99_Accurate_Solution.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Input Handling and Data Preprocessing"
      ],
      "metadata": {
        "id": "xqyLNKCzLXG7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Importing the libraries"
      ],
      "metadata": {
        "id": "gk6N0j1miwEh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "import os"
      ],
      "metadata": {
        "id": "QcLGNBOIi1CN"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Importing the dataset\n"
      ],
      "metadata": {
        "id": "UJyPeVg9ifuO"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "6uwoWS2VhqHC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99184f15-98c9-4df4-958e-c17c9d490911"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/problems\n"
          ]
        }
      ],
      "source": [
        "%cd /content/problems"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "folder_path = '.'\n",
        "csv_files = [os.path.join(folder_path, f) for f in os.listdir(folder_path) if f.endswith('.csv')]\n",
        "\n",
        "# Print the file paths\n",
        "for file_path in csv_files:\n",
        "    print(file_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OF3Pm7ixxK5g",
        "outputId": "6d018816-b9e9-4033-933b-e2e88e68b808"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "./occlusion1_sol.csv\n",
            "./frag01_sol.csv\n",
            "./occlusion2_sol.csv\n",
            "./frag1.csv\n",
            "./isolated_sol.csv\n",
            "./isolated.csv\n",
            "./frag2.csv\n",
            "./occlusion2.csv\n",
            "./occlusion1.csv\n",
            "./frag0.csv\n",
            "./frag2_sol.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Utility Functions"
      ],
      "metadata": {
        "id": "V6_dq2JxjUCo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def group_segments_by_curve(csv_file, tolerance=1e-5):\n",
        "\n",
        "\n",
        "    # Read the CSV file\n",
        "    df = pd.read_csv(csv_file, header=None)\n",
        "    df.columns = ['segment_no', 'sub_segment', 'x', 'y']\n",
        "\n",
        "    # Initialize variables\n",
        "    segments = []\n",
        "    for segment_no in df['segment_no'].unique():\n",
        "        segment_df = df[df['segment_no'] == segment_no]\n",
        "        for sub_segment in segment_df['sub_segment'].unique():\n",
        "            sub_segment_df = segment_df[segment_df['sub_segment'] == sub_segment]\n",
        "            x_start = sub_segment_df['x'].values[0]\n",
        "            y_start = sub_segment_df['y'].values[0]\n",
        "            x_end = sub_segment_df['x'].values[-1]\n",
        "            y_end = sub_segment_df['y'].values[-1]\n",
        "            start_point = (x_start, y_start)\n",
        "            end_point = (x_end, y_end)\n",
        "            segments.append(((segment_no, sub_segment), start_point, end_point))\n",
        "\n",
        "    # Lists to store curves\n",
        "    closed_curves = []\n",
        "    open_curves = []\n",
        "\n",
        "    # Function to check if two points are approximately equal within tolerance\n",
        "    def points_match(point1, point2, tol):\n",
        "        return np.linalg.norm(np.array(point1) - np.array(point2)) < tol\n",
        "\n",
        "    # Iterate over segments\n",
        "    for i in range(len(segments)):\n",
        "        current_segment = segments[i]\n",
        "        cur_segment_id = current_segment[0] #tuple (segment_no, segment_id)\n",
        "        cur_start_point = current_segment[1]\n",
        "        cur_end_point = current_segment[2]\n",
        "\n",
        "        # Flag to determine if added to an existing curve\n",
        "        added_to_curve = False\n",
        "\n",
        "        # Check if current segment matches any open curve\n",
        "        for curve in open_curves:\n",
        "            first_segment_id = curve[0]\n",
        "            last_segment_id = curve[-1]\n",
        "\n",
        "            first_segment = next(seg for seg in segments if seg[0] == first_segment_id)\n",
        "            last_segment = next(seg for seg in segments if seg[0] == last_segment_id)\n",
        "\n",
        "            if points_match(last_segment[2], cur_start_point, tolerance):\n",
        "                # Add to the existing curve\n",
        "                curve.append(cur_segment_id)\n",
        "                added_to_curve = True\n",
        "                if points_match(first_segment[1], cur_end_point, tolerance):\n",
        "                    # Curve is closed\n",
        "                    closed_curves.append(curve)\n",
