeyww3jttdf4zW9+w9///nf8fr/Twsi+m1xrf5DKy8vbrDfQ7sCurq6u2R95aLxAy/5Dt63HuuOOO7j//vv5xz/+weuvv46qquy///5cf/31rX6zO336dP76178CVij1gx/8gIEDB/LXv/6VZDLJu+++y0EHHYTL1bFfW9vz8/z73/+ec+Fus7+prKura/Fby/79++f8TNpj/fr1XHPNNXzwwQe43W5GjhzJ2LFjgW3/8W7p8Vurr63pe7Curo4hQ4Y0O1dLP/em7w+7FZG9z5dffsl1113Hl19+id/vZ/To0U4LwOzn0qdPn2YXHkVFRW2GhdvL/jd03XXXcd111zXbvnXrVsDqTjhgwABeeeUVZs+ezezZs5k0aRLXXnut8/MQQoiewP47areYas2GDRsIBoNOqHLcccfxyiuvsHz5coqLi/noo4+cbmL19fUYhsGDDz7ofIDM5vV6c9az/5YoisIjjzziDKr88ssv43a7Oeyww7juuuuca6Cmx9l/B+y/D+35e1RXV0efPn2adS2y/z619GF/W1pqudH02iFbe64T7O54Tz/9NNFolIEDB7Lnnns2ex1beqxtXae0ZfPmzZx99tmsXbuWO+64w7me1HWdK664giOPPJIDDjggpxWSYRik0+ltXl/V19czc+ZMPv74Y66++uqclnN265amX+QcfPDBPPfcc6xevTon6GuP7Ot9t9tNSUlJq62Xm7auCYfDbf4MofHayH5/ZSsuLqahoYGGhgZM02xzn7Y0veayQ52Wrs3tsduaPseWHhva/77x+/0dCgVDoVCLQ6NEIpE2w8xgMMioUaNybjQTCoWYNGkSX331lbPeUmsru37bG5aK3k9CKdErLFy4kCFDhnDjjTfmlJumycyZM3n22Wc7LZTqqLy8PIYPH57TNSjbtr6pOfbYYznrrLNYt24dr732GkcccYTzhyY/Px+wmlu31JzV/sDfGQoKClq8Hat92127m2B75OXlcckll3DJJZewZs0a/vOf/zB//nyuu+46FixY0OIxhxxyCPPnz2fZsmUsW7aMq666ikGDBpFIJPjkk0/46KOPWgwVOlteXh77779/TjdLm33B1qdPn2YDh0JjAJKt6TgA2a3uDMPgrLPOwu128+KLLzJu3DhcLherVq1yArqOqKmpyVm3xyxoqwtaQUFBi7dWbunn3vT52fsUFRU5zfjtAddHjhyJqqq8/fbbvP766znH2Rd52d+4VVZW7pA70Nj/hi699FKmTp3abLv9Ycnj8fDb3/6W3/72t5SVlfHmm28yf/58Lr744l36tupCiJ6nqKiIvffem9dff50LLrigxdYF4XCY9957zxnyAKwuZiUlJfzjH/+gpKQEr9frtFoNBoMoisKpp57a4hdh2/qA379/f6699lr++Mc/snz5cv75z3/y4IMP0qdPH2f8nm1pz9+jgoICampq0HU9J5iyv2DoyLXK9mrPdcKCBQt47LHHuO666zj88MOdD9t2l8AdYcWKFZxxxhkkEgkeeeQR9t13X2fb5s2b+fzzz/n888+bDS4/f/585s+fz3/+859Wr1m3bNnCaaedxsaNG7n99tv54Q9/mLN92LBhQPMW3qlUCmgearaH3fVsexQWFm4zMLL//ldWVjJy5MicbRUVFQwZMoS8vDwURWkxpKmoqHAC385ghzlNx5tqbfyp9qqvr+/Qv4sRI0bwv//9D8Mwcn63rFu3LqfVV1PDhg1rsYV/Op3G5/MB1oD84XCY6urqnGu+devWUVpa6uwndj3SRk70eBUVFbz77rscffTRTJs2LWf63ve+x5FHHsnbb7/d4RYq26vpxd/UqVPZvHkzRUVFTJw40Znee+89HnrooWbf5jV14IEHUlxczBNPPMGyZcucrnvQ2DS3pqYm59zV1dXcddddLYYg7a13U/vuuy+bNm3K6eoF8Morr+B2u50m2tuyadMmpk+f7oyHMHLkSM4880z2339/ysrKWj1u4sSJ9O3bl/nz5+P1etljjz3o168fI0eO5N577yWRSLQ5KGdnNfm17yQ0btw45/XeY489eOyxx5yBzPfbbz82btyYMzB3bW1ts9cuFAo1e19++umnznJNTQ1r167lhBNOYOLEic7F7DvvvAO0v5Wb7Y033shZf/311/H7/W1+O7nvvvvy2WefNfvG/ZVXXqGkpMS50Myul+21115DURSmTp3KmjVrqK2t5eSTT2b06NHOz6Ol5xKLxXIGvY9EIrz11ltMmzatQ8/X1tbPfuTIkRQVFbFx48acf0P9+/fntttu46uvviIej3PEEUfwyCOPAFbYe9JJJ3H00Ue3+Z4VQojuMnPmTNauXcvtt9/ebJuu6/zxj38kHo/nDH6taRo/+tGPePPNN/nnP//JYYcd5rTkCIVCjB8/njVr1uT8rtxtt92455572rzz22effcb+++/PF198gaIojBs3jgsvvJAxY8Z06Hdoe/4eTZ06lXQ6nTPmkr0P4HTx35HdgNpznbB48WJGjx7NT3/6UyeQKi8v55tvvunUISdsmzdv5rTTTkNRFJ555pmcQAqsYRLs3gbZE8DPf/5zXnzxxZybxGQLh8OccsopbN26lUcffbRZIAVWiyhFUZp9ifPf//6XwsLCNsOMHaG0tJTNmzfnlDW9Ht9rr73weDz87W9/yyn/5JNPKCsrY5999iEQCLDHHnvwj3/8I+dLxoaGBt56660ODynRlgEDBjB06NBmN83517/+td3nrKurIxaLdehL7AMPPJBIJMK7777rlNnDMhxwwAGtHjd9+nS+/vrrnMHfa2pq+PTTT53Xaf/99wfI+febTCZ566232jy32PlJSynR47388suk0+lWu7D9+Mc/5oUXXuD555/vkvrk5+fz2WefObdoPf7443nqqac47bTTOOeccxg4cCDvv/8+Dz74IL/61a/aNebD0UcfzVNPPUX//v1zPpjvvvvuHHvssVx99dVs2rSJPfbYw2mSPXjwYIYPH96hegP8+9//5uCDD252gXD88cfz5z//mfPOO49Zs2YxePBg/vvf/7Jw4UJmzpzpHL8tpaWlDBgwgBtuuIFwOMzQoUNZunQpb7/9NmeffXarx6mqysEHH8zLL7/MgQce6AQ006ZN45lnnmHKlCltfiOVn5/PV199xaJFi9odoLXk3HPP5cQTT+Tss8/mF7/4BV6vl+eee4433njDGYT7uOOOc16riy66iLy8PO6///5mTZIPOeQQXnvtNfbaay+GDRvGSy+9lNMaraioiNLSUp5++mkGDBhAfn4+7777rnNnno6OKfGPf/yDoqIipk+fzqJFi3j66ae58MILmzUfz3baaafxyiuvcOqppzJz5kwKCwt5+eWX+fDDD7nppptyLuy//PJLrrrqKo455hi+/PJL7r77bk444QSGDx/uDF55//3343K5cLlcvP76685Fb/ZzcbvdXHnllVx00UWEQiEWLFhAPB5v8a6Q7ZGfn8+nn37Kxx9/nDPGAlj/vi688EKuueYaNE1jxowZ1NfXM3/+fMrLy5kwYQI+n48JEyZw77334na72X333Vm7di1/+ctfOOKII7arTkIIsSMddNBBXH755dxyyy18/fXX/PSnP6Vfv35s3LiRZ555hq+//pobb7yxWffj4447jkceeQRVVZt107NvqnLxxRdz7LHHOnfZ+/zzz9v8/Tx+/Hh8Ph+XXnop559/PsXFxbz//vt8/fXXnHzyye1+Tu35e3TwwQczbdo0/vCHP1BeXs7YsWNZtGgRDz74ID/5yU+cwazz8/OprKzk7bffZty4ca0GLtujPdcJe+65J/Pnz2fBggXsvfferFu3jgceeIBkMtnhv+3V1dWsX7++2dih2W644Qaqqqq47rrrCIfDOXcutgf5bq3lUb9+/XK2rV+/nurqamesq7vvvptvv/2W888/H5fLlXNuj8fD+PHjGTJkCL/61a946KGHcLlc7Lvvvrz55pu88sorXH311c618JYtW9iyZYtzg5od5YADDuDPf/5zTqtsOxx86623KCgoYOzYsZx11lnMmzcPt9vNjBkz2LhxI3fddRejR4/mJz/5CQAXX3wxZ5xxBmeddRa//OUvSaVSLFiwgGQy6dw0oDPYd+b7/e9/zx//+Ed+8IMfsHz5cudu29sTtNpjzx544IGAFTCuWrWKoUOHtto6fd9992Xq1KlOj4fCwkLuuece8vLynIHVwbopQDKZZPz48QCcfPLJvPTSS5x11llceOGF+P1+7rvvPhRF4YwzzgCszwg/+clPmDNnDolEguHDh/Poo49SX1/f7O6RYtcioZTo8V566SV22203xowZ0+L2yZMnM3jwYF544YU2B+DrLCeddBJLly7lzDPPZM6cOfzoRz/i6aef5rbbbmPu3Lk0NDRQWlrKxRdfzOmnn96ucx533HE8/vjjOXc6s82ZM4cHHniAZ599li1btlBUVMRRRx3F7373u222wso2bdo09t9/f2677TY++OCDZt3o/H4/Tz75JLfddht33XUX4XCYkSNH5tyBpr3uvfdebr/9du666y5qamoYOHAgM2fOzLnbTEumT5/Oyy+/nBPM2aHUtgacPv3007nppps444wzePTRRztU32xjx47l6aef5o477uDSSy/FNE3GjBnDvHnznDuleDweHnnkEW6++WZmz56Ny+XiZz/7WbMLrCuuuIJ0Os3NN9+My+XiqKOO4uKLL+YPf/iDs8/8+fO58cYbufzyy/F4PIwePZr77ruPm266iU8++STnTnbbcsEFF7Bo0SKee+45Bg4cyDXXXJNzAdGSkpISnnnmGW677TZuuOEGUqkUY8eOZf78+c3uDHPeeeexdOlSzjnnHPLy8vjNb37DzJkzAetib/78+dxyyy1ccMEFBINBxo0bx1NPPcWZZ57JJ5984nQj6du3LxdffDG33347FRUV7LXXXjz11FPNms+31znnnMP8+fM588wzWxzj42c/+xnBYJCHHnqI5557jkAgwD777MOtt97qjF9y/fXXc+edd/LII49QUVFBUVERJ5xwAhdccMF21UkIIXa00047jUmTJvH4449z8803U11dTUlJCQcccAA33nijE9BkGzt2LGPGjKGmpob99tsvZ9uBBx7Iww8/zL333susWbNwu91MmDCBRx99tM07dnm9Xh555BHnrnX19fUMHz6c66+/nuOPP77dz6c9f4/suw/ffffdPPbYY1RXVzN48GAuuuiinO50xx9/PG+//bbzRdu2rj86oj3XCWeffTY1NTU88cQTzJs3j4EDB3Lcccc59a+vr2/3l31vvfUWV1xxBU888USLLYrtliZAi10lp06dypNPPtnu5zd//nz+8pe/sGLFCqCxpc4999zDPffck7NvaWkp//3vfwG48sorGTBgAM899xwLFixgyJAh3HDDDfzsZz9z9n/hhRe499572+wq2BkOP/xw5s2bxxdffOG0Ft9tt9045phjePrpp3n33Xf529/+5oSoTz31FM899xyFhYUceeSR/O53v3O+0Ntvv/149NFHufvuu7nooovweDxMmTKFm2++udlA/t/Vj370I6LRKA8//DALFy5kt91246qrruKqq65q8wvG1rzzzjvsueeezjh0y5Yt4+STT2bOnDlt/tu89957+dOf/sQtt9yCYRjss88+3HnnnTnjw1133XVs2rTJ+fkXFBTwzDPPMHfuXK6//npSqRT77LMPf/7znxk4cKBz3PXXX09+fj4PPvgg0WjU+R2T3TJf7HoUc3tH0hNCCJHDDpA6cvHXGTZu3Mj3v//9bV5kCCGEEEJ0p5NOOok777yzxZvrdKZzzjmHPn36MGfOnB36OJ3pb3/7G+PHj8/5ku6tt97i7LPP5q9//WuHbrwSjUY56KCDuPnmm50B74XoqWRMKSGEEEIIIYQQO9RHH31ELBZr9Y5ynenCCy/kX//6V68aG/KVV17hzDPP5NVXX+WTTz5h4cKF/PGPf2Tq1KkdvhPws88+y2677das1bsQPZF03xNCCCGEEEIIsUMNHjyYhx9+uEuG29h99905++yzufXWW1u8IUBPdPPNNzvDgVRXV1NcXMyRRx7JrFmzOnSe6upqHnvsMZ588skuea2F+K6k+54QQgghhBBCCCGE6HLSfU8IIYQQQgghhBBCdLntDqWSySTHHHMMH330kVO2YcMGTj31VPbee2+OOuoo/ve//+Uc8/7773PMMcew1157cfLJJ7Nhw4btr7kQQgghhBBCCCGE6LW2K5RKJBJcdNFFrFy50ikzTZPzzjuP4uJiFi5cyHHHHcfMmTOdweXKyso477zzOP7443nxxRfp27cv5557Lu3tPWiaJuFwuN37CyGEEEKIXHI9JYQQQoiepMOh1KpVq/j5z3/O+vXrc8o//PBDNmzYwPXXX8+oUaM4++yz2XvvvVm4cCEAL7zwAnvssQenn346u+22G3PmzGHTpk0sWrSoXY8biUSYPHkykUiko1UWQgghhBDI9ZQQQgghepYOh1KLFi1i2rRpPPfccznln3/+OePHjycQCDhlkydPZsmSJc72KVOmONv8fj8TJkxwtgshhBBCCCGEEEKIXYerowf88pe/bLG8oqKCfv365ZQVFRWxZcuWdm3vdqYBVYvASIIrBK48cOeBpxA0X3fXTgghhBBCCCGEEGKn0uFQqjWxWAyPx5NT5vF4SCaT7dre7b59Gj44ueVtqtcKp9wF4C0CbzF4S8BXAr6BEBicmUrBNwDUTntZhRBC9EKmaWJiOssAJmbOctNt29p3W/tnr7e1z7b2a8++bZW395htHdfSdnsfVVEZUjAEl/y9FUIIIYTo1Trtas7r9VJbW5tTlkwm8fl8zvamAVQymSQ/P7+zqvDd9N0XiveH2CZIRyEdAT0GmGAkIF5uTQ3bOI+igX8QBIZAYCiERkDBOMgbC/4BoKjWPorWZFkDxWXNVa0rnrEQQnQLwzSc0Maet1TW2rzpvkC7jgOan8swcs5p0DjHzNQ1s7+zjIlpZPYBDMNwllt6LHvZ1ixIajJvnDU/T9NztXW+ls7ZWhkKrZdllSuKkhsUZS1mb1NQcurZ9Li2tiuZB25tu2Ea+Fw+igPF5HnzEEIIIUTvohsmmqpse8cebGd4Dj1Fp4VS/fv3Z9WqVTlllZWVTpe9/v37U1lZ2Wz7uHHjOqsK303BWDj8PWvZ0MFMgR6HRA0ktkK8AhIVEN8CiUqIV0Ky2lpOVEKyBlK1YOoQ3WBNvJ/7GO58CA6HvN0gb3frMT19rHAKO6RSrXBKdVsttDSPNVfdmXJXY3jVbLJDLvnHIYRozg5Wsic7kHFCmSblrZVlL+uGjm5ak2EaGIbhLOuGjkFjWbOwyF7O/M/6f2MIZNe7pX0wsYKTFuZKk9+Dpmk235fG/RQUFEVxApHs4+1tTZe3tS3nvNnnVnOPb1qPpo/fkf06um9vlNST1MRrursaQgghhNhOmqpwwbOfsWpruLursl1G9wtx14mTursaO41OC6X22msvFixYQDwed1pHLV68mMmTJzvbFy9e7Owfi8X46quvmDlzZmdVofOoGqBZY0l5CiFvRO5207TGntJj1pSqt0KrVD3EyqwWValaK7iKroOG1VZIlaqH2i+syRYYAn2nZKa9QfWDmYZ0HMw6K+QydesxHYoVPDkBVHagpTWGWk6I5QHNvY0gKzNHbTyXs7xzfJARoqczTTM3zMmEQU7YY68berNwyS5PG2nSZtpZduZZ53BCpUyrIKcVkN1SKBMK2a1Zclq52L+KslrW2IGLoiioinX/DFVRc0Ka7PXsfe2ARiGz3kJI1FaZEEIIIYTofVZtDbOsrL67qyF6gE4LpaZOncrAgQO54oorOPfcc3nzzTf54osvmDNnDgA//elPefjhh1mwYAEzZsxg3rx5DB48mGnTpnVWFbqOooDmtSYKwT8Q8saAHoVUgxVQxcshHbZCHbs1VHgN1C+H2i+tKfJtY6uqjX+xQqHCvaD/ITDgMPD1a70OppGZdGtOJrgydTBSVvdDmu5DY8Bkmo3rioYVdGWCKHs5Z3KB4s6EXFnBl2oHWVoLYVYL6znbMscI0YvZgY5u6o0thprM7cDIKTN0kkaStJ4mZaRIG9Y8J2zKtC7KDovslkO27G5O2eGPHQDZIU/2sqqoqGrr25uGQ0IIIYQQQgixo3RaKKVpGvPnz+eqq67i+OOPZ9iwYcybN49BgwYBMHjwYO655x5uuukm5s2bx6RJk5g3b97O88FHUcAVtCb/ANDHWN374uUQ2wx6whpjqmA8DDneOiZZB7WfQ+UHUPmhFU7VfGpNy++APpNg4OFWQOUpbPJ4dsjzHX+EdpCFmdUiy8iaZ0IuYrnbnGMgdxASp4KNr0t2IJUdftmBmNMl0W7Z5W4SeG0r3Gppmx2S7STvL7FDGabRrFWRvW4HSXZ5Mp0kqSdJGklSeqpZmKSbutVVDd36J5LVbcxucWQHP9mTpmhOaORSXc2225MQQgghhBBC7Cy+U6KxYsWKnPVhw4bx1FNPtbr/9OnTmT59+nd5yN5D81otqPwDITgCohutKVFl3cHPHQJPAfQ72JrA2r71f7Dl31ZY5QRUt1vB1JAToHBi5wYtimKFQQC4O++80ELAZS/brbqMTFfFRPNy57i26p7dqivTjTF73Qmm7MDL7sLoymrx1VILr6aD0LcQeslg9D2WHTBlTyk95Szrpk4inSCpJ4nrcZLpZGMARWNLJmc8o6yxi7LDJE3RrLmqOUFS0zIJkYQQQgghhBCidXIv5a7gKbCm4DAreIqshVQd+Ppb4YgtMBiGn2hNsS2w5Q0o+zs0fGPNy/5udRMsPRYGHNp2976ewBnvaged3+7CiJk7d0It3Qq9zNZaebUQeimKVd5qt0Ytq/WXZgVdaE26NbYn8GraoqvpmF7SwiubbuikjJQTLmUvp400sXSMRDpBLBVzWi454yqZ6WZ3K1OxgiNN0Zy5W3PjU3xOqGS3XBJCCCGEEEIIsWNIKNWV3CHrjnu+ftCwEqJl4Mlv3jUPrC6AI34Fw0+CumWw/kWrBVXDN7D8Vmsq3BMGfB/6f9/af1fjhDs7gBNeZbfwaqFbYzqK1YzGaBKStdXKK3vMrrZaeGUNVu8MXO9uJexqZbwuJ+jqmd0Z7bApqTd2hbPn8VScWDpGLBWzxl/KavlkGIbTcgnIaamkqVbA5Ff9zrq0WBJCCCGEEEKInkdCqe7g7QvuyeDtB+FVENkIgYGZAKEJRYHCPaxp7IVWa6kt/268i1/tF7D8Tug7GQYdZbWgcoW6/CntdHK6NXayFlttNWnhlT2OV86YX9DyGF6Ac1fGrFZdOa27MsGU6iGndZfqySy3FnK1IwRroYWXYRrW2EtNp3SSSCpCJBmxxmUyUqR1645xpmE9t+yxlezJ77JCJpfqQpPuk0IIIYQQQgjR60ko1V1UF+SNsAKquq8agynV0/oxngIY/gtrim+F8v/Clv9AzWdQ/Yk1fXUz9JsOQ34MfafsuJZEYvvldGvs5H+COeFW0+VMd0Y9uztja627MiNz59Q5N/AyTJOEniZpGiSMNElDJ6GniehponqSqJ4iZZikMUkbJmYmwFIUDZfqxq25cWleAqobl+bBpQVRFVfzrpPZoZcQosfI7lab1JM5LR2TetIZz63pun2TgOyuuPZdKLOPyR4PLnt7Qk8wus9oDhp6UHe/BEIIIYQQ4juSUKq7eQqg7ySo9UH0W2sQdFdw28f5+sGwE60pthnK/mG1oop8C1v+ZU3+Uhh8nDUGla94Rz8T0RN0cpfGpJ4ioSdJpJMk9DhJPUUkFSGcjBBLJ6wPl2nrwyJYA4O7FBW3ouFWFQKqhltVcamKlWuZmUnPTDl1zwq9UHDu0IgKqh2G2a27MnM0a9B5uzUYWuMxzvnU3G3ZLcdyHkeI3sE0TRJ6wml9mEgnnPWEniCZtuZOWTpBykg5A/y3OBlWK0Y7MMrelh0kZYdPutn0H3HX+aL8C+754T3d9vhCCCGEEKJzSCjVE2g+6LMnuANQv8LqutXSOFOt8Q+EUafDyNOg/mvY+Aps/gfENsHK+bDqAeh3CAz9mdXNTz6AiyxpI008nSSuJ0no1jycjFKfipDQkyT1FCkjnWlIZaKpGh7VjVv1EHAF8Pit7nXfmTN2l91lEZq15jJSja2/cga4b+F82Y29cgKvrKBKyZ67ce6saI/F5Qxanx1uqbnhlnM+yO3GmD2XVl47O7u7ajwdJ5aKEU/Hm00JPbHN9UQ64YRMiXSCuB7PWbeDpp7Io3lwq25cqstZdmutr7tUl7WuWPPs7S7VlbNun9etWXeJHRga6CwLIYQQQojeS0KpnkLVrDvraX6o/RKStR0LpsD6AFww3pp2v8C6e9/Gv1jjTpX/x5pCI61watDR4ArsiGcieqikniKuJ4ilE8TTSSKpGPXJCJF0jKSeImmknB58blXDo7nxqG4CXh8e1b3j70Sn2MEOdOqvJqerIjghV9M7NhppING4L2R1fdxWvVto4eWM76VkLatYrbWyJzUzgH3TgKu11l5N59ldHKXV17aYpklSTxJLx4imokRTUeLpuLNsl8dSMWeQ/Zx5OkY8ZQVIznomhEroiW55Tqqi4tW8eDQPXpcXt+rG6/I6ZR7NYy27rLm93d7W0uRW3Y3LmhuPmpk32W6X2YFRV92tMqknqYnXdMljCSGEEEKIHUtCqZ5EUSA41Fqu+RxQrO5928Plh8E/sqaGlbD+BauLX3iNNe7UN/dC6XEw7OcQGNxpT0F0v5SeJqbHiaWtAKohGaEuGbZaQqWtsVpAQVUUvJoHj+Ym3xPCo7l2zrvU5Yzh1ckDpOcEXllBl3NHxsxYXU7wZeYGX9mtwrbZ2gtyW11lhV92S63sQe7tOzc63RztQeuzw6+sQEvJCrqatvLKCcK0Lg2+dEMnlo4RToaJJCNEUhGiqSjhZJhoKuoMmm8HS5GUtU8sFXPm9rZoKtolXc68mhefy4fXZc2dSWuhzOXDq3mdIMk+zlm2t2XK7HWP5sHn8qEpWpeFQUIIIYQQQnQ2CaV6ouBQ60Ns7ZfWB0J33nc7X95uMOFKGDMLNv0N1j8P0fWw7s+w7hnodzAMPhaK9wdVukP0FoZpEE8niabjxNJxIqkYtYkwkXSMeDqZCZ9AUzW8mgev6ibo8+ORLi+dJyfw2sFyujfa4Rc44Vd2uZHKlOmNx7an1Re00PKrSWsvJ8jSssb3cmW1+MoEXiikTYNwKkY4FSGcitGQjBJORWhIRggno4RT1no4GSGSCZqsecQJl6KpaCe+iI28mpeAO0DAHcDn8uF3+wm4AtbcLnP5c7b7XZnJ7XcCJb/L72y3gyS5O6QQQgghhBDtI6FUTxUcZrWgqF0KKOAOffdzukMw/ESrdVTl+7DuOaj8ALa+bU3ufOj/fRh4hDX4uiIfrHoKwzSIpuJE09ZUlwhTmwwTTyeI6wlM00RRVHyaB6/mpq8vX8KnnU1O98YdpIWWX0k9SX0yTH0qTH0iYi0nozQkI9QnIzSkojSkotSnooSTMepTMSuASsWJdmKXNpeqEXIHCLr8BN0Bgu4AAXfucsAdIOgOEvAECLgCmXnImruD1uQJ4Xf50TQX0uVRCCGEEEKI7iWhVE+lKNb4T6YJdUut1giar5POrULJgdYUXgsb/mLdrS9RaY1BtfEv4C2BAT+AQUdC/jj50NaFTNMkmmn5FE3HqU00UBNvIK4nMuM+mbhVF17NQ9Dto68vf+fsdic6VdrQqU+GqUuGqU2EqUs0UJsMU5cIU5+0wqbaZGY5U2Z3+/yufJqHPE+QkNtPnjtAyO0n6PYTcvsJufyE3D5r2e0j6PJaZS6vsx7QvHg1+z2e6fLY7lZfADEwEpCogUTTsb7s5czg9ordzTGr1VeLXR+zuze2cIdHGe9LCCGEEEKIbZJQqidTFMgbCXrMGhcqUGqFU50pNALGXQRjL4Dqz2DzP2HLfyBRkene92cIDLFaTw08wtpfdKqEbg06HknFqEuGqYrXE0vFSehJTKwAyufyku8J4dW6YMBx0ePZwWVNop6aeAM1iXpqE2FrnmygNt5gzRNhahMN1GXCpu2lKgp57iB5ngD5nhD5niB57gD5niD5niChzHKeJ0DIHSDPHcxa93fO3Rk7yunSaLf+ssfxMhrLzSZdHp39s6ac1mNtyBnzq4VB6XMGrXdlhV8q4ALNlRt6QdZ4X0ructM7SOaMLdZksH3RbcrLy7nxxhv58MMP8Xq9HHXUUVx00UV4vV42bNjA1VdfzZIlSxg0aBBXXnklBx54oHPs+++/z0033cSGDRvYa6+9uPHGGxkyZIiz/bHHHuPhhx8mHA7zwx/+kKuvvhq/398dT1MIIYQQ4juRUKqnU1TI3x2MOEQ3WYOS74gPGooGRVOsafylVre+sn/C1ncgugFWP2RNebtZ4dSAH1ghmegQuxueNZZOjMpYLQ2pCLF0At000BQVv8tLyOOnWCuUAGoXkjbSVMfrrSlRT3W8jupEPTWZ9ex5TaKBZGbMsI7Kcwco9OZR4AlR4A3lzj0h8jxBCr2hTOAUyoRO/t7XGs9pAQWdPsB9S7IDLmcwdT03/DKz1skaFN85rp2P1dYdH1tqAWa3AlPtZSUTjNnbodUwjOzgi+aP5WynSTDWdN9di2mazJo1i/z8fJ5++mnq6uq48sorUVWVSy+9lPPOO48xY8awcOFC3njjDWbOnMnf//53Bg0aRFlZGeeddx7nn38+Bx10EPPmzePcc8/llVdeQVEUXn/9de69917mzp1LUVERV1xxBXPnzuWaa67p7qcthBBCCNFhEkr1BpoHCsZDOgrxLeAfuGMfT/VAv+nWlI5a401tft0KqhpWWtM391p1GnA4DDgM/AN2bJ16Kd3QMwM9R6lLhKmI1xJNxUhkukT5XF78mpf8QAiXDI680zFMg7pEmKp4HZXxWipjdVTFa6mK12WmWqrj9VTF66hLhjt8fp/moY83nz7ePAp9eRR68ij05tHHm0cfbz6F3hCF3jwnhMrzBOV9tqM43fZgh/5pzWm5ld0CLOuOj9BKCzAjd1tHgjDIhFLZgZhdmB1QgXM3yKZBGWoLXRubzJ0pcx7n3DQ+rpGCZEPjc+2B1qxZw5IlS3jvvfcoLi4GYNasWdx8880cfPDBbNiwgWeffZZAIMCoUaP44IMPWLhwIeeffz4vvPACe+yxB6effjoAc+bM4YADDmDRokVMmzaNJ554glNOOYUZM2YAcN1113HGGWdwySWXSGspIYQQQvQ6Ekr1Fq4gFO4BVZ9AsgY8fbrocQMw6IfWlKyD8jetgKp6MdR9ZU0r7oTCPaH/obt8QGWHUA2pCDXxeipjdUTTcVJGGkVRCLi85HuCeLU+0gqqFzNNk7pkmIpYDRWx2sy8hspYLRVxa14Zq6UqUUfa0Ld9wgxNUenjzaevL5++vgL6ePMp8uU7ZVYAZS/n4XN5d+CzFD1Szh0fuzhgbDoQfk53R7sse6D87O6RWfvkdJHMLjMaT5NNaVKmp60pHQHyO/EJdp6SkhIeeughJ5CyhcNhPv/8c8aPH08gEHDKJ0+ezJIlSwD4/PPPmTJlirPN7/czYcIElixZwpQpU/jyyy+ZOXOms33vvfcmlUqxfPlyJk2atGOfmBBCCCFEJ5NQqjfxFlmtk2o+B+rAU9C1j+8pgCE/tqZEZSag+jfUfAa1X1jTijuhYAIM+D70mwHBIds4ae9mmAaRVIz6ZISaRD0V0VonhFIVhaDbL3fC62XShk5lvJbyaBUVsRrKo9VsjdawNVbthE8VsdoOdZ8r9OZR5CugyFdAsa+QIn8BRb5Cirz5FPkLnW35nmDv6yYndh05gVg3SkUhspmONfPqWvn5+Rx00EHOumEYPPXUU3zve9+joqKCfv365exfVFTEli1bANrcXl9fTyKRyNnucrkoLCx0jhdCCCGE6E0klOptAoOtcUlqvwRM8BR2Tz28xTD0Z9YUr4Dy/1oDpNd8BnXLrGnF3RAaDf1nQP9DIG/MTjG2SDQVd+5itjVWQ0MyQkJPoSmqhFA9nGEaVMfr2RKtcqbyaFUmeKqmPFZNVbwWo53dggq9efTz96HYX0iJLzP3F1LsK6Q4U17kLcCtya9aIXZlc+fO5auvvuLFF1/ksccew+Px5Gz3eDwkk1a37lgs1ur2eDzurLd2vBBCCCFEbyKflHobRYHQcGteu7Rru/JlS6cbl119oPSn1hSvgoq3rKnmUwivsqbVD4JvIPQ72Jr67gNq7whu0kaa+mSEukSY8mg1dckwsXQcBQW/20eBJw+fy7PtE4kdLqWnKY9VsTlSSVmkki3RSrZEqtgcrcwEUNWkjPQ2z+NSNUp8fegf6EuJv/m8xN+HYl+hhI9CiG2aO3cujz/+OHfccQdjxozB6/VSW1ubs08ymcTn8wHg9XqbBUzJZJL8/Hy8Xq+z3nS7jCclhBBCiN5IQqneKjgMUK0uc4lq8PbdMY8Ti0M0bs3jCYjGIBy1Qim7MYnd+MkZHmQ4cCr4/w+MZWAsgdQyiG+G9c9ZkxaEPvtC/4Og3wFWy6seJJaOU5cIUx2vpzxaTTgdRTcMvJqboNtPka9AxoTqBmlDZ2usmrJwBZsiFZRFKiiLVLI5UsHmSCVbYzWY2+jSoyqKEzj1DxQxIFDkLPcP9KWfvy99ffnSjU4I8Z3Nnj2bZ555hrlz53LEEUcA0L9/f1atWpWzX2VlpdMlr3///lRWVjbbPm7cOAoLC/F6vVRWVjJq1CgA0uk0tbW1lJSUdMEzEkIIIYToXBJK9WbBIdbdiGq/gESVNebUd5VKQ2091IehqhYiUSuMMkwrfHK7waWBy+XcPTxXVlBjBIEpYO4DxMD4GvgKlGWgh6HyLWtaBnhHQf4U6Ps96DsR/EHwdF0rFMM0aEhGqU00UB6rojYRJpqKoSgqIbeffv4+uFT559IVwskoG8Nb2RjZysbwVjaFt7IpspVN4Qq2RKvQzbYHDvdqbgYEihkYLGZgoIgBwWIGBooZEChiYLCYEn+h/CyFEDvcvffey7PPPsvtt9/OkUce6ZTvtddeLFiwgHg87rSOWrx4MZMnT3a2L1682Nk/Fovx1VdfMXPmTFRVZeLEiSxevJhp06YBsGTJElwuF2PHju3CZyeEEEII0Tnkk1lvFygFFKj93Bp8fHtaHOk61DZAdQ1sqbRaQpkmeL3g90JBHqjftdVIITAQONS6G1NiNcQ+hdQXYKy31itWQ8VzgA/UseDZA/pMgYLh4PeBzwtejzV1Qisl3dCpT0aojtexOVpJXSJC0kjh1TyE3H76evOlNdQOYJomNYl6NoTL2dCwlY3hcjaEy9kYLmdjeCt1yXCbx7tVFwODxZQGSxgULGFgsIRBwWIGBYsZGCyRn5sQotutXr2a+fPnc9ZZZzF58mQqKiqcbVOnTmXgwIFcccUVnHvuubz55pt88cUXzJkzB4Cf/vSnPPzwwyxYsIAZM2Ywb948Bg8e7IRQv/zlL7nmmmsYM2YM/fr149prr+XnP/+5dN8TQohdhG6YaKpc64qdh4RSO4PAIGte+0XHg6naeli9DrZWWd3vggHoVwTaDrzVuKKCbzdr4v9Ar4XEF5D8AhJLwYxYXf7iS2DzU1DWB8zdQB0DnnHgK4G8EOQFrODM57HmXvc2660bOnVJq1teWaSC+mSElJ4m4PbRx5eHV5OxoTpLQzLCuoYtrLen8BY2NJSzPryFSCrW5rFFvgJKg/0oDfVjcMgKnwaH+jMoWEKJv1C61gkherT//Oc/6LrOfffdx3333ZezbcWKFcyfP5+rrrqK448/nmHDhjFv3jwGDbL+lg8ePJh77rmHm266iXnz5jFp0iTmzZvnhO1HH300mzZt4pprriGZTHL44YdzySWXdPlzFEII0T00VeGCZz9j1da2v8jtqQ7ZvYRLjpDWvaKRYprtvM1UNwuHw0yePJnFixcTCoW6uzo9U2wz1HwOKODbRjCVTsOGzbBmIySS0K+v1SWvu5kGpNZmAqplkFoFNOmupfQHcyQYI8EcAWof8HjA44KAH/KD4Pdb3f98Xgyvm1ozTlWsjrJoJfXJMGlDJ+j2k+8Oyp3RvoO0kWZjeCvfNmxmXf1m1jVszgRRm6lJNLR6nIJC/0BfhoT6MzjUn8GhfgzJ68/gYD8Gh/oTcPu68FkIIXqTZCpKTWQzB+1xJnmhQd1dnV5HrqeEEKL3O/rud1lWVt/d1dgux+41kLt/sU+vfg4TBuXz2qyDursaOw35NL4z8Q+05jWftz34eV0DrPoWNldCfgiKCruqhtumqOAZZU2hn4ARh9QKK6BKfg3pdWCWA+WgfmAdoxYDoyE1CqqHQnlfTAPqzDhVSpwyotRqKdI+N6FQASX+fNy+EOC2Hk9sUzgV49v6Mr6tL2NtfRnfNpTxbf1mNoa3tjnGU7GvkGH5Axka6s+QvAHOfHCon7RKE0IIIYQQQohdnIRSOxv/QDB1qP4M0hFwBRu3mSaUbYVv1kIsAQOKe0brqLaoPvDuZU0ARgSSKyD5lTVPrwOj0pr40NpF81PnGsRWpT+VehFJYyAleh7uehNqaoDaxkHb3S5rrKqgD7w+q7WVx924bRcbm6g20cDa+jLW1G1ibf0m1tRv4tv6MrbGalo9xu/yMixvYGYawFBnPoCgW8Y4EUIIIYQQQgjRsh6eSIjt4i+F/AjUfQWqG1SPdVe9tRtg9XorhBnUr7truX3UIPj2sSYAI0YqsZx4Yhkkv8FvbMRlxuhjrqYPq9kdQIW4VkTEN5iIWkpUHUSUfphp1Xpd6sNQXWuNqaVgjUvl0qxgKuCzBln3uBvDKq/H2t6LNSQjrK7bxOq6jayp38iaOiuAqorXtXpMsa+Q4fkDGZE/iGF5gxiRP4jh+YPo5+8jA4sLIYQQQgghhOgwCaV2RooCodGQikLkWzD7wMr1UFYORX2skKWXM0yD2nSUilQ9mxIe6vWxaOp4Ctw+ipUaQsZ6gvpGgsZGfGY1PrMKX7qKIj4HwEQlppQQdQ8k5h1IVB1IVB2AofggrVtjbqXTUFVr3Z3QTqxcGrgyraiCfiu0sltVuTPBVQ8KrBJ6krX1Zayq3cjq+g2srt3I6rqNlMeqWz1mQKCIkQWljMwvZUR+KSPyrQAqzxNs9RghhBBCCCGEEKKjJJTaWakaFI6Dyi3wxf8g5oUBJT2/u942RPUEVekwGxNVVKUipE2dPM3HIE8ftMz4UDH8xLRBVLitYzQzQlDfRNDYRMDYRNAow22GCZjlBNLlwBLn/AmlD1F1ADF1ADFff6KBASSVwszYU6YVVKV0a3D4SDQTWClWCyuXy5rcrsYWVm67O6Brh3YJNE2T8mgVK+s2sLJ2Aytr17OqbgPrG7agm0aLx/T392VUwWBGFpQyqmCwE0BJlzshhBBCCCGEEF2hdycUom1bqmBVDGJAibvXBlK6aVCdCrMlVcfmZC2RdAKf6qavK4hXdW/7eCVIvWsM9YyxCkwTt9lAwCgjYGx2Jo9Zh9eswavX0Ef/uvF43MTVfsQyU9zdj5i3hJRS0BgwmUZjCysnsDJAybSwsrsEujSr+2TAZ90x0A6qXK52t7JK6inW1G3im9r1fFO7jm8yAVR9MtLi/vmeIKMLBjOqYAijCgYzumAIowpKpeWTEEIIIYQQQohu1TtTCtE2w4C1a+Hrr8ETgtHToH4FpOrAXdDdtWu3iB6nMhVmQ6KK6lQEU4FCzU9fb/C7jWGkKKSUfOrUfOoY6xRrZpSAsQW/sQW/UU7A2ILPqEAjRdCwWlpl0/EQV4uJKyXWXC0m7i0m4euLqWTfZtvMBFaZqS5sdQs0M4FVdisrl2YFVj4veDw0kGRFbDMrIptYUb+Rb+o2sLa+rMU73mmKxvD8gexWMITRhUPZrXAIuxUMoUTGfBJCCCGEEEII0QNJKLWzSaXgm29g5UooLIS8PKs8bxTUfwOpBnDndWsV22KYBtXpCJuTtWxO1hLW4wRVL/09+biUHTtWk64EaNBG0qCNbCw0dbxmDX6jHL9Rjs+owG9U4DWr0EgSNMoIUgZZGZGJQlIpIK4WkVCKSKhFxJW+JDxFJL2FmEqgyZM2QNepitexvLaM5Vs2syJRzorkVjbp9S3WNd/lZ0xoMGMKhjCmcCi7FY1gRJ/BeFyeHfDKCCGEEEIIIYQQnU9CqZ1JOGy1jtqwAfr3B1/WgOa+fmDqVjClaOAKtH6ebhA3klSkGtiQqKIy1YAJFGoB+nqKureVj6KRUIpJqMXUMqGx3NStAdSNCnxGJT6z0lnWSOA1a/HqtcDqnNPZgdW3eh4fJzx8Gjf4IhHny1gdW9PRFqswyF3IGG9/dvf2Y4yrmN21IvoTsF6XtAJ1GoQjULbGamEV9DeOY+VxN45zpao77nUSQgghhBBCCCE6SEKpnUVFBSxbBjU1UFra8vhRvgGgpyG82hq4W+veu/CZpkm9HmNLspaNiRrq0lH8qod+rnzcag9/aypaptteSW65aeIigteowmdU4TMrqUtuZVlsK5/HGvg0ofNJvJYtem3zUwK7u2Fvn5cJvjzG+4sZ4x9AwF1CUikgqRSSUkKZQdedB8zqGpiG2gRUVls3C8Rs7BKYPZaV15u5U2DWHQOle58QQgghhBBCiC7Wwz/5i20yDFi/3mohZRgwZEjrAYOiQLAUSEP4W/D2AdXblbUFrIHLK1MNbEpUsyVVT8JIUaD5Gert5lZRnaBOj/F1dAtfxTbxVbSMr6ObKE8174KnAmO8Qfb2epjsgyneOFM8MUIqQCIzVYKx3FrMMFFJKvkklXxSSgFJtcBaduWTdOeTUvJJOXcLpDGsyhnLyjoTmpYZZF0Dv79x8HVP1qDrbre0sBJCCCGEEEIIsUNIKNWbRaPW+FHr1kEoZI0htS2KCoGhYOgQXQ++YlC2fQe7zhA3kmxN1rM+UUVVKoyCQl9XEL87v0sev7PFjRQrYptZFtnIsugmvopuYkOyutl+CgojfMWM85cyLjCIcYFBjPENwK/ljv/0jZnCY9bhMWrxmrV4zFo8RmZu1uMx61EwrK6BZq11UPPxzjFRSCkhUkpeJqTKI+UJkfLmkVTySCl5pJUQKcMP6cxdA2vrG1tYKViBlaZm7grogYAX/D4rpPK6pYWVEEIIIUQH6IaJpvbu66ad4TkIIXoeCaV6q/JyWL4cqqut8aO8HWjxpGoQGg7oEN0EvhJQdtxboT5tddHbkKimLh0loHkY4CnY4QOXdybdNFgbr2Bp1AqglkU3sjq2FR2j2b6DPX0ZFxjEhIAVQo31DyKobfvnYypuZ/yqhhZ30HGbYSu4Mutwm/V4jDo8Zr21bNbjNsMoGHjMBjxmA1DW+uOhkCZASguR1kKk/CFSSoi0EiRlBEgbflKGl3TET7rOjWlkLkJaamHl9WTGsXI3jmclhBBCCCEA0FSFC579jFVbw91dle0yul+Iu06c1N3VEELshOSTY2+TSsHq1bBqldWtqq3uem1RXRAcaXX5i2+2BkLvxJDIMA2qUmE2JWvYnKwlbiTJ1wIM8fZFVdrRHcwwUONJtFgSNZZAiydR40nUZBo1mUJJpFHTaZS0gZLWUXQdxTDAMME0UQwz93yKgqkqmKoKmoppTy4N06VhuDRMtwvD7cL0uNjiSrJEqeILKvhCL+erZDlRM9msmkWuEBMCgxnvhFClFO6oQeQVjZRSQIoCIq3tYxq4zAgeswG3WY/bmYdxZ4Iqt9mAy4ygYOImgtuIAOW0kK9ZXNaUxkdaCZImQNr0k9L9pOu9pGu9pA0/afyktSBpLYTuykcPFmYGXfdI6yohhBBC7PJWbQ2zrKzlOyuLriGtvYToeSSU6k3SafjqKyuUKi6GYPC7nU9zQ94oQIf4VvD2/c5jTCWMVOYuepVUpBoAhT5agH5aCFd9FHd1Oe7qetw1Ydy1YVx1Edx1EVz1EbSGGK5IHC0cQ4smUExzm4/XGaJuWDwQPhoMH5XCh4NhY0Hz/UIJmLwF9i13MaXKwz61PgaYfkx/Et2/Bd1fjRH4Bj3gQw960f2ZedBHOuhDD/rRgz70oA9zR7UkUlTSSh5p8oBBre9nGriI4jbCTkhlBVgRXGbYmhPBbYZxmVEUDFzEcZlxoCrzWICWmZrSwaxTSNf5SJs+dPyklQBpNYDuCpH2FpB2h9A9eaQ9Bei+PHR3PmlXAF0LNhnMXQixszJNE8M0MTEwmiybpomBiWEamKaJibU9rceRjxNCCNG1SkLenSLQ6e0t1g7ZvYRLjhjb3dUQolNJKNVbZAdSAwZ0rLteWzQP5I2xwqjoJnAFwR3q8GkakhEqyzdSvX4NlG2leGuY0VVRfJX1eCpq8VTVo+itNcVpnamq6H4Phi8zeVwYXjeGx43p1jC1TCsnlwZ2S6hMqyibYprWWEmmiWnorPHG+SQvwqd5ET4pjLEsL4HeJANRDZhQpTJtE0xbb/C9jTCuAjQTIJ2Zoh1+PjbD47aCqpAPPeQnHfI783TIj56XNc8LONv1oK9zWhopKmmsbnsxBrS9r2mgEc8EVxFcZgSXGc2aR3GRtWzG0EiiKCZuYriVGFCTOReQykxt0BUfaS2I7sqEWFoAPXtyZeaqP2s5gK750TU/pto146QJsTPKDopMEwwMp8zAxDQzoRGNwZHZwrphGplfvSYKmXssYKKgYGbWFEVBQUFV1Mw8s6xYywoKKiqqqqCpKi7FhUYAnxnA6+r6G3UIIcSuKt/v2mkCnd7cYm1UyXdslCA6xc4S0vaU5yChVG+g69bd9Vat6vj4Ue2heawWU64gRL6FRBV4+rTcWiWegPVlsL4Mc30ZyfUbMdZvIrC5ihHJNCPaeBhTUUgXBkn2ySPVN490QYh0QYBUQZB0fpB0nj8noNH9XkyP6zuFMPXpGMuiG/kyupGlkQ0sjW6iXo8126/YlcfE4GD2CAxmj+BgxvkHEbDHgTJNkmmdL+NJ1HjK6UqoxRJW18JYEi2aQI0n0CJxtFgCLZqZInG0aNyaR+JOCzA1mcKTTEFNi6NHtf4aqirpkA89L0A6P2AFVnZwlee3yrO35VuBFq7v0DVTUdEJoCsBEpS07xAzhcuM4SKKlgmqXGbMWsZejuEyo2hGDBcxNDOOplhplWbG0dJxSFdtV5UNxY2u+dA1P4bqz4RZPivE0vwYmg9dzWzX/OiqzykzVB+61jg3FelyKHqGtsKi7EDIblmUHRbpdplpOPtaFOvtbZIJiqx1OyRSMuuaouUERQoKmqKiqiqqouBSXKiKmilT0NBwqdakqRoqijVXMiFTJnyyJqXx2Kz17P1y7sxqJCFZY/3tEkII0aUk0BFi5whpe9I4cRJK9XS6bg1ovnKlFUj5fDvmcRQVAoNAC0B4NUQ2Q1Uavt0Cq9fD2o2wbhOUbYXMhxkFyI7HTFUlWVJAYkBfEgP6kOhXSLK4kGS/ApLFhaT6hL5bOLINaVNnTbyCLyMbWBrdyJeRDXybqGy2n1dxMS4wKBNADWGPwGD6u/NzP/RkUxRMtwvd7ULP22Yjn7YZRmNYFYnjCsfQwrHGeUPT9ahTpiVSKIaBuz6Kuz4Km9r/sOmAz2p1ld8ksMoJtxqX9Tz/d+piaCpuUoqbFB27s6JiptGIZ0KpCC49gqZH0IyYNZHApSTQ1CSakkJTktYyCesYMwGAaqZQ0ync6Y6Ffi0+F9TGkEr1YWheDNWbCbC8WetejJzt1tyaPJm5L7NsrZuq/AreGVghEFld0AynVZG1bAVCeiYQsrqmGZntJmBmfq2azu8hKzSyAqHs5exWRYoCKo2tiuxyTVFRVBUtExZpqmoFRIoLt6rhUl2oioKmaC2GQyqNZfZj5GxXsusgga0QQgghdk29OaTtSeQTUU+WSlmBlN1CakcFUomE1S1wxQrr8VZkHjOeaLlaeX7qBvclUtoXY8gA0oP7kxhURKKkcIeGTk1VphpYGtnIl9ENfBnZyNexMmJG88HIh3j6OuHTnsEh7Obv3313/lNVqxVYyN/hQ5VkygqoGuywygqsXA1RNHu5PpJbHo6jmCauaBxXNI63vKbdj6cHvFlBlZ90XrCxVVZ+INMqy++0ckvnBTC9363bnKm4rG6FSgg8xa3tBWkdUmmrW2tSt8JbAFNHVdJorjSamkLT0mhqZl1Lo2ppNDIhFklUM4FmJhrnRgLNiKPpcdTMwPYKBi49Cvr2d9ds9fmiZYVUnqzwKmtdyd1u5pS7rTLFnbWPO2vdjens5wa0Xa7Vlx0KZY9XpJutd0EzMmFRbqsiINOKqHHeWGa3LLICokyIk2lBZHc/0zQVl2IFQi5VRVPsueqERM26sDXtztZCWJQdSLXaskgIIYQQQogeSkKpnioeh2XLYN26zg+kTBPWroX33rOmJUusD/dNeb2YwweSHNqXmqElbBxcSPmgPFx9Cil0B7s02IkbKZZHy1ga3WhNkY1sSdU12y+oepkQKGViJoSaGBxMoWvnaKpretykitykijrQ+kg30CKxrMAqhqsuK7iqjzYGXPWN4ZZimk4XxI4EWYbHnRNc5XYrbNLNMGSt60EfaB0Z2FwBl8uaWqqDaWDoBindsMIq3YCEbt1p0jCs97+TLSjWXSxVe26NTYamgVtFdZtomo6q6WhKGpWkFXopSVQziUoSjRSqmUA1k2hmEtVINE56AtVIomWXGQkUezwddKcVWFcwUXKCKiu8cmMqLiu4UtyYztxlbVdcOWWN+1jbnTLFlVl2WcuZc5qKZu2nuDDUTDkauqJhoGBAm4NbNx+fqLGFkZlpZaQ4r2jzwKhZC6IWWhWpqhUYaaqWCY60zPhFWk5g5LQSaqH7mUJjK6LsFkiaEyJJSCSEEEIIIURTEkr1ROEwLF0KZWUwaBC4O2HQ5mgUPv4Y3n/fmjZvzt1eUAC77w5jx8Luu2OM2Y3K/nlsjJezuWEVqXg5hYpGf18Ryne8Q9+2GKbB+kSVEz4tjW5kZawcndyB0lUURvr6ZcaCskKoEb5iVLlzWyNNRc8PoucHSZS21vKoCcOwuhbWN4ZVVsurzB0Sc8oz83AMRTessbKqUniqOtaMNR30Od0G03l+0qGANb5Y1jhjTccca3WsLEUFl9rO324mGCYYOuimFVrpBqSTEDMwDGuyd7VuNagC3kx4pVqPZ4dammoFWi7NCs38mfBMUzPbNVBAUU1U7JBLRyXVuE4a1UiiZMIsxUii2pOZRDVSznarzF5PZbZb+yhmytrXbOxwqmCiGUkgCXqHfkQ7jK5oVlDlzF2ZIKtxTiY4Q3WB4gZ7WbWX3ShqY7liz1UPij1XXCiK25rUxklV3c7+1vEmKKa1rCjWXQ8UV+bnnNlH0TL7ao3bhBBCCCGEEB0moVRPU11tBVLV1TB4sPUhdnuYptUl78MPrRDqs8+s7oA2jwemTIH997emIUNAUYjrCSqTtWyIlVNRvwFFUegbGo4vNBTiW61B0JU4uPOwPpx/d1uT9XwV3cSy6EaWRTexLLqJiNG862CRK+QMRD4xMJhxgVKCmtx9qdOpKnqe1T2v5Q6cLTBN1Ggip+ug1a2wSVfD+mjjWFnhGFrUegRXJI4rEoctHauq7vOgB31WYBW0AytfZjlzZ8OgXeaztgetZdPjBpTGQKlDvw3tMCvT+speThuQTOVuy3qN7CZapqKgKwq6plplTrilWUGLKy8TbGUFXKritOSyjjcxVQVdtfIyQ1WtFkQqmAroqmK1GjN0FCMFZioTdCVRTR3VTKGZaTQzhWqkUU1r0swUmqmjoVtzM51Zt7enUQ1r3WXqqFj7WOe05oqZRjHSKGYqs2zNMVJOmyabZupg9pCEbLspVlClZgVWitZ8Ulsqz4RaLe2fMzXdR816HLVJudq8nMz7C63J9qxlWjmepmVqpizTHTRnn+ztWfvknNveLq3HhBBCCCF2dRJK9RS6Dhs2WOM6JZNWINXRC/aqKvjkEyuI+ugj2Lo1d3tpaWMIte++TpdAwzSoTdWzNVHNpkQF9akIXtVDP09f3NkDMQeHgqcwE07VgOYGV4iOhFO16QhfR8tYFt3EV9EyvopuorKFwai9iptxgYFOCGUNRl4gXWB6KkXBCPpIBn0kB3TguLSOyxng3Q60YlY3wkhm/Kxw4/hYzgDwkTgAWjyJFk92uGUWgOF2oYd8pDMhlR7wOYGVvZ7OKfdaywFr2fB52u5CmD1WUaa7WWOZgWnYLbHSmLoVHhkpA8PUMU0D08gEX6bV5dCKs+z/KqiAqmioqoKqapllFUVRcbk0fIqGS3Pj8nhwuzy43B5c7hCay42quVBd1t3SVJfLGv9Ic6NqqrVN1RrLXVnlWd3StuvfomlaAVQmJMPImjKhVWN5ukl5C9vNdGaevZ7KKk/nLhtp6/GblbWw3UxbLejMTJm9rUmLzcwTs/bTW+gGLdqgNAmwmi4rTYIve38gfxwUH9h9VRdCCCGEEJ1CQqmeIBKBb76xxo/Ky4Pidnazqq21xoP65BOra97q1bnbvV6YPBmmTYMDDoBhw3KCrkg6RlWqjk2xrVQl60ibafJdIQb7+rXSBU4Bd4EVRKVqIV4JiVrrnO4gKLm3565NR1ge3czXsTK+jpaxPFZGWbK22Vntbnh7BAYzIVjKhMBgRvpKum8wctF1XBrpwhDpwlDHjtMNtGi8SVAVy10Px6y7HGZCLM1ejyZQTBM1lUatCeOu2b7buJoKpP0eUgEvKb+HdMBatsv0gCcTYnnRfR7SAS9GwIvht8oMvxcz4EPxh3Cp1l3RNDLjGDljEWUGyc7cEc2eW+MWqaimgmqaaIbV60wzQdVNVJSsrogmJO0WXZnQxh57STGtJ2KvO2NrZT78a5nWLM5cy+qemGnB5dZAczWOxWUfmzNWV2abM4aXCqoXVD+4s8p6A9PIBFd6VqCVHXQZucGWqWfCrayAy9Qz+2Wv602ObzrZ+xtZ63pjGUYb+9plWcc22z8zoTd5jJaOMRsDumbHGx1o+WafR6dJA7ptS4Wt5yiE2OnphommypeSQgixs5JQqjsZBpSXW62jqqth4ECrW11rIhH43/+sEGrJEmuw8qbGjIGpU+F734NJk6xgKkvKSFOVrGVLopryRBURPYZf9dLXk49XbeOxsykaeIrAXQipMGaigvJYGSuiW1iRrGZFvJIVsS0tDkQOMNRbxPhAKeP9gxgfKGVsYCC+9j622OkZWQNZ2wNet9jqyGdi+lwYRSFMgo0tkTItiaxGRWbWZ91MKyMDPIkU3nACdzSFNxrHHU3hiSRwRzNTJIknmsAVSeCKWpMdaGnROEpaRzHBHU3ijja/42OHKAr4veD3Q8BnTX4fBPzW3J9d5rP283tzy/0+8GWVbW+336bdEU3TCrbseSqdW2Zm7Wfar3T2Bwc77KIxqFLsZSUr8MoKsOwxuFyZAMztyoRh9vFK68GXmr1P1r5q1uPZ5dv987Jb8bitIcZEy5zgymwMstBbLnfWs0IwaP14IwHufKurqxBip6epChc8+xmrtm7fl0g9wSG7l3DJEWO7uxpCCNEjSSjVHUzTCqHWrLEGM/d4YOjQlj8oRSLw7rvwxhvW2FDJJh+AR4yAffaxuuNNmQKFhc1OYZgGNal6KhI1lCUqqU+FURWNAleIog52iUsYSdZGy1gZWc83kfWsjKxnZWQDdemWLxSGevuyu38Q4wKDGO8fxNjAIEJaJ95JUHS51rqk6RgtbssOi3TTaBYa2SGGmQkwFHDukqahWHdLw2oxZG/zqJnWRKi4VBUNDU1RcCvW3dNy7pBm3xUNxWlllL3NbpGkoTr7tflvwjStcaMiUYjEsuaZ5WhmuaV5NAbReGOZHehE49ZU1Uk/JLe7MbjyexsDK583d8op81hzb2bu91qhdvb2QGZw+Y6GOvbztLsjGmbz9bSR6XoXbyX0Aifkclp6ZT2GHXbZQRRZYRRkLWe1/HJltfCyQy+XfQdGV26Q1fScTYOv7HVnmebhmL19Z6co1phXAHRyeGQkIdn+u4IKIXq/VVvDLCvreFf9nmJUyc5xJ2ghhNgRJJTqavX1Vje99eutcaT69cttHbWtAcqHDoWDDrJaQe29d4shFFgtourSYWqS9WxJVFGbakA3dfJcQQb5+qFt425RaSPNhng5a6JlrIluZFVkA6uiG9kQ24LRQj8LDZURgVJ2Dw5ld/8Axnj7MtYVJGR/ftQ81qRIi6gdqfUxjHJDoeygyDTJOcZ04iGzyU+6cTwjRcEKiVoJjlyKhqaqmeBIw63a3dI03G2ERjnd0zLbrbLcfbuVoljBjdcDfQu3/zx2uJUdVNnLsXjjcjwTWMUSEItZ82hmHos3TtF448DqqZQ11e+Ab5VV1QqovJmgypu17PG0Pvc2mTzuTLnbWne7m2zLbNfa+fNuKfQym4RfJplujXYLnXhjWdP9yQrCFCWTg9ndHWkMVRUlq/VXJohCyQ2rlBbW1awQTFEyYZgGWtY2pztkk6BLUVs5ZwutwbKDMGUb60IIIYQQQnQxCaW6gq5DZSVs3gxbtlgfLEtKrG446TQsXw6ff25NS5Y0H6B86FA47DBr2m23Vj88RPU4dakw1ak6yhPVNKQjmKZJQPNT4umTO2h5RjgdY0N8C99Gy/g2ttmZr4ttJt3KuCAFrhCjg0MYExzKbsGhjAkOZURgUPPuf6YO6ag1pWpBj4HekPnQ5LUGSlcyd0DbRWx7AGyzWWhkB0VNu6c1RkaKfU83JzDSsAa8VqHFlkZuxW5hZAVHLkVDUxqDo8YWRS0HRrkhUeN64z69ZHyg7pQdbvUp+O7nM02re10sDvFE4zyamWeXxRNWqGWXJZLWciLZWJ5INO4bT2SCGqwAx27Z1RVcWmNo5Wlpygqx3G7wuLKW3Vb3v6bLblfu/u6WtmXWXZq13la3PyMTeNmBmH13xqYhV3a3SGeiMUzMntuBmNNCDBpbh2WFYzmhGc1DMPs4J9CyQ7WsEIsmAZWq5nartJe1JutOoNXkcbODupz1lvZpUkZWWXadsycznXtnSyGEEEII0Wt1aSiVSCS47rrr+Ne//oXP5+P000/n9NNP78oqdB3DsFpF1dZad9WrrrZaPNTVNd5lb+VKa4DzaDT3WHuA8v32s6YmA5TbEnqSsB6lIR1lS6KKulSYmB5HVTSCmo8B3mI0VKpSdXwdXktZvIKN8a1silewIb6FDbFyqloZ9wnAr3oZEShlZGAQowKDGR0cwujAEIo9he3r8qdo4M6zJn8J6AnQ45COQKohE1KFAdPqKoPbmisuOnJHv86w/WEROcERQG77IrupmLWsgBPmtBQa2eGPpjYObu3KCovs5ZwgqGlXtFYCo+yASe5i2E6mue19muqu11ZRGsOZgrzOPbdpQlq3gqpYwmrhZYdY2WX2ejxpdTVuOk8kIZFqXE6mso7L7JNMWY9lS+uQjgGxzn1OHaUqmYHdM5MrO8xyNW5zaY3rLZZpWeWu3LGz7G3Zc62FMqc8U2YPQG+HSVpWSyv7V1DTIMzMag1mBzxpvfk+9r+BnOPNVkIzaPySwcw8dva/BzP330d2q7LsoIqm5eQGVmYaXEk4dJr190UIIYQQQvRaXRpK3XLLLSxdupTHH3+csrIyLrvsMgYNGsSRRx7ZldXYMUwTqqpg1SorcFq+HDZutFpHlZdb86pWBowJBmHiRNhrL2vac0/w5Y67ZJgGMT1BRI8R0WNUJmupStRRlqigKllHOB0loseoSTVQkaxhS6KK8kQ1WxJVJM1Uy4+b0dedz3DfAIb5BzLcN4DhvgGM9A+iv6eP1eLF+QCTmSfirX9Yd/Zrsu4UuIACIAQkwUyAmYREFIwI9m3VTTskUjQMRcNUVExUDFQMJdNNzbTjIXLCI7t7YWOolPWNupkZzwhy8iI7GFKw4jAVFUzTCXYUrG5qHlS0TPjjQsWVCYVcaLhUrXHcIvs40zouJxwyM8GUolrLKKhqpoWRifWa2x/AnDxLcerfuCHrg7vzOXA7QhTn/G0d2yRoaWnXbZ2iPfvnfH5t42StBT/ZxyhKy+ewW5W0Va/vymxhxa5Ps59t1n4mjS1Xst+32e/Z7FYtZM2zx1JSWivPaj3TYnmTY52HVxrDl1AXjIuh642BlR1aZa/b4VUqlVvebD2dW55KZ+2TWU61NE83DupuM8zG8/YmzphZTcKsnDK1cVnVcgeat7flLLcwt49ruq2lcjV7Uqx9VKVxWVEauzHaY4DZg9srijWmVIE7t2u7EEIIIYTolboslIpGo7zwwgs8+OCDTJgwgQkTJrBy5UqefvrpnhNKxWJW66ZIpHEKh62yhobGlk9VVVBRYc0rK63QqarKOn5b+vaF0aNh991ht93QdxtNuLSEBiNGOBWmLtlA5db3qEzUUJmqozpZR3mymq3JGqrTDdSmw9TpEer0KA1G+1oOqCj0cxUwyN2XUndfSt3FDPGWMNRTwhBPP2vgcaeXR2ZBV6xuPSiNLYXsMEgxc8Mgu+UQja2GDAxAsZaV7H0ah7M2FTLvQJd1JyXyMt0ydBTSKEYa1UyhGElU0iimgYIdFFnn0BQNVdPQFDcaLjTVhUt1oalua1lz41JdmVZJqhX+KFkhkR0GqVld0xQts5+KqmaFSU6XtCbdTLLnLXH2abLe2nHb2p59rtYfdFs7tHJYB45rtW4dfOy2du/wuZS21zt63o6+jE3DruxQo2mrE3v/ZuVNWqk07eZl6I3dwNJGY5cxXc+aZw0sjtl4Rz37cbK7lmU/nmHkrjuvg5I1plLmhVGy98l6j7fWFctezg6/Wtovu/WM3ZUuFGh5e3YLmpbO+V2YmdcyO6RKp7PCK92a22VpvTHUSuuN++l6prVX2grC9LR1rG6vZ/a15/a+aT1zbEtlWZOeblxuqUubYYKRqePOpDAEx57d3bUQQgghhBDfUZeFUsuXLyedTjNp0iSnbPLkydx///0YhoHa9Jv5Lhb719954aofU+1KYSg4k66AruYupzOTrkC6H6QGQipTlnKrJANekj43Sa+LpNdFzKuRcCvEXBAnTcz8hpjxJXEzRXJFGlZsf701RaWPp4Bib1/6+vpQ5C2k2NeXEn8x/fzFFPv60jdQhKZoVqik4ARDJia1ikINNLbOoHFGZnwiUFBULTP8SKajmWqNWaQoWG2HMqGNlim3giEXLs1lBUeq1limupz9m04KzcsVQDXTWZOemdIoehLFTFq3CNfj1gd2Mw1kbitu6s27l2S3WFE06wkrVvso67nbt3wnM89qTdJ0Oft1c16/rP2cdSG6Qc4d7Mhazgq5moVfZisDhbcxGdmPYzSGZrppretGblBmGC08NuCMxZRVd+d5ZJe1EOrlBGw0Cc2gMUizVzM7mfbOZG2091Gat2hT1czdCGk5lG5PWdbv2Jz11kLubR1vb7NfVzuYtEMsvUlZ2g4w041hlrNf1rK9vx2G6kbr++eU2etGbrkd9NnHGXrzx8s+zl7OWc88p1GlVtfFXdQuNRyCEEIIIXZqXXZFV1FRQZ8+ffBk3WmuuLiYRCJBbW0tffv27aqqtOjpyv9w5jGd0RXAwBr7pEkrJhNo4/SqouJ3+fBpPoLuAEFPiEBmnu/NJ9+bT543jwJvAYX+Qvr6+tLH34cCbwGaYoVCSqabmR3maKpmBUJNlxU7IGoMk+zjssOipgFRe7d1G9PMhFBpq2WAmc4KpuzJaJwbKadllrVvOnc/7A/NmdYj2Ms0rpP1wZkmH5id8VbIKsv+VNySzPacrmc5n6Qbl1vrntaWZt0AW3jsbdVtW+fuVt1Rh57wvLdTp/xzzQ5mte07RUvjHdlhUUstuprul9Oyq4WyFsdIgtyAq5VjmtYPmgR7Wb8HnMHgs35XtBSYNT2Xc0yTx7frmb3e+KLlbs85Jqswk7fjzt4/84M3tebHNj3ptrpqt3BImy0Gc3ZQcledoqyCpr/nFMX6nR0KNe9mugvZqYdDEEIIIcQupctCqVgslhNIAc56Mpnsqmq06qgfXcwp/65gc91GNNWFYgc2amOQo6oqmupCVVRcigtVVXGrbifwsYMej+bBrVrdxryaF7/bj8/lw+/243f7yXPnEfAECLgD+F1+8jx5eDSP1fqIxnApe24HQdnBU1vLuyRFwRok3bXdn41zZAdSOcFUdiBll9Ok3GyhnKxt5JZlP2bTslY/ILbxKbDFD5JtBU7t/ODZkePbPPY7nLdd5+/EY3JP8B2P722Pu716W317qOzwyl7PnueU0fK2lvZttkzb+7QaKrWwbVvnau96q2WZueaG/KLm23cBvWI4BNFj6IaJpu6i14VCCCF6hS4Lpbxeb7PwyV73NRnUuzsMyhvEY8c/0d3VED2J071PCCGE6Bl6+nAIO5OdIdDRVIULnv2MVVvD3V2V7XLI7iVccsTY7q6GEEKIHajLQqn+/ftTU1NDOp3GlRkHoqKiAp/PR35+/jaPNzPfmIbDvfOPqhBCCCF6l2Aw2ONaH3/X4RDkeqpj7n9rNWV17buxTE8zcXABP5s8hHQ8ipHsnc8hGYsSDocZnq9iJN3dXZ3t1t9Pr38e8hx6BnkOPcPO8ByG56tddi2wreupLgulxo0bh8vlYsmSJUyZMgWAxYsXM3HixHZ9qxeJRACYPn36Dq2nEEIIIQRY1ymhUKi7q5Hjuw6HINdTu47XgD91dyW+ozXAztCPYWd4HvIcegZ5Dj3DzvIcJs/pmsfa1vVUl4VSfr+fH//4x1x77bXcdNNNbN26lUceeYQ5c9r3SvTr14+33367R35rKYQQQoidTzAY7O4qNPNdh0OQ6ykhhBBCdKVtXU916f2Ur7jiCq699lpOOeUUQqEQ559/Pocffni7jlVVlQEDBuzgGgohhBBC9FzfdTgEuZ4SQgghRE+imOZ3vh2VEEIIIYToArFYjGnTpvHII484wyHMmzePDz74gKeeeqqbayeEEEII0TFyixYhhBBCiF4ieziEL774gjfeeINHHnmEk08+uburJoQQQgjRYdJSSgghhBCiF4nFYlx77bX861//IhQKccYZZ3Dqqad2d7WEEEIIITpMQikhhBBCCCGEEEII0eWk+54QQgghhBBCCCGE6HISSgkhhBBCCCGEEEKILiehlBBCCCGEEEIIIYTochJKAYlEgiuvvJIpU6Zw4IEH8sgjj3R3lXqt8vJyZs2axdSpUznooIOYM2cOiUSiu6vVq5111llcfvnl3V2NXiuZTHLdddex7777sv/++3P77bcjQ+ltn82bN3P22Wezzz77cOihh/LYY491d5V6lWQyyTHHHMNHH33klG3YsIFTTz2Vvffem6OOOor//e9/3VjD3qOl13LJkiWceOKJTJo0iSOOOIIXXnihG2vYc8k1T8f8+9//Zvfdd8+ZZs2a1d3V6lHkd1vHtPR63XDDDc3eZ0899VQ31rJ7tfV5Qt5budp6reR91dy6des444wzmDRpEocccggPPfSQs03eW7naeq06+73l6owK93a33HILS5cu5fHHH6esrIzLLruMQYMGceSRR3Z31XoV0zSZNWsW+fn5PP3009TV1XHllVeiqiqXXXZZd1evV3rttdd4++23+clPftLdVem1brjhBj766CMefvhhIpEIF154IYMGDeLEE0/s7qr1Or/73e8YNGgQL730EqtWreL3v/89paWl/OAHP+juqvV4iUSCiy++mJUrVzplpmly3nnnMWbMGBYuXMgbb7zBzJkz+fvf/86gQYO6sbY9W0uvZUVFBWeeeSa/+MUv+NOf/sSyZcu44oorKCkp4ZBDDum+yvZAcs3TMatWrWLGjBnMnj3bKfN6vd1Yo55Ffrd1TEuvF8Dq1au5+OKLc673QqFQV1evR2jr88Sll14q760s2/rsJe+rXIZhcNZZZzFx4kT+8pe/sG7dOi666CL69+/PMcccI++tLG29Vj/60Y86/b21y4dS0WiUF154gQcffJAJEyYwYcIEVq5cydNPPy0XaB20Zs0alixZwnvvvUdxcTEAs2bN4uabb5ZQajvU1tZyyy23MHHixO6uSq9VW1vLwoULefTRR9lzzz0BOP300/n8888llOqguro6lixZwuzZsxk+fDjDhw/noIMO4oMPPpBQahtWrVrFxRdf3KyF3ocffsiGDRt49tlnCQQCjBo1ig8++ICFCxdy/vnnd1Nte7bWXss33niD4uJiLrroIgCGDx/ORx99xKuvviqhVBa55um41atXM2bMGEpKSrq7Kj2O/G7rmNZeL7DeZ2eccYa8z2j788TBBx8s760s2/rsJe+rXJWVlYwbN45rr72WUCjE8OHD2W+//Vi8eDHFxcXy3srS1mtlh1Kd+d7a5bvvLV++nHQ6zaRJk5yyyZMn8/nnn2MYRjfWrPcpKSnhoYcecn4p2sLhcDfVqHe7+eabOe644xg9enR3V6XXWrx4MaFQiKlTpzplZ511FnPmzOnGWvVOPp8Pv9/PSy+9RCqVYs2aNXz66aeMGzeuu6vW4y1atIhp06bx3HPP5ZR//vnnjB8/nkAg4JRNnjyZJUuWdHENe4/WXku7y0JT8vcnl1zzdNzq1asZPnx4d1ejR5LfbR3T2usVDocpLy+X91lGW58n5L2Vq63XSt5XzfXr148777yTUCiEaZosXryYjz/+mKlTp8p7q4m2Xqsd8d7a5VtKVVRU0KdPHzwej1NWXFxMIpGgtraWvn37dmPtepf8/HwOOuggZ90wDJ566im+973vdWOteqcPPviATz75hFdffZVrr722u6vTa23YsIHS0lJefvll7r//flKpFMcffzy//e1vUdVdPpPvEK/XyzXXXMPs2bN54okn0HWd448/np/97GfdXbUe75e//GWL5RUVFfTr1y+nrKioiC1btnRFtXql1l7LwYMHM3jwYGe9qqqK1157bZf8drMtcs3TMaZpsnbtWv73v//xwAMPoOs6Rx55JLNmzcp5DXdV8rutY1p7vVavXo2iKNx///288847FBYWctppp+2yQze09XlC3lu52nqt5H3VtkMPPZSysjJmzJjBEUccwU033STvrVY0fa2WLl3a6e+tXT6UisVizS4s7PVkMtkdVdppzJ07l6+++ooXX3yxu6vSqyQSCf74xz9yzTXX4PP5urs6vVo0GmXdunU8++yzzJkzh4qKCq655hr8fj+nn356d1ev11m9ejUzZszgtNNOY+XKlcyePZv99tuPY489trur1iu19vdH/vZ8N/F4nPPPP5/i4mL+7//+r7ur06PINU/HlJWVOa/ZnXfeycaNG7nhhhuIx+P84Q9/6O7q9Vjyu61j1qxZg6IojBw5kl/96ld8/PHHXH311YRCIekeT+7niccee0zeW23Ifq2WLVsm76s23H333VRWVnLttdcyZ84c+b3Vhqav1YQJEzr9vbXLh1Jer7fZm81el0Bg+82dO5fHH3+cO+64gzFjxnR3dXqVe++9lz322CPnmw+xfVwuF+FwmNtuu43S0lLA+pDxzDPPSCjVQR988AEvvvgib7/9Nj6fj4kTJ1JeXs59990nodR28nq91NbW5pQlk0n52/MdRCIRzj33XL799lv+/Oc/4/f7u7tKPYpc83RMaWkpH330EQUFBSiKwrhx4zAMg0suuYQrrrgCTdO6u4o9kvxu65gf//jHzJgxg8LCQgDGjh3Lt99+yzPPPLPLhwdNP0/Ie6t1TV+r3XbbTd5XbbDH7E0kEvz+97/npz/9KbFYLGcfeW9Zmr5Wn376aae/t3b5/iv9+/enpqaGdDrtlFVUVODz+cjPz+/GmvVes2fP5tFHH2Xu3LkcccQR3V2dXue1117jjTfeYNKkSUyaNIlXX32VV199NWcMENE+JSUleL1eJ5ACGDFiBJs3b+7GWvVOS5cuZdiwYTl/nMePH09ZWVk31qp369+/P5WVlTlllZWVzZqPi/YJh8OcccYZrFy5kscff1zG0WiBXPN0XGFhIYqiOOujRo0ikUhQV1fXjbXq2eR3W8coiuJ8uLONHDmS8vLy7qlQD9HS5wl5b7WspddK3lfNVVZW8sYbb+SUjR49mlQqRUlJiby3srT1WoXD4U5/b+3yodS4ceNwuVw5g5gtXryYiRMnypgz2+Hee+/l2Wef5fbbb+foo4/u7ur0Sk8++SSvvvoqL7/8Mi+//DKHHnoohx56KC+//HJ3V63X2WuvvUgkEqxdu9YpW7NmTU5IJdqnX79+rFu3LqeVxZo1a3LG8REds9dee7Fs2TLi8bhTtnjxYvbaa69urFXvZBgGM2fOZOPGjTz55JPstttu3V2lHkmueTrm3XffZdq0aTnfnn/99dcUFhbK+FttkN9tHXPXXXdx6qmn5pQtX76ckSNHdk+FeoDWPk/Ie6u51l4reV81t3HjRmbOnJkTnixdupS+ffsyefJkeW9laeu1evLJJzv9vbXLX4H4/X5+/OMfc+211/LFF1/wxhtv8Mgjj3DyySd3d9V6ndWrVzN//nzOPPNMJk+eTEVFhTOJ9istLWXYsGHOFAwGCQaDDBs2rLur1uuMHDmSQw45hCuuuILly5fz7rvvsmDBAn7xi190d9V6nUMPPRS3280f/vAH1q5dy3//+1/uv/9+fv3rX3d31XqtqVOnMnDgQK644gpWrlzJggUL+OKLLzjhhBO6u2q9zosvvshHH33EDTfcQH5+vvO3p2k3j12dXPN0zKRJk/B6vfzhD39gzZo1vP3229xyyy385je/6e6q9Wjyu61jZsyYwccff8zDDz/M+vXr+fOf/8zLL7+8yw4z0NbnCXlv5WrrtZL3VXMTJ05kwoQJXHnllaxatYq3336buXPncs4558h7q4m2Xqsd8d5STNM0O7H+vVIsFuPaa6/lX//6F6FQiDPOOKNZ+ie2bcGCBdx2220tbluxYkUX12bncfnllwPwpz/9qZtr0js1NDQwe/Zs/v3vf+P3+/nlL3/Jeeedl9MdQ7TPqlWruPHGG/niiy/o27cvJ510Eqeccoq8lh2w++6788QTTzBt2jQA1q1bx1VXXcXnn3/OsGHDuPLKK9l///27uZa9Q/ZrecYZZ/C///2v2T5Tp07lySef7Iba9VxyzdMxK1eu5KabbmLJkiUEg0FOPPFE+RvSAvnd1jFNX6833niDu+++m2+//ZbS0lIuvPBCDj/88G6uZffY1ucJeW812tZrJe+r5srLy5k9ezYffPABfr+fX/3qV5x99tkoiiLvrSbaeq06+70loZQQQgghhBBCCCGE6HK7fPc9IYQQQgghhBBCCNH1JJQSQgghhBBCCCGEEF1OQikhhBBCCCGEEEII0eUklBJCCCGEEEIIIYQQXU5CKSGEEEIIIYQQQgjR5SSUEkIIIYQQQgghhBBdTkIpIYQQQgghhBBCCNHlJJQSQgghhBBCCCGEEF1OQikhRI+3++67c/HFFzcrf+mllzj00EO7oUZCCCGEEEIIIb4rCaWEEL3C3/72Nz744IPuroYQQgghhBBCiE4ioZQQolcoLS3l+uuvJ5lMdndVhBBCCCGEEEJ0AgmlhBC9wu9+9zvKy8t5+OGHW91ny5YtXHDBBUydOpVp06Zxww03OCHWSy+9xK9//Wvuvvtupk2bxpQpU5gzZw6maTrHP/vssxx66KFMmjSJX//616xYsWKHPy8hhBBCCCGE2FVJKCWE6BX69+/PrFmzuP/++9mwYUOz7clkklNOOYVYLMaTTz7JnXfeyVtvvcUtt9zi7PPZZ5+xdu1annnmGa6++mqeeOIJ3n//fQD++9//cu+993L11Vfzl7/8hcmTJ3PyySdTV1fXZc9RCCGEEEIIIXYlEkoJIXqNX//61wwbNowbb7yx2bZ3332X8vJy5s6dy+67785+++3HNddcwzPPPEMkEgFA13Vmz57NyJEjOe644xg7dixffvklAA899BBnn302M2bMYPjw4fzud7+jtLSUV155pUufoxBCCCGEEELsKlzdXQEhhGgvTdO49tpr+eUvf8kbb7yRs2316tUMHz6cgoICp2yfffYhnU6zfv16AIqKigiFQs72UChEOp12jp87dy633367sz2RSPDtt9/uwGckhBBCCCGEELsuCaWEEL3KPvvsw09/+lNuvPFGfvOb3zjlXq+32b66rufMPR5Ps33sMaV0XefKK69kv/32y9meHWIJIYQQQgghhOg80n1PCNHr/P73vycajeYMej5ixAi+/fZbamtrnbIlS5bgcrkYOnToNs85YsQItmzZwrBhw5zp/vvvZ8mSJTvgGQghhBBCCCGEkFBKCNHr9OnTh9///vds2rTJKTvggAMYMmQIl156KStWrODDDz9k9uzZHHPMMeTn52/znKeddhqPP/44L7/8MuvXr2fu3Ln84x//YNSoUTvyqQghhBBCCCHELku67wkheqUTTjiBhQsXsnXrVsAab2r+/PnMnj2bn//85wSDQX70ox9x0UUXtet8Rx11FJWVldx9991UVlYyevRo7rvvPoYPH74Dn4UQQgghhBBC7LoU0x5QRQghhBBCCCGEEEKILiLd94QQQgghhBBCCCFEl5NQSgghhBBCCCGEEEJ0OQmlhBBCCCGEEEIIIUSXk1BKCCGEEEIIIYQQQnQ5CaWEEEIIIYQQQgghRJeTUEoIIYQQQgghhBBCdDkJpYQQQgghhBBCCCFEl5NQSgghhBBCCCGEEEJ0OQmlhBBCCCGEEEIIIUSXk1BKCCGEEEIIIYQQQnQ5CaWEEEIIIYQQQgghRJf7f0fq7jHnjLybAAAAAElFTkSuQmC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      " ] @@ -560,7 +588,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The plots above show what happens in the four different scenarios. We observe that in the model where none of the policies were imposed, the probability of the overshoot being too high is relatively low, $0.24$. On the other hand, when both policies were imposed, the probability of the overshoot being to high was relatively higher $0.81$. \n", + "The plots above show what happens in the four different scenarios. We observe that in the model where none of the policies were imposed, the probability of the overshoot being too high is relatively low, $0.05$. On the other hand, when both policies were imposed, the probability of the overshoot being to high was relatively higher $0.72$. \n", "\n", "To identify which of `lockdown` and `mask` is the cause, we analyze the models where only one of the policies were imposed. Interestingly, the effect of the interventions is somewhat nuanced. Implementing both interventions increases the risk of overshoot as compared to the no intervention model, but individual interventions would have even worse consequences, which means that the two interventions while jointly increasing the risk to some extent mitigate each other's contribution to that risk as well.\n", "\n", @@ -571,7 +599,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Causal Explanations using `SearchForExplanation`\n" + "## Causal Explanations using SearchForExplanation\n" ] }, { @@ -585,7 +613,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 253, "metadata": {}, "outputs": [], "source": [ @@ -639,14 +667,14 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 254, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.1267)\n" + "tensor(0.1255)\n" ] } ], @@ -680,14 +708,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The above probability itself is not directly related to our query. It is the probability that the overshoot is too high in the antecedents-intervened workd and not too high in the alternatives-intervened world, where antecedent interventions are preempted with probabilities $0.5$ at each site, and witnesses are kept fixed at the observed values with probability $0.5+0.2$ at each site. But more fine-grained queries can be answered using the 10000 samples we have drawn in the process. \n", + "The above probability itself is not directly related to our query. It is the probability that the overshoot is too high in the antecedents-intervened world and not too high in the alternatives-intervened world, where antecedent interventions are preempted with probabilities $0.5$ at each site, and witnesses are kept fixed at the observed values with probability $0.5+0.2$ at each site. But more fine-grained queries can be answered using the 10000 samples we have drawn in the process. \n", "\n", "We first compute the probabilities that different sets of antecedent candidates have causal effect over `os_too_high` conditioned on the fact that lockdown and masking were actually imposed in the factual world." ] }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 271, "metadata": {}, "outputs": [], "source": [ @@ -698,11 +726,6 @@ " for i, v in mask.items():\n", " mask_intervened &= trace.nodes[i][\"value\"] == v\n", "\n", - " # Conditioning on masking and lockdown being actually imposed\n", - " with mwc:\n", - " mask_tensor = (gather(trace.nodes[\"mask\"][\"value\"], IndexSet(**{\"mask\": {0}, \"lockdown\": {0}})) == 1) & (gather(trace.nodes[\"lockdown\"][\"value\"], IndexSet(**{\"mask\": {0}, \"lockdown\": {0}})) == 1)\n", - " mask_intervened &= mask_tensor\n", - "\n", " print(\n", " mask,\n", " (\n", @@ -712,19 +735,30 @@ " )" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We specifically compute the following four probabilities that compute if the set of antecedent varables under consideration have a causal effect over the outcome or not conditioned on lockdown and masking being imposed.\n", + "1. $P(\\mathit{oth}_{\\mathit{ld}, m}, \\mathit{oth}'_{\\mathit{ld}', m'} | \\mathit{ld}, m)$\n", + "2. $P(\\mathit{oth}_{\\mathit{ld}}, \\mathit{oth}'_{\\mathit{ld}'} | \\mathit{ld}, m)$\n", + "3. $P(\\mathit{oth}_{m}, \\mathit{oth}'_{m'} | \\mathit{ld}, m)$\n", + "4. $P(\\mathit{oth}, \\mathit{oth}' | \\mathit{ld}, m)$" + ] + }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 272, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0} 0.19745628535747528\n", - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1} 0.3155815303325653\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0} 1.559821827257224e-09\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1} 1.8681491908978387e-09\n" + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 0.19184289872646332\n", + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 0.28547579050064087\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 2.4801263265317175e-09\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 2.821781475148555e-09\n" ] } ], @@ -733,7 +767,7 @@ "compute_prob(\n", " importance_tr,\n", " log_weights,\n", - " {\"__cause____antecedent_lockdown\": 0, \"__cause____antecedent_mask\": 0},\n", + " {\"__cause____antecedent_lockdown\": 0, \"__cause____antecedent_mask\": 0, \"mask\": 1, \"lockdown\": 1},\n", " mwc_imp\n", ")\n", "\n", @@ -741,7 +775,7 @@ "compute_prob(\n", " importance_tr,\n", " log_weights,\n", - " {\"__cause____antecedent_lockdown\": 0, \"__cause____antecedent_mask\": 1},\n", + " {\"__cause____antecedent_lockdown\": 0, \"__cause____antecedent_mask\": 1, \"mask\": 1, \"lockdown\": 1},\n", " mwc_imp\n", ")\n", "\n", @@ -749,7 +783,7 @@ "compute_prob(\n", " importance_tr,\n", " log_weights,\n", - " {\"__cause____antecedent_lockdown\": 1, \"__cause____antecedent_mask\": 0},\n", + " {\"__cause____antecedent_lockdown\": 1, \"__cause____antecedent_mask\": 0, \"mask\": 1, \"lockdown\": 1},\n", " mwc_imp\n", ")\n", "\n", @@ -757,11 +791,18 @@ "compute_prob(\n", " importance_tr,\n", " log_weights,\n", - " {\"__cause____antecedent_lockdown\": 1, \"__cause____antecedent_mask\": 1},\n", + " {\"__cause____antecedent_lockdown\": 1, \"__cause____antecedent_mask\": 1, \"mask\": 1, \"lockdown\": 1},\n", " mwc_imp\n", ")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As the above probabilities show, `{lockdown=1}` has the most causal role on overshoot being too high when both lockdown and masking were imposed." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -775,12 +816,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can also compute degree of responsibilities assigned to both lockdown and mask as follows:" + "We can also compute degree of responsibilities assigned to both lockdown and mask as follows. To compute degree of responsibility of lockdown and mask, we specifically compute the probability that these factors were a part of the cause of the outcome. Mathematically, we compute the following:\n", + "1. Degree of responsibility of lockdown: $\\sum_{\\mathit{ld} \\in C} P(\\mathit{oth}_{C}, \\mathit{oth}'_{C'} | \\mathit{ld}, m)$\n", + "2. Degree of responsibility of mask: $\\sum_{\\mathit{m} \\in C} P(\\mathit{oth}_{C}, \\mathit{oth}'_{C'} | \\mathit{ld}, m)$" ] }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 273, "metadata": {}, "outputs": [ { @@ -788,27 +831,34 @@ "output_type": "stream", "text": [ "Degree of responsibility for lockdown: \n", - "{'__cause____antecedent_lockdown': 0} 0.24750958383083344\n", + "{'__cause____antecedent_lockdown': 0, 'mask': 1, 'lockdown': 1} 0.2363203763961792\n", "\n", "Degree of responsibility for mask: \n", - "{'__cause____antecedent_mask': 0} 0.09557109326124191\n" + "{'__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 0.09837335348129272\n" ] } ], "source": [ "print(\"Degree of responsibility for lockdown: \")\n", - "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_lockdown\": 0}, mwc_imp)\n", + "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_lockdown\": 0, \"mask\": 1, \"lockdown\": 1}, mwc_imp)\n", "print()\n", "\n", "print(\"Degree of responsibility for mask: \")\n", - "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_mask\": 0}, mwc_imp)" + "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_mask\": 0, \"mask\": 1, \"lockdown\": 1}, mwc_imp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Fine grained analysis of `overshoot` using sample traces" + "As the output shows, `lockdown=1` has a higher degree of responsibility than `mask=1`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fine grained analysis of overshoot using sample traces" ] }, { @@ -820,7 +870,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 276, "metadata": {}, "outputs": [], "source": [ @@ -836,6 +886,7 @@ " ).bool()\n", " for key, val in masks.items():\n", " mask_tensor = mask_tensor & (trace.nodes[key][\"value\"] == val)\n", + "\n", " data_to_plot = data_to_plot.squeeze()[torch.nonzero(mask_tensor.squeeze())]\n", "\n", " os_too_high = gather(\n", @@ -862,7 +913,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 277, "metadata": {}, "outputs": [], "source": [ @@ -876,6 +927,7 @@ " \"__cause____antecedent_mask\": 0,\n", " \"__cause____antecedent_lockdown\": 1,\n", " \"__cause____witness_mask_efficiency\": 0,\n", + " \"lockdown\": 1, \"mask\": 1\n", " },\n", " 1,\n", ")\n", @@ -886,6 +938,7 @@ " \"__cause____antecedent_mask\": 1,\n", " \"__cause____antecedent_lockdown\": 0,\n", " \"__cause____witness_lockdown_efficiency\": 0,\n", + " \"lockdown\": 1, \"mask\": 1\n", " },\n", " 1,\n", ")" @@ -893,7 +946,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 293, "metadata": {}, "outputs": [ { @@ -901,14 +954,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "factual: 24.31097984313965 counterfactual mask: 21.902610778808594 counterfactual lockdown: 20.758800506591797\n", + "factual: 24.26240348815918 counterfactual mask: 26.32572364807129 counterfactual lockdown: 21.432722091674805\n", "Probability of overshoot being high\n", - "factual: 0.7299000024795532 counterfactual mask: 0.5736842155456543 counterfactual lockdown: 0.5078909397125244\n" + "factual: 0.5996000170707703 counterfactual mask: 0.8367347121238708 counterfactual lockdown: 0.28977271914482117\n" ] }, { "data": { - "image/png": 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", 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", 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      " ] @@ -947,6 +1000,7 @@ "plt.ylabel(\"pr\")\n", "plt.xlabel(\"overshoot\")\n", "plt.title(\"Counterfactual - Necessity World\")\n", + "plt.axvline(x=(overshoot_threshold), color = \"red\", linestyle = \"--\", label=\"overshoot too high\")\n", "sns.despine()\n", "\n", "print(\"Overshoot mean\")\n", @@ -974,12 +1028,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can have similar plots for sufficiency worlds (indicated by 2) where variables are intervened on to be their antecedent value. The resulting plots show that when `mask` is set to be 1, there is a higher probability of high overshoot but the distribution is more flat as compared to the distribution when `lockdown` is set to `, that has higher peaks." + "The above histogram also takes into account the context that is being kept fixed. If `lockdown` is being intervened on, keeping `lockdown_efficiency` fixed would hinder the effect of intervention. Thus to obtain the relevant samples, we also filter for the appropriate context. Once we have filtered for the context, we take the samples and plot them as density above. The histogram above plots three quantities. It plots $P(\\mathit{os} | \\mathit{ld}, m)$ as the factual distribution of overshoot, $P(\\mathit{os}_{\\mathit{ld}'} | \\mathit{ld}, m)$ as `counterfactual_lockdown` and $P(\\mathit{os}_{\\mathit{m}'} | \\mathit{ld}, m)$ as `counterfactual_mask`. These distributions help in comparing how necessity interventions for the two antecedents affect the overshoot." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can have similar plots for sufficiency worlds (indicated by 2) where variables are intervened on to be their antecedent value. The resulting plots show that when `mask` is set to be 1, there is a higher probability of high overshoot but the distribution is more flat as compared to the distribution when `lockdown` is set to 1, that has higher peaks." ] }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 286, "metadata": {}, "outputs": [], "source": [ @@ -993,6 +1054,7 @@ " \"__cause____antecedent_mask\": 0,\n", " \"__cause____antecedent_lockdown\": 1,\n", " \"__cause____witness_mask_efficiency\": 0,\n", + " \"lockdown\": 1, \"mask\": 1\n", " },\n", " 2,\n", ")\n", @@ -1003,6 +1065,7 @@ " \"__cause____antecedent_mask\": 1,\n", " \"__cause____antecedent_lockdown\": 0,\n", " \"__cause____witness_lockdown_efficiency\": 0,\n", + " \"lockdown\": 1, \"mask\": 1\n", " },\n", " 2,\n", ")" @@ -1010,7 +1073,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 292, "metadata": {}, "outputs": [ { @@ -1018,14 +1081,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "factual: 24.31097984313965 counterfactual mask: 26.651079177856445 counterfactual lockdown: 22.560808181762695\n", + "factual: 24.26240348815918 counterfactual mask: 26.453907012939453 counterfactual lockdown: 26.533231735229492\n", "Probability of overshoot being high\n", - "factual: 0.7299000024795532 counterfactual mask: 0.8868421316146851 counterfactual lockdown: 0.7044476270675659\n" + "factual: 0.5996000170707703 counterfactual mask: 0.6938775777816772 counterfactual lockdown: 0.6761363744735718\n" ] }, { "data": { - "image/png": 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", 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", 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      " ] @@ -1064,6 +1127,7 @@ "plt.ylabel(\"pr\")\n", "plt.xlabel(\"overshoot\")\n", "plt.title(\"Counterfactual - Sufficiency World\")\n", + "plt.axvline(x=(overshoot_threshold), color = \"red\", linestyle = \"--\", label=\"overshoot too high\")\n", "sns.despine()\n", "\n", "print(\"Overshoot mean\")\n", @@ -1087,6 +1151,13 @@ ")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As for the earlier histogram, we can describe the above histogram as follows. It plots three quantities. It plots $P(\\mathit{os} | \\mathit{ld}, m)$ as the factual distribution of overshoot, $P(\\mathit{os}_{\\mathit{ld}} | \\mathit{ld}, m)$ as `counterfactual_lockdown` and $P(\\mathit{os}_{\\mathit{m}} | \\mathit{ld}, m)$ as `counterfactual_mask`. Again, these distributions help in comparing how sufficiency interventions for the two antecedents affect the overshoot." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1096,14 +1167,14 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 281, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.3526315789473684\n" + "0.0\n" ] } ], @@ -1112,6 +1183,7 @@ " \"__cause____antecedent_mask\": 1,\n", " \"__cause____antecedent_lockdown\": 0,\n", " \"__cause____witness_lockdown_efficiency\": 0,\n", + " \"lockdown\": 1, \"mask\": 1\n", " }\n", "with mwc_imp:\n", " data_nec = gather(\n", @@ -1130,6 +1202,7 @@ " ).bool()\n", " for key, val in masks.items():\n", " mask_tensor = mask_tensor & (importance_tr.nodes[key][\"value\"] == val)\n", + "\n", " data_suff = data_suff.squeeze()[torch.nonzero(mask_tensor.squeeze())]\n", " data_nec = data_nec.squeeze()[torch.nonzero(mask_tensor.squeeze())]\n", "\n", @@ -1142,6 +1215,7 @@ " \"__cause____antecedent_mask\": 0,\n", " \"__cause____antecedent_lockdown\": 1,\n", " \"__cause____witness_mask_efficiency\": 0,\n", + " \"lockdown\": 1, \"mask\": 1\n", " }\n", "with mwc_imp:\n", " data_nec = gather(\n", @@ -1159,6 +1233,7 @@ " ).bool()\n", " for key, val in masks.items():\n", " mask_tensor = mask_tensor & (importance_tr.nodes[key][\"value\"] == val)\n", + "\n", " data_suff = data_suff.squeeze()[torch.nonzero(mask_tensor.squeeze())]\n", " data_nec = data_nec.squeeze()[torch.nonzero(mask_tensor.squeeze())]\n", "\n", @@ -1178,12 +1253,12 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 264, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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      " ] @@ -1239,7 +1314,7 @@ " label=\"Mean Overshoot\",\n", ")\n", "ax.axhline(y=(os_mask_nec) * 36 / 45, color=\"white\", linestyle=\"--\")\n", - "ax.text(13, 2, 'pr(masking caused overshoot): %.4f' % pr_mask.item(), color=\"white\")\n", + "ax.text(13, 2, 'pr(masking is a cause ): %.4f' % pr_mask.item(), color=\"white\")\n", "\n", "ax.legend(loc=\"upper left\")\n", "\n", @@ -1250,14 +1325,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "It is evident from the plot above that counterfactual for lockdown has more probability mask in the top right quadrant (low overshoot in necessity world and high overshoot in sufficient world). This gives us a more clear picture into why lockdown has more causal role in overshoot being too high as compared to masking." + "The above heatmaps plot the distributions $P(\\mathit{os}_{\\mathit{ld}}, \\mathit{os}_{\\mathit{ld}'}|\\mathit{ld, m})$ and $P(\\mathit{os}_{\\mathit{m}}, \\mathit{os}_{\\mathit{m}'}|\\mathit{ld, m})$ respectively. It is evident from the plot above that counterfactual for lockdown has more probability mass in the top right quadrant (low overshoot in necessity world and high overshoot in sufficient world). This gives us a more clear picture into why lockdown has more causal role in overshoot being too high as compared to masking." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## For advanced readers: Looking into different contexts" + "## Looking into different contexts (For advanced readers)" ] }, { @@ -1273,7 +1348,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 282, "metadata": {}, "outputs": [], "source": [ @@ -1285,6 +1360,7 @@ " \"__cause____antecedent_lockdown\": 0,\n", " \"__cause____witness_lockdown_efficiency\": 0,\n", " \"__cause____witness_mask_efficiency\": 1,\n", + " \"lockdown\": 1, \"mask\": 1\n", " },\n", " 1,\n", ")\n", @@ -1297,6 +1373,7 @@ " \"__cause____antecedent_lockdown\": 0,\n", " \"__cause____witness_lockdown_efficiency\": 0,\n", " \"__cause____witness_mask_efficiency\": 0,\n", + " \"lockdown\": 1, \"mask\": 1\n", " },\n", " 1,\n", " )\n", @@ -1305,7 +1382,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 291, "metadata": {}, "outputs": [ { @@ -1313,14 +1390,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "mask_efficiency fixed: 20.179100036621094 mask_efficiency not fixed: 22.04108238220215\n", + "mask_efficiency fixed: 19.134967803955078 mask_efficiency not fixed: 26.356489181518555\n", "Probability of overshoot being high\n", - "mask_efficiency fixed: 0.4541666805744171 mask_efficiency not fixed: 0.6267281174659729\n" + "mask_efficiency fixed: 0.05000000074505806 mask_efficiency not fixed: 0.8035714030265808\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
      " ] @@ -1351,6 +1428,7 @@ "plt.ylabel(\"pr\")\n", "plt.xlabel(\"overshoot\")\n", "plt.title(\"Counterfactual Lockdown\")\n", + "plt.axvline(x=(overshoot_threshold), color = \"red\", linestyle = \"--\", label=\"overshoot too high\")\n", "sns.despine()\n", "\n", "print(\"Overshoot mean\")\n", @@ -1370,6 +1448,19 @@ ")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use the notation $\\mathit{os}^m$ to describe the variable $\\mathit{os}$ when $m$ is kept fixed. \n", + "\n", + "The above histogram plots the following distributions:\n", + "1. `mask_efficiency fixed`: $P( \\mathit{os}^{\\mathit{me}}_{\\mathit{ld}'} | \\mathit{ld}, m)$\n", + "2. `mask_efficiency not fixed`: $P( \\mathit{os}_{\\mathit{ld}'} | \\mathit{ld}, m)$\n", + "\n", + "The plot clearly shows that depending on the fact that `mask_efficiency` was kept fixed on the factual value or not, the `overshoot` variable changes." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1379,7 +1470,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 284, "metadata": {}, "outputs": [], "source": [ @@ -1391,6 +1482,7 @@ " \"__cause____antecedent_lockdown\": 1,\n", " \"__cause____witness_mask_efficiency\": 0,\n", " \"__cause____witness_lockdown_efficiency\": 1,\n", + " \"lockdown\": 1, \"mask\": 1\n", " },\n", " 1,\n", ")\n", @@ -1402,6 +1494,7 @@ " \"__cause____antecedent_lockdown\": 1,\n", " \"__cause____witness_mask_efficiency\": 0,\n", " \"__cause____witness_lockdown_efficiency\": 0,\n", + " \"lockdown\": 1, \"mask\": 1\n", " },\n", " 1,\n", ")" @@ -1409,7 +1502,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 290, "metadata": {}, "outputs": [ { @@ -1417,14 +1510,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "lockdown_efficiency fixed: 21.957277297973633 lockdown_efficiency not fixed: 21.78769874572754\n", + "lockdown_efficiency fixed: 26.546525955200195 lockdown_efficiency not fixed: 25.787281036376953\n", "Probability of overshoot being high\n", - "lockdown_efficiency fixed: 0.5786407589912415 lockdown_efficiency not fixed: 0.563265323638916\n" + "lockdown_efficiency fixed: 0.8561151027679443 lockdown_efficiency not fixed: 0.7894737124443054\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
      " ] @@ -1455,6 +1548,7 @@ "plt.ylabel(\"pr\")\n", "plt.xlabel(\"overshoot\")\n", "plt.title(\"Counterfactual Mask\")\n", + "plt.axvline(x=(overshoot_threshold), color = \"red\", linestyle = \"--\", label=\"overshoot too high\")\n", "sns.despine\n", "\n", "print(\"Overshoot mean\")\n", @@ -1473,6 +1567,17 @@ " oth_mask_notfix.item(),\n", ")" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly to the earlier histogram, the above plot shows the following distributions:\n", + "1. `lockdown_efficiency fixed`: $P( \\mathit{os}^{\\mathit{le}}_{\\mathit{m}'} | \\mathit{ld}, m)$\n", + "2. `lockdown_efficiency not fixed`: $P( \\mathit{os}_{\\mathit{m}'} | \\mathit{ld}, m)$\n", + "\n", + "The plot clearly shows that `lockdown_efficiency` has little effect on how intervening on `mask` affects `overshoot`." + ] } ], "metadata": { From 220962637123a39ae999382bfd8e2f8efdc711c7 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Wed, 28 Aug 2024 15:43:54 -0400 Subject: [PATCH 078/111] tweaks --- docs/source/explainable_sir.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index ecdb5469..3a64a328 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -800,7 +800,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As the above probabilities show, `{lockdown=1}` has the most causal role on overshoot being too high when both lockdown and masking were imposed." + "As the above probabilities show, `{lockdown=1}` has the most causal role on overshoot being too high among all the possible sets of causes when both lockdown and masking were imposed." ] }, { From b7966081f0f150c9b7d917f0c9b670f2bd67bf6e Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Thu, 29 Aug 2024 10:38:34 -0400 Subject: [PATCH 079/111] revised the sir notebook --- docs/source/explainable_sir.ipynb | 171 ++++++++++++++---------------- 1 file changed, 78 insertions(+), 93 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 3a64a328..0322cc67 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -13,7 +13,7 @@ "source": [ "The **Explainable Reasoning with Chirho** package aims to provide a unified, principled approach to computations of causal explanations. We showed in an earlier [tutorial](https://basisresearch.github.io/chirho/explainable_categorical.html) how Chirho provides a handler `SearchForExplanation` to carry out the program transformations needed to compute causal queries and explanations, focusing on discrete variables (we assume the reader is familar with it). In this notebook we illustrate the usage of `SearchForExplanation` for causal models with continuous random variables in the context of a dynamical system.\n", "\n", - "We take an epidemiological dynamical system model (described in more detail in this [tutorial](https://basisresearch.github.io/chirho/dynamical_intro.html)) and show how the but-for analysis is not sufficiently fine-grained to allow us to derive the right conclusions about effects of different policies during a pandemic. Next, we illustrate how various causal explanation queries can be computed using `SearchForExplanation` and inference algorithms. We also demonstrate how more detailed causal queries can be answered by post-processing the samples obtained using the handler. " + "We take an epidemiological dynamical system model (described in more detail in this [tutorial](https://basisresearch.github.io/chirho/dynamical_intro.html)) and show how the but-for analysis is not sufficiently fine-grained to allow us to derive the right conclusions about the effects of different policies during a pandemic. Next, we illustrate how various causal explanation queries can be computed using `SearchForExplanation` and inference algorithms. We also demonstrate how more detailed causal queries can be answered by post-processing the samples obtained using the handler. " ] }, { @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 200, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -91,7 +91,7 @@ "pyro.set_rng_seed(seed)\n", "\n", "smoke_test = \"CI\" in os.environ\n", - "num_samples = 10 if smoke_test else 300" + "num_samples = 10 if smoke_test else 700" ] }, { @@ -112,8 +112,8 @@ "\n", "1. Find the time at which the number of infected individuals is at its peak, `t_max`.\n", "2. Determine the proportion of susceptibles at `t_max` in the whole population, `S_peak`.\n", - "3. Find the proportion of susceptibles (those not infected) at the end of the logging period, `S_final`.\n", - "4. Calculate the additional ratio of infected individuals since the peak as `S_peak - S_final`.\n", + "3. Find the proportion of susceptibles (those who have never been infected yet) at the end of the logging period, `S_final`.\n", + "4. Calculate the additional ratio of infected or infected and later removed individuals since the peak as `S_peak - S_final`.\n", "\n", "This quantity is of interest because epidemic mitigation policies often have multiple goals that need to be balanced. One goal is to increase `S_final`, i.e., to limit the total number of infected individuals. Another goal is to limit the number of infected individuals at the peak of the epidemic to avoid overwhelming the healthcare system. A further goal is to minimize the proportion of the population that becomes infected after the peak, that is, the overshoot, to reduce healthcare and economic burdens. Balancing these objectives involves making trade-offs.\n", "\n", @@ -129,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 201, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -169,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": 202, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -217,7 +217,7 @@ "source": [ "The number $0.15$ is the overshoot you get if $\\beta = 0.03, \\gamma = 0.5$. This value is observed by simulating the SIR dynamics model with these values and calculating the overshoot directly.\n", "\n", - "Also, note that the above dynamical system introduces the variables: `S` - susceptible, `I` - infected, `R` - recovered and `l` - effect of intervention. These variables evolve over time and their dynamics are captured by the model. As we add features to our model, we also add new variables to this list. And even further on in the notebook, we will describe the probabilities we compute in terms of these variables." + "Also, note that the above dynamical system introduces the variables: `S` - susceptible, `I` - infected, `R` - recovered and `l` - effect of intervention. These variables evolve over time and their dynamics are captured by the model. As we add features to our model, we also add new variables to this list. Further on in the notebook, we will describe the probabilities we compute in terms of these variables." ] }, { @@ -233,12 +233,12 @@ "source": [ "\n", "\n", - "Now suppose we are uncertain about $\\beta$ and $\\gamma$, and want to construct a Bayesian SIR model that incorporates this uncertainty. Say we induce $\\beta$ to be drawn from the distribution `Beta(18, 600)`, and $\\gamma$ to be drawn from distribution `Beta(1600, 1600)`. This adds the random variables `beta` and `gamma` to the list of random variables that our model captures." + "Now suppose we are uncertain about $\\beta$ and $\\gamma$, and want to construct a Bayesian SIR model that incorporates this uncertainty. Say we induce $\\beta$ to be drawn from the distribution `Beta(18, 600)`, and $\\gamma$ to be drawn from distribution `Beta(1600, 1600)`. This converts the parameters of the original dynamical system into random variables `beta` and `gamma` in our model." ] }, { "cell_type": "code", - "execution_count": 203, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -279,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 204, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -302,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 249, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -360,7 +360,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now that we have our full-fledged model of SIR dynamics along with interventions, we have a complete list of random variables in question: `S` - susceptible, `I` - infected, `R` - recovered, `l` - effec of intervention, `ld` - lockdown, `m` - masking, `le` - lockdown efficiency, `me` - mask efficiency, `je` - joint efficiency, `os` - overshoot, and `oth` - overshoot is too high. We use these notations in the rest of the notebook to describe the probabilities we are computing." + "Now that we have our full-fledged model of SIR dynamics along with interventions, we have a complete list of random variables in question. In our explanation we will abbreviate them as follows. `S` - susceptible, `I` - infected, `R` - recovered, `ld` - lockdown, `m` - masking, `le` - lockdown efficiency, `me` - mask efficiency, `je` - joint efficiency, `os` - overshoot, and `oth` - overshoot is too high. We use these notations in the rest of the notebook to describe the probabilities we are computing." ] }, { @@ -389,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 250, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -435,32 +435,12 @@ }, { "cell_type": "code", - "execution_count": 251, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "dict_keys(['lockdown', 'mask', 'beta', 'gamma', 'lockdown_efficiency', 'mask_efficiency', 'joint_efficiency', 'S', 'I', 'R', 'l', 'overshoot', 'os_too_high'])" - ] - }, - "execution_count": 251, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "samples.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 252, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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ilHHjxikOh0NRFPU5Hj16dL7td5+f//3vfx7vmTt37lSaNGmi1K1bVzl06NBl2+R+biMiIpTJkydr16ndbldee+01JSIiQhkwYIC2fn7v97nldx0V97V3ue0GDx6sREREKM8884zH5+/x48e1z4sFCxZo83Of39yfR+7PsIJ069YtzzaKol7P7sf5/fff53uct956y+N5mTRpkhIREaH07t1bmxcbG6vUrVtXadq0qXLkyBGPY2zZskWpU6eOEhER4fH+437ec79333vvvUpERISyfv16j33ExsYqtWvX9vgu0qFDB6VOnToe14bL5VImTJigREREKMOHD8/zeHJfr9fidSaEECVFMqWEELeN7t27ExQUBIBer7692e122rdvzyuvvIKvr6/H+u5uDadPny50v7GxsSxfvpzw8HDefPNNjxH/qlSponVV+OSTT7T5X375JaB258ld+yQkJISXX36ZatWq5dutJbdFixaRmZlJr1696NWrl8ey+++/n169epGRkcFXX31VpHORn8WLF5OcnMwjjzzC3Xff7bHsoYceol27dpw6dUob3jsuLg6A8PBwjyLDISEhvPnmm0yaNEnLIilM2bJl6dixI0888YTHfH9/f7p16wbk/7wYjUbGjh3r8Rw89NBDmM1mEhMTSUxMBGD79u3s37+fOnXqMHDgQG1dk8nEG2+8QWho6GXb6DZ//nwAXn31VY+MM7PZzBtvvEGVKlXYu3cvGzduBKBNmzYEBwezbt06j659oHbRMBgM2mO8kmvLrUGDBtSvX1+7r9friY+PB9Tzq9PptGWVKlXirbfeYsqUKXleBwADBw6kVq1a2v3KlStr3b5ydzN0n4sJEyZ4PM9+fn688847+Pv78+uvv2pdgq7mGi4q97YjRoygTJky2vx69eoxaNCgIu3jSs9bQdq1a6edH51O57HPgowePZqwsDDtfqtWrejTpw82m63A7qnX2tU8Xw8++CDNmjXT7gcFBdGjRw8Aj6wO97m+dCCJRx55hJEjR/J///d/V/04Ro0aRUhIiHa/ffv2VKhQAYfDwbFjx656/yEhIYwcORKDwQCoz/GAAQMAz8f6zz//sHnzZmrVqsWwYcM83jMbNGjAoEGDsNvtRcrWdatcuTJDhw7Vrimj0ciYMWMIDQ1l7dq1nDx58qof37Vw5swZfv31V/z9/Xn77bc9Xj+VK1fWstY+/vjjPNuGh4d7fB4V9hmWkZFBvXr1uO+++/J8hoWHh2uZgfl9noSGhuZ5Xvr16wfAoUOHtHkJCQl06tSJQYMG5RlVsEmTJtSsWbPAY+Tmzpb6/vvvPeYvX74cp9Pp8ZqLj4/HaDRSqlQpbZ5Op+OZZ55h9OjRWkZVQUridSaEENeKBKWEELeN3D+q3bp27cqcOXM8fixlZ2ezb98+raaF0+kstNbI1q1bcTqdREVFeQQN3Fq2bIler2fbtm04nU4URWHLli3o9XratWuXZ/2OHTvy888/079//0Ifz5YtWwDo0qVLvsvvueceADZv3pxnWX7nIj+bNm0C8Dg/ubVu3dpjvejoaEwmEytWrGDgwIEsXryY8+fPA9C8eXN69+5dpFELx44dy6xZs7QfdQCJiYmsX7+ebdu2AeRbo6Ny5cpasM3NbDYTHBwMoNU0cp+7/OrlWCwWWrVqddk2glqPbPv27ej1ejp16pRnudFo1EaMdJ8jk8lE165dsdvt/Prrr9q6//zzD6dOnaJ58+baD43iXlu55de1pmnTpoD6Q+/FF1/khx9+ICkpCVCvu549e3r8WHdr1KhRnnlly5YF1Ho4AOfOneP06dOEhIQQExOTZ31/f3/tfLuvyau5hovC5XKxdetWDAYDLVu2zLP8cl3V3K70vBWkqK8/t1KlSuX7GnTXf7rS81NcV/N8NWjQIM88d+Hr3LXGmjRpAqi1m9555x02b96M3W7HbDbTv39/2rZte1WPwdvbO9/Xhvs1l5mZeVX7BzXgeenIf5e+XuDie0LTpk3zDay431+L8/zefffdeUaNtFgstGjRotj7up7c11KLFi3w8/PLszwmJobw8HDOnz+fJ5BWlC67br6+vkyaNClP18zY2Fj+/PNP9u/fD+T/eVK3bt0859J9nVitVm0Uxjp16jBt2jSPz2yn08nx48f54YcfSElJAdBqRRakW7duWCwWVq5c6fGaWLZsGXq9nnvvvVeb16RJE7Kzs+nduzczZ85k165duFwuQkNDefTRR7X3rIJc79eZEEJcSzL6nhDithEYGJjv/PT0dBYtWsSaNWs4cuQI8fHxKIrikb2gFFIHyZ3RtHr16kK/LGdlZXl8OQ0JCcHb2/tKHgpwMSupfPny+S6vUKECcPEvorkVdC4u5a7bNHjw4ELXcweeypYty5QpUxg9ejR//fWXVti3Zs2adOrUiT59+hR5BKYDBw7w1VdfsWvXLo4fP05GRgaA9rzk95wEBATkuy/3Dwv3jwj3ucv9V+bc3OfucpKTk7Hb7QQHB+f7wyr3vnI/Dz179uSLL77gxx9/5MEHHwTUWiKAlj0Cxb+2cgdGLg3OgVrjaOTIkUydOpWff/6Zn3/+GZ1OR926denSpQsPPfRQvtdGfiM3uQOGl57TwoKOl56Lq7mGi8L9/ISEhODl5ZVneUHHvdSVnreCFGfdwtrpzvy6tFDz9XKt33MuvYYAhg0bxunTp1m3bh1z585l7ty5+Pj40KpVK3r27FnkQGJB/P39881Mc79HFPZeX1T5vQ/l91jdr+/PP/+czz//vMD9ud9fi6Kg58YdFLvW18qRI0eYM2dOnvnVq1fn2WefLXC7y11LoF5P8fHxxMXFedRly++97XK2b9/ON998w969ezl58qQ2yEFxP09yB6lcLpcWTHQ6nfzyyy+sWLGCQ4cOcfbsWW0AhMKOkVtgYCCdOnXixx9/5Ndff6Vnz57s2bOHQ4cO0apVK+05BJg4cSKDBg1i7969zJgxgxkzZhAUFESbNm247777uOOOOwo91vV+nQkhxLUkQSkhxG0jv79EHzp0iMcff5zExESCg4OpX78+3bp1IzIykmbNmnHnnXdedr/uHxk1atSgdu3al12/KCM8FcXlvuC622U2m/MsK6y7Q27utrZr167AoAuoj93tnnvuoXXr1qxatYq///6bTZs2cejQIQ4dOsT8+fOZN28eDRs2LPS4H3/8MW+//Tag/rhp164d1atXJyoqipMnTzJ+/Ph8tytKN6iirJc7Q6swRfkB6z6HuZ+H+vXrU61aNbZs2UJcXBxhYWH88ssv+Pj4eGRcFffayq2gx9i/f3+6d+/Ob7/9xt9//82WLVvYs2cPe/bs4bPPPuPLL7+kcuXKHtsU5Xq5knNxNdfwtaDX64v8XF/JeSvsuMVhsVgKXX5pNkdBrva952qer6K+Nv39/fn000/ZvXs3v//+Oxs2bGDPnj38+uuv/Prrr3Tp0oXp06cXv/HFbMfVKOox3OcrKiqq0GLdxWnz5V4rl2ZwFaSo10pCQoIWUM8tJiam0KBUcdpw6WMq7nM4btw4vvzyS/R6PbVq1eLuu++mevXqNGjQgHXr1vHBBx/ku11Rj5OZmcnjjz/Orl278PLyol69erRs2ZKaNWvSuHFjJkyYoGWGXc7999/Pjz/+yPLly+nZs6fWla93794e65UpU4YlS5awZcsWVq9ezfr16zl48CDLly9n+fLlDBgwgOHDhxd4nOv9OhNCiGtJglJCiNva+PHjSUxM5P/+7/8YOnSoxw9Ud1bT5YSHhwNQu3Zt3nnnncuub7fbMZlMpKSkkJ2dnSeDw2q18u2331KtWjWaN29e4H5KlSrFsWPHOHPmjFazIjf3SEvFqY+U3zGOHz9Ov379tO4fReHv78+9996rdTfYu3cv7777LmvXruW9997Tag/l59SpU0ydOhV/f38++OADrZuBW2HbFpU7W6ugul3uv+JfTlBQkPZcpqen5xu4K+h56NmzJ9OmTePXX3+levXqxMfH07NnT49uesW9tooqNDSUPn360KdPH1wuF9u3b2fSpEns2bOHjz76iIkTJxZ7n+6sszNnzhS4jvtcuGsjXe9rODg4GIvFQnJyMhkZGXnqPiUkJBQrUHM9zltRFHQ9us917uw09w/p/B5X7lHurkRJvOe4RUVFERUVxUsvvURaWho///wzEydOZOXKlWzdujXP+8KtyP36btmyJS+99NI12eflrhV3to07MJo7cyu33N0MC9OsWTOPunJFVZT3C3cNpty11Ipr8+bNfPnll5QtW5aPP/7Y4w8ogEcX6iv16aefsmvXLpo3b8706dPzZFgV9VwC3HHHHZQvX55NmzaRkpLCr7/+SkBAQL6ZSzqdjpiYGK27dGJiIkuWLGHatGnMmzePxx577LLd5f8LrzMhxK1PakoJIW5rO3fuBOCZZ57JkzGRe0jkgr64w8V6M1u2bPGoA+G2e/duOnfuzPPPP4+iKJhMJqKionA6naxduzbP+ps2bWL8+PFad46C/lrrPu6lQ1O7/fzzzwD51vcpKvcx3N3wLjVlyhTuvfdevvnmG0AtuN2uXTuWLVvmsV7dunUZNmwYcLFLYEHctTGaNWuW7xdi9zm7mm427mDfqlWr8vx4L+h5yY/JZCI6OhqXy6UVe8/N4XDw+++/A3nrcvXo0QOdTscff/yhPVc9e/b0WKe419blTJo0iVatWnn81V6v19OkSRMtq6E4XYVyK1euHOXLl+fChQv51q1JS0vTXlPux3Ul13BxsiR0Oh133HEHLpeLVatW5Vn+559/Fmk/xT1v1zob59ixY/kWSXb/oM59ftxBTXfNq9zc73e5Faet1/s958KFC/Tu3Zvu3bt7zPf39+fBBx/Uar1d6TV6rVyr59d9PtesWZPvZ8xvv/3G3XffzRtvvFHkfeb33pWRkcHatWu1axYuXifuwR9yO3ToUL61ta7ldd24cWN0Oh3r1q0jPT09z/KNGzeSlJREhQoVilSHsCDua75z5855AlJOp1MbgOJqPk927NgBwKOPPponIBUbG8uRI0eAwr9HuOl0Onr37o3dbmfGjBmcP3+erl27emRLHj58mO7du+cpRh4aGsrAgQOJjIxEUZQCu2reKq8zIYRwk6CUEOK25q7Bc+kP1i1btvDmm29q9/MrgupWsWJFOnTowPnz5xk1apTHF+zExERGjRrFiRMnPEbuevTRRwH1x27uH5tJSUlMmTIFuFhbyP1lNDMz0+NL7YMPPoiPjw/fffcd3333nUeblixZwvfff4+Pj0+eUbKK46GHHsLHx4cvvviCFStWeCxbvXo1CxYsYP/+/URFRQFqofGzZ88yZ84cj7oyiqJoheNzjwiXH/dz8s8//3j8YLLb7bz33nusWbMGUDPKrlT9+vVp3Lgxx44dY8qUKdp5dblcTJ48+bKjJOX2+OOPA2qAbu/evR7tHTduHCdPnqRWrVo0btzYY7ty5coRExPDpk2b+P333ylVqlSezLgrubYKU7ZsWeLj43n33Xc99uVwOLSAgvu5vBLuc/H6669rWTOg/igeNmwY6enptGvXTqsjcyXXsLsrT34/ZAtr05QpU7Qfh6DWwpk2bVqR9lHc8+Zu49VmJrkpisKIESM8jr1y5UqWLFmCv78/DzzwgDbfXUR9wYIFHj+058+fz549e/Lsu6D3l/xc7/ec4OBgnE4nBw8ezJMRefr0aW1QgdyjXN4I7nNW1GuwIM2aNaN27drs3buXKVOmeHzOnDhxggkTJnD06FGqVq1a5H1u2rTJY7Q+m83G66+/TkpKCnfffbeWKVW1alXMZjOnTp1i9erV2vqpqamMGzcu330X97VXGPd7W3p6OsOGDdNqBoKacff6668DFz8rr5R7kIsNGzZ4BPazsrIYPXq0Nore1XyeuD+z/vjjD4/X3NmzZxk8eLBWW6qox+jduzd6vZ6FCxdq93OrUqUKcXFxrF27ll9++cVj2Z49ezhy5Ag+Pj55gnBut8rrTAgh3KT7nhDitvbEE08wadIkhg8fzqJFiwgPD+fkyZP8+++/BAUFER4eTnx8PPHx8YXWVHrzzTc5ceIEK1asYN26dURFRaHT6di6dSuZmZk0atTIo3tG165d2bBhA4sXL+aee+4hJiYGg8HAtm3bSEtL47777uOuu+4C1C+8AQEBpKam0qdPHypVqsQ777xD6dKlmTx5MkOHDmXEiBHMnz+fqlWrcuzYMfbv34+3tzdTpkwpcjHn/OQ+xtChQ5k1axbVqlXj3Llz2g/c1157Tat31KFDBzp16sRvv/1Gp06daNSoEb6+vhw8eJDjx48TFhbGCy+8UOgxY2JiqFOnDvv27aNLly7aX/Z37dpFYmIiNWvW5NChQyQkJFzx4wI1INivXz/mz5/Pn3/+Sa1atdi/fz8nTpygQYMG/PPPP0XaT8eOHRkwYACffvopDzzwAI0bNyY4OJh//vmH8+fPU758eaZNm5ZvHaGePXuyadMmEhMTGTBgQL7rFPfaKszDDz/MTz/9xPbt22nfvj0NGjTAbDazb98+zp49S7Vq1XjiiSeKtK/8PPbYY+zYsYOff/5Zu669vb3ZunUrFy5cIDIy0qOL25Vcw+7aO+6RHe+8806PoMylWrZsycCBA/noo4+49957tQLAGzdupG7dukW6jop73txtnD17Njt27LjqwsFVq1bl0KFDdOrUiSZNmhAfH8+OHTswmUxMmTLFo7tcv379+OWXX1i5ciV33XUXkZGRHDp0iGPHjnnUqHEr6P0lPyXxnjNu3Dgee+wxJk2axDfffEP16tVJT09n27ZtWK1WnnrqqULrL5UEd+2wP//8k6effpro6GieeeaZYu9Hp9Mxbdo0Hn/8cebNm8eKFSuoW7cu2dnZbN26FbvdTpcuXYoVmImOjmbixIl89913VKxYUXsfioiIYPTo0dp6Pj4+9O3bl3nz5vHcc89pr9UtW7YQGBhITExMnozH4r72Lmf8+PEcP36c1atX06FDB5o0aUJWVhabN2/GZrPRrVs3Lah8pe6++25mzpzJwYMH6dixIw0bNsRms7Fjxw7S0tKuyefJo48+ys8//8y3337L9u3bqVmzJklJSezYsQNFUbTXSFGPUa5cOVq0aMHatWupWbNmnj/kGI1G3nzzTYYMGcILL7xA3bp1qVChAhcuXNBGYn3ttdfyHaDC7VZ4nQkhhJtkSgkhbmv9+/dn6tSpREVFcfDgQf744w+ysrJ47LHHWL58uRYY+uOPPwrdT2hoKN988w0vvPACpUqVYsuWLezcuZOqVavy2muvMW/evDwj7U2YMIG3336bunXrsm3bNtavX0/58uUZM2YMEyZM0NbT6/W88847VK9enX379rFu3Tqt3lXnzp359ttv6datG4mJifz++++kpqZy//33s2TJEo+i2Veqc+fOLFmyhB49epCWlsaff/5JQkIC7dq1Y8GCBR4/GnQ6He+++y4vv/wyVapUYfv27fz555+4XC4ee+wxli1bdtmR7QwGA/Pnz6d///6EhISwdu1atm7dSsWKFRk3bhzfffcdAQEB7Nq166p+SFSuXJnFixfz8MMPk52dzR9//IGvry+zZ8/WhmIvquHDhzN79myaNWvG/v37+fPPP/H19eXZZ5/lu+++o1q1avlu16VLF+26uLTrntuVXFsFsVgsfPLJJwwcOJDQ0FA2bdrE2rVr8fHx4ZlnnmHx4sXFHhkuN71ez7Rp05g0aRL16tVj+/btrFu3jjJlyjBs2DAWL16cp95Qca/hjh070r9/f3x8fPj777/Ztm3bZdv18ssv895771G3bl22bt3Knj176NWrF3Pnzi3S4yrueXv44Ye1emp///13vhlKxVGmTBm+/PJL6tWrx9q1azl06BDt2rVj0aJFtG/f3mPdqKgovvjiC1q3bk1CQgJr1qwhLCyMefPm0a1btzz7Luz9JT/X+z2nYcOGfPnll3Tp0oXU1FRWr17N3r17adSoEe+//z6vvPLKVe3/Wqhbty4vv/wy4eHhrFu3jvXr11/xvqpWrcqyZct48skn8fHxYd26dezfv5969eoxadIk3n333SIX4wf12ps4cSJWq5XVq1ej1+t58skn+eqrr7SsIbdXX32V1157jerVq7N9+3Z2797N3XffzeLFi/Ot43Qlr73ChIaGsmjRIp5//nlCQ0O110p0dDTTpk1j6tSpxR4U4FJ+fn5888033HfffVgsFv766y927dpFnTp1mDZtGgsWLECn07F27VrsdvsVHaNBgwZ8+eWXtG7dWrtmT5w4QceOHfn6668ZOnQocPnvEbk1atQIyJsl5da5c2c++eQT2rRpw9mzZ1m1ahWHDx+mTZs2zJ8/n4cffrjQ/d8KrzMhhHDTKddibFwhhLiNtG7dmri4OFavXn1VGQFCCCGEEJfq0aMHx44d46+//tK6BwohxH+VZEoJIUQuKSkpJCUlodPp5IuiEEIIIa6J7OxsFEVh3rx5HDhwgK5du8r3DCGEQGpKCSEEoBYgHzBgAElJSTgcDho1alTkLlNCCCGEEIXp3LkzFy5cwGaz4ePjw+DBg290k4QQ4qYgmVJCCIE6ElJCQgIXLlwgJiaGyZMn3+gmCSGEEOI20bBhQxRFITIykg8//PCy9ReFEOK/QmpKCSGEEEIIIYQQQogSJ5lSQgghhBBCCCGEEKLESVBKCCGEEEIIIYQQQpQ4CUoJIYQQQgghhBBCiBInQSkhhBBCCCGEEEIIUeIkKCWEEEIIIYQQQgghSpwEpYQQQgghhBBCCCFEiZOglBBCCCGEEEIIIYQocRKUEkIIIYQQQgghhBAlToJSQgghhBBCCCGEEKLESVBKCCGEEEIIIYQQQpQ4CUoJIYQQQgghhBBCiBInQSkhhBBCCCGEEEIIUeIkKCWEEEIIIYQQQgghSpwEpYQQQgghhBBCCCFEiZOglBBCCCGEEEIIIYQocRKUEkIIIYQQQgghhBAlToJSQgghhBBCCCGEEKLESVBKCCGEEEIIIYQQQpQ4CUoJIYQQQgghhBBCiBInQSkhhBBCCCGEEEIIUeIkKCWEEEIIIYQQQgghSpwEpYQQQgghhBBCCCFEiZOglBBCCCGEEEIIIYQocRKUEkIIIYQQQgghhBAlToJSQgghhBBCCCGEEKLESVBKCCGEEEIIIYQQQpQ4CUoJIYQQQgghhBBCiBInQSkhhBBCCCGEEEIIUeIkKCWEEEIIIYQQQgghSpwEpYQQQgghhBBCCCFEiZOglBBCCCGEEEIIIYQocRKUEkLcdBRFudFNuCK3aruFEEKI2418Jov8yHUhxM1HglJC/Ac99thj1KlTh927d+e7vH379owYMeKqjxMZGcmMGTOKtc3ixYuZPHnyVR+7pB06dIiHH37YY96VPH4hhBCiqLZt28bzzz9Py5YtiYqKokOHDrz++uscOXLkRjfNw4wZM4iMjCyx423bto2BAweW2PFuNkOGDMnzPW7EiBFERkYWOJ05c6ZI+96zZw9169Zl6dKlHvPT09OZPHkyHTt2pGHDhnTv3p2FCxficrmK1Xb3tZJ7qlOnDs2aNeO5557j0KFDRd7Xp59+yiuvvAJAamoqr776Klu3bi1We67UiBEjaN++faHrLF26lMjISE6fPl3k/RZlmwsXLnDnnXdy6tSpIu83t4yMDMaNG0fLli2Jjo7mqaee4ujRo5fdLiEhgZdffplmzZrRuHFjhg4dSlxcnMc6ixcvzvf6Gz9+/BW1VdwejDe6AUKIG8PpdDJy5EiWLl2K2Wy+LsdYtGgRZcqUKdY2c+bMISYm5rq053r65Zdf2LFjh8e8K3n8QgghRFF89NFHvPvuu7Rq1YrXXnuN8PBwTpw4wVdffUWvXr2YNGkSXbt2vdHNvCEWL1580wXmSoLL5WLSpEmsXLmSXr16eSwbNGgQffr08ZiXkpLCCy+8QExMDOXKlbvs/m02GyNGjMDhcHjMVxSFF198kd27dzNkyBCqVavGhg0bmDBhAsnJyTz33HPFfiyLFi3S/u90Ojl79izTpk2jb9++rFixgvDw8EK3P3LkCB9++CHLly8H4N9//+X777/nvvvuK3Zbrpc777yTRYsWUapUqWu63+DgYPr3789rr73GggUL0Ol0xdr+5Zdf5p9//mHYsGH4+fkxc+ZM+vXrx4oVKwgMDMx3G4fDwVNPPUV6ejpvvPEGDoeDqVOn8uSTT7J06VJMJhOgPg9Vq1blf//7n8f2YWFhV/ZgxW1BglJC/Ef5+/tz6NAhZs2axUsvvXRdjtGwYcPrst9bxX/98QshhLg+/vjjD6ZOncrzzz/P4MGDtfkxMTHce++9vPzyy4wYMYKIiAhq1qx5A1sqSsr+/fuZMGECu3fvxsvLK8/ySpUqUalSJY95zz//PIGBgbzzzjtFCly89957pKWl5Zm/b98+1qxZw3vvvcfdd98NQPPmzUlJSeHjjz9m0KBBxQ6MXPodqnHjxpQtW5a+ffvy3XffXTYT7u2336Zbt26ULl26WMctSSEhIYSEhFyXfT/yyCPMmTOH3377jc6dOxd5ux07dvDHH3/w0Ucf0bZtWwCaNGlChw4d+PLLL3n22Wfz3e6XX35h3759rFixgho1agBQu3ZtunXrxs8//0yPHj0ANSgVFRUl35GFB+m+J8R/VO3atbn33nv5+OOP2bNnT6HrOp1OFi5cSPfu3alfvz533nkn77zzDlartdDtcndf27RpE5GRkWzYsIEBAwbQoEEDWrZsydtvv43T6QTUboNnzpzhu+++80hNPnv2LEOHDiUmJoYGDRrw+OOPs2/fPu04p0+fJjIyknnz5nHXXXfRoEED5syZQ2RkJH/88YdHm/79918iIyP57bffALBarUyZMoW2bdtSr149unfvzk8//eSxTfv27Zk+fTqTJ0+mRYsW1K9fnyeffJLjx48Daqr5zJkz8zzmS7vvxcXFMXLkSNq2bUv9+vW5//77WbVqVZ5ztnDhQkaNGkVMTAzR0dG88MILJCQkaOucPHmSZ555hmbNmtGgQQMeeugh/vrrr0KfCyGEELePmTNnUq1atXwzUEwmE+PHj8dgMDB37lwABgwYQO/evfOsO2jQIO3HIsDWrVt59NFHadCgATExMQwfPpykpCRt+dKlS6lTpw6LFy+mZcuWxMTEcPjw4SJ/Lv3555/06NGDqKgounTpwrJlyzyWF+Vz0mq1MmvWLO666y6ioqLo3LkzH330kdZNbMSIEXz33XecOXOGyMjIPN3M3GbMmMFdd93Fb7/9Rrdu3YiKiqJnz57s2LGDnTt38sADD1C/fn26devGhg0bPLY9ePAgTz/9NI0aNaJRo0Y899xzebpK7d+/n8GDB3PHHXdQt25dWrduzYQJE8jOztbWKcpnvru71qZNm/J9HG7Dhw/H6XSyaNEiQkNDC10X4K+//uLXX39l5MiRBAQEXHb97du388UXXzBmzJh8lz/00EM0b97cY161atXIzMwkMTHxsvsvinr16gFoXQ1nzJhBp06dmDlzJjExMbRq1YqUlBQOHjzIn3/+Sbdu3QD1O2i/fv0A6NevH4899pi2z59++onevXsTHR1Ny5YtGTNmDCkpKR7H3b17N08++STNmjWjUaNGPPPMM0XuRrh06VK6dOlCVFQUPXr08Hhd5NcV77vvvuOee+7R1t+wYQN16tTJcx3/888/9OnTh6ioKO68804+/vhjj+Vms5kuXbrw4YcfavPc38ULek0ArF27Fh8fH1q1aqXNCwkJoWnTpoV+11y7di1Vq1bVAlIANWrUoHr16tp2iqJw4MABateuXeB+xH+TBKWE+A977bXXCA4OZuTIkdhstgLXGzNmDJMmTaJjx47MmTOHvn378sUXXzBo0KBiF4x85ZVXaNy4MR988AHdunXj448/ZvHixYD6JTs8PJy2bdtq6cxJSUn06dOHvXv3Mnr0aKZOnYrL5aJv3755UvNnzJjBU089xZQpU+jVqxeVKlVixYoVHuv8+OOPBAUF0bZtWxRF4bnnnuPrr7/miSeeYM6cOURHR/PSSy/l+aK8YMECjh49yqRJk5gwYQJ79uxh+PDhADzwwAPcf//9gJpu/sADD+R53AkJCdx///1s3bqVl156iRkzZlC+fHmee+45LbXcbdq0abhcLt59911effVV/vjjD9566y1ATc1/+umnycrKYsqUKcyePZugoCCeffZZTpw4UaznQgghxK0nKSmJPXv20K5duwKzT4KCgmjRooUW0OnRowd79+71+JxITU3l77//pmfPngBs2bKF/v374+XlxXvvvcdrr73G5s2b6devn0cgxel08umnnzJx4kRGjhxJ1apVi/y5NGbMGPr378+cOXMoU6YMI0aMYP/+/UDRPicVReGZZ57h448/5oEHHuCDDz7grrvu4r333mPs2LGAGmhr27Yt4eHhLFq0iDvvvLPAc3n+/Hn+97//8cwzz/D++++TmprKkCFDGDp0KA888ACzZs1CURReeukl7RwcO3aMPn36kJiYyOTJk5k4cSKnTp3i4Ycf1oIvcXFx9O3bl6ysLP73v/8xd+5cunbtyueff86CBQs82lDYZz5c7OJVt27dAh8HwJQpU/jqq6+oVatWoeu5z+PkyZOJiYnhrrvuuuz6WVlZjBw5kqeffjrf2mB169Zl/PjxBAUFecz//fffr2k20LFjxwA8Mr7Onj3LX3/9xbRp0xg5ciSBgYH88MMPhIeHa9k4devW1YJpY8aM0a6V2bNnM3ToUBo2bMj06dN57rnnWLlyJY899pj2fG/cuFGrGfrWW28xYcIEzp07R58+fS7bRfTcuXN89NFHvPDCC8yYMQOdTseQIUMKDNItW7aMESNG0KhRI2bPnk2XLl0YNGiQ9sfb3N544w26du3KRx99RHR0NG+//XaeP8Tedddd7NmzRztvdevWvexr4siRI1SoUAGDweAxv1KlStp+CtquSpUqeebn3u7kyZNkZGSwe/duunTpQt26dfMNTov/Hum+J8R/WGBgIOPHj+fZZ58tsBvf4cOH+fbbb3n55Ze1VOmWLVtSqlQpXn31Vf7++28tvbcoHnjgAe0vu82bN+f333/nzz//pE+fPtSpUwez2UxISIj2ReKzzz4jOTmZr776ivLlywPQpk0b7rnnHt5//32mT5+u7fvuu+/2qBXQo0cPPv30U7Kzs/Hy8kJRFH766SfuuusuzGYz69atY82aNUybNo177rkHgNatW5OVlcU777xDt27dMBrVt8mAgABmz56tfUifPHmSGTNmcOHCBcqUKaPVjiooHXnevHkkJSWxcuVK7XG0bduW/v37M2XKFLp164Zer/6dICIigkmTJmnb7tq1i19++QWAxMREjh49qn3pBqhfvz4zZ84sNLAohBDi9uDOEnF/lhSkcuXKrFq1ipSUFDp37sy4ceP48ccftc/gX3/9FafTqWWTTJ06lapVq/Lhhx9qn3UNGjSga9euLFmyhL59+2r7fuaZZ7QftvHx8UX+XJowYQJt2rQB1B+rnTp1YvPmzdSqVatIn5Nr1qxh/fr1vPvuu1q9rJYtW+Ll5cX7779Pv379qFmzJiEhIZjN5st2EcrKymLs2LFamw4fPszUqVOZOHGi9semzMxMhgwZwrFjx6hduzYzZ87E29ub+fPn4+fnB6jfZzp27MjHH3/M8OHDOXjwILVr1+b999/X1mnRogXr1q1j06ZNHl3PCvvMh6J38SpOIfnVq1dz5MgRXn/99SKtP3XqVHx8fHj66ac5f/58kbb57LPP2Lx5MyNGjNC+3xRH7rpV2dnZ7N+/n7feegt/f3+P7D6Hw8Hw4cNp0qSJNm/jxo1ERUVpQVs/Pz8tg6dGjRrUqFGDlJQU5syZw4MPPuiR/RUREUHfvn21a37q1KlUrlyZjz76SHtdtGrVik6dOjF9+nTef//9Ah+Dy+Vi1qxZVK9eHQCLxUL//v3ZuXMnHTp0yLP++++/T7t27ZgwYQKgfic1mUxMnTo1z7pDhw7VgmUNGzbkt99+Y+PGjbRr105bJyoqCoANGzZQtWpV/Pz8LvuaSEtL067Z3Hx9fcnIyCh0u8qVKxe63b///guoPRxGjBiB0Whk2bJlDB8+HJvNxoMPPlho28TtSzKlhPiPa9++PT169ODjjz9m7969eZZv3rwZIE+x1K5du2IwGC6bTn6p6Ohoj/tlypQhMzOzwPU3bNhA7dq1KV26NA6HA4fDgV6vp02bNqxfv95j3UvTgXv06EFmZqb2l6Pt27dz9uxZ7a/CGzZsQKfT0bZtW23fDoeD9u3bEx8f75GaHRUV5fFXI3cQKisrq0iPe/PmzURHR+f5EdGjRw/tC73bpV8YypQpox0nLCyMGjVqMHr0aIYPH84PP/yAy+Vi5MiRUjdECCH+A9wZyu7CwQVxf2YpioKPjw8dO3b06J6+YsUKmjdvTunSpcnKyuKff/7Rsojdn4cVK1akevXqrFu3zmPfuT9vi/O5lDtwUKFCBUDN2IKifU5u3rwZo9GYJ7vHHaRwf2cpjkaNGnk8FlCDcW7u7B93Ozdu3EhMTAxeXl7aefLz86NJkyba95JWrVrxxRdfYLFYOHz4MKtWrWLOnDkkJSXlCdQV9pl/vSxcuJDatWvTokWLy667adMmFi1axKRJk7Q/1F3OF198waRJk7j77rvp37//FbWxbt262tS4cWP69u2LzWbTsupzu/T736lTp7TrqyA7d+7EZrNpQVm3Jk2aUL58eTZv3kxmZia7d+/m7rvv9vgOGBAQQLt27S57vQUHB2sBKbh4zedXl+vEiROcPXs2z7Vd0GAFuV9L3t7ehIWFadeom7+/PwEBAcUa3a+wHhCF1QUrynZNmzblgw8+4LPPPqNdu3a0bt2aqVOn0qJFC6ZPn17s3hfi9iGZUkIIXn/9dTZs2MDIkSNZsmSJxzJ3v/pLvwAYjUaCg4Pz/WAtzKXFN/V6faEfQsnJyZw4caLAtPXcX9x8fHw8llWuXJno6GhWrFjB3XffzYoVK6hUqZL2BTQ5ORlFUTy+kOYWFxenfdHx9vbO026gyEMdp6SkULFixTzz3V+Ac3+RyO9Y7nOk0+n49NNPteKVy5Ytw2Qy0bFjR8aNG1fgqChCCCFuD+6gjTtjqiCnTp3C19dXC6r07NmT5cuXs3//fsLCwti0aZPWTSw1NRWXy8XcuXO1OlS5WSwWj/u5P2+L87mUezv356j7860on5MpKSkEBwfn6Vrk/o5S3O8kQL5ZIZd+DueWnJzMTz/9lKf+JKBlNLm74y1cuJDMzEzKli1L/fr185zH/I51ue9FVys5OZlNmzYxdOjQy66bkZHByJEjeeqpp6hRowYOh0P73uNyuXA4HB6BKpfLxZQpU5g3bx7dunVj8uTJxS5w7vbtt99q/zeZTISHhxdYK8vX19fjfnp6eqHPIVz8fpvfqG9hYWGkpaWRlpaGoiiFrlOYS7+Xus9Fft8d3bXbLn2MBY1KV9Trxtvbm/T09ELbmZufn59HTTO3jIwM/P39C90uv0yq9PR0bbvQ0FCPTC63tm3bsn79ehISEi47qqK4PUlQSghBYGAgb7zxBs899xyzZ8/OswzU9Pzcf7202+1cuHCB4ODg69o2f39/YmJiePXVV/NdbjabC92+R48eTJo0ibS0NH755Rct1dm9bx8fnzz1HdzyS0O+UoGBgcTHx+eZ755XnPNYunRp3njjDcaOHcv+/fv55ZdfmDt3LsHBwVqdBCGEELen0NBQGjZsyMqVK3nhhRfy7RqVnp7OunXraN++vTavefPmhIeH8/PPPxMeHo7FYtFG5fL19UWn09G/f/98MzMu9wP/WnwuFeVzMjAwkAsXLuB0Oj0CU3Fxcdo615u/vz8tWrTgiSeeyLPMHaD56KOPmD9/PuPGjaNz587aj3J3l8Abac2aNTgcjiLVktqzZw9nzpxh1qxZzJo1y2PZqFGjGDVqFAcOHADAZrPx8ssv8+uvvzJgwABeffXVKw5IwcWuZ1ciKCjosgEj9/fbhIQEqlWr5rEsPj6eihUr4u/vj06nyzdIEx8fn6eG1tVwZ+BfWm/qaovEp6amFut1UbVqVdauXYvL5fJ4bzlx4oRH1ld+27m75+V28uRJ6tevD6gDKZw6dYpevXp5rGO1WjEYDPKH1f8w6b4nhACgY8eOdOvWjY8++shjpJ2YmBiAPAXDV6xYgdPppHHjxte0HZd+uY6JieHYsWNUrVqVqKgobfr+++/59ttv8/y19FL33HMPiqLw/vvvk5iY6FGHICYmhszMTBRF8dj3wYMHmTVrlkc9g+K2+1JNmzZlx44def6yvXz5csLDw4scANuxYwctWrRg165d6HQ6ateuzUsvvURERARnz54tcnuFEELcugYPHsyxY8d499138yxzOp2MHTuW7Oxs/u///k+bbzAY6N69O3/88Qe//PILHTt21DI5/Pz8qFOnDkePHvX4PKxZsyYzZswotKv+tfpcKsrnZExMDA6Hw6PmknsdQPtOciU1jIrKPeJg7dq1tfNUr1495s+fr43su23bNmrUqMF9992nBaRiY2M5ePBgkTOsr5d//vmHMmXKXLYmGahd6L799luPac6cOYB6DebOZho5ciS//fYbI0eOZPjw4VcVkLpa5cuX59y5cx7zLv2+2KBBA8xmMz/++KPH/K1bt3L27FkaNWqEj48P9erV4+eff/YoNp6Wlsaff/55Tb8DlylThkqVKmnXkNuvv/56xftMSUkhKyuLcuXKFXmbVq1akZGRwZo1a7R5SUlJbN26lZYtWxa63ZEjRzh8+LA27/Dhwxw5ckTbbuPGjYwYMcKjYLrL5WLlypVER0df9g/N4vYlmVJCCM3o0aPZuHGjx1+EatSoQa9evZg+fTpZWVk0bdqUf//9l5kzZ9KsWTNat259TdsQEBDAvn372Lx5M/Xr16d///58//339O/fnwEDBhAcHMxPP/3EN998w8iRIy+7P/dIe19++SXR0dEewZ+2bdvStGlTBg0axKBBg6hevTq7du1i+vTptG7dulijxbiHU/7xxx9p0KBBni4ITzzxBMuXL6d///4MHjyYoKAgli1bxsaNG3nrrbeK/AW6Tp06eHl58eqrr/L8888TFhbG+vXr+ffff7XhjoUQQtzeWrduzYgRI5gyZQr//vsv9913H6VKleL06dN89dVX/Pvvv0ycODHPSGw9e/bk008/Ra/X5+mmN3ToUAYOHMjLL79Mjx49tFH2/vnnHwYNGlRgW67V51JRPifbtGlDs2bNeP3114mNjaVWrVps3ryZuXPn0qtXL62YdUBAAAkJCfz111/Url2bUqVKFePsFm7QoEH06dOHp59+mocffhiLxcKiRYv4/ffftcFX6tevz+zZs/noo49o2LAhJ06c4MMPP8RmsxW7XlRSUhInT56kRo0a+XY1LK4DBw5o5yk/J0+eJCkpiYYNG+Ln55cnY8ldn6h8+fLast9//50ff/yR9u3b07BhQ3bu3OmxjXsgm/Pnz3P+/Hnt/vXSsmVLvvzySxRF0YJj7uDgn3/+SWBgILVq1WLgwIHMmjULk8lEu3btOH36NO+//7723Rfg5Zdf5sknn2TgwIE88sgj2O12PvroI2w2mzZowLXgHpnvlVdeYezYsXTq1In9+/drGWpXEmjdtm0boAaMQM2gPHz4MJUqVSrwO27Tpk2JiYlh2LBhDBs2jKCgIGbMmIG/v79Hb4PDhw9js9moU6cOoP4R+IMPPuCpp57i5ZdfBtQC+REREdx9990A9OnTh6+//ppnnnmGF154AW9vb7788ksOHjzIwoULi/34xO1DglJCCE1QUBBvvPEGgwcP9pg/ceJEKleuzJIlS5g7dy6lSpWiX79+DBo06Jr/NXLAgAG89dZbPPnkk8ybN48mTZrw9ddfM3XqVN544w2sVitVqlTxGBnncnr27Mnvv/9O9+7dPebr9Xo++ugj3n//fT788EMSExMpXbo0TzzxRLG/aHTu3Jnvv/+eESNGcP/99/PGG294LA8PD+err75i6tSpTJgwAbvdTq1atZg9e3a+I7AUxGKx8Omnn2qjA6WmplKlShXGjx9P7969i9VmIYQQt64nnniC6OhoPvvsMyZPnkxSUhLh4eG0bNmSiRMn5ht4qFWrFhEREVy4cIHmzZt7LGvVqhWffPIJM2fOZMiQIZhMJurWrcu8efMKHbHrWn0uFeVzUqfT8eGHHzJ9+nTmz59PUlISFSpUYOjQoR7d6Xr37s1ff/3Fc889x5AhQzxGu7tatWrVYuHChUybNo1XX30VRVGIiIhg1qxZWjuffvppLly4wIIFC5g1axZly5alZ8+eWvtTU1O1P2Zdzp9//snIkSNZsGABzZo1u+r2JyYmFjpS3+zZs/nuu++0bnlF4c7mWb16NatXr86zfNWqVVSoUIHFixczc+ZM7f710rlzZ2bNmsWuXbu0ovU1a9akW7duLFy4kDVr1vDjjz9qQdQvvviCRYsWERQUxF133cWLL76oZRE2b96cefPmMX36dIYOHYrZbKZJkyZMnjz5mg8w0717dzIzM/nkk09YsmQJNWvW1LpJXlqfqij+/vtv6tevr2XF7d27l379+jFp0qRCX5szZ87kf//7H1OmTMHlctGoUSPee+89j+5148aN48yZM9rzbTabmTdvHhMnTmT06NGYTCZatmzJyJEjtW6tYWFhLFy4UHuNZ2RkEBUVxfz58z0GFxD/PTpFytwLIYQQQgghhLjO+vbty3vvvXfdC1o/88wzBAcHM2nSpOt6nGvpxx9/pE6dOh41rv7880+efvppvv/++zyZj4XJzMykdevWTJ48mY4dO16P5gpxzUimlBBCCHEZTqcTu91+o5shrhGTyXTZenRCCCGurU2bNpGVlVXgiHLX0ksvvcQjjzzC888/X6yaSjfS8uXLmTZtGi+++CJly5blxIkTTJ8+nZiYmGIFpAC+/vpratasWaxsfCFuFMmUEkIIIQqgKArnz58nOTn5RjdFXGNBQUGUKVPmhhbjFUKI/5IzZ87g4+NTIqMkgjoK4v79+/MdEOBmdOHCBaZOncrff/9NUlISYWFhdOnShSFDhuDr61vk/SQlJXHvvffy+eefX9ORpIW4XiQoJYQQQhTg3LlzJCcnU6pUKXx8fCSAcRtQFIXMzEzi4uIICgqibNmyN7pJQgghhBD/WdJ9TwghhMiH0+nUAlKhoaE3ujniGvL29gYgLi6OUqVKSVc+IYQQQogb5IqHzbLZbHTr1o1NmzZp806dOkX//v1p2LAh99xzD2vXrvXYZv369XTr1o0GDRrQr18/Tp06deUtF0IIIa4jdw2pKxnxRtz83M+r1AoTQgghhLhxrigoZbVaGTp0KIcOHdLmKYrCc889R1hYGEuWLKFnz54MHjyYs2fPAnD27Fmee+45evfuzbfffktISAiDBg2iqL0HFUUhPT29yOsLIYQQ14J02bs9/VefV/k+JYQQQoibSbGDUocPH+bBBx/k5MmTHvM3btzIqVOnGD9+PNWrV+fpp5+mYcOGLFmyBIDFixdTr149BgwYQM2aNZk0aRJnzpxh8+bNRTpuRkYGjRs3JiMjo7hNFkIIIYQQyPcpIYQQQtxcih2U2rx5M82aNWPRokUe8//55x/q1Knj0c2hcePG7Ny5U1vepEkTbZm3tzd169bVlgshhBDiv+fnn38mMTERgBkzZvDYY48BsHTpUtq3b1/gdiNGjGDEiBEl0kYhhBBCCHF9FLvQ+SOPPJLv/Pj4eEqVKuUxLzQ0lPPnzxdp+U0h9RC4bGDyA6M/GP3AYL7RrRJCCCFuS2fOnOHFF19k1apVAAwYMEALSgkhhBBCiNvfNRt9LysrC7PZM4BjNpux2WxFWn7DnVgE6/rknW/wBlMgmIPVW0soWMLBKxwspcC7LPiUB+/y6q1RCuIKIcTtrrB6PAqXqdWjFGGdQvbnPraiKGj/8pnnPo57uXs/hf1fO16ubS9dr9Dt8rmfZ/tcj/98svqHqRPJJ8jyztK2i0uM43zaeexOOwcSDuTZl6/ZF0VRbuq6ULGxsUycOJGNGzdisVi45557GDp0KBaLhVOnTjF69Gh27txJuXLleO2112jVqpW27fr163nrrbc4deoUDRo0YOLEiVSsWFFbPn/+fD755BPS09O5++67GT16tDaioBBCCCHEreSaBaUsFgvJycke82w2G15eXtrySwNQNpuNgICAa9WEqxNQCwLqQNZZcGaqGVMAzix1yi5iRpcpCHwqgm8l8K0CflVz9h0JBh/QGdRJbwD0F+/rDKA3gu6KB0QUQogbTguKKAouxeURECnqssvdAkVe5nKpx3EpLvW4uHC6nNhddmwOG1anFZvTRrYjG7vTjtVp1W4NLgNR5ijiMuIw2o0Xj+NygTMzT1CooLZp8woI8mj/12I2eddx6iwoxYi//LLkF35a/BOpSalUqFqBxwY/htPpZOJLE1n4x0JtvQ/+9wEAz4x4hoz0DOZOmcve7XtBB9F3RNP/xf74+Kp/bPnpm59YuXQlaSlpRNSLYMDQAZQqWwpFUVj2+TJ+X/47tmwbkfUj6f9Cf8JKhwHQt11fnnrlKb7/8ntSL6TSqEUjnnz5Sby8vRjQewAAA+4bwMDhA0k4n8C/O//l9fdeJ8uZhUtx8fGsj/l12a94+3jT/eHudOndhUxHJgoKOi6elN9++41p06Zx5swZatasyauvvkpMTEzRT9o1pCgKQ4YMISAggIULF5KSksJrr72GXq/n1Vdf5bnnniMiIoIlS5bw+++/M3jwYH766SfKlSunDQzz/PPP07p1a2bNmsWgQYNYvnw5Op2OlStXMnPmTN5++21CQ0MZOXIkb7/9NmPGjLkhj1UIUTROl4JBf/MG0ovidngMQoibzzULSpUuXZrDhw97zEtISNC67JUuXZqEhIQ8y2vXrn2tmnB1ghtAt73gsquTIwNsF8CacHHKTgBbYs79JLAl5dxPVNd1WcGeDCnJkLL7kgPo1awq38rgH5ETqIoAgxcXg1M5twYL6M05kwX0ppyAVc7k/r/elHNrKPnzJYS4qeQO9LgUl8fkXpZ7eWHzct93upxaIMepONV5ilOb73K5tP9fGmxyt0tBDQZp2Tc58xxOB1anlWxHNlaHlWxnNlanFatDDRRpk8OGzWXD6rBqwSSby4bdab9467x4655nd9kLvC2Kyr6V+aDlBxgyDGDVTjS1dvwffqm7rtMzmb+0gAYciJ4LhWQGuQM0xw8f56sPv2Lo+KFUrFqRn5f8zPQ3pjNk9BAALAaLtr5BZ0CHDm+jN19+9iVpyWn8b87/cDgdvDf+PX768ieeeO4Jfln2C98t+I7BwwdTPbI6Cz5YwKzxs3j/0/dZ/u1yNq7ayIjxIwgOCWbpl0t5e/jbfLDwA4xG9WvGknlLePG1FwkJDeGdN99h4fsLGfnmSGbOn8ng/oOZOX8mVatVZdHnizAbzJTxK0OQJYiE2ATiTsYxe95sDvx7gHcnvkt03WiaxDTxCEjt37+f4cOHM27cOOrXr89ff/3FU089xfLly6lcufL1eloKdPToUXbu3Mm6desIC1ODc0OGDGHy5Mm0adOGU6dO8fXXX+Pj40P16tXZsGEDS5Ys4fnnn/cYGAZg0qRJtGzZUqvpuWDBAh5//HHatWsHwLhx43jyyScZNmyYZEsJcRMz6HW88PUODsel3+imXJEapfx4v0/0jW6GEOI2dM2CUg0aNOCjjz4iOztby47atm0bjRs31pZv27ZNWz8rK4t9+/YxePDga9WEa0NvUiejj9pFj4i86ygucGZfzKKyJasBK2scZJ5Rb23JkB0HGcch/QjYUyDrjDolrFf3ozOpgamQJhDSGPxrgd4FtmzACS4n4IJLu4m4s6rIudUbQe8NRm+1u6HelBPQMuUKbpkkC0uIGyh3EMipOC/+3+X0CCDlXnbpZHfacSpOHE4HDpdDDQ4pThwuR94AVE4QyD0f8JjvDhrpdDot48TdHSp31g6A1WHVgkfuKc99h5UsR5ZHgOnS5bnnuQNMNwODzoDJYMJsMGPSmzAZTJj0Jir6VcRkMGExWDCY1OCNDtDrr9lHZ5F5m7yoGVITnU4POnLaotP+n9vZf86i1+lpEtmEGjVr0KhqI3Z13qU9p9WCq2nr+pv9AagSVIX0hHSC/INoXKsx3t7eVH6vMgoKFQIqsPrH1Tza71H69FK7uVcbU40Fny0g2BTMsoXLGPH6CNq2aQtAozcb0bl9Zw7tOETbO9V5A/5vAN06dwNg5GsjGTRwEGPHjqVKmSrq8ctUoWxwWbyMXhj1RoK9gvEx+WCxWHhr0lsEBQXRsE5D9u3Yx8/LfqZl85Yej/mTTz7hwQcfpHv37gD069ePLVu28NVXX92QYujh4eF8/PHHWkDKLT09/aoGhmnSpAm7d+/2+O7UsGFD7HY7+/fvJzpafjAKcTM7HJfO3rOpN7oZQghxU7lm36xjYmIoW7YsI0eOZNCgQfzxxx/s2rWLSZMmAXDffffxySef8NFHH9GuXTtmzZpFhQoVaNas2bVqQsnR6dWglbt+lE8FNVDlyAB7mhqMyo5T7xtMYAwEZwakHYbU/WoWVfJuNbsqZa86HfsMjL4QGgOl20Gptur9SykKKM6cyaHeOq3qsbJz7mvt1KnBKIxqOwzeavF2o8/FLCyDJScby1zoX+CF+K9yB47c2UFallBOACm/eTaHDYfiwO6043Dl3Co5gSOXS8sqcmce5Q4ouWnBIff9nH96nR6dTr3V6/T5zjPmBE2yHdlk2jO1KcOWQYY9g0x7Jum2dDLsGeptrvm5/++eilP/6EpZDBa8jF54Gb2wGCxYjDmT4eKtl9ELs8GMxWBRb3Pmmw3mfO8XNLkDT+7/GwrKNnWAMdVIef/yWLws2mylw19kODOv+znxYPDBWMT36OYtmlOjZg0e7P0gtWrXom27tvS+rzcnTpwodLuHH32YoUOG0qFtB5o1a0aHzh24+567ATh+/DhP13kaULPdQsJCePHlF8nMyCQ2NpYRw0agz/WHD6vVyvHjx2mlqHWSohpE4XA5AIioHYHT6eTYsWMEBgcCaFlsLpf6OrA77ThdTsqVL4evvy92px0FhZq1arL8u+V52n7kyBF+/vlnj5GB7Xa7R52mkhQQEEDr1q21+y6Xiy+++II77rjjqgaGSU1NxWq1eiw3Go0EBQXdXAPHCCFuO+F+ltui+97t8BiEuN1cs6CUwWBg9uzZjBo1it69e1O5cmVmzZpFuXLlAKhQoQIzZszgrbfeYtasWURHRzNr1qybukhpsej0YPJXJ59y4MhSg05ZsWA9B04bBEVBeHN1fUVRs6aSdkDCBkjcpGZTxf6hTnoLhLeEsp0hvLUaPAI1cKQzoj51loJak3MMlxq4cndJtKWoXQ9zfhiALifLKiebyugHpgA140pvUYNYBq+crCwhbl0uxYXDlZNd5HJqWUbu++7/u7uT5e4iljvYdGlmkzujKHd2kU6nBo8MeoMWJMo9mQwmbbk7oKQFmC55P3QpLtKsaaRaU0m2JpNqTc0zpdnSSLOmedym29JJt6Vf02CSDh0+Jh98TD54G73xMfvgY/TB2+SNt9E7z62X0evivJz57qDTpZPFYLm1Pgt0uvz/aFDCchcOz33f4mVh3sJ5bN+6nTV/rmH5suUsXrSYt6a8BXAxQ00Bu8OO3qDH7rTTsElDvv/le9b8uYZ1a9YxcdxE1q1dx7i3xmE0GLVaXLnZ7Gr9xUlvT6JylcoXs7Z0EBB4sWakyWTCoDOomV05xbGMBiPmnBFu3UFFo96IXq/Hy6QGII1GI96mi13STDoTFrPFYx6A0+nkqaee4t577/WY787cvtHefvtt9u3bx7fffsv8+fOveGCY7Oxs7X5B2wshxPUQ4G2ULohCiOviqqINBw4c8LhfuXJlvvjiiwLXb9u2LW3btr2aQ946jDnd6bzLgr0KZJ6FzNNq/SlLKJj81AwrnwpQobua4ZTyL8SvhXO/QuZJiF2tTqZAKN8dKt4HvhUve2iNTg+6nIBTfhQFFLta1N1lB2us2kYUQKdmV+kt6o8vY4DaZoNPTtDKS2pZiRsid4CpoMnmtF2sUeSwqt3bXC6tu5s7sHRpdzWdTq2xo9fpPYJKBp1By6jJPa84gRR3gCkhK4Hk7OQCpxRrCinZKaRYU0i1pl51YMlsMONn9sPP5Kfemv3wNfuqtyZffM2+nrc5/3cHoHxMPviafPEyet1agaObSL5FzHMFlAqap0NX4POf+7nI3X1Ph45du3axddNWnhz4JE1jmvL8S8/T6c5O7Ni2AwB7th0/Xz8Azp09R+XKlfE2efP5Z59TM7ImD9z/AA/c/wA///Qzo0eNxs/sR+UqlTlx5AR3d1Yzp1IupNC1a1e+WfwNoaGhZCRnUKdmHUAdxOSVl19hwIABVAivAMDJIydpXF/tzr/r4C5MJhN1IupoA6S4g5hGgxG9Tq924zMYOX3qNC67S6uVtHfPXqpXq47ZYPY4B1WrVuX06dMe9aOmTJlC1apVeeCBB4r6VF0Xb7/9Np999hnTpk0jIiLiqgaGsVgs2v1Ll0s9KSFESZAuiEKIa01SYK43nQ7MwerkW0mtOZVxTM2K8ip9MQtJZ4CgeupU42lIO6gGp879AtmxcPwLdQptBpX7QHirq+9up9MVHLRSlJxglVXNsMqOVTOv0KtZWwaLOtKgOSgnUOWj3kqgSlwBd70ku+tid7fc/89dm8jmtHlmNykOj25vAHrUoJJRb7wYVDKY8NJ5aYGl4gaV8uNwOUjKSiIxM5HELHW6kHVBu03KTlJvs5JIzk7Gmbt7bTH4mHwIsAQQYAkg0BKIv8WfAHOAemsJwN/sr83zM/vhb/HH3+yPn9kPi/EyGZUiX06XE5fThUExXCz8nhNcKkoQyf1/9zV2aeBIqwWlQ+uG6c6yAzz/n0/tqHz3mzMvxC+Ejz/8mHKly9GiRQu2bNlCVmYW93S5h88+/YzPPv6Mhx56iJUrV7L/3/1Uq1oNi9FCQnwCS75dwqRJkwgKCmLVb6uoU6cOJoOJfo/1Y9KkSdSKrEX16tWZNm0aFSpUoFLFSvTv35/333+fsLAwqlWrxuzZs9m+fTsTJ07U2jR9+nTKly+PxWJhwoQJ9OrVC19fX6xWtYL8/v37CQ4OzvM8WK1Whg8fzvPPP8+2bdtYuXIlX3/9dZ71+vfvT9++fYmKiuLOO+9k9erVzJ8/n88++6y4T/019eabb/LVV1/x9ttv06VLF+DqBoYJCgrCYrGQkJBA9erVAXA4HCQnJxMeHl4Cj0gIIYQQ4tqSoFRJMvlDYC3wKqXWl8o6q2YfmYI8A0w6HQREqlPEIIhfD6e+VW8TN6mTf02o9gSU6aAGtK41nS5X8CnXfMWlBquc2Wr7M3JqlBjMavaUOwBn9FG7Axq8pVbVf5iiKGpXOOfF0dDc/7c6rWTZs8hyZGnZTO5Jq6+Uc+kYdAYMOjXIZNCrASZvvbc6PyfIdC3ZnXbiM+OJz4wnITOB+Iyc25z7CZkJJGYlkpydXOx9+5p8CfYOJtgrmCCvIG0KtARqt4FegR63JoPp8jsWHjxGCbyk/pc72y53EXjtestJFNXr9JhdZkoppTyuR3ftrtxBpEuDSUCx719LderUYeLEicyePZs333yTcuXK8fbbb1OrVi3efPNNpk2bxueff06nTp3o27cvFy5cAOCFF14gLS2NZ599lszMTJo2bcrbb78NQM+ePYmNjWXcuHGkp6cTExPD9OnTAXjyySfJyMhgzJgxpKenU69ePT755BMCAwO1Nt17772MGDGC1NRUunbtyqhRowAICQmhR48evPjii7zyyit5Hkvt2rUpXbo0Dz74IMHBwbz11lvUq1cvz3oNGzZkypQpzJgxgylTplCpUiWmTp1K06ZNr/n5LaqZM2fy9ddf8+6773LXXXdp869mYBi9Xk9UVBTbtm3TanLu3LkTo9FIrVq1SvDRCSGEEEJcGzrl0hSDm1R6ejqNGzdm27Zt+Pn53ejmXD2XE7JOQ+ohcKSr3fwuV7sp8wyc/BZOLQF3kV2fSlB9AJS7+/oEp4rKac0ZjTBb7QqITq1HZfQFS5haq8rkpwaqZBTA24bdqQaZLp2yHFlk2DPIsmfhcDq0rCen4tR+9OvQYdQbMelNWrDJqDdq0/VidViJzYglNj1Wvc2IJS4jjriMOGIzYonPiOdC9oUi78+gMxDiHUKoT6h66x1KsFcwId4hBHsHE+odSpBXECHeIQR5BWk1dETR5VdoPvetu4B27swlrSumXs2aM6AGMA16AxajBbPerI2s577+3EFOo96IQWfAaXcSezqWqlWr4uXlpQWgRPFERkayYMGCm25gk+zsbI4dO6Y9v9fSkSNH6N69OwMHDqRv374ey9yBuIiICG1gmDlz5rBixQrKlSvH6dOnueeeexg8eLA2MMzRo0f5/vvv0el0rFixgjFjxjB58mRKlSrFa6+9xh133MHrr79epLbddt+nhLiFdJ2+5pbt+tajQVmmP9zoln4MdcsFsGJI68uvKIQoUZIpdaPoDeBbGcwhkLJPreXkXUYN5BTEpzzUegGq9YeTi+DE12rtqd1vwLEvIPJ5CGtxYzKT3FlVbopLDVA5MyAtUe0OaDCDwRcs4WAOzCmq7itBqpuYw+XA6rBidVq1Ok1Wh5U0WxoZ9gwt+8nutHt0TTPqjJgMarDJZDDhbfIufJSzayjNmsbZ9LOcSzvHufRz2m1seiznM86TlJVUpP2Y9CZK+ZYizCeMcJ9wwnzC8kyh3qEEegVe80yt252iKB7F5nMXoXfX/XJzB4K0TLmcwJGXwctjdD6zwawtdwc2cweY3PeL81xl67KJ18VrdcSEKKpVq1bhdDqZM2cOc+bM8Vh24MCBqxoYpmvXrpw5c4YxY8Zgs9no3Lkzw4YNK/HHKIQQQghxLUhQ6kYz+UNwtNrNLf2oWqPJ5F/4NuZAqDEQqvSFk4vh6GeQfhi2vQAhTSFyCATWLpHmF0inz+nC53NxnjNbzaZKP6QGrQwWMPqr3RlN7iCVT8H7FNeFFmxyWsl2qIXB023ppFnTLo5E57Rp3Zx06DDpTZgMJkx6E15mr+ue3XRpe8+mneVM2hnOpJ65+P+0M5xLO0eaLe2y+/AyelHatzRl/MpQyrcUpX1LU8q3lMcUaAmUrJhiKqz4PFws+K3T6zDqjBcDRgbjxdH5coJN7qBmQZMEicTNbODAgQwcOLDA5Vc7MMzl9i+EEEIIcauQoNTNwGBWC5wbfSF1v1pc3BJ2+e2MvmrWVIVecHQenFgESVtgQz+ocC9EPKcGuW4WBi91MucUs3VmgyMDUvYDihqYMweDd+mcAFWAFE6/Rpwup0ex8GxHNum2dFKtqVgdVmwutdudu2udSa8GnMwGM15mrxLLcnKzOqycTj3NqdRTnEw5yanUU5xOPc3p1NOcTz9/2RHpQrxDKONXhrJ+ZSnrV1b9v796W8a3DAGWAAk4FYO7EL3DdbErpnvKXYvJXe/LqFOzkgIsAdqoahbDxUCTO6iZu/umPB+3r0tH6hVCiOJwuhQMevmMEEKI25UEpW4WOj34V1cDM8m7wZpQtMAUqJlTtV6ESg/CoTlw7mc4/R3ErlYDUxXuvTm7yLmDVBbU7n3OTLAlQNYZ0JvULCrvMmqgyhSkBu9EoRwuh1Y8PNuRTaY9kxRrChm2DDUjymlFURStnpPZYMZsMONr9sWkN5VoYEBRFOIz4zmefJzjycc5kXKCkyknOZF8gnPp5woNPHkbvSkfUJ4K/hUo51+O8gHlKe9fnnL+5SjrVxZvkwyNXlSXjnzoHvHQoThwPwV6vd4jgORv8cfX5Iu30Ruz0awFMN3Zc+5bCTQJIYS4Wga9jhe+3sHhuPQb3ZQrdmdkOMO6yGAEQgiRHwlK3Wx8yqk1oS78A9ZEsIQWb9sGb0LF3rBvstqlb+9bcPp7qDsKAiKuX7uvlk6nZn4ZfdX7LrtaAD5lv3rf6AdeYWpXP3Nw4bW3/gNciksLPmXZ1aLiyVnJZNgz1PpPDhugFnu2GNR6O/5mf0INoSXe7cnpcnI27SxHk49y7MIxjl44qgaiUo6Tac8scDtfky+VAitRMbAilQIqUSGggjaFeodKwKOInC6nVvfL3RXT4XJoQT+dTqdlxpkMJvwsfngbvfE1+WqBJrPBM/AkXeeEEEKUpMNx6bdscW2A6uG+N7oJQghx05Kg1M3Iu6yaOZT8D1iTwBJSvO1DoqHFF2q9qcMfQMpe2PAYVO0H1f/PsyD5zUpvUoNP5mBQnGo3v4wTkH4sZ0S/8IsBKuPtnRVjc9rIsmeRac8ky5HFhawLarc7p1p0XEHNfPIyemE2mAmyBGH2MZd40EZRFOIy4jiUdIgjF45wJOkIRy4c4XjycaxOa77bGHQGKgRUoHJQZaoEVqFSYCWqBKm3wV7BEngqAkVRsDltWuDJ/X93/Sa9Xq8FlbyN3oT5hOFr8tWKg186yTkXQgghhBBClBQJSt2sfMoBOYEp24WLdZiKSm+EKg9DmU7w7xS1K9/Reept3dfVwNWtQmdQa0yZAtQC6Y5MyDx1MUDlVUqdLCG3fAaV1WEl055Jpj2TDFsGSVlJZNgzyHZkY3Pa1CLjBhNeRi8188m75DOfALId2Ry9cJSDiQc5mHiQw0mHOZR0qMAi4xaDhcpBlakWVI2qwVWpGqROFQIqYDKYSrj1tx6X4lKDTU67Wnw+p74TOrRrwqxXs5iCvYPxN/vjZVKDlO5MOYvRUmLF6IUQQgghhBCiKOQXys3MpzygwIWdYE8Hk1/x9+EVBtFT4Pxq+Heymm20+Smo9BBEDL71sox0evU8mPzUbDJHhhqgyjie08Wv9MUAlf7mDnY4XA4ybBlk2DNIt6aTlJVEui2dLEcWDpcDvV6vjkRmsBDqHXrDgjdp1jQOJB5gf8J+DiQe4EDCAY6nHNdG48vNoDNQOagy1YOrUyOkBtWDq1M9uDrl/MuVaKH0W5GiKFr3OqvDqmU+KShqtpNezWTyt/jjZ/bDz+yHxWDBYrRot1LHSQghhBBCCHErkaDUzc6nAjiyIWW3mv10pZlAZdpDaBM48L5aY+rkIkhYD1FjIbjhNW1yidHpcgWoXDld/I5B+lEw+YN3OfAKV4uk3+CAiKIoZDmySLelaxlQydnJWgaUXqfHy+ilZkBZ/G9YRku6LZ1/4//l34SL0+nU0/muG+QVRERoBBEhEdQIqUFEaARVgqpgloL0hXJnPbmfe5vTpnW1MxlMWAwWfEw++Fv88bf4YzFY8DJ6YTGqt5LtJIQQQgghhLhdyK+bW4F/NXVkurRDapDqSn+UmgKg3mgo3RH2vKlmGG16Cqr0hZrP3hq1pgqi06uBKJO/WoPKngap+yHtoNr10aeCWjTe6K8Gs64zp8upZkDZ0km1ppKQkaB1w3MpLiwGC94m7xuaAWV32jmYdJA9cXvYG7eXfQn7OJF8It9R78r5lSMyLJLI0EhqhdUiMjSSMJ8wycophEtxYXVYtdpfNpdn8XmLwUKYTxgBlgB8TD5a0MldG0yIq3H69Gk6dOjAqlWrqFChQqHrLlq0iGnTpmG1Wlm8eDE1atS4omPabDaWLVvGgw8+eEXbL126lJkzZ7J69eor2l4IIYQQQtx6JCh1K9DpIaAWOLMg+zx4l7+6wEp4c2i1CPa/C2d+gONf5GRNjYfA22C4Wp0BzEHq5LKDPUXtAmnwAkuYmkFlCbumQTiny0m6LZ00WxrJ2ckkZiaSbktXs6D0erwN6mhmN3LUuNj0WHbH7WZX7C72xO1hf+J+bE5bnvXK+pWldnht6oTVoVZYLWqF1SLIK6jkG3yLUBRFCzxZnVY180lR0Ol1eBnUAFNpv9Ja8MkdePIyekmXRnHdlC1blrVr1xIScvmBMt5++2369evHfffdR5kyZa74mCtWrOCDDz644qCUEEIIIYT475Gg1K3CYIbAuuDMzglMlb26/Zn81a57pdvBnolql7eNj0ONp6Bq/yvPxrrZ6E1qAMoCOLIgOw4yTqtZY95lwbu0mklVzGLhLsVFmjWNNFsaF7IukJCZQKY9E7vLjh49vmZfgr2CsRhvTPaZ0+XkUNIhdp7fyT+x/7ArdhexGbF51gu0BFI3vC51S9Wlbnhd6oTXIcS7mKM9/oc4XA6yHdlaAMrhcqDX6bVC4mE+YQR5BeFt8sbb6I23yRsvo9cNKUYv/tsMBgPh4eFFWjctLY2YmBjKly9/VcdUlLxZlkIIIYQQQhTmNok8/EeY/CCoHiRuBVuymgl0tUq1gVb1Ye8kiF0Fhz6AuLVQfxz4VgaXC7KsYLd7bqfTgckEFhMYbpFsD6O3OikutXtf2iFIP6J26/OpoAavjD75bqooChn2DFKtqSRnJROfGa9lQhl0BnzNvoR4h9ywbldWh5W98XvZfm47O8/vZHfcbjLsGR7r6HV6aobUpH7p+tQrVY+oUlFUDKgoXfAK4K775A5AuRQXRr0Ri1Gt+VQuoBz+Zn98TD5aEEoyn/4jFAUyM0v2mD4+xcqQzd19r0OHDkyZMoW5c+dy/Phx6tevz+TJk6lYsSKRkZEAPP7448TExPD5559z8OBB3nzzTf755x/Kli1Lv3796Nu3r7bv77//njlz5nDu3Dlq167NmDFjSEtLY+TIkQBERkayatUqypcvz+zZs/nqq6/Izs6mSZMmjBkzhnLlygEQGxvLqFGj2Lp1K1WrVqVt27bX8IQJIYQQQohbgQSlbjWWULWLXdJOMHhfmy5o5iBo+D849zPsmwIpe2Dtw+DXB5zNwWYHh0P9Ieam14PRCCYDWCzg5wO+3uDlBV5msJjBy1Ii9ZuKTacHc6A6uWxgS1EDfUbfnOypsmAOwaY4tSBUXGYcqdZUsuxZ6PV6fI03NhMq25HNrthdbDu3je3ntrMnbg92l2fg0NfkS4PSDahfuj4NSjegbqm6+JjyD7r919mcNrLsWVrxcQUFs8GMl9GLYJ9gQrxC8DH5aAEoi8Eiwbz/KkWBVq1g/fqSPW7LlrBmzRW/p86YMYM333yT0NBQXnjhBd577z2mTp3K2rVradWqFTNmzCAmJobs7GyeeuopevXqxZtvvsnRo0cZPXo0vr6+3HvvvaxZs4ZRo0YxatQoWrRoweeff87TTz/NqlWreO211/j000/59ttvCQkJ4YsvvuCHH35g6tSphIWF8emnnzJgwAB++OEHTCYTL7zwAj4+PixevJhDhw4xatQogoODr/GJE0IIIVThfhacLgWD/tb+Dnc7PAYhcpOg1K3Ip6KaKZV2FHwrFLvrWR6KAmkZkB0FllFg/xQ4BGkLQL8Z/J4ESziQ681PcYHdAQ4nZGRCcqoauEIHBj2YTWpgyt8X/P3U/3tb1ACWl1kNat0M9GZ1hD5FQbGnk5G8j5S4bSQ4IUExk44RxWDB2+iNn8mPMO8bU9zb5rSxO243W89uZevZrfkGoUK9Q2lUthENyzQkukw01YOrS+ZOPuxOO1mOLC0LCtRR77yMXoT7hhPsFYyv2VcLQt2oQvTiJnYLBiSfeOIJmjdvDsDDDz/MwoULAbQufoGBgQQFBbF48WJCQ0N58cUXAahSpQpnzpxhwYIF3HvvvSxatIhu3brx8MMPA/Dqq69iMplISUnB39/fo9vgxx9/zNixY2nWrBkA48ePp1WrVqxZs4aKFSuyY8cO/vjjD8qVK0fNmjXZs2cPv/zyS0meFiGEEP8hAd5GDHodL3y9g8Nx6Te6OVekRik/3u8TfaObIcQ1JUGpW5FODwGRahe07Nirqy+Vmg4nzsD5eLDawNcfwkeC9Q9I+xpc+yFtLOgeB6/muX6M5WRK5cfpVLOrbHY4nwCnz4MC6HVgNqtd/vx8IcBPzaa6gcEqp8tJqi2DZGsa5zMTSbGmk2XPxOjKxk8HZb2CMRjD1Swqo7nEfoy6FBcHEw+y6cwmtpzZwo7zO7A6rR7rlPItReOyjWlctjGNyjaSrnj5cLqcWgDKPfKhOwMqzCeMYK9g/Cx+WgDKeLvUUhPXj06nZizd5N33LlW5cmXt/35+ftgv7ZKd4+jRo+zfv5/o6ItfeJ1OJ4acbtrHjh2jT58+2jKz2czw4cPz7CcjI4Pz58/z0ksvoc/1vp6dnc3x48exWq0EBQVpXfkAoqKiJCglhBDiujscl87es6k3uhlCiBzyC+xWZfCCwNqQuEXtfmYOLN72VpsaLDpxBrKyISQIwkMvLjd2Bks9SPkQ7EchZQ5kb4XA/qAPuEzbDOBtAG8vz/lOF9hsarAqPgnOxuYNVvn7QoC/GqzyyukCeI27ATpcDpKt6SRmJ3M+M4k0WwZ2lwNvowU/szfhPrm6jzgzIeucOpkCwCunMLrB+5q1xy02PZaNZzay6cwmNp/ZTHJ2ssfyEO8QmpRrQtNyTWlStgkVAipIECoX9yh4WfYsshxZOBUnep0eb6M3AZYAqgRVwd/ij6/JVzKgxNXR6cDX90a3olhMpqJd7w6Hg+bNmzNmzJh8lxsL+mPEJZxOJwDvv/8+VatW9VgWGBjIhg0b8hRGL2obhRBCCCHE7UOCUrcySygE1IILO9UgVVHqSykKxCbC0ZOQlAyBAWpAKj/GchAyGjJ+gPTvwboFEvZDwADwalL89hr0aqCqsGBVbCKcyRWsctem8vPJyazyysmsyqlbVcSgjN3p4II1laTsFM5lJJJqT0dRFHxN3oR6BWIuKEBh8FEnlxMcaZCyXw1IeYWphdFNAaC7si5yVoeVHed3sOH0Bjac3sDRC0c9lvuYfGhctjEx5WNoWq4p1YOrSxAqF/dIeO5aUOjAYlCLkJf2K02gVyC+Jl98zb54Gb0uv0MhBFWrVmXVqlVUqFBBy476/vvv2b17N6+//jqVK1dm//792vpOp5NOnTrx9ttve7w/BQQEEBoaSnx8PHfeeScANpuNoUOH8uSTTxIREUFKSgonTpzQsrj+/fffknugQgghhBDipiBBqVudbyWwJ0P6MbXWVGFBC6cTjp2GwyfUbKZypS/fXU5nBL9eYIlWs6YcpyH5ffBqAQGPgt7/6h9DgcGqnG6AVhuci4dT59BqVrmDUv5+4O+jdv/LHazS67VAVEJWMuczE0mzZwIKfiYfyviEYSxOvSW9QS0Ibw7KyZ46A1lnwRSYK3vq8oGPs2lnWXdqHetPrWfL2S1qMMV9CJ2eOuF1uKP8HTQr34yo0lHSnSwXm9NGpj2TLHsWdpcdg96At9GbIO8gwrzD8LP44WdWu+Lpr7bOmhD/UT169GDmzJmMGTOGAQMGcPr0aSZOnMgTTzwBwGOPPcaAAQNo0qQJjRo14vPPP0dRFOrWrUt8fDwpKSkcP36cChUq0L9/f9577z1CQ0OpVq0as2fPZvv27UycOJHAwECaN2/Oa6+9xujRozl9+jRffPEFvrdYBpoQQgghhLg68ov3VqfTg3+E2oXPGqcGSPKTbYWDx+DkOQgOVEfKKw5TFQgdD+lLIWMFZK8H2x4I6A9eTa/2UeSvoG6ADsfFYFVabM7IgIDRgMOkJ1nvIMFk56zRSprBAUYjfj4BlPULxnAtuofkzp6yp0HKv+p9LXvKX8uecrgc7IrdxZqTa1h3ch1Hkz2zocJ9wmleoTnNKzQnpnwMgV7F7IZ5m3J3xcu0Z5Jpz0RRFEwGE94mbyoGViTYOxhfky9+Zr8bNgKiELcjPz8/5s6dy1tvvcW9995LUFAQffv25emnnwagadOmjB07llmzZhEfH0+9evX44IMP8PLy4o477qBy5cp0796dL7/8kieffJKMjAzGjBlDeno69erV45NPPiEwUH2fmzZtGqNHj6ZPnz6UK1eOxx57jKVLl97Ihy+EEEIIIUqYBKVuB0YftRtf0lZwpIPRz3N5ShrsPwoJSVAqVB0Z70roTOD/EFgaQ+rH4DgDydPB0hQCHgdDCQVUjEZ18lEDay7FRYoji4TsZM5mJpBsTQeHAz+XgbI6szoCnTENjHFq3Spfb7VLoNmsnguzEUym4hdZ1xvAEgRKIDizIPM0ZJ0hTTGxPukUf5/dwfrTm0izpWmbGHQG6peuT8uKLWlRsQU1Q2pKlzzUIFSWI4tMeybZjmwURcFitOBr9qW8f3kCvQLxM6uZUDKioBCXV6FCBQ4cOACg3br17t2b3r17a/cvXV63bl1tdL783H///dx///155gcFBeUJKr300ku89NJL+e4nODiYmTNneswbPHhwgccVQgghhBC3HwlK3S68S4N/DUjeCz5e4O72lZQMew9BWiaUKaV2fbta5hoQ+qZaZyrjh5xaU/vAvw94t1Gzt0pAmiOLJEcGZ2xJXLBnYFOc+JkslPEujdGjzpOiZlPZHWpR99R0cLkAnVq3ypQT5PK2qIEui0UNVJlNarDKZCy8W6ROx3lrJn+e+Ye/z2xjW/x+nIpLWxxo8adFxZa0rtSGOyrcQYDlMoXi/wNciksrSJ5lz0Kn0+Fl9MLP7EfVoKoEeAVIVzwhhBBCCCGEuM1JUOp24lcNbMmQdR58K6gBqT0HIcsK5Upd22PpTOB/v9p1L2UuOE5A6ieQtRYCnwBj+Wt3LEWBjCzIyMSWmUlq+gWS0hJJsaZhc9qwYKSazoxRb0AxGVGMBhSjAZfZhNPHgtPbgtPHAt7ecGmvRZdLDVbZHZCSDonJaldAlJyMLIMamPKyqME+sxlMRhSTgSNZsfwZt4s/zmzjQPIJj91WDShHmzJRtA6vQVRoFQzmYLVrpfG/ObpU7kyoLHsWAN5mdVS8asHVCLBcDEJJ5pgQQgghhBBC/DdIUOp2ojep3fjsqXD2KBxKUGtJlQ67fsc0VYbQcZC5EtKXgP0AJIwC3+7g1x105svvw+6A0+fUYubn4uBsnPr/CymQnIpyIRWd3Q6AGQjLmYrLZTbhCPDBEeCDPcAHR6Av9mB/7CH+2EMCsIX4YwsPxB7ir9azcjjVDCuHA5KtuOIT2Wc7z+qsw/yRfYRTjmRt33p0NAisSttS9WlTNppKQeXVbCuDAVwOsKfn1J7yBq9QMIeCOVAtJH8bUhSFbEc2GfaMi5lQJi8CLAFUD66uZUJ5G70lCCWEEEIIIYQQ/1G35y/i/zJzINhLw671oHhD2XLX/5g6A/jeA14xkPoZWHdCxjI1ayrgEbA0udj9zWqDA0fh3yNw9BQcOQknz6oj7RW0+5xbp9GAy8uEy8uMy8usZkXpcrrg6XSgKOgcTvR2p3prtaPPsmKwqgEtvc2OOSEFc0JKoQ9H0euxhfpjCw8iq3QQa6vqWV46lV8s54glU1vPrDPQzLsKd3rXoLW5MiF6b8jUwdELag0rU04XQG8vtWugyQx6qzpSovEk+ASBdxkwBYHRt/AugreAbEc2GbYMshxZKCh4G73xt/hrmVD+Fn8JQgkhhBBCCCGE0EhQ6naTlAQH48AQDn7poDi1keCuO0MYBA0F61ZIXQiuBIidDocqw8FKsO8cHDqmZiBdytcbKpSBsqWwlg4mOcyX8/46LvgbcQX54R0Shtnb58ra5XRiyLJhSM/CmJaFMSUDY1omppR0TElp2mROTMWUmIridLLOL4XFVVNYWvsEcbnqxvtZoetRA10TAmmjlMNYphTZFYLIrmDGWjYQxWRUuwQ6HOrjzMqGtIxcQTcdmAw5gbRzYNoLPr7gGwZ+pcAnFLz81C6DRtNNHaiyOW1k2DLIdGTiUlxYDBb8zH5UDqpMoFcg/mZ/6Y4nhBBCCCGEEKJAEpS6naSkwK5dkJ0NVRtB6r9gTVBrGZUUnQ4SK8H6DrDxD9gbD/YTQK6aS6FBUKcm1KwMVStC9Uo4w4NJdKRzxnqB8/YUsl02AgzeBBi8r77QtcGA088bp583tjL5r+JUXOxIP87vyXtYfWEfSa6LGVGBDgNdT3tx3x6Fe3Zm4uVwAkk500WKXo+1TDDZFcLJqlSK7IrhZFUsRXb5MBSLu5aUktMt0AkOI2Q7IC0dbHGg7AajBbyCwDsYLAHg66fWwrJYLgaqTKaLRdhLsAi4w+Ugw5ZBhj0Dh8uB2WDGz+xHDf8aBHkH4W/2x8/sJ0EoIYQQ4ibhdCkY9PK5LIQQ4uYlQanbRXq6GpBKSYHy5dXgkF9VSM4EewqYAq/v8c/Ewl+b4K/NcNiz6DdhJqhnh1pApAWqdga/LqAzk+2yEWdL5WTaYRLt6eh1eoIM3pQyXf8R6lyKi50ZJ/kteQ+rk/eR6EjXlgUavLkzsDYdgurS1K8qpiZGuBf2WW1Yzl3A62wCXmcTsZxJwOtMAt6n4zFkWvE6m4jX2USCNu/X9qXodVjLhJBVqTRZlUupAatKpckuFwIGH8D9WBVwZIE1HbLTINsCqT6g8wKdRc140wGGnFpVJpMarPLyUgNX7kCVMdeogYYrf4m7FBeZ9kwybBlYnVaMeiM+Jh8qBVYixDtEK05u0JdQJp4QQgghisWg1/HC1zs4HJd++ZVvQndGhjOsS60b3QwhhBDXkQSlbgdZWbB7NyQmXgxIAZj8wa8KpOwHgxfoLdf2uKnpsHoD/LoGDhy7OF+vh4a1IaYBxNSHSuXAtg/SvlJH6cv4BlfW78Sa2/OvqxrJzmx8DGbKmAMxXueuhoqisDfzNCuT9/B78h7i7WnasgCDN+0Ca9MxqC5N/avl2xbFYia7Smmyq1ySfaYomC6k4XU6Aa+TcXifjldvT8VhTMvSglXBG/dpm7iMBjWrqkppsiqX1m7tweGgU8CRDc5M0GWpBdLNIWDyBczgyOkimJ4OycngcqqjBuoAveHiyIFGI3h552RbmT0DViaTujwnqORRnNyRhQ4dPiYfSvmVIswnTK0LZfbHZPhvjiAohBBC3IoOx6Wz92zqjW7GFake7nujmyCEEOI6k6DUrc5qhT174Px5NSClv6Q7l1dpcGRAxgnwKnX19aVcLti+F37+C9Zth5xR8dDrIboOtG0GrRpDoL/ndpa6uExvkJH5J5aMZZhdSZTN/pZAXTDnTe24YKx/3bqiKYrCoexYfr2wm1+Td3PWlqwt89N7cWdgLToF16OZf/UrD4rpdNhDArCHBJBWv1rug2NMTsf7ZBzeJ2K1W69T8RiybfgcP4/P8fMeu3L4+5BVuRSZVcqowarK4WSXU3A5MtQAksEHzEFq7SljAHDJeXM6wZkzaqDNBpmZ6v8Vd1vRsq1seoUMo4sMgwvFYsbLy48A7yCqB5Qj0DcUf99gvHwC1OCVEEIIIYQQQghxDckvzVuZ3Q779sGpU2pAypBPQEWnA9/K4MyG7LicwNQVBH8ysmDl3/D973A6VxClWkW4qy10aA5B+Xe5cyhO4u1pnMxOJM4eAoZ+1OVfKjo24KNcoJptKVn2NZw3tyHJUO+aFWY/aU3k1wu7WXlhN8es8dp8b72ZtoG16BRUj+b+NTDrr+PLQKfDEexPWrA/aQ2qX5zvcmGOS1YDVSdi8T4ei/fJWLzOJmJMy8R/z3H89xzXVld0OqxlQ8iqFE5WpRCyKoSQVaUU1nJlwStYHb3P4K2eO0POZM6bGedQnGQ6s8iwpmG32zA7dfjZTdTU+xKc6U2AzoKP3oVOfw6M8TkZVzlZVd45GVc+Pjl1rcwXb93/l3pSQogStHTpUmbOnMnq1atvdFOEEEKI6y7cz3Jb1Iq7HR6DuHYkKHWrcjph/344dgzKli08k0VvBL9q4LKBNQm8wop+nDOxsOQX+HWtOpIcgI8XdGqlBqNqVi4wEGFzOYi1p3A8O4EkRzpG9IQa/bDoTVwgnBRzM8LtmyhjX4e3Ek9V6xLK6VZz3tSSRGM0iq743cTibKn8mrybXy/sZl/WWW2+WWekZUBNugRH0SogAi+9udj7vqb0emxlQrCVCSGlWW1tts5qx+t0PN4nYvE5Hov3ifN4n4jDlJyeqwvgxd04LSayK4aQVTGMrCplyapejazq1XGElQadQa0L5bSS6cwiW7FhQI+v0ZsKfuUJNQcSYPTDz+iN4dJAoKLkjCCYK+MqI0P9v8ulLs95HGp3wJyugO7Ala/vxfnuyR28yi94KoQQQgghhChUgLfxlq8VV6OUH+/3ib7RzRA3EQlK3YpcLjh0CA4fhtKl1R/6l2P0VgNTKf8WrfD5vsPwzQpYu+1iAKJyebi3I3RsCT7eBW6a5bRxzpbMSWsiFxzpeOvNlDHlrRfl0lmINbch3hRDKfsmStk3YFEuUNn2I+XsfxJnjCHB1BSHrvB6AimOTFYl72Nl8i62p59AyemnZkBPjH81ugRHcWdgbfwMXpc/TzeYYjGRVb0cWdXLeYztZ0xOz8moUoNU3idi8T4Vh8Fqx/dwLL6HY4G92vrWAG9SK4WTWrksPtUqQ40IfCPq4h8YToDRD9PlssN0uovBpMI4nWrGnjt4lZwM8fHqfPd1o9NdzLgyGtXi7L6+F0cVdGdZ5c6+kqwrIYQQQggh8nUr14oT4lISlLrVKAocPQoHDkBYmDryWlGZA8G/GqQeUOtMGS8J9igKbP4HvvwB9hy8OL9ZQ3jgLmhYp9BAQYYzm3PWZE5YE0lxZuKv96KCOQT9ZboLunRenDe3JdbUnDDHDkrb12JRUihvX01Z+98kGaOIM95BlqGstk2W08ZfqftZeWEXG9KO4FCc2rIGvpXoEhRFx6C6hJj8in5+bmKOID/Sgvw8uwA6nVjOJWE6fg7T8bN4n4jF/2QCvnEpWFKzCN9zkvA9J4FNF7cpUwqqVYfqNaBaNXWqWlXtkncl3F0FC5M768puV2tcpaaq/3cHrtz7MuYqwO4eVdDb27OLYO4AltEowStR4hRFIdOeWaLH9DH5oCvGtX769Gk6dOjAkCFDmD9/Pt27d6d58+ZMmzaNM2fOULNmTV599VViYmIAcDgcTJ8+naVLl5KVlUXLli0ZN24cwcHBWK1Wpk+fzo8//khKSgp33HEHY8eOpWzZsrz00kuYzWYmT56sHfvll1/Gy8uLiRMncu7cOcaNG8eGDRsIDQ2ld+/ePPvssxgMBpYuXco333xDaGgoGzduZOzYsXTv3p3Zs2fz1VdfkZ2dTZMmTRgzZgzlypUDIDY2llGjRrF161aqVq1K27Ztr+2JFkIIIYQQJUqCUrcSRYHjx9U6UkFBVxZI8CoFDiukH1F/zBt8wOmCtVvUYNThE+p6RoOaEfXA3VClQqG7THNkccZ2gZPWRNKd2QQafKhkDi3WDygARWcm3tSMeGMTQpx7KGXfiK/rDGGOHYQ5dpCkq8CSrAosSUvlr9RDZLvs2rYRXmXoHBxFl+AoypqDintWroJycSQ8h1O91elAr1Nrd+l1FwM3Rj1qlfGr41CcZDitZDitOML1mEtVwq95BOGmABSjD4pdh9+pRHQnzsCxU3D0BJw4C4kpcD5OndZv8Nxp2bJqcOrSKSD/OmHFkjvryrvgDDuP7oIOB6SkqCNKOhyewavcWVcFdRnMXe/KZJIug+KaURSFVvNasf7U+hI9bsuKLVnzxJpiv69u376dJUuWkJmZySOPPMK4ceOoX78+f/31F0899RTLly+ncuXKvP/++yxbtoy33nqLcuXKMXbsWMaOHcv06dMZO3Ys27dvZ/LkyQQFBfHOO+8waNAglixZQteuXXnttdew2+2YTCZsNht//PEHM2fORFEUBg8eTK1atfjuu++Ij49nzJgx6HQ6nnvuOQB27NjBM888w9ChQwkODuaLL77ghx9+YOrUqYSFhfHpp58yYMAAfvjhB0wmEy+88AI+Pj4sXryYQ4cOMWrUKIKDg6/HKRdCCCGEECVAglK3khMnYPdu8PcHv6vIAPKtALgg5TCs3QiLVsLJnPpLXhbo0QHuuwvCCv+in+LI5LQ1idO2JDKdVoIMvlS2FKNeVUF0BpKMDUgy1MfLeZKDKatZkXKMpemnueA6ra1W0RxAl+BougTXp6pX+NUf93JcLrDZ1Qwfmx1cCqDkBEcM6uTjp853OsGV04XNalUDf05nzgh4ihokMbmzgnK2LSBg5VCcZDltZLis2BQnRp0eH72FSl6hhJj8CDB442fwwuDOSLMAtYOgdnXPHaVlwLETcPSYGqw6eR5OxkJyOpw7p07rL/mhHRoKVaqoAarKldX/V6midhu9dKTHq+UOMl3OpcGry3UZNJnUboI+Purk7jJ4aeBKRhgURaS7BsHlkvL4449TqVIlhg0bxoMPPkj37t0B6NevH1u2bOGrr75i+PDhfPPNNwwfPpw2bdoAMG7cOH7++WdSUlL4/vvvmTt3LnfccQcA77zzDnfeeSfr1q2jTZs2uFwuNm3aRKtWrVi7di1eXl40a9aMjRs3cvbsWRYvXoxer6datWoMHz6ckSNHakEpnU7Hs88+i1dO1u/HH3/M2LFjadasGQDjx4+nVatWrFmzhooVK7Jjxw7++OMPypUrR82aNdmzZw+//PJLSZ9WIYQQQghxjcivsFuFOyDl53f12SsOB/y2DeZ9Amdj1Xl+PtCrszoF+he4qaIoJDsyOWVL5Iz1AlaXnWCjL2GWgrcpLpfiYnfmaX69sJvfk/eS6LhYxK+sQcdD/gqP+EMTSyrZ+r0k6Q0kuRpg01/Lv5YrYLWrBb5tdjUgpdOBOSeIERyonjOzSZ2MRjXIlDtQoyjqdu4MKrt7skNmtjqiod0O2dnqOooCOh1OvY5Mg4tMvQubHnRGA34GL8qagwg1+eNv8MLf4HX5ulCX8veF+nXUyc1lhaQEOHYSTsXC6QQ4GQen4yA+Uc1USkyEbds892WxQKVK6lS58sXbihXVLL7rqSjBK0XxrHWVkaEGr3JnXV1a68pkUrOtfH3VroOXBq6kSLtADaKseWLNTd99z618+fIAHDlyhJ9//plFixZpy+x2O61atfp/9s47Tooqe/tPhc6TA0NGEIacBEEElrCmNevqGlGENayBXTCBu5iRFX8GEAyYs76Yw645r2JAARMZYWByno6V7vvHraoO05NgmMT58rncWFW3urumq54+51xUVVWhuroaw4cPt/sGDhyIq666Chs2bIBhGBg9erTdl5GRgf79+2P79u2YOnUqjjrqKLz//vuYMmUK3n//fRx77LGQJAnbt29HdXU1xo0bZ29rGAbC4TCqqqoAANnZ2bYgFQgEUFxcjPnz50OM+VsaDofx+++/IxKJICMjw3blA4CRI0eSKEUQBEEQBNGJIVGqM7BrF7BxI39YTm8iQHljRCLAW28BTz4JFBfztvRU4NQpwKnHA2kZDW7KGEOl5kdBpBJ7lSpoho4s2YdujlZw7zL3/0twLz6o/hkfVv+CErXG7kuXPJiZMQzHZozEWF9fZLBdyFHXgemb4GHl6KV+jF7qx/CLfVAlD0O1NKzlApWmAmFThNL1qMuZywlkZ3ALG7eTJ6ezeXGMhBjXPVeSYPSMAaoGXYkgGPIjGAogEglCjKjwRQR0M9zI0d1IVSSkim44JQfglAGnADgNwGHsv7WS6AJyevE0ngFMAfQwT4EQUFgF7Knk+d5S7ga4Zy//LG3dylMi6elcnEpMvXvzvraIASUIUSGpIRJjXUUigN9fP9aVZUUVG6Td5+PlWMHKSq1tQUZ0OARBgM/Z+AIMHQWXywUA0HUdF198MU499dS4frfbDbkRkdfaPhFd12EYBgDg+OOPx6JFi/Cvf/0LH3/8MVatWgWAx6kaMGAAHnjggXrbp6am1tu/rvPYgMuXL0f//v3jxqenp+Prr78Gi702ATiaWoyBIAiCIAiC6NCQKNWRsWJI/fILF0X2VZAKhYBXXgGefRYoL+dt2dnArFnAaacCRgkQKAC0ICDHx6kymIEK1Y/dkQoUKdXQwZAt+eBxNGPFvyZgjOHXUCE+rP4ZH1f/ir1Kld3nFZ2Ylj4Ux2aOxMSUAXFWQXU4FHXSoRBZGJnab8jSNiDV2IkUowApSgH64D0ExB6oloahRhqMkJgXL4QYBhBReNI07m7nlLnFU04mtyhyObm1jLuZAlQL0AwNQS2CoBaGoqsQBQG+NC+65eQhx5OBFIcXqQ4PXBq4lVY4wucaDAE1fm5ZVWuJJ+ZOHaalltPJz8OxD5e2IACCiwtVjnTADSCrFzAkzC2qDA3cxVAGygJAUR1QWA7sKQYKTLGqpITHgqqpAX7+uf4xUlK4OGWlXr2iKS+vbV3omhPryhKuLKurYJCfm6ZFrecsi6vYAO0+X7yrYKx45XCQcEW0Of3798eePXvQr18/u23ZsmXo378/zjzzTGRmZmLTpk0YPHgwAOC3337DpZdeiv/85z+QZRnr16/H1KlTAQBVVVXYtWuXLRwdeeSR0HUdTzzxBNxuN8aPH28fs7CwEFlZWbYI9b///Q+vvvoqli1bVm+OaWlpyM7ORllZGaZPnw4AUBQFCxYswNy5c5Gfn4+amhrs2rXLPo/ffvvtwLxgBEEQBEEQRJtAolRHRde5Fcrmzdxdb19c9mprgTVrgBde4K5LAH/wnzULOPXU6Mp9zAsITiD4O8BUwJEOnRkoV+uwO1KOIqUagIBsOQVucf9+lTaYgV+De/FR9a/4qOYXFCrVdp9bdGBq2mAckzECk9IGNXksQ3CjwjEWFY6xcBi1yNB/Q6b2C1KMXfAZRfAZReilfgQFqahh/VGj9EOd3guG6OGiU6oPSEsBPKb45HYdEPesiK4gZIpQmqFDFiV4ZTd6eLOR7c5AisODVKePW0LFIoPPKS0hfpimAWElKqxFFKDOD/iDvBwIchEFpmDikKNuho5mxm2yEEzRBqZ7JjMAQwF6yEA3FzAqBxCGApILEN2A5gRKaoC9Zdw1dG8RULAHKCjgcZ/8fmDTJp4SkSSgWzegZ89o6tEjmufmtn3cp1jhqiFiXQVVFairA6qq6rsKWvuxgrN7vdHg7MlWFiRXQaIVmT17Ns477zyMHDkS06dPx8cff4wnn3wSTz31FABg1qxZWL58OfLy8pCdnY0lS5ZgzJgxSElJwZlnnonbbrsNt912G9LT0/F///d/6N69OyZPngwAkGUZxxxzDB566CGceeaZtpvhlClT0KtXL1x77bWYP38+6urqsHjxYhx55JGQGvh8z549G/fddx+ys7NtK6sffvgBS5YsQXp6OiZNmoQbbrgBixcvxp49e/Dss8/C5+scVmsEQRAEQRBEfUiU6ogoCl9hb8cO/iDe0lX2iouB558HXnuNW0kB3BLloouAE06o/4AtiEBKH0B2QavditK67dhl6ChV6yAJInLlNDhbGr8oBo3pWO/fjY9rfsWnNb+hVK21+ywh6o8ZwzA5NR8ead8ssFQxDWXiRJQJh0EOVyND+w3pwnakSQVwCnXIFTYi17URDCICrn6oSxuO2oxRCPiywMT9t/qyMJiBkBZBSIsgrEXAADglGV7ZjX4pPZDpTkWKw4sUhxcOaR9fU1kGUmQe0yoWxrhlVaxYFQoBdUHuihcKA7Wmu9q+ClaCCEhunhym5R7TTUuqMMBqgFwG5KYB43IAcSwg+QA5BdAYUFwFFJYBhUU87d3LU2Eh/9xbAdcTY1gBXKTJzeUCVffuPOXlRfO8PL4IQFu4B8a9Js1wFTSMeOGqpoZbLTYWnN3p5KKVxxNvcZUoYrX1+RKdkjFjxmDZsmW4//77sWzZMvTt2xd33303Dj/8cADAJZdcgrq6OvzjH/+ApmmYPn06Fi9eDAC4/vrrceedd2LevHlQFAVHHnkknnzySThjPvMnnHACXnrpJZxwwgl2myRJePDBB3HbbbfhL3/5C7xeL4477jhcf/31Dc5z7ty5CAQCuPHGG+H3+zFixAg89thjSDcthe+9914sXrwYZ599Nnr27IlZs2bh1VdfPRAvGUEQBEEQBNEGkCjV0QgEuCBVUMAfthuI55GU337jVlHvvccfdgFg0CDggguAo49uVHCI6ApKmIFdzEBFsAQOQ0GetzscYguOH0NQj2Bt3XZ8VrMJX9ZuQY0eDQrsFZ2YnJaPozKGY3LaILj3VRTS9GggclXlbQ4ZmjMF5VnTUJ56PASngFRjF9KDPyOt7ie4IyVIiexEStlO9Ch7G4YgI+jtD39KPvwpg+H3DYQuNy9oO2MMEV1FWOcilGpoEAUBHtmNVIcXh6T1QKrDhxSnBz7ZA0k8wJYvgsAtwJLFr7JcFi13QMUMsF4X5G6B4Qhfnc+y7rFd0qxkWvhISdzOBAmQvIAEwNI7mQEYKreqUiuAcAkABmSKQLYPGD0UkA4DZB8XuCADVX6gpBwoKuUilSVQWUnTuOBqxUNLhsfDra3y8niemHJygKystnefE0V+LTd2PRtG/KqCwSC3drRiXAkCzyUpXrxyu/l5ezz1La5iE7kMHlT07t0bmzdvjms74YQT4kSjWBwOBxYuXIiFCxfW6/N4PLj55ptx8803N3i8iRMn1jseAPTp0werV69Ous3pp5+O008/Pa5NkiTMnz8f8+fPT7pNZmYmVq5cGdd25ZVXNjgvgiAIgiAIomNDolRHgTEej2fTJu7606tX81yVNA34+GPgxRd5MHSLceOACy8EJk1q1JIioIVQHCnHrlAxalQ/vJIbPbJGQw6XAZEKQFK5cNCMJdALI1X4snYLvqzdgu/9O6Ewze5Llzz4Q/oQzEwfhgmpA+BqqRugpgOqwlfEs+JAWWJJegqQmsJd3dwu7opnuoYwALXIQS346k8OpRxpdb8itfYXpPp/hVOtRkpgK1ICW4GSdwAAEWc3BHyHIuAbgIC3P0KevjAkDxRd5RZQugJFVwAATskBj+RGn5Q8ZLhS4XN44HO44ZHdLTu/A40ocjdFj7v+6oqGwUUqy8pKUbh7YMC0sFJUIOSPvu5A1MpKlqIWVg456nImiNylT0oQYZjOY1MZCqBWA5GyaFwsWQZ6y0DfboB0CI9vJrm4CyGkqGhVWsZFquJifs2UlPByTQ23DNu1i6eGkCQuTjWUsrOj4lVbuguKYtMWV0C8cKVp/LwrKuLdBYF48Uo24415PGasNHe8O2FiTgIWQRAEQRAEcQDITXFBNxgksXNb+3eFc+gokCjVEVAUYNs2YPt2/kDYp0/TLjm7dwNvvgm8/XY0eLksc4uos88GYpb2ToQxhiq1FkWRcuwJlSKgh5Eme9Hb3Q2iYD6MpngARyoQKgYilYAzzRQHokQMFesDu7G2bhv+V7sVO8Klcf29nJmYlj4E09KHYLSvL2ShOZZCfEU6bv1kPnQzmOKHg8dXSvVFxSe3m/c1E9WZg4rsP6Ai+w8AY3AqpUjxb0GqfzNS/FvgjhTBpZTCpZQiq+prc0YCAo5s1Lp7I+jpC6QcCmf6YLhS+sHr8MIruw+8FdSBRBSjgl4yVI2LVaoaFa/CEW5hFQyb9SB/r3Sd65fM3K8cI1xJVu6sL1YBANNM6yoNUKuASKkpWDEucskOU7TKAcQ+gOwBRCcgOgBBBhQDKKsEyiqAsnKgtJTHsSotjaaqKj5HS8xqivR0LlJlZfGUnQ1kZkbrWVlARgZv83rbxpXOEpmaIla40nVuhRkbpN3CsoqzRCxJilp1Wa6DsYJVYlmWyYWQIAiCIAiCaBZpHhmSKODvL/6IbaX+9p7OPjGwWwqWnz22vafRZSBRqj1hjFs4bN3KLT+aih/l9wOffAK89Rbwww/R9qws4M9/5iknp8HNFUNFuVKNvaFSlCpVUA0VmY405DgzkowWAVc2t5IKlwCRchgQsEUL4Hv/Lnxbtx3r/LsQYaq9hQQRo3x9MDktH1PS8nGou5sd8DYpms4fkC33O4MBYGbMHDnBAspauawVP7KCgKAjG5Vp4xHxjUIkR4Gg1CEzsge5yl5kRgqQHt4Dp1qFFLUcKWo5ULcesLQ3yQ34+gO+Q4CUfjz39QM8vblg0lWwXPgawhIRrffSKocjXLQKhU1hS+XiiBq1oIMALoJIEhevZAmQHPy1dUpRix2mm6KVaWWlB4GIFrWyAgBRBlIlIE0CBvUExP7RAOyizIUrXQCqa4CKaqC8AqioAioqubBbXs6vRyvpenQlwR07mn6dXC4uUFkiVWYmL6enR9vT0+OT+wBa1DVXvGKMn2usiOX388URdD3qCmy5D4pivBWWJPFr0xKy3O54wSp2HAlZBEEQBEEQBIBtpX78Uljb9ECiy0OiVHtRU8NdjAoK+INe797JV9vy+4HPPwc++ABYuzYaO0kUgSOOAE45BfjDHxpcHYwxhlotgLJIFXaHi1Gr+uEQZGQ40uBuIqi4xnRsCxVjfe0mrKv6GT/UbkGNHoobkyOn4ojUQ3FE2kBMSh2IdDlRVGOm650WtXyyHnItyxmXE8jJALweHnDb5QJcjlZdfUwzNER0FYquIqIrUA0e7FsSRbglJ7yyCz282Uh1DoRXngSv7IZHdnELKKUKqN0M1G4C6rYD/u2Afyegh4Ha33hKxJUNePtwgcrTA/D0NPMegLsbt+7pKjQlWgGmYGW+/7EilmqKV6EIEIlEhS1LJDFY1PJKEPhnwrLAkszVEiXRFEpMUZNZwlUIiOg8vlXcfCWghwT0TAWETEDI5++H6DSTDDABqA0C1bVAZQ1QVc3z6mqgssqsV3LLq6oqPvdIpPkWWBYuFxenrBU209N5sHarnpbG61ZbSkq03pSbX3OJDbDeHCyhykqaxlccTBSxYrGER0uYEkX+N8sK4G7lsdZaseXYNkkiQYsgCIIgCIIguggkSrU1gQAXonbt4vFvcnK4i4wFY9yN76uvgK+/Bn780VwtzWTAAOCYY4CTTuLBnBs6jBZCpVqDveEyVCo1iDAVqZIXPd3dIAnJ48VUq3X41b8TP9dtw4barfipbhuCejhujFd0YYyvDyZ4e+AIXz8c6u0NQXIDOjOtY4JRawsrvo0Vc8jlBLLSAZ/HfCA1VxFzto5Aoxk6VEOFomtQDQ0RXYFuGPyZW5ThlBxwSU7kejKR6vTCI7vhlpzwyC64JGfDVl3OTCDnCJ4sDA0IFgCB3wH/70BgFy8HCwC1hsfjilQAVeuT7FAAXFmAO48nVzfAnQu4cqLJmQU4M7jbWlegJS5nqha1okssRxQuYkUUU8BSAN3g7mi6FfMq5n0UBUCUouKVIACSAIiMJ0EDRBXQA1y8YnrU+koAkAkgSwSEXEDIM98PkQd3F2Xu0ipIQMQwRaw6oLYOqKnj5ZpaXq6p5UHLq2u5IF1by8WbSCTqWthSXC4uUsWm1FSe+3zRNp8vmlJSuDWm18vrXm/LxV9LGGouliWWrpvvkx4N5O73x/clYlllWcmqWysQWoHdLUGruUkUWyRssdhYXUSXgd5XgiAIgiCI9odEqbbAMLhVhbWKmN8fDaasaTy4+YYNwPr1PJWVxW9/yCE8VtRRRwGHHpr0EIwxBPQQqtU6lClVKI1UIaiH4RKdSJdT4E6I4VOuVGNrYDe2BHZjs38XfvXvwJ5w/Qdjn+TBqNSBGJuaj8N9gzDU1RuyrgPhWiBcBQQrAF0BHE7A6QOcHiAjDfC6uQjlkLno1Aqud7phQDU0aAYXnRQz5w8WDJIowSk64BBlZLhSkOrwwefwwCU54JZdcEvOxsWnliDKQEp/nhK1QbUWCO6JplARECo08yKAqVHRqubXho8hSFyYcmZxYcyRznNnhlk2c0c6j//lSOfulp1ZyGqJxY5hmIJVjOuZbrZZwoeqAmErFpYS7VNNccQwBRNmilkComVR4OKFqUNB0E0RSwAES9QCFzcEA0gBT0g1k4kg8PdSAADTyocJQDAC+ENAIAzUhYDaEOAP8ra6IOAP8BURA0Ggzm/WzRyIWmdVVOzfa+5yRQUqS6zyeKK51xtd3c9Kbnd8u7UCYGxuuV621BIrllghK7asKNw6y2ozjPgg7xaWiCWK8cmyvooN8O50xltkiSIcggBoGoJ1dfBY1qjW34+GcqLTEAzyVWEdDVgaEwRBEARBEAeeNhWlIpEIbrnlFrz//vtwu92YM2cO5syZ05ZTaDsMIxqXZc8eHq8mEIjWt24FtmzhuXljbONyAePH85XzjjwS6Ns36SFUQ0OdFkCtFkBxpALVah1CegQOQUaq7EOmnIpytQY/1W3DzmAhdoYK8XuwEDuCe1Gh1iTdZ19nNwz39MEoV1+Mdh2CQ109IIkiIJkPa4LELZ1ycgGPF5AMACHAqAEQBiTdfOjzAKKLB7VuBpqh88R0aIZmWj1p0A3dNFxhEAURDlGGQ+JWT5muNKQ4PXBLLrhMKyieHO0beNyRBqQP4ykRxrg7YLgkmiLlPIXL+Gp0kQpubcX0qHjVbETAkQLIMcmu+3guefnKdrKPv0927uW5nVz8/e6oiCLgFFtmaWcYpnBlCli2G5opZhlmWTe48KGYwpblemptbyXGTFEECcIWzP9MwUoEzwWNl8G4uOVzASlOoEe62S+afaxhcVE3uLtjMAT4w1zcCoa5uBUwy1YKhM2A9JHoNoEQj/OlmW52lrhVWbmv70RyXE7A5eYx4axYU253tGzFoIod4zLHOJ3xY2Jd/GLbElPiqoGWYBUraMUKW5YVlzUuwVpLApCRmopSTQMiEXidTgixx4gVomLFqcRyMsGqoW2b20/sM4wxBINBlJaWIiMjA1IruooTXQtaWYkgCIIgDjxtKkotW7YMP//8M5566ikUFhbi+uuvR8+ePXHccce15TQODIzxh7pt27jYtGkTd9MrLOTL1RcVNfzQ5/MBo0YBo0fzNGoUf+iK2z1DyIggqIcR0EKoUKpRolRib6gUZUo1alQ/arUAKpQqFIXLsSdcir2RMihMS3pIAQL6OnKQ7+6FQe5eGOY7BMNS+iPNlxl9OHSZlgOyuXS8w7QoaEjw0UOAFgCUGuhKBfRIFXQtAh0GNEGGLkjQIUCDCIOxOC8pURQhCxIckgxZkOBzeuCV3fA5PXCIMpym+51TdMApcWuoVrF4amsE03XPlQWkD214nKEBSjWglJt5VUyq5qKVWmOW63jZiAAwuKWW2kpBA0UXDzouuZOUXTwGkx1M3MlFSNGVEKPJWT9mk1UWZLPPES3H5WaAcqGV4ghZljL7arVnCVi2OKVHy4ltsQKYJWpZ8dUsKy1rnMb4WLBou2X5YxsAWa6JAgAPILuATAHINoUwmKKWEGvBZZ23ub1obi+YIlw4wgWqUAQIh7lVWTAMhBWzLSYPx9QjChBSTHdKM0UUHszeImK2tWX8SkniIqVloemI+dsVV4/5e2aVZTM2msMZ7ZNldHc4gDHjUTpwcP0A7Ymfybg++78kfYnbJYxNNqaxfTQoWgkJu02sxxw3cVySYpKNDzwNHnL/55KRkYHu3bvv936IrktnXx1q+uBcXHvskPaeBkEQBEE0SpuJUsFgEGvWrMEjjzyC4cOHY/jw4di6dSuee+65jiNKhUI81ksgwK2XAgHuIlJXx9vr6nhQ44oK7mJXUcEtoEpLeR4KNX2MrCxg0CAgPx/Iz4d+6AD4e+WijoXhV/2oVfwoL/kS5ZEqlKs1qFRqUKpWoThSiSqtDlWaHzV6ANV6AH4j3OThJIjo6crBIb5e6J/aB/3T+qF/Wj8cmnkoPG5f/RWyBBEGM5Im3YjAiISgMz2mTQezbZmsh2gnRLkHJFmFbKiQDBWS7oeLqfCIIlwAPLIDsijBIbngkN1wSB44ZBcckhsOyQVBOsjdKUQZcOfw1FwMxRSk6gDND6h+QDPLWpALhpo/WrdERN0qh6Jle58RnhqwrGs7hJg4TlISwcrKJS6axrWJMXUxpi7Bjg8V22a52yF2bExutwsx5Zj22DZZABwi4I0ZC4HnjHE3PjM+e/1k9hnWOFOUMlh0jKHzeG66JWaZ4pZluWWAD2TmdlrMPsCnBC8DPDLAUnibFVSexVp9mdY+tpJsWQsJUaELgimwGVyMshY3UFQuVqkqtzyLJNQVFVD0qGWaYsYKU/Xoao5WkHzLci1iWq8pMbHrAC72hXSgGX+KW/DJQw88gm5eL9ScnNa1VpKtOFliNLfKohjjQmqVhWg8LMn8HFl1e6xZFpJsY28bU5fE+H5rH1LM8YXYNqssA7K5nX0eZuw2WeafLcu6Vo49Byl+33Gx3iTEnbcQ2x/rigkgLQPodTgXxfcBh8NBFlJEs+jMq0Mdmutr7ykQBEF0SXJTXF3CmrajnEObiVKbNm2CpmkYO3as3TZu3Dg89NBDMAwDYqLbRRsTev8/WPPPU1EpqzAE2EkXAF2ML2tm0gVA6waoPQDVbFMdIhSvC4rbAcUlQ3HJCLkkRBwCQjIQhoYw24ygsRFhQ4WyRQO27Pu8ZUFCtjsLeZ5c5HqzkePNRZ6vG7qn9kD31F7ITekGUZTAwGAwA4wxMDBwh7AgmMEABTwB5rOnAAkSz0UJoiBChAhRFOEQHfBKXjhEB5yyE07RCafkhCRKkEUZkmDmZj02iczgK9bpYcAIA3okXhTRVcAIAlqtuWJaQoyYpMJAzEO+JRJYD9BWu20JEPPw3BmtrJpCdEYDpe8PjHEhSg+Z75eVR8z3LjaPcDHMCAOGyuOLWUKWofC2pLnCrcGYFm1jWrSNJa7gZq6qpye3/CP2E+vS2F9EAE4ztQUMgA5AA6AmSVqSXEvSrif0W3VdMOsMkhaEpO2OHk+LObbeRDlJDHdiP+njBn7YCuSkt/dM2oWDKhwCQRAEQXQw0jxyp7emHdgtBcvPHtv0wDagzUSpsrIyZGZmwhmzjHlOTg4ikQiqq6uRlZXVVlNJynPlH+HiE9WmBzaJGWMp8ad6Bv6Q0wCiIJqrwbnhc/rgc/h47vQh1ZmGNFcaUl2pSHelI9OTiSxPFrI8WUhzpkEURAiCAEEQeBmCLQ6JomiLRbFCkSiIdpIEKVoWpbh2qx5b3mcEERDNGEeJMMMUJ1QeCNwWLEyBgmlc8LD7rLG6ua3OX2RbzGL8vbBdoGLa7Xg/9sSaqMd2CUgaULnhk256360ikLV0Hy0YL7p5asp4rTWFPnslvAShytDMPutzkZhitrPrRnw/YtsScuszY5eN6GcnblsWLVt9dj9LaEPMfmM/h9a2CfWkbUje32AZMcdONgYx9ZhyvX3ElOvtK7ENMe0tuUb2AwH8W0wG4D4QB2il87DEs4aSgaiIZX6M4voSxyaOa6xs/mms15+YWCN9DY1ljWzPkvSzJNs2lCNJPRaHKXIfpHTpcAgEQRAE0UnozNa0HYk2E6VCoVCcIAXAriuKkmyTNuX4k67GhR+UoahmDyRRhiBySyExRpQRRRGSKejIgmxbDkmiFGch5JSccIgOyKIMl+SCx+GBW3bD4/DA4/AgxZECn9MHr8MLj+xBqjMVTskJUeSCUqy4JAhC0twWomLKsdt0OgSRu2HsiyuGLTzEigKJeeLDfgMP4cnqQJKH7mR9if1J6vUErcb6kz0Qx7Q1Jo4l7WvpA3ZLxh8oEaKNxI0GD9/Ox+/sJApgdlvi5zjZNRi3oyTbNnTtNOfabUk/Guhv7FiNbQPE/R2KPT5LmE/i/owk6ozdlmQOiftLOkejgfEJc63XFrvbZO9n/GEbHJ90TknOnbGoiyoApPYDsnriYKRThEMgCIIgCIJoJm0mSrlcrnrik1V3uw/IT9wtomdqTzx5+tPtPQ1iX7Dd+AiCIAiia9PRwyEQBEEQBEG0hDYTpfLy8lBVVQVN0yDL/LBlZWVwu91IS0trcntm/mrq93dOn02CIAiCIDoXPp+vw1kf7284hLa6n+oowVP3l0PSRBhK51x8Jc/D32c6h/anK5wHnUPHgM6hY9AVzuGQNLHNtJWm7qfaTJQaOnQoZFnG+vXrMX78eADAunXrMHLkyGb9qhcIBAAA06ZNO6DzJAiCIAiCAPh9SkpKkjiI7cj+hkOg+6mDhx0AOrsPQFc4B6BrnAedQ8eAzqFj0FXOYdzStjlWU/dTbSZKeTwenHrqqbj55ptxxx13oLS0FI8//jiWLm3eK9GtWzd89tlnHfJXS4IgCIIguh4+n6+9p1CP/Q2HQPdTBEEQBEG0JU3dT7WZKAUAixYtws0334wLL7wQKSkpuOqqq3DMMcc0a1tRFNG9e/cDPEOCIAiCIIiOy/6GQ6D7KYIgCIIgOhICY7S8FEEQBEEQRGcgFAph4sSJePzxx+1wCKtWrcLXX3+NZ599tp1nRxAEQRAE0TJoiRaCIAiCIIhOQmw4hI0bN+LDDz/E448/jgsuuKC9p0YQBEEQBNFiyFKKIAiCIAiiExEKhXDzzTfj/fffR0pKCubOnYvZs2e397QIgiAIgiBaDIlSBEEQBEEQBEEQBEEQRJtD7nsEQRAEQRAEQRAEQRBEm0OiFEEQBEEQBEEQBEEQBNHmkChFEARBEARBEARBEARBtDkkSgGIRCK44YYbMH78eEyZMgWPP/54e0+p01JSUoJ58+ZhwoQJmDp1KpYuXYpIJNLe0+rUXHLJJVi4cGF7T6PToigKbrnlFhx++OE48sgjcc8994BC6e0bRUVFuPTSS3HYYYdh5syZePLJJ9t7Sp0KRVFw4okn4ptvvrHbCgoKMHv2bIwZMwbHH388vvzyy3acYech2Wu5fv16nH322Rg7diyOPfZYrFmzph1nePDy6quvYvDgwfXSkCFDko4/+eST643dsmVLG89632mN6/rtt9/GUUcdhdGjR+OKK65AZWXlgZ72PtMa19748ePrveeBQOBAT32fSXbOt99+e71zePbZZxvcx5NPPompU6di7NixuOGGGxAKhdpi6vtM4jkvXLgw6XXd0KqfNTU19cZOnDixLU+hWTT23NJVr+PGzrmrXsuNnXNXvJYbOt8OfR0zgt16663spJNOYj///DN7//332dixY9l///vf9p5Wp8MwDPaXv/yF/fWvf2Vbtmxh3333HTv66KPZv//97/aeWqfl7bffZvn5+ez6669v76l0WhYvXsyOOeYYtmHDBvbVV1+xiRMnshdeeKG9p9Up+ctf/sL+8Y9/sJ07d7IPPviAjR49mr3//vvtPa1OQTgcZldccQXLz89na9euZYzxv5knnXQSu/rqq9m2bdvYQw89xEaPHs327t3bzrPt2CR7LUtLS9n48ePZ3XffzXbu3MnefvttNnLkSPbJJ5+072QPQkKhECstLbVTYWEhO/roo9mSJUvqjdU0jY0cOZJ9++23cduoqtoOM285rXFdb9iwgY0aNYq99tpr7LfffmPnn38+u+SSS9ryNJpNa1x7xcXFLD8/n+3evTvuPTcMow3PpPkkO2fGGJs9ezZ7+OGH484hGAwm3ce7777Lxo0bxz7++GO2YcMGdvzxx7NbbrmlrU6hxSQ759ra2rhz/fHHH9mIESPYBx98kHQf33//PZswYULcNuXl5W15Gk3S2HNLV72OGzvnrnotN/V82tWu5cbOtyNfxwe9KBUIBNjIkSPjvmhWrVrFzj///HacVedk27ZtLD8/n5WVldltb731FpsyZUo7zqrzUlVVxf7whz+wP//5zyRK7SNVVVVs2LBh7JtvvrHbHn74YbZw4cJ2nFXnpLq6muXn57PNmzfbbVdeeWWH+zLuiGzdupWdfPLJ7KSTToq7yf/qq6/YmDFjWCAQsMdeeOGFbMWKFe011Q5PQ6/l888/z4477ri4sYsXL2YLFixoj2kSMTz00EPsqKOOYpFIpF7f77//zoYMGcLC4XA7zGz/aK3r+tprr437ji8sLGSDBw9mu3fvPrAn0EJa69r73//+xyZPnnzA59saNHTOjDE2depU9sUXXzRrP+eee27c+//dd9+xUaNGNfjg2540ds6xzJkzh11zzTUN7uf//b//x84666wDNc1WobHnlq56HTd2zl31Wm7q+bSrXcsteR7vSNfxQe++t2nTJmiahrFjx9pt48aNw4YNG2AYRjvOrPORm5uLRx99FDk5OXHtfr+/nWbUubnzzjtxyimnYODAge09lU7LunXrkJKSggkTJthtl1xyCZYuXdqOs+qcuN1ueDwevPrqq1BVFTt27MAPP/yAoUOHtvfUOjzffvstJk6ciJdeeimufcOGDRg2bBi8Xq/dNm7cOKxfv76NZ9h5aOi1tMzTE6Hvn/aluroajzzyCK6++mo4nc56/du2bUOPHj3gcrnaYXb7R2td1xs2bMD48ePteo8ePdCzZ09s2LDhgMx7X2mta2/btm3o37//AZlja9PQOfv9fpSUlOCQQw5pch+6ruOnn36Ke4/HjBkDVVWxadOm1p7yftPQOcfy9ddf47vvvsOCBQsaHLNt27ZmvT7tSWPPLV31Om7snLvqtdzYOXfFa7m5z+Md7TqW2+QoHZiysjJkZmbG3Szl5OQgEomguroaWVlZ7Ti7zkVaWhqmTp1q1w3DwLPPPosjjjiiHWfVOfn666/x/fff46233sLNN9/c3tPptBQUFKBXr154/fXX8dBDD0FVVZx++un429/+BlE86DX5FuFyuXDjjTfitttuw9NPPw1d13H66afjzDPPbO+pdXjOPffcpO1lZWXo1q1bXFt2djaKi4vbYlqdkoZey969e6N37952vaKiAu+88w6uuuqqtpoakYQXXngB3bp1w3HHHZe0f/v27XA4HLj00kvx888/o3///rjuuuswatSoNp5py2mt67q0tLRT/B1orWtv+/btCIVCmDVrFnbu3ImhQ4fihhtu6JAPtw2d8/bt2yEIAh566CF8/vnnyMjIwEUXXYTTTjut3tja2lpEIpG491iWZWRkZHS49xho+JxjWb16NU477TT06NGjwTHbt2+Hpmk444wzUFJSgvHjx2PRokX1PuvtSWPPLV31Om7snLvqtdzYOXfFa7m5z+Md7To+6J/KQqFQvV/vrLqiKO0xpS7DXXfdhV9//RXz589v76l0KiKRCG666SbceOONcLvd7T2dTk0wGMSuXbvw4osvYunSpbj++uvxzDPPUIDufWT79u2YMWMGXnrpJSxduhTvvvsu3nzzzfaeVqeloe8f+u7ZP8LhMK666irk5OTgrLPOau/pHLQwxrBmzRqcf/75DY7ZuXMnampqcOaZZ2L16tU49NBDceGFF6KoqKgNZ9q6tPS6DofDXebvQHOuvR07dqCmpgZ/+9vf8MADD8DtdmP27Nmdyqpxx44dEAQBAwYMwOrVq3HmmWdi8eLF+OCDD+qNDYfDANBl3uOCggKsXbsWs2bNanTcjh074Pf7sWjRItx7770oLS3FZZddBl3X22imLSf2ueVguY4belbrytdy7DkfDNdysve4I17HB72llMvlqvdBsuokCOw7d911F5566ince++9yM/Pb+/pdCpWrlyJESNGxKncxL4hyzL8fj/uvvtu9OrVCwBQWFiIF154AXPmzGnn2XUuvv76a7z88sv47LPP4Ha7MXLkSJSUlODBBx/EySef3N7T65S4XC5UV1fHtSmKQt89+0EgEMDll1+O33//Hc8//zw8Hk97T+mg5aeffkJJSQlOOOGEBsfcdtttCIfDSElJAQDcfPPN+OGHH/DGG2/gsssua6uptiotva4bug/tbJ/d5l57jz32GFRVhc/nAwD83//9H6ZNm4ZPPvkEJ510UltOeZ859dRTMWPGDGRkZAAAhgwZgt9//x0vvPACjj766LixlmtqV3iPAeC9997D0KFDmwwt8c4770AQBPtzv2LFCkyZMgUbNmzAYYcd1hZTbRGJzy0Hw3Xc0LNaV76WE8950KBBXfpabug97ojX8UFvKZWXl4eqqipomma3lZWVwe12Iy0trR1n1nm57bbb8MQTT+Cuu+7Cscce297T6XS88847+PDDDzF27FiMHTsWb731Ft566624uGdE88jNzYXL5bIFKQDo379/p/4Vvr34+eef0a9fv7gbsmHDhqGwsLAdZ9W5ycvLQ3l5eVxbeXl5h3Jv6Ez4/X7MnTsXW7duxVNPPdXh45l0db744guMHz8e6enpDY6RZdkWpADYv1iXlJS0xRQPCC29rhsan5ube8Dm2Nq05NpzOp32QyzAH/R69+7dqd5zQRDsh1iLhj63GRkZcLlcce+xpmmorq7uVO+xxRdffIE//vGPTY7zeDxx9wvZ2dnIyMjokO9zsueWrn4dN/Ss1pWv5WTn3JWv5caexzvidXzQi1JDhw6FLMtxgevWrVuHkSNHUsyZfWDlypV48cUXcc899zT66yjRMM888wzeeustvP7663j99dcxc+ZMzJw5E6+//np7T63TMXr0aEQiEezcudNu27FjR5xIRTSPbt26YdeuXXG/EO3YsSMu/gDRMkaPHo1ffvnFNgkH+PfP6NGj23FWnRPDMHDllVdiz549eOaZZzBo0KD2ntJBz8aNG5v8JXXWrFlYuXKlXTcMA5s3b8aAAQMO9PQOGC29rkePHo1169bZ9aKiIhQVFXWavwMtufYYYzjqqKPw6quv2m2Wm31nes+XL1+O2bNnx7Vt2rQp6TmIooiRI0fGvcfr16+HLMsYMmTIgZ5qq8IYw08//dTkde33+3H44Ydj7dq1dltJSQmqqqo63Pvc0HNLV76OGzrnrnwtN3TOXfVabux5vKNexwe96uLxeHDqqafi5ptvxsaNG/Hhhx/i8ccfxwUXXNDeU+t0bN++HQ888AAuvvhijBs3DmVlZXYimk+vXr3Qr18/O/l8Pvh8PvTr16+9p9bpGDBgAKZPn45FixZh06ZN+OKLL7B69Wqcc8457T21TsfMmTPhcDjwr3/9Czt37sTHH3+Mhx56qEl/dKJhJkyYgB49emDRokXYunUrVq9ejY0bN+KMM85o76l1Ol5++WV88803uP3225GWlmZ/9yS6XxBtx9atW+u5Bui6jrKyMlvcnjlzJp588kl89NFH2LFjB2699VbU1dUlDTLbWWjqulYUBWVlZXZMjnPOOQdvvPEG1qxZg02bNuG6667D9OnT0adPn/Y8jWbT1LUXe76CIGD69Om4//778c0332Dr1q247rrr0L17d0ybNq19T6QFzJgxA9999x0ee+wx7N69G88//zxef/11OyxAOByOu/c999xz8dhjj+HDDz/Exo0bcfPNN+Mvf/lLh3X5aYi9e/ciEAgkdfmJPeeUlBSMGzcOS5cuxcaNG/HLL79g/vz5mDp1KgYPHtzW026Qxp5buup13Ng5d9VrubFz7orXclPP4x32OmYECwaD7LrrrmNjxoxhU6ZMYU888UR7T6lT8vDDD7P8/Pykidh3rr/+enb99de39zQ6LbW1tezaa69lY8aMYZMmTWL3338/MwyjvafVKdm6dSubPXs2O+yww9hRRx3FnnjiCXotW0h+fj5bu3atXf/999/Zeeedx0aMGMFOOOEE9r///a8dZ9e5iH0t58yZk/S75/zzz2/nWR68jBw5kn3++edxbQUFBXHvm2EY7MEHH2TTp09nI0aMYOeddx7bvHlze0x3v2jJdb127VqWn5/PCgoK7LZXXnmFTZs2jY0ZM4ZdccUVrLKysk3n31Jacu0lnm84HGZLly5lkydPZqNHj2aXXnopKywsbLdzaS6J7/EHH3zATjrpJDZy5Eh23HHHsffee8/ue+WVV+rd+z788MNs0qRJbNy4cWzRokUsHA632dz3lcRzXr9+PcvPz2eRSKTe2MRzrq6uZgsXLmQTJ05kY8eOZddccw2rrq5uk3k3l6aeW7riddzYOXfVa7mp97mrXctNnW9HvY4FxhhrfamLIAiCIAiCIAiCIAiCIBrmoHffIwiCIAiCIAiCIAiCINoeEqUIgiAIgiAIgiAIgiCINodEKYIgCIIgCIIgCIIgCKLNIVGKIAiCIAiCIAiCIAiCaHNIlCIIgiAIgiAIgiAIgiDaHBKlCIIgCIIgCIIgCIIgiDaHRCmCIAiCIAiCIAiCIAiizSFRiiAIgiAIgiAIgiAIgmhzSJQiCKLDM3jwYFx99dX12l999VXMnDmzHWZEEARBEARBEARB7C8kShEE0Sl4++238fXXX7f3NAiCIAiCIAiCIIhWgkQpgiA6Bb169cKtt94KRVHaeyoEQRAEQRAEQRBEK0CiFEEQnYJ//OMfKCkpwWOPPdbgmOLiYvz973/HhAkTMHHiRNx+++22iPXqq69i1qxZWLFiBSZOnIjx48dj6dKlYIzZ27/44ouYOXMmxo4di1mzZmHz5s0H/LwIgiAIgiAIgiAOVkiUIgiiU5CXl4d58+bhoYceQkFBQb1+RVFw4YUXIhQK4ZlnnsF9992HTz/9FMuWLbPH/Pjjj9i5cydeeOEFLF68GE8//TS++uorAMDHH3+MlStXYvHixXjttdcwbtw4XHDBBaipqWmzcyQIgiAIgiAIgjiYIFGKIIhOw6xZs9CvXz8sWbKkXt8XX3yBkpIS3HXXXRg8eDAmTZqEG2+8ES+88AICgQAAQNd13HbbbRgwYABOOeUUDBkyBD/99BMA4NFHH8Wll16KGTNm4JBDDsE//vEP9OrVC2+++WabniNBEARBEARBEMTBgtzeEyAIgmgukiTh5ptvxrnnnosPP/wwrm/79u045JBDkJ6ebrcddthh0DQNu3fvBgBkZ2cjJSXF7k9JSYGmafb2d911F+655x67PxKJ4Pfffz+AZ0QQBEEQBEEQBHHwQqIUQRCdisMOOwx//vOfsWTJEvz1r3+1210uV72xuq7H5U6ns94YK6aUruu44YYbMGnSpLj+WBGLIAiCIAiCIAiCaD3IfY8giE7HNddcg2AwGBf0vH///vj9999RXV1tt61fvx6yLKNv375N7rN///4oLi5Gv3797PTQQw9h/fr1B+AMCIIgCIIgCIIgCBKlCILodGRmZuKaa67B3r177bbJkyejT58+uO6667B582asXbsWt912G0488USkpaU1uc+LLroITz31FF5//XXs3r0bd911F/773//i0EMPPZCnQhAEQRAEQRAEcdBC7nsEQXRKzjjjDLzyyisoLS0FwONNPfDAA7jtttvwl7/8BT6fDyeddBIWLFjQrP0df/zxKC8vx4oVK1BeXo6BAwfiwQcfxCGHHHIAz4IgCIIgCIIgCOLgRWBWQBWCIAiCIAiCIAiCIAiCaCPIfY8gCIIgCIIgCIIgCIJoc0iUIgiCIAiCIAiCIAiCINocEqUIgiAIgiAIgiAIgiCINodEKYIgCIIgCIIgCIIgCKLNIVGKIAiCIAiCIAiCIAiCaHNIlCIIgiAIgiAIgiAIgiDaHBKlCIIgCIIgCIIgCIIgiDaHRCmCIAiCIAiCIAiCIAiizSFRiiAIgiAIgiAIgiAIgmhzSJQiCIIgCIIgCIIgCIIg2hwSpQiCIAiCIAiCIAiCIIg2h0QpgiAIgiAIgiAIgiAIos0hUYogCIIgCIIgCIIgCIJoc0iUIgiCIAiCIAiCIAiCINocEqUIgiAIgiAIgiAIgiCINodEKYIgCIIgCIIgCIIgCKLNIVGKIIgWwxg7KI+9P3TWeRMEQRBEZ4S+d4lk0OeCIDoeJEoRRBdm1qxZGDx4cFwaP348LrjgAnz77bct3l9xcTEuueQS7N27126bOXMmFi5c2OJ9DR48GPfff3+LtlmzZg3uvPPOFh+rvdm6dSvOOeecuLZ9OX+CIAiCiGXdunW46qqrMHnyZIwcORJ//OMf8a9//Qvbt29v76nFcf/992Pw4MFtdrx169bhkksuabPjdQR++eUXXHzxxTjiiCMwceJEzJkzB7/88kvcGMYYHnvsMRxzzDEYOXIkjj32WDz33HNN7nvv3r34+9//jkmTJmHixIm4/PLLsXv3brvfen8bSi2550y2r2HDhmHixIm44oorsHXr1mbv6/HHH8c111wDAKitrcV1112H77//vtnb7w8LFy7EzJkzGx3z6quvYvDgwdizZ0+z99ucbaqqqjB9+nQUFBQ0e7+xBAIB3HLLLZg8eTLGjh2Liy++GDt27Gh0m5kzZzb4/se+DmVlZfjXv/6FGTNmYOzYsTj99NPxn//8Z5/mSXQd5PaeAEEQB5Zhw4bhpptuAgDouo6qqiq88MILmDt3Ll599VUMGjSo2fv66quv8Nlnn7XKvF566SV07969Rds8+OCDmDBhQqscvy1599138eOPP8a17cv5EwRBEITF6tWrcc8992DKlCm44YYbkJubi127duGFF17AaaedhqVLl+KEE05o72m2C2vWrOlwwtyBZNeuXTj//PMxYsQILFmyBIIg4PHHH8e5556L1157DQMGDAAALFu2DM888wzmzZuHkSNH4vPPP8ett94KWZZx1llnJd13OBzGnDlzoGkaFi9eDJfLhRUrVmDWrFl46623kJaWhjPPPBNTp06N205VVcyfPx+5ubkYNWpUi8/ppZdessu6rqOwsBD33nsvzjvvPLzzzjvIzc1tdPvt27fj4YcfxptvvgkA+O233/DGG2/gz3/+c4vncqCYPn06XnrpJXTr1q1V95uZmYnZs2fjhhtuwNNPPw1BEFq0/dVXX40NGzbg2muvRUpKClauXIkLLrgA77zzDtLT05Nus3LlSiiKEte2fv16LF26FGeffTYAQFEU/PWvf0VdXR3mzZuHbt264b333sP8+fOhKApOPfXUfTpfovNDohRBdHFSUlIwZsyYuLYjjzwSkyZNwquvvorrr7++XeaVOKeDjYP9/AmCIIh955NPPsHdd9+Nq666CldeeaXdPmHCBJx66qm4+uqrsXDhQuTn57foxyeic/LMM8/A4/Hg4YcfhtfrBQAcccQRmDlzJp599lnceOON2LNnD5588kksXrwY5557LgBg0qRJKCoqwpdfftmgKPX999/j999/x5NPPolJkyYBAPr3748//elP+Oijj3Daaaehe/fu9X5oW7p0KQKBAF588UW43e4Wn1PifdK4cePQo0cPnHfeeXjttdeatIS76667cOKJJyIvL6/Fx24rsrKykJWVdUD2fe655+LBBx/EBx98gGOOOabZ2/3444/45JNPsHr1akybNg0AMH78ePzxj3/E888/j7/97W9Jtxs2bFhc3e/3Y8GCBZg+fbr9Xn366afYtGkT1qxZYwuVkydPRmFhIR599FESpQ5iyH2PIA5CPB4PXC5XvV9O/vOf/+D000/H2LFjMXnyZNx4442oqakBwM2FFy1aBAD44x//GOeyp6oqli1bhsmTJ2PMmDGYM2cOdu3a1egcYt3XvvnmGwwePBhff/015syZg9GjR2Py5Mm46667oOs6AG4WvHfvXrz22mtxZsuFhYVYsGABJkyYgNGjR+PCCy/Er7/+ah9nz549GDx4MJ544gkcd9xxGD16NB588EEMHjwYn3zySdycfvvtNwwePBgffPABACASiWDZsmWYNm0aRowYgZNOOqmeifHMmTOxYsUK3HnnnTjyyCMxatQozJ07F7///jsAboa+cuXKeuec6L5XWlqKRYsWYdq0aRg1ahTOOOMMfPTRR/Ves+eeew7//Oc/MWHCBIwdOxZ///vfUV5ebo/ZvXs3LrvsMkycOBGjR4/GWWed1WrWbQRBEETHYOXKlRgwYACuuOKKen0OhwO33norJEnCI488AgCYM2cOTj/99HpjL7/8cpx88sl2/fvvv8f555+P0aNHY8KECbj++utRWVlp97/66qsYNmwY1qxZg8mTJ2PChAnYtm1bs797Pv30U5x88sm269jrr78e19+c78JIJIJVq1bhuOOOw8iRI3HMMcdg9erVMAwDAHebeu2117B3714MHjwYr776atLX8P7778dxxx2HDz74ACeeeCJGjhyJU045BT/++CPWr1+PM888E6NGjcKJJ56Ir7/+Om7bLVu24NJLL8Vhhx2Gww47DFdccUU9V6lNmzbhyiuvxBFHHIHhw4dj6tSpuP322xEOh+0xzflet9y1vvnmm6TnAQADBgzAnDlzbEEKALxeL7p372672X344YdwuVw444wz4ra97777Gg0nEIlEAAA+n89uy8jIAABUV1cn3Wbz5s145plncOWVV6J3794N7ruljBgxAgDsMBL3338/jj76aKxcuRITJkzAlClTUFNTgy1btuDTTz/FiSeeCIDfZ15wwQUAgAsuuACzZs2y99nYva/FTz/9hLlz52LixIk47LDDcNlllzXbjfDVV1/Fsccei5EjR+Lkk0+Ouy6SueK99tprOP744+3xX3/9NYYNG1bvc7xhwwacffbZGDlyJKZPn45HH300rt/pdOLYY4/Fww8/bLdZ99sNXRMA8OWXX8Lr9WLKlCl2W1ZWFg4//PAW3U8+8MADqKysxI033mi3paSk4KyzzsLIkSPjxg4YMCDOHZQ4+CBRiiC6OIwxaJoGTdOgqirKyspw9913Q1GUOBPmBx54AAsWLMCYMWOwYsUKXHHFFXjvvfcwa9YshMNhTJ8+3f51ZOXKlbj88svtbf/zn/9g69at+Pe//42bbroJP//8M+bPn9/iuV5zzTUYN24cHnroIZx44ol49NFHsWbNGvuYubm5mDZtmm3qXFlZibPPPhu//PILFi9ejLvvvhuGYeC8886rZ7Z///334+KLL8ayZctw2mmnoW/fvnjnnXfixrz99tvIyMjAtGnTwBjDFVdcgRdffBEXXXQRHnzwQYwdOxbz58+vdxP99NNPY8eOHVi6dCluv/12/Pzzz7YF2plnnmnfAL700ks488wz6513eXk5zjjjDHz//feYP38+7r//fvTq1QtXXHGFbXZuce+998IwDNxzzz247rrr8Mknn+COO+4AABiGgUsvvRShUAjLli3DAw88gIyMDPztb39rUiQkCIIgOgeVlZX4+eefMWPGjAbdcjIyMnDkkUfags7JJ5+MX375Je67oLa2Fp9//jlOOeUUAMB3332H2bNnw+1247777sMNN9yAb7/9FhdccEGckKLrOh5//HEsWbIEixYtQv/+/Zv93XPjjTdi9uzZePDBB9G9e3csXLgQmzZtAtC870LGGC677DI8+uijOPPMM/HQQw/huOOOw3333WeHKrj88ssxbdo05Obm4qWXXsL06dMbfC2Li4vx73//G5dddhmWL1+O2tpazJs3DwsWLMCZZ56JVatWgTGG+fPn26/Bzp07cfbZZ6OiogJ33nknlixZgoKCApxzzjmoqKgAwMW18847D6FQCP/+97/xyCOP4IQTTsAzzzyDp59+Om4OjX2vA1EXr+HDhzd4Hueeey7++te/xrXt2rULW7dutS3lfvvtN/Tr1w/fffcdTjvtNAwfPhwzZ86Mc5NLxpQpU3DooYfirrvuQkFBAcrKynDbbbfB6/XiqKOOSrrNsmXL0Lt3b1x44YWN7rul7Ny5EwDQt29fu62wsBCfffYZ7r33XixatAjp6el46623kJuba1tbDR8+3BZHbrzxRvuz0tS9LwCsXbvWjgt6xx134Pbbb0dRURHOPvvsJl1Ei4qKsHr1avz973/H/fffD0EQMG/ePPtzksjrr7+OhQsX4rDDDsMDDzyAY489Fpdffrn9A20sN998M0444QSsXr0aY8eOxV133VXvx9bjjjsOP//8s/26DR8+vMlrYvv27ejduzckSYpr79u3r72fpigsLMTTTz+NuXPnolevXnb7kUceiVtvvTXu75aqqvjss88wcODAZu2b6KIwgiC6LOeffz7Lz89Pmh566CF7XHV1NRsxYgRbvHhx3Pbfffcdy8/PZ88++yxjjLFXXnmF5efns4KCAnvMjBkz2LRp05iiKHbbvffey/Lz81ldXV2Dc8vPz2crVqxgjDG2du1alp+fz+699964MTNnzmSXXnpp3LGuv/56u37PPfewkSNHsj179thtkUiE/fGPf2RXXXUVY4yxgoIClp+fz2644Ya4fa9YsYKNGTOGhUIhxhhjhmGw6dOnsxtvvJExxtiXX37J8vPz2TvvvBO33TXXXMMmT57MVFW15zRjxgymaZo95v7772f5+fmssrLSPlZ+fn6D579s2TI2fPjwuPNgjLELL7yQTZ48mem6bm9zzjnnxI1ZuHAhGzNmDGOMsdLSUpafn8/efPNNu7+2tpbdcccdbMuWLYwgCILo/GzcuDHuu7kh/v3vf7P8/HxWXV3NAoEAGzNmDFu5cqXdv2bNGjZkyBBWXFzMGGPsrLPOYieeeGLc99mOHTvY0KFD690HvP766/aY5nz3WN+Dn332mT1m165dLD8/nz311FOMseZ9F3766acsPz+fvf3223FjVq1axfLz8+3jXX/99WzGjBmNvj7J5vTwww+z/Px8tmbNGrvt3XffZfn5+ezXX39ljDG2YMECduSRR8bd41RVVbFx48axf//734wxxr744gt23nnn1bsPOvHEE9mcOXPselPf6/tKKBRiZ511FhszZoz9ev71r39lEydOZEcccQR79tln2VdffcX+9a9/sfz8fPbiiy82ur8ffviBTZgwwb6HHDFiBPvyyy+Tjv3tt99Yfn4++3//7//t09yt90VVVTvV1dWx7777jp122mls3LhxrLS0NG7sd999F7ePM844g/3tb3+La7PuNdeuXcsYa/697xlnnMGOP/74uOuipqaGTZgwgc2bN6/B87j++utZfn4+27Ztm9321Vdfsfz8fPbhhx8yxurfV0+fPj3uvpex6GfylVdeidvm+eeft8cEg0E2fPhwdscdd8RtW1tby/Lz89lzzz3X4DwTmTNnDjv77LPrtd9zzz1s+PDhzdrHHXfcwcaOHcuqq6ubHLtkyRKWn5/P3nvvvWbPkeh6kKUUQXRxhg8fjpdffhkvv/wy1qxZg8ceewwXXngh7r33Xtx7770AeCBCRVFsM2eL8ePHo1evXk2umjJq1Cg4HA67bplq19bWtmiuY8eOjat3794dwWCwwfFff/01hg4diry8PNsaTBRF/OEPf8BXX30VN3bo0KFx9ZNPPhnBYND+VemHH35AYWGh/Yvx119/DUEQMG3aNHvfmqZh5syZKCsrizPbHjlyZNwvSlZchVAo1Kzz/vbbbzF27Ni4X5OsOZaVlcWteJIYY6F79+72cXJycjBw4EAsXrwY119/Pd566y0YhoFFixZRTBGCIIguAjOXtI/93k2G9b3EGLOtWmJd0N955x1MmjQJeXl5CIVC2LBhg20pbH3n9enTB4ceeij+97//xe079ju1Jd8948ePt8uJ9wrN+S789ttvIcsyjjvuuHpjrH20lMMOOyzuXABg9OjRdpvlqmbNc+3atZgwYQLcbrf9OqWkpGD8+PH2vceUKVPw7LPPwuVyYdu2bfjoo4/w4IMPorKysl4w6Ma+1/cFv9+PSy+9FD/99BPuuusu+/VUVRVVVVW45ZZbcN5552HSpEm47bbbMGXKFDvMQDIsa7khQ4bg4YcfxiOPPII//OEPuPLKK5OuZPfcc88hOzvbvp/aV4YPH26ncePG4bzzzoOiKLblfCyJ93gFBQVNug025943GAzip59+wp/+9Ke4+7y0tDTMmDGjyc9bZmYmDj30ULtuzamurq7e2F27dqGwsLDeZ7uhxQpiryWPx4OcnJx6992pqalIS0tr0ep+1t+XZDQnYHokEsHLL7+MM844o8Gg6NZxli1bhqeeegpz585tUdwroutBgc4Joovj8/nq+W5PmTIFwWAQjz76KC644ALbd966GYslJycn6ZdnLLExDABAFLnebcV3aC6JgTBFUWz0y7G6uhq7du1q0KQ99qYucY79+vXD2LFj8c477+BPf/oT3nnnHfTt29e+Oa2urgZjLO5mNZbS0lL7Jsjj8dSbN9D886+pqUGfPn3qtVvvR+xNRrJjWa+RtdqOFdjy9ddfh8PhwFFHHYVbbrml0ZsDgiAIonNgiQxWXJ2GKCgogM/ns0WVU045BW+++SY2bdqEnJwcfPPNN7abWG1tLQzDwCOPPGLHoYrF5XLF1WO/U1vy3RO7nfVdaX2HNee7sKamBpmZmfVciyyRoqn7lWSkpKTUa0v8ro2luroa//nPf5IuY28Frbbc8Z577jkEg0H06NEDo0aNqvc6JjtWU/c+jVFUVIRLL70UO3fuxL333hvnXufz+ewf22KZOnUqvvzyS5SXlye9D3zooYeQl5eHRx55BE6nEwC/jzz77LNxxx13xMUn0nUdH3zwAY4//nh77L7y8ssv22WHw4Hc3FxkZ2cnHRsb7wrgwlxj7yGAZt371tXVgTHWavfHlqiT7P7Qit2WeI7Jjg00/3Pj8Xjg9/sbnWcsKSkpcTHNLAKBAFJTU5vc/ssvv4Tf78dJJ53U4BhFUbBw4UK88847mDt3Lq677rpmz4/ompAoRRAHKSNGjMCaNWuwZ88e+4axvLzcXjbYoqysLOlNYkcgNTUVEyZMaPDLrKkbopNPPhlLly5FXV0d3n33XTtmgLVvr9dbL/aDRb9+/fZ94gmkp6ejrKysXrvVlpmZ2ex95eXl4eabb8ZNN92ETZs24d1338UjjzyCzMxMO4YCQRAE0XnJzs7GmDFj8N577+Hvf/+7Le7E4vf78b///Q8zZ8602yZNmoTc3Fz