benchmark/results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "b3edae65-ec1c-4318-b825-1ea20cea1f3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "pd.options.display.float_format = '{:.3f}'.format\n",
    "dataset_type = {\n",
    "    \"imagenet1k\": \"natural\",\n",
    "    \"imagenetv2\": \"natural\",\n",
    "    \"imagenet-r\": \"natural\",\n",
    "    \"imagenet_sketch\": \"specialized\",\n",
    "    \"objectnet\": \"natural\",\n",
    "    \"imagenet-a\": \"natural\",\n",
    "    \"imagenet-o\": \"natural\",\n",
    "    \n",
    "    \"vtab/cifar10\": \"natural\",\n",
    "    \"vtab/cifar100\": \"natural\",\n",
    "    \"mnist\": \"specialized\",\n",
    "\n",
    "    \"vtab/flowers\": \"natural\",\n",
    "    \"cars\": \"natural\",\n",
    "    \"vtab/svhn\": \"natural\",\n",
    "    \"fer2013\": \"natural\",\n",
    "    \"renderedsst2\": \"specialized\",\n",
    "    \"vtab/pets\": \"natural\",\n",
    "    \"vtab/caltech101\": \"natural\",\n",
    "    \"voc2007_multilabel\": \"natural\",    \n",
    "    \"voc2007\": \"natural\",\n",
    "    \"sun397\": \"natural\",\n",
    "    \"fgvc_aircraft\": \"natural\",\n",
    "    \"country211\": \"natural\",\n",
    "    \"vtab/dtd\": \"natural\",\n",
    "    \"gtsrb\": \"natural\",\n",
    "    \"stl10\": \"natural\",\n",
    "\n",
    "    \"vtab/diabetic_retinopathy\": \"specialized\",\n",
    "    \"vtab/eurosat\": \"specialized\",\n",
    "    \"vtab/resisc45\": \"specialized\",\n",
    "    \"vtab/pcam\": \"specialized\",\n",
    "\n",
    "    \"vtab/clevr_count_all\": \"structured\",\n",
    "    \"vtab/clevr_closest_object_distance\": \"structured\",\n",
    "\n",
    "    \"vtab/dsprites_label_orientation\": \"structured\",\n",
    "    \"vtab/dsprites_label_x_position\": \"structured\",\n",
    "\n",
    "    \"vtab/smallnorb_label_elevation\": \"structured\",\n",
    "    \"vtab/smallnorb_label_azimuth\": \"structured\",\n",
    "\n",
    "    \"vtab/dmlab\": \"structured\",\n",
    "    \"vtab/kitti_closest_vehicle_distance\": \"structured\",\n",
    "    \n",
    "    \"mscoco_captions\": \"retrieval\",\n",
    "    \"flickr8k\": \"retrieval\",\n",
    "    \"flickr30k\": \"retrieval\",\n",
    "}\n",
    "\n",
    "def extract_arch(model):\n",
    "    vit, size, patch_size, *rest = model.split(\"-\")\n",
    "    return vit+\"-\"+size+\"-\"+patch_size\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "62911961-311b-43dc-acf4-7af886be969d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"benchmark.csv\")\n",
    "vtab_plus = list(map(lambda s:s.strip(), open(\"datasets.txt\").readlines()))\n",
    "df = df[df.dataset.isin(vtab_plus)]\n",
    "df.loc[:, \"dataset_type\"] = df.dataset.apply(lambda d:dataset_type[d])\n",
    "df.loc[:, \"model_arch\"] = df.model.apply(extract_arch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "2ee732b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"model_fullname\"]=df[\"model_fullname\"].str.replace(\"/fsx/rom1504/open_clip/good_models/\", \"openclip \")\n",
    "df[\"model_fullname\"]=df[\"model_fullname\"].str.replace(\"/fsx/rwightman/\", \"openclip \")\n",
    "df[\"pretrained\"]=df[\"pretrained\"].str.replace(\"/fsx/rom1504/open_clip/good_models/\", \"openclip \")\n",
    "df[\"pretrained\"]=df[\"pretrained\"].str.replace(\"/fsx/rwightman/\", \"openclip \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "84305a59",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_retrieval = df[df[\"dataset_type\"] == \"retrieval\"]\n",
    "df = df[df[\"dataset_type\"] != \"retrieval\"]\n",
    "df = df.drop([\"image_retrieval_recall@5\", \"text_retrieval_recall@5\"], axis=1)\n",
    "\n",
    "dataset_type = {k:v for k,v in dataset_type.items() if v != \"retrieval\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a550373-8f81-4465-98e4-1c4e9e0f4e5a",
   "metadata": {},
   "source": [
    "# Accuracy of all models on all datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b24eb94a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "c9373820-7e48-480a-b2e9-6bec50b3dc63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='dataset', ylabel='acc1'>"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "#order = df.sort_values(by=\"dataset_type\").dataset.unique()\n",
    "order = list(dataset_type.keys())\n",
    "ax = sns.barplot(\n",
    "    x=\"dataset\", y=\"acc1\", \n",
    "    data=df,\n",
    "    order=order,\n",
    "    hue=\"model_fullname\"\n",
    ")\n",
    "ax.set_xticklabels(ax.get_xticklabels(),rotation = 90)\n",
    "ax"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02e6ee8f-d3a3-48da-8f6b-5e568c947796",
   "metadata": {},
   "source": [
    "# Zooming on a specific architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "8ab3d735-f6ea-4c80-a076-19777dbc7573",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='dataset', ylabel='acc1'>"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "order = list(dataset_type.keys())\n",
    "d = df[df.model_arch==\"ViT-B-32\"]\n",
    "ax = sns.barplot(\n",
    "    x=\"dataset\", y=\"acc1\", \n",
    "    data=d,\n",
    "    order=order,\n",
    "    hue=\"model_fullname\"\n",
    ")\n",
    "ax.set_xticklabels(ax.get_xticklabels(),rotation = 90)\n",
    "ax"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9056c48c-4070-4501-a91a-da050f812217",
   "metadata": {},
   "source": [
    "# Accuracy averaged over all models for each dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "id": "809fc438-1f91-4c73-bdc2-e72b72abc11b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rom1504/CLIP_benchmark/.env/lib/python3.8/site-packages/seaborn/algorithms.py:98: RuntimeWarning: Mean of empty slice\n",
      "  boot_dist.append(f(*sample, **func_kwargs))\n",
      "/home/rom1504/CLIP_benchmark/.env/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n",
      "  r, k = function_base._ureduce(a,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='dataset', ylabel='acc1'>"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "order = list(dataset_type.keys())\n",
    "ax = sns.barplot(\n",
    "    x=\"dataset\", y=\"acc1\", data=df,\n",
    "    order=order\n",
    ")\n",
    "ax.set_xticklabels(ax.get_xticklabels(),rotation = 90)\n",
    "ax"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cd85d6a-2b71-44e6-a4b6-96a58a9400b7",
   "metadata": {},
   "source": [
    "# Grouping over architecture for each dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "169f49c6-deec-4534-a120-635e7537f276",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rom1504/CLIP_benchmark/.env/lib/python3.8/site-packages/seaborn/algorithms.py:98: RuntimeWarning: Mean of empty slice\n",
      "  boot_dist.append(f(*sample, **func_kwargs))\n",
      "/home/rom1504/CLIP_benchmark/.env/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n",
      "  r, k = function_base._ureduce(a,\n",
      "/home/rom1504/CLIP_benchmark/.env/lib/python3.8/site-packages/seaborn/algorithms.py:98: RuntimeWarning: Mean of empty slice\n",
      "  boot_dist.append(f(*sample, **func_kwargs))\n",
      "/home/rom1504/CLIP_benchmark/.env/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n",
      "  r, k = function_base._ureduce(a,\n",
      "/home/rom1504/CLIP_benchmark/.env/lib/python3.8/site-packages/seaborn/algorithms.py:98: RuntimeWarning: Mean of empty slice\n",
      "  boot_dist.append(f(*sample, **func_kwargs))\n",
      "/home/rom1504/CLIP_benchmark/.env/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n",
      "  r, k = function_base._ureduce(a,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='dataset', ylabel='acc1'>"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j1EiSybIPACULSTpQhCWmpLm6wySmpNkaCwAAVhiS7r4s2y6b4gAAu9DdHQAAAAAAH0GSDgAAAACAjyBJBwAAAAB41ZQpU9SqVSu7w/BJjEkHAAAAgCLm8LTmXq2v5qRrnyGiZ8+eunjxolatWpXj2oYNG9SxY0ft2LFDo0aNkiTVrl1bhw4dyrO8QYMGaeHChW7nsj7j5+enyMhIdevWTc8//7wqVqyYZ1mJiYkaN26cvvnmG+3fv1+jR4/W7Nmzc9yXmpqqxx9/XMuWLdOJEydUq1YtzZ49W927d7+Gr4BnSNKBIiwjsGyu+wAAAIBdYmNj1bt3b/3888+qXr2627UFCxaoTZs2atGihevc1q1blf7bpJGbNm1S7969tXfvXoWGhkqSSpcunWs906ZN07Bhw5Senq59+/bp/vvv1+jRo/XWW2/lGdv58+cVHh6uiRMn6oUXXsj1ngsXLuiWW25RRESE3nvvPVWrVk2HDh1ShQoV8vNlKDCSdKAIS2vUze4QAAAAADe33XabwsPDtXDhQk2cONF1Pi0tTUuXLtWMGTM0ZcoUrVixQgkJCQoPD3fdExYWJkmKiIi4alJcrlw5ValSRZJUrVo1DRo0SP/617+u+Ezt2rUVHx8vSZo/f36u98yfP18nTpzQpk2bFBgY6HrOWxiTDgAosWJeinHbspszbqXbBgAAri4gIEADBw7UwoULZYxxnV+6dKnS09PVv39/y+s8evSoVq5cqXbt2nlc1gcffKD27dtrxIgRioyMVLNmzfT000+7WvsLG0k6UILExcVpwIABGjBggOLi4uwOBwAAAMXU0KFDdeDAAa1fv951bsGCBerdu7fKly9vSR2PPPKIQkJCVLp0aVWvXl0Oh0OzZs3yuNwff/xR7733ntLT0/XRRx/piSee0MyZM/WPf/zDgqivjiQdKEGcTqeSk5OVnJwsp9NpdzhAoeODKQAA7NG4cWN16NDB1aV8//792rBhg2JjY/NdVkhIiGsbPny46/z48eOVkJCgnTt3avXq1ZKkHj16uFq883ruajIyMhQREaFXX31VrVu3Vr9+/fT4449r3rx5+Y69IBiTDgAotjI/mAIAAN4XGxurUaNGae7cuVqwYIHq1aunG2+8Md/lJCQkuPYzJ5OTpMqVK6t+/fqSpAYNGmj27Nlq37691q5dq86dO+f53NVERUUpMDBQ/v7+rnNNmjRRUlKSLly4oKCgoHy/hvwgSQcAAAAAWK5v376Ki4vT4sWLtWjRIj344INyOBz5LiczEb+azKT67Nmz+Xouu5iYGC1evFgZGRny87vc+Xzfvn2Kiooq9ARdIkkHAK/JPjHZxlEbbYoEAACg8IWEhKhfv36aMGGCTp06pcGDB1ta/q+//qqkpCQZY3TkyBE9/PDDCg8PV4cOHa74XGYLe1pampxOpxISEhQUFKSmTZtKkh588EHNmTNHcXFxGjVqlH744Qc9/fTTGj16tKXx54UkHQAAAACKmJqTdtkdwjWJjY3VG2+8oe7du6tq1aqWlj1p0iRNmjRJkhQeHq4//vGP+vTTT1WpUqUrPhcdHe3a37ZtmxYvXqxatWrp4MGDkqQaNWrok08+0UMPPaQWLVqoWrVqiouL0yOPPGJp/HkhSQcAAAAAFIr27du7LcOWacqUKZoyZUqO8506dcr1/uwyE+qCuJby27dvr6+++qrAdXiCJB0oRuLi4lyztoeHh0sFG4YDFBuJKWmuZUwSU9JsjQUAAOBakKQDxUiOmaxJ0ouUOeNWuh2PnNnTpkgAAABgF9ZJBwAAAADAR5CkAwAAAADgI0jSAQAAAADwEYxJBwCLZJ+4Lz4+3uaIAAAAUNSQpAMlyLmkJMnh+H0/yuaAipkcE/fBdhmBZXPdBwAA8FUk6UAxcin1mCT/LPtAyZbWqJvdIQAAAOQLY9IBAAAAAF41ZcoUtWrVyu4wfBIt6QAAAABQxMS8FOPV+jaO2njN9/bs2VMXL17UqlWrclzbsGGDOnbsqB07dmjUqFGSpNq1a+vQoUN5ljdo0CAtXLjQ7dzChQs1ZswYpaam5rjf4XBo+fLl6tWrV55ljh49Whs3btR3332nJk2aKCEhIc979+/fr+joaPn7++dan9VI0gEAAAAAlomNjVXv3r31888/q3r16m7XFixYoDZt2qhFixauc1u3blV6erokadOmTerdu7f27t2r0NBQSVLp0qULJc6hQ4fq66+/1s6dO/O85+LFi+rfv79uuOEGbdq0qVDiyI7u7gAAAAAAy9x2220KDw/P0fqdlpampUuXKjY21q27e3h4uKpUqaIqVaooLCxMkhQREeE6V758ectjfPHFFzVixAjVrVv3ivdNnDhRjRs3Vt++fS2PIS8k6QAAAAAAywQEBGjgwIFauHChjDGu80uXLlV6err69+9vY3TXbs2aNVq6dKnmzp3r1Xrp7g4AKDGyr2Wv+jYHBABAMTV06FDNmDFD69evV6dOnSRd7ureu3dvy1rGT548qZCQEEvKyu5///ufBg8erLffftvV7d5baEkHipGw4HRV+m0LC063OxzA52SuZZ+cnOxK1gEAgPUaN26sDh06aP78+ZIuT762YcMGxcbG5ruskJAQ1zZ8+HDX+XLlyikhISHHllW3bt1cz/7hD3+45jqHDRumAQMGqGPHjvmO11O0pAPFyGPRqW7H/eXdT/2Aoi57S3uDgM42RwQAQNEVGxurUaNGae7cuVqwYIHq1aunG2+8Md/lZE28s7Zq+/n5qX79K3eLe/3113X27FlJUmBg4DXXuWbNGn3wwQd6/vnnJUnGGGVkZCggIECvvvqqhg4dmo9XkD8k6QAA/CazpT1TgygbgwEAoIjr27ev4uLitHjxYi1atEgPPvigHA5Hvsu5WiJ+JdWqVSvQc5s3b3bNOC9J77//vp599llt2rSpwGVeK5J0oAQJNZJksuwDAAAAhSMkJET9+vXThAkTdOrUKQ0ePNjukFz279+vtLQ0JSUl6ezZs67W+qZNmyooKEhNmjRxu/+bb76Rn5+fmjVrVuixkaQDJciQdPdx6rtsigMAAAAlQ2xsrN544w11795dVatWtTscl/vuu0/r1693HUdHR0uSfvrpJ9WuXdumqC4jSQcAAACAImbjqI12h3BN2rdv77YMW6YpU6ZoypQpOc536tQp1/uzGzx4cJ4t89fy/Lp16656z7XWZzVmdwcAlBiXUo/lug8AAOArSNIBAAAAAPARdHcvIWJeinE7LirdYwAAAACgJCFJLyayr+0bHx9vc0QAAAAAgPwiSS8msq/tCwAAAAAoekjSAQAlRlhwutv+URtjAQAAyA1JOiRJc8atdDseObOnTZEAQOF5LDrV7bi/Qt2OzyUlSQ7H7/tR3ooMAADgMmZ3BwAAAADAR5CkAwAAAADgI0jSAQAAAABeNWXKFLVq1cruMHwSY9IBAAAAoIhZ3/FGr9Z34xfrr/nenj176uLFi1q1alWOaxs2bFDHjh21Y8cOjRo1SpJUu3ZtHTp0KM/yBg0apIULF7qdy/qMn5+fIiMj1a1bNz3//POqWLFinmV9+eWXeuSRR7Rnzx6dOXNGtWrV0gMPPKCHHnrIdc/06dO1bNky7dmzR6VLl1aHDh307LPPqlGjRtf8NfAESToAAAAAwDKxsbHq3bu3fv75Z1WvXt3t2oIFC9SmTRu1aNHCdW7r1q1KT7+8AsumTZvUu3dv7d27V6Ghlyd4LV26dK71TJs2TcOGDVN6err27dun+++/X6NHj9Zbb72VZ2xly5bVyJEj1aJFC5UtW1ZffvmlHnjgAZUtW1b333+/JGn9+vUaMWKE/vjHP+rSpUt67LHH1KVLF+3evVtly5b16GtzLUjSAQAAAACWue222xQeHq6FCxdq4sSJrvNpaWlaunSpZsyYoSlTpmjFihVKSEhQeHi4656wsDBJUkREhCpUqHDFesqVK6cqVapIkqpVq6ZBgwbpX//61xWfiY6OVnR0tOu4du3aWrZsmTZs2OBK0rP3AFi4cKEiIiK0bds2dezY8epfAA8xJh0AAAAAYJmAgAANHDhQCxculDHGdX7p0qVKT09X//79La/z6NGjWrlypdq1a5ev57799ltt2rRJN96Y9/CBkydPSvr9A4TCRpJeTCSmpOW6DwAAAADeNnToUB04cEDr1/8+ln3BggXq3bu3ypcvb0kdjzzyiEJCQlS6dGlVr15dDodDs2bNuqZnq1evruDgYLVp00YjRozQfffdl+t9GRkZGjNmjGJiYtSsWTNL4r4aknQAAAAAgKUaN26sDh06aP78+ZKk/fv3a8OGDYqNjc13WSEhIa5t+PDhrvPjx49XQkKCdu7cqdWrV0uSevTo4Rrfntdz0uUJ7L755hvNmzdPs2fPzrOb/IgRI/Tdd9/p3XffzXfcBcWYdAAAAACA5WJjYzVq1CjNnTtXCxYsUL169a7YrTwvCQkJrv3MyeQkqXLlyqpfv74kqUGDBpo9e7bat2+vtWvXqnPnznk+J0l16tSRJDVv3lzJycmaMmVKjm74I0eO1P/93//piy++yDEBXmEiSQcA4DehRpJMln0AAFBQffv2VVxcnBYvXqxFixbpwQcflMPhyHc5mYn41fj7+0uSzp49m6/nMjIydP78edexMUajRo3S8uXLtW7dOldC7y0k6QAA/GbIb93jMu2yKQ4AAIqDkJAQ9evXTxMmTNCpU6c0ePBgS8v/9ddflZSUJGOMjhw5oocffljh4eHq0KFDns/MnTtXNWvWVOPGjSVJX3zxhZ5//nmNHj3adc+IESO0ePFivf/++ypXrpySkpIkSeXLl89zOTgrMSYdAAAAAFAoYmNjlZKSoq5du6pq1aqWlj1p0iRFRUWpatWquu2221S2bFl9+umnqlSpUp7PZGRkaMKECWrVqpXatGmjuXPn6tlnn9W0adNc97zyyis6efKkOnXqpKioKNe2ZMkSS+PPCy3pAAAAAFDE3PjF+qvf5APat2/vtgxbpilTpmjKlCk5znfq1CnX+7M7ePBggeIZNWqURo0adcV7rqX+wkRLOgAAAAAAPoIkHQAAAAAAH0GSDgAAAACAjyBJBwAAAADARzBxXDGREVg2130AvisuLk5Op1OSFB4ergYBnW2OCAAAAHYjSS8m0hp1szsEAPnkdDqVnJzsOm4QZWMwsM2ccSvdjkfO7GlTJAAAwBeQpAMA4IHsPSLi4+Pdrse8FON2vHHURq/FBgAAih6SdACwSGJKmmuij8SUNFtjgfdk7xEBAADgCZJ0AB6hlTBv2VtYVd/mgAAAAODzmN0dAApJZgtrcnKyK1kHAACANGXKFLVq1cruMHwSLekAAAAAUMRkn3i0sOVnYtOePXvq4sWLWrVqVY5rGzZsUMeOHbVjxw6NGjVKklS7dm0dOnQoz/IGDRqkhQsX5jvm3HzxxReaMWOGtm3bpsTERC1fvly9evXK8/7hw4frn//8p1544QWNGTPGkhiuhiQdAAAPMBcBAADuYmNj1bt3b/3888+qXr2627UFCxaoTZs2atGihevc1q1blZ6eLknatGmTevfurb179yo0NFSSVLp0actiO336tFq2bKmhQ4fqL3/5yxXvXb58ub766itVrVrVsvqvBUk6AABelH2uggYBnW2OCAAAa912220KDw/XwoULNXHiRNf5tLQ0LV26VDNmzNCUKVO0YsUKJSQkXJ675zdhYWGSpIiICFWoUOGK9fzjH//Qiy++qLNnz6pfv36qXLmyVq1apYSEhDyf6datm7p1u/ry1UePHtWoUaP0ySefqEePHle930ok6QAKFWtAo6S52oSB2WeDbxDlzegAACh8AQEBGjhwoBYuXKjHH39cDodDkrR06VKlp6erf//+euGFFzyq45133tFTTz2ll19+WTExMXr33Xc1c+ZM1alTx+P4MzIydO+992r8+PH6wx/+4HF5+cXEcQAAeCAjsKwygn7bAssyYSAAAJKGDh2qAwcOaP369a5zCxYsUO/evVW+fHmPy3/ppZcUGxurIUOGqGHDhpo0aZKaN2/ucbmS9OyzzyogIECjR4+2pLz8IkkHAMADaY266VSzu3Sq2V1Ka3T17nMAAJQEjRs3VocOHTR//nxJ0v79+7VhwwbFxsbmu6yQkBDXNnz4cEnS3r171bZtW7f7sh5v2LDB7bl33nnnmuratm2b4uPjtXDhQlcPAG+juzsAAAAAwHKxsbEaNWqU5s6dqwULFqhevXq68cYb811O1jHmmZPJXU2bNm3cnouMjLym5zZs2KDjx4+rZs2arnPp6ekaN26cZs+erYMHD15TOZ4gSQcAwEKXUo9J8s+yDwBAydS3b1/FxcVp8eLFWrRokR588MECtU7Xr18/x7lGjRpp69atGjhwoOvc1q1bXfulS5fO9bmruffee9W5s/ukrl27dtW9996rIUOG5Lu8giBJBwDAi84lJUm//YNyLilJYuI4AEAxFRISon79+mnChAk6deqUBg8ebFnZo0aN0rBhw9SmTRt16NBBS5Ys0c6dO1W3bt0rPpeWlqb9+/e7jn/66SclJCQoLCxMNWvWVKVKlVSpUiW3ZwIDA1WlShU1atTIsvivhDHpAAAAAIBCERsbq5SUFHXt2tXS9cbvvvtuTZgwQX//+9913XXX6aefftLgwYNVqlSpKz73zTffKDo6WtHR0ZKksWPHKjo6WpMmTbIsNk/Rkg4AAAAARUxRWda2ffv2MsbkOD9lyhRNmTIlx/lOnTrlen9unnjiCT3xxBOu41tuueWqXdzzU34mb4xDz4okHQAAAABQpJw5c0bz5s1T165d5e/vr3/961/6/PPP9dlnn9kdmsdI0gEAsFBYcLrb/lEbYwEAoLhyOBz66KOP9NRTT+ncuXNq1KiR/vOf/+SY9K0oIkkHAMBCj0Wnuh33l/tSMaFGkkyWfQAAkF+lS5fW559/bncYhYIkHcAVxcXFyel0SpLCw8MVHx9vc0RA0TYkPd3teJdNcQAAAN9Ekg7gipxOp5KTk+0OAwAAACgRWIINAAAAAHxYfmcjhz2s+j6RpAMAAACADwoMDJR0eSZz+L4LFy5Ikvz9/T0qh+7uAAAAAOCD/P39VaFCBR0/flySVKZMGTkcDpujQm4yMjLkdDpVpkwZBQR4lmaTpAMAAACAj6pSpYokuRJ1+C4/Pz/VrFnT4w9SSNIBAIDH5oxb6XY8cmZPmyIBgOLF4XAoKipKERERunjxot3h4AqCgoLk5+f5iHLbk/S5c+dqxowZSkpKUsuWLfXSSy+pbdu2ed4/e/ZsvfLKKzp8+LAqV66su+66S9OnT1epUqW8GLXvy75slurbHBAAAACAAvP39/d4rDOKBluT9CVLlmjs2LGaN2+e2rVrp9mzZ6tr167au3evIiIicty/ePFiPfroo5o/f746dOigffv2afDgwXI4HJo1a5YNr8B35Vg2iyQdAAAAAHyerbO7z5o1S8OGDdOQIUPUtGlTzZs3T2XKlNH8+fNzvX/Tpk2KiYnRgAEDVLt2bXXp0kX9+/fXli1bvBw5AAAAAADWs60l/cKFC9q2bZsmTJjgOufn56fOnTtr8+bNuT7ToUMHvf3229qyZYvatm2rH3/8UR999JHuvfdeb4UNoJiKeSnG7XjjqI02RQLYI/swqfj4eJsjAgCgZLItSf/ll1+Unp6uyMhIt/ORkZHas2dPrs8MGDBAv/zyi66//noZY3Tp0iUNHz5cjz32WJ71nD9/XufPn3cdnzp1ypoXAABAMZJjmBQAALCFrd3d82vdunV6+umn9fLLL2v79u1atmyZPvzwQz355JN5PjN9+nSVL1/etdWoUcOLEQMAAAAAcO1sa0mvXLmy/P39c3xqn5yc7FoLMLsnnnhC9957r+677z5JUvPmzXX69Gndf//9evzxx3Od7n7ChAkaO3as6/jUqVMk6gAAAAAAn2RbS3pQUJBat26t1atXu85lZGRo9erVat++fa7PnDlzJkcinrkMgTEm12eCg4MVGhrqtgEAAAAA4ItsXYJt7NixGjRokNq0aaO2bdtq9uzZOn36tIYMGSJJGjhwoKpVq6bp06dLknr27KlZs2YpOjpa7dq10/79+/XEE0+oZ8+erBkIAIAXZZ9orkFAZ5sjAgCgeLA1Se/Xr5+cTqcmTZqkpKQktWrVSqtWrXJNJnf48GG3lvOJEyfK4XBo4sSJOnr0qMLDw9WzZ0899dRTdr0EAABKpOwTzTWIsjEYAACKEVuTdEkaOXKkRo4cmeu1devWuR0HBARo8uTJmjx5shciA1CcsLwUAAAAigLbk3QA8AaWlwIAAEBRQJIOAAByoPcJAAD2IEkvpi6lHpPkn2UfAIBrR+8TAADsQZIOwHa02AEAAACXkaQDsF1xabHLCCyb6z5QFCSmpMkvy35UxRBb4wEAoKQiSQcAi6Q16uZ+4ugqewIBAABAkUWSfhUxL8W4HW8ctdGmSAB7ZG9dAwAAAFB4/K5+CwAAAAAA8AZa0oupsOB0t/2jNsYCFAdzxq10Ox45s+dVn2GVBRQn2XuWRSYFSw6HJOlcUpIUZUdUAAAUPyTpxdRj0alux/0Vak8gAAAAAIBrRnd3ACVC1vH0vjK2/lxSUq77gC/I2vuDniAAAHgPSToAAAAAAD6CJB0AAAAAAB/BmHQPFWQyKQAAAAAAclPik/S4uDg5nU5JUnh4uOLj422OyDuyv+4GAZ1tjggAAAAAUOKTdKfTqeTkZLvD8Lrsr7sBS+cAAAAAgO0Ykw4AAAAAgI8o8S3pAABAyggs67Yf5p/iOg4LTtdRO4ICAKAEIkkHAABKa9TN7fixcjPcjvsr1O041EiSybIPAACsQJIOwFJMSgiUDEPS092Od9kUBwAAxQ1JOgBL+eqkhNm78gIAAAC+iCQdQImQvSsvgKJnzriVbscjZ/a0KRIAAAoPSTqAK6IFGoC3xLwU43a8cdRGmyIBAMA+JT5JT0xJc61Dl5iSZmssgC+iBRoAAADwHtZJBwAAAADAR5CkAwAAAADgI0jSAQAAAADwESV+TDpKnuzreMfHx9scEQAAAABcRpKOEif7Ot4AAAAA4CtI0gHkS/aeCKpvc0AAAABAMUKSXkKdS0qSHI7f96NsDghFRo6eCCTpAK4Rw40AALg6knQAAOAVDDdCYeEDIADFCUk6AAAAijQ+AAJQnLAEGwAAAAAAPqLEt6RnBJbNdR8AAAAAAG8r8Ul6WqNudocA4CpiXopxO944aqNNkQAAAACFi+7uAAAAAAD4CJJ0AAAAAAB8BEk6AAAAAAA+giQdAAAAAAAfQZIOAAAAAICPIEkHAAAAAMBHlPgl2AAAAABPsVwoAKuQpAOwXWJKmqtbT2JKmlfqjIuLk9PplCSFh4dL9b1SLQAAhWrOuJVuxyNn9rQpEgAFRZIOwFLnkpIkh+P3/SibA8qD0+lUcnLy7ydI0gEAAOADGJMOAAAAAICPoCUdAHKRvTt8g4DONkcEAACAkoAkHQBykb07fAMf7bYPAACA4oUk3QfYMRtoqJEkk2XfN2RvvYyPj7c5IgCAVeyYJBIAgKKGJN0LfDHxHJKe7na8y6Y4sssxmRd8zqXUY5L8s+wXTcXldRRlvvjeCAAAYDeSdC8g8QSAnHhvBAAAyInZ3QEAAAAA8BG0pAMAAFtkH/Kg+jYHBACADyBJBwAAtsgx5IEkHQAAknQULXbMhA8AAAAA3kKSXgTMGbfS7XjkzJ42RQIAAAC7ZG+skGiwAIojkvRsGB8HAAAAALALSXo2jI8DAAAAANiFJB1AkVeQISFhwelu+0ctjwoAAADIP5J0ACXSY9Gpbsf9FWpPIADylH0IWoOAzjZHBABA4SNJB4BcnEtKkhyO3/ejbA4IKIGyD0FrwO8hAKAE8LM7AAAAAAAAcBlJuhckpqTlug8AAAAAQFYk6QAAAAAA+AjGpGdzKfWYJP8s+wAAAEVPQVa+AADYjyQdAADYgg/GPRPzUozb8cZRG22KBABgJbq7AwAAAADgI2hJBwAAXpERWNZ9/9IpG6MBAMA3kaSjWGH8HQD4rrRG3dyOI/8736ZIAN8UFxcnp9MpSQoPD1d8fLzNEQGwA0k6AAAA4AOcTqeSk5PtDgOAzRiTDgAAAACAj6AlPZuw4HS3/aM2xgKUVNm7+6m+zQEBAAAAXkKSns1j0alux/0Vak8ggI/yxgdZObr7ZUvSsyfxDQI6Wx5DqJEkk2UfgLedS0qSHI7f96NsDggAAC8gSc8nbyQHRZUV67UmpqS5xmAkpqRZEBWs5gsfZGVP4hsUwj/uQ9LT3Y53WV8FAAAAkANJej4VJDnIseQMAAAAAAC5IEn3guxLzgAAAAAAkBtmdwcAAAAAwEeQpAMAAAAA4CPo7g7AUsyKDgAAABQcSToASzErOgAAAFBwdHcHAAAAfEDW5WdZihYouWhJh9fMGbfS7XjkzJ42RQIAAAAAvomWdAAAAAAAfAQt6QAAAEAho0chgGtFkg4AAABcRVxcnJxOpyQpPDxc8fHxNkcEoLgiSc+nc0lJksPx+36UzQEBQAlFqxQAb3I6nUpOTrY7DAAlAEk6ABSSsOB0t/2jNsYCAACAooEkHQAKyWPRqW7H/RXqdhxqJMlk2QdKFj7IAgAgJ5J0ALDJkPR0t+NdNsVhl8SUNNcSI7mtBxzzUozb8cZRG70QFbzpah9kAQBQEpGkAwAAn0RvEwBASUSSDgAAfFJJ720CACiZSNIBAAAAH5ARWDbXfQAlC0k6AAAA4APSGnWzOwQAPsDv6rcAAAAAAABvoCU9n5jEBgAAAABQWEjS84lJbAAAAEqeqy0bCQBWIUnHNYuLi5PT6ZQkhYeHKz4+3uaIAAAAAKB4IUnHNXM6nUpOTrY7DAAAAAAotkjSAQAAUKLQOxCALyNJt0H2Pwyqb3NAJQxjygAAKNnoHQjAl5Gk2yDHHwaSdJeMwLK57gMAAABASUCSDp+S1qib3SEAAOAT6JINACUTSToAAIAPoku2b7Gjt9+1DJHMfk+DgM5eiQ1A4SFJBwD4BObrAODL7Ojtdy1DJLPf0yDKC4EBKFQk6QAAn8B8HQAAAHJNcg0AAAAAAGxGkm6DS6nHct33dVmXK2PpMgAAAACwnu1J+ty5c1W7dm2VKlVK7dq105YtW654f2pqqkaMGKGoqCgFBwerYcOG+uijj7wULQAAAFC8xMXFacCAARowYIDi4uLyfR2AtWwdk75kyRKNHTtW8+bNU7t27TR79mx17dpVe/fuVURERI77L1y4oFtuuUURERF67733VK1aNR06dEgVKlTwfvAAAABAMXC1lQRYaQDwLluT9FmzZmnYsGEaMmSIJGnevHn68MMPNX/+fD366KM57p8/f75OnDihTZs2KTAwUJJUu3Ztb4YMAAAAAEChsS1Jv3DhgrZt26YJEya4zvn5+alz587avHlzrs988MEHat++vUaMGKH3339f4eHhGjBggB555BH5+/t7K3QAKLLmjFuZ49zImT1tiAQAAAC5sS1J/+WXX5Senq7IyEi385GRkdqzZ0+uz/z4449as2aN7r77bn300Ufav3+//va3v+nixYuaPHlyrs+cP39e58+fdx2fOnXKuhcBwBIZgWVz3QcAAABKmiK1TnpGRoYiIiL06quvyt/fX61bt9bRo0c1Y8aMPJP06dOna+rUqV6OFEB+pDXq5n7i6Cp7AgEAAABsZtvs7pUrV5a/v3+OSSiSk5NVpUqVXJ+JiopSw4YN3bq2N2nSRElJSbpw4UKuz0yYMEEnT550bUeOHLHuRQAAAAAAYCHbkvSgoCC1bt1aq1evdp3LyMjQ6tWr1b59+1yfiYmJ0f79+5WRkeE6t2/fPkVFRSkoKCjXZ4KDgxUaGuq2AQAAAADgi2xdJ33s2LF67bXX9Oabb+r777/Xgw8+qNOnT7tmex84cKDbxHIPPvigTpw4obi4OO3bt08ffvihnn76aY0YMcKul1AgYcHpqvTbFhacbnc41ywjsKwygn7bGDcMAABQqC6lHst1H0DxZuuY9H79+snpdGrSpElKSkpSq1attGrVKtdkcocPH5af3++fI9SoUUOffPKJHnroIbVo0ULVqlVTXFycHnnkEbteQoE8Fp3qdtxfRaN1P8e44WIs5qUYt+ONozbaFAkAAIC14uLi5HQ6JUnh4eE2RwMgO9snjhs5cqRGjhyZ67V169blONe+fXt99dVXhRwVAAAAUDw5nc4c80IB8B22dncHAAAAAAC/I0kHAAAAAMBHkKQDAAAAAOAjbB+TDgAAgJwSU9JcrSmJKWm2xMBEqvbKugpQWHC6jtoYCwDvIUkHAAAA8inHDOn1ra+jqK4IBMAzJOk+KPubfoOAzjZHBAD2470RgC/JMUN6ISTpAEomknQflP1Nv0GUjcEAgJdcSj0myT/LvjveGwEAQEnAxHEAfE7WBC23ZA3FQ0ZgWWUE/bYFlrU7HAAoks4lJeW6D6DooiUdAGCLtEbd3I4j/zvfpkgAAAB8By3pAAAAAAD4CJJ0AAAAAAB8BN3dgQKYM26l2/HImT1tigQAAABAcUJLOgAAAAAAPoIkHQAAAAAAH0F3dx90LilJcjh+32ctYAC/iYuLk9PplCSFh4crPj7e5ogAAABgJZJ0AChCnE6nkpOT7Q4DAFCC5GhAAlCoSNIBoBiJeSnG7XjjqI02RQIAAICCIEkHAAAoArIPd1F9mwMCABQKknQAAIAiIMdwF5J0ACiWSNLh02g1AADg2mT/m9kgoLPNEQEACoIkHT6NVgMAmVj5Ariy7H8zG/A7AgBFEuukAwAAAADgI2hJ90GhRpJMln0AAFDSZASWzXUfsNql1GOS/LPsA7ATSboPGpKe7na8y6Y4AACAfdIadXM/cXSV5XVkH8ceHx9veR0ofuaMW+l2PHJmT5siAYonknQAAIASKsfcLwAA25Gko8CYeR12YQIxAIDd6CIOoLBYOnFccnKypk2bZmWR8GGZn74nJye7knUAKKiw4HRV+m0LC06/+gMAAADFkKUt6UlJSZo6daomTZpkZbEAgBLgsehUt+P+CrUnEAAAABvlK0nfuXPnFa/v3bvXo2AAAAAAFK7ElDRXd9rElDSFO2wNB0A2+UrSW7VqJYfDIWNyrguWed7h4LccAAAA8AaW7gWKn3wl6WFhYXruued0880353r9v//9r3r2ZAkGAAAAwBtYuhcofvKVpLdu3VrHjh1TrVq1cr2empqaays7Sqbss783COhsc0QAAAC4mpiXYtyOIxVsUyRAyZSvJH348OE6ffp0ntdr1qypBQsWeBwUiofsa682KMAyWSxvAgAAAKAkyVeSfuedd17xesWKFTVo0CCPAgIAAAAAoKSydJ10AAAAAABQcAVK0nv37q1nn302x/nnnntOffr08TgoAACyCzVSeWNU3hhmMAYAAMVWvrq7Z/riiy80ZcqUHOe7deummTNnehoTAMBm2Sd+jI+PtzkiZjAGAAAlQ4GS9LS0NAUFBeU4HxgYqFOnTnkcFADAXtknfgQAAIB3FKi7e/PmzbVkyZIc59999101bdrU46AAAAAAACiJCtSS/sQTT+gvf/mLDhw4oJtuukmStHr1av3rX//S0qVLLQ0QAAAAgH0uzwNisuwDKEwFStJ79uypFStW6Omnn9Z7772n0qVLq0WLFvr888914403Wh0jAAAAruJcUpLkcPy+H2VzQCg2mBME8K4CJemS1KNHD/Xo0cPKWAAAAOBFiSlprrGPiSlptsYCALisQEn61q1blZGRoXbt2rmd//rrr+Xv7682bdpYEhwAwDfFvBTjdrxx1EabIgEAACheCjRx3IgRI3TkyJEc548ePaoRI0Z4HBSKhkupx3LdBwAAAAAUTIFa0nfv3q3rrrsux/no6Gjt3r3b46AAAACAoiwuLk5Op1OSFB4ergYBnW2OCEBRUaCW9ODg4FzXz01MTFRAQIGHuQM5hAWnq9JvW1hw+tUfAAAA8AFOp1PJyclKTk52JesAcC0KlFF36dJFEyZM0Pvvv6/y5ctLklJTU/XYY4/plltusTRAlGyPRae6HfdXqD2BAAAAAIAXFChJf/7559WxY0fVqlVL0dHRkqSEhARFRkbqrbfesjRAAAAAAABKigIl6dWqVdPOnTv1zjvvaMeOHSpdurSGDBmi/v37KzAw0OoYAQAAAAAoEQo8gLxs2bK6/vrrVbNmTV24cEGS9PHHH0uSbr/9dmuiAwAAAACgBClQkv7jjz/qzjvv1K5du+RwOGSMkcPhcF1PT2eCLxQd2WdfjY+PtzkiAAByurzcqX+WfcAaWSfnDQtO11EbYwFQwCQ9Li5OderU0erVq1WnTh19/fXXOnHihMaNG6fnn3/e6hiBQpU5+yoAAEBJxES9gG8pUJK+efNmrVmzRpUrV5afn5/8/f11/fXXa/r06Ro9erS+/fZbq+MELJMRWDbXfQC/S0xJc63RmZiSZmssAOCLrtb6fC4pSfqtp+m5pCQpyovBASjSCpSkp6enq1y5cpKkypUr69ixY2rUqJFq1aqlvXv3WhogYLW0Rt3cTxxdZU8gAADYjA+uC64otz7zfQd8W4GS9GbNmmnHjh2qU6eO2rVrp+eee05BQUF69dVXVbduXatjBAAAQCHI/sF19nlaVP/Kz88Zt9LteOTMnpbGh8KRo8FC39kSB4DcFShJnzhxok6fPi1JmjZtmm677TbdcMMNqlSpkpYsWWJpgCi66OYFAEDRkmOelqsk6QAA6xUoSe/atatrv379+tqzZ49OnDihihUrus3yDgAFwSyzAAAAKKkKvE56dmFhYVYVBVyz7N3yGgR0tjkiWKEoj/MrbEzoBgAAULxZlqQDdsjeLa8BXeoBAAAAFGF+V78FAAAAAAB4A0k6AAAAAAA+gu7uAAAAgMVCjSSZLPsAcG1I0gEAAACLDUlPdzveZVMcAIoeknQAQA4ZgWVz3QcAAEDhIkkHAOSQ1qib3SEAAACUSCTpKLCw4HS3/aM2xgIAAAAAxQFJOgrssehUt+P+CrUnEAC2i4uLk9PplCSFh4crPj7e5ogAAACKJpJ0AIDHnE6nkpOT7Q4DAACgyGOddAAAAAAAfARJOgAAAAAAPoIkHQAAAAAAH0GSDgAAAACAj2DiOAAAgCKApU8BoGQgSUeRdi4pSXI4ft+PsjkgAJKkOeNWuh2PnNnTpkiA4uNqS5+GGkkyWfatl325xQYBnQunIgAowUjSAQAAioEh6elux7sKoY7syy024MNxALAcSToA4Kqyt56pvs0BAQAAFFMk6QCAq8reekaSDgAAUDiY3R0AAAAAAB9Bkg4AAAAAgI8gSQcAAIAk6VLqsVz3AQDeQ5IOAAAAAICPIEkHAAAAAMBHkKQDAAAAAOAjWIINhSbUSJLJsg8AAAAAuBKSdBSaIenpbse7bIoDAAAAAIoKursDADx2Likp130AAADkD0k6AOCqWJYJAADAO0jSAQAAAADwESTpAAAAAAD4CCaOQ5HGDPIAAAAAihOSdBRpzCAPAAASU9Jc3UMTU9JsjQUAPEWSDgAAgBJtzriVbscjZ/a0KRIAIEkHAADAb8KC0932j9oYCwCUVCTpAAAAkCQ9Fp3qdtxfofYEAgAlGLO7AwAAAADgI2hJB1DkMKs/AE/EvBTjdrxx1EabIgEAICeSdABFDrP6ex/jVAEAALyDJB0AcFWMUwUAAPAOxqQDAAAAAOAjaEkHgCIkI7BsrvsAAAAoHkjSAaAISWvUze4QAAAAUIjo7g4AAAAAgI+gJR0A4DGWxQMAALAGSToAwGMsiwcAAGANursDAAAAAOAjSNIBAAAAAPARJOkAAAAAAPgIn0jS586dq9q1a6tUqVJq166dtmzZck3Pvfvuu3I4HOrVq1fhBggARVRcXJwGDBigAQMGKC4uzu5wAAAAcBW2J+lLlizR2LFjNXnyZG3fvl0tW7ZU165ddfz48Ss+d/DgQf3973/XDTfc4KVIAaDocTqdSk5OVnJyspxOp93hAAAA4CpsT9JnzZqlYcOGaciQIWratKnmzZunMmXKaP78+Xk+k56errvvvltTp05V3bp1vRgtAAAAAACFx9Yk/cKFC9q2bZs6d+7sOufn56fOnTtr8+bNeT43bdo0RUREKDY21hthAgAAQNK5pKRc9wEA1rF1nfRffvlF6enpioyMdDsfGRmpPXv25PrMl19+qTfeeEMJCQnXVMf58+d1/vx51/GpU6cKHC8AAACKvnNJSZLD8ft+lM0BAUAWtnd3z49ff/1V9957r1577TVVrlz5mp6ZPn26ypcv79pq1KhRyFGiqLmUesxt//C05jZGAwAAAKAks7UlvXLlyvL391dycrLb+eTkZFWpUiXH/QcOHNDBgwfVs2dP17mMjAxJUkBAgPbu3at69eq5PTNhwgSNHTvWdXzq1CkSdQAAAACAT7I1SQ8KClLr1q21evVq1zJqGRkZWr16tUaOHJnj/saNG2vXrl1u5yZOnKhff/1V8fHxuSbfwcHBCg4OLpT4AQAAAACwkq1JuiSNHTtWgwYNUps2bdS2bVvNnj1bp0+f1pAhQyRJAwcOVLVq1TR9+nSVKlVKzZo1c3u+QoUKkpTjPAAAAAAARY3tSXq/fv3kdDo1adIkJSUlqVWrVlq1apVrMrnDhw/Lz69IDZ0HAAAAAKBAbE/SJWnkyJG5dm+XpHXr1l3x2YULF1ofEAAAAAAANvCJJB0AUDBxcXFyOp2SpPDwcKm+zQEBAADAIyTpAFCEOZ1O9xUySNIBAACKNAZ7AwAAAADgI0jSAQAAAADwESTpAAAAAAD4CJJ0AAAAAAB8BEk6AAAAAAA+gtndAQAAspgzbqXb8ciZPW2KBABQEtGSDgAAAACAjyBJBwAAAADAR5CkAwAAAADgI0jSAQAAAADwESTpAAAAAAD4CJJ0AAAAAAB8BEk6AAAAAAA+giQdAAAAAAAfQZIOAAAAAICPCLA7AAAAACvFxcXJ6XRKksLDwxUfH29zRAAAXDuSdAAAUKw4nU4lJyfbHQYAAAVCd3cAAAAAAHwESToAAAAAAD6CJB0AAAAAAB9Bkg4ARdil1GO57gMAAKBoIkkHAAAAAMBHkKQDAAAAAOAjWIINAAAA1yTUSJLJsg8AsBpJOgAAAK7JkPR0t+NdNsUBAMUZ3d0BAAAAAPARJOkAAAAAAPgIknQAAAAAAHwESToAAAAAAD6CJB0AAAAAAB/B7O4AAAD5FBcXJ6fTKUkKDw9XfHy8zREBAIoLknQAAIB8cjqdSk5OtjsMAEAxRJIOAAAAwKfMGbfS7XjkzJ42RQJ4H0k6Sryw4PRc9wEAAADA20jSUeI9Fp1qdwgAAAAAIInZ3QEAAAAA8Bkk6QAAAAAA+AiSdAAAAAAAfARJOgAAAAAAPoIkHQAAAAAAH0GSDgAAAACAj2AJNgAAUGy0Hr9IoSlprlaIxJQ0W+MBACC/aEkHAAAAAMBHkKQDAAAAAOAjSNIBAAAAAPARJOkAAAAAAPgIknQAAAAAAHwESToAAAAAAD6CJdiAaxAXFyen0ylJCg8PV4OAzjZHBAAAAKA4IkkHroHT6VRycrLruEGUjcEAWYQFp7vtH7UxFgAAAHiOJB0AirDHolPdjvsr1J5AAAAAYAmSdAAoxs4lJUkOx+/79AIBvCLmpRi3442jNtoUCQCgqGHiOAAAAAAAfARJOgAAAAAAPoIkHQAAAAAAH0GSDgAAAACAj2DiOAAAAACFKi4uTk6nU5IUHh6u+Ph4myMCfBdJOgAAQD4lpqS5uiMmpqTZGgtQFDidTiUnJ9sdBlAkkKQDAAB42ZxxK92OR87saVMkAICr8XZPEJJ0AAAAAJbKntQARZm3e4KQpAMAAACwFN3bUZJY3dJOkg4AAAAAQAFZ/aEUS7ABAAAAAOAjaEkHAAAA4FUxL8W4HW8ctdGmSADfQ0s6AAAAAAA+giQdAAAAAAAfQZIOAAAAAICPIEkHAAAAAMBHkKQDAAAAAOAjmN0dAAAUKxmBZXPdBwCgKCBJBwAAxUpao252hwAAQIGRpAMAAKBECTWSZLLsA4DvIEkHrsG5pCTJ4fh9P8rmgAAAQIENSU93O95lUxwAkBsmjgMAAAAAwEfQkg4AAAAAwG/i4uLkdDolSeHh4V6vnyQdAAAAAIDfOJ1OJScn21Y/3d0BAAAAAPARJOkAAAAAAPgIknQAAAAAAHwESToAAAAAAD6CJB0AAAAAAB/B7O4AAAAAirzsy2bFx8fbHBGKi0upxyT5Z9kvXCTpAAAAAIo8u5fNQsl1LilJcjh+3/cQSToAACjWsreuqb7nZWYEls11HwAAT5GkAwCAYi1H65oFSXpao26eFwIAQC5I0gEAQImWvaW9QUBnmyMCih9vj+kFijKSdAAAUKJlb2lvEGVjMACAEo8kHQAAAMVazEsxbsdP8y8wAB/GOukAAAAAAPgIknQAAAAAlkpMSct1H8DVkaQDAAAAAOAjGJADAADgocJYix0AYI/ElDRXa3ZiSprCHd6tnyQdAADAQ4WxFjsAoGSiuzsAAAAAAD6ClnTgGoQaSTJZ9gEAAADAeiTpwDUYkp7udrzLpjgAAAAAFG90dwcAAAAAwEeQpAMAAAAA4CPo7g4AAADAVtmXMWwQ0NnmiAD7kKQDAAAAsFX2ZQwbRNkYDGAzursDAAAAAOAjaEkHAAAAACAPYcHpue4XFpJ0AAAAAECRkH3+gvj4+EKv87Ho1EKvIyuSdAAAAABAkZB9/oLCkBFYNtd9byFJBwAAJdq5pCTJ4fh9nwmrAKBES2vULduZ77xav09MHDd37lzVrl1bpUqVUrt27bRly5Y8733ttdd0ww03qGLFiqpYsaI6d+58xfsBAAAAACgqbE/SlyxZorFjx2ry5Mnavn27WrZsqa5du+r48eO53r9u3Tr1799fa9eu1ebNm1WjRg116dJFR48e9XLkAAAAAABYy/YkfdasWRo2bJiGDBmipk2bat68eSpTpozmz5+f6/3vvPOO/va3v6lVq1Zq3LixXn/9dWVkZGj16tVejhwAAAAAAGvZmqRfuHBB27ZtU+fOnV3n/Pz81LlzZ23evPmayjhz5owuXryosLCwwgoTAAAAgAfCgtNV6bfNG0tYAUWZrRPH/fLLL0pPT1dkZKTb+cjISO3Zs+eaynjkkUdUtWpVt0Q/q/Pnz+v8+fOu41OnThU8YAAAUORcSj0myT/LPgBvy76EVX+F2hMIUATY3t3dE88884zeffddLV++XKVKlcr1nunTp6t8+fKurUaNGl6OEgAAFHdZk38+CAAAeMLWJL1y5cry9/fPsc5dcnKyqlSpcsVnn3/+eT3zzDP69NNP1aJFizzvmzBhgk6ePOnajhw5YknsAAAAAABYzdYkPSgoSK1bt3ab9C1zErj27dvn+dxzzz2nJ598UqtWrVKbNm2uWEdwcLBCQ0PdNgAAAAAAfJGtY9IlaezYsRo0aJDatGmjtm3bavbs2Tp9+rSGDBkiSRo4cKCqVaum6dOnS5KeffZZTZo0SYsXL1bt2rWVlJQkSQoJCVFISIhtrwMAAAAA4Fvi4uLkdDolSeHh4YqPj7c5oquzPUnv16+fnE6nJk2apKSkJLVq1UqrVq1yTSZ3+PBh+fn93uD/yiuv6MKFC7rrrrvcypk8ebKmTJnizdABAAAA5CIjsGyu+4C3OZ3OHMOrfZ3tSbokjRw5UiNHjsz12rp169yODx48WPgBAQAAACiwtEbdsp35zpY4gOzmjFvpdjxyZk+Pyww1kmSy7HvGJ5J0AAAAAACKoiHp6ZaWR5IOAMWY1Z/sAgBQGM4lJUkOx+/7Ufkv4/Lyh/5Z9t0VxbHJKJlI0gGgGMv+ye4um+IAAMBuRXFsMkomW5dgAwAAAADgWiWmpOW6X5yQpAMAAAAA4CPo7g4AAEo05m4AAPgSknQAAFCiWTF3Q1hwutv+UQ9jAgBYw4pJCb2NJB0AAMBDj0Wnuh33V6g9gQC4ZoWxXjZgBcakAwAAAADgI2hJBwAAKGTZ12duENDZ5ogAAL6KJB0AABRrvjBePPv6zA2KwJhIoLgpimOTUTKRpAMAgGKN8eIAgKKEMekAAAAAAPgIWtIBAAAAFDsxL8W4HUcq2KZIgPyhJR0AAAAAAB9BSzoAAACAIif7qglAbkKNJJks+76PJB0AAKCQMas0YL3sqyagZMgILOu2n/3Dmvj4eLf7h6Snux3vKvwQPUaSDgAAAKDYK4otqsgprVE3t2Pn0VXF7sMaknQAAAAAtvJGAl0UW1RRMpGkAwAAALBVQRLoxJQ01yzYiSlpalTq9zLCgtN11LrwAK8iSQcAAABQ5D0Wnep23F+h9gQCeIgl2AAAAAAA8BEk6QAAAACAIulS6rFc94syursDAAAUMmaVBqyXfSkuoLggSQcAAChkzCoNWC/7UlzSd7bEAViNJB0AAAAAUCSFBafnum+nOeNWuh2PnNkzX8+TpAMAAAAAiqTss/oXB0wcBwAAAACAj6AlHQAAAABQLMS8FON2/HQRTHmLXsQAAABAFszyDaA4IUkHAABAkZZzlm8AKLoYkw4AAAAAgI+gJR0AAADFSlxcnJxOpyQpPDxcqm9zQACQDyTpAAAAKFacTqeSk5N/P0GSDqAIobs7AAAAAAA+giQdAAAAAAAfQZIOAAAAAICPIEkHAAAAAMBHkKQDAAAAAOAjSNIBAAAAAPARLMEGAAAAACVIXFycnE6nJCk8PFzx8fE2R4SsSNIBAAAAoARxOp1KTk7O8zpJvL1I0gEAAAAALldL4lG4SNIBAAAAoBjL3jIO30aSDgAAAADFGC3jRQtJOgAAAAAgT3PGrXQ7Hjmzp02RlAwk6QAAAABQjCWmpLnW3k5MSVNUxRC36zEvxbgd11ItL0VWNGX/ej1tcVrNOukAAAAAAJdzSUm57sM7aEkHAAAAgGIsI7Cs2/6l1GOS/CXpt334EpJ0AAAAACjG0hp1czuO/O98myLBtaC7OwAAAAAAPoIkHQAAAAAAH0GSDgAAAACAjyBJBwAAAADARzBxHAAAAADAJdRIksmyD28iSQcAAACAEiQsON1t/2i260PS092Od3khJvyOJB0AAADFCmtAA1f2WHSq23F/hdoTCHLFmHQAAAAAAHwESToAAAAAAD6CJB0AAAAAAB9Bkg4AAAAAgI8gSQcAAAAAwEeQpAMAAAAA4CNI0gEAAAAA8BEk6QAAAAAA+AiSdAAAAAAAfARJOgAAAAAAPoIkHQAAAAAAH0GSDgAAAACAjyBJBwAAAADAR5CkAwAAAADgI0jSAQAAAADwESTpAAAAAAD4CJJ0AAAAAAB8RIDdAQAAAAAASpa4uDg5nU5JUnh4uOLj422OyHeQpAMAAKBYCQtOd9s/amMsQEl1tSTc6XQqOTnZjtB8Hkk6AAAAipXHolPdjvsr1J5AgBKMJLzgSNIBAAAAALaaM26l2/HImT1tisR+JOkAAAAAAI9k795+KfWYJH9J+m0f14okHQAAAADgkat1b495KcbtODIpWHI4JEnnkpKkqEINr0ghSQcAAAAAWIoJHAuOJB0AAAAAYCkmcCw4P7sDAAAAAAAUbYkpabnuI/9I0gEAAAAA8BF0dwcAAAAAeFWokSSTZR+ZSNIBAAAAAB7JCCyb635ehqSnux3vsjyiooskHQAAAADgkbRG3bKd+c6WOIoDknQAAAAA8BFxcXFyOp2SpPDwcMXHx9scEbyNJB0AAAAAfITT6VRycrLdYcBGzO4OAAAAAICPoCUdAAAAAIoIusMXfyTpAAAAAGCBgiTQ2Z+5lHpMkr8k/bbvLnt3+DnjVrpdHzmzZ0HDh48gSQcAAACAa3C1JLwg48kZg47sSNIBAAAA4BoURkKdmJLmmigsMSVN4Q736zEvxbgdRyYFS47LN51LSpKiLA0HPoAkHQAAAAAKAV3RURAk6QAAAACQi+zd2wtDRmBZt/0w/xTXcVhwuo5muz/USJLJso/ihiQdAAAAAHJxte7tVnRFT2vUze34sXIz3I77K9TteEh6utvxrqtXgSKGddIBAAAA4BpknW09t5nXASuQpAMAAABALhJT0nLdBwoTSToAAAAAAD6CMekAAAAAkIvsk7rp0qkr3s+kbrACSToAAAAA5CL7pG5N9r/m2s9t5nUmdYMVSNIBAAAA4Bo8Fp3qdpx95nXACoxJBwAAAADAR5CkAwAAAADgI0jSAQAAAADwESTpAAAAAAD4CJJ0AAAAAAB8hE8k6XPnzlXt2rVVqlQptWvXTlu2bLni/UuXLlXjxo1VqlQpNW/eXB999JGXIgUAAAAAoPDYnqQvWbJEY8eO1eTJk7V9+3a1bNlSXbt21fHjx3O9f9OmTerfv79iY2P17bffqlevXurVq5e+++47L0cOAAAAAIC1bE/SZ82apWHDhmnIkCFq2rSp5s2bpzJlymj+/Pm53h8fH69bb71V48ePV5MmTfTkk0/quuuu05w5c7wcOQAAAAAA1gqws/ILFy5o27ZtmjBhguucn5+fOnfurM2bN+f6zObNmzV27Fi3c127dtWKFStyvf/8+fM6f/686/jkyZOSpFOnTkmS0s+fdbv/18B0t+NLZy+5HZ92P9TZ82fcjjPLzYo6ilYdVtXjDb7w9aKOol2Ht+qhDs/q8Ibi8rXKXoe36qEO6rCjDm/VQx3U4St1eKuewqgjsx5jTI76cjA2Onr0qJFkNm3a5HZ+/Pjxpm3btrk+ExgYaBYvXux2bu7cuSYiIiLX+ydPnmwksbGxsbGxsbGxsbGxsbHZuh05cuSqebKtLeneMGHCBLeW94yMDJ04cUKVKlWSw+G4pjJOnTqlGjVq6MiRIwoNDS2UOKnDt+rwVj3UQR1FtQ5v1UMd1FFYistroQ7qKCzF5bVQR8mrw1v15LcOY4x+/fVXVa1a9ar32pqkV65cWf7+/kpOTnY7n5ycrCpVquT6TJUqVfJ1f3BwsIKDg93OVahQoUDxhoaGFuoPE3X4Xh3eqoc6qKOo1uGteqiDOopyPdRBHUW1Dm/VQx3UUZTryU8d5cuXv6b7bJ04LigoSK1bt9bq1atd5zIyMrR69Wq1b98+12fat2/vdr8kffbZZ3neDwAAAABAUWF7d/exY8dq0KBBatOmjdq2bavZs2fr9OnTGjJkiCRp4MCBqlatmqZPny5JiouL04033qiZM2eqR48eevfdd/XNN9/o1VdftfNlAAAAAADgMduT9H79+snpdGrSpElKSkpSq1attGrVKkVGRkqSDh8+LD+/3xv8O3TooMWLF2vixIl67LHH1KBBA61YsULNmjUrtBiDg4M1efLkHN3mqaP41uGteqiDOopqHd6qhzqooyjXQx3UUVTr8FY91EEdRbmewqzDYcy1zAEPAAAAAAAKm61j0gEAAAAAwO9I0gEAAAAA8BEk6QAAAAAA+AiSdAAAAAAAfARJOoqERYsW6fz58znOX7hwQYsWLbIhIgAAAACwHrO7F2MXL17Urbfeqnnz5qlBgwZ2h+MRf39/JSYmKiIiwu38//73P0VERCg9Pd2myHxTUlKSvv76ayUlJUmSqlSponbt2qlKlSqW1nPy5Em3OsqXL29p+YsWLVK/fv1yLG1x4cIFvfvuuxo4cKCl9UnSqVOntGbNGjVq1EhNmjSxpMyzZ8/KGKMyZcpIkg4dOqTly5eradOm6tKliyV1SNLq1au1evVqHT9+XBkZGW7X5s+f73H56enp8vf3dx1//fXXOn/+vNq3b6/AwECPy7dLenq6du3apVq1aqlixYp2h3NVH3zwwTXfe/vttxe4nm3btql169YFft7XDB06VPHx8SpXrpzb+dOnT2vUqFGW/I7kJSUlRStXriyU96zCdvz4ce3du1eS1KhRoxx/hz1V2H9HBg0apNjYWHXs2NHScrMqTv+jnDt3Tjt37sz170hB309OnTp1zfeGhoYWqI7c7N+/XwcOHFDHjh1VunRpGWPkcDgsK1+SMjIytH///ly/Xlb9zKWmpmrLli251lEU31MK0969e/XSSy/p+++/lyQ1adJEo0aNUqNGjSyrIzU1Ve+9954OHDig8ePHKywsTNu3b1dkZKSqVatmWT0yyNXixYvzvPb3v//di5F4pnLlymbfvn1eq+/8+fPmyJEj5tChQ26bpxwOhzl+/HiO8wkJCaZixYoel5/df//7X/Pxxx+b999/322zyrp168xtt91m6tWrZ+rVq2d69uxpvvjiC4/LTUtLM3fffbfx9/c3AQEBJiIiwkRERJiAgADj7+9v7rnnHnP69GmP63nttddMkyZNjJ+fn9vWpEkT8/rrr3tcfiY/Pz+TnJyc4/wvv/xi/Pz8LKmjT58+5qWXXjLGGHPmzBnToEEDExgYaAICAsx7771nSR233HKLeeWVV4wxxqSkpJjIyEhTvXp1U6pUKfPyyy9bUseUKVOMn5+fadu2rbnjjjtMr1693DZPHDt2zMTExBh/f3/TsWNHc+LECdOjRw/jcDiMw+EwDRs2NMeOHbPkdRhjTHp6ep7nrXg/iYuLc/2cXrp0ycTExBiHw2HKli1r1q5d63H5V3P48GEzZMiQAj+f+XW/2ubp74jD4TD16tUzTz31lDl69KhHZeWlWbNmZtq0aebw4cOFUn5Web2fOJ1O4+/vX6h1JyQkePz9aNWqlYmOjr6mzQqnTp0y99xzjwkICHD9TAUEBJi7777bpKamely+t/6O3HHHHSYwMNDUr1/fPPXUU+bnn3+2rOxMDocj15+to0ePmlKlSnlU9tdff20uXbrkOl65cqXp2LGjqVq1qmndurV58803PSo/q48//tiEh4db/n6S+fyVNiveszL98ssv5uabb3aVeeDAAWOMMUOGDDFjx461pA5jjNm8ebOpU6eOK34r338zffDBB6ZcuXLG4XCY8uXLmwoVKrg2q/7/vXTpknn99ddN//79zc0332z+/Oc/u21FpY733nvPBAQEmD/96U/moYceMg899JBp3769pf/P7dixw4SHh5v69eubgIAA18/W448/bu69915L6shEkp6H8uXLm48++ijH+TFjxpgqVap4XP6HH35oYmNjzfjx483333/vdu3EiROW/cCOGTPGPPLII5aUdSX79u0z119/veVvupn/lPj5+ZnmzZu7/RPSokULU65cOdOnTx/LXseBAwdMixYtXHFnfbO16g33rbfeMgEBAaZv374mPj7exMfHm759+5rAwEDzzjvveFR2bGysadCggVm1apXbH/VLly6ZTz75xDRs2NDcd999HtXx3HPPmTJlyphHH33UrF271uzevdvs3r3brF271kyYMMGULVvWzJgxw6M6Mnnjw5nIyEiTkJBgjDHmnXfeMfXr1zenT582L7/8smnVqpUldVSqVMl89913xpjL/5i2aNHCpKenm3//+9+mcePGltRRpUoVs2jRIkvKyu7ee+81HTp0MB988IHp16+f6dChg7nhhhvMzz//bA4dOmRiYmLMiBEjPK7n5MmTpk+fPqZUqVImIiLCPPHEE24/x0lJSZb8HlarVs1s3brVGGPM8uXLTdWqVc3evXvNxIkTTYcOHTwu/2qsSNi8weFwmGHDhrk+6OvRo4dZvny52/fEijoqVapk/P39TdeuXc17771nLl68aFn5xlz+uUpNTTUOh8Ps37/fnDx50rWdOHHCvPnmmyYqKsrjOq60bdiwwePv+ZQpU1zbo48+akJDQ3P8MxoaGmoeffRRj+rJ1LdvX9ffk8zXsWrVKtOoUSPTr18/j8r25t8RY4w5fvy4mTlzpmnRooUJCAgwt956q1m6dKm5cOGCR+Vm/g338/MzTz31lOs4Pj7ezJo1y/Tq1cvjvyNZP1z64IMPjJ+fnxk4cKCZO3euue+++0xAQIBZtmyZR3Vkql+/vvnb3/5mkpKSLCkv07p16655s8K9995runbtao4cOWJCQkJcidSqVatM06ZNLanDGGNatmxp+vTpY3bv3m1SUlJMamqq22aFBg0amLi4OEsaWPIyYsQIU7ZsWdO3b18TFxdnxowZ47YVlTrq1q1rnnjiiRznJ02aZOrWrWtJHTfffLMZP368Mca4/Wxt3LjR1KpVy5I6MpGk5+H//u//TPny5c2GDRtc50aOHGmqVq2aI6nOr3feecf4+/ubHj16mOuvv96UKlXKvP32267rVv0jaszlmENDQ03r1q3N/fff7/pjnrlZpUOHDqZjx47mo48+Mt9++61JSEhw2woq8x8Sh8Nh/v73v7v9k/L000+bxYsXm/Pnz1v2Om677TZzxx13GKfTaUJCQszu3bvNhg0bTNu2bS1p6TbGmMaNG5tZs2blOD9z5kyPE7YKFSqYjRs35nn9yy+/NBUqVPCojpo1a5olS5bkef3dd981NWrU8KgOb344U6pUKVcr3r333uv6UOvQoUOmbNmyltRRunRpVwtwnz59zJQpU4wxl1tUS5cubUkdYWFhZv/+/ZaUlV1UVJTZvHmzMcaY//3vf8bhcJjPP//cdX316tWW/AEcPXq0adiwoVm6dKl57bXXTK1atUyPHj1cv+NJSUnG4XB4XE9wcLA5cuSIMcaYYcOGmbi4OGOMMT/++KMpV66cx+Vn74GTfXvhhRcKJUk/e/aspeVltg5evHjRvPfee6Z79+7G39/fREZGmocfftjs3bvXkjqOHj1qli9fbnr27GkCAgJMeHi4GTdunNm9e7cFr+LqrXj+/v7mH//4R6HWYWXLmjGXP5CdOHFijvOTJk3yqJdGVmXKlHH7HyjTF198YcqUKeNR2d74O5KXbdu2mZEjR5pSpUqZypUrmzFjxhS4x2Ht2rVN7dq1jcPhMDVq1HAd165d2zRs2NB06dLFfPXVVx7Fm7WV/vrrr8/xIcxTTz1l/vSnP3lUR6Zy5coV2t8Rb8r64XvWROrAgQOW/V035vLvyA8//GBZeXnVkRl/YalUqZL58MMPi3wdpUuXzvX7sW/fPsv+1woNDXX9jmT92Tp48KAJDg62pI5MAdZ1nC9eevTooZdfflm33367PvvsM73xxht6//33tXbtWjVs2NCjsmfMmKFZs2Zp9OjRkqR///vfGjp0qM6dO6fY2Fgrwnf57rvvdN1110mS9u3b53bNynE5CQkJ2rZtmxo3bmxZmZI0efJkSVLt2rXVr18/lSpVytLys9u8ebPWrFmjypUry8/PT35+frr++us1ffp0jR49Wt9++63Hdfz444/q2bNnjvO33367HnvsMY/KzsjIUFBQUJ7Xg4KCcoxnyq/jx4+refPmeV5v3ry5fvnlF4/q6NWrl6TLP1ddu3ZVSEiI61pQUJBq166t3r17e1RHpho1amjz5s0KCwvTqlWr9O6770q6PIbUqp+3+vXra8WKFbrzzjv1ySef6KGHHpJ0+Wtp1fi7++67T4sXL9YTTzxhSXlZpaSkuMZZhYWFqUyZMqpVq5brev369ZWYmOhxPStWrNCbb76pTp06Sbr8c9CjRw/17NnTNQ7bivetyMhI7d69W1FRUVq1apVeeeUVSdKZM2fcxtwXVK9eveRwOGSuMOWLVe+/6enpevrppzVv3jwlJydr3759qlu3rp544gnVrl3bkr8pAQEB6t27t3r37q2jR49q/vz5WrhwoZ5//nnFxMToiy++8Lj8Xr16qVevXkpMTNTChQu1YMECvfDCC2rXrp3uu+8+DR06tMDlr127VsYY3XTTTfrPf/6jsLAw17WgoCDVqlVLVatW9eg1lCtXTo8//rjatWuX6/UffvhBDzzwgEd1ZLV06VJ98803Oc7fc889atOmjSXj6ytVqpTr+PDy5ct7PHeDN/6O5CYxMVGfffaZPvvsM/n7+6t79+7atWuXmjZtqueee8713nytfvrpJ0nSn//8Zy1btqzQ57TYt2+fZs+e7Xaud+/emjFjhiXl33XXXVq3bp3q1atnSXmZdu7cec33tmjRwuP6Tp8+7ZoDJqsTJ07kmOPGE+3atdP+/ftVv359y8rMrmvXrvrmm29Ut27dQqsjKCioUF+Dt+ro1KmTNmzYkKOeL7/8UjfccIMldQQHB+c6x8K+ffsUHh5uSR0ulqb8xdDcuXNNcHCwqV69umWflpUtW9b8+OOPbufWrFljQkJCzCuvvGJpS7q3tGnTJtdP3K2UkpJiXnvtNfPoo4+a//3vf8aYy5+IWzm+rEKFCq7vTd26dc2aNWuMMcbs37/fsk/h6tWrZ+bNm5fj/CuvvGLq16/vUdkDBgww0dHRZvv27Tmubd++3bRu3drcfffdHtVxww03mIEDB+baJfXSpUtm4MCBpmPHjh7VkWnhwoXm3LlzlpSVl7lz55qAgABToUIF07JlS9d46BdffNF06tTJkjqWLl1qAgMDjZ+fn7nllltc559++mlz6623WlLH6NGjTYUKFUzHjh3NyJEjLe01U7NmTfP111+7jh955BHX76Axl7tvV65c2aM6jLn8KXj298ZTp06Z9u3bm5tuusn8+OOPlrw3Tp482ZQvX940btzY1KxZ0/Uz9sYbb1jSIlW1alWzYsWKPK9/++23lr3HT5061dStW9e8/fbbpnTp0q5P9d99912PX0teY7gzff7552bAgAGFVsfatWvNPffc41HLV8WKFY3T6TTGGDN48GBz6tSpApd1JZ06dTLPPvtsntcTEhIs6QWSKTIy0ixYsCDH+QULFpiIiAhL6vjnP/9pOnfubBITE13nEhMTTZcuXXL9G5Yf3vw7cuHCBfPee++ZHj16mMDAQNO6dWvzyiuvmJMnT7ruWbZsmce9zAqLw+Ewa9euNTt27DC1atUyW7Zscbu+Z88eExISYkldp0+fNt27dzeDBg0yzz//vFv3/fj4+AKXm30IYWHNo5GpW7durp4mISEh5scffzTp6emmT58+pnfv3pbUYczln5umTZuaBQsWmG+++cbs2LHDbbPC66+/bmrWrGkmT55s3nvvvUKZK+n55583f/vb30xGRoYl5Xmzjqxfi1deecWEh4ebESNGmLfeesu89dZbZsSIESYiIsI1L5CnYmNjTa9evcyFCxdcP1uHDh0y0dHRrl55VmF29yzGjh2b6/mlS5fquuuuc/tkcdasWQWup2rVqlq2bJn+9Kc/uZ1fv369brvtNsXFxWn69OmWzwb6r3/9S7fffrvKli1rSXlZP0n65ptvNHHiRD399NNq3rx5jpmePW0t3Llzpzp37qzy5cvr4MGD2rt3r+rWrauJEyfq8OHDli3DdsMNN2jcuHHq1auXBgwYoJSUFE2cOFGvvvqqtm3bpu+++87jOl555RWNGTNGQ4cOVYcOHSRJGzdu1MKFCxUfH+9Ra0tKSooGDBigTz75RBUrVnTNNHv8+HGlpqaqa9euWrx4sSpUqFDgOnbu3KmuXbvq4sWL6tixoyIjIyVJycnJ+uKLLxQUFKRPP/1UzZo1K3AdmY4cOSKHw6Hq1atLkrZs2aLFixeradOmuv/++z0uP9O2bdt0+PBh3XLLLa5W+w8//FAVKlRQTEyMJXUkJSUpMTFRLVu2lJ/f5dUvt2zZotDQUEt6oPz5z3/O85rD4dCaNWsKXPYdd9yhm266SXFxcblenzt3rpYtW6bVq1cXuA5Jaty4sWbNmqXu3bu7nU9LS1OXLl105swZ7dq1y5L3xvfee09HjhxRnz59XD9fb775pipUqKA77rjDo7Jvv/12tWrVStOmTcv1+o4dOxQdHe1xrxbpci+Gf/7zn7r55ptVrlw57dixQ3Xr1tWePXvUvn17paSkFLhsPz8/JSUlWT6bd37rOHXqVIH/hoSEhGjnzp2qW7eu/P39lZSUZH1rh6TXXntNZ8+edfWQyy45OVnz5s1z9Q7z1DPPPKOpU6dq2LBhatu2raTLqy3Mnz9fTzzxhB599NEClRsdHe3Wy+OHH37Q+fPnVbNmTUnS4cOHFRwcrAYNGmj79u0Fjt+bf0cqV66sjIwM9e/fX8OGDVOrVq1y3JOamqro6GhXy3hB/Pzzz/rggw90+PBhXbhwwe2aJ/8z+vn5ufXMmTVrlsaMGeO6/u677+rJJ5/Uf//73wLXkemNN97Q8OHDVapUKVWqVMntZ8HhcOjHH38sULmHDh265nuz9tIqqO+++04333yzrrvuOq1Zs0a33367/vvf/+rEiRPauHGjZT0FMv+WZ5X5vXI4HJb8rcqtjqx1WVHHnXfeqbVr1yosLEx/+MMfcvwPv2zZsgKV+5e//MXteM2aNZbXcaWvT1ZWfa1Onjypu+66S998841+/fVXVa1aVUlJSWrfvr0++ugjy3IsiSXY3Fzpn9ysPP2Ht1evXmrZsqWmTp2a49q6det022236ezZs5Yn6aGhoUpISLCsy0zmH45MJpelLax6o7r55pvVunVrPffcc27/iG7atEkDBgzQwYMHPSo/0yeffKLTp0/rL3/5i/bv36/bbrtN+/btU6VKlbRkyRLddNNNltSzfPlyzZw5022JiPHjx3ucHGTas2ePNm/e7LasTfv27S0bjvDrr7/q7bff1ldffZWjjgEDBljWhfuGG27Q/fffr3vvvVdJSUlq2LChmjVrph9++EGjRo3SpEmTPCr/4sWLaty4sf7v//7PsuXWcqujdOnSSkhIsOQfTl+0ZcsWlSlTxuPXN3r0aCUmJmrp0qU5rv3666+65ZZbtHXrVo/fTwp7ab8NGzbo9OnTuvXWW3O9fvr0aX3zzTe68cYbPapHkkqXLq09e/aoVq1abu+Nu3fvVtu2bZWWllbgstevX6+YmBgFBBTeyLghQ4boxRdfzLEsmlVuueUWJScnq3Xr1nrzzTfVr18/lS5dOtd7C3MJtsLw73//W/Hx8W5/R+Li4tS3b98Cl5nb/yV58fQDB2/9HXnrrbfUp0+fQh0ut3r1at1+++2uD8iaNWumgwcPyhjjShQLKnuCGxISokqVKrmOMxsprFiKq0qVKho9erQeffTRa05+fNXJkyc1Z84c7dixQ2lpabruuus0YsQIRUVFWVbH1T58sOIDB28YMmTIFa8vWLCgUMq1og67bNy40e1nq3PnzpbXQZJug/Xr12vTpk2aMGFCrtfXrl2rRYsWWf4Dm/UfOCusX7/+mu/19J/R8uXLa/v27apXr57b6zh06JAaNWqkc+fOeVT+lZw4cUIVK1a0fG1NXF3FihX11VdfqVGjRnrxxRe1ZMkSbdy4UZ9++qmGDx9e4E/1s6pWrZo+//zzQkvSJalu3bpavny5WrZsWWh1FAcpKSk6duyY/vCHP+R6/ddff9X27ds9fj8pTmsat27dWg899JDuuecet/fGadOm6bPPPtOGDRvsDtFWycnJeuGFF3TgwAEtW7ZMXbt2zXNM6vLly70cne9KT0/Xxo0b1aJFC496XpUUbdu