        "                    open_curves.remove(curve)\n",
        "                break\n",
        "            elif points_match(first_segment[1], cur_end_point, tolerance):\n",
        "                # Add to the existing curve\n",
        "                curve.insert(0, cur_segment_id)\n",
        "                added_to_curve = True\n",
        "                if points_match(first_segment[1], cur_start_point, tolerance):\n",
        "                    # Curve is closed\n",
        "                    closed_curves.append(curve)\n",
        "                    open_curves.remove(curve)\n",
        "                break\n",
        "\n",
        "        # If not added to any existing curve, start a new open curve\n",
        "        if not added_to_curve:\n",
        "            if points_match(cur_start_point, cur_end_point, tolerance):\n",
        "                # Curve is closed\n",
        "                closed_curves.append([cur_segment_id])\n",
        "            else:\n",
        "                open_curves.append([cur_segment_id])\n",
        "\n",
        "    return segments, closed_curves, open_curves"
      ],
      "metadata": {
        "id": "aLCoYvSUjX5h"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_all_points_from_curve(curve, csv_file):\n",
        "    \"\"\"Extract all points for the segments in the given curve from the CSV file.\"\"\"\n",
        "    df = pd.read_csv(csv_file, header=None)\n",
        "    df.columns = ['segment_no', 'sub_segment', 'x', 'y']\n",
        "\n",
        "    # Create a set of tuples from the curve list for filtering\n",
        "    curve_set = set(curve)\n",
        "\n",
        "    # Filter the DataFrame to include only rows with (segment_no, sub_segment) in the curve\n",
        "    curve_segments = df[df.apply(lambda row: (row['segment_no'], row['sub_segment']) in curve_set, axis=1)]\n",
        "\n",
        "    # Extract points\n",
        "    points = curve_segments[['x', 'y']].values.tolist()\n",
        "    return points"
      ],
      "metadata": {
        "id": "r3CRR64txju-"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def read_csv(csv_path):\n",
        "    \"\"\"Read the CSV file into a NumPy array\"\"\"\n",
        "    np_path_XYs = np.genfromtxt(csv_path, delimiter=',')\n",
        "    path_XYs = []\n",
        "    for i in np.unique(np_path_XYs[:, 0]):\n",
        "        npXYs = np_path_XYs[np_path_XYs[:, 0] == i][:, 1:]\n",
        "        XYs = []\n",
        "        for j in np.unique(npXYs[:, 0]):\n",
        "            XY = npXYs[npXYs[:, 0] == j][:, 1:]\n",
        "            XYs.append(XY)\n",
        "        path_XYs.append(XYs)\n",
        "    return path_XYs"
      ],
      "metadata": {
        "id": "QGs5-I--xll4"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Shape Identification & Regularisation"
      ],
      "metadata": {
        "id": "yqIjKDdxx9Bj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Importing the libraries"
      ],
      "metadata": {
        "id": "nvWFTQ5iyE8z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from scipy.signal import savgol_filter\n",
        "from io import BytesIO\n",
        "from PIL import Image as PILImage"
      ],
      "metadata": {
        "id": "K56E-4QeyB2K"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Utility Functions"
      ],
      "metadata": {
        "id": "xpRSioTKyaJH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For Checking Shape Lines"
      ],
      "metadata": {
        "id": "eJF6Ggkbyffn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def check_straight_line(points, tolerance):\n",
        "\n",
        "    if len(points) < 2:\n",
        "        return True  # Less than two points is trivially a straight line\n",
        "\n",
        "    # Fit a line to the points\n",
        "    X = np.array([p[0] for p in points]).reshape(-1, 1)\n",
        "    y = np.array([p[1] for p in points])\n",
        "    model = LinearRegression().fit(X, y)\n",
        "    slope, intercept = model.coef_[0], model.intercept_\n",
        "\n",
        "    # Calculate deviations from the line\n",
        "    deviations = np.abs(np.array([p[1] for p in points]) - (slope * np.array([p[0] for p in points]) + intercept))\n",
        "\n",
        "    # Check if all deviations are within tolerance\n",
        "    return np.all(deviations < tolerance)"
      ],
      "metadata": {
        "id": "BT5LYhmQyQnT"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "For Regularising Open Curves"
      ],
      "metadata": {
        "id": "9Uap4Zblypfa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def regularize_open_curves(csv_file, open_curves, tolerance=2, verbose=True):\n",
        "\n",