897//RW5uLlwul22dYAkWs2fPTmqZ0dQDfmt89zTnuzA9PR1VVVXQdT1OmCotLbXHHGhSU1Nx5JFH4qKLLqrXJ8v80Wr16tV48sknccstt+CYY46xH+YTg4y3Jps3b8bcuXMRiUTw+OOP4/DDD4/r79evHxhjUFU1ThzTNA1A/R8GLfbu3YsRI0bE3VOJoohx48bhueeeixu7YcMGVFVV4U9/+tN+n0/iD6otISMjo0nBqDn3vqmpqRAEIalIU1ZWZgu+rYFlZZ8Yb6qh+FPNpba2tkXXRf/+/fHll1/CMIy4vy27du2Ks/pqiE8//RS9e/du8P2rq6vDJZdcgvXr1+OGG25o9bhjROeE3PcI4iBl48aNkCQJffr0wejRo+F0OvH222/Hjfn+++9RWFhoWwslu/FtSxKPP2HCBOzcuRP9+/fHyJEj7fTGG2/g5ZdfrvdLaiLHH388GGNYvnw5Kioq4lYgmjBhAoLBIBhjcfvesmULVq1aZd/E7cu8Ezn88MPx448/1vvV+80330Rubm6zBbAff/wRRx55JDZu3AhBEDB06FDMnz8f+fn5KCwsbPZ8CYIgiI7NlVdeiZ07d+Kee+6p16frOm666SaEw+G44NeSJOGkk07CJ598gnfffRdHHXWUbcmRkpKCYcOGYceOHXHfeYMGDcL999/f6MpvrfXd05zvwgkTJkDTNLz77rv1xgDAuHHjABzY+xVrxcGhQ4far9OIESPw5JNP2qv3rlu3DgMHDsSf//xnW5AqKSnBli1bWmxF3hyKiopw0UUXQRAEvPDCC/UEKQC2hVTiIi8ff/wxBg8enNRiDOAro23cuDHO7ZAxhh9//LHej5YbNmyALMsYNWrU/p7SftGrVy8UFRXFtSXeEzbn3tfr9WLEiBH473//GxdsvK6uDp9++qn9eWsNunfvjr59+9qfIYv3339/n/dZU1ODUCiEnj17NnubKVOmIBAI4IsvvrDbKisr8f3332Py5MlNbr9+/foGvQw0TcNll12Gn376Cffeey8JUoQNWUoRRBfH7/dj/fr1dl1RFHz88cd45ZVXcNZZZ9mm5pdccglWrVoFh8OBGTNmYM+ePVi+fDkGDhyI0047DQD3oQeADz74AH/4wx+a9YtJa5KWloZff/0V3377LUaNGoXZs2fjjTfewOzZszFnzhxkZmbiP//5D/7f//t/WLRoUZP7s1bae/755zF27Ng48WfatGk4/PDDcfnll+Pyyy/HoYceio0bN2LFihWYOnWq/bo1d94AX91v9OjR9W7iLrroIrz55puYPXs2rrzySmRkZOD111/H2rVrcccddzT75nrYsGFwu9247rrrcNVVVyEnJwdfffUVfvvtN3spZIIgCKLzM3XqVCxcuBDLli3Db7/9hj//+c/o1q0b9uzZgxdeeAG//fYblixZgiFDhsRtd8opp+Dxxx+HKIr13PQWLFiASy65BFdffTVOPvlke5W9DRs2xK24m0hrffc057vwD3/4AyZOnIh//etfKCkpwZAhQ/Dtt9/ikUcewWmnnWav4JWWloby8nJ89tlnGDp0KLp169aCV7dxLr/8cpx99tm49NJLcc4558DlcuGll17Chx9+iBUrVgDgsTYfeOABrF69GmPGjMGuXbvw8MMPQ1GUFseLqqysxO7duzFw4MAGhaPbb78dFRUVuOWWW+rd96WkpGDgwIGYOHEiZsyYgaVLlyIUCmHQoEF4/fXX8cMPP+CBBx6wx+/evRuVlZV2rKvLL7/cXt3vwgsvhCzLeOWVV7B+/Xr7fC22bNmC3r17J3VTBPhqh8XFxRg2bNh+u/c1xuTJk/H888+DMWa7zFni4Keffor09HQMGTKkWfe+V199NebOnYtLLrkE5557LlRVxerVq6EoCq644opWm7O1Mt8111yDm266CUcffTQ2bdqEVatWAdg3oXXdunUAuNAE8GeCbdu2oW/fvg3exx5++OGYMGECrr32Wlx77bXIyMjA/fffj9TU1DiPgm3btkFRFAwbNsxu03UdO3bsqBeny+K5557D999/j7POOgvdu3eP+5wC9eOrEQcPJEoRRBfn119/xVlnnWXXXS4X+vbti/nz52Pu3Ll2u3Uj+eyzz+Kll15CRkYGjjvuOPzjH/+wf0mdOHEijjzySNx99934+uuvsXr16jY9lzlz5uCOO+7A3Llz8cQTT2D8+PF48cUXcffdd+Pmm29GJBLBIYccgiVLljTbRP6UU07Bhx9+WM/3XRRFrF69GsuXL8fDDz+MiooK5OXl4aKLLmrxTcgxxxyDN954AwsXLsQZZ5yBm2++Oa4/NzcXL7zwAu6++27cfvvtUFUVQ4YMwQMPPIA//vGPzT6Oy+XC448/jrvvvhtLlixBbW0tDjnkENx66604/fTTWzRngiAIomNz0UUXYezYsXjqqadw5513orKyErm5uZg8eTKWLFmSdIn1IUOGID8/H1VVVZg0aVJc35QpU/DYY49h5cqVmDdvHhwOB4YPH44nnnii0YfF1vruac53oSAIePjhh7FixQo8+eSTqKysRO/evbFgwYI4d7rTTz8dn332Ga644grMmzcPl1xySbPn0RRDhgzBc889h3vvvRfXXXcdGGPIz8/HqlWr7HleeumlqKqqwtNPP41Vq1ahR48eOOWUU+z519bW2j9YNcWnn36KRYsW4emnn8bEiRPr9SuKgk8//RQAkrpKTpgwAc888wwAYPny5Vi5ciWeeOIJVFZWYuDAgVi5cmWcm+cDDzyA1157DZs3bwbA3eieffZZLF++HNdccw0cDgcGDx6Mp59+GhMmTIg7Vnl5eaPxK9esWYOVK1fio48+ajIQ+f5wzDHHYNWqVdi4caMdtH7QoEE48cQT8dxzz+GLL77A22+/3ax730mTJuGJJ57AihUrsGDBAjidTowfPx533nlnqy8ic9JJJyEYDOKxxx7DK6+8gkGDBuGf//wn/vnPf9aLT9UcPv/8c4waNcqOQ/fLL7/gggsuwNKlSxu9NleuXIl///vfWLZsGQzDwGGHHYb77rsv7r295ZZbsHfvXnz88cd2W3V1NTRNa/CzbVl9vfTSS3jppZfq9VufOeLgQ2D7GkmPIAiCIAiCIAiCIJrJeeedh/vuu6/eCnqtzWWXXYbMzEwsXbr0gB6nNXn77bcxbNiwuBhXn376KS699FK88cYb9SwfGyMYDGLq1Km488474wLeE0RHhGJKEQRBEARBEARBEAeUb775BqFQqMEV5VqT+fPn4/333+9UMTXffPNNXHzxxXjrrbfw/fff45VXXsFNN92ECRMmtEiQAoAXX3wRgwYNapHFPUG0F2QpRRAEQRAEQRAEQRxQ9u7dC6/X2yarJAJ8FcRNmzYlXRCgI1JVVYW7774bn3/+OSorK5GTk4Njjz0W8+bNg8/na/Z+Kisrceqpp+KZZ55p1dWiCeJAQaIUQRAEQRAEQRAEQRAE0eaQ+x5BEARBEARBEARBEATR5uyzKKUoCk488UR88803dltBQQFmz56NMWPG4Pjjj8eXX34Zt81XX32FE088EaNHj8YFF1yAgoKCfZ85QRAEQRAEQRAEQRAE0WnZJ1EqEolgwYIF2Lp1q93GGMMVV1yBnJwcvPLKKzjllFNw5ZVX2sHlCgsLccUVV+D000/Hyy+/jKysLFx++eVorvcgYwx+v7/Z4wmCIAiCIIh46H6KIAiCIIiORItFqW3btuEvf/kLdu/eHde+du1aFBQU4NZbb8Whhx6KSy+9FGPGjMErr7wCAFizZg1GjBiBOXPmYNCgQVi6dCn27t2Lb7/9tlnHDQQCGDduHAKBQEunTBAEQRAEQYDupwiCIAiC6Fi0WJT69ttvMXHiRLz00ktx7Rs2bMCwYcPg9XrttnHjxmH9+vV2//jx4+0+j8eD4cOH2/0EQRAEQRAEQRAEQRDEwYPc0g3OPffcpO1lZWXo1q1bXFt2djaKi4ub1d/uKNXAtkcAQwHkFMCRaqYMwJkBODPNciYgSu07V4IgCIIgCIIgCIIgiE5Oi0WphgiFQnA6nXFtTqcTiqI0q7/d2b0GWH9dMwYKXJhy5fDkzgM8PQFvb8DTC/D1BVIH8XYSrwiCIAiCIAiCIAiCIJLSaqKUy+VCdXV1XJuiKHC73XZ/ogClKArS0tJaawr7R58/A3VbgdotgB4AtCCgBQDND6h1PNeDABigVPJUt6Xh/UleU6zqw0WqtHwgbSjg7QvIbkCQkidRNstyTLvQZi8DQRAEQRAEQRAEQRANoxsMkti5n9M7yjm0miiVl5eHbdu2xbWVl5fbLnt5eXkoLy+v1z906NDWmsL+4coCxi6r325o3KXPULhIFSoBQnuAwC4gXAKES4FwMRCpAJQqIFIGRMq5gOXfxlPpJ9H9SR4gNR9IG8xT6mBA9gEQuPhkCVEQuaWVIAGiExBdgOTmSZAAQeQJoilaWXUh2pesP3EMQRAEQRAEQRAEQRDNRhIF/P3FH7Gt1N/eU9knBnZLwfKzx7b3NAC0oig1evRorF69GuFw2LaOWrduHcaNG2f3r1u3zh4fCoXw66+/4sorr2ytKRwYRJkneHlsKW8vAIfxPkPj4pPqB5QaQCnjwpVSA6hmChUCddu4FZZ/J6CHgOoNPAEABCB9KJBzJJA9EUgbwkUkpgPM4LlWB7BqwDDbwMwxscs5C7zdFrfEaBlm2RajhBjBSkwQwmTYQpYgx/THilsx+0JCOXZMU/3J9kFWYQRBEARBEARBEEQHZ1upH78U1rb3NDo9rSZKTZgwAT169MCiRYtw+eWX45NPPsHGjRuxdOlSAMCf//xnPPbYY1i9ejVmzJiBVatWoXfv3pg4cWJrTaHtEWVATAMcaYC3J8CGcDc/pYqLUUold93reQIXtJgB+HcA1RuB6p94HiwAan7lafujfF+5fwB6HAtkH26KRC2AMQAsKl7ZOQNgJOQaYBhmHTy3x8ZsA5j9lggmRMv1xDFE2+MEKcQLULZAlZhbgpYllCVaj4kALJGsOWJZA337vA1BEETbwxgDA4vLAdRra6wvsS1xv431N1RO3M4qJ443DP5dYsDg7db2iC/zr56Evti6OcYpOTG823A4pfhYlQRBEARBEETnotVEKUmS8MADD+Cf//wnTj/9dPTr1w+rVq1Cz549AQC9e/fG/fffjzvuuAOrVq3C2LFjsWrVKghd6UFfEKKr9nn7cAuncDl39QsWcDe91EN5fKm+Z/BtwmVA+Vqg/Cug4htArQUK3+bJkQF0PwrodSKQMaL5c4gVgtqLOPELMeUY0csWw2KFNMMUyhLHNiSWmdZhtpUYYo4ZI54lWpIly1simNnukqabZZx7pGS6Xsa4SiYTu+r1NVJvcPtEAa0LXU8EcYBhjMFgBhjMPKbeWDl2fEvaDMOAASOaxxzTTlZ/TFuDQlCiWAPwP5FJhKXE8XwoQ3QzFvNnVODbJPkTKgiCPbahsjXe2k/i97wAXhcEIa6ciAAhrt0aqzMdgiBgYNZAEqUIgiAIgiA6OfslSm3evDmu3q9fPzz77LMNjp82bRqmTZu2P4fsPAgCt3pypHGXv3AJd98L7AUcPsCZxce4c4HeJ/FkaNx6qvgDoPhDbnFV8DJPacO4kNXjGB5XqqMjCOAWTe09kQSSiVy2IBbbHyOYMSMqmLFY6zMg3roM0T77SS+Zi2WyeoKwFidIISo+2S9orICVWE8inNmWZQl9se0tEb8S3UQbFdIaautoHw6iPYgTZJKINJaw09CY2H7GGHRDh854MpgB3eC5xjQwg9l99YQmxrgVj3ltJ/YlWvVYlyuAaDlRnBHq91lCS6zg0tI2URDriTXJxgKo1x47PrHcWVB0BVXhqvaeBkEQBEEQBNEKtJqlFNEIkgvw9QU8PYBQMV+1L1gAuLvFC0yiDGQdxtOQq4GK74DC/3CBqvZX4Odbgc33AbmTebD01EE8d2W126l1OmxRJZp1KBoUzWKFrlhLMiAqlum830jsTxDeku7bIlE4a6TNEsPiXDQbsTqztkkUqBoV0GLdNRMC9dfbd3OEtca2acY+29sCsR2IFYMsgcdghi36JOuLTZqh8cQ06IYOzYjm9YQmJLdWMkyRmMV8Vm3xB6hnkRMr3gDgIk6McBMr6oiCyOuiUG+7ZGM7o4hDEARBEARBEB0VEqXaEtEB+PpwEaluOxD4nbe5cuo/7IoykDuJp6ELgD1vAgWv8FhVhf8F8N/oWFc2X8UvbXB0ZT9v74PyAbrT09FFs1jquWg2ZHWWTAQzhTMjtt3aR8I+Y7e195tIzGtm7yfJGAGIF6uQREizxiZaoiUIWJZ4BksAS1jx0nbljBXbGhPRmlluhvjGGKAzI5oMAzoMGIyZbbotIlllzdCgGipUXY0rN+RaZolIhi12Wq+cYL5NpmBkijyWoGOJPLFtkiDZolBiP4C4MkEQBEEQBEEQXQcSpdoD2QdkjOSue7VbgOAebkUlOpKPd2YCAy4E+p8PVHwP1PwE1G7lK/oFC4BIBRD5iselspB8QNogvppf6mC+wp/vkJYHTieIhuioLpoNkdQ6rBEhrTFLs6RiWoIA1ywxLRH+YhowoOm6LSpphg4dBnTGeAKDZvA+lelQDB2qrkM1dCiGBp1xRzTd4LGC7DoYmGFZGXEBS4AIJgACREiiDEGUIAkSRFGCCAmiwOuyJHLxSOBtoiiaYpElzCUR+WyLoiQWc/b5xnyABEuwFBJecwFgifuM3T6xnSAIgiAIgiCIzgApFO2FIHAhypHBV94L7AI8eY3HixIkIGciTxZaEKjbxl0CazcDdZu5FZYeAKrW82QhuoDUgUDaUCB9CM9TDiWhijg4aAcrNMMUlDRDh8Y0Li7ZbZpdjugKFENFRFOhGCo0Q4cB08KJ6dzCydAB072NwwUvgQmQBAGiIEAyLYpEUYIkCHBIot0nQoAowLQ4ShDmEoU1aGaKEeB0Fj1sU9gWa02MqWcRFtuZpC1uGwCNCV/JFhVIZllmW7YlsY5LtJ4DGhDdkhy/nvCWMO9621hNCceNPb962xEEQRAEQRBE54bUiPZG9gCZo7gYVbcNcJrB0Zu9vZdvnzkq2mZoXOSq3cSFKivXA0DNLzwVmGNFJxeqfAMAX7+Y1If3EQRhoxs6NKZD1TWeG5otMGmGDsVQEdZURHQFEUPh1k7QoRumtZMZh0lANB4SIEASuRWSJIi2sOQQZLgdvMzbpa4Ty6ieFZndgXox1GLb4wS0xH2Z5aRiW+w+YvbZ4DxaiThxC42Ib6if19vW/C+pEGa5Nja0GEGSvlihLm5RhWTzakAgSzqX2P7E84wV1RLOuUFxLsl5M72ZlocEQRAEQRBER4dEqY6A6ODudZIHqP0NMBQeZ2qf9ycDqYfy1OsE3sYM7iZYu4kfo8bMNT+31Kr5NXEn3JLL148Haff1A7xm7u4GildFdBUMZkA1NKi6xnMzaYYORVcR0iKIGApCWsS0eNJtcUo3uHuZIMAMtC1CFkRIomQLSQ7RAY9kiUsSJJGunfoCRLvN5MARJ6yxBOHNaksUyqz+RKEsVnQz4scaiUJbA/uJG5NEqGtM40m0fGuqnrhtY+JT3MCGrOMQ32eogKYA+gQAqY1MnCAIgiAIgujokCjVURBEIHUAt5iq3sjjRLmyW3f/vr489TiGt9lC1WZuWRXcxfPALkALAKG9PMXGqgL4HL3mviyhyhKvWmLlRRAHkESxSTFUux7RFYT0CIJqGBFdTRCadESfsgXIogTZFJlkUYZbdEJ2mHGWRKmdz5LosBwMwltT1IvdFteJ+HhrycS5ZNsyQDf4d5ShHoBJEwRBEARBEG0JiVIdDW9PAIzHglKqAWfGgTtWrFAVC2OAUgEEdkdFKiuF9gJ6mMewqttSf5+OjBjLqj5R4crbh7sqEkQrYDADis7jL6m6BsXQeF1XEdTCCGkRbtlkxnFSDc1eJU6AAEEQIIsyF5wECW6JC02yIJMlE0G0FnHufGg9YY4JQKSmlXZGEARBEARBtCckSnVEvL24FVP1BkARAGd62x5fELj7oCsHyDosvs/QuDBlCVZBS7jaDUTKALUaqK7m1l6JuLrxWFW2lZUpWnl6AZKrLc6M6CRohgbFtGhSDNUUoDQElJAtOqlMM2M7abZBhiAAsiDZgpNTcsJHYhNBEARBEARBEESHhESpjoqvDw/mWv0Tt2hydJC4GaIcddfD1Pg+LQgEC7hAFdwFBKzybkCtASKlPFWuS9ipALi7m4JVnxgLqz6ApycJVl0Qgxk8GLhp3RTRFYQ1BUEtjDo1gIjOLaBUppnudAAgwCFywckhynBLTqQ6vHCIctcJAE4QBEEQBEEQBHEQQaJUR8bXLypMiQ4ey6kjI3uBtME8JaJUxwhWBaaVVQEXrvQAEC7iqeLbhA0FwJ1nilW9Y5JZl71tcWbEPqAZGiIxglNEVxDQQvArIYS0iB3jSTM0MACiGb/JITngEGWkOr22xRNBEARBEARBEATR9SBRqiMjCEBKfx7DqXYzd+sTO+lb5szgKWNkfDtjgFIZFaiCu3nw9eBuILDHFKyKear8Lsl+s0yRqhfgiRWtegHO7IQly4nWRjd0RHQVYT1iC091ShB+NYiQHrHjPjFzBTFJlOAUuejkld1wSjLkzvqZJgiCIAiCIAiCIPYLehrs6AgikJYP6EEguJcLLl1JaBEEvsqgKxvIHBPfxxigVHHBKlhgilV7omW1hgtaSmXyGFaSm8ersgWrXma9p+kW2MEtzzoQSozwFNYVBNUQapQAAmoIqsFjPxnmClqWe51TciDNmQKHSPGcCII4+CgpKcGSJUuwdu1auFwuHH/88ViwYAFcLhduv/12PPPMM3HjFy9ejPPPPx8A8Pbbb+O+++5DWVkZpkyZgttuuw1ZWVkAAMYY7r77brz88sswDANnnHEGrrnmGoj0d5YgCIIgiE4IiVKdAdEBpA3lMZvCJYCne3vPqG0QBMCVxVPm6Pr9al2MULUHCO3hwl1wD3+d9DDg385TMlzZXKTymCKVt6dZ78FjXB1kFjyMMSiGipAWQdhcva5OCaJW9SOsK4hoKlRD5a52ggiX5IBTdCDF4UGmK42EJ4IgCBPGGObNm4e0tDQ899xzqKmpwQ033ABRFHH99ddj+/btuPrqq3HaaafZ26SkpAAANm7ciH/+85+45ZZbMGTIECxZsgSLFi3Cww8/DAB44okn8Pbbb2PlypXQNA3XXnstsrOzMXfu3HY5V4IgCIIgiP3h4Hrq7sw4UoD04TxIuFLNXeEOdhypQPpQnhIxFCBUBAQL+WqBwb1R0Sq0F9ACQKSCp2RWVhABd64pWPWIyXsA7h6AJw8QnQf8FA8Uis7Fp5C5kl2tEkSNUmcHH9cMDQAgmxZPLtGBFLcXDon+ZBAEQTTFjh07sH79evzvf/9DTk4OAGDevHm48847bVFq7ty5yM3Nrbfts88+iz/96U849dRTAQDLli3DjBkzUFBQgD59+uDpp5/GvHnzMH78eADANddcg+XLl5MoRRAEQRBEp4SeMDsT7hwuwFT/xMUWd06nFkYOKKIzZpXABBgD1FouToUKTaGqMCYVcVErXMJT1Y9JDmC6Hbp7cMs1W7DqHs0dKQf8NJtCN3QuPunc8smvBFEdqUNQD9uWTwAXn1ySA07JiTSnj+I8EQTRbDRDNwVtxXT1VaGYAnfEUO322NU2IwYvK7qKiKFEy2YcOmusVVbNfak6dxfOT++B94b/tb1PvUFyc3Px6KOP2oKUhd/vh9/vR0lJCQ455JCk227YsAEXX3yxXe/Rowd69uyJDRs2wOl0oqioCIcffrjdP27cOOzduxelpaXo1q3bATkfgiAIgiCIAwU9eXY2vH0AyQMEfufClCgDrpyDztVsvxAEwJnOU/qw+v3MACKVpmhVxIOsW2KVlYwIECnnqean5MeRfXzlQHd3nntiyu48wN2tVeNaKbqKoGn5FFBDqIn4UaP47QdC7nYnwCU54ZacZPlEEF0YzdDsGHDReHCRhDYeKy6i87bYlTIT67xNtcuWVWVEV6Azo83Pb2sNsy06OyJpaWmYOnWqXTcMA88++yyOOOIIbN++HYIg4KGHHsLnn3+OjIwMXHTRRbYrXzJxKTs7G8XFxSgrKwOAuH5L+CouLiZRiiAIgiCITgc9kXY2BIG7lbmyuUDl38lFEsnNYy8JUnvPsPMjiNwKzZ2TPJaVFYA9bIlUxdyiyhawingQdi0A+Hfw1BCODC5OxQpVdr0b4OoGyJ56m4W1CIJaGEEtbFo/+eHXgghrCjRDhyAADtEBlxls3CU5IHSlAPkE0cnRDQNh04IxZF7PIS0S12ZZOYaTtId1xWxX4rYLm0KSzvR2OS+n6OAuv5IDbskJpxl7ziU54ZJ47pBk3me2OyXZzLmrsMPc3tqX03IjlpxwiDJEpsMLDQ7J0S7nuC/cdddd+PXXX/Hyyy/jl19+gSAIGDBgAM4//3x89913WLx4MVJSUnD00UcjHA7D6Yy3gnY6nVAUBeFw2K7H9gGAoihtd0IEQRAEQRCtBIlSnRVB5G5jrlwuhPh3cDc02Qc4M3k/cWCIDcCePjz5GC1kuv8Vm0KVVS6JJj0MqNU81W1p8HBMToHuzIbmzEJITodfTEGd6EOd6EVQSkPEkQHBmQW37Eaqx0uudwTRyuiGgZAWRkAL81wNJ61bwhIXjCP12kJ2mVsntQWiIMAtueCWnHDLTrgkJzySCy6ZW0y6JCc8ssu2oHTFjLOEJLcUX48da9UtcUlsg+8eRQ2iKlB0wI/TWtx111146qmncO+99yI/Px+DBg3CjBkzkJGRAQAYMmQIfv/9d7zwwgs4+uij4XK56glMiqLA4/HECVAul8suA4DHU/8HDIIgCIIgiI4OPb12dkQJ8PbiVjWhIi5OBQq4a5ojnQsoRNsje4CUQ3hKBmOAVhcVqEIlQKQURrAIergECJdCipRBNMIQND9kzQ85uAtuAJnJdgcBqiMjJmVCdaSbudkmZ0B1pJE1HXFQYAlJfi2EgBqTtHBMOYSgGoZfDSFotge1EAJq2LZEDKghhA+ggCRAgFd2wS274JFd8EgueGQ3L5uJC0pWOTrObQpLsX1u2Wn28e0cokxWku3IbbfdhhdeeAF33XUXjj32WACAIAi2IGUxYMAArF27FgCQl5eH8vLyuP7y8nLk5uYiLy8PAFBWVobevXvbZQBJg6YTBEEQBEF0dEiU6iqIDsDXl7t9Bfdyt77gbu4e5kxv79kRiQgCDDkFQZeMgJSFgPMQVIRrUOsOIKRFoBoaREFECnSksSBSDD88Wg0cShUcahWcahUcajUcZi6AwWm2NwaDAE1ONQWrDKhyOjRHBq/L6VAd6dDMXJd8JGoS7YJmaPCrIdQpQfhVK4XicyVk9wVMN9aAFi8+tTaSIMIru+F1eOAzhSOfwwOP7IJXdsMju+12r+yGW3bxusPN+01ByeuIll2Sk0SjLsrKlSvx4osv4p577sFxxx1nty9fvhw//vgjnnzySbtt06ZNGDBgAABg9OjRWLduHU4//XQAQFFREYqKijB69Gjk5eWhZ8+eWLdunS1KrVu3Dj179qR4UgRBEARBdEpIlOpqSC4gdQBfAS64hwdE9+/irmaO1Pae3UGNoqv8AVoNoTpSh8pILYJq2HbjsdxoctwZ9QKQB82UFGZA1mq5UGWKVlywMpNWY5ZrIMCAQ6uFQ6sFQgWNztcQJFug0uS0aB5T5u1p0ORUssAibDRDh18NolYJoFYJwK8GUacEUacGUKsE4VcDZj0Iv5WbY/xqsFUtk2RRQorDC5/shs/h4Un2wOtwwyd74HPwdq/Vb/e54wQor8MNp0ix2YjmsX37djzwwAO45JJLMG7cONuaCQBmzJiB1atX47HHHsPRRx+NL7/8Eq+//jqefvppAMA555yDWbNmYcyYMRg5ciSWLFmC6dOno0+fPnb///3f/6F79+4AgLvvvhtz5sxp+5MkCIIgCIJoBUiU6qrIHiBtEODtyd35ArsApZIHSJdT2nt2XR7GGEJaxLbkKA9Xo1bhVlCaoUMWJXgkV+sEIRdEaI4MaI4MwNu/kUkZkLW6OKFKVmti6rW8rlVD1oMQmQ6nWgmnWtmsaWhSClQ5DZoj1RSsUnndLGuOVGhSKlRHGnQpBYxiX3VoDGbAr4ZQq/hREwmgTg2gRvGjNhJArRpAnRJAjcLzWitXg6hTAgi2kpWSV3YjxeFBisNr5z6r7rTavfDJHqQ43NG6g4tNKQ4vnJ0oGDbRdfjoo4+g6zoefPBBPPjgg3F9mzdvxvLly7FixQosX74cvXr1wt13342xY8cCAMaOHYtbb70VK1asQE1NDSZPnozbbrvN3n7u3LmoqKjAlVdeCUmScMYZZ2D27NlteXoEQRAEQRCthsAYY+09iebg9/sxbtw4rFu3DikpJKq0GLWOi1PB3TzAtiuLB0UnWgWDGQiY7ka1ih/l4Wo7+DHAraAsdx5Z7PgWRYKhQNZq4VBr4dBqTLGqFrJVN/tkrRayVgcBLf8zokleaHIKNCmVi1ZyCnQzj7abZTmVhKx9hDGGgBZGTaQONYofNRE/z5WA3VarBOw+y7qpVgmA7cP7GotPdiPF6UOqw4s0pxcpDh9SnV6kOrxINdtTnVxISnV4kWL2WcJSZ7hWiLbHCnQ+dcTFSE3p2d7T6XTQ/RRBEARBtA4nrPgCvxTWtvc09onhPdPwzryp7T0NAGQpdfDgSAUyhvGg6IHdQGgPEKkkcWof0Q3djm1THalDebgGATWIiKby1a5kF3wON7Ld6Z3S3YeJTqjOHKjOnGYMNnggdo2LVA6tDnKMYOUw89gkgEHWg5D1IIDSZs9LF92mYJUCXU6BJvvMcnyuyT7odu4FE51N77wTYAlM1ZG6mORHtVKHmiRlLj75oRn6Ph/TLTmR7kpFmsOLNFcK0pw+pDl8SHP6kOrkuZVS7XYuLJGoRBAEQRAEQRBEY5AodbDhTAecI3lQ9FhxypnJxalOKKC0BZYIVacGUBWuRXmoBkEtDMVQIQoifLIb6c5UuL1dQ/xoEYIIzZEGzZHWvPHMgKQHTCErVqwy67qfi1q6ueqg5oekByCAQTLCkJQwXChv+jgxGILTFqg0yQdd9kGXfGbZGy1LXlPY8vKy5IUhug/YdWEwA7VKAJXhWlRFalEVqUO1mVeF61CtcOGpKlyLasWP6kgdVEPbp2O5JAfSnSk8uaw8FelOH9KdKUgz29PsOheYyP2NIAiCIAiCIIgDBYlSByv1xKkiQKkARBfgzAAkd3vPsF3h8XR44OWqSC0qwjXwKyFzVTwBPocHWe40emDfFwQRupwKXU5FBD2atw0zIOlBLlLpfkiaH7IWiJZNkUuKywO2mCUyBU5VAZpYnTDpoSFAl3zQJY8tVHHRKlq3yproRi1klGkGylUN5aqKcjWCcvNzVBmutYPcc6GpDsY+eFC7JScyXKnR5ExBhivVFpsyXKnR3JWCDGcK3LKrxcchCIIgCIIgiNZGNxgkkYwhCA6JUgc7ljiVeigPhB4sBJRyIFwKSF7u9id1/YdZxhiPCaUGURWuRVm4Gn4lCMVQIQkiiVDtjSBCl7nLXqQl2zEDkh7iIpUegKQFY8oBs8zbJC3I2/WgmQIQmQ4wBr/iR4nuR4kOFGtAiQ6UWHlMuVQHQvsQhildkpHldCHL4UG2y4dMpw+ZrlRkutOQ4UpHmjsTGe4spHqzkeHKhNtxcIvGBEEQBEEQROdFEgX8/cUfsa3U395T2SemD87FtccOae9pdBlIlCI4spcnTy9Aq+MufaFCQKkCDIW79jnSALHriDIhLYw6JYiaiB8loQr4lRBCWgSiKMIru5DpToVLOgjd8boSgshd9WQflISuiK6gIlyDilANysPVqNCrUaHVoDxUw9vD1agIV6MyXAulhS5zXgHIkwXkigx5MtBNAvIkIFfi5VwJ6CbzPEcCnIIGQAMQAGJdE1UzxXxfM4jQJTcM0V0/F10wJA/PRTcMyQVdtMaYbaLLHO+KSU5AEPfpJSYIgiAIgiCIlrKt1N9pg4QfmksxmVsTEqWIeASBi0+ONO7ap9ZEBapIOWCoXLxypAGdLHi0qmuoU/mqYqWhSlRH/AiqIQCAR3Yj1elFjiejUwYmJ6IE1BDKQtUoD1ejPFSN8lCVXa4I19jlOjXYov36HB5ku9KR5U5DtjsdWe50M09DljsdWa40sy0NHtm0ZGIMohGBaIRjLLDC3HrLCEHXQyjTQzxWlh6CaJfD5jYh3m6EIRncRkyAwQPE60EuWLUSuuhMEKpcprjlgiG56olYuplzoctppmh/tO40RS8Kek4QBEEQBEEQRDwkShFRDAPQdUDTeK7rgAFATwcML6BVA+FKIFwCKIWApnLXPsmXXKAShJgEAAIgioBo5TFlwcwlEZBlnov7Z7nBGINfDaJWCaAiVIOycBUCagg6M+CSnPA5PMh0pUIkC5FOQVhTUB6uQmmwCmXhKi48hXheFiM8BbVws/fpEGXkuDOQ7U5Htic9WnZnINudhhxPhik4pcMt74MIKwgwJDcMyQ3NkdHy7WNhBkQjYopUSXIjDFEPQzJCEPVIQhsXxkTdbLOEMiNqPyYZilmv2795NoAhSDBEF5jgMMWqhFxwwhAdYJagJThsQYu3OcwxTjC7zMfwfcaXmeCghRsIgiAIgiAIooNDotTBCGNAOAwEAjxVVwN1dYCqRsUoncfSsfPYYMwMgOEAVAVQigAtwC2oRAcXqUTTSsR+IBTMjZhZNucAARDB3YYsYcoSoySJl52OmOQEZIn3yRIXrxwyrzt4OWKoqFUCqA7XoSRUgToliLCuQBYl+GQP8rxZkEX62HckDGagMlyLslAVSkNVKAtV8jxYhbJwNUqDlSgLVbXIssknu5HtyUCOOwO5nkzkuDOQY4lOZnu2Ox1pTl/nsYwTRBiSB4bkab19MgOioZgilSVkRWxRq167nZT47fQIRKZGx+oRCEyBaKgQwP92iEyHqLfMOm1/MWIEKkvUYmJ8GxNks89hil1Wm9Puix3Lx8hmm9xAmyNmW5lcIwmCIAiCIAiiAejp/GDAMLj45PcDtbVAWRmvh8NcHHKYgo8o8rLbHWPJFCMYJYMxQA8Aqp+7+ak1gB4xBSYPd/VrzG3HMPg+DMbLhgHoZh5RgFDEtNgy28GiVlcADFFAnaChVlBRxgKoFBQEZB3M6YDHzZe07+bycNHKkAHDFMKINkEzNJSFqlESrESpKTaVWuUgr5eHq6EZerP255Kc6ObJRK4nA9nuDHTzZCHXk4EcTyZyPBnoZgpQXgoE3jwE0bbkOiAwBsEWq9QYQcusMy5uCbbIZSZL0KpXj24jGqotfPGkQGBREQwAF8r0VvRx3EcYxKiAZQtWVl3mglZT9Zg2uy5IZptUvz02N8cYdltsf/xYEtAIgiAIgiCItoREqa6IJULV1XErKEuEikS4wOR2A14vkJW13y5yEARATuHJ0x3QwoDmB9RqIFLFhSqmA5KTi1SiO17gso7fgnAzEUNFjRZEtRpASbgatUoAiq7CaQA+yOjBZEiGCrAqANUAWNT6Spa5lZXbCXhcpvWVaXEVa321v6/LQUBUcKpAsSk0lQQrUBqsQolZrgzXgqHp5egECMh2p3PByZtpCk9ZZs5TN08mfA5P57FsIgBBABOc0EUnmic77ieMQWC6KYRZwpVZZpaoZeaGCoFpEJkSX7e2YSoEQ0vYzqzb4zRz/7E5HxP3MsAwXSMTw+13PBiEBMFKakDIStIOMWFMtA67LJpjE/chArF1iPX7wMuqriFg0N9ogiAIgiCIrgCJUl2FUIhbQVVXA6Wl3CrKEqG8XiA9nYtRBxrZzZM7B/Bp3LVPCwBKBbemUmqj4yQ3ILqa3CVjDH49jBo9hHK1DuVqHQJ6BAwMXtGFTG86XE2tCmjHyzJjZlVHgPKqqBshADhM10BJ4mKVxwm4Xdx6zHQPtMtdXBixXOqKg+UoDlaiOFiBkmAFSoKVdl4RrmmW4OQQZeR6MpDnzUY3Txa6eTN5Iv6cNQABAABJREFUbopO3bxZyHanQxYpEDaxnwimoAK5dd0cW0qMOMYFLC2hHBWvBCNhHHQItmCm8f3YYpe1rc5zWyTT49vtbfToPhrKE65hAdy6Daz9LcwaY5DoQWTkFe09DYIgCIIgCGI/IVGqs6LrXISqqeEiVHU1F6YAwOMB0tLaRoRqDFEGnOk8eXuaVlQBQKsDlCpe1qu5JiR5zHhUXKRSDQ21eghVagClWi1qtBDCugqHKCFFdKGHMwNSS9xMLDdER0PiFQM0gwtWmg4EgkBtHX+dBQBMiIpWDhlwOQGvm4tXDkfUyspplju4aBVQQygOVvAUqLDLJVYKVTbLpc4hyujmyUKeNxN53mzkeU2xyZuF7p5sdPNmIoOCyRMHGzHiGIC2sRLbV5huilTJhKv6fSLTAGY0Mq6BBKtsQIjbh9UfXwczYraxtuPtYBoqnN2RIdAtDEEQBEEQRGeH7ug6G6EQsGsXUFISDU7ucgE+H5CZ2bHdziwrKmQDrC+ghwAtCKhcpApGKlGj+FGhB1BqKPAzBh0ivJILaZIb3RxpB3BygiksNWStw0wrKzOF/UBFddTSSkB8EHaXi7sHupxxgdjjgrMfIAxmoCJcg+JABYqC5SgOVqAoUG4KUDxvTtBwURCQ685EnjcLed5sdDdFp7yYnFYvJIhOju0u13lQ1CCqAkWY2sHFf4IgCIIgCKJpSJTqTITDwMaNwJ493BIqO5tb6nRGzJXE6hhDtaGhRAygCgJCYBAFGT6BIU8EZMPgK/sJzIxN5UL7RCoXzHhUDVwyzHIPNLhoVesHqmq426C1+KCUsHKgy3QRdCYIV7Gxr5Kg6hpKQpUoDpSjMFDOhSczLwpUoCRU0SwrpzSnzxaaeJ6D7t5sdDfrOZ5McqkjCIIgCIIgCIIgDhgkSnUWIhHgp5+AvXuB3r0bFkc6OKqhoUbzo0qpRXGkArVaAIqhwiU6kSKnI9uVZwayZoCuAHoYMMzg6VoIUGsBQ+fucaLDTE60+5J6ggjIYuNXlBXTytABRQFCYaBc431WaCtRRFgwUMwCKDLqUAg/ivU6FGm1KFKqURSuQlmk6VhOoiCgm4eLSz18OehuCU6+bFN4yobP0Y4xdwiCIAiCIAiCaDG6wSCJZC1MdB06p7JxsBGJAD//DBQUAL16dTpBKqiHUaP6Ua5UoVSpgl8LgjEGr+RBpiMVLjGZtZfAraIkF4B0AHmAoQFGhCfdFKr0CBerGDNFHRkQHIDkQIf7eJuWUiFdQZFQh0JWjSJUc7FJqUahUoUipRqVWqDJXbkEGd3lNPRwpKOHMwPd3Zno4c1Gd18OevhykevNgux0ApIISKbVlSR2bPdOgiAIgiAIgiAaRRIF/P3FH7Gt1N/eU9knpg/OxbXHDmnvaRAdiA721E7UQ1G4ILVrV6cRpAxmwK8FUa35URqpRIVSg5AehiCISJW86O7KgSzsg1uYKPMEX8zBVMBQokKVHgS0CKAGuEUSAIgSIEhRy6oDbFUVNhQUKTW2yFRoCk6FpvhU1QzRySs60cOZYaeeZt7dkYEejjRkCR4IBuPWV7oO6Aag6kAlAyprAaHWdBk0hShJjLoPupyAU+Zug1YcLLtfNNtkXqaYLQRBEARBEATRodhW6scvhbXtPY194tBcX9ODiIOKjq9wHMyEQsAvvwC7dwM9e3ZoQUqz3fLqUKyUo1YNIGwo3C1P8iLbkW665bUyttAU88eN6dyCylB40kKAEeIClhaMWlUJllhliV0SeEfjhA0VxUoNipQq7FWqUWQKTi2xdPKJLvRyZqJ7jODUM0aESpM8rfB6MS5WGQbPdR2ImG6Dus5fB8sL0DqULHFXRMlMssxXF7RELFvkkqLWV5brIglZBEEQBEEQBEEQRAvouCrHwU5dHbeQKi7usBZSYT2CarUOlWotSiKVqNMC0JkBr+RGupyKPKmdgrALEiB7AXhjGplpVWWlCKCFTbFKM0UsblmlGBqK9SAKNT+K1DoUqrUoVGpta6cKrWlT2VjRqZctOmXa4lOq3BbxnISodZSjGcOZJV7FCFlqBAiEuIhlMAAsKjoxAKIQFarEGMHKaYpZDjNouyRF+yzBS5QASeBiliiQayFBEARBEARBEMRBRsdTOgigqoqvsldVxQUpqWOsgMYYg18PolqtQ1mkChVqDQJaCIIgIkXyIM+ZBVnsqB8pgQdEF53QDA3FioIizY+94TIUhUtRGC5BYbgcRZEKlKm1TQYS94oO9HRwsamXKxM9nJmm6JSJXm0mOrUyzQnWHgfjQpWhAzqLClmaaY1lmOKWOZSvQmiKWqIYFaJik7UCoSxxUcsSr6yxlpuhKETbJYlELYIgCIIgOjRdITh1VzgHgiA6Hh1VQTh4KS7mLnvBIF9lr51doTRDQ60WQI3mR1G4HDWqH2FDgVOQkSJ5kClnQVJ0iCEFglINUVUhKioERYWoahAUFYKqQVR5Lmga79d0CJrG+zQtWtd0nnQzaTxmkqAbZhvPoRsQDF4HYxBMQUQwDGgwUORW8btXxe4UFb/7NOxO0bErRcPvqToKvQaMJvQLrwr0rZNwSJ2IQ2pF9PNL6Fcn4pBaCX3rBGQpAgRBB1ABJlSY7m8CmCgAAsBsoUUAM8UTJnLXNiaKfJyZM9N6iJdFPk4S7TIzy3FtsmS3M8ksy1K03e6XwOSYPjMZsmT2SWCO+Pbmu+AJUTGoRX9JkohZhgGoKncvZCzGvdBUswTL1ZC/vpAkPkfLfdCehyloyRK30rJdCsV4YcvKk7WT+yFBtBjGGBiY6RVsifrRutVn+QyzuD4AzC6BxezP2re9DQBNC7fhmREEQbQOnT049cBuKVh+9tj2ngZBEF0QEqU6CqoK7NgBbN3KH7h79jwwxzEMoKYGqKgAysuBykqeQiGewmFowQC0kB9qKAA15AfCYWRGFHSLaJAVLiqJEQWiovBg222MJgJ70oDfM5KnPWmA3oTo5FaBfjVA/yrgkGqgf7WZm/WcICBAB6AfwDPpuBiyBOaQowKWKVwZZlv9ugzDIYHJZu6QwRzRfsMhx7c5om3W+Gjd7HdG63FCmWFwUYuZuSVqaRpfGCC2zX4INrdNZq0lJOSiGBW2JKl+MHhrW1sUi8lFicfRt6y4rLEEgWTCTXLRJirG1Bdtkgk2se32HliMwANmbh/ts0faf8J5QbCEXybYc4TZE3MmvEUQ4i4va3tBEGD9s9sF2G1WvDw7N8vWeCsXIfLtBL6NKAi8TXLA5ZDhkl0tfg8IgiDak84cnLqrQNZeBNHxIFGqI1BTA2zaBOzdC2RnAykpLd9HOMyFJktsSpZbSW9caJHN5G7hFAyHA4bTAeZ0mMKCg4sKTkeM2OAAky1RwmFa6MhxeUQWUehWUOAMo8AZQoEjhL1yAAVSAHukAIqFAHShcTHMAQk9hVT0FjLQU8xAb4nnvcQM9JYykS36IOaJAAQw63vJfEAqN5ON+dQmWAKHGSBcsCx5zCRYQgmDXRZ0DTB0nusaBDPBzlUIugpB07hFmGn9JWgaYDBeNgwzB7cMMxgE3eyzcwOCZuUxlmWaEbU80wyIsZZoKi+LWv3Pg6jpQJL29oKJQlTwcsowZJnnlqDllHm/M0bccsbmpgjmlM3PnQhDlkwhTYIhSWAO0cwlGKYVmiHxcZa1GQD+OUl0R7SEKct6TEgQuUQhasVliV6xboeWyJUodiXuSzDdFQUcVFZdlvBisMaFnP0VcVoq4HCEhLb64o3Vbr1dyYSbhkSbRMHG2r4hwUYUxPi6aI6FEC3H9Yv8GIg9plDv2Mna4sY31J5wXvXON0GMSmxPuuCDoQBKFdBecQsJgiAOQnJTXF1C0OnsFmvTB+fi2mOHtPc0CKJVIVGqPdE0oLAQ2LyZu+s1FdC8rAz49FO+Gl+syFReDvhb9oeVZWRAz8qAkpkOf5oLYbcMxSmBud2QPD5IXh+Y2w3D5QRzOWEkJifPmdMBw8WFpuZahIT1CIoiPH5TUbgcxZFyu14cLkepUtVkTCeHIKO7Kxs93bno6cpBD3cuetj1XGQ70yEKDc8n1KJXqy2wLHt0nqCbgdf1mHYzSDvTeHB2xng/LDHMQNKXzRI1BAGmGU9USIFobi9CVE0RS42KWaKmmXXN7NdNl8sYUUs1x6gaL2vx7XH9mm66dsbsU9UgKlp034q5H92InoLB+HaKCjS9uOEBI070skUuS/SKsf4yrcksizPDdKk0HDIMWTRFMTl+jEOMbivLMJwSmNMZL645HVHByhatrADyVnwtMSp+xcTuYgCYADBB4GXRFPsggIngrqXMbLPGCYhJ/Ca0SVGHGQ0KOoIA+zMa/ajWF3ME6784I7eWCTnW9W/1i9a2YrQsCiIXZwTRFnAES/KxhBtrjCnmCILAJZ4WiDfJhJuGRJvoudY/52YJNgRBEARxAEjzyF1G0OnMFmuH5vqaHkQQnQwSpdoDxoDSUu6uV1IC+Hw8flQyKiqAjz8GPvgA+PFHxPxUXx+XC8jJ4dZW2dlxZZadjWCmDzXpbpT5RJSzOvi1EBgz4JHcSJG8cLfCr86MMVRrdSiOVKA4XIHiSIUpOJXzcrgc1Vpdk/txCg70cOeghyvHznua5eaITp0PU0Bo9iVpCVF6NLeELRhRkQoGF7AMDYBmClpWvCYDgArL+ssQdb5KX9xKfZbdnMv0sTGtgQRuW2Ga7PChlvgFIWas9R5ZY1uAYURFLSUqeImKBkE1hS2rPUbY4v2mm2msWGaP5fsUI6oZ14xvIypq/PEUtZ4VGR+ntew8WhlDEqGbIphuWnsZpuWXblp5WW26bPaZYpdhWoMZTlP4sqzFHBKYwxF123TGuFs6LatHCczlAnM5AJcLgtMB0emAIDsgyjK3whEdECUBgiBBFCVIkghRkCBJsinoiBAkkeeiCEEUIIgSt+4RzSTwNt4XO1a0haRk1jck4hAEQRDEgYcEHYIgWhsSpdoSxviKert2AQUF3KqhR4946yhdB379FfjqK+B//wN++y1eiBo1Chg9Oio4xeYpKXHuPCE9jDotiBrVjxKlArVqABGjFrIqwSd50MOVA6mFwk5YV1CiVKAkUoniSAVKIlx4ik0RQ2lyPz7Jje6ueNGJl7PRw5WLLEcaPUA2igAIEk8txhK0mClmAUCsUGVErbAYzDEsasVlGAA03maYbbYboylyWYnB3KcVqLzp02KMgQkiDAAQGZhTAHOC1yGBQQZjbm69A8Ytehi4xY9guXEJUYugOLcuIcadK+oCFnVeirfRExgg6lz8khRTzFJ1yKoOSeGWX5JmQFa4G6Sk6JA03iepOkRVj8k1iOY+4trU6H5j81gRTYj5GyDqBkTdAMLqPrz3rYxguiVaQeUdklmXY4LOx6yoaNWtNkfMWCt3Osx2R8wYB+C0cis5AbeT5y6z7HIALjfvdzmilmOWSBpnYZaknJjsbZPUDyL3SYIgCIIgCII4UJAo1RaoKnex27OHW0hpGheR3G4emHnDBp7Wr+d5TU389sOHA0cfDRx1FNC9e4OHiegK6vQg6rQASiNVqNH8CGrcUc0juZEupzZqDaUYKkqVKpRGKlES4cJTiVLJc7PeHCsnAMh2pKOHOwfdXdno4cpBnpl3d2WjhzsHqZKXRKd2gjHAgBVLSwKDWQcAgcFIcLuK1i3BJzHeDgNjOs8N3XTfYmDQTfcuS+QCj7Nl7tWajMAYIBh8LGMQBD5GYODxtMAgCIzXYZg5j+kVa69l2WwJAs8lJprOigJEAZAgQGSMu2OJIq9DgGTG4LHi6kRdsyxrHED0JnO1MmPzmPF6oq5d8fV49zExwVVLSBA2hBiDMrOfgcf30jRAUfnfE0UDVDMpGm9TdbNPjW+PrdfbR0y/qkb3GduvJuSxixswZvZ1AIEsGYIQI4TFBq2X6rdbZUmKF9RkKSqqSVJUKJPMNqcjOsbpAGRLQJOjZacjXlCzRDiXVXaYgl6sICdFhS87ob5whiRticJZQ9sRBEEQBEEQRDvTpqJUJBLBLbfcgvfffx9utxtz5szBnDlz2nIKbYeuc3GpqoqLUVVVPG5UdTXw++98lb0tW4Bt27gwFUtKCnDEEcCRRwKTJgG5uUkPEdYj8Osh+LUgypVqVKt1COghGGBwC054JQ8y3akQIMCvB1EYKUNppBLlSjVKlEqURapsEapUqUSl2jxTXI/oMkWmbOS5stHdlY08V5YtOnVzZcEpOpreURfHqBdbhyWUEQ3cbAZxbkz4MWICMBu8xusCosHYTauhhGA8UVEoTmwRYuLuIL6eGJdHECDZwZJFSGYMHkHgoo5kBlgWBatPMMuSHUfHjskTc6zEODu8LX5+SfsZF6lEQeBlwRKpBPN52xLDrHI0CH20HruyWMwYq273s+h2ttWXkWS/lvhmRPdrj0+yf/P9qzcPFpubZRGAE4BTAq+4EsbEjGWx5xR7rMSxSYiJ+dRgu65zgUqzcg1QEtqsFFc3hTVVj+/XYscm9sVsY+d6/Lax2xlG/LwZi+6vM2Kt+CiJMeWYlSBFEZBjxiQG0o8dL8dsF1eWo3XLik2WEqzfYsqSxIW2egKflCDq/X/27jtOqur+//jrlumzlV2WKr2LiigESxQTS4yx+42xBSVqvook9hKjKCoKPzs2VOxRv9ZoNEUSexAVBRRF6W0pu7Btdqff+/vjzr07M1vYhWULfJ4+7uPOLXPnzHjZnfvecz5XJaNnXKOhYHYgmPpKkh6iQX2wZoe4RhyUThqECiGEEEKIVmnXUGrmzJl8++23PPPMM5SWlnLttdfSq1cvjjvuuPZsxu6RTFqFyFessAqX//CDNURv82arbtTGjVDdROhTUGANyTvgAGs+YkSDgudJM0ldMkJdMkJtIkxZrJLyWAWbo9uoiFv1oezhetvjVZTHKim357HKFg2pA/CoLrq7CynxFKbm3SjxFKSFT906XS+n7NDG7qdjDdky0oZrZYY/0FRwREaxZvu49tAyJTUsjFTh5rS8xyn8nF6w2A5jgMYLJKf1qmkq+NEVa4uqalYPn1T4Y4U+aUWcGzxuItTJCoWcx0paXx6l4fE60//3Li09OMoOkTKW07en7ZMdpqWvahBGtXSfjB2aeT2y2tTI/s22Oftl0gO8RnZoLDzMeHratqSRFmTFIRHPDL7sEM2eJ9KW43Hr53h62JZIpoVwaSFbMi0os8Mze79kMjMwc5aTDbelr2tM0rCmvYVCfYF+Le2ulFr6ulTtutFD4LVDIC+no1sthBBCCCF2QbuFUnV1dbzyyis8/vjjjBo1ilGjRrF8+XJeeOGFzhNKhcNW76ba2vqppsaaqquteUWFFT6Vl8P27dbjrVut5eweT40pKYEhQ2DoUBg6lMSQQYS651OTqCMUC1EZr6F84/uUxyrYFqtiW7zKGj4Xr6AiXkNVsjY11REyIq16e3mqnyI9l2I9l2I9j+72pOXR3ZVHiZ5PntpE4BS3JrOmBpNqkk6okxbkpAIgp/ePghMQWfumBUCp0Mje1wmSsIMdO/wxUUxrTmrolLXd6hFj9weyohRIv7m56oQ+6cFM/RAtNVV4WcOarPBHRTXtYV2aNQwsrcdPem8fNe24qj2cC8UaQkZmuFM/aIvMYAilvoBzevDTXEH7dM7/q8YCjJY8rw3t6Jitec22PNauHqelr9WWx9rZY7T6c1Oy5i04xs68dmu2NVhuxf6pmvwZ65rbP70nTmvbmv3cXf1/m0wP0xINp2QyFbIl6vdN35YetBnJzP1aMren9GU7QDOytidSteWSSSugS6TuFppI28fenr7e3tYUk/ogbkcdoT7/DpI7U1NPCCGEEEJ0Ju0WSi1btoxEIsGYMWOcdWPHjuXRRx/FMAxUtWPvpBb+5zu8cuMpbNfjGArOlFQgqWY+TqSmpAqJnhDvA/HUurhbI+ZzW5NHJ+bRCXtUoi6VsG4SMROEzW8JGwsJR2LEv2niL+QtpCoqBa5cCjx5FLrzKEhNhZ48Cj0FFLhzKfDkUuguRNesL/BGasiX3ZMIAAVqTIUa57rKTAVAOGVunADI7kFjxy9KqgcQOMGMNdoiNbzLCWM0q/ePPcwLFS11563GljN68NihTVbvnQY9fLLWp/cUyjhWav0ua2kI1FbP252vmb69uX3bar/sdW15rJ09zs681s4eZ0fHTV+3o3Y014aWfq6t3d6abW35WbR0ubFtjbWjta/VksfpGnan3PE8mz3kze1u+jiNvW5zj5t7zo6eu6vPMVJDXZNp4VZTc3u/9HWxGHTvbtXe2kvtVeUQxF4vaZhoqvTUFkKIPVW7hVJlZWUUFBTgdtcX2i4qKiIajVJZWUlhYWF7NaVRL2z7Dxee0BY1KpJAODWlMYBmOlLpqo5X8+LTvQRcAfxuPwFXkIA7QK4n15q8ueS6cynwFZDvzafQV0iOOwddtf43pgcvqpIKeEgVdVa0+gLPirVeV3VUVc0Mdhp5rNoFnVu4X1PPEUJ0cS0J4Fq7vbUBVmvDqZYGUs2ta65t6cFfR86zp/R2ZrdxR/u1dt/mPovGHtt/hEofpt7aUNrrbTDMfW+yR5dDECKLpir84aWvWbE11NFN2WlHDivm6mOHd3QzhBCiU2q3b3ThcDgjkAKc5VhLhr3tZsf/6kp++14Zm6o2oKk6iqpZoUqq546maFY9n9SyrliBjkt1oakauqqjKdbcrbnRVR1d1fFoHnwuH17di9/lx+vykuvOJeAO4Nf9+Fw+gu4gHt2TUcenqXl68NNcUCSEEG2uqZ8t8jNH7Ehb9D5LX1aUvbanVJcohyBEG1uxNcTS0pbdkKczGlQc6OgmCCFEp9VuoZTH42kQPtnLXq+3vZrRpF45vXj61Gc7uhlCCCHEnmdHtcNEi3X2cgiic5Ghb0IIITq7dgulSkpKqKioIJFIoKe63JeVleH1esnNzd3h8+27moVCXbfrrhBCCCG6jkAg0Ol6H+9qOQT5PtVye0qg8+gHKymtCu94x05odJ88zhjbl/65Kkas6/aOLPFZ/+a68vuQ99A5yHvoHPaE99A/V2237wI7+j7VbqHUiBEj0HWdRYsWcdBBBwGwcOFCRo8e3aK/6tXW1gJwxBFH7NZ2CiGEEEKA9T0lGAx2dDMy7Go5BPk+JbqSd4A7O7oRbWAV0NXHY8h76BzkPXQOe8p7GDujfV5rR9+n2i2U8vl8nHzyyUybNo077riDrVu3MnfuXGbMaNkn0b17dz788MNO+VdLIYQQQux5AoHOVwdmV8shyPcpIYQQQrSnHX2fatdb11x//fVMmzaN3/72twSDQS677DKOOeaYFj1XVVV69Oixm1sohBBCCNF57Wo5BPk+JYQQQojORDHN5u7DLIQQQgghOotwOMz48eOZO3euUw7hoYceYv78+Tz//PMd3DohhBBCiNaRW7QIIYQQQnQR6eUQlixZwrx585g7dy7nnXdeRzdNCCGEEKLVpKeUEEIIIUQXEg6HmTZtGv/6178IBoNMnjyZSZMmdXSzhBBCCCFaTUIpIYQQQgghhBBCCNHuZPieEEIIIYQQQgghhGh3EkoJIYQQQgghhBBCiHYnoZQQQgghhBBCCCGEaHcSSgHRaJQbbriBgw46iMMOO4y5c+d2dJO6rC1btjB16lTGjRvH4YcfzowZM4hGox3drC7toosu4rrrruvoZnRZsViMW265hYMPPphDDjmEe+65Bymlt3M2bdrExRdfzIEHHshRRx3F008/3dFN6lJisRgnnHACCxYscNatX7+eSZMmccABB3D88cfzySefdGALu47GPstFixZx5plnMmbMGI499lheeeWVDmxh1yXfiVrmvffeY9iwYRnT1KlTO7pZnYb8vGuZxj6n2267rcG59fzzz3dgKztOc9cVcj7Va+5zkvOp3tq1a5k8eTJjxozhyCOP5IknnnC2yflUr7nPaXedT/ouH2EPMHPmTL799lueeeYZSktLufbaa+nVqxfHHXdcRzetSzFNk6lTp5Kbm8sLL7xAVVUVN9xwA6qqcu2113Z087qkd955hw8//JBTTjmlo5vSZd12220sWLCAJ598ktraWi6//HJ69erFmWee2dFN63L++Mc/0qtXL15//XVWrFjBVVddRe/evTn66KM7ummdXjQa5corr2T58uXOOtM0ufTSSxk6dCivvfYa8+bNY8qUKbz77rv06tWrA1vbuTX2WZaVlXHhhRfym9/8hjvvvJOlS5dy/fXXU1xczJFHHtlxje2C5DtRy6xYsYKJEycyffp0Z53H4+nAFnUe8vOuZRr7nABWrlzJlVdemfHdLxgMtnfzOlxz1xXXXHONnE8pO7r+kvPJYhgGF110EaNHj+aNN95g7dq1XHHFFZSUlHDCCSfI+ZTS3Of0q1/9aredT3t9KFVXV8crr7zC448/zqhRoxg1ahTLly/nhRdekC9grbRq1SoWLVrEp59+SlFREQBTp07lrrvuklBqJ1RWVjJz5kxGjx7d0U3psiorK3nttdd46qmn2G+//QC44IILWLx4sYRSrVRVVcWiRYuYPn06/fv3p3///hx++OHMnz9fQqkdWLFiBVdeeWWDHnqfffYZ69ev56WXXsLv9zNo0CDmz5/Pa6+9xmWXXdZBre3cmvos582bR1FREVdccQUA/fv3Z8GCBbz99tsSSrWCfCdquZUrVzJ06FCKi4s7uimdivy8a5mmPiewzq3Jkyfv9edWc9cVP/3pT+V8StnR9ZecT5by8nJGjBjBtGnTCAaD9O/fnwkTJrBw4UKKiorkfEpp7nOyQ6ndcT7t9cP3li1bRiKRYMyYMc66sWPHsnjxYgzD6MCWdT3FxcU88cQTzg9EWygU6qAWdW133XUXJ510EoMHD+7opnRZCxcuJBgMMm7cOGfdRRddxIwZMzqwVV2T1+vF5/Px+uuvE4/HWbVqFV999RUjRozo6KZ1ep9//jnjx4/n5Zdfzli/ePFiRo4cid/vd9aNHTuWRYsWtXMLu46mPkt7uEI2+f3TOvKdqOVWrlxJ//79O7oZnY78vGuZpj6nUCjEli1b5Nyi+esKOZ/qNfc5yflUr3v37tx3330Eg0FM02ThwoV88cUXjBs3Ts6nNM19TrvzfNrre0qVlZVRUFCA2+121hUVFRGNRqmsrKSwsLADW9e15ObmcvjhhzvLhmHw/PPP85Of/KQDW9U1zZ8/ny+//JK3336badOmdXRzuqz169fTu3dv3nzzTR599FHi8Tinnnoq//u//4uq7vWZfKt4PB5uuukmpk+fzrPPPksymeTUU0/ljDPO6OimdXpnnXVWo+vLysro3r17xrpu3bqxefPm9mhWl9TUZ9mnTx/69OnjLG/bto133nlnr/sL566S70QtY5omq1ev5pNPPuGxxx4jmUxy3HHHMXXq1IzPbm8kP+9apqnPaeXKlSiKwqOPPspHH31Efn4+559//l5ZxqG56wo5n+o19znJ+dS4o446itLSUiZOnMixxx7LHXfcIedTI7I/p2+//Xa3nU97fSgVDocbfIGwl2OxWEc0aY8xa9YsvvvuO1599dWObkqXEo1Gufnmm7npppvwer0d3Zwura6ujrVr1/LSSy8xY8YMysrKuOmmm/D5fFxwwQUd3bwuZ+XKlUycOJHzzz+f5cuXM336dCZMmMCJJ57Y0U3rkpr6/SO/e3ZNJBLhsssuo6ioiF//+tcd3ZwuRb4TtUxpaanzWd13331s2LCB2267jUgkwo033tjRzeuU5Oddy6xatQpFURg4cCDnnHMOX3zxBX/+858JBoN7/VD59OuKp59+Ws6nJqR/TkuXLpXzqREPPPAA5eXlTJs2jRkzZsjPpyZkf06jRo3abefTXh9KeTyeBiecvSyBwM6bNWsWzzzzDPfeey9Dhw7t6OZ0KbNnz2bffffN+KuH2Dm6rhMKhbj77rvp3bs3YF1MvPjiixJKtdL8+fN59dVX+fDDD/F6vYwePZotW7bwyCOPSCi1kzweD5WVlRnrYrGY/O7ZBbW1tVxyySWsWbOGv/zlL/h8vo5uUpci34lapnfv3ixYsIC8vDwURWHEiBEYhsHVV1/N9ddfj6ZpHd3ETkd+3rXMySefzMSJE8nPzwdg+PDhrFmzhhdffHGvDhGyryvkfGpc9uc0ZMgQOZ8aYdfrjUajXHXVVZx22mmEw+GMfeR8avg5ffXVV7vtfNrrx6+UlJRQUVFBIpFw1pWVleH1esnNze3AlnVd06dP56mnnmLWrFkce+yxHd2cLuedd95h3rx5jBkzhjFjxvD222/z9ttvZ9T4EC1TXFyMx+NxAimAAQMGsGnTpg5sVdf07bff0q9fv4xf0CNHjqS0tLQDW9W1lZSUUF5enrGuvLy8QRdy0TKhUIjJkyezfPlynnnmGamhsRPkO1HL5efnoyiKszxo0CCi0ShVVVUd2KrOS37etYyiKM4Fn23gwIFs2bKlYxrUCTR2XSHnU0ONfU5yPtUrLy9n3rx5GesGDx5MPB6nuLhYzqeU5j6nUCi0286nvT6UGjFiBLquZxQyW7hwIaNHj5aaMzth9uzZvPTSS9xzzz388pe/7OjmdEnPPfccb7/9Nm+++SZvvvkmRx11FEcddRRvvvlmRzety9l///2JRqOsXr3aWbdq1aqMkEq0TPfu3Vm7dm1GL4pVq1Zl1PERrbP//vuzdOlSIpGIs27hwoXsv//+HdiqrskwDKZMmcKGDRt47rnnGDJkSEc3qUuS70Qt8/HHHzN+/PiMv6x///335OfnS92tJsjPu5a5//77mTRpUsa6ZcuWMXDgwI5pUAdr6rpCzqdMTX1Ocj7V27BhA1OmTMkIUL799lsKCwsZO3asnE8pzX1Ozz333G47n/b6bxg+n4+TTz6ZadOmsWTJEubNm8fcuXM577zzOrppXc7KlSt5+OGHufDCCxk7dixlZWXOJFqud+/e9OvXz5kCgQCBQIB+/fp1dNO6nIEDB3LkkUdy/fXXs2zZMj7++GPmzJnDb37zm45uWpdz1FFH4XK5uPHGG1m9ejX/+c9/ePTRRzn33HM7umld1rhx4+jZsyfXX389y5cvZ86cOSxZsoTTTz+9o5vW5bz66qssWLCA2267jdzcXOd3T/bwDtE8+U7UMmPGjMHj8XDjjTeyatUqPvzwQ2bOnMnvfve7jm5apyU/71pm4sSJfPHFFzz55JOsW7eOv/zlL7z55pt7ZcmB5q4r5Hyq19znJOdTvdGjRzNq1ChuuOEGVqxYwYcffsisWbP4/e9/L+dTmuY+p915PimmaZpt0P4uLRwOM23aNP71r38RDAaZPHlygxRQ7NicOXO4++67G932ww8/tHNr9hzXXXcdAHfeeWcHt6RrqqmpYfr06bz33nv4fD7OOussLr300oxhF6JlVqxYwe23386SJUsoLCzk7LPP5re//a18lq0wbNgwnn32WcaPHw/A2rVr+dOf/sTixYvp168fN9xwA4ccckgHt7JrSP8sJ0+ezCeffNJgn3HjxvHcc891QOu6LvlO1DLLly/njjvuYNGiRQQCAc4880z53ZJFft61TPbnNG/ePB544AHWrFlD7969ufzyyznmmGM6uJXtb0fXFXI+WXb0Ocn5VG/Lli1Mnz6d+fPn4/P5OOecc7j44otRFEXOpzTNfU6763ySUEoIIYQQQgghhBBCtLu9fvieEEIIIYQQQgghhGh/EkoJIYQQQgghhBBCiHYnoZQQQgghhBBCCCGEaHcSSgkhhBBCCCGEEEKIdiehlBBCCCGEEEIIIYRodxJKCSGEEEIIIYQQQoh2J6GUEEIIIYQQQgghhGh3EkoJITq9YcOGceWVVzZY//rrr3PUUUd1QIuEEEIIIYQQQuwqCaWEEF3C3/72N+bPn9/RzRBCCCGEEEII0UYklBJCdAm9e/fm1ltvJRaLdXRThBBCCCGEEEK0AQmlhBBdwh//+Ee2bNnCk08+2eQ+mzdv5g9/+APjxo1j/Pjx3HbbbU6I9frrr3PuuefywAMPMH78eA466CBmzJiBaZrO81966SWOOuooxowZw7nnnssPP/yw29+XEEIIIYQQQuytJJQSQnQJJSUlTJ06lUcffZT169c32B6Lxfjtb39LOBzmueee47777uODDz5g5syZzj5ff/01q1ev5sUXX+TPf/4zzz77LP/9738B+M9//sPs2bP585//zBtvvMHYsWM577zzqKqqarf3KIQQQgghhBB7EwmlhBBdxrnnnku/fv24/fbbG2z7+OOP2bJlC7NmzWLYsGFMmDCBm266iRdffJHa2loAkskk06dPZ+DAgZx00kkMHz6cb775BoAnnniCiy++mIkTJ9K/f3/++Mc/0rt3b9566612fY9CCCGEEEIIsbfQO7oBQgjRUpqmMW3aNM466yzmzZuXsW3lypX079+fvLw8Z92BBx5IIpFg3bp1AHTr1o1gMOhsDwaDJBIJ5/mzZs3innvucbZHo1HWrFmzG9+REEIIIYQQQuy9JJQSQnQpBx54IKeddhq33347v/vd75z1Ho+nwb7JZDJj7na7G+xj15RKJpPccMMNTJgwIWN7eoglhBBCCCGEEKLtyPA9IUSXc9VVV1FXV5dR9HzAgAGsWbOGyspKZ92iRYvQdZ199tlnh8ccMGAAmzdvpl+/fs706KOPsmjRot3wDoQQQgghhBBCSCglhOhyCgoKuOqqq9i4caOz7tBDD6Vv375cc801/PDDD3z22WdMnz6dE044gdzc3B0e8/zzz+eZZ57hzTffZN26dcyaNYu///3vDBo0aHe+FSGEEEIIIYTYa8nwPSFEl3T66afz2muvsXXrVsCqN/Xwww8zffp0/ud//odAIMCvfvUrrrjiihYd7/jjj6e8vJwHHniA8vJyBg8ezCOPPEL//v1347sQQgghhBBCiL2XYtoFVYQQQgghhBBCCCGEaCcyfE8IIYQQQgghhBBCtDsJpYQQQgghhBBCCCFEu5NQSgghhBBCCCGEEEK0OwmlhBBCCCGEEEIIIUS7k1BKCCGEEEIIIYQQQrQ7CaWEEEIIIYQQQgghRLuTUEoIIYQQQgghhBBCtDsJpYQQQgghhBBCCCFEu5NQSgghhBBCCCGEEEK0OwmlhBBCCCGEEEIIIUS7k1BKCCGEEEIIIYQQQrQ7CaWEEEIIIYQQQgghRLuTUEoIIYQQQgghhBBCtDsJpYQQQgghhBBCCCFEu5NQSgghhBBCCCGEEEK0OwmlhBBCCCGEEEIIIUS7k1BKCCGEEEIIIYQQQrQ7CaWEEEIIIYQQexTTNDu6CaITkvNCiM5HQikhRJs699xzGTZsGGeeeWaT+1x++eUMGzaM6667rk1f+8EHH2TYsGFtesyW2rBhA8OGDeP111/vkNcXQgix91m4cCGXXXYZhx56KKNHj+ZnP/sZN954IytXruzopmVo79/PCxcu5KKLLmq31+sMli5dyoUXXshPfvITxo8fzwUXXMDSpUsz9jFNkyeffJJjjjmG0aNHc+yxx/LCCy+06nXuvPNOzj333Ix19v/fpqbPP/+8xcdv7FgjR45k/PjxXHrppSxfvrzFx5o7dy5XXXUVANXV1VxzzTV8+eWXLX7+rrjuuus46qijmt3n9ddfZ9iwYWzYsKHFx23JcyoqKjjyyCNZv359i4+brra2lltuuYVDDz2UMWPGcOGFF7Jq1aodPu+HH37gd7/7HePGjeOwww7j2muvpby8vMn9n3vuuR1+RmLvoHd0A4QQex5VVVm0aBGbN2+mR48eGdvq6up4//33O6hlQgghxJ5hzpw53HPPPRx22GHccMMNFBcXs3btWl588UVOOeUUZsyYwS9/+cuObmaHeOWVVzpdMLc7rV27lnPOOYd9992X22+/HUVRmDt3LmeddRZvvPEGAwcOBGDmzJk899xzTJ06ldGjR/PRRx9x6623ous6v/71r3f4OnPnzuWpp55i3LhxGevPOOMMDj/88Ix18Xicyy+/nOLiYvbbb79Wv6eXX37ZeZxMJiktLeXee+/l7LPP5p133qG4uLjZ569cuZLHHnuMt956C4Dvv/+ev/71r5x22mmtbsvucuSRR/Lyyy/TvXv3Nj1uQUEBkyZN4oYbbuDZZ59FUZRWPf/KK69k8eLFXH311QSDQWbPns15553HO++8Q15eXqPPKS8v57e//S09e/ZkxowZRKNR/t//+39ceOGF/N///R8ulytj/3feeYc777yTkpKSnX6fYs8hoZQQos2NHDmSFStW8I9//INJkyZlbHv//ffx+Xzk5uZ2TOOEEEKILu7999/n7rvv5rLLLmPKlCnO+nHjxnHyySdz5ZVXct111zF06FCGDBnSgS0V7eG5557D5/Px2GOP4ff7AfjJT37CUUcdxfPPP89NN93Ehg0bePrpp/nzn//MWWedBcCECRPYtGkTn3zySbOh1Pr167nrrrv4z3/+Q05OToPtPXr0aPBHyBkzZlBbW8tLL72E1+tt9Xs64IADMpbHjh1Lz549Ofvss3njjTd22BNu1qxZnHDCCZ069CgsLKSwsHC3HPuss87ikUce4b333uOYY45p8fO+/vpr3n//febMmcMRRxwBwEEHHcTPfvYz/vKXv/C///u/jT7v3//+NxUVFfzf//0f++yzDwA5OTn87ne/4+uvv3aCzG3btnH//ffz8ssvk5+fv2tvUuwxZPieEKLN+f1+jjjiCP7xj3802Pbuu+9y7LHHouuZmfj27du55ZZbmDhxIvvuuy/jxo3j0ksvzeievG7dOn7/+98zfvx49t9/f37961/z4YcfNtmO0tJSjjzySE499VSqq6ub3O+bb75h8uTJjB8/ngMPPJDf//73Gd3DFyxYwLBhw5g/fz4XXHAB+++/P4ceeiizZs0imUw2OF5lZSWjR4/mnnvuyVgfDocZO3YsjzzySJNtEUIIIXZk9uzZDBw4kEsvvbTBNpfLxa233oqmaTz++OMAXHDBBZx66qkN9r3kkks48cQTneUvv/ySc845h/33359x48Zx7bXXsn37dmf766+/zsiRI3nllVc49NBDGTduHCtWrGjx7+cPPviAE0880Rk69uabb2Zs37p1K9dffz1HHHEE++23H6effjr//ve/M/aJRqM89NBDHHfccYwePZpjjjmGOXPmYBgGYA2beuONN9i4cWOzw+offPBBjjvuON577z1OOOEERo8ezUknncTXX3/NokWLOOOMM9hvv/044YQTmD9/fsZzf/zxRy6++GIOPPBADjzwQC699NIGQ6WWLVvGlClT+MlPfsKoUaM4/PDDue2224hEIs4+w4YN44UXXuBPf/oT48aNY8yYMfzhD3/IGPJkD9dasGBBo+8DYODAgVxwwQVOIAXWd7EePXqwbt06AObNm4fH4+H000/PeO59993Hgw8+2OSxwQqY1q5dyzPPPMOIESOa3ResYVzPPfccU6ZMoU+fPjvcv6X23XdfADZu3AhY/w+PPvpoZs+e7QwZq6qq4scff+SDDz7ghBNOAKzvceeddx4A5513Xsbww3fffZdTTz2VMWPGcOihh3LTTTdRVVWV8bo7+p7YnNdff51jjz2W0aNHc+KJJ2b8u2hsKN4bb7zB8ccf7+w/f/58Ro4c2eA8Xrx4MWeeeSajR4/myCOP5IknnsjY7na7OfbYY3nsscecdfb32eZKTXzyySf4/X4OO+wwZ11hYSEHH3xws9+5o9EoAMFg0Flnh06VlZXOukcffZRPPvmEBx98kIkTJzZ5PLF3kVBKCLFbHH/88c4QPlsoFOKjjz5yviTYTNPk4osv5tNPP+Wqq67iySefZMqUKcyfP5+bb74ZAMMwuPjiiwmHw8ycOZOHH36Y/Px8/vd//5e1a9c2eP2ysjImTZpEfn4+Tz31VJM9sz777DN+85vfAHDHHXdw2223sWnTJs4888wGXf+vuuoqxo4dy6OPPsoJJ5zAE088wSuvvNLgmPn5+fz85z/n7bffziio+d5771FXV8fJJ5/csg9RCCGEyLJ9+3a+/fZbJk6c2OSwnPz8fA455BAn0DnxxBNZunRpxu/L6upqPvroI0466SQAvvjiCyZNmoTX6+W+++7jhhtu4PPPP+e8887LCFKSySRz587l9ttv5/rrr2fAgAEt/v180003MWnSJB555BF69OjBddddx7JlywBr+M/pp5/Ol19+yeWXX86DDz5I7969ufTSS50hWKZp8vvf/54nnniCM844g0cffZTjjjuO++67z/m+cMkll3DEEUdQXFzMyy+/zJFHHtnkZ7l582buvPNOfv/733P//fdTXV3N1KlTueKKKzjjjDN46KGHME2Tyy+/3PkMVq9ezZlnnsm2bdu46667uP3221m/fj2/+c1v2LZtG2CFa2effTbhcJg777yTxx9/nF/+8pc899xzPPvssxltuPfeezEMg3vuuYdrrrmG999/nzvuuMPZbg/xGjVqVJPv46yzzuJ3v/tdxrq1a9eyfPlyp6fc999/T79+/fjiiy845ZRTGDVqFEcddVTGMLmm/PGPf+Stt97i4IMP3uG+YA0T7NOnD7/97W9btH9LrV69GsDpiQPWHyA//PBD7r33Xq6//nry8vJ4++23KS4udnpbjRo1iptuugmwzkH7XHn44Ye54oorOOCAA3jggQe49NJL+ec//8m5557r/P9uzffEbJs2bWLOnDn84Q9/4MEHH0RRFKZOneqcJ9nefPNNrrvuOg488EAefvhhjj32WC655JJG/wA6bdo0fvnLXzJnzhzGjBnDrFmzGpTHOO644/j222+dz23UqFE7/DexcuVK+vTpg6ZpGev32Wcf5ziN+cUvfkFxcTG33norW7duZf369cycOZPi4mIOOeQQZ78zzzyTf/7zn63qvSX2fDJ8TwixWxx55JH4fL6MIXzvvfce3bp1Y+zYsRn7bt26FZ/Px7XXXstBBx0EwPjx41m3bp3zZWnbtm2sWrXK+bIJsN9++zF79mxisVjG8SoqKjj//PPxer089dRTTY5/B7j77rvp168fc+bMcX4BH3bYYRx99NE88MAD3H///c6+Z5xxhvNX6QkTJjBv3jw++OCDRou6n3baabz77rssWLCAn/zkJ4D1ZeOQQw6hZ8+eLf4chRBCiHR2L5HevXs3u1+/fv3497//TVVVFccccwy33HILf/vb35zfY//6179IJpPOH4ruvvtuBgwYwGOPPeb8Ptx///355S9/yWuvvcbZZ5/tHPv3v/+9c2FbVlbW4t/Pt912Gz/96U8B6yL36KOP5vPPP2f48OE89dRTbN++nX/+85/OezviiCOYNGkSM2fO5IQTTuDjjz/mv//9L/fcc49TL+vQQw/F6/Vy//33c9555zFkyBAKCwtxu90NhoBlC4fD3HzzzU6bVqxYwd13383tt9/u9Ciqq6tj6tSprF69mhEjRjB79mx8Ph9PP/200ytkwoQJ/PznP+eJJ57g2muv5ccff2TEiBHcf//9zj6HHHIIn376KQsWLMgYejZ06FBmzJjhLC9ZsiSjp/nODPGKRCJce+21uN1uzjnnHMAKM7ds2cJVV13FlClTGDhwIO+++64T1jQ3fG/o0KEtfu1ly5bxySefcNtttzXoFd8aiUTCeRyJRFi2bBl33HEHOTk5Gb37EolExvdHsIKk0aNHO6FtMBhk8ODBAAwePJjBgwdTVVXFI488wv/8z/84n4H9Xs8++2znnG/N98RshmHw0EMPMWjQIAA8Hg+TJk1i0aJF/OxnP2uw//3338/EiRO57bbbADj88MNxuVzcfffdDfa94oornLDsgAMO4L333uOzzz7L6H00evRoAObPn8+AAQMIBoM7/DdRU1OT0dvJFggEqK2tbfJ5xcXF3HLLLVxxxRX8/e9/ByAvL49nn30243j2ZyFEOukpJYTYLbxeL0cddVTGF6t33nmHX/ziFw3+sltSUsKzzz7L2LFj2bBhA59++inPPfccX331lfOFtqioiMGDB/PnP/+Za6+9lrfffhvDMLj++usb1Mv43e9+x/Lly7nhhhsoKChoso11dXV88803/OIXv8j4i1Bubi4TJ05scLeYMWPGZCz36NGDurq6Ro99yCGH0KtXL/76178C1l9j58+fzymnnNJke4QQQogdsXvgZhcOzmb/XjNNE7/fz89//nPeffddZ/s777zDhAkTKCkpIRwOs3jxYo444ghM0ySRSJBIJOjbty+DBg3i008/zTh2+hCu1vx+Tg8O7GFd9vD6zz//nDFjxjQI20488UQn+Pr888/RdZ3jjjuuwT72MVrrwAMPzHgvYIVxNnsIkt3Ozz77jHHjxuH1ep3PKRgMctBBB/Hf//4XsEKL559/Ho/Hw4oVK/j3v//NI488wvbt2xsEddkhQY8ePQiHw61+H7ZQKMTFF1/MN998w6xZs5zPMx6PU1FRwS233MLZZ5/NhAkTmD59OocddhizZ8/e6dfL9sILL9CtWzenB97OGjVqlDONHTuWs88+m1gsxuzZsxsUOc8eUrh+/fodDhtctGgRsVisQe/9gw46iN69e/P555+3+ntitoKCgowQxm5TTU1Ng33Xrl1LaWlpg3O7qZsVpP9b8vl8FBUVNShVkZOTQ25ubqvu7pfewz9bcwXT3377baZMmcJRRx3Fk08+ycMPP8yQIUO44IIL9qqbDoidIz2lhBC7zS9+8QumTJnC5s2b8Xg8zJ8/nz/+8Y+N7vvWW29xzz33sGnTJvLz8xkxYkRGYUz7TjJ20cY333wTl8vFz3/+c2655ZaM3lDhcJg+ffpw99138/LLL6OqjefvNTU1mKbpfAlNV1RU1OBLQ3ahTlVVm/zlraoqp556Kk899RQ333wzf/3rXwkGgxx99NGN7i+EEEK0hB0y2D2mmrJ+/XoCgYATqpx00km89dZbLFu2jKKiIhYsWOAME6uursYwDB5//HGnDlU6j8eTsZxeu6g1v5/Tn2f/brZ/j1ZVVdG3b98Gr23/jq6urqaqqoqCgoIGQ4vskKKxi/0daaxXiM/na3L/yspK3n333YyAz2b3aLKH473wwgvU1dXRs2dP9ttvvwafY2Ov1dx3ix3ZtGkTF198MatXr+bee+/l5z//ubMtEAigKIrTm812+OGH88knn1BeXt7o96HWSCaTvPfeexx//PG43e5dOtarr77qPHa5XBQXF9OtW7dG9w0EAhnLoVCo2f+HgFM3qrnvgK39npgt/XyH+lDHrn+Wzq7dlv0em/p/0tLzxufzEQqFmm1numAwmFHTzFZbW9tokXvb7NmzGTNmDPfee6+z7tBDD+X444/n/vvv54EHHmhxG8TeR0IpIcRu89Of/pRAIMA//vEP/H4/ffr0cYpUpvvyyy+59tprOffcc5k8ebJzp5SZM2eycOFCZ7+SkhKmTZvGzTffzLJly/jHP/7B448/TkFBgVMfAOCZZ57h+++/58ILL+TZZ59tcAdAW05ODoqiNPrLt6ysbJfvCnLqqafy0EMP8dFHH/H3v/+d448/vtEvpEIIIURLdevWjQMOOIB//vOf/OEPf2j0Dy+hUIhPP/2Uo446ylk3YcIEiouL+fvf/05xcTEej8ep62IHFpMmTWq0Z8aOLvBb+vu5OXl5eZSVlTVYb68rKCggLy+PiooKkslkRjC1detWZ5/dLScnh0MOOYTzzz+/wTZ7uNqcOXN4+umnueWWWzjmmGOci/nsIuNt6YcffmDy5MlEo1Hmzp3boP5Tv379ME2TeDye8V3EHia3M3fIy7Z48WIqKir4xS9+scvHsoee7Yz8/PwdBkZ2WFpeXs7AgQMztpWVldG3b9/d/j0xnX33wux6U03Vn2qp6urqVv27GDBgAJ988gmGYWT8bFm7dm2zQ+82btyYEYKCdU7tu+++LS4KL/ZeMnxPCLHbuN1ufv7zn/PPf/6Tv//97012Qf76668xDIPLLrvMCaSSyaTTDd4wDL7++msOOeQQlixZgqIojBgxgssvv5yhQ4dSWlqacbzi4mJ++tOf8otf/IL777+/yW7Lfr+ffffdl7///e8ZRSRramr44IMPGtS+aq3evXszYcIEnn32Wb7//vtG73wkhBBCtNaUKVNYvXp1g7u8gvX78+abbyYSiWQUv9Y0jV/96le8//77/OMf/+DnP/+505MjGAwycuRIVq1axejRo51pyJAhPPjgg83e+a01v5+bc/DBB/P111836AH21ltvUVxcTL9+/Rg3bhyJRKLB3X3tQuj27+2meki3BfuOgyNGjHA+p3333Zenn36a9957D4CFCxcyePBgTjvtNCeQ2rJlCz/++GOjvWR21aZNmzj//PNRFIUXX3yx0YLkdg+pd955J2P9f/7zH4YNG9Zoj7HWWrx4Mbqus99+++3ysXZF79692bRpU8a67N51+++/P263m7/97W8Z67/88ktKS0s58MADd/v3xHQ9evRgn332cc4h27/+9a+dPmZVVRXhcJhevXq1+DmHHXYYtbW1fPzxx8667du38+WXX3LooYc2+byBAwfy1VdfZfTWikajLF26tNEekEKkk55SQojd6vjjj+fiiy9GVVVuvPHGRvexv7zceuutnHbaaVRVVfHCCy84d+Spq6tj5MiReL1errnmGi677DKKior473//y/fff+/c5jfbDTfcwMcff8zNN9/Mk08+2eg+V155JZMnT+aiiy7irLPOIh6PM2fOHGKxWKO32m6t008/nSuuuIJBgwZl1KgQQgghdtbhhx/Oddddx8yZM/n+++857bTT6N69Oxs2bODFF1/k+++/5/bbb2f48OEZzzvppJOYO3cuqqo2GKZ3xRVXcNFFF3HllVdy4oknOnfZW7x4MZdcckmTbdmZ38+NOf/883nrrbeYNGkSU6ZMIT8/nzfffJPPPvuMO+64A1VV+elPf8r48eO58cYb2bJlC8OHD+fzzz/n8ccf55RTTnGKWefm5lJeXs6HH37IiBEj6N69eys+3eZdcsklnHnmmVx88cX85je/wePx8PLLLzNv3jxniNJ+++3Hww8/zJw5czjggANYu3Ytjz32GLFYrNX1orZv3866desYPHhwk8HRbbfdxrZt27jlllsIhUIsWrTI2WYX+R4/fjwTJ05kxowZhMNhhgwZwptvvslXX33Fww8/7Oy/bt06tm/fvsOC2I358ccf6dOnT5O9wjdv3szmzZsZOXLkLg/va86hhx7KX/7yF0zTdIbM2eHgBx98QF5eHsOHD+eiiy7ioYcewuVyMXHiRDZs2MD999/P4MGDnRqgu/t7os2+M99VV13FzTffzNFHH82yZct46KGHgJ0LWu3RBocddhhg9aBcsWIF++yzT5PF8w8++GDGjRvH1VdfzdVXX01+fj4PPvggOTk5TmF1sG4KEIvFGDlyJAB/+MMfuPTSS/nDH/7A6aefTiwW45lnnmHLli2NFmoXIp2EUkKI3eqQQw4hNzeXnj17Ntntd/z48dx000089dRT/OMf/6CoqIjx48cze/ZsLr30UhYuXMgRRxzB3LlznbviVFdX079/f2699dYmeyB1796dK664gltvvZU333yTk08+ucE+EyZM4KmnnuKBBx7giiuuwO12c9BBB3HXXXc1KNC6M4444ggURZFeUkIIIdrU+eefz5gxY3jmmWe466672L59O8XFxRx66KHcfvvtTkCTbvjw4QwdOpSKigomTJiQse2www7jySefZPbs2UydOhWXy8WoUaN46qmnmg0oPB5Pq38/N6a4uJgXX3yRu+++m9tuu414PM7w4cN5+OGHnTuVKYrCY489xgMPPMDTTz/N9u3b6dOnD1dccUXGcLpTTz2VDz/8kEsvvZSpU6dm3O1uVw0fPpwXXniBe++9l2uuuQbTNBk6dCgPPfSQ086LL76YiooKnn32WR566CF69uzJSSed5LS/urqa3NzcFr3eBx98wPXXX8+zzz7L+PHjG2yPxWJ88MEHAI0OlRw3bhzPPfccYN3dbfbs2c6dDgcPHszs2bMzhnk+/PDDvPHGG/zwww+t/WgoLy9v9o7Hr7zyCrNnz+bf//73DguR74pjjjmGhx56iCVLljh/EBwyZAgnnHACL7zwAh9//DF/+9vfnBD1+eef5+WXXyY/P5/jjjuOP/7xj04vwt39PTHdr371K+rq6njyySd57bXXGDJkCH/605/405/+1KA+VUt89NFH7Lfffk4duqVLl3LeeecxY8aMZv9tzp49mzvvvJOZM2diGAYHHngg9913X8b/21tuuYWNGzfyn//8B4Cf/exnzJkzh4cffpgpU6YQCATYb7/9ePXVVxuE40JkU8ydraQnhBBih959912uueYaPvzwwyYLdAohhBBC7A3OPvts7rvvvgZ30Gtrv//97ykoKGDGjBm79XXa0t/+9jdGjhyZUePqgw8+4OKLL+avf/1rq8Kduro6Dj/8cO66664GtZ6E6Gykp5QQQuwG8+bN45tvvuGll17i1FNPlUBKCCGEEHu1BQsWEA6Hd/kufy1x+eWXc9ZZZ3HZZZe1qqZSR3rrrbe49957+eMf/0jPnj1Zu3YtDzzwAOPGjWt1b6OXXnqJIUOGOL33hOjMpKeUEELsBk8//TT33XcfY8eO5b777mv2NrpCCCGEEHu6jRs34vf72+UuiWDdBXHZsmWN3hCgM6qoqODuu+/mo48+Yvv27RQVFXHssccydepUAoFAi4+zfft2Tj75ZJ577jn69eu3G1ssRNuQUEoIIYQQQgghhBBCtLvdd79UIYQQQgghhBBCCCGasNOhVCwW44QTTmDBggXOuvXr1zNp0iQOOOAAjj/+eD755JOM5/z3v//lhBNOYP/99+e8885j/fr1O99yIYQQQgghhBBCCNFl7VQoFY1GueKKK1i+fLmzzjRNLr30UoqKinjttdc46aSTmDJlCqWlpQCUlpZy6aWXcuqpp/Lqq69SWFjIJZdcQktHD5qmSSgUavH+QgghhBAik3yfEkIIIURn0upQasWKFfzP//wP69aty1j/2WefsX79em699VYGDRrExRdfzAEHHMBrr70GwCuvvMK+++7LBRdcwJAhQ5gxYwYbN27k888/b9Hr1tbWMnbsWGpra1vbZCGEEEIIgXyfEkIIIUTn0upQ6vPPP2f8+PG8/PLLGesXL17MyJEj8fv9zrqxY8eyaNEiZ/tBBx3kbPP5fIwaNcrZLoQQQgghhBBCCCH2Hnprn3DWWWc1ur6srIzu3btnrOvWrRubN29u0fYOF6uClY+DEQM9CHoOuHLAnQ+ufHAX1D9WtY5tqxBCCCGEEEIIIUQX1+pQqinhcBi3252xzu12E4vFWrS9w637P/j66hbsqIKnENzdwFsM3hLw9QJ/H/D3Bn8/yBkMnu6gys0NhRBib2TX6zExneX0x63Z1txya56zo+ftaF1L17f0OTt6XmPb7X1URaVvXl90tc2+xgghhBBCiA7QZt/mPB4PlZWVGetisRher9fZnh1AxWIxcnNz26oJu6bvaVD9I9Qsh0QIEnWQrLUex2useTIMGBAtt6aaH5o+nh4AX2/w94WcIZA7DHJHQKAfqC5QtPpJ1dOW9ax1EmwJIfYsdghjzw3TaLDONFPrs9a1Zt7YazW2zTAMDIyM13TalNVGA2s7Jpnts7elhTqNPU7/DJxtZAVBzmzHIVNjx2tsnp7tNLYOxZ4p9cdX0l8gtUpRnOdn7Ju1f/a29Odlb29uW2PPN0wDr+6lyF9EjienwWchhBBCCLG7JQ0TTVV2vGMn1lneQ5uFUiUlJaxYsSJjXXl5uTNkr6SkhPLy8gbbR4wY0VZN2DWeQjhwVsP1Rjw1xSBRC+HNULcRatdAdCtEtlhTtBxilRAtsx4naqHmR2va8u/642l+yBkKucOtoCpniBVgoVjDAhUVsMMqDRQXqB7QPKB5rUBLdVnhVWOPhRCiCXboYk922OIsZ4Uyja1rbDlpJDEwSBpJkmbSWmcmnfWmkVpObUt/rt0u579GQqQG+6RCIed92csKO5w7gUdqnaJYv4iVVKKiKAoKirPe3pa+X/Y2+3nNbVcVtcG+TR2/qX2yX8fZtgv7dkWxZIyKSEVHN0MIIYQQezFNVfjDS1+zYmuoo5uyUwZ3D3L/mWM6uhlAG4ZS+++/P3PmzCESiTi9oxYuXMjYsWOd7QsXLnT2D4fDfPfdd0yZMqWtmrB7OGGP36op5e8N3az3hJGAZB3EQ1ZNqliZFUZFKyFRZa0Lb4TQCqhZAaHV1v6Vi6zJegHIGwVFE6BovBVSoYCZBDMBRtjqpWUmUuvS/yqtpHpWpSZVt0Iv3Qe6PxVouUFzW3PVY+0jhOiU7CAoaSTrH5vJjOAofVv2lDAS1mQmSBpJEkYCw7DWpx/HCYao7w3U2Ho7wLF7qWQvp/eesYOV7Lma6u3Z2LKqqA22Zwc0qqI2CHyygx8hhBBCCCHa24qtIZaWVnd0M7q8Nksoxo0bR8+ePbn++uu55JJLeP/991myZAkzZswA4LTTTuPJJ59kzpw5TJw4kYceeog+ffowfvz4tmpC+1N1UHPBlQv+XmAOtwKkWAWESyG2HXKHQu9fWYGWaVgBVeU3ULEEKpdYoVXVN9a0co5VSL3kCOh5LBSOtXpMNcU0U8FVIhVaxSG2DSJx67WcK0atPlxTPVYRdz0Aure+F5bqrg+2hBAtZgdFduhjP06f2+GS/ThuxIklYySNJDHDmseT8cyQCcMaVmb3RiKZORyqiSFOdtCTHujYy852NXO5qcdCNMXuDZcwEsSTcWtuxOuD0bSpsfV2aGoHpk1tt7elL8eSMfrn9+fwfQ7v6I9BCCGEEELsojZLIDRN4+GHH+ZPf/oTp556Kv369eOhhx6iV69eAPTp04cHH3yQO+64g4ceeogxY8bw0EMP7Vl/7VYU6459rhyrllSiBiLlULsW6tZbQVDOEGvo3j5nWM+JbIGy+VD+X9i2AOKVsOGv1uTpBj2Oht4nWM9p7PWUFgzbs0MrI57q2VVlrbPHs6h66jipmlaaB1RfasigO7VNr5+nDxmUC1exB2juAjhjWzJBNBkllowRT8adQCm9R1N6T6bsYWJ27yI7OGps0hQNl+pyAiVN0TLCJrF3SRpWCBNNRokn4xnzWDKWcS7Gk/H6dangM56MO48TRsLZbj+OG/H6UCm1b9yIk0gmnMfpoZO9T8JIdOjn4lJd3Hj4jR3aBiGEEEIIset2KZT64YfMQt/9+vXj+eefb3L/I444giOOOGJXXrLrUBSrB5Ur1xryF9liDd+r3Qguv3X3PkWx7t7X92RrMhJQ8TVs+hds+Q9Et8Hal6wpbzT0OwNKfmaFRa2h6oBu1aTKZppWDyvDHh4Yh1gEzG2p3ldmw2NlFGR3g2YHWL60OleutN5ZqRBrTwogRaeU3nujqSn9gj6aiDoX6Om9meyAyTooTi0iAE3RrPBI1RoESdnr9qjQXZA0kkSTUSKJCNGENbcne336cjQRbXQeS8aIJCIZ52B6yJR+fibNZEe/7RbTFA1d1Vs1aWrmcxo7hr1OUzW0VO/hfvn9cGlSR1EIIYQQoquTsVrtQfNAYB/w9bQKpdcst3pOebtnBkWqDt0OtqaR10D5Aih9B7a8bw3vW/INuO6BvqdC/7OsIYG7SlFASdWc2hHTrK91ZSbra2olaupDLeugqfdjh1d2vSuf9X41f1oPrFTdq/QAS4g0pmk22lvDfhxLxojE64OA9JpKdsCUzh7Wpimac0GsKqoTKqWHTqLrSRpJwokw4XiYungd4YQ1r4vXEY6HneVIIuLsZ8+ddYlwfbiUiDrLsWRsxw3YjTRFw625G0wuzWU9VusfuzRX5rLqwqW5cKnWsq7qzn7p2+z1uqrj0lLLqeOkB0XZz9FVvd1CWCl0LoQQQgix55BQqj2pLgj0te70V7PSGtan6uApajgMTnVB98OsKVpuDedb/7rV42rVXFj7IvQ9HQacbT2/PdiF1Vty2qQHWPbwwVhlKtBK74Fl33XQRbPF2tWsufRA6fLssCl9iJE9jyQiTkgQSUSsIXRmgkTSGkaXXmTbDpDSe1W4NXdG6CQ6t4SRIBQLURurJRS35rWxWmrj1lQXr3OW7cd18TpnOX2KJqPt0maP5sGre/HqXjy6B6+WmutePJoHj+5x5tnr3Jobr+7Frbkz1qWvT588mscJhYQQQgghhNiTyDfcjqAHIH80eIuh+keoXW8VSm+ql5CnCAZNhgG/ha0fwKqnoPoHWPMcrPs/q+fUoMlt03OqraQHWM11OMkIr+LNFGtX0+pauep7XOmBtMAqfXJJcNWB0gOn7KkuXkddrI5IMuL0eLKHz9k1mBSUzF4Zmguf6nNCJxkW13mYpkldvI6aWA010RprnnocioUIxULUxBp/XBurJRQL7ZYgSVM0fC4ffpcfn+6zHut+fC6fs+zVvfh0ax+v7s1YlzF3+fBoHme7W3NLfS8hhBBCCCHagIRSHUVRrOF8rnyo+s7qNeXtbvUQaoqqQ4+fW3Wlyj+FFU9aw/rWvggb34KB50O/MxuvHdVZZYRXzbTbHi5o17+KVYFZDkaSzODKrmdl97iyg6vs0Mpt9dASO8Uwjfr6N4loRuAUiocIx8MZRZNNw/p/pCiZYZNbc+N3+Z2hc6LjJIwEVZEqqqPVVEXr5/a6mliNtT5STXWsmppoDdXRakKxUJvVPfLqXgKuAAFXgKA7SMBtPfa7/ATcqXnWsl/343en5qn1Pt2HW3NLeCmEEEIIIUQnJ6FUR9N9ULC/VW+pZjmYqeLozVEUKD4Mig617tj3wwNQ8yP8OBvWvQJD/hd6Hb9n3RlP0UDTgGaKvDcIrrZbwx3Ta12pdo2rVG8rPQB6MFWw3ZMWWHn2+tAqYSQaFGiui9VRE68hHA87w+4SRgITE9M0M2rNuDU3AVcAl+aSXiXtzA6YKiIVVEYqM6aqaJU1j1RRGU3NI5XUxmt36TV1VSfXk0vQHSTHnWNNnhyC7qCzzn6cPQVcAQLugAxPE0IIIYQQYi8jVwCdgapD3nArJKn+HoxYy+pEKQoU/QS6jYPSv8Pyh60Q5ptpsP4NGHUd5AzZ7c3vNHYUXNlDBY2YNVQwUQOxCuuxzRkimCrMrget4MoJrDz1AdYeELQkjWSDu4fZQ6vC8bAzBM8wDTCt+k12cWSv5iXXnduuBY73VqZpEoqF2BbexvbwdmeqiFQ4jysjlc68Olpt1d1qJQWFoDtInjePPI815XhyyPPkkevJdR7neHLIdVvLuZ5cctw5eHWvnAdCCCGEEEKIVpFQqrNQVMgZaAVTlUsgug083Vr+3N6/hB4/g7UvwconoXIx/Pcc2Od/YMjFVriyt7OHCjbXG8NIq23VaGiVKsiuuetrWumBzLBK9XS6mlb2LejtqS5eR1W0itpYrXMLenuInV3Dya25yXHnyNC63SiSiFBeV+5M28Lb2FZnBU/2YzuIiqefhy2U58kj35tPgbeAfG8++d588rx5zuN8T359AOXNI8edI/+vhRBCCCGEEO1GQqnOxt/LmlcusgIRd0HLn6t5YeAk6HkcLLsHtvzHqje1+T0YeS2UTNwdLd6zqHZo1URtLyOR6mkVs+4mGNmaOTxQc4HiToVWAXDlWP9fVE99YLUbe1nFkrGMW9vXRGuoilQ5PaHiSSvYUBTFueNXjjuHblo3GWLXhuLJOOV15ZTVlbG1divldeVsrd1KWV0Z2+q2UVZXRlldGaFYqFXHDbgCFPoKM6YCbwEFvgIKvYUU+Aqc5VxPrgyHE0IIIYQQQnRqcsXSGfl7WUFH5RIr+GjtXfV8PWDMTCibD9/PhLr18PXVVpH0EVe3vAeWaMgJrfwNt5mG1avKiFuhVSIE4Q3WsEH7uYorrZdVjlWIPT2sUj3N9+RKMUyDcDxMXbyOcCJMKBqiMlpJXazOCp+MOJigqZpzK/qAO4BLdckQq10US8bYWruVzaHNbKndwtbarc6yHTxtD29v8fE8mocifxHd/N3o5utmPfZ1o5u/G4W+QuuxrxsFvgK8ehe6iYEQQgghhBBC7ICEUp1VoC9gQMViq1fNjoqfN6Z4AhS+BCsehzXPweZ5sO0LGHEl9PxFpxpetkdQVCtY0pqoaeX0sopbveAiW6wgy36u6k4VYE/VsnIFQfWQQKPOMKgzktQl405harsAuWEaaIqGR/fg1b0E3UFcmqv93vcexDRNKiIVbAptYlPNJid42hza7DxuaeDkUl0U+4spDhQ78yJ/kfXYbz0u8hcRdAclKBRCCCGEEELslSSU6sz8+4CRhMpvrB42ehNDypqjeWDYFKuX1Le3WnfpW3KTFVCNuqFlBdVF21CbqWdlJsGIk0iEqavbTF2sltpYiO3RENXxMBHTJG4AmguXKxevO48cd4BunhxUpwi7BFE7Ypom28PbKa0ppTRUyqaaTZTWlLIpZM03hzYTTUZ3eByP5qEkWEJJwJq6B7pnTCWBEvK9+RI2CSGEEEIIIUQzJJTqzBQFggMgGYHqZeDv3aKhXY3KGw4TnoXVz1g9p7Z+ZPXCGnW9FViJdmeYBnXxCHWJCLXxMNsj1VTFQoQTUaeotVvz4PMGKVRU3KqSKsKegPhWsOteZwwLDFhDAlVXKqhy73WBVSQRobSmlI3VG9lYs5EN1RvYWLORjdUbKQ2VEklEmn2+gkKRv4iewZ70yOlBj0APegR7OCFUj2AP8jx5EjgJIYQQQgghxC6SUKqzUxTIHQLJWqjbAP6+Oz/sTtVh0GTofoTVW6rmR1h0HfQ4GoZeCv4+bdt2kSGeTBCK1xGK11EVC7EtUk04HiFqxDBME7fmwqd5KPDm4NHcLTuoadYHVckoJGohnKjf3mRg5UkLrrpeYFUbq2V99Xo2VG9gffV61ldZjzfUbGBr7dZmn6ug0D3QnV45vegZ7GnNc3rSK2jNSwIlMvxRCCGEEEKI3SRpmGiq/IFXWCSU6gpUF+SNhEQdRDaDr+euHS9nMEx4BlY+Cauesu7Ot/k9KDgQep9g9ZzSGynkLVolkohSE68jFKtjW6QqrRdUAk1R8ekectx+irRdGOalKFbgRCMhVosDK0+q8Lo/s3eV5gal435ExJIx1letZ13VOtZWrWVd1TrWV1vL28Lbmn1uwBWgd25v+uT2oXdOb3rnWI975fSiR7AH7paGfkKITsEwDQzTIGkkiSVjHd0cIYQQQuwCTVX4w0tfs2Jr6+5E3VkcOayYq48d3tHN2GNIKNVV6AHI3xe2fWkVyXYX7NrxVBcM+T10PxyWPwrln0HFV9b0/SwoOcoKqArHWkW4xQ7VxSPUxGupidVRHq6kOl5LOBHFNA1cmgu/7qXYl4++s0MwW6vFgVUkdafA7MDKnXanQH/mUMA26mFl13haXbmatVVrWVO5hrVVa1lbuZZNoU0YdiH4RuR789kndx/65Pahb15f+ub2pU9uH/rk9pHhdUJ0IkkjaQVKZjIjXGpqHQpg4swVRUFRFFRUVFVFUzSC7mD7/SwVQgghRJtbsTXE0tLqjm7GThlUHOjoJuxR5BtdV+LpZvWYqvgaVO/OFT7PljcKDnrQuhPcxndh49tQtw5K37Embwn0Oh56/RKC/Xf99fYgkUSU6lgt1bFaysIVVMdqCafqFXl1Dz7dS4EnB7UzhnrNBlaGdadAM95MYOVKC6wCO6xhZZgGm0ObWVWxitWVq1lVsYq1lWtZXbmamlhNk80MuAL0y+vHPnn7NJiC7mAbfRhCiKaYppkRHCWMRKPBUlOBEgqoihUkqYqKpmrOY7fmxq26cWkuXJoLt+bGpbrQVM15jr1/+lxVVFyqS4bZCiGEEELsASSU6mr8fSBeDTU/gNYHFK1tjustgUHnw8BJ1t3+Sv8Gm/5lhVWrnrKmvFFWQNXzWHDnt83rdiHxZILqWIiaeB1bw9upjIYIxyOYmHh1D37dS6E3t3OGUK2hqK0cEripfrOisTlSw8rQVlbWlLOqZgurqtazunpDkwXGFRR65fSif35/+uf3p19eP/rl96N/Xn8KfYXS40mIXZQeLCWNZEaIZD+2t9v7o1j/NsEKlVRFRVd157GmavhcPlxqfZjk1t1OcKSrepOhUnq4tCfbsmULt99+O5999hkej4fjjz+eK664Ao/Hw2233cZzzz2Xsf+f//xnzjnnHAD+9re/cd9991FWVsZhhx3G9OnTKSwsBKz/P3fffTevvvoqhmFw+umnc9VVV6Gqe/bnKYQQQog9k4RSXY2iWDWh4tUQ3gL+Xrt+zMpqqA1j/VkbMLtD3gWQdx6EPoeKeVD1JVQttaZl90DRIdDrOKtouubd9TZ0QqZp1hclD1dTHqmgNh52ipIHXL7O2xNqd0nrYVUZrWFF5RZWVK1nRdUGVlatZ1XVRmqbCJ90RaNfTgkDc3vRP28fBuQPZEBBf/bJH4jHFeiyRdeFaE/pPZbSA6akmXTWmZhWsGRTyAyEFA1Vre+p5NE9uDQXHs1jhUlZoVJ2wGSHUxIYN800TaZOnUpubi4vvPACVVVV3HDDDaiqyrXXXsvKlSu58sorOeWUU5znBINW788lS5bwpz/9iVtuuYXhw4dz++23c/311/PYY48B8NRTT/G3v/2N2bNnk0gkuPrqq+nWrRuTJ0/ukPcqhBBCCLErJJTqijQP5A2H8s8hVrnzvZZqw7C+FNZvgljcChzSrmOs4ReFwBmgHg/qYuBLMNdD2cfWpHoh71AoORpKDgFf1y6QHkvGqYqFqIqG2FxXTnWslmgyjq5qBHQfJf5u6Gob9U7rIhJGgjXVm1heuY4fq9axonI9yyvXUx6pbHR/TdHol9ODQXl9GJTXh4F5vRmY05s+/gJ0Ja2nlZEAIlD9XdqQQFfqLoGBzNpVqscqui4XwWIPkx0opT9OGAkMMuuqKYqCrugZPY5cmoscPQe35sajefDoVriUHjDZwVL6ur0qUG9nq1atYtGiRXz66acUFRUBMHXqVO666y4nlJo8eTLFxcUNnvv888/zi1/8gpNPPhmAmTNnMnHiRNavX0/fvn159tlnmTp1KgcddBAAV111Fffff7+EUkIIIYTokiSU6qrcBVYwtf1r0HxWUNVS8QRs3AxrNkKoDgrzoHgH9akMAxL7QPJ4iK2H6AIwvgBjO1T825qWBcFzMBRNhG5jIRiAgA+0zh3i1MbDVEZrKA9XUR6pIBQPY5oGPt1LnjsHr7733KktFKvjh8q1LK9cxw+V6/ixYi2rqjcSNxKN7t87UMzg/L4MyuvD4Dxrvk+wBy6tFT9aMoYExqy7TKYNCbQCK72+hpXmT7s7oNsKshSXFOQXnYJpmlaolAqX7KlByJT6A4CqqvVBUSpsCrgCeHRPg4CpqUnby4LyrqC4uJgnnnjCCaRsoVCIUCjEli1b6N+/f6PPXbx4MRdeeKGz3LNnT3r16sXixYtxu91s2rSJgw8+2Nk+duxYNm7cyNatW+nevftueT9CCCGEELuLhFJdmb+vdSe+mlUQ6Nuyi/LqEPywGjaXQW4Qepe0rPeJqoJbBVzgGwIMAfNsiC+HyH8h/DmYNRB9Hza+DxtyQT0APOOg2xgoyLMCqoAf3B07RMswDWpidVRGa9hUW05VLERdIoKuagRdfnr6i9D2gtoc2yJVLKtY40w/VqxjY+3WRvcNuHwMyevLkPy+DMnfxwmgAq42KLa/o7sEmqmi60YcEuWpHlYpqg7ooOlWrz3ND7q34V0CFflRJ3aeHTQ5AVNW4GRioqBgmiaKavVkcnomaTo+lw+v7sWreZ2hcrqq41JdGeGSS3OhKZoMi9sD5ObmcvjhhzvLhmHw/PPP85Of/ISVK1eiKAqPPvooH330Efn5+Zx//vnOUL7GwqVu3bqxefNmysrKADK228HX5s2bJZQSQgghRJcjV2pdmaJCzjCIVUG0zCpW3pwt5fDDKqt3VK8S0HYxeFEUcA+1ppxzIfYdRD6DyJdANZgfQeQj2JgH60dbIZV3OOTlWr2zAn4rqPJ6dvuwrKSRpCoWoiJSzaa6cqqitcSMOB7NTY7LTzdv3h59IVgeruT7itV8t321E0KVhSsa3beHvxvDCvoxNL8fQ/P3YUj+PvQOFHfM56MoVi8oXNBYZxD7LoFGAuJVENtmBVnpz03vZaX7U2GVS3pZ7eXs4t/p4VJG0JQq9o1JRtBkTwFXwAmb3Jo7I2iyH9vLe/LPFtEys2bN4rvvvuPVV19l6dKlKIrCwIEDOeecc/jiiy/485//TDAY5OijjyYSieB2Z4b0brebWCxGJBJxltO3AcRisfZ7Q0IIIYQQbURCqa5O90HeCNj2BSRCoAcb7pNMwrpSWL7W6vHUawfh1c5QNPCMtqbcSRD9FiILIPoVmFWgfgJ8AtFc2DoaSkeCMhi8fiucKsyrH+7XRkP+kkaSymgN2yPVlNaVUx0LkTSS+HQvBd4cPNqeOSyvMlrDd9tX8932VSzdvorvt69utP6TgkK/nJ4ML+jHsIJ+DCvoz9D8fcj35LR/o3eW01OqkW1mMhVaJSAZsf59hBPU36c+u5eVz+plpbjrgyvVDq0kVOgq7GFyjQZNdgHw1J3l0ofN6ZruDJvzuXxO0W+X5soImuzeTRI0iZaaNWsWzzzzDPfeey9Dhw5lyJAhTJw4kfz8fACGDx/OmjVrePHFFzn66KPxeDwNAqZYLIbP58sIoDwej/MYwOdrg56rQgghhBDtTEKpPYG3u3VHvsql4PemLrZT4gn4cTWs2QB5OVbws7spLvCOsSYzDtFvIPJFKqCqBj4F7VNQ/GCOhppRsG0gmF5w6+D1Qn4u5OdYAZU/1ZuqBTKDqDKqoiEM0yDg8lHsLWhdraMuIJyIsKxiLUu3rWTp9lV8t30VG2vLGuynKgr9c3oxonAAIwr6M7xgAEPz98Hv2jPvnAhYQammAY2cO6ZphVZOL6tqiG0HM62otF3LSk0fGuixzm/VnVovPa3ai33HuYSRIJ6MZ4RNBkZ91qioGT2a/C4/PpcPn+7D6/I2CJjs0EmCJrE7TJ8+nRdffJFZs2Zx7LHHAlaxejuQsg0cOJDPPvsMgJKSEsrLyzO2l5eXU1xcTEmJ9UelsrIy+vTp4zwGGi2aLoQQQgjR2e1ZV+h7s+BAq75UZDP4rS+qxBOwbBWs3QDdu4G7A3oGKS7wHmhNZiI1xO9LiC4EoxriC4AF1gW+ewTo+0FiBJSGrd5dKlZI5fdl9qbye0G3Tl/DNKiKhtgWqWJjbZnTIyrg8lHiL0RX94zTPGkYrK7eyLfbV7J020q+3baSldUbMNJv/Z6yT04PRhUOZETBAEYWDmBYQT98+h4cQLWWoqTqTDXVyyq9llUyFVqlDQ2ErJ5WHtBSva0UPbOnleoCNOlt1Yz0gMkOnOJGnKSZdAqC20Po7EDJ7/Ljd/vxu/x4NE9Gjya35nYeyx3mREeZPXs2L730Evfccw/HHXecs/7+++/n66+/5umnn3bWLVu2jIEDBwKw//77s3DhQk499VQANm3axKZNm9h///0pKSmhV69eLFy40AmlFi5cSK9evaSelBBCCCG6pD3jal1YF765wyFWDbFKIADLVlrBTveiDi8uDlgX6579rMmcBPEVVjgVWQjJLRD7xpoA9L7gPwBc+0Gyj1UHa1sFGCa4XZgeFzV+ne1+2GDUUKnGSLg0At4A3X0Fe0QQVRmt4ZttK/hm2wq+3WYFUbWJSIP9uvsKGFU4iFHdBjKycCAjCvqT426HHnF7sh3VsmrQ0yoE8UorwHKOoVrnvKKl9bbypfW20lNzV32vrD1MeoHwuBF3Qqe4Ece00yYTq1eTVt9zKc+b5/Rusus1uVRXxmO545zozFauXMnDDz/MRRddxNixY53eTAATJ05kzpw5PPnkkxx99NF88sknvPnmmzz77LMA/OY3v+Hcc8/lgAMOYPTo0dx+++0ceeSR9O3b19n+//7f/6NHjx4A3H333VxwwQXt/yaFEEIIIdrAnncVtDdz50PeMNj8OaxZBxvKoaSTBFLZFLW+SHrwTEiWQuRriH5t3dEvsd6aeNsa5ucZDfn7EdaHsS2WpDS0ifLtlUTjYfyKm26eAG63D3xhyAlbw/28bvB4wNX5T3O7F9Ti8uUs2bacb8pXsC60ucF+Pt3DiIIBjO42mH27DWLfboMo9hV0QIv3cjvqaQVpNa2SVq+reCXEyrN6W2lYvahSPa4UTyq4ctcXaVez5p2kx5VdKNwOmdJ7OJmmad2RTlEy7jDn031083fD7/Lj1b0Ngia35pZhdGKP8O9//5tkMskjjzzCI488krHthx9+4P777+eBBx7g/vvvp3fv3tx9992MGTMGgDFjxnDrrbfywAMPUFVVxaGHHsr06dOd50+ePJlt27YxZcoUNE3j9NNPZ9KkSe359oQQQggh2oximo2M/emEQqEQY8eOZeHChQSDjRTzFpZoBP77OixfBPsM75ghe7vKqIHoEogutupRmaGMzVV0Y5van7A+lKg+CFNxQyIBsbg1JRJWjypdswI5jxtyAuDzWo/dbvC4rKLvHaQ2HuabbStYUr6cJaneULXxcIP9+uX0ZL9ug9m322BGdxvEwLw+6NJDZM9hpAIrM5FZmD39x7KiWr2tnB5XqaGCqietGHt6/au26XWVPpwuvZeTgVV3yy4Ubk8ezUPAHbCG0+ke3JrbGUaXfnc6IUTHku9TQgghOoNfPvAxS0urO7oZO+XE/XvywG8O7NLvYVSvXN6ZenhHNwOQnlJ7lkQCvl8GZQr07g9mDdCto1vVemoOpvcQqlwHUO6qoia6FH98Gd1ZS765hTy2kWdsg9hCjJhGrdqXam0QNZ4B1Pp6WxfvAIkkxOMQjkBVjXWhryhWLSq3K3WnP38qqHJZQZXbvVt6omyt287X5T+wqOxHFpcvZ0XVuga1oPy6l327DWK/bkMYXTSYfQsHkeeRC4Y9mt1TqrFi7DY7rCKZqm8VgnhVal36sXTrWGoqvFI8oKUCLCUtrFJ0TEUjjkk8aRA302o5mfXH1BTN6cHk0T0UugqdHk524OQETxI4CSGEEEIIIXaCXEXsKQwDfvgBVq2CPv2AYqheCola0LtOfaGoEac8XsPGaAVliRpiRpyg2oNc3wBCioZm1pGbXElucgW5yVW4zSpyjDXkGGsgDknchLR9qFEHUKP1p87by+ohZTMNqwB8LAHbq6Bsu3PXLtypUMqfKqxu97Jyuay7Amot66Fkmiarq0v5uuwHFpX/yOLyHyitLW+wX09/EfsXDWH/oqHsVzSEwXl90Tqw95bopJy7CNLMUEG7xpXV88pIRoknaognI6meTkniRgLDNDFVFVXR0TU3Ls2DS/eR587F78kn4CnArftxu3y49QBu3YfbFcCl+1MBmhBCCCGEEEK0HQml9gSmCcuXw48/QkmJVUcJD/j7Q81y0FL1aTop0zSpSNSyNV7NxmgF1ckwbkUnX/fjdWW2O6n4qdBHU6GPBtPEY24nJ7mS3OQqcpKr0QmTl1xBXnKFE1LVqn2p0foR0vpRq/bGdLsbDms0UmFVPAHbKmHrtvqwStetUMrrtQIrr8da53GBSyehqfxQtZ6vy3/g67JlLCr7kapY5pBDVVEYmt+P/YuGcEDRMPYvGkJ3f+Fu/VzFnitpGFYdJyORmpKpWk7WdkUBt+rC5c7HpbrI0z0EdB9+3YNbVXCjWHPFmlSM1LDBCohXQBxr2KB9h0FVSw0V9NbfZTB96KCaXrhd71S1r4QQQgghhBCdl4RSe4LVq2HZMigstIITm6+nNdQnUgreHp3uItHuFbU+up3yeA1J0yBH89LHXdCy27grClGlG1G1G+WucWAa+Iyt5BiryUmuJphci06YXGMlucZKiIOJSp3ak5C6DyFtH2rVvsTVXKu+lMdtTekMwxoWGU9AdQi2VxIzEiyNbear+Ca+jm1kSaSUOjOW8TSP6mJ04SAOKB7KAd2HM7rbYAIuXxt+emJPljCSJIwEMSd0ShBP2kPrTFRFtUInTcejuenm9RFw+fBqHtyaC4/mwq26cGsuXDtbODy7UHsyCom6+vpXjda9Sr/boLu+/pXmzRg+mHHHQWdoofQSFEIIIYQQYm8joVRXt2EDfPcd5OZCdsFSVYNgf0jWQmw7eDq+vpRpmlQl69gSq2JDWq+oQj2AR93F3lyKSljrQVjrwVbXBDANvGYZOck1BJNrCRprcZs1BIyNBIyNlCTmAxBT8gipfajV+lCr9qFO7WkVTwdQVSK6wjfRzXwVXsNXtWv4tnYDUTOznk+O6uEAd2/GuHsxxt2L4Z4SXC4PJHQod0FoqzWM0KVnTnrLhwWKPUcirYdTLHXXuoSRxMQKejRVw6XouDQdv+4h4MonqPvx6G7cqhVEuVPBk0vbTT/G04cN7oiZ6mll1A8hJFEDZmX9uoxjK/UBlpLqiaW40npjeVI9PLWs4Cor+JIeWUIIIYQQQnRpEkp1ZVu2wLffWsP1cnMb30f3QnAQVH8P8WpwNbHfbpYwk06tqC3xamJGnBzN13ivqGQSLRxDjcZRY3HUaBwlFkeNJ1ESSRR7nkyiGCYYJophNPq6pqqS0PxUqKPYru6LrkXwqOV41TJ86lY8egVuVxWFrioKXUvBAyEdPkjm85+wl0/CMb4OV5IwM49fqAcYE+jPmGA/xgb7M8jbPfN92D2sEkmr0HpNLSQNSIUOaJp1d0BdA7cHvG5rWGB2YOWS0KoratDTKVXXyf7/r6s6LlVHVzVyXH4CLqunk0dzpXo6uVM9nfSuUUBcUUFxQ0s7O5lGRg0sjCQYYUiE6teZ4Px7sV4kFV6pgB1mqakwK9UrS3VbYZYTYKWFWOl3MHSWpXeWEEIIIYQQHakLXO2IRm3bBkuWWENoCndQm8iTD8EBUP2D1RNBa6dhZIZB3fZtVGzdyLatpcQqtuOtjjC6NoE3FEWrCaPXRtBqw2i1EbS6KFo4ihpL7PjYbajWBf/tCx/0t6bPe0NCq8zYp1cdHFGmcEiVh0Nqc+hPIbFALolAjKR/HcnAVpIBL8mAl0TQRzLoIxnwYvrdjb0kJJNWYJVIQF0dVNfUh1aKYk2aVh9O+TxWaOV2Wct2oGUHV1IgvV1ZNZ0STl2nmGHdwc5MBSmaquFWXeiqRlD3EfAVEnT7G/Ry8mgutL2xgLiipgKhVvSOzCjmnkzrnZUeZqXWN3g9u2eWCqj1gZQ9zNDppeVO66HV0kl6agkhhBCi/SQNE02V7x9izyGhVFdUXQ3ffAORCPTq1bLneEsgGYHQKvDqu1743DStGkuby6xpyzbrTnZl2zC3bscs34ZSUYPfMPADvXfmJVQVw+OyJreO6dIxXBqmrmFqGqauYqoqKAqmqkD2D2fDRLF7URkmStJASSYJmwk+7xbho5IYH/eM80VJknhWLtCnCo5cY01HrIVB20HBBCKpqaxF7yHpdZMM+kjk+Ejk+EkEfSRy/SRy/SRz/M7jeG4g9TiA6XHV97RKJiEWs3pbJRKpOj5KqgB7aniVHV550npbZQdXug6aKhfQLWSYhlPHKZ7W48lM1VFSFQWXquPWXHh1D0WufAK6D6/uceo5WeFTF+np1BXYwdLO/NrK6Jll1C8bMUiGs7abDZ+vKGk9q9KCLdWepwVbigs0V1ZwpTb+OD0gk4BLCCGEEC2gqQp/eOlrVmwN7XjnTujIYcVcfezwjm6G6ETkaqmrqa21ekhVVUHvVkQ9igL+vmBEoW4jeLunLop2oKoG1pXChs2wcTNs2AKlW2DTVqiLNP5SqQnAVCCREyBeELRCl7wAidyAE9IkA14rtAl6Sfq8GD43SZ8Hw+fGdLXN6Rk14nxTu54vQ6tZGFrDt3VbiGfVuClx5TE22J+DfPtwsKs3fRN+tMEJ1EiMZDjK2roKfHVb8IS34amtwF1XjVYXhTBQS/28FszUsmKCFomhRWK4y6ta3N6k1536nPzE8wIk8oLE8wPWuryAtS4/QDzgI+HWrIvocARCdVaIlR5cqao1OUMF3VZvK68nLbBKbVPThhTu4RfHhmkQSyac2k71oZMBKPV3r9Os4KnAk0vQbRUSzx5it9tqOom2szM9s9LZvbRIC7ScYCsBiXDDbWD9O3JCLiWzLU6vrfQpVVsrvRi8fYdDJS3Aygiz1MbXZR9bCCGEEHuMFVtDLC2t7uhm7JRBxYGOboLoZORqqiupq7MCqfJyK5BqbXCgahDob91FK1IO3uL6i5WKKlizAVZvsOZrS60wqnoHCXy3fIySIsLFuVQUeNhe4CHaLQd39+4o3QqI5wWskKMdxYwE39StZ2FoDV/WrObbug3EsgqTl7hyOTA4gIOCAxgb7E9vd0HGHcoSqckWYkCD19HMWnzGFvzGZnzGFnzGZnxGGSoJMLCCqhqssCoERkglURsgGfJi1LgwQypKjYlaE0erCqPX1KEmkk6Q5dlSscP3aiqKE/bFC4LE84NWiFVgBVnxVAAYz/GRCHghErN6YRlGZrkeLS28UlUruHK7wOMCl6u+R5YdWqUvd8IAK3t4XdxIZvR0Sg+dXKnQKeDypno6uTPuXufWdrFXoej6nF5au8gJt8y0XlnJrKGJ4Sb2cxpDxj9eRaE+gEo9RkkLppS0QMsOt+zC8WnzxsKsBsGW0vw+KJ3y54EQQgghhOi8JJTqKsJhK5DatAn69Nn5GkKaB3IGg/kjbFkD73wJ//gItjfTk6ekCPr2hF7doU8P6N0DenWnpluAzUqYddFtVCXqCGge8nU/uqLRnlWh4kaCpXUbUz2hVrOkdn2Du+MV6TkclFMfQvVxF2aEUDsjqQQIaQMJaQPrV5oGHrMCr7EVn2cLvtwyvGYZXqMclQRuarCSqkwxJYdaehONFhKvCZCs9mLUuKFaQauOolfV4qqqRa8MWfOqWvTqOhTTxJXa5lu3tdn2mqpCIjdAPD9YH2Cl5vG8APFcnxViBX0kvXGoMet7Xtk9PuxaV4pSH2C59FSA5a7veaVpVtCVPm/DIYR2IfH0Xk7pNZ1URXWG13k0N4Uen9PTyVpXHzq5VH2XzwUhWiQj3GqjsNM0sXpppQdZRto6u0dXvH5d+jZnXbMNrw+30oMpJ4RKD7/U+pCLVA9MZ7hi+nL6sXYwT38N57V06/eZEEIIIYTo0iSU6goiEesue6WlVg+pXb0bW3UEnv03vPo6RKLWOkWxQqf+faB/b2vet6c1eeu/+JumSUWilo2xCkpja6lLxsjVvPT1FDa8i95ukjCTLKsr5cvQar4MrWZR7ToiRjxjn256kAOD/Tko1RtqH0+39gkeFJWo0o2o2o0qRtSvd8KqMrxmOV6jHI9hzV3U4TZrcFNDjnsddMOaUuIEiKoFRJVCatV9iCoFxNR8okYuRsiFqypshVUVIVxVIfSKEK7KWlyVIWt9ZcgKsAwTV2qZNc2/DcOlW2FVYZB4fk59eFVg9b6Kp+pfxQNezEQSasNWgGWk9+BIzfXUBajdG8ulW72v3Lo1nNBeb/e80lQSpkFMNYgrEMcgYSZJGEnMVA8RTdVwKTouTcevewik1XRyp4VRMrxO7PEUBetuhLDbfqWnB1lO6NVE+GUmwIzU79vo80htU7LmGW8sbZY2BNEOpnQfFB4Mun/3vGchhBBCCNEu5Gqts4tGYelSWL/eCqT0XfhfVl4Ozz0Hr71mBV0Ag/eB/5kIEw4Dv7fJpyZNg/J4DRui29kcqyRhGuTrfoo8OTvfnhZKmEl+CG9mYWg1X9asZlHtWuqMWMY++ZqfscEBTm+o/p6iztX7JSOsyqSZYTzGNrzmNjzGNjzGdjzmdjzGdlzU4aIWl1FLkA2QWQoL060Q755DrCSfmJJHVM0nqhQSUgYQU/KIqbkk8YFhoFfVWaFURQ2uylr0yhoryKoIOWGVXhFCr4ugxhN4yirxlFXu8K0lAl6rZlh+kHhBjhVe5QdI5OcQz/NbAVaOn3iODwwToy5CwqghloiRMJLESRI3kiRN0yrEpai4dB1ddeHSXORoLgIuPwFfEI/Xj9vjxe22Jo/qRdft4YWp3hmdfGihEF1ORvDVjpxgy8wMtow4JGpTPcOEEEIIIURXJqFUZ1ZX1zaB1ObN8Mwz8Ne/WndyAxg5Ei66CMbvD9U/QrwSkrmgZf7VOW4k2BqvZl20nK2xGlQUCl1BvOruq7OTMJP8mAqhFobW8HVoLbVGNGOfXM2X0RNqoLe43XpqtbWk4qNO60MdfRpsU80IHqMCj1mRCqoqcJsVuI1KPGalNSTQrMZtpgodNnKNlsRFXMkl5sslHsgl1ieXuJJLVClKhVc5JJQgZmpYkRKNZ4RUVoiVFl5V1Dg9s9REEr02gl4bgQ3lzb5PU4FY0Es0P0As3y7YHsQsyEUtzEcrKEAtyEPPz0PPzbXqORkqmmFC0oC6JITiVk00s4K0cvrWnRfV1HBCe7igXSPLro/ldtX3zNJUK8TS0vbPGGqo7fwQWSFE21DSbpuhZK1P1nZEi4QQQgghRBuTUKqzqqmBb76xAqWdDaTWroVnn4V33oFEqsbSfvvB734HEybU9yLJGw51myBaBrFqcOUQUV1sjlWxNlJORaIOt6JR4srFtRtub5/eE+qrJkKooOplTLCfE0IN8ZV02RCqNQzFS1jrSZieDTeaBrpZi9usxGNW4TYrcRtVuE1rchlVuKhDI45mWj2xMJp+rQR+4krQmvJzrLpTA4MklCAhpRsJJUAEPxHTQxzTquFUW4e2rRpXVS2eylq8lXX4qsL4KuvwVtXhqarDU2nVwFIME09NBE9NBNZv2/GbzwlCQS7k50BBHuTnWlNBXmp9LuTlWI/9vvoC7kl7SkIkYQ0tNIz6+lgZd0NLD7SU+jDLDqdcrvqaWS7dmpx9tMxQq7F10lNLCCGEEEIIIZokoVRntH27FUhVVkLfvq3vsfHdd/D00/D++/UX4AcdZIVRY8c2vFDWA5A7GBI9CIXWs6l6OevCm6jCJOjOp5c7H60NA6C4keC7cClfhdbwVWgNi2vXNRiOZ4dQY4P9OSg4kCG+kjZtwx5BUUkoOSTIoY6+je9ixnGb1bhSvalcRmpu1mRMKkl06tDNOnzm1mbDKxOI4yOuBEh6Aph9cmCfXBQ1B0Xtjqrloal56FoeupYLag4YLqiugYpq2F5pzSuq0qa05coaK0SqCVnTuhZ8FroGeakAyw6q0pftACs/F3KDkBOo/3dlh1npgZZhQCwO4WjmNmc4EVhFmK2ZE2o54VZanaz0QMvlqi8Eb+/vhFppoZgTjKn1y0IIIYQQQgixh5FQqrPZtMkasheJWD2kWtrTIpmEjz+Gl16CL7+sX//Tn8Jvfwv779/kU03TpDJeQ2m0jA2xSuo0N7mBPuyTiKAkwxCvsoIr1b1TbylixPi2dgNf1a7l69AaltRuIGpmFibP0byMCfRnbLA/Y1M9oSSE2nWm4iKqdCNsFhA3kyQUw5qnpnjqdvNuInipI6CE8RPGb4bxKWF8hPGYtehmCM0MoZq1KJi4CeM2w2CWNxtg1XOBGrQCobwADAqCGrAmpReoQ0BJLeODkAJVCaiKQUXICq2qqqGyOhVgpS3XRSCRhG0V1tQSqppqSw7kpea5QcjNqX+cPQ/4Gg+HTDMt0EoVe7eXY6meWun7pAdb9r9vk/pQSlFSQZSSFVBp9eGWrtUHXPb29GLy6WGWmrVeenAJIYQQQgghOgkJpTqLeBxWroQVK6yhej0bGa7VmFAI3noLXn4ZNm601mkaHHccnHceDBrU5FMN06A8VsnGyFZKI+XEjTgFrlyKAv2sHcwkxGsgVgmJKuux5gLNC0rTAVVVoo5FtetYFFrLotq1fB/eRCKrIG2+5mdM0AqhDgz2Z7C3+14xHK+tmaZJ0r47nVkfOMWNJEm7QDAKqgK6ouNSVHQ08nQfPtWNX/XgUXVcqo5b0XArOi7Vmjf6/8NMglGTmqrTplBqXgNmKG1dCKvQVRyMCmtqKX9q6uW2AivFB6ofFD+o3UDpa61LuKFagWoTqg2oTkJVHKqjUBWBqjBU1UJVDVSFoLbOCogqU6FWS6mq1cMqN1jf2yo3aA0zzA2k5qn1OYH69f4mwqxs2T22GoRd0frAyzCsx2b9/+O0/0lpwZZqPdbS51khl542112Nh2NOz66ssCt7m4RdQgghhBBCiFaQUKozqKqCZcusUKmoCAKB5vc3TViyBN58E957r/5Oerm5cMopcMYZ0KNHk0+PGXHKohWsC2+mLFaBoigU6Dn4tKy77ykauPOtKRm27nYUq7DmhhVQmYqHDYkQi2vXsbh2HYtC61gdLWvwmsWuHA4M9GdMsD8HBvsxwFPcue6O18lkh03ZcxRrHwUFDQUXGjrgQiNP8eJTdXyKjtvUcKHiNlVcporbUHEpKmpSqQ89TBPMWOomV/bQtOzbs6dTADeoxaCW1IcRDXroKNakxIA6UOrqgyqzFozUZNam1telHqfmZuq8NmPWREWjhdwB8Kam7s01222FWMliCLmhRoeQBjUK1AA1JoRSoVZNHKrjUBO1pnDc+qyqaqypNVQFAn4IpsKqoD81D9TPnXX++uWg33qeayd+TJtmVoBlh1hNhFymWR+EpUptOeGWmdarKz3osv8fK0pWry6tfoiipoFLs4L2jGBLscKxpoKtjHMoq9eYEEIIIYQQYo8hoVRHSiSgtBR++MG6096OCppv3myFUG+9BatX168fOBB+/Ws4/njw+Zp8enU8xNZoBRsiW6iM1+BW3XR3F7aseLnmA81HRM/h+6rvWVK9giU1K1hSu46KZLjB7v08RYwJ9OOAYD/GBPrRy52/94ZQpmFd5BsGSSNJIpkkYSRImgnrsWmkllNDv7D21U0FHdWZgqoLr6LjVzy4FQ2XouKyezapLlyaC11RUVR7eJaSFgylXdjraXedUzXQU3epU+yhXdT3eEn/f2YXCbfDq2QC4knrPI4nrd5+yaQVcsTjmT1+kgqYfmtSilOde5T6EENLtSP9DniqCUoUzHBaWBWunzf6OGJNRmqdGcFJsuxwSwFyUlNLxYFQ1lQD1KYe12pQq9Svq00FXNFUOFRTa02bduL88bisoYMBX31QFfBb4bUdXvl91jq/N7UtfdkHXs+u92JKD7pMIy30MutDrUQSjFh90OXM03t1kTls0e7ppVIfVNl1uuxzMT0A09ICL+duialzWNMzAywlPehqJODa0XYhhBBCCCHEbiWhVEcwTdi6FVatgi1brIvLPn0a37esDP79b/jXv6zeUTavF44+Gk46yaoX1cQFZ9xIsD1eRWmkjC3R7YSTUXJ0P7283XdYs8k0TdZHtvBtzUq+qVnBtzUr+bF2nRWepHEpGiP8vdnf15MDfCXs7+1BvjsHFBfobrrGaWZfcJuAfaFN1kV11gV26qI7YRokUz2YkpgkSFuHaXU2UVRQQUVF13R0VUNXdLyaC6/qxufy4nP5cOmu1OTG5fLgcrnRU8tKY/WGsnupNFaPqL2HViWT9b1x7KLh6esSqeAqkbR6+UViEIlay4kERA0wkvVFxzGtHk6m3zq+PQwt/U556YGWHXQ5vXziqaAqUh9aOVM0tT5av+zM0yY9Ct4odIumwq0omcW0mujCFScVUpEKr7Kmuqy5/bgOsLPeaNyatrdiqGE2XQGfljbp4HeBL31yg98Nfg/47MmbmvtS633WpLlSRd01UHRAs3pWZsxT6zPWNfMzJ/vfmv3vLH3ZMCCeyPx3mP48zMygK2NoY1r4Rdq/E7JCL3ueEZqmzis9PQxT60MwpbnjqZnHtcMu598sje+zt4b4QgghhBBir9IV0oI9h2lCRQWsXQsbNlgXHj17ZvaOSiTg22/hv/+1pmXL6rcpCowZA8cea03BYKMvY5gGlfGaVL2oMqoTIVRFJV/Podhd0GTzymOVLK1Zxfeh1SytWcnS0CqqE7UN9uvmymO/3CHsnzOE/XKHMDzYH7fqsuoNJSPWUL94yBrmF6+1AgawLuAUF6h66oK1tRddLQiOSOu9Qdo2M+1iVUldrJrOOCXnItRUFJKKSRJIqiYJTJKYqceQ1EwMt3UxqWgqaC5U3YWuamiqhq678Kgu8l0efC4fXpcHl2YFTbqqW0GTrqNrVtCkuVx7Xo8MuxcLrtY9z+5ZlUhYAZUdZCUSaSFXqkdWNAGxGERj9fvF4vUhmGH3Okv7f4wCihc0fyM1kexgK6tmUnPMRCq0itUHVc5Qw1j9424xMONp2+zH8fr1xNOW49ZyMga1UahNQF3cmtca9YFVenjV2HIYSI2AJGFCTcKa2oKL+iGTntTU1OP0dW7Ao9ZPXg08WZOup8Kr9EBLrZ9j18XS6h/T1OPUsqLUPxcVTBVrDKySNleteTK1bG8zsuYmacvUH0tJPd9+LXu9vU1R6sM5Ne19qKn3pqQtY5+Deqo3Y2qu6dY6OxBz7taYFWY1FYal9zyz90vflv2c9BBNCCGEEEKI3UBCqfYQj0N5uRVEbd1qXWQXF4PHA9GoFUItWgSLF1vzmqyaNfvtZ/WK+tnPoHvjRXOSZpKqeIiKeDWbo9uoiNWQMBMEdT89PcUZvaJM06Q0WsaPoXX8WLuOH2rX8n1oNWWxhkWo3YqL4cF+7JszmNE5gxmdM4gST7fGh+IpKqje1IV/HiTiVt2aRBRiYYikgiozZg39sjuamHYPCvtiTLM3NBoc1V9cqVkXT0rqws26YDVUSKoKSV21gqbU9WYSSGI46+znKop18ajpOpqqo6kamu7CpWkEXR58uheP5sKtuXCpOrpq9XhyqTq6ouHSrLnmtF+0ih0EtbaGkmnWh1hOL6xEfU+rZHrAFbfuiBe3p3h98BVLH26YOg4KKGnnYWMFxFUN606COZk91LTdMAzMNIBEWnhlP05gBVuJ1OPU3IhZQ4PrIlAXtu4EWBeBuhiEo9ZUF7NqZtXFIRKHugREEhBOQCQJ4SREDGuyO4SlcjNaWV7LYtDsLRs1UuFVau5OW3alrXdlrXdnrU9/nD7Pfuyi9fk4pHpc7aQWdLJrETs4a+2kNLY+9XMVtfF9FbV+2Xmc9hw7VIP6EI6sfZ3nK40cKzvASz+WmvkcTMgZDMWH78KHJ4QQYm+UNEw0VXojC9GZtGsoFY1GueWWW/jXv/6F1+vlggsu4IILLmjPJrSfZNIqYF5RYYVR27dbF4dVVVY9qB9/tKaVK60L43S5uTB+PBx6KPzkJ1bx80bUJSPUJGqpjNewObqN6ngtCZL4VS/d3HmoisrmSDnf1axmdd1GVodLWV23kZV1G6ltpA6UisIATw9G+vZhlG8fRnn2YYinJ7pVVdu6kKpKAlvrn2T/TLc7pahq/YWKmrrYUH3gDQDF1o6KFQuhJa0LZyUKpGr9KEb9X/M1FVPRSKo6hqaTVBSSqoqhqiSxezQZGIpCEgPTHjqTejpYd5zTFBVVVdEUFZeiEdTcuDUdt+bCq3msoXSqhq5oTtBkPbYnyW47NUWxgqydKQgOmXe4s0MtJ8RK1gdU9vZkMhVsxa1gK31oYtwOtrJqLjUYTmYPJcsKuZoq9u30XlHrh8xpO7ghgs2/cx9Lo+IJK9yKpAKtSATCESvoikRT6yOpwCuSGp6ZmqL2c1LDNaOpnm7RuLXO7t2YpL6nV3txKeBSwa1YPebcirVOT81dihVe6am5s0x9sKWnLeuAbqam1GMXoBmZ2zR7HyNrf1oWeilpdbraQmOHasPDt6lNOgy5DvJaUxxOCCHE3k5TFf7w0tes2Brq6KbslCOHFXP1scM7uhlCtKl2vdqeOXMm3377Lc888wylpaVce+219OrVi+OOO649m7F7JJNWL6gVK6ywadkyK4zatMmqG1VaCtVN1ITp1s2qC3XAAdZ82LAGBc+TZpK6ZIS6ZIRQoo6yaCVbotvZFC1je7yKqlgtVfFqymMVlEbKWR/ZyqbYNpJN9EjQ0Rjk6clQXx+GBvowItCfYYF++Nz+VG8jLbP4tdMjxL4wVuoDqGbWWaERJDExFBMDSJoGRqrmkmE/NmIYySgYcRQzgZmMgxFDMaKoRgyNJBomKiYaJi5FIahaPZY8uhuP7kFXPei6C011oatudM1tPdZc1mNFlYBJNLSzPbTSpQdbRqqnld3jyqmrlX4nvLShinawZReNt59jGtY6p8B4Mm24qpHqbdNIzSR7boe7jQ3nsgOxxnoe2p+JvS597tKtEKCtgwDTtD6HaKw+3IrFrLDKXheLp0KsVLAVSwVa0Vj9vnF7OTXPWI5b83jqeWZa2hI3rc+6rm3f1i6xh5a69My7GOpa1qSm3fFQrV92pS+rmY/T12lK2rq0x6qSWlbS9lPS1qetcyZSf1Cw9yO1H6l/Z2bqd4l9Z8dUzTgzVTcM0tYZ9f+PnMf2TSNiECvctV5mQggh9lortoZYWtrEdVknN6i4hX+UFKILabcr9Lq6Ol555RUef/xxRo0axahRo1i+fDkvvPBC5wmlwmGrJ1Ntbf1UU2NN1dXWvKLCKj5eXm71ftq61Zq2bbMujHakRw8YMsQKnoYOJTF4IDVFOYQSddTEQlTGqinf8G+2xSopj1WyPV7Nluh2tsQrqEiEqEyEqEzWUpmsJWzu+PU8ios+/h4MCPZhQO4+DMjrz4C8fvTPH4DL40td5KTqlSiKExKZpknSTDaY29vrpwRG6mLCtOs2GaAYdo8lBU3RUO3eSmioqopLdeHX/LhUF27djVt149bc1pC51BA4LdVzSVM1NNNEI4mOiWYaaIoBRmq4UiJVrNqIQjKaGsJkWDWuzCgkwtYFZ4a04VgZQ0/Sh46oadvT500MORF7r7YIttLZBbydIMusD7PsbenrnLviZa1LpupzJbKKxycSmfXWkiYYqXpTRlZIkL5skhbmKPUzMy0Is7c7Q3yVtJ6TpA3foj78Sg/R3C5rcgK11HY17XnZ25x1pP2bzHqt7M83kapPFounhViJ+uVYrH6Yp/PYDrbsx2nDQDPmWY/t8DH7cTxRfxfLZNbPKCP1/yTeRnXAOhtn+KuaVhsrPSzT6v/Qkb0dEwb1gl929JsQQgghhBC7qt1CqWXLlpFIJBgzZoyzbuzYsTz66KMYhoHawcVUw/98h1duPIXtehxDwZmSCiTVzMeJ1JRUIdEL4n0hnloXd2vEfG5r8ujEPDphj0rUpRLWTSJmgrD5DWHjS8J1MeJLdu1PvR7VTZGvG939RXT3F1Ps705xsDs9c3vTM6cXBb4CwAqMDNPATP23mYhVlDzrekdRFVRUK0RKBUnpyy7NhUu1ina7NbdVvFt1oWu6EyapipoRLDW1brcxUsMCzbS5kUwtNzIZceviz0zV5zFSz8FIC7dSf723Uzf79vbOX/Wh8XEuShPr7c1pF+4ZtV7St2ftYz9MX59xnOzXz36d9PWN7JuxMrstjb6JrMUm2tDkclOHbWy/5p7bzLaudiczRUkrGL8bZd+9Lj3wsh9D4/s4IXT6zQTSt5NZn8sJz9LWOb3AsgI3+9+T3SPMPq7dZjN9nb2ezDZkrKd+GagP0OxF0wpAvG7wuJ1dnNeDVM8eMrfZx7G3Z59nO1qXfhxn2GhafbRE2nI8azl9P3t7xp0vs/c3MmuvJbO2ZT+3sbtlGkbWkFZ7StT/f00amcNgm2IHgySxCpS10sYtDYe+70X2qnIIQgghhNijtVsoVVZWRkFBAW6321lXVFRENBqlsrKSwsLC9mpKo17Y9h8uPKEtvuA2URDFAJrp2KSrOl7Ni0/3EnAH8LsCBNwBAq4AuZ5ccj255HnzyPXkUugtpMBfQDdfNwJ6AFVVURQFBcUJj3RVR1VVdMUqyG2vs3seOaFT2mT3aEoPkbKXGy1w3tmoqTtc7SrTyBpKkirQ7AwjyR5mkhZSZQRWZtq6rMfpx3Qepx0vY1sjx8l4PfuKOWve2EV6Y/vZ+2avy/xQmnjcyLLZ1DGaen5Tz2lJW1qwe4t3UnawT/a/gR294I6O19hTsl5jh59lU6/pHKCVz08dwv5npDV2zGxNvcZO/HvMDracu2eSFU5lB1RkrTMa2d7IMck6VvpxMl4r7T1mB3P2sZ0gL+3fnbM9/TXS20nqRoFpr2HfPdLM/vzS2pe2mNG2Bv8004ZxN7lf9s+H7GM09nOisdfK+hnhhJCpzyFpZg5hbarXX6PrqB++OngAuD1NNHbPt0eXQxBCCCHEXqXdQqlwOJwRSAHOcqwlw952s+N/dSW/fa+MTVUb0FQdRbUCGFVND2bqe/3oiu4MQ9NSxbDt4WZ2DyJd1fFoHnwuH17di9/lx+vykuvOtYIn3Y/P5SPoDuLRPSgoTrjU2FxV1GYfq4raNUKjriL9blJdTXa41FxglbE+63GTIVUTF6g72rfR/Rt7ThPrmgxndhC67ExAtiv7tTpE2onX2G3Pb+yQu+GYO37RDnjNttYG76GxMKipeUu3ZTetuec0e4wWPM5+rR0dv6XLqg55xeyNukQ5BCGEEEKIFmq3UMrj8TQIn+xlr9fbXs1oUq+cXjx96rMd3Qwh2kb2cD3JKoUQYo/Q2cshCCH2XEnDRFPlS6UQom21WyhVUlJCRUUFiUQCPXVnubKyMrxeL7m5uTt8vpn6S2ko1DVv3ymEEEKIriUQCHS6Hsi7Wg5Bvk+13J5wAS7vofPYU97Hox+spLQqvOMdO6HRffI4Y2xf+ueqGDFXRzdnp5T4rJ/f8h461p7wHvrnqu32XWBH36faLZQaMWIEuq6zaNEiDjroIAAWLlzI6NGjW/RXvdraWgCOOOKI3dpOIYQQQgiwvqcEg8GObkaGXS2HIN+nhBB7q3eAOzu6EbtoFdDVx/bIe+gcVgFjZ7TPa+3o+1S7hVI+n4+TTz6ZadOmcccdd7B161bmzp3LjBkt+yS6d+/Ohx9+2Cn/aimEEEKIPU8gEOjoJjSwq+UQ5PuUEEIIIdrTjr5PtVsoBXD99dczbdo0fvvb3xIMBrnssss45phjWvRcVVXp0aPHbm6hEEIIIUTntavlEOT7lBBCCCE6E8U0O+S2SkIIIYQQopXC4TDjx49n7ty5TjmEhx56iPnz5/P88893cOuEEEIIIVpHbtEihBBCCNFFpJdDWLJkCfPmzWPu3Lmcd955Hd00IYQQQohWk55SQgghhBBdSDgcZtq0afzrX/8iGAwyefJkJk2a1NHNEkIIIYRoNQmlhBBCCCGEEEIIIUS7k+F7QgghhBBCCCGEEKLdSSglhBBCCCGEEEIIIdqdhFJCCCGEEEIIIYQQot1JKAVEo1FuuOEGDjroIA477DDmzp3b0U3qsrZs2cLUqVMZN24chx9+ODNmzCAajXZ0s7q0iy66iOuuu66jm9FlxWIxbrnlFg4++GAOOeQQ7rnnHqSU3s7ZtGkTF198MQceeCBHHXUUTz/9dEc3qUuJxWKccMIJLFiwwFm3fv16Jk2axAEHHMDxxx/PJ5980oEt7Doa+ywXLVrEmWeeyZgxYzj22GN55ZVXOrCFoqO89957DBs2LGOaOnVqRzdrjyM/z9pPY5/1bbfd1uA8f/755zuwlV1bc9cvcl63reY+azmv297atWuZPHkyY8aM4cgjj+SJJ55wtnWmc1vvsFfuRGbOnMm3337LM888Q2lpKddeey29evXiuOOO6+imdSmmaTJ16lRyc3N54YUXqKqq4oYbbkBVVa699tqObl6X9M477/Dhhx9yyimndHRTuqzbbruNBQsW8OSTT1JbW8vll19Or169OPPMMzu6aV3OH//4R3r16sXrr7/OihUruOqqq+jduzdHH310Rzet04tGo1x55ZUsX77cWWeaJpdeeilDhw7ltddeY968eUyZMoV3332XXr16dWBrO7fGPsuysjIuvPBCfvOb33DnnXeydOlSrr/+eoqLiznyyCM7rrGi3a1YsYKJEycyffp0Z53H4+nAFu155OdZ+2nsswZYuXIlV155Zcb3w2Aw2N7N2yM0d/1yzTXXyHndhnZ0rSjnddsyDIOLLrqI0aNH88Ybb7B27VquuOIKSkpKOOGEEzrVub3Xh1J1dXW88sorPP7444waNYpRo0axfPlyXnjhBQmlWmnVqlUsWrSITz/9lKKiIgCmTp3KXXfdJaHUTqisrGTmzJmMHj26o5vSZVVWVvLaa6/x1FNPsd9++wFwwQUXsHjxYgmlWqmqqopFixYxffp0+vfvT//+/Tn88MOZP3++hFI7sGLFCq688soGPfQ+++wz1q9fz0svvYTf72fQoEHMnz+f1157jcsuu6yDWtu5NfVZzps3j6KiIq644goA+vfvz4IFC3j77bcllNrLrFy5kqFDh1JcXNzRTdkjyc+z9tPUZw3WeT558mQ5z9tAc9cvP/3pT+W8bkM7ulaU87ptlZeXM2LECKZNm0YwGKR///5MmDCBhQsXUlRU1KnO7b1++N6yZctIJBKMGTPGWTd27FgWL16MYRgd2LKup7i4mCeeeML5IWMLhUId1KKu7a677uKkk05i8ODBHd2ULmvhwoUEg0HGjRvnrLvooouYMWNGB7aqa/J6vfh8Pl5//XXi8TirVq3iq6++YsSIER3dtE7v888/Z/z48bz88ssZ6xcvXszIkSPx+/3OurFjx7Jo0aJ2bmHX0dRnaQ8ByCa/f/Y+K1eupH///h3djD2W/DxrP0191qFQiC1btsh53kaau36R87ptNfdZy3nd9rp37859991HMBjENE0WLlzIF198wbhx4zrdub3X95QqKyujoKAAt9vtrCsqKiIajVJZWUlhYWEHtq5ryc3N5fDDD3eWDcPg+eef5yc/+UkHtqprmj9/Pl9++SVvv/0206ZN6+jmdFnr16+nd+/evPnmmzz66KPE43FOPfVU/vd//xdV3esz+VbxeDzcdNNNTJ8+nWeffZZkMsmpp57KGWec0dFN6/TOOuusRteXlZXRvXv3jHXdunVj8+bN7dGsLqmpz7JPnz706dPHWd62bRvvvPOO/CV7L2OaJqtXr+aTTz7hscceI5lMctxxxzF16tSM73li58nPs/bT1Ge9cuVKFEXh0Ucf5aOPPiI/P5/zzz9fSj3spOauX+S8blvNfdZyXu9eRx11FKWlpUycOJFjjz2WO+64o1Od23t9KBUOhxt8UbGXY7FYRzRpjzFr1iy+++47Xn311Y5uSpcSjUa5+eabuemmm/B6vR3dnC6trq6OtWvX8tJLLzFjxgzKysq46aab8Pl8XHDBBR3dvC5n5cqVTJw4kfPPP5/ly5czffp0JkyYwIknntjRTeuSmvr9I797dk0kEuGyyy6jqKiIX//61x3dHNGOSktLnX9X9913Hxs2bOC2224jEolw4403dnTz9mjy86z9rFq1CkVRGDhwIOeccw5ffPEFf/7znwkGgzKcvg2kX788/fTTcl7vRumf9dKlS+W83o0eeOABysvLmTZtGjNmzOh0P7P3+lDK4/E0+PDtZQkEdt6sWbN45plnuPfeexk6dGhHN6dLmT17Nvvuu2/GXxLEztF1nVAoxN13303v3r0B66LlxRdflFCqlebPn8+rr77Khx9+iNfrZfTo0WzZsoVHHnlEQqmd5PF4qKyszFgXi8Xkd88uqK2t5ZJLLmHNmjX85S9/wefzdXSTRDvq3bs3CxYsIC8vD0VRGDFiBIZhcPXVV3P99dejaVpHN3GPJT/P2s/JJ5/MxIkTyc/PB2D48OGsWbOGF198US7ed1H29Yuc17tP9mc9ZMgQOa93I7tGcTQa5aqrruK0004jHA5n7NOR5/ZeP36lpKSEiooKEomEs66srAyv10tubm4Htqzrmj59Ok899RSzZs3i2GOP7ejmdDnvvPMO8+bNY8yYMYwZM4a3336bt99+O6PumWiZ4uJiPB6PE0gBDBgwgE2bNnVgq7qmb7/9ln79+mX8sho5ciSlpaUd2KquraSkhPLy8ox15eXlDbpTi5YJhUJMnjyZ5cuX88wzz0hdir1Ufn4+iqL8f/buO76KKv//+Gtmbk0PJISmIGJCka6JiK6Au8IqNtRVUYqC4iqiKzYQFQVEZW0ICLjY609FvpZdd8XuiriygquC0ntJSL3JrXPn98fcO7k3BQKEFPg89zE7M2fKPfc6JHfeOeeMtX7iiSfi9/spKSlpxFod/eTnWcNRFMW6cY/q1KkTe/bsaZwKHSVqun+R6/rIqOmzluu6/hUUFLBs2bK4ss6dOxMMBsnMzGxS1/YxH0p17doVm80WN6jXypUr6dGjh4w5cwjmzp3LG2+8weOPP855553X2NVpll5++WXef/99li5dytKlSxk8eDCDBw9m6dKljV21ZqdXr174/X42bdpklW3cuDEupBJ106pVK7Zs2RLXsnTjxo1x4/iIg9OrVy9+/vlnfD6fVbZy5Up69erViLVqnsLhMBMmTGD79u28/PLLnHTSSY1dJdEIvvrqK/Ly8uL++rtmzRrS0tJkjNAjTH6eNZynnnqKMWPGxJWtXbuWTp06NU6FjgK13b/IdV3/avus5bquf9u3b2fChAlxwd5PP/1EixYt6NevX5O6to/51MXtdnPRRRcxbdo0fvzxR5YtW8Zzzz3HqFGjGrtqzc6GDRuYP38+1113Hf369SM/P9+aRN21a9eODh06WFNiYiKJiYl06NChsavW7HTq1ImBAwcyefJk1q5dy1dffcWiRYu48sorG7tqzc7gwYOx2+1MnTqVTZs28emnn7JgwQJGjhzZ2FVrtnJzc2nTpg2TJ09m3bp1LFq0iB9//JFLL720savW7Lz99tusWLGCGTNmkJKSYv3uqdrtQhzd+vTpg9PpZOrUqWzcuJEvvviCRx99lHHjxjV21Y568vOs4QwaNIj//Oc/LF68mK1bt/Laa6+xdOlSGZbgEO3v/kWu6/q1v89aruv616NHD7p3786UKVNYv349X3zxBbNnz+aGG25octe2YhiG0Siv3IR4vV6mTZvGv/71L5KSkhg7dmy1pFYc2KJFi3jsscdq3Pbrr782cG2OHnfffTcADz/8cCPXpHkqKytj+vTpfPzxx7jdbkaMGMFNN90U171D1M369euZOXMmP/74Iy1atOCqq65i9OjR8lkehJycHF566SXy8vIA2LJlC/fccw+rV6+mQ4cOTJkyhdNPP72Ra9k8xH6WY8eO5euvv662T25uLi+//HIj1E40lnXr1vHQQw+xatUqEhMTueKKK+Rn/hEiP88aTtXPetmyZcyZM4fNmzfTrl07/vKXv3DOOec0ci2bpwPdv8h1XX8O9FnLdV3/9uzZw/Tp01m+fDlut5urr76a8ePHoyhKk7q2JZQSQgghhBBCCCGEEA3umO++J4QQQgghhBBCCCEanoRSQgghhBBCCCGEEKLBSSglhBBCCCGEEEIIIRqchFJCCCGEEEIIIYQQosFJKCWEEEIIIYQQQgghGpyEUkIIIYQQQgghhBCiwUkoJYQQQgghhBBCCCEanIRSQogmLycnh0mTJlUrX7JkCYMHD26EGgkhhBBCCCGEOFwSSgkhmoUPPviA5cuXN3Y1hBBCCCGEEELUEwmlhBDNQrt27XjwwQcJBAKNXRUhhBBCCCGEEPVAQikhRLNw6623smfPHhYvXlzrPrt37+aWW24hNzeXvLw8ZsyYYYVYS5YsYeTIkcyZM4e8vDxOOeUUZs2ahWEY1vFvvPEGgwcPpk+fPowcOZJff/31iL8vIYQQQgghhDhWSSglhGgWsrKymDhxIgsWLGDbtm3VtgcCAUaPHo3X6+Xll1/mySef5PPPP+fRRx+19vnhhx/YtGkTr7/+Ovfeey8vvfQS33zzDQCffvopc+fO5d577+Xdd9+lX79+jBo1ipKSkgZ7j0IIIYQQQghxLJFQSgjRbIwcOZIOHTowc+bMatu++uor9uzZw+zZs8nJyaF///7cd999vP7665SXlwOg6zrTp0+nU6dOXHjhhXTp0oX//e9/APztb39j/PjxDBo0iI4dO3LrrbfSrl073nvvvQZ9j0IIIYQQQghxrLA1dgWEEKKuNE1j2rRpjBgxgmXLlsVt27BhAx07diQ1NdUq69u3L6FQiK1btwLQsmVLkpKSrO1JSUmEQiHr+NmzZ/P4449b2/1+P5s3bz6C70gIIYQQQgghjl0SSgkhmpW+fftyySWXMHPmTMaNG2eVO53Oavvquh43dzgc1faJjiml6zpTpkyhf//+cdtjQywhhBBCCCGEEPVHuu8JIZqd22+/nYqKirhBz0844QQ2b95McXGxVbZq1SpsNhvHH3/8Ac95wgknsHv3bjp06GBNCxYsYNWqVUfgHQghhBBCCCGEkFBKCNHspKenc/vtt7Njxw6rbMCAARx33HHceeed/Prrr3z77bdMnz6dYcOGkZKScsBzXnPNNbz44ossXbqUrVu3Mnv2bP7xj39w4oknHsm3IoQQQgghhBDHLOm+J4Roli699FLeeecd9u7dC5jjTc2fP5/p06fzpz/9icTERM4//3xuu+22Op3v3HPPpaCggDlz5lBQUEDnzp155pln6Nix4xF8F0IIIYQQQghx7FKM6IAqQgghhBBCCCGEEEI0EOm+J4QQQgghhBBCCCEanIRSQgghhBBCCCGEEKLBSSglhBBCCCGEEEIIIRqchFJCCCGEEEIIIYQQosFJKCWEEEIIIYQQQgghGpyEUkIIIYQQQgghhBCiwUkoJYQQQgghhBBCCCEanIRSQgghhBBCCCGEEKLBSSglhBBCCCGEEEIIIRqchFJCCCGEEEIIIYQQosFJKCWEEEIIIYQQQgghGpyEUkIIIYQQQgghhBCiwUkoJYQQQgghhBBCCCEanIRSQgghhBBCCCGEEKLBSSglhBBCCCGEEEIIIRqchFJCCCGEEEIIIYQQosFJKCWEEEIIIYQQQgghGpyEUkIIIYQQQoijjmEYjV0F0QTJdSFE0yKhlBACgJEjRzJy5Mgj/jrbt28nJyeHJUuWHNRxK1asICcnhxUrVhyhmjUNgwcP5u67727sagghhGgmVq5cyc0338yAAQPo0aMHZ599NlOnTmXDhg2NXbU4Tz/9NDk5OQ32eitXruT6669vsNdrCn7++Weuu+46TjvtNPLy8rj22mv5+eef4/YxDIPFixdzzjnn0KNHD4YMGcKrr756UK/z008/0b1792rf5b755htycnKqTePHjz+o8999993VztG9e3fOOOMM7rjjDnbt2lXnc02fPp0nnngCgN27d3P99dezY8eOg6rPoarLd+tD+XdRl2M2btzI4MGDKS0tPahzRxUUFDBp0iTy8vLo168ft912G3v37j3gcd9//z0jRoygb9++DBw4kBkzZuDxeOL2Wb9+PePHj+fUU08lLy+Pu+66i/z8/EOqpzg62Bq7AkIIIYQQQoiDt2jRIh5//HHOOOMMpkyZQmZmJlu2bOH111/n4osvZtasWZx33nmNXc1G8dZbbzW5YO5I2rJlC1dffTUnn3wyM2fORFEUnnvuOUaMGMG7775Lp06dAHj00Ud5+eWXmThxIj169ODLL7/kwQcfxGazcfnllx/wdQKBAHfffTehUKjatjVr1pCUlMTixYvjylNSUg76/WRmZjJ37lxrPRQKsWnTJv7617/yww8/8MEHH+ByufZ7juXLl/Pxxx/zz3/+EzBDsy+++OKg63IkXXbZZZx55pn1ft5OnTpx9tlnM2PGDB599NGDOjYUCnHdddfh8XiYNm0aoVCIxx57jLFjx7JkyRLsdnuNx61bt45rrrmGfv368eSTT7Jnzx7++te/sn37dhYsWADAnj17GDVqFMcffzyzZ8/G6/XyxBNPcM011/Duu+/Wem5xdJNQSgghhBBCiGbms88+47HHHuPmm29mwoQJVnlubi4XXXQRkyZN4u677yY7O5uTTjqpEWsqGsLLL7+M2+1m4cKFJCQkAHDaaacxePBgXnnlFe677z62b9/OCy+8wL333suIESMA6N+/P7t27eLrr7+uUyj15JNPUlZWVuO2NWvWkJOTQ+/evQ/7/TgcjmrnOeWUU7Db7dx111188sknBwxcZ82axZgxY3C73YddnyOldevWtG7d+oic+/rrr2fgwIGMHj2a7t271/m4jz76iF9++YUPP/yQzp07A9C1a1eGDRvGP/7xDy644IIaj3v//fdRFIV58+aRmJgIgK7r3H///ezYsYN27drx1ltvUVZWxjPPPEN6ejoALVq0YNSoUXz77bdHJKATTZ903xNCHJR///vfjBgxgn79+pGXl8ekSZOqNaPeuHEjEyZMIDc3l1NPPZXx48fX+tdKwzCYPHkyPXv25Ouvv7bK33jjDYYMGULPnj25+uqr2blzZ7VjN2/ezMSJExkwYAC9e/dm5MiRrFy5EoDi4mK6devGCy+8YO2/a9cucnJyuOOOO6yycDhMXl4eCxcutLoW/uMf/2DixIn06dOH3Nxcpk6dSkVFxX4/l7179zJ58mTOOussevbsyaWXXsonn3wSt09OTg6vvvoq99xzD7m5ufTp04dbbrmFgoKCGs95ySWXcMUVV1QrHzNmDNdcc81+6yOEEOLoNnfuXDp16sRNN91UbZvdbufBBx9E0zSeffZZAK699lqGDx9ebd8bb7wx7ibz+++/5+qrr6ZXr17k5uZy1113UVhYaG1fsmQJ3bp146233mLAgAHk5uayfv16tm7dyg033EBeXh69evXi8ssvr7FVyueff84FF1xgdR1bunRp3Pa6/D71+/3MmzePoUOH0qNHD8455xwWLVpEOBwGzO5f7777Ljt27NjvkAFPP/00Q4cO5eOPP2bYsGH06NGDCy+8kB9++IFVq1Zx2WWX0bNnT4YNG8by5cvjjv3tt98YP348ffv2pW/fvtx0001s27Ytbp+1a9cyYcIETjvtNLp3786ZZ57JjBkz8Pl81j51+W6wZMmSAw5h0KlTJ6699lorkAJISEigdevWbN26FYBly5bhdDq59NJL44598sknefrpp2s9d9R///tfK+Cqydq1a+natesBz3M4evToAWB1wbv77rsZPXo0999/P3379uXcc89F13U+//xzfvvtNyu4WrJkCZMnTwbg7LPPtoZK0HWdV199lfPPP5+ePXsycOBA/vrXv+L3++Nety7ff2tiGAbPPvssAwcOpGfPnlx++eX8+OOP1vaauuItXryYs88+m549e3LFFVfw6aef1vjf/0D/ljIzMznttNNYuHChVVaXa+nrr7/mhBNOsAIpgM6dO3PiiSfut6WZ3+/HZrPFhYBpaWmA+b0cYMSIEbz22mtWIAVYraOqfubi2CGhlBCizpYuXcq1115LmzZtePzxx5k8eTI//PADl19+Ofv27QPMZrmXX345mzdvZtq0acyePZuCggJGjx5t/UKKNWPGDD744APmzp3LGWecAcArr7zC/fffz1lnncX8+fPp1asX9957b9xx69evZ/jw4Wzfvp2pU6fy17/+FUVRGD16NN999x1paWn07t2bb775xjom+oXy+++/t8pWr15NcXExAwcOtMruv/9+2rVrx/z58xk7dixvv/02zzzzTK2fS0FBAZdeeinff/89f/nLX3j66adp164dN910E++9917cvk888QThcJjHH3+cO++8k88++4yHHnqoxvNeeuml/PDDD2zZssUq27VrFytWrKjxxkIIIcSxobCwkJ9++olBgwahKEqN+6SlpXH66adbgc4FF1zAzz//HPc7pbS0lC+//JILL7wQgP/85z+MGTMGl8vFk08+yZQpU/juu+8YNWpUXJCi6zrPPfccM2fOZPLkyZxwwgmMHz8er9fLo48+yvz580lLS+PPf/5z3OsB3HfffYwZM4ZnnnmG1q1bc/fdd7N27Vqgbr9PDcPghhtu4G9/+xuXXXYZCxYsYOjQoTz55JPcf//9gBm0nXXWWWRmZvLmm2/G/Y6vavfu3Tz88MPccMMNPPXUU5SWljJx4kRuu+02LrvsMubNm4dhGPzlL3+xPoNNmzZxxRVXsG/fPh555BFmzpzJtm3buPLKK63vQ3v37uWqq67C6/Xy8MMP8+yzz3Leeefx8ssv89JLL8XV4UDfDQYOHMibb76539YuI0aMYNy4cXFlW7ZsYd26dVZLuTVr1tChQwf+85//cPHFF9O9e3cGDx7Mm2++Wet5o7xeL5MnT2b8+PE1jmfk9/vZtGkTO3bs4MILL+Tkk09m0KBBLF68uF4HFt+0aRMAxx9/vFX2/fffs2vXLubNm8ekSZPQNI333nuP3r17k5WVBZif4Z///GfADHRvvPFGwLweZ82axe9//3ueeeYZrrrqKl555RVuvPFGq951+f5bm5UrV/Lxxx9z7733Mnv2bPbu3cuf//znGrs/Ruv217/+lT/+8Y/Wd+Bbb721xn33928paujQoXz66aeUl5dbn8OBrqUNGzbQsWPHauXHH3+89fnX5JJLLgHMFmpFRUWsW7eOefPmkZ2dTZcuXQCzVVQ0WPT7/axatYoHH3yQ448/3roPEMcgQwghDMO4+uqrjauvvrrW7bquGwMGDDCuvfbauPItW7YY3bt3Nx555BHDMAzj4YcfNnr27Gns3bvX2mfXrl3GwIEDjc8//9zYtm2bkZ2dbbzzzjvGX//6V6N79+7GZ599Zu0bDoeN/v37G7feemvc69x3331Gdna28e233xqGYRi33HKLkZeXZ5SVlVn7BINBY8iQIcYll1xiGIZhLFy40Ojdu7cRCAQMwzCM22+/3bj44ouN7OxsY9u2bYZhGMZTTz1lDBo0yDAMw6rb7bffHvfaI0eONIYNG1brZ/Poo48a3bt3N7Zv3x5XPnr0aGPAgAGGruuGYRhGdna2ceWVV8btc/fddxu9e/e21gcNGmTcddddhmEYRmlpqdGzZ0/jqaeesrY/88wzRr9+/Qyv11trfYQQQhzdfvzxRyM7O9t45ZVX9rvfww8/bGRnZxvFxcVGeXm50bt3b2Pu3LnW9rfeesvo0qWLsXv3bsMwDOPyyy83hg0bZoRCIWufjRs3Gl27drVe65133jGys7ONpUuXWvvs3bvXyM7ONt577z2rrLS01HjooYeM3377zTAMw5gzZ46RnZ1tfPHFF9Y+W7ZsMbKzs40XX3zRMIy6/T79/PPPjezsbOODDz6I22fevHlGdna29Xp33XWX9fu9NjXVaeHChUZ2drbx1ltvWWUfffSRkZ2dbfzyyy+GYRjGbbfdZpx++ulx30GKioqMfv36GQ8//LBhGIbx1VdfGVdddVXcPoZhGMOGDYv7LlWX7waHwuv1GpdffrnRu3dv6/McN26ckZeXZ5x22mnGK6+8YnzzzTfG1KlTjezsbOONN97Y7/mmT59uXHTRRUYwGIz7LhcVvSbPOecc4x//+IfxzTffGNOnTzdycnKMxx9//KDqHv1vFwwGramoqMj48ssvjcGDBxuDBw+2vgfdddddRnZ2trFr1664c/Tv39+YMWNGXFn02o1+B1y3bp2RnZ1tLFy4MG6/pUuXGtnZ2cbnn39e5++/Nbn66quNnj17GkVFRVbZ//t//8/Izs421qxZYxhG5TVoGIZRXl5u9OzZ05g+fXrcee69996478B1+bcUtWbNGuu91NWQIUOMSZMmVSufNGmScc455+z32Ndee83o0qWLkZ2dbWRnZxuDBg0ydu7cWeO+55xzjpGdnW307NnT+PLLL+tcP3H0kZZSQog62bRpE/n5+QwbNiyu/Pjjj6dPnz589913gPkXod69e5OZmWnt07p1az777DPOOussq+zVV19l0aJFnHfeeXF/wdy4cSP79u1j0KBBca/zxz/+MW79u+++Y9CgQSQlJVllNpuN8847j59++ony8nLOOussKioqWL16NQDffvsto0ePxu1285///AeAL7/8stpfUKuOYdC6dev9dt/77rvv6NOnD+3atYsrv+CCC8jPz2fjxo37PbfX663xvMnJyZxzzjlxra3effddzj333AMO7imEEOLoZURacBxoUGBN06z9ExIS+P3vf8/f//53a/uHH35I//79ycrKwuv1snr1as466ywMwyAUChEKhTjuuOM48cQT+fe//x137thuWhkZGXTu3Jl7772Xu+66i/fff59wOMzkyZOrjWd1yimnWMvt27cHsJ4QVpffp9999x02m42hQ4dW2yd6joPVt2/fuPcC0KtXL6ss2gUpWs9vv/2W3NxcXC6X9TklJSVxyimnWC20zzjjDF555RWcTifr16/nk08+4ZlnnqGwsJBAIBD3+gfz3aAuPB4P48eP53//+x+zZ8+2Ps9gMEhRUREPPPAAV111Ff3792f69OmcccYZcYOKV7VixQrefPNNZs2ahc1W85DEHTt2ZNGiRbz++usMHTqU/v37M3XqVC699FIWL15c6zhUtdmxYwfdu3e3pry8PMaNG0fLli2ZN29e3PegtLS0uHGZKioq2Ldvn3V91SZ6rVQdm+q8885D0zRWrFhR5++/tencubN1/UDlNV/T57Fq1Sp8Pl+1a7vqa0ft799SVPS//fbt2/dbz1jGflq21dYyE8wHL0ybNo0rr7ySF154gSeeeILExETGjBlT41AV999/P4sXL6Z///7ccMMNfPXVV3Wuozi6yEDnQog6iXa9i35Zi5WRkcEvv/xi7XegLwFgjjtwxhln8MEHHzB69Gi6desGQElJCUBcX3MgLuSK7ldbXQzDwOPxkJOTQ5s2bfjmm29IT09n7969nH766fTt25fvvvuOs846i59//plbbrkl7hxVB8RUVXW/v6BLSko47rjjaqwLxH9BONhzX3rppbz33nt8//33aJrG5s2beeSRR2rdXwghxNEveqN5oEfbb9u2jcTEROum+MILL+S9995j7dq1ZGRksGLFCqubWGlpKeFwmGeffdYahyqW0+mMW48duyj6pLdnnnmGjz/+mKVLl2K32/n973/PAw88QGpqao3Hqar59/Ho78G6/D4tKSkhPT3dCtyiot8TDjb8AOL+wBW1v8Gxi4uL+fvf/x4X8EW1aNECwOqO9+qrr1JRUUGbNm3o2bNntc+xptc60HeD/dm1axfjx49n06ZNPPHEE/z+97+3tiUmJqIoStwfCQHOPPNMvv76awoKCqp9tyovL2fy5Mlcd911dO7cmVAoZI3dFQ6HCYVC2Gw2kpOTq50XzO5i0SchHswA6JmZmXFDJzgcDlq3bh13LcW+r1jRayD2WqtJ9Dtn1e+YNpuN9PR0ysrK6vz9tzZV6xC95qOfYazo2G3RayiqZcuWBzx31X9LUdFry+Px7LeesZKSkqzufrE8Hg/Jyck1HhMKhZg/fz7nn39+3JhjeXl5/P73v2fx4sXcddddccecfvrpgDkg/3nnncezzz4rA50foySUEkLUSfQLbU1/6cjPz7dCpOTk5LgBUaOWL19O+/btrb+w3HLLLYwaNYrzzjuPqVOn8tZbb6FpmnWeqn30q45HlZqaWmtdoDLUOuuss1i+fDktW7bkhBNOIDMzk7y8PP7f//t/fP3117hcLvLy8g7ik6guNTXVet391eVQ5Obmcvzxx/PRRx+hqiqdOnWql6faCCGEaL5atmxJ7969+ec//8ktt9xi3ZDG8ng8/Pvf/2bw4MFWWf/+/cnMzOQf//gHmZmZOJ1OzjnnHKAysBgzZkyNTzU70BPMsrKymDZtGvfffz9r167lo48+4tlnnyU9Pd0a6+lA6vL7NDU1laKiInRdjwum9u7da+1zpCUnJ3P66afX+NCRaEuiRYsW8cILL/DAAw9wzjnnWDfzVQcZr0+//vorY8eOxe/389xzz3HqqafGbe/QoQOGYRAMBuPCsej4RjW1wv7pp5/YsWMH8+bNY968eXHb7rnnHu655x5+/fVXfvnlF1atWsUVV1wRdz1Gx+GqGrQciMPhsMYeOljRa6Bqq6GqogFXfn5+XOu8aIuy9PT0On//rQ/R1l779u2jU6dOVnlN36vrKvoZHEw9TzjhBNasWVOtfOvWrfTs2bPGYwoLC/F6vXGtDgHr+/e6desAs5Wh3++PCzBtNhs5OTn89ttvda6jOLpI9z0hRJ1EA50PPvggrnzbtm2sWrXK+iV0yimnsHr16rhfoPv27WPcuHFxT+zIyMjA5XJx33338fPPP/P8888DZvPvNm3a8NFHH8W9zmeffRa3fuqpp/LZZ5/F/eVH13U+/PBDevTogcPhAMy/0P3vf//jyy+/JDc3FzD/IrN9+3beeOMNBgwYYO17qE499VR++OGHan+xfu+998jMzKRDhw6HfG5FURg+fDjLli3j008/5eKLLz6sugohhDg6TJgwgU2bNvH4449X2xZ9DLvP54sb/FrTNM4//3w+++wzPvroI37/+99brS2SkpLo1q0bGzdupEePHtZ00kkn8fTTT+/3aV0//PADp59+Oj/++COKotC1a1f+8pe/kJ2dXePTc2tTl9+nubm5hEKhat8Tol3d+/XrB1BjUFdfok8c7Nq1q/U5nXzyybzwwgt8/PHHgDmcQefOnbnkkkusQGrPnj389ttvNbaSOVy7du3immuuQVEUXn/99WqBFGAFAR9++GFcefTpbjW1GOvevTtvv/123BRtwTRhwgTefvttwHwa4QMPPFDtKYV///vfadeuXZ1a0dcXh8NBZmZmtafjVb0mot8Lq34eH374Ibqu069fvzp//60PXbp0ITk52bqGov71r38d8jl3794NQNu2bet8zBlnnMGGDRtYv369VbZ+/Xo2bNjAgAEDajymZcuWpKWlWU/BjiosLGTz5s1WC8j/+7//484774z7/u7xePjhhx9qHEBfHBukpZQQwrJ7925eeOGFauXZ2dmcfvrp3HbbbUyePJlJkyZxwQUXUFRUxNy5c0lNTbX+WjhmzBiWLl3KuHHjGD9+PHa73XoqyPnnn1+tWf1ZZ53F0KFDefrppxkyZAjHHXcct99+O5MmTWLq1KkMHTqUVatW8frrr8cdN2HCBL788ktGjRrF9ddfj91u55VXXmHbtm387W9/s/Y77bTTUFWVzz//3Pri3r17dxITE1m5ciUzZ8487M/tmmuu4b333mPMmDFMmDCBtLQ0li5dyrfffstDDz102F+Mhw8fbj2qOfqEJCGEEMe2M888k7vvvptHH32UNWvWcMkll9CqVSu2b9/O66+/zpo1a5g5c6b11KuoCy+8kOeeew5VVat107vtttu4/vrrrd/z0afsrV692npaWU26deuGy+Xizjvv5OabbyYjI4NvvvmGNWvWMGrUqDq/p7r8Pv3d735HXl4eU6dOZc+ePXTp0oXvvvuOZ599losvvth6jH1KSgoFBQV88cUXdO3alVatWh3Ep7t/N954I1dccQXjx4/nyiuvxOl08uabb7Js2TLmzJkDQM+ePZk/fz6LFi2id+/ebNmyhYULFxIIBA56vKjCwkK2bt1K586dawyOwHya8b59+3jggQfweDysWrXK2paUlETnzp3Jy8tj0KBBzJo1C6/Xy0knncTSpUv573//y/z58639t27dSmFhIb179yYpKalai6Xo+ETt2rWztg0ZMoS//e1v3HXXXdx66620atWKDz74gE8//ZQ5c+ZY34Viz30kDRgwgP/+979xZSkpKQB8/PHH/O53v6Nz585cfPHFzJkzB6/Xy6mnnsqaNWuYO3cueXl5nHnmmaiqWqfvv/UhKSmJcePGMWfOHNxuN7m5uXz33XfWd+BD+T65cuVK3G63Nf5UXa6lc889lwULFnDdddcxadIkAB577DGys7Pjxnj95ZdfcDgcdO7cGU3TuPnmm5k+fTqJiYn88Y9/pKioiIULF6JpGtdeey0A48aN46OPPuLPf/4zY8eOJRAI8Oyzz1JeXs7NN9980O9PHB0klBJCWLZu3cqsWbOqlV966aWcfvrpDB8+nMTERBYuXMhNN91EUlISZ555JrfddpvVH79Nmza89tprzJ49m7vvvhuHw0FeXh5PPPEEqampNY71MGXKFL7++mvuvfdeXnjhBYYNG4aqqsyfP5//+7//Izs7mwcffJDbbrvNOuakk07itddesx7NqygKPXv25KWXXoob+NHtdpOXlxfXUspms3HKKafUOMj5ocjMzOT111/nscceY8aMGQSDQbp06cL8+fM5++yzD/v8WVlZdOnShYyMDOvRxkIIIcQ111xDnz59ePHFF3nkkUcoLCwkMzOTAQMGMHPmTCugidWlSxeys7MpKiqif//+cdvOOOMMFi9ezNy5c5k4cSJ2u53u3bvz/PPP7zdEcDqdPPfcczz22GPMnDmT0tJSOnbsyIMPPsjw4cPr/H7q8vtUURQWLlzInDlzeOGFFygsLKR9+/bcdtttcQHB8OHD+eKLL7jpppuYOHEi119/fZ3rcSBdunTh1Vdf5YknnuDOO+/EMAyys7OZN2+eVc/x48dTVFTESy+9xLx582jTpg0XXnihVf/S0lIrJDmQzz//nMmTJ/PSSy/VOORAIBDg888/B6ixq2Rubi4vv/wyAE899RRz587l+eefp7CwkM6dOzN37ty4bp7z58/n3Xff5ddff63zZ+J2u3n++ed54oknmDNnDkVFRZx00knMnTs3blyrQzn3oRgyZAjvv/8+e/bssb475eXlcfrpp/PYY4+xfPlyFi1axMyZM+nQoQPvvPMOzz77LK1atWLUqFHceOONVghUl++/9WX8+PEYhsGbb77J4sWL6dWrF7fffjuzZs064BhZNYl+1412zTzQtQRmS7Pnn3+emTNncu+992K32xkwYACTJ0+OG+h+woQJtGvXzrq2rr76apKTk3n++edZsmQJ6enpnHLKKcydO9dqKXXiiSfy6quv8thjj3HnnXcSCoXIzc2t9eeVODYoxqGOoieEEKJB7Nmzh0GDBjFnzpy4L3ZCCCGEEM3NOeecc1hd0urCMAwuuOAChgwZwoQJE47oa9WXUCjEBx98QF5eHm3atLHKX331VWbMmMGKFSvqHGSC+SCEP/zhD7z99tvWA4WEaIqkpZQQQjRRa9as4ZNPPuGf//wnHTt2jPsrphBCCCFEc/N///d/cYN4HymKonDHHXcwZcoUxowZU2tXtabEZrPx7LPP8uKLL/LnP/+Z9PR0fvvtN5588kkuuuiigwqkAJ577jmGDh0qgZRo8qSllBBCNFGrVq1i7NixZGVl8fjjj1cbF0QIIYQQojnZuHEjrVu3PqSuaIfi/vvvJyUlxRobqanbtm0bjz/+OCtWrKC0tJS2bdtywQUXWOO01tWGDRsYN24c7777rvUEQSGaKgmlhBBCCCGEEEIIIUSDO3LPShVCCCGEEEIIIYQQohaHHEoFAgGGDRvGihUrrLJt27YxZswYevfuzbnnnsvXX38dd8w333zDsGHD6NWrF6NGjWLbtm2HXnMhhBBCCCGEEEII0WwdUijl9/u57bbbWLdunVVmGAY33XQTGRkZvPPOO1x44YVMmDCBnTt3ArBz505uuukmhg8fzttvv02LFi248cYbqWvvQcMw8Hg8dd5fCCGEEELEk+9TQgghhGhKDjqUWr9+PX/605/YunVrXPm3337Ltm3bePDBBznxxBMZP348vXv35p133gHgrbfe4uSTT+baa6/lpJNOYtasWezYsYPvvvuuTq9bXl5Ov379KC8vP9gqCyGEEEII5PuUEEIIIZqWgw6lvvvuO/Ly8njzzTfjylevXk23bt3inqTQr18/Vq1aZW0/5ZRTrG1ut5vu3btb24UQQgghhBBCCCHEscN2sAeMGDGixvL8/HxatWoVV9ayZUt2795dp+2NLlAC6xdBOAC2JLAnR6Y0cKSBIz2ynA6q1siVFUIIIYQQQgghhGjeDjqUqo3X68XhcMSVORwOAoFAnbY3uq3/D1bdWYcdFTOYcmaYkysL3G0hoT2420Hi8ZB8klku4ZUQQgghhBBCCCFEjeotlHI6nRQXF8eVBQIBXC6Xtb1qABUIBEhJSamvKhye4y6BsnVQ+hvo5RCqgFA5hDwQLDPnegVgQKDQnMp+q/18WkIkrDrODKmSsyGlCyR2BJsTFK3KZDNDrNj1aJkQQghxjKs6MLeiKI1UEyGEEEIc6/SwgaY27+8iTeU91FsolZWVxfr16+PKCgoKrC57WVlZFBQUVNvetWvX+qrC4XG2gD6PVi8Ph8wufeGAGVJ594B3O5RvAd8e8O015/4CCBSDP99c1ivAs96c9n5WeT7NZQZUyTmQmmPO7clgGKCoVYIq1QymNCeokUlzgmoDVFCUyDwyocQs16Ws8S9AIYQ4mhmGgYFR4xyocVvV46rudzDb97dvdLmm/aPbwuEwAGEi86rrRrjyPEbMa0XrE7NedT+rLpFt0XPHHm99jlXq5rQ56dumLy6b69D+wwghhBBCHAZNVbjljR9Yv9fT2FU5JJ1bJfHUFX0auxpAPYZSvXr1YtGiRfh8Pqt11MqVK+nXr5+1feXKldb+Xq+XX375hQkTJtRXFY4M1RYJgRLMsaUS2gF9zW3hEOjeSGuqUvDlR5ZLIFBkjlPl2wVlkXDKswl0HxT/aE7bABRI6QqZ/aFlnrmsKGDokSkEQZ+5HA5B5AagVlZQpWCFT0SDqJht0aAq2ioL1XyfVqstW0wwdoCQq6b1amGZBGBCiPpjGAZhI2wGJ0a4xvX9LcfuX9v26FwP64QJEw6HzXnM60W3RddjJ6geOkXrHvu/SN5S475V94/uF3sMBuaPWRTzOAUwzJZE0e21LUP1Y6PLVVsiKZjr0fKq67Xtt799D/Y8AHpYxxvyEtSDEkoJIYQQotGs3+vh552ljV2NZq/eQqnc3FzatGnD5MmTufHGG/nss8/48ccfmTVrFgCXXHIJixcvZtGiRQwaNIh58+bRvn178vLy6qsKDU+1gRoZEN3dxmwBFSo3AynvLrOLX+gkaPtHcxwqw4DyTVD0IxT/zwymKrZC6S/mtGGxOch6q99BmyFmSKUe5H8iwwDCVeZGJMyKrBvhysDLiN03HNk39tgDiGutFVmODcPiArCY0MvqrhgNwPYXaqmRbowHCsAk+BKiMdQUyNQ2RcOfuLKY4w0MQuEQoXAI3dAJh83gR0c352E9bt9oa5ww8es1batLkEP0x0h0ORLiKChxc+Cgy1RFrRbU1LQvUK08dv+qy8eagB6gyFfU2NUQQgghhBD1oN5CKU3TmD9/Pvfccw/Dhw+nQ4cOzJs3j7Zt2wLQvn17nn76aR566CHmzZtHnz59mDdv3tH1pVpRwJ5kTgntzVZTgULwbIaKnWbXu8QTzDGmjr/EPMZXAPu+hfxvYN8Ks5XVzr+bkz0VWp8N7c6H1JPrFrooCqBV3lgdaUZsmBU7rxKAhYOAr4YALDqHyj/bV6VUhltxgVdNwZfdDL5Uew2tvqq2/IpZr6ksbv0ouk7FMc1q3WOEzdCnhvXYstjyoB4kGA6ih3UzNArr5npkuxUuxbYcCkeCqEhZnJjVaOsdBSU+xIlZBqqVqYqKoir73a+m8EcIIYQQQgjR+A4rlPr111/j1jt06MArr7xS6/5nnXUWZ5111uG8ZPOhKGYLKnuyOeC5bw94tpgtqDSn+eQ+RQVXBrQbZk6Gbrag2vUv2L3MDLS2LTGnlBw47jKzBZXN3djvrpIV4BxB1Vp/VW0JFht8eeO3GTqVrb9qfRMx4VQk1Isddyu2m6MVeNkqAy9rjK+YEKta6FVb+RH+7MRRIxoU6YZep3kwHCSoBwmFQwTCAUJ6ZesjwzBqnUe7iFktiCKioU60tY+maPHBEAo21Ra3T+y2qq2EhDjaffzxx9WGKBgyZAhz5szhl19+4f777+e3336jc+fOPPDAA5x88snWfh988AFPPvkk+fn5nHHGGUyfPp0WLVoAZsvExx57jLfffptwOMyll17K7bffjqrK7xMhhBBCND/11lJK7IdqN1tOudqY4VTZOqjYZgZTtsTK/RQN0nubU5fboOi/sOPvsPtjKP0Vfp4Bvz4JmQMig6VnQ8pJ5nmOZke69Vdc2FW1tVe4srWXUTXwimkRZvX9qVr3KmNxxbbwUqNze5Wwyw6avTLMiguytP2EXJoEXU1cNFiKhkNWN7VImGS1PtKDBMIBAnqAoB7Er/utrms6Zpe22BZMVbuaRWmKZgVDqqKiqZoVHtmxW+vRuQRHQtSf9evXM2jQIKZPn26VOZ1OKioquP766zn//PN5+OGHef311xk/fjwff/wxCQkJ/Pjjj9xzzz088MADdOnShZkzZzJ58mQWLlwIwPPPP88HH3zA3LlzCYVC3HHHHbRs2ZKxY8c21lsVQgghhDhkEko1JFWDhLbmk/48m8GzwRwM3dWq+thRqg1a5ppTl1thx/uw9W3w7oBd/zSnKEeLSECVAymRJ/slHhcJNMQBWaHXEfi8rKArtoWXHhNqBSHkr9LlsZbWXYoSeUpj1acu7i/oclTOq3VjPEDgFd2uynVUk7ARtkKl2qaAHsAf8uPX/QRCAavrW9XxkSB+8OpoKyNN0dBUzVq2a3acitNajwZNQoimZ8OGDWRnZ5OZmRlX/vbbb+N0OrnzzjtRFIV77rmHL7/8ko8++ojhw4fzyiuv8Mc//pGLLroIgEcffZRBgwaxbds2jjvuOF566SUmTpzIKaecAsDtt9/OU089JaGUEEIIIZolCaUag+aC1C5mC6eydVCxA9xZZnlNHGlwwkjoeBUUrjS7+JWtg7LfoHyb2c1v37fmFPsaVlCVYz7VL6mTGVaIhnMkWi5VbdlVtVVXtaArdtsB6mk9rTH2yYpafEuu6FxzxG+vS8DVxMfoCoVD1rhJ0eW4gEn34w168Yf8lWMrGSFrHtfdLdK0z6ba4sIlu2bHpbisQCnaDU4IcXTZsGEDp59+erXy1atX069fv8pB6xWFvn37smrVKoYPH87q1au57rrrrP3btGlD27ZtWb16NQ6Hg127dnHqqada2/v168eOHTvYu3cvrVq1OvJvTAghhBCiHkko1ZhcGWBPMbvmedabT+izJ9e+v6JCy1PNKUr3Qdl6M6Aq/c08V9lvZnnxj+ZkHW83B1lPyYHUbpDaFZJOlKCquTkSLbtqCres9RCE/RCKbeVVU2uumC6MihKpX5V5tDw23FIjXRZVe+SJjLWFXIfWksswDGt8parzgB7AG/LiDXnxBX1mAGWEzPGXoiFTTOulaLikKZrZBU6z41bdcaGTEEIYhsGmTZv4+uuvWbhwIbquM3ToUCZOnEh+fj6dO3eO279ly5asW7cOoMZwqWXLluzevZv8/HyAuO0ZGWYX/t27d0soJYQQQohmR0KpxqY5IK07aG4oW2ve/B/MGFGaC9JONqcoQ4fyrVC61gypSteaU8gDpb+Y0/Z3zX0VuzkuVUokpErtZj4hsGp3QnF0q+8WXfsNuSKD0ofKY7bHhlzRcKuGkKtKS64wEAiHCRoQAAIGBA2DgAEVegiv7scbChIyDEKGTsgw0I0wRky3R5tqx6Y6sGkObKoDpy0RzWGGThIyCSEOxc6dO/F6vTgcDp588km2b9/OjBkz8Pl8Vnksh8NBIBAAwOfz1brd5/NZ67HbAOt4IYQQQojmRJKHpkBRIaUz2BKg+CfzCX3uNodxPg2STjCntn80ywzDHI+qdC2UrIHSNeY8VAYlv5jTtsjxqrOyNVVKV0jtDonHywDaou7qKeQK6iEC4SCBkJ+A7iegm4N/lwfKqQiaLZyC4RDBcICQrmOgo0TG69IUBbtqw6Zo2FUVt6JhU80pbgB6Yp+yGPukxeiTFbXKgedrfMqiWsO2aGs26ZYnxLGoXbt2rFixgtTUVBRFoWvXroTDYe644w5yc3OrBUiBQACXy+zC73Q6a9zudrvjAiin02ktA7jdTejJvEIIIYQQdSShVFOS0BY0JxStOvxgqipFMZ8AmNAeWv/eLIsGVSVrzNZT0bAqVF69658tsTKgSu1mzl1ZctMtDose1vHrQfx6gEA4iF8P4gv5KQ95KQ968etBgnqIYGTcpuhITWa4ZMNmS8Cl2khWbdjq2n0u7gmKerQmlYPPh0Ng+Mx9iOxTddD5WPsNuGyVIVe1gCu2O+P+Ai618rxCNANhI0xAD+AL+ayB/q15dLnKenT/aPDsjwmhow8KiC77Q346t+jMmcef2dhvdb/S0tLi1k888UT8fj+ZmZkUFBTEbSsoKLC63mVlZdW4PTMzk6ysLADy8/Np3769tQxUG1BdCCGEEKI5kFCqqXG2hPTeUPgDeHeDu/WRe63YoKrNH8wyIxzp+vdLZQuq0l/NoKrwe3OKcrQ0A6q07pASCascqUeuvqJZCuoh/HoAvx7AF5l7Al7KQ168IT/BsNkaygqdFAWbquFQbdhVGy5HghlA1ddT5qxWTHDYPwIPGHD5Kvchdiyu2up2MAGXDTTbQQZc0oLrWGYYBqFwyBxHLejFF/KZ46mFfHFTTWVVJ3/Ij0+PX48Nn460baXbCOrBI/46h+qrr77i9ttv5/PPP7daMK1Zs4a0tDT69evHs88+i2EYKIqCYRj897//5YYbbgCgV69erFy5kuHDhwOwa9cudu3aRa9evcjKyqJt27asXLnSCqVWrlxJ27ZtZTwpIYQQQjRLEko1Rc6WkN7LbDHl2wuuBvyiqaiQ1NGc2p5rloVD4NkYCal+NifPBgjsg/yvzCkqoX2kNVUkpErpUvtTBcVRQw/rVuDkCwXw6X48AS+lQQ9+PUhADxIIByMNjgxzkHDVhkOzk2h3kaYm11/o1JAaJeCKbDusFlyxXRQ1s+41dVGklqDLOrcEXEdKUA9SEazAG/JSHijHG/JSEaywyiqCFXiDXmu96nLcPCaE0q1rq2HYVBtOzYnT5rTmDs2BS3NZZQ6bA6fmxGVz4dAc5vaYZYdmbo/OVUUlIzEDu9Z0H9LRp08fnE4nU6dO5aabbmLbtm08+uijjBs3jqFDh/LYY48xc+ZMrrjiCt544w28Xi9//KPZ3f7KK69k5MiR9O7dmx49ejBz5kwGDhzIcccdZ23/61//SuvW5h+tHnvsMa699tpGe69CCCGEEIdDQqmmypUJaT0jwVSB+aS+xqLaICXbnI67yCzTfWYLqpJfoOQnc16xDSq2m9Ouf5r7KZr5hL9oUJXWXQZSb8YCehCf7o8ETwEqgl5KAuWUB70E9CD+cCAueHJodhyqjRRHEg5NBg7frwYNuGroorjfFlwxT1GsFnCp5gMTqo7FpUZDqxoCrbiWXdFz1/Ng+w3MMAz8uh9PwENFsILyQDnlwfLK9WC5VRYNlzwBjxUmxZZXBCsIho9sKyBN0XDb3bhsLty2+LnL7jLnkeDIKo9MTpszfj0SKFnzmADKdgR+1gf0AEW+ono/b31KSkpi8eLFPPTQQ1xyySUkJiZyxRVXMG7cOBRFYeHChdx///38v//3/8jJyWHRokUkJCQAZqD14IMPMmfOHEpKShgwYADTp0+3zj127Fj27dvHhAkT0DSNSy+9lDFjxjTSOxVCCCGEODyKYezvT+1Nh8fjoV+/fqxcuZKkpKTGrk7D8e6Coh9AdTX9rnGBksgA6j9Vtqry76u+n+YyW1DFjk/lbictLpqQgB7EG/Lj0/14Q348wQqK/WVma6hQkJARImwYqIqKU7PjUO04NTt21Y6mNt9gQdQgLuCKhFixAVe0LG4e3k/ARXzAZQVW0aAr0k1RtcWHXLFdD+NacVUprylAO+BbNKgIVlAWKMMT8BxwioZN5YHyuPUj0QrJqTlx290k2BJw293msj2hct1mrkeX48oi69HAKXa5KbcyOpBoKHXm8WeS7Exu7Oo0O8fs9ykhhBCinp035yt+3lna2NU4JN3bpvDhxKYxPqc0V2nq3G0g5IXi/5mDoDflrnCOVMg4zZzAvGn174Xin2OCqjWgl5stwIpWVR5rT40EVJEppXvjtg47RuhhHW/IjzcaPgXM8KlC9+EPBa3WGpqq4dQcOFU7iS43dtWGIiHisSGuBVc9MMIxQVc0wIoGXDqEg5Xl0ZBrv90UzTArGNYpDfgoC/ooDXopDfnwBL2URtY9QR9lQS+lwQo8QS9lwXLKAhV4AuWUBcsJG+F6eXsKCgn2BJIcSXHzREciifZEczk6d8SvR5fddrc1PxItjYQQQgghhGgq5Ntuc5DUEXSv2V0uoV3z6fqmKOYT+lpnQevBZpkRhvItlWNTlfwMpesgWAIFy80pytkqElJFnvqX0rXptxZrwvx6gIqgD2/IT3nQS3GgjLJAhfnkOz2IAaiKglNz4NIcJLkSsGvN5FoTzYeiRgKumscQ8+sBSvweSgMVFAc8lAY8lPg9lATKKQ14KA2UUxKZl0WX/eV462FwbU1RSba7SbK7SLK5I8vRKYEku5tEewJJjgSS7EmR5WQSHYkkOZNJcCSTYHejKvZImBfbbbGmll5VBqMXQgghhBDiGCN3nM2BoprjOekVULHDHEy8ubZSUVRIOsGc2g0zy8IBKFtX+bS/kp/Bs8lsZbV3L+z9vPJ4d7vKoColMpC6XbofxDLHtglQHvRSEWktUuQrpTzkxa8HCYXNLkYOzY5Lc5DiSMKp2aXlk6h3vlCAYn8pxX4PxYEyiv3mVBLwRJY9kWVzXhLpIno4kuwJpDgSSLYnkuJIJMmRQIo9kWRHAsmORGvZ3C+RJHtCZN2NS3NGGoRFuh9Gx9qq2j2xapdFDDDKwFcGvloqpkBl+BTbtVCNjL8V7X4Y030RLWZ8rprG5qoadtVSLoQQQgghRBMloVRzodrNMCZUYY4z5W5z9NxsqI7KgdCjQhWRgdR/joxTFRlI3bvDnHZ/XLlvwnFmK6rUrub8GAuqfCE/FSEf5UEvZYEKCv0lVIT8+EJ+wkYYRVFxRVo/pTgSpTuQOCSGYeAN+Sn0l1LkK6XIX0qhv5RifxlFvjKKIsvF/jKK/Ob6oQZMmqKS4kgkxZFEqiOJVGciqY4kUhxJpDjM5WRHIqnRfZyJJNvNgKl+xjTT9tua65DEjslljT0VjumeGO26GPuExRrG51Kosq7EB11WC6yY8rhxujTzfamRiZgQK64FV2yoFTvu18GN1yWEEEIIIcT+yN1pc2JLhLSTofhHKN9qdmWzpx6dNwa2BGjRx5yigqVQurayRVXpWvDujDz1bxvs/lflvgnHmeGUNeWAI63B30Z9C+ohykPeSABVTqGvFE/Iiy/kRzfCaIqKy+bEpTlIcyTLoONiv8JGmNJAOft8JRT6StjnK6XQV0Khv4RCX6k5RUKoQn8p/kMImWyqRpojmTRnMmnOJNKcyaQ6kiLryZHQKSlSZs4T7e6j70mNVqAD9fqr19AjIVVMC66qA9PHjdPFgQejh5pbdqFE3kdsqyyt5sCr6iD0sU9tjG0lpsS8hlLl9Y7G321CCCGEECKOhFLNjbMlZPQ3u/F5NpnhlDMd7CmNXbMjz54CLXPNKSpQXBlUla4xW1fFBVUxLapcrc1wypq6mONWNdEbH8MwrBZQnmAF+3wllAbK8Yb8BMMh1GgLKJtTAigRpzzoZZ+vhAJvMft8JezzmfOC6LK3hEK/GTod7BPjnJqDdGcyLZwppLnMebozhTRnMunOFNJdyaQ5ks25M4VEm0u6hh5JyhFo1QVVWnZVCbuIGZje8FcOXA91C7ygSguvOoRe1qRC2IBgOYRD9fuehRBCCCFEg5NQqjnSXJB8otmFr2IbeLZAYCs4W4Dt2Om2Bpitn2Kf+AeVQVXpWjOkKl1rfk6+3ea094vKfe1p5nhdyTmReTYkdmiUweRD4RCeSABV6i+nwFdMRdCHPxzAMAwcmgO3zUmGK00GID8GGYZBedBLvq+IfG8xBd5i8r1FFHiLKfBFpsiyN3Rwg36nOpJo4UqhhSuVFs4UWrpSreV0VwotXWbwlO5MwW1zSsh0LIhr2VXPrGAr9imMUHPoFYgJvSLbgn4I+cwHgJB+ZOoohBBCCCEahNzZNme2BLPFj7stlG+Diq3gKwRXhrntWFVTUBX0QNmvkZAqElSVb4ZgMez7zpyiVAcknQjJJ5khVcpJ5nI9t0bz6wE8AfPx9NHuUrGtoBJsTpIcbjK0NAkBjnJBPUS+r4i9FUXke4vY6y2MzIso8JohVL636KDGaEqwuchwpdHClUKGO42WrlRaRtdd0fVU0p0pEnKKhqVEWkcdagsvtQJCu+q5UkIIIYQQojHIncjRwJ4Mad0goR2UbwHvdvBHwinN1di1axrsSdCinzlF6X7wbIDS3yoDq7L15lMOS9eYUyxXFiR3hqSTzHlyZ0jsWOdWVX49QFmggrKA2QqqJOChIuhDN8I4VBvuSIggAcHRxa8H2FtRxB5vIXsrCtnrLWRPhTlFw6d9vpI6ny/ZnkCmO50MdxoZrjQy3GnmemQ5Gjgl2OXfvhBCCCGEEKJpk7vfo4kjFRw9IfE4M5yq2G52gXBmgOZs7No1PZrTfKJharfKMiNsjtdV9ltkWmcGVd6d4NtjTvn/rtxfsZnd/ZI7R1pXdYakTuBuiz8cskKofF8RJX4PFSE/4XAYp81Bos1FVkJLbOoR6iIjjjg9HKbAV8zuigJ2V+xjd/k+M3DyRpa9hRT7y+p0LrtqI9OdRqa7Ba3c6WS60815QjqZrnRaJZjBk8sm/5aFEEIIIYQQRwcJpY5GjnRzrCR3+0jLqR2AYracUh2NXbumTVHNUC/xOGh9dmV50GMGVJ71ZkhVtg7KNoBebra28myIO42uOvA5WlPhaEW5IwvV1Y6UhA6kutuiSUuoZsMX8rOrooCd5QXsKi9gT8U+M3yq2Meu8gLyvUXo1ng4tXNqDrISWpDlbkFWQgtaJbSgVSR8ykpoSSt3OmnOZOmmKYQQQgghhDimyN3x0UqJhFDOluCPtJzy7jJDF2cGqPbGrmHzYk+CFn3MKULXQ5R7NuMv/oVg6a+ono24vdtJCu5FCwdI9W0l1bc17jS66sDnbIvP3Q6fqy0+V1u8rrb4na3MVleiQZUHvewqL2BneT47ywvYHQmgdpcXsLOioE6tnDRFIyshndYJLclKaElWQovKZXcLshJakuJIlMBJCCGEEEIIIaqQu+CjnaKAq5UZRPnyzcG9vbvNcZCcLSWcOgiGYVAR8lEaKKfIV0q+rwhPwEsgnIw96TQS0weRYHNhU8Dp34vbux23bzsu3w7c3h04/bvQwgESvZtJ9G6OPzcaPmdWJKhqjd/Vxlx2tkG3JTbOGz4K+PUAu8r3RUKnvewoz2enxwygdpbnUxLwHPAciXY3bRIyaJPYktYJGbRJzKB1QksreMpwpaGpagO8GyGEEEIIIYQ4ukgodaxQVHBngSsTfHsj4dQeCacOIKiHKA14KA2Us6eikJKAB2/Ih6KoJNpctHCl4NCqf3Z+Vxv8rjYUc2ploaHj9O/B7duJKzp5d+Dy70QLB3D7d+L274QqY14Hbcn4nW3wuVrjc7bB78wygytnFsYx3h3TMAwK/aVs9+xhhyefHeV72eExw6cdnr3ke4sxMPZ7jlRHEm0SM2iTkEHbxEzaJLakTWImbRJa0iYxg2SHhIJCCCGEEEIIcSRIKHWsUVRwtwZnJvjzJZyqItoaqsTvYZ+vmHxvMZ5gBXo4jNvuJNHmpqUr9dC6Yikafldb/K62VV40jD1YVBlU+Xbh8u/C5duFI1iEPVSGPVRGUvlv1U4ZsLfA58zCHwmp/M4sfM4sAo5MwkfJkxdD4RC7yvex3bOH7Z695lRuLu/w7MWnB/Z7vNvmpG1iJm0TM2kXmbdNMpfbJGaSZHc30DsRQgghhBBCCBFLQqljlapVD6d8eyNjTrU8pgZED4VDlAbKKfF72FNhPi3NpwdQFYVEu/vIPyFPUQk6WhJ0tKQspUfcJlX34fTvxuXbHZnvwuU3l216BY5gIY5gIXjWVDttwJ6G39EKv7MVfmcWAWemtR6ypZhdO5uIgB5kZ3k+2zx72Fa2h22ePWz3mPNd5QX7HUxcVRSy3C1pm5RJ+8RWtEtqRbtI6NQusZUMIC6EEEIIIYQQTZSEUse6aDjlamWOOVWx1RwQHQWcLeAoaW1TlS/kp9hfRqGvlD0VhXhCFYR0HbfdSZLDTYaW1iSCjLDmwpvQEW9Cx/gNhoGmeyJh1R6c/j24IvPKwKoYR7CY5BpaWOmqg4AjE78jMxJWxcwdGUdkHKugHmJHeT7bPLvZWrabbWV72Oox57sr9u23m51Ts9MuEji1T2rFcUlZ1nLbhEzs8kRDIYQQQgghhGh25E5OmGLHnPIXQPk28O2GcMgMp2wJ+z/eMEDXQQ9DSDeXw2FzPVpuGOZkHhB9YbPFTjQAUhRQInNVBVUBTYssq2DTzEk7uJZLhmHgCVZQ7C8j31vEPl8J5UEviqKSZHfTyp2OTW1G/xwUBd2WTHlSMuVJJ1XbrIU8OP17rcDKGcjH6c/HEdiLI1BojmHl24Hbt6PG0+uqG78zg4DDnPyODAKOlpEpI9LSqvrg3mEjzJ6KQraU7WJrmRk+bY2EUAdq8ZRgc9E+KYvjklpxXHJWZNmcZ7rTUGt4PSGEEEIIIYQQzVczugsXDUJRI0/ry4RAEVRsg/LtULIDSISwC4IhCAYhEABfEHx+czkaPulhMCLhVDSEMjDDJgziGsQoSqQsplVSdNEwzFBK1cy5ooIWnTRwOMAVmRwOsNnAYTcnmw3dplJqmC2idlfso9hfhjfkx6nZSbInkJaUfNQGHbotiQpbEhWJnaptU8JBHIF9OAL5ZnAVyMfhLzDngQLsoVK0sJcE7zYSvNtqPH9BWOPncAprdBe/hjTW+XXW+7xs9nrwh0O11sulOTg+uTXHJWVF5q05PhJAHfJYXUIIIYQQQgghmiUJpUQlwwCfDyoqwOs1p5IgFBrg8YBnHfg9YHOBlmSGRdFWS9GgyGkzWzRZrZuUwxu7yDAqW1yFw5XLwRD4AlCkR8KvMCgQNMIUG34KFT+7qaBMCRJ0argTkkhxp9DKmQCKHXQNNAOO4FBRTZWh2s2B0V2tKathu6r7cAT2ofj2sqd0E1tLtrHJs5eN5cVs8FbwWyBEga4DRTWe3w50skO2Q+FEt5tO7hSOT8rg+KQsWiS2IeRoQdCRTsCeTtCeCor8GBJCCCGEEEKIY5HcDR7LfD4zbCovh9JSKCoygyifzwyDFAXsdnC6ITMHsk4CvRh8eyBYZoZS9uQjOyi6Eum+t5/uev5wkKJQOfuCHvYESvCEKggHdRLRyDCc2CsUKC0Ho7yy9ZXNBvZIyyqXE9wucz1aZtPM964enS2pYpUFytlStovNpbvYXLbTnJfuYptnD7qh13pcliuZTgkpnOh0ke1U6WIP01X1cqJSilv3YDaJqzCn0G4o/gmK489hoBCyJRO0pxO0pxG0p1lhVXQ9ZEsjaE/FOMafDCmEEEIIIYQQRxsJpY4Vum6GT9EAat8+M5Dyes2gRtPA5YKEBEhP308IlGQOjB4oMsOpQBGEdbAngZbQYE90q9D9FIcqyA+Wkh8sozzsBxSSVCdZznRsrv00gQqHIRQyW1h5/VBWbra+ivYrtNnMll82m9kt0O00uwhGuwfabZGxrWwHPbZVYzEMgz3eQjaX7oxMu9hUZi7v85XUepzZ3a4NHVPa0CHZnDomt+H45NYk2KsPgu8HfgGUcAB7sBhHoBB7sDAyL8IRLDLngSLswWIUdOyhUuyhUvBu2e97CGmJBG2phOypZmhlSyVkTyFoM9dDthSC9hRCthSMY+jpkUIIIYQQQgjRXEkodbTSdTN0KiszW0Dt22d2ywsEzODI5QK3G9LSDj5YUW3mgOjODAiWmgOj+/PNgdE1p9l6Sqn/Vi3luo+iUAV7AyXkh8qo0APYUEnWXLS1H8RA2Kpqhk215RZ6pEtgKATlFVBaZpZFhr+qDK0i41o5HWZLK1skrLLbKlti2bQGC+oAQuEQ2z172VS605o2l+5kc9lOvCF/rcdlutPpGBM+dUxpS8eUtrRypx/SuFuG6iDgbEXA2Wo/O4WxhcqwB4uxB4si8ypTqAR7sBjVCGHTy7Hp5eDfecDX11W3FVAFbSmE7CmEbMmR9eTIsrkesiVJiCWEEEIIIYQQjUBCqaOFYcR3wysoMEOpQMAMnRITzQDK6ay/11QUcKSak7sdBIvN1lP+YnOMJ1ui+dS+QxxM3DAMPLqPolA5uwMlFOnlVIQCOFSNJM1FS0fSkRkYO9pd0FlTUGFAKPJEwVAIKrxQ6jFbXxmR7YoSH1w5nZXdBO2R1lXRllbREOsguwn6QgG2lO2KBE87rPBpq2c3oXDNXe40ReO4pCw6prThhJS2dEhua85T2pBkdx/0x3TYFJWQ3Wz55KVD7fsZBppejj1Ygi1Ugj1YEgmsSrEFS7CHSrAFS815qBTV0NHCXjS/F/x76lQVXXURsiXFhFXJ5rqWRMiWhB6ZRyddSyKsOhs0cBRCCCGEEEKIo42EUs2Z32+GUMXFsHev2SrK5zMDjoQEaNHCbMnTEGwusLU2n9wXLAV/odmCypcfGRA9CbTq3b2qMgyDskgQtStQTFGoHF84iEOxkaK5aOk8QkFUnSmRQKm20IrIgOy6GV6F9Ej3wMiTCYHK4CpmkHi73Tyf027+N4sEV+WE2FSxl00Vu9lUvptNZTvZWLKTneX5GHGPMazktjnpmNw20trJDKBOSGlL+8Qs7Foz/CevKOi2JHRbEtBu//saBppegS1UGgmxyszlUCm2UCm2YHS9LLKtDIUwWtiHFvDhDBTUuVphxRYJqBIr51oiuq3KPG45AV1LxFCb4X8HIRpA2AgTNgxrbhBGNwyMmPKg7mvsagohhBBCiHoid0bNTTAIu3eb3fEKCszWUYZhdsVLToaMjMZtvaGo4Egzp4T2ECwBXwEEi8zxpzSHGVCplS22DMOgVPdGgqgiikJe/OEALsVBsuailT2l0d7OIVFVc9rfvy4j8hTBkA66TqmnhE0Fe9kUKGBjYB+bQoVsDO1jj+6p9RSptgROSGzNCUmt6Zhshk+dUtvTKjkT1WY7NlvxKAq6zQyB/K42B97fCrE8Zmile6ywyhbyoIU85ja9LLKPB033oBo6qhHCESw2WwgepLDiIGSLhlQJ6Jo7EmpFlyvLzHlsWQJh1XXILRCFqC96OBwJjcIYhlEZJhENlcI1lBkYhoHZtFSx5taSYrbqVFBQFQVVUSvnqNhVG24tgQS7DaetHlv+CiGEEEKIRiGhVHMSCsHPP8OmTWbLmqQkaNOm6Q62rTlAyzTHnwqVm0/s8+dDsJRwqIBSDIoM2KVXUBSqIBAO4VbtpGpuXM0tiKqj4lA5G335bPTtZaMvn02R+b5Q7eFTS1sSnRwZnGBvwQm2FpxgS6eTmk664qpsNeZRwKtBfgFoRWBTwWaPtLyymddLtFtitKWXFtNS6xh4ymCNYkMssup2jGGghv1WQGXTy9FC5dh0T2ReXjnXy7GFzLmmV2DTKwBQjQCOYMAMaw+Rrrriwqqwte5GV12EI3NdcxPWXOiqG11zWfuFVac1l4Dr6FctJDqMAAkMFEVBQTEDJAVUVFQ1EiChoikqTs2OTbGhqSoO1Y6mmqGSTbWhKaoVOGmKFjdXFbXG7WqkjHCg8o8cQgghhGh29LCBph6Df0AXNZJQqrkIheCXX2DjRmjdun7HhmoItkTCmpsSzU2hdze7gtso8e4hECojwYB0exJOe2KkBVXz/gFlGAb7Qh4reNrsyzcDKH8+RaHyWo/LsqdygiuDTq5WdHK14gRXJic4M0mx1WW8p5ixrqJdB/0VUKpHxruK6eqnKGYYFe1CqMYM2u6IPHEwWh4NrSTEqqQohDUXAc0FZBzcsUYYTffGhFTlkfUKcwpVWdcrzPVwhVWuGuaYYVrYhxb2HVawFaWrjkhYZYZW5rLTnKtOwqqTsOYyg6zouuoirMWuV07msQ5Q5FfMoYhvdRQfGFmtkqgSLkWWo9FR1SAprtXRfgIkm6pFgqPKeXxIVLl8oGCpcbtaCyGEEKKp0lSFW974gfV7a//DfFM2MCeTO4Z0aexqHDXkjqE50HVYswbWr292gZRu6JQEPRQGStnlL6Ak6CFIiARbIunpJ+M0whCqMLv5hcohWGHeR6kusDmBJtoKDPPGcU+wNBI67WWTPxI++fIp1b21HtfOkW4GTpHQqZOrFR1dGSTVYcyt2sWMdXXgmpuhVViHsGF2IfQFoMJnXmuGYU6KUjmPhliaGpkiTxd02M2pamilaaAqEmRVpahWy6xDPkU4EAmofGjhClTdG1n3mgO8615U3QysKpdjy3yoYfN4BXOcMy0cQAsHsIdK6+udAhBWtEhQ5agyjywrjkiZI24fI7pubbdXliv2uGOMyHpjtvYyDMMKi3QjHDcOUmz5/lohxf5zA6zuajV1YVNVxWpxZNc0bIoNu6ph1+yoKGiqVmNQVNtcAiQhhBBCNLT1ez38vLN+v3s2lBMzD/27vKhOQqmmTtdh7VozkMrKahaBVCgcojjkoShQyk5/AaVBDyFDJ1Fz09KRikO1xx+gucHZEnQ/6N5IOFVidvcL66DazBZUmgNo+BtP3QizK1DMpmj4FGn1tNlXQHnYX+MxKgrtnS3o6MzkRFcmJ7ha0cmVSQdnBu5G73ISCbDqGvgZYdCNyhBL182nOnp9kUHdoy2xoje1MQO5K2plIKWqZoBlt5ktsjRbZYssVa0eZsWWCYuhOgipDkL21MM8kYFiBM3gKuxHDfsjwZYPVfdHyqIhlh8tso9qrfus48xjK5eVSFsd1dBR9QqIdFs8ksKKZgVUYdWOoUTCLMUeCbmiy3Z0xUZYic5t6NakmXPMeQgVXbURQiOEhq5oGJF9w4oWOVbDUO2gOlCwRVogxYdImqZarY6i3djMuYZWQ8sja65GjlertkySgFcIIYQQQhwdJJRqyoLBykCqVStwHU5LmiMrGA5RHCxjX6CY3f59lIbKCRthEjU3mY507HV52pjmNCdHGhitQfeZIVWw1Ayq/BWAAZp5A4gaCan0MKo/gOYLoPqCqL6AOfmD1qT5zXUlGDLLAiFzOToP6QRDQTY5KvgtwcdviX5+TQ7wa3KQdSkhfLVU36bDSUUKXQugW4FCl3zong85BQauUCEYhcCvNR8czXAUBUNRzCBHUTBUBVQFQ1XNdU2NlKnmsqZiRJbRYspsGoamYdhUwjabuR6d7Bpha9lmLtsjyw4bYbvNXLbbMBzx87DTXrmfw0bY6Txwi6xoWKVHAitdN69nn79y3cD876kAhlL5mdg0c0GLCatsNnNyRObR1lqRz6Uy3FLMpz1qMWGYtACpmaJgKGbAVa8MA8UIoYYDcaFV5XrsPBC/bsSuB63tSmRdsbYHUY0gqhGyXlY1dDB0s0tjIzFQQLWDEv0ZZTPDKjV+3dweMyn2yDZ7/DbFVr08WqZEylVb5fHRsqr7KLYqZZG5osm/DyGEEEII0agklGqqfD5zUPMtW8wWUk0wkPLrAYpDZRQEitnjL6QsZIZGSVoCWY4W2OoSROk6mqcCW1k5mqfcXPZUmMvlFWjlXnPu8aCVm2VqeQVahRfNa4ZPmj904NeJUeqEtRnmtCYD1mSZy+tbgF5LAwRnCLoUQNd86BaZuhZA50Jw6NHxmoyaD66NUbnQ3G4LDVWNBFR2wg47YactMrdjROY1TbrTUbnuqmHZbkNXVcJ2M2TDiHQ1DHjNVlpVW2YpxC/HdjNU1MpWVzbNbKEVXY7tUhjbUis22LJabCnS9fBgKAo6GkHVia7YMdTE/XZhq2k8pKrzWsdDMsBGCAc6TsLYDQO7EsZu6NjRsRFCM3RUI4gWCbI0I4QSNgMt1QhGwq4gSjiAEg6ZcyNoDqYdnfTocpXycIDYf/cKRqQsAHrt48c1KYpWJaiKLlctj65rVbZp8dsVLbJPtEyrcg61sjx2n+hxaDGvodV8nBEGR3pjf3JCCCGEEKIeSCjVFHk88L//wa5d0Lat+eS0JqJC91EcLCPfX0x+oJDykBcUhSTNTVtScJZ4sJXuw1ZSZk6lZdhKPOa81IOt1INWVo6tzIOttBytovaxlw6WoSiEXXZ0lx3daWNHuo21mQprWxj8mqbzW3KQ35IC7HYGaz1HUtjGieFkTiSVTqTSWUnnRC2dto4U1A42jE5aZQslVeU3Ta1s6aQqVZYjcVNMiyizopGbWCNyE2tgBi4YKGFzPCclEsCYcwMlHDYn3QxllFBkPaSj6OHIpJvlIT1mOYQa1M2ykG62CgvqVuswJRhCCeqowcqWY2owhBI7DwRRI9uilHAYzWe2TjtSDEWJC6x0l8NcdtkJOx3obgdhpyPy39xhhWK6KxKUOTT0mMBMj7b8skdbh0TDhMhytLWapmK11IoGW9HWWNFWW/ZIi61ogBUNxKL7Vg231JjzNMGWKXV9MpsZLkXGSSI6JlJlhFRTiGQ+nU1BU1QUzLGQ7KoNTdVwaDY0zHn0iWwHOx6SpjZy985wyAyrrCArGBNeBWO2RctC5twIVa5b24OR8qrroZjzh8DQ488b3Sdu/1D8cjgEkTHE4kRamFFLV+Qmy5YMF25u7FoIIYQQQojDJKFUU1NYaAZSRUXQvn3jjqej6xiFhVQU7KQ8fyee/B0E8vegFhWTXuKhfakPZ2k59uJSbMVlaL5Dv6nRE1yEkhLRkxLQo/PEBPREN6GkRMIJLnM9wY2e6EZ3uwgnuvE4VDZrpWw09rElsIvN3p1s8e5miy+finDtgUlLWxIdXRmc4MykY8yg45n25BoH/K2/6KwZC4fNACsaUvmDKP5IaOUPxsxDqP6Y7pOBoNmtMjr3B+O3+wNoseWR8EsxjCMWfIUddjPUcjniwi7dGQ29KoMu3Wkzwy+nzQy5HDazzGE3j7HbCbttGDZbZeAUG27FhlFW90KtMuSy26q01IrpdqhFj4uEWooKmjk8eViJmWoJkaLL8SFSpIooGBhmlKRghkeY4VFNT2azq7bIgNqRsZE0e41jH9UUHlUdG+mookZaDFGXp2Q2MiNcPagyguZ4cXEhll4l3KppXa+yrx6/r7VPlXVre7jKceEatlddj6l/So483VEIIYQQ4igg3+iaCl2Hbdvg11/NsXfatz8yLSoMA0pKYN8+cyooMOeFheZyYSHGvn0Y+wpQiktQDINEIBFoVYfTh20aodQUQqlJhFKTCaUkE0pJQk9JIhQz6clJhFIS0ZMSCSUlmK1OaqEbYXb5C9jq3c1W7y62eNeawdO+3ezx76v1OA2Vdq4MTnC1oqMzgw6OFE6wp9LRkUKyYsdsHaNWdi3Rmk6LtCZJVTGcKrrTjn4kX0cPRwIsc4wwzR+oHCfMFzTHDouUWeOI+QOo3gCaP4jq85vL0XHFvAHrHEo4MgB3wAzJKKm/LlaGqppBljs24HKgO23oTnMectrQHeY85LARtGvoTo2A04Zu1wi6bATtNgJOjaDTRsihYTg0IBJKRUIuRTUDHiXaskjVUG02VM2Gptlw2mzY7U5sNjs2zYbd5sKu2tE0G5qmRfbTrP2j59A0G6qmRfYzl1VVkyezHS0UFZToeHzNWDgAgaIm2epQCCGEEEIcHAmlmoLycvjtN9i8GVJSICPj4M/h91cPmqrOo1No/2MwRW59AbMLVTA1iVB6GqG0FEJpKQTTI/PIeigtmVBqCsHUZMKJ7kO6UQgbYfb6C9nq28M27x62+Xaz1buHrd7d7PDtJWjUXudUWxId3K3p4G5Dh4S2dIwst3dlVR9gPdpNRY92hYkMpq4HIFhu/tU++iGoNiAyhopqw3zyn9wEHXGaStjtJOyu5ydNGgZKIBQJsgJxYZXqDaB5/Sg+P0ok4FK9/pgwLGAdp0UCL5vPbOGlBc1rRgmHsVX4oKJ+B9o2bBqGy4HhcppzpwNcTnA6wOlAcTnBYc4VhwPFYUdxOsDhAKfd3M/hAJcjshwpczkjg8ZHuh9GuyrGtcyKdl2MjscVHXQ+Oi5Xle6JSmyLsNhujDEtvva3XQghhBBCCHFMkVCqMYXDsHu3GUgVFUHr1ubNY20KCuDzz80WVbEhU0EBlJYe1EsbqSmEW7YgmJ5KRWoCZWluPKlu/GnJ0LIltpatMFq2IJSaXG9dCEPhELv8+9ju28N2395I+LSHHb69bPfuJWDUPtaTQ7HT3t3KDJ7crTne3Ybj3a3p6G5Dmj257pVQNNASoOpbCofAiI4BExnrJfr0v3DQXA7HtA9SI4PuRgflVSOtWTjKuiY1UZVd1QzCkW5pkQ5qVle16LZw7DbCGC4DxaVgpEVCRxKIjoukoFSOhxRdJjK4Nma5XVWxoWFXNTRU7IaC069j8wXRfEFs/mAkuAqi+fxma61I8KX6zKBLiQZgXj+KL2A+2MDrB6/PfEKh1wehSNgV0lE8XvAcgU6kdhs4nZWBVTTsckTWY0Mthz1+skfmTnv8/vaY4+y2mLG51AOHX4oS83RF1fx3ZYuO52Wr4bgqIVfV5apBWG2vKYQQQgghhGgUEko1BsMww6RNm2DnTvOm8Ljjar45KiyETz+Ff/0LfvihcpDsmtjtZiurli3j5y1aQEYGvhYplKW6KEqyscsowxOqIBAO4lBsJNkSSNBc1ngvtcdD+3tbBiUhDzt9+ez057Pdl88O397IlM9uXwF6TQPtRmiKRjtXJse7WnOcO4vj3a05zpVFB3cbWjlboB3JsWiiraKq5W9GzLgrMfNwINLiKhpkRUKr2P88scGVosQ8XerYaHEVDY6MGsIhczlcY3AUNsw5YM2VyOdlxAyorSkqSjQ0igmPotucqoZNiQmQFBWbomGPjHUUHS9JjY6jFDmPuayixe5jvU4t1+BB5KJ1FgyZAZWvSlhllVXd5jfDrdhttU2RbowEQ+bkOYJPiottnRUbeEUDLXtMgBUNv+y2yrk9Oo8sO2yR/SPLDkflPjZbJPiKDaaID6+gMpiKHfMrOpi9okTmqhmIaVpkzK9Ii7LYkIsqr1NTABYNx6yQrOo24vcTQgghhBDiGCKhVEMrKYEtW8zWTuEwtGoV3zpK12HNGvj3v+Gbb+CXX+KDqB49oFevytApOmVkmF3/Ym5qguEQpSEPZaEK9voLKQ558IZKIABu1UW6PRnnQYwtYhgGpaFydvkL2O0vYKevgF3+AjOEigRR5fr+uy45VTvtXK1o52oVCZ9a0d6VRXt3Fq2dLbEpjfwkrWoUUO2AvYbACiBcw2C/kWXdb4ZYeiAyQK8f9HB8i6vIS5g3zdGWVmrlejTMiutUWT+MaiGRGSDphGvcdqDgyHwr0fAocq9dpdWRgoIWExw5VA27oqKhYYtpgWRTtCohUeV5tLhWTJXhUTQ4ig6+3exFg5jkxPo9r2GY49bFBlf+QGQ5Mvf7a1gPVN/fX6XMHzD3C8bE2tHysiMYfEWpamUAVlOrrrjAq2roFQ22NHPZppmhl6bFPHFRqx6U2exgj5RrWjyBO8EAAKEpSURBVOXFH9dSC7MwNpiK1jd2X6slV8ykxQ5yH7OuavHHx4ZcVHntmkKx/U51OEYIIYQQQoh6IKFUQwiFzJZRO3ea3fX8fjNEcrvNm7cff4TVq83phx/M4CpWt27whz/A738PbdrU/jLhEGXBCsr0CgoDJewLllAe8qIbYZyqg0TNTborudYb9kA4yN5AEXv8+9jjL2S3f19kKrCWKw4QOgG0tKfSzpVJO1cW7V2ZVgjV3tWKlo7UoyMwsKh1GDTYMEOpaHhF5IlU0Xnc493NfYywThi/2eEsbD5BzTAqw6BoSGQoBmFFwQDCKIQNsxNaWDGzB0NRzEGqFRUMcz8z/DK7qimKOSC8oqhm+639tDjaX3AUGwaZxysxLYzMuUZ8iyMVRQbQbgyKEmmR5IDUI9HECzN8DQQqAymv31z3BSLzmBArOsVtj8z9QfPnpT+ybJ0zZjkQE4CFw5UhWWNQVTPIsseEXo7YAMtWfdkWXY4JtqwxvKKTrfJJjZqtsgWXplWGZVXDM+sJjzbQIgGVFZhBtVZeRMpiQyioPaiqMURTYgKz2FZoSkwXztrOFfP6+31NIqF/OWTsp+WwEEIIIYRoFho0lPL7/TzwwAP861//wuVyce2113Lttdc2ZBUajq5DWZk5VtS2bebc46lsKbVunfmkvQ0bzJuuWImJkJcHp59uTq1qfu5dMBzCEzJDqKJAKQXBYrwhP0EjhF2xkWhzk+VsiYaKR69gb6CQ/EAR+f7iyHIhe/xF5EeCqMJg3calSrcn08aZQVtXZty8nSuTNs5MXFozf7JTLWK7o1W2FgrHBUWV2+K7rhlGOBIcmeGSoWCmRkq0C5ATFKc5GDcABqphoGCgGAYq5qQYmHOMSJsqHS1sYFfMhlw2w0DDwA7YVBXViNwXWnPzOCsgUhRUQ4kLizRFQVU0c4reJBpKZV2NyDz2JtKIWTfbSEW6iMXua74vs+VfOL4FYG3dUuP2qaWcWvapdnwdXmN/r1lTQa11qoO4MCBuQ2V5ba1SaryRp+Ygodr2Kjf7R4KmgttlTkeaYZjBVDSgCgTi163yYJXtQQjGbPMHzT8SxO0XMpej5db2UOWx4Zj/7uFwpEVZ4Mi/74NlBWBa5TxapsUMYB87iL2tylzTKvfV1Pht0VZd0X1i59Ggyjom2gqsyrF2zWwZah0be06tspWYoYNDh9ZngiOlsT9ZIYQQokHpYQNNPULf4YRoBA0aSj366KP89NNPvPjii+zcuZO77rqLtm3bMnTo0IasxpERDkN+Pqxfb4ZNa9fC9u2wa5fZOmrXruotoKLS0swueb17m/Nu3cwv/bGnN8J4dT8Vuo9y3cu+QAn5gSJ2+wooCJbgCVZQrvvw6BUUBUspCBRTECwx54Fi/OG63SQ5VTutHC1o7WxJlrMFrZ0ZZDlb0MaZQWtnBq2dLesQOkVu0owq85hNletG/Iaajql62nAk8Il2M4sMbB0XAMV0R4vbJyZEsoKlSGBkBkIKhhGOf1FDidy7R1sRmd3UVBQUo7J1kRnFqGiRcMcWnRtqpKWRig07NtVmhkSR7eZxMQNqx7RWUlXVDI1iuqtVBkiamTmoSuXnFQ27MCAUBsWIdCeMtNaKhkFGdFs0HApHtlPZiouYY9Hj91Oin40Rec1wzOuHI0FVuPp/cOv3p1q5zQqzjPhQJTaxUWPG4VJjt0Vb3VXpVhQbvsTuo8ZUwgoEqx6jVpZBpGVZ7Hpkn9jXjftiUFvYFGFE/s8wKj8ewyDu2q9pvdrxYQjXdGzVYyLzcOx5o/vFvDUjdgXrP4l1DkWpXK/aigUi/41iympq5RL9b6dU+dyrtY5Rq5+/NopSOV5VY9D1+NArGDLDq2BMoBW7HIjZHgxCUI9fD4XMfUKhyjG/gkFz4PvofrHLwZh9Y4+pqrby5iY69lf28XD2qMaujRBCCNHgNFXhljd+YP1eT2NX5ZAMzMnkjiFdGrsaoglpsFCqoqKCt956i2effZbu3bvTvXt31q1bx6uvvtp0Qimv1wyOyssrp7IycyotrWz5VFBgTvv2mUHU3r3mctUWTzXJyoKTToLsbMjOJtS5E56sdMqC5XiC5ZQEyyjY8Rn5gWL2BYopDJawx1/EnmAhRcEyikPllIQrKNHL8YQP7tHzKaqbDFsKmbZUWmmRuT2VVloqWfY0smxppGoJ1rhAFgUIAEEwSgvRlcoWQYYB4cjdbRizO5m5jcg6la2HIqeNjk2EokTCInNcIkOJBECqErmfrgwpFEWx7pmVSLcvc4wiMw5SFQUlOvB1ZOwic/wiBVWxoamqOaGhRZ6apkW6oGmKWaZYLYQUVNVmhkWqZnY1U7XKEEmN7KNoZmikaCiqWrmfNRZUDTfs1W7cleo33nHBSg3zw93nUMqqLluJRjTYCscEUlXm1r5EQqyYYCRuH+LLouWGTmXLqphyYubRwC2ubrF1VSrLFaXy9au9n8hytVA0NiytGqRW2edgWHWJK6zhXEpMvZTKMDE2gDJigkAjZh+U+KAr7hhqn8d+FuGYECscOW84bHbTi24PR8qMsLmsh831cCQ8iwZohGPOVTWIo3I70deK+ayIqReY77PWzzbyf1YQRpXgMnaspyrboq8XLY/dh5h9rf0Vs6liggLYQXHE/Nuv5fUPywHOEf3vEw28dN1c1kOVoVZIjwmz9Mp9QqHKbdZ+NazrB1iuWqaHaymvYZ+Qbta/JuEwBMKwe59ZJyGEEOIYtH6vh593HtzT15uKEzPrebxU0ew1WCi1du1aQqEQffr0scr69evHggULCIfDqNGb9Ubi/eeHvDX1YgptQcIK1qQroKvxy6HIpKsQagPB9hCMlAUdGgG3w5ycNgJOG16nit+u4rUZ+AjhDf+EN7wSrz9A8Ccdfjr0equKSro9hXRnKi0cqaQ7Ukl3mvMWzrTKZUc6ds38z23eFkbCoGirFqBMUSgziLS8iQRBioFiKNa9n6JGW/aoVgikRMYOwgptImFRpCuYomhoqoKGhqpGQiGlctmm2tBiwh8FpXKumK2FomXm+c0ASFHUasfEbo/uo1RrQUM93RiKJstqDVRT2BS7jRq2U7mPda79lVH9dWrap1p5Ddv2d1yt22s7vsq2Gj8Lat5Wa0BnVKlODeFetf2jwVYksDKIjKVWNRgLV9k3GoYZlUFXbKhVbR+j8vzEhmWx4Vh0/5hlq15VtkXrZQVzYLUIjP28ou8hHLstprzqZxzbii229VlcYKrEHxdbHs0slarbASVcucH6WUdMGWBXwaHF7xOzS+VL13Qeqv/srPEcse/rAMdXPU90Hv3vGQqbgVXYqAyrAkFwJ4DDXfO5jgHH1HAIQghRj6TrmxBNT4OFUvn5+aSnp+OIedJcRkYGfr+f4uJiWrRo0VBVqdGr+z7lumHBA+94QDrgjUwxwpitjWphUzRcNjdum4sEewKJjkQS7YkkOBJJcaaQ4kwh1ZVKiiOFdHc6aa40WrhbkOxIxqaa/xnNlj6qFeZoqmZ1AdMUzQp4NMUs1yKhUPSYqsfHru9v24HWZTBr0eDiuu8JcZBq6kpZbQrvfz+oDM5q65IZVxaueZ+almP3r23fqoFdNDSzAsGa6lFl3+g+Vqul/XwelR9e3Kza51m1LC5Qjd22v2MAdyI4kzhWHdXDIQghxBEkXd+EaHoaLJTyer1xgRRgrQcCjT8o7LnnT2L0x/nsKtmOptpQot25IqFNbMseVVGxKTZUVcWu2itb+yjm3KE5sKk2bKoNp+bEbXfjsrlw29247W6S7ckkOhNJsCWY645knDan1W2uaqATO48GPlWDIgmBhBCinkiLykNTW/hUW7BU03Jd91VV8wm2x6BmMRyCEOKodLS0MpKub0I0LQ0WSjmdzmrhU3Td5WqAJzQdQNvktrww/KXGroYQQgjRPEmY1yCa+nAIR5Oj4QZc3kPTcTS8D2llJIQ4EhoslMrKyqKoqIhQKIQt8mS5/Px8XC4XKSkHfqSzEfkrqcfTPH8ICiGEEKJ5SUxMbHKtjw93OISG+j51NNyAAyz4fAM7S7wH3rEJ6tE+lcv6HSfvoQk4Gt5H9D2EfBWEA83zPQS8FXg8HjqmqIQD9sauziHJciPvoQk4Gt5DxxS1wbKVA32farBQqmvXrthsNlatWsUpp5wCwMqVK+nRo0ed/qpXXl4OwFlnnXVE6ymEEEIIAeb3lKSkpjV21eEOhyDfp44dHwIPN3YlDtPR8B7g6HgfR8N72Ag0934x8h6ahqPlPfSb1TCvdaDvUw0WSrndbi666CKmTZvGQw89xN69e3nuueeYNatun0SrVq344osvmuRfLYUQQghx9ElMbHpjdxzucAjyfUoIIYQQDelA36caLJQCmDx5MtOmTWP06NEkJSVx8803c84559TpWFVVad269RGuoRBCCCFE03W4wyHI9ykhhBBCNCWKYcQ9y1kIIYQQQjRRXq+XvLw8nnvuOWs4hHnz5rF8+XJeeeWVRq6dEEIIIcTBkUe0CCGEEEI0E7HDIfz4448sW7aM5557jlGjRjV21YQQQgghDpq0lBJCCCGEaEa8Xi/Tpk3jX//6F0lJSYwdO5YxY8Y0drWEEEIIIQ6ahFJCCCGEEEIIIYQQosFJ9z0hhBBCCCGEEEII0eAklBJCCCGEEEIIIYQQDU5CKSGEEEIIIYQQQgjR4CSUAvx+P1OmTOGUU07hjDPO4LnnnmvsKjVbe/bsYeLEieTm5nLmmWcya9Ys/H5/Y1erWbv++uu5++67G7sazVYgEOCBBx7g1FNP5fTTT+fxxx9HhtI7NLt27WL8+PH07duXwYMH88ILLzR2lZqVQCDAsGHDWLFihVW2bds2xowZQ+/evTn33HP5+uuvG7GGzUdNn+WqVau44oor6NOnD0OGDOGtt95qxBqKhvTxxx+Tk5MTN02cOLGxq3XUkJ9dDaemz3rGjBnVru9XXnmlEWvZfO3vPkWu6fqzv89Zruf6s2XLFsaOHUufPn0YOHAgf/vb36xtze16tjV2BZqCRx99lJ9++okXX3yRnTt3ctddd9G2bVuGDh3a2FVrVgzDYOLEiaSkpPDqq69SUlLClClTUFWVu+66q7Gr1yx9+OGHfPHFF1x88cWNXZVma8aMGaxYsYLFixdTXl7OX/7yF9q2bcsVV1zR2FVrdm699Vbatm3LkiVLWL9+Pbfffjvt2rXjD3/4Q2NXrcnz+/1MmjSJdevWWWWGYXDTTTeRnZ3NO++8w7Jly5gwYQJ///vfadu2bSPWtmmr6bPMz8/nuuuu48orr+Thhx/m559/ZvLkyWRmZjJw4MDGq6xoEOvXr2fQoEFMnz7dKnM6nY1Yo6OH/OxqODV91gAbNmxg0qRJcd8Fk5KSGrp6zd7+7lPuvPNOuabryYHuB+V6rh/hcJjrr7+eHj168O6777JlyxZuu+02srKyGDZsWLO7no/5UKqiooK33nqLZ599lu7du9O9e3fWrVvHq6++KqHUQdq4cSOrVq3i3//+NxkZGQBMnDiRRx55REKpQ1BcXMyjjz5Kjx49GrsqzVZxcTHvvPMOzz//PD179gTg2muvZfXq1RJKHaSSkhJWrVrF9OnT6dixIx07duTMM89k+fLlEkodwPr165k0aVK1Fnrffvst27Zt44033iAhIYETTzyR5cuX884773DzzTc3Um2btto+y2XLlpGRkcFtt90GQMeOHVmxYgXvv/++hFLHgA0bNpCdnU1mZmZjV+WoIj+7Gk5tnzWY1/fYsWPl+j5M+7tP+d3vfifXdD050P2gXM/1o6CggK5duzJt2jSSkpLo2LEj/fv3Z+XKlWRkZDS76/mY7763du1aQqEQffr0scr69evH6tWrCYfDjViz5iczM5O//e1v1g+gKI/H00g1at4eeeQRLrzwQjp37tzYVWm2Vq5cSVJSErm5uVbZ9ddfz6xZsxqxVs2Ty+XC7XazZMkSgsEgGzdu5L///S9du3Zt7Ko1ed999x15eXm8+eabceWrV6+mW7duJCQkWGX9+vVj1apVDVzD5qO2zzLaPaAq+f1zbNiwYQMdO3Zs7GocdeRnV8Op7bP2eDzs2bNHru96sL/7FLmm68/+Pme5nutPq1atePLJJ0lKSsIwDFauXMl//vMfcnNzm+X1fMy3lMrPzyc9PR2Hw2GVZWRk4Pf7KS4upkWLFo1Yu+YlJSWFM88801oPh8O88sornHbaaY1Yq+Zp+fLlfP/997z//vtMmzatsavTbG3bto127dqxdOlSFixYQDAYZPjw4fz5z39GVY/5TP6gOJ1O7rvvPqZPn85LL72ErusMHz6cyy67rLGr1uSNGDGixvL8/HxatWoVV9ayZUt2797dENVqlmr7LNu3b0/79u2t9X379vHhhx822b8IivpjGAabNm3i66+/ZuHChei6ztChQ5k4cWLcdztx8ORnV8Op7bPesGEDiqKwYMECvvzyS9LS0rjmmmtkWIdDsL/7FLmm68/+Pme5no+MwYMHs3PnTgYNGsSQIUN46KGHmt31fMyHUl6vt9qXluh6IBBojCodNWbPns0vv/zC22+/3dhVaVb8fj/3338/9913Hy6Xq7Gr06xVVFSwZcsW3njjDWbNmkV+fj733Xcfbreba6+9trGr1+xs2LCBQYMGcc0117Bu3TqmT59O//79ueCCCxq7as1Sbb9/5HfP4fH5fNx8881kZGRw+eWXN3Z1xBG2c+dO69/Sk08+yfbt25kxYwY+n4+pU6c2dvWOSvKzq+Fs3LgRRVHo1KkTV199Nf/5z3+49957SUpKkq7zhyn2PuWFF16Qa/oIif2cf/75Z7mej4A5c+ZQUFDAtGnTmDVrVrP8GX3Mh1JOp7Paf6DougQCh2727Nm8+OKLPPHEE2RnZzd2dZqVuXPncvLJJ8f9lUEcGpvNhsfj4bHHHqNdu3aAeQPz+uuvSyh1kJYvX87bb7/NF198gcvlokePHuzZs4dnnnlGQqlD5HQ6KS4ujisLBALyu+cwlJeXc+ONN7J582Zee+013G53Y1dJHGHt2rVjxYoVpKamoigKXbt2JRwOc8cddzB58mQ0TWvsKh515GdXw7nooosYNGgQaWlpAHTp0oXNmzfz+uuvy038Yah6nyLX9JFR9XM+6aST5Ho+AqLjD/v9fm6//XYuueQSvF5v3D5N/Xo+5vuvZGVlUVRURCgUssry8/NxuVykpKQ0Ys2ar+nTp/P8888ze/ZshgwZ0tjVaXY+/PBDli1bRp8+fejTpw/vv/8+77//fty4Z6JuMjMzcTqdViAFcMIJJ7Br165GrFXz9NNPP9GhQ4e4X2jdunVj586djVir5i0rK4uCgoK4soKCgmpNrkXdeDwexo4dy7p163jxxRdlzIpjSFpaGoqiWOsnnngifr+fkpKSRqzV0Ut+djUcRVGsG/ioTp06sWfPnsap0FGgpvsUuabrX02fs1zP9aegoIBly5bFlXXu3JlgMEhmZmazu56P+VCqa9eu2Gy2uIG/Vq5cSY8ePWTMmUMwd+5c3njjDR5//HHOO++8xq5Os/Tyyy/z/vvvs3TpUpYuXcrgwYMZPHgwS5cubeyqNTu9evXC7/ezadMmq2zjxo1xIZWom1atWrFly5a4lqUbN26MG8dHHJxevXrx888/4/P5rLKVK1fSq1evRqxV8xQOh5kwYQLbt2/n5Zdf5qSTTmrsKokG8tVXX5GXlxf3V+E1a9aQlpYm44IeIfKzq+E89dRTjBkzJq5s7dq1dOrUqXEq1MzVdp8i13T9qu1zluu5/mzfvp0JEybEBXo//fQTLVq0oF+/fs3uej7mUxe3281FF13EtGnT+PHHH1m2bBnPPfcco0aNauyqNTsbNmxg/vz5XHfddfTr14/8/HxrEnXXrl07OnToYE2JiYkkJibSoUOHxq5as9OpUycGDhzI5MmTWbt2LV999RWLFi3iyiuvbOyqNTuDBw/GbrczdepUNm3axKeffsqCBQsYOXJkY1et2crNzaVNmzZMnjyZdevWsWjRIn788UcuvfTSxq5as/P222+zYsUKZsyYQUpKivW7p2p3DHH06dOnD06nk6lTp7Jx40a++OILHn30UcaNG9fYVTtqyc+uhjNo0CD+85//sHjxYrZu3cprr73G0qVLZQiCQ7C/+xS5puvP/j5nuZ7rT48ePejevTtTpkxh/fr1fPHFF8yePZsbbrihWV7PimEYRmNXorF5vV6mTZvGv/71L5KSkhg7dmy1FFcc2KJFi3jsscdq3Pbrr782cG2OHnfffTcADz/8cCPXpHkqKytj+vTpfPzxx7jdbkaMGMFNN90U19VD1M369euZOXMmP/74Iy1atOCqq65i9OjR8lkehJycHF566SXy8vIA2LJlC/fccw+rV6+mQ4cOTJkyhdNPP72Ra9k8xH6WY8eO5euvv662T25uLi+//HIj1E40pHXr1vHQQw+xatUqEhMTueKKK+TnfD2Tn10Np+pnvWzZMubMmcPmzZtp164df/nLXzjnnHMauZbNz4HuU+Sarh8H+pzleq4/e/bsYfr06Sxfvhy3283VV1/N+PHjURSl2V3PEkoJIYQQQgghhBBCiAZ3zHffE0IIIYQQQgghhBANT0IpIYQQQgghhBBCCNHgJJQSQgghhBBCCCGEEA1OQikhhBBCCCGEEEII0eAklBJCCCGEEEIIIYQQDU5CKSGEEEIIIYQQQgjR4CSUEkIIIYQQQgghhBANTkIpIUSTl5OTw6RJk6qVL1myhMGDBzdCjYQQQgghhBBCHC4JpYQQzcIHH3zA8uXLG7saQgghhBBCCCHqiYRSQohmoV27djz44IMEAoHGrooQQgghhBBCiHogoZQQolm49dZb2bNnD4sXL651n927d3PLLbeQm5tLXl4eM2bMsEKsJUuWMHLkSObMmUNeXh6nnHIKs2bNwjAM6/g33niDwYMH06dPH0aOHMmvv/56xN+XEEIIIYQQQhyrJJQSQjQLWVlZTJw4kQULFrBt27Zq2wOBAKNHj8br9fLyyy/z5JNP8vnnn/Poo49a+/zwww9s2rSJ119/nXvvvZeXXnqJb775BoBPP/2UuXPncu+99/Luu+/Sr18/Ro0aRUlJSYO9RyGEEEIIIYQ4lkgoJYRoNkaOHEmHDh2YOXNmtW1fffUVe/bsYfbs2eTk5NC/f3/uu+8+Xn/9dcrLywHQdZ3p06fTqVMnLrzwQrp06cL//vc/AP72t78xfvx4Bg0aRMeOHbn11ltp164d7733XoO+RyGEEEIIIYQ4VtgauwJCCFFXmqYxbdo0RowYwbJly+K2bdiwgY4dO5KammqV9e3bl1AoxNatWwFo2bIlSUlJ1vakpCRCoZB1/OzZs3n88cet7X6/n82bNx/BdySEEEIIIYQQxy4JpYQQzUrfvn255JJLmDlzJuPGjbPKnU5ntX11XY+bOxyOavtEx5TSdZ0pU6bQv3//uO2xIZYQQgghhBBCiPoj3feEEM3O7bffTkVFRdyg5yeccAKbN2+muLjYKlu1ahU2m43jjz/+gOc84YQT2L17Nx06dLCmBQsWsGrVqiPwDoQQQgghhBBCSCglhGh20tPTuf3229mxY4dVNmDAAI477jjuvPNOfv31V7799lumT5/OsGHDSElJOeA5r7nmGl588UWWLl3K1q1bmT17Nv/4xz848cQTj+RbEUIIIYQQQohjlnTfE0I0S5deeinvvPMOe/fuBczxpubPn8/06dP505/+RGJiIueffz633XZbnc537rnnUlBQwJw5cygoKKBz584888wzdOzY8Qi+CyGEEEIIIYQ4dilGdEAVIYQQQgghhBBCCCEaiHTfE0IIIYQQQgghhBANTkIpIYQQQgghhBBCCNHgJJQSQgghhBBCCCGEEA1OQikhhBBCCCGEEEII0eAklBJCCCGEEEIIIYQQDU5CKSGEEEIIIYQQQgjR4CSUEkIIIYQQQgghhBANTkIpIYQQQgghhBBCCNHgJJQSQgghhBBCCCGEEA1OQikhhBBCCCGEEEII0eAklBJCCCGEEEIIIYQQDU5CKSGEEEIIIYQQQgjR4CSUEkIIIYQQQgghhBANTkIpIYQQQgghhBBCCNHgJJQSQgghhBBCCCGEEA1OQikhhBBCCCGEEEII0eAklBJCCCGEEEIIIYQQDU5CKSEOgWEYjV2FQ9ac634gR/N7E/sn/+2FEELEkt8LoiZyXQjR9EgoJZqdSZMmkZOTw3PPPVdt2+DBg7n77rsBWLFiBTk5OaxYsaLWc8XuX1effPIJd91118FVugnYvXs3119/PTt27LDKDuX9NxUjR45k5MiR1vpbb73FI488Yq0vWbKEnJwctm/f3hjVq1dPP/00OTk5jVqHnJwcnn766cM+T13eS9V/u3fffTeDBw+2tle9bufPn8/ixYsP6jWEEOJosHLlSm6++WYGDBhAjx49OPvss5k6dSobNmxo7KrFaeifyytXruT6669vsNdrCn7++Weuu+46TjvtNPLy8rj22mv5+eefa91/165d9OvXr06/27ds2cItt9zCGWecQb9+/bjyyitZvnx5tf2WLFnC+eefT48ePRg8eDBz585F1/WDeh/RayV26tatG3l5edx0002sW7euzud67rnnuP322wEoLS3lzjvv5Pvvvz+o+hyqqt9danIo31XrckxRUREDBw5k27ZtdT5vrPLych544AEGDBhAnz59uO6669i4ceMBjystLWXatGnWcZdffnm16yQUCvHkk09y1lln0atXL0aMGMHq1asPqZ7i6CGhlGhWysrKWLZsGdnZ2bz55puH/deOuXPncuONNx7UMS+88AK7du06rNdtDN988w1ffPFFXNmhvP+m4v777+f++++31p955hmKi4sbr0Ki3nTv3p0333yT7t2717i96nX71FNP4fV6rfXLLruMN99884jXUwghGtOiRYu46qqr8Hq9TJkyhcWLF3PDDTfwyy+/cPHFF/Phhx82dhUbzVtvvdXkgrkjacuWLVx99dX4fD5mzpzJrFmzCAQCjBgxosYwwTAMpkyZgsfjOeC5i4qKuPrqq9m4cSNTpkzhiSeeICMjg2uvvZbvvvvO2u/VV19lypQpnHnmmSxatIjLLruMBQsW8NRTTx3Se3rzzTet6eWXX2bq1KmsWbOGq666ivz8/AMev2HDBhYuXMgdd9wBwJo1a/i///s/wuHwIdXnSBg4cCBvvvkmrVq1qtfzpqenM2bMGKZMmXJI90qTJk3io48+YtKkSTzyyCPs2bOHUaNGUVJSUusxuq5z3XXX8cknn3DHHXcwZ84cUlJSuP7661m7dq2138MPP8wLL7zAuHHjeOKJJ9A0jTFjxrBly5ZDeq/i6GBr7AoIcTA++OADAO655x5Gjx7Nt99+S//+/Q/5fN26dauvqjVLzfn9d+7cubGrII6QpKQkevfuXev2A123rVu3pnXr1vVcKyGEaDo+++wzHnvsMW6++WYmTJhglefm5nLRRRcxadIk7r77brKzsznppJMasaaiIbz88su43W4WLlxIQkICAKeddhqDBw/mlVde4b777ovb/7XXXqtTyxeApUuXUlRUxNtvv01WVhYAAwYM4MILL2Tx4sXk5uZSUVHBY489xtixY60QqH///pSWlvLNN99w2223HfR7qvo9oF+/frRp04arrrqKd99994At4WbPns2wYcOsOjdFLVq0oEWLFkfk3CNGjOCZZ57h448/5pxzzqnzcT/88AOfffYZixYt4qyzzgLglFNO4eyzz+a1117jz3/+c43Hvf/++/z0009WSy4wfx5dcMEF/Pvf/6ZLly7s2rWL119/nXvuuYcRI0YAcMYZZzBkyBCeffZZZsyYcZjvWjRX0lJKNCvvvPMO/fv357TTTqNDhw688cYbh3W+2G5A27dvJycnh3/84x9MnDiRPn36kJuby9SpU6moqADMLmPfffcd3333XVz3ouLiYu677z5OP/10evTowZ/+9KdqzVVzcnKYO3cuw4cPp2fPnsydO5euXbvyyiuvxO1XWFhI9+7deeGFFwAIh8MsWrSIP/zhD5x88skMGTKEl19+Oe6YkSNHcs8997Bo0SIGDhxIjx49uOKKK/jxxx8Bs6nv5MmTATj77LOt91y1G1RZWRmzZs3i97//PT169GDYsGG8/fbb1T6zOXPm8Mgjj3D66afTs2dPxo4dy+bNm+Pew6RJk6zuBBdeeCFLly6t9b/DhAkTuOCCC+LKRo8ezcknn4zP57PKZs6cyZAhQ6z3HO2+N3jwYHbs2MG7775brUnz6tWrueKKK+jRowcDBw7kb3/7W631iPrtt98YP348ffv2pW/fvtx0003VmkBv376dG2+8kb59+zJgwACeeeYZ7rnnnrguhTV1eaupC8Nbb73F8OHD6d27Nz179uTCCy/kH//4xwHrGVuXnJwcPvzwQ2644QZ69erFwIEDmTdvXtxfBAcPHsxDDz3E6NGj6dmzJ/fccw8Ae/fuZfLkyZx11ln07NmTSy+9lE8++aTa63g8Hm6//Xb69OlD//79mTFjRlwLJV3XWbRoEcOGDaNnz5707t2bK664gm+//bbauZYtW8aQIUPo0aMHl112Wdy/lwN1vY29bqOf5dy5c63lmj7jZcuWMXz4cHr06MGAAQOYMWOG9e8awOfzMW3aNH73u99x8sknM3To0LgugUII0ZTMnTuXTp06cdNNN1XbZrfbefDBB9E0jWeffRaAa6+9luHDh1fb98Ybb4z7/fv9999z9dVX06tXL3Jzc7nrrrsoLCy0ti9ZsoRu3brx1ltvMWDAAHJzc1m/fj1bt27lhhtuIC8vj169enH55ZdXa50N8Pnnn3PBBRfQo0cPhgwZUu27QV1+H/n9fubNm8fQoUPp0aMH55xzDosWLbJ+39199928++677Nixg5ycHJYsWVLjZ/j0008zdOhQPv74Y4YNG2Z9X/nhhx9YtWoVl112GT179mTYsGHVvtPV5XvC2rVrmTBhAqeddhrdu3fnzDPPZMaMGXHfa3Jycnj11Ve55557yM3NpU+fPtxyyy0UFBTEfeYHGo6iU6dOXHvttVYgBZCQkEDr1q3ZunVr3L7btm3jr3/9K9OnT6/1fLGysrIYM2ZMXLijaRodOnSwzv3vf/+b8vLyuO9AAHfddVe175GH4+STTwawhqJ4+umn+cMf/sDcuXPJzc3ljDPOoKSkhN9++43PP/+cYcOGAeb3ilGjRgEwatSouHr+/e9/Z/jw4fTp04cBAwZw3333VWsR9L///Y+xY8eSl5dH3759ueGGG+rcjXDJkiXW950LLrgg7t9FTV3x3n33Xc4991xr/+XLl9OtW7dq1/GBvt86HA6GDBnCwoULrbLo96va/k0AfP311yQkJHDGGWdYZS1atODUU0+t8d901D//+U9OPfXUuO9fTqeTf/7zn4wdOxaA5cuXEwqF+MMf/hBXz4EDB+733OLoJ6GUaDbWrVvH//73Py666CIALrroIj755JO4X9z14f7776ddu3bMnz+fsWPH8vbbb/PMM89Y27p160a3bt2s7kV+v5/Ro0fzySef8Je//IW5c+fSunVrxo0bV+1LzIIFCzj//POZM2cOQ4YMITc3t1rz+o8++gjDMDjvvPMAmDZtGnPmzOGCCy5gwYIFDB06lIceeoh58+bFHffPf/6TTz75hKlTp/L4449TUFDAzTffjK7rDBw40PrLRm1d9nw+HyNGjOD9999n3LhxzJ8/n379+nHPPfewYMGCuH1feuklNm7cyKxZs5gxYwY//fRT3Dhbd9xxBxs2/P/27jtAivL+4/h7ZrbvXoG7ox0dRIqoCEKsiDFq1GhiTH4mJtZYoohRY49GRSWKXUHF3mLFGI1JTExssaEoKihIk3ZwXL/bvjszvz9mZ273Gnd4XIHvKxln5pmyz+4td7OffZ5nVnPdddfx4IMPMn78eC677LIWwwmA6dOn880331BVVQVYF5yfffYZqVSKJUuWOPu98847zJgxo9nx9957LyUlJUyfPr1ZM+hrr72Wo48+mgULFjBp0iTmzp3Lm2++2WI9ANauXcuJJ55IVVUVN998MzfeeCMbNmzgF7/4hVM/+8Lrm2++4YYbbuCqq67i5Zdf5t///ner523N008/zTXXXMNhhx3GAw88wK233orH4+H3v/89W7Zs6dC5rr32WkKhEPfccw/HHXcc9957L7fddluzx5s4cSLz58/nhBNOoLKykhNOOIFPPvmECy+8kHvuuYfS0lLOO+88XnnllZxjn3zySSKRCHfeeSdnn302L7zwgjNWA8Ctt97K/Pnz+b//+z8eeughZs+eTW1tLRdccEFOeAVWa8eTTz6Ze+65h2AwyJlnnsmXX37ZwVcPp5veCSec0GqXvVdffZXzzjuPkSNHMm/ePGbOnMkrr7zCueee6zRrv+mmm3jnnXe47LLLePjhh/n+97/PLbfcwsKFCztcJyGE2JGqq6tZunQpM2bMQFGUFvcpLCxk//33dwKdY489lmXLluV0kamvr+edd97huOOOA+Djjz/m1FNPxefzceedd3LllVeyaNEiTj755JwgRdd1HnnkEW688UauuOIKRowYwdlnn00sFuOWW25h/vz5FBYW8tvf/rZZl5xrrrmGU089lfvuu48BAwZw+eWXO1172vP3yDRNzjnnHB566CGne9iRRx7JnXfe6XTpP/fcc5k+fTolJSU899xzHHLIIa2+llu2bOFPf/oT55xzDnfddRf19fXMmjWLiy66iJ/97GfMmzcP0zS58MILndegPdcJW7dudbpW/ulPf+LBBx/k6KOP5sknn+SJJ57IqcMdd9yBYRjcfvvtXHrppbz55pvcdNNNzna7i1drXdrBahXzm9/8Jqds3bp1rFy5MqelnGEYXH755fzwhz/k4IMPbvV82Y466qicv/UAdXV1fPzxx865v/76a/Ly8qisrOSkk05ijz324IADDmD+/PmdOrD42rVrARg6dKhTVlZWxttvv80dd9zBFVdcQUFBAa+++iolJSVOa6sJEyY4rcWuueYa570yf/58LrroIvbee2/uvvtuzjvvPF5//XV+/etfOz/vDz/8kF/84heAda1www03sHnzZk488cRtdhHdvHkzCxYs4IILLuCee+5BURRmzZrlvE+aevnll7n88svZZ599mD9/PkcccQTnnntui+Nytef69sgjj2Tp0qXO62YPj9DWv4nVq1czePBgNE3LKR86dKhznpYsX76c0aNH89hjj3HooYcyYcIEjj/++JwxvFavXk0wGKSkpCTn2GHDhrF161YikUir5xc7N+m+J3qNhQsXUlhY6Awa+JOf/IR77rmHF198kXPOOafTHmf69OlOwLLffvvx3nvv8dZbb3HxxRczevRoQqEQ0Nis+Pnnn2f58uU8//zz7LXXXgAcfPDB/PrXv+bWW2/N+VA7ZcoUTjvtNGf9uOOO48orr6SsrIxBgwYB8Nprr7H//vtTUlLC2rVref7557noooucZsoHHnggiqLwwAMP8Mtf/pI+ffoA1sCBDz/8sFO/SCTCZZddxtdff80ee+zh/AEfN24cgwcPbva8X3rpJb755hueffZZJk2aBMBBBx1EOp1m/vz5nHjiiRQWFgKQn5/P/PnznT9Y69ev55577qGmpoY+ffqwaNEizjvvPA477DDAar5bWFiIx+Np9TUH6xuUY445hk8//RRN0xgxYgQff/wx3/ve99iwYQPffvtti6HU+PHj8Xg89O3bt1lz74suusi5mNh7773597//zYcfftjiecAKuPx+P4899pjzWu63334cdthhPPTQQ1x22WX85S9/YfPmzfz1r391vhHac889OfLII1s8Z1s2bNjAGWeckRMUlpaWcvzxx7N48WInnGyPCRMmcOuttwLWezAajfL444/z29/+1nkugwYNyrm4nDt3LtXV1bz++uuUlpYC1s/j1FNP5ZZbbuGYY45BVa3vL0aNGsW8efNQVZXp06ejKAo33XQT33zzDWPGjGHr1q1ceOGFOd9Aer1ezj//fFasWJHzs7nuuuuc12u//fbj+9//Pg8++CB33313h14/+5wDBgxoscufaZrceuutHHTQQc5rAzB8+HBOPfVU3n77bQ455BAWLVrEAQcc4Lze06ZNIxAIUFRU1KH6CCHEjma3ErF/Z7dm2LBh/Oc//6Guro7DDz+c6667jr/97W9O66p//etf6LrutCa57bbbGDFiBA888IDz932vvfbi6KOPZuHChZx00knOuc855xzng21FRQVr1qxxwiDAaRGeTCZz6nTDDTc4YcjQoUP5wQ9+wKJFixg7diyPPvroNv8evfvuu7z//vvcfvvtzu/rAw44AJ/Px1133cXJJ5/MbrvtRt++ffF4PG12BQeIxWL88Y9/dOq0atUqbrvtNm688UZOOOEEAKLRKLNmzWLt2rWMGzeuXdcJ33zzDePGjeOuu+5y9tl///157733+Oijj3K6no0ZM4Y5c+Y461988QX//Oc/nfXt6eIVj8e57LLL8Hg8/OpXv3LKH3/8cTZu3Njsy8aOMAyDq6++mnA47ARh1dXV6LrOWWedxSmnnML555/Pe++9xz333EM8Ht+u7nvpdDrn+SxfvpybbrqJvLy8nNZ96XSayy67jClTpjhlH374IRMnTnRC21Ao5Az7MHr0aEaPHk1dXR333XcfP//5z3O6N44ZM4aTTjrJec/fdtttDBs2jAULFjj/Lg488EB+8IMfcPfdd7c5ZpZhGMybN49Ro0YB1jXRqaeeypIlS/j+97/fbP+77rqLGTNmON3YDjroINxud7MvGKF917cTJ04ErOvrESNGbHN4BLB6Tdjv2WzBYLDN0Ki6upp//vOfFBQUcOmll+L3+1mwYAGnn346zz//PGPHjm3z3GC1yLeXxa5FWkqJXiGVSvHKK69w2GGHEY/Hqa+vJxgMMnnyZJ5//vlOHbSw6S/rAQMG5HTzaeqDDz6gpKSECRMmkE6nSafT6LrOjBkzWLp0aU4T4HHjxuUce/jhh+P1evn73/8OWN+oLF682PnW8sMPP8Q0TQ499FDn3Ol0mkMPPZREIsHixYudc2UHZoDTzLppC5XWLFq0iNLSUieQsh177LEkEomcO2NMnDgx5xsUe/we+7GmTZvGPffcw6xZs3jhhReorKzksssuY5999mnxsfv168f48eN5//33Aes13Weffdh3332dQTTfeecd8vPzmTx5cruejy37IsXv91NcXEx9fX2r+3/44YdMnToVn8/nvN6hUIgpU6Y49fvkk08YMmRIThPlwYMHN3vt2uPyyy/n97//PfX19SxZsoS//vWvPP300wDNLua3xW5FaDviiCNIpVJ89tlnTlnT9+CiRYuYNGlSsw83xx57rPNBw3bkkUc6ARXgjFHw8ccfA9YHmlNOOYXq6mo++eQTFi5c6Hy7nf1c3G53zvgGXq+Xgw8+2DlPZ1qzZg1btmxp9m9o3333JRQK8d577wHWe/b555/nzDPP5KmnnmLDhg2cd955bX6bKIQQ3cFueeJ2u9vcz/47bZomgUCAww47zLneAOtLsP3224/+/fsTi8X4/PPPmT59OqZpOr8rhwwZwqhRo5zflbbsvyXFxcWMHj2aq6++mssuu4xXX30VwzC44oormo1nlf032f6CzP6b3J6/R4sWLcLlcjX7EsgOKbIH3m6v7GuT4uJiAOdLRsD5Qs6uZ3uuEw488ECeeuopvF4vq1at4j//+Q/33Xcf1dXVzf62t3Td2d5rt5aEw2HOPvtsvvzyS+bOneu8nqtXr+bOO+/k+uuvJy8vb7vOnUqluOSSS3j99de56qqr2HPPPZ3yaDTKmWeeydlnn833vvc9Lr74Yn72s5/x6KOPtmtA9aYmTJjgTJMnT+akk04imUw6reOzNb222bBhQ4tfwGZbsmQJyWTSCWVtU6ZMobS0lEWLFhGNRvnyyy/54Q9/mHPdm5+fz4wZM7b5fuvTp48TSEHje76hoaHZvuvWraOsrKzZe7u1Lyfbc32bl5dHfn5+h+7u11bLttZaZoL1HmhoaODhhx/myCOPZPr06TzwwAMEg0GnG/G2Ws1lX2OKXYu0lBK9wltvvUVVVRUvvvhii33T3333Xefbue/K7/fnrKuq2uYv0draWioqKlptVl1RUUFBQQFATl9/sL65Oeyww3jttdf4zW9+w9///nf8fr/Twsi+m1xrf5DKy8vbrDfQ7sCurq6u2R95aLxAy/5Dt63HuuOOO7j//vv5xz/+weuvv46qquy///5cf/31rX6zO336dP76178CVij1gx/8gIEDB/LXv/6VZDLJu+++y0EHHYTL1bFfW9vz8/z73/+ec+Fus7+prKura/Fby/79++f8TNpj/fr1XHPNNXzwwQe43W5GjhzJ2LFjgW3/8W7p8Vurr63pe7Curo4hQ4Y0O1dLP/em7w+7FZG9z5dffsl1113Hl19+id/vZ/To0U4LwOzn0qdPn2YXHkVFRW2GhdvL/jd03XXXcd111zXbvnXrVsDqTjhgwABeeeUVZs+ezezZs5k0aRLXXnut8/MQQoiewP47areYas2GDRsIBoNOqHLcccfxyiuvsHz5coqLi/noo4+cbmL19fUYhsGDDz7ofIDM5vV6c9az/5YoisIjjzziDKr88ssv43a7Oeyww7juuuuca6Cmx9l/B+y/D+35e1RXV0efPn2adS2y/z619GF/W1pqudH02iFbe64T7O54Tz/9NNFolIEDB7Lnnns2ex1beqxtXae0ZfPmzZx99tmsXbuWO+64w7me1HWdK664giOPPJIDDjggpxWSYRik0+ltXl/V19czc+ZMPv74Y66++uqclnN265amX+QcfPDBPPfcc6xevTon6GuP7Ot9t9tNSUlJq62Xm7auCYfDbf4MofHayH5/ZSsuLqahoYGGhgZM02xzn7Y0veayQ52Wrs3tsduaPseWHhva/77x+/0dCgVDoVCLQ6NEIpE2w8xgMMioUaNybjQTCoWYNGkSX331lbPeUmsru37bG5aK3k9CKdErLFy4kCFDhnDjjTfmlJumycyZM3n22Wc7LZTqqLy8PIYPH57TNSjbtr6pOfbYYznrrLNYt24dr732GkcccYTzhyY/Px+wmlu31JzV/sDfGQoKClq8Hat92127m2B75OXlcckll3DJJZewZs0a/vOf/zB//nyuu+46FixY0OIxhxxyCPPnz2fZsmUsW7aMq666ikGDBpFIJPjkk0/46KOPWgwVOlteXh77779/TjdLm33B1qdPn2YDh0JjAJKt6TgA2a3uDMPgrLPOwu128+KLLzJu3DhcLherVq1yArqOqKmpyVm3xyxoqwtaQUFBi7dWbunn3vT52fsUFRU5zfjtAddHjhyJqqq8/fbbvP766znH2Rd52d+4VVZW7pA70Nj/hi699FKmTp3abLv9Ycnj8fDb3/6W3/72t5SVlfHmm28yf/58Lr744l36tupCiJ6nqKiIvffem9dff50LLrigxdYF4XCY9957zxnyAKwuZiUlJfzjH/+gpKQEr9frtFoNBoMoisKpp57a4hdh2/qA379/f6699lr++Mc/snz5cv75z3/y4IMP0qdPH2f8nm1pz9+jgoICampq0HU9J5iyv2DoyLXK9mrPdcKCBQt47LHHuO666zj88MOdD9t2l8AdYcWKFZxxxhkkEgkeeeQR9t13X2fb5s2b+fzzz/n888+bDS4/f/585s+fz3/+859Wr1m3bNnCaaedxsaNG7n99tv54Q9/mLN92LBhQPMW3qlUCmgearaH3fVsexQWFm4zMLL//ldWVjJy5MicbRUVFQwZMoS8vDwURWkxpKmoqHAC385ghzlNx5tqbfyp9qqvr+/Qv4sRI0bwv//9D8Mwcn63rFu3LqfVV1PDhg1rsYV/Op3G5/MB1oD84XCY6urqnGu+devWUVpa6uwndj3SRk70eBUVFbz77rscffTRTJs2LWf63ve+x5FHHsnbb7/d4RYq26vpxd/UqVPZvHkzRUVFTJw40Znee+89HnrooWbf5jV14IEHUlxczBNPPMGyZcucrnvQ2DS3pqYm59zV1dXcddddLYYg7a13U/vuuy+bNm3K6eoF8Morr+B2u50m2tuyadMmpk+f7oyHMHLkSM4880z2339/ysrKWj1u4sSJ9O3bl/nz5+P1etljjz3o168fI0eO5N577yWRSLQ5KGdnNfm17yQ0btw45/XeY489eOyxx5yBzPfbbz82btyYMzB3bW1ts9cuFAo1e19++umnznJNTQ1r167lhBNOYOLEic7F7DvvvAO0v5Wb7Y033shZf/311/H7/W1+O7nvvvvy2WefNfvG/ZVXXqGkpMS50Myul+21115DURSmTp3KmjVrqK2t5eSTT2b06NHOz6Ol5xKLxXIGvY9EIrz11ltMmzatQ8/X1tbPfuTIkRQVFbFx48acf0P9+/fntttu46uvviIej3PEEUfwyCOPAFbYe9JJJ3H00Ue3+Z4VQojuMnPmTNauXcvtt9/ebJuu6/zxj38kHo/nDH6taRo/+tGPePPNN/nnP//JYYcd5rTkCIVCjB8/njVr1uT8rtxtt92455572rzz22effcb+++/PF198gaIojBs3jgsvvJAxY8Z06Hdoe/4eTZ06lXQ6nTPmkr0P4HTx35HdgNpznbB48WJGjx7NT3/6UyeQKi8v55tvvunUISdsmzdv5rTTTkNRFJ555pmcQAqsYRLs3gbZE8DPf/5zXnzxxZybxGQLh8OccsopbN26lUcffbRZIAVWiyhFUZp9ifPf//6XwsLCNsOMHaG0tJTNmzfnlDW9Ht9rr73weDz87W9/yyn/5JNPKCsrY5999iEQCLDHHnvwj3/8I+dLxoaGBt56660ODynRlgEDBjB06NBmN83517/+td3nrKurIxaLdehL7AMPPJBIJMK7777rlNnDMhxwwAGtHjd9+nS+/vrrnMHfa2pq+PTTT53Xaf/99wfI+febTCZ566232jy32PlJSynR47388suk0+lWu7D9+Mc/5oUXXuD555/vkvrk5+fz2WefObdoPf7443nqqac47bTTOOeccxg4cCDvv/8+Dz74IL/61a/aNebD0UcfzVNPPUX//v1zPpjvvvvuHHvssVx99dVs2rSJPfbYw2mSPXjwYIYPH96hegP8+9//5uCDD252gXD88cfz5z//mfPOO49Zs2YxePBg/vvf/7Jw4UJmzpzpHL8tpaWlDBgwgBtuuIFwOMzQoUNZunQpb7/9NmeffXarx6mqysEHH8zLL7/MgQce6AQ006ZN45lnnmHKlCltfiOVn5/PV199xaJFi9odoLXk3HPP5cQTT+Tss8/mF7/4BV6vl+eee4433njDGYT7uOOOc16riy66iLy8PO6///5mTZIPOeQQXnvtNfbaay+GDRvGSy+9lNMaraioiNLSUp5++mkGDBhAfn4+7777rnNnno6OKfGPf/yDoqIipk+fzqJFi3j66ae58MILmzUfz3baaafxyiuvcOqppzJz5kwKCwt5+eWX+fDDD7nppptyLuy//PJLrrrqKo455hi+/PJL7r77bk444QSGDx/uDF55//3343K5cLlcvP76685Fb/ZzcbvdXHnllVx00UWEQiEWLFhAPB5v8a6Q7ZGfn8+nn37Kxx9/nDPGAlj/vi688EKuueYaNE1jxowZ1NfXM3/+fMrLy5kwYQI+n48JEyZw77334na72X333Vm7di1/+ctfOOKII7arTkIIsSMddNBBXH755dxyyy18/fXX/PSnP6Vfv35s3LiRZ555hq+//pobb7yxWffj4447jkceeQRVVZt107NvqnLxxRdz7LHHOnfZ+/zzz9v8/Tx+/Hh8Ph+XXnop559/PsXFxbz//vt8/fXXnHzyye1+Tu35e3TwwQczbdo0/vCHP1BeXs7YsWNZtGgRDz74ID/5yU+cwazz8/OprKzk7bffZty4ca0GLtujPdcJe+65J/Pnz2fBggXsvfferFu3jgceeIBkMtnhv+3V1dWsX7++2dih2W644Qaqqqq47rrrCIfDOXcutgf5bq3lUb9+/XK2rV+/nurqamesq7vvvptvv/2W888/H5fLlXNuj8fD+PHjGTJkCL/61a946KGHcLlc7Lvvvrz55pu88sorXH311c618JYtW9iyZYtzg5od5YADDuDPf/5zTqtsOxx86623KCgoYOzYsZx11lnMmzcPt9vNjBkz2LhxI3fddRejR4/mJz/5CQAXX3wxZ5xxBmeddRa//OUvSaVSLFiwgGQy6dw0oDPYd+b7/e9/zx//+Ed+8IMfsHz5cudu29sTtNpjzx544IGAFTCuWrWKoUOHtto6fd9992Xq1KlOj4fCwkLuuece8vLynIHVwbopQDKZZPz48QCcfPLJvPTSS5x11llceOGF+P1+7rvvPhRF4YwzzgCszwg/+clPmDNnDolEguHDh/Poo49SX1/f7O6RYtcioZTo8V566SV22203xowZ0+L2yZMnM3jwYF544YU2B+DrLCeddBJLly7lzDPPZM6cOfzoRz/i6aef5rbbbmPu3Lk0NDRQWlrKxRdfzOmnn96ucx533HE8/vjjOXc6s82ZM4cHHniAZ599li1btlBUVMRRRx3F7373u222wso2bdo09t9/f2677TY++OCDZt3o/H4/Tz75JLfddht33XUX4XCYkSNH5tyBpr3uvfdebr/9du666y5qamoYOHAgM2fOzLnbTEumT5/Oyy+/nBPM2aHUtgacPv3007nppps444wzePTRRztU32xjx47l6aef5o477uDSSy/FNE3GjBnDvHnznDuleDweHnnkEW6++WZmz56Ny+XiZz/7WbMLrCuuuIJ0Os3NN9+My+XiqKOO4uKLL+YPf/iDs8/8+fO58cYbufzyy/F4PIwePZr77ruPm266iU8++STnTnbbcsEFF7Bo0SKee+45Bg4cyDXXXJNzAdGSkpISnnnmGW677TZuuOEGUqkUY8eOZf78+c3uDHPeeeexdOlSzjnnHPLy8vjNb37DzJkzAetib/78+dxyyy1ccMEFBINBxo0bx1NPPcWZZ57JJ5984nQj6du3LxdffDG33347FRUV7LXXXjz11FPNms+31znnnMP8+fM588wzWxzj42c/+xnBYJCHHnqI5557jkAgwD777MOtt97qjF9y/fXXc+edd/LII49QUVFBUVERJ5xwAhdccMF21UkIIXa00047jUmTJvH4449z8803U11dTUlJCQcccAA33nijE9BkGzt2LGPGjKGmpob99tsvZ9uBBx7Iww8/zL333susWbNwu91MmDCBRx99tM07dnm9Xh555BHnrnX19fUMHz6c66+/nuOPP77dz6c9f4/suw/ffffdPPbYY1RXVzN48GAuuuiinO50xx9/PG+//bbzRdu2rj86oj3XCWeffTY1NTU88cQTzJs3j4EDB3Lcccc59a+vr2/3l31vvfUWV1xxBU888USLLYrtliZAi10lp06dypNPPtnu5zd//nz+8pe/sGLFCqCxpc4999zDPffck7NvaWkp//3vfwG48sorGTBgAM899xwLFixgyJAh3HDDDfzsZz9z9n/hhRe499572+wq2BkOP/xw5s2bxxdffOG0Ft9tt9045phjePrpp3n33Xf529/+5oSoTz31FM899xyFhYUceeSR/O53v3O+0Ntvv/149NFHufvuu7nooovweDxMmTKFm2++udlA/t/Vj370I6LRKA8//DALFy5kt91246qrruKqq65q8wvG1rzzzjvsueeezjh0y5Yt4+STT2bOnDlt/tu89957+dOf/sQtt9yCYRjss88+3HnnnTnjw1133XVs2rTJ+fkXFBTwzDPPMHfuXK6//npSqRT77LMPf/7znxk4cKBz3PXXX09+fj4PPvgg0WjU+R2T3TJf7HoUc3tH0hNCCJHDDpA6cvHXGTZu3Mj3v//9bV5kCCGEEEJ0p5NOOok777yzxZvrdKZzzjmHPn36MGfOnB36OJ3pb3/7G+PHj8/5ku6tt97i7LPP5q9//WuHbrwSjUY56KCDuPnmm50B74XoqWRMKSGEEEIIIYQQO9RHH31ELBZr9Y5ynenCCy/kX//6V68aG/KVV17hzDPP5NVXX+WTTz5h4cKF/PGPf2Tq1KkdvhPws88+y2677das1bsQPZF03xNCCCGEEEIIsUMNHjyYhx9+uEuG29h99905++yzufXWW1u8IUBPdPPNNzvDgVRXV1NcXMyRRx7JrFmzOnSe6upqHnvsMZ588skuea2F+K6k+54QQgghhBBCCCGE6HLSfU8IIYQQQgghhBBCdLntDqWSySTHHHMMH330kVO2YcMGTj31VPbee2+OOuoo/ve//+Uc8/7773PMMcew1157cfLJJ7Nhw4btr7kQQgghhBBCCCGE6LW2K5RKJBJcdNFFrFy50ikzTZPzzjuP4uJiFi5cyHHHHcfMmTOdweXKyso477zzOP7443nxxRfp27cv5557Lu3tPWiaJuFwuN37CyGEEEKIXHI9JYQQQoiepMOh1KpVq/j5z3/O+vXrc8o//PBDNmzYwPXXX8+oUaM4++yz2XvvvVm4cCEAL7zwAnvssQenn346u+22G3PmzGHTpk0sWrSoXY8biUSYPHkykUiko1UWQgghhBDI9ZQQQgghepYOh1KLFi1i2rRpPPfccznln3/+OePHjycQCDhlkydPZsmSJc72KVOmONv8fj8TJkxwtgshhBBCCCGEEEKIXYerowf88pe/bLG8oqKCfv365ZQVFRWxZcuWdm3vdqYBVYvASIIrBK48cOeBpxA0X3fXTgghhBBCCCGEEGKn0uFQqjWxWAyPx5NT5vF4SCaT7dre7b59Gj44ueVtqtcKp9wF4C0CbzF4S8BXAr6BEBicmUrBNwDUTntZhRBC9EKmaWJiOssAJmbOctNt29p3W/tnr7e1z7b2a8++bZW395htHdfSdnsfVVEZUjAEl/y9FUIIIYTo1Trtas7r9VJbW5tTlkwm8fl8zvamAVQymSQ/P7+zqvDd9N0XiveH2CZIRyEdAT0GmGAkIF5uTQ3bOI+igX8QBIZAYCiERkDBOMgbC/4BoKjWPorWZFkDxWXNVa0rnrEQQnQLwzSc0Maet1TW2rzpvkC7jgOan8swcs5p0DjHzNQ1s7+zjIlpZPYBDMNwllt6LHvZ1ixIajJvnDU/T9NztXW+ls7ZWhkKrZdllSuKkhsUZS1mb1NQcurZ9Li2tiuZB25tu2Ea+Fw+igPF5HnzEEIIIUTvohsmmqpse8cebGd4Dj1Fp4VS/fv3Z9WqVTlllZWVTpe9/v37U1lZ2Wz7uHHjOqsK303BWDj8PWvZ0MFMgR6HRA0ktkK8AhIVEN8CiUqIV0Ky2lpOVEKyBlK1YOoQ3WBNvJ/7GO58CA6HvN0gb3frMT19rHAKO6RSrXBKdVsttDSPNVfdmXJXY3jVbLJDLvnHIYRozg5Wsic7kHFCmSblrZVlL+uGjm5ak2EaGIbhLOuGjkFjWbOwyF7O/M/6f2MIZNe7pX0wsYKTFuZKk9+Dpmk235fG/RQUFEVxApHs4+1tTZe3tS3nvNnnVnOPb1qPpo/fkf06um9vlNST1MRrursaQgghhNhOmqpwwbOfsWpruLursl1G9wtx14mTursaO41OC6X22msvFixYQDwed1pHLV68mMmTJzvbFy9e7Owfi8X46quvmDlzZmdVofOoGqBZY0l5CiFvRO5207TGntJj1pSqt0KrVD3EyqwWValaK7iKroOG1VZIlaqH2i+syRYYAn2nZKa9QfWDmYZ0HMw6K+QydesxHYoVPDkBVHagpTWGWk6I5QHNvY0gKzNHbTyXs7xzfJARoqczTTM3zMmEQU7YY68berNwyS5PG2nSZtpZduZZ53BCpUyrIKcVkN1SKBMK2a1Zclq52L+KslrW2IGLoiioinX/DFVRc0Ka7PXsfe2ARiGz3kJI1FaZEEIIIYTofVZtDbOsrL67qyF6gE4LpaZOncrAgQO54oorOPfcc3nzzTf54osvmDNnDgA//elPefjhh1mwYAEzZsxg3rx5DB48mGnTpnVWFbqOooDmtSYKwT8Q8saAHoVUgxVQxcshHbZCHbs1VHgN1C+H2i+tKfJtY6uqjX+xQqHCvaD/ITDgMPD1a70OppGZdGtOJrgydTBSVvdDmu5DY8Bkmo3rioYVdGWCKHs5Z3KB4s6EXFnBl2oHWVoLYVYL6znbMscI0YvZgY5u6o0thprM7cDIKTN0kkaStJ4mZaRIG9Y8J2zKtC7KDovslkO27G5O2eGPHQDZIU/2sqqoqGrr25uGQ0IIIYQQQgixo3RaKKVpGvPnz+eqq67i+OOPZ9iwYcybN49BgwYBMHjwYO655x5uuukm5s2bx6RJk5g3b97O88FHUcAVtCb/ANDHWN374uUQ2wx6whpjqmA8DDneOiZZB7WfQ+UHUPmhFU7VfGpNy++APpNg4OFWQOUpbPJ4dsjzHX+EdpCFmdUiy8iaZ0IuYrnbnGMgdxASp4KNr0t2IJUdftmBmNMl0W7Z5W4SeG0r3Gppmx2S7STvL7FDGabRrFWRvW4HSXZ5Mp0kqSdJGklSeqpZmKSbutVVDd36J5LVbcxucWQHP9mTpmhOaORSXc2225MQQgghhBBC7Cy+U6KxYsWKnPVhw4bx1FNPtbr/9OnTmT59+nd5yN5D81otqPwDITgCohutKVFl3cHPHQJPAfQ72JrA2r71f7Dl31ZY5QRUt1vB1JAToHBi5wYtimKFQQC4O++80ELAZS/brbqMTFfFRPNy57i26p7dqivTjTF73Qmm7MDL7sLoymrx1VILr6aD0LcQeslg9D2WHTBlTyk95Szrpk4inSCpJ4nrcZLpZGMARWNLJmc8o6yxi7LDJE3RrLmqOUFS0zIJkYQQQgghhBCidXIv5a7gKbCm4DAreIqshVQd+Ppb4YgtMBiGn2hNsS2w5Q0o+zs0fGPNy/5udRMsPRYGHNp2976ewBnvaged3+7CiJk7d0It3Qq9zNZaebUQeimKVd5qt0Ytq/WXZgVdaE26NbYn8GraoqvpmF7SwiubbuikjJQTLmUvp400sXSMRDpBLBVzWi454yqZ6WZ3K1OxgiNN0Zy5W3PjU3xOqGS3XBJCCCGEEEIIsWNIKNWV3CHrjnu+ftCwEqJl4Mlv3jUPrC6AI34Fw0+CumWw/kWrBVXDN7D8Vmsq3BMGfB/6f9/af1fjhDs7gBNeZbfwaqFbYzqK1YzGaBKStdXKK3vMrrZaeGUNVu8MXO9uJexqZbwuJ+jqmd0Z7bApqTd2hbPn8VScWDpGLBWzxl/KavlkGIbTcgnIaamkqVbA5Ff9zrq0WBJCCCGEEEKInkdCqe7g7QvuyeDtB+FVENkIgYGZAKEJRYHCPaxp7IVWa6kt/268i1/tF7D8Tug7GQYdZbWgcoW6/CntdHK6NXayFlttNWnhlT2OV86YX9DyGF6Ac1fGrFZdOa27MsGU6iGndZfqySy3FnK1IwRroYWXYRrW2EtNp3SSSCpCJBmxxmUyUqR1645xpmE9t+yxlezJ77JCJpfqQpPuk0IIIYQQQgjR60ko1V1UF+SNsAKquq8agynV0/oxngIY/gtrim+F8v/Clv9AzWdQ/Yk1fXUz9JsOQ34MfafsuJZEYvvldGvs5H+COeFW0+VMd0Y9uztja627MiNz59Q5N/AyTJOEniZpGiSMNElDJ6GniehponqSqJ4iZZikMUkbJmYmwFIUDZfqxq25cWleAqobl+bBpQVRFVfzrpPZoZcQosfI7lab1JM5LR2TetIZz63pun2TgOyuuPZdKLOPyR4PLnt7Qk8wus9oDhp6UHe/BEIIIYQQ4juSUKq7eQqg7ySo9UH0W2sQdFdw28f5+sGwE60pthnK/mG1oop8C1v+ZU3+Uhh8nDUGla94Rz8T0RN0cpfGpJ4ioSdJpJMk9DhJPUUkFSGcjBBLJ6wPl2nrwyJYA4O7FBW3ouFWFQKqhltVcamKlWuZmUnPTDl1zwq9UHDu0IgKqh2G2a27MnM0a9B5uzUYWuMxzvnU3G3ZLcdyHkeI3sE0TRJ6wml9mEgnnPWEniCZtuZOWTpBykg5A/y3OBlWK0Y7MMrelh0kZYdPutn0H3HX+aL8C+754T3d9vhCCCGEEKJzSCjVE2g+6LMnuANQv8LqutXSOFOt8Q+EUafDyNOg/mvY+Aps/gfENsHK+bDqAeh3CAz9mdXNTz6AiyxpI008nSSuJ0no1jycjFKfipDQkyT1FCkjnWlIZaKpGh7VjVv1EHAF8Pit7nXfmTN2l91lEZq15jJSja2/cga4b+F82Y29cgKvrKBKyZ67ce6saI/F5Qxanx1uqbnhlnM+yO3GmD2XVl47O7u7ajwdJ5aKEU/Hm00JPbHN9UQ64YRMiXSCuB7PWbeDpp7Io3lwq25cqstZdmutr7tUl7WuWPPs7S7VlbNun9etWXeJHRga6CwLIYQQQojeS0KpnkLVrDvraX6o/RKStR0LpsD6AFww3pp2v8C6e9/Gv1jjTpX/x5pCI61watDR4ArsiGcieqikniKuJ4ilE8TTSSKpGPXJCJF0jKSeImmknB58blXDo7nxqG4CXh8e1b3j70Sn2MEOdOqvJqerIjghV9M7NhppING4L2R1fdxWvVto4eWM76VkLatYrbWyJzUzgH3TgKu11l5N59ldHKXV17aYpklSTxJLx4imokRTUeLpuLNsl8dSMWeQ/Zx5OkY8ZQVIznomhEroiW55Tqqi4tW8eDQPXpcXt+rG6/I6ZR7NYy27rLm93d7W0uRW3Y3LmhuPmpk32W6X2YFRV92tMqknqYnXdMljCSGEEEKIHUtCqZ5EUSA41Fqu+RxQrO5928Plh8E/sqaGlbD+BauLX3iNNe7UN/dC6XEw7OcQGNxpT0F0v5SeJqbHiaWtAKohGaEuGbZaQqWtsVpAQVUUvJoHj+Ym3xPCo7l2zrvU5Yzh1ckDpOcEXllBl3NHxsxYXU7wZeYGX9mtwrbZ2gtyW11lhV92S63sQe7tOzc63RztQeuzw6+sQEvJCrqatvLKCcK0Lg2+dEMnlo4RToaJJCNEUhGiqSjhZJhoKuoMmm8HS5GUtU8sFXPm9rZoKtolXc68mhefy4fXZc2dSWuhzOXDq3mdIMk+zlm2t2XK7HWP5sHn8qEpWpeFQUIIIYQQQnQ2CaV6ouBQ60Ns7ZfWB0J33nc7X95uMOFKGDMLNv0N1j8P0fWw7s+w7hnodzAMPhaK9wdVukP0FoZpEE8niabjxNJxIqkYtYkwkXSMeDqZCZ9AUzW8mgev6ibo8+ORLi+dJyfw2sFyujfa4Rc44Vd2uZHKlOmNx7an1Re00PKrSWsvJ8jSssb3cmW1+MoEXiikTYNwKkY4FSGcitGQjBJORWhIRggno4RT1no4GSGSCZqsecQJl6KpaCe+iI28mpeAO0DAHcDn8uF3+wm4AtbcLnP5c7b7XZnJ7XcCJb/L72y3gyS5O6QQQgghhBDtI6FUTxUcZrWgqF0KKOAOffdzukMw/ESrdVTl+7DuOaj8ALa+bU3ufOj/fRh4hDX4uiIfrHoKwzSIpuJE09ZUlwhTmwwTTyeI6wlM00RRVHyaB6/mpq8vX8KnnU1O98YdpIWWX0k9SX0yTH0qTH0iYi0nozQkI9QnIzSkojSkotSnooSTMepTMSuASsWJdmKXNpeqEXIHCLr8BN0Bgu4AAXfucsAdIOgOEvAECLgCmXnImruD1uQJ4Xf50TQX0uVRCCGEEEKI7iWhVE+lKNb4T6YJdUut1giar5POrULJgdYUXgsb/mLdrS9RaY1BtfEv4C2BAT+AQUdC/jj50NaFTNMkmmn5FE3HqU00UBNvIK4nMuM+mbhVF17NQ9Dto68vf+fsdic6VdrQqU+GqUuGqU2EqUs0UJsMU5cIU5+0wqbaZGY5U2Z3+/yufJqHPE+QkNtPnjtAyO0n6PYTcvsJufyE3D5r2e0j6PJaZS6vsx7QvHg1+z2e6fLY7lZfADEwEpCogUTTsb7s5czg9ordzTGr1VeLXR+zuze2cIdHGe9LCCGEEEKIbZJQqidTFMgbCXrMGhcqUGqFU50pNALGXQRjL4Dqz2DzP2HLfyBRkene92cIDLFaTw08wtpfdKqEbg06HknFqEuGqYrXE0vFSehJTKwAyufyku8J4dW6YMBx0ePZwWVNop6aeAM1iXpqE2FrnmygNt5gzRNhahMN1GXCpu2lKgp57iB5ngD5nhD5niB57gD5niD5niChzHKeJ0DIHSDPHcxa93fO3Rk7yunSaLf+ssfxMhrLzSZdHp39s6ac1mNtyBnzq4VB6XMGrXdlhV8q4ALNlRt6QdZ4X0ructM7SOaMLdZksH3RbcrLy7nxxhv58MMP8Xq9HHXUUVx00