2Vbdu3TR16lTX72FERITuvvtu3XrrrXrwwQftDvGahIWFaevWrZaPSc/N7t27c+11UNC12IHihonjbHDjjTde8Z/MP//5z9fcqm8nK1qBrpVXJ2r4TebwgKyTDBVWHVZ2j7mSlJQUrVy50pJP3JOSkvT111+7WkCioqLUtm1bValSxeOyM128eNH1D/Xnn3/u+uPduHFjSyYqk6QRI0bo2Wef1euvv15oLYaPP/64HnvsMb311luF9vN0+vRpPfPMM1q9erWOHz+eoyu1FR9o5MWqn6uKFSuqYsWKebZ0BwcH56vbZF5y6/UjXe6umttEWZ46f/68JFk6YVGmSZMmadCgQTp69KgyMjK0bNky7d27V4sWLdL//d//eVz+hQsXtGLFihw9czp06KA77rjjihNV5tfhw4eVmJgoPz8/1a1b1621sKAiIyP1zDPPSJLq1Kmjt956y5Jy8ys5OVn//Oc/Pe79k6lu3braunVrjteSmpqq6667zuPfd39/f3Xp0kXff/+9LUn6xYsXlZiY6Opm7wlvvDd+//33+te//iXp8kSIZ8+eVUhIiKZNm6Y77rijUJL0IUOG6KmnnvJ40sOsBg0apCVLlng8ie2V/Pjjj7rzzju1a9cut278me/JVnxIumDBAoWEhKhPnz5u55cuXaozZ85o0KBBHteR6cCBA5o9e7arR0vTpk0VFxdn6Qcd69ev1/PPP+9Wx/jx4y2bDC2T0+nU3r17JUmNGjWy9P/rm266ScuWLcvxfnLq1Cn16tXLo94mWZ0+fVrr16/P9QOgvIYj5cfo0aNVv379HGXNmTNH+/fvzzGxo0csHeFeAuzfv9+yNcxvvvlms2DBArfJSwrThg0bCm0Srvnz55t///vfOc7/+9//NgsXLvS4fG9O1JCpXLlyhb7shTfqyMqK9ZnT0tLM3Xffbfz9/U1AQICJiIhwraPs7+9v7rnnHsvW82zbtq155JFHzBdffGFKlSrlWlJl8+bNplq1apbU0atXL1OuXDkTFRVlunTpYu688063zQqtWrUyISEhJjg42DRs2NBtSbno6GhL6vjrX/9qoqKizMMPP2xeeOEFM3v2bLetMFm97ndek4n98ssvHtXjzaX9Pv30U9OtWzdToUIF1zJcFSpUMN26dTOfffaZJXVk+uKLL0znzp1NeHi4KV26tImJiTGffPKJx+X+8MMPpm7duqZUqVLmxhtvNH379jV9+/Y1N954oylVqpSpX7++JROqzp0719SsWTPHsmUxMTHmm2++8bh8X2D170jWZbmySkpKMkFBQZbU0bp1a7elFr3Jyq+XN94bIyMjXUsGNmnSxDWhV0JCgsdLfmWfjCxzCwwMNMuXL7d0krJRo0aZ8uXLF8oEpJm8sdRtgwYNXBP/ZrVu3TrTsGFDS+ow5vK660FBQaZt27aur1Hbtm1NcHCw+fTTTy2p46233jIBAQGmb9++rgn8+vbtawIDA80777xjSR1paWlmyJAhxt/f3zWJX0BAgBk6dKhl/8/l9Z6VnJxsAgICLKlj+/btpkqVKiY0NNT4+/ub8PBw43A4TNmyZU2dOnUsqaNq1aq5/l3atm2bZf+XZqIlPZ/S0tLy1c37Sv7whz9owoQJ+tvf/qYePXronnvuUffu3XNMpuCJrJ9cXX/99a7zVn9yNX36dP3zn//McT4iIkL333+/x59azpw5U3fddZciIiJ09uxZ3Xjjja6JGp566imPys6L8cJIEKvryK23QVa//vqrx3XExcVpy5Yt+vDDD9W5c2fXklXp6elavXq1Ro0apbi4OL322mse1/Xss8/qzjvv1IwZMzRo0CBXd/EPPvjANVmSpypUqGDZcm55yVxSrjB9/PHH+vDDDy2b6C4rb/xcZWUKqaXbW0v7vfnmm7rvvvt011136YUXXnCbFOvTTz9V9+7d9cYbb+jee+/1uC7p8twNn332mSVlZfXggw+qefPm+vbbb3OMDz516pQGDhyoESNG6JNPPilwHc8//7xeeOEFTZgwQaVKldKsWbPUv39//fGPf9TixYvVsWNHrV+/Xm3atPH05UgqvFaWqy0xldk65anMpQily3OoZP19yHwPrl27tiV1/eMf/9Df//53Pfnkk2rdunWOHl9WjRkvbIX53pjpT3/6k7788ks1adJE3bt317hx47Rr165cJwnOr1atWuW5pGPv3r0tnaRs165dio6OlqQcE+VaNeTPG0vdHj58WHXq1MlxvlatWjp8+LDH5Wd69NFH9dBDD7l662Q9/8gjj+iWW27xuI6nnnoqx/KAo0eP1qxZs/Tkk09qwIABHtcxduxYrV+/XitXrnT9nnz55ZcaPXq0xo0b51qmtCCyvjfu3r3b1SNLuvyetWrVKtcSr5566KGH1LNnT82bN0/ly5fXV199pcDAQN1zzz15TnybX//73/9y/T8kNDTU+mUjLU35i4Hsy01k3x5++GFLPw1PT083n3zyiRk0aJAJDQ01FStWNMOGDTPr1q2zpHxvfHJljDHBwcHmp59+ynH+p59+MqVKlbKsng0bNpi5c+eaZ5991vLWqOxCQkIKvZXb6joylzDJa7NiiZMKFSqYjRs35nn9yy+/tHQpm0uXLpkTJ064nfvpp5+uuDRUSVS7dm1XS47VvPFzZYz3WroXLlxozp4963E5eWnQoIGZM2dOntfnzp3r8XKL2W3dutUsWrTILFq0yLLW59KlS5tdu3bleX3nzp0eL01Zu3Zt89FHH7mO9+7daypVquRammv06NFuyxZ6ojBbWa60xJSVvyPZy8y6BQUFmYYNG5qVK1d6XE/WurL//lvxWrL3Jsq+NW7c2LL/tQrzvTHTgQMHXK3ZaWlp5oEHHjDNmzc3f/nLX8zBgwc9Krtly5amR48e5vvvvzcHDx40Bw8eND/99JMJCAgwn332metcUeGNpW5r1KiR6/JkK1assLS1Mzg42Ozbty/H+b1795rg4GBL6ggKCsq1x9IPP/xgWR2VKlUya9euzXF+zZo1Hi+vmvX9I7f3xzJlypg33njDozoylS9f3uzZs8e1n/l7/9VXX5lGjRpZUscf/vAH89JLL+U4/+KLL5omTZpYUkcmWtKzGTNmjKKiovIcZ5f9k3dP+fn5qUuXLurSpYvmzZunlStX6qmnntIbb7zh0aei3vzkSrrcYr5z584cn+Dv2LHD0vF/119/vVuPgML08ccfW/o18kYd5cqV0+OPP6527drlev2HH37waIk3ScrIyLjiONSgoCBLlpbK5O/vr4oVK7qds6qlKNOlS5e0bt06HThwQAMGDFC5cuV07NgxhYaGurW2+rInn3xSkyZN0ptvvqkyZcpYWrY3fq4k77V0Dxo0SKmpqXr77bd14MABjR8/XmFhYdq+fbsiIyM9/p08fPjwFWd6vfnmmzVu3DiP6sj0888/q3///tq4caNrrF9qaqo6dOigd99917W8XEFUqFBBBw8ezHPW/oMHD3o8Xvn48eNukzY2aNBAJ0+elNPpVFRUlIYOHWrZe35htrKEhYXpueee080335zr9f/+97/q2bOnR3VIcr231qlTR1u3blXlypU9LjMva9euLbSyd+/erb/+9a+5tnZKUmJiovbt22dJXYX53pgp64S8ZcuW1bx58ywre8uWLXr44YfVu3dvvf32266Wbunykr5FZQbxTM2aNdOOHTtUp04dtWvXTs8995yCgoL06quvWjaxcf/+/TV69GiVK1dOHTt2lHR5XHdcXJz++te/WlKHJIWHhyshIUENGjRwO5+QkGDZ0pU1atTQ6tWrVb9+fbfzn3/+uWrUqGFJHWfOnHH1+MoqIiJCZ86c8ajsn376ScYY1a1bV1u2bHEb5x4UFKSIiAhXj0xPBQYGulYliIiI0OHDh9WkSROVL19eR44csaSOsWPHauTIkXI6na4Vn1avXq2ZM2daOx5doiU9u9q1a5slS5bkef3bb7+1tCU9U2JionnhhRdM69atjcPhMO3atfOoPG9+cmWMMQ8//LCpVauWWbNmjbl06ZK5dOmSWb16talVq5YZN26cJXV8/vnnZsKECSY2NtYMGTLEbbPKn//8Z5OSkpLj/MmTJy2bi6Aw6+jUqZN59tln87yekJBgHA6HR3UMGDDAREdHm+3bt+e4tn37dtO6dWtz9913F7j86OhoV8t5ZstqXpsVDh48aBo3bmzKlClj/P39XT0bRo8ebR544AFL6rh06ZKZMWOG+eMf/2giIyNNxYoV3baCyv71KVeunAkJCTHNmjWz9GvljZ+rrBYuXFho82cYc3mMZ3h4uKlfv74JCAhwfc8ff/xxc++993pc/nXXXWfGjx+f5/WHH37YXHfddR7XY4wxXbt2Ne3atXO1HhhjzJ49e0z79u1N165dPSr7iSeeMBUrVjSzZs0yO3bsMElJSSYpKcns2LHDzJo1y4SFhZnJkyd7VEerVq3Mq6++6jpevXq1KVOmjMnIyDDGXH4t5cqV86iOTIXZytKlSxfz5JNP5nnd6t+Roq5169bm5ZdfzvO6p/9reeu9MdPhw4fNkSNHXMdff/21iYuLM//85z8tKd8YYz766CNTvXp18/TTT5v09HQTEBBg/vvf/1pWfqatW7ea8ePHm379+hXKHC2rVq0y//nPf4wxl1uDGzVqZBwOh6lcubJZvXq1JXWcP3/e9O3b1zgcDhMYGGgCAwONv7+/GTJkiDl//rwldRhjzNSpU02FChXMM888Y7744gvzxRdfmOnTp5sKFSqYadOmWVLHyy+/bIKCgszw4cNdvaUeeOABExwcbObNm2dJHTfddJPp06ePWw+zM2fOmD59+pibb77Zkjq84ZZbbnGN07/vvvtM27Ztzdtvv226du1q2rZta1k9L7/8sqlWrZorp6pTp4558803LSs/Ey3p2bRu3Vrbtm3Lc53RvMYFFcSpU6f0n//8R4sXL9a6detUt25d3X333VqyZInHs0J685Mr6fIn1QcPHtTNN9/smiE7IyNDAwcOtGTM+NSpUzVt2jS1adNGUVFRhbYc2rp163LtLXHu3DnLljIqzDoGDBigs2fP5nm9SpUqHq9tO2fOHA0YMECtW7dWxYoVXZ8WHz9+XKmpqeratavmzJlT4PLvuOMO1yzY3hjLHRcXpzZt2uTo9XHnnXdq2LBhltQxdepUvf766xo3bpwmTpyoxx9/XAcPHtSKFSs8mu3ZG18fyTs/V1nddNNNcjqdrlbgLVu2aPHixWratKnuv/9+j8t/6KGHNHjwYD333HNu63N3797dkvF9M2fO1G233aZVq1apc+fObmPSV69erR9//FEffvihx/VIvy/p2ahRI9e5Ro0a6aWXXvJ45t9p06apbNmymjFjhsaNG+d63zXGqEqVKnrkkUf08MMPe1THhAkTdM899+jzzz9XqVKltGzZMo0ePdpV17p16/Jsyc+vwmxlGT58uE6fPp3n9Zo1a3q8rOqLL754zfdaMYvxqlWrFBIS4urJMHfuXL322mtq2rSp5s6dm6OHU37ExMRccZx+1hbQgvDWe2OmAQMG6P7779e9996rpKQkde7cWc2aNdM777yjpKQkS2b179atm7755hsNGTJEH3/8sQVR5/Tuu+9q4MCB6tq1qz799FN16dJF+/btU3Jysu68805L6ujatatrv379+tqzZ0+uS93+/PPPqlq1aoHWaw8KCtKSJUv05JNPaseOHSpdurSaN29uea+DJ554QuXKldPMmTNdyypXrVpVU6ZMseR3ULo8N0iVKlU0c+ZM/fvf/5YkNWnSREuWLNEdd9xhSR3x8fHq2rWrqlev7pr7Z8eOHSpVqpRHc45ktWjRoitet2LVoaeffto1R85TTz2lgQMH6sEHH1SDBg00f/58j8vP9OCDD+rBBx+U0+lU6dKlC63HJeukZ7N7926dOXMmz0lqLl68qGPHjlnyi166dGlVrFhR/fr10913323ZxDh2+uGHH5SQkGD5G2JUVJSee+45yyZayi5zeECrVq20Zs0at2WyMocH/POf/9TBgwd9ug5v2rNnT45lmdq3b6/GjRvbHFn+VKpUyZXkZF1n+uDBg2ratKnHXb0kqV69enrxxRfVo0cPlStXTgkJCa5zX331lRYvXmzBKyk+brjhBrd/eBs2bKhmzZrphx9+0KhRozz+h7d8+fLavn276tWr5/Y9P3TokBo1aqRz5855/BoOHjyoV155RV999VWO35Hhw4dbNmSjYcOGevvtt3NMpLhlyxYNGDBA+/fvt6SeH3/8UcnJyZIuv468uikXxMcff6y3335b58+fV9euXd0+HPvf//4nSZYMm+rSpYsGDx6sAQMGaNiwYdq5c6dGjx6tt956SykpKfr66689rqMwZf+aO51OnTlzxm2YQ5kyZRQREWHJsmLNmzfXs88+q+7du2vXrl1q06aNxo0bp7Vr16px48Yef+hQnFSsWFFfffWVGjVqpBdffFFLlizRxo0b9emnn2r48OGWL4H54osvau3atXrppZc8GtKSXYsWLfTAAw9oxIgRrvfGOnXq6IEHHlBUVJSmTp1qWV1XExoaqoSEBMu6wBe2zOQw6we/RcmZM2f0zjvvaM+ePZIufxBw9913q3Tp0paUn/1DvYsXL+rMmTMKCgpSmTJldOLECUvqKVYsb5vHNfv0009Nenp6odezaNEi06FDBxMVFeWaXGTWrFlmxYoVltUxderUXJdpOHPmjJk6darH5YeFhZn9+/d7XE5evDE8wFtDEC5cuGBuuummXCczKarOnz9vjhw5Yg4dOuS2WaFChQquLoNZJ/LbsGGDiYiIsKSOMmXKuOKtUqWK2bZtmzHm8mRDoaGhltQRGxub68QvRVGFChVc3ZLj4+NNhw4djDHGfPLJJ5YsoxIeHu4arpH1e/7pp5+a6tWre1y+N61YscK0bdvWbN261XVu69at5k9/+pNZvny5fYH5oK1bt7omqUpOTjZdu3Y15cqVM9ddd51recei4p133jExMTE5hjnccMMN5u2337akjrJly7omhJ08ebLp3bu3MebyUkORkZGW1OENderUMb/88kuO8ykpKZYty5T1a9WzZ0/zzDPPGGOMOXTokKWT5xa2MmXKuF5HWFiY2blzpzHGmN27d5sqVap4NRZPJta9dOmSef31103//v3NzTffbP785z+7bfAN+/btMzfffLNZtWqV3aFcs6SkJHPPPfeYqKgo4+/vn2MiXSvR3T0PWZcuy8rKpcumT5+uP/7xj4VaxyuvvKJJkyZpzJgxeuqpp1yT0VWsWFGzZ8+2rKvM1KlTNXz48ByTspw5c0ZTp071uOXrvvvu0+LFi/XEE094VE5evDE8wFtDEAIDA6+6HFBhSklJ0cqVKy3purRv3z7FxsZq06ZNbueNhUvOdOnSRbNnz9arr74q6fKQlrS0NE2ePFndu3f3uHxJql69uhITE1WzZk3Vq1dPn376qa677jpt3brV1bXfU06nU7feeqvCw8P117/+Vffcc4+r25oVfvnlF82fPz9H74kOHTpo8ODBbj/Pnrp48aLr6/L555/r9ttvlyQ1btxYiYmJHpd/++23a9q0aa6ugw6HQ4cPH9YjjzxS6MvxWSF7t9DTp0+rXbt2rqFGly5dUkBAgIYOHepxt9/du3drzpw5ufaaGTlypJo2bepR+ZnS09Pd3v++/vprnT9/Xu3bt7dkWVJjjCIiIlxd5yMiIrRq1SqPy80qMTFRq1evVlhYmDp37uw2webp06c1c+ZMS7o9S5e72b733ns5hjm88MILuuuuu3T33Xd7XEdQUJCrJ9Hnn3/uek8PCwu76rKMV5Kfv08tWrQocD2ZDh48mOvfivPnz+vnn3/2uHzp8pK68+bNU48ePfTZZ5/pySeflKT/Z+/M42ra/v//OqdBpzkpQypSbkUpfMxjUYarDDdDIRHX1U1kvi7XPE8ZrplkniJzSIVMhSJTgxKuZKYypN6/P/qd/e10ynDOOidlPx+P/Xhk7az3Op1z9t5rrff79cJ///3HTDx37ty5qFq1KgYPHizRvmnTJjx79gwTJkyQO4aBgQG3I2xiYoLExETY2dnh9evXTLLKlEVAQACCg4PRtWtX1K9fn2mJZMOGDREREQEDAwM4Ojp+se9r167JFKNy5cpISkpClSpVpK73xZF1B/rQoUPo3Lkz1NTUJKwdS0J8D2aNlZUV5s2bh/79+3M7+N/L196Dosj6fhRl0KBByMjIwJQpUxRafgsA/CS9FJRRmxwdHa3wGCtWrMD69evRvXt3CR/Hxo0bY+zYsUxiAKX7GickJEikdcvKhw8fsG7dOpw+fRr29vZSD21LliyRq39xWj5LVfKyiCGmf//+2Lhxo5R3pzLIyMiAj48Pk0m6j48PVFVVceTIEYVdDBcvXgxXV1fY2triw4cP8PT0RHJyMqpUqYKdO3cyidGjRw9ERESgadOm8Pf3596fjIwMCe9TeQgLC8OrV6+wd+9e7NixA0uWLIG1tTW8vLzg6ekpV3p1bGwsXF1doampiQ4dOqBu3boACmusly9fjnnz5iE8PJxZyY6iH3gXL16M3377DcbGxnj//j3atm2LzMxMNG/enImGxtdISEhAw4YNZV5kYq4gWwrHjx9H9+7d0bBhQ7i7u0vU1p86dQoNGzZEWFiYRI3p9/LkyRN4eHjg0qVLaNmyJQ4ePIgBAwbg2LFjAAof4qKiolC9enW5XgsRwdLSErdu3ZJSYmZBbGwsXFxcUFBQgLy8PJiYmODgwYOoV68eACA7O5vJgrWYJ0+e4PPnz1Lt+fn5XFmCvLRq1QqBgYFo2bIlrly5gt27dwMoXDyVJ8X6S77fwP9p/8i7EPstnvKsyjbmz5+PHj16YOHChfD29uYWSA8dOiRViiIra9euLbE0ql69eujbty+TSXqbNm1w6tQp2NnZwcPDAwEBAThz5gxOnTpVqnPBj8iuXbuwZ88eZgvtRSmqm+Pu7q6Q55KlS5dyafNLly5VSIzu3bsjMzMTxsbGX1zMZbUhUhqqqqr477//ZP7/ytafOH/+PM6dOwcHBweFx+Jr0otR0WqTRSIR7t69C3Nzc4nay+TkZNjb239RDOpbEK/wvXnzBrq6uhIXkvz8fGRnZ2P48OFYtWqVXHHat2//xfMsrWK2bt2KNWvWIC0tDRcvXoS5uTmWLl0KCwsLZpkHio7h7++PkJAQWFlZoVGjRtDS0pI4L8+ixtd2UG7cuIG2bdsyuahraWnh6tWrCq9z//z5M3bt2oUbN24gOzsbDRs2ZFqLVZxLly7hwoULsLKyYmLLVBKPHj3Czp07sWnTJiQnJ5f4QP+tNGvWDA0aNMCaNWukHhaICMOHD8eNGzdw8eJFeYcNoHCRtEePHnj79i28vb05wZe//voLd+/eRWhoKJM458+fl3jPv2SbxpKEhAQ4OjoqZcFOHho0aAB3d3fMmDGjxPPTpk1DaGioXJk7AwcORGpqKiZOnIjt27fj4cOHUFFRwc6dO5Gfnw9PT084ODjIJUYppl69eti4cSOaNWsmd1/F6dixI0xNTbFhwwbk5ORgwoQJ2LNnD06dOgVHR0c8ffoUNWrUYPaw261bNzx+/BgbNmxAw4YNAQBXr17FsGHDYGJi8tWdsW8hIyMDI0aMwMOHDzFy5EgMGTIEQKHwYn5+/ncJ2RXlwYMH3/y78ujaiAXHSloQUFNTQ61atTiRRxbk5+fj7du3ErW36enpnE6AvGhoaODOnTtSCwv379/nFpnl5eXLl/jw4QNq1KiBgoICLFiwgLtX/f3333KJBX4vRZ9Zv5caNWogKiqKW1DmKXuKX5OICE+ePMHKlSthamqqMDFE1tja2mL79u0SVogKg2nyfAWgItUmExHZ2NhwtedF63uWL1/OxHokODiYNm/eTAKBgIKCgig4OJg7duzYQRcuXJA7hjL5999/qUqVKjRr1iwSiUTc32vz5s3Url27chOjXbt2pR7y1mMV/fyWdIjPs6Bx48Z07tw5Jn2VJXPmzCnxO71x40audpElnz59ogMHDlCvXr1IQ0ODatSoIVd/GhoadOfOnVLP37lzh3nd5efPnzkrPjFpaWn09OlTpnEUQXHbouKHk5OTQqw8379/T2/evJE45EFDQ0Oi5rk4d+/elft9r169Ol28eJGIiF68eEECgYBOnz7NnY+IiCALCwu5Yog5dOgQtWrVim7evMmkv6IYGBjQvXv3JNrmzp1LBgYGdOXKFcrMzGT6nmdlZVHnzp1JIBCQuro6qaurk1AopM6dO5eL74iY6OhoysvLk2rPy8uj6OhoJjFq1apFz549Y9LX18jKyqJz587RuXPnKCsri2nflpaWtHXrVqn2kJAQZrX1PxI6Ojoy16QvWrSIRowYwVk5Kgpl6B0IhcISv9PPnz9ndk3ZsmVLibanHz9+ZGYtVnyuIxQKqWrVqtSvXz/677//mMQoyrt375jeD8WEh4eTi4sLp92gSPhJejHS09MpLS2NBAIBxcbGUnp6Onf8999/9Pnz53IRQ8z69evJxMSEdu3aRVpaWrRz506aNWsW9zMroqKiSrzRssLHx4fevn0r1Z6dnc3UJ93GxoYTWyq6qHHz5k0yNDQsNzEUia6uLs2fP5+ioqJKPNavX8/sxhEREUHNmzenyMhIev78uUIuuKampjRgwADasGGDzA8EX8Pc3JxiYmKk2i9dukS1atViFufMmTPk6+tLBgYGpKenRz4+PnT69Gm5H1Rq1ar1xRv1li1byNzcXK4YRcnNzZUQokxPT6elS5cyFZc5ffo0de3alSwsLMjCwoK6du1Kp06dYtK3qqoqde7cmQYNGlTi4ebmxuw7kp2dTX5+fmRkZFTiopk8WFtb0+LFi0s9v3jxYrn9xTU0NCgjI4P7t5aWFiUnJ3P/fvDgAYlEIrliiNHX1+cmsxoaGmRgYCBxyIOBgQElJCRItS9cuJD09fUpNDRUIQsz9+7do7CwMAoLC5NaJJAXZUwOlBFDGYifRVRUVLhJiKqqKg0ePLhEUV1ZmD9/PhkaGtKmTZu4Z8aNGzeSoaEhzZkzh0mMo0ePlnidDQ8Pp2PHjjGJ8a3IIxzXvXt30tPTo9q1a9Ovv/6qEL93osKJZ0mf38zMTFJTU1NojMePHzNbHK8o38P79+9Tly5dSFNTU2GbR0XvI9ra2kzvI8Xha9KLUdFqk319fSESifD3338jNzcXnp6eqFGjBoKCgtC3b19mcdq2bYvU1FRs3rwZqampCAoKgrGxMY4fPw4zMzOuLk9WtmzZgnnz5klZW7x//x4hISHM/A/T0tJKTGGpVKnSFz1wf7QYikScWtm2bdsSz+vr65daa/i9iNOPnZycJNKsiaFw3Jw5c3D27FnMnz8fQ4cOhYmJCdq2bYu2bduiXbt2TOpXMzMzS6ypNTIyYiKEBhSK/Lx8+RKdOnXCunXr0K1bN2aidGPHjsWwYcNw9epVODs7S/l+r1+/HosWLWISCyis8+vZsyeGDx+O169fo2nTplBTU8Pz58+xZMkS/PHHH3L1/++//yIgIAC//fYbAgICABSWIHTp0gVLly6Fn5+fXP3b2NigV69eXHpwceLj43HkyBG5YogZP348IiMjsXr1agwYMACrVq3C48ePsXbtWrk1KWbMmAFPT09ERUWV6Pd+4sQJue0DjY2N8eTJE5iamgIA/vzzT4kSsFevXkmV68iKImv569evjwsXLkgJnY0dOxYFBQXo168fkzitW7eGu7s73NzcULduXe5QBKVdxz9+/CghiidvjJLqbV+8eMHsfQcKhfuio6ORkZEhpQXEws86MDAQ0dHROHz4MFq2bAmgsJxm5MiRGDNmDFavXi13jHHjxuHFixcYMWIE9xo0NDQwYcIEzqNbXiZOnFjidaOgoAATJ05E586d5Y4xePBgBAUFST3P5eTkwN/fn3ueu337NmrUqCFTDH19fWa+7iWhDL0DcTmJQCDAhg0bJLy48/PzcfbsWWalgKV9Dx89eiTx2n50+vfvDyLCpk2bULVqVYXU8itLEwYAn+7+JZRhXaaMGGJycnIUlgYXFRVFIpGIOnToQOrq6tzq59y5cznbFll48+YNvX79mgQCAaWkpEjsor58+ZK2bNlC1atXZ/UyFF4eoKwYRIV2Q+PGjaM+ffowXUVet24dBQUFlXo+MzOTpk2bJlcMMaXt1osP1vz333+0c+dO8vLyIlVVVWYrr8pIU1y3bh29evWKSV8lsWvXLmratCmpqqpK7BQ1bdqUdu/ezTSWoaEhJSYmElFhNpC9vT3l5+fTnj17yNraWu7+TUxMaMWKFVLtK1eulLs0gIho0KBBNGLEiFLP3759m1kGhampKWe9p6Ojw+1Ch4SEUOfOneXuPyYmhvr06UNmZmZcWrWZmRn16dOHSTmTm5sbLVu2rNTzK1euJCcnJ7njKJr169dT//79Sz0/b948Ju/5li1bqGfPnqStrU3W1tY0fvx4On/+PNO03qCgIAoKCiKhUEizZ8/m/h0UFERLliyh7t27k4ODg1wxxPcioVBIXbp0kbg/ubm5Ua1atcjV1ZXJ67l27RpVq1aNdHV1SUVFhYyMjEggEJCWlhaz66+hoWGJFphnzpyhKlWqMIkh5t27d3TlyhW6efNmiSnK8qChoVFiGm9aWhppamoyiVHaru2zZ89IRUWFSQxFUzRlu3gat7q6OtWtW5cOHz4sV4xatWpRrVq1SCAQkKmpKffvWrVqUd26dcnFxYUuXbokVwwHBwdydHQkoVBIdnZ25OjoyB329vako6NDHh4ecsUQU1BQQHv27KE//viDevXqpZDsBi0trS+WaJU3+J30UlCGdZmy7NHEaGpqSlmksWLixImYNWsWAgMDJVZHnZyc5BL80dfXh0AggEAgKHG3QCAQYPr06TL3X5zAwED4+fnhw4cPICJcuXIFO3fuxNy5c7Fhw4ZyE2PXrl0YOHAgXF1dcfLkSbi4uCApKQlPnz6Ve3V56NChXzxftWpV/PPPP3LFENO2bVt8+PABN27cQFZWlsKyT3Jzc3H+/HlERUUhMjIS169fR/369dGuXTsm/Q8dOhSjRo1CXl4enJycAAAREREYP348xowZwyxGUd6+fYszZ87gl19+gY2Njdz99+nTB3369EFeXh6eP38OAKhSpQoTe6zi5ObmcteRkydPomfPnhAKhWjWrNl3iU6VxuvXr9GpUyepdhcXFyYKyWvWrPliloeNjQ3S0tLkjgMUCj2JhZV0dXU5O55WrVrJnXEAAC1atECLFi3k7qc0wsLCvnj+f//7X6lZO9/LsWPHoKKiIqVGf/LkSeTn58u1S+jr6wtfX99Sz0+YMIHJZ2vgwIEYOHAgPn78iIiICISFhcHDwwP5+fno2rUr3Nzc4OrqKpfo5dKlSwEU7q6tWbNGwhpPXV0dtWrVwpo1a+R6HeLdOSKCjo6OxHjV1dXRrFmzr95rvpXRo0ejW7duWLNmDfT09HDp0iWoqamhf//+XCaNvOTm5nKZJkUxNjZmbl2mra2N//3vf3j79i2OHz/O7BoPFL4v9+/fl3IDSUlJkTuz4e3bt6DCMlu8e/cOGhoa3Ln8/HwcO3aMicCeMhA/i9SuXRuxsbGoUqUK8xjie0T79u0RGhqqENE+sTJ6fHw8XF1dJXbrxd91Vrako0aNwtq1a9G+fXuF7XL/73//w8OHDyWsKRXJhw8fpDJzdHV12QUoyxWCH5mKUpucmZlJ/fv3p+rVq5OKigrTesWiaGlp0f3794lI8rWkpaVRpUqVZO43KiqKIiMjSSAQUGhoqMQu6oULF+jx48dMxl+Ubdu2kaWlJbcqamJiQhs2bChXMezs7GjlypVE9H/vR0FBAQ0dOpSmTp0qd/+fPn0iJycnSkpKkruvL3H8+HFu16Mk0REWNG/enDQ0NMjR0ZFGjx5NBw8elBIsk5eCggIaP348aWhocN89TU1Nmj59OrMYHh4e3O5wbm4uWVlZkZqaGqmqqtK+ffuYxVEGdnZ2FBQURBkZGaSrq8vt2MbFxVHVqlXl7r9fv360YMECqfaFCxdSnz595O5fmdjZ2XFZJc7OzjRmzBgiKtwNNTExKcuh/XDY2dnR0aNHpdqPHz9O9vb2TGJMnTqVy4pTJpcuXaK//vqL6tevTyKRiLp27Urnz5+Xq8927dopNDuHiGjcuHESNdtpaWnM9Sf09PS43TU9PT26ffs2ERX+zeTVVBDj5OREHh4e9P79e64tNzeXPDw8yNnZmUkMZVzjhw0bRnZ2dpSSksK1JScnk729PQ0ZMkSuvr8mOquiokKzZs2S9yVw7N27lzw8PKhp06YSO8QsMxYrCsHBwRKfXUVgYGBQ4vWXJSkpKdShQwcKDg6muLg4SkhIkDhYoEgdmOLwO+mlUFFqkwcNGoSMjAxMmTJFYT7TQOGO95MnT6RqcK5fvw4TExOZ+xXvoKSlpcHMzExh4y+Kl5cXvLy8kJubi+zsbIWs7Co6RmpqKrp27QqgcDU0JycHAoEAo0ePhpOTk9zZB2pqanLZLn0r/v7+8PDwwNSpU0vcpWDB3bt3oaWlBWtra1hbW8PGxob5irVAIMD8+fMxZcoU3LlzByKRCFZWVsxqxgHg7NmzmDx5MgDgwIEDICK8fv0aW7ZswaxZs+ReDU9ISMDhw4dRuXJl9O7dW2Ln4O3btxg1ahQzbYipU6fC09MTo0ePhrOzM5o3bw6gcMeThe2Jra0tZs+ejaioKK7vS5cuISYmBmPGjJGwlpK3XrWgoAApKSklZoK0adNGrr4BwMfHBwkJCWjbti0mTpyIbt26YeXKlcjLy5PLalHMsWPHEBoaisqVK8PHx0dix+7Vq1fo1asXzpw5I3ccAEhOTkZkZGSJfysW/uLJycmwtbWVare2tkZKSorc/QOFmQGzZ89G27ZtMWTIEPTq1Yvp97w0mjZtiqZNm2L27NlITU3FoUOH5Na7cHBwKNF+TyAQQENDA5aWlnB3d5fQEPherl+/jpCQEE5/olmzZkz1J4DC+5XYjs3Y2BgZGRmwsbGBnp4eHj58KHf/QGGdaqdOnVCzZk3OIz0hIQEaGhoIDw9nEkPR13gAWLBgATp16gRra2vUrFkTQGFdcuvWreXWHYmMjAQRwcnJCfv375f43Kirq8Pc3FzmGvTiLF++HJMnT8agQYMQFhYGHx8fpKamIjY2Vm7NkeIoQu8gMDAQM2fOhJaWFgIDA7/4uyyu897e3nL38TX09PRkstP7Hp49e4bU1FT4+PhwbWILRlY6RorUgZGC6ZS/AlFRapO1tbXp+vXrTPr6EmPGjKFWrVrRkydPuLrI8+fPk4WFBZP65E2bNtGePXuk2vfs2UPBwcFy91/RMDExoRs3bhBR4e7Rjh07iIjowoULpKuryyTGqFGjaMKECUz6Kg0dHR2JFX1FUFBQQAkJCRQUFEQ9e/akKlWqUI0aNahfv360bt06hcZmSVGV7AEDBnDvzYMHD0hLS0uuvsPDw0ldXZ3q1atHZmZmZGhoSGfOnOHOs7aXIiJ68uQJXbt2jfLz87m2y5cvf9EK7lspWtv3pUPeetWLFy9S7dq1S6xbVJRibnp6Ou3fv5/JrsH27dtJRUWFunbtSq1atSINDQ3atm0bd57l+75u3TpSUVGhqlWrUoMGDcjBwYE7WN0Pq1atShEREVLtp06dIiMjIyYxiAproP39/alKlSqkr69Pw4cPpytXrjDrvyixsbEUEhJCISEhFBcXx7Tvdu3aka6uLmlpaVHDhg2pYcOGpK2tTXp6etS0aVPS19cnAwMDunXrlswxFK0/QUTUsWNH2r59OxER+fr6UpMmTWjbtm3k6upKTZo0YRKDqFD3Z926dRQYGEiBgYG0fv16ys3NZda/Iq/xRSkoKKDw8HBasGABrVixgpkVnpj09HSJ67oi+OWXX7jnnqLP11OmTCE/Pz9mcRSld1A0i0WRlrpiPn/+TAsXLqT//e9/VLVqVYUolgcHB1Pfvn2ZfieKY2NjQz179qRLly5RWlqahHsWqwwnRevAFIWfpJeCMqzLlBHDxsaGrl27xqSvL/Hx40fy9fXlRKXU1NRIKBRS//79mVjKWVlZSUwKxERFRVHdunXl7l+MMsoDlBGjX79+nHXSjBkzyMjIiHx9fcnc3JyZQMeff/5Jurq61KhRIxo2bBiNHj1a4mCBj48P81KDL1FQUECxsbHk7e3NVDhOGVhZWdHu3bspOzubjIyMuMlIfHy83OUzzZs3p7/++ouICv9G8+fPJ21tbTp+/DgRKWaSXhFo0KABeXh40O3bt+nVq1f0+vVrieNHx8HBQUIkcvfu3aSlpcV9J1m+72ZmZjRv3jwmfZWGIlN5S+LTp0+0f/9++vXXX0lNTY3s7Oxo2bJlTN77hw8fUqtWrUggEHAP0gKBgFq2bEkPHz5kMHqipUuXUs+ePSXsLl+/fk2//fYbLVu2jHJycsjd3Z1cXFxkjiESiejBgwdEVJjOLV7Uz8jIYGa9Fxsbyz0/PH36lFxdXUlHR4caNmxI8fHxcvf/6dMnsrCw4NLoFYUir/HK5tWrVxQeHk5bt26lLVu2SBwsEIlE3KTMyMiIe5+TkpKocuXKTGIQEbVt25aGDh1K+fn53GJARkYGtWnThvbv388sjqKZMmUKVa9enRYtWkQaGho0c+ZMGjJkCBkaGn5RKPh7yM3NJVdXV9LW1qb69esrpARBU1NTwsJTEWhpaXHXLBMTE7p8+TIRFdq/sVwsI+LT3UtFGdZlyoixbNkyTJw4EWvXrpUSAmGJuro61q9fj6lTp+LmzZvIzs6Go6MjrKys8P79e7lEbAAgIyOjRDsLc3NzZGRkyNV3UZRRHqCMGCtXrsSHDx8AAJMnT4aamhouXLiAXr164e+//2YSIzExkbNjS0pKkjjH6jWtXLkSHh4eOHfuHOzs7KREylhY51y7dg1RUVGIiorC+fPn8e7dO9jZ2cHf35+ZYJUyGDVqFLy8vKCtrQ1zc3NO9O7s2bOws7OTq+9bt25h69atAArf2/Hjx6NmzZr47bffsGvXLvzvf/+Td/jo2bPnN/9uaGioTDHE9lXu7u5MrPW+RnJyMvbt2wdLS0um/RZNx/8a8nxHkpOT0a1bN+7fvXv3hpGREdzc3JCXl8fU4ujVq1fw8PBg1l9JKDKVtySICHl5efj06ROICAYGBli5ciWmTJmC9evXo0+fPjL37evri7y8PNy5c4cTSbp37x58fHzg6+uLEydOyD3+hQsX4tSpUxJCSHp6epg2bRpcXFwQEBCAqVOnwsXFReYYlpaWOHjwIHr06IHw8HCMHj0aAJCVlcVMgKlx48bcz8bGxkz+NkVRU1Pj7reKRJHX+KJERERg6dKluHPnDoBCoctRo0ZxlqjycvjwYXh5eSE7Oxu6uroSzwsCgQADBw6UO0a1atXw8uVLmJubw8zMDJcuXUKDBg2QlpbGzCIWKBRcW7t2LYRCIVRUVPDx40dYWFhgwYIF8Pb2/q772rciFoUVl+ixYPv27Vi/fj26du2KadOmoV+/fqhTpw7s7e1x6dIlJs9a3t7euHr1Kvr3768w4TgnJyckJCQwv+cWxcLCgivBtba2xp49e9CkSRMcPnwY+vr6bIMxnfJXUBRpXaboGPr6+qSurk5CoZC0tbUVksJCROTv719ie3Z2NrVr107u/k1NTSksLEyq/eDBg0zFkZRRHqCoGKNHj6bs7GwiIoqOjqa8vDzmMZTNhg0bSFVVlbS1tcnc3JxpKrIYFRUVaty4MY0ZM4YOHTpULnY4SyM2NpZCQ0Pp3bt3XNuRI0fkFpAyMjIqMZV2586dpKmpSatXr5Z7R3XQoEHc4e3tTbq6umRqasrZs5iZmZGuri4NGjRI5hjKsK8qSvv27blsA5YoK12/evXqdPHiRan2qKgo0tbWpsmTJzPbSR88eDCtXr2aSV9fQtGpvESFAod+fn5UuXJlql69Ok2YMEFid2f58uVkbGwsVwwNDY0Ss+Ti4uKY7UBraWmVaCsWGRlJ2traRESUmppKOjo6MsfYu3cvl3nXsWNHrn3OnDnUqVMnmfstiadPn9LZs2fp7NmzlJWVxbTv2bNnk7e3t8Lvu4q6xotZtWoVqaqqUt++fTnbvX79+pGamhonRisvVlZWFBAQICEYyJohQ4ZwWRkrV67kLIL19fVp8ODBzOJUqVKFE9C1srLiBA/v3LnDzLJOGYKBmpqa3O5wtWrV6OrVq0RU+P1mVSKpqalJ586dY9JXaaxdu5ZMTU3pn3/+oX379lFYWJjEwYIlS5Zw2QWnTp0iDQ0NqlSpEgmFwi/aicoCP0mv4AQHB3/xYIWFhYWUanh2dja1atWKWrVqJXf/48ePJ3Nzczpz5gx9/vyZPn/+TBEREWRubs6pGbNAGeUBioqhqqpKmZmZRFS6D2l5o2rVqjR79myF1q8VTeUsj7Rq1YoWLlyoUKX9jh070sKFC0s8t2PHDu4hmxXjx48nX19fiVKZz58/07Bhw2js2LFy9//hwwc6evQoDRs2jKpXr07Gxsbk4+NDBw4ckLterqiSbGhoKNna2tLmzZsVpjSrSNzd3Ut1g4iMjCQtLS1m7/ucOXOoSpUq5O3tTYsWLZLw5maRbvnp0ydSUVGhmzdvMhht6dSvX59UVVWpS5cudODAgRLLvZ49e0YCgUCuOFZWVlyaZVEuX75MderUkatvMZ6enlS7dm0KDQ2lhw8f0sOHDyk0NJQsLCw4T/idO3dSo0aN5IqjSP0JIqK3b99S//79uXI8gUBAqqqq5OXlxWxRtnv37qSjo0PVq1cnFxcXhXhAKwMTExNuQliUlStXUo0aNZjE0NTU5GrEFUV+fr7EgsnOnTvJ39+fli9fTh8/fmQWRxl6B1WrVuXS9bdv306WlpaUk5ND//77Lzk4ODCJUbduXc5zvWXLljR37lwiItq1axczvY5ffvlF4fe9kpyAypMOTHH4SXopVJTaZGWRkpJC1atXp6VLlxJR4U2xefPm1Lp1a253Vx4+fvxIvXv35urd1dTUSEVFhXx8fJhecMPDw8nFxYXS0tKY9amsGJaWlvTXX39RVFQUCQQCOnjwIEVHR5d4sCI2NpbGjRtHffr0UchDiYGBgcKF48TExcXR1q1baevWrdwqcnlAGbvDoaGhNGrUqFLPb9++nUnGjJgqVapwlklFuXv3LtN6QjEs7avEDwNfelBQ5AMDS6KiomjOnDmlnj9z5oxcmQ1FUWRGgJjatWszqUH+EjNmzKBHjx4pNAZRYRZZkyZNKDY2lmuLjY2lZs2acdau8vLu3Tvy9fXlsvGEQiGpq6vT0KFDufv69evXlSJOKw+9e/fmdjnfvHlDb968oRMnTtAvv/zCzHKxaCZQSYesFM2QK677oggdGC0trRJrepOSkpjV2/bo0YN2797NpK/SePDgQYn3wIKCAm7HmAVf0jtg9b1QhmDghAkTaPbs2URUODFXVVUlS0tLUldXZyYQfOTIEXJ1dVXo87Uy2LJlC3348EGq/ePHj8w0FcQIiBgWZ1QgOnfujIyMDPz5558l1g27u7uXixhAoR3X5s2bkZqaiqCgIBgbG+P48eMwMzNDvXr1mMQAgBs3bqB9+/b4559/sHPnTlSqVAlHjx6FlpYWsxhJSUlISEiASCSCnZ0dzM3NmfUNAAYGBsjNzcXnz5+hqakpVQP98uXLHzbGwYMHMXz4cGRlZXGWEyXByoZi165dGDhwIFxdXXHy5Em4uLggKSkJT58+RY8ePbB582a5Y4wePRpGRkb466+/5O6rNLKystCnTx9ER0dz9USvX79G+/btsWvXLhgZGSksNks+fvyIiIgIhIWF4fDhw8jPz0fXrl3h5uYGV1dXuXUh8vPzERMTA3t7e/Z1V8UwMDBAcHCw1DUwLCwMgwYNwqtXrxQWW2xfZWpqit9+++27//+DBw+++XdlvX4p256norBx40aEhoZi69atctmGlUZeXh6sra1x5MgRCas6VhgYGEg8J+Tk5ODz589QVS2UFxL/rKWlxeReJSY7Oxv3798HUFiPqa2tzaxvZaClpYXw8HC0atVKov3cuXPo1KkTM8tbRdC+fXscOHAA+vr6aN++fam/JxAImFghenp6wtHREePGjZNoX7RoEeLi4rBr1y65Y2zcuBEzZsyAj49PiVozbm5ucsdQUVHBkydPpOxtX7x4AWNjYybPQMqibt26mDVrFrp27YratWtj165dXO21s7Mznj9/zjzmpUuXcOHCBVhZWUnoksiDMp6vlYEyP1v8JL0UdHR0cO7cOTg4OJTrGNHR0ejcuTNatmyJs2fP4s6dO7CwsMC8efMQFxeHffv2MY138eJFdOzYEU2bNsWRI0fknhgU59OnT0hLS0OdOnW4BxOWbNmy5YvnWXhJKjqGWIzl3r17pfqv6+npyRUDAOzt7fH777/Dz88POjo6SEhIQO3atfH777+jevXqcnuxA4WiVyEhIWjQoAHs7e2lLuosJiB9+vTB/fv3ERISwj1Y3759G97e3rC0tMTOnTvljlEWXL58GYcOHcKhQ4eQmpoKJycnTJo0CS1btpS5Tw0NDdy5c6dEEUeWBAYGIiQkBH/99ReaNGkCoPD1zJs3DwMGDGA28YyLi+PEkWxtbdGoUSMm/Sqab31wBwq9ieXl2LFjUFFRgaurq0R7eHg4CgoK0LlzZ7ljKANHR0ekpKQgLy8P5ubmUgvI165dkzuGiYkJTp8+rZBJ+tfuHUVRhu9xecHMzAxHjx6VEle7ceMGunTpgkePHpXRyH48Zs2ahUWLFqFly5Zo3rw5gMIJW0xMDMaMGSMh5iermJjYs74kWG0iCIVCPH36VGqR/cGDB7C1tWW2MOPk5ITQ0FCpheu3b9+ie/fuTBZO/v33XwQEBHCCgdeuXYNQKMSKFSsQGhrK5Br/4cMHaGhoyN3Pl1DUs6+yhFTFlPbZSkhIQPv27ZkuNvCT9FKwtbXF9u3b4ejoWK5jNG/eHB4eHggMDOQmUhYWFrhy5Qp69uwp183J0dGxRHXGBw8ewNjYWGKCLu/DT25uLvz9/bkveVJSEiwsLODv7w8TExNMnDhRrv4rGtHR0WjZsqVCFjLEaGlp4datW6hVqxYMDQ0RFRUFOzs73LlzB05OTnjy5IncMZSxc6Cnp4fTp09LKZRfuXIFLi4ueP36tdwxyhp5d4fFNG7cGPPnz4ezszPD0UlTUFCARYsWISgoiPscVa9eHQEBARgzZgxUVFTk6v/Ro0fo168fYmJiJLInWrRogV27dnHK3/Iyd+5cVK1aFYMHD5Zo37RpE549e4YJEyYwiaNo7O3tMW/ePHTp0kWi/cSJE5gwYQISEhLkjlH8b1ScTZs2yR3jawuH//zzj9wx5syZg6SkJGzYsEGh11+eb2fdunXYu3cvtm7dimrVqgEAMjMzOfXt33//Xe4YtWvX/qJatTgTgSWKUPn+1gVYgUCgkNckL+LMoqCgIAwdOhSamprcufz8fFy+fBkqKiqIiYlhEk8oFCIzM1NqQyQrKwsmJibIy8tjEicuLg4PHz5Ex44duUyWo0ePQl9fX66FdzG6urro0aMH+vfvD2dn5y8upPxoFP/MPnv2DLm5uRL3dk1NTRgbG8v1mRXPeRISElCvXj2J63t+fj7S0tLQqVMn7NmzR+YYxeHvIKWgDOsyZcS4efMmduzYIdVubGwsd4pM9+7d5fr/38OkSZOQkJCAqKgodOrUiWvv0KEDpk2bxnSSrozyAEXEePv2LbfK7ejoiNzc3FJ/l4W1jYGBAd69ewegcPcoMTERdnZ2eP369Rdjfw8sVoi/RkFBgdQOPVBoq1NQUKDw+KwpaXe4Tp06nLWRPMyaNQtjx47FzJkz0ahRI6mdSFaWSUKhEOPHj8f48ePx9u1bpn0DyrGvAoC1a9eWeP2tV68e+vbty2SSPnjwYAQFBUFHR0eiPScnB/7+/kwmt8nJybC1tZVqt7a2RkpKitz9A5AqYcjLy0NiYiJev34NJycnJjFYTMK/RmxsLCIiInDy5EnY2dlJfUdktQ/8Eh8+fMCnT58k2lh+X8o7q1evRkpKCszMzGBmZgag0Na1UqVKePbsGdauXcv9rqwbCqNGjZL4d15eHq5fv44TJ05IpY7LSu/evdGmTRv8+eefeP/+PRo3boz09HQQEXbt2oVevXrJHSMtLY3BSMuO69evAyi0P7x58ybU1dW5c+rq6mjQoAHGjh0rd5wbN25wP9++fRuZmZncv/Pz83HixAmYmJjIHUdM48aN0bhxY1ChlhgEAgG6du3KrP8tW7Zgx44dcHd3h56eHvr06YP+/ftL2BfKQtHnUvG9vDRkvWYV/czu2LED//77LzZu3Chxbx86dKjci3HiOU98fDxcXV0lyn7U1dVRq1YtJt/BovA76aVQnmuTi1KzZk3s2bMHLVq0kNhJP3DgAMaOHYvU1FS5YygDc3Nz7N69G82aNZN4HSkpKWjYsOFXv/zfijLKAxQVo2idjFAoLHFVX3xxZ5FO5unpicaNG3P1sStWrIC7uztOnTqFhg0bKuRhVBG4u7vj9evX2LlzJ2rUqAEAePz4Mby8vGBgYIADBw6U8Qi/DWXsDhddXS/6+WL5uRLz+fNnREVFITU1FZ6entDR0cF///0HXV1duWtiRSIRLly4IJXFdPXqVbRu3ZrZIlNp5QH379+Hra0tE2/l0urjnj9/jmrVquHz589yx6hWrRp27NghNVk+ffo0PD09kZWVJXeMkigoKMAff/yBOnXqYPz48QqJwRofH58vnmeh1QEULsJMmDABe/bswYsXL6TOl6eaW0XzPaVXrBdyVq1ahbi4OCbve7Vq1RAeHo4GDRpgx44d+Oeff5CQkIAtW7Zg3bp13ARVFlq3bg13d3e4u7vDyspK7rF+iRkzZnzx/NSpU+WO4ePjg6CgIIUtVhV9xippGiUSibBixYqvZgh9KyEhIVi4cCGSk5MBFNapjxs3DgMGDGDSv5h3795h37592LlzJ86cOQMLCwv0799f5vdE2c+lderUwb59+0q8t//2229MFqG2bNmCvn37olKlSnL39TX4nfRSWLZsWYWIId6t2bt3LwQCAQoKChATE4OxY8di4MCBzOI8fPgQAoGAmwhcuXIFO3bsgK2tLYYNGyZ3/8+ePSuxvjonJ+eLKWbfy8SJEzFr1iyuPECMk5MTVq5c+UPHOHPmDCeGdObMGaZ/l5JYuXIlN8mYPHky1NTUcOHCBfTq1Qt///23QmOzZOXKlXBzc0OtWrVgamoKoPDzXL9+fWzbtq2MR/ftKGN3WBmZDUBhyUynTp2QkZGBjx8/omPHjtDR0cH8+fPx8eNHrFmzRq7+TU1NS0xDzM/P5xZqWGBqaoqYmBipSXpMTIzccd6+fcvtqrx7906injA/Px/Hjh0rVZPie3F3d8eoUaNw4MAB1KlTBwCQkpKCMWPGMBF5Kg2hUIjAwEC0a9dO5kl65cqVkZSUhCpVqkgJrxWHxcI4q0n41xg/fjwiIyOxevVqDBgwAKtWrcLjx4+xdu1azJs3TyljKC8oI4OiNDp37oxJkyYx+Vy8efOGu8efOHECvXr1gqamJrp27Sr3bv3QoUMRFhaG6dOno2bNmnBzc4ObmxtatGjB/Fmi+MJ3Xl4e0tLSoKqqijp16jCZpBf/e7MuDUhLSwMRceWjReuT1dXVYWxsLHdZlpglS5ZgypQp+PPPP7nU9vPnz2P48OF4/vw5kyw5MTo6OvDx8YGPjw9u374NLy8vTJ8+Xeb3RNnPpU+ePClxYTo/Px9Pnz5lEsPJyQnPnj1T2HxHAqZa8Tw/HB8/fiRfX1/OH1TsZ9y/f/8S/VtlpVWrVhQSEkJEhZ6nOjo61Lx5c6pSpQpNnz5d7v5bt25Ny5cvJyIibW1tun//PhER/fnnn+Tq6ip3/2K0tLS4vrW1tTkvz7S0NKpUqVK5iaEoitrBREdHS/iQlmcKCgro5MmTtHz5clq+fDmdOnWqrIf03WhoaNC1a9ek2uPi4kgkEpXBiGTH3d2d+vfvTx8/fpT4jkRGRpKlpaXc/SvDvoqIaP78+WRoaEibNm2i9PR0Sk9Pp40bN5KhoeEXrc2+BbGNW2mHiooKzZo1i8nreP36NTVr1oxUVVU5WzRVVVVq3749vXr1ikmM0jh69ChVqVJF5v8fHBzM2eUEBwd/8WBBaX+TN2/eUPv27ZnEICIyNTWlyMhIIiLS0dHhbLNCQkKoc+fOzOJUJIrabJZ0rVQE8+fPJ3NzcyZ9WVlZ0e7duyk7O5uMjIwoIiKCiIji4+PJ0NCQSYwPHz7Q0aNHadiwYVS9enUyNjYmHx8fOnDgAOXm5jKJURJv3ryhHj16cM+R8uLh4cH5vefm5pKVlRWpqamRqqoq7du3j0kMZVGrVq0Srb2Cg4OpVq1aTGO9f/+edu/eTe7u7lSpUiUyMzNjZsGmDH799VdydHSUsNGNi4ujhg0bUrdu3ZjEUPR8pyj8JP0LpKSk0OTJk6lv37709OlTIiI6duwYJSYmlqsYRIV+ikePHqXdu3dTUlIS076JiPT19Tlf46CgIGrRogURFXqCs/C4PXfuHGlra9Pw4cNJQ0ODAgICqGPHjqSlpUVxcXFy9y/GxMSEYmJiiEhyAh0aGkoWFhblJsamTZtoz549Uu179uyR62FUVVWVMjMziYhIKBRyn1messfKyoouX74s1X758mWqU6cOszhnz54lLy8vat68OecHHRISQufOnWMWo3Llytz1pPhClqwLDvr6+mRgYMAdYu9ndXV1iZ8NDAyYvY6CggIaP348aWhocJNnTU1NJjfyqKgoioyMJIFAQKGhoRQVFcUdFy5coMePHzN4Bf9HQUEBhYeH04IFC2jFihUUHR3NtP/ins+jRo2iPn36kLa2Nvn5+TGNpUgEAkGJ18WnT5+SqqoqszhaWlqc37OJiQn33b9//z4z7+SKwtOnT6l9+/YkEAi4779AICAnJyfKyspiEsPBwYEcHR25w8HBgapVq0YqKiq0du1aJjFWrVpFqqqqpK+vT/b29pSfn09ERMuXL6d27doxiVGcS5cu0V9//UX169cnkUhEXbt2pfPnzysk1o0bN5gtaFStWpXi4+OJiGj79u1kaWlJOTk59O+//5KDgwOTGGKSkpJo7dq1NHPmTJo+fbrEwYJKlSqV6l3PamPnxIkTNHDgQNLV1aXKlSvTsGHDmF/ja9euTYMGDZLyGH/27BmTeQIRUVZWFnXu3JkEAoHEvb1z587MnlcVPd8pCp/uXgrF64Znz54NY2NjJCQkYOPGjQqpTVZEDDFFBVMUQV5eHlefcfr0aS4F0tramonKd6tWrRAfH4958+bBzs4OJ0+eRMOGDXHx4kUpWxV5UEZ5gDJizJ07V0IMR4yxsTGGDRsms9VFrVq1sHz5cri4uICIcPHiRRgYGJT4u23atJEphjJQtmWHMli4cCH8/f2xatUqTuwlLi4OAQEBWLRoEZMY+/fvx4ABA+Dl5YVr167h48ePAArTMOfMmYNjx44xiVNQUFBifdqjR4+kBNK+FWWUFxVHIBBg/vz5mDJlCu7cuQORSAQrKysmtWxt27YFUJh2aWZmpvA0QoFAABcXF7i4uCik/+L1tEKhEEZGRli8eDGzus6isBZbU7aQlIWFBffeW1tbY8+ePWjSpAkOHz4sZQf1s+Pv7493797h1q1bUjabI0eOZGKzWVxIV/z5bdeuHTPl9REjRqBJkyacyrdYI8TCwgKzZs1iEqM4TZs2RdOmTTF79mzOJYTFM11JvHnzBm/evGHWl6JKA4qyfv16/PHHH6hSpQqqVasmcR0WCARMUvctLS2xZ88e/PXXXxLtu3fvZqYf0KNHD/z6668ICQlBly5dShTTlZf09HSoqqqidevWOHToEOe0kJ+fjwcPHjCJYWRkhGPHjiEpKQl3794FUDgPqVu3LpP+AcXPd4rCC8eVgiKty5QZQ2xHURyBQAANDQ1YWlrC3d2du5jJStOmTdG+fXt07doVLi4uuHTpEho0aIBLly7ht99+Kzc+pJ8+fYKfnx+Cg4ORn58PVVVV5Ofnw9PTE8HBwUxqjJQRQ0NDA3fv3pVyDUhPT4eNjQ3ev38vU78HDx7E8OHDkZWVBYFAUKJgCsDO61RRlHebGTHFa2xzcnLw+fNnzhpE/LOWlhaTeltHR0eMHj0aAwcOlLhmXb9+HZ07d5aYmMhDnz59oKenh3Xr1kFHRwc3btyAkZER3N3dYWZmprS6X1akpKQgNTUVbdq0gUgk4oRyZKXohPBr2NvbyxynKNHR0Vi0aJGEc8C4cePQunVrJv3n5uaCiDg19PT0dBw8eBA2NjZS/uyyokixNWULSS1duhQqKioYOXIkTp8+jW7duoGIkJeXhyVLliAgIIBJnIpARbPZ/PTpE9LS0lCnTh2F2fyV5BLCiuKL5ESEJ0+eYOvWrWjbtm2JjhjfS926dTFr1ix07doVtWvXxq5du+Dk5ISEhAQ4OzvL7W4kxtzcHCNGjFConeb+/fvRp08fdOjQgatJj4mJQUREBPbs2YMePXrIHePdu3cyL4B/KyoqKkhOTsbYsWNx+fJlHDx4EP/73//w9OlT1KhRQ67rr1j40M3NjemEvCSUOt9hui9fgagotcnt2rUjXV1d0tLSooYNG1LDhg1JW1ub9PT0qGnTplwK6K1bt+SKExkZSfr6+iQUCsnHx4drnzRpEvXo0UOmPt+8eSPx85cO1rXRii4PUHQMU1NTCgsLk2o/ePAgmZiYyN3/u3fvSCAQUFJSEr1+/brE40fmRx/ft/K1GlvW9bYikYjS0tKISPKalZqaylRPISMjg2xtbcnGxoZUVVWpWbNmZGhoSL/88gvzEov3799LXU9Y8fz5c3JycuLqx8V/Lx8fHwoMDJS5X3F/AoHgi4dQKGTyOrZu3UqqqqrUu3dvCgoKoqCgIOrduzepqanR9u3bmcTo2LEjrV69moiIXr16RVWrVqWaNWuShoYG/fvvv0xijBgxgmxsbGjfvn0kEolo06ZNNHPmTKpZsyZt27ZNrr7T09MpLS2NBAIBxcbGchoE6enp9N9//zHVgCkt/v79+ykhIUGhccoj2tradP36dan2a9eukY6ODrM4ii5fzMnJocGDB5OKigqpqKhw15M///yT5s6dyyTGw4cPqVWrVlKlAS1btqSHDx8yiSHWtRAfFhYW1LRpU5o0aRK9ffuWSYyipQENGjRQWGmAjo4O9z4okri4OPLy8uKe4728vJjrKij681u0FGjixIkkEolo69atlJmZKfe9asuWLdSzZ0/S1tYma2trGj9+PJ0/f54KCgpYDF0CRcx3SoOfpJdCRalNXrp0KfXs2VPiwfP169f022+/0bJlyygnJ4fc3d3JxcVF7lifP3+mly9fSrSlpaVJPFSfP39eqh6lNIrWPH+LSJK1tTWdOXNG7tdRERg/fjyZm5vTmTNn6PPnz/T582eKiIggc3NzGjNmDJMYUVFR5VY4TigUcrWIyhC/qijUrl2bE9Ures3asmUL2djYMI2Vl5dH27Zto3HjxtEff/xB69evZyZclJ2dTX5+fmRkZFTi9YQVAwYMIFdXV3r48KHE3+vEiRNka2src79FJ4BfO1hgbW1NS5YskWpfvHgxWVtbM4lhaGjIPQyuX7+eq7nds2cPsxgVSWxty5YtJd5LP378WKLI1M+Mm5sbtWnTRkKn4dGjR9S2bVvq3r07kxhRUVEkEomoQ4cOpK6uzn3X586dS7169WISY+TIkdSoUSM6d+4caWlpcTEOHjzIrM7a1dWVmjZtytXcEhHdvXuXmjdvzlSkVxnExsZSaGgovXv3jms7cuQI05r6wYMHc4uL5RllfH6L6xht3bqVNDQ0yMfHh9l9V1nCh98y32EBn+5eCuJ0jL1796Ju3bq4du0anj59ioEDB2LgwIFMLD2UEcPExASnTp2Cra2tRPutW7fg4uKCx48f49q1a3BxcWGW/vMldHV1ER8fDwsLi6/+bnR0NFq2bAlVVVVER0d/8Xc/fvyIgwcP4syZM1wdiiwoozxAGTE+ffqEAQMGYO/evVw6XEFBAQYOHIg1a9ZAXV1dpn7fvn3L1W1+zZteUf6kLNDT08OlS5dgY2MDoVCIp0+fSliolHdY19uKmTt3LrZt24ZNmzahY8eOOHbsGB48eIDRo0djypQp8Pf3lztGXl4erK2tceTIEa5+lDV+fn6IjIzEzJkzS7Sv8vLyYhKnqK9x0fKA+/fvw97eHtnZ2UziKJpKlSrh1q1bsLS0lGhPSUlB/fr1mfi9a2pq4u7duzAzM0Pv3r1Rr149/PPPP3j48CF++eUXJt712trauH37NszMzFCzZk2EhoaiSZMmSEtLg52dHbP3Izk5GZGRkcjKykJBQYHEORY1qoCk/3BRXrx4AWNj4x+63EjZPHz4EG5ubrh165aUzeahQ4c4KyV5UEb5orm5OXbv3o1mzZpJxEhJSUHDhg2/ek/+FkQiES5cuFCiz3Tr1q2ZfA+LIv67sHgPyoK5c+diyZIl6Nq1K+zs7KRquVlp2uTn5+PAgQMSJQju7u7Myh2U8fkVCoXIzMyUuGZdvHgRPXr0wLNnzxRyzbp8+TIOHTqEQ4cOITU1FU5OTpg0aRJXNvCjwwvHlcKcOXPg5+cHU1NT5Ofnw9bWlqsbZuUBrYwYb968QVZWltQk/dmzZ9wFXV9fX+qBXlF8z5qQWBip+M+l4eDggCtXrsg0LjHXr1/HtWvXkJ+fz3lNJyUlQUVFBdbW1vj3338xZswYnD9/Xupv+iPFUFdXx+7duzFz5kwkJCRAJBLBzs4O5ubmMvUnxsDAgHsw1NfXL7Gulv5/ve2P/JDYoUMHtG/fnpsE9ujRo9SFizNnzihzaDKjqHrbGzduoH79+hAKhZg0aRIKCgrg7OyM3NxctGnTBpUqVcLYsWOZTNABQE1Njcmk70scPnwYISEhaNeuHXx8fNC6dWtYWlrC3Nwc27dvZzZJz8nJgaamplT7y5cvmYjHAUBISMgXz7MQozQ1NUVERITUJP306dPcpEdeLC0tcfDgQfTo0QPh4eGc929WVhazBT9liK0pQ0gKQKm6Bo8ePYKenh6TGBUFU1NTXLt2DadPn+YW8W1sbNChQwdmMW7evFliLbWxsTGzDZBnz55JLcoAhdcZVsKRpqamyMvLk2rPz89HjRo1mMQoKCjArFmzsHjxYm5hTEdHB2PGjMHkyZM5QbzvJTAwEDNnzoSWllapmyFilixZIlOM4qxbtw7a2tqIjo6W2kwSCARMJum3bt2Cm5sbMjMzuWfG+fPnw8jICIcPH0b9+vXljqGMz2/xBUugcHEgISFBrs21L8FK+LBhw4aIiIiAgYEBHB0dv/h9u3btmjxDloCfpJeCuro61q9fjylTpiAxMRHZ2dlwdHRkpqSorBju7u4YPHgwFi9ezImmxMbGYuzYsZwa6ZUrVxQutMCCr60kGhsbIy4uTq4Y4h3szZs3cw+Gb968ga+vL1q1aoWhQ4fC09MTo0ePRnh4+A8Xo7h4hvhgxZkzZ7gd/jNnzihcUVpRbNu2DcHBwUhNTUV0dDTq1atX4mSqPDF+/HhERkZi9erVJe4Oy4qjoyO3MGNhYYHY2FiMGzcOKSkpyM7Ohq2tLbS1tRm+ksKd7vnz52PDhg0KEUZ6+fIll82jq6vLieq1atUKf/zxB7M4rVu3RkhICGbOnAkAnJvDggUL0L59eyYxiguE5eXlITc3F+rq6tDU1GQySR8zZgxGjhyJ+Ph4tGjRAkChcFFwcDCCgoLk7h8o3GEWX/ecnZ3RvHlzAMDJkyeldvVkxcfHBwkJCWjbti0mTpyIbt26YeXKlZzYGgtmzZqF2bNnK0xISvyAKBAI4OzsLPH9yM/PR1paGjp16qSQ2OUZgUCAjh07omPHjgrpX19fH0+ePJESJb1+/TozVf/GjRvj6NGj3IKo+P67YcMG7vsiL8pwCZk8eTI2btyIefPmcTua58+fx7Rp0/DhwwfMnj1bpn6vX7/OLTAUd4soCsvnlrS0NGZ9lYavry/q1auHuLg4zk3n1atXGDRoEIYNG4YLFy7IHUMZn9/SqFq1KqpWrcq0z5KED+vUqcMt/n4v7u7u3MJ6cScHhcI0eZ7nh+Pdu3fk6+vLeQWKvYCHDh1K2dnZRER0/fr1EkVVFEHRuszvITExkSwsLEhTU5PzIdXS0qJatWrRzZs3mY2vRo0aJYroJSYmUo0aNYiI6OrVq2RoaPhDxlCmeEZFoV27dhWiJl1R9baVK1emS5cuEVGhNgQrX+Ev0b17d9LR0aHq1auTi4sL9ejRQ+KQFzs7O4qKiiIiImdnZ06nISgoiImwopibN2+SsbExderUidTV1em3334jGxsbqlq1KqWkpDCLU5ykpCRydnamEydOMOszNDSUWrZsSZUrV6bKlStTy5Yt6eDBg8z6JyJ68uQJXbt2jRN5IiK6fPky3blzh2kcMYoQW1O0kNS0adNo2rRpJBQKaezYsdy/p02bRnPmzKEdO3bQx48fFRa/vHL69GmaNGkSDRkyhHx8fCQOFowZM4ZatWpFT5484a6/58+fJwsLC5o2bRqTGOfOnSNtbW0aPnw4aWhoUEBAAHXs2JG0tLQoLi5O5n7FAsLiQ/y8WNRnWl1dnQwMDJi8jurVq5cqbCt+BipvfPz4ke7evasQnR4NDY0Sxdtu3rxJGhoaTGIo4/NLRLR3717y8PCgpk2bcs/y4oMFyhA+VCb8TnopVJTaZG1tbaxfvx5Lly7lrKQsLCwkdr4cHBxk7l9ZKGMlEVBOeYAiY4j1DD5+/IiIiAiEhYXBw8MD+fn56Nq1K9zc3ODq6gqRSCTz+MVs3rwZ2tra8PDwkGjfu3cvcnNzZfZiVxZt2rSBu7s71q5dWyF8hRW1O9yrVy+0bdsW1atXh0AgQOPGjUu1CWRlV6evr49evXox6asklLGjCgD169dHUlISVq5cCR0dHWRnZ6Nnz57w8/ND9erVmcUpjpWVFebNm4f+/fszSSP09fVF//79cf78eQajK51q1apx3rlimjRporB45ubmJZYA2dnZ4dixYzKl8nt4eODkyZMYPnw4iyFKIdaqmTFjBpydnfld829g+vTpmDFjBho3bsxdx1ijjPLFVq1aISEhAXPnzoWdnR1OnjyJhg0b4uLFi7Czs5O532XLljEZ37fy8uXLEr3jra2tmViFKpPc3Fz4+/tjy5YtAApLFy0sLODv7w8TExNMnDhR7hh169bF06dPUa9ePYn2rKwsqRIkWVHG53f58uWYPHkyBg0ahLCwMPj4+CA1NRWxsbHw8/NjEsPX1xd5eXm4c+cOVxpw7949+Pj4wNfXFydOnGASR2mU9SrBj4oyrMuUZY/2IyHrLoMyVhKJiDw9Pal27doUGhpKDx8+pIcPH3Jq+/379yciop07d1KjRo1+6BjFuXTpEv31119Uv359EolE1LVrV7kVTq2srEpU04+KiqK6devK1bcyqGhZB4rcHT5+/DitWLGCBAIBzZw5k5YtW1biUV5RlH3VgwcPSv08PXjwgGms4ly/fp2ZvZSbmxtVqlSJatasSePGjaP4+Hgm/f6oyJrxRUQ0Z84cqlKlCnl7e9OiRYs4yzrxwYqf7T2Rh2rVqlFISIhSYinKWvXTp0/k4+PD2faWZ5o0aUL+/v5S7X/++Sc1bdpU5n6LZ1x96WCFMhT3jx49SvXq1aO9e/dyz4x79+4lOzs7Onr0KFP7UEVaA//yyy+0Y8cOIpK8xk6ZMoX8/PyYxNDQ0CjRmi4uLo5EIpHM/RbPNvnSwRJ+kl4KyrAuU5Y9WmxsLI0bN4769OmjsAvVtyLrw4+9vT1FRERItUdERFD9+vVZDI2IlFMeUNYlCCkpKbRkyRLau3evXP1UqlSJ88wuSlpaGtOFE0WjLMsORbNkyRJuEnDq1CnS0NCgSpUqkVAoZDaBHjRoEDMf26+Rl5dHp06dojVr1nAxHz9+LGGnIyvKsq8qbjkj5vnz58wsZ8LCwiSOgwcP0urVq6levXrUqVMnJjGIiF6+fElr166ltm3bklAoJFtbW5o9e3aJ14DyjjyT9OIe0EWP2rVrMx3nz/SeyEPlypUVVl7SqlUrWrhwId27d08h/RdFV1dXqZP09+/fS0wAWUwCiQoX8rW0tMjGxoYGDx5MgwcPJhsbG9LW1qazZ8/K3O+gQYO4w9vbm3R1dcnU1JR73jUzMyNdXV0aNGgQk9dBRGRmZkYXL14kIsnrRnJyMrNFUoFAwB3iZ8aS/s3SPlQRiEQizhbUyMiIW1hMSkqiypUrM4lhZWVFly9flmq/fPky1alTR+Z+g4ODuWPx4sVkYGBAffv25RZf+/btSwYGBiValcoDb8FWCsqwLlNGjF27dmHgwIFwdXXFyZMn4eLigqSkJDx9+hQ9evTA5s2bZeq3OE5OTggNDZVKG3779i26d+8uk0J2UTuR8+fPY/z48Zg2bRqaNWsGALh06RJmzJiBefPmoUuXLnKNvzjZ2dmllgeUpxgliWewwszMDCtXroSbm5tEe1hYGPz8/JhYdpQF5d2yQ8yDBw9w9epVWFpawt7evqyH8108ePAAnTp1QkZGBj5+/MilEAYEBODjx49Ys2aNXP0ry76qNHu/Bw8ewNbWFjk5OUxiFEUgEMDIyAhOTk5YvHixQtLqHz16hJ07d2LTpk1ITk7G58+fmccoS4paEJUXKvp7Ig8TJkyAtrY2pkyZwrzvkJAQhIWF4eTJk6hZsybc3Nzg5uaGFi1aME+r9/b2hoODg8ziV9+ColxCivP48WP8+++/Emr7I0aMYKYgP2HCBLx8+RJr1qzhyrPy8/MxYsQI6OrqYuHChUziaGpqIjExERYWFhLXjYSEBLRp0wZv3ryRO8bXLIiL8i1OSGK+poBfFBZlYBYWFti/fz8cHR3RuHFjDB06FL///jtOnjyJvn37Mil1CAsLw5w5c6SED/39/TFhwgQmom+9evVC+/bt8eeff0q0r1y5EqdPn8bBgwfljiGGr0kvhfJemyxmzpw5WLp0Kfz8/KCjo4OgoCDUrl0bv//+O9OHt6ioqBLH+eHDB5w7d06mPotbfBERevfuzbWJ15e6devG3O5LW1tb4RMbRcZ49OgR+vXrh5iYGG7h5PXr12jRogV27drFxJO0X79+GDlyJHR0dNCmTRsAhTeTgIAA9O3bV+7+ywpWlh3KJiQkBH369OEUSMX1tp8+fUJISAgTlW9lERAQgMaNGyMhIQGGhoZce48ePTB06FC5+ycF21eJH34EAgGmTJki4RyQn5+Py5cvM9MCKcnWRpHk5eUhLi4Oly9fRnp6OnNVXp7vh39PvsyHDx+wbt06nD59Gvb29lJe1vJMQJSpA2NlZYUZM2YgJiYGjRo1gpaWlsR5FnZfinIJKY6JiYnMKu7fwqZNm3D+/HkJ/RQVFRUEBgaiRYsWzCbpylDc/9aJ94gRI1CvXj1UqVLlm37/Swr4RWG12OTk5IRDhw7B0dERPj4+GD16NPbt24e4uDj07NlT5n4NDAwkxpiTk4OmTZtyzhefP3+GqqoqBg8ezGSSHh4ejvnz50u1d+rUiYkGQVH4SXopKMO6TBkxUlNT0bVrVwCFlm9iP83Ro0fDyckJ06dPl7lvoNBDWczt27eRmZnJ/Ts/Px8nTpyQ2b4hMjJSrrHJSlxcHPbs2YOMjAyphYfQ0NByEUMZ4hkzZ85Eenq6hA1QQUEBBg4ciDlz5sjdv7JhbdmhbHx8fNCpUyep3eF3797Bx8enXE3Sz507hwsXLkh519eqVQuPHz+WuV9l2VeJH36ICDdv3pR4Herq6mjQoAHGjh0rdxxlEhkZiR07dmD//v0oKChAz549ceTIETg5OZX10H4oBg8e/MXzmzZtYhaLf0++jRs3bnCLYomJiRLnWE1AKlWqhC5duqBLly5Yu3Ytl5E1ZcoUeHp6MsnI2rhxI/T19XH16lVcvXpV4hwrT+7Dhw8jJCQE7dq1g4+PD1q3bg1LS0uYm5tj+/bt8PLykjuGMkRnP3/+jLt373LPP2Lu3r3LdGFzzpw56Ny5M27fvo3Pnz8jKCgIt2/fxoULF75rB5wF27Ztw9ixY795kq7sZ+x169Zxf3s/Pz8YGhriwoULcHNzw++//y5zv8oWPjQ0NERYWBjGjBkj0R4WFiaxqcACfpJeCmvXrsXo0aPRt29fLm1MVVUV3t7eWLp0KYBCJcoNGzb80DEMDAzw7t07AIUrl4mJibCzs8Pr16+Rm5src79iHBwcuIfekh4MRCIRVqxYIVPfxVcPX79+jY0bN0pMpIYMGcJk50vM18oDykuM6OhoXLhwQeIG9csvv2DFihVo3bo1kxjq6urYvXs3Zs6ciYSEBIhEItjZ2ZWolvwjo4ysA2Wg6N1hZVJQUFBidsyjR4+go6Mjc7/ixc+EhAS4urpKlJioq6ujVq1aTFTlxQ8/Pj4+CAoKgq6urtx9lgYRYd++fYiMjERWVpbUAyiLRT8TExO8fPkSnTp1wrp169CtWzcuY4NHklevXkn8Oy8vD4mJiXj9+jXTyTP/nnw7ZbHgr4iMLGV4civKJaQoc+fOxdq1a6XajY2NMWzYMCaTdB8fHwwZMgSpqamcQ8Tly5cxb948+Pj4yN2/mFatWiE+Ph7z5s1jqrgvCyyql1NSUpCamoo2bdpAJBKV+lwhC0KhUKI8q2/fviVmXY4YMQIzZsz45sUGZTsJTZ8+Hb6+voiKikLTpk0BFH62Tpw4gfXr17MNxrTCvQLy7t07SkhIoISEBCaCRcqO0a9fP1q8eDEREc2YMYOMjIzI19eXzM3NmQjHpaenU1paGgkEAoqNjaX09HTu+O+//+jz589yxyAqFL8zNDQkExMTTgSkZs2aZGhoSFevXmUSg6hQIXvlypVE9H8iIAUFBTR06FCaOnVquYmhKPEMIuUK5SgDV1dXatq0Kd29e5dru3v3LjVv3pxcXV3LcGTfhoODAzk6OpJQKCQ7OzsJ31F7e3vS0dEhDw+Psh7md9G7d28aOnQoERV+R+7fv0/v3r0jJycnJqI/QqGQjh8/Lnc/PwIjR46kSpUqUadOncjb21tCPImVQNK6devo1atXTPoqD2zfvp0T8WRBfn4+DRs2jObPn8+sz5/tPSkvxMbGUkhICIWEhMjlXV4U8T2Xtdp2SSjSJUSMMkRn8/Pzaf78+VSjRg1OZK1GjRo0f/58Zs+lPxryCF4+f/6cnJycOAE6cT8+Pj4UGBjIcphfRVYXqOIoSviQqNAxydPTk3vW8vT0pEuXLjHrXwwvHFfBefnyJT58+IAaNWqgoKAACxYswIULF2BlZYW///6b8xz/0RGnXK1fv16izsTX1xf379/H2bNnmcTR0tLCrVu3UKtWLRgaGiIqKgp2dna4c+cOnJycmNQmKyOGIsUzlCmUowxEIhEuXLgAR0dHifarV6+idevWTDJOFIm4ZGXGjBkIDAwsdXe4eOr4j8yjR4/g6uoKIkJycjIaN26M5ORkVKlSBWfPnpVK6f9e3N3dER4eDiMjI/Tr1w9eXl5o0KABk7F/T20di13uypUrY9u2bczFMysiI0eOhKWlpVRa8MqVK5GSkqLQtMl79+6hXbt25ULfoiLQs2dPBAcHQ1dX96vfSRbfQ0VmZCnznrt06VKoqKhg5MiROH36NLp16wYiQl5eHpYsWYKAgAC5YyhbdFas8VRSRlNMTAwaN278XVkob9++5foqKnBcEorMoiqOPIKXAwcORFZWFjZs2AAbGxuun/DwcAQGBuLWrVsKGHHJyPM6lCV8+C3MmzcPw4cPlxLU/h74dPcvUBFqkytXrsz9LBQKmYsaFGXr1q1Ys2YN0tLScPHiRZibm2Pp0qWwsLCAu7u7XH3HxcVJTNCBwtKA8ePHc5NQFii6PECRMZQlnqFMoRxlYGpqiry8PKn2/Px8ZkqziuSff/4BUDhJd3Z2ZlJTXdbUrFkTCQkJ2LVrF27cuIHs7GwMGTIEXl5eTD5XYWFhePXqFfbu3YsdO3Zg8eLFsLa2hpeXFzw9PVGrVi2Z+1Z2aYGenl65UiEvS/bv349Dhw5Jtbdo0QLz5s1T6CQ9NTWVV1xXInp6etz9UFdXV+ELyIrUgVHmPbeoBkuHDh1w9+5d5i4hyhad/dJEuXPnzoiPj/+ua6iBgQHnDlJc4FgM/f80cWVOCuXh5MmTCA8Pl1pMsrKywoMHD8poVN+PsoQPv4U5c+agd+/eck3S+XT3Uti5cyepqanRr7/+Surq6vTrr79S3bp1SU9Pj1kKoTJiiHn69CndvHmTS6sXH6z4999/qUqVKjRr1iwSiURcqsrmzZupXbt2cvdvbGxM4eHhUu0nTpwgY2NjufsXo+jyAEXGKOrj+LVDEVy6dIn++usvql+/PolEIuratSudP39eIbFYcvDgQWrSpAnFxsZybbGxsdSsWTM6cOBA2Q3sO3Fzc6NKlSpRzZo1ady4cZwHKc/XefjwIS1YsICsra1JRUWlrIfzXQQHB1Pfvn0pNze3rIfyw1OpUiVKTk6Wak9OTqZKlSoxiTF69GiJY9SoUdSnTx/S1tYmPz8/JjF4fjw0NDTo2rVrUu1xcXEkEokUElMR99wtW7bQhw8fpNo/fvxIW7Zskavvon317t2bBAIBqampkZqaGqmoqJCPjw99/PiRSYxvRZYU8aioKMrLy+N+/tKhTORJd9fW1ubKKYr2Exsby8zD/HvGIuvrMDU1pcjISCIqTJsXX+9DQkKoc+fOrIb4TcjzOsTwk/RSqCi1yXFxcVSvXj0SCoVcXY74EAqFTGIQEdnY2HATmqIfzJs3b5KhoaHc/fv7+1PNmjVp165dlJGRQRkZGbRz506qWbMmBQQEyN2/mBcvXtDjx4+JqLCmae7cudStWzcKDAykly9flpsYZU1KSgotWbKE9u7dW9ZDKRF9fX0yMDDgDnV1dRIKhaSuri7xs4GBQVkP9bt4+fIlrV27ltq2bUtCoZBsbW1p9uzZJdb//WiEhYV988GST58+0YEDB6hXr16koaFBNWrUYNq/osnNzSVXV1fS1tam+vXrS2gSODo6lvXwfijq1atHK1askGpfvnw52djYMInRrl07icPJyYn69OlDa9eu5R7seZRL+/btS6zff/PmDbVv355JDEXqwHwLrO65QqGQnj59KtX+/Plzps+MRERJSUm0Z88eOnz4MKWnpzPt+1uRZyKVl5dH06dPp4cPHzIelWwMHz6cnj17JtP/7dy5M/39999E9H86MPn5+eTh4UG9evViOcyvIs97oqWlRQ8ePCAiIhMTE+47ef/+fdLS0mI2xm+BxSSdr0kvhYpSm9ygQQPUqVMHEyZMQNWqVaXSclgpcYtEIty9exfm5uYS9STJycmwt7fH+/fv5er/06dPGDduHNasWcOlDKqpqeGPP/7AvHnzeFXbL/DhwwepUgqWdVIlWZf96GzZsuWbf1fZyqGsePToEXbu3IlNmzYhOTn5h0+1Lar6ChRaChW/PYmvXyxSCEuyr/Ly8oKTkxPT1Nh9+/aVWtJ07do1ufvv3bs3IiMj8dtvv5V4jReXQ/AU2p/9+eefGDduHKe0HhERgcWLF2PZsmUYOnRoGY+QRxEIhUJkZmZKaVlkZWXBxMSkxHKn70WROjDFUeQ9VygU4unTpzAyMpJoT0hIQPv27Tm1d2Wgq6v73ano34s89c/i/3/z5k25SqS+xrRp0zB16lSpe+SbN28wfPhw7Ny5U+4YiYmJcHZ2RsOGDXHmzBm4ubnh1q1bePnyJWJiYlCnTh25Y3wr8rwn9vb2WLFiBdq2bYsOHTrAwcEBixYtwvLly7FgwQLmegdfQt7PFsDXpJdKea5NLsr9+/exf/9+WFpaMumvNGrXro34+HipSf+JEydgY2Mjd//q6uoICgrC3LlzkZqaCgCoU6cONDU15e67JLKyskq0M2JVk6XoGMoQzyjP1mXldeL9reTl5SEuLg6XL19Geno6qlatWtZD+ipFvwenT5/GhAkTMGfOHDRv3hwAcPHiRfz999+YM2eO3LGUZV+1fPlyTJ48GYMGDUJYWBh8fHyQmpqK2NhY+Pn5MYlx9OhRhIeHo1WrVkz6q8gMHjwYHz9+xOzZszFz5kwAQK1atbB69WoMHDiQaaxnz57h3r17AArtL4tPengUz40bN7ifb9++jczMTO7f+fn5OHHiBExMTGTuX1k6MGIUec91dHTk7HSdnZ0l9H/y8/ORlpamdL2T8rCH6OTkhOjoaIVO0jdu3IiTJ09i27Zt3IQvKioKAwcORLVq1ZjEqF+/PpKSkrBy5Uro6OggOzsbPXv2hJ+fH6pXr84kxrfSv39/mTeSfHx8kJCQgLZt22LixIno1q0bVq5cyQkfljf4SXoptGnTBqdOnYKdnR08PDwQEBCAM2fO4NSpU3B2di43MZydnZGQkKDwSXpgYCD8/Pzw4cMHEBGuXLmCnTt3Yu7cuXL5vBdHU1NTod6TV69ehbe3N+7cuVPiLh6Lya0yYihDPEORQjllhaKzDhRNSbvDR44cYerPrAxGjRqFNWvWSEw8XV1doampiWHDhnG7SLIybdo0eHh4yCfo8g38+++/WLduHfr164fg4GCMHz8eFhYWmDp1KrMdKVNT03L1GS1r/vjjD/zxxx949uwZRCKRhBsCC3JycuDv74+QkBBu4UlFRQUDBw7EihUrFLawzCONg4MDN/Es6RooEomwYsUKmftXpNBgSSjyniteREhISICrq2upLiEVDXmzpjp37oyJEyfi5s2baNSoEbS0tCTOF1ewl4UbN27g999/h4ODAxYvXoykpCQEBQVh3LhxnLsLC/T09DB58uQv/s73epgXXSj7GuINqtWrV3/z/ymOMoQPlYq8OfcVlYpSm/zs2TPq0qULTZs2jfbt26fQ2s5t27aRpaUlV/NuYmJCGzZsYBpD0djb21OPHj3o0qVLlJaWJuH7zqpmShkxlCGeURZCOYogOzub/Pz8yMjIiIRCodRRXqhRowZpaGhQ9+7dae/evSWK/5QXNDQ06ObNm1