        "    open_plots = []\n",
        "    for curve in open_curves:\n",
        "        points = extract_all_points_from_curve(curve, csv_file)\n",
        "        x = [p[0] for p in points]\n",
        "        y = [p[1] for p in points]\n",
        "\n",
        "        # Smooth the y-values using Savitzky-Golay filter\n",
        "        if len(y) > 5:  # Ensure enough points for the filter\n",
        "            y_smoothed = savgol_filter(y, window_length=5, polyorder=2)\n",
        "        else:\n",
        "            y_smoothed = y\n",
        "\n",
        "        # Check if the curve is a straight line\n",
        "        if check_straight_line(points, tolerance):\n",
        "            if verbose:\n",
        "                print(\"Line detected\")\n",
        "            st_line_points = [x[0], y[0], x[-1], y[-1]]\n",
        "            open_plots.append([st_line_points, \"Line\"])\n",
        "        else:\n",
        "            # Plot the smoothed curve\n",
        "            open_plots.append([points, \"Irregular\"])\n",
        "\n",
        "    return open_plots"
      ],
      "metadata": {
        "id": "SW8V47yxyhqQ"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Shape Identification Using OpenCV Contours + Mathematical Formulas (Higher accuracy than CNN)"
      ],
      "metadata": {
        "id": "G8poevclyyy9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def identify_shapes(contour):\n",
        "\n",
        "    # Approximate contour\n",
        "    epsilon = 0.02 * cv2.arcLength(contour, True)\n",
        "    approx = cv2.approxPolyDP(contour, epsilon, True)\n",
        "\n",
        "    # Calculate area and perimeter\n",
        "    area = cv2.contourArea(contour)\n",
        "    perimeter = cv2.arcLength(contour, True)\n",
        "\n",
        "    # Filter out small contours\n",
        "    if area < 500:\n",
        "        return None\n",
        "\n",
        "    # Calculate the centroid\n",
        "    M = cv2.moments(contour)\n",
        "    if M[\"m00\"] == 0:  # To prevent division by zero\n",
        "        return None\n",
        "    cx = int(M[\"m10\"] / M[\"m00\"])\n",
        "    cy = int(M[\"m01\"] / M[\"m00\"])\n",
        "\n",
        "    # Shape classification\n",
        "    shape_name = \"Irregular\"\n",
        "    if len(approx) == 3:\n",
        "        shape_name = \"Triangle\"\n",
        "    elif len(approx) == 4:\n",
        "        # Check for rectangle or square using aspect ratio\n",
        "        x, y, w, h = cv2.boundingRect(approx)\n",
        "        aspect_ratio = float(w) / h\n",
        "        if 0.99 <= aspect_ratio <= 1.01:\n",
        "            shape_name = \"Square\"\n",
        "        else:\n",
        "            shape_name = \"Rectangle\"\n",
        "    elif len(approx) > 4:\n",
        "        # Calculate distances from centroid to all contour points\n",
        "        distances = [np.sqrt((cx - p[0][0])**2 + (cy - p[0][1])**2) for p in contour]\n",
        "\n",
        "        # Detect peaks and valleys\n",
        "        peaks = (np.diff(np.sign(np.diff(distances))) == -2).nonzero()[0] + 1\n",
        "        valleys = (np.diff(np.sign(np.diff(distances))) == 2).nonzero()[0] + 1\n",
        "\n",
        "        # Check for circle\n",
        "        mean_dist = np.mean(distances)\n",
        "        max_dist_deviation = 0.15 * mean_dist  # Allowable deviation (increased for rough circles)\n",
        "        if all(abs(d - mean_dist) < max_dist_deviation for d in distances):\n",
        "            shape_name = \"Circle\"\n",
        "        elif len(approx) == 10 and abs(len(peaks) - len(valleys)) <= 2:\n",
        "            shape_name = \"Star\"\n",
        "        else:\n",
        "            # Fit ellipse and check its properties\n",
        "            if len(contour) >= 5:\n",
        "                ellipse = cv2.fitEllipse(contour)\n",
        "                (center, axes, orientation) = ellipse\n",
        "                major_axis, minor_axis = max(axes), min(axes)\n",
        "                axis_ratio = minor_axis / major_axis\n",
        "\n",
        "                # Calculate focal points\n",
        "                focal_distance = np.sqrt(major_axis**2 - minor_axis**2) / 2\n",
        "                angle_rad = np.deg2rad(orientation)\n",
        "                focal1 = (int(center[0] + focal_distance * np.cos(angle_rad)),\n",
        "                          int(center[1] + focal_distance * np.sin(angle_rad)))\n",
        "                focal2 = (int(center[0] - focal_distance * np.cos(angle_rad)),\n",
        "                          int(center[1] - focal_distance * np.sin(angle_rad)))\n",