UV4vV42bNjA1VdfzZIlSxg0aBBXXnklBx54oHPs+++/z0033cSGDRvYa6+9uPHGGxkyZIiz/bHHHuPhhx8mHA7zwx/+kKuvvhq/398dT1MIIYQQ4juRUKqnU1TI3x2MOEQ3WYOS74gPGooGRVOsafylVre+sn/C1ncgugFWP2RNebtZ4dSAH1ghmegQuxueNZZOjMpYLQ2pCLF0At000BQVv8tLyOOnWCuUAGoXkjbSVMfrrSlRT3W8jupEPTWZ9ex5TaKBZGbMsI7Kcwco9OZR4AlR4A3lzj0h8jxBCr2hTOAUyoRO/t7XGs9pAQWdPsB9S7IDLmcwdT03/DKz1skaFN85rp2P1dYdH1tqAWa3AlPtZSUTjNnbodUwjOzgi+aP5WynSTDWdN9di2mazJo1i/z8fJ5++mnq6uq48sorUVWVSy+9lPPOO48xY8awcOFC3njjDWbOnMnf//53Bg0aRFlZGeeddx7nn38+Bx10EPPmzePcc8/llVdeQVEUXn/9de69917mzp1LUVERV1xxBXPnzuWaa67p7qcthBBCCNFhEkr1BpoHCsZDOgrxLeAfuGMfT/VAv+nWlI5a401tft0KqhpWWtM391p1GnA4DDgM/AN2bJ16Kd3QMwM9R6lLhKmI1xJNxUhkukT5XF78mpf8QAiXDI680zFMg7pEmKp4HZXxWipjdVTFa6mK12WmWqrj9VTF66hLhjt8fp/moY83nz7ePAp9eRR68ij05tHHm0cfbz6F3hCF3jwnhMrzBOV9tqM43fZgh/5pzWm5ld0CLOuOj9BKCzAjd1tHgjDIhFLZgZhdmB1QgXM3yKZBGWoLXRubzJ0pcx7n3DQ+rpGCZEPjc+2B1qxZw5IlS3jvvfcoLi4GYNasWdx8880cfPDBbNiwgWeffZZAIMCoUaP44IMPWLhwIeeffz4vvPACe+yxB6effjoAc+bM4YADDmDRokVMmzaNJ554glNOOYUZM2YAcN1113HGGWdwySWXSGspIYQQQvQ6Ekr1Fq4gFO4BVZ9AsgY8fbrocQMw6IfWlKyD8jetgKp6MdR9ZU0r7oTCPaH/obt8QGWHUA2pCDXxeipjdUTTcVJGGkVRCLi85HuCeLU+0gqqFzNNk7pkmIpYDRWx2sy8hspYLRVxa14Zq6UqUUfa0Ld9wgxNUenjzaevL5++vgL6ePMp8uU7ZVYAZS/n4XN5d+CzFD1Szh0fuzhgbDoQfk53R7sse6D87O6RWfvkdJHMLjMaT5NNaVKmp60pHQHyO/EJdp6SkhIeeughJ5CyhcNhPv/8c8aPH08gEHDKJ0+ezJIlSwD4/PPPmTJlirPN7/czYcIElixZwpQpU/jyyy+ZOXOms33vvfcmlUqxfPlyJk2atGOfmBBCCCFEJ5NQqjfxFlmtk2o+B+rAU9C1j+8pgCE/tqZEZSag+jfUfAa1X1jTijuhYAIM+D70mwHBIds4ae9mmAaRVIz6ZISaRD0V0VonhFIVhaDbL3fC62XShk5lvJbyaBUVsRrKo9VsjdawNVbthE8VsdoOdZ8r9OZR5CugyFdAsa+QIn8BRb5Cirz5FPkLnW35nmDv6yYndh05gVg3SkUhspmONfPqWvn5+Rx00EHOumEYPPXUU3zve9+joqKCfv365exfVFTEli1bANrcXl9fTyKRyNnucrkoLCx0jhdCCCGE6E0klOptAoOtcUlqvwRM8BR2Tz28xTD0Z9YUr4Dy/1oDpNd8BnXLrGnF3RAaDf1nQP9DIG/MTjG2SDQVd+5itjVWQ0MyQkJPoSmqhFA9nGEaVMfr2RKtcqbyaFUmeKqmPFZNVbwWo53dggq9efTz96HYX0iJLzP3F1LsK6Q4U17kLcCtya9aIXZlc+fO5auvvuLFF1/ksccew+Px5Gz3eDwkk1a37lgs1ur2eDzurLd2vBBCCCFEbyKflHobRYHQcGteu7Rru/JlS6cbl119oPSn1hSvgoq3rKnmUwivsqbVD4JvIPQ72Jr67gNq7whu0kaa+mSEukSY8mg1dckwsXQcBQW/20eBJw+fy7PtE4kdLqWnKY9VsTlSSVmkki3RSrZEqtgcrcwEUNWkjPQ2z+NSNUp8fegf6EuJv/m8xN+HYl+hhI9CiG2aO3cujz/+OHfccQdjxozB6/VSW1ubs08ymcTn8wHg9XqbBUzJZJL8/Hy8Xq+z3nS7jCclhBBCiN5IQqneKjgMUK0uc4lq8PbdMY8Ti0M0bs3jCYjGIBy1Qim7MYnd+MkZHmQ4cCr4/w+MZWAsgdQyiG+G9c9ZkxaEPvtC/4Og3wFWy6seJJaOU5cIUx2vpzxaTTgdRTcMvJqboNtPka9AxoTqBmlDZ2usmrJwBZsiFZRFKiiLVLI5UsHmSCVbYzWY2+jSoyqKEzj1DxQxIFDkLPcP9KWfvy99ffnSjU4I8Z3Nnj2bZ555hrlz53LEEUcA0L9/f1atWpWzX2VlpdMlr3///lRWVjbbPm7cOAoLC/F6vVRWVjJq1CgA0uk0tbW1lJSUdMEzEkIIIYToXBJK9WbBIdbdiGq/gESVNebUd5VKQ2091IehqhYiUSuMMkwrfHK7waWBy+XcPTxXVlBjBIEpYO4DxMD4GvgKlGWgh6HyLWtaBnhHQf4U6Ps96DsR/EHwdF0rFMM0aEhGqU00UB6rojYRJpqKoSgqIbeffv4+uFT559IVwskoG8Nb2RjZysbwVjaFt7IpspVN4Qq2RKvQzbYHDvdqbgYEihkYLGZgoIgBwWIGBooZEChiYLCYEn+h/CyFEDvcvffey7PPPsvtt9/OkUce6ZTvtddeLFiwgHg87rSOWrx4MZMnT3a2L1682Nk/Fovx1VdfMXPmTFRVZeLEiSxevJhp06YBsGTJElwuF2PHju3CZyeEEEII0Tnkk1lvFygFFKj93Bp8fHtaHOk61DZAdQ1sqbRaQpkmeL3g90JBHqjftdVIITAQONS6G1NiNcQ+hdQXYKy31itWQ8VzgA/UseDZA/pMgYLh4PeBzwtejzV1Qisl3dCpT0aojtexOVpJXSJC0kjh1TyE3H76evOlNdQOYJomNYl6NoTL2dCwlY3hcjaEy9kYLmdjeCt1yXCbx7tVFwODxZQGSxgULGFgsIRBwWIGBYsZGCyRn5sQotutXr2a+fPnc9ZZZzF58mQqKiqcbVOnTmXgwIFcccUVnHvuubz55pt88cUXzJkzB4Cf/vSnPPzwwyxYsIAZM2Ywb948Bg8e7IRQv/zlL7nmmmsYM2YM/fr149prr+XnP/+5dN8TQohdhG6YaKpc64qdh4RSO4PAIGte+0XHg6naeli9DrZWWd3vggHoVwTaDrzVuKKCbzdr4v9Ar4XEF5D8AhJLwYxYXf7iS2DzU1DWB8zdQB0DnnHgK4G8EOQFrODM57HmXvc2660bOnVJq1teWaSC+mSElJ4m4PbRx5eHV5OxoTpLQzLCuoYtrLen8BY2NJSzPryFSCrW5rFFvgJKg/0oDfVjcMgKnwaH+jMoWEKJv1C61gkherT//Oc/6LrOfffdx3333ZezbcWKFcyfP5+rrrqK448/nmHDhjFv3jwGDbL+lg8ePJh77rmHm266iXnz5jFp0iTmzZvnhO1HH300mzZt4pprriGZTHL44YdzySWXdPlzFEII0T00VeGCZz9j1da2v8jtqQ7ZvYRLjpDWvaKRYprtvM1UNwuHw0yePJnFixcTCoW6uzo9U2wz1HwOKODbRjCVTsOGzbBmIySS0K+v1SWvu5kGpNZmAqplkFoFNOmupfQHcyQYI8EcAWof8HjA44KAH/KD4Pdb3f98Xgyvm1ozTlWsjrJoJfXJMGlDJ+j2k+8Oyp3RvoO0kWZjeCvfNmxmXf1m1jVszgRRm6lJNLR6nIJC/0BfhoT6MzjUn8GhfgzJ68/gYD8Gh/oTcPu68FkIIXqTZCpKTWQzB+1xJnmhQd1dnV5HrqeEEKL3O/rud1lWVt/d1dgux+41kLt/sU+vfg4TBuXz2qyDursaOw35NL4z8Q+05jWftz34eV0DrPoWNldCfgiKCruqhtumqOAZZU2hn4ARh9QKK6BKfg3pdWCWA+WgfmAdoxYDoyE1CqqHQnlfTAPqzDhVSpwyotRqKdI+N6FQASX+fNy+EOC2Hk9sUzgV49v6Mr6tL2NtfRnfNpTxbf1mNoa3tjnGU7GvkGH5Axka6s+QvAHOfHCon7RKE0IIIYQQQohdnIRSOxv/QDB1qP4M0hFwBRu3mSaUbYVv1kIsAQOKe0brqLaoPvDuZU0ARgSSKyD5lTVPrwOj0pr40NpF81PnGsRWpT+VehFJYyAleh7uehNqaoDaxkHb3S5rrKqgD7w+q7WVx924bRcbm6g20cDa+jLW1G1ibf0m1tRv4tv6MrbGalo9xu/yMixvYGYawFBnPoCgW8Y4EUIIIYQQQgjRsh6eSIjt4i+F/AjUfQWqG1SPdVe9tRtg9XorhBnUr7truX3UIPj2sSYAI0YqsZx4Yhkkv8FvbMRlxuhjrqYPq9kdQIW4VkTEN5iIWkpUHUSUfphp1Xpd6sNQXWuNqaVgjUvl0qxgKuCzBln3uBvDKq/H2t6LNSQjrK7bxOq6jayp38iaOiuAqorXtXpMsa+Q4fkDGZE/iGF5gxiRP4jh+YPo5+8jA4sLIYQQQgghhOgwCaV2RooCodGQikLkWzD7wMr1UFYORX2skKWXM0yD2nSUilQ9mxIe6vWxaOp4Ctw+ipUaQsZ6gvpGgsZGfGY1PrMKX7qKIj4HwEQlppQQdQ8k5h1IVB1IVB2AofggrVtjbqXTUFVr3Z3QTqxcGrgyraiCfiu0sltVuTPBVQ8KrBJ6krX1Zayq3cjq+g2srt3I6rqNlMeqWz1mQKCIkQWljMwvZUR+KSPyrQAqzxNs9RghhBBCCCGEEKKjJJTaWakaFI6Dyi3wxf8g5oUBJT2/u942RPUEVekwGxNVVKUipE2dPM3HIE8ftMz4UDH8xLRBVLitYzQzQlDfRNDYRMDYRNAow22GCZjlBNLlwBLn/AmlD1F1ADF1ADFff6KBASSVwszYU6YVVKV0a3D4SDQTWClWCyuXy5rcrsYWVm67O6Brh3YJNE2T8mgVK+s2sLJ2Aytr17OqbgPrG7agm0aLx/T392VUwWBGFpQyqmCwE0BJlzshhBBCCCGEEF2hdycUom1bqmBVDGJAibvXBlK6aVCdCrMlVcfmZC2RdAKf6qavK4hXdW/7eCVIvWsM9YyxCkwTt9lAwCgjYGx2Jo9Zh9eswavX0Ef/uvF43MTVfsQyU9zdj5i3hJRS0BgwmUZjCysnsDJAybSwsrsEujSr+2TAZ90x0A6qXK52t7JK6inW1G3im9r1fFO7jm8yAVR9MtLi/vmeIKMLBjOqYAijCgYzumAIowpKpeWTEEIIIYQQQohu1TtTCtE2w4C1a+Hrr8ETgtHToH4FpOrAXdDdtWu3iB6nMhVmQ6KK6lQEU4FCzU9fb/C7jWGkKKSUfOrUfOoY6xRrZpSAsQW/sQW/UU7A2ILPqEAjRdCwWlpl0/EQV4uJKyXWXC0m7i0m4euLqWTfZtvMBFaZqS5sdQs0M4FVdisrl2YFVj4veDw0kGRFbDMrIptYUb+Rb+o2sLa+rMU73mmKxvD8gexWMITRhUPZrXAIuxUMoUTGfBJCCCGEEEII0QNJKLWzSaXgm29g5UooLIS8PKs8bxTUfwOpBnDndWsV22KYBtXpCJuTtWxO1hLW4wRVL/09+biUHTtWk64EaNBG0qCNbCw0dbxmDX6jHL9Rjs+owG9U4DWr0EgSNMoIUgZZGZGJQlIpIK4WkVCKSKhFxJW+JDxFJL2FmEqgyZM2QNepitexvLaM5Vs2syJRzorkVjbp9S3WNd/lZ0xoMGMKhjCmcCi7FY1gRJ/BeFyeHfDKCCGEEEIIIYQQnU9CqZ1JOGy1jtqwAfr3B1/WgOa+fmDqVjClaOAKtH6ebhA3klSkGtiQqKIy1YAJFGoB+nqKureVj6KRUIpJqMXUMqGx3NStAdSNCnxGJT6z0lnWSOA1a/HqtcDqnNPZgdW3eh4fJzx8Gjf4IhHny1gdW9PRFqswyF3IGG9/dvf2Y4yrmN21IvoTsF6XtAJ1GoQjULbGamEV9DeOY+VxN45zpao77nUSQgghhBBCCCE6SEKpnUVFBSxbBjU1UFra8vhRvgGgpyG82hq4W+veu/CZpkm9HmNLspaNiRrq0lH8qod+rnzcag9/aypaptteSW65aeIigteowmdU4TMrqUtuZVlsK5/HGvg0ofNJvJYtem3zUwK7u2Fvn5cJvjzG+4sZ4x9AwF1CUikgqRSSUkKZQdedB8zqGpiG2gRUVls3C8Rs7BKYPZaV15u5U2DWHQOle58QQgghhBBCiC7Wwz/5i20yDFi/3mohZRgwZEjrAYOiQLAUSEP4W/D2AdXblbUFrIHLK1MNbEpUsyVVT8JIUaD5Gert5lZRnaBOj/F1dAtfxTbxVbSMr6ObKE8174KnAmO8Qfb2epjsgyneOFM8MUIqQCIzVYKx3FrMMFFJKvkklXxSSgFJtcBaduWTdOeTUvJJOXcLpDGsyhnLyjoTmpYZZF0Dv79x8HVP1qDrbre0sBJCCCGEEEIIsUNIKNWbRaPW+FHr1kEoZI0htS2KCoGhYOgQXQ++YlC2fQe7zhA3kmxN1rM+UUVVKoyCQl9XEL87v0sev7PFjRQrYptZFtnIsugmvopuYkOyutl+CgojfMWM85cyLjCIcYFBjPENwK/ljv/0jZnCY9bhMWrxmrV4zFo8RmZu1uMx61EwrK6BZq11UPPxzjFRSCkhUkpeJqTKI+UJkfLmkVTySCl5pJUQKcMP6cxdA2vrG1tYKViBlaZm7grogYAX/D4rpPK6pYWVEEIIIUQH6IaJpvbu66ad4TkIIXoeCaV6q/JyWL4cqqut8aO8HWjxpGoQGg7oEN0EvhJQdtxboT5tddHbkKimLh0loHkY4CnY4QOXdybdNFgbr2Bp1AqglkU3sjq2FR2j2b6DPX0ZFxjEhIAVQo31DyKobfvnYypuZ/yqhhZ30HGbYSu4Mutwm/V4jDo8Zr21bNbjNsMoGHjMBjxmA1DW+uOhkCZASguR1kKk/CFSSoi0EiRlBEgbflKGl3TET7rOjWlkLkJaamHl9WTGsXI3jmclhBBCCCEA0FSFC579jFVbw91dle0yul+Iu06c1N3VEELshOSTY2+TSsHq1bBqldWtqq3uem1RXRAcaXX5i2+2BkLvxJDIMA2qUmE2JWvYnKwlbiTJ1wIM8fZFVdrRHcwwUONJtFgSNZZAiydR40nUZBo1mUJJpFHTaZS0gZLWUXQdxTDAMME0UQwz93yKgqkqmKoKmoppTy4N06VhuDRMtwvD7cL0uNjiSrJEqeILKvhCL+erZDlRM9msmkWuEBMCgxnvhFClFO6oQeQVjZRSQIoCIq3tYxq4zAgeswG3WY/bmYdxZ4Iqt9mAy4ygYOImgtuIAOW0kK9ZXNaUxkdaCZImQNr0k9L9pOu9pGu9pA0/afyktSBpLYTuykcPFmYGXfdI6yohhBBC7PJWbQ2zrKzlOyuLriGtvYToeSSU6k3SafjqKyuUKi6GYPC7nU9zQ94oQIf4VvD2/c5jTCWMVOYuepVUpBoAhT5agH5aCFd9FHd1Oe7qetw1Ydy1YVx1Edx1EVz1EbSGGK5IHC0cQ4smUExzm4/XGaJuWDwQPhoMH5XCh4NhY0Hz/UIJmLwF9i13MaXKwz61PgaYfkx/Et2/Bd1fjRH4Bj3gQw960f2ZedBHOuhDD/rRgz70oA9zR7UkUlTSSh5p8oBBre9nGriI4jbCTkhlBVgRXGbYmhPBbYZxmVEUDFzEcZlxoCrzWICWmZrSwaxTSNf5SJs+dPyklQBpNYDuCpH2FpB2h9A9eaQ9Bei+PHR3PmlXAF0LNhnMXQixszJNE8M0MTEwmiybpomBiWEamKaJibU9rceRjxNCCNG1SkLenSLQ6e0t1g7ZvYRLjhjb3dUQolNJKNVbZAdSAwZ0rLteWzQP5I2xwqjoJnAFwR3q8GkakhEqyzdSvX4NlG2leGuY0VVRfJX1eCpq8VTVo+itNcVpnamq6H4Phi8zeVwYXjeGx43p1jC1TCsnlwZ2S6hMqyibYprWWEmmiWnorPHG+SQvwqd5ET4pjLEsL4HeJANRDZhQpTJtE0xbb/C9jTCuAjQTIJ2Zoh1+PjbD47aCqpAPPeQnHfI783TIj56XNc8LONv1oK9zWhopKmmsbnsxBrS9r2mgEc8EVxFcZgSXGc2aR3GRtWzG0EiiKCZuYriVGFCTOReQykxt0BUfaS2I7sqEWFoAPXtyZeaqP2s5gK750TU/pto146QJsTPKDopMEwwMp8zAxDQzoRGNwZHZwrphGplfvSYKmXssYKKgYGbWFEVBQUFV1Mw8s6xYywoKKiqqqqCpKi7FhUYAnxnA6+r6G3UIIcSuKt/v2mkCnd7cYm1UyXdslCA6xc4S0vaU5yChVG+g69bd9Vat6vj4Ue2heawWU64gRL6FRBV4+rTcWiWegPVlsL4Mc30ZyfUbMdZvIrC5ihHJNCPaeBhTUUgXBkn2ySPVN490QYh0QYBUQZB0fpB0nj8noNH9XkyP6zuFMPXpGMuiG/kyupGlkQ0sjW6iXo8126/YlcfE4GD2CAxmj+BgxvkHEbDHgTJNkmmdL+NJ1HjK6UqoxRJW18JYEi2aQI0n0CJxtFgCLZqZInG0aNyaR+JOCzA1mcKTTEFNi6NHtf4aqirpkA89L0A6P2AFVnZwlee3yrO35VuBFq7v0DVTUdEJoCsBEpS07xAzhcuM4SKKlgmqXGbMWsZejuEyo2hGDBcxNDOOplhplWbG0dJxSFdtV5UNxY2u+dA1P4bqz4RZPivE0vwYmg9dzWzX/OiqzykzVB+61jg3FelyKHqGtsKi7EDIblmUHRbpdplpOPtaFOvtbZIJiqx1OyRSMuuaouUERQoKmqKiqiqqouBSXKiKmilT0NBwqdakqRoqijVXMiFTJnyyJqXx2Kz17P1y7sxqJCFZY/3tEkII0aUk0BFi5whpe9I4cRJK9XS6bg1ovnKlFUj5fDvmcRQVAoNAC0B4NUQ2Q1Uavt0Cq9fD2o2wbhOUbYXMhxkFyI7HTFUlWVJAYkBfEgP6kOhXSLK4kGS/ApLFhaT6hL5bOLINaVNnTbyCLyMbWBrdyJeRDXybqGy2n1dxMS4wKBNADWGPwGD6u/NzP/RkUxRMtwvd7ULP22Yjn7YZRmNYFYnjCsfQwrHGeUPT9ahTpiVSKIaBuz6Kuz4Km9r/sOmAz2p1ld8ksMoJtxqX9Tz/d+piaCpuUoqbFB27s6JiptGIZ0KpCC49gqZH0IyYNZHApSTQ1CSakkJTktYyCesYMwGAaqZQ0ync6Y6Ffi0+F9TGkEr1YWheDNWbCbC8WetejJzt1tyaPJm5L7NsrZuq/AreGVghEFld0AynVZG1bAVCeiYQsrqmGZntJmBmfq2azu8hKzSyAqHs5exWRYoCKo2tiuxyTVFRVBUtExZpqmoFRIoLt6rhUl2oioKmaC2GQyqNZfZj5GxXsusgga0QQgghdk29OaTtSeQTUU+WSlmBlN1CakcFUomE1S1wxQrr8VZkHjOeaLlaeX7qBvclUtoXY8gA0oP7kxhURKKkcIeGTk1VphpYGtnIl9ENfBnZyNexMmJG88HIh3j6OuHTnsEh7Obv3313/lNVqxVYyN/hQ5VkygqoGuywygqsXA1RNHu5PpJbHo6jmCauaBxXNI63vKbdj6cHvFlBlZ90XrCxVVZ+INMqy++0ckvnBTC9363bnKm4rG6FSgg8xa3tBWkdUmmrW2tSt8JbAFNHVdJorjSamkLT0mhqZl1Lo2ppNDIhFklUM4FmJhrnRgLNiKPpcdTMwPYKBi49Cvr2d9ds9fmiZYVUnqzwKmtdyd1u5pS7rTLFnbWPO2vdjens5wa0Xa7Vlx0KZY9XpJutd0EzMmFRbqsiINOKqHHeWGa3LLICokyIk2lBZHc/0zQVl2IFQi5VRVPsueqERM26sDXtztZCWJQdSLXaskgIIYQQQogeSkKpnioeh2XLYN26zg+kTBPWroX33rOmJUusD/dNeb2YwweSHNqXmqElbBxcSPmgPFx9Cil0B7s02IkbKZZHy1ga3WhNkY1sSdU12y+oepkQKGViJoSaGBxMoWvnaKpretykitykijrQ+kg30CKxrMAqhqsuK7iqjzYGXPWN4ZZimk4XxI4EWYbHnRNc5XYrbNLNMGSt60EfaB0Z2FwBl8uaWqqDaWDoBindsMIq3YCEbt1p0jCs97+TLSjWXSxVe26NTYamgVtFdZtomo6q6WhKGpWkFXopSVQziUoSjRSqmUA1k2hmEtVINE56AtVIomWXGQkUezwddKcVWFcwUXKCKiu8cmMqLiu4UtyYztxlbVdcOWWN+1jbnTLFlVl2WcuZc5qKZu2nuDDUTDkauqJhoGBAm4NbNx+fqLGFkZlpZaQ4r2jzwKhZC6IWWhWpqhUYaaqWCY60zPhFWk5g5LQSaqH7mUJjK6LsFkiaEyJJSCSEEEIIIURTEkr1ROEwLF0KZWUwaBC4O2HQ5mgUPv4Y3n/fmjZvzt1eUAC77w5jx8Luu2OM2Y3K/nlsjJezuWEVqXg5hYpGf18Ryne8Q9+2GKbB+kSVEz4tjW5kZawcndyB0lUURvr6ZcaCskKoEb5iVLlzWyNNRc8PoucHSZS21vKoCcOwuhbWN4ZVVsurzB0Sc8oz83AMRTessbKqUniqOtaMNR30Od0G03l+0qGANb5Y1jhjTccca3WsLEUFl9rO324mGCYYOuimFVrpBqSTEDMwDGuyd7VuNagC3kx4pVqPZ4dammoFWi7NCs38mfBMUzPbNVBAUU1U7JBLRyXVuE4a1UiiZMIsxUii2pOZRDVSznarzF5PZbZb+yhmytrXbOxwqmCiGUkgCXqHfkQ7jK5oVlDlzF2ZIKtxTiY4Q3WB4gZ7WbWX3ShqY7liz1UPij1XXCiK25rUxklV3c7+1vEmKKa1rCjWXQ8UV+bnnNlH0TL7ao3bhBBCCCGEEB0moVRPU11tBVLV1TB4sPUhdnuYptUl78MPrRDqs8+s7oA2jwemTIH997emIUNAUYjrCSqTtWyIlVNRvwFFUegbGo4vNBTiW61B0JU4uPOwPpx/d1uT9XwV3cSy6EaWRTexLLqJiNG862CRK+QMRD4xMJhxgVKCmtx9qdOpKnqe1T2v5Q6cLTBN1Ggip+ug1a2wSVfD+mjjWFnhGFrUegRXJI4rEoctHauq7vOgB31WYBW0AytfZjlzZ8OgXeaztgetZdPjBpTGQKlDvw3tMCvT+speThuQTOVuy3qN7CZapqKgKwq6plplTrilWUGLKy8TbGUFXKritOSyjjcxVQVdtfIyQ1WtFkQqmAroqmK1GjN0FCMFZioTdCVRTR3VTKGZaTQzhWqkUU1r0swUmqmjoVtzM51Zt7enUQ1r3WXqqFj7WOe05oqZRjHSKGYqs2zNMVJOmyabZupg9pCEbLspVlClZgVWitZ8Ulsqz4RaLe2fMzXdR816HLVJudq8nMz7C63J9qxlWjmepmVqpizTHTRnn+ztWfvknNveLq3HhBBCCCF2dRJK9RS6Dhs2WOM6JZNWINXRC/aqKvjkEyuI+ugj2Lo1d3tpaWMIte++TpdAwzSoTdWzNVHNpkQF9akIXtVDP09f3NkDMQeHgqcwE07VgOYGV4iOhFO16QhfR8tYFt3EV9EyvopuorKFwai9iptxgYFOCGUNRl4gXWB6KkXBCPpIBn0kB3TguLSOyxng3Q60YlY3wkhm/Kxw4/hYzgDwkTgAWjyJFk92uGUWgOF2oYd8pDMhlR7wOYGVvZ7OKfdaywFr2fB52u5CmD1WUaa7WWOZgWnYLbHSmLoVHhkpA8PUMU0D08gEX6bV5dCKs+z/KqiAqmioqoKqapllFUVRcbk0fIqGS3Pj8nhwuzy43B5c7hCay42quVBd1t3SVJfLGv9Ic6NqqrVN1RrLXVnlWd3StuvfomlaAVQmJMPImjKhVWN5ukl5C9vNdGaevZ7KKk/nLhtp6/GblbWw3UxbLejMTJm9rUmLzcwTs/bTW+gGLdqgNAmwmi4rTYIve38gfxwUH9h9VRdCCCGEEJ1CQqmeIBKBb76xxo/Ky4Pidnazqq21xoP65BOra97q1bnbvV6YPBmmTYMDDoBhw3KCrkg6RlWqjk2xrVQl60ibafJdIQb7+rXSBU4Bd4EVRKVqIV4JiVrrnO4gKLm3565NR1ge3czXsTK+jpaxPFZGWbK22Vntbnh7BAYzIVjKhMBgRvpKum8wctF1XBrpwhDpwlDHjtMNtGi8SVAVy10Px6y7HGZCLM1ejyZQTBM1lUatCeOu2b7buJoKpP0eUgEvKb+HdMBatsv0gCcTYnnRfR7SAS9GwIvht8oMvxcz4EPxh3Cp1l3RNDLjGDljEWUGyc7cEc2eW+MWqaimgmqaaIbV60wzQdVNVJSsrogmJO0WXZnQxh57STGtJ2KvO2NrZT78a5nWLM5cy+qemGnB5dZAczWOxWUfmzNWV2abM4aXCqoXVD+4s8p6A9PIBFd6VqCVHXQZucGWqWfCrayAy9Qz+2Wv602ObzrZ+xtZ63pjGUYb+9plWcc22z8zoTd5jJaOMRsDumbHGx1o+WafR6dJA7ptS4Wt5yiE2OnphommypeSQgixs5JQqjsZBpSXW62jqqth4ECrW11rIhH43/+sEGrJEmuw8qbGjIGpU+F734NJk6xgKkvKSFOVrGVLopryRBURPYZf9dLXk49XbeOxsykaeIrAXQipMGaigvJYGSuiW1iRrGZFvJIVsS0tDkQOMNRbxPhAKeP9gxgfKGVsYCC+9j622OkZWQNZ2wNet9jqyGdi+lwYRSFMgo0tkTItiaxGRWbWZ91MKyMDPIkU3nACdzSFNxrHHU3hiSRwRzNTJIknmsAVSeCKWpMdaGnROEpaRzHBHU3ijja/42OHKAr4veD3Q8BnTX4fBPzW3J9d5rP283tzy/0+8GWVbW+336bdEU3TCrbseSqdW2Zm7Wfar3T2Bwc77KIxqFLsZSUr8MoKsOwxuFyZAMztyoRh9vFK68GXmr1P1r5q1uPZ5dv987Jb8bitIcZEy5zgymwMstBbLnfWs0IwaP14IwHufKurqxBip6epChc8+xmrtm7fl0g9wSG7l3DJEWO7uxpCCNEjSSjVHUzTCqHWrLEGM/d4YOjQlj8oRSLw7rvwxhvW2FDJJh+AR4yAffaxuuNNmQKFhc1OYZgGNal6KhI1lCUqqU+FURWNAleIog52iUsYSdZGy1gZWc83kfWsjKxnZWQDdemWLxSGevuyu38Q4wKDGO8fxNjAIEJaJ95JUHS51rqk6RgtbssOi3TTaBYa2SGGmQkwFHDukqahWHdLw2oxZG/zqJnWRKi4VBUNDU1RcCvW3dNy7pBm3xUNxWlllL3NbpGkoTr7tflvwjStcaMiUYjEsuaZ5WhmuaV5NAbReGOZHehE49ZU1Uk/JLe7MbjyexsDK583d8op81hzb2bu91qhdvb2QGZw+Y6GOvbztLsjGmbz9bSR6XoXbyX0Aifkclp6ZT2GHXbZQRRZYRRkLWe1/HJltfCyQy+XfQdGV26Q1fScTYOv7HVnmebhmL19Z6co1phXAHRyeGQkIdn+u4IKIXq/VVvDLCvreFf9nmJUyc5xJ2ghhNgRJJTqavX1Vje99eutcaT69cttHbWtAcqHDoWDDrJaQe29d4shFFgtourSYWqS9WxJVFGbakA3dfJcQQb5+qFt425RaSPNhng5a6JlrIluZFVkA6uiG9kQ24LRQj8LDZURgVJ2Dw5ld/8Axnj7MtYVJGR/ftQ81qRIi6gdqfUxjHJDoeygyDTJOcZ04iGzyU+6cTwjRcEKiVoJjlyKhqaqmeBIw63a3dI03G2ERjnd0zLbrbLcfbuVoljBjdcDfQu3/zx2uJUdVNnLsXjjcjwTWMUSEItZ82hmHos3TtF448DqqZQ11e+Ab5VV1QqovJmgypu17PG0Pvc2mTzuTLnbWne7m2zLbNfa+fNuKfQym4RfJplujXYLnXhjWdP9yQrCFCWTg9ndHWkMVRUlq/VXJohCyQ2rlBbW1awQTFEyYZgGWtY2pztkk6BLUVs5ZwutwbKDMGUb60IIIYQQQnQxCaW6gq5DZSVs3gxbtlgfLEtKrG446TQsXw6ff25NS5Y0H6B86FA47DBr2m23Vj88RPU4dakw1ak6yhPVNKQjmKZJQPNT4umTO2h5RjgdY0N8C99Gy/g2ttmZr4ttJt3KuCAFrhCjg0MYExzKbsGhjAkOZURgUPPuf6YO6ag1pWpBj4HekPnQ5LUGSlcyd0DbRWx7AGyzWWhkB0VNu6c1RkaKfU83JzDSsAa8VqHFlkZuxW5hZAVHLkVDUxqDo8YWRS0HRrkhUeN64z69ZHyg7pQdbvUp+O7nM02re10sDvFE4zyamWeXxRNWqGWXJZLWciLZWJ5INO4bT2SCGqwAx27Z1RVcWmNo5Wlpygqx3G7wuLKW3Vb3v6bLblfu/u6WtmXWXZq13la3PyMTeNmBmH13xqYhV3a3SGeiMUzMntuBmNNCDBpbh2WFYzmhGc1DMPs4J9CyQ7WsEIsmAZWq5nartJe1JutOoNXkcbODupz1lvZpUkZWWXadsycznXtnSyGEEEII0Wt1aSiVSCS47rrr+Ne//oXP5+P000/n9NNP78oqdB3DsFpF1dZad9WrrrZaPNTVNd5lb+VKa4DzaDT3WHuA8v32s6YmA5TbEnqSsB6lIR1lS6KKulSYmB5HVTSCmo8B3mI0VKpSdXwdXktZvIKN8a1silewIb6FDbFyqloZ9wnAr3oZEShlZGAQowKDGR0cwujAEIo9he3r8qdo4M6zJn8J6AnQ45COQKohE1KFAdPqKoPbmisuOnJHv86w/WEROcERQG77IrupmLWsgBPmtBQa2eGPpjYObu3KCovs5ZwgqGlXtFYCo+yASe5i2E6mue19muqu11ZRGsOZgrzOPbdpQlq3gqpYwmrhZYdY2WX2ejxpdTVuOk8kIZFqXE6mso7L7JNMWY9lS+uQjgGxzn1OHaUqmYHdM5MrO8xyNW5zaY3rLZZpWeWu3LGz7G3Zc62FMqc8U2YPQG+HSVpWSyv7V1DTIMzMag1mBzxpvfk+9r+BnOPNVkIzaPySwcw8dva/BzP330d2q7LsoIqm5eQGVmYaXEk4dJr190UIIYQQQvRaXRpK3XLLLSxdupTHH3+csrIyLrvsMgYNGsSRRx7ZldXYMUwTqqpg1SorcFq+HDZutFpHlZdb86pWBowJBmHiRNhrL2vac0/w5Y67ZJgGMT1BRI8R0WNUJmupStRRlqigKllHOB0loseoSTVQkaxhS6KK8kQ1WxJVJM1Uy4+b0dedz3DfAIb5BzLcN4DhvgGM9A+iv6eP1eLF+QCTmSfirX9Yd/Zrsu4UuIACIAQkwUyAmYREFIwI9m3VTTskUjQMRcNUVExUDFQMJdNNzbTjIXLCI7t7YWOolPWNupkZzwhy8iI7GFKw4jAVFUzTCXYUrG5qHlS0TPjjQsWVCYVcaLhUrXHcIvs40zouJxwyM8GUolrLKKhqpoWRifWa2x/AnDxLcerfuCHrg7vzOXA7QhTn/G0d2yRoaWnXbZ2iPfvnfH5t42StBT/ZxyhKy+ewW5W0Va/vymxhxa5Ps59t1n4mjS1Xst+32e/Z7FYtZM2zx1JSWivPaj3TYnmTY52HVxrDl1AXjIuh642BlR1aZa/b4VUqlVvebD2dW55KZ+2TWU61NE83DupuM8zG8/YmzphZTcKsnDK1cVnVcgeat7flLLcwt49ruq2lcjV7Uqx9VKVxWVEauzHaY4DZg9srijWmVIE7t2u7EEIIIYTolboslIpGo7zwwgs8+OCDTJgwgQkTJrBy5UqefvrpnhNKxWJW66ZIpHEKh62yhobGlk9VVVBRYc0rK63QqarKOn5b+vaF0aNh991ht93QdxtNuLSEBiNGOBWmLtlA5db3qEzUUJmqozpZR3mymq3JGqrTDdSmw9TpEer0KA1G+1oOqCj0cxUwyN2XUndfSt3FDPGWMNRTwhBPP2vgcaeXR2ZBV6xuPSiNLYXsMEgxc8Mgu+UQja2GDAxAsZaV7H0ah7M2FTLvQJd1JyXyMt0ydBTSKEYa1UyhGElU0iimgYIdFFnn0BQNVdPQFDcaLjTVhUt1oalua1lz41JdmVZJqhX+KFkhkR0GqVld0xQts5+KqmaFSU6XtCbdTLLnLXH2abLe2nHb2p59rtYfdFs7tHJYB45rtW4dfOy2du/wuZS21zt63o6+jE3DruxQo2mrE3v/ZuVNWqk07eZl6I3dwNJGY5cxXc+aZw0sjtl4Rz37cbK7lmU/nmHkrjuvg5I1plLmhVGy98l6j7fWFctezg6/Wtovu/WM3ZUuFGh5e3YLmpbO+V2YmdcyO6RKp7PCK92a22VpvTHUSuuN++l6prVX2grC9LR1rG6vZ/a15/a+aT1zbEtlWZOeblxuqUubYYKRqePOpDAEx57d3bUQQgghhBDfUZeFUsuXLyedTjNp0iSnbPLkydx///0YhoHa9Jv5Lhb719954aofU+1KYSg4k66AruYupzOTrkC6H6QGQipTlnKrJANekj43Sa+LpNdFzKuRcCvEXBAnTcz8hpjxJXEzRXJFGlZsf701RaWPp4Bib1/6+vpQ5C2k2NeXEn8x/fzFFPv60jdQhKZoVqik4ARDJia1ikINNLbOoHFGZnwiUFBULTP8SKajmWqNWaQoWG2HMqGNlim3giEXLs1lBUeq1limupz9m04KzcsVQDXTWZOemdIoehLFTFq3CNfj1gd2Mw1kbitu6s27l2S3WFE06wkrVvso67nbt3wnM89qTdJ0Oft1c16/rP2cdSG6Qc4d7Mhazgq5moVfZisDhbcxGdmPYzSGZrppretGblBmGC08NuCMxZRVd+d5ZJe1EOrlBGw0Cc2gMUizVzM7mfbOZG2091Gat2hT1czdCGk5lG5PWdbv2Jz11kLubR1vb7NfVzuYtEMsvUlZ2g4w041hlrNf1rK9vx2G6kbr++eU2etGbrkd9NnHGXrzx8s+zl7OWc88p1GlVtfFXdQuNRyCEEIIIXZqXXZFV1FRQZ8+ffBk3WmuuLiYRCJBbW0tffv27aqqtOjpyv9w5jGd0RXAwBr7pEkrJhNo4/SqouJ3+fBpPoLuAEFPiEBmnu/NJ9+bT543jwJvAYX+Qvr6+tLH34cCbwGaYoVCSqabmR3maKpmBUJNlxU7IGoMk+zjssOipgFRe7d1G9PMhFBpq2WAmc4KpuzJaJwbKadllrVvOnc/7A/NmdYj2Ms0rpP1wZkmH5id8VbIKsv+VNySzPacrmc5n6Qbl1vrntaWZt0AW3jsbdVtW+fuVt1Rh57wvLdTp/xzzQ5mte07RUvjHdlhUUstuprul9Oyq4WyFsdIgtyAq5VjmtYPmgR7Wb8HnMHgs35XtBSYNT2Xc0yTx7frmb3e+KLlbs85Jqswk7fjzt4/84M3tebHNj3ptrpqt3BImy0Gc3ZQcledoqyCpr/nFMX6nR0KNe9mugvZqYdDEEIIIcQupctCqVgslhNIAc56Mpnsqmq06qgfXcwp/65gc91GNNWFYgc2amOQo6oqmupCVVRcigtVVXGrbifwsYMej+bBrVrdxryaF7/bj8/lw+/243f7yXPnEfAECLgD+F1+8jx5eDSP1fqIxnApe24HQdnBU1vLuyRFwRok3bXdn41zZAdSOcFUdiBll9Ok3GyhnKxt5JZlP2bTslY/ILbxKbDFD5JtBU7t/ODZkePbPPY7nLdd5+/EY3JP8B2P722Pu716W317qOzwyl7PnueU0fK2lvZttkzb+7QaKrWwbVvnau96q2WZueaG/KLm23cBvWI4BNFj6IaJpu6i14VCCCF6hS4Lpbxeb7PwyV73NRnUuzsMyhvEY8c/0d3VED2J071PCCGE6Bl6+nAIO5OdIdDRVIULnv2MVVvD3V2V7XLI7iVccsTY7q6GEEKIHajLQqn+/ftTU1NDOp3GlRkHoqKiAp/PR35+/jaPNzPfmIbDvfOPqhBCCCF6l2Aw2ONaH3/X4RDkeqpj7n9rNWV17buxTE8zcXABP5s8hHQ8ipHsnc8hGYsSDocZnq9iJN3dXZ3t1t9Pr38e8hx6BnkOPcPO8ByG56tddi2wreupLgulxo0bh8vlYsmSJUyZMgWAxYsXM3HixHZ9qxeJRACYPn36Dq2nEEIIIQRY1ymhUKi7q5Hjuw6HINdTu47XgD91dyW+ozXAztCPYWd4HvIcegZ5Dj3DzvIcJs/pmsfa1vVUl4VSfr+fH//4x1x77bXcdNNNbN26lUceeYQ5c9r3SvTr14+33367R35rKYQQQoidTzAY7O4qNPNdh0OQ6ykhhBBCdKVtXU916f2Ur7jiCq699lpOOeUUQqEQ559/Pocffni7jlVVlQEDBuzgGgohhBBC9FzfdTgEuZ4SQgghRE+imOZ3vh2VEEIIIYToArFYjGnTpvHII484wyHMmzePDz74gKeeeqqbayeEEEII0TFyixYhhBBCiF4ieziEL774gjfeeINHHnmEk08+uburJoQQQgjRYdJSSgghhBCiF4nFYlx77bX861//IhQKccYZZ3Dqqad2d7WEEEIIITpMQikhhBBCCCGEEEII0eWk+54QQgghhBBCCCGE6HISSgkhhBBCCCGEEEKILiehlBBCCCGEEEIIIYTochJKAYlEgiuvvJIpU6Zw4IEH8sgjj3R3lXqt8vJyZs2axdSpUznooIOYM2cOiUSiu6vVq5111llcfvnl3V2NXiuZTHLdddex7777sv/++3P77bcjQ+ltn82bN3P22Wezzz77cOihh/LYY491d5V6lWQyyTHHHMNHH33klG3YsIFTTz2Vvffem6OOOor//e9/3VjD3qOl13LJkiWceOKJTJo0iSOOOIIXXnihG2vYc8k1T8f8+9//Zvfdd8+ZZs2a1d3V6lHkd1vHtPR63XDDDc3eZ0899VQ31rJ7tfV5Qt5budp6reR91dy6des444wzmDRpEocccggPPfSQs03eW7naeq06+73l6owK93a33HILS5cu5fHHH6esrIzLLruMQYMGceSRR3Z31XoV0zSZNWsW+fn5PP3009TV1XHllVeiqiqXXXZZd1evV3rttdd4++23+clPftLdVem1brjhBj766CMefvhhIpEIF154IYMGDeLEE0/s7qr1Or/73e8YNGgQL730EqtWreL3v/89paWl/OAHP+juqvV4iUSCiy++mJUrVzplpmly3nnnMWbMGBYuXMgbb7zBzJkz+fvf/86gQYO6sbY9W0uvZUVFBWeeeSa/+MUv+NOf/sSyZcu44oorKCkp4ZBDDum+yvZAcs3TMatWrWLGjBnMnj3bKfN6vd1Yo55Ffrd1TEuvF8Dq1au5+OKLc673QqFQV1evR2jr88Sll14q760s2/rsJe+rXIZhcNZZZzFx4kT+8pe/sG7dOi666CL69+/PMcccI++tLG29Vj/60Y86/b21y4dS0WiUF154gQcffJAJEyYwYcIEVq5cydNPPy0XaB20Zs0alixZwnvvvUdxcTEAs2bN4uabb5ZQajvU1tZyyy23MHHixO6uSq9VW1vLwoULefTRR9lzzz0BOP300/n8888llOqguro6lixZwuzZsxk+fDjDhw/noIMO4oMPPpBQahtWrVrFxRdf3KyF3ocffsiGDRt49tlnCQQCjBo1ig8++ICFCxdy/vnnd1Nte7bWXss33niD4uJiLrroIgCGDx/ORx99xKuvviqhVBa55um41atXM2bMGEpKSrq7Kj2O/G7rmNZeL7DeZ2eccYa8z2j788TBBx8s760s2/rsJe+rXJWVlYwbN45rr72WUCjE8OHD2W+//Vi8eDHFxcXy3srS1mtlh1Kd+d7a5bvvLV++nHQ6zaRJk5yyyZMn8/nnn2MYRjfWrPcpKSnhoYcecn4p2sLhcDfVqHe7+eabOe644xg9enR3V6XXWrx4MaFQiKlTpzplZ511FnPmzOnGWvVOPp8Pv9/PSy+9RCqVYs2aNXz66aeMGzeuu6vW4y1atIhp06bx3HPP5ZR//vnnjB8/nkAg4JRNnjyZJUuWdHENe4/WXku7y0JT8vcnl1zzdNzq1asZPnx4d1ejR5LfbR3T2usVDocpLy+X91lGW58n5L2Vq63XSt5XzfXr148777yTUCiEaZosXryYjz/+mKlTp8p7q4m2Xqsd8d7a5VtKVVRU0KdPHzwej1NWXFxMIpGgtraWvn37dmPtepf8/HwOOuggZ90wDJ566im+973vdWOteqcPPviATz75hFdffZVrr722u6vTa23YsIHS0lJefvll7r//flKpFMcffzy//e1vUdVdPpPvEK/XyzXXXMPs2bN54okn0HWd448/np/97GfdXbUe75e//GWL5RUVFfTr1y+nrKioiC1btnRFtXql1l7LwYMHM3jwYGe9qqqK1157bZf8drMtcs3TMaZpsnbtWv73v//xwAMPoOs6Rx55JLNmzcp5DXdV8rutY1p7vVavXo2iKNx///288847FBYWctppp+2yQze09XlC3lu52nqt5H3VtkMPPZSysjJmzJjBEUccwU033STvrVY0fa2WLl3a6e+tXT6UisVizS4s7PVkMtkdVdppzJ07l6+++ooXX3yxu6vSqyQSCf74xz9yzTXX4PP5urs6vVo0GmXdunU8++yzzJkzh4qKCq655hr8fj+nn356d1ev11m9ejUzZszgtNNOY+XKlcyePZv99tuPY489trur1iu19vdH/vZ8N/F4nPPPP5/i4mL+7//+r7ur06PINU/HlJWVOa/ZnXfeycaNG7nhhhuIx+P84Q9/6O7q9Vjyu61j1qxZg6IojBw5kl/96ld8/PHHXH311YRCIekeT+7niccee0zeW23Ifq2WLVsm76s23H333VRWVnLttdcyZ84c+b3Vhqav1YQJEzr9vbXLh1Jer7fZm81el0Bg+82dO5fHH3+cO+64gzFjxnR3dXqVe++9lz322CPnmw+xfVwuF+FwmNtuu43S0lLA+pDxzDPPSCjVQR988AEvvvgib7/9Nj6fj4kTJ1JeXs59990nodR28nq91NbW5pQlk0n52/MdRCIRzj33XL799lv+/Oc/4/f7u7tKPYpc83RMaWkpH330EQUFBSiKwrhx4zAMg0suuYQrrrgCTdO6u4o9kvxu65gf//jHzJgxg8LCQgDGjh3Lt99+yzPPPLPLhwdNP0/Ie6t1TV+r3XbbTd5XbbDH7E0kEvz+97/npz/9KbFYLGcfeW9Zmr5Wn376aae/t3b5/iv9+/enpqaGdDrtlFVUVODz+cjPz+/GmvVes2fP5tFHH2Xu3LkcccQR3V2dXue1117jjTfeYNKkSUyaNIlXX32VV199NWcMENE+JSUleL1eJ5ACGDFiBJs3b+7GWvVOS5cuZdiwYTl/nMePH09ZWVk31qp369+/P5WVlTlllZWVzZqPi/YJh8OcccYZrFy5kscff1zG0WiBXPN0XGFhIYqiOOujRo0ikUhQV1fXjbXq2eR3W8coiuJ8uLONHDmS8vLy7qlQD9HS5wl5b7WspddK3lfNVVZW8sYbb+SUjR49mlQqRUlJiby3srT1WoXD4U5/b+3yodS4ceNwuVw5g5gtXryYiRMnypgz2+Hee+/l2Wef5fbbb+foo4/u7ur0Sk8++SSvvvoqL7/8Mi+//DKHHnoohx56KC+//HJ3V63X2WuvvUgkEqxdu9YpW7NmTU5IJdqnX79+rFu3LqeVxZo1a3LG8REds9dee7Fs2TLi8bhTtnjxYvbaa69urFXvZBgGM2fOZOPGjTz55JPstttu3V2lHkmueTrm3XffZdq0aTnfnn/99dcUFhbK+FttkN9tHXPXXXdx6qmn5pQtX76ckSNHdk+FeoDWPk/Ie6u51l4reV81t3HjRmbOnJkTnixdupS+ffsyefJkeW9laeu1evLJJzv9vbXLX4H4/X5+/OMfc+211/LFF1/wxhtv8Mgjj3DyySd3d9V6ndWrVzN//nzOPPNMJk+eTEVFhTOJ9istLWXYsGHOFAwGCQaDDBs2rLur1uuMHDmSQw45hCuuuILly5fz7rvvsmDBAn7xi190d9V6nUMPPRS3280f/vAH1q5dy3//+1/uv/9+fv3rX3d31XqtqVOnMnDgQK644gpWrlzJggUL+OKLLzjhhBO6u2q9zosvvshHH33EDTfcQH5+vvO3p2k3j12dXPN0zKRJk/B6vfzhD39gzZo1vP3229xyyy385je/6e6q9Wjyu61jZsyYwccff8zDDz/M+vXr+fOf/8zLL7+8yw4z0NbnCXlv5WrrtZL3VXMTJ05kwoQJXHnllaxatYq3336buXPncs4558h7q4m2Xqsd8d5STNM0O7H+vVIsFuPaa6/lX//6F6FQiDPOOKNZ+ie2bcGCBdx2220tbluxYkUX12bncfnllwPwpz/9qZtr0js1NDQwe/Zs/v3vf+P3+/nlL3/Jeeedl9MdQ7TPqlWruPHGG/niiy/o27cvJ510Eqeccoq8lh2w++6788QTTzBt2jQA1q1bx1VXXcXnn3/OsGHDuPLKK9l///27uZa9Q/ZrecYZZ/C///2v2T5Tp07lySef7Iba9VxyzdMxK1eu5KabbmLJkiUEg0FOPPFE+RvSAvnd1jFNX6833niDu+++m2+//ZbS0lIuvPBCDj/88G6uZffY1ucJeW812tZrJe+r5srLy5k9ezYffPABfr+fX/3qV5x99tkoiiLvrSbaeq06+70loZQQQgghhBBCCCGE6HK7fPc9IYQQQgghhBBCCNH1JJQSQgghhBBCCCGEEF1OQikhhBBCCCGEEEII0eUklBJCCCGEEEIIIYQQXU5CKSGEEEIIIYQQQgjR5SSUEkIIIYQQQgghhBBdTkIpIYQQQgghhBBCCNHlJJQSQgghhBBCCCGEEF1OQikhRI+3++67c/HFFzcrf+mllzj00EO7oUZCCCGEEEIIIb4rCaWEEL3C3/72Nz744IPuroYQQgghhBBCiE4ioZQQolcoLS3l+uuvJ5lMdndVhBBCCCGEEEJ0AgmlhBC9wu9+9zvKy8t5+OGHW91ny5YtXHDBBUydOpVp06Zxww03OCHWSy+9xK9//Wvuvvtupk2bxpQpU5gzZw6maTrHP/vssxx66KFMmjSJX//616xYsWKHPy8hhBBCCCGE2FVJKCWE6BX69+/PrFmzuP/++9mwYUOz7clkklNOOYVYLMaTTz7JnXfeyVtvvcUtt9zi7PPZZ5+xdu1annnmGa6++mqeeOIJ3n//fQD++9//cu+993L11Vfzl7/8hcmTJ3PyySdTV1fXZc9RCCGEEEIIIXYlEkoJIXqNX//61wwbNowbb7yx2bZ3332X8vJy5s6dy+67785+++3HNddcwzPPPEMkEgFA13Vmz57NyJEjOe644xg7dixffvklAA899BBnn302M2bMYPjw4fzud7+jtLSUV155pUufoxBCCCGEEELsKlzdXQEhhGgvTdO49tpr+eUvf8kbb7yRs2316tUMHz6cgoICp2yfffYhnU6zfv16AIqKigiFQs72UChEOp12jp87dy633367sz2RSPDtt9/uwGckhBBCCCGEELsuCaWEEL3KPvvsw09/+lNuvPFGfvOb3zjlXq+32b66rufMPR5Ps33sMaV0XefKK69kv/32y9meHWIJIYQQQgghhOg80n1PCNHr/P73vycajeYMej5ixAi+/fZbamtrnbIlS5bgcrkYOnToNs85YsQItmzZwrBhw5zp/vvvZ8mSJTvgGQghhBBCCCGEkFBKCNHr9OnTh9///vds2rTJKTvggAMYMmQIl156KStWrODDDz9k9uzZHHPMMeTn52/znKeddhqPP/44L7/8MuvXr2fu3Ln84x//YNSoUTvyqQghhBBCCCHELku67wkheqUTTjiBhQsXsnXrVsAab2r+/PnMnj2bn//85wSDQX70ox9x0UUXtet8Rx11FJWVldx9991UVlYyevRo7rvvPoYPH74Dn4UQQgghhBBC7LoU0x5QRQghhBBCCCGEEEKILiLd94QQQgghhBBCCCFEl5NQSgghhBBCCCGEEEJ0OQmlhBBCCCGEEEIIIUSXk1BKCCGEEEIIIYQQQnQ5CaWEEEIIIYQQQgghRJeTUEoIIYQQQgghhBBCdDkJpYQQQgghhBBCCCFEl5NQSgghhBBCCCGEEEJ0OQmlhBBCCCGEEEIIIUSXk1BKCCGEEEIIIYQQQnQ5CaWEEEIIIYQQQgghRJf7f0fq7jHnjLybAAAAAElFTkSuQmC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", 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      " ] @@ -588,7 +568,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The plots above show what happens in the four different scenarios. We observe that in the model where none of the policies were imposed, the probability of the overshoot being too high is relatively low, $0.05$. On the other hand, when both policies were imposed, the probability of the overshoot being to high was relatively higher $0.72$. \n", + "The plots above show what happens in different scenarios. We observe that in the model where none of the policies were imposed, the probability of the overshoot being too high is relatively low, $\\approx 0.05$. On the other hand, when both policies were imposed, the probability of the overshoot being to high was relatively high at $\\approx 0.7$. \n", "\n", "To identify which of `lockdown` and `mask` is the cause, we analyze the models where only one of the policies were imposed. Interestingly, the effect of the interventions is somewhat nuanced. Implementing both interventions increases the risk of overshoot as compared to the no intervention model, but individual interventions would have even worse consequences, which means that the two interventions while jointly increasing the risk to some extent mitigate each other's contribution to that risk as well.\n", "\n", @@ -613,7 +593,7 @@ }, { "cell_type": "code", - "execution_count": 253, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -667,14 +647,14 @@ }, { "cell_type": "code", - "execution_count": 254, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.1255)\n" + "tensor(0.1328)\n" ] } ], @@ -708,14 +688,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The above probability itself is not directly related to our query. It is the probability that the overshoot is too high in the antecedents-intervened world and not too high in the alternatives-intervened world, where antecedent interventions are preempted with probabilities $0.5$ at each site, and witnesses are kept fixed at the observed values with probability $0.5+0.2$ at each site. But more fine-grained queries can be answered using the 10000 samples we have drawn in the process. \n", + "The above probability itself is not directly related to our query. It is the probability that the overshoot is both too high in the antecedents-intervened world and not too high in the alternatives-intervened world, where antecedent interventions are preempted with probabilities $0.5$ at each site, and witnesses are kept fixed at the observed values with probability $0.5+0.2$ at each site (see the tutorial on categorical variables for explanation of why this stochasticity is in general useful). \n", "\n", - "We first compute the probabilities that different sets of antecedent candidates have causal effect over `os_too_high` conditioned on the fact that lockdown and masking were actually imposed in the factual world." + "However, more fine-grained queries can be answered using the 10000 samples we have drawn in the process. We first compute the probabilities that different sets of antecedent candidates have causal effect over `os_too_high` conditioned on the fact that lockdown and masking were actually imposed in the factual world." ] }, { "cell_type": "code", - "execution_count": 271, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -739,7 +719,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We specifically compute the following four probabilities that compute if the set of antecedent varables under consideration have a causal effect over the outcome or not conditioned on lockdown and masking being imposed.\n", + "We specifically compute the following four probabilities. In each of the computations, we condition on lockdown and masking actually being implemented in the factual workd. Then we take an interventional setting and compute the probability that this setting has a causal power over the outcome. For instance, in 1., we assume lockdown (`ld`) and masking (`m`) have been implemented, and we ask about the joint prbability that both (a) removing both interventions, i.e. intervening for both `ld` and `m` to not happen - which we mark by the apostrophe - would lead to `oth` not happening, $\\mathit{oth}'_{\\mathit{ld}', m'}$, and (b) intervening for both to happend would lead to `oth`, $\\mathit{oth}_{\\mathit{ld}, m}$ (which, given the stochasticity between these interventions and the outcome, might be non-trivial).\n", + "\n", "1. $P(\\mathit{oth}_{\\mathit{ld}, m}, \\mathit{oth}'_{\\mathit{ld}', m'} | \\mathit{ld}, m)$\n", "2. $P(\\mathit{oth}_{\\mathit{ld}}, \\mathit{oth}'_{\\mathit{ld}'} | \\mathit{ld}, m)$\n", "3. $P(\\mathit{oth}_{m}, \\mathit{oth}'_{m'} | \\mathit{ld}, m)$\n", @@ -748,17 +729,17 @@ }, { "cell_type": "code", - "execution_count": 272, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 0.19184289872646332\n", - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 0.28547579050064087\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 2.4801263265317175e-09\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 2.821781475148555e-09\n" + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 0.24283304810523987\n", + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 0.2902735471725464\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 2.3861892461951584e-09\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 2.636660445531902e-09\n" ] } ], @@ -800,7 +781,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As the above probabilities show, `{lockdown=1}` has the most causal role on overshoot being too high among all the possible sets of causes when both lockdown and masking were imposed." + "As the above probabilities show, `{lockdown=1}` has the most causal role on the overshoot being too high among all the possible sets of causes when both lockdown and masking were imposed." ] }, { @@ -823,7 +804,7 @@ }, { "cell_type": "code", - "execution_count": 273, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -831,10 +812,10 @@ "output_type": "stream", "text": [ "Degree of responsibility for lockdown: \n", - "{'__cause____antecedent_lockdown': 0, 'mask': 1, 'lockdown': 1} 0.2363203763961792\n", + "{'__cause____antecedent_lockdown': 0, 'mask': 1, 'lockdown': 1} 0.2677857577800751\n", "\n", "Degree of responsibility for mask: \n", - "{'__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 0.09837335348129272\n" + "{'__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 0.1170731708407402\n" ] } ], @@ -851,7 +832,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As the output shows, `lockdown=1` has a higher degree of responsibility than `mask=1`." + "As the output shows, `lockdown=1` has a higher degree of responsibility than `mask=1`.\n", + "\n", + "The reader might have the impression that the numbers are relatively low: what one needs to remember though, that these are computed with stochastic witness preemptions in the background and that in this model the witnesses are downstream from the interventions, so part of the time some of the interventions are blocked as their effects are stochastically chosen to be witnesses and fixed at the actual values. The role of witnesses will be investigated in more detail in the next section." ] }, { @@ -865,12 +848,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this section, we use the samples we obtained earlier to analyze the distribution of `overshoot` variable in different counterfactual worlds. We first define a function to obtain histogram data from the samples in a particular world and then we demonstrate the plots for `overshoot` distribution in different settings." + "In this section, we use the samples we obtained earlier to analyze the distribution of `overshoot` variable in different counterfactual worlds. We first define a function to obtain histogram data from the samples in a particular world and then we inspect the marginal distribution plots for `overshoot` in different settings." ] }, { "cell_type": "code", - "execution_count": 276, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -908,12 +891,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We first plot the distribution of `overshoot` in the factual world (indicated by 0) and necessity counterfactual worlds (indicated by 1) where intervened variables are set to their alternative value. One can see how the distribution changes in the counterfactual worlds. When `mask` is set to 0, the probability of high overshoot is lower than that when `lockdown` is set to 0. This agrees with the intuition that `lockdown` has a higher role in inducing high overshoot." + "We first plot the distribution of `overshoot` in the factual world (indicated by 0) and necessity counterfactual worlds (indicated by 1) where intervened variables are set to their alternative value. One can see how the distribution changes in the counterfactual worlds. When `mask` is set to 0, high overshoot is still quite likely, whereas when `lockdown` is set to 0, this visibly shifts the distribution towards the lower values of overhead. This agrees with the intuition that `lockdown` has a higher role in inducing high overshoot." ] }, { "cell_type": "code", - "execution_count": 277, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -946,7 +929,7 @@ }, { "cell_type": "code", - "execution_count": 293, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -954,14 +937,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "factual: 24.26240348815918 counterfactual mask: 26.32572364807129 counterfactual lockdown: 21.432722091674805\n", + "factual: 24.302181243896484 counterfactual mask: 26.837486267089844 counterfactual lockdown: 21.276477813720703\n", "Probability of overshoot being high\n", - "factual: 0.5996000170707703 counterfactual mask: 0.8367347121238708 counterfactual lockdown: 0.28977271914482117\n" + "factual: 0.6021000146865845 counterfactual mask: 0.8484848737716675 counterfactual lockdown: 0.32460734248161316\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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      " ] @@ -1035,12 +1018,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can have similar plots for sufficiency worlds (indicated by 2) where variables are intervened on to be their antecedent value. The resulting plots show that when `mask` is set to be 1, there is a higher probability of high overshoot but the distribution is more flat as compared to the distribution when `lockdown` is set to 1, that has higher peaks." + "We can have similar plots for sufficiency worlds (indicated by 2) where variables are intervened on to have their antecedent values. While this might seem redundant, this investigates probabilistically the impact of the implemented interventons: after all, it might be the case that the observed outcome is an unusual one and that usually those interventions do not lead to the outcome of interest. The resulting plots show that when `mask` is set to be 1, there is a higher probability of high overshoot, but that this distribution is more flat than the distribution for `lockdown` being set to 1, which has higher peaks." ] }, { "cell_type": "code", - "execution_count": 286, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -1073,7 +1056,7 @@ }, { "cell_type": "code", - "execution_count": 292, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1081,14 +1064,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "factual: 24.26240348815918 counterfactual mask: 26.453907012939453 counterfactual lockdown: 26.533231735229492\n", + "factual: 24.302181243896484 counterfactual mask: 26.423648834228516 counterfactual lockdown: 27.30312728881836\n", "Probability of overshoot being high\n", - "factual: 0.5996000170707703 counterfactual mask: 0.6938775777816772 counterfactual lockdown: 0.6761363744735718\n" + "factual: 0.6021000146865845 counterfactual mask: 0.6717171669006348 counterfactual lockdown: 0.717277467250824\n" ] }, { "data": { - "image/png": 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", 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", 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      " ] @@ -1155,19 +1138,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As for the earlier histogram, we can describe the above histogram as follows. It plots three quantities. It plots $P(\\mathit{os} | \\mathit{ld}, m)$ as the factual distribution of overshoot, $P(\\mathit{os}_{\\mathit{ld}} | \\mathit{ld}, m)$ as `counterfactual_lockdown` and $P(\\mathit{os}_{\\mathit{m}} | \\mathit{ld}, m)$ as `counterfactual_mask`. Again, these distributions help in comparing how sufficiency interventions for the two antecedents affect the overshoot." + "The histogram plots three quantities. It plots $P(\\mathit{os} | \\mathit{ld}, m)$ as the factual distribution of overshoot, $P(\\mathit{os}_{\\mathit{ld}} | \\mathit{ld}, m)$ as `counterfactual_lockdown` and $P(\\mathit{os}_{\\mathit{m}} | \\mathit{ld}, m)$ as `counterfactual_mask`. Again, these distributions help in comparing how sufficiency interventions for the two antecedents affect the overshoot." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can also combine samples from both sufficiency and necessity worlds to draw conclusions. We first visualize samples where only lockdown was intervened on and then we analyze samples where masking was intervened on." + "We can also combine samples from both sufficiency and necessity worlds to focus on a context-sensitive counterpart of the joint probability of necessity and sufficiency directly (see the previous tutorial for more explanation). We first visualize samples where only lockdown is considered as an intervention. Then we analyze masking as an intervention." ] }, { "cell_type": "code", - "execution_count": 281, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1253,12 +1236,12 @@ }, { "cell_type": "code", - "execution_count": 264, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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      " ] @@ -1300,8 +1283,8 @@ "ax.set(xticks=range(0, 36, 2), xticklabels=bin_edges[0:36:2].tolist())\n", "ax.set(yticks=range(0, 36, 2), yticklabels=bin_edges[0:36:2].tolist())\n", "ax.set(\n", - " xlabel=\"Overshoot in Sufficiency Intervention\",\n", - " ylabel=\"Overshoot in Necessity Intervention\",\n", + " xlabel=\"Overshoot under sufficiency intervention\",\n", + " ylabel=\"Overshoot under necessity intervention\",\n", " title=\"Overshoot in counterfactual mask\",\n", ")\n", "ax.axvline(x=(overshoot_threshold) * 36 / 45, color=\"red\", linestyle=\"--\", label=\"Overshoot too high\")\n", @@ -1325,14 +1308,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The above heatmaps plot the distributions $P(\\mathit{os}_{\\mathit{ld}}, \\mathit{os}_{\\mathit{ld}'}|\\mathit{ld, m})$ and $P(\\mathit{os}_{\\mathit{m}}, \\mathit{os}_{\\mathit{m}'}|\\mathit{ld, m})$ respectively. It is evident from the plot above that counterfactual for lockdown has more probability mass in the top right quadrant (low overshoot in necessity world and high overshoot in sufficient world). This gives us a more clear picture into why lockdown has more causal role in overshoot being too high as compared to masking." + "The above heatmaps plot the distributions $P(\\mathit{os}_{\\mathit{ld}}, \\mathit{os}_{\\mathit{ld}'}|\\mathit{ld, m})$ and $P(\\mathit{os}_{\\mathit{m}}, \\mathit{os}_{\\mathit{m}'}|\\mathit{ld, m})$ respectively. It is evident from the plot above that counterfactual for lockdown has more probability mass in the top right quadrant (low overshoot in the necessity world and high overshoot in the sufficient world). This gives us a more clear picture into why lockdown has more causal role in overshoot being too high as compared to masking." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Looking into different contexts (For advanced readers)" + "## Looking into different contexts (for curious readers)" ] }, { @@ -1340,15 +1323,17 @@ "metadata": {}, "source": [ "`SearchForExplanation` allows the users to perform an even finer grained analysis by visualizing distributions of random variables when different contexts are kept fixed in the model. To illustrate this, we consider the following two scenarios:\n", - "1. Intervene on `lockdown=1` while keeping `mask_efficiency` fixed or not.\n", - "2. Intervene on `mask=1` while keeping `lockdown_efficiency` fixed or not.\n", + "1. Intervene on `lockdown=1` while keeping `mask_efficiency` fixed (or not).\n", + "2. Intervene on `mask=1` while keeping `lockdown_efficiency` fixed (or not).\n", + "\n", + "The key motivation for looking into this is the intuition that there is some part of the actual context in which removing lockdown would significantly lower the overshoot, whereas there is no corresponding part of the actual context in which removing masking would lead to lower overshoot - which is the core of the assymetricity between the two intervetnions in our example.\n", "\n", - "We first intervene on `lockdown` being 1 and analyze how the distribution of `overshoot` change as we keep the `mask_efficiency` fixed or not." + "We first intervene on `lockdown` being 1 and analyze how the distribution of `overshoot` change as we keep the `mask_efficiency` fixed (or not)." ] }, { "cell_type": "code", - "execution_count": 282, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -1382,7 +1367,7 @@ }, { "cell_type": "code", - "execution_count": 291, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1390,14 +1375,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "mask_efficiency fixed: 19.134967803955078 mask_efficiency not fixed: 26.356489181518555\n", + "mask_efficiency fixed: 18.7790470123291 mask_efficiency not fixed: 25.793893814086914\n", "Probability of overshoot being high\n", - "mask_efficiency fixed: 0.05000000074505806 mask_efficiency not fixed: 0.8035714030265808\n" + "mask_efficiency fixed: 0.08130080997943878 mask_efficiency not fixed: 0.7647058963775635\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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      " ] @@ -1458,19 +1443,19 @@ "1. `mask_efficiency fixed`: $P( \\mathit{os}^{\\mathit{me}}_{\\mathit{ld}'} | \\mathit{ld}, m)$\n", "2. `mask_efficiency not fixed`: $P( \\mathit{os}_{\\mathit{ld}'} | \\mathit{ld}, m)$\n", "\n", - "The plot clearly shows that depending on the fact that `mask_efficiency` was kept fixed on the factual value or not, the `overshoot` variable changes." + "The plot clearly shows that depending on the fact that `mask_efficiency` was kept fixed on the factual value or not, the `overshoot` variable changes. Crucially, if we keep the mask efficiency at the actual value, the probability of the overshoot being too high drops down to $\\approx 0.08$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We then intervene on `mask` being 1 and analyze how the distribution of `overshoot` change as we keep the `lockdown_efficiency` fixed or not." + "We then run an analogous analysis for masking - we intervene on `mask` being 1 and analyze how the distribution of `overshoot` change as we keep the `lockdown_efficiency` fixed or not." ] }, { "cell_type": "code", - "execution_count": 284, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -1502,7 +1487,7 @@ }, { "cell_type": "code", - "execution_count": 290, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1510,14 +1495,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "lockdown_efficiency fixed: 26.546525955200195 lockdown_efficiency not fixed: 25.787281036376953\n", + "lockdown_efficiency fixed: 27.030378341674805 lockdown_efficiency not fixed: 26.37188148498535\n", "Probability of overshoot being high\n", - "lockdown_efficiency fixed: 0.8561151027679443 lockdown_efficiency not fixed: 0.7894737124443054\n" + "lockdown_efficiency fixed: 0.8642857074737549 lockdown_efficiency not fixed: 0.8103448152542114\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
      " ] @@ -1576,7 +1561,7 @@ "1. `lockdown_efficiency fixed`: $P( \\mathit{os}^{\\mathit{le}}_{\\mathit{m}'} | \\mathit{ld}, m)$\n", "2. `lockdown_efficiency not fixed`: $P( \\mathit{os}_{\\mathit{m}'} | \\mathit{ld}, m)$\n", "\n", - "The plot clearly shows that `lockdown_efficiency` has little effect on how intervening on `mask` affects `overshoot`." + "The plot clearly shows that `lockdown_efficiency` as a context has little effect on how intervening on `mask` affects `overshoot`. Again, crucially, whichever context senting we choose here, withdrawing the masking policy does not radically change the fact that the overshoot is still very likely to be too high." ] } ], From d7429a455ac7d856c26d0b36d391ab6215bbad88 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Thu, 29 Aug 2024 11:11:10 -0400 Subject: [PATCH 080/111] fix num samples --- docs/source/explainable_sir.ipynb | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 0322cc67..3866a6aa 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -91,7 +91,7 @@ "pyro.set_rng_seed(seed)\n", "\n", "smoke_test = \"CI\" in os.environ\n", - "num_samples = 10 if smoke_test else 700" + "num_samples = 10 if smoke_test else 10000" ] }, { @@ -397,7 +397,6 @@ "# propagating the changes, as the decisions are upstream from ds\n", "\n", "# no interventions\n", - "num_samples = 10000\n", "policy_model_none = condition(\n", " policy_model, {\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(0.0)}\n", ")\n", From b0d8e1d125bdd97829ae3834765655772ee8de12 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Thu, 29 Aug 2024 11:17:50 -0400 Subject: [PATCH 081/111] small fixes --- docs/source/explainable_sir.ipynb | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 3866a6aa..2526739c 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -1226,7 +1226,6 @@ " for i in range(len(data_nec)):\n", " if (data_nec[i] < overshoot_threshold) & (data_suff[i] > overshoot_threshold):\n", " sum += 1\n", - " print(sum / len(data_nec))\n", "\n", "a = torch.transpose(torch.vstack((data_nec.squeeze(), data_suff.squeeze())), 0, 1)\n", "hist_mask_2d, _ = torch.histogramdd(a, bins = [36, 36], density=True, range=[0.0, 45.0, 0.0, 45.0])\n", From ff0739eeff8afec9bb72375717c9661bba2f9f9f Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Thu, 29 Aug 2024 11:19:30 -0400 Subject: [PATCH 082/111] toc change --- docs/source/explainable_sir.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 2526739c..43a50582 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -30,7 +30,7 @@ "- [But for Analysis with Bayesian SIR Model with Policies](#but-for-analysis-with-bayesian-sir-model-with-policies)\n", "- [Causal Explanations using `SearchForExplanation`](#causal-explanations-using-searchforexplanation)\n", "- [Fine-grained Analysis of `overshoot` using Sample traces](#fine-grained-analysis-of-overshoot-using-sample-traces)\n", - "- [For Advanced Readers: Looking into Different Contexts](#looking-into-different-contexts-for-advanced-readers)" + "- [For Advanced Readers: Looking into Different Contexts](#ooking-into-different-contexts-for-curious-readers)" ] }, { @@ -1313,7 +1313,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Looking into different contexts (for curious readers)" + "## Looking into different contexts for curious readers" ] }, { From ca71be54c30731e3413867f37844dba6cd7d2ede Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Thu, 29 Aug 2024 16:06:17 -0400 Subject: [PATCH 083/111] formulae clarified with small changes --- docs/source/explainable_sir.ipynb | 121 ++++++++++++++++++------------ 1 file changed, 73 insertions(+), 48 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 43a50582..47d976ab 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -129,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -169,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -238,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -279,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -302,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -360,7 +360,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now that we have our full-fledged model of SIR dynamics along with interventions, we have a complete list of random variables in question. In our explanation we will abbreviate them as follows. `S` - susceptible, `I` - infected, `R` - recovered, `ld` - lockdown, `m` - masking, `le` - lockdown efficiency, `me` - mask efficiency, `je` - joint efficiency, `os` - overshoot, and `oth` - overshoot is too high. We use these notations in the rest of the notebook to describe the probabilities we are computing." + "Now that we have our full-fledged model of SIR dynamics along with interventions, we have a complete list of random variables in question. In our explanation we will abbreviate them as follows. `S` - susceptible, `I` - infected, `R` - recovered, `l` - the effect of intervention, `beta`, `gamma` - the parameters of the SIR dynamics model, `ld` - lockdown, `m` - masking, `le` - lockdown efficiency, `me` - mask efficiency, `je` - joint efficiency, `os` - overshoot, and `oth` - overshoot is too high. We use these notations in the rest of the notebook to describe the probabilities we are computing." ] }, { @@ -389,9 +389,17 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Variables in the model: dict_keys(['lockdown', 'mask', 'beta', 'gamma', 'lockdown_efficiency', 'mask_efficiency', 'joint_efficiency', 'S', 'I', 'R', 'l', 'overshoot', 'os_too_high'])\n" + ] + } + ], "source": [ "# conditioning (as opposed to intervening) is sufficient for\n", "# propagating the changes, as the decisions are upstream from ds\n", @@ -429,7 +437,16 @@ "lockdown_samples = lockdown_predictive()\n", "\n", "predictive = Predictive(policy_model, num_samples=num_samples, parallel=True)\n", - "samples = predictive()" + "samples = predictive()\n", + "\n", + "print(\"Variables in the model:\", samples.keys())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the above list of variables match our list of variables earlier when we constructed the full-fledged SIR model." ] }, { @@ -581,6 +598,17 @@ "## Causal Explanations using SearchForExplanation\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we dive into the code below, let us first define some notation. We use small case abbreviations to refer to the value of the variables under consideration. For example, $\\mathit{ld}$ refers to `lockdown=1` and $\\mathit{ld}'$ refers to `lockdown=0`. We place interventions in the subscripts, i.e. $\\mathit{os}_{\\mathit{ld}}$ refers to the `overshoot` under the intervention that `lockdown=1`. Later on in the notebook, we also employ contexts that are kept fixed in the intervened worlds. We place these contexts in the superscript. For example, $\\mathit{os}_{\\mathit{ld}}^{\\mathit{me}}$ refers to the variable `overshoot` when `lockdown` was intervened to be 1 and `mask_efficiency` was kept fixed at its factual value. \n", + "\n", + "We use $P(.)$ to denote the distribution described by the model (`policy_model` in this notebook). We also induce a distribution over the sets of interventions and the sets of context nodes kept fixed. We denote these distributions by $P_a(.)$ and $P_w(.)$ respectively. As an example, $P_a(\\{ld\\})$ refers to the probability that the set of interventions under consideration is $\\{ld\\}$. These distributions are determined using the parameters `antecedent_bias` and `witness_bias` given to the handler `SearchForExplanation`. For more details, please refer to the [documentation](https://basisresearch.github.io/chirho/explainable.html#chirho.explainable.handlers.explanation.SearchForExplanation)\n", + "\n", + "Now let's dive into the code and we use this notation to describe the quantities we are computing. " + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -679,7 +707,7 @@ " witness_bias=0.2,\n", ")(policy_model)\n", "\n", - "logp, importance_tr, mwc_imp, log_weights = importance_infer(num_samples=10000)(query)()\n", + "logp, importance_tr, mwc_imp, log_weights = importance_infer(num_samples=num_samples)(query)()\n", "print(torch.exp(logp))" ] }, @@ -698,7 +726,7 @@ "metadata": {}, "outputs": [], "source": [ - "def compute_prob(trace, log_weights, mask, mwc):\n", + "def compute_prob(trace, log_weights, mask):\n", " mask_intervened = torch.ones(\n", " trace.nodes[\"__cause____antecedent_lockdown\"][\"value\"].shape\n", " ).bool()\n", @@ -718,12 +746,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We specifically compute the following four probabilities. In each of the computations, we condition on lockdown and masking actually being implemented in the factual workd. Then we take an interventional setting and compute the probability that this setting has a causal power over the outcome. For instance, in 1., we assume lockdown (`ld`) and masking (`m`) have been implemented, and we ask about the joint prbability that both (a) removing both interventions, i.e. intervening for both `ld` and `m` to not happen - which we mark by the apostrophe - would lead to `oth` not happening, $\\mathit{oth}'_{\\mathit{ld}', m'}$, and (b) intervening for both to happend would lead to `oth`, $\\mathit{oth}_{\\mathit{ld}, m}$ (which, given the stochasticity between these interventions and the outcome, might be non-trivial).\n", + "We specifically compute the following four probabilities. In each of the computations, we condition on lockdown and masking actually being implemented in the factual world. Then we take an interventional setting and compute the probability that this setting has a causal power over the outcome. For instance, in 1., we assume lockdown (`ld`) and masking (`m`) have been implemented, and we ask about the joint prbability that both (a) removing both interventions, i.e. intervening for both `ld` and `m` to not happen - which we mark by the apostrophe - would lead to `oth` not happening, $\\mathit{oth}'_{\\mathit{ld}', m'}$, and (b) intervening for both to happend would lead to `oth`, $\\mathit{oth}_{\\mathit{ld}, m}$ (which, given the stochasticity between these interventions and the outcome, is non-trivial). Note that in computing these probabilities, we also marginalize over all the possible contexts to be kept fixed, i.e. all possible subsets of $W = \\{\\mathit{le}, \\mathit{me}\\}$\n", + "\n", + "1. $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{oth}^w_{\\mathit{ld}, m}, \\mathit{oth}'^w_{\\mathit{ld}', m'} | \\mathit{ld}, m)$\n", + "\n", + "2. $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{oth}^w_{\\mathit{ld}}, \\mathit{oth}'^w_{\\mathit{ld}'} | \\mathit{ld}, m)$\n", + "\n", + "3. $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{oth}^w_{m}, \\mathit{oth}'^w_{m'} | \\mathit{ld}, m)$\n", "\n", - "1. $P(\\mathit{oth}_{\\mathit{ld}, m}, \\mathit{oth}'_{\\mathit{ld}', m'} | \\mathit{ld}, m)$\n", - "2. $P(\\mathit{oth}_{\\mathit{ld}}, \\mathit{oth}'_{\\mathit{ld}'} | \\mathit{ld}, m)$\n", - "3. $P(\\mathit{oth}_{m}, \\mathit{oth}'_{m'} | \\mathit{ld}, m)$\n", - "4. $P(\\mathit{oth}, \\mathit{oth}' | \\mathit{ld}, m)$" + "4. $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{oth}^w, \\mathit{oth}'^w | \\mathit{ld}, m)$" ] }, { @@ -748,7 +779,6 @@ " importance_tr,\n", " log_weights,\n", " {\"__cause____antecedent_lockdown\": 0, \"__cause____antecedent_mask\": 0, \"mask\": 1, \"lockdown\": 1},\n", - " mwc_imp\n", ")\n", "\n", "# only lockdown executed, masking preempted\n", @@ -756,7 +786,6 @@ " importance_tr,\n", " log_weights,\n", " {\"__cause____antecedent_lockdown\": 0, \"__cause____antecedent_mask\": 1, \"mask\": 1, \"lockdown\": 1},\n", - " mwc_imp\n", ")\n", "\n", "# only masking executed, lockdown preempted\n", @@ -764,7 +793,6 @@ " importance_tr,\n", " log_weights,\n", " {\"__cause____antecedent_lockdown\": 1, \"__cause____antecedent_mask\": 0, \"mask\": 1, \"lockdown\": 1},\n", - " mwc_imp\n", ")\n", "\n", "# no interventions executed\n", @@ -772,7 +800,6 @@ " importance_tr,\n", " log_weights,\n", " {\"__cause____antecedent_lockdown\": 1, \"__cause____antecedent_mask\": 1, \"mask\": 1, \"lockdown\": 1},\n", - " mwc_imp\n", ")" ] }, @@ -796,9 +823,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can also compute degree of responsibilities assigned to both lockdown and mask as follows. To compute degree of responsibility of lockdown and mask, we specifically compute the probability that these factors were a part of the cause of the outcome. Mathematically, we compute the following:\n", - "1. Degree of responsibility of lockdown: $\\sum_{\\mathit{ld} \\in C} P(\\mathit{oth}_{C}, \\mathit{oth}'_{C'} | \\mathit{ld}, m)$\n", - "2. Degree of responsibility of mask: $\\sum_{\\mathit{m} \\in C} P(\\mathit{oth}_{C}, \\mathit{oth}'_{C'} | \\mathit{ld}, m)$" + "We can also compute degree of responsibilities assigned to both lockdown and mask as follows. To compute degree of responsibility of lockdown and mask, we specifically compute the probability that these factors were a part of the cause of the outcome. Mathematically, we compute the following where $W = \\{\\mathit{le}, \\mathit{me}\\}$ and $C = \\{\\mathit{ld}, m\\}$:\n", + "\n", + "1. Degree of responsibility of lockdown: $\\sum_{w \\subseteq W} \\sum_{\\mathit{ld} \\in C} P_w(w) P_a(C | \\mathit{ld} \\in C) \\cdot P(\\mathit{oth}^w_{C}, \\mathit{oth}'^w_{C'} | \\mathit{ld}, m)$\n", + "\n", + "2. Degree of responsibility of mask: $\\sum_{w \\subseteq W} \\sum_{\\mathit{m} \\in C} P_w(w) P_a(C | \\mathit{m} \\in C) \\cdot P(\\mathit{oth}^w_{C}, \\mathit{oth}'^w_{C'} | \\mathit{ld}, m)$" ] }, { @@ -820,11 +849,11 @@ ], "source": [ "print(\"Degree of responsibility for lockdown: \")\n", - "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_lockdown\": 0, \"mask\": 1, \"lockdown\": 1}, mwc_imp)\n", + "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_lockdown\": 0, \"mask\": 1, \"lockdown\": 1})\n", "print()\n", "\n", "print(\"Degree of responsibility for mask: \")\n", - "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_mask\": 0, \"mask\": 1, \"lockdown\": 1}, mwc_imp)" + "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_mask\": 0, \"mask\": 1, \"lockdown\": 1})" ] }, { @@ -1010,7 +1039,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The above histogram also takes into account the context that is being kept fixed. If `lockdown` is being intervened on, keeping `lockdown_efficiency` fixed would hinder the effect of intervention. Thus to obtain the relevant samples, we also filter for the appropriate context. Once we have filtered for the context, we take the samples and plot them as density above. The histogram above plots three quantities. It plots $P(\\mathit{os} | \\mathit{ld}, m)$ as the factual distribution of overshoot, $P(\\mathit{os}_{\\mathit{ld}'} | \\mathit{ld}, m)$ as `counterfactual_lockdown` and $P(\\mathit{os}_{\\mathit{m}'} | \\mathit{ld}, m)$ as `counterfactual_mask`. These distributions help in comparing how necessity interventions for the two antecedents affect the overshoot." + "The above histogram also takes into account the context that is being kept fixed. If `lockdown` is being intervened on, keeping `lockdown_efficiency` fixed would hinder the effect of intervention. Thus to obtain the relevant samples, we also filter for the appropriate context. Once we have filtered for the context, we take the samples and plot them as density above. The histogram above plots three quantities. It plots $P(\\mathit{os} | \\mathit{ld}, m)$ as the factual distribution of overshoot, $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{ld}'} | \\mathit{ld}, m)$ as `counterfactual_lockdown` where $W = \\{\\mathit{me}\\}$ and $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{m}'} | \\mathit{ld}, m)$ as `counterfactual_mask` where $W = \\{\\mathit{le}\\}$. These distributions help in comparing how necessity interventions for the two antecedents affect the overshoot." ] }, { @@ -1137,7 +1166,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The histogram plots three quantities. It plots $P(\\mathit{os} | \\mathit{ld}, m)$ as the factual distribution of overshoot, $P(\\mathit{os}_{\\mathit{ld}} | \\mathit{ld}, m)$ as `counterfactual_lockdown` and $P(\\mathit{os}_{\\mathit{m}} | \\mathit{ld}, m)$ as `counterfactual_mask`. Again, these distributions help in comparing how sufficiency interventions for the two antecedents affect the overshoot." + "The histogram plots three quantities. It plots $P(\\mathit{os} | \\mathit{ld}, m)$ as the factual distribution of overshoot, $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{ld}} | \\mathit{ld}, m)$ as `counterfactual_lockdown` where $W = \\{\\mathit{me}\\}$ and $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{m}} | \\mathit{ld}, m)$ as `counterfactual_mask` where $W = \\{\\mathit{le}\\}$. Again, these distributions help in comparing how sufficiency interventions for the two antecedents affect the overshoot." ] }, { @@ -1161,11 +1190,12 @@ } ], "source": [ + "# Collecting samples for joint distribution of overshoot under necessity and sufficiency interventions on lockdown\n", "masks = {\n", " \"__cause____antecedent_mask\": 1,\n", - " \"__cause____antecedent_lockdown\": 0,\n", - " \"__cause____witness_lockdown_efficiency\": 0,\n", - " \"lockdown\": 1, \"mask\": 1\n", + " \"__cause____antecedent_lockdown\": 0, # Intervening only on lockdown\n", + " \"__cause____witness_lockdown_efficiency\": 0, # Excluding lockdown efficiency fron the context candidates\n", + " \"lockdown\": 1, \"mask\": 1 # Conditioning on lockdown and masking being imposed in factual world\n", " }\n", "with mwc_imp:\n", " data_nec = gather(\n", @@ -1188,16 +1218,17 @@ " data_suff = data_suff.squeeze()[torch.nonzero(mask_tensor.squeeze())]\n", " data_nec = data_nec.squeeze()[torch.nonzero(mask_tensor.squeeze())]\n", "\n", - "\n", - "a = torch.transpose(torch.vstack((data_nec.squeeze(), data_suff.squeeze())), 0, 1)\n", + "a = torch.transpose(torch.vstack((data_nec.squeeze(), data_suff.squeeze())), 0, 1) # Joint distribution\n", "hist_lockdown_2d, rough = torch.histogramdd(a, bins=[36, 36], density=True, range=[0.0, 45.0, 0.0, 45.0])\n", "pr_lockdown = (hist_lockdown_2d[:16, 16:].sum()/hist_lockdown_2d.sum())\n", "\n", + "\n", + "# Collecting samples for joint distribution of overshoot under necessity and sufficiency interventions on mask\n", "masks = {\n", " \"__cause____antecedent_mask\": 0,\n", - " \"__cause____antecedent_lockdown\": 1,\n", - " \"__cause____witness_mask_efficiency\": 0,\n", - " \"lockdown\": 1, \"mask\": 1\n", + " \"__cause____antecedent_lockdown\": 1, # Intervening only on mask\n", + " \"__cause____witness_mask_efficiency\": 0, # Excluding mask efficiency fron the context candidates\n", + " \"lockdown\": 1, \"mask\": 1 # Conditioning on lockdown and masking being imposed in factual world\n", " }\n", "with mwc_imp:\n", " data_nec = gather(\n", @@ -1219,15 +1250,7 @@ " data_suff = data_suff.squeeze()[torch.nonzero(mask_tensor.squeeze())]\n", " data_nec = data_nec.squeeze()[torch.nonzero(mask_tensor.squeeze())]\n", "\n", - " data_nec = data_nec.squeeze()\n", - " data_suff = data_suff.squeeze()\n", - "\n", - " sum = 0\n", - " for i in range(len(data_nec)):\n", - " if (data_nec[i] < overshoot_threshold) & (data_suff[i] > overshoot_threshold):\n", - " sum += 1\n", - "\n", - "a = torch.transpose(torch.vstack((data_nec.squeeze(), data_suff.squeeze())), 0, 1)\n", + "a = torch.transpose(torch.vstack((data_nec.squeeze(), data_suff.squeeze())), 0, 1) # Joint distribution\n", "hist_mask_2d, _ = torch.histogramdd(a, bins = [36, 36], density=True, range=[0.0, 45.0, 0.0, 45.0])\n", "pr_mask = (hist_mask_2d[:16, 16:].sum()/hist_mask_2d.sum())" ] @@ -1273,7 +1296,7 @@ "ax.axhline(y=(os_lockdown_nec) * 36 / 45, color=\"white\", linestyle=\"--\")\n", "\n", "ax.legend(loc=\"upper left\")\n", - "ax.text(13, 2, 'pr(lockdown caused overshoot): %.4f' % pr_lockdown.item(), color=\"white\")\n", + "ax.text(13, 2, 'pr(lockdown has causal role over high overshoot): %.4f' % pr_lockdown.item(), color=\"white\")\n", "\n", "ax = axs[1]\n", "hist_mask = hist_mask_nec.unsqueeze(1) * hist_mask_suff.unsqueeze(0)\n", @@ -1295,7 +1318,7 @@ " label=\"Mean Overshoot\",\n", ")\n", "ax.axhline(y=(os_mask_nec) * 36 / 45, color=\"white\", linestyle=\"--\")\n", - "ax.text(13, 2, 'pr(masking is a cause ): %.4f' % pr_mask.item(), color=\"white\")\n", + "ax.text(13, 2, 'pr(masking has causal role over high overshoot): %.4f' % pr_mask.item(), color=\"white\")\n", "\n", "ax.legend(loc=\"upper left\")\n", "\n", @@ -1306,7 +1329,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The above heatmaps plot the distributions $P(\\mathit{os}_{\\mathit{ld}}, \\mathit{os}_{\\mathit{ld}'}|\\mathit{ld, m})$ and $P(\\mathit{os}_{\\mathit{m}}, \\mathit{os}_{\\mathit{m}'}|\\mathit{ld, m})$ respectively. It is evident from the plot above that counterfactual for lockdown has more probability mass in the top right quadrant (low overshoot in the necessity world and high overshoot in the sufficient world). This gives us a more clear picture into why lockdown has more causal role in overshoot being too high as compared to masking." + "The above heatmaps plot the joint distributions arising from necessity and sufficient interventions, particularly $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{ld}}, \\mathit{os}^w_{\\mathit{ld}'}|\\mathit{ld, m})$ where $W = \\{\\mathit{me}\\}$ and $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{m}}, \\mathit{os}^w_{\\mathit{m}'}|\\mathit{ld, m})$ where $W = \\{\\mathit{le}\\}$.\n", + "\n", + "It is evident from the plot above that counterfactual for lockdown has more probability mass in the top right quadrant (low overshoot in the necessity world and high overshoot in the sufficient world). This gives us a more clear picture into why lockdown has more causal role in overshoot being too high as compared to masking." ] }, { From ca1bc4c9f13e7bd98e0dbb48c862991124bfd63b Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Thu, 29 Aug 2024 16:10:03 -0400 Subject: [PATCH 084/111] tweaks --- docs/source/explainable_sir.ipynb | 104 ++++++++++++++---------------- 1 file changed, 48 insertions(+), 56 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 47d976ab..af8b5504 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -129,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -169,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -238,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -279,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -302,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -389,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -451,12 +451,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
      " ] @@ -620,7 +620,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -674,14 +674,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.1328)\n" + "tensor(0.1287)\n" ] } ], @@ -722,7 +722,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -759,17 +759,17 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 0.24283304810523987\n", - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 0.2902735471725464\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 2.3861892461951584e-09\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 2.636660445531902e-09\n" + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 0.20909090340137482\n", + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 0.3085271418094635\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 2.3285480210688547e-09\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 3.2006626238256786e-09\n" ] } ], @@ -832,7 +832,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -840,10 +840,10 @@ "output_type": "stream", "text": [ "Degree of responsibility for lockdown: \n", - "{'__cause____antecedent_lockdown': 0, 'mask': 1, 'lockdown': 1} 0.2677857577800751\n", + "{'__cause____antecedent_lockdown': 0, 'mask': 1, 'lockdown': 1} 0.2582375407218933\n", "\n", "Degree of responsibility for mask: \n", - "{'__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 0.1170731708407402\n" + "{'__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 0.10722610354423523\n" ] } ], @@ -881,7 +881,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -924,7 +924,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -957,7 +957,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -965,14 +965,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "factual: 24.302181243896484 counterfactual mask: 26.837486267089844 counterfactual lockdown: 21.276477813720703\n", + "factual: 24.31097984313965 counterfactual mask: 26.97050666809082 counterfactual lockdown: 21.247312545776367\n", "Probability of overshoot being high\n", - "factual: 0.6021000146865845 counterfactual mask: 0.8484848737716675 counterfactual lockdown: 0.32460734248161316\n" + "factual: 0.6075999736785889 counterfactual mask: 0.8814433217048645 counterfactual lockdown: 0.3526315689086914\n" ] }, { "data": { - "image/png": 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BgwevHCD+IgcHBzHJsbS0RKNGjTSmZPDz80NcXBwePXokzh0EPG85LW5CxNu3b8Pe3l6j9UdbhZ/1hIQEjVaf27dv61wXGTaOAaLX1ogRI2BlZYVPP/0UycnJRbYnJCSItwa3a9cOADRuFQaAyMhIje2A+ov0v//9r0a5qKiol7YAacPS0rLYLrEuXbrgwoULOHHiRJFtGRkZKCgoKPUxpVJpkZaew4cPF7n9+91330VaWhq2bdtWpI7C/S0tLcWY/qlu3bq4ffu2RnfdtWvXikyCJ5VKYWJionEd7927h59//lnHM1MLCAhAYGCg+NI1AQLUrUAFBQUIDw8vEmunTp1w9OhR3Lhxo8h+L56rNtevS5cuUCqV+Oabb4qUKSgoEK9renp6kffM09MTAMQuw7S0NI3tEolEbI3655QOL7K0tERmZuZLt/fs2ROnTp3C5s2bUb16dZ261Pz9/XHt2jWcOnWqyB8jfn5+uHjxIs6fPw+5XC52Xzs7O8PT0xP79u3T+H9148YNnDp1SuMzqYvCuLdu3aqx/p+ffXr9sQWIXlv16tXDihUrMHnyZHTt2lWcCVqhUODChQs4cuSIOPOyh4cHPvjgA+zcuRMZGRlo1qwZ/vzzT0RHRyM4OBgtW7YU6+3bty8+++wzTJgwAYGBgbh27RpOnjyp0WqiKy8vL+zYsQPffPMNXF1dUaNGDbRq1QohISH4v//7P4wePRoffPABvLy8kJubixs3buDo0aP4+eeftR70/E/vvPMO1q5di1mzZsHPzw83btzAgQMHiiQK77//Pvbt24fQ0FBcunQJAQEByM3NRWxsLD788EMEBwfDwsICjRo1wuHDh+Hm5obq1avjzTffhLu7O/r06YNNmzYhJCQEffr0QUpKCn744Qc0atRIY4xWu3btEBkZiREjRqB79+5ISUnB9u3bUa9ePVy/fr3U17YsCluBoqOji2ybOnUqzpw5g379+qFv375o1KgR0tPTceXKFcTGxuLs2bMAtLt+zZs3x7/+9S989913uHr1Klq3bg0zMzPcvXsXR44cwZw5c9C5c2dER0djx44dCA4ORr169ZCdnY2oqCjY2NiIX+yffvop0tPT0bJlS9SsWRMPHjzA999/D09PT43WzX/y8vJCTEwMQkND4ePjAysrK415mrp3744vvvgCP/30Ez788MOXtmYVJyAgAHv37sWff/6JgQMHamzz8/NDZmYmMjMziwxMnj59OkaOHIl//etf6NOnj3gbvK2tLcaPH6/18V/k6emJ7t27Y/v27cjMzISfnx9Onz6Nv//+u1T1keFiAkSvtQ4dOmD//v2IiIjAzz//jB07dkAmk0Eul2PmzJno16+fWHbRokVwcXFBdHQ0jh07BkdHR3z88cdFftH269cP9+7dw+7du3HixAkEBAQgMjKyTI8QGDduHB48eIDw8HBkZ2ejefPmaNWqFSwtLbF161Z89913OHLkCPbt2wcbGxu4ublhwoQJWo3BeJnRo0cjNzcXBw4cQExMDN566y189913WLlypUY5qVSKDRs2YN26dTh48CB+/PFHVK9eHf7+/hrjXBYtWoSFCxciNDQU+fn5GD9+PNzd3dGwYUMsW7YMYWFhCA0NRaNGjbB8+XIcPHhQTBIAoFWrVli8eDE2bNiAJUuWwMXFBdOmTcP9+/f1lgABz8cC/bOFz9HREbt27cLatWvx008/YceOHahevToaNWokDvIFtL9+n3/+Oby9vfHDDz/gyy+/hFQqRZ06dfDee++Jc1k1b94cf/75J2JiYpCcnAxbW1v4+vpixYoVYuJaOHh7+/btyMjIgJOTE7p06YIJEya8cubnAQMG4OrVq9i7dy82bdqEOnXqaCRAjo6OaN26NX799VeN+aS08eK4nn+2AL355puoVq0aMjIyiszZFRgYiPDwcISFhSEsLAympqZo1qwZ/v3vf5eqRa/QkiVLYG9vjwMHDuDnn39GixYtsH79+lK3KpFhMhEqY7QjEREZvHHjxuHGjRsaEy8SGSqOASIiohI9efKkVK0/RFUVu8CIiOilEhMTERcXh927d8PU1BT/+te/9B0SUblgCxAREb3UuXPnMH36dNy7dw9Lly6Fk5OTvkMiKhccA0RERERGhy1AREREZHSYABEREZHRYQJUDEEQkJWVVSnPQyIiIqLKxwSoGNnZ2QgICND6SeJEZMSyswETE/WLvzOIDAYTICIiIjI6TICIiIjI6DABIiIiIqPDBIiIiIiMDh+FQURUFqamwNChz5eJyCDw01oGSqUS+fn5+g6DyCiYmZlBKpXqO4yizM2BTZv0HQUR6YgJUCkIgoBHjx7h6dOn+g6FyKhUr14dtWrVgomJib5DISIDxwSoFAqTH2dnZ1hZWfGXMVEFEwQBOTk5ePLkCQDgjTfe0HNELxAEICdHvWxlpZ4PiIiqPCZAOlIqlWLy4+DgoO9wiIyGpaUlAODJkydwdnauOt1hOTmAjY16OSsLsLbWbzxEpBXeBaajwjE/VlZWeo6EyPgUfu449o6IyooJUCmx24uo8vFzR0TlhQkQERERGR0mQEZEEATMnTsXzZs3h1wux9WrV/Ud0kvNnDkTY8eO1XcYRET0muIg6HKUlgakp1fOsezsAHt73fb57bffEB0djS1btqBu3bqw17WCf1izZg2OHTuG//znP2Wqh4iIqLIxASpH6enA4cNAdnbFHsfaGujSRfcEKDExEU5OTvD396+YwIiIiAwEE6Bylp2tvhO2qpk5cyaio6MBAHK5HHXq1MH8+fOxbt063Lx5E1KpFE2aNMGcOXNQr149cb9Hjx5h+fLlOHnyJBQKBRo0aIDPPvsM8fHx+Prrr8X6ACA0NBTNmzdHhw4dsG/fPnh6egIAMjIy0KxZM2zZsgUtWrSAUqnE3Llzcfr0aSQnJ+ONN97AgAEDMLTwcQJEhkQqBfr0eb5MRAaBCZCRmDNnDurWrYuoqCjs3r0bUqkU586dw/DhwyGXy5GTk4PVq1dj3Lhx+M9//gOJRILs7GwMGjQINWvWxDfffAMnJydcuXIFKpUKXbt2xc2bN3HixAlERkYCAGxtbZGcnFxiLCqVCrVq1cLq1atRvXp1XLhwAfPmzYOTkxO6du1a0ZeCqHxZWAC7duk7CiLSERMgI2Frawtra2tIpVI4OTkBADp16qRRZsmSJWjVqhVu3boFd3d3HDx4EKmpqdi9ezeqV68OAHB1dRXLW1lZadSnLTMzM0ycOFH8uW7durh48SKOHDnCBIiogqXlpiE9T/vBinbmdrC3LNt4QaKqiAmQEbt79y7CwsLwxx9/IC0tDYIgAAAePnwId3d3XL16FW+99ZaY/JSnbdu2Yc+ePXjw4AHy8vKQn58PDw+Pcj8OEWlKz0vH4ZuHkZ1f8mBFazNrdHmzCxMgei0xATJio0ePRp06dbBo0SI4OztDpVKhe/fu4iy7FhYWOtcpkahnVihMpgCgoKBAo8yhQ4ewbNkyzJgxA35+frC2tkZERAT++OOPMpwNkZ5kZxvcozCy87ORpaiCgxWJKhHnATJSaWlpuHPnDsaMGYNWrVqhYcOGSP/HPfyFcwW97Kn3ZmZmUKlUGutq1KgBAEhKShLX/XO+obi4OPj5+WHgwIF466234OrqioSEhHI4KyIiIu0wATJSdnZ2qF69Onbu3Im///4bsbGxWLp0qUaZbt26wdHREePGjcP58+eRmJiIo0eP4sKFCwCAOnXq4N69e7h69SpSU1OhUChgYWGBJk2aYP369YiPj8fZs2fx1VdfadTr6uqKy5cv48SJE7hz5w6++uor/Pnnn5V16kREREyAypu1tbo1vCJf5dHCLpFI8OWXX+LKlSvo3r07QkNDMX36dI0yMpkMGzduhIODA0aNGoUePXpg/fr14lO4O3XqhLZt22LIkCFo1aoVDh48CEA9mFqpVKJXr15YsmQJPvnkE416+/fvj3fffReTJ09Gv3798PTpUwwYMKDsJ0VERKQlE+HFwRoEAMjKykJAQADOnz8Pm8K+/f959uwZ7ty5g/r16xcZI1PVZ4ImMnSv+vzpjYGNAbr79C52/7VbqzFANjIb9HmrD9yqu1V8YESVjIOgy5G9PZMSIiIiQ8AuMCIiIjI6bAEiIioLqRQonMCTj8IgMhhMgIiIysLCAjh0SN9REJGO2AVGRERERocJEBERERkdJkBERGWRna2+9d3aWr1MRAaBY4CIiMoqJ0ffERCRjtgCREREREaHCRAZtGPHjqFjx47w9PTE4sWL9R3OK505cwZyuRwZGRkllt27dy+aNm1abscuTX26xEtEZGiqRAK0bds2BAUFwcfHB3379sWlS5deWvbHH39Er1690LRpUzRp0gQ9e/bEvn37NMoIgoDVq1ejTZs28PX1xbBhw3D37t2KPQkAyC8AnuVVziu/oOLPp5zdu3dPfMJ8eZk3bx46deqE48ePY9KkSWWuryJiJCKiqkfvY4BiYmIQGhqKBQsWoHHjxti8eTNCQkJw5MgRODg4FClvZ2eHMWPGoEGDBjAzM8Mvv/yC2bNnw8HBAW3btgUAbNiwAVu3bsXSpUvh4uKC1atXIyQkBDExMTA3N6+4k1EqgZSngEpVcccAAIkEcKgOmOn97dOb/Px8KBQKpKSkoE2bNqhZs6a+QyIiIgOi9xagyMhI9OvXD71790ajRo2wYMECWFhYYM+ePcWWb9GiBTp27IiGDRuiXr16GDp0KORyOc6fPw9A3fqzZcsWjBkzBsHBwfDw8MDy5cvx5MkTHDt2rOJPSKUClBX8KmWCpVKpsGHDBnTs2BHe3t545513sG7dOgDA9evXMWTIEPj6+qJFixaYO3cusl+4o2Xw4MFFupjGjh2LmTNnij8HBQXh22+/xaxZs+Dn54d33nkHO3fuFLd36NABAPD+++9DLpdj8ODB4rZdu3ahS5cu8PHxQefOnbFt2zZxW2GrTExMDAYNGgQfHx8cOHAA/v7+ACD+Hzhz5gzS0tIwZcoUtG3bFo0bN0aPHj3Ep9Rrcx1eFqM2579v3z706tULfn5+aN26NaZOnYqUlBSt3httbN++HcHBwfD29kanTp2KtHxmZGRg3rx5CAwMhI+PD7p3745ffvml2LpSU1PRq1cvjBs3DgqFAgDw66+/olOnTvD19cXgwYNx//79IvsdPXoU3bp1g7e3N4KCgrBx40Zx2/fff4/u3buLPx87dgxyuRw7duwQ1w0bNgxffvklAGDNmjViC25QUBACAgIwefJkZGWV/JBOIqKy0msCpFAocOXKFQQGBorrJBIJAgMDceHChRL3FwQBsbGxuHPnDpo1awZA/WWZlJSkUaetrS0aN26sVZ2vs5UrV2LDhg0YO3YsYmJisGLFCjg6OiInJwchISGws7PD7t278dVXX+H333/HwoULdT5GZGQkvL29sW/fPgwYMADz58/H7du3AaiTHADYtGkTTp48iTVr1gAA9u/fj9WrV2Py5MmIiYnBlClTEBYWhujoaI26V6xYgSFDhiAmJgYtWrTAkSNHAKi/SE+ePAk/Pz8oFAp4eXlh/fr1OHjwIPr164fp06drdKu+7Dq8KkZtFBQUYNKkSdi/fz/Wrl2L+/fvayRIZfHTTz9hyZIlGD58OA4cOID+/ftj9uzZOH36NAB1Ujdy5EjExcXhiy++QExMDKZOnQqJpOhH/OHDhxgwYADc3d0RFhYGmUyGhw8fYvz48Wjfvj327duHvn37YuXKlRr7Xb58GZ988gm6du2KAwcOYPz48Vi9ejX27t0LAGjWrBlu3bqF1NRUAMDZs2dhb2+Ps2fPAlC32l28eBEtWrQQ60xISMDPP/+Mb7/9Ft999x3OnTuHDRs2lMs1qzQSCdCunfpVzPUmoqpJr30oaWlpUCqVRbq6HBwcxC/N4mRmZuLtt9+GQqGARCLBZ599htatWwMAkpKSxDr+WWdycnI5n4HhyMrKwpYtWzBv3jx88MEHAIB69eqhadOmiIqKgkKhwLJly2BlZQVAPbZm9OjRmDZtmpgcaOPtt9/GwIEDAQAjR47Epk2bcObMGTRo0AA1atQAAFSvXh1OTk7iPmvWrMHMmTPx7rvvAgDq1q2LW7duYefOnWKsgLqlp7AMAHFwrp2dnVhfzZo1ERISIpYZPHgwTp48icOHD8PX1/eV1wHAS2PURp8+fcTlunXrYs6cOejTpw+ys7NhbW2tU13/FBERgQ8++EC8tvXr18fFixexceNGtGzZEr///jsuXbqEmJgY1K9fX4zhn27fvo2PPvoIwcHBmDNnDkxMTAAAO3bsQL169cSErUGDBrhx44ZGMhIZGYlWrVph3LhxYgy3bt1CREQEevXqBXd3d9jZ2eHs2bPo3Lkzzp49i48++ghbtmwBAFy6dAkFBQXw8/MT6xQEAaGhobCxsQEAvPfee4iNjcXkyZPLdL0qlaUlcPy4vqMgIh0Z5CASa2tr7Nu3Dzk5OYiNjcXSpUtRt25djb8sSdPt27ehUCjQsmXLItvi4+Mhl8vF5AcA/P39oVKpcOfOHZ0SILlcLi6bmJjA0dHxld1AOTk5SEhIwJw5czB37lxxfUFBAWxtbTXKent7l3h8pVKJb7/9FkeOHMHjx4/FsUIWFhYAXn0dyury5cv4+uuvce3aNaSnp0MQBADqFpdGjRqVqe7bt2/jX//6l8Y6f39/Mbm4evUqatWqJSY/xXn27BkGDhyI7t27Y86cORrb4uPj4evrq7GuSZMmRWIo7CL8ZwxKpRJSqRTNmjXD2bNnERgYiFu3bmHAgAEIDw9HfHw8zp07B29vb1haWor716lTR0x+AMDZ2blcuw2JiF5GrwmQvb09pFJpkV94KSkpr/zSlUgkcHV1BQB4enoiPj4e69evR4sWLcS/2lNSUuDs7KxRp4eHRwWchWEo6+BvExMT8Qu9UEFB0TvRTE01/0sVt9+Lcv43gdzChQvRuHFjjW3/7L55MUF7mYiICGzZsgWzZ8+GXC6HpaUllixZgvz8fAClvw4lnX9hN2KbNm2wYsUK2Nvb4+HDhwgJCRGPXZEKE7xXkclkCAwMxPHjxzFixIgKGTjevHlzREVF4b///S/eeust2NjYoGnTpjh79izOnTuH5s2ba5T/5/8XAK/8/0JEVF702mEtk8ng5eWF2NhYcZ1KpUJsbKxGM3lJVCqVOJDTxcUFTk5OGnVmZWXhjz/+0KnO142bmxssLCzEMSMvatiwIa5fvy4mIwAQFxcHiUQitijUqFFD7F4E1C0tN2/e1CkGMzMzcd9Cjo6OcHZ2RmJiIlxdXTVexXXhlCQuLg4dOnRAz5494eHhgbp162pMgfCq6/CyGIGSz//27dt4+vQppk2bhqZNm6Jhw4bl2pLRoEEDxMXFaayLi4sTW5bkcjkePXqEO3fuvLQOiUSC5cuXw8vLC0OGDMHjx4/FbQ0bNsSff/6pUf6PP/7QKgY3NzdIpVIA6gTo1q1bOHLkiJjsNG/eHLGxsYiLiyuSAL0WsrMBJyf1i4/CIDIYeh+xN3z4cERFRSE6Ohrx8fGYP38+cnNz0atXLwDA9OnTNQZjfvfddzh16hQSExMRHx+PjRs3Yv/+/XjvvfcAqP9SHzJkCNatW4eff/4Z169fx/Tp0+Hs7Izg4GC9nGNVYG5ujpEjR+KLL77Avn37kJCQgIsXL2LXrl3o0aMHZDIZZs6ciRs3buD06dNYuHAhevbsKbbEtWzZEr/++iuOHz8uvk+6TpDn4OAACwsLnDhxAsnJycjMzAQATJw4EevXr8eWLVtw584dXL9+HXv27EFkZKTO5+nq6orff/8dcXFxiI+Px7x58zTGfr3qOrwqxpLOv3bt2jAzM8PWrVuRmJiIn3/+Gd98843O8b/MiBEjEB0dje3bt+Pu3buIjIzETz/9hI8++giAOslo2rQpJk6cKH4+fv31V/z2228a9UilUqxYsQJyuRxDhw4Vk7r+/fvj7t27WLZsGW7fvo0DBw4UGYT+0UcfITY2FmvXrsWdO3cQHR2Nbdu2iTEA6kTMzs4OBw8eFJOdFi1a4NixY1AoFOKde6+d5GT1i4gMht7HAHXt2hWpqakICwtDUlISPD09ER4eLn7xPnz4UKMrJCcnBwsWLMCjR49gYWGBBg0a4IsvvkDXrl3FMiNHjkRubi7mzZuHjIwMBAQEIDw8vGLnACpUGXeBlPIYY8eOhVQqRVhYGJ48eQInJyf0798flpaWiIiIwOLFi9GnTx9YWlri3Xff1biDqXfv3rh27RpmzJgBqVSKYcOG6TzmytTUFJ9++inWrl2LsLAwNG3aFFu3bkXfvn1hYWGBiIgILF++HFZWVnB3d8fQoUN1PscxY8YgMTERISEhsLS0RL9+/RAcHCwmMq+6Dq+KsaTzr1GjBpYuXYpVq1Zh69at8PLywowZMzBmzBidz6E4wcHBmD17NjZu3IglS5agTp06WLJkiUYMa9aswbJlyzBlyhTk5ubC1dUVU6dOLVKXqakpVq1ahcmTJ2Po0KHYunUrateujTVr1iA0NBTff/89fH19MXnyZMyePVvcz8vLC1999RXCwsKwbt06ODk5YeLEieIfK4D6D5CAgAD8+uuvCAgIAKBOimxsbFC/fn2tujGJiCqDicAO9yKysrIQEBCA8+fPawzQBNQDSe/cuYP69esXHXeRX6CeDLEySKVGPREiGadXfv70JTsbKPw9kZWlfip8FXb36V3s/ms3shQlz7dkI7NBn7f6wK26W8UHRlTJ+A1ansxMmZQQEREZAH5bE+nBiBEjxNnL/+njjz/G6NGjKzkiIiLjwgSISA8WL16MZ8+eFbvNzs6ukqMhIjI+TICI9IAPb32NSCTA/2YS56MwiAwHEyAiorKwtATOndN3FESkI/65QkREREaHCRAREREZHSZARERlkZMDuLmpXy88ToaIqjaOASIiKgtBAP7++/kyERkEtgCRQTt27Bg6duwIT09PLF68WN/hvNKZM2cgl8t1foZaZbl37x7kcjmuXr2q71CIiCocW4DKUVpuGtLz0ivlWHbmdrC3tK+UY5WXe/fuoUOHDti3bx88PT3Lpc558+ahV69eGDx4MKzL4REEFREjERFVPUyAylF6XjoO3zyM7PzsCj2OtZk1urzZxeASoPKUn58PhUKBlJQUtGnThvPqEBGRTtgFVs6y87ORpciq0FdpEyyVSoUNGzagY8eO8Pb2xjvvvIN169YBAK5fv44hQ4bA19cXLVq0wNy5c5Gd/fw4gwcPLtLFNHbsWI0nxgcFBeHbb7/FrFmz4Ofnh3feeQc7d+4Ut3fo0AEA8P7770Mul2Pw4MHitl27dqFLly7w8fFB586dsW3bNnFbYddMTEwMBg0aBB8fHxw4cAD+/v4AgKFDh0Iul+PMmTNIS0vDlClT0LZtWzRu3Bg9evTAwYMHtb4OL4tRm/Pft28fevXqBT8/P7Ru3RpTp05FSkqKVu/NP+3duxdNmzbFL7/8gk6dOqFx48aYOHEicnNzER0djaCgIDRr1gyLFi2C8oUH8JYUQ3p6OqZOnYqWLVvC19cX7777Lvbs2VNsDEqlErNmzULnzp3x4MGDUp0HEVFVxRYgI7Jy5Urs2rULs2bNQkBAAJ48eYI7d+4gJycHISEh8PPzw+7du5GSkoJPP/0UCxcuxNKlS3U6RmRkJCZOnIjRo0fj6NGjmD9/Ppo1a4YGDRpg165d6Nu3LzZt2oRGjRrBzMwMALB//36sXr0a8+bNg6enJ65evYq5c+fCysoKH3zwgVj3ihUrMHPmTHh6ekIikeDIkSPo3Lkz1qxZAz8/P9jZ2SEtLQ1eXl4YOXIkbGxscPz4cUyfPh316tWDr6/vK68DgJfGqI2CggJMmjQJDRo0QEpKCpYuXYqZM2diw4YNOl3DQs+ePcPWrVvx5ZdfIjs7G+PHj8f48eNha2uL9evXIzExERMmTIC/vz+6du2qVQyrV69GfHw8NmzYAHt7eyQkJBT7SA6FQoEpU6bg/v372L59O2rUqFGqcyAiqqqYABmJrKwsbNmyBfPmzROTinr16qFp06aIioqCQqHAsmXLYGVlBUA9tmb06NGYNm0aHB0dtT7O22+/jYEDBwIARo4ciU2bNuHMmTNo0KCB+CVavXp1ODk5ifusWbMGM2fOxLvvvgsAqFu3Lm7duoWdO3dqJEBDhw4VywAQBxPb2dmJ9dWsWRMhISFimcGDB+PkyZM4fPgwfH19X3kdALw0Rm306dNHXK5bty7mzJmDPn36IDs7u1Tjk/Lz8zF//nzUq1cPANCpUyfs378fp06dgrW1NRo1aoQWLVrg9OnTYgJUUgwPHjyAp6cnfHx8AAAuLi5FjpudnY1Ro0ZBoVBgy5YtsLW11Tl2o2JiArz11vNlIjIITICMxO3bt6FQKNCyZcsi2+Lj4yGXy8XkBwD8/f2hUqlw584dnRIguVwuLpuYmMDR0fGV3UA5OTlISEjAnDlzMHfuXHF9QUFBkS9eb2/vEo+vVCrx7bff4siRI3j8+LE4VsjCwgLAq69DWV2+fBlff/01rl27hvT0dAj/uyX64cOHaNSokc71WVpaiskPADg6OqJOnToayZSjoyNSU1O1juHDDz/ExIkT8ddff6F169YIDg4WuxILTZ06FbVq1cLmzZvF60avYGUFXLmi7yiISEdMgIyEubl5mfY3MTERv0wLFRQUFClnaqr5X6q4/V6U87+J4xYuXIjGjRtrbJP848GSLyZoLxMREYEtW7Zg9uzZkMvlsLS0xJIlS5Cfnw+g9NehpPMv7EZs06YNVqxYAXt7ezx8+BAhISHisXVV3LUsbp1KpdI6hnbt2uGXX37Br7/+ilOnTmHYsGEYOHAgZsyYIdbZrl077N+/HxcuXECrVq1KFTsRUVXHQdBGws3NDRYWFjh9+nSRbQ0bNsT169fFZAQA4uLiIJFIUL9+fQDqrqGkpCRxu1KpxM2bN3WKoXA8zYuDdh0dHeHs7IzExES4urpqvOrWratT/YVxd+jQAT179oSHhwfq1q2Lu3fvittfdR1eFiNQ8vnfvn0bT58+xbRp09C0aVM0bNiw1AOgS0vbGGrUqIEPPvgAK1aswOzZszUGqgPAhx9+iKlTp2Ls2LE4e/ZsZYVPRFSp2AJkJMzNzTFy5Eh88cUXMDMzg7+/P1JTU3Hz5k306NEDYWFhmDlzJsaPH4/U1FQsXLgQPXv2FLu/WrZsiaVLl+L48eOoW7cuNm3apPOEfg4ODrCwsMCJEydQq1YtmJubw9bWFhMnTsSiRYtga2uLtm3bQqFQ4PLly8jIyMDw4cN1OoarqyuOHj2KuLg42NnZITIyEsnJyWjYsGGJ16Fv374vjbGk869duzbMzMywdetWfPjhh7hx4wa++eYbnWIvK21iWL16Nby8vPDmm29CoVDg+PHj4rV50eDBg6FUKvHxxx9jw4YN4hgpKkZODtCsmXr53Dl1lxgRVXlMgMqZtVnZJ+OrqGOMHTsWUqkUYWFhePLkCZycnNC/f39YWloiIiICixcvRp8+fWBpaYl3331X4xbv3r1749q1a5gxYwakUimGDRuGFi1a6HR8U1NTfPrpp1i7di3CwsLQtGlTbN26FX379oWFhQUiIiKwfPlyWFlZwd3dHUOHDtX5HMeMGYPExESEhITA0tIS/fr1Q3BwMDIzM0u8Dq+KsaTzr1GjBpYuXYpVq1Zh69at8PLywowZMzBmzBidz6G0tInBzMwMq1atwv3792FhYYGAgACsWrWq2PqGDRsGQRAwatQohIeHFxkrRP8jCMBffz1fJiKDYCK8aoCGkcrKykJAQADOnz8PGxsbjW3Pnj3DnTt3UL9+/SIDRDkTNFHFetXnT2+ys4HC3xNZWUA5zEheke4+vYvdf+1GliKrxLI2Mhv0easP3Kq7VXxgRJWMLUDlyN7SnkkJERGRAWACRKQHI0aMwPnz54vd9vHHH2P06NGVHBERkXFhAkSkB4sXLy52BmZAPbEjERFVLCZARHrAh7cSEekXEyAiorIwMQFcXZ8vE5FBYAJUSoWz7xJR5amSnzsrK+CFyTaJyDAwAdKRTCaDRCLBgwcP4OTkBJlMBhP+1UdUoQRBgEKhQFJSEiQSCWQymb5DIiIDxwRIR4WPh3j48CEePHig73CIjIqVlRXq1atX5DlxRES6YgJUCjKZDPXq1UNBQUGRZ0YRUcWQSqUwNTWtei2uubnA22+rl3/7DbC01G88RKQVJkClZGJiAjMzM/HhmURkpFQq4L//fb5MRAaB7chERERkdJgAERERkdFhAkRERERGhwkQERERGR0mQERERGR0eBcYEVFZOTrqOwIi0hETICKisrC2BpKS9B0FEemIXWBERERkdJgAERERkdFhAkREVBa5ucA776hfubn6joaItMQxQEREZaFSAb/++nyZiAwCW4CIiIjI6DABIiIiIqNTJRKgbdu2ISgoCD4+Pujbty8uXbr00rJRUVEYMGAAmjVrhmbNmmHYsGFFys+cORNyuVzjFRISUtGnQURERAZC72OAYmJiEBoaigULFqBx48bYvHkzQkJCcOTIETg4OBQpf+bMGXTr1g3+/v6QyWQIDw/HRx99hEOHDqFmzZpiubZt2yI0NFT8WSaTVcr5EBERUdWn9xagyMhI9OvXD71790ajRo2wYMECWFhYYM+ePcWWX7lyJQYOHAhPT080bNgQixYtgkqlQmxsrEY5mUwGJycn8WVnZ1cZp0NEREQGQK8JkEKhwJUrVxAYGCiuk0gkCAwMxIULF7SqIzc3FwUFBUUSnLNnz6JVq1bo1KkTPvvsM6SlpZVr7EREIisr9YuIDIZeu8DS0tKgVCqLdHU5ODjg9u3bWtWxYsUKODs7ayRRbdu2RceOHeHi4oLExESsWrUKI0eOxM6dOyGVSsv1HIjIyFlbA9nZ+o6CiHSk9zFAZbF+/XrExMRgy5YtMDc3F9d369ZNXC4cBB0cHCy2ChEREZFx02sXmL29PaRSKVJSUjTWp6SkwLGEpytHRERg/fr1iIiIgIeHxyvL1q1bF/b29vj777/LHDMREREZPr0mQDKZDF5eXhoDmAsHNPv5+b10vw0bNuCbb75BeHg4fHx8SjzOo0eP8PTpUzg5OZVL3EREomfPgG7d1K9nz/QdDRFpSe9dYMOHD8eMGTPg7e0NX19fbN68Gbm5uejVqxcAYPr06ahZsyamTp0KQN3tFRYWhpUrV6JOnTpISkoCAFhZWcHa2hrZ2dn4+uuv0alTJzg6OiIxMRFffPEFXF1d0bZtW72dJxG9ppRKICbm+TIRGQS9J0Bdu3ZFamoqwsLCkJSUBE9PT4SHh4tdYA8fPoRE8ryh6ocffkB+fj4mTpyoUc/48eMxYcIESKVS3LhxA/v27UNmZiacnZ3RunVrTJo0iXMBEREREQDARBAEQd9BVDVZWVkICAjA+fPnYWNjo+9wiKgqy84GCn9PZGWp7wqrwu4+vYvdf+1GliKrxLI2Mhv0easP3Kq7VXxgRJVM7xMhEhEREVU2JkBERERkdJgAERERkdFhAkRERERGR+93gRERGTRra4D3khAZHLYAERERkdFhAkRERERGhwkQEVFZPHsG9O2rfvFRGEQGgwkQEVFZKJXA7t3qFx+FQWQwmAARERGR0WECREREREaHCRAREREZHSZAREREZHSYABEREZHRYQJERERERoePwiAiKgsrKyAr6/kyERkEJkBERGVhYqJ+HhgRGRR2gREREZHRYQJERFQWeXnAsGHqV16evqMhIi0xASIiKouCAmDzZvWroEDf0RCRlpgAERERkdFhAkRERERGhwkQERERGR0mQERERGR0mAARERGR0WECREREREaHM0ETEZWFlRXw5MnzZSIyCEyAiIjKwsQEcHLSdxREpCN2gREREZHRYQJERFQWeXnAuHHqFx+FQWQwmAAREZVFQQHwzTfqFx+FQWQwmAARERGR0WECREREREaHCRAREREZHSZAREREZHSYABEREZHRYQJERERERoczQRMRlYWlJXDnzvNlIjIITICIiMpCIgHc3PQdBRHpiF1gREREZHSYABERlYVCAfz73+qXQqHvaIhIS0yAiIjKIj8fWLFC/crP13c0RKQlJkBERERkdJgAERERkdGpEgnQtm3bEBQUBB8fH/Tt2xeXLl16admoqCgMGDAAzZo1Q7NmzTBs2LAi5QVBwOrVq9GmTRv4+vpi2LBhuHv3bgWfBRERERkKvSdAMTExCA0Nxbhx4xAdHQ0PDw+EhIQgJSWl2PJnzpxBt27dsGXLFvzwww9444038NFHH+Hx48dimQ0bNmDr1q2YP38+oqKiYGlpiZCQEOTl5VXWaREREVEVpvcEKDIyEv369UPv3r3RqFEjLFiwABYWFtizZ0+x5VeuXImBAwfC09MTDRs2xKJFi6BSqRAbGwtA3fqzZcsWjBkzBsHBwfDw8MDy5cvx5MkTHDt2rDJPjYiIiKoovSZACoUCV65cQWBgoLhOIpEgMDAQFy5c0KqO3NxcFBQUwM7ODgBw7949JCUladRpa2uLxo0ba10nERERvd70OhN0WloalEolHBwcNNY7ODjg9u3bWtWxYsUKODs7iwlPUlKSWMc/60xOTi6HqImIXmBpCVy+/HyZiAyCQT8KY/369YiJicGWLVtgbm6u73CIyBhJJICXl76jICId6bULzN7eHlKptMiA55SUFDg6Or5y34iICKxfvx4RERHw8PAQ1zs5OYl16FonERERGQe9JkAymQxeXl7iAGYA4oBmPz+/l+63YcMGfPPNNwgPD4ePj4/GNhcXFzg5OWnUmZWVhT/++OOVdRIRlYpCAcyfr37xURhEBkPvXWDDhw/HjBkz4O3tDV9fX2zevBm5ubno1asXAGD69OmoWbMmpk6dCkDd7RUWFoaVK1eiTp064pgfKysrWFtbw8TEBEOGDMG6devg6uoKFxcXrF69Gs7OzggODtbbeRLRayo/H1iwQL38738DMpl+4yEireg9AeratStSU1MRFhaGpKQkeHp6Ijw8XOyuevjwISSS5w1VP/zwA/Lz8zFx4kSNesaPH48JEyYAAEaOHInc3FzMmzcPGRkZCAgIQHh4OMcJEREREQDARBAEQd9BVDVZWVkICAjA+fPnYWNjo+9wiKgqy84GCn9PZGUB1tb6jacEd5/exe6/diNLkVViWRuZDfq81Qdu1d0qPjCiSqb3iRCJiIiIKhsTICIiIjI6TICIiIjI6DABIiIiIqOj97vAiIgMmoUFcPbs82UiMghMgIiIykIqBZo103cURKQjdoERERGR0WELEBFRWSgUwOrV6uVJkzgTNJGBYAJERFQW+fnA9Onq5bFjmQARGQh2gREREZHRYQJERERERocJEBERERkdJkBERERkdJgAERERkdFhAkRERERGh7fBExGVhYUF8Msvz5eJyCAwASIiKgupFHjnHX1HQUQ6YhcYERERGR22ABERlUV+PrB+vXp51CjAzEy/8RCRVpgAERGVhUIBjB+vXh42jAkQkYFgFxgREREZHSZAREREZHSYABEREZHRYQJERERERkfnBCg/Px+zZs1CYmJiRcRDREREVOF0ToDMzMzw448/VkQsRERERJWiVF1gwcHB+Pnnn8s7FiIiw2NuDhw8qH6Zm+s7GiLSUqnmAXJ1dcXatWsRFxcHLy8vWFpaamwfMmRIuQRHRFTlmZoC3brpOwoi0lGpEqDdu3fD1tYWly9fxuXLlzW2mZiYMAEiIiKiKq1UCdD//d//icuCIABQJz5EREYnPx/Ytk29PHAgZ4ImMhClvg1+165d6N69O3x8fODj44Pu3btj165d5RkbEVHVp1AAw4erXwqFvqMhIi2VqgVo9erV2LRpEwYNGoQmTZoAAC5evIglS5bgwYMHmDRpUnnGSERERFSuSpUA7dixAwsXLkT37t3FdR06dIBcLsfChQuZABEREVGVVqousIKCAnh7exdZ7+XlBaVSWeagiIiIiCpSqRKgnj17YseOHUXWR0VFoUePHmUOioiIiKgilaoLDFDfCn/q1Ck0btwYAHDp0iU8ePAA77//PkJDQ8Vys2bNKnuURERURFpuGtLz0rUuLzWRIq8grwIjIjIcpUqAbty4gbfeegsAkJCQAACoXr06qlevjhs3bojleGs8EVHFSc9Lx+Gbh5Gdn61VeScrJwTUDqjgqIgMQ6kSoK1bt5Z3HEREhsncHIiKer5cybLzs5GlyNKqrLWZdQVHQ2Q4St0FRkREUD8Ko29ffUdBRDoq9USIRERERIaKLUBERGVRUABER6uXP/hA3SJERFUeP6lERGWRlwf066dezspiAkRkINgFRkREREaHCRAREREZHb0nQNu2bUNQUBB8fHzQt29fXLp06aVlb968iQkTJiAoKAhyuRybNm0qUmbNmjWQy+Uar86dO1fgGRAREZGh0WsCFBMTg9DQUIwbNw7R0dHw8PBASEgIUlJSii2fm5sLFxcXTJ06FU5OTi+t980338TJkyfF1/bt2yvqFIiIiMgA6TUBioyMRL9+/dC7d280atQICxYsgIWFBfbs2VNseV9fX8yYMQPdunWDTCZ7ab1SqRROTk7iq0aNGhV1CkRERGSA9JYAKRQKXLlyBYGBgc+DkUgQGBiICxculKnuv//+G23atEGHDh0wdepUPHjwoKzhEhER0WtEb/drpqWlQalUwsHBQWO9g4MDbt++Xep6fX19ERoaivr16yMpKQlr167FwIEDceDAAdjY2JQ1bCIiTTIZEBn5fJmIDMJrN2FFu3btxGUPDw80btwY7du3x+HDh9GX09UTUXkzMwOGDdN3FESkI711gdnb20MqlRYZ8JySkgJHR8dyO061atXg5uYmPrWeiIiISG8JkEwmg5eXF2JjY8V1KpUKsbGx8PPzK7fjZGdnIzEx8ZV3jRERlVpBAXDokPpVUKDvaIhIS3rtAhs+fDhmzJgBb29v+Pr6YvPmzcjNzUWvXr0AANOnT0fNmjUxdepUAOqB0/Hx8eLy48ePcfXqVVhZWcHV1RUAsGzZMrRv3x61a9fGkydPsGbNGkgkEnTv3l0/J0lEr7e8PKDw9wsfhUFkMPT6Se3atStSU1MRFhaGpKQkeHp6Ijw8XOwCe/jwISSS541UT548wfvvvy/+vHHjRmzcuBHNmzfH1q1bAQCPHj3ClClT8PTpU9SoUQMBAQGIiorirfBEREQk0vufKoMGDcKgQYOK3VaY1BRycXHB9evXX1nfl19+WW6xERER0etJ74/CICIiIqpsTICIiIjI6DABIiIiIqPDBIiIiIiMjt4HQRMRGTSZDPj66+fLRGQQmAAREZWFmRkwbpy+oyAiHbELjIiIiIwOW4CIiMpCqQROnFAvt20LSKX6jYeItMIEiIioLJ49A9q3Vy9nZQHW1vqNh4i0wi4wIiIiMjpMgIiIiMjoMAEiIiIio8MEiIiIiIwOEyAiIiIyOkyAiIiIyOjwNngiorIwMwOWL3++XMUplUBmBpD+rOSyKnMgL6/iYyLSByZARERlIZMB//63vqPQmkoFJCQA95NLLutcHcj3qfCQiPSCCRARkZHJLwAUipLLFeRXfCxE+sIEiIioLJRKIC5Ovezvz0dhEBkIJkBERGXx7BnQvLl6mY/CIDIYvAuMiIiIjA4TICIiIjI6TICIiIjI6DABIiIiIqPDBIiIiIiMDhMgIiIiMjq8DZ6IqCzMzIDPPnu+TEQGgQkQEVFZyGTA/Pn6joKIdMQuMCIiIjI6bAEiIioLlQq4elW97OkJSPh3JZEhYAJERFQWubmAt7d6mY/CIDIY/FOFiIiIjA4TICIiIjI6TICIiIjI6DABIiIiIqPDBIiIiIiMDhMgIiIiMjq8DZ6IqCzMzIBp054vE5FBYAJERFQWMhnwxRf6joKIdMQuMCIiIjI6bAEiIioLlQpISFAv16vHR2EQGQgmQEREZZGbC9Svr17mozCIDAb/VCEiIiKjwwSIiIiIjI7eE6Bt27YhKCgIPj4+6Nu3Ly5duvTSsjdv3sSECRMQFBQEuVyOTZs2lblOIiIiMj56TYBiYmIQGhqKcePGITo6Gh4eHggJCUFKSkqx5XNzc+Hi4oKpU6fCycmpXOokIiIi46PXBCgyMhL9+vVD79690ahRIyxYsAAWFhbYs2dPseV9fX0xY8YMdOvWDTKZrFzqJCIiIuOjtwRIoVDgypUrCAwMfB6MRILAwEBcuHChytRJRERErx+93QaflpYGpVIJBwcHjfUODg64fft2lamTiOiVTE2BsWOfL1eivDwgIx3IyNOuvLUACELFxkRkKDgPEBFRWZibA2vX6uXQ+fnA7TvAk6da7lAXEBpVZEREhkNvCZC9vT2kUmmRwckpKSlwdHSsMnUSEVVlBfmAQqFlWWXFxkJkSPQ2Bkgmk8HLywuxsbHiOpVKhdjYWPj5+VWZOomIXkkQgKQk9Yv9S0QGQ69dYMOHD8eMGTPg7e0NX19fbN68Gbm5uejVqxcAYPr06ahZsyamTp0KQD3IOT4+Xlx+/Pgxrl69CisrK7i6umpVJxFRucrJAZyd1ctlfBRGWm4a0vPStSorNZEC0jxIpKU+HJFR02sC1LVrV6SmpiIsLAxJSUnw9PREeHi42F318OFDSF54sOCTJ0/w/vvviz9v3LgRGzduRPPmzbF161at6iQiqqrS89Jx+OZhZOdnl1jWycoJvk4BfPYqUSnpfRD0oEGDMGjQoGK3FSY1hVxcXHD9+vUy1UlEVJVl52cjS5FVYjlrMz50lags+LcDERERGR0mQERERGR0mAARERGR0WECREREREZH74OgiYgMmqkpMHTo82UiMgj8tBIRlYW5ObBpk76jICIdsQuMiIiIjA5bgIiIykIQ1LNBA4CVFWBiot94iEgrbAEiIiqLnBzAxkb9KkyEiKjKYwJERERERocJEBERERkdJkBERERkdJgAERERkdFhAkRERERGhwkQERERGR3OA0REVBZSKdCnz/NlIjIITICIjFV+AaBU6raPiYl64r+KKg+okwgzA/rVZGEB7Nql7yiISEcG9FuGiMqVUgmkPAVUKu3Km5oC1ayBp5na7aNreQCQSACH6oaVABGRQeJvGSJjplIBSm2TE5Vu++hanoioEnEQNBFRWWRnq7v6TEzUy0RkEJgAERERkdFhAkRERERGhwkQERERGR0mQERERGR0mAARERGR0WECREREREaH8wAREZWFVAp07fp8mYgMAhMgIqKysLAADh3SdxREpCN2gREREZHRYQJERERERocJEBFRWWRnA9bW6hcfhUFkMDgGiIiorHJy9B0BEemICRARUQVIy01Del661uWlJlLkFeRVYERE9CImQEREFSA9Lx2Hbx5Gdr523WJOVk5oUjMAmRlA+rOSy1sLgCDoHpeJCWBmCshkJZc1NdO9fiJDwQSIiAxffgGgVGpfXipVZwEVLDs/G1mKLK3KWptZQ6UCEhKA+8la7FAXEBrpFo9ECkglAt5wLoC5VUGJ5atbFUBmqtLtIEQGggkQERk+pRJIeQqotPiylkgAh+qVkgCVRn4BoFCUXK5Ah3yvkPquFwGqXAXyn+aWWF4FM5igFM1MRAagav4GICLSlUoFKNlaoQ2VUoCyoOTERqVk8kOvLyZARERlIZEA7do9XyYig8AEiIioLCwtgePH9R0FEemICRARGRcTE/W/z3S85VzXgdMqFVBQoH5pQ1kAiUTg81SJKgkTICIyLiYm6kHTTzO1GzQNlG7gtCAAuXnAs5IHGwMApAqYQIDERPtDEFHpMQEiIuNUXoOms7MBNzf18t276kdiFBJ0mayHA46JKhMTICKiskrWZuIeIqpKmAAREVUhpmZaztIsfT6ciYh0VyUSoG3btiEiIgJJSUnw8PDA3Llz4evr+9Lyhw8fxurVq3H//n24ublh2rRpaFd4GyqAmTNnIjo6WmOfNm3aICIiosLOgYiobNTZjLNDAaRmJQ+crulQACkHTROVmt4ToJiYGISGhmLBggVo3LgxNm/ejJCQEBw5cgQODg5FysfFxWHq1KmYMmUK2rdvjwMHDmDcuHHYu3cv3N3dxXJt27ZFaGio+LNMmz+piIj0yASAoO0szVYKgIOmiUpN77N2RUZGol+/fujduzcaNWqEBQsWwMLCAnv27Cm2/JYtW9C2bVuMGDECDRs2xCeffIK33noL33//vUY5mUwGJycn8WVnZ1cZp0NEVCaFszSX9FKpOGiaqCz0mgApFApcuXIFgYGB4jqJRILAwEBcuHCh2H0uXryIVq1aaaxr06YNLl68qLHu7NmzaNWqFTp16oTPPvsMaWlp5R4/ERERGSa9doGlpaVBqVQW6epycHDA7du3i90nOTkZjo6ORconv3AXRtu2bdGxY0e4uLggMTERq1atwsiRI7Fz505I2WFOVHWVdpJCbefzqQgSCdC06fNlIjIIeh8DVBG6desmLsvlcsjlcgQHB4utQkRURZVmkkJTU6CadcnlKoqlJXDunP6OT0SlotcEyN7eHlKpFCkpKRrrU1JSirTyFHJ0dNRo7SmpPADUrVsX9vb2+Pvvv5kAERkCXSYplFTNJ8ArlUCeAnj2TLvyCmtOhUhUmfTaXiuTyeDl5YXY2FhxnUqlQmxsLPz8/Irdp0mTJjh9+rTGut9//x1NmjR56XEePXqEp0+fwsnJqVziJiIqiUoFpKcDSUnavXKy9R0xkXHRe4f18OHDERUVhejoaMTHx2P+/PnIzc1Fr169AADTp0/HypUrxfJDhgzBiRMnsHHjRsTHx2PNmjW4fPkyBg0aBADIzs7GsmXLcPHiRdy7dw+xsbEYO3YsXF1d0bZtW72cIxG9xnJy1I/CcHNTL79ApVK3BGnzKu0wpsKJE7V5mZqV+WyJXht6HwPUtWtXpKamIiwsDElJSfD09ER4eLjYpfXw4UNIXhhY6O/vjxUrVuCrr77CqlWr4ObmhrVr14pzAEmlUty4cQP79u1DZmYmnJ2d0bp1a0yaNIlzARFR6bxqcHbuM+Dvv58vS9Q3WkgkQoXO1GxiYgITE+0nTgQAR/sCmJhwBmkioAokQAAwaNAgsQXnn7Zu3VpkXZcuXdClS5diy1tYWHDGZyIqX68anP1iq09yGpCTB8hkFZ5oFNat7cSJACDY5mvsS2TMqkQCRERkEIobnP3iz8r/bVepgEqacaNw4kRtyxKRmt7HABERERFVNrYAERFpQakEsjMAVf4/NuQC1f+3+DQdgAIwtQEE28qNj4h0wwSIqCrKL1B/4+rCxAQQdOji0OfsyQZIpQISEoGMVM31kmdAy/8tX7sKqCwAJ1dAwgSIqEpjAkRUFSmVQMpT3WdD1nYGZX3PnmygCgoAhUJznSTfBNm11XehKvJNoJIAygKOLyCq6pgAEVVVpZkNWdt9qujsyYZIZW6Jc4v/T99hEJGO+EcKERERGR22ABERkV6lpakfG6ILOzvA3r5i4iHjwASIiKoeiQ6N07qUrQCSvFwEfN4NAHB+3iGozC31Gk9VoEtCI5Wq55L8v/8DsrV8Hpq1NdClCxMgKhsmQESviZfepl0MiQVgbVtpc/Vpz8QEacospCMFMNHyjjaVBHZKAfZ6m95YgPWDG+IyqZOfw4e1S2icnICAAHXZrKyKj42oEBMgotfEy27TLk41J0Beq2omQOl56Th8IwbZzzK12sXashq6ePWEvYn2953rkiwCgNQSMLfSunqC9gmNNW9GJD1hAkT0GinuNu1iy2n5xa8v2XlZyMrTLgEqTReYLskiANR4A6jvpPNhiKgKYwJERFqrjG42laBO4p490668zFS9j660TRYLyxLR64UJEJERKmw0yShFN1BldLNlZAJJWrbOgFMaEVEpMAEiMkISaem7gSqjm02l0v5JIIVP/8jMApS5JZfneB4iApgAEVVJeXnAs3RA0LLrpbRf6q9DN1DhI9DuPwBSH5ZcvvzH85jgmYOLuExEhoEJEFEVlJ8P3LkL5GRoV74qDtItTTdbWZ6irnXLVDkncipzS5xecbp8KyWiCscEiKiKylcYdutMabrZKu0p6iaAqbkEMi3nLDSVSdi4Q/SaYQJERBVKl262yniKusTUBBnIgqJWCiT22t0+lmslQQYESEyZBRG9LpgAEZFRkUhMkKFIx6HrMUhO1W6uIWfHaujv0BMSadHmKYkiF01C+wAALs7aDZW2zUoGoHBy7fv31d2y2pBK1WPYiKo6JkBEZJQyc7OQnqNdAmT57BXtUoKAanf/EJdfJyYm6m7MU6eAhATt9il8tAVRVccEiIiIXik3V/vndPHRFmQo9PsYZSIiIiI9YAJERERERocJEBERERkdjgEiIqKXkphIYGkJ2NhoV97K6vkkmERVGRMgokqQlgakp2tX1swMsOEn06AobGroO4QKYWFmDokpUKPBXcictdzHAjCztYNMZl+xwRGVEX/NElWC9HTg8GEgO7vksvXqAR3bVnxMVD5U5lb4fc0lfYdRIcxMzZCVn4kD104h8ZEW/3kBvOFojRE1u8DMjAkQVW1MgIgqSXa2drcS52rxRHOiypSRk41ULe+DtyrFQ3mJ9IE9tURERGR02AJERKQFiYkEpjIJZJaafzdKFLnwDB0EALg663uoZJaQyvi3JVFVxwSIiKgE5mbmgATIdUqGxFqlsU2SmwO7q7HqZbcUwNIKz6rJYIYCmEj48FSiqooJEBFRCWRSM2TmZyHm2k94kpyhuS1Pgeb/W951cS8U5jI0cHkD7f0CxYeJElHVwwSIiEhLmc+KPkBVlvf8MenpOVlQKM2Q9axaZYdGRDpiAkRUCSQS7R8SaWmJ16flwAQwNZdAZqldcY6dIaLKwgSISFf5BYBSqdMuzvYmaNdCgEJRcllrG8BCpoJUWsr4qgiJqQkykAVFrRRI7AWt9uHYGSKqLEyAiHSUl6NE3sOnEApUJRcGILUwhczBGqqUTOSklryPRS1TmNS2hsTQEyCJCTIU6Th0PQbJqZkl7wBw7AwRVRomQEQ6ys8Hbt9SISdDuwSoxhsq1K8BFOSpoMgteZ8ChXb1GorM3KLjZl7GUMfO5Mn4q5TI0PBTS1QK+Qpo1Z0FAAUFFRsL6ZfC3AwfL5ug7zCISEdMgIh0HNMjMzX88TmlpsOg5soc0PyySQqLw4HWRAQwASJCWl4a0tOTAWgxUNdEAhurGjA1N75BKroOaq6sAc2vmqRQn3ERUdXGBIiMXnpeBg7fOITsZyWPU7G2rIZOb/WERGpbCZFVLboOaq6sAc2vmqSwMuIyyy/A+MiDAICvh3dHvhl/rRIZAn5SyegplUBqZhYytRioq1BKIGh3R/drS9tBzZU9oLm4SQqLU95xmagENL56R1wmIsPABIiMnkoFpKcDKU9LLlvq0SM6jJ0xlUl0nkBQ130KyxMRGasqkQBt27YNERERSEpKgoeHB+bOnQtfX9+Xlj98+DBWr16N+/fvw83NDdOmTUO7du3E7YIgICwsDLt27UJGRgb8/f0xf/58uLm5VcLZGKhSTO6XpsxCer52tzcDgJ25Hewt7XU7Rm4a0vPSK/QYgDoJ0ub0VaW4Q13XsTN5NqZIhgKK2hmQ2Gt3QF33ybWSIAMCJKbMgojIOOk9AYqJiUFoaCgWLFiAxo0bY/PmzQgJCcGRI0fg4OBQpHxcXBymTp2KKVOmoH379jhw4ADGjRuHvXv3wt3dHQCwYcMGbN26FUuXLoWLiwtWr16NkJAQxMTEwNzcvLJP0TAoleomEG2/4U1NkS7LwOHbPyI7P7vE4tZm1ujyZhedk5P0vHQcvnlY62ME1euCrGR7rXM5c3Othj6XSanGztgF4vB17ca0lGYfZ8dq6O9gnGOZiIiAKpAARUZGol+/fujduzcAYMGCBTh+/Dj27NmDUaNGFSm/ZcsWtG3bFiNGjAAAfPLJJ/j999/x/fff4/PPP4cgCNiyZQvGjBmD4OBgAMDy5csRGBiIY8eOoVu3bpV3coZGpQKUWiZAEhWUSuBRSjbSn2WVWLyaOZBTB0jM1L6hydQUyJEAD5OzkZFX8jHsLIBnzsDp40BSknbHcHcH5E21K1tWuo6d0XZMS2n2sXzGW8HJsHG2cCorvSZACoUCV65cwccffyyuk0gkCAwMxIULF4rd5+LFixg2bJjGujZt2uDYsWMAgHv37iEpKQmBgYHidltbWzRu3BgXLlxgAlSOVCogIQG4n1xy2TqOQJ4HcO437ZOTevWAN5sBt+8AT55qdwyVHMjJAbJKzpcAAM+eaVeOiKoOmQwQBODuXe33sbMD7HXvHafXmF4ToLS0NCiVyiJdXQ4ODrh9+3ax+yQnJ8PR0bFI+eRk9bdw0v++XYurs7BMSYT/3eaTpe236GsgLz0PitQ8CFq2AEllSmRb5sBWZgoHS1mJ5W1lplA8y4GlZRaqaXkTjkwGPMvNhp25KZQ6HMPBIUvriQptbIDcnBxYm5kj36rk0cOWpubIyckFZDLIqmk3xbPKVIWc7BxYmZnDTotjmEvMkJOdA0tT7cqXZp/X5TxKs095H0MmlSJLom5Rs7OygMJcZpDnURwLqbq8vaUpntmV/BkEgBpWun0Oq1dXtwo7O0Pr3w3Vq6tvXLh+XbsZ2WUyoEkTwMxMu/rJ8FlbW8OkhGZCvXeBVUXZ2erxJi8OrKayO4p1r8UxtiOywo+xAWsrfJ/X5TxKs095H2N/o0bqha/3V9gxyqN8afYJxzc6H6MyPodEr3L+/HnY2Ni8soxeEyB7e3tIpVKkpKRorE9JSSnSylPI0dGxSEvOi+WdnJzEdc7OzhplPDw8tIrL2dkZv/76q1YZJBEREVUt1tbWJZbRawIkk8ng5eWF2NhYccCySqVCbGwsBg0aVOw+TZo0wenTpzXGAf3+++9o0qQJAMDFxQVOTk6IjY2Fp6cnAHVX1h9//IEPP/xQq7gkEglq1apV+hMjIiKiKk3vt4IMHz4cUVFRiI6ORnx8PObPn4/c3Fz06tULADB9+nSsXLlSLD9kyBCcOHECGzduRHx8PNasWYPLly+LCZOJiQmGDBmCdevW4eeff8b169cxffp0ODs7i0kWERERGTe9jwHq2rUrUlNTERYWhqSkJHh6eiI8PFzs0nr48CEkkud5mr+/P1asWIGvvvoKq1atgpubG9auXSvOAQQAI0eORG5uLubNm4eMjAwEBAQgPDyccwARERERAMBEEIz9yUZERERkbPTeBUZERERU2ZgAERERkdFhAkRERERGhwkQERERGR0mQERERGR0mABVAdu2bUNQUBB8fHzQt29fXLp0Sd8hGY1z585h9OjRaNOmDeRyufhQ3UKCIGD16tVo06YNfH19MWzYMNzV5QmMpLXvvvsOvXv3hp+fH1q1aoWxY8cWeSZgXl4eFixYgBYtWsDPzw8TJkzQ+hl/pLvt27ejR48e8Pf3h7+/P/71r3/h119/Fbfz/dCv9evXQy6XY/HixeI6vifaYwKkZzExMQgNDcW4ceMQHR0NDw8PhISEFHk8CFWMnJwcyOVyfPbZZ8Vu37BhA7Zu3Yr58+cjKioKlpaWCAkJQV5eXiVH+vo7e/YsBg4ciKioKERGRqKgoAAhISHIyckRyyxZsgS//PILvvrqK2zduhVPnjzB+PHj9Rj1661WrVqYNm0a9u7diz179qBly5YYN24cbt68CYDvhz5dunQJP/zwA+RyucZ6vic6EEiv+vTpIyxYsED8WalUCm3atBG+++47PUZlnNzd3YWffvpJ/FmlUgmtW7cWwsPDxXUZGRmCt7e3cPDgQX2EaFRSUlIEd3d34ezZs4IgqK+9l5eXcPjwYbHMrVu3BHd3d+HChQt6itL4NGvWTIiKiuL7oUdZWVnCu+++K5w6dUoYNGiQsGjRIkEQ+BnRFVuA9EihUODKlSsIDAwU10kkEgQGBuLChQt6jIwA4N69e0hKStJ4f2xtbdG4cWO+P5UgMzMTAGBnZwcAuHz5MvLz8zXej4YNG6J27dq4ePGiPkI0KkqlEocOHUJOTg78/Pz4fujR559/jnbt2mlce4CfEV3p/VEYxiwtLQ1KpRIODg4a6x0cHIqMfaDKl5SUBADFvj/sU69YKpUKS5Ysgb+/v/iYm+TkZJiZmaFatWoaZR0cHMT3isrf9evX0b9/f+Tl5cHKygpr165Fo0aNcPXqVb4fenDo0CH89ddf2L17d5Ft/IzohgkQEVU5CxYswM2bN7F9+3Z9h2L06tevj3379iEzMxNHjx7FjBkz8P333+s7LKP08OFDLF68GBs3buSzLcsBEyA9sre3h1QqLTLgOSUlRXwYLOmPk5MTAPX74ezsLK5PSUmBh4eHvsJ67X3++ec4fvw4vv/+e9SqVUtc7+joiPz8fGRkZGj8hZuSkiK+V1T+ZDIZXF1dAQDe3t74888/sWXLFnTp0oXvRyW7cuUKUlJS0KtXL3GdUqnEuXPnsG3bNkRERPA90QHHAOmRTCaDl5cXYmNjxXUqlQqxsbHw8/PTY2QEAC4uLnByctJ4f7KysvDHH3/w/akAgiDg888/x08//YTNmzejbt26Gtu9vb1hZmam8X7cvn0bDx48QJMmTSo5WuOlUqmgUCj4fuhBy5YtceDAAezbt098eXt7o0ePHuIy3xPtsQVIz4YPH44ZM2bA29sbvr6+2Lx5M3JzczUyfKo42dnZSEhIEH++d+8erl69Cjs7O9SuXRtDhgzBunXr4OrqChcXF6xevRrOzs4IDg7WY9SvpwULFuDgwYP45ptvYG1tLY5ZsLW1hYWFBWxtbdG7d28sXboUdnZ2sLGxwaJFi+Dn58df7hVk5cqVePvtt/HGG28gOzsbBw8exNmzZxEREcH3Qw9sbGzEMXGFrKysUL16dXE93xPtMQHSs65duyI1NRVhYWFISkqCp6cnwsPD2QVWSS5fvowhQ4aIP4eGhgIAPvjgAyxduhQjR45Ebm4u5s2bh4yMDAQEBCA8PJz97xVgx44dAIDBgwdrrA8NDRX/IJg9ezYkEgkmTpwIhUKBNm3avHQOJyq7lJQUzJgxA0+ePIGtrS3kcjkiIiLQunVrAHw/qiK+J9ozEQRB0HcQRERERJWJY4CIiIjI6DABIiIiIqPDBIiIiIiMDhMgIiIiMjpMgIiIiMjoMAEiIiIio8MEiIiIiIwOEyAiMnozZ87E2LFj9R0GEVUiJkBERJUkKCgImzZt0ncYRAQmQET0mlMoFPoOgYiqICZARFSpFAoFFi1ahFatWsHHxwcffvghLl26BJVKhbfffhvbt2/XKP/XX3/Bw8MD9+/fBwBkZGRgzpw5aNmyJfz9/TFkyBBcu3ZNLL9mzRr07NkTu3btQlBQEHx9fQEAR44cQY8ePeDr64sWLVpg2LBhyMnJ0ThWREQE2rRpgxYtWmDBggXIz88Xt6Wnp2P69Olo1qwZGjdujBEjRuDu3bsa+x89ehTdunWDt7c3goKCsHHjRnHb4MGDcf/+fYSGhkIul0Mul5fL9SSi0mECRESVavny5Th69CiWLl2K6OhouLq6YsSIEcjIyEC3bt1w8OBBjfIHDhyAv78/6tSpAwCYNGkSUlJSsGHDBuzduxdeXl4YOnQonj59Ku6TkJCAo0eP4uuvv8a+ffvw5MkTTJ06Fb1790ZMTAy2bNmCjh074sVHIZ45cwYJCQnYvHmzGFt0dLS4febMmbh8+TLWrVuHnTt3QhAEjBo1SkySLl++jE8++QRdu3bFgQMHMH78eKxevRp79+4FoE7MatWqhYkTJ+LkyZM4efJkRV1iItKGQERUSbKzswUvLy9h//794jqFQiG0adNG2LBhg/DXX38JcrlcuH//viAIgqBUKoW2bdsK27dvFwRBEM6dOyf4+/sLeXl5GvUGBwcLP/zwgyAIghAWFiZ4eXkJKSkp4vbLly8L7u7uwr1794qNa8aMGUL79u2FgoICcd3EiROFTz75RBAEQbhz547g7u4unD9/Xtyempoq+Pr6CjExMYIgCMKUKVOE4cOHa9S7bNkyoWvXruLP7du3FyIjI7W7WERUodgCRESVJiEhAfn5+fD39xfXmZmZwdfXF/Hx8fD09ETDhg3FVqCzZ88iNTUVnTt3BgBcv34dOTk5aNGiBfz8/MTXvXv3kJCQINZZu3Zt1KhRQ/zZw8MDrVq1Qo8ePTBx4kRERUUhPT1dI7ZGjRpBKpWKPzs5OSElJQUAEB8fD1NTUzRu3Fjcbm9vj/r16yM+Ph4AcPv2bY3zAgB/f3/8/fffUCqVZbpuRFT+TPUdABHRi3r06IEDBw5g1KhROHjwINq0aQN7e3sAQHZ2NpycnLB169Yi+9na2orLlpaWGtukUikiIyMRFxeHU6dOYevWrfjyyy8RFRWFunXrAgBMTTV/HZqYmGh0kRHR64UtQERUaerVqwczMzPExcWJ6/Lz8/Hnn3+iUaNGAIDu3bvj5s2buHz5Mo4ePYr33ntPLOvl5YXk5GRIpVK4urpqvF5s8SmOiYkJAgICMHHiROzbtw9mZmY4duyYVnE3bNgQBQUF+OOPP8R1aWlpuHPnjhh3gwYNNM4LAOLi4uDm5ia2LJmZmUGlUml1TCKqWEyAiKjSWFlZ4cMPP8Ty5cvx22+/4datW5g7dy6ePXuGPn36AABcXFzg5+eHOXPmQKlUIigoSNw/MDAQTZo0wbhx43Dy5Encu3cPcXFx+PLLL/Hnn3++9Lh//PEHvv32W/z555948OABfvzxR6SmpqJBgwZaxe3m5oYOHTpg7ty5+O9//4tr167h3//+N2rWrIkOHToAAD766CPExsZi7dq1uHPnDqKjo7Ft2zZ89NFHYj116tTBuXPn8PjxY6SmppbmEhJROWEXGBFVqmnTpkEQBEyfPh3Z2dnw9vZGeHg47OzsxDI9evTAggUL8P7778PCwkJcb2JigvXr1+Orr77CrFmzkJaWBkdHRzRt2hSOjo4vPaaNjQ3OnTuHzZs3IysrC7Vr18bMmTPRrl07reMODQ3F4sWLMXr0aOTn56Np06ZYv349zMzMAKhbp7766iuEhYVh3bp1cHJywsSJE9GrVy+xjokTJ2LevHkIDg6GQqHA9evXdbl0RFSOTAR2chMREZGRYRcYERERGR0mQERERGR0mAARERGR0WECREREREaHCRAREREZHSZAREREZHSYABEREZHRYQJERERERocJEBERERkdJkBERERkdJgAERERkdFhAkRERERG5/8B69ToBpxzph4AAAAASUVORK5CYII=", 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", 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      " ] @@ -1051,7 +1051,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -1084,7 +1084,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -1092,14 +1092,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "factual: 24.302181243896484 counterfactual mask: 26.423648834228516 counterfactual lockdown: 27.30312728881836\n", + "factual: 24.31097984313965 counterfactual mask: 27.37161636352539 counterfactual lockdown: 26.888933181762695\n", "Probability of overshoot being high\n", - "factual: 0.6021000146865845 counterfactual mask: 0.6717171669006348 counterfactual lockdown: 0.717277467250824\n" + "factual: 0.6075999736785889 counterfactual mask: 0.7061855792999268 counterfactual lockdown: 0.7263157963752747\n" ] }, { "data": { - "image/png": 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", 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", 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      " ] @@ -1178,17 +1178,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 32, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0\n" - ] - } - ], + "outputs": [], "source": [ "# Collecting samples for joint distribution of overshoot under necessity and sufficiency interventions on lockdown\n", "masks = {\n", @@ -1257,12 +1249,12 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 43, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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      " ] @@ -1296,7 +1288,7 @@ "ax.axhline(y=(os_lockdown_nec) * 36 / 45, color=\"white\", linestyle=\"--\")\n", "\n", "ax.legend(loc=\"upper left\")\n", - "ax.text(13, 2, 'pr(lockdown has causal role over high overshoot): %.4f' % pr_lockdown.item(), color=\"white\")\n", + "ax.text(21, 2, 'pr(lockdown has causal role \\n over high overshoot): %.4f' % pr_lockdown.item(), color=\"white\")\n", "\n", "ax = axs[1]\n", "hist_mask = hist_mask_nec.unsqueeze(1) * hist_mask_suff.unsqueeze(0)\n", @@ -1318,7 +1310,7 @@ " label=\"Mean Overshoot\",\n", ")\n", "ax.axhline(y=(os_mask_nec) * 36 / 45, color=\"white\", linestyle=\"--\")\n", - "ax.text(13, 2, 'pr(masking has causal role over high overshoot): %.4f' % pr_mask.item(), color=\"white\")\n", + "ax.text(21, 2, 'pr(masking has causal role \\n over high overshoot): %.4f' % pr_mask.item(), color=\"white\")\n", "\n", "ax.legend(loc=\"upper left\")\n", "\n", @@ -1356,7 +1348,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -1390,7 +1382,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -1398,14 +1390,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "mask_efficiency fixed: 18.7790470123291 mask_efficiency not fixed: 25.793893814086914\n", + "mask_efficiency fixed: 18.844215393066406 mask_efficiency not fixed: 26.454023361206055\n", "Probability of overshoot being high\n", - "mask_efficiency fixed: 0.08130080997943878 mask_efficiency not fixed: 0.7647058963775635\n" + "mask_efficiency fixed: 0.10000000149011612 mask_efficiency not fixed: 0.8999999761581421\n" ] }, { "data": { - "image/png": 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", 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", 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      " ] @@ -1478,7 +1470,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1510,7 +1502,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1518,14 +1510,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "lockdown_efficiency fixed: 27.030378341674805 lockdown_efficiency not fixed: 26.37188148498535\n", + "lockdown_efficiency fixed: 27.033872604370117 lockdown_efficiency not fixed: 26.814355850219727\n", "Probability of overshoot being high\n", - "lockdown_efficiency fixed: 0.8642857074737549 lockdown_efficiency not fixed: 0.8103448152542114\n" + "lockdown_efficiency fixed: 0.8768116235733032 lockdown_efficiency not fixed: 0.8928571343421936\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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      " ] From 43a2e541a42cf78b62d458402cf5c519297c2b38 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Thu, 29 Aug 2024 16:13:37 -0400 Subject: [PATCH 085/111] tweaks --- docs/source/explainable_sir.ipynb | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index af8b5504..55196b12 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -1249,12 +1249,12 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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tKlWquF43b95clStXliTFxMScdx1hYWEFaheoAnnfn1tbdHS0NYUAAGAB8uiFoaTm0bJly1pdQpGLiIhQRESE1WUAPlVyvuYCiqGUlBR98803evDBB13h74zLLrtM3bt31yeffCKn06k2bdros88+c71vjNF1112n//73v5Kkn376Sd27d1fDhg3VpUsXLV682NV25MiRGjlypLp27apWrVpp586d+uqrr3TTTTcpNjZWnTp10rJly3L0v2zZMnXs2FGNGjXSoEGDdPz4cdd7P//8s+655x41btxYHTp00EcffZRj2c8//1y33HKLGjZsqO7du2vdunWSss86mz9/vubPn5/nJQh5XZ5z9iXBn3/+ufr06aMpU6aoRYsWatasmcaPH58jVE6dOlWtWrVSixYtNG/evBzrz8zM1Isvvqj/b+/Og6oq/weOvw0jEFADNbe8XtxyAQRT1EgNyPReFjUlJREFTa3c18ItqRytkGQRU1OJSNBAxVIWhYKcZCxTccSKLkhixShYWSKCvz+ce+JwETH7uv0+rxlHOedwnud8zjNzPz7nfp7j6uqKq6sr8+fPp7y8XNV2VFQUffv2ZeXKlSb9KykpISgoCGdnZwYMGEBoaCiVlZWAaYlvzWsJCAggNzeXqKgounXrppS5BAYGEhAQYFL6UlRURHBwMM7OzgwZMoTY2FjAtETm3LlzTJs2DScnJ9zd3YmMjKSqqqrBsdqyZQvu7u44OzsTHBxMcXEx33zzDT169ODChQvKcXl5eTg5OfHnn3+axKSu+5eWloanpycODg5MnTpVibGHh4fyt7GsJj09HZ1Oh5OTE6NHjyY3N1c5X0BAAKGhoXh4eDBkyBBmzJjBokWLVG3OmzePkJCQ24rH4cOHee211zh79qzqntW8nzca03C9TOjjjz/Gz88PBwcHfH19ycvLqzdWQgghxL1C8lE1yUevu5/y0Zol2wEBAaxfv57g4GAcHR157rnnyM7OBq6PwdzcXCIjI5X7ebP+jx07lldeeYU+ffqwZ88enJyc+Pvvv5W2c3JycHFx4fLly1y7do2oqCjc3Nx48sknmTZtGiUlJcqx3bp1Y/fu3Xh5edGrVy/8/f0pLi5WrgFgwoQJREREmJRsFxQUEBwcjIuLC08//TSRkZFUV1cD18fnvHnzWL58OS4uLgwYMICNGzeaxEmIe41MSApxF508eZJr167h4OBQ5/4+ffqQn5/P1atXGTZsGOnp6cq+7777jvLycjw8PCgtLWXq1KmMGjWKlJQUJk+ezOLFizly5Ihy/O7du5k9ezYbNmzAxsaGhQsXMnXqVPbv38/zzz/P3LlzlWQIIDk5mbCwMGJjYzl58qTyoVZQUEBgYCB9+/YlKSmJGTNmsHr1aqVvSUlJhIaGMnXqVHbt2sXAgQN56aWX+PXXXwkKCmL48OEMHz6cnTt3/quYHT16FIPBwCeffMLSpUuJjY3l0KFDACQkJBAbG8vbb7/N1q1bVQkzQFhYGHl5eWzcuJHY2Fj+/PNPZs2apTrm22+/5dNPP2XChAkmbYeGhtKkSRN27dpFVFQUqampqnUHbyQiIgJnZ2eCgoLIyclRylwiIiJM1imsqKggKCgIKysrEhMTWbZsGWvXriUzM1N13LVr13j11Vexs7MjOTmZVatWkZKSQkxMTINitX37diIjI5k/fz7JyclYWVkxa9YsXFxceOyxx1Rjbd++fQwePBhra+ubXitATEwMYWFhxMXFceLECbZs2QKgJOQ7duxAp9ORn5/PokWLmD59Onv27MHHx4cpU6ZQVFSknCspKYl33nmHyMhIfH19yczMVJLuK1eukJmZiV6vv614ODs78/rrr9O6dWtycnJo06aN6nrqG9M17/FLL73Enj17sLGx4c0332xQrIQQQoi7TfLRWyf56HX3aj4aExODXq9n7969PPHEEyxdupTq6mpCQkKUGERERDS4/507dyYxMRE3NzcsLS358ssvlf1paWm4u7tjYWFBXFwcKSkpvPfeeyQkJGBnZ0dQUJCSuxrjHRISQlJSEmVlZYSHhwMoYzEiIoKgoCDV9Vy4cAF/f39atWrFjh07WL58OXFxccokMUBqaiqPPPIIycnJBAcH8+6772IwGG4aKyHuJpmQFOIuMj7lbdq0aZ37jdvLy8vR6/V89dVXylPB1NRU5UP5448/ZuDAgYwfPx6NRoOvry8vvPAC27ZtU87l4OCAu7s7jo6O/Prrr1RWVtK6dWvatWtHUFAQ0dHRPPLII8rxCxYswNHREScnJ4YPH05+fj4AiYmJ9OjRg7lz52Jvb8/IkSMZP348mzZtAuCjjz4iICCAESNGYG9vz/z58+natStxcXFYWVkp5Qf/trSiqqqK0NBQ7O3t8fX15YknnuDEiRNK3wIDA3nmmWfo3r27alLo77//Ji4ujjfeeANHR0e6devGmjVryM3N5fTp08pxgYGBdOjQgY4dO5q0ffbsWWxsbGjbti0uLi588MEHDVp/pnnz5jz88MM0adKEli1bKmUuzZo1MykNzsnJ4cKFC7z99tt06dIFd3d3lixZYrJu09dff01JSYkSC1dXVxYtWqRKTOqLVUJCAhMnTkSn09GxY0eWLVuGq6srFRUV6HQ69u/fr5xn//796PX6m16n0cyZM5Wx4+3trbRpvOe2trZYWFiwefNm/Pz88Pb2RqPRMGHCBAYNGqT6hsOQIUNwcXGhV69eDBo0iOrqamVR+JycHCwsLHB1db2teJibm2NjY4OZmRktW7Y0+XZIfWPaaOTIkXh6eqLVapk0aZJ8Q1IIIcR9Q/LRWyf56HX3aj46ePBgRo0aRYcOHZg+fTrnzp2jtLQUGxsbJQbNmzdvUP8bNWrE9OnT6dSpE7a2tgwdOpS0tDTl2jIyMtDpdABs2rSJhQsX4urqSqdOnVi5ciUXL15UvqEJMGnSJAYMGEDXrl0ZN26ckjMax2KzZs2wsrJSXc/evXuxtLQkNDSUTp064enpyaxZs5Txbry/ixYtQqPRMHnyZJo3by75qLjnyRqSQtxFzZo1A66XyrRu3dpk/2+//QZc/4Bp1aoVLVu25IsvvkCv15OWlsaCBQsA+Omnn8jMzMTZ2Vn53crKSrRarfKzcW0YgO7duzNkyBAmTZqEVqvFw8ODMWPGYGlpqRzToUMH5d82NjZUVFQA159IOzo6qvrp7OzM9u3blf2vvPKKan/v3r2VN8bdLjs7O9WTUWtra+Xtj7Xb7ty5s7LAdXFxMZWVlYwdO1Z1vurqagoLC+nZsyegjlNtkydP5vXXXyc9PZ1Bgwah0+no0aPHf3JdRgaDAa1Wq7rG559/HkD1dsaCggLKy8vp06eP6louX75MWVkZUH+sDAaDcs0ALVq0UMqhvby82Lp1K2VlZRQXF1NWVnZLi2prNBpVmzWfCtdUUFDAvn37SEhIULZVVlbi5uam/Fzzfpibm+Pp6UlaWhpubm6kpaXx3HPPYWZmdtvxqE9DxnTN/zDUd81CCCHEvUby0Vsn+eh192o+WjsvA+rM+Rra/5prOer1el5++WWuXLnC0aNHldz10qVL/PLLL8yZM0c1cXv58mUKCwuVnxuaJ9fuZ8+ePWnc+J/pG2dnZ0pLS/n9998BaN++veqhupWVVYPyXCHuJpmQFOIucnBwwMzMjLy8vDoTwLy8PLp164a5uTkAOp2O1NRUNBqN6kP56tWreHt7M23aNNXv1/zQqvm0uVGjRmzYsIHjx49z4MAB0tPTiY+PJz4+HhsbG4Abvkmx5nmMqqurlbVW6tpfVVWlrHFSn0aNGplsq/1BaoxFTTXXoam9SLkxBsb+xcfHm7yFz87OTikPqqv/RsY1jzIyMsjKymLmzJlMmTKFOXPmmBxrbO9W1bxn9bl69Sr29vZER0eb7DPew/piVV873bt3p0OHDmRkZFBYWIiHh0e9camtoW+nrqqqYsqUKYwYMUK1vWbSV7tdnU7Ha6+9xpIlSzh48CBRUVHA7cejPg0Z0/JGbiGEEPcryUfVJB+9//PRuvKyunK+hvS/dpt9+/alSZMmHDp0iOzsbDw9PTE3N+fy5csAvP/++6pJePhn0v9GfbuZG413+OceN/SahbiXSMm2EHeRra0tnp6eREdHmyQM586dY+fOnfj5+SnbjGUyqampuLu7K0+QtVotRUVFaDQa5c+BAwdISUmps92CggJWr16No6Mjc+bM4bPPPqNNmzaqcoIb0Wq1HDt2TLXt6NGjygdvXfuPHTum7K8ryTMyfpBeunRJ2VZzQfGb6dKli1ICYvxd41PDxx9/HDMzM8rLy5UYWVtbs2rVKs6fP9+g869du5bz588zbtw4NmzYwOzZs5WSDXNzc1W/jQtU36qOHTtSVFSkWix79erVJmsSarVaSkpKsLW1Va7n559/Zt26dfXG2Eij0ShlTwBlZWX0799fibeXlxeZmZnKNyD+C7X7pdVq+fnnn1XjNiEhQbUuT20DBw6kqqqKLVu2YGFhwZNPPqmc63biUd8xNxvTQgghxP1M8lE1yUcf7Hz0dvv/0EMPMWzYMLKysjhw4IDSr6ZNm2JnZ0dpaalyrjZt2vDOO+/c9lqOWq2WkydPqr5NefToUWxtbU3K7YW4n8iEpBB3WUhICBcvXmTKlCkcOXKEkpIS0tPTmTBhAv369cPf3185tnv37rRq1Yq4uDiGDx+ubPf39ycvL4+1a9dSWFhISkoKYWFhtG3bts42mzZtyieffEJ0dDTFxcVkZWVx9uzZBpV7+Pv7c+rUKcLCwjAYDCQnJxMfH8+LL74IwMSJE4mLi2PXrl0YDAbeffdd8vPzGT16NACWlpacPXtW9UIQoxYtWtCmTRs2b95McXExSUlJZGVlNTiW48ePJzY2ltTUVL7//ntCQkKUJ+vW1taMGTOGFStWcPjwYX788UcWLlxIUVER7du3b9D5f/rpJ1auXEl+fj4//PADX3zxhRKzXr16sW/fPo4fP87x48dZt25dg/tdk5ubGy1atGDZsmUUFBRw4MABtm/fripjNh7Xrl07FixYwOnTpzly5AhLly7F0tLSZA3EugQEBLBt2zYyMjIwGAwsX76c9u3bK7Hw8vIiJyeH0tJSnnrqqX91LbUZ/8OSn5/PpUuXmDhxIp9//jmxsbGcOXOGrVu3snXr1jrXSzJq3LgxQ4cOJSYmhmHDhinJ4u3Gw9LSkosXL1JYWGjyLYibjWkhhBDifif56D8kH32w89EmTZpQWFjI+fPn/3X/9Xo9u3fvpqKigv79+yvbJ06cSHh4OAcPHqSwsJAlS5bw7bffYm9v3+C+/fDDD/zxxx+q7d7e3ly5ckW5HxkZGURERDBu3LgGTfwKca+SCUkh7rLHHnuMxMREtFot8+fPZ9iwYYSHhzN27FhiYmJMSlV0Oh1mZmYMGjRI2dauXTtiYmLIzs7Gy8uL8PBwFi9ejI+PT51ttmzZkoiICFJTU9Hr9axcuZK5c+eaJBl1adu2LRs2bCA7Oxtvb2/Wr1/P4sWLlXVldDodc+bMYd26dfj4+JCbm8uHH35Ip06dAPD19cVgMODj42NSRvDQQw/x1ltvcfz4cWUh69plP/Xx9fVl5syZhIaG4u/vz1NPPaVaoH3x4sUMGDCAmTNn4ufnR+PGjfnggw8alDABrFixghYtWhAQEICfnx+tWrUiJCQEuL5AdY8ePRg/fjzz5s3j5ZdfbnC/a2rcuDHR0dH89ttvjBw5krfeeouFCxearJljZmbG+vXrqa6uxs/PjxkzZjB48GCWLFnSoHZ8fX0JCgrijTfeYNSoUVRUVKiSVo1GQ+fOnXn22Wf/s3JkW1tbfHx8mD17Njt27KB3796sWbOG+Ph4dDodiYmJvPfee/Tt27fe8+j1ev766y/Vk/LbjUf//v3RaDR4e3tz6tQp1b6bjWkhhBDifif56D8kH32w89ExY8aQnZ3N5MmT/3X/e/fuzaOPPsrQoUNVZefBwcGMHj2aZcuWMWLECEpKSti8ebOqZLs+AQEBrFmzxuSt59bW1mzatIkzZ84wYsQIQkNDCQwM5NVXX731AAhxD2l0TRYWEEIIUUt1dTXPPPMMq1evVj35FUIIIYQQ4k6QfFSIB5u81EYIIYRKVlYWOTk5WFhY0K9fv7vdHSGEEEII8f+M5KNCPPhkQlIIIYTK5s2bMRgMhIeH3/DtlkIIIYQQQvyvSD4qxINPSraFEEIIIYQQQgghhBB3jDxqEEIIIYQQQgghhBBC3DEyISmEEEIIIYQQQgghhLhjZEJSCCGEEEIIIYQQQghxx8iEpBBCCCGEEEIIIYQQ4o6RCUkhhBBCCCGEEEIIIcQdIxOSQgghhBBCCCGEEEKIO0YmJIUQQgghhBBCCCGEEHeMTEgKIYQQQgghhBBCCCHuGJmQFEIIIYQQQgghhBBC3DH/B7AFJJwt4YgQAAAAAElFTkSuQmCC", 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      " ] @@ -1272,8 +1272,8 @@ "ax.set(xticks=range(0, 36, 2), xticklabels=bin_edges[0:36:2].tolist())\n", "ax.set(yticks=range(0, 36, 2), yticklabels=bin_edges[0:36:2].tolist())\n", "ax.set(\n", - " xlabel=\"Overshoot in Sufficiency Intervention\",\n", - " ylabel=\"Overshoot in Necessity Intervention\",\n", + " xlabel=\"Overshoot under sufficiency Intervention\",\n", + " ylabel=\"Overshoot under necessity Intervention\",\n", " title=\"Overshoot in counterfactual lockdown\",\n", ")\n", "ax.axvline(x=(overshoot_threshold) * 36 / 45, color=\"red\", linestyle=\"--\", label=\"Overshoot too high\")\n", @@ -1576,7 +1576,7 @@ "1. `lockdown_efficiency fixed`: $P( \\mathit{os}^{\\mathit{le}}_{\\mathit{m}'} | \\mathit{ld}, m)$\n", "2. `lockdown_efficiency not fixed`: $P( \\mathit{os}_{\\mathit{m}'} | \\mathit{ld}, m)$\n", "\n", - "The plot clearly shows that `lockdown_efficiency` as a context has little effect on how intervening on `mask` affects `overshoot`. Again, crucially, whichever context senting we choose here, withdrawing the masking policy does not radically change the fact that the overshoot is still very likely to be too high." + "The plot clearly shows that `lockdown_efficiency` as a context has little effect on how intervening on `mask` affects `overshoot`. Again, crucially, whichever context setting we choose here, withdrawing the masking policy does not radically change the fact that the overshoot is still very likely to be too high." ] } ], From e68d10346cf3db7eade7084d0091005a3cd309fc Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Thu, 29 Aug 2024 16:27:11 -0400 Subject: [PATCH 086/111] despine call --- docs/source/explainable_sir.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 55196b12..1245813c 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -1502,7 +1502,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1517,7 +1517,7 @@ }, { "data": { - "image/png": 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", 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", 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      " ] @@ -1549,7 +1549,7 @@ "plt.xlabel(\"overshoot\")\n", "plt.title(\"Counterfactual Mask\")\n", "plt.axvline(x=(overshoot_threshold), color = \"red\", linestyle = \"--\", label=\"overshoot too high\")\n", - "sns.despine\n", + "sns.despine()\n", "\n", "print(\"Overshoot mean\")\n", "print(\n", From 0bec84eba910fa8cfacd07d236d35b8f6834cddb Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Fri, 30 Aug 2024 07:43:11 -0400 Subject: [PATCH 087/111] grammar and small fixes --- docs/source/explainable_sir.ipynb | 166 +++++++++++++++--------------- 1 file changed, 83 insertions(+), 83 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 1245813c..a8f9097a 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The **Explainable Reasoning with Chirho** package aims to provide a unified, principled approach to computations of causal explanations. We showed in an earlier [tutorial](https://basisresearch.github.io/chirho/explainable_categorical.html) how Chirho provides a handler `SearchForExplanation` to carry out the program transformations needed to compute causal queries and explanations, focusing on discrete variables (we assume the reader is familar with it). In this notebook we illustrate the usage of `SearchForExplanation` for causal models with continuous random variables in the context of a dynamical system.\n", + "The **Explainable Reasoning with Chirho** package aims to provide a unified, principled approach to computations of causal explanations. We showed in an earlier [tutorial](https://basisresearch.github.io/chirho/explainable_categorical.html) how Chirho provides a handler `SearchForExplanation` to carry out the program transformations needed to compute causal queries and explanations, focusing on discrete variables (we assume the reader is familiar with it). In this notebook, we illustrate the usage of `SearchForExplanation` for causal models with continuous random variables in the context of a dynamical system.\n", "\n", "We take an epidemiological dynamical system model (described in more detail in this [tutorial](https://basisresearch.github.io/chirho/dynamical_intro.html)) and show how the but-for analysis is not sufficiently fine-grained to allow us to derive the right conclusions about the effects of different policies during a pandemic. Next, we illustrate how various causal explanation queries can be computed using `SearchForExplanation` and inference algorithms. We also demonstrate how more detailed causal queries can be answered by post-processing the samples obtained using the handler. " ] @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -106,18 +106,18 @@ "metadata": {}, "source": [ "\n", - "We start with building the epidemiological SIR (Susceptible, Infected, Recovered/Removed) model, one step at a time. We first encode the deterministic SIR dynamics. Then we add uncertainty about the parameters that govern these dynamics - $\\beta$ and $\\gamma$. These parameters have been described in much detail in the [dynamical systems tutorial](https://basisresearch.github.io/chirho/dynamical_intro.html). We then incorporate the resulting model into a more complex causal model that involves two policy mechanisms: imposing lockdown and masking restrictions.\n", + "We start with building the epidemiological SIR (Susceptible, Infected, Recovered/Removed) model, one step at a time. We first encode the deterministic SIR dynamics. Then we add uncertainty about the parameters that govern these dynamics - $\\beta$ and $\\gamma$. These parameters have been described in detail in the [dynamical systems tutorial](https://basisresearch.github.io/chirho/dynamical_intro.html). We then incorporate the resulting model into a more complex causal model that involves two policy mechanisms: imposing lockdown and masking restrictions.\n", "\n", - "Our outcome of interest is overshoot, the proportion of the population that remains susceptible after the epidemic peak but eventually becomes infected as the epidemic continues. One way to compute it is to:\n", + "Our outcome of interest is overshoot, the proportion of the population that remains susceptible after the epidemic peaks but eventually becomes infected as the epidemic continues. One way to compute it is to:\n", "\n", - "1. Find the time at which the number of infected individuals is at its peak, `t_max`.\n", + "1. Find when the number of infected individuals is at its peak, `t_max`.\n", "2. Determine the proportion of susceptibles at `t_max` in the whole population, `S_peak`.\n", "3. Find the proportion of susceptibles (those who have never been infected yet) at the end of the logging period, `S_final`.\n", "4. Calculate the additional ratio of infected or infected and later removed individuals since the peak as `S_peak - S_final`.\n", "\n", - "This quantity is of interest because epidemic mitigation policies often have multiple goals that need to be balanced. One goal is to increase `S_final`, i.e., to limit the total number of infected individuals. Another goal is to limit the number of infected individuals at the peak of the epidemic to avoid overwhelming the healthcare system. A further goal is to minimize the proportion of the population that becomes infected after the peak, that is, the overshoot, to reduce healthcare and economic burdens. Balancing these objectives involves making trade-offs.\n", + "This quantity is of interest because epidemic mitigation policies often have multiple goals that must be balanced. One goal is to increase `S_final`, i.e., to limit the total number of infected individuals. Another goal is to limit the number of infected individuals at the peak of the epidemic to avoid overwhelming the healthcare system. A further goal is to minimize the proportion of the population that becomes infected after the peak, that is, the overshoot, to reduce healthcare and economic burdens. Balancing these objectives involves making trade-offs.\n", "\n", - " Suppose we are working under constraint that the overshoot should be lower than 24% of the population, and we implement two policies, lockdown and masking, which together seem to lead to the overshoot being too high. In fact, only one of them is responsible, and we are interested in being able to identify which one. " + " Suppose we are working under the constraint that the overshoot should be lower than 24% of the population, and we implement two policies, lockdown and masking, which together seem to lead to the overshoot being too high. Only one of them is responsible, and we are interested in being able to identify which one. " ] }, { @@ -129,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -169,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -217,7 +217,7 @@ "source": [ "The number $0.15$ is the overshoot you get if $\\beta = 0.03, \\gamma = 0.5$. This value is observed by simulating the SIR dynamics model with these values and calculating the overshoot directly.\n", "\n", - "Also, note that the above dynamical system introduces the variables: `S` - susceptible, `I` - infected, `R` - recovered and `l` - effect of intervention. These variables evolve over time and their dynamics are captured by the model. As we add features to our model, we also add new variables to this list. Further on in the notebook, we will describe the probabilities we compute in terms of these variables." + "Also, note that the above dynamical system introduces the variables: `S` - susceptible, `I` - infected, `R` - recovered, and `l` - effect of the intervention. These variables evolve over time and their dynamics are captured by the model. As we add features to our model, we also add new variables to this list. Further on in the notebook, we will describe the probabilities we compute in terms of these variables." ] }, { @@ -238,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -273,13 +273,13 @@ "metadata": {}, "source": [ "\n", - "Now we incorporate the Bayesian SIR model into a larger model that includes the effect of two different policies, lockdown and masking, where each can be implemented with $50\\%$ probability (these probabilities won't really matter, as we will be intervening on these, the sampling is mainly used to register the parameters with Pyro). We encode their efficiencies which further affect the model. Crucially, these efficiencies interact in a fashion resembling the structure of the stone-throwing example we discussed in the tutorial on categorical variables. If lockdown is present, this limits the impact of masking as agents interact less and so masks have fewer opportunities to block anything. We assume the situation is asymmetric: masking has no impact on the efficiency of lockdown. The model also computes `overshoot` and `os_too_high` for further analysis.\n", + "Now we incorporate the Bayesian SIR model into a larger model that includes the effect of two different policies, lockdown and masking, where each can be implemented with $50\\%$ probability (these probabilities won't really matter, as we will be intervening on these, the sampling is mainly used to register the parameters with Pyro). We encode their efficiencies which further affect the model. Crucially, these efficiencies interact in a fashion resembling the structure of the stone-throwing example we discussed in the tutorial on categorical variables. If a lockdown is present, this limits the impact of masking as agents interact less and so masks have fewer opportunities to block anything. We assume the situation is asymmetric: masking has no impact on the efficiency of lockdown. The model also computes `overshoot` and `os_too_high` for further analysis.\n", "\n" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -302,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -360,7 +360,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now that we have our full-fledged model of SIR dynamics along with interventions, we have a complete list of random variables in question. In our explanation we will abbreviate them as follows. `S` - susceptible, `I` - infected, `R` - recovered, `l` - the effect of intervention, `beta`, `gamma` - the parameters of the SIR dynamics model, `ld` - lockdown, `m` - masking, `le` - lockdown efficiency, `me` - mask efficiency, `je` - joint efficiency, `os` - overshoot, and `oth` - overshoot is too high. We use these notations in the rest of the notebook to describe the probabilities we are computing." + "Now that we have our full-fledged model of SIR dynamics along with interventions, we have a complete list of random variables in question. In our explanations, we will abbreviate them as follows. `S` - susceptible, `I` - infected, `R` - recovered, `l` - the effect of intervention, `beta`, `gamma` - the parameters of the SIR dynamics model, `ld` - lockdown, `m` - masking, `le` - lockdown efficiency, `me` - mask efficiency, `je` - joint efficiency, `os` - overshoot, and `oth` - overshoot is too high. We use these notations in the rest of the notebook to describe the probabilities we are computing." ] }, { @@ -377,19 +377,19 @@ "\n", "Suppose now we introduced both policies, and this resulted in an overshoot. What intuitively is the case is that lockdown limited the efficiency of masking, and it was in fact the lockdown that in this particular context caused the overshoot (this is consistent with saying that in the context where only masking has been implemented, masking would be responsible for the resulting overshoot being too high).\n", "\n", - "We might try to use the but-for analysis to idenitfy which of the policies causes overshoot to be too high. To do so, we investigate the following four scenarios:\n", + "We might try to use the but-for analysis to identify which of the policies causes overshoot to be too high. To do so, we investigate the following four scenarios:\n", "\n", "1. None of the policies were applied\n", "2. Both lockdown and masking were enforced\n", "3. Only masking was imposed\n", "4. Only lockdown was imposed\n", "\n", - "The hope is that by looking at these we will be able to indentify the culprit. We create these four models by conditioning on the policies being imposed as required (in fact, this has the same effect as intervening here, as the sites are upstream from the dynamical system model; we could emulate 1-4 using `do` with the same estimates). For the sake of completeness, we also illustrate the consequences of following a stochastic policy and deciding randomly about the interventions." + "The hope is that by looking at these we will be able to identify the culprit. We create these four models by conditioning on the policies being imposed as required (in fact, this has the same effect as intervening here, as the sites are upstream from the dynamical system model; we could emulate 1-4 using `do` with the same estimates). For the sake of completeness, we also illustrate the consequences of following a stochastic policy and deciding randomly about the interventions." ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -446,17 +446,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note that the above list of variables match our list of variables earlier when we constructed the full-fledged SIR model." + "Note that the above list of variables matches our list of variables earlier when we constructed the full-fledged SIR model." ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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      " ] @@ -584,11 +584,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The plots above show what happens in different scenarios. We observe that in the model where none of the policies were imposed, the probability of the overshoot being too high is relatively low, $\\approx 0.05$. On the other hand, when both policies were imposed, the probability of the overshoot being to high was relatively high at $\\approx 0.7$. \n", + "The plots above show what happens in different scenarios. We observe that in the model where none of the policies were imposed, the probability of the overshoot being too high is relatively low, $\\approx 0.05$. On the other hand, when both policies were imposed, the probability of the overshoot being too high was relatively high at $\\approx 0.7$. \n", "\n", - "To identify which of `lockdown` and `mask` is the cause, we analyze the models where only one of the policies were imposed. Interestingly, the effect of the interventions is somewhat nuanced. Implementing both interventions increases the risk of overshoot as compared to the no intervention model, but individual interventions would have even worse consequences, which means that the two interventions while jointly increasing the risk to some extent mitigate each other's contribution to that risk as well.\n", + "To identify which of `lockdown` and `mask` is the cause, we analyze the models where only one of the policies was imposed. Interestingly, the effect of the interventions is somewhat nuanced. Implementing both interventions increases the risk of overshooting as compared to the no-intervention model, but individual interventions would have even worse consequences, which means that the two interventions while jointly increasing the risk to some extent mitigate each other's contribution to that risk as well.\n", "\n", - "Crucially, the analysis does not allow us to distinghuish the intuitive role that the lockdown played, as opposed to masking (whose impact has been limited by the presence of lockdown). So, we need a more fine-grained analysis where we not only control the variables being intervened on (that is, the policies), but also pay attention to what context we are in. We achieve that level of sensitivity by stochastically keeping part of the context (that is, other variables in the model) fixed (see the tutorial for categorical variables for a more extensive explanation of this method and simpler examples). The key idea is that starting with the scenario in which both interventions have been implemented, there is a context such that if we keep it fixed, removing lockdown would significantly lower the overshoot, but there is no context that we could keep fixed such that if in that context we remove the masking policy, the overshoot would decrease. In the next section, we show how this analysis can be carried out with the help of `SearchForExplanation`." + "Crucially, the analysis does not allow us to distinguish the intuitive role that the lockdown played, as opposed to masking (whose impact has been limited by the presence of lockdown). So, we need a more fine-grained analysis where we not only control the variables being intervened on (that is, the policies) but also pay attention to what context we are in. We achieve that level of sensitivity by stochastically keeping part of the context (that is, other variables in the model) fixed (see the tutorial for categorical variables for a more extensive explanation of this method and simpler examples). The key idea is that starting with the scenario in which both interventions have been implemented, there is a context such that if we keep it fixed, removing the lockdown would significantly lower the overshoot, but there is no context that we could keep fixed such that if in that context we remove the masking policy, the overshoot would decrease. In the next section, we show how this analysis can be carried out with the help of `SearchForExplanation`." ] }, { @@ -602,11 +602,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Before we dive into the code below, let us first define some notation. We use small case abbreviations to refer to the value of the variables under consideration. For example, $\\mathit{ld}$ refers to `lockdown=1` and $\\mathit{ld}'$ refers to `lockdown=0`. We place interventions in the subscripts, i.e. $\\mathit{os}_{\\mathit{ld}}$ refers to the `overshoot` under the intervention that `lockdown=1`. Later on in the notebook, we also employ contexts that are kept fixed in the intervened worlds. We place these contexts in the superscript. For example, $\\mathit{os}_{\\mathit{ld}}^{\\mathit{me}}$ refers to the variable `overshoot` when `lockdown` was intervened to be 1 and `mask_efficiency` was kept fixed at its factual value. \n", + "Before we dive into the code below, let us first define some notation. We use small case abbreviations to refer to the value of the variables under consideration. For example, $\\mathit{ld}$ refers to `lockdown=1` and $\\mathit{ld}'$ refers to `lockdown=0`. We place interventions in the subscripts, for instance, $\\mathit{os}_{\\mathit{ld}}$ refers to the `overshoot` under the intervention that `lockdown=1`. Later on in the notebook, we also employ contexts that are kept fixed in the intervened worlds. We place these contexts in the superscript. For example, $\\mathit{os}_{\\mathit{ld}}^{\\mathit{me}}$ refers to the variable `overshoot` when `lockdown` was intervened to be 1 and `mask_efficiency` was kept fixed at its factual value. \n", "\n", - "We use $P(.)$ to denote the distribution described by the model (`policy_model` in this notebook). We also induce a distribution over the sets of interventions and the sets of context nodes kept fixed. We denote these distributions by $P_a(.)$ and $P_w(.)$ respectively. As an example, $P_a(\\{ld\\})$ refers to the probability that the set of interventions under consideration is $\\{ld\\}$. These distributions are determined using the parameters `antecedent_bias` and `witness_bias` given to the handler `SearchForExplanation`. For more details, please refer to the [documentation](https://basisresearch.github.io/chirho/explainable.html#chirho.explainable.handlers.explanation.SearchForExplanation)\n", + "We use $P(.)$ to denote the distribution described by the model (`policy_model` in this notebook). We also induce a distribution over the sets of potential interventions and the sets of context nodes potentially kept fixed. We denote these distributions by $P_a(.)$ and $P_w(.)$ respectively. As an example, $P_a(\\{ld\\})$ refers to the probability that the set of interventions under consideration is $\\{ld\\}$. These distributions are determined using the parameters `antecedent_bias` and `witness_bias` given to the handler `SearchForExplanation`. For more details, please refer to the [documentation](https://basisresearch.github.io/chirho/explainable.html#chirho.explainable.handlers.explanation.SearchForExplanation)\n", "\n", - "Now let's dive into the code and we use this notation to describe the quantities we are computing. " + "Now let's dive into the code, using this notation to describe the quantities we are computing. " ] }, { @@ -615,12 +615,12 @@ "source": [ "\n", "\n", - "We first setup a function for performing importance sampling through the model that returns cumulative log probabilities of the samples, sample traces, a handler object for multiworld counterfactual reasoning, and log probabilities. We use these objects later in the code to subselect the samples." + "We first introduce a function for performing importance sampling through the model that returns cumulative log probabilities of the samples, sample traces, a handler object for multi-world counterfactual reasoning, and log probabilities. We use these objects later in the code to subselect the samples." ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -662,26 +662,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Then, we setup the query as follows:\n", + "Then, we set up the query as follows:\n", "1. `supports`: We extract supports of the model using `ExtractSupports` and enrich it with additional information of `os_too_high` being a Boolean (constraints for deterministic nodes currently need to be specified manually).\n", "2. `antecedents`: We postulate `lockdown=1` and `mask=1` as possible causes.\n", "3. `alternatives`: We provide `lockdown=0` and `mask=0` as alternative values.\n", "4. `witnesses`: We include `mask_efficiency` and `lockdown_efficiency` as candidates to be included in the contexts potentially to be kept fixed.