LtCQkJzHx0lYFIJOK+00ZGRpyYX1JSEjMxniNHjpCrq2u50B0oa2bOnEn3799XaIxhw4aRhYUFHTt2jPPmPXr0KNWpU4eGDx+u0Ng8kqSnp1NaWhoJBAKKjY2VuM/+999/5c4vWxn3XKFQSMePH2fSl7ywqOlVdIziWk+K0n0iIpo0aRIntnf69GmmfX8r3+thLv47fOlvxPJvpQzhw2+lc+fO9N9//8nVBz9Jr+AcOnSI9PT0lHIBEZOTk1Oi8Eh5QFtbu0T13/IWQxniGWUtlMOKESNGkI2NDe3bt49EIhFt2rSJZs6cSTVr1qRt27aV9fC+mXXr1pUojlQead26NXXs2JEyMzO5tszMTHJxcaE2bdqU4ci+j9q1a3MP1Y0aNaI1a9YQEVF4eDgzUUJ9fX1O7FBbW1tCELG8CR8qGnt7exIKhdS8eXNatWqVzCJLX8LQ0JBbIC3KmTNnqEqVKszj8fx4vH//nlugER8sUMY990dyCZFnAq0MsUBlsnz5ctLU1CRPT0/65ZdfyNbWtkzem+99T4pvQn3pYIGyhA9TUlJo8uTJ1LdvXy7esWPHKDExkVkMIn6S/lUUbV2m6Bjm5ubk5+cn8bDLUzru7u60b9++ch/Dzs6Os/9wdnamMWPGEBFRUFAQmZiYMIlRUazLfiTLDp5CkpOTqX79+qSurk516tShOnXqkLq6OtWrV0/hC1wsGTJkCE2bNo2IiFauXEkikYg6dOhA+vr6NHjwYCYxysJusTyTmJhIkyZNotq1a5Oamhp16dKFtm/fTjk5OUz6F4lEdPv27RLjampqMonB8/2EhIRQixYtqHr16tyEYMmSJXTw4EEm/SsjI0tZ99wfxSXke3dtiyIQCEqcrD19+pRUVVXlHZpScXV1JUNDQ065Pzc3l4YPH04aGho0f/58pY5FGdkN8iAQCCgrK0uqPT4+ntmidVRUFHcvV1dX5/4ec+fOZa6Ez6u7l0JFqU3W0dFBfHy8wpUZnz59irFjxyIiIgJZWVlSr4fFa1EGz58/h7e3N5o0aYL69etDTU1N4jyL+iJlxFi6dClUVFQwcuRInD59Gt26dQMRceIZAQEBMvVbklCOWBwH+D+hHC0tLaWqwMqDtrY2bt++DTMzM9SsWROhoaFo0qQJ0tLSYGdnh+zs7LIe4k8JEeHUqVO4e/cuAMDGxgYdOnRgqryuaAoKClBQUMB9P3bt2oULFy7AysoKv//+O9TV1ZU2lnnz5mH48OEKr8MvT8TExGDHjh3Yu3cvPnz4gLdv38rdp7OzMwwNDRESEgINDQ0AwPv37+Ht7Y2XL1/i9OnTcsfg+T5Wr16NqVOnYtSoUZg9ezYSExNhYWGB4OBgbNmyBZGRkXLH8PPzQ2RkJGbOnFmiDoyXl5dM/Zb1PbcsXUJkUccW10A7ODjgzJkzqFy5MndOLBa4du1apKenMxtndHQ0Fi1aJKG4P27cOLRu3ZpJ/x07dsSWLVtQo0YNifajR4/C19eXiRPUtyKvYvnWrVuxZs0apKWl4eLFizA3N8eyZctQu3ZtuLu7yzwusfBhQkIC6tWrV6rw4Z49e2SOIaZ58+bw8PBAYGCgxN/jypUr6NmzJ1MFeV44rhQGDx6MunXrYuPGjSXa2pSXGD179kRkZKTCJ+mDBg1CRkYGpkyZgurVq5erB+miXLx4ETExMTh+/LjUOVYLJ8qIoSjxDGUL5SgDCwsLpKWlwczMDNbW1tizZw+aNGmCw4cP8xOaMkQgEMDFxQUuLi5lPRSZEQqFErY5ffv2Rd++fctkLHPmzEHv3r35z3QRtLS0IBKJoK6uzjmtyMuyZcvQqVMn1KxZEw0aNABQKMZVqVIlnDx5kkkMnu9jxYoVWL9+Pbp37y4hnNq4cWOMHTuWSYzDhw8jJCQE7dq1g4+PD1q3bg1LS0uYm5tj+/btMk/Sy/KeqwiXkLy8PFhbW+PIkSNfdf45fvz4d6vvK1ossDjbtm2Dj48PevbsiZEjRwIoXPxzdnZGcHAwPD095Y5x6tSpEtu7du2Kmzdvcv/euXMn3NzcpMTrfhSKL5aJn3X19fWxbNkyuSbpyhQ+vHnzJnbs2CHVbmxsjOfPnzOJwcF0X74CUVFqk2fNmkVVqlQhb29vWrRoEQUFBUkcrNDW1qbr168z66+sUEZ5gDJi/EjiGT86S5Ys4b4Lp06dIg0NDapUqRIJhUJatmxZGY/u5+X06dM0adIkGjJkCPn4+Egc5YVNmzbRnj17pNr37Nmj9FT0Hz1NUVncv3+fZs2aRba2tqSiokJOTk60YcMGev36NbMYOTk5tG7dOgoMDKTAwEBav3495ebmMuuf5/vQ0NDgUtyLfg+SkpKYCVEqQwdGWZw5c4Z8fX3JwMCA9PT0yMfHh06fPk0FBQVM+q9Ro0aJJSEsULZYoLW1NS1ZskSqffHixWRtbc001teQpzzgW5HnPmJjY8OVZRTt5+bNm2RoaMhkfMoQPjQxMaGYmBgiknwdoaGhZGFhwTQWv5NeCsqwLlNGjA0bNkBbWxvR0dGIjo6WOCcQCLiVP3kxNTUtF56WX+PFixcYPXq0Qn2llRHDx8cHnTp1grGxsUT7u3fv4OPjw9wTuDxbl1U4y44KwPTp0zFjxgw0bty4XGfmzJ07F2vXrpVqNzY2xrBhw+Dt7V0Go/p5adasGWJjY2Fvbw8fHx/069dPLp/skpg7dy6qVq2KoUOHSrRv2rQJz549w4QJE5jG4/k6tWvXRnx8PMzNzSXaT5w48dXd3G9F2RlZirrnmpiY4OXLl+jUqRPWrVuHbt26oVKlSnL3WxQ/Pz/Mnz8fGzZskEhLZoH4PRbbHyqa+/fvo1u3blLtbm5u+Ouvv5QyBjHKeAaXx8M8LS0Njo6OUu2VKlVCTk6OvEMDAPz666/o3r07jIyM0K9fP3h5eXEZTazo27cvJkyYgL1790IgEKCgoAAxMTEYO3Ys82drfpJeChs2bIC3tzcSExMVVjesjBhpaWly9/EtLFu2DBMnTsTatWtRq1YtpcRUBMooD1BGDCIqcWLz6NEj6OnpMYmRk5ODCRMmYM+ePXjx4oXU+fKiQxASEoI+ffpwDyLm5uYwNzfHp0+fEBISwvyiy/N11qxZg+DgYAwYMKCshyIXGRkZqF27tlS7ubk5MjIyymBEPzfOzs7YtGkTbG1tFRZj7dq1JaZC1qtXj3u441EugYGB8PPzw4cPH0BEuHLlCnbu3Im5c+diw4YNTGL4+PggISEBbdu2xcSJE9GtWzesXLmS04FhgTLuudOmTYOHh4dCy2JiY2MRERGBkydPws7OTio9OzQ0VO4Y4sWywYMHS7SzXiwzNTVFRESE1Gbb6dOnYWpqyiSGolC2h7kyFsvCwsLw6tUr7N27Fzt27MDixYthbW0NLy8veHp6MpmfzJkzB35+fjA1NUV+fj5sbW2Rn58PT09P/P333/K/iCLwk/RSKM+1yYGBgZg5cya0tLQQGBhY6u8JBAIsXrxYphjF6dOnD3Jzc1GnTh1oampKLTiUFxGxunXrYtKkSTh//jzs7OykXgeLzANFxhCLZwgEAjg7O5cqnsGC8ePHIzIyEqtXry5RKKe8oOysA56v8+nTJ7Ro0aKshyE3xsbGuHHjhtSDQUJCAgwNDctmUD8xs2fPVniMzMxMVK9eXardyMhIqQJPPP+Hr68vRCIR/v77b+Tm5sLT0xM1atRAUFAQM40IZWRkKeOeWzwDRBHo6+szqw8uDWUtlo0ZMwYjR45EfHw8d8+KiYlBcHAwgoKCmMRQFOL6/dJ24MXnWM15lLFYBhSKLQ4bNgzDhg2TED6cOnUqE+FDdXV1rF+/HlOmTEFiYiKys7Ph6OgIKysrBqOXhJ+kl4K/vz/69++PKVOmKCwtWVExrl+/jry8PO7n0mCZQlpRBMWUUR6gyBjKFM9QlFCOslFG1gHP9+Hr64sdO3ZgypQpZT0UuejXrx9GjhwJHR0dtGnTBkChEnBAQECZCcj9zJS2aC0QCKChoQFLS0u4u7tLKEJ/L6ampoiJiZHKoIiJiZFSZ+ZRPJ8/f8aOHTvg6uoKLy8v5ObmIjs7W2pRVl6UkZFVUe65mzdvVngMZS2W/fHHH6hWrRoWL17MKYfb2Nhg9+7dcgmhKQNlZdqKUcZiWVEUIXxYFDMzM5iZmTHtszi8BVspKMO6TFn2aDw/HyoqKjh69CizXfOSKO/WZcq07OD5PgICAhASEgJ7e3vY29tLZZuwSh9VNJ8+fcKAAQOwd+9e7vNVUFCAgQMHYs2aNUq1YOvSpQs2btxY4oPrz0L79u1x7do15Ofn45dffgEAJCUlQUVFBdbW1rh37x4EAgHOnz8vc0r8ggULsGDBAixcuJBTl46IiMD48eMxZswYTJo0idnr4fk2NDU1cefOHak0W5aoqKjgyZMnUpP/Fy9ewNjYmMlOZHm/54qZNWsWvLy8SiwFYoWVlRX++ecf9O/fX6J969at+Oeff3D//n2FxS4JZSivy2uPpkwUtVgGAJGRkdixYwf279+PgoIC9OzZE15eXnBycpJ5c/JLWcnFYfl8wu+kl0JFqU1WJqmpqdi8eTNSU1MRFBQEY2NjHD9+HGZmZqhXr15ZD69UlFEeoOwSBGWIZ5R36zJlZh3wfB83btyAg4MDACAxMVHiXHkSkVNXV8fu3bsxc+ZMJCQkQCQSwc7Ojslk4erVq2jUqNE3//6xY8fkjlneEe+Sb968mRM/evPmDXx9fdGqVSsMHToUnp6eGD16NMLDw2WKMW7cOLx48QIjRozghL00NDQwYcIEfoJeRjRp0gTXr19X6CRdGRlZ5f2eK2bv3r34559/0LRpU/Tv3x+9e/dGlSpVmMYYOnQoRo0ahby8vBIXy5TN77//jqZNmyp0Am1ubi61oP09KMrDvDhZWVm4d+8egML7uZGREbO+FSV8+KWs5KKwfj7hd9JLYfbs2Vi2bBm6du2qsNpkZcRQFtHR0ejcuTNatmyJs2fP4s6dO7CwsMC8efMQFxeHffv2lfUQS6V9+/Y4cOAA9PX10b59+1J/TyAQ4MyZMz9sjOIUFc84d+4cc/GMpUuXQkVFBSNHjsTp06fRrVs3EBEnlBMQECD/i1ACysg64Pm5aN26Ndzd3eHm5oa6desqJIZQKISFhQUGDx6MQYMG8anU34CJiQlOnToltUt+69YtuLi44PHjx7h27RpcXFzk9rvNzs7GnTt3IBKJYGVlxVwhm+fb2bNnDyZNmoTRo0ejUaNGUruZ8tSMKzMjq6Lcc4HC79z27duxa9cuPHr0CB07doSXlxe6d+8OTU1NufsnIkycOBHLly+XWiybOnWq3P1/Lz/6LndxD/PExERYWFggODgYW7ZsQWRkpNwx3r17hxEjRmDnzp2c+r6Kigr69OmDVatWMVnMWr9+vcKFD5UJP0kvhS+l4QgEAiapMsqIoSyaN28ODw8PBAYGSlyMrly5gp49e+LRo0dlPcSfmqLiGcnJyUzEM4rz4MGDcmld5u7ujvDwcIVmHfDIRkpKClJTU9GmTRuIRKJSd6t+NEJCQhAWFoaTJ0+iZs2acHNzg5ubG1q0aMFs/EKhEL6+vggLC8PLly/h6uoKX19fdOvWDSoqKkxiVDS0tbVx5MgRtGvXTqI9KioK3bp1w7t373D//n04ODjg7du3ZTNIHuYIhUKpNlaiWNOnTwcAzJgxA4GBgaVmZCmitKW83nOLExMTgx07dmDv3r348OED0+/ej7JY9r2TdAMDg2++V7AQZra1tcWcOXPQvXt3ibEmJiaiXbt2ci9aAoUC09evX8eKFSvQvHlzAIUC2gEBAXBwcMCuXbvkjqEM3rx5g/z8fCntkpcvX0JVVZWp/TA/Sedhgra2Nm7evInatWtLfMHT09NhbW2NDx8+lPUQf1ry8vJw9OhRbNu2DUePHkXlypXx+PFjufstLpQj5tOnT9i1a1e5UkVXdNYBz/fx4sUL9O7dG5GRkRAIBEhOTuZ2jQ0MDJi5Uiiajx8/IiIiAmFhYTh8+DDy8/PRtWtXuLm5wdXVFSKRSOa+hUIhMjMzUblyZYSFhWHTpk0IDw9HlSpV4O3tjSFDhihsF7+84uXlhYsXL2Lx4sX43//+B6DQDmrs2LFo0aIFtm7dil27dmHRokWIi4sr49HysOLBgwdfPM8iDV4ZGVkV6Z5blPj4eGzbtg27du3Cixcv8P79e2Z9/ygLvd87Sd+yZcs39+3t7S3rsDhEIhHu3r0Lc3NzibEmJyfD3t6eyXuipaWF8PBwtGrVSqL93Llz6NSpEzOvdEXTuXNndOvWDSNGjJBoX7NmDQ4dOsS0tIyfpBehItYmK4uaNWtiz549aNGihcQX/MCBAxg7dixSU1PLeog/HYoQzyiKMoRyygJlZB3wfJmBAwciKysLGzZsgI2NDXc9CQ8PR2BgIG7dulXWQ5SJy5cv49ChQzh06BBSU1Ph5OSESZMmoWXLlt/dl3iSXvT79/jxY2zatAnBwcFIT0/nyo9+Zm7cuIH69etDKBQiOzsbo0ePRkhICPe9VlVVhbe3N5YuXQotLS3Ex8cDAKeJwPPz0LVrV2zYsEEmcUVlZGRVpHtuWloaduzYgR07duDevXto27YtPD098dtvvzFJe/7RFnp/9HR3W1tbzJ07F+7u7hJjXbFiBTZv3oxr167JHcPMzAxHjx6FnZ2dRPuNGzfQpUuXcpNxW7lyZcTExEh5u9+9exctW7bEixcvmMXiheOKoAzrsrKwR1MGYt/JvXv3QiAQoKCgADExMRg7dmy5Xd0tzyhKPKMoFdG6TNGWHTzfxsmTJxEeHo6aNWtKtFtZWX11V+xHpmnTpmjatClmz56N1NRUHDp0SGY7oJK+eyYmJpgyZQqmTJmCiIgIbNq0Sd4hl3scHR25iY29vT1iY2OxdOlSrpzMwsJCIkWZn5z/vJw9e1bmHcOwsDCJjKzFixczz8iqKPfcZs2aITY2Fvb29vDx8UG/fv1gYmLCNMbo0aOhpqaGjIwMiclUnz59EBgYWK42wQDFCzMrw8P877//RmBgILZu3Ypq1aoBKLTKGzduXLmyW/348WOJmzd5eXlMs0AAAMTDw4CPHz+Sr68vqaqqkkAgIDU1NRIKhdS/f3/6/PlzWQ/vp2PdunX06tUrhfTt4OBAjo6OJBQKyc7OjhwdHbnD3t6edHR0yMPDQyGxFcWZM2fI19eXDAwMSE9Pj3x8fOj06dNUUFBQ1kP7KdHW1qakpCTu59TUVCIiio2NpcqVK5fl0GQiNjaWQkJCKCQkhOLi4pj0KRAI6OnTp0z6qshUrlyZLl26RESFf7OsrKwyHhHPj0rRa428PHz4kBYsWEDW1takoqIiV18V7Z77119/0a1btxQao2rVqhQfH09Eku9ramoqaWlpKTR2SdSrV48yMjJk+r9RUVEkEomoQ4cOpK6uzr2WuXPnUq9evZiNcdu2bWRpaUkCgYAEAgGZmJjQhg0b5OpT/NkVH9ra2qSmpkZ16tShOnXqkJqaGmlra5OjoyOjV6F42rVrR3/++adU+4gRI6hVq1ZMY/E76TxMUFdXx/r16zFlyhQkJiYiOzsbjo6OsLKyKuuh/ZQMHTpUYX1XNOsyZWQd8HwfrVu3RkhICGbOnAkAXHbOggULvuiO8KPx6NEj9OvXDzExMZza7OvXr9GiRQvs2rVLKlPge4iMjJQSruGRplevXmjbti2qV68OgUCAxo0blyqsV57EWnl+XFhnZFW0e+7s2bMBFNbSp6WloU6dOhKq+CzIyckpUSX+5cuXZXJ/L24l+j1MnDgRs2bN4oSZxTg5OWHlypUshgegULPDy8uLqYe5+LNbkZg1axY6dOiAhIQEODs7Ayi094uNjcXJkyeZxuJr0nl4eGSioliXVTTLjorArVu34OTkhIYNG+LMmTNwc3PDrVu38PLlS8TExKBOnTplPcRvolOnTnj9+jW2bNmCX375BQBw7949+Pj4QFdXFydOnCjjEf4cnDhxAikpKRg5ciRmzJgh8aBblPJkYcXDHnnrhpWhA1MR7rnv37/Hn3/+yYmjJSUlwcLCAv7+/jAxMcHEiRPljtGlSxc0atQIM2fOhI6ODm7cuAFzc3P07dsXBQUFctkCK1t5XZnCzEU9zK2trZl6mFck4uPjsXDhQsTHx0MkEsHe3h6TJk1ivjHJ76TzMKE0ETyBQAANDQ1YWlrC3d2d3/mpQPz666/o3r17ubcuU2TWAc/3k5eXh5EjR+Lw4cM4deoUdHR0kJ2djZ49e8LPz08mUaeyIjo6GhcuXOAm6ADwyy+/YMWKFWjdurXc/X/69AkHDx7ExYsXkZmZCQCoVq0aWrRoAXd3d4XYPpVHxJOaq1evIiAgoNRJOg+PrCgjI6ui3HMnTpyIhIQEREVFSSw4dOjQAdOmTWMySV+wYAGcnZ0RFxeHT58+Yfz48RILvfKwbNkyucf3Pejr6+PJkydSts3Xr19nVsuvDA/zioSDgwO2b9+u8Dj8TjoPE9q3b49r164hPz+feyBNSkqCiooKrK2tce/ePQgEApw/fx62trZlPFoeVvDWZTyKwMjICBcuXCj35TJ169bFtm3b0KRJE4n2K1euwNPTEykpKTL3nZKSAldXV/z3339o2rQpl1L79OlTXL58GTVr1sTx48dhaWkp12vg4flZkGcnXVkZWRXhnmtubo7du3ejWbNmEn/zlJQUNGzYkJlP+ps3b7By5UokJCQgOzsbDRs2LHcLvQAwduxYXL58GXv37kXdunVx7do1PH36FAMHDsTAgQPxzz//yB1DGR7m+fn5WLp0Kfbs2YOMjAx8+vRJ4jyLrANF8fbtW87//GufT5Y+6bxwHA8Tli5dSj179qQ3b95wba9fv6bffvuNli1bRjk5OeTu7k4uLi5lOEoeRcJSKIfn52bUqFE0YcKEsh6G3Bw8eJCaNGlCsbGxXFtsbCw1a9aMDhw4IFffHTp0IHd3d4lrrpg3b97w11senu9kzpw5ChNcVQTl9Z4rEok48bOiom7x8fGkq6tblkOTiZSUFJo8eTL17duXE/M8duwYJSYmMulfGcLMmpqadO7cOan2s2fPkqamJpMYU6ZMoerVq9OiRYtIQ0ODZs6cSUOGDCFDQ0MKCgpiEkNRCIVC7r0VCAQkFAqlDnE7S/iddB4mmJiY4NSpU1K75Ldu3YKLiwseP36Ma9euwcXFBc+fPy+jUfIoiry8PBw9ehTbtm3D0aNHUblyZTx+/Lish8VTTvH390dISAisrKzQqFEjaGlpSZxfsmRJGY3s6xSvV8zJycHnz585YSTxz1paWnLtHGhqauLKlSuoX79+iedv3ryJpk2bIjc3V+YYPDwVgblz56Jq1aoYPHiwRPumTZvw7NkzTJgwoYxGJjvl+Z7bpk0beHh4wN/fn6sXr127Nvz9/ZGcnCyzVseNGzdQv359CIVC3Lhx44u/q62tDVNTU6ipqckUS0x0dDQ6d+6Mli1b4uzZs7hz5w4sLCwwb948xMXFyVX7XpyMjAyFCTMrw8O8Tp06WL58Obp27QodHR3Ex8dzbZcuXcKOHTvkjqEooqOj0bJlS6iqqiI6OvqLv9u2bVtmcfmadB4mvHnzBllZWVKT9GfPnnGpIfr6+lLpLTzlm5KEco4cOQInJ6eyHhpPOSYxMRENGzYEUFg2UxQWAkyKRFn1ivr6+khPTy91kp6ens6LIfLwAFi7dm2JE4B69eqhb9++5WqSXhHuuXPmzEHnzp1x+/ZtfP78GUFBQbh9+zYuXLjw1QnQl3BwcEBmZiaMjY3h4OAAgUCAL+1D6unpYc2aNejTp4/MMZWlvA4UTqRNTU0BsL8PKsPDPDMzk1sE0NbWxps3bwAUai386D7pRSfeLCfhX4OfpPMwwd3dHYMHD8bixYvxv//9DwAQGxuLsWPHchYMV65cQd26dctwlDws4a3LeBRFZGRkWQ9BZry9vZUSx9fXFwMHDsSUKVPg7OwsUZMeERGBWbNmwd/fXylj4eH5kcnMzCyxDtnIyAhPnjwpgxHJRkW557Zq1Qrx8fGYN28e7OzscPLkSTRs2BAXL16U2sn9HtLS0jg18rS0tC/+7sePH7F3715MmDBBrkn6zZs3S1wAMjY2Zpo1unHjRixduhTJyckAACsrK4waNQq+vr4y9+no6Cgx2U9OToaZmRnMzMwAFO7cV6pUCc+ePcPvv/8u3wsAULNmTTx58gRmZmaoU6cO977HxsaWu8/x69evceXKFWRlZXFCe2IGDhzILA4/Sedhwtq1azF69Gj07dsXnz9/BgCoqqrC29sbS5cuBVBo57Bhw4ayHCYPQ6ZNm8Zbl/HwfCMfPnyQyiSSR2BmxowZ0NLSwsKFCzFmzBjuYYuIUK1aNUyYMAHjx4+Xa8w8PBUBU1NTxMTESKljx8TEoEaNGmU0qu+nIt1z69Spg/Xr1zPt09zcvMSfS2PEiBG4evWqXDGVobw+depULFmyBP7+/hKibqNHj0ZGRgZmzJghU7/K9jDv0aMHIiIi0LRpU/j7+6N///7YuHEjMjIyMHr0aKWORR4OHz4MLy8vZGdnQ1dXV2KhQyAQMJ2k8zXpPEzJzs7G/fv3AQAWFhbQ1tYu4xHx8PDwlA05OTmYMGEC9uzZgxcvXkidz8/PZxLn/v37ePr0KYBCC7biD4w8PD8zCxYswIIFC7Bw4UIuLTwiIgLjx4/HmDFjMGnSpDIeYcXnexTbWaljv3r1Chs3bsSdO3cAADY2Nhg8eDBTK2BlKK8bGRlh+fLl6Nevn0T7zp074e/vX251ni5evIiLFy/CysoK3bp1K+vhfDN169ZFly5dMGfOHGhqaio0Fj9J5+Hh4eHhUQB+fn6IjIzEzJkzMWDAAKxatQqPHz/G2rVrMW/ePHh5eZX1EHl4KjxEhIkTJ2L58uVcNouGhgYmTJiAqVOnlvHofg6EQuFX66iJCAKBgMni5dmzZ9GtWzfo6emhcePGAICrV6/i9evXOHz4MNq0aSN3DAD49OkT/Pz8EBwcjPz8fKiqqiI/Px+enp4IDg6GioqK3DH09fURGxsrJRSXlJSEJk2a4PXr13LH4Pl2tLS0cPPmTZmsGr8XfpLOw4y4uLhS/Q9DQ0PLaFQ8PDw8ZYOZmRlCQkLQrl076Orq4tq1a7C0tMTWrVuxc+dOHDt2TK7+b9++jZUrV+LixYvIzMwEULiT3rx5c/z5559SQp48PD8z2dnZuHPnDkQiEaysrMpdHWx55nsE4VgIc9nZ2aF58+ZYvXo1N1HOz8/HiBEjcOHCBdy8eVPuGEVRpPK6v78/1NTUpFxNxo4di/fv32PVqlVyx1CUh/mhQ4e++Xfd3NxkiqFsevbsib59+6J3794Kj8VP0nmYsGvXLgwcOBCurq44efIkXFxckJSUhKdPn6JHjx7YvHlzWQ+Rh4eHR6loa2vj9u3bMDMzQ82aNREaGoomTZogLS0NdnZ2yM7Olrnv48ePo3v37mjYsCFcXV0lhONOnTqFq1evIiwsDK6urqxeDg8PD0+5QCQSIT4+Hr/88otE+7179+Dg4ID3798zjymeTrFQXg8MDOR+/vz5M4KDg2FmZoZmzZoBAC5fvoyMjAwMHDgQK1askDve1KlTsWHDBowZMwZ///03Jk+ejPT0dBw8eBBTp07FyJEjZepXKBR+0++xyqBQFEUXG549e4YZM2bAx8cHdnZ2UjZ+LBcb+Ek6DxPs7e3x+++/w8/PDzo6OkhISEDt2rXx+++/o3r16pg+fXpZD5GHh4dHqdjb22PFihVo27YtOnToAAcHByxatAjLly/HggUL5PKebdCgAdzd3UsVDZo2bRpCQ0O/6hfMw1MR6dmzJ4KDg6Grq4uePXt+8Xf5TD/lsnnzZmhra8PDw0Oife/evcjNzWXikNGyZUuMGzdOfgRwGAAARnlJREFUShzt4MGDmDdvHi5duiR3DDGKUF5v3779N/2eQCDAmTNnZI4jpjx7mCuDslps4NXdeZiQmpqKrl27AgDU1dWRk5MDgUCA0aNHw8nJiZ+k8/Dw/HT4+PggISEBbdu2xcSJE9GtWzesXLkSeXl5UqmL30tSUtIXa9r79euH+fPnyxWDh6e8oqenx+1oFldg5ilb5s6di7Vr10q1GxsbY9iwYTJP0osuSI4cORIBAQFISUnhdp8vXbqEVatWYd68ebINvAQUpbyubBvSH8nD3M7ODseOHeM84X8EitusKQt+ks7DBAMDA7x79w5AoZdnYmIi7Ozs8Pr1a+Tm5pbx6Hh4eHiUT1FbmQ4dOuDu3bu4evUqLC0tYW9vL1fftWrVwtGjR6XSOcUcPXr0m2yIeHgqIkVL7IKDg8tuIDxSZGRklOhAYW5ujoyMDJn7dXBwgEAgQNEE4ZJsKD09PeXyRi/K6tWrsX79egnldTc3N9jb28Pf31/mSbqy+ZE8zNPT05GXl6fUmLLy4cMHaGhoKKx/fpLOw4Q2bdrg1KlTsLOzg4eHBwICAnDmzBmcOnUKzs7OZT08Hh4eHqUTEhKCPn36cA855ubmMDc3x6dPnxASEiKXn+qMGTPg6emJqKgodOjQQaImPSIiAidOnPjpUxR5eADAyckJoaGhUv7ib9++Rffu3ZmkC/N8O8bGxrhx4wZq1aol0Z6QkABDQ0OZ+01LS5NzZN9PXl4epx5flEaNGuHz58/M4ihamLmieJgrg/z8fMyZMwdr1qzB06dPkZSUBAsLC0yZMgW1atXCkCFDmMXia9J5mPDy5Ut8+PABNWrUQEFBARYsWIALFy7AysoKf//9NwwMDMp6iDw8PDxKRUVFBU+ePIGxsbFE+4sXL2BsbCx37dqFCxewfPnyEtXdAwICuPRLHp6fGaFQiMzMTKnvYVZWFkxMTMrNrl1FYcKECdi9ezc2b97MWaFFR0dj8ODB+O2337Bo0aIyHuG3owzl9bIQZi5LD3OxrpUyLM5kYcaMGdiyZQtmzJiBoUOHIjExERYWFti9ezeWLVuGixcvMovFT9J5eHh4eHgUgFAoxNOnT2FkZCTRnpCQgPbt28tsa8PDw/N1xDXKDg4OOHPmDCpXrsydy8/Px4kTJ7B27Vqkp6eX0Qh/Tj59+oQBAwZg7969UFUtTOgtKCjAwIEDsWbNGqirqzOJk5qaimXLluHOnTsAAFtbWwQEBKBOnTpy9ats5fWfTZj5R5+kW1paYu3atXB2dpYY6927d9G8eXO8evWKWSw+3Z2HKVlZWcjKypISWZC3/pKHh4envODo6AiBQACBQABnZ2fuQRQonBykpaWhU6dOZThCHp6Kj7hGWSAQwMnJSeq8SCRiMoni+T7U1dWxe/duzJw5EwkJCRCJRLCzs2OqoREeHg43Nzc4ODigZcuWAICYmBjUq1cPhw8fRseOHWXu+/r16xL/btSoEYDCRQEAqFKlCqpUqYJbt27JHKMoihJmroge5srg8ePHsLS0lGovKChgnpXDT9J5mHD16lV4e3vjzp07KJ6c8aP7H/Lw8PCwRGz7k5CQAFdXV2hra3Pn1NXVUatWLfTq1UvuOMeOHUNoaCgqV64MHx8f2NjYcOdevXqFXr168fW2PD8taWlpICJYWFjgypUrEhkt6urqMDY2hoqKShmO8OeidevWcHd3h5ubG+rWrcsdimDixIkYPXq0lJL7xIkTMWHCBLkm6cpWXleUMHNxe7rS4J/hJbG1tcW5c+ekFpX27dsHR0dHprH4dHceJjRo0AB16tTBhAkTULVqVSm7E15lmIeH52dDRUUFR48eVciu+Y4dOzBw4EB06tQJb968QVxcHDZs2MDZsj19+hQ1atTgH654eHh+CEJCQhAWFoaTJ0+iZs2acHNzg5ubG1q0aMHcIk9DQwM3b96ElZWVRHtSUhLs7e3x4cMHpvEUiaenJxo3bozAwEDMnDkTK1asgLu7O06dOoWGDRsyEY77kdixYwfc3d2hpaVV1kMpkbCwMHh7e2PSpEmYMWMGpk+fjnv37iEkJARHjhyRawGoOPwknYcJOjo6uH79eokpIDw8PDw/I+7u7ggPD4eRkRH69esHLy8vNGjQgEnfjo6O8PHxwciRIwEAe/bsweDBgxEUFIQhQ4bwk3QeniJs3boVa9asQVpaGi5evAhzc3MsXboUFhYWcHd3L+vh/VR8/PgRERERCAsLw+HDh5Gfn4+uXbvCzc0Nrq6uEIlEcscwNTXFkiVL4OHhIdG+Z88ejB07Vi6rt+IoWnn9RxJmltfDPCIiAkuXLuV0AmxsbDBq1Ch06NCB5TAVzrlz5zBjxgwkJCQgOzsbDRs2xNSpU+Hi4sI2EPHwMMDd3Z327dtX1sPg4eHh+aF4+fIlrV27ltq2bUtCoZBsbW1p9uzZlJaWJle/WlpadP/+fYm2M2fOkLa2Nq1evZoyMzNJKBTKFYOHpyLw77//UpUqVWjWrFkkEokoNTWViIg2b95M7dq1K+PR8Vy6dIn++usvql+/PolEIuratSudP39erj6nT59O+vr6NG/ePDp79iydPXuW5s6dS/r6+jRjxgxGIyfauXMnqamp0a+//krq6ur066+/Ut26dUlPT48GDRrELM6Pgra2Nvf9+V5WrVpFqqqq1LdvXwoKCqKgoCDq168fqamp0cqVKxmPtGLA76TzMOH58+fw9vZGkyZNUL9+faipqUmc50UneHh4fnYePXqEnTt3YtOmTUhOTpbLR7dGjRoIDQ3lFIXFREdH49dff0VAQADmzp3L76Tz/PTY2tpizpw56N69u4Qac2JiItq1a4fnz5+X9RB5/j+pqak4dOgQTE1N8dtvv8ncDxFh2bJlWLx4Mf777z8AhdfMcePGYeTIkczS6xWlvP727dtv/l1dXV2ZYsiCPMrrNWvWxMSJE/Hnn39KtK9atQpz5szB48ePWQ1Tofj6+qJ///5o166dwmPxk3QeJhw+fBgDBgwo8cLCi07w8PD87OTl5eHo0aPYtm0bjh49isqVK8v1UNK9e3c0aNCgxIfAqKgo/Prrr3j//j1/7eX56RGJRLh79y7Mzc0lJhnJycmwt7fH+/fvy3qIPyVxcXES9mhilXTWiEXXdHR0pM7FxMSgcePGqFSpkkx9a2lp4datW6hVqxYMDQ0RFRUFOzs73LlzB05OTnjy5IlM/QqFwq8uJBCR0p+v5Zmka2trIz4+XqosNjk5GY6OjsjOzmY1TIVStIytb9++8PLygoODg0Ji8eruPEzw9/dH//79MWXKFFStWrWsh8PDw8PzQxAZGYkdO3Zg//79KCgoQM+ePXHkyJESLaG+h9GjR+PChQslnmvXrh0OHz6MkJAQuWLw8FQEateujfj4eCkB2xMnTkg4IvAoh0ePHqFfv36IiYmBvr4+AOD169do0aIFdu3ahZo1azKNV9LkXEznzp0RHx8vsye3opTXla0grwzc3Nxw4MABjBs3TqI9LCwMv/76axmN6vsJCwvDq1evsHfvXuzYsQNLliyBtbU1vLy84OnpiVq1ajGLxU/SeZjw4sULjB49mp+g8/Dw8Px/TExM8PLlS3Tq1Anr1q1Dt27dZN6xKU7btm3Rtm3bUs+3b98e7du3ZxKLh6c8ExgYCD8/P3z48AFEhCtXrmDnzp2YO3cuNmzYUNbD++nw9fVFXl4e7ty5g19++QUAcO/ePfj4+MDX1xcnTpxQ2ljkTSZu06YNTp06BTs7O3h4eCAgIABnzpzBqVOn4OzsLHO/X7q2l8aIESMwY8YMVKlSRea4rFm+fDn3s62tLWbPno2oqCg0b94cAHDp0iXExMRgzJgxZTVEmTAwMMCwYcMwbNgwiTK2qVOnylXGVhw+3Z2HCd7e3mjdujV8fX3Leig8PDw8PwTr16+Hh4cHt1ukCI4dOwYVFRW4urpKtIeHh6OgoACdO3dWWGwenvLC9u3bMW3aNKSmpgIorE+ePn06hgwZUsYj+/kQiUS4cOGClKf01atX0bp1a7l2oL8XedK3gR9LeV1XV1eurIBv4Xv/XrVr1/6m3xMIBLh//748QysTWJexFYffSedhQt26dTFp0iScP38ednZ2UsJxYpsgHh4enp+FoUOHKjzGxIkTMW/ePKl2IsLEiRP5SToPDwAvLy94eXkhNzcX2dnZMDY2Lush/bSYmpoiLy9Pqj0/Px81atQogxHJTuXKlbmfhUIhJk6cWGZjUcae69q1a78rYzYtLU2Boyk7FFXGVhx+J52HCV9aLSuvK2Q8PDw8PzoikQh37tyRqoNLT09HvXr1kJOTUzYD4+H5wcjKysK9e/cAANbW1jAyMirjEf2chIWFYc6cOVi1ahUaN24MoFBEzt/fHxMmTED37t2VNhZZdtIrovI6oFgP87y8PFhbW+PIkSPlXgeiaBmbl5cX0zK24vA76TxMqKirZTw8PDw/Mnp6erh//77UJD0lJQVaWlplMygenh+Id+/eYcSIEdi5cycKCgoAACoqKujTpw9WrVoFPT29Mh5hxcfAwEBCrTwnJwdNmzaFqmrhNOTz589QVVXF4MGDlTpJl8WKTV9f/4dUXpeHf//9FwEBAfjtt98QEBAAoLBevEuXLli6dCn8/Pzk6l9NTQ0fPnxgMdQyZ9q0ad9Uxvbo0SPUqFEDQqFQ5lj8TjqPzAQGBmLmzJnQ0tJCYGBgqb8nEAiwePFiJY6Mh4eH5+fg999/x8WLF3HgwAHUqVMHQOEEvVevXvjf//7HC2Px/PT06dMH169fx4oVKzjBqosXLyIgIAAODg7YtWtXGY+w4rNly5Zv/l1vb28FjkQSWXafo6Ojv/l3ZRGAk5Uf3cN8zpw5SEpKwoYNG7jFmYoMC40AfpLOIzPt27fHgQMHoK+v/0UVYYFAgDNnzihxZDw8PDw/B2/evEGnTp0QFxfHWRc9evQIrVu3RmhoqEJF63h4ygNaWloIDw9Hq1atJNrPnTuHTp068SUhFRAnJ6cSr39v375F9+7dlf5Mqgzl9R/dw7xHjx6IiIiAtrY27OzspDK9QkND5Y7xIyFv+QHAp7vzyEFRH8eK6OnIw8PD86Ojp6eHCxcu4NSpU0hISIBIJIK9vT3atGlT1kPj4fkhMDQ0LDGlXU9PT6nq2zzSfPjwAZ8+fZJoY1HHHRUVJdWvON65c+fk7v972bZtG8aOHavQSXr//v1l/tspw8NcX18fvXr1YtLXzwI/Sefh4eHh4SnHCAQCuLi4wMXFpayHwsPzw/H3338jMDAQW7duRbVq1QAAmZmZGDduHKZMmVLGo/v5yMnJwYQJE7Bnzx68ePFC6rw8ddw3btzgfr59+zYyMzMl+j1x4gRMTExk7l9W5ElaPnHiBLS1tblMkFWrVmH9+vWwtbXFqlWruIWm1atXf1e/yvYw37x5M5N+fib4dHceHh4eHp5yTHR0NBYtWsSp8tra2mLcuHFo3bp1GY+Mh6dscHR0lBD3Sk5OxsePH2FmZgYAyMjIQKVKlWBlZYVr166V1TB/Svz8/BAZGYmZM2diwIABWLVqFR4/foy1a9di3rx58PLykrlvoVDIve8lTW9EIhFWrFiBwYMHyxxDFuRJfbazs8P8+fPRpUsX3Lx5E//73/8QGBiIyMhIWFtbyzz5LSsP82fPnnEuC7/88kuFdVng0915eHh4eHh+YrZt2wYfHx/07NkTI0eOBADExMTA2dkZwcHB8PT0LOMR8vAoH2UqhPN8H4cPH0ZISAjatWsHHx8ftG7dGpaWljA3N8f27dvlmqSnpaWBiGBhYYErV65ITADV1dVhbGwMFRUVFi9DaaSlpcHW1hYAsH//fvz666+YM2cOrl27hi5dusjVrzLJycmBv78/QkJCJFwWBg4ciBUrVkBTU1Op41E0sjgHFIefpPPw8PDw8JRTZs+ejQULFmD06NFc28iRI7FkyRLMnDmTn6Tz/JT8888/ZT0EnlJ4+fIlt7uoq6uLly9fAgBatWqFP/74Q66+zc3NAYCbBFYE1NXVkZubCwA4ffo0Bg4cCACoXLnyd3m2l4ayPMwDAwMRHR2Nw4cPo2XLlgCA8+fPY+TIkRgzZsx3p+v/6LBIVJfdvI2Hh4eHh4enTLl//z66desm1e7m5qb0nRIeHh6er2FhYcFdm6ytrbFnzx4AhTvsrNwotmzZgqNHj3L/Hj9+PPT19dGiRQs8ePCASQxl0apVK87y+MqVK+jatSsAICkpiXP0kAdleZjv378fGzduROfOnaGrqwtdXV106dIF69evx759+xQenxWDBw/Gu3fvpNpzcnIkyihu377NLRrJCj9J5+Hh4eHhKaeYmpoiIiJCqv306dMwNTUtgxHx8PxY5OfnY9GiRWjSpAmqVauGypUrSxw8ysXHxwcJCQkAgIkTJ2LVqlXQ0NDA6NGjpdTFZWXOnDkQiUQAgIsXL2LlypVYsGABqlSpIpF1pCzkUV5fuXIlVFVVsW/fPqxevZoTvjt+/Dg6derEZHx+fn6YP38+Pn/+zKS/ksjNzUXVqlWl2o2NjblMgfLAli1b8P79e6n29+/fIyQkhPu3qamp3KUVvHAcDw8PDw9POWX16tUYNWoUBg8ejBYtWgAorEkPDg5GUFAQfv/99zIeIQ9P2TJ16lRs2LABY8aMwd9//43JkycjPT0dBw8exNSpUzktB56y4cGDB7h69SosLS1hb2/PpE9NTU3cvXsXZmZmmDBhAp48eYKQkBDcunUL7dq1w7Nnz5jE+Vbl9R8dZXiYOzs7w9DQECEhIdDQ0ABQOLH19vbGy5cvcfr0abljKJK3b9+CiGBgYIDk5GQJvYP8/HwcPnwYEydOxH///ccsJj9J5+Hh4eHhKcccOHAAixcv5tTdbWxsMG7cOLi7u5fxyHh4yp46depg+fLl6Nq1K3R0dBAfH8+1Xbp0CTt27CjrIf5UhISEoE+fPqhUqZJE+6dPn7Br1y6u5loejI2NER4eDkdHRzg6OiIwMBADBgxAamoqGjRogOzsbLljAIpTXi9OamoqNm/ejNTUVAQFBcHY2BjHjx+HmZkZ6tWrJ3f/Pj4+XzzP4nUkJibC1dUVHz9+RIMGDQAACQkJ0NDQQHh4OJPXoUiKOgeUhEAgwPTp0zF58mRmMflJOg8PDw8PTznF19cX/fv3R7t27cp6KDw8PyRaWlq4c+cOzMzMUL16dRw9ehQNGzbE/fv34ejoiDdv3pT1EH8qVFRU8OTJExgbG0u0v3jxAsbGxnL5pIvx8vLC3bt34ejoiJ07dyIjIwOGhoY4dOgQ/vrrLyQmJsodAwC0tbWRmJiIWrVqYdq0aUhMTMS+ffs45fWiPu2yEh0djc6dO6Nly5Y4e/Ys7ty5AwsLC8ybNw9xcXHlqp47NzcX27dvx927dwEULih7eXlxpQk/MtHR0SAiODk5Yf/+/RKlMurq6jA3N0eNGjWYxuTV3Xl4eHh4eMopz549Q6dOnWBkZIR+/frBy8uL26Xg4eEBatasiSdPnsDMzAx16tTByZMn0bBhQ8TGxkrt5vIoHiIqcUfy0aNH0NPTYxJj1apV+Pvvv/Hw4UPs378fhoaGAICrV6+iX79+TGIAildeBwrr9mfNmoXAwEDo6Ohw7U5OTli5ciWTGGIU7WGuqamJoUOHMu1TWbRt2xZAoXWdmZkZE4u1r8FP0nl4eHh4eMopYWFhePXqFfbu3YsdO3Zg8eLFsLa2hpeXFzw9PVGrVq2yHiIPT5kirrdt2rQp/P390b9/f2zcuBEZGRllIiL2s+Lo6AiBQACBQABnZ2eoqv7fFCQ/Px9paWnMhND09fVLnMBOnz6dSf9ixMrrLVu2xJUrV7B7924A7JTXAeDmzZsllmQYGxvj+fPnTGIoy8M8OTkZkZGRyMrKkrLJmzp1KpMYiubOnTt4+PChUnQI+HR3Hh4eHh6eCsKjR4+wc+dObNq0CcnJyQpV6+XhKY9cvHgRFy/+v/buPazmdP0f+HtVU0pSmRKVkiSRJpqDkkOxa4YOgxjaIoeMQTlOZhszwjQucpYzo5BUDkWYNhJTM3wLa+xhSEqymzG7xE5Mter3R1frZ8nMNtZnfVYt79d1dV32sz499/PU/mPunudz39+ja9euL2xfSKrRmCAvWbIEc+bMgaGhofwzXV1d2NraYsSIEdDV1RUk3vnz57F161bcvn0bycnJsLS0xJ49e9C5c2d5gqWs4uJifPLJJ7h79y7Cw8MxadIkAMDs2bMhk8mwfv16pWNYWVkhKSkJ7u7uaNOmDaRSKezs7HD48GHMmzcPBQUFSseYOnUqTp06hY0bNzbpYT5kyBBBephv374d06ZNw5tvvgkLCwuFk2iJRIJLly4pHUMMz9chcHNzw9y5cwWvQwAwSSciItIINTU1SE9Px969e5Geng5TU1Pcu3dP3csiIpLT1tZGenq6YKfmL3Lw4EGMGzcOwcHB2LNnD65duwY7Ozts3LgRx48fx/Hjx1UWW2jz5s3DhQsXkJycDAcHB1y6dAm//vorQkJCEBISgi+//FLpGG+++SZSUlKa1DbJzMzEqFGjBKmGb2Njg08++QSRkZFKz6VOYtQhaMTr7kRERC1YZmYmEhIScPDgQdTV1WH48OE4duwYvLy81L00IrVIS0vD+++/jzfeeANpaWl/+qy/v79IqyIAGDZsGAIDA1VaR2PZsmXYsmULQkJCkJiYKB/38PDAsmXLBI2l6srr0dHRmD59OqytrSGTyeDk5ASZTIaxY8fi888/F2AH4vQwf/DgAYKCggSZS53EqEPQiCfpRERELZSlpSXKy8vh6+uL4OBg+Pn5sRgWvfa0tLTwyy+/wNzcHFpaWn/4nEQiEaSaOP01z9bROH/+vOB1NAwMDHDt2jXY2toqXBG/ffs2nJyc8PTpU+U3AXErr9+9exdXr15FZWUlXF1d0bVrV8HmFqOH+aRJk/D222/j448/VnoudfL390d1dTU8PDywdOlSFBYWwtLSEhkZGZgxYwZu3rwpWCyepBMREbVQixcvRlBQEIyNjdW9FKJm49miVM8XqCL1MzExQVhYGMLCwhTqaHzxxReC1NGwsLDArVu3miT83333Hezs7JSev5EYldeXLFmCefPmwdraGtbW1vLxJ0+eYOXKlYIUXFu3bh18fHxgZWX1wh7mQrC3t8eiRYvwww8/wNnZGW+88YbC5+Hh4YLEUbWNGzfik08+QUpKCjZv3gxLS0sAwIkTJwR/hYMn6UREREREJCpV1dH4+uuvsXfvXuzatQtDhgzB8ePHcefOHcyePRuLFi3CzJkzBVh9w/vJV69eRefOnRVO7IuKiuDo6CjIib0YfeUB1fcw79y58x9+JpFIcPv2bUHiaBKepBMRERGRxvgrVbVbygmeJlF1HY0FCxagrq4O3t7eqKqqQv/+/aGnp4d58+YJlqADDa3eSktLmySgly9flp+wKuuP+spLpVKYmpoKEgNQfQ/zwsJClc0tNlXXIWjEk3QiIiIi0hjPJ02//fYbqqqq5K+FVFRUwMDAAObm5jzBE5mYdTSqq6tx69YtVFZWwsnJSaHtmxBUWXndxMQEEokEDx8+hJGRkUKiLpPJUFlZiY8//hixsbFCbEUjepiLQcw6BEzSiYiIiEgjJSQkYNOmTdi5cye6desGALhx4wamTJmCqVOnIjg4WM0rfL1s375d9Doajx49wpkzZ9CtWzd0795dsHmrq6sxffp07N69GzKZDDo6OvLK67t374a2tvYrzx0XF4f6+npMnDgRa9euRdu2beWfNfaV79u3rxDbUFkP8zlz5rz0s6tXr36lGGLr27cvgoKC5HUIGl9xuHjxIoYPH46SkhLBYjFJJyIiIiKN1KVLF6SkpMDV1VVhPC8vDyNHjtSoa7jUYNSoUejfvz9mzJiBJ0+e4K233kJhYSHq6+uRmJiIESNGCBpPlZXXs7Ky4O7u3qTQmpBU1cN80KBBL/WcRCLBmTNnBI2tKmLUIWjEd9KJiIiISCOVlpa+sGK4TCbDr7/+qoYVkaqdO3cOCxcuBAAcPnwYdXV1qKioQFxcHJYtWyZYkq6qyuuPHj2CkZERAMDV1RVPnjzBkydPXvhs43PKUFUP88zMTMHnVDcx6hA0+uPmkURERERELZi3tzemTp2qcGU3Ly8P06ZNw+DBg9W4MlKVhw8fyouqnTx5EiNGjICBgQGGDh2K/Px8weJERUWhsrKyyXhVVRWioqJeeV4TExPcv38fQENSaGJi0uSrcVwIQUFByMjIEGQuTffRRx8hMjISv/zyCyQSCerq6pCdnY158+YhJCRE0Fg8SSciIiIijbRr1y6MHz8ebm5u8ivDtbW18PHxwY4dO9S8OlIFa2trfP/99zA1NcXJkyeRmJgIoOHEuFWrVoLFUVXl9TNnzsi/X4zTaFX1MB8+fPhLP3vo0KFXiiG26OhoTJ8+HdbW1pDJZHBycpLXIfj8888FjcV30omIiIhIo928eVPeA9rR0REODg5qXhGpyqZNmxAREQFDQ0PY2Njg0qVL0NLSwoYNG3Do0CGlE1+xKq/X1tYiOjoaEydOhJWVlVJz/RlV9TAPDQ196We/+eabV4qhLqqsQ9CISToRERERaRRPT08EBATA39+fCflrKDc3F3fv3sWQIUPkrdfS09NhbGwMDw8PpeYWs/J6mzZtcPXqVdja2goyHwlLJpPh6tWrsLGxEez1g0ZM0omIiIhIo8THxyM1NRUZGRmwsrKCv78//P394e7u/sIryqSZGtMcVfzOxai8HhAQgOHDh2P8+PEqi0Evb9asWXB2dsakSZMgk8kwYMAA5OTkwMDAAMeOHcPAgQMFi8UknYiIiIg00u+//47Tp08jNTUVR48ehUwmw9ChQ+Hv7w8fHx/o6+ure4mkAvHx8Vi5cqW8UJyDgwPmz5+PcePGKTXvs5XXHz169KfPClF5fcuWLYiKikJwcDD69OmD1q1bK3zu7+//SvOqo4d5SkoKkpKSUFxcjOrqaoXPXrUXu9isrKxw5MgRuLm54ciRI/jkk09w9uxZ7NmzB2fOnEF2drZgsZikExEREdFr4cKFC0hLS0NaWhoKCgrg5eWFzz77TOkr0NR8rF69GosWLcKMGTPkv9fvvvsOsbGxWLZsGWbPnv3Kc2tra6O0tBTm5ubQ0tJ64Ql9Y0E5mUz2ynEaaWn9cSMuZWKI3cN8/fr1WLhwISZMmIBt27YhNDQUBQUF+L//+z9Mnz4dX331ldIxxNCqVSvcunULVlZWCAsLg4GBAdauXYvCwkK4uLj8zz/c/BVM0omIiIjotVNQUIC0tDRYW1tj5MiR6l4OCaRz586Iiopq0hIrLi4OixcvRmFh4SvPnZWVBQ8PD+jo6CArK+tPnx0wYMArx9E0jo6O+PLLLzFmzBi0adMGUqkUdnZ2+OKLL1BeXo6NGzeqe4kvxcbGBtu3b4e3tzc6d+6MzZs3Y+jQofjpp5/Qr18/PHjwQLBYbMFGRERERBotNzcX169fBwA4OTmhT58+6NKli1KnqtQ8lZaWwt3dvcm4u7s7SktLlZq7MfGura1FVlaWyiuva4ri4mL570RfXx///e9/AQDjxo3De++912KS9NDQUIwaNQodOnSARCLB4MGDATTc0HF0dBQ0FpN0IiIiItJIJSUlGDNmDLKzs2FsbAwAqKiogLu7OxITE5lgaSB7e3skJSXhH//4h8L4gQMHBGuVpaOjg5UrVzY5rVeF06dPY82aNfI/MnXv3h2zZs2SJ4ivQuwe5hYWFigvL4eNjQ06deqEH374AS4uLigsLERLutS9ePFi9OzZE3fv3kVQUBD09PQANLwGsWDBAkFjMUknIiIiIo00efJk1NTU4Pr16+jWrRsA4MaNGwgNDcXkyZNx8uRJNa+QhBYVFYXRo0fj3Llz8nfSs7Ozcfr0aSQlJQkWx8vLC1lZWSptj9bY833kyJGIiIgAAPzwww/44IMPsGbNGkyfPv2V5n22bZwYvLy8kJaWBldXV4SGhmL27NlISUlBbm7uX/qDQXPwoldjVFF9n++kExEREZFG0tfXR05ODlxdXRXG8/Ly4OnpiaqqKjWtjFQpLy+vyenz3Llzm/z/QBmqqrz+LCsrKyxYsAAzZsxQGI+NjUV0dDTu3bundAwx1NXVoa6uDjo6DefDiYmJyMnJQdeuXTF16lTo6uqqeYUvLysrCzExMQqvz8yfPx+enp6CxmGSTkREREQaycHBAXv37sU777yjMH7x4kWMHTsWt27dUtPKqKVTVeX1ZxkaGuLKlSuwt7dXGM/Pz4erqysqKyuVjkEvb+/evQgNDcXw4cMVbmkcPnwYu3fvxtixYwWLxSSdiIiIiDRSamoqoqOjERsbCzc3NwANReRmzpyJyMhIBAYGqneBJIi/0vpKiP7lYhk7dixcXV0xf/58hfGYmBjk5uYiMTFRkDhi9DB/+vQpfvzxR9y/fx91dXUKnwlx60AM3bt3R1hYWJOCk6tXr8b27dvlp+tCYJJORERERBrDxMREoX/148ePUVtbK79q2/jv1q1bo7y8XF3LJAH9Uc/yZwnZv1wsy5YtQ0xMDDw8PNC3b18ADe+kZ2dnY+7cuQp/cAgPD3+lGGL0MD958iRCQkLwn//8p8lnLel3oqenh59++qnJzYZbt26hZ8+eePr0qWCxmKQTERERkcaIi4t76WdVUfCJxPe/epY/S8j+5aqovP6szp07v9RzEokEt2/ffqUYYvQw79q1K/72t7/hiy++QPv27ZWeT13s7e0xf/58TJ06VWF8y5YtWLVqFfLz8wWLxSSdiIiIiIjoL3i28vqzp9wpKSlKVV4Xm4GBAa5fvw4bGxuYm5vjn//8J1xcXJCfn4/33nsPZWVlSscwMjLC5cuX0aVLFwFWrD6bN2/GrFmzMHHiRHnf9+zsbOzevRvr1q1rkrwrgy3YiIiIiEjjPX36tMn7ti3p/WR6Od988w0MDQ0RFBSkMJ6cnIyqqirBbk9ER0djzZo1CpXXw8PD4eHhgejoaJUk6TKZDFevXoWNjQ1MTEwEmVOMHuYjR47E2bNnW3ySPm3aNFhYWGDVqlXydn7du3fHgQMHEBAQIGgsnqQTERERkUZ6/PgxIiMjkZSU9MITwZbyLiy9PAcHB2zduhWDBg1SGM/KykJYWBhu3LghSBwxKq/PmjULzs7OmDRpEmQyGfr374/vv/8eBgYGOHbsGAYOHKh0jMmTJ8Pa2hpffvklYmNjMX/+fHh4eMh7mO/cuVPpGFVVVQgKCoKZmRmcnZ3xxhtvKHz+qu/TazKepBMRERGRRvr000+RmZmJzZs3Y9y4cYiNjcW9e/ewdetWLF++XN3LIxUoLi5+4bvcNjY2KC4uFiyOv78/Dh8+3KTyempqKoYNGyZIjJSUFPz9738HABw9ehRFRUX4+eefsWfPHixcuBDZ2dlKx9i2bZu82vr06dPRrl075OTkwN/fX7Dr2/v370dGRgZatWqFs2fPKhT5k0gkTNJfgCfpRERERKSROnXqhPj4eAwcOBBGRka4dOkS7O3tsWfPHuzfvx/Hjx9X9xJJYJ06dcLGjRubtPVKTU3F9OnTUVJSIkgcMSqvt2rVCrdu3YKVlRXCwsJgYGCAtWvXorCwEC4uLn+p9Zw6WVhYIDw8HAsWLPjT/vLN0fPdIv6MkN0ieJJORERERBqpvLwcdnZ2ABreP2/8j+h+/fph2rRp6lwaqciYMWMQHh6ONm3aoH///gAarrpHRETgo48+EizOzp07YWJigmvXruHatWvycWNjY4Ur4sqcFLdv3x7Xrl1Dhw4dcPLkSWzevBlAw/VxbW1t5TbwDFX3MK+ursbo0aNbXIIOAGvXrlVLXCbpRERERKSR7OzsUFhYiE6dOsHR0RFJSUl45513cPToURgbG6t7eaQCS5cuRVFREby9vaGj05Dq1NXVISQkBNHR0YLFKSwsFGyuPxIaGopRo0ahQ4cOkEgk8tZuFy5cgKOjoyAxxOhhPn78eBw4cAD/+Mc/lJ5LbOpq08jr7kRERESkkdasWQNtbW2Eh4fj1KlT8PPzQ319PWpqarB69WpERESoe4mkIvn5+bhy5Qr09fXh7OwMGxsblcZTReV1oOG99Lt37yIoKAhWVlYAgLi4OBgbGwtSUVyMHubh4eGIj4+Hi4sLevXq1aRw3OrVq1USV2jHjx+HtrY2fHx8FMYzMjIgk8nw/vvvCxaLSToRERERvRbu3LmDvLw82Nvbo1evXupeDqmRkZERrly5In8d4q8So/K6GMToYf58pf1nSSQSnDlzRmWxhdSrVy8sX74cH3zwgcL4yZMnERkZCalUKlgsXncnIiIiIo0UHx+P0aNHQ09PD0BDhW8bGxtUV1cjPj4eISEhal4hqYuy55Sqqry+fv36l35WiKroYvQwz8zMVNncYsrPz4eTk1OTcUdHR9y6dUvQWDxJJyIiIiKNpK2tjdLSUpibmyuMl5WVwdzcnH3SX2Nt2rSBVCp95ZN0VVVef7593G+//Yaqqip5DYWKigoYGBjA3Nwct2/ffqUYz1JHD/NHjx7hzJkzcHR0FOzdejFYWFggISEBXl5eCuOnTp3C2LFjcf/+fcFi8SSdiIiIiDRSfX39C9snlZSUoG3btmpYEWkKVVVef7YgXUJCAjZt2oSdO3eiW7duAIAbN25gypQpLaqH+ahRo9C/f3/MmDEDT548gZubG4qKilBfX4/ExESMGDFC6RhiCAgIwKxZs3D48GH5zYNbt25h7ty5glTBfxaTdCIiIiLSKK6urpBIJJBIJApVvoGGAl+FhYXw9fVV4wqppROj8vqiRYuQkpIiT9ABoFu3blizZg1GjhyJ4OBgpWMsXLgQUVFRKu1hfu7cOSxcuBAAcPjwYdTX16OiogJxcXFYtmxZi0nSV6xYAV9fXzg6OsqL+JWUlMDT0xMxMTGCxmKSTkREREQaJTAwEAAglUrh4+MDQ0ND+We6urqwtbVtMYkBqcaLblj8FYsXL0bPnj3lldcb6x5oa2tjwYIFQiwRpaWlqK2tbTIuk8nw66+/ChJDjB7mDx8+hKmpKYCGImsjRoyAgYEBhg4divnz56ssrtDatm2LnJwc/POf/4RUKoW+vj569eqF/v37Cx6L76QTERERkUbS1tZGeno6T82pCWXfSReDn58f7t27hx07dqB3794AgLy8PISFhcHS0hJpaWlKx5g9ezbMzMxU2sPcwcEBy5Ytw9ChQ9G5c2ckJibCy8sLUqkU3t7eL+zR3pI5Ozvj+PHjsLa2fuU5eJJORERERBpp2LBhCAwMhJmZGcaMGYPg4GC4uLioe1mkIjU1NXB0dMSxY8fQvXv3P332xIkTsLS0/Evzi115fdeuXRg/fjzc3NzkBd1qa2vh4+ODHTt2KD0/0HAqv2LFCnz77bcq62E+a9YsBAcHw9DQEDY2NvL2dOfOnYOzs7PS8zc3RUVFqKmpUWoOnqQTERERkcZ68OABkpOTkZCQgPPnz8PR0RHBwcEYO3YsbG1t1b08EpilpSVOnTr1P5P0VyF25fVGN2/exM8//wygod2Xg4ODYHOL1cM8NzcXd+/exZAhQ+Svn6Snp8PY2BgeHh6CxGguhLilwSSdiIiIiF4LJSUl2L9/P3bt2oX8/PwXvu9LLVt0dDRu3ryJHTt2KBQMFNr/qryuTFE3T09PBAQEwN/fX9CEXGyN+wgICEDXrl3VvRzRMEknIiIiInoJNTU1SE9Px969e5Geng5TU1Pcu3dP3csigX344Yc4ffo0DA0N4ezsjNatWyt8fujQIUHidOnSBSkpKXB1dVUYz8vLw8iRIxVaqf1V8fHxSE1NRUZGBqysrODv7w9/f3+4u7srXfDufxGyh7k696FOQiTpqivjR0RERESkZpmZmZgyZQrat2+PCRMmwMjICMeOHUNJSYm6l0YqYGxsjBEjRsDHxwcdO3ZE27ZtFb6EosrK6yEhITh48CD+85//YNWqVaioqEBQUBAsLCwwceJEHDlyBE+ePFEqRqNRo0Zh48aNACDvYT5q1Cg4Ozvj4MGDSs0t5j40DU/SiYiIiEgjWVpaory8HL6+vggODoafn5+8VRaRMsSovP68CxcuIC0tDWlpaSgoKICXlxc+++wzpd7ptrCwwLfffgsXFxckJCTgyy+/hFQqRVxcHLZt24bLly8LuIMGqthHc8Lr7kREREREf2D79u0ICgqSF/Yizbds2TIEBwc3KfImtN9++w3jx4/HyZMnm1Re3717N8zNzVUav6CgAGlpabC2tsbIkSNfeR59fX3cvHkT1tbWCAkJQceOHbF8+XIUFxfDyckJlZWVAq66KaH20ZwkJCQgICCgyasWfwWTdCIiIiIi0gguLi7417/+hXfffRd///vfMWrUKLz55psqi6fKyuuNcnNzcf36dQCAk5MT+vTpI9jcYvYwV+U+VGX9+vUICwtDq1at/mcLPiHa7jVikk5ERERERBrjp59+wr59+5CYmIiSkhIMGTIEwcHBCAwMhIGBgVJzi1l5vaSkBGPGjEF2drZCmzd3d3ckJibCyspK6RibNm1CRESEvIf5pUuXoKWlhQ0bNuDQoUPIzMxUOoYY+1CVzp07Izc3F+3atfvT2xkSiUTQtntM0omIiIiISCNlZ2cjISEBycnJePr0KR49eqTUfGJWLPf19UVFRQXi4uIU2ryFhobCyMgIJ0+eFCSOqnuYi7UPTcIknYiIiIiINNKVK1ewd+9eJCYmoqysTLBq4r///jtOnz6N1NRUHD16FDKZDEOHDoW/vz98fHygr6+vdAx9fX3k5OS8sM2bp6cnqqqqXnluMXuYq3IfYlqyZAnmzZvX5DbGkydPsHLlSnzxxReCxWILNiIiIiIi0hiFhYX46quv0KNHD7i5ueHy5cuIiorCL7/8IlgMPT09fPDBB9i6dSv+/e9/Iy0tDR06dMCiRYvQrl07DBs2DNnZ2UrFsLa2Rk1NTZNxmUyGjh07KjX3lClT8P3336N3797o3r07IiMjkZ2dDVWc36pyH2KKiop6YSG9qqoqREVFCRqLSToREREREWmE9957D/b29khJSUFoaCju3LmD06dPY9KkSYL2SX/eu+++i6+++gpXr17F1atX4e3tjdLSUqXmXLlyJWbOnInc3Fz5WG5uLiIiIhATE6PU3GL2MFflPsRUX1//wlcapFIpTE1NBY3F6+5ERERERKQRFi5ciODgYDg5OYkST+iK5SYmJgqJ4OPHj1FbWwsdHR0AkP+7devWKC8vVyrWiwjVw1zd+xBS414ePnwIIyMjhX3JZDJUVlbi448/RmxsrGAxmaQTEREREZFGqa6uRmFhIbp06SJPDIWkqorlcXFxL/3s+PHjXynGy1Kmh3lz2oey4uLiUF9fj4kTJ2Lt2rUKNzJ0dXVha2uLvn37ChqTSToREREREWmEJ0+eYMaMGfIk8ebNm7Czs8PMmTNhaWmJBQsWCBJH0yqWt8Qe5mLLysqCh4eHSv7o8zwm6UREREREpBEiIiKQnZ2NtWvXwtfXFz/++CPs7OyQmpqKxYsX4/Lly4LEEbti+dOnT1FdXa0wZmRkpPS8YvcwV9U+xKCtrY3S0lKYm5srjJeVlcHc3BwymUywWCwcR0REREREGuHIkSPYuHEj+vXrp/DucI8ePVBQUCBYHDEqlj9+/BgzZsyAubk5WrduDRMTE4UvIUyePBk1NTW4fv06ysvLUV5ejuvXr6Ourg6TJ08WJIYY+xDDH51t//7779DV1RU0lurP6omIiIiIiETw22+/NTnpBBoSxRdV5n5VjRXLY2Nj4ebmBkD4iuWffvopMjMzsXnzZowbNw6xsbG4d+8etm7diuXLlwsSIysrCzk5OfIr+wDQrVs3bNiwAZ6enoLEEGMfqrR+/XoAgEQiwY4dO2BoaCj/TCaT4dy5c3B0dBQ0JpN0IiIiIiLSCG5ubkhPT8fMmTMBQJ6Y79ixQ+niXi+qWP7uu+82qVg+ceJEBAYGKhULAI4ePYr4+HgMHDgQoaGh8PT0hL29PWxsbLBv3z4EBwcrHUOMGwFi7EOV1qxZA6DhJH3Lli3Q1taWf9ZYOG7Lli2CxmSSTkREREREGiE6Ohrvv/8+rl27htraWqxbtw7Xrl1DTk4OsrKylJp77dq1wizyJZWXl8POzg5Aw3vbja3K+vXrh2nTpgkSQ4wbAWLsQ5UKCwsBAIMGDcKhQ4dEuaLPJJ2IiIiIiDRCv379cOXKFSxfvhzOzs7IyMhA79698f3338PZ2VmpucVuFWZnZ4fCwkJ06tQJjo6OSEpKwjvvvIOjR4/Ki7y9CrFvBKhqH2LLzMwULRaruxMREREREb0iVVUsX7NmDbS1tREeHo5Tp07Bz88P9fX1qKmpwerVqxEREfFK84rdw1xV+xDDnDlzsHTpUrRu3Rpz5sz502dXr14tWFwm6URERERE1GI9evTopZ8Vqt3X48ePERkZiaSkJJSVlTX5XMh2XI3u3LmDvLw82Nvbo1evXoLPL5aWtI9Bgwbh8OHDMDY2xqBBg/70WSFP2pmkExERERFRi6WlpfU/K7fX19dDIpEIljxPnz4dmZmZWLp06QsrlgtRDC0+Ph6jR4+Gnp6ewnh1dTUSExMREhKidIxnqepGgNj70ARM0omIiIiIqMX6KwXhBgwYIEjMTp06ySuWGxkZ4dKlS7C3t8eePXuwf/9+HD9+XOkY2traKC0tbdJSrqysDObm5oL8wUGMGwFi7EMMmZmZf3iaHhsbi+nTpwsWi4XjiIiIiIioxRIq8f4rxKhY3nj6/7ySkhK0bdtWkBhi9DAXYx9iGD58OE6dOoU+ffoojK9btw6LFi1ikk5ERERERPS8b775BoaGhggKClIYT05ORlVVlWAV2lVZsdzV1RUSiQQSiQTe3t7yqutAw8l2YWEhfH19ldxBA1X2MBdzH2JYuXIl3n//fZw7dw6Ojo4AgFWrVmHJkiVIT08XNBaTdCIiIiIi0ghff/01tm7d2mTc3NwcYWFhgiXpoaGhkEqlGDBgABYsWAA/Pz9s3LhRXrFcGY1tz6RSKXx8fGBoaCj/TFdXF7a2thgxYoRSMRqp8kaAmPsQw+TJk1FeXo7Bgwfju+++w4EDBxAdHY3jx4/Dw8ND0Fh8J52IiIiIiDRCq1at8PPPP8PW1lZhvKioCN27d8eTJ09UElcVFcu1tbWRnp6u0tPmXr16YcOGDRgwYAAGDx6Mt956CzExMVi/fj1WrFiBkpISpWOIsQ8xRUZGYufOnZDJZDhx4gTee+89wWPwJJ2IiIiIiDSCubk5fvzxxyZJulQqRbt27QSL83zFchsbG9jY2KC6uhrx8fGCVCwfNmwYAgMDYWZmhjFjxiA4OBguLi5Kz/ssVd4IaCTGPlRl/fr1TcYsLS1hYGCA/v374+LFi7h48SIAIDw8XLC4PEknIiIiIiKNEBkZiQMHDuCbb75B//79ATRUf584cSJGjhyJmJgYQeKIVbH8wYMHSE5ORkJCAs6fPw9HR0cEBwdj7NixTf4QIQRV9TAXex9C6dy580s9J5FIcPv2bcHiMkknIiIiIiKNUF1djXHjxiE5OVleqKyurg4hISHYsmULdHV1BYmjpaWFX3/9FWZmZgrjUqkUgwYNkr/bLaSSkhLs378fu3btQn5+Pmpra5WeUx09zFWxD03DJJ2IiIiIiDTKzZs3IZVKoa+vD2dnZ9jY2Agyb2PFcqlUih49evxhxfKkpCRB4jWqqalBeno69u7di/T0dJiamuLevXtKzyt2D3NV7UOV+vfvj4CAAPj7+6Nr166ixOQ76URERERE1KJ5enrKEykHBwf5l9DErliemZmJhIQEHDx4EHV1dRg+fDiOHTsGLy8vQeYXq4e5qvehSpMmTUJaWhoWL14MKysr+Pv7w9/fH+7u7i/82QmBJ+lERERERNSixcfHIzU1FRkZGaIkUmJULLe0tER5eTl8fX0RHBwMPz+/JtfSX5WYNwJUuQ8x/f777zh9+jRSU1Nx9OhRyGQyDB06FP7+/vDx8YG+vr5gsZikExERERGRRhArkQoICMC3336r0orl27dvR1BQEIyNjQWdFwCioqIAAEuWLMGcOXP+8EaAEO/wq3If6nThwgWkpaUhLS0NBQUF8PLywmeffSZIz3Qm6UREREREpJFUmUi11Irlz9K0HubqUlBQgLS0NFhbW2PkyJFKz8cknYiIiIiINJ7QidSzWmrFcjFuBGia3NxcXL9+HQDg5OSEPn36CB6DSToREREREWkUMRKpRi2xYvmzNOFGgBhKSkowZswYZGdny6/uV1RUwN3dHYmJibCyshIsFpN0IiIiIiLSCGImUi+qWB4cHAwvLy+VVf1WtZZ6I0AMvr6+qKioQFxcHLp16wYAuHHjBkJDQ2FkZISTJ08KFotJOhERERERaQSxEilNqVj+rJZ+I0DV9PX1kZOTA1dXV4XxvLw8eHp6oqqqSrBY7JNOREREREQaISsrCzk5OfIEHQC6deuGDRs2wNPTU7A4ixcv1piK5S25h7mYrK2tUVNT02RcJpOhY8eOgsZikk5ERERERBpBrERqypQpgs2lTs/eCNi2bZtG3AhQlZUrV2LmzJmIjY2Fm5sbgIbaBxEREYiJiRE0Fq+7ExERERGRRkhNTUV0dHSTRGrmzJmIjIxEYGCgehfYzGhqD3OhmJiYKNQXePz4MWpra6Gj03DW3fjv1q1bo7y8XLC4TNKJiIiIiKjFUlciRZovLi7upZ8dP368YHGZpBMRERERUYulrkSKSFWYpBMRERERERG9pKdPn6K6ulphzMjISLD5WTiOiIiIiIg0jqoTKXq9PH78GJGRkUhKSkJZWVmTz2UymWCxtASbiYiIiIiISI0eP36MGTNmwNzcHK1bt4aJiYnCF9Gr+vTTT3HmzBls3rwZenp62LFjB6KiotCxY0fEx8cLGotJOhERERERaQQxEyl6vRw9ehSbNm3CiBEjoKOjA09PT3z++eeIjo7Gvn37BI3FJJ2IiIiIiDSCmIkUvV7Ky8thZ2cHoOG1icZOAf369cO5c+cEjcUknYiIiIiINIKYiRS9Xuzs7FBYWAgAcHR0RFJSEoCGPwwJ3WeeSToREREREWkEMRMper2EhoZCKpUCABYsWIDY2Fi0atUKs2fPxvz58wWNxRZsRERERESkEdasWQNtbW2Eh4fj1KlT8PPzQ319PWpqarB69WpERESoe4mkIe7cuYO8vDzY29ujV69egs7NJJ2IiIiIiDSSKhMper3Ex8dj9OjR0NPTUxivrq5GYmIiQkJCBIvFJJ2IiIiIiDSCmIkUvV60tbVRWloKc3NzhfGysjKYm5uzTzoREREREdHzQkND8fDhwybj//3vfxEaGqqGFZGmqK+vh0QiaTJeUlKCtm3bChpLR9DZiIiIiIiI1ETMRIpeD66urpBIJJBIJPD29oaOzv9PoWUyGQoLC+Hr6ytoTCbpRERERETUoqkjkaLXQ2BgIABAKpXCx8cHhoaG8s90dXVha2uLESNGCBqT76QTEREREVGLFhUVBQBYsmQJ5syZ84eJlK6urrqWSC2ctrY20tPTRfljD5N0IiIiIiLSCGImUvR6CQgIwLfffgszMzOMGTMGwcHBcHFxUUksFo4jIiIiIiKNMGzYMAQGBsLa2hqffvoppFKpupdEGiI1NRWlpaVYtGgRLl68iN69e6NHjx6Ijo5GUVGRoLF4kk5ERERERBrjwYMHSE5ORkJCAs6fPw9HR0cEBwdj7NixsLW1VffySEOUlJRg//792LVrF/Lz81FbWyvY3EzSiYiIiIhII6kykaLXV01NDdLT07F3716kp6fD1NQU9+7dE2x+XncnIiIiIiKNU1NTg9zcXFy4cAFFRUVo3769updELVxmZiamTJmC9u3bY8KECTAyMsKxY8dQUlIiaBy2YCMiIiIiIo2RmZmJhIQEHDx4EHV1dRg+fDiOHTsGLy8vdS+NWjBLS0uUl5fD19cX27Ztg5+fH/T09FQSi9fdiYiIiIhIIzybSAUHB6s0kaLXy/bt2xEUFARjY2OVx2KSTkREREREGkHMRIpIVZikExERERERETUTLBxHRERERERE1EwwSSciIiIiIiJqJpikExERERERETUTTNKJiIg03MCBAzFr1ix1L4OIiIheApN0IiIikjt79iwkEgkqKipEjbt48WK89dZbosYkIiJqjpikExERERERETUTTNKJiIg0yOPHjxESEgJDQ0N06NABq1atUvh8z549cHNzQ5s2bWBhYYGxY8fi/v37AICioiIMGjQIAGBiYgKJRIIJEyYAAE6ePIl+/frB2NgY7dq1w7Bhw1BQUCCft7q6GjNmzECHDh3QqlUr2NjY4Ouvv5Z/XlFRgcmTJ8PMzAxGRkbw8vKCVCoFAOzevRtRUVGQSqWQSCSQSCTYvXu3Cn9KREREzReTdCIiIg0yf/58ZGVlITU1FRkZGTh79iwuXbok/7ympgZLly6FVCrFkSNHUFRUJE/Era2tcfDgQQDAjRs3UFpainXr1gFoSP7nzJmD3NxcnD59GlpaWvjwww9RV1cHAFi/fj3S0tKQlJSEGzduYN++fbC1tZXHDQoKwv3793HixAnk5eWhd+/e8Pb2Rnl5OUaPHo25c+eiR48eKC0tRWlpKUaPHi3OD4yIiKiZ0VH3AoiIiEgYlZWV2LlzJ/bu3Qtvb28AQFxcHKysrOTPTJw4Uf5vOzs7rF+/Hm+//TYqKythaGgIU1NTAIC5uTmMjY3lz44YMUIh1q5du2BmZoZr166hZ8+eKC4uRteuXdGvXz9IJBLY2NjIn/3uu+9w8eJF3L9/H3p6egCAmJgYHDlyBCkpKQgLC4OhoSF0dHRgYWEh+M+FiIioJeFJOhERkYYoKChAdXU13n33XfmYqakpunXrJv/feXl58PPzQ6dOndCmTRsMGDAAAFBcXPync+fn52PMmDGws7ODkZGR/JS88fsmTJiAK1euoFu3bggPD0dGRob8e6VSKSorK9GuXTsYGhrKvwoLCxWuzBMRERFP0omIiF4bjx8/ho+PD3x8fLBv3z6YmZmhuLgYPj4+qK6u/tPv9fPzg42NDbZv346OHTuirq4OPXv2lH9f7969UVhYiBMnTuDUqVMYNWoUBg8ejJSUFFRWVqJDhw44e/Zsk3mfPa0nIiIiJulEREQao0uXLnjjjTdw4cIFdOrUCQDw4MED3Lx5EwMGDMDPP/+MsrIyLF++HNbW1gCA3NxchTl0dXUBADKZTD5WVlaGGzduYPv27fD09ATQcIX9eUZGRhg9ejRGjx6NkSNHwtfXF+Xl5ejduzd++eUX6OjoKLyn/nzcZ2MSERG9rpikExERaQhDQ0NMmjQJ8+fPR7t27WBubo6FCxdCS6vh7bZOnTpBV1cXGzZswMcff4x//etfWLp0qcIcNjY2kEgkOHbsGD744APo6+vDxMQE7dq1w7Zt29ChQwcUFxdjwYIFCt+3evVqdOjQAa6urtDS0kJycjIsLCxgbGyMwYMHo2/fvggMDMSKFSvg4OCAf//730hPT8eHH34INzc32NraorCwEFeuXIGVlRXatGkjf3+diIjodcJ30omIiDTIypUr4enpCT8/PwwePBj9+vVDnz59AABmZmbYvXs3kpOT4eTkhOXLlyMmJkbh+y0tLREVFYUFCxagffv2mDFjBrS0tJCYmIi8vDz07NkTs2fPxsqVKxW+r02bNlixYgXc3Nzw9ttvo6ioCMePH4eWlhYkEgmOHz+O/v37IzQ0FA4ODvjoo49w584dtG/fHkBDYTpfX18MGjQIZmZm2L9/vzg/MCIiomZGUl9fX6/uRRARERERERERT9KJiIiIiIiImg0m6URERERERETNBJN0IiIiIiIiomaCSToRERERERFRM8EknYiIiIiIiKiZYJJORERERERE1EwwSSciIiIiIiJqJpikExERERERETUTTNKJiIiIiIiImgkm6URERERERETNBJN0IiIiIiIiomaCSToRERERERFRM/H/ADnQz/uj32KmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "order = list(dataset_type.keys())\n",
    "ax = sns.barplot(\n",
    "    x=\"dataset\", y=\"acc1\", \n",
    "    data=df,\n",
    "    order=order,\n",
    "    hue=\"model_arch\"\n",
    ")\n",
    "ax.set_xticklabels(ax.get_xticklabels(),rotation = 90)\n",
    "ax"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b397daeb-e12c-4bae-887a-8c4d7bfd7db7",
   "metadata": {},
   "source": [
    "# Grouping over pre-training data source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "c7c746f2-820c-4939-80dc-796efef941e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rom1504/CLIP_benchmark/.env/lib/python3.8/site-packages/seaborn/algorithms.py:98: RuntimeWarning: Mean of empty slice\n",
      "  boot_dist.append(f(*sample, **func_kwargs))\n",
      "/home/rom1504/CLIP_benchmark/.env/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n",
      "  r, k = function_base._ureduce(a,\n",
      "/home/rom1504/CLIP_benchmark/.env/lib/python3.8/site-packages/seaborn/algorithms.py:98: RuntimeWarning: Mean of empty slice\n",
      "  boot_dist.append(f(*sample, **func_kwargs))\n",
      "/home/rom1504/CLIP_benchmark/.env/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n",
      "  r, k = function_base._ureduce(a,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='dataset', ylabel='acc1'>"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "order = list(dataset_type.keys())\n",
    "d = df.copy()\n",
    "ax = sns.barplot(\n",
    "    x=\"dataset\", y=\"acc1\", \n",
    "    data=d,\n",
    "    order=order,\n",
    "    hue=\"pretrained\"\n",
    ")\n",
    "ax.set_xticklabels(ax.get_xticklabels(),rotation = 90)\n",
    "ax"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b914d109-eec9-40f4-b0f8-23eb179dc49a",
   "metadata": {},
   "source": [
    "# Best results from each pre-training source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "c70befe3-0c72-4f6c-b57f-0d1a67ae2553",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1235910/2264146503.py:4: FutureWarning: \n",
      "\n",
      "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
      "\n",
      "  ax = sns.barplot(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='dataset', ylabel='acc1'>"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "order = list(dataset_type.keys())\n",
    "d = df.copy()\n",
    "ax = sns.barplot(\n",
    "    x=\"dataset\", y=\"acc1\", \n",
    "    data=d,\n",
    "    order=order,\n",
    "    hue=\"pretrained\",\n",
    "    estimator=np.max,\n",
    "    ci=None\n",
    ")\n",
    "ax.set_xticklabels(ax.get_xticklabels(),rotation = 90)\n",
    "ax"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eea94f09-18c1-445b-a4e2-7418f25c816b",
   "metadata": {},
   "source": [
    "# Detailed results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccba4abe-53bc-4092-b05b-dafc0eccb4c5",
   "metadata": {},
   "source": [
    "### All results (acc1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "f03782e4-be8f-4e3b-a699-c516da5c62a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>model_fullname</th>\n",
       "      <th>ViT-B-16 laion400m_e32</th>\n",
       "      <th>ViT-B-16 openai</th>\n",
       "      <th>ViT-B-16-plus-240 laion400m_e32</th>\n",
       "      <th>ViT-B-32 laion2b_e16</th>\n",
       "      <th>ViT-B-32 openai</th>\n",
       "      <th>ViT-B-32 openclip b_290.pt</th>\n",
       "      <th>ViT-B-32-quickgelu laion400m_e32</th>\n",
       "      <th>ViT-H-14 openclip h_256.pt</th>\n",
       "      <th>ViT-L-14 laion400m_e32</th>\n",
       "      <th>ViT-L-14 openai</th>\n",
       "      <th>ViT-L-14 openclip epoch_160.pt</th>\n",
       "      <th>ViT-L-14-336 openai</th>\n",
       "      <th>ViT-g-14 openclip g_90.pt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cars</th>\n",
       "      <td>0.837</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.844</td>\n",
       "      <td>0.842</td>\n",
       "      <td>0.594</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.792</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.896</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country211</th>\n",
       "      <td>0.181</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.147</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.318</td>\n",
       "      <td>0.264</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fer2013</th>\n",
       "      <td>0.429</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.446</td>\n",
       "      <td>0.477</td>\n",
       "      <td>0.409</td>\n",
       "      <td>0.469</td>\n",
       "      <td>0.427</td>\n",
       "      <td>0.518</td>\n",
       "      <td>0.501</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.480</td>\n",
       "      <td>0.466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fgvc_aircraft</th>\n",
       "      <td>0.175</td>\n",
       "      <td>0.242</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.230</td>\n",
       "      <td>0.197</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.428</td>\n",
       "      <td>0.251</td>\n",
       "      <td>0.317</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.329</td>\n",
       "      <td>0.378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gtsrb</th>\n",
       "      <td>0.435</td>\n",
       "      <td>0.436</td>\n",
       "      <td>0.495</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.331</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imagenet-a</th>\n",
       "      <td>0.332</td>\n",
       "      <td>0.501</td>\n",
       "      <td>0.368</td>\n",
       "      <td>0.262</td>\n",
       "      <td>0.314</td>\n",
       "      <td>0.263</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.466</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.539</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imagenet-r</th>\n",
       "      <td>0.779</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.693</td>\n",
       "      <td>0.764</td>\n",
       "      <td>0.734</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imagenet1k</th>\n",
       "      <td>0.670</td>\n",
       "      <td>0.684</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.633</td>\n",
       "      <td>0.665</td>\n",
       "      <td>0.629</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.727</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.765</td>\n",
       "      <td>0.767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imagenet_sketch</th>\n",
       "      <td>0.523</td>\n",
       "      <td>0.481</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.529</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.633</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imagenetv2</th>\n",
       "      <td>0.596</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.560</td>\n",
       "      <td>0.582</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.656</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.677</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mnist</th>\n",
       "      <td>0.666</td>\n",
       "      <td>0.495</td>\n",
       "      <td>0.570</td>\n",
       "      <td>0.637</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.729</td>\n",
       "      <td>0.764</td>\n",
       "      <td>0.768</td>\n",
       "      <td>0.549</td>\n",
       "      <td>0.787</td>\n",
       "      <td>0.690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>objectnet</th>\n",
       "      <td>0.515</td>\n",
       "      <td>0.554</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.488</td>\n",
       "      <td>0.442</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.439</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.599</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>renderedsst2</th>\n",
       "      <td>0.547</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.578</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.526</td>\n",
       "      <td>0.641</td>\n",
       "      <td>0.563</td>\n",
       "      <td>0.699</td>\n",
       "      <td>0.593</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stl10</th>\n",
       "      <td>0.970</td>\n",
       "      <td>0.983</td>\n",
       "      <td>0.969</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.971</td>\n",
       "      <td>0.966</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.984</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.989</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sun397</th>\n",
       "      <td>0.696</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.670</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.743</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voc2007</th>\n",
       "      <td>0.768</td>\n",
       "      <td>0.784</td>\n",
       "      <td>0.763</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.766</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.757</td>\n",
       "      <td>0.776</td>\n",
       "      <td>0.756</td>\n",
       "      <td>0.783</td>\n",
       "      <td>0.805</td>\n",
       "      <td>0.782</td>\n",
       "      <td>0.810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/caltech101</th>\n",
       "      <td>0.835</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.819</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.842</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/cifar10</th>\n",
       "      <td>0.917</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.940</td>\n",
       "      <td>0.898</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.974</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.957</td>\n",
       "      <td>0.966</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/cifar100</th>\n",
       "      <td>0.710</td>\n",
       "      <td>0.669</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.756</td>\n",
       "      <td>0.702</td>\n",
       "      <td>0.847</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.746</td>\n",
       "      <td>0.839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/clevr_closest_object_distance</th>\n",
       "      <td>0.245</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/clevr_count_all</th>\n",
       "      <td>0.288</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.232</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.163</td>\n",
       "      <td>0.278</td>\n",
       "      <td>0.242</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.311</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/diabetic_retinopathy</th>\n",
       "      <td>0.087</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.263</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.338</td>\n",
       "      <td>0.238</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.733</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.733</td>\n",
       "      <td>0.434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/dmlab</th>\n",
       "      <td>0.151</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.189</td>\n",
       "      <td>0.195</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.142</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.224</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/dsprites_label_orientation</th>\n",
       "      <td>0.030</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/dsprites_label_x_position</th>\n",
       "      <td>0.031</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/dtd</th>\n",
       "      <td>0.513</td>\n",
       "      <td>0.446</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.540</td>\n",
       "      <td>0.443</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.543</td>\n",
       "      <td>0.679</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.628</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/eurosat</th>\n",
       "      <td>0.503</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.482</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.717</td>\n",
       "      <td>0.618</td>\n",
       "      <td>0.627</td>\n",
       "      <td>0.651</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0.648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/flowers</th>\n",
       "      <td>0.693</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.689</td>\n",
       "      <td>0.663</td>\n",
       "      <td>0.716</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.802</td>\n",
       "      <td>0.754</td>\n",
       "      <td>0.792</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.784</td>\n",
       "      <td>0.776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/kitti_closest_vehicle_distance</th>\n",
       "      <td>0.190</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.274</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.288</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.208</td>\n",
       "      <td>0.224</td>\n",
       "      <td>0.229</td>\n",
       "      <td>0.269</td>\n",
       "      <td>0.146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/pcam</th>\n",
       "      <td>0.605</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.504</td>\n",
       "      <td>0.622</td>\n",
       "      <td>0.586</td>\n",
       "      <td>0.546</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.486</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.553</td>\n",
       "      <td>0.613</td>\n",
       "      <td>0.551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/pets</th>\n",
       "      <td>0.892</td>\n",
       "      <td>0.889</td>\n",
       "      <td>0.904</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.873</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.919</td>\n",
       "      <td>0.931</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/resisc45</th>\n",
       "      <td>0.585</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.613</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.609</td>\n",
       "      <td>0.546</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.673</td>\n",
       "      <td>0.635</td>\n",
       "      <td>0.667</td>\n",
       "      <td>0.637</td>\n",
       "      <td>0.717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/smallnorb_label_azimuth</th>\n",
       "      <td>0.059</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/smallnorb_label_elevation</th>\n",
       "      <td>0.098</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.112</td>\n",
       "      <td>0.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/svhn</th>\n",
       "      <td>0.386</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0.363</td>\n",
       "      <td>0.385</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0.279</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.382</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.554</td>\n",
       "      <td>0.603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "model_fullname                       ViT-B-16 laion400m_e32  ViT-B-16 openai  \\\n",
       "dataset                                                                        \n",
       "cars                                                  0.837            0.645   \n",
       "country211                                            0.181            0.228   \n",
       "fer2013                                               0.429            0.458   \n",
       "fgvc_aircraft                                         0.175            0.242   \n",
       "gtsrb                                                 0.435            0.436   \n",
       "imagenet-a                                            0.332            0.501   \n",
       "imagenet-r                                            0.779            0.777   \n",
       "imagenet1k                                            0.670            0.684   \n",
       "imagenet_sketch                                       0.523            0.481   \n",
       "imagenetv2                                            0.596            0.619   \n",
       "mnist                                                 0.666            0.495   \n",
       "objectnet                                             0.515            0.554   \n",
       "renderedsst2                                          0.547            0.611   \n",
       "stl10                                                 0.970            0.983   \n",
       "sun397                                                0.696            0.643   \n",
       "voc2007                                               0.768            0.784   \n",
       "vtab/caltech101                                       0.835            0.825   \n",
       "vtab/cifar10                                          0.917            0.908   \n",
       "vtab/cifar100                                         0.710            0.669   \n",
       "vtab/clevr_closest_object_distance                    0.245            0.158   \n",
       "vtab/clevr_count_all                                  0.288            0.204   \n",
       "vtab/diabetic_retinopathy                             0.087            0.063   \n",
       "vtab/dmlab                                            0.151            0.159   \n",
       "vtab/dsprites_label_orientation                       0.030            0.020   \n",
       "vtab/dsprites_label_x_position                        0.031            0.030   \n",
       "vtab/dtd                                              0.513            0.446   \n",
       "vtab/eurosat                                          0.503            0.556   \n",
       "vtab/flowers                                          0.693            0.714   \n",
       "vtab/kitti_closest_vehicle_distance                   0.190            0.270   \n",
       "vtab/pcam                                             0.605            0.506   \n",
       "vtab/pets                                             0.892            0.889   \n",
       "vtab/resisc45                                         0.585            0.585   \n",
       "vtab/smallnorb_label_azimuth                          0.059            0.056   \n",
       "vtab/smallnorb_label_elevation                        0.098            0.119   \n",
       "vtab/svhn                                             0.386            0.305   \n",
       "\n",
       "model_fullname                       ViT-B-16-plus-240 laion400m_e32  \\\n",
       "dataset                                                                \n",
       "cars                                                           0.844   \n",
       "country211                                                     0.188   \n",
       "fer2013                                                        0.446   \n",
       "fgvc_aircraft                                                  0.185   \n",
       "gtsrb                                                          0.495   \n",
       "imagenet-a                                                     0.368   \n",
       "imagenet-r                                                     0.804   \n",
       "imagenet1k                                                     0.691   \n",
       "imagenet_sketch                                                0.544   \n",
       "imagenetv2                                                     0.614   \n",
       "mnist                                                          0.570   \n",
       "objectnet                                                      0.538   \n",
       "renderedsst2                                                   0.578   \n",
       "stl10                                                          0.969   \n",
       "sun397                                                         0.698   \n",
       "voc2007                                                        0.763   \n",
       "vtab/caltech101                                                0.833   \n",
       "vtab/cifar10                                                   0.927   \n",
       "vtab/cifar100                                                  0.737   \n",
       "vtab/clevr_closest_object_distance                             0.159   \n",
       "vtab/clevr_count_all                                           0.232   \n",
       "vtab/diabetic_retinopathy                                      0.132   \n",
       "vtab/dmlab                                                     0.149   \n",
       "vtab/dsprites_label_orientation                                0.026   \n",
       "vtab/dsprites_label_x_position                                 0.043   \n",
       "vtab/dtd                                                       0.557   \n",
       "vtab/eurosat                                                   0.580   \n",
       "vtab/flowers                                                   0.711   \n",
       "vtab/kitti_closest_vehicle_distance                            0.284   \n",
       "vtab/pcam                                                      0.544   \n",
       "vtab/pets                                                      0.904   \n",
       "vtab/resisc45                                                  0.613   \n",
       "vtab/smallnorb_label_azimuth                                   0.055   \n",
       "vtab/smallnorb_label_elevation                                 0.108   \n",
       "vtab/svhn                                                      0.363   \n",
       "\n",
       "model_fullname                       ViT-B-32 laion2b_e16  ViT-B-32 openai  \\\n",
       "dataset                                                                      \n",
       "cars                                                0.842            0.594   \n",
       "country211                                          0.165            0.172   \n",
       "fer2013                                             0.477            0.409   \n",
       "fgvc_aircraft                                       0.230            0.197   \n",
       "gtsrb                                               0.365            0.331   \n",
       "imagenet-a                                          0.262            0.314   \n",
       "imagenet-r                                          0.759            0.693   \n",
       "imagenet1k                                          0.655            0.633   \n",
       "imagenet_sketch                                     0.529            0.423   \n",
       "imagenetv2                                          0.572            0.560   \n",
       "mnist                                               0.637            0.485   \n",
       "objectnet                                           0.488            0.442   \n",
       "renderedsst2                                        0.537            0.590   \n",
       "stl10                                               0.965            0.971   \n",
       "sun397                                              0.685            0.625   \n",
       "voc2007                                             0.789            0.766   \n",
       "vtab/caltech101                                     0.831            0.819   \n",
       "vtab/cifar10                                        0.940            0.898   \n",
       "vtab/cifar100                                       0.752            0.645   \n",
       "vtab/clevr_closest_object_distance                  0.167            0.172   \n",
       "vtab/clevr_count_all                                0.198            0.235   \n",
       "vtab/diabetic_retinopathy                           0.270            0.263   \n",
       "vtab/dmlab                                          0.189            0.195   \n",
       "vtab/dsprites_label_orientation                     0.027            0.024   \n",
       "vtab/dsprites_label_x_position                      0.031            0.035   \n",
       "vtab/dtd                                            0.540            0.443   \n",
       "vtab/eurosat                                        0.502            0.507   \n",
       "vtab/flowers                                        0.689            0.663   \n",
       "vtab/kitti_closest_vehicle_distance                 0.166            0.274   \n",
       "vtab/pcam                                           0.504            0.622   \n",
       "vtab/pets                                           0.893            0.873   \n",
       "vtab/resisc45                                       0.617            0.538   \n",
       "vtab/smallnorb_label_azimuth                        0.052            0.060   \n",
       "vtab/smallnorb_label_elevation                      0.109            0.118   \n",
       "vtab/svhn                                           0.385            0.124   \n",
       "\n",
       "model_fullname                       ViT-B-32 openclip b_290.pt  \\\n",
       "dataset                                                           \n",
       "cars                                                      0.860   \n",
       "country211                                                0.166   \n",
       "fer2013                                                   0.469   \n",
       "fgvc_aircraft                                             0.246   \n",
       "gtsrb                                                     0.493   \n",
       "imagenet-a                                                0.263   \n",
       "imagenet-r                                                0.764   \n",
       "imagenet1k                                                0.665   \n",
       "imagenet_sketch                                           0.536   \n",
       "imagenetv2                                                0.582   \n",
       "mnist                                                     0.692   \n",
       "objectnet                                                 0.490   \n",
       "renderedsst2                                              0.566   \n",
       "stl10                                                     0.966   \n",
       "sun397                                                    0.687   \n",
       "voc2007                                                   0.791   \n",
       "vtab/caltech101                                           0.839   \n",
       "vtab/cifar10                                              0.935   \n",
       "vtab/cifar100                                             0.756   \n",
       "vtab/clevr_closest_object_distance                        0.190   \n",
       "vtab/clevr_count_all                                      0.157   \n",
       "vtab/diabetic_retinopathy                                 0.735   \n",
       "vtab/dmlab                                                0.158   \n",
       "vtab/dsprites_label_orientation                           0.034   \n",
       "vtab/dsprites_label_x_position                            0.030   \n",
       "vtab/dtd                                                  0.557   \n",
       "vtab/eurosat                                              0.482   \n",
       "vtab/flowers                                              0.716   \n",
       "vtab/kitti_closest_vehicle_distance                       0.259   \n",
       "vtab/pcam                                                 0.586   \n",
       "vtab/pets                                                 0.907   \n",
       "vtab/resisc45                                             0.609   \n",
       "vtab/smallnorb_label_azimuth                              0.062   \n",
       "vtab/smallnorb_label_elevation                            0.115   \n",
       "vtab/svhn                                                 0.410   \n",
       "\n",
       "model_fullname                       ViT-B-32-quickgelu laion400m_e32  \\\n",
       "dataset                                                                 \n",
       "cars                                                            0.792   \n",
       "country211                                                      0.147   \n",
       "fer2013                                                         0.427   \n",
       "fgvc_aircraft                                                   0.168   \n",
       "gtsrb                                                           0.420   \n",
       "imagenet-a                                                      0.217   \n",
       "imagenet-r                                                      0.734   \n",
       "imagenet1k                                                      0.629   \n",
       "imagenet_sketch                                                 0.493   \n",
       "imagenetv2                                                      0.551   \n",
       "mnist                                                           0.374   \n",
       "objectnet                                                       0.439   \n",
       "renderedsst2                                                    0.526   \n",
       "stl10                                                           0.955   \n",
       "sun397                                                          0.670   \n",
       "voc2007                                                         0.757   \n",
       "vtab/caltech101                                                 0.833   \n",
       "vtab/cifar10                                                    0.908   \n",
       "vtab/cifar100                                                   0.702   \n",
       "vtab/clevr_closest_object_distance                              0.159   \n",
       "vtab/clevr_count_all                                            0.163   \n",
       "vtab/diabetic_retinopathy                                       0.338   \n",
       "vtab/dmlab                                                      0.172   \n",
       "vtab/dsprites_label_orientation                                 0.019   \n",
       "vtab/dsprites_label_x_position                                  0.029   \n",
       "vtab/dtd                                                        0.543   \n",
       "vtab/eurosat                                                    0.516   \n",
       "vtab/flowers                                                    0.683   \n",
       "vtab/kitti_closest_vehicle_distance                             0.288   \n",
       "vtab/pcam                                                       0.546   \n",
       "vtab/pets                                                       0.868   \n",
       "vtab/resisc45                                                   0.546   \n",
       "vtab/smallnorb_label_azimuth                                    0.045   \n",
       "vtab/smallnorb_label_elevation                                  0.097   \n",
       "vtab/svhn                                                       0.279   \n",
       "\n",
       "model_fullname                       ViT-H-14 openclip h_256.pt  \\\n",
       "dataset                                                           \n",
       "cars                                                      0.935   \n",
       "country211                                                0.300   \n",
       "fer2013                                                   0.518   \n",
       "fgvc_aircraft                                             0.428   \n",
       "gtsrb                                                     0.584   \n",
       "imagenet-a                                                0.592   \n",
       "imagenet-r                                                0.893   \n",
       "imagenet1k                                                0.780   \n",
       "imagenet_sketch                                           0.666   \n",
       "imagenetv2                                                0.708   \n",
       "mnist                                                     0.729   \n",
       "objectnet                                                 0.697   \n",
       "renderedsst2                                              0.641   \n",
       "stl10                                                     0.984   \n",
       "sun397                                                    0.752   \n",
       "voc2007                                                   0.776   \n",
       "vtab/caltech101                                           0.850   \n",
       "vtab/cifar10                                              0.974   \n",
       "vtab/cifar100                                             0.847   \n",
       "vtab/clevr_closest_object_distance                        0.168   \n",
       "vtab/clevr_count_all                                      0.278   \n",
       "vtab/diabetic_retinopathy                                 0.238   \n",
       "vtab/dmlab                                                0.142   \n",
       "vtab/dsprites_label_orientation                           0.026   \n",
       "vtab/dsprites_label_x_position                            0.031   \n",
       "vtab/dtd                                                  0.679   \n",
       "vtab/eurosat                                              0.717   \n",
       "vtab/flowers                                              0.802   \n",
       "vtab/kitti_closest_vehicle_distance                       0.111   \n",
       "vtab/pcam                                                 0.536   \n",
       "vtab/pets                                                 0.944   \n",
       "vtab/resisc45                                             0.696   \n",
       "vtab/smallnorb_label_azimuth                              0.055   \n",
       "vtab/smallnorb_label_elevation                            0.111   \n",
       "vtab/svhn                                                 0.561   \n",
       "\n",
       "model_fullname                       ViT-L-14 laion400m_e32  ViT-L-14 openai  \\\n",
       "dataset                                                                        \n",
       "cars                                                  0.896            0.777   \n",
       "country211                                            0.231            0.318   \n",
       "fer2013                                               0.501            0.490   \n",
       "fgvc_aircraft                                         0.251            0.317   \n",
       "gtsrb                                                 0.500            0.502   \n",
       "imagenet-a                                            0.466            0.707   \n",
       "imagenet-r                                            0.848            0.879   \n",
       "imagenet1k                                            0.727            0.755   \n",
       "imagenet_sketch                                       0.596            0.596   \n",
       "imagenetv2                                            0.656            0.697   \n",
       "mnist                                                 0.764            0.768   \n",
       "objectnet                                             0.599            0.691   \n",
       "renderedsst2                                          0.563            0.699   \n",
       "stl10                                                 0.980            0.994   \n",
       "sun397                                                0.726            0.675   \n",
       "voc2007                                               0.756            0.783   \n",
       "vtab/caltech101                                       0.842            0.839   \n",
       "vtab/cifar10                                          0.947            0.957   \n",
       "vtab/cifar100                                         0.774            0.761   \n",
       "vtab/clevr_closest_object_distance                    0.149            0.161   \n",
       "vtab/clevr_count_all                                  0.242            0.190   \n",
       "vtab/diabetic_retinopathy                             0.072            0.733   \n",
       "vtab/dmlab                                            0.186            0.167   \n",
       "vtab/dsprites_label_orientation                       0.026            0.023   \n",
       "vtab/dsprites_label_x_position                        0.030            0.032   \n",
       "vtab/dtd                                              0.603            0.551   \n",
       "vtab/eurosat                                          0.618            0.627   \n",
       "vtab/flowers                                          0.754            0.792   \n",
       "vtab/kitti_closest_vehicle_distance                   0.208            0.224   \n",
       "vtab/pcam                                             0.486            0.516   \n",
       "vtab/pets                                             0.919            0.931   \n",
       "vtab/resisc45                                         0.673            0.635   \n",
       "vtab/smallnorb_label_azimuth                          0.052            0.046   \n",
       "vtab/smallnorb_label_elevation                        0.110            0.114   \n",
       "vtab/svhn                                             0.382            0.571   \n",
       "\n",
       "model_fullname                       ViT-L-14 openclip epoch_160.pt  \\\n",
       "dataset                                                               \n",
       "cars                                                          0.926   \n",
       "country211                                                    0.264   \n",
       "fer2013                                                       0.537   \n",
       "fgvc_aircraft                                                 0.369   \n",
       "gtsrb                                                         0.561   \n",
       "imagenet-a                                                    0.539   \n",
       "imagenet-r                                                    0.874   \n",
       "imagenet1k                                                    0.752   \n",
       "imagenet_sketch                                               0.633   \n",
       "imagenetv2                                                    0.677   \n",
       "mnist                                                         0.549   \n",
       "objectnet                                                     0.655   \n",
       "renderedsst2                                                  0.593   \n",
       "stl10                                                         0.989   \n",
       "sun397                                                        0.743   \n",
       "voc2007                                                       0.805   \n",
       "vtab/caltech101                                               0.850   \n",
       "vtab/cifar10                                                  0.966   \n",
       "vtab/cifar100                                                 0.834   \n",
       "vtab/clevr_closest_object_distance                            0.161   \n",
       "vtab/clevr_count_all                                          0.311   \n",
       "vtab/diabetic_retinopathy                                     0.211   \n",
       "vtab/dmlab                                                    0.224   \n",
       "vtab/dsprites_label_orientation                               0.020   \n",
       "vtab/dsprites_label_x_position                                0.032   \n",
       "vtab/dtd                                                      0.628   \n",
       "vtab/eurosat                                                  0.651   \n",
       "vtab/flowers                                                  0.759   \n",
       "vtab/kitti_closest_vehicle_distance                           0.229   \n",
       "vtab/pcam                                                     0.553   \n",
       "vtab/pets                                                     0.932   \n",
       "vtab/resisc45                                                 0.667   \n",
       "vtab/smallnorb_label_azimuth                                  0.056   \n",
       "vtab/smallnorb_label_elevation                                0.109   \n",
       "vtab/svhn                                                     0.463   \n",
       "\n",
       "model_fullname                       ViT-L-14-336 openai  \\\n",
       "dataset                                                    \n",
       "cars                                               0.793   \n",
       "country211                                         0.345   \n",
       "fer2013                                            0.480   \n",
       "fgvc_aircraft                                      0.329   \n",
       "gtsrb                                              0.516   \n",
       "imagenet-a                                         0.774   \n",
       "imagenet-r                                         0.891   \n",
       "imagenet1k                                         0.765   \n",
       "imagenet_sketch                                    0.611   \n",
       "imagenetv2                                         0.707   \n",
       "mnist                                              0.787   \n",
       "objectnet                                          0.718   \n",
       "renderedsst2                                       0.707   \n",
       "stl10                                              0.994   \n",
       "sun397                                             0.687   \n",
       "voc2007                                            0.782   \n",
       "vtab/caltech101                                    0.838   \n",
       "vtab/cifar10                                       0.950   \n",
       "vtab/cifar100                                      0.746   \n",
       "vtab/clevr_closest_object_distance                 0.158   \n",
       "vtab/clevr_count_all                               0.199   \n",
       "vtab/diabetic_retinopathy                          0.733   \n",
       "vtab/dmlab                                         0.159   \n",
       "vtab/dsprites_label_orientation                    0.024   \n",