        "\n",
        "                # Check the ellipse property: sum of distances to focal points is constant\n",
        "                sum_distances = []\n",
        "                for p in contour:\n",
        "                    d1 = np.linalg.norm(np.array(p[0]) - np.array(focal1))\n",
        "                    d2 = np.linalg.norm(np.array(p[0]) - np.array(focal2))\n",
        "                    sum_distances.append(d1 + d2)\n",
        "\n",
        "                # Allow a small deviation\n",
        "                max_deviation = 0.1 * np.mean(sum_distances)\n",
        "                if max(sum_distances) - min(sum_distances) <= max_deviation:\n",
        "                    shape_name = \"Ellipse\"\n",
        "\n",
        "    # Return shape information\n",
        "    return {'shape': shape_name, 'approx': approx}"
      ],
      "metadata": {
        "id": "uYe5hIZ3ysFA"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Utility Function For plotting a curve"
      ],
      "metadata": {
        "id": "_x8w52tQzASA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_curve(curve):\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(8, 8))\n",
        "\n",
        "    x_min, x_max = float('inf'), float('-inf')\n",
        "    y_min, y_max = float('inf'), float('-inf')\n",
        "\n",
        "    points = curve[0]\n",
        "    x = [p[0] for p in points]\n",
        "    y = [p[1] for p in points]\n",
        "    ax.plot(x, y, 'o', color='black', markersize=5)\n",
        "    x_min = min(x_min, *x)\n",
        "    x_max = max(x_max, *x)\n",
        "    y_min = min(y_min, *y)\n",
        "    y_max = max(y_max, *y)\n",
        "\n",
        "    # Set limits based on the shapes\n",
        "\n",
        "    ax.set_xlim(x_min - 10, x_max + 10)\n",
        "    ax.set_ylim(y_min - 10, y_max + 10)\n",
        "    ax.set_aspect('equal')\n",
        "    ax.axis('off')  # Hide the axes\n",
        "    return fig, ax"
      ],
      "metadata": {
        "id": "EfbxqaQqy-oz"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "For Regularising Closed Shapes"
      ],
      "metadata": {
        "id": "KO4IRFQ4zGmZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def regularize_closed_curves(csv_file, closed_curves, margin, verbose=True):\n",
        "\n",
        "    final_closed_plots = []\n",
        "    for curve in closed_curves:\n",
        "        points = extract_all_points_from_curve(curve, csv_file)\n",
        "        adjusted_points = [[x + margin, y + margin] for x, y in points]\n",
        "        closed_plot = [adjusted_points, \"Original\"]\n",
        "\n",
        "        # Generate the plot using plot_curve\n",
        "        fig, ax = plot_curve(closed_plot)\n",
        "\n",
        "        # Convert the plot to a NumPy array (image format)\n",
        "        fig.canvas.draw()\n",
        "        image = np.frombuffer(fig.canvas.tostring_rgb(), dtype='uint8')\n",
        "        image = image.reshape(fig.canvas.get_width_height()[::-1] + (3,))\n",
        "        plt.close(fig)  # Close the figure to free memory\n",
        "\n",
        "        # Convert the image to grayscale\n",
        "        gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "        # Apply thresholding to get a binary image\n",
        "        _, thresh_image = cv2.threshold(gray_image, 200, 255, cv2.THRESH_BINARY)\n",
        "\n",
        "        # Find contours in the binary image\n",
        "        contours, _ = cv2.findContours(thresh_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
        "\n",
        "        i = 0\n",
        "\n",
        "        # Process each contour\n",
        "        for contour in contours:\n",
        "            # Remove outermost contour that represents image border\n",
        "            if i == 0:\n",
        "                i = 1\n",
        "                continue\n",
        "            # Only 1 contour needed per curve\n",
        "            if i > 1:\n",
        "                continue\n",
        "            # Calculate the centroid\n",
        "            M = cv2.moments(contour)\n",
        "            if M[\"m00\"] == 0:  # To prevent division by zero\n",
        "                continue\n",
        "            cx = int(M[\"m10\"] / M[\"m00\"])\n",
        "            cy = int(M[\"m01\"] / M[\"m00\"])\n",
        "            centroid = (cx, cy)\n",
        "\n",
        "            shape_info = identify_shapes(contour)\n",
        "            if shape_info:\n",
        "                # Check if the shape is a known geometric shape\n",
        "                if shape_info['shape'] in [\"Triangle\", \"Square\", \"Rectangle\", \"Circle\", \"Ellipse\", \"Star\"]:\n",