\n", "5. `consequents`: We put `os_too_high=1` as the outcome whose causes we wish to analyze.\n", "6. `antecedent_bias`, `witness_bias`,: We set these parameters to have equal probabilities of intervening on cause candidates, and to slightly prefer smaller witness sets. Please refer to the documentation of `SearchForExplanation` for more details.\n", - "7. `consequent_scale` is set to effectively include values near 0 and 1 depending on whether the binary outocomes differ across counterfactual worlds." + "7. `consequent_scale` is set to effectively include values near 0 and 1 depending on whether the binary outcomes differ across counterfactual worlds." ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.1287)\n" + "tensor(0.1328)\n" ] } ], @@ -715,14 +715,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The above probability itself is not directly related to our query. It is the probability that the overshoot is both too high in the antecedents-intervened world and not too high in the alternatives-intervened world, where antecedent interventions are preempted with probabilities $0.5$ at each site, and witnesses are kept fixed at the observed values with probability $0.5+0.2$ at each site (see the tutorial on categorical variables for explanation of why this stochasticity is in general useful). \n", + "The above probability itself is not directly related to our query. It is the probability that the overshoot is both too high in the antecedents-intervened world and not too high in the alternatives-intervened world, where antecedent interventions are preempted with probabilities $0.5$ at each site, and witnesses are kept fixed at the observed values with probability $0.5+0.2$ at each site (see the tutorial on categorical variables for an explanation of why this stochasticity is in general useful). \n", "\n", - "However, more fine-grained queries can be answered using the 10000 samples we have drawn in the process. We first compute the probabilities that different sets of antecedent candidates have causal effect over `os_too_high` conditioned on the fact that lockdown and masking were actually imposed in the factual world." + "However, more fine-grained queries can be answered using the 10000 samples we have drawn in the process. We first compute the probabilities that different sets of antecedent candidates have a causal effect over `os_too_high` conditioned on the fact that lockdown and masking were actually imposed in the factual world." ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -746,7 +746,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We specifically compute the following four probabilities. In each of the computations, we condition on lockdown and masking actually being implemented in the factual world. Then we take an interventional setting and compute the probability that this setting has a causal power over the outcome. For instance, in 1., we assume lockdown (`ld`) and masking (`m`) have been implemented, and we ask about the joint prbability that both (a) removing both interventions, i.e. intervening for both `ld` and `m` to not happen - which we mark by the apostrophe - would lead to `oth` not happening, $\\mathit{oth}'_{\\mathit{ld}', m'}$, and (b) intervening for both to happend would lead to `oth`, $\\mathit{oth}_{\\mathit{ld}, m}$ (which, given the stochasticity between these interventions and the outcome, is non-trivial). Note that in computing these probabilities, we also marginalize over all the possible contexts to be kept fixed, i.e. all possible subsets of $W = \\{\\mathit{le}, \\mathit{me}\\}$\n", + "We specifically compute the following four probabilities. In each of the computations, we condition on lockdown and masking actually being implemented in the factual world. Then we take an interventional setting and compute the probability that this setting has a causal power over the outcome. For instance, in 1., we assume lockdown (`ld`) and masking (`m`) have been implemented, and we ask about the joint prbability that both (a) removing both interventions, i.e. intervening for both `ld` and `m` to not happen - which we mark by the apostrophe - would lead to `oth` not happening, $\\mathit{oth}'_{\\mathit{ld}', m'}$, and (b) intervening for both to happend would lead to `oth`, $\\mathit{oth}_{\\mathit{ld}, m}$ (which, given the stochasticity between these interventions and the outcome, is non-trivial). Note that in computing these probabilities, we also marginalize over all the contexts that potentially can be kept fixed, i.e. all possible subsets of $W = \\{\\mathit{le}, \\mathit{me}\\}$\n", "\n", "1. $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{oth}^w_{\\mathit{ld}, m}, \\mathit{oth}'^w_{\\mathit{ld}', m'} | \\mathit{ld}, m)$\n", "\n", @@ -759,17 +759,17 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 0.20909090340137482\n", - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 0.3085271418094635\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 2.3285480210688547e-09\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 3.2006626238256786e-09\n" + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 0.24283304810523987\n", + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 0.2902735471725464\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 2.3861892461951584e-09\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 2.636660445531902e-09\n" ] } ], @@ -807,23 +807,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As the above probabilities show, `{lockdown=1}` has the most causal role on the overshoot being too high among all the possible sets of causes when both lockdown and masking were imposed." + "As the above probabilities show, `{lockdown=1}` has the most causal role in the overshoot being too high among all the possible sets of causes when both lockdown and masking were imposed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Note that one could also compute above queries by giving specific parameters to `SearchForExplanation` instead of subselecting the samples, as we did in the tutorial for explainable module for models with categorical variables. Here, however, we illustrate that running a sufficiently general query once produces samples that can be used to answer multiple different questions.\n", + "Note that one could also compute the above queries by giving specific parameters to `SearchForExplanation` instead of subselecting the samples, as we did in the tutorial for the explainable module for models with categorical variables. Here, however, we illustrate that running a sufficiently general query once produces samples that can be used to answer multiple different questions.\n", "\n", - "Also, we use the log probabilities above to identify whether a particular combination of intervening nodes and context nodes have causal power or not, which is made possible by the fact that our handler adds appropriate log probabilities to the trace (see the previous tutorial and documentation for more explanation). One can also obtain these results by explictly analyzing the sample trace as we do in the next section." + "Also, we use the log probabilities above to identify whether a particular combination of intervening nodes and context nodes have causal power or not, which is made possible by the fact that our handler adds appropriate log probabilities to the trace (see the previous tutorial and documentation for more explanation). One can also obtain these results by explicitly analyzing the sample trace as we do in the next section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can also compute degree of responsibilities assigned to both lockdown and mask as follows. To compute degree of responsibility of lockdown and mask, we specifically compute the probability that these factors were a part of the cause of the outcome. Mathematically, we compute the following where $W = \\{\\mathit{le}, \\mathit{me}\\}$ and $C = \\{\\mathit{ld}, m\\}$:\n", + "We can also compute a relatively natural interpretation of what the degree of responsibilities assigned to both lockdown and mask is as follows. We compute the probability that these factors were a part of the cause of the outcome. Mathematically, we compute the following, where $W = \\{\\mathit{le}, \\mathit{me}\\}$ and $C = \\{\\mathit{ld}, m\\}$:\n", "\n", "1. Degree of responsibility of lockdown: $\\sum_{w \\subseteq W} \\sum_{\\mathit{ld} \\in C} P_w(w) P_a(C | \\mathit{ld} \\in C) \\cdot P(\\mathit{oth}^w_{C}, \\mathit{oth}'^w_{C'} | \\mathit{ld}, m)$\n", "\n", @@ -832,7 +832,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -840,10 +840,10 @@ "output_type": "stream", "text": [ "Degree of responsibility for lockdown: \n", - "{'__cause____antecedent_lockdown': 0, 'mask': 1, 'lockdown': 1} 0.2582375407218933\n", + "{'__cause____antecedent_lockdown': 0, 'mask': 1, 'lockdown': 1} 0.2677857577800751\n", "\n", "Degree of responsibility for mask: \n", - "{'__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 0.10722610354423523\n" + "{'__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 0.1170731708407402\n" ] } ], @@ -862,7 +862,7 @@ "source": [ "As the output shows, `lockdown=1` has a higher degree of responsibility than `mask=1`.\n", "\n", - "The reader might have the impression that the numbers are relatively low: what one needs to remember though, that these are computed with stochastic witness preemptions in the background and that in this model the witnesses are downstream from the interventions, so part of the time some of the interventions are blocked as their effects are stochastically chosen to be witnesses and fixed at the actual values. The role of witnesses will be investigated in more detail in the next section." + "The reader might have the impression that the numbers are relatively low: what one needs to remember though, is that these are computed with stochastic witness preemptions in the background and that in this model the witnesses are downstream from the interventions, so part of the time some of the interventions are blocked as their effects are stochastically chosen to be witnesses and fixed at the actual values. The role of witnesses will be investigated in more detail in the next section." ] }, { @@ -876,12 +876,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this section, we use the samples we obtained earlier to analyze the distribution of `overshoot` variable in different counterfactual worlds. We first define a function to obtain histogram data from the samples in a particular world and then we inspect the marginal distribution plots for `overshoot` in different settings." + "In this section, we use the samples we obtained earlier to analyze the distribution of `overshoot` variable in different counterfactual worlds. We first define a function to obtain histogram data from the samples in a particular world, and then we inspect the marginal distribution plots for `overshoot` in different settings." ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -924,7 +924,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -957,7 +957,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -965,14 +965,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "factual: 24.31097984313965 counterfactual mask: 26.97050666809082 counterfactual lockdown: 21.247312545776367\n", + "factual: 24.302181243896484 counterfactual mask: 26.837486267089844 counterfactual lockdown: 21.276477813720703\n", "Probability of overshoot being high\n", - "factual: 0.6075999736785889 counterfactual mask: 0.8814433217048645 counterfactual lockdown: 0.3526315689086914\n" + "factual: 0.6021000146865845 counterfactual mask: 0.8484848737716675 counterfactual lockdown: 0.32460734248161316\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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      " ] @@ -1039,19 +1039,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The above histogram also takes into account the context that is being kept fixed. If `lockdown` is being intervened on, keeping `lockdown_efficiency` fixed would hinder the effect of intervention. Thus to obtain the relevant samples, we also filter for the appropriate context. Once we have filtered for the context, we take the samples and plot them as density above. The histogram above plots three quantities. It plots $P(\\mathit{os} | \\mathit{ld}, m)$ as the factual distribution of overshoot, $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{ld}'} | \\mathit{ld}, m)$ as `counterfactual_lockdown` where $W = \\{\\mathit{me}\\}$ and $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{m}'} | \\mathit{ld}, m)$ as `counterfactual_mask` where $W = \\{\\mathit{le}\\}$. These distributions help in comparing how necessity interventions for the two antecedents affect the overshoot." + "The above histogram also takes into account the context that is being kept fixed. If `lockdown` is being intervened on, keeping `lockdown_efficiency` fixed would hinder the effect of the intervention. Thus to obtain the relevant samples, we also filter for the appropriate context. Once we have filtered for the context, we take the samples and plot them as density above. The histogram above plots three quantities. It plots $P(\\mathit{os} | \\mathit{ld}, m)$ as the factual distribution of overshoot, $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{ld}'} | \\mathit{ld}, m)$ as `counterfactual_lockdown` where $W = \\{\\mathit{me}\\}$ and $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{m}'} | \\mathit{ld}, m)$ as `counterfactual_mask` where $W = \\{\\mathit{le}\\}$. These distributions help in comparing how necessity interventions for the two antecedents affect the overshoot." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can have similar plots for sufficiency worlds (indicated by 2) where variables are intervened on to have their antecedent values. While this might seem redundant, this investigates probabilistically the impact of the implemented interventons: after all, it might be the case that the observed outcome is an unusual one and that usually those interventions do not lead to the outcome of interest. The resulting plots show that when `mask` is set to be 1, there is a higher probability of high overshoot, but that this distribution is more flat than the distribution for `lockdown` being set to 1, which has higher peaks." + "We can have similar plots for sufficiency worlds (indicated by 2) where variables are intervened on to have their antecedent values. While this might seem redundant, this investigates probabilistically the impact of the implemented interventions: after all, it might be the case that the observed outcome is an unusual one and that usually, those interventions do not lead to the outcome of interest. The resulting plots show that when `mask` is set to be 1, there is a higher probability of high overshoot, but that this distribution is flatter than the distribution for `lockdown` being set to 1, which has higher peaks." ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -1084,7 +1084,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1092,14 +1092,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "factual: 24.31097984313965 counterfactual mask: 27.37161636352539 counterfactual lockdown: 26.888933181762695\n", + "factual: 24.302181243896484 counterfactual mask: 26.423648834228516 counterfactual lockdown: 27.30312728881836\n", "Probability of overshoot being high\n", - "factual: 0.6075999736785889 counterfactual mask: 0.7061855792999268 counterfactual lockdown: 0.7263157963752747\n" + "factual: 0.6021000146865845 counterfactual mask: 0.6717171669006348 counterfactual lockdown: 0.717277467250824\n" ] }, { "data": { - "image/png": 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", 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", 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      " ] @@ -1178,7 +1178,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -1249,12 +1249,12 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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      " ] @@ -1321,9 +1321,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The above heatmaps plot the joint distributions arising from necessity and sufficient interventions, particularly $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{ld}}, \\mathit{os}^w_{\\mathit{ld}'}|\\mathit{ld, m})$ where $W = \\{\\mathit{me}\\}$ and $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{m}}, \\mathit{os}^w_{\\mathit{m}'}|\\mathit{ld, m})$ where $W = \\{\\mathit{le}\\}$.\n", + "The above heatmaps plot the joint distributions arising from necessity and sufficient interventions, particularly $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{ld}}, \\mathit{os}^w_{\\mathit{ld}'}|\\mathit{ld, m})$ where $W = \\{\\mathit{me}\\}$ and $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{m}}, \\mathit{os}^w_{\\mathit{m}'}|\\mathit{ld, m})$, where $W = \\{\\mathit{le}\\}$.\n", "\n", - "It is evident from the plot above that counterfactual for lockdown has more probability mass in the top right quadrant (low overshoot in the necessity world and high overshoot in the sufficient world). This gives us a more clear picture into why lockdown has more causal role in overshoot being too high as compared to masking." + "It is evident from the plot above that the counterfactual for lockdown has more probability mass in the top right quadrant (low overshoot in the necessity world and high overshoot in the sufficient world). This gives us a clearer picture into why lockdown has higher causal role in the overshoot being too high as compared to masking." ] }, { @@ -1337,18 +1337,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "`SearchForExplanation` allows the users to perform an even finer grained analysis by visualizing distributions of random variables when different contexts are kept fixed in the model. To illustrate this, we consider the following two scenarios:\n", + "`SearchForExplanation` allows the users to perform an even finer-grained analysis by visualizing distributions of random variables when different contexts are kept fixed in the model. To illustrate this, we consider the following two scenarios:\n", "1. Intervene on `lockdown=1` while keeping `mask_efficiency` fixed (or not).\n", "2. Intervene on `mask=1` while keeping `lockdown_efficiency` fixed (or not).\n", "\n", - "The key motivation for looking into this is the intuition that there is some part of the actual context in which removing lockdown would significantly lower the overshoot, whereas there is no corresponding part of the actual context in which removing masking would lead to lower overshoot - which is the core of the assymetricity between the two intervetnions in our example.\n", + "The key motivation for looking into this is the intuition that there is some part of the actual context in which removing lockdown would significantly lower the overshoot, whereas there is no corresponding part of the actual context in which removing masking would lead to lower overshoot - which is the core of the assymetricity between the two interventions in our example.\n", "\n", - "We first intervene on `lockdown` being 1 and analyze how the distribution of `overshoot` change as we keep the `mask_efficiency` fixed (or not)." + "We first intervene on `lockdown` being 1 and analyze how the distribution of `overshoot` changes as we keep the `mask_efficiency` fixed (or not)." ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -1382,7 +1382,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1390,14 +1390,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "mask_efficiency fixed: 18.844215393066406 mask_efficiency not fixed: 26.454023361206055\n", + "mask_efficiency fixed: 18.7790470123291 mask_efficiency not fixed: 25.793893814086914\n", "Probability of overshoot being high\n", - "mask_efficiency fixed: 0.10000000149011612 mask_efficiency not fixed: 0.8999999761581421\n" + "mask_efficiency fixed: 0.08130080997943878 mask_efficiency not fixed: 0.7647058963775635\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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      " ] @@ -1470,7 +1470,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1502,7 +1502,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1510,14 +1510,14 @@ "output_type": "stream", "text": [ "Overshoot mean\n", - "lockdown_efficiency fixed: 27.033872604370117 lockdown_efficiency not fixed: 26.814355850219727\n", + "lockdown_efficiency fixed: 27.030378341674805 lockdown_efficiency not fixed: 26.37188148498535\n", "Probability of overshoot being high\n", - "lockdown_efficiency fixed: 0.8768116235733032 lockdown_efficiency not fixed: 0.8928571343421936\n" + "lockdown_efficiency fixed: 0.8642857074737549 lockdown_efficiency not fixed: 0.8103448152542114\n" ] }, { "data": { - "image/png": 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", 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", 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      " ] @@ -1572,7 +1572,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Similarly to the earlier histogram, the above plot shows the following distributions:\n", + "Similar to the earlier histogram, the above plot shows the following distributions:\n", "1. `lockdown_efficiency fixed`: $P( \\mathit{os}^{\\mathit{le}}_{\\mathit{m}'} | \\mathit{ld}, m)$\n", "2. `lockdown_efficiency not fixed`: $P( \\mathit{os}_{\\mathit{m}'} | \\mathit{ld}, m)$\n", "\n", From 7900524d15d96b0882defd023458e11cfdf9c7ec Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Fri, 30 Aug 2024 14:31:29 -0400 Subject: [PATCH 088/111] tweaks --- docs/source/explainable_sir.ipynb | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index a8f9097a..86b2bcec 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -30,7 +30,7 @@ "- [But for Analysis with Bayesian SIR Model with Policies](#but-for-analysis-with-bayesian-sir-model-with-policies)\n", "- [Causal Explanations using `SearchForExplanation`](#causal-explanations-using-searchforexplanation)\n", "- [Fine-grained Analysis of `overshoot` using Sample traces](#fine-grained-analysis-of-overshoot-using-sample-traces)\n", - "- [For Advanced Readers: Looking into Different Contexts](#ooking-into-different-contexts-for-curious-readers)" + "- [Looking into Different Contexts for Curious Readers](#ooking-into-different-contexts-for-curious-readers)" ] }, { @@ -746,7 +746,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We specifically compute the following four probabilities. In each of the computations, we condition on lockdown and masking actually being implemented in the factual world. Then we take an interventional setting and compute the probability that this setting has a causal power over the outcome. For instance, in 1., we assume lockdown (`ld`) and masking (`m`) have been implemented, and we ask about the joint prbability that both (a) removing both interventions, i.e. intervening for both `ld` and `m` to not happen - which we mark by the apostrophe - would lead to `oth` not happening, $\\mathit{oth}'_{\\mathit{ld}', m'}$, and (b) intervening for both to happend would lead to `oth`, $\\mathit{oth}_{\\mathit{ld}, m}$ (which, given the stochasticity between these interventions and the outcome, is non-trivial). Note that in computing these probabilities, we also marginalize over all the contexts that potentially can be kept fixed, i.e. all possible subsets of $W = \\{\\mathit{le}, \\mathit{me}\\}$\n", + "We specifically compute the following four probabilities. In each of the computations, we condition on lockdown and masking actually being implemented in the factual world. Then we take an interventional setting and compute the probability that this setting has a causal power over the outcome. For instance, in 1., we assume lockdown (`ld`) and masking (`m`) have been implemented, and we ask about the joint probability that both (a) removing both interventions, i.e. intervening for both `ld` and `m` to not happen - which we mark by the apostrophe - would lead to `oth` not happening, $\\mathit{oth}'_{\\mathit{ld}', m'}$, and (b) intervening for both to happend would lead to `oth`, $\\mathit{oth}_{\\mathit{ld}, m}$ (which, given the stochasticity between these interventions and the outcome, is non-trivial). Note that in computing these probabilities, we also marginalize over all the contexts that potentially can be kept fixed, i.e. all possible subsets of $W = \\{\\mathit{le}, \\mathit{me}\\}$\n", "\n", "1. $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{oth}^w_{\\mathit{ld}, m}, \\mathit{oth}'^w_{\\mathit{ld}', m'} | \\mathit{ld}, m)$\n", "\n", @@ -1341,7 +1341,7 @@ "1. Intervene on `lockdown=1` while keeping `mask_efficiency` fixed (or not).\n", "2. Intervene on `mask=1` while keeping `lockdown_efficiency` fixed (or not).\n", "\n", - "The key motivation for looking into this is the intuition that there is some part of the actual context in which removing lockdown would significantly lower the overshoot, whereas there is no corresponding part of the actual context in which removing masking would lead to lower overshoot - which is the core of the assymetricity between the two interventions in our example.\n", + "The key motivation for looking into this is the intuition that there is some part of the actual context in which removing lockdown would significantly lower the overshoot, whereas there is no corresponding part of the actual context in which removing masking would lead to lower overshoot - which is the core of the asymmetry between the two interventions in our example.\n", "\n", "We first intervene on `lockdown` being 1 and analyze how the distribution of `overshoot` changes as we keep the `mask_efficiency` fixed (or not)." ] @@ -1452,8 +1452,6 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We use the notation $\\mathit{os}^m$ to describe the variable $\\mathit{os}$ when $m$ is kept fixed. \n", - "\n", "The above histogram plots the following distributions:\n", "1. `mask_efficiency fixed`: $P( \\mathit{os}^{\\mathit{me}}_{\\mathit{ld}'} | \\mathit{ld}, m)$\n", "2. `mask_efficiency not fixed`: $P( \\mathit{os}_{\\mathit{ld}'} | \\mathit{ld}, m)$\n", From d6a63d777fcf0643df4d620330e5be8654a98655 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Fri, 30 Aug 2024 14:57:17 -0400 Subject: [PATCH 089/111] clean up --- docs/source/counterfactual_sir.png | Bin 161069 -> 0 bytes docs/source/explainable_categorical.ipynb | 2 +- docs/source/inference.ipynb | 120 ---------------------- 3 files changed, 1 insertion(+), 121 deletions(-) delete mode 100644 docs/source/counterfactual_sir.png delete mode 100644 docs/source/inference.ipynb diff --git a/docs/source/counterfactual_sir.png 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zhRj{z@6!Tb=%Y|%fMR*Qw%=6bLIa}1;ZgFkt)GG=QJe17sIEO=i$l|%{iuo>v|jlG zdR1fo3{Zmpg@^-NxwA!)YeUHL(Pd`}>m!&8!G`+ddsvWy^ar5i-izowI}o{X+&{a_ zuUGiHcmoUeU8p5`%^@Pc23T@}`EvD}A+)m#(flAwi=H^vRS2#N@ruE?V#zxhtNIbs z^Smw;kpK2Tj`sza20>QQC=ePKM0k+0KLUd)AEa1-YWfR_>;y)7oJ_ll|E?NW1h4>l z)Nux)yMid+4IDdEE4^0xlR?syW;F{fcR;8PU%Y>U&hP=r6&4<5EjhDv2AMOjC@GMu4 z*L@4-n$_&fm|1>$>Bhcgv)ph`*yAR;s5x|}3!AY{yu=FL! zAHeN{IW88=oP@1xSCzVxBzZ zUmON#w}o-2tJqIfgT&fiyQ;bYY>(J9Ub*}=tpx7??H~X@=nXn@6Vi}&H(wy>;s2aU{_TwEc+&XH|Mm}XJO2*{>)%5<=*$0~ c&%E|x)@^*6i}C~u1^g%dQeL7^Oz+eG0%o#^82|tP diff --git a/docs/source/explainable_categorical.ipynb b/docs/source/explainable_categorical.ipynb index eee6cddf..f12d638e 100644 --- a/docs/source/explainable_categorical.ipynb +++ b/docs/source/explainable_categorical.ipynb @@ -461,7 +461,7 @@ "source": [ "query = SearchForExplanation(\n", " supports=forest_fire_supports,\n", - " antecedents={\"smile\": torch.tensor(1.0)},\n", + " antecedents={\"match_dropped\": torch.tensor(1.0)},\n", " consequents={\"forest_fire\": torch.tensor(1.0)},\n", " witnesses={}, \n", " alternatives={\"match_dropped\": torch.tensor(0.0)},\n", diff --git a/docs/source/inference.ipynb b/docs/source/inference.ipynb deleted file mode 100644 index d4b99e30..00000000 --- a/docs/source/inference.ipynb +++ /dev/null @@ -1,120 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Optional, Callable\n", - "import math\n", - "\n", - "import pyro.distributions as dist\n", - "import torch\n", - "\n", - "import pyro\n", - "from chirho.counterfactual.handlers.counterfactual import \\\n", - " MultiWorldCounterfactual\n", - "from chirho.explainable.handlers import SearchForExplanation\n", - "from chirho.explainable.handlers.components import ExtractSupports\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "def model():\n", - " a = pyro.sample(\"a\", dist.Normal(loc=torch.tensor(0.0), scale=torch.tensor(1.0)))\n", - " b = pyro.sample(\"b\", dist.Normal(loc=torch.tensor(0.0), scale=torch.tensor(1.0)))" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "with ExtractSupports() as s:\n", - " model()\n", - "\n", - "query = SearchForExplanation(\n", - " supports=s.supports,\n", - " alternatives={\"a\": torch.tensor(0.5)},\n", - " antecedents={\"a\": torch.tensor(-0.5)},\n", - " antecedent_bias=0.0,\n", - " witnesses={},\n", - " consequents={\"b\": torch.tensor(0.0)},\n", - " consequent_scale=1e-8,\n", - " )(model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "How can I compute the probability that `a=-0.5` is a sufficienct and necessary cause of `b=0` using `SearchForExplanation`?" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "def importance_infer(\n", - " model: Optional[Callable] = None, *, num_samples: int\n", - "):\n", - " \n", - " if model is None:\n", - " return lambda m: importance_infer(m, num_samples=num_samples)\n", - "\n", - " def _wrapped_model(\n", - " *args,\n", - " **kwargs\n", - " ):\n", - "\n", - " guide = pyro.poutine.block(hide_fn=lambda msg: msg[\"is_observed\"])(model)\n", - "\n", - " max_plate_nesting = 9 # TODO guess\n", - "\n", - " with pyro.poutine.block(), MultiWorldCounterfactual() as mwc_imp:\n", - " log_weights, importance_tr, _ = pyro.infer.importance.vectorized_importance_weights(\n", - " model,\n", - " guide,\n", - " *args,\n", - " num_samples=num_samples,\n", - " max_plate_nesting=max_plate_nesting,\n", - " normalized=False,\n", - " **kwargs\n", - " )\n", - "\n", - " return torch.logsumexp(log_weights, dim=0) - math.log(num_samples), importance_tr, mwc_imp, log_weights\n", - "\n", - " return _wrapped_model" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 199dab42d84c2b24b8296cdaa36ea27c129b1712 Mon Sep 17 00:00:00 2001 From: PoorvaGarg Date: Fri, 30 Aug 2024 15:01:41 -0400 Subject: [PATCH 090/111] html corrected --- docs/source/explainable_sir.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 86b2bcec..5fd96f72 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -28,9 +28,9 @@ " - [Bayesian SIR Model](#bayesian-sir-model)\n", " - [Bayesian SIR Model with Policies](#bayesian-sir-model-with-policies)\n", "- [But for Analysis with Bayesian SIR Model with Policies](#but-for-analysis-with-bayesian-sir-model-with-policies)\n", - "- [Causal Explanations using `SearchForExplanation`](#causal-explanations-using-searchforexplanation)\n", - "- [Fine-grained Analysis of `overshoot` using Sample traces](#fine-grained-analysis-of-overshoot-using-sample-traces)\n", - "- [Looking into Different Contexts for Curious Readers](#ooking-into-different-contexts-for-curious-readers)" + "- [Causal Explanations using SearchForExplanation](#causal-explanations-using-searchforexplanation)\n", + "- [Fine-grained Analysis of overshoot using Sample traces](#fine-grained-analysis-of-overshoot-using-sample-traces)\n", + "- [Looking into Different Contexts for Curious Readers](#looking-into-different-contexts-for-curious-readers)" ] }, { From e08ea78a8b3893cb80c7065cbbd13c97ae6a248a Mon Sep 17 00:00:00 2001 From: Sam Witty Date: Wed, 6 Nov 2024 13:11:32 -0500 Subject: [PATCH 091/111] Requested changes to `explainable_sir.ipynb` (#573) * edit intro * progress * remove plate and more edits --- docs/source/explainable_sir.ipynb | 154 ++++++++++++++++-------------- 1 file changed, 80 insertions(+), 74 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 5fd96f72..a74e4f3c 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -11,9 +11,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The **Explainable Reasoning with Chirho** package aims to provide a unified, principled approach to computations of causal explanations. We showed in an earlier [tutorial](https://basisresearch.github.io/chirho/explainable_categorical.html) how Chirho provides a handler `SearchForExplanation` to carry out the program transformations needed to compute causal queries and explanations, focusing on discrete variables (we assume the reader is familiar with it). In this notebook, we illustrate the usage of `SearchForExplanation` for causal models with continuous random variables in the context of a dynamical system.\n", + "The **Explainable Reasoning with ChiRho** module aims to provide a unified, principled approach to computations of causal explanations. We showed in an earlier [tutorial](https://basisresearch.github.io/chirho/explainable_categorical.html) how ChiRho provides `SearchForExplanation`, an effect handler that transforms causal probabilistic programs to compute causal explanations and other related causal queries. In that tutorial we focused on discrete variables. In this notebook, we illustrate the usage of `SearchForExplanation` for causal models with continuous random variables in the context of a dynamical system.\n", "\n", - "We take an epidemiological dynamical system model (described in more detail in this [tutorial](https://basisresearch.github.io/chirho/dynamical_intro.html)) and show how the but-for analysis is not sufficiently fine-grained to allow us to derive the right conclusions about the effects of different policies during a pandemic. Next, we illustrate how various causal explanation queries can be computed using `SearchForExplanation` and inference algorithms. We also demonstrate how more detailed causal queries can be answered by post-processing the samples obtained using the handler. " + "We take an epidemiological dynamical system model (described in more detail in our [dynamical systems tutorial](https://basisresearch.github.io/chirho/dynamical_intro.html)) and show how the but-for analysis is not sufficiently fine-grained to explain the effects of different policies during a pandemic. Next, we illustrate how various causal explanation queries can be computed by combining ChiRho's `SearchForExplanation` and Pyro's probabilistic inference. We also demonstrate how more detailed causal queries can be answered by post-processing the samples obtained using the effect handler. " ] }, { @@ -106,18 +106,18 @@ "metadata": {}, "source": [ "\n", - "We start with building the epidemiological SIR (Susceptible, Infected, Recovered/Removed) model, one step at a time. We first encode the deterministic SIR dynamics. Then we add uncertainty about the parameters that govern these dynamics - $\\beta$ and $\\gamma$. These parameters have been described in detail in the [dynamical systems tutorial](https://basisresearch.github.io/chirho/dynamical_intro.html). We then incorporate the resulting model into a more complex causal model that involves two policy mechanisms: imposing lockdown and masking restrictions.\n", + "We start with building the epidemiological SIR (Susceptible, Infected, Recovered) model, one step at a time. We first encode the deterministic SIR dynamics. Then we add uncertainty about the parameters that govern these dynamics - $\\beta$ and $\\gamma$. These parameters have been described in detail in the [dynamical systems tutorial](https://basisresearch.github.io/chirho/dynamical_intro.html). We then incorporate the resulting model into a more complex causal model that involves two policy mechanisms: imposing lockdown and masking restrictions.\n", "\n", "Our outcome of interest is overshoot, the proportion of the population that remains susceptible after the epidemic peaks but eventually becomes infected as the epidemic continues. One way to compute it is to:\n", "\n", - "1. Find when the number of infected individuals is at its peak, `t_max`.\n", - "2. Determine the proportion of susceptibles at `t_max` in the whole population, `S_peak`.\n", - "3. Find the proportion of susceptibles (those who have never been infected yet) at the end of the logging period, `S_final`.\n", - "4. Calculate the additional ratio of infected or infected and later removed individuals since the peak as `S_peak - S_final`.\n", + "1. Find the time when the number of infected individuals is at its peak, which we denote `t_max`.\n", + "2. Determine the proportion of susceptible individuals at `t_max` in the whole population, which we denote `S_peak`.\n", + "3. Find the proportion of susceptible individuals (and have thus never been infected) at the end of the logging period, `S_final`.\n", + "4. Return the difference between proportions of peak and final susceptible individuals, `S_peak - S_final`.\n", "\n", "This quantity is of interest because epidemic mitigation policies often have multiple goals that must be balanced. One goal is to increase `S_final`, i.e., to limit the total number of infected individuals. Another goal is to limit the number of infected individuals at the peak of the epidemic to avoid overwhelming the healthcare system. A further goal is to minimize the proportion of the population that becomes infected after the peak, that is, the overshoot, to reduce healthcare and economic burdens. Balancing these objectives involves making trade-offs.\n", "\n", - " Suppose we are working under the constraint that the overshoot should be lower than 24% of the population, and we implement two policies, lockdown and masking, which together seem to lead to the overshoot being too high. Only one of them is responsible, and we are interested in being able to identify which one. " + "Suppose we are working under the constraint that the overshoot should be lower than 24% of the population, and we implement two public health policies, lockdown and masking, which together seem to lead to the overshoot being too high. Only one of them is responsible, and we are interested in being able to identify which one. " ] }, { @@ -153,7 +153,7 @@ " return dX\n", "\n", "\n", - "# l is a parameter describing the strenght of the intervening policies\n", + "# l is a parameter describing the strength of the intervening policies\n", "# it is a value between 0 and 1, and (1-l) is the fraction of the original unintervened beta\n", "class SIRDynamicsPolicies(SIRDynamics):\n", " def __init__(self, beta0, gamma):\n", @@ -184,7 +184,10 @@ "# Computing overshoot in a simple SIR model without interventions\n", "# note it's below the desired threshold\n", "\n", + "total_population = 100\n", "init_state = dict(S=torch.tensor(99.0), I=torch.tensor(1.0), R=torch.tensor(0.0))\n", + "assert init_state[\"S\"] + init_state[\"I\"] + init_state[\"R\"] == total_population\n", + "\n", "start_time = torch.tensor(0.0)\n", "end_time = torch.tensor(12.0)\n", "step_size = torch.tensor(0.1)\n", @@ -203,8 +206,8 @@ "\n", "def get_overshoot(trajectory):\n", " t_max = torch.argmax(trajectory[\"I\"].squeeze())\n", - " S_peak = torch.max(trajectory[\"S\"].squeeze()[t_max]) / 100\n", - " S_final = trajectory[\"S\"].squeeze()[-1] / 100\n", + " S_peak = torch.max(trajectory[\"S\"].squeeze()[t_max]) / total_population\n", + " S_final = trajectory[\"S\"].squeeze()[-1] / total_population\n", " return (S_peak - S_final).item()\n", "\n", "\n", @@ -312,7 +315,7 @@ "lockdown_time = torch.tensor(1.0)\n", "mask_time = torch.tensor(1.5)\n", "\n", - "def policy_model():\n", + "def policy_model() -> State[torch.Tensor]:\n", "\n", " lockdown = pyro.sample(\"lockdown\", dist.Bernoulli(torch.tensor(0.5)))\n", " mask = pyro.sample(\"mask\", dist.Bernoulli(torch.tensor(0.5)))\n", @@ -340,7 +343,9 @@ " lockdown_sir, init_state_lockdown, start_time, logging_times[-1]\n", " )\n", "\n", - " trajectory = lt.trajectory\n", + " return lt.trajectory\n", + "\n", + "def overshoot_query(trajectory: State[torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:\n", "\n", " t_max = torch.max(trajectory[\"I\"], dim=-1).indices\n", " S_peaks = pyro.ops.indexing.Vindex(trajectory[\"S\"])[..., t_max]\n", @@ -353,7 +358,11 @@ " event_dim=0,\n", " )\n", "\n", - " return overshoot, os_too_high" + " return overshoot, os_too_high\n", + "\n", + "def overshoot_model():\n", + " trajectory = policy_model()\n", + " return overshoot_query(trajectory)" ] }, { @@ -384,7 +393,7 @@ "3. Only masking was imposed\n", "4. Only lockdown was imposed\n", "\n", - "The hope is that by looking at these we will be able to identify the culprit. We create these four models by conditioning on the policies being imposed as required (in fact, this has the same effect as intervening here, as the sites are upstream from the dynamical system model; we could emulate 1-4 using `do` with the same estimates). For the sake of completeness, we also illustrate the consequences of following a stochastic policy and deciding randomly about the interventions." + "The hope is that by looking at these we will be able to identify the culprit. We create these four models by conditioning on the policies being imposed as required (in fact, this has the same effect as intervening here, as the sites are upstream from the dynamical system model; we could emulate 1-4 using ChiRho's `do` with the same estimates). For the sake of completeness, we also illustrate the consequences of following a stochastic policy and deciding randomly about the interventions." ] }, { @@ -405,38 +414,38 @@ "# propagating the changes, as the decisions are upstream from ds\n", "\n", "# no interventions\n", - "policy_model_none = condition(\n", - " policy_model, {\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(0.0)}\n", + "overshoot_model_none = condition(\n", + " overshoot_model, {\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(0.0)}\n", ")\n", "unintervened_predictive = Predictive(\n", - " policy_model_none, num_samples=num_samples, parallel=True\n", + " overshoot_model_none, num_samples=num_samples, parallel=True\n", ")\n", "unintervened_samples = unintervened_predictive()\n", "\n", "# both interventions\n", - "policy_model_all = condition(\n", - " policy_model, {\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(1.0)}\n", + "overshoot_model_all = condition(\n", + " overshoot_model, {\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(1.0)}\n", ")\n", "intervened_predictive = Predictive(\n", - " policy_model_all, num_samples=num_samples, parallel=True\n", + " overshoot_model_all, num_samples=num_samples, parallel=True\n", ")\n", "intervened_samples = intervened_predictive()\n", "\n", - "policy_model_mask = condition(\n", - " policy_model, {\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(1.0)}\n", + "overshoot_model_mask = condition(\n", + " overshoot_model, {\"lockdown\": torch.tensor(0.0), \"mask\": torch.tensor(1.0)}\n", ")\n", - "mask_predictive = Predictive(policy_model_mask, num_samples=num_samples, parallel=True)\n", + "mask_predictive = Predictive(overshoot_model_mask, num_samples=num_samples, parallel=True)\n", "mask_samples = mask_predictive()\n", "\n", - "policy_model_lockdown = condition(\n", - " policy_model, {\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(0.0)}\n", + "overshoot_model_lockdown = condition(\n", + " overshoot_model, {\"lockdown\": torch.tensor(1.0), \"mask\": torch.tensor(0.0)}\n", ")\n", "lockdown_predictive = Predictive(\n", - " policy_model_lockdown, num_samples=num_samples, parallel=True\n", + " overshoot_model_lockdown, num_samples=num_samples, parallel=True\n", ")\n", "lockdown_samples = lockdown_predictive()\n", "\n", - "predictive = Predictive(policy_model, num_samples=num_samples, parallel=True)\n", + "predictive = Predictive(overshoot_model, num_samples=num_samples, parallel=True)\n", "samples = predictive()\n", "\n", "print(\"Variables in the model:\", samples.keys())" @@ -456,7 +465,7 @@ "outputs": [ { "data": { - "image/png": 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", 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Zo9jtdkVR1Of4nXfeybH9rvPz3//+1+0986+//lIaNGighIeHK0ePHr1hm1zPbWhoqDJx4kTtOrXZbMpbb72lhIaGKn379tWWz+n9PqucrqOCvvZutN4rr7yihIaGKgMGDHD7/D116pT2eTF//nxtetbzm/XzyPUZlpvOnTtnW0dR1OvZdZw//vhjjvt5//333Z6XCRMmKKGhoUqPHj20aZcvX1bCw8OVhg0bKsePH3fbx44dO5SaNWsqoaGhbu8/ruc963v3I488ooSGhiqbN29228bly5eVGjVquH0Xadu2rVKzZk23a8PpdCrjxo1TQkNDlWHDhmU7nqzXa2G8zoQQoqhIppQQ4r7RpUsXAgICANDr1bc3m81GmzZteOONN/D29nZb3tWt4dy5c3lu9/LlyyxfvpyQkBDee+89txH/KlasqHVV+PLLL7Xp33zzDaB258la+yQoKIjXX3+dypUr59itJatFixaRmppK9+7d6d69u9u8xx57jO7du5OSksLChQvzdS5ysnjxYuLj43n66ad56KGH3OY9+eSTtG7dmrNnz2rDe1+5cgWAkJAQtyLDQUFBvPfee0yYMEHLIslLqVKlaNeuHc8//7zbdF9fXzp37gzk/LwYjUZGjx7t9hw8+eSTmM1mYmJiiImJAWD37t0cOnSImjVr0r9/f21Zk8nEu+++S3Bw8A3b6DJv3jwA3nzzTbeMM7PZzLvvvkvFihU5ePAgW7duBaBly5YEBgayadMmt659oHbRMBgM2jHezLXlUqdOHWrXrq091uv1REdHA+r51el02rzy5cvz/vvvM2nSpGyvA4D+/ftTvXp17XGFChW0bl9Zuxm6zsW4cePcnmcfHx8+/PBDfH19WbNmjdYl6Fau4fxyrTt8+HBKliypTa9VqxYDBw7M1zZu9rzlpnXr1tr50el0btvMzTvvvEOxYsW0x82bN6dnz55YrdZcu6cWtlt5vp544gkaNWqkPQ4ICKBr164AblkdrnN9/UASTz/9NCNGjOD//u//bvk4Ro4cSVBQkPa4TZs2lC1bFrvdzsmTJ295+0FBQYwYMQKDwQCoz3Hfvn0B92Pdu3cv27dvp3r16gwdOtTtPbNOnToMHDgQm82Wr2xdlwoVKjBkyBDtmjIajYwaNYrg4GA2btzImTNnbvn4CsP58+dZs2YNvr6+fPDBB26vnwoVKmhZa1988UW2dUNCQtw+j/L6DEtJSaFWrVo8+uij2T7DQkJCtMzAnD5PgoODsz0vvXv3BuDo0aPatKtXr9K+fXsGDhyYbVTBBg0aUK1atVz3kZUrW+rHH390m758+XIcDofbay46Ohqj0Ujx4sW1aTqdjgEDBvDOO+9oGVW5KYrXmRBCFBYJSgkh7htZf1S7dOrUiVmzZrn9WEpPT+fvv//Walo4HI48a43s3LkTh8NBRESEW9DApVmzZuj1enbt2oXD4UBRFHbs2IFer6d169bZlm/Xrh0rV66kT58+eR7Pjh07AOjYsWOO8x9++GEAtm/fnm1eTuciJ9u2bQNwOz9ZtWjRwm25yMhITCYTK1asoH///ixevJhLly4B0KRJE3r06JGvUQtHjx7NjBkztB91ADExMWzevJldu3YB5Fijo0KFClqwzcVsNhMYGAig1TRynbuc6uVYLBaaN29+wzaCWo9s9+7d6PV62rdvn22+0WjURox0nSOTyUSnTp2w2WysWbNGW3bv3r2cPXuWJk2aaD80CnptZZVT15qGDRsC6g+9//znP/z000/ExsYC6nXXrVs3tx/rLvXq1cs2rVSpUoBaDwfg4sWLnDt3jqCgIKKiorIt7+vrq51v1zV5K9dwfjidTnbu3InBYKBZs2bZ5t+oq5rLzZ633OT39edSvHjxHF+DrvpPN3t+CupWnq86depkm+YqfJ211liDBg0AtXbThx9+yPbt27HZbJjNZvr06UOrVq1u6Rg8PT1zfG24XnOpqam3tH1QA57Xj/x3/esFrr0nNGzYMMfAiuv9tSDP70MPPZRt1EiLxULTpk0LvK3byXUtNW3aFB8fn2zzo6KiCAkJ4dKlS9kCafnpsuvi7e3NhAkTsnXNvHz5Mr///juHDh0Ccv48CQ8Pz3YuXddJRkaGNgpjzZo1mTJlittntsPh4NSpU/z0008kJCQAaLUic9O5c2csFgurV692e00sW7YMvV7PI488ok1r0KAB6enp9OjRg+nTp7Nv3z6cTifBwcE888wz2ntWbm7360wIIQqTjL4nhLhv+Pv75zg9OTmZRYsWsWHDBo4fP050dDSKorhlLyh51EFyZTStW7cuzy/LaWlpbl9Og4KC8PT0vJlDAa5lJZUpUybH+WXLlgWu/UU0q9zOxfVcdZteeeWVPJdzBZ5KlSrFpEmTeOedd/jjjz+0wr7VqlWjffv29OzZM98jMB0+fJiFCxeyb98+Tp06RUpKCoD2vOT0nPj5+eW4LdcPC9ePCNe5y/pX5qxc5+5G4uPjsdlsBAYG5vjDKuu2sj4P3bp14+uvv+bnn3/miSeeANRaIoCWPQIFv7ayBkauD86BWuNoxIgRTJ48mZUrV7Jy5Up0Oh3h4eF07NiRJ598MsdrI6eRm1wBw+vPaV5Bx+vPxa1cw/nhen6CgoLw8PDINj+3/V7vZs9bbgqybF7tdGV+XV+o+XYp7Pec668hgKFDh3Lu3Dk2bdrE7NmzmT17Nl5eXjRv3pxu3brlO5CYG19f3xwz01zvEXm91+dXTu9DOR2r6/X91Vdf8dVXX+W6Pdf7a37k9ty4gmKFfa0cP36cWbNmZZtepUoVXnrppVzXu9G1BOr1FB0dzZUrV9zqsuX03nYju3fv5rvvvuPgwYOcOXNGG+SgoJ8nWYNUTqdTCyY6HA5WrVrFihUrOHr0KBcuXNAGQMhrH1n5+/vTvn17fv75Z9asWUO3bt04cOAAR48epXnz5tpzCDB+/HgGDhzIwYMHmTZtGtOmTSMgIICWLVvy6KOP0rhx4zz3dbtfZ0IIUZgkKCWEuG/k9Jfoo0eP8txzzxETE0NgYCC1a9emc+fOhIWF0ahRIx544IEbbtf1I6Nq1arUqFHjhsvnZ4Sn/LjRF1xXu8xmc7Z5eXV3yMrV1tatW+cadAH12F0efvhhWrRowdq1a/nzzz/Ztm0bR48e5ejRo8ybN4+5c+dSt27dPPf7xRdf8MEHHwDqj5vWrVtTpUoVIiIiOHPmDGPHjs1xvfx0g8rPclkztPKSnx+wrnOY9XmoXbs2lStXZseOHVy5coVixYqxatUqvLy83DKuCnptZZXbMfbp04cuXbrw66+/8ueff7Jjxw4OHDjAgQMH+N///sc333xDhQoV3NbJz/VyM+fiVq7hwqDX6/P9XN/MectrvwVhsVjynH99NkdubvW951aer/y+Nn19fZkzZw779+/nt99+Y8uWLRw4cIA1a9awZs0aOnbsyNSpUwve+AK241bkdx+u8xUREZFnse6CtPlGr5XrM7hyk99r5erVq1pAPauoqKg8g1IFacP1x1TQ53DMmDF888036PV6qlevzkMPPUSVKlWoU6cOmzZt4tNPP81xvfzuJzU1leeee459+/bh4eFBrVq1aNasGdWqVaN+/fqMGzdOywy7kccee4yff/6Z5cuX061bN60rX48ePdyWK1myJEuWLGHHjh2sW7eOzZs3c+TIEZYvX87y5cvp27cvw4YNy3U/t/t1JoQQhUmCUkKI+9rYsWOJiYnh//7v/xgyZIjbD1RXVtONhISEAFCjRg0+/PDDGy5vs9kwmUwkJCSQnp6eLYMjIyOD77//nsqVK9OkSZNct1O8eHFOnjzJ+fPntZoVWblGWipIfaSc9nHq1Cl69+6tdf/ID19fXx555BGtu8HBgwf56KOP2LhxIx9//LFWeygnZ8+eZfLkyfj6+vLpp59q3Qxc8lo3v1zZWrnV7XL9Ff9GAgICtOcyOTk5x8Bdbs9Dt27dmDJlCmvWrKFKlSpER0fTrVs3t256Bb228is4OJiePXvSs2dPnE4nu3fvZsKECRw4cIDPP/+c8ePHF3ibrqyz8+fP57qM61y4aiPd7ms4MDAQi8VCfHw8KSkp2eo+Xb16tUCBmttx3vIjt+vRda6zZqe5fkjndFxZR7m7GUXxnuMSERFBREQEr732GklJSaxcuZLx48ezevVqdu7cme194V7ken03a9aM1157rVC2eaNrxZVt4wqMZs3cyiprN8O8NGrUyK2uXH7l5/3CVYMpay21gtq+fTvffPMNpUqV4osvvnD7Awrg1oX6Zs2ZM4d9+/bRpEkTpk6dmi3DKr/nEqBx48aUKVOGbdu2kZCQwJo1a/Dz88sxc0mn0xEVFaV1l46JiWHJkiVMmTKFuXPn8uyzz96wu/y/4XUmhLj3SU0pIcR97a+//gJgwIAB2TImsg6JnNsXd7hWb2bHjh1udSBc9u/fT4cOHXj11VdRFAWTyURERAQOh4ONGzdmW37btm2MHTtW686R219rXfu9fmhql5UrVwLkWN8nv1z7cHXDu96kSZN45JFH+O677wC14Hbr1q1ZtmyZ23Lh4eEMHToUuNYlMDeu2hiNGjXK8Qux65zdSjcbV7Bv7dq12X685/a85MRkMhEZGYnT6dSKvWdlt9v57bffgOx1ubp27YpOp2P9+vXac9WtWze3ZQp6bd3IhAkTaN68udtf7fV6PQ0aNNCyGgrSVSir0qVLU6ZMGeLi4nKsW5OUlKS9plzHdTPXcEGyJHQ6HY0bN8bpdLJ27dps83///fd8baeg562ws3FOnjyZY5Fk1w/qrOfHFdR01bzKyvV+l1VB2nq733Pi4uLo0aMHXbp0cZvu6+vLE088odV6u9lrtLAU1vPrOp8bNmzI8TPm119/5aGHHuLdd9/N9zZzeu9KSUlh48aN2jUL164T1+APWR09ejTH2lqFeV3Xr18fnU7Hpk2bSE5OzjZ/69atxMbGUrZs2XzVIcyN65rv0KFDtoCUw+HQBqC4lc+TPXv2APDMM89kC0hdvnyZ48ePA3l/j3DR6XT06NEDm83GtGnTuHTpEp06dXLLljx27BhdunTJVow8ODiY/v37ExYWhqIouXbVvFdeZ0II4SJBKSHEfc1Vg+f6H6w7duzgvffe0x7nVATVpVy5crRt25ZLly4xcuRIty/YMTExjBw5ktOnT7uN3PXMM88A6o/drD82Y2NjmTRpEnCttpDry2hqaqrbl9onnngCLy8vfvjhB3744Qe3Ni1ZsoQff/wRLy+vbKNkFcSTTz6Jl5cXX3/9NStWrHCbt27dOubPn8+hQ4eIiIgA1ELjFy5cYNasWW51ZRRF0QrHZx0RLieu52Tv3r1uP5hsNhsff/wxGzZsANSMsptVu3Zt6tevz8mTJ5k0aZJ2Xp1OJxMnTrzhKElZPffcc4AaoDt48KBbe8eMGcOZM2eoXr069evXd1uvdOnSREVFsW3bNn777TeKFy+eLTPuZq6tvJQqVYro6Gg++ugjt23Z7XYtoOB6Lm+G61y8/fbbWtYMqD+Khw4dSnJyMq1bt9bqyNzMNezqypPTD9m82jRp0iTtxyGotXCmTJmSr20U9Ly52nirmUkuiqIwfPhwt32vXr2aJUuW4Ovry+OPP65NdxVRnz9/vtsP7Xnz5nHgwIFs287t/SUnt/s9JzAwEIfDwZEjR7JlRJ47d04bVCDrKJd3guuc5fcazE2jRo2oUaMGBw8eZNKkSW6fM6dPn2bcuHGcOHGCSpUq5Xub27Ztcxutz2q18vbbb5OQkMBDDz2kZUpVqlQJs9nM2bNnWbdunbZ8YmIiY8aMyXHbBX3t5cX13pacnMzQoUO1moGgZty9/fbbwLXPypvlGuRiy5YtboH9tLQ03nnnHW0UvVv5PHF9Zq1fv97tNXfhwgVeeeUVrbZUfvfRo0cP9Ho9CxYs0B5nVbFiRa5cucLGjRtZtWqV27wDBw5w/PhxvLy8sgXhXO6V15kQQrhI9z0hxH3t+eefZ8KECQwbNoxFixYREhLCmTNn+OeffwgICCAkJITo6Giio6PzrKn03nvvcfr0aVasWMGmTZuIiIhAp9Oxc+dOUlNTqVevnlv3jE6dOrFlyxYWL17Mww8/TFRUFAaDgV27dpGUlMSjjz7Kgw8+CKhfeP38/EhMTKRnz56UL1+eDz/8kBIlSjBx4kSGDBnC8OHDmTdvHpUqVeLkyZMcOnQIT09PJk2alO9izjnJuo8hQ4YwY8YMKleuzMWLF7UfuG+99ZZW76ht27a0b9+eX3/9lfbt21OvXj28vb05cuQIp06dolixYgwePDjPfUZFRVGzZk3+/vtvOnbsqP1lf9++fcTExFCtWjWOHj3K1atXb/q4QA0I9u7dm3nz5vH7779TvXp1Dh06xOnTp6lTpw579+7N13batWtH3759mTNnDo8//jj169cnMDCQvXv3cunSJcqUKcOUKVNyrCPUrVs3tm3bRkxMDH379s1xmYJeW3l56qmn+OWXX9i9ezdt2rShTp06mM1m/v77by5cuEDlypV5/vnn87WtnDz77LPs2bOHlStXate1p6cnO3fuJC4ujrCwMLcubjdzDbtq77hGdnzggQfcgjLXa9asGf379+fzzz/nkUce0QoAb926lfDw8HxdRwU9b642zpw5kz179txy4eBKlSpx9OhR2rdvT4MGDYiOjmbPnj2YTCYmTZrk1l2ud+/erFq1itWrV/Pggw8SFhbG0aNHOXnypFuNGpfc3l9yUhTvOWPGjOHZZ59lwoQJfPfdd1SpUoXk5GR27dpFRkYGL7zwQp71l4qCq3bY77//zosvvkhkZCQDBgwo8HZ0Oh1TpkzhueeeY+7cuaxYsYLw8HDS09PZuXMnNpuNjh07FigwExkZyfjx4/nhhx8oV66c9j4UGhrKO++8oy3n5eVFr169mDt3Li+//LL2Wt2xYwf+/v5ERUVly3gs6GvvRsaOHcupU6dYt24dbdu2pUGDBqSlpbF9+3asViudO3fWgso366GHHmL69OkcOXKEdu3aUbduXaxWK3v27CEpKalQPk+eeeYZVq5cyffff8/u3bupVq0asbGx7NmzB0VRtNdIfvdRunRpmjZtysaNG6lWrVq2P+QYjUbee+89Bg0axODBgwkPD6ds2bLExcVpI7G+9dZbOQ5Q4XIvvM6EEMJFMqWEEPe1Pn36MHnyZCIiIjhy5Ajr168nLS2NZ599luXLl2uBofXr1+e5neDgYL777jsGDx5M8eLF2bFjB3/99ReVKlXirbfeYu7cudlG2hs3bhwffPAB4eHh7Nq1i82bN1OmTBlGjRrFuHHjtOX0ej0ffvghVapU4e+//2bTpk1avasOHTrw/fff07lzZ2JiYvjtt99ITEzkscceY8mSJW5Fs29Whw4dWLJkCV27diUpKYnff/+dq1ev0rp1a+bPn+/2o0Gn0/HRRx/x+uuvU7FiRXbv3s3vv/+O0+nk2WefZdmyZTcc2c5gMDBv3jz69OlDUFAQGzduZOfOnZQrV44xY8bwww8/4Ofnx759+27ph0SFChVYvHgxTz31FOnp6axfvx5vb29mzpypDcWeX8OGDWPmzJk0atSIQ4cO8fvvv+Pt7c1LL73EDz/8QOXKlXNcr2PHjtp1cX3XPZebubZyY7FY+PLLL+nfvz/BwcFs27aNjRs34uXlxYABA1i8eHGBR4bLSq/XM2XKFCZMmECtWrXYvXs3mzZtomTJkgwdOpTFixdnqzdU0Gu4Xbt29OnTBy8vL/7880927dp1w3a9/vrrfPzxx4SHh7Nz504OHDhA9+7dmT17dr6Oq6Dn7amnntLqqf355585ZigVRMmSJfnmm2+oVasWGzdu5OjRo7Ru3ZpFixbRpk0bt2UjIiL4+uuvadGiBVevXmXDhg0UK1aMuXPn0rlz52zbzuv9JSe3+z2nbt26fPPNN3Ts2JHExETWrVvHwYMHqVevHp988glvvPHGLW2/MISHh/P6668TEhLCpk2b2Lx5801vq1KlSixbtox+/frh5eXFpk2bOHToELVq1WLChAl89NFH+S7GD+q1N378eDIyMli3bh16vZ5+/fqxcOFCLWvI5c033+Stt96iSpUq7N69m/379/PQQw+xePHiHOs43cxrLy/BwcEsWrSIV199leDgYO21EhkZyZQpU5g8eXKBBwW4no+PD9999x2PPvooFouFP/74g3379lGzZk2mTJnC/Pnz0el0bNy4EZvNdlP7qFOnDt988w0tWrTQrtnTp0/Trl07vv32W4YMGQLc+HtEVvXq1QOyZ0m5dOjQgS+//JKWLVty4cIF1q5dy7Fjx2jZsiXz5s3jqaeeynP798LrTAghXHRKYYyNK4QQ95EWLVpw5coV1q1bd0sZAUIIIYQQ1+vatSsnT57kjz/+0LoHCiHEv5VkSgkhRBYJCQnExsai0+nki6IQQgghCkV6ejqKojB37lwOHz5Mp06d5HuGEEIgNaWEEAJQC5D37duX2NhY7HY79erVy3eXKSGEEEKIvHTo0IG4uDisViteXl688sord7pJQghxV5BMKSGEQB0J6erVq8TFxREVFcXEiRPvdJOEEEIIcZ+oW7cuiqIQFhbGZ599dsP6i0II8W8hNaWEEEIIIYQQQgghRJGTTCkhhBBCCCGEEEIIUeQkKCWEEEIIIYQQQgghipwEpYQQQgghhBBCCCFEkZOglBBCCCGEEEIIIYQochKUEkIIIYQQQgghhBBFToJSQgghhBBCCCGEEKLISVBKCCGEEEIIIYQQQhQ5CUoJIYQQQgghhBBCiCInQSkhhBBCCCGEEEIIUeQkKCWEEEIIIYQQQgghipwEpYQQQgghhBBCCCFEkZOglBBCCCGEEEIIIYQochKUEkIIIYQQQgghhBBFToJSQgghhBBCCCGEEKLISVBKCCGEEEIIIYQQQhQ5CUoJIYQQQgghhBBCiCInQSkhhBBCCCGEEEIIUeQkKCWEEEIIIYQQQgghipwEpYQQQgghhBBCCCFEkZOglBBCCCGEEEIIIYQochKUEkIIIYQQQgghhBBFToJSQgghhBBCCCGEEKLISVBKCCGEEEIIIYQQQhQ5CUoJIYQQQgghhBBCiCInQSkhhBBCCCGEEEIIUeQkKCWEEEIIIYQQQgghipwEpYQQQgghhBBCCCFEkZOglBBCCCGEEEIIIYQochKUEkIIIYQQQgghhBBFToJSQgghhBBCCCGEEKLISVBKCCGEEEIIIYQQQhQ5CUoJIe46iqLc6SbclHu13UIIIcT9Rj6TRU7kuhDi7iNBKSH+hZ599llq1qzJ/v37c5zfpk0bhg8ffsv7CQsLY9q0aQVaZ/HixUycOPGW913Ujh49ylNPPeU27WaOXwghhMivXbt28eqrr9KsWTMiIiJo27Ytb7/9NsePH7/TTXMzbdo0wsLCimx/u3bton///kW2v7vNoEGDsn2PGz58OGFhYbnezp8/n69tHzhwgPDwcJYuXZrrMsnJyTf9XdJ1rWS91axZk0aNGvHyyy9z9OjRfG9rzpw5vPHGGwAkJiby5ptvsnPnzgK36WYMHz6cNm3a5LnM0qVLCQsL49y5c/nebn7WiYuL44EHHuDs2bP53m5WKSkpjBkzhmbNmhEZGckLL7zAiRMnbrje1atXef3112nUqBH169dnyJAhXLlyxW2ZxYsX53j9jR079qbaKu4PxjvdACHEneFwOBgxYgRLly7FbDbfln0sWrSIkiVLFmidWbNmERUVdVvaczutWrWKPXv2uE27meMXQggh8uPzzz/no48+onnz5rz11luEhIRw+vRpFi5cSPfu3ZkwYQKdOnW60828IxYvXnzXBeaKgtPpZMKECaxevZru3bu7zRs4cCA9e/Z0m5aQkMDgwYOJioqidOnSN9y+1Wpl+PDh2O32PJebMGFCvoNcuVm0aJH2b4fDwYULF5gyZQq9evVixYoVhISE5Ln+8ePH+eyzz1i+fDkA//zzDz/++COPPvroLbWrMD3wwAMsWrSI4sWLF+p2AwMD6dOnD2+99Rbz589Hp9MVaP3XX3+dvXv3MnToUHx8fJg+fTq9e/dmxYoV+Pv757iO3W7nhRdeIDk5mXfffRe73c7kyZPp168fS5cuxWQyAerzUKlSJf773/+6rV+sWLGbO1hxX5CglBD/Ur6+vhw9epQZM2bw2muv3ZZ91K1b97Zs917xbz9+IYQQt8f69euZPHkyr776Kq+88oo2PSoqikceeYTXX3+d4cOHExoaSrVq1e5gS0VROXToEOPGjWP//v14eHhkm1++fHnKly/vNu3VV1/F39+fDz/8MF+Bi48//pikpKQ8l/njjz9YuXIlvr6+BTuA61z/Hap+/fqUKlWKXr168cMPP9wwE+6DDz6gc+fOlChR4pbacTsFBQURFBR0W7b99NNPM2vWLH799Vc6dOiQ7/X27NnD+vXr+fzzz2nVqhUADRo0oG3btnzzzTe89NJLOa63atUq/v77b1asWEHVqlUBqFGjBp07d2blypV07doVUINSERER8h1ZuJHue0L8S9WoUYNHHnmEL774ggMHDuS5rMPhYMGCBXTp0oXatWvzwAMP8OGHH5KRkZHnelm7r23bto2wsDC2bNlC3759qVOnDs2aNeODDz7A4XAAarfB8+fP88MPP7ilJl+4cIEhQ4YQFRVFnTp1eO655/j777+1/Zw7d46wsDDmzp3Lgw8+SJ06dZg1axZhYWGsX7/erU3//PMPYWFh/PrrrwBkZGQwadIkWrVqRa1atejSpQu//PKL2zpt2rRh6tSpTJw4kaZNm1K7dm369evHqVOnADXVfPr06dmO+frue1euXGHEiBG0atWK2rVr89hjj7F27dps52zBggWMHDmSqKgoIiMjGTx4MFevXtWWOXPmDAMGDKBRo0bUqVOHJ598kj/++CPP50IIIcT9Y/r06VSuXJmXX3452zyTycTYsWMxGAzMnj0bgL59+9KjR49syw4cOFD7sQiwc+dOnnnmGerUqUNUVBTDhg0jNjZWm7906VJq1qzJ4sWLadasGVFRURw7dizfn0u///47Xbt2JSIigo4dO7Js2TK3+fn5nMzIyGDGjBk8+OCDRERE0KFDBz7//HOcTiegdpv64YcfOH/+PGFhYbl2M5s2bRoPPvggv/76K507dyYiIoJu3bqxZ88e/vrrLx5//HFq165N586d2bJli9u6R44c4cUXX6RevXrUq1ePl19+OVtXqUOHDvHKK6/QuHFjwsPDadGiBePGjSM9PV1bJj+f+a7uWtu2bcvxOFyGDRuGw+Fg0aJFBAcH57ksqMGjNWvWMGLECPz8/G64/O7du/n6668ZNWpUrsskJCTw9ttvM3To0Hxts6Bq1aoFoGVhTZs2jfbt2zN9+nSioqJo3rw5CQkJHDlyhN9//53OnTsD6nfQ3r17A9C7d2+effZZbZu//PILPXr0IDIykmbNmjFq1CgSEhLc9rt//3769etHo0aNqFevHgMGDMh3N8KlS5fSsWNHIiIi6Nq1q9vrIqeueD/88AMPP/ywtvyWLVuoWbNmtut479699OzZk4iICB544AG++OILt/lms5mOHTvy2WefadNc38Xz6nq5ceNGvLy8aN68uTYtKCiIhg0b5vldc+PGjVSqVEkLSAFUrVqVKlWqaOspisLhw4epUaNGrtsR/04SlBLiX+ytt94iMDCQESNGYLVac11u1KhRTJgwgXbt2jFr1ix69erF119/zcCBAwtcMPKNN96gfv36fPrpp3Tu3JkvvviCxYsXA+qX7JCQEFq1aqWlM8fGxtKzZ08OHjzIO++8w+TJk3E6nfTq1Stbav60adN44YUXmDRpEt27d6d8+fKsWLHCbZmff/6ZgIAAWrVqhaIovPzyy3z77bc8//zzzJo1i8jISF577bVsX5Tnz5/PiRMnmDBhAuPGjePAgQMMGzYMgMcff5zHHnsMUNPNH3/88WzHffXqVR577DF27tzJa6+9xrRp0yhTpgwvv/yyllruMmXKFJxOJx999BFvvvkm69ev5/333wfU1PwXX3yRtLQ0Jk2axMyZMwkICOCll17i9OnTBXouhBBC3HtiY2M5cOAArVu3zjW7JSAggKZNm2oBna5du3Lw4EG3z4nExET+/PNPunXrBsCOHTvo06cPHh4efPzxx7z11lts376d3r17uwVSHA4Hc+bMYfz48YwYMYJKlSrl+3Np1KhR9OnTh1mzZlGyZEmGDx/OoUOHgPx9TiqKwoABA/jiiy94/PHH+fTTT3nwwQf5+OOPGT16NKAG2lq1akVISAiLFi3igQceyPVcXrp0if/+978MGDCATz75hMTERAYNGsSQIUN4/PHHmTFjBoqi8Nprr2nn4OTJk/Ts2ZOYmBgmTpzI+PHjOXv2LE899RQxMTGAGlzr1asXaWlp/Pe//2X27Nl06tSJr776ivnz57u1Ia/PfLjWxSs8PDzX4wCYNGkSCxcupHr16nku5zqPEydOJCoqigcffPCGy6elpTFixAhefPHFPGuDvffee1SpUiVbN8HCcvLkSQC3jK8LFy7wxx9/MGXKFEaMGIG/vz8//fQTISEhWjZOeHi4FkwbNWqUdq3MnDmTIUOGULduXaZOncrLL7/M6tWrefbZZ7Xne+vWrVrN0Pfff59x48Zx8eJFevbsecMuohcvXuTzzz9n8ODBTJs2DZ1Ox6BBg7Tr5HrLli1j+PDh1KtXj5kzZ9KxY0cGDhyo/fE2q3fffZdOnTrx+eefExkZyQcffJDtD7EPPvggBw4c0M5beHj4DV8Tx48fp2zZshgMBrfp5cuX17aT23oVK1bMNj3remfOnCElJYX9+/fTsWNHwsPDcwxOi38f6b4nxL+Yv78/Y8eO5aWXXsq1G9+xY8f4/vvvef3117VU6WbNmlG8eHHefPNN/vzzTy29Nz8ef/xx7S+7TZo04bfffuP333+nZ8+e1KxZE7PZTFBQkPZF4n//+x/x8fEsXLiQMmXKANCyZUsefvhhPvnkE6ZOnapt+6GHHnKrFdC1a1fmzJlDeno6Hh4eKIrCL7/8woMPPojZbGbTpk1s2LCBKVOm8PDDDwPQokUL0tLS+PDDD+ncuTNGo/o26efnx8yZM7UP6TNnzjBt2jTi4uIoWbKkVjsqt3TkuXPnEhsby+rVq7XjaNWqFX369GHSpEl07twZvV79O0FoaCgTJkzQ1t23bx+rVq0CICYmhhMnTmhfugFq167N9OnT8wwsCiGEuD+4skRcnyW5qVChAmvXriUhIYEOHTowZswYfv75Z+0zeM2aNTgcDi2bZPLkyVSqVInPPvtM+6yrU6cOnTp1YsmSJfTq1Uvb9oABA7QfttHR0fn+XBo3bhwtW7YE1B+r7du3Z/v27VSvXj1fn5MbNmxg8+bNfPTRR1q9rGbNmuHh4cEnn3xC7969qVatGkFBQZjN5ht2EUpLS2P06NFam44dO8bkyZMZP3689sem1NRUBg0axMmTJ6lRowbTp0/H09OTefPm4ePjA6jfZ9q1a8cXX3zBsGHDOHLkCDVq1OCTTz7RlmnatCmbNm1i27Ztbl3P8vrMh/x38SpIIfl169Zx/Phx3n777XwtP3nyZLy8vHjxxRe5dOlSjsv8+uuvrF27lp9//rnANYxykrVuVXp6OocOHeL999/H19fXLbvPbrczbNgwGjRooE3bunUrERERWjt8fHy0DJ6qVatStWpVEhISmDVrFk888YRb9ldoaCi9evXSrvnJkydToUIFPv/8c+110bx5c9q3b8/UqVP55JNPcj0Gp9PJjBkzqFKlCgAWi4U+ffrw119/0bZt22zLf/LJJ7Ru3Zpx48YB6ndSk8nE5MmTsy07ZMgQLVhWt25dfv31V7Zu3Urr1q21ZSIiIgDYsmULlSpVwsfH54aviaSkJO2azcrb25uUlJQ816tQoUKe6/3zzz+A2sNh+PDhGI1Gli1bxrBhw7BarTzxxBN5tk3cvyRTSoh/uTZt2tC1a1e++OILDh48mG3+9u3bAbIVS+3UqRMGg+GG6eTXi4yMdHtcsmRJUlNTc11+y5Yt1KhRgxIlSmC327Hb7ej1elq2bMnmzZvdlr0+Hbhr166kpqZqfznavXs3Fy5c0P4qvGXLFnQ6Ha1atdK2bbfbadOmDdHR0W6p2REREW5/NXIFodLS0vJ13Nu3bycyMjLbj4iuXbtqX+hdrv/CULJkSW0/xYoVo2rVqrzzzjsMGzaMn376CafTyYgRI6RuiBBC/Au4MpRdhYNz4/rMUhQFLy8v2rVr59Y9fcWKFTRp0oQSJUqQlpbG3r17tSxi1+dhuXLlqFKlCps2bXLbdtbP24J8LmUNHJQtWxZQM7Ygf5+T27dvx2g0ZsvucQUpXN9ZCqJevXpuxwJqMM4lICDArZ1bt24lKioKDw8P7Tz5+PjQoEED7XtJ8+bN+frrr7FYLBw7doy1a9cya9YsYmNjswXq8vrMv10WLFhAjRo1aNq06Q2X3bZtG4sWLWLChAnaH+quFxsby6hRo3jzzTdvGCzNr/DwcO1Wv359evXqhdVq1bLqs7r++9/Zs2e16ys3f/31F1arVQvKujRo0IAyZcqwfft2UlNT2b9/Pw899JDbd0A/Pz9at259w+stMDBQC0jBtWs+p7pcp0+f5sKFC9mu7dwGK8j6WvL09KRYsWLaNeri6+uLn59fgUb3y6sHRF7Bxvys17BhQz799FP+97//0bp1a1q0aMHkyZNp2rQpU6dOLXDvC3H/kEwpIQRvv/02W7ZsYcSIESxZssRtnqtf/fVfAIxGI4GBgTcseHm964tv6vX6PD+E4uPjOX36dK5p61m/uHl5ebnNq1ChApGRkaxYsYKHHnqIFStWUL58ee0LaHx8PIqiuH0hzerKlSvaFx1PT89s7Qa0GhY3kpCQQLly5bJNd30BzvpFIqd9uc6RTqdjzpw5WvHKZcuWYTKZaNeuHWPGjMl1VBQhhBD3B9eP/huNbnb27Fm8vb21oEq3bt1Yvnw5hw4dolixYmzbtk3rJpaYmIjT6WT27NlaHaqsLBaL2+Osn7cF+VzKup7rc9T1+Zafz8mEhAQCAwOzdS1yfUcp6HcSIMeskOs/h7OKj4/nl19+yVZ/EtAymlzd8RYsWEBqaiqlSpWidu3a2c5jTvu60feiWxUfH8+2bdsYMmTIDZdNSUlhxIgRvPDCC1StWhW73a5973E6ndjtdoxGI++++y5Vq1blsccec8twcgU4DQZDgbOnvv/+e+3fJpOJkJCQXGtleXt7uz1OTk7O8zmEa99vcxr1rVixYiQlJZGUlISiKHkuk5frv5e6zkFO3x1dtduuP8bcRqXL73Xj6elJcnJynu3MysfHx62mmUtKSkqexet9fHxyzKRKTk7W1gsODnbL5HJp1aoVmzdv5urVqzccVVHcnyQoJYTA39+fd999l5dffpmZM2dmmwdqen7Wv37ZbDbi4uIIDAy8rW3z9fUlKiqKN998M8f5ZrM5z/W7du3KhAkTSEpKYtWqVVqqs2vbXl5e2eo7uOSUhnyz/P39iY6OzjbdNa0g57FEiRK8++67jB49mkOHDrFq1Spmz55NYGCgVidBCCHE/Sk4OJi6deuyevVqBg8erAV3skpOTmbTpk20adNGm9akSRNCQkJYuXIlISEhWCwWbVQub29vdDodffr0yTEz40Y/8Avjcyk/n5P+/v7ExcXhcDjcAlNXrlzRlrndfH19adq0Kc8//3y2ea5Mos8//5x58+YxZswYOnTooP0od3UJvJM2bNiA3W7PVy2pAwcOcP78eWbMmMGMGTPc5o0cOZKRI0dy+PBhVq9eDVwrRO5y/vx5li1bxvz582nUqFGB2unqenYzAgICbhgwcn2/vXr1KpUrV3abFx0dTbly5fD19UWn0+UYpImOjtYCvoXBlYF/fb2p3OpP5VdiYmKBXheVKlVi48aNOJ1Ot/eW06dPu2V95bSeq3teVmfOnKF27dqAOpDC2bNn6d69u9syGRkZGAwG+cPqv5h03xNCANCuXTs6d+7M559/7jbSTlRUFEC2guErVqzA4XBQv379Qm3H9V+uo6KiOHnyJJUqVSIiIkK7/fjjj3z//ffZ/lp6vYcffhhFUfjkk0+IiYlxq0MQFRVFamoqiqK4bfvIkSPMmDHD7a99BW339Ro2bMiePXuy/WV7+fLlhISE5DsAtmfPHpo2bcq+ffvQ6XTUqFGD1157jdDQUC5cuJDv9gohhLh3vfLKK5w8eZKPPvoo2zyHw8Ho0aNJT0/n//7v/7TpBoOBLl26sH79elatWkW7du20TA4fHx9q1qzJiRMn3D4Pq1WrxrRp0/Lsql9Yn0v5+ZyMiorCbre71VxyLQNo30lu9Jl8K1wjDtaoUUM7T7Vq1WLevHnayL67du2iatWqPProo1pA6vLlyxw5ciTfGda3y969eylZsmS+utmFh4fz/fffu91mzZoFqNegK5vp+mW+//57QkJCaN26Nd9///0Ni7QXtjJlynDx4kW3add/X6xTpw5ms5mff/7ZbfrOnTu5cOEC9erVw8vLi1q1arFy5Uq3YuNJSUn8/vvvhfoduGTJkpQvX167hlzWrFlz09tMSEggLS2N0qVL53ud5s2bk5KSwoYNG7RpsbGx7Ny5k2bNmuW53vHjxzl27Jg27dixYxw/flxbb+vWrQwfPtytYLrT6WT16tVERkbe8A/N4v4lmVJCCM0777zD1q1b3f4iVLVqVbp3787UqVNJS0ujYcOG/PPPP0yfPp1GjRrRokWLQm2Dn58ff//9N9u3b6d27dr06dOHH3/8kT59+tC3b18CAwP55Zdf+O677xgxYsQNt+caae+bb74hMjLSLfjTqlUrGjZsyMCBAxk4cCBVqlRh3759TJ06lRYtWuSrsGjWdoM6ul+dOnWydUF4/vnnWb58OX369OGVV14hICCAZcuWsXXrVt5///18f4GuWbMmHh4evPnmm7z66qsUK1aMzZs3888//2jDHQshhLi/tWjRguHDhzNp0iT++ecfHn30UYoXL865c+dYuHAh//zzD+PHj882Elu3bt2YM2cOer0+Wze9IUOG0L9/f15//XW6du2qjbK3d+9eBg4cmGtbCutzKT+fky1btqRRo0a8/fbbXL58merVq7N9+3Zmz55N9+7dtWLWfn5+XL16lT/++IMaNWpQvHjxApzdvA0cOJCePXvy4osv8tRTT2GxWFi0aBG//fabNvhK7dq1mTlzJp9//jl169bl9OnTfPbZZ1it1gLXi4qNjeXMmTNUrVo1x66GBXX48GHtPOXkzJkzxMbGUrduXXx8fLJlLLnqE5UpU0abl1NWk9lsJiAgwG3epUuXuHTpkjawze3SrFkzvvnmGxRF0brMuYKDv//+O/7+/lSvXp3+/fszY8YMTCYTrVu35ty5c3zyySfad1+A119/nX79+tG/f3+efvppbDYbn3/+OVarVRs0oDC4RuZ74403GD16NO3bt+fQoUNahtrNBFp37doFqAEjUDMojx07Rvny5XP9jtuwYUOioqIYOnQoQ4cOJSAggGnTpuHr6+vW2+DYsWNYrVZq1qwJqH8E/vTTT3nhhRd4/fXXAbVAfmhoKA899BAAPXv25Ntvv2XAgAEMHjwYT09PvvnmG44cOcKCBQsKfHzi/iGZUkIITUBAAO+++2626ePHj+fll1/mp59+on///ixYsIDevXsze/bsQv9rZN++fbl69Sr9+vXjwIEDlChRgm+//ZYyZcrw7rvvMmDAAPbt28f48ePp06dPvrbZrVs3HA4HXbp0cZuu1+v5/PPP6dSpE5999hn9+vXj22+/5fnnn2fKlCkFaneHDh2IiIhg+PDhfPnll9nmh4SEsHDhQsLDwxk3bhyDBw/m4sWLzJw5023EwBuxWCzMmTOHatWqMX78ePr168fatWsZO3YsPXr0KFCbhRBC3Luef/55Fi5ciJ+fHxMnTuT5559n6tSphIaGsnTpUm1Qj6yqV69OaGgowcHBNGnSxG1e8+bN+fLLL7l06RKDBg3izTffxGAwMHfu3DxH7Cqsz6X8fE7qdDo+++wzevbsybx58+jfvz+rVq1iyJAhWn0sgB49elCmTBlefvnlQh9uvnr16ixYsACdTsebb77JoEGDiI6OZsaMGVp3SFfAav78+bzwwgt8+eWXdOvWjVdeeYWjR49mK0idl99//50nn3wyx8FobkZMTIz2h7SczJw5kyeffLJQ9nW9xYsX8+STT2rdLW+XDh06EBcXx759+7Rp1apVo3PnzixYsIA33ngDgFdffZXRo0ezdetWBgwYwPTp03nwwQf55ptvtCzCJk2aMHfuXNLT0xkyZAjvvPMOJUqU4LvvviM0NLRQ292lSxfGjh3Lli1bGDBgACtWrGDkyJFA9vpU+fHnn39Su3ZtLSvu4MGDPPnkk/z+++95rjd9+nTatm3LpEmTGD58OCVKlGDevHlu3evGjBnDK6+8oj02m83MnTuX8PBw3nnnHcaOHUvdunX58ssvtW6txYoVY8GCBYSFhTFu3Dj+85//kJaWxrx589wGFxD/PjpFytwLIYQQQgghhLjNevXqxccff3zbC1oPGDCAwMBAJkyYcFv3U5h+/vlnatas6Vbj6vfff+fFF1/kxx9/zJb5mJfU1FRatGjBxIkTadeu3e1orhCFRrrvCSGEEDfgcDiw2Wx3uhmikJhMphvWoxNCCFG4tm3bRlpaWq4jyhWm1157jaeffppXX321QDWV7qTly5czZcoU/vOf/1CqVClOnz7N1KlTiYqKKlBACuDbb7+lWrVqtG3b9ja1VojCI5lSQgghRC4UReHSpUvEx8ff6aaIQhYQEEDJkiULPEy5EEKIm3P+/Hm8vLyKZJREUEdBPHToUI4DAtyN4uLimDx5Mn/++SexsbEUK1aMjh07MmjQILy9vfO9ndjYWB555BG++uqrQh1JWojbRYJSQgghRC4uXrxIfHw8xYsXx8vLSwIY9wFFUUhNTeXKlSsEBARQqlSpO90kIYQQQoh/Lem+J4QQQuTA4XBoAang4OA73RxRiDw9PQG4cuUKxYsXl658QgghhBB3yE0Pm2W1WuncuTPbtm3Tpp09e5Y+ffpQt25dHn74YTZu3Oi2zubNm+ncuTN16tShd+/enD179uZbLoQQQtxGrhpSNzPijbj7uZ5XqRUmhBBCCHHn3FRQKiMjgyFDhnD06FFtmqIovPzyyxQrVowlS5Zow55euHABgAsXLvDyyy/To0cPvv/+e4KCghg4cCD57T2oKArJycn5Xl4IIYQoDNJl7/70b31e5fuUEEIIIe4mBQ5KHTt2jCeeeIIzZ864Td+6dStnz55l7NixVKlShRdffJG6deuyZMkSABYvXkytWrXo27cv1apVY8KECZw/f57t27fna78pKSnUr1+flJSUgjZZCCGEEEIg36eEEEIIcXcpcFBq+/btNGrUiEWLFrlN37t3LzVr1nTr5lC/fn3++usvbX6DBg20eZ6enoSHh2vzhRBCCPHvs3LlSmJiYgCYNm0azz77LABLly6lTZs2ua43fPhwhg8fXiRtvBmXL19m0KBBREVF0aJFCyZMmEBGRgZw6+UO5s2bR4sWLYiMjOStt94iLS2tyI5LCCGEEKIwFTgo9fTTT/PWW29pRUJdoqOjKV68uNu04OBgLl26lK/5d5yiQOIRiD8AyacgIwYc1jvdKiGEEOK+df78ef7zn/9oQZW+ffsybdq0O9yqW6coCoMGDSItLY0FCxYwZcoU1q9fz8cff3zL5Q5Wr17N9OnTGTt2LP/73//Yu3cvH3zwwZ08XCGEEEKIm1Zoo++lpaVhNpvdppnNZqxWa77m33FnvoNNPbNPN3iCyR/MAeq9JRgsIeBRHCzFwbMUeJUBzzLqvVEK4gohxP0ur3o8Cjeo1aPkc7m8tqmo0xQUrS2KomSflvlvRV1Bm+/27yzr5vTvrNvJ2pYCP86yX9f8y/GXATidcJp0r3RtneiYaC4lX8LmsHHk6hH3bQDeJm8URblr60KdOHGCv/76i02bNlGsWDEABg0axMSJE2nZsiVnz57l22+/xcvLiypVqrBlyxaWLFnCq6++6lbuAGDChAk0a9ZMy1SfP38+zz33HK1btwZgzJgx9OvXj6FDh2b7g6EQQgghxN2u0IJSFouF+Ph4t2lWqxUPDw9t/vUBKKvVip+fX2E14db4VQe/mpB+Eeyp4FRT7HGkqbf0fGZ0mQLAqyx4V1BvPpXArwb4hoLRG3R60Bmy3FyPjeq9XoalFkLcu7TASOa9U3Fmm6YomdOvm5af+5z2kdc8p9OpBV+cTidOnNr+bQ4bGY4MMhwZWO1W9d5hxeq0YrVb0Tv0VDdWJzolGqPNeG27ihPsqTm34bpgUbZ2kUOwCPdA0PWBGwCH3qKFpfITzFq1ZBW/LP6FxNhEylYqy7OvPIvD4WD8a+NZsH6Bttyn//0UgAHDB5CSnMLsSbM5uPsg6CCycSR9/tMHL2/1jy2/fPcLq5euJikhidBaofQd0pfipYqjKArLvlrGb8t/w5puJax2GH0G96FYCTUY06t1L1544wV+/OZHEuMSqde0Hv1e74eHpwfP93gegL49+tJ/WH+uXrrKP3/9w9sfv02aPQ2n4mT2jNmsWbYGTy9PujzVhY49OpJiTUFBQce1oNSvv/7KlClTOH/+PNWqVePNN98kKiqqAFdv4QkJCeGLL77QAlIuycnJt1TuoEGDBuzfv59XXnlFm1+3bl1sNhuHDh0iMjLy9h6YEOKmOZwKBv3dGUjPr/vhGIQQd59CC0qVKFGCY8eOuU27evWq1mWvRIkSXL16Ndv8GjVqFFYTbk1gHeh8EJw29WZPBWuc2o0vIxoyrmbex4A1BtKvqvOtMZnT4tRAli0eEuIh4cB1O9CrWVXeFcA3DPzD1ECVwQK4glOZASq9GXQmdZ7BAnpTZtDKCPqs96Zrj+/SvxYLIYpG1kCPU3FqN9f06+fltPz10xRFweF04MSJw+nAoTjUaYpDm644Mx9nzssabHK1S0HBifNawCZLIMfhdJBuTyfdnk6GI4N0RzoZtoxrAaLMW4YjA5vDdm2a04rNblMDSA4rNocNm1Odb3Pa1McOdb7dYVeXd9iwO+1YHVbsTvsNgzsVvCvwabNPMaQYIEM70VTf83/4JO67zc+ouyS/OhyOnJ2v9/rTR0+z8LOFvDb2NcpVLMfKJSuZ+u5UBr0zCACzQc1a1qHDoFP/EOJh9OCb/31DYlwiE2ZNwOFw8PHYj1nxzQqef/l5Vi1bxQ/zf+DlN1+mSlgVvvrsK2aMncHHcz7mp8U/sXXtVoaNHUZQUBBLvlnCB29+wKxvZmE0ql8zvp/7Pa+99RqBwYFMfm8yX3/yNSPGjmD6vOm80ucVps+bTqXKlfh2/reYDCZK+pTE3+LP1ctXuXLmCjPmzODIP0f46P2PqBtel4ZRDd0CUocOHWLYsGGMGTOG2rVr88cff/DCCy+wfPlyKlSoUNhPxw35+fnRokUL7bHT6eTrr7+mcePGt1TuIDExkYyMDLf5RqORgICAu6ccghAiRwa9jsHf7uHYleQ73ZSbUrW4D5/0lMC3EKLwFVpQqk6dOnz++eekp6dr2VG7du2ifv362vxdu3Zpy6elpfH333+7/bXvrqA3qTejF3gUA6plX0ZxZmZQpWcGr+KvBa5Sz2UGqWIh/QqknILk42BLgLTz6u3qZnU7OhP4hUFQfQhuAL7VAQfYk0FxqPtR7Gq9K3XHmesZsmRXGdXsKoMH6D3B6KkGtVzHof07814IcUdkDQ45FMe1fzsdOU6//uZwOrA77dicNpxOJ3anXQsG2Z32HANLWfcJqP92BYdc7yuu3/WuhzqdlvHjeux0OtWAkT1dCxqlO9LJsGeQZk+7FlDKDC6l29V5bo+vn5/52O60F+0TkQuDzoDJYMKkN2E2mDHqjZTyKYVRb8RsMGMwGtDpdOgU0OsL7aMz3zyNHlQNrIpOr5aC1Ol0WlBGhw71f/Xxxb0X0ev0NAxrSNVqValfuT77Ou7TntMqgVW07fqafQGoFFCJ5KvJBPoF0qB6Azw9PanwcQUUFMr5lWP9z+t5pvczPNXjKXUbo6ow/3/zCTIFseybZQx/ezitWrYCoN579ejQpgPH9hyj1QPqtH7/14/OHToDYH7LzMD+A3l39LtULFkRgIolK1IqsBSeJk9MehOBHoF4m7yxWCy8P+F9AgICiAyP5O+//mbVslU0b9Lc7fx8+eWXPPHEE3Tp0gWA3r17s2PHDhYuXHhXFEP/4IMP+Pvvv/n++++ZN2/eTZc7SE9P1x7ntr4Q4u517EoyBy8k3ulmCCHEXaXQvllHRUVRqlQpRowYwcCBA1m/fj379u1jwoQJADz66KN8+eWXfP7557Ru3ZoZM2ZQtmxZGjVqVFhNKDo6vdoVz+it1pjyLgfOzGCSPQnSLqsBKkeqGgwyBajzko9B4iGI3w/x+9TsqoQD6u3k/8DoA8FRUKI1FG+pbj8nrmCV05EZvLKDNQGUmMwgFmi/PrRMKhMYTGDIbLfBIzMLy6K20ZWRJYRw4woIubKDrr93BZOyTnNl7diddi07x65kBo6ydCHLml3kCii5aMEh1+PM//Q6PTqdeq/X6XOcZtQb0ev0OBUnSdYk0mxppNpSSbYmk2JLIdWWSoo1RXucYk1xv89cxrVchiMjp1NTqHTosBgteBg9sBgs6r8NHliMFu2x9m+DBbPBrC1vNpgx6U14GD0wGUxYDBbt3mwwq/Nd0zODTiaD6do8vQlDTl2n7WBMNFLWtywWD4s2WWn7BymO1Nt+TtwYvDDlMyO2SdMmVK1WlSd6PEH1GtVp1boVPR7twenTp/Nc76lnnmLIoCG0bdWWRo0a0bZDWx56+CEATp06xYs1X9SWDS4WzGuvv0ZqaiqXL19m+NDh6HXXxk7JyMjg9KnTWkC0dt3aOBQHAGE1w3A4HJw8eZKAwAAA9TWSGVxVULA5bTgUB6XLlMbb1xubwwZAaPVQfvzhx2xtP378OCtXrnQbGdhms9G8efNsyxa1Dz74gP/9739MmTKF0NDQWyp3YLFYtMfXz5d6UkIIIYS4FxVaUMpgMDBz5kxGjhxJjx49qFChAjNmzKB06dIAlC1blmnTpvH+++8zY8YMIiMjmTFjxl1bpLTA9AYw+6s3r7JgT4GMWEi7CBlX1OBRQB0o1lhdXlHUrKnYPXB1C8RsU7OpLq9Tb3oLhDSDUh0gpEVmN79MOj3ozPkbO9Fpzwxg2cCRoQbH0mzXsq90+szugWY1UGXyBZMP6D0yA1eZN12BB2oU4q7hyjJyZRXl9NihOLSuYVm7iTkUB06nEweOa93ZsnSHQ0HLUnEVXtahw6A3aEGirDeTweQWUDLoDW6Bpetl2DNIzEgkISOBxIzEbLckaxJJGUlu98nWZJKsSaTb0wv1PBp0BrzN3ngaPfEyeeFp8sTLqN5n/beH0QNPozrN05j5OHO6K+jkmu5hvBZ4umc+D3S63P9ocAe5gpgeHh78b8H/2LVzF3/+8SfLly1n8aLFvP/B+wBu2Wk2uw2D0YDNaSOyQSTLVy3nz9//ZNOGTYwfM55NGzcx5v0xGA1GLciatZZTulW9xt7/4H0qVKzg1qXOz99PC7QajNeCfoojc5rBoAUDjXojRr0Rg059Pbiy1YwGIx5GDy2jz6AzYDGpwcisHA4HL7zwAo888ojbdFeg50557733WLhwIR988AEdO3YEbq3cQUBAABaLhatXr1KliprxZrfbiY+PJyQkpAiOSAghhBCicN1SUOrw4cNujytUqMDXX3+d6/KtWrWiVatWt7LLe4crk8qrrNqdL+UspF9QM6gsxdRudl5l1VvZLmrQKuEfiN4IF9dA6plrASqTP5TpDOUeBe/yBWuH3ggY1cBSThTHtTpajhSwxamBLMgMWFnAYAaDjzoCodFLHZHQ4JkZrLpHfkSK+4ZTcbpnIOVwy7CrNYlc9zllOWUtTA1ogSVXlpFep8egM2iBJIvO4jYttyBSXjLsGcSnxxOXHkd8erzbLSEjQb1PT3B7XBhZSl4mL3zMPviYfNR7sw/eZm/13uSNt9kbb5M3XiYv7bHr314mLy0QZTaY753A0V0qp2Lm2vScpuWwbFY61Iy6rM/L/r372bl9J31f6Ev9hvV5efDLdGzdkd07dgOQlpqGj48POnRcOH+BChUrYDFY+Gr+V4SFhvHoo4/y2KOPsfKXlYx6exTeJm8qVKzA6eOn8W6vBuPi4+Pp0qkLixYvIjg4mJT4FGpWrQmoWTtvvP4Gffv1pWzxsgCcOXaG+hFqd/4DRw9gMpkIDw3XMoY8TZ54m70xGdSsNS+TF2aDmXPnzqHYFS0L6O8Df1OlShUsRvcgZqVKlTh37pxb/ahJkyZRqVIlHn/88YI9SYVk+vTpfPvtt3z00Uc8+OCD2vRbKXeg1+uJiIhg165dWqb5X3/9hdFopHr16kV4dEIUHSmuLYQQ97eiL4zxb6PTqbWpLMFgq6gGp1JOq1lRHsWvZSDpDBBQS71VfRGSjqjBqYurIP0ynFqg3oKjoEJPCGleONlLOgMYDDkHrRQHOKxqAXfrVUi7AChZglUeYA4Ek58arDJ6q8EqyaoSBeQaCU0rUJ1576qhlG5PJ82WRoY9A6vT6hZgsmd2Wc2avZG1G5tBp2ZjuDIvXEGlHLtrFZCiKKTYUriaepWYtBhiU2PV+7RY4tLj1Pu0OGLT1fsUW8pN7Uev0+Nn8cPP7IefR+a9xQ9fi696b/ZV/232w8fsg6/FV5vmbfIulGP9t3IbNS/rqHg5PVb/ka3bpXZtZvlNdX09KEDrguma7sq6u75mlCsYkzUrKeu0QJ9AvvjsC0qXKE3TJk3ZsWMHaalpPNzxYebPnc/Xc77mySefZPXq1Rw+dJgqlavgafIkNjqW/37/XyZMmEBAQADrfltHzZo1MRvN9H62NxMmTKBGWA2qVKnClClTKFu2LBXKVaBPnz5M/WQqIcVCqFy5MjNnzmTPnj1UrVJVO55p06ZRtmxZLBYL48aNo3v37nh7e5ORoQZdDx06RGBgYLbzn5GRwbBhw3j11VfZtWsXq1ev5ttvv822XJ8+fejVqxcRERE88MADrFu3jnnz5vG///2vAM924Tl+/DgzZ86kf//+1K9fn+joaG3erZY7ePrppxk1ahShoaEUL16cd999lyeeeEK674n71r1eIBzggbAQhna8twPHIT6W+yJAeD8cgxD3GwlKFRWdLjOAEwAeJdSgU8pZ8AhWa0ldv6xfmHoLHQjRm+Hs9+p9zHb15lMVKveBUu3VwNJtabNBzejiui+6ikPtCuhIVzO6XJlVBg81KGUOUG8Gb7UrYG5ZWuK+pyiKNhra9SOkpdvSSbOnkWZLU0dIy8xycjjVYFPWH+GuwJIrqKR17cmS1VTY7U7ISCA6JZqrqVeJTo0mOlX9t+sWkxpDTFpMgTOZDDoDAR4BBHoGEmDJvPcIIMAjAH+LP/4e/m73AR4BeJm8Cv0Y/01yqv2VtVaY69/XB5yMipFSSimtm2e2oFBmEMktgJRHAfLrg0m5Pb5VtcJr8f7495k5cybj3htH6dKl+eCDD6hRowbvvfceU6ZM4auvvqJ9+/b06tWLuLg4AAYPHkxSUhIvvfQSqampNGzYkA8++ACAbt26cfnyZcaMGUNycjJRUVFMnToVgH79+pGSksKoUaNITk6mVq1afPnll/j7+2tteuSRRxg+fDiJiYl06tSJkSNHAhAUFETXrl35z3/+wxtvvJHtWGrUqEGJEiV44oknCAwM5P3336dWrVrZlqtbty6TJk1i2rRpTJo0ifLlyzN58mQaNmxYKOe0oNauXYvD4WDWrFnMmjXLbd7hw4dvqdxBp06dOH/+PKNGjcJqtdKhQweGDh1a5McoRFG61wuEVwm5+7p8F5Sfp/GeDxDKCIJC3J10StbKunex5ORk6tevz65du/Dx8bnxCnc7R8a1kfkUJ3iUvHFXuNTzanDqzFK1qx2AVzk1OFW6U2ZXvTtEUdSMKteohA4b6PXqiIAmX7XLoslXDcAZvaXb333C5rCR4cjIVospxZZCqjWVNHualu3kKmIMgIKWyZTT7XZm9jgVJ7FpsVxOucyV5CtcTrms/jvlinaLTo3G6sj/SFbeJm+CvYIJ9gwmyDNIuwV6BBLsGUygZyCBHoEEegbia/aVLnA3yVUcPmsdMFd3zqzZc25F4hXQ6/VaYDNrppyryLnZaNaKoLuWMegNOK1O4i/FU7FSRTw91OB81kCSPI/5ExYWxvz58++6gU3S09M5efIklSpVuuO1p4rSffd9SvwrdJq64Z4OSnWtU4qpT9W7p4/jfjiG8NJ+rBjU4k43QwhxHcmUulMMFjUTyhwMCQch9Rx4lso7sORVBsIGQ+Xn4cx3cGohpJ6FA++pXftCX4aQlncm4KPTXSuK7qI41CCVLVHtggjqfKM3WELUbn8mPwlS3cVcQacM+7XAU5o9TRulzWpXs6BsThtOpxpw0ul0bgEms8GMt8n7tgebsrb5csplLiRd4GLyRS4lX+Jiknp/KeUSl5MvY3Pa8rWtAI8AQrxCCPEKoZhXMUK81ftgz2DtPtgrOFvRZZE/WYNMWQNLWf99/YiEWnApM3hk1BvxNnljMV4bkS9r0eysgc6s3TkNOsMNg0rp6ekk6ZOK7NoVQgghhBDi30aCUneaRzEw1FcDU2nn1a59N+ruZvKDKv8HFXrB2SVwYi4kn4Ddr0NgXQgdBIG1i6T5edIZMjOjMv8S68qmsqdC0mFwOtXugUYftb6WyT8zSCV1MYqKq3udK/CUbk8n3Z5OsjWZZGuyFoiyOq1qcEBRAwMmvUnNMjGY8DR5ahkmRdXmmLQYziWe43zSec4nnud80nkuJF3gQtIFolOjr2Vk5UKHjmJexSjhU4IS3iUo7l1cuy/uXVwNRHmHYDaYi+SY7jeu0Q2vvzkUNdDkGrEQwKjLEjDSG/A0eGIxWrSR+UwGkxZYMulN2YJMxjuZISqEEEIIIYS4JfJt/m5g8oHASLUeU/IJsARmrzOVE6MnVHoGynaDE/+D0wsh7i/Y1hdKPwxhg9Ruc3cLt2yqoCxBqmR15EFQz4ElCDwyM6mMfiAZCrfM5lCLhWc4MrSi4cnWZBIzEt263mUdgc5sMGM2mPEwemA2mIu0ppFTcXIl5QrnEs9xJuEMZxPPci7xnHafbk/Pc32LwUIp31KU8ilFSZ+SlPIpRSnfUpT0LklJn5KEeIdIMOMm5DTyoatrpjaSoS5710wvkxeeJk88jZ54mDy0AJMr4OQKNpkMJqmddR+6fqReIYQQQgghXORX2d3CYAb/cDUok/C3+uPOlM9aDyZfCHsFKjwBRz+F8z/BhV/gyh/qSH7ln7iz9aZykzVIZUGtreVIVbv6pZ4FvQmMvuBZMrNIvL/a7VHkypXtlGZP0zKeEjMS1ZHrHBnYHDYURdG62FkMapcnX7MvRr2xyGvkpFhTOJVwilPxpzgdf5ozCWc4naDe51VAXK/TU8K7BGX9ylLatzRlfMu43Qd5Bkm9nwJSFMVt9MOsGU6KoqgBS50Oo86oBZN8LD54Gj3xNHlq15LJYNICTq57CTQJIYQQQgghcnIXRir+xfQG8K0K6NTufDqdWm8pvzyKQ8QoKNcD/p4EiX/DoY/g3I8QPvLu6NKXF53evbuf0wq2ZEg4pD42eqsZVJYQNUj1L+7mZ3faSbWlkmZLI82eRlJGEgnpCaTZ07TMJ0DLeLIYLARYAjAbzHckWBOfHs+JuBPa7VT8KU4lnOJKypVc1zHoDJT2LU05/3KU9ytPWb+ylPMvR1lfNRBlMpiK8AjufU7FidWhjnKYdSREV80mV6DSFUzyt/jjZfbC0+ip1WvK2m2zKLtsCiGEEEIIIe5PEpS62+h04FsZcGR2adOB0atg2wioBU3mwrnlcGS6OsLftn5qxlTowIIFuu4kvVntymcJUoum21Mg5TQknwSDl9o10bNEZoCqgOfoHqEoipb5lGpLJdWaSlxGHMkZyaTb09WC3QoY9AYsBgsWowVfs+8dC9gkW5M5Hnec47HHtfsT8SeITYvNdZ1gz2AqBFSggr96qxhQkfL+5SntW1q62BWAK9Mpa+DJ6rC6ZTmZ9WpgyWK0EOQZhLfZW+ueef1NspuEEEIIIYQQt5v84rsb6fTgW03tzpZwCDyL37j4ebZtGKBcdyjRGg5/onbpO7MIrvwONUdA8ea3pem3jc5wbbQ+RQFHCqRfgNTTmQGqkHs+QOUKQKXaUkm1pZKYkUhcWhyp9lTS7ek4FSc6dFrwKdAj8I5lPjkVJ+cTz3Mk9ghHYo5wNPYox2KOcSH5Qq7rlPEtQ6WASlQKrKTeB1SiQkAF/Cx+Rdjye5urML3NaSPDnqGNfOgqQJ+1FpifxQ9fiy+eRk9tVDpX1pxkmQkhhBBCCCHuBhKUulvp9OAbqgamkg6DZ2m1xlJBmQMgYjSU6ggHJ6gj/O3+D5R+CKq/AWb/wm757afTXevmly1A5a12Y3QFqAoazCtCGfYMUmwpagAqPZHYtFhS7Cmk29NRFAU9em0EsgBLwB3rKmVz2DgRf4JDVw9x+OphDscc5mjsUVJtqTkuX9y7OFUCq1A1qCqVAytTJbAKlQIq4Wn693a3LIisGU9aEfrMbnZ6nR6TwYRZb8bL5IWvxRcfsw8Wo0ULVrpqO0lNLSGEEEIIIcTdToJSdzO9AfzCwGGFlJPgXU4NVt2MYo2h+SI4+hmcWgAXVkLMDgh/C4q3LNx2F6XrA1T2FLVIespJtUi6R4nMAFXQHS327lScpFhTSLYmk2xNJiYthuSMZNLsaTicDnQ6nVow2uhJoCXwjgWg7E47x2KPcejqIf6O/ptDVw9xNPaomo1zHbPBTJXAKoQGh1ItqBrVgqtRNbAq/h73YKDzDnDVeMqwZ6j3jgytvpPJYNKym0K8Q/A1++Jh9NCCTh5GD8l2EkIIIYQQQtzzJCh1t9Mbwb+6Oipd2kXwKnPz2zJ4QPXBULIN7H9Xrc+0ewiU7gQ13lBH8buX6XTqiIUmHzXDzJ4MKScg+YSaEeZZBjyKqaP43eZ6OTaHTQtAxafHq1lQthSsdis6ndoFz9Pkia/F947VTVIUhfNJ5zlw5QAHow9yMPogh68eznHUOx+zD2HBYVQvVp2w4DDCgsOoEFBBaj7lg1NxkmHPIMORoXW5UxQFnV6nZTUFegWqhcVNXlp2nGQ8iVtx7tw52rZty9q1aylbtmyeyy5atIgpU6aQkZHB4sWLqVq16k3t02q1smzZMp544ombWn/p0qVMnz6ddevW3dT6QgghhBDi3iO/KO8FRk8ICIeYnZB+VQ2s3IqACGi6IDNr6mu4sELNmooYpWZU3Q90+ms1qJx2sCdBwgFIMqtZU15lwBJcaEXfrQ4rSRlJahZUagxx6XGk2dKwO+0Y9AY8jZ74W/zx8L5z3QlTrCkcjD7Ivsv7OHDlAAeiDxCfHp9tOR+zDzWK1bh2C6lBGd8yEhy5AUVRtMBThiNDKzKu0+vwMKjFxEv4lMDP4oenSc2KcwWgZBQ7UdhKlSrFxo0bCQoKuuGyH3zwAb179+bRRx+lZMmSN73PFStW8Omnn950UEoIIYQQQvz7SFDqXmEOUANTsbvBlnTrWU2urKkSD8D+MZB6Bna+AuUfh9BBaiDsfqE3qvWlzIHgyABbPMRcVANSniXAo5Q6wl8BanbZHDaSrEkkZSRxNfUq8enxpNhScDgdmA1qvZ8Q75A7mkl0KfkSf136i72X97L38l6OxR7DqTjdljHpTYQGh1KreC3CQ8IJDwmnnH85GXntBuxOO+n2dNLt6WTYM9Qi9Dqd1uWumFcxNfPJ7KV2yzSpASg5r6KoGAwGQkJC8rVsUlISUVFRlClzC5m4oHU/FUIIIe5GIT4WHE4Fg/7e/kPr/XAMQmQlQal7iWcp8A2DhP1gsIDefOvbDKyjZk0dmQpnFqu3q1shYoyaUZWeARlWsNrU7nGg3ut1YDKBxQwm47V5dzuDBQzFrxVITzkDyafVoJ9nGfAIUbOrrjsep+IkKSOJJGsSMakxXE29SqotFbvTjtlgxtvkTQnvEncsCOVUnJyMO8meS3vYc2kPf136i8spl7MtV8qnFBElIogort5Cg0MxGwrhOrqPuTKf0u3patdGBQx6Ax5GD7xN3pT1K6uNcufKgJLMp/ucokBqzoX+bxsvrwK9z2btvte2bVsmTZrE7NmzOXXqFLVr12bixImUK1eOsLAwAJ577jmioqL46quvOHLkCO+99x579+6lVKlS9O7dm169emnb/vHHH5k1axYXL16kRo0ajBo1iqSkJEaMGAFAWFgYa9eupUyZMsycOZOFCxeSnp5OgwYNGDVqFKVLlwbg8uXLjBw5kp07d1KpUiVatWpViCdMCCGEcOfnacSg1zH42z0cu5J8p5tzU6oW9+GTnpF3uhlCFCoJSt1rfCqpmVKpp8CrXOEEg4yeUHMYFH8A9r2rFgrf1g9MHYCOYAMcjmvLu/ZpMqqBKbMJfLzAxxs8LOBpAYsFPMygv0szQ7IWSHfawZaoBvuSLWAJAc8ypOm9SLCnE58ez+XkyyRbk7E6rBj1RrxN3hT3Ln5Hg1DHYo+x6+Iudl/cze6Lu0nISHBbxqAzUL1YdeqUqEPtErWpXaI2xb2L35H23gtc3e9cGVCu4u4mvQkPowfFvIoR4BGAt9kbT6MnXiYvLEbLHW61KHKKAs2bw+bNRbvfZs1gw4abfs+fNm0a7733HsHBwQwePJiPP/6YyZMns3HjRpo3b860adOIiooiPT2dF154ge7du/Pee+9x4sQJ3nnnHby9vXnkkUfYsGEDI0eOZOTIkTRt2pSvvvqKF198kbVr1/LWW28xZ84cvv/+e4KCgvj666/56aefmDx5MsWKFWPOnDn07duXn376CZPJxODBg/Hy8mLx4sUcPXqUkSNHEhgYWMgnTgghhHB37EoyBy8k3ulmCCEySVDqXqM3gH8Y2BMh/TJ43nz9D02GFWLi4YofOF4H53eg3w221aA/CIEvgqWi+zqKAjYbWO1qFtWlq2C7lNlGHZjNYDGpgSo/n7s7WKU3giUIh8mfxLQYEqMPcDl1PfEOhVSDNzpzIF4eIQR6BN6xIISiKJyIO8GOCzu0QNT1QSiLwUKdEnWoW7IukSUjqVW8Fp6m+6gbZiHLsGeQZk9zC0C5RrYr5VOKQM9APE1q8MnL5CVF3cU190pmaBbPP/88TZo0AeCpp55iwYIFAFoXP39/fwICAli8eDHBwcH85z//AaBixYqcP3+e+fPn88gjj7Bo0SI6d+7MU089BcCbb76JyWQiISEBX19ft26DX3zxBaNHj6ZRo0YAjB07lubNm7NhwwbKlSvHnj17WL9+PaVLl6ZatWocOHCAVatWFeVpEUIIIYQQd5j8yroXGb3Av4ZanPxW6ks5HGow6dQ5iE8CowH8gsHzNUjfCQlzwHkO4t4Fnx7g3Ql0md2SdJmBJ3MOXb8cTrBmdvmLjoXzmd3Isgar/HzAN0uwysOidgUs4h97VoeN+Iwk4jISuZQaQ5I1FZvTjkVvxtvgJJA09A6HGnzTh4A+APRFE5i6lHyJbee3seP8DnZc2EFMWozbfC+TF3VL1qVeyXrUL1Wf6sWqYzLkvy7Wv4nNYdMCUBl2dXRBs9HsFoByBZ+8TF7S/U7kTqdTM5bu8u5716tQoYL2bx8fH2w2W47LnThxgkOHDhEZea1rgMPhwGBQXxMnT56kZ8+e2jyz2cywYcOybSclJYVLly7x2muvoc/yR4j09HROnTpFRkYGAQEBWlc+gIiICAlKCSGEEEL8y0hQ6l7lUTxLfSmPAhXpRlEgNgFOnVWDUh4eUCrEPXvJowGYqkHiHMjYDcmLIWMX+PcH4w2K4Rr04Omh3rLKGqy6dBXOXgQFNRhmMas3Xx/w876WUXUbglVp9nTiM5K5mhbHlbQ4km1pgIK30ZNiHgGYDNe9LFzF0dOj1eLoHsXBEgjG7LWnbkWKNYWdF3ey7dw2tp3fxumE027zLQYLdUvWpUHpBjQo1YAaITUkeycHTsVJuj2dVFsq6Y50nE4nJr0JT5MnwV7BBHkE4WPxwdvkLQEocXN0OvAunJE7i4rJlL/PCLvdTpMmTRg1alSO843G/L3nODK7fH/yySdUqlTJbZ6/vz9btmzJVhg9v20UQgghhBD3D/lFey/zqQi2BLUGlFfZ/AVIrDY4cQbOXFSDUyWKQW4/Mgz+EPAfSN8EiV+B7QRcfQd8HwOvB6GgI4nlGqxyqF0IM6yQdBnO2AGdGqwym3IOVlkyA1b5DAql2tKJy0gkOi2O6LQ4km2p6HUGfE1elPIqhiGv7oQGi3pTnGBPhZRTkHYOTP5qgMoccFPZU07FyZGYI2w5t4Ut57aw99JeHMq12l0GnYHwkHAalmlIw9INqV2ithQlz4HVYSXNlkaaPQ2b04YOHR4mtQh5hYAK+Jp98TZ7423ylkwyIW6gUqVKrF27lrJly2rZUT/++CP79+/n7bffpkKFChw6dEhb3uFw0L59ez744AN0Wd6P/fz8CA4OJjo6mgceeAAAq9XKkCFD6NevH6GhoSQkJHD69Gkti+uff/4pugMVQgghhBB3BQlK3cv0RvALUwNTGTHgUSzv5ZNT4fBxuBgNwYHZg0M50enAszmYa0LCF2DdD0kL1e59/i+AsdStH4fBAF6e6i0re2a9quuDVQa9GqzysLgXWPcwXwtaGQxaIOpyagzRafGk2TMw6PX4mbwp61MCfUGDajo9mHzUm8Oaed6vgiFr9pRvnsG6xIxEtp3bxuZzm9l8dnO2Lnll/crSuExjGpdtTIPSDfAx+xSsjfc5RVG0LKg0RxqKU8FkULOgyviVIcAjAB+zmgXlYfRw+5EshLixrl27Mn36dEaNGkXfvn05d+4c48eP5/nnnwfg2WefpW/fvjRo0IB69erx1VdfoSgK4eHhREdHk5CQwKlTpyhbtix9+vTh448/Jjg4mMqVKzNz5kx2797N+PHj8ff3p0mTJrz11lu88847nDt3jq+//hrveywDTQghhBBC3BoJSt3rTD7gVx1id6pZPEavnJeLjoXDJyAhCUoVVwNBBWEIgsChkPYHJC0A21G4OhJ8uoP3w9dqTRUmo1G9XR+scjiuBavcCqxDmgHijDYuG6xcMVtJ1YPJYsHXy49gryB05kLKlDGYwRB8XfbUWTAFuGVPKYrCqfhTbDizgY1nNrL3sns2lKfRk4ZlGtKkbBOalG1CWb+yhdO++4TD6SDNnkaqLZUMRwYo4GnyxNvkTfmA8vhZ/PA2eeNt9paujEIUAh8fH2bPns3777/PI488QkBAAL169eLFF18EoGHDhowePZoZM2YQHR1NrVq1+PTTT/Hw8KBx48ZUqFCBLl268M0339CvXz9SUlIYNWoUycnJ1KpViy+//BJ/f38ApkyZwjvvvEPPnj0pXbo0zz77LEuXLr2Thy+EEEIIIYqY/Iq7H3iWAp+qkHgIvMu6B4gURa3ddPik+u/SJW6+DpJOB14PgKUWJMwF6z5I/g7St4P//4Gpwg03USgMBvA0aJleVqedWHsyl9PjuZIWS3JyKiaHE1+nkWDM6HTJYEpQA1wmE3h7qjeTSS26bsqcfjMjAuaQPWVPu8zuuPP8GX2MDRf/4nzSRbdVKgVUolm5ZjQt15S6JetKl7ws7E47qbZUUm2pWB1W9Ho9XkYvAj0DKe5dXMuC8jJ5SRaUEHkoW7Yshw8fBtDuXXr06EGPHj20x9fPDw8P10bny8ljjz3GY489lm16QEBAtqDSa6+9xmuvvZbjdgIDA5k+fbrbtFdeeSXX/QohhBBCiPuPBKXuBzod+FZRi3GnXQavzNGMFEUdWe/QCTXbyP8mR+m7nqEYBL4B6RshcQHYT0HMKPB+CLwfAX0+ugXeIqfiJM6eQrQtiQsZcSQ509Ghw8/sQTnPgOsCFgrYHWCzq9lVKalqthWoAS6jQQ1MeVjA2ytzVEGj2kXQlBm0ukEAJNmaysaLe/nzwm42X9xHsu3ayFwmvZGGJevQrHxLmld4gDJ+NygU/y9ic9i0IJTNacOoN+Jl8qKUTymCvILwMfvga/bFYiyaEQ+FEEIIIYQQQhQdCUrdLwwWtRtfzHawJoDJD06eU7vs+XqrdZcKk04Hni3AHAGJ8yFjB6SsgPRt4NcHLHUKd3+KAjY7ycnxxCfFcSXhMskZyTgdDnwxUVJnRq/XoxhTUQwGFKMBp9mIw9OM09OCYsrsCnhdT0CcTjVYZbNDQjLExKsjAqJkdh/MErDytKg1q0wmMBuJtifzR/R+fr+wm51X/sbuvNYtL9DiS4vSkbQsVZuooAp46RQweIEuDaxxYPIF3b/v5Wdz2EixpZBqS8XutGPSm/Aye1HOvxyBnoH4mn3xMftIQXIhhBBCCCGE+Bf49/0qvp9ZgsA3FGL/gtNX4ehZ8PNVi4HfLoYACBwE6bvV4JTjKsR9CB5R4NtLrUWVH0kpcOEyXLgCF6+oxdhj4iE+ASU2AeIT0dns+AA+QEErLzmNBhxeFux+Xth9vbD7eWP388IW6IMt0BdbkC+2ID+sxfyw+3urXfnsDrXYut0B8UlwNY5ztnjWpx1jffpx9lndu+VV9CpOq5DatCoVSXhINQwW87XaXYoCjjR11L6082D0AY8Sau0pg/fNd6m8y+UWhCrvX54gzyB8LWoQSupBCSGEEEIIIcS/j/wSvN94loNLe2H/bihe+fYGpLLyqKeO0Je8FFJXq3WmMv4C725qtz5dZuaLosClaPjnOJw4A8fPwPGzEBOX66avD9c4zSYcnmYUkxFFpwO9DsWgB6eCzuFAb3egszvQZ9jRW20A6O0O9ImpmBJTs+/g+u0bDViL+WMN8cdaPJAD5cz8WDKRlZ6XOay4j5ZXy1KaB7yq8IC5EhWNgWDXwTkbXDqSWavKqNa+8vRQ/232BINOLY6ecQiMnmAOBEsxMPuD/t7upnZ9TSgJQgkhhBBCCCGEyI38MryfKAqcOg3nrFCsNJjTgCIKSoFaS8rvafBspmZN2Y5A0mI4shaO1YZ/UuHg0dwDUMEBKCVDSC8ZSEKINzH+JhL9LOgD/TEHB4OPN04PMxgKUJDc4cCQZkWfloEhJQNjUqp6S0zFmJCCKS4JU1wy5thETLFJmOKS0NkdHHXGsrhYLEvCTvJ38cxtKWBwQquzejpf8aO9rTSBxUqSXjaE9DK+ZBTzV7sJOp1qhpXDAelWSE4FhxN0CqC7VsfKaABTEpgugFEHnr7gXQK8Q8ArCMwed30GlcPpINWWSootBavDikFvwNvkTRm/MmoQyuyLr8VXglBCCCGEEEIIIbKRX4r3kzNn4J9/oFhJMAVBwt9gTwFjIdeTuhFrcTjQEbbqYedhiI0Ffr8232iAqhWgWkWoXA6qlCe9QgmuGG2cyYgh1pYCOggweOFtUDOHnDfbFoMBh48nDh9PbCG5L6YoCsfTr/Bb7H7Wxh3gpD1Wm2dy6mhzyYNHDyr02J1OcJoTiM+8/X1tG3odGSUCSS9XnLRyIWqwKvPe6eEaYU8Bu/Nat8CMDPXfTic448FxNDOjygc8g8E38+bhodayMmYWX3cVYdfdxIiBt8CpOEmzpZFqSyXNnoZep8fb5E0JnxKEeIXga/HF1+wrNaGEEEKIu4DDqWDQ391/4BJCCPHvJkGp+8X583DgAHh5gY+POs2rAiQfVYug3+6i2ilpsHk3/LkdduwHm+3aPLMeqivqLRSoGQXFngRDMAn2VC5Z4zmbcZaktHQ8DSaKm3wxFVFmzcn0aNbE7ee3+IOczIjWppt0Bpr4VqNtQE1a+oXhW88THobTGVYuX4zFciEGjwsxWM5fxeP8VTzOXcWYmo7HxVg8LsYSsP2Qti1Fp8NaPIC0CiVIK18881aCjDLBKNm6VyrquctIguSzkHQOsIDBRy2UbvDILMCeefPI7Bro6eketDIZ1Xu94ZbPUbo9nWRrMun2dAA8TB4EeAYQ6h2Kn8VPRscTQggh7lIGvY7B3+7h2JXkO92Um/JAWAhDO1a/080QQghxG0lQ6n5w+bIakDKZICDg2nSv0mBPhvRLalHtwu4KZrfD9n2wegNs2+seiCpTEhrXgYa1oXZ1MMRD8mJI3wrKFpTonVwxRnFAV4dExYS/wZNyliB0RdBd7WxGDGviDrAmfj/H069o012BqHYB4bT0D8PH4JFtXcViJq1iSdIqlrxuhoIxLhmP89F4no3G42w0nmev4HE2GlNCCpbLcVgux7kHqwx60ssUU4NVWW7WEH8wBasV3RWbWiDdkQwGq1oUXe8LigEUJyQlQlys2j3QxWC4NnKgxaIGrDw8sgesTCZ1ueuyrawOq9Ylz6k4sRgs+Jp9qRxYGT+LH34WPzxN1w9jKIQQQoi70bEryRy8kHinm3FTqoQUcba/EEKIIidBqXtdTAzs3692/ype3H2e3gA+FcGRAtZYsAQXzj5PnYdVf8CvmyA+y5eccqWgVSNoFQWVyl4XBCuO1e9F4k1N8Uj5Hj/nGUrYN1GM7VwxNeGyoSmO2xiQumRN4Lf4A6yJ28/faRe06Uadgca+VWgfUItW/tVzDETli06HPciX5CBfkiMqu80yxifjcfYKnqev4HnmCp5nLuN55gqG1IzMx1dgw35teYeXhbQKJUh1BaoqliCtfAmcJr0aZHTGgcGsjuDnGQBGXzUbzlUS3uEaNdAOqWmQmAQOOyiutgKGa9lWdrORVKNCiknBrgeT2QMfrwCq+pQiwLcYfj7BeFt8iyRgKIQQQgghhBDi30OCUveyhAQ1IJWeDqVK5byM0RN8qkDiP2BLApPvze3L4VC75y37Ff7659r0AD9o1ww6NFfrQ+UQuEh1ZHDJmsCZjBji7A4s+seobLxIOfsfeDsvUMr2J8Vt27hiasQVUxPsusL5q1icPYXf4g+yOm4ff6Wc0aYb0NPQtxLtAyJo7V8DP+PtzfqxB/iQHODjHqxSFExXE/A8fTnzdgXP05fxOH8VQ2oGPv+cweefM27byQgJIK1CcTVTq3wwaWX9SC/hAxZPtW6YKUDt4mf0VINUlpy71DmddlKtqaRYk8lIS8OQAt6KkbI6L4INvvga9PgabBjM0WCMA9MpdVteXurNbL6WaXX9TV+0Na6EEP9uS5cuZfr06axbt+5ON0UIIYQQQtwECUrdq1JS1IBUQgKUKZP3spZA8KkMiYdBbwRDAYIwCUnw83r4aS1EZxb/1uugcSQ82BIa1VEzbnKQaE/jojWOsxmxJDrS8NV7UMYciEGnJ4UADhmr4+84RGnrOryUy5Sy/UkJ2xauGutxydQMmz4g/+3MlOxIZ338P6yO38eOpJM4spRIj/SuQMfACNr41yTI5FPgbRcqnQ5bSAC2kAASG4Rdm2yzYzkfg+fpy3idvoTnKTVoZY5JxBIdjyU6noCdR7TlnUYD6WWLkVY+iLSyQWpWVaVy2EqWBZM3GLxQ0JPhtJLsSCPNodaF8jJ6Usy3BCGWQPyM3vgavTHrsxQndzrV7ph2u3qflARxcepjRbkWfNTr1e6Crq6AHh45B6+y/ttovOtHFRRCCCGEEOJuE+JjuS8GMLgfjkEUHglK3YvS09WA1NWrakAqPz/wPUqAIx2ST4KHEXQ3GB3twmX4fhWs+hMyrOq0AF94uDV0bg0liuW4mqIoxNlTOG+N43xGLGlOK/4GL8qbg7N3/9LpSDDWIMEQRoDjECVtf+LtvEBx+zZC7DuINdTiiqkJqYa8g27pThsbE4+wOm4/mxKPYFXs2ryanqXpEBhB+4BalDD73/A03WmKyUh6xRKkVyxBHLW16YbkNDWj6tTlLNlVlzGkW/E6dRmvU5fdtmP3tpBUNpCEckHEVyiFo1JF/KuEUblUVfxNPvgavfDMq6uiXq9mR+WSbaXJ2lXQZoPERHW0RVfwCtTrU6tzlVnPKmvw6vqgVdbglRBCCCGEEAIAP0/jPT+AQdXiPnzSM/JON0PcReRX373GaoWDB+HiRTUgld/uUjodeJUFezqkXwCP4qDLYWS2wydg4c+wcee1oELVCvDYg2q9KHPOwSyn4iTGlsy5jFguWOOwK04Cjd6EmPzy0TY98caaxBtq4Os8QUnrn/g5TxLs2EewYx/J+nJcMTUmzlBTa7NdcbA96QSr4/bze8I/pDgztM1VsoTQMTCCDoERlC+sOlp3mMPHk+TwiiSHV7w20enEfCUezzNXsJy6iPnUJbzOXMb7QhzGlAwCD18i8PAl4O9r6wT6QaWKUKUKVAmFqtWgcmXwy8fzlBODQb3lFbxSlOzBq7Q0iI5Wp2cNXmUdWdBkUou0e3urQSxX4Or6DCzJuhJFTFEUUm2pRbpPL5NXgeq6nTt3jrZt2zJo0CDmzZtHly5daNKkCVOmTOH8+fNUq1aNN998k6ioKADsdjtTp05l6dKlpKWl0axZM8aMGUNgYCAZGRlMnTqVn3/+mYSEBBo3bszo0aMpVaoUr732GmazmYkTJ2r7fv311/Hw8GD8+PFcvHiRMWPGsGXLFoKDg+nRowcvvfQSBoOBpUuX8t133xEcHMzWrVsZPXo0Xbp0YebMmSxcuJD09HQaNGjAqFGjKF26NACXL19m5MiR7Ny5k0qVKtGqVavCPdFCCCHEPeBeHsBAiOtJUOpeYrOpAanTp9WAlCGHoFJe9EbwrQSKFdKvgkeIOvKaosC+Q/DNT7DzWsFtGtWBxx+GujVy/eHvVJxE25I4k3GVS9YEFCDY6I2H3lzw49PpSDJUIcmzCl6O8xS3byXQfgAf51l8Ms6SgTcrrVVZmKywJv4Y8Y5rPwpLmvzpEBjBg4G1qeZR4s4U5XY61SBL5rFoN70OrQh5IXEoTlKdGaQEGbAFhmCoWwIvvYViJh+C8CDgYiLep6+gP3UeTp5Vi9Nfioa4RIjbB7v3uW8wOAgqVVYDVJUqqbeKFSE4+NaDPlmDTXlRlGuBK7sdMjLUbqqXLqnnVadTl9Hrr2VSubKuPD2vdRm8PnhlNhf8tSJELhRFofnc5mw+u7lI99usXDM2PL+hwO9tu3fvZsmSJaSmpvL0008zZswYateuzR9//MELL7zA8uXLqVChAp988gnLli3j/fffp3Tp0owePZrRo0czdepURo8eze7du5k4cSIBAQF8+OGHDBw4kCVLltCpUyfeeustbDYbJpMJq9XK+vXrmT59Ooqi8Morr1C9enV++OEHoqOjGTVqFDqdjpdffhmAPXv2MGDAAIYMGUJgYCBff/01P/30E5MnT6ZYsWLMmTOHvn378tNPP2EymRg8eDBeXl4sXryYo0ePMnLkSAIDA2/HKRdCCCGEEEVAglL3CldA6uRJtaj5zXZtMljAtyooRyDtCuy7qAajDh5V5+v10LYpPPkwVCqX62YcipMrtkROp1/lsjUBg05PsNEHi/4G3QLzKdVQhlOGRzlrbM+l5PX8Gr+PxUkpnLXv1ZYJMlhoFxBBh8A61PYuh15XREW27Xaw2tSb3TWqnXKtvhKZjxXUQJWiZGYD6dCGwDMYwJjZpc1kVP+dR/udipNUp5VUh5V0xYoePd4GCyXNfhQz+eFr8MDX4IFJn3ldVCsB1aq5byQtHU5fgFPn4NQZNVh1+gJEx0NMrHrbudN9HV/fawGqChXU+4oV1aBoYXev0+muZUDlxeG4Vu/KblfrqsXEqNOybitr1lXWQu0eHtmDVvnZrxCZdIUcZL6dnnvuOcqXL8/QoUN54okn6NKlCwC9e/dmx44dLFy4kGHDhvHdd98xbNgwWrZsCcCYMWNYuXIlCQkJ/Pjjj8yePZvGjRsD8OGHH/LAAw+wadMmWrZsidPpZNu2bTRv3pyNGzfi4eFBo0aN2Lp1KxcuXGDx4sXo9XoqV67MsGHDGDFihBaU0ul0vPTSS3h4qN2Jv/jiC0aPHk2jRo0AGDt2LM2bN2fDhg2UK1eOPXv2sH79ekqXLk21atU4cOAAq1atKurTKoQQQgghCokEpe4Fdjv88w+cOKEGpMw3kYWUld4Ddl2EL2fDscwR3kwmeKglPNkJSobk3hTFwRVrIqczorliTcKo01PC5HctGFJIjqddYU38ftbE7eesNVab7qfX093byVO+0NYrA51uP3FGB7FOO0n6SnkGdm6K3aF2mXQFoJyKGkQymcDPB3y91S6NWnAp8zw4nYCiLu90ZmZRZd7b7ZCWoQaJrDZISXMvIG404jQYSDU4STU4SMeBHh1eBjPBJh9CzL74GjzxNXgULAjo6QHVK6u3rFLS4PQpOHkGTp+DM5fh7BW4HKsWON+3T71lZTBA2bJqoMp1K19evRVGdlVeXF0G8+LKunIFr1JT1XpXNlv2WleurCujUQ1aeXurmVfXZ12ZzVLnSqDT6djw/Ia7vvueS5nMgTCOHz/OypUrWbRokTbPZrPRvHlz4uLiiI+PJzw8XJtXtWpVXn31Vfbu3YvT6aROnTravICAACpVqsTx48dp0aIF7dq1Y82aNTRv3pw1a9bQsWNHDAYDx48fJz4+nvr162vrOp1O0tPTiYuLAyA4OFgLSKWkpHDp0iVee+019Fm6pqenp3Pq1CkyMjIICAjQuvIBRERESFBKCCGEEOIeJr+w7naugNSxY1Cy5K0FpOx2+PVXmDtXDXABeFjg4cbQ81EIzr0LhM1p57ItkVPp0Vy1JWHWGSlp9seYU12qm3Qq/Sq/xu/n1/iDnEi/ok236Ey09A+jQ0AtmvpVw5dkitl347DvxaLEU8y+h2L2Pdh0PsQZahJvrEmSvkLONbPy4nSqRd2tNjV4cX0Ays9HPV8eZjXzxlgIx+5wQIYNhzWDtNRkUlMSyUhJRpdhxdOmJ9BhpLjOC1+DBV+TNx4WL7Ubpt4MukJ6+Xp7Qs0a6s3FmQGpSXD2HJy5BP/P3nkHSFHe//89bdv1xtEEETi6gCAEQSkxSqyxGytioomoiV1MUIwFhZ8apYgYu0b9YostRo09igUFbCBNOLjjets67fn98czMzu7tNTiu8Xnpw1PnmefZ3bmdee/n+Ty7q4BdlcDuCmDXHiAa48tId+xo3F8gABx0EBeo+vfn6YMO4un8/I7xAdVaqyu3nytN45sHlJa6rNsQXyYoy/z6s4Ur289VsnhFSwV7PIIgIM2T1tnDaBVey9+bYRj4/e9/j9/85jcJ9T6fD3IzYqu3CX9xhmHANPnupscddxzmz5+Pv/71r3jvvfewfPlyANxP1SGHHIIVK1Y0Oj4jI6NR/4a1/Pn+++/HoEGDEtpnZWXhs88+A7OvSwuFLBwJgiAIgiC6NSRKdWVUFfjhB75kr7Cw5Z3QmuvnjTeAJ54Adu3iZenpwNlnA2ecCGAPoDUAzGgk5Kimjj1qLbZHK1GtB+EXFfTxZLebGPVztBL/rf0e79Z+h83R+A5ysiBhSsYQHJszBkdlDkNAis9dRQ5KPL9EiTITaWYx8vT1yNG/h8KC6KV/gV76F9AQQJ08HLXSMDRIh8AUkl47ZgKqzl+bmAq+rE4AvB4gLQBkpnHLIr+3/QQoF7ppIKJHEdZjUA0VgiwikJuO3N69UeDPRobsR4Yhw2cAiKp8nMEwUB/kolldPY9h+a3yKDwolnAi7aPFmOgF0r3AiHxgBACmcyf5ZgzQVaCqHiitAUqqgZIaS6wqBUr3cKukTZt4SMbn40v/+vfnoV+/eOjTZ+8/43uLbSHla2YnQrfFVTTKrcfcFldAYx9X9lJBr7fxUkESrohOYNCgQdi1axcGDhzolC1evBiDBg3CGWecgZycHGzcuBHDhg0DAPz444+49NJL8eabb0KWZaxbtw5HHnkkAKCmpgY7duxwhKMjjjgChmHgscceg8/nw8SJE51zlpSUIDc31xGh/ve//+Gll17C4sWLG40xMzMTeXl5qKiowIwZMwAAqqri6quvxsUXX4yioiLU1dVhx44dzjx+/PHH/fOCEQRBEARBEB0CiVJdlUgE+O47oLiYW0jtzcN6KAS88grwzDNAuWV5lJ0N/Pa3wJlncn9BAKBnAQ3bgGg54MsHBAVRU8UetQ4/RytRowcREL3o58mBtI/L4xhj2BatwHt13+Pd2u+x1WURJUHE5IzBOCZnNKZnDkeG7G++M0FESBqIkDQQxZ7jkGFsQ47xA7L1jVAQRr7+NfL1r2FCQoMwEHXmIWjQ+iGqZ8ctaXxeoCCXC1A+Lw9N7DC4L6iGhrAeRUSPQTN1SIKIgOJDL38O8nxZyPCkIV3xwye34n3WdG6pFI1xQS0SAepDQDgChKNAbT238gKLLyv0WFZDitz6HRvdCDKgpANIB7wAAoVAPw0wo4ChckETAmBKQHk9sKce2FML7CoDSvYAxbuBsjIu6mzdykMq8vOBvn3jIlXfvjzu02ffLQX3lpactLuXCmoaEAwCNTXxJZk27mWC5OOK6GDmzJmDc889F2PGjMGMGTPw3nvv4fHHH8cTTzwBADj//PNx//33o7CwEHl5ebjjjjswbtw4pKen44wzzsBtt92G2267DVlZWfh//+//oXfv3pg6dSoAQJZlHHPMMVi5ciXOOOMMZ5nhtGnT0K9fP1x33XW46qqr0NDQgAULFuCII46A1IQwO2fOHPz9739HXl6eY2X19ddf44477kBWVhamTJmCm266CQsWLMCuXbvw9NNPIy2te1itEQRBEARBEI0hUaor0tAAfPstf4jfG4fSlZXA888DL7zA+wKAggLg/POBU07h/nLcyOlA5jCgwYNw6GeUAvhZC6JODyNd8qG/J3efnIgzxvBDpATv1/6A9+p+wM5YlVPHhahDMCt7FGZkjUC2HNi7cwgy6uUi1MtF2CFqyNC2IEvfhGxhK7xiHbLYNmQJ2wAPoPoy0RAYgfrM0QjmjILqbdqH1t5gMhNRXUXEiCGix8AYgyLK8MteHJReiBxfJtIVP9KVADzSXggPiiU0ZSQ9iFlLARGLxeNQBGgIcQErErQsfIC4YKUAHrntgpUgcqf5khewp8BMwNSA/h6gbyZg9gEwIr7ckClAVRTYUw3sLue7AZaUASUlwO7d3MKqspKHZB9WNnl5iSJVYWE8LiwEcnI6Znmgm9YsFXTvLKhplpBY39jiyr1U0PZxZe8saJ/DDrZ4RVZXRCsYN24cFi9ejKVLl2Lx4sUYMGAA7rnnHhx++OEAgEsuuQQNDQ3485//DF3XMWPGDCxYsAAAcMMNN+Duu+/GlVdeCVVVccQRR+Dxxx+HxyUSH3/88Xj++edx/PHHO2WSJOHBBx/EbbfdhjPPPBOBQACzZ8/GDTfc0OQ4L774YoRCIdx8880IBoMYPXo0HnnkEWRlZQEA7rvvPixYsABnn302+vbti/PPPx8vvfTS/njJCIIgCIIgiA6ARKmuRmUl32WvtpYLUm154Ny8mYtRb77Jl3sB3K/PeecBJ5zQrJVJvamhRFRQbMTQENqJLCUDA7wFe+VYF+DL/r4KbsdHdRvxUf0mlGv1Tp0iSJicMRi/zBqF6VnDkdmSRVRzJPuBYgAkEQ2egWhIH4ZdGWnwyrXI1jYhM/I90kNb4DHrkRf8HHnBz4ESIObJQzBtGILpwxBKG4KIv3+bHKbHDBURPYaoHoNq6BAFAT7ZizTFhwHphcj0pCNN8SNd8UMS96OAIElAQAICKZaiabr1Oqmu5YARoMFaDhgJAroGmADA+PwdCys5bnHV3OfBLVTZMAZAt6ypokBODMgOAMMHAdJQQPBYxwSAoAGUVfFQWgHsKef+nUpLuXAVi/Fd9qqquBVhKhQF6NWLC1S9ejUO+fk8dLTDcrdwlSwKu0nl40rXueBo9wPERStbnPL7ebCtrlIFctJ+QNG/f39sSlpCe/zxxyeIRm4URcGNN96IG2+8sVGd3+/HwoULsXDhwibPN3ny5EbnA4CDDjoIq1atSnnMqaeeilNPPTWhTJIkXHXVVbjqqqtSHpOTk4Nly5YllF1++eVNjosgCIIgCILo2tBTSlfBMLjT6J9+4g+h/fq1zuJD14GPPgKeew74+ut4+ZgxwAUXAEcd1aSwxRhDjVaP3dFylEQrETajyA70xQBPLoToHkCtATwZgNA6a55KrQH/q9+M/9X/hDUNWxA2VafOL3owLbMIM7NG4IjMoUiXmvHh0xSGwQUUtwAluvxAZQSAgJ87Ivf5HD9QMfRGGYajDCdDMFWkBzcjo+E7ZDb8gED4Z3jVKnjVT5FX8yk/jehFODAIobTBCAUGIRw4GKqnFyAIUA0NUUuEUg0+P4+kwC/50C+9F7K9GUhT/EiT/fDL3r0W9dod27oKKSzRbMFK07iFlapyAaghzJcExlQgFOY+uAAAjH+m3DsOKhIgyY19WQkCAAWQkz5DjAFM45ZVRhhQ6wDRBPoA6JsFTMgHhEMBycMFK8kH1EeB8mqgvAooqwDKK4GycmDPHh6qq/kcdu/moSkEAcjNjQtUySEvj8e5uc37mtoftMbHlb2Dox3snQV13dr10cLayTEh2MKVvbugLWy5fWK1tFyRIAiCIAiCIAiinaAnj65AMMidQu/cCWRl8aV2LbFrF/Dqq8Drr8f9RUkSMHMmd2A+dmyTopZu6qhUa1EcKUNZrBoGDGTLmcj3ZPMGcjqgpAGRMiBWBUgKL0Nifzoz8G2oGGsatuJ/9T9hY6Q0oT5fzsBRWcMwPWs4JqYPglds7VI1a6lTTOOCiWaJIZLILXdsAcrn5b6gvJ5W+4FiogcNmaPQkDkKJQBEI4q00BakBzchPfQT0kLbIJlRZAQ3IiO40TlOE32o8/ZFg7c/ov7+YBmD4csZiYA3BwHFhzR5P1tB7U8cwSoFjHERMKYmilfhCBCO8WVoqgqEDZcoIvCPii1cyRJPK660IHArKdGy3nO/fczgjtVNnQtWWj1gGrzPQgB9MgEhFxBHcSsr0QdIfsBgQFUdUFnDQ1klUFkFVFQCFRV8OWxlJRc3bYurVM7Y3aSlcZEqL4+LVMkhJyceMjI6ZumgKMYdpjeHW7wyjPiyQbssuU9bjJIkLk55vXEByy1cNRUIgiAIgiAIogUK0r0wTAZJ7CI/3u8lPWEOXQV6kuhMTJMvTdq0iVs69OnTvF+aYBD44APgtdeAtWvj5dnZwKmnAqedxpctNUFIj6BCrUFxpAxVai1kQUaOkgmflOLhVvID6QMATyYQ3gPEqsCkALZpQXwV3I7PG7ZibXA7QmYs4bCRgX6YmjEU07KGYYS/T8u+qHRLzLCtn2wH3YpltZGVDqSnWU7IPdZuZu33sTUlH2rSh6PUPxix3KMR06MIRPcgJ7oD+epuZEd3Iz1WAsWMIj+yDfmRbUAtAFt/8/UG0g4G0g/mcdpAIHAQ4OvVpiWAXRbBskTzNiGAMMbFKvv9U11CYjhi+bKK8vc4EuXiiGE5RQfj/SeIV6Ir72ks8jAGwOCClakBehAw63ge4IZgAwAMzAWEXnw3SVHhuwlKXu6wvT4cF6+qa4GqGqCq2hWquHilqnyzgFCIC8YtIcv8WszO5iKVnc7O5mKzO22HQGD/CVmtFa8A/p7Y4pVu7UoZDsfzbr9X9nvmDrbzdndIJVw5760cFycJgiAIgiCIA4ZMvwxJFPCn577BlvJgZw9nrxjSKx33nz2+s4fRYyBRqjNgjD/4bt/OfeX4fED//qkf0IJB4MMPgXffBdas4Q/+AG87ZQpw0kl8iV4TD54GM1Ct1mNPrBIl0UqEjAjSJD/6+AogC81b9pgM2KaFsC60E2trv8Xa+i2oNsIJbbKlACZnDMYvMobgiMyhyFPSU02Yi0+a7SvHtczIFiO8HiAvi1tBeSw/OT5Puzpx1gwdMVNDzFChGhp0UwdjgCLJ8EoKArIXfdPykZE/DH7Zi4Dsg1/2QgQDQtuB+k1AwxYegluBWAUQ3cND1ZrEk4kewN8PCPQH/H0Bfx8r7gv4CwElu2c8kAuC9X614OTbFqp0PS5i2WJkVOW78sVU/jmJaZZ4pfMPoSDwPgTRJVrZgoht2SPGRQ5mIi5cGdYOgUEgZvB+RAAFAAp8gNgPEAZYAqItYMl8yWrYAOoagOp6oKYeqKnjOxvW1PJQXQPU1vGd9kIhPh/bUXtrURQgM7PpkJERj5PTXm/7fYZscak1MJYoYtlWWNFovMy+vp33LoWQJUlx0cxjL7lNEq/ccXKaIAhiP0O/ghMEQew/tpQH8X1JfcsNiR4PiVIdTV0d8PPPQHExf1grLEy0jmIM2LoV+PRTHtatS1xqM2gQcOyx3HF5794pT8EYQ50eRGWsFrtjFai1nIxnyxnxJXopqNdD+KFhG74PbsP6+p/wbf0WNCSJUF5BxthAPxwe6IspmUNRlH4wREGGIzxFY9byIJeFhSBwEUGWucVTXjb3/aQoXHhSWhA12oBu6lANHaqpQTU0qKYOxhgfgiDDK3Pxqbc/DxmeAHyyF34reFNZjNlkDOXBjVoLhH7mIbiDx+GdQHg3YKpcyAptT92f6OXWVL5CwFcAeK3gKwA8eYDXCtJ+tKTpKFojXAHWDnVGXLyyBSz7s6TpXMCKxSwBSwdiVr1pcjGEMWuVqRA/ty1iiJagIVriligAkgAIJgCT92FGAcPkwpaoA9kAshUAeQByrWWHtsN3iYtZgghoJlAfAerC3BdXXRioD3JRqz7Ixaz6BqCunof6+rh1mb2UsK3IMhen0tMbh7S0xLQ7pKdzC61AgOebs85MhdtXVWtxC1n2e2UYXHS30/b7l3wuUXS9h2J8qaF7F0I7pBK+koO7v1YPn7XciOh20PtKtER3/yV/xrACXHfs8M4eBkEQBEE0C4lSHYGu84fOkhLukDkW446U/X5et3EjF5/Wr+dxRUXi8QcfDPzqV8DRRwODB6c8hclM1Osh1GoNKI1Wolqrh8o0pIl+9PbkQRYT3+pqtQ4/hXZic2gnNoV24IeG7dgZ3dOoX7/oxeiMwRifNQwT04sw2jcAHk0DItU81O/kDsdlL+DxA4olMqWnAWn+uBihyNYW9vv2kdNNgwtPpg7NCrqhg4E/XMiiDEXklk/5/mxkKAEEFD98kgdeyQO/7IVHah8BDJ5swDMOyBmXWM4M7o8rXAyEdwGRUiBSYoVSQK0CzJhVX9z8OUQv4MkFPDnW+axYyYrHShagZMaD5O+eQpYgNO/fKhnDsAQr21pHj5fZsarGnePH1LgwEjO5FZVhiVGMIe4zzUqLQlwEkaxYgFVuWgKVyYPEgBwPkC0BLA2ACTT1vCuIvH9V40JWKAI02CEEBKN8Z8SglQ9FuNAVCln1obi/qJoaHvYFRYkLVLZY5Q72zn6BQNzHVKq824m6bfXkzHkvhCwgLma5hSx77tGoJSS6BMnkZYaMxUUo5710pW0/WclO3612iiAAuo5wQwP8smv3yZZiolsQDvMfXZS2CrPEAUV3/iV/cEFaZw+BIAiCIFqkQ0WpWCyGW2+9FW+//TZ8Ph/mzp2LuXPnduQQOg7D4NYQtbXcH01tLX+Iqqvj+Z9+iodIJPFYrxc4/HC+PO+II4CDDkp5iqgRQ70eQr0WQmmsEvV6CKqpwSd6kC1nwCPKqFRrsa7+J2yPlGB7uAQ/h0uwNbwLVVpdyj77efIx0j8Ah/oGYpx3IIZ6+kC2HXgzGdAYIPuA/EGAbwQgawDC3K+PqFnWUH5ADvAlbK30q8QY44IT0y3hyeCCEzNgmAYAQBAESIIERZQhixICshcZSi4Csg8+2QOPpMAneeGVFHgkpWV/VvsLQQICfXnA5Mb1pgZEy4FoGQ+xCiBaEY/VKiBWzZ18mzEgWspDW84vZwBKOo/ldJ6W0gA5jb83chq3wnLSfivvt9J+vuOd5OP9dUVsaxdvG44xTS5E6UkWVrotUFmxYVqClmvJqWPlY/LrwBZCzCRhy3KX5cRucUsQANGy5hK8QIYPyMwC+tmiBuPtAS58wV6O6BK5GOMWYyFLuApFLcfzUSAUs/JRIGKlQ1HuzysSs9pYedVaCqxp/O9SXeq/CXuNbRnp88Wdpvu88bTb/5TPF1/C5/UCXmsDA68X8Lh9VVn1Hk88to+1haVk3KKW/V7ZeVVtXGa/xoIAiTFkZ2aiXNeBWAwBj4fvqOkWn1IJUu42yWk3remnuXpir2CMIRwOo7y8HNnZ2ZBoOShBEARBEESn0aGi1OLFi/Hdd9/hiSeeQElJCW644Qb07dsXs2fP7shh7B9sP1FbtnDH5Rs38h3ySkr4rl+lpXzL+lSkpwOHHsrDuHHAmDH8gcuFyUyEjSjCRhQhI4LyWA0q1BqURitQpdahXg+jXg+iMlaDklgldkfLsTtagRjTUp5SgICDlDwM9fbFUF8/jEo7GCPSD0a2N4s/BNoPk7J7u3iXJUGy4GNqgBHijqdjNWBqAwyjGjozYQAwBQW6IMJgInSBWzwxFt++XhAEyAIXm2RRgkdSkOVNR5rsdwQnj6i4YrmR9Ve3QVSAQD8emkOPcIFKrUkKdYBWC2h1fAmh1sB3qNPqAaZxSy2tlod2Ga+Xi1N27KS9XHh0dsCzdtMTvXHn4gmxx4qT0oJdZvlyEmW+RC5lfh8fHh0Lmb387BgG93Nl2OKVEReq7Ngtfhgm942l6nFrLns5oslcIhnj+WSRy17+yoC40gUA1uufnQPkii7BwhK1BMQDwP1oMSuGwNsZBrfajEaBsArEonz5bTgWF7GiUZ6Pqq5g5SMqtz6zy2NWMC31TNeBoM4tuzoKUbD+RsmJscf1t0uRLetN20rK/TfOPsbj/M3rrcjA+EkoHzqCL0MW3C8skoQjxOuEhMLEtkJSeVPtk/tv1E9r+hYaddlkvynbJs8nxRg7g1SvYRvJzs5G7yaWwRMEQRAEQRAdQ4c91YfDYaxevRoPP/wwRo0ahVGjRmHz5s145plnuo4oFYlw66ZQiO88FQoBDQ081NfzuKaGi08VFfFdusrKeDrZ4ikVeXnA0KE8DBsGY8hgBPsVoMEII6iFUKfWo7LsE1SqtahUa1Gt1aEsVo0ytRo1ehC1ehB1Rgi1RghBM9ri6SSI6OPNw8H+vhiU0R8HZwzAoKyBGJIzGAFvRspdsRi4g3TGWEJsMhOmGYERCcJkZryMmXHfHAIAIQuC4ock65CYDsmIQDJCUJiODEGAT5TgVTzwyl7IkheK7IMi+azYC0XyQm6vJXbdGdkPyP25s/TWwBhgRAG9HtCCgN7ARUItyGM9ZIUgoIe5JZYdGxErHbHyrs+WGeOhSyBYO+pZQpUgJQpWjYJVLrrKIPI8RKtMdLUXrXJ3nRg/TrBECUGyxiImHSPGx+j4QxIAj7tOjAtNDAATEmMAMOGqdwUIlnhl5U0TMBgPjshli1uIC1uGGBe9mBBf5sYAeBigAGAiwALgWxha54LgEsVcVmCOeGEtabTnBgC6aVmY6dxpve0LTNPjlmcxywpN1azYldaSyrSkds4ujy6Rz8ZkcXGsnRAA9MFj6BUIQMvPb19LJXtpqB3svO3U37Gyc8V2vYB4WrSWm9ppIWn5qZ2XrLQgcoHSvTwVQnwMosCvEVFIPKfk6s89ZlEEFOsakuzPvb0hgWD5c3ONRwT/wcO2InOf17280jm/FG9nX1fZOUD/SVyc3QsURSELKYIgCIIg9oqCdG+P2Ayjq8yhw0SpjRs3Qtd1jB8/3imbMGECVq5cCdM0IYqdtNTKIvL2m1j9l9+gWtZgCnCCIVjPc660bgVDAPRegNYH0KwyTRGh+j08eGWoXhkRr4SYIiAiA1FBR8TciIi5HlFDg7pRBzbu/bhlQUKeLxeF/nwU+PORH8hHYVov9E7vi96ZfVGQXgBRUsDAwBjjAhIYqsBQBUtEs1YHwXqOE0QBIkSIghVE0clLogSv5IUiK/CIHngkHmRRhiRKPBZ47C6TBREy07m4YUQt8STCRRIjzC2tmA4YDZaFTwqHxwkOppOFAdt6wZUWrLx9vPshuyciCJaQ5ecO1PcFxlzvVST+nrnfPzMGGDHu1N2I8thUXeWaq0xrHDPdijWrXLfSery+kWMmZn1O9FSjJvaWJOOffUJGx9rgmgA0V9BTpN1xctpdZiTVG/F6SQ9DMnY2KnfS7tgd7DKifTnIC3y9BcjP6uyRdAoHlDsEgiAIguhiZPrlbr8ZxpBe6bj/7PEtN+wAOuzRoaKiAjk5OfB44juc5efnIxaLoba2Frm5uR01lJQ8U/lf/P6E1Evd2oYJIGoFFwz8QacJREGEX/bBJ/mQ5klDmhJAmicdaUoaMryZyPRmIsObgSxvFnL8Ocj15yLXn4tMTyZEQYQgCBAEgachOMKQKIpOWhKlhLQjOrmCJMTL7fbJaWGvRR0P4lYY7teGucQJ3SVW6HwpGjMAwxI33HWmzl9vZi2pYm7zEtNlDeIqd5ZBubAdIvNM4/q9phV97bNAtrfHt/E40ctDs13uB7GPGdZnwoh/HkzX5yIh6Py9ZoYV23kzsR3MpHa2zyZX3ilLTrs/W3YdS2rnapPw2UtqD9sxN2tjWWvSgGNu5Xzm3W3g6h+uOiT1kdyn+3ik6M/dhytO2OUsqY99RQT3K7Z3BjMdgz1lW6AykShgmYgLWKYrb7jyelI+VZum0qnKWDPtzBTtUpWxpLLktu48UtSxpL6S08n9u1EsMfsApUe7QyAIgiCIbkJ33gyjK9FholQkEkkQpAA4eVVtv6UWe8txJ16DC9+pQGndLkiiDEGUuMhjiTeSIHGBR5QhCiJkgQs+iqg0Ens8kgeKqEAWZXglL/yKHz7ZB7/ih1/xI0PJQMATQEAJwC/7keHJgEfyQBS5oCQIQqPYFpvsMkeIcqXdbboVgsD9EcHTYtNGNBIR3A/zrrz7gTzlgzxS54GmH7QT6pLKk5+gUm493lSbFto2t415a87Tapo7rh1FhQ7pN9WpOvBcRNO4r8eEfHIbJLZLKXilKm/iumpRQGui39b00eQhKf7WsKTzsqTXIeHPitm4X6eMJeYbnbKp+qS/W01dF+7yZvtwjb3Z+mbyyfO2sX2tmQxIHwjk9k091h5Ot3CHQBAEQRAE0Uo6TJTyer2NxCc77/P5OmoYTdI3oy8eP/XJzh4G0VYcPz4EQRAE0fPp6u4QbLqKnwqCIAiCILo2HSZKFRYWoqamBrquQ7a27a6oqIDP50NmZmaLx9uOtIPB7rlmkyAIgiCI7kVaWlqXsz7eV3cIHXk/tfKDrSipa8UmMF2QMf2zcMaEg3BwpghT7Z6brxT6+ftMc+h8esI8aA5dA5pD16AnzOHgTLHDtJWW7qc6TJQaMWIEZFnGunXrMHHiRADA2rVrMWbMmFb9qhcK8W3Fp0+fvl/HSRAEQRAEAfD7lPT09M4eRgL76g6B7qdaxxsA7ursQewj2wB09zUAPWEOQM+YB82ha0Bz6Br0lDlMWNQx52rpfqrDRCm/34/f/OY3WLhwIe68806Ul5fj0UcfxaJFrXslevXqhQ8//LBL/mpJEARBEETPIy0trbOH0Ih9dYdA91MEQRAEQXQkLd1PdeTG3Zg/fz4WLlyICy+8EOnp6bjiiitwzDHHtOpYURTRu3fv/TxCgiAIgiCIrsu+ukOg+ymCIAiCILoSAmO0BRVBEARBEER3IBKJYPLkyXj00UcddwjLly/HZ599hqeffrqTR0cQBEEQBNE2usYWLQRBEARBEESLuN0hbNiwAe+++y4effRRXHDBBZ09NIIgCIIgiDZDllIEQRAEQRDdiEgkgoULF+Ltt99Geno6Lr74YsyZM6ezh0UQBEEQBNFmSJQiCIIgCIIgCIIgCIIgOhxavkcQBEEQBEEQBEEQBEF0OCRKEQRBEARBEARBEARBEB0OiVIEQRAEQRAEQRAEQRBEh0OiFIBYLIabbroJEydOxLRp0/Doo4929pC6LWVlZbjyyisxadIkHHnkkVi0aBFisVhnD6tbc8kll+DGG2/s7GF0W1RVxa233orDDz8cRxxxBO69916QK729o7S0FJdeeikOO+wwzJo1C48//nhnD6lboaoqTjjhBHz++edOWXFxMebMmYNx48bhuOOOwyeffNKJI+w+pHot161bh7PPPhvjx4/Hsccei9WrV3fiCHs2L730EoYNG9YoDB8+PGX7k046qVHbn376qYNHvfe0x7X7+uuv4+ijj8bYsWMxb948VFdX7+9h7zPtcZ1NnDix0XsfCoX299D3mlRzvv322xvN4emnn26yj8cffxxHHnkkxo8fj5tuugmRSKQjhr5PJM/7xhtvTHmNN7XLZ11dXaO2kydP7sgptJrmnlV66nXd3Jx78jXd3Lx76nXd1Jy7/DXNCPa3v/2NnXjiiey7775jb7/9Nhs/fjz797//3dnD6naYpsnOPPNM9rvf/Y799NNP7Msvv2S/+tWv2F133dXZQ+u2vP7666yoqIjdcMMNnT2UbsuCBQvYMcccw9avX88+/fRTNnnyZPbss8929rC6JWeeeSb785//zLZv387eeecdNnbsWPb222939rC6BdFolM2bN48VFRWxNWvWMMb438wTTzyRXXPNNWzLli1s5cqVbOzYsWz37t2dPNquTarXsry8nE2cOJHdc889bPv27ez1119nY8aMYe+//37nDraHEolEWHl5uRNKSkrYr371K3bHHXc0aqvrOhszZgz74osvEo7RNK0TRt522uPaXb9+PTv00EPZyy+/zH788Ud23nnnsUsuuaQjp9Fm2uM627NnDysqKmI7d+5MeO9N0+zAmbSeVHNmjLE5c+awhx56KGEO4XA4ZR9vvfUWmzBhAnvvvffY+vXr2XHHHcduvfXWjprCXpFq3vX19Qnz/eabb9jo0aPZO++8k7KPr776ik2aNCnhmMrKyo6cRqto7lmlp17Xzc25J1/TLT2X9sTrurk5d/Vr+oAXpUKhEBszZkzCl8/y5cvZeeed14mj6p5s2bKFFRUVsYqKCqfstddeY9OmTevEUXVfampq2FFHHcVOO+00EqX2kpqaGjZy5Ej2+eefO2UPPfQQu/HGGztxVN2T2tpaVlRUxDZt2uSUXX755V3yS7mrsXnzZnbSSSexE088MeGm/9NPP2Xjxo1joVDIaXvhhReyBx54oLOG2uVp6rX85z//yWbPnp3QdsGCBezqq6/ujGEecKxcuZIdffTRLBaLNar7+eef2fDhw1k0Gu2Eke0b7XXtXnfddQnf4yUlJWzYsGFs586d+3cCe0l7XWf/+9//2NSpU/f7eNuDpubMGGNHHnkk+/jjj1vVzznnnJPwOfjyyy/ZoYce2uTDbmfT3LzdzJ07l1177bVN9vN///d/7Kyzztpfw2w3mntW6anXdXNz7snXdEvPpT3xum7Ls3hXu6YP+OV7GzduhK7rGD9+vFM2YcIErF+/HqZpduLIuh8FBQX4xz/+gfz8/ITyYDDYSSPq3tx99904+eSTMWTIkM4eSrdl7dq1SE9Px6RJk5yySy65BIsWLerEUXVPfD4f/H4/XnrpJWiahm3btuHrr7/GiBEjOntoXZ4vvvgCkydPxvPPP59Qvn79eowcORKBQMApmzBhAtatW9fBI+w+NPVa2ibqydD3z/6ntrYWDz/8MK655hp4PJ5G9Vu2bEGfPn3g9Xo7YXT7Rntdu+vXr8fEiROdfJ8+fdC3b1+sX79+v4x7X2mv62zLli0YNGjQfhlje9PUnIPBIMrKynDwwQe32IdhGPj2228T3utx48ZB0zRs3LixvYfcLjQ1bzefffYZvvzyS1x99dVNttmyZUurXqPOprlnlZ56XTc35558TTc37556Xbf2WbwrXtNyh52pi1JRUYGcnJyEG6n8/HzEYjHU1tYiNze3E0fXvcjMzMSRRx7p5E3TxNNPP41f/OIXnTiq7slnn32Gr776Cq+99hoWLlzY2cPpthQXF6Nfv3545ZVXsHLlSmiahlNPPRV//OMfIYoHvCbfJrxeL26++WbcdtttePLJJ2EYBk499VScccYZnT20Ls8555yTsryiogK9evVKKMvLy8OePXs6YljdkqZey/79+6N///5OvqqqCm+88QauuOKKjhraAcuzzz6LXr16Yfbs2Snrt27dCkVRcOmll+K7777DoEGDcP311+PQQw/t4JG2nfa6dsvLy7vVtd5e19nWrVsRiURw/vnnY/v27RgxYgRuuummLvlQ29Sct27dCkEQsHLlSnz00UfIzs7GRRddhFNOOaVR2/r6esRisYT3WpZlZGdnd7v32s2qVatwyimnoE+fPk222bp1K3Rdx+mnn46ysjJMnDgR8+fPb/S572yae1bpqdd1c3Puydd0c/Puqdd1a5/Fu+I1fcA/lUUikUa/7Nl5VVU7Y0g9hiVLluCHH37AVVdd1dlD6VbEYjHccsstuPnmm+Hz+Tp7ON2acDiMHTt24LnnnsOiRYtwww034KmnniIH3XvJ1q1bMXPmTDz//PNYtGgR3nrrLbz66qudPaxuS1PfP/Tds29Eo1FcccUVyM/Px1lnndXZw+nRMMawevVqnHfeeU222b59O+rq6nDGGWdg1apVGDx4MC688EKUlpZ24Ejbl7Zeu9FotMdd6625zrZt24a6ujr88Y9/xIoVK+Dz+TBnzpxuZcG4bds2CIKAQw45BKtWrcIZZ5yBBQsW4J133mnUNhqNAkCPeq+Li4uxZs0anH/++c2227ZtG4LBIObPn4/77rsP5eXl+MMf/gDDMDpopHuH+1nlQLmum3o+6+nXtHveB8p1neq97qrX9AFvKeX1eht9oOw8CQJ7z5IlS/DEE0/gvvvuQ1FRUWcPp1uxbNkyjB49OkHpJvYOWZYRDAZxzz33oF+/fgCAkpISPPvss5g7d24nj6578dlnn+GFF17Ahx9+CJ/PhzFjxqCsrAwPPvggTjrppM4eXrfE6/WitrY2oUxVVfru2QdCoRAuu+wy/Pzzz/jnP/8Jv9/f2UPq0Xz77bcoKyvD8ccf32Sb2267DdFoFOnp6QCAhQsX4uuvv8a//vUv/OEPf+ioobYrbb12m7rX7K6fz9ZeZ4888gg0TUNaWhoA4P/9v/+H6dOn4/3338eJJ57YkUPea37zm99g5syZyM7OBgAMHz4cP//8M5599ln86le/SmhrL1HtSe/1f/7zH4wYMaJFVxJvvPEGBEFwroEHHngA06ZNw/r163HYYYd1xFDbTPKzyoFwXTf1fNbTr+nkeQ8dOrTHX9dNvddd9Zo+4C2lCgsLUVNTA13XnbKKigr4fD5kZmZ24si6L7fddhsee+wxLFmyBMcee2xnD6fb8cYbb+Ddd9/F+PHjMX78eLz22mt47bXXEvyeEa2joKAAXq/XEaQAYNCgQd36F/rO4rvvvsPAgQMTbs5GjhyJkpKSThxV96awsBCVlZUJZZWVlV1uuUN3IRgM4uKLL8bmzZvxxBNPdAv/Jt2djz/+GBMnTkRWVlaTbWRZdgQpAM6v02VlZR0xxP1CW6/dptoXFBTstzHuL9pynXk8HufhFeAPd/379+9W770gCM6Dq01Tn9/s7Gx4vd6E91rXddTW1nbL9xrg1/gvf/nLFtv5/f6E+4O8vDxkZ2d32fc61bNKT7+um3o+6+nXdKp59/Trurln8a56TR/wotSIESMgy3KCE7u1a9dizJgx5HNmL1i2bBmee+453Hvvvc3+cko0zVNPPYXXXnsNr7zyCl555RXMmjULs2bNwiuvvNLZQ+t2jB07FrFYDNu3b3fKtm3bliBSEa2jV69e2LFjR8IvRdu2bUvwRUC0jbFjx+L77793TMMB/v0zduzYThxV98Q0TVx++eXYtWsXnnrqKQwdOrSzh3RAsGHDhhZ/MT3//POxbNkyJ2+aJjZt2oRDDjlkfw9vv9HWa3fs2LFYu3atky8tLUVpaWm3u9bbcp0xxnD00UfjpZdecsrsJfXd6b2///77MWfOnISyjRs3ppyDKIoYM2ZMwnu9bt06yLKM4cOH7++htjuMMXz77bctXuPBYBCHH3441qxZ45SVlZWhpqamS77XTT2r9OTruqk59/Rruql59+Trurln8a58TR/wqovf78dvfvMbLFy4EBs2bMC7776LRx99FBdccEFnD63bsXXrVqxYsQK///3vMWHCBFRUVDiBaD39+vXDwIEDnZCWloa0tDQMHDiws4fW7TjkkEMwY8YMzJ8/Hxs3bsTHH3+MVatW4be//W1nD63bMWvWLCiKgr/+9a/Yvn073nvvPaxcubLFNelE00yaNAl9+vTB/PnzsXnzZqxatQobNmzA6aef3tlD63a88MIL+Pzzz3H77bcjMzPT+e5JXopBtC+bN29utATAMAxUVFQ4AvasWbPw+OOP47///S+2bduGv/3tb2hoaEjpULa70NK1q6oqKioqHN8bv/3tb/Gvf/0Lq1evxsaNG3H99ddjxowZOOiggzpzGm2mpevMPW9BEDBjxgwsXboUn3/+OTZv3ozrr78evXv3xvTp0zt3Im1g5syZ+PLLL/HII49g586d+Oc//4lXXnnFcQEQjUYT7nPPOeccPPLII3j33XexYcMGLFy4EGeeeWaXXubTFLt370YoFEq5zMc97/T0dEyYMAGLFi3Chg0b8P333+Oqq67CkUceiWHDhnX0sJuluWeVnnpdNzfnnnxNNzfvnnpdt/Qs3qWvaUawcDjMrr/+ejZu3Dg2bdo09thjj3X2kLolDz30ECsqKkoZiL3nhhtuYDfccENnD6PbUl9fz6677jo2btw4NmXKFLZ06VJmmmZnD6tbsnnzZjZnzhx22GGHsaOPPpo99thj9Fq2kaKiIrZmzRon//PPP7Nzzz2XjR49mh1//PHsf//7XyeOrnvhfi3nzp2b8rvnvPPO6+RR9mzGjBnDPvroo4Sy4uLihPfGNE324IMPshkzZrDRo0ezc889l23atKkzhrtPtOXaXbNmDSsqKmLFxcVO2YsvvsimT5/Oxo0bx+bNm8eqq6s7dPx7S1uus+R5R6NRtmjRIjZ16lQ2duxYdumll7KSkpJOm0trSX6v33nnHXbiiSeyMWPGsNmzZ7P//Oc/Tt2LL77Y6D73oYceYlOmTGETJkxg8+fPZ9FotMPGvi8kz3vdunWsqKiIxWKxRm2T511bW8tuvPFGNnnyZDZ+/Hh27bXXstra2g4Zd1to6VmlJ17Xzc25J1/TLb3XPfG6bmnOXfmaFhhjbP/IXQRBEARBEARBEARBEASRmgN++R5BEARBEARBEARBEATR8ZAoRRAEQRAEQRAEQRAEQXQ4JEoRBEEQBEEQBEEQBEEQHQ6JUgRBEARBEARBEARBEESHQ6IUQRAEQRAEQRAEQRAE0eGQKEUQBEEQBEEQBEEQBEF0OCRKEQRBEARBEARBEARBEB0OiVIEQRAEQRAEQRAEQRBEh0OiFEEQXZ5hw4bhmmuuaVT+0ksvYdasWZ0wIoIgCIIgCIIgCGJfIVGKIIhuweuvv47PPvuss4dBEARBEARBEARBtBMkShEE0S3o168f/va3v0FV1c4eCkEQBEEQBEEQBNEOkChFEES34M9//jPKysrwyCOPNNlmz549+NOf/oRJkyZh8uTJuP322x0R66WXXsL555+PBx54AJMnT8bEiROxaNEiMMac45977jnMmjUL48ePx/nnn49Nmzbt93kRBEEQBEEQBEEcqJAoRRBEt6CwsBBXXnklVq5cieLi4kb1qqriwgsvRCQSwVNPPYW///3v+OCDD7B48WKnzTfffIPt27fj2WefxYIFC/Dkk0/i008/BQC89957WLZsGRYsWICXX34ZEyZMwAUXXIC6uroOmyNBEARBEARBEMSBBIlSBEF0G84//3wMHDgQd9xxR6O6jz/+GGVlZViyZAmGDRuGKVOm4Oabb8azzz6LUCgEADAMA7fddhsOOeQQnHzyyRg+fDi+/fZbAMA//vEPXHrppZg5cyYOPvhg/PnPf0a/fv3w6quvdugcCYIgCIIgCIIgDhTkzh4AQRBEa5EkCQsXLsQ555yDd999N6Fu69atOPjgg5GVleWUHXbYYdB1HTt37gQA5OXlIT093alPT0+HruvO8UuWLMG9997r1MdiMfz888/7cUYEQRAEQRAEQRAHLiRKEQTRrTjssMNw2mmn4Y477sDvfvc7p9zr9TZqaxhGQuzxeBq1sX1KGYaBm266CVOmTEmod4tYBEEQBEEQBEEQRPtBy/cIguh2XHvttQiHwwlOzwcNGoSff/4ZtbW1Ttm6desgyzIGDBjQYp+DBg3Cnj17MHDgQCesXLkS69at2w8zIAiCIAiCIAiCIEiUIgii25GTk4Nrr70Wu3fvdsqmTp2Kgw46CNdffz02bdqENWvW4LbbbsMJJ5yAzMzMFvu86KKL8MQTT+CVV17Bzp07sWTJEvz73//G4MGD9+dUCIIgCIIgCIIgDlho+R5BEN2S008/HS+++CLKy8sBcH9TK1aswG233YYzzzwTaWlpOPHEE3H11Ve3qr/jjjsOlZWVeOCBB1BZWYkhQ4bgwQcfxMEHH7wfZ0EQBEEQBEEQBHHgIjDboQpBEARBEARBEARBEARBdBC0fI8gCIIgCIIgCIIgCILocEiUIgiCIAiCIAiCIAiCIDocEqUIgiAIgiAIgiAIgiCIDodEKYIgCIIgCIIgCIIgCKLDIVGKIAiCIAiCIAiCIAiC6HBIlCIIgiAIgiAIgiAIgiA6HBKlCIIgCIIgCIIgCIIgiA6HRCmCIAiCIAiCIAiCIAiiwyFRiiAIgiAIgiAIgiAIguhwSJQiCIIgCIIgCIIgCIIgOhwSpQiCIAiCIAiCIAiCIIgOh0QpgiAIgiAIgiAIgiAIosMhUYogCIIgCIIgCIIgCILocEiUIgiCIAiCIAiCIAiCIDocEqUIgiAIgiAIgiAIgiCIDodEKYIgCIIgCIIgCIIgCKLDIVGKIIg2wxg7IM+9L3TXcRMEQRBEd4S+d4lU0OeCILoeJEoRRA/m/PPPx7BhwxLCxIkTccEFF+CLL75oc3979uzBJZdcgt27dztls2bNwo033tjmvoYNG4alS5e26ZjVq1fj7rvvbvO5OpvNmzfjt7/9bULZ3syfIAiCINysXbsWV1xxBaZOnYoxY8bgl7/8Jf76179i69atnT20BJYuXYphw4Z12PnWrl2LSy65pMPO1xX4/vvv8fvf/x6/+MUvMHnyZMydOxfff/99QhvGGB555BEcc8wxGDNmDI499lg888wzLfa9e/du/OlPf8KUKVMwefJkXHbZZdi5c2eT7YPB4F7fH9qfFXcYOXIkJk+ejHnz5mHz5s2t7uvRRx/FtddeCwCor6/H9ddfj6+++qrNY9obbrzxRsyaNavZNi+99BKGDRuGXbt2tbrf1hxTU1ODGTNmoLi4uNX9ugmFQrj11lsxdepUjB8/Hr///e+xbdu2Zo+ZNWtWo/fNDk29Dk899VSLrxFxYCB39gAIgti/jBw5ErfccgsAwDAM1NTU4Nlnn8XFF1+Ml156CUOHDm11X59++ik+/PDDdhnX888/j969e7fpmAcffBCTJk1ql/N3JG+99Ra++eabhLK9mT9BEARB2KxatQr33nsvpk2bhptuugkFBQXYsWMHnn32WZxyyilYtGgRjj/++M4eZqewevXqLifM7U927NiB8847D6NHj8Ydd9wBQRDw6KOP4pxzzsHLL7+MQw45BACwePFiPPXUU7jyyisxZswYfPTRR/jb3/4GWZZx1llnpew7Go1i7ty50HUdCxYsgNfrxQMPPIDzzz8fr732GjIzMxsds2jRooQfMPeG559/3kkbhoGSkhLcd999OPfcc/HGG2+goKCg2eO3bt2Khx56CK+++ioA4Mcff8S//vUvnHbaafs0rvZkxowZeP7559GrV6927TcnJwdz5szBTTfdhCeffBKCILTp+GuuuQbr16/Hddddh/T0dCxbtgwXXHAB3njjDWRlZaU8ZtmyZVBVNaFs3bp1WLRoEc4+++xG7d944w3cddddKCwsbNPYiJ4JiVIE0cNJT0/HuHHjEsqOOOIITJkyBS+99BJuuOGGThlX8pgONA70+RMEQRB7z/vvv4977rkHV1xxBS6//HKnfNKkSfjNb36Da665BjfeeCOKiora9OMT0T156qmn4Pf78dBDDyEQCAAAfvGLX2DWrFl4+umncfPNN2PXrl14/PHHsWDBApxzzjkAgClTpqC0tBSffPJJk6LUV199hZ9//hmPP/44pkyZAgAYNGgQfv3rX+O///0vTjnllIT2H374If79738jIyNjn+aUfJ80YcIE9OnTB+eeey5efvnlFi3hlixZghNOOKFLix65ubnIzc3dL32fc845ePDBB/HOO+/gmGOOafVx33zzDd5//32sWrUK06dPBwBMnDgRv/zlL/HPf/4Tf/zjH1MeN3LkyIR8MBjE1VdfjRkzZiS8V1VVVbj//vvx/PPPIzs7u+0TI3oktHyPIA5A/H4/vF5vo19O3nzzTZx66qkYP348pk6diptvvhl1dXUAuLnw/PnzAQC//OUvE0yyNU3D4sWLMXXqVIwbNw5z587Fjh07mh2De/na559/jmHDhuGzzz7D3LlzMXbsWEydOhVLliyBYRgAuFnw7t278fLLLyeYLZeUlODqq6/GpEmTMHbsWFx44YX44YcfnPPs2rULw4YNw2OPPYbZs2dj7NixePDBBzFs2DC8//77CWP68ccfMWzYMLzzzjsAgFgshsWLF2P69OkYPXo0TjzxRLz55psJx8yaNQsPPPAA7r77bhxxxBE49NBDcfHFF+Pnn38GwM3Qly1b1mjOycv3ysvLMX/+fEyfPh2HHnooTj/9dPz3v/9t9Jo988wz+Mtf/oJJkyZh/Pjx+NOf/oTKykqnzc6dO/GHP/wBkydPxtixY3HWWWe1m3UbQRAE0TVYtmwZDjnkEMybN69RnaIo+Nvf/gZJkvDwww8DAObOnYtTTz21UdvLLrsMJ510kpP/6quvcN5552Hs2LGYNGkSbrjhBlRXVzv1L730EkaOHInVq1dj6tSpmDRpErZs2dLq754PPvgAJ510krN07JVXXkmob813YSwWw/LlyzF79myMGTMGxxxzDFatWgXTNAHwZVMvv/wydu/ejWHDhuGll15K+RouXboUs2fPxjvvvIMTTjgBY8aMwcknn4xvvvkG69atwxlnnIFDDz0UJ5xwAj777LOEY3/66SdceumlOOyww3DYYYdh3rx5jZZKbdy4EZdffjl+8YtfYNSoUTjyyCNx++23IxqNOm1a871uL9f6/PPPU84DAA455BDMnTvXEaQAIBAIoHfv3s4yu3fffRderxenn356wrF///vfm3UnEIvFAABpaWlOmS0m1NbWJrStq6vDX//6V1x33XUpLaj2ldGjRwOAY4W1dOlS/OpXv8KyZcswadIkTJs2DXV1dfjpp5/wwQcf4IQTTgDA7zMvuOACAMAFF1yA888/3+mzuXtfm2+//RYXX3wxJk+ejMMOOwx/+MMfWr2M8KWXXsKxxx6LMWPG4KSTTkq4LlItxXv55Zdx3HHHOe0/++wzjBw5stHneP369Tj77LMxZswYzJgxA//4xz8S6j0eD4499lg89NBDTpl9v93UNQEAn3zyCQKBAKZNm+aU5ebm4vDDD2/T/eSKFStQXV2Nm2++OaF85cqV+OSTT7B06VLMnDmz1f0RPRsSpQiih8MYg67r0HUdmqahoqIC99xzD1RVTTBhXrFiBa6++mqMGzcODzzwAObNm4f//Oc/OP/88xGNRjFjxgzn15Fly5bhsssuc4598803sXnzZtx111245ZZb8N133+Gqq65q81ivvfZaTJgwAStXrsQJJ5yAf/zjH1i9erVzzoKCAkyfPt0xda6ursbZZ5+N77//HgsWLMA999wD0zRx7rnnNjLbX7p0KX7/+99j8eLFOOWUUzBgwAC88cYbCW1ef/11ZGdnY/r06WCMYd68eXjuuedw0UUX4cEHH8T48eNx1VVXNbqJfvLJJ7Ft2zYsWrQIt99+O7777jvHAu2MM85wbgCff/55nHHGGY3mXVlZidNPPx1fffUVrrrqKixduhT9+vXDvHnzHLNzm/vuuw+maeLee+/F9ddfj/fffx933nknAMA0TVx66aWIRCJYvHgxVqxYgezsbPzxj39sUSQkCIIgugfV1dX47rvvMHPmzCaX5WRnZ+OII45wBJ2TTjoJ33//fcJ3QX19PT766COcfPLJAIAvv/wSc+bMgc/nw9///nfcdNNN+OKLL3DBBRckCCmGYeDRRx/FHXfcgfnz52PQoEGt/u65+eabMWfOHDz44IPo3bs3brzxRmzcuBFA674LGWP4wx/+gH/84x8444wzsHLlSsyePRt///vfHVcFl112GaZPn46CggI8//zzmDFjRpOv5Z49e3DXXXfhD3/4A+6//37U19fjyiuvxNVXX40zzjgDy5cvB2MMV111lfMabN++HWeffTaqqqpw991344477kBxcTF++9vfoqqqCgAX184991xEIhHcddddePjhh3H88cfjqaeewpNPPpkwhua+14H4Eq9Ro0Y1OY9zzjkHv/vd7xLKduzYgc2bNzuWcj/++CMGDhyIL7/8EqeccgpGjRqFWbNmJSyTS8W0adMwePBgLFmyBMXFxaioqMBtt92GQCCAo48+OqHtbbfdhsGDB6dcrtUebN++HQAwYMAAp6ykpAQffvgh7rvvPsyfPx9ZWVl47bXXUFBQ4FhbjRo1yhFHbr75Zuez0tK9LwCsWbPG8Qt655134vbbb0dpaSnOPvvsFpeIlpaWYtWqVfjTn/6EpUuXQhAEXHnllc7nJJlXXnkFN954Iw477DCsWLECxx57LC677DLnB1o3CxcuxPHHH49Vq1Zh/PjxWLJkSaMfW2fPno3vvvvOed1GjRrV4jWxdetW9O/fH5IkJZQPGDDA6aclSkpK8OSTT+Liiy9Gv379EurOPvts/Oc//2mT9RZxAMAIguixnHfeeayoqChlWLlypdOutraWjR49mi1YsCDh+C+//JIVFRWxp59+mjHG2IsvvsiKiopYcXGx02bmzJls+vTpTFVVp+y+++5jRUVFrKGhocmxFRUVsQceeIAxxtiaNWtYUVERu++++xLazJo1i1166aUJ57rhhhuc/L333svGjBnDdu3a5ZTFYjH2y1/+kl1xxRWMMcaKi4tZUVERu+mmmxL6fuCBB9i4ceNYJBJhjDFmmiabMWMGu/nmmxljjH3yySesqKiIvfHGGwnHXXvttWzq1KlM0zRnTDNnzmS6rjttli5dyoqKilh1dbVzrqKioibnv3jxYjZq1KiEeTDG2IUXXsimTp3KDMNwjvntb3+b0ObGG29k48aNY4wxVl5ezoqKitirr77q1NfX17M777yT/fTTT4wgCILo/mzYsCHhu7kp7rrrLlZUVMRqa2tZKBRi48aNY8uWLXPqV69ezYYPH8727NnDGGPsrLPOYieccELC99m2bdvYiBEjGt0HvPLKK06b1nz32N+DH374odNmx44drKioiD3xxBOMsdZ9F37wwQesqKiIvf766wltli9fzoqKipzz3XDDDWzmzJnNvj6pxvTQQw+xoqIitnr1aqfsrbfeYkVFReyHH35gjDF29dVXsyOOOCLhHqempoZNmDCB3XXXXYwxxj7++GN27rnnNroPOuGEE9jcuXOdfEvf63tLJBJhZ511Fhs3bpzzev7ud79jkydPZr/4xS/Y008/zT799FP217/+lRUVFbHnnnuu2f6+/vprNmnSJOcecvTo0eyTTz5JaPP2228nnC/5nq212O+LpmlOaGhoYF9++SU75ZRT2IQJE1h5eXlC2y+//DKhj9NPP5398Y9/TCiz7zXXrFnDGGv9ve/pp5/OjjvuuITroq6ujk2aNIldeeWVTc7jhhtuYEVFRWzLli1O2aeffsqKiorYu+++yxhrfF89Y8aMhPtexuKfyRdffDHhmH/+859Om3A4zEaNGsXuvPPOhGPr6+tZUVERe+aZZ5ocZzJz585lZ599dqPye++9l40aNapVfdx5551s/PjxrLa2ttl2rblOiQMDspQiiB7OqFGj8MILL+CFF17A6tWr8cgjj+DCCy/Efffdh/vuuw8Ad0Soqqpj5mwzceJE9OvXr8Wd+g499FAoiuLk+/fvD4D/CtsWxo8fn5Dv3bs3wuFwk+0/++wzjBgxAoWFhY41mCiKOOqoo/Dpp58mtB0xYkRC/qSTTkI4HHZ+Vfr6669RUlLi/GL82WefQRAETJ8+3elb13XMmjULFRUVCWbbY8aMSfhFyXZgHolEWjXvL774AuPHj2/0a9JJJ52EioqKhB1Pkn0s9O7d2zlPfn4+hgwZggULFuCGG27Aa6+9BtM0MX/+fPIpQhAE0UNg1pb27u/dVNjfS4wxx6rFvQT9jTfewJQpU1BYWIhIJIL169c7lsL2d95BBx2EwYMH43//+19C3+7v1LZ890ycONFJJ98rtOa78IsvvoAsy5g9e3ajNnYfbeWwww5LmAsAjB071imzl6rZ41yzZg0mTZoEn8/nvE7p6emYOHGic+8xbdo0PP300/B6vdiyZQv++9//4sEHH0R1dXUjZ9DNfa/vDcFgEJdeeim+/fZbLFmyxHk9NU1DTU0Nbr31Vpx77rmYMmUKbrvtNkybNs1xM5AK21pu+PDheOihh/Dwww/jqKOOwuWXX+7sZGcv07r++usbvX97y6hRo5wwYcIEnHvuuVBV1bGcd5N8j1dcXOx8vpqiNfe+4XAY3377LX79618n3OdlZmZi5syZLX7ecnJyMHjwYCdvj6mhoaFR2x07dqCkpKTRZ7upzQrc15Lf70d+fn6j++6MjAxkZma2aXc/++9LKlrjMD0Wi+GFF17A6aef3qRTdIJIhhydE0QPJy0tDWPGjEkomzZtGsLhMP7xj3/gggsucNbO2zdjbvLz81N+ebpx+zAAAFHkerft36G1+Hy+Rv009+VYW1uLHTt2NGnS7r6pSx7jwIEDMX78eLzxxhv49a9/jTfeeAMDBgxwbk5ra2vBGEu4WXVTXl7u3AT5/f5G4wZaP/+6ujocdNBBjcrt98N9k5HqXPZrZO+2Yzu2fOWVV6AoCo4++mjceuutdHNAEATRA7Af+lva3ay4uBhpaWmOqHLyySfj1VdfxcaNG5Gfn4/PP//cWSZWX18P0zTx8MMPO36o3Hi93oS8+zu1Ld897uPs70r7O6w134V1dXXIyclptLTIFilaul9JRXp6eqOy5O9aN7W1tXjzzTcb+ZgE4DittpfjPfPMMwiHw+jTpw8OPfTQRq9jqnO1dO/THKWlpbj00kuxfft23HfffQnL69LS0pwf29wceeSR+OSTT1BZWZnyPnDlypUoLCzEww8/DI/HA4DfR5599tm488478dJLL2HhwoUYMmQITj/9dOi67hxrC5ySJLV5B7gXXnjBSSuKgoKCAuTl5aVs6/Z3BXBhrrn3EECr7n0bGhrAGGu3+2P7NUh1f2j7bkueY6pzA63/3Pj9fgSDwWbH6SY9PT3Bp5lNKBRqlfP6Tz75BMFgECeeeGKrz0kQJEoRxAHK6NGjsXr1auzatcu5YaysrHS2DbapqKhIeZPYFcjIyMCkSZNw/fXXp6y3b56a4qSTTsKiRYvQ0NCAt956y/EZYPcdCAQa+X6wGThw4N4PPImsrCxUVFQ0KrfLcnJyWt1XYWEhFi5ciFtuuQUbN27EW2+9hYcffhg5OTmODwWCIAii+5KXl4dx48bhP//5D/70pz854o6bYDCI//3vf5g1a5ZTNmXKFBQUFODf//43CgoK4PV6Hb8utmAxZ86clJYZLT3gt8d3T2u+C7OyslBTUwPDMBKEqfLycqfN/iYjIwNHHHEELrrookZ1sswfrVatWoXHH38ct956K4455hjnYT7ZyXh7smnTJlx88cWIxWJ49NFHcfjhhyfUDxw4EIwxaJqWII7ZIlLyD4M2u3fvxujRoxPuqURRxIQJE/DMM88AAP7zn/8AiDsidx/7yiuv4Mknn8TkyZPbNJ/kH1TbQnZ2douCUWvufTMyMiAIQkqRpqKiol13j7Ot7JP9TTXlf6q11NfXt+m6GDRoED755BOYppnwt2XHjh0JVl9N8cEHH6B///779P4RBx60fI8gDlA2bNgASZJw0EEHYezYsfB4PHj99dcT2nz11VcoKSlxrIVS3fh2JMnnnzRpErZv345BgwZhzJgxTvjXv/6FF154odEvqckcd9xxYIzh/vvvR1VVVcIORJMmTUI4HAZjLKHvn376CcuXL0/4JbCt407m8MMPxzfffNPoV+9XX30VBQUFrRbAvvnmGxxxxBHYsGEDBEHAiBEjcNVVV6GoqAglJSWtHi9BEATRtbn88suxfft23HvvvY3qDMPALbfcgmg0muD8WpIknHjiiXj//ffx1ltv4eijj3YsOdLT0zFy5Ehs27Yt4Ttv6NChWLp0abM7v7XXd09rvgsnTZoEXdfx1ltvNWoDABMmTACwf+9X7B0HR4wY4bxOo0ePxuOPP+7s3rt27VoMGTIEp512miNIlZWV4aeffmqzFXlrKC0txUUXXQRBEPDss882EqQAOBZSyZu8vPfeexg2bFhKizGA7+y3YcOGhGWHjDF88803zo+WtpsIdygoKMDMmTPxwgsvNOukfX/Qr18/lJaWJpQl3xO25t43EAhg9OjR+Pe//53gbLyhoQEffPCB83lrD3r37o0BAwY4nyGbt99+e6/7rKurQyQSQd++fVt9zLRp0xAKhfDxxx87ZdXV1fjqq68wderUFo9ft25dk6sMCKIpyFKKIHo4wWAQ69atc/KqquK9997Diy++iLPOOssxNb/kkkuwfPlyKIqCmTNnYteuXbj//vsxZMgQnHLKKQDgbO/7zjvv4KijjmrVLybtSWZmJn744Qd88cUXOPTQQzFnzhz861//wpw5czB37lzk5OTgzTffxP/93/9h/vz5LfZn77T3z3/+E+PHj08Qf6ZPn47DDz8cl112GS677DIMHjwYGzZswAMPPIAjjzzSed1aO26A7+43duzYRpZnF110EV599VXMmTMHl19+ObKzs/HKK69gzZo1uPPOO1t9cz1y5Ej4fD5cf/31uOKKK5Cfn49PP/0UP/74o7MVMkEQBNH9OfLII3HjjTdi8eLF+PHHH3HaaaehV69e2LVrF5599ln8+OOPuOOOOzB8+PCE404++WQ8+uijEEWx0TK9q6++GpdccgmuueYanHTSSc4ue+vXr0/YcTeZ9vruac134VFHHYXJkyfjr3/9K8rKyjB8+HB88cUXePjhh3HKKadgyJAhAPj3bmVlJT788EOMGDECvXr1asOr2zyXXXYZzj77bFx66aX47W9/C6/Xi+effx7vvvsuHnjgAQDc1+aKFSuwatUqjBs3Djt27MBDDz0EVVXb7C+quroaO3fuxJAhQ5oUjm6//XZUVVXh1ltvbXTfl56ejiFDhmDy5MmYOXMmFi1ahEgkgqFDh+KVV17B119/jRUrVjjtd+7cierqasfX1WWXXebs7nfhhRdClmW8+OKLWLdunTPfVFYxHo8H2dnZCXV79uzBnj17MHLkyBat2feFqVOn4p///CcYY86SOVsc/OCDD5CVlYXhw4e36t73mmuuwcUXX4xLLrkE55xzDjRNw6pVq6CqKubNm9duY7Z35rv22mtxyy234Fe/+hU2btyI5cuXA9g7oXXt2rUAuNAE8GeCLVu2YMCAAU3exx5++OGYNGkSrrvuOlx33XXIzs7G0qVLkZGRkbCiYMuWLVBVFSNHjnTKDMPAtm3bGvnpIoiWIFGKIHo4P/zwA8466ywn7/V6MWDAAFx11VW4+OKLnXL7RvLpp5/G888/j+zsbMyePRt//vOfnV9SJ0+ejCOOOAL33HMPPvvsM6xatapD5zJ37lzceeeduPjii/HYY49h4sSJeO6553DPPfdg4cKFiMViOPjgg3HHHXe02kT+5JNPxrvvvtto7bsoili1ahXuv/9+PPTQQ6iqqkJhYSEuuuiiNt+EHHPMMfjXv/6FG2+8EaeffjoWLlyYUF9QUIBnn30W99xzD26//XZomobhw4djxYoV+OUvf9nq83i9Xjz66KO45557cMcdd6C+vh4HH3ww/va3v+HUU09t05gJgiCIrs1FF12E8ePH44knnsDdd9+N6upqFBQUYOrUqbjjjjscgcbN8OHDUVRUhJqaGkyZMiWhbtq0aXjkkUewbNkyXHnllVAUBaNGjcJjjz3WyBm3m/b67mnNd6EgCHjooYfwwAMP4PHHH0d1dTX69++Pq6++OmE53amnnooPP/wQ8+bNw5VXXolLLrmk1eNoieHDh+OZZ57Bfffdh+uvvx6MMRQVFWH58uXOOC+99FLU1NTgySefxPLly9GnTx+cfPLJzvjr6+udH6xa4oMPPsD8+fObXAKnqio++OADAEi5VHLSpEl46qmnAAD3338/li1bhsceewzV1dUYMmQIli1blrDMc8WKFXj55ZexadMmAFxwevrpp3H//ffj2muvhaIoGDZsGJ588klMmjSpTa/d6tWrsWzZMvz3v/9t0RH5vnDMMcdg+fLl2LBhg+O0fujQoTjhhBPwzDPP4OOPP8brr7/eqnvfKVOm4LHHHsMDDzyAq6++Gh6PBxMnTsTdd9/d7pvInHjiiQiHw3jkkUfw4osvYujQofjLX/6Cv/zlL438U7WGjz76CIceeqjjh+7777/HBRdcgEWLFjV7bS5btgx33XUXFi9eDNM0cdhhh+Hvf/97gn+4W2+9Fbt378Z7773nlNXW1kLX9VZ/tgnCRmB760mPIAiCIAiCIAiCIFrJueeei7///e+NdtBrb/7whz8gJycHixYt2q/naU9ef/11jBw5MsHH1QcffIBLL70U//rXvxpZPjZHOBzGkUceibvvvjvB4T1BdEXIpxRBEARBEARBEASxX/n8888RiUSa3FGuPbnqqqvw9ttvdyufmq+++ip+//vf47XXXsNXX32FF198EbfccgsmTZrUJkEKAJ577jkMHTq0TRb3BNFZkKUUQRAEQRAEQRAEsV/ZvXs3AoFAh+ySCPBdEDdu3JhyQ4CuSE1NDe655x589NFHqK6uRn5+Po499lhceeWVSEtLa3U/1dXV+M1vfoOnnnqqXXeLJoj9BYlSBEEQBEEQBEEQBEEQRIdDy/cIgiAIgiAIgiAIgiCIDmevRSlVVXHCCSfg888/d8qKi4sxZ84cjBs3Dscddxw++eSThGM+/fRTnHDCCRg7diwuuOACFBcX7/3ICYIgCIIgCIIgCIIgiG7LXolSsVgMV199NTZv3uyUMcYwb9485Ofn48UXX8TJJ5+Myy+/3HEuV1JSgnnz5uHUU0/FCy+8gNzcXFx22WVo7epBxhiCwWCr2xMEQRAEQRCJ0P0UQRAEQRBdiTaLUlu2bMGZZ56JnTt3JpSvWbMGxcXF+Nvf/obBgwfj0ksvxbhx4/Diiy8CAFavXo3Ro0dj7ty5GDp0KBYtWoTdu3fjiy++aNV5Q6EQJkyYgFAo1NYhEwRBEARBEKD7KYIgCIIguhZtFqW++OILTJ48Gc8//3xC+fr16zFy5EgEAgGnbMKECVi3bp1TP3HiRKfO7/dj1KhRTj1BEARBEARBEARBEARx4CC39YBzzjknZXlFRQV69eqVUJaXl4c9e/a0qr7TUeuALasAUwXkdEDJsEI24MkGPDlWOgcQpU4eLEEQBEEQBEEQBEEQRPemzaJUU0QiEXg8noQyj8cDVVVbVd/p7Pw/YN31rWgocGHKmwd48wFfIeDvCwT6A/5+QNoAIGMo4OtN4hVBEARBEARBEARBEEQTtJso5fV6UVtbm1Cmqip8Pp9TnyxAqaqKzMzM9hrCvnHQaUDDZqD+J8AIAXoY0EOAHgS0Bh4bYQAMUKt5aNjcdH+SPy5WpQ8BMocBmcOBtEGA7AMEqXEQZVde5oGELYIgCIIgCIIgCILoMhgmgyQKnT2MfaKrzKHdRKnCwkJs2bIloayystJZsldYWIjKyspG9SNGjGivIewb3lxg/OLG5abOl/SZKhepImVAZBcQ2glEy4FYGS+LVQJaLRCtAGIVgBEBglt5KP8w3p/oBTKGABnDgKzhPPZkAYwBgpgkVIlcmJK8/DjRa6VlACIgCFZsBQiudGvKOv8DSBAEQRAEQRAEQRDdCUkU8KfnvsGW8mBnD2WvGNIrHfefPb6zhwGgHUWpsWPHYtWqVYhGo4511Nq1azFhwgSnfu3atU77SCSCH374AZdffnl7DWH/IMqWCBTgvqUC/QAcxutMg1tP6UFAq+eClJ1WawCtDoiUcmGqYQsQ3AaYMaDuex52WefIKALyfwHkTQayR3NBihlW0AEtytOmATATgHsbZyExLwhwRCcntvzZO8KUu96y0IJLBLPrBdkllAnxY939NzqXkNh/U/Wp+iKRjCAIgiAIgiAIgugGbCkP4vuS+s4eRren3USpSZMmoU+fPpg/fz4uu+wyvP/++9iwYQMWLVoEADjttNPwyCOPYNWqVZg5cyaWL1+O/v37Y/Lkye01hI5HlADRcoju78PFJT3EBanoHiBWBaQPBfrMBrw5/Jjgz0Ddt0DNBqD2WyC0HWj4iYftT/JlfwXTgD7HAPlHcMuotsAYADMpZpaY5c4bPDZjgG6VJdTbZXY6SfwCuIjEksrs8gThCokilSNAJce2YCa7hCpbGLPEMYj8dRckNBa0WhLNmil3H5vyGIIgiK4NYwwMDMz6u2yn3XFr2rnL2iPtPqedTq5rKs+Ya2yuvCRKOCTnECiS0s6vIkEQBEEQBNGRtJsoJUkSVqxYgb/85S849dRTMXDgQCxfvhx9+/YFAPTv3x9Lly7FnXfeieXLl2P8+PFYvnw5hJ5kHSMIgJLOQ9pBgBYE1CouRIV3c4EpbSCQMRjo/xt+jFoDVH4OVH7KY7UK2PMOD3Ia0GsG0O8EIHdi6yyJBAHc6mn/TbNFkgUxt8jllJmWxmUJZrZoZpoAokltk483XecBEgUzVzpZNGskOiEuSKUSzwQh6RjbmswSxwR3sAUzGa0TypqzFmuhbcrje9B1RBCdiMlMRwRxpxmz8kkCTqoyd5zcR7NtTRMmzHhsHWsyMx5ggpnMaeecoylRyC0GWf/x/1MLVE0ewzNgYBAg8Pb2nx37dwsrLQiCcx6A16U8JqnOPs6ddyNYBzMwKJKCvhl9SZQiCIIgCILo5uyTKLVp06aE/MCBA/H000832X769OmYPn36vpyye2ELVP6+3P9UaAcQ2QNICuAt4MKDJwfoO5sHZgL1PwKlligVLQNK3uAhbSB3xt7vBEDpIs7hm6IrCGOpSBDLgCYtw1KKYIz7FXOLabagBrjS8Ye3RpZlDslWZ4IrSrYwSxLNEgSs5LwtnFlWZLZVGSSXxZdkWZq10kospUDWyjYp23W1DwXR1XALPQliTJJA465PFm6S6wzTgMGsYMZjEyaPTRMGMxKEJmcsMBOEHLtPuz7BgscWYuxLPEUsoPE14BZgBAgQBMGJmysD0GRb0RLeBTGxPFU6VT+p6ux0Z6MaKmqiNZ09DIIgCIIgCKIdaDdLKaIZRIX7ovL1BmLl3L9UqBjw5QFyerydIAJZo3gYdiVQuwEo+TcPoR3AxnuBn5YDeZOAjKFAZhHf2S/tIEuIIJqlq4plQAqrMKB50cxVzwyrudm43t0fc9XDjpoSziyatTZLIUKltDhLzttCmFscs63Q3E75XWWpBK7WimKp2jdrgeZK09LNRthij2Ea8TQzGglITQXDNKCbOnRTd9IGDCedLDS5hSJbLHIshhhr/BF2NF7BEY9sUUUUxEZpADztEn6cvCgklLcmJgiCIAiCIAii9ZAo1ZGIEvc95ckFQj9zB+hqPeDrZTkbdyGIQM44HoZdyYWpnS8AwS1Axcc82Eg+Lk5lFnG/VpnDuGgl+TpwcsQ+4Ygh8ahLkuCjDGhkMdaceOZedmkLZE1anbn7QaIw1iIJZilolYiWIFKhcbuEZZp2O7eAJsTTont5Z3OCWBPWZE2NdW/6Ergw47YQsoUhx1rIEpWS05qhQWMadEOHznRouuZYGzkikb3UzBaREBeOmn53EkUhURAdESi5TBZlJw2gyTRBEARBEARBEN0TEqU6A8nLhSNvPtCwmfub8hc2LSLJacCA0/nyvfofuIP0hi2Wg/StgBEF6r7jwUHkS/4yhwGZw60wjDtlJ4i9JcEyqYuT0vqsKR9nbhEt1dLNJto02RdSCGlCXC9ryULNEdREmGAwGINhmtBhWmkGg5nQmcHTVhvNNKCaOlRDhw4DqmnAME0YYDCZwPsCuJAkWNOCaPny4WlAgCBKEG1BSBQhQoQgiJAECYIgQRIEKKIEERIEu40gQRRkiKIt3CWJggmWdK7yBEE2WWxLPh6ucoIgCIIgCIIgujskSnUm3jzuH0rO4CKTJ7N5f1GCEF/eZ8MMvhSw4Seg3trFr34Td5ge2s5D6Vvx9v5+cZEqawSPPdn7bYoE0Wl0MeszbqFkQmc6dNPgopK9fM1OMwOqoUHVVUQNFaoZg27oMBmDbhowYcA0DeimYfkvShTLBAGQBBEiBEiOxREgQYAiiJCsZWaSLEEUADFB4LGXgjIAqjVoOAZue01TAhRc5cllCccAjUQqt6+1RpsT2IKp21LNZbmW7JPNbS3XrDjmPncq0S3FmBuJay30nbKcIAiCIAiCIHouJEp1NqLCxSHJz52cGzHAV9D64wUJSD+Yhz7HxMtjlVycqt8E1G/kcWR3PJT9N97W1wdIHwSkDeDWVQEr9vXqHhYxBNHB2EviNFO3hCUuKOmOvyReF9VVxEwVMUOFZhiW7yRriZzLd1IcAaIgQBIlSILIBSZBhCxK8Mh2Wuw+S9dYsoUZ0NiazD3/VBZpcC35dPXrtn5Ltl5z95dsJYekJvtKs6Kbq641whvQhPhm51spwDn1ySJcqvEki2XucyMp765395U8l+TXpIn5NSnMpRoDQRAEQRAE0RMhUaorIIhAxiGAHABqvwfCJdz31L7ckHvzgYJ8oGBqvEyrt0SqH4G6H7lYFS4GoqU8VH6a2IfkswSqJLEqbSAtAyR6HIwxR2TSTN1K605ZzODWS1Gdi0y2CGULTIZpIP5AzSAIImRBhCRKEC2BSRFk+BS+DM4u69HOsbuYtVq7kyCqpRDeGolurrpUGw60WnwDGgtwdpukMSGpmdBCvi00ZxGW0KgpUc59vLsuWahCoiBn6IAWA4zJezlwgiAIgiAIoqtAolRXwt+b+5uqWQ9ESvddmEpGyQTyDufBRmvgS/5CO/kOf3aI7Oa+qhqsJYHJeHJSCFYDgEB/crBOdClMZnKRydAdsUk1NWgGjyN6DFFdRdSIcaHJsJbWMd3SAyxvS5bAJAsSZMuSySMrVlqCJHYDyyWifUm2jOqJwltTpLSCAxJFuqZEOTtOFvKS+3bVma68HgW0IGDG9mkKBEEQBEEQROdDolRXw5MD5IwFqr8Bonu4MLU/UTKA3Ak8uDF1IFJiiVU/A+GdVnonECsH1BoeatcndSgAvkJLoBqQGPv7Nt5lkCD2AcOyalJNDaqhQzU0aKaOmKEioscQ0iKIGRrfPc6yeHLvDidYS+NsoUkRZPg9Xkdo6tFWTASxL3SmFZygAGqwg09KEARBEARB7A9IIeiKeHKAnHFcmIrs4RZUHY0oW1ZQAwBMS6zTw3zZX2gnELatq4p5rDdwMS26B6j6IvE4QeLCVOAgS6w6KJ729SbBimiEZnCBiYtOGlRTR0xXEdajCGkRRA017tfJsWziz8tcaJIhixK8kgdpih+yIJNFE0EQBEEQBEEQRBeBVICuijeXW0zVrAOiZdz6qKsgB4DMYTy4YQzQ6iyRamfcuipczNNG1EoXN/ZfJchcsEo7KC5W2cHfhwSrHopu6ogZGheeDA0xQ0PUiCGoRhDSI9wKyrJ+4hZOAgSAWzeJMhRRRkD2QbHEJ7JsIgiCIAiCIAiC6D7Qk35XxpcP5BzKLabUGm5B1ZURBMCTzUPO2MQ6xoBYRVykcotV4V2AqVrpnSn6lbglVeAgLlr5+1lxfyDQj3xYdWEYY1BNLjrZ4lPM0BBUw2jQQogZcd9OjugkCFBEGYoowSMq8Hm8UESycCIIgiAIgiAIguhpkCjV1fH1ArJGcIsp0QPIaZ09or1DEPhcfL2AvImJdcwEouVxK6pwMV8OGN7F02aMO16P7Aaq1jTu21vAHaz7+/E40C+e9+TQtuL7GbfwZO9MF9FjaFDDCOoRqIYK1eA72TEwAAIUUYIiKfCIMnyeABRRIdGJIAiCIAiCIAjiAINEqe5A4CDux6n+By5MiUpnj6h9EUTuN8vfO3FnQMCysKqMW1Q5wRKt9CC3wIpVADXfNO5b8ltiVT8r7uvK9yUrqzZgmAaihoqoHuOxoaJBDaFeDVnL73RopgaAOxBXRBkeUYZH8iBdCUARZVpeRxAEQRAEQRAEQTiQKNUdEAQgYwigR4DQdr50TThArEoEAfAV8JC8QyBjgFYPRNxi1W4eR3Zz6ysjAgS38JAKT57lfL0vj/39uA8rf18ukvU0AbAV6KaOiC08WTvY1akhhLQI9/tkqmAs7kzcIykkPBEEQRAEQRAEQRBthkSp7oIoA1nDASMMREq4cCJInT2qzkUQAE8WD1mjGtebKhAp5UJVxBarSuJ5PQSoVTzUfZvqBHxpoFuk8vfleZ9l2dWNLa0M00BEjyFixBDRYwhrEdTGggjpEcQMFZrBl9sJggivpMAjKsjwBJAnZUE8UERRgiAIgiAIgiAIYr9BolR3QvYD2aOB2m+B0G5ACQCe3APHaqqtiB4gbSAPyThWViWWv6oSIFxi5Ut5bMaAWDkPtetTn8OTy8UpX2+XWOUSrZSsTvdpxRhD1BKeIpblU63agKAacXa9M8EgCiJ8kgceSUG2NwMeUSGrJ4Ig2oRhmvzviqkh6uyqyTc4UA0todzecZP7pNO4/zknbdWb8ePssqgew9DM3jhy9O87e7oEQRAEQRDEPkKiVHfDkwXkTwYie4DgNm79I6eTQ++2kmBlNaJxPWPcgiqyJy5SRUqBaKmV38Ot1tRqHup+SH0e0Qv4Ci3hqpCLVb5Cq8yK29F5vWEaCOtRbvmkR1EXC6FWbUBUjyFmqDCYCRECPJICr+RBpicdXonEJ4LoqTDGoJl6oj84K45ZvuHsDQqiRgxRXbXqNESNmGvXTDVhM4Ooq9wWnqKGBoMZHTKv3aFyaIbWIeciCIIgCIIg9h8kSnVHRIX7lfIVcrEkuA0I7eQCSxewzOkRCALgzeche3TjetvSKmoJVJE98XTUCrEqbm0V3slDU8hpcaHK1wvw9orvVOgr5EsIlcxG76tm6AjrUSfUROtRr4YQNWLQTB2MMciiDK+kICD7kOPNpB3uCKKLYYtGYT2KqHs5rR7jeSvY1o5OrKuIGDHnmKju3oQgLi5FjRhMxjplbooocwFc5CK4LYZ7RBle2eOUK5IMr8Tz3EcdzyuiAq+kQLH+jnmsZcQiM5GvKFCkA8/nH0EQBEEQRE+DRKnujOQB0g/mVjjh3UDwZ74rnSebixjE/sNtaZU5PHUbUwUiZUC0LC5UOXkr6EHu2yq4jYcmYKIXpjcPupIHVclGUEpDg5CGoJiOoJiGqJIJ05MHj5KOLE8GWT8RxH6AMYaYoSKkRxHRowhpcVE4bKUjehRhPWbFiWlbYHKnI3oUBjM7ZPyStUTXJ3vhkzzwSp7EvGzlrToeuHDkk73wSkrqOivPRSfF8kHn2W8iuKqFURMq3S99EwRBEARBEB0LiVI9AckHZAzmvozCu7g4FdsBePMAJb2zR3fgInq4RVvaQU230UNAtMISqcqBaBnMaBnMyB6waBnEWAUkvQGCGYMUKYEUKYEXQAaAPqm6k9KgKdmukJOYl7OhKVkwu7GDdoJoCyYzHV9qIT2CkBZ1pa28HkHYKY8ibNWFtWhcgLLi/Wl1pIgy/LIXftkHv+S10lwQsvM+O7aEpIQ6S1Sy2/G83c4DWaSv/I6krKwMd9xxB9asWQOv14vjjjsOV199NbxeL4qLi7FgwQKsW7cOffv2xU033YRp06Y5x3766ae48847UVxcjLFjx+KOO+7AQQfFv0sef/xxPPLIIwgGg/j1r3+NBQsWwO/3d8Y0CYIgCIIg9gm6Q+1JyAEgs4jvEBfeBYR2cH9H3lzud4rocjApgKivN0JSNoKeg1DrbUCNtwGR9ChihgoBAnyCgCxEkGGEEDDqoWg18Gg1ULQaKGoNPFotFK0GItMgGyHIRgj+6O5mz2uIPkuoyoImZ0FXsizRKhOakg3dFTN6kCU6Cc3QEdTCCGoRK3anuXAU1MJWHM87aUtsYmh/ISkg+3hQrNgSkwKu4JO9SFN8VrnXEZwClujkk70IyF6nrSwe4Duq9iAYY7jyyiuRmZmJZ555BnV1dbjpppsgiiKuv/56zJs3D0VFRXjxxRfx7rvv4vLLL8ebb76Jvn37oqSkBPPmzcMVV1yBI488EsuXL8dll12GV199FYIg4D//+Q+WLVuGJUuWIC8vD/Pnz8eSJUtw8803d/a0CYIgCIIg2gw9bfZElHQgazgQ6AuEivmSvlgNt5ySA509ugMa3dQR0qIIamE0qCFURmsR0qKIGioYY1BECT7Zm3L3u5gVUsIYJCMMRauFolVD0eqsdI0V10LReZlkxiCZUUixPfDF9rQ8ZinNEq4yrDiTx3ImNCUTupwJXc6CpmTAFP3k04xw0Awd9VoIDWoYQSuO58NoUMNosK4FW0ziZSEENb47ZHshCSLSlQDSFB8Csh8B2Yd0xY80xe+IS+l2WvZb7XwIKH6kWfVp1nE+2QORdj0lmmHbtm1Yt24d/ve//yE/Px8AcOWVV+Luu+/GUUcdheLiYjz33HMIBAIYPHgwPvvsM7z44ou44oorsHr1aowePRpz584FACxatAhTp07FF198gcmTJ+PJJ5/EhRdeiJkzZwIAbr31Vlx88cW47rrryFqKIAiCIIhuB4lSPRklE8geBQT6c0fokWIgVm2JU3Tj2hFE9ZhjtVEdq0NNtAERnTsiFwQBPsmDgOxDri9z3x5yBQGGnAZDTkPU36/ZpqIR4aKVXucIVrKTr3Ol6yHAcKyvmlbE4piCbIlUGdDkTOgKTzt5Od2p1+UMGFIAoIf7Lo1u6qhXQ6hXw6hXg1baDkFHaKpXQ2iwym2hKdpOopJf9iJdCSBd8aeM0xR/o3RA9iPd40eazMvJzxrRkRQUFOAf//iHI0jZBINBrF+/HiNHjkQgEP+RaMKECVi3bh0AYP369Zg4caJT5/f7MWrUKKxbtw4TJ07Et99+i8svv9ypHzduHDRNw8aNGzF+/Pj9OzGCIAiCIIh2hkSpAwFPFuAZw30bhXbwpX2xKhKn2hnGGMJ61LEAqYzUokELIaxHwRigiBL8lgDl6cRdo0zJj5jkRwy9m2/ITEhGCIpWD9kWrPR6K18PRbdirQ6y3gDJjEJkOjxaNTxadavGwiBYQhUXqXQpw8rzMkNOhy6lu9qkw5DSSMjaC3TTQIMaQp0aRG0siHo1iDo1iLpYEHWWwFRniU51saAjOoX06D6fO10JINMTQLqShgxPABlKABmeNCsOIF3hZemKH+meNKQrfivPRSZa1kZ0NzIzM3HkkUc6edM08fTTT+MXv/gFKioq0KtXr4T2eXl52LOHW642V19fX49YLJZQL8sysrOzneMJgiAIgiC6EyRKHUh4snlIG2CJU7tJnNoHTGY6vmzqYg2ojNYiqEYQNbhJkc/yH5Plydhvu1DtVwQRhpwBQ84A0Lz1FQAIZgyK1gDZEqtkvcESrhp40Owy3kYyoxDAoFhlrYVBgCEFHIFKl9OhS2mWgBWPeb27XRog9Iw/ebppWOJSgxV42l1mp23hqUEL79M505UAsjxpyPSkI8MTQKYnDRlKGrK8PM70pCHDY8WW6JTpSUOa7O+en3+CaEeWLFmCH374AS+88AIef/xxeDyehHqPxwNV5ZaFkUikyfpoNOrkmzqeIAiCIAiiO9EzntCItuHJ4SFtoEucqgSUDB4EskpIhclM7vdGjaA21oCKaC3CWgRRXYUoCPArPqR7/MiXsg/IZUJM9EL1eqF681tuDEAwdUhG0BGvJD1oCVjBeN4IQtbjQTIjEMDiSwrbiCF6uUAlpcGQA6nTUoAHOQ26nZYCMEXffvOXFdVjqIk1oCZWz+NoA2rttBXXxRpQYwlO+yIwcXEpHVnedGR70pHpSUOWNx2ZnnRkufK2AGWLTyQsEcTesWTJEjzxxBO47777UFRUBK/Xi9ra2oQ2qqrC5+O7onq93kYCk6qqyMzMhNfrdfLJ9eRPiiAIguguGCaDJB54z0tEakiUOpBxi1PRci5OhUv5g7eSyXfsOwDFFRvbEqpBDTsiVEgLI2ZokAQJftmDTE86egU8LXdGNIKJMnQxG7qSjVYvEGM6ZD3EBSqDx7IRhOQuM4KQ9ZBVH+LCl8FFHO7kPdbq5YUJp7YstAzJD0NKs+JAyjgKLyoMARW6gQpdQ4WmoUqLoioWRo0aRE203hKbuOAU0VvhsCsJAYIjIOV4M5HlSUe21xabMnjszUC2JUBxwSmdlsIRRAdy22234dlnn8WSJUtw7LHHAgAKCwuxZcuWhHaVlZXOkrzCwkJUVlY2qh8xYgSys7Ph9XpRWVmJwYMHAwB0XUdtbS0KCgo6YEYEQRAEse9IooA/PfcNtpQHO3soe8WMYQW47tjhnT2MHgOJUkRcnEo/hDtCj5YD0T3c95QgAZ5MQErr8QKV7ROK+9RpQHmkBiEtgpihQRAEBGQvsjwZ8MkkQnUaggxdyYKuZLXtOMs/lmSE44KVEYakW2UGL5P0sCsftkIIIjMQMxnKtBD2GCGU6ZUoM8CDzuNyK11uANVm26fmEQTkKR7kKT7keHzI9QSQ40lHjjcD2b5MZHuzkOXPRaY3G1mBfKR7cyFJ9CecILoqy5Ytw3PPPYd7770Xs2fPdsrHjh2LVatWIRqNOtZRa9euxYQJE5z6tWvXOu0jkQh++OEHXH755RBFEWPGjMHatWsxefJkAMC6desgyzKGD6ebY4IgCKL7sKU8iO9L6jt7GHvF4IK0zh5Cj4KeaIg4ogL4C3kwhnKBKlIGxMp5WlT48r4eJFBF9Zizk1h5uBoNWhgRPQoBAgKKD5medBKhegIu/1iqN15smCZqYvWojNaiKlqHKr0OVZqVjtahKlqLqmg9qqK1CGqRNp1SBJAvCeglCygUTRTKQIEE9HKFAlecITIIQgx8m8O6xM7s4qTvbUP0whR9MCQfj0UvTMkHQ/TBlHwwRa+Tttuaotdq722UNkUvmCD3mOubIDqLrVu3YsWKFbjkkkswYcIEVFRUOHWTJk1Cnz59MH/+fFx22WV4//33sWHDBixatAgAcNppp+GRRx7BqlWrMHPmTCxfvhz9+/d3RKhzzjkHN998M4qKitCrVy8sXLgQZ555Ji3fIwiCIAiiW0KiFJEayQcE+vKghwHVFqgq4wKVnAHI3Uug0gwdDdb29eWRatTGgohoUTAw+GUf0hQf8nxZB6RPqJ5EzFBRGalFZbQWlZE6K+b5KjuO1qEmVg+TsVb3q4gycn1ZyPNlIc+XiVxvlpXPRI4vE7neTKc+05MG0d4lkDEITLMsr6KQzAhEIwLJCEM0oqg1o6g3IpCMCN/F0Ig47SQjAtGMQTKiEE1eLoCbYtnLERW9rplRtw0GCabogSF5E8QqU/RYIlfjsnhspSVXWvBY9fG2tHsi0dP573//C8Mw8OCDD+LBBx9MqNu0aRNWrFiBv/zlLzj11FMxcOBALF++HH379gUA9O/fH0uXLsWdd96J5cuXY/z48Vi+fLnzvXT88cdj9+7duPnmm6GqKo455hhcd911HT5HgiAIgiCI9oBEKaJlJD/g6Q3IvQBfCIhWAaFyIFIJaCUABEAKWAJVkr8at7gjilYQeLmdFsX9JmzZzsnr1RCqIvWojPIleQYz4ZU8SFP8yPFmxMUDokujGToqo7WoiNSgIsLjyihPVzr5WtSrrXeCLgoCcryZyPNlId+XzQUnfxZyvVnI92cj15eFfB8XnzKUwN4JloIAJnigix7oStsPT8ARuGyxyorNKEQjasUxiGbUVcbzomG1M2NW2io3YxCZzocKg4thZtssw9qCKciOUMUSRCvFEbJYUt4UPWBOXrHydtrKW+WmqFhpxSkjIYzoSC655BJccsklTdYPHDgQTz/9dJP106dPx/Tp0/e6f4IgCIIgiO4CiVIHMroOhEJAOAzEYoBhAKrKg6bxelXl5aYJMBaPGQM0w2pTD8S2AkYEAAMkLxeyRCUuNgnWP4KQKEoJ4A+LtjjlUQBFBmQZUCRAVgBJtIIMyJKVlngbWeLBtTOYvSSvJlaP8kgNGtQQYoYGWZSQJvtRGMiFLNJHvyvBGEOt5cerIlKD8ki1IzyVR6pRGalFeaQGtbGGVvfpERXk+7OQ78tBvj8b+b5s5Pu56OTEvmzkeDO7185yLoGrXWE6JCPGBSpX4MKVagld7qBagbcRrLzkqheYVWbEILL4bmEi0yEaOmDs/S6CbcUUJJdQZQleggImylasuGK56bwgO2KXc6xTJsfbC7J1PjmxXpBJICMIgiAIgiAIC3oyP1BgDIhEgGCQh+pqoK4OiEa5AAVwoUgQuODjWDVZQZLi9bZlk5AJCL152tQBPQhoQUCr5Ev+jBgXpiQfIHitdpagZce20GUCMDQgHLXyJmBYAhjs5VUCT4uiJURxgcoQBTQIGuplA+VCBDWihhA0CJIEvzeALF8afJ70uIhFdCi6aaAyWovycDXKIzVWXI2ysC081aA8UgPN1FvVnyLKKLBEpgJ/Dgr8cdHJThf4c/bequlARZBhyDIM7CfHjZaFly1kxUUtK1gClpBQpiWWMQ2iqfG805cKwdSs43VLCLOOQ9zjvMgMgBmQzFbv9bjfYBAtsSouVNniFQ+SS8SSeL0leCXkXe2ZaB3nOoYlpJtq01zMA4loBEEQBEEQxP6CRKmeCmPcAqqhgYtPFRXcKipiLcnxeAC/H8jN5el9RgG8fgAFABsI6CEeYtWAVg8YtbyZ7OMilejb+1MZJiJaBHVqELVqCGUqd0Id01V4mYh0QUE2PBAFAUB93JJKsqyqvArg93GrLEnillmOdZbMy4hWoZs6KiK1KAtXoyxSxQWncA3KItUoC1ehPFKDqmhtq/025XozHaGpwJ+DXgFX2oqzPOkkNnVHLAsvQ/TAQHrHnJMZXMSyxCyeVl1leryOqRBMHSLTLJFLi+dtMYzpzrHxOt2pS1nGdGdppPNSwIRkqgDU1OPuYjAIjYSqeGiqXAJDorDF24qu48RG7eJtJTAkHgNBBIMEzdShsn34DiEIgiAIgiC6DCRK9SRUlQtQdXVAeTkXpCIRbqHk8wGBABeh9vdSJUHku/QpGYC/N6BHLZGqAVBruEWVUcPbSX4uVAnNO9oxmYl6I4J6PYJyrR7VeghhIwZBEJDm9yI3LQOelEvyGLe40g3AtJYbRmNAVW28noELUbZoJctAwAf4vICiJIpWngNHtDKZiapoHcrCVdgTrsKeMBeaylxxVbQODC0LTpIgoZc/B70CuTz256JXIDHO92VDkehPEtGOCBJMSQLgg9GZ42DMEai4YMXFsHjaFq80CMyAYCblEwQvK+9qJzADoqv/eJ1d7jqmhVhkjV8pAdzKDUzrhBcvNYcIHkTGXdvZwyAIgiAIgiD2EXoC7M6YJl+KV1cHVFUBlZXcGooxbgUVCAB5eZ2/O57s4wF5ABvA/cjoIUBtALRa7pPK1LiTdMnHfVKJHsRMDXV6GDV6CHvUegSNCFRmwCPISJe8yPEEWuGgXIgLTs1hGDzoBhBTgXCE5xngLBm0/Vgplmjl9yVaWCndy9IqqIYtsakKe8KVKAtXY0+4yhGhyiM10M2WH+VlUUKhPxeFgVz08ueiMJCHAn8OegdyLREqF7m+THImTxy4CAJfqgcuvneqQNYSjAEwLKHKHSzRytQBmEmCVitCUp9gSX04fZpWmQkkHB+vZ6aGCqUXCsV93TWAIAiCIAiC6GxIlOpumCYXoGprgT17uDVULMYtetLTgT59urYoIoiAnM6Dr5BbLxkRwAiBxeoQjJWjLlqOilgtqowwQgAgKAjIAeTK6U1YQ7UDtnDV1EpG0+RWVoYlWoXCcafvQFy0skUqv5dbWnmURIfsdno/v0e2H6c9oUpHeCp1pfeEqxDSWt5dTRJE5PtzHNGpdyAPhQEuPNkx7V5IED0IQQBg+aPq7LE0gaqFURMqRWFnD4QgCIIgCILYZ0iU6k6YJvDTT8DmRrvqTAABAABJREFUzVwcCQSArCy+NK+booOhjumoNVSUGRHUmQxRQYLiyUI6y0JfpkNkMe4E3dT4DnyCwq2p0IFCiCjGfW/5U9S7RSs12dIK3Ee7ZO0cKEtcRPR69lq4iuoxR2gqDVdaaW7xtCdUhfJINQxmNnm8TZYnHYWBPPS2Q1qeS3zKQ74vG7LYhUVOgiAIgiAIgiAIottColR3wTS5GLVxI1+SFwh09oj2mogRRa0WRLVWh7JYNYJ6GAYzEZB8yFQy0MuX72rNAEMFjChgRrk/Kj3KnaebJhd7RMUKHnSoUOXGLVqlhAG6aS0R1IFIDAiGGwtXlhgVhIpSM4QSIYRSswF79HqUxmpRGqtBabQGNWpDi0OSBAm9LWumPmn5ceHJyhcGcuGXu6+gSRAEQRAEQRAHGobJIIm06Q/RcyBRqjtgC1I//sgdlXczQcpkJhr0MOr0IMpj1ajW6hHWIxAEEemSH4WeXMhNLssTuFWU5AWQBaAQYIYlUsV4rAcBIwboYS7w2EKVIAOSB0BXsPQRHEso5lHQYERRojagVK1BqVqLUrUWJU66Dg2t2LY+ICjorWShj5KNPr6cuOAUyEeftHzkpeVAkmVuXWZbaO1vJ/cEQRAEQRAEQew3JFHAn577BlvKg509lL1ixrACXHfs8M4eBtGFIFGqq8MYsHUrt5DKzQXS0jp7RK1CM3XU6UHUqg3Yo1aiTgshZqrwih6kSwHk+jIh7K0DdkEC5DQArtfC1LhIZca4QGWELYuqIPdbBQCixIUqUeai1X6yqmKMod6IWEJTXGwqscSnUrUWITPWYj9ZUgB9PFno48mOByUbvZVM9JEykMk8EEyT7y5oWlZYQQYEAaCKB0m0/F3ZQQa8Cl8y6PHEhSp7uaAkJu5E2NlO8gmCIAiCIAiCSGBLeRDfl9R39jD2isEF3eN5lug4SJTqyhgGsGULF6Sys7u8IBU2oqjTgqhSa1Gu1qBBD4ExBr/kQ7acAZ/U3PK2fcRewof0eJmpA6YaD3qEi1WmZllVuZyUCzIXu0SZp9G0GNNeolOenI7eniz09eQ4cR8lC308OejjyUJA8u7bawIGmMzaWdCM+7uKRLmI5ThqF3hbwbLmEsS4mCVLXLzyyNwPli1i2aKVaLezBC9RIGssgiAIgiAIgiAIolWQKNVVUVUuRm3dyn1IdUFBqqlleaIgIU3yobc3H7LQiUvnRMsqCu7ljsyyqrKD5a/KiHARy+B+nuqNKEq0epTqQZRoDSjVGrBbq0OpykNrRac+nmz09eSgr8vaqa8nG709WfCJ+1GkAwAIcZGoNTunMxMwmLUjomWBFVWBcLKIZfUN8P4lMVHIkqS4iKVYjtttwcqxxhK55ZokWLFIYhZBEARBEARBEMQBBolSXZFIBPjuO6C4GCgs7FK766ValqeaGjyisu/L8joEARA9CJo6StQ6lMQqURKtQGmsEiXRcpRGK1ASq0LQiLTYU54UQG9PJvp6stHPk4M+3mz08eSiryfPEp1aowR1IQTR+ovQBiHRFqsMV6w3ZY0FOBZZohgXzJy0JWApEqDIlpiVZIHlXo4oSnGhiyy0CIIgCIIgCIIguh0kSnU16uqA778HysqAfv34g3knYy/Lq1RrUK7WIKiHnWV5OUoGvIICGAZETYcQCkJUNYiqBkHTIGg6RE2DoFqxrkPQeBDttG7Eyw0jnjcMCIZppU0nD7vcMLhPJdO0fCsxCIzn60UdxX4VO/0adqap2OHXsDNNw440HTvTdNR6zRbnnR8RcXBQxsFBCQNCMgYEJQwMyhgYlHBQWITfEME9q9cBQh2YAK69CAKYKMRjUQATuX8mZgkoTOSiChMFMEtoYXa5JIKJIpjEyyCJYJLE8wmBlzn1chOxJMJUJCstgSkSmGz1J/O0aZXxpYxtEBWd5Xtt+URZywpNw7LMssQrXQdiKhexkgUtwS1sgQtRgpAkbIn8erEcykORXIKW1FjYEoW4hZdA4hZBtASzLCUZGBjjMVxpuxwJaTsHgDEnzZhT6vTtrnPKwaw663yMwTBVunkhCIIg9grauY4guh50X9dVME1g925g0yYgFAL6999/D8eaBlRXA1VVPK6uBsJhIBoFolGwcBhqJAgtEkQs3AAjEoYci6JfTMfBmgE5pkFSdQiqCjGmQlRVCCZr+bztSK0P+Dkb2JHF459zgB3ZVjobqPG33Ed+CBhUCxzsCoNqeDywDghoJgB1P82g62JaQhVTZFfaEq+ctOwSs2QwRYKpyE5bU7HKrDqmyI3KTI8Mptj9yGAe2enDVGQwn9fpH5LrWrCXGTLTErdcopaqJpZZD8iJPsKSrLXgWoIoIr4E0fabJUtJFlsu8SrZ2ktIZQFGIteBiCOytFK8aUm4SSXaJNbFRRs7bZfDOR9Px4lbMAoQAMG1QjdFW1uvFiBAEATY/zktBDhltsUsbwenvas3iBDjx4j8GNE6XhREnhfsvFUGAQLT4THD8Mldx4qYIAiC6B7QznUE0fUgUaorEIkAmzcD27cDgQAXpNoKY0B9PReaKisTYzvY+bq6ZrsSAHitkN5sy9Rw4UKB6VG46KAoliBhx7IlZFgChCw7IgiTuNBR6TFR7I9hlzeGYm8Mu5UIipUIipUwdksh1Itai+PIYT70FzLRD1noJ2RaIRv9hSz0FbKQlusByxNcekX8gWlXQk/Oz/0Q+NMjz1hpwU6bzKrnQbDEEcESTwRD51ZepgGYOgRd57Fh8Dpdg2DoPOgGP9awrcT40jg7LZh2mlnBbBx0OzbisR2svKgbjV43UTcA3eD+pLoITBQtEUtyPkOOyOVx5+WEfDwtJdVJzmeNyRJMSeR92WWiCFMWrc+nZcUmW8saBcsxPBMStS7besu24LJFLsFemuja4dBOu5cuCohbqiULW7DzsPq1RTChR+6QmCzoxAWZvRdzzAQrn30TcQT7H+Zu0bSAY4spABKFnRbEmwThRhAckUaEmCDaiBAhilZbCE5atI+BCFEUef9wn09odN5UZQntGx0rNBKjEsoEIWm+iaKW3XebMFVArQGkbrZEmiCIA5qeYKHTE+YA0M51BNHVIFGqM2EMKC/nDs2rq4FevZr3H1VfD3z8MbBrV6LgVFnJj9daFmqcU0sSWG4O9NxsRLPSEPZJUD0idK8Hoi8AKZAGweeH6fPA9HjisdcD5vXA9CgwrZh5PFwA8HrAFCXRqiUFBjNRqdaiNFaJPdFKlMYqURqrwp5YJUqtfNRsWRDJUTLQx1uAvr589PHmo7c3H/18BejjzUcfXz4CUtOvpQGgS38VMcMKZmIMw7IAMgCmcefsTLdiE4AVGLPaIOHhGUD8iVrg75OgI1HY0hgEw4BoAIJmcHFLN/nyTN2AoHExi6d5mZgUC5qV1gxrqaYVazpfymkf67TTIdptrVhwmWwIpgkpqgLRjnoDGsMEobEo5nGJY27xy7YYcy+PdItgtuClyNbySkug9cSt0xwRzqOAeRUu3nplS/xyCV6CmLgTohQPTJTABAZTFACIYALjyzYZwCyrMAbAFK2PiCDweQrMikVAYDABLsw1I+yYTYg6bs3XFpSSauD+gCaIGEnWOE1Z4ohugcOxtBEcC5zmhRzJssLh4lG8Li7itEXA2Rfxpinhpmv76SMIgiBaQ3e30Dn84BwsOGFUZw+DIIgeCIlSnUV1NbBjB3dmLklNL9draAA+/BB45x3g88/5EqXmyMzku/Xl58fj3FwgPx9qTiYasv2oy/CgxKei3oggZmpQBBnpsh9pkh+isO9LjSJGFGWxauyJVWFPrIqLT7EqlEYrURarQplaDZ01ttBJJk/JQl9fAXp789DHm2+JT1yE6u3Ng78Z0anbI0g8tAVHvDIBGC5hyi1UWeIV07lPJxhgkg7GrPZcokDcEsy9416qcbrHaxfYwfZPJbhiq40gug5O8ZljDDBMR9hqJFypWlK5JXZpcfHLbuPkY9w6TdQMCKrWuG/VdQ6VH++2JBMYs8pbuAb3M6bErbgMRYYpizBlCYYlgJmKlbfKTUsAc8Qw9/JJWQSTuZBmKgqYIjoWjKZHBmyrRo8M5lEARYHg9QAeD5jfC8HjgejzQpRkCLIEUZQgSQpEUYQoyhAlEYIoQhDsIECQJCu2xCFRtpZtiRBEd511rChY5TzNrYZIzCEIgiC6J93dQqe7C2u09I0guiYkSnU0DQ3Azp1ckNI0oKAA8Hrj9YwBP/0EfPYZ8OmnwPr1gOEScIYMAQ49NC462cKTLT55PE5T3dRRr4fQoIdRodagRqtH2GjgTspNH7LkDPgkD9qCzgxUqrUoswQnW3yy83tiVajTW/6ikiCi0JuL3t489LYsm7i1Ux76+gpQ6M2FV2zb2A549kbIcnAJUI5gaAtVpkucMuNiVYJFluESw9zWXSzeN1g8MMTLXJqXyRi30nH73lHAg2Av2RLBBB9PC4JrWZbAJTV7SZWAeF6In920bXlsSxsmWFZC/CABglXHhTlJNyFoOiTVgKQZEDUDsm5AtvO6AVnjAppdL2kGJDWej9fpkJLaipYQJlrCmFtYEyyhzG01JhomRMOEHOtcccxBluIO5u1dE2XZSkuuvO2jS0ls63G1V+w6xcpbwRLFoMj876VX4WVeDy/3cbEMXg/gs+rt9pIlmApi0vLKpLTbyb87715WaeeT2xMEQRD7jZ6yZKyn0N2FNYIguh4kSnUEhsGX2ZWU8F31wmEuJqWlccunH37g4tO6dTxUVSUef8ghwDHHAEcfDRx8cJOn0U0dQa0BDXoI1WoDqrRahPQIdGbAIypIl/zI9mY0aQ1lMBNVai3K1WrsiVWjLFaF8lgNylQuPpXHqlGh1jgP9c0RkHyOhRMXnlxpXz4KPDmQ2sEqi2gbic6STWf5lckcrzpcGLLamwAYE8DspV9M4GKPYC3VElL0ZVtZAdwCC5YvLqsWDBCs47lwBQhCfKc9QWAQme26ifFlWAzcNxcfCbdaArPssayYMYgCg8gYJEGEwARIguW7XBAgMtHJS/aSL9vnOUu08RJgOVwWAMErWOcRua7B7Lxr6Rbise3Hh+fF+DIyy2KIvyD2OyK4Mqn9m4GBO3bXNEDVAc0AVM1y7K7ztGbwet2d13lQdV7n5F1pTXf168przRxjJFk52j7Iuipu0SzZgb3tRN+9a6MtormPSy63xS5Z4uKYLbZ55LgY5lEai2pucc0ul+VEAc6pV+I7Q7oFMkccc/kTSy5L1a6pYwmCILo43d06ByALHYIgiOboUFEqFovh1ltvxdtvvw2fz4e5c+di7ty5HTmEjsM0uQ+o2lruA6q6GggGuZPx4mJuDbV5M4+jSY5y/H5g4kTgiCOAKVOadHyumTqCehhBI4xqtQHVWh3CRhSqqUEWJKRJfvTy5EAWZYSNKCrUGmwM7kClWoNytQblsWqUqzWosNKVai0MmC1OTRIk9PLkOGJToTcXhS7RqdCbi3QpcEAvnWlJ/HH737FknCRnzixu0WP763H77nE5dIYl3jhL45wyIV4HAILgSB22Q+QEUSXJb44tXgqCAMlxoCxCEkTH545k9SQKVrnA85IgWc6dEd8xS4jvrOWkk3zzuOsT/AI1SosJPnsaCa1uKy077xLLnCWKjqWWy6IrIe/qI8HSy1XGkurt451zmonH2RZngCvt7s9tQWbNQwQXKQJwHQfXcUjqI7ksqT7RQ3fjNo36dzXUTUuIskUslwCmu9NN1OlNtTd4e/s4XY8LXlpSmeaqc58nlTjW1UWz5hAEyz+Y5PIVlpyWAFm0nOlbfsXsHSNtX2PudrYIZx/vtHUJcJItvIku8U5yWbvJcUs32e7XqrOd+jtBSTyvu060+rTnpMiJApz9GriFNYBfI2LLP44QBNFz6M7WOQBZ6BAEQTRHh4pSixcvxnfffYcnnngCJSUluOGGG9C3b1/Mnj27I4exfzBN7nB861Zg0ybuvHzXrrh1VGkpUFOT+tiMDL4kb+xYHsaMSViGBwAmMxExYggbUYSMCKrVOlSotSiLVqFSq0W9HkLEiCGoh1GjN6BSrUWVWodKrRZVai1CRus8REsQke/NQaEn1xKbctHLw0Une7ldjpLZpaycmhRtWFzwaWwJxB9oTJdg5OzSZfXD5QJLGmIMzBJ4BEGI6xYC40u+BDhOoN1iXCrxxy322MIMAEfQEQSRW/JAtMQdnpYFyTlGtkSf+DGJYo3b905rhJ14+6aP7VYioyAm+tHuCSQLRQmCGVKUJQtMdlkTglMjIaup45tpm2qciQNsoi7V8a2pT0pbvsgcizFbONM1njdsKzMtbvWVXO+IbTpguEWw5GAkpZPzrnLDnXYJaO5yM8UPAox1b1Ftb7AtwyR7Z0o77XLkL4jA6KHAK9MA2oCPIAiCIAiiW9NholQ4HMbq1avx8MMPY9SoURg1ahQ2b96MZ555puuIUpEIt2QKhfgSu1CI+4BqaOBWTw0NXFiqrAQqKrj1U0UF30GvshKIxVo+R34+UFQEDB0KFBVBH3IIgn3yEDQiCGoh1GkNqCz9CJVqLSrVWtRo9ShTq1Gm1qBaq0etEUKdEUK9EUa9GWnT9NJEL/KlTBTImSiQs9BLznLiXnIWCuUs5EoZqQUnA0AYYKEoGCLQwSw/PfHlXo7Fj8sKyBGLAJimq94qTRCL3NY/sMUfLvoAtiWQiOQHWnuZlL2kChC4mxhnSZXbqia+NIsvs+LWPpIlxMiCCJEJcWsfcAsgSZASBBvHogeAKEqJS76scaQUgiBwB852X0Cj9s2KP8l1jghhvyZG6vbNOStvqu99oTV97c35WjqmPebQXmNvqs2+jnFfX4N2fQ3dSw/3od+9rUtVLwv8m83nqmvpvUheBteadq0ZW1swzbgo5g6pypLrktsYRvNlqeLkevfx9tiaOs5u4y5Lno9pJrZpCsbifTTH2h8BvacpzwRBEP+fvfuOj6pM////OmVqJpWE0HtHRMAFsazCruK6rn0/69oWZS2ryCq6trWgqKj87NhQsa/6tbG2LeLaRVQUUBSld0JC6kymn/P748yczKSRUDIJXM+H53HqzNwzHpI571z3fYQQYv/TZqHUihUriMVijBo1yt42ZswYHn30UQzDQG3sznNtKPifd3jl+pMp16MYCvYUVyCupi/HElNcgVgXiHaHaGJb1KES8bqIuB3W5NQIujTCToWgBiElRtD4gaDxLcFYhOiPcfhx19utopDrzKHAmUu+M5d8V441d+ZS4Mojz5lLgSuHfGceHt2TiH6UugqgRICEohBGYQuJHl/UVf2YppnYlhyWJBmwWHdQs8YKTomAUsfQQbHH9FFQ0VUtveuXoqKp6eGPqjZW/ZPa3at+l7CUoKj+Y1KrgZS66Ce5fbe1JOzZneP35mvs7LjU/c0d25Lj6m9vzWu3dFtLH9PadrXmNXZlf0se25LPdWfHtPT/TUv3tfaxLf0cWvp6zT1faz7Lxra3dFtj64pSty11ub6mjmvsMYpS1wUveWOM5HFNHd/Y6zU2b+z4xvY1F8Q1d3xTj0kNn5KBVXJKDcmS66n7IhHrJiGO/bdMar8aDkEIIYQQ+7Q2C6VKS0vJz8/HmdItrbCwkHA4TGVlJQUFBW3VlEa9sON/nH98dA88kwEEE1O9zc0M16QpGh7djVt343V4yXJkkeX0keXMItuZTa47lxxXDjnOHPI8eeS788n35JPjzEFXdSuMSQ1kFKvSR1M0FFVBQ7PCn0SXr2T4o6t62mNSl9OCnSb2teY4IcQ+YE+HW7sa1DW2vrPllu63x/5qZn9z85a+RlOPac3rNHdM6tSSbfVfP9ml0DAa7mvuOVOPb6qdYHXHSwZLrQ2+3W5rjKr91D49HIIQQggh9itt9o0uGAymBVKAvR6JRNqqGU067ndX8Kf3StlatQlN1VHUxPg9iSDHCnHqgh1d0VFVFYfqQFM1dFVHU6y5U3Oiqzq6quPSXHgcHty6G4/Dg8fhIduRTZYrC6/utdad2bh0V6LSiAZhTv15Y4GQBEBCiDaxt7olin1fawLE5pbBOt/200qpDjEcghBCCCFEC7VZKOVyuRqET8l1t9vdVs1oUrfsbjx9yrOZboYQQgixb9pZlz7RIu19OATRvsQNE02Vf29CCCHarzYLpYqLi6moqCAWi6EnSu5LS0txu93k5OTs9PHJO5v5/f692k4hhBBCCICsrKx2V328u8MhtNX3qX0hDNkX3gPAox+uZktV626O016M6JHL78f0pE+OihHpuNWRxR7r31xHfh/yHtoHeQ/tw77wHvrkqG2Wrezs+1SbhVJDhw5F13WWLFnCwQcfDMDixYsZMWJEi/6qFwgEADjyyCP3ajuFEEIIIcD6nuLz+TLdjDS7OxyCfJ8SHck7wB2ZbsQesAbo6P0x5D20D/Ie2od95T2MmdU2r7Wz71NtFkp5PB5OOukkZsyYwe2338727duZN28es2a17JPo3LkzH330Ubv8q6UQQggh9j1ZWVmZbkIDuzscgnyfEkIIIURb2tn3qTa9dc21117LjBkz+NOf/oTP5+PSSy/lmGOOadFjVVWlS5cue7mFQgghhBDt1+4OhyDfp4QQQgjRniim2dx9mIUQQgghRHsRDAYZN24c8+bNs4dDeOihh1i4cCHPP/98hlsnhBBCCNE6cosWIYQQQogOInU4hGXLlrFgwQLmzZvHOeeck+mmCSGEEEK0mlRKCSGEEEJ0IMFgkBkzZvDf//4Xn8/HlClTmDx5cqabJYQQQgjRahJKCSGEEEIIIYQQQog2J933hBBCCCGEEEIIIUSbk1BKCCGEEEIIIYQQQrQ5CaWEEEIIIYQQQgghRJuTUAoIh8Ncd911HHzwwRx++OHMmzcv003qsEpKSpg2bRpjx47liCOOYNasWYTD4Uw3q0O74IILuOaaazLdjA4rEolw880384tf/IJDDz2Ue+65BxlKb9ds3bqVCy+8kNGjRzNx4kSefvrpTDepQ4lEIhx//PEsWrTI3rZx40YmT57MQQcdxHHHHcenn36awRZ2HI19lkuWLOH0009n1KhRTJo0iVdeeSWDLRTt0XvvvcfgwYPTpmnTpmW6We2W/MxqvcY+s1tvvbXBeff8889nsJXtQ3PXDHKeNa65z0zOs6atX7+eKVOmMGrUKI466iieeOIJe5+ca41r7jPbG+eavrsN3hfcddddfP/99zzzzDNs2bKFq6++mm7dunHsscdmumkdimmaTJs2jZycHF544QWqqqq47rrrUFWVq6++OtPN65DeeecdPvroI04++eRMN6XDuvXWW1m0aBFPPvkkgUCAyy+/nG7dunH66adnumkdzmWXXUa3bt14/fXXWbVqFVdeeSXdu3fn6KOPznTT2r1wOMwVV1zBypUr7W2maXLJJZcwaNAgXnvtNRYsWMDUqVN599136datWwZb27419lmWlpZy/vnn88c//pE77riD5cuXc+2111JUVMRRRx2VucaKdmXVqlVMmDCBmTNn2ttcLlcGW9R+yc+s1mvsMwNYvXo1V1xxRdp3OZ/P19bNa1eau2a46qqr5DxrxM6us+Q8a5xhGFxwwQWMGDGCN954g/Xr1zN9+nSKi4s5/vjj5VxrRHOf2e9+97u9cq7t96FUbW0tr7zyCo8//jjDhw9n+PDhrFy5khdeeEFCqVZas2YNS5Ys4bPPPqOwsBCAadOmceedd0ootQsqKyu56667GDFiRKab0mFVVlby2muv8dRTT3HggQcCcN5557F06VIJpVqpqqqKJUuWMHPmTPr06UOfPn044ogjWLhwoYRSO7Fq1SquuOKKBhV6X3zxBRs3buSll17C6/XSv39/Fi5cyGuvvcall16aoda2b019lgsWLKCwsJDp06cD0KdPHxYtWsRbb70loZSwrV69mkGDBlFUVJTpprRr8jOr9Zr6zMA676ZMmSLnXYrmrhl++ctfynnWiJ1dZ8l51riysjKGDh3KjBkz8Pl89OnTh/Hjx7N48WIKCwvlXGtEc59ZMpTa0+faft99b8WKFcRiMUaNGmVvGzNmDEuXLsUwjAy2rOMpKiriiSeesH9QJvn9/gy1qGO78847OfHEExkwYECmm9JhLV68GJ/Px9ixY+1tF1xwAbNmzcpgqzomt9uNx+Ph9ddfJxqNsmbNGr755huGDh2a6aa1e19++SXjxo3j5ZdfTtu+dOlShg0bhtfrtbeNGTOGJUuWtHELO46mPstkN4b65PePSLV69Wr69OmT6Wa0e/Izq/Wa+sz8fj8lJSVy3tXT3DWDnGeNa+4zk/OsaZ07d+a+++7D5/NhmiaLFy/mq6++YuzYsXKuNaG5z2xvnWv7faVUaWkp+fn5OJ1Oe1thYSHhcJjKykoKCgoy2LqOJScnhyOOOMJeNwyD559/nkMOOSSDreqYFi5cyNdff81bb73FjBkzMt2cDmvjxo10796d+fPn8+ijjxKNRjnllFP4y1/+gqru95l8q7hcLm688UZmzpzJs88+Szwe55RTTuH3v/99ppvW7p1xxhmNbi8tLaVz585p2zp16sS2bdvaolkdUlOfZY8ePejRo4e9vmPHDt555539+i+dIp1pmqxdu5ZPP/2Uxx57jHg8zrHHHsu0adPSvgMK+Zm1K5r6zFavXo2iKDz66KN8/PHH5OXlce655+73wzI0d80g51njmvvM5DxrmYkTJ7JlyxYmTJjApEmTuP322+Vc24n6n9n333+/V861/T6UCgaDDb6MJNcjkUgmmrTPmD17Nj/88AOvvvpqppvSoYTDYW666SZuvPFG3G53ppvTodXW1rJ+/XpeeuklZs2aRWlpKTfeeCMej4fzzjsv083rcFavXs2ECRM499xzWblyJTNnzmT8+PGccMIJmW5ah9TU7x/53bN7QqEQl156KYWFhfzhD3/IdHNEO7Flyxb739x9993Hpk2buPXWWwmFQlx//fWZbl6HID+zWm/NmjUoikK/fv0466yz+Oqrr7jhhhvw+XzS9T1F6jXD008/LedZC6R+ZsuXL5fzrAUeeOABysrKmDFjBrNmzZKfaS1Q/zMbPnz4XjnX9vtQyuVyNTjxkusSCOy62bNn88wzz3DvvfcyaNCgTDenQ5kzZw4HHHBA2l9DxK7RdR2/38/dd99N9+7dAevC5MUXX5RQqpUWLlzIq6++ykcffYTb7WbEiBGUlJTwyCOPSCi1i1wuF5WVlWnbIpGI/O7ZDYFAgIsvvph169bxj3/8A4/Hk+kmiXaie/fuLFq0iNzcXBRFYejQoRiGwd/+9jeuvfZaNE3LdBPbPfmZ1XonnXQSEyZMIC8vD4AhQ4awbt06XnzxRQkLEupfM8h5tnP1P7OBAwfKedYCyXF6w+EwV155JaeeeirBYDDtGDnX0tX/zL755pu9cq7t9/1XiouLqaioIBaL2dtKS0txu93k5ORksGUd18yZM3nqqaeYPXs2kyZNynRzOpx33nmHBQsWMGrUKEaNGsVbb73FW2+9lTbumWiZoqIiXC6XHUgB9O3bl61bt2awVR3T999/T+/evdN+UQ8bNowtW7ZksFUdW3FxMWVlZWnbysrKGpSSi5bx+/1MmTKFlStX8swzz8jYGqKBvLw8FEWx1/v37084HKaqqiqDreo45GdW6ymKYl+8JfXr14+SkpLMNKidaeyaQc6z5jX2mcl51rSysjIWLFiQtm3AgAFEo1GKiorkXGtEc5+Z3+/fK+fafh9KDR06FF3X0wY0W7x4MSNGjJAxZ3bBnDlzeOmll7jnnnv47W9/m+nmdEjPPfccb731FvPnz2f+/PlMnDiRiRMnMn/+/Ew3rcMZOXIk4XCYtWvX2tvWrFmTFlKJluncuTPr169Pqyxds2ZN2jg+onVGjhzJ8uXLCYVC9rbFixczcuTIDLaqYzIMg6lTp7Jp0yaee+45Bg4cmOkmiXbmk08+Ydy4cWl/Ff/xxx/Jy8uT8UNbSH5mtd7999/P5MmT07atWLGCfv36ZaZB7UhT1wxynjWtqc9MzrOmbdq0ialTp6aFJt9//z0FBQWMGTNGzrVGNPeZPffcc3vlXNvvUxePx8NJJ53EjBkzWLZsGQsWLGDevHmcc845mW5ah7N69Woefvhhzj//fMaMGUNpaak9iZbr3r07vXv3tqesrCyysrLo3bt3ppvW4fTr14+jjjqKa6+9lhUrVvDJJ58wd+5c/vjHP2a6aR3OxIkTcTgcXH/99axdu5b//e9/PProo5x99tmZblqHNXbsWLp27cq1117LypUrmTt3LsuWLeO0007LdNM6nFdffZVFixZx6623kpOTY//uqd8FROy/Ro0ahcvl4vrrr2fNmjV89NFH3HXXXfz5z3/OdNM6DPmZ1XoTJkzgq6++4sknn2TDhg384x//YP78+fv9EALNXTPIeda45j4zOc+aNmLECIYPH851113HqlWr+Oijj5g9ezYXXXSRnGtNaO4z21vnmmKaprmH2t9hBYNBZsyYwX//+198Ph9TpkxpkACKnZs7dy533313o/t++umnNm7NvuOaa64B4I477shwSzqmmpoaZs6cyXvvvYfH4+GMM87gkksuSevCIVpm1apV3HbbbSxbtoyCggLOPPNM/vSnP8ln2QqDBw/m2WefZdy4cQCsX7+ev//97yxdupTevXtz3XXXceihh2a4lR1D6mc5ZcoUPv300wbHjB07lueeey4DrRPt0cqVK7n99ttZsmQJWVlZnH766fL7YCfkZ1br1f/MFixYwAMPPMC6devo3r07l19+Occcc0yGW5lZO7tmkPOsoZ19ZnKeNa2kpISZM2eycOFCPB4PZ511FhdeeCGKosi51oTmPrO9ca5JKCWEEEIIIYQQQggh2tx+331PCCGEEEIIIYQQQrQ9CaWEEEIIIYQQQgghRJuTUEoIIYQQQgghhBBCtDkJpYQQQgghhBBCCCFEm5NQSgghhBBCCCGEEEK0OQmlhBBCCCGEEEIIIUSbk1BKCCGEEEIIIYQQQrQ5CaWEEO3e4MGDueKKKxpsf/3115k4cWIGWiSEEEIIIYQQYndJKCWE6BDefvttFi5cmOlmCCGEEEIIIYTYQySUEkJ0CN27d+eWW24hEolkuilCCCGEEEIIIfYACaWEEB3CZZddRklJCU8++WSTx2zbto2//vWvjB07lnHjxnHrrbfaIdbrr7/O2WefzQMPPMC4ceM4+OCDmTVrFqZp2o9/6aWXmDhxIqNGjeLss8/mp59+2uvvSwghhBBCCCH2VxJKCSE6hOLiYqZNm8ajjz7Kxo0bG+yPRCL86U9/IhgM8txzz3Hffffx4Ycfctddd9nHfPvtt6xdu5YXX3yRG264gWeffZbPP/8cgP/973/MmTOHG264gTfeeIMxY8ZwzjnnUFVV1WbvUQghhBBCCCH2JxJKCSE6jLPPPpvevXtz2223Ndj3ySefUFJSwuzZsxk8eDDjx4/nxhtv5MUXXyQQCAAQj8eZOXMm/fr148QTT2TIkCF89913ADzxxBNceOGFTJgwgT59+nDZZZfRvXt33nzzzTZ9j0IIIYQQQgixv9Az3QAhhGgpTdOYMWMGZ5xxBgsWLEjbt3r1avr06UNubq69bfTo0cRiMTZs2ABAp06d8Pl89n6fz0csFrMfP3v2bO655x57fzgcZt26dXvxHQkhhBBCCCHE/ktCKSFEhzJ69GhOPfVUbrvtNv785z/b210uV4Nj4/F42tzpdDY4JjmmVDwe57rrrmP8+PFp+1NDLCGEEEIIIYQQe4503xNCdDhXXnkltbW1aYOe9+3bl3Xr1lFZWWlvW7JkCbqu06tXr50+Z9++fdm2bRu9e/e2p0cffZQlS5bshXcghBBCCCGEEEJCKSFEh5Ofn8+VV17J5s2b7W2HHXYYPXv25KqrruKnn37iiy++YObMmRx//PHk5OTs9DnPPfdcnnnmGebPn8+GDRuYPXs2//rXv+jfv//efCtCCCGEEEIIsd+S7ntCiA7ptNNO47XXXmP79u2ANd7Uww8/zMyZM/m///s/srKy+N3vfsf06dNb9HzHHXccZWVlPPDAA5SVlTFgwAAeeeQR+vTpsxffhRBCCCGEEELsvxQzOaCKEEIIIYQQQgghhBBtRLrvCSGEEEIIIYQQQog2J6GUEEIIIYQQQgghhGhzEkoJIYQQQgghhBBCiDYnoZQQQgghhBBCCCGEaHMSSgkhhBBCCCGEEEKINiehlBBCCCGEEEIIIYRocxJKCSGEEEIIIYQQQog2J6GUEEIIIYQQQgghhGhzEkoJIYQQQgghhBBCiDYnoZQQQgghhBBCCCGEaHMSSgkhhBBCCCGEEEKINiehlBBCCCGEEEIIIYRocxJKCSGEEEIIIYQQQog2J6GUEEIIIYQQQgghhGhzEkoJIYQQQgghhBBCiDYnoZQQQgghhBBCCCGEaHMSSgkhhBBCCCGEEEKINiehlBBCCCGEEGKfYppmppsg2iE5L4RofySUEkLsUWeffTaDBw/m9NNPb/KYyy+/nMGDB3PNNdfs0dd+8MEHGTx48B59zpbatGkTgwcP5vXXX8/I6wshhNj/LF68mEsvvZTDDjuMESNG8Ktf/Yrrr7+e1atXZ7ppadr69/PixYu54IIL2uz12oPly5dz/vnnc8ghhzBu3DjOO+88li9fnnaMaZo8+eSTHHPMMYwYMYJJkybxwgsvtOp17rjjDs4+++xmj/H7/UycOHGXvuclz5XUadiwYYwbN45LLrmElStXtvi55s2bx5VXXglAdXU1V111FV9//XWr27QrrrnmGiZOnNjsMa+//jqDBw9m06ZNLX7eljymoqKCo446io0bN7b4eVMFAgFuvvlmDjvsMEaNGsX555/PmjVrdvq4n376iT//+c+MHTuWww8/nKuvvpqysrK0Y2KxGPfddx9HHnkkI0eO5IwzzmDp0qW71E6x75BQSgixx6mqypIlS9i2bVuDfbW1tXzwwQcZaJUQQgix75g7dy5nnnkmwWCQ6667jieffJKLLrqIH374gZNPPpl33nkn003MmFdeeaXdBXN70/r16znrrLMIhULcdtttzJo1i0gkwhlnnJEWJtx1113ce++9nHbaacydO5eJEydyyy238PLLL7fodebNm8dTTz210+NmzZrF5s2bd/n9ALz88sv29Nxzz3H99dfz448/cuaZZ1JaWrrTx69evZrHHnuMv/3tbwD8+OOP/POf/8QwjN1q15501FFH8fLLL9O5c+c9+rz5+flMnjyZ6667bpcqw6644gr+/e9/c8UVV3DnnXdSUlLCOeecQ1VVVZOPKSsr409/+hM7duxg1qxZXHfddXz11Vecf/75RKNR+7g77riDp59+mj//+c/ce++9aJrG5MmTWb9+/S69V7Fv0DPdACHEvmfYsGGsWrWKf//730yePDlt3wcffIDH4yEnJyczjRNCCCE6uA8++IC7776bSy+9lKlTp9rbx44dy0knncQVV1zBNddcw6BBgxg4cGAGWyrawnPPPYfH4+Gxxx7D6/UCcMghhzBx4kSef/55brzxRjZt2sTTTz/NDTfcwBlnnAHA+PHj2bp1K59++il/+MMfmnz+jRs3cuedd/K///2P7OzsZtvy0Ucf8a9//Wunx+3MQQcdlLY+ZswYunbtyplnnskbb7yx00q42bNnc/zxx1NcXLxb7dibCgoKKCgo2CvPfcYZZ/DII4/w3nvvccwxx7T4cd9++y0ffPABc+fO5cgjjwTg4IMP5le/+hX/+Mc/+Mtf/tLo495//30qKir4f//v/9GrVy8AsrOz+fOf/8y3337L2LFj2bp1Ky+++CJ///vf7XPw8MMPZ9KkSTz++OPceuutu/muRUcllVJCiD3O6/Vy5JFH8u9//7vBvnfffZdJkyah6+mZeHl5OTfffDMTJkzggAMOYOzYsVxyySVp5ckbNmzgoosuYty4cYwcOZI//OEPfPTRR022Y8uWLRx11FGccsopVFdXN3ncd999x5QpUxg3bhyjR4/moosuSisPX7RoEYMHD2bhwoWcd955jBw5ksMOO4zZs2cTj8cbPF9lZSUjRozgnnvuSdseDAYZM2YMjzzySJNtEUIIIXZmzpw59OvXj0suuaTBPofDwS233IKmaTz++OMAnHfeeZxyyikNjr344os54YQT7PWvv/6as846i5EjRzJ27FiuvvpqysvL7f2vv/46w4YN45VXXuGwww5j7NixrFq1qsW/nz/88ENOOOEEu+vY/Pnz0/Zv376da6+9liOPPJIDDzyQ0047jffffz/tmHA4zEMPPcSxxx7LiBEjOOaYY5g7d65dAXPNNdfwxhtvsHnz5ma71T/44IMce+yxvPfeexx//PGMGDGCE088kW+//ZYlS5bw+9//ngMPPJDjjz+ehQsXpj32559/5sILL2T06NGMHj2aSy65pEFXqRUrVjB16lQOOeQQhg8fzhFHHMGtt95KKBSyjxk8eDAvvPACf//73xk7diyjRo3ir3/9a1qXp2R3rUWLFjX6PgD69evHeeedZwdSYH0X69KlCxs2bABgwYIFuFwuTjvttLTH3nfffTz44INNPjdYlU/r16/nmWeeYejQoU0eV1VVxfXXX8/f/va3vfLHxwMOOADArsJ68MEHOfroo5kzZ47dZayqqoqff/6ZDz/8kOOPPx6wvsedc845AJxzzjlp3Q/fffddTjnlFEaNGsVhhx3GjTfe2KAiaGffE5vz+uuvM2nSJEaMGMEJJ5yQ9u+isa54b7zxBscdd5x9/MKFCxk2bFiD83jp0qWcfvrpjBgxgqOOOoonnngibb/T6WTSpEk89thj9rbk99nmhpr49NNP8Xq9HH744fa2goICfvGLXzT7nTscDgPg8/nsbXl5eYD1vRhg4cKFxGIxjj766LR2HnXUUc0+t9j3SSglhNgrjjvuuAZd+Px+Px9//LH9JSHJNE0uvPBCPvvsM6688kqefPJJpk6dysKFC7npppsAMAyDCy+8kGAwyF133cXDDz9MXl4ef/nLXxot+S0tLWXy5Mnk5eXx1FNPNfnl6IsvvuCPf/wjALfffju33norW7du5fTTT29Q+n/llVcyZswYHn30UY4//nieeOIJXnnllQbPmZeXx69//WveeuuttLLp9957j9raWk466aSWfYhCCCFEPeXl5Xz//fdMmDABRVEaPSYvL49DDz3UDnROOOEEli9fnvb7srq6mo8//pgTTzwRgK+++orJkyfjdru57777uO666/jyyy8555xz0oKUeDzOvHnzuO2227j22mvp27dvi38/33jjjUyePJlHHnmELl26cM0117BixQrA6v5z2mmn8fXXX3P55Zfz4IMP0r17dy655BLefPNNwPq+cNFFF/HEE0/w+9//nkcffZRjjz2W++67z/6+cPHFF3PkkUdSVFTEyy+/zFFHHdXkZ7lt2zbuuOMOLrroIu6//36qq6uZNm0a06dP5/e//z0PPfQQpmly+eWX25/B2rVrOf3009mxYwd33nknt912Gxs3buSPf/wjO3bsAKxwLdm18o477uDxxx/nt7/9Lc899xzPPvtsWhvuvfdeDMPgnnvu4aqrruKDDz7g9ttvt/cnu3gNHz68yfdxxhln8Oc//zlt2/r161m5cqVdKffjjz/Su3dvvvrqK04++WSGDx/OxIkTW9R177LLLuPNN9/kF7/4RbPHzZw5k/79+zc7rujuWLt2LYBdiQPWHyA/+ugj7r33Xq699lpyc3N56623KCoqsquthg8fzo033ghY52DyXHn44YeZPn06Bx10EA888ACXXHIJ//nPfzj77LPt/9+t+Z5Y39atW5k7dy5//etfefDBB1EUhWnTptnnSX3z58/nmmuuYfTo0Tz88MNMmjSJiy++uNE/gM6YMYPf/va3zJ07l1GjRjF79uwGw2Mce+yxfP/99/bnNnz48J3+m1i9ejU9evRA07S07b169bKfpzG/+c1vKCoq4pZbbmH79u1s3LiRu+66i6KiIg499FD7ubOysigqKkp7bO/evdm+fTuBQKDJ5xf7Num+J4TYK4466ig8Hk9aF7733nuPTp06MWbMmLRjt2/fjsfj4eqrr+bggw8GYNy4cWzYsMH+srRjxw7WrFljf9kEOPDAA5kzZw6RSCTt+SoqKjj33HNxu9089dRT5ObmNtnOu+++m969ezN37lz7F/Dhhx/O0UcfzQMPPMD9999vH/v73//e/qv0+PHjWbBgAR9++GGjX75OPfVU3n33XRYtWsQhhxwCWF82Dj30ULp27driz1EIIYRIlawS6d69e7PH9e7dm/fff5+qqiqOOeYYbr75Zt5++23799h///tf4vG4/Yeiu+++m759+/LYY4/Zvw9HjhzJb3/7W1577TXOPPNM+7kvuugi+8K2tLS0xb+fb731Vn75y18C1kXu0UcfzZdffsmQIUN46qmnKC8v5z//+Y/93o488kgmT57MXXfdxfHHH88nn3zC559/zj333MNvf/tbAA477DDcbjf3338/55xzDgMHDqSgoACn09mgC1h9wWCQm266yW7TqlWruPvuu7ntttvsiqLa2lqmTZvG2rVrGTp0KHPmzMHj8fD000/bVSHjx4/n17/+NU888QRXX301P//8M0OHDuX++++3jzn00EP57LPPWLRoUVrXs0GDBjFr1ix7fdmyZWmV5rvSxSsUCnH11VfjdDo566yzACvMLCkp4corr2Tq1Kn069ePd9991w5rmuu+N2jQoJ2+5nvvvcf777/P22+/3WRY2hqxWMxeDoVCrFixgttvv53s7Oy06r5YLJb2/RGsIGnEiBF2O3w+HwMGDABgwIABDBgwgKqqKh555BH+7//+z/4Mku/1zDPPtM/51nxPrM8wDB566CH69+8PgMvlYvLkySxZsoRf/epXDY6///77mTBhgt2N7YgjjsDhcHD33Xc3OHb69Ol2WHbQQQfx3nvv8cUXXzBhwgT7mBEjRgBWhVLfvn3x+Xw7/TdRU1OTVu2UlJWV1WxoVFRUxM0338z06dP517/+BUBubi7PPvus/XzNPTdYf7xOLov9i1RKCSH2CrfbzcSJE9O+WL3zzjv85je/afBlpbi4mGeffZYxY8awadMmPvvsM5577jm++eYb+wttYWEhAwYM4IYbbuDqq6/mrbfewjAMrr322gbjZfz5z39m5cqVXHfddeTn5zfZxtraWr777jt+85vfpP1FKCcnhwkTJvDll1+mHT9q1Ki09S5dulBbW9vocx966KF069aNf/7zn4D119iFCxdy8sknN9keIYQQYmeSFbgOh6PZ45K/10zTxOv18utf/5p3333X3v/OO+8wfvx4iouLCQaDLF26lCOPPBLTNInFYsRiMXr27En//v357LPP0p47tQtXa34/pwYHPXr0ALC713/55ZeMGjWqQdh2wgkn2MHXl19+ia7rHHvssQ2OST5Ha40ePTrtvYAVxiUluyAl2/nFF18wduxY3G63/Tn5fD4OPvhgPv/8c8AKLZ5//nlcLherVq3i/fff55FHHqG8vLxBUFc/JOjSpQvBYLDV7yPJ7/dz4YUX8t133zF79mz784xGo1RUVHDzzTdz5plnMn78eGbOnMnhhx/OnDlzdvn1wAq8brzxRq666qqdhqUtNXz4cHsaM2YMZ555JpFIhDlz5jSotKnfpXDjxo32+dWUJUuWEIlEGlTvH3zwwXTv3p0vv/yy1d8T68vPz7cDKag752tqahocu379erZs2dLg3E6Gr/Wl/lvyeDwUFhY2GKoiOzubnJycVt3dr7mB0ZsLG9966y2mTp3KxIkTefLJJ3n44YcZOHAg5513nl1RtrNB11VVoon9lVRKCSH2mt/85jdMnTqVbdu24XK5WLhwIZdddlmjx7755pvcc889bN26lby8PIYOHYrb7bb3K4rCvHnz7EEb58+fj8Ph4Ne//jU333xzWjVUMBikR48e3H333bz88stN/pKrqanBNE37S2iqwsLCBl8aUtsD1i/Ppn7BqqrKKaecwlNPPcVNN93EP//5T3w+X1o/eiGEEKK1khf9O7u72caNG8nKyrJDlRNPPJE333yTFStWUFhYyKJFi+xuYtXV1RiGweOPP26PQ5XK5XKlraeOXdSa38+pj0v+bk7+Hq2qqqJnz54NXjv5O7q6upqqqiry8/MbdC1KhhSNXezvTGOVGx6Pp8njKysreffdd9MCvqRkRVOyO94LL7xAbW0tXbt25cADD2zwOTb2Ws19t9iZrVu3cuGFF7J27Vruvfdefv3rX9v7srKyUBTFrmZLOuKII/j0008pKytr9PtQS8yYMYMBAwZw2mmnpVU4JQNOTdNaXT316quv2ssOh4OioiI6derU6LH1q2v8fn+z/w8Be9yo5r4DtvZ7Yn2p5zvUhTqN3QEwOXZb/ffY1P+Tlp43Ho8Hv9/fbDtT+Xy+tDHNkgKBQLOD18+ZM4dRo0Zx77332tsOO+wwjjvuOO6//34eeOABfD5fo9VWyfbt7uD4ouOSUEoIsdf88pe/JCsri3//+994vV569OhhD1KZ6uuvv+bqq6/m7LPPZsqUKfadUu666y4WL15sH1dcXMyMGTO46aabWLFiBf/+9795/PHHyc/Pt8cHAHjmmWf48ccfOf/883n22Wcb3AEwKTs7G0VRGv3lW1paan+R31WnnHIKDz30EB9//DH/+te/OO644xr9QiqEEEK0VKdOnTjooIP4z3/+w1//+tdG//Di9/v57LPPmDhxor1t/PjxFBUV8a9//YuioiJcLpd9V65kYDF58uRGKzN2doHf0t/PzcnNzaW0tLTB9uS2/Px8cnNzqaioIB6PpwVT27dvt4/Z27Kzszn00EM599xzG+xL3sRl7ty5PP3009x8880cc8wx9sV2/UHG96SffvqJKVOmEA6HmTdvXoPxn3r37o1pmkSj0bTvIskQqf4f3lrjP//5D0CD73ibN29m/vz5PPvss4wbN65Vz5nserYr8vLydhoYJcPSsrIy+vXrl7avtLSUnj177vXviam6dOkC0GC8qabGn2qp6urqVv276Nu3L59++imGYaT9bFm/fn1a1Vd9mzdvTgtBwTqnDjjgAHtQ+H79+uH3+ykvL0/rkrp+/Xq6d+++W+eg6NikRk4Isdc4nU5+/etf85///Id//etfTZYgf/vttxiGwaWXXmoHUvF43C6DNwyDb7/9lkMPPZRly5ahKApDhw7l8ssvZ9CgQWzZsiXt+YqKivjlL3/Jb37zG+6///4my5a9Xi8HHHAA//rXv9IGkaypqeHDDz9sMPZVa3Xv3p3x48fz7LPP8uOPPzZ65yMhhBCitaZOncratWsb3OUVrN+fN910E6FQKG3wa03T+N3vfscHH3zAv//9b37961/blRw+n49hw4axZs0aRowYYU8DBw7kwQcfbPbOb635/dycX/ziF3z77bcNKsDefPNNioqK6N27N2PHjiUWizW4u29yIPTk7+292Q0oecfBoUOH2p/TAQccwNNPP817770HwOLFixkwYACnnnqqHUiVlJTw888/N1ols7u2bt3Kueeei6IovPjii40OSJ6skHrnnXfStv/vf/9j8ODBjVaMtdSrr77aYCoqKmLChAm8+uqrzQ7Svjd0796drVu3pm2rX103cuRInE4nb7/9dtr2r7/+mi1btjB69Oi9/j0xVZcuXejVq5d9DiX997//3eXnrKqqIhgM0q1btxY/5vDDDycQCPDJJ5/Y28rLy/n666857LDDmnxcv379+Oabb9KqtcLhMMuXL7crIJMDnqf++41EInz44YfNPrfY90mllBBirzruuOO48MILUVWV66+/vtFjDjzwQABuueUWTj31VKqqqnjhhRfsO/LU1tYybNgw3G43V111FZdeeimFhYV8/vnn/Pjjj/Ztfuu77rrr+OSTT7jpppt48sknGz3miiuuYMqUKVxwwQWcccYZRKNR5s6dSyQSafRW26112mmnMX36dPr37582RoUQQgixq4444giuueYa7rrrLn788UdOPfVUOnfuzKZNm3jxxRf58ccfue222xgyZEja40488UTmzZuHqqoNuulNnz6dCy64gCuuuIITTjjBvsve0qVLufjii5tsy678fm7Mueeey5tvvsnkyZOZOnUqeXl5zJ8/ny+++ILbb78dVVX55S9/ybhx47j++uspKSlhyJAhfPnllzz++OOcfPLJ9mDWOTk5lJWV8dFHHzF06FA6d+7cik+3eRdffDGnn346F154IX/84x9xuVy8/PLLLFiwgAceeACwvtc8/PDDzJ07l4MOOoj169fz2GOPEYlEWj1eVHl5ORs2bGDAgAFNBke33norO3bs4Oabb8bv97NkyRJ7X3KQ73HjxjFhwgRmzZpFMBhk4MCBzJ8/n2+++YaHH37YPn7Dhg2Ul5fvdEDsVI1VNTmdTvLy8tL2bdu2jW3btjFs2DCcTmeLn7+1DjvsMP7xj39gmqbdZS4ZDn744Yfk5uYyZMgQLrjgAh566CEcDgcTJkxg06ZN3H///QwYMMAeA3Rvf09MSt6Z78orr+Smm27i6KOPZsWKFTz00EPArgWtyd4Ghx9+OGBVUK5atYpevXo1OXj+L37xC8aOHcvf/vY3/va3v5GXl8eDDz5Idna2PbA6WDcFiEQiDBs2DIC//vWvXHLJJfz1r3/ltNNOIxKJ8Mwzz1BSUmIP1N69e3dOPvlkZs2aRTgcpk+fPjz11FNUV1c3uHuk2L9IKCWE2KsOPfRQcnJy6Nq1a5Nlv+PGjePGG2/kqaee4t///jeFhYWMGzeOOXPmcMkll7B48WKOPPJI5s2bZ98Vp7q6mj59+nDLLbc0WYHUuXNnpk+fzi233ML8+fM56aSTGhwzfvx4nnrqKR544AGmT5+O0+nk4IMP5s4772wwQOuuOPLII1EURaqkhBBC7FHnnnsuo0aN4plnnuHOO++kvLycoqIiDjvsMG677TY7oEk1ZMgQBg0aREVFBePHj0/bd/jhh/Pkk08yZ84cpk2bhsPhYPjw4Tz11FPNBhQul6vVv58bU1RUxIsvvsjdd9/NrbfeSjQaZciQITz88MP2ncoUReGxxx7jgQce4Omnn6a8vJwePXowffr0tO50p5xyCh999BGXXHIJ06ZNS7vb3e4aMmQIL7zwAvfeey9XXXUVpmkyaNAgHnroIbudF154IRUVFTz77LM89NBDdO3alRNPPNFuf3V1NTk5OS16vQ8//JBrr722yS5wyUoToNGukmPHjuW5554DrLu7zZkzx77T4YABA5gzZ05aN8+HH36YN954g59++qm1H81OvfLKK8yZM4f3339/pwOR745jjjmGhx56iGXLltl/EBw4cCDHH388L7zwAp988glvv/22HaI+//zzvPzyy+Tl5XHsscdy2WWX2VWEe/t7Yqrf/e531NbW8uSTT/Laa68xcOBA/v73v/P3v/+9wfhULfHxxx9z4IEH2uPQLV++nHPOOYdZs2Y1+29zzpw53HHHHdx1110YhsHo0aO577770saHu/nmm9m8eTP/+9//APjVr37F3Llzefjhh5k6dSpZWVkceOCBvPrqq2nh+C233EJOTg6PP/44tbW19s+Y3r17t/r9iX2HYu7qSHpCCCF26t133+Wqq67io48+anKATiGEEEKI/cGZZ57Jfffd1+AOenvaRRddRH5+PrNmzdqrr7Mnvf322wwbNixtjKsPP/yQCy+8kH/+858NKh+bU1tbyxFHHMGdd97ZYKwnIdobqZQSQoi9YMGCBXz33Xe89NJLnHLKKRJICSGEEGK/tmjRIoLB4C7f5a81Lr/8cs444wwuvfTSVo2plElvvvkm9957L5dddhldu3Zl/fr1PPDAA4wdO7ZVgRTASy+9xMCBA+3qPSHaM6mUEkKIveDpp5/mvvvuY8yYMdx3331ym1shhBBC7Nc2b96M1+ttk7skgnUXxBUrVjR6Q4D2qKKigrvvvpuPP/6Y8vJyCgsLmTRpEtOmTSMrK6vFz1NeXs5JJ53Ec889J93iRIcgoZQQQgghhBBCCCGEaHN7736pQgghhBBCCCGEEEI0YZdDqUgkwvHHH8+iRYvsbRs3bmTy5MkcdNBBHHfccXz66adpj/n88885/vjjGTlyJOeccw4bN27c9ZYLIYQQQgghhBBCiA5rl0KpcDjM9OnTWblypb3NNE0uueQSCgsLee211zjxxBOZOnUqW7ZsAWDLli1ccsklnHLKKbz66qsUFBRw8cUX09Leg6Zp4vf7W3y8EEIIIYRIJ9+nhBBCCNGetDqUWrVqFf/3f//Hhg0b0rZ/8cUXbNy4kVtuuYX+/ftz4YUXctBBB/Haa68B8Morr3DAAQdw3nnnMXDgQGbNmsXmzZv58ssvW/S6gUCAMWPGEAgEWttkIYQQQgiBfJ8SQgghRPvS6lDqyy+/ZNy4cbz88stp25cuXcqwYcPwer32tjFjxrBkyRJ7/8EHH2zv83g8DB8+3N4vhBBCCCGEEEIIIfYfemsfcMYZZzS6vbS0lM6dO6dt69SpE9u2bWvR/oyLVMHqJ8CIgO4DRzbo2eDMS0z54EgsKzI+vBBCCCGEEEIIIcTuaHUo1ZRgMIjT6Uzb5nQ6iUQiLdqfcRv+H3x7ZQsOVMHVKTEVgbsYPN3A2wO83cHbG7IHgrsIFGWvN1sIIUT7khyrx8S011OXW7Mvdb0lx6SOE9TabTt7zZZub82+5p6zsccmj1EVlZ65PdHVPfY1RgghhBBCZMAe+zbncrmorKxM2xaJRHC73fb++gFUJBIhJydnTzVh9/Q8Bap/hpqfIRZImfwQq7Hm8RBgQLjUmljR9PPpPvB0h6xe4BsAOUMgdyh4eoDmBEVLn1Q9ZV1P2SZVWUKIfUcyhDFMw16uP29uX2vmQKP70tphGBgkXs80MTDs1wcwTCOtPQYGmNjHpj6u/msml5Ovl/oZpO2jrl11s50HU809n72v3vM2tQ0lOVPqnj/17yrJTYk/tiSfI/V4RVHSQ6SUxfr70l6nlfsN08Ctuyn0FpLtykYIIYQQoq3FDRNN7dhFKO3lPeyxUKq4uJhVq1albSsrK7O77BUXF1NWVtZg/9ChQ/dUE3aPqxOMnp2+zTTBjFld+owIRP0Q2ga1myCwAUIldVN4B0QrIFQKkXIrxKr5yZp4r+459SzIHmSFVNlDrKoq3QMoVgClakAynFKtYEp1geoGzWVNip4IrBygOtKXpTpLCNEI00yEPYnQJzVssZdT9jV2fOq21MfFzTiGYRAzY5imSdyMEzfiVsBj1B1T//F2u7C2Wf81EizVD4QSx6JY4UVj7xUF+xjMlDAlZV9yW/I5FEVBQbG3t2Zfcr3Jxzby+JY8R+ox9Y9LXd/ZczZ1bEcUiUeoCFVkuhlCCCGE2I9pqsJfX/qWVdv9mW7KLhnQ2cf9p4/KdDOAPRhKjRw5krlz5xIKhezqqMWLFzNmzBh7/+LFi+3jg8EgP/zwA1OnTt1TTdjzFKUu7CHLGlcqqyd0+oW134hBvNYKqyKVVvVUvNYanypSAdFKCG6BmlVQsxIC66zqq4pvrQms8Cn3ACg8FArHQfYAKwwjDkYc4mGI1YIZtwIyM+UqC8UKsRS9LqjSPNake0F1JiZHIthySnAlRDuVDGziZrwu7DHi6cFPSphUf4obcWJGjJgRs5fjZpy4aS03FyylhUwplUDJ6pRk1Upj61AXpNSfq4lKTwVrOfV4VVHT9jf2+NSAprFtQgghhBBCZMqq7X6Wb6nOdDM6vD0WSo0dO5auXbty7bXXcvHFF/PBBx+wbNkyZs2aBcCpp57Kk08+ydy5c5kwYQIPPfQQPXr0YNy4cXuqCW1P1UHNAUcOeLuBaVgVUpEKqN1iVU5lD4YeJ4Ij1wqWalZB5TJrqlgGoa1QudSaVj0Czk5QfCR0nQT5o5rvvmeadWGVEbPmkUowy6z1uv4WiTAqEUrpPqtiS/dYYZXmTux3SJdBIVohNUiKG/FG58nAyN5mxIkYEWsej6StJ0OjOHVVRfW7lzXoIpWQiGvs8CcZ+qRuU1DQFA1d1e19qccCaduF2BnDNIjGo8SMGFEjagejuzIlA9RksJpcT12OGTEi8Qi9c3tzRK8jMv32hRBCCCHEbtpjoZSmaTz88MP8/e9/55RTTqF379489NBDdOvWDYAePXrw4IMPcvvtt/PQQw8xatQoHnrooX3rwkdRrYDKkQPeXhCthnAZBNZD7UYrCMoZZI0t1fsP1mOCW6H0cyj7HHZ8BZEdsPF1a3J1hq5HQ/ffWRVUDV5PsSqk0EFrpl2mAUbUmswohLdDMJKouiIxfpUjZXwrZyKscllVV2ldBPXEsSnzfen/odjvpFYZpV4E25VGKcuRWIRIPELEiBCJRYiZsYbVTIZBnLhdbWQXNoLd3SwZAmmKhqIo9lxVVFRVxaW47GPqB0ti/xMzYkTjUevcS5nC8bC1PXE+RoyIfVzUiKY9JrmeOo/EI3XPbUSIxWPpxxlRYvGYHTgl9yXDoagRxTCNjHwmDtXB1YddnZHXFkIIIYQQe85uhVI//fRT2nrv3r15/vnnmzz+yCOP5Mgjj9ydl+w4FAWcudbk7WGNReVfB4HN4MgCZ4F1jKcr9DrVmowolC+Grf+Bkg+s8GjdC9aUPxp6/R6KJ1jBUKvaotaNR9WYZJWVGUt0GQxag7ub8cSUNlptXXfB5GDsmjsRYnms10gNrewKLEfr2y1EKxmmsdNqjEg8QjgWJhQPWcFSshKDxDhIRmL8o9TBoBsJk1RFRVOtua7qDbapUnG4T4oZMUKxEKFYyDqPYiFC8brl5Lllr8fDhGNhwvEwkXiEUCxkn4PheGJ7LFK3nAycYnXLcTOe6bfdYgoKuqo3mByqo9Htmqqlryta2rbG1hVFoXdebxyaI9NvVwghhBBC7CZJCdqC5oKs3uDuaoVTNSuhdgO4i61AJ0l1QOEh1jT8WquCass7sP1jqPjGmlyFVjjV6w/g8O2Z9qmJaquWMA0rqDJidV0Ho1V1wRakVGClBFjJCivVY3UbtCuwUkMrR6JKS05LUceuykhUbySXk5UaoViIUNQKCaJGNL2rT2Lg7VQqVnCUvNjVFA2H5sCtuO1QKXnhKzqmSDxCbbSWYDRozWNBazlmbUuuB2NBQrGQfVwobi0nQ6fk/uQxoVgo4wGRpmg4NWfa5NAcOFUnTt2JU7XWHarD2p44Rlf1uuMT+5JBUfI5UtdTgySHln58cp9DczQInjS1ubLdPUMGOhdCCCGE2HfI1X9b0pyQ1cu601/NKqtbn+q0gqb6F8CqE4qPsqZQCWycD5tet7oDrnwE1j5ndQHs/Udw5rXde1DUxF0BW/AX6rQAK3EXw1gthBMVWal9mlQ9PcDSvFZ4pXtTQitn+lwqUTq01C5J9bsWpYYG0XjU7iaXHLAbsE+fZLVFcqwkh+bAo3rs0Ekqlto/0zQJxUIEogH8ET/+iJ9ANEBtpNbeFogGqI3WUhutJRBJWY4GCEaD1jxmhUsxI7bX26yg4NbduHU3Lt2FS3Ph0l3WNi19W+o+l+bCqTnTtjs1p7Vcb92hOezjk8GRLqG9EEIIIYTYh8i320zQsyDvQHB3huqfrfGmPN2arhByF8PAC6H/ebBtAax5CvxrYPWTsO4fVtVUvz+BI7tt38fOtDTAMs30wdrNKETKIBSzgi17wHY1fWwrzZOovPLWdRu07zgowVUmJbvJpU7ReNQOmgLRQNrYNMkQIXlnt2TAlAycvJo3rSuPaF+i8Sg1kRpqwjVpc3/Eb8+TU024xg6ZkvNAJLBXKpCcmhOP7sHr8OLRPXgc1rJbd+NxeKxtie3JuVt349E9duCU3ObWUpZ1Nw7VIdV0QgghhBBC7CYJpTIlOZ6UIxeqfrSqpjz1uvPVpzqg22+sO/OVfAirn4Can2Ht07DpDeh/vjU2VUuqmNoTRamrhmpOcsB2M2bNI1Vg7kiEWUbieZT0LoGaO1F1lVUXVmn1givRatF4tMH4N+FY2A4YwvFwWjc7wL77W7Law6E6cDlcdvgkF/iZZZom/oifqnAV1eFqqsPVVIYq7eXqcDU1kRqqwlXUhGvs9epwNaFYaI+0QVVUfE4fXoe3bu6w5lnOLGvuyCLLmYVH99jbvLoXj8NDliOx7rDWpapICCGEEEKI9k2+sWea7oX8A63wpGal1RVvZxVPigpdJlqDnpd+DD/NgcBaWPH/wYaXYdBUKJ64790VLzlgO00M2A4pwVXUCqsiVWCWWcv282h1wZXmAC3LGp9Lc6eEVa6Uaqt97HNsAcM00gdjTgzanKx2Sb2jV7I7XXKA4+RYMz6nzw6cRNtKBkwVoQoqQ5WNTlXhKqpCVVSGK6kKWUHU7lYrZTmyyHZlk+20Jp/LZ82ddfPGpixHFj6nD7fulnBSCCGEEEKI/YhcLbYHqgNyh1qhSPWP1thLrk47f5yiQOcjofAw2PwmrHzM6gq45GroNA6GXWUNsL4/aUlwZY9xFYV4GGJ+CG5K9BI0U6qtEqGU5rWCQs2d6Ca4b4RWpmmmBU7heJjaaC014RoC0YAVPMUixBID2CdDp+RAyTmunDYb2FhAKBZiR+0OKkIV7AjuoCJYQXmwnIpQ3bwiWGEHUbs6rpJLc5HnziPXlUuOK4dsVzZ57jyyndnkuHLsKduVTY4zxw6hfE6fnAtCCCGEEEKIVpFQqr1QVMjubwUflcsgXA6ugpY9VtWh5ynQ9VhY+6w17VgEn55ujTXVb3Lz3QL3N8m7DTb1maRVWyXHt9qaGN+KhqGV7kuptEqEVZorsZz5f2JxI27fQSwUC9nBU02kxr4dffIudaqi2oMqe3QPua5cqXTai2JGjPJgOWW1ZZTVlrEjuIMdtTuseWK5PFjOjuAOaqO1rX5+j+4h351PniePPHceea48ct25duiU586zl3PdueS6cnHpzQS6QgghhBBCCLEHydVme+Ptbs0rlkCksnV31tO9MPAi6H48/HAXlH1ujTu15V8w/FooPGQvNHgftLNqq2RolQyuwiWJSqvUAdmTdwp0gZ6dCK1SqqySodUerLIyTCP9dvfRoDX+T6TG6ooXD2Mm2uhQrbt6uTW3BE97QdyIUxGqoDRQyvba7ZQGSimtLaU0UEpZsIyyQBllwTIqghWY9l0od86pOcl359PJ04kCTwH5nnzy3fn2coHbmifDJrcuYbQQQgghhBCi/ZIr0fbI2x3MOFQsBRRw5rby8T1gzP1Q8gGsuBuCm+HrqdD9BBhyefu7S19HkwyttKZCq3hdaBWvhWgVBKJ1++3xrJwp41l56oKqFlRZhWNWV7tgLEhtpNYenDrZHc8wDVRVxalat5bPdmbTSeuEKncj3G0xI0ZZbRklgRK2+7db80DdfHtgO2W1ZS0en0lTNAo8BRR6C+nk6WTNvVbo1MnTyQ6gOnk7keXIkjGXhBBCCCGEEPsMCaXaq6xeYMSh6jsrBGltkKQo1mDohYfAz3Ngw/+zxp0q+xyGXQPFR+2VZgusgdQ1rfHugaaZGM8q0kTXwJQqK82DqWURVHSChkltPE5NLEJFJEBtPEo4FiZqRFEV1ap80hPhk0fCp91RE65hq38rW/1b2ebfZk8l/hJKAiWU1pbag7s3R1VUCjwFdPZ2pjCr0Jp7Cyn0FlKUVWQtewrJ9+TL/y8hhBBCCLHfiBsmmip/aBUWCaXaM18fwIDK7xMhxS50xdG91oDnXY+B726B2g3w7ZXQdRIMvar1VVhi9yiKVSWlOhrsMk2TUKyWQLiG2kgl1eEqyoMVBKNBQvEIJqCqDly6B7fTR44zB92Ra91BUHValVWqCsgP+OZUh6vZUrPFnrb6t9rzrTVbCUQDO30OTdEoziqmc1Znin2JeVYxRVlF1txbRCdvJ+kWKYQQQgghRD2aqvDXl75l1XZ/ppuyS44aXMTfJg3JdDP2GXLF1J4pCvj6QjwE1T9Z3fp29SI3/yA47B+w6nFY+xxs/Q+UL4YDroeiw/dos0XLROMx/NFaArEg1eEA5eEqAtEQoXgY0wRNVXHr2XhdhRToTlRIv3NgtBLCZXVPqOqg6HV3DNSzEoOuO61B2bXE4Oz7ePevaDzKVv9WNldvZnONNW2q3sTmms1sqdmCP7LzX3557jy6+rrSxdfFnoqziu3lAk+BVDcJIYQQQgixi1Zt97N8S3Wmm7FL+hdlZboJ+xQJpdo7RYWcQdbYRLWbrfGidjVU0Nww+FIongDf3QSB9bD4MuhxIgy4CNxFe7Tpoo5pmtTGQlYIFQ2yI1RFdSRAbSxEzIijKioe3YVXd1Pgzmk68NCcgLOxF7DCKjNqBVeRHdYA7KYJKCl3HNStcax0b2LcqsRg7B0ssArFQmyq3sSm6k1srN7IxqqN9vq2wLaddq8r8BTQLbsbXX1dG8y7+LrgcXja6J0IIYQQQgghxP5LQqmOQHVAzlCI1UKoBDxddu/58g6AQ1+Anx+G9S/Cpn/CpregaDx0/x10/qVVYSN2WdyIE4gF8UeCVEf8lIUqU6qgTJyaE6/uorMnf8908Up2C8QBWiP7jcYCq8Q+VUs8TgfVY1VYae5EYJWstHJYAWkbihkxttRsYX3letZXrWdD1QY2Vm9kQ9UGSgIlzT7WpbnontOdHjk96J7d3Z66ZXejW3Y3CZ2EEEIIIYQQoh2QUKqjcPggd7jV5S5SCc683Xs+zQ1Dp1sDnv88ByqXQeln1uTIscac6vZb6zU7SPVMJsWNOP5okJpogIpQNeXhavyRIJHEQOQe3UWWw00nd25m7p5mV0o1ss+IgxmxAqtoJURK6wVWemLcqmRg5UqMYeWsC652I7CqClWxtnIt66vWs65ynT3fXL252TvY+Zw+euX0okduD3rm9KRnTk965PSgR04POnk6yV3qhGinTNMkbsYxTAPDNIgb1nJT20xM62eSAgoKJibZzmz5Ny6EEEIIsQ+QUKojcRdC7lAo/9YKlXZl4PP6CkbDIfOsrnyb34bN70B4O2x4xZqy+ljhVLff7H6F1j7EMA380VqqI1YIVRasojYWImJE0RSVLIeHfHc2Lq0DVJypGuBpJrCK1o1h1aLAypESWlmBlWmabA9sZ23lWtZWrmVNxRrWV65nbeVaKkIVTTbNpbnondubXrm9Gky5rgwFfELs51JDpbgRb3TZMA1iRiwtUEoNllRFRVM1NEVDVVR73aW4cOgOnKoTh+bAoTpwaA5rv6LZj3FoDrIc7Xs8h5KSEm677Ta++OILXC4Xxx13HNOnT8flcnHrrbfy3HPPpR1/ww03cNZZZwHw9ttvc99991FaWsrhhx/OzJkzKSgoAKzP/+677+bVV1/FMAxOO+00rrzySlRVxrkTQgghRMcjoVRH4+0JkWrw/5wYX6qxJGEXZPWGQZfAwItgx1dWQFXyAQTWwcqHYOXDUDAGuh0HXSaC7tszr9uBBKJBqiMBqsI1lAQrCERrCcfrQqgCdw5OreFd9To0VQO0xgPQRgIr0zApDVWz2l/C6ppS1vhLWFO9jbXVWwnEgk2+THFWMX3y+tAnrw+9c3vTO683fXL7UJRVJAOKC7GXpAZJqZVJqeFSzIhhmmbdTT1NUJSGoZKmJoIiLQuH6sCpO3FqThyqA03V0FU9LVRqar6vBM2maTJt2jRycnJ44YUXqKqq4rrrrkNVVa6++mpWr17NFVdcwcknn2w/xuezfq8uW7aMv//979x8880MGTKE2267jWuvvZbHHnsMgKeeeoq3336bOXPmEIvF+Nvf/kanTp2YMmVKRt6rEEIIIcTukFCqo1EUyBkI0SoIloC32+4/Z0UV1IYg+Rdt+kCnqVDwZ6j+HCoWgH8ZlH9tTT/caY071e1YKDzUqozZB0XjMaojfqojAUpqy6mK+AnGQigoeB1u8lwdpBJqL6mJhVhVtYmVlRtYXbXJnmqitY0erykqPbMK6ZtdTN/sbvTN6UafvF70zu2L15OffodAzWndSVAIsVPJyqXUgCluxokZsbTgya5aSlBUBV3R0wImTdXwODw4VSdO3YlLc+HQHHaopKu6fWxyOTVwEpY1a9awZMkSPvvsMwoLCwGYNm0ad955px1KTZkyhaKihjcYef755/nNb37DSSedBMBdd93FhAkT2LhxIz179uTZZ59l2rRpHHzwwQBceeWV3H///RJKCSGEEKJDkqu+jkhzWd34dny1e+NLBYKwcQts3AqRqBV4pVywoJhgdgPOBrUC1KXA12CUwLb3rEn1Qf4RUHwMFI8Fl2u3316mmKZJIBqkKuKnPFTF9mAFgWiQuGHgcbjI0j2ZGxMqg2JGnA0121hZtYGVlRtYWbmRVZUbKQmWN3q8pqj09HWhf253+uX2oF9Od/rldqeXrwsOTQczbo1fZSQGXo+VQ3WZ9WCFxKDqDiuY0ryJOwU66o1h5ZCxzsQ+K2bEGg2YUiuYTLPuh7WiKHYopKoquqLb4ZJLc+HSXXZ3uNQgKTVoSg2b9refcXtDUVERTzzxhB1IJfn9fvx+PyUlJfTp06fRxy5dupTzzz/fXu/atSvdunVj6dKlOJ1Otm7dyi9+8Qt7/5gxY9i8eTPbt2+nc+fOe+X9CCGEEELsLRJKdVSuAsgZDBVLQPNYQVVLRWOweRus2wz+WuiUB56djE9ldIbYAIidCJE1EFkExmIwqmHHv6zph1xwj4OiiZB/IPi8kOUBrf3+9TxuxKmOBKgM11BSW05luIZgPIymqPgcXoq9ndD3o7/+B6JBVlZu4KdKK4D6uWI9q6o2ETGijR7fxduJgXk96Z/bgwG51rx3dtfmuzEqmnVONHbOmoYVVpkxiIcg5odgrG6/qlsVVKojJbBy1QutHHuuW6sQe0AyUIoZMTtoSi4nu8cpijWAd9p4S6pmB0xehxeX7sKluXDr7rQgKbmcOkm4lFk5OTkcccQR9rphGDz//PMccsghrF69GkVRePTRR/n444/Jy8vj3HPPtbvyNRYuderUiW3btlFaWgqQtj8ZfG3btk1CKSGEEEJ0OBJKdWRZvayxfPxrrbGmWnIBUu2Hn9bCtlLI8UH34pY9TlXBqYLTAd7hwHAwJ0PkRwh9DqGvwKyC0H9h439hQwGoB4FrLBQcAPm5VkDl9YA7s9VUkXjUrobaVruDmkgtUSOGR3fhc3oo1PL2i4u5ilA1P1WuZ0XFOn6qWM9PFevZ6C+xuvjU49FdDMztyYC8XgzK68mAXGvyOb17tlGKmgirGgusTCusMhMVVpEdEC5pOPC6qiduBOAB3WNVVaVVWelSZSV2i2maxIxYWrBkrxtWN7lk0ASgKmpaJZJTd5KtZ+PW3Lh1N07NmRYoJSuaJGDad8yePZsffviBV199leXLl6MoCv369eOss87iq6++4oYbbsDn83H00UcTCoVwOtO7hjudTiKRCKFQyF5P3QcQiUTa7g0JIYQQQuwhEkp1ZIoK2YMgUmXdMc9d3PzxJWXw0xoI1EK3YtB2cwBpRQXXcGvKmQzh7yD0BYS/AcrB/B+E/gdbCmDTCFAOBNdAyMmGgrxEJZUXvO69HhKEYmEqwzWUBivYHqzAHw0CJl7dTaE7z+pWtg/bEarix/K1/FixlhUV61hRsZ6S2h2NHtvZk8/g/N4MyuvNwLxeDMrrRQ9f58wPOK4oVsCEo5k7BSa6BUb9EKmwKq+S6ldZJSsMk9VVyeBKxrLaLyXHZUoNmFKnZBWTaZr2WEx29zdNx+vw4tatkMmlu3CojkZDpuTA32L/MXv2bJ555hnuvfdeBg0axMCBA5kwYQJ5eXkADBkyhHXr1vHiiy9y9NFH43K5GgRMkUgEj8eTFkC5Et3lk8d6PJ62e1NCCCGEEHuIXH11dLoXcoZYA5DH/I3fFc8wYP1mWLUeUKBL5z0fAikOcI+2JjMM4aUQ+hLC34JZDupHwEcQzYcdI6BkGNAHXIkufoV54PNZy3uoy18oFqYiXMP22gpKQ+X4I7WoiobP4aGrtxBtH719dmW4hh/L1/JDxVp+KF/Dj+Vr2R6saPTYXtldGJLfh8F5vRmc35vBeb3Jd+e0cYv3EPtOgU1UWRlRIFZXZWXGrCor07QqARU9MZaVIxFYedO7BNpdA+XHZkeS7CpXf0obm0kBBSV9nCVNJ8uRhcfhsauZkuGSQ3WkLeuqLpVMolEzZ87kxRdfZPbs2UyaNAmwxgBLBlJJ/fr144svvgCguLiYsrKytP1lZWUUFRVRXGz98am0tJQePXrYy0Cjg6YLIYQQQrR3cnW1L/AUQ/YAqFxuVR2pKf9bYzH4eS2s3WRVKGVn7f32KC5wj7WmtIBqKZgVwMegfQyKz6qeCgyHin5gOsCpg9ttdffL9bW6y184HqE8VM322gq2B3cQiAZRFZUcZxbd20O1zx4WioVZUbGO78vX8EP5GpbvWMPmwPYGxyko9MnpypD8vgzN78PQgr4MzOuFz7Gf/GVdUayB03E2XmWVHHzdjEE8DLFAXWiVfLySCKQ0Z133QNVl/XtTUwZnl9CqTTQVNkWNaN1d5pS6rnPJyevw4nF48Oge3A53g4DJoTkkaBJ7xJw5c3jppZe45557OPbYY+3t999/P99++y1PP/20vW3FihX069cPgJEjR7J48WJOOeUUALZu3crWrVsZOXIkxcXFdOvWjcWLF9uh1OLFi+nWrZuMJyWEEEKIDkmunvYVvn5Wd6XQNvBaX1SJxmDFGli/CYo6gcvZ/HPsDWkBVQTC30P4awh9A6Yfop8Dn1sVKa5hoB0I8SGwOQjr46BihVReDxTkWt39kkGVwzp9o/EY5eEqtteWUxJMVkSp5Dp95Pty9pkLS8M0WFu9he93rOb7HatZXr6a1VWbiKd2UUvo5evC0IK+DCvoy9D8vgzO703W/hJA7Yrk4OuNjWUF9UKrxADsDUKrZPdAPaV7YOJOgaojEV4lx7Tat8LRPSl1vKaoEa1bjkcxTMO6QyMNwyafy4dH99h3nEuGS/Xn0nVOtIXVq1fz8MMPc8EFFzBmzBi7mglgwoQJzJ07lyeffJKjjz6aTz/9lPnz5/Pss88C8Mc//pGzzz6bgw46iBEjRnDbbbdx1FFH0bNnT3v///f//X906dIFgLvvvpvzzjuv7d+kEEIIIcQeIKHUvkJ1WN34IlUQqbSqkJKBVOdO4MxAIFWf4qzr4pcTh8jPEF5sTfEyCC8BlljH6n3AOxIcB0LcZ42DtaPCCgF0jbjLQYXbpMwDW5QANWoUxeUi25Ozz1REVYZr+G7HKr7bsSoRQq0hEA02OK7Ik8/wgn4ML+jHsIJ+DCvoS7azDSri9ictDq0S41qldg9MUnVAB023KqyS1VbJca5UvS7A2kcHY09WN6WGTcnACQUwra5Nuqqjazq6ouPSXRQ4CuzKJqfmTAuZkt3q9oV/82Lf8f777xOPx3nkkUd45JFH0vb99NNP3H///TzwwAPcf//9dO/enbvvvptRo0YBMGrUKG655RYeeOABqqqqOOyww5g5c6b9+ClTprBjxw6mTp2KpmmcdtppTJ48uS3fnhBCCCHEHqOYptnwVlvtkN/vZ8yYMSxevBifr5Fxk4QlsB5KvoK1NbC5DDoXWnfMa89ME2IbrVAq/C1EV5N2Na/4wHUApnMENfpAykImm2u3U1FbhRGNkq26yHZloblcVgWVL8vq7ud2WmGco/1nr3HDYG31ZpaWrWTZjpV8V7aKDf5tDY7z6C6G5vflgE79ramgP529BRlosWgV+86BiTGtjDjW+Fbx5AFW9ZRdbaWB6k4EV+66CqxkYGUHV+0niIkb8QaVTTEjRsyMWQeYoKpq2jhMHt2Dx+nBq3txak47YEoNnnS1/f/7FaIjke9TQggh2oPfPvAJy7dUZ7oZu+SEkV154I+jO/R7GN4th3emHZHpZgBSKbXvcXSBDTFY8yP0GNz+AymwKkIcvazJdwIY1db4U+Gl1h39TD+EvkAJfUEOYNIJRe1HYd4ggnpfTMUJsSiEo1BZA6XlgAK6ZgVSbhdke61ugC6nNTkd1uDWGRKIBvluxyqWla1kWaIaqrEqqN7ZXTmw0wAO6DSAEZ360y+3B7p0P+p4dnbnQKhXbRWHaDVEK1KCK6xqIiWl4kpJjm/lqusaaFdcJZbRdrvqyjANovGoHTo1CJywutM51MR4TJrVnS7LkYVH9zQZOEl1kxBCCCGEEPs3CaX2JfE4rPgJSk3o2hvw02R3o/ZMzSHuPoxybSTb9QoC4R/Ijf1MMRvJNUvIZQe5xg6IfIUR0QioPanW+lPj7kvA293qagUQi0M0CoEgVFZb1SqKAo7EgOpZXquyyg6qdKuyai90myqpLWdJ2U8sKf2ZpWUrWVW1AaNekaJXd3NAp/4c2GkgIwoHcEBBf3Jd8lfs/YbdRbAZpmEFVyQCLCMAsepGugom7kSoaIlwypUIrlwp1VZWVZapaMRMiJom0Xpd65KFtIqq2IGTQ3M0GjjVD532lbHchBBCCCGEEHuPhFL7CsOAn36CVaugWy9QiqB6uXUXMb3jjC/kj4coi9awMVxOedQPCuSovVE9g/ErCroZIDu+hpz4KnLia3CaVWQb68g21kEU4jjxa72oUfvi1/oQcHcDj7vuBUwDIjFrEPgdlVBSRuIWXVZVldNp3cHQ67GqqZwOK7By6DsPDBKSA5J/W/oTS8t+Zknpz2ytLWtwXLesQg7sNJCRhYM4sHAgA3J7omWwekt0AIqauIsgzVRcmdZ5blpVV3EjQjRSQ8yIEI1FiBpxokbMCkVVFdBw6C50zYFD85DtyCbLnYvXnYvLmYVT8+B0eHE6vDg0D05HForq2CfHvBJCCCGEEEK0LQml9gWmCWvWwM8/Q+fOVjc13ODtAzUr6+4A1k7FzDg7on62RiopiVYRiIfJUl10ceaiK+lX3jEliwp9BBX6CDBNXOaOREi1huz4WnSC5MZXkRtflQipHATUnvi13tRovalVe2AkK6NSGYYVVEWjVli1fYd9S3l03aqicrshyw0utxVSuRyg68R0hRWV6/m29Ce+Kf2JZWUrqYr4055eVRQG5/VhZOFADioaxMjCQRR58vfq5yr2fdad6uKJCidrsqqc4onCKdPuVud0eHG6dPJ1D1kON27NiVNVcSoKTlXBiYJDMdFIjn9VnehCCIRT7i6YHPNKcycqr9ygu+q6DKaOeWV3JZQup0IIIYQQQoiGJJTaF6xfDz/8APn54PHUbfd0hagfQlvBXdzuKhsC8RCl0Ro2hHdQEQ2gKAq5modCV3bLnkBRCCuFhNVCyhxjwTTwmNvJjq8lO74WX3w9OkFyjDXkGGsgCiYqtWoX/GovAlov/GpPokqOVTHSVFgVS1RWVfuhvJKwEeP7yFa+jW7lm8hmloW2EDKjaQ9za05G5PfjoM6DOahoCAd06k+Ww4MQrWEmutRFkoOHJ6Z4YpwphcSd6lQNh6qT7fCS5fDgc3pxqjpOzYFTddhzh7aLP/JNo+EdBqNVKetG+vHJboNoViCl6IkQKzH+leZMCbjqze1lqRoUQgghhBBiXyehVEe3eTMsXw4+nzWlUjXw9YZ4ACLl4OqUmTamMEyD8liALZEKtkYqm62KajVFJah0Iah2YbtjPJgGbrMMX3wd2fH1+Iz1OM1qsowtZBlbIPYFABElm4Dak4DanYDWg1q1G4aSGItLVQlqsDS8hW+C6/jWv57vazcRNeNpL52jujnI2Y1Rzu6McnVjiLsLusMJMR0qXBAut7oCOhxWlZWuW3OH3u7CQtF2DNOwgqZ4XeAUMWKYpgEoKAroio5T03GoOj5XDlkON1m6xwqa7NBJx6k60PZWRZKiWpPaworL1PDKjIMZhUio7g6E9W/6qqhWkKXoiUBLS1RYOevGw7KDLC0luEp9THJdwiwhhBBCCCE6CgmlOrLt2+H7762gIze38WN0D/j6Q/UPVlccR07btjEhbEQpjdawMVxGabQGE8jTvI1XRRkGaiiCGo6iRmKJeRQlGkOJxVFiBmosDnEDxTBQDDMxjk7Kha6igAKmqhJTfZSrI9ihjkTTgri1MtxKKR5tO25tB05nDU7HD+Q7fgAn1GjwQTiX90NuPq0NsyxYRYz0i+hOuo/Rvj6MyurNaF8f+rmL0u8kFo8nBlqvq7Cy2qckugQmBrV2OKxugJ7EnQGTYZWuWct6Cwa/Fu1W3DDswcOtudXVzh5AXMGuYHJoDvJc2fgcHty6C6fqwJUMnhLhU4cZPFxN3CGwpTdaSN5x0IhjDeIeByMIMX9KuAV1o7knPgc1GUIlgyo1EWY5rKos1WEFWopWL7hKCbPS9kmgJYQQQgghRFuSUKqjKi+H776zwo/i4uaPdeWBrx9U/5S4SGujbmRxg5ryUspLNlFeuoV4ZSV5NVF6+qO4/CE0fwjdH0SrDaEFQmi1YdRgGC0c3flz70F+J3zWEz7sY01fdYe4WgVU2cf0DMCRZSqH1rgYX5tDT7WAiC+PmDdOPGsz8awdxLLcxLM8xLPcxHweDK+rYXdAsLo6xeMQS3QNDIWtcaxM07rmVhUriNJUK5RyOMDtsqZklVVqaOVIXIyLNmeFTtG0KifrrnVQN56T1Y3OpTkpcHnwOT24NVdiW133Ooeqd5zQaU+zg6FWPMY068KsZBdCM1YvzIqnhNUpgZaS8pqodRVWipaozkoGW8m7FWoNJ7XeevJ5ZPwsIYQQQuxFccNEU/fT74xinyShVEdUXW0FUrW10K1byx7jLoZ4CPxrwa3v/sDnpgkVVbCtDLaVQskOKCuH7Tswy8oxSstRq2rINkyygd678hKKguHUMVwOTKcDw6Fh6imTpmIq1p3zzEZCGcU0wTBRDMOax+IocYOAEmNRpzAfd43wcbcYX3cxiNd7eJ8KOHI9HLUOjlwHfSpBwQCCialk5+1XFeJZHmLZyclL3OchluMllu1Nn+dmWcvZXtAUK7CKx63QKhiCmoA1vlVqoUiyikpT64Irl9PqJqhpdYGVprXq7oGiTmroFEkZSDw1dEpWOrl1F4WOPLL0ZKVTXRi1W+M5icYpicHXd+XXmGnUjZNlGtjVWWYUYqF6+836D068fkqYlRZsqSkDvTtTBnt3pIdfactq4wFZ8vmEEEIIIRI0VeGvL33Lqu3+nR/cDh01uIi/TRqS6WaIdkSukjqa2lorkKqshO7dW/44RQFvT+uCK7QF3J0TF0HNME0oq4CNW2HzNthUAltKYHMJbN0OkcYrmhTq7lZvKhDL9RHNyyKWk1UXvuR4ifmsqqK4z0Msy43hdRF3OzE81tx07pnxlkJGhCWBDSz2r2NxzVqW15YSJ31g5u7OfEZ7e3OwqwcHa93oVexB7RNFPSRCNBhmfW0VntrtuII7cAUqcdRWowfCVj5VmzIFwAyAEgXFMNFratFralvcVlNRiPs8RHO99ucVzU18bsnPMTfL2p/tJO5wWOFVMAT+Wms52U0QrGBKVetCKqfDmtyulIorLSXgSizvJxU79bvXJSudDNNEgbTQyau7yHLk4dO9uHQnTlW3AqdExZOuyo/TDsMOe3bj/5ldnZUMtlKCrFik4bbU7oeKUhd2KQpWGKWmBFyJuZqcJwKtZNiVvMOhWj/ISnl8o9vq798//p0LIYQQ+5pV2/0s31Kd6Wbskv5FWZlugmhn5CqqIwkGYdkyayypHj1af0GhapDdF8wIhMrAXWRdnJgmbN8B6zbB2k3WfMMW2LDVCjuafD4FCguIF3eitjCb8nwXFfkuYoW5OAuLMDvlEcv1tnmFTsiI8n1gI1/71/K1fy3f124mVm9g8q6OPMb4+nBwdl9GZ/Whmys/bX+4Ba+jmUHcxna8xjY8RgkeYxseYzsaEYgAAcCfmAJg+hVifi9xvxvDr2PWKCg1JmpNFL0miF4TRDFTg6yynbbB0DUrpMrzEc332cFVND+bWF4W0RwvUZ+HWI6HuMthdRU0jIZVV1riAlVPVl45walb4105HOmBVQcKsJoKnVpa6SShk2iS3W1vD1Sd2uGVUS/MMoAoxMIp2xPdFqk3jl6D9jUSdqEk1pX0Ci/7TomNDSTfWLClNL3N3l7/9ZV2/bNCCCGEEEJkhlxldRShkBVIbd1qVUjt6hhCmguyB4D5M2xZDf9cBP/5xKqyaYyqQrfO0KMLdO8C3YutqVtnKvPdbDX8bIqU44+H8KlucnUvmqLSlqNCRYwY39Vu5OuatSz2r+P72k1EzFjaMcWOXCuE8vXlYF/fBiHUrogrHgJabwJaSudE08BpVuN2leDJ2o6nqBS3YU0aERwEcBBo8FwxPPjN7kRqc4nV+IjVuDGqdahWUauiOKoD6FUBHJV+a14VsMbgisVx7qjGuWPnfykxHLpVaZXnI5qfbQVZyfVE9VU020s024MZjUGNkVJ5laAo6QGWqlpdA52OuoHaU7sVpo6NpenW8h68MI0Z8UTgFLfHdYrFk8PSS/c60QEoidrS3b37aH12qGWmz+3l5KDyYYilBGNpxwM0E3wlx+eywyfF2kZqCJUagimNj8+lqIkKMDURkKUe38Rc1cFZIEGXEEIIIUQHJ1dhHUE4bHXZ27zZCqR2t/JoRw089Q788y3r7nBgPWfPLtCnR2LqDj27WQGUo+40MUyD8liATeFytgS3ETai5Gleejo7tdkgzVEjxvLazXztX8ti/1qWBTYSrhdCFTmyGZMIoA729aW7M79t2qeoRJQ8Imoe1Qyu226aOMwqPEYpLnMHbqPMCqvMHTjNanSC+JTNkLUZsoAudQ+N4Sai5hFWCvCrvYgo+YSVPKJRH0a1jlYdtcKqSj+OSj+OisRylbXsqPRbAVY0hqu0CldpVYNm1xf3uuzqq2hetlWFlVzP8dpTzOu27jIYCNYFWPY1rFkXXilqIqRSE3cYdFiVWE6ntS21i6GmgqphqgoxDaKmQdRMCZ2MRJUIoKkaTtWBrmrNdq+T0Ensd9pqPKrUoMsOvhLLdmWXUVfdZUTqttsBGHWVYGDtS+3iCA3X9WwoHAcO395/j0IIIYQQYq+Rq7T2LhKB77+HjRutQErfjf9lW7fC00/Dm29CNFHLNKw//GECjD3UqnZpQsyMUxqtYUNoB9ujVRhAgeal2JGz6+1poZgZ5wc7hFrH0sAGQkZ6LVYn3cfolEqoXq62C8laRFGIKnlE1TxgYNou1YzgMsrtsMplVuAyduAyKxKBVQjd2IaXbRCv97w+iPqyiPTItcIwJZewmk+N0oeIkkNUySWq+FAi8USVlR9HeV1gpVfWBVdWoFWDGomh1YbRasO4t+xo9m2ZikIsx1vXdTDfl6jA8hHL91nVVzlZ1kDvXjfETYiGIBAkHrMqnGJmjCiJ4AkDwxrMCRQVh+7AoTlwOJzkurx4XVlkuX04XS6cDhcuzYPT4cTpdKM7nPUqs9p310Ih9gmZGIzdiECkguaruIQQQgghREcgoVR7FgzumUBq40Z46il45x2rmgVg9Gg4/3wYOQRqfoboDojngOZNe2jYiLI9Ws26UBnlMT86KoV6Ns69OL5OMoRa7F/HYv9aljQSQuVpXqsSKtsKofq4CttXCNUKhuIkqHUhmFoelaCYUVxmJU6jPBFWlVvrZiUuowKNsNUl0AiQxZZGn99EJaJkE83JIZKbQ7R3DlE1m5DSiajSh6iSTVTJxlBcYJpogZDVRbCixgqqymsS634clTV2qKVXBVAME0eiO+HO7khoqAqRXC+hXC/hPC/hXC+xPKsCK56Xg5qfi6OgAD03Bz3bh1PRcZoKTkPFaSioYROCBsSDYNYmKiesd4iqWN0DU7sVJquynA6rIsulg+5IH/w9NcCq3+VwV7vICiGEEEIIIYRoEQml2quaGiuQ2rZt1wOplSvhmWfgv/+1BrYGGDsW/vxnK5RKyh0CtVshXAqRanBkU4vG1mgV60NlVMVr8ahOujhy0ff0uCdY3fF+CG5hsX8t3/jXsTSwkaARSTsmV/My2tfb7pLXz12Euh/cKt1UHISUIkJqUaP7NTOI06jEaVbhNCtTlqtxmlU4zBoUDFxmFS6z+W57cZxEFR9RLZtoJx+xQp+1rvgIK52IKlnEFB8RvMQUjWg0CjV+1PJq9Moa9IoanJUB3FW1uCpr8VTV4qoK4qoM4KwJohom7ooA7oqGY2o1oCqQkw35OZCXA/m51jw55acu51rBk2Fa53k8npgbEAlBTaBu3e5WBNbYN2ZdQJUaaKlq3d0KHXpictQFWKracOysZBCWGmp10KBUCCGEEEIIIdqChFLtUUWFNah5ZWXrx5AyTViyxOqm99lnddsPO8wKo0aMaPgYPQtyBkCsC9X+DWyp+pmNoW3UKJDjzKeHs2CPBkBhI8ry2s18419nh1BhM70SKlfzMNrXZ78LoVorrngIah6CdG38ADOOw/TjNKtxmNX23GHUJJZrcJg1aESsySzHbZbv9HVjOIjgJerJIt4jC6OXD0XNQVHzUdQ+qFoOupqDruWgaXlguKHSDxXVUFGZmFclpnrL1X4rYKqstqaWcDrqQqrc7MRyNuTmQK4vfXtuNmR56wKj5N0I4ynzeBxCMagNpe83DazBlk0wk6GWYt3ZMnWeOk5Wc8GWljw+JchKhlupIZeEW0IIIYQQQoh9kIRS7c22bbB8udV1r3v3ll+MxmLwv//Biy9ag6KDdVH7q1/Bn/4EQ4Y0+VDTNKmIVrMpuJ0tkUqCmoe8rN70itWixINgVILuA9W5S28pEA/zXWAj3wTW8a1/PctrNze4O55VCdXHvkOehFB7iKJZ40qRi2maxMw4MdMgqsaJmXGiiXXFDOGmFjcBvATxEsRDEDe1eKjFadbiMANoZg0KcXSi6FSBWWWPZ7yThoCSBfk+6JQFqs9aV7NAKQa1X2I5C0wPVJtQFYeqKFTWWmFVVY0VUlVUQ2UVVCbWwxGIRGH7DmtqCV2zwqmcbCu0ys2GHF/dtpyUbckpy9P4v8dkaGUHW2ZKpVbMGgQ+9Zi0QZwTwRZKXTilKIlwSkkPqRyJcEvTrEHidT2lSis1EEudNxJySQWXEEIIIYQQop2QUKq9iEZhzRqry52uQ9cmKl/qq6qCN96AV16BksSYPg4H/O53cPbZ0LNnkw+Nm3HKIpVsCm5nW3gHMTNGvp5DkS/fOsCMQ7QGIpUQq4JoNWhO0NygNB1QlUf9LAls4NvAepb41/NzcBvxeqlFcmDy0b4+jM7qQ193oYRQuyEZNqXPrWUzpbuaQ1HRVQ0dDbfmpEB1kKW5cam6NYaTouNQNWtZ1Rt21zRNazwnowaM6pTJnzKvsSazxlo3Q1h32fJD3N9wsPam5CSmniooXlC9oHgS83xQuoPqgbADqlWoAaoNqIpBdQyqwlAdhqogVAagym8FW6GwdcfAHZXW1FKaBtlZ6UFVjs/alu2DnMQ+X8p6tg+87paFQMlKrNSuhsnlWMxqdzwl2ErObYmAS1GsxWRIpSQCLkWpV42lWSGXriXuiKhZ43KlBltKvTArGZQpKcfUD76EEEIIIYQQooUklGoPqqpgxQrYvBkKCyErq/njTRMWL4Z//hPef9+6Qx9AQQGceqo1FRY2+fBwPML2SDnrg1vZEalCRaXAkYtbqxc0KRo486wpHoRYAKKVEE0ED5oDU3GxPlrFksBGlgY2sDSwgQ3hhtUqXR15jPb1YZSvN6N9fejpLOiwA5O3pcbDJmsOJmYiiNAUFV3R0E0reMrChUfV8OLEqWg4TNWao+I0FRyo6IZq3Q0vbNaFG8mxvMzEtuRA4smiHrAWFDURDBUkQpDUsEIFXUkJKgxQakEJpgRaATAD1twIWIGVUZvYVpvYVouVYBl1gVZTshJTw7HiU6iguCGaB34X+HXwa1CjJCYTagyoiVuhVk0UaiJQE4ZwzOrS15ouhfbLquDzWuGVLysxb2Tdl7rda3Ux9Hlb1303KRlYpYZbqRVcsTgYYWubvd+o+/8ONOimiAIqdWEX9Su6lLqwKlnFleyq6EhUdqWGWI1VhKWGXvUru1KPE0IIIYQQQuwTJJTKpFgMtmyBn36C2tqdD2i+ebM1aPmbb1p31EsaNAjOOAOOOca6y1gjTNOkOhagJLyDjaESqqMBPKqLLs5O6C25k57mAc1DrZ7FD+EVLKteybKalXwX2EhVPNTg8P7uzozK6s1Bvt4clNWbLs7cnb/Gvs40wIR4PE7cjBEz4sTiMeJGoqopbs2TwYBimqgo6GZyUslVdTyKjltx4FJ1HOg4FBWHouFQNJyqjkN1oGiJC/fUKpnkMikX+LqecvFff0yklEqZZHez1OAiHk8POuJxqxtdMuCIxSCS7NIGGE4wHKDkJT4P0ruZaY0MFK7FQAnXhVRmMBFeBdOXjUTgZYasyUjsT64DVrhVC3ot5GFNLRXBqsTyNzIFAL8CATVl3bSmaOLzqfZb065wO8HnBq8nEVZ5rODal2XNs5LbPNYxWd7EsYl1rwc87l177foaC7tMs+48MAyIxhL/D1L3J88bqEs4U5POlC6Maup5mnL+plZ9Jc/d1OArOUi9ricqxZoIv+oHXKmhqqo0frwQQgghhBBir5BQKhNME7Zvt7rrlZRYF5Y9ejR+7LZtsGABvPeeNdZUktcLkybBySfD0KFNXjhFjCjlkSo2h0opCe8gbEbJ0bLo4e680+5yhmmwPriV72tW813Nar6vWcWqwEYMuzuYxaU4GJ7VnZHuroz0FnOgu5gcR5bVxU9zgOJo1ceTWckL7MRAScnlZBBjmIlwKXkMmPG4FSxhEDcN4macmGkSV+LEDJMYcavaRLW6VqmJbnGaqqGrKk5FJ1f34na48OguHLoDh8OJQ3Pi0J04nIl13W2FTa2pNMnUhXZqt7N4SkgVi1tTPDGPRiEctcKsSCSxPwZhA4x4IuwyEudQDpg5gJIeXmka6PXDrJTBwU0DzHBdQGWGwAglQqtwvX3hetsi1rIjDN4wdA4ntiW22/8WTBrtlxjBCqkC1AVW9afaxFR/PZmlhSLWRCsrtFLpCnhV8Gjg0etNDvA6wOMErxM8LmvZ4wKvC9wuK9iy527QnUAiDEKzqirRQNGxyqmSy7oVJNnz5HFNnH/1Q67Uf3fJ9ZgBRrQu+KofjtW/w2Jq7pVa8ZUaepEafCX/jZAegiXvuGiHuBpoSmKb1vDfV2r1YP3nTT2mqUBMaaN/q0IIIcR+JG6YaKr8bhWiPZFQqi2ZpnVnvfXrYdMm64Kja9f06qhYzLrz3uefW9PPP9ftU1UYMwaOPRaOPtoKphphmAYV0WpKwxVsDpfij9aiKir5jmw8WuMVE6ZpUhIp58eatfzgX8vymtUs968hEA82OLbY1YkR2f0ZmT2IA3MGMiirFw5Vt8agioesKeqHeADitVYAAdZ4NejWXHGQ0h+stR9kvaDIqkCy1g2rS1qjx9Trkpa8YLXXAUWx4gUVYpjEFYgrJnHFtEInxSQGmJqK4tQwNat6Q3d40TQdTdfRVR2XpuPR3bh0K2hyOpzouo5Dc6JruhU2aQ50XUd3uPa9qozkRbyjlT9iUsOsZHhVfz0Wh3A4PcyKG9ayfQe9eMpA4mCPtaTqoOWCmt/wbnd2V7HE8s6YJhBND6ns5cREBMxove3RetujiW3J9Zi1Hg+DPwK1UQhEE/M41Bp1wVUwMaWuJwOtWiCcaGvMhOq4NRFp3f+TxjgAd2JyJab66/WntH0KOFVwa+BSwaXVTXoiuLLDLrVuXn9ZSy5ridCrkWNSt6eum4kwylQT85T1uFJvW8pxBtZzGKTsw9qvJNYVLXF+aCTSLeq6vSqJduh1FYyodcEWiYpFJfE+1OS+5Jhfie6RarJSTLeeK9k9skGoVi8Iqx94KfUe09RxQgghxD5AUxX++tK3rNq+ixXsGXbU4CL+NqnpG1gJ0RFJKNUWolEoK7OCqO3brQvsoiJwuSAUskKoJUusaelSCATqHqsocNBBVgj1q19Bp06NvkTcjFMZraEiUsPWcBmV0RriZpxsPYuu7iK0lKoowzTYEirlp8AGfg6s5yf/en70r2VHtKrB87pUJ0N9fRiRPYADsgcwIrs/nV0Fjb9PRQM9y+rqp+eBEbMCqlgIoiGIBiAWtKocYjErQMLEOg1TLiJRdhocpV1EUe8CKqWyIa6pGArENYWYCoamJIKmROiEgaEAqoKiavZFma7qqKqGrjvQVA2n7iBbd+F2WJNTdVgDhqs6DlW3lhUNh1ZXBSV2wa6EWcmuhMkqrNSqrGS3wuT+SMTqXhaOWv8u4/H0boap3RKb7V5WrzJNc4HqqduWGnjtjuLG3q8BJIIrM4YVZiXW07bHIBaB2gDUhqA2CMEQ+IMQDNdNgQgEoxBMzmMQilnzYNyawnEIGnV3WUxkaNTs6htLVpY1Ul2mYQVXznqTCysMS+5z1NvurLc9dTl1Xn95V/Lx+uPL70a+bj/XTu9gubPnSgm/0hqmpiwn9yWDpmYmpbHllMcpKeuKWrdNSQZ/pB9D/eMa29bM86dtNyF7ABQdsZsfmhBCiP3Rqu1+lm/ZjQr0DOpftJOxh4XogNo0lAqHw9x8883897//xe12c95553Heeee1ZRPaTjxuDWBeUWGFUeXl1rhRlZWwbp01jtRPP1ld+GKx9Mfm5cH48XDooXDIIZCf3+DpTdMkaISpjvqpjNZQEimnOhogZsbxam6KnPkowJZwGd/XrGZN7WbW1m5hbXAza2s3E2hkHCgNlf7urgz19GK4pzfDPb3o7yxGR8O+K1hlFChJCYoaYf+1PfmXdycoLnDlWReQyYtmNXFxTRiIWt3biFtdsZIDZmsODFQMTSemqBiqhqGqxBQwFJO4mahmwsBUFOu6LDn2DCaqoqIpGpqioqnWslPVcKoOXLoDl+rEpTutQcJVzQ6X9GTQlFiXkKkdUxSrSqS58diakjoeVjLQSt0Wr9eNMBq1Qi17ShlDKxpLdDOLJzKXRLiVFqoqNAi4Gu2+lVKtYu9LPcZJc3fAtLmwBoDfE0zTeo/BkHUnwGAoMYXTt4USYVconLItOaVsD0UgnJhCUewB1uPUVX61FYcCDtWq3tIVa+5QrElX6pYdJNZJWU8s64DDtOY6oCeWHYBmJo416vZpZuKYxHGakdhvWvPW5plKasK1B5hNLLcXJR/C4BsgNzvTLRFCCCGEELuhTUOpu+66i++//55nnnmGLVu2cPXVV9OtWzeOPfbYtmzG3hGPW1VQq1ZZXe5WrLDCqK1brXGjNm+GmibKCjp1glGjrIqogw6CgQMb3HErbsapjYcIxIL4Y0G2R8opDVewJVTKjlg1NdEAVZFqtkesbZtC29kWKSfexJ/fnYrOAFdXBrl7MNDbg2FZfRjo7YlbdyW6iaSM15Mco0dLjp2i1IVO9QcfrrfNVBTimBiKiQHEFcOaY2KYBoZpEDfiGEaUeDyMaUTAiKKYMcx4GIwIihFBJ45KDJUoOtYA4C5Nx6FquDVr/CWX7kHTHOiqE03V0TUnmupA1xzoihNNk4BJ1LOr3QxTJaurksGVHWzF69ZTtyfvhBeLQjSePtZWLJby+EbGUzKMREAMjVZyJbspAg0qCJPvN7VrVuoA4grp26h3nKKA02FNezoIME0r4EsGVaGwVdWWth5NhFiJQCsSsSrewpG6Y6PJ9cQ8bT3R3TOaeJyZkrRETev/RW0jlVuZoipWdz1H6h0ME90b9dRltW6g9+TA77qavp7snqonJvumAkrKeGxKYkoekzhv7DtpKvWm5HMk25qsEDTrHqspdfvVxLKmJIq3EuermTJ+X7J0zDCaWE4cG49CJK/RQjshhBBCCNGxtFkoVVtbyyuvvMLjjz/O8OHDGT58OCtXruSFF15oP6FUMGhVNwUCdVNNjTVVV1vzigooLbW645WXW0HU9u2wY4d1YdQcRYEuXay75Q0aBIMHE+3fB3+nbGpitfgjfiqj1ZRtXEBZpJIdkUp2RKspCZezPVpBeayGyliAqniAyniAoLnzsWFcqpOe7mL6ZvekX3Zv+ub2pm9uH3rn9UF3uuoqTDQrUDLBDosM08DEJG7EG8xTjzHMGIZpJHraKZjJi70YKKqChoaqqqiKiopVraQqKg7NgVO1xlZyaA6cmhOn5rQqm1QNLVGxpKkammmiEUfHRDMNNMVANeNWxVU8Yo2/YwTBiFjdBs14YkrsN2usC0/rfwRpXQGT48wkx3ZJHeclrftJva4q9buYiP2TXam1h54vGT6l3tkwGVTV35e6bNbbFk92YUxUb8VSQ7J4vcfU3fUxbbBwk/Tx2JLts9543Sztn1ZKWKYo6WGX3W0rdVu9/U4HuJx1+6h3XP3HNtiv2C+f9nr1P+PkQPuRaEqIFatbT3b1jMZSlpPBVnI5pWIubV5vORZrfDkas/4/ReN1Y+8lGabV1Tka3aXTqN1LVsMm/+hRf3w3LeUPIPX3Y0L/bvDbTL8JIYQQQgixu9oslFqxYgWxWIxRo0bZ28aMGcOjjz6KYRiouzv2ym4K/ucdXrn+ZMr1KIaCPcUVa9Dr1OVYYoqrEOsG0Z4QTWyLOjUiXhcRt4OISyfi0gm6NEIOhZBuEjSjhMwl1BpfEvRHiC3dvT/1ujUXhe4Cir2dKfQW0tlbRGdfMV2zu9E1pzt5nnxMTEzTtEMmgK0EwQzWjQ0DiSILxQqPFBVFUdAULW2uKzpu3Y2u6ThVpzXekubEoTqsrnKJMCk5b2xbcq7srSDHSIRRqcGUEU9ZrzcZ0USQlRiPJxlqkahKMeMp418l/lJvX7QntpuNBF4tkVrVkjZ2S+r+esckF1O3pz1P2gs08jqp2xs5Nm1j/bY0+ibqrTbRhibXm3raFr5eS563I4WGipKoXNnLr5N6E4C0u9hR78YAqaFXE8c1uLFA6nMbdV0Zk3evjBv1luvdaS/5b8tIaafd5tRtKf/WUrenhmp218nEZ5s2AH5y0bQCEJfTustg6nOnPib5zzv131/9sMx+3hZss3cpdeFgLNmdNHWw/8T2aKxuPXWstLiRfiOAeCPLqcc0tq/Reeo4bSnzZKiZrA6MJ7qupj42eWxz518sOa7YLgRvm7bt/A9B+7D9ajgEIYQQQuzT2iyUKi0tJT8/H6ezbhyUwsJCwuEwlZWVFBQ0MXh2G3lhx/84//g98Rfp5IAo9Rg0e9MrXdVxa248upssZxZZjiyynD6ynFlkO7PJdeeS68olx5VDviefAk8BnTydyHJkoSiKNaHYYZJdYUQiBFI1dEW3tyeDp9Tl5PhL9felhkuqktnwsEXU5N27dpNppIROyeXkRXj9ffVCqgaBldn4shknvYuKkf58yddLC8XMJl4veYFeb17/At6+jq9/PI08R4MPpYnlRtbNpp6jqcc39ZiWtKUFh+90p9KCY/bGcY09tF6IttPPsqnXtp+g9Q9vkOPtLNhr7DWSNy9o5cD1qXfKTLtrJvUCqvrHpKzTxHPUD7iot54WgjXyeqnLRmLdMOqOrx/ope1v5DWS+xSsH1u6kfZP3b4TYd0HVO9zqP+5NPGZpi039bOgicc3+hxNHJ92rpp1YZVd+Wemj91mminBZLLKL/EZJMOtZKBpVwUaMKAvuBq/m+z+YJ8eDkEIIYQQ+5U2C6WCwWBaIAXY65F28NfO4353BX96r5StVZvQVB1FtSp5VDU1kNHsYEZXdFRVxaFad2fTVd3ububUnImBsnVcmguPw4PH4cGre3E73GQ7s8lyZuHVvXgcHnxOHy7dhUJduNTYXFXUtG3JACq5nFwXe0jqXaQ6kvoXhfXXU+eNhlAtCamauEDd2bGNHt/YY5rY1mQ400zosivh2C4d05LX2gOvsdce39xT78XnbvpFM/Cae9suvKfGwqCm5i3e18Lnb+6Yli7Xf73WPH9zx6s65BY1PHY/0CGGQxBCCCGEaKE2C6VcLleD8Cm57nZn/q+d3bK78fQpz2a6GULsvrRgUtl5gYsQQogOo70PhyCE2HfFDRNNlS+WQog9q81CqeLiYioqKojFYuiJW7eXlpbidrvJycnZ6eOTg2f7/f692k4hhBBCCICsrKx2V4G8u8MhyPepltsXLsDlPbQf+8r7ePTD1WypCma6GbtkRI9cfj+mJ31yVIyII9PN2SXFHuvnt7yHzNoX3kOfHLXNvgvs7PtUm4VSQ4cORdd1lixZwsEHHwzA4sWLGTFiRIv+qhcIBAA48sgj92o7hRBCCCHA+p7i8/ky3Yw0uzscgnyfEkLsr94B7sh0I3bTGqCj9+2R99A+rAHGzGqb19rZ96k2C6U8Hg8nnXQSM2bM4Pbbb2f79u3MmzePWbNa9kl07tyZjz76qF3+1VIIIYQQ+56srKxMN6GB3R0OQb5PCSGEEKIt7ez7VJuFUgDXXnstM2bM4E9/+hM+n49LL72UY445pkWPVVWVLl267OUWCiGEEEK0X7s7HIJ8nxJCCCFEe6KYZkZuqySEEEIIIVopGAwybtw45s2bZw+H8NBDD7Fw4UKef/75DLdOCCGEEKJ15BYtQgghhBAdROpwCMuWLWPBggXMmzePc845J9NNE0IIIYRoNamUEkIIIYToQILBIDNmzOC///0vPp+PKVOmMHny5Ew3SwghhBCi1SSUEkIIIYQQQgghhBBtTrrvCSGEEEIIIYQQQog2J6GUEEIIIYQQQgghhGhzEkoJIYQQQgghhBBCiDYnoRQQDoe57rrrOPjggzn88MOZN29eppvUYZWUlDBt2jTGjh3LEUccwaxZswiHw5luVod2wQUXcM0112S6GR1WJBLh5ptv5he/+AWHHnoo99xzDzKU3q7ZunUrF154IaNHj2bixIk8/fTTmW5ShxKJRDj++ONZtGiRvW3jxo1MnjyZgw46iOOOO45PP/00gy3sOBr7LJcsWcLpp5/OqFGjmDRpEq+88koGWyjam/fee4/BgwenTdOmTct0s/YZ8vOtbTT2Od96660Nzu3nn38+g63smJq7hpFzec9p7nOWc3nPWb9+PVOmTGHUqFEcddRRPPHEE/a+9ng+65luQHtw11138f333/PMM8+wZcsWrr76arp168axxx6b6aZ1KKZpMm3aNHJycnjhhReoqqriuuuuQ1VVrr766kw3r0N65513+Oijjzj55JMz3ZQO69Zbb2XRokU8+eSTBAIBLr/8crp168bpp5+e6aZ1OJdddhndunXj9ddfZ9WqVVx55ZV0796do48+OtNNa/fC4TBXXHEFK1eutLeZpskll1zCoEGDeO2111iwYAFTp07l3XffpVu3bhlsbfvW2GdZWlrK+eefzx//+EfuuOMOli9fzrXXXktRURFHHXVU5hor2o1Vq1YxYcIEZs6caW9zuVwZbNG+Q36+tY3GPmeA1atXc8UVV6R9V/T5fG3dvA6tuWuYq666Ss7lPWRn14pyLu8ZhmFwwQUXMGLECN544w3Wr1/P9OnTKS4u5vjjj2+X5/N+H0rV1tbyyiuv8PjjjzN8+HCGDx/OypUreeGFFySUaqU1a9awZMkSPvvsMwoLCwGYNm0ad955p4RSu6CyspK77rqLESNGZLopHVZlZSWvvfYaTz31FAceeCAA5513HkuXLpVQqpWqqqpYsmQJM2fOpE+fPvTp04cjjjiChQsXSii1E6tWreKKK65oUKH3xRdfsHHjRl566SW8Xi/9+/dn4cKFvPbaa1x66aUZam371tRnuWDBAgoLC5k+fToAffr0YdGiRbz11lsSSgnAunAfNGgQRUVFmW7KPkV+vrWNpj5nsM7tKVOmyLm9G5q7hvnlL38p5/IesrNrRTmX94yysjKGDh3KjBkz8Pl89OnTh/Hjx7N48WIKCwvb5fm833ffW7FiBbFYjFGjRtnbxowZw9KlSzEMI4Mt63iKiop44okn7B8ySX6/P0Mt6tjuvPNOTjzxRAYMGJDppnRYixcvxufzMXbsWHvbBRdcwKxZszLYqo7J7Xbj8Xh4/fXXiUajrFmzhm+++YahQ4dmumnt3pdffsm4ceN4+eWX07YvXbqUYcOG4fV67W1jxoxhyZIlbdzCjqOpzzLZBaA++f0jklavXk2fPn0y3Yx9jvx8axtNfc5+v5+SkhI5t3dTc9cwci7vOc19znIu7zmdO3fmvvvuw+fzYZomixcv5quvvmLs2LHt9nze7yulSktLyc/Px+l02tsKCwsJh8NUVlZSUFCQwdZ1LDk5ORxxxBH2umEYPP/88xxyyCEZbFXHtHDhQr7++mveeustZsyYkenmdFgbN26ke/fuzJ8/n0cffZRoNMopp5zCX/7yF1R1v8/kW8XlcnHjjTcyc+ZMnn32WeLxOKeccgq///3vM920du+MM85odHtpaSmdO3dO29apUye2bdvWFs3qkJr6LHv06EGPHj3s9R07dvDOO+/IX7EFYHUZWbt2LZ9++imPPfYY8XicY489lmnTpqV9/xOtJz/f2kZTn/Pq1atRFIVHH32Ujz/+mLy8PM4991wZ9qGVmruGkXN5z2nuc5Zzee+YOHEiW7ZsYcKECUyaNInbb7+9XZ7P+30oFQwGG3whSa5HIpFMNGmfMXv2bH744QdeffXVTDelQwmHw9x0003ceOONuN3uTDenQ6utrWX9+vW89NJLzJo1i9LSUm688UY8Hg/nnXdeppvX4axevZoJEyZw7rnnsnLlSmbOnMn48eM54YQTMt20Dqmp3z/yu2f3hEIhLr30UgoLC/nDH/6Q6eaIdmDLli32v7f77ruPTZs2ceuttxIKhbj++usz3bx9kvx8axtr1qxBURT69evHWWedxVdffcUNN9yAz+eTrvW7IfUa5umnn5ZzeS9J/ZyXL18u5/Je8MADD1BWVsaMGTOYNWtWu/3ZvN+HUi6Xq8H/hOS6BAK7bvbs2TzzzDPce++9DBo0KNPN6VDmzJnDAQcckPaXBLFrdF3H7/dz99130717d8C6OHnxxRcllGqlhQsX8uqrr/LRRx/hdrsZMWIEJSUlPPLIIxJK7SKXy0VlZWXatkgkIr97dkMgEODiiy9m3bp1/OMf/8Dj8WS6SaId6N69O4sWLSI3NxdFURg6dCiGYfC3v/2Na6+9Fk3TMt3EfY78fGsbJ510EhMmTCAvLw+AIUOGsG7dOl588UW5kN9F9a9h5FzeO+p/zgMHDpRzeS9Ijk0cDoe58sorOfXUUwkGg2nHtIfzeb/vv1JcXExFRQWxWMzeVlpaitvtJicnJ4Mt67hmzpzJU089xezZs5k0aVKmm9PhvPPOOyxYsIBRo0YxatQo3nrrLd566620cc9EyxQVFeFyuexACqBv375s3bo1g63qmL7//nt69+6d9ktr2LBhbNmyJYOt6tiKi4spKytL21ZWVtagrFq0jN/vZ8qUKaxcuZJnnnlGxqUQafLy8lAUxV7v378/4XCYqqqqDLZq3yU/39qGoij2RXxSv379KCkpyUyDOrjGrmHkXN7zGvuc5Vzec8rKyliwYEHatgEDBhCNRikqKmqX5/N+H0oNHToUXdfTBvdavHgxI0aMkDFndsGcOXN46aWXuOeee/jtb3+b6eZ0SM899xxvvfUW8+fPZ/78+UycOJGJEycyf/78TDetwxk5ciThcJi1a9fa29asWZMWUomW6dy5M+vXr0+rLF2zZk3aOD6idUaOHMny5csJhUL2tsWLFzNy5MgMtqpjMgyDqVOnsmnTJp577jkGDhyY6SaJduSTTz5h3LhxaX8d/vHHH8nLy5OxQ/cS+fnWNu6//34mT56ctm3FihX069cvMw3qwJq6hpFzec9q6nOWc3nP2bRpE1OnTk0L9L7//nsKCgoYM2ZMuzyf9/vUxePxcNJJJzFjxgyWLVvGgv+fvfuOj6LO/zj+mpmt6ZQQigoiBgTpGkT0R/FOOMWGeCIKKqh4gugJKk1FAVE5GwIq9q6HhbPceScqlhNROUFFkd5JSEL69p35/TG7k90UCBBS4PP0Mc7Md8p+Z7Nkd9/5fr+zbBnPP/88o0ePru+qNTqbNm1i0aJFXH/99fTu3Zvc3FxrEjXXpk0b2rZta02JiYkkJibStm3b+q5ao9O+fXsGDBjA1KlTWbduHV999RWLFy/miiuuqO+qNTqDBg3CbrczY8YMtmzZwmeffcZTTz3FqFGj6rtqjVZWVhatWrVi6tSpbNiwgcWLF/PTTz8xfPjw+q5ao/P222+zcuVKZs+eTUpKivXeU7HLhTg29ezZE6fTyYwZM9i8eTNffPEFDz30ENddd119V+2oJb/f6sbAgQP5/vvvee6559i+fTuvv/46S5culSEKDtL+vsPIa7n27O95ltdy7enatStdunRh2rRpbNy4kS+++IJ58+Zx4403NtjXs2IYhlGvNWgAvF4vM2fO5D//+Q9JSUmMHTu2UlIrDmzx4sU8/PDDVW77/fff67g2R48pU6YA8MADD9RzTRqnkpISZs2axSeffILb7WbkyJGMHz8+rhuHqJmNGzcyZ84cfvrpJ5o2bcqVV17J1VdfLc/lQejYsSMvv/wyffr0AWDbtm1Mnz6dNWvW0LZtW6ZNm8aZZ55Zz7VsHGKfy7Fjx/L1119X2icrK4tXXnmlHmonGpoNGzZw//33s3r1ahITExkxYoS8F9Qy+f1WNyo+z8uWLWP+/Pls3bqVNm3a8Ne//pVzzz23nmvZuBzoO4y8lmvHgZ5neS3XnpycHGbNmsWKFStwu91cddVVjBs3DkVRGuTrWUIpIYQQQgghhBBCCFHnjvnue0IIIYQQQgghhBCi7kkoJYQQQgghhBBCCCHqnIRSQgghhBBCCCGEEKLOSSglhBBCCCGEEEIIIeqchFJCCCGEEEIIIYQQos5JKCWEEEIIIYQQQggh6pyEUkIIIYQQQgghhBCizkkoJYRo8Dp27MikSZMqlb/77rsMGjSoHmokhBBCCCGEEOJwSSglhGgUPvzwQ1asWFHf1RBCCCGEEEIIUUsklBJCNApt2rThvvvuIxAI1HdVhBBCCCGEEELUAgmlhBCNwq233kpOTg7PPfdctftkZ2dzyy23kJWVRZ8+fZg9e7YVYr377ruMGjWK+fPn06dPH0477TTmzp2LYRjW8W+++SaDBg2iZ8+ejBo1it9///2IX5cQQgghhBBCHKsklBJCNAoZGRlMnDiRp556ih07dlTaHggEuPrqq/F6vbzyyis89thjLF++nIceesja58cff2TLli288cYb3HXXXbz88st88803AHz22WcsWLCAu+66i/fee4/evXszevRoioqK6uwahRBCCCGEEOJYIqGUEKLRGDVqFG3btmXOnDmVtn311Vfk5OQwb948OnbsSN++fbn77rt54403KCsrAyAcDjNr1izat2/PRRddRKdOnfj5558BePbZZxk3bhwDBw6kXbt23HrrrbRp04b333+/Tq9RCCGEEEIIIY4VtvqugBBC1JSmacycOZORI0eybNmyuG2bNm2iXbt2pKamWmW9evUiFAqxfft2AJo1a0ZSUpK1PSkpiVAoZB0/b948HnnkEWu73+9n69atR/CKhBBCCCGEEOLYJaGUEKJR6dWrF5deeilz5szhuuuus8qdTmelfcPhcNzc4XBU2ic6plQ4HGbatGn07ds3bntsiCWEEEIIIYQQovZI9z0hRKMzefJkPB5P3KDnJ554Ilu3bqWwsNAqW716NTabjRNOOOGA5zzxxBPJzs6mbdu21vTUU0+xevXqI3AFQgghhBBCCCEklBJCNDpNmjRh8uTJ7Nq1yyrr168fxx9/PHfccQe///473377LbNmzWLo0KGkpKQc8JzXXnstL730EkuXLmX79u3MmzePf/3rX5x00klH8lKEEEIIIYQQ4pgl3feEEI3S8OHDeeedd9i7dy9gjje1aNEiZs2axZ///GcSExO54IILuO2222p0vvPOO4+8vDzmz59PXl4eHTp04Mknn6Rdu3ZH8CqEEEIIIYQQ4tilGNEBVYQQQgghhBBCCCGEqCPSfU8IIYQQQgghhBBC1DkJpYQQQgghhBBCCCFEnZNQSgghhBBCCCGEEELUOQmlhBBCCCGEEEIIIUSdk1BKCCGEEEIIIYQQQtQ5CaWEEEIIIYQQQgghRJ2TUEoIIYQQQgghhBBC1DkJpYQQQgghhBBCCCFEnZNQSgghhBBCCCGEEELUOQmlhBBCCCGEEEIIIUSdk1BKCCGEEEIIIYQQQtQ5CaWEEEIIIYQQQgghRJ2TUEoIIYQQQgghhBBC1DkJpYQQQgghhBBCCCFEnZNQSgghhBBCCCGEEELUOQmlhBBCCCGEEEIIIUSdk1BKCCGEEEIIIYQQQtQ5CaWEEEIIIYQQRxXDMOq7CqIBkteFEA2PhFJCCABGjRrFqFGjjvjj7Ny5k44dO/Luu+8e1HErV66kY8eOrFy58gjVrGEYNGgQU6ZMqe9qCCGEaARWrVrFzTffTL9+/ejatSvnnHMOM2bMYNOmTfVdtThPPPEEHTt2rLPHW7VqFTfccEOdPV5DsHbtWq6//nrOOOMM+vTpw5gxY1i7dm3cPoZh8Nxzz3HuuefStWtXBg8ezGuvvXZQj/PLL7/QpUuX/X6O++yzzw755x19rcROnTt3pk+fPowfP54NGzbU+FzPP/88kydPBqC4uJg77riDH3744ZDqdbCmTJnCoEGD9rvPu+++S8eOHdm5c2eNz1uTYwoKChgwYAA7duyo8XljlZWVce+999KvXz969uzJ9ddfz+bNmw943O+//851111HVlYWZ511FnfeeSd5eXlx+2RnZ3Pbbbdxxhln0KtXL8aPH8/WrVsPqZ7i6CGhlBBCCCGEEI3M4sWLufLKK/F6vUybNo3nnnuOG2+8kV9//ZVLLrmEjz76qL6rWG+WLFnS4IK5I2nbtm1cddVV+Hw+5syZw9y5cwkEAowcOTIuTHjooYd49NFHGT58OIsXL2bQoEHcd999vPXWWzV6nEAgwJQpUwiFQtXus3LlSiZNmnTY1/TWW29Z0yuvvMKMGTP47bffuPLKK8nNzT3g8Zs2beLpp5/m9ttvB+C3337jH//4B7quH3bdasuAAQN46623aNGiRa2et0mTJlxzzTVMmzbtkFqGTZo0iY8//phJkybx4IMPkpOTw+jRoykqKqr2mLy8PK6++mry8/OZO3cu06ZN4/vvv+f6668nGAwC4PF4uOaaa/jtt9+YOXMmf/vb38jJyeGqq66isLDwUC9XHAVs9V0BIYQQQgghRM19/vnnPPzww9x8881MmDDBKs/KyuLiiy9m0qRJTJkyhczMTE4++eR6rKmoC6+88gput5unn36ahIQEAM444wwGDRrEq6++yt13383OnTt58cUXueuuuxg5ciQAffv2Zc+ePXz99ddcfvnlB3ycxx57jJKSkiq3lZaW8swzz/DMM8+QnJyMx+M5rGvq0aNH3Hrv3r1p1aoVV155Je+9994BW8LNmzePoUOHkpGRcVj1OJKaNm1K06ZNj8i5R44cyZNPPsknn3zCueeeW+PjfvzxRz7//HMWL15M//79ATjttNM455xzeP311/nLX/5S5XGffvopBQUF/P3vf+eEE04AIDk5meuuu44ff/yRrKws/v3vf7NlyxY+/PBD6/dSZmYm55xzDh9//DEjRow4zKsWjZW0lBJCHJT//ve/jBw5kt69e9OnTx8mTZrEnj174vbZvHkzEyZMICsri9NPP51x48ZV+xdLwzCYOnUq3bp14+uvv7bK33zzTQYPHky3bt246qqr2L17d6Vjt27dysSJE+nXrx89evRg1KhRrFq1CoDCwkI6d+7Miy++aO2/Z88eOnbsaP3VDEDXdfr06cPTTz9tdS3817/+xcSJE+nZsydZWVnMmDHjgB+u9u7dy9SpU+nfvz/dunVj+PDhfPrpp3H7dOzYkddee43p06eTlZVFz549ueWWWyo1bY669NJLq3yDvuaaa7j22mv3Wx8hhBBHrwULFtC+fXvGjx9faZvdbue+++5D0zSeeeYZAMaMGcOwYcMq7XvTTTdx4YUXWus//PADV111Fd27dycrK4s777yTffv2WdvfffddOnfuzJIlS+jXrx9ZWVls3LiR7du3c+ONN9KnTx+6d+/O5ZdfzhdffFHp8ZYvX86FF15odR1bunRp3PaavJf6/X4WLlzIkCFD6Nq1K+eeey6LFy+2WsBMmTKF9957j127du13uIAnnniCIUOG8MknnzB06FC6du3KRRddxI8//sjq1au57LLL6NatG0OHDmXFihVxx65fv55x48bRq1cvqwtSxa5S69atY8KECZxxxhl06dKFs88+m9mzZ+Pz+ax9avK5INpda3/DF7Rv354xY8ZYgRRAQkICLVu2ZPv27QAsW7YMp9PJ8OHD44597LHHeOKJJ6o9d9T//vc/K+Cqyttvv83f//537r77bq666qoDnu9QnHrqqQDs2rULMH+Gf/zjH1mwYIHVZayoqIj169ezfPlyhg4dCpitt0aPHg3A6NGj44ar+Oc//8mwYcPo2bMn/fr14+67767UIujnn39m7Nix9OnTh169enHjjTfWuBvhu+++y+DBg+natSsXXnhh3L+Lqrrivffee5x33nnW/itWrKBz586VXsdr1qxhxIgRdO3alQEDBvDss8/GbXc4HAwePJinn37aKosOhbG/rpdff/01CQkJnHXWWVZZ06ZNOf3006v8Nx3l9/sBSEpKssrS0tIArFZQf/jDH3jjjTfignK73R53vDg2SSglhKixpUuXMmbMGFq1asUjjzzC1KlT+fHHH7n88svJz88HICcnh8svv5ytW7cyc+ZM5s2bZzXprapp7uzZs/nwww9ZsGCB9Qb46quvcs8999C/f38WLVpE9+7dueuuu+KO27hxI8OGDWPnzp3MmDGDv/3tbyiKwtVXX813331HWloaPXr04JtvvrGOiX6ojB1PYM2aNRQWFjJgwACr7J577qFNmzYsWrSIsWPH8vbbb/Pkk09W+7zk5eUxfPhwfvjhB/7617/yxBNP0KZNG8aPH8/7778ft++jjz6Krus88sgj3HHHHXz++efcf//9VZ53+PDh/Pjjj2zbts0q27NnDytXrqzyy4UQQoij3759+/jll18YOHAgiqJUuU9aWhpnnnmmFehceOGFrF27Nu79pLi4mC+//JKLLroIgO+//55rrrkGl8vFY489xrRp0/juu+8YPXp0XJASDod5/vnnmTNnDlOnTuXEE09k3LhxeL1eHnroIRYtWkRaWhp/+ctf4h4P4O677+aaa67hySefpGXLlkyZMoV169YBNXsvNQyDG2+8kWeffZbLLruMp556iiFDhvDYY49xzz33AGbQ1r9/f9LT03nrrbfi3t8rys7O5oEHHuDGG2/k8ccfp7i4mIkTJ3Lbbbdx2WWXsXDhQgzD4K9//av1HGzZsoURI0aQn5/Pgw8+yJw5c9ixYwdXXHGF9Vlo7969VtfKBx54gGeeeYbzzz+fV155hZdffjmuDgf6XBDt4tWlS5dqr2PkyJFcd911cWXbtm1jw4YNVgDw22+/0bZtW77//nsuueQSunTpwqBBg2rUdc/r9TJ16lTGjRtX7VhRgwYN4rPPPjuirV22bNkCYLXEAdi9ezdffPEFjz76KFOnTiU1NZUPPviA9PR0q7VVly5drDDt7rvvtl4rixYt4rbbbqNHjx7Mnz+f8ePH8+9//5tRo0ZZP+9vv/2WK664AoD777+f2bNns2fPHkaMGHHALqJ79uxh8eLF3HLLLTzxxBMoisLEiROt10lFS5cuZcqUKfTq1YtFixYxePBgbrrpJsLhcKV9Z86cyfnnn8/ixYvp2bMn8+bN4/PPP4/bZ8iQIfzyyy/W89alS5cD/pvYtGkTxx13HJqmxZWfcMIJ1nmq8qc//Yn09HTuu+8+9u7dy44dO3jooYdIT0/nzDPPBMyWU7169QLMrqDr1q1jypQpNGnShD/96U/VnlscAwwhhDAM46qrrjKuuuqqareHw2GjX79+xpgxY+LKt23bZnTp0sV48MEHDcMwjAceeMDo1q2bsXfvXmufPXv2GAMGDDCWL19u7Nixw8jMzDTeeecd429/+5vRpUsX4/PPP7f21XXd6Nu3r3HrrbfGPc7dd99tZGZmGt9++61hGIZxyy23GH369DFKSkqsfYLBoDF48GDj0ksvNQzDMJ5++mmjR48eRiAQMAzDMCZPnmxccsklRmZmprFjxw7DMAzj8ccfNwYOHGgYhmHVbfLkyXGPPWrUKGPo0KHVPjcPPfSQ0aVLF2Pnzp1x5VdffbXRr18/IxwOG4ZhGJmZmcYVV1wRt8+UKVOMHj16WOsDBw407rzzTsMwDKO4uNjo1q2b8fjjj1vbn3zySaN3796G1+uttj5CCCGOXj/99JORmZlpvPrqq/vd74EHHjAyMzONwsJCo6yszOjRo4exYMECa/uSJUuMTp06GdnZ2YZhGMbll19uDB061AiFQtY+mzdvNk455RTrsd555x0jMzPTWLp0qbXP3r17jczMTOP999+3yoqLi43777/fWL9+vWEYhjF//nwjMzPT+OKLL6x9tm3bZmRmZhovvfSSYRg1ey9dvny5kZmZaXz44Ydx+yxcuNDIzMy0Hu/OO++03turU1Wdnn76aSMzM9NYsmSJVfbxxx8bmZmZxq+//moYhmHcdtttxplnnhn3+aOgoMDo3bu38cADDxiGYRhfffWVceWVV8btYxiGMXTo0LjPUTX5XHAovF6vcfnllxs9evSwns/rrrvO6NOnj3HGGWcYr776qvHNN98YM2bMMDIzM40333xzv+ebNWuWcfHFFxvBYDDuc1x1os/toYgeGwwGramkpMT4/vvvjUsuucTo3bu39Rkzuu/3338fd47hw4cbf/nLX+LKvv3227jPkYWFhcapp55q3HXXXXH7ff/993H/voYPH26cd955cf8uioqKjKysLGPixInVXsedd95pZGZmGhs3brTKvvnmGyMzM9NYtmyZYRjl/56in0kHDBhgjBs3Lu480ddk9PmOHvP6669b+3g8HqNLly7G/fffH3dscXGxkZmZabz22mvV1rOiMWPGGCNGjKhU/sgjjxhdunTZ77HLli0zunXrZmRmZhqZmZnG6aefbvz222/VPk5mZqbRqVOn/b6WxLFBWkoJIWpky5Yt5ObmWk2ho0444QR69uzJd999B5h3vOnRowfp6enWPi1btuTzzz+3+qYDvPbaayxevJjzzz8/7i82mzdvJj8/n4EDB8Y9TsW/oHz33XcMHDgwrpmwzWbj/PPP55dffqGsrIz+/fvj8XhYs2YNYP616+qrr8btdvP9998D8OWXX1b6i1HFcQxatmy53+573333HT179qRNmzZx5RdeeCG5ublxg4xWdW6v11vleZOTkzn33HPjWltFm3W7XK5q6yOEEOLoZUQGLo52e6lOtKWDYRgkJCTwhz/8gX/+85/W9o8++oi+ffuSkZGB1+tlzZo19O/fH8MwCIVChEIhjj/+eE466ST++9//xp37lFNOsZabN29Ohw4duOuuu7jzzjv54IMP0HWdqVOnVhrP6rTTTrOWjzvuOMBssQU1ey/97rvvsNlsDBkypNI+0XMcrGjLjei1AHTv3t0qi3ZBitbz22+/JSsrC5fLZT1PSUlJnHbaaVbr7LPOOotXX30Vp9PJxo0b+fTTT3nyySfZt28fgUAg7vEP5nNBTZSWljJu3Dh+/vln5s2bZz2fwWCQgoIC7r33Xq688kr69u3LrFmzOOuss1iwYEG151u5ciVvvfUWc+fOxWaru+GIu3TpYk29e/fmyiuvJBAIsGDBgrjPmBD/egTYsWOH9fqqzurVqwkEApU+15522mm0adOG7777Do/Hw88//8yf/vSnuJZDKSkpDBw48ICvtyZNmnDSSSdZ69E6VTUu17Zt29i9e3el1/b5559f5blj/y253W6aN29uvUajkpOTSUlJOai7+xn7GRi9upaZAB988AETJkxg0KBBPPfccyxatIiTTz6ZMWPGVNmi7C9/+QsvvvgiF198MVOnTmXJkiU1rqM4+shA50KIGol2vYt+YIvVvHlzfv31V2u/A30QAHOshbPOOosPP/yQq6++ms6dOwNY/fibNGkSt3/FDyBFRUXV1sUwDEpLS+nYsSOtWrXim2++oUmTJuzdu5czzzyTXr168d1339G/f3/Wrl3LLbfcEncOt9sdt66q6n7fpIuKijj++OOrrAsQ9yHhYM89fPhw3n//fX744Qc0TWPr1q08+OCD1e4vhBDi6BYNGaLj6lRnx44dJCYmWqHKRRddxPvvv8+6deto3rw5K1eutLqJFRcXo+u6NVB1RU6nM249duwiRVF4/vnnrUGVly5dit1u5w9/+AP33nsvqampVR6nqubfxqPvgTV5Ly0qKqJJkyaVuhZFPyNUNwj3/sT+cSuq4nt1rMLCQv75z3/GBXxR0UGro93xXnvtNTweD61ataJbt26VnseqHutAnwv2Z8+ePYwbN44tW7bw6KOP8oc//MHalpiYiKIocX8gBDj77LP5+uuvycvLq/S5qqysjKlTp3L99dfToUMHQqGQNXaXruuEQqEjFlS9/fbb1rLdbic9PZ1mzZpVuW9iYmLcemlp6X5/hlD+ebO6z5IlJSWUlJRgGMZ+99mf2Nc7lIc6Vd0BMDp2W8VrrOqxoeavG7fbTWlp6X7rGSspKanKsU7LyspITk6u9rgFCxbQs2dPHn30UausX79+nHfeeTz++OPMnz8/bv9oqNa3b1927drFU089xWWXXVbjeoqji4RSQogaiX6oreqNKjc31wqRkpOT4wZFjVqxYgXHHXec9YZ8yy23MHr0aM4//3xmzJjBkiVL0DTNOk/F/vYVx6NKTU2tti5QHmr179+fFStW0KxZM0488UTS09Pp06cPf//73/n6669xuVz06dPnIJ6JylJTU6u8PXHFuhyKrKwsTjjhBD7++GNUVaV9+/aV/qoqhBDi2NGsWTN69OjBv//9b2655RYr3IlVWlrKf//7XwYNGmSV9e3bl/T0dP71r3+Rnp6O0+m07soVDSyuueaaKltmHOgLfkZGBjNnzuSee+5h3bp1fPzxxzzzzDM0adLEGr/nQGryXpqamkpBQQHhcDgumNq7d6+1z5GWnJzMmWeeWeUNR6IBzeLFi3nxxRe59957Offcc60v8xUHGa9Nv//+O2PHjsXv9/P8889z+umnx21v27YthmEQDAbjwrFQKARQZQvsX375hV27drFw4UIWLlwYt2369OlMnz6d33///QhcDXTt2vWQj01LSztgYBQNS/Py8mjfvn3cttzcXI4//niSk5NRFKXaz5vRz8a1oWXLlkDlz7/VjT9VU8XFxQf17+LEE0/k66+/Rtf1uN8t27Zti2v1VdGuXbviQlAwX1OnnnqqNSj8Tz/9xM6dOznvvPPi9uvSpQs//vhjjesojj7SfU8IUSPRQOfDDz+MK9+xYwerV6+2mr+fdtpprFmzJi6Yys/P57rrrou7a0fz5s1xuVzcfffdrF27lhdeeAGAdu3a0apVKz7++OO4x6k4eOPpp5/O559/HvfXn3A4zEcffUTXrl1xOByAOUDozz//zJdffklWVhZg3iZ5586dvPnmm/Tr18/a91Cdfvrp/Pjjj5X+av3++++Tnp5O27ZtD/nciqIwbNgwli1bxmeffcYll1xyWHUVQgjR+E2YMIEtW7bwyCOPVNoWDoe555578Pl8cYNfa5rGBRdcwOeff87HH3/MH/7wB6slR1JSEp07d2bz5s107drVmk4++WSeeOKJ/d757ccff+TMM8/kp59+QlEUTjnlFP7617+SmZlZ5Z1zq1OT99KsrCxCoVClzwjRbu69e/cGqDKoqy3ROw6ecsop1vN06qmn8uKLL/LJJ58A5lAGHTp04NJLL7UCqZycHNavX19lK5nDtWfPHq699loUReGNN96oFEgBVgupjz76KK78s88+o2PHjlW2GOvSpQtvv/123BS98cuECRPiWjM1JG3atKl0Z+iKreu6d++Ow+Go9Ln2hx9+YPfu3fTq1YuEhAROPfVU/vWvf8UNNl5SUsLy5cut11ttaNmyJSeccIL1Gor6z3/+c8jnLCoqwuv10rp16xofc9ZZZ1FWVsZXX31lle3bt48ffviBfv36VXtc+/bt+d///hfXWsvv97N27VqrBeSXX37J7bffHvezCYfDfPvtt9UOoC+ODdJSSghhyc7O5sUXX6xUnpmZyZlnnsltt93G1KlTmTRpEhdeeCEFBQUsWLCA1NRU6y+G11xzDUuXLuW6665j3Lhx2O126y47F1xwQaW/XPXv358hQ4bwxBNPMHjwYI4//ngmT57MpEmTmDFjBkOGDGH16tW88cYbccdNmDCBL7/8ktGjR3PDDTdgt9t59dVX2bFjR9xtcc844wxUVWX58uXWh/cuXbqQmJjIqlWrmDNnzmE/b9deey3vv/8+11xzDRMmTCAtLY2lS5fy7bffcv/99x/2h+Nhw4ZZt2uO3iVJCCHEsevss89mypQpPPTQQ/z2229ceumltGjRgp07d/LGG2/w22+/MWfOHDp16hR33EUXXcTzzz+PqqqVuunddttt3HDDDdZ7fPQue2vWrOGmm26qti6dO3fG5XJxxx13cPPNN9O8eXO++eYbfvvtN0aPHl3ja6rJe+n//d//0adPH2bMmEFOTg6dOnXiu+++45lnnuGSSy6hQ4cOgDnmT15eHl988QWnnHIKLVq0OIhnd/9uuukmRowYwbhx47jiiitwOp289dZbLFu2zOqi1K1bNxYtWsTixYvp0aMH27Zt4+mnnyYQCBz0eFH79u1j+/btdOjQocrgCMw7Gefn53PvvfdSWlrK6tWrrW1JSUl06NCBPn36MHDgQObOnYvX6+Xkk09m6dKl/O9//2PRokXW/tu3b2ffvn306NGDpKSkSi2WouMTtWnT5qBbM2VnZ5OdnU3nzp0P+w+C+9OvXz9ef/11DMOwWuhHw8Hly5eTmppKp06duOGGG1i4cCF2u52BAweyc+dOHn/8cTp06GD9EXDSpEmMHTuWG264gZEjRxIMBlm8eDGBQIDx48fXWp2jd+abPHky99xzD3/84x9Zt26d1ULtUD5Lrlq1CsC6u3VpaSkbN27khBNOsLqaVnT66aeTlZXF7bffzu23305aWhpPPPEEycnJ1l0IwbwLdiAQsIbfuOWWWxg/fjy33HILw4cPJxAI8NJLL5GTk8PDDz8MwIgRI3jzzTcZN24cEyZMwG638/rrr7N+/Xqee+65g74+cfSQUEoIYdm+fTtz586tVD58+HDOPPNMhg0bRmJiIk8//TTjx48nKSmJs88+m9tuu80az6FVq1a8/vrrzJs3jylTpuBwOOjTpw+PPvooqampVTannjZtGl9//TV33XUXL774IkOHDkVVVRYtWsQ//vEPMjMzue+++7jtttusY04++WRef/11HnnkEaZOnYqiKHTr1o2XX3650uCPffr0iWspZbPZOO2006oc5PxQpKen88Ybb/Dwww8ze/ZsgsEgnTp1YtGiRZxzzjmHff6MjAw6depE8+bNycjIOOzzCSGEaPyuvfZaevbsyUsvvcSDDz7Ivn37SE9Pp1+/fsyZM8cKaGJ16tSJzMxMCgoK6Nu3b9y2s846i+eee44FCxYwceJE7HY7Xbp04YUXXthvt3Gn08nzzz/Pww8/zJw5cyguLqZdu3bcd999DBs2rMbXU5P3UkVRePrpp5k/fz4vvvgi+/bt47jjjuO2226L6043bNgwvvjiC8aPH8/EiRO54YYbalyPA+nUqROvvfYajz76KHfccQeGYZCZmcnChQuteo4bN46CggJefvllFi5cSKtWrbjooous+hcXF5OSklKjx1u+fDlTp07l5ZdfrnK4gUAgwPLlywGq7CqZlZXFK6+8AsDjjz/OggULeOGFF9i3bx8dOnRgwYIFcd08Fy1axHvvvXdEuuUtWbKEBQsW8Omnn9Zo/NFDde6557Jw4UJ++ukna9D6k08+maFDh/Laa6/x1Vdf8eGHH1oh6quvvspbb71FWloaQ4YM4dZbb7VaEfbt25cXXniB+fPnc9ttt+FwODjttNN48MEHKw3kf7guuOACPB4Pzz33HO+88w4nn3yy1U2y4vhUNfHll1/SrVs3axy6tWvXMnr0aObOnbvff5sLFizggQce4KGHHkLXdXr16sVjjz0WNz7cvffey65du/jss88AOOecc1i8eDGLFi1iwoQJJCYm0q1bN95++20rHG/evDlvvPEG8+bN45577sHj8dCtWzdeeumluM/u4tijGIc6kp4QQog6kZOTw8CBA5k/f36l/vpCCCGEEI3FlVdeyWOPPVbpBja17cYbb6RJkyZV/rG1ofrwww/p3Llz3BhXy5cvZ9y4cfzjH/+o1PJxfzweD2effTYPPvigfHYUDZ6MKSWEEA3Ub7/9xoIFC7juuuto165d3F8yhRBCCCEak5UrV+L1equ9o1xt+utf/8p//vOfgxrXrL69//77XH/99XzwwQf88MMPvPPOO9xzzz1kZWUdVCAF8Oabb3LyySfXSot9IY40aSklhBAN1OrVqxk7diwZGRk88sgjB/2BRAghhBCiodi1axcJCQl1cpdEMO+CuG7duipvCNAQFRQU8PDDD/Pll1+yb98+mjdvzuDBg5k4cSKJiYk1Ps++ffu4+OKLeeWVVw7rZjtC1BUJpYQQQgghhBBCCCFEnZPue0IIIYQQQgghhBCizh1yKBUIBBg6dCgrV660ynbs2ME111xDjx49OO+88/j666/jjvnmm28YOnQo3bt3Z/To0ezYsePQay6EEEIIIYQQQgghGq1DCqX8fj+33XYbGzZssMoMw2D8+PE0b96cd955h4suuogJEyZYg8vt3r2b8ePHM2zYMN5++22aNm3KTTfdRE17DxqGQWlpaY33F0IIIYQQ8eTzlBBCCCEakoMOpTZu3Mif//xntm/fHlf+7bffsmPHDu677z5OOukkxo0bR48ePXjnnXcAWLJkCaeeeipjxozh5JNPZu7cuezatYvvvvuuRo9bVlZG7969KSsrO9gqCyGEEEII5POUEEIIIRqWgw6lvvvuO/r06cNbb70VV75mzRo6d+5MQkKCVda7d29Wr15tbT/ttNOsbW63my5duljbhRBCCCGEEEIIIcSxw3awB4wcObLK8tzcXFq0aBFX1qxZM7Kzs2u0vd4FCmHjM6AHwJYE9uTIlAaONHA0iSw3AVWr37oKIYQQQgghhBBCNHIHHUpVx+v14nA44socDgeBQKBG2+vd9iWw+o4a7KiYwZSzuTm5MiChDbjbQMJxkHACJHcwyyW8EkIIIYQQQgghhKhSrYVSTqeTwsLCuLJAIIDL5bK2VwygAoEAKSkptVWFw3P8pVCyAYrXQ7gMQmUQ8kCoFIIl5jzsAQwI7DOnkvXVn09LhITWkHC8GVIlZ0JKJ0hoCzYXKFr1k2qLLEfnSp09DUIIIURDFh2gW5H3RiGEEELUk7BuoKmN+7NIQ7mGWgulMjIy2LhxY1xZXl6e1WUvIyODvLy8SttPOeWU2qrC4XE2hZ4PVS7XQ2aXPj1gBlXeHPDugrJt4MsB317wZYM/DwIF4M81l8NlZshVsgFyPis/n+aOBFQdIbmjObclmtsUNRJCqUA0oNJAdYDqBM0JmisSVqmAEjkmZpnIuqLELFex3Tq2/l+EQghxtDMMAwMjbg5UKjMwarx/xf32t72q5dj9o8sH2s8wDHR0AHRdL9+GgW7occdE12PLY//DqHD+mMcArMeJvUtctF4OzUGvVr1w2VyH9gMRQgghhDgMmqpwy5s/snFvaX1X5ZB0aJHE4yN61nc1gFoMpbp3787ixYvx+XxW66hVq1bRu3dva/uqVaus/b1eL7/++isTJkyorSocGarNnEgwx5ZKaAP0MrfpYbP1VKgUgsXgyzWDq2Ah+AvMuW8PlGw0p9ItEPZC4RpzAkCB1M7Q/ExofobZmgoDDB2MsDmFSsAoKl+v9jbOinlsbCAF5WGUFUwp5j7RwMoKuSq00lKjrbUqBlwVlysGYtUtSwAmhKg90ZBDN3QzLDH0uPWqtsWGOLFlVW2vdC5dJ2yE0Q3dnNAxdDOkCethaz16XHSqNkCKCWmA8qCmimApdv/ovrH7Kyjmfgo1Wo6cBEVRrPNEl6vbL5YS2Rgtr7h+sPtVt00hZjlSHtJDeIIeguGghFJCCCGEqDcb95aydndxfVej0au1UCorK4tWrVoxdepUbrrpJj7//HN++ukn5s6dC8Cll17Kc889x+LFixk4cCALFy7kuOOOo0+fPrVVhbqnaqBGBkR3tzJbQIU9Zospbzb48yGUCa3OMwMtgNLNUPhT+eTZCUVrzWnTM2BPhRb/B60GQ9PTIoHYQTJ0In+CBvRIwGWYZUSWrdArBIYvfl/rHHoVAVgk+Iorig2tIssoxLXGioZT0dCrUgAWXa4YZGlUCrn2F4BFgzghRJ2KBjsHM1UKbyLnCOthwkZk0s0QKKSHzG0xZXFBUjQ4QreCGitQim2hY5SHObGhS9yvNiW+rGJIoiiKNT+ksmi5WnlfYL/niC2vuHysCIQDFPgK6rsaB/TJJ59U+sPb4MGDmT9/Pr/++iv33HMP69evp0OHDtx7772ceuqp1n4ffvghjz32GLm5uZx11lnMmjWLpk2bAua/tYcffpi3334bXdcZPnw4kydPRlXl/U8IIYQQjU+thVKaprFo0SKmT5/OsGHDaNu2LQsXLqR169YAHHfccTzxxBPcf//9LFy4kJ49e7Jw4cKj68O0ophd8WyJ5qDnoTIzmCrbBt49oNoh6URIyYQThpvH+PZC3orI9C0Ei2DXB+bkaAot/wBtLoDUg+jmaLWQqv1LrJIRE3bFhWAVyghDyF+hzKiw735UF35VCqsigVc0+FJtgA00exUhlkbVgVd1AdlR9HoVxyQr+IkJd6Ktf2LDntgWQdZ+epigHiSkh8zJCJll4WBcsFQxbALKWxZV+HdutcqJrseELaqixoUyqqJWCmoUFDRFQ1GVKrdFzwHEHS/EkbZx40YGDhzIrFmzrDKn04nH4+GGG27gggsu4IEHHuCNN95g3LhxfPLJJyQkJPDTTz8xffp07r33Xjp16sScOXOYOnUqTz/9NAAvvPACH374IQsWLCAUCnH77bfTrFkzxo4dW1+XKoQQQghxyA4rlPr999/j1tu2bcurr75a7f79+/enf//+h/OQjUs0oHK3NsOnsm3mmFSqDVzpZsjhagHHXWROeggKVkP2J5C9zBxMffvfzSn1VDjhMjOk0pz1fWXxFAVzDKwj/DhGbJAVO49p/UVkDLCwXs3+FZtEVLyOqkIvrUJrL3t5C69oKy+rbD+tvSqFYBVbgckXZVG92DAppIfKWxPp4UoBU7QsEAoQNIKEwiGC4SBBPRjf0ggdXY9pvRTpfmZRYhfLg53YKTb4URUVVa2irMJ+QhwLNm3aRGZmJunp6XHlb7/9Nk6nkzvuuANFUZg+fTpffvklH3/8McOGDePVV1/lT3/6ExdffDEADz30EAMHDmTHjh0cf/zxvPzyy0ycOJHTTjsNgMmTJ/P4449LKCWEEEKIRqnWWkqJ/VBt5p343C3NcKpkI5TtNAdXtyfH79fsNHM65XbIXwm7/wnZn0LRL/DzL7DuUWhxdmSQ9JMh+WSwN5A7GB5pR7p7XlxLr6pCrzBVtvaqtqtjbN0rBl4alQIvRTPDrdgWXoo9vmtjxVZc1nI1YZeqHbnnSxw0wzDiQqVoi6NoUBS77A/5CepBAuEAgXDADJQqtmTSdcKEzSCpii5nCgqaqlUKklRFxabaqixXpQusELVi06ZNnHnmmZXK16xZQ+/evctbBSoKvXr1YvXq1QwbNow1a9Zw/fXXW/u3atWK1q1bs2bNGhwOB3v27OH000+3tvfu3Ztdu3axd+9e6+YyQgghhBCNhYRSdUlRzWDK0RTKtprjSwWLwZVReewo1Qbp/cyp0yTYuRR2vGPe8W/XB8AH5fu6Wkbu5he5q19KR7NMWiQcHKvF1xEIcqxQK7bVVjgm3AqDHjTHJKsYikXH+Ypt4aUo5a2+lKrCrtguidGAKybkUiPzSq25IuGWGg25tMrbJbQAzIDJ6sZWzRQMB/GFfQRCAQK6GS7FtXDSw+WhEhAdjNpcNAMlTTFDpWi4ZFNtlcokSBKiYTEMgy1btvD111/z9NNPEw6HGTJkCBMnTiQ3N5cOHTrE7d+sWTM2bNgAUGW41KxZM7Kzs8nNzQWI2968eXMAsrOzJZQSQgghRKMjoVR90BzmuFKudCheD55dZjc+m7vq/Z1N4aQxcOJos/VU4S9Qsh5KNoB3N/iyzWnvF+XH2FPLA6rkjuaYVAnHS6BQXxTFDIVqW5Wtu6oIu0I6NW/ZFb07o0bVrbq08u6Kqj0SejkqDFZ/gFAr2tWxgXVd1A3d6uYWDJtjJ8WOoeQL+vCH/XhDXitgChkhQuGQ1XIpKtoKQlM0K1yKzh2ao1JrJSHE0WP37t14vV4cDgePPfYYO3fuZPbs2fh8Pqs8lsPhIBAIAODz+ard7vP5rPXYbYB1vBBCCCFEYyKhVH1yNIGmvcGWbAZMelL5XfqqEtt6KipYYh5b/LsZVBX/DqWbzAHT878zpygtMRJUdTJDqpRTIPEECaoasyPRuit24HmrdVe4fN0IRbowVgi6DL36c0bDrGi4VXFweaVid0V7pDWXVrOAq9L2+JAr2mopGjbFzn1BH96QF2/QS0APxHWtiw7crSgKBgYqkZZKqoZNtWFTbTgVJ5qjPHQSQog2bdqwcuVKUlNTURSFU045BV3Xuf3228nKyqoUIAUCAVwuF2AOhl7VdrfbHRdAOZ1OaxnA7a7mD1tCCCGEEA2YhFL1TbWZAZE9EYp+NbvnOVvUvOWIPRma9jKnKD0AJZuheB2U/A5Fv5nBVbgMCv5nTlFaIqR2gpTOZj1SO4O7TYNpuSLqQdzA9bX0KyKuhVbFFlv7a80F+xuU3kAhaOgEdYOAoRPQwwQNg4ARxhc28IQDeMJmWQgIGRAydIxoazBUVFXDpjmwqXZsmgOX6sBmc6Jpydi0yBhf8u9BCHGQ0tLS4tZPOukk/H4/6enp5OXlxW3Ly8uzut5lZGRUuT09PZ2MjAwAcnNzOe6446xloNKA6kIIIYQQjYGEUg2BokBiW9DcUPgzeHcdXjCkOsygKbVTeZkeMsexKl5nhl/F68xWVeEy2LfKnKLsKWYrqtTOkNrFnLtknApxGA5xkPqwHiagBwmEQ5G5OXmDPsqCZXhCPoJhP8FwiJAeIGy16NJRFbArKjZVxYZCgqpgVzS0aHe5aNYVuWljeR2jg9JXGJuLSPfEaDdF646LsXdQjN2/qrswRgM/CbmEOJp99dVXTJ48meXLl1stmH777TfS0tLo3bs3zzzzTHkrTMPgf//7HzfeeCMA3bt3Z9WqVQwbNgyAPXv2sGfPHrp3705GRgatW7dm1apVVii1atUqWrduLeNJCSGEEKJRklCqIXG1gKanQcHqww+mKlJtkNzBnNoMNcuiQVXRr1D8W2S+3hx8PX+lOUU5m5cHVNH5sXLXP3HE6IZOIBzEHw7iDwcI6OZyWcBLWciLN+QnqIcIRbrTGRgoKKiqik3RsKs27LYE3A4bNlXDdqjd5yp1WQQoD7jMbovRLotGhZZcETHj0JvrRzrkiq5LyCUaHsMwCIQD+MN+/CE//rAfX8hnrQfCAXwhX6V9AuFA3HJ0Pbpf9LgOTTtw9gln1/dlVqtnz544nU5mzJjB+PHj2bFjBw899BDXXXcdQ4YM4eGHH2bOnDmMGDGCN998E6/Xy5/+9CcArrjiCkaNGkWPHj3o2rUrc+bMYcCAARx//PHW9r/97W+0bNkSgIcffpgxY8bU27UKIYQQQhwOCaUaGkcqNOkRCab2gLvVkfvCGRtUcaFZpgehZGMkoPrVnJdsAn+eOZB67GDqCcdHAqpISJXSETTXkamraLTCehh/OIgv7LfCJ0/QR0nQgyfoI2iECIbNAcXBHCBciwZOqo0Emwu7aoZOypH6t1DbXRbrKuRCBTU2nLJFxuGKveNiVUFWNQFX3DZxNDIMg6AexBv04guZ46n5Qr6DnqIhk7VeRfBkVNX1tpasz19PINxwB/ZOSkriueee4/777+fSSy8lMTGRESNGcN1116EoCk8//TT33HMPf//73+nYsSOLFy8mISEBMAOt++67j/nz51NUVES/fv2YNWuWde6xY8eSn5/PhAkT0DSN4cOHc80119TTlQohhBBCHB7FMKq9BVeDUlpaSu/evVm1ahVJSUn1XZ0jL1AI+36EUBkktKrfuoR9kW5/a82QqmgteHZW3k/RzIDLCqq6QNKJtTsIt2iQdEPHHw7gCwXwhQP4wwFKgx5KA148IZ/Z7U4PohsGYKAqKg7Njl214VDtRz50auziQq5IkBUbckWDreh+1uD0+zln7ODzFUMuIneLtFpy2WKCrpguiLGD10vIVauiLY28IS9lgTK8IS+eoAdP0IM36MUTisyDHmug/thlb8icrIH8I+W+kM/s5lqHVEXFZXPh1Jw4NAdOmxOX5sJpi6xrTpw2Z9z26HJ0ii1TFZWWSS35c5c/k+xMrtNrORocc5+nhBBCiCPk/PlfsXZ3cX1X45B0aZ3CRxMbRqtzaSnVUDnSoEl3KPixvMVUfdFcZuutJj3KywKF5gDqxWuhMBJWBfLNcaqKf4cd70aOdUfGp4rp+nckW3+JIyqkh/CG/PjCAXwhP96Qn6JAKWVBL/5I8BQKh1EU0FTNCpySHQnYVTuaKuHEIYlryVULqgy59JhgKzL4fLQFV1zotZ86WgG0dughVyNuyaUbOp6gh7JAGWXBsvJ5ZNkT9FRajoZJZcEyK3SKTkc6PLKpNlw2F26bO27usrvMeSRIipY7bU6rLLo9dr/Y7dFll82FTa3djxqBcIACX0GtnlMIIYQQQtQPCaUaMmdTMwjKXwX+fHA2q+8alXOkQXpfcwLzy6ovJ9Ka6pdIi6rfIOypfMc/R5P4salSu5hlosGIhk/RAKo04KE4UEZZyIs/HCAYjozvpKg4VBsOzU6CzUWalnzo4zqJunPEQi4i3RVrEnLVtCUXZl2JjskVncd2U9QAG2i2/Yda++nGGDLClAZK46YSfwmlwfL1skBZ3PbY4Kk0UIon6DkiXdacmpNERyJumxu33U2CPYEEWwJuuxu3zVyPLleaR5ZdNhcJ9gQrfHLb3bUeFgkhhBBCCHGw5BNpQ+dsBmldYN//IFQKtgba1F5RwN3SnFqeY5YZYSjbFmlJFQmqSjZAoABy/2tOUe7WkYCqM6R0htRTwJZYP9dyDNENHV8ogCfkwxvyURb0UugvrRA+meM8uTQHDs1OmjMZh2qXrnaiXFzIVQuhZLUhV8yYXBVCLkMPUxYZq6w46KUkMhUHvZQEPJSEfJQEfZFyn7W9NOilNOTFE/Iffr0jNEUjyZFEgj3Bmic6Ekm0J5rL9kQSHeZydD26XHFy29xoEvQKIYQQQoijlIRSjYG7NaR4oPAXcNtBc9Z3jWpG0SCpvTkdd4FZFvabwZTVmmqtGVx5d5tT9rLowZDYzgynokFVSqYMpH4YguGQFT55Qj6KAqUU+UutQch1w0BVFJyaA6fmkPBJ1Avd0CkJeCgKlFIcKKXQX0pxoIziQClF0blVVkZx0JyXBGqnu5tbc5Bkd5Nkd5Fsc5Fkd5FoM9eTbC4SI/Mkm5NEu5skRwKJNhdJ9gQSbG6SHG6cmhNFjXZFrDAAvbUcaalljckVM49t6WUEzTulVhr/SwghhBBCiMZPQqnGQFHMYCdcBqVbIKFN4x08XHNC2qnmFBUsgeLfykOqol/NroBlW8xp9z/N/RQNkk4yg6qUSFiV3AFUR/1cSwMWCAfxhHx4gmbrp33+YkqDHnyhgHWXO7tmx6U5SLS7aOpKQW2g4/SIxiukh61gqdBfQmGghEJ/CUV+s6woUBKZm0GTGUSVHVYXOLtqI8WRSLI9kRRHAsmORJLtCSRHllPsiSQ5EqztSZFtSfYEkg62S1vs4PJxY3NVvMti7AD01YzNFXu3RSV23Kxo18XY7ouRgAv1AGFXxa6KseN3VQjEGvBYXUIIIYQQ4ugloVRjoWpmEBPygme3OVj40TIeiD0ZmmWZU5Q/3xyTqmhteWAV2Acl682Jf5j7KTZIPinSkqqTGVglndR4WpPVgmA4RFnIiyfoozToYZ+vmNKQGUCF9DAK4LSZrZ+aulJwaPb6rrJopILhEAX+Yvb5iyn0l7DPV0xBZLnAXxJZLqXAX2y1ZjrUgCnR5iLFkUSqM4kURyKpjiRSHUmkOBNJcSSRYk80l+2JpDjKJ6fmqLvWfYpae10Wo6odhN68cyWGbracwlezsCuuvjFhlBVcxbS+ssbqqjjtJ/CKW1bjA66Krb6khZcQQgghhKjgKEk1jhHRVkaFgDcbNDs4mh094VQsZzNocZY5QflA6laLql+heB0Ei8rv+BcV16Kqkzkln3xUdP0L62E8kbGfSoNe9vmLKAl48Ib8VgDlsjlxaQ6S3QkykLHYL8MwKAt6yfcXsc9XHJmK2Be77i+mIDIvDXoO6XFSHImkOZJJcyaR6kwmzZFEmjPZWk51moGTNXckYdeO0ddubQ9CHxU7TpfVsgsgHAnAjJixumLCruoGpY9t2RVdR6069KrUMssWE3ipMaFX9LiK4ZYav00Pml3BhRBCCCFEo3eMfupvxKKtinw5Zlc+7x4zbHE2bbxd+moidiD1jIFmmWGY11/8m9mqqniduRwsqtyiChWS2kFyR0jpGAmrMsGeUk8XVDO+kJ/SoDcyALnZMsUb9hEIm13wnJoDt81Jc1fasfslXlTiDwfI9xWR5y0k31dEvq8wsl5kre/zFZPvKyIQ6c5ZU5qikuZMpokzhSbOZJq4InNnCmnOJNKcKZHt5pTiSJI7MjYEtT0YfUVGhdZc0UCrYquvKkOvGrTyil6DokI4DKEQhM4EZ3LtX4sQQgghhKgz8i22MVI1SGgNrhbgy4aSTeDZZd6tztHk2BkXRFHM5yGhdcwd/wzzObFCqnVmK6pAPpRuNqc9/yo/h6uVGU4lZ5phVfLJ5sDy9dDNRDd0qwVUacBDnq+QkqAHb8iHbhjYVRtum5M0ZzJOTcbROhZ5Qz5yvYXkegvI8xWS6y0kz1tghU95vkLyvObr5mAk2MxxxZo6U2nmSjWXI+tNXMk0dZplac5kUhyJMv6YqMzqyniExLb0UsogkBu5M6MQQgghhGjMJJRqzFQbJBwHzkg4VboZynaAIwXsacfm+B2KYo635W4FLQeVl/vyzICqJBJSFa8H7y7w7TGnvV+U72tLMsOp5JPNsCq5gznVcve/kB4yA6igh0J/Cfm+YjxBL/5wAEVRcGlOXDYnaY5kNFVCgKNZSA+T7ysi17uPvd4C9noKyPUWsNdbQJ63wAyifAWUBb01PqdDtdPcnUpTZyrN3Wk0c0UDp1Sau1Jp5kqjqSuFZq5UXLZjZww20UjFtvRS5KOLEEIIIcTRQj7ZHQ00BySeAK4Ms8VU2RbwbAd7qjkdi+FURa7m4IoZowrMu/6VrDcDqpL1ZlhVuhlCpVDwozlZFEg4PhJQnWzOkzpE7oRYs8AoGA5RGvRQEvRQ4Csh31eIN+QnoAfRFJWEyMDOLpu0gjqaBMMh9kbCphzPPnPZs48czz5yvPvI9RaQ7ytEr0n3JcBtc5LuakJzd5o5udJIj8xjy5LsCXU34LcQQgghhBBCHAIJpY4mmhOS25uthDy7oGwrlG0HZ5MGP3ZSvbAnQ9Pe5hSlB83nLRpUlWwwp0CBGfR5tkPOZ+X7ay5Iam8OrJ7cwZwnnQTO5oSMMCUBM4Ta5zPH8vGG/AT1kNUVr5krVcaCasTCus4+fxHZZflke/LJ9uSZYZNnHzmefHK8+8j3FdXoXJqiRgKmJqQnNKGFuwnp7qaReaTc3YREu/sIX5UQQgghhBBC1A35Nnw0srkhpQMktALPTijdBoHt4GgK9qT6rl3DptrLu+5xfnm5Pz8SUG00p9KNZquqsK/8boAxQloipc6WFNtbUGRPx+dqjTOhLUnuDGyavW6vSRwyX8hPtiefPWV57PHkmcFTXABVQLgG49o4VDstEprQwt2UjIRmZESWW7ib0CKhKS3cTWniTJFumkIIIYQQQohjioRSRzNbojl4t7sNeHaYraYC+8w79dkknDoozmbm1PwMq8gIB/EUb8Jf9Buh4vUopZtw+3aSEMjDFi4jzbOJNDbFnSakJeBztY6bvK7WBBzpx84A9Q2IJ+hjd1kuezx57C7LI7ssL7Kez56yXAr8JQc8h6aopLub0DKhWSRwamrNW7rNeZozWbrSCSGEEEIIIUQFEkodC+xJkHqKOf5R2Q4zoPIXmN36JJw6KP5wgOJAGUX+UvZ69lEcKMMbSkNL6ktC2gAS7W6cCrh8e3D5duH27sTl243btxOnPwdb2ENS2UaSyjbGnVdX7PicLfG5WuF3tcLnbIXPZU66Jt21DpU/HGBPWT67y3LZVbqX3Z5cdpfmsjsSPhUFSg94jkSbi5aJzWmV0JxWic1omdCclonNrBAq3dVEWjgJIYQQQgghxCGQUOpYYk+BtC7mHfs8OyWcqgHd0CkJeCgKlJLnLWCfv5iygBcDA7fNRZLDTXN3WlwrGAPwJpyAN+EECmLOpehBnP5s3L7duLy7cPl2m5M/G9UIkuDbQYJvR6U6BG2p+Fwt8Tlb4Xe1xOfMwO9sid/ZAkM9tgdFNwyDfF8RO0v3sqtsrxk8leWyKxJC7fUWHPAcKY7ESODUnNaJ6bRKNJejZckyYLgQQgghhBBCHBESSh2LHKnmFBdO7ZMxpyJ8IT9FgVIKfMXkeAsoDXgI6EEcqo1Eu5tWiemH1DLGUO343Mfjcx8PTWI36DgCeZGQag8u/57Icjb2UJE5lRaRXPp7/PlQCDiaRgKqjEhYlYHf2SISWDkP85loGILhELs9uews3RuZcthVmmvOy3LxhwP7PT7B5qJ1YjqtE9Npk5hO6yQzeGqTmE6rhOYkORLq6EqEEEIIIYQQQsSSUOpYFg2nEo+Hsmg4lR9pOZUMx0jrkNjWULneAvb5ivCEfGBAgt1FU1cKjiM5OLmiEnC2IOBsQXFqj7hNWqgMpz8Hl38PTl82Ln+2ue7LRtO9OAP5OAP5ULK20mkD9iZmQOUwQ6qAM91aDtlSGtTP1xcKsKtsLztKc9hZksOOUnPaWZpDticf3TCqPVZVFFomNKN1YjrHJWWY4VOSGUC1SWwh4zkJIYQQQgghRAMloZSIdOvrbIZT3t3mgOieHWa5PeWoHIA7GA5RGCih0FdCtjefYn8Zfj2AU7WTZE8gLTEZtQFcd9iWiMfWHk9i+/gNhoEtVIzTnxMJrXKs5ejYVY5gAY5gAcn8Xvm8qoOAIx2/Iz0SVqUTcDTH7zTnYS2x1kMrfzjArtJctpdms6Mkm+0lOewozWZH6V5yPPn7PdapOTguqYU5JWZwfHIL2iS24LikDFomNMOuya8yIYQQQgghhGhs5JucKGdPBntHs1ufNxvKtprd+2wJ4GgCinbgc4TDENYhFAZdj58MwDAiy1W0fFEUUFVQlZhlFTTNLNM00CJlh8AT9FHoLyHfV8hebwFlQS+6oZNgd9PElYxTa0TjMykKIXsqIXsqZUmZlTZroVKc/r3lQVUgF6c/F0dgL47APjQ9gNu3C7dvV5WnD6suAo5mkbCqGQFH88jcXA7a06oMK0N6mGxPHttKstleks32kj1sL8lhe0k22Z58DKpv8ZRod3NCUkuOS2rB8ckZHJ+UwXFJ5ryZK1VaOwkhhBBCCCHEUUZCKVGZLRGSTzLv1ufLgdKtULYLwgooSRBUIBCEUMice3zg80MwaIZSoTDoBuhhM4jSdTB00IG4UEIBhcoBlaJUCKjU8jBKjSzb7eC0g9NhTnYbaDZzHpkMm0aJ4afQX0qOJ599vmK8IR+qopJkTyAjoRk2tQZBWyMUtiXhsSVVbmGFOeC6I5CPIxJUOQO5OAK5OPx5OAN52ENFaLqv2tDKMCAnrLLWSOa3sIv1QY31gTAbfT62e0sIGnq19Uq0uTg+uSXHJ2VwQnJLjk9qyQmRAEq62QkhhBBCCCHEsUVCKRFP18HnA6/XnPtCUJgAeXlQugvK8szwSUsCzWmGR5oGNpsZFmkaOByRECkSKMW2fqoJwyifwnp5y6rociAIXr+5HApF9jUPDaNTpAYpVAJk66UUKgF8Dg1nQiLJrmSauRJRHHYwbOyn0c5RzVDt+F0t8btaUlLFdkX34wjkgzeb3UVb2F60gy2luWwuK2STz8N6f4giXQeKIlM8lwId7JDpgPYuN+3dKbRNasYJSS1JScgg5GhK0NGEgL0JQXvaMX8HQSGEEEIIIYQ4VkkodSzTdfB4oKzMnBcXQ2FhJIzymWGPopiBkzMVmjWFpj4w9kEgH8J+s1WVLbF2x51SYgIs7cAtmYJ6iMKQh32hUrIDhRT7PYTCARIMG6lhGxlBFYrLwCgDDDMks9nArpktrtwucDnLW1k57GCLBm1HZ0uqKMMwKPSXsLVkD1uLd7O1eA9bS8z5Hk9utQOMKyi0cadyYkIKJ7tcdHCodLLrnKL5aEcRzlAxCgbgNadQDhT+CoWVzxXSEgna0ypPtuhyKkF7GrrqalCDswshhBBCCCGEODwSSh1LAgEoLTWnwkIoKDDDqEDADKDsdnA6ITERmjatZuymFKAFhErBVwC+bPDtBdVujklVR61e/HqQwpCHvGAJOYEiSsI+DAwSVSfprlTs6n5e2roOwZDZysrrg5Iys4xIiytNi4RSkdDK5TSDK7stEmZF5o7GE1qFdZ3dZblsLdnNluLdVgC1rWQPRYHSao9LsifQNrklbZNb0S6lNW2TW9E2uSXHJ2dUOQZXGbAWwAhhDxbiCOzDHiwonwcLsQejZYWoRgBbuAxbuKza8a2sa1AdhGypZkhlSyNkTyFoSyVoT7HKQ7YUgvYUdNUtAZYQQgghhBBCNHASSh3N/H4oKTGnvDwoKjJDqHDYDFXcbkhJMYOog/0Cb0uCpCRwt4RggTn2VKAI9JA5MHptt54CvOEAhWEPewNF5AZLKNP9gEKS6qSlIxVbTQZiBzNsi45FVZXouFgVQ6toq6Ho4OvRgMrtNIMrh90ss8Vss2mHPDD7ofCFAmwv2cOWEjN42hIJn7aXZBPQg1Ueo6DQKrEZ7ZJb0zalFW2TW3FiJIA65AHGFRtBR3OCjubV72MYaGEPdiuoKoosR6ZQoVWm6T40PYAWMMfAOhBdsZkBlS2FkD0lspxMyJZCyJYcM6UQsiUR1hIkxBJCCCGEEEKIOiah1NEkFDK74BUXmyFUYaHZNU/XzeApIQEyMszApLZodtBagDMdQiXg32e2nIq2nrJFxp46RGVhH4UhD3sDxeSGSvCEA6gopGguWtvTUGs5+AIid/nTqg+tomNZhcNmKzOvLzK2VWS7ghlGRc/jcIArOiC7vbwVVjTAstsOOrgqDXrZWryLzUW72VK8iy2RAGp3WW61d7hzavZISyczdGqX0pp2ya04IbklLtuh/4wOmaIQtiUStiXic7fZ765q2IctVBQJqYqwh4qwBYuwh4oj8yLswWJsoWI03YdqhHAE9+EI7jN7EB6AgUbIlhSZksvnmlkWtiVZyyFbMmEtkZAtsWZ3pBRCCCGEEEIIUSUJpRozwzBDp6Ii2LcPcnPN9VDIDEKSkqBVq7rpYqYoYE8xJ3drCBaCL8+cB/aB5gZ7Iij2A56qNOyjIFRGTqCI/FApnrAfu2IjWXPR1JFY/3doU1Xz+a2OoZuDsociLa48HiguibS2iuwTF1xF7yboMO8o6HBYXQgLDR+bPXvZUpbNltI9bCnZw5biXez1FlT78CmORNolt+bElNbl4VNKa1olNEerw1ZbtUnXXAQ0FwFnxgH3VXQ/9mAJtlARtlBJJLgyAytzvQRbqCSyXoqm+1AIm8FWqPLA7fsT0hIiAVWSOdfMkC2kJUSCrETC1j7ly2HNXestCYUQQgghhGgMwrqBpkovBWGSUKqx0XUzgCoshL17zUDK5zNDjMREaNGidltCHQrNYbaecrWAUJnZrc+3N6Z7n9tsQRVpZWIYhhVEZQeK2BcqxRsO4lA1kjU3zRxJ9R9EHQxFBZu6/59DTHBlhELkl+SzOXcvmwN5bAnmsyW4jy2hfRTo1TfzSXekcGJiS9oltuTElFacmNKGE9Pa0DShKYpNO2a7oxmqk4DTScC5n66DMRQ9gC1UGgmqIvNwibWuhUqxhUsj28rQwqXYwh4AbGEPtrCnRl0KKzIDrejkjoRV0WVzHooEWHq0TC3fpquH0O1WiHqiGzq6YWAYBmFDx8Bct8qJlFtlOjqGebOFyHazszEYeohE1da43heEEEIIYdFUhVve/JGNe6sf27YhG9AxndsHd6rvahw1JJRqTHQdNmyA9evNrmPRMaFatKjvmlUvenc+d0sIlkCgGPx7MXx5lIR9FKCzJxygIOzDpwdxRlpEpdtT6rvmtc4wDHKCxWzx7WWzL5ctvlw2+/eyxZdLSdhX7XGt7Wmc6GhGe3sz2tmacqKtCSeqqSQrkS53BlCsQJkCOdmg5ZqhmN1ujnPldJS3yrLZzJZZsd0Lj+EAC8BQHQQdTQk6mh7EQeFIQGUO0q7FBVZlaKGymLkHLRzZN1SGaphje0UDrUOuN0p5gKW60TWXuW4tu8qXVTdhzYWuuiIhl4uwGl13RQIuabl1rDMDIaNSYGSGQ2ZQVB4qRfY1dGs9eg6zKaiBglIeJimgKRoKCqqioCqqNVdQsCkaTs2OXbVhU2zYVA2bqmFXbWiqhopizhUF1QhhC5WSaE+sz6dLCCGEEIdh495S1u4uru9qHJKT0uUzSG2SUKqxMAzYuBHWrYMmTcxWUY2JomLYUyhWNAqAPSEfBcEC/IF8XIRJVuy0sCea408pdXMHvyNFN3T2BIrM8MkfCZ98Zvjk0QNVHqOicJyzKe2c6ZzkSudEVwvau9Jp62yOu4q73FVi6BA2zLBSD0NIB39kUPvoIO2KUj7XIkFUdCB2uy0SYtnM7oPR8mj3QpsGaiTA0tQ6Hby9QVI0cwB1ewr+gz1UD6KFPWhhjxlchT1oIQ+aHpmHo5PXnOve+PWwFwUDBeOwg61YYdWBrroJa050K7CKLjsJq050LWY5Uq5r0XUnuuqIzMsnY393whSHzGpNVMPwyCrHiARHoCiKdf+GaC6toJjhkQIqKoqioKlmcKSiomkqNkXDptqwaxoaseGRGgmbVDRFtYInTdHigqjY9fL9DuJ3ih6AgO2YDtOFEEIIIY4W8m2hMYgGUr/91ugCKd3QKQ6VURAsZo8vj4JgCQE9iFt1kpp8Ii71ZAj7IFQKwSIIeyFcYgYgqjMySHrDDEBCRphd/gK2WMFTLlt8e9niy8NvVH2nOw2Vtq5mnOg0Q6d2rnTau9I5wdkMp3rg8baqpajmv2ZbDcYPiwZYetjsQhjWweuHMq8ZYhlVDJQeDbE0NTLZzAAr2hortuWVFgmz1Eg3Rs0GqiJBVoSh2gmpqYTsqQcdaJknMFB1P6ruiwmrfGi6FzXsNdd1H1o4sh5d1n1oYR9qdF/dbwVcgHl3Qz2APVSrl4uBhq46IgGXA12JhleOmCDLgWGt29EVR8w+kW2KvXx7tFxxxKzbQWl4b2nR1kdmdzS9im5s1XdbMwwj0uYIKrY8im1lpCk1C4/smg27aosLhuIDo+rDpGi5dJkTQgghhBC1qeF9ghfxDAM2bYJff4W0tEYRSIWNMEXBUgqCxez25VEcKiOgB0nQXDSxJ+NUK7T8iXbxc7UwA6qwBwIlZlDlLzT30exmSKXaMbuG1J2AHmK7P5+t/lyr290WXy7b/HkEjXCVx9gVjXbO5rRzpXOSqwUnutJp72rB8c6m2Or7jm3RAIsa1iM2xNKN+LsO6nqFlliYP55ogKWo5S2rtMidBu2RQMtmKw+vtJgWWKoaE2wpdTNQf2OiKGaLJc1FyJ52eOcyDBQjYIZVuj8y95l3MAz7zTLdG7PsjwRi/ki4ZYZjZlkgcg5zu4JuVpew2dprP+Oj1RYDFV21YyjlQZW5bEdX7Bix80jQZai2mG02wooNHRth1UYYjbCiEVZshKxljTA2gopKGI0QaqRMRVds5mOiRhomGijRwCdSZrZCigRJ1XRbs6vl3dcqhkUHCo2i5RIeCSGEEEKIxkBCqYYsHC7vspeWZt5Nr4EK6SEKQ6UUBIrZ7c+jJFhGkBAJqoum9hQcNWoFpJh36dPc4GgGetBsORXyQijSiioYGQxPc5gTNpSQjuoNoPkCqP4Aqjcy9wcrT8EQaiCEEohZDoZRgiG8RpCNLg/r3X5+T/Lze3KA9SlBNiWHCVfTyMcdhE75Cp3zFE7Jg1PyoEsOnFhoYNdzwcgFfq3yUo3ol0ZVMZcjk2F+c8VQVXNdU81lNbJsTRpGpOWSoanoNg3Dppnl9siyTSsvt9vMdbu5rNttGHbNnDts6LbI3F4+1x02DIcd3RFZttswnI4Dd5uJhlXhmHm4YpBV/lxYoVZsmKUqFQKtSIssu1YeaEW7ImoxYZYaE2ZJC639UxQMxUlIddb+qfVQeYAVE1rFzwOoRsAMvYwAqh60til6ZN2I7hs0y41A3Loa0ypRQUfT/YAfqs6L64SBCqodQ7GZQXp0UuygOqx1RXWgVNpui1mOlttiyquaxyxH1ytur7StwlzGFBNCCCGEEPVAQqmGKhg0w6hNm8wuew0wkAroQQqDJewLFJHtz6c4VIZu6CRqbpo70rAfaCwZw0DxB7CVlKGVlGEr86CVlqGVeNDKytDKvGhlHrTSyLysDM3jQSvzoHq8aL4AmjeAEtZrXGcDyEuA39JhXXP4rTn81tKcb0+r/rgUnxk4nZILXXKhc6653LYIVMOgPGGpucbajsFQFXSHHd0ZCauc9vLgymkv32ZNjiqXw64KZS47ul1D12zoNs18gnTdHB8rEDS7F8YGWgqAAUakg1PsWFmqUh5OqZE7Idq08lBLs5UP8B4bXkXCPwm2Dp+hRlsbma07q+rGFo4dKLuK7mzRefkPvLwLm6IoZtc1FDRCONDRjBA2dDQ9hJ0QdsLYMcy5EcZmhLArOqoeRDNCKEYoMg+iGiFUPYiiB1ENc65E53oQ9ACKYc7Ro/OY5RgKOuh+lHoOxw6OWiGo0iJhlRYTXGkVtlVVpkXKKuxTqVyrYoqEY9H9iO5fYTJ0cKbX9xMmhBBCCCFqgYRSDZHXa3bX27oVWrYEl6u+a2Txhf0UBkvICxSSE9hHacgLGCQqTlr7nbhKPNgKc7AVlWArLsFWXBpZLsVWXIpWXIqtpAxbSSlaSRlqsPYGsNHtGrrLTthpJ+iysaWpyrp0ld+b6vyeEmZDcoD1iX4K7NV/S2ymO+igp3CSkUp7JY2T1CacpKaRnpgEaTaMTLNVEqqKR1X4VVOtEMSIBBeGWt7qqcrkKZpfGUZk2UDRjUhXKgP0yLqul5eHdZRw2AzgdMNaVsI6SihcPo9OkXU1ZLYCi5arkVZh0XI1EEIJma3F1EAINRhCqTBXA0HUQPnPSdENMxD0VT1oe23RHZHgyuUwJ6edcHTZ5TCXneXbw05bJOCyEY5p3RWOhmd2G2F7JGQyYn8QMaFWtKUWsS22iB/k3RbpghjbQis2ENNiwi1FBa1CQNZIulVVGkQ7MtbRwYRIsWPrA1Y3top3X4sdAyk67lF5NzZbpa5r1c7VyLnUgxw4+3AZBhjhykGVHgQjFFNW3Xqw/Ji49ch2qywAesy6EapivWJ5hbkRMuta+SceCdcCjSNI0xKgzfn1XQshhBBCCHGYJJRqaIqLYe1a2LMH2rQxuyzVF58PIz8fb+4ePHm78ezdhT9/L+q+AtKKymhd7MFVVIa9qARbYQmKXvMWS7EMTSOUnEA4KZFwUgKhRHMeTnQTTkwwlxMiy4kudLebcIKLcIKbQofOFqWQzcY+tnl3s9W7i23ebHb4cqsd70kBWjnSrDGfTnSlc6LTHHQ8zZZQ5TFlh3RlRxHDsEIsNRA0Ayu/GVZZXSUj26JdJZXIsuYLmvtEj/EHUH1Bc19fZDlyDs1f3hXLDMOCUFw7d5eL0m0auttROeByOSKhpqO8JVek1VfYYUN32sygLBJylZc50B22SGuqSIsehfhWW4oS3wpL08pbbkUHiVdjgi21POyMa8EVDbmiY3VF9q0uQKrqTmwVA6TYgbRjWyMRHf8oZlDtiiFS7NhHdtW23xCpuuDI3NbIx0BSlPIWRlT9O6RBMfRIiBYTUsWGVpWWw5GpuvXYsnDMPjHnRjfHpYstj92fcMz2ilOovM7RKbmD2RVSCCGEEEI0ahJKNRSGAbt2we+/Q2kpHHfckRngORSCffsgP9+c8vLMKT/fKjci2xSPBwXzK1ZNv2aFE9yEUpMJpSYRSkk2l1OS4qZwShKh5CRCyYmEkxPR3a79th4J6EF2+fayzZvNdm8227x7zHnRHvYFi6s9zqnaOcGVQTtXBu1czWnrSONEezJt7cm4FMXsBgbl3UNUDdBpqHf7q1eKguGwE3bYCeM+co+j62Z45YsEXZFxwjRv+brmC8Rt16LBVuy+sftFlqPdPNVQGLXEi62kdgfeDlfRossKtmJacoUdGiGHjZDTbLkVctoIOmyEnJo5d2gEnXYCLhshm4KhKoQVo3y8seikKiiRMlXTUDUbis2GptpQNBXNpqHY7KiKhqaZLZBsmh1bZG7X7NjtdlTNhqqqaJoNVdXMuWYeo2o2NGuw7fg7tjXqEOlYp0RbAdbjHz0Ohx6AQEF910IIIYQQQtQCCaUaAp8PNmyAzZvB7TYDqYNhGFBWVh4uVTfPz4fCQnP//Yj9qhm22wg2SSHcJI1gWgqhJinmPHY5NYVQWjKhlGQMx6F9yQnpIXb789jhzWGHL5vt3hx2REKoPf489P2M2ZTuaEJbd0vaulvRzt2atgmtaOduRUtns6q78MR2rdH9EPKB7o0MrO4z/1offSJUG2CLdL+yUeM71olDo6pW66XapgRDVssszRdA9frjAi/N648Pwzw+czlaHg26IoPqa74ANl95y64j0aXRUBRzzC6XA8PpQHc7wOnAcDnNucMBLgc4HCguB4rD3K46HCgOO4rTiepyWuXm5DSPsdviW2FZXRZjWnVFl6MDy9tUczwuu1ZerlB+nrgWYWr88v5agDWibo1CCCGEEEKI2iOhVH0yDNi7F9avh9xcyMjY//hR2dmwfDls3145dPL5av64mobRtCl6syYEmqTiSU2gOM1NSaoLf5NkaNIMW3oLaNoMPdFda18W/XqA3b5cdvr2stO7lx2+HHb6ctjhzWG3P49wNd3tABI0Fye4WtI2oZU1b+tuyQmuliTaDrLlTvSOVnEMswtJ3DgvfvOOf2F/5cAKIl+kYwbjVTUiAxAdXH3EYSnvqmZExjwyCGN2TTM7rBnmgNoY6M7IlGJDR8MggWjntegA2uaSqby7WqQLGwrmGma3MxScQQOHL4jDH8bhC2MPhLBHgi/NG0CLbdHlDaD4Aqg+vzn3+lH8AfD6Ubx+8PlQfH6IhFuKYY7fRS2HXebJFTOkckVCqtjAKhpiOezlYZbdDk67OXdEpmiZw24e47KDwwkOm3mMqpV3N1RVM8BSKgResWXRoEtVzQBMjQZhWvldGNWY4IsqArS4cytVh2EVu1UKIYQQQggh6oWEUvWloMAcyHznTvNL0fHHV/3lKC8Pli2D//wHfvpp/+dMTIRmzaB588rz5s3xpyVTnOamMEElJ1RAcbAMvx7ErthIsrlJ0NxokZZFhzI6lGEY5AeL2O3LtcKnXb5cdvly2OXLZW+gIPKlv2pO1cHxrgyOd2dwvCuDE9wtramZPfUIdxdSqgmrwBwLJTp2SswgwmE/GAEIR1tdRUKr2EtUlPKwyrr7VDS4Oja+DMeHQ+ZSbIBUKTiqYn/A/PkbBrGjICmRwbRVVBTFvBObEhMeqShoiopTVbErGjbFHEzbpmjYFS0yzpGChmqFT1pMCKVFx09SFGuA7vLtR/Dnp+vgD4DXBz5/zNwfM/dVmFc1BcqXo/sEg9EfTPm2I0VV40Ou2KArGmQ57JUnu83cZreZwZddK1+O3e60gc1hhmDRll8VgygFrPBKoXIYFtvSywq/1PKB7DU1PhCrGHjFni/u8SuEXhXrVDEck4BMCCGEEEIcgySUqmslJWZLp+3bIRAwA6PY1lGhEPzyC3zzjTmtW1e+TVGgZ0/o3j0ubLKW3fEthkJ6iOJQGSUhD7mBAgqCxXjCuRhlBm7NRaotGZdW825ShmFQECxmjz+fPf489vhy2e3PY48vj91+M4jy6ftv0ZGouWjjyuA4VwtOcGdwXCSEOs6VQbojrW7vmFVjqjmgbrWD6kYH8I0ODBw7CHDs3bjCYPjNwKFieAWRL8ma+UWZyB3gogGWGlNW5S39Dl35XdXKw6CqQqNomV6hzLDaF1WMHMsHzVYU0FBRFNW8imqCI5uiYkPDrpqBUTRAig2F4oOjmADJ2q5aYdMRD4+OFFUFt8ucals4bIZV/piwyh+oHGjFlUX3j5QFAhXK/eAPli/rkVeCroPHZ051IRpcOasLu6KBVsyyFXzZyu+saC1r5V0XHTHbbZFQzKaax9ps5oD1auTfZjR4UmLDK6gUjsUGWFQIq6KhmHVHx+hg+NFB7iNBWey5ouemwrkVpfLjxa5H6xwbmlU8RqmwjxBCCCGEELVAQqm6EA6bg4jv3m12wfN4zCCpRQvzy93q1ea0Zo05LymJP75bN/jjH+Gcc8xjqhHSQ5SGvZSEytgXKCEvWIAn5CNkhHGodpI0N2nO5Gq/pPvCAfYG9pHjzyfHv49sf7417fHnke3Px3+A0ElBoYWzKW2czWnjyqCNK53jXC1o42rBce4WpNmSj8IBkqOh0QHG04q7m5Qec0cpvbz1lR4CzG6ERjiEHg5goKMHw+iRfaPBkKEY6ERbIoGOgqFE5gbmftYXTvNnbhD50mpEv4yaXdWiAU5saKSZJVYIpEVCIw0Nm2qGRzZVw1ZFCFSxxVFsoBTbAqlRB0eNlaZBotucjgTDgGAoElwFysOt6Ho00PIHzW3+yHLF7YHo9pgptiwYWQ7FdKkNhszJU7uD2NeYTYtvzRUbeMWGYLGBlxV0aeXhVqV55A6NWoVlTYs5T4X9o8uxLbys3weR/8UFWTFlVQZTWASsy8kAAJ89SURBVL8zKgVUVY0jZnXJVOLLKgZpFUO7qh6zYpkRgnAZNN//+IhCCCHE0SisG2jRP4QJcRSo01DK7/dz77338p///AeXy8WYMWMYM2ZMXVah7ui6GS4VFsKOHWYoVVRUvr5+vTlt3FjenSYqJQX69IF+/eCMM8xWUFUI6iFKQx5Kwx72BUrIDxbiDfsJ6EFsikai5qaFowmaolEcKiPbn09uoIDcQCF7/fvYGyiw5jn+fIpCpQe8LAWF5o40Wjmb0dqVTitnOq1dzWnlbE4bVwtaOpthV4/erDPaqoiYVkNGZBj26LJudUMzy6PL5S2PzPGODAUUs4mROfa8qoFiAwzQQLGDYhioGCiYc9UAxdDN73eRdQ0dzQAbOpphYFOMyFwlOjS7CqiKgWoo1nm02PGS4sZMUlDVSJCkatbc7IIYfQOMtNqyvqTGtuCKrBsxb5ZG7LFGeSsx3XyGQC8vixuI36hyMW6f/Q3cb1RzfJXHHcS+1T3mAW4iEKe6cLZieaX1CgtxQUOFYyq2nIltkRMbUtQmRSlvmZSUWPvnryism0FWbGgViIRcgWB54BUIVi4PBiuUh8rLosFXIGYKhiqsV/jdHQqbk7eOWobVlKKUB2GaVh6MVQyxoqGXrYoAzFqODnyvVljWykMpW4VukNZydJwwLb5bZPT8qhKZazHHxrQai7YWM0Lg1KHl2eBIqe9nVwghhKhTmqpwy5s/snHvgb+7NUQDOqZz++BO9V0N0YDUaXrw0EMP8csvv/DSSy+xe/du7rzzTlq3bs2QIUPqshpHhq6bg5Zv2mSGTevWmeHT7t2Qk2POi4urPrZpU7NLXo8e5rxTJ/ODfuzpDR1v2I8n7KMs7CU/UMTewD6yffnkB4ooDpXhCXspDfsoCBaTFygkL1AQmRcRMIJVP3YFLtVBhrMZLWOmDGczWjmb0crZnIxDDp0iX9aNCvOYTfG7G/EbK4QQVuBjREOg6HK0PGyFQHEBUWQfPRKM6JF185HMsMkcuyj6yDoYSqQjmhFpOYTZsseIaWEUKVeig2CjYFdUbIbZ4ijawsjskhZdjgRARiQQMmJaFUH8oNqRuVqx21psKyNFKX++FGJaYUVDHwMIm/tEy3Q9coxh7osR05JLN/fXDbPbYXSbda7IMQbl58CIPHZsPYzyekXFBSjRVlJG1SGJFXhFQ5Uqyq2gJXJyRa3iXEr5tig1pjy6T9xxVXRxiq2/da4Kxx1obKCq/h1Ahaepmn8DsU9nteeJvIANI+ZnFbNu6PGPZf28zNd+eVoa8/jWtsj1VtUFLC4Ei3ke4gKx2FY3FY+JbqvQxW1/tCPY1fFAoq3CgjFhlrUcgmA06IrZHgqVh1/BkLlu7V9xPWiGXNF9re0VlkMhCIbLjw2Fq6hnFSFaYxQNvTJPgHNG13dthBBCiHqxcW8pa3dX892ygTspvQ7+aCkalToLpTweD0uWLOGZZ56hS5cudOnShQ0bNvDaa681nFDK6zVbM5WVlU8lJeZUXGzOCwrMwcdzc83WT3v3mlN+vvmX9gPJyIDMTGsKnXwSpemplIQ8lAZKKQqWkLdrObmBAvIDRewLFpLjLyAnWMC+YDFF4TKKQmUU6h7K9IP7a3yqmkC6LZV0WwrptlQybKm0sKWSbkulpS2NFvZUkhV3fPe6aM4RAPxgGHlmC6FI+KDr0TGFIuGPYpjfd5Xy8YhQiGs1ZCiUj0UUaSWkR774GoqB2ZYn5huzopRnG4rZWotIKx/zO6zZDUyxuqJFIhxFse6eZkfFptoirYPM7mcaGlp07CLVVh4uqZGuZWqk+5oSaTGkmINhKzHbVUUr3y9uH818HitOkeux5vvbp6ryisdXV3aw+x+orKrlaHAVDcAqhlvmTzSmPBKEVSyPblOIPy723NY5o8tQHo7pkcmIKa+iDtZjEBOuxIQ6cdmHEQlmiDm24r+LmODN2i9muaZiAyVFiQSF8VWJO58RedDo8xDdXqmBWSR40vUKgVT0MSsEVXrsPtFtkTJdjzxmZFnXzbBSN8yWSuHIz0IPRn4ckbLoOfWYcMz8hVD+GMQ8fux1WMdVeDJUFatysa3xrF9WxPy7iZRXDMKq6y5G7D7R5ZpsB+yKOWEHxVEhwKzi8cs31lxNW7YZkZ9LNAALhyPhVSTgCunlZcHI9lC4PNyKXQ9F1oMV5lZ57PEVtsWWhfUK2/X4ffSYfULhyv8OonQdAjpk55v1E0IIIYQQjVqdhVLr1q0jFArRs2dPq6x379489dRT6LqOWs93HfL++yOWzLiEfbYguoI1hRUIq/HLocgUViHUGoLHQzBSFnRoBNwOc3LZCDhseJ0qfruK12bgM0J4jZ/x6j/g9QYI/hQ+cOX2Q1M0mthTaOJMpakjlSaRqakztbzMmUoTRxp2zW4GQYYZDlntgwwdVIViQ6FIKW8VRCToMYMixfw6qURGGVIjbYIiLXYURUFR1UibEzOYIdIFTFEUc+BqVDMEinQJs2k2lMjcDIc0VFWLNLDQ4oIeJRL2qIpiLStqtMwMgKLhUfm+5fscVJBzJLo0ifoT2+rOCkaM+PK41kgV9z/APhXLKrbyq7Z8f9tiQ6j9bDuU7ZVarsVcZ1Xbqts37pxUKKtQfyMSQMSFY3p5eGKFZDGTXmF/XS9ftoKsCuWx++rh+O26UV5mRAIzPVxeF73i+WKu16A8IKvqNRQb4sU+z7EBX+xzY/4yreK5jIRcRkwgE7tfpWMqHlth2QrfwArGnHZwxQZmxO9fZTkVtlf4HRltpVlxv6rOUdXxFcuioa+hlwdoulEeVvkD4E4CxxEaF60ROKaGQxBCiFok4zEJ0fDUWSiVm5tLkyZNcDjK72DWvHlz/H4/hYWFNG3atK6qUqXX8j/j+qG10bUhDHgjUwwds7VRNWyqDZfmwm1zkWhPJMGRQKI9iURHIinOFFKcKaS6UklxppDmSqOJqwlN3E1IdiSjRUIZJRIimSGOgqaYQY+iKlaroGhYo0Va+NhUW9wxscuqotZ424H2FaLexLZKkc8goqYqBl1GxRCqivKqArKK+1dar2bbgY6rbv/o3KqLHn83xIrhnxXkxe5DeTBmHbOfehtUroO5UqFeMZviWkJVcWzcaYzKy4mJ4EziWHVUD4cghBBHkIzHJETDU2ehlNfrjQukAGs9UJNub0fYeRdM4upPctlTtBNNtVndr6ItdaKteKLBjk2xoaoqdtWOpmqRrmHm3KE5sKk2bKoNp+bEbXfjsrlw29247W5SHCkkOhJJsCXgtrtJciThtDkjnc+IC3miAU/FoCdaFrtf7LIQQojDUJPxrES8qoKp/ZUdzjGqCu5js6VUoxgOQQhRydHQQudouAaQ8ZiEaGjqLJRyOp2VwqfoustVDwPUVtA6uTUvDnu5vqshhBBCNE7SBbpONPThEI4mR8MXcLmGhuNoaaFzNFyDEKJhqbNQKiMjg4KCAkKhELbIneVyc3NxuVykpBz4ls5G5K+kpaWN85egEEIIIRqXxMTEBtf6+HCHQ6irz1NHS5Dw1PJN7C7yHnjHBqjrcalc1vt4uYYGIHodIZ8HPdA4ryPg9VBaWnpUXEO7FBU9YK/v6hySDDdyDQ3A0XAN7VLUOstWDvR5qs5CqVNOOQWbzcbq1as57bTTAFi1ahVdu3at0V/1ysrKAOjfv/8RracQQgghBJifU5KSGtbYVYc7HIJ8njp2fAQ8UN+VOExHwzXA0XEdm4HG3qdErqFhkGtoGDYDvefWzWMd6PNUnYVSbrebiy++mJkzZ3L//fezd+9enn/+eebOrdkz0aJFC7744osG+VdLIYQQQhx9EhMb3tgdhzscgnyeEkIIIURdOtDnqToLpQCmTp3KzJkzufrqq0lKSuLmm2/m3HPPrdGxqqrSsmXLI1xDIYQQQoiG63CHQ5DPU0IIIYRoSBTDiLt/sxBCCCGEaKC8Xi99+vTh+eeft4ZDWLhwIStWrODVV1+t59oJIYQQQhwcuUWLEEIIIUQjETscwk8//cSyZct4/vnnGT16dH1XTQghhBDioElLKSGEEEKIRsTr9TJz5kz+85//kJSUxNixY7nmmmvqu1pCCCGEEAdNQikhhBBCCCGEEEIIUeek+54QQgghhBBCCCGEqHMSSgkhhBBCCCGEEEKIOiehlBBCCCGEEEIIIYSocxJKAX6/n2nTpnHaaadx1lln8fzzz9d3lRqtnJwcJk6cSFZWFmeffTZz587F7/fXd7UatRtuuIEpU6bUdzUarUAgwL333svpp5/OmWeeySOPPIIMpXdo9uzZw7hx4+jVqxeDBg3ixRdfrO8qNSqBQIChQ4eycuVKq2zHjh1cc8019OjRg/POO4+vv/66HmvYeFT1XK5evZoRI0bQs2dPBg8ezJIlS+qxhuJwfPLJJ3Ts2DFumjhxYn1X66gkv5fqVlXP9+zZsyu93l999dV6rGXjt7/vI/L6PjL295zLa7z2bdu2jbFjx9KzZ08GDBjAs88+a21rjK9xW31XoCF46KGH+OWXX3jppZfYvXs3d955J61bt2bIkCH1XbVGxTAMJk6cSEpKCq+99hpFRUVMmzYNVVW5884767t6jdJHH33EF198wSWXXFLfVWm0Zs+ezcqVK3nuuecoKyvjr3/9K61bt2bEiBH1XbVG59Zbb6V169a8++67bNy4kcmTJ9OmTRv++Mc/1nfVGjy/38+kSZPYsGGDVWYYBuPHjyczM5N33nmHZcuWMWHCBP75z3/SunXreqxtw1bVc5mbm8v111/PFVdcwQMPPMDatWuZOnUq6enpDBgwoP4qKw7Jxo0bGThwILNmzbLKnE5nPdbo6CS/l+pWVc83wKZNm5g0aVLcZ72kpKS6rt5RY3/fR+644w55fR8BB/oOKK/x2qXrOjfccANdu3blvffeY9u2bdx2221kZGQwdOjQRvkaP+ZDKY/Hw5IlS3jmmWfo0qULXbp0YcOGDbz22msSSh2kzZs3s3r1av773//SvHlzACZOnMiDDz4oodQhKCws5KGHHqJr1671XZVGq7CwkHfeeYcXXniBbt26ATBmzBjWrFkjodRBKioqYvXq1cyaNYt27drRrl07zj77bFasWCGh1AFs3LiRSZMmVWqh9+2337Jjxw7efPNNEhISOOmkk1ixYgXvvPMON998cz3VtmGr7rlctmwZzZs357bbbgOgXbt2rFy5kg8++EBCqUZo06ZNZGZmkp6eXt9VOWrJ76W6Vd3zDebrfezYsfJ6ryX7+z7yf//3f/L6PgIO9B1QXuO1Ky8vj1NOOYWZM2eSlJREu3bt6Nu3L6tWraJ58+aN8jV+zHffW7duHaFQiJ49e1plvXv3Zs2aNei6Xo81a3zS09N59tlnrV9GUaWlpfVUo8btwQcf5KKLLqJDhw71XZVGa9WqVSQlJZGVlWWV3XDDDcydO7cea9U4uVwu3G437777LsFgkM2bN/O///2PU045pb6r1uB999139OnTh7feeiuufM2aNXTu3JmEhASrrHfv3qxevbqOa9h4VPdcRrsKVCTvP43Tpk2baNeuXX1X46gmv5fqVnXPd2lpKTk5OfJ6r0X7+z4ir+8jY3/PubzGa1+LFi147LHHSEpKwjAMVq1axffff09WVlajfY0f8y2lcnNzadKkCQ6Hwypr3rw5fr+fwsJCmjZtWo+1a1xSUlI4++yzrXVd13n11Vc544wz6rFWjdOKFSv44Ycf+OCDD5g5c2Z9V6fR2rFjB23atGHp0qU89dRTBINBhg0bxl/+8hdU9ZjP5A+K0+nk7rvvZtasWbz88suEw2GGDRvGZZddVt9Va/BGjhxZZXlubi4tWrSIK2vWrBnZ2dl1Ua1Gqbrn8rjjjuO4446z1vPz8/noo48a9F8FRdUMw2DLli18/fXXPP3004TDYYYMGcLEiRPjPquJwyO/l+pWdc/3pk2bUBSFp556ii+//JK0tDSuvfZaGbbhMOzv+4i8vo+M/T3n8ho/sgYNGsTu3bsZOHAggwcP5v7772+Ur/FjPpTyer2VPuRE1wOBQH1U6agxb948fv31V95+++36rkqj4vf7ueeee7j77rtxuVz1XZ1GzePxsG3bNt58803mzp1Lbm4ud999N263mzFjxtR39RqdTZs2MXDgQK699lo2bNjArFmz6Nu3LxdeeGF9V61Rqu79R957Do/P5+Pmm2+mefPmXH755fVdHXGQdu/ebf3beOyxx9i5cyezZ8/G5/MxY8aM+q7eUU9+L9WtzZs3oygK7du356qrruL777/nrrvuIikpSbrG15LY7yMvvviivL7rQOxzvnbtWnmNH0Hz588nLy+PmTNnMnfu3Eb7O/yYD6WcTmelH1J0XQKBQzdv3jxeeuklHn30UTIzM+u7Oo3KggULOPXUU+P+4iAOjc1mo7S0lIcffpg2bdoA5heeN954Q0Kpg7RixQrefvttvvjiC1wuF127diUnJ4cnn3xSQqlD5HQ6KSwsjCsLBALy3nMYysrKuOmmm9i6dSuvv/46bre7vqskDlKbNm1YuXIlqampKIrCKaecgq7r3H777UydOhVN0+q7ikc1+b1Uty6++GIGDhxIWloaAJ06dWLr1q288cYb8oW9FlT8PiKv7yOv4nN+8skny2v8CIqOPez3+5k8eTKXXnopXq83bp/G8Bo/5vuvZGRkUFBQQCgUsspyc3NxuVykpKTUY80ar1mzZvHCCy8wb948Bg8eXN/VaXQ++ugjli1bRs+ePenZsycffPABH3zwQdy4Z6Jm0tPTcTqdViAFcOKJJ7Jnz556rFXj9Msvv9C2bdu4N7XOnTuze/fueqxV45aRkUFeXl5cWV5eXqVm16JmSktLGTt2LBs2bOCll16S8SsasbS0NBRFsdZPOukk/H4/RUVF9VirY4P8XqpbiqJYX9aj2rdvT05OTv1U6ChS1fcReX0fWVU95/Iar315eXksW7YsrqxDhw4Eg0HS09Mb5Wv8mA+lTjnlFGw2W9zgX6tWraJr164y5swhWLBgAW+++SaPPPII559/fn1Xp1F65ZVX+OCDD1i6dClLly5l0KBBDBo0iKVLl9Z31Rqd7t274/f72bJli1W2efPmuJBK1EyLFi3Ytm1bXMvSzZs3x43jIw5O9+7dWbt2LT6fzypbtWoV3bt3r8daNU66rjNhwgR27tzJK6+8wsknn1zfVRKH6KuvvqJPnz5xf+n97bffSEtLk3E+64D8Xqpbjz/+ONdcc01c2bp162jfvn39VOgoUd33EXl9HznVPefyGq99O3fuZMKECXHB3i+//ELTpk3p3bt3o3yNH/Opi9vt5uKLL2bmzJn89NNPLFu2jOeff57Ro0fXd9UanU2bNrFo0SKuv/56evfuTW5urjWJmmvTpg1t27a1psTERBITE2nbtm19V63Rad++PQMGDGDq1KmsW7eOr776isWLF3PFFVfUd9UanUGDBmG325kxYwZbtmzhs88+46mnnmLUqFH1XbVGKysri1atWjF16lQ2bNjA4sWL+emnnxg+fHh9V63Refvtt1m5ciWzZ88mJSXFeu+p2E1DNHw9e/bE6XQyY8YMNm/ezBdffMFDDz3EddddV99VOybI76W6NXDgQL7//nuee+45tm/fzuuvv87SpUtliIHDsL/vI/L6PjL295zLa7z2de3alS5dujBt2jQ2btzIF198wbx587jxxhsb7WtcMQzDqO9K1Dev18vMmTP5z3/+Q1JSEmPHjq2U6IoDW7x4MQ8//HCV237//fc6rs3RY8qUKQA88MAD9VyTxqmkpIRZs2bxySef4Ha7GTlyJOPHj4/rGiJqZuPGjcyZM4effvqJpk2bcuWVV3L11VfLc3kQOnbsyMsvv0yfPn0A2LZtG9OnT2fNmjW0bduWadOmceaZZ9ZzLRuH2Ody7NixfP3115X2ycrK4pVXXqmH2onDsWHDBu6//35Wr15NYmIiI0aMkN/bR5D8XqpbFZ/vZcuWMX/+fLZu3UqbNm3461//yrnnnlvPtWy8DvR9RF7fte9Az7m8xmtfTk4Os2bNYsWKFbjdbq666irGjRuHoiiN8jUuoZQQQgghhBBCCCGEqHPHfPc9IYQQQgghhBBCCFH3JJQSQgghhBBCCCGEEHVOQikhhBBCCCGEEEIIUecklBJCCCGEEEIIIYQQdU5CKSGEEEIIIYQQQghR5ySUEkIIIYQQQgghhBB1TkIpIYQQQgghhBBCCFHnJJQSQjR4HTt2ZNKkSZXK3333XQYNGlQPNRJCCCGEEEIIcbgklBJCNAoffvghK1asqO9qCCGEEEIIIYSoJRJKCSEahTZt2nDfffcRCATquypCCCGEEEIIIWqBhFJCiEbh1ltvJScnh+eee67afbKzs7nlllvIysqiT58+zJ492wqx3n33XUaNGsX8+fPp06cPp512GnPnzsUwDOv4N998k0GDBtGzZ09GjRrF77//fsSvSwghhBBCCCGOVRJKCSEahYyMDCZOnMhTTz3Fjh07Km0PBAJcffXVeL1eXnnlFR577DGWL1/OQw89ZO3z448/smXLFt544w3uuusuXn75Zb755hsAPvvsMxYsWMBdd93Fe++9R+/evRk9ejRFRUV1do1CCCGEEEIIcSyRUEoI0WiMGjWKtm3bMmfOnErbvvrqK3Jycpg3bx4dO3akb9++3H333bzxxhuUlZUBEA6HmTVrFu3bt+eiiy6iU6dO/PzzzwA8++yzjBs3joEDB9KuXTtuvfVW2rRpw/vvv1+n1yiEEEIIIYQQxwpbfVdACCFqStM0Zs6cyciRI1m2bFnctk2bNtGuXTtSU1Otsl69ehEKhdi+fTsAzZo1IykpydqelJREKBSyjp83bx6PPPKItd3v97N169YjeEVCCCGEEEIIceySUEoI0aj06tWLSy+9lDlz5nDddddZ5U6ns9K+4XA4bu5wOCrtEx1TKhwOM23aNPr27Ru3PTbEEkIIIYQQQghRe6T7nhCi0Zk8eTIejydu0PMTTzyRrVu3UlhYaJWtXr0am83GCSeccMBznnjiiWRnZ9O2bVtreuqpp1i9evURuAIhhBBCCCGEEBJKCSEanSZNmjB58mR27dpllfXr14/jjz+eO+64g99//51vv/2WWbNmMXToUFJSUg54zmuvvZaXXnqJpUuXsn37dubNm8e//vUvTjrppCN5KUIIIYQQQghxzJLue0KIRmn48OG888477N27FzDHm1q0aBGzZs3iz3/+M4mJiVxwwQXcdtttNTrfeeedR15eHvPnzycvL48OHTrw5JNP0q5duyN4FUIIIYQQQghx7FKM6IAqQgghhBBCCCGEEELUEem+J4QQQgghhBBCCCHqnIRSQgghhBBCCCGEEKLOSSglhBBCCCGEEEIIIeqchFJCCCGEEEIIIYQQos5JKCWEEEIIIYQQQggh6pyEUkIIIYQQQgghhBCizkkoJYQQQgghhBBCCCHqnIRSQgghhBBCCCGEEKLOSSglhBBCCCGEEEIIIeqchFJCCCGEEEIIIYQQos5JKCWEEEIIIYQQQggh6pyEUkIIIYQQQgghhBCizkkoJYQQQgghhBBCCCHqnIRSQgghhBBCCCGEEKLOSSglhBBCCCGEEEIIIeqchFJCCCGEEEIIIYQQos5JKCWEEEIIIYQQQggh6pyEUkIcAsMw6rsKh6wx1/1AjuZrE/snP3shhBCx5H1BVEVeF0I0PBJKiUZn0qRJdOzYkeeff77StkGDBjFlyhQAVq5cSceOHVm5cmW154rdv6Y+/fRT7rzzzoOrdAOQnZ3NDTfcwK5du6yyQ7n+hmLUqFGMGjXKWl+yZAkPPvigtf7uu+/SsWNHdu7cWR/Vq1VPPPEEHTt2rNc6dOzYkSeeeOKwz1OTa6n4b3fKlCkMGjTI2l7xdbto0SKee+65g3oMIYQ4GqxatYqbb76Zfv360bVrV8455xxmzJjBpk2b6rtqcer69/KqVau44YYb6uzxGoK1a9dy/fXXc8YZZ9CnTx/GjBnD2rVrq91/z5499O7du0bv7du2beOWW27hrLPOonfv3lxxxRWsWLEibp/S0lIefPBB/vCHP9CjRw8uuOACXnvtNXRdP6jriL5WYqfOnTvTp08fxo8fz4YNG2p8rueff57JkycDUFxczB133MEPP/xwUPU5VBU/u1TlUD6r1uSYgoICBgwYwI4dO2p83lhlZWXce++99OvXj549e3L99dezefPmAx5XXFzMzJkzreMuv/zySq+TUCjEY489Rv/+/enevTsjR45kzZo1h1RPcfSw1XcFhDgYJSUlLFu2jMzMTN566y2uvfZaFEU55PMtWLCApKSkgzrmxRdfPOTHq0/ffPMNX3zxRVzZoVx/Q3HPPffErT/55JNkZWXVU21EberSpQtvvfUWHTp0qHJ7xdft448/zoQJE6z1yy67jLPPPvuI11MIIerT4sWLeeSRRzjrrLOYNm0a6enpbNu2jTfeeINLLrmEuXPncv7559d3NevFkiVLGlwwdyRt27aNq666ilNPPZU5c+agKArPP/88I0eO5L333qN9+/Zx+xuGwbRp0ygtLT3guQsKCrjqqqtIS0tj2rRpJCUlsWTJEsaMGcNLL71EVlYWhmFw66238vPPPzNx4kTat2/PihUrmD17NoWFhYwfP/6gr+mtt96ylsPhMLt37+bRRx/lyiuv5KOPPiI9PX2/x2/atImnn36a999/H4DffvuNf/zjH1x66aUHXZcjZcCAAbz11lu0aNGiVs/bpEkTrrnmGqZNm8bLL7980N+VJk2axJo1a7j99ttJSkpiwYIFjB49mo8++ojU1NQqjwmHw1x//fXs3r2b22+/nWbNmvHyyy9zww03sGTJEjp16gTAAw88wNtvv82kSZNo06YNL7zwAtdccw1Lly6lbdu2h33tonGSUEo0Kh9++CEA06dP5+qrr+bbb7+lb9++h3y+zp0711bVGqXGfP3VBRai8UtKSqJHjx7Vbj/Q67Zly5a0bNmylmslhBANx+eff87DDz/MzTffHBfKZ2VlcfHFFzNp0iSmTJlCZmYmJ598cj3WVNSFV155BbfbzdNPP01CQgIAZ5xxBoMGDeLVV1/l7rvvjtv/9ddfr1HLF4ClS5dSUFDA22+/TUZGBgD9+vXjoosu4rnnniMrK4tff/2Vr776iscee4w//elPAPTt25eioiKeffZZbrrppoMORip+DujduzetWrXiyiuv5L333jtgS7h58+YxdOhQq84NUdOmTWnatOkROffIkSN58skn+eSTTzj33HNrfNyPP/7I559/zuLFi+nfvz8Ap512Gueccw6vv/46f/nLX6o87oMPPuCXX36xWnKB+fvowgsv5L///S+dOnViz549vPHGG0yfPp2RI0cCcNZZZzF48GCeeeYZZs+efZhXLRor6b4nGpV33nmHvn37csYZZ9C2bVvefPPNwzpfbDegnTt30rFjR/71r38xceJEevbsSVZWFjNmzMDj8QBml7HvvvuO7777Lq57UWFhIXfffTdnnnkmXbt25c9//nOl5qodO3ZkwYIFDBs2jG7durFgwQJOOeUUXn311bj99u3bR5cuXawWWbqus3jxYv74xz9y6qmnMnjwYF555ZW4Y0aNGsX06dNZvHgxAwYMoGvXrowYMYKffvoJMJv6Tp06FYBzzjnHuuaK3aBKSkqYO3cuf/jDH+jatStDhw7l7bffrvSczZ8/nwcffJAzzzyTbt26MXbsWLZu3Rp3DZMmTbK6E1x00UUsXbq02p/DhAkTuPDCC+PKrr76ak499VR8Pp9VNmfOHAYPHmxdc7T73qBBg9i1axfvvfdepSbNa9asYcSIEXTt2pUBAwbw7LPPVluPqPXr1zNu3Dh69epFr169GD9+fKUm0Dt37uSmm26iV69e9OvXjyeffJLp06fHdSmsqstbVV0YlixZwrBhw+jRowfdunXjoosu4l//+tcB6xlbl44dO/LRRx9x44030r17dwYMGMDChQvjms0PGjSI+++/n6uvvppu3boxffp0APbu3cvUqVPp378/3bp1Y/jw4Xz66aeVHqe0tJTJkyfTs2dP+vbty+zZs/F6vdb2cDjM4sWLGTp0KN26daNHjx6MGDGCb7/9ttK5li1bxuDBg+natSuXXXZZ3L+XA3W9jX3dRp/LBQsWWMtVPcfLli1j2LBhdO3alX79+jF79mzr3zWAz+dj5syZ/N///R+nnnoqQ4YMiesSKIQQDcmCBQto3759lS1Q7HY79913H5qm8cwzzwAwZswYhg0bVmnfm266Ke7994cffuCqq66ie/fuZGVlceedd7Jv3z5r+7vvvkvnzp1ZsmQJ/fr1Iysri40bN7J9+3ZuvPFG+vTpQ/fu3bn88ssrtc4GWL58ORdeeCFdu3Zl8ODBlT4b1OT9yO/3s3DhQoYMGULXrl0599xzWbx4sfV+N2XKFN577z127dpFx44deffdd6t8Dp944gmGDBnCJ598wtChQ63PKz/++COrV6/msssuo1u3bgwdOrTSZ7qafE5Yt24dEyZM4IwzzqBLly6cffbZzJ49O+5zTceOHXnttdeYPn06WVlZ9OzZk1tuuYW8vLy45/xAw1G0b9+eMWPGWIEUQEJCAi1btmT79u1x++7YsYO//e1vzJo1q9rzxcrIyOCaa66JC3c0TaNt27Zx57788ssr/aG4ffv2eDwe8vPza/RYB3LqqacCWENRPPHEE/zxj39kwYIFZGVlcdZZZ1FUVMT69etZvnw5Q4cOBczPFaNHjwZg9OjRcZ/V/vnPfzJs2DB69uxJv379uPvuuykqKop73J9//pmxY8fSp08fevXqxY033ljjboTvvvuu9XnnwgsvjPt3UVVXvPfee4/zzjvP2n/FihV07ty50uv4QJ9vHQ4HgwcP5umnn7bKop+vqvs3AfD111+TkJDAWWedZZU1bdqU008/vcp/01H//ve/Of300+M+fzmdTv79738zduxYAFasWEEoFOKPf/xjXD0HDBiw33OLo5+EUqLR2LBhAz///DMXX3wxABdffDGffvpp3Bt3bbjnnnto06YNixYtYuzYsbz99ts8+eST1rbOnTvTuXNn3nrrLbp06YLf7+fqq6/m008/5a9//SsLFiygZcuWXHfddZU+xDz11FNccMEFzJ8/n8GDB5OVlcVHH30Ut8/HH3+MYRhWk/uZM2cyf/58LrzwQp566imGDBnC/fffz8KFC+OO+/e//82nn37KjBkzeOSRR8jLy+Pmm28mHA4zYMAA6y8bCxYs4Kabbqp03T6fj5EjR/LBBx9w3XXXsWjRInr37s306dN56qmn4vZ9+eWX2bx5M3PnzmX27Nn88ssvceNs3X777WzatIl7772XZ555hs6dO3PnnXdWGU4A9O/fn/Xr11sfWvx+Pz/++CPBYJDVq1db+3355ZcMHDiw0vELFiwgPT2d/v37V2oGPXPmTM4//3wWL15Mz549mTdvHp9//nmV9QDYsmULI0aMID8/nwcffJA5c+awY8cOrrjiCqt+ZWVljBo1ivXr1zN79mymT5/O/7d33/Ft1Pcfx193p+1tx1nOInsQIASSsgmlQIFCS2l/LbSUUUYhhLIJFAoESCFsSICwV5mhFEpbRlsoZYUEAiSQkEWWM+x4akt39/vjdGdJlh07cTySz/PxuMdNnb6SZVt66/P93quvvsrbb7/d4nlb8uyzz3Lddddx5JFH8tBDD3H77bfj8Xi47LLL2LRpU7vOdf3115Ofn899993HiSeeyP33388dd9zR7P7Gjx/PnDlzOPnkk6murubkk09mwYIFXHzxxdx3331UVFRwwQUXOCXvtqeffppQKMTdd9/Nueeey0svveSM1QBw++23M2fOHP7v//6PRx55hBkzZlBXV8dFF12UEV6BVe142mmncd9995GXl8fZZ5/NV1991c5nr6m8/+STT84o9U/3+uuvc8EFFzB06FBmz57N1KlTee211zj//POdAU9vueUW/vvf/3LllVfy6KOP8v3vf5/bbruNefPmtbtNQgixM9XU1LB48WKmTJnSYvVJcXExBx54oBPonHDCCSxZsoQ1a9Y4xzQ0NPDf//6XE088EYBPP/2U008/HZ/Px913383VV1/N/PnzOe200zKCFF3Xeeyxx7j55puZPn06e+yxB+eeey6RSITbbruNOXPmUFxczO9+97uM+wO47rrrOP3003nggQfo27cvV111FUuXLgVo0/8j0zQ577zzeOSRR/jZz37mvC+6++67nW79559/Pocddhjl5eW88MILHH744S0+l5s2beJPf/oT5513Hvfccw8NDQ1MmzaNSy65hJ/97GfMnj0b0zS5+OKLneegLe8TtmzZwqmnnkokEuFPf/oTDz/8MMcddxxPP/00Tz31VEYb7rrrLgzD4M477+SKK67gP//5D7fccouz3+7iNW7cuBYfxymnnMJvf/vbjG1r1qxh+fLlGZVyhmFw1VVX8cMf/pBDDz20xfOlO/bYYzP+1wPU19fz6aefOuceN24cN954I8XFxRnHvfPOOx1aDbR69WoABg0a5GyrrKzkvffe46677mL69OkUFRXx+uuvU15e7lRbjRs3zqkWu+6665zXypw5c7jkkkvYZ599uPfee7ngggt48803+fWvf+38vD/++GN++ctfAtZ7hZtuuomNGzfyi1/8YptdRDdu3MjcuXO56KKLuO+++1AUhWnTprUY0r366qtcddVV7LvvvsyZM4ejjz6a888/H13Xmx3blve3xxxzDIsXL3aeN3t4hNZ+J1auXMmAAQPQNC1j+6BBg5zz5LJ06VKGDx/OE088wRFHHMG4ceM46aSTMsbwWrlyJXl5ec26Xg4ePJgtW7YQCoVaPL/YtUn3PdFjzJs3j+LiYmfQwJ/85Cfcd999vPzyy5x33nkddj+HHXaYE7AccMABfPDBB7z77rtceumlDB8+3BnLxv5H9+KLL7J06VJefPFF9t57bwAOPfRQfv3rX3P77bdnfKjdb7/9OOOMM5z1E088kauvvprKykr69+8PwBtvvMGBBx5IeXk5q1ev5sUXX+SSSy5xypQPPvhgFEXhoYce4pRTTqGkpASwBg589NFHnfaFQiGuvPJKvvnmG/bcc0/nH/iYMWMYMGBAs8f9yiuv8O233/L8888zYcIEAA455BCSySRz5szhF7/4hfNmo7CwkDlz5jj/sNauXct9991HbW0tJSUlzJ8/nwsuuIAjjzwSsMp3i4uL8Xg8LT7nYH2Dcvzxx/PZZ5+haRp77LEHn376Kd/73vdYt24d3333Xc5QauzYsXg8HkpLS5uVe19yySXOm4l99tmHt99+m48//jjnecAKuPx+P0888YTzXB5wwAEceeSRPPLII1x55ZX85S9/YePGjfz1r391vhHaa6+9OOaYY3KeszXr1q3jrLPOyggKKyoqOOmkk1i4cGG7xgMZN24ct99+O2C9BsPhME8++SS/+93vnMfSv3//jDeXs2bNoqamhjfffJOKigrA+nmcfvrp3HbbbRx//PGoqvX9xbBhw5g9ezaqqnLYYYehKAq33HIL3377LSNHjmTLli1cfPHFGd9Aer1eLrzwQpYtW5bxs7nhhhuc5+uAAw7g+9//Pg8//DD33ntvu54/+5x9+/bN2eXPNE1uv/12DjnkEOe5ARgyZAinn3467733Hocffjjz58/noIMOcp7vyZMnEwgEKCsra1d7hBBiZ7OrROy/2S0ZPHgw//rXv6ivr+eoo47ihhtu4G9/+5tTXfXWW2+h67pTTXLHHXewxx578NBDDzn/3/fee2+OO+445s2bx6mnnuqc+7zzznM+2FZVVbFq1SonDAKcivB4PJ7RpptuuskJQwYNGsQPfvAD5s+fz+jRo3n88ce3+f/o/fff58MPP+TOO+90/l4fdNBB+Hw+7rnnHk477TRGjBhBaWkpHo+n1a7gAJFIhD/+8Y9Om1asWMEdd9zBzTffzMknnwxAOBxm2rRprF69mjFjxrTpfcK3337LmDFjuOeee5xjDjzwQD744AM++eSTjK5nI0eOZObMmc76l19+yT//+U9nfXtCnWg0ypVXXonH4+FXv/qVs/3JJ59k/fr1zb5sbA/DMLj22msJBoPNgrB0Tz75JPPnz+eqq65y3ke0RzKZdJaj0ShLly7llltuoaCgIKO6L5lMcuWVV7Lffvs52z7++GPGjx/vhLb5+fnOsA/Dhw9n+PDh1NfX88ADD/Dzn/88o3vjyJEjOfXUU53X/B133MHgwYOZO3eu83tx8MEH84Mf/IB7772Xe+65p8XHYBgGs2fPZtiwYYD1nuj0009n0aJFfP/73292/D333MOUKVOcbmyHHHIIbre72ReM0Lb3t+PHjwes99d77LHHNodHAKvXRK7xZvPy8loNjWpqavjnP/9JUVERV1xxBX6/n7lz53LmmWfy4osvMnr06FbPDVZFvr0sdi9SKSV6hEQiwWuvvcaRRx5JNBqloaGBvLw8Jk6cyIsvvtjuK3u0JvuPdd++fTO6+WT76KOPKC8vZ9y4cSSTSZLJJLquM2XKFBYvXpxRAjxmzJiM2x511FF4vV7+/ve/A9Y3KgsXLnS+tfz4448xTZMjjjjCOXcymeSII44gFouxcOFC51zpgRnglFlnV6i0ZP78+VRUVDiBlO2EE04gFotlXBlj/PjxGd+g2OP32Pc1efJk7rvvPqZNm8ZLL71EdXU1V155Jfvuu2/O++7duzdjx47lww8/BKzndN9992X//fdn/vz5gFUlVVhYyMSJE9v0eGzpb1L8fj+9evWioaGhxeM//vhjJk2ahM/nc57v/Px89ttvP6d9CxYsYODAgRklygMGDGj23LXFVVddxWWXXUZDQwOLFi3ir3/9K88++yxAszfz22JXEdqOPvpoEokEn3/+ubMt+zU4f/58JkyY0OzDzQknnOB80LAdc8wxGW8s7TEKPv30U8D6QPOb3/yGmpoaFixYwLx585xvt9Mfi9vtzhjfwOv1cuihhzrn6UirVq1i06ZNzX6H9t9/f/Lz8/nggw8A6zX74osvcvbZZ/PMM8+wbt06Lrjggla/TRRCiK5gV3i63e5Wj7P/T5umSSAQ4Mgjj3Teb4D1JdgBBxxAnz59iEQifPHFFxx22GGYpun8rRw4cCDDhg1z/lba0v+X9OrVi+HDh3Pttddy5ZVX8vrrr2MYBtOnT282nlX6/2T7CzL7f3Jb/h/Nnz8fl8vV7EsgO6Sw3zO0R/p7k169egE4XzICzhdydjvb8j7h4IMP5plnnsHr9bJixQr+9a9/8cADD1BTU9Psf3uu951tfe+WSzAY5Nxzz+Wrr75i1qxZzvO5cuVK7r77bm688UYKCgq269yJRILLL7+cN998k2uuuYa99tor53HPPPMMM2fO5Ic//CGnn376dt3XuHHjnGnixImceuqpxONxpzo+XfZ7m3Xr1uX8AjbdokWLiMfjTihr22+//aioqGD+/PmEw2G++uorfvjDH2a87y0sLGTKlCnbfL2VlJQ4gRQ0veYbGxubHbtmzRoqKyubvbZb+nKyLe9vCwoKKCwsbNfV/ey/L7m0Ni5YIpGgsbGRRx99lGOOOYbDDjuMhx56iLy8PKcbcWvnBrYrvBS7BqmUEj3Cu+++y9atW3n55ZebjXEE8P777zvfzu0ov9+fsa6qaqt/ROvq6qiqqmqxrLqqqsq5UkV6X3+wvrk58sgjeeONN/jtb3/L3//+d/x+v1NhVFdXB7T8D2nz5s2tthtoc2BXX1+f80om9hu09H9027qvu+66iwcffJB//OMfvPnmm6iqyoEHHsiNN97Y4je7hx12GH/9618BK5T6wQ9+QL9+/fjrX/9KPB7n/fff55BDDsHlat+fre35ef7973/PeONus7+prK+vz/mtZZ8+fTJ+Jm2xdu1arrvuOj766CPcbjdDhw51rlCyrX/eue6/pfbasl+D9fX1DBw4sNm5cv3cs18fdhWRfcxXX33FDTfcwFdffYXf72f48OFOBWD6YykpKWn2xqOsrKzVsHB72b9DN9xwAzfccEOz/Vu2bAGs7oR9+/bltddeY8aMGcyYMYMJEyZw/fXXOz8PIYToDuz/o3bFVEvWrVtHXl6eE6qceOKJvPbaayxdupRevXrxySefON3EGhoaMAyDhx9+2PkAmc7r9Wasp/8vsa/0Zg+q/Oqrr+J2uznyyCO54YYbMq7WlX47+/+A/f+hLf+P6uvrKSkpada1yP7/lOvD/rbkqtzIfu+Qri3vE+zueM8++yzhcJh+/fqx1157NXsec93Xtt6ntGbjxo2ce+65rF69mrvuust5P6nrOtOnT+eYY47hoIMOyqhCMgyDZDK5zfdXDQ0NTJ06lU8//ZRrr702o3Iu/Vy33XYbjz/+OMcffzy33nrrdl8lO/39vtvtpry8vMXq5ezqmmAw2OrPEJreG9mvr3S9evWisbGRxsZGTNNs9ZjWZL/nsp+LXO/N7bHbsh9jrvuGtr9u/H5/m66yaMvPz885NEooFGo1zMzLy2PYsGEZF5rJz89nwoQJfP311856rmoru33bG5aKnk9CKdEjzJs3j4EDB3LzzTdnbDdNk6lTp/L88893WCjVXgUFBQwZMiSja1C6bX1Tc8IJJ3DOOeewZs0a3njjDY4++mjnH01hYSFglUDnKme1P/B3hKKiomZjP4AVqgFON8G2KCgo4PLLL+fyyy9n1apV/Otf/2LOnDnccMMNzJ07N+dtDj/8cObMmcOSJUtYsmQJ11xzDf379ycWi7FgwQI++eSTnKFCRysoKODAAw/M6GZps9+wlZSUNBs4FJoCkHTZ4wCkV90ZhsE555yD2+3m5ZdfZsyYMbhcLlasWOEEdO1RW1ubsW6PWdBaF7SioiLnZ5wu1889+/HZx5SVlTll/PaA60OHDkVVVd577z3efPPNjNvZb/LS36hWV1fvlCvQ2L9DV1xxBZMmTWq23/6w5PF4+N3vfsfvfvc7Kisr+c9//sOcOXO49NJLm437JoQQXamsrIx99tmHN998k4suuihndUEwGOSDDz5whjwAq4tZeXk5//jHPygvL8fr9TpVq3l5eSiKwumnn57zi7BtfcDv06cP119/PX/84x9ZunQp//znP3n44YcpKSlxxu/Zlrb8PyoqKqK2thZd1zOCKfsLhva8V9lebXmfMHfuXJ544gluuOEGjjrqKOfDtt0lcGdYtmwZZ511FrFYjMcee4z999/f2bdx40a++OILvvjii2aDy8+ZM4c5c+bwr3/9q8X3rJs2beKMM85g/fr13Hnnnc4V9tLF43EuvfRS3nrrLc4880yuuOKK7Q6koKnr2fYoLi7eZmBk//+vrq5m6NChGfuqqqoYOHAgBQUFKIqSM6SpqqpqNobWjrDDnOzxpnZ0kPiGhoZ2/V7sscce/O9//8MwjIy/LWvWrMmo+so2ePDgnBX+yWQSn88HWAPfB4NBampqMt7zrVmzhoqKCuc4sfuRGjnR7VVVVfH+++9z3HHHMXny5Izpe9/7HscccwzvvfdeuytUtlf2m79JkyaxceNGysrKGD9+vDN98MEHPPLII82+zct28MEH06tXL5566imWLFnidN2DptLc2trajHPX1NRwzz335AxB2trubPvvvz8bNmzI6OoF8Nprr+F2u1ss0c62YcMGDjvsMGc8hKFDh3L22Wdz4IEHUllZ2eLtxo8fT2lpKXPmzMHr9bLnnnvSu3dvhg4dyv33308sFmt1UM6OKvm1ryQ0ZswY5/nec889eeKJJ5yBzA844ADWr1+fMTB3XV1ds+cuPz+/2evys88+c5Zra2tZvXo1J598MuPHj3fezP73v/8F2l7lZnvnnXcy1t988038fn9GN4Rs+++/P59//nmzb9xfe+01ysvLGTx4sLPNbpftjTfeQFEUJk2axKpVq6irq+O0005j+PDhzs8j12OJRCIZg96HQiHeffddJk+e3K7Ha2vtZz906FDKyspYv359xu9Qnz59uOOOO/j666+JRqMcffTRPPbYY4AV9p566qkcd9xxrb5mhRCiq0ydOpXVq1dz5513Ntun6zp//OMfiUajGWP+aJrGj370I/7zn//wz3/+kyOPPNKp5MjPz2fs2LGsWrUq42/liBEjuO+++1q98tvnn3/OgQceyJdffomiKIwZM4aLL76YkSNHtutvaFv+H02aNIlkMpkx5pJ9DOB08d+Z3YDa8j5h4cKFDB8+nJ/+9KdOILV582a+/fbbDh1ywrZx40bOOOMMFEXhueeeywikwBomwe5tkD4B/PznP+fll1/OuEhMumAwyG9+8xu2bNnC448/njOQApg+fTpvv/0206dP58orr9yhQGpHVVRUsHHjxoxt2e/H9957bzweD3/7298yti9YsIDKykr23XdfAoEAe+65J//4xz8yvmRsbGzk3XffbfeQEq3p27cvgwYNanbRnLfeemu7z1lfX08kEmnXl9gHH3wwoVCI999/39lmD8tw0EEHtXi7ww47jG+++SZj8Pfa2lo+++wz53k68MADATJ+f+PxOO+++26r5xa7PqmUEt3eq6++SjKZbLEL249//GNeeuklXnzxxU5pT2FhIZ9//rlzidaTTjqJZ555hjPOOIPzzjuPfv368eGHH/Lwww/zq1/9qk1jPhx33HE888wz9OnTJ+OD+ahRozjhhBO49tpr2bBhA3vuuadTkj1gwACGDBnSrnYDvP322xx66KHNvu046aST+POf/8wFF1zAtGnTGDBgAP/+97+ZN28eU6dOdW6/LRUVFfTt25ebbrqJYDDIoEGDWLx4Me+99x7nnntui7dTVZVDDz2UV199lYMPPtgJaCZPnsxzzz3Hfvvt1+o3UoWFhXz99dfMnz+/zQFaLueffz6/+MUvOPfcc/nlL3+J1+vlhRde4J133nEG4T7xxBOd5+qSSy6hoKCABx98sFlJ8uGHH84bb7zB3nvvzeDBg3nllVcyqtHKysqoqKjg2WefpW/fvhQWFvL+++87V+Zp75gS//jHPygrK+Owww5j/vz5PPvss1x88cXNysfTnXHGGbz22mucfvrpTJ06leLiYl599VU+/vhjbrnllow39l999RXXXHMNxx9/PF999RX33nsvJ598MkOGDHEGr3zwwQdxuVy4XC7efPNN501v+mNxu91cffXVXHLJJeTn5zN37lyi0WjOq0K2RWFhIZ999hmffvppxhgLYP1+XXzxxVx33XVomsaUKVNoaGhgzpw5bN68mXHjxuHz+Rg3bhz3338/brebUaNGsXr1av7yl79w9NFHb1ebhBBiZzrkkEO46qqruO222/jmm2/46U9/Su/evVm/fj3PPfcc33zzDTfffHOz7scnnngijz32GKqqNuumZ19U5dJLL+WEE05wrrL3xRdftPr3eezYsfh8Pq644gouvPBCevXqxYcffsg333zDaaed1ubH1Jb/R4ceeiiTJ0/mD3/4A5s3b2b06NHMnz+fhx9+mJ/85CfOYNaFhYVUV1fz3nvvMWbMmBYDl+3RlvcJe+21F3PmzGHu3Lnss88+rFmzhoceeoh4PN7u/+01NTWsXbu22dih6W666Sa2bt3KDTfcQDAYzLhysT3Id0uVR717987Yt3btWmpqapyxru69916+++47LrzwQlwuV8a5PR4PY8eO5Z133uFvf/sbRxxxBPvss0/GMdB0QZpNmzaxadMmZ31nOeigg/jzn/+cUZVth4PvvvsuRUVFjB49mnPOOYfZs2fjdruZMmUK69ev55577mH48OH85Cc/AeDSSy/lrLPO4pxzzuGUU04hkUgwd+5c4vG4c9GAjmBfme+yyy7jj3/8Iz/4wQ9YunSpc7Xt7Qla7bFnDz74YMAKGFesWMGgQYNarE7ff//9mTRpktPjobi4mPvuu4+CggJnYHWwLgoQj8cZO3YsAKeddhqvvPIK55xzDhdffDF+v58HHngARVE466yzAOszwk9+8hNmzpxJLBZjyJAhPP744zQ0NLQ6aL7Y9UkoJbq9V155hREjRjBy5Mic+ydOnMiAAQN46aWXOuVbmVNPPZXFixdz9tlnM3PmTH70ox/x7LPPcscddzBr1iwaGxupqKjg0ksv5cwzz2zTOU888USefPLJjCud2WbOnMlDDz3E888/z6ZNmygrK+PYY4/l97///TarsNJNnjyZAw88kDvuuIOPPvqoWTc6v9/P008/zR133ME999xDMBhk6NChGVegaav777+fO++8k3vuuYfa2lr69evH1KlTM642k8thhx3Gq6++mhHM2aHUtgacPvPMM7nllls466yzePzxx9vV3nSjR4/m2Wef5a677uKKK67ANE1GjhzJ7NmznSuleDweHnvsMW699VZmzJiBy+XiZz/7WbM3WNOnTyeZTHLrrbficrk49thjufTSS/nDH/7gHDNnzhxuvvlmrrrqKjweD8OHD+eBBx7glltuYcGCBRlXstuWiy66iPnz5/PCCy/Qr18/rrvuuow3ELmUl5fz3HPPcccdd3DTTTeRSCQYPXo0c+bMaXZlmAsuuIDFixdz3nnnUVBQwG9/+1umTp0KWG/25syZw2233cZFF11EXl4eY8aM4ZlnnuHss89mwYIFTjeS0tJSLr30Uu68806qqqrYe++9eeaZZ5qVz7fVeeedx5w5czj77LNzjvHxs5/9jLy8PB555BFeeOEFAoEA++67L7fffrszfsmNN97I3XffzWOPPUZVVRVlZWWcfPLJXHTRRdvVJiGE2NnOOOMMJkyYwJNPPsmtt95KTU0N5eXlHHTQQdx8881OQJNu9OjRjBw5ktraWg444ICMfQcffDCPPvoo999/P9OmTcPtdjNu3Dgef/zxVq/Y5fV6eeyxx5yr1jU0NDBkyBBuvPFGTjrppDY/nrb8P7KvPnzvvffyxBNPUFNTw4ABA7jkkksyutOddNJJvPfee84Xbdt6/9EebXmfcO6551JbW8tTTz3F7Nmz6devHyeeeKLT/oaGhjZ/2ffuu+8yffp0nnrqqZwVxXalCZCzq+SkSZN4+umn2/z45syZw1/+8heWLVsGNFXq3Hfffdx3330Zx1ZUVPDvf//bOebf//43//73v5ud0+4a+NJLL3H//fe32lWwIxx11FHMnj2bL7/80qkWHzFiBMcffzzPPvss77//Pn/729+cEPWZZ57hhRdeoLi4mGOOOYbf//73zhd6BxxwAI8//jj33nsvl1xyCR6Ph/32249bb7212UD+O+pHP/oR4XCYRx99lHnz5jFixAiuueYarrnmmla/YGzJf//7X/baay9nHLolS5Zw2mmnMXPmzFZ/N++//37+9Kc/cdttt2EYBvvuuy933313xvhwN9xwAxs2bHB+3kVFRTz33HPMmjWLG2+8kUQiwb777suf//xn+vXr59zuxhtvpLCwkIcffphwOOz8jUmvzBe7H8Xc3pH0hBBCZLADpPa8+esI69ev5/vf//4232QIIYQQQnSlU089lbvvvjvnxXU60nnnnUdJSQkzZ87cqffTkf72t78xduzYjC/p3n33Xc4991z++te/tuvCK+FwmEMOOYRbb73VGfBeiO5KxpQSQgghhBBCCLFTffLJJ0QikRavKNeRLr74Yt56660eNTbka6+9xtlnn83rr7/OggULmDdvHn/84x+ZNGlSu68E/PzzzzNixIhmVe9CdEfSfU8IIYQQQgghxE41YMAAHn300U4ZbmPUqFGce+653H777TkvCNAd3Xrrrc5wIDU1NfTq1YtjjjmGadOmtes8NTU1PPHEEzz99NNdOuC8EG0l3feEEEIIIYQQQgghRKeT7ntCCCGEEEIIIYQQotNtdygVj8c5/vjj+eSTT5xt69at4/TTT2efffbh2GOP5X//+1/GbT788EOOP/549t57b0477TTWrVu3/S0XQgghhBBCCCGEED3WdoVSsViMSy65hOXLlzvbTNPkggsuoFevXsybN48TTzyRqVOnOoPLVVZWcsEFF3DSSSfx8ssvU1payvnnn09bew+apkkwGGzz8UIIIYQQIpO8nxJCCCFEd9LuUGrFihX8/Oc/Z+3atRnbP/74Y9atW8eNN97IsGHDOPfcc9lnn32YN28eAC+99BJ77rknZ555JiNGjGDmzJls2LCB+fPnt+l+Q6EQEydOJBQKtbfJQgghhBACeT8lhBBCiO6l3aHU/PnzmTx5Mi+88ELG9i+++IKxY8cSCAScbRMnTmTRokXO/v3228/Z5/f7GTdunLNfCCGEEEIIIYQQQuw+XO29wSmnnJJze1VVFb17987YVlZWxqZNm9q0v8uZBlR/AkYc3PngKgB3AbiLQfOBXE5TCCGEEEIIIYQQosO0O5RqSSQSwePxZGzzeDzE4/E27e9y3z0LH52We5/qAXeRNXnLwFsO3l7gKwdfPwgMSE0V4O8Paoc9rUIIIXog0zQxMZ1lABMzYzl737aO3dbx6esdtW17trf1Ntu6Xa799jGqojKwaCAu+X8rhBBCCNGjddi7Oa/XS11dXca2eDyOz+dz9mcHUPF4nMLCwo5qwo4pmwTlB0F4AyRDoIchGQEMq3oqVmVNwRWtn0fRwNc3FVINhvw9oGg0FI6xAixVs45paVJdoGz3RRGFEKLbswMb0zQxTMNZTp8bptFs247MgRb32W0wDAMDo8X2ZbTVNJuOTS2nP7Zcy+mP325Prjlm8yAn13m2db7sc7a2DYWWt6VtVxQlMyhKW0zfp6BktDX7dq3tV1J33NJ+wzTwuXz0CvSiwFuAEEIIIXoW3TDR1J7dE2lXeAzdRYeFUn369GHFiszAprq62umy16dPH6qrq5vtHzNmTEc1YccUjoIf/M9aNnQwE6DHIVaTCqS2QKwaIptS61shvtXaFquGeC3E68BMQmSDNW39JPM+XPmQNxgKRkLBKCgaZVVcKSpgB1OqFUypHlC91lzzguJKBVapSdUANUeopXXyEyeE6CnsYMUJYVLLdgCUva+1benruqFjYKAbOrqpW9tM3dluGiZJI+mEOOnhDtC0bqUxuQMlOwRKOwYTKzjJmjuBR4592ccoqa7ZdhCiKAoKirPd3pd+XFv3pW/Ldd7s+23tHNnHOPtydC1v6fhcx/ZEcT1ObbS2q5shhBBCiO2kqQoXPf85K7YEu7op22V473zu+cWErm7GLqPDQqm9996buXPnEo1GneqohQsXMnHiRGf/woULneMjkQhff/01U6dO7agmdBxVAzRrLClPIRQMaX6MHgcjCskwJBpTAVUdRDZCdCMk6qywKrQWgiuteTII9UusyebrB6UTrUqt0gmgBazKrGQE0FMBmZ5154o1xpUdYqFac0VNBVZuUNypYMudmlIVWHZ45QRaOeYZ55OqLSE6ix3Y6KbuhEG6kbactj3XlNSTJI0kSTOJYRgkjSS6qVvbjGTzsAkDTDKCKRPT2e5UqmRX0WStpwcu6XNVUTOCkex1TdGahTEtzXMdI4QQQggheqYVW4IsqWzo6maIbqDDQqlJkybRr18/pk+fzvnnn89//vMfvvzyS2bOnAnAT3/6Ux599FHmzp3LlClTmD17NgMGDGDy5Mkd1YTOpXmsyV0I/r7ACNCjVkAV25oKphrBNK1gS/FAeA00LIO6r6wpuNI6rvJv1oQKReOgz+HQ9wcQ6J/7vk0TMKzB2e0JPbWchGQstZzahpG6DTSVCtirdhilWPeP0rRuB1ROhZaWCru0tMqtXEGWRvNgK9eyfb9C9Gx2hVD23A6VnKqhVLCU1JMkjARJI0lCT82NhHOcaZro6E1VRanKI7urWLMuVCmpuMYJf1RFtZaztmmpikp7f/qyHfioEkgLIYQQQgghdrIOC6U0TWPOnDlcc801nHTSSQwePJjZs2fTv78VrAwYMID77ruPW265hdmzZzNhwgRmz569a33brfmsyVcOBcOtLn2xagivh2Qt+HpDwQgYcIJ1fDIItV9C9UdQ/TGEVkP9V9b07X1WQNX3B9D/GKubn01RcLr77ShTtwIrU8cqjbBDrLSwKxnLDMIwm25nNcg+Wea57aDLCaPS19NDL3cqoLKDLndm4NViqJVrPT0Qk9BLtI0d+iSNJLqhOxVG6cv2voSRIKbHSCQTxI04Cd0KkwzDQMeap1c1YaaNh5PWdcwOgXJOqopX8WZsy640EkIIIYQQQoiebodCqWXLlmWsDx48mGeeeabF4w877DAOO+ywHbnLnkN1pa7OV26NIxXZCKE1VkDlygNPqTXGVPmB1gTWeFVV/4NN70DNwqauft/eB70Ph0EnQ+l+HRu0KFoqU9oJVzAy00Os9HlLoZcdkKVVdylKWtZlDwpjtx0yQq42VXqljc3lDCq/rYqutuwT3YkdJtmTXZWUPkWTUWLJGDE9RlyP56xuMgzDCZHsMYpUrEojOyzSFGvZrbmdIMnepskYb0IIIYQQQgjRIrmWcmdwBaBgmHVFvuhmaFxujTHl6w0uf9Nx/r5W8DToZKvCatO/oPKfVuXU5n9ZU2CQVWnV90jrfN3Zzg5snOqtrO6M9jZTp+VKL3s5V7vTgjBFwUolsoMvO5iyQzAtVfGVNhC9M75XepDVQvVXrm6PGYGbsLu6JYxERpe3pJEknowT1aNEkhFiyZgTPOmGnjH4djpN1dAUDU3VcKkuNEXDrboztksXNiGEEEIIIYTYeSSU6kyaF/IGWV3xGldaY0wl6qxwKrsrnrcXDP4/a2pcDmvnQeXfIbwWvr3fmgpHQZ/vW1388gZ2yUPqUk43xp14H61We9nhVjI1OH2InOFYq48hrWujHURlV3spqtWlUdHSujXaA9hrzcOsZl0Yc43lZd9P1wde6WFTXI9nBE/hRJhIMkIkEcmsdDKTmEbTINwKihMs2SGTT/OhuZsCJ+n2JoQQQgghhBDdi4RSXcEVgOI9rcqoxuUQWg/+PtZ4VLkUjIBxV8GoC2HjW7Dpbat7X8Mya1o+B4rGQ8WxVkDlKe7Uh7NL2+nVXrnG6Uqv/tLBSACRzH1O6AVpAxVlhmDZXQ+bVXilXbGxWaVX+pheOQazz9m1sXkAppsGcT2eMSWMBNFklFA8RCQRIabHSJpJ58px9gDe9tXZXKrLmbxurxUySRWTEEIIIYQQQvR4Ekp1FUWxxptyF1nBUnAVuAvAU9TybVx5MPAn1hSvg83vWuNPbZ3fNED6N3dA+cEw4MdQfkDzCizRvWSEXu6OPXdGxVaObo5O4BVtXv3VpiovhaQJcSNJzNCJ6wZxI0lUTxLWY4T0ONFkkgQmSdMkaZiYigaqiqK6cWte3Jobl+IlT3PjUt24PF4U1a70UrLm6V0Z7UCue1R7CbG7sqsXs6sc7QDaroS0LwrgHJvVDTe7S26udae7rh5neOlwDhl0SFc/fCGEEEIIsYMklOpqmgeKx1mBU+NSiMbAW77tD9qeYhj4Y2uKVcPGN2HDG9D4LWx515p8faDiBGsMKn+/nf5QRDfTAVVepmmSMJLE9DgxPZGaxwnGIwTjQSJ6jKSeIK7H0A0dMFJd6VRcioJb1QgoGm5Vw+VSrJe1aQAR0COQKhLL3X5wwihSy04IZVd72dVfWlZll2Zt01xN3TxzDYLvdAFN24eatV+I7scOZ+J6PGPA/pgeI56MO+v2toSeaDomGXO6y8aSsYwqxoyqxlSY5CynhUn2stHS2Hw72YLKBdx51J1dct9CCCGEEKLjSCjVHSgqFAy1gqn6JRDeAIH+bQ8UvL1gyKnW1LgC1r8GlW9Yg6qvfBhWPmJVTw36OfSavHO7o4keKaEnieoxoqnQKZKIEUyGaYyHiRsJ4nqCuG51rVMAl+rCrbrwaG7yXT5cagmujr7SnFPdlTaeF+ldGM2mii8z3lQJRtZtWiv4cgIqexD7rLArI/BKD77sweyzbufMswfHVzPvK1cVmOjRTNN0ruoYSUaIJqPNplgyRlRPW05GiemxjP0xPZYxj+q5t+um3tUPOSeP5sGtunFrbtyqG4/mQVO1jO0u1YVH9TjL1t+Tptu4VJezL9c6QP/C/ri1Dq4uFUIIIYQQnU5Cqe7E38e6Gl/tFxCuhEBF+ys1CobDmEtg1FTY/B9Y9yrUfApV71tTYCAM/CkMONHqLih2G6ZpEjcSRJIxokkrgAomwtTHgk4YldCTGJioKLg0Fx7VhUd1E/D68Kjuzh0sXFF37iD20LxbI1njdhnJ1HHbCLwUcodfTrVXKqhywi8lM/yCpuqu9GovZ8wvNSvkygq4lFzBWNZtJAADrAqjSCKSMYh+OBl2toUTYaLJqLM/ezmaaAqd7NvboVJXBUV2+ON1eXGrbrwuL17Ni0fzWNs1Lx6XxwmCfC6fsy/7ts52ze0c3+q2VJjk0TyddkGBuB6nNlq70+9nR23evJmbb76Zjz/+GK/Xy7HHHssll1yC1+tl3bp1XHvttSxatIj+/ftz9dVXc/DBBzu3/fDDD7nllltYt24de++9NzfffDMDBzZd0OSJJ57g0UcfJRgM8sMf/pBrr70Wv9+fqxlCCCGEEN2ahFLdjbsQSvZOBVMbti+YAlA90O9oawp+B+tehg2vQ3gdLLsbVjwEFcfDoP+D/CEd/CBEV0voSSJ6lEgyRiQZoyEWoj4RJJqMEUuN1QKgqRpezYNHdVHsLej84KmrOcHXThp7zQm8oCn4gmYVX5ipAMxMC8BSx7Wl4svWrMuj3W0xRwCmuKxjVXdW9VfWwPhK9rJd9aU1P2YndHk0TZNIMkIoHiKUCBGMBwknws56KBFywqRQ3FoOJUJNIVMyTDgedoKnmB7r0PbloikaPpev2eR1ea255s3crnmdIMk+zlm296W22esezYPP5cOtutE6ukpR7DDTNJk2bRqFhYU8++yz1NfXc/XVV6OqKldccQUXXHABI0eOZN68ebzzzjtMnTqVv//97/Tv35/KykouuOACLrzwQg455BBmz57N+eefz2uvvYaiKLz55pvcf//9zJo1i7KyMqZPn86sWbO47rrruvphCyGEEEK0m4RS3ZEdTNV8vmPBlC1/CIy5DEacD5X/gLUvWAOrr33JmnodaI07VX5wy1cAFN2SYRpO8BRJRgklItTFgoSSEaJJa8wXsLrbeTQ3Xs1DoScPlyq/+p3CGbMKdlrwla4tXR6dACyaFnilX82xFQpNYRTZFVh2t0R7fC4NA5VQMk5jIkZQj9GYiBBMhK15PEwoaa0H42GCqTDJWg8RSoQJpcKmnTFukaZo5Hny8Lv8+N1+Zx5wBTLX3QF8Lp+zbi/bgZJ9rLPu8ku3MsGqVatYtGgRH3zwAb169QJg2rRp3HrrrRx66KGsW7eO559/nkAgwLBhw/joo4+YN28eF154IS+99BJ77rknZ555JgAzZ87koIMOYv78+UyePJmnnnqK3/zmN0yZMgWAG264gbPOOovLL79cqqWEEEII0ePIJ9Puyl0IpROsYCqyAfw7GEwBuAIw6Kcw8CSrS993z0HV/6D6Q2vS8qDPFOh/DJTuBxJcdCu6oRNORq0pEaUhHqIu3kgkESNmxDFNE0VR8WkevJqbUl8hHvlwvHvZCV0ek0aS+niIxniIhliQhniQxniQBntbIkRjPEyjM7dCp8Z4hFAyitmmEq9tU1HIc/vIc/lTcx8Bt588t5+AKzV3B6xtrgB+d4A8Tx6B1H6/O0CeK0DAk0fAHbCCozZ3e9w5VWBi11VeXs4jjzziBFK2YDDIF198wdixYwkEAs72iRMnsmjRIgC++OIL9ttvP2ef3+9n3LhxLFq0iP3224+vvvqKqVOnOvv32WcfEokES5cuZcKECTv3gQkhhBBCdDBJHbozdyGU7AM1n0Fsi3U1vY6gKFA2yZpC62D9q9bV+6KboPJv1uQphb5HWt3/iveSD2OdzDANwokooWQkVf3USF0sSFSPEdet6ie36sLn8pLv8VOmFaHu5mMFidbphkFjIkRdLEh9vJH6WJD6eDA1D6WWG2mIh6iPh2hIBU/hZHSH79ujuinwBMhz+ylwB8h3B8h3+1PzAPme1HJa4JRvz11eAm4vfs1t5W0ZlWBp69vs8hgFIwrRGoiSOQ5Yzis9pg9an1UF5gRXLQ1+nzV2WPa50ucZY4ypWcfkuvKk6AkKCws55JBDnHXDMHjmmWf43ve+R1VVFb179844vqysjE2bNgG0ur+hoYFYLJax3+VyUVxc7NxeCCGEEKInkVCqu/MUQfE4K5iK14GnuGPPnzcQRl0IIy+Aui+tcGrj2xCvgbUvWpOvH/Q7ygqoCkbIB6OdwOp6F3UCqJpoA1E9RkyPA1iDE2ue3XPcJ5FTJBmlJtpAbawxFVo2UhtroC4WdOZ1sUbqUgFUQzy0Q1VL+e4ARZ48Cjx5FHryKHAHrHlqOX17gSdAvjsvNffj1Twd+Mh3gm11e7SPATAStDj2l9nO5zfj6o/QFFLRcojljOWVmqtpIRkaqHZYBk64BrkDr5wBWvrYY9nVYultEe0xa9Ysvv76a15++WWeeOIJPJ7M3wmPx0M8bv29j0QiLe6PRqPOeku3F0IIIYToSSSU6gl8vaFoLNQusgYlduV1/H0oqlWVVbIPjL4Uts6Hjf+Eze9CdCOsftKa8oakBlA/CvIGd3w7dgOGaRBKRAgmIjTEg2yN1BNMRogkoximiVt14Xd5KfTk49UkgNpdJA2dulgjW6P11MYarHm0gZpYQ/N5rNEJLNsrz+2n2JNPkSefIm/mvNCTT5EnjyJvAYWePIo8+RR4AhS489DUXbgSrzOu9JgtYxD8rOovZz1tbodlTihmH9fOQfGzrxSZMxhLD6DACqZS+1oLx5wqMi2tamwbwZhzH+nhmH2fSlO70m9rJtsfAHahWbNm8eSTT3LXXXcxcuRIvF4vdXV1GcfE43F8PmtMR6/X2yxgisfjFBYW4vV6nfXs/TKelBBCCCF6IgmleorAQNAjULcE/C7QvDvvvlQXlB9oTXrUGndq45tQ9QGEvrOu3LfiISgYCX1/AP1+AIEBO689PZxu6DQmrIGc7VAhlIgQSyZQFQWfy4vf5aXYU7Brf/DfDZmmSX08yNZoPdWROrZG69kateeZU30s2O5KJq/mpthbSIm3gBJvAcXeAoo9BRT7UnNvAcXe/NS8gCIZ5L77yBgEv4ukh15AU2WYvZ4VnDULx+xzGE23aW3Q/OxALGNfdrdG+wZq07IdVhkJSMZBnwQUtPdRd6oZM2bw3HPPMWvWLI4++mgA+vTpw4oVKzKOq66udrrk9enTh+rq6mb7x4wZQ3FxMV6vl+rqaoYNGwZAMpmkrq6O8vLyTnhEQgghuppumGiqfGktdh3y6aSnUBTIH24FU40rrRCoMz5caj5rbKm+R0IiCFvehY1vwdZPoPFba1o+GwrHQN/vQ5/vW10Cd2O6oTtVUDXRRrZG64gkY8SNBJqikuf2U+QpwBfo5l2aRIvssKk6UkdVpJaqSB1V0VqqI3XWFLW2b43WkzCSbT6vqiiUeAsp8RZS6iuk1FtIqa+IUl/TNiuAstb9Lq9U0ont5wRB9oZOuEJkLhlVY5DZjdKWVgVmxCEZagrGuqn777+f559/njvvvJNjjjnG2b733nszd+5cotGoUx21cOFCJk6c6OxfuHChc3wkEuHrr79m6tSpqKrK+PHjWbhwIZMnTwZg0aJFuFwuRo8e3YmPTgghRFfRVIWLnv+cFVuCXd2U7XL4qHIuP1r+Z4kmEkr1JKpmhT9GwhqgPFDRuVfIc+dDxfHWFK+zuvZtegu2LoCGb6zp2/tTFVRHQJ8jIG+PXX78Ebs7XkM8RG2sgapwHeFklLiRwKVqBFw+ynxFuDX5desJkobO1mg9m8Nb2RKpZUu4hi2RGrZEaqmK1LIlXEt1tJaY3vYPxEWefMp8RZT5iujlL3aWS+1tvmJKfYUUSbWc2B21t2rMBKjbac3pCCtXrmTOnDmcc845TJw4kaqqKmffpEmT6NevH9OnT+f888/nP//5D19++SUzZ84E4Kc//SmPPvooc+fOZcqUKcyePZsBAwY4IdQpp5zCddddx8iRI+nduzfXX389P//5z6X7nhBC7EZWbAmypLKhq5uxXYaV74ShaESPJp+SexrNa10ND7ommLJ5imHgj60pVmNVUG36F9QsSKugehACg6DPFGsqGpt2NameLZyI0hC3BpLeEqklmAgT05sqoUp9hXg0d1c3U2QxTZPaWAObwlutKbSVzeEaNkes+ZZwDdXROnRnbJ/WFXnyKfeX0NtfQi9/Mb38xZT7Usu+YieAkteCELuXf/3rX+i6zgMPPMADDzyQsW/ZsmXMmTOHa665hpNOOonBgwcze/Zs+vfvD8CAAQO47777uOWWW5g9ezYTJkxg9uzZTlXkcccdx4YNG7juuuuIx+McddRRXH755Z3+GIUQQgghOoJimj1jtNBgMMjEiRNZuHAh+fn5Xd2crqfHoG6xNcZTZ3Xla42ZGm8kXgdb3rNCqq3zwUyrJvGWQfmh0PtQKNvf6hrYQySNJA3xEA3xEJvDNdTFGokkoygoBNw+Ai4/Ppd0x+tqSSPJ5nANG8Nb2RiqYmOoOiOA2hTeSrwNXX40RaWXv4Q+/lJ6B0ro7S+lt7+E3oHSVABVQrm/uPtfVU6IXVA8EaY2tJFD9jybgvz+Xd2cHkfeTwkhRM933L3v99hKqRP27se9v9y3Rz+Gcf0LeWPaIV3djF2GVEr1VJoXive0lsNrwL8TK6ZicYhErbm9HIqAbkAyCYYBRnq2ORD4NeT9DPQlYHwB8S8hthXW/8WaVC8U7Qu9D4G+h4K/785p+w4IJ6LUx4PUROvZEq4lmAyTNHR8moc8t58yX5GM59PJdMOgKlLDhlAVlc5UzcbUfEukBmMbObuCQi9/MX38pfTNK6OPv4w+gVL6BOx5KaXeIulGJ4QQQgghhBA7mYRSPZkTTJkQWgt5A1KX5N5B4SgEQxAMQ209NIYgGgNdt/arKrhcoKUu2W1/eM8OaAwP6HuDuRcocTCWgbkYlK/BqIPaj6xp2W3gHgQFE6F0EpTtC/588Ho6dTwqwzRojIepizWyObKV2qhVDaUqGnluH739JXLlsk4QSkRYH9zChtAWax7cwoZQFRuCW9gYriZp6K3e3qO66Rsoo29eGf0CveiX14u+gV6p9TJ6+0tlfC8hhBBCCCGE6Abkk1lPZwdTZhLCldYYU+0dtymZhIagFT5V1VjL0ZjVHc/jAb8PyvNA29HAqzdwiHXexFqIfAaxRWCsttZr1kLNX2CFB9QR4N4TiiZC6Ujw+cDvtYKqDgyrdEOnPh6kNtrAxnA19bEQcSOBV/OQL9VQO01drJH1wc2sa9zMuuBm1gW3sCFoLdfGGlu9rUvV6BfoRf+88tTUi36pef+8ckp9hai7yNhlQgghhBBCCLErk1BqV6D5oHg8GOnBVBuDlLoGWLEGqrZaXfA8HsjzQ0nRzqtSUhTwDLYmfgJGI8QWQ+xLiH8FRj0YSyC2BLa8AJsLwBwO6ijwjAZvPygsgIIAeL1NYZXP26Y2J40kdTEriKoMV9MQD6IbOn6XjxJfgYwT1EGCiQjrGjexpnETaxs3si64mbWNm1gX3ExDPNTqbYu9BQzI601Ffm8q8supyOvNgPzeVOSVU+4vla51QgghhBBCCLELkFBqV+HKg5K9oOZziGyCQL/Wj9d1WL8JVq6FaBx6l1ld8rqCWgD+A6zJNCG5FmJfQfxriC8FpRGUz4HPIQ7ES6BxGBh7gDkM1F5WOOVxQ34e5AdSQVVTWJXUoDbWyNZIHZvCW2mIhzBNk4DbR7mvRLpzbaekoVMZqmJN40bWNGy05o0bWdO4ia3R+lZv29tfwoD8PgzM78OA/N4MLOjDgDxrOd8T6KRHIIQQQgghhBCiq8gn8V2Ju9CqmKr5HKJbwNc793HBsFUdtWGTFeL0b+G4rqAo4B5sTRwPZhziKyC+BOLfQGIVUAssAHWBdRu1yKqkig2F8GCo7A2mgm4a1LmSVCsxNqoRGjwmptdDnj+fPoEiXD4vuN1NY2KJFoUTUdY0bmR1QyWrGyr5rrGSNQ0bWRvc1OoYT2W+IgYV9GVgfp/UvC+DCqwgyufyduIjEEIIIYQQQgjR3UgotavxllpjTNUuhHgdeIoz91fXwtKVUN8I5WVWdVF3pnjAO9aaAIwoJJZbAVV8mRVSGfVgLAQWAmCqHqLaQKrowya9hK2JMtxGAX2CLlxmAswQaFXgdlnVYX4vBPxW9z+3y3pOPB5reTcTjIdZ1bCBVQ0bWF1fyerU8qbw1hZv49U8DC7oy+CCfqmpL4NSc6l4EkIIIYQQQgjRkt3vU/fuwN8HjHFQswhUt9W1zzRhw2ZYugoMA/r36dQr23UY1Qfe8dYEViVVYhXx6BL0+DLcydW4iOLXVzKIlQwCcEFMKSKkDiCs9iesVRCiL0bSBYkk1Adha531HEFTWOV2Q8BnDfTudlldAV2p0KqHB1ahRITVDRtYUb+eVfVW8LSqfj1bIrUt3qbEW8Aehf0ZUljBkIJ+qeX+9AmUysDiQgghhBBCCCHarWd/shYtCwwCPQp1S8ANrN0CK9ZaFUFFBV3dug4RN5JsTYbZFPOxRR9BmMH4XC76aCFKzY3kG+vI09fjM6vwmvV49XpK9SWQsG4fVcoIu/oR9vQjrPUjrPZDVwKQ1K2wKh6HUBh0AxQTUMClWcGUy9UUWHncTUGVOzXvJoFfQk+ypnEjK+rXsbJ+vTOvDFW3eJve/hKGFg1gj8L+qamCPQr7U+zdNV43QgghhBBCCCG6BwmldlWKAvnDIVQHi96HLTqUlVqhVA9mmAZ1yTBbEg1siNXSqEfQUClyBeiVCk1MithKf7YyEQDVjBEwNpCnbyDPqCRgbMBr1uEzt+LTt1KqL3aCqrhSSETtQ1jrS8Tdl0heH6JKGSgaYEIyCQkdEgnYGrUGjDdN6/l2aaBpmRVWdljlce/UwMo0TbZG61let5bl9etYXreOFXVrWd1Y2eKYT2W+IoYVDWBY4QCGFQ1gaFEFQwsrpMudEEIIIYQQQohOIaHUrqwxCCujViBVZFpjJ/VQET1OdbKR9bGtbE2ESJo6BZqfCk/JNruOGYqXoDaUoDbU2aaZIfL0SvzGRgKpyWfW4DEb8OgNFOnLm26PRlTtRUTpQ1TtTcRdTtTbm5hSAs59m1aFVVK3gqutdU2BFaSqq1KBlc8Lfo91dUCXK7PCyqVt87lIGkm+a9jIt3Vr+bZuDd/WrWV53VpqY405j89z+RhWNJDhxQMZVjSA4UVWCCWVT0IIIYQQQgghupKEUruqTZtgyRIIBmHE9yC0AmJV4O3dbbqWbYtuGtQkgmxK1LMpXkcwGcOnuil15eFVd2yAdl3Jo8E1ggZGONtUM4rf2EzA2Izf2JSatqARJ2BsJsBmSCs6MnARVXsRVXpZc7WcqLsXMW8ZhpKXdm8mJA0rrEomoS4G1UkwU/sUpalLoDs18HqqyipEkuXRTSwLVbKsYR3fprrfJYxks8ekKgoD8/syonggI4oHMaLImvcNlKH0kJ+5EEIIIYQQQojdh4RSuxrDgO++g2++sbqSDRhgbVeHQ/03EK8Bb1mXNnFbQnqUqkQj62I11CSCoECR6megt3SnhiuG4iOkDSakDW7aaBp4zHr8xmb8xmZ8RhV+swqfUYVKkoCxiQCbMsIqsLoBRpUyYmoZUbWMmFJKzFNGzFuCqWQFaoYBuk5drIFltRtZunEjy2KbWZrYwrpkHWaOtuZpXkbmD2Bk0UArhCodwrDSQfjcvg5/XoQQQgghhBBCiJ1BQqldSTIJy5bBihVQUABFRU373PlQMAIavoFEHbiLu6qVORmmQU0yxMZ4HZXxWkJ6jDzVS19PES5l213adhpFJa6UEFdLqGd003bTwGPWpYVU1fiMKnzGVlyErW6AZgMYqzNOZ6KQUArYoBcyP+7ls6jJF7EYX0XqqUwEczaht6uAkb4+jPL0ZpSrnJFaKf3VQlQUMIAGDUIx2Lgq1TXQlzmGlX3FQJfWY6rkhBBCCCGEEELs+iSU2lVEIvD117BmDfTuDf4cA5p7i6FgODQsg0QjuLt+TKGIHqcq0cD6eA3ViUZMoFgLUObJ795dzhSVuFJKXC2lgVEZuzQzjM/YitfYis+sJpTYwteRLXwZqeezqM6CWAPrkg05TzvMDXt7vYz35zPGV8Yof18KPb2JK0XElWISSn5q0PUU06qysroH6lAftMazMlNXC1TIvGKg0zXQkxrLKm3SujD8E0IIIYQQYifTDRNN7cafMYTYDUkotStoaIDFi61xpPr3typkWuIrBzMJDcutQbpdeS0fu5MYpkFtMsSmeD2V8Voak1ECmoferkLcas9+SYb0GN+EN/N1ZANfhzfwTbiSDfHanMcO8wTYx+dlohf288bZ3xOiWAOIpaatYH5rLaaYqCSUAuJKoRVUqUUklELirkLi7kISSiEJpagpuLJDq0RqAPbaGFTVNI1npaqpKwZmhVZ2lZUnbSB2tfUB5YUQQgghRG67QhiyKzwGTVW46PnPWbEldw+F7u7wUeVcfvTobR8oRA/SsxMAkTmg+YABbat28fUFQ4fGFVZ3Li2w89sJJIwkVYlG1sa2Up1oRDcNijQ/g7w9cyDuuJFkeXQzS0Lr+Tq8gSXhDXwXq8bMMQrUQE8pYwMVjAn0Z0ygP6P8/cjXMsd/WmUm8Zj1eMw6PEYdHrMOr1mHx6hPba9HwXCWYV2zsazA7iKYT0IpSIVUBSS0fBKuAuK+AhJKAUkln4SSB6aSGoBdh1gcwpHUVQOtM6FpTZVWPi8EfOD1NIVWbrcVXEmVlRBCCCFEi3p6GDK8dz73/GJCVzejQ6zYEmRJZe5eC93dsPLOLygQYmeTUKqnSiZh5UpYvtwKDCoq2j5ekKJAoMKqogmuAo8K2s4bIDukR9kcb2BtbCu1yRAexdUhV9DrTIZpsDZWw5LwepaEN7AkvJ5vI5tImM1ToT7uIsYGKhgX6M+YQAVj/P0pdOXoTpnFVFzElDJilEGujMc0cJtBPGY9brPeGrfKqMedGr/KmjemgqtGPGYjUNny/aGQJEBCzbdCKm8+CSWfpJJHgtTc9JPUfSSTYDYkoKYuq2tgqpLK64WA1wqu7K6BHrd0CxRCCCGESOnJYUh5vneXqJQSQnQ/Ekr1RI2NsHQprFsHZWWQn9/+cygK5A0ADAh+Bx4FNG+HNdE0Ter1MJWxOtbHawjqUQpUHxWeEjSlDd3ATBM1lkCNxNAicdRoaoonUONJ1HgCJaGjJFOTbqDoOoppgmGCaaKkCpZMJfV4FQVTUzFVFVNTQVMxXBpm2mR4XJhuF9XuJIuUrXxJFV/pW1gc30SjEW3WzCItwLhAf8YFBjA2UMHYQAVl7u34ebSFolqVTxQCA1t43gxcZgi32ZgKqhqbTR6zEZcZQsHETQi3EQI2W4Omt3jfkHT7SHrySCqBVHAVIGn6SBo+kmEvyUaPtWz40BU/uivPCqZ8PsjzWcGVXVnldlv7emCFnBBCCCHE7qbQ7+rx1V7S9U10lF0lpO0uj0FCqZ6muhq+/NIaR6qiwqpU2V6KCnmDrIqp0Fpw+cFduEPN002DrYlGNsRqqUzUkTCSFGsBBrlLcQUjeGq24K5pxF3biKs+hLsuhKs+hKshhCsYQQtGcAWjaKEoitFaStJxYhp83g8+qYBPBsDHA2B1SfPjfAnYdxPsv1lj4lYPE+v9DEr4MAIGun8zhr8O3b8CPc+L7vei5/nQAz70QGo5z08yz4ee58P0uHZOIKOoJJUCkhQQoX/Lx5kGLsK4jSDuVEhlhVYhXGbQmhPCbQZxmWEUDFxEcZlRMLc2P59Gs+ouE4Wk6ScZ9qEHfVaAhRedAEk1QFLLQ/cWkPQVo3sK0H2FJL1F6P4icPWcKjohhBBC7Dzd5UOTsPTkai/p+iY6yq4Q0nanLrkSSvUk1dWwaBHEYtb4UR0Raigq5A8BV8CqmIpuAW9Z5hXe2iCmx9m6ZQNb16wksWEjeVsa2G9rGH91I94tdbi3NqAmcwyAtA2momD4POg+D4bPjeFxY3jdmB4XhttlVT65XJguqwIKVcFUFFAVrP5lpjU+kmmimCamYbDWE2NhYYiFBWEWFIf5qjBGPMfDHb1VYfIGhclrDb63HvbcAm4DrIGcIqlp+xguDT3fj57nI5nvQ8/3k8z3O/NkQaBpW0HatjwfaB0w4LiikiSfpJZPhL6tH2saaERTwVUIlxnCZYbT5mFcpC2bETTiqUqsMG4l3EJ3RCCamrLoeEiqAXQ1gK4FSLry0F156O58dJe1LXPyp839mKpnx58jIcQ2GaaBYZqAiWGamJiYqblhWssGJoZpOMummVoHTNNwtpmpv9cmJgoKJta/OdPuMgxYByQp0Fw9cixCIUT79fQPfiAVOkLsqnpySNudSCjVU6QHUn23ESK0l6KCv29mMOUpzd2dLxaHtZWwbiOsrSSxdj3G2g1oG6vpH020VpsDQKIoj0RJAYnSfBJF+SSL8qypMI9kQVMwo+f70P1eDO+OXfUtpMf4OryBr8Lr+Cq0nsXh9dQmQ82OK3HlsWeggj0DA9kzbwDjAhVNA5GbJmZSZ0k0jhpNoNldCaNW10ItEkNNzbVwFC0cs6ZQtGndXg5FUQwTNamj1gVx17XvDZapKFaQVZAKqVJza7K3BUgWprYVWttNzw5UHikqOgF0JUCM8rbdxEzgMiO4CKOlgiqXGUFLbbOWo7jMMBpN+zQSAGjE0Yw4GHWQJOMKhG1hKC50zY+h+p2gSld91rbUsqH5nO2Gs9+HkTU3FY90MxTdXnoQZAdEGcuYVgCUFhwZ2QFSRmBkWsPHYQdC9gUcrGVFUTBN61dDVVRUrL/TqqKgoKAo1mSvq6ioqoJL1XCpGqqi4lI1NDQ0VUVTVDRFQ1VVVOzbqiiQmqfOpSioZhIt0UieW77xFmJ30dM/+EmFjhBCtExCqZ6gqgq++GLnBFLp3IVQOBrC6yC4DjbXwto6WLUBvltvTZWbrTGb7Juk3dxUFeK9ioj1LSXWp4R472Li5cXEy4uIlxeTKMnHdO+8l5xhGqyOVvNVeB1Lwuv5KrSeldEtza6G51I0Rvv7sWdgAHvmDWDPwAAqPCUtf+uuKJhuF7rbhV5AKjbZTqaJGok5XRS1YARXKILWmJoHU9uCEVyNEbTGcNNyJIZimtZ6MAIba9p8t7rP44RWycIcwZW9XNC0zfRuf5BlKm4Sijs1/lXbKWYSjSiaGU2FVhFr3Qij6RE0PYxmpEIsJY5GzJorcTQlhkYcANVMoiYbgcbtfgzOY0FJC6q8qRDLi6F6W1hvWjZUr7OvabsnNXkxFRlXa1emG0ZaAJSqFEoLiExoVkG07YAInGAIxfn7pqSFQekBkapkLisoaIqKoqpoioJLcaEqVihkh0X2sr2eHg5lLzuhU9qyHUbZYZVz3x31WjfiVvWl/O4IIYQQQvR4Ekp1d1u2WGNI7cxAKpGwruS3dCksW2ZNy5dDJHf3tES+n4aKEhorStEH9sEY2JdYvzLivYt3auiUrTYZYnFoPV+F17E4ZF0VL2Q0L6np5y52wqe98gYy0t+36678pygYAR/xwHZc7TCppwKqsBViNYZxOfPUtoawNT5X2n7FMNCicbRoHG9VfZvvTve608Irf1M1W1p4pRf6SRakthcGMHw7VlVkKi6rW6HSjsHidR0SSUjqkEygJsNWpRVRNDWBpumoSsJaVhNoWhLVXiaOShzNjKOZMVQzZs2NKFrqtaRgohkRNGP7u2u2+HhR0kIqD4ZihVZm+jbVbYVZitsJtUzVnTre07SuuFPHejAVt3NbU7HP4U6FYB3Q/XMXYYdEJga6mRYapbqUZQdG1jH2PjPVrSy9kqipi5mSFtY4VT5Zgc22AiJNtUIda1nLqCDS0kIhK/xJry5SmoVF6XO7HUIIIYQQQnQ1CaW6K9O0rq63ZIm13pGBlGnCmjXwv//Bhx/C559bwVQ2jweGDCQxuIz6igI2DCqlsn8xZkkBJe58PGrnvXziRpJvI5tYHLa64H0VWseGeG2z4/yqh7GB/lYVVGAA4/MG0std0Gnt3KlcGsnifJLF7QhsTNOqyLJDqoawE2I56w1Ny/ZxalJHiyXQYvV4qtseZBkuLatLYcAJrLK7Gur5AafLJq72jWGWQdOsyW4DRRikKtpMA3TTCq4MHXQDEkZq3bB+F5w8QbHOoyhWexQF1WWgug00zUB16ahaEk1JopJAU+KoZiIVaiVQiVnrZhzViKEZMVQjhmrEU/O0dTMJ2IFXzAnAOoOhuDAVV0ZgZa8bitsJuEzVZYVbiiu1zdW0T3Fhqi7nOGebc15X2jGutH1Ny/Y6aDmDzKYQKHeVkeFUFdljGuGESeljFmGPVZQWHNnjFClp1TyKApqiZXQ50xQVVVVxKVYo5FI0p4LIrbqcoCgz+MkRGKWql7KDoQ6vIBJCCCGEEKKHkVCqO9L1psqlvDwoLt7xc0ajsGABfPCBFURt2JC5v7AQRo2C0aNh1CjMESOo7VfCRr2WDcG1BEPrKTCilLkCaJ5CYOdVW5imyfp4DYtTY0AtDq/n28gmEmbzgdL38JazZ94AxqfGghrqK8fVzkHad2mKkhqjy0+8X2nbbmN3McyuvEqrwNIasoKtxghqImmNlVUbxF3bvrGydL+3aUyxgkDasj3OWCrIyvNZoVZq3LFtjpWlqNZfubaEXnaAZYdXuoERAyMCSQMwFDBdoLjB9ANmZpClqdb9aQqoGrhd1uRzWVfJ1FRrfDRNA9VEVawKLhUdlQRqKuxSzUQqyIqjmjFUI4FirxvxVOgVb9qesW6HYgkUM4FqWOdSaLqSpWomwUyiGTlGmO8iuqJh4MJUNAxnalpvCrK05nPVBYoLVDeK4gLV+hkpqj25UtvT1nGjaNbx9nGq6k5b1lBVj7MNxWVd/EFNzRUtdZ9a0z5Fkyo0IYQQQggh2klCqe4mHrfCqFWroKQE8ttRFZPOrob6+GMrhFq40OoCaHO7Yd994aCD4MADYfBgUBQSRpLqeB3rI5vZ0vg1CSNBsbuAQWX7oiQaIbYZYjWg+ayB0TsgnNqaCPJ1eANLwlYXvCXhDTTozbtKFWsBpxvennkDGOevoMDl3+H7F1nSuhjG+5S07TamiRpLWGNgpXUptKuwtPRtjakxtOyxscAaJD4Sw7ulrl1NNTzu5lcvzPM5g+Un8+wrGvrQ7e151nbT48qs0LEDrJyXCmypAYY1xppphVhOsJWIQyRq/R7qeloPr6aKHUNRMLIDLVUB1QuuPCtIc2ng1kBzWfvcWuoYNS3kUq1ugJp1N4aqYCgKpmJ3TQOMBOhxFDMBRgwlFWhhxFGMBKSCLNVMohkJVFNHNRNoqblqJtHQ0UwdzUyimnpqX9LaZ+qoJFO3S6YmvWluJFBMHcVMoprNqzI1U0dDbxoyqcdSU+FVWnDlBFapUAs1K9zKPlbNsU9tYX9ryyqQagu5jkubk2ObffvsbYoGKFnH2cektqOmHa+2vp5+WyGEEEIIsduRUKo7qa21AqmNG6FPH/C1c9yh+nqrGurjj61p48bM/X37WgHUgQfCpEkQCDi7GhJBtsRqWR/dTF2iEZfiosRdiE/zNN3eUwTufIjXQmQzxGpBc4Mrn7aGUw3JCEsjlakQyppvTjTvHuZRXIzy92XPwADGtWUwctG1FAXD58HweUiUF7f9drqBFopaYVUwkhFeWdsj1sDv9v5gJDVIfMS6imE8gacmATXtH9DccGlpIVUqtEqfAqntAR96nteapy0bfo8VCqkAWuao/2mcwavtcYmc7mU6hm5gGDqmkcQwkhhxA9PUMYxUFzVDxzCsrmuYJoqiYmJYA1ybpnXfioaiKiiKiqpay6qioWoaisuF6nJlzlUNl8uHS3Ph1ty43G5cmguX242muqyrn2kuVE1LzVUUzbpda93R2jxekWmCmQQjCWbCCsyMRGqbPY9b+5ttz9rvnCd9nshct5eNBJh65jZnrre+bOqZ58tRtZl6VaWCyR26HMLuKyPwUrACK3ueHmQpUDgWDju4a9srhBBCCCF2mIRS3YGuW1VNy5db1UwVFVZ3n20JBq2r8i1YAPPnw7ffpioxUtxumDABvvc9qyJq6NCMb6PjRoKt8Xoqo1VsidUQNeIUuAL09/VGa6kbiqKBtxe4iyDRALFqiNVZH85dAVCaQqyGZIRlkY18E6nkm3AlS8OVrIs3v2KcgsIQby+r+ilQwbjAAIb7euPuxDGrRBfRVPTCAHphgHaNqmQYaOFYU3AVygys7DBLC0Wdqxq6ghG0sHXVQ8Uwra6GdUHcde3ramgzFUj6PCQCXhIBD8mAh4TfQzLgJRHwkvR7rO1+L3rAgx7wkgx4UXxezDwv+L3g94HXg+Zx41ECuBQVDRWXqqKhpQa/tsYyUu0xiGga2Fo1TVQTNANUw0QFVHvZMFEMUBMmasxaV820AbkVPRWuRKx1FauqRlWaqrYyuiWmuh7aFVwuV9PcrtrKmJSmuV0Rlr5Pc4PqAZfa86pkTBMw0oKr7CArbZtpZAVbRo79abe3gy0zaXUlxUjN026P3nQ7e5+ZdruM+0g71jRS5891vJF239u6Tfo2M/U6MjNv56ynnqs2Pa+psC815lqr6r5s23FCCCGEEKJbk0/9Xa2hwQqj1q6FoiLo1avlY6NRqwJqwQL47DNYscLqPpRu6FDYf3844ACYOBH8md3bDNOgPhFkS7yGDdEqGhJBXIqLYnc+vbU2jjkEoLrBWwaeYsx4A9Xh71hW9zXLwhtZFtvKsmhVzoHIASo8JYwNVDA20J+xgQpG+/uTp3nbft9it+Bc9Yz0Aa7TtvnA8HkxyzwYZmFqq5k2ALZ1LKlBrSF1bTQTtGgcdyiKOxTHE4rhjSRwh2J4wnE84RiuUAxPKIYrHMMdiuGKWNtc4RhaOIaa1FFMcEfiuCNx2LoDj1NRUPxe63c14LMmvw8C/tQ8bd1etqeAz6qotAOugA/yvFbw0172wO+GmeqWmNU1UddTFUr2cUbTsvUDs55hxbQSOzv/UkmFXGpaMJUKvOwAzA680oMvLS38UtMCrWbnUDP3Q9qxZAZh9vE7SlGAzAH2RSvsYMpMC67IDrjs15KetpzjGEzQY9aXIF11FVUhhBBCCNFhJJTqKuGwFUStWQORCPTrZ1U2ZYtGrcHJ33nHulpeJGuspYoKa2yoSZOsMCpHqGWaJkE97FRF1SYaiRsJCl15rVdF5RA3EnwXrmR5eB3LQ2v5NrSWFaF11CQach5f4S5mdKA/owP9GRPozxh/f4pcgZzHip7FDojsK54ZOUIk52poOcIis8UBhKw0Q0mrDFIU1co27EqhVHdRu2pIU9VUlZGWqjJSUxVG9pXQ7O5mqdsXpSqN7CusKQoaOY5LVSRldEczTYgnIBSGUCRtnloOp5ZzzsMQjlrjTYUiYBgopmltC0d3KNzK4PVYQZUvFValz9Mnv7f5Nnvyepqv+73tD2KcACsVcBnp66llO/Cy100zaxmcCi9nnrbNDr7soIv00CsrAEsPsOzwywnE1Kb19EBLIS1YIy0cSwu6lPT72sZxuxs7xFOgQ952GHGrG7kQQgghhOjxJJTqbNGodeW71autKqnS0swgyTStsOqjj6xpwYLMAcr79YODD7a65U2YAOXlOe/GNE0akyHqkkE2RavZGq8nYsQJqF5K3AV4VU/O29mSps6G6BZWhTawKrKBFaF1rAyvZ01kE3qO8VRUFIYE+jMqbzCj8gYy0teH0d4SCo04JFNBmuYFV+v3KzrGNquMmi0bOxQa2WMKKShpYY+CW3HjVjVcaWGRplgBkkvRnBBIAStcUprCouYBURsCo86gKFZA4/VAafH2n8c0IRZPBVKRtHnECq3CUSuEjsSagqxI6phILG09CtGYdayR+pnF4tZE+8fa2iaXBl4v+FLPQcZy+uQFr7tp7slxjMfe7raWvR5r3edN7XO3LcSxK7xMw8qpzLTQy9mXmpJGanypVNjVLARLnc++uqIdiCmkVYDZVWE0D6JyBWLpYVl6KKalz9OCMfsqjbmCNbLWs+8/O/zKtb+144UQQgghhOhEEkp1BtOEujrYsgXWr7cGJC8qgkGDrH0rVlhjQ33xBSxaBJWVmbfv3x++/3048kgYO7bFDw8JI0lDMkhDMsTGaDV1iUZiRgKv6qHQFcjZPS+sR1kX2cx3kUq+C29MzStZE9lEPMdVsgDytQAj8gYyIm8QI/MGMSJvEMMCFfhydcEzdUiGINFojUGVCFnjoqguazwZzUtHXMGvp7Arh6zlbYdFLVUd2RSU1LpdQWJvxQmJlLRQJzs0UtFwaXZlkYpLceFurcIoR2jUdJySVnHURYFRT6IoTVVIpUU7fj7ThEQiLaRKze319G3pUyTWfFssnlpOzWOxpsArqUMybFV9dQa3uym0skMsj9va7skxpW93uzK3uV2pZVfzbRlzV/P1XN3+TDNzMlJhVnoIZodkdgBmkArFaArCss9ln8epECMrIFPSf91zh2TOlDooI9iC5kEXWd0js8YVyzVWmKI2VZXlCuCy25GrbdnbaMNxZtKqrhNCCCGEED1ep4ZSsViMG264gbfeegufz8eZZ57JmWee2ZlN6DymCaGQFUZt2ADV1dDYaF1hr7LSCqK+/daaglkDLacPUH7ggTBsWM4gSjd1gskIjckQW+MNVCdqCSUj6BgEVB9FrgK8qpvaRAMrw+vZEK1KTVtYF9nE2uhmquN1LT4Er+phD39/9ghUMCxvAMMDAxieN5A+ntK2hw2KBu5CazL7gh61pkRjKqxqsD6UKVghlepOjRPSeUHVtqqKnCumpQVF7e2OpijWS8IOi7K7pNlhkeIERtlhkZaqMGpl0Ov00Km7VhmJnUdRUoHNTqhGNE1IJDNDq1jcCquc4CpudWvM2J81xe3lRNNyPGFN6ccYab9LiYQ1dTVVtcIsV1ZY5XI1X7bHwsrYlr5dAy1rnr7fpVm30dLWNa3p3OnjbaVvz1V9BZmBFzSFZnbwZQdjSb0pSMvel377jDDOfoJyhWdY1WVkHWMdmAqb7OUcQZp9vvRgymooaHE4YjK4C3bghyqEEEIIIbpap4ZSt912G4sXL+bJJ5+ksrKSK6+8kv79+3PMMcd0ZjN2ntraprBp2TJrvKjKSti0yZqqq3Pfzu+H8eNhr71g772tKZA57pJpmsSMOGE9SkiPUJtoZEusho3RajbHa2hIBAnqERqSIaritWyKbWVzrIZNsa3EjHirzS5y5TPE15ch/r4M8fVjiK8ve/j7099b5ozdk/FhJBbN3NbUyLTlHNucjR6gDNRiMGJAFBIRK6QywmAmMYxUDKQoGKoLw1QxFRVDUTBRMLPGKrICIqsSKTM0AmegaxNQUhUFaYUFoFgXHlMUFDOzykg1SRvHyBp/yIVVDeRKhTwuNFypMYxUcI5VFAXVbLqtEwyl6pucgMhMC4vscCm9EsLmVETYk709R7CU/bOxU7GM9eY32yHZ99Fsfwttyz5Hrgee6yYdkadtz3PgPI5t7G/tvpSsDSZZP5OsD/j2h3S7csXel6uixK7oyf6An/O2WNUuLVWsOO1VmqqPCvNbeIAdKJlsCqrs0CqeHmClgqp41pS9zV5PJJvWs5cTydRxyaZtydT2dIZhBXC0/ve027HHy3KlAjBn2d7uyhxLK2N/+nhbueY5ttmBWFuW06/O6Iy7lVpOHwTfrr5SU1eCNOJQoFk/JyFEq3TDRFOVbR8ohBBCdJFOC6XC4TAvvfQSDz/8MOPGjWPcuHEsX76cZ599tvuEUpGINc5TKNQ0BYPWtsZGa15XB1u3QlWVNa+utrrlVVc3H4Q8l+JiGDHCmkaORB82lODAPgSJEYwHaUiEqN76MdWVdVTHa6mJN7IlUcPmeC01iQbqkkHq9BD1eohGI9pKdU4TBYVyVyH93aX0d5dS4S5jkKc3A73lDPKUU+gKZH4IVRSri0nEGsvK6XKmKE1dyBTT6mWipAIgaFZBZIVEqWUltayYqSuhOYkQqKB4/JgeH/Zl0RUziWrqqEYcjDiqGUfFQDGN1GeVVHWQquJRXLhUD6qi4dLcuBQXmurCpbpwKS5U1ZU27lFq/CJVawqB1FRYlHZOFc0Z50hVVOsYeyBlJe15yhUIpR+T+glk7Ms4roV9OW7a7D62paW2bc+52mubp2xrpV2OcKQ7sdvTWtCWLtdxzjYzM8xNH+TbMMDQrQoXPXU1PHvcJN2+Ul7alN5tzKB5lUt6pUzqrpuupGdat0kPxuxVxQ531VR70/uQ0byrFWnLzbqTtXScvV9pGmy92f60Y5wKmhzn3BF2dZgdUMUT1nJSb1q2Q61cy85cT02p0MteTupNx6Vvy57retN5dL3pfHraue3tuV5femp/vBtUm3WkkgI44dyuboUQ3Z6mKlz0/Oes2BLc9sHd0OGjyrn86NFd3QwhhBA7UaeFUkuXLiWZTDJhwgRn28SJE3nwwQcxDAO1Iy7TvQMib/2dl675MTWuBIaCM+kK6GrmcjI16Qoke0OiHyRS2xJulbjfQ9zvJu51E/dqRLwaMbdK1AVRRSdqrCBsLCFixomvTMLK7W+3ikKxp4hevhLKvCWU+Urp5Suhl78X5f5elPvLKPOX4dJcmAqp0IjUskG9olDf1IciM3DBCrRQUkGNoqCgpb60VlPBkIqiqCiKgqZqKCi4VDeaqqKpLjRVQ1M0NNUKjDRFS4U81lxRmsKi7ElJC5JUI4lqJlBN3Qqs0FOhVcK6PLiR6hZoJlPjt+hpk5H2wNLGX3I+yKppczt4Sm2zP/imzzO2k2MfaetkHStEJ8genyh77CMz7Zj07lkZgVaOddOkaTDxHPdhpIVlhgF66ng9LSwzTCskyR5cHDN3iOY8nvTtaWFaRte09O00racHazbFvs/UilPpl6tEMW1RU60rGbYWTm9rW3qwlr6eHtBl3zZjnqN9ipJ6nvWmIEo3rLDKSJvbAZa9TU/brmcdp5tW+GX/zDLOY7S8LT0oTb+/7NvpacekB6w596eHsDoM7W9Vee2mdqvhEMQOW7ElyJLK3FdJ7u6Gled1dROEEELsZJ32jq6qqoqSkhI8aeOd9OrVi1gsRl1dHaWlzQfh7kzPVv+Ls4/viG+SDSCamtKYQCunVxUVv8uHT/OR584j4AmQ584nz5NHobeQAm8Bhd5CCr2FlPhKKPGVUBoopdBTiEt1WeMSpQIeO8yxA6H0ZU3VUFFxpQKj7FAoPQjKFRi1tt8eVLtLGXoqmEo2hVJGekClA3ZoZYCeAJKpKpQEkNpuh1t2NYi9bH9whqb1jIqRrOX0D8npgZgzb8m29mcd06zrXPbt00O59M3Zt2trG1o4X/a5u1x3aEO2btImOzfd4e8D0gNYbftO0VJglhFE5TiOHMe2FFjlOp7089L8ftLPQ479RnobU38XMraljjfsvyW0sC/rdhn3ReZ605PWfDH7tgpN/+U96cdrWD8rdwvnzwoEW5Kry3b28rbant09V8nan6sa0TQgPz/34PO7iV1+OAQhhBBC7DY6LZSKRCIZgRTgrMfjXT9Gx7E/upTfvF3Fxvr1aKoLxQ5sUhU9mqKhpqp/1NRVylRVxa26ncDHDno8mgeX6sKtuvFoHnxuH36XH7/bj1/zk+/NJ9+TT8AdwO/yk+/Jx6t5UdWmYCc95Mmep+9raXm3pdoftnJcCXB7mGldmjKCqexAKscypN02Pagia39btmctt/QBMtd6zg+VLX3Q3EYQ1tZuaq2dp8VztOXc23Peduqo81gn68Bz7Yju0o6daRd8jLnCsPR5xjZa2dfC347s27bl2GY3yD6uhcfQ6vlyrLe4LTXX3FBY1nz/bqBHDIcghBBCCNFGnRZKeb3eZuGTve7z+TqrGS3qX9CfJ056qqubIbobexBoIYQQohvo7sMh7EpkkHAhhBBi5+u0UKpPnz7U1taSTCZxpcaBqKqqwufzUVhYuM3bm6lvTIPBnjlQoxBCCCF6lry8vG5XfbyjwyF01vupXSXQefDdlVTWt+FCNt3Q+AFF/GziQIYUqhhxd1c3Z7v08Vuv1Z78GGDXeBzyGLoHeQzdw67wGIYUqp2WrWzr/VSnhVJjxozB5XKxaNEi9ttvPwAWLlzI+PHj2/StXigUAuCwww7bqe0UQgghhADrfUp+fn5XNyPDjg6HIO+ndh9vAH/q6kbsoFXArtCPYVd4HPIYugd5DN3DrvIYJs7snPva1vupTgul/H4/P/7xj7n++uu55ZZb2LJlC4899hgzZ7btmejduzfvvfdet/zWUgghhBC7nry87nflrx0dDkHeTwkhhBCiM23r/VSnXk95+vTpXH/99fzmN78hPz+fCy+8kKOOOqpNt1VVlb59++7kFgohhBBCdF87OhyCvJ8SQgghRHeimGaHXmpKCCGEEELsJJFIhMmTJ/PYY485wyHMnj2bjz76iGeeeaaLWyeEEEII0T5yiRYhhBBCiB4ifTiEL7/8knfeeYfHHnuM0047raubJoQQQgjRblIpJYQQQgjRg0QiEa6//nreeust8vPzOeusszj99NO7ullCCCGEEO0moZQQQgghhBBCCCGE6HTSfU8IIYQQQgghhBBCdDoJpYQQQgghhBBCCCFEp5NQSgghhBBCCCGEEEJ0OgmlgFgsxtVXX81+++3HwQcfzGOPPdbVTeqxNm/ezLRp05g0aRKHHHIIM2fOJBaLdXWzerRzzjmHq666qqub0WPF43FuuOEG9t9/fw488EDuvPNOZCi97bNx40bOPfdc9t13X4444gieeOKJrm5SjxKPxzn++OP55JNPnG3r1q3j9NNPZ5999uHYY4/lf//7Xxe2sOfI9VwuWrSIX/ziF0yYMIGjjz6al156qQtbKDrS22+/zahRozKmadOmdXWzegT5u7Njcj1/N910U7PX4zPPPNOFrex+Wvs8IK+/tmntOZTXYNusWbOGs846iwkTJnD44YfzyCOPOPvkdbhtrT1/Hf0adHVEg3u62267jcWLF/Pkk09SWVnJlVdeSf/+/TnmmGO6umk9immaTJs2jcLCQp599lnq6+u5+uqrUVWVK6+8squb1yO98cYbvPfee/zkJz/p6qb0WDfddBOffPIJjz76KKFQiIsvvpj+/fvzi1/8oqub1uP8/ve/p3///rzyyiusWLGCyy67jIqKCn7wgx90ddO6vVgsxqWXXsry5cudbaZpcsEFFzBy5EjmzZvHO++8w9SpU/n73/9O//79u7C13Vuu57Kqqoqzzz6bX/7yl/zpT39iyZIlTJ8+nfLycg4//PCua6zoECtWrGDKlCnMmDHD2eb1eruwRT2D/N3ZMbmeP4CVK1dy6aWXZrw3y8/P7+zmdVutfR644oor5PXXBtv6TCWvwW0zDINzzjmH8ePH85e//IU1a9ZwySWX0KdPH44//nh5HW5Da8/fj370ow5/De72oVQ4HOall17i4YcfZty4cYwbN47ly5fz7LPPSijVTqtWrWLRokV88MEH9OrVC4Bp06Zx6623Sii1Herq6rjtttsYP358Vzelx6qrq2PevHk8/vjj7LXXXgCceeaZfPHFFxJKtVN9fT2LFi1ixowZDBkyhCFDhnDIIYfw0UcfSSi1DStWrODSSy9tVqH38ccfs27dOp5//nkCgQDDhg3jo48+Yt68eVx44YVd1NruraXn8p133qFXr15ccsklAAwZMoRPPvmE119/XUKpXcDKlSsZOXIk5eXlXd2UHkP+7uyYlp4/sF6PZ511lrweW9Da54FDDz1UXn9tsK3PVPIa3Lbq6mrGjBnD9ddfT35+PkOGDOGAAw5g4cKF9OrVS16H29Da82eHUh35Gtztu+8tXbqUZDLJhAkTnG0TJ07kiy++wDCMLmxZz1NeXs4jjzzi/PG0BYPBLmpRz3brrbdy4oknMnz48K5uSo+1cOFC8vPzmTRpkrPtnHPOYebMmV3Yqp7J5/Ph9/t55ZVXSCQSrFq1is8++4wxY8Z0ddO6vfnz5zN58mReeOGFjO1ffPEFY8eOJRAIONsmTpzIokWLOrmFPUdLz6XdtSGb/P/ZNaxcuZIhQ4Z0dTN6FPm7s2Naev6CwSCbN2+W12MrWvs8IK+/tmntOZTXYNv07t2bu+++m/z8fEzTZOHChXz66adMmjRJXodt0NrztzNeg7t9pVRVVRUlJSV4PB5nW69evYjFYtTV1VFaWtqFretZCgsLOeSQQ5x1wzB45pln+N73vteFreqZPvroIxYsWMDrr7/O9ddf39XN6bHWrVtHRUUFr776Kg8++CCJRIKTTjqJ3/3ud6jqbp/Jt4vX6+W6665jxowZPPXUU+i6zkknncTPfvazrm5at3fKKafk3F5VVUXv3r0ztpWVlbFp06bOaFaP1NJzOWDAAAYMGOCsb926lTfeeEO+8dwFmKbJ6tWr+d///sdDDz2Eruscc8wxTJs2LeO9m8gkf3d2TEvP38qVK1EUhQcffJD//ve/FBcXc8YZZ8gwC2la+zwgr7+2ae05lNdg+x1xxBFUVlYyZcoUjj76aG655RZ5HbZD9vO3ePHiDn8N7vahVCQSafamxl6Px+Nd0aRdxqxZs/j66695+eWXu7opPUosFuOPf/wj1113HT6fr6ub06OFw2HWrFnD888/z8yZM6mqquK6667D7/dz5plndnXzepyVK1cyZcoUzjjjDJYvX86MGTM44IADOOGEE7q6aT1SS/9/5H/PjolGo1x44YX06tWL//u//+vq5ogdVFlZ6fyu3H333axfv56bbrqJaDTKH/7wh65uXo8jf3d2zKpVq1AUhaFDh/KrX/2KTz/9lGuvvZb8/Hzpyt6C9M8DTzzxhLz+tkP6c7hkyRJ5DbbTvffeS3V1Nddffz0zZ86Uv4PtlP38jRs3rsNfg7t9KOX1epu9AO11CQS236xZs3jyySe56667GDlyZFc3p0e5//772XPPPTO+IRHbx+VyEQwGueOOO6ioqACsDzjPPfechFLt9NFHH/Hyyy/z3nvv4fP5GD9+PJs3b+aBBx6QUGo7eb1e6urqMrbF43H537MDQqEQ559/Pt999x1//vOf8fv9Xd0ksYMqKir45JNPKCoqQlEUxowZg2EYXH755UyfPh1N07q6iT2K/N3ZMT/+8Y+ZMmUKxcXFAIwePZrvvvuO5557TgKBHLI/D8jrr/2yn8MRI0bIa7Cd7PF5Y7EYl112GT/96U+JRCIZx8jrsGXZz99nn33W4a/B3b7/Sp8+faitrSWZTDrbqqqq8Pl8FBYWdmHLeq4ZM2bw+OOPM2vWLI4++uiubk6P88Ybb/DOO+8wYcIEJkyYwOuvv87rr7+eMe6ZaJvy8nK8Xq8TSAHssccebNy4sQtb1TMtXryYwYMHZ/zDHjt2LJWVlV3Yqp6tT58+VFdXZ2yrrq5uVlIu2iYYDHLWWWexfPlynnzySRlvYxdSXFyMoijO+rBhw4jFYtTX13dhq3om+buzYxRFcT6I2YYOHcrmzZu7pkHdWK7PA/L6a59cz6G8Btumurqad955J2Pb8OHDSSQSlJeXy+twG1p7/oLBYIe/Bnf7UGrMmDG4XK6Mgc0WLlzI+PHjZcyZ7XD//ffz/PPPc+edd3Lcccd1dXN6pKeffprXX3+dV199lVdffZUjjjiCI444gldffbWrm9bj7L333sRiMVavXu1sW7VqVUZIJdqmd+/erFmzJqOydNWqVRnj+Ij22XvvvVmyZAnRaNTZtnDhQvbee+8ubFXPZBgGU6dOZf369Tz99NOMGDGiq5skOsj777/P5MmTM77V/uabbyguLpZxP7eD/N3ZMffccw+nn356xralS5cydOjQrmlQN9XS5wF5/bVdS8+hvAbbZv369UydOjUjKFm8eDGlpaVMnDhRXofb0Nrz9/TTT3f4a3C3T138fj8//vGPuf766/nyyy955513eOyxxzjttNO6umk9zsqVK5kzZw5nn302EydOpKqqyplE21VUVDB48GBnysvLIy8vj8GDB3d103qcoUOHcvjhhzN9+nSWLl3K+++/z9y5c/nlL3/Z1U3rcY444gjcbjd/+MMfWL16Nf/+97958MEH+fWvf93VTeuxJk2aRL9+/Zg+fTrLly9n7ty5fPnll5x88sld3bQe5+WXX+aTTz7hpptuorCw0Pnfk91NRPQ8EyZMwOv18oc//IFVq1bx3nvvcdttt/Hb3/62q5vWI8nfnR0zZcoUPv30Ux599FHWrl3Ln//8Z1599VUZEiBNa58H5PXXNq09h/IabJvx48czbtw4rr76alasWMF7773HrFmzOO+88+R12AatPX874zWomKZpdmD7e6RIJML111/PW2+9RX5+PmeddVaz9E9s29y5c7njjjty7lu2bFknt2bXcdVVVwHwpz/9qYtb0jM1NjYyY8YM3n77bfx+P6eccgoXXHBBRlcQ0TYrVqzg5ptv5ssvv6S0tJRTTz2V3/zmN/JctsOoUaN46qmnmDx5MgBr1qzhmmuu4YsvvmDw4MFcffXVHHjggV3cyp4h/bk866yz+N///tfsmEmTJvH00093QetER1q+fDm33HILixYtIi8vj1/84hfyd7wd5O/Ojsl+/t555x3uvfdevvvuOyoqKrj44os56qijuriV3ce2Pg/I62/btvUcymuwbTZv3syMGTP46KOP8Pv9/OpXv+Lcc89FURR5HbZBa89fR78GJZQSQgghhBBCCCGEEJ1ut+++J4QQQgghhBBCCCE6n4RSQgghhBBCCCGEEKLTSSglhBBCCCGEEEIIITqdhFJCCCGEEEIIIYQQotNJKCWEEEIIIYQQQgghOp2EUkIIIYQQQgghhBCi00koJYQQQgghhBBCCCE6nYRSQgghhBBCCCGEEKLTSSglhOj2Ro0axaWXXtps+yuvvMIRRxzRBS0SQgghhBBCCLGjJJQSQvQIf/vb3/joo4+6uhlCCCGEEEIIITqIhFJCiB6hoqKCG2+8kXg83tVNEUIIIYQQQgjRASSUEkL0CL///e/ZvHkzjz76aIvHbNq0iYsuuohJkyYxefJkbrrpJifEeuWVV/j1r3/Nvffey+TJk9lvv/2YOXMmpmk6t3/++ec54ogjmDBhAr/+9a9ZtmzZTn9cQgghhBBCCLG7klBKCNEj9OnTh2nTpvHggw+ybt26Zvvj8Ti/+c1viEQiPP3009x99928++673Hbbbc4xn3/+OatXr+a5557j2muv5amnnuLDDz8E4N///jf3338/1157LX/5y1+YOHEip512GvX19Z32GIUQQgghhBBidyKhlBCix/j1r3/N4MGDufnmm5vte//999m8eTOzZs1i1KhRHHDAAVx33XU899xzhEIhAHRdZ8aMGQwdOpQTTzyR0aNH89VXXwHwyCOPcO655zJlyhSGDBnC73//eyoqKnjttdc69TEKIYQQQgghxO7C1dUNEEKIttI0jeuvv55TTjmFd955J2PfypUrGTJkCEVFRc62fffdl2Qyydq1awEoKysjPz/f2Z+fn08ymXRuP2vWLO68805nfywW47vvvtuJj0gIIYQQQgghdl8SSgkhepR9992Xn/70p9x888389re/dbZ7vd5mx+q6njH3eDzNjrHHlNJ1nauvvpoDDjggY396iCWEEEIIIYQQouNI9z0hRI9z2WWXEQ6HMwY932OPPfjuu++oq6tzti1atAiXy8WgQYO2ec499tiDTZs2MXjwYGd68MEHWbRo0U54BEIIIYQQQgghJJQSQvQ4JSUlXHbZZWzYsMHZdtBBBzFw4ECuuOIKli1bxscff8yMGTM4/vjjKSws3OY5zzjjDJ588kleffVV1q5dy6xZs/jHP/7BsGHDduZDEUIIIYQQQojdlnTfE0L0SCeffDLz5s1jy5YtgDXe1Jw5c5gxYwY///nPycvL40c/+hGXXHJJm8537LHHUl1dzb333kt1dTXDhw/ngQceYMiQITvxUQghhBBCCCHE7ksx7QFVhBBCCCGEEEIIIYToJNJ9TwghhBBCCCGEEEJ0OgmlhBBCCCGEEEIIIUSnk1BKCCGEEEIIIYQQQnQ6CaWEEEIIIYQQQgghRKeTUEoIIYQQQgghhBBCdDoJpYQQQgghhBBCCCFEp5NQSgghhBBCCCGEEEJ0OgmlhBBCCCGEEEIIIUSnk1BKCCGEEEIIIYQQQnQ6CaWEEEIIIYQQQgghRKeTUEoIIYQQQgghhBBCdLr/B8XTm33U8AM0AAAAAElFTkSuQmCC", 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      " ] @@ -575,9 +584,7 @@ " fontsize=16,\n", " y=1.05,\n", ")\n", - "sns.despine()\n", - "\n", - "# plt.savefig(\"counterfactual_sir.png\")" + "sns.despine()" ] }, { @@ -604,7 +611,7 @@ "source": [ "Before we dive into the code below, let us first define some notation. We use small case abbreviations to refer to the value of the variables under consideration. For example, $\\mathit{ld}$ refers to `lockdown=1` and $\\mathit{ld}'$ refers to `lockdown=0`. We place interventions in the subscripts, for instance, $\\mathit{os}_{\\mathit{ld}}$ refers to the `overshoot` under the intervention that `lockdown=1`. Later on in the notebook, we also employ contexts that are kept fixed in the intervened worlds. We place these contexts in the superscript. For example, $\\mathit{os}_{\\mathit{ld}}^{\\mathit{me}}$ refers to the variable `overshoot` when `lockdown` was intervened to be 1 and `mask_efficiency` was kept fixed at its factual value. \n", "\n", - "We use $P(.)