       "vtab/dsprites_label_x_position                     0.031   \n",
       "vtab/dtd                                           0.557   \n",
       "vtab/eurosat                                       0.619   \n",
       "vtab/flowers                                       0.784   \n",
       "vtab/kitti_closest_vehicle_distance                0.269   \n",
       "vtab/pcam                                          0.613   \n",
       "vtab/pets                                          0.938   \n",
       "vtab/resisc45                                      0.637   \n",
       "vtab/smallnorb_label_azimuth                       0.048   \n",
       "vtab/smallnorb_label_elevation                     0.112   \n",
       "vtab/svhn                                          0.554   \n",
       "\n",
       "model_fullname                       ViT-g-14 openclip g_90.pt  \n",
       "dataset                                                         \n",
       "cars                                                     0.928  \n",
       "country211                                               0.287  \n",
       "fer2013                                                  0.466  \n",
       "fgvc_aircraft                                            0.378  \n",
       "gtsrb                                                    0.497  \n",
       "imagenet-a                                               0.571  \n",
       "imagenet-r                                               0.886  \n",
       "imagenet1k                                               0.767  \n",
       "imagenet_sketch                                          0.652  \n",
       "imagenetv2                                               0.696  \n",
       "mnist                                                    0.690  \n",
       "objectnet                                                0.675  \n",
       "renderedsst2                                             0.646  \n",
       "stl10                                                    0.986  \n",
       "sun397                                                   0.754  \n",
       "voc2007                                                  0.810  \n",
       "vtab/caltech101                                          0.852  \n",
       "vtab/cifar10                                             0.971  \n",
       "vtab/cifar100                                            0.839  \n",
       "vtab/clevr_closest_object_distance                       0.177  \n",
       "vtab/clevr_count_all                                     0.332  \n",
       "vtab/diabetic_retinopathy                                0.434  \n",
       "vtab/dmlab                                               0.190  \n",
       "vtab/dsprites_label_orientation                          0.031  \n",
       "vtab/dsprites_label_x_position                           0.035  \n",
       "vtab/dtd                                                 0.681  \n",
       "vtab/eurosat                                             0.648  \n",
       "vtab/flowers                                             0.776  \n",
       "vtab/kitti_closest_vehicle_distance                      0.146  \n",
       "vtab/pcam                                                0.551  \n",
       "vtab/pets                                                0.943  \n",
       "vtab/resisc45                                            0.717  \n",
       "vtab/smallnorb_label_azimuth                             0.059  \n",
       "vtab/smallnorb_label_elevation                           0.113  \n",
       "vtab/svhn                                                0.603  "
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metric = \"acc1\"\n",
    "df_metric = pd.pivot(df, index=\"model_fullname\", columns=\"dataset\", values=metric).T.dropna()\n",
    "df_metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "208d6deb-2f0c-47e8-884a-b3c36da17a57",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>model_fullname</th>\n",
       "      <th>ViT-B-16 laion400m_e32</th>\n",
       "      <th>ViT-B-16 openai</th>\n",
       "      <th>ViT-B-16-plus-240 laion400m_e32</th>\n",
       "      <th>ViT-B-32 laion2b_e16</th>\n",
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       "      <th>ViT-B-32 openclip b_290.pt</th>\n",
       "      <th>ViT-B-32-quickgelu laion400m_e32</th>\n",
       "      <th>ViT-H-14 openclip h_256.pt</th>\n",
       "      <th>ViT-L-14 laion400m_e32</th>\n",
       "      <th>ViT-L-14 openai</th>\n",
       "      <th>ViT-L-14 openclip epoch_160.pt</th>\n",
       "      <th>ViT-L-14-336 openai</th>\n",
       "      <th>ViT-g-14 openclip g_90.pt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cars</th>\n",
       "      <td>0.838</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.846</td>\n",
       "      <td>0.844</td>\n",
       "      <td>0.597</td>\n",
       "      <td>0.862</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.896</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country211</th>\n",
       "      <td>0.182</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.171</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.147</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.318</td>\n",
       "      <td>0.263</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fer2013</th>\n",
       "      <td>0.392</td>\n",
       "      <td>0.417</td>\n",
       "      <td>0.394</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.433</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.489</td>\n",
       "      <td>0.534</td>\n",
       "      <td>0.491</td>\n",
       "      <td>0.481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fgvc_aircraft</th>\n",
       "      <td>0.175</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.232</td>\n",
       "      <td>0.197</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.426</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.317</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.332</td>\n",
       "      <td>0.378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gtsrb</th>\n",
       "      <td>0.401</td>\n",
       "      <td>0.370</td>\n",
       "      <td>0.432</td>\n",
       "      <td>0.351</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.439</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.447</td>\n",
       "      <td>0.466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imagenet-a</th>\n",
       "      <td>0.341</td>\n",
       "      <td>0.483</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.279</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.581</td>\n",
       "      <td>0.473</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imagenet-r</th>\n",
       "      <td>0.764</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.744</td>\n",
       "      <td>0.679</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.721</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imagenet1k</th>\n",
       "      <td>0.670</td>\n",
       "      <td>0.684</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.656</td>\n",
       "      <td>0.633</td>\n",
       "      <td>0.665</td>\n",
       "      <td>0.629</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.727</td>\n",
       "      <td>0.754</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.766</td>\n",
       "      <td>0.767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imagenet_sketch</th>\n",
       "      <td>0.523</td>\n",
       "      <td>0.482</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.529</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.633</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imagenetv2</th>\n",
       "      <td>0.596</td>\n",
       "      <td>0.620</td>\n",
       "      <td>0.615</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.560</td>\n",
       "      <td>0.582</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.709</td>\n",
       "      <td>0.654</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.678</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mnist</th>\n",
       "      <td>0.667</td>\n",
       "      <td>0.526</td>\n",
       "      <td>0.568</td>\n",
       "      <td>0.628</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.688</td>\n",
       "      <td>0.371</td>\n",
       "      <td>0.733</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.758</td>\n",
       "      <td>0.543</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>objectnet</th>\n",
       "      <td>0.502</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.527</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.427</td>\n",
       "      <td>0.482</td>\n",
       "      <td>0.427</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.586</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.701</td>\n",
       "      <td>0.665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>renderedsst2</th>\n",
       "      <td>0.546</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.579</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.526</td>\n",
       "      <td>0.641</td>\n",
       "      <td>0.564</td>\n",
       "      <td>0.699</td>\n",
       "      <td>0.593</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stl10</th>\n",
       "      <td>0.970</td>\n",
       "      <td>0.983</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.967</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.989</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sun397</th>\n",
       "      <td>0.680</td>\n",
       "      <td>0.653</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.678</td>\n",
       "      <td>0.635</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.751</td>\n",
       "      <td>0.713</td>\n",
       "      <td>0.682</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voc2007</th>\n",
       "      <td>0.804</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.805</td>\n",
       "      <td>0.807</td>\n",
       "      <td>0.805</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.851</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.864</td>\n",
       "      <td>0.849</td>\n",
       "      <td>0.863</td>\n",
       "      <td>0.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/caltech101</th>\n",
       "      <td>0.901</td>\n",
       "      <td>0.909</td>\n",
       "      <td>0.918</td>\n",
       "      <td>0.903</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.909</td>\n",
       "      <td>0.909</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.934</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.939</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/cifar10</th>\n",
       "      <td>0.917</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.974</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.957</td>\n",
       "      <td>0.967</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/cifar100</th>\n",
       "      <td>0.711</td>\n",
       "      <td>0.669</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.847</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.747</td>\n",
       "      <td>0.839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/clevr_closest_object_distance</th>\n",
       "      <td>0.167</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.195</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.177</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/clevr_count_all</th>\n",
       "      <td>0.282</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.182</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.307</td>\n",
       "      <td>0.195</td>\n",
       "      <td>0.319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/diabetic_retinopathy</th>\n",
       "      <td>0.252</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.221</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.234</td>\n",
       "      <td>0.207</td>\n",
       "      <td>0.217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/dmlab</th>\n",
       "      <td>0.172</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.182</td>\n",
       "      <td>0.171</td>\n",
       "      <td>0.173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/dsprites_label_orientation</th>\n",
       "      <td>0.033</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/dsprites_label_x_position</th>\n",
       "      <td>0.032</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/dtd</th>\n",
       "      <td>0.510</td>\n",
       "      <td>0.449</td>\n",
       "      <td>0.554</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.443</td>\n",
       "      <td>0.562</td>\n",
       "      <td>0.547</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.632</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/eurosat</th>\n",
       "      <td>0.511</td>\n",
       "      <td>0.547</td>\n",
       "      <td>0.589</td>\n",
       "      <td>0.511</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.526</td>\n",
       "      <td>0.720</td>\n",
       "      <td>0.630</td>\n",
       "      <td>0.638</td>\n",
       "      <td>0.664</td>\n",
       "      <td>0.631</td>\n",
       "      <td>0.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/flowers</th>\n",
       "      <td>0.667</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.673</td>\n",
       "      <td>0.665</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.663</td>\n",
       "      <td>0.799</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.746</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/kitti_closest_vehicle_distance</th>\n",
       "      <td>0.257</td>\n",
       "      <td>0.352</td>\n",
       "      <td>0.408</td>\n",
       "      <td>0.325</td>\n",
       "      <td>0.406</td>\n",
       "      <td>0.340</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.308</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/pcam</th>\n",
       "      <td>0.605</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.504</td>\n",
       "      <td>0.622</td>\n",
       "      <td>0.586</td>\n",
       "      <td>0.546</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.486</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.553</td>\n",
       "      <td>0.613</td>\n",
       "      <td>0.551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/pets</th>\n",
       "      <td>0.892</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.904</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.866</td>\n",
       "      <td>0.943</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.931</td>\n",
       "      <td>0.937</td>\n",
       "      <td>0.943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/resisc45</th>\n",
       "      <td>0.593</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.615</td>\n",
       "      <td>0.624</td>\n",
       "      <td>0.542</td>\n",
       "      <td>0.615</td>\n",
       "      <td>0.554</td>\n",
       "      <td>0.706</td>\n",
       "      <td>0.678</td>\n",
       "      <td>0.642</td>\n",
       "      <td>0.676</td>\n",
       "      <td>0.646</td>\n",
       "      <td>0.726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/smallnorb_label_azimuth</th>\n",
       "      <td>0.060</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/smallnorb_label_elevation</th>\n",
       "      <td>0.098</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/svhn</th>\n",
       "      <td>0.369</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.403</td>\n",
       "      <td>0.379</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.406</td>\n",
       "      <td>0.589</td>\n",
       "      <td>0.487</td>\n",
       "      <td>0.559</td>\n",
       "      <td>0.568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "model_fullname                       ViT-B-16 laion400m_e32  ViT-B-16 openai  \\\n",
       "dataset                                                                        \n",
       "cars                                                  0.838            0.647   \n",
       "country211                                            0.182            0.228   \n",
       "fer2013                                               0.392            0.417   \n",
       "fgvc_aircraft                                         0.175            0.241   \n",
       "gtsrb                                                 0.401            0.370   \n",
       "imagenet-a                                            0.341            0.483   \n",
       "imagenet-r                                            0.764            0.761   \n",
       "imagenet1k                                            0.670            0.684   \n",
       "imagenet_sketch                                       0.523            0.482   \n",
       "imagenetv2                                            0.596            0.620   \n",
       "mnist                                                 0.667            0.526   \n",
       "objectnet                                             0.502            0.536   \n",
       "renderedsst2                                          0.546            0.611   \n",
       "stl10                                                 0.970            0.983   \n",
       "sun397                                                0.680            0.653   \n",
       "voc2007                                               0.804            0.835   \n",
       "vtab/caltech101                                       0.901            0.909   \n",
       "vtab/cifar10                                          0.917            0.908   \n",
       "vtab/cifar100                                         0.711            0.669   \n",
       "vtab/clevr_closest_object_distance                    0.167            0.168   \n",
       "vtab/clevr_count_all                                  0.282            0.215   \n",
       "vtab/diabetic_retinopathy                             0.252            0.211   \n",
       "vtab/dmlab                                            0.172            0.170   \n",
       "vtab/dsprites_label_orientation                       0.033            0.018   \n",
       "vtab/dsprites_label_x_position                        0.032            0.029   \n",
       "vtab/dtd                                              0.510            0.449   \n",
       "vtab/eurosat                                          0.511            0.547   \n",
       "vtab/flowers                                          0.667            0.691   \n",
       "vtab/kitti_closest_vehicle_distance                   0.257            0.352   \n",
       "vtab/pcam                                             0.605            0.506   \n",
       "vtab/pets                                             0.892            0.885   \n",
       "vtab/resisc45                                         0.593            0.592   \n",
       "vtab/smallnorb_label_azimuth                          0.060            0.052   \n",
       "vtab/smallnorb_label_elevation                        0.098            0.118   \n",
       "vtab/svhn                                             0.369            0.350   \n",
       "\n",
       "model_fullname                       ViT-B-16-plus-240 laion400m_e32  \\\n",
       "dataset                                                                \n",
       "cars                                                           0.846   \n",
       "country211                                                     0.188   \n",
       "fer2013                                                        0.394   \n",
       "fgvc_aircraft                                                  0.188   \n",
       "gtsrb                                                          0.432   \n",
       "imagenet-a                                                     0.381   \n",
       "imagenet-r                                                     0.791   \n",
       "imagenet1k                                                     0.692   \n",
       "imagenet_sketch                                                0.545   \n",
       "imagenetv2                                                     0.615   \n",
       "mnist                                                          0.568   \n",
       "objectnet                                                      0.527   \n",
       "renderedsst2                                                   0.579   \n",
       "stl10                                                          0.970   \n",
       "sun397                                                         0.685   \n",
       "voc2007                                                        0.816   \n",
       "vtab/caltech101                                                0.918   \n",
       "vtab/cifar10                                                   0.927   \n",
       "vtab/cifar100                                                  0.737   \n",
       "vtab/clevr_closest_object_distance                             0.170   \n",
       "vtab/clevr_count_all                                           0.233   \n",
       "vtab/diabetic_retinopathy                                      0.231   \n",
       "vtab/dmlab                                                     0.148   \n",
       "vtab/dsprites_label_orientation                                0.026   \n",
       "vtab/dsprites_label_x_position                                 0.043   \n",
       "vtab/dtd                                                       0.554   \n",
       "vtab/eurosat                                                   0.589   \n",
       "vtab/flowers                                                   0.686   \n",
       "vtab/kitti_closest_vehicle_distance                            0.408   \n",
       "vtab/pcam                                                      0.544   \n",
       "vtab/pets                                                      0.904   \n",
       "vtab/resisc45                                                  0.615   \n",
       "vtab/smallnorb_label_azimuth                                   0.055   \n",
       "vtab/smallnorb_label_elevation                                 0.109   \n",
       "vtab/svhn                                                      0.403   \n",
       "\n",
       "model_fullname                       ViT-B-32 laion2b_e16  ViT-B-32 openai  \\\n",
       "dataset                                                                      \n",
       "cars                                                0.844            0.597   \n",
       "country211                                          0.164            0.171   \n",
       "fer2013                                             0.465            0.359   \n",
       "fgvc_aircraft                                       0.232            0.197   \n",
       "gtsrb                                               0.351            0.320   \n",
       "imagenet-a                                          0.284            0.324   \n",
       "imagenet-r                                          0.744            0.679   \n",
       "imagenet1k                                          0.656            0.633   \n",
       "imagenet_sketch                                     0.529            0.423   \n",
       "imagenetv2                                          0.572            0.560   \n",
       "mnist                                               0.628            0.458   \n",
       "objectnet                                           0.475            0.427   \n",
       "renderedsst2                                        0.537            0.590   \n",
       "stl10                                               0.965            0.972   \n",
       "sun397                                              0.678            0.635   \n",
       "voc2007                                             0.805            0.807   \n",
       "vtab/caltech101                                     0.903            0.879   \n",
       "vtab/cifar10                                        0.941            0.900   \n",
       "vtab/cifar100                                       0.753            0.645   \n",
       "vtab/clevr_closest_object_distance                  0.183            0.162   \n",
       "vtab/clevr_count_all                                0.182            0.220   \n",
       "vtab/diabetic_retinopathy                           0.221            0.219   \n",
       "vtab/dmlab                                          0.168            0.164   \n",
       "vtab/dsprites_label_orientation                     0.025            0.022   \n",
       "vtab/dsprites_label_x_position                      0.031            0.034   \n",
       "vtab/dtd                                            0.537            0.443   \n",
       "vtab/eurosat                                        0.511            0.490   \n",
       "vtab/flowers                                        0.673            0.665   \n",
       "vtab/kitti_closest_vehicle_distance                 0.325            0.406   \n",
       "vtab/pcam                                           0.504            0.622   \n",
       "vtab/pets                                           0.891            0.870   \n",
       "vtab/resisc45                                       0.624            0.542   \n",
       "vtab/smallnorb_label_azimuth                        0.054            0.063   \n",
       "vtab/smallnorb_label_elevation                      0.109            0.121   \n",
       "vtab/svhn                                           0.379            0.133   \n",
       "\n",
       "model_fullname                       ViT-B-32 openclip b_290.pt  \\\n",
       "dataset                                                           \n",
       "cars                                                      0.862   \n",
       "country211                                                0.167   \n",
       "fer2013                                                   0.433   \n",
       "fgvc_aircraft                                             0.246   \n",
       "gtsrb                                                     0.435   \n",
       "imagenet-a                                                0.279   \n",
       "imagenet-r                                                0.752   \n",
       "imagenet1k                                                0.665   \n",
       "imagenet_sketch                                           0.537   \n",
       "imagenetv2                                                0.582   \n",
       "mnist                                                     0.688   \n",
       "objectnet                                                 0.482   \n",
       "renderedsst2                                              0.566   \n",
       "stl10                                                     0.967   \n",
       "sun397                                                    0.685   \n",
       "voc2007                                                   0.805   \n",
       "vtab/caltech101                                           0.909   \n",
       "vtab/cifar10                                              0.936   \n",
       "vtab/cifar100                                             0.755   \n",
       "vtab/clevr_closest_object_distance                        0.139   \n",
       "vtab/clevr_count_all                                      0.150   \n",
       "vtab/diabetic_retinopathy                                 0.200   \n",
       "vtab/dmlab                                                0.166   \n",
       "vtab/dsprites_label_orientation                           0.034   \n",
       "vtab/dsprites_label_x_position                            0.030   \n",
       "vtab/dtd                                                  0.562   \n",
       "vtab/eurosat                                              0.494   \n",
       "vtab/flowers                                              0.700   \n",
       "vtab/kitti_closest_vehicle_distance                       0.340   \n",
       "vtab/pcam                                                 0.586   \n",
       "vtab/pets                                                 0.907   \n",
       "vtab/resisc45                                             0.615   \n",
       "vtab/smallnorb_label_azimuth                              0.063   \n",
       "vtab/smallnorb_label_elevation                            0.116   \n",
       "vtab/svhn                                                 0.422   \n",
       "\n",
       "model_fullname                       ViT-B-32-quickgelu laion400m_e32  \\\n",
       "dataset                                                                 \n",
       "cars                                                            0.793   \n",
       "country211                                                      0.147   \n",
       "fer2013                                                         0.399   \n",
       "fgvc_aircraft                                                   0.166   \n",
       "gtsrb                                                           0.393   \n",
       "imagenet-a                                                      0.235   \n",
       "imagenet-r                                                      0.721   \n",
       "imagenet1k                                                      0.629   \n",
       "imagenet_sketch                                                 0.494   \n",
       "imagenetv2                                                      0.551   \n",
       "mnist                                                           0.371   \n",
       "objectnet                                                       0.427   \n",
       "renderedsst2                                                    0.526   \n",
       "stl10                                                           0.955   \n",
       "sun397                                                          0.661   \n",
       "voc2007                                                         0.791   \n",
       "vtab/caltech101                                                 0.909   \n",
       "vtab/cifar10                                                    0.908   \n",
       "vtab/cifar100                                                   0.703   \n",
       "vtab/clevr_closest_object_distance                              0.167   \n",
       "vtab/clevr_count_all                                            0.158   \n",
       "vtab/diabetic_retinopathy                                       0.259   \n",
       "vtab/dmlab                                                      0.158   \n",
       "vtab/dsprites_label_orientation                                 0.020   \n",
       "vtab/dsprites_label_x_position                                  0.031   \n",
       "vtab/dtd                                                        0.547   \n",
       "vtab/eurosat                                                    0.526   \n",
       "vtab/flowers                                                    0.663   \n",
       "vtab/kitti_closest_vehicle_distance                             0.365   \n",
       "vtab/pcam                                                       0.546   \n",
       "vtab/pets                                                       0.866   \n",
       "vtab/resisc45                                                   0.554   \n",
       "vtab/smallnorb_label_azimuth                                    0.045   \n",
       "vtab/smallnorb_label_elevation                                  0.097   \n",
       "vtab/svhn                                                       0.280   \n",
       "\n",
       "model_fullname                       ViT-H-14 openclip h_256.pt  \\\n",
       "dataset                                                           \n",
       "cars                                                      0.935   \n",
       "country211                                                0.299   \n",
       "fer2013                                                   0.506   \n",
       "fgvc_aircraft                                             0.426   \n",
       "gtsrb                                                     0.544   \n",
       "imagenet-a                                                0.581   \n",
       "imagenet-r                                                0.880   \n",
       "imagenet1k                                                0.780   \n",
       "imagenet_sketch                                           0.666   \n",
       "imagenetv2                                                0.709   \n",
       "mnist                                                     0.733   \n",
       "objectnet                                                 0.685   \n",
       "renderedsst2                                              0.641   \n",
       "stl10                                                     0.985   \n",
       "sun397                                                    0.751   \n",
       "voc2007                                                   0.851   \n",
       "vtab/caltech101                                           0.944   \n",
       "vtab/cifar10                                              0.974   \n",
       "vtab/cifar100                                             0.847   \n",
       "vtab/clevr_closest_object_distance                        0.195   \n",
       "vtab/clevr_count_all                                      0.256   \n",
       "vtab/diabetic_retinopathy                                 0.233   \n",
       "vtab/dmlab                                                0.166   \n",
       "vtab/dsprites_label_orientation                           0.027   \n",
       "vtab/dsprites_label_x_position                            0.031   \n",
       "vtab/dtd                                                  0.681   \n",
       "vtab/eurosat                                              0.720   \n",
       "vtab/flowers                                              0.799   \n",
       "vtab/kitti_closest_vehicle_distance                       0.272   \n",
       "vtab/pcam                                                 0.536   \n",
       "vtab/pets                                                 0.943   \n",
       "vtab/resisc45                                             0.706   \n",
       "vtab/smallnorb_label_azimuth                              0.056   \n",
       "vtab/smallnorb_label_elevation                            0.110   \n",
       "vtab/svhn                                                 0.557   \n",
       "\n",
       "model_fullname                       ViT-L-14 laion400m_e32  ViT-L-14 openai  \\\n",
       "dataset                                                                        \n",
       "cars                                                  0.896            0.777   \n",
       "country211                                            0.231            0.318   \n",
       "fer2013                                               0.450            0.489   \n",
       "fgvc_aircraft                                         0.248            0.317   \n",
       "gtsrb                                                 0.450            0.439   \n",
       "imagenet-a                                            0.473            0.675   \n",
       "imagenet-r                                            0.833            0.865   \n",
       "imagenet1k                                            0.727            0.754   \n",
       "imagenet_sketch                                       0.596            0.596   \n",
       "imagenetv2                                            0.654            0.697   \n",
       "mnist                                                 0.759            0.758   \n",
       "objectnet                                             0.586            0.674   \n",
       "renderedsst2                                          0.564            0.699   \n",
       "stl10                                                 0.981            0.994   \n",
       "sun397                                                0.713            0.682   \n",
       "voc2007                                               0.831            0.864   \n",
       "vtab/caltech101                                       0.934            0.933   \n",
       "vtab/cifar10                                          0.947            0.957   \n",
       "vtab/cifar100                                         0.774            0.761   \n",
       "vtab/clevr_closest_object_distance                    0.144            0.177   \n",
       "vtab/clevr_count_all                                  0.231            0.187   \n",
       "vtab/diabetic_retinopathy                             0.220            0.206   \n",
       "vtab/dmlab                                            0.193            0.178   \n",
       "vtab/dsprites_label_orientation                       0.026            0.024   \n",
       "vtab/dsprites_label_x_position                        0.031            0.032   \n",
       "vtab/dtd                                              0.604            0.550   \n",
       "vtab/eurosat                                          0.630            0.638   \n",
       "vtab/flowers                                          0.726            0.793   \n",
       "vtab/kitti_closest_vehicle_distance                   0.179            0.372   \n",
       "vtab/pcam                                             0.486            0.516   \n",
       "vtab/pets                                             0.916            0.933   \n",
       "vtab/resisc45                                         0.678            0.642   \n",
       "vtab/smallnorb_label_azimuth                          0.053            0.046   \n",
       "vtab/smallnorb_label_elevation                        0.108            0.114   \n",
       "vtab/svhn                                             0.406            0.589   \n",
       "\n",
       "model_fullname                       ViT-L-14 openclip epoch_160.pt  \\\n",
       "dataset                                                               \n",
       "cars                                                          0.926   \n",
       "country211                                                    0.263   \n",
       "fer2013                                                       0.534   \n",
       "fgvc_aircraft                                                 0.365   \n",
       "gtsrb                                                         0.517   \n",
       "imagenet-a                                                    0.536   \n",
       "imagenet-r                                                    0.860   \n",
       "imagenet1k                                                    0.753   \n",
       "imagenet_sketch                                               0.633   \n",