        "                    final_closed_plots.append([points, shape_info['shape']])\n",
        "                    if verbose:\n",
        "                        print(f\"{shape_info['shape']} detected\")\n",
        "                else:\n",
        "                    # Draw original contour for unknown shapes\n",
        "                    final_closed_plots.append([points, \"Irregular\"])\n",
        "            i += 1\n",
        "\n",
        "    return final_closed_plots"
      ],
      "metadata": {
        "id": "1pePHE9IzFrj"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def regularize(csv_file, verbose=True):\n",
        "\n",
        "    segments, closed_curves, open_curves = group_segments_by_curve(csv_file, tolerance=1)\n",
        "\n",
        "    margin = 100  # Define a margin size\n",
        "    closed_plots = []\n",
        "    open_plots = []\n",
        "\n",
        "    if closed_curves:\n",
        "        # Regularize closed curves\n",
        "        closed_plots = regularize_closed_curves(csv_file, closed_curves, margin, verbose=verbose)\n",
        "\n",
        "    if open_curves:\n",
        "        # Regularize open curves\n",
        "        open_plots = regularize_open_curves(csv_file, open_curves, verbose=verbose)\n",
        "\n",
        "    # Combine both closed and open plots\n",
        "    all_plots = closed_plots + open_plots\n",
        "\n",
        "    return all_plots"
      ],
      "metadata": {
        "id": "9hlKttrqzMpJ"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_regularized_image(plot_list, filename=\"regularized_image.png\", padding=0.5):\n",
        "    # Create a new figure for combined plots\n",
        "    fig, ax = plt.subplots(figsize=(8, 8))\n",
        "    ax.axis('off')\n",
        "\n",
        "    # Plot all curves using plot_curve on the same axis\n",
        "    for curve in plot_list:\n",
        "        points, shape_name = curve\n",
        "\n",
        "        if shape_name == \"Line\":\n",
        "            x_start, y_start, x_end, y_end = points\n",
        "            ax.plot([x_start, x_end], [y_start, y_end], color='black', linewidth=3)\n",
        "\n",
        "        elif shape_name == \"Triangle\":\n",
        "            # Convert points to a numpy array\n",
        "            points = np.array(points)\n",
        "\n",
        "            # Calculate the centroid (mean of the points)\n",
        "            centroid_x = np.mean(points[:, 0])\n",
        "            centroid_y = np.mean(points[:, 1])\n",
        "\n",
        "            # Calculate distances from centroid to each point\n",
        "            distances = np.sqrt((points[:, 0] - centroid_x) ** 2 + (points[:, 1] - centroid_y) ** 2)\n",
        "            mean_distance = np.mean(distances)\n",
        "\n",
        "            # Create an equilateral triangle around the centroid\n",
        "            angles = np.linspace(0, 2 * np.pi, 4)[:-1]  # 3 points for an equilateral triangle\n",
        "\n",
        "            triangle_points = []\n",
        "            for angle in angles:\n",
        "                x = centroid_x + mean_distance * np.cos(angle)\n",
        "                y = centroid_y + mean_distance * np.sin(angle)\n",
        "                triangle_points.append([x, y])\n",
        "\n",
        "            triangle_points = np.array(triangle_points)\n",
        "\n",
        "            # Draw the triangle by connecting the points\n",
        "            ax.plot(triangle_points[:, 0], triangle_points[:, 1], color='black', linewidth=3)\n",
        "            ax.plot([triangle_points[-1, 0], triangle_points[0, 0]], [triangle_points[-1, 1], triangle_points[0, 1]], color='black', linewidth=3)\n",
        "\n",
        "        elif shape_name == \"Circle\":\n",
        "            # Convert points to a numpy array\n",
        "            points = np.array(points)\n",
        "\n",
        "            # Calculate the centroid (mean of the points)\n",
        "            centroid_x = np.mean(points[:, 0])\n",
        "            centroid_y = np.mean(points[:, 1])\n",
        "\n",
        "            # Calculate the radius as the mean distance from the centroid to each point\n",
        "            distances = np.sqrt((points[:, 0] - centroid_x)**2 + (points[:, 1] - centroid_y)**2)\n",
        "            radius = np.mean(distances)\n",
        "            circle = plt.Circle((centroid_x, centroid_y), radius, color='black', linewidth=3, fill=False)\n",
        "            ax.add_patch(circle)\n",
        "\n",
        "        elif shape_name == \"Ellipse\":\n",
        "            # Convert points to a numpy array\n",