$ to denote the distribution described by the model (`policy_model` in this notebook). We also induce a distribution over the sets of potential interventions and the sets of context nodes potentially kept fixed. We denote these distributions by $P_a(.)$ and $P_w(.)$ respectively. As an example, $P_a(\\{ld\\})$ refers to the probability that the set of interventions under consideration is $\\{ld\\}$. These distributions are determined using the parameters `antecedent_bias` and `witness_bias` given to the handler `SearchForExplanation`. For more details, please refer to the [documentation](https://basisresearch.github.io/chirho/explainable.html#chirho.explainable.handlers.explanation.SearchForExplanation)\n", + "We use $P(.)$ to denote the distribution described by the model (`overshoot_model` in this notebook). We also induce a distribution over the sets of potential interventions and the sets of context nodes potentially kept fixed. We denote these distributions by $P_a(.)$ and $P_w(.)$ respectively. As an example, $P_a(\\{ld\\})$ refers to the probability that the set of interventions under consideration is $\\{ld\\}$. These distributions are determined using the parameters `antecedent_bias` and `witness_bias` given to the handler `SearchForExplanation`. For more details, please refer to the [documentation](https://basisresearch.github.io/chirho/explainable.html#chirho.explainable.handlers.explanation.SearchForExplanation).\n", "\n", "Now let's dive into the code, using this notation to describe the quantities we are computing. " ] @@ -615,7 +622,7 @@ "source": [ "\n", "\n", - "We first introduce a function for performing importance sampling through the model that returns cumulative log probabilities of the samples, sample traces, a handler object for multi-world counterfactual reasoning, and log probabilities. We use these objects later in the code to subselect the samples." + "We first introduce a function for performing importance sampling through the model that returns cumulative log probabilities of the samples, sample traces, an effect handler object for multi-world counterfactual reasoning, and log probabilities. We use these objects later in the code to subselect the samples." ] }, { @@ -663,7 +670,7 @@ "metadata": {}, "source": [ "Then, we set up the query as follows:\n", - "1. `supports`: We extract supports of the model using `ExtractSupports` and enrich it with additional information of `os_too_high` being a Boolean (constraints for deterministic nodes currently need to be specified manually).\n", + "1. `supports`: We extract the support of each distribution in the model using `ExtractSupports`. We also encode our knowledge that `os_too_high` is a Boolean. Note that constraints for deterministic nodes currently need to be specified manually when using `ExtractSupports`, as we do here.\n", "2. `antecedents`: We postulate `lockdown=1` and `mask=1` as possible causes.\n", "3. `alternatives`: We provide `lockdown=0` and `mask=0` as alternative values.\n", "4. `witnesses`: We include `mask_efficiency` and `lockdown_efficiency` as candidates to be included in the contexts potentially to be kept fixed.\n", @@ -687,7 +694,7 @@ ], "source": [ "with ExtractSupports() as s:\n", - " policy_model()\n", + " overshoot_model()\n", "\n", "supports = s.supports\n", "supports[\"os_too_high\"] = constraints.independent(\n", @@ -705,7 +712,7 @@ " consequents={\"os_too_high\": torch.tensor(1.0)},\n", " consequent_scale=1e-8,\n", " witness_bias=0.2,\n", - ")(policy_model)\n", + ")(overshoot_model)\n", "\n", "logp, importance_tr, mwc_imp, log_weights = importance_infer(num_samples=num_samples)(query)()\n", "print(torch.exp(logp))" @@ -722,31 +729,30 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ - "def compute_prob(trace, log_weights, mask):\n", + "def compute_prob(trace, log_weights, mask, verbose=True):\n", " mask_intervened = torch.ones(\n", " trace.nodes[\"__cause____antecedent_lockdown\"][\"value\"].shape\n", " ).bool()\n", " for i, v in mask.items():\n", " mask_intervened &= trace.nodes[i][\"value\"] == v\n", "\n", - " print(\n", - " mask,\n", - " (\n", - " torch.sum(torch.exp(log_weights) * mask_intervened.squeeze())\n", - " / mask_intervened.float().sum()\n", - " ).item(),\n", - " )" + " prob = (torch.sum(torch.exp(log_weights) * mask_intervened.squeeze()) / mask_intervened.float().sum()).item()\n", + "\n", + " if verbose:\n", + " print(mask, prob)\n", + " \n", + " return prob" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We specifically compute the following four probabilities. In each of the computations, we condition on lockdown and masking actually being implemented in the factual world. Then we take an interventional setting and compute the probability that this setting has a causal power over the outcome. For instance, in 1., we assume lockdown (`ld`) and masking (`m`) have been implemented, and we ask about the joint probability that both (a) removing both interventions, i.e. intervening for both `ld` and `m` to not happen - which we mark by the apostrophe - would lead to `oth` not happening, $\\mathit{oth}'_{\\mathit{ld}', m'}$, and (b) intervening for both to happend would lead to `oth`, $\\mathit{oth}_{\\mathit{ld}, m}$ (which, given the stochasticity between these interventions and the outcome, is non-trivial). Note that in computing these probabilities, we also marginalize over all the contexts that potentially can be kept fixed, i.e. all possible subsets of $W = \\{\\mathit{le}, \\mathit{me}\\}$\n", + "We specifically compute the following four probabilities. In each of the computations, we condition on lockdown and masking actually being implemented in the factual world. Given this factual world, each equation represents the probability that a given collection of policy interventions would have changed whether the overshoot was too high. For instance, in equation 1., we assume lockdown (`ld`) and masking (`m`) have been implemented, and we ask about the joint probability that both (a) removing both interventions, i.e. intervening for both `ld` and `m` to not happen - which we mark by the apostrophe - would lead to `oth` not happening, $\\mathit{oth}'_{\\mathit{ld}', m'}$, and (b) intervening for both to happen would lead to `oth`, $\\mathit{oth}_{\\mathit{ld}, m}$. Given the stochasticity between these interventions and the outcome, computing these probabilities is non-trivial. Note that in computing these probabilities, we also marginalize over all the contexts that potentially can be kept fixed, i.e. all possible subsets of $W = \\{\\mathit{le}, \\mathit{me}\\}$\n", "\n", "1. $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{oth}^w_{\\mathit{ld}, m}, \\mathit{oth}'^w_{\\mathit{ld}', m'} | \\mathit{ld}, m)$\n", "\n", @@ -759,7 +765,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -775,28 +781,28 @@ ], "source": [ "# no preemptions on lockdown and masking, i.e. both interventions executed\n", - "compute_prob(\n", + "_ = compute_prob(\n", " importance_tr,\n", " log_weights,\n", " {\"__cause____antecedent_lockdown\": 0, \"__cause____antecedent_mask\": 0, \"mask\": 1, \"lockdown\": 1},\n", ")\n", "\n", "# only lockdown executed, masking preempted\n", - "compute_prob(\n", + "_ = compute_prob(\n", " importance_tr,\n", " log_weights,\n", " {\"__cause____antecedent_lockdown\": 0, \"__cause____antecedent_mask\": 1, \"mask\": 1, \"lockdown\": 1},\n", ")\n", "\n", "# only masking executed, lockdown preempted\n", - "compute_prob(\n", + "_ = compute_prob(\n", " importance_tr,\n", " log_weights,\n", " {\"__cause____antecedent_lockdown\": 1, \"__cause____antecedent_mask\": 0, \"mask\": 1, \"lockdown\": 1},\n", ")\n", "\n", "# no interventions executed\n", - "compute_prob(\n", + "_ = compute_prob(\n", " importance_tr,\n", " log_weights,\n", " {\"__cause____antecedent_lockdown\": 1, \"__cause____antecedent_mask\": 1, \"mask\": 1, \"lockdown\": 1},\n", @@ -832,7 +838,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -849,11 +855,11 @@ ], "source": [ "print(\"Degree of responsibility for lockdown: \")\n", - "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_lockdown\": 0, \"mask\": 1, \"lockdown\": 1})\n", + "_ = compute_prob(importance_tr, log_weights, {\"__cause____antecedent_lockdown\": 0, \"mask\": 1, \"lockdown\": 1})\n", "print()\n", "\n", "print(\"Degree of responsibility for mask: \")\n", - "compute_prob(importance_tr, log_weights, {\"__cause____antecedent_mask\": 0, \"mask\": 1, \"lockdown\": 1})" + "_ = compute_prob(importance_tr, log_weights, {\"__cause____antecedent_mask\": 0, \"mask\": 1, \"lockdown\": 1})" ] }, { @@ -881,7 +887,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -919,12 +925,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We first plot the distribution of `overshoot` in the factual world (indicated by 0) and necessity counterfactual worlds (indicated by 1) where intervened variables are set to their alternative value. One can see how the distribution changes in the counterfactual worlds. When `mask` is set to 0, high overshoot is still quite likely, whereas when `lockdown` is set to 0, this visibly shifts the distribution towards the lower values of overhead. This agrees with the intuition that `lockdown` has a higher role in inducing high overshoot." + "We first plot the distribution of `overshoot` in the factual world (indicated by `world=0`) and necessity counterfactual worlds (indicated by `world=1`) where intervened variables are set to their alternative value. One can see how the distribution changes in the counterfactual worlds. When `mask` is set to 0, high overshoot is still quite likely, whereas when `lockdown` is set to 0, this visibly shifts the distribution towards the lower values of overhead. This agrees with the intuition that `lockdown` has a higher role in inducing high overshoot." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -957,7 +963,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -972,7 +978,7 @@ }, { "data": { - "image/png": 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", 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", 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      " ] @@ -1046,12 +1052,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can have similar plots for sufficiency worlds (indicated by 2) where variables are intervened on to have their antecedent values. While this might seem redundant, this investigates probabilistically the impact of the implemented interventions: after all, it might be the case that the observed outcome is an unusual one and that usually, those interventions do not lead to the outcome of interest. The resulting plots show that when `mask` is set to be 1, there is a higher probability of high overshoot, but that this distribution is flatter than the distribution for `lockdown` being set to 1, which has higher peaks." + "We can have similar plots for sufficiency worlds (indicated by `world=2`) where variables are intervened on to have their antecedent values. While this might seem redundant, this investigates probabilistically the impact of the implemented interventions: after all, it might be the case that the observed outcome is an unusual one and that usually, those interventions do not lead to the outcome of interest. The resulting plots show that when `mask` is set to be 1, there is a higher probability of high overshoot, but that this distribution is flatter than the distribution for `lockdown` being set to 1, which has higher peaks." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -1084,7 +1090,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1099,7 +1105,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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      " ] @@ -1178,7 +1184,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -1249,12 +1255,12 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { - "image/png": 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Gq3a9e/fWsmXLdN9998kYo1WrVumOO+6QJJ06dUr79+/XY489pieeeMK9TE5OTp4LTzdu3DjX44EDB+qpp57SV199pSuvvFLdu3dXw4YNc7U5c9qpJPe1YlJSUlS7dm3t3r1b/fr1y9W+RYsWWrBggSRp165dqlmzpmrVquV+vkGDBqpYsaJ2796tpk2berX9vji7XpvNpurVqyslJcVdT3x8vKKiotxtEhISci3/yy+/aOPGjXnmS9L+/ft18cUXS5IaNWpUYB2VK1dW3759NXjwYLVr105t27bVNddcoxo1ahR527yxbds22e12tWrVyqv2v/zyi7Zv366VK1e65xlj5HK5dPDgQdWvX19Swa/rtm3bdPnll3u89lDXrl01YcIErVmzRr169dLy5cvVpk0bxcbG+rRtBdWQn+J8Njp27KiIiAh99tln6tWrlz755BOVL19eV155paTTr9v+/ftzXXtJkjIzM7V//3734wYNGshut7sfx8TE6P/bu/+Yquo/juPPm8mUrnMKcc3rr4mjBmsyV/6MP9wgplcaiWxqZQmlFumsMRnOHwwSSK/Oabqggi0Snc5ou4Q4nUNRy1HinAwQMPRqUjQGJqgUfr9/OO+Xw0W4V/F7v197PTb+uOd8OOfzOfd84L3POZ/35+LFi160mn772X3dr4+/vz9ms9kwxUdERKQ3ikcVjw60f0o86km8dfnyZW7dukVCQoJh+19//eW2sOOLL75o+Gyz2di8eTPnzp0jPDwch8NBWFiY63p4ep90rzPc60N9xdA93bx5k99//90t7p0yZQo1NTWGbb311ZaWFledRQaSBihFBsC4ceMwmUw0NDQQFRXltr+hoYHhw4e7EjPPmzcPu91OVVUVt2/fpqmpiblz5wK4crNkZGQwefJkw3GeesqYNtbf39/wOT4+nldeeYWysjJOnTpFbm4uKSkphvx93ZMzm0wmAI/zBg4kk8nkFkD//fffbuV6JpPu7ff60tHRwezZs0lOTnbb1z2Ztycr+mVlZfHWW29RXl7OoUOH2L59O/n5+YSHh3vcHm95u7hKR0cHCxcuNHzn93UP5Pu6rv2d08/Pj9jYWL799luioqJwOBwer87dnbff7aP0DT8/P6Kjo3E4HNhsNoqLi5k7d66rDh0dHYSFhWG3293O2z2h+qPej97ouciAyWTySV8VEZH/D4pHvad41DNPcjzqbbx1v2/k5ORgsVjc6tRdz+/z2WefZfr06RQXFxMeHk5xcTGLFi0yHNuT+8RX8agv+6r8M2iRHJEBMGLECGbNmkVhYSG3b9827GtubsbhcDBnzhzXH/VRo0bx8ssv43A4cDgczJw5k4CAAAACAwMJCgrC6XQyfvx4w8/YsWP7rctzzz3HokWL+Oyzz1i6dCn79+/3uB0TJ07k7Nmzhm1nz55l0qRJAAQHB9PU1MT169dd++vr67lx44brKdrgwYM9+qc1cuRImpubXZ+7urqoq6vzuK7361NbW8udO3dc286dO2coExYWRl1dHVar1e169gyoPREaGsry5cvZt28fISEhFBcX99oeoNcE6d4KCQnh7t27VFRUeFy/+vp6t7aOHz/e45Ugn3/+eaqrq2ltbX1gmfj4eE6fPk1hYSFdXV28+uqrHh3bU/eDoa6uLte2R+0bMTExnDx5krq6On788UdiYmJc+8LCwrh8+TIBAQFux+75pkh/9e7v/u+vn4mIiDwMxaOKRxWPPv54NDg4GD8/P3799Ve3tnUffH2QmJgYSkpKqKysxOl0uh4KwMDdJ4MHDzbUuSez2UxQUJDiUfmfowFKkQGyfv16Ojs7SUxMpKKiguvXr3PixAkSEhKwWCx89NFHhvKvvfYa33//PaWlpYaBEoBVq1aRm5vL119/zS+//EJtbS0HDx4kPz+/zzps2rSJ8vJynE4nVVVVnDlzxqvX7999912KioooLCyksbGR/Px8jhw54prCMHPmTEJCQkhOTqaqqorz58+zZs0apk6d6prCYLVauXr1KtXV1bS0tNDZ2dnruaZPn87x48cpKyujoaGBtLQ0w8p/npg3bx4mk4l169ZRX1/P8ePHycvLM5RZvHgxbW1tfPzxx5w/f54rV65QXl5Oampqn/+4e3I6nWzdupXKykquXbvGyZMnaWxsZOLEia72XLhwge+++47GxkZ27NjhdYDbmzFjxvD666+zdu1ajh49itPp5MyZM5SUlPRa/r333qOyspL09HSqq6tpbGzk6NGjpKene3xOm81GYGAgSUlJ/PzzzzidTg4fPmxYtS84OJjJkydjt9ux2WxeP1nvT0BAAEOGDKG8vJw//viDP//8E3j4vgH3VmAMDAwkOTmZMWPGGN4IiYmJYcSIEbz//vv89NNPruv8ySef0NTU5HG9rVYrFRUV/Pbbbw+cHtRfPxMREXlYikcVjyoeHThWqxWTyURZWRktLS20t7djNptJSEggKyuLoqIirly5QlVVFQUFBRQVFfV7zKioKNrb20lLS2PatGmGtzAH6j6xWq388MMPNDc309bW1muZxMREvvjiC0pKSrh06RJ2u52amhrXyuUivqAp3iIDZMKECRw8eJCdO3eyevVq2traCAwMJDIykqSkJLf8KdHR0aSnpzNo0CAiIyMN++Lj4xkyZAhfffUVmzdvxt/fn5CQEN5+++0+63D37l3S09NpamrCbDYTERFBamqqx22IjIxk7dq15OXlkZmZidVqJTMzk2nTpgH3XuvfvXs3GRkZvPnmm5hMJiIiIgy5iaKjozly5AhLlizhxo0bZGVlMX/+fLdzxcXFUVNTQ0pKCoMGDeKdd95xncdTzzzzDJ9//jkbN24kNjaWSZMmkZyczMqVK11lLBYLe/fuxW63k5iYSGdnJ6NHjyYiIsJtilJfhg4dyqVLlygqKqK1tZWgoCDeeOMNFi5cCEBERAQffPABW7Zs4c6dO8TFxREbG+t1fsLepKWlsW3bNtLS0mhtbWX06NEsX76817IvvPACBQUFbN++ncWLFwMwduxYw9PZ/vj5+ZGXl8enn37KsmXL6OrqIjg4mI0bNxrKLViwgMrKSuLi4h6+cQ/w9NNPs27dOnbt2sWOHTt46aWXKCgoeOi+AffuX5vNxpdffklSUpJh39ChQ/nmm2+w2+18+OGHtLe3Y7FYmDFjBmaz2eN6r1q1ig0bNhAZGUlnZye1tbVuZfrrZyIiIg9L8eh/2qV4VPHoo7JYLKxcuZKtW7eSmppKbGws2dnZrF69mpEjR5KTk8PVq1cZNmwYoaGhrFixot9jms1mZs+ezaFDh8jMzHQ730DcJykpKWRnZ3PgwAEsFgvHjh1zK7NkyRJu3rxJdna2K6fk7t27mTBhgsfnERlopn89rmQFIiLyRNu1axelpaWGBOgiIiIiIv8tikdFnhya4i0iIl5pb2/n4sWL7Nmzp9fk5yIiIiIij5PiUZEnjwYoRUTEKxkZGcyfP5+pU6c+luk0IiIiIiJ9UTwq8uTRFG8RERERERERERHxGb1BKSIiIiIiIiIiIj6jAUoRERERERERERHxGQ1QioiIiIiIiIiIiM9ogFJERERERERERER8RgOUIiIiIiIiIiIi4jMaoBQRERERERERERGf0QCliIiIiIiIiIiI+IwGKEVERERERERERMRnNEApIiIiIiIiIiIiPvNvKINv28hrpmUAAAAASUVORK5CYII=", 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", 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      " ] @@ -1348,7 +1354,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -1382,7 +1388,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1397,7 +1403,7 @@ }, { "data": { - "image/png": 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", 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", 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      " ] @@ -1468,7 +1474,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -1500,7 +1506,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1515,7 +1521,7 @@ }, { "data": { - "image/png": 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", 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      " ] @@ -1594,7 +1600,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.10.13" } }, "nbformat": 4, From 9d2c4997634adf5eeb72b5d475f62845c4323441 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Wed, 4 Dec 2024 14:30:56 -0500 Subject: [PATCH 092/111] added thresholds to but-for histograms --- docs/source/explainable_sir.ipynb | 31 +++++++++++++++++++++++++------ 1 file changed, 25 insertions(+), 6 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index a74e4f3c..0a771489 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -276,7 +276,7 @@ "metadata": {}, "source": [ "\n", - "Now we incorporate the Bayesian SIR model into a larger model that includes the effect of two different policies, lockdown and masking, where each can be implemented with $50\\%$ probability (these probabilities won't really matter, as we will be intervening on these, the sampling is mainly used to register the parameters with Pyro). We encode their efficiencies which further affect the model. Crucially, these efficiencies interact in a fashion resembling the structure of the stone-throwing example we discussed in the tutorial on categorical variables. If a lockdown is present, this limits the impact of masking as agents interact less and so masks have fewer opportunities to block anything. We assume the situation is asymmetric: masking has no impact on the efficiency of lockdown. The model also computes `overshoot` and `os_too_high` for further analysis.\n", + "Now we incorporate the Bayesian SIR model into a larger model that includes the effect of two different policies, lockdown and masking, where each can be implemented with $50\\%$ probability. These probabilities won't really matter, as we will be intervening on these, the sampling is mainly used to register the parameters with Pyro. The sampling here, hower, illustrates that the model in principle could incorporate uncertainties of this sort. We encode their efficiencies which further affect the model. Crucially, these efficiencies interact in a fashion resembling the structure of the stone-throwing example we discussed in the tutorial on categorical variables. If a lockdown is present, this limits the impact of masking as agents interact less and so masks have fewer opportunities to block anything. We assume the situation is asymmetric: masking has no impact on the efficiency of lockdown. The model also computes `overshoot` and `os_too_high` for further analysis.\n", "\n" ] }, @@ -289,6 +289,7 @@ "# a utility function\n", "# allowing for interventions on a dynamical system\n", "# within another model\n", + "# to avoid conflicts arising from repeated sites in the trace\n", "\n", "def MaskedStaticIntervention(time: R, intervention: Intervention[State[T]]):\n", "\n", @@ -460,12 +461,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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      " ] @@ -506,12 +507,18 @@ " unintervened_samples[\"R\"], axs, coords=(0, 0), color=colors[2], label=\"recovered\"\n", ")\n", "\n", + "for ax in axs[:, 1]:\n", + " ax.set_xlim(0, 35)\n", + "\n", "axs[0, 1].hist(unintervened_samples[\"overshoot\"].squeeze())\n", "axs[0, 0].set_title(\"No interventions\")\n", "axs[0, 1].set_title(\n", " f\"Overshoot mean: {unintervened_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {unintervened_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", ")\n", - "\n", + "axs[0, 1].axvline(unintervened_samples['overshoot'].squeeze().mean().item(), color=\"red\", \n", + " linestyle=\"--\", label=\"mean overshoot\")\n", + "axs[0, 1].axvline(overshoot_threshold, color=\"black\", linestyle=\"--\", label=\"overshoot threshold\")\n", + "axs[0, 1].legend()\n", "\n", "add_pred_to_plot(\n", " intervened_samples[\"S\"], axs, coords=(1, 0), color=colors[0], label=\"susceptible\"\n", @@ -530,6 +537,9 @@ "axs[1, 1].set_title(\n", " f\"Overshoot mean: {intervened_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {intervened_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", ")\n", + "axs[1,1].axvline(intervened_samples['overshoot'].squeeze().mean().item(), color=\"red\",\n", + " linestyle=\"--\", label=\"mean overshoot\")\n", + "axs[1, 1].axvline(overshoot_threshold, color=\"black\", linestyle=\"--\", label=\"overshoot threshold\")\n", "\n", "\n", "add_pred_to_plot(\n", @@ -548,6 +558,9 @@ "axs[2, 1].set_title(\n", " f\"Overshoot mean: {mask_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {mask_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", ")\n", + "axs[2, 1].axvline(mask_samples['overshoot'].squeeze().mean().item(), color=\"red\",\n", + " linestyle=\"--\", label=\"mean overshoot\")\n", + "axs[2, 1].axvline(overshoot_threshold, color=\"black\", linestyle=\"--\", label=\"overshoot threshold\")\n", "\n", "add_pred_to_plot(\n", " lockdown_samples[\"S\"], axs, coords=(3, 0), color=colors[0], label=\"susceptible\"\n", @@ -565,6 +578,9 @@ "axs[3, 1].set_title(\n", " f\"Overshoot mean: {lockdown_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {lockdown_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", ")\n", + "axs[3, 1].axvline(lockdown_samples['overshoot'].squeeze().mean().item(), color=\"red\",\n", + " linestyle=\"--\", label=\"mean overshoot\")\n", + "axs[3, 1].axvline(overshoot_threshold, color=\"black\", linestyle=\"--\", label=\"overshoot threshold\")\n", "\n", "add_pred_to_plot(samples[\"S\"], axs, coords=(4, 0), color=colors[0], label=\"susceptible\")\n", "add_pred_to_plot(samples[\"I\"], axs, coords=(4, 0), color=colors[1], label=\"infected\")\n", @@ -576,6 +592,9 @@ "axs[4, 1].set_title(\n", " f\"Overshoot mean: {samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", ")\n", + "axs[4, 1].axvline(samples['overshoot'].squeeze().mean().item(), color=\"red\",\n", + " linestyle=\"--\", label=\"mean overshoot\")\n", + "axs[4, 1].axvline(overshoot_threshold, color=\"black\", linestyle=\"--\", label=\"overshoot threshold\")\n", "\n", "\n", "fig.tight_layout()\n", @@ -1586,7 +1605,7 @@ ], "metadata": { "kernelspec": { - "display_name": "chirho", + "display_name": "new-pyro", "language": "python", "name": "python3" }, @@ -1600,7 +1619,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.12.3" } }, "nbformat": 4, From 39407c3e641ca65541a8c9463d055db7c28fe680 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Wed, 4 Dec 2024 14:38:02 -0500 Subject: [PATCH 093/111] add labels and modify titles in the but for analysis --- docs/source/explainable_sir.ipynb | 27 ++++++++++++++++++--------- 1 file changed, 18 insertions(+), 9 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 0a771489..f4279068 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -461,12 +461,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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Jhx3jzz//XDIajVKzZs2k69evy+Umk0kaPXq0ZDQapaZNm6a7x8nOezkrFy5cyHC56Oho6e2335aMRqP07bffppue22v5vn37pGvXrqUrv3jxovxZdOzYMadpmR273bt3S0ajUWrfvn2G++a4Fv/f//2fXDZ37lzJaDRKn376abp7BJPJJO3Zsydb287rexpBEITURJdGQRDS8fLyYuzYsZn+Ctm6dWsMBoNTmUKhoEePHoSEhHD+/HkuXryYrW0tWrQIk8nEe++9R4sWLZym+fn5MWXKFACWL18ul0dGRrJy5UrA/otohQoV0tWlbt26OcpDc/PmTTZv3oxCoWDixIlOvza7uroyefJktFotR44c4fDhwxmu4/3336d06dLZ3qbNZpO7JM2aNSvdftSsWZN33nkHs9nMmjVr5PLIyEgAGjVqlG6dOp2OmjVrZrsO8GivZ+XKlXnvvfdQqVRymdFopH379gDs2bPHaf7vvvsOgCFDhjjl8lCr1YwaNYrChQvnqO5gPzcsFgvNmzenY8eOTtMaNWpE165dAZy6V7i5udGiRQtsNhu//PJLunU6Wne1b98etfpBY+i5c+ciSRITJkxId5xTd99xnJ9pdezYUe7ylNrdu3cBewuRjHLSVKlSJdMWEMOGDSM4ONipbODAgbi7u3PlyhXCw8Pl8oMHD3Ls2DH0ej2TJk1yet0LFizIxIkTAdi4cSO3bt2Sp+XmGD+q8PBw/vzzTxQKBRMmTHB6P3t5eTFhwoQcrS8+Pp7k5GSKFy+e4Whufn5+j5Rf5u2336Zq1aq5Wlan0zFhwgR0Op1cVqRIEfl8Wr16dabdoZ6URz0HcnKtcLwfKlWqlOE1oWzZsnmaF7FIkSLpPvOKFi1Kz54909UtL+V0u7n5vMyuwoULExoa6nS9K1++vNyle+nSpTleZ35ISUlh1apVgL0rcupWgBqNhjFjxuDt7c2NGzfYsmVLhuvI7Xu5bNmyGS7n6enJmDFjANi8eXOmy+fkWg5Qq1Ytihcvnm49ZcqU4Z133nno9lKrV68e5cuX58yZMxw8eNBp2q1bt9i+fTt6vd6ppZXjXqRBgwbpWsBpNBrq1q2brW3n9T2NIAhCaiLgJQhCOtkJFl29epWVK1cyZcoURo8eTWhoKKGhofIXlcuXLz90Ozabjb///huwB10yUqVKFQwGA6dPn5a/8O3btw+z2UzlypUJDAzMya5l6sCBA3I3nbSBJ7CPLlS/fn15+xnJaRehU6dOERERQYkSJTLdj1q1agE45fIKCgoCYPz48ezatStPvgjn9vVs0qRJhl09HIGD1LlSbt++zdWrVwHkL7mpabVaWrVqleO679+/H7B3dcnIq6++CtiDPalz0XTu3BlAztPkYDab+f33352WBXt+luPHj6PT6eQcMmnVrl0bINOgaGbnSOXKlVGpVPz888+sWrWKiIiIDOfLSEZ1cXFxkb8IpX4NHMeqQYMGcvef1AIDA6lQoQI2m02eN/VyOT3GjyL1ezKjrosVK1bMNC9SRgoWLIifnx9nz55l2rRpXLhwIU/q6ZCbc9fhpZdeyjCw06RJE7y8vIiPj+fkyZOPUr1H9qjnQE6uFWXKlMHV1ZW///6bhQsXcv369Ueuf1bq1q2LXq/PVt3ya7u5/bzMrldeeSXDbrOO4OaVK1ce23HIS//99x+JiYl4eXllmCtKr9fLxy+zz/JHeS9brVb27t3L/PnzGT9+PKNGjSI0NJRFixYBWd8b5eRa7pCQkMCmTZuYNWsWn376qfzZvXXr1oduLy1Hzj9HwNDhhx9+wGKx0K5dOzw8PORyx73IzJkz2bp1KwkJCdneVmqP455GEATBQeTwEgQhHT8/v0ynWa1WJk6cyJo1a5AkKdP54uPjH7qd6Ohoeb6MftnLaH5fX1/CwsIAMmylkVuOG8mscsKUKFHCad7UChUqlOEXl6w4vsRdu3btoV/co6Ki5H/37duXQ4cOsWfPHvr164dGoyEgIICaNWvSunVr+eYxOx719SxatGiG5Y5WSqmTUTtaDBUoUABXV9cMl8tNTp6HvXaOLwspKSlER0dTqFAhwB6cKl68OJcvX+bw4cNynrKdO3cSFRVFcHCwU4ufGzduIEkSycnJVKlSJcs63bt3L8PyzOpYokQJRo0axYwZM5g4cSITJ07Ez8+PqlWr0rhxY1q1apVpi8vMWro4XoPUXx6ye56fOXMmXbAyq+UyO8aPwnG+ZFVXf3//HA12MGPGDIYNG8bSpUtZunQpXl5eBAUF8dJLL9G+fftHGtkxq+vmw2S1j35+fkRHRzu1uMsPj3oO5ORa4ebmxtSpUxk1ahRffvklX375JYULF6Zq1ao0aNCAtm3bZnoNyY2c1C0v5WS7uf28zK7MXlc3Nze8vLyIjo7m9u3bOVpnbmzbto1t27alK3/11VepUaPGQ5d3/FiQ1fsxq8/yhy2blStXrjBkyJAs83dmdW+Uk2s5wI4dOxg1alSWA3hk517MoX379nzxxRf8+eefRERE4OPjg8lkYu3atQD06NHDaf4OHTqwe/dufv/9d4YOHYpKpaJs2bJUr16dli1bZruFV17e0wiCIKQlAl6CIKSTultNWsuXL+eHH36Quz+EhITg7e0t/zL84YcfsmHDhiyDJw42m03+d2atBlLLTtLg/JLVMcuM4xgVLlxYbj2WmdTd2fR6PUuXLuX48ePs2rWLI0eOcOTIEU6cOMHSpUvp3r0748aNy1YdHvX1VCqf3YbCCoWCTp06MWfOHNavXy8HvBwjQjlagDk4jkFuE34DWSYe79WrF6+88go7duzg0KFDHDp0iD/++IM//viDuXPnsmrVKnx8fNIt9yy/Bk9ajRo12LFjBzt37uTAgQMcOXKEf/75h7///ps5c+Ywf/78bH9JSys314DHJfW19WmR0/O0ZcuW1KtXj+3bt3Po0CEOHz7Mn3/+yZ9//smcOXP4v//7vxy18MvLuuWVnGz3afi8zM7nukNuz8HTp0+na3UL9tbO2Ql45YXcvpeHDRvG+fPnadKkCf369aNs2bK4ubmh0WgwmUwP/aEkJ+fD7du3ef/990lOTqZfv360a9cOf39/DAYDSqWSf/75J1sJ41PT6/W89tprLF68mB9//JEhQ4awdetW7t69S40aNdK1flcqlcycOZNBgwaxc+dODh8+zOHDh/n+++/5/vvvadKkCfPnz3fqxpzZdvPqnkYQBCEtEfASBCFHNm3aBMCECRMyzEV05cqVbK+rQIEC6HQ6kpOTGTFiRLZbVzh+Bb106VK2t/Uwjl+ts+o645iWV79wFylSBLDnI5o2bVqOlw8KCpJ/+bRYLGzbto2RI0eyevVqWrZsSZ06dR66jrx8PR/Gcdzu3btHQkJChi00HK33crrea9eucf36dYxGY7rpjuHktVptupEHO3XqxLx589i4cSOffPIJ8fHx7Nq1C51Ol270RsfrpVAo+Oyzzx7Ll2Rvb29ef/11eZSsixcv8sknn3DkyBG++OILpk+f/kjrz+15/ijH+FHrmtU5kZvzRafT0apVK7nbUlRUFF9++SVr1qxh9OjR/PXXX7mr8CNwHL+MOPYx9evhCGZk1oXo5s2beVg75O0/6XPA3d2djh07yt3qwsPDmTRpEtu3b2fSpEmZ5sp7HuX28zK7MjsH4+Pj5RZEjmsgPL5zcOjQoQwdOjRXywLyjwJZXRvy+rMc7Nfqs2fPUqhQIebNm+eUCw2Qu/PnlR07dpCcnEzz5s35+OOP003P7fZ69OjB0qVLWbNmDQMHDpTfY2lbd6VWrlw5udu5JEn8+++/fPjhh/z111/88ssv2R5hMS/uaQRBENISPwsLgpAjMTExQMZN/s+fP8+ZM2eyvS6VSkW9evWAB4GX7KhTpw4ajYaTJ09mO6+N4+bcYrFkOL1mzZoolUpOnz6d4T5ERESwa9cu4EGepkflSER+4cKFLLtAZIdaraZVq1ZyS7Hsvg55+Xo+TJEiReRuTxs2bEg33WQyZTvBbmqOPGcZtQoA+OmnnwB76560X0KKFStG3bp1iY+PZ+vWrfz2229yYu60eex8fX0JCAggISFBPhcet7Jly9KvXz/A3vLhUTmO1a5du+T8bKmdOnWK06dPo1QqnRIF5/YYP+x9l5WaNWuiUCg4depUhoMmnDlzJkfdGTNTsGBB+QvjzZs35fcEPKh/XuUly8zu3bvlxM2p/e9//yM6OhpXV1enPH+OL+qZDSbxv//9L8PyR3k9HuV9lleKFi3KsGHDgLx5PzwOj3KMs5Lbz8vs2rx5c4ZdN3/99VcASpYsmS4IDhmfg5IkyfnG0npcx8fBkccsOjqa7du3p5uenJzMxo0bgbz7LIcHn6U+Pj4Znv+//fZbnm0r9fYy6gYpSZKchzKnihUrRrNmzYiIiGDOnDkcOXIEHx+fdIMkZMYxaFDbtm2B3L9Pc3tPIwiCkJYIeAmCkCOOvFmrVq1y6rIQERHByJEjc3wTO2TIEDQaDZ9//jnr16/PsBvEuXPn5ASsYM+X1a1bNwCGDx/OuXPnnOaXJIm9e/cSFxcnlzl+mc7sC2KxYsVo1aoVkiQxduxYpxxMiYmJjB07lpSUFEJCQuSub49Ko9EwZMgQJEliyJAh6UZGggcJcI8ePSqXrVq1KsPWbXfu3OHEiRPy/mRHXr+eD9OnTx/APtph6tfCarUyffr0HCVrd+jduzdqtZpt27bJX84c/vnnH3mEy7fffjvD5R2/Pq9bt04enTGzX6Tfe+89wD76144dO9JNlySJY8eO8c8//+RoH/bu3cv//vc/zGZzuvXt3LkTyP5rmpUaNWoQHBxMcnIyY8eOJSkpSZ4WFRXF2LFjAXtS7NT5hXJ7jB3vu9wkiC9WrBjNmzfHZrMxfvx4p1w0MTExjB8/PkddrMLCwli7dm2GOW0cr6Wnp6fTKJmOL/WPGpB+mOTkZMaPH09ycrJcdvv2bbnl5xtvvOHUHbZOnTpyt6XUgwtIksTy5cszHX2uYMGCaDQa7t69m2Xen4w86vssJ06dOsXGjRudjoeD47XKy1Ea89KjHOOHyc3nZXZFREQwffp0p+DuxYsXWbBgAfDg2u3g6Pr766+/Or2/zWYzn3/+Of/991+G23FcE65evZruepcXtFqt3Bpp+vTpTi29zGYzU6ZM4c6dO/j7++e6a3pGSpUqhUql4ty5c+mS4e/YsYNly5bl2bbgwcAGW7ZscfrctFqtfPXVV04D3eRU7969Afjmm28A6Nq1a4ZBvF9++UW+50gtPj5evi5lJx9aXt7TCIIgpCW6NAqCkCODBg1i165d/Pjjj+zbt49KlSoRHx/PgQMHKF68OM2bN+fPP//M9voqV67M559/Lo9k9OWXX1KuXDkKFChATEwM586d49atW7Ru3drpF8aPP/6YGzdusGPHDjp06EBwcLCc3Pn8+fPcvn2b7du3y610goOD8fHx4dSpU3Tq1Amj0YharaZ06dJyC5qxY8dy6dIljh07RvPmzalduzYqlYoDBw4QFRWFv78/M2fOzNPj2bNnT27evMmSJUvo0aMH5cuXp0SJEuh0Ou7cucOZM2eIjY1l/Pjx8nDnP/74IxMnTsTf35/y5cvj5ubGvXv3OHjwIMnJydSpUyfD0akyktev58P06NGD3bt389dff9GhQwdq166Np6cnx44d486dO3Tr1o3vv/8+R+sMCAhg7NixjB8/nhEjRvDdd99RunRpbt68yZEjR5AkiaFDh2aaJ61Zs2Z4enqyd+9ewH6DnlnXiaZNm/LJJ58wffp0Bg8eTMmSJSldurT8Gpw5c4bIyEj69+//0LxsqZ09e5apU6fi5uZGpUqV8PHxISUlhVOnThEWFoa7uzvDhw/P0XHJzBdffEGfPn3Yvn07L7/8MjVq1MBisbBv3z7i4+OpXLmyHPhyyO0xrl+/PgaDgW3bttGtWzdKlSqFUqmkWrVq2ermMnbsWM6cOcP+/ft5+eWXqVWrFpIksW/fPnkUtowCjxmJjY1lzJgxTJgwgQoVKshJuq9evcqpU6dQKBR8/PHHTvlmmjVrxvz581mxYgXnz5+nSJEiKJVKmjZtmmEX4Nzq2LEjO3fupFmzZlSvXp2UlBT27dtHYmIiISEhcqsmh6JFi9KzZ0+WL1/Om2++SfXq1fHy8uLMmTOEh4czYMAA+ctqahqNhqZNm7JlyxY6duxI9erV5XxFU6ZMybKOj/o+y4mbN2/y/vvvo9PpqFSpEkWLFsVisXDu3DkuX76MRqPJsBvX0+BRjvHD5PbzMjveeOMN1q5dy86dOwkODiYmJkYeEbl58+Z0797daf7q1avz8ssvs337drp06UL16tXRarWcOnWK+Ph4evfuzfLly9Ntp1ixYgQGBnLixAnatWtHYGAgWq2WAgUK8NFHHz3S8XEYNmwYJ06cYO/evbRu3ZratWvj6urK0aNHuXnzJl5eXnz11VeZDgSSGwULFqRHjx7ye7JGjRr4+Phw+fJlTp48yeDBg1m4cGGeba9JkyZUrlyZkydP0rJlS2rVqoVer+f48eNERETQv39/vv3221ytu0aNGlSqVIlTp06h0Wjo2rVrhvNt3bqVkSNH4uPjQ8WKFfHw8CA2NpbDhw8TFxeH0Wjktddee+j28vKeRhAEIS0R8BIEIUeCg4P5+eef+fLLL/nvv//YsWOH/OVr8ODBTJ48OcvlMxqW/pVXXqFKlSqsWLGCPXv2cPjwYaxWK97e3pQoUYIePXqkGybcxcWFBQsW8Mcff7B+/XpOnDjBiRMn8PLyomTJkvTp04fChQs7zb9kyRJmz57N0aNHOXPmDDabjVq1askBrwIFCvDDDz+wYsUKNm7cyO7du7HZbPj7+/P666/z9ttv51lumtRGjBhBs2bNWL16NYcPH2bXrl1oNBoKFy5MrVq1aNy4sdOXl/fff5+dO3dy7Ngxjh07RlxcHIUKFSIoKIguXbrQpk2bbHcpetTXM6eUSiXz5s1jxYoV/PTTT+zfvx+DwUD16tWZP38+p06dynHAC+y/QFeoUIElS5Zw+PBhzp49i5ubG40aNaJ379689NJLmS6r1Wpp06YNq1evBux5vTI6Tx169+5NnTp1WLlyJfv27WPv3r0olUq8vb2pWLFiutcrO5o2bUp8fDwHDx7k6tWrHDt2DJ1OR5EiRRgwYAA9evRwyp/zKIoXL866dev4v//7P7Zt28bOnTtRKpWULl2aV155hd69e2eYtDk3x9jb25tvv/2W+fPnc/LkSY4ePYrNZsNqtWYr4FW4cGF+/PFH5s+fz59//slff/1FoUKFaN26NcOHD2fGjBk52u/Ro0dz4MABzp8/L3f78/HxoWPHjvTq1cup2yBAhQoVmDt3LkuWLOHYsWPs3bsXSZIoUqRInga8/P39+emnn/jyyy/5999/iYmJoVixYrRt25b+/ftn+HqMHj2aYsWKsXbtWo4cOYKrqyshISF8+eWXxMfHZxjwApg4cSJeXl7s2rWLLVu2yK1sshOMeZT3WU4EBwfz4YcfcvDgQS5evMjp06dRqVQUKVKEHj160LNnzzwdpTevPcoxfpjcfF5mR3BwMF27dmXOnDns3r2bxMRESpUqxauvvkrPnj0zvCZ++eWXLFiwgA0bNrB//348PDyoW7cuw4cPz7DFssPcuXP54osv2LdvH5s2bcJiseDn55dnAS8XFxc58fqvv/7KwYMHMZlMFC1alF69etG/f//HMtrk6NGjCQgIYPXq1Zw4cQKVSoXRaGT27Nm0bt06TwNearWaFStW8M0337Blyxb27t2Lm5sbISEhzJkzh4SEhFwHvABeeuklTp06RcuWLZ3upVJ7++238ff358iRI5w6dYro6Gi8vLwoV64cbdu2pXPnzhgMhoduKy/vaQRBENJSSDnpDyAIgpBLM2bMYMmSJXz88cdygEkQBEEQBEF4elitVpo3b05YWBg//PADISEh+V0lQRCEXBM5vARBeOwkSZLzeZQuXTqfayMIgiAIgiBkZM2aNYSFhRESEiKCXYIgPPNE+1BBEB6rjz/+mHPnznHmzBl8fX3zrMuLIAiCIAiC8OguXbrEkiVLuHv3Lrt27UKpVDJixIj8rpYgCMIjEwEvQRAeq99++w1XV1caNmzIyJEjM8xFIwiCIAiCIOSPO3fu8NNPP6HRaChXrhxDhw7NsxGpBUEQ8pPI4SUIgiAIgiAIgiAIgiA8V0QOL0EQBEEQBEEQBEEQBOG5IgJegiAIgiAIgiAIgiAIwnNFBLwEQRAEQRAEQRAEQRCE54oIeAmCIAiCIAiCIAiCIAjPFRHwEgRBEARBEARBEARBEJ4rIuAlCIIgCIIgCIIgCIIgPFdEwEsQBEEQBEEQBEEQBEF4roiAlyAIgiAIgiAIgiAIgvBcEQEvQRAEQRAEQRAEQRAE4bkiAl6CIAiCIAiCIAiCIAjCc0UEvARBEARBEARBEARBEITnigh4CYIgCIIgCIIgCIIgCM8VEfASBEEQBEEQBEEQBEEQnisi4CUIgiAIgiAIgiAIgiA8V0TASxAEQRAEQRAEQRAEQXiuiICXIAiCIAiCIAiCIAiC8FwRAS9BEARBEARBEARBEAThuSICXoIgCIIgCIIgCIIgCMJzRQS8BEEQBEEQBEEQBEEQhOeKCHgJgiAIgiAIgiAIgiAIzxUR8BIEQRAEQRAEQRAEQRCeKyLgJQiCIAiCIAiCIAiCIDxXRMBLEARBEARBEARBEARBeK6IgJcgCIIgCIIgCIIgCILwXBEBL0EQBEEQBEEQBEEQBOG5IgJegiAIgiAIgiAIgiAIwnNFBLwEQRAEQRAEQRAEQRCE54oIeAmCIAiCIAiCIAiCIAjPFRHwEgRBEARBEARBEARBEJ4rIuAlCIIgCIIgCIIgCIIgPFdEwEsQBEEQBEEQBEEQBEF4roiAlyAIj93cuXMJCAjI72o8M0JDQ2natGl+V0MQBOGF0qtXL9q2bZvf1Xhh2Ww22rZty8KFC5/4tsV9Ss48Lfcp77//PsOHD8/varxwvv32W1q1aoXNZsvvqgBw48YNAgICWLJkyUPnfZT3umPZqKioLOczm800atSIVatW5Wo7Qt4SAS9BeAGtW7eOgIAAqlSpwu3bt9NNf5pu+g8fPszcuXOJjY3N76rkqdu3bzN37lxOnz6d31URBEHIM+fPn+ejjz6iQYMGBAYGUr9+fT788EPOnz+f31V7qi1atIht27bldzXy1YYNGwgPD6dnz575XZUcEfcp+ad///5s3bqVM2fOPNJ6Vq9ezbBhw2jcuDEBAQGEhoZmOF+vXr0ICAjI8FG5cuUcbdNsNtO6detsBWp+++03AgICCAkJydE2UnMEhRyPihUr0rhxY959990cvcbx8fEsXryY/v37o1TaQwlJSUnMnTuXffv25bp+zxONRsNbb73FokWLSElJeaR1mUwmPv/8c+rXr09QUBCvvfYau3fvzvbyt2/fZvjw4dSoUYNq1aoxePBgrl+/nm6+zM7rb7755pHq/zRQ53cFBEHIPyaTiW+++YZPP/30sW5n8ODBDBgwIFfLHjlyhHnz5tGpUyc8PDzyuGb5JyIignnz5uHn50fFihWdpk2aNAlJkvKpZoIgCLmzdetWPvjgA7y8vOjSpQv+/v6EhYXx008/sWXLFmbPnk3z5s3zu5pPpa+//pqWLVvSrFmz/K5KvlmyZAlt2rTB3d39iW9b3Kek9yzcp1SqVInAwED+7//+jxkzZuR6PYsXLyYhIYEqVapw586dTOcbNGgQr776qlNZUlIS48aN46WXXsrRNleuXEl4ePhD50tISODzzz/HYDDkaP2Zadu2LQ0bNsRms3Hx4kW+//57/v77b3788cd0r3NGfvrpJywWi9MP40lJScybN48hQ4ZQu3btPKnn4/Io7/Wc6Ny5MzNnzuT3339Pd87kRGhoKFu2bKF3796UKlWK9evXM2DAAL777jtq1KiR5bIJCQn07t2buLg4Bg4ciEajYdmyZfTs2ZNffvmFAgUKOM3/0ksv0aFDB6eySpUq5bruTwsR8BKEF1jFihX58ccfGTBgAL6+vo9tO2q1GrX66brcJCUlodfr87saGdJoNPldBUEQhBy5du0aI0aMoHjx4qxatYqCBQvK03r37k2PHj0YMWIEv/32G8WLF39i9UpMTMyzL4rC43Pq1CnOnDmTacuax03cp+TM03Sf8sorrzB37lwSEhJwdXXN1TpWrFhBsWLFUCgUWbaiyiio9euvvwLQrl27bG8vMjKS+fPn069fP+bMmZPlvAsXLsTV1ZXatWuzffv2bG8jM5UqVXIKajha/Xz//fdMnDgxw2VSX0fXrVtH06ZN0Wq1j1yX/PCk3useHh7Ur1+f9evX5zrgdfz4cf744w9GjBhB3759AejYsSNt27Zl5syZ/PDDD1kuv3r1aq5cucLatWsJCgoCoEGDBrRr146lS5fywQcfOM1fqlSpdAGv54Ho0igIL7CBAwdis9n49ttvHzqvxWJh/vz5NGvWjMDAQJo2bcqsWbMwmUwPXTaj/vIBAQFMnDiRbdu20bZtWwIDA2nTpg1///2303KOX+xefvlluXntjRs35Hl+/fVXOnfuTFBQELVq1eL9999P94uZo4vmiRMn6NGjB8HBwcyaNYuBAwfy8ssvZ1jnrl270rlzZ6eynGzrwoUL9OrVi+DgYBo0aOB0jPft2yd/+I0aNUrer3Xr1gEZ58ZITExk2rRpNGrUiMDAQFq2bMmSJUvS/cKaneMK9ibpU6ZMoWnTpgQGBlK3bl3eeustTp48meHxEARByMrixYtJSkpi0qRJTsEugIIFCzJx4kQSExPla+HmzZsJCAhg//796db1ww8/EBAQwLlz5+SyixcvMmzYMGrVqkWVKlXo3Llzui9/ju76+/fvZ/z48dStW5dGjRoBObvmZXX9doiMjGT06NHUq1ePKlWq0L59e9avX59uvuxcuwMCAkhMTGT9+vXy50FWgZ99+/YREBDAxo0bmTdvHg0aNCAkJIRhw4YRFxeHyWRiypQp1K1bl5CQEEaNGpXhZ3V2PtMOHjwod/UKDAykUaNGfPbZZyQnJzvNFxoaSkhICLdv3+add94hJCSEOnXqMH36dKxWa6b74rBt2zY0Gk26FguO+4erV68SGhpKjRo1qF69OqNGjSIpKclpXnGf8mLep9SrV4/ExET27NnjVH7z5k0uXryY4bFLy8/PD4VCka1509qwYQMGgyHT1ykjM2fOpHTp0rRv3z7L+a5cucKyZcsYNWrUYwvS1KlTB0A+Z7O6jl6/fp2zZ89Sr149efkbN25Qt25dAObNmyefK3PnzpXn2bt3L927d6dq1arUqFGDwYMHZ/janDp1in79+lGtWjVCQkLo06cPR48ezdH+rFmzRr4GdOnShePHjztNz+i9npyczOTJk6lduzYhISEMGjSI27dvp9sPh7i4uIdej8B+bh46dIjo6Gin8osXL3Lz5s2H7svmzZtRqVR07dpVLtNqtbz66qscOXLkoS0Et2zZQpUqVeRgF0DZsmWpW7cumzZtynCZ5OTkR+6G+bR5un7KEAThifL396dDhw78+OOP9O/fP8tWXmPGjGH9+vW0bNmSt956i+PHj/P1119z8eJF5s+fn6vtHzp0iK1bt9K9e3dcXV1ZsWIFw4YN46+//qJAgQI0b96cK1eusGHDBkaNGiU3vXV8mVq4cCFfffUVr7zyCq+++ipRUVGsXLmSHj168Msvvzh1LYiOjqZ///60adOG9u3bU6hQISpXrszIkSM5fvy404dBWFgYR48eZcSIEXJZTrYVExNDv379aN68Oa+88gpbtmxh5syZGI1GGjVqRNmyZRk2bBhz5syha9euVK9eHbD/ypYRSZIYPHiwfANasWJFdu3axYwZM7h9+zajR4/O0XEFGDduHFu2bKFnz56ULVuW6OhoDh06xMWLF3Och0IQBOGvv/7Cz88v0y4WNWvWxM/Pj//9738ANG7cGIPBwKZNm6hVq5bTvBs3bqR8+fIYjUbAnhesW7du+Pr60r9/f3m5d999l7lz56brJjlhwgQKFizIu+++S2JiIpD9a97Drt9g/0LQq1cvrl27Ro8ePfD392fz5s2EhoYSGxtLnz59gOxfu2fMmMGYMWMICgri9ddfB6BEiRIPPebffPMNOp2OAQMGcPXqVVauXIlarUahUBAbG8uQIUM4duwY69atw8/PjyFDhsjLZvczbfPmzSQnJ9OtWze8vLw4fvw4K1eu5NatW+laplitVvr27UtQUBAjRoxg7969/N///R/Fixene/fuWe7LkSNHMBqNmbYceu+99/D39+eDDz7g1KlTrF27loIFC/Lxxx/L84j7lBfzPqVcuXLodDoOHz7sdC0YOXIk+/fv5+zZszl70XMgKiqKPXv28Morr2S7Jenx48f55ZdfWL169UODbJ999hm1a9emUaNGmQYoHtW1a9cA8PLycirP6Dp65MgRwLmbW8GCBRk/fjzjx4+nefPm8mvgCCrt2bOH/v374+/vz5AhQ0hOTmblypV069aNdevW4e/vD9iv8z169MDV1ZV+/fqhVqtZs2YNvXr1YuXKlQQHBz90XzZs2EBCQgJdu3ZFoVCwePFihg4dKgfUMxMaGsqmTZvo0KEDwcHBHDhwIMtuj9m5HgFUrlwZSZI4cuQITZo0kctbt25NrVq1WLFiRZb7c/r0aUqVKoWbm5tTueNacPr0aYoWLZrhsjabjbNnz9KlS5d006pUqcI///xDfHy807rXr1/P6tWrkSSJsmXLMnjw4By1XHxqSYIgvHB+/vlnyWg0SsePH5euXbsmVapUSZo0aZI8vWfPnlKbNm3kv0+fPi0ZjUbpk08+cVrPtGnTJKPRKO3duzfL7c2ZM0cyGo1OZUajUapcubJ09erVdNtZsWKFXLZ48WLJaDRK169fd1r+xo0bUsWKFaWFCxc6lZ89e1aqVKmSU3nPnj0lo9Eoff/9907zxsXFSYGBgdK0adOcyr/99lspICBACgsLy/W21q9fL5elpKRIL730kjR06FC57Pjx45LRaJR+/vnndMdr5MiRUpMmTeS///zzT8loNEoLFixwmm/o0KFSQECA0zHM7nGtXr26NGHChHTbFgRByKnY2FjJaDRKgwcPznK+QYMGSUajUYqLi5MkSZI++OADqW7dupLFYpHniYiIkCpUqCDNmzdPLuvTp4/Utm1bKSUlRS6z2WxS165dpRYtWshljs+2bt26Oa1TkrJ3zcvu9XvZsmWS0WiUfv31V7nMZDJJXbt2lapWrSrvX06u3VWrVpVGjhyZZf0c/v33X8loNEpt27aVTCaTXP7BBx9IAQEBUr9+/Zzm79q1q9NnSk4+05KSktJt/+uvv3b6jJQk++eW0Wh0et0kSZI6duwoderU6aH71LBhQ6dj7OC4fxg1apRT+bvvvivVqlVL/lvcp7zY9yktWrRId9479jOncvJeXLFihWQ0GqWdO3dma36bzSa9+uqr0gcffCBJkiRdv35dMhqN0uLFi9PN+9dff0mVKlWSzp8/L0mS/ZhXrVo1m3uRnmNbc+fOlSIjI6U7d+5I+/btkzp27CgZjUZpy5YtkiRlfR2dPXu2ZDQapfj4eKfyyMhIyWg0SnPmzEm33Q4dOkh169aV7t27J5edPn1aqlChgjRixAi57J133pEqV64sXbt2TS67ffu2FBISIvXo0SNb+1arVi0pOjpaLt+2bZtkNBqlHTt2yGVp3+snTpyQjEajNGXKFKd1hoaGptun7F6PUtffaDRK33zzjVO50WiUevbsmeU+SZIktWnTRurdu3e68vPnz2d4vUjN8ZqkvSZLkiStXLlSMhqN0sWLF+Wyrl27SsuWLZO2bdsmrV69Wmrbtq1kNBqlVatWPbSeTzvRpVEQXnDFixenffv2/Pjjj0RERGQ4j+MX+bfeesup/O2333aanlP16tVz+hW7QoUKuLm5ZTh6SFp//vknNpuNV155haioKPnh7e1NyZIl040U4+Likq7pv5ubGw0bNmTTpk1OTe43btxI1apVKVasWK62ZTAYnPrAu7i4UKVKlWztV0b+/vtvVCoVvXr1cip/++23kSQpXTeA7BxXDw8Pjh07luEonYIgCDmRkJAA8ND8OY7pjvlfeeUVIiMjnbo1btmyBZvNRuvWrQF7q5d///2XV155hfj4ePn6e+/ePerXr8+VK1fSXcdef/11VCqVU1l2r3nZuX7//fffFC5c2Clps0ajoVevXiQmJnLgwAF5vpxcu3OqQ4cOTq0WgoKCkCQp3S/6QUFBhIeHY7FYgJx9pul0OvnfiYmJREVFERISgiRJnDp1Kl2dunXr5vR39erVnbr3ZSY6OjrLhO9vvPGG0981atQgOjqa+Ph4QNynvOj3KZ6enty7d8+pbMWKFY+1dRfYWxQVLFgw2wnr161bx7lz5/joo4+ynM9kMjF16lTeeOMNypUrlxdVlc2dO5e6devy0ksvyS1VP/roI1q0aOE0X0bX0ejoaNRqdbZzpUVERHD69Gk6derk1IKsQoUK1KtXT35fWq1Wdu/eTbNmzZxyPPr4+NC2bVsOHTokv9ez0rp1azw9PeW/HS2Oszqvd+3aBZCuFWpWo8U+7Hrk4KhL2nPz7NmzD23dBfbWxC4uLunKHfnT0nYtT83RLTGr5VN3Xfzhhx/o06cPL7/8Mt26dePnn3/GaDQye/bsLLfzLBBdGgVB4J133uG3337jm2++YcyYMemmh4WFoVQq03WxKFy4MB4eHoSFheVquxk1w/X09MzW0N5XrlxBkqR0H9AOaXMd+Pr6ZnjRb926Ndu2bePIkSNUq1aNa9eucfLkSafm9zndVpEiRdI1U/f09Mz1jVdYWBg+Pj7pmjSXLVtWnp5ado7rRx99RGhoKI0bN6Zy5co0atSIjh07PtFk0oIgPB/SBrIykzYw1rBhQ9zd3dm4caOcA2bjxo1UrFiR0qVLA/buNpIk8dVXX/HVV19luN7IyEinLvmOLjKpZfeal53rd1hYGCVLlkSpdP7d2HFNduRmyem1O6ccwQ4Hx+iGaT8D3N3dsdlsxMXFUaBAgRx9pt28eZM5c+awY8cOYmJinOZL++VOq9Wmy9/m6emZbrnMSFmM+pd2Xx3BsZiYGNzc3MR9ygt+nyJJUq5zcOXW9evXOXLkCD179sxWfq34+HhmzZpF3759M+2G5rBs2TLu3bvH0KFD86q6sq5du9KqVSsUCgUeHh6UL18+w/Muo+toTjmuhY7reWply5bln3/+ITExkYSEBJKSkjKdz2azER4eTvny5bPcXtrj6gg4ZfV+vXnzJkqlMt3+lixZMtNlHnY9cnBc03J7bup0ugxzEDoCVal/kEjLEdTKavmsBh5wcXGhR48ejBs3jhMnTjx0RMinmQh4CYLg1Morqz7reX0zkfaXI4esbnodbDYbCoWCb7/9NsP1pM2lkNmHQpMmTdDr9WzatIlq1aqxadMmlEolrVq1yvW2MtuvJyU7x7V169bUqFGDP//8k927d7NkyRK+/fZb5s6dK+epEQRByA53d3cKFy780C/LZ8+exdfXV/5C4OLiQrNmzfjzzz8ZN24ckZGRHD582GnkKJvNBthbijRo0CDD9aYNcmR0E5/da15+X79zIm3A7WHljs+A7H6mWa1W3nrrLTnfU5kyZTAYDNy+fZvQ0FD5tXF4lGPn5eWV5ZfSh+2Tg7hPeTHvU2JjY7MMUDwOv//+O5D90RmXLFmC2WymdevWcqvHW7duAfb637hxAx8fH1JSUli4cCHdu3cnPj5eDiwnJiYiSRI3btxAr9dTqFChXNW7ZMmSTknnM5PRddTLywuLxZIu99PT4lHerzmR3euRI9jvyEuXU4ULF86wheOdO3cAewu4zHh5eeHi4iLPm9Pl4UEAMbs/WjytRMBLEAQABg8ezG+//ZbhaFR+fn7YbDauXr0q/1oHcPfuXWJjY/Hz83ts9crs5rVEiRJIkoS/v3+Gvwhll8FgoHHjxmzevJlRo0axceNGatSo4dRaIK+2lVpObsr9/PzYu3dvuhuMS5cuydNzw8fHhx49etCjRw8iIyPp1KkTixYtEgEvQRByrEmTJvz4448cPHgww1+CDx48SFhYmNNoU2Dv1rh+/Xr27t3LxYsXkSSJV155RZ7uaM2h0Wiy9SUtK3l1zfPz8+Ps2bPYbDanLz6Oa7Lj1//Hde1+VNn9TDt37hxXrlxh+vTpdOzYUS7fvXt3ntepTJky2er6mBlxn/Li3qdYLBbCw8PTjRr5uG3YsIESJUpQtWrVbM0fHh5OTEwMbdq0STdt0aJFLFq0iF9++QV3d3cSExNZvHgxixcvTjfvyy+/zMsvv8yCBQsedRdyrEyZMoB9ZMYKFSrI5ZmdK45r4eXLl9NNu3TpEgUKFMBgMKDVatHr9ZnOp1QqH9oqLreKFSuGzWbjxo0blCpVSi6/evXqI6/bcU1LfU3KiQoVKrBv375076tjx44BULFixUyXVSqVGI1GTpw4kW7a8ePHKV68+EODlo6uoGlb7j5rRA4vQRAA+81S+/btWbNmTbpfAxw3Ft99951T+dKlS52mPw56vR6wDwGcWosWLVCpVMybNy/dLyqSJKXrL5+V1q1bExERwdq1azlz5ozTl6283paDY7+y0y2iYcOGWK1WVq1a5VS+bNkyFAoFDRs2zNG2rVZruuNZqFAhfHx8sjV8uyAIDxcfH8+2bdsyHHr9edS3b190Oh3jxo1Ld02Mjo5m3Lhx6PV6+vXr5zStXr16eHl5sXHjRjZt2kRQUJBTl6VChQpRq1Yt1qxZk2GeyaioqIfWLa+veQ0bNuTOnTts3LhRLrNYLKxYsQKDwUDNmjXl+bJ77TYYDNn6PMgL2f1McwTzUs8jSRLLly/P8zpVrVqV8+fP5/ozSNynvLj3KRcuXCAlJYWQkBCn8ps3bz626++pU6e4ePGiUx6/tK5duyaPgAjQq1cv5s+f7/SYOHEiAJ07d2b+/Pn4+/tTqFChdPPNnz+f2rVro9VqmT9/PgMHDnws+/UwjmOcNoiS2bni4+NDxYoV+eWXX5ymnTt3jt27d8vvS5VKxUsvvcT27dudAt93795lw4YNVK9e/bG1KKtfvz4Aq1evdipfuXLlI6/75MmTKBSKdEHRixcvyt09s9KqVSusVitr1qyRy0wmE+vWrSM4ONgpCJjR+d6yZUv+++8//vvvP7ns0qVL/Pvvv04tRDP6HI2Pj+e7776jQIECz/zo7aKFlyAIskGDBvHrr79y+fJlp37yFSpUoFOnTqxZs4bY2Fhq1qzJf//9x/r162nWrBl16tR5bHVyXGRnz55N69at0Wg0NGnShBIlSvDee+/xxRdfEBYWRrNmzXB1deXGjRts27aN119/nb59+2ZrG40aNcLV1ZXp06ejUqlo2bKl0/S83FbqdXp4ePDDDz/g6uqKwWBI90XPoWnTptSuXZvZs2cTFhZGQEAAu3fvZvv27fTp0ydbw9enlpCQQKNGjWjZsiUVKlTAYDCwZ88e/vvvP0JDQ3O0LkEQ7IYPH07NmjXp2bMnycnJdOnShbCwMCRJYtasWemuK8+bUqVKMW3aND7++GPatWvHq6++ir+/P2FhYfz000/cu3ePWbNmpbteaTQamjdvzh9//EFSUhIjR45Mt+5x48bRvXt32rVrx+uvv07x4sW5e/cuR48e5datW/z2229Z1i2vr3ldu3ZlzZo1hIaGcvLkSfz8/NiyZQuHDx9m9OjR8heznFy7K1euzN69e1m6dCk+Pj74+/sTHByc47plR3Y/08qUKUOJEiWYPn06t2/fxs3NjS1btjyWwJyjxcr+/fvlL6A5Ie5TXtz7lD179qDX69O1AB05ciT79+/PVl6yHTt2cObMGQDMZjNnz56VW1A1bdrUqTUTZK8745tvvimvG+znSdrAgSO4U65cOZo1ayaXp/63w7Zt2/jvv//STVu3bh2jRo1i6tSp6QY9yGvFixfHaDSyd+9eXn31Vblcp9NRrlw5Nm3aRKlSpfDy8qJ8+fIYjUZGjBhB//796dq1K6+++irJycmsXLkSd3d3hgwZIq/jvffeY8+ePXTv3p3u3bujUqlYs2YNJpOJjz/++LHtU2BgIC1btuS7774jOjqa4OBgDhw4wJUrV4BH6ya9Z88eqlWrlq5LY+vWralVq9ZDE9cHBwfTqlUrZs2aRWRkJCVLlmT9+vWEhYUxZcoUp3kzOt+7d+/O2rVrGThwIG+//TZqtZply5ZRqFAheUAPgFWrVrFt2zaaNGlCsWLFiIiIYN26ddy8eZMZM2ZkmOPtWSICXoIgyEqWLEn79u1Zv359ummTJ0/G39+f9evXs23bNry9vRk4cKDTh9XjEBQUxPDhw/nhhx/YtWsXNpuN7du3YzAYGDBgAKVKlWLZsmXMnz8fsCdifemll3LUtF2r1dK0aVN+//136tWrl2FehLzaloNGo2HatGnMmjWL8ePHY7FYmDp1aoY3kkqlkoULFzJnzhw2btzIunXr8PPzY8SIEU4fWNml0+no1q0bu3fvZuvWrUiSRIkSJeQvlYIg5NzBgwcZPHgwYB8xTZIkDhw4wPr161m4cOFzH/ACe/fEMmXK8M033/DTTz8RHR2Nl5cXtWvXZuDAgRiNxgyXa926NWvXrkWhUKRruQL2L4M///wz8+bNY/369URHR1OwYEEqVarEu++++9B65fU1T6fTsWLFCmbOnMn69euJj4+ndOnS6b5w5uTaHRoaytixY/nyyy9JTk6mU6dOjy3gBdn7TNNoNCxatIjJkyfz9ddfo9Vqad68OT169HAa4S8vBAYGEhAQwKZNm3IV8AJxn/Ki3qds3ryZ5s2bP1ILoK1btzrd+546dUoehbRIkSJOAS+bzcYff/xB5cqV5S5++SkxMRGw53t6Erp06cJXX31FcnKyU965yZMnM2nSJKZOnYrZbGbIkCEYjUbq1avH4sWLmTNnDnPmzEGtVlOzZk0+/vhjp3OpfPnyrFq1ii+++IKvv/4aSZIICgri888/f6zXQoDp06fj7e3NH3/8wZ9//km9evWYPXs2rVq1ynWwJy4ujn/++Ydx48Y9Ut1mzJjBl19+yW+//UZMTAwBAQEsWrRIbkmcFTc3N1asWMFnn33GwoULsdls1K5dm1GjRjl1U6xWrRpHjhyRP7f1ej1BQUFMmTJFHlDmWaaQ8jqLmyAIgiA8paxWK2azOb+rITxBGo3miSRoDgoKYsuWLRQtWpQRI0bg4+PDRx99xM2bN2nTpg1Hjhx57HUQhGfZL7/8wsSJE9m5c6c86pkgZOX06dN06tSJ9evXZ5nP6Hk2fPhwuSXtkxAXF0ezZs346KOPeO21157INvPD6dOn6dixI59//jnt27fP8fLLli1j8eLFbNu2LcvRFIXHT7TwEgRBEJ57kiRx69YtoqOj87sqQj7w8vKiSJEij3XY+qJFi3LkyBE8PT3ZtWsXs2bNAuw5TZ717gCC8CS0b9+exYsXs2rVKrm1pCBk5ZtvvqFly5YvbLBLkiT279/P559//sS26e7uTt++fVmyZAldunTJdMTCZ0na1mpgzweoVCqz1ZIqLbPZzLJlyxg8eLAIdj0FRAsvQRAE4bkXHh5OdHQ0Pj4+GAyGxxr4EJ4ekiSRmJhIREQEXl5ej22UJ7DnwPjss88wGAwULVqUX375BaVSyYoVK9i6detDc3UIgiAIgvDkzZs3jxMnTlCnTh1UKhV///03f//9N127dpUHFhCeXSLgJQiCIDzXrFYr586dw8fHJ8O8J8LzLzIykoiICIxG42Pt3vjff/9x69Yt6tWrh6urKwA7d+7E3d2d6tWrP7btCoIgCIKQO7t372bevHlcvHiRxMREihYtSocOHRg0aBBqtegQ96wTAS9BEAThuZacnMzly5cpVaqUPHS28GJJSkriypUrlC5d+rF3LzCZTNy4cYMSJUqIG2VBEARBEIR89Ox3uhUEQRCEbBDdGF9cT+K1T0pKYvTo0VStWpW2bdsSHh4OwKRJk/jmm28e+/YFQRAEQRAEZyLgJQiCIAiC8Ii++OILzpw5w/Lly9FqtXJ53bp12bhxYz7WTBAEQRAE4cWUrwGvAwcOMGjQIOrXr09AQADbtm1zmi5JEl999RX169cnKCiIN998kytXrjjNEx0dzYcffki1atWoUaMGo0ePJiEhIUf1kCSJ+Ph4RO9OQRAEQXh89u3bR0BAALGxsQCsW7eOGjVqZLnM3Llz6dChw5Oo3iPZvn07Y8eOTbc/5cuX59q1a/lUq8dL3D8JgiAIgvA0y9eAV2JiIgEBAYwbNy7D6d9++y0rVqxg/Pjx/Pjjj+j1evr27UtKSoo8z0cffcSFCxdYunQpixYt4uDBg4wdOzZH9UhISKB69eo5DpQJgiAIgpCxXr16MWXKFKeykJAQ/vnnH9zd3fOpVo9PVFRUhoMiJCUlPbfdacX9kyC8oJKSoHFj+yMpKb9r88JJSkqicePGNG7cmCRx/AUhS/maTbVRo0Y0atQow2mSJLF8+XIGDx5Ms2bNAJgxYwb16tVj27ZttGnThosXL7Jr1y5++uknqlSpAsCYMWMYMGAAI0aMwNfX94ntiyAIgiAIWXNxcaFw4cL5XY3HIjAwkJ07d9KrVy+n8rVr11K1atX8qZQgCMLjoNfDzp35XYsXll6vZ6c4/oKQLU9tDq8bN25w584d6tWrJ5e5u7sTHBzMkSNHADhy5AgeHh5ysAugXr16KJVKjh8//sTrnKF7R+HScri+Hm5tg7v7IOYMJN0Ga8pDFxcEQRAeE0kCS8KTfeSw69fmzZtp164dQUFB1K5dmzfffJPExMQMW0+98847hIaGyn+vWrWKFi1aUKVKFerVq8ewYcPkaTabjW+//ZbmzZsTGBhI48aNWbhwoTw9PDyc4cOHU6NGDWrVqsXgwYO5ceOGPD00NJR33nmHefPmUadOHapVq8bYsWMxmUzy9P3797N8+XICAgIICAjgxo0b6bo0Omzbtk2ua9++feWE75lZu3Ytr7zyClWqVKFVq1asWrUqR8f1cXj//feZNWsW48aNw2q1snz5ct5++23WrVvH+++/n+31fP3113Tp0oWQkBDq1q3LO++8w6VLl5zmSUlJYcKECdSuXZuQkBCGDh3K3bt3nea5efMmAwYMIDg4mLp16zJ9+nQsFovTPPv27aNTp04EBgbSvHlz1q1bl/sDIAiCIAiC8JR5asfLvnPnDkC67gGFChWSb+ru3r1LwYIFnaar1Wo8PT3l5fOVzQp/NgRLXObzqPTgUgC0he0PXWHQFwODPxiK2x/uZcGlIDynXSIEQRCeOEmCP+vD3T1PdrPe9bC+vNN+Pb8f+5Lu/0OSJOT/JImIOxF8+OGHDHt/GI2bNiYxIZHDhw8TkxyDxWYhxZJCVFIUSPZ1mKwmki3J3Em4w+lTp5kyZQqjJ44mMDiQ2OhYjh09xs24m0iSxNdzv2bjLxsZ/N5gKletTNTdKK5ducbV6KuYLWYGvzmYClUqMH3hdFQqFd8v/Z4+b/Vh3sp5aNQaYlNi2bN3DyZMTJozidu3bjNnyhysWiu9Bvbi1YGvcur8KUqULkG3ft2QJIkoVRRXoq8AcPrOaQxJBm7E3iApKYlZc2fRf2R/1Bo1i2ctZuCQgUycPxGAW/G3SLIkEZcSh7vWnd9++42vvvqKsWPHUrFiRU6fPs2nn36KwWCgU6dOT/T1TK1GjRr8+uuvfPPNNxiNRnbv3k2lSpX44YcfCAgIyPZ69u/fT48ePahSpQpWq5VZs2bRt29f/vjjDwwGAwCfffYZ//vf//jyyy9xd3dn0qRJDBkyhB9++AEAq9XKwIED8fb25ocffiAiIoKRI0ei0Wj44IMPALh+/ToDBw7kjTfeYObMmezdu5cxY8ZQuHBhGjRokPcHSBCEx85qk1Apn+z3hfzYpiAIQnY9tQGv54JSBYFj7K27LAlgTbz/K3+8/RkJrEn2vu9JN7Nel9otVQCsPHgEgGdlcC0JClX6h1IFCnWqMrW9TBAEIQ85gjQ2yfbQf2dVlp1p6ea12bBhn98m2ezL3p/u+Ntms2EymTBYDMSlxJEkJd3fhg1PmwXNEz5eCeZEzt/+D0mBnOjbEfBK6/K5y1gsFkpUK0GcLg50ENw8mLDkMJLMScSkxHDp3oOWP4nmRDDB1ZirnLx4EhedCyWrlsRisGBwNVDXry43426SlJjEuu/X0Wd4H4KbBAPgU8AHn/I+3Em8wz9//oPZaqb3+73l3FNvffQW/dv1Z++/ewmqGYTJakKlVvHWR2+h1Wkp4F+Azm915vtF39OhTweUWiVKlRKViwqth33EQpNkIuV+y+ZkazIqqwqzzYzFYqH30N6UrlgagIGhA/m4z8ecPXmWshXLyudEsiUZd607c+fOJTQ0lBYtWgBQvHhxLly4wJo1a/I14AVQokQJJk+e/EjrWLJkidPf06ZNo27dupw8eZKaNWsSFxfHzz//zMyZM6lbty5gD4C1bt2ao0ePUrVqVf755x85v6m3tzcVK1Zk+PDhzJw5kyFDhuDi4sIPP/yAv7+/3CqwbNmyHDp0iGXLlomAlyA8o1RKBcN/OMKFiPjHuh1tShJLJ7yOSqnALfwGuLo+1u0JzhISEihVqhQAV65cwVUcf0HI1FMb8HLk+IiMjMTHx0cuj4yMpEKFCgB4e3sTFRXltJzFYiEmJubpyRFSaYT9AfYWBZIFbGawJkPyXUiJgORbEH8VUu7YH8kRkHwbTFH3H/fsQbLY0/bHra0P1q8y3A+AVQCPiuBuBLUBFMr7D0fAS2kPeqm0oHQBpRZUOlCq7wfDNOmflU/t6SEIQhpOQZ6HPByBJUcgIaNyq81qf0gPnh3lNmxYbJb0Qao0LZTSPd//t8VmIdmaTLI5mWRLMinWFFIsKfKzyWqS/zZZ7UESk8Uk/+0oM1vN8rPZZpanpf63yWqipGtJFr20CGuM1flTr8o8lLbkJ/o62ZQ6wEYmMS4nJcuWpHK1yozqO4qgmkEE1QyiTuM6uLu7o1AoUCqUaFX2YJLjb7VCjUFjoHbd2qwvsp4PenxAtdrVqFa7GnUb1UWn13Hr4i3MZjN16tTBS+eFAoW8DoCIqxHcvnmbfm36OdXHbDKTdDcJX1df9Go95cqXo6R3SfuyKKhXox5Lk5aiTlRTtGhRXFQuuLq44u/hL89z12Bvoe3v7o+7hzveem9UahVNajdBqVSiUCgo5VUKd3d3THdMlK5bmgK6ArioXPDQepCYmMi1a9f45JNP+PTTT+W6WSyWfEmEHx+f/S+Vbm5uudpGXJy9lbinpycAJ06cwGw2O6V8KFu2LMWKFZMDXkePHsVoNOLt7S3PU79+fcaPH8+FCxeoVKkSR48elQNmqef57LPPclVPQRCeDhci4jl5M/bhMz4CvSkZz4SYx7oNIWtpu7ELgpCxpzai4e/vT+HChdm7dy8VK1YE7DeWx44do1u3boB9tKfY2FhOnDhBYGAgAP/++y82m42goKB8q3umFApQaOzBJLUBtAUB44Ppks0eCLMk2rtBJt8Bcwyk3IOUW/Z/J0dA/EWIuwDxl+2txqKP2R8AKMGrCnjXBe/a4FYWe38XK0hmMCfb/22z2reX+luXHCBT24NdSg0o9aB2BbXufqAs7eOpPYUE4ankCDClDiA5BZMcAacMyiw2C2abGavNisVmcXo4BbDStHoC7P++Xw7IQQ7H36TqjeCYplQokSSJFGsKSeYkki32AFWiOZEkSxIp1hT7v81JpFhSSLIkkWSxz5dkTsrw72RLMiar6ckd8DQUKFAoFChQ2AMsaq1TWVbPSpSgsB+X1NOAhy6f4Tz3/+00DQX2/+3ly5ct59jRY+zds5e/fv2Ln/7vJ5avWo7BxYCn1pMyBcrI+6ZVaHFzcaOkZ0nwhLU/reXQgUPs3buXH//vR9YuW8vK71fiX8AegPJ19aWoW9H0x8isoFKlSkyelr6lUoECBXDXu6NVaVGr1BTQFZCnuWvtASd3F3c8tB6olWpcVC64uzwIROk1egBcNa64alxxUbmgQIFBY0CpdE4rqlFp0Kl1qJQq+XgkJiYCMGnSJIKDg53mT7v8k1CjRo2HjsAoSRIKhYLTp0/neP02m43PPvuMatWqYTTa7xfu3r2LRqPBw8PDad5ChQrJ6Rzu3r3rFOwC5L8fNk98fDzJycnodLoc1zczVqsVs9mcZ+sThKeNRqNBpRI9KQRBEJ42+RqtSEhI4Nq1a/LfN27c4PTp03h6elKsWDF69+7NwoULKVmyJP7+/nz11Vf4+PjIozaWLVuWBg0a8OmnnzJhwgTMZjOTJk2iTZs2z+YIjQqlPRCmNgDe4FbantjeEehKumVv6VW0JWgLAQqIvwTRx+HecftzUtiDANiFRaD1hiLNoGgrexfIrG7MJdv9FmgW+7M1xd71MvmWPUgm11P1INil1ILaHTRu9jKVo+XY/ZZkIu+Y8ByRJElu8WSxWZxaP2X07GhpZLFZ7K2QrGYskgWrzWpfF1Z7tzxH6ytsSLb7ASmFQu7qpkAhf2lWKpRODwX2Mnma8kEZgMlqIsGcQHxKvP3ZFE+cKc7+SIkjwZxAXEoc8aYH0xNMCfK/E82JmXa5e1QKFOg1enRqHXq1Hq1ai06tc3poVVqnf2vVWqdnR7mL2sVenurfLioXXFQu6NHjkeRBCa8SaHXax7Ivj4tCoaBqSFWqhlRlwKABtGnRhr+2/0WBAgW4e+fBr7tWq5ULFy5Qs2ZNuUytVlO7bm1q163NwEEDafRSI/bv30/9BvXR6XTs37efTv7puwBWqFiBrZu3UqBgAdxc7a2SUp8DjnP1/NnzJCQloNPqkJA4evQoBoMBbx9vzDYzKrUKi8V+7js4/m2ymuzvB5sFi8XCsePHCKwSiITE1StXiYuLw7+kPyarCZvNJgdkvb298fHx4fr167Rv3z5Pj3VuLF++/LGuf8KECZw/f57Vq1c/1u08LpIkcevWLaKjo/O7KoLw2Hl5eVGkSJGHBsEFQRCEJydfA14nTpygd+/e8t9Tp04FoFOnTkybNo3+/fuTlJTE2LFjiY2NpXr16ixevBit9sEXlpkzZzJp0iT69OmDUqmkRYsWjBkz5onvy2Oj0oLKB3Q+9q6LpihIvAFJ4YDCnsPLIwBKvGafPykc7uyBu7sh8gCk3IWrP9gfej8o1gr8O4G+SPptKZSguN9yKyuOgJjNbG9hZo6BBAtyazGl5kHwS+0BLh6g1NkDYSq9/VncDAj5yBG4MlvNWKX0raUcAS2zzezUxc4RrErb+soqWe2nvwL52dEiRaVQoVQoUSlVzsEqpRKtQpsugOUIVKWtb7IlmZiUGKKTo4lOjiYmJYaY5BhiUmKITYklJtn+HGuKtT/ff1hslnTryw2VQoWbixsGjQGDxoCrxtX+bxcDBvX9MhdX9Go9eo3ePk1tkANaBo1BnpY6gPVEvhhYQJH87F1z/jv+H/v37aduvboUKFiA/47/x7179yhVuhRavZbZn8/m7//9jV9xP1YtX0VcXJzc3XTX/3YRdiOMkOohuLu7s+efPdhsNvyK+6FQK+j1Zi++mvUVCpWCoKpBRN+L5tLFS3To3IHmrzTnu6Xf8f7Q9xn47kB8fHy4FX6Lv7b/Re+3euNbxP6DktlsZvK4yfQd0Jfwm+F8u/Bbunbriova3mrL39+fUydOcff2XVwNrnh6eaJV2z+/9Rr7uaBVaVGr1Xw540tCR4eiUquYOnkqwcHB1K5eGwAXlQtKhRKNyp5xbdiwYUyePBl3d3caNGiAyWTixIkTxMbG8tZbbz3R16hWrVqPbd0TJ05k586drFy5kiJFHnxme3t7YzabiY2NdWrlFRkZKadz8Pb2TjdataP7S+p50naJuXv3Lm5ubnnWussR7PLx8cFgMIhAgPBckiSJxMREIiIiAChaNH3LWUEQBCF/5GvAq3bt2pw9ezbT6QqFguHDhzN8+PBM5/Hy8uKLL754HNV7+qi0oC8KOl9IiYSEa5Acbs8NpvOxt7jSF4USXewPmwnu7ofwLRCx09766+ISuLgUfBrag2SFauU8+KRUA2p74CotSbJ3nbSZ7rdOC4OEK/ZpCuWD3GEaL3sgTKW3t2hTGUT3SOGROFpROQJVqf92jGiXbE2Wu9RZbdYHAaz7gSu5ex/Yu65hD1SlDlqpFCo0So1TuaOFVXZJkkSSJYmopCj5cS/5HlFJUUQnR3Mv6R73ku2PmGR7kMuR7Ds3VAoV7lp3PFw8cNO64e7ijpvLg+fMHq4aV1xd7IGtJxacek5I9qETnVpGZadMwYOWfVq9lkMHD7F6xWoSEhIoUrQIwz8cTp36dbCYLZw7e45xn4xDpVLRvVd3atSsgQIFKqUKL08vVi9fzeJFizGZTJQoUYLpn08nsKK9+/+Qd4egc9GxeOFiIiIiKFy4MK93fV1+7VesXMHsL2YT+kEoCQkJ+Pr6UqdOHXwL+uLu4o5GqaFu3bqUK1OOAW8NwGQy0bZtWz547wNcXOw/mgzoN4DQ0FC6dOhCcnIy27dvx0Vln6ZV21vpqVVq9Ho9AwYMIHREKLdv36ZGjRpMmTJFDnClbbX42muvodPpWLJkCTNmzMBgMGA0GunTp89jflUfLiYmhp9++omLFy8CUK5cOTp37oyXl1e21yFJEpMmTeLPP/9kxYoVFC9e3Gl6YGAgGo2GvXv30rJlSwAuXbrEzZs3qVq1KgBVq1Zl0aJFREZGyqNd79mzBzc3N8qVKyfP8/fffzute8+ePfI6HpXVapWDXWlH3BaE541eb++uHRERYc89rFC+MCMXWm0ST7ozpxgZUhCE7FJITt/wXkzx8fFUr16dQ4cO5TqpbL6QJHvgK+68PfDlUgA0HhnPa0mCiP/BjV8h6sCDctfSUPYtKNLi8QecJKs9CGZLuf9sxh5VuB88U3vc3wc3e94wlUGMLPmCkyQJs80sJyVP/Wyymh7kiDIn2wNbkgWL1SK3wHJwfFlWKVSolCrUSrX879TPjxrQsdgsRCVFcTfxLncS7xCZGMndxLtEJtmfHcGtu4l3cxXA0ig1eOo88dJ54an1xFPnaX/WeuKh9cBD64Gn1tMe3Lr/t4fWA71a/2IHqyygjlVTvETxbHVpTBuEyurv1AEqBzkvF6me7x/+tDm8HK0B0+b6ymr51GWOv1Mv9ySEhoYSGxvLggULntg2H0VycjKXL1+mdOnSeZqbKrUDBw4waNAg3N3d5byiJ0+eJDY2lkWLFjl1N83K+PHj2bBhAwsWLKB06dJyubu7u1z3cePG8ffffzN16lTc3NzkkSF/+OEHwB5s6tixIz4+Pnz88cfcuXOHESNG8Nprr/HBBx8AcP36ddq1a0f37t3p0qUL//77L1OmTOHrr7/O9iiNWd0/OY55qVKl5GCAIDzPkpKSuHLlinydeRKjJabWOKAwH7esQJs5u55I0vrTs1+1/xEfz/Dfzz2xfS3n48ZXb4Q8kW09rRISEuRrbnx8vBilURCyIJrUPMsUCtB5g4unvbVX3AUw3wBdkfTBK7Xe3p2xWCt73q9rayHsD0i4DMfHwoVvoMzbUKz14wt8KVT385MZnMttJnuyftMdeys0pPtdOfWgKQhaL1C72R+qZyv/jpA5SZKcRtJz5LtyJD9PNCWSbE3GYn3QYstqs8rdBhUKhRy4UivVqJVqtCotKhf73xl1DXwUCaYEbifcJiIhwulxJ/GO/ZFwh6ikqBzlu9KpdRTSF6KAvgAFdPcf9//tpfOigN7+7KX1wkvnhUEjugRllyORv9VmxWax4S65y4n/M2pp5ZAu2JQqqXzaZPWZJZrPaB1ppwnPn4kTJ9K6dWvGjx8vJ6+2Wq1MmDCBiRMn8vvvv2drPd9//z0AvXr1ciqfOnUqnTt3BmD06NEolUqGDRuGyWSifv36jBs3Tp5XpVKxaNEixo8fT9euXdHr9XTq1Ilhw4bJ8xQvXpyvv/6aqVOnsnz5cooUKcLkyZOzHezKLnHOCy+KtOf6kxgtMbWyhZ9c0MOmUHCsSHm8DBpKKpVPfF9fdEqlkho1asj/FgQhcyLg9TxQasC9rH3Ux9hzkBgGet+MuxwCuJWBSiPB+C5cXQtXVtrzgp2YaO/yaHwXijR/cnm2HCM+OlqnSdL9IFgSJF2HhEv2YJnKYJ9HV/hBovzM9lHIdzbJJgewTFYTKVb7c6I5kQRzAknmpActtmxme3fC+4EslUKFRqVBrVSjUWnQa/RyUOtxsNqs3Em8Q3hcOOHx4dyKv0V4fDi3429zK+EWt+Nvk2BOyNa6VAoVhQyF8NZ7258N3ngbvCmkL0QhfSEKGgran/UFMWgMD1+h4DSaZeo8a6kHCUgbvJJb7ilVaKz2rnGOIOnDRlKEB62u0v5bEDJz9epVvvrqK6eR2lQqFW+++Sa//PJLtteTVaoHB61Wy7hx45yCXGn5+fnx7bffZrme2rVr56hugiAIACkaLR36zKZ9cFHmiBacT5xer+fAgQMPn1EQBBHweq64FICC1eyBofgL4FLQHhTKjNrN3p2xZFe4/jNcXmlvYXVsNFxZDRXegwJVn1TtH1Ao7rfw0gJe9jKb5X4rsLuQdBNQ2FuLabzsATCNB2jc7cE/4YkxW82kWFNIsaTIzwlm+wh/iaZETDaTvYVWqlHaVEoVLioXuUWWm4vbY2mRlZokSUQmRRIWF0ZYbBg3424SFmd/Do8L53bCbXvi+Ydwc3HD19UXX1dffFx9KOxamMKGwvi4+uBt8KawoTBeOi9UoivuQzmCWE6DBaQKaDlaYQEolArUCrW9O+r9Z61G6zSqoyNA6nikbvlnMVm4fu06ri6u6FxEkDwvTJs2Lb+r8NSpVKkSly5dokyZMk7lly5dokKFCvlUK0HIP02bNqV37968+eab+V0VQRAE4QUlAl7PG6UGvCrbuwPGnra3lNIWzHoZtQFK94Lir8KVVXD5O4g5Afv6gW8TqPBhxqM6PklKNSjdHgTwJKs9L5npjr11mlJlz/ul9bHvr8bT/rdolfHIrDYryRZ7svcUawrJlmTiTfHEJsfKrbZMNhOSTQKFvWWNI6BlUBvQuGjkVjWPu563E25zPfY612OucyP2Btdj7c9hcWEkW5KzXF6tVOPr6ktRt6IUdS9KEbci+Lr6ys++br6iRVY2SJLkNHCA42G2mu151RxdUu8HsRxBKY1Kg7vGHZ1ah16lx0VtP4c0ygct/VIHtHISIE22JjvluhKEx6F3795MmTKFq1evEhwcDMCxY8dYtWoVH330EWfOnJHnFQEwQcgbN27c4OWXX+aXX36hYsWK+V0dQRAE4SkjAl7PI4XS3sVRrYPok5AcYR/F8WHUeijXD4p3hPNf2xPc3/4L7u6FcgOgZPenZyRFhcoe/HIEwGwWsCTYc5LFX7if/8vTHqjTeNofotVNlsxWsz0B/P3gVqI5kZiUGBJMCXKXRMlmb3KjUWnQKDW4qFzw0HrgonJ5rC20UotJjuFKzBWuRl/lasxVrsVc42rMVW7E3sBkNWW6nFKhxNfVFz93P4q5F6OYezH8PPwo6laUYu7FKKQvJFpmPYQjB5ajK2rqgFbqUQbVSjVqlRq1Qo1OrcOgMaBX69FpdGiUGvn8cTw7glii26DwLHMkg//8888znKZQKJAkCYVCwenTp5909QQhT5lMJnlEVuHFozMns23xO+hdVNDhQn5X54WTmJhIpUqVADh16hQGg/hBVhAy85REL4Q8p1CAwR8UGrh3FFLugtY7e8tqvSHwE3tXx1PT7MufnQNhG6Dy6Pzp5vgwSrU9eb+Lpz0HmDUZzDGQfNs+Te1uD365FAQXrxe666PFZiHJbB/dMMmcRLwpnpiUGBJNiaRYUzBbzUhIKBVKtCotLioX3F3cKaQq9MSCWpIkEZEQweXoy1y6d4nL0Ze5En2FK9FXuJd8L9Pl1Eo1/h7+FPco7vTs7+FPUbeiaFQv7uueHVabNcNRMVNztLbSKDV4aj3Ra/QYNAa0aq0cBE0dEBVBROFFsX379vyugpBHevXqhdFoRKlU8ssvv6DRaHjvvfdo27YtkyZNYvPmzXh7ezNmzBgaNWokL3fu3DlmzJjBoUOH0Ov1vPTSS4waNYqCBe0t7f/++28WLlzI+fPnUalUVK1alU8++YQSJUoAD1orzZ07lxUrVnD8+HFKlizJhAkTCAnJfFS6mzdvMmnSJP79918UCgUNGjTg008/xdvbm8uXL9OqVSs2btxI2bJl5WWWLVvGypUr2bZtW7bq3qtXL8qXL49KpeK3337DaDSyfPly5s2bx88//8zdu3fx8vKiVatWjBkzRt5OcnIyo0aNYvPmzXh6ejJ48GC6du0qTz979ixTpkzh6NGj6PV6WrRoQWhoqDzqnM1mY8GCBfz4449ERUVRtmxZPvzwQxo2bAjAyy+/DEDHjh0BqFWrFitWrMjdCy9km0IC/9gI+x9S9gfrEfKGJElcvXpV/rcgCJnLVcCrd+/ezJs3Dw8PD6fy+Ph43nnnHZYvX54nlRPygN4XpCoQfRRM9+x5vrLLvRzU+sY+muPZr+yjO+7rByVeA+MQe5fBp5FCYW+tpr6fRNNmBks8xNzvTqJxt49kqfO2H4/nOPiVbEkmyZxEojmRRHMiUclRxKfE27siWkygACVKtGotWpUWVxdXNErNE21pE50czYWoC1yIusDFexe5eO8il+5dIt6U+fDWvq6+lPQqSUnPB48SniUo4lZEBFiy4BgZ0zEipiOg5ciXpVQq7QErpQatWkshfSF73iu1DheVi/zQqDRPtFWfIDwL/Pz88rsKz46ELAYBUalAp8vevEolpE6YndG8rrm7V1m/fj39+vVj7dq1bNy4kfHjx/Pnn3/SvHlzBg4cyLJlyxgxYgQ7d+5Er9cTGxtLnz59eO211xg1ahQpKSnMnDmT9957T74vTkpK4q233iIgIIDExES++uor3n33XX799VenkdZmz57NyJEjKVmyJLNnz+bDDz9k69atqNXpb9ttNhvvvPMOBoOBFStWyCODvv/++6xYsYLSpUsTGBjI77//znvvvScv99tvv9G2bVuAbNXdcUy6desmjyS6ZcsWli1bxqxZsyhfvjx379516roLsHTpUoYNG8agQYPYsmUL48ePp2bNmpQpU4bExET69u1LSEgIP/30E5GRkYwZM4ZJkybJeQKXL1/O0qVLmThxIhUrVuTnn3/mnXfeYcOGDZQqVYq1a9fy2muvsWzZMsqVK4dG8/ze0wmCIAg5l6uA1/79+zGbzenKU1JSOHTo0CNXSshjhmL2nFfRx8AUY28FlV0KJfi3A58GcG6uvZvjtbUQ8Y+9FZh3ncdX77yi1NgDWy4F7nd9jIe48/aHHPwqfD/49Ww2epQkiRRrCgmmBBLNicSlxBGVFEWiJZEUSwoWmwWlwh7M0Kl1eGm9cDG4PNHAlsVm4Wr0Vc5FneN85HnOR9kfdxPvZji/SqGiuGdxSnuVtj8K2J9LepZErxEjAmXGMTqm42G22rseorB3N9SoNLgoXdCqtRTUF8Tdxd0e8Lwf9Ewd1BIEIWdu377NoUOHiIqKwmazOU3r3bt3PtXqKeSWxYA6rVvDH388+NvHBxITM563USPYufPB36VKwd00nym5bP1QoUIF3nnnHQAGDhzIt99+S4ECBXj99dcBePfdd/n+++85e/YsVatWZeXKlVSqVEnu2grw2Wef0ahRIy5fvkzp0qVp2bKl0zY+++wz6taty4ULFzAajXL522+/TePGjQEYNmwYbdq04erVq04ttBz27t3LuXPn2L59O0WLFgVgxowZtGnThuPHjxMUFES7du1YtWqVHPC6fPkyJ0+elLvfZqfuAKVKlWLEiBHyPP/73//w9vamXr16aDQaihUrRlBQkFP9GjZsSI8ePQDo378/y5YtY9++fZQpU4YNGzZgMpmYPn263CVr7NixDBo0iI8++ghvb2+WLFlC//79adOmDQAff/wx+/bt47vvvmPcuHFyCzQvLy8KFy6c9YsqCIIgvHBy9O0+9a82Fy5c4M6dO/LfNpuNXbt24evrm3e1E/KOa/H7Qa/j9xO8Z3GzmREXLwj8FIq0gJNT7CMlHhwC/h2gwvs5X19+Uart++LiZT8e5rgHwS8XT9AXBW1h+/SnuPWKyWoiwWQfDTHeFM/dhLskWhJJsiQhSRJKlOjUOrRqLZ5aT9RPOJCXYknhfNR5ztw9w9nIs5yNPMvFqIukWFMynN/P3Y+yBctStkBZyhUsR9kCZSnhWQIXlcgPkhFHS63UD4tkkZPBa1X27oXuWnfcXNxwc3FDq3oQ1HJ0PxQ5swQh76xbt46xY8ei0WgoUMC5NbVCoRABr2dMQECA/G+VSoWXl5dTUMrb254mIjIyErDfI+/bty/DrofXrl2jdOnSXLlyhTlz5nDs2DHu3bsnd0UKDw93WnfqbTuCOI7ufGldvHiRIkWKyMEugHLlyuHh4cGlS5cICgqiTZs2zJgxg6NHj1K1alV+//13KleuLK8vO3UHqFy5stO0Vq1a8d1339GsWTMaNGhAo0aNaNKkiVNLtNT7olAo8Pb2lo/ZxYsXCQgIcMo/VK1aNWw2G5cvX0an0xEREUG1atWctlutWrV0LckEQRAEISM5+hbcsWNHFAoFCoWCPn36pJuu0+mc+u0LTxnXkvbufdEnwOACylwEE7xrw0s/wLn5cO1He4uvu/shaDwUrJ7nVX6sFKoHwS+bBSxxEHMaFOfsrb0MfqAtZM//lY+BAZtkI9GcSIIpwd5yKzmK2ORYkixJWCUrChT2ke3Uery0Xk+8S5/ZauZ81HlO3TnFqTunOHP3DBfvXcQqWdPNa9AYKF+wPMZCRsoVLIexoJEyBcrg6vKUdo/NZ1abVR4wQB4NU5LkllpalRaDxkAR9yK4ubjZg5wqrRzsfNKBTiHv9erViwoVKvDJJ59ka/6LFy8yatQoTp8+TZkyZfj1118fcw1zJiAggPnz59OsWbP8rkqec3RPGzhwoFP3NCED8Zl3WUeV5jMsIiLzedMe5ytXcl2ltNJ2H1QoFE5ljh8MHEGrxMREmjRpwkcffZRuXY6g1aBBg/Dz82Py5Mn4+Phgs9lo27Ztul4TqbvlObaTtsVgThQuXJg6deqwYcMGqlatyoYNG+jWrZs8PTt1B9DrnVtXFy1alM2bN7Nnzx727NnDhAkTWLJkCStWrJD3IaPjKHIOCYIgCE9Kjr4Nbd++HUmSaNasGWvXrpWbEYP9w7lQoUKo0t6oCE8PhQLcyoA53j6aoWvx3LViUhug0sdQ5GX4bwIkhcH+QVCqO5R/B1TavK/746ZUp+r2aLYnvL93FFQ6exJ/fTF78Eule+iqHpXFZiHeFG9PJp8cw93EuySa7QnlkUCr1qJX6/Fx9XniAQ1Jkrgee50TESc4eeckJyJOcC7yXLrE5gBeOi8qelckoFAAFbwrYCxkxN/DX+R9ykDqwFayJRmLzYJNsqFSquRuht4GbzlJvE6tkx+i6+Hzbe7cuRnm7clqfr1ez+bNm/Ns1KbnOUiVl5KTk2nTpo0IdmVHTvJqPa5581jlypXZsmULfn5+Gb5n7927x+XLl5k8eTI1atQA4ODBg4+83bJly3Lr1i3Cw8PlVl4XLlwgNjbWqUVYu3bt+Pzzz2nTpg3Xr1+ndevW2a57VnQ6HU2bNqVp06Z0796dV155hXPnzqVrDZZZ3devX09iYqJ8vTp8+DBKpZLSpUvj5uaGj48Phw8fplatWvJyhw8flrtOOgJrVmv6H9kEQRAEIUefao6ErKIZ8TNMqQLPCmBNgKRweyum3CpYDV5aDWe+hBvr4coquLsXgiaCR4U8q/ITp9TYg1xawJIEyRGQcMOe70tfFHS+9/N95U1w12KzEJcSR7wpnqikKCKTIkkyJ2GymlAqlBg0Bjy0HmhV2ifeBS3RnMjJiJMcjzjOf7f/47+I/4hJiUk3n4fWg0relahYuKL87OvqK7rMpeHItZZiSZFbbaUeEVOr1lLUrSgeWg/0Gr1TcEsECl9MXl5eOZr/2rVrNG7cWCRQzwddunRh8+bNDBgwIL+rIuSD7t278+OPP/LBBx/Qr18/vLy8uHr1Khs3bmTy5Ml4enri5eXFmjVrKFy4MDdv3uSLL7545O3Wq1cPo9HIRx99xOjRo7FarYwfP55atWpRpUoVeb7mzZszfvx4xo8fT+3atZ1SkDys7pn9mL1u3TqsVivBwcHo9Xp+++03dDodxYoVy1bd27Vrx5w5cwgNDWXIkCFERUUxadIkOnToIHcZ7du3L3PnzqVEiRJUqFCBdevWcebMGWbOnAlAoUKF0Ol07Nq1iyJFiqDVanF3d8/t4RSySVLAuUIlcNepKSru9Z44hUJBpUqV5H8LgpC5XDcPuXLlCvv27SMyMjJdM+shQ4Y8csWEx0itB6/KEHkQUiLtLZdyvS5Xe/J6n0ZwcpJ9JMe9b0L5QVC6l73b4LPMMdqjJNm7PMZdgPiL97s8FrcHxjQ5y19mtVmJM8XJieUjkyJJNCVitpnRKDXoNXoK6gvmS/6qu4l3OXrrKEdvHeXY7WOcizyXrmuiRqmhgncFAn0CqVy4MoE+gfi5+4kP3DQsNgvJlmRSLPZWWzbJhkKhwEVlTxjvbfDGS+eFQWMQga38IkmZJ8N+XAyGHHWRTt2lsWnTprz++utcvXqVzZs34+npyeDBg+natSvwIFfOyZMnmT9/PkOGDGHo0KGEh4czbdo0du/ejVKppHr16nzyySf4+/vL2/npp59YunQpV69excvLixYtWjB27FiaNm0K2BN0g/2Hrx07dgCwbds25s+fz4ULF/Dx8aFTp04MGjRIbiFy5coVPvnkE44fP07x4sWz3S3zWfXhhx8ycOBAdu3ahdFoTNdSZtSoUflUM+FJ8PX15fvvv2fmzJn07dsXk8lEsWLFaNCgAUqlEoVCwezZs5k8eTJt27aldOnSjBkzhl69ej3SdhUKBQsWLGDSpEn07NkThUJBgwYN+PTTT53mc3Nzo0mTJmzatInPPvssR3XPjIeHB9988w3Tpk3DZrNhNBpZtGhRuhx2mdHr9SxZsoQpU6bw6quvotfradGiBaGhofI8vXv3Jj4+nmnTpsl5zBYsWECpUqUAe5fJMWPGMH/+fObMmUONGjVYsWJFNo+ekFvJGh0t+i2gfXBR5uRRa+LsKuymxWqTUCmf/H1nfm03LYPBwMmTJ/O7GoLwTFBIuehI/+OPPzJ+/HgKFCiAt7e30xddhULB+vXr87SSj1t8fDzVq1fn0KFDuGU1ctDzJjEMog7Zc1ip86AbgCkaTkyGiJ32vwtUg6AJ9lZRkgQmM5gtD77sKRSgUoKLJl9zZOWYzWwf7dKaCCoD6H1BV9QeOMygi6EkSSSaE4lNieVe0j3uJN4h3hSPyWpCrVTjqnHF1cU1X7on3oy7yeFbhzkcfpijt45yPfZ6uvl8XX0J8g2iik8VgnyDMBYyimTyaZitZpItyfYAlzUFm2RDrVQ/yK2m98LdxV0ObunV+ieea+1FlpycLI80ptPd75YsSVC/PuzZ82Qr89JLsGtXtq95aQNeCQkJDBs2jPr167NlyxZmz57NH3/8QZkyZbhz5w5vvfUWDRo04O2338ZgMODi4kKHDh2oWrUqffr0Qa1Ws2DBAk6ePMlvv/2Gi4sLq1evZtq0aXz44Yc0bNiQuLg4Dh8+zJtvvklUVBR169Zl6tSpNGjQAJVKRcGCBTl48CADBw5kzJgx1KhRg2vXrvHpp5/SuXNnhgwZgs1mo0OHDhQqVIjQ0FDi4uL47LPPOHXqVL50j8zwHMhjCxYsYM6cOZQuXVpuneKgUChYvnz5Y9lufsrq/ulJHHNBeJqkPefbzNnFyZuxT2z77YOLMqdbtSe63fzc5vAfjnAhIot8gHmsnI8bX72RfmAHQRCebrn6hr1w4ULee+890Wz/WacvBu5xEHMKDNoMgzU54uIFIZ9D2G9w+gu4dxh2dQXPN8ESbA94We63FnJ82VMpQesCBj24GUCvBZ3OXqbXpk9e+zRQakB3/8uMJQESrkH8Vfv+G4qDrjBmpY7YlFhiU2K5HX+bmJQYksxJKJQKXNWu+daC62bcTQ7ePMih8EMcCj/ErfhbTtMVKChfsDxVi1QluEgwwb7BFHEr8sTr+TQzWU1ycMvRJVGj1KBT6yhgKEABbQFcXVzl4FZ+dEUVsukZfF0aNmxIjx49AOjfvz/Lli1j3759lClThsKFC6NSqTAYDHKi6V9//RWbzcaUKVPk83Dq1KnUrFmT/fv3U79+fRYuXMhbb73lNBiNIz+OI1enh4eHU/LqefPmMWDAADp16gRA8eLFGT58OJ9//jlDhgxhz549XLp0icWLF8tdp95//3369+//mI9Q/lm6dCmfffYZnTt3zu+qCIIgPPcuRMQ/0YCiIAjPplxFOGJiYnjllVfyui7Ck6ZQgHtZMEdD8i0w+D90kSxJEsQlgKU66D+BuG/AdhXuLQBlNXDtBa4eIKWa32qzB8ISkuDmbXuZQgEaDehcwNUAHm6g1z0Ihulc0o/MlF/UrqB2RbKaSUyKICb6ApFmM3dsSuLRYlO7obvfgquQvtATD3xEJkZy4OYBDtw8wMGbBwmLC3OarlKoqFy4MtWKVrMHuXyDcdeK3BcOFpuFJHOS3HIrdXDL2+BNAd2D4JZBYxAJ5J8lCoW9pdVT3qUxLUe3RbC3GvL29iYyMjLT+c+cOcO1a9eoVq2aU3lKSgrXrl0jMjKSiIgI6tatm6N6nDlzhsOHD7No0SK5zGq1kpKSQlJSEhcvXqRIkSJOeYJCQp7vX8ZdXFzSHWdBEITnkc6czG/ffYC7Tg0d/svv6rxwEhMTqVmzJgAHDhzIs0FqBOF5lKuAV6tWrfjnn3+chjQWnlFKjT3BvCkWTPfsualyIz4RroVBWASkpIDBAwp9CqZNEL8ebIftI0N6DgRt2pF7nIe5xmazd31MMUFkNNy6A9wPhLloQasBd1fwcAed9n4gTGtvFfYEA0o2yUacKZHolDhuJd4lOiWeRHMyKsy4YcVX5YJaaQKlCygVT6RuyZZkDocfZl/YPvaH7ed81Hmn6Y4AV/Vi1aletDrBvsHoNfpM1vZisUk2ksxJJFnsAS4JCbXC3i3RS+9FIX0h3FzcRHDreaJQ5OuobrmRNi+UQqEgq8wEiYmJVK5cWU7wnFrBggVzHYRPTExk6NChtGjRIt00rfYZHKk3D/Tu3ZuVK1cyZsyY/K6KIAjCY6WQwBh5zf5HzrPjCI9IkiROnTol/1sQhMzlKuBVsmRJvvrqK44dO5ZhYtbevXvnSeWEJ8TFyz5yY9QRe04qVQ6+rJgtEB4Bl67bg16FvKBwwVTr7gguVSBmEVhvwb1pYGgB7l1BkUmXPuX9bo7aNNNtNnsQzGSG25Fw45a9tZhaZZ9Xp7W3BnN3vd8lUmcvc8m7wIRNshGTEk9UcgzhiXeJSUkgxWpGp3bBXWOgkM7zwRdIm9We6D72LCh19m6QWm/QeORZMn9Jkrh47yJ7b+xl7429HL11FJPV5DSPsZCR2n61qVGsBlV9q+Lq8mx9wX9cTFYTieZEki3JmG1mFCjQaXS4alwp6VUSNxc3Ob+ayFkmPKsqV67Mpk2bKFSoUKY5Kv38/Ni7dy916tTJcLpGo8FqdR68olKlSly+fJmSJUtmuEzZsmW5desWERER+Pj4AHD06NHc78gz4Pjx4/z777/89ddflC9fPt290bx58/KpZoIgCIIgCC+mXAW81qxZg8FgYP/+/ezfv99pmkKhEAGvZ5HBH0xREHcJXItDdkaKuxcD567AnSh7kMnPN+NWTC5lodAkiPsBkrZD4lZIOQFeA0FTJvt1VCrvd21Mk/zWarUHwpJTIDYeLBb7r00uLvZgl14Hnm727pGOlmA5CITZJBvRKXFEJcfeD3LFY7ZaMGh0eGnd0akzC9yp7MFEsCe4TwqzPzReoCtin6bKeSLfeFM8+8P2s+f6Hvbe2MvthNtO031dfantV5va/rWpVawWBfS5bLX3HLFJNpItyfYAlzUZm82Gi8oFvUaPn4cfXjovOcClU+tEzi3hudGuXTuWLFnC4MGDGT58OL6+vty8eZM///yTfv36UaRIEYYOHcq4ceMoVKgQDRs2JCEhgcOHD8ujxzkCYtWqVcPFxQVPT0/effddBg0aRLFixWjZsiVKpZIzZ85w7tw53n//ferVq0epUqUIDQ1lxIgRxMfHM3v27Hw+Go+Xh4dHhi3eBEEQBEEQhPyRq4CXY0hy4TmiUIJ7gH30wZQ7oPPNfF5JgvA7cPaSPdBUtPDDk8srdfbk9bpqEPMtWG9C5ARwbQ9uHUDxCAnzVSp70ntDmq55jm6RCYkQFQ02CZDsgS7N/UCYh+uDQJgjGKZ1cWrJdfN+kMtis6BX6yik88Qlp93ZVAb7w2YB8/2BAtQG0BYGXSFQe2QaZJQkiasxV/nn2j/svr6bw+GHsUoPWltoVVqqF6tOXf+61PWvS0nPki98wMZis8gBrhRrir31lvpB6y0PrQeuGlfcXNzEaInCc02v17Ny5UpmzpzJkCFDSEhIwNfXl7p168otvjp16kRKSgrLli1jxowZeHl50apVK3kdI0eOZNq0aaxduxZfX1927NhBgwYNWLRoEfPnz+fbb79FrVZTpkwZXnvtNQCUSiXz5s3jk08+4dVXX8XPz48xY8bQr1+/fDkOT8LUqVPzuwqCIAiCIAhCKo84LJ/wXFHr7fm8og7YRx9UZ9D1zWqFK2Fw/oq9BVWRwunnyYo2CLw/g9jvIHkfJPwCKYfBcwBoMu4ak2satf1BqkSOkmQPhJnM9kDYvRj7PgGSWk2cykKU2kKYOoVojQWTSoHB4EYhgwcubq6PnodLqQZtgfsJ+xMh8Tok37zf6svH3upLqcVis3D01lF2XdvF31f/5nrsdafVlPAswUvFX6Kefz2qFa2GVv1i5sxxsNgsJJgSSLIkYbaaUSqVGNQGChkK4W3wxs3FDTcXN/Rq/QsfDBSeLStWrJD/ndGPTb/++muWfwMULlyY6dOnZ7mdN954gzfeeCPDaU2bNqVp06bpyhs0aECDBg0yXWfp0qVZvXq1U9nZs2ezrIcgCIIgCIIg5JVcBbxGjRqV5XTxK+czTO8LbuXsLZBcdc65pkxmOHsZroZBAU9wzWWyc6U7eA2BpJoQuwws1yByHLi1t7f4epTWXg+jUNhbeLlocATCEq0pRFriCU+MJDIpmqT4JFxtSgpKGlwUGlDGg+auPcBn0NlbkmldHqxHo354C7eM6nF/hEdsZjDHkJBwg713r/K/22fZHX6UWFOcPLtaqaZ60erUL1Gf+sXrU9yzeB4elGeP2WomwZxAojkRi82CRqmRuycW0BWQA1wveiBQEIQna/PmzWzatInw8HDMZrPTtPXr1+dTrQRBEARBEF5MuYosxMbGOv1tsVg4f/48sbGxmSa9FZ4hbmXsIzYm3QZDMXuZyQxnLsK1cPApaA/+PCp9bXCpYA96pRy0j+aYfBA8+trzfj1GFslKpDme2+YYbpliiLcko1Vq8HL1xMc9Tas1x6iRZrN91MjbdwEFKLB3jdSoQKezBwi1WnuZVgNqtT0glkWLoqjkGP4OO8LOsEPsv30Sk+3BFyRPFzfq+9emYamm1Cle/4VONp82wOXIv1XCswQF9QXlAJcYOVEQhPyyfPlyZs+eTefOndm+fTudO3fm+vXr/Pfff/To0SO/qycIgpBnJAXc8PBB76KikGg5/8QpFAp50BjRc0EQspargNf8+fPTldlsNsaPH0/x4i92y5PngsoFPAIgMsae0wvDg2CXr/f9boJ5tS1P8BoGyf9C7AqwXIeoCfaRHN1etef+elSSZB9BMj6B+LgYYuIiiYyNJCklASQohho9alAqkdQqJI0Km1qFTeuC1aDFZtBi1WuxuRqcg1eOQJjFYk+WHxVtHzUSyR7sUqvsD532fqswLWjU3LLG8ted4/x16yhH757Flmo44eJuvjT2q06jokFU8fRBZbPaW4GZ7oDCcj/X1/P/weboougIcKmVagwuBkp6laSArgDuWnfcXNxQK0WvbEEQng6rV69m0qRJtG3blnXr1tG/f3+KFy/OV199RUxMTH5XT3gOhIaGEhsby4IFC/K7KpmaO3cu27Zty7B79eOUF8dm3bp1fPbZZxw8eDDTefJr/542yRod9Qf/H+2DizLHYHj4AkKeMhgMXLlyJb+rIQjPhDz7tqhUKnnzzTfp3bs3/fv3z6vVCvlFWxDcy8PtQ3AtCW7cyftgl4NCAfq6oA2E2FWQvBsSt9xv7dXbnug+K5Jkb3l1NQxuRsDN2/bnO1FwLwbpXgwKswUAt/sPv1xU06ZWYfEw2B/uBswF3TEXcMdcyANTQXdMhT0xFfbC4uUGVps9EGaxQkw8YbevsiPxAtuTznPCfMtpvRVd/WjsE0yTIiGULuCPwpFUX6kEyWbPp5ZwBZKu23N9aQvneoTHp5XFZiHRnEiiORGT1WQPcGkMFPcsTkF9QRHgEgThqRceHk5ISAgAOp2OhIQEADp06EDXrl0ZO3ZsflZPELKtadOm9O7dmzfffDPL+QICApg/fz7NmjV7MhUTBEEQhBzK02+P169fx2Kx5OUqhfzkUgyu/wsXTkGJCo8n2JWa0h28BkFKPYhdCta7ED0btFXBvReofezzRUXDf2fhzCW4cBUuXoOYuExX62gPZdGqsem12HRabDoXbC5qUCqQFAp7cMlmQ2mxorj/UCabUCWZUCWmoLg/zSUqDpeozLcFYNOoMRX25FxpN34OsPGLbwzHtQ+6ASuAYH1xmroaaaIrQ1FcwSTB1Ti4cc5+nNXq+y3DdPZ8YWoXUEmgjICE26B1A603aAuBxsM519ozwCbZSDQnkmBKIMWaglKpxE3jRlG3ohQyFMJD64G71l0EuARBeGZ4e3sTExODn58fRYsW5ejRo1SoUIEbN24gpWrJKwgZMZlMuORFuohn0Iu874IgCMLjlatvk2mT0kuSxJ07d9i5cyedOnXKk4oJ+cxqhbPn4Y4CivkDCYDXk9m2NggKTYWEXyFhE9w9Cjv+g7P+cCYJwiLSL6NUQDFfJD9fkosUIMrXjdteamI8XVAVKICukDcqbS4TmEsSyhQzqrhE1LGJaGITUEcnoImORxMVhyYqDpfIGFzuxBBujWVtJQs/Vo7kgF/kg+rZoNFV6HJaQetILwoU0JPsn0yy/x2S/SWS/QtjddM7d5OMS7CPImmzIecMc3SVVN0F9UXQa0HvBe5FwOANOk97gEz5dAXAJEki2ZJMgjmBJHMSCoUCvUaPt6s3hQ2F5QCXi0rc8AqC8GyqU6cOO3bsoFKlSnTp0oWpU6eyZcsWTpw4QfPmzfO7ekIOmEwmZsyYwR9//EF8fDyBgYGMGjWKoKAgbDYbjRs3ZtCgQXTv3l1e5tSpU3L+Nj8/P2JjY5k+fTrbt2/HZDIRGBjI6NGjqVChAvCga1zPnj1ZuHAhN2/e5MyZM2zevJn58+dz9epV9Ho9FStWZMGCBRhSdRtbsmQJS5cuxWw207p1a0aPHo1GY89hGRMTw/+zd97xUZTbH362pjdCAAm9JJQQukgvIkhTRISLglgQQZRiQVAvCoIgYKF4UcEGeu3gT5Ci2C8iIKCAFGmhQyC9bLbO74/Z3ewmm5CEsiGc5/N5MzPvvDNzpuyUb84576xZs/jxxx+xWCy0bduW5557jjp16riX37BhAwsXLuTYsWNUqVKF4cOH88ADDwAwYsQITp06xezZs93v+756V3X13Dpu3DgAYmNjvXqS/eqrr1i4cCEZGRl06dKFF198kdDQUPc2GjZsiE6n4+uvvyYuLo4VK1bwzz//MHfuXLZv305QUBAdO3Zk6tSpVKpUCaDMx0av16PRaEp0bAry9ttv8/7772MymejTp4/bluudAKuZz/47hchgAwwsOgRUuDKYTCa6dOkCwC+//EJQUBk7EhOE64AyCV579+71mtZqtVSqVIkpU6Zw5513XhbDBD/icMDBg3DoENSoA0oltddGe97VC6M7kQL/C4Tfa8C+Y6DYgWPqPA1QrxY0bQgN6kCDWlhqVeO8No8T5lTOWzNxoBCpCyb8ctir0ageYYFGrDGRmArMvmDNYmP633ybvptdOfmeXFoFOuREMPB4MHfutlHzaAa6PAuQppY//vFaj6VSGHk1q2Cq5Sy1q5BXswqOIKdQpzjUEEmbDawK5CmQlqN6wtn3giEAAiIgqBKEVILQcDWZvtHoTK7v7FnSYLgqgpjFblHzcNlysTvsBBmCCDOGUS+qHhGBEYQHhBOorzhhmYIgXN+8+OKLOBwOAO655x4iIyPZuXMnPXr0YOjQoX62rnzhCvf0hU6nIzAwsERttVqt14eer7YhIaXv8GXu3Lls2LCBOXPmEBsby7Jlyxg1ahTffvstkZGR9OvXjzVr1ngJXqtXr6ZVq1bExqpJEyZMmEBAQABLly4lLCyMTz/9lJEjR7JhwwYiIyMBOH78OBs2bGDx4sVotVqSk5N54okneOqpp+jZsyc5OTn88ccfXh6CW7ZsISYmhg8++IDjx48zadIkGjduzJAhQwA1l9WxY8dYsmQJoaGhzJs3j9GjR/PNN99gMBjYs2cPEydO5NFHH6Vv377s3LmT6dOnExkZyaBBg1i0aBG33347Q4YMca/TF1988QXt27dn9uzZdO7cGZ1Hb9XHjx/n+++/58033yQzM5OJEyeydOlSJk2a5G6zatUqhg0bxscffwyoHWKNHDmSu+66i6lTp2I2m5k/fz4TJ05k+fLll3xsjqfm8tyTT3L65AmemfUKwSEhvP/mIu57YBRLVnyOXq/nXGYeDkXh4DnVk//XH75j4aJFjJ04mSaJLfhxw1o+/Oi/VKse627jid1qITkzjx/+d4RxPZuU+Hq7FtEqCs3PHlQnnPc94erhcDjcueYccvwFoVjKJHitWLHictshlBcUBY4ehQMHICZGTbSuVIbgmmoeqcAqVy587tQ5+Ol3+HELHD3hPa9ONCTkQOM8aAhEGiGsE7m6mpy1ZHDcfIx0Wy5GjY7K+jCMVzgULtNm4oeMvWxI28X27CQcarZ6NGhoGVKbXlEJdI9oQrRB/W9mCpCiKBhSswg4fYHAkxcIOnmewJPnCTx5AWNKpjtcMvyvw17bMleNwlS7KqbaVTDVrkZunaqYb6gEugLnwZYHeVlgOgY5p+B8CGiCQB8EGr3aXu8KlwxUS1CQGkJpMOSLYgY96Ep//OwOuxqmaM3BYrdg1BkJNYbSIKwBkUGRhAeEE2IIkd5kBEGokGi1WrRarXu6X79+9OvXz48WlV9cnj6+6Nu3L9988417ukqVKuTm5vps27VrV3766Sf3dJ06dbhw4YJXm9KGk+bm5vLJJ58we/ZsunbtCqhi5qZNm/jiiy8YNWoUt912G++99x6nT5+mevXqOBwOvvnmG8aOHQvAH3/8wa5du9i8ebM7VO/pp59m48aNbNiwwS2AWq1W5s6d6/Ya+vvvv7HZbNxyyy1u4Sw+Pt7LvoiICKZNm4ZOp6N+/fp07dqVzZs3M2TIEJKSkvjhhx/4+OOPadVKzX86f/58unXrxsaNG+nTpw/vvfce7du3d3tm1a1bl0OHDvHOO+8waNAgIiMj0el0hISEEBNToNdqD1w2h4eHF2qnKAqzZ892n+fbbruNzZs3ewlederUYfLkye7p//znPzRp0oTHH3/cXffSSy/RtWtXjh49Sm5u7iUdmyNHjrJl0y/MXLiU+k0SAXh0ynTGDBvALz/9QPuuN2OxOVAUMFntAKz67L/0uHUAnXsPAGDwfQ+z448tWCwWdxuv/bbZsdgVzqQX/NeoIAiC4C8uSRVITU3lyJEjANSrV0/cfCsCJ0/C3r0QEQEuF3GNRhW8bNlgvgCBVS/f9kx58PNWWPsT/H0wv16vg9bNoENLuLE5VIkGxaKGOOasBushSJ1OlrYhRzQ3ouhiqW6MRKfRFrmpSyXPYeGXjAOsT9vNb1kHsSn5LzvNgmvQK6oZPSObEmMI970CjQZrdDjW6HCym9XzmqXNyVMFsOPJBJ1IJuhYMkHHz2FIyybgXBoB59KI3Lrf3d5h1GOqWQVTnaqY6qgimKl2NezhMYCieuPZTeox05khIAo0gYAebApkZ0N6uhq66kKnzRfEDEYIcopixgCnGOYhjOn1KGgw2UzkWHIw2UzuPFzVw6rn5+EyhqErZ+GVgiAIV4JffvmF4OBg2rRpA8BHH33EZ599RoMGDZg2bRoRERF+tlAoCcePH8dqtboFIwCDwUBiYiKHD6v/kGrcuDH169dnzZo1jB49mq1bt5Kamsqtt94KqCGAubm5tGvXzmvdeXl5HD9+3D1dvXp1r3fnRo0a0b59ewYMGECnTp3o1KkTvXv39rp2GjRo4OVNFRMTwz//qB7jhw8fRq/X07x5c/f8qKgo6tat67b9yJEj3HzzzV52tWrViuXLl2O3273WXVZiY2O9RM0qVaqQkpLi1aZp06Ze0/v372fLli3ujh88OX78OJ06dbqkY3Py2FF0Oh0NGuVvNywiguo1a3Py2FGf+3HqeBK9Bgzyqotr0ow9f26/2CEQBEEQygllErxyc3N58cUX+b//+z+3G6VOp+P222/n3//+t8QRX6ucPQt79qheP2Fh3vN0BgitCxm5YM0AwyW+uB86Bl9vhB9+V0UvUBPHt2oK3dpBpzYQ5h2GoGAgJaAHZx31icj7hhqOv6nqOEgVDpGqac5ppTsWTdSl2VUAm2JnS9Zh1qft5qeMfZgcFve8hoFV6RXVjF6RzYgNuLTtOkICyYmvSU58Ta96XWYOQcfOqQLYsXPuojNbCTl8mpDDp73aW6LDMdWuSm7daqpXWN0q5FUxQu4ZNRRUFwzGCAgMA12Yt7ee3Q52m5pDLC8PcrLV8EnXP8c1YNFAjtZKjsaGYjQQFBxORFhl4sJuIDwwivDQaIyBIflhlNorJ0AKgnB9UNIe4/zNvHnzePLJJwFV8Jg9ezYPPPAAW7ZsYc6cOYXyn17PZGdnFzmvoOCSnOwjb6cTbYFnTFJS0iXZVRoGDBjA6tWrGT16NGvWrKFTp05ERanvAjk5OcTExPiMiAjzeL8q+L6s0+l477332LFjB5s2bWLFihW89tprfPbZZ9Ssqb4f6PXer+4ajabcdYpQ0EYo7GlXcN9zc3Pp3r27+zfkSUxMTIU5NoIgCMLVpUyC15w5c9i2bRtLliyhdevWAGzfvp2ZM2cyZ84cpk+fflmNFK4Cqamq2KXRQFQR4o0hDELrQcZ+0Oaq4klpsNth0w5YtQF2eSRAja0KfbpCr84QHVl4McXBeWsmJ8wpnLVk4AAqGW8nU9ON6pYfiLLvJdr2J5Vsu0jRt+CsoTNmbXTpbPNAURT25J5kXdouvkvfQ5otPydIrDGK3lHN6B2ZSP2gKmXeRkmxh4eQ3ayet0eYw0HAuTSCks4RlHSWoKRzBCedJeBcmhoamZJJxI58bzmHQY+pZgymulUx1axEbs0oTHUqY4+sBMZI0IeCPkQNedTpVI8u1/YVB7l2Ezk2ExbFitGuIZQgGmpCidIEEZ5rJDhHj+bsBeACaA65PcDcoZNBQWoJCPDIJ2ZQx41GtZ2EOQqCcI1z8uRJ6tevD8C3335Ljx49ePzxx/n7778ZPXq0n60rX5Qmr9aValsUtWrVwmAwsGPHDnfonNVqZffu3YwcOdLdrn///rz++uvs2bOHDRs2eL37Nm3alAsXLqDT6ahRo0aptq/RaGjdujWtW7dm3LhxdO/enY0bN3L//fdfdNn69etjs9n466+/3B5qaWlpHD16lAYNGgBqRMaOHTu8ltuxYwd16tRxi40Gg6FEeYEMBgN2e+HQvrLQtGlTNmzYQGxsrE/BDC7t2NSoXRe73c6h/X8T31QNaczKyOD0iWPUqF3X5zKxtepwcN8euvbq6677Z++eMuydIAiC4C/KJHi5enfxdNXu2rUrAQEBTJw4UQSva42sLNi9G0wmqF69+LYBMRBiguwjEGgAjeHi6zflwTc/wpcbINnp0q7TQZe2MKAHJDbyKXhYHTaSrZkcN18g2ZKFTqMlWh9KgFbdZh5VOBL4L4Ltp6hu3UiE/TCVbTuItu0kTZfAGWMX8rQlD79MyrvA+rS/WJ+2m5OWVHd9lD6EWyIT6BOVSEJwDf/noNJqMd8QjfmGaNLb5ydF1ebmEXTsHMFHzxF07Kwqhh1LRpdnIeTIGUKOnPFajaVSKKZa0ZhqVcZU5wZM9ethql2H3IAAchx2TA4zWo2GUH0w1YNiqGyMJFwfSqg+CF1RedwcDrBaVc8wm00NnUxLc3qKefyXVafLF8YMBlUYCwlx5hQzFBbGDIbCOcsE4TrEYrG4cwJdS1yrdpcGg8FAXp7qsfzbb78xcOBAQM0rVJxHk1C+CA4OZtiwYcydO5eIiAiqV6/OsmXLyMvLY/Dgwe52NWrUoGXLljz77LPY7XZ3r4UAHTp0oEWLFowbN46nnnqKOnXqkJyczM8//0zPnj1p1qyZz23/9ddfbN68mY4dOxIdHc1ff/1Famoq9erV89m+IHXq1OHmm2/m3//+N9OnTyc0NJT58+dTtWpVdxjjAw88wODBg3njjTfo27cvf/75Jx999BHPP/+8ez2xsbFs27aNfv36YTAYikxZEhsby+bNm2nVqhVGo/GSwnbvvvtuPvvsMx5//HFGjRpFZGQkx44dY+3atcycOZM9e/Zc0rGJrVmLth268OYrLzF60hSCgoP5aOl/qFQ5hrYdu/pcpu+gobwx90Xqxzcmvmlzfv1+PSePHaHKDbFl3k/h2iUmNAC7Q0GnvbrfAf7YpiBUJMokeOXl5VG5cuVC9dHR0e6XPeEawWRSxa70dIgtwQNco4HgGuDIg9xTxSexT8uAVd/C199DltNLKjIM+vdQS4zvFyiLw8Y5awZJeRe4YM0iQKOnmjECfRHbydXFckg3kmD7CW6w/kKk/QCV7LupZNpNhq4+yfoOZOoa+BTVLliz+C59D+tS/2KvKT88MEhrpFtEI26NSqRdWP0it12ecAQHktO4NjmNa3tUFuMNlpqNMTWbiD+P5TfXaci5IQpznRvQNGiIIb4JgY3qYKxUq2ThiVqt6skVEFB8O5cgZrOpApnJBOfPq9MuNJp8QUyvV9cZHKyWgACPRPsFBDJ/C5LCNYOiKORafSfDvlIEG4JLJZqPGDGChg0botPp+Prrr4mLi+Pf//43c+fOZfv27QQFBdGxY0emTp3q/ih1OBy88847fPbZZ5w5c4bKlSszdOhQd0LtAwcOMGvWLP7880+CgoLo1asXU6ZMISQkhP/973+MHTuWTZs2ER6en49w5syZ/PPPPyxfvhxQk3K/+uqr7Nmzh6ioKG655RYef/xxgp25H3v06MGdd97JsWPH2LhxI7169WLOnDkXXS4lJYVnn32W3377jcqVKzNx4sTLcdivCq1atWL27Nm0atWK3bt38/rrrwNqmF21atX8a5xQKp588kkURWHy5Mnk5OSQkJDAsmXLCgk6AwYMYPr06QwcONCrZ0mNRsPbb7/N66+/ztSpU0lLS6Ny5cq0adPG5/uzi9DQULZt28YHH3xAdnY21atXZ8qUKe7k+SVh9uzZzJo1izFjxmC1WmnTpg1vv/02BoP6z8KmTZvy+uuvs3DhQpYsWUJMTAzjx49n0KD8XFXjx49n2rRp9OzZE4vFwoEDB3xu6+mnn2bOnDl8/vnnVK1alR9++KHEdhakatWqfPzxx8yfP58HH3wQi8VC9erV6dy5s5oj9DIcm0cm/5v33niVOc8+gc1mpXFiS5556bUiPco6dr+Fc6dPseLtxVgtFtp17k6vAXfy5x+/l3k/KxIpQeEE6LUU3QVFxSI8SI9Oq2HCJzs5lHx1/onRoEooC/5VOK8dUOy9RBCEfDRKGYLbR44cSWRkJHPnziXA+WGbl5fH008/TUZGBu+///7ltvOKkp2dTevWrdm+fXuxPQdVOMxm2LVLTVQfG1s6Dxq7GTIPgDnVKXp5fMCdT4VPv1G9uixWtS62GgztC7d0VMUJX+Y4rJy1ZJCUd55UWzbB2gCi9CGlTkQfZD9DNesvRNn3onEmoDJpYkg23ESqPpEsB/yUsY91aX+xNeuIu4dFHVpuCq9Pn6jmdA1vRJCuYnoj2BQ7edmZ6I6dISQpmagTKUQeTyXs+Hl0OUUI1kGBULcO1G8ADRpC/fpQr57ak+eVEpgcDm9RzHNY0FvMJYx5eosFB3uLYZ5DEcWuK/Ly8jh69Ch169Z1f5QqikKn9zrx24nfrqotHWt25Nf7fy2x6DVixAj27NnDsGHDGDx4MFlZWYwZM4a77rqL22+/HbPZzPz587HZbG4xat68eXz++edMnTqV1q1bk5yczNGjR7nrrrvIzc2lV69etGzZkscee4yUlBSee+452rZty5w5c7Db7XTp0oWJEydy1113ARSqO378OLfffjsTJkygW7dupKam8uKLL9KoUSN3nqoePXqQkZHBuHHj6Nmzp3t/LrbcQw89RHJyMtOnT0ev1zNz5kz27dvHpEmTLimHl69r4HJz+vRppk+fzpkzZxgxYoT7+L300ks4HA6ee+65K7Jdf1Lc+9PVOOaCUFIOnsvy2bPi5USxWUg+fZKfTyvMHNyafgt/5e/TmVd0m57c1vwGFg5rdVW3e71s01/bbVo9nG/Gd74q2xKEikqZPLyeeeYZRo0aRZcuXWjUqBGg9q5iNBp59913L6uBwhXCalV7YzxxovRiF4AuQM3nZbc4e26MgXMX4OPVsP4XNfE5QHw9+Fd/6Nha7QXQB2aHlTOWdI7mnSfdmkuILoAaxkpoy9jjokl3A0d1QznlSKOK9Xcq23agc5xnV+pqPsz6htU5YFLyc1M0C67BrVHNuSWyKZUMV0nwdDjUY2SzqbnNbM6XMJcXlUYDWg1otKDTgNaZX0tfek8zh+Ig12Ehx27GotjQajSEBAUS2SyBSq1CCdcFEaoLRIdGDTk9cgKSTsLRE2o5fkYNS927Xy2ehIaqwle9elCnTv54lSqXnrBeq83P81UcvrzFkpPVY+wSxrTawqJYcLAqjBUUwyTh/nWDhmtD+KxTpw6TJ08G4D//+Q9NmjTh8ccfd89/6aWX6Nq1K0ePHiUmJobly5czbdo07rjjDkDNSeTqOXDNmjVYLBZefvllt1fVtGnTGDNmDE8++SSVK1emb9++rFmzxi3YbN68mczMTHr37g3AW2+9xYABA9wCVJ06dXj22WcZMWIEL7zwgvsfYTfddBMPPPCA285nn3222OVOnz7NL7/8wueff05ioppjZ9asWfTtm58/pzxTvXp13nrrrUL1zzzzjB+sEQRBEARBEMokeMXHx/Ptt9+yevVqjhw5AqjJOwcMGCD/xbsWsNng77/h6FE1Z1cRrtwXxRAK4Q3hwCb4+DP4fpsq3gAkxsPwgWqvi0V4MuQ5LJw2p5OUd550Wy5hukBqBESVWegqtH5NBKutjVmflsf36XtIt1sBVeiKM8Bd4WH0impNeMhN2DWlTMBfGhwOMFtUbzerFRyKKv4Z9KqAFRwMgcb8tnY72BX1PDkc6rjVDHbnPEXxCPfzKM7jpigKJoeFXIcFk8OCBg1BOiOVDWFUMYYTpgskXBeEQevjvFetrJb2Hu7TNhucOqeKX0eSnCLYWTh9Qc3RtWuXWjwJCoLataFuXXVYp45aata8eLhjaXElyC8Ou93bQywjAy5cyD+eoB5Tz7xiQUHquXEl3PcUxEQUu+bRaDT8ev+v5T6kEdQQJBf79+9ny5YttGxZOMTh+PHjZGVlYbFYuOmmm3yu6/Dhw8THx7vFLlBD8RwOB0ePHqVy5coMGDCAoUOHcu7cOapWrcrq1avp1q2bO8Rx//79HDhwgNWrV7vXoSgKDofDK3F7QkKC17YvttzRo0fR6/Vey9WvX98rtFIQBEEQBEEQSkqZlI633nqL6OhohgwZ4lX/xRdfkJqaKr0RlWfsdti3D44cgRtuUD/ay8rJk/Duu/DNGlWMAVXgGjFQTURfBC6h62jeeTKcQlfNgLJ7dHmiKAoHTGfZkL6Lb9P2cM6a4Z4XrQ+lf0QtRoSa6W44glaTBfyEI/dXMnRxpOoTydA1RNFcQiij4gCLzSlwWVQxRauFACOEBENYsBoeGBgARqdQU5x3ncOhCmQuwcZiVT3DzBbIyQVTHkqOiTxrHjmKmTyHDUWvI8gQRFhACPWDqxNuDCFcH0Sgtoz7pddD7Vi1dPP4iDbnwbFjkHRM9QI7fh5OJMPpZNXLav9+tXii0agia61aqhBWu7Y6XqsWVK165QQkVw+UxYltrhBKq1OYzMiAlBTvEEqXKOYqQUH5yfY9xTBPUUwot2g0GkKMl96r25UmKCjIPZ6bm0v37t158sknC7WLiYnhxIkTl7y9xMREatWqxdq1axk2bBjfffcdc+bM8bLhX//6FyNGjCi07A033ODT7pIsd/To0Uu2XRAEQbjyBFjNfPD580R/HQADf/W3OdcdJpOJPn36ALBu3bpCz1tBEPIpk+D16aefMn/+/EL1DRs2ZNKkSSJ4lVdsNjhwAA4dUsWFsvaYdewYvPcerFuX79HVrg0M7QKN64AxyudiJruF05Y0kvIukGHPJVwXdNmErqS8C2xI28W36Xs4Zr7grg/RBtAjsgm3RiXSJrSuOx/YbiWbSrbdRNv+JNhxhij7PqLs+7BjJEMXR5q+acnEL5sV8qyquGW3q4KI0aAKK1UqqeJWUKAqeBnLIH5otaBF9QQLMEKIKuqZ7RZybMGYLLkoFiuBdg2hDj11CCLCDGEmB8E2DZpsG9jzAJMqXBldXkoG1SvsUggIhLh4tQAodnVb5hw4fU4Vv06lwKlUOJkMJ05Ddg6cOqWWzZu912c0Qo0aqhdYzZr54zVqqNdrWT0RS0pJQigLimLp6YWT7bt6oHSVkBC1BAYWFsOMRul9UigVTZs2ZcOGDcTGxvpMtFynTh0CAwP5/fffqVmzZqH59evXZ9WqVeTm5rq9vHbs2IFWq6Vu3brudgMGDGD16tVUrVoVrVZLt27d3POaNGnCoUOHqF27dsHVF8vFlqtXrx42m409e/a4QxqPHDlCZubVy9EiCIIgXBytonDTiT3qhMNRfGPhsuNwOPj555/d44IgFE2ZviDPnz9PTExMofpKlSpx/vz5SzZKuAJYLGrOrqNH1fxKZQk9PXpU9ejasCH/4dahAzz0EDRrBnkXIOuQmtPLGO0OZfQldNUyRpc6rKcgJ8wpfJe+h+/S9nAw75y73qjR0yk8jl5RzegUHkegtrDQZNOEkmxoT7KhPYGOc0Tb/iLKtocAJZ1K9j1Usu/BgZ5MXT0ydPFk6OKxEqp6Vpkt+V4/Br0qWsRUgrAQ1XMr0HhZE6MrioLJZibXlofJZgYUAnUBhBiDqB1WjXBjKGGGYEIMQfnH1BVGabZAnkXtoCA7FzKzIc+seodZrYAmX6TzLGXxtNLoQB+iloZVoIEdHGY1z5vdrHq/ZeTBmTQ4k+4Uws7CyTNw6rR6jR45opaC6HSqZ1iNGmrOudhYddo1vFohTyURxTzziZnNatin1ertJeYKwzQY8nufDAkpOnRSRDHByd13381nn33G448/zqhRo4iMjOTYsWOsXbuWmTNnEhAQwEMPPcS8efMwGAy0atWK1NRUDh48yF133cWAAQNYuHAhU6ZM4dFHH3Unjr/99tu9enwaMGAAixYt4s0336R3794YPa75hx56iKFDhzJjxgzuuusugoKCOHToEL/99hvTpk0r0vaLLVevXj06d+7M888/zwsvvIBOp+Oll16SVAmCIAiCIAhCmSiT4HXDDTewY8eOQv893r59O1WqVLkshgmXkbw8NWfXsWNQrVrpcyj9/Te8/z789FP+R3vnzvDgg+CZoyWwMmj1kHUY8pLJNYRx2pJJkvkCmXYTEZdB6ErKu8APGX/zffpeDpjOuOt1aGkXVp/eUc3oGtGIUF3JP5DytFU5ZezFKcMtBDtOE2XfQ5RtLwFKGpH2f4i0/wOsJtdRmUxNXTKD48mObIISHJYvcF1GQcLucGCy5ZFry8NstwAQpA8k1BBE3XA1RDG0oMBVEK0237vME0VRwyLNFlX4Mlsg16SKYa6h1aLmDdOQL8oYDWoIpsFQciFPowNdsFpcBNkgpgokmMFuBRTQGkDRQUounM2CMxfgVDKcOqsKYadOqYLRiRNq8UVoaL4AVq2aGq7rKtWqQUTE1euZ0SVmFfWRrijeXmI5OaqnWMHQSU9RzJVkPzjYWxTzHF5pDzihXFC1alU+/vhj5s+fz4MPPojFYqF69ep07twZrVOofuSRR9DpdCxcuJDk5GRiYmL417/+Bahhhu+88w6zZs1i8ODBBAUF0atXL6ZMmeK1ndq1a5OYmMiuXbsKJV1v1KgRK1as4PXXX+fuu+8GoGbNmhdNLl+S5WbPns1zzz3H8OHDqVy5MhMmTGDhwoWXdtAEvyLeB8J1g6IACspFGwqCIAhXizJ9Id1111289NJL2Gw2d2LczZs3M2/ePK8emYRyQE4O7N4Np0+rYkBJP4oVBbZsgeXLYevW/Pru3VWhq1ERObqMkWSH1OZU6g6OX9hGli6ACH1YmYUuRVH4x3SWnzL28UPGXg7nJbvn6dDSNqwuPSMT6B7RmAj9JSSet6l5sXItoeRa23BK05pAQwaRxmNEaI8QYj9BsPYCwVygmnUbjgsGckLqkxXaiGxHI7JDGqBoy5aM3Wy3YLKZMdnMWB02dBotQfoAKgVGUDkwgjCnwBWkD7hkrzg0GjU0MsAI4QV6pLTb873BzE7vpKxcNQzRbFHzclltgAKKRg2zNBg8EucbiuyJM3/7rgTzHnmTFJvqARYTDNE6aBIJNACdEbRG0ARAugXOpqqJ8s8mqyGTZ86oYlhqqupF9c8/avFFUJAqfFWrpoZHVqmiDqtWVeuqVFHFpKuBZx6woigoimVnQ1pa0aKYZ6J9V/ikpxjmWcRb7JpixYoVherq1KnD4sWLi1xGq9UyduxYxo4d63N+fHw8y5cvv+i2P//88yLnJSYmFtsr8w8//FCm5WJiYgr1dDhw4MDiDS0nmM1mVqxYwZYtW0hJSUFRvD97V61a5SfL/IPRaESr1XL69GliYmIwGo2X/gwThDJit1pQXD1iXwEUuw1TZhppuTZyrPIPKEEQhPJCme7Io0aNIj09nenTp2O1WgEICAhg1KhRPPzww5fVQOESSE5WE9SnpamhYCX50M3Lg/Xr4b//zQ8t0+ng1lth5EioV6/IRTOs2ZzKS+aE6Rw5CkQFVqWWNRsNlMq7xqbY+TP7OD9n7ufnjH2ctqS756meXPXoEdmUrhGNiNKXIeG0za56MZmdPfY5FNV7yWiAShHu0MS8wEDOBvTgrFaL3ppJWNbfhGftITxzD0ZrKmHZ+wnL3g9nwaHRYQqqTXZIA3JCGpATUh+LMabQftscNkw2M3l2C2a7BUVRMOoMBOkCqRlajajAMEL0gYQagwnQXULy/LKg00FIkFoKYrWpHmEWa/4w2ymGWZ3jrh4oUdQeIz2FMINOFWV8hUq6RTAPFAcoVjUc0pEJwVaoZ4B6N4CuFmgMoAtQvcesGkhOh7MX4Ox5tZxLVgWxM2dUQcxkUkNyi0uKHRqqCl8xMflDV6lcWR1GR18dT6rSiGKuYjarAvfZs/m59TzX5RLGjEZVGAsKyhfGfBXxGBOEUvHMM8+wadMmevfuTWJi4nUv7rjywp05c4bTp0/72xzhOic5Mw+L/cr5XtkdDnadzeOTPVl0iq96xbYjCIIglI4yfdFoNBqeeuopHnnkEQ4fPkxgYCB16tTxyvEh+BGbDZKS1AT1oIpdF3vxPnkSvvoKVq1Se6cD9YP4tttg+HA1NMwHDsVBqjWTk6ZkzuSdJ89hoZIhnMohNdUk5uZUyDurDgMiAN+iW5oth82Zh/g18wCbMw+R7chzzwvQGGgf3oBuEY3oEt6IcH0peiJx9Wxo8RC3XCJMeKgqbgUFqmGJgQFFioI2QzhpldqTVqk9KAoB5jOEZe8nNEsVvYzWNEJyjxCSewTOfwuAVRdCdmAt0gJjSTHeQLqxGpaAqgQYgog0hhEdGEGYUQ1NDNYHotOWY88bl3jlC5tNFQ8tFo+h0zvMlKdO5zpFGVdkiytcUu9cr94piOl16rWq0aqeXQW95lxCmMMGdhNYM8FhhyggKhAS6oImThXRdMGgDwKbBi5kQHIKJF+AcxfUYXIynDunDrOz84uvHGIuNBqIjFQFsOjo/GGlSurQczw8/Mr1Oumy5WKiGHgn2rfZIDcXMjOd58Mj1Kigx5grLDMwML8XSpcnma/hdf5xLwg//fQTb7/9Nq1bt/a3KeUGo9FIrVq1sNls2O1XzrtGEC7GnBV/cCg5+4qs26FAjsVBlkXCGYXLS0xoAHaHgk57dd+x/LFNQbhSXNK/8ENCQtw9KQnlhOxsVeg6dkz98A4LK7qt2Qw//qgKXX/8kV9/ww0wdCjcfnuRy1sdNs5b0jhhOkuyOQ0HCpUM4VTRVcpvpNFBYAwYQsB0ThW9dEbQB2NTFPbknGRz1iE2Zx5kn+mMV9aDSF0wncLj6BbRmJvC6xOovYiYqjjAYlM9kVw9JkJhccuVcyswUBVXyoJGgzmwOubA6lyo3APF4UCTd4bA7IOE5BwkIvcoEebTGOw5ROXsIypnHy6/OEVrRAmpizasIYTWgdC6EFIXDLFls6U84BJHfHmGuXKGWZyheRZXMndn7rAckzptsjo97zx6O9SgCpAuIUyncwpjOvU6KiiwKIoqhik2VRCzpqoCnAKEAqEaaHADaGo684sZQRukeorlWSElA86nwYU0uJAK51MgJQXOX4ALzmK3qx6TaWlw8GDxx0Wng6go9XcYFZVfIiMLT0dGqr+1KxFuWJJE+6CKX3a7d9L9vLz8ac9QSsg/765zZDSq+cUCApy/L723IFawSGilUMGoWrUqISFl8Dqu4Gg0GgwGA4aLifOCcAW5YFI4lSWi6+Uk1xCATquhbAk9hJIQHqRHp9Uw4ZOdXoKtzWxCZ1RzxQ76zyb0AaVwBrgIDaqEsuBfLS/b+gTB30jMSkXBYlG9tI4cUUWv6tV9e37YbGpuru++U8WunBy1XqOB9u3hjjvUhPRFhDNl2XJINqdywnSOdGsWBo2eysZIjD56QnSjC8YeXIND1ky2XfiDbVlH2Gk6Ta7D4tWsYWBVOkXE0zk8jqbBNdBpfHjHKA5VFHEVmzOnFE5vF6MeKkep4kuAU9wKCCi7uFUAq92G2W7B7LBitluwOxyAglEXSEBEa8JiOmM1hmPS6Qg1nyEo9wiGrIOQdQCyj6Cx56HJOqBOe6LRQ3AsBNeC4JoQXEOdDoqFoBtUUeZaxDNnWFHYbN7n1OWNZ7GCyax6iZnNal2e2SnKFHhp1WlVAcUliukMoA8EvbNeo3EKNk4xzGEHey5Ys1SBTAEigAg9NKwK2uqqKIZOTaqvNarnKMsMaVmQmgEp6ZDqKmnOkqoOMzNVO11CWUmPVXi4mmA/MlIdRkTk17nGPUtYmBqKeTnEI61WLSX5KHWJY54CWXa2mnzfVV9w31znx32e9PkCmau4Bc4CYprnUIQyoZzy9NNPM3/+fKZPn05s7DX8TwxBEISLYDIG0uTxL7mt+Q0sFKH/inMoOZu/T2d61dWY9AUAB1KsgNUPVgnCtYEIXtc6Doeas+fwYfXDOjy8cAhjZqYqcv32G/zyS37IIqhJu2+7TfXmqlbN5yYsDisplgzOmi+QbE4jx24iTBdM9cAqvkUpIM9uZl92En9mHmBn5gF2ZR4i257r1SZCF0i70Dq0D4/npvCGxBjCnXMUsDnAZs4XQ2x2td4VdmXUq15bIUGq15ZLVDEaLjmMzO5wYHVYsThsWOyqsOVw9rxj0BoI0BsI1gdQPaQyYYYQgvQB7qLXev6kYoE2+ZOKA3JPQdZByD4COUch+yjkHAOHWR3mHPNtVEA0BFZzlqoQWCW/BMRAQOVrVxRziRvF/XNKUbxFTpc3mOv6yMtTvblMTo8kiwVMHqKMouSLXi7xRasFnV719HILZlrQAhoFcKjCmCMP7Dnq+dPbIQaICQKCQHODKoxptIBzqNGCXQOZJsjIdZYsSM+C9EyPkgGZWapIlJWt2paRoZbjx0t+/DQaNVF9eLgqfnkKYaGh3uMFS0iIWgICSheSWBpxzHX+XOKY65y4PMhc0w6H93mC/PPkyv/mErw8e6oMCPAtinkWp4eaIr3FXbcUTCB/JWjWrBlms5mePXsSGBhYyKNpq2cHMIJwnSKhUoIgCMLVRASvaxWzGc6fhxMn1BxERqPaC6NOp+bo2b0b/vxTDVXctcvb46JSJejZE3r1gsREnwKR1WEj3ZpFiiWdU+bzZFlz0Gl0ROhDqWyM9GprcVg5knuKA9nH+Dv7CHuyDnM45wR2vD8ug3WBtAyPp21EE9qGNaChEog2LwXyslSvGCVH9czBlUtIlx+OGBykhicGOEOzjJcWEmVz2LDYbVgdarE4rNgddkCjOiVpjRh0eiKMoYQbQwgxBBGoNxKoU4Uto64MoRkaLYTUVAs98usVB+Sdg5zjkHtCHZpOgek05J5WxRZziloy/i56/fowVRgLiAZjJTBGOYeR6rghwjkeCYZw1WvpWkGjUcVMYwm9j1wiqa3A0O4UyswWtbhCLe0OddzuyPdeUpyeg4BbbHUJMFotaDXOIaB1qE01DrVoFYjQQHgQ1DCCEuljn3CKZRqwoeY8yzKpw8xcyMqBrDzIzFE7BcjMUTsJyMx2DrNUjzdFyc8/VlZ0OlUACw5WBTDPYcHxgiUwsPB4YKD3fcUzP1hpcIlhnqKYp1Dmda48j63G63wZdDqIjiY3NZUguz1/fsHiWrakQ8lbds2Qm6v+w+VKhtU9/vjjJCcnM2nSJCpXrnzdJ60XBF/4Cs+6knSLj+Gp3kX0LC4IgiBUeETwupZwOFRvrQsXVA8Ql6dWWprq4fXPP7B3r5rDq2BIUb160LGjWlq29CkW5drzyLLlcMGSzllzCtk2E6AQqgsmNrAKCnAm7zzbM/ZxxHSaIzknOZhznCOm09iVwnkRKhsiSAyuR8ugerQMrE0DQ1X0rqT1VqfXRnA4hFpBZwFtLugdqpgVFAaBYWAMLPVHpc1hdxYbNsXuFrVUQUvFoDWg1+owag1EGEMJNQYRog8iQGckUG8kQGckQGco4LF1hdBo1bDFoBuAdt7zFAWsGWA6qyb/dw3zksF8Pn/osIAtSy05SSXbri5QFcH0YaoAZgh1joeBPhT0IR7DYNA5h/oQ0AWpSeF1pT8/Vxyt1unxV8L2ipIvhtk8xBWbXRXA7B5CmdWVj8zmLZBZFXXc5cnkUFRBS8H1B3CJMFqnWALoNKo3mQYIDIagYKgane9lpiG/KHZ39K57lVabmgct21ly8iA3L388J0+d7xrm5nmPm/LybXZ5l10uAgJU70tXb5Cu5Peu8cBACHR5aAaqnUcEeMwrGO5YsAQ5w5aNPvK5gXpenAn7dQ4HkWYzyc7w0mCjURUjPIWygt5lLooSvHyN+xLQiltXaeqEMqEoCrm5uSQnJxMZGYnuCobE7ty5k08//ZRGjeTjWhCKw1d41pWifoyE210JAmwWlqx6iaobAuCOjf4257pDsVk4v+olAGLueAaN/hr6J7YgXGUqjOD10Ucf8c4773D+/HkaNWrEv//974qRUN+Vm2vvXrUkJeV7dZ0+rRZfYTrVqkGLFqq41b69mtPLA0VRMDnM5NhMal4uSxrpliySLalcsGSQbcshw5bDGfMFTprOcsKUzCnzeayKrfC2gHBtEHEB1WkcWJOEoNo0Da1L1YBoNAaD+hHr+kDV61UvHYMB9Ib8hNYA9jywZYMlU004bs+BvEwUwK4xYNcasKPFBtgddmyK3T1UlHwlQKfVYdDo0evUYWhAMCGGQIL1gRh1BoxaAwE6I0adngCdEW0RYZnlAo0m3ysrooiPKEVRj5s5BcwX1GJJ8yipqmhmSVeLNQNQ1ONtzwPOXYqBTvErSBW/3OMBzulAtadFnbPHRV1g/rjW6KwzOscNznpXzixD/rjWkF9c9S7vqEtFoym+F8ricDg8RDGHh9eRR53DKYC5RDSrNd8DzWrzaOdczqE4w/uc61ecda7j7SWk6UETBqHhEKZxCmXOoUsd0zqXdYWQuIQ0rUZdv9mieoXmWdScabm5qhiWa3bmUDOr43nO8TzntNkCJouz3ukxl2fJF4zMZrVkXIWPGoMh3wPQ6Ax1NDgT6TvnVQsIgFv7knxTB7VOo3U68DmPqZfnlqu+BCJX/kS+Q6DnPI3zhCnONh4Dn8sVqvM17jH0mOU1UnC6qLpC++GrybUvvEVGRlKtiLD9y0W9evXIy8u7eENBEIRrHK3DQY8jzg6vpAfWq47icGByHn/F4agAT2lBuHJUCMFr7dq1zJ49m+nTp9O8eXM++OADHnzwQdavX090dLS/zVOx29UPyZwcyMpSS2amWtLSnL3BnVe9t86cgXPn1JKSoi5XHJGREB8PcXHqsEUL7FVjyLaZyLJkcT4vjfOnD3PenMp5cxpnzBc4Y77ABUsmqdZMUm2ZZNhzSbdnYylC0HIRoDFQO7Aa9YKqUze0BvXD69IoOo6qodVUcUuvU4Usz8TTGg0OxYFDcWB32N3jDsWG3WbGblHr7E4BS8GIoq0M5IJiBnsuOlsuOsWKXuNABxg0OsIMQQQGBBOsD8GgD1SLLhCj3ohBq3eXCh9WotGoXlmGMLXnx4uhOFSBzJqZX2zZagJ3W5ZzmO0sOWDNVhO823JVEdKWC3aTa2XqPPtFrtErhcYAWj1o9PlDz3F3nc45rnMWz3Fn0bqS1BeoR4s7T5e7Xps/jca7Do95Go1znc7wOqPGo97VTqOKIYoG1D4Q8osDULTqPEVxTiv57RWc4poD7IqH2ObhceZaxu4hvLjq0KhimkELhgAICUTN3o/TPqcdGq2HB5RLUHHO1+jyp3H2ymm2eoSOmlXh3uzspdNV5xlaanZ6zlk8xwsUs8W5jHPa0xPL6gxNzSnmUgFu+G0bVYKDsVaufPW8p1xefTqd6tGn1amdKWidOeN0HiGyOme9FmdnC1qnF6DGmcPM5R2o8Qirda5To8lfh8Zjnsa1HdTrXudaVutj+x7b1OrAoPNo55kXTave67Xa/B5UXW1c6/HchkarCpAaj23rnb8193Y99l/ntMfl3ugSIaPqQWDlUp8Cg8FwRT27XDzxxBPMmTOHSZMmERcXVyh8MjQ09IrbIAiCIAiXQkxogN9y7UmOP+FKUCEEr/fee48hQ4Zw5513AjB9+nR++uknvvzyS0aPHu0/wxSFf4bczI5Dv+Kw23Bo1G9OuxavcbsGbFp13KZVo/2sYWCLUMctOrAEGrCGBGIONmIODsAUbMAcoMdk1JKrtZOrnCTXcZhcx/+Rvd1EnlL23joqBURSNbAyVYJjqBpchdiw6twQdgPVw2KJDolBY9CjoIpYCgoOxcEpxQGYnbutqJ2FuDph1IBWo0WLFq1Wi06jU6c1WnRaHQG6AIx6I0atEaPOSIA+AJ1Wh16rzy8aHXrFil6xqQUbWLJUAcaeBw6r2tuew6QKMi5cwoJLzHALFS5hQpM/dNWBc7yIsKRrHY3WGcIYfvG2RaE4nB5iuWAzqQKYu+SpSfhdHmTuabPH0FJg3KpOOywe066hTR33dU0rVrBLzzSXjOsyvxRNwKU/GZylxBvV+KjzGFcK/gY1oBjATv59xqZRw0qtgMVj3LPeWXQ2Gzrrmfx5NtR12QCbazmPade43WPac9yKU2B0thMuDy69y3krJlQHi2fC3VP8a1cxjBo1CoD77rvPq15RFDQaDfv27fODVYIgCIJQcsKD9Fc91x5AgyqhLPhXy6u2PeH64ZoXvCwWC3///TcPP/ywu06r1dKhQwd27tzpR8vAbrNyY9zPZCRcjp7Biuhy1lK4yhOdRkeIIZhgfTAhxhBCDSGEB4YTHhBJeGA4EQERVAqqRKWgSkQHR1MpqBIBugA0Gg0aNO5wP7dApVGFKK1Wi16rd08btAZ0Wp1bxPJs76orOO4aXrIHlsOeL5QoNg/xyyWWmL0FFMWmCjaKVR26XF4U53lSHM5pKJw0qSCe8zzG3bmA3C4y3vN8rqe46ZIuVwYu9fjrjM4eIiMv3ZbiUBTnubOr51Wxe5xP17jdee5t+eOKPb84bKjn1q5eN4o9f9pVh915fbiGDo82Dh91HtePazkUjzYO7zauea7rDed8d/sCbcGjvce0u63HOlyuYUqBodc43usq2O6qoRQYFkFRl70WVVgLvqxGXRou7zmX+GUvUHzVFSyOi9Q5LjKtFFim4LTHLc9r+YJFKUOd4mPac7y0x9K1PEC6Hc7sKeVKri7Lly/3twllpsKmhRAEQRDKxNXMtScIV5JrXvBKS0vDbrcXCl2Mjo7myJEjfrJKRWcwMqH7FNbv+T80Wi0avQGtTo9Wp0Or0aHVaNC6hCCPoV6rd3s46bQ6t+eTXqvHoDOoXlC6AIw6I8GGYEIMIYQGhBJqDCVYH0xoQChhxjBCjCEE6FRvKQ0at4jlOdRqtIXGtRptoWlXXblEqwNtEBBUsvYuccGnMFFQkChOOHCNU8S0x7BgvVvswHs5n8sX/FL03E6Beh+jxVcW9RVazNepz21fjEsQUsq0vatJebevDLh/B76ub89r1fO3gHdbd7sils+f6Vtw82pbcF0F1+llvI91e9ZTRH1R7YpYb6H1u6od3vWe4qPLfqVA24LrKjQfvI6j57TX4fRs78M+L1t91Rc4noUObVHrKtjYx3lRnMs4nPNcvWu6p1156zynPeaHREKbeynP3Hjjjf42oUxcE2khBEEQBEEQysA1L3iVd6b3nMX0nrP8bYbgicaZ1+aS4rcEQRAEIZ9t27YVO79t27ZXyZLSUW7TQghXFMmVIwiCIFwPXPOCV1RUFDqdjpSUFK/6lJQUKlcuWXJbxfmf6OzsqxenLAiCIAhC+SEkJOSSPJlHjBhRqM5zfeUxh9elpoWQ96drmzd/OszpDNPFG14mmtWI4K7WNakTrsVhKVGyx0umapB6fVb0bV7t7RrNNrK1zry3cnyv+jZtZhunnMc/PlqPPuDy2eOv41snXCvPkmuUS31/utJoFKXcxwtdlLvuuovExET+/e9/A+BwOOjWrRvDhw8v0X8nz549S9euXa+0mYIgCIIglFO2b99+ST0pZmVleU1brVb27dvHggULmDRpEu3bt79UEy87586do0uXLnzyySe0bJmfLHju3Lls27aNzz//vNjl5f1JEARBEK5vLvX96UpzzXt4Adx///08/fTTJCQkkJiYyAcffIDJZGLQoEElWr5KlSr8/PPP5V6dFARBEAThyhASEnJJy4eFhRWq69ixIwaDgTlz5rBy5cpLWn95RN6fBEEQBOH65lLfn640FULw6tu3L6mpqSxcuJDz58/TuHFjli1bVuKQRq1WS7Vq1a6wlYIgCIIgXG9ER0dz9OhRf5vhk0tNCyHvT4IgCIIglGcqhOAFMHz4cIYPH+5vMwRBEARBuA7Zv39/obrk5GSWLl1Ko0aN/GDRxTEajTRt2pTNmzfTs2dPQE0LsXnzZnmnEgRBEAThmqfCCF6CIAiCIAj+YuDAgWg0GgqmRm3RogWzZpXf3povNS2EIAiCIAhCeUUEL0EQBEEQhEvk+++/95rWarVUqlSJgIAAP1lUMi41LYQgCIIgCEJ5pUL00igIgiAIgiAIgiAIgiAILsTDSxAEQRAE4TKwefNmNm/eTEpKCg6Hw2ve7Nmz/WSVIAiCIAjC9YkIXoIgCIIgCJfI4sWLeeONN0hISCAmJgaNRuNvkwRBEARBEK5rJKRREARBEAThEunUqRNPPvkkAwcO9LcpgiAIgiAIAqD1twGCIAiCIAjXOlarlVatWvnbDEEQBEEQBMGJCF5XkI8++ogePXrQrFkz7rrrLnbt2uVvk8o9b731FnfeeSctW7akffv2PPLIIxw5csTfZl1zvP3228THxzNr1ix/m3JNcO7cOZ588knatWtHYmIiAwYMYPfu3f42q1xjt9t5/fXX6dGjB4mJifTs2ZM33ngDcRouzLZt2xgzZgydOnUiPj6ejRs3es1XFIUFCxbQqVMnEhMTue+++0hKSvKPseWI4o6b1Wpl3rx5DBgwgBYtWtCpUycmT57MuXPn/Gbv4MGDWb16td+27w/kPadoFi1aRHx8vFe59dZb/W2W35H74cW52DGaMmVKoWvrwQcf9JO1/qEk3wtms5np06fTrl07WrZsyWOPPcaFCxf8ZPHVpyTHaMSIEYWupWnTpvnJ4qvPf//7XwYMGECrVq1o1aoVQ4cO5eeff3bPv96vIRcXO07l/TqSHF5XiLVr1zJ79mymT59O8+bN+eCDD3jwwQdZv3490dHR/jav3LJ161buuecemjVrht1u59VXX+XBBx/km2++ITg42N/mXRPs2rWLTz75hPj4eH+bck2QkZHBsGHDaNeuHUuXLiUqKopjx44RERHhb9PKNUuXLuXjjz/m5ZdfpkGDBuzZs4epU6cSFhbGvffe62/zyhW5ubnEx8dz55138uijjxaav3TpUlasWMGcOXOoUaMGCxYs4MEHH2Tt2rUEBAT4weLyQXHHLS8vj7179zJ27FgaNWpEZmYms2bNYuzYsaxcudIv9prNZj777DM2b95MfHw8er33K9bUqVP9YteVQt5zLk7Dhg1577333NM6nc6P1pQP5H54cS52jAA6d+7s1RGG0Wi8WuaVC0ryvfDSSy/x888/8/rrrxMWFsaLL77Io48+yieffOJn668OJf2mGjJkCOPHj3dPBwUF+cNcv1CtWjWefPJJateujaIofPXVV4wbN45Vq1bRsGHD6/4acnGx4wTl/DpShCvC4MGDlenTp7un7Xa70qlTJ+Wtt97yo1XXHikpKUpcXJyydetWf5tyTZCdna306tVL2bRpkzJ8+HBl5syZ/jap3DNv3jxl2LBh/jbjmmP06NHK1KlTveoeffRR5YknnvCTRdcGcXFxynfffeeedjgcSseOHZVly5a56zIzM5WEhARlzZo1/jCxXFLwuPnir7/+UuLi4pRTp05dJau8GT58eJFlxIgRfrHpSiLvOcWzcOFC5bbbbvO3GeUauR9eHF/3vqeffloZO3asnywqnxT8XsjMzFSaNm2qrFu3zt3m0KFDSlxcnLJz504/WelffH1TybdCYdq2bat89tlncg1dBNdxUpTyfx2Jh9cVwGKx8Pfff/Pwww+767RaLR06dGDnzp1+tOzaIysrC0C8bUrIjBkz6Nq1Kx06dGDJkiX+Nuea4IcffqBTp06MHz+ebdu2UbVqVe6++26GDBnib9PKNS1btuSzzz7j6NGj1K1bl/3797N9+3amTJnib9OuKU6ePMn58+fp0KGDuy4sLIzmzZuzc+dO+vXr50frri2ys7PRaDSEh4f7ZfsrVqzwy3b9gbznlIxjx47RqVMnAgICaNGiBU888QTVq1f3t1nlFrkflpytW7fSvn17wsPDuemmm5g4cSJRUVH+NstvFPxe2LNnD1ar1etaql+/PtWrV+fPP/+kRYsW/jDTrxT1TbV69Wq+/vprYmJi6N69O4888kj58s65StjtdtavX09ubi4tW7aUa6gICh4nF+X5OhLB6wqQlpaG3W4v5NIfHR0t+ahKgcPh4KWXXqJVq1bExcX525xyzzfffMPevXv54osv/G3KNcWJEyf4+OOPuf/++xkzZgy7d+9m5syZGAwG7rjjDn+bV24ZPXo02dnZ9OnTB51Oh91uZ9KkSdx2223+Nu2a4vz58wA+nxfXY56IsmI2m5k/fz79+vUjNDTU3+ZUeOQ95+IkJiYye/Zs6taty/nz53njjTe45557WL16tVyjRSD3w5LRuXNnbrnlFmrUqMGJEyd49dVXeeihh/j000+vy7BZX98LFy5cwGAwFPoHSHR0tPs6u54o6puqf//+VK9enSpVqnDgwAHmz5/P0aNHWbx4sR+tvbocOHCAf/3rX5jNZoKDg3njjTdo0KAB+/btk2vIg6KOE5T/60gEL6HcMn36dA4ePMh///tff5tS7jlz5gyzZs3i3XffvW5yXFwuFEUhISGBxx9/HIAmTZpw8OBBPvnkExG8imHdunWsXr2aV155xf1iMHv2bKpUqSLHTbiqWK1WJkyYgKIoTJ8+3d/mCAIAXbt2dY83atSI5s2b0717d9atW8ddd93lR8uEax1PTzdXguiePXu6vb6uN+R74eIUdYyGDh3qHo+PjycmJob77ruP48ePU6tWrattpl+oW7cuX331FVlZWWzYsIGnn36aDz/80N9mlTuKOk4NGjQo99eR9NJ4BYiKikKn05GSkuJVn5KSQuXKlf1k1bXFjBkz+Omnn/jggw+oVq2av80p9/z999+kpKQwaNAgmjRpQpMmTdi6dSsrVqygSZMm2O12f5tYbomJiaF+/fpedfXq1eP06dN+sujaYO7cuYwePZp+/foRHx/PwIEDGTlyJG+99Za/TbumiImJAZDnRRmxWq1MnDiR06dP8+6774rnzFVC3nNKT3h4OHXq1OH48eP+NqXcIvfDslGzZk13hzvXG0V9L1SuXBmr1UpmZqZX+5SUFPd1dr1Qmm+q5s2bA1xX15LRaKR27dokJCTwxBNP0KhRI5YvXy7XUAGKOk6+KG/XkQheVwCj0UjTpk3ZvHmzu87hcLB582avWFehMIqiMGPGDL777js++OADatas6W+TrgluuukmVq9ezVdffeUuCQkJDBgwgK+++uq6dHEvKa1ateLo0aNedUlJScTGxvrJomuDvLw8NBqNV51Op0NRFD9ZdG1So0YNYmJivJ4X2dnZ/PXXX/K8uAgusevYsWO8//7713X+mquNvOeUnpycHE6cOHFdfiiVFLkflo2zZ8+Snp5+XV1bF/teSEhIwGAweF1LR44c4fTp09dN7qWyfFPt27cP4Lq6lgricDiwWCxyDV0E13HyRXm7jiSk8Qpx//338/TTT5OQkEBiYiIffPABJpOJQYMG+du0cs306dNZs2YN//nPfwgJCXHHSIeFhREYGOhn68ovoaGhhfKcBQcHExkZKfnPLsLIkSMZNmwYb775Jn369GHXrl189tlnzJgxw9+mlWu6d+/Om2++SfXq1d0hje+99x533nmnv00rd+Tk5Hh5dZw8eZJ9+/YRERFB9erVuffee1myZAm1a9emRo0aLFiwgCpVqtCzZ08/Wu1/ijtuMTExjB8/nr179/LWW29ht9vdz4uIiAiMRqO/zL5ukPec4nn55Zfp3r071atXJzk5mUWLFqHVaunfv7+/TfMrcj+8OMUdo4iICBYvXkzv3r2pXLkyJ06cYN68edSuXZvOnTv70eqry8W+F8LCwrjzzjuZM2cOERERhIaGMnPmTFq2bHndiBUXO0bHjx9n9erVdO3alcjISA4cOMDs2bNp27YtjRo18rP1V4dXXnmFLl26cMMNN5CTk8OaNWvYunUr77zzjlxDHhR3nK6F60ijyL/jrxgffvgh77zzDufPn6dx48Y899xzbhc/wTfx8fE+62fPni0v0aVkxIgRNGrUiGeffdbfppR7fvzxR1599VWSkpKoUaMG999/v/TSeBGys7NZsGABGzduJCUlhSpVqtCvXz/GjRsnYkMBtmzZwr333luo/o477mDOnDkoisLChQv57LPPyMzMpHXr1jz//PPUrVvXD9aWH4o7bo8++ig333yzz+WWL19Ou3btrrR5AvKeUxyTJk1i27ZtpKenU6lSJVq3bs2kSZPKRT4TfyL3w4tT3DF64YUXGDduHHv37iUrK4sqVarQsWNHJkyYcF2FfZbke8FsNjNnzhy++eYbLBYLnTp14vnnny83XidXmosdozNnzvDUU09x8OBBcnNzueGGG+jZsyePPPLIdZMe4JlnnuH3338nOTmZsLAw4uPjeeihh+jYsSMg15CL4o7TtXAdieAlCIIgCIIgCIIgCIIgVCgkh5cgCIIgCIIgCIIgCIJQoRDBSxAEQRAEQRAEQRAEQahQiOAlCIIgCIIgCIIgCIIgVChE8BIEQRAEQRAEQRAEQRAqFCJ4CYIgCIIgCIIgCIIgCBUKEbwEQRAEQRAEQRAEQRCECoUIXoIgCIIgCIIgCIIgCEKFQgQvQRAqLFu2bCE+Pp7MzEx/myIIgiAIglDhmTJlCo888oi/zRAEQQBE8BIEoQIxYsQIZs2a5Z5u2bIl//vf/wgLC/OjVYIgCIIgCMLlpEePHrz//vv+NkMQhHKOCF6CIFRYjEYjMTExaDQaf5siCIIgCIJwTWOxWPxtgiAIQqkQwUsQhArBlClT2Lp1K8uXLyc+Pp74+HhWrlzpFdK4cuVK2rRpw48//kjv3r1p3rw548ePx2QysWrVKnr06EHbtm2ZOXMmdrvdvW6LxcLLL79M586dadGiBXfddRdbtmzx164KgiAIgiBgsViYOXMm7du3p1mzZgwbNoxdu3bhcDjo0qUL//3vf73a7927l0aNGnHq1CkAMjMzefbZZ7npppto1aoV9957L/v373e3X7RoEbfffjuff/45PXr0IDExEYD169czYMAAEhMTadeuHffddx+5uble23rnnXfo1KkT7dq1Y/r06VitVve8jIwMJk+eTNu2bWnevDmjRo0iKSnJa/kNGzbQr18/EhIS6NGjB++++6573ogRIzh16hSzZ892v/MJgiD4Qu9vAwRBEC4Hzz77LElJSTRs2JDx48cDcOjQoULt8vLyWLFiBa+99ho5OTk8+uijPProo4SFhfH2229z4sQJHnvsMVq1akXfvn0BmDFjBocOHeK1116jSpUqfPfdd4waNYrVq1dTp06dq7mbgiAIgiAIAMydO5cNGzYwZ84cYmNjWbZsGaNGjeLbb7+lX79+rFmzhrvvvtvdfvXq1bRq1YrY2FgAJkyYQEBAAEuXLiUsLIxPP/2UkSNHsmHDBiIjIwE4fvw4GzZsYPHixWi1WpKTk3niiSd46qmn6NmzJzk5Ofzxxx8oiuLezpYtW4iJieGDDz7g+PHjTJo0icaNGzNkyBBA/SflsWPHWLJkCaGhocybN4/Ro0fzzTffYDAY2LNnDxMnTuTRRx+lb9++7Ny5k+nTpxMZGcmgQYPcQtyQIUPc6xQEQfCFCF6CIFQIwsLCMBgMBAYGEhMTA8CRI0cKtbNarbzwwgvUqlULgN69e/P111+zadMmQkJCaNCgAe3ateP333+nb9++nD59mpUrV/Ljjz9StWpVAB588EF+/fVXVq5cyeOPP371dlIQBEEQBAHIzc3lk08+Yfbs2XTt2hWAF198kU2bNvHFF19w22238d5773H69GmqV6+Ow+Hgm2++YezYsQD88ccf7Nq1i82bN2M0GgF4+umn2bhxIxs2bGDo0KGA+t40d+5cKlWqBMDff/+NzWbjlltucQtnBT2sIiIimDZtGjqdjvr169O1a1c2b97MkCFDSEpK4ocffuDjjz+mVatWAMyfP59u3bqxceNG+vTpw3vvvUf79u0ZN24cAHXr1uXQoUO88847DBo0iMjISHQ6HSEhIe53PkEQBF+I4CUIwnVFUFCQW+wCqFy5MrGxsYSEhHjVpaamAvDPP/9gt9u59dZbvdZjsVjc//0UBEEQBEG4mhw/fhyr1eoWjQAMBgOJiYkcPnyYUaNGUb9+fdasWcPo0aPZunUrqamp7veZAwcOkJubS7t27bzWm5eXx/Hjx93T1atXd4tdAI0aNaJ9+/YMGDCATp060alTJ3r37k1ERIS7TYMGDdDpdO7pmJgY/vnnHwAOHz6MXq+nefPm7vlRUVHUrVuXw4cPA+o/LG+++WYvu1q1asXy5cux2+1e6xYEQSgOEbwEQbiu0Ou9b3sajcZnncPhANT/oOp0Or788stCL1jBwcFX1lhBEARBEIQyMmDAAFavXs3o0aNZs2YNnTp1IioqCoCcnBxiYmJYsWJFoeU8e7cOCgrymqfT6XjvvffYsWMHmzZtcqeJ+Oyzz6hZsybg+13LM+RREAThaiFJ6wVBqDAYDAa3UHW5aNy4MXa7ndTUVGrXru1VxI1eEARBEAR/UKtWLQwGAzt27HDXWa1Wdu/eTYMGDQDo378/Bw8eZM+ePWzYsIHbbrvN3bZp06ZcuHABnU5X6P3G06PLFxqNhtatWzN+/Hi++uorDAYDGzduLJHd9evXx2az8ddff7nr0tLSOHr0qNvuevXqee0XwI4dO6hTp477n49X4p1PEISKhwhegiBUGGJjY/nrr784efIkqampl+VFqG7dugwYMIDJkyfz7bffcuLECXbt2sVbb73FTz/9dOlGC4IgCIIglJLg4GCGDRvG3Llz+eWXXzh06BD//ve/ycvLY/DgwQDUqFGDli1b8uyzz2K32+nRo4d7+Q4dOtCiRQvGjRvH//73P06ePMmOHTt47bXX2L17d5Hb/euvv3jzzTfZvXs3p0+f5ttvvyU1NZV69eqVyO46depw88038+9//5s//viD/fv389RTT1G1alV3GOMDDzzA5s2beeONNzh69CirVq3io48+4oEHHnCvJzY2lm3btnHu3Dl3GgpBEISCSEijIAgVhgceeIApU6bQr18/8vLymD179mVZ7+zZs1myZAlz5swhOTmZyMhIWrRoQbdu3S7L+gVBEARBEErLk08+iaIoTJ48mZycHBISEli2bJlXPq0BAwYwffp0Bg4cSGBgoLteo9Hw9ttv8/rrrzN16lTS0tKoXLkybdq0oXLlykVuMzQ0lG3btvHBBx+QnZ1N9erVmTJlijtxfkmYPXs2s2bNYsyYMVitVtq0acPbb7+NwWAAVO+z119/nYULF7JkyRJiYmIYP348gwYNcq9j/PjxTJs2jZ49e2KxWDhw4EBpDp0gCNcJGkUCqgVBEARBEARBEARBEIQKhIQ0CoIgCIIgCIIgCIIgCBUKEbwEQRAEQRAEQRAEQRCECoUIXoIgCIIgCIIgCIIgCEKFQgQvQRAEQRAEQRAEQRAEoUIhgpcgCIIgCIIgCIIgCIJQoRDBSxAEQRAEQRAEQRAEQahQiOAlCIIgCIIgCIIgCIIgVChE8BIEQRAEQRAEQRAEQRAqFCJ4CYIgCIIgCIIgCIIgCBUKEbwEQRAEQRAEQRAEQRCECoUIXoIgCIIgCIIgCIIgCEKFQgQvQRAEQRAEQRAEQRAEoUIhgpcgCIIgCIIgCIIgCIJQoRDBSxAEQRAEQRAEQRAEQahQiOAlCIIgCIIgCIIgCIIgVChE8BIEQRAEQRAEQRAEQRAqFCJ4CYIgCIIgCIIgCIIgCBUKEbwEQRAEQRAEQRAEQRCECoUIXoIgXDKLFi0iPj6e1NTUMi0/ZcoUevTocZmtqriMGDGCESNG+NsMQRCEa4YRI0bQv39/f5tx3eJwOOjfvz9Llixx113qu0NpWLlyJfHx8ezevbtMy7tsFUpGeXmvmzRpEhMmTPC3GdcdS5cu5dZbb8XhcPjbFABOnjxJfHw877zzzkXbXspvvaT3NKvVSteuXfnoo4/KtB2hdIjgJQgVENeLnWdp3749I0aM4Oeffy7zet988002btx4GS29dH7++WcWLVrkbzMuO4cOHWLRokWcPHnS36YIgiAAcPDgQZ588kk6d+5MQkICnTp14oknnuDgwYP+Nq1cUx6fnVebNWvWcObMGYYPH35Ft/PRRx+xcuXKK7qN0rJjxw4WLVpEZmamv025rJw7d45Fixaxb98+f5tSJA899BDffvst+/fvL/M6zpw5w+LFixk8eDBt27alXbt2jBgxgt9++63IZX777TfuvfdeWrduTcuWLRk0aBBr164t0fbWrl3LkCFDaNOmDe3atWP48OH89NNPxS7z9ddfEx8fT8uWLUuza164RCFXady4Md26dWPcuHGlOsfZ2dksW7aMhx56CK1WlRpMJhOLFi1iy5YtZbavImEwGLj//vt58803MZvNl7Qui8XCvHnz6NSpE4mJidx1111s2rSpRMv26NGj0Peiq/Tq1cur7X//+1/Gjx9Pt27diI+PZ8qUKZdk99VEBC9BqMCMHz+euXPn8vLLLzNq1CjS0tIYPXo0P/74Y5nW99Zbb12Rl/YXX3yR9evXl2nZn3/+mcWLF19mi/zPoUOHWLx4MadOnSo075133inRf6kEQRAuF99++y133HEHv//+O4MGDeL5559n8ODBbNmyhTvuuIPvvvvO3yaWW67Us/Na4p133qFfv36EhYVd0e18/PHHrFq16rKvd+zYsezatatMy+7cuZPFixdXOMErOTmZxYsX+xRDLuW97nLSpEkTEhISePfdd8u8ju+//56lS5dSu3ZtJk6cyCOPPEJOTg73338/X375ZaH2X375JQ888AAGg4HHH3+cyZMn06ZNG86cOXPRba1YsYJJkyYRFRXFE088wdixY8nKyuLhhx/m22+/9blMTk4O8+bNIzg4uMz76En//v2ZO3cuL730Ev379+f3339nyJAhJRa9vvjiC2w2m5dHrclkYvHixWzduvWy2HgluZTfemkYNGgQaWlprF69+pLWM2XKFN5//30GDBjAs88+i06nY/To0fzxxx8XXfaZZ55h7ty5XmXixIkAdOzY0avtsmXL2LJlCw0aNECv11+SzVeba8taQRBKRZcuXWjWrJl7evDgwXTs2JE1a9bQvXt3P1rmjcFg8LcJXiiKgtlsJjAw0N+m+MRoNPrbBEEQriOOHz/O5MmTqVmzJh999BGVKlVyz7v33nu55557mDx5Ml9//TU1a9a8anbl5uZeto884cqxd+9e9u/ff015BBREr9eXu488k8lEUFCQv83wSXl6r+vTpw+LFi0iJyeHkJCQUi/frl07fvzxR6/73rBhw7j99ttZuHAhd955p7v+5MmTzJgxg+HDh/Pcc8+VelsffvghzZo1480330Sj0QDqu3vnzp1ZtWpVIa8bgCVLlhASEkK7du34/vvvS73NgjRp0oTbb7/dPd2qVSvGjh3Lxx9/zIwZM3wu43kvXrlyJT169CAgIOCSbfEHV+u3Hh4eTqdOnVi1ahWDBw8u0zp27drFN998w+TJk3nwwQcBGDhwIP3792f+/Pl88sknxS7fs2fPQnX/+c9/ABgwYIBX/YoVK6hevToajeaSPAn9gXh4CcJ1RHh4OAEBAYVu5Lm5ucyZM4euXbuSkJBA7969eeedd1AUxd0mPj6e3NxcVq1a5XZ3LfjympWVxZQpU2jTpg2tW7dm6tSpmEymi9pVMNeDZ6z9p59+Ss+ePUlISODOO+/0+q/LlClT3PHvnm64LhwOB++//z79+vWjWbNmdOjQgWnTppGRkeG1/R49evDwww/z66+/MmjQIBITE/nkk0/o37+/z1xZDoeDzp07M378+DJv648//mDw4ME0a9aMm2++ma+++srdZuXKle6cE/fee697v1yu4L5yeKWkpPDMM8/QoUMHmjVrxm233Vbov9wlPa4A58+fZ+rUqXTp0sUdujR27FgJsRSE65Bly5ZhMpl48cUXvT76ACpVqsSMGTPIzc1l6dKlAKxfv574+Hif/83/5JNPiI+P559//nHXHT58mPHjx3PjjTfSrFkzBg0aVOjDzRWqv3XrVl544QXat29P165dATWEZtasWfTo0YOEhATat2/P/fffz99//11o+4cOHWLEiBE0b96czp07u232pCT3U7h8z05PtmzZQnx8PGvXrmXx4sV07tyZli1bMn78eLKysrBYLMyaNYv27dvTsmVLpk6disViKbSe//u//3M/z2688UYmTZpUyMPkjz/+cIeoJCQk0LVrV1566SXy8vK82k2ZMoWWLVty7tw5HnnkEVq2bMlNN93Eyy+/jN1uL3JfXGzcuBGDwUCbNm18zk9LS2PChAm0atWKdu3aMXPmzEJhPjabjTfeeMP93OrRowevvvqq17736NGDgwcPsnXrVvexLvistFgszJ49m5tuuokWLVowbty4EuUQ85XXJz4+nhkzZrBx40b69+9PQkIC/fr145dffvFabu7cuQDcfPPNbrs8n6UlOVeuHHR79uzhnnvuoXnz5rz66qs8/PDD3HzzzT5tHjp0KIMGDfKqK822ivutbNmyxf2RPnXqVPd+ucJJfeXwKsnvpaTHFUr+u+/QoQO5ubmFQhBPnz7N4cOHfR47Txo2bFjovmc0GunatStnz54lOzvbXf/JJ59gt9vd73A5OTmF9q84srOziY6OdotdAKGhoYSEhPj8J2xSUhLvv/8+U6dOvWIizU033QTgvmaLuxefOHGCAwcO0KFDB/fyJ0+epH379gAsXrzYfa14piPZvHkzd999Ny1atKBNmzaMHTvW57nZu3cvo0aNolWrVrRs2ZKRI0fy559/lmp/Lvb+6+u3npeXx8yZM2nXrh0tW7ZkzJgxnDt3rtB+uCjp91CHDh3Yvn076enpXvWHDx/m9OnTF92X9evXo9PpGDp0qLsuICCAwYMHs3PnzhJ5FRZkzZo11KhRg1atWnnVx8bGel2X1xLl618VgiBcVrKzs90vcikpKaxYsYLc3Fxuu+02dxtFURg7dqz75aVx48b8+uuvzJ07l3PnzvHMM88AMHfuXJ577jkSExMZMmQIALVq1fLa3sSJE6lRowaPP/44e/fu5fPPP6dSpUo89dRTZbJ/zZo15OTkMHToUDQaDcuWLeOxxx5zvzwPHTqU5ORkNm3a5H6h9GTatGmsWrWKQYMGMWLECE6ePMlHH33E3r17+fjjj73+A3n06FGeeOIJhg4dypAhQ6hbty59+vRh8eLFnD9/npiYGHfb7du3k5ycTN++fcu0rWPHjjFhwgQGDx7MHXfcwZdffsmUKVNo2rQpDRs2pG3btowYMYIVK1YwZswY6tWrB0D9+vV9Hqe8vDxGjBjB8ePHueeee6hRowbr169nypQpZGZmMnLkyFIdV4DHHnuMQ4cOMXz4cGJjY0lNTWXTpk2cOXOGGjVqlOFsCoJwrfLjjz8SGxtbpGDRtm1bYmNj3Tkiu3XrRnBwMOvWrePGG2/0art27VoaNmxIXFwcoOYFGzZsGFWrVuWhhx5yLzdu3DgWLVrELbfc4rX89OnTqVSpEuPGjSM3NxeA559/ng0bNjB8+HDq169Peno627dv5/DhwzRt2tS9bEZGBqNGjeKWW26hT58+bNiwgfnz5xMXF+f+YCvp/fRyPjt98fbbbxMYGMjo0aM5duwYH374IXq9Ho1GQ2ZmJo8++ih//fUXK1euJDY2lkcffdS97JIlS1iwYAF9+vRh8ODBpKam8uGHH3LPPffw1VdfER4eDqgfS3l5eQwbNozIyEh27drFhx9+yNmzZ1m4cKGXPXa7nQcffJDExEQmT57M5s2beffdd6lZsyZ33313sfuyc+dO4uLiivT6mThxIrGxsTzxxBP8+eefrFixgszMTK/n+nPPPceqVavo3bs3999/P7t27eKtt97i8OHDvPHGG4AanvPiiy8SHBzMmDFjAKhcubLXtmbOnEl4eDiPPvoop06d4oMPPmDGjBm8/vrrFz0nvti+fTvffvstd999NyEhIaxYsYLx48fz448/EhUVxS233EJSUhJr1qxh6tSpREVFAbgFlJKeK4D09HQeeugh+vXrx2233UZ0dDRNmzbl6aefZteuXSQmJrrbnjp1ij///JPJkye760qzrYv9VurXr8/48eNZuHAhQ4cOpXXr1gCFPpJdlPT3UtLjCiX/3Tdo0IDAwEB27NjhdT95+umn2bp1KwcOHCjdSXdy/vx5goKCvLzsfvvtN+rVq8fPP//s3reIiAjuvvtuxo8f785pVRQ33ngjGzZsYMWKFXTv3h2z2cyHH35IVlYW9957b6H2L730Eu3ataNr166sW7euTPtxMY4fPw5AZGSkV72ve/HOnTsB1UvMRaVKlXjhhRd44YUXuOWWW9znwCUq/fbbbzz00EPUqFGDRx99lLy8PD788EOGDRvGypUr3e+cBw8e5J577iEkJIRRo0ah1+v59NNPGTFiBB9++CHNmze/6L6U5P3XF1OmTGHdunXcfvvtNG/enG3btjF69Ogi25f0e6hp06YoisLOnTu9Im/69u3LjTfeyIoVK4rdn3379lGnTh1CQ0O96l33gn379nHDDTcUuw5P9u7dy+HDh933zwqDIghChePLL79U4uLiCpWEhARl5cqVXm2/++47JS4uTvnPf/7jVf/YY48p8fHxyrFjx9x1LVq0UJ5++ulC21u4cKESFxenTJ061at+3Lhxyo033nhRe59++mmle/fu7ukTJ04ocXFxyo033qikp6e76zdu3KjExcUpP/zwg7tu+vTpSlxcXKF1btu2TYmLi1O+/vprr/pffvmlUH337t2VuLg45ZdffvFqe+TIESUuLk5ZsWKFV/0LL7ygtGjRQjGZTGXe1rZt29x1KSkpSkJCgjJnzhx33bp165S4uDjl999/L7Rvw4cPV4YPH+6efv/995W4uDjl//7v/9x1FotFGTp0qNKiRQslKytLUZSSH9eMjAwlLi5OWbZsWaFtC4JwfZGZmanExcUpY8eOLbbdmDFjlLi4OPf95vHHH1fat2+v2Gw2d5vk5GSlUaNGyuLFi911I0eOVPr376+YzWZ3ncPhUIYOHar06tXLXed6rg0bNsxrnYqiKK1bt1amT59erH3Dhw9X4uLilFWrVrnrzGaz0rFjR+Wxxx5z15X0fno5np2++P3335W4uDilf//+isVicdc//vjjSnx8vDJq1Civ9kOHDvV6fp48eVJp3LixsmTJEq92Bw4cUJo0aeJV73qGefLWW28p8fHxyqlTp9x1Tz/9tBIXF+d13hRFUQYOHKjccccdF92nLl26eB1jF653hzFjxnjVv/DCC0pcXJyyb98+RVEUZd++fUpcXJzy7LPPerWbM2eOEhcXp2zevNld169fP6/nowvX9XPfffcpDofDXf/SSy8pjRs3VjIzM4vdB5etnsTFxSlNmzb1OtcuWz3fG5YtW6bExcUpJ06c8Fq+NOfKdf1+/PHHXm2zsrIKvT8oiqIsXbrU6zyWZVsX+63s2rVLiYuLU7788stCx6vge11pfi8lPa4l+d276NWrV6Hfjms/y0JSUpLSrFkz5amnnvKqb9WqldK2bVslISFBef3115X169crjz/+uBIXF6fMnz//ouu9cOGCMnLkSK9393bt2ik7duwo1PbHH39UmjRpohw8eFBRFPWYt2jRokz7oyj574iLFi1SUlJSlPPnzytbtmxRBg4cqMTFxSkbNmxQFKX4e/Frr72mxMXFKdnZ2V71KSkpSlxcnLJw4cJC27399tuV9u3bK2lpae66ffv2KY0aNVImT57srnvkkUeUpk2bKsePH3fXnTt3TmnZsqVyzz33lGjfSvJdUfC3vmfPHiUuLk6ZNWuW1zqnTJlSaJ9K+z107tw5JS4uTnn77be96uPi4nzexwrSr18/5d577y1Uf/DgQZ/3i4vhuqceOnSo2HaleaaVBySkURAqMNOmTeO9997jvffeY968ebRr147nnnvOK/HlL7/8gk6nK+T2/8ADD6AoSiEX8uL417/+5TXdpk0b0tPTvdy9S0Pfvn2JiIjwWh+oLtMXY/369YSFhdGxY0dSU1PdpWnTpgQHBxfqKaZGjRp07tzZq65u3bo0btzYq2cdu93Ohg0b6NGjh9u9vLTbatCggZenRKVKlahbt26J9ssXv/zyCzExMV4JQg0GAyNGjCA3N5dt27Z5tb/YcQ0MDMRgMLB169ZCIZmCIFxf5OTkAFw0941rvqt9nz59SElJ8Qpr3LBhAw6Hw+0dm56ezu+//06fPn3cHsmpqamkpaXRqVMnkpKSOHfunNd2hgwZgk6n86oLDw/nr7/+KtS2IMHBwV65aYxGI82aNfO695b0fno5n52+uP322708DhITE1EUxStfkKv+zJkz2Gw2AL777jscDgd9+vTxeh5VrlyZ2rVrez2PPEOkcnNzSU1NpWXLliiKwt69ewvZNGzYMK/p1q1blyjMPT093ct7qCD33HOP17SrJ0fXMXR5Dt5///1e7R544AGv+SVhyJAhXmE5bdq0wW63++wgpiR06NDBy2OvUaNGhIaGluh5XppzBer1WjBEMTQ0lC5durBu3Tqv0Lm1a9fSokULqlevXqZtleS3UhpK+3spyXEt6e8eICIigrS0NK+6FStWlMm7y2QyMWHCBAIDA3niiSe85uXm5pKRkcFjjz3GhAkT6N27N6+88gqdO3dm+fLlF30fDgwMpG7dutxxxx0sWLCAl156iZiYGB577DGOHTvmbucKzf3Xv/5FgwYNSr0PxbFo0SLat29Px44d3d6uTz75ZKH8Yb7uxenp6ej1+hLnSktOTmbfvn3ccccdXh5kjRo1okOHDu7ftt1uZ9OmTfTs2dMrT2SVKlXo378/27dvL9G3Rlm+K3799VeAQp6sxfU4W9LvIZctBa/NAwcOXNS7C1SPZF95fV350wqGpxeHw+Hgm2++oUmTJkVGlFyrSEijIFRgEhMTvZLW9+/fn4EDBzJjxgy6deuG0Wjk1KlTVKlSpZA7rOtmV5qXQNeLlQvXC25GRkah9ZeEgm64rgdDSXo6OnbsGFlZWe68AQVJSUnxmi4qTK9v3768+uqrnDt3jqpVq7J161ZSUlLo06dPmbfly704IiKizOLSqVOnqF27diFXedc5LJgH4GLH1Wg08uSTT/Lyyy/TsWNHmjdvTrdu3Rg4cKBXaKcgCBWfgkJWURQUxrp06UJYWBhr16513xvXrl1L48aNqVu3LqCGyiiKwoIFC1iwYIHP9aakpFC1alX3tK979ZNPPsmUKVPo1q0bTZs2pWvXrgwcOLBQAv1q1aoVykESERHh9dFb0vvp5Xx2+qLg89TVu2HB+3dYWBgOh4OsrCyioqJISkpCURSfya0Brzw/p0+fZuHChfzwww+Fnj8FP8wCAgIK5TEqzXNLKSaPUe3atb2ma9WqhVardYtpp06dQqvVFgoFjYmJITw8/LK8p5S1B8WinuclWV9pzhVA1apVfX7c9u3bl40bN7Jz505atWrF8ePH+fvvv73CBEu7rZL8VkpDaX8vJTmuJf3dg3r9XY78Q3a7nUmTJnHo0CGWLl3qdW8CVbDKzc31EsxBff/+9ddf2bdvH23bti1y/RMmTECv1/Pmm2+6626++WZ69+7Na6+95g69ff/990lLS+Oxxx675H0qyNChQ7n11lvRaDSEh4fTsGFDn9fd5Uhv4bqfup4JntSvX5///e9/5ObmkpOTg8lkKrKdw+HgzJkzNGzYsNjtleW74vTp02i12kL7W/C+5UlJv4dc98WyXpuBgYE+czi6ciCWpvOtrVu3cu7cOe67774y2VKeEcFLEK4jtFot7dq1Y/ny5Rw7duyiD4ayrN8Xxb3oFkfB/xyVZn0Oh4Po6Gjmz5/vc37BF/eiHgp9+vThlVdeYd26ddx3332sW7eOsLAwunTpUuZtFbVfV4uSHNf77ruPHj16sHHjRv73v/+xYMEC3n77bT744AOv3AyCIFRswsLCiImJueiH7oEDB6hatar7Zd5oNNKzZ0++++47nn/+eVJSUtixYwePP/64exmHwwGoXh4FPWxdFBQ5fPX81bdvX9q0acN3333Hpk2beOedd1i6dCmLFi1y5+YC/997S0NRz9OLPWcdDgcajYalS5f63F9XT2p2u53777/fnaupXr16BAcHc+7cOaZMmeI+Ny4u5dhFRkaWSlAq6uPvcggW5e09pSTnykVR7yndu3cnKCiIdevW0apVK9atW4dWq+XWW28t87b8/VspyXEt6e8eVEGjOIGipDz33HP89NNPzJ8/3+c/OatUqUJSUlKh3HGu98DiBOITJ07w66+/8uKLL3rVR0ZG0qpVK3bs2AGoCdGXLFnC3XffTXZ2tluczs3NRVEUTp48SVBQENHR0WXax9q1a3slnS8KX/fiyMhIbDYb2dnZZfpH95XmUn6vpaGk9xnX9eDKS1daYmJifHo4nj9/HlCvx5KyevVqtFot/fr1K5Mt5RkRvAThOsPVo5IrwWRsbCybN28u9HA6cuSIe355pqgX4Fq1arF582ZatWpVqv9wFKRmzZokJiaybt06hg8fzrfffkvPnj29/tt1ubblSWle7GNjYzlw4AAOh8PrIes6hwX/01RSatWqxQMPPMADDzxAUlISAwcO5N133y1S2BMEIZ/s7Gx+//136tate82HB3Tv3p3PPvuMP/74w2fi+j/++INTp0559RQF6j8MVq1axebNmzl8+DCKonh5x7o8MQwGQ4k+sIqjSpUq3HPPPdxzzz2kpKRwxx138Oabbxb68L0YJb2fltdnZ61atVAUhRo1avj0hnDxzz//kJSUxMsvv8zAgQPd9Zs2bbrsNtWrV6/Y0Mdjx455eeUcO3YMh8Ph9qiIjY3F4XBw7Ngxr9/ShQsXyMzM9DrW5bEXseLeU0pyri5GcHAw3bp1Y/369UydOpW1a9fSpk0bL++jy7UtT0r7nnIlfi8l+d3bbDbOnDlTqNfI0vLyyy+zcuVKnnnmmUIeXC6aNm3qDsX2vKaTk5OBwv8A9eTChQsAPns+tdls7vqMjAxyc3NZtmwZy5YtK9T25ptv5uabb+Y///lPyXfuMuHqZOnkyZM0atTIXV/UteK6nx49erTQvCNHjhAVFUVwcDABAQEEBQUV2U6r1ZYqOXtpqF69Og6Hg5MnT1KnTh13vWeIaVlx3RfL+o7QqFEjtmzZUuh39ddffwHQuHHjEq3HYrHw7bffcuONNxbyWqwISA4vQbiOsFqtbNq0CYPB4L65dunSBbvdzkcffeTV9v3330ej0Xh5MgUHB5fZ7f9K4eodp6Bdffr0wW63+3zg22y2Uu1H3759+fPPP/nyyy9JS0vz+mC73Nty4dqvrKysi7bt0qUL58+f98o1ZrPZWLFiBcHBwcW6z/vCZDIV6hK+Vq1ahISE+HSdFgRBDUX58MMPATVvxp133snEiRO57bbb2LBhg5+tuzQefPBBAgMDef755wvlGklPT+f5558nKCiIUaNGec3r0KEDkZGRrF27lnXr1pGYmOj1ERgdHc2NN97Ip59+6v4g9MTVy3Bx2O32QvfJ6OhoqlSpUqb7VUnvp+X12dmrVy90Oh2LFy8u5E2gKIr7/LnEPM82iqKwfPnyy25TixYtOHjwYJHno+AxdP2OXMfQJV588MEHXu3ee+89r/mgPjvL63tKweu0pOeqJPTt25fk5GQ+//xz9u/fX+g95XJuy0VR71++KM3vpSSU5nd/6NAhzGYzLVu29Ko/ffo0hw8fLtH2li1bxrvvvsuYMWMK9XztiSs/4RdffOGuczgcrFy5ksjISBISEtz1x48fd/eACLhDqdeuXet1js6ePcsff/zhFi+io6N54403CpV27doREBDAG2+8wcMPP1yi/brcuI7xnj17vOqLulaqVKlC48aN+eqrr7zm/fPPP2zatMn929bpdHTs2JHvv//eSzy/cOECa9asoXXr1lfMo6xTp04A/Pe///Wqd92nLoW///4bjUZDixYtvOoPHz5cKB2JL2699Vbsdjuffvqpu85isbBy5UqaN2/uJQIWd73//PPPZGZmMmDAgLLtSDlHPLwEoQLzyy+/uP97lpqayurVq0lKSmL06NHuB0OPHj1o164dr732GqdOnSI+Pp5Nmzbx/fffM3LkSK9wkqZNm7J582bee+89qlSpQo0aNUrUDfCVxNX19MyZM+nUqRM6nY5+/fpx4403MnToUN566y327dtHx44dMRgMJCUlsX79ep599lkvd//i6NOnDy+//DIvv/wykZGRhTwRLue2XDRu3BidTsfSpUvJysrCaDRy0003+XRRHzp0KJ9++ilTpkzh77//JjY2lg0bNrBjxw6eeeaZUr8EJCUlcd9993HrrbfSoEEDdDodGzdu5MKFCxXS1VkQLgd//PEHY8eOBdQE0YqisG3bNlatWsWSJUvo3bu3ny0sO3Xq1GHOnDk89dRTDBgwgMGDB1OjRg1OnTrFF198QVpaGq+++mqh8EODwcAtt9zCN998g8lk4umnny607ueff567776bAQMGMGTIEGrWrMmFCxf4888/OXv2LF9//XWxtuXk5NC1a1d69+5No0aNCA4O5rfffmP37t1MmTKl1Pta0vtpeX121qpVi4kTJ/LKK69w6tQpevbsSUhICCdPnmTjxo0MGTKEBx98kHr16lGrVi1efvllzp07R2hoKBs2bLgiYpHL22Tr1q3uj0dPTp48yZgxY+jcuTN//vknX3/9Nf3793d7iDRq1Ig77riDTz/9lMzMTNq2bcvu3btZtWoVPXv25KabbnKvq2nTpnz88cf85z//oXbt2lSqVKnI/JpXC9d7ymuvvUbfvn0xGAx07969xOeqJHTt2pWQkBBefvlldDpdofvN5dyW5zrDw8P55JNPCAkJITg4uJCo7aI0v5eSUJrf/W+//UZQUFChd7enn36arVu3XjRc+7vvvmPevHnUqVOHevXq8X//939e8zt27OgOYbz55ptp3749b731FmlpacTHx/P999+zfft2ZsyY4RUd4MqV9MMPPwCq99edd97J559/zsiRI+nVqxc5OTn897//xWw2u0WsoKAgevbsWcjOjRs3snv37kLzVq5cydSpU5k9e3ahTg8uNzVr1iQuLo7NmzczePBgd31gYCANGjRg3bp11KlTh8jISBo2bEhcXByTJ0/moYceYujQoQwePJi8vDw+/PBDwsLCePTRR93rmDhxIr/99ht33303d999Nzqdjk8//RSLxcJTTz11xfYpISGB3r1788EHH5Cenk7z5s3Ztm0bSUlJwKV5lf7222+0atWqUEhj3759ufHGGy+auL558+bceuutvPrqq6SkpFC7dm1WrVrFqVOnmDVrllfb4q731atXYzQai31P+eGHH9i/fz+gOlAcOHDA/Y/+Hj16eHn0lTdE8BKECszChQvd4wEBAdSrV48XXnjBq/cQrVbLkiVLWLhwIWvXrmXlypXExsYyefJkdw9ILqZMmcK0adN4/fXXycvL44477vC74NWrVy9GjBjBN998w9dff42iKG5RZsaMGSQkJPDJJ5/w2muvodPpiI2N5bbbbqNVq1Yl3ka1atVo2bIlO3bs4K677vLqOcvF5dqWi5iYGKZPn85bb73Fs88+i91uZ/ny5T4Fr8DAQFasWMH8+fNZtWoV2dnZ1K1bt8wvN9WqVaNfv35s3ryZr7/+Gp1OR7169Xj99dev6Y92QbiSZGVluRPg/vrrr/Tq1YugoCC6devGvHnz/GzdpdOnTx/q1avH22+/zRdffEF6ejqRkZG0a9eOhx9+mLi4OJ/L9e3bl88//xyNRlPI6wTUXmu//PJLFi9ezKpVq0hPT6dSpUo0adKEcePGXdSuwMBAhg0bxqZNm/j2229RFIVatWq5hbTSUtL7aXl+do4ePZo6derw/vvv88YbbwDqfb1jx47usC6DwcCbb77JzJkzeeuttwgICOCWW27hnnvu8eqd73KQkJBAfHw869at8yl4vf766yxYsIBXXnkFvV7P8OHDmTx5slebmTNnUqNGDVatWsXGjRupXLkyDz/8sNcHMcC4ceM4ffo0y5YtIycnhxtvvNHvgldiYiITJkzgk08+4ddff8XhcPD9998THBxconNVEgICAujRowerV6+mQ4cOPt8VLte2XBgMBubMmcOrr77KCy+8gM1mY/bs2T4Fr9L8XkpCaX7369ev55ZbbimzB5DrIz8pKanQdQmwfPlyt+Cl0Wh44403eP3111m3bh0rV66kbt26zJs3j9tuu+2i23rhhRdo1KgRX3zxBa+88goAzZo14+WXXy61t74LVwqTq9Xp0J133smCBQvIy8vzSvMxc+ZMXnzxRWbPno3VauXRRx8lLi6ODh06sGzZMhYuXMjChQvR6/W0bduWp556yutaatiwIR999BGvvPIKb731FoqikJiYyLx58674t8jLL79M5cqV+eabb/juu+/o0KEDr732GrfeeqvPhP4lISsri//97388//zzl2Tb3Llzef311/n666/JyMggPj6eN998s8TXS3Z2Nj/99BPdunVzd47ii2+//ZZVq1a5p/fu3evuzbdatWrlWvDSKJc7S5sgCIIgCMJ1Ie6IAAABAABJREFURu/evZkwYQLdunXj5ptv5tVXX6V9+/bs37+fkSNHsmXLFn+bKAh+46uvvmLGjBn89NNP7h7LBOFKs2/fPu644w5WrVpV4nxGFY0JEya4vXGvBllZWfTs2ZMnn3ySu+6666ps0x/s27ePgQMHlljMLMj777/PsmXL2Lhx42XL/yv4RnJ4CYIgCIIgXCL33nsvTz31FF27diUmJoZ27doBsG3btiK9nwTheuG2226jevXqhXI4CcKV5O2336Z3797XrdilKApbt25l4sSJV22bYWFhPPjgg7zzzjuFenu9VsnLyytU98EHH6DVasvkeWe1Wnn//fcZO3asiF1XAfHwEgRBEARBuAzs3r2bs2fP0qFDB0JCQgD46aefCAsLo3Xr1n62ThAEQRCE0rJ48WL27NnDTTfdhE6n45dffuGXX35h6NChzJgxw9/mCRdBBC9BEARBEITLhMVi4eTJk9SqVQu9XlKlCoIgCMK1zKZNm1i8eDGHDx8mNzeXG264gdtvv50xY8bIc/4aQAQvQRAEQRCES8RkMvHiiy/y1VdfAbBhwwZq1qzJiy++SNWqVRk9erR/DRQEQRAEQbjOkBxegiAIgiAIl8grr7zC/v37Wb58OQEBAe769u3bs3btWj9aJgiCIAiCcH3iV8Fr27ZtjBkzhk6dOhEfH8/GjRu95iuKwoIFC+jUqROJiYncd999JCUlebVJT0/niSeeoFWrVrRp04ZnnnmGnJycUtmhKArZ2dmIs5sgCIIgCGXh+++/Z9q0abRp08arvmHDhhw/ftxPVl1Z5P1JEARBEITyjF8Fr9zcXOLj43n++ed9zl+6dCkrVqzghRde4LPPPiMoKIgHH3wQs9nsbvPkk09y6NAh3nvvPd58803++OMPpk2bVio7cnJyaN26damFMkEQBEEQBIDU1FSio6ML1ZtMJjQajR8suvLI+5MgCMLVx2Qy0a1bN7p164bJZPK3OUVjMkG3bmopz3YKFRq/Zlnr2rUrXbt29TlPURSWL1/O2LFj6dmzJwBz586lQ4cObNy4kX79+nH48GF+/fVXvvjiC5o1awbAc889x+jRo5k8eTJVq1a9avsiCIIgCML1S0JCAj/99BMjRozwqv/8889p0aKFf4wSBEEQKhxBQUH89NNP/jbj4gQFwbVgp1ChKbfdCpw8eZLz58/ToUMHd11YWBjNmzdn586d9OvXj507dxIeHu4WuwA6dOiAVqtl165d3HLLLf4w3Zv9r8H538AQBvowdWiIAGNUfgmIhsAqEFAZtAZ/WywIgiAIQimZNGkSDz30EIcOHcJut7N8+XIOHz7Mzp07WbFihb/NEwRBEARBuO4ot4LX+fPnAQqFB0RHR3PhwgUALly4QKVKlbzm6/V6IiIi3Mv7FYcddk0DW3bJlzFEQmBVCKoOwdUhKBZC6kBYAwhrqM7T6NSi1V0pywVBEARBKAVt2rTh//7v/3j77beJi4tj06ZNNGnShE8++YT4+Hh/mycIgiAIwlXE7lDQaa+9lAbXqt1FUW4FrwqBVgc3/wQnV4ElHWw5qvhlzQJbJlgz1XGrcxwHWNPVknXA9zp1wRBSG0LrQWh9CIuDyAQwhKveYVojaPWg0ecLYxq9aovGWe+eX3EuZEEQBEHwN7Vq1WLmzJn+NkMQBEGowOTk5FCnTh0AkpKSCAkJ8a9BRZGTA047SUqC8mrnFUKn1TDhk50cSi6F84ufaVAllAX/aulvMy4r5VbwiomJASAlJYUqVaq461NSUmjUqBEAlStXJjU11Ws5m81GRkaGe3m/E91aLQVRFHBYwWEBh1kVwkxnIDsJco9D7ikwJ4M5BcznwXQW8s6BPRcy96nFhUanCmDhjSE8HiKaqaGTnrg9wjwEMK0RtAFOAUznIYZpAa1TENOq00XVuetd4xofQxHWBEEQhIpHdnbJX2JDQ0OvoCWCIAjC9YQr4qncc63YeYU4lJzN36cz/W3GdU25Fbxq1KhBTEwMmzdvpnHjxoD6YvnXX38xbNgwAFq2bElmZiZ79uwhISEBgN9//x2Hw0FiYqLfbC8RGg3ojGohVM3jFVIbKt+kCmEuTzBzGpgvgC0LFDtYs8F0CrIPQdYhyNyvCmJZB9VyCkCren1V7gAxHSEsHnCoxWFX16PYnOtMV8U3xQEoainYvbhGU6DOKWJ5CVq+RK4C4pfL48wtkOm8693TLlHNxzrwtd6StPNYp8avnZMKgiAIJUDxeO4oKGjL4b27TZs2F+2BUVEUNBoN+/btK7adIAiCIAiCcHnxq+CVk5PD8ePH3dMnT55k3759REREUL16de69916WLFlC7dq1qVGjBgsWLKBKlSruXhvr169P586d+fe//8306dOxWq28+OKL9OvX79ruoVFryE9oH1IL7GawpKkeXnnJqvdWdBt1PhrIOwtpf0H6LkjdDtmH1fH0XXDoTQiuATfcqpbQOpdun+ISxhxFDD3mK3Z1GUeBeTg82rnqCoptGme9Z5Wn+KZRm/gS2jyLW+yigCjmIbqh9fCA0xQQ4HSlF9VKUl+wTTn8mBME4eqhKAoKyiUPi1oXUOq6gvMBHA4HDhzuceeSOBRH/raV/Dr3uHMZxeFcDw6vtgW36VoXgEajIS46juph1a/gGSg9y5cv97cJgiAIgiAIQhH4VfDas2cP9957r3t69uzZANxxxx3MmTOHhx56CJPJxLRp08jMzKR169YsW7aMgIAA9zLz58/nxRdfZOTIkWi1Wnr16sVzzz131ffliqILgKBqarGZ1FDH7CTIPanm9AqsAtVvVQuo4Y8XflN7h7ywWW13eJlawhtBzcFqW11g2ezxEm78TEGRzS284axzzYd8kc2heri5x/FodzERriAuUc71H37nuEbje4jzmHmJXgWnXUKbFlWA8wwhdXrIaV3echcT1IoT4S7S1md7CU8Vrk0KCjAOxaGOF6hziS4lGS+qzu6w41Ac2BV7oe05FFUscjgcXnU+RSdPIch5H/PVrri2KLg9kBRFcd+yNBqNe77X/xaKmg9e61KbquMajcZrvKTzC7bxNe0i3ZSOJdJSqnN+Nbjxxhv9bYIgCIIgCIJQBBpFKfZr/rogOzub1q1bs3379msnx4bdonp85SSpIY3GKDVxfUFsuZD8C5xZr4pfLo8rfRjUuF0tIbXLh3hVUfDygMNj3PlTKxQ+6ktgcwlxF1sX+eOeaDQF5nl8PGrAW9jCW/gqOK0p4DnnJcgV9JIrKNLpKFZAK67Oa7oEYpzXcsK1gJcIVMJSUDxyCVY2hw2bw6aKTA512u6wY1fsbuHJ7W2kqN5GbvFI8fZO8pzn1rDRuAUjz/GCgpKncOMSenwNfbW5nHVQWDS6ljmZeZLm1ZpTJ7KOv00ployMDL744gsOHz4MQIMGDRg0aBCRkZH+NewKcU2+PwmCIFzj5OTkuO+52dnZ5TtpvevZkJ193SWtB+i38NdrKodX0+rhfDO+s7/NuKyU2xxewkXQGSGkpur1lX1MDWO0noTAamrieRf64HzvL0s6nFoDxz9X84AlfagWXQiEN4Swhmq+r/B4NQm+LqDIzQvFUJ484ApSSFgrRngrVO8U2hyWkq/DpacXDEV1DQrp7R7zKHAcC3rJFRK7fIlfLjHOJdS5csi51uvLW644Qe1i3m++vOZKumz5paDHUsFpz7qC7ax2KzZFFZ9cIpTNYcOm2LwEKDv2fG+pAl5TkB/a5sLtgYRT6HGKPVqNFg3OoVP88RzXaXRotIXrXcu61ueaJwglZdu2bYwZM4awsDB3XtEVK1bwxhtv8Oabb9K2bVs/WygIgiAIgnB9IYLXtY7WAOENIDAaMv+B3NNgjFBLQYyRUHc41LlbDXk89hmkbAN7DqT9qRYXGh2E1FVDIMMbQUQjCItTBTTh2sXLq8uvlhSNz3DSgp5tnrng8Gjv4QnnKE7cK2qdGrfXTmHPOY2POvKXKeQ15yFw+RS98K7z1eMpBTzlfIW5FiOsKWiwKw5sih27Q8GuOLDjwO5wqOOKA7uiuMdtDjsWp6eUxW7FptixOezYFQcKYHeovk8Ol5cVqpeWW7DzPDyafCFKq9G6i0tYcglTLgEqQBNQSKxytROEa4EZM2bQt29fXnjhBXQ6HQB2u53p06czY8YMVq9e7WcLBUEQhIqAVqulTZs27vFyi1YLTjspz3YKFRoRvCoKxiio1BoCYiBzH+RZIDDGd1uNFmI6qcVhU8Misw6qglnWAcg8ANYMtSfI7ENweo1rQTX8MbyxKoCFN1bFMBHBhMvJtSDKeeJTTPPolKFgm4LecO6cczYf61KLzW5zCk92r9A9m0P1orI5varMdisWuw2LYsPiXMaBKmqpHleqJ5VDUZx6m8aZ60nj1Mq06FziFOpQp9Gh0WjQa3QYtfn1Wq1rqHPus6fnmg6UAt5siqcg6KzTeop6znW41oXndVBQ0MNjvICASME6Cix7kXVScP2CUDKOHTvGggUL3GIXgE6n47777uOrr77yn2GCIAhChSIoKIht27b524yLExQE14KdQoVGBK+KhFYPYfVUASp9j+rtFVSNYkPrtHoIa6CW6n3UOkVR84Nl7lfFL9fQnKyKYzlJcGadcwUaCK3rFMEaq2GRIbXBGC0fi8L1QSkEOkVRsDns2FDFKJtTrMoXs+xY7TbMDgtmmxWLw+r2tLK7wwYVbIrNGeLn2qDi4RGlQ68xojVonIKVFqNG5x7XarToXP9l8xLY3FbiLbi58OwAwkPIK+RVR4F2BddTMHS2wOyi8HSwK8bZzqe4hUe9l6hWcBkoJIC58sd5hauSP10ov5ynmOcpAha0wcO2IsW3gvM86gsJe76EQCgk5nndl31tVygrTZo04ciRI9SrV8+r/siRIzRq1MhPVgmCIAiCIFy/iOBVEQmqpubfSt+j9tAYVN07r9fF0Gjye4Ws2i2/3pyiil8Z+1Qvsox9zh4jj6jl9Df5bfUhqvAVUsdjWEfNO6Y1Xp79FIRygN1hx+qwYXWoIpbXULFjtlkw2cyY7RYsDqs7dNDuFrE8BCNwC1c6jQ6dRoteq4pVBq0enV6HXqu9fKF+15o3XXEUKd45h4UEvILtPUU8xcc0FC/kFdxOKcQ8KFrE89XOp/ebVwPvNj5FMY/porzmCop8XiG02gK2FPDU8xL68F6np50XE+Rc09Z0sJuLPzZ+5t5772XWrFkcO3aM5s2bA/DXX3/x0Ucf8eSTT7J//353WxHABEEQBEEQrjwieFVUjFFQqRWk/62KXsGxpRO9fBEQDTEd1eLCfEEVvjL2qiJY9lEwnQFbjlqXsbfASrQQdINTBKvtLYoFVBYvA6Fc4PLEsjisbjHLalcFLKvDhsmWh8lmxmQzY3XYnCKWze2FpUHj1i50Wm/xSqfREmAwoHN6Xem1umJtEUpIRRLvisJLtCuNsOcaFhD3XOv06gUWb4HPPSiw7YLrLyj0FWxWUkGvqPY5KRBdH4gvxUquLo8//jgA8+bN8zlPo9G4e/Lct2/f1TZPEARBqCDk5ubSpEkTAPbu3UtwcDlNL5ObC0472bsXyqudQoVGBK+KjD4EohIBBUynVdFLc5k/rgMqQ5XOanFhN0PuCWf44zGPkqQKYaZTarnwm/e6dCEQUstZakNwrXyvMP31142tcGVwKA4sdjVc0Gq3YXEKWi5PLJNdFbNsih2b3e5O3O5CA+i0+QKWXqvHqDWgMwSh1+jzwwUF4XJT0Jurogp7vshOKZ1g5ge+//57f5sgCIIgXAcoisKxY8fc4+UWRQGnnYV7ZheEq0OZBK97772XxYsXEx4e7lWfnZ3NI488wvLlyy+LccJlQBcIkc3U/+DnnIKQGhSb0+uybDMgPy+YJ4oClpQCIpizmE6rvUVmOsMlCxIQU0AIqw3BNZ2ea4Yruz/CNYPLM8sVPqgKW6qYlWvLI8dqIs9uUT217DZnLix1WZeQZdDq0Wt1GDR6gowBTq8sNXG7IAhCUcTGxvrbBEEQBEEQBMGDMgleW7duxWq1Fqo3m81s3779ko0SLjP6YKfoZVcT2QfH+id0UKNRPcICKqs9SnrisKqhlwWFsNzjYEkD83m1pBa4vjQ6NUQy2OkZ5h7WdCbsl3CxiobqlWXFbLc4eyW0YrLlkWPNI8dmcntuWR02HIoD0KABtzeWQasnSBdAmCHYnR9LEAThcnDu3Dm2b99OamoqDnd+PpV7773XT1YJgiAIgiBcn5RK8PJMuHro0CHOnz/vnnY4HPz6669UrVr18lknXD4MoWp4Y+pONcdWcHV/W+SN1qD29hhat/A8aybkHHcKYCfyhbCc42A3qUJZ7snCIZIagyruBddUi0sIC64JQVVFDCunKIriJWjl2cyY7VZyrCayrDlukUsVtPITvRu0eowuQcsYqCZ5l/BCQRCuEitXrmTatGkYDAaioqK85mk0GhG8BEEQBEEQrjKlErwGDhyIRqNBo9EwcuTIQvMDAwN57rnnLptxwmXGEA6RCZDyh+o1ZYy6+DLlAZfdkQne9YqiJs13C2An8oWw3JOgWJ15xJIKr9NLDKvhUWqqHmMSJnlFcYlaeTYLZruFPLuFPJuZLEsu2TYTZrvF6aWlepJq0KDX6d2iVrgxVAQtQRDKFQsWLGDcuHE8/PDDaOXeJAiCIAiC4HdKJXh9//33KIpCz549+fzzz6lUqZJ7nsFgIDo6Gp1OvGbKNQHRENEE0naCNkANd7xW0WggMEYt0W285yl2MJ1TBbDck04R7ISznCpeDEOrhkMG14CgWKcw5hTEgmqo3nJCibA77G4xK88pbGVassmy5Kp5tpyilgJoNRoMWgNGrZ5AnZFwQwh6reTOEgTh2iAvL49+/fqJ2CUIgiAIglBOKJXg5UrI6hnaKFyDBNdQe0vM3Ava6hXTm0mjU8M2fYVuusWwE/nhkO7xE+Awq0n0Tad9r9sQkS+EuYfV1WFgNdBef52f2hw2/p+9+45zotr7OP6ZTNr2TlvAAu4i0lEQREFEuYBYsKAieBXEhthQ0WvDBnaxIXbF3uARC9gutouAimJF6b1sL+mZef6YZDbZzrKQ3eX35jWvmTlTchKyKd+cc8YT8OEOevEEvJT73RT7yin3u/EF/Xg1H7puZJTWUCstu2onyZaATT3wHi8hRMtzxhlnsGjRIiZPnhzrqgghhGjBFEWha9eu5nKTpSgQqmdMxo8WggYOWg+wYcMGli1bRn5+fpWBWadMmbLXFRP7kKIYV1AMlBstoOL3w5Ubm5KoMKx/9DZdB29+RQDm3loRirm3Gl1B/cXGVPJHNSe3gLO1ce648NTWCMbi2hqt0Zrx2GGaruEOeEOTB1fAQ5G3jDK/y7giYtCPjo6iKDhUO3aLjSR7PBlqigwOL4Ro0a677jouueQSvvnmG3JycrBaoz9i3XTTTTGqmRBCiJYkPj6e33//PdbVqFt8PDSHeooWrUGB19tvv80dd9xBWloamZmZUcmyoigSeDUHFiukHA5BjzH2lT3FaLl0oKfvigLOTGNK7111e6DcaPnl2mJ0jXRvC4ViW42LAWhe8Gw3Jqq5YqmiGq3A4tpWTM7wchsjLGsiLe68QR/ugBeX3wi2ir1llPjKjcHkNR+armMJBVsO1U6yPRGHamvavzQJIfabgBbAE/TjC12Awph8oVafEcuhsNyn+SKWjXJ/MIBX8xP0lXFbu4EcHOs7VYu5c+fy7bffcsghVS++Iq+LQgghhBD7X4MCrzlz5nD11VdLs/3mzhpvjH3l2gJl641xrhzpYEuKdc2aLmsCJB1mTJXpGngLjAAs3CXSvc0IwlxbwbMj1J1yqzFVSwFHZigIax0KwdoaV5V0tjEmW3KjBpO6rhvBVqjFVpnPRaG3BFfAY3zh1ALm+FoOVVpsCdFcabqGL+iPHlcvELpoRNBY90aUeUP7hYMq4wITfjxBr3kVVW/owhPeiBArHGQFda3uSu2BYzd/T59OJzfqORvTiy++yL333suYMWNiXRUhhBBCCEEDA6/i4mJGjBjR2HURsaA6IKmTEbC4NkPZBqPbniPDCHdE/SmWitZhaT2rbteD4NltBGDubUYrMPeO0Pp28Ow0Woh5dxtTTVSnEYaZU6hlmLNVaGpj/N9VE4qFuyS6Ah5cfqPVVqGvNPTl14uu61gUCw7VjtPqINmegPUAHJNMiFjQdR1vKFAK/52Guw+7A17coXIjkPKa3Ys9odaY4b9jd0SYFR5XLxxgxYo9FJgbLUJt2EPL4XJ7uMwSWraEtqtW7KoNi9/FyEOGxKz+9WG32+nTp0+sqyGEEKKFc7lcHHXUUQCsWLGC+PgmehEylwtC9WTFCqOLoxD7WYO+yf7rX//i22+/5dxzz23s+ohYscZDcq4x5lTZBnBvBm+hEd6ozljXrmVQVKPFVlwboJrukroOvoJQ+LXDCMM8OyvCMM9OY3vQA+Ubjakmajy6sxWaPQO/PR2vNZVSNYkixUmJEk+JEo9HTcSq2nCqdhJsTtKdydJqS4h6MsIpn9kyMtz11xXw4gq4cflD84jx7sL7VLSojAizAh7cQS+aru+X+oeDJqfVgVO14wx1TXZa7ThVB06r3eyu7FBtOFVHaB5RbjVCKWfEftFzI9Cyq9a9fm3ZsvsP4qxN+71owoQJvPrqq9xyyy2xrooQQogWTNd1/vjjD3O5ydJ1CNWTplxP0aI1KPA66KCDmD17Nr/88ku1A7NOmDChUSonYsCWBGndIaE9lG4Az1bjBcqZCRZ7rGvXsimK0bLOkQF0q36foAc8uyoCsIhJcxtzS6AUgi6U8g2o5RtQASeQArSPOJWOit+Wgs+Wht+Wht+easxtqcZkTcVnTyOoJsrYbqLF8AX9lPvdlAfclPndxnJ4Crgp83so97txBcJlofVQWBUucwc8jd5lL1I4ZIq3OomzOnBaHcRZHcSpDnPdqdqNsohlp+rEaTWWHao9Yn+7ue5Q7agWCbcb26pVq/j+++/573//y2GHHVbls9ETTzwRo5oJIYQQQhyYGhR4vfXWW8THx7N8+XKWL18etU1RFAm8WgJ7GqSngrc9lG8A1zZjoHtHpjEXsaE6IaEjWnx780t6qc9FvqeYUn857oAXgh4SgqWk6OUkBctJCJZgDxRh8xVi9xdg8xdi8xejEMTuL8DuL6j1JjVFJWBNxW9LqQjDwstmeQoBawp6ExlwX7RM3qCPUp+LMr8xGctuSv3llPndRrkvHFy5KPdXhFrGMW78WqDR6xVnNYKpBGsccVYHCTYncVanGVYlWOOIs4XDKycJoblxnBFehfeNszqJUx0SSDVDycnJnHTSSXt9nrlz5/Lpp5+ybt06nE4nvXv3Ztq0aRx66KHmPl6vl1mzZvHxxx/j8/kYNGgQt99+O5mZmeY+27Zt44477mDZsmXEx8dz2mmncd1110UFccuWLWPWrFn8888/tG3blssuu0zGIBNCCCFEi9Gg5OLLL79s7HqIpkhRwJlltDiK72AMbO/eboz7ZU+X4Gs/8gX95hf2Ik8pBd5iXAEPvqAfJXSlxDjVQXJ8IlaLah5XFpqq0IPY/MXY/EXY/AXY/UWhICxyXoQtUIpFD2L352P359dZz4Aaj9+aSsCWjN+aYs6NQCw5aq5Li8EDTkALUOpzUeIvp9RXHrFsBFglvorlUr/L2MfvosxnrDdmWBVvdZJgiyPe6iTRFkdCeDLLK63bjKDKKDfCrHhbHPFWh3QFFgDMnDmzUc6zfPlyxo0bR/fu3QkGgzz88MNMnDiRjz76yByn5d577+Wrr77i0UcfJSkpibvuuospU6bw5ptvAhAMBrnkkkvIzMzkzTffZNeuXdx4443YbDauvfZaADZv3swll1zCOeecw4MPPsjSpUu55ZZbyMrK4thjj22U+yKEEEIIEUuSWIi6KRZj3ClHljG2VNl6o8WXNd64qqN82WtU4asmhlux5HmKKPGV4/J70HQN1aISb3WSYk/Codoadrl7RcVvT8dvTwcOrXk3LYA1UFQRgPmLjKAsUFyx7C/CGijGogexBl1Ygy7wbquzCkGLMxR+JROwJhnL1orlgC0pYj1JArImQtd1XAEPxb4ySrzlxtxnzEt95ZRETqFgq9hnzF0Bz17fvoJCoi2ORFs8Sfb4UFgVT6It3iw35/Y4EqyRZXFmmCUtqERT9fzzz0etz5o1iwEDBvD7779z1FFHUVpaynvvvceDDz7IgAEDACMAGzlyJD///DO9evXi22+/Zc2aNbz44otkZmZy+OGHc9VVV/Hggw8yZcoU7HY7b775Ju3bt2f69OkAdOrUiR9//JGXXnpJAi8hhBBCtAgNCrxuuummWrc31q+coomxqBCfbVwJ0L0DytYZV3a0JhpdICX4apBwgBBu8bLbXUip34Un4EVHN1pvWR20js+Iar21X+pmseK3Z+K3Z9axo44adJnhlxGKlWD1F2ELlGDzF2ONmFv0AKrmQfV5cPh21asuRkCWaIRh1kQzCDOmRAJqaG5uSwRFMv3aBLQARd4yirylFPvKKPaFlr3GsjEPh1oV60E9uFe3m2CLI9mWQJI9niR7Akm2inmyPYFEW1xUeaItjiRbPIn2BGlRJZq0RYsW8cknn7B9+3b8fn/Utvnz5zfonKWlpQCkpKQA8Ntvv+H3+xk4cKC5T6dOnWjXrp0ZeP3888/k5OREdXEcNGgQd9xxB2vWrKFr1678/PPPZmAWuc+9997boHoKIYQQQjQ1Dfo2WFJSErUeCAT4559/KCkp4eijj26UiokmzGKDhA7gbA2e7UaLr/LNxoD39lQJvuqg67ox9pbfRYmvjN3uQsp8bjxBL4qimANVN6urJioKQWsCQWsCkF37vrqORXNj85cY4VigFKu/xAjEAsbcGijF6i8NrZehEIwIyPLqXa2KkCyJoJpQEYipiQStCQRUYz2oxofmCQSsCc0yKNN0jRJfOUXeUgq9pRRFTWUU+aqul/vdDb49u8VGiiORZHsCyfYEUuyJJNkTSLEnRM2NYCu83WiJtb+DWyH2h1deeYVHHnmEMWPG8MUXXzBmzBg2b97Mr7/+yrhx4xp0Tk3TuPfee+nTpw85OTkA5OXlYbPZSE5Ojto3IyOD3bt3m/tEhl2AuV7XPmVlZXg8HpzOpn1VTCGEOFApisJBBx1kLjdZigKhesoFsESsNOhb3ZNPPlmlTNM07rjjDjp06LDXlRLNhGqHhIPA2cYY26tsHbi2GGN82VKMAdaF2YLL6OZVxi53IWU+F96gDwWFOJuTRHscmWpq037TaiyKgqbG41Xj8dKm7v1DrcesgdJQGFYWWg5NwbJKZWWowXIU9AaFZBAOyhIIqsYUUBMIWuNDy/GhsCy8PT4UmBnLumJrlDf1cIBV4CmhwFtCoaeEQm9o8pSGlktDAVcJxb4ytAZc8llBMQIrRyKp9qTQPJEURyIpoXmyvaIs2Z5Iij0Bp9Wx1/dRiJbk9ddf56677uLkk0/m/fff5+KLL6ZDhw7Mnj2b4uLiBp1zxowZ/PPPP7z++uuNXFshhBDNVXx8PBs2bIh1NeoWHw97Wc+gpqNaDoDvR2KfabRmDBaLhX//+99MmDCBiy++uLFOK5oD1QGJBxvjfHl2g3srePNB8xndHW3JB9wA9+5wwOUtZ5e7gFKfy2jBFQq4ku0JONS0AyPg2lsRrcfqFZAB6FooJCvDGixDDUSEYsFyYz1YboZj1kA5arDMGH8MzKAM6h6ovzJNsRJU4yLCsDgzFHMpDnYGVXYEdXYGdPICQfICfnb7fOT5PeT7XOR7y80gK6hre3z7ibZ40hxJpEZMaY4kUuyJpDmSSXUkkupIMgOuJHt882lJKEQTtn37dnr37g2A0+mkvLwcgFNPPZWxY8dy22237dH57rzzTpYsWcKrr75KmzYVr32ZmZn4/X5KSkqiWnnl5+eTlZVl7rNq1aqo8+XlGcF/5D7hssh9EhMTpXWXEEKIJkG1KFz15krW7Kr2MlxN1pDcLK4f3iXW1RA08qD1mzdvJhBo/Eu+i2ZCdRpdHePbg78YPHlG+OXZATpgSzS6PSotrzuTL+gPXWGunJ2uAop9ZXiCXnRdJ856gLXgagoUC0FrIkFrIt49OU7XQgGYKxSAlaMGyiNCMZdRFnShBstRAy4IlJPnLWe3z8XOIOwMBNgRLGVHoNRYD8KOgDEv2vP8ijTVQqbVSobNRqbNQbrdSYYtnjRHAunhUMuZQoojjeS4NKzWBIKqk6Aah2Zxoltse36jQog9lpmZSXFxMdnZ2bRt25aff/6ZLl26sGXLFvQ9aH2p6zp33XUXn332GfPmzavScr5bt27YbDaWLl3K8OHDAVi3bh3btm2jV69eAPTq1Yunn36a/Px8MjIyAPjf//5HYmIinTt3Nvf5+uuvo879v//9zzyHEEII0RSs2VXG79tK6t6xCemUlRDrKoiQBgVelQel13Wd3bt3s2TJEk4//fRGqZhoxhTFGMvLnmq0/PIVGS2/PNuMqzsqCliTQuFX82xZEtSCoTG4yslzF1LoLaXM70LXjUHmE2xxzWsMLmFQLAStSQStSbgDKex2F5EXKCLPo5DnDpDv8ZDnCRpzdyn5niKKvGXo1P/LrFVRyLJaaWWz0lpVaKVCazVIa0uQtpaAsW6FLNWYbIoG+EJTefTJ/KGplh+9NEVFs8QRVB1oFidBixNNrZhrFkdoOTS3hMpUB5rFUWndGSqzN9u/XSH2laOPPpovv/ySrl27csYZZzBz5kwWL17Mb7/9xoknnljv88yYMYMPP/yQp556ioSEBHPMraSkJJxOJ0lJSZxxxhnMmjWLlJQUEhMTufvuu+ndu7cZVg0aNIjOnTtzww03cP3117N7924effRRxo0bh91uXPH2nHPO4bXXXuP+++/njDPO4Pvvv+eTTz5h7ty5jf7YCCGEaDxut5vjjjsOgK+//pq4uLgY16gGbjeE6snXX0NTrado0RoUeP3xxx9R6xaLhfT0dKZPn84ZZ5zRKBUTLYTFCs5MYwp2Al8heHeDe2dE+JVotP5q4i2/yv1uSnzlFHpK2O0xBpr3aX7sFisJtjjaxmehWiQEaOo8AS+73UXsdhey21PIbncR+ea6Mc93F1Ee8NT7nKpiIc2RTGZcKunOZDKcqWSE5pHr6Y4Uku0JNbf00zUsmgcl6KYw6KIk6EHV3FiCbtTwpLlRgx6jLLyseULbPVjCZbpxhTiLHsQSNLp2NiZNsUeEYg4zCDMCtOj1qGXVgabYI7bZI6aK9cYaC02I/eWuu+5C04xmnOPGjSM1NZWVK1cydOhQxo4dW+/zvPHGGwCMHz8+qnzmzJmMGTMGgJtvvhmLxcLUqVPx+XwMGjSI22+/3dxXVVWefvpp7rjjDsaOHUtcXBynn346U6dONffp0KEDc+fOZebMmbzyyiu0adOGu+++m2OPPbbBj4EQQoh9T9M0fvjhB3O5ydI0CNWTplxP0aI1KPCaN29eY9dDtESaBsGgMYWXNSfo2aCmg78AXHlG6y//RqPbozXemCKvkqcoFV98LQpYLBXz8LbwsmoBVTXmexk++YMBSnxllER0U3QHPCiKhYTQVRTtqnQXayoCWpACTzG73IXsdheyy11IXmi+25yKKPO76n3OOKuDTGcqmXGpxtyZSkZcKpnOFCPcCoVcqY6kxmnNp1jQ1Hg0NR4/GXt3Lj0YEYaF5poHS9AbCtG8EWUeVM2LRfOGlj2hZW+o3FhXtYoOohbdhyXgA0r3rp61qAjGwpMNTbGjh5ctdnMf3WJDU2wVgVlo3+htNjNM0yw2dCV8HptZ1tSDd9F0WSwWLBHvO6NGjWLUqFF7fJ7Vq1fXuY/D4eD222+PCrkqy87O5tlnn631PP3792fBggV7WkUhhBBCiGZhr8bwKigoYN26dQAceuihpKenN0qlRDOiaVBeXjGVlIDPB15vpaArNNd1Y1nXjQlAUyBoAV8J+LaA5jb2sdpBjQclMlQKHVM56FIwuliplYMvFew2sNnAYTXmVqtRbg1NqgpWK7pqoRw/JX4X+Z4iMxwJahpOq51EWzwZzhQZhysGvEEfu92F7HQVsMtVwC53IbvcBexyhebuQvI9RfW+SqFTtdMqLp3MuBSy4tLJikslMy7NDLeyQgFXgq0ZN71WVGOwfxLwN9Y5dQ2L5jPCsIjJCMV8oRAtPPmitiuaD4vmqwjWQuvh/RTdb5TrQfPmLLoPS9AHwVrq1Mh0LFEBmDG3Vr+u2NAt1tDchqYY5RVloblijVo2zmONWFYjzhs6h6KiWayAKi3dmomvv/6a+Ph4jjzySABee+013n77bTp37sxtt91GSkpKjGsohBBCCHFgaVDg5XK5uOuuu/i///s/sxmlqqqceuqp3HrrrU23H7HYe34/lJUZU1ER5Ocb/bO9oZYf4UDJYjGDJGw2Y9liqQiowvPKtAAEysBXCr58Y1nzgcUOapwxML5iCQVnOhCa61porkNQMwIznx80L5SE1rXQPmEKBAhSYglQrPvYqZdRhBePqmCx20iIT6a1MxGrzQGaGroNX0VgJhqFN+hjl6uQna58droLzFBrp7tiXuStXysiVbGQ6UwlKy6NrPg0spxptIpPM9bj0siKSyUrLp0Eq1OCy4ZQLMa4X+o+vIKbHqwUhvmw6D4zMKso82PRwuV+IxzT/Ma6ua2i3KL5QqGaMSlmuR8lIlFT0EIt2bz7NWiriY4SCsHUiKBMrQjMQmFalbLwfhZrpWPU0Ha1SrlusaKjRhynRkwR61RTVmVfywE3ztsDDzzAtGnTAKOV1syZM7noootYtmwZs2bNqjL+qRBCCCGE2LcaFHjNmjWLFStWMGfOHPr27QvAjz/+yN13382sWbOYMWNGo1ZSxFAwaIRbJSVGuBUOuPx+I/iJj4fkZHA4GqcVgsVaMeC9ng0BlxF6eQshUAyeUPBhdRrhl8UBe5g9uYM+ioMu8v1l7PIVU+ZzE9D9xKGSqNvJ9FlQPJoR6OmFQKgFmRnihYK8OAc4HUYLMqu1otxuM5YlUCGoaez2GGHWDlc+O8rz2ekqYKc7NHflU1jPMMuh2mkVl0br+HSy4tJpHZ9mzOOMeav4NNIdKTKOWnOnqGhqHJq6H3840bWIMMyHogdCoVjkesAMyRQ9gEX3o2jhuR+LHgjNje0VZYGKMvO81ZUHzPNGPRzoKLofdD+qVv9x5ZqCirDOUiUUg+qCMktUqEbEero/gKX90bG+S7XasmULnTp1AuDTTz9l6NChXHvttfz+++9Mnjw5xrUTQgghhDjwNCjwWrx4MY899hj9+/c3ywYPHozD4eDqq6+WwKu5c7uNgKuwEHbuNAIvn89oqRUfD1lZRrCzrykWYzB7WyLEtYGgF/xlECg1Br8PlEGwMNSVMQ6scdFjf4VoukZp0ENxwMUufwn5gTJcQR8WxUKixU6ruFSsdY7bE2o5FgiNSeb1gdsDgYDRqiwcboVDL6vVCMPi7EYYaLWCLdTazd5yWoiV+sqNICsUZpnLoSnPXUhQr3uQSodqp3V8Om3iM2gdl06r+HRaxaXTOt6YWsWl1z7YuxB7Q7GgK8Zg+zFv1KXrgFYRjOnBUPBmzM2ATA+iaP7Q9nBZoGLd3D+IogdD2yK260EULXq94vhg9H5Vyqspo+rfeUVYt/cPSxqwu3AFHHLK3p9sH7HZbHg8Rij5v//9j9NOOw2AlJQUysoa96IRQgghhBCibg1KLTweD5mZmVXKMzIyzA97ohnRNCgtheJi2LULCgrA5TJCnIQESE+H0GXMY0p1GBMZoHeEoAsC5Ub3R38R+IqMLpGKil+xUaxoFAZ87PAXUxIMXVFRsZGoOsiwJ+5heKJUjAlWEz00Tpk/aARhRV7IC1R82VOoCMNsVoh3GqGYzRYKw0Lbmkggpukaee4itrvy2OHKZ3t5PjvKQ8uhsnK/u87zWC0qreLSjDArPsMIseJC89B6yh7/fwjRQikKoKI1gdeAPaJrKLoWFYRBeF2rITDTUKgoQw9G70vF8u7yAjKzhsT6XtaqT58+zJw5kz59+vDrr7/y6KOPArBhwwbatGkT28oJIYRoUar7Lt4kNZd6iharQYFXr169eOyxx7j//vtxOByAEYI98cQT9OrVqzHrJ/alYBA2bICtW43Ay+czgq3EREhL2+urHO5TigWsicbkbA1aAJevkGLPbvLc29nl2kS5vxRdDxCn2kmzJuGwJ4cGwN9HwYpiAasFrDVcuTE8cH8gYLQQK3eFBvAH0CuNe2YFh91oIWa3V3SZtEV0ndzLL8QBLcBOVwHby/PY7spjW3keO0LL28vz2enOJ6DV3d4lxZ5I24RM2sRnhEKt9Ij1TNKdyY1zBUMhRNOlWIwuiXt3LZwabeEPMiyOfXLuxnLbbbcxY8YMFi9ezO23307r1q0BYzD7Y489Nsa1E0II0VIkJCSwe/fuWFejbgkJ0BzqKVq0Bn0yvfnmm5k0aRLHHXccXbp0AeCvv/7CbrfzwgsvNGoFxT6iabB6tTElJBgBl6Npf5moTNd1SgPlFAXK2OUtIN9XjDvoQVGsJCV1prViwar7IeAGfwkEPRAMdStRraDYQbUD+ymMCQ/ab7NBdcMTaZoRhgWDRvjo9oRaiOmY44ipqnEFSqtqnMcZGkcs3DosfOVJq4pP0dnpLWZb+e5QiBUKtULh1m53QZ1XNVQVC63i0mmbYIRXbRIyaBufaQZarePTibPuwwHMhRCimWjXrh1z586tUn7zzTfHoDZCCCGEEKJBgVdubi6ffvopCxcuZN26dQCcfPLJjB49GqdTvvw2eZoGf/9tTFlZ0IyuqhnQAhQHyijyl7Ldm0eJvxyv5sdusZGkxpNhS6naNc4O0BaCPmMcsHDwFQ7CNC00KL0tNO3HECySxVJH11EdAhWtxPwuFzsLt7HNV8i2QAnbgyVsC03bAyXsDpbVOXSO3WKlTSjAapeQSZuETDPQahufQWZcGlZLM+taJYQQQgghhAAgqOmoFhk6RByYGhR4zZ07l4yMDM4+++yo8nfffZeCggK5GlFTpuuwZg389RdkZDSLsMsb9FEUKCXfV8xObwGlgXI0NOIsTlKsSTjV+owvZjGu6qg6gRTA6AaJ5jWmoCc0CL7XCMJ0PRSCWY1ukKqNBv65NFhQ19jlL2Grt5DtviIj2IqY7/aXoNURaTkUK+2sybRVk2mnJtFGTaatmkS2NYU2jjTSbYlYwt0j7TZjUm0QtILLAt4yo8WYqoa6a1qNYE7G2xJCCCGEEGK/c7vdjBgxAoBPPvmEuDq+z6kWhaveXMmaXfv3Aip2n5cZc68H4PZLHsBn37PeRENys7h+eJd9UTVxAGnQN/i33nqLBx98sEr5YYcdxjXXXCOBV1Ol67B2Lfz5p9GFMT4+1jWqUXnATZG/lN2+IvJ8hZQFXCiKQqIaT2t7OlZLI4RPFqsxkVBRpvlB81WEYEEXBLzgLwctCOjGWF0Wm3FFSNUKqDRkXDBN1ygIlLPVV8g2byFbfdHB1g5fMcFqrnwWyaFYaWNPJdueSlt7Gu3sqbS1p9IutJxmreHKhrpmXHUyqBn3KxAAj9do7aZpFVeeDM+taqhLploxhpgjdMXJ8ED8kd0tLZHLEpAJIYQQQgjRGDRN46uvvjKX62PNrjJ+31ayL6tVRZzPQ481PwPw17Zi3PY96wnWKSuh7p2EqEODUoPdu3eTlZVVpTw9Pb15DKB3IAoG4Z9/jDG7UlONgembEE3XKA24KPKXstObT6G/FFfQg1VRSbLGk+1stX8GPg93a4wMwfRgKAALh2EeCLiMcMzvDQVhVLQIwwjSdEWlJOhlm6/ICLVCwdY2XyFbfUXs8BXh1QO1VseqqLS1pdDWnkq2I4129rRQoGWEWhnWBl7d0Bxgvx77hkOwyHDM64PSUPfKyDHGdMCiGGGXGZCFBuO3hQIyu71iu6pGhGmhOqlWY7uEZEIIIYQQQgghGqhBgVfbtm356aef6NChQ1T5jz/+SKtWrRqlYqIR+f1Gq661a41LwzaRll3h8bgKfaXs8OVRHBqPy2mxk2StYTyuWFBUUOONhlyRtADuQCnb3DvZ5tnFVo8x3+YtCIVaxZRrvlpPbUGhdVSgVdE6q509jSxbUuyvcGiGUfXcXwsFY0E9FJBp4PNDudsIyDS9okFcuEdm5ZDMooRCMmvFFB6UX1EqWpNZLKF5xDESlgkhhBBCCCHEAa9BgddZZ53FvffeSyAQ4OijjwZg6dKlPPDAA1x00UWNWkGxlzwe+P132LgRWreGGF9UwBP0UhwoixqPK6hrxKtO0mxJOCz1GY8rQjCI4g9gCQRQ/AGU0LoSDKIEQlMwPBktkpSgZkxaEMxloxWTomsQ1I25puPTA2ynlC2KMW22lLLZUsZm1ZjyLJ46q5gVdNAhEEeHQBztg046+J10CDrpGHDQRnNiwwIWBd1itHTSLV6w5KGrhaH1cLliznVL6BjVYiyrFnPZXLeG1lULulU1lq0WdFVFVy37Lhja04AM3QjBzJAs3N0ydKVKs5tlaF8F0JWK0EwNh2CWUHfTiLDMqlaEZZHBmBmUWSqOU5Xo7RKaCbFP6bqOjo6ug26m3xXr4W2gE9Tr12UjlrxeL/PmzWPZsmXk5+ejV7oK7vz582NUMyGEEI1BBn8XovlpUOA1adIkioqKmDFjBn6/HwCHw8GkSZO45JJLGrWCYi8UFhqD02/fDu3aGV3K9iW/H8rLwe02gja3G93txuMqwVVWSFl5AeXlRQTdLlRfgIP9Ok6/huoPYvH6UHx+LD4/Fl/FsuIPhOZ+LP6AMfcFUAKhSavrOoS1CyqwNRk2pML6VFifVjHfkApbk0Cro4FVigcOKYRDioz5oRHLBxVDvN8LeIGivarrvqCroQDMakGzquayblWNSVXRbeHl0D5WFd1qNcvNsvC6zWqua+Z+VmM/m4puq7RsM/Yx5la0ULlutaOHW3TVRDOCSUIBpRmaBX1VxyQLd7007jlmN8xwYBYZdFlC4Vc4NAsP2h/ubmnuFxEcmoFbROgWPr8Qe6FqMFR9KKRHlQPh4yLOQeXl0Lp5Bt28hdDx0eWYtxc+OkxBQQFFN/+8ovMePWrPyGEPFRQURSH8zyxXMMsS7XGosW7tWoebb76Z7777juHDh9OjR4+m0UJZCCFEo4nV4O+VBbxuc3nMU99hddQ8aL0M/C4OdA0KvBRF4frrr+fyyy9n7dq1OJ1ODj74YOz2PWydI/YNTYPNm42wy+uF9u0b/qVb16GsDPLyjCk/v+pyeF5SdSBEBYgLTRl7c5/qW11VNQIT1QhdglYL25MUNqTChhSdDSkaG5M0NiYG2ZgYYHNCgEAd36Higgod3A4O8hhTB4+Tjh4HB3mdHORxkhqwokd+r0kKTR3Ao4PRBiwUuBjfLlHMAAajdVloXQmFN0owaLQ4C7dCCw0yb6wHUbSgsW+4xZqmoYRaR1W0YNMrloPVt44wt/mq9thsCnRFqQjO7BVhmGYLr1cOy0Jze0R5aD/Nbosus1rRbZaKuRoO74xlXbWgqRZ0m2r8/0S2Mgt/KVeoGNg/HHShVIRgFiU0VyOCs4hlMxirHLZZajifRVqg7YG9CYo0M+SJDokaGhAp4THuIKKVYkU4hI65vxkIRT3VqoZCkYGQsV4xV8y5Yh5vwWIcoxjnsihGmUWxRK9bQvuiYLEY57AoFnN7+Hilcr0q3W61ZUpFfWosj7hf5n1wbSUuqW1jPTX2iSVLlvDMM8/Qt2/fWFdFCNHCNeeWRs257hCbwd8r03wVPUz+3F6Kxe6vcV8Z+F0c6PbqUncJCQn06NGjseoiGoPbbQxMv3GjMTB9ZmbN+3o8sGFD1SCrcpjl9e5RFTSblYDTTsBhQ3M6wOlEdzrRHXY0hx3N4UBz2Ixlu1Gm221odlvUsm4LldltRosfmy0UaNhCrYSsBK0WdlPO1mAR2wMFbPPms92zm23ePLZ78tjuzcNfj4HhWzsyyHZk0c5ZMYXX023JNf5SXxiaYkIPRkxapeUAaAFjYH0CEPCFwrGAMQWCKAE/SkALdfvUjGUtiBIwgjcloKMENCzmuhbqKhouD1YcH5qMVngRy4Egij9yOWAc5w9g8Ye6n/qDoS6pof0iwjlF11F8ASy+ALhi9UBjBGFmcBYZqhlzM2gLt2wLtXLTrKEANnJurWhRV7G/Lfq48LLTjma3Ro9NFp7CrctUNWI5tK5W6ppZOVCrHNBZLGChooWaQpVQTdd1NDMAqhoc6Wg1ti7SQt3RqguNNF2rMzAiqssbVB0ELhTrmMGRkRSFq185LKocqEA9QiJLOPCpCH8ULKhK1YDICJHC+1n2KhyqLhiqKRQyy6q7r5WCpWbH6mjyIW/r1q1JSJAvFkKIfa+ptDTaU0cdnMatJx8R62q0CIrNEesq1IurmdRTtFx7FXiJJiQQgG3bjIHpCwuhTRtwVPMC4/fD99/Dp5/CV1+Bq54pQjg8y8iAzEz0jAy86cmUpyVQkGRjd7JKYZKNYGIc8fZEEtV4bJa9f3oFtAA7fQXs8Oaz3bOL7d7dbHfnsb3QCLN2ePMJ6MFaz6FiobUjnbbOLNo5smjnzKRtaN7OmUWWPa3Jd5WplqIaU33pQXRdQw+HY4TDMa1SYBYIXXkyaARmeqgFGpox6XpEWXX1iqwfhJKU0JxQABMqUwgthx9/i9GSzQzPKoIxM0wLLVt8gahyY90fvb8vFLD5Qsd4/SiB8LHBUHfZYMW+4dv0BYxWeOFaBYIQCKKyZ+FvY9EVBc1uhGCaGYgZgVkwNNesFoLmurGs2SLLVII2S8UxtnB5KMhzGAGyZrcSDLWm0+w2gnYbutOKFrrKpqJaUVQVxaKgWKyhuQUlFJApigVFsWAJdfFUlFArIYsaakWkoqoqltAxFlXFolhQrSqKxWosKzUHRlHrdYRFlioBU/2DIouiNP+QSOxXN954Iw8++CAzZswgOzs71tURQrRwTaGl0Z7qlJXQbMO6ptQ10GJ30vHa92JdjTq57U66NoN6ipZNAq/mTtNg1y5Ytw527oSEBOjYMfqX8OJiI+T67jv45hsoLa3YlpJiDGafmRkVaFVZdzrxBL2UBMop9Jewy1tIacC4qqJdsZJojaeV6tzjKwqWB9xGmBUKr3aE5tu9+ezw5LHbV4hGDcFKSDjQauPIJNuZRVtnJu0cWeZ6liMN654EQy3VngZkpnDIFQyFXMGK0Cu8LbLMDM8CFftogYhtOuh+Qv07Q8eHjzVuUVcgaAPMYecsQKjLdLgpj6JEzMMbKiZd19EUo4MYSmioLyXcAkkJTZGticLrGKFgKCgzwjM/ijd0cQRf0Fj3RYdxqi9gjEfnD2DxBVH94fWgud3i11BDIZ4aOr5iv0Bo39C2Si3dVG8A1Vt7a8V9TVcUc1wzPTy+mdW4iqZutaKELxJgXlXTWFZsNhSb1RhHMLzNbgRoxrLdWLbZjHKbDRw2cDqM/Z0OsDvAGSqz2yEuYlu4RVu4tVpkS7bKrdoUJfqpYqHqtsiWcULUU/fu3fF6vQwbNgyn04mt0riZy5cvj1HNhBA1ae7d65qr5hrWCSGaHwm8miu/H3bvhi1bYMcO4wtfu3bGl8fycvj1V/j5Z1ixwljWIsZwysyEE0+Ek06Cbt1q/FLn1wKUBsopCRSwq7CAokAZ7qDRZzzO4iTFmoRTrXncNr8WYJevgJ3eAnZ689npLQiFWvnsDM1Lg3W3MLMpVto6MmnjzKCtI9NYdmTS1ml0Q8yUQGsfC3Vxq+Yxju6iVtE1TQuFWBXjIIXXNHRNQw+3NkNH0wOhrmwammbsg66hmfvr6HrQHC9LQUfXIkO3yDAuFKpRKdMIdXULLysY6ZeigEUPrYPRXc08xoJqV7A4FBQcqEocKhYs4W1m9zYFVTGOVEItnIzubRFjJYU6x1WsG8GwrlgIYkEL3bZitl5SUDTM7p+KL4ji84davmkofuOCDvgC4A+Az19pHjBeI8y5v+o2fwB8PmPuDc39lY/3R712KLpunqtJfT2wWKLHRrNawaZWhG6R22wRZTZbxZU81dB65HHh8C4c1NltFZO10npkUGezgtUWEeqFt4dDPWt0qGY8WSqCNyLWK+8TFdpV2kfE1LXXXsuuXbu45ppryMzMlJaBQjQDzbG1UVNqaSSEEE2dBF7NiaYZrbPy8oxB6QsLjbKCAqOF199/w59/GmN4RQZcAJ06wTHHGFOvXtUOYu/XApQFXJQGXRT4isn3F+MKeAjoQRwWOwlqHGmOJCyKBU/QR56/iF1lBezyFrDLV8gubwE7fRXhVoG/pNK4O9VLtibQxpFBa0dGKMyKmDszSbcl73HLsZYoHC5poVZRWuXxk8AMmsKhU/jxjxwnqfJ5jJhIqxh3KRQsmS2pdEKDaxsBUXjsJRTFDD3C4xdV7npm7FbRtQzAYrFiwYpihkbhLmxGUKRiwWIxzqgqasS2arqcmcsRARPhgEkPhVcKiq4bQZcezgl0M9hSQncvarvxiEdPZhfHyKCt8npkt0+9Yhmt0nnC5REt3EKPv7lswQhVnHbzZivGrIqsX0R51J9bpTqbZZWfWZUolc4T1EJBWDAUitU2hfYJhJbDIZs/GCqLXK50vkAw+jyBSseFupWay5E0DXyacXvNRXhcNvMKoJHrasVYbNbqyirtG3lBBFWNCO3U6MAvKuyLWLaHr0ZqjT7OZjVuP1xWOUwMh4RWtWrAWHnMOXNAtcrLVLMP1Rxb/YU3mpKVK1fy1ltv0aWLfBEVojlpbq2NpKWR0AM+ds+/F4Cs029GsTbNC8c5Aj7mhOp52ek3422i9RQtW4sJvF577TWef/55du/eTZcuXbj11ltbxoD6Xq/RiuuPP4xp/Xoj7Nq1yxiza/v26sdSatcOevaEPn1gwABjTK8Iuq7j0by4gh7KAm7yfUUUBkop8BaT5y+izO/CpXlD4VcJeb5C8nxF7ArNiwP1+yXMrtho7UgPTRm0CU2tHRm0tqfTxpFBgrXmS+nGkhkS1avlUsTA3BFhU3UtoCqHS0aWFBpkO7ysKxEtmhRzNTLggYqWSJXDpsiWRBaMsZasihq6AlsoVELBYlFRQ+ezKmqVYMkMksxlJeo2awqdzCu5QcRy9P7S+iFCVJgWGWQRsa5X2heqDb2iQq0awrQat0dtrP7cVepcad/azhN5u1H7VXd/a9mm6aHwy18xhYMxnx8CEevhbeb+4fAsELFPRJnfXxGuBUKt3YLBiHNUsx7eP7I8cqr8AwQY+waDQDMK6faEohihV/iCCpFXHg1fYMGiVloP7x9RHj4+ORFm3gUDD471PavRoYceisfjqXtHIYQQYi/omoZ73Q/mclP9RG3RNIaG6mmp7rOQEPtBiwi8Pv74Y2bOnMmMGTPo2bMnL7/8MhMnTmTRokVkZGTEunqGQMAYIL683GilVVJSMRUWGldD3L3baL21fbsxHteuXcZ6XR+g09MhN9eYcnKgZ0+CrTIp85VT6i8jz5PHri1/szvUEmu7N4/t3nzyfcUUBEooCpZTHCynKFCOR/fV+y45FCutrKm0sqaYU2ubsd4mVJ6mJlYfbPhDU3kJYPyqFtUKKdRyKVxS0Zqp4ipu4bG96necsd1o91MxByOg0aOCpXATl8hBryPDpvAa0eEQFQGPRQGrEmrFhGIETOHBuEPranhA7yqBUUXoZKFqUFTlNs0rxlXeXhE2NVjUsZFvVMFa9qvv+Rphvz1R1zn39fa61Hb8Xt32XnZ3a+htV1eu1LG9IXWJLKtpubr9q9u3MZ93kYGgpkUEbL6K5cjQLVipLOCvVB6selwgUBGcRa5H7RMMnSMYsd1fsV+wUlCnBaPLgpXCvMj7omnVh3nh+x8IXfyisSxbDQNHNt75Gtl1113HrFmzuOaaa8jJyakyhldiYmKMaiaEEEIIcWBqEYHXiy++yNlnn80ZZ5wBwIwZM1iyZAnvvfcekydPjl3FdJ2/zxrKT2u/QQsG0RQIKhC0ELUcVCBgiZ78KeBPg8Dh4FPBF2fDH+/EG+/Am+DAHWfF47Didii4lCBufRMu7R9cQR9lP7jx6A1vNeCw2Mmwp5BhTyHNFprbU8hwpJBuSyHdmUaGLYUEm9GkWld0IwpRqGjZhE65rlNezfnNgCk0Nw41mjAZAU04aoKKgKlqtzlLuAVTxFXdVMVqzFGxWBQsuoLVYjW7x1VuaRTVUkmPbp1UETpFtFAiupVVRWun6G58dT0v6lVW2z57un99hPev7bj61r0hZQ2tb0PPWdt+9TlHXfXdm+211ac+567Lnv4fN9axe3pcQ///atuvruMiy4w+vBXzmkRur2m52mMwuhGq1ur3rxzA7WlYV3lbQ/atrR5hmmYEYuF5TZMW6sobGZ5FHlP5PJFznw9Gjapa7yZk0qRJAPz73/+OKg+/3/35558xqJUQQgghxIGr2QdePp+P33//nUsuucQss1gsDBw4kJUrV8awZhAM+OmX+zXF3RujCWe4SVRpdLG39qNURSXBFk+CLYEEewIJtgRSHCkkOZNJdiST4kghLS6NVGcqac400uPTSbAmoFiU6IApMiBSwmMrqUb3uHBXuNCyOVnUKsdVBEyWRtsmRLOxL8KzvQ3e9ub8Db3txrq9+gaVlcPcmo6rbl6f89S2z57Upbpt4RZU4bpUXo9crq7elfevvF/kPjWdu656K4oxdlek+j4OldW0j8UCTmf1xzQRr7zySqyr0GAtdlgIIYQQQhzQmn3gVVhYSDAYrNJ1MSMjg3Xr1sWoVgbVZueq429i0W8LUCwWFKsNi9WKxaJiUVSjhZIlHBypqBarMZaSxYpVtaIqKlaLFZvFhl21G8uqDYfqwK7acVgdxFnjSLQnkmBPINGeSLw1nkRHIkn2JBLtiTitzoqBvivNqwuP6rMshGigPen+J0RTU5+wrq7t9Q3AqtsW1zTHewzr169frKvQIM1iWAghhBBCiAZo9oFXUzdj2N3MGHZ3rKshhBBC7J19MdZZC7JixYpatx911FH7qSZ7pskOCyGajaCmo1rkdUEIIUTT0+wDr7S0NFRVJT8/P6o8Pz+fzMzMep0jPI5UWVn9rjwohBBCiJYlISFhr1oxjx8/vkpZ5Pma4hheezssRFP8/NScw5fmXPenl6xlW7E71tXYI93bp3BW3w4cnGxB89nqPqCJaB1n/M01t3qD1L2xBLwBtlqMcYNzM6xYHTXXJ5b1tnsDlIXqmZNhxVdLPavTlB7zPdVc635wsmWP39P39vPTvtbsAy+73c4RRxzB0qVLGTZsGACaprF06VLOP//8ep2jvNwYWn3w4MH7rJ5CCCGEaLp+/PHHvbqSYuUWXn6/nz///JPZs2dzzTXX7G319om9HRZCPj+J5uwjYFasK9EA64DmOmKg1L3xdO7cGYBNr9xQ636xrnffUD2po57ViXXd90Zzrfs6oO/MPTtmbz8/7WvNPvACuPDCC7nxxhvp1q0bPXr04OWXX8btdjNmzJh6Hd+qVSu++uqrJp9OCiGEEGLfSEhI2Kvjk5KSqpQdc8wx2Gw2Zs2axfvvv79X52+K5POTEEIIcWDb289P+1qLCLxGjhxJQUEBjz32GLt37+bwww/nueeeq3eXRovFQps2bfZxLYUQQghxoMnIyGD9+vWxrka19nZYCPn8JIQQQoimrEUEXgDnn39+vbswCiGEEEI0pr/++qtK2a5du3j22Wfp0qVLDGpUt8YYFkIIIYQQoqlqMYGXEEIIIUSsnHbaaSiKYg7kHtarVy/uueeeGNWqbns7LIQQQgghRFMlgZcQQgghxF764osvotYtFgvp6ek4HI4Y1ah+9nZYCCGEEEKIpkrRK/8UKYQQQgghhBBCCCFEMyYtvIQQQgghGsHSpUtZunQp+fn5aJoWtW3mzD28zrcQQgghhNgrEngJIYQQQuylJ554gieffJJu3bqRlZWFoiixrpIQQgghxAFNujQKIYQQQuylQYMGMW3aNE477bRYV0UIIYQQQgCWWFdACCGEEKK58/v99OnTJ9bVEEIIIYQQIRJ47UOvvfYaQ4cOpXv37px11lmsWrUq1lVq8ubOncsZZ5xB7969GTBgAJdffjnr1q2LdbWanWeeeYbc3FzuueeeWFelWdi5cyfTpk2jf//+9OjRg9GjR/Prr7/GulpNWjAY5NFHH2Xo0KH06NGDYcOG8eSTTyKNhqtasWIFl156KYMGDSI3N5fPP/88aruu68yePZtBgwbRo0cP/v3vf7Nhw4bYVLYJqe1x8/v9PPDAA4wePZpevXoxaNAgbrjhBnbu3Bmz+p555pksXLgwZrcfC/I5p2aPP/44ubm5UdO//vWvWFcr5uT1sG51PUbTp0+v8tyaOHFijGobG/X5vuD1epkxYwb9+/end+/eXHnlleTl5cWoxvtffR6j8ePHV3ku3XbbbTGq8f73+uuvM3r0aPr06UOfPn0YO3YsX331lbn9QH8OhdX