       "imagenetv2                                                    0.678   \n",
       "mnist                                                         0.543   \n",
       "objectnet                                                     0.643   \n",
       "renderedsst2                                                  0.593   \n",
       "stl10                                                         0.989   \n",
       "sun397                                                        0.735   \n",
       "voc2007                                                       0.849   \n",
       "vtab/caltech101                                               0.939   \n",
       "vtab/cifar10                                                  0.967   \n",
       "vtab/cifar100                                                 0.833   \n",
       "vtab/clevr_closest_object_distance                            0.174   \n",
       "vtab/clevr_count_all                                          0.307   \n",
       "vtab/diabetic_retinopathy                                     0.234   \n",
       "vtab/dmlab                                                    0.182   \n",
       "vtab/dsprites_label_orientation                               0.022   \n",
       "vtab/dsprites_label_x_position                                0.032   \n",
       "vtab/dtd                                                      0.632   \n",
       "vtab/eurosat                                                  0.664   \n",
       "vtab/flowers                                                  0.746   \n",
       "vtab/kitti_closest_vehicle_distance                           0.308   \n",
       "vtab/pcam                                                     0.553   \n",
       "vtab/pets                                                     0.931   \n",
       "vtab/resisc45                                                 0.676   \n",
       "vtab/smallnorb_label_azimuth                                  0.057   \n",
       "vtab/smallnorb_label_elevation                                0.110   \n",
       "vtab/svhn                                                     0.487   \n",
       "\n",
       "model_fullname                       ViT-L-14-336 openai  \\\n",
       "dataset                                                    \n",
       "cars                                               0.793   \n",
       "country211                                         0.345   \n",
       "fer2013                                            0.491   \n",
       "fgvc_aircraft                                      0.332   \n",
       "gtsrb                                              0.447   \n",
       "imagenet-a                                         0.735   \n",
       "imagenet-r                                         0.878   \n",
       "imagenet1k                                         0.766   \n",
       "imagenet_sketch                                    0.610   \n",
       "imagenetv2                                         0.708   \n",
       "mnist                                              0.778   \n",
       "objectnet                                          0.701   \n",
       "renderedsst2                                       0.707   \n",
       "stl10                                              0.995   \n",
       "sun397                                             0.692   \n",
       "voc2007                                            0.863   \n",
       "vtab/caltech101                                    0.933   \n",
       "vtab/cifar10                                       0.950   \n",
       "vtab/cifar100                                      0.747   \n",
       "vtab/clevr_closest_object_distance                 0.181   \n",
       "vtab/clevr_count_all                               0.195   \n",
       "vtab/diabetic_retinopathy                          0.207   \n",
       "vtab/dmlab                                         0.171   \n",
       "vtab/dsprites_label_orientation                    0.025   \n",
       "vtab/dsprites_label_x_position                     0.032   \n",
       "vtab/dtd                                           0.556   \n",
       "vtab/eurosat                                       0.631   \n",
       "vtab/flowers                                       0.786   \n",
       "vtab/kitti_closest_vehicle_distance                0.374   \n",
       "vtab/pcam                                          0.613   \n",
       "vtab/pets                                          0.937   \n",
       "vtab/resisc45                                      0.646   \n",
       "vtab/smallnorb_label_azimuth                       0.046   \n",
       "vtab/smallnorb_label_elevation                     0.113   \n",
       "vtab/svhn                                          0.559   \n",
       "\n",
       "model_fullname                       ViT-g-14 openclip g_90.pt  \n",
       "dataset                                                         \n",
       "cars                                                     0.929  \n",
       "country211                                               0.288  \n",
       "fer2013                                                  0.481  \n",
       "fgvc_aircraft                                            0.378  \n",
       "gtsrb                                                    0.466  \n",
       "imagenet-a                                               0.564  \n",
       "imagenet-r                                               0.875  \n",
       "imagenet1k                                               0.767  \n",
       "imagenet_sketch                                          0.652  \n",
       "imagenetv2                                               0.696  \n",
       "mnist                                                    0.683  \n",
       "objectnet                                                0.665  \n",
       "renderedsst2                                             0.646  \n",
       "stl10                                                    0.986  \n",
       "sun397                                                   0.752  \n",
       "voc2007                                                  0.858  \n",
       "vtab/caltech101                                          0.944  \n",
       "vtab/cifar10                                             0.971  \n",
       "vtab/cifar100                                            0.839  \n",
       "vtab/clevr_closest_object_distance                       0.227  \n",
       "vtab/clevr_count_all                                     0.319  \n",
       "vtab/diabetic_retinopathy                                0.217  \n",
       "vtab/dmlab                                               0.173  \n",
       "vtab/dsprites_label_orientation                          0.030  \n",
       "vtab/dsprites_label_x_position                           0.036  \n",
       "vtab/dtd                                                 0.683  \n",
       "vtab/eurosat                                             0.645  \n",
       "vtab/flowers                                             0.781  \n",
       "vtab/kitti_closest_vehicle_distance                      0.182  \n",
       "vtab/pcam                                                0.551  \n",
       "vtab/pets                                                0.943  \n",
       "vtab/resisc45                                            0.726  \n",
       "vtab/smallnorb_label_azimuth                             0.060  \n",
       "vtab/smallnorb_label_elevation                           0.115  \n",
       "vtab/svhn                                                0.568  "
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metric = \"mean_per_class_recall\"\n",
    "df_metric = pd.pivot(df, index=\"model_fullname\", columns=\"dataset\", values=metric).T.dropna()\n",
    "df_metric"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f9eb124-635a-4076-ae90-0199a862202c",
   "metadata": {},
   "source": [
    "### Imagenet robustness results (acc1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "e1c11461-63a1-49e5-9199-d8be38ab8f09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>model_fullname</th>\n",
       "      <th>ViT-B-16 laion400m_e32</th>\n",
       "      <th>ViT-B-16 openai</th>\n",
       "      <th>ViT-B-16-plus-240 laion400m_e32</th>\n",
       "      <th>ViT-B-32 laion2b_e16</th>\n",
       "      <th>ViT-B-32 openai</th>\n",
       "      <th>ViT-B-32 openclip b_290.pt</th>\n",
       "      <th>ViT-B-32-quickgelu laion400m_e32</th>\n",
       "      <th>ViT-H-14 openclip h_256.pt</th>\n",
       "      <th>ViT-L-14 laion400m_e32</th>\n",
       "      <th>ViT-L-14 openai</th>\n",
       "      <th>ViT-L-14 openclip epoch_160.pt</th>\n",
       "      <th>ViT-L-14-336 openai</th>\n",
       "      <th>ViT-g-14 openclip g_90.pt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>imagenet-a</th>\n",
       "      <td>0.332</td>\n",
       "      <td>0.501</td>\n",
       "      <td>0.368</td>\n",
       "      <td>0.262</td>\n",
       "      <td>0.314</td>\n",
       "      <td>0.263</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.466</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.539</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imagenet-r</th>\n",
       "      <td>0.779</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.693</td>\n",
       "      <td>0.764</td>\n",
       "      <td>0.734</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imagenet1k</th>\n",
       "      <td>0.670</td>\n",
       "      <td>0.684</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.633</td>\n",
       "      <td>0.665</td>\n",
       "      <td>0.629</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.727</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.765</td>\n",
       "      <td>0.767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imagenet_sketch</th>\n",
       "      <td>0.523</td>\n",
       "      <td>0.481</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.529</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.633</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imagenetv2</th>\n",
       "      <td>0.596</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.560</td>\n",
       "      <td>0.582</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.656</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.677</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>objectnet</th>\n",
       "      <td>0.515</td>\n",
       "      <td>0.554</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.488</td>\n",
       "      <td>0.442</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.439</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.599</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "model_fullname   ViT-B-16 laion400m_e32  ViT-B-16 openai  \\\n",
       "dataset                                                    \n",
       "imagenet-a                        0.332            0.501   \n",
       "imagenet-r                        0.779            0.777   \n",
       "imagenet1k                        0.670            0.684   \n",
       "imagenet_sketch                   0.523            0.481   \n",
       "imagenetv2                        0.596            0.619   \n",
       "objectnet                         0.515            0.554   \n",
       "\n",
       "model_fullname   ViT-B-16-plus-240 laion400m_e32  ViT-B-32 laion2b_e16  \\\n",
       "dataset                                                                  \n",
       "imagenet-a                                 0.368                 0.262   \n",
       "imagenet-r                                 0.804                 0.759   \n",
       "imagenet1k                                 0.691                 0.655   \n",
       "imagenet_sketch                            0.544                 0.529   \n",
       "imagenetv2                                 0.614                 0.572   \n",
       "objectnet                                  0.538                 0.488   \n",
       "\n",
       "model_fullname   ViT-B-32 openai  ViT-B-32 openclip b_290.pt  \\\n",
       "dataset                                                        \n",
       "imagenet-a                 0.314                       0.263   \n",
       "imagenet-r                 0.693                       0.764   \n",
       "imagenet1k                 0.633                       0.665   \n",
       "imagenet_sketch            0.423                       0.536   \n",
       "imagenetv2                 0.560                       0.582   \n",
       "objectnet                  0.442                       0.490   \n",
       "\n",
       "model_fullname   ViT-B-32-quickgelu laion400m_e32  ViT-H-14 openclip h_256.pt  \\\n",
       "dataset                                                                         \n",
       "imagenet-a                                  0.217                       0.592   \n",
       "imagenet-r                                  0.734                       0.893   \n",
       "imagenet1k                                  0.629                       0.780   \n",
       "imagenet_sketch                             0.493                       0.666   \n",
       "imagenetv2                                  0.551                       0.708   \n",
       "objectnet                                   0.439                       0.697   \n",
       "\n",
       "model_fullname   ViT-L-14 laion400m_e32  ViT-L-14 openai  \\\n",
       "dataset                                                    \n",
       "imagenet-a                        0.466            0.707   \n",
       "imagenet-r                        0.848            0.879   \n",
       "imagenet1k                        0.727            0.755   \n",
       "imagenet_sketch                   0.596            0.596   \n",
       "imagenetv2                        0.656            0.697   \n",
       "objectnet                         0.599            0.691   \n",
       "\n",
       "model_fullname   ViT-L-14 openclip epoch_160.pt  ViT-L-14-336 openai  \\\n",
       "dataset                                                                \n",
       "imagenet-a                                0.539                0.774   \n",
       "imagenet-r                                0.874                0.891   \n",
       "imagenet1k                                0.752                0.765   \n",
       "imagenet_sketch                           0.633                0.611   \n",
       "imagenetv2                                0.677                0.707   \n",
       "objectnet                                 0.655                0.718   \n",
       "\n",
       "model_fullname   ViT-g-14 openclip g_90.pt  \n",
       "dataset                                     \n",
       "imagenet-a                           0.571  \n",
       "imagenet-r                           0.886  \n",
       "imagenet1k                           0.767  \n",
       "imagenet_sketch                      0.652  \n",
       "imagenetv2                           0.696  \n",
       "objectnet                            0.675  "
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Imagenet robustness results\n",
    "metric = \"acc1\"\n",
    "df_metric = pd.pivot(df, index=\"model_fullname\", columns=\"dataset\", values=metric).T.dropna()\n",
    "df_metric[(df_metric.index.str.startswith(\"imagenet\")) | (df_metric.index==\"objectnet\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36d7adac-b3b1-4421-af4f-f7d820592e11",
   "metadata": {},
   "source": [
    "# Robustness plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec10d993-7934-476b-94d7-8cde3fedc9ee",
   "metadata": {},
   "source": [
    "Here, following \"Measuring Robustness to Natural Distribution Shifts\n",
    "in Image Classification\" (https://arxiv.org/pdf/2007.00644.pdf, https://share.streamlit.io/modestyachts/imagenet-testbed-website/main/website.py),\n",
    "we show  the deviation from the line fit of (x=imagenet1k accuracy, y=imagenetv2/imagenet-1/imagenet_sketch) which was used\n",
    "to measure robustnest improvements separately from accuracy improvements in imagenet1k, as the two are correlated.\n",
    "\n",
    "In the plot below, deviation from the line are improvements in robustness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "id": "3fa79a28-b555-45d2-950d-efa415084004",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc3e3207e50>"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ggFkvZ//+XJPogHp2REREnM+NGzB9ujnhOCbGrI/TqxdMmAC+vo6OLssp2REREXEWhgHLl8OQIXDqlNnWoAHMnm0WCsylbB7GatKkCV988QU3btzIjHhEREQkPfbvh8ceMysdnzoFJUrAokWwcWOuTnQgHclOrVq1GDJkCH5+frz00kts2bIlM+ISERGRtLh0Cfr1g5o1Yd06cHc3l5EfPWpWQc6hWzzYk83JzsyZM/nnn3+YN28eFy5coHHjxlSpUoX33nuP8+fPZ0aMIiIicqdbt+D9980tHj780Fxa/vTTcOSIuZ+Vl5ejI8w20rUaK2/evHTo0IEffviBM2fO0LVrV0aPHk1gYCDt27dn7dq19o5TREREkqxZY/bkvPYaXLkC1avD2rXwzTdQurSjo8t2MrT0fNu2bYwdO5Zp06ZRvHhxRowYQdGiRXnyyScZMmSIvWIUERERgLAw6NABmjeHgwehSBGYOxd27YJHH3V0dNmWzdtFXLhwgS+//JJ58+YRGhpKmzZt6N27Ny1btsTyv3HBjRs30qpVK65du5YpQdtC20WIiEiOd+0aTJoE06ZBbKy5d9Wrr8K4cVC4sKOjyxQO3S6iZMmSlC1bll69etGzZ0+KFSuW4poaNWpQt27dDAUmIiKS6yUmmiuq3ngD/vnHbGveHGbOhKpVHRpaTmJzsrNmzRoaNWp0z2u8vb1Zt25duoMSERHJ9bZvN7d4SFr1XKaMWSiwbVutsLKRzXN2SpYsSWhoaIr20NBQTiUVMBIREZH0iYiAF16Ahx4yEx0vL3MI69AhaNdOiU462Jzs9OzZk7/++itF+9atW+nZs6c9YhIREcl9YmNh6lRzKfn8+WZb9+5w7BgMH27Wz5F0sTnZ2b17Nw0bNkzRXq9ePfbs2WOPmERERHIPw4Aff4Rq1cy5Odeumb06mzfDggUQEODoCHM8m5Mdi8XC1atXU7RHRUWRkJBgl6BERERyhUOHoFUrcx7O8ePg52cmOJs3Q716jo7Oadic7DRu3JhJkyYlS2wSEhKYNGkSjzzyiF2DExERcUpXrkBICNSoAb/9Bm5uZq/OsWPm0FWeDJXBkzvYvBprypQpNG7cmIoVK1pXZW3YsIHo6GhVThYREbmXhAT4739h1Cj491+zrW1bs35OuXKOjc2J2Zw6VqlShX379tGpUycuXLjA1atX6d69O0eOHKFatWqZEaOIiEjO98cf5u7jr7xiJjqVK8OqVfDDD0p0MpnNFZRzGlVQFhERh/r7bxg61Ny3CqBgQXjrLejbF1xdHRpadubQCspJYmJiCA8PJy4uLll7jRo1MhSQiIiIU4iJgSlTzOXkN2+a83D69DF3JE9l9wHJPDYnOxcvXuSFF17g119/TfW8VmSJiEiuZhiwdKnZm3P6tNnWpIm5xUPNmo6MLNeyec5OSEgIkZGRbN26FU9PT1auXMmCBQsoX748K1asyIwYRUREcobdu83E5tlnzUQnKMhMfNatU6LjQDb37Kxdu5YffviBOnXqkCdPHkqVKsXjjz+Ot7c3kyZNonXr1pkRp4iISPZ18SKMHGmutDIM8PSEESNgyBDzv8WhbO7ZuX79OsWLFwegUKFCXLx4EYDq1auza9cu+0YnIiKSncXHw4wZ5hYPn35qJjpdusDRozB6tBKdbMLmZKdixYocPXoUgAceeICPP/6Ys2fP8tFHH+Hv72/3AEVERLKlVavMooCDBkFUFNSqBRs2wKJFEBjo6OjkNjYPYw0YMIBz584BMHbsWFq1asXChQtxc3NjftLGZSIiIs4qNNRMcH76yTwuXhwmToSePcHFxaGhSeoyXGcnJiaGI0eOEBQURNGiRe0Vl92ozo6IiNhFdDSMH2+uqoqPh7x5YcAAc7jKx8fR0Tkde35/2zSMFR8fT9myZTl8+LC1LV++fDz44IPZMtERERHJsMREmDcPKlSAd981E50nnoADB+C995To5AA2DWO5urpy8+bNzIpFREQke9m8GV5/HXbsMI8rVDAnJP/nP46NS2xi8wTlfv36MWXKFG7dupUZ8YiIiDje2bPw/PPQoIGZ6BQoYPbi7N+vRCcHsnmC8vbt21mzZg2//fYb1atXx8vLK9n55cuX2y04ERGRLHXzprkD+cSJ5nYPFgv06gUTJoCvr6Ojk3SyOdkpWLAgHTt2zIxYREREHMMw4PvvYfBgCAsz2xo0gNmzzZ3KJUezOdmZN29eZsQhIiLiGAcOmKuq1q41j0uUMDfv7NLF7NmRHM/mOTsiIiJO4dIl6N8fHnjATHTc3WHUKLP6cdeuSnSciM09O8HBwVju8QY4efJkhgISERHJVLduwccfw5gxcPmy2dahgzkBOTjYsbFJprA52QkJCUl2HB8fz+7du1m5ciVDhw61V1wiIrleQmICG8I3cO7qOfwL+NMoqBEuedJfodfe98uR1q41h6wOHDCPq1eHWbPg0UcdG5dkqnRtF5GaDz74gB1JdQhERCRDlh9ezoCVAzgTfcbaVtK7JLNazaJD5Q4Ov1+OExZmTj7+7jvzuHBhsxrySy+ZlZDFqWV4u4gkJ0+epGbNmkRHR9vjdnaj7SJEJKdZfng5Ty99GoPkH88WzCkE33b61qYExd73y1GuXYNJk8zl5LGx5t5Vr74K48aZCY9kWw7bLuJevv32WwrrjSMikiEJiQkMWDkgRWICWNtCVoaQkJjgkPvlGIYBX30FFSuaNXNiY6F5c9i711xOru+rXMXmvrtatWolm6BsGAYRERFcvHiRDz/80K7BiYjkNhvCNyQbarqTgcHp6NNsCN9A09JNs/x+OcL27ea8nM2bzeMyZWD6dGjbViuscimbk5327dsnO86TJw/FihWjadOmVKpUyV5xiYjkSueunsvW12VrERHw5pvmpp0AXl7mUvKQEPDwcGho4lg2Jztjx47NjDhERATwL+Cfra/LlmJjzaGpd96Bq1fNtu7dzbk6AQGOjU2yBZvn7Pzyyy+sWrUqRfuqVav49ddfbbpX6dKlsVgsKX769esHwM2bN+nXrx9FihQhf/78dOzYkfPnz9sasohIjtEoqBElvUtaJw/fyYKFQO9AGgU1csj9shXDgJ9+gmrVYNgwM9F56CFz+GrBAiU6YmVzsjN8+HASElJOZDMMg+HDh9t0r+3bt3Pu3Dnrz+rVqwF45plnABg4cCA//vgj33zzDX/88Qf//PMPHTo46YoBEXFKCYkJrD+1nsX7F7P+1Pr7TgR2yePCrFazAFIkKEnHM1vNTHN9HHvfL9s4fBieeALatIHjx8HPz0xwNm+GevUcHZ1kMzYvPff09OTw4cOULl06WfupU6eoWrUq169fT3cwISEh/PTTT4SGhhIdHU2xYsVYtGgRTz/9NABHjhyhcuXKbN68mXppfDNr6bmIOEpGatuk9ruFPQsz4OEBjGw00ubkJLX7BXoHMrPVzJy17DwyEt56C95/36yE7OYGAwfCyJFQoICjoxM7cujScx8fn1S3hDh+/DheXl7pDiQuLo6vvvqKXr16YbFY2LlzJ/Hx8TRv3tx6TaVKlQgKCmJz0gz7VMTGxhIdHZ3sR0QkqyXVtrlzJdTZ6LM8vfRplh9efs/f71C5A6cGnOKtpm9R2NNcJn35xmXGrh9L6Vml7/v7d7vfuh7rWNRhEet6rCNsQFjOSXQSEuCTT6B8eZg500x02raFgwdh8mQlOnJPNic77dq1IyQkhBMnTljbjh8/zuDBg2nbtm26A/n++++JjIykZ8+eAERERODm5kbBggWTXefr60tERMRd7zNp0iR8fHysP4GBgemOSUQkPexV2+aHoz8wbv04Lt+4nKw9rQnTnVzyuNC0dFO6VO9C09JNc87Q1Z9/Qp068PLL8O+/ULkyrFoFP/wA5co5OjrJAWxOdqZOnYqXlxeVKlUiODiY4OBgKleuTJEiRXjvvffSHchnn33GE088QUAGJ5SNGDGCqKgo68/p06czdD8REVvZUtvmbnJtMcDbhYdD587QpAns2QMFC5r7WO3dCy1aODo6yUFsXnru4+PDX3/9xerVq9m7dy+enp7UqFGDxo0bpzuIv//+m99//53ly///Xyl+fn7ExcURGRmZrHfn/Pnz+Pn53fVe7u7uuLu7pzsWEZGMskdtm1xZDDBJTAy8+y5MmQI3bkCePNCnD7z9NhQr5ujoJAdK1+5nFouFFi1a0MJOmfW8efMoXrw4rVu3trbVrl0bV1dX1qxZQ8eOHQE4evQo4eHh1K9f3y6vKyKSGexR2yZXFQNMYhjwzTcwdKjZqwNmr87MmVCzpiMjkxzO5mGs119/ndmzZ6dof//99wkJCbE5gMTERObNm0ePHj3Ie9vOsz4+Prz44osMGjSIdevWsXPnTl544QXq16+f5pVYIiKOYI/aNrmiGODtdu82E5vOnc1EJygIli6FdeuU6EiG2ZzsLFu2jIYNG6Zob9CgAd9++63NAfz++++Eh4fTq1evFOdmzJjBk08+SceOHWncuDF+fn7JhrpERLIje9S2cepigLe7eNGceFy7NmzYAJ6e5tLyI0fgmWe0l5XYhc11djw8PDhw4ADl7pgBf/z4capVq8bNmzftGmBGqc6OiDhKRmvbJC1fB5JNVE5KgL7t9G3OWTp+p/h4+OADGDcOoqLMtmefhalTQatoBft+f9s8Z6dcuXKsXLmS/v37J2v/9ddfKVOmTIaCERFxJh0qd6BdxXZsCN/Auavn8C/gT6OgRmle8t2hcge+7fRtqoUJc1wxwNutWmVuznnkiHlcq5a5yqpRDu+lkmzL5mRn0KBB9O/fn4sXL9KsWTMA1qxZw7Rp05g5c6a94xMRydGSatukV0YTpmwlNBQGDTL3swJzZdXEifDCC+CSA/8eyTFsHsYCmDt3LhMmTOCff/4BzA09x40bR/fu3e0eYEZpGEtExMGio2H8eHNVVXw85M0Lr78Oo0ebtXNEUmHP7+90JTtJLl68iKenJ/nz589QEJlJyY6IiIMkJpqbc44YAefPm22tWsGMGVCpkmNjk2zPoXN2bldMxZ1ERCQ1mzebvTc7dpjH5cubSc5//qMVVpLl0pXsfPvttyxdupTw8HDi4uKSndu1a5ddAhMRyYkSEhOcY35Nep09C8OHw1dfmccFCsCYMWbi4+bm2Ngk17K5zs7s2bN54YUX8PX1Zffu3Tz00EMUKVKEkydP8sQTT2RGjCIiOcLyw8spPas0jy54lK7Lu/LogkfTtUN5jnTzJkyYABUqmImOxQK9esGxYzBkiBIdcSibk50PP/yQTz75hDlz5uDm5sawYcNYvXo1r7/+OlFJtRJERHKZpJo4d+5nld4dynMMw4DvvoMqVWDUKHNfqwYNYNs2+OwzuMdehiJZxeZkJzw8nAYNGgDg6enJ1atXAXj++edZvHixfaMTEckBcu0O5QcOQPPm0KEDhIVBiRKwcCFs3Ah16jg6OhErm5MdPz8/Ll++DEBQUBBbtmwBICwsjAws7BIRybFs2aHcKVy+DP37wwMPwNq14O4OI0eaRQK7dtUEZMl2bE52mjVrxooVKwB44YUXGDhwII8//jidO3fmqaeesnuAIiLZXa7ZofzWLXOLh/Llzf+bmAgdO8Lhw2YdnWxchkRyN5tXY33yySckJiYC0K9fP4oUKcJff/1F27Ztefnll+0eoIhIdpcrdihfuxYGDDCHrgCqVTO3ePhfJX2R7CxDRQVzAhUVFJHMlpCYQOlZpTkbfTbVeTsWLJT0LknYgLCctww9LMxcTbX8fxOsCxeGd96BPn3MSsgimcSe3982D2OJiEhyLnlcmNVqFvD/O5InSTqe2Wpmzkp0rl0zV1dVrmwmOi4u5jyd0FB49VUlOpKjKNkREbGDpB3KS3iXSNZe0rsk33b6NufsUG4Y5oqqihXNujmxsfDYY7BnD8yZY/bsiOQwGsYSEbGjHF1BeccOs9Lx5s3mcXAwTJ8O7dpphZVkuWyzN5aIiCTnkseFpqWbOjoM20REwJtvwrx55rGXl7mUfOBA8PBwbGwidqBkR0Qkt4qNhdmzzQnH/ysQy/PPw6RJZoFAESdhtzk7hw8fpkyZMva6nYiIZBbDgB9/NJePDxtmJjp168Jff8EXXyjREadjt2QnLi6Ov//+2163ExGRzHD4MDzxBLRtC8ePg6+vOXy1ZQvUr+/o6EQyRZqHsQYNGnTP8xcvXsxwMCIikkkiI+Gtt+D9981KyK6u5pyckSNBizfEyaU52Zk1axY1a9a864zoa9eu2S0oERFb5OgVUJktIcHcfXzkSPj3X7OtbVt47z1z2weRXCDNyU65cuUYOHAgzz33XKrn9+zZQ+3ate0WmIhIWiw/vJwBKwck24izpHdJZrWalXNq22SWP/80t3jYs8c8rlwZZsyAli0dGpZIVkvznJ06deqwc+fOu563WCza9VxEstTyw8t5eunTKXYcPxt9lqeXPs3yw8sdFJmDhYdD587QpImZ6Pj4wMyZsHevEh3JldJcVDAiIoLY2FhKlSqV2THZlYoKijinpP2o7kx0kuTo/ajSKyYG3n0XpkyBGzfMQoB9+phLy4sVc3R0IjZxSFFBPz+/DL2QiIg9bQjfcNdEB8DA4HT0aTaEb8h5Rf5sZRjwzTcwdKjZqwPQuLG5K3nNmg4NTSQ7yNDS81dffZV/kya8iYhkoXNXz9n1uhxr925zuKpzZzPRCQyEr7+G9euV6Ij8T4aSna+++oro6Gh7xSIikmb+Bfztel2Oc/EivPwy1K4NGzaAp6e5tPzIEejUSXtZidwmQ8mOJiSLiKM0CmpESe+SWEj9S92ChUDvQBoFNcriyDJZfLw52bh8efjkE3MIq3NnM8kZMwby5XN0hCLZjt0qKIuIZCWXPC7MajULIEXCk3Q8s9VM55qc/Ntv8MADZjHAqCioVctcXr5kCQQFOTo6kWwrQ8nO1atXtR+WiDhMh8od+LbTt5TwTr6XU0nvknzb6VvnqbMTGmoWAmzZ0tzuoWhRs1dn+3Zo5GQ9VyKZIM1Lz2+XkJDAd999x+HDhwGoXLky7du3J2/e7LeJupaeizg/p62gHB0NEyaYhQDj4yFvXujfH8aOhYIFHR2dSKay5/e3zcnOwYMHadu2LREREVSsWBGAY8eOUaxYMX788UeqVauWoYDsTcmOiOQ4iYmwYAGMGAHnz5ttrVqZSU+lSo6NTSSL2PP72+ZhrN69e1O1alXOnDnDrl272LVrF6dPn6ZGjRr06dMnQ8GIiOR6mzfDww9Dr15molO+PPz0E/zyixIdkXSyedxpz5497Nixg0KFClnbChUqxIQJE6hbt65dgxMRyTXOnoXhw+Grr8zjAgXM1VWvvw5ubo6NTSSHs7lnp0KFCpxP6la9zYULFyhXrpxdghIRyTVu3oSJE6FiRTPRsVjMXp1jx2DIECU6InZgc8/OpEmTeP311xk3bhz16tUDYMuWLbz99ttMmTIlWZFBzZEREbkLw4Dvv4fBgyEszGyrXx9mz4Y6dRwamoizsXmCcp48/98ZZPlfhc6kW9x+bLFYSEhIsFec6aYJyiKS7Rw4AAMGwNq15nFAAEydCl27qvKxyP84ZCPQJOvWrcvQC4qI5FqXLpnLxufONVdcububQ1XDh0P+/I6OTsRp2ZzsNGnSJDPiEBFxXrduwccfmxOOL1822zp0gPfeg+Bgx8YmkgukqwpgZGQkn332mbWoYNWqVenVqxc+Pj52DU5EJMdbu9YcsjpwwDyuVg1mzYJmzRwbl0guYvNqrB07dlC2bFlmzJjB5cuXuXz5MtOnT6ds2bLs2rUrM2IUEcl5wsKgY0d47DEz0SlcGD74AHbvVqIjksVsnqDcqFEjypUrx6effmrdHuLWrVv07t2bkydP8ueff2ZKoOmlCcoikqWuXYNJk2DaNIiNBRcX6NsX3nrLTHhEJE0cul2Ep6cnu3fvptIdlTwPHTpEnTp1iImJyVBA9qZkR0SyhGHAokUwbBj884/Z9thjMHOmOXQlIjZx6HYR3t7ehIeHp2g/ffo0BQoUyFAwIiI50o4d0LAhPPecmegEB8N338Hq1Up0RLIBm5Odzp078+KLL/L1119z+vRpTp8+zZIlS+jduzddunTJjBhFRLKniAiz2vFDD5l7Wnl5mdWQDx2C9u1VM0ckm7B5NdZ7772HxWKhe/fu3Lp1CwBXV1f69u3L5MmT7R6giEi2ExdnVjp++224etVse/55c65OiRKOjU1EUrB5zk6SmJgYTpw4AUDZsmXJly+fXQOzF83ZERG7MQz4+WcYNAhCQ822unXNxOd/2+eIiH04dM5Or169uHr1Kvny5aN69epUr16dfPnycf36dXr16pWhYEQkYxISE1h/aj2L9y9m/an1JCQ6fssWp3HkCPznP9CmjZno+PrCvHmwZYsSHZFszuaeHRcXF86dO0fx4sWTtf/777/4+flZh7ayC/XsSG6x/PByBqwcwJnoM9a2kt4lmdVqFh0qd3BgZDlcZKQ5XDVnjlkJ2c0NBg6EN98EfaaIZBqH7I0VHR2NYRgYhsHVq1fx8PCwnktISOCXX35JkQCJSNZYfng5Ty99GoPk/3Y5G32Wp5c+zbedvlXCY6uEBPj8cxg5Ei5eNNvatjXr55Qr59jYRMQmaU52ChYsiMViwWKxUKFChRTnLRYLb731ll2DE5H7S0hMYMDKASkSHQADAwsWQlaG0K5iO1zyuDggwhxowwZzi4fdu83jypVhxgxo2dKxcYlIuqQ52Vm3bh2GYdCsWTOWLVtG4dsqgbq5uVGqVCkCAgIyJUgRubsN4RuSDV3dycDgdPRpNoRvoGnpptb2hMQENoRv4NzVc/gX8KdRUCMlQ+Hh8MYbsGSJeezjY1Y+fvVVcHV1bGwikm5pTnaSdjsPCwsjKCgIi+pHiGQL566es/k6ze+5Q0wMvPsuTJkCN26Y9XH69IF33oFixRwdnYhkkM2rsUqVKqVERyQb8S/gb9N1SfN77uwNSprfs/zwcrvHmG0ZBnzzjTlMNW6cmeg0bgy7dsFHHynREXESNic7IpK9NApqREnvklhI/R8hFiwEegfSKKjRfef3AISsDMkdS9b37IGmTaFTJ3P4KjAQvv4a1q+HmjUdG5uI2JWSHZEcziWPC7NazQJIkfAkHc9sNROXPC42ze9xWhcvwiuvQO3a8Oef4OkJY8eadXQ6ddIWDyJOSMmOiBPoULkD33b6lhLeybcqKOldMtmy8/TM73Ea8fEwaxZUqAAffwyJidC5s5nkjBsH2bQKvIhknM17Y4lI9tShcgfaVWx3zxVWts7vcRq//QYhIXD4sHlcs6a5xUOjRo6MSkSyiE3Jzi+//MLy5cspXLgwvXr1olKlStZzV65coWPHjqxdu9buQYpI2rjkcUm2vPxOSfN7zkafTXXejgULJb1L0ijISZKA48fNfax+/NE8LlrU3JW8Vy9wyeXL7EVykTQPYy1atIi2bdsSERHB5s2bqVWrFgsXLrSej4uL448//siUIEXEPmyZ35OjXb1q1supUsVMdPLmNbd4CA2Fl15SoiOSy6Q52Xn33XeZPn06P/30Exs2bGDBggW8/PLLfPbZZ5kZn4jYWVrn9+RIiYkwf745L2fqVHOeTqtWsH8/TJ8OBQs6OkIRcYA0D2OFhobSpk0b63GnTp0oVqwYbdu2JT4+nqeeeipTAhQR+0vL/J4cZ8sWeP112L7dPC5f3tzi4T//0QorkVwuzT073t7enD9/Plnbo48+yk8//cTQoUOZM2dOugI4e/Yszz33HEWKFMHT05Pq1auzY8cO63nDMBgzZgz+/v54enrSvHlzQkND0/VaIvL/kub3dKnehaalm+bcROeff6B7d6hf30x0ChQwqyEfOACtWyvREZG0JzsPPfQQv/76a4r2Jk2a8OOPPzJz5kybX/zKlSs0bNgQV1dXfv31Vw4dOsS0adMoVKiQ9ZqpU6cye/ZsPvroI7Zu3YqXlxctW7bk5s2bNr+eiDiRmzfNycYVKsCXX5pJzQsvwLFjMGQIuLk5OkIRySbSPIw1cOBA/vrrr1TPNW3alB9//JEvvvjCphefMmUKgYGBzJs3z9oWHBxs/W/DMJg5cyajRo2iXbt2AHzxxRf4+vry/fff8+yzz9r0eiLZkTbktJFhwA8/wODBcPKk2daggVlDp04dx8YmItmSxTCMlOtPs0iVKlVo2bIlZ86c4Y8//qBEiRK8+uqrvPTSSwCcPHmSsmXLsnv3bmreVr69SZMm1KxZk1mzZqW4Z2xsLLGxsdbj6OhoAgMDiYqKwtvbO9P/JhFbaENOGx04YNbLWbPGPC5RwpyI3KWLhqtEnEx0dDQ+Pj52+f62uYJy8+bNmT9/PtHR0Rl6YTCTmblz51K+fHlWrVpF3759ef3111mwYAEAERERAPj6+ib7PV9fX+u5O02aNAkfHx/rT2BgYIbjFMkM2pDTBpcvw2uvmcUA16wBd3cYOdKsfty1qxIdEbknm5OdqlWrMmLECPz8/HjmmWf44YcfiI+PT9eLJyYm8uCDDzJx4kRq1apFnz59eOmll/joo4/SdT+AESNGEBUVZf05ffp0uu8lklm0IWca3boFH35orqx6/31ISIAOHcxKyOPHQ/78jo5QRHIAm5OdWbNmcfbsWb7//nu8vLzo3r07vr6+9OnTx+aigv7+/lSpUiVZW+XKlQkPDwfAz88PIMUqsPPnz1vP3cnd3R1vb+9kPyLZjTbkTIN16+DBB6FfP7Nnp1o1s1dn2TK4bW6fiMj9pGsj0Dx58tCiRQvmz5/P+fPn+fjjj9m2bRvNmjWz6T4NGzbk6NGjydqOHTtGqVKlAHOysp+fH2uSxucxx/C2bt1K/fr10xO6SLaQqzfkvJ+wMOjYEZo1M4sBFipk9urs3m22iYjYKEMbgUZERLBkyRK++uor9u3bx0MPPWTT7w8cOJAGDRowceJEOnXqxLZt2/jkk0/45JNPALBYLISEhDB+/HjKly9PcHAwo0ePJiAggPbt22ckdBGHyrUbct7L9eswebJZIyc21tzSoW9fc0fyIkUcHZ2I5GA2JzvR0dEsW7aMRYsWsX79esqUKUO3bt34+uuvKVu2rE33qlu3Lt999x0jRozg7bffJjg4mJkzZ9KtWzfrNcOGDeP69ev06dOHyMhIHnnkEVauXImHh4etoYtkG7luQ857MQxYvBiGDYOzZ822Zs3MpeTVqjk2NhFxCjYvPff09KRQoUJ07tyZbt26USeb17Ww59I1EXtKWo0FJEt4kjbkzPH7VKXFjh0wYAAk1fAKDoZp06B9e62wEsnl7Pn9bXPPzooVK3jsscfIkydd031E5H+SNuRMrc7OzFYznTvRiYgwl47Pm2f27Hh5wZtvwqBBoF5bEbEzm3t2mjVrxvLlyyl4x+7B0dHRtG/fnrVr19ozvgxTz45kB/eqkpyrKijHxcHs2fD223D1qtn23HPmXJ0SJe79uyKSqzi0Z+ePP/4gLi4uRfvNmzfZsCEXL5MVuYv7VUlO2pDT6f38MwwcCEkb+dapYyY+WlkpIpkszcnOvn37AHO/qkOHDiWrYJyQkMDKlSspoX+ZiSSTNC/nzknISVWSc8W8nCNHzOGppI2EfX1h0iTo0QM0HC4iWSDNyU7NmjWxWCxYLJZU6+l4enoyZ84cuwYnkpPdr0qyBQshK0NoV7Gdcw5bRUaaw1Vz5piVkF1dzZ6dkSNBQ8oikoXSnOyEhYVhGAZlypRh27ZtFCtWzHrOzc2N4sWL4+LihB/YIulkS5VkpxrGSkiAzz83k5qLF822J5+E6dPNbR9ERLJYmpOdpKrGiYmJmRaMiDPJlVWSN2wwl5Lv3m0eV6oEM2ZAq1aOjUtEcrV0DZh/+eWXNGzYkICAAP7++28AZsyYwQ8//GDX4ERyslxVJfn0aejSBRo3NhMdHx8zydm3T4mOiDiczcnO3LlzGTRoEP/5z3+IjIwkIcHclblQoULMnDnT3vGJ5FhJVZKTigTeyYKFQO/AnF0lOSbGnJdTsSIsWWIWAuzTx1xxFRJiztMREXEwm5OdOXPm8OmnnzJy5Mhkc3Tq1KnD/v377RqcSHaTkJjA+lPrWbx/MetPrSchMeGu17rkcWFWq1kAKRKepOOZrWbmzMnJhgFLl0LlyjB2LNy4AY0awc6d8PHHcNucPhERR7O5zk5YWBi1atVK0e7u7s7169ftEpRIdnS/ejmpccoqyXv3mvNy/vjDPA4MhPfeg2ee0RYPIpIt2ZzsBAcHs2fPHuuE5SQrV66kcuXKdgtMJDvJSL2cDpU70K5iu5xfJfnff2HUKPj0U0hMNLd1eOMNcwPPfPkcHZ2IyF3ZnOwMGjSIfv36cfPmTQzDYNu2bSxevJhJkybx3//+NzNiFHEoe9TLydFVkuPj4cMPYdw4s3YOQOfOMHUqBAU5MjIRkTSxOdnp3bs3np6ejBo1ipiYGLp27UpAQACzZs3i2WefzYwYRRwq19bLAfjtN3Oi8eHD5nHNmjBrlrnqSkQkh7A52QHo1q0b3bp1IyYmhmvXrlG8eHF7xyWSbeTKejnHj8PgwbBihXlctChMmAAvvggqHioiOUy6kp0k+fLlI5/G6sXJ5ap6OVevwvjxZo2c+HjImxf69zdXXBUs6OjoRETSxeal5+fPn+f5558nICCAvHnz4uLikuxHxNnkino5iYmwYAFUqGDOxYmPh5YtzaKAM2Yo0RGRHM3mnp2ePXsSHh7O6NGj8ff3x6KlpuLkkurlPL30aSxYkk1UzvH1cgC2bDGXkm/bZh6XK2cmOK1baym5iDgFi2EYKZeY3EOBAgXYsGEDNWvWzKSQ7Cs6OhofHx+ioqLw1k7LTiEhMcEhy7hTq7MT6B2Yc+vl/PMPDB8OX35pHhcoAKNHw+uvg7u7Y2MTkVzPnt/fNvfsBAYGYmN+JGI36SnsZy9OUy/n5k2z52bCBEgqBPrCCzBxIvj5OTY2EZFMYHPPzm+//ca0adP4+OOPKV26dCaFZT/q2XEedyvslzSUdK/CfoK5xcMPP5irrE6eNNvq1YPZs6FuXcfGJiJyB3t+f9uc7BQqVIiYmBhu3bpFvnz5cL1jo7/Lly9nKCB7U7LjHBISEyg9q/Rd691YsFDSuyRhA8JyXk9LVjh40JyXs2aNeRwQAFOmQLdumpcjItmSQ4extLO5OEKuLuyXEZcvm8vG586FhARzLs7gwTBiBOTP7+joRESyhM3JTo8ePTIjDpF7ypWF/TLi1i345BNzwnFSb+tTT5kbdpYp49jYRESyWLqKCp44cYJ58+Zx4sQJZs2aRfHixfn1118JCgqiatWq9o5RcqnbV12dv34+Tb/jFIX9MmrdOnPIav9+87haNZg5Ex57zKFhiYg4is1FBf/44w+qV6/O1q1bWb58OdeuXQNg7969jB071u4BSu60/PBySs8qzaMLHqXr8q4MXDUQF8vd5+I4RWG/jAoLg6efhmbNzESnUCF4/33YvVuJjojkajYnO8OHD2f8+PGsXr0aNzc3a3uzZs3YsmWLXYOT3Clp1dWdc3QSjIRUr3eKwn4Zcf26OVxVuTIsWwZ58kC/fhAaav7fvBnaFUZEJMez+VNw//79LFq0KEV78eLF+ffff+0SlOReCYkJDFg5IMXy8tu5WFySJT4lvUvm3MJ+GWEYsHgxDBsGZ8+abc2amUNW1as7NDQRkezE5mSnYMGCnDt3juDg4GTtu3fvpkSJEnYLTHKn+626ArOHZ0bLGfh6+ebcwn4ZtXOnWen4r7/M4+BgmDYN2rfXUnIRkTvYPIz17LPP8sYbbxAREYHFYiExMZFNmzYxZMgQunfvnhkxSi6S1tVUvl6+dKnehaalm+auROf8eXjxRbMI4F9/Qb58ZiXkQ4fM1VZKdEREUrA52Zk4cSKVKlUiMDCQa9euUaVKFRo3bkyDBg0YNWpUZsQouUhaV1PlulVXcXFmz02FCvD55+YQVrducOwYvPkmeHg4OkIRkWzL5grKScLDwzlw4ADXrl2jVq1alC9f3t6x2YUqKOcsSZWSz0afTXXeTq6slPzzzzBwoDnhGKBOHZg1Cxo0cGxcIiKZyKEVlJMEBQURFBSUoRcXuZNLHhdmtZrF00ufxoIlWcKT61ZdHTkCgwbBr7+ax76+MGkS9OhhrrgSEZE0sTnZGTRoUKrtFosFDw8PypUrR7t27ShcuHCGg5PcqUPlDnzb6dtUdzfPFauuoqLg7bfNDTpv3QJXVwgJgVGjQL2TIiI2s3kY69FHH2XXrl0kJCRQsWJFAI4dO4aLiwuVKlXi6NGjWCwWNm7cSJUqVTIlaFtoGCvnur2Ccq5YdZWQAPPmmXNwLl402558EqZPh2w6TCwiklkcOoyV1Gszb94864tHRUXRu3dvHnnkEV566SW6du3KwIEDWbVqVYaCk9zNJY9L7tnUc+NGcyn57t3mcaVKMGMGtGrl2LhERJyAzT07JUqUYPXq1Sl6bQ4ePEiLFi04e/Ysu3btokWLFtmiyKB6diRbO33aLAq4ZIl57OMD48aZlY9dXR0amoiII9nz+9vmWY5RUVFcuHAhRfvFixeJjo4GzMKDcXFxGQpMxKnduGHOy6lY0Ux0LBbo08dcSh4SokRHRMSO0jWM1atXL6ZNm0bdunUB2L59O0OGDKF9+/YAbNu2jQoVKtg1UBGnYBjw7bcwZAiEh5ttjRqZS8lr1XJsbCIiTsrmYaxr164xcOBAvvjiC27dugVA3rx56dGjBzNmzMDLy4s9e/YAULNmTXvHazMNY0m2sXcvDBgAf/xhHgcGwrvvQqdOqnwsInIHe35/p7uo4LVr1zh58iQAZcqUIX/+/BkKJLMo2RGH+/dfc9n4p59CYqJZ7XjYMHjjDXO7BxERSSFbFBXMnz8/NWrUyNCLizi1+Hj48ENzwnFkpNnWqZPZm6OCnCIiWSZdyc6OHTtYunQp4eHhKSYiL1++3C6BieRov/1mTjQ+fNg8rlnTnJfTuLEjoxIRyZVsXo21ZMkSGjRowOHDh/nuu++Ij4/n4MGDrF27Fh8fn8yIUSTnOH4c2rWDli3NRKdoUfj4Y9ixQ4mOiIiDpGvX8xkzZvDjjz/i5ubGrFmzOHLkCJ06ddJeWZJ7Xb0Kw4dD1aqwYgXkzWv27Bw7Zi4pd3Hiys8iItmczcnOiRMnaN26NQBubm5cv34di8XCwIED+eSTT+weoEi2lpgICxZAhQowZQrExUGLFrBvn1kBuVAhR0coIpLr2ZzsFCpUiKtXrwJmNeUDBw4AEBkZSUxMjH2jE8nOtm6F+vWhZ0+IiIBy5eDHH2HlSqhc2dHRiYjI/9g8Qblx48asXr2a6tWr88wzzzBgwADWrl3L6tWreeyxxzIjRpHs5Z9/zCGrL780jwsUgNGjzb2t3N0dG5uIiKRgc7Lz/vvvc/PmTQBGjhyJq6srf/31Fx07dmTUqFF2D1Ak27h50xyamjABrl8323r2hEmTwM/PoaGJiMjdpbuoYE6hooKSYYYBP/wAgwfD/wppUq8ezJ4N/9syRURE7CtbFBW8cOECFy5cIDExMVm7Cg2KUzl40FxV9fvv5nFAgDkRuWtXyGPzlDcREXEAm5OdnTt30qNHDw4fPsydnUIWi4WEhAS7BSfiMJcvw9ixMHcuJCSYc3EGD4YRIyCbbo0iIiKpsznZ6dWrFxUqVOCzzz7D19cXizYwFGdy65a5h9Xo0XDpktn21FPw3ntQpoxjYxMRkXSxOdk5efIky5Yto1y5cpkRj4jjrFtnDlnt22ceV6sGM2eCVhmKiORoNk86eOyxx9i7d29mxCLiGKdOwdNPQ7NmZqJTqBC8/z7s3q1ER0TECdjcs/Pf//6XHj16cODAAapVq4arq2uy823btrVbcCKZ6vp1mDzZ3IU8NtaccNy3L7z1FhQp4ujoRETETmxOdjZv3symTZv49ddfU5zTBGXJEQwDFi+GYcPg7Fmz7dFHzV3Jq1d3bGwiImJ3Ng9jvfbaazz33HOcO3eOxMTEZD9KdCTb27kTHnkEunUzE53SpWHZMlizRomOiIiTsjnZuXTpEgMHDsTX1zcz4hHJHOfPQ+/eZhHAv/6CfPlg/Hg4fBg6dACtKhQRcVo2JzsdOnRg3bp1mRGLiP3FxZnLxsuXh88+M4ewunWDY8dg5Ejw8HB0hCIikslsnrNToUIFRowYwcaNG6levXqKCcqvv/663YITyZCff4aBAyE01DyuU8ecl9OggWPjEhGRLGXz3ljBwcF3v5nFwsmkvYOyCe2NlQsdPWomOUmT6H19zc06e/TQFg8iIjmEQ/fGCgsLy9ALimSaqCh4+21zg85bt8DV1SwSOGoUKNEVEcm1HPrP3HHjxmGxWJL9VKpUyXr+5s2b9OvXjyJFipA/f346duzI+fPnHRixZEsJCfDf/5rzcqZPNxOdJ5+EAwdg6lQlOiIiuVyaenYGDRrEO++8g5eXF4MGDbrntdOnT7cpgKpVq/J70o7SQN68/x/SwIED+fnnn/nmm2/w8fGhf//+dOjQgU2bNtn0GuLENm6EAQNg1y7zuFIlmDEDWrVybFwiIpJtpCnZ2b17N/Hx8db/vpv0bAqaN29e/Pz8UrRHRUXx2WefsWjRIpo1awbAvHnzqFy5Mlu2bKFevXo2v5Y4kdOn4Y03zOKAAD4+5i7l/fubw1ciIiL/k6Zk5/al5vZedh4aGkpAQAAeHh7Ur1+fSZMmERQUxM6dO4mPj6d58+bWaytVqkRQUBCbN2++a7ITGxtLbGys9Tg6Otqu8YqD3bhhLiWfPBliYsz6OL17mzVzihd3dHQiIpINOXTOzsMPP8z8+fNZuXIlc+fOJSwsjEaNGnH16lUiIiJwc3OjYMGCyX7H19eXiIiIu95z0qRJ+Pj4WH8CAwMz+a+QLGEY8O23ULkyjBljJjqNGpkVkT/5RImOiIjclc2rsezpiSeesP53jRo1ePjhhylVqhRLly7F09MzXfccMWJEsnlF0dHRSnhyun37zHk569ebx4GB5uadnTqp8rGIiNxXtio6UrBgQSpUqMDx48fx8/MjLi6OyMjIZNecP38+1Tk+Sdzd3fH29k72IznUv/+au5DXqmUmOh4e5rycI0egc2clOiIikibZKtm5du0aJ06cwN/fn9q1a+Pq6sqaNWus548ePUp4eDj169d3YJSS6eLjYc4ccyn5Rx9BYqLZi3PkCIwbZ+5rJSIikkYOHcYaMmQIbdq0oVSpUvzzzz+MHTsWFxcXunTpgo+PDy+++CKDBg2icOHCeHt789prr1G/fn2txHJmv/9uDlkdOmQeP/CAWSSwcWPHxiUiIjmWQ5OdM2fO0KVLFy5dukSxYsV45JFH2LJlC8WKFQNgxowZ5MmTh44dOxIbG0vLli358MMPHRmyZJYTJ2DwYPjhB/O4SBGYMMFcaeXi4tjYREQkR7N5b6ycRntjZXNXr8LEiWbl47g4yJsX+vUz5+YUKuTo6ERExEEcujeWiF0kJsJXX8Hw4XDunNnWogXMnGkuLxcREbETJTuS9bZuNeflbN1qHpcta27x8OSTWmElIiJ2l61WY4mTO3cOevSAevXMRCd/fpgyBQ4ehDZtlOiIiEimUM+OZL6bN83hqQkT4No1s61nT5g0Ce5RM0lERMQelOxI5jEMWLECBg2CkyfNtnr1zKXkdes6NjYREck1NIwlmePgQWjZEtq3NxOdgAD48kvYtEmJjoiIZCklO2JfV67A66+bxQBXrwY3N3jzTTh6FJ57DvLoLSciIllLw1hiH7duwaefwujRcOmS2da+PUybBmXKODQ0ERHJ3ZTsSMatX28uJd+3zzyuWhVmzYLHHnNoWCIiIqBhLMmIU6fgmWfg0UfNRKdQIXMDzz17lOiIiEi2oZ4dsd3162Z9nHffNZeV58kDffvCW2+Ze1qJiIhkI0p2JO0MAxYvhmHD4OxZs+3RR80hq+rVHRubiIjIXSjZkbTZudOcl7Npk3lcurQ5+fipp1T5WEREsjXN2ZF7u3ABevc2a+Ns2gT58sH48XDoEHTooERHRESyPfXsSOri4szJxm+/DdHRZlu3buZcnRIlHBubiIiIDZTsSEq//AIDB8KxY+Zx7drmFg8NGjg2LhERkXTQMJb8v6NHoXVr8+fYMSheHD77DLZtU6IjIiI5lpIdgagoGDwYqlUze3VcXWHIEAgNhV69tMWDiIjkaBrGys0SEmD+fHPvqgsXzLbWrWH6dKhQwaGhiYiI2IuSndxq40ZzKfmuXeZxxYowYwY88YRj4xIREbEzjU/kNqdPQ9eu0KiRmeh4e5v1cvbtU6IjIiJOST07ucWNG/DeezB5MsTEmPVxevc2a+YUL+7o6ERERDKNkh1nZxiwbJk54fjvv822Rx4xt3h48EHHxiYiIpIFlOw4s337zHk569ebxyVLmpt3du6sysciIpJraM6OM/r3X3MX8lq1zETHwwNGj4YjR+DZZ5XoiIhIrqKeHWcSHw9z58LYsRAZabY98wxMnWpu3CkiIpILKdlxFqtXQ0iIuUEnwAMPmPNymjRxaFgiIiKOpmGsnO7ECWjfHlq0MBOdIkXgo49g504lOiIiIqhnJ+e6ehUmTjSrHcfFgYsL9O9vDmEVKuTo6ERERLINJTs5TWIifPUVDB8O586ZbY8/DjNnQpUqDg1NREQkO1Kyk5Ns3WouJd+61TwuW9bs2WnTRiusRERE7kJzdnKCc+egRw+oV89MdPLnhylT4OBBaNtWiY6IiMg9qGcnO4uNNTfnnDABrl0z23r0gEmTwN/fsbGJiIjkEEp2siPDgBUrYPBgc7UVwMMPw+zZ8NBDjo1NREQkh9EwVnZz6BC0bGkuJz9xwuzB+eIL+OsvJToiIiLpoGQnu7hyxZx8XKOGWSDQzQ1GjIBjx+D55yGP/qcSERFJDw1jOVpCAnzyibl31aVLZlv79jBtGpQp49DQREREnIGSHUdav97szdm3zzyuWtWsl9O8uSOjEhERcSoaG3GEU6fMDToffdRMdAoVgjlzYM8eJToiIiJ2pp6drHT9ulkf59134eZNcx7OK6/AW29B0aKOjk5ERMQpKdnJCoYBS5bAsGFw5ozZ1rSpuSt5jRoODU1ERMTZKdnJbDt3mvNyNm0yj0uVMicfd+igysciIiJZQHN2MsuFC9C7N9StayY6+fLBO+/A4cPQsaMSHRERkSyinh17i4szJxu//TZER5ttXbuac3VKlnRsbCIiIrmQkh17+uUXGDjQLAQIULu2OS+nYUPHxiUiIpKLaRjLHo4ehdatzZ9jx6B4cfjsM9i2TYmOiIiIgynZyYioKBgyBKpVM3t1XF3N42PHoFcvbfEgIiKSDWgYKz0SEmD+fHjzTXMiMpi9OtOnQ4UKDg1NREREklOyY6tNm+D112HXLvO4YkWYMQOeeMKxcYmIiEiqNM6SVmfOmKuqHnnETHS8vc16Ofv2KdERERHJxtSzcz83bsB778HkyRATY9bH6d0bxo83JyKLiIhItqZk524MA5YtMycc//232fbII+ZS8gcfdGxsIiIikmZKdlKzb5+5xcP69eZxyZLm5p2dO6vysYiISA6jOTu3+/df6NsXatUyEx0PDxgzxqyj8+yzSnRERERyIPXsAMTHw9y5MHYsREaabc88Y/bmlCrl0NBEREQkY5TsrF4NISFw6JB5/MAD5rycJk0cGpaIiIjYR+4dxjpxAtq3hxYtzESnSBH46CPYuVOJjoiIiBPJfT07V6/CxIlmteO4OHBxgX79YNw4KFTI0dGJiIiIneWeZCcxEb74AoYPh3PnzLbHH4eZM6FKFYeGJiIiIpkn9yQ7zZubQ1QAZcuaPTtt2miFlYiIiJPLPXN2du6E/PnNSsgHD0Lbtkp0REREcoHc07PTtau57YO/v6MjERERkSyUe5KduXPNzTtFREQkV8k2w1iTJ0/GYrEQEhJibbt58yb9+vWjSJEi5M+fn44dO3L+/HnHBSkiIiI5TrZIdrZv387HH39MjRo1krUPHDiQH3/8kW+++YY//viDf/75hw4dOjgoShEREcmJHJ7sXLt2jW7duvHpp59S6LY6N1FRUXz22WdMnz6dZs2aUbt2bebNm8dff/3Fli1bHBixiIiI5CQOn7PTr18/WrduTfPmzRk/fry1fefOncTHx9O8eXNrW6VKlQgKCmLz5s3Uq1cv1fvFxsYSGxtrPY6KigIgOjo6k/4CERERsbek723DMDJ8L4cmO0uWLGHXrl1s3749xbmIiAjc3NwoWLBgsnZfX18iIiLues9Jkybx1ltvpWgPDAzMcLwiIiKStS5duoSPj0+G7uGwZOf06dMMGDCA1atX4+HhYbf7jhgxgkGDBlmPIyMjKVWqFOHh4Rl+WM4mOjqawMBATp8+jbdWqiWjZ5M6PZe707O5Oz2bu9OzubuoqCiCgoIoXLhwhu/lsGRn586dXLhwgQcffNDalpCQwJ9//sn777/PqlWriIuLIzIyMlnvzvnz5/Hz87vrfd3d3XF3d0/R7uPjozfSXXh7e+vZ3IWeTer0XO5Oz+bu9GzuTs/m7vLkyfj0YoclO4899hj79+9P1vbCCy9QqVIl3njjDQIDA3F1dWXNmjV07NgRgKNHjxIeHk79+vUdEbKIiIjkQA5LdgoUKEC1atWStXl5eVGkSBFr+4svvsigQYMoXLgw3t7evPbaa9SvX/+uk5NFRERE7uTw1Vj3MmPGDPLkyUPHjh2JjY2lZcuWfPjhhzbdw93dnbFjx6Y6tJXb6dncnZ5N6vRc7k7P5u70bO5Oz+bu7PlsLIY91nSJiIiIZFMOLyooIiIikpmU7IiIiIhTU7IjIiIiTk3JjoiIiDg1p0h2xo0bh8ViSfZTqVIl6/mmTZumOP/KK684MOKsdfbsWZ577jmKFCmCp6cn1atXZ8eOHdbzhmEwZswY/P398fT0pHnz5oSGhjow4qxzv2fTs2fPFO+dVq1aOTDirFG6dOkUf7fFYqFfv34A3Lx5k379+lGkSBHy589Px44dOX/+vIOjzhr3eza59fMmISGB0aNHExwcjKenJ2XLluWdd95Jtq9Rbv2sScuzya2fNQBXr14lJCSEUqVK4enpSYMGDZJtI2WX943hBMaOHWtUrVrVOHfunPXn4sWL1vNNmjQxXnrppWTno6KiHBhx1rl8+bJRqlQpo2fPnsbWrVuNkydPGqtWrTKOHz9uvWby5MmGj4+P8f333xt79+412rZtawQHBxs3btxwYOSZLy3PpkePHkarVq2SvXcuX77swKizxoULF5L9zatXrzYAY926dYZhGMYrr7xiBAYGGmvWrDF27Nhh1KtXz2jQoIFjg84i93s2ufXzZsKECUaRIkWMn376yQgLCzO++eYbI3/+/MasWbOs1+TWz5q0PJvc+lljGIbRqVMno0qVKsYff/xhhIaGGmPHjjW8vb2NM2fOGIZhn/eN0yQ7DzzwwF3PN2nSxBgwYECWxZOdvPHGG8Yjjzxy1/OJiYmGn5+f8e6771rbIiMjDXd3d2Px4sVZEaLD3O/ZGIb5AdSuXbusCSgbGzBggFG2bFkjMTHRiIyMNFxdXY1vvvnGev7w4cMGYGzevNmBUTrG7c/GMHLv503r1q2NXr16JWvr0KGD0a1bN8Mwcvdnzf2ejWHk3s+amJgYw8XFxfjpp5+StT/44IPGyJEj7fa+cYphLIDQ0FACAgIoU6YM3bp1Izw8PNn5hQsXUrRoUapVq8aIESOIiYlxUKRZa8WKFdSpU4dnnnmG4sWLU6tWLT799FPr+bCwMCIiImjevLm1zcfHh4cffpjNmzc7IuQsc79nk2T9+vUUL16cihUr0rdvXy5duuSAaB0nLi6Or776il69emGxWNi5cyfx8fHJ3jOVKlUiKCjI6d8zd7rz2STJjZ83DRo0YM2aNRw7dgyAvXv3snHjRp544gkgd3/W3O/ZJMmNnzW3bt0iISEhxYbgnp6ebNy40X7vG7ulZw70yy+/GEuXLjX27t1rrFy50qhfv74RFBRkREdHG4ZhGB9//LGxcuVKY9++fcZXX31llChRwnjqqaccHHXWcHd3N9zd3Y0RI0YYu3btMj7++GPDw8PDmD9/vmEYhrFp0yYDMP75559kv/fMM88YnTp1ckTIWeZ+z8YwDGPx4sXGDz/8YOzbt8/47rvvjMqVKxt169Y1bt265cDIs9bXX39tuLi4GGfPnjUMwzAWLlxouLm5pbiubt26xrBhw7I6PIe689kYRu79vElISDDeeOMNw2KxGHnz5jUsFosxceJE6/nc/Flzv2djGLn7s6Z+/fpGkyZNjLNnzxq3bt0yvvzySyNPnjxGhQoV7Pa+cYpk505XrlwxvL29jf/+97+pnl+zZo0BJJub4axcXV2N+vXrJ2t77bXXjHr16hmGkbs/gO73bFJz4sQJAzB+//33zA4v22jRooXx5JNPWo+V7Py/O59NanLL583ixYuNkiVLGosXLzb27dtnfPHFF0bhwoX1Dyvj/s8mNbnps+b48eNG48aNDcBwcXEx6tata3Tr1s2oVKmS3d43TjOMdbuCBQtSoUIFjh8/nur5hx9+GOCu552Jv78/VapUSdZWuXJl6zCfn58fQIqVNOfPn7eec1b3ezapKVOmDEWLFs0V7x2Av//+m99//53evXtb2/z8/IiLiyMyMjLZtbnhPXO71J5NanLL583QoUMZPnw4zz77LNWrV+f5559n4MCBTJo0CcjdnzX3ezapyU2fNWXLluWPP/7g2rVrnD59mm3bthEfH0+ZMmXs9r5xymTn2rVrnDhxAn9//1TP79mzB+Cu551Jw4YNOXr0aLK2Y8eOUapUKQCCg4Px8/NjzZo11vPR0dFs3bqV+vXrZ2msWe1+zyY1Z86c4dKlS7nivQMwb948ihcvTuvWra1ttWvXxtXVNdl75ujRo4SHhzv9e+Z2qT2b1OSWz5uYmBjy5En+leLi4kJiYiKQuz9r7vdsUpPbPmsAvLy88Pf358qVK6xatYp27drZ731j7+4oRxg8eLCxfv16IywszNi0aZPRvHlzo2jRosaFCxeM48ePG2+//baxY8cOIywszPjhhx+MMmXKGI0bN3Z02Fli27ZtRt68eY0JEyYYoaGhxsKFC418+fIZX331lfWayZMnGwULFrSOF7dr1y5XLAe937O5evWqMWTIEGPz5s1GWFiY8fvvvxsPPvigUb58eePmzZsOjj7zJSQkGEFBQcYbb7yR4twrr7xiBAUFGWvXrjV27Nhh1K9fP8WQoDO727PJzZ83PXr0MEqUKGFdXr18+XKjaNGiyYY2c+tnzf2eTW7/rFm5cqXx66+/GidPnjR+++0344EHHjAefvhhIy4uzjAM+7xvnCLZ6dy5s+Hv72+4ubkZJUqUMDp37mwdHw8PDzcaN25sFC5c2HB3dzfKlStnDB06NFfUvUjy448/GtWqVTPc3d2NSpUqGZ988kmy84mJicbo0aMNX19fw93d3XjssceMo0ePOijarHWvZxMTE2O0aNHCKFasmOHq6mqUKlXKeOmll4yIiAgHRpx1Vq1aZQCpvhdu3LhhvPrqq0ahQoWMfPnyGU899ZRx7tw5B0TpGHd7Nrn58yY6OtoYMGCAERQUZHh4eBhlypQxRo4cacTGxlqvya2fNfd7Nrn9s+brr782ypQpY7i5uRl+fn5Gv379jMjISOt5e7xvLIZxWwlHERERESfjlHN2RERERJIo2RERERGnpmRHREREnJqSHREREXFqSnZERETEqSnZEREREaemZEdEREScmpIdkds0bdqUkJAQR4eRI+hZ5Q5r1qyhcuXKJCQkpPseK1eupGbNmvfcHkEkMynZEbnN8uXLeeeddxwdRpY5deoUFovFun9TkoMHD9KxY0dKly6NxWJh5syZGX6t9evXY7FYUmwgmlnOnTtH165dqVChAnny5FFilk7Dhg1j1KhRuLi4ALB7925q1apF/vz5adOmDZcvX7Zee+vWLWrXrs22bduS3aNVq1a4urqycOHCLI1dJImSHZHbFC5cmAIFCjg6DIeLiYmhTJkyTJ48OcfuSB0bG0uxYsUYNWoUDzzwgKPDSbe4uDiHvfbGjRs5ceIEHTt2tLb17t2bZs2asWvXLqKiopg4caL13LRp02jYsCEPPfRQinv17NmT2bNnZ0ncIndSsiNymzuHZkqXLs348ePp3r07+fPnp1SpUqxYsYKLFy/Srl078ufPT40aNdixY4f1dy5dukSXLl0oUaIE+fLlo3r16ixevDjZ61y9epVu3bpZd/mdMWNGiteOjY1lyJAhlChRAi8vLx5++GHWr19vPT9//nwKFizIqlWrqFy5Mvnz56dVq1acO3cu2Wv997//pXLlynh4eFCpUiU+/PBD67ng4GAAatWqhcVioWnTpgDUrVuXd999l2effRZ3d/c0Pbuff/4ZHx+fVP/1furUKR599FEAChUqhMVioWfPnta/8/XXX6d48eJ4eHjwyCOPsH37duvvJvUI/fzzz9SoUQMPDw/q1avHgQMH7hlP6dKlmTVrFt27d8fHxydNf0NCQgIvvvgiwcHBeHp6UrFiRWbNmpXius8//5yqVavi7u6Ov78//fv3t56LjIzk5ZdfxtfXFw8PD6pVq8ZPP/0EwLhx46hZs2aye82cOZPSpUtbj3v27En79u2ZMGECAQEBVKxYEYAvv/ySOnXqUKBAAfz8/OjatSsXLlxIdq+DBw/y5JNP4u3tTYECBWjUqBEnTpzgzz//xNXVlYiIiGTXh4SE0KhRo7s+jyVLlvD444/j4eFhbTt8+DAvvfQSFSpUoEuXLhw+fBiAkydP8tlnnzFhwoRU79WmTRt27NjBiRMn7vp6IplFyY7IfcyYMYOGDRuye/duWrduzfPPP0/37t157rnn2LVrF2XLlqV79+4kbTN38+ZNateuzc8//8yBAwfo06cPzz//fLKu/UGDBrFp0yZWrFjB6tWr2bBhA7t27Ur2uv3792fz5s0sWbKEffv28cwzz9CqVStCQ0Ot18TExPDee+/x5Zdf8ueffxIeHs6QIUOs5xcuXMiYMWOYMGEChw8fZuLEiYwePZoFCxYAWGP6/fffOXfuHMuXL0/XM1q0aBFdunRh4cKFdOvWLcX5wMBAli1bBsDRo0c5d+6cNYkYNmwYy5YtY8GCBezatYty5crRsmXLZMMjAEOHDmXatGls376dYsWK0aZNG+Lj49MV790kJiZSsmRJvvnmGw4dOsSYMWN48803Wbp0qfWauXPn0q9fP/r06cP+/ftZsWIF5cqVs/7+E088waZNm/jqq684dOgQkydPtg4BpdWaNWs4evQoq1evtiZK8fHxvPPOO+zdu5fvv/+eU6dOWRNGgLNnz9K4cWPc3d1Zu3YtO3fupFevXty6dYvGjRtTpkwZvvzyS+v18fHxLFy4kF69et01jg0bNlCnTp1kbQ888ACrV6/m1q1brFmzhho1agDwyiuvMHXq1Lv2jAYFBeHr68uGDRtsehYidmHXrUtFcrgmTZoYAwYMsB6XKlXKeO6556zH586dMwBj9OjR1rbNmzcbwD13/W7durUxePBgwzDMHZBdXV2Nb775xno+MjLSyJcvn/W1//77b8PFxcU4e/Zssvs89thjxogRIwzDMIx58+YZgHH8+HHr+Q8++MDw9fW1HpctW9ZYtGhRsnu88847Rv369Q3DMIywsDADMHbv3n3X2EuVKmXMmDEjRXvSs3r//fcNHx8fY/369Xe9h2EYxrp16wzAuHLlirXt2rVrhqurq7Fw4UJrW1xcnBEQEGBMnTo12e8tWbLEes2lS5cMT09P4+uvv77na94Za3r069fP6Nixo/U4ICDAGDlyZKrXrlq1ysiTJ89dd2QeO3as8cADDyRrmzFjhlGqVCnrcY8ePQxfX99ku4WnZvv27QZgXL161TAMwxgxYoQRHBxsxMXFpXr9lClTjMqVK1uPly1bZuTPn9+4du3aXV/Dx8fH+OKLL5K1HThwwGjcuLERFBRkdOnSxYiKijK++OILo127dsaZM2eMFi1aGGXLlk31GdWqVcsYN27cPf8ukcyQ14F5lkiOkPQvVwBfX18AqlevnqLtwoUL+Pn5kZCQwMSJE1m6dClnz54lLi6O2NhY8uXLB5jd/fHx8cnmNfj4+FiHKwD2799PQkICFSpUSBZLbGwsRYoUsR7ny5ePsmXLWo/9/f2tQxvXr1/nxIkTvPjii7z00kvWa27dupXmYZ37+fbbb7lw4QKbNm2ibt26Nv/+iRMniI+Pp2HDhtY2V1dXHnroIevwSJL69etb/7tw4cJUrFjRek3+/Pmt55577jk++ugjm2NJ8sEHH/D5558THh7OjRs3iIuLsw49XbhwgX/++YfHHnss1d/ds2cPJUuWTPG/m62qV6+Om5tbsradO3cybtw49u7dy5UrV6wrm8LDw6lSpQp79uyhUaNGuLq6pnrPnj17MmrUKLZs2UK9evWYP38+nTp1wsvL665x3LhxI9kQFkDVqlX5448/rMeXLl1i7Nix/Pnnn7z22ms0aNCA5cuXU7duXR5++GHatGljvdbT05OYmBibn4dIRinZEbmP2788LBbLXduSvnzeffddZs2axcyZM6levTpeXl6EhITYNNH02rVruLi4sHPnzhRDILd/sd/5xWaxWKzDadeuXQPg008/5eGHH052na3DKndTq1Ytdu3axeeff06dOnWszyKr3b6azNvbO933WbJkCUOGDGHatGnUr1+fAgUK8O6777J161bA/LK+l/udz5Mnj/V/nySpDcXdmYBcv36dli1b0rJlSxYuXEixYsUIDw+nZcuW1vfV/V67ePHitGnThnnz5hEcHMyvv/6abA5YaooWLcqVK1fuec2gQYMICQmhZMmSrF+/nvHjx+Pl5UXr1q1Zv359smTn8uXLFCtW7J73E8kMSnZE7GzTpk20a9eO5557DjCToGPHjlGlShUAypQpg6urK9u3bycoKAiAqKgojh07RuPGjQEziUhISODChQv3nEB6L76+vgQEBHDy5MlU59EA1t6D9NZQKVu2LNOmTaNp06a4uLjw/vvv3/Xa1F6rbNmyuLm5sWnTJkqVKgWYX/7bt29PsVR8y5Yt1ud15coVjh07RuXKlQGsc2YyatOmTTRo0IBXX33V2nb7hNoCBQpQunRp1qxZY51wfbsaNWpw5swZjh07lmrvTrFixYiIiMAwDGtieOey/9QcOXKES5cuMXnyZAIDAwGSTYpPeu0FCxYQHx9/196d3r1706VLF0qWLEnZsmWT9ailplatWhw6dOiu59esWcPhw4eZN28eYP5vm5S83ZnE3bx5kxMnTlCrVq17/7EimUATlEXsrHz58qxevZq//vqLw4cP8/LLL3P+/Hnr+QIFCtCjRw+GDh3KunXrOHjwIC+++CJ58uSxfgFWqFCBbt260b17d5YvX05YWBjbtm1j0qRJ/Pzzz2mO5a233mLSpEnMnj2bY8eOsX//fubNm8f06dMB81/7np6erFy5kvPnzxMVFQWYy5337NnDnj17iIuL4+zZs+zZs4fjx4+neI0KFSqwbt06li1bds9aNqVKlcJisfDTTz9x8eJFrl27hpeXF3379mXo0KGsXLmSQ4cO8dJLLxETE8OLL76Y7Pfffvtt1qxZw4EDB+jZsydFixalffv29/z7k/6Ga9eucfHiRfbs2XPPL+/y5cuzY8cOVq1axbFjxxg9enSylWFgrqiaNm0as2fPJjQ0lF27djFnzhwAmjRpQuPGjenYsSOrV68mLCyMX3/9lZUrVwLmar+LFy8ydepUTpw4wQcffMCvv/56z78BzMm9bm5uzJkzh5MnT7JixYoU9aD69+9PdHQ0zz77LDt27CA0NJQvv/ySo0ePWq9p2bIl3t7ejB8/nhdeeOG+r9uyZUs2btyY6rmbN2/Sv39/PvnkE/LkMb9KGjZsyAcffMDevXtZtmxZsmRqy5YtuLu7JxuOFMkyDp4zJJKtpDZB+c7JuYDx3XffWY/vnOR76dIlo127dkb+/PmN4sWLG6NGjTK6d+9utGvXzvo70dHRRteuXY18+fIZfn5+xvTp042HHnrIGD58uPWauLg4Y8yYMUbp0qUNV1dXw9/f33jqqaeMffv2GYZhTlD28fFJFtt3331n3Pn/1gsXLjRq1qxpuLm5GYUKFTIaN25sLF++3Hr+008/NQIDA408efIYTZo0SfY33fmTdD61Z3Xo0CGjePHixqBBg+76fN9++23Dz8/PsFgsRo8ePQzDMIwbN24Yr732mlG0aFHD3d3daNiwobFt2zbr7yRNUP7xxx+NqlWrGm5ubsZDDz1k7N27966vkyS1v+H2ycB3unnzptGzZ0/Dx8fHKFiwoNG3b19j+PDhKSYVf/TRR0bFihWt/7u89tpr1nOXLl0yXnjhBaNIkSKGh4eHUa1aNeOnn36ynp87d64RGBhoeHl5Gd27dzcmTJiQYoLy7e+VJIsWLTJKly5tuLu7G/Xr1zdWrFiRYnL53r17jRYtWhj58uUzChQoYDRq1Mg4ceJEsvuMHj3acHFxMf7555/7Pr9Lly4ZHh4expEjR1KcGz58uHXSfZLQ0FCjbt26hre3t9G3b18jISHBeq5Pnz7Gyy+/fN/XFMkMFsO4YwBZRLLc9evXKVGiBNOmTUvRo5HbrV+/nkcffZQrV65QsGBBR4eT47344otcvHiRFStWpOn6oUOHEh0dzccff5zu1/z333+pWLEiO3bssNZ2EslKGsYScYDdu3ezePFiTpw4wa5du6xzatq1a+fgyMRZRUVFsXHjRhYtWsRrr72W5t8bOXIkpUqVytC+VqdOneLDDz9UoiMOownKIg7y3nvvcfToUdzc3KhduzYbNmygaNGijg5LnFS7du3Ytm0br7zyCo8//niaf69gwYK8+eabGXrtOnXqpChOKJKVNIwlIiIiTk3DWCIiIuLUlOyIiIiIU1OyIyIiIk5NyY6IiIg4NSU7IiIi4tSU7IiIiIhTU7IjIiIiTk3JjoiIiDg1JTsiIiLi1P4P1vrHVmQJ6lYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_metric = pd.pivot(df, index=\"model_fullname\", columns=\"dataset\", values=\"acc1\").T.dropna()\n",
    "dataset = \"imagenetv2\"\n",
    "line_fits_data = {\n",
    "    # slopes and intercepts from https://share.streamlit.io/modestyachts/imagenet-testbed-website/main/website.py\n",
    "    \"imagenetv2\": (1.112, -20.433),\n",
    "    \"imagenet-r\": (1.549, -104.556),\n",
    "    \"imagenet_sketch\": (0.931, -45.373)\n",
    "}\n",
    "x=np.linspace(0, 100,100)\n",
    "slope, intercept = line_fits_data[dataset]\n",
    "y=x*slope+intercept\n",
    "plt.xlim(55,90)\n",
    "plt.ylim(40,90)\n",
    "d = df_metric.T[[\"imagenet1k\", dataset]]*100\n",
    "plt.scatter(d[\"imagenet1k\"], d[dataset], color=\"green\")\n",
    "plt.plot(x,y, color=\"red\")\n",
    "plt.xlabel(\"imagenet1k top-1 accuracy (%)\")\n",
    "plt.ylabel(f\"{dataset} top-1 accuracy (%)\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f52c332-cf8f-4aa4-b47b-7f58078f25f9",
   "metadata": {},
   "source": [
    "### All results (mean_per_class_recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "f81989e8-e234-40dc-9889-5e2713e178b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>model_fullname</th>\n",
       "      <th>ViT-B-16 laion400m_e32</th>\n",
       "      <th>ViT-B-16 openai</th>\n",
       "      <th>ViT-B-16-plus-240 laion400m_e32</th>\n",
       "      <th>ViT-B-32 laion2b_e16</th>\n",
       "      <th>ViT-B-32 openai</th>\n",
       "      <th>ViT-B-32 openclip b_290.pt</th>\n",
       "      <th>ViT-B-32-quickgelu laion400m_e32</th>\n",
       "      <th>ViT-H-14 openclip h_256.pt</th>\n",
       "      <th>ViT-L-14 laion400m_e32</th>\n",
       "      <th>ViT-L-14 openai</th>\n",
       "      <th>ViT-L-14 openclip epoch_160.pt</th>\n",
       "      <th>ViT-L-14-336 openai</th>\n",
       "      <th>ViT-g-14 openclip g_90.pt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cars</th>\n",
       "      <td>0.838</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.846</td>\n",
       "      <td>0.844</td>\n",
       "      <td>0.597</td>\n",
       "      <td>0.862</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.896</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country211</th>\n",
       "      <td>0.182</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.171</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.147</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.318</td>\n",
       "      <td>0.263</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fer2013</th>\n",
       "      <td>0.392</td>\n",
       "      <td>0.417</td>\n",
       "      <td>0.394</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.433</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.489</td>\n",
       "      <td>0.534</td>\n",
       "      <td>0.491</td>\n",
       "      <td>0.481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fgvc_aircraft</th>\n",
       "      <td>0.175</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.232</td>\n",
       "      <td>0.197</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.426</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.317</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.332</td>\n",
       "      <td>0.378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gtsrb</th>\n",
       "      <td>0.401</td>\n",
       "      <td>0.370</td>\n",
       "      <td>0.432</td>\n",
       "      <td>0.351</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.439</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.447</td>\n",
       "      <td>0.466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imagenet-a</th>\n",
       "      <td>0.341</td>\n",
       "      <td>0.483</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.279</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.581</td>\n",
       "      <td>0.473</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imagenet-r</th>\n",
       "      <td>0.764</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.744</td>\n",
       "      <td>0.679</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.721</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imagenet1k</th>\n",
       "      <td>0.670</td>\n",
       "      <td>0.684</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.656</td>\n",
       "      <td>0.633</td>\n",
       "      <td>0.665</td>\n",
       "      <td>0.629</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.727</td>\n",
       "      <td>0.754</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.766</td>\n",
       "      <td>0.767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imagenet_sketch</th>\n",
       "      <td>0.523</td>\n",
       "      <td>0.482</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.529</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.633</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imagenetv2</th>\n",
       "      <td>0.596</td>\n",
       "      <td>0.620</td>\n",
       "      <td>0.615</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.560</td>\n",
       "      <td>0.582</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.709</td>\n",
       "      <td>0.654</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.678</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mnist</th>\n",
       "      <td>0.667</td>\n",
       "      <td>0.526</td>\n",
       "      <td>0.568</td>\n",
       "      <td>0.628</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.688</td>\n",
       "      <td>0.371</td>\n",
       "      <td>0.733</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.758</td>\n",
       "      <td>0.543</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>objectnet</th>\n",
       "      <td>0.502</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.527</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.427</td>\n",
       "      <td>0.482</td>\n",
       "      <td>0.427</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.586</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.701</td>\n",
       "      <td>0.665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>renderedsst2</th>\n",
       "      <td>0.546</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.579</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.526</td>\n",
       "      <td>0.641</td>\n",
       "      <td>0.564</td>\n",
       "      <td>0.699</td>\n",
       "      <td>0.593</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stl10</th>\n",
       "      <td>0.970</td>\n",
       "      <td>0.983</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.967</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.989</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sun397</th>\n",
       "      <td>0.680</td>\n",
       "      <td>0.653</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.678</td>\n",
       "      <td>0.635</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.751</td>\n",
       "      <td>0.713</td>\n",
       "      <td>0.682</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voc2007</th>\n",
       "      <td>0.804</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.805</td>\n",
       "      <td>0.807</td>\n",
       "      <td>0.805</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.851</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.864</td>\n",
       "      <td>0.849</td>\n",
       "      <td>0.863</td>\n",
       "      <td>0.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/caltech101</th>\n",
       "      <td>0.901</td>\n",
       "      <td>0.909</td>\n",
       "      <td>0.918</td>\n",
       "      <td>0.903</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.909</td>\n",
       "      <td>0.909</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.934</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.939</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/cifar10</th>\n",
       "      <td>0.917</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.974</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.957</td>\n",
       "      <td>0.967</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/cifar100</th>\n",
       "      <td>0.711</td>\n",
       "      <td>0.669</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.847</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.747</td>\n",
       "      <td>0.839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/clevr_closest_object_distance</th>\n",
       "      <td>0.167</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.195</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.177</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/clevr_count_all</th>\n",
       "      <td>0.282</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.182</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.307</td>\n",
       "      <td>0.195</td>\n",
       "      <td>0.319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/diabetic_retinopathy</th>\n",
       "      <td>0.252</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.221</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.234</td>\n",
       "      <td>0.207</td>\n",
       "      <td>0.217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/dmlab</th>\n",
       "      <td>0.172</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.182</td>\n",
       "      <td>0.171</td>\n",
       "      <td>0.173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/dsprites_label_orientation</th>\n",
       "      <td>0.033</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/dsprites_label_x_position</th>\n",
       "      <td>0.032</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/dtd</th>\n",
       "      <td>0.510</td>\n",
       "      <td>0.449</td>\n",
       "      <td>0.554</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.443</td>\n",
       "      <td>0.562</td>\n",
       "      <td>0.547</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.632</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/eurosat</th>\n",
       "      <td>0.511</td>\n",
       "      <td>0.547</td>\n",
       "      <td>0.589</td>\n",
       "      <td>0.511</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.526</td>\n",
       "      <td>0.720</td>\n",
       "      <td>0.630</td>\n",
       "      <td>0.638</td>\n",
       "      <td>0.664</td>\n",
       "      <td>0.631</td>\n",
       "      <td>0.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/flowers</th>\n",
       "      <td>0.667</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.673</td>\n",
       "      <td>0.665</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.663</td>\n",
       "      <td>0.799</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.746</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/kitti_closest_vehicle_distance</th>\n",
       "      <td>0.257</td>\n",
       "      <td>0.352</td>\n",
       "      <td>0.408</td>\n",
       "      <td>0.325</td>\n",
       "      <td>0.406</td>\n",
       "      <td>0.340</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.308</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/pcam</th>\n",
       "      <td>0.605</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.504</td>\n",
       "      <td>0.622</td>\n",
       "      <td>0.586</td>\n",
       "      <td>0.546</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.486</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.553</td>\n",
       "      <td>0.613</td>\n",
       "      <td>0.551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/pets</th>\n",
       "      <td>0.892</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.904</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.866</td>\n",
       "      <td>0.943</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.931</td>\n",
       "      <td>0.937</td>\n",
       "      <td>0.943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/resisc45</th>\n",
       "      <td>0.593</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.615</td>\n",
       "      <td>0.624</td>\n",
       "      <td>0.542</td>\n",
       "      <td>0.615</td>\n",
       "      <td>0.554</td>\n",
       "      <td>0.706</td>\n",
       "      <td>0.678</td>\n",
       "      <td>0.642</td>\n",
       "      <td>0.676</td>\n",
       "      <td>0.646</td>\n",
       "      <td>0.726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/smallnorb_label_azimuth</th>\n",
       "      <td>0.060</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/smallnorb_label_elevation</th>\n",
       "      <td>0.098</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtab/svhn</th>\n",
       "      <td>0.369</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.403</td>\n",
       "      <td>0.379</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.406</td>\n",
       "      <td>0.589</td>\n",
       "      <td>0.487</td>\n",
       "      <td>0.559</td>\n",
       "      <td>0.568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "model_fullname                       ViT-B-16 laion400m_e32  ViT-B-16 openai  \\\n",
       "dataset                                                                        \n",
       "cars                                                  0.838            0.647   \n",
       "country211                                            0.182            0.228   \n",
       "fer2013                                               0.392            0.417   \n",
       "fgvc_aircraft                                         0.175            0.241   \n",
       "gtsrb                                                 0.401            0.370   \n",
       "imagenet-a                                            0.341            0.483   \n",
       "imagenet-r                                            0.764            0.761   \n",
       "imagenet1k                                            0.670            0.684   \n",
       "imagenet_sketch                                       0.523            0.482   \n",
       "imagenetv2                                            0.596            0.620   \n",
       "mnist                                                 0.667            0.526   \n",
       "objectnet                                             0.502            0.536   \n",
       "renderedsst2                                          0.546            0.611   \n",
       "stl10                                                 0.970            0.983   \n",
       "sun397                                                0.680            0.653   \n",
       "voc2007                                               0.804            0.835   \n",
       "vtab/caltech101                                       0.901            0.909   \n",
       "vtab/cifar10                                          0.917            0.908   \n",
       "vtab/cifar100                                         0.711            0.669   \n",
       "vtab/clevr_closest_object_distance                    0.167            0.168   \n",
       "vtab/clevr_count_all                                  0.282            0.215   \n",
       "vtab/diabetic_retinopathy                             0.252            0.211   \n",
       "vtab/dmlab                                            0.172            0.170   \n",
       "vtab/dsprites_label_orientation                       0.033            0.018   \n",
       "vtab/dsprites_label_x_position                        0.032            0.029   \n",
       "vtab/dtd                                              0.510            0.449   \n",
       "vtab/eurosat                                          0.511            0.547   \n",
       "vtab/flowers                                          0.667            0.691   \n",
       "vtab/kitti_closest_vehicle_distance                   0.257            0.352   \n",
       "vtab/pcam                                             0.605            0.506   \n",
       "vtab/pets                                             0.892            0.885   \n",
       "vtab/resisc45                                         0.593            0.592   \n",
       "vtab/smallnorb_label_azimuth                          0.060            0.052   \n",
       "vtab/smallnorb_label_elevation                        0.098            0.118   \n",
       "vtab/svhn                                             0.369            0.350   \n",
       "\n",
       "model_fullname                       ViT-B-16-plus-240 laion400m_e32  \\\n",
       "dataset                                                                \n",
       "cars                                                           0.846   \n",
       "country211                                                     0.188   \n",
       "fer2013                                                        0.394   \n",
       "fgvc_aircraft                                                  0.188   \n",
       "gtsrb                                                          0.432   \n",
       "imagenet-a                                                     0.381   \n",
       "imagenet-r                                                     0.791   \n",
       "imagenet1k                                                     0.692   \n",
       "imagenet_sketch                                                0.545   \n",
       "imagenetv2                                                     0.615   \n",
       "mnist                                                          0.568   \n",
       "objectnet                                                      0.527   \n",
       "renderedsst2                                                   0.579   \n",
       "stl10                                                          0.970   \n",
       "sun397                                                         0.685   \n",
       "voc2007                                                        0.816   \n",
       "vtab/caltech101                                                0.918   \n",
       "vtab/cifar10                                                   0.927   \n",
       "vtab/cifar100                                                  0.737   \n",
       "vtab/clevr_closest_object_distance                             0.170   \n",
       "vtab/clevr_count_all                                           0.233   \n",
       "vtab/diabetic_retinopathy                                      0.231   \n",
       "vtab/dmlab                                                     0.148   \n",
       "vtab/dsprites_label_orientation                                0.026   \n",
       "vtab/dsprites_label_x_position                                 0.043   \n",
       "vtab/dtd                                                       0.554   \n",
       "vtab/eurosat                                                   0.589   \n",
       "vtab/flowers                                                   0.686   \n",
       "vtab/kitti_closest_vehicle_distance                            0.408   \n",
       "vtab/pcam                                                      0.544   \n",
       "vtab/pets                                                      0.904   \n",
       "vtab/resisc45                                                  0.615   \n",
       "vtab/smallnorb_label_azimuth                                   0.055   \n",
       "vtab/smallnorb_label_elevation                                 0.109   \n",
       "vtab/svhn                                                      0.403   \n",
       "\n",
       "model_fullname                       ViT-B-32 laion2b_e16  ViT-B-32 openai  \\\n",
       "dataset                                                                      \n",
       "cars                                                0.844            0.597   \n",
       "country211                                          0.164            0.171   \n",
       "fer2013                                             0.465            0.359   \n",
       "fgvc_aircraft                                       0.232            0.197   \n",
       "gtsrb                                               0.351            0.320   \n",
       "imagenet-a                                          0.284            0.324   \n",
       "imagenet-r                                          0.744            0.679   \n",
       "imagenet1k                                          0.656            0.633   \n",
       "imagenet_sketch                                     0.529            0.423   \n",
       "imagenetv2                                          0.572            0.560   \n",
       "mnist                                               0.628            0.458   \n",
       "objectnet                                           0.475            0.427   \n",
       "renderedsst2                                        0.537            0.590   \n",
       "stl10                                               0.965            0.972   \n",
       "sun397                                              0.678            0.635   \n",
       "voc2007                                             0.805            0.807   \n",
       "vtab/caltech101                                     0.903            0.879   \n",
       "vtab/cifar10                                        0.941            0.900   \n",
       "vtab/cifar100                                       0.753            0.645   \n",
       "vtab/clevr_closest_object_distance                  0.183            0.162   \n",
       "vtab/clevr_count_all                                0.182            0.220   \n",
       "vtab/diabetic_retinopathy                           0.221            0.219   \n",
       "vtab/dmlab                                          0.168            0.164   \n",
       "vtab/dsprites_label_orientation                     0.025            0.022   \n",
       "vtab/dsprites_label_x_position                      0.031            0.034   \n",
       "vtab/dtd                                            0.537            0.443   \n",
       "vtab/eurosat                                        0.511            0.490   \n",
       "vtab/flowers                                        0.673            0.665   \n",
       "vtab/kitti_closest_vehicle_distance                 0.325            0.406   \n",
       "vtab/pcam                                           0.504            0.622   \n",
       "vtab/pets                                           0.891            0.870   \n",
       "vtab/resisc45                                       0.624            0.542   \n",
       "vtab/smallnorb_label_azimuth                        0.054            0.063   \n",
       "vtab/smallnorb_label_elevation                      0.109            0.121   \n",
       "vtab/svhn                                           0.379            0.133   \n",
       "\n",
       "model_fullname                       ViT-B-32 openclip b_290.pt  \\\n",
       "dataset                                                           \n",
       "cars                                                      0.862   \n",
       "country211                                                0.167   \n",
       "fer2013                                                   0.433   \n",
       "fgvc_aircraft                                             0.246   \n",
       "gtsrb                                                     0.435   \n",
       "imagenet-a                                                0.279   \n",
       "imagenet-r                                                0.752   \n",
       "imagenet1k                                                0.665   \n",
       "imagenet_sketch                                           0.537   \n",
       "imagenetv2                                                0.582   \n",
       "mnist                                                     0.688   \n",
       "objectnet                                                 0.482   \n",
       "renderedsst2                                              0.566   \n",
       "stl10                                                     0.967   \n",
       "sun397                                                    0.685   \n",
       "voc2007                                                   0.805   \n",
       "vtab/caltech101                                           0.909   \n",
       "vtab/cifar10                                              0.936   \n",
       "vtab/cifar100                                             0.755   \n",
       "vtab/clevr_closest_object_distance                        0.139   \n",
       "vtab/clevr_count_all                                      0.150   \n",
       "vtab/diabetic_retinopathy                                 0.200   \n",
       "vtab/dmlab                                                0.166   \n",
       "vtab/dsprites_label_orientation                           0.034   \n",
       "vtab/dsprites_label_x_position                            0.030   \n",
       "vtab/dtd                                                  0.562   \n",
       "vtab/eurosat                                              0.494   \n",
       "vtab/flowers                                              0.700   \n",
       "vtab/kitti_closest_vehicle_distance                       0.340   \n",
       "vtab/pcam                                                 0.586   \n",
       "vtab/pets                                                 0.907   \n",
       "vtab/resisc45                                             0.615   \n",
       "vtab/smallnorb_label_azimuth                              0.063   \n",
       "vtab/smallnorb_label_elevation                            0.116   \n",
       "vtab/svhn                                                 0.422   \n",
       "\n",
       "model_fullname                       ViT-B-32-quickgelu laion400m_e32  \\\n",
       "dataset                                                                 \n",
       "cars                                                            0.793   \n",
       "country211                                                      0.147   \n",
       "fer2013                                                         0.399   \n",
       "fgvc_aircraft                                                   0.166   \n",
       "gtsrb                                                           0.393   \n",
       "imagenet-a                                                      0.235   \n",
       "imagenet-r                                                      0.721   \n",
       "imagenet1k                                                      0.629   \n",
       "imagenet_sketch                                                 0.494   \n",
       "imagenetv2                                                      0.551   \n",
       "mnist                                                           0.371   \n",
       "objectnet                                                       0.427   \n",
       "renderedsst2                                                    0.526   \n",
       "stl10                                                           0.955   \n",
       "sun397                                                          0.661   \n",
       "voc2007                                                         0.791   \n",
       "vtab/caltech101                                                 0.909   \n",
       "vtab/cifar10                                                    0.908   \n",
       "vtab/cifar100                                                   0.703   \n",
       "vtab/clevr_closest_object_distance                              0.167   \n",
       "vtab/clevr_count_all                                            0.158   \n",
       "vtab/diabetic_retinopathy                                       0.259   \n",
       "vtab/dmlab                                                      0.158   \n",
       "vtab/dsprites_label_orientation                                 0.020   \n",
       "vtab/dsprites_label_x_position                                  0.031   \n",
       "vtab/dtd                                                        0.547   \n",
       "vtab/eurosat                                                    0.526   \n",
       "vtab/flowers                                                    0.663   \n",
       "vtab/kitti_closest_vehicle_distance                             0.365   \n",
       "vtab/pcam                                                       0.546   \n",
       "vtab/pets                                                       0.866   \n",
       "vtab/resisc45                                                   0.554   \n",
       "vtab/smallnorb_label_azimuth                                    0.045   \n",
       "vtab/smallnorb_label_elevation                                  0.097   \n",
       "vtab/svhn                                                       0.280   \n",
       "\n",
       "model_fullname                       ViT-H-14 openclip h_256.pt  \\\n",
       "dataset                                                           \n",
       "cars                                                      0.935   \n",
       "country211                                                0.299   \n",
       "fer2013                                                   0.506   \n",
       "fgvc_aircraft                                             0.426   \n",
       "gtsrb                                                     0.544   \n",
       "imagenet-a                                                0.581   \n",
       "imagenet-r                                                0.880   \n",
       "imagenet1k                                                0.780   \n",
       "imagenet_sketch                                           0.666   \n",
       "imagenetv2                                                0.709   \n",
       "mnist                                                     0.733   \n",
       "objectnet                                                 0.685   \n",
       "renderedsst2                                              0.641   \n",
       "stl10                                                     0.985   \n",
       "sun397                                                    0.751   \n",
       "voc2007                                                   0.851   \n",
       "vtab/caltech101                                           0.944   \n",
       "vtab/cifar10                                              0.974   \n",
       "vtab/cifar100                                             0.847   \n",
       "vtab/clevr_closest_object_distance                        0.195   \n",
       "vtab/clevr_count_all                                      0.256   \n",
       "vtab/diabetic_retinopathy                                 0.233   \n",
       "vtab/dmlab                                                0.166   \n",
       "vtab/dsprites_label_orientation                           0.027   \n",
       "vtab/dsprites_label_x_position                            0.031   \n",
       "vtab/dtd                                                  0.681   \n",
       "vtab/eurosat                                              0.720   \n",
       "vtab/flowers                                              0.799   \n",
       "vtab/kitti_closest_vehicle_distance                       0.272   \n",
       "vtab/pcam                                                 0.536   \n",
       "vtab/pets                                                 0.943   \n",
       "vtab/resisc45                                             0.706   \n",
       "vtab/smallnorb_label_azimuth                              0.056   \n",
       "vtab/smallnorb_label_elevation                            0.110   \n",
       "vtab/svhn                                                 0.557   \n",
       "\n",
       "model_fullname                       ViT-L-14 laion400m_e32  ViT-L-14 openai  \\\n",
       "dataset                                                                        \n",
       "cars                                                  0.896            0.777   \n",
       "country211                                            0.231            0.318   \n",
       "fer2013                                               0.450            0.489   \n",
       "fgvc_aircraft                                         0.248            0.317   \n",