        "            points = np.array(points)\n",
        "\n",
        "            # Calculate the centroid (mean of the points)\n",
        "            centroid_x = np.mean(points[:, 0])\n",
        "            centroid_y = np.mean(points[:, 1])\n",
        "            centroid = np.array([centroid_x, centroid_y])\n",
        "\n",
        "            # Center the points by subtracting the centroid\n",
        "            centered_points = points - centroid\n",
        "\n",
        "            # Calculate the covariance matrix of the centered points\n",
        "            covariance_matrix = np.cov(centered_points, rowvar=False)\n",
        "\n",
        "            # Eigenvalues and eigenvectors of the covariance matrix\n",
        "            eigenvalues, eigenvectors = np.linalg.eigh(covariance_matrix)\n",
        "\n",
        "            # Calculate the major and minor axes lengths\n",
        "            major_axis_length = 2 * np.sqrt(eigenvalues[1])\n",
        "            minor_axis_length = 2 * np.sqrt(eigenvalues[0])\n",
        "\n",
        "            # Calculate the angle of the major axis with respect to the x-axis\n",
        "            angle = np.arctan2(eigenvectors[1, 1], eigenvectors[0, 1])\n",
        "\n",
        "            # Draw the ellipse\n",
        "            ellipse = plt.Ellipse(centroid, major_axis_length, minor_axis_length, np.degrees(angle), color='black', linewidth=3, fill=False)\n",
        "            ax.add_patch(ellipse)\n",
        "\n",
        "        elif shape_name in [\"Rectangle\", \"Square\"]:\n",
        "            # Convert points to a numpy array for easier manipulation\n",
        "            points = np.array(points)\n",
        "\n",
        "            x_min, y_min = np.min(points, axis=0)\n",
        "            x_max, y_max = np.max(points, axis=0)\n",
        "\n",
        "            # Calculate width and height\n",
        "            width = x_max - x_min\n",
        "            height = y_max - y_min\n",
        "\n",
        "            # Calculate the centroid\n",
        "            centroid_x = (x_min + x_max) / 2\n",
        "            centroid_y = (y_min + y_max) / 2\n",
        "\n",
        "            # Draw the rectangle or square\n",
        "            rect = plt.Rectangle((centroid_x - width / 2, centroid_y - height / 2), width, height, color='black', linewidth=3, fill=False)\n",
        "            ax.add_patch(rect)\n",
        "\n",
        "        elif shape_name == \"Star\":\n",
        "            points = np.array(points)\n",
        "\n",
        "            # Calculate the centroid of the star\n",
        "            centroid_x = np.mean(points[:, 0])\n",
        "            centroid_y = np.mean(points[:, 1])\n",
        "            centroid = np.array([centroid_x, centroid_y])\n",
        "\n",
        "            # Calculate the distance of each point from the centroid\n",
        "            distances = np.sqrt((points[:, 0] - centroid_x) ** 2 + (points[:, 1] - centroid_y) ** 2)\n",
        "\n",
        "            # Sort the points by distance to centroid\n",
        "            sorted_indices = np.argsort(distances)\n",
        "            sorted_points = points[sorted_indices]\n",
        "\n",
        "            # Extract highest and lowest points\n",
        "            num_peaks = 5\n",
        "            num_valleys = 5\n",
        "\n",
        "            # Extract the top 5 peaks and bottom 5 valleys\n",
        "            peaks = sorted_points[-num_peaks:]\n",
        "            valleys = sorted_points[:num_valleys]\n",
        "\n",
        "            # Calculate the mean distances\n",
        "            mean_peak_distance = np.mean([np.sqrt((p[0] - centroid_x) ** 2 + (p[1] - centroid_y) ** 2) for p in peaks])\n",
        "            mean_valley_distance = np.mean([np.sqrt((v[0] - centroid_x) ** 2 + (v[1] - centroid_y) ** 2) for v in valleys])\n",
        "\n",
        "            # Create star points with equal angular separation\n",
        "            num_points = num_peaks + num_valleys\n",
        "            angles = np.linspace(0, 2 * np.pi, num_points, endpoint=False)\n",
        "\n",
        "            # Generate star points based on calculated mean distances\n",
        "            star_points = []\n",
        "            for i, angle in enumerate(angles):\n",
        "                if i % 2 == 0:  # Peak\n",
        "                    x = centroid_x + mean_peak_distance * np.cos(angle)\n",
        "                    y = centroid_y + mean_peak_distance * np.sin(angle)\n",
        "                    star_points.append([x, y])\n",
        "                else:  # Valley\n",
        "                    x = centroid_x + mean_valley_distance * np.cos(angle)\n",