1ODX155GM4bWPfPzxx8ycOZMZM2bQs2dPXn75ZSZOnMiiRYvIyMiIdfWarOXLlzNu3Di6d+9OMBjk4YcfZuLEiXz00UfEx8fHunrNwqpVq3jzzTfJzc2NdVWaheLiYs4991z69+/Ps88+S1paGhs3biQlJSXWVWvSnn32Wd544w3uu+8+OnfuzG+//cZNN91EUlISEyZMiHX1mhSXy0Vubi5nnHEGU6ZMqbL92WefZd68ecyaNYv27dsze/ZsJk6cyMcff4zD4YhBjZuG2h43j8fDH3/8wWWXXUaXLl0oKSnhnnvu4bLLLuP999+PSX29Xi9vv/02S5cuJTc3F6s1+iPWTTfdFJN67SvyOaduhx12GC+++KK5rqpqDGvTNMjrYd3qeowAjj322KgLYdjt9v1VvSahPt8X7r33Xr766iseffRRkpKSuOuuu5gyZQpvvvlmjGu/f9T3O9XZZ5/N1KlTzfW4uLhYVDcm2rRpw7Rp0zjooIPQdZ0FCxZwxRVXMH/+fA477LAD/jkUVtfjBE38eaSLfeLMM8/UZ8yYYa4Hg0F90KBB+ty5c2NYq+YnPz9fz8nJ0ZcvXx7rqjQLZWVl+kknnaR/9913+vnnn6/ffffdsa5Sk/fAAw/o5557bqyr0exMnjxZv+mmm6LKpkyZol933XUxqlHzkJOTo3/22WfmuqZp+jHHHKM/99xzZllJSYnerVs3/cMPP4xFFZukyo9bdX755Rc9JydH37p1636qVbTzzz+/xmn8+PExqdO+JJ9zavfYY4/pp5xySqyr0aTJ62Hdqnvtu/HGG/XLLrssRjVqmip/XygpKdGPOOII/ZNPPjH3WbNmjZ6Tk6OvXLkyRrWMreq+U8l3haqOOuoo/e2335bnUB3Cj5OuN/3nkbTw2gd8Ph+///47l1xyiVlmsVgYOHAgK1eujGHNmp/S0lIAaW1TT3feeSeDBw9m4MCBzJkzJ9bVaRa+/PJLBg0axNSpU1mxYgWtW7fmvPPO4+yzz4511Zq03r178/bbb7N+/XoOOeQQ/vrrL3788UemT58e66o1K1u2bGH37t0MHDjQLEtKSqJnz56sXLmSUaNGxbB2zUtZWRmKopCcnByT2583b15MbjcW5HNO/WzcuJFBgwbhcDjo1asX1113He3atYt1tZoseT2sv+XLlzNgwACSk5M5+uijufrqq0lLS4t1tWKm8veF3377Db/fH/Vc6tSpE+3atePnn3+mV69esahmTNX0nWrhwoV88MEHZGVlcfzxx3P55Zc3rdY5+0kwGGTRokW4XC569+4tz6EaVH6cwpry80gCr32gsLCQYDBYpUl/RkaGjEe1BzRN495776VPnz7k5OTEujpN3kcffcQff/zBu+++G+uqNCubN2/mjTfe4MILL+TSSy/l119/5e6778Zms3H66afHunpN1uTJkykrK2PEiBGoqkowGOSaa67hlFNOiXXVmpXdu3cDVPt+cSCOE9FQXq+XBx98kFGjRpGYmBjr6rR48jmnbj169GDmzJkccsgh7N69myeffJJx48axcOFCeY7WQF4P6+fYY4/lxBNPpH379mzevJmHH36Yiy++mLfeeuuA7DZb3feFvLw8bDZblR9AMjIyzOfZgaSm71Qnn3wy7dq1o1WrVqxevZoHH3yQ9evX88QTT8SwtvvX6tWrOeecc/B6vcTHx/Pkk0/SuXNn/vzzT3kORajpcYKm/zySwEs0WTNmzOCff/7h9ddfj3VVmrzt27dzzz338MILLxwwY1w0Fl3X6datG9deey0AXbt25Z9//uHNN9+UwKsWn3zyCQsXLuShhx4yPxjMnDmTVq1ayeMm9iu/389VV12FruvMmDEj1tURAoDBgweby126dKFnz54cf/zxfPLJJ5x11lkxrJlo7iJbuoUHiB42bJjZ6utAI98X6lbTYzR27FhzOTc3l6ysLP7973+zadMmOnbsuL+rGROHHHIICxYsoLS0lMWLF3PjjTfy6quvxrpaTU5Nj1Pnzp2b/PNIrtK4D6SlpaGqKvn5+VHl+fn5ZGZmxqhWzcudd97JkiVLePnll2nTpk2sq9Pk/f777+Tn5zNmzBi6du1K165dWb58OfPmzaNr164Eg8FYV7HJysrKolOnTlFlhx56KNu2bYtRjZqH+++/n8mTJzNq1Chyc3M57bTTuOCCC5g7d26sq9asZGVlAcj7RQP5/X6uvvpqtm3bxgsvvCAtZ/YT+Zyz55KTkzn44IPZtGlTrKvSZMnrYcN06NDBvODOgaam7wuZmZn4/X5KSkqi9s/PzzefZweKPflO1bNnT4AD6rlkt9s56KCD6NatG9dddx1dunThlVdekedQJTU9TtVpas8jCbz2AbvdzhFHHMHSpUvNMk3TWLp0aVRfV1GVruvceeedfPbZZ7z88st06NAh1lVqFo4++mgWLlzIggULzKlbt26MHj2aBQsWHJBN3OurT58+rF+/Pqpsw4YNZGdnx6hGzYPH40FRlKgyVVXRdT1GNWqe2rdvT1ZWVtT7RVlZGb/88ou8X9QhHHZt3LiRl1566YAev2Z/k885e668vJzNmzcfkF+U6kteDxtmx44dFBUVHVDPrbq+L3Tr1g2bzRb1XFq3bh3btm07YMZeash3qj///BPggHouVaZpGj6fT55DdQg/TtVpas8j6dK4j1x44YXceOONdOvWjR49evDyyy/jdrsZM2ZMrKvWpM2YMYMPP/yQp556ioSEBLOPdFJSEk6nM8a1a7oSExOrjHMWHx9PamqqjH9WhwsuuIBzzz2Xp59+mhEjRrBq1Srefvtt7rzzzlhXrUk7/vjjefrpp2nXrp3ZpfHFF1/kjDPOiHXVmpzy8vKoVh1btmzhzz//JCUlhXbt2jFhwgTmzJnDQQcdRPv27Zk9ezatWrVi2LBhMax17NX2uGVlZTF16lT++OMP5s6dSzAYNN8vUlJSsNvtsar2AUM+59Tuvvvu4/jjj6ddu3bs2rWLxx9/HIvFwsknnxzrqsWUvB7WrbbHKCUlhSeeeILhw4eTmZnJ5s2beeCBBzjooIM49thjY1jr/auu7wtJSUmcccYZzJo1i5SUFBITE7n77rvp3bv3ARNW1PUYbdq0iYULFzJ48GBSU1NZvXo1M2fO5KijjqJLly4xrv3+8dBDD3HcccfRtm1bysvL+fDDD1m+fDnPP/+8PIci1PY4NYfnkaLLz/H7zKuvvsrzzz/P7t27Ofzww7nlllvMJn6ierm5udWWz5w5Uz5E76Hx48fTpUsX/vOf/8S6Kk3ef//7Xx5++GE2bNhA+/btufDCC+UqjXUoKytj9uzZfP755+Tn59OqVStGjRrFFVdcIWFDJcuWLWPChAlVyk8//XRmzZqFrus89thjvP3225SUlNC3b19uv/12DjnkkBjUtumo7XGbMmUKJ5xwQrXHvfLKK/Tv339fV08gn3Nqc80117BixQqKiopIT0+nb9++XHPNNU1iPJNYktfDutX2GN1xxx1cccUV/PHHH5SWltKqVSuOOeYYrrrqqgOq22d9vi94vV5mzZrFRx99hM/nY9CgQdx+++1NptXJvlbXY7R9+3auv/56/vnnH1wuF23btmXYsGFcfvnlB8zwADfffDPff/89u3btIikpidzcXC6++GKOOeYYQJ5DYbU9Ts3heSSBlxBCCCGEEEIIIYRoUWQMLyGEEEIIIYQQQgjRokjgJYQQQgghhBBCCCFaFAm8hBBCCCGEEEIIIUSLIoGXEEIIIYQQQgghhGhRJPASQgghhBBCCCGEEC2KBF5CCCGEEEIIIYQQokWRwEsIIYQQQgghhBBCtCgSeAkhWqxly5aRm5tLSUlJrKsihBBCCNHiTZ8+ncsvvzzW1RBCCEACLyFECzJ+/Hjuuecec7137958++23JCUlxbBWQgghhBCiMQ0dOpSXXnop1tUQQjRxEngJIVosu91OVlYWiqLEuipCCCGEEM2az+eLdRWEEGKPSOAlhGgRpk+fzvLly3nllVfIzc0lNzeX999/P6pL4/vvv8+RRx7Jf//7X4YPH07Pnj2ZOnUqbreb+fPnM3ToUI466ijuvvtugsGgeW6fz8d9993HscceS69evTjrrLNYtmxZrO6qEEIIIQQ+n4+7776bAQMG0L17d84991xWrVqFpmkcd9xxvP7661H7//HHH3Tp0oWtW7cCUFJSwn/+8x+OPvpo+vTpw4QJE/jrr7/M/R9//HFOPfVU3nnnHYYOHUqPHj0AWLRoEaNHj6ZHjx7079+ff//737hcrqjbev755xk0aBD9+/dnxowZ+P1+c1txcTE33HADRx11FD179mTSpEls2LAh6vjFixczatQounXrxtChQ3nhhRfMbePHj2fr1q3MnDnT/MwnhBDVsca6AkII0Rj+85//sGHDBg477DCmTp0KwJo1a6rs5/F4mDdvHo888gjl5eVMmTKFKVOmkJSUxDPPPMPmzZu58sor6dOnDyNHjgTgzjvvZM2aNTzyyCO0atWKzz77jEmTJrFw4UIOPvjg/Xk3hRBCCCEAuP/++1m8eDGzZs0iOzub5557jkmTJvHpp58yatQoPvzwQ8477zxz/4ULF9KnTx+ys7MBuOqqq3A4HDz77LMkJSXx1ltvccEFF7B48WJSU1MB2LRpE4sXL+aJJ57AYrGwa9currvuOq6//nqGDRtGeXk5P/zwA7qum7ezbNkysrKyePnll9m0aRPXXHMNhx9+OGeffTZg/Ei5ceNG5syZQ2JiIg888ACTJ0/mo48+wmaz8dtvv3H11VczZcoURo4cycqVK5kxYwapqamMGTPGDOLOPvts85xCCFEdCbyEEC1CUlISNpsNp9NJVlYWAOvWrauyn9/v54477qBjx44ADB8+nA8++IDvvvuOhIQEOnfuTP/+/fn+++8ZOXIk27Zt4/333+e///0vrVu3BmDixIl88803vP/++1x77bX7704KIYQQQgAul4s333yTmTNnMnjwYADuuusuvvvuO959911OOeUUXnzxRbZt20a7du3QNI2PPvqIyy67DIAffviBVatWsXTpUux2OwA33ngjn3/+OYsXL2bs2LGA8bnp/vvvJz09HYDff/+dQCDAiSeeaAZnlVtYpaSkcNttt6GqKp06dWLw4MEsXbqUs88+mw0bNvDll1/yxhtv0KdPHwAefPBBhgwZwueff86IESN48cUXGTBgAFdccQUAhxxyCGvWrOH5559nzJgxpKamoqoqCQkJ5mc+IYSojgReQogDSlxcnBl2AWRmZpKdnU1CQkJUWUFBAQB///03wWCQf/3rX1Hn8fl85q+fQgghhBD706ZNm/D7/WZoBGCz2ejRowdr165l0qRJdOrUiQ8//JDJkyezfPlyCgoKzM8zq1evxuVy0b9//6jzejweNm3aZK63a9fODLsAunTpwoABAxg9ejSDBg1i0KBBDB8+nJSUFHOfzp07o6qquZ6VlcXff/8NwNq1a7FarfTs2dPcnpaWxiGHHMLatWsB4wfLE044Iapeffr04ZVXXiEYDEadWwghaiOBlxDigGK1Rr/sKYpSbZmmaYDxC6qqqrz33ntVPmDFx8fv28oKIYQQQjTQ6NGjWbhwIZMnT+bDDz9k0KBBpKWlAVBeXk5WVhbz5s2rclzk1a3j4uKitqmqyosvvshPP/3Ed999Zw4T8fbbb9OhQweg+s9akV0ehRBif5FB64UQLYbNZjODqsZy+OGHEwwGKSgo4KCDDoqapBm9EEIIIWKhY8eO2Gw2fvrpJ7PM7/fz66+/0rlzZwBOPvlk/vnnH3777TcWL17MKaecYu57xBFHkJeXh6qqVT7fRLboqo6iKPTt25epU6eyYMECbDYbn3/+eb3q3alTJwKBAL/88otZVlhYyPr16816H3rooVH3C+Cnn37i4IMPNn983Bef+YQQLY8EXkKIFiM7O5tffvmFLVu2UFBQ0CgfhA455BBGjx7NDTfcwKeffsrmzZtZtWoVc+fOZcmSJXtfaSGEEEKIPRQfH8+5557L/fffz9dff82aNWu49dZb8Xg8nHnmmQC0b9+e3r1785///IdgMMjQoUPN4wcOHEivXr244oor+Pbbb9myZQs//fQTjzzyCL/++muNt/vLL7/w9NNP8+uvv7Jt2zY+/fRTCgoKOPTQQ+tV74MPPpgTTjiBW2+9lR9++IG//vqL66+/ntatW5vdGC+66CKWLl3Kk08+yfr165k/fz6vvfYaF110kXme7OxsVqxYwc6dO81hKIQQojLp0iiEaDEuuugipk+fzqhRo/B4PMycObNRzjtz5kzmzJnDrFmz2LVrF6mpqfTq1YshQ4Y0yvmFEEIIIfbUtGnT0HWdG264gfLycrp168Zzzz0XNZ7W6NGjmTFjBqeddhpOp9MsVxSFZ555hkcffZSbbrqJwsJCMjMzOfLII8nMzKzxNhMTE1mxYgUvv/wyZWVltGvXjunTp5sD59fHzJkzueeee7j00kvx+/0ceeSRPPPMM9hsNsBoffboo4/y2GOPMWfOHLKyspg6dSpjxowxzzF16lRuu+02hg0bhs/nY/Xq1Xvy0AkhDhCKLh2qhRBCCCGEEEIIIUQLIl0ahRBCCCGEEEIIIUSLIoGXEEIIIYQQQgghhGhRJPASQgghhBBCCCGEEC2KBF5CCCGEEEIIIYQQokWRwEsIIYQQQgghhBBCtCgSeAkhhBBCCCGEEEKIFkUCLyGEEEIIIYQQQgjRokjgJYQQQgghhBBCCCFaFAm8hBBCCCGEEEIIIUSLIoGXEEIIIYQQQgghhGhRJPASQgghhBBCCCGEEC2KBF5CCCGEEEIIIYQQokWRwEsIIYQQQgghhBBCtCgSeAkhhBBCCCGEEEKIFkUCLyGEEEIIIYQQQgjRokjgJYQQQgghhBBCCCFaFAm8hBBCCCGEEEIIIUSLIoGXEEIIIYQQQgghhGhRJPASQhwQli1bRm5uLosWLYp1VQB4/PHHyc3NjXU1hBBC7Efjx4/n5JNPjnU1DliapnHyySczZ86cWFelWu+//z65ubn8+uuvsa4KANOnT2fo0KF7fNyaNWvo2rUrf//99z6olahJU3x+hz/vFhQU1Lnv0KFDmT59eoNuZ+jQoVxyySV17vf111/Tu3fvetVHtAwSeAkh9rnwB7jc3Fx++OGHKtt1XWfw4MHk5ubW681KCCHEgeuff/5h2rRpHHvssXTr1o1BgwZx3XXX8c8//8S6ak3a008/zeeffx7rasTUhx9+yPbt2zn//PNjXZUWrXPnzgwePJjHHntsr87z6aefcvXVV3PCCSfQs2dPhg8fzqxZsygpKal2/7KyMu6//36GDh1Kt27dOPbYY5k6dSput7vO25ozZw6XXnopAwcOJDc3l8cff7xedbzwwgvJzc3lzjvv3KP7FikcCoWnnj17MnLkSB555BHKysrqfZ7qnt8//fQTjz/+eI2P2YHmuOOOo2PHjsydO3evz7V27VomTpxI79696devH9dff329gzSv18vcuXMZOXIkPXv2NJ+r1b2PlZSUcOutt3L00UfTq1cvxo8fz++//77X9T9QWGNdASHEgcPhcPDhhx9y5JFHRpUvX76cHTt2YLfbY1QzIYQQzcGnn37KtddeS2pqKmeccQbt27dn69atvPvuuyxevJhHHnmEE088MdbVbJLmzp3L8OHDGTZsWKyrEjPPP/88o0aNIikpKdZVafHOOeccJk+ezKZNm+jYsWODznHrrbfSqlUrTjnlFNq1a8fq1at59dVX+eqrr5g/fz5Op9Pct7S0lPPPP58dO3YwduxYOnbsSEFBAT/++CM+n4+4uLhab+vRRx8lKyuLww8/nG+//bZe9fv000/5+eefG3TfqnPHHXcQHx+Py+Xiu+++4+mnn2bZsmW88cYbKIpS5/HVPb9XrlzJE088wemnn05ycnKj1XVfWLRoUb3u594aO3Ys999/P1deeSWJiYkNOseOHTsYN24cSUlJXHPNNbhcLl544QX+/vtv3nnnnTq/00ybNo0vv/ySs846iyOOOIKdO3fy+uuvM3bsWBYuXEh2djZgtNqbPHkyq1evZuLEiaSlpfH6668zfvx43n//fQ4++OAG1f9AIoGXEGK/GTx4MIsWLeKWW27Baq14+fnwww854ogjKCoqil3lhBBCNGmbNm3ihhtuoEOHDrz22mukp6eb2yZMmMC4ceO44YYb+OCDD+jQocN+q5fL5SI+Pn6/3Z5omD/++IO//vqrwV2mxJ4ZOHAgKSkpzJ8/n6uuuqpB53jsscfo379/VFm3bt248cYbWbhwIWeddZZZ/tBDD7Ft2zbef//9Bv39f/HFF7Rv356CggIGDBhQ5/5er5dZs2YxadKkvW7JFjZ8+HDzde3cc8/lyiuvNEO13r17V3uM2+0mLi6uRTy/99cP38OHD+fuu+9m0aJFnHnmmQ06x9NPP43b7eb999+nXbt2APTo0YMLL7yQ+fPnM3bs2BqP3blzJ59++ikXXXQRN954o1l+5JFHcsEFF/DZZ5/x73//GzBCwJUrVzJ79mz+9a9/ATBixAiGDx/O448/zkMPPdSg+h9IpEujEGK/GTVqFEVFRXz33Xdmmc/nY/HixYwePbraY55//nnOOecc+vfvT48ePRgzZky143B99913nHvuuRx55JH07t2b4cOH8/DDD9daH5/PxyWXXELfvn356aefat03Pz+fm2++mYEDB9K9e3dOOeUU5s+fH7XPli1byM3N5fnnn+ett95i2LBhdOvWjTPOOINVq1bVev7zzz+fU045pdptw4cPZ+LEibUeL4QQLd1zzz2H2+3mrrvuigq7ANLT07nzzjtxuVw8++yzgPFFITc3l+XLl1c515tvvklubm7UGENr165l6tSp9OvXj+7duzNmzBi++OKLqOPCXfSXL1/OHXfcwYABAxg8eDBgdKe65557zO5UAwYM4MILL6y268maNWsYP3682ZUlXOdI9XnfASNwmzVrFoMHD6Zbt24MHz6c559/Hl3XzX1yc3NxuVzMnz/f7DZV2xfj8LiXH3/8MU888QTHHnssvXv3ZurUqZSWluLz+bjnnnsYMGAAvXv35qabbsLn81U5z//93/8xZswYevToQb9+/bjmmmvYvn171D4//PADU6dOZciQIXTr1o3Bgwdz77334vF4ovabPn06vXv3ZufOnVx++eX07t2bo48+mvvuu49gMFjjfQn7/PPPsdlsVVqZh7uTrV+/nmnTptG3b1+OPvpoHn30UXRdZ/v27Vx22WX06dOHY445hhdeeCHqeJ/Px+zZsxkzZgx9+/alV69enHfeeXz//fdV6vDRRx8xZswYevfuTZ8+fRg9ejQvv/xyrfUuLi7mzDPP5LjjjmPdunW17rt582bzOdyzZ0/OPvtslixZErVP5P/tnDlzOO644+jevTsXXHABGzdurPHcuq4zdOhQLrvssirbvF4vffv25bbbbjPLbDYb/fr1q/I35Ha7Wbt2bb26flUOuwCzheLatWvNspKSEt5//33OPvtsOnTogM/nq/b5WJv27dvv0f7PPvssuq7v089nRx99NGB8voSKMQB/++03xo0bR8+ePc3PutU9vx9//HHuv/9+AE444QTzbz98vkAgwJNPPml+Xh06dCgPP/xwtY/da6+9xqhRo8xu5DNmzNijbpKlpaVMnz6dI488kr59+3LTTTdV6Wpa3Rhef/31F+effz49evTguOOO46mnnuK9996Luh+RfvjhB84880y6d+/OCSecwIIFC6rsk5GRQW5ubpXnZmlpKWvXrqW0tLTO+/Ppp58yZMgQM+wCI+Q9+OCD+eSTT2o9NtxNNTMzM6o8KysLMHrEhC1evJjMzExOOukksyw9PZ0RI0bwxRdf7PHz/EAkLbyEEPtNdnY2vXr14qOPPjK/IHz99deUlpYycuRI5s2bV+WYV155haFDhzJ69Gj8fj8fffQRV111FXPnzmXIkCGAMZ7LJZdcQm5uLlOnTsVut7Nx48ZaQyyPx8Pll1/Ob7/9xosvvkiPHj1q3Xf8+PFs2rSJcePG0b59exYtWsT06dMpKSnhggsuiNr/ww8/pLy8nLFjx6IoCs899xxXXnml+WGkOqeeeiq33HILf//9Nzk5OWb5qlWr2LBhQ7UfMIUQ4kDy3//+l+zs7CqBRdhRRx1FdnY2X331FQBDhgwhPj6eTz75hH79+kXt+/HHH3PYYYeZr7f//PMP5557Lq1bt+biiy82j7viiit4/PHHq3STnDFjBunp6VxxxRW4XC4Abr/9dhYvXsz5559Pp06dKCoq4scff2Tt2rUcccQR5rHFxcVMmjSJE088kREjRrB48WIefPBBcnJyzPfG+r7v6LrOZZddxrJlyzjzzDM5/PDD+eabb7j//vvZuXMnN998MwD3338/t9xyCz169ODss88GqFc3s2eeeQan08nkyZPZuHEjr776KlarFUVRKCkpYcqUKfzyyy+8//77ZGdnM2XKFPPYOXPmMHv2bEaMGMGZZ55JQUEBr776KuPGjWPBggVm96pFixbh8Xg499xzSU1NZdWqVbz66qvs2LGjSsuZYDDIxIkT6dGjBzfccANLly7lhRdeoEOHDpx33nm13peVK1eSk5NT4/vwNddcQ6dOnbjuuuv46quvmDNnDqmpqbz55pscffTRTJs2jYULF3LffffRvXt3jjrqKMD48vrOO+9w8sknc9ZZZ1FeXs67777LpEmTeOeddzj88MMB44e5a6+9lgEDBjBt2jQA1q1bx08//VTlc0RYQUEBF110EcXFxbz66qu1/p/l5eVxzjnn4Ha7GT9+PGlpacyfP5/LLruMxx57rMpz+Nlnn0VRFC666CLKysp47rnnmDZtGu+8806151cUhdGjR/P8889TVFREamqque3LL7+krKysyg93RxxxBF988QVlZWVm17FVq1YxYcIEpkyZwpVXXlnj/antfgKkpaWZZT/++CNer5eDDjqIqVOn8vnnn6NpGr169eL22283/w8ay7Zt23j22We59957o7pVNrZNmzYBRD3WRUVFXHzxxYwaNYpTTjmFjIwMoPrn94knnsiGDRv48MMPuemmm8zHLPyDwS233ML8+fMZPnw4F154IatWrWLu3LmsXbuWJ5980jzP448/zhNPPMHAgQM599xzWb9+PW+88Qa//vorb7zxRo1/U5Guvvpq2rdvz7XXXssff/zBO++8Q3p6Otdff32Nx+zcudP825g8eTLx8fG1dhfcuHEjV111FWeeeSann3467733HtOnT+eII47gsMMOi9r3iCOOqDKm4WeffcZNN93EzJkzGTNmTK31ys/Pp1u3blW29ejRg6+//rrGY8F47W3Tpg0vvvgihxxyCF27dmXXrl088MADtG/fnlGjRpn7/vnnn3Tt2hWLJbqdUvfu3XnrrbdYv369XASrLroQQuxj7733np6Tk6OvWrVKf/XVV/XevXvrbrdb13Vdnzp1qj5+/Hhd13X9+OOP1ydPnhx1bHi/MJ/Pp5988sn6hAkTzLIXX3xRz8nJ0fPz82usw/fff6/n5OTon3zyiV5WVqaff/75ev/+/fU//vijzvq/9NJLek5Ojv5///d/UfUYO3as3qtXL720tFTXdV3fvHmznpOTo/fr108vKioy9/3888/1nJwc/csvvzTLHnvsMT0nJ8dcLykp0bt3764/8MADUbd911136b169dLLy8vrrKcQQrRUJSUlek5Ojn7ZZZfVut+ll16q5+TkmK/L1157rT5gwAA9EAiY++zatUvv0qWL/sQTT5hlF1xwgX7yySfrXq/XLNM0TR87dqx+0kknmWXh97Nzzz036py6rut9+/bVZ8yYUWv9zj//fD0nJ0efP3++Web1evVjjjlGv/LKK82y+r7vfPbZZ3pOTo7+1FNPRd3OlVdeqefm5uobN240y3r16qXfeOONtdYvLPyeefLJJ+s+n88sv/baa/Xc3Fx90qRJUfuPHTtWP/744831LVu26Icffrg+Z86cqP1Wr16td+3aNaq88vu8ruv63Llz9dzcXH3r1q1m2Y033qjn5ORE/b/puq6fdtpp+umnn17nfTruuOOiHuOw8PvxrbfeapYFAgH9uOOO03Nzc/W5c+ea5cXFxXqPHj2iHsdAIBD1vAnvN3DgQP2mm24yy+6++269T58+VZ43kSI/L+3atUsfNWqUfsIJJ+hbtmyp8/7dc889ek5Ojr5ixQqzrKysTB86dKh+/PHH68FgUNf1iv/bESNGRNX75Zdf1nNycvTVq1ebZTfeeGPU/+u6dev0nJwc/fXXX4+67UsvvVQ//vjjdU3TosoXLlyo5+Tk6L/88otZFr79xx57rM77VJ2bb75ZP/zww/X169ebZeHPgf369dPPPPNM/YMPPtBfe+01feDAgfpRRx2l79y5s97nz8/Pr7N+V155pT527FhzPScnp86//dqEn4Pr1q3T8/Pz9c2bN+tvvvmm3q1bN33gwIG6y+XSdb3i9eONN96oco6ant/PPfecnpOTo2/evDmq/M8//9RzcnL0//znP1Hls2bN0nNycvSlS5fqum48HkcccYR+0UUXmc8hXdf1V199Vc/JydHffffdet23yL8FXdf1K664Qu/Xr19U2fHHHx/1t3XXXXfpubm5UZ/VCwsL9X79+lW5T8cff3yV539+fr7erVs3fdasWVXq9fTTT+s5OTl6Xl6eWRb++3vvvfdqvU+rVq2q8joedt999+k5OTlVXhMq++WXX/Rhw4bpOTk55nT66afru3btitqvV69eVR47Xdf1JUuW6Dk5OfrXX39d6+0IXZcujUKI/WrEiBF4vV7++9//UlZWxpIlS2rszghE/XJWXFxMaWkpffv25Y8//jDLw78Sf/HFF2iaVuvtl5aWMnHiRNatW8e8efPq9avf119/TVZWVtSl5G02G+PHj8flcrFixYqo/UeOHElKSoq5Hm6NsHnz5hpvIykpiRNOOIGPPvrI7IYSDAb55JNPOOGEE2R8GCHEAa28vByAhISEWvcLbw/vP2LECPLz86O6NS5evBhN0xg5ciRgtJj4/vvvGTFiBGVlZRQUFFBQUEBhYSGDBg1iw4YN7Ny5M+p2zj77bFRVjSpLTk7ml19+qbJvZfHx8Zx66qnmut1up3v37lHvEfV93/n6669RVZXx48dH3cZFF12Erut1tjSoy6mnnhrVeqNHjx7ous4ZZ5wRtV+PHj3Yvn07gUAAMFpKaJrGiBEjzMezoKCAzMxMDjroIJYtW2YeG/k+73K5KCgooHfv3ui6HvVeH3buuedGrfft27fark2VFRUV1Tpod+RYPqqq0q1bN3RdjypPTk7mkEMOifq/UlXVbHGiaRpFRUUEAgG6detW5bOK2+2OGtahJjt37uT888/H7/fz2muvmQNY1+arr76iR48eUS0gExISGDt2LFu3bmXNmjVR+48ZMyaqpUx9Pqsccsgh9OzZk4ULF5plRUVFfPPNN4wePbrKgOPhx7uwsNAs69+/P6tXr25Q666FCxfy7rvvcuGFF0YN1h3+e1cUhZdeeonRo0dz3nnn8eSTT1JcXMxrr722x7dVk++//55PP/3UbD3ZmP71r38xYMAATjjhBG677TYOOugg5s6dGzXgvt1ur7b1UV3P78rCLWEvvPDCqPKLLrooavv//vc//H4/EyZMiGpldNZZZ5GYmGjuV5dzzjknav3II4+kqKio1qtQfvPNN/Tq1Svqs3pqamqN3xs6d+4c9fxPT0+v8vcaVt1zc8yYMaxevbrW1l1gdOGF6sccC3dHrNwlu7rbP/zww5k8eTJPPvkkN954I1u3buWqq64yzx8+T3W3Ey6L3FdUT7o0CiH2q/T0dAYMGMCHH36Ix+MhGAwyfPjwGvf/73//y5w5c/jzzz+j+qlHfqgaOXIk77zzDrfccgsPPfQQAwYM4MQTT+Rf//pXlSbA9957Lz6fj/nz51dp3lyTrVu3ctBBB1U5V6dOnQCjaXuktm3bRq2Hw6+6xjo47bTT+Pjjj/nhhx846qij+N///kdeXl7UFyMhhDgQVQ6yalI5GDvuuONISkri448/Ngei/vjjjzn88MM55JBDAKPbkK7rzJ49m9mzZ1d73vz8fFq3bm2uVzfez7Rp05g+fTpDhgzhiCOOYPDgwZx22mlVBtBu06ZNlWAgJSWF1atXm+v1fd/ZunUrrVq1qnKlsfB+W7durfb+1Ffk+DSAefW3yu9zSUlJaJpGaWkpaWlpbNiwAV3Xo8adiRR54Zpt27bx2GOP8eWXX1JcXBy1X+Uvww6Ho8r4bSkpKVWOq4keMa5ZZdXd1+puLykpqcpFdubPn88LL7zA+vXr8fv9Znnk8+S8887jk08+4eKLL6Z169Ycc8wxjBgxguOOO65KXa6//nqsVisff/yxOa5PXbZt20bPnj2rlB966KHm9sghEyrf33AAUNdnlVNPPZW77rqLrVu3kp2dzaJFi/D7/dV+Vqnt8d5TP/zwA//5z38YNGgQ11xzTdS2cGh6/PHHR4XivXr1on379qxcubJR6hAIBLjnnns49dRTax0Ko6Eef/xxEhMTsVqttGnTptourK1bt66xS9+ePN5bt27FYrFUuY2srCySk5PN147wa034eRRmt9vp0KFDvV9janq+FRcX13ilxK1bt9KrV68q5TV17a38ugQ1vz6EH6uGXBUyHGpVN35WOICqratraWkp48aNY+LEiWbACMYFGcaPH897771ndtF2Op3V3k64LHK8L1E9CbyEEPvdySefzK233kpeXh7HHXdcjb9I/fDDD1x22WUcddRR3H777WRlZWGz2Xjvvff48MMPzf2cTievvfYay5YtY8mSJXzzzTd8/PHHvPXWW7zwwgtRv8KfcMIJfPzxxzzzzDPcf//9Vb5MNIbKv/qH1fVBZNCgQWRmZvLBBx9w1FFH8cEHH5CVlcXAgQMbvY5CCNGcJCUlkZWVFRUKVWf16tW0bt3a/AJlt9sZNmwYn332Gbfffjv5+fn89NNPXHvtteYx4ZbBF110Eccee2y15638Bau6LxkjR47kyCOP5LPPPuO7777j+eef59lnn+Xxxx83x+aCmt8jmqKa3iNrKg+/z2mahqIoPPvss9Xe33Cr5WAwyIUXXmiOa3booYcSHx/Pzp07mT59epVW23vz2KWmptYa5lR3n+rzfv5///d/TJ8+nWHDhjFx4kQyMjJQVZW5c+dGtSzJyMhgwYIFfPvtt3z99dd8/fXXvP/++5x22mncd999Uec/6aSTWLBgAa+88grXXXfdnt7Veqnr/7Amo0aNYubMmSxcuJBLL72UDz74gG7dulUJRKAiPIscb6sh/vrrLy677DIOO+wwHnvssajAFKBVq1ZA1UHAwXjc92Rw9dosWLCA9evXM2PGjCqtCsvLy9myZQsZGRlRLbL2xJFHHlklYK2spiClrud3TRoS+DREQ59ve2JPXh/25rkZfr7t3r27yrbdu3eTmppa6xUnFy9eTF5eHkOHDo0q79evH4mJifz0009m4JWVlVXt7ezatSuqLqJmEngJIfa7E088kdtvv52ff/6ZRx55pMb9Fi9ejMPh4Pnnn49643jvvfeq7GuxWBgwYAADBgzgpptu4umnn+aRRx5h2bJlUYHRsGHDGDRoENOnTychIYEZM2bUWd/s7GxWr16NpmlRb9jhqyVV/tWqoVRV5eSTT2b+/PlMmzaNzz//vNpuM0KIpq+srIzvv/+eQw45xGxtI/bO8ccfz9tvv80PP/xQ7cD1P/zwA1u3bq1yOfgRI0Ywf/58li5dytq1a9F1nREjRpjbwy2wbDbbXv/A0KpVK8aNG8e4cePIz8/n9NNP5+mnn44KvOqjvu872dnZLF26NGpQ8Mj96tMVbl/o2LEjuq7Tvn17syVddf7++282bNjAfffdx2mnnWaW16fb35469NBD69X1cU8tXryYDh068MQTT0SFB5UH3AcjgB06dChDhw5F0zTuuOMO3nrrLS6//HIOOuggc7/zzz+fjh078thjj5GUlMTkyZPrrEe7du1Yv359lfLG/qySmprKkCFDWLhwIaNHj+ann36qsXvfli1bsFgstT4H6rJp0yYmTZpEeno6zz77bLXdmsMXhaiuO/GuXbuqDeMaYvv27fj9/irdasEIwxYsWGBe9XB/q+n5XVOglZ2djaZpbNy4Meo9Ki8vj5KSEvO1I/y8WbduXVRrVZ/Px5YtW/bpj7LZ2dnVXjk0PJj/3tiyZQtpaWl1BozVad26Nenp6fz2229Vtq1atYouXbrUenx+fj5AlavL6rqOpmlR5V26dOHHH3+s8l6watUq4uLi9upv60AhY3gJIfa7hIQE7rjjDq688soqv25EUlUVRVGiXvi3bNlS5TLClbsWAGZ//+qaAZ922mnccsstvPnmmzzwwAN11ve4445j9+7dfPzxx2ZZIBBg3rx5xMfHm1dqagynnnoqxcXF3HbbbbhcripXPBJCNE1XXXUVr776KmCMuXHGGWdw9dVXc8opp7B48eIY165lmDhxIk6nk9tvvz1q3BUw3gduv/124uLimDRpUtS2gQMHkpqayscff8wnn3xCjx49or64ZWRk0K9fP9566y3zV/NIBQUFddYtGAxWuZR9RkYGrVq1atBl4+v7vnPccccRDAarjFH00ksvoShKVHe5+Pj4RmvpUpeTTjoJVVV54oknqrTg0HXd/P8Lf4GL3EfXdV555ZVGr1OvXr34559/GvT/UZvwj1KR9+GXX37h559/jtqv8nPWYrGYV1errk5XXHEFF110EQ899BCvv/56nfUYPHgwq1atiuq+53K5ePvtt8nOzqZz5871vk91OfXUU1mzZg33338/qqpGXVUu0u+//07nzp3NrrAAbrebtWvX1uvvavfu3Vx00UUoisLzzz9fYzhx6KGH0qVLF7744ouo83777bds3749KpQpLS1l7dq1Vf5e62PkyJE8+eSTVSYwHv8nn3xyn3R1rI+ant/h1maV7284hH/55Zejyl988cWo7QMHDsRmszFv3ryo5/i7775LaWnpHof5e2LQoEH8/PPP/Pnnn2ZZUVFR1BhyDfX7779X6S65J8+Nk046iSVLlrB9+3azbOnSpWzYsIF//etfZpnf72ft2rVR7y3h8eciX9/BGIvY5XJFjVn2r3/9i7y8PD799FOzrKCggEWLFnH88cfX2pJMGKSFlxAiJk4//fQ69xk8eDAvvvgikyZN4uSTTyY/P5/XX3+djh07RnVrefLJJ/nhhx8YPHgw2dnZ5n5t2rShb9++1Z77/PPPp6ysjEceeYSkpCQuvfTSGusxduxY3nrrLaZPn87vv/9OdnY2ixcvNn/VrGnsgYbo2rUrOTk5LFq0iE6dOkVdyl4I0XSFu2CDMWC3ruusWLGC+fPnM2fOnFrHKhT1c/DBBzNr1iyuv/56Ro8ezZlnnkn79u3ZunUr7777LoWFhTz88MNVuh/abDZOPPFEPvroI9xuNzfeeGOVc99+++2cd955jB49mrPPPpsOHTqQl5fHzz//zI4dO/jggw9qrVt5eTmDBw9m+PDhdOnShfj4eP73v//x66+/Mn369D2+r/V93xk6dCj9+/fnkUceYevWreTm5vLdd9/xxRdfcMEFF0Q9FkcccQRLly7lxRdfpFWrVrRv377aMZ8aQ8eOHbn66qt56KGH2Lp1K8OGDSMhIYEtW7aYrZcnTpzIoYceSseOHbnvvvvYuXMniYmJLF68eJ8EcyeccAJPPfUUy5cvZ9CgQY123iFDhvDpp59yxRVXMGTIELZs2cKbb75J586dcblc5n633HILxcXFHH300bRu3Zpt27bx6quvcvjhh9fYCvTGG2+krKyMO++8k4SEhFrH9Jw8eTIfffQRF198MePHjyclJYUFCxawZcsWHn/88UYdwmHw4MGkpqayaNEijjvuODIyMqrs4/f7WbFiRZXWUKtWrWLChAlMmTKlzoHrJ02axObNm5k0aRI//vgjP/74o7ktMzOTY445xly/6aabuOiiizjvvPM455xzKC0t5cUXX+Tggw+OqsNnn33GTTfdxMyZM6MGJ1+wYAHbtm0zBxtfsWIFTz31FGAEfNnZ2XTq1KnG/6v27dtXadk1fvx4li9fXmdX7MZQ0/M7/DnykUceYeTIkdhsNo4//ni6dOnC6aefzltvvUVJSQlHHXUUv/76K/Pnz2fYsGEcffTRgDH27iWXXMITTzzBpEmTGDp0KOvXr+f111+ne/fu+/SH2UmTJvHBBx9w4YUXcv755xMfH88777xD27ZtKSoqanB3zPz8fFavXm12Gwyr6blRnUsvvZRFixYxYcIEJkyYgMvl4vnnnycnJyfqoh47d+5k5MiRnH766cyaNQswWisfdthhPPnkk+bYexs3buS1114jKysr6kIZw4cPp1evXtx0002sWbOGtLQ03njjDYLBYIMu/HAgksBLCNFkDRgwgHvuuYdnn32We++9l/bt2zNt2jS2bt0a9eFh6NChbN26lffee4/CwkLS0tLo168fV155ZdSvipVdeumllJaWmqHXuHHjqt3P6XQyb948HnzwQebPn09ZWRmHHHJIvd4QG+LUU0/lgQcekMHqhWhGSktLzQtUfPPNN5x00knExcUxZMiQerUkFfUzYsQIDj30UJ555hneffddioqKSE1NpX///lxyySVRg3JHCl/cRFGUqO6MYZ07d+a9997jiSeeYP78+RQVFZGenk7Xrl254oor6qyX0+nk3HPP5bvvvuPTTz9F13U6duxoBml7qr7vOxaLhTlz5vDYY4/x8ccf8/7775Odnc0NN9wQNRgywPTp07ntttt49NFH8Xg8nH766fss8AIjgDn44IN56aWXzFYwbdq04ZhjjjFbd9tsNp5++mnuvvtu5s6di8Ph4MQTT2TcuHGN/h7YrVs3cnNz+eSTTxo18BozZgx5eXm89dZbfPvtt3Tu3JkHHniARYsWRV0d9JRTTuHtt9/m9ddfp6SkhKysLEaMGMGVV15Zaxg1Y8YMXC4XN998MwkJCTV2l8vMzDRbrr/66qt4vV5yc3N5+umnGTJkSKPdXzC6Zo4cOZLXX3+9xv+npUuXUlRUVK8fOGvy119/AfDcc89V2davX7+owOvoo4/mueeeY/bs2Tz88MPExcUxbNgwrr/++jqv7grGcBmR/1/Lli0zrybat2/fBnUPLi8vr/dFB/ZWTc/vHj16cNVVV/Hmm2/yzTffoGkaX3zxBfHx8dx99920b9+e+fPn8/nnn5OZmckll1zClClTos595ZVXkp6ezquvvsrMmTNJSUnh7LPP5tprr426imtja9u2La+88or5+pCens64ceOIi4vj7rvvbvCA7Z9++il2u73a94I9qdurr77KrFmzeOihh7DZbAwePJjp06fX2erKbrfz2muv8dRTT7FkyRI+/PBD82/7mmuuiWrJqKqqOe7wvHnz8Hq9dO/enZkzZzZaV92WTtEbc6Q4IYQQe+3ll19m5syZfPnll4025oYQYt8aPnw4V111FUOGDOGEE07g4YcfZsCAAfz1119ccMEF5hcnIUTsLFiwgDvvvJMlS5bUeMEcUT/33nsv7777Lt999121g7RffvnlKIpihp0HmrKyMvr378/NN99c4w+qje1AeX7fc889vPXWW6xcubJB49yedtpp9OvXr8ax50TLImN4CSFEE6LrOu+++y5HHXWUhF1CNCMTJkzg+uuvZ/DgwWRlZdG/f3/A6BZTU6sjIcT+dcopp9CuXbsqY56JPeP1evnggw8YPnx4tWHX2rVrWbJkCVdddVUMatc0/PDDD7Ru3Zqzzjprv91mS3x+h7uYhhUWFvLBBx/Qt2/fBoVdX3/9NRs3buSSSy5prCqKJk5aeAkhRBPgcrn48ssvWbZsGW+//TZPPfUUJ5xwQqyrJYTYA7/++is7duxg4MCBZheaJUuWkJSUVON4gkII0Vzk5+fzv//9j8WLF/P5558zf/78qAG2hWhsp556Kv369aNTp07k5eXx3nvvsWvXLl566aVGvWiUaLkk8BJCiCZgy5YtnHDCCSQnJ3PeeedxzTXXxLpKQogGCF+qvWPHjlitMlSqEKLlWLZsGRMmTCAjI4PLL7+c888/P9ZVEi3cww8/zOLFi9mxYweKotC1a1emTJkSdeVNIWojgZcQQgghxF5yu93cddddLFiwAIDFixfToUMH7rrrLlq3bs3kyZNjW0EhhBBCiAOMjOElhBBCCLGXHnroIf766y9eeeWVqCtHDRgwgI8//jiGNRNCCCGEODDFNPBasWIFl156KYMGDSI3N5fPP/88aruu68yePZtBgwbRo0cP/v3vf7Nhw4aofYqKirjuuuvo06cPRx55JDfffDPl5eV7VA9d1ykrK0MauwkhhBCiIb744gtuu+02jjzyyKjyww47jE2bNsWoVvuWfH4SQgghRFMW08DL5XKRm5vL7bffXu32Z599lnnz5nHHHXfw9ttvExcXx8SJE/F6veY+06ZNY82aNbz44os8/fTT/PDDD9x22217VI/y8nL69u27x0GZEEIIIQRAQUEBGRkZVcrdbjeKosSgRvuefH4SQoj9z+12M2TIEIYMGYLb7d7fNw5DhhjT/r5tIRogpqOpDh48mMGDB1e7Tdd1XnnlFS677DKGDRsGwP3338/AgQP5/PPPGTVqFGvXruWbb77h3XffpXv37gDccsstTJ48mRtuuIHWrVvvt/sihBBCiANXt27dWLJkCePHj48qf+edd+jVq1dsKiWEEKLFiYuLY8mSJbG6cYjVbQvRAE328kFbtmxh9+7dUVdgSEpKomfPnqxcuZJRo0axcuVKkpOTzbALYODAgVgsFlatWsWJJ54Yi6pH++tRyFsKtiSwJhlzWwrY0yomRxY4s8CeARY11jUWQgghxB665ppruPjii1mzZg3BYJBXXnmFtWvXsnLlSubNmxfr6gkhhBBCHHCabOC1e/dugCrdAzIyMsjLywMgLy+P9PT0qO1Wq5WUlBTz+JjSgrDqVgiU1fMABezpENcG4toZU3w2JB4KiZ0hqTM4MkGxgKIacyGEEPuUruvo6FHL4TGLGrIcPk99ttVnvaH7RJZVd2x9961cXt02s6zS4xh1vohhoKqra4eUDmTGZ9JUHXnkkfzf//0fzzzzDDk5OXz33Xd07dqVN998k9zc3FhXTwghhGjSgpqOamm6QwA09fqJ6jXZwKtFsKhwwpeweT74iyBQDv4yCJSCvwT8paHlYmMZHXz5xlT8e/XntCZDwsGQ1AkSO0FyF0g+HKwJYLGBag+FYdbQXAWLtWI9vGyR/3ohxL6n6ZoZbtS0rOuh9dByddv3dK7pmnlOTdPQ0MxzVi7X0dE13VgOhTTmcg3BFVQKrSIDndAutR1b6/HGgrmuoKDrOoqiVNlulkWeOvxZTK+0Xk1Z+NyR51JCG6vcZqXbrYlC9LbIfWvbVtu5yv3lpMWlNenAC6Bjx47cfffdsa6GEEKIFqy8vJyDDz4YgA0bNpCQkLA/bxxCt82GDdCIt61aFK56cyVrdtW3scj+07lVIrPP6R3raogGaLKpR1ZWFgD5+fm0atXKLM/Pz6dLly4AZGZmUlBQEHVcIBCguLjYPD7mMo4ypsp0HTSfMQW9RuDl3grlG6B8s7Hs2WWEX97d4NpmLAdKoHiVMYVZ7JCUEwq/ukDKEaA6K7YrSkX4hWoEcYrV2MfiBNVhTBYbKDZjbk52aUkmRAsQDoBqmyKDIjMUqmabjk5ACxDUggT1IEEtiKZrBPQAuqYbZXqwamgVDrPQMDKaimALqAiaQgFOZPgS3o6CEbrUMQ8HKQoKiqKY87CGlANYFEuN28LHhZcbsr26dQFbSrbEugrVKiur/4fyxMTEfVgTIYQQB5Jwj6cY3fg+O/WaXWX8vq1kn51fHHiabODVvn17srKyWLp0KYcffjhgfLD85ZdfOPfccwHo3bs3JSUl/Pbbb3Tr1g2A77//Hk3T6NGjR8zqXi+KUhE02ZLAmQlJhwCDIOgzWn4FysCTB74Co2UYQaM1mGsLlP5jTCWrjbLi34wJjJAqvS9kHgOtBkFcNuhB0AOheRB0P/g8obKAEcCZ3xQtoRZgVlBtYHGAmgDW+FAwZq+YWxzSWkyIRhQOj8KBUU3rkWWarhEIBghoAQK6EUQFtIA5RYZPmq6ZgVNUmBXRuqmmVj0AKNHhjyUUiEcGQQqKsR4RHFkUC4olet04XfXrlUMlIZqiI488ss7nafhv6s8//9xPtRJCCCGEEBDjwKu8vJxNmzaZ61u2bOHPP/8kJSWFdu3aMWHCBObMmcNBBx1E+/btmT17Nq1atTKv2tipUyeOPfZYbr31VmbMmIHf7+euu+5i1KhRzfsKjaod1AxwZEDCQRBwG6GXewd484yB7rMGGYPfA5RvhKKfofAXKPgR3NuMgfLzlsJfDxpdHtuNgLYnGWOA1UXXQPMbQZgWMMI3X6GxbPZnUStagalxYE00gjuLPdRSLNRtUrFFdKmUL6+i5dJ13WztVHkeDp3CZeGAyqf58Af9+DSfGVgF9WBUFzxN16LWjRuruN3IbmjhoCk8hYOkcBilKipWizWqLDKYCi+LA4ema/iDfvyan4AWMJ6PQV/UekAL4NeMfcLr4bLwPuHlhkxBLUhAr7Qe8TcT0ALYVBtPj3qag1MPjvVDFuWVV16JdRWEEEIIIUQNYhp4/fbbb0yYMMFcnzlzJgCnn346s2bN4uKLL8btdnPbbbdRUlJC3759ee6553A4HOYxDz74IHfddRcXXHABFouFk046iVtuuWW/35d9yhoH1mxjAHt/mRF8lW+E8k1gTzZCscSDof1pRkut8g2w+zvY/S0UroSSP43pr0chox8cdJYRmCk1XBFSsRgtuHBUvx2M8EsPGF0yA6VGIKcFos9hjiVmNbpRWhxGN0o1LjTWmC0iHKu0LF+6RYzoul6ldVTlL+LhL/vegBdf0GcGBJGtsSJbYpljLEV0uwsHUJEBlWpRUVCwqbao8shJtAyaruENePEGvebzyBs01n0BYzn83AovRz7fapxCIWo4SDUDrIh1v1ZRFtSDsX4o6m1d4bpYV6GKfv36xboKQgghhBCiBopeeRTdA1BZWRl9+/blxx9/bD5jbATcRkuu8g1Gl0ZHltHlsDJvAez4DLYtguJfK8rjsqHjWdD+FLAlN379dC0UiAUjulOGWozpQWN75Ld/i7UiILOExxeLM8K+cKsxMxSLXJYAQNRO1/UqrVUqt07xBr14/B58mhEahFucBLWKVlmVu/VZMAIqi2JBVdRql8Pr0mqq+dB0DU/Ag9vvxh1w4wl4zHVPwBNVVu0U9OANePEEQvPQuhluhQIuv+aP9V2tlqqo2FQbNosNq8WKXbVjtVixqca6zVKxzWqxYlWtUetR28JloWPDLQyrm1SLilWJWI4oVxUVb8DLyJyRTa6FV2XFxcW8++67rF27FoDOnTszZswYUlNTY1uxfaRZfn4SQohmrry83HzNLSsr2/+D1odf78vKGnXQeoBRj33TJMfwOqJdMh9NPTbW1RANIIMvNVfWOONKjfHZULoOytcZV350tooOgRzpcNBYY3JtMa4YuWWBMSj+6kdhzVzoeDYccr7RVbKxKBZQ7FCfPErXK0KxcMsxfzFo+RHji0WcN7LLpMUBarwR9pnBWOW5rfHul2gyAlrAbKVSuWuVx28ED+HQITLACmiBqKvlKYpihlThudVixaE4zFZXElo1TZqu4fK7zKncX47LZ8zdATflPmMeuY/b78YVcJmBVrjMHXDj9rvxBr37/X5YLVYcqgO7asdhNeZO1YlNteGwOsxtdU02i61iWbVht4TmobJwgBXeN7zNDLJCLQuboqY6aH2kFStWcOmll5KUlGSOKzpv3jyefPJJnn76aY46qpoL2AghhBBCiH1GAq/mTnVCyuHGeF+lf4Nrc82tveLbQ+6V0Pli2L4INrwJZWtg/cuw6e19E3zVh6IYARZWqKGXpUkPRnSn9FeMLxYVjCkVV6IMjyEWDsXMFmORoZhdQrEmRNd1s4ug2V0w1C0rHEq4/K4q4wCZ3QYxBlCPbCliU23EWeLMFiYSXsWeN+Cl1FdKma8sair1lVLuK6fMV0a5v2Je7iuvdr4vOa1O4qxxxFnjcNqMZafVGTU5VAdxtriodXM5FFaFy8PrDmtFmV21o1rqeuETzcGdd97JyJEjueOOO1BV4/80GAwyY8YM7rzzThYuXBjjGgohhGgJLBYLRx55pLm8n28cQrfN/r5tIRpAAq+WQFEgrjXYU6F0rXH1Rs0P9pTq91edxnhf2aca43ytecYY42v9y7DpHTj033DwecZ+TY2igqpS6/hiEBGKBUJXpMwDT6g7pXk1SjWii2Q4FEuoFIpFTvKltDEEtWDUuEThKdxCx+13R7Xa0jSN0EUBje5WEV2mHDaHuSwh1v6l6zrugJsSbwnF3mJKvaXmvMRbQqnPmJd4S8wgq9Rbai77gr5Gq4uqqMTb4om3xZNgSyDeHk+81ViPs8WRYEsgzhZnrFvjzH3jbHHmuhluhcocVkeTbe0kmqaNGzcye/ZsM+wCUFWVf//73yxYsCB2FRNCCNGixMXFsWLFiljdOMTqtoVoAAm8WhLVYbT2UuOg5A/w+MFZy1UZFQVaHWsMYB8ZfP3zlNH1MXcKtDmpeQ4gb6lHi7FqQ7HtofHFoGJssdBA+qoT1ASwJVaEYKrd6FZpsYduU4RbaIXHKgrPw611PAGPOah2QAuYQVV47KBwt6sEW4LZSkvsWwEtYARXnmKKPEXG5C2i2FNMsdcoK/YWU+Ixwq1wyBWIvFBFAygoJNoTa5wSbAnG3J5grifYE4y5LcEIuOwJOFSHBJ4i5rp27cq6des49NBDo8rXrVtHly5dYlQrIYQQQogDl3xDb2kUBZIOAasTin4D93Zwtqk9tDKDr2Ng+2L4+wkj+PnlP7DxLTjiZkjqvP/uw/5SVyim6xVdJyO7T5YHgNCg++FWYhabEXxZE41WYqqzUiDmaFEtxHRdN4MsT8CDN+jF7Tda+5T7y81uiOZ4WQpRg2An2BKwOY110fiCWpAiTxGFnkIK3AUUuAso9BRS6C6MmofDrRJvidEltAFsFhvJjuSoKcmeZMwdSeZ6kiPJmNuTSLQnkuRIIt4WL62oRIsxYcIE7rnnHjZu3EjPnj0B+OWXX3jttdeYNm0af/31l7mvBGBCCCGEEPuefNtsqeLaGiFL0a/GAPVx2XW31FIs0G4EtD4eNrwK616GolXwv3Fw8DjodLHR3e9AoSgVV4SsidlKzAdBV2iw/QDmYFKKWtH6S42LCMRCIZgabh3maJIt6QJaIOoKdG6/m1JvKaW+UrxBr9n1EN0Y/D08ILZTdZJsT5auho1I0zWKPcXkufLId+eb8wJ3Afmu0Dy0XuQpalCAlexIJtWRSoozhVRnKimOFFKcKVHzVGcqyY5kUhwpJDuScVqd8n8sBHDttdcC8MADD1S7TVEUdF1HURT+/PPP/V09IYQQLYTL5aJr164A/PHHH8THVzN28767cQjdNn/8AfvztoVoAAm8WjJHOqT1gsKVRkuv+Hb1O051QqdJkD0a/nwQdv4X1r8C2z+DI240ukAKg9lKrIbxzrRQCzE93EKsALQgFaOrh8YQU+2h7pJJoa6TkYGYY593lwxqQXNAeE/AQ7m/nGJvMeW+crwBY6yt8Be18BXlwqGWTZUB//eGrusUeYrY7dptTOUV8zx3HnnleeS588h35RPUg/U+r4JCijOFdGc6aXFppMelk+pMJc1ZsRxeT3UaIZe0uBOi4b744otYV0EIIcQBQNd1Nm7caC7v5xuH0G2zv29biAZo0LebCRMm8MQTT5CcnBxVXlZWxuWXX84rr7zSKJUTjcCeAqk9oGAluHdAXJv6H+tsDb0fgF1fwx/3G90cf7wa2p0Mh18LtuQ6T3HAs1hDYVU1LePMLpM+IxTz5UePIaZYKq4gGW4dZkus6CJphmL2elcn3BXR5XeZVzss9BRS5iurCLbQUVDMq8klO5Kxq3bpetYAQS1IgbuAneU72VG2g13lu9hVvoud5TvZVb7LDLf8mr/e50x1ppIZn0lGXIYxxRvz9Lj0qCnVmSoBlhD7UXZ2dqyrIIQQQgghIjTo29Dy5cvx+6t+QfN6vfz44497XSnRyBzpkNYDCn4CT17tA9lXp9VxkH4k/PM0bHwDtn0I+cvgiP9AK2nt1WB1dZnUg6Hxw3wRrcPCg4QroNpACY0TZk0yAjHVabYQ0xQb7mAQV8AItkp9pRS6C3EHjFZcmq6hYHRDdKhGsCWDf++ZMl8ZO8p2sKNsB9vLtrOjbAc7y4xwa0f5DnaX7653q6w0ZxpZCVlkxRtTZnwmmfGZZCWEluMyyYjPkBBLiCZs586d/PjjjxQUFBhXmI0wYcKEGNVKCCHEgS6o6agW+YwvDjx79M0pcsDVNWvWsHv3bnNd0zS++eYbWrdu3Xi1E43HmWWEXoU/g68I7Kl7drw13mjV1eYE+HUGuDbBT1cb3R4Pv84IW0TjUlRQ1eq7S+p6RcuwoBfdV4Lb76Lc76Y84KU44KPI78GtaXh1FU11YrXG4bQn4rQnkRqXjqo6m+S4YU1Jma+MbaXbzGl72Xa2l25nW9k2dpTtoMRbUuc5VEUlMz6T1gmtaZ3YmlYJrYwpvhVZCVm0TmhNZnymdA0Vopl7//33ue2227DZbKSlpUVtUxRFAi8hhBAxo1oUrnpzJWt2le3VeRxeN++Hlsc89R1eR+OM7zwkN4vrh8sFXUTj26PA67TTTkNRFBRF4YILLqiy3el0cssttzRa5UQji2trBCQFK0NXFGzAC1RaTzjmdfhnDmx4HbYuhPwfoMcMSO/T+HUW1fJqfsr9Hsr9bop9ZeR7SvAEvHgCXnRdx6roxKkqSVYrGYqORfeA5gFPIfisoFhDXSXjjUH0zStKhibFdkCEYUEtyK7yXWwp2cKW0i1sLdnK1tKt5rzYW1znOVIcKbRObE3bxLa0TWxL68TWtEloY8wT25ARl4Hagq7QKYSo3uzZs7niiiu45JJLsFikC7gQQoimZc2uMn7fVvePtbWJ83nM5T+3l+K2139Yjtp0ykpolPMIUdkeBV5ffPEFuq4zbNgw3nnnHdLT081tNpuNjIwMVFW+2DVp8R3AXwolf0F8+4YNhq46ocs1xtUcV91uXAVy+SVwyHg47FIjMBGNRtf1UMstN6U+F/meYkr95bj8HoK6hqpYiLM6SLA5SXcm1z7Wlq4bXSV1n9E90pcPnp0V2y0RYZg1wQjELPbQeGG20PhhzaslUkALsL10O5tKNrG5eDNbSrawuWQzm0s2s610GwGzm2j1UhwpZCdl0y6pHW2T2hrzULjVJrENCXZ5gxZCgMfjYdSoURJ2CSGEEEI0EXuUdoQHZI3s2iiaGUWB5BwIlIWu3Ni+4S150nrBMa/Bnw/D1g+MKzlu+9gY1L79aEg4qFGrfqAIakEz3Cr2lpHnKcLl9+AJegFwWh3EqQ5ax2dg3dOWQ4piBFpYofKhlcMwb17EmGFUhGGqPdQyLDE6BAsPsB8Duq6zq3wXm4o3sbF4I5uKN5nT1tKttY6jZbPYaJfUjuzkbLKTsmmf3J7spGwz5JJASwhRH2eccQaLFi1i8uTJsa6KEEKIFkxRFLp27Wou70+6An9ndDSXhWjqGjz68YYNG1i2bBn5+flVBmadMmXKXldM7EMWG6QcAUE3eHcZV2NsKGsidL/NGNj+j5lGSLL+JWNK6w3ZpxjjflnjG6v2LU5QC1Lmd1PqL6fQU0K+p5hyvwef5kdVVOKsdpLs8WSqqfv2Ta3OMCwAuh+CPgi4jKt+hlmsRjdI1WGEYbb4igH1zfneD7buC/rYVLyJDUUbKqbiDWwq3oTL76rxOIfqoH1yezqmdKRDcgc6pHSgQ3IH2ie3Jys+S7ocCrGf6bpOUA8S1IJoukZQN+aarkWVhffzhgL/puy6667jkksu4ZtvviEnJwerNfo176abbopRzYQQQrQk8fHx/P777zG5bY/NyUmTnorJbQvREA36Bvr2229zxx13kJaWRmZmZtSXcEVRJPBqDmyJkNIVCn4AfwnYkvfufK2HQNYxsOsbo7XX7v9B4Upj+vN+aDPMCL/Seh0QY0PVRtd1yvwuSnzlFHlLyfMUUeZzhwIuCwm2ONKcSTjUJtQ1NHxFSWzVh2Ga3wjEgp5Q68HIlmG2ijDMGm9M/8/efcdJTe19HP8kmbod2AWk1116RwRBFFCuIhZUFCkWsCvYRa9XBQuoWEERFQugoo+C96Iogr0gXYoU6b0s2/uU5PkjM7Mz21iWhVmW31vzSibJJGdCdmbynXNOFJt5l0l/7bAiYVi+J5+daTvZkb4jMN6Vtot9WfvQDZ2SaIpGveh6NI5tTKPYRiFD7cjaZTf1FEIcF38Q5Q+oSguvPLoHAwMMQCEwVhQFDQ1VVdEUDVVRURUVTdWwa3asFis21YZVM8cWzUItZ60wv+qyzZgxg99++42mTZsWWyZ3vxVCCCGEOPUqFHhNnz6de++9V6rtn+4ctSE6CdLWguY88eZoqhXq9jOH/COw/yuzU/vcveZ4/wKzCWW9QVDvEoioXzmv4zSQ58kn05VDRkE2h/NSyXblku91oSoqkRaz7y3b6XqXPkUxa3BRQkBn6EFhWC54MkH3NS9UwKUr7MpJYUd2MtuzjrAt8wA7MvZyIPuweZFcgkhrJE1rNKVpXFMaxzamSVwTmsQ1oX50fbnToRDHyR9ceXRPsfAqeGwYhhlYARhmgOMPqDTFHFRVxapaidQisWm+sEqzYVWtWFRL4boljC2qxdyeop224dD777/Pc889x5AhQ05oOzNmzOC7775jx44dOBwOOnfuzIMPPkizZs0C64wcOZLly5eHPO/aa69l4sSJgccHDhzgqaeeYtmyZURERHDFFVfwwAMPhNQ8W7ZsGZMnT2br1q2cddZZ3HHHHSdcfiGEEEKIqqJCgVdGRgYXX3xxZZdFhENkY3Clm6HUifTnBeD2QGa2r/mbBWKugOjLIW8jHP0OUn6C3H2wbYY51OgC9S+BOgPMGmfViFf3kuU2++BKzksjtSCTPI95VxOnxUGMLZLalprH2Eo1oKig2TEMG4cKUvgnfQ/bMvayLX0v2zL2sifrEN5SamzF2SJpFlOPZrENaBrbmKY1mtE0rhnxUXVRtJJrhglxJisaUnl0T2A6UNvK8AXJvrd6f00rTQ0NoJxWJ1bVit1iN2tcab7QStEC4VXRx/7nnqlsNhtdupz43YqXL1/O8OHDad++PV6vl5dffpnRo0fz9ddfExFR2D3A0KFDGTt2bOCx01l452Wv18ttt91GfHw8c+fO5ciRIzzyyCNYrVbuv/9+APbu3cttt93Gddddx5QpU1i6dCmPP/44CQkJ9OnT54RfhxBCiJMjNzeX7t27A7BixYqQz4aTzeHO538fmp8jl93wMvlWxynbtxAVUaGrxX/961/89ttvDBs2rLLLI0411WJ2Yu9KN+/YZ48//m0YBhxJgZ37IDXdfBxMUUC9EJTzQd0ArAB9C6StNoe/n4e4nlD3X3DWeWA7Pd848z0FZLiyScvP5HBeGtmuXFy6G5tmJcoaQQ179BnRrC7f42JH5j7+SdvDP+l72Jqxh63pe8kupY+tKGsELWIb0Dy2Ac184+bRdalhi/DVDHObfYcZOrgPQfphs4mkajGbRFoiweIsbCZZiX2GCREuwbWtgoMrr+57bHhDakEqBNW4UrVAiGW32s3Qyhdc2TRbIKwKDqyKhlinay2rcBo1ahRz5szh8ccfP6HtzJw5M+Tx5MmT6dmzJ3///XfgAgfA4XCQkJBQ4jZ+++03tm3bxvvvv098fDytW7dm3LhxTJkyhbvvvhubzcbcuXNp0KAB48ePB6B58+asWrWKDz74QAIvIYSowgzDYOPGjYHpU0kxIDFlT2BaiKquQleEjRs35rXXXmPt2rUldsw6atSoSimcOEWs0RDbClJXgSfPDA/KKzMbdh+A/YdAVaFOAmhFQh3DAN0AXQdvX/CcC+6j4PoT9BXAYUj7xRw2RYKzByT0h1pdIDISIhzmtqsYwzDIceeR7soiOTeNo/np5LjzUBSFiNO9mWI5Zbpy2JK2iy1pu9mSvpstabvZlXUAvYQPX4uq0TS6Hi3iGtEitgEt4hrSIrYhtZ01yri4thd/lzK85t0j/c0k3RlmGAZmrRXF32eYLTQM899FUvWFYXJBL06h4KaD/qFooAWE1LyyKL4Qyjd2as5AeOXQHIFaV2UNElydOuvWrePPP//kxx9/pGXLlsW+G02bNq1C283KygIgNjY2ZP6CBQv43//+R0JCAhdccAF33nlnoJbXX3/9RWJiIvHxhT9i9e7dm6eeeopt27bRpk0b/vrrL3r27Bmyzd69e/Pcc89VqJxCCCGEEFVNhQKvTz/9lIiICJYvX16sDwlFUSTwOh0560FkGmT/A1pDsylaWQwD9h+GrbsgNw/ia4K9lE7WFQU0xQzCrBbADkQCjcEYCp7dkPcb5C0FIxPyfoA9P8DuOFC7QMQ5EN8BYqIhKgIinWAJT+0dwzACnc0fzDlKekEWed4CrKqFaGsEcVHVtxZXekEWm1J3siltF5vTdrElbRf7c5JLXDfOHk1iXCOS4hrTIq4hiXGNaBJdD6tWCf9uigaahnkeFRHSZ1g+eLIK+wyDoLtJWs27SWoRZgCm2Xy1xvyBWPX8NxSVy18LKxBgFQm0AjWwfH1eBTf9s6gW7FY7TqsZYjksjkBfV/6haKBVXd9bqouYmBguuuiiSt2mrus899xzdOnShcTExMD8Sy+9lHr16lG7dm22bNnClClT2LlzZyBUO3r0aEjYBQQeJycnl7lOdnY2+fn5OBynZ21rIYQQQgi/Cl19/vDDD5VdDhFuigIxLcCdDvmHwXlW6et6PLBzL2zbA04H1K97Yvu1NjGH6OvBtRHyl0L+CiAdjB8g5wfIrgVGR9C6gLMZ1IiFuBhfABYBtpNXk0o3dDIKsgMhV1pBFgVeN06LjWhbBAmWGidt3+GS6cphY+oONqXuZGPaTjal7uRQbkqJ69aLjCepRhOS4hrTqkYTEuMakVBmra2TyNdnWMlhWPDdJF3gyTVrivmpGmDx3T3SYYZhFkdhQKb4a4hV71p7Z7qSamKVGmKpZi2s4FAq0hqJ0+rEYXEEOm63qJaQMMs/TwKs6mXSpEmVvs0JEyawdetWPv7445D51157bWA6KSmJhIQEbrzxRvbs2UOjRo0qvRxCCCGEEKcj6eRGFNIcEJMEqSvAnV1yR/J5+Watrt0HIL6GGXhVFkUFeztziLkBCtZB/p+QvwaUFFB+MAOwvATIbQ972oHaCCIiICYKasWZ4VekExwlBB7HQTd0Ml05pOVnsj/nCBkFObh0NxEWB3H2aByWUmqznYbyPQVsTtvNxtQd/J26g79Tt7Mv+0iJ6zaKqkurmk1oXaMJrWo0IalGE2Jskae4xBVU1t0kwawJZrjNUMydafZpZ+iA4msqGVQ7TI0wm0qqdl9fYv4mkxbpO6yKMgyj1BAr0B+WryaWpmhYNIsZZmkWIqwROCwOszaWrwN3f4AVPC1NCEVlmjhxIj/99BNz5syhbt2yf1jq2LEjALt376ZRo0bEx8ezbt26kHWOHj0KEOj3Kz4+PjAveJ2oqCip3SWEEEKIaqFCV2aPPvpomctPxq+c4hRxJEBUS8jYYAZgatApkpUDf2+Fo6lQN8HXPPEkUWzg6GYOej4UrIH85VCwFoxk4AfQfgC1Fng6wJG2cKCRGZo5HRAV6QvAnBDhNMfH6AfMMAyy3Dmk5mdyIDuZtIKsQMhVXfrj0g2dXZkH2ZCynQ2p29mQsp3tGXtLvFNig6jatK7RlDY1m9K6ZjNaxTUmynbq7gJzyqkaoJnnfVGGHtRvmK92WIGHQJ/himoGXf7wS4vw/f0E1QqTQOykCQ6v3F538SALs1P3os0Fox3ROC1OIqwRWFVrILwqOpYQS5TXt99+yzfffMPBgwdxu90hy+bPn1+ubRiGwdNPP83ixYuZPXs2DRs2POZzNm3aBBSGWZ06deKtt94iJSWFWrVqAfDHH38QFRVFixYtAuv88ssvIdv5448/6NSpU7nKKYQQQghR1VXoyiszMzPkscfjYevWrWRmZnLOOedUSsFEGEU1MWu35B+CiAbmvKwc2PAPpGVAvTqnthN51QHOnuag54NrbWH4paeA/iPwI1iiwdYRlPaQ3gyOHDWbsdntZsf3NWLMfsAiHGYI5qsFluPOIzU/g/3ZyaQWZFDgNUOuGo5o7NrpXZMroyCb9Snb2JCy3RynbifHnVdsvVqOWNrVak6bms1oW7MZrWs0JdZeQg2/M5WilqN2mCeo77BsX3NJA7OGmOrrI0wrIRCzhAZjEogFGIaBW3eHBFn+x4ZhmDXvDMwQy1cjKzjIirRGlhpiWatBgC2qllmzZvHKK68wZMgQvv/+e4YMGcLevXtZv349w4cPL/d2JkyYwFdffcWbb75JZGRkoM+t6OhoHA4He/bsYcGCBfTt25e4uDi2bNnCpEmT6N69O61atQLMzudbtGjBww8/zEMPPURycjKvvvoqw4cPx2Yz38euu+46PvroI1544QWuuuoq/vzzT7755htmzJhR+QdHCCFEpVEUhcaNGwemTyVDgX0xtQPTQlR1FbqyeuONN4rN03Wdp556qly/RIoqTrVCTCtwZYArHQqshWFX3YTw3jFRdYCjhzkYLijYAAUrzWaPRhYU/Ab8Blghoo0vAGsLbivsOQjefaAquGwWUmxeDkd4Oay6yNV07BFRxEbE4LCcWHPIcPHX3lp3dCtrU7ay7uhWdmcdLLaeXbPRpmZT2tVsTrtaLWhbqxl1nDWlFsuJ8NcOK6nvMCgjEPPx1xBTLKBZQHWC5izSmb6lMBBTtFPxqk4q3dCLhVhF+8ny36nQ3+dVpC2SCGsEEdYI7BY7VtUa6CfLPy3NCkW4fPzxxzz99NNceumlzJs3j1tuuYWGDRvy2muvkZGRUe7tfPLJJwCMHDkyZP6kSZMYMmQIVquVpUuXMmvWLHJzcznrrLO46KKLuPPOOwPraprGW2+9xVNPPcW1116L0+nkyiuvZOzYsYF1GjZsyIwZM5g0aRKzZs2ibt26PPPMM/Tp0+cEj4QQQoiTKSIigl27doVl3/lWB73veC8s+xaiIiqtKoGqqtx4442MGjWKW265pbI2K8LFFmf257VvKWzPgNRsOKt2eMOuohQbOLqYQ4wXXP9AwWpz8B4xa4AVrDXXtdTHiOhIliWRg55a7MtJJSszC9XjJVZxUMviQLHnQkQmREeatb8cNrDZTmqH+Cci31PAhtQdrD36D2uP/sP6o9vIcucWW69RdF3a12pB+1pmwNUitiEW9fQPTE4rxwrEDG/xJpNGUJNJ8DUv1oKaTTp9tcSCAjGlaoRi/ppZbq+7eJjlq5mlKIpZ08rX/1WsPRanzUmEpXiYZdNsWFUrmpy3ogo7ePAgnTt3BsDhcJCTkwPA5ZdfzrXXXssTTzxRru1s2bKlzOVnnXUWc+bMOeZ26tevzzvvvFPmOj169ODLL78sV7mEEEIIIU43ldp2Zu/evXg8nmOvKE4PRk3YngcHdkGT1lUr7CpK0cDe2hyM68Gzzxd4rcFwb0Xx7Efx7CeGhTixUlNrTE5MItmWlhQoNcGrg9sFmdmQmm4GDZpq9lPmsJshmNMBdps52Kyn/Hik5mew9uhW/kr+h7+ObmFz2m68hjdkHYdmo22t5nSo1ZIO8S1oX6sFcfboU1pOUQGKBlpZgZhhhmKGxwzGvLngySpHKGYvbDoZHIb5a4uhmZ35Hyd/E0N/qOWvpWUYQTWzgjp0j7HHhNTM8gdYNs0WCLXkjoXidBcfH09GRgb169fnrLPO4q+//qJVq1bs27cv8LchhBBCCCFOnQoFXkU7pTcMg+TkZH766SeuvPLKSimYCLOCAti4CfKc0KAxeDNBqxHuUpWPouC11CfFiOGQ0oGjyiGi3P9QX9lLgr4bGzkk6NtI0LeBGwqUODK15mRZm5PlaIJHqWlux+sFl9u8M2VGlhk6KApYrWCzFN4R0m4Hu9U331qhAKEowzA4kJPMGl+4tSZ5S4nNExOcNegUn0iH+JZ0jG9JYlwjLKr0AVXtKIqvby+LWVGsJMVCsbxSQjFfbTPFH4zZC4MxxYKOigdwG+A2dNy6gdswQm5s4A+zLKp5B8NIWySR1shAmBVcM8um2STMEmeEc845hx9++IE2bdpw1VVXMWnSJBYtWsSGDRu48MILw108IYQQ1UReXh7nnXceAL/88gtOp/OU7dvuLuCzj8cDMPT6yRRYT8+uYMSZo0JXxhs3bgx5rKoqNWvWZPz48Vx11VWVUjARRm43bNwI+/dDw2bgSYeMjeYFtHbq3lArIsebT7I7i70FqaS5czAUiNOiUZ29OKQoHDJ0nPohYr1bifFuJ1Lfi91IJ8GzigTPKgBylTpkaU3J0pqS7WiM1xlUQ8rQweUBtwdS0uHwUUAprA1mtfruDOkwm0PabWY4Zi27RphhGOzOOsiqI5tZk7yZNclbOJyXWmy95rEN6BSfSKf4RDomJHJWRLz0VyRM5QnFAK/Xjdubj9tTgNubjdtbgFsvwPB6QTFQULCqFqyaDatmI8oaQaQtighbDDZrNDZbNDZLJDarE5slAovFGVRzzBp6Z1chziBPP/00um4Gw8OHDycuLo41a9bQr18/rr322jCXTgghRHWh6zorV64MTJ9KqmHQ8dDWwLQQVV2Frkxmz55d2eUQVYWuw+bNsHMn1KsHFgtotSCyEWTvMPu1qmIdZuuGTqonhwOuNA660snxFhCp2qlji8FStKyKSp5WjzytHofoi2oUEOXdTYx3O9H6DiL0w0QYh4nwHKaO508A8pTaZGlNyNaakK02wm2PMYOsYF6vGYJ5PJCcaj4ObhZptYDTF4TZbehWjR35yazK2M7qlH9YnbyZtIKskE1qikabmk3plJBI5/gkOsYnyp0TxTF5dC8e3YNL9+D2DWbfWQAGqqJiVS3YNDt2ayQ1LU4irQ4cFjt2zYZNtfgGFZuioATXGjPc4DliVv/y3+zTH7T5B9Xia0bpAH8YFhyI+ddR/P2OSe0vUT2oqooa9MPGoEGDGDRoUBhLJIQQQghxZjuhn+JTU1PZsWMHAM2aNaNmzZqVUigRJoYBW7fC9u1Qp45ZKwnMC1pnfbN5VMFRcNQJbzl98nWXrzZXCkfdWRgoxGlO4o+jzypdsZNpSSTTkgiAxcghyruLGO8Oory7cBrJOI0jOD1HqO1ZDkCBUoNsrRHZaiNy1IbkqbXN/pdK6oNJ18HtQXe52X5kO6tydrGqYB+rXfvJ0PNDVrWrFtrFNKFLrUQ6JyTRPiERpzOyUppIiurDo3vNvrN0rxloed14dK+v1aKBpmpYFQtWzUKExU6kNY4oSwR2iy/M0qy+YMuKVauE2liGHhSIec1QzJVfeFfKkF//FF/IFdSkUrGGNqtUbYUd8AfCsaCxUrF+x4Q42X755RciIiLo1q0bAB999BGfffYZLVq04IknniA2NjbMJRRCCCGEOLNU6GonNzeXp59+mv/+97+BapSapnH55Zfzn//855S2IxaVaPdus3ZXjRrgcIQu06wQ2QQ8OeBOB2tcGApoyvDkcsiVzr6CNDI8uThVG7UtMVgroSmVR4kk3dKWdEtbACxGNlHe3UR7dxGl78GpH8JupGH3pFEL8w6QXuzkqPXJ0RqQozYgR2uAiwh25CezMnsnq7J3sjp7Nxne0DsoOhQrHZ0N6OJoQFdLPdpYErCpGhQocEiF5J1m6Oiwg9Nu1iqzWAqbTlotYKlate3EiStWQysQaJnBkaZq2FQrFlUjyuIk0lmTCIsDh8WGTbNiU32BlmY5Nf25Kap5x1TVdux1g/sZMzyge0H39zXm9Q1FqscrWlC/YxZzWrX5QjLfOKTmmFYYjIWEZBKUiZPrxRdf5MEHHwTMOy1OmjSJm2++mWXLljF58uRi/Z8KIYQQQoiTq0JXQ5MnT2bFihVMnz6drl27ArBq1SqeeeYZJk+ezIQJEyq1kOIUOHAA/v4boqMhqpRmc9ZoiGoGGZtByQVLxCkrntfQSXFnsb8gjYPuDAp0FzFaBI3VWCy5BWjZaVhy8lHzCtDyXah5LtR8F2qB2xxcblS3B8XjRXF7Ubw6iteLohugGyjB7d99F8WGqmCoKqgKhqaSo9Unx1IPTcvDpmVh0bKxWTPRrAVE23awP2IHPzrhRzv8pMLRItfWTtVGp8hGdI1qQteoprSOqFe8yaW5Z/B4fYMH0gvgqL/jcV/H+RZLYfjltJuhmM3qm68FBWMWucivYry67quhVRhqebyekBpaRQOtSKsTu2YNqZ11ygKtyhTcz1h5BAKyIoMnG4yMMkIyfzNLFfOulf4aZTazRplm94Vm1uIhWUjts6DH0vRSHMO+ffto3rw5AN999x39+vXj/vvv5++//+bWW28Nc+mEEEKcTF7dQFPlO7cQVU2FrpYWLVrE66+/To8ePQLz+vbti91u595775XA63Rz9Chs2GCGJMdqcuGoDZ58yN7uu8NbOWp1HC+XC5LTIDkVz9EUspMPk5N8GD01nfpZebTIKsCekYuWlYtW4K78/ZeDAeysAT80NYcfa8OhIi0pI1zQew+cv8scuh5xYbHvRI/YjydyNe7IKDyRkXgjnXgjHXiinXgjnXiinXiinHijI8zpmAj02OjC4ErXzSDM6zWPVW5eYZ9hAApm6KVp5thuN/tes9l8tcJ8QZimFdYSk1Cs0piBlick1AruQ6tok8Noa5hraFVlxxuQ+Rl6kZBMDwrK/I/9zS0VQm5jqahBAZfqqzmmhgZmqrUwMAsJxfxhmW+MWjw0k7+1astqtZKfbzZV/+OPP7jiiisAiI2NJTs7O4wlE0IIcbJpqsK4uWvYduTkv997CvIC00Pe/B2LvezWVecnJfDQwFYnu1hCVEkVuprKz88nPj6+2PxatWoFvuyJ00RmJqxfb96ZsW7d8j0nor7ZDCl3vxmAHW8n9l6veXfD/Yfh4BE4mAyHkuHQUTiSAumZgVUtQJxvKIsnwoE30oHutOF12vE6begOO7rdim6zoNutGFYLulXDsPgGzbz49NfiMi98/Qprfim6Dl6dQ+Txpz2F3yNS+S0yg332gpAy2L0K5yTb6HvAwgV7dHrs9uDI9aAUBF1Mu7yoWXlYyMNB8bswlka3aHhiIvBERxSO4yLxxJiDOy4ST2wk7thIPDFOvA4b6L6aYpnZkG6+hsCFfaCWmGZ2rF80FPOHZYEaY/4+yoRu+AMtL26vO1BLy/DVMlIVxdcpvBWHxU68NY5IixOHxY5NtfjCLCt2zSqB1smiqL7Aynr8zzX0oEBMDwrPXOadakPmFb07kf/vq2ho5guV/TXJQu5qaQuqTRYUkKEWmacWTocskwCtqujSpQuTJk2iS5curF+/nldffRWAXbt2Ube8n69CCCFOW9uOZPP3gcxjr3iCdFc+qjMGgE0Hs1BtZVcAaJ4QWan7T/HtW4jTQYWutjp16sTrr7/OCy+8gN1udtKdn5/PtGnT6NSpU2WWT5xMOTmwbp0ZetWvX/7nqZrZn5e3APKP+kKvEi66cvNgzwHYtd8c7zkA+w6ZIZfHW+YuvDYLubWi8NSIRq8Zi6dmNO64KDyxvoAnNgJPdCTeaCdep90MbSpZuieXldk7WZm1gxXZO9ldcDRkuYZK+8gGdI9qRteoprSPbIBdLbzA3hh4MTrWvAwic/YRkXOAiKzDOLKTseVmouQakEPIYGSDkaNBtoKSpaO4dVSPF1tqFrbU0Ds5lsbQVDP8io00j1tcFG7/dI0o3DERuGMi8EQ58DgdUFBaKKaZtVs0X/hlt5nBmL+mWCAYCwrITvNgzDAMPLoXl6+Gllv34PK60Q0dUFAUAoGWVbNSwx5DpNWB0+IIBFmV2im8OLUCYVkF/+0MA9CDgjEdCKplprvNvhDxBjXZDL6luL/GmW8cKI8vMPOPFSUoDLMEBWlBoZrqD8eKhmVBA8ealkCtvJ544gkmTJjAokWLePLJJ6lTx7zByy+//EKfPn3CXDohhBDVhWpz0HDsx2HZd57NQdcw7VuIiqjQN/rHHnuMMWPGcN5559GqlVk9cvPmzdhsNt57771KLaA4SfLyzLDr6FEz7DreixrNbvbn5XWZd2601oQ//4JN22HnXti5z6zFVRqrFerXhnp1MOrGk5MQw+FaNg7EWcis6SAytibR1lPXRxhAjreANTm7WZm1g+XZO9iadzjQUTiAikKriHp0j2pKt6imdIpsjFMrR5NOTcUdVYP0qBqk0z4wWzHcOPRkIvRDOPXDgcFKDgpBgWABkA1kgTs7AndWFN4sJ3qWFSNLQck0UDNdWDNysGTkYMnJR/Hq5Q7IDFXFHRcUhsVF464ZZc6LiQwJx3SrxWxOGVyzxR+MKWphAGbzdapv94Vjqhpao8yigWYxp0/xBbU/0DKbG7oD0x7dAyhmi1DVbHJoUy1E2WOItkbgtNixa7aQvrSsqgVFAgERTFEwO9ivpOA3EJrpJU8bXjDyigdtGCU32SwsaGFohlIYcvmnlSIBm2oxX1dwn2eqP2xTCA3LlNDQLLDNUqZLHJ9+f1f16tVjxowZxeY/9thjYSiNEEIIIYSoUOCVlJTEd999x4IFC9ixYwcAl156KYMHD8ZR9O5+ourJzzfDrkOHzLBLrWDtKGsURDaD/30EHy+E/cnF16kVB43qQeP65rjhWVC/DiTUxKMYJLuz2JOfwmF3OqBQU4ukbnlCpEpQoLtZn7OXFdk7WZG1g79z9+NFD1mnuaM23aOa0S2qKV2jmhBtqbw7kBqKlTytHnlavZD5FiMbh34Ep55sjtUjOOxHsdbKwUouVnKLbUvHQoFSg2y1CQXeONxZkeiZdvRMK2QYWDLysKZnY0nPwZqWZYZj6dlYM3NR9PKHY16nHXeNKHOIi/aNfWFZdATuWF845rSbF926bl50ByqtFAnHNMUMP23WwpDMH4T5m1GqKljUcgdkwYFW8GAGWiarr1N4m2ol1uYkyuYkwuIIaW5odgxvRZXOykU4BcKhShYIyIKDsqLzvJjNuwvAE7QeBAVvwZ0HBgdrwY8VX4vxoNcSCNgoEnIFh17BfZ9ZILIxOIp3pyCEEEIIIURJKhR4zZgxg1q1ajF06NCQ+Z9//jmpqalyN6KqzOUy78a4f78ZdlW0+ZnXC4sXwzvvwO7d5rzoCDjvbGjWCJo2gKYNIab4HR9duocj7nR25SeT4s7GoqgkWGKwneT+jDyGl025B1iRvYOVWTtZm7OHAsMTsk59Ww3OjjabKHaPakYtayl3rDyZ5VSiyNaiyNaahczXjFwc+lFzMI5i11Nw6EexG6moeHAayTi9vtAx2jfUBwMFlxKDS6lBgVqDbKUJBUoNXGosLm80RqaGNT0PS3oW1rRsMxBLzzan07OxpprzVZcbLa8ALa8Ax4GUMl+Doam446LMoUYU7hpmOOaJi8Qd62tSGRuBO8qJ4fZAdq6v5ljIVkKbVapmQGZoGm6LituqmGNFx42OVwVD1VAtGhaLFYvFis1qJ9YeSaQ1gkirI+Quh/6aWhJoiTNSoCbaKdqfERSWBYIyo0htNL2wmac/aMMw3xc8WWCvBUjgJYQQ4symuws48n9PAlD7mgmoVvsp27fdXcCHvn3fcM0ECk7hvoWoiAolDJ9++ilTpkwpNr9ly5bcd999EnhVVf6wa/duM+yyVOCf3x90vfsu7NplzouNhWuHwMA2EGEHa1yJtXDydReHXBnszj9KqicHp2qlri0WS2U1/SlCN3T+yTvMyuwdrMzeyZrs3eTooR3Nx1ui6R7dNFCLq569xkkpS2XwKhHkaI3I0RqFLjC82IxM7EYKDj0Vu56C3UjDrqdiN9JQcWM3MrAbGUTru4pt13AquCNicNWPxaXE4lJjyVcSyFSa41ZicCkxeIhAzXcXD8TSzDDMkhYUlmXmmk0qUzKxpRy7405PhMPXlDIKd81oXHGR5MdGkB8XQW6Mg5xoGzlRNvIjbWalD13HYqjYDBWrDlGKlUjVRoRiw66ATVOxWQzsFgWbBVSLCyw62ArMJpZ2W2F/Y2pQTTJ/sFb0sRDixJ1oTbUcuSGOEEIIAYBhULB3Q2D6VFINg3N8+1ZP8b6FqIgKBV7JyckkJCQUm1+zZk2Sk0to1ibCLy8PNmyAvXvhrLOOP+zyeAqDLn+NrpgYuP56uO46iIoyO7DP3gH5h8ASCZYoUFRyvPkcLEhnd0EKGZ5cojQH9W010Cq5Zo1hGOzITzY7ms/ewersXWR480LWidWcZu2taDPgamKPP/37YFI0XEoNXNQgq2h2aBhYjGwzADPSsOlp2I10bHo6NiMdm5GBihebkYHNyDCfU8L9BAxU3EoUrpoxuGvF4FaicStR5CsJZClNzcdqFB4iwGuYgZi/dli6GYRZ0vzjwsBMc3ux5OZjyc3Hsb+MPt8AQ1HQY6Mw4qKhZixKjTjUmnEoNWKhRizUiIG4CIiNNmsbopgBrVcHVz5k5ZhNLL3+mia+f3f/P7+mmXfr9Dej1HzNLq1WsFl8TS6thXex1LTCdTS1sJ8yLei5p/u5JYQQQgghhBCnqQoFXmeddRarV6+mYcOGIfNXrVpF7dq1K6VgohJlZ8P69XDw4PHX7HK54Ouv4cMPYd8+c15MDAwfDtdeawZdfo54M+QqOAp5B8nM2c0Br4s93nxydDcxmoNG9lqVFjAZhsHugqOszN7FquydrMreSaonJ2SdCNVGl6gmdPN1NJ/orHtmNWFTFDxKNB6iyaFR8eWGjtXIxuoLvGxGBjY9A5uRidXI9I2zUNCx+R6XxUDBTSSuiAjyIyLIrxdBPk6yFCcF1MBFPbxKJF4tCsOIxFmgEZvpJiqjAEd6Drb0bKxpOVjSstDSMlHTMiEtEzIyUXQDLT0L0rNg14Fjv/aYKIiLCQrDgobgx7HREOE0fyHzB2ReX/9jLjfkFfiCMm9hn2RFwzJ/QKaoheGXP/Ty901mtYQGZsHr+MdF56lylzwhhBBCCCGEqIgKBV7XXHMNzz33HB6Ph3POOQeApUuX8uKLL3LzzTdXagHFCcrIKLwbY4MG5e+zKycH/vtfmDMHjhwx58XGmjW6igZdQQzNTro1hn2uTPbrh8jLP0oc0NDiRNGsJ3TxbhgGuwqO+sKtXazO3kWKJztkHbtipWNkQ7pFmwFXm4j6J63JZLWgqLiVGNzEkEvDktcxvFiNbDQ9A81IR9MzsRhZvkAsG7uRg50c7OShYGAjG5uRTcgZUrTGs78WmWqDmtEQHw1qNKhR5qDUBbVF4WPdCdkKpHsgvQDSsiAtA1IzzHFahhmMpWVAZhboBmRmm8OecoRjFs0MvmKjC0OwuGiIjTHHMUGPY6PMMM3/t2QY5v78gZh/8Org8kBOXuh8f2CmGGD4OvZWg2qEqaqvpplaON/iC8wsFrO2WaAmWVBIphapWRZcY03CMyFOuoKCAmbPns2yZctISUnBKNLUY/78+WEqmRBCCCHEmalCgdeYMWNIT09nwoQJuN1uAOx2O2PGjOG2226r1AKKCjIMs0bXpk1mDa8GDcrXH9GhQ/DppzBvnhl6ASQkwIgRMGQIOEu+S6Fu6KS6M9mbd4gD+Udx625qOOuTENUU3FngSgN3BuhZoNnBEkHgDl2l0A2d7flHWJW9izXZu1mTs6tYDS6bYqFDZEO6RTWlS1QT2kU0OOmd31dHHsPrG3Q8hhd3YNrXoTQKqhKFVYnDqmlYFBWnaiNSsxOh2rGpFmyAnTxsRjZWIweLkQ16ZilDFuABXKCnmEN5RAPRCjRyghoBSiSokaBEgFrLfGw4IUeDTCBDhwwPZLjNoCw9DzJyIT0b0jPNIS8fPF5ISTeH8oqONIOvmGhzHBs0HRNlhmYxUYXr1Yg1+w8rSdGgTDcKp90e0PND19GNwL9LaHCmFNYyIyg0U5WSwzOrViQ8U8ybAwTW9z9fDQ3RgpdJiCYEAI899hi///47AwcOpEOHDqd/c3khhBBCiNNchZIBRVF46KGHuPPOO9m+fTsOh4MmTZpgs5VyMSdOLbcbtm+Hf/4Bu91sxljWF2/DgFWr4Isv4IcfzJoqAI0bm00XL70USvm39egekl3p7Mk7yJGCNHQMalljcGqOwpVscWCLBU8uuLPBlQqudPN6XXOAxQGouHUPm/IOsCZ7N3/l7GZtzl4yi/TBZfcFXF2imgQCLrtqPZGjVa15fQGWJ2js9oVbhj8wwUBTVKyKhkXRsCoaUZqDSM2OU7VhQ8WGhtU39k8r4KvdBHj9d1yzgxFr7lxRzM2r+Jr7+ef55isuIBvIBSML9GwzCNOzzcHwjfUc3+McMPJ9+8kFby5QSr9fKhDnG0pkBdUJSiy4a0OWBbItkKlCpgKZBmTpkOWFTDdkuiCjALLyISvPPHezcsxh/+Hy/4PYbWYAFh0FMb5xdGRhKBYVWfg4OtJ8HBUBMRG+EOsYDKMwEAsOx0oLz/zr+58bcss+o/DfS/Pdzc8fpilKaLgWCNKCOvy3WgvDsZAhuBZbcK22Isv884Q4Tfz000+8/fbbdO3aNdxFEUIIIYQQVDDw8ouMjKRDhw6VVRZRGTIyYPNm2L/frJkVEVH6uqmpZv9c8+fDnj2F87t1M4Ouc88t9YIzz5sfCLpSCjKwKBrxtjhspYZPiq8j+0izry9PDtn5yaxL38hfObtYm3uADXmHKDA8Ic9yqjY6+gOuyCa0iagvNbgIqpHl9eDRveZgePB4vRiGbgYyho6KgsUACyoWQyUKC05Dw6nYcWDBiopV8QVaioZN1bAYGorqCz4MQNV9wZUBihfwhRH+AAuCAhDfgC8A0w3fpNcMVPzN/wx/EKOBHgVEms8zfDtVgu+WGNS0T9VByQMl3xwbOaDnmgGYHjRt5IKeFzo2csFw+Y6gG3Q3kGmGY7G+oX45Dr6OmdNl+sbZQBZmYJat+gb/PN0MzrI95vMKXOZwNO34/9EjHEEhmC8ICx4iIyHS6Zv2DVFO37SzYndlDfn38v2b+ecFaqR5QS8IWhbcdJPQJpwEn1eY/86Bc0ktPIf8/aH5gy9/v2fBd9AMbqrpr2lWNCzzT/vPy5KW++cLcYLq1KlDZGRkuIshhBDiDKBY7WHbd24Y9y3E8ZLkoLrIzzfvwLhjhzldWuf02dnw00+waBEsX15YmysiAv71L7jqKkhKKnEXhmGQ4cnmUP5R9uUfIcuTi1O1U9cRf8x+sgzDYE/+IdZlbmVd1jbWZW5lR+5+Xy2jQrGag07OenSOrE+nyKa0im6ERTnNaw76A6jgoMcInjbX8XrN4MprePHoHt+0jgdf00LDCOQFmqJiwWxaaNEsRKtWnKodp2bDbrFiVa1YNQtW1YLVYsNqsWGz2FCtFtAsZojkr5UTfNF/rFAgZBwUcAVqbfkDLwoDLiNoWvcdC6+3SCfx3sKO4j0es5Zivtu8aUKBy1zucvmCFgU8NtAtoMQUBiuBJndFOoIPjDUztDPywcjzBWF55uPAtO+xkW/WhPJPGwW+ZQVmuBObDzEFgDvoHzo0rA09B4A8zCAsh8KgrOh0TtC8HCAXKPBtIzffHA6Xs/lnUTYVIi3gtEKkFZw2iPAP9qDBaY4jI8DpMN8bIpy+cQQ4nKBYzAHNN7YFPS5nM67g2mhGkTDN/9jtNce5+YUhWuA8MgoDUpSgcdC8skK14FpqwU0+g8+X4Dtu+ptzBv4Gimy7tL+X4L+p4OcGLxfVwiOPPMKUKVOYMGEC9euXJz0XQgghjp9qc9Do/i/Csu88m4M2Ydq3EBUhgdfpzus1++rats2ssVWjBsTHh65z6BD8/jv89hssW2YGB35t2sCVV8LAgaXWBsvz5pPiymB//hGOujJw625iLFE0dNQptY+SdHcWG7N3siFrGxuytvN31g4yinQwD9DAUZtOMYl0ikmiY0wLmthqoOj54M4FdyZ48nz9PSmgWUG1mh2dH6P/rwopTzBV4kV3kQtvX59KOgZeQ8eLjkcx8KjgxcCDgVcx0AHD/zI0DdVmwWKxYrE40DQLdqsVpy0Cu2bDabFjs9qxamaQZbFYsWo2rBYLVqsdVdWKB1VaNbqg9gdiHq8ZiAU/9nrMsdtj3lXRX4vK7THXLdBB95rr6DqF4YgVFBuoNUro/F0DSzk6fDe8Zq0xo6CUwVU4RBdAggsoAMNdZLm7cJrgx25wuyDHXRiAFR0XnQ4e8igMzFy6+bef5uKEKICjjMGugEMBhxo0aOZg940dFnBawKaBw2o+tlhAteBrAwuKZo41LWhekXHItFp82vC1qTX804o59g9eJWgdCtcLGQh9bqCdri8sM4pMq4ov/FPN/tAoEoSVFBaramH4XFLYVvRv2x+2+belljBdatimIDXbTo727dtTUFDAgAEDcDgcWK2hNZ6XL18eppIJIcTpz6sbaP4WCEIIUU4SeJ2u8vPNuyfu2WPegdHphIYNzQuY9HT46y9YvdqsxbVtW+hzmzQxA66BA6FRoxI3X+B1kebOJNmVxqGCFLI9edhVG3GWaBxaaI2rLE8OW7L3sDlnFxuzdrAxewf78o8U26ZNsdImuinto1vQMaYl7aJbEG+LK2HvkWCrZYYJXl+NGm+e2fm9Jx+8OWaAYeBrBuerWYJqDv7aUIHAqmg/RRDSxCrQ5IrCi0OCL0pVDMXAq4FHU/BqKl5VQdfAo4JHVcwAq0itDUXTsKgWLJoFTbOgqRpOqx27xYbdasdpc2LRrFgtNiyqhlW1BMZW1Vxf+PhDANtx9NdmGIUBmcdrhmQeb2hI5vHVHHO5zTsqut3mem63r8ZZUM0zgKDTJzARCMnsZr9gRZvT+YOLE+ng3TAANxgeMwQjKBALPPYtD17PcIPXZd4pMjcPcvIhp8CsMZXjglwX5PkCtXwP5PqGPK9v0CFfhzwD8v219TCDtLxSC+sb9ON7jSpgLzLYgsa2UuaVZ7AWmT6enMefcVWE/xDoRYKywLjodAmPDaXs5cUG/3nmf5G+MC4wXwlarhQGZ4pa8qD61lWD1/eHi5jBpBK8naAyBNYtup+S9lv4fltYxqCDb3ghplUF/yFOjfvvv58jR45w3333ER8fL53WCyFEJdJUhXFz17DtSPEf0MPt/KQEHhpYtT+jhDhTSeB1OvF6ITPTDLr27TP76zIMM/Dats3su2vjRrNZYzBVhXbtoHdv6NMHWrQoduFtGAY53jwyPTkkF6ST7Eol25OLoqjEWiJp6KiDy3CzJ+8QO3L3B4Z/snezvyC5xOI2tNemXWQT2kU0pX1EU1o66mFFLbzDXLYXjBQC/QMFt0wqRgMlzlejJuii3uurJYObQJMyTfV1nm0Di9U32MFqQ1dAVxQ8vhpWXsXAi4HXV/tKVwyz+aCqmsXQzAswRVXN0EpR0VTNbFKoaET6mhDaVRt2iw2LomFRtZAAy6IUBllyAXQKKYrZ95P1ON/mdD00IAt+HByCeXXw+IMy/+AubKLp9q1j6IWhmRFaCzBwvhft3D3Qj1XRGjwWs4ajGjS/PGKO89iVdlwKXL6mlXmQV+Ab50JujhnC5+WZQ24e5BeYd8DMyzen813mkOcb57uhwA0eXyqkc4wgrRJZAKsCNt9gVXxhmGIGYkUHi+ELzQx8neKB1TAHS/B6Rcb+aYv/eXroOhrHDtMq4y3DKDI+XRkJ0P0/4S5FqdasWcOnn35Kq1Zy0SOEECfDtiPZ/H0gM9zFKKZ5wqntv9HwuEie/xwACVc+hmI5dd2/2D0upvv2fceVj1FwCvctREVUm8Dro48+YubMmSQnJ9OqVSv+85//VI8O9fPzYfduM8jauNGc3rsXDh82O6Y/UrwmFQDNmkHnzuZwzjkQFxeyWDd08rwF5HjzyHBnk+xKI92dxcH8o6S6M8ny5JLuzuRAfjJ78w6xN/8Ih1ypxfrc8qtnqUGSowGtnA1pG9GYNhFNiLFEhtSSCtSgsljMx/7Op1XV7FfK30eOGlSzQPX3m2Ne8Ouq76aAim6OMdDxohsuvLrbHHvz8brzMLw5GN4CMLwo5GPo2SiARdFQFcVX68qCplpxqHZsFgd2zYHNYsWmObBoNjRfeGUGWWZwpSmqL9SqNn8+Ipiqmv1dUcG7f+pBAZc//PJ37u4NmhfSh5nHDM48Qc0zA8/z313RX1PRKAzSdMzH/hsFGMGJcZHmc6X1vRbS11TwOLjpnO+4OB3mUCvuRI9yIY/HF44VmOMCly8gKyhsnppXYNbEy/ctc/nH7tD1gpu0Bua5zGDN318hmNm4xzBrroWb1fc+aNHMcNai+eapvvnBY9+0poLVV3PQP19TzWa4/mlN8TXLDZqvErQcX+jme6+1KOZz/M8LtBr1rasphWNfhbHA+hpm/3iAeR76avgZRuFjQw9aFjzfP62Hzi+6br4O1k6n6B+lYpo1a0Z+fn64iyGEEKKaM3SdvB0rA9On8ud0Vdfp59u36m8BIUQVVi2u2BcuXMikSZOYMGECHTt25MMPP2T06NF8++231KpVK9zFM3k8kJNjDllZ5pCZaQ5paZCSYoZXyclmn1uHD5tDSgoUFJS97Tp1zI7mk5KgVSvo2BFPTBRZ7myyXNkk5+/hyL6/OJKfQrIrjYMFKRwsOEqKO4NUdxZp3izSPTmke3PwHqMZUpTmpFlEfZpHNaRpTGNaxjUnsWZLYiNqmEGWP8wKumg2u8oxMFDwGl4Mw8BreNENvcTBq3vRDbevNpQbw99ZuwGKoqChoaoqmqKhKiqqYkNTnTg1G1bVik2zBQazc3cdDR3N8GLBQEPHohhoXjcWPGi6C8Vwg+4xa5Dh9TWZ9AIeX36ggG+RGST4+w4q0vwnuDlRoHlOcFMi6TOn2vPX0qoMuh4aoOlGYVhWdL7uD8GCOoP393fm8YVqgZppniIdxlNYA00PChpC+rGDYm06/fmaQmFz4eA7dwb3Iec/Jv73Bn8zYv86dhs47L5NBwVvIdsKWuZ/btHtlMbrLQzEXO6gaVfJ/b+53GaNvQLf2D/Pv76/Rp/LHTTtKVw3ZOwtfBxoVu3j9prD6c5fK7Fo5/9F+8YLNPMN6qcssEwJXS+4SbBiQLOq/cX6gQceYPLkydx3330kJiYW68MrKioqTCUTQgghhDgzVYvA6/3332fo0KFcddVVAEyYMIGffvqJL774gltvvTV8BTMM/rmmH6u3/4ru9aIrZh/JXpWQaa/i7wuqcHDHgbumOe3SwBVhwxXhoCDSTkGEjTynlQK7hTybQq7iIdfYRq6+kZzcfHJ+L6DAcB+zeCXRFJV4R01qOxOoE1GbupF1qBdTn3rR9Tgrtj6xzpqgEAinDMMgy/CSaeSagZRHKawF5r/+VRVUVF84paKqauCxpmrYFJvvroJWLJoFm2rDqlnRFM3XfLB84xP9t8Lw+EIvX/DlD8D8j/2D19d3ku4u8hzd19zSFzwQVKPB/9gIpAMljCkyTWg/NoHpIvP9z/OHAQRf+AcHAEXnK0VWKWFZYF/BSlqmlLw85GllbKfoc6Tpp8l/0X+y3qmD75QYHHj5gzYoPj84FPPXOAt+7L+5g+67w6I/ZAtu2lk0uAvczdMXrAVq/hC0LLjGD6HzAjWCirw2IPR89a/jC1YcdnAEVcVXlCKZnhL0Z1EkUCvpccg4aN/B2/A3kfWHYbpuBl7eoBsyeHwhWOAGDaX0RVdi/3SlPPbXGgzc7KGUdQNNeIND0iLPK+1c8vh/FajY588xLV0JfS8/OduuBGPGjAHgxhtvDJlvGAaKorBp06YwlEoIIYQQ4sx12gdeLpeLv//+m9tuuy0wT1VVevXqxZo1a8JYMvB63Jyd9AsZ7SvjV2nf3duCGRTega0UFtVCpCWCSFskUdYoIm2RxNhjiXHEEGOPIc4eR82ImtRy1qKmsyY1nTWxaBYUzJBKURRUpXBs8XWmbvZL5ZtWLVhVayDQ0lStcNpXC8s/L/ixf7pK9GulKKD47gJZESGhll7KdJF1ijX9CW4CpBeOi23PKL7tsrZlFrCUYKBI5z5GkceUsl7w8qI1Vkp6fsg6pU2XsNtiM5USVlJKWa+EDQafa8XKXcpzyr28yD5KPC4V2F6Jz/E7nudy7CAxUEsLs5kaQePy7cA3Vile1mPsN/iOpyHTegnzikz7z+my1jN3VGRfpYVnQY8DTUkpDPOKbqPEffqf79+1v0ad79DYMPsEC5RLAaxgWAv35S9H0WNVdH6xv+eSHxZ/XgnbKO1vNXi2P7AMab7rCzj9IWcgSA268YNOUF94QTULg8PUwHb84apvuU2Di84v4QVVHbNmzQp3ESqs2nYLIYQQQogz2mkfeKWlpeH1eos1XaxVqxY7inbefoppVhvjLniUbzd8iaKqKBYrqsWCqmqovn6kVH/wo2poqiUQKlk0C5pSGCbZNLPWU3CTPYfFgcPiIMoWRZQtimh7NE6L05y2RRNlj8Ku2VEUBQUlZKwqapnT/sDKP0+VpnhlU3zNHI8vHTg1QmrFBAdWpYVc5Z1fwrJSlxedLmu9ostKWafczyvr+eVYdszg6ljbrsh6x7v/k7TvU7q949l1GPddWYoGV0XDtsC4SDAXvLy07RxrnWNtq7zTZf39GaXML8/j0tap27z4elXI2WefHe4iVMhp0S2EEEIIIUQFnPaBV1U3YcAzTBjwTLiLIc5kIU0hw1oSIYSotlasWFHm8u7du5+ikhyfKtsthBDilPLqBpoqXxSFENXLaR941ahRA03TSElJCZmfkpJCfHx8ubZh+H5Jzs7OrvTyCSGEEKLqi4yMPKEm9iNHjiw2L3h7VbEPrxPtFkK+P1Weqh42SPlO3OlQxrd+2s6BjLxwF6OY9g1iuaZrQ5rEqOiuCnY/chLVcZrvg6eqfJ4CD/t9NwNKqmXBYi97n5VZPluBh2zfvhNrWXAdY9/ldaqP4fFqEqPKZ10pTvT708l22gdeNpuNtm3bsnTpUgYMGACArussXbqUESNGlGsbOTk5APTt2/eklVMIIYQQVdeqVatO6E6KRWt4ud1uNm3axGuvvcZ99913osU7KU60Wwj5/iSEOBW+BiaHuxBl2AGc6l4cW7RoAcCeWQ8fc93KLl9X374px77LKxzH8HjsALpOCncpqqYT/f50sp32gRfATTfdxCOPPEK7du3o0KEDH374IXl5eQwZMqRcz69duzY///xzlU8nhRBCCHFyREZGntDzo6Oji80799xzsVqtTJ48mXnz5p3Q9qsi+f4khBBCnNlO9PvTyVYtAq9LLrmE1NRUXn/9dZKTk2ndujXvvvtuuZs0qqpK3bp1T3IphRBCCHGmqVWrFjt37gx3MUp0ot1CyPcnIYQQQlRl1SLwAhgxYkS5mzAKIYQQQlSmzZs3F5t35MgR3nnnHVq1ahWGEh1bZXQLIYQQQghRVVWbwEsIIYQQIlyuuOIKFEUJdOTu16lTJ5599tkwlerYTrRbCCGEEEKIqkoCLyGEEEKIE/T999+HPFZVlZo1a2K328NUovI50W4hhBBCCCGqKsUo+lOkEEIIIYQQQgghhBCnManhJYQQQghRCZYuXcrSpUtJSUlB1/WQZZMmyf3MhRBCCCFOJQm8hBBCCCFO0LRp03jjjTdo164dCQkJKIoS7iIJIYQQQpzRpEmjEEIIIcQJ6t27Nw8++CBXXHFFuIsihBBCCCEANdwFEEIIIYQ43bndbrp06RLuYgghhBBCCB8JvE6ijz76iH79+tG+fXuuueYa1q1bF+4iVXkzZszgqquuonPnzvTs2ZM777yTHTt2hLtYp523336bpKQknn322XAX5bRw+PBhHnzwQXr06EGHDh0YPHgw69evD3exqjSv18urr75Kv3796NChAwMGDOCNN95AKg0Xt2LFCm6//XZ69+5NUlISS5YsCVluGAavvfYavXv3pkOHDtx4443s2rUrPIWtQso6bm63mxdffJHBgwfTqVMnevfuzcMPP8zhw4fDVt6rr76aBQsWhG3/4SDfc0o3depUkpKSQoZ//etf4S5W2Mn74bEd6xiNHz++2Lk1evToMJU2PMpzvVBQUMCECRPo0aMHnTt35p577uHo0aNhKvGpV55jNHLkyGLn0hNPPBGmEp96H3/8MYMHD6ZLly506dKFa6+9lp9//jmw/Ew/h/yOdZyq+nkkfXidJAsXLmTSpElMmDCBjh078uGHHzJ69Gi+/fZbatWqFe7iVVnLly9n+PDhtG/fHq/Xy8svv8zo0aP5+uuviYiICHfxTgvr1q1j7ty5JCUlhbsop4WMjAyGDRtGjx49eOedd6hRowa7d+8mNjY23EWr0t555x0++eQTnn/+eVq0aMGGDRt49NFHiY6OZtSoUeEuXpWSm5tLUlISV111FXfffXex5e+88w6zZ89m8uTJNGjQgNdee43Ro0ezcOFC7HZ7GEpcNZR13PLz89m4cSN33HEHrVq1IjMzk2effZY77riDefPmhaW8BQUFfPbZZyxdupSkpCQsltCvWI8++mhYynWyyPecY2vZsiXvv/9+4LGmaWEsTdUg74fHdqxjBNCnT5+QG2HYbLZTVbwqoTzXC8899xw///wzr776KtHR0Tz99NPcfffdzJ07N8ylPzXKe001dOhQxo4dG3jsdDrDUdywqFu3Lg8++CCNGzfGMAy+/PJL7rrrLubPn0/Lli3P+HPI71jHCar4eWSIk+Lqq682JkyYEHjs9XqN3r17GzNmzAhjqU4/KSkpRmJiorF8+fJwF+W0kJ2dbVx00UXG77//bowYMcJ45plnwl2kKu/FF180hg0bFu5inHZuvfVW49FHHw2Zd/fddxsPPPBAmEp0ekhMTDQWL14ceKzrunHuueca7777bmBeZmam0a5dO+Orr74KRxGrpKLHrSRr1641EhMTjf3795+iUoUaMWJEqcPIkSPDUqaTSb7nlO311183LrvssnAXo0qT98NjK+m975FHHjHuuOOOMJWoaip6vZCZmWm0bdvW+OabbwLrbNu2zUhMTDTWrFkTplKGV0nXVHKtUFz37t2Nzz77TM6hY/AfJ8Oo+ueR1PA6CVwuF3///Te33XZbYJ6qqvTq1Ys1a9aEsWSnn6ysLACpbVNOEydOpG/fvvTq1Yvp06eHuzinhR9++IHevXszduxYVqxYQZ06dbj++usZOnRouItWpXXu3JnPPvuMnTt30rRpUzZv3syqVasYP358uIt2Wtm3bx/Jycn06tUrMC86OpqOHTuyZs0aBg0aFMbSnV6ys7NRFIWYmJiw7H/27Nlh2W84yPec8tm9eze9e/fGbrfTqVMnHnjgAerVqxfuYlVZ8n5YfsuXL6dnz57ExMRwzjnncO+991KjRo1wFytsil4vbNiwAbfbHXIuNW/enHr16vHXX3/RqVOncBQzrEq7plqwYAH/+9//SEhI4IILLuDOO++sWrVzThGv18u3335Lbm4unTt3lnOoFEWPk19VPo8k8DoJ0tLS8Hq9xar016pVS/qjOg66rvPcc8/RpUsXEhMTw12cKu/rr79m48aNfP755+Euymll7969fPLJJ9x0003cfvvtrF+/nmeeeQar1cqVV14Z7uJVWbfeeivZ2dlcfPHFaJqG1+vlvvvu47LLLgt30U4rycnJACV+XpyJ/URUVEFBAVOmTGHQoEFERUWFuzjVnnzPObYOHTowadIkmjZtSnJyMm+88QbDhw9nwYIFco6WQt4Py6dPnz5ceOGFNGjQgL179/Lyyy9zyy238Omnn56RzWZLul44evQoVqu12A8gtWrVCpxnZ5LSrqkuvfRS6tWrR+3atdmyZQtTpkxh586dTJs2LYylPbW2bNnCddddR0FBAREREbzxxhu0aNGCTZs2yTkUpLTjBFX/PJLAS1RZEyZMYOvWrXz88cfhLkqVd/DgQZ599lnee++9M6aPi8piGAbt2rXj/vvvB6BNmzZs3bqVuXPnSuBVhm+++YYFCxbw0ksvBb4YTJo0idq1a8txE6eU2+1m3LhxGIbBhAkTwl0cIQDo27dvYLpVq1Z07NiRCy64gG+++YZrrrkmjCUTp7vgmm7+DqIHDBgQqPV1ppHrhWMr7Rhde+21gemkpCQSEhK48cYb2bNnD40aNTrVxQyLpk2b8uWXX5KVlcWiRYt45JFHmDNnTriLVeWUdpxatGhR5c8juUvjSVCjRg00TSMlJSVkfkpKCvHx8WEq1ell4sSJ/PTTT3z44YfUrVs33MWp8v7++29SUlIYMmQIbdq0oU2bNixfvpzZs2fTpk0bvF5vuItYZSUkJNC8efOQec2aNePAgQNhKtHp4YUXXuDWW29l0KBBJCUlccUVV3DDDTcwY8aMcBfttJKQkAAgnxcV5Ha7uffeezlw4ADvvfee1Jw5ReR7zvGLiYmhSZMm7NmzJ9xFqbLk/bBiGjZsGLjhzpmmtOuF+Ph43G43mZmZIeunpKQEzrMzxfFcU3Xs2BHgjDqXbDYbjRs3pl27djzwwAO0atWKWbNmyTlURGnHqSRV7TySwOsksNlstG3blqVLlwbm6brO0qVLQ9q6iuIMw2DixIksXryYDz/8kIYNG4a7SKeFc845hwULFvDll18Ghnbt2jF48GC+/PLLM7KKe3l16dKFnTt3hszbtWsX9evXD1OJTg/5+fkoihIyT9M0DMMIU4lOTw0aNCAhISHk8yI7O5u1a9fK58Ux+MOu3bt388EHH5zR/decavI95/jl5OSwd+/eM/JCqbzk/bBiDh06RHp6+hl1bh3reqFdu3ZYrdaQc2nHjh0cOHDgjOl7qSLXVJs2bQI4o86lonRdx+VyyTl0DP7jVJKqdh5Jk8aT5KabbuKRRx6hXbt2dOjQgQ8//JC8vDyGDBkS7qJVaRMmTOCrr77izTffJDIyMtBGOjo6GofDEebSVV1RUVHF+jmLiIggLi5O+j87hhtuuIFhw4bx1ltvcfHFF7Nu3To+++wzJk6cGO6iVWkXXHABb731FvXq1Qs0aXz//fe56qqrwl20KicnJyekVse+ffvYtGkTsbGx1KtXj1GjRjF9+nQaN25MgwYNeO2116hduzYDBgwIY6nDr6zjlpCQwNixY9m4cSMzZszA6/UGPi9iY2Ox2WzhKvYZQ77nlO3555/nggsuoF69ehw5coSpU6eiqiqXXnppuIsWVvJ+eGxlHaPY2FimTZvGwIEDiY+PZ+/evbz44os0btyYPn36hLHUp9axrheio6O56qqrmDx5MrGxsURFRfHMM8/QuXPnMyasONYx2rNnDwsWLKBv377ExcWxZcsWJk2aRPfu3WnVqlWYS39qvPTSS5x33nmcddZZ5OTk8NVXX7F8+XJmzpwp51CQso7T6XAeKYb8HH/SzJkzh5kzZ5KcnEzr1q15/PHHA1X8RMmSkpJKnD9p0iT5En2cRo4cSatWrfj3v/8d7qJUeT/++CMvv/wyu3btokGDBtx0001yl8ZjyM7O5rXXXmPJkiWkpKRQu3ZtBg0axF133SVhQxHLli1j1KhRxeZfeeWVTJ48GcMweP311/nss8/IzMyka9euPPnkkzRt2jQMpa06yjpud999N/379y/xebNmzaJHjx4nu3gC+Z5Tlvvuu48VK1aQnp5OzZo16dq1K/fdd1+V6M8knOT98NjKOkZPPfUUd911Fxs3biQrK4vatWtz7rnnMm7cuDOq2Wd5rhcKCgqYPHkyX3/9NS6Xi969e/Pkk09WmVonJ9uxjtHBgwd56KGH2Lp1K7m5uZx11lkMGDCAO++884zpHuCxxx7jzz//5MiRI0RHR5OUlMQtt9zCueeeC8g55FfWcTodziMJvIQQQgghhBBCCCFEtSJ9eAkhhBBCCCGEEEKIakUCLyGEEEIIIYQQQghRrUjgJYQQQgghhBBCCCGqFQm8hBBCCCGEEEIIIUS1IoGXEEIIIYQQQgghhKhWJPASQgghhBBCCCGEENWKBF5CCCGEEEIIIYQQolqRwEsIUW0tW7aMpKQkMjMzw10UIYQQQohqb/z48dx5553hLoYQQgASeAkhqpGRI0fy7LPPBh537tyZ3377jejo6DCWSgghhBBCVKZ+/frxwQcfhLsYQogqTgIvIUS1ZbPZSEhIQFGUcBdFCCGEEOK05nK5wl0EIYQ4LhJ4CSGqhfHjx7N8+XJmzZpFUlISSUlJzJs3L6RJ47x58+jWrRs//vgjAwcOpGPHjowdO5a8vDzmz59Pv3796N69O8888wxerzewbZfLxfPPP0+fPn3o1KkT11xzDcuWLQvXSxVCCCGEwOVy8cwzz9CzZ0/at2/PsGHDWLduHbquc9555/Hxxx+HrL9x40ZatWrF/v37AcjMzOTf//4355xzDl26dGHUqFFs3rw5sP7UqVO5/PLL+b//+z/69etHhw4dAPj2228ZPHgwHTp0oEePHtx4443k5uaG7GvmzJn07t2bHj16MGHCBNxud2BZRkYGDz/8MN27d6djx46MGTOGXbt2hTx/0aJFDBo0iHbt2tGvXz/ee++9wLKRI0eyf/9+Jk2aFPjOJ4QQJbGEuwBCCFEZ/v3vf7Nr1y5atmzJ2LFjAdi2bVux9fLz85k9ezavvPIKOTk53H333dx9991ER0fz9ttvs3fvXu655x66dOnCJZdcAsDEiRPZtm0br7zyCrVr12bx4sWMGTOGBQsW0KRJk1P5MoUQQgghAHjhhRdYtGgRkydPpn79+rz77ruMGTOG7777jkGDBvHVV19x/fXXB9ZfsGABXbp0oX79+gCMGzcOu93OO++8Q3R0NJ9++ik33HADixYtIi4uDoA9e/awaNEipk2bhqqqHDlyhAceeICHHnqIAQMGkJOTw8qVKzEMI7CfZcuWkZCQwIcffsiePXu47777aN26NUOHDgXMHyl3797N9OnTiYqK4sUXX+TWW2/l66+/xmq1smHDBu69917uvvtuLrnkEtasWcOECROIi4tjyJAhgSBu6NChgW0KIURJJPASQlQL0dHRWK1WHA4HCQkJAOzYsaPYem63m6eeeopGjRoBMHDgQP73v//x+++/ExkZSYsWLejRowd//vknl1xyCQcOHGDevHn8+OOP1KlTB4DRo0fz66+/Mm/ePO6///5T9yKFEEIIIYDc3Fzmzp3LpEmT6Nu3LwBPP/00v//+O59//jmXXXYZ77//PgcOHKBevXrous7XX3/NHXfcAcDKlStZt24dS5cuxWazAfDII4+wZMkSFi1axLXXXguY35teeOEFatasCcDff/+Nx+PhwgsvDARnRWtYxcbG8sQTT6BpGs2bN6dv374sXbqUoUOHsmvXLn744Qc++eQTunTpAsCUKVM4//zzWbJkCRdffDHvv/8+PXv25K677gKgadOmbNu2jZkzZzJkyBDi4uLQNI3IyMjAdz4hhCiJBF5CiDOK0+kMhF0A8fHx1K9fn8jIyJB5qampAPzzzz94vV7+9a9/hWzH5XIFfv0UQgghhDiV9uzZg9vtDoRGAFarlQ4dOrB9+3bGjBlD8+bN+eqrr7j11ltZvnw5qampge8zW7ZsITc3lx49eoRsNz8/nz179gQe16tXLxB2AbRq1YqePXsyePBgevfuTe/evRk4cCCxsbGBdVq0aIGmaYHHCQkJ/PPPPwBs374di8VCx44dA8tr1KhB06ZN2b59O2D+YNm/f/+QcnXp0oVZs2bh9XpDti2EEGWRwEsIcUaxWELf9hRFKXGeruuA+Quqpml88cUXxb5gRUREnNzCCiGEEEJU0ODBg1mwYAG33norX331Fb1796ZGjRoA5OTkkJCQwOzZs4s9L/ju1k6nM2SZpmm8//77rF69mt9//z3QTcRnn31Gw4YNgZK/awU3eRRCiFNFOq0XQlQbVqs1EFRVltatW+P1eklNTaVx48Yhg1SjF0IIIUQ4NGrUCKvVyurVqwPz3G4369evp0WLFgBceumlbN26lQ0bNrBo0SIuu+yywLpt27bl6NGjaJpW7PtNcI2ukiiKQteuXRk7dixffvklVquVJUuWlKvczZs3x+PxsHbt2sC8tLQ0du7cGSh3s2bNQl4XwOrVq2nSpEngx8eT8Z1PCFH9SOAlhKg26tevz9q1a9m3bx+pqamV8kWoadOmDB48mIcffpjvvvuOvXv3sm7dOmbMmMFPP/104oUWQgghhDhOERERDBs2jBdeeIFffvmFbdu28Z///If8/HyuvvpqABo0aEDnzp3597//jdfrpV+/foHn9+rVi06dOnHXXXfx22+/sW/fPlavXs0rr7zC+vXrS93v2rVreeutt1i/fj0HDhzgu+++IzU1lWbNmpWr3E2aNKF///785z//YeXKlWzevJmHHnqIOnXqBJox3nzzzSxdupQ33niDnTt3Mn/+fD766CNuvvnmwHbq16/PihUrOHz4cKAbCiGEKEqaNAohqo2bb76Z8ePHM2jQIPLz85k0aVKlbHfSpElMnz6dyZMnc+TIEeLi4ujUqRPnn39+pWxfCCGEEOJ4PfjggxiGwcMPP0xOTg7t2rXj3XffDelPa/DgwUyYMIErrrgCh8MRmK8oCm+//Tavvvoqjz76KGlpacTHx9OtWzfi4+NL3WdUVBQrVqzgww8/JDs7m3r16jF+/PhAx/nlMWnSJJ599lluv/123G433bp14+2338ZqtQJm7bNXX32V119/nenTp5OQkMDYsWMZMmRIYBtjx47liSeeYMCAAbhcLrZs2XI8h04IcYZQDGlQLYQQQgghhBBCCCGqEWnSKIQQQgghhBBCCCGqFQm8hBBCCCGEEEIIIUS1IoGXEEIIIYQQQgghhKhWJPASQgghhBBCCCGEENWKBF5CCCGEEEIIIYQQolqRwEsIIYQQQgghhBBCVCsSeAkhhBBCCCGEEEKIakUCLyGEEEIIIYQQQghRrUjgJYQQQgghhBBCCCGqFQm8hBBCCCGEEEIIIUS1IoGXEEIIIYQQQgghhKhWJPASQgghhBBCCCGEENWKBF5CCCGEEEIIIYQQolqRwEsIIYQQQgghhBBCVCsSeAkhhBBCCCGEEEKIakUCLyGEEEIIIYQQQghRrUjgJYQQQgghhBBCCCGqFQm8hBBCCCGEEEIIIUS1IoGXECLsli1bRlJSEt9++22Fnj916lSSkpIquVSnl5EjRzJy5MhwF0MIIaqtkSNHcumll4a7GGcsXde59NJLmT59emDevHnzSEpKYt++fSdln+PHj6dz584Vfn6/fv0YP358JZbo9LJv3z6SkpKYN2/ecT/3vvvuY9y4cSehVKIsCxcu5OyzzyYnJyfcRQlISkpi4sSJx1zvRN4P/M9dv379MdcdOnQoL7zwwnHvQ4SHBF5CiGKO501fCCGEKK+tW7fy4IMP0qdPH9q1a0fv3r154IEH2Lp1a7iLVqW99dZbLFmyJNzFCKuvvvqKgwcPMmLEiHAXRZwCt9xyC9999x2bN2+u8DYOHjzItGnTuPrqq+nevTs9evRg5MiR/PHHH6U+548//mDUqFF07dqVzp07M2TIEBYuXHhc+3W73VxyySUkJSUxc+bMYsunT5/O7bffTq9evUhKSmLq1KnH/dqKSkpKCgytWrWid+/e3HzzzSxbtqzc2/B6vUydOpURI0YQGRkZmC/vP6FuueUWPv74Y5KTk09oO7qu884779CvXz/at2/P4MGD+eqrr8r9/A0bNnDbbbdx7rnn0rlzZwYPHsysWbPwer0h6z333HNceeWVnH322XTs2JGLL76YqVOnVqlQ82SSwEsIIYQQQpx03333HVdeeSV//vknQ4YM4cknn+Tqq69m2bJlXHnllSxevDjcRayyZsyYccZfcM6cOZNBgwYRHR0d7qKIU6BNmza0a9eO9957r8Lb+P7773nnnXdo3Lgx9957L3feeSc5OTncdNNNfPHFF8XW/+KLL7j55puxWq3cf//9PPzww3Tr1o2DBw8e137nzJlT5nNeffVVNmzYQOvWrY/7NZXl3HPP5YUXXmDy5Mlcd911bNmyhRtuuIGff/65XM//8ccf2blzJ9dee23I/NPl/efyyy9n3bp11K9f/6Tup3///kRFRfHxxx+f0HZeeeUVpkyZwrnnnst//vMf6tWrxwMPPMDXX399zOdu2LCB6667jv3793PLLbfwyCOP0LBhQ5599lkmTZoUsu769evp2rUr99xzD//+97/p0aMHb7/9NmPGjEHX9RN6DacDS7gLIIQQQgghqrc9e/bw8MMP07BhQz766CNq1qwZWDZq1CiGDx/Oww8/zP/+9z8aNmx4ysqVm5tLRETEKdufqJiNGzeyefPmM7p54JkouCZKcI2j8urRowc//vhjyPvNsGHDuPzyy3n99de56qqrAvP37dvHxIkTGTFiBI8//niFy5ySksIbb7zBmDFjeP3110tc5/vvv6dBgwakpqbSs2fPCu+rqCZNmnD55ZcHHl944YVcdtllzJo1i759+5b4nIKCAqxWK6qq8sUXX9ClSxfq1KlTaWU6lTRNQ9O0k74fVVUZOHAg//3vfxk7diyKohz3Ng4fPsz777/P8OHDeeKJJwC45pprGDFiBC+88AL/+te/ynwtn376KWCGq3FxcQBcd911jBgxgvnz54ecw5988kmx5zdq1Ijnn3+edevW0alTp+Mu/+lEangJISps48aNjBkzhi5dutC5c2duuOEG/vrrr2LrZWZm8txzz9GvXz/atWvHeeedx8MPP0xqamqp23a5XNx222107dqV1atXB+avXLmSq666ivbt2zNgwADmzp1b4vM9Hg9vvPEGAwYMoF27dvTr14+XX34Zl8sVWGfSpEn06NEDwzAC855++mmSkpKYNWtWYN7Ro0dJSkoK/JLj73Ns4cKFTJ8+nfPOO4/27dtzww03sHv37ko7dv6mpatWrWLSpEmcc845dOrUibvuuqvMY5eTk0OnTp145plnii07dOgQrVu3ZsaMGeUqpxBCVIZ3332XvLw8nn766ZCLT4CaNWsyceJEcnNzeeeddwD49ttvSUpKYvny5cW2NXfuXJKSkvjnn38C87Zv387YsWM5++yzad++PUOGDOH7778PeZ7/PXX58uU89dRT9OzZM3ARmJ2dzbPPPhv4nOrZsyc33XQTf//9d7H9b9u2jZEjR9KxY0f69OkTKHOwlJQUHnvsMXr16kX79u257LLLmD9/frH1cnNzmTx5Mn379qVdu3YMHDiQmTNnhnwuJSUlkZuby/z58wNNlsoKfoI/o6ZNm0afPn3o3LkzY8eOJSsrC5fLxbPPPkvPnj3p3Lkzjz76aMhno99///tfhgwZQocOHTj77LO57777itVaWblyJWPHjuX888+nXbt29O3bl+eee478/PyQ9fx9YR0+fJg777yTzp07c8455/D8888Xa35TkiVLlmC1WunWrdsx1wX46KOPGDRoUKDZ7IQJE8jMzCy23tq1a7nlllvo3r07nTp1YvDgwXz44YdlbnvTpk2cc845jBw5MtAkyDAM3nzzTc477zw6duzIyJEjS22mu3fv3sC52rFjR4YOHcpPP/0UWG4YBj169AippaHrOt26daN169Yhr+Ptt9+mTZs2gXKc6HEu77Hz92dXnr+FYF988QVJSUls3Lix2LK33nqL1q1bc/jw4cC8Xr16kZubW6wJ4oEDB9i+ffsxX0vLli2Lvd/YbDb69u3LoUOHyM7ODsyfO3cuXq830G9YTk5OyN9heU2ZMoWmTZty2WWXlbpOgwYNjnu7FZGUlESNGjUCfVr53xu+/vprXnnlFfr06UPHjh3Jzs6moKCAX3/9lV69ehXbRlnvP+W9FjjWeV8eS5Ys4dJLL6Vdu3YMGjSIX375JWR5SX146brO1KlT6d27d+Bvc9u2baX2r+dyucr1nbtXr17s37+fTZs2hczfs2cPe/bsKddrcbvdXH/99YF5iqIwbNgwDh06xJo1a8p8fnZ2Nna7nZiYmJD5CQkJOByOY+7fXwuupPfF6kZqeAkhKmTr1q0MHz6cyMhIxowZg8Vi4dNPP2XkyJHMmTOHjh07AuYXhuHDh7N9+3auuuoq2rRpQ1paGj/88AOHDx8u9kUEID8/nzvvvJMNGzbw/vvv06FDBwC2bNnC6NGjqVmzJvfccw8ej4epU6dSq1atYtt4/PHHmT9/PgMHDuSmm25i3bp1zJgxg+3bt/PGG28A0K1bNz744AO2bt1KYmIiYH55V1WVlStXMmrUqMA8gO7du4fs45133kFRFG6++Ways7N59913efDBB/m///u/Sjl2fs888wwxMTHcfffd7N+/nw8//JCJEyfy6quvlrj9yMhIBgwYwDfffMOjjz4a8gvRV199hWEYDB48uMwyCiFEZfrxxx+pX79+qYFF9+7dqV+/fqDpzfnnn09ERATffPMNZ599dsi6CxcupGXLloH37a1btzJs2DDq1KnDLbfcEnjeXXfdxdSpU7nwwgtDnj9hwgRq1qzJXXfdRW5uLgBPPvkkixYtYsSIETRv3pz09HRWrVrF9u3badu2beC5GRkZjBkzhgsvvJCLL76YRYsWMWXKFBITEwPhWX5+PiNHjmTPnj0MHz6cBg0a8O233zJ+/HgyMzO54YYbADPYuOOOO1i2bBlXX301rVu35tdff+n6oFQAAQAASURBVOWFF17g8OHDPPbYYwC88MILPP7443To0IGhQ4cC5q/zx/L222/jcDi49dZb2b17N3PmzMFisaAoCpmZmdx9992sXbuWefPmUb9+fe6+++7Ac6dPn85rr73GxRdfzNVXX01qaipz5sxh+PDhfPnll4GLrG+//Zb8/HyGDRtGXFwc69atY86cORw6dKhY7Rav18vo0aPp0KEDDz/8MEuXLuW9996jYcOGIRd9JVmzZg2JiYlYrdZjvu6pU6cybdo0evXqxbBhw9i5cyeffPIJ69ev55NPPgls4/fff+e2226jdu3ajBo1ivj4eLZv385PP/0U+Dcqat26dYwZM4Z27drx5ptvBi4sX3vtNaZPn07fvn3p27cvf//9NzfffDNutzvk+UePHuW6664jLy+PkSNHUqNGDebPn88dd9zB66+/zoUXXoiiKHTp0oUVK1YEnrdlyxaysrJQVZXVq1dz/vnnA7Bq1Spat24dUvvpRI5zeY8dlO9voaiBAwcyceJEFixYQJs2bUKWLViwgLPPPjukdlGLFi1wOBysXr065O/4kUceYfny5WzZsqXM11Oa5ORknE4nTqczMO+PP/6gWbNm/Pzzz4G/wdjYWK6//nrGjh2Lqh67nsi6dev48ssv+fjjjytU66eyZWRkkJmZSePGjUPmv/nmm1itVkaPHo3L5cJqtbJhwwbcbnexf5ey3n/K+322POf9saxatYrvvvuO66+/nsjISGbPns3YsWP58ccfqVGjRqnPe+mll3j33Xe54IIL6NOnD5s3b2b06NEUFBSUuH55v3O3a9cOgNWrV4ccsxtvvBGAH374oczXs2nTJiIiImjevHnIfP81z6ZNm8oM+M8++2wWLlzIE088wU033YTD4eCXX35h8eLFPPTQQ8XW93g8ZGZm4na72bp1K6+++iqRkZGB/VVrhhBCFPHFF18YiYmJxrp160pd58477zTatm1r7NmzJzDv8OHDRufOnY3hw4cH5r322mtGYmKi8d133xXbhq7rhmEYxp9//mkkJiYa33zzjZGdnW2MGDHC6NGjh7Fx48Zi+2zfvr2xf//+wLxt27YZrVu3NhITEwPzNm3aZCQmJhr//ve/Q54/efJkIzEx0Vi6dKlhGIaRkpJiJCYmGh999JFhGIaRmZlptGrVyhg7dqzRq1evwPOefvpp4+yzzy5W3osvvtgoKCgIrPfhhx8aiYmJxpYtW0o9bsdz7Pz/DjfeeGNg34ZhGM8995zRunVrIzMzMzBvxIgRxogRIwKPf/31VyMxMdH4+eefQ/Y9ePDgkPWEEOJky8zMNBITE4077rijzPVuv/12IzEx0cjKyjIMwzDuv/9+o2fPnobH4wmsc+TIEaNVq1bGtGnTAvNuuOEG49JLLw15P9Z13bj22muNiy66KDDP/546bNiwkG0ahmF07drVmDBhQpnlGzFihJGYmGjMnz8/MK+goMA499xzjXvuuScw74MPPjASExON//73v4F5LpfLuPbaa41OnToFXt/ixYuNxMRE48033wzZzz333GMkJSUZu3fvDszr1KmT8cgjj5RZPj//Z9Sll15quFyuwPz777/fSEpKMsaMGROy/rXXXmtccMEFgcf79u0zWrdubUyfPj1kvS1bthht2rQJmZ+Xl1ds/zNmzDCSkpJCPqsfeeQRIzExMeTfzTAM44orrjCuvPLKY76m8847L+QY+/n/Tffu3WsYhvm53rZtW+Pmm282vF5vYL05c+YYiYmJxueff24YhmF4PB6jX79+xgUXXGBkZGSEbDP48/aRRx4xOnXqZBiGYaxcudLo0qWLceutt4aca/593nrrrSHPffnll43ExMSQf7dnn33WSExMNFasWBGYl52dHSiLv8zvvvuu0bp168C5MmvWLOOCCy4wrr76auPFF180DMMwvF6v0a1bN+O5554LKW9Fj3N5j51hlP9vYe/evUZiYqLxxRdfBObdf//9Ru/evUP28ffffxdbz++iiy4qds76918Ru3btMtq3b2889NBDIfO7dOlidO/e3WjXrp3x6quvGt9++61x//33G4mJicaUKVOOuV1d142rr77auP/++w3DKHzt7777bqnP8X8Pff311yv0WoIlJiYajz32mJGSkmKkpKQYa9euNW644QYjMTHReO+99wzDKHxv6N+/f7G/3c8++6zU77Clvf+U9/tsec/7sl5b27ZtQ94T/d/1Z8+eHZhX9P0gOTnZaNOmjXHnnXeGbG/q1KnF/jaP5zu3X9u2bY0nn3wyZN4FF1wQ8n5amltvvdXo379/sfm5ubnlOuc8Ho8xceJEo23btkZiYqKRmJhotG7d2vj4449LXH/NmjWB9RITE42BAwcaf/755zHLWR1Ik0YhxHHzer38/vvvDBgwIKSvldq1a3PppZeyatWqQDXx7777jlatWpX4603RX7+ysrIYPXo0O3bsYPbs2SGdeXq9Xn777TcGDBhAvXr1AvObN29O7969Q7bjryFw0003hcy/+eabQ5bXrFmTZs2aBWpwrV69Gk3TGD16NEePHmXXrl2A+atSly5dipV3yJAh2Gy2wGP/LzF79+4t8bj5X0d5j53f0KFDQ/bdrVs3vF4v+/fvL3U/vXr1onbt2ixYsCAw759//mHLli1lVrMXQojK5m9udaw+ePzL/etffPHFpKSkhDRrXLRoEbquc8kllwCQnp7On3/+ycUXX0x2djapqamkpqaSlpZG79692bVrV0gTKTDfU4v2jRITE8PatWuLrVtURERESB85NpuN9u3bh7zv//LLLyQkJHDppZcG5lmtVkaOHElubm6g5s4vv/yCpmmMHDkyZB8333wzhmEUa65zvC6//PKQGjkdOnTAMIyQfov88w8ePIjH4wFg8eLF6LrOxRdfHDieqampxMfH07hx45C7vgU3ncnNzSU1NZXOnTtjGEaJzdaGDRsW8rhr164hzY9Kk56eXqzpTkn++OMP3G43o0aNCqmRc8011xAVFRX4/N+4cSP79u1j1KhRxbZbUs2cP//8kzFjxtCzZ0+mTp0a8tnv3+eIESNCnltSLbGff/6ZDh06hNTciIyM5Nprr2X//v1s27YNKPyc9zdrWrlyJV27dqVbt26B7yz//PMPmZmZJdYCqchxLu+x8yvP30JJLr/8co4cORJyHi1YsACHw8FFF11UbP3Y2FjS0tJC5s2ePbtCtbvy8vIYN24cDoeDBx54IGRZbm4uGRkZ3HPPPYwbN46BAwfy0ksv0adPH2bNmlXsu1lR8+bN459//uHBBx887nJVls8//5yePXvSs2dPrrnmGlavXs1NN91U7Fy84oorijV7S09PB8zjXR7H8322vOd9WXr16hVSs7VVq1ZERUWVeb4tXboUj8dTrGZjWXd6PZ7v3CWdmz/88MMxa3eBWRM4+H3Ez263B5aXRdM0GjZsSO/evXn++ed55ZVXuOCCC3jmmWdKvMFAixYteP/99wP9y0VERARqOFd30qRRCHHcUlNTycvLo2nTpsWWNW/eHF3XOXjwIC1btmTPnj0lfoEpyXPPPYfL5WL+/Pm0bNmy2D7z8/OLVcsGaNq0acgXsf3796OqarEmHwkJCcTExIR8aHXr1i3w3JUrV9KuXTvat29PXFwcK1euJD4+ns2bN4dcuPgFB29A4EtzWe3hj+fYnch+VFVl8ODBfPLJJ+Tl5eF0OlmwYAF2u51//etfpT5PCCEqW9EgqzRFg7HzzjuP6OhoFi5cGOjYeeHChbRu3TrwHrpnzx4Mw+C1117jtddeK3G7KSkpIc2kSuo/58EHH2T8+PGcf/75tG3blr59+3LFFVcU60C/bt26xQKR2NjYkIvv/fv307hx42JNoPxNVw4cOBBYr3bt2kRFRZW4Xlk/apRH0c8O/90NzzrrrGLzdV0nKyuLGjVqsGvXLgzDKPWz22IpvHw4cOAAr7/+Oj/88AMZGRkh6xUNCOx2e7FuDGJjY4s9rzRGOfpT8h/bZs2ahcy32Ww0bNgwcEz9F8n+ZrFlKSgo4LbbbqNt27a8+uqrIa8/eJ9NmjQJmV+zZs1i4cGBAweKdVsQXN4DBw6QmJhImzZtcDqdrFy5kj59+rBq1Sruuece4uPjmT17NgUFBaxatQoww6xgFT3O5T12fuX5WyjJueeeS0JCAv/73//o2bMnuq7z1VdfBe58V5RhGJXSPNDr9XLfffexbds23nnnnWIdszscDnJzc4t937v00kv59ddf2bRpU7GuLfyys7N5+eWXGT16dLG/r1Opf//+geA1MjKSFi1alHhTjrL6ECvP3xkc3/fZ8p73ZSnpuMbGxpb5Xdh/The9HoiLiys12Due79wncm46HI4S+070N7U8Vj9cb7/9NrNmzWLRokWBz8xLLrmEkSNHMmHCBM4///yQ96qoqKhA/2wDBgxgwYIF3HnnncyfP59WrVpV6DWcLiTwEkJUGf3792fhwoW8/fbbvPDCC+XqL6Es5fkQ6tq1K5999hl79+4N/ILq7z9j1apV1K5dO9BZbFGlla+8XxbKq6L7ueKKK5g5c2agk8+vvvqK888/X27pLoQ4paKjo0lISDjmhfCWLVuoU6dO4KLXZrMxYMAAFi9ezJNPPklKSgqrV6/m/vvvDzzHf0v1m2++mT59+pS43aIXO/5f0INdcskldOvWjcWLF/P7778zc+ZM3nnnHaZOnRrSH9GpuANYZSnts+NYnym6rqMoCu+8806Jr9d/Ae31ernpppsCfTk1a9aMiIgIDh8+zPjx44vd7v5Ejl1cXFzYOle22Wycd955/PDDD/z6669ccMEFJ32fVquVDh06sHLlSnbv3k1ycjLdunWjVq1aeDwe1q5dy8qVK2nWrFmxcOtUnaMV3Y+maQwePJjPPvuMp556itWrV3PkyJFSa5+X1AdVRTz++OP89NNPTJkypcQ7I9auXZtdu3YRHx8fMt9/fMsKDGfOnInb7eaSSy4J1KQ7dOhQoPz79u2jdu3aJdboqUx169Yt1ul8SUoKU/x3+svIyKBu3bqVXbQTVtr5Fs7v3JmZmWX2H1aWhIQEli1bViw0S05OBszzsSwff/wxPXr0KFZzun///kyaNCnww0tpLrroIh5++GG+/vrrah94SZNGIcRxq1mzJk6nk507dxZbtmPHDlRVDfwS06hRo1LvVFTUgAEDeO655/jqq6+YMGFCsX06HI4S74JYtBz169dH1/Vi6x49epTMzMzAnUmg8JfR33//nfXr1weCre7du7Ny5UpWrVpFRERESKfFJ+J4jt2J8v9KvGDBAlauXMmBAwdCmh8IIU6e7OxslixZUq47iZ0JLrjgAvbt2xdojlXUypUr2b9/f6Azbr+LL76YtLQ0li5dyjfffINhGFx88cWB5f4aWFarlV69epU4lFRrpCS1a9dm+PDhvPnmm3z//ffExcXx1ltvHfdrrV+/Prt37y4W+OzYsQMorEFQv359jhw5UqwmlH+94M+qU6lRo0YYhkGDBg1KPJ7+W9j/888/7Nq1i/Hjx3PrrbcyYMCAQHP6ytasWbNyNX30H1v/MfRzuVzs27cvcEz9503wnT5LoygKU6ZM4ZxzzmHcuHEhTfGC9+nvBsEvNTW1WEhSr169Uj//g7cFZg30devW8ccff1CjRg2aNWtGXFwcLVu2ZOXKlaxcubLUGkcVUd5jVxkuv/xysrOz+eGHH/jf//5HzZo1i3VPAWZH2wcPHizWsffxev7555k3bx6PPvpoiTX2gcD3vKLNmo8cOQJQ4k2W/A4ePEhGRgaDBg2if//+9O/fn+HDhwPm3Sf79+9f5T8L/LWtyvN3Bsf3ffZ4zvvK5N9u0bsmpqWllbtmaWkOHz6M2+2u8LnZunVr8vLyip0Xa9euDSwvy9GjR4t9xgCBG2X4m6iXxuVyBWr2VncSeAkhjpumaZx77rl8//33IR+MR48e5auvvqJr166BC4yLLrqIzZs3s3jx4mLbKenXkiuuuILHH3+cuXPn8uKLL4bss3fv3ixZsiRQRRnMW9H/9ttvIdvw/xpf9Nbi77//fshyML/01qlThw8++ACPx0OXLl0A84vmnj17+Pbbb+nYsWOxJgwVdTzHrjJcfvnl/P7773z44YfExcVx3nnnVdq2hRCFxo0bx5w5cwCz742rrrqKe++9l8suu4xFixaFuXThN3r0aBwOB08++WSxPk/S09N58skncTqdjBkzJmRZr169iIuLY+HChXzzzTd06NAhpJlhrVq1OPvss/n0008DF6bBSrqdfFFer7fYl/5atWpRu3btEpucHMt5551HcnIyCxcuDMzzeDzMnj2biIiIQEhx3nnn4fV6+eijj0Ke/8EHH6AoSsj7dURExCmr4XTRRRehaRrTpk0r9jltGEbg389fEyJ4HcMwmDVrVqWXqVOnTmzduvWY/x69evXCarUye/bskHJ9/vnnZGVlBT7/27ZtS4MGDZg1a1ax41rSdxObzca0adNo3749t99+O+vWrSu2zzlz5oQ8t+h3EDC/f6xbty7QNxeYfUd99tln1K9fnxYtWgTmd+vWDZfLxYcffhiofQ7mD3X//e9/OXLkSLHmjCeivMeuMrRq1YqkpCQ+//xzvvvuOwYNGlTi96xt27ZRUFBA586dQ+YfOHCg3AHSu+++y3vvvcftt99e6t03gUC/gJ9//nlgnq7rzJs3j7i4uMBd+cAMUIJDlJEjR/LGG2+EDBMnTgTM/l7feOONMpsRVgXt2rUL3K2xqJLef47n++zxnPeVqWfPnlgsFj755JOQ+UXfcyvCf5yKnptFz43S9O/fH6vVyscffxyYZxgGc+fOpU6dOiHbPXLkCNu3bw+562vTpk35448/Qj5PvV4v33zzDZGRkYGazf47Mxblv6N88HldXUmTRiFEqb744gt+/fXXYvNHjRrFvffeyx9//MH111/P9ddfj6ZpfPrpp7hcrpDb4Y4ePZpFixYxbtw4rrrqKtq2bUtGRgY//PADEyZMKLEa7YgRI8jOzuaVV14hOjqa22+/HYB77rmHX3/9leHDhzNs2DC8Xi9z5syhRYsWIU1lWrVqxZVXXsmnn35KZmYm3bt3Z/369cyfP58BAwZwzjnnhOyvW7dufP311yQmJgba9Ldp04aIiAh27drF4MGDK+V4+pX32FWGSy+9lBdffJHFixczbNiwct3SXQhx/FauXMkdd9wBmJ1+G4bBihUrmD9/PtOnT2fgwIFhLmF4NWnShMmTJ/PQQw8xePBgrr76aho0aMD+/fv5/PPPSUtL4+WXXy7W/NBqtXLhhRfy9ddfk5eXxyOPPFJs208++STXX389gwcPZujQoTRs2JCjR4/y119/cejQIf73v/+VWbacnBz69u3LwIEDadWqFREREfzxxx+sX7+e8ePHH/drvfbaa/n0008ZP348f//9N/Xr12fRokWsXr2axx57LHAR2K9fP3r06MErr7zC/v37SUpK4vfff+f777/nhhtuCDkWbdu2ZenSpbz//vvUrl2bBg0alNgnTmVo1KgR9957Ly+99BL79+9nwIABREZGsm/fPpYsWcLQoUMZPXo0zZo1o1GjRjz//PMcPnyYqKgoFi1adFKCuf79+/Pmm2+yfPnyEmsC+dWsWZPbbruNadOmMWbMGPr168fOnTv5+OOPad++faDZnKqqPPXUU9xxxx1cccUVDBkyhISEBHbs2MG2bduYOXNmsW07HA5mzJjBqFGjuOWWW5g9ezaJiYnUrFmTm2++mRkzZnDbbbfRt29fNm7cyC+//FKsudOtt97K119/zS233MLIkSOJjY3lyy+/ZN++fUydOjWkOVWnTp2wWCzs3LmTa6+9NjC/e/fugQv4krpbqKjyHrvKcsUVV/D8888DlLrtP/74A6fTWayZ3iOPPMLy5cuP2Ux68eLFvPjiizRp0oRmzZrx3//+N2T5ueeeG2jC2L9/f3r27MmMGTNIS0sjKSmJ77//nlWrVjFx4sSQ5og33ngjQKBz8rZt2xZrCeAPgVq0aMGAAQNCln355ZccOHAg0DH5ihUrePPNNwHzh0p/bbply5YxatQo7r77bu65554yX+uJstvt9O7dm6VLlzJu3LiQZaW9/5T3++zxnPeVKT4+nlGjRgUCzz59+rBly5bA3+aJ9A33xx9/UK9ePdq0aRMyv+i5UZq6desyatQoZs6cicfjoX379ixZsoSVK1cyZcqUkCacL7/8MvPnz+f7778PBKe33HILDz30EEOHDmXo0KE4HA6+/vpr/v77b+69997A9/3ly5fzzDPPMHDgQBo3bozb7WbVqlV89913tGvX7oy4kZUEXkKIUhX9RcRvyJAhtGzZko8++oiXXnqJGTNmYBgGHTp04MUXXwz5Eh4ZGclHH33E1KlTWbx4MfPnz6dWrVr07NmzWIehwW6//XaysrICodfw4cNp1aoVM2fOZNKkSbz++uvUrVuXe+65h+Tk5GJfep555hkaNGjA/PnzWbJkCfHx8dx2223cfffdxfbVtWtXvv7665BfSi0WC506deKPP/6o1F9QgXIfu8oQHx/Pueeey88//yzNGYU4ibKysgKB+a+//spFF12E0+nk/PPPD6mteia7+OKLadasGW+//Taff/456enpxMXF0aNHD2677bZSOy2+5JJL+L//+z8URQlpzujXokULvvjiC6ZNm8b8+fNJT0+nZs2atGnThrvuuuuY5XI4HAwbNozff/+d7777DsMwaNSoUSBIO14Oh4PZs2czZcoU5s+fT3Z2Nk2bNmXSpEkMGTIksJ6qqkyfPp3XX3+dhQsXMm/ePOrXr8/DDz8cuKuw3/jx43niiSd49dVXyc/P58orrzxpgReYF6hNmjThgw8+4I033gDMC7Rzzz2Xfv36AWYY+dZbb/HMM88wY8YM7HY7F154IcOHD6/0z5t27dqRlJTEN998U2bgBeaPYzVr1mTOnDlMmjSJ2NhYhg4dyv333x/yo0+fPn348MMPeeONN3jvvfcwDIOGDRsydOjQUrcdFRXFzJkzGTFiBDfffDMfffQRjRs35t5778VmszF37lyWLVtGhw4deO+997jttttCnh8fHx+owT5nzhwKCgpISkrirbfeKtacNyIigtatW7N+/fqQ7yH+kOuss86q9Gav5T12lWHw4MFMmTKFhg0b0qFDhxLX+fbbb7nwwgsrXPN98+bNgNnc9OGHHy62fNasWYHAS1EU3njjDV599VW++eYb5s2bR9OmTXnxxRcrPRT44osvQu4+u2zZskBT2a5duwb+Xf130UtISKjU/Zfmqquu4p577uHgwYMh3WuU9v5T3u+zx3PeV7YHH3wQh8PB//3f/7F06VI6derEzJkzuf766yvcp5qu6yxatIirr776hEKzBx98kNjYWD799FPmzZtHkyZNePHFF8v1Q/tll11GjRo1ePvtt5k5c2bgc2bChAlcd911gfUSExPp0aMH33//PcnJyYHPt7vuuovRo0ef9H7lqgLFqOye3oQQQlQpd911F//880+JzUqFEJVj4MCBjBs3jvPPP5/+/fvz8ssv07NnTzZv3swNN9xQrN8fIcTx+fLLL5k4cSI//fRT4M5p4vSVmppKnz59uPPOO0sMpjdt2sSVV17J/Pnzj9mfUXX1wgsv8PXXX7N48eJTEkx4vV4uueQSLr74Yu69996Tvr9w8bf+uPfeewM1s4/HkiVLeOCBB1i8ePFJ6bNQVC7pw0sIIaqxI0eOSO0uIU6BUaNG8dBDD9G3b18SEhLo0aMHYDZVOdbt1oUQx3bZZZdRr169Sul/R4Tf/Pnz8Xq9pX4/efvttxk4cOAZG3aBWfPrzjvvPGW1cDRNY9y4cXz88cfk5OSckn2ebP5mo8H8/eudffbZFdrmO++8w/DhwyXsOk1IDS8hhKiG9u7dy+rVq/n8889Zv349ixcvPmVV4oU4U61fv55Dhw7Rq1evwK3Cf/rpJ6Kjoyu9abQQQpyOli5dyvbt23nttdfo0aMH06ZNC3eRRDU2b9485s+fz3nnnUdERASrV6/mq6++onfv3iX21SeqHwm8hBCiGvLffrtevXo88sgj/Otf/wp3kYQ4I7hcLvbt20ejRo0q7e6uQghRXYwcOZI1a9bQuXNnpkyZUmZ/rkKcqL///psXX3yRTZs2kZOTQ61atbjooou49957Az9MiepNAi8hhBBCiBOUl5fH008/zZdffgnAokWLaNiwIU8//TR16tTh1ltvDW8BhRBCCCHOMNKHlxBCCCHECXrppZfYvHkzs2bNwm63B+b37NmThQsXhrFkQgghhBBnprAGXitWrOD222+nd+/eJCUlsWTJkpDlhmHw2muv0bt3bzp06MCNN97Irl27QtZJT0/ngQceoEuXLnTr1o3HHnvsuDvZMwyD7OxspLKbEEIIISri+++/54knnqBbt24h81u2bMmePXvCVKqTS74/CSGEEKIqC2vglZubS1JSEk8++WSJy9955x1mz57NU089xWeffYbT6WT06NEUFBQE1nnwwQfZtm0b77//Pm+99RYrV67kiSeeOK5y5OTk0LVr12pzNwohhBBCnFqpqanUqlWr2Py8vDwURQlDiU4++f4khBCnXl5eHueffz7nn38+eXl54S4MnH++OYS7LEKUIKy9qfbt25e+ffuWuMwwDGbNmsUdd9zBgAEDAHjhhRfo1asXS5YsYdCgQWzfvp1ff/2Vzz//nPbt2wPw+OOPc+utt/Lwww9LJ4hCCCGEOCXatWvHTz/9xMiRI0Pm/9///R+dOnUKT6GEEEJUO06nk59++incxTA5nVBVyiJECars7YP27dtHcnIyvXr1CsyLjo6mY8eOrFmzhkGDBrFmzRpiYmICYRdAr169UFWVdevWceGFF4aj6KE2vwLJf4A1GizR5tgaC7YahYM9HhwJ5li1hrvEQgghhDhO9913H7fccgvbtm3D6/Uya9Ystm/fzpo1a5g9e3a4iyeEEEIIccapsoFXcnIyQLHmAbVq1eLo0aMAHD16lJo1a4Yst1gsxMbGBp4fVroX1j0BnuzyP8daA5x1wFnPHCLqQ2RTiG4B0S3BngCK5htUqKbNJIQQQojTSbdu3fjvf//L22+/TWJiIr///jtt2rRh7ty5JCUlhbt4QgghxGnJqxtoatW85q3KZROmKht4VQuqBv1/gn3zwZUOnhwz/HJngScT3JnmtNs3jQ7uNHPI3FzyNi1RENkYopqbQ0wSxLYBS6RZO0y1meNAKGYxx6ol6LHF91hu0imEEOLE+DssNzDKfFzWOuWZF2GNQK3in1uNGjXimWeeCXcxhBBCVGM5OTk0adIEgF27dhEZGRnOwoCvLOzaBSehLJqqMG7uGrYdOY5KJKdAi9pRvHZd53AXQxxDlQ28EhISAEhJSaF27dqB+SkpKbRq1QqA+Ph4UlNTQ57n8XjIyMgIPD/sanU1h6IMA3Q36C7QC8wgLPcgZO+C3N2Qtx/yj4ArBfKTIf+g+diTDRl/m4OfYjVrgMW0hthWENPWDMACFw2KWRMsJADTzOepdl9ApgYFYZqv5pjqm6/6povMU1TfttUS1g1aJrXQhBDVkGEYGBjohh6YLmsMnNA6JS0rOi+4XIbhK1sJ04Gxf7roOhgYurk9Hd0c63rIuv59+/dZ0uPgYxW8PGTdoHn+yaLbURWV1gmtaRTbqIL/WidHdnb5v3xHRUWdxJIIIYQ4k/hbPFUJp6As245k8/eBzJO+H1H9VNnAq0GDBiQkJLB06VJat24NmF8s165dy7BhwwDo3LkzmZmZbNiwgXbt2gHw559/ous6HTp0CFvZy0VRQLOZA1Fgr2XW3Eo4xwzC/DXBXGmQf9R8rLvAkwW5ByB7K2RtM2uCuVIhc5M57MMMrmp0goRzIaE3RDUFw2sOuhfwgu4BPdfcnqEDhhnCofvGYAZWBD2mcL7/NRQNvSA0IAuEY8E1zHyBG8E1z9TSg7NjLQtMS7AmRHUUHCz5wyV/MHO8j0ub1nUdr+HFa3jN/eg6Ojpe3YuOjqEbhctKCI6Cy+n/z/y/fOsBhdMKgbHie78tukxRlJB5Coq5/aDlfv5tKIoSMl3Scv/joncVPN51SttX8HpFt1d0XrAjOUfQDb3Y/HDr1q3bMe/AaBgGiqKwadOmU1QqIYQQQggBYQ68cnJy2LNnT+Dxvn372LRpE7GxsdSrV49Ro0Yxffp0GjduTIMGDXjttdeoXbt24K6NzZs3p0+fPvznP/9hwoQJuN1unn76aQYNGnR636FRtRZ2aB/ZCLwuM9TKP2zW8rLFQXx3czkq5O6D9LWQthZSV0LuXnOcuhK2vAZRLaDev+Csf4GzbuWUMSQcM4JCM73IfC8YHjDyg56jF3lOcKCmFF7sBfPXUIMitcjKCtYsvppsQU04FY0yQ7VSl/trvQkhgumGLxTyBUHBQ3BAVNbg0T14dS8ew4OuFz72Gl48uie0FpVhmAFUUA0lfxBSNIgCQiq6Bh4XnTYKw6CSxv5mdEXn++dpilYsCCq6TlnTJYVHIlRVbco4a9asSt/m1KlTmTZtWsi8pk2b8u233wJQUFDA5MmTWbhwIS6Xi969e/Pkk08SHx8fWP/AgQM89dRTLFu2jIiICK644goeeOABLJbCr33Lli1j8uTJbN26lbPOOos77riDIUOGVPrrEUIIIYQIl7AGXhs2bGDUqFGBx5MmTQLgyiuvZPLkydxyyy3k5eXxxBNPkJmZSdeuXXn33Xex2+2B50yZMoWnn36aG264AVVVueiii3j88cdP+Ws5qTSbGVQ564I33wy+sndBzn6wOMF5FkQ2hPqXmuvn7DHvDHn0d0hZAdnb4J9p5lCjCzS6BupcYIZAFaUogAan4hotJFzzBWbF5pUSrIUEcCW+EHxXu4TWFCtSc8wffgX6RwvqCy0wTy0chwRlweFZ0OOQcE0udsWp4Q+ovIY3MA6eV+K07sWtu/HoHtxec+zRPWZAFVSDKngIBFNGYc2mkkIdf+0XVVFRFRUFc9ofBAVPa4qGohafHxxK+bchAZI4Fc4+++yTst2WLVvy/vvvBx5rmhaYfu655/j555959dVXiY6O5umnn+buu+9m7ty5AHi9Xm677Tbi4+OZO3cuR44c4ZFHHsFqtXL//fcDsHfvXm677Tauu+46pkyZwtKlS3n88cdJSEigT58+J+U1CSGEEEKcamENvHr06MGWLVtKXa4oCuPGjWPcuHGlrhMXF8dLL710MopXNWkOs+mjsx7kHYKcXWZ/X9Y4sMWa60Q2Mocm15md4R/6Hg58A2mrCwd7bWg0BBpcaTanrMpORbhm+GqjlVhbzR+cecFTUEqgBqHV0oKrqQX3oVZKmBYSovkDNCto1hLCsZLCNQnSzgSGYQSCKo/uCQmtgmtF+ccujwu37sblNcdur9sMsnzN9UJqZ/ma7QU3j/M3m/MHTCUNCgoW1RISQhVdLuGTOFNkZGTw+eefs337dgBatGjBkCFDiIuLO67taJpWYl+kWVlZfPHFF0yZMoWePXsCZgB2ySWX8Ndff9GpUyd+++03tm3bxvvvv098fDytW7dm3LhxTJkyhbvvvhubzcbcuXNp0KAB48ePB8wa86tWreKDDz6QwEsIIYQQ1UaV7cNLHINqNWt1OeuaNbqytkHOPnDWMZf5WWOg4ZXmkHcI9v0X9n4BBUdg61vm4KwH0YkQk2je9TE6CRx1zqywRFHMsOlkCK6NFhyqBQdqugfIKyFMC27yWbStZ1lBWlD4pVpLqJFmKSUkKxKmlbjsDDovThJ/cBWoKeUb/EGWP8xyeV0UeAtweVyB0Kq0mlhF+3wCAqGTpmjmWNVQULBq1mLBVXCzPCHE8VuxYgW333470dHRgX5FZ8+ezRtvvMFbb71F9+7dy72t3bt307t3b+x2O506deKBBx6gXr16bNiwAbfbTa9evQLrNm/enHr16gUCr7/++ovExMSQJo69e/fmqaeeYtu2bbRp04a//vorEJgFr/Pcc8+d4FEQQgghhKg6JPA63alWiG5u1tLK2gq5+8EabfbzVZSzLrS8DZrfBIeWwO5Pzbs95h0whyM/Fa5rjTPDr5hWENvaHDvrS9hRESE11CrxT+6YQZrXvAGCN7dwfplNPP3NO4Pvslmktpi/JppqMWug+WukqVZff2klBWdFQ7TqF6AZhhEIqvxN/4o2/8v35FPgLaDAU0CBtyC0NpZvOnBnO98hUTFDquDASlM0rKq12HwhRHhNnDiRSy65hKeeeirQBNHr9TJhwgQmTpzIggULyrWdDh06MGnSJJo2bUpycjJvvPEGw4cPZ8GCBRw9ehSr1UpMTEzIc2rVqkVycjJg3rkrOOwCAo+PtU52djb5+fk4HI7jPwBCCCFOCVVV6datW2A6zIUBX1kId1mEKIEEXtWFLc7sn8ueYN6tMf8IOGqXvK5qg3qXmIM7EzL/gax/IHOLOeTsBHc6pCwzBz9rjBl8/T975x0fRbX+4WdmtqX3UEIHE2roIkpRLooIWEDhhxQLiGLBehH0XhVFwXoV8Sp2il1BL0pR7CJKEUEBkV5CCwmp23fm98fsbnazG0hCYEM4D5/DzJw5M/PO7GTKd973PfFtvCJYG907rA4IFmckp0xIK+d9FjTuFdHc4eZDmN4GdPskhTIBTSLYgyxAQPMPwwloYYSz8iJaDQs/vtxVLo8rZOj0OLG77dhcNuwee5B3lltzl+Wt8qpXPi8qRVYwyAYUScGkmPx1wsNKIDiz2bNnDy+88EJQvi1FUbj++uv59NNPK72evn37+sdbt25Nx44dueiii1i6dKkQogQCgUBAVFQUa9asibQZOlFRUFtsEQjCIASvuoSsQGwzUKKg8E+998aohl5vnQowxkNKN7348Dj0RPeFf0GRtxRv18WxvNV68S+fAAltA0o7MKeGbkdw5uD38Kohwglo5b3Q3OVENX8oZ5guOwNDNv1hnEqAB5pS5nUmGXSBVzYECWQaMi7Vg1P14NI0nKobl6ri0jzY3E5sHic2tx1nuZDD8qGDBtngL4qkYDaa9XFZqbW9ygkEglND27Zt2blzJy1atAiq37lzJ61bt672euPj42nWrBl79+7l/PPPx+VyUVRUFOTllZeX58/5lZqaysaNG4PWcfToUYCgNr66wDaxsbFCVBMIBAKBQFBnEIJXXSSqnp7cvuCPMtGrKj0yKmZduEpoV1anuqB4h+49VrjZK4JtA1chHF2lFx+WemXiV0J7SGgNhtia2z/BmcUpF9A8ZWGdmhtUBy6nC6fHgdOj575yup04NTdWlwOrx4HN5cSluXGrKi5NRQU9QbsmIcsKBtmIUTFikA1EKWYMshmDwYwsG0Hx5kPz7ZOkguTWh3j0fGxagBcbAeKc8OASCOosY8eO5fHHH2fPnj107NgRgA0bNvDOO+9w33338ddff/nbVkUAKy0tZd++faSlpdG+fXuMRiOrVq1iwIABgC6oHThwgE6dOgHQqVMnXnnlFfLy8khJ0Tul+fnnn4mNjaVVq1b+Nj/88EPQdn7++Wf/OgQCgUAgEAjqAkLwqquYEiC5qy5Ole6B6IyqiV7lkY26cJXQWk+AD6A6ddGrcLO3bIKSXWA/rJfD33oXliC2eZmIltAe4lqdnD2CsxdJxq2pODweHB4nTo8Lh8eFw+Ok1G2j2GnFpbpxedy4ND0RvHdBDLKCQY7CaInDJBuIRsYoyyiSXOZZ5hfSAsZVKzhL9Pma57gdcpYJXFLZeNjeOBXAUNYTZ5AwFuC1RqD3Wrk2AsEZjqqpeucM3tx2vlBhn1jt67ShxFlC+/T2kTb3uNxzzz0APP3002Hn6aK63vvpli1bKlzPk08+yUUXXUTDhg05cuQIL774IrIsM3jwYOLi4hg2bBgzZ84kISGB2NhYpk+fTufOnf1iVa9evWjVqhWTJ0/mn//8J7m5uTz//POMGjUKk8kEwP/93//xzjvv8NRTTzFs2DB++eUXli5dypw5c2r+wAgEAoGgRrFarbRt2xaAzZs3Ex0dHUljwGsLmzdDJG0RCMIgFIe6jCEKEtsDKpTuO3nRqzyyKdQTzF2qe38VboaCP/Wh/SCU7NRLjjdpr2zWk+IntNdtTGgPUQ2EB4wA0JPAO1VdxPKJWQ6PixKnlWJXKQ6PC5fHjVN16WGGSEgSGGUjRlnBKBuwmKK94YaVzetVA/m/NC2gd81AAU0r641Ts+t1gW3C9SEAZUIZXuELqZyA5g3VlA0E9cQZJJzJYYSyCkQ1gQA9d53D49Dz07ntevHYy8bddhxuR9m4xxFU75v2D711DneZoOUI8MCsLAmWBO7occcp3POT4+uvv66R9Rw6dIh77rmHgoICkpOT6dq1Kx9++CHJyckAPPDAA8iyzKRJk3A6nfTq1YuHH37Yv7yiKLzyyis88sgjjBgxgqioKK666iomTZrkb9O4cWPmzJnDjBkzmDdvHvXr12f69On07t27RvZBIBAIBKcOTdPYs2ePfzzCxoDXlvCdYgkEkUXSqvFXMnbsWGbPnh3SS1BJSQm33nor8+bNqzEDTwclJSV07dqVdevWERtbB0PvPHY49jvYDuqil3Sae3Rz5Hk9wP6Egk26J5i7OLSdKVkXz3wCWEI7MNbB30Pgx+kVs+weJw6PE5vLQYnbSpHT6n0Z1nNnad7EWUZZwagYMckGjLLB21thHfZ08nmahRPPyo+H8z4rT2APnAQKZ75i1OtlY5mQJns7BAjJlRbG+0yEb542VE3F5rJhc9uwuWxY3VZ9OmDc6rJic9v8nSuUn7a77frybhsOt8M/ryoiVE2iSApmgxmzYsakmPShQR9GG6N5bsBz9GrSKyK2CcJT55+fBAKBoBZSWlrqv+aWlJQQExMTSWPAd/0vKYFTZMugWT+y6UDRKVl3dWnXMJ4vJokPRbWdarn7rF69GpfLFVLvcDhYt27dSRslqGEUCyRm6y/H1gNe0es0igTmFEjvrRfQX9Ct+7weYH/qw+K/wZkPuT/qBQAJYpqVCWCJ7SG2pQiFPMPQNA27x4HdrQtbdreDYqeVIlcJdm9Iosuji1oSEgbFgFk2YlJMxBqjMcqGs7f3Qkn2hknWkPcZgQIagCfY+wxHgIdaYO+bgTbhT9zv73UTr4jmF86kMo8zn3CGwZv/rJyHGWEEs7BeaWc2mqZhd9spdZVS6iyl1FWK1WWlxFmC1WUNGre5bZQ69flWtxWr0zt0BQtZpwOLweIvZsVMlDEKi2LRhSmDmShDFGZFHw9sZzFY/OKVb+ibZzbogpZv2qSYMBv0Dh8qYn/RfhrFNzot+3wyHD58mHXr1pGfn4+qqkHzxo4dGyGrBAKBQCAQCM5OqqQcBCZc3b59O7m5uf5pVVX58ccfqVevXs1ZJ6g5DNGQ2AGOucs8vSKFJENMU71kDNLrPHYo2lomgBVuAtsBKN2ll8BQyIQ2waGQlnrCm6QW4BO2bG5d3LJ5HBQ5SyhylmJ3O3GqLjyqLqYYZAMmxYhZNhJjjjq7Ra3Thd8jC2okmj3I0yxQJPMJaHavyOYV13ydCxzPvkABTfIOZZ/gpQSEbCplXminIXzTrbopcZZUqvgErRKXdzpA3NLDb2sWCYloYzRRxiiiDFH6uHdoMVqIMuj1vvm+oU+c8td766KMZfNMikn0NloFFi5cyEMPPYTRaCQpKSloniRJQvASCAQCgUAgOM1U6a3nyiuvRJIkJEniuuuuC5lvsVj417/+VWPGCWoYY6wuEuWtBcdRMKdG2qIyFAskddSLD0eeLnz5PMEKN+k5wo79rhcf5pSyEMjE9hDfVoRCnmKcHhc2twOb247N7aDIWUqBswS724HD40TVNCQJDLLurRVtsJCoxFUhn5ag1uMXqGrS+4zwnQdoLtAcwR0JlPc+C+w8AILCN1UNSt0Oitx2il12ilw2il0Oil02it0OilxWSlw2fdqbJ67EWUqxs5Ril35e1xSyJBNtjCbGGEOMMYZoU7R/OtpYNh5jiiHKEEWMyVtv0EWtGGMMUcYyYctisAixuJbwwgsvcNttt3HzzTcj1+VQa4FAIBAIBIIzhCoJXl9//TWaptG/f38++ugjfwJVAKPRSEpKCooiXmhrNaYkSGgL+b+BuwQMtVgYMqdAeh+9gP6SW7onQAD7E4q368LYke/1AvhDIRPaQWI7fX/jztGT7AuqhM9ry+qyY3XbKXXZOOYoptRtw+F24VL10GajbMCsmIg2WEgyx9ftvFqCmifI+6zie4hb9fi9BgscJf7xQu+wyFlCobOUIodvupRiVynFTivqcZObVY4oxUSsMUovBguxxuiyaVMMMcZoYkwxxBpjiTXFEmOOI9oYRawxjlhjDDGmOCxGC5JsKPNgC+ehVkfCOM827HY7gwYNEmKXQCAQCAQCQS2hSoJXRoYeBhcY2ig4A4lqCPGlunAUbTpzhCBJhtjmemk0RK/z2PVeIX1hkIWbwZZTFgp54HPvskaIP6esV8mEtrooJl4q/Wiahs3toNRtw+qyU+Qs4ZijWA9R9DhQNQ1ZkrEoJiyKiVhLNEZF5FMTVA+36qbAUUKBo5gCp3foLYWOEgqcJRR65xU6Sih0llDisp7UNs2KiXhTDHHGaOJMMcSbook16sM4YwxxpmhijdH+NrHe6ThDFDFGMwZJqaADAa3MGy0whNPfA2ep7p3qPhLsiVa+E4GQXjiVgDxoXm+6kI4EAtbhD9s8Qb3glDBs2DCWLVvGhAkTIm2KQCAQCOowkiTRtm1b/3iEjQGvLSLFjKA2Uu231d27d/Prr7+Sl5cXkpj19ttvP2nDBKcQSdKTv7tKdVEoptHp77mxplAskNRJLz6cx8p6g/QVV6G3p8jNwEfeZaMhvrVXAGuji2BRGWfFxdrnuVXq9dwqsBdzzFmM3e3A7nGiaioGWcEivLYElcTlcXPMUUS+o4hjdu/QUUyBo4h8e5FX3PLVFVN8EuJVnDGaBHMs8aZYEkwxJJhiifMPo0kwxRFviibeFEu8KcZfTIqxBva0hq6VWhjRLCiM0wOqq6zd8ToSCMQnlgWJaAH50PwCmFdI8xVfbrSgPGc+8S1gOlydENMAuPfee7n55pv58ccfyczMxGAIfsSaOnVqhCwTCAQCQV0iOjqaTZs2RdoMnehoqC22CARhqJbg9eGHH/LII4+QlJREampqkLIsSZIQvM4EZAUS2+iJpa05YIjRwx3rwguLKQnSe+kF9BdGW45X8PJ6gRVtAY8Vjv2mFx/GeF0Ei28LCa0hvo3uEXeGi2Auj5tSt40Sl5UiRyn5jkJKXXbsHgeaBoosE6WYiTFaSLbEi0TVAgCsLjv5jkKO2grJdxSSby8kz15Evr0wWNiyF1VLwJKQSDDHkmCKJckcR6K3JJhiSTTHkmCK04fe8QRTDHGmmLqRCy4ojLMG8Xua+XKdQVlvnF4PtOoIaRDgNQZhPdLCimlyQGcDYXr0DBovJ54FCmqnIOF/TTNnzhx++uknmjdvHjIv4l/gBQKBQCAQCM5CqiV4vfzyy9x1113Cbf9MR7FASje918aSnVC6D0wJYEw44wWeICQJohvppcElep3mgZJdZV5fhZuheBu4iiBvtV58GBMgPksXv+Jb695gtdgTTNM0f76tEpeVPHshRc5SbG4HbtWDJElEGcxEGczCc+ssxK16yLcXctReQJ69kDybb7yAo97pPLsucNmqmKxdkRSSzHF6scSTZI73C1lJ5niSLHFBdXHGGHH+1TSSfMJcaNUirEdamLrKimkhnQxAsJdYoDeZBKV5kJQBNKvZ/apB3nrrLZ544gmGDh0aaVMEAoFAIBAIBFRT8CosLGTgwIE1bYsgEshGiGkClnTd06tkF5TuBXMyGOMibd2pQ1IgrpVeGl2u16kuKN4BRZuhcIvuBVa8XQ+HLC+CGWIhLtPrDZall5hmuhfDacajenTvLaeNImcJufYCrC47NrcdAJNiItpgJi0qEUME7BOcHjyqSr6jkFzbMXJtBeTajnHUN7QXkGvTRa18exFaFRK4WxQTKZYEUiwJJFniSTbr48mWeL2YE0iyxJFsTiDOFC28A+sqp8ojzYffM60CQc1VDB7nKdp4zWAymejSpUukzRAIBAJBHcdqtdK9e3cA1qxZQ3R0dCSNAa8trFmjhzgKBLWIar39Xnrppfz000+MHDmypu0RRArFAnEtIaoBWPdByW49F5Y5FQxnyYVLNuphjAmtobG3TnXqolfhFj05ftFW3RPMXRIaDimbILaVVwDL1AWxuFZ6uGgN4lbdlLhsFDtLKXAUc9RWiNVtx6m6kCWZKIMemphiSRBhNHUEq8vOEVs+R2zHOGI9xhFbPrneaZ+4lWcvQNUqJ2QpkkyyJYFUS6JfzEqN8o0nkmKJ9w4TiDZaTvHeCQQEeKZVRO0PYx07diwLFizgX//6V6RNEQgEAkEdRtM0Nm/e7B+PsDHgtYVI2yIQhKFaglfTpk154YUX2LBhQ9jErGPHjq0R4wQRwBCtCzZRDaBkD9j2gSMfLKm6KHa2IZv0ZPYJbcvqVLceAlq0VRfBirdC0d96TrCizXoJJLoRxJ3jFcDO0UsV8oK5VTfFTivFLivH7EXk2XWBy626UWRFTypvicOsnCG9bQqCsLntHLbmc8iax2FrPkds+frQW3fEdqzSvRMqkuwVr5JIi0okzZJEalSiXiyJpEXp04mmOBFKKBDUMBs3buSXX37h22+/5Zxzzgl5Npo9e3aELBMIBAKBQCA4O6mW4PXBBx8QHR3N6tWrWb16ddA8SZKE4FUXMMZDUge9B8fi3WDP0VV7S6ouAp3NyAbdgys+Exii12mqHhJavNUrhHk9wRy5YN2vl8Pflq1DidE96uJa6V5hca30njNNCXhUD8UuK8XOUvLtxeTZC7C67bhUN0bZQLTBQqolEaMiwhNrOx5V5ai9gIOlRzlkzfOKWkc5ZM3nsHe6yFlaqXVFGyzUi04mLSqJelHJpEUnkR6VRFpUsneYJHKyCQQRJD4+nksuuSTSZggEAoFAIBAIvFTrjfmbb76paTsEtRVTEiQngqMxlO7SE9xLih7qKPJBlSHJENNYL/X7l9U7j+neX8XbykrJLvCUQsFGvQTgMiZRbGpAgSmdY8Z0bJaGKNFNRf6tWorT4+KwNZ+D1qMcKM3lUGkeB61HOViay8FS3TvLo3lOuJ4YYxT1opKpH51CenQy9aKTSY/Sh7rIlUysMeo07JFAIKguM2bMiLQJAoFAIBAIBIIAxBu04MRIku7ZZU4G+xFdsLEdAsUEpmQhfB0PUxKk9tCLD9WNo2gb9oLNeLyeYBZbDtHufIyuYyS7jpFcGhwW6TClYrc0xGbJwG7JwB6Vgc3SEFU5S/KrRQi36uaQNZ+DpbnklOZysPQoB0p1cetgaS65toITJoBXJIV60UnUi06hfnQq9aNTqB+dQr3oZP94rEn8jgKBQCAQCAQCgUBQk1RLqZg6depx54uvnHUUSYao+mBOA/thPY+V7aCe28ucrHt+CcLiC1MsdJSQazvGMUcRVk8KWkxPohIuItYYRbSkEmXPIcq2D4sthyj7fqJs+zG6CzE7j2J2HiWhKNgjzGlMxG5pqBdzQ+yWBtgtDXEZkyqdI+xsRtM0ChzF5JQeYX9JLjmlRzjgHeaU5HLYlnfCRPBmxUTDmFQaRKdSPyaVBtEpNIhJpX50Kg1iUkm1JIowQ4HgLGHZsmUsXbqUgwcP4nK5guYtWrQoQlYJBAKBQCAQnJ1US/AqKioKmna73Wzbto2ioiLOO++8GjFMUIuRFYhuCJZ0sB/SPb5Kc/SE9+YkIXx5sbntFDpKKHAUc9iWT4nThlN1YVKMxBqiSIyNQ5bKhBAVKI1pRWlMq6D1KO5iouwHsNhysNhziLLnYLEfwOQ6hslVgMlVQHxxsEeYR7ZgN9fHYWmA3VIfu7kBDkt97Ob6qMrZFRrnVj0csh5lf8kR9pccZn+JLmbtLzlMTmkuVrf9uMubZCMNYlLJiEmjQUwqDWPSaOgfTyXJHC96wxQIBMybN4///Oc/DB06lK+//pqhQ4eyb98+/vjjD0aNGhVp8wQCgUBQR5AkiaZNm/rHI2wMeG0RH9sFtZFqCV4vvfRSSJ2qqjzyyCM0btz4pI0SnCHIBr0HQks9PcSxZKeeuN0QrYfynWXCl6qpFDutFDpLOGLNJ99RhNVlQ0IixhhFsiUek2Ks8no9hjhKYrMoic0Kqpc9Vl0Isx/AYj/oH5odh1FUOzG23cTYdoesz2VIwG6pj8NcTxfFzPVxmNNxmOuhnqE9cTo9Lg6U5rKv5DD7ig+zr+Qw+0v04cHSo3g0tcJlJSTSo5LIiNWFrIzYdDJi0r3jaaRYEoKESYFAIAjHu+++y2OPPcbgwYNZuHAhN910E40bN+aFF16gsLAw0uYJBAKBoI4QHR3N7t27I22GTnQ01BZbBIIw1FjyJVmWuf766xk7diw33XRTTa1WcCYgG/Vk7ZZ6Z53Hl8vjptBZwjF7EYdseRQ5Ssu8uIzRJJnjTplYoirRYT3CJNWNyXnEK4IdxOLQRTCL/RBGd6FeSgqJK9kauj+GBBzmel4BLN0vhDlMabgN8RH9cuPyuMkpzWVfySH2Fh9iX/Fh9pYcYn/JYQ5Zjx96aJKNZMSm0Sg2nUYx9ciITdfHY9NpEJOKWTnLex4VCAQnzcGDB+ncuTMAFouF0lK9B9YrrriCESNG8NBDD0XSPIFAIBAIBIKzjhrNNr5v3z7cbndNrlJwJqGYIKYJRDXQPb5Kd4P1ACjmOpXc3ua2U+Ao4ajtGEdsxyh12VA1lWhjFEmWuIiLJ5pswGFpiMPSkPI+BbLHisV+CLPjEBbHIcz2w/rQcQSDp8QviMWW/h2yXo9sxmFKx2lOw2FOw2FKw2nSx52m1BoJlVQ1lcPWfPYUH2Rv8aGyUnLohJ5a0QYLjWLr0Tg2ncZx9WgUW887XY+0qEThpSUQCE4pqampFBYWkpGRQYMGDfj9999p3bo1+/fvRztBLkCBQCAQCAQCQc1TLQWifFJ6TdPIzc3lu+++46qrrqoRwwRnMD6Pr6j63uT2e3QBTDaAOUWfHw5VBZcbPCq43fq0RwWPB1RNn9a04BKIJAUXWQZFLjdU9HGDoo9XwmNJ0zRKXFY9F1dgqKIkE2eMpl50Cgb5zPBiU5VorDEtsMa0CJmnuEsxOw57yxHMziP+caOrAEV1EG3fR7R9X9h1u5VYr/iVgtOU6i0pOLxDjxLrP95FzlL2FB1kT7GvHGJP8UH2lxzG4XGFXT9AlMFM49j6NImrR+PY+jSOraePx9UnWeTSEggEEeS8887jm2++oW3btgwbNowZM2awfPly/vzzTy6++OJImycQCASCOoLNZqNPnz4A/PDDD0RFRTA/r80GXlv44QeIpC0CQRiqJXht3hycIFuWZZKTk5kyZQrDhg2rEcMEdQDZ6M3x1QAcR6BoJ+TvA4cLpFjwyGB3gM0ONgc4neD26CKXpurjqgZogAQSXpHLO453WpLKxC9J1pctaxAsfknlRDCTCSwmiDLr4wYDmIyoBpkizUmBZuegs4ACRzF2jxOTbCDWFFMnxRWPIQarIbwYJqlOTM48vxBmchzF7MzF5MjF7MzF4CnF4CnBYC0hxrpLX58Gu12wywV/OWGLS+Yvl4GtTg9H3Z4K7TDIilfIqk+T2Po0jWtA47h6NI1rQIoloc4dd4FAUDd47LHHUFXdC3XUqFEkJiayfv16+vXrx4gRIyJsnUAgEAjqCqqqsnbtWv94hI0Bry1E2haBIAzVErzmz59f03YI6hKqqqv9VmvZsLAQSuxQokLJEbBvBs0NxjgwxYDRqHtdmUy6EKUoZaLUyaJpZR5iqs9jzOs9VlIKBUXg9uBR3RRqdvLdVg5qpRRJTpyyRpQpmvjYeNJj4sEk6cKcUQWjQbdbrvuhcppswmFpgMPSIGSe1WVnb+Fu9uX/zd7CPewuPsTO0nx22UpxBnnhqYDTP9VQgSyTtxj1YQuLhYbRyaimFFymWJxGEy6jistkx6kV4HJLuA1xunApEAgEtQhZlpED7geDBg1i0KBBEbRIIBAIBAKB4OzmpJIq5efns3PnTgBatGhBcnJyjRglOIPQNF3UKi3VS0GBXux2cDh0YUmWwez1oEptBPWbgWYD+xGw54LHCgYjGKJOTYJ7SQJFCiueuTUPBW4rea5iDjkLKfLYcGsq0ZhJ1mIxaRK4PHDMCkeLyzzJFEUX6AwGsJh1LzGL2e8h5p9nNNQZQeyYvYhdRQfYVXSA3UUH2FWsjx+25lW4jFkx0ji2Ps3j6tMiOoFzoqLINCtkGlSS1SJMrnxMznyMrnwMHitgB8cBvVSAhoLLmIDLmFhxMSTgMsaDVDfyxgkEgtrPDz/8QHR0NN26dQPgnXfe4cMPP6RVq1Y89NBDJCQkRNhCgUAgEAgEgrOLar0NWq1WHnvsMT777DO/G6WiKFxxxRX8+9//jmwcseDUoqq6sFVSonttHT2qT9vtuhhkNILFArGxkJJyHLEnTvfuimoIjjw915f9qJ7nyxgLsvmU7YJLdesil7uEg44Cijw2NDRiZDPpxngM5UW3cKezx6OHXLrcUFwKxwq9brzecDujN0eYooQKYkZvqYWCmKZpHLEdY1dRDruKDrCzMIfdxfqw0FlS4XJJ5jiaxzekaVxDmsU3oHl8Bs3iG1A/OiVssvgSbwlE9ti84tcxTK58jE7v0HUMo6sAkzMfg7sICY8ulLnyT7g/LkMcbkOCLpAZEnAbE3AZ4nEZE7z18bgNetHqSKcKAoEgMjz99NPcd999AGzdupUZM2Zw44038uuvvzJz5syQ/KcCgUAgEAgEglNLtd7wZs6cyZo1a3j55Zfp2rUrAOvWrWP69OnMnDmTadOm1aiRggiiabqgVVwMx45Bbq4eomi360JNVBRER0NycvWEG0MUGBrpCe6dx3SvL+cxUI+BIUYvNRC+5lTdFLhLOeoq4ZCzgGKPHYBYxUx9U0KoyHUifGKWOVyPjBq4vYn33R4oKoFjnjJBTKLMQ0zxhnFGBQhihnKCmKHmvd58vSHuKNzPrqIcdhYdYFdhDruKcih12ytcrmFMKs3iG9I8LoPm8Q1pFq8LXInmuJO3SYnCHpWBPSqj4kaaG6OrCKOrwCuEHcPoKvROe4u7EKOrEAkVo7sYo7uYKPv+E27frUT7xS+XIQ63MR63Ic5bF4vL4JvWiyZHtjdOgUBQu9i/fz8tW7YE4Msvv6Rfv37cc889bNq0iQkTJkTYOoFAIBAIBIKzj2oJXsuXL2fWrFn06NHDX9e3b1/MZjN33XWXELzOdBwOKCrSPbgOH9bFLptNF7SioyE+HtLTa3absgEsaWBOBXeJ1+vLG/IoK2CIBcVSpVU6VTfH3KUc9YYrlnjsIEGsbKGBKRHllOWBkrwhjRUJVeUEsVIrFBWX9UIJZT1KGryCl9kIURY9XFJRyrzEFN9QDtvjpKqpHCw9ys6iHHYW5rCzKEcXtooPYHM7wlqnSDKNYuvRIj5DF7fiG9IiPoOm8fWJMlTtN6hxJAMuUzIu0wnCpzUVg7sEo6sAg7tQF8ncBRhcRRjdhf6h0VWIwV2MhIrBY9XDKh2HKmWKRzbhNsThUWK9IlisXhR96DHE4lbicBtivG1i8ShRIv+YQFBHMRqN2O36B4Off/6ZK6+8EoCEhARKSir2kBUIBAKBINJ4VA1FFh1DCeoe1RK87HY7qampIfUpKSn+hz3BGYSm6aJWYSHk5ZWFKWqa7sEVGwupqWEFlRpHkvRQR1+4o6tQD3V0HQNngS56GWNAMoZdPHIiV1U4kSCGLn653Xpifbdb780yr8A7U9M7rvSLYgqqInNQsrLTc4wdrqPssuWy03qIXdbD2D3OsJswyApN4xr4Ba0WCRk0j8+gSWx9jMoZHt4nybqHljH+xG01FcVTitFVhMFdhMFdjMFdhNFdhMFdUlbnKsbgKcbgLkbWPCiqE8WZB1ScwyxkU0h4lGivCKYXtxKDxxCDW4nW6wzeOiUGjyHaOx7tFctOQY47gaAWoWkaqqahoeLRVP+0W6u4Z9faQpcuXZgxYwZdunThjz/+4Pnnnwdg9+7d1K9fP7LGCQQCgaBOEe5d/GRQZIk731/P9iNV+0Bjdth4K0bPUXnDf1fiMNdsaqMLs9L454DWNbpOwdlFtd5qO3XqxKxZs3jqqacwm/VcS3a7ndmzZ9OpU6eatE9wKvF4YM8eOHBA9+iy2/XwuthYaNBAF1MiiWICJU33/HKX6oKXPRechaC6veGQsTg1LYzIJREnm2uRyFVFZFn/LcqhaiqHnIXstB9hh+0wO4uOsMuRy07HUeyaK+yqjCg0MyXT3JRKC0saLWLq0yKuIY3i62Mwmb3hmbLuTaYo4NEAT+R//9OFJOMxxOExxAHHCaf0oWnIqg2jqxjFU+wVxUp0UcyjjyvuEu94KYpvqNqR0DB4SjF4Sqtlqke2+MUvfRhdbjpKL3LAuLeosj7U5PBisUBQVTyqioaKqmmomneIb1wXqzyaioZXxNJU/7Tm78G17EOK75uKhIQiKUgSyOg9HyaaYzDW8jx7Dz30ENOmTWP58uU8/PDD1KtXD9CT2ffu3TvC1gkEAoGgrhATE0Nubm6Nr3f7kRI2HSiq8nIdb39HH8lzAeHfR6pLy7SYGl2f4OyjWk+PDzzwAOPHj6dPnz60bq0rrn/99Rcmk4k333yzRg0UnCJUFbZtgy1b9DDFhATwPpzXSnz5vKIagKsYp/0oBda9HC3Zw0FnESVooJiJU2LOXJErAE3TOOwqZIf9CDvtR9hpz2WH/Qi77LnY1PAeW0ZJoak5lRaWdFpY0mhpSae5JY1GSiIGVQPVo4tZHg8UeaDgkDeEUgI0r8eYXCaAKQYwGfQ8ZSZTWc4xnzgmy2AIGD8dHoC1AUlCVaJxKNFA5f9mJNWF4rHqgpin1Du0YvCUorhLMXj0acVdWlbvsepF1cNPFdWOotpP6llClQyossUrhFnwyFGogcOgOou3rW9oRpWDxzXJcPb89mcoPjGqTIBS8XgFKL9YRcD8AGEK8IpTEpLki7rWkCQJCQlZkpElCRkZSZJQZFmvR0ZRZAySgkE2YJBlDJIBo2LAICkosowiKfqykowiyUFD33r9bawHMMXVbi+phg0bMmfOnJD6Bx54IALWCAQCgUAgEAiqJXhlZWXx5ZdfsnjxYnbu3AnA4MGDGTJkCBZLhHP8CE6Mpuli119/6aGK0dGRtqhSuFQ3x1xFHHUWcNCZR4mqgiGWOCWWBh6bLgp4bOjeSWaQan9ScU3TOOIq0j22vMLWTq+wVaqGz7FlkBSamlNoYUmnpSXdK3Cl08icVPXk+4Goqi6KuVVdFHM6wWbXxzVvGKVutS5wGBRd6JIVr/Bl1AUyk7Esv5gihwpkSsU5x+oymmzELes9RVZ9YTeKx+YVwqwobmuZGOax6vNU79DbTvHYkD12b70VxSuUypob2aN7oNXIfqF4xS+9eGQLatB02bgqmwPmmcrNM3lL4LgJpNrt1VPTqFo5jym/GFUW4ucTo8pC/vS2Xuna+78+JXk7ypAlWfeWkgJEKm+dUTZglA0osoJRVjBIBgyy4q2TKydIhWkj1eTfuCI8EwUCgUAgEAgEVaNabxJz5swhJSWF4cOHB9V//PHH5Ofni96IajOaBjt26GJXcnKtF7ucqosCV7EucjmOUuK2AhCrRNMgqn6ZJ5fm0cUud6me98tjA0+xLsbIZl0AI3JeX5qmkesq9nprlQlbO+25lKjh894pyDS1pHpFrTS/wNXInHxywlZFyLJXlKpEW1XViydAJHNaodgrlmmBjcMIZIrk7YXSJ5KZdBEsIC9ZmT1nr0jmRzL4wy7D+/dVAk3VRTDV7hXE7N5xO4pqQ/bYvON2fVx1eAWzsnayakdWHfq4N4RWwqMn+/dYa2x3g8xGCRLAgopkQpONAdP6uBYwrsrGgOly47723mlfXWW91so8okI9p8qLU2UiVsXilM+LSpEUb1ifLhoFilOKogtUBlnRi2TAKCsYFSOy1+NKkb1t5fIiVZlAFShUyWe4R6xAIBAIBGcTNpuNgQMHArB06VKiomo2b1ZVMLsczP3oYQCuu2YaDqM5YrYIBOGoluD1wQcf8Mwzz4TUn3POOdx9991C8KqtaBrs3AmbNkFiIsTUzphop+rye3IdcuQFi1zmtPDhipK3J0dDLFjSwWMHtxVcJeAuBkeB3k4x6gKYbCQwd0yl8HiQnW5khwvJ5UZ2upFcHmSXW592e8Dt5oi7hO3qMbZzjO1SEdvkQrYpxRTL7rCrVTSJ5s4oMh3RZDliyLJHk+WIpoUjCqNfpPMAB4FDaBL6y7gsoUkSSBKa7BvKer0so3m9qzRF9k/rRUEz+IbecYPBO66gGr31RgOqQUEz6uNBAkBVxDEoJ5B5hy47lNp0gUzVyn4On1gmS2UimBwghvl6qDQavHnHAub5x73CmiyV1Z+tYpkPScZjiMFDDC5STn59mqdM/FId5cbtumCmOpA9jrL5atm4rDrDDJ3+acl7Ikh4UFQbimo7eZurgEcyoEoGPJIRVVK8Q4M3JNSABwVVNuhimaQPNdmI5q3zTUuyCUk2IRtMSJIJWTEjGyxIkhFkI7JiRpKMSIoJSTEjyyZkxYQsm/XlFCOKbEZWTCiyGUkW4pRAIBAIBGczqqry/fff+8cjiaxpnLfvT/+4QFDbqJbglZubS1paWkh9cnLyKUmgJ6gBPB7Yvl337EpI0BPT1yIcHicF7uJyIpdErBJVschVIRLIFmRVQrHLKCWgFLlRiouQi/NRSkpQSm0oNieyw43iUJEdLhSbC9nuRHY4vfNcyE6XLnA5vYKWF1WCffGwOS20FFUQ1auo0Cof2h2BtrnQLlcfz8zTMHusgBU4ehJH8dSi+kQwk8E/VI0GNO9QNRnQTEa93jvUTEZUs3fabCwblh83G1EtpqB5SJTlHfOJZB5vmKXmzUUGvqRC+L1kJK/IJXkFM0nWPcrk44hlcoBQ5l8mUDwTglkIkoKqRKMqp8BLVNOQNLcuXHvsSKodPA7wCmh4BTJ84pjqRPKJZZoLRXXpQ82ForrLxjW3t7iQveOy6hsPTozma2uklvU8LBl0wV42lo1LBpANeu+1QUNDxcMK65TQ+ZJB/6gglx9XTjBPCW4TMs+g/60JDzOBQCAQCAQCwSmgWoJXgwYN+O2332jcuHFQ/bp160hPT68RwwQ1iMulC13bt0NKSq3x7LJ7HBxzFXPUeYwjzmOUuKx674qBnlyahmy1YygsxlBU7B2WYCgqQSkuxVBcgqGoFKW4BENJKUqxFaW0FKXEGiRQVRe3DNuTYEsT2JIKW7yi1l+pUFpBijBFhRYlBloXm2hdbKZ1qYUsq5mW9ihMskH3xpJltHoS1JcokmWQ0L21ZAm8OXdC8CaLRkMXBABUDUlV9WlVK5tWNSSPiqSqSB4PeFR92qNPS+7yQw+yWx9KrrLpQHQPNg+KLXxusZpGNRm8ApgJj8UrkFlMqBZfnalMJIsy4TGbUKNMqCaDPu5d3mNSUI1GVJOCx2hAMyq6SKZqlIWTlQ38QhmBHmYBgpnBG3LpE8z84prXo0wut4zPy0yizvd8WVGIX1konxoc2uft0U8L+CIoIXmTpQeE8knRyEossrEsQbriDd0zykZ/3imjYkCRDUiSrBdZRpIUPcG65P2dJMXrGVk21Otl0Fy6qKr6iqNsPKjeCarLO99Vrs43DBjXfOPu4Pmar66i6TC9E2hu8Li9+QrrClIYEUwJEN8UUKKh+8sQ2yzSxgoEAoFAIBAIzhCqJXhdc801PPHEE7jdbs477zwAVq1axdNPP82NN95YowYKThKHAzZvhl279F4YI9ypgNVjp7Aol2OHdlOSuw81Lw9LoZXmxQ5iCm0YC4sxFBRjKCzSxa3C4pMSrjRZwhMTgycmqqxER6FGecejzKgWE8VRKtujrGwzF7HNWMQOQwk7pGL2UIKL8K7CBkmhiTmFFpY0mpv1HFvNLWk0NadglEP/tHKqvRcRQvUKZC43ssvj9XJz60NX2TAwxFMvLiSnq2za6yknBYzLDl/Rvez0oQvFUfaC71ue4pp9sdckqUwk84pmHotXLLN4hTOLEU+A95nHZNTrjAZ9aDKUCWteDzZkGb+HWaBo5hfBAsQwg9fLzJfQP1A4CxLM5GDxTQoQz2rA6yxUhNKChKiKBSoIEgv1I+vNNxU+Oboi6bmnTLLRn4OqLFm63mNfuNxT5XNO+YfyKRAPJRPItaizC03T8xP6RTOXLnipLl0Y03zDQJHMXa6Nb9odOq1VUKd5wtR7Qsf97QKmQ9p5ytpVcC3VhXzvtjiOoF68HRr0PwUHumZwOBzMnz+fX3/9lby8vCAhF2DRokURskwgEAgEAoHg7KRagtf48eMpKChg2rRpuFz6C6rZbGb8+PHcfPPNNWqg4CQoKIAtW+DgQWjYUE8QfqpwuSAvD44e1Yfeoh09ivvoEdS8XLS8fEz5BTRwOGlQxdV7LGbc8bF44mNx+0qcdzouRp8XG4MnLgZ3TDSeuBg8sdGoFrNfFNA0jVznMXbbDrLHdpA91oPstu1nt+0Ahxx5FW7bLBloZk6hhTmZZqZEWphSaB6VSiNzGgbFrIfoRDAh/ilD9ub+MhoqfE2tcTRNF8scLhS7VxArNyyr10tQO7sTxR44L2DcK6ZJmobinVeTfxEeX1hmgIAWVjzzhmzqXmsG/7Rq8QloJjxmA6rZpCfs9+Vl08CjSGgSeCTNO5TRDDIeWUJTZH2+LOGR8c4H8OZ4k71eTDJeDxoJWVF0UUoxICsysqzodV4PKrNi9CZFLxOofMnSw/XKp3tdVSBQeT2tBFVAkspCC4lcQtoawyfg+QUytZxAplYw3wOuYmh4aaT34Lg88MADrFy5kgEDBpCdnS3Od4FAIBAIBIIIUy3BS5Ik/vnPf3LrrbeyY8cOLBYLzZo1w2SqRV/Gz2ZUFfbv18MYbTbIyNC9SKqD1aqLWBUVn8hVWBh2cQnCigqq2YQrKR5XYgLupHjcifG4EuNxJ8ThTorHleAdT4zHHR+LZq78uVXitrLXdoi9xX+x58gh9toPscd6kL32Q1g9FefjSTLG0SyqIc2iGtA0uiHNoxrSPLoh9c0pyJoaEL7kALdN75VOdYC7JCDRekCeGtkAKFQ5Of7ZjCShmXWhyBNfw+tW9VxtfiHM5hPCAsZtATnc7E4Um6NMMLM5kO0uv4jmmyep+o+v+DzUCktrzGSPUcFtMeL2imb+YgnwOvPmQtNMRrCYwGREMpuRzEZkiwXJYgazGdls0odRUchGI4oseR3IJGRJQ1EkZFnyDmVkoxHJ4M11ZpD1ME5fb5k+DzQp0BtN0sU0WfPOU73zPN4hZV5rQgg4O/EJeBiAKvbiVLr/VFhUo3z33Xe8+uqrdO3aNdKmCAQCgUAgEAiopuDlIyYmhuzs7JqyRVAT2Gzw99+we7eeq6tRo4rbFhbC1q3HF7Ss1kpvWjMYcCcnYkuMxZoYgy0pFk9SIlJqKqSk4EpKwJ2UgCspHjXKclIvvTaPnX22w+y1H2a/7TB77Yf0adsh8lzhxTcABZkMSzpNoxvQLKqBLnBFN6BpVAMSjXEVb1CSvQJWICp4AkKNVCe47aDa9GmPQ0+67kNWvJ41AYmd66JnWG1FllGjzKhRZgL7y9RzTWl66B7eED7/tIaGVq5NWVs0DcWtotgcGGxuDA4XBpsDg92Nye7C4HBjdLgw2F0Ybfq0webEYNfrDA6X19vM5RfUZJtDz8MGKC4PisuDubiGE6cbDWAx68Vs8o7rghhmXTTThyYwG/WhyVhWzKay9oFto7xDX9il5BPGfEIXwR0CGJSyoaKUDQPzoUkBywfWBYaIlm8Trr1AcIqpV68eMbUkR6ZAIBAI6jbR0aeg06BqYjVW8SOWQHAaOSnBS1CLcLv10MUdO+DYMUhPD5+vy2qFH36A5cth1Sp9uRMRFQWpqUFFTUnGkRRPcXIMeXEKR+JkCqL0l8sYJYpYJTpsHqvKomkaRe5S9tuPsN9+WB/a9PF99sMcdRYcd/kUYwKNo+rRNEoXs5pE1adZVAMyLOknZVcwMihmQj0VtHL5dbxDtx00h1ckc3g9xAKCBSVvknPkgB7NvNNnuZeYL3+UFiRCaeVEKDXsPN+/Mry9OaInSJckvDmnZP1ISxIKMhISMnoonlFRMKBgkGUMKN4k6QqKL4F6sozsTbKuePNXKQG5rILr9JxWFaJp4HLrvVHaHWBzgN0OVt+0XS++aX+dd9weMO5b1rcurzcaLrdeimvOG63s8Eq6AOYT0nyCma/OL5yVE9GMgeOG4HZmgz40+uZ56xRDmZBGoKgWIHgF5lFTlLJ8ab7OBHw9c8pKqEhWkZAWmEMtsI1PiJPC2CJEtzrP/fffzzPPPMO0adPIyMiItDkCgUAgqKPExMRQWnoKnuGqgc1koe09n0TaDIGgQoTgdaajaXDkCOzcCYcOlXl1yQEv1Lm5sHKlXn7+WU9k76NRI2jQoEzMSkkpG09L04feL9Z2j4MidymFrhIOOfIodpdiV52YJCOxhigaKlHHf5Evh0N1ctB+lAOOoxywHyHHnust+niJ5/jeZQmGWBpH1aOxpT5NourR2FKPJlH1aRJVn1hDJL96SCAbAaMe0Vie8gmhAxNKq3avt5jHK4r5ctiUrVofer3FkL1eY76Xb9+f9On1HAsUovyeUWGEKLUCwconSUkh0lSZSKD3zleWCN0nSOmilIRR8vbW5xWmFPQk6UZJ8baR/esIFJ58YpRfsPIKVb42pz0PjySViT0Jx/E6rCqapufaCxLDKigVzXOUm+dwlo273GXb8dUVFtec/eHwC2IBAppPVAsUzYyGMkHNJ5r5i7GsE4HAYjKBUfG2N5aNGwyg+IQ0398dhAhugeJXUOcFcllnBOWHivdv2e8hF0Z8C1xveWHNNw3Hnx847SuCk6ZDhw44HA769++PxWLBWC5v5urVqyNkmUAgEAgEAsHZiRC8zlRcLj3kcP9+3bNLUfTE9AaDHqr4++96Wb1aD1sMpHFjGDBAL82bV7wJ1U2xu5Qi6wGOOPIpcJdg8+bAipItJBjiqKdUnFurxG3jkOMohxx5HPQOD9hzOeg4ykH70eOGHvpIMyXRyJJOI0s6GZZ0r8BVj0ZR9Yg3nKGhI5I3ZOt4OWz8vZr5ejjz6OOqN5mzxwW4A8QxF3hD7IIEMi8qGiqgIaFKEhqgahKq5O21EH0RFckrSElB4XtlPlJlK5YkSe9cDS3IU0oKEI90QUpGAhRJxiQrGCUZpZwoZfAJTIGClE+UKidEhROtIiJMnWlIklcUMtWskObD4wG7UxfFgsQw79DpLDff5R136m39xVvna19+6AkIE3a69FJS87tzXPzeaIYAAc07bTCUiWwGQ5lY5s+HZiibNhr0ME5fT52+PGkGJXie0ajnUTN6vdp861C882UpVMDyiXCB0+XnS2HEMJ/4FuTZ5h0qcrBoV369gd52IduqQLQjYB6EbyNJ+u9crtfD2sY999zDkSNHuPvuu0lNTRXXJIFAIBAE4VE1FFncGwSC04kQvM4kNA2Ki3WPrX379F4YfcLXzp26sLV5sx7WGIgkQdu20KsX9O4NWVlhv+i7VDclbivFHit5jgLy3UWUum14NBWzbCJGiSLJHIcsyX4x67AznyOOfA478jniPKbXeadP5KEFEK1YaGhOo6EljYbmVBpF6cJWhiWdhuZULErdjQnXfB5Ofm8oNcAryjdHj0LTJBlVk9BkBVUzoMrmsmVUD5rmQRe8fB5hunwFKqgqEhoybmRVQ0JF1jQkza3nF0fTBSk0zJKMQdPFKQMyCpLeKx8yihzgDSUp3iL7h4qk9+4ny0qZMBU4TzZQ5qJ2GkO8Al+Sy78waxVOlFvuOOss3+B4bSv7wh54bMofJ6nCiQDRoPxy5epPxbFXFIiJ0supxOMpE8ecrjLxzOkMrvcNneHqfOPeeS538HyXq1w7V7ANLm+b2hFNECCMGcqENkMYUS1wWD5nWvk6g1IW6mkoHw4aUOfLw6YErsuXn63cuvzrU3TBTlbCiHQQVqhz5kGXDMiq+CNNpFm/fj0ffPABrVu3jrQpAoFAIKiFKLLEne+vZ/uRk/tS53E5+O3NfwPQ5cbHUE4yh9aFWWn8c0D17l1mt5OXFz0BwMSrHsBhEJ3YCWoXdUbweuedd3jjjTfIzc2ldevW/Pvf/64bCfUdDti7VxeyNm/Wk9Hv3QuHD8OBA/owHE2bQpcu0LkznHceJCcHzdY0DbvqoNRjp8RtJd9ZSL6riDxnIbnOY5S4rZR67BS7reS7CjnqLOCos4AjzmPkOo8dt7fDQOINMdQ3p1LfnEIDcwoNvMJWA0sqDcypJBhiI/AV3Cs6aGVDLTC8Lmhc9QtRep1Hd6Lyj2tezygVTS0L4UPT0CStTPzQdC8of8Ce5n2X0yTdiYGyED19CLKkTyneeoOk6AKUpKBoem4pA7I3l1SgtxNIsoSieadlgr2lAovq0belanq6I1Svx5jqzS/mHWoauD1e9c0d3M4ntuHweph585J5j0PZQfD6kfnrAuI0fb+FJIEmeQ9Q2bErE2ik4HF/nYw3JtK7DsoJOwGhZb7tlK/3VwWEg5YP0ZXL5VMLOXePJ1RJYZuFEHDOBNdrYRpVsLxW7hz3j2tlh17y1fmOie98lQJWFDBdkaeOb15gvqzjeRJBubxb5X6DEwlyigLRUXo5XfjOf58AFiiIuVzgdOviWeC0y1UmpPnGfcv56wPaHXe83HRg7j/QbXN7dPHvTEKSQjsuCBTWDAHjFgWezYKsnpG2ukJatGiB3V7DHUwIBAKBoE6x/UgJmw4UndQ6VKed3M2/ALA5pxDZFCZvcxVomVb9qBlZVem3c61/XCCobdQJwWvJkiXMmDGDadOm0bFjR+bOncu4ceNYtmwZKSkpkTZPx+2G0lK9FBfrpahIL8eOQV6enovr6FE9RPHwYX06L09POn080tN1r62sLGjdGrKzcSfG695azhKO2g9wZP8fHHHmk+s4pocWOnI56iwi311EgbuEQo+VAk8JTq0SSey9xMgW6hkSqGdIJN2YQLqSQD1jIvUMidQ3JFLPmEi0ZCYgR7iOB7ACpVbvSLC3ExKoqi4YBXtB4a9TJQ1NDVgG/J5RSAFheN6hJPnEpkCxpOyFXn/H9iYql8ukJ1+onC8sT5J1rydZ1sUnxSs2GSSD15NJxiDJGBRDQKhdoLeTPpQIEKF84Xthin/b5fPyVCQ6hGtzvLqK5h1vez7CjfvFLN8NTw0WuDS1TFjxj3vb+wQ3vxjjFcj8wozPg823nFrWxlfnG/fZoPmEs0ChTSqzB8qGEqF1/v0L3Ldyp49fnPMJdeU8q0I0Md/CAcdfUwNWKpWJfGr5ejVgGUIJ2q0AW7Ryv4/qOx7eOjVACAup17zOgt7j7dHKxlVNzzOneo+7x6PPU73L+9ZBgE3+QsCQgN8ywHbNt1OBxypAHPVN+wXOgPqg8xv0vHcB0z6xUw74ncItR7m6KEnvjdJ3bSsvnlbJg66y7co196j6PcXl1oUul6tMkHN7yub55rt97dzB4775nnLz/KWCaV97TwXzfHU+O92eUJEO8HfQAFAZrW7TXzCwaofsdHLvvfcyc+ZM7r77bjIzM0NyeMXGxkbIMoFAIBAIBIKzkzoheL311lsMHz6cYcOGATBt2jS+++47PvnkEyZMmBA5wzSNv6/px287fkT1eFAl8EjgkQka90jgloOLKxFcyfq4UwFntAlnjAVHlAlHjBlblBGHWcFmkrHKbqzqDqzqFqw2ByWr7Dg01wnNq4hoxUKKMYFkUwLJxnhSzIkkGxNIMSWSbIon2ZRIiiUBixLlFaD0/QFNzwmlaWgS5GmQ538X9HpwoCFJMpqmIcmSPvR60UiS7HUU0ZOxy5IuNvnrUbzT3vxQkowiGVBkPaxOkWQU2aCLUIriFaMUZNknIOnL6yF3Utm4JHtFKblsnlw2LVHWztdWqkg4Cjc82TaCk8Pv2RQovAUKW5WdR+j8sG2puH3gsNr1vvWqofNDPOrC2Rxgb8j6w9lQbrv+0XDiYMC21ACB0i+ceQVNNVDcCpgfKJKVF8A0r7gWJJgFTKuqd345Ic63jBpmGLTdcseg/La1gLpA8aa851yQd12AuBouNFULrvIvIwUsB8ECZ4Vhq4ARMMmASS8h1xT/f8HaP+HqpdB55eeX35+QbQXM9B13nyCm+sYD6jTvb+gvPtHMA3YXDPwHtZnx48cDcP311wfV6/c5iS1btkTAKoFAIDi7EHmyBAJBIGe84OV0Otm0aRM333yzv06WZc4//3zWr18fQcvA43ZxbtYPFHaoCfdOp7cEoHHCr+IGyUCMMVovphhijLEkWOKJtyQSb44nwZxAUlQSiZZEkqOSSY5KJsoQBZI3ATmSX2SSvN5IkiQFiUmKL5eTd7y80BS4XE1M++pEQmBBpSgfsiiovYTNcRaurqK2x2l/vPWogUJa+aKWtauoBM1XK6gvV+fbps+0cMuUH/eLiL7tBMxXy4l/ECoIQoB3nxZ+XuCx8q+n/DEvJ4yWn+8/1GEEUu0EdWFFVUCSISaD2sy8efMibUK1qbNpIQQCwVlHTeXJqmlOJk+WQCCoPme84HXs2DE8Hk9I6GJKSgo7d+6MkFU6itHEnRdNZdmfnyLJMpLBiGwweL2FvOFtsk8cUlBkPQzOIBswKLqnkkE2YJSNmBQTRsXoHzcpJswGM1GGKGJMMcSZ4og1xRJtjCbWFKtPm2MxK2a/WBU4DBSNAgWlyowLBALBKSHs9aWCa05NXoqUGlxXXaG86BY4DFdXlbbVnR9Tu3vmPffccyNtQrU4I9JCCARnIbXZU6k22wY1kyerpjmZPFkCgaD6nPGCV21nWv/pTOs/PdJmCAQCgUBQeaqUi0wAsGbNmuPO7969+2mypGrU2rQQgipRmwUIYVv1qO2eSrXZNoFAIPBxxgteSUlJKIpCXl5eUH1eXh6pqamVWofm/XpcUlK7LtoCgUAgEAhODzExMSflxTxmzJiQusD11cYcXiebFuJse36qzeIIwCvf7eBAoS3SZgTRoVEC13RtLGyrIj7b3HYrqrN22ea0WSkpKanVtjWLl1GdxhMvcBqpF0WN2eZ2uMnxdjCVlWLAYD659Z2MbSaHmxKvLZkpBpwnaUtN2naqaRYvnzX3v+Nxss9PpxpJ04KSZ5yRXHPNNWRnZ/Pvf/8bAFVVufDCCxk9enSlvk4eOnSIvn37nmozBQKBQCAQ1FLWrVt3Uj0pFhcXB027XC62bNnCCy+8wN13303Pnj1P1sQa5/Dhw/Tp04f333+fzp07++ufeuop1qxZw0cffXTc5cXzk0AgEAgEZzcn+/x0qjnjPbwAbrjhBu6//37at29PdnY2c+fOxWazMXTo0Eotn56ezvfff1/r1UmBQCAQCASnhpiTzBEWFxcXUnfBBRdgNBqZOXMmCxcuPKn110bE85NAIBAIBGc3J/v8dKqpE4LXZZddRn5+PrNmzSI3N5c2bdrw+uuvVzqkUZZl6tevf4qtFAgEAoFAcLaRkpLCrl27Im1GWE42LYR4fhIIBAKBQFCbqROCF8Do0aMZPXp0pM0QCAQCgUBwFvLXX3+F1B05coTXXnuN1q1rZxJlk8lEu3btWLVqFf379wf0tBCrVq0Sz1QCgUAgEAjOeOqM4CUQCAQCgUAQKa688kokSaJ8atROnTrx+OOPR8iqE3OyaSEEAoFAIBAIaitC8BIIBAKBQCA4Sb7++uugaVmWSU5Oxmw2R8iiynGyaSEEAoFAIBAIait1opdGgUAgEAgEAoFAIBAIBAKBwIfw8BIIBAKBQCCoAVatWsWqVavIy8tDVdWgeTNmzIiQVQKBQCAQCARnJ0LwEggEAoFAIDhJZs+ezUsvvUT79u1JS0tDkqRImyQQCAQCgUBwViNCGgUCgUAgEAhOkl69enHfffdx5ZVXRtoUgUAgEAgEAgEgR9oAgUAgEAgEgjMdl8tFly5dIm2GQCAQCAQCgcCLELxOIe+88w79+vWjQ4cOXHPNNWzcuDHSJtV65syZw7Bhw+jcuTM9e/bk1ltvZefOnZE264zj1VdfJSsri8cffzzSppwRHD58mPvuu48ePXqQnZ3NkCFD+OOPPyJtVq3G4/Hw/PPP069fP7Kzs+nfvz8vvfQSwmk4lDVr1nDLLbfQq1cvsrKyWLFiRdB8TdN44YUX6NWrF9nZ2Vx//fXs3r07MsbWIo533FwuF08//TRDhgyhU6dO9OrVi8mTJ3P48OGI2Xv11VezePHiiG0/EojnnIp58cUXycrKCiqXXnpppM2KOOJ6eGJOdIymTJkScm6NGzcuQtZGhsq8LzgcDqZNm0aPHj3o3Lkzd9xxB0ePHo2QxaefyhyjMWPGhJxLDz30UIQsPv28++67DBkyhC5dutClSxdGjBjB999/759/tp9DPk50nGr7eSRyeJ0ilixZwowZM5g2bRodO3Zk7ty5jBs3jmXLlpGSkhJp82otq1evZtSoUXTo0AGPx8Nzzz3HuHHj+OKLL4iOjo60eWcEGzdu5P333ycrKyvSppwRFBYWMnLkSHr06MFrr71GUlISe/bsISEhIdKm1Wpee+013nvvPZ588klatWrFn3/+ydSpU4mLi2Ps2LGRNq9WYbVaycrKYtiwYdx+++0h81977TXmz5/PzJkzadSoES+88ALjxo1jyZIlmM3mCFhcOzjecbPb7WzevJmJEyfSunVrioqKePzxx5k4cSILFy6MiL0Oh4MPP/yQVatWkZWVhcEQ/Ig1derUiNh1qhDPOSfmnHPO4a233vJPK4oSQWtqB+J6eGJOdIwAevfuHdQRhslkOl3m1Qoq877wxBNP8P333/P8888TFxfHY489xu233877778fYetPD5V9pxo+fDiTJk3yT0dFRUXC3IhQv3597rvvPpo2bYqmaXz66afcdtttLFq0iHPOOeesP4d8nOg4QS0/jzTBKeHqq6/Wpk2b5p/2eDxar169tDlz5kTQqjOPvLw8LTMzU1u9enWkTTkjKCkp0S655BJt5cqV2ujRo7Xp06dH2qRaz9NPP62NHDky0maccUyYMEGbOnVqUN3tt9+u3XvvvRGy6MwgMzNT++qrr/zTqqpqF1xwgfb666/764qKirT27dtrn3/+eSRMrJWUP27h2LBhg5aZmanl5OScJquCGT16dIVlzJgxEbHpVCKec47PrFmztMsvvzzSZtRqxPXwxIS79t1///3axIkTI2RR7aT8+0JRUZHWrl07benSpf4227dv1zIzM7X169dHyMrIEu6dSrwrhNK9e3ftww8/FOfQCfAdJ02r/eeR8PA6BTidTjZt2sTNN9/sr5NlmfPPP5/169dH0LIzj+LiYgDhbVNJHn30Ufr27cv555/Pyy+/HGlzzgi++eYbevXqxaRJk1izZg316tXj2muvZfjw4ZE2rVbTuXNnPvzwQ3bt2kXz5s3566+/WLduHVOmTIm0aWcU+/fvJzc3l/PPP99fFxcXR8eOHVm/fj2DBg2KoHVnFiUlJUiSRHx8fES2P3/+/IhsNxKI55zKsWfPHnr16oXZbKZTp07ce++9NGzYMNJm1VrE9bDyrF69mp49exIfH895553HXXfdRVJSUqTNihjl3xf+/PNPXC5X0LnUsmVLGjZsyO+//06nTp0iYWZEqeidavHixfzvf/8jLS2Niy66iFtvvbV2eeecJjweD8uWLcNqtdK5c2dxDlVA+ePkozafR0LwOgUcO3YMj8cT4tKfkpIi8lFVAVVVeeKJJ+jSpQuZmZmRNqfW88UXX7B582Y+/vjjSJtyRrFv3z7ee+89brjhBm655Rb++OMPpk+fjtFo5Kqrroq0ebWWCRMmUFJSwsCBA1EUBY/Hw913383ll18eadPOKHJzcwHC3i/OxjwR1cXhcPDMM88waNAgYmNjI21OnUc855yY7OxsZsyYQfPmzcnNzeWll15i1KhRLF68WJyjFSCuh5Wjd+/eXHzxxTRq1Ih9+/bx3HPPcdNNN/HBBx+clWGz4d4Xjh49itFoDPkAkpKS4j/PziYqeqcaPHgwDRs2JD09na1bt/LMM8+wa9cuZs+eHUFrTy9bt27l//7v/3A4HERHR/PSSy/RqlUrtmzZIs6hACo6TlD7zyMheAlqLdOmTWPbtm28++67kTal1nPw4EEef/xx3nzzzbMmx0VNoWka7du355577gGgbdu2bNu2jffff18IXsdh6dKlLF68mGeffdb/YDBjxgzS09PFcROcVlwuF3feeSeapjFt2rRImyMQANC3b1//eOvWrenYsSMXXXQRS5cu5ZprromgZYIznUBPN1+C6P79+/u9vs42xPvCianoGI0YMcI/npWVRVpaGtdffz179+6lSZMmp9vMiNC8eXM+/fRTiouLWb58Offffz8LFiyItFm1joqOU6tWrWr9eSR6aTwFJCUloSgKeXl5QfV5eXmkpqZGyKozi0cffZTvvvuOuXPnUr9+/UibU+vZtGkTeXl5DB06lLZt29K2bVtWr17N/Pnzadu2LR6PJ9Im1lrS0tJo2bJlUF2LFi04cOBAhCw6M3jqqaeYMGECgwYNIisriyuvvJLrrruOOXPmRNq0M4q0tDQAcb+oJi6Xi7vuuosDBw7w5ptvCs+Z04R4zqk68fHxNGvWjL1790balFqLuB5Wj8aNG/s73DnbqOh9ITU1FZfLRVFRUVD7vLw8/3l2tlCVd6qOHTsCnFXnkslkomnTprRv3557772X1q1bM2/ePHEOlaOi4xSO2nYeCcHrFGAymWjXrh2rVq3y16mqyqpVq4JiXQWhaJrGo48+yldffcXcuXNp3LhxpE06IzjvvPNYvHgxn376qb+0b9+eIUOG8Omnn56VLu6VpUuXLuzatSuobvfu3WRkZETIojMDu92OJElBdYqioGlahCw6M2nUqBFpaWlB94uSkhI2bNgg7hcnwCd27dmzh7fffvuszl9zuhHPOVWntLSUffv2nZUvSpVFXA+rx6FDhygoKDirzq0TvS+0b98eo9EYdC7t3LmTAwcOnDW5l6rzTrVlyxaAs+pcKo+qqjidTnEOnQDfcQpHbTuPREjjKeKGG27g/vvvp3379mRnZzN37lxsNhtDhw6NtGm1mmnTpvH555/z3//+l5iYGH+MdFxcHBaLJcLW1V5iY2ND8pxFR0eTmJgo8p+dgOuuu46RI0fyyiuvMHDgQDZu3MiHH37Io48+GmnTajUXXXQRr7zyCg0bNvSHNL711lsMGzYs0qbVOkpLS4O8Ovbv38+WLVtISEigYcOGjB07lpdffpmmTZvSqFEjXnjhBdLT0+nfv38ErY48xztuaWlpTJo0ic2bNzNnzhw8Ho//fpGQkIDJZIqU2WcN4jnn+Dz55JNcdNFFNGzYkCNHjvDiiy8iyzKDBw+OtGkRRVwPT8zxjlFCQgKzZ89mwIABpKamsm/fPp5++mmaNm1K7969I2j16eVE7wtxcXEMGzaMmTNnkpCQQGxsLNOnT6dz585njVhxomO0d+9eFi9eTN++fUlMTGTr1q3MmDGD7t2707p16whbf3p49tln6dOnDw0aNKC0tJTPP/+c1atX88Ybb4hzKIDjHacz4TySNPE5/pSxYMEC3njjDXJzc2nTpg3/+te//C5+gvBkZWWFrZ8xY4Z4iK4iY8aMoXXr1jz44IORNqXW8+233/Lcc8+xe/duGjVqxA033CB6aTwBJSUlvPDCC6xYsYK8vDzS09MZNGgQt912mxAbyvHrr78yduzYkPqrrrqKmTNnomkas2bN4sMPP6SoqIiuXbvy8MMP07x58whYW3s43nG7/fbb+cc//hF2uXnz5tGjR49TbZ4A8ZxzPO6++27WrFlDQUEBycnJdO3albvvvrtW5DOJJOJ6eGKOd4weeeQRbrvtNjZv3kxxcTHp6elccMEF3HnnnWdV2Gdl3hccDgczZ87kiy++wOl00qtXLx5++OFa43VyqjnRMTp48CD//Oc/2bZtG1arlQYNGtC/f39uvfXWsyY9wAMPPMAvv/zCkSNHiIuLIysri5tuuokLLrgAEOeQj+MdpzPhPBKCl0AgEAgEAoFAIBAIBAKBoE4hcngJBAKBQCAQCAQCgUAgEAjqFELwEggEAoFAIBAIBAKBQCAQ1CmE4CUQCAQCgUAgEAgEAoFAIKhTCMFLIBAIBAKBQCAQCAQCgUBQpxCCl0AgEAgEAoFAIBAIBAKBoE4hBC+BQCAQCAQCgUAgEAgEAkGdQgheAoFAIBAIBAKBQCAQCASCOoUQvAQCQZ3l119/JSsri6KiokibIhAIBAKBQFDnmTJlCrfeemukzRAIBAJACF4CgaAOMWbMGB5//HH/dOfOnfnpp5+Ii4uLoFUCgUAgEAgEgpqkX79+vP3225E2QyAQ1HKE4CUQCOosJpOJtLQ0JEmKtCkCgUAgEAgEZzROpzPSJggEAkGVEIKXQCCoE0yZMoXVq1czb948srKyyMrKYuHChUEhjQsXLqRbt258++23DBgwgI4dOzJp0iRsNhuLFi2iX79+dO/enenTp+PxePzrdjqdPPnkk/Tu3ZtOnTpxzTXX8Ouvv0ZqVwUCgUAgEAhwOp1Mnz6dnj170qFDB0aOHMnGjRtRVZU+ffrw7rvvBrXfvHkzrVu3JicnB4CioiIefPBBzjvvPLp06cLYsWP566+//O1ffPFFrrjiCj766CP69etHdnY2AMuWLWPIkCFkZ2fTo0cPrr/+eqxWa9C23njjDXr16kWPHj2YNm0aLpfLP6+wsJDJkyfTvXt3OnbsyPjx49m9e3fQ8suXL2fQoEG0b9+efv368eabb/rnjRkzhpycHGbMmOF/5hMIBIJwGCJtgEAgENQEDz74ILt37+acc85h0qRJAGzfvj2knd1uZ/78+fznP/+htLSU22+/ndtvv524uDheffVV9u3bxx133EGXLl247LLLAHj00UfZvn07//nPf0hPT+err75i/PjxLF68mGbNmp3O3RQIBAKBQCAA4KmnnmL58uXMnDmTjIwMXn/9dcaPH8+XX37JoEGD+Pzzz7n22mv97RcvXkyXLl3IyMgA4M4778RsNvPaa68RFxfHBx98wHXXXcfy5ctJTEwEYO/evSxfvpzZs2cjyzJHjhzh3nvv5Z///Cf9+/entLSUtWvXommafzu//voraWlpzJ07l71793L33XfTpk0bhg8fDugfKffs2cPLL79MbGwsTz/9NBMmTOCLL77AaDTy559/ctddd3H77bdz2WWXsX79eqZNm0ZiYiJDhw71C3HDhw/3r1MgEAjCIQQvgUBQJ4iLi8NoNGKxWEhLSwNg586dIe1cLhePPPIITZo0AWDAgAH873//Y+XKlcTExNCqVSt69OjBL7/8wmWXXcaBAwdYuHAh3377LfXq1QNg3Lhx/PjjjyxcuJB77rnn9O2kQCAQCAQCAWC1Wnn//feZMWMGffv2BeCxxx5j5cqVfPzxx1x++eW89dZbHDhwgIYNG6KqKl988QUTJ04EYO3atWzcuJFVq1ZhMpkAuP/++1mxYgXLly9nxIgRgP7c9NRTT5GcnAzApk2bcLvdXHzxxX7hrLyHVUJCAg899BCKotCyZUv69u3LqlWrGD58OLt37+abb77hvffeo0uXLgA888wzXHjhhaxYsYKBAwfy1ltv0bNnT2677TYAmjdvzvbt23njjTcYOnQoiYmJKIpCTEyM/5lPIBAIwiEEL4FAcFYRFRXlF7sAUlNTycjIICYmJqguPz8fgL///huPx8Oll14atB6n0+n/+ikQCAQCgUBwOtm7dy8ul8svGgEYjUays7PZsWMH48ePp2XLlnz++edMmDCB1atXk5+f73+e2bp1K1arlR49egSt1263s3fvXv90w4YN/WIXQOvWrenZsydDhgyhV69e9OrViwEDBpCQkOBv06pVKxRF8U+npaXx999/A7Bjxw4MBgMdO3b0z09KSqJ58+bs2LED0D9Y/uMf/wiyq0uXLsybNw+PxxO0boFAIDgeQvASCARnFQZD8GVPkqSwdaqqAvoXVEVR+OSTT0IesKKjo0+tsQKBQCAQCATVZMiQISxevJgJEybw+eef06tXL5KSkgAoLS0lLS2N+fPnhywX2Lt1VFRU0DxFUXjrrbf47bffWLlypT9NxIcffkjjxo2B8M9agSGPAoFAcLoQSesFAkGdwWg0+oWqmqJNmzZ4PB7y8/Np2rRpUBFu9AKBQCAQCCJBkyZNMBqN/Pbbb/46l8vFH3/8QatWrQAYPHgw27Zt488//2T58uVcfvnl/rbt2rXj6NGjKIoS8nwT6NEVDkmS6Nq1K5MmTeLTTz/FaDSyYsWKStndsmVL3G43GzZs8NcdO3aMXbt2+e1u0aJF0H4B/PbbbzRr1sz/8fFUPPMJBIK6hxC8BAJBnSEjI4MNGzawf/9+8vPza+RBqHnz5gwZMoTJkyfz5Zdfsm/fPjZu3MicOXP47rvvTt5ogUAgEAgEgioSHR3NyJEjeeqpp/jhhx/Yvn07//73v7Hb7Vx99dUANGrUiM6dO/Pggw/i8Xjo16+ff/nzzz+fTp06cdttt/HTTz+xf/9+fvvtN/7zn//wxx9/VLjdDRs28Morr/DHH39w4MABvvzyS/Lz82nRokWl7G7WrBn/+Mc/+Pe//83atWv566+/+Oc//0m9evX8YYw33ngjq1at4qWXXmLXrl0sWrSId955hxtvvNG/noyMDNasWcPhw4f9aSgEAoGgPCKkUSAQ1BluvPFGpkyZwqBBg7Db7cyYMaNG1jtjxgxefvllZs6cyZEjR0hMTKRTp05ceOGFNbJ+gUAgEAgEgqpy3333oWkakydPprS0lPbt2/P6668H5dMaMmQI06ZN48orr8RisfjrJUni1Vdf5fnnn2fq1KkcO3aM1NRUunXrRmpqaoXbjI2NZc2aNcydO5eSkhIaNmzIlClT/InzK8OMGTN4/PHHueWWW3C5XHTr1o1XX30Vo9EI6N5nzz//PLNmzeLll18mLS2NSZMmMXToUP86Jk2axEMPPUT//v1xOp1s3bq1KodOIBCcJUiaCKgWCAQCgUAgEAgEAoFAIBDUIURIo0AgEAgEAoFAIBAIBAKBoE4hBC+BQCAQCAQCgUAgEAgEAkGdQgheAoFAIBAIBAKBQCAQCASCOoUQvAQCgUAgEAgEAoFAIBAIBHUKIXgJBAKBQCAQCAQCgUAgEAjqFELwEggEAoFAIBAIBAKBQCAQ1CmE4CUQCAQCgUAgEAgEAoFAIKhTCMFLIBAIBAKBQCAQCAQCgUBQpxCCl0AgEAgEAoFAIBAIBAKBoE4hBC+BQCAQCAQCgUAgEAgEAkGdQgheAoFAIBAIBAKBQCAQCASCOoUQvAQCgUAgEAgEAoFAIBAIBHUKIXgJBAKBQCAQCAQCgUAgEAjqFELwEggEAoFAIBAIBAKBQCAQ1CmE4CUQCAQCgUAgEAgEAoFAIKhTCMFLIBAIBAKBQCAQCAQCgUBQpxCCl0AgEAgEAoFAIBAIBAKBoE4hBC9BxPj111/Jyspi2bJlkTYFKLPn119/rVXrOhtYuHAhWVlZ7N+/P9Km1BgvvvgiWVlZVWqbn59/iq2KHLXxb8J33v3xxx81ts4xY8YwePDgE7bbv38/WVlZLFy40F8X7pzp168fU6ZMqfS2x4wZUzWDBYJaRGX/fgSnBlVVGTx4MC+//HKkTQlLVlYWjz76aKTN8JOVlcWLL75Y69ZV1wl3/4wE27dvp23btvz9998RteNs5KabbuJf//pXpM3wU5XnyZN5VqvsPVKcm8EIwessY+vWrUyaNImLLrqIDh060Lt3b2644Qbmz58f1O6VV15hxYoVEbLy1PLOO+9E/CZ5PA4fPsyLL77Ili1bIm1KjVOXz6vKcLbvv+Dk2b59Oy+++GKdEocFNcu2bdu477776N27N+3bt6dXr17ce++9bNu2LdKm1WrE9Rk+//xzDh48yOjRo6u03OLFi3n77bdPjVER5vvvv6/1QlRdPXdr+3nVqlUr+vbty6xZs05qPV9++SV33XUX//jHP+jYsSMDBgxg5syZFBUVHXe5vXv30qFDh0oLLTt27OCpp57iiiuuoHPnzvTq1YsJEyaEXba6Nh0PnyjkKx06dGDAgAE8+uijHD16tNLrWbduHStXruSmm27y14lno2Bq6twE/b30zjvvpFu3bnTp0oWJEyeyb9++Si/vdDp55ZVXuPTSS+nQoQPnn38+EyZM4NChQyHtnn76aXr16kV2djbXXHMNK1euPGn7AQw1shbBGcFvv/3G2LFjadiwIddccw1paWkcPHiQDRs2MG/evCC1ec6cOQwYMID+/ftH0OJTw3vvvUdSUhJDhw4Nqu/evTsbN27EaDSe9DZOZl1Hjhxh9uzZZGRk0KZNm5O2pTZR0Xl1xRVXMGjQIEwmU4Qsq3kmTpzIhAkTgurq8t+VoGpkZGSwceNGDIbj34aXLVuGJEn+6e3btzN79mzOPfdcGjVqFNT2jTfeOCW2Cs4cvvzyS+655x4SExMZNmwYjRo1Iicnh48//pjly5fzn//8h4svvjjSZtZKxPVZv4YMGjSIuLi4Ki33+eefs23bNq6//vpTY1gE+f7773nnnXe44447QuZt3LgRRVFqZDsns666eu5WdF5V9v55Ovi///s/JkyYwN69e2nSpEm11vHvf/+b9PR0Lr/8cho2bMjWrVtZsGAB33//PYsWLcJisYRd7oknnsBgMOB0Oiu1nY8//piPP/6YSy65hGuvvZbi4mI++OADRowYweuvv875559/0jZVhkmTJtGoUSOcTifr1q3jvffe4/vvv+fzzz8nKirqhMu/8cYb9OzZk6ZNm/rrjvdsVNs4Xc9qNXFulpaWMnbsWIqLi7n55psxGo28/fbbjB49mk8//ZSkpKTjLu9yubj55ptZv34911xzDVlZWRQVFbFhwwaKi4upX7++v+2UKVNYvnw5Y8eOpVmzZixatIgJEyYwd+5cunXrVi37fUT+SiE4bbzyyivExcXx8ccfEx8fHzQvLy8vQlbVHmRZxmw217p11RRWq5Xo6OhImxEWRVFq7KGxtmAwGGrFw9jZhKqquFyuWve3Fw5JkiplZ1VE4LokGAuqzt69e5k8eTKNGzfmnXfeITk52T9v7NixjBo1ismTJ/O///2Pxo0bnza7avO9R1DG5s2b+euvvyodQi2gRu81te2+5XA4MBqNyHLtCwaq7P3zdHD++eeTkJDAokWLuPPOO6u1jlmzZtGjR4+guvbt23P//fezePFirrnmmpBlfvzxR3766SfGjx9f6RDkQYMGcfvttxMTE+OvGzZsGJdddhkvvvhikOBVHZsqS58+fejQoQMA11xzDYmJibz11lt8/fXXFYbr+e4jeXl5fP/99zzyyCPV3n6kOV3PajVxbr777rvs3r2bjz76iOzsbAB69+7NkCFDeOutt7jnnnuOu/zbb7/NmjVrePfdd/3Lh2Pjxo188cUXTJ48mXHjxgFw5ZVXMnjwYJ555hnef//9atnvo/ZdxQSnjL1799KqVasQsQsgJSXFP56VlYXVamXRokV+t9PAB6DNmzczfvx4unTpQufOnbnuuuv4/fffQ9ZZVFTEE088Qb9+/Wjfvj19+vRh8uTJIXmKVFXl5Zdf9l8Ar7vuOvbs2RPUZu3atUyaNIkLL7yQ9u3b07dvX5544gnsdntQu9zcXKZOnUqfPn38oRwTJ070u7j269ePbdu2sXr1av+++TzbKsoxtGHDBm666Sa6d+9Op06dGDJkCHPnzj3usQ63Ll/c9fbt2xkzZgwdO3akd+/evPbaa0HLXX311QBMnTrVb2NgCOaGDRsYN24cXbt2pWPHjowePZp169YFbd+XC2j79u3ce++9dO/enWuvvZY33niDrKwscnJyQmx+9tlnad++PYWFhdXa1p49e5gyZQrdunWja9euTJ06FZvN5m93vPOqohxe77zzDoMGDfL/ltOmTQtxp67McfUxf/58Bg0aRMeOHenevTtDhw5l8eLFIe18aJpGjx49mDFjhr9OVVW6detGmzZtgmx59dVXadu2LaWlpUHHpTL776O4uPi4x/B4VOa3Av1vadiwYXTo0IH+/fvz/vvvh9h6vPwY5fOM5OTk8MgjjzBgwACys7Pp0aMHkyZNqrZbuc+WHTt2cOedd9KlSxd69OjB9OnTcTgcIbY8+uij/O9//2PQoEF06NCBH3/8Eaj8dQrAbrfz0EMP0aNHD7p06cLkyZOD/g4AVqxYwYQJE+jVqxft27enf//+vPTSS3g8nrDr/PPPP/m///s/srOz6devH++9917Q/MrmIAnM4bVw4UL/Q8vYsWP955HvOhMuL4TT6WTWrFlcfPHF/mvnU089FfJFeOXKlYwcOZJu3brRuXNnBgwYwHPPPXdc2wS1i9dffx2bzcZjjz0WJHYBJCcn8+ijj2K1Wv3XxmXLlpGVlcXq1atD1vX++++TlZUVlP9jx44dTJo0iXPPPZcOHTowdOhQvv7666DlfNfy1atX88gjj9CzZ0/69u0LQElJCY8//rj/maBnz57ccMMNbNq0KWT7lbme5+Xl8cADD3D++efToUMHLr/8chYtWhTSzmq1MnPmTPr27Uv79u0ZMGAAb7zxBpqm+dtU5vociO8ev2TJEmbPnk3v3r3p3LkzkyZNori4GKfTyeOPP07Pnj3p3LkzU6dODeuF8dlnnzF06FCys7M599xzufvuuzl48GBQm8o+/0yZMoXOnTtz+PBhbr31Vjp37sx5553Hk08+WeF1KpAVK1ZgNBpDvqSf6HcbM2YM3333HTk5Of5j169fP//ylf2dVFVl7ty5DBkyhA4dOnDeeecxbty4sOFWK1asYPDgwbRv355Bgwbxww8/BM2v7H3J5XIxe/ZsLrnkEjp06ECPHj0YOXKkP4xmypQpvPPOOwBBoVg+wuXdOnz4MA888ID/XtGvXz8efvjhE3rhlF9XTTxb+eyZOnUq559/vv94ffzxx0Hb9p3PX3zxBf/5z3/o3bs3HTt2ZNOmTWRlZYX9vX788UeysrL49ttvq7WtJUuWHPfZ/3jnVUX3z1WrVnHttdfSqVMnunXrxsSJE9mxY0dQm8oeV6jcfdFoNHLuueeGXAttNhs7duyoVG7W8sIS4PfWK28/6Oft448/ztixY6vkudO+ffsgsQsgKSmJbt26sXPnzpOy6WQ477zzAPx/n75r2d69e7npppvo3Lkz9913HwDfffcdbrc7SJw70bMRVO59AmDp0qX+a3KPHj247777OHz4cKX3xel0MmPGDM477zw6derEbbfdFnIOhHtWy8nJ4ZZbbqFTp0707NmTJ554wv83Fi73bWXukTVxbi5fvpwOHToEiVUtW7akZ8+eLF269LjLqqrKvHnz6N+/P9nZ2bjd7grfaZYtW4aiKIwYMcJfZzabufrqq1m/fn3IfbGqCPeDs4iMjAzWr1/P33//TWZmZoXtnnrqKf71r3+RnZ3N8OHDAfwX1G3btjFq1ChiYmIYP348BoOBDz74gDFjxrBgwQI6duwI6C6Qo0aNYseOHQwbNoy2bdty7NgxvvnmGw4fPhz0MP7aa68hSRI33ngjJSUlvP7669x333189NFH/jbLli3DbrczcuRIEhMT2bhxIwsWLODQoUNB8cl33HEH27dvZ/To0WRkZJCfn8/KlSs5ePAgjRo14oEHHuCxxx4jOjqaW265BYDU1NQKj8XKlSu5+eabSU9PZ+zYsaSmprJjxw6+++47rrvuuir/BoWFhYwfP56LL76YgQMHsnz5cp555hkyMzPp27cvLVu2ZNKkScyaNYsRI0bQtWtXALp06QLoN/ObbrqJ9u3bc/vttyNJEgsXLuS6664Lq57feeedNG3alLvvvhtN07jooot4+umnWbp0KePHjw9qu3TpUi644AISEhKqta277rqLRo0acc8997B582Y++ugjkpOT+ec//wkc/7wKx4svvsjs2bM5//zzGTlyJLt27eK9997jjz/+4L333gsKFz3RcQX48MMPmT59OgMGDGDs2LE4HA62bt3Khg0bGDJkSFgbJEmiS5curFmzxl+3detWiouLkWWZ3377jQsvvBDQcwq0adMm5GHCR2X2/0THsCIq+1tt3bqVcePGkZyczB133IHb7ebFF18MEryryh9//MH69esZNGgQ9evXJycnh/fee4+xY8fyxRdfVMo9PRx33XUXGRkZ3Hvvvfz+++/Mnz+foqIinnrqqaB2v/zyC0uXLmXUqFEkJSWRkZFR6euUj0cffZT4+Hhuv/12/3l24MAB5s+f7w8nXLRoEdHR0dxwww1ER0fzyy+/MGvWLEpKSrj//vuD1ldYWMiECRMYOHAggwYNYunSpTzyyCMYjUa/oF0dunfvzpgxY5g/fz633HILLVq0APSHj3CoqsrEiRNZt24dw4cPp2XLlvz999/MnTuX3bt389///hfQr+s333wzWVlZTJo0CZPJxJ49e/jtt9+qbavg9PPtt9+SkZFRoet/9+7dycjI4PvvvwfgwgsvJDo6mqVLl3LuuecGtV2yZAnnnHOO/1lh27ZtjBw5knr16nHTTTf5l7vtttt48cUXQ8Ikp02bRnJyMrfddhtWqxWAhx9+mOXLlzN69GhatmxJQUEB69atY8eOHbRr186/bGWu53a7nTFjxrB3715GjRpFo0aNWLZsGVOmTKGoqMh/f9Y0jYkTJ/o/JrVp04Yff/yRp556yi9MQNXvTz5effVVLBYLEyZMYM+ePSxYsACDwYAkSRQVFXH77bezYcMGFi5cSEZGBrfffrt/2ZdffpkXXniBgQMHcvXVV5Ofn8+CBQsYNWoUn376qf/jZGWffwA8Hg/jxo0jOzubyZMns2rVKt58800aN27Mtddee9x9Wb9+PZmZmSGpGE70u91yyy0UFxdz6NAhpk6dCuC/D1b2dwJ48MEHWbhwIX369OHqq6/G4/Gwdu1aNmzY4PcGAf1e++WXX3LttdcSExPD/PnzmTRpEt9++60/vKay96XZs2czZ84crrnmGrKzsykpKeHPP/9k06ZNXHDBBYwYMYIjR46wcuXKkHtPOA4fPszVV19NcXExw4cPp0WLFhw+fJjly5djt9ur5dlxMs9WR48eZfjw4UiSxKhRo0hOTuaHH37gwQcfpKSkJCRU8L///S9Go5Fx48bhdDpp1aoVjRs3ZunSpVx11VVBbZcsWUJCQgK9evWq1rZO9Ox/vPMqHD///DM33XQTjRo14vbbb8dut7NgwQJGjhzJwoULQ8LcTnRcq3JfbNeuHV9//TUlJSXExsYCusfK2LFjuf3228OGw54IX06rcCFjc+fOpaioiFtvvZUvv/yyyusuT25uLomJiSdl08mwd+9egCAb3G63/yPu/fff7w+hXL9+PYmJiWRkZPjbnujZqLLvEwsXLmTq1Kl06NCBe+65h7y8PObNm8dvv/0WdE0+HtOnT/c/T+bk5DB37lweffRRnn/++QqXsVqtXHfddeTm5vrfNT///PMKO3mqzD3Sx8mcm6qqsnXrVoYNGxYyr0OHDvz0009B6y3P9u3bOXLkCFlZWfz73/9m0aJFuFwuMjMzefDBB/1CJ8CWLVto1qxZyLp87y9btmyhQYMGFdp6QjTBWcNPP/2ktWnTRmvTpo02YsQI7amnntJ+/PFHzel0hrTt1KmTdv/994fU33rrrVq7du20vXv3+usOHz6sde7cWRs1apS/7oUXXtAyMzO1L7/8MmQdqqpqmqZpv/zyi5aZmakNHDhQczgc/vlz587VMjMzta1bt/rrbDZbyHrmzJmjZWVlaTk5OZqmaVphYaGWmZmpvf7668c9DoMGDdJGjx4dUu+z55dfftE0TdPcbrfWr18/7aKLLtIKCwvD7kNFlF+Xpmna6NGjtczMTG3RokX+OofDoV1wwQXaHXfc4a/buHGjlpmZqX3yySch27zkkku0G2+8MWj7NptN69evn3bDDTf462bNmqVlZmZq99xzT4htI0aM0K666qqgug0bNgTZVp1tTZ06NWidt912m3buuecG1VV0Xn3yySdaZmamtm/fPk3TNC0vL09r166dduONN2oej8ffbsGCBVpmZqb28ccf++sqe1wnTpyoDRo0KGTbJ+L111/X2rRpoxUXF2uapmnz5s3TLrroIu3qq6/Wnn76aU3TNM3j8WjdunXTnnjiCf9yvuNSmf2vyjEsT1V+q1tvvVXr0KGD/29G0zRt+/btWps2bYJs3bdvX9hzUNM0LTMzU5s1a1bQdsqzfv36kN8k3N9EOHzH4pZbbgmqf+SRR7TMzExty5YtQba0bt1a27ZtW1Dbyl6nfOfdVVddFXQdfO2117TMzExtxYoVx93Pf//731rHjh2Drl++8/HNN9/01zkcDu2KK67Qevbs6d9OuGMc7py56KKLgs6ZpUuXVngcR48eHXRt+/TTT7XWrVtra9asCWr33nvvaZmZmdq6des0TdO0t956S8vMzNTy8vJC1ik4MygqKtIyMzO1iRMnHrfdLbfcomVmZvqvZ/fcc4/Ws2dPze12+9scOXJEa926tTZ79mx/3XXXXacNHjw46FxXVVUbMWKEdskll/jrfH9TI0eODFqnpmla165dtWnTph3Xvspez99++20tMzNT++yzz/x1TqdTGzFihNapUyf//n311VdaZmam9t///jdoO3fccYeWlZWl7dmzx19X0fU5HL7r2eDBg4OuHffcc4+WlZWljR8/Pqj9iBEjtIsuusg/vX//fq1Nmzbayy+/HNRu69atWtu2bYPqK/P8o2madv/992uZmZlBv5umadqVV14Zcs8PR58+fYKOsY/K/G4TJkwI2j8flf2dVq1apWVmZmqPPfZYyDoC72uZmZlau3btgn63LVu2aJmZmdr8+fP9dZW9L11++eXahAkTjrtv06ZNC7kuB9oTeD+cPHmy1rp1a23jxo3H3Y/KrKsmnq0eeOAB7YILLtDy8/OD6u+++26ta9eu/uPkO5//8Y9/hBy7Z599VmvXrp1WUFDgr3M4HFq3bt2CbKvqtirz7F/ReRXu/um7xx47dsxft2XLFq1169ba5MmT/XWVPa5VuS8uXrxYy8zM1DZs2OCv8+1n4G9aFR544AGtTZs22q5du4Lqjxw5onXu3Fl7//33NU0ru+aGO+cqw5o1a7SsrCzt+eefr7ZNlcVn688//6zl5eVpBw8e1L744gvt3HPP1bKzs7VDhw5pmlZ2LXvmmWdC1jFy5Miw17OKno0q+z7hdDq1nj17aoMHD9bsdru/3bfffqtlZmZqL7zwQqX27frrrw/6W3/iiSe0Nm3aaEVFRf668s9qb775ppaZmal99dVX/jq73a5deuml1X6X9HEy52ZeXl7Ye4qmlR2/HTt2VLj8l19+qWVmZmrnnnuudskll2iffPKJ9sknn2iXXHKJ1q5du6Dn+UGDBmljx44NWce2bdu0zMxM7b333juurSdChDSeRVxwwQW8//779OvXj7/++ovXX3+dcePG0adPnxB3x3B4PB5WrlxJ//79g/J/pKenM3jwYNatW0dJSQmgJ85t3bp12OS4gQmYAYYOHRr01cv3dTqwB4jA5IhWq5X8/Hw6d+6Mpmls3rzZ38ZoNLJ69eqQcKTqsHnzZvbv38/YsWNDVP3y+1BZoqOjueKKK/zTJpOJDh06VKq3iy1btrB7926GDBnCsWPHyM/PJz8/H6vVSs+ePVmzZg2qqgYt83//938h6xk4cCCbNm3yf1EB3bvLZDL53ZVrYlvdunWjoKDAf05UhZ9//hmXy8XYsWOD8kdcc801xMbG+j0UfFTmuMbHx3Po0CE2btxYJVu6deuGx+Nh/fr1gB5e0rVrV7p168batWsB+PvvvykqKjrppIrVOYaV/a08Hg8//fQT/fv3p2HDhv7lW7Zs6f9CWx0C/zZdLhfHjh2jSZMmxMfH+/82q8OoUaOCpn29hpUPXenevTutWrXyT1flOuVjxIgRQV4NI0eOxGAwBJ1ngftZUlJCfn4+3bp1w2azhYQCGAyGILdsk8nEiBEjyMvLCxu+dapYtmwZLVu2pEWLFv7zIj8/3/9Vzff10Hd9+/rrr0P+rgVnBr5Q6uN5QATO97UfOHAgeXl5QWGNy5cvR1VVLrvsMgAKCgr45ZdfGDhwoP/cz8/P59ixY/Tq1Yvdu3eHhHwMHz48JC9jfHw8GzZsOGF4SGWu5z/88ANpaWlB+V6MRiNjxozBarX6vXJ/+OEHFEUJCR+58cYb0TQt5HpSVa644oqga0d2djaapoV8Ec/OzubgwYO43W4AvvrqK1RVZeDAgUF/m6mpqTRt2jToy35lnn8CGTlyZNB0165dKxViXlBQENaDobK/Wzgq+zt9+eWXSJIU5AHno/zz1vnnnx/kfde6dWtiY2MrfGY83n0pPj6ebdu2sXv37irvW3lUVWXFihX+XtBPtB+VpbrPVpqm8eWXX9KvXz80TQs6z3r16kVxcXHI/ejKK68MSUZ+2WWX4XK5gjyJVq5cSVFRkf8aUZ1tVebZv7IcOXKELVu2cNVVVwV5CbVu3Zrzzz8/5JkRTnxcq3Jf9LU9duyYv65Hjx5s3bq1Wt5dixcv5uOPP+aGG26gWbNmQfOeeeYZGjdufFI5tHzk5eVx77330qhRo5Coj6rYVFWuv/56f7j73XffTUxMDLNnz6ZevXpB7cpfy0C/TvkiUSpDZd8n/vzzT/Ly8hg5cmRQfrgLL7yQFi1a8N1331Vqez4vRx++d4hwqWR8/Pjjj9SrV49//OMf/jqz2ez32CxPVd4lT+bc9KURCeeZ6jtG5VONBOJ7zigtLeXtt99m6NChDB06lLfeegvQ0zD4qMgD1red8iH8VUWENJ5lZGdnM3v2bJxOJ3/99RcrVqzg7bff5s477+TTTz8NenEsT35+PjabjebNm4fMa9myJaqqcvDgQc455xz27t3LJZdcUimbAl++oeyPMzC2+sCBA8yaNYtvvvkmRMzy3ZxMJhP33XcfTz75JBdccAEdO3bkwgsv5MorryQtLa1StgTiu3AcL/yzqtSvXz/koSchIYGtW7eecFnfA1n58KlAiouLg24E4XoqufTSS5k5cyZLlizhlltuQdM0li1bRp8+ffyupNXZVkW/Y2FhYYXurhVx4MABAL9bsg+TyUTjxo1DbhyVOa433XQTP//8M9dccw1NmzblggsuYPDgwf6w0Ypo27YtUVFRrF27lt69e7Nu3TruuOMOUlNTmT9/Pg6Hw58r60TrOhHVOYaV/a2cTid2uz2oVxsfzZs3D/tAWBnsdjtz5sxh4cKFHD58OCgvTnFxcbXWCYTY2aRJE2RZDnlxK3+OV+U6VdG2YmJiSEtLCzrPtm3bxvPPP88vv/wS8qJRfj/T09NDknT7HhBzcnLo1KlTmD2uefbs2cOOHTvo2bNn2Pm+zkouu+wyPvroI/71r3/x7LPP0rNnTy6++GIuvfTSWpmwWBBKeSGrIsoLY3369CEuLo4lS5b4z5MlS5bQpk0b/9/Q3r170TSNF154gRdeeCHsevPy8oJeVsLde+677z6mTJnChRdeSLt27ejbty9XXnllSAL9ylzPc3JyaNq0acj56Qth8d1DcnJySE9PD7l++tod7yWkMpS/Zvt6NywfehEXF4eqqhQXF5OUlMTu3bvRNK3C56TADk8q8/zjw2w2h+RvS0hIqPRHwMDrt4/K/m7hqOzvtHfvXtLT0ysVVhUurCUhISHombGy96VJkyZx6623MmDAADIzM+nVqxdXXHEFrVu3PqEd5cnPz6ekpCTo3lITVPfZKj8/n6KiIj744AM++OCDCtsEEu7vtnXr1rRo0YKlS5f6RZYlS5aQlJTk/3hSnW1V5tm/svjOo4ru+z/99FNI5xknOq5VuS+G+7upLmvXruXBBx+kV69e3H333UHzfv/9dz777DPefvvtk743W61Wbr75ZkpLS3n33XeP+7HkeDZVh4ceeojmzZujKAqpqak0b948ZH8MBkNQD36BVOV4V/Z94njnUIsWLcLmxA1Hdc7rnJwcmjRpEnLfqyisvirvkidzbvrEpnD5B31C1/E6j/CJ5126dAm6bjds2JAuXbr4HQl8bY+3nZPpFRSE4HXWYjKZyM7OJjs7m2bNmjF16lSWLVsW9uvaqaaii7bvj9Tj8XDDDTf4Y5ZbtGhBdHQ0hw8fZsqUKUFfXq6//nr69evHihUr+Omnn3jhhRd49dVXmTt3Lm3btj0t+3M8TqYnQt/xmDx5Mm3atAnbpvxLdrgLUb169ejWrRtLly7llltu4ffff+fAgQP+hJDV3daJfsdTSWWOa8uWLVm2bBnfffcdP/74I19++SXvvvsut912G5MmTapwOaPRSHZ2NmvXrmXPnj3k5ubSrVs3UlJScLvdbNiwgbVr19KiRYuQF42qUp1jWNnfqrLdVkPFX6LDJT5+7LHH/PnCOnXqRFxcHJIk+fPG1RQV2XSyN8HKUFRUxOjRo4mNjWXSpEk0adIEs9nMpk2beOaZZ2qtV5SqqmRmZvrzn5TH9zBpsVh45513+PXXX/1/H0uWLOGDDz7gzTffrHM9qNZF4uLiSEtLO+HHk61bt1KvXj3/i7LPs/err77i4YcfJi8vj99++y2o5yXf+X3jjTfSu3fvsOst/2Ae7t5z2WWX0a1bN7766itWrlzJG2+8wWuvvcaLL74YlHfkTDrfKrpmn+harqoqkiTx2muvhd1f3/21Ks8/cHLHLjExMexLWWV/t9NFRfsYeL+p7H2pe/fufPXVV3z99desXLmSjz/+mLlz5zJt2rQa8aCpCar7bOU7Ny6//PKQ/Fs+AhPwQ8X308suu4xXXnmF/Px8YmNj+eabbxg0aJBfmK3OtiL5zFiZ7Vflvuj7uznZ3FZ//fUXEydO5JxzzmHWrFkhPX0//fTTdOvWjUaNGvk//vk8d3Jzczlw4ECI4BIOp9PJHXfcwdatW3njjTeO+2H/RDZVh+zs7LBekIGYTKawv1FF16nawuk4r6tynT+ZczMxMRGTyURubm7IPF9denp6hcv75oXLlZ2SksKWLVv802lpaWG9iCuzncogBC8B7du3B3SX4OORnJxMVFQUu3btCpm3c+dOZFn2K7hNmjRh27ZtNWLf33//ze7du3nyySe58sor/fW+XnTK06RJE2688UZuvPFGdu/ezZVXXsmbb77JM888A1Terdz39fLvv/8O6g3kVFORfT57YmNjT9qegQMHMm3aNHbu3MmSJUuIiorioosuOiXbqg6+G/bOnTuDviI7nU72799fbZuio6O57LLLuOyyy/w3/FdeeYWbb775uF8punXrxmuvvcbPP/9MUlISLVq0QJIkzjnnHNauXcvatWuDjt/ppLK/VXJyMhaLJaQHVCDkb9rnuVf+ocL3BSyQ5cuXc+WVVwb1CuVwOE7Kuwt076TA337Pnj2oqhr2C3QgVblOBa47MHlmaWkpubm59OnTB4DVq1dTUFDA7Nmz6d69u79dRWFCR44cCfma7PPEC0y0Wh2qEhbTpEkT/vrrL3r27HnC5WRZpmfPnvTs2ZOpU6fyyiuv8J///Idff/01ItcAQdW56KKL+PDDD1m7dm3Y8Oq1a9eSk5MTFG4L+v1g0aJFrFq1ih07dqBpGgMHDvTP9/0dGo3Gkz4X0tPTGTVqFKNGjSIvL4+rrrqKV155pcrCSUZGBlu3bkVV1aAXDF94se8ekpGRwapVq0IS6/ranezfY3Vp0qQJmqbRqFGjsB4FPqr6/HMytGjRosJr2ol+t4quL5X9nZo0acJPP/1EQUFBpby8TkRV7kuJiYkMGzaMYcOGUVpayujRo3nxxRf9gldlr7nJycnExsbW2LPvyZKcnExMTAyqqp703+1ll13G7Nmz+fLLL0lNTaWkpIRBgwadkm0FUtlj7zuPKrrvJyUlhXykrQyVvS/u378fWZaP+7d8Ivbu3cv48eNJTk7mtddeC+txdfDgQXJycoJC33xMnDiRuLg4f6qNilBVlfvvv59Vq1bx/PPPh3RYUlWbTjctWrQIm6i/onOlsu8TgedQea/4Xbt2VUpIrC4ZGRls374dTdOC9iMw9Ux1OZlzU5ZlMjMz+fPPP0Pmbdy4kcaNGx/Xy9TXCUo4IevIkSNBTgKtW7fm119/DblXb9iwAaDCD/qVRcQqnEX88ssvYRVmXyhToLtndHR0yMuuoihccMEFfP3110EPRUePHuXzzz+na9eu/pP0kksu4a+//uKrr74K2V5VVW7fQ1LgcpqmMW/evKB2NpstJJa4SZMmxMTEBHm3REVFVerrQLt27WjUqBHz5s0LaX8qv0D5eg8qv8327dvTpEkT3nzzzbBhK5XpXtbHgAEDUBSFL774gmXLlvl76zoV2wok3HkVjvPPPx+j0cj8+fODjvXHH39McXFxtb4qB8avg/71qGXLlmiahsvlOu6y3bp1w+l0MnfuXLp27eq/IXXt2pXPPvuMI0eOVCqcsbL7XxUq+1spikKvXr1YsWJFkHC1Y8cOfvrpp6BlYmNjSUpKCnlwevfdd0PWH+5L0/z588N6g1UFX1fwPhYsWADgF6EqoirXKR8ffPBB0Dnw3nvv4Xa7/dsKdw1yOp1hjwfovQsFhnU4nU4++OADkpOTg3qjqw6+60NlBMWBAwdy+PBhPvzww5B5drvd33teQUFByHzfw0VVPAMFwZSUlLBixYoa78K9IsaNG4fFYuHhhx8Oud4VFBTw8MMPExUVFZKr5fzzzycxMZElS5awdOlSsrOzg14MUlJSOPfcc/nggw/CfhirzP3A4/GEnLMpKSmkp6dX6xzr06cPubm5LFmyxF/ndruZP38+0dHRfmG6T58+eDyekOvJ22+/jSRJQdeTU3F9rohLLrkERVGYPXt2yPOEpmn+36+yzz81QadOndi2bVvQ71HZ3y0qKirsNamyv9Mll1yCpmnMnj07ZB3Ved6q7H2p/N9JTEwMTZo0Cdk3OHGonSzL9O/fn2+//ZY//vgjZP6pfG6s6Jl9wIABLF++nL///jtkmao8x7Vs2ZLMzEyWLFnCkiVLSEtLC/r4U5PbCqSi86o86enptGnThk8//TToOPz999+sXLmyWs+MVbkvbtq0iVatWvlDmkF/J9mxY0el9j03N5cbb7wRSZJ44403KowWePTRR3nppZeCii8/4f333+//sA/6M8KOHTtCjt9jjz3GkiVLePjhh4+beqayNp1uOnXqRGFhYUi+qoqejSr7PtG+fXtSUlJ4//33g37f77//nh07dvh7ZD8V9OrVi8OHDwfl03Y4HGGf3arKyZ6bAwYM4I8//gi6pu3cuZNffvmFSy+9NKjtjh07gt4vYmNj6dOnD+vXrw96DtqxYwfr168PEo0vvfRSPB5PyLPzwoUL6dix48n10Ijw8DqrmD59OjabjYsvvpgWLVrgcrn47bffWLp0KRkZGQwdOtTftl27dqxatYq33nqL9PR0GjVqRMeOHbnrrrv4+eefufbaa7n22mtRFIUPPvgAp9Pp78oX9Afv5cuXc+eddzJs2DDatWtHYWEh33zzDdOmTatSfoQWLVrQpEkTnnzySQ4fPkxsbCzLly8Pubnv3r2b66+/nksvvZRWrVqhKAorVqzg6NGjQV+i2rVrx3vvvcd///tfmjZtSnJyctgcN7Is88gjjzBx4kSuvPJKhg4dSlpaGjt37mT79u288cYbVTn8lcaXWPX9998nJiaG6Oho/wvI9OnTuemmmxg8eDBDhw6lXr16HD58mF9//ZXY2FheeeWVSm0jJSWFHj168NZbb1FaWupPPOpDluUa21YgFZ1X5UlOTubmm29m9uzZjB8/nn79+rFr1y7effddOnTowOWXX17lbY8bN47U1FS6dOlCSkoKO3fuZMGCBfTt2/eEOcY6deqEwWBg165dQd4R3bt357333gOoVML6yu5/VajKb3XHHXfw448/MmrUKEaOHInH42HBggW0atUqJBTqmmuu4dVXX+XBBx+kffv2rF27NuzX0wsvvJDPPvuM2NhYWrVqxe+//87PP/980l/p9+/fzy233ELv3r35/fff+d///sfgwYMrde2o7HXKh8vl4vrrr2fgwIH+86xr167+r6idO3cmISGBKVOmMGbMGCRJ4rPPPqvwBSY9PZ3XXnuNnJwcmjVrxpIlS9iyZQuPPfZYUILr6tCmTRsUReG1116juLgYk8nEeeedR0pKSkjbK664gqVLl/Lwww/z66+/0qVLFzweDzt37mTZsmW8/vrrdOjQgZdeeom1a9fSt29fMjIyyMvL491336V+/fonnZfubOLOO++ke/fujB49GrvdzrBhw8jJyUHTNJ577jkGDBhwSrffrFkzZs6cyT//+U+GDBnC1VdfTaNGjcjJyeHjjz/m2LFjPPfccyHhh0ajkYsvvpgvvvgCm80WNh/gww8/zLXXXsuQIUMYPnw4jRs35ujRo/z+++8cOnSI//3vf8e1rbS0lL59+zJgwABat25NdHQ0P//8M3/88UeQF05lGTFiBB988AFTpkxh06ZNZGRksHz5cn777TceeOAB/zW9X79+9OjRg//85z/k5OSQlZXFypUr+frrr7nuuuuCjsWpuD5XRJMmTbjrrrt49tlnycnJoX///sTExLB//35WrFjB8OHDGTduXKWff2qCf/zjH/z3v/9l9erV/o5MKvu7tWvXjiVLljBjxgw6dOhAdHQ0/fr1q/TvdN5553HFFVcwf/589uzZQ+/evVFVlXXr1tGjRw9/pyWVpbL3pUGDBnHuuefSrl07EhMT+eOPP1i+fHnQ9nwfKaZPn06vXr1QFCXomTKQe+65h5UrVzJmzBiGDx9Oy5Ytyc3NZdmyZbz77rthOwWoCSo6d++9915+/fVXhg8fzjXXXEOrVq0oLCxk06ZNrFq1KqizihNx2WWXMWvWLMxmM1dffXVI6FZNbitwv8KdV+GYPHkyN910EyNGjODqq6/GbrezYMEC4uLiqpWupbL3RZfLxZo1a0ISrG/cuJGxY8dy++23nzA5+Pjx49m3bx/jx49n3bp1QfmiUlNTueCCCwDCdjDkuxZ07949KEzwq6++YurUqcyYMcP/fvf222/z7rvv0rlzZywWC5999lnQui6++GL/x+/K2gQwZcoUFi1axNdff31CL/yT5cILL8RgMPDzzz8HPY8f79moMu8TRqOR++67j6lTpzJ69GgGDRpEXl4e8+bNIyMjg+uvv/6U7dOIESNYsGAB9957L2PHjiUtLY3Fixf7I0+q2+FFTZyb1157LR999BE333wzN954IwaDgbfffpuUlBRuvPHGoLaXXXYZ5557LvPnz/fX3XPPPaxatYrrrruOsWPHAjBv3jwSEhK45ZZb/O06duzIpZdeynPPPUdeXh5NmzZl0aJF5OTk8Pjjj1dr/wMRgtdZxOTJk1m2bBnff/+936OhYcOGXHvttUycODHoRjxlyhQeeughnn/+eex2O1dddRUdO3bknHPO4Z133uHZZ59lzpw5aJpGdnY2Tz/9dNCDYUxMDO+88w4vvvgiX331FYsWLSIlJYWePXuG9MJxIoxGI6+88grTp09nzpw5mM1mLr74YkaNGhXUS0X9+vUZNGgQq1at4n//+x+KotCiRQuef/75oBeN2267jQMHDvD6669TWlrKueeeW2FS5969ezN37lxeeukl3nzzTTRNo3HjxhX2nFETGI1GZs6cyXPPPccjjzyC2+1mxowZNG7cmB49evDBBx/w3//+lwULFmC1WklLSyM7OzskTOVEXHbZZfz888/ExMSE/fpVk9vyUdF5FY477riD5ORkFixYwIwZM0hISGD48OHcc8891RINRowYweLFi3nrrbewWq3Ur1+fMWPGcOutt55w2ejoaNq0acMff/wR9KDjE7kaNGhQqdCYqux/Vajsb9W6dWveeOMNZsyYwaxZs6hfvz533HEHubm5IYLXbbfdRn5+PsuXL2fp0qX06dOH119/PeRv5cEHH0SWZRYvXozD4aBLly689dZbJ+zx50Q8//zzvPDCCzz77LMYDAZGjx7N5MmTK7VsZa9TPh566CEWL17MrFmzcLlcDBo0iH/961/+h4ykpCReeeUVnnzySZ5//nni4+O5/PLL6dmzJ+PGjQtZX0JCAjNnzmT69Ol8+OGHpKam8tBDD9XIdSMtLY1p06YxZ84cHnzwQTweD/PmzQsreMmyzEsvvcTbb7/NZ599xldffUVUVBSNGjVizJgxfhf3fv36kZOTwyeffMKxY8dISkri3HPP5Y477gj6Kig4PmvXrmXixImA/rKhaRpr1qxh0aJFvPzyy6dc8ALdq69Fixa8+uqrfPzxx/4QsR49enDzzTdXmKvFl6BZkqSgcEYfrVq14pNPPmH27NksWrSIgoICkpOTadu2LbfddtsJ7bJYLIwcOZKVK1fy5ZdfomkaTZo08QtpVcVisTB//nyeeeYZFi1aRElJCc2bNw96uQP9b+Dll19m1qxZLFmyhIULF5KRkcHkyZNDHtZP1fW5IiZMmECzZs14++23eemllwD9OeaCCy7wv9RX9vmnJmjfvj1ZWVksXbrU/2Jd2d/t2muvZcuWLSxcuJC3336bjIwM+vXrV+nfCWDGjBlkZWXx8ccf89RTTxEXF0f79u3p3LlzlfelsvelMWPG8M0337By5UqcTicNGzbkrrvuCrquX3LJ/7d333FWVPf/x18zc/vdyhZgAVGpIiIgBkUsMSQmoonBrxp/RmJiiyWW2I0NG0RNIka/iiVGiIkaY/wGjWJJTNQQQUXpSIelbW+3T/n9MXPv3rsFlmXh7i6fZx6TmTkzd+7hclnvfe/nnPkWF154IW+++SZ/+9vfsCyr3cCrb9++vPLKK8yePZv58+fT1NRE3759Oemkk/brfJPtvXeLi4v585//zBNPPMG7777Ln/70JwoKChg6dGjGvK0dcfrpp/Poo48SiUTa/BnRlc+V1N77qi2TJk3i2Wef5bHHHkvNNXXsscdy0003degGCy119L+LCxcupK6urt25yzpi9erVQOZd65K+9rWvZYRL+yL5PEuWLMmYMDzp/fffTwVee9OncDiMz+fbb4FuuuLiYk466STeeuutjM+3u/ts1NHvE9OmTcPn8/HMM8/wyCOPEAgEmDJlCjfddNN+/bMFg0FeeOEF7r//fubOnUsgEOCss85i3Lhx/OxnP9vtlCu70xXvzZycHObNm8eDDz7Ik08+iWmaTJw4kdtuu61DVX9Dhw7lD3/4A4888ghPPvkkiqJw3HHHcfPNN7fKAx566CEeffRR/va3v1FfX8+IESN46qmnMqpJO0uxDtTsgEIIIbql3/72tzz++OMdulvogerLwoULu00JvRAdMWbMGBYsWED//v25+eabKS0t5cYbb2T79u1MnTq1zS8YQnQnr7/+Ovfeey8ffPDBAfnyKkRPd+WVV6IoSiq0PhhNmjSJ733ve7u9W3hX+vTTT7nwwgt56623UnfA7o1+//vfM3PmTP7973/vdbEIyHsznczhJYQQQgixj/r378+SJUsIh8N8+OGHqd+ANzQ04PF4stw7Ifbsu9/9LmVlZa3mPBNCtLZ+/Xo++OADrr322mx3JWvWrl1LNBrl0ksvPWDPOWHCBE444YQ2q896qmg0mrEfi8V4+eWXOfTQQzsVdsl7M5MMaRRCCCGE2EfTp0/npptuIhAI0L9/fyZOnAjA4sWLd3vbdyG6C1VVeeONN7LdDSF6hCFDhrBy5cpsdyOrhg0bxueff37An7c3hV0AV199NWVlZYwcOZKmpib+9re/sWHDhowbEewNeW9mksBLCCGEEGIfXXDBBYwZM4adO3cyadKk1KTOgwYN4rrrrstu54QQQgjRLU2ePJlXX32V+fPnYxgGQ4cO5Te/+U2rm4qJzpE5vIQQQgghukg8Hqe8vJxDDjkEl0t+ryiEEEIIkS0yh5cQQgghxD6KRCLcfvvtjB07ljPOOIMdO3YAcN999/H0009nuXdCCCGEEAcfCbyEEEIIIfbRr371K1avXs3cuXMzbiN+/PHH8/e//z2LPRNCCCGEODhlNfBavHgxP/3pT5k8eTIjRozgvffeyzhuWRazZ89m8uTJjBkzhosuuohNmzZlnFNXV8cNN9zA+PHjmTBhArfffjuhUGiv+mFZFk1NTcjoTiGEEEJ0xvvvv89dd93FhAkTMtqHDRvGli1bstSr/Us+PwkhhBCiO8tq4BUOhxkxYgR33313m8efeeYZ5s2bxz333MMrr7yC3+/n4osvJhaLpc658cYbWbduHc8//zxPPfUUn376KXfdddde9SMUCnHMMcfsdVAmhBBCCAFQU1NDUVFRq/ZIJIKiKFno0f4nn5+EEOLAi0QinHLKKZxyyilEamrglFPsJRLJdteE6HayOpvqySefzMknn9zmMcuymDt3LldccQVTpkwB4KGHHmLSpEm89957TJ06lfXr1/Phhx/y6quvctRRRwFwxx13cNlll3HzzTfTt2/fA/ZnEUIIIcTBa/To0XzwwQdceOGFGe1//vOfGTt2bHY6JYQQotfx+/188MEHzQ3p20KIDN329kHl5eVUVlYyadKkVFtubi5HH300S5YsYerUqSxZsoS8vLxU2AWkbgW+dOlSvvnNb2aj65kq/g01n4ErF9y59tqTD57C5kXzZbuXQgghhNgH119/PZdeeinr1q3DMAzmzp3L+vXrWbJkCfPmzct294QQQgghDjrdNvCqrKwEaDU8oKioiKqqKgCqqqro06dPxnGXy0V+fn7q8VllGvDB6aDvodRfC4C3GLwl4CsGX38IDIDAQPAPhJwhkHM4uLy7v44QQogul5yfyMLCsiwsrFT7vmwnr9mRYx3Z3905e2rb0/V2d26r/nT0GC2ulzYNVFt9HZQ/iOJAMd3VhAkT+L//+z+efvpphg8fzscff8yoUaN46aWXGDFiRLa7J4QQQvRohmmhqT1rioCe2OfeptsGXr2CqsG4X8PWv9ihlxG213oTJBrtNZbdHt5iL+1fDHyldgiWMxTyRkDeEZA/0q4cUzT7HEWzF9XVvK240vblxpxCiAPLtEwsy7LXaaFRsj3Z1vJ4e20dOZZ+TvrSVnvqMZaFiYlpmqn+QdtBV8aatGNpwc3uwqzdPt7eaHOf5Gcmi9S8UOnPqSgKFhYKCpZlpfbthyrNz6/Qqq3lY9Pbmjvd/DztUcg8ln7u7o7t7lqhRIhCf2G3DrwADjnkEO6///5sd0MIIUQvFgqFOPTQQwHYtGIFwSOPtA9s2gTBYNb6tb9pqsK1Ly1hXUVTtrvSIUNLc5j9g3HZ7sZBr9sGXiUlJQBUV1dTWlqaaq+urmbkyJEAFBcXU1NTk/E4Xdepr69PPT7rhl1mL0mWBWYCzDiYMYjVQHQHNG2BSDlEK5xlJ8SrIVYL0V1gRu226E6o+TTtCVQIDnYCsJH24usLKHbgpqhAMgTTQHGD6gXNaw+lVN1pwZhzfjIky2h3Hi+E6LFahj/tLW0FQi2PGaaBYTUvpum0kbZtGRkBE5AKlZIhTXrw1dbxdKk2hQ6tk2GKgoKiKKl1eltb20Cr89PPU51fHChq68e0vF5Hju+uD8JW3lCe7S60qamp4x+6c3Jy9mNPhBBCHEySI56cnex15ABbV9HEiu0N2e6G6EG6beA1cOBASkpKWLhwIUcccQRgf7D88ssvOf/88wEYN24cDQ0NLF++nNGjRwPw3//+F9M0GTNmTNb6vluKAprHXsgBbxHkDYNkpmcmQA/b1V/xOohV2dVg0QqI10KiBpo2QdM6aFxrt4U22suOt+1rBAZC8SQomgiFY0H1gGWApYMZsa9t6U5bG7cSV5S0arAWQZjqdkIzt101pnrsdaq6LG2N6oRkqtOupm1raW1CiHTpgVJy27TMVvvpbaZlohs6uqmjWzqGaZAwEximgW7qqfNSYRZpVVdp1U3pw9CA5ooimquBWgY3yQBIVdSM7fSgKf1Ycr+967QVUAnRHU2YMGGP79Hkv5lVq1YdoF4JIYQQQgjIcuAVCoXYsqV5GF95eTmrVq0iPz+fsrIypk+fzpNPPsngwYMZOHAgs2fPprS0NHXXxiFDhnDiiSdy5513MmPGDBKJBPfddx9Tp07tuXdoVN3OpPb59jxelgmJBjvYCm+DRD30mWBPdu/OtYOwuqVQu9ReN6yCcDlsecVeVC+UnAD9v22vtQ7MA2aZzmLYa5y1pYMetUOy1DGz7dAsNV5GyQy7UNKCr2SwlhyC6XaGXyaHYbrbCMraCc2UFsGaDN8UWZKqfEoLmtLb0te6oRM34ySMBAkzgW7qJIyEXRllWRlhlmnaIVWyUqq9oWgKCpqqZYRKybBJQUFTNFyqK6Mt/TwJmYTouLlz52a7C0IIIYQQoh1ZDbyWL1/O9OnTU/szZ84E4Pvf/z6zZs3i0ksvJRKJcNddd9HQ0MAxxxzDs88+i9fbHNo88sgj3HffffzoRz9CVVW+9a1vcccddxzwP8t+o6jgKbCX4GA78ArvsEOt2GY7GOv7Dehnh4DoIaheDFX/gcr/2EMgd/3DXlxB6HsqDDob8o+0A6f2nlNR2ee3h2XRHIglQzQLO0CzmkM0MwZ62jlY7VefpcYs0RyYZYRo6WFaWlUa6fOatZjTLPUYrY3ArGXQln6OBGq9WTKwarkYlhNkmQYJI0HMiBE34qklGWilqq9MEwM7wEoOt0ufx0hTNBTFDqKSIZSqqLhUF27caGpmuyrvOyG6ja997WvZ7oIQQgghhGhHVgOviRMnsmbNmnaPK4rCtddey7XXXtvuOQUFBfzqV7/aH93rfhTVruzyFELwEIjshNBmCG2x7+7oCjqh1in2YlnQuAZ2LIAd79hzgW2bby95R8Ah50D/b9lzee2X/irYQyL3z+VTlWgZAZmZWaFmJuybAqSHb6nKNJz8LH34luLsp1WmKQqtqtPUtABMddGxQK2N7fRhoC3PkTnTulRyiF8yuEoYiYwgK2bEiOkxokaUmB5LhVqpai1Lbx7qlzZxdzKo0lQNTdHQVA2P5mnVLlVTQvR+9fX1vPrqq6xfvx6AoUOHMm3aNAoKCrLbMSGEEEKIg1C3ncNL7IE7B9xD7WGPTc4cXokG8JY4AQx2UJOcyH74z6D2Syj/P9j5jj30cfm9sPrX0PfrdoVY0cTmx/YEGVVW7q69dkeq09Dtmw8Y6cFbi0At49ZqyYQtramtICyjQi0Znrkyt1VX+0M5M/ZbVqb1rrnTLMuywyszQcJIEDfiGUMDI3qESCJCVI+m2tOHGtoXsVeKouBSXbhUVyq48mpeNFVLtUloJYRoz+LFi/npT39Kbm5ual7RefPm8cQTT/DUU09x7LHHZrmHQgghhBAHlx6Ubog2ufxQMAp8pfYk9pFt4Oljz++VTlGhzzh7GXkdlL8OW/5i3yEyWfXlzrOHPA44AwqObn/I48Fgf1enQfNcaK0q1JL7yQq1tKo1rMxwLb2/GfvJUKuN4Z7J6rTUHTqT86U564wbDbQxtLOtedRaHe+Kl8ciYSYyhgsmQ62IHiGUCBFNRFOBl27Yk7WnDxtMDg1MLj7Nh8vtSoVYQgjRVe69915OP/107rnnHjTNrtA1DIMZM2Zw7733Mn/+/Cz3UAghRG+gqioTJkywt10ucLbtz/hCiHTyja+38BXb83k1bbaHMRoxu60tngI4/CI4bDrUfgE734Wd/4B4tR2Elb9u3+mxbKodfvn7H7A/xkFFUeyQCej6CrUWAVmq6swZ6okBeqyNqrVkJVvaXGmZnW6eOy011DNt6Gd6RVlqqKe7RWWaHZIZpkXc1IlbBjE9Qdw0iFs6YT1GKBElokdJmAYJy0Q3DUzLSj2PS3FlBlken1RhCdGDtazWbDn8uD5Wn+0u7tHmzZuZPXt2KuwC0DSNiy66iNdffz17HRNCCNGr+P1+Fi9e3NyQvi2EyCCBV2+iuiFvKLiDULfCvqujv3/7FTeKCn3G28sRN0LNEtj+Jux8354Uf90cWPc0FB8Pg6ZByeSeNeTxYLa/hi22rEKjRVBmmUACzBhWwiRhxonqMeJmnJgeJ6bHaNKjhBMxwkbMDrRMA900UtV0murCrbpxqxoe1UNQdePSXGiqKy0kTIZqzl0+SdtuOedaeqVbRkintDim2teRwEz0YslgKWbE2rzpQqqS0owT1+Opu4imjjlVl8lQqr3tjDanAjNhZh5LzqOXDLf25Jkzn+GS8ZccgFepc0aNGsWGDRs4/PDDM9o3bNjAyJEjs9QrIYQQQoiDl6QXvZG/P2h+qFtuB1f+/s6dCndD0aBogr0ccbN9V8dtb0CNc8fHqv+AtxQGnWWHX952qsdE75Yx1NOFZVnEzQQxI07M0InqMaJ6nCY9TGM8TMyIEzd0Eqkvs5Z990EtiNudT1Bz4XaqtOzDLW8ukD6XmtNmRmke0mk6j0ufN60Df4Zk0IUTgCWDMDUZgCUr0ZwgDac6TXVCsYybGqiZ10it22kTwmFapn2jBD1KzLDXye1Uux5LHUu2J0OqqB4lbsQz2mK6s04LspKPS+5bHfqHkn0ezWP/vFDd5HhyGJA7INtd2q3p06fzwAMPsHnzZo4++mgAvvzyS1588UVuvPFGVq9enTpXAjAhhBBCiP1PAq/eylMAfY6B+pUQ3gz+AR2vznL5YcBUewlthfLXoHw+xCrsiq/1v4N+34TB50HB6P36xxDdg2mZxIw4ESfQihoxGuNh6uNNqVBLN3UsLBQUXJoLr+rGo3nIcQdwq66ODTXc73f2TAZokBriCWQO67TsudOSwz2hY4Fa+k0IgOZgTGleJ4eDkn4TgeS2y6lSa1GFllGNpjY/T6sqNQnXupppmUQSkdTNDyJ6hHAiTFSPNrfrkdR+MrBKtenNbVE9SjQRJWpEM4KsbHM7/07TF7fmtKmt25LnuzX3brcz2pL7yWOq2wm+m891qa5UwNXW8OTyhnKOKDkii6/Unv385z8H4OGHH27zmKIoWJaFoiisWrXqQHdPCCFELxEOhxk1ahQAKz/9lEByDq+VKyEQyGLPhOh+JPDqzVx+KDjK3g5ttu/ouLdDEoODYMS1MOwKe56vLa9A3VLY8Za95I+GQy+w7/Qowx17PMuyUsFWRI8RMWLURRtpTISIGXYll+lUW7lVFx6tE6FWNmUEatoeTt5LVotKNJztVMVaskJNp1WFWsbNCPb0Z4DMQCt9CKeSFoKRNkyzxZKaS62N4Z8ZFWtaO0M/u2egljAShBIhwokwoXgotR1OhDPaw4mwfeODeCgVYkUSEcK6s3YecyADKY/mwat58bl8eF1evJoXr8uLT7P3fS5fqs2jefC5fKnHJM/3aJ6M7fTzkkv6MbfmRu2Gf4891fvvv98l15kzZw7vvPMOGzZswOfzMW7cOG688caMoZKxWIxZs2bx97//nXg8zuTJk7n77rspLm6uvt6+fTv33HMPn3zyCYFAgLPOOosbbrgBl6v5v9WffPIJs2bNYu3atfTv358rrriCadOmdcmfQwghxP5hWRabN2+2t00TnO2MG1gJIYBOBl7Tp0/n8ccfJy8vL6O9qamJK6+8krlz53ZJ50QX0DxOFZYJoXIIlHUumFI9UPZte6lfCZtfhh3vQP1y+PI28PWDwT+Agd9rfYdI0S3ppk5EjxHWo0T0GA2xEHXxRqJGnJgRxzBNFAXn7oYegm4fhd48NLkDTNuUZPXVftZulVra3TuTwVtGuJYexpmpu1m2G7BlDP1so4oso0LNtYcwLX2Yp0Zm8KhiAmE9QmMsRGMiRFM8RJMTUjXFm2iKNxGK29vJtlAilAq1kuu4Ee/61xtQUPC7/fhd/ua1y4/P7Utt+91+fC4fPpcvY9+ree1zXT78br+9n3YsGUhpahcHsOKAGzCga4ZcLlq0iAsuuICjjjoKwzD49a9/zcUXX8ybb75JwPnN/YMPPsi//vUvHn30UXJzc7nvvvu4+uqreemllwD77pCXX345xcXFvPTSS1RUVHDLLbfgdrtTlWhbt27l8ssv5wc/+AGPPPIICxcu5I477qCkpIQTTzyxS/4sQgghhBDZ1KnAa9GiRSQSiVbtsViMzz77bJ87JbqY5rUrvSwTItvtSi9lH75c5Y+CMTNgxDWw5VXY8meI7oQ1j8JXT0DpiVB2OpRMsoMykXVxI0FYjxJORAklItTFG2mMh4kaMRKmjmlauDUXXs2NT/OQ78nBJV/Au6f9WaWWzjLaDtWSlWyWkTb00yRqxGiINdGQCNEYj9CQiNDY7hKlKW07pEe7dF4pr+Yl6PYTdAcJuP0E3QEC7gB+d8DZttvtdXI/iN8TwO/y2+cm105I1e2rF0W3sGvXLj777DNqamowTTPj2PTp0zt0jeeeey5jf9asWRx//PGsWLGCY489lsbGRv7yl7/wyCOPcPzxxwN2AHb66afzxRdfMHbsWD766CPWrVvH888/T3FxMUcccQTXXnstjzzyCFdffTUej4eXXnqJgQMHcuuttwIwZMgQPvvsM37/+99L4CWEEEKIXmGvAq/0CVfXrVtHZWVlat80TT788EP69u3bdb0TXUfzQcEYwILwdggM3Pe70XmLYNjlcPiPYPvbsPlP0LTenvB+1z/AnQd9v2GHX4VHd8shUL1RwtAJ63awFUpEqI7WE9IjRHQ73FIVBY/qxqt5KPDm4lHd8mVepMSMOHWxJupjjdTHm6iPh6iPNdEQb0rtN8SbaEi1h2iIh4ibrX8Jsrc8qosct99eXD5y3H6Cbp+zba+DLi9Bl8fedvsIaF5y3Ml2HwGXNzOs3e0Q0OSx9KGgFihRsBKQCIGedvOC5Hxs6cNASX9syxsVtDfHWvox0Vu89tpr3HXXXbjdbgoLCzOOKYrS4cCrpcbGRgDy8/MBWL58OYlEgkmTJqXOGTJkCGVlZanA64svvmD48OEZQxwnT57MPffcw7p16xg1ahRffPFFKjBLP+fBBx/sVD+FEEIIIbqbvQq8zjrrLBRFQVEUfvSjH7U67vP5uOOOO7qsc6KLufyQfyQYUYjuAn+/rrmu5rPv3jjwe9C4Fra/BTvehlgllP/VXnz9oOw70P/bkDuka55XYFqmXbWl2+FWTbSB+ngTUT1O3EygouB1efBpHop9Bbg1mWftYGKYJg3xJmpjjdTGGqiNNVLXcok3Ueu8b+pi9pDWztIUlRx3gDxPkFxP0F67A852gFx3kFxPIHVOjjuQ2s9x+/FqXVwRmppTLVk9ll6tlj401BnqaRlgxdPmV0sbApp8yO6GgUKLoaCQCscyAjZazLPmDPVESbuJQVq72uIGCKQ/Vsl8zjaHkipt76e2RVeYPXs2V111FZdffjlqFw39Nk2TBx98kPHjxzN8+HAAqqqqcLvdraaVKCoqSv0isqqqKiPsAlL7ezqnqamJaDSKz+frkj+DEEIIIUS27NW33/fffx/LspgyZQp//vOf6dOnT+qY2+2mqKgITZNhUN2aO9cOvWo+g3idfTfHrqIokDfcXkZcbT/H9rfsye6jO2HD8/aSO9yeC6zft7oudDtI6KZOUyJCUyJMfayJqmg9kUSUqDO5tkfz4Hd5KfLlS7jVS4UTUWpi9VRH66mNNlAdbaA21kBNch1roDZqr+tjTZ0aKqgpKvmeHPK9OeR7cshztgs8OeR5gqn9PCfUSrYFXb7uVS2opAdNsF+HgEKLgC395gXQOmBLnmO0eFz6jQzS9vd0h1ArbZvdhW4t7hqaDNnS16pG5o0L0m5o0Fboln5TgzbvGNryuVtU23Wn98w+iEajTJ06tcvCLoAZM2awdu1a/vjHP3bZNYUQQgghDhZ79Y04OSFr+tBG0QP5Sux5uGqW2HNsufbD7WsVDYq+Zi+jboGKD2H736FqITR+BWu+gjW/hcJx0P9b0O8b4Cnc83UPMnEjQVMiTGM8TG2sgepofWpooqao+F1ecj0BirWC7hU0iL2imzpV0XqqI/VUReuojtZR7exXR+ud/Qaqo3WdqsBKhleF3jwKvbkUeHMp8OZQ4M2jwJPcdtaeXIJuv7yfOqNVwJYFVhuVa6RVuKXf2CB5zEzQfIfQFiFdR0O3dHuscmsZutFc0UaLAC25hCogfuhevxwH0tlnn83bb7/NZZdd1iXXu/fee/nggw/4wx/+QL9+zb8cKi4uJpFI0NDQkFHlVV1dTUlJSeqcpUuXZlyvqqoKIOOcZFv6OTk5OVLdJYQQ3ZiiKIwaNcreVlVwtvfmF0iGaaGp8llP9H6dLgHZtGkTn3zyCdXV1a0mZr366qv3uWNiPwsMAj0MDStB7b9/J5fXfND/m/YSr7Pn99r+NtR+3rysehiKJkK/b0Lfk+35vw5CCUOnMRGiMR6mOlpHbayRUCKCYZl4VBd+l0+GJvYghmlSE6unIlxLZaSGykgdldFaKiN1VEVqqYrUUeX8Pe8Nn+ahyJdPH18+hd48inx5FDjrQl8efbx5drjly5MbEBxsMm5qkAV7rHJLv2sozUNMMyrdIDN4A8LlkNi7fycH2g033MDll1/Ohx9+yPDhw3G5Mn9O33bbbR26jmVZ3Hfffbz77rvMmzePQYMGZRwfPXo0brebhQsXctpppwGwYcMGtm/fztixYwEYO3YsTz31FNXV1RQVFQHwn//8h5ycHIYOHZo659///nfGtf/zn/+kriGEEKJ7CgQCrFixorkhfbuDNFXh2peWsK6iqQt7tv+cMqKEm04bme1uiB6oU9+aX3nlFe655x4KCwspLi7OqARQFEUCr55AUSB3KOgRCG2w79youvf/83oKYNA0e4nshJ3vwo4F0LAaqv5jLytcUHwc9JsCpSfbwzB7KdMyaYyHaUyEqI7YFTwhPYJuGnhUFwG3n76BIgksuqG4kaAiUktFuIaKSA27nHVFuNZeR2qpjtZhWh0ri9EUjWJfPkW+fIr8Bc52gbOfTx+vc8yXT8At1Reim9pvVW67uvqCXW7OnDl89NFHHHbYYa2O7U3F5IwZM3jjjTf43//9X4LBYGrOrdzcXHw+H7m5uZx99tnMmjWL/Px8cnJyuP/++xk3blwqrJo8eTJDhw7l5ptv5qabbqKyspJHH32UCy64AI/H/gXXD37wA1588UUeeughzj77bP773//y1ltvMWfOnH1/MYQQQnR76yqaWLG9Idvd6JAhJcFsd0H0UJ0KvJ588kmuu+66LivbF1miuqDgSHs7vBH8ZQcm9Ery94PDLrSX0GbY8Q7sfM++02PlR/aiuOxhkX1Phb6ndO2cY1kSTkRpcCYI3xWppSkeJm4mcKkaQZefUn8hLlUquLLJtEyqo/XsDFWzM1zNrrC9trdr2BWupibWsQ8ImqJS5Mun2F9Iqb+QEn8Bxf5Cin0FlCT3fQXke3NQ5a59QvRYzz//PA8++CDTpk3bp+v86U9/AuDCCy/MaJ85c2bq2rfffjuqqnLNNdcQj8eZPHkyd999d+pcTdN46qmnuOeeezjvvPPw+/18//vf55prrkmdM2jQIObMmcPMmTOZO3cu/fr14/777+fEE0/cp/4LIYQQQnQXnfpWXV9fz3e+852u7ovIBs3jhF4mhDeDf4AdhB1owcEw9FJ7adpgB18737O3U5VfD0LhWDv4Kj3ZrkrrAQzToCEeoj7exK5wDXWxRiJ6FAWFgNtHoS+36+9OJ3ZLNw0qI7VsD1WyI1TFjnAV20NV7HS2d4VrSJj6Hq/j1dz09RdRGiikxN+HvoFCSv19nGCrD30DfSj05qF14STWQojuyePxMH78+H2+zpo1a/Z4jtfr5e67784IuVoaMGAAzzzzzG6vM3HiRF5//fW97aIQQogsCofDHHvssQAs/te/CJx8sn1g8WII7Ie5mYXowTqVbHz729/mo48+4vzzz+/q/ohs0DxQMNreDm12hjfuh9DLMCASg1i8eYnHQTfsY3pyDheASeCfBJ6doH8O0cUQ39g859fqX0NgCJSeCP1Otu882Y2qY6J6jPp4EzXRBioiNTTGwyRMHZ/mIej2U+TLl0nB9yPLsqiJNbCtqcJeQpVsd5YdoSp2hmswLGO311AVhRJfIf2CxfQLFNEvUETfQB/6Out+gSLyPTny9yiEAGD69On84Q9/4I477sh2V4QQQvRilmWxcuVKe9s0wdmmg9NoCHEw6VSqMXjwYGbPns2XX37Z5sSs06dP75LOiQNI8zqhl2lPDhwY4Nyifh/oOjSFIRSGukaoqYdoDBIJey5iBVCdW9InK2BSd+xy1qYfzOPBOg7MKmA5KCuAjRBeD5vWw6bfg5oPuROgcCKUHg/BIvAeuKopy7JoSoSpjzdREa6hOlpPWI+CBUG3Xyaa3w90U2dHqJqtTbsob9pFeVMF5U0VbAvZIdee7mboUjX6B4rpHyymLFhCv0AR/YPFqbYSf6HMnSaE6LClS5fy3//+l3/+858MGzas1Wejxx9/PEs9E0IIIYQ4OHXqG/jLL79MIBBg0aJFLFq0KOOYoigSePVUmhfyR4OpQ3iHE3rtRfWKZdkBV2MI6uqhug5CEbt6S9PA74O8HHC7mgOuvVICHAGcA3oDRJZAbAnoK8Csh/r37WWTAsph4DkK8idA0WjwB8DntRdP18xTZlkWjYkQtdFGdoSqqI01EDXiuFUXue4ABcFcmZNpH+mmzvZQFVsad7KlcSdbm3aytamCrY272BmuwrDMdh+roFAa6MOAYAkDgiWU5ZRQltwOllDsL5C/HyFEl8nLy+Nb3/pWtrshhBBCCCEcnQq8/vGPf3R1P0R34fLblV41n0NkBwTKOva4eAI2lcOWHXYVl6ZCMADFheDaD5VNrjzIPdleLB3iayD2JcSXgr4NrA0Q2wAV/we7/MBQUEeCexQEBkF+DuQE7SqwvQjCLMuiIR6iNtbQHHLpCfwuD7meAKWuPl3/Z+3lLMuiMlLL5sYdzuKEW4272Baq2G2o5dU8DMwptZdgXwbklDAwpy8Dc0rpFyjCox3AmzAIIQ5qM2fOzHYXhBBCCCFEGhljJVpz50LhGKj+HKK7wNd39+fXNcBXm6CiCgoL7JDrQFJc4D3SXvh/YFRBbCnElkF8JRAGloG1DOJAvADqhoJ5OFiHg7sveJzgKzcIOQHwesHnAa8Hy+uhIRHOCLliRgK/y0u+J5e+AZlwviNiRpytjbvY1LidTQ072NSwnU2NO9jSuNMe/tkOn+ZhUE5fBuX245DcvgzK6efs96XYVyBzaAkhhBBCCCGEaKVTgddtt9222+PyW85ewFMIhUfZlV7xWnu/JdOE8p2wbjPEEtC/r13ZlW1aMQROtRfLgMRGiC+D2EpIrAPqgE9B/dR5QCHow6DxMKg7BIxiQCOkJKjREmxTQtS4DWJeDb8vSH4gD5+/EDwue6imyBBKRNjUsIONDdvY0LCNjQ3b2diwje2hSsx2JtPUFJUBwVIOye3H4Nz+zrofg3L7UeovlFBLCNEjvP3227z11lvs2LGDRCKRceyvf/1rlnolhBBCCHFw6lTg1dDQkLGv6zpr166loaGB4447rks6JroBX6l998PaJaC6wZXTfCyegHWbYGO5PTSwf0G2erl7igaeofaS832wYhBfa1d+xVdDYgOYtWAuAuz56EyXn0Z1IBVWKTv1QiJGP/LDAXyWaj9eqbGHabpddiVYjt+uDnO7weu2127X3s1/1gNF9Cgb6rezvr6cDQ3lbKi3A66d4ep2H5PjDnBYXhmH5vZncF4Zh+b159Dc/gwIlsqk/kKIHm3u3Ln85je/Ydq0abz//vtMmzaNrVu3smzZMi644IJsd08IIUQvoSgKgwcPtrdVFZzt3v7dQ4jO6NQ3zCeeeKJVm2ma3HPPPQwaNGifOyW6kcBAMMJQtxL8bnti+3AUVq+H7bug5MDeDXGfKV7wjrYXACuGEV9LLLoMM/4VPmMzLitCvrGWfNYyTAVLVYh6SghpAwmpAwip/YnoRWDg3IGy3r7rJJYdhLk0O/QK+CDgt8MvjxOEeZwwrAfRTZ1NDTtYV1/O+vqtrKsvZ0N9OdtCle0+psiXz+F5AzgsbwCH5ZXZIVdeGUW+fKnWEkL0Sn/84x+57777OOOMM3jttde49NJLGTRoELNnz6a+vj7b3RNCCNFLBAIBNm3a1NyQvi2EyNBl37xVVeWiiy5i+vTpXHrppV11WZFtigI5Q8GIQOMG0PNh1Ub7Doz9SvbPhPQHSKMeoVpvojzmpkYfjqUMJ8/joVStI9csJ8fYStDciteqw29V4NcrKOZzAExcRNS+hH1lhAJlhNX+RNVSLEMBXbeX6jqoqMZOwxQ7CHO5msMwv8+pEvPY7V6PfU6WWJZFdbSer+q2sLZuC+vqy1lXt4WNjdvRTaPNxySDrcPzB3B43kCG5NshV743p83zhRCit9qxYwfjxo0DwOfzEQqFAPje977Heeedx1133ZXN7gkhhBBCHHS6NK3YunUruq535SVFd6BqkHcEbCuHZR+DkQNlpaB2g/m69pJuGVQlGtkRq2NXop6wGSeoeil15eFW7X8OUXKIagOpdNvDc11mI0GznKC5jaCxjYC5DRdRe9/cRolzbQuViFpCRO1P2NuPiL8vEbUvupIDWHYIljAgHrcrwwwTlHbCsIAvrSLMWXdhGJas2vqqbgtf1W121luoizW2eX7Q5WNI/kCG5A9iSP5AhhYMYkjeAAp9eV3WJyGE6MmKi4upr69nwIAB9O/fny+++IKRI0dSXl6O1c78hUIIIYQQYv/pVODVclJ6y7KorKzkgw8+4Pvf/36XdEx0I5YFW3fAhhhYHihSe1zY1aBHqEw0UB6roU4Po6BQ4ApQ4t5zYKOrudSrR1DPEXaDZeG1agiYOwiY2wkY2wmYO3ARIWDuImDuoijt8Qklh4hSSkQtJeLqS9RTQkQtxVR8zhlpYVgsGYYZgAIKzjBJZ86w9Mqw5FDJ5LF2/k7CiShr67ewpnazvdRtZn19OQmzdTitKgqH5PRnWMEghhYMYli+ve4fKJahiEIIsRvHHXcc//jHPxg1ahRnn302M2fOZMGCBSxfvpxvfvOb2e6eEEKIXiISiXDSSScB8O8FC/Cfdpp94N//Br8/iz0TovvpVOC1cuXKjH1VVenTpw+33norZ599dpd0THQTug5ffQVr10JuIZQcC/WrIVEH7oJs9263EqZOtd7E9lgtuxINRMw4uaqPfp58XMo+VEspCjGliJhaRC3JucAs3FY9AXMnAXMHfnMnfnMXXqsWt9WE22oiz9yQcZm4kkdUKSaqlhBRS4h5iol6i0ko+c2TTlom6Ib995AMw0zTDiHBvktksjrM56XBY7ImVsHqyA5Wh7axuqGcLaFdWLSuLgi6fAwrOIRhBYcwomAwwwoOYUj+AHwub+dfGyGEOEjdd999mKYJwAUXXEBBQQFLlizh1FNP5bzzzsty74QQQvQWpmny6af23eZNXQdnG+e/QUKIZp0KvObNm9fV/RDdUTQKq1bBxo1QUgKBgN2eOwQa1oAeAlcwu31sQ5MRZVe8nq2xGuqNMKplV3OVdqCaq9MUhYRSQL1aQD0jU82qFcdnVuA3d+E3K/CbFfisCjxWIx6rAY/V0CoIM/AQVYtSoVpUKSLmKSLm64NOcxjWoEdY1VTOqsZtrI5sZ1V0B9v0tidGLnHlMiJYxoi8QQzPG8SIPodSlleK6naGTPawij0hhOhuVFVFTftZOnXqVKZOnZrFHgkhhBBCHNz2aQ6vmpoaNmywv6wffvjh9OnTp0s6JbqBxkZYvhx27ICyMjsUSfKVgBGDxnWgaKD52r/OAWJaJtWJJrbHa9mZqCdkxMhVffR3F6Ap2QtzTMVDWBtIWBuY0a5ZEXxmFT6zEp9Vaa/NarxWLRpxguYOguyw7wQJNJrweRQ+iWksjrn5LGqyMRFv8zkHeAoZ6S9jpL8fI719GeEqoY/ltavEDBPqLaivBa0BNNWuEPN4IeAFn9euFksOleyBd5UUQohs+Pe//00gEGDChAkAvPjii7zyyisMHTqUu+66i/z8/Cz3UAghhBDi4NKpb7LhcJj77ruP//u//0uV72uaxve+9z3uvPNO/DJ2uGerqYFly6C2FgYOtIfNtRQYAFYCmjaBtw+ongPeTYCIEbfn5orXUJVoxEKhQPNT7M3NSn86ylD8hLRBhLRBGe2KpYNRxcbwOlaGt7A8UsmXkQa+iiecQYkGqRQMONwNE7xwjBfG++AoXy5BVy4xRSOuJogrTcQVjbhaQFzJw1SSwxUt0E17rjBdt4dK1jfYQyWdefRTQyVd6XOHOQGYJ21esbbeH0IIcZB5+OGHufHGGwFYs2YNM2fO5Cc/+QmffPIJs2bNajX/qRBCiN7BMC00Vea6FaI76lTgNWvWLBYvXsyTTz7JMcccA8Bnn33G/fffz6xZs5gxY0aXdlIcQDt22JVd0agddrU3UbmiQGAQmDqEt9pVX8qBqQSyLIt6I8z2WB3b47U06lH8mjvjTos9hWmZbIlVszy8jRXhclaEt/FVZCe6ZbQ6t587n9H+Ysb6cxjn8zDea9FPbcBr1uKx6tBIAI1gNpLDlvRcLEXHR1zNJ6HkEVfyiSt5JDz5xH25TlseJl7AssOwhAGJBFRH7X0L+5iqOmGYZleB+X3g96bdUVKqw4QQB5fy8nKGDBkCwDvvvMOpp57Kz3/+c1asWMFll12W5d4JIQ5WPTGM6Wl91lSFa19awrqKpgPyfHoskto+/5n/8jdne9r/fkzMu+fCk1NGlHDTaSP3eJ4QvUGnvo0uWLCAxx57jIkTJ6baTj75ZLxeL9ddd50EXj2RYdhzda1ZY4cVZWV7fozqgpzDwDQguh18pfYQx/3VRcukOtHItlgtOxL1xM0E+VqAQd4+PeYOgrV6iOWhcpaFt7I8VM7K8HaazGir8wq0AKMCAzjSWY4IDKDInZNxTghYn9yxLFyE8Jh1eK1aPGYdHqsej1WHx6zHY9XjImovZhTY1W4fDTwklNzUEvfkkvDlois5ze2WD0N32UMkI1FoDDVPpq8omdVhQX/znSW9HrvN67HPEUKIXsLtdhON2j/P//Of/3DWWWcBkJ+fT1PTgfkSJIQQLR3oMGZfDS3NYfYPxmW7G3ttXUUTK7Y3HJDnMuPN3x3W7GxMba/a0UjEk9jj44eUdL85mIXYXzoVeEWjUYqLi1u1FxUVpT7siR4kHLaDrk2boLAQcvdiOKDqgtzDAQOiFU6lV9cGGXFTpyLRwJZYFZXxRlRFpY8rgG9/TkLfBRKmzprITpaHy1kW2srycDnb4rWtzvMqbkYG+jM6MNAJuAZS5inYuxBPUdDJQddyCDOwzVNUK2pPlG/W47YanECsAbdVj8dsxG014CKKRhzNqsZnVe/2KU00OwRz55DwBJ1ALIiuBEmYfnTDRyLhQ6/youseOwizFHA7YZjbbQ+VDPjA47ErwpJVYTJMUgjRw4wfP56ZM2cyfvx4li1bxqOPPgrApk2b6NevX3Y7J4Q4qB3IMGZfleR4e1yFVzao/ubvQdX+7v2dSIhs6lTgNXbsWB577DEeeughvF57TqBoNMrjjz/O2LFju7J/Yn+rqoIVK+x5u/r1s4OHvaV5IGcIWAZEq8BX3CWhV8SIsytRz+ZoFTV6CJ/ipp8nH1fLaxsGroYwrvowrlAErTFir0NRtEgMNRxDi8RRY3HUmI4aT6DGEii6YS+GszYtMC0Uy16nKICiYGkqlqqAqtrbLg3TpWG5XVgujS15sKg4xqcFUT7ND7MsGCKmWrQ0lHzGaH0Z7S1jVHAgh+eVoQYC9gTy+5Gp+IgqPqJqabvnqFYct2WHX26ryV6bjc52o7M04SKKiuGEZm3fGdK+IOCxFx0/uhJAJ4Bu+dHjXvSoE4hZfnR86FouuisH3ZePESwEX8CeL8zjtt+bMkRSCNFN3XXXXcyYMYMFCxZw991307dvX8CezP7EE0/Mcu+EEF1Bgpj9L8/v6lFVadkYHqh6fAy65o8AxIBjnG3RvfTE8Lan9bcjOvXt8fbbb+eSSy7hpJNOYuRI+x/46tWr8Xg8/O53v+vSDor9aNcu+PJLiMft+brUfQhcXD7IHQbKervSy1PY6bs3NugRdsRr2RqrIdJQR1F1jCOro/iqGvDUNOCubsRd04C7tgl3fQhXY7jz/e6kqAs+7w8LB8LCQfDfgbCtjV+uFIXhuHKYWG6vj90OBdF6oB74KuNcw+fBCPowAl57HfShB/3N+zl+9By/s/Zh5ATs/Vw/ptfd/nxre8FUPMSUImIU7fY8xUrgskJOENbUatvet9cuwihYuIjgsiKAUzmmOktbovaiW150y4eBH13xY6h+dHcuhifXXrtz0D15GF5729CC6K4gpuqFLN6dUwjRPsuyMC0LCwvLWdv7JpYFJhZYFmba8bbmNexuysrKmDNnTqv222+/PQu9EULsDz0piIGePVdTT6lKk+GBoj09LbztqcOJ96RTgdeIESN45513mD9/Phs2bADgjDPO4Mwzz8Tn61zIIQ6wigo77DIM6N+/a67p8kPeSNAC9kT2ZgzcHbgNe2MIa8s2Qps2EdqyBXPbTkp31nD4rgbc4ViHntpSleYwKNePEfQ3h0d+L6bfg+H1YHrdWB43pteF6XZhaRqWW7MrtjTVruRSFeyyLgALxYKdZhNL9J18ae7iC6uClVYNCcXM6INmKRyRyOWYcB7HNOVwbL2fwxs1tGjCrirLi6F54oSjcbviLBJDC8dQdfuLnBaNo0XjqTxob5guDSPXj55rh2B6biD1Wuh5Abs9N2Cfkxe023P9nR46aCluEkoBCQo6cLLphF1OAGaF05YQLiuCC2fttGvYf+8uJYZLiWEHhNiT5sedZXdPiYKh+jG0IIYrgKH5MbSAs/gz12py3+e0+TFVey2hmTgYZIROloWJiWmZTggFVmo7edxZW2bquAXOvoXl3NxCURQsK3nbV7sNFBQFFBRURUVpua8oKIDq/NtTUVEVhVJ/AT6Xt3XnhRDiAOspQQxIGCNEd9CTfmb0Rp0KvObMmUNRURHnnntuRvurr75KTU2N3I2ou6ustMMuXQdnyEWXSU5k786Bpo0Q2QW+IvsOjqEIbNoKG8phUzlsLMfasg2ltgEFyHGWlhJ5AeKlBcSLC0gU5RIvyiPRJ49En1wSBTno+UH0HH+XDQmMmzprIjtYGtrKsvBWloW2sivR4oeUAn1cQY4KDOKo4CCOCg5klH8Afi1zSOjWDjyfktDRwjG0cBQtlFxHm9dNEVyhKFpTFC0UwdUUQWuKOusIqm7YS20T7tq9++2B3jIUywva26n95BJ0QjTf3lcCKio69txeHWYZqYowzQo76yguwmhWBJcVddZhNNPZJoJGDFUx7IoyM4zLDMOe5+5sl6F6UuGXHYT5MDRfam2ovlZtpurD1LzOMa/Tbq8txdUllXiidzMtu9LJwg6aTMsCJ5Cyw6Zku5lRBZURSqXCKPuxNoVk6GSz2+2QSUFRlFTAlAyf1OQxFDRFRVVVXIqGpqqoiopL1VDR0FQFTdHQFBVN1VCdACt5LU1Rm69J6+snw6/UczvbirNPqByCJQf6r0IIIYTodsxEjIo/3w3AoLNuZ97rDwLwo3NmEHPLL4eESNepwOvll1/mkUceadU+bNgwrr/+egm8urOqquZhjPtrEl1FgUYFltXDyk/hq69g407YUdX6VGcdKcohNqCEeFkxsf59iPUvItavD/HSAkxfJ+YV2wuViQY73AptZWloK6sjO4hbesY5GirD/H05KjiIMU7INcBT2CV3h7TcLvR8F3p+J34LZ1mo0bgdijVGcDWGcTVGnP1w835yu8FZQvbNJVxNdoDGjpqOPZ1TSZcKxPKDzcFYRkAWQM+1wzMz4N37kEfR7En4lbYi0D081EqgWRE0PYxLb0LTw2hmxG4jhqbE0dSEs8TRlASaEkclhmZF0cwYKvbfv2bG0cw4bn0385TtBQsVU/U6AVjLxYPRYr/9deZiKenbEqrtT2Z62OQMw2uujEq2N1dEGZaZEUDhtNuaAyhFIVUNpSg0Vz+lhUBAaltFxa260FQVl+JKhUouVcvYTg+fkgFTMtRSUtdK204dV9HU1iFUT7kjrhBCCNFrWRaxrcsBUC2T41LbrecOFuJg16nAq7KykpKS1r9p7dOnD5WVlfvcKbGf7NwJy5Z1fdhlmrBqFfznP/YE+KtWQXXb4/LMogLCg0upGlhA1cAC9EP64jpkIErA33X92Y3knROXhbemQq6didZhRr4WYExwIGOCh3BUYBBHBlpXb3ULioLp92L6vSRKCjr+OMPA1RR1ArCQPdF/o73tarBDsuS2u945Ho6imBbuhjDuho7Pm2ZpamYglh6M5bbRntvJkCz5fIobXXGje/KItfVXZpn2UN6EYVc5Gqa9hlQhjKLoqKphh2Iue61qOprLtNeqjkYcVUmgWTFU4vbaiqNaMTs0M6NoRgzVjKFadpmZgmmHb2akU3+2Dv35UTBVd0YIZqpuTMWDlbZtqm4s1Y2puO12xZ3W5sFSXc1tGWtXi303luLCUlyYqrvL79K6139+q+V8UGaqLT2Qygyo0tqdCilb+nuwOYzSFC0jCEoPhDRFxaO5cStOGKVqaGi4VK1VANWymklNVkFBaju9Aqrl8yVDMCGEEEIIIURrnQq8+vfvz+eff86gQYMy2j/77DNKS9u/A5zIEsuCrVth+XJ7zqauCLtCIVi0CD78ED7+uHXApapw6KEwciTm8GE0Di6lvCTGVmUHEcsg31dMnhbEvZ+rBXbF61kWLmd5aCtLw1tZHW5dvaWiMMRXaldvBQ9hTHAQgzx9enclg6bZ1Vn5QaCDw4R0I7NKLLnt3DjA1WhvuxvCTkVZBC0aRzFM+wYDezHc0tLU1FxkujPvWGqOMmfdas6yXD9WR+7iqKjgUsHlbv/5sTAME8Mw7EDMMEE3IOyEZfZJqbNRFHBp9rU11X7/axp4XeBygVtBVU00zUC1EqiqjqokUEmgYa9TixVHNeN2UJa21swYiukcs9KPJ1DNGEpyeBoWmulMdJaFeb4tlBbBmLNWNDsUS1ssRcNI7WvNCxqGs23grBXV3kZ1znNhKiom9nUs55xk+IbqsodSO/uK5gbVhYKGprhRNDdu1Y1LdeFyqqTcmis1XE9TtIxgSUsLoNrabzkUUAghhBBCCJFdnQq8zjnnHB588EF0Xee4444DYOHChTz88MP85Cc/6dIOin1kmrB+vV11FQxCQUHnr7VlC3z0kb18/nlzVQzY1z7uOBg/Ho44AoYPJ+FxURWvY0tkB5WxOgzLS6GSR0miAeJ1gAKuAJlVFJ0XMeKsimxnebic5aFyVoTLW8+9BeRrfkYHB3JUYBBjgoM4MjCQoCbj3ffIpaEX5qIX5nb4IUos0RyMNSbDsUhmm7PWnG0tlrBDsvoQ7vrQXnXR8LoxUiGYv/nOlrnpd7hsufZh+ltWlCl2YNXRSf1N016MtLURh0g0dcxM3n1OUZywzLKroTSP3ZYMylTFCcnc4HbZi+YEaIrTL1VxQjXVDtlUBQUTRTWc4Ex3wrA4qpVwgrJEKixTzDiqpaMk28w4mAkUK55qw0zY4ZzTrpo6qqU717O3NSthX6c5/UPBss839nBngW5BbQ7G0gKy5m2tRbvWRrvW4liyzVnSH9NyUdtqT7apaftqG+ep7RxT97Dt3B5V0WToaxeLxWLMmzePTz75hOrq6rRKQdtf//rXLPVMCCGEEOLg1KnA65JLLqGuro4ZM2aQSNhDdbxeL5dccgmXX355l3ZQ7INoFNauhXXroE8fyNnL+ZCiUfj0U3uo4n/+A+XlmccHDoTJk+HEE+2gy21XzDTpYSpiVWyt3UVdvBGXotHHk4dXdcaXeYshXmtPaB+rtu/q6PKzN8GXYZlsjFayPFzOivA2VoTLWR+pwKDFnRNRGervy1GBgamQ6xBvkVRgHCCW103Cm0+iuAN363Qo8YQzpDItCEsOsWx0Ju1PtjWG7fnKmiIopoUWS6DF6vFU7d2cW5aqouf4MHLsu3vqQWc7x29vO/updqdND/owAj67uktV9/4nqmWCYTlrs3k/EbVv8mCamIaRmg/KVJLD9JzJyxUwVTtuMlUFU1GwVOy1pmFqCqbLCcY0L6j+VFhmqUpzWObWUDR7snFF09BUF4qqoLpcGZVLdvWThktJDs9TcaHiUhQ0dDTLQLMMJ3Cz9+2QTEexkoGZU+VmGShOu2ImUJx91dLBjIOpg6WDmUjbTzj7yW3nuJV+rrOf/nhLB6utcjfTvnaWquGyT80MwpLbtNWmtGhzpYVnaUvGfhuPo71zWx5T0h7vDN3MOTwbL1KH3X777Xz88cecdtppjBkzRv47I4QQQgiRZZ0KvBRF4aabbuLKK69k/fr1+Hw+Dj30UDyebjjH0cGqshJWr7bXffuCz7fnxxgGrFkDn3xiD1f84gtIpN3iTtNg3Dg75Jo8GQYPTlUIGJZBTayW7dEqdsaqCBlRcjQ//XzFuFrO6aNodujlyrUrvWJVTvDlBVcQyJyXxrIstsVrWRnexsrwNlaEt7E6soOI2bqCpMSdy+jAQHtp586JonuzPG4SRW4SRXkdf5Bp2ne6bIzgCkWa5yRz7mRph2ROW/LOl06bqhsoprnXc5OlM7xuOwALeNGDPvSAx94OeEkEvOgBDwm/l3jAQyLgaV773Pa234PlsofCWaqFguJMbJ42fxMKiqKiYv8MVi0F1bLsoM8CtwUuCzRLxW2CS1dxJRRcJmiAajmLomBHC5a9aIpdWKaCqipoqoKqWmiaardpKqrbjeJ2O5Vnmr1ODd1MVpmpmW2qav98SN9PVqbt7Z0+u4JlZQZhGaFYyzajdbtlpO0bLc4z0s7T7eAy/bhltnicmXbMaF5MHTuES64NIHks/Vppj0nuZ5yfds5umc196wkKx0LhUdnuRbs++OADnn76aY455phsd0UIIYQQQtDJwCspGAwyZsyYruqL6Aq6Dps22XdGtCwYNKj9L5e6bodiS5bYQxSXLIGmFvMs9e0LkybBCSfAscfaQxfThPQIlfFatkZ2UZtowAIKXLkUewr23FfNC/6+4C2ERANEK7GitWzT61kdb2BVtIJV4e2sjuygwWg9ybdf9TAqUMaRgYEcGRjAkYGB9PN0vJJI9CKqmqrISo9BkxOTJyciT24nJzC3TBPiCVxNEdSmCFooagdhoSjuUNSe2D8UxR2K4U6uw/Hm7agdCNuVZQk8NY2d/iOYHhdWwOcs/tSagA+CfvD7IehD8ftRAj4I+FEDAZSA324LOucGfHYg1eEnNtMWq3ltOdt6AiLx5nOS7fYLjF2BY4Hl3HFQUZqHXiqkqshSgVdynjNNsyvjNK05SEsOI215fnpY1jJMS52nZJ7XsrpGUUBxg9r+3G29UkZAZrbebxmQJc/BbOMxaeGalbZgtNhPf4yVeRyrxfOk7eO8vzKu4WybJvT7ZpZexI7p27cvwWAn7rYrhBBC7CXF3TwdS9gtU7MI0Z59CrxEN2JZdjXXhg2wYwcUFkJuG3MtGYYdbL37LvzjH1Bbm3k8GIQJE2DiRPja1zKquJLiZoKaeD07Y9XsitUQMiIENT99PX1wqR1/S+mmzsbIdtY0beGr0GbWNG1iTWgzTW2EW25FY5ivL6OcYGtUYACH+orR5C5lPZplWRik3zGvjVAKq3lIXxttQGrdzA5fFOeueip29ZTq3A1PQXEqphRUrwfD54WSPqiKiomGqtprU01Ohq5goaIrCrG0iivVMNEiMVzhGGpjBC0cRW2KoIYjKKEoSiiCGoqghCL2EMVQOHMdiUDUjujUuA7xJqjr+OT+7XK7we+1wy+/z67w9Hsh4LfXfh/4vPZ28lhq7U1bp53n9dph057/Up3ALC1AS28znIn/zXhzeJZ+Dk7Ylv5zJznnGYpdAJoMwdIDtfTAS0kLvZLVaJpqz6flUp0hqFqLqjMl87pqW9vtPQ+Zx7uT1BDBHh70hcrBvRdVn1lwyy238MgjjzBjxgwGDBiQ7e4IIYTopVSPj0N+/hcAYsAoZ1sI0ZoEXr1Bba1d1bV1q/2lq6wss8IjHIbFi+15uP71L6iqaj6Wm2sPUxw/3l6GD2+zOiRh6tQlGqmO17EtVkljIoSqqOR3oJrLsiwq47WsD5ezLrSVtaGtrAtvZWN4O4k2htJ4FDdDAwMYEejPKG8fjvAUMsSTj9sVAM1nV2mIA8JKBk3pYVNa6GRYZofCqeYheknNgZSaCqLUzDCK5nZNVXEpKhqafUc9NNzOXFJq2jU0xX6kvaY5mFKU5rmo2tzfh+DUD/TZhxdZ1yEctUOwcBTCEXsJRdK209oj0bTzonZoFo7a7Qnn31MiYS8NXRCepfO47QAsuXg9zraz9ibbPPa2z2PvJ7c9ntZrr7Mkr+1x7z40ahmgparRnLVlNd9V07SAaGbFWvo1UjcPcG4k0LJSzaI5YFMUO+RCad5W2tpWmyvckpVsqmIHbJrSOmxLD83Sn0fZTaCmtPPcbT1WHDBHHXUUsViMKVOm4PP5cLsz/1u1aNGiLPVMCCGEEOLgJIFXT2UYUFNjV3OVl9tfbktK7C+W0SgsXQpffmkHXUuWZM7FlZsLX/86fOtbdjVXO8OfokaMer2Jaqeaq1EPAxY5WoABvtJWIYFlWeyK17ApvJ2N4e1sjGxnQ7ic9aFyGo2250bK0QIMDx7CiJzBDAsewsjgYA4PDEirFDNBj4DeZM/3pYft+W1UF6gee1gkB+eXuraG6JmW2SqU2lMYBcnbBdj/byW30r73t6yQUrEDIwXQFBWPquFOC6Q07AnOXYqK5gRZGmoqgNJaBFGZ56QFYE4VVq/mckFejr3sq4RuB18RJxCLxiASc4KymP2zIRmYRWPNxyPOfmodaz4ejTnD0oB4wl66OkhryeN2QjAPeN3NgZg7bdvrydxva3HvbtvlbHucu2E6x1wt7l5omvY/imS4RotwLb3NTA/aWpzXcoHmdeofG07glmxKq2qjRQAGzQGZQuvQCyUzKMuYS03NDOHS91PhW1vX203Q1laVW6uAroNtPdTPf/5zKioquP766ykuLu79P7uEEEIIIbqaLL/NAAAim0lEQVQ5Cbx6Esuyq7Wqq2HLFnudSEBdnb3/1VewahWsXGlXjaQbMKB5Lq6JE1N3VEynmzqNepgGPURlvJbaRAMhPYKiqPYE9N4iNFSqE/UsbVxHeaSCrdGdbIk0LxEz1mbXNVQG+vsyLDiIoYFBDAsewrDgIPp79/SlQLUnsncFwVcCehSMCCQam0Mwy7IDMMUNmhsUF83fCA880zLt78dtVUBZdsTUkaF6ycF6zZqropJVU8lwKGMy8xZhlFfVMqqj7IBJwa1oaC0qpDIropR2A6qDLpTqKdwucHdReJZkWXbIlR6QRWMQSwZi8bT9OMSS+852zHlMPNHcFo1BPA6xhL2Oxu0QPykZrBHquj/H3kgGYi5XcxjmTgZkTpvLlXmey5W57XZlnps+4b877RxNy1y7XHboljrPCaE0ZzhmMrDSnDsZps+nlhoSmravG2lBG+0Hb+nnQGbgZp/UYphp2s+mZHDVMhRLnd5GwJV6XIuALiNUSwvnYpVw5EDIObTr/p672JIlS3j55ZcZOXJktrsihBCiF7P0OJV/fRCAAWfeyJz5jwBwxfdvJ+aSm3UJka7XBF4vvvgizz33HJWVlYwcOZI777yzd0yor+uwbZs9ufzKlbBxoz18cdcu2LnTPma0cSeuoiJ7qOLYsXDcce3OxRXSIzQZYWrijVQn6qiK1VERr6FBDxHSI9QmGtkZr2ZXrJod0Sq2x6qItXF3xCRN0Rjk68thgf4c6i9jSGAAQwIDGOzrhyc5WXR6hpMcWtTyQKsKiNQzADn2l3otAUYUjJgdfukhsELO3cwsu1pBcYGigaLZczIpYFlmc9CEs91GRRTY4VXzuc2BVeaXwcw+2pEQaXfTc9qcu+MploLmBEYuNDRFweVURGmKistScalOENVizqnUXfas9EqrzMqrVDiVHKrX6jVsh2VHcbs/3sX2dM0OPWUbJ+2Hru6V3eV/u+tbq8elVfu0VTkDbVfkpLeTVo3Tsr0jFTaK0jzsML+NeQG7imE0B2Lp4VgqGEs0t8edoCxj3xnGGYvblW7Jc2JOe6rN2Y+ntbX8GZocEtrdJSf+TwZkrrSJ/5Pt7e1ratpj1MzztBZtGfvtrDMqx3Zzx86MRUm7yUDL/RahV8MOOKz1/I7dyeGHH040Gs12N4ToUQzTQlPll2ZC7A3LNIls+BQA1dA5Nblt7uZzvBAHqV4ReP39739n5syZzJgxg6OPPpoXXniBiy++mLfffpuioqJsd89mGBAK2RVajY320tBgL7W19vDEykp72bnTXioq7Cqu0B6qHPLyYMQIe/6t4cPh6KMxy/rTpIdpjNvzblVs/4jKeB2VsRp2xqrZEaumKl5Htd5Ard5InRGiXg8RsdoPs5JUFPq5Cihz92GQp4TB7mIOcZdwiKeEge4iXKqW+YCIApEGe9v5Mm1ZTpWTQmalE1bqf6YCpmUP8zFT57Q8357w3B7ak4NlJYAEWM6XXaMJFAPFMlBM0wmPsAMkzWUHUaoLVdFQFAUNDbfqwqW40ZzKKJcz5E5TVCegUjOrodT0Kqu0IMqppmreTrbbYZbScn6djECDjNcr7aTMUKTNgKKdx2bst/PhUmnr+u2cty/SH9/WtdoLftp7THtBTU9itQh8U6FvckhdcoL3tIneDbN5zirDaD4nOTeV0XIoXfLayaogMo9jOZlnepkPrat9Wu23CM5I286o9mkRvrV1LDk/WMtjLauDWlYE7QvTtMOvRALienNAlgzGdN1u1/Xm9kT6tmEfa3le+lo3ms83jObH60bz8fR1+uN0o7mSK51hOEHhvv3xewSvG54ZAkdOznZP2nXDDTcwa9Ysrr/+eoYPH95qDq+cnC6svBSil9BUhWtfWsK6iv08VL4LnDKihJtOkwpOIYToSXpF4PX8889z7rnncvbZZwMwY8YMPvjgA/7yl79w2WWXZa9jlsVX536Dz9f9G9MwMBUwFDBUMrYNBXTV3tZVSKiQyAU9HxIjIK5B3OciEfARC3qJBbxE/W6iPhcRj0pYM4iYOwibmwib82n6Mkrkiz0HV+0Jan6KvYUU+Qop8RXR119Mqa+YEn8Rpf5iSvzFuFSXMzTPdEIrO5TaiXPHvLShLPYQPNK+GyvO2p7TSXGCIUVxQiM1OSDPCYoUFU112cPvFBWX5sKlulDR7MnMVTucSk56riav66xVy0S1Es5ioGKgmjqqGUe1nAUTxTRQFQsFEyyj9Z3i7E4DTjWCojrVY4pdTYZityW/nJO+nbZODwlIPyZEF0kFX07IlTHRextD2lLntji/5WPanJcKJ2xzAjjTCeBa3qXRMDPvyJhaTDtkazXEjsznhbYDutThFsN/04svleRQveS/tbTnArstI+NzKouSd6pMntPWOrndMqxOD6fbe0zGOu1xbbVblv06607IlZqY3wnrkuFXsk03moPQNo+lHdfTjluWHbSl7qZpZp7X1uMzzm25b7Z9rfT3Q1v7ZotSyOT7rBu75JJLALjooosy2i3LQlEUVq1alYVeCdH9ratoYsX2hmx3Y4+GlASz3QUhhBB7qccHXvF4nBUrVnD55Zen2lRVZdKkSSxZsiSLPQNDT/C14f+ifnRXlJfqQJOzpNlDrqUpGkF3gIA7QNAdIOjOId+XR643nzxvHnnePIoCRRT4Cujj70ORv4iAO2CHT8n/Kfaipu6Ap9mBk6o2byvN28nwSUkLn1IhFJltezoneXy/Mw2wdHsx09ap8MtZTKfdTDSfn3FusnrGTPuinrZYLdbJL+yZE+d0sNO7OzftmKKkfbnPSAEyH9/h0G135+3hGl3yHFnWk8PJVv+U9ubPkhbWdoX04CoVoqW102I/uU1bbS3Pbev89HPbCtDS+tUylDPT/p0mK+haBoEZlXhpz5P88Z8M9iDz+dP3W/57bu+85L5m2aO83cljKlhqi2u2cb322jK22zgn/WdJ8t9BxrG0xygtA8V2AkZIqxhMa0iFoU5YakTh2HGt+9+NzJ07N9td6LReOy2EEEIIIQ5qPT7wqq2txTCMVkMXi4qK2LBhQ5Z6ZdPcHq79+m28vfx1FFVFcblRNReqpqEqml2NpDoTh6samhMcJUOj5NqjevBoHlyqC7fmxqN58GpePJrHCbKCBD1Bcjw5BNwBcjw55HpyyfHm4NN8qKraHFylrZOBUnvbyfCp5XavpGrY3xy9nb9GKuxqI/RKtrcKu3bTZl+0+dptttG6vc1j7R1v71g7x1s+zmrn+u31q5UOfinvyOP29FR7d1Inzu2s/fQc+2POtR6tm7welrWHdSfPa+ucjH/Xuzm/3eu00/fdbbc6tptrdOTff3pY329Q6/O7ka997WvZ7kKn9IhpIYQQQgghOqHHB17d3Ywp9zNjyv3Z7oY4EBTFHuIohBDioLN48eLdHj/22GMPUE/2TredFkLsNZkAXgghhMjU4wOvwsJCNE2juro6o726upri4uIOXcNyfoPc1NT9J8wUQgghRNcLBoP7VMV84YUXtmpLv153nMNrX6eF6M2fn3pqePTUB+vZXt+972iadNTAfM45ZhCH5qmYcfeeH5Blff32e72n9BekzwdCNvqrx3S2OTe/GtrHTZOzPbzIRdy75z70tNcYpM8HwqF5aqf+e76vn5/2N8Wyev64l3POOYcxY8Zw5513AmCaJqeccgo//OEPO/TbyZ07d3LyySfv724KIYQQopv67LPP9ulOio2NjRn7iUSCVatWMXv2bK6//nqOP/74fe1il9u1axcnnXQSL730EuPGNc+R9tBDD7F48WL+/Oc/7/bx8vlJCCGEOLjt6+en/a3HV3gB/PjHP+aWW25h9OjRjBkzhhdeeIFIJMK0adM69PjS0lL+9a9/dft0UgghhBD7RzC4b3dgy83NbdV2wgkn4Ha7mTVrFq+99to+Xb87ks9PQgghxMFtXz8/7W+9IvA6/fTTqamp4bHHHqOyspIjjjiCZ599tsNDGlVVpV+/fvu5l0IIIYQ42BQVFbFx48Zsd6NN+zothHx+EkIIIUR31isCL4Af/vCH/PCHP8x2N4QQQghxEFq9enWrtoqKCp555hlGjhyZhR7tmcfj4cgjj2ThwoVMmTIFsKeFWLhwoXymEkIIIUSP12sCLyGEEEKIbDnrrLNQFIWWU6OOHTuWBx54IEu92rN9nRZCCCGEEKK7ksBLCCGEEGIfvf/++xn7qqrSp08fvF5vlnrUMfs6LYQQQgghRHfVK+7SKIQQQgghhBBCCCFEklR4CSGEEEJ0gYULF7Jw4UKqq6sxTTPj2MyZM7PUKyGEEEKIg5MEXkIIIYQQ++jxxx/niSeeYPTo0ZSUlKAoSra7JIQQQghxUJMhjUIIIYQQ+2jy5MnceOONnHXWWdnuihBCCCGEANRsd0AIIYQQoqdLJBKMHz8+290QQgghhBAOCbz2oxdffJFTTz2Vo446inPOOYelS5dmu0vd3pw5czj77LMZN24cxx9/PFdeeSUbNmzIdrd6nKeffpoRI0bwwAMPZLsrPcKuXbu48cYbmThxImPGjOHMM89k2bJl2e5Wt2YYBo8++iinnnoqY8aMYcqUKTzxxBNI0XBrixcv5qc//SmTJ09mxIgRvPfeexnHLcti9uzZTJ48mTFjxnDRRRexadOm7HS2G9nd65ZIJHj44Yc588wzGTt2LJMnT+bmm29m165dWevv//zP/zB//vysPX82yOec9v32t79lxIgRGcu3v/3tbHcr6+Tn4Z7t6TW69dZbW723Lr744iz1Njs68n0hFosxY8YMJk6cyLhx4/jZz35GVVVVlnp84HXkNbrwwgtbvZfuuuuuLPX4wPvjH//ImWeeyfjx4xk/fjznnXce//rXv1LHD/b3UNKeXqfu/j6SObz2k7///e/MnDmTGTNmcPTRR/PCCy9w8cUX8/bbb1NUVJTt7nVbixYt4oILLuCoo47CMAx+/etfc/HFF/Pmm28SCASy3b0eYenSpbz00kuMGDEi213pEerr6zn//POZOHEizzzzDIWFhWzevJn8/Pxsd61be+aZZ/jTn/7EL3/5S4YOHcry5cu57bbbyM3NZfr06dnuXrcSDocZMWIEZ599NldffXWr48888wzz5s1j1qxZDBw4kNmzZ3PxxRfz97//Ha/Xm4Uedw+7e92i0SgrV67kiiuuYOTIkTQ0NPDAAw9wxRVX8Nprr2Wlv7FYjFdeeYWFCxcyYsQIXK7Mj1i33XZbVvq1v8jnnD0bNmwYzz//fGpf07Qs9qZ7kJ+He7an1wjgxBNPzLgRhsfjOVDd6xY68n3hwQcf5F//+hePPvooubm53HfffVx99dW89NJLWe79gdHR71Tnnnsu11xzTWrf7/dno7tZ0a9fP2688UYGDx6MZVm8/vrrXHXVVfz1r39l2LBhB/17KGlPrxN08/eRJfaL//mf/7FmzJiR2jcMw5o8ebI1Z86cLPaq56murraGDx9uLVq0KNtd6RGampqsb33rW9bHH39s/fCHP7Tuv//+bHep23v44Yet888/P9vd6HEuu+wy67bbbstou/rqq60bbrghSz3qGYYPH269++67qX3TNK0TTjjBevbZZ1NtDQ0N1ujRo6033ngjG13sllq+bm358ssvreHDh1vbtm07QL3K9MMf/rDd5cILL8xKn/Yn+Zyze4899pj13e9+N9vd6Nbk5+GetfWz75ZbbrGuuOKKLPWoe2r5faGhocE68sgjrbfeeit1zrp166zhw4dbS5YsyVIvs6ut71TyXaG1Y4891nrllVfkPbQHydfJsrr/+0gqvPaDeDzOihUruPzyy1NtqqoyadIklixZksWe9TyNjY0AUm3TQffeey8nn3wykyZN4sknn8x2d3qEf/zjH0yePJlrrrmGxYsX07dvX/7f//t/nHvuudnuWrc2btw4XnnlFTZu3Mhhhx3G6tWr+eyzz7j11luz3bUepby8nMrKSiZNmpRqy83N5eijj2bJkiVMnTo1i73rWZqamlAUhby8vKw8/7x587LyvNkgn3M6ZvPmzUyePBmv18vYsWO54YYbKCsry3a3ui35edhxixYt4vjjjycvL4/jjjuO6667jsLCwmx3K2tafl9Yvnw5iUQi4700ZMgQysrK+OKLLxg7dmw2uplV7X2nmj9/Pn/7298oKSnh61//OldeeWX3qs45QAzD4O233yYcDjNu3Dh5D7Wj5euU1J3fRxJ47Qe1tbUYhtGqpL+oqEjmo9oLpmny4IMPMn78eIYPH57t7nR7b775JitXruTVV1/Ndld6lK1bt/KnP/2JH//4x/z0pz9l2bJl3H///bjdbr7//e9nu3vd1mWXXUZTUxPf+c530DQNwzC4/vrr+e53v5vtrvUolZWVAG3+9+JgnCeis2KxGI888ghTp04lJycn293p9eRzzp6NGTOGmTNncthhh1FZWckTTzzBBRdcwPz58+U92g75edgxJ554It/85jcZOHAgW7du5de//jWXXnopL7/88kE5bLat7wtVVVW43e5WvwApKipKvc8OJu19pzrjjDMoKyujtLSUNWvW8Mgjj7Bx40Yef/zxLPb2wFqzZg0/+MEPiMViBAIBnnjiCYYOHcqqVavkPZSmvdcJuv/7SAIv0W3NmDGDtWvX8sc//jHbXen2duzYwQMPPMDvfve7g2aOi65iWRajR4/m5z//OQCjRo1i7dq1vPTSSxJ47cZbb73F/Pnz+dWvfpX6YDBz5kxKS0vldRMHVCKR4Nprr8WyLGbMmJHt7ggBwMknn5zaHjlyJEcffTRf//rXeeuttzjnnHOy2DPR06VXuiUniJ4yZUqq6utgI98X9qy91+i8885LbY8YMYKSkhIuuugitmzZwiGHHHKgu5kVhx12GK+//jqNjY0sWLCAW265hT/84Q/Z7la3097rNHTo0G7/PpK7NO4HhYWFaJpGdXV1Rnt1dTXFxcVZ6lXPcu+99/LBBx/wwgsv0K9fv2x3p9tbsWIF1dXVTJs2jVGjRjFq1CgWLVrEvHnzGDVqFIZhZLuL3VZJSQlDhgzJaDv88MPZvn17lnrUMzz00ENcdtllTJ06lREjRnDWWWfxox/9iDlz5mS7az1KSUkJgPz3opMSiQTXXXcd27dv53e/+51Uzhwg8jln7+Xl5XHooYeyZcuWbHel25Kfh50zaNCg1A13DjbtfV8oLi4mkUjQ0NCQcX51dXXqfXaw2JvvVEcffTTAQfVe8ng8DB48mNGjR3PDDTcwcuRI5s6dK++hFtp7ndrS3d5HEnjtBx6PhyOPPJKFCxem2kzTZOHChRljXUVrlmVx77338u677/LCCy8waNCgbHepRzjuuOOYP38+r7/+emoZPXo0Z555Jq+//vpBWeLeUePHj2fjxo0ZbZs2bWLAgAFZ6lHPEI1GURQlo03TNCzLylKPeqaBAwdSUlKS8d+LpqYmvvzyS/nvxR4kw67Nmzfz+9///qCev+ZAk885ey8UCrF169aD8otSR8nPw87ZuXMndXV1B9V7a0/fF0aPHo3b7c54L23YsIHt27cfNHMvdeY71apVqwAOqvdSS6ZpEo/H5T20B8nXqS3d7X0kQxr3kx//+MfccsstjB49mjFjxvDCCy8QiUSYNm1atrvWrc2YMYM33niD//3f/yUYDKbGSOfm5uLz+bLcu+4rJyen1TxngUCAgoICmf9sD370ox9x/vnn89RTT/Gd73yHpUuX8sorr3Dvvfdmu2vd2te//nWeeuopysrKUkMan3/+ec4+++xsd63bCYVCGVUd5eXlrFq1ivz8fMrKypg+fTpPPvkkgwcPZuDAgcyePZvS0lKmTJmSxV5n3+5et5KSEq655hpWrlzJnDlzMAwj9d+L/Px8PB5Ptrp90JDPObv3y1/+kq9//euUlZVRUVHBb3/7W1RV5Ywzzsh217JKfh7u2e5eo/z8fB5//HFOO+00iouL2bp1Kw8//DCDBw/mxBNPzGKvD6w9fV/Izc3l7LPPZtasWeTn55OTk8P999/PuHHjDpqwYk+v0ZYtW5g/fz4nn3wyBQUFrFmzhpkzZ3LssccycuTILPf+wPjVr37FSSedRP/+/QmFQrzxxhssWrSI5557Tt5DaXb3OvWE95Fiya/j95s//OEPPPfcc1RWVnLEEUdwxx13pEr8RNtGjBjRZvvMmTPlQ/ReuvDCCxk5ciS/+MUvst2Vbu+f//wnv/71r9m0aRMDBw7kxz/+sdylcQ+ampqYPXs27733HtXV1ZSWljJ16lSuuuoqCRta+OSTT5g+fXqr9u9///vMmjULy7J47LHHeOWVV2hoaOCYY47h7rvv5rDDDstCb7uP3b1uV199Nd/4xjfafNzcuXOZOHHi/u6eQD7n7M7111/P4sWLqauro0+fPhxzzDFcf/313WI+k2ySn4d7trvX6J577uGqq65i5cqVNDY2UlpaygknnMC11157UA377Mj3hVgsxqxZs3jzzTeJx+NMnjyZu+++u9tUnexve3qNduzYwU033cTatWsJh8P079+fKVOmcOWVVx400wPcfvvt/Pe//6WiooLc3FxGjBjBpZdeygknnADIeyhpd69TT3gfSeAlhBBCCCGEEEIIIXoVmcNLCCGEEEIIIYQQQvQqEngJIYQQQgghhBBCiF5FAi8hhBBCCCGEEEII0atI4CWEEEIIIYQQQgghehUJvIQQQgghhBBCCCFEryKBlxBCCCGEEEIIIYToVSTwEkIIIYQQQgghhBC9igReQohe65NPPmHEiBE0NDRkuytCCCGEEL3erbfeypVXXpntbgghBCCBlxCiF7nwwgt54IEHUvvjxo3jo48+Ijc3N4u9EkIIIYQQXenUU0/l97//fba7IYTo5iTwEkL0Wh6Ph5KSEhRFyXZXhBBCCCF6tHg8nu0uCCHEXpHASwjRK9x6660sWrSIuXPnMmLECEaMGMFrr72WMaTxtddeY8KECfzzn//ktNNO4+ijj+aaa64hEonw17/+lVNPPZVjjz2W+++/H8MwUteOx+P88pe/5MQTT2Ts2LGcc845fPLJJ9n6owohhBBCEI/Huf/++zn++OM56qijOP/881m6dCmmaXLSSSfxxz/+MeP8lStXMnLkSLZt2wZAQ0MDv/jFLzjuuOMYP34806dPZ/Xq1anzf/vb3/K9732PP//5z5x66qmMGTMGgLfffpszzzyTMWPGMHHiRC666CLC4XDGcz333HNMnjyZiRMnMmPGDBKJROpYfX09N998M8ceeyxHH300l1xyCZs2bcp4/IIFC5g6dSqjR4/m1FNP5Xe/+13q2IUXXsi2bduYOXNm6jOfEEK0xZXtDgghRFf4xS9+waZNmxg2bBjXXHMNAOvWrWt1XjQaZd68efzmN78hFApx9dVXc/XVV5Obm8vTTz/N1q1b+dnPfsb48eM5/fTTAbj33ntZt24dv/nNbygtLeXdd9/lkksuYf78+Rx66KEH8o8phBBCCAHAQw89xIIFC5g1axYDBgzg2Wef5ZJLLuGdd95h6tSpvPHGG/y///f/UufPnz+f8ePHM2DAAACuvfZavF4vzzzzDLm5ubz88sv86Ec/YsGCBRQUFACwZcsWFixYwOOPP46qqlRUVHDDDTdw0003MWXKFEKhEJ9++imWZaWe55NPPqGkpIQXXniBLVu2cP3113PEEUdw7rnnAvYvKTdv3syTTz5JTk4ODz/8MJdddhlvvvkmbreb5cuXc91113H11Vdz+umns2TJEmbMmEFBQQHTpk1LBXHnnntu6ppCCNEWCbyEEL1Cbm4ubrcbn89HSUkJABs2bGh1XiKR4J577uGQQw4B4LTTTuNvf/sbH3/8McFgkKFDhzJx4kT++9//cvrpp7N9+3Zee+01/vnPf9K3b18ALr74Yj788ENee+01fv7znx+4P6QQQgghBBAOh3nppZeYOXMmJ598MgD33XcfH3/8Ma+++irf/e53ef7559m+fTtlZWWYpsmbb77JFVdcAcCnn37K0qVLWbhwIR6PB4BbbrmF9957jwULFnDeeecB9uemhx56iD59+gCwYsUKdF3nm9/8Zio4a1lhlZ+fz1133YWmaQwZMoSTTz6ZhQsXcu6557Jp0yb+8Y9/8Kc//Ynx48cD8Mgjj3DKKafw3nvv8Z3vfIfnn3+e448/nquuugqAww47jHXr1vHcc88xbdo0CgoK0DSNYDCY+swnhBBtkcBLCHFQ8fv9qbALoLi4mAEDBhAMBjPaampqAPjqq68wDINvf/vbGdeJx+Op334KIYQQQhxIW7ZsIZFIpEIjALfbzZgxY1i/fj2XXHIJQ4YM4Y033uCyyy5j0aJF1NTUpD7PrFmzhnA4zMSJEzOuG41G2bJlS2q/rKwsFXYBjBw5kuOPP54zzzyTyZMnM3nyZE477TTy8/NT5wwdOhRN01L7JSUlfPXVVwCsX78el8vF0UcfnTpeWFjIYYcdxvr16wH7F5bf+MY3Mvo1fvx45s6di2EYGdcWQojdkcBLCHFQcbkyf+wpitJmm2magP0bVE3T+Mtf/tLqA1YgENi/nRVCCCGE6KQzzzyT+fPnc9lll/HGG28wefJkCgsLAQiFQpSUlDBv3rxWj0u/u7Xf7884pmkazz//PJ9//jkff/xxapqIV155hUGDBgFtf9ZKH/IohBAHikxaL4ToNdxudyqo6ipHHHEEhmFQU1PD4MGDMxYpoxdCCCFENhxyyCG43W4+//zzVFsikWDZsmUMHToUgDPOOIO1a9eyfPlyFixYwHe/+93UuUceeSRVVVVomtbq8016RVdbFEXhmGOO4ZprruH111/H7Xbz3nvvdajfQ4YMQdd1vvzyy1RbbW0tGzduTPX78MMPz/hzAXz++ecceuihqV8+7o/PfEKI3kcCLyFErzFgwAC+/PJLysvLqamp6ZIPQocddhhnnnkmN998M++88w5bt25l6dKlzJkzhw8++GDfOy2EEEIIsZcCgQDnn38+Dz30EP/+979Zt24dd955J9FolP/5n/8BYODAgYwbN45f/OIXGIbBqaeemnr8pEmTGDt2LFdddRUfffQR5eXlfP755/zmN79h2bJl7T7vl19+yVNPPcWyZcvYvn0777zzDjU1NRx++OEd6vehhx7KN77xDe68804+/fRTVq9ezU033UTfvn1Twxh/8pOfsHDhQp544gk2btzIX//6V1588UV+8pOfpK4zYMAAFi9ezK5du1LTUAghREsypFEI0Wv85Cc/4dZbb2Xq1KlEo1FmzpzZJdedOXMmTz75JLNmzaKiooKCggLGjh3LKaec0iXXF0IIIYTYWzfeeCOWZXHzzTcTCoUYPXo0zz77bMZ8WmeeeSYzZszgrLPOwufzpdoVReHpp5/m0Ucf5bbbbqO2tpbi4mImTJhAcXFxu8+Zk5PD4sWLeeGFF2hqaqKsrIxbb701NXF+R8ycOZMHHniAn/70pyQSCSZMmMDTTz+N2+0G7OqzRx99lMcee4wnn3ySkpISrrnmGqZNm5a6xjXXXMNdd93FlClTiMfjrFmzZm9eOiHEQUKxZEC1EEIIIYQQQgghhOhFZEijEEIIIYQQQgghhOhVJPASQgghhBBCCCGEEL2KBF5CCCGEEEIIIYQQoleRwEsIIYQQQgghhBBC9CoSeAkhhBBCCCGEEEKIXkUCLyGEEEIIIYQQQgjRq0jgJYQQQgghhBBCCCF6FQm8hBBCCCGEEEIIIUSvIoGXEEIIIYQQQgghhOhVJPASQgghhBBCCCGEEL2KBF5CCCGEEEIIIYQQoleRwEsIIYQQQgghhBBC9Cr/Hy0IxX5hEs5fAAAAAElFTkSuQmCC", "text/plain": [ "
      " ] @@ -507,13 +507,22 @@ " unintervened_samples[\"R\"], axs, coords=(0, 0), color=colors[2], label=\"recovered\"\n", ")\n", "\n", + "axs[0, 0].set_title(\"No interventions\")\n", + "for ax in axs[:, 0]:\n", + " ax.set_xlabel(\"time\")\n", + " ax.set_ylabel(\"count\")\n", + "\n", + "\n", "for ax in axs[:, 1]:\n", " ax.set_xlim(0, 35)\n", + " ax.set_xlabel(\"overshoot\")\n", + " ax.set_ylabel(\"num samples\")\n", "\n", "axs[0, 1].hist(unintervened_samples[\"overshoot\"].squeeze())\n", - "axs[0, 0].set_title(\"No interventions\")\n", + "\n", + "\n", "axs[0, 1].set_title(\n", - " f\"Overshoot mean: {unintervened_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {unintervened_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", + " f\"Overshoot mean (no interventions): {unintervened_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {unintervened_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", ")\n", "axs[0, 1].axvline(unintervened_samples['overshoot'].squeeze().mean().item(), color=\"red\", \n", " linestyle=\"--\", label=\"mean overshoot\")\n", @@ -535,7 +544,7 @@ "\n", "axs[1, 1].hist(intervened_samples[\"overshoot\"].squeeze())\n", "axs[1, 1].set_title(\n", - " f\"Overshoot mean: {intervened_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {intervened_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", + " f\"Overshoot mean (both interventions): {intervened_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {intervened_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", ")\n", "axs[1,1].axvline(intervened_samples['overshoot'].squeeze().mean().item(), color=\"red\",\n", " linestyle=\"--\", label=\"mean overshoot\")\n", @@ -556,7 +565,7 @@ "\n", "axs[2, 1].hist(mask_samples[\"overshoot\"].squeeze())\n", "axs[2, 1].set_title(\n", - " f\"Overshoot mean: {mask_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {mask_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", + " f\"Overshoot mean (mask only): {mask_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {mask_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", ")\n", "axs[2, 1].axvline(mask_samples['overshoot'].squeeze().mean().item(), color=\"red\",\n", " linestyle=\"--\", label=\"mean overshoot\")\n", @@ -576,7 +585,7 @@ "\n", "axs[3, 1].hist(lockdown_samples[\"overshoot\"].squeeze())\n", "axs[3, 1].set_title(\n", - " f\"Overshoot mean: {lockdown_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {lockdown_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", + " f\"Overshoot mean (lockdown only): {lockdown_samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {lockdown_samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", ")\n", "axs[3, 1].axvline(lockdown_samples['overshoot'].squeeze().mean().item(), color=\"red\",\n", " linestyle=\"--\", label=\"mean overshoot\")\n", @@ -585,12 +594,12 @@ "add_pred_to_plot(samples[\"S\"], axs, coords=(4, 0), color=colors[0], label=\"susceptible\")\n", "add_pred_to_plot(samples[\"I\"], axs, coords=(4, 0), color=colors[1], label=\"infected\")\n", "add_pred_to_plot(samples[\"R\"], axs, coords=(4, 0), color=colors[2], label=\"recovered\")\n", - "axs[4, 0].set_title(\"All interventions with equal probabilities\")\n", + "axs[4, 0].set_title(\"Stochastic interventions with equal probabilities\")\n", "axs[4, 0].legend_.remove()\n", "\n", "axs[4, 1].hist(samples[\"overshoot\"].squeeze())\n", "axs[4, 1].set_title(\n", - " f\"Overshoot mean: {samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", + " f\"Overshoot mean (stochastic interventions): {samples['overshoot'].squeeze().mean().item():.2f}, Pr(too high): {samples['os_too_high'].squeeze().float().mean().item():.2f} \"\n", ")\n", "axs[4, 1].axvline(samples['overshoot'].squeeze().mean().item(), color=\"red\",\n", " linestyle=\"--\", label=\"mean overshoot\")\n", From d1293797f4f49810494f4c868d36bdd6477e5613 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Mon, 9 Dec 2024 08:21:10 -0500 Subject: [PATCH 094/111] diseentangled histograms --- docs/source/explainable_sir.ipynb | 247 +++++++++++++++++++----------- 1 file changed, 154 insertions(+), 93 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index f4279068..9b213fe5 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -461,7 +461,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -757,7 +757,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -793,7 +793,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -802,8 +802,8 @@ "text": [ "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 0.24283304810523987\n", "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 0.2902735471725464\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 2.3861892461951584e-09\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 2.636660445531902e-09\n" + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 2.38618436121385e-09\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 2.636655116461384e-09\n" ] } ], @@ -866,7 +866,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -915,7 +915,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -958,7 +958,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -991,9 +991,19 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 16, "metadata": {}, "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "name": "stdout", "output_type": "stream", @@ -1003,21 +1013,14 @@ "Probability of overshoot being high\n", "factual: 0.6021000146865845 counterfactual mask: 0.8484848737716675 counterfactual lockdown: 0.32460734248161316\n" ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
      " - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "width = 45/36\n", - "plt.bar(\n", + "fig, axes = plt.subplots(3, 1, figsize=(8, 8), sharex=True) \n", + "\n", + "width = 45 / 36\n", + "\n", + "axes[0].bar(\n", " bin_edges[:36].tolist(),\n", " hist_fact_nec,\n", " align=\"center\",\n", @@ -1025,7 +1028,13 @@ " alpha=0.5,\n", " color=\"blue\",\n", ")\n", - "plt.bar(\n", + "axes[0].axvline(x=overshoot_threshold, color=\"red\", linestyle=\"--\", label=\"overshoot too high\")\n", + "axes[0].set_title(\"Factual\")\n", + "axes[0].set_xlabel(\"overshoot\")\n", + "axes[0].set_ylabel(\"frequency\")\n", + "axes[0].legend()\n", + "\n", + "axes[1].bar(\n", " bin_edges[:36].tolist(),\n", " hist_lockdown_nec,\n", " align=\"center\",\n", @@ -1033,7 +1042,13 @@ " alpha=0.5,\n", " color=\"pink\",\n", ")\n", - "plt.bar(\n", + "axes[1].axvline(x=overshoot_threshold, color=\"red\", linestyle=\"--\", label=\"overshoot too high\")\n", + "axes[1].set_title(\"Counterfactual - Lockdown\")\n", + "axes[1].set_xlabel(\"overshoot\")\n", + "axes[1].set_ylabel(\"frequency\")\n", + "\n", + "\n", + "axes[2].bar(\n", " bin_edges[:36].tolist(),\n", " hist_mask_nec,\n", " align=\"center\",\n", @@ -1041,12 +1056,16 @@ " alpha=0.5,\n", " color=\"green\",\n", ")\n", - "plt.legend([\"factual\", \"counterfactual_lockdown\", \"counterfactual_mask\"])\n", - "plt.ylabel(\"pr\")\n", - "plt.xlabel(\"overshoot\")\n", - "plt.title(\"Counterfactual - Necessity World\")\n", - "plt.axvline(x=(overshoot_threshold), color = \"red\", linestyle = \"--\", label=\"overshoot too high\")\n", + "axes[2].axvline(x=overshoot_threshold, color=\"red\", linestyle=\"--\", label=\"overshoot too high\")\n", + "axes[2].set_title(\"Counterfactual - Mask\")\n", + "axes[2].set_xlabel(\"overshoot\")\n", + "axes[2].set_ylabel(\"frequency\")\n", + "\n", + "plt.suptitle(\"Counterfactual distribution of overshoot (Necessity Worlds)\")\n", + "\n", "sns.despine()\n", + "plt.tight_layout()\n", + "plt.show()\n", "\n", "print(\"Overshoot mean\")\n", "print(\n", @@ -1066,7 +1085,7 @@ " oth_mask_nec.item(),\n", " \" counterfactual lockdown: \",\n", " oth_lockdown_nec.item(),\n", - ")" + ")\n" ] }, { @@ -1085,7 +1104,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -1118,9 +1137,19 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 18, "metadata": {}, "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "name": "stdout", "output_type": "stream", @@ -1130,21 +1159,13 @@ "Probability of overshoot being high\n", "factual: 0.6021000146865845 counterfactual mask: 0.6717171669006348 counterfactual lockdown: 0.717277467250824\n" ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
      " - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "width = 45/36\n", - "plt.bar(\n", + "fig, axes = plt.subplots(3, 1, figsize=(8, 8), sharex=True) \n", + "\n", + "\n", + "axes[0].bar(\n", " bin_edges[:36].tolist(),\n", " hist_fact_suff,\n", " align=\"center\",\n", @@ -1152,7 +1173,13 @@ " alpha=0.5,\n", " color=\"blue\",\n", ")\n", - "plt.bar(\n", + "axes[0].axvline(x=overshoot_threshold, color=\"red\", linestyle=\"--\", label=\"overshoot too high\")\n", + "axes[0].set_title(\"Factual\")\n", + "axes[0].set_xlabel(\"overshoot\")\n", + "axes[0].set_ylabel(\"density\")\n", + "axes[0].legend()\n", + "\n", + "axes[1].bar(\n", " bin_edges[:36].tolist(),\n", " hist_lockdown_suff,\n", " align=\"center\",\n", @@ -1160,7 +1187,12 @@ " alpha=0.5,\n", " color=\"pink\",\n", ")\n", - "plt.bar(\n", + "axes[1].axvline(x=overshoot_threshold, color=\"red\", linestyle=\"--\", label=\"overshoot too high\")\n", + "axes[1].set_title(\"Counterfactual - Lockdown\")\n", + "axes[1].set_xlabel(\"overshoot\")\n", + "axes[1].set_ylabel(\"density\")\n", + "\n", + "axes[2].bar(\n", " bin_edges[:36].tolist(),\n", " hist_mask_suff,\n", " align=\"center\",\n", @@ -1168,12 +1200,17 @@ " alpha=0.5,\n", " color=\"green\",\n", ")\n", - "plt.legend([\"factual\", \"counterfactual_lockdown\", \"counterfactual_mask\"])\n", - "plt.ylabel(\"pr\")\n", - "plt.xlabel(\"overshoot\")\n", - "plt.title(\"Counterfactual - Sufficiency World\")\n", - "plt.axvline(x=(overshoot_threshold), color = \"red\", linestyle = \"--\", label=\"overshoot too high\")\n", + "axes[2].axvline(x=overshoot_threshold, color=\"red\", linestyle=\"--\", label=\"overshoot too high\")\n", + "axes[2].set_title(\"Counterfactual - Mask\")\n", + "axes[2].set_xlabel(\"overshoot\")\n", + "axes[2].set_ylabel(\"density\")\n", + "\n", + "\n", + "plt.suptitle(\"Counterfactual distribution of overshoot (Sufficiency Worlds)\")\n", + "\n", "sns.despine()\n", + "plt.tight_layout()\n", + "plt.show()\n", "\n", "print(\"Overshoot mean\")\n", "print(\n", @@ -1212,7 +1249,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -1283,12 +1320,12 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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      " ] @@ -1382,7 +1419,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -1416,9 +1453,19 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 22, "metadata": {}, "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "name": "stdout", "output_type": "stream", @@ -1428,21 +1475,12 @@ "Probability of overshoot being high\n", "mask_efficiency fixed: 0.08130080997943878 mask_efficiency not fixed: 0.7647058963775635\n" ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
      " - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "width = 45/36\n", - "plt.bar(\n", + "fig, axes = plt.subplots(2, 1, figsize=(8, 8), sharex=True) \n", + "\n", + "axes[0].bar(\n", " bin_edges[:36].tolist(),\n", " hist_lockdown_fix,\n", " align=\"center\",\n", @@ -1450,7 +1488,13 @@ " alpha=0.5,\n", " color=\"blue\",\n", ")\n", - "plt.bar(\n", + "axes[0].axvline(x=overshoot_threshold, color=\"red\", linestyle=\"--\", label=\"overshoot too high\")\n", + "axes[0].set_title(\"Mask Efficiency Fixed\")\n", + "axes[0].set_xlabel(\"overshoot\")\n", + "axes[0].set_ylabel(\"density\")\n", + "axes[0].legend()\n", + "\n", + "axes[1].bar(\n", " bin_edges[:36].tolist(),\n", " hist_lockdown_notfix,\n", " align=\"center\",\n", @@ -1458,12 +1502,16 @@ " alpha=0.5,\n", " color=\"pink\",\n", ")\n", - "plt.legend([\"mask_efficiency fixed\", \"mask_efficiency not fixed\"])\n", - "plt.ylabel(\"pr\")\n", - "plt.xlabel(\"overshoot\")\n", - "plt.title(\"Counterfactual Lockdown\")\n", - "plt.axvline(x=(overshoot_threshold), color = \"red\", linestyle = \"--\", label=\"overshoot too high\")\n", + "axes[1].axvline(x=overshoot_threshold, color=\"red\", linestyle=\"--\", label=\"overshoot too high\")\n", + "axes[1].set_title(\"Mask Efficiency Not Fixed\")\n", + "axes[1].set_xlabel(\"overshoot\")\n", + "\n", + "\n", + "plt.suptitle(\"Counterfactual Lockdown: mask efficiency fixed vs. stochastic\")\n", + "\n", "sns.despine()\n", + "plt.tight_layout()\n", + "plt.show()\n", "\n", "print(\"Overshoot mean\")\n", "print(\n", @@ -1502,7 +1550,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1534,9 +1582,19 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 24, "metadata": {}, "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "name": "stdout", "output_type": "stream", @@ -1546,21 +1604,14 @@ "Probability of overshoot being high\n", "lockdown_efficiency fixed: 0.8642857074737549 lockdown_efficiency not fixed: 0.8103448152542114\n" ] - }, - { - "data": { - "image/png": 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RsYYCj4iIiNg9BR47dfjwYfz8/Dh8+PC/2k5cXBwdO3YscfmOHTsSFxf3r/ZZlk6fPs3DDz9MYGAg0dHR7Ny5ky5duhAYGMiKFSvw8/Njy5YtV9xOScuVFZPJxJAhQ2jevDl9+/Zl1KhRjBo16prv51r9nImIlDYNWv4XzpyB9PSy21/lylC1atnt73rw2WefcfDgQVatWkXVqlV54YUXqF+/PjExMVSpUoWIiAgqV658xe1s2rSpROXKysaNG9m4cSMffPABNWvWNAb5iohcrxR4/oX0dPjqKzh/vvT35eUF3bsr8FxrGRkZNGzYkMaNGwNw7tw5WrZsyY033giAd9GVOFdQo0aNUqvj1Th37hy+vr40a9bM1lURESkX1KX1L50/X3hVamm//m2oSk9P58UXX+SWW24hNDSU559/nvS/NU8lJibSp08fgoKC6Nq1K19++WWxbRQUFDBixAjuuusuzp49C8CHH35IREQEISEhzJ07t1j5d955h06dOhldK3/88QcAgwcPZurUqUbZF154gQ4dOhjvN23aRPv27YHC7qJPP/2UO+64g2bNmvHggw9y6NChEh/7119/TY8ePQgKCuLee+9l69atAMyaNYtZs2axbds2/Pz86Nu3L1u3bmXOnDn4+fkZ+y7qqsrMzGTcuHG0bt2a1q1b8+KLL2IymYqVy8nJ4eWXXzbKPffcc8ZNK4u6gNatW0fnzp0JDAxk0KBBFje13LBhA3fffTdBQUHceeedbN68mezsbEJCQli3bp1RLjc3l9atW7N582aL442Li2PUqFEcOXIEPz8/4/2oUaMwm808/PDDPPLII0b5mTNnEhERQUZGxmU/r6J9Tpo0ibCwMG677TZ++OGHEp8Hu+LkBPfeW/iy5R2fRaTEFHiuE8OGDWP37t3MmzePd999l+TkZGNMx6lTpxgwYAD+/v6sXLmSQYMGER0dTVJSksU2XnnlFZKSkli0aBE+Pj5s3LiRyZMn89RTT7F8+XJ+/fVX/vrrL6P8nDlziImJYcyYMaxcuZK6devy+OOPk5mZSXh4uMWYl23btnH06FGOHTsGwI8//kh4eLixfNasWYwdO5a4uDjOnDnDjBkzSnTcSUlJREdHM3jwYD777DPuvPNOBg4cSEpKCgMGDGDAgAEEBwezadMmZsyYQXBwMAMGDGDTpk3FtvXCCy8QHx/P3LlziYmJIT4+/qL1mD59Ort27WLhwoUsXbqUjIwMnnzySYsy8+bNY/r06Sxbtoxff/2Vd999F4C9e/cyePBgbr/9diPkDRkyhHPnztG5c2fWrl1rbOOnn37C2dmZVq1aWWy7R48ejBkzhtq1a7Np0yZ69OhhLHNwcGDixIn88ssvrF27ln379rFgwQImTZqEt7f3ZT+vovPw3Xff8fbbb/PWW2+xdOnSEp0Hu+PuDitWFL7c3W1dGxEpAXVpXQeSkpLYunUra9asoVGjRgC89tpr9OjRg/379xvjT1544QUcHR256aabSE9PJzs729jGwoULWbNmDR988AG+vr4ArFixgsjISHr16gUUBqKiVhmz2cyyZct45pln6NSpEwCTJk3i9ttv57PPPiM8PJzJkydz7tw5srOzSUtLIygoiB07dtCjRw82b97ME088Yey/f//+tG3bFoA+ffoQGxtbomNftGgR999/P5GRkQA88sgjbNu2jQ8++IBRo0bh6emJi4uL0SXl4uKCp6dnsS6q9PR01qxZw7vvvktoaCgAL730Ert377Yol5WVxbJly/jkk0+MVqJp06bRunVr/vjjD7z+d4O6ESNG0Lx5cwAiIyP59ddfAfj4448JCQlhyJAhAERFRZGZmcnZs2fp2bMnTz/9NCaTCTc3N9asWUO3bt2K3XTP3d2dSpUq4eTkdNGutsaNGzNo0CCmTZuGr68vd955J+3atbvi5xUdHc2KFSuIjo6mZcuWAIwZM4aoqKgSnQuxjasZa6jxgmKPFHiuA/v378fHx8cIO1D4R69y5crs37+fAwcO0LRpUxwdLzT49e/f31j3xIkTvPnmm9SuXdviD2hycjIPPPCA8b5q1arUq1cPKGw1KgoxRVxcXGjWrJmxXp06ddi+fTtZWVkEBwfTsGFD4uPjadOmDfv27eOWW24x1m3QoIEx7e3tXeKbzSUnJ/PVV1+xfPlyY15ubq5F61FJpKSkkJ+fT0BAgDEvLCyMsLAwi3KHDh0iNzfX4nOBwu69gwcPGutf6ngOHDhgsQ+Ap556yljH1dWVjRs30r59e9avX8+8efOsOo4iUVFRfP755xw4cIB33nnHmH+5z+vMmTOcPn0af39/Y1lgYOBV7V/KjrVjDTVeUOyVAs91wNXV9aLz8/Pzyc/Px9n58j8GDg4OLFq0iDFjxvD222/z9NNPG8vMZrNFWRcXFwDc3Nwuuc+CggIAbr31VrZu3YrJZCIkJIRGjRoxd+5cfv75ZwIDA/Hx8Sm2XWvl5+czcOBAoxWqiLuV3RAl3X9+fj4A77//frG7c1evXt0Yq3Op7V3uXDg7O9O1a1fWrl2Li4sL3t7ehISElKhe/3T69GlSU1MxmUzs3r3b6BYryef193N+teelwjt/vsI8WuJq7s+oezqKPdIYnutAo0aNOHv2LPv37zfm7du3j4yMDBo1akTDhg35448/LP6QPfXUU8b//GvUqEHbtm15/vnniYmJMcZz/Oc//zG6YqDwiqeiZZUqVcLX15edO3cay3Nzc/ntt9+MlqZ27dqxdetWduzYQVhYGKGhoezZs4e1a9caXSzX4tgPHz5MgwYNjNfy5cvZsGGDVdupV68eTk5OFuOa1q9fz913333Rcmlpacb+vL29mTJlCqdOnbrifho0aFBs7NQDDzxgDCKPjIxkw4YNfPvtt3Tr1u2q7zY8adIkWrVqxeOPP86LL75ITk4OcPnPq2rVqvj6+lqc899///2q9i9lp4p3Hu1amugcXrJXu5Ymqnjn2braItecAs91oHHjxtx2221ER0eTmJhIYmKiMQ7jv//9L5GRkaSlpTFt2jQOHjxIXFwc33zzDbfeeqvFdnr06EGLFi2YNGkSAA8//DBfffUVH330EcnJyYwbN85i3E+/fv2YOXMm3377LcnJycZVTUWDaNu0acOePXtISUmhWbNmVKtWjfr161/TwNOvXz9Wr17N0qVL+fPPP1m8eDGLFy+mYcOGVm3H29ubXr16MXnyZBITE/n111958803adOmTbFy9913HxMmTGDLli3s27ePkSNHkpKSYlzqfjl9+vRh+/btvPvuu6SkpDB//nz27t1rdJ2Fhobi4eHBypUr6dmzp1XHUGTdunVs3LiRsWPHMmjQIEwmE3PmzAEu/3k5ODjw0EMPMXPmTH766Sd+/fVXpkyZclV1kLLjZM4n70QamSmnS/TKO5GGkznf1tUWuebUpfUvlVVL9r/dz9SpU3n55Zfp168fTk5OdOrUidGjRwPg4+PD/PnzeeWVV3jvvfeoV68eb7zxBv7+/sUG5Y4dO5bevXuzbt06unTpwpQpU5gxYwanT5/mnnvusRjfMWDAADIyMnjxxRfJyMggODiY9957j2rVqgGF4SAwMBAHBwej2y0sLIy0tLRrdv+YFi1aMG3aNGbNmsW0adOoX78+b7zxhjHo1hpjxoxh8uTJ9O/fHxcXF3r06GHRvVdk1KhRTJ06lREjRpCbm0vLli1ZsGBBiZ7oXb9+fWbNmsUbb7zB9OnT+c9//sO8efOoVasWUNi92K1bN7799tur+owyMjKYNGkSAwcONMZbjRo1iueee46ePXte8fN64oknyMrK4umnn8bJyYmhQ4fy0ksvWV0PKVu52QXkZBWUqOz12ksp9s/B/M9BGNehjIwMQkNDiY+PL3ajuezsbA4cOECjRo2KjfvQnZbFFp599lkaNGjAiBEjbF0Vm7rc72apq0BjeDJOmti94TTnz5Ys8Hj5OOJ/WzW8fS8+Dk+kolILz79QtaoCiJSdnTt38ttvv/HNN9/wxRdf2Lo6IiIVigKPVFhr16697AMxQ0NDLS65rug2btxITEwMTz/9dInGA4mIyAUKPFJhhYeHs2rVqksuL/NujlI2fPhwhg8fbutqCBQ+TqLoDtZ6tIRIhaDAIxWWl5eXcedikTLl7g4Xed6ciJRfuixdRERE7J4Cj4iIiNg9BR4REWudP194KbqXV8kfUiUiNqUxPCIiVyMz09Y1EBErqIVHRERE7J5NA4/JZGLMmDGEhYURHh5OTEzMJct+9tlndO3alebNm/PAAw+QmJhosfyLL76gc+fOBAUFMXToUE6fPl3a1S/XDh8+jJ+fH4cPH/5X24mLi6Njx44lLt+xY0fi4uL+1T7L0unTp3n44YcJDAwkOjqanTt30qVLFwIDA1mxYgV+fn5s2bLlitspabmKYvfu3ezYseOSy2fNmkVoaChhYWEsXbrUqp8Ra1S0nycRKb9s2qU1bdo0du3axZIlSzhy5AjR0dHUqVOHbt26WZTbvn07Y8eO5eWXXyYkJIT333+fgQMH8u233+Ll5UViYiJjx45l4sSJNGnShMmTJzN69Gjmz59fugeQmwf5ZfiQPScncFEv5LX02WefcfDgQVatWkXVqlV54YUXqF+/PjExMVSpUoWIiAgqV658xe1s2rSpROUqiqFDhzJs2DBCQkKKLUtPT2f27NlMmjSJW2+9lerVq3PHHXfYoJYiIiVns7+emZmZrFixgoULFxIQEEBAQAB79+4lNja2WOBJTU1lyJAh3HXXXUDhl3FMTAzJyck0b96cZcuW0b17d3r16gUUBqkOHTpw6NAh4wGJpSI/H06lQUHJnlHzrzg6QvUqCjzXWEZGBg0bNqRx48YAnDt3jpYtWxp3Mv7ns9UupUaNGqVWx/ImIyMDgLZt21K3bl3A/m7yKCL2x2ZdWklJSeTl5REcHGzMCw0NJSEhgYJ/BIju3bszePBgoPCBgYsXL6Z69erGH6mEhATCwsKM8jfccAN16tQhISGh9A+koADyy+D1L0NVeno6L774IrfccguhoaE8//zzpP/tyaeJiYn06dOHoKAgunbtypcXualaQUEBI0aM4K677uLs2bMAfPjhh0RERBASEsLcuXOLlX/nnXfo1KkTzZs3p2/fvvzxxx8ADB48mKlTpxplX3jhBTp06GC837RpE+3btwcKu4s+/fRT7rjjDpo1a8aDDz7IoUOHSnzsX3/9NT169CAoKIh7772XrVu3AoXdMrNmzWLbtm34+fnRt29ftm7dypw5c/Dz8zP2XdRVlZmZybhx42jdujWtW7fmxRdfxGQyFSuXk5PDyy+/bJR77rnnSEtLAy50Na5bt47OnTsTGBjIoEGDjOUAGzZs4O677yYoKIg777yTzZs3k52dTUhICOvWrTPK5ebm0rp1azZv3lzsmEeNGsWUKVN46qmnCAoKon379hZ3pTaZTLz22mu0b9+eFi1a8MQTT3D06FEA+vbty19//cXo0aOLPbrj8OHDRvdV586dGTVqlEW354wZM2jdurXxs7V582YCAgLYtWsXAHv27KFv3740b96crl27Ehsba7H9y/08iYj8GzYLPKmpqVStWhVXV1djnq+vLyaTyeLL/+82b95McHAws2fPZsyYMcZddk+cOEHNmjUtylavXp1jx46VWv0rmmHDhrF7927mzZvHu+++S3JysvHH7NSpUwwYMAB/f39WrlzJoEGDiI6OJikpyWIbr7zyCklJSSxatAgfHx82btzI5MmTeeqpp1i+fDm//vorf/31l1F+zpw5xMTEMGbMGFauXEndunV5/PHHyczMJDw83GLMy7Zt2zh69Khxzn788UfCw8ON5bNmzWLs2LHExcVx5swZZsyYUaLjTkpKIjo6msGDB/PZZ59x5513MnDgQFJSUhgwYAADBgwgODiYTZs2MWPGDIKDgxkwYACbNm0qtq0XXniB+Ph45s6dS0xMDPHx8Retx/Tp09m1axcLFy5k6dKlZGRk8OSTT1qUmTdvHtOnT2fZsmX8+uuvvPvuuwDs3buXwYMHc/vttxshb8iQIZw7d47OnTuzdu1aYxs//fQTzs7OtGrV6qLHHhsbS0BAAF988QVdunRh/PjxnDt3DoDx48fz9ddfM3XqVD788EPy8vIYMmQIBQUFzJo1i9q1azNmzBjGjh1rsc0bbriBFStWALBixYpiy4cMGUKVKlWYOXMmJpOJ8ePH89hjj9GsWTOys7MZOHAgoaGhfPbZZ0RHRzN37lwjiF3p56lccXSE9u0LX4669kOkIrBZ/0hWVpZF2AGM9zk5ORdd5z//+Q9xcXF89913jBo1ihtvvJEWLVqQnZ190W1dajvXm6SkJLZu3cqaNWto1KgRAK+99ho9evRg//79xviTF154AUdHR2666SbS09PJzs42trFw4ULWrFnDBx98gK+vL1D4By8yMtLoSnzllVeMVhmz2cyyZct45pln6NSpEwCTJk3i9ttv57PPPiM8PJzJkydz7tw5srOzSUtLIygoiB07dtCjRw82b97ME088Yey/f//+tG3bFoA+ffoUaxm4lEWLFnH//fcTGRkJwCOPPMK2bdv44IMPGDVqFJ6enri4uBhdUi4uLnh6ehbrokpPT2fNmjW8++67hIaGAvDSSy+xe/dui3JZWVksW7aMTz75xGglmjZtGq1bt+aPP/4wQvqIESNo3rw5AJGRkfz6668AfPzxx4SEhDBkyBAAoqKiyMzM5OzZs/Ts2ZOnn34ak8mEm5sba9asoVu3bjhd4llOfn5+DBw4EIAnn3ySpUuXsnfvXho3bsynn37KwoULadOmDQCvv/46ERER/Pjjj7Rr1w4nJycqVapEpUqVLLbp5OREtWrVAKhWrVqx5a6urkyaNIkBAwZw6tQpnJ2dGTZsGACff/451atX56mnngKgYcOG/PXXXyxdupRevXpd9uep3PHwgO+/t3UtRMQKNgs8bm5uxQJJ0ftLjQfw9fXF19cXf39/EhIS+PDDD2nRosUlt+Xh4VE6la9g9u/fj4+PjxF2ABo3bkzlypXZv38/Bw4coGnTpjj+7X+q/fv3N9Y9ceIEb775JrVr17YIAsnJyTzwwAPG+6pVqxpjpk6dOmWEmCIuLi40a9bMWK9OnTps376drKwsgoODadiwIfHx8bRp04Z9+/Zxyy23GOs2aNDAmPb29iY3N7dEx56cnMxXX33F8uXLjXm5ubkWrUclkZKSQn5+PgEBAca8sLAwi65UgEOHDpGbm2vxuUBh997BgweN9S91PAcOHLDYB2AEhAYNGuDq6srGjRtp374969evZ968eZesc8OGDS32AZCXl8fBgwcpKCiwODdVqlShUaNGJCcn065duyt9HJfVqlUrIiMjiYuLIzY21vjPyP79+0lKSrLoxs7PzzcC2+V+nkRE/i2bBZ5atWpx5swZ8vLycHYurEZqairu7u74+PhYlE1MTMTJycniD0Hjxo1JTk42tnXy5EmLdU6ePHldDSS9nH+2fhXJz88nPz/f+PwvxcHBgUWLFjFmzBjefvttnn76aWOZ2Wy2KOvi4gIUBtpL7bNojNatt97K1q1bMZlMhISE0KhRI+bOncvPP/9MYGCgxc9B0XatlZ+fz8CBA41WgyLWDrIt6f7z/3fV3vvvv4+np6fFsurVqxvdtZfa3uXOhbOzM127dmXt2rW4uLjg7e190auoLldns9lconPzbxQUFLBnzx6cnJz4+eefjVCYl5dH27ZtGTdu3CXXvdTPk4jIv2Wzzmd/f3+cnZ3ZuXOnMS8+Pp7AwECLlgYobOafPn26xbzffvuNm266CYCgoCDi4+ONZUePHuXo0aMW/4O9njVq1IizZ8+yf/9+Y96+ffvIyMigUaNGNGzYkD/++MPij81TTz3FO++8AxRegdS2bVuef/55YmJiSElJAQq7GIu6YqDw6p2iZZUqVcLX19fi/Obm5vLbb78ZLU3t2rVj69at7Nixg7CwMEJDQ9mzZw9r1679160Mfz/2w4cP06BBA+O1fPlyNmzYYNV26tWrh5OTk8W4pvXr13P33XdftFxaWpqxP29vb6ZMmcKpU6euuJ8GDRoUGzv1wAMPGIPIIyMj2bBhA99++y3dunXDwcHBquMoquM/f/fOnDlDSkqKRSvg1Vq6dKnRKjh//nzjPyaNGjXiwIED3HjjjcZns3PnTt577z3g8j9P5c7581CjRuFLj5YQqRBsFng8PDzo1asXEyZMIDExkfXr1xMTE8MjjzwCFLb2FI0h+b//+z9+/vlnlixZwsGDB5k5cyaJiYn069cPKBzT8emnn7JixQqSkpIYOXIkERERag7/n8aNG3PbbbcRHR1NYmIiiYmJREdH07JlS/773/8SGRlJWloa06ZN4+DBg8TFxfHNN99w6623WmynR48etGjRgkmTJgHw8MMP89VXX/HRRx+RnJzMuHHjLMb99OvXj5kzZ/Ltt9+SnJxsXNXUo0cPANq0acOePXtISUmhWbNmVKtWjfr161/TwNOvXz9Wr17N0qVL+fPPP1m8eDGLFy+26O4pCW9vb3r16sXkyZNJTEzk119/5c033zTGwPy93H333ceECRPYsmUL+/btY+TIkaSkpBiXul9Onz592L59O++++y4pKSnMnz+fvXv3Gq0koaGheHh4sHLlSnr27GnVMRTx8vLivvvuY9KkSWzZsoWkpCSef/55ateubZxzT09P9u/ff8kLCC7lyJEjvPXWW0RHR9O1a1ciIiIYN24cZrOZO++8k+zsbMaNG0dycjI//PADkydPpnr16sCVf57KnZMnC18iUiHY9PKC0aNHExAQwKOPPsrEiRMZPnw4Xbp0ASA8PJzVq1cDEBAQwOzZs/n444+58847+eGHH1i0aBG1atUCIDg4mJdeeok5c+bQp08fKleuzJQpU8rmIBwdwakMXv/ySpCpU6dSr149+vXrx2OPPcZ//vMf5syZA4CPjw/z589n+/bt3HHHHSxcuJA33ngDf3//YtsZO3YsP/30E+vWrSMsLIwpU6Ywf/587r33XqpVq2axzoABA7jvvvt48cUX6d27N8eOHeO9994zBr16e3sTGBhI06ZNjW63sLAwqlatSrNmzf7V8RZp0aIF06ZN4/3336dHjx589NFHvPHGG7Rs2dLqbY0ZM4YmTZrQv39/Bg4cSOvWrS2694qMGjWKtm3bMmLECO6//36cnZ1ZsGDBJQcX/139+vWZNWsWn3zyCXfccQdr165l3rx5xs+6g4MD3bp1o3bt2v/qM4qOjuaWW25hxIgR9OnTBzc3NxYvXmych6KB4S+88IJV233ppZdo1qyZcSPC0aNH89tvv/HRRx/h7e3NwoULOXjwIL169eKFF17goYceYtCgQQBX/HkSEfk3HMz/7DS/DmVkZBAaGkp8fHyxG81lZ2dz4MABGjVqVHzch+60LDbw7LPP0qBBA0aMGGHrqtjUZX83S9v581D0XZGRUfjU9HIq46SJ3RtOc/5sycZnefk44n9bNbx9Lz7WS6Si0l/Pf8PFWQFEyszOnTv57bff+Oabb/jiiy9sXR0RkQpFf62lwlq7dm2xOwH/XWhoqDHw2h5s3LiRmJgYnn766RKNBxIRkQsUeKTCCg8Pt3hcwj/Z2/Odhg8fzvDhw21dDRGRCkmBRyosLy8v487FImXK0RGKbjqpR0uIVAgKPCIi1vLwgG3bbF0LEbGC/mtSQtfiDrQicu3od1JErKEWnitwdXXF0dGRI0eOUKNGDVxdXa/q7rYicm2YzWZycnJITU3F0dHxko9OERH5OwWeK3B0dKRRo0YcPXqUI0eO2Lo6IvI/np6e1K9fv9ijaMpEZiY0bVo4/fvv8I/npolI+aPAUwKurq7Ur1+fvLw84+GQImI7Tk5OODs726611WyGoud86d6tIhWCAk8JOTg44OLioqc3i4iIVEAatCwiIiJ2T4FHRERE7J4Cj4iIiNg9BR4RERGxexq0LCJiLQeHC5el675cIhWCAo+IiLU8PeG332xdCxGxgrq0RERExO4p8IiIiIjdU+AREbFWZiYEBBS+MjNtXRsRKQGN4RERsZbZXPgMraJpESn31MIjIiIidk+BR0REROyeAo+IiIjYPQUeERERsXsKPCIiImL3dJWWiIi1HBygQYML0yJS7inwiIhYy9MTDh60dS1ExArq0hIRERG7p8AjIiIidk+BR0TEWllZ0LJl4Ssry9a1EZES0BgeERFrFRTA9u0XpkWk3FMLj4iIiNg9BR4RERGxewo8IiIiYvcUeERERMTuKfCIiIiI3dNVWiIiV8PX19Y1EBErKPCIiFjLywtSU21dCxGxgrq0RERExO4p8IiIiIjdU+AREbFWVhZERBS+9GgJkQpBY3hERKxVUAA//HBhWkTKPbXwiIiIiN2zaeAxmUyMGTOGsLAwwsPDiYmJuWTZ77//nrvuuovg4GAiIyP55ptvLJaHhYXh5+dn8Tp//nxpH4KIiIhUADbt0po2bRq7du1iyZIlHDlyhOjoaOrUqUO3bt0syiUlJTFs2DBGjhxJ+/bt2bRpE08++SQff/wxTZo04fjx45w7d47169fj7u5urOfp6VnWhyQiIiLlkM0CT2ZmJitWrGDhwoUEBAQQEBDA3r17iY2NLRZ4vvjiC9q0acMjjzwCQIMGDfj222/56quvaNKkCcnJydSoUYN69erZ4lBERESknLNZ4ElKSiIvL4/g4GBjXmhoKPPmzaOgoABHxwu9bXfffTe5ubnFtnHu3DkA9u3bR6NGjUq/0iIiIlIh2WwMT2pqKlWrVsXV1dWY5+vri8lkIi0tzaJs48aNadKkifF+7969bN68mbZt2wKQnJxMVlYWffv2JTw8nIEDB3LgwIEyOQ4RuU55eha+RKRCsFngycrKsgg7gPE+JyfnkuudPn2a4cOHExISQqdOnQDYv38/6enpDB48mLlz5+Lu7k6/fv3IyMgovQMQkeuXlxecP1/48vKydW1EpARs1qXl5uZWLNgUvf/7wOO/O3nyJP3798dsNjNz5kyj22vRokXk5ubi9b8vntdff5327dvz3XffERkZWYpHISIiIhWBzQJPrVq1OHPmDHl5eTg7F1YjNTUVd3d3fHx8ipU/fvy4MWh56dKlVKtWzVjm6upq0Vrk5ubGjTfeyPHjx0v5KERERKQisFmXlr+/P87OzuzcudOYFx8fT2BgoMWAZSi8ouvxxx/H0dGRZcuWUatWLWOZ2Wymc+fOxMXFWZRPSUnhpptuKvXjEJHrUHY29OxZ+MrOtnVtRKQEbNbC4+HhQa9evZgwYQKvvPIKJ06cICYmhilTpgCFrT2VKlXC3d2d+fPn8+eff/Lee+8Zy6Cw66tSpUpEREQwa9Ys6tatS7Vq1XjrrbeoXbs27du3t9XhiYg9y8+H1asvTItIuWfTGw+OHj2aCRMm8Oijj+Lt7c3w4cPp0qULAOHh4UyZMoXevXuzdu1asrOzue+++yzWv/vuu3n11Vd5/vnncXZ25tlnnyUjI4M2bdqwYMECnJycbHFYIiIiUs44mM1ms60rYWsZGRmEhoYSHx+Pt7e3rasjIuXd+fNQ9F2RkVGur9TKOGli94bTnD9bsoecevk44n9bNbx93Uq5ZiJlSw8PFREREbunwCMiIiJ2T4FHRERE7J4Cj4iIiNg9m16lJSJSIXl5ga73EKlQ1MIjIiIidk+BR0REROyeAo+IiLWys+G++wpferSESIWgwCMiYq38fPj448KXHi0hUiEo8IiIiIjdU+ARERERu6fAIyIiInZPgUdERETsngKPiIiI2D0FHhEREbF7erSEiIi1PD0hI+PCtIiUewo8IiLWcnAofJ6WiFQY6tISERERu6fAIyJiLZMJ+vUrfJlMtq6NiJSAAo+IiLXy8mDJksJXXp6tayMiJaDAIyIiInZPgUdERETsngKPiIiI2D0FHhEREbF7CjwiIiJi9xR4RERExO7pTssiItby9IQTJy5Mi0i5p8AjImItBweoUcPWtRARK6hLS0REROyeAo+IiLVMJhg6tPClR0uIVAgKPCIi1srLg7lzC196tIRIhaDAIyIiInZPgUdERETsngKPiIiI2D0FHhEREbF7CjwiIiJi9xR4RERExO7pTssiItby8IADBy5Mi0i5p8AjImItR0do2NDWtRARK6hLS0REROyeAo+IiLVycuD55wtfOTm2ro2IlIACj4iItXJz4fXXC1+5ubaujYiUgAKPiIiI2D2bBh6TycSYMWMICwsjPDycmJiYS5b9/vvvueuuuwgODiYyMpJvvvnGYvkXX3xB586dCQoKYujQoZw+fbq0qy8iIiIVhE0Dz7Rp09i1axdLlixh/PjxzJ49mzVr1hQrl5SUxLBhw7jnnntYtWoVDzzwAE8++SRJSUkAJCYmMnbsWIYNG8by5cs5e/Yso0ePLuvDERERkXLKZpelZ2ZmsmLFChYuXEhAQAABAQHs3buX2NhYunXrZlH2iy++oE2bNjzyyCMANGjQgG+//ZavvvqKJk2asGzZMrp3706vXr2AwiDVoUMHDh06RL169cr60ERERKScsVkLT1JSEnl5eQQHBxvzQkNDSUhIoKCgwKLs3XffzXPPPVdsG+fOnQMgISGBsLAwY/4NN9xAnTp1SEhIKKXai4iISEVis8CTmppK1apVcXV1Neb5+vpiMplIS0uzKNu4cWOaNGlivN+7dy+bN2+mbdu2AJw4cYKaNWtarFO9enWOHTtWegcgIiIiFYbNurSysrIswg5gvM+5zH0tTp8+zfDhwwkJCaFTp04AZGdnX3Rbl9uOiMhV8/CAXbsuTItIuWezwOPm5lYskBS9d3d3v+g6J0+epH///pjNZmbOnImjo+Nlt+WhLyIRKQ2OjhAQYOtaiIgVbNalVatWLc6cOUNeXp4xLzU1FXd3d3x8fIqVP378OA899BA5OTksXbqUatWqWWzr5MmTFuVPnjxJjRo1Su8AREREpMKwWeDx9/fH2dmZnTt3GvPi4+MJDAw0Wm6KZGZm8vjjj+Po6MiyZcuoVauWxfKgoCDi4+ON90ePHuXo0aMEBQWV6jGIyHUqJwcmTCh8qetcpEKwWeDx8PCgV69eTJgwgcTERNavX09MTIxx6XlqairZ2dkAzJ8/nz///JOpU6cay1JTU42rtPr06cOnn37KihUrSEpKYuTIkUREROiSdBEpHbm5MHFi4UuPlhCpEGw2hgdg9OjRTJgwgUcffRRvb2+GDx9Oly5dAAgPD2fKlCn07t2btWvXkp2dzX333Wex/t13382rr75KcHAwL730EjNnziQ9PZ1bb72VSZMm2eKQREREpBxyMJvNZltXwtYyMjIIDQ0lPj4eb29vW1dHRMq78+eh6LsiIwO8vGxbn8vIOGli94bTnD9bcOXCgJePI/63VcPb162UayZStvTwUBEREbF7CjwiIiJi9xR4RERExO4p8IiIiIjds+lVWiIiFZK7O2zdemFaRMo9BR4REWs5OUHLlrauhYhYQV1aIiIiYvfUwiMiYq2cHHjrrcLpJ58EV1fb1kdErkiBR0TEWrm5MHJk4fSQIQo8IhWAurRERETE7inwiIiIiN1T4BERERG7p8AjIiIidk+BR0REROyeAo+IiIjYPV2WLiJiLXd3+O67C9MiUu4p8IiIWMvJCSIibF0LEbGCurRERETE7qmFR0TEWrm5sGBB4XRUFLi42LY+InJFCjwiItbKyYFhwwqn+/VT4BGpANSlJSIiInZPgUdERETsngKPiIiI2D0FHhEREbF7VxV4tm/fTk5OzrWui4iIiEipuKqrtIYOHcqSJUto0qTJta6PiIhcxpkzkJ5esrIuLuCta3FFgKsMPP/5z39ITExU4BGR65ObG3zxxYXpMpSeDl99BefPX7ls/fpwe7vSr5NIRXBVgady5cqMGzeOmTNncuONN+Lq6mqxfOnSpdekciIi5ZKzM/TsabPdnz8PGRlXLpeVVfp1Eakorirw+Pv74+/vj9lsJi0tDQcHB6pUqXKNqyYiIiJybVxV4Bk8eDAzZ85kxYoVnD59GoBatWrx0EMPERUVdU0rKCJS7uTmQmxs4fRDD+lOyyIVwFUFnqlTp7J27Vqee+45mjVrRkFBAb/++iszZ84kJyeHYUW3XBcRsUc5OdC/f+H0ffcp8IhUAFcVeFauXMmcOXNo1aqVMa9JkybUrVuX5557ToFHREREypWrug+Ph4cHLhf5H42Pjw8ODg7/ulIiIiIi19JVBZ6RI0cyZswYvvvuO9LS0sjIyGD79u28+OKLPProoxw5csR4iYiIiNjaVXVpPffcc0Dh4OWiFh2z2QzA7t27efPNNzGbzTg4OLB79+5rVFURERGRq3NVgeebb7651vUQERERKTVXFXjq1q17reshIiIiUmr0lBUREWu5ucFHH12YFpFyT4FHRMRazs6F998RkQrjqq7SEhEREalI1MIjImKtvDxYubJw+u67C1t8RKRc02+piIi1TCa4//7C6YwMBR6RCkBdWiIiImL3bBp4TCYTY8aMISwsjPDwcGJiYq64zvbt2+nUqVOx+WFhYfj5+Vm8zp8/XxrVFhERkQrGpu2w06ZNY9euXSxZsoQjR44QHR1NnTp16Nat20XL//HHHzz55JO4/eMy0OPHj3Pu3DnWr1+Pu7u7Md/T07NU6y8iIiIVg80CT2ZmJitWrGDhwoUEBAQQEBDA3r17iY2NvWjg+fDDD5k6dSr16tUjIyPDYllycjI1atSgXr16ZVV9ERERqUBs1qWVlJREXl4ewcHBxrzQ0FASEhIoKCgoVn7Dhg1MnTqVfv36FVu2b98+GjVqVJrVFRERkQrMZoEnNTWVqlWr4urqaszz9fXFZDKRlpZWrPzcuXPp0qXLRbeVnJxMVlYWffv2JTw8nIEDB3LgwIHSqrqIiIhUMDYLPFlZWRZhBzDe5+TkWLWt/fv3k56ezuDBg5k7dy7u7u7069evWNeXiMg14eoK775b+PrH95iIlE82G8Pj5uZWLNgUvf/7wOOSWLRoEbm5uXh5eQHw+uuv0759e7777jsiIyOvTYVFRIq4uMBFutdFpPyyWeCpVasWZ86cIS8vD+f/3bQrNTUVd3d3fHx8rNqWq6urRWuRm5sbN954I8ePH7+mdRYREZGKyWZdWv7+/jg7O7Nz505jXnx8PIGBgTg6lrxaZrOZzp07ExcXZ8zLzMwkJSWFm2666VpWWUSkUF4efPll4Ssvz9a1EZESsFkLj4eHB7169WLChAm88sornDhxgpiYGKZMmQIUtvZUqlTpit1bDg4OREREMGvWLOrWrUu1atV46623qF27Nu3bty+LQxGR643JBHfcUTitR0uIVAg2vdPy6NGjCQgI4NFHH2XixIkMHz7cuBIrPDyc1atXl2g7zz//PF27duXZZ5/lvvvuIy8vjwULFuDk5FSa1RcREZEKwsFsNpttXQlby8jIIDQ0lPj4eLy9vW1dHREp786fh6LviowM+N8FE2Xh4EH4+OPC3V5Jo0ZwZxcT+zaf5vzZ4vc3uxgvH0f8b6uGt6/blQuLVCB6eKiIiIjYPQUeERERsXsKPCIiImL3FHhERETE7ulaShERa7m6wuzZF6ZFpNxT4BERsZaLCwwdautaiIgVFHhERCoQB4eSXwXv4VFYXkQUeERErJefDxs3Fk63awdleJPTKt55tGuZX6InWnh5g7trQVlWT6TcUuAREbFWdjZ06FA4XcY3HnQy55N3Io3Mc1e+kaB7bWcc6njhqMAjosAjIlLR5GYXkJN15cCTl1OyuyuLXA90WbqIiIjYPQUeERERsXsKPCIiImL3FHhERETE7inwiIiIiN3TVVoiItZycYFp0y5Mi0i5p8AjImItV1d4/nlb10JErKAuLREREbF7auEREbFWfj7s2FE4HRJSpo+WEJGro8AjImKt7Gxo1apwuowfLSEiV0ddWiIiImL3FHhERETE7inwiIiIiN1T4BERERG7p8AjIiIidk+BR0REROyeLksXEbGWiwuMH39hWkTKPQUeERFrubrChAm2roWIWEFdWiIiImL31MIjImKtggLYvbtw2t8fHPV/R5HyToFHRMRaWVnQrFnhtB4tIVIh6L8lIiIiYvcUeERERMTuKfCIiIiI3VPgEREREbunwCMiIiJ2T4FHRERE7J4uSxcRsZaLCzz33IVpESn3FHhERKzl6gqvvWbrWoiIFdSlJSIiInZPLTwiItYqKIA//yycrl9fj5YQqQAUeERErJWVBY0aFU7r0RIiFYL+WyIiIiJ2z6aBx2QyMWbMGMLCwggPDycmJuaK62zfvp1OnToVm//FF1/QuXNngoKCGDp0KKdPny6NKouIiEgFZNPAM23aNHbt2sWSJUsYP348s2fPZs2aNZcs/8cff/Dkk09iNpst5icmJjJ27FiGDRvG8uXLOXv2LKNHjy7t6ouIiEgFYbPAk5mZyYoVKxg7diwBAQHcfvvtPP7448TGxl60/IcffsgDDzxA9erViy1btmwZ3bt3p1evXjRp0oRp06bxww8/cOjQodI+DBEREakAbBZ4kpKSyMvLIzg42JgXGhpKQkICBQUFxcpv2LCBqVOn0q9fv2LLEhISCAsLM97fcMMN1KlTh4SEhFKpu4iIiFQsNgs8qampVK1aFVdXV2Oer68vJpOJtLS0YuXnzp1Lly5dLrqtEydOULNmTYt51atX59ixY9e0ziIiIlIx2eyy9KysLIuwAxjvc3JyrNpWdnb2Rbdl7XZERErE2RmGDLkwLSLlns1+U93c3IoFkqL37u7u12RbHh4e/66SIiIX4+YGc+bYuhYiYgWbdWnVqlWLM2fOkJeXZ8xLTU3F3d0dHx8fq7d18uRJi3knT56kRo0a16SuIiIiUrHZLPD4+/vj7OzMzp07jXnx8fEEBgbiaOVt2oOCgoiPjzfeHz16lKNHjxIUFHStqisicoHZDKmpha9/3CZDRMonm3VpeXh40KtXLyZMmMArr7zCiRMniImJYcqUKUBha0+lSpVK1L3Vp08f+vbtS4sWLQgMDGTy5MlERERQr1690j4MEbkeZWZC0YUS/+LREmfOFK5eUs7O4OV0VbsSue7ZdLTd6NGjmTBhAo8++ije3t4MHz7cuBIrPDycKVOm0Lt37ytuJzg4mJdeeomZM2eSnp7OrbfeyqRJk0q7+iIi/4pjQR7nT+Xzt579y/Lyhqo1C3BS6BGxmoP5n7ctvg5lZGQQGhpKfHw83t7etq6OiJR3589D0XfFv2jhyThpYt/WNDLPFb/32MVUq+1Mo0Avdv+UTtrJK6/jW9eZ/4RWKnF5AC8fR/xvq4a3r1uJyotUFLqeUkTEhnKzC8jJKlkYycspWTkRKU5PSxcRERG7p8AjIiIidk+BR0REROyexvCIiFjL2RkeffTCtIiUe/pNFRGxlpsbLF5s61qIiBXUpSUiIiJ2Ty08IiLWMpsL77YM4OkJDg62rY+IXJFaeERErJWZWXjjQW/vC8FHRMo1BR4RERGxewo8IiIiYvcUeERERMTuKfCIiIiI3VPgEREREbunwCMiIiJ2T/fhERGxlpMT3HvvhWkRKfcUeERErOXuDitW2LoWImIFdWmJiIiI3VPgEREREbunwCMiYq3z5wufn+XgUDgtIuWeAo+IiIjYPQUeERERsXsKPCIiImL3FHhERETE7inwiIiIiN1T4BERERG7pzsti4hYy8kJevS4MC0i5Z4Cj4iItdzd4csvbV0LEbGCAo+IyDVw5gykp5e8vIsLeOsbWKTM6NdNROQaSE+Hr74q+Y2X69eH29uVbp1E5AIFHhERa50/DzVrFk6fOAFeXsbsjIySbSIrq5TqJiIXpcAjInI1MjNtXQMRsYIuSxcRERG7p8AjIiIidk+BR0REROyeAo+IiIjYPQUeERERsXu6SktExFqOjtC+/YVpESn3FHhERKzl4QHff2/rWoiIFfRfExEREbF7CjwiIiJi9xR4RESsdf481KhR+Crpw7NExKY0hkdE5GqcPGnx1sHBeKRWiXh4FK4jImXDpoHHZDIxceJE1q1bh7u7OwMGDGDAgAEXLfv7778zfvx49uzZw80338zEiRNp1qyZsTwsLIxz585ZrLNjxw68rPkGEhG5SlW882jXMp+8vJKV9/IGd9cCnJxKt14iUsimgWfatGns2rWLJUuWcOTIEaKjo6lTpw7dunWzKJeZmUlUVBSRkZG8+uqrfPDBBwwaNIivv/4aT09Pjh8/zrlz51i/fj3u7u7Gep6enmV9SCJynXIy55N3Io3McwUlKu9e2xmHOl44KvCIlAmbBZ7MzExWrFjBwoULCQgIICAggL179xIbG1ss8KxevRo3NzdGjhyJg4MDY8eOZcOGDaxZs4bevXuTnJxMjRo1qFevno2ORkQEcrMLyMkqWeDJyylZORG5Nmw2aDkpKYm8vDyCg4ONeaGhoSQkJFBQYPlFkJCQQGhoKA7/6/B2cHAgJCSEnTt3ArBv3z4aNWpUZnUXERGRisVmgSc1NZWqVavi6upqzPP19cVkMpGWllasbM2aNS3mVa9enWPHjgGQnJxMVlYWffv2JTw8nIEDB3LgwIFSPwYRERGpGGwWeLKysizCDmC8z8nJKVHZonL79+8nPT2dwYMHM3fuXNzd3enXrx8ZGRmleAQict1ydISwsMKXHi0hUiHYbAyPm5tbsWBT9P7vA48vV7ao3KJFi8jNzTWuyHr99ddp37493333HZGRkaV1CCJyvfLwgG3bbF0LEbGCzQJPrVq1OHPmDHl5eTg7F1YjNTUVd3d3fHx8ipU9+Y97Xpw8edLo5nJ1dbVoAXJzc+PGG2/k+PHjpXwUIiIiUhHYrC3W398fZ2dnY+AxQHx8PIGBgTj+o4k4KCiIX375BbPZDIDZbGbHjh0EBQVhNpvp3LkzcXFxRvnMzExSUlK46aabyuRYREREpHyzWeDx8PCgV69eTJgwgcTERNavX09MTAyPPPIIUNjak52dDUC3bt04e/YskydPZt++fUyePJmsrCy6d++Og4MDERERzJo1iy1btrB3715GjhxJ7dq1ad++va0OT0TsWWYmNGxY+MrMtHVtRKQEbDrabvTo0QQEBPDoo48yceJEhg8fTpcuXQAIDw9n9erVAHh7ezN//nzi4+Pp3bs3CQkJLFiwwLix4PPPP0/Xrl159tlnue+++8jLy2PBggU46RamIlIazGZISSl8/a/lWUTKNwezWb+tGRkZhIaGEh8fj7e3t62rIyLl3fnzUPRdkZEBXl5knDSxe8Npzp8t2Q0Ffes685/QSuz+KZ20k6WzztXsw8vHEf/bquHt61ai8iIVha6nFBEREbunwCMiIiJ2T4FHRERE7J5Nn5YuIkJuHuTnW7eOkxO4WPH1VRb7EJFyTb/NImJb+flwKg0KSvj0cEdHqF7FujByrffh4ABNm16YFpFyT4FHRGyvoADySxhGysM+PD3ht9+uzbZEpExoDI+IiIjYPQUeERERsXsKPCIi1srMhICAwpceLSFSIWgMj4iItcxm+P33C9MiUu6phUdERETsnlp4RMSmTCbITgdzXsnKOziDuw+4uZduvUTEvijwiIhN5ebCgYOQebZk5T194ObaoEdbiog1FHhExOZycyAnp2RlXUpYTkTk7zSGR0REROyeWnhERKzl4AANGlyYFpFyT4FHRMRanp5w8KCtayEiVlCXloiIWFKjldghtfDI9Sc3r/Dp2dZwcrLu6dwiFZSTswOuLkC2yYqV9Psh5Z9+QuX6k58Pp9IKn55dEo6OUL2KvtDlgqwsuO22wukNG8DDw7b1uYYcnR1wKMiHU+dK9jui3w+pIPQTKtenggLIL2HgEfmnggLYvv3CtD3S74jYGY3hEREREbunwCMiIiJ2T4FHRERE7J7G8IiI3dMDSkVEgUdE7J4eUCoiCjwicl245g8o9fX9V/URkbKlwCMiYi0vL0hNtXUtRMQKGrQsIiIidk+BR0REROyeurRE5Jo6cwbS00tW1sUFvMvht5CTU+G/hw5d/LFrDtlZ1OrXHYDji7/CuZJHuTyOq+H4v/8Gnz0LBblXLq8r2qSisJNfUREpL9LT4auv4Pz5K5etXx9ub1f6dbKWo1PhlV3ffnvxoTouOQU8ueUHAFbFFXDDzeXzOK6Go1PhUyX+PARnT1+5vK5ok4pCgUdErrnz5yEj48rlsrJKvy7/RmbmxY/j71dxZWSU/+O4Gnl5JbuqrURXtImUAwo8InJNOTiUxU7KZh+enuDtXXzR3//Ie3sXPiy9TI5bRK6aAo+UL7l5Fx80cTlOTuCiH+VScRXno4q3E66upXc+nJwdcHUBsk0lXsfVucAYl1MSjs4OeLhD6yAT2dkXWZ51Yd8d2prwqOGMu6t1+xCRsqW/ElK+5OfDqbTCQQQl4egI1aso8JSWqzgfTm5VcCnF8+Ho7IBDQT6cOleyejk74+DhhaM1gcepcB95qefIPF18H47ZmcZ05p9ncHXMx6GOdfsQkbKlvxJS/hQUQH4J/8BK6Suv56Ok9XK8+rrnmQrIybpI4DFdmJeTXUBeTjn8fETEggKPiMhVyHf1sHUVRMQKCjwiIlYqcPNk4/y9tq6GiFhBd1oWERERu6fAIyIiInZPXVoiIlZyzM0mYHYUAL8NWwBc5GY9IlKuKPBIiVnzjKQilStD1aqlU5/yzNrPysEBXF3BVMJby1hbvoi158Nkgux0MOeVrLyjC5hdrauTtax91pOTB7h5XuNKFBRQPfFbY1pEyj+bBh6TycTEiRNZt24d7u7uDBgwgAEDBly07O+//8748ePZs2cPN998MxMnTqRZs2bG8i+++IIZM2aQmppKeHg4kyZNolq1amV1KNcFa56RBODlBd27X5+Bx9rPqkYNCA2FjRtLto615eHqzkduLhw4CJlnS1bepxrULeVfO2uf9VTtBmhUo3TrJCLln00Dz7Rp09i1axdLlizhyJEjREdHU6dOHbp162ZRLjMzk6ioKCIjI3n11Vf54IMPGDRoEF9//TWenp4kJiYyduxYJk6cSJMmTZg8eTKjR49m/vz5Njoy+1XSZySJdZ+Vl5d161hb/t/IzSnZM5Wg8PlLZaWkz3oqyzqJSPlls0HLmZmZrFixgrFjxxIQEMDtt9/O448/TmxsbLGyq1evxs3NjZEjR9K4cWPGjh2Ll5cXa9asAWDZsmV0796dXr160aRJE6ZNm8YPP/zAoUOHyvqwREREpByyWeBJSkoiLy+P4OBgY15oaCgJCQkU/KNPPCEhgdDQUBz+93Q+BwcHQkJC2Llzp7E8LCzMKH/DDTdQp04dEhISSv9AREREpNyzWeBJTU2latWquLpeGOHo6+uLyWQiLS2tWNmaNWtazKtevTrHjh0D4MSJE5ddLiIiItc3m43hycrKsgg7gPE+5x8d85cqW1QuOzv7ssuvxGw2A5ChwSmXlZUFPj7gXMKfGk9PyM62coxJtqlwR9Y8PDQjA/JKcLlOGe7D2s/Ky6twPEpJ17G2PFzd+cg4b8LsnIWDa8k+qwJHR85nZuDllUuVKlcu7+EB5zOt20e+gzMZ5x0ocCzZOtaWL8k6DuZsMv53uZiDazb5Di7XfB/XYp2y2IfZ2ZGMzAzIsOJ3UOQa8/LyMnqBLsVmgcfNza1YICl67+7uXqKyReUutdzDo2TPujn/v8tc2rdvX/IDkBJ54w1b10D+TufjGrr55sJ/X73XtvUQEeLj4/H2vvz9sGwWeGrVqsWZM2fIy8vD+X//RU1NTcXd3R0fH59iZU+ePGkx7+TJk0Y31qWW16hRsmtRa9asyQ8//FCihCgiIiLli1fRpauXYbPA4+/vj7OzMzt37jQGHMfHxxMYGIijo+XQoqCgIBYuXIjZbMbBwQGz2cyOHTt44oknjOXx8fH07t0bgKNHj3L06FGCgoJKVBdHR0dq1659DY9OREREyhObDVr28PCgV69eTJgwgcTERNavX09MTAyPPPIIUNjak52dDUC3bt04e/YskydPZt++fUyePJmsrCy6d+8OQJ8+ffj0009ZsWIFSUlJjBw5koiICOrVq2erwxMREZFyxMFcNGLXBrKyspgwYQLr1q3D29ubxx57jH79+gHg5+fHlClTjFabxMRExo8fT3JyMn5+fkycOJGmTZsa24qLi2PmzJmkp6dz6623MmnSJKpej7f4FRERkWJsGnhEREREyoLNurREREREyooCj4iIiNg9BR4RERGxewo8IiIiYvcUeGzAZDIxZswYwsLCCA8PJyYmxtZVui7l5ORwxx13sGXLFmPeoUOH6NevHy1atKBHjx5s2rTJhjW8Phw/fpwRI0bQqlUr2rVrx5QpUzCZTIDOh62kpKTw2GOPERwcTEREBO+8846xTOfEdqKiohg1apTx/vfff+e+++4jKCiIe+65h127dtmwduWfAo8NTJs2jV27drFkyRLGjx/P7NmzWbNmja2rdV0xmUw888wz7N2715hnNpsZOnQovr6+fPLJJ9x1110MGzaMI0eO2LCm9s1sNjNixAiysrKIjY3lzTff5LvvvmPGjBk6HzZSUFBAVFQUVatWZeXKlUycOJG3336bzz//XOfEhr788kt++OEH431mZiZRUVGEhYURFxdHcHAwgwYNIjMz04a1LN9sdqfl61VmZiYrVqxg4cKFBAQEEBAQwN69e4mNjaVbt262rt51Yd++fTz77LP8844MP//8M4cOHeLDDz/E09OTxo0bs3nzZj755BOGDx9uo9rat/3797Nz505+/PFHfH19ARgxYgRTp07ltttu0/mwgZMnT+Lv78+ECRPw9vamYcOGtG3blvj4eHx9fXVObCAtLY1p06YRGBhozFu9ejVubm6MHDkSBwcHxo4dy4YNG1izZo1x/zqxpBaeMpaUlEReXh7BwcHGvNDQUBISEigo6dO75V/ZunUrrVu3Zvny5RbzExISaNq0KZ6ensa80NBQdu7cWcY1vH7UqFGDd955xwg7RTIyMnQ+bKRmzZrMmDEDb29vzGYz8fHxbNu2jVatWumc2MjUqVO56667uLnogbUUfl+FhoYaz390cHAgJCRE5+IyFHjKWGpqKlWrVsXV1dWY5+vri8lkIi0tzXYVu448+OCDjBkzBg8PD4v5qampxgNpi1SvXp1jx46VZfWuKz4+PrRr1854X1BQwLJly2jTpo3ORznQsWNHHnzwQYKDg+natavOiQ1s3ryZ7du3M2TIEIv5OhfWU+ApY1lZWRZhBzDe5+Tk2KJK8j+XOjc6L2Xntdde4/fff+fpp5/W+SgHZs6cybx589i9ezdTpkzROSljJpOJ8ePHM27cONzd3S2W6VxYT2N4ypibm1uxH8ii9//8gZay5ebmVqyVLScnR+eljLz22mssWbKEN998k//+9786H+VA0ZgRk8nEc889xz333ENWVpZFGZ2T0jN79myaNWtm0Qpa5FJ/S3QuLk2Bp4zVqlWLM2fOkJeXh7Nz4cefmpqKu7s7Pj4+Nq7d9a1WrVrs27fPYt7JkyeLNRvLtTdp0iQ++OADXnvtNbp27QrofNjKyZMn2blzJ507dzbm3XzzzeTm5lKjRg32799frLzOSen48ssvOXnypDHmsyjgrF27ljvuuIOTJ09alNe5uDx1aZUxf39/nJ2dLQaWxcfHExgYiKOjToctBQUF8dtvv5GdnW3Mi4+PJygoyIa1sn+zZ8/mww8/ZPr06fTs2dOYr/NhG4cPH2bYsGEcP37cmLdr1y6qVatGaGiozkkZeu+99/j8889ZtWoVq1atomPHjnTs2JFVq1YRFBTEL7/8Ylxtajab2bFjh87FZegvbBnz8PCgV69eTJgwgcTERNavX09MTAyPPPKIrat23WvVqhU33HADo0ePZu/evSxYsIDExETuvfdeW1fNbiUnJzN37lwGDhxIaGgoqampxkvnwzYCAwMJCAhgzJgx7Nu3jx9++IHXXnuNJ554QuekjNWtW5cGDRoYLy8vL7y8vGjQoAHdunXj7NmzTJ48mX379jF58mSysrLo3r27ratdbjmY/3kzEil1WVlZTJgwgXXr1uHt7c1jjz1Gv379bF2t65Kfnx9Lly6ldevWQOEdZseOHUtCQgINGjRgzJgx3HLLLTaupf1asGABb7zxxkWX/fHHHzofNnL8+HEmTZrE5s2b8fDw4OGHH2bQoEE4ODjonNhQ0V2WX331VQASExMZP348ycnJ+Pn5MXHiRJo2bWrLKpZrCjwiIiJi99SlJSIiInZPgUdERETsngKPiIiI2D0FHhEREbF7CjwiIiJi9xR4RERExO4p8IiIiIjdU+ARkevK4cOH8fPz4/Dhw6Wy/VOnTvHVV1+VyrZF5Oop8IiIXEOvv/46P/zwg62rISL/oMAjInIN6eb1IuWTAo+IlKljx47x5JNP0qpVK1q3bs3LL79MTk4O7dq145NPPjHKmc1mbrvtNj799FMAtm/fTu/evWnevDmRkZGsXbvWKDtq1ChGjRrFnXfeSdu2bTl48CCrV6+ma9euBAYG0qNHD9avX29Rj/Xr19O5c2eCgoJ44oknSE9PN5b98ssv9OnThxYtWtCxY0c++OADi3Xj4uLo3r07zZs3p3fv3mzbtg2AWbNmsXLlSlauXEnHjh2v+WcnIldPgUdEykxOTg6PPvooWVlZvPfee8yYMYPvv/+eadOm0a1bN77++muj7M6dO0lLS6NTp06kpqYyaNAgevfuzeeff87jjz/OqFGj2L59u1H+008/5amnnmL+/PlUqlSJkSNHMmjQINasWcM999zDM888Q1pamlF+5cqVTJ8+naVLl/Lbb7+xcOFCoPAJ7o8++igtW7YkLi6O4cOHM3XqVKNucXFxTJo0iUGDBrFq1SpuueUWoqKiOH78OAMGDKB79+50796djz/+uGw+VBEpEWdbV0BErh8bN27k+PHjfPTRR1SuXBmAcePGMXjwYJYsWUL//v3JyMjA29ubtWvX0r59e7y9vXnnnXe45ZZbePjhhwFo0KABu3fvZsmSJYSFhQEQGBhotKr8/vvv5ObmUrt2berWrcuAAQPw8/PDzc2NjIwMAJ5//nmaN28OQPfu3UlKSgLgo48+omnTpjzzzDMA3HTTTSQnJ/POO+9w++23895779G3b1969eoFwHPPPce2bdtYtmwZzz77LO7u7gBUq1atDD5RESkptfCISJlJTk6mYcOGRtgBCAkJIS8vDy8vL2rUqGEM+F23bh09evQAYP/+/Xz33XcEBwcbr2XLlnHw4EFjO3Xr1jWm/f39iYiIoH///nTr1o3XX3+dG2+8EQ8PD6NM/fr1jelKlSphMpmMOhYFoSLBwcEkJydfcnmLFi2M5SJSPqmFR0TKjJubW7F5+fn5xr89evRg7dq1NGjQgDNnzhAREQFAXl4ekZGRPPHEExbrOjtf+Ar7+7YdHByYP38+iYmJfPPNN3z99de8//77vP/++1SqVAkAR8eL/3/vYnUsKCgw6nmpYygoKLjcoYuIjamFR0TKTKNGjTh48KDFWJqdO3fi7OxM/fr16dmzJz/++CNr166lY8eORotMo0aNSElJoUGDBsbrm2++4fPPP7/ofpKTk5k6dSrNmzfn6aef5ssvv+SGG25g48aNJapjQkKCxbxffvmFRo0aXXJ5QkKCsdzBwaHEn4eIlB0FHhEpM7feeiv16tVj5MiR/PHHH/z8889MmjSJO+64Ax8fH/z9/alZsybLli2je/fuxnoPPvggu3bt4s033+TgwYN8/vnnTJ8+nTp16lx0Pz4+PnzwwQfMnTuXQ4cO8f333/PXX3/RtGnTK9bxwQcfZPfu3UyfPp0DBw6wcuVK3n//fR566CEA+vXrx7Jly1i1ahUHDhzg9ddfJykpiXvvvRcADw8P/vrrL44fP34NPjERuVYUeESkzDg5OTF37lwA7r//fp555hk6derESy+9ZJTp0aMHTk5O3Hbbbca8unXrMm/ePDZu3Mgdd9zBjBkzjMvQL6ZGjRrMmjWLtWvX0rNnT1566SWeeeYZwsPDr1jHOnXqMH/+fDZu3EhkZCRvv/02o0aN4p577jHq9/TTTzNz5kzuvPNOtm7dSkxMDI0bNwbgrrvu4sCBA9x55526J49IOeJg1m+kiIiI2Dm18IiIiIjdU+ARERERu6fAIyIiInZPgUdERETsngKPiIiI2D0FHhEREbF7CjwiIiJi9xR4RERExO4p8IiIiIjdU+ARERERu6fAIyIiInZPgUdERETs3v8DuYokeA4RBPMAAAAASUVORK5CYII=", - "text/plain": [ - "
      " - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "width = 45/36\n", - "plt.bar(\n", + "\n", + "fig, axes = plt.subplots(2, 1, figsize=(8, 8), sharey=True) \n", + "\n", + "\n", + "axes[0].bar(\n", " bin_edges[:36].tolist(),\n", " hist_mask_fix,\n", " align=\"center\",\n", @@ -1568,7 +1619,13 @@ " alpha=0.5,\n", " color=\"blue\",\n", ")\n", - "plt.bar(\n", + "axes[0].axvline(x=overshoot_threshold, color=\"red\", linestyle=\"--\", label=\"overshoot too high\")\n", + "axes[0].set_title(\"Lockdown Efficiency Fixed\")\n", + "axes[0].set_xlabel(\"overshoot\")\n", + "axes[0].set_ylabel(\"density\")\n", + "axes[0].legend()\n", + "\n", + "axes[1].bar(\n", " bin_edges[:36].tolist(),\n", " hist_mask_notfix,\n", " align=\"center\",\n", @@ -1576,12 +1633,16 @@ " alpha=0.5,\n", " color=\"pink\",\n", ")\n", - "plt.legend([\"lockdown_efficiency fixed\", \"lockdown_efficiency not fixed\"])\n", - "plt.ylabel(\"pr\")\n", - "plt.xlabel(\"overshoot\")\n", - "plt.title(\"Counterfactual Mask\")\n", - "plt.axvline(x=(overshoot_threshold), color = \"red\", linestyle = \"--\", label=\"overshoot too high\")\n", + "axes[1].axvline(x=overshoot_threshold, color=\"red\", linestyle=\"--\", label=\"overshoot too high\")\n", + "axes[1].set_title(\"Lockdown Efficiency Not Fixed\")\n", + "axes[1].set_xlabel(\"overshoot\")\n", + "\n", + "\n", + "plt.suptitle(\"Counterfactual Mask: lockdown efficiency fixed vs. stochastic\")\n", "sns.despine()\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", "\n", "print(\"Overshoot mean\")\n", "print(\n", From 689e523f3d5256fed3e78599b8d4ef4e6628c3b8 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Mon, 9 Dec 2024 08:31:49 -0500 Subject: [PATCH 095/111] add colorbar and explain more (heatmap) --- docs/source/explainable_sir.ipynb | 48 ++++++++++++++----------------- 1 file changed, 22 insertions(+), 26 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 9b213fe5..a1be7e72 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -1320,14 +1320,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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      " + "
      " ] }, "metadata": {}, @@ -1337,9 +1337,10 @@ "source": [ "fig, axs = plt.subplots(1, 2, figsize=(16, 8))\n", "\n", + "# Heatmap for counterfactual lockdown\n", "ax = axs[0]\n", - "hist_lockdown = hist_lockdown_nec.unsqueeze(1) * hist_lockdown_suff.unsqueeze(0)\n", - "ax.imshow(hist_lockdown_2d, cmap=\"viridis\")\n", + "hist_lockdown_2d = hist_lockdown_nec.unsqueeze(1) * hist_lockdown_suff.unsqueeze(0)\n", + "im = ax.imshow(hist_lockdown_2d, cmap=\"viridis\")\n", "ax.set(xticks=range(0, 36, 2), xticklabels=bin_edges[0:36:2].tolist())\n", "ax.set(yticks=range(0, 36, 2), yticklabels=bin_edges[0:36:2].tolist())\n", "ax.set(\n", @@ -1349,21 +1350,18 @@ ")\n", "ax.axvline(x=(overshoot_threshold) * 36 / 45, color=\"red\", linestyle=\"--\", label=\"Overshoot too high\")\n", "ax.axhline(y=(overshoot_threshold) * 36 / 45, color=\"red\", linestyle=\"--\")\n", - "\n", - "ax.axvline(\n", - " x=(os_lockdown_suff) * 36 / 45,\n", - " color=\"white\",\n", - " linestyle=\"--\",\n", - " label=\"Mean Overshoot\",\n", - ")\n", + "ax.axvline(x=(os_lockdown_suff) * 36 / 45, color=\"white\", linestyle=\"--\", label=\"Mean Overshoot\")\n", "ax.axhline(y=(os_lockdown_nec) * 36 / 45, color=\"white\", linestyle=\"--\")\n", - "\n", "ax.legend(loc=\"upper left\")\n", "ax.text(21, 2, 'pr(lockdown has causal role \\n over high overshoot): %.4f' % pr_lockdown.item(), color=\"white\")\n", "\n", + "\n", + "cbar1 = fig.colorbar(im, ax=ax, orientation=\"vertical\", fraction=0.046, pad=0.04)\n", + "cbar1.set_label(\"density\")\n", + "\n", "ax = axs[1]\n", - "hist_mask = hist_mask_nec.unsqueeze(1) * hist_mask_suff.unsqueeze(0)\n", - "ax.imshow(hist_mask_2d, cmap=\"viridis\")\n", + "hist_mask_2d = hist_mask_nec.unsqueeze(1) * hist_mask_suff.unsqueeze(0)\n", + "im = ax.imshow(hist_mask_2d, cmap=\"viridis\")\n", "ax.set(xticks=range(0, 36, 2), xticklabels=bin_edges[0:36:2].tolist())\n", "ax.set(yticks=range(0, 36, 2), yticklabels=bin_edges[0:36:2].tolist())\n", "ax.set(\n", @@ -1373,28 +1371,26 @@ ")\n", "ax.axvline(x=(overshoot_threshold) * 36 / 45, color=\"red\", linestyle=\"--\", label=\"Overshoot too high\")\n", "ax.axhline(y=(overshoot_threshold) * 36 / 45, color=\"red\", linestyle=\"--\")\n", - "\n", - "ax.axvline(\n", - " x=(os_mask_suff) * 36 / 45,\n", - " color=\"white\",\n", - " linestyle=\"--\",\n", - " label=\"Mean Overshoot\",\n", - ")\n", + "ax.axvline(x=(os_mask_suff) * 36 / 45, color=\"white\", linestyle=\"--\", label=\"Mean Overshoot\")\n", "ax.axhline(y=(os_mask_nec) * 36 / 45, color=\"white\", linestyle=\"--\")\n", "ax.text(21, 2, 'pr(masking has causal role \\n over high overshoot): %.4f' % pr_mask.item(), color=\"white\")\n", - "\n", "ax.legend(loc=\"upper left\")\n", "\n", - "sns.despine()" + "cbar2 = fig.colorbar(im, ax=ax, orientation=\"vertical\", fraction=0.046, pad=0.04)\n", + "cbar2.set_label(\"density\")\n", + "\n", + "sns.despine()\n", + "plt.tight_layout()\n", + "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The above heatmaps plot the joint distributions arising from necessity and sufficient interventions, particularly $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{ld}}, \\mathit{os}^w_{\\mathit{ld}'}|\\mathit{ld, m})$ where $W = \\{\\mathit{me}\\}$ and $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{m}}, \\mathit{os}^w_{\\mathit{m}'}|\\mathit{ld, m})$, where $W = \\{\\mathit{le}\\}$.\n", + "The above heatmaps plot the joint distributions arising from necessity and sufficient interventions, particularly $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{ld}}, \\mathit{os}^w_{\\mathit{ld}'}|\\mathit{ld, m})$ where $W = \\{\\mathit{me}\\}$ and $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{m}}, \\mathit{os}^w_{\\mathit{m}'}|\\mathit{ld, m})$, where $W = \\{\\mathit{le}\\}$. \n", "\n", - "It is evident from the plot above that the counterfactual for lockdown has more probability mass in the top right quadrant (low overshoot in the necessity world and high overshoot in the sufficient world). This gives us a clearer picture into why lockdown has higher causal role in the overshoot being too high as compared to masking." + "The key to interpreting this histogram is the fact that we plot two different counterfactual distributions against each other here. Along the x-axis we show what overshoot a model expects under the sufficiency intervention. And here, we see that for both interventions the model expects most of the density to lie above the overshoot threshold (red). So both intereventions are, approximately, sufficient. On the y-axis, however, we show the counterfactual distribution in the necessity world, where the intervention does **not** take place. Note that the scale starts in the upper left corner. This means that the higher the density mass is on the plot, the **lower** is the expected overshoot without that intervention, i.e. \"the more necessary\" a given intervention is for the outcome. Here, only lockdown seems to play a necessary role. Ideally, however, we are interested in whether an intervention is **both necessary and sufficient**, the probablity of which is the proportion of density mass in the upper-right quandrant determined by the dashed red lines. It is evident from the plot above that the counterfactual for lockdown has more probability mass in the top right quadrant (low overshoot in the necessity world and high overshoot in the sufficient world). This gives us a clearer picture into why lockdown has higher causal role in the overshoot being too high as compared to masking." ] }, { From 903b0137af4b0504055934882ad708ea253292bd Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Mon, 9 Dec 2024 08:41:01 -0500 Subject: [PATCH 096/111] add refs for degree of responsibility --- docs/source/explainable_sir.ipynb | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index a1be7e72..2710968f 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -861,7 +861,10 @@ "\n", "1. Degree of responsibility of lockdown: $\\sum_{w \\subseteq W} \\sum_{\\mathit{ld} \\in C} P_w(w) P_a(C | \\mathit{ld} \\in C) \\cdot P(\\mathit{oth}^w_{C}, \\mathit{oth}'^w_{C'} | \\mathit{ld}, m)$\n", "\n", - "2. Degree of responsibility of mask: $\\sum_{w \\subseteq W} \\sum_{\\mathit{m} \\in C} P_w(w) P_a(C | \\mathit{m} \\in C) \\cdot P(\\mathit{oth}^w_{C}, \\mathit{oth}'^w_{C'} | \\mathit{ld}, m)$" + "2. Degree of responsibility of mask: $\\sum_{w \\subseteq W} \\sum_{\\mathit{m} \\in C} P_w(w) P_a(C | \\mathit{m} \\in C) \\cdot P(\\mathit{oth}^w_{C}, \\mathit{oth}'^w_{C'} | \\mathit{ld}, m)$\n", + "\n", + "\n", + "For earlier accounts of the degree of responsibility in the original actual causality framework, see Chapter 6. of *Actual Causality* by Joseph Y. Halpern. While the above is on an implementation of the original notion, it is definitely inspired by the discussion there. " ] }, { From 52343da12bbc4e06a745beae2e21061ad87c3c14 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Mon, 9 Dec 2024 09:25:15 -0500 Subject: [PATCH 097/111] explanation of low prob values wip --- docs/source/explainable_sir.ipynb | 94 +++++++++++++++++++------------ 1 file changed, 58 insertions(+), 36 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 2710968f..fbe7f9cd 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -106,7 +106,7 @@ "metadata": {}, "source": [ "\n", - "We start with building the epidemiological SIR (Susceptible, Infected, Recovered) model, one step at a time. We first encode the deterministic SIR dynamics. Then we add uncertainty about the parameters that govern these dynamics - $\\beta$ and $\\gamma$. These parameters have been described in detail in the [dynamical systems tutorial](https://basisresearch.github.io/chirho/dynamical_intro.html). We then incorporate the resulting model into a more complex causal model that involves two policy mechanisms: imposing lockdown and masking restrictions.\n", + "We start with building the epidemiological SIR (Susceptible, Infected, Recovered) model, one step at a time. We first encode the deterministic SIR dynamics. Then we add uncertainty about the parameters that govern these dynamics: $\\beta$ and $\\gamma$. These parameters have been described in detail in the [dynamical systems tutorial](https://basisresearch.github.io/chirho/dynamical_intro.html). We then incorporate the resulting model into a more complex causal model that involves two policy mechanisms: imposing lockdown and masking restrictions.\n", "\n", "Our outcome of interest is overshoot, the proportion of the population that remains susceptible after the epidemic peaks but eventually becomes infected as the epidemic continues. One way to compute it is to:\n", "\n", @@ -115,9 +115,9 @@ "3. Find the proportion of susceptible individuals (and have thus never been infected) at the end of the logging period, `S_final`.\n", "4. Return the difference between proportions of peak and final susceptible individuals, `S_peak - S_final`.\n", "\n", - "This quantity is of interest because epidemic mitigation policies often have multiple goals that must be balanced. One goal is to increase `S_final`, i.e., to limit the total number of infected individuals. Another goal is to limit the number of infected individuals at the peak of the epidemic to avoid overwhelming the healthcare system. A further goal is to minimize the proportion of the population that becomes infected after the peak, that is, the overshoot, to reduce healthcare and economic burdens. Balancing these objectives involves making trade-offs.\n", + "Epidemic mitigation policies often have multiple goals that must be balanced. One typical goal is to increase `S_final`, i.e., to limit the total number of infected individuals. Another goal is to limit the number of infected individuals at the peak of the epidemic to avoid overwhelming the healthcare system. Yet another goal is to minimize the proportion of the population that becomes infected after the peak, that is, the overshoot, to reduce healthcare and economic burdens. Balancing these objectives involves making trade-offs. To properly think through such trade-offs, one needs a decent counterfactual picture of what these outcomes might be under various interventions. In what follows, we focus on overshoot.\n", "\n", - "Suppose we are working under the constraint that the overshoot should be lower than 24% of the population, and we implement two public health policies, lockdown and masking, which together seem to lead to the overshoot being too high. Only one of them is responsible, and we are interested in being able to identify which one. " + "Suppose we are working under the constraint that the overshoot should be lower than 24% of the population, and we implement two public health policies, lockdown and masking, which together seem to lead to the overshoot being too high. As an example, we will work with an example when only one of them holds most of the responsibility, and we are interested in being able to identify which one. " ] }, { @@ -129,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -137,7 +137,6 @@ "# dI = beta * SI - gamma * I\n", "# dR = gamma * I\n", "\n", - "\n", "class SIRDynamics(pyro.nn.PyroModule):\n", " def __init__(self, beta, gamma):\n", " super().__init__()\n", @@ -169,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -218,7 +217,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The number $0.15$ is the overshoot you get if $\\beta = 0.03, \\gamma = 0.5$. This value is observed by simulating the SIR dynamics model with these values and calculating the overshoot directly.\n", + "The number $0.15$ is the overshoot you get if $\\beta = 0.03, \\gamma = 0.5$, which, say, are the true parameters of the epidemic. This value is observed by simulating the SIR dynamics model with these values and calculating the overshoot directly.\n", "\n", "Also, note that the above dynamical system introduces the variables: `S` - susceptible, `I` - infected, `R` - recovered, and `l` - effect of the intervention. These variables evolve over time and their dynamics are captured by the model. As we add features to our model, we also add new variables to this list. Further on in the notebook, we will describe the probabilities we compute in terms of these variables." ] @@ -241,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -276,13 +275,13 @@ "metadata": {}, "source": [ "\n", - "Now we incorporate the Bayesian SIR model into a larger model that includes the effect of two different policies, lockdown and masking, where each can be implemented with $50\\%$ probability. These probabilities won't really matter, as we will be intervening on these, the sampling is mainly used to register the parameters with Pyro. The sampling here, hower, illustrates that the model in principle could incorporate uncertainties of this sort. We encode their efficiencies which further affect the model. Crucially, these efficiencies interact in a fashion resembling the structure of the stone-throwing example we discussed in the tutorial on categorical variables. If a lockdown is present, this limits the impact of masking as agents interact less and so masks have fewer opportunities to block anything. We assume the situation is asymmetric: masking has no impact on the efficiency of lockdown. The model also computes `overshoot` and `os_too_high` for further analysis.\n", + "Now we incorporate the Bayesian SIR model into a larger model that includes the effect of two different policies, lockdown and masking, where each can be implemented with $50\\%$ probability. These probabilities won't really matter, as we will be intervening on these, the sampling is mainly used to register the parameters with Pyro. It does, hower, illustrate that the model in principle could incorporate uncertainties of this sort. We encode the intervention efficiencies which further affect the model. Crucially, these efficiencies interact in a fashion resembling the structure of the stone-throwing example we discussed in the tutorial on categorical variables. If a lockdown is present, this limits the impact of masking as agents interact less and so masks have fewer opportunities to block anything. We assume the situation is asymmetric: masking has no impact on the efficiency of lockdown. The model also computes `overshoot` and `os_too_high` for further analysis.\n", "\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -306,7 +305,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -387,7 +386,7 @@ "\n", "Suppose now we introduced both policies, and this resulted in an overshoot. What intuitively is the case is that lockdown limited the efficiency of masking, and it was in fact the lockdown that in this particular context caused the overshoot (this is consistent with saying that in the context where only masking has been implemented, masking would be responsible for the resulting overshoot being too high).\n", "\n", - "We might try to use the but-for analysis to identify which of the policies causes overshoot to be too high. To do so, we investigate the following four scenarios:\n", + "We might try to use the but-for analysis to identify which of the policies causes overshoot to be too high. Recall from our previous tutorial, that the key idea behind it is to evaluate whether \"$B$ wouldn't have been the case but for $A$ happening\". This query invites one to ask what would happeni if a candidate cause was removed, and declares it responsible just in case the outcome is different in that scenario. To apply it in this case, we investigate the following four scenarios:\n", "\n", "1. None of the policies were applied\n", "2. Both lockdown and masking were enforced\n", @@ -461,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -623,7 +622,7 @@ "\n", "To identify which of `lockdown` and `mask` is the cause, we analyze the models where only one of the policies was imposed. Interestingly, the effect of the interventions is somewhat nuanced. Implementing both interventions increases the risk of overshooting as compared to the no-intervention model, but individual interventions would have even worse consequences, which means that the two interventions while jointly increasing the risk to some extent mitigate each other's contribution to that risk as well.\n", "\n", - "Crucially, the analysis does not allow us to distinguish the intuitive role that the lockdown played, as opposed to masking (whose impact has been limited by the presence of lockdown). So, we need a more fine-grained analysis where we not only control the variables being intervened on (that is, the policies) but also pay attention to what context we are in. We achieve that level of sensitivity by stochastically keeping part of the context (that is, other variables in the model) fixed (see the tutorial for categorical variables for a more extensive explanation of this method and simpler examples). The key idea is that starting with the scenario in which both interventions have been implemented, there is a context such that if we keep it fixed, removing the lockdown would significantly lower the overshoot, but there is no context that we could keep fixed such that if in that context we remove the masking policy, the overshoot would decrease. In the next section, we show how this analysis can be carried out with the help of `SearchForExplanation`." + "Crucially, the analysis does not allow us to distinguish the intuitive role that the lockdown played, as opposed to masking (whose impact has been limited by the presence of lockdown). So, we need a more fine-grained analysis where we not only control the variables being intervened on (that is, the policies) but also pay attention to what context we are in. We achieve that level of sensitivity by stochastically keeping part of the context (that is, other variables in the model) fixed (see the tutorial for categorical variables for a more extensive explanation of this method and simpler examples). The key idea is that starting with the actual scenario in which both interventions have been implemented, there is a context such that if we keep it fixed, removing the lockdown would significantly lower the overshoot, but there is no context that we could keep fixed such that if in that context we remove the masking policy, the overshoot would decrease. In the next section, we show how this analysis can be carried out with the help of `SearchForExplanation`." ] }, { @@ -637,7 +636,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Before we dive into the code below, let us first define some notation. We use small case abbreviations to refer to the value of the variables under consideration. For example, $\\mathit{ld}$ refers to `lockdown=1` and $\\mathit{ld}'$ refers to `lockdown=0`. We place interventions in the subscripts, for instance, $\\mathit{os}_{\\mathit{ld}}$ refers to the `overshoot` under the intervention that `lockdown=1`. Later on in the notebook, we also employ contexts that are kept fixed in the intervened worlds. We place these contexts in the superscript. For example, $\\mathit{os}_{\\mathit{ld}}^{\\mathit{me}}$ refers to the variable `overshoot` when `lockdown` was intervened to be 1 and `mask_efficiency` was kept fixed at its factual value. \n", + "Before we dive into the code below, let us first define some notation. We use small case abbreviations to refer to the value of the variables under consideration. For example, $\\mathit{ld}$ refers to `lockdown=1` and $\\mathit{ld\\,}'$ refers to `lockdown=0`. We place interventions in the subscripts, for instance, $\\mathit{os}_{\\mathit{ld}}$ refers to the `overshoot` under the intervention that `lockdown=1`. Later on in the notebook, we also employ contexts that are kept fixed in the intervened worlds. We place these contexts in the superscript. For example, $\\mathit{os}_{\\mathit{ld}}^{\\mathit{me}}$ refers to the variable `overshoot` when `lockdown` was intervened to be 1 and `mask_efficiency` was kept fixed at its factual value. \n", "\n", "We use $P(.)$ to denote the distribution described by the model (`overshoot_model` in this notebook). We also induce a distribution over the sets of potential interventions and the sets of context nodes potentially kept fixed. We denote these distributions by $P_a(.)$ and $P_w(.)$ respectively. As an example, $P_a(\\{ld\\})$ refers to the probability that the set of interventions under consideration is $\\{ld\\}$. These distributions are determined using the parameters `antecedent_bias` and `witness_bias` given to the handler `SearchForExplanation`. For more details, please refer to the [documentation](https://basisresearch.github.io/chirho/explainable.html#chirho.explainable.handlers.explanation.SearchForExplanation).\n", "\n", @@ -703,20 +702,20 @@ "3. `alternatives`: We provide `lockdown=0` and `mask=0` as alternative values.\n", "4. `witnesses`: We include `mask_efficiency` and `lockdown_efficiency` as candidates to be included in the contexts potentially to be kept fixed.\n", "5. `consequents`: We put `os_too_high=1` as the outcome whose causes we wish to analyze.\n", - "6. `antecedent_bias`, `witness_bias`,: We set these parameters to have equal probabilities of intervening on cause candidates, and to slightly prefer smaller witness sets. Please refer to the documentation of `SearchForExplanation` for more details.\n", - "7. `consequent_scale` is set to effectively include values near 0 and 1 depending on whether the binary outcomes differ across counterfactual worlds." + "6. `antecedent_bias`, `witness_bias`: We set these parameters to have equal probabilities of intervening on cause candidates, and to slightly prefer smaller witness sets. Please refer to the documentation of `SearchForExplanation` for more details.\n", + "7. `consequent_scale` is set to effectively lead to probabilities near 0 and 1 depending on whether the binary outcomes differ across counterfactual worlds." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.1328)\n" + "tensor(0.1357)\n" ] } ], @@ -750,14 +749,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The above probability itself is not directly related to our query. It is the probability that the overshoot is both too high in the antecedents-intervened world and not too high in the alternatives-intervened world, where antecedent interventions are preempted with probabilities $0.5$ at each site, and witnesses are kept fixed at the observed values with probability $0.5+0.2$ at each site (see the tutorial on categorical variables for an explanation of why this stochasticity is in general useful). \n", + "The handler performs two stochastic searches at the same time (which antecedents to intervene on? which witnesses to keep at the actual values?) while inspecting the distribution of the outcome under these scenarios. For this reason, the most direct probabilistic summaries are impacted by both uncertainties present in the original model *and* the probabilistic search distributions. Since the way the latter are set up, the users might be interested in them (for instance, they might prefer smaller causal sets, or they may prefer higher context sensitivity, both of which can be tuned and are mirrored in the resulting log probabilities). But also, this means there is some nuance as to how to interpret and how to decompose them. We illustrate the process for the sake of users' understaning.\n", "\n", - "However, more fine-grained queries can be answered using the 10000 samples we have drawn in the process. We first compute the probabilities that different sets of antecedent candidates have a causal effect over `os_too_high` conditioned on the fact that lockdown and masking were actually imposed in the factual world." + "The above probability itself, which potentially of interest in some applications, is only related to our current query. It is the probability that the overshoot is both too high in the antecedents-intervened world and not too high in the alternatives-intervened world, where antecedent interventions are preempted with probabilities $0.5$ at each site, and witnesses are kept fixed at the observed values with probability $0.5+0.2$ at each site (see the tutorial on categorical variables for an explanation of why this stochasticity is in general useful). Given how search and model stochasticities are composed here, we expect these values to not be to high - but them being not too high does not mean low causal role.\n", + "\n", + "Now, more fine-grained queries can be answered using the 10000 samples we have drawn in the process. We first compute the probabilities that different sets of antecedent candidates have a causal effect over `os_too_high` conditioned on the fact that lockdown and masking were actually imposed in the factual world." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -780,7 +781,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We specifically compute the following four probabilities. In each of the computations, we condition on lockdown and masking actually being implemented in the factual world. Given this factual world, each equation represents the probability that a given collection of policy interventions would have changed whether the overshoot was too high. For instance, in equation 1., we assume lockdown (`ld`) and masking (`m`) have been implemented, and we ask about the joint probability that both (a) removing both interventions, i.e. intervening for both `ld` and `m` to not happen - which we mark by the apostrophe - would lead to `oth` not happening, $\\mathit{oth}'_{\\mathit{ld}', m'}$, and (b) intervening for both to happen would lead to `oth`, $\\mathit{oth}_{\\mathit{ld}, m}$. Given the stochasticity between these interventions and the outcome, computing these probabilities is non-trivial. Note that in computing these probabilities, we also marginalize over all the contexts that potentially can be kept fixed, i.e. all possible subsets of $W = \\{\\mathit{le}, \\mathit{me}\\}$\n", + "We specifically compute the following four probabilities. In each of the computations, we condition on lockdown and masking actually being implemented in the factual world. Given this factual world, each equation represents the probability that a given collection of policy interventions would have changed whether the overshoot was too high. For instance, in equation 1., we assume lockdown (`ld`) and masking (`m`) have been implemented, and we ask about the joint probability that both (a) removing both interventions, i.e. intervening for both `ld` and `m` to not happen - which we mark by the apostrophe - would lead to `oth` not happening, $\\mathit{oth}'_{\\mathit{ld'}, m'}$, and (b) intervening for both to happen would lead to `oth`, $\\mathit{oth}_{\\mathit{ld}, m}$. Given the stochasticity between these interventions and the outcome, computing these probabilities is non-trivial. Note that in computing these probabilities, we also marginalize over all the contexts that potentially can be kept fixed, i.e. all possible subsets of $W = \\{\\mathit{le}, \\mathit{me}\\}$\n", "\n", "1. $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{oth}^w_{\\mathit{ld}, m}, \\mathit{oth}'^w_{\\mathit{ld}', m'} | \\mathit{ld}, m)$\n", "\n", @@ -793,47 +794,68 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 0.24283304810523987\n", - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 0.2902735471725464\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 2.38618436121385e-09\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 2.636655116461384e-09\n" + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 0.22131147980690002\n", + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 0.32633280754089355\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 0.005042018368840218\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 3.008263638193398e-09\n", + "both interventions executed 0.22131147980690002 \n", + " only lockdown executed 0.32633280754089355 \n", + " only masking executed 0.005042018368840218 \n", + " no interventions executed 3.008263638193398e-09 \n", + "\n" ] } ], "source": [ "# no preemptions on lockdown and masking, i.e. both interventions executed\n", - "_ = compute_prob(\n", + "both = compute_prob(\n", " importance_tr,\n", " log_weights,\n", " {\"__cause____antecedent_lockdown\": 0, \"__cause____antecedent_mask\": 0, \"mask\": 1, \"lockdown\": 1},\n", ")\n", "\n", - "# only lockdown executed, masking preempted\n", - "_ = compute_prob(\n", + "\n", + "# # only lockdown executed, masking preempted\n", + "lockdown = compute_prob(\n", " importance_tr,\n", " log_weights,\n", " {\"__cause____antecedent_lockdown\": 0, \"__cause____antecedent_mask\": 1, \"mask\": 1, \"lockdown\": 1},\n", ")\n", "\n", - "# only masking executed, lockdown preempted\n", - "_ = compute_prob(\n", + "# # only masking executed, lockdown preempted\n", + "masking = compute_prob(\n", " importance_tr,\n", " log_weights,\n", " {\"__cause____antecedent_lockdown\": 1, \"__cause____antecedent_mask\": 0, \"mask\": 1, \"lockdown\": 1},\n", ")\n", "\n", - "# no interventions executed\n", - "_ = compute_prob(\n", + "# # no interventions executed\n", + "no_interventions = compute_prob(\n", " importance_tr,\n", " log_weights,\n", " {\"__cause____antecedent_lockdown\": 1, \"__cause____antecedent_mask\": 1, \"mask\": 1, \"lockdown\": 1},\n", + ")\n", + "\n", + "print(\n", + " \"both interventions executed\", \n", + " both, \"\\n\",\n", + "\n", + " \"only lockdown executed\", \n", + " lockdown, \"\\n\",\n", + "\n", + " \"only masking executed\",\n", + " masking, \"\\n\",\n", + "\n", + " \"no interventions executed\",\n", + " no_interventions, \"\\n\",\n", + "\n", ")" ] }, @@ -841,7 +863,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As the above probabilities show, `{lockdown=1}` has the most causal role in the overshoot being too high among all the possible sets of causes when both lockdown and masking were imposed." + "As the above probabilities show, `{lockdown=1}` has the most causal role in the overshoot being too high among all the possible sets of causes when both lockdown and masking were imposed. Note, however, that the search probabilities still come into the computation of those values, so they should not be interpreted as, say, \"the probability that the overshoot will be too high in the most damaging possible context\". " ] }, { From 8e2ede677ad56ccf5c2092ad60c632308a0f47de Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Thu, 19 Dec 2024 03:04:06 -0500 Subject: [PATCH 098/111] react to remark about "relatively low" --- docs/source/explainable_sir.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index fbe7f9cd..541693f6 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -386,7 +386,7 @@ "\n", "Suppose now we introduced both policies, and this resulted in an overshoot. What intuitively is the case is that lockdown limited the efficiency of masking, and it was in fact the lockdown that in this particular context caused the overshoot (this is consistent with saying that in the context where only masking has been implemented, masking would be responsible for the resulting overshoot being too high).\n", "\n", - "We might try to use the but-for analysis to identify which of the policies causes overshoot to be too high. Recall from our previous tutorial, that the key idea behind it is to evaluate whether \"$B$ wouldn't have been the case but for $A$ happening\". This query invites one to ask what would happeni if a candidate cause was removed, and declares it responsible just in case the outcome is different in that scenario. To apply it in this case, we investigate the following four scenarios:\n", + "We might try to use the but-for analysis to identify which of the policies causes overshoot to be too high. Recall from our previous tutorial, that the key idea behind it is to evaluate whether \"$B$ wouldn't have been the case but for $A$ happening\". This query invites one to ask what would happen if a candidate cause was removed, and declares it responsible just in case the outcome is different in that scenario. To apply it in this case, we investigate the following four scenarios:\n", "\n", "1. None of the policies were applied\n", "2. Both lockdown and masking were enforced\n", @@ -749,9 +749,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The handler performs two stochastic searches at the same time (which antecedents to intervene on? which witnesses to keep at the actual values?) while inspecting the distribution of the outcome under these scenarios. For this reason, the most direct probabilistic summaries are impacted by both uncertainties present in the original model *and* the probabilistic search distributions. Since the way the latter are set up, the users might be interested in them (for instance, they might prefer smaller causal sets, or they may prefer higher context sensitivity, both of which can be tuned and are mirrored in the resulting log probabilities). But also, this means there is some nuance as to how to interpret and how to decompose them. We illustrate the process for the sake of users' understaning.\n", + "The handler performs two stochastic searches at the same time (which antecedents to intervene on? which witnesses to keep at the actual values?) while inspecting the distribution of the outcome under these scenarios. For this reason, the most direct probabilistic summaries are impacted by both uncertainties present in the original model *and* the probabilistic search distributions. Since the way the latter are set up, the users might be interested in them (for instance, they might prefer smaller causal sets, or they may prefer higher context sensitivity, both of which can be tuned and are mirrored in the resulting log probabilities). But also, this means there is some nuance as to how to interpret and how to decompose them. Let's talk this through.\n", "\n", - "The above probability itself, which potentially of interest in some applications, is only related to our current query. It is the probability that the overshoot is both too high in the antecedents-intervened world and not too high in the alternatives-intervened world, where antecedent interventions are preempted with probabilities $0.5$ at each site, and witnesses are kept fixed at the observed values with probability $0.5+0.2$ at each site (see the tutorial on categorical variables for an explanation of why this stochasticity is in general useful). Given how search and model stochasticities are composed here, we expect these values to not be to high - but them being not too high does not mean low causal role.\n", + "The above probability itself, which potentially is of interest in some applications when one-number summaries are a good enough approximation, is only related to our current query. It is the probability that the overshoot is both too high in the antecedents-intervened world and not too high in the alternatives-intervened world, where antecedent interventions are preempted with probabilities $0.5$ at each site, and witnesses are kept fixed at the observed values with probability $0.5+0.2$ at each site (see the tutorial on categorical variables for an explanation of why this stochasticity is in general useful). Given how search and model stochasticities are composed here, we expect these values to not be very high - but that being so, does not mean low causal role.\n", "\n", "Now, more fine-grained queries can be answered using the 10000 samples we have drawn in the process. We first compute the probabilities that different sets of antecedent candidates have a causal effect over `os_too_high` conditioned on the fact that lockdown and masking were actually imposed in the factual world." ] @@ -921,7 +921,7 @@ "source": [ "As the output shows, `lockdown=1` has a higher degree of responsibility than `mask=1`.\n", "\n", - "The reader might have the impression that the numbers are relatively low: what one needs to remember though, is that these are computed with stochastic witness preemptions in the background and that in this model the witnesses are downstream from the interventions, so part of the time some of the interventions are blocked as their effects are stochastically chosen to be witnesses and fixed at the actual values. The role of witnesses will be investigated in more detail in the next section." + "The reader might have the impression that the numbers are relatively low: what one needs to remember though is (1) our explanation of how those one-number summaries have stochastic search probabilities mixed in (which may be of interest, as they track causal set size and context sensitivity), and (2) in this model the witnesses are downstream from the interventions, so part of the time some of the interventions are blocked as their effects are stochastically chosen to be witnesses and fixed at the actual values. We go beyond these one-number summaries and investigate the role of witnesses in more detail in the next section." ] }, { From 4e32370a07c6d6efa94de677b9f0c879337f15c2 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Thu, 19 Dec 2024 03:06:40 -0500 Subject: [PATCH 099/111] react to remark on "filtering for relevant context" --- docs/source/explainable_sir.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 541693f6..4b97fc58 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -1117,7 +1117,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The above histogram also takes into account the context that is being kept fixed. If `lockdown` is being intervened on, keeping `lockdown_efficiency` fixed would hinder the effect of the intervention. Thus to obtain the relevant samples, we also filter for the appropriate context. Once we have filtered for the context, we take the samples and plot them as density above. The histogram above plots three quantities. It plots $P(\\mathit{os} | \\mathit{ld}, m)$ as the factual distribution of overshoot, $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{ld}'} | \\mathit{ld}, m)$ as `counterfactual_lockdown` where $W = \\{\\mathit{me}\\}$ and $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{m}'} | \\mathit{ld}, m)$ as `counterfactual_mask` where $W = \\{\\mathit{le}\\}$. These distributions help in comparing how necessity interventions for the two antecedents affect the overshoot." + "The above histogram also takes into account the context that is being kept fixed. If `lockdown` is being intervened on, keeping `lockdown_efficiency` fixed would hinder the effect of the intervention. Thus to obtain the relevant samples, we also condtition on the appropriate context by rejecting samples. Once we have done so, we take the samples and plot them as density above. The histogram above plots three quantities. It plots $P(\\mathit{os} | \\mathit{ld}, m)$ as the factual distribution of overshoot, $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{ld}'} | \\mathit{ld}, m)$ as `counterfactual_lockdown` where $W = \\{\\mathit{me}\\}$ and $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{m}'} | \\mathit{ld}, m)$ as `counterfactual_mask` where $W = \\{\\mathit{le}\\}$. These distributions help in comparing how necessity interventions for the two antecedents affect the overshoot." ] }, { From 1d698ab021ec50806ba978a75e35a10251e85e8c Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Thu, 19 Dec 2024 03:14:49 -0500 Subject: [PATCH 100/111] added assumptions --- docs/source/explainable_sir.ipynb | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 4b97fc58..98a511e6 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -120,6 +120,28 @@ "Suppose we are working under the constraint that the overshoot should be lower than 24% of the population, and we implement two public health policies, lockdown and masking, which together seem to lead to the overshoot being too high. As an example, we will work with an example when only one of them holds most of the responsibility, and we are interested in being able to identify which one. " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Assumptions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We make a range of assumptions in this tutorial:\n", + "\n", + "(1) all dynamics in the system are deterministic, meaning that the system's behavior can be precisely described by its current state and the governing equations without random variability, and all the stochasticity is delegated to parameter uncertainty and perhaps observational noise.\n", + "\n", + "(2) The dynamical system model is known except for the prameters, and accurately captures the process we want to model.\n", + "\n", + "(3) There are no confounders between the model parameters, i.e. these are not systematically influenced by unobserveed variables in a way that would bias our causal effect estimates.\n", + "\n", + "There are many models where we could relax or abandon these assumptions, which would lead to other modeling decisions. The general point, however, of how to use *a* model with `SearchForCauses` would, *mutatis mutandis*, hold as soon as one has a causal model." + ] + }, { "cell_type": "markdown", "metadata": {}, From 5fb5d98093b1dcfd7c68ced586094876c96dc165 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Thu, 19 Dec 2024 03:29:40 -0500 Subject: [PATCH 101/111] reacted to remark on importance sampling --- docs/source/explainable_sir.ipynb | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 98a511e6..c56536ae 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -714,6 +714,19 @@ " return _wrapped_model" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The key idea here is that once we transform the original model using `SearchForExplanation` the trace will not only contain the original sites, but also sites representing witness preemptions, sites representing which antecedents were intervened on, and sites representing differences between the values of the outcome variable across counterfactual worlds. At a high level, we'd be using a trace with the following structure:\n", + "\n", + "| A | Witness A | Antecedent A | B | Witness B | Antecedent B | ... | Outcome | Outcome Diff |\n", + "|---|-----------|--------------|---|-----------|--------------|-----|---------|--------------|\n", + "| | | | | | | | | ... |\n", + "\n", + "where each of these is tracked in three worlds: actual, one with the necessity interventions/preemptions, and one with the sufficiency preemptions. This collection of values will come with a corresponding \"table\" of log probabilities, which will be the usual log probs wherever values are sampled from a distribution, and soft equality or soft non-equality penalty terms expressed in the log prob space. This way, we can exploit the already existing sampling method to obtain samples and one-number log-prob summaries that capture answers to counterfactual queries. Potentially, there are other ways to run inference with the transformed models, we will look at conditioning by rejection sampling, but will not discuss other inference methods in this tutorial.\n" + ] + }, { "cell_type": "markdown", "metadata": {}, From a3877ea8b2217a3c0b385f87a1ac2b8fa61e02f4 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Thu, 19 Dec 2024 03:33:37 -0500 Subject: [PATCH 102/111] added an explanation about alternatives --- docs/source/explainable_sir.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index c56536ae..5088c95c 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -734,8 +734,8 @@ "Then, we set up the query as follows:\n", "1. `supports`: We extract the support of each distribution in the model using `ExtractSupports`. We also encode our knowledge that `os_too_high` is a Boolean. Note that constraints for deterministic nodes currently need to be specified manually when using `ExtractSupports`, as we do here.\n", "2. `antecedents`: We postulate `lockdown=1` and `mask=1` as possible causes.\n", - "3. `alternatives`: We provide `lockdown=0` and `mask=0` as alternative values.\n", - "4. `witnesses`: We include `mask_efficiency` and `lockdown_efficiency` as candidates to be included in the contexts potentially to be kept fixed.\n", + "3. `alternatives`: We provide `lockdown=0` and `mask=0` as alternative values. If we don't specify them, the search simply samples the values using uniform/wide distributions for those sites - which makes sense if the site has continuous support or if we only know the outcome but not the antecedent values. For this simpler application, however, searching through `lockdown=1` or `mask=1` as alternatives would not conceptually do, as we already know what the alternative values should be.\n", + "4. witnesses`: We include `mask_efficiency` and `lockdown_efficiency` as candidates to be included in the contexts potentially to be kept fixed.\n", "5. `consequents`: We put `os_too_high=1` as the outcome whose causes we wish to analyze.\n", "6. `antecedent_bias`, `witness_bias`: We set these parameters to have equal probabilities of intervening on cause candidates, and to slightly prefer smaller witness sets. Please refer to the documentation of `SearchForExplanation` for more details.\n", "7. `consequent_scale` is set to effectively lead to probabilities near 0 and 1 depending on whether the binary outcomes differ across counterfactual worlds." From e020cd32909eaa0c58a08c13fec482f591abd21f Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Thu, 19 Dec 2024 03:46:05 -0500 Subject: [PATCH 103/111] added explanation of "necessity" and "sufficiency" worlds --- docs/source/explainable_sir.ipynb | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 5088c95c..75d90d02 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -662,6 +662,8 @@ "\n", "We use $P(.)$ to denote the distribution described by the model (`overshoot_model` in this notebook). We also induce a distribution over the sets of potential interventions and the sets of context nodes potentially kept fixed. We denote these distributions by $P_a(.)$ and $P_w(.)$ respectively. As an example, $P_a(\\{ld\\})$ refers to the probability that the set of interventions under consideration is $\\{ld\\}$. These distributions are determined using the parameters `antecedent_bias` and `witness_bias` given to the handler `SearchForExplanation`. For more details, please refer to the [documentation](https://basisresearch.github.io/chirho/explainable.html#chirho.explainable.handlers.explanation.SearchForExplanation).\n", "\n", + "At a high level, we will transform the original model into one that runs through three \"possible worlds\" at the same time. (1) the \"actual world\", where the model is played in its original unintervened form, (2) the \"necessity world\", where the intention is to investigate the extent to which changing antecedent values to alternative values would change the outcome, and particular interventional settings are rewarded for the outcome being different from the actual value, and (3) the \"sufficiency world\", where we investigate the result of fixing the causal candidates at their actual values, and investigating the extent to which they ensure the outcome being as is (accounting for stochasticity involving other sites), rewarding settings for proximity to the actual value. All this happens across multiple runs which contain different interventional settings to perform search through causal nodes and witnesses, and ultimately we will care about causal candidates that are both necessary and sufficient, that is in a qantity of interest that would be obtained by accounting for information obtained across those possible worlds. If this seems confusing, please take a look at more detailed explanations in the tutorial on explanation with categorical variables.\n", + "\n", "Now let's dive into the code, using this notation to describe the quantities we are computing. " ] }, From d25988a6f29a4fffe92094929034992f31f9221c Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Thu, 19 Dec 2024 04:00:50 -0500 Subject: [PATCH 104/111] added conclusion --- docs/source/explainable_sir.ipynb | 59 +++++++++++++++++++------------ 1 file changed, 37 insertions(+), 22 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 75d90d02..82c134aa 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -13,7 +13,7 @@ "source": [ "The **Explainable Reasoning with ChiRho** module aims to provide a unified, principled approach to computations of causal explanations. We showed in an earlier [tutorial](https://basisresearch.github.io/chirho/explainable_categorical.html) how ChiRho provides `SearchForExplanation`, an effect handler that transforms causal probabilistic programs to compute causal explanations and other related causal queries. In that tutorial we focused on discrete variables. In this notebook, we illustrate the usage of `SearchForExplanation` for causal models with continuous random variables in the context of a dynamical system.\n", "\n", - "We take an epidemiological dynamical system model (described in more detail in our [dynamical systems tutorial](https://basisresearch.github.io/chirho/dynamical_intro.html)) and show how the but-for analysis is not sufficiently fine-grained to explain the effects of different policies during a pandemic. Next, we illustrate how various causal explanation queries can be computed by combining ChiRho's `SearchForExplanation` and Pyro's probabilistic inference. We also demonstrate how more detailed causal queries can be answered by post-processing the samples obtained using the effect handler. " + "We take an epidemiological dynamical system model (described in more detail in our [dynamical systems tutorial](https://basisresearch.github.io/chirho/dynamical_intro.html)), expand it to a causal model with two interacting policies: lockdown and masking, where the former dampens the effect of the latter (the stronger the lockdown, the less the masking matters as people interact less anyway). Suppose both policies have been implemented, resulting in an undesirable overshoot (roughly, the ratio of people who infected after the peak of the epidemic). We want to be able to isolate the role of lockdown and masking in this outcome - in particular, we want to recover the intuition that since lockdown dampened the masking effect, it was lockdown that caused the overshoot, even if masking alone without lockdown would also have led to overshoot. We show how the but-for analysis is not sufficiently fine-grained to explain the effects of different policies during a pandemic. Next, we illustrate how the analysis can be run and various causal explanation queries can be computed by combining ChiRho's `SearchForExplanation` and Pyro's probabilistic inference. We also demonstrate how more detailed causal queries can be answered by post-processing the samples obtained using the effect handler. " ] }, { @@ -151,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -190,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -262,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -303,7 +303,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -327,7 +327,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -482,7 +482,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -745,14 +745,14 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.1357)\n" + "tensor(0.1328)\n" ] } ], @@ -795,7 +795,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -831,21 +831,21 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 0.22131147980690002\n", - "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 0.32633280754089355\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 0.005042018368840218\n", - "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 3.008263638193398e-09\n", - "both interventions executed 0.22131147980690002 \n", - " only lockdown executed 0.32633280754089355 \n", - " only masking executed 0.005042018368840218 \n", - " no interventions executed 3.008263638193398e-09 \n", + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 0.24283304810523987\n", + "{'__cause____antecedent_lockdown': 0, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 0.2902735471725464\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 0, 'mask': 1, 'lockdown': 1} 2.38618436121385e-09\n", + "{'__cause____antecedent_lockdown': 1, '__cause____antecedent_mask': 1, 'mask': 1, 'lockdown': 1} 2.636655116461384e-09\n", + "both interventions executed 0.24283304810523987 \n", + " only lockdown executed 0.2902735471725464 \n", + " only masking executed 2.38618436121385e-09 \n", + " no interventions executed 2.636655116461384e-09 \n", "\n" ] } @@ -1382,12 +1382,12 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
      " ] @@ -1443,6 +1443,7 @@ "\n", "sns.despine()\n", "plt.tight_layout()\n", + "plt.suptitle(\"Counterfactual heatmap of overshoot under necessity and sufficiency interventions\")\n", "plt.show()\n" ] }, @@ -1452,7 +1453,7 @@ "source": [ "The above heatmaps plot the joint distributions arising from necessity and sufficient interventions, particularly $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{ld}}, \\mathit{os}^w_{\\mathit{ld}'}|\\mathit{ld, m})$ where $W = \\{\\mathit{me}\\}$ and $\\sum_{w \\subseteq W} P_w(w) \\cdot P(\\mathit{os}^w_{\\mathit{m}}, \\mathit{os}^w_{\\mathit{m}'}|\\mathit{ld, m})$, where $W = \\{\\mathit{le}\\}$. \n", "\n", - "The key to interpreting this histogram is the fact that we plot two different counterfactual distributions against each other here. Along the x-axis we show what overshoot a model expects under the sufficiency intervention. And here, we see that for both interventions the model expects most of the density to lie above the overshoot threshold (red). So both intereventions are, approximately, sufficient. On the y-axis, however, we show the counterfactual distribution in the necessity world, where the intervention does **not** take place. Note that the scale starts in the upper left corner. This means that the higher the density mass is on the plot, the **lower** is the expected overshoot without that intervention, i.e. \"the more necessary\" a given intervention is for the outcome. Here, only lockdown seems to play a necessary role. Ideally, however, we are interested in whether an intervention is **both necessary and sufficient**, the probablity of which is the proportion of density mass in the upper-right quandrant determined by the dashed red lines. It is evident from the plot above that the counterfactual for lockdown has more probability mass in the top right quadrant (low overshoot in the necessity world and high overshoot in the sufficient world). This gives us a clearer picture into why lockdown has higher causal role in the overshoot being too high as compared to masking." + "The key to interpreting this heatmap is the fact that we plot two different counterfactual distributions against each other here. Along the x-axis we show what overshoot a model expects under the sufficiency intervention. And here, we see that for both interventions the model expects most of the density to lie above the overshoot threshold (red). So both intereventions are, approximately, sufficient. On the y-axis, however, we show the counterfactual distribution in the necessity world, where the intervention does **not** take place. Note that the scale starts in the upper left corner. This means that the higher the density mass is on the plot, the **lower** is the expected overshoot without that intervention, i.e. \"the more necessary\" a given intervention is for the outcome. Here, only lockdown seems to play a necessary role. Ideally, however, we are interested in whether an intervention is **both necessary and sufficient**, the probablity of which is the proportion of density mass in the upper-right quandrant determined by the dashed red lines. It is evident from the plot above that the counterfactual for lockdown has more probability mass in the top right quadrant (low overshoot in the necessity world and high overshoot in the sufficient world). This gives us a clearer picture into why lockdown has higher causal role in the overshoot being too high as compared to masking." ] }, { @@ -1729,6 +1730,20 @@ "\n", "The plot clearly shows that `lockdown_efficiency` as a context has little effect on how intervening on `mask` affects `overshoot`. Again, crucially, whichever context setting we choose here, withdrawing the masking policy does not radically change the fact that the overshoot is still very likely to be too high." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`SearchForExplanation` can be used in any `ChiRho` causal model to investigate the causal role of variables of interest in outome variables having the values that they do in particular runs. This is achieved by transforming the causal model into one that allows us to investigate multiple causal settings with \"necessity\" and \"sufficiency\" interventions and their impact on the outcome variable across different possible worlds. Looking back at the heatmap, \"Counterfactual heatmap of overshoot under necessity and sufficiency interventions\" with the accompanying explanation illustrates how paying attention to both conjoining sufficiency and necessity and to searching through actual contexts to be fixed allows us to break the *prima facie* symmetries which makes the usual but-for analysis blind to such a fine-grained analysis." + ] } ], "metadata": { From fe308848b6924075f042c44470ea5f1251a3e6ad Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Thu, 19 Dec 2024 04:01:42 -0500 Subject: [PATCH 105/111] expand conclusion --- docs/source/explainable_sir.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 82c134aa..e9a9a6e8 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -1742,7 +1742,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "`SearchForExplanation` can be used in any `ChiRho` causal model to investigate the causal role of variables of interest in outome variables having the values that they do in particular runs. This is achieved by transforming the causal model into one that allows us to investigate multiple causal settings with \"necessity\" and \"sufficiency\" interventions and their impact on the outcome variable across different possible worlds. Looking back at the heatmap, \"Counterfactual heatmap of overshoot under necessity and sufficiency interventions\" with the accompanying explanation illustrates how paying attention to both conjoining sufficiency and necessity and to searching through actual contexts to be fixed allows us to break the *prima facie* symmetries which makes the usual but-for analysis blind to such a fine-grained analysis." + "`SearchForExplanation` can be used in any `ChiRho` causal model to investigate the causal role of variables of interest in outome variables having the values that they do in particular runs. This is achieved by transforming the causal model into one that allows us to investigate multiple causal settings with \"necessity\" and \"sufficiency\" interventions and their impact on the outcome variable across different possible worlds. Looking back at the heatmap, \"Counterfactual heatmap of overshoot under necessity and sufficiency interventions\" with the accompanying explanation illustrates how paying attention to both conjoining sufficiency and necessity and to searching through actual contexts to be fixed allows us to break the *prima facie* symmetries which makes the usual but-for analysis blind to such a fine-grained analysis. Moreover, the obtained samples can be used to answer more specific causal queries if need be." ] } ], From 349a801375b6c55dac6887d8854a129ff4cbcadf Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Thu, 19 Dec 2024 04:04:03 -0500 Subject: [PATCH 106/111] update TOC --- docs/source/explainable_sir.ipynb | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index e9a9a6e8..7b0c0ff1 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -24,13 +24,15 @@ "\n", "- [Setup](#setup)\n", "- [Bayesian Epidemiological SIR Model with Policies](#bayesian-epidemiological-sir-model-with-policies)\n", + " - [Assumptions](#assumptions)\n", " - [SIR Model and Simulation](#sir-model-and-simulation)\n", " - [Bayesian SIR Model](#bayesian-sir-model)\n", " - [Bayesian SIR Model with Policies](#bayesian-sir-model-with-policies)\n", "- [But for Analysis with Bayesian SIR Model with Policies](#but-for-analysis-with-bayesian-sir-model-with-policies)\n", "- [Causal Explanations using SearchForExplanation](#causal-explanations-using-searchforexplanation)\n", "- [Fine-grained Analysis of overshoot using Sample traces](#fine-grained-analysis-of-overshoot-using-sample-traces)\n", - "- [Looking into Different Contexts for Curious Readers](#looking-into-different-contexts-for-curious-readers)" + "- [Looking into Different Contexts for Curious Readers](#looking-into-different-contexts-for-curious-readers)\n", + "- [Conclusion](#conclusion)" ] }, { From a264ba676bedfcbf131becdaf2e7cead79770b21 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Thu, 19 Dec 2024 04:09:23 -0500 Subject: [PATCH 107/111] add a practical implication to the conclusion --- docs/source/explainable_sir.ipynb | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index 7b0c0ff1..f79cc761 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -1744,7 +1744,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "`SearchForExplanation` can be used in any `ChiRho` causal model to investigate the causal role of variables of interest in outome variables having the values that they do in particular runs. This is achieved by transforming the causal model into one that allows us to investigate multiple causal settings with \"necessity\" and \"sufficiency\" interventions and their impact on the outcome variable across different possible worlds. Looking back at the heatmap, \"Counterfactual heatmap of overshoot under necessity and sufficiency interventions\" with the accompanying explanation illustrates how paying attention to both conjoining sufficiency and necessity and to searching through actual contexts to be fixed allows us to break the *prima facie* symmetries which makes the usual but-for analysis blind to such a fine-grained analysis. Moreover, the obtained samples can be used to answer more specific causal queries if need be." + "`SearchForExplanation` can be used in any `ChiRho` causal model to investigate the causal role of variables of interest in outome variables having the values that they do in particular runs. This is achieved by transforming the causal model into one that allows us to investigate multiple causal settings with \"necessity\" and \"sufficiency\" interventions and their impact on the outcome variable across different possible worlds. Looking back at the heatmap, \"Counterfactual heatmap of overshoot under necessity and sufficiency interventions\" with the accompanying explanation illustrates how paying attention to both conjoining sufficiency and necessity and to searching through actual contexts to be fixed allows us to break the *prima facie* symmetries which makes the usual but-for analysis blind to such a fine-grained analysis. Moreover, the obtained samples can be used to answer more specific causal queries if need be.\n", + "\n", + "A practical policy-making implication is that developing causal models for problems at hand and employing a strategy analogous to the one discussed in this notebook is a promising strategy for a fine-grained and probabilistically aware responsibility attribution." ] } ], From 257419d03f3f0665836f149ad2d17916c93aaf39 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Thu, 19 Dec 2024 04:09:57 -0500 Subject: [PATCH 108/111] conclusion->conclusions --- docs/source/explainable_sir.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/explainable_sir.ipynb b/docs/source/explainable_sir.ipynb index f79cc761..fbf3c0f8 100644 --- a/docs/source/explainable_sir.ipynb +++ b/docs/source/explainable_sir.ipynb @@ -32,7 +32,7 @@ "- [Causal Explanations using SearchForExplanation](#causal-explanations-using-searchforexplanation)\n", "- [Fine-grained Analysis of overshoot using Sample traces](#fine-grained-analysis-of-overshoot-using-sample-traces)\n", "- [Looking into Different Contexts for Curious Readers](#looking-into-different-contexts-for-curious-readers)\n", - "- [Conclusion](#conclusion)" + "- [Conclusions](#conclusions)" ] }, { @@ -1737,7 +1737,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Conclusion" + "## Conclusions" ] }, { From 1c4940816969986577ee99c61c282c043db7612b Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Thu, 19 Dec 2024 04:25:02 -0500 Subject: [PATCH 109/111] remove python 3.8 from CI as no longer available --- .github/workflows/test.yml | 2 +- .github/workflows/test_notebooks.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index ec3a8e68..08cdf72d 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -12,7 +12,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: [3.8, 3.9, '3.10'] + python-version: [3.9, '3.10'] os: [ubuntu-latest] # , macos-latest] steps: diff --git a/.github/workflows/test_notebooks.yml b/.github/workflows/test_notebooks.yml index fbd3b2ab..2d3a881e 100644 --- a/.github/workflows/test_notebooks.yml +++ b/.github/workflows/test_notebooks.yml @@ -16,7 +16,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [3.8] + python-version: [3.9, 3.10] steps: - uses: actions/checkout@v2 From e44e55e35481369d94579a61fa2d446c18274c07 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Thu, 19 Dec 2024 04:27:19 -0500 Subject: [PATCH 110/111] typo in CI --- .github/workflows/test_notebooks.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/test_notebooks.yml b/.github/workflows/test_notebooks.yml index 2d3a881e..3d948d4c 100644 --- a/.github/workflows/test_notebooks.yml +++ b/.github/workflows/test_notebooks.yml @@ -16,7 +16,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [3.9, 3.10] + python-version: [3.9, '3.10'] steps: - uses: actions/checkout@v2 From 85cd5894a99e48273a38b4cd51308c10307db732 Mon Sep 17 00:00:00 2001 From: rfl-urbaniak Date: Thu, 19 Dec 2024 04:30:55 -0500 Subject: [PATCH 111/111] remove 3.8 from lint CI --- .github/workflows/lint.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index 2bdd8cfc..bbea6fff 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -11,7 +11,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [3.8, 3.9, '3.10'] + python-version: [3.9, '3.10'] steps: - uses: actions/checkout@v2