       "gtsrb                                                 0.450            0.439   \n",
       "imagenet-a                                            0.473            0.675   \n",
       "imagenet-r                                            0.833            0.865   \n",
       "imagenet1k                                            0.727            0.754   \n",
       "imagenet_sketch                                       0.596            0.596   \n",
       "imagenetv2                                            0.654            0.697   \n",
       "mnist                                                 0.759            0.758   \n",
       "objectnet                                             0.586            0.674   \n",
       "renderedsst2                                          0.564            0.699   \n",
       "stl10                                                 0.981            0.994   \n",
       "sun397                                                0.713            0.682   \n",
       "voc2007                                               0.831            0.864   \n",
       "vtab/caltech101                                       0.934            0.933   \n",
       "vtab/cifar10                                          0.947            0.957   \n",
       "vtab/cifar100                                         0.774            0.761   \n",
       "vtab/clevr_closest_object_distance                    0.144            0.177   \n",
       "vtab/clevr_count_all                                  0.231            0.187   \n",
       "vtab/diabetic_retinopathy                             0.220            0.206   \n",
       "vtab/dmlab                                            0.193            0.178   \n",
       "vtab/dsprites_label_orientation                       0.026            0.024   \n",
       "vtab/dsprites_label_x_position                        0.031            0.032   \n",
       "vtab/dtd                                              0.604            0.550   \n",
       "vtab/eurosat                                          0.630            0.638   \n",
       "vtab/flowers                                          0.726            0.793   \n",
       "vtab/kitti_closest_vehicle_distance                   0.179            0.372   \n",
       "vtab/pcam                                             0.486            0.516   \n",
       "vtab/pets                                             0.916            0.933   \n",
       "vtab/resisc45                                         0.678            0.642   \n",
       "vtab/smallnorb_label_azimuth                          0.053            0.046   \n",
       "vtab/smallnorb_label_elevation                        0.108            0.114   \n",
       "vtab/svhn                                             0.406            0.589   \n",
       "\n",
       "model_fullname                       ViT-L-14 openclip epoch_160.pt  \\\n",
       "dataset                                                               \n",
       "cars                                                          0.926   \n",
       "country211                                                    0.263   \n",
       "fer2013                                                       0.534   \n",
       "fgvc_aircraft                                                 0.365   \n",
       "gtsrb                                                         0.517   \n",
       "imagenet-a                                                    0.536   \n",
       "imagenet-r                                                    0.860   \n",
       "imagenet1k                                                    0.753   \n",
       "imagenet_sketch                                               0.633   \n",
       "imagenetv2                                                    0.678   \n",
       "mnist                                                         0.543   \n",
       "objectnet                                                     0.643   \n",
       "renderedsst2                                                  0.593   \n",
       "stl10                                                         0.989   \n",
       "sun397                                                        0.735   \n",
       "voc2007                                                       0.849   \n",
       "vtab/caltech101                                               0.939   \n",
       "vtab/cifar10                                                  0.967   \n",
       "vtab/cifar100                                                 0.833   \n",
       "vtab/clevr_closest_object_distance                            0.174   \n",
       "vtab/clevr_count_all                                          0.307   \n",
       "vtab/diabetic_retinopathy                                     0.234   \n",
       "vtab/dmlab                                                    0.182   \n",
       "vtab/dsprites_label_orientation                               0.022   \n",
       "vtab/dsprites_label_x_position                                0.032   \n",
       "vtab/dtd                                                      0.632   \n",
       "vtab/eurosat                                                  0.664   \n",
       "vtab/flowers                                                  0.746   \n",
       "vtab/kitti_closest_vehicle_distance                           0.308   \n",
       "vtab/pcam                                                     0.553   \n",
       "vtab/pets                                                     0.931   \n",
       "vtab/resisc45                                                 0.676   \n",
       "vtab/smallnorb_label_azimuth                                  0.057   \n",
       "vtab/smallnorb_label_elevation                                0.110   \n",
       "vtab/svhn                                                     0.487   \n",
       "\n",
       "model_fullname                       ViT-L-14-336 openai  \\\n",
       "dataset                                                    \n",
       "cars                                               0.793   \n",
       "country211                                         0.345   \n",
       "fer2013                                            0.491   \n",
       "fgvc_aircraft                                      0.332   \n",
       "gtsrb                                              0.447   \n",
       "imagenet-a                                         0.735   \n",
       "imagenet-r                                         0.878   \n",
       "imagenet1k                                         0.766   \n",
       "imagenet_sketch                                    0.610   \n",
       "imagenetv2                                         0.708   \n",
       "mnist                                              0.778   \n",
       "objectnet                                          0.701   \n",
       "renderedsst2                                       0.707   \n",
       "stl10                                              0.995   \n",
       "sun397                                             0.692   \n",
       "voc2007                                            0.863   \n",
       "vtab/caltech101                                    0.933   \n",
       "vtab/cifar10                                       0.950   \n",
       "vtab/cifar100                                      0.747   \n",
       "vtab/clevr_closest_object_distance                 0.181   \n",
       "vtab/clevr_count_all                               0.195   \n",
       "vtab/diabetic_retinopathy                          0.207   \n",
       "vtab/dmlab                                         0.171   \n",
       "vtab/dsprites_label_orientation                    0.025   \n",
       "vtab/dsprites_label_x_position                     0.032   \n",
       "vtab/dtd                                           0.556   \n",
       "vtab/eurosat                                       0.631   \n",
       "vtab/flowers                                       0.786   \n",
       "vtab/kitti_closest_vehicle_distance                0.374   \n",
       "vtab/pcam                                          0.613   \n",
       "vtab/pets                                          0.937   \n",
       "vtab/resisc45                                      0.646   \n",
       "vtab/smallnorb_label_azimuth                       0.046   \n",
       "vtab/smallnorb_label_elevation                     0.113   \n",
       "vtab/svhn                                          0.559   \n",
       "\n",
       "model_fullname                       ViT-g-14 openclip g_90.pt  \n",
       "dataset                                                         \n",
       "cars                                                     0.929  \n",
       "country211                                               0.288  \n",
       "fer2013                                                  0.481  \n",
       "fgvc_aircraft                                            0.378  \n",
       "gtsrb                                                    0.466  \n",
       "imagenet-a                                               0.564  \n",
       "imagenet-r                                               0.875  \n",
       "imagenet1k                                               0.767  \n",
       "imagenet_sketch                                          0.652  \n",
       "imagenetv2                                               0.696  \n",
       "mnist                                                    0.683  \n",
       "objectnet                                                0.665  \n",
       "renderedsst2                                             0.646  \n",
       "stl10                                                    0.986  \n",
       "sun397                                                   0.752  \n",
       "voc2007                                                  0.858  \n",
       "vtab/caltech101                                          0.944  \n",
       "vtab/cifar10                                             0.971  \n",
       "vtab/cifar100                                            0.839  \n",
       "vtab/clevr_closest_object_distance                       0.227  \n",
       "vtab/clevr_count_all                                     0.319  \n",
       "vtab/diabetic_retinopathy                                0.217  \n",
       "vtab/dmlab                                               0.173  \n",
       "vtab/dsprites_label_orientation                          0.030  \n",
       "vtab/dsprites_label_x_position                           0.036  \n",
       "vtab/dtd                                                 0.683  \n",
       "vtab/eurosat                                             0.645  \n",
       "vtab/flowers                                             0.781  \n",
       "vtab/kitti_closest_vehicle_distance                      0.182  \n",
       "vtab/pcam                                                0.551  \n",
       "vtab/pets                                                0.943  \n",
       "vtab/resisc45                                            0.726  \n",
       "vtab/smallnorb_label_azimuth                             0.060  \n",
       "vtab/smallnorb_label_elevation                           0.115  \n",
       "vtab/svhn                                                0.568  "
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metric = \"mean_per_class_recall\"\n",
    "pd.pivot(df, index=\"model_fullname\", columns=\"dataset\", values=metric).T.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dba407aa-4103-44aa-bddc-3046ec73aa77",
   "metadata": {},
   "source": [
    "### All results (mAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "id": "99130646-f092-43a0-a657-c4e64053dcb5",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>model_fullname</th>\n",
       "      <th>ViT-B-16 laion400m_e32</th>\n",
       "      <th>ViT-B-16 openai</th>\n",
       "      <th>ViT-B-16-plus-240 laion400m_e32</th>\n",
       "      <th>ViT-B-32 laion2b_e16</th>\n",
       "      <th>ViT-B-32 openai</th>\n",
       "      <th>ViT-B-32 openclip b_290.pt</th>\n",
       "      <th>ViT-B-32-quickgelu laion400m_e32</th>\n",
       "      <th>ViT-H-14 openclip h_256.pt</th>\n",
       "      <th>ViT-L-14 laion400m_e32</th>\n",
       "      <th>ViT-L-14 openai</th>\n",
       "      <th>ViT-L-14 openclip epoch_160.pt</th>\n",
       "      <th>ViT-L-14-336 openai</th>\n",
       "      <th>ViT-g-14 openclip g_90.pt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>voc2007_multilabel</th>\n",
       "      <td>0.784</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.796</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.801</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.807</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "model_fullname      ViT-B-16 laion400m_e32  ViT-B-16 openai  \\\n",
       "dataset                                                       \n",
       "voc2007_multilabel                   0.784            0.789   \n",
       "\n",
       "model_fullname      ViT-B-16-plus-240 laion400m_e32  ViT-B-32 laion2b_e16  \\\n",
       "dataset                                                                     \n",
       "voc2007_multilabel                            0.785                 0.793   \n",
       "\n",
       "model_fullname      ViT-B-32 openai  ViT-B-32 openclip b_290.pt  \\\n",
       "dataset                                                           \n",
       "voc2007_multilabel            0.760                       0.796   \n",
       "\n",
       "model_fullname      ViT-B-32-quickgelu laion400m_e32  \\\n",
       "dataset                                                \n",
       "voc2007_multilabel                             0.762   \n",
       "\n",
       "model_fullname      ViT-H-14 openclip h_256.pt  ViT-L-14 laion400m_e32  \\\n",
       "dataset                                                                  \n",
       "voc2007_multilabel                       0.801                   0.785   \n",
       "\n",
       "model_fullname      ViT-L-14 openai  ViT-L-14 openclip epoch_160.pt  \\\n",
       "dataset                                                               \n",
       "voc2007_multilabel            0.790                           0.820   \n",
       "\n",
       "model_fullname      ViT-L-14-336 openai  ViT-g-14 openclip g_90.pt  \n",
       "dataset                                                             \n",
       "voc2007_multilabel                0.804                      0.807  "
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For multi-label classification tasks\n",
    "metric = \"mean_average_precision\"\n",
    "pd.pivot(df, index=\"model_fullname\", columns=\"dataset\", values=metric).T.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b75cf72d",
   "metadata": {},
   "source": [
    "## All results (retrieval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "48f5ea9d",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>model_fullname</th>\n",
       "      <th>ViT-B-16 laion400m_e32</th>\n",
       "      <th>ViT-B-16 openai</th>\n",
       "      <th>ViT-B-16-plus-240 laion400m_e32</th>\n",
       "      <th>ViT-B-32 laion2b_e16</th>\n",
       "      <th>ViT-B-32 openai</th>\n",
       "      <th>ViT-B-32 openclip b_290.pt</th>\n",
       "      <th>ViT-B-32-quickgelu laion400m_e32</th>\n",
       "      <th>ViT-H-14 openclip h_256.pt</th>\n",
       "      <th>ViT-L-14 laion400m_e32</th>\n",
       "      <th>ViT-L-14 openai</th>\n",
       "      <th>ViT-L-14 openclip epoch_160.pt</th>\n",
       "      <th>ViT-L-14-336 openai</th>\n",
       "      <th>ViT-g-14 openclip g_90.pt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>flickr30k</th>\n",
       "      <td>0.882</td>\n",
       "      <td>0.855</td>\n",
       "      <td>0.889</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.855</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.929</td>\n",
       "      <td>0.889</td>\n",
       "      <td>0.935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flickr8k</th>\n",
       "      <td>0.620</td>\n",
       "      <td>0.564</td>\n",
       "      <td>0.644</td>\n",
       "      <td>0.622</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.629</td>\n",
       "      <td>0.579</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.598</td>\n",
       "      <td>0.712</td>\n",
       "      <td>0.627</td>\n",
       "      <td>0.730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mscoco_captions</th>\n",
       "      <td>0.636</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.662</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.558</td>\n",
       "      <td>0.654</td>\n",
       "      <td>0.608</td>\n",
       "      <td>0.734</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0.724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "model_fullname   ViT-B-16 laion400m_e32  ViT-B-16 openai  \\\n",
       "dataset                                                    \n",
       "flickr30k                         0.882            0.855   \n",
       "flickr8k                          0.620            0.564   \n",
       "mscoco_captions                   0.636            0.584   \n",
       "\n",
       "model_fullname   ViT-B-16-plus-240 laion400m_e32  ViT-B-32 laion2b_e16  \\\n",
       "dataset                                                                  \n",
       "flickr30k                                  0.889                 0.881   \n",
       "flickr8k                                   0.644                 0.622   \n",
       "mscoco_captions                            0.662                 0.647   \n",
       "\n",
       "model_fullname   ViT-B-32 openai  ViT-B-32 openclip b_290.pt  \\\n",
       "dataset                                                        \n",
       "flickr30k                  0.834                       0.884   \n",
       "flickr8k                   0.532                       0.629   \n",
       "mscoco_captions            0.558                       0.654   \n",
       "\n",
       "model_fullname   ViT-B-32-quickgelu laion400m_e32  ViT-H-14 openclip h_256.pt  \\\n",
       "dataset                                                                         \n",
       "flickr30k                                   0.855                       0.941   \n",
       "flickr8k                                    0.579                       0.745   \n",
       "mscoco_captions                             0.608                       0.734   \n",
       "\n",
       "model_fullname   ViT-L-14 laion400m_e32  ViT-L-14 openai  \\\n",
       "dataset                                                    \n",
       "flickr30k                         0.908            0.872   \n",
       "flickr8k                          0.675            0.598   \n",
       "mscoco_captions                   0.681            0.611   \n",
       "\n",
       "model_fullname   ViT-L-14 openclip epoch_160.pt  ViT-L-14-336 openai  \\\n",
       "dataset                                                                \n",
       "flickr30k                                 0.929                0.889   \n",
       "flickr8k                                  0.712                0.627   \n",
       "mscoco_captions                           0.711                0.616   \n",
       "\n",
       "model_fullname   ViT-g-14 openclip g_90.pt  \n",
       "dataset                                     \n",
       "flickr30k                            0.935  \n",
       "flickr8k                             0.730  \n",
       "mscoco_captions                      0.724  "
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metric = \"image_retrieval_recall@5\"\n",
    "pd.pivot(df_retrieval, index=\"model_fullname\", columns=\"dataset\", values=metric).T.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "id": "cbc65601",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>model_fullname</th>\n",
       "      <th>ViT-B-16 laion400m_e32</th>\n",
       "      <th>ViT-B-16 openai</th>\n",
       "      <th>ViT-B-16-plus-240 laion400m_e32</th>\n",
       "      <th>ViT-B-32 laion2b_e16</th>\n",
       "      <th>ViT-B-32 openai</th>\n",
       "      <th>ViT-B-32 openclip b_290.pt</th>\n",
       "      <th>ViT-B-32-quickgelu laion400m_e32</th>\n",
       "      <th>ViT-H-14 openclip h_256.pt</th>\n",
       "      <th>ViT-L-14 laion400m_e32</th>\n",
       "      <th>ViT-L-14 openai</th>\n",
       "      <th>ViT-L-14 openclip epoch_160.pt</th>\n",
       "      <th>ViT-L-14-336 openai</th>\n",
       "      <th>ViT-g-14 openclip g_90.pt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>flickr30k</th>\n",
       "      <td>0.968</td>\n",
       "      <td>0.963</td>\n",
       "      <td>0.971</td>\n",
       "      <td>0.964</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.963</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.993</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.974</td>\n",
       "      <td>0.987</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flickr8k</th>\n",
       "      <td>0.767</td>\n",
       "      <td>0.728</td>\n",
       "      <td>0.792</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.699</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.739</td>\n",
       "      <td>0.856</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.766</td>\n",
       "      <td>0.822</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mscoco_captions</th>\n",
       "      <td>0.796</td>\n",
       "      <td>0.768</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.795</td>\n",
       "      <td>0.748</td>\n",
       "      <td>0.798</td>\n",
       "      <td>0.768</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.822</td>\n",
       "      <td>0.792</td>\n",
       "      <td>0.840</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "model_fullname   ViT-B-16 laion400m_e32  ViT-B-16 openai  \\\n",
       "dataset                                                    \n",
       "flickr30k                         0.968            0.963   \n",
       "flickr8k                          0.767            0.728   \n",
       "mscoco_captions                   0.796            0.768   \n",
       "\n",
       "model_fullname   ViT-B-16-plus-240 laion400m_e32  ViT-B-32 laion2b_e16  \\\n",
       "dataset                                                                  \n",
       "flickr30k                                  0.971                 0.964   \n",
       "flickr8k                                   0.792                 0.761   \n",
       "mscoco_captions                            0.810                 0.795   \n",
       "\n",
       "model_fullname   ViT-B-32 openai  ViT-B-32 openclip b_290.pt  \\\n",
       "dataset                                                        \n",
       "flickr30k                  0.949                       0.963   \n",
       "flickr8k                   0.699                       0.778   \n",
       "mscoco_captions            0.748                       0.798   \n",
       "\n",
       "model_fullname   ViT-B-32-quickgelu laion400m_e32  ViT-H-14 openclip h_256.pt  \\\n",
       "dataset                                                                         \n",
       "flickr30k                                   0.941                       0.993   \n",
       "flickr8k                                    0.739                       0.856   \n",
       "mscoco_captions                             0.768                       0.860   \n",
       "\n",
       "model_fullname   ViT-L-14 laion400m_e32  ViT-L-14 openai  \\\n",
       "dataset                                                    \n",
       "flickr30k                         0.978            0.974   \n",
       "flickr8k                          0.806            0.766   \n",
       "mscoco_captions                   0.822            0.792   \n",
       "\n",
       "model_fullname   ViT-L-14 openclip epoch_160.pt  ViT-L-14-336 openai  \\\n",
       "dataset                                                                \n",
       "flickr30k                                 0.987                0.981   \n",
       "flickr8k                                  0.822                0.780   \n",
       "mscoco_captions                           0.840                0.810   \n",
       "\n",
       "model_fullname   ViT-g-14 openclip g_90.pt  \n",
       "dataset                                     \n",
       "flickr30k                            0.991  \n",
       "flickr8k                             0.846  \n",
       "mscoco_captions                      0.854  "
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metric = \"text_retrieval_recall@5\"\n",
    "pd.pivot(df_retrieval, index=\"model_fullname\", columns=\"dataset\", values=metric).T.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e30ecf3c-40a3-4c66-836f-4fbd3e3b0e37",
   "metadata": {},
   "source": [
    "## Aggregating over datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b26606d9-591d-45a1-8cd9-9853bfcc5053",
   "metadata": {},
   "source": [
    "See VTAB (https://arxiv.org/pdf/1910.04867.pdf, Section E) for a discussion about different aggregation \n",
    "strategies and how much they correlate. They find that all aggregation strategies have high\n",
    "Kendall score with the simple top-1 mean accuracy over datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d14d866e-702f-4cd2-97b0-f6e2c97394d8",
   "metadata": {},
   "source": [
    "### Ranking the models over mean top-1 accuracy over all datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "id": "6d0ece3a-de4d-4080-ae82-925bf563819b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1235910/453967910.py:1: FutureWarning: ['dataset', 'model', 'pretrained', 'task', 'dataset_type', 'model_arch'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n",
      "  df.groupby(\"model_fullname\").agg(['mean', 'std', 'median']).sort_values(by=(\"acc1\", \"mean\"), ascending=False)\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">acc1</th>\n",
       "      <th colspan=\"3\" halign=\"left\">acc5</th>\n",
       "      <th colspan=\"3\" halign=\"left\">mean_per_class_recall</th>\n",
       "      <th colspan=\"3\" halign=\"left\">mean_average_precision</th>\n",
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       "      <th></th>\n",
       "      <th>mean</th>\n",
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       "      <th>std</th>\n",
       "      <th>median</th>\n",
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       "      <th>std</th>\n",
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       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_fullname</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ViT-L-14-336 openai</th>\n",
       "      <td>0.567</td>\n",
       "      <td>0.287</td>\n",
       "      <td>0.637</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.558</td>\n",
       "      <td>0.292</td>\n",
       "      <td>0.631</td>\n",
       "      <td>0.804</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-g-14 openclip g_90.pt</th>\n",
       "      <td>0.565</td>\n",
       "      <td>0.290</td>\n",
       "      <td>0.648</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.238</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.563</td>\n",
       "      <td>0.297</td>\n",
       "      <td>0.646</td>\n",
       "      <td>0.807</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-H-14 openclip h_256.pt</th>\n",
       "      <td>0.564</td>\n",
       "      <td>0.301</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.836</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.298</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.801</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-L-14 openai</th>\n",
       "      <td>0.558</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.635</td>\n",
       "      <td>0.823</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.917</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.291</td>\n",
       "      <td>0.638</td>\n",
       "      <td>0.790</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-L-14 openclip epoch_160.pt</th>\n",
       "      <td>0.546</td>\n",
       "      <td>0.288</td>\n",
       "      <td>0.593</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.904</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.290</td>\n",
       "      <td>0.593</td>\n",
       "      <td>0.820</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-L-14 laion400m_e32</th>\n",
       "      <td>0.518</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.802</td>\n",
       "      <td>0.243</td>\n",
       "      <td>0.901</td>\n",
       "      <td>0.522</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.586</td>\n",
       "      <td>0.785</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-B-32 openclip b_290.pt</th>\n",
       "      <td>0.508</td>\n",
       "      <td>0.281</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.783</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.796</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-B-16-plus-240 laion400m_e32</th>\n",
       "      <td>0.493</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.862</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.785</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-B-16 laion400m_e32</th>\n",
       "      <td>0.484</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.515</td>\n",
       "      <td>0.779</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.882</td>\n",
       "      <td>0.488</td>\n",
       "      <td>0.278</td>\n",
       "      <td>0.511</td>\n",
       "      <td>0.784</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-B-32 laion2b_e16</th>\n",
       "      <td>0.481</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.504</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.249</td>\n",
       "      <td>0.858</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.511</td>\n",
       "      <td>0.793</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-B-16 openai</th>\n",
       "      <td>0.475</td>\n",
       "      <td>0.273</td>\n",
       "      <td>0.501</td>\n",
       "      <td>0.783</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.854</td>\n",
       "      <td>0.483</td>\n",
       "      <td>0.269</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.789</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-B-32-quickgelu laion400m_e32</th>\n",
       "      <td>0.458</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.757</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.858</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.762</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-B-32 openai</th>\n",
       "      <td>0.449</td>\n",
       "      <td>0.262</td>\n",
       "      <td>0.443</td>\n",
       "      <td>0.764</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.854</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.267</td>\n",
       "      <td>0.443</td>\n",
       "      <td>0.760</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  acc1               acc5               \\\n",
       "                                  mean   std median  mean   std median   \n",
       "model_fullname                                                           \n",
       "ViT-L-14-336 openai              0.567 0.287  0.637 0.826 0.239  0.926   \n",
       "ViT-g-14 openclip g_90.pt        0.565 0.290  0.648 0.833 0.238  0.935   \n",
       "ViT-H-14 openclip h_256.pt       0.564 0.301  0.666 0.836 0.239  0.925   \n",
       "ViT-L-14 openai                  0.558 0.286  0.635 0.823 0.239  0.917   \n",
       "ViT-L-14 openclip epoch_160.pt   0.546 0.288  0.593 0.820 0.239  0.904   \n",
       "ViT-L-14 laion400m_e32           0.518 0.296  0.596 0.802 0.243  0.901   \n",
       "ViT-B-32 openclip b_290.pt       0.508 0.281  0.557 0.783 0.246  0.886   \n",
       "ViT-B-16-plus-240 laion400m_e32  0.493 0.282  0.544 0.780 0.248  0.862   \n",
       "ViT-B-16 laion400m_e32           0.484 0.280  0.515 0.779 0.246  0.882   \n",
       "ViT-B-32 laion2b_e16             0.481 0.280  0.504 0.778 0.249  0.858   \n",
       "ViT-B-16 openai                  0.475 0.273  0.501 0.783 0.233  0.854   \n",
       "ViT-B-32-quickgelu laion400m_e32 0.458 0.272  0.493 0.757 0.254  0.858   \n",
       "ViT-B-32 openai                  0.449 0.262  0.443 0.764 0.239  0.854   \n",
       "\n",
       "                                 mean_per_class_recall               \\\n",
       "                                                  mean   std median   \n",
       "model_fullname                                                        \n",
       "ViT-L-14-336 openai                              0.558 0.292  0.631   \n",
       "ViT-g-14 openclip g_90.pt                        0.563 0.297  0.646   \n",
       "ViT-H-14 openclip h_256.pt                       0.572 0.298  0.666   \n",
       "ViT-L-14 openai                                  0.550 0.291  0.638   \n",
       "ViT-L-14 openclip epoch_160.pt                   0.551 0.290  0.593   \n",
       "ViT-L-14 laion400m_e32                           0.522 0.296  0.586   \n",
       "ViT-B-32 openclip b_290.pt                       0.494 0.285  0.537   \n",
       "ViT-B-16-plus-240 laion400m_e32                  0.500 0.280  0.545   \n",
       "ViT-B-16 laion400m_e32                           0.488 0.278  0.511   \n",
       "ViT-B-32 laion2b_e16                             0.484 0.280  0.511   \n",
       "ViT-B-16 openai                                  0.483 0.269  0.506   \n",
       "ViT-B-32-quickgelu laion400m_e32                 0.459 0.276  0.494   \n",
       "ViT-B-32 openai                                  0.450 0.267  0.443   \n",
       "\n",
       "                                 mean_average_precision             \n",
       "                                                   mean std median  \n",
       "model_fullname                                                      \n",
       "ViT-L-14-336 openai                               0.804 NaN  0.804  \n",
       "ViT-g-14 openclip g_90.pt                         0.807 NaN  0.807  \n",
       "ViT-H-14 openclip h_256.pt                        0.801 NaN  0.801  \n",
       "ViT-L-14 openai                                   0.790 NaN  0.790  \n",
       "ViT-L-14 openclip epoch_160.pt                    0.820 NaN  0.820  \n",
       "ViT-L-14 laion400m_e32                            0.785 NaN  0.785  \n",
       "ViT-B-32 openclip b_290.pt                        0.796 NaN  0.796  \n",
       "ViT-B-16-plus-240 laion400m_e32                   0.785 NaN  0.785  \n",
       "ViT-B-16 laion400m_e32                            0.784 NaN  0.784  \n",
       "ViT-B-32 laion2b_e16                              0.793 NaN  0.793  \n",
       "ViT-B-16 openai                                   0.789 NaN  0.789  \n",
       "ViT-B-32-quickgelu laion400m_e32                  0.762 NaN  0.762  \n",
       "ViT-B-32 openai                                   0.760 NaN  0.760  "
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"model_fullname\").agg(['mean', 'std', 'median']).sort_values(by=(\"acc1\", \"mean\"), ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4791ba73-3c54-4ef0-93a4-59b75b00378e",
   "metadata": {},
   "source": [
    "### Compute rank of the model for each dataset (1 = best, lower is better), then average the ranks over the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "aa0b6dda-b531-4954-b2bd-79d8c910d96d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_fullname</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ViT-g-14 openclip g_90.pt</th>\n",
       "      <td>3.286</td>\n",
       "      <td>2.295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-H-14 openclip h_256.pt</th>\n",
       "      <td>3.786</td>\n",
       "      <td>3.355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-L-14 openclip epoch_160.pt</th>\n",
       "      <td>4.529</td>\n",
       "      <td>2.440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-L-14-336 openai</th>\n",
       "      <td>4.871</td>\n",
       "      <td>3.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-L-14 openai</th>\n",
       "      <td>5.343</td>\n",
       "      <td>3.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-L-14 laion400m_e32</th>\n",
       "      <td>6.829</td>\n",
       "      <td>2.925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-B-32 openclip b_290.pt</th>\n",
       "      <td>7.114</td>\n",
       "      <td>3.332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-B-16-plus-240 laion400m_e32</th>\n",
       "      <td>7.971</td>\n",
       "      <td>2.285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-B-16 laion400m_e32</th>\n",
       "      <td>8.543</td>\n",
       "      <td>2.822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-B-16 openai</th>\n",
       "      <td>9.029</td>\n",
       "      <td>2.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-B-32 laion2b_e16</th>\n",
       "      <td>9.086</td>\n",
       "      <td>2.513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-B-32 openai</th>\n",
       "      <td>9.514</td>\n",
       "      <td>4.111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViT-B-32-quickgelu laion400m_e32</th>\n",
       "      <td>11.100</td>\n",
       "      <td>2.727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   mean   std\n",
       "model_fullname                               \n",
       "ViT-g-14 openclip g_90.pt         3.286 2.295\n",
       "ViT-H-14 openclip h_256.pt        3.786 3.355\n",
       "ViT-L-14 openclip epoch_160.pt    4.529 2.440\n",
       "ViT-L-14-336 openai               4.871 3.037\n",
       "ViT-L-14 openai                   5.343 3.038\n",
       "ViT-L-14 laion400m_e32            6.829 2.925\n",
       "ViT-B-32 openclip b_290.pt        7.114 3.332\n",
       "ViT-B-16-plus-240 laion400m_e32   7.971 2.285\n",
       "ViT-B-16 laion400m_e32            8.543 2.822\n",
       "ViT-B-16 openai                   9.029 2.875\n",
       "ViT-B-32 laion2b_e16              9.086 2.513\n",
       "ViT-B-32 openai                   9.514 4.111\n",
       "ViT-B-32-quickgelu laion400m_e32 11.100 2.727"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metric = \"acc1\"\n",
    "df_metric = pd.pivot(df, index=\"model_fullname\", columns=\"dataset\", values=metric).T.dropna()\n",
    "df_metric.rank(axis=1,ascending=False).agg([\"mean\", \"std\"]).T.sort_values(by=\"mean\",ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ed19b84-14c0-46ea-80b1-de99fd18370b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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