        "                    y = centroid_y + mean_valley_distance * np.sin(angle)\n",
        "                    star_points.append([x, y])\n",
        "\n",
        "            star_points = np.array(star_points)\n",
        "\n",
        "            # Draw the star by connecting the points\n",
        "            ax.plot(star_points[:, 0], star_points[:, 1], color='black', linewidth=3)\n",
        "            ax.plot([star_points[-1, 0], star_points[0, 0]], [star_points[-1, 1], star_points[0, 1]], color='black', linewidth=3)\n",
        "\n",
        "        else:\n",
        "            # For other shapes, plot the points as they are\n",
        "            x = [p[0] for p in points]\n",
        "            y = [p[1] for p in points]\n",
        "            ax.plot(x, y, '.', color='black', linewidth=2)\n",
        "\n",
        "    # Set equal scaling and title\n",
        "    ax.set_aspect('equal', 'box')\n",
        "\n",
        "    # Save plot to an in-memory buffer\n",
        "    buf = BytesIO()\n",
        "    plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=padding)\n",
        "    plt.close(fig)\n",
        "\n",
        "    # Load the image from the buffer and return it\n",
        "    buf.seek(0)\n",
        "    image = PILImage.open(buf)\n",
        "    image_path = 'regularized_output.png'\n",
        "    image.save(image_path)\n",
        "    return image_path"
      ],
      "metadata": {
        "id": "GYHSs-K_zRyc"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Function to Plot Test Case and Rgularised Output (Side by Side)"
      ],
      "metadata": {
        "id": "56z_cKj-zWKj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_side_by_side(csv_file):\n",
        "\n",
        "    # Extract the base name of the file without the extension\n",
        "    base_name = os.path.splitext(os.path.basename(csv_file))[0]\n",
        "\n",
        "    # Read original paths from CSV\n",
        "    original_paths = read_csv(csv_file)\n",
        "\n",
        "    # Regularize curves\n",
        "    regularized_plots = regularize(csv_file)\n",
        "\n",
        "    # Create a new figure with two subplots\n",
        "    fig, axes = plt.subplots(1, 2, figsize=(16, 8))  # Two subplots side by side\n",
        "\n",
        "    # Plot the original image\n",
        "    ax1 = axes[0]\n",
        "    for XYs in original_paths:\n",
        "        for XY in XYs:\n",
        "            ax1.plot(XY[:, 0], XY[:, 1], c='k', linewidth=3)\n",
        "    ax1.set_aspect('equal')\n",
        "    ax1.axis('off')  # Hide axes for the original image\n",
        "    ax1.set_title(\"Original Image\")\n",
        "\n",
        "    # Plot the regularized image\n",
        "    ax2 = axes[1]\n",
        "    regularized_image_path = get_regularized_image(regularized_plots, filename = f\"{base_name}_regularized.png\", padding=0)\n",
        "    regularized_image = cv2.imread(regularized_image_path)\n",
        "    ax2.imshow(regularized_image)\n",
        "    ax2.axis('off')  # Hide axes for the regularized image\n",
        "    ax2.set_title(\"Regularized Image\")\n",
        "\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "AVO2G2eVzVfx"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Calling the Regularisation & Shape Identification"
      ],
      "metadata": {
        "id": "L00jxldWzfok"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_side_by_side('frag0.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        },
        "id": "30rl59Tuzl38",
        "outputId": "544913bc-2d18-4c92-b1d4-98f472f9f8ce"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Circle detected\n",
            "Rectangle detected\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 2 Axes>"
            ],
            "image/png": 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KtgYNGsDV1RUTJ07EwYMH0aFDBwwYMAAeHh5ITEzEvXv3sGfPHrx48QI2Njbo2LEjGjZsiMmTJ+PFixdwc3PD3r171ebTK0g8piXSDQzriLRc9+7dUalSJSxYsEAK6KytrdGsWTNMnToVVatW/aLn37RpExwcHLB9+3bs27cPLVu2xM6dO1GxYkUYGRnlyXuYOnUqEhMTsW3bNuzcuRPu7u44cuQIJk+enCfPnxcGDBiAsLAwrFq1CidOnICbmxu2bNmC3bt349y5c9J+crkcR44cwZgxY7Bx40bIZDJ4eXlh5syZaNiwocrPzM3NDbdu3cLs2bOxYcMGREdHw87ODrVq1cKMGTM08C6JiIiI/k+XLl1QtmxZLF68GEOGDMn1sYuzszPOnj2L0aNHY/78+bC1tcWIESNgamqK0aNHqxwPDRkyBIGBgVi3bh2OHz+ORo0a4eTJk2jRosUHa9PX18ehQ4cwevRoLFiwAEZGRvDy8sLIkSNRo0aNz37PkZGRSExMxA8//KC2bf369XB1dYWJiQnOnz+P+fPnY/fu3di0aRPMzc1RoUIFzJ49GxYWFgDerZ568OBBjB07Flu2bIEgCOjUqROWLFmCWrVqfXaNX4LHtES6QRCzjk0joiLP19cXtWrVwpYtW9C7d29Nl1Mo7N+/H15eXrh06RIaNmyo6XKIiIiICtzYsWOxatUqJCQk5LgYAmk3HtMSaQfOWUdUxCUnJ6u1LV26FDKZDI0bN9ZARdov688sc24Sc3NzuLu7a6gqIiIiooKT9XgoOjoamzdvhqenJ4O6QoLHtETai8NgiYq4RYsW4fbt22jWrBn09PRw7NgxHDt2DD/88AOcnZ01XZ5WGjVqFJKTk/HVV18hNTUVe/fuxZUrVzB//nwYGxtrujwiIiKifPfVV1+hadOmqFy5MsLDw7Fu3Tq8ffsW06dP13RplEs8piXSXhwGS1TEnTx5ErNnz8bDhw+RkJCAUqVKoW/fvpg2bRr09JjnZ2fbtm1YsmQJnj17hpSUFJQrVw7Dhw/HyJEjNV0aERERUYGYOnUq9uzZg9evX0MQBLi7u2PmzJlo2bKlpkujXOIxLZH2YlhHRERERERERESkJThnHRERERERERERkZZgWEdERERERERERKQlGNYRERERERERERFpCYZ1REREREREREREWoJhHRERERERERERkZZgWEdERERERERERKQlGNYRERERERERERFpCYZ1REREREREREREWoJhHRERERERERERkZZgWEdERERERERERKQlGNYRERERERERERFpCYZ1REREREREREREWoJhHRERERERERERkZZgWEdERERERERERKQlGNYRERERERERERFpCYZ1REREREREREREWoJhHRERERERERERkZZgWEdERERERERERKQlGNYRERERERERERFpCYZ1REREREREREREWoJhHRERERERERERkZZgWEdERERERERERKQlGNYRERERERERERFpCYZ1REREREREREREWoJhHRERERERERERkZbQ03QBRERERESaJIoiMjIykJ6erulSiIiISAsYGBhALpdDEASNvD7DOiIiIiIq0jIyMnDq1CkcPXpU06UQERGRFujZsyfq168PPT3NxGYM66jIS0lJQXR0NGJiYhAbG4v4+PgP3hISEpCUlITU1FSVW1pamtr93NDT04OhoaF0MzAwULlvaGgIIyMjmJmZoVixYtLt/fuZ3xcvXhxWVlawsrKCubm5xq4CEBERFSYKhQI3btzA8uXLNV0KERERaYEqVaqgbt26Gnt9hnWkUxITExEeHo6IiAiEh4cjMjJSCuLev73flpycrNGa09LSkJSUlOfPK5fLVcK77G52dnawt7eXbsWLF2fAR0RERERERKRBDOtIq4miiOjoaISHh0shXGYQl93X/Ai9CiuFQoGoqChERUXl+jH6+vpqAZ69vb1am6OjI4M9IiIiIiIionzAsI40IjU1FWFhYQgLC0NoaGiOX8PDwznZcwFKT09HcHAwgoODP7qvkZERHB0dP3grUaIEDAwMCqByIiKivPX777+jXr16kMlkmi6FiIiI8lFGRgZOnjyJefPmaboUCcM6yjOiKOLNmzcfDN8yv8bGxmq63GxZWFjA3NxcZW64rDczMzOYmJiozSuX9WZgYPDRnmeZq899bP675ORkJCQkSHPmvf818/u3b98iNja2wMLNlJQUPH/+HM+fP//gfnZ2dioBnpOTk1qoZ2FhwV56RESkVSpVqoR69epBLpdruhQiIiLKR+np6QgICNB0GSoY1tFHpaenIzw8/KMBXFhYGFJTUzVdLoB3vb7s7e1ha2sLGxsbWFlZwdraWm3OtvfbLC0tC/0BuSiKSExMVJujL7tbVFSUNLw4Li4u32rKHLrs4+OT4z4mJia56qWnqZV4iIio6JHJZJDJZIX+2ICIiIg+TKFQaF3nEZ75FlGiKCI+Pj5XveA+Zc6z/FS8eHFp7rSPfTUzM9O6X7aCIAgCzMzMYGZmhlKlSuX6campqdLcfx+7RUdH53ndSUlJePr0KZ4+fZrjPoIgwN7ePtueee/fzM3N87w+IiIiIiIiooLCsE7HZGRkIDIyMlchnKZXQQUAAwMDODg4wMHBASVKlMjxq52dHQwNDTVdrs4yNDSEs7MznJ2dP7pvWloaQkNDpbntcrqlpKTkaY2iKErzHN66dSvH/czMzHIM8pycnODs7Aw7OzvOQURERERERERaiWFdIZGYmIjQ0NCPhnCRkZFQKpWaLheWlpYfDN8yv3JF0cLHwMAALi4ucHFxyXEfURQRGxuL4OBgvH79OsdALz96bSYkJMDf3x/+/v457qOnp6cS3jk5OUm3zPv29vYc+kREREREREQFjmGdBimVSkRFReWqF1xCQoKmy4Wenh7s7e0/GsI5ODjAyMhI0+WSBgmCIM0FWK1atRz3S01NRUhIiEqAlzXcCwkJQVpaWp7Wl5GRgaCgIAQFBeW4j1wuR8mSJXMM85ycnODg4MB59IiIiIiIiChP8SwzHyQnJ+dqMYbw8HAoFApNlwtzc/OP9oBzcHCAtbU1hw5SnjI0NESZMmVQpkyZHPcRRRFRUVE59s7LDPfyeoVhhUKBV69e4dWrVznuI5fLUaJEiWyDvMz7XBiDiIiIiIiIPgXPIHNJFEXExMSoBG45hXD5ubJmbslkMtjb2+cqhDMxMdF0uUQ5EgQBtra2sLW1Rc2aNXPcLykpSa2XXtZQLyQkJE8DcoVCgdevX+P169c57iOTyeDg4JBjmOfs7IySJUsy0CMiIiIiIiIADOuQmpqK8PDwj/aCCwsLQ3p6uqbLhampqRS0fSiEs7Gx4XxbVKSYmJigXLlyKFeuXI77KBQKhIeHSwFb5u3Vq1fS98HBwXn6u65UKhESEoKQkBDcuHEj231kMhkcHR1RqlQptZuzszNKlSoFS0tLzu9IRERERERUBBSpsE4URZQpUwYREREA3s1bpQ0BXGbPodwsyGBmZqbpcokKrcx56EqWLIm6detmu49SqURERESOYV7mLS/n0VMqldKQ28uXL2e7j5mZWbZhXubN0dERBgYGeVYTERERERERaUaRCusEQUBwcDAyMjIK5PWMjIxUgracQjg7OzsOgSPSEpnDVh0cHFC7du1s98lcHCanMC/zfmpqap7VlZCQgIcPH+Lhw4fZbhcEAQ4ODjmGec7OzrCxsWHvPCIiIiIiIi1X5BKivDhRtba2zlUvOHNzc54YE+kgmUwGOzs72NnZwd3dPdt9RFFEdHT0B8O8V69eISUlJU9qEkVRmkvz+vXr2e5jZGQkhXelS5dWu5UoUYKLyBAREREREWlYkQvrRFHMtt3AwCBXizHY29tzqBkRfZQgCLCxsYGNjU2OC2NkBnovX77M8RYWFpbj59anSklJwZMnT/DkyZNstxsYGOQY5DHMIyIiIiIiKhhFKqxLS0tTGwJ74MABeHp6onjx4uwFR0QF6v1AL6ceemlpaQgODv5goJeQkJAn9aSlpeHZs2d49uxZttv19fU/GuZxYRsiIiIiIqIvU6TCuujoaLW2+vXrw8rKSgPVEBF9nIGBAcqUKYMyZcpku10URcTFxX0wzAsODoZSqfziWtLT0/H8+XM8f/482+1ZwzxXV1fpVrZsWVhZWfGiCBERERER0UcUqbAuKipKrY1BHREVZoIgwNLSEpaWlqhevXq2+2RkZCA0NBQvX75EUFAQXr58iRcvXqjc8mIxjI+Feebm5lJw936I5+rqilKlSkFfX/+LayAiIiIiIirsilRYFxkZqXLfysqKq7ASkc7T09ODs7MznJ2d0bBhQ7XtSqUSERERagFeXod5b9++ha+vL3x9fdW2yeVylCpVSq03Xub3xYsX/+LXJyIiIiIiKgyKVFKVtWedjY2NhiohItIeMpkMDg4OcHBwQP369dW2Zw3zgoKC1MK8L13VVqFQIDAwEIGBgTh9+rTa9uLFi6uEeOXKlUP58uVRvnx5ODg4cHgtERERERHpDIZ1RET0QR8L80RRVOuZFxAQgMDAQDx//hxBQUFQKBRfVENsbCxu376N27dvq20zMzNDuXLlVAK8zJudnR2DPCIiIiIiKlQY1hER0RcRBAH29vawt7dHvXr11LZnZGTg1atXeP78OQICAhAQECB9//z5c8TFxX3R6yckJOQ4vLZYsWIoX758tkGejY0NgzwiIiIiItI6RSqsyzpnna2trYYqISIqOvT09D64om1MTIxaiJf5/atXr75oJdv4+HjcuXMHd+7cUdtmYWEhBXkVK1ZExYoVUalSJVSoUAGmpqaf/ZpERERERERfokiFdexZR0SkfaysrGBlZYXatWurbUtLS8PLly9VeuI9f/4cT58+xbNnz75o4Yu4uDjcunULt27dUtvm7OyMSpUqoVKlSlKIV6lSJZQsWZK98YiIiIiIKF8xrCMiIq1lYGAgzUeXlVKpxOvXr/H06VMpvMv8/vnz50hLS/vs13316hVevXqFkydPqrSbmZlJ4d37IV758uVhZGT02a9HRERERESUiWEdEREVSjKZDKVKlUKpUqXQokULlW0KhQKvXr1SC/GePn2KgIAApKenf9ZrJiQkZLvQhSAIKF26tEqI5+bmhipVqsDKyuqz3yMRERERERU9RSqs45x1RERFg1wuR+nSpVG6dGm0atVKZVtGRgZevnwphXhPnjzBkydP8PjxYwQFBUEUxU9+PVEUERgYiMDAQBw7dkxlm729PapUqSKFd5nfW1tbf9F7JCIiIiIi3VRkwjpRFNmzjoiIoKenB1dXV7i6uqJ169Yq25KTk/H06VM8fvwYjx8/hr+/v/Q1MTHxs14vPDwc4eHhOHPmjEq7vb29WoBXpUoVhnhEREREREVckQnrEhMT1SYiZ1hHRETvMzY2RvXq1VG9enWVdlEUERwcrBbiPX78GK9fv/6s18oM8c6ePavSbmdnpxLgVatWDdWqVYOFhcVnvy8iIiIiIio8ikxYl7VXHcCwjoiIckcQBDg5OcHJyQktW7ZU2ZaQkIAnT56oBHiPHj2Cv7//Zy1yERERgYiICLUQz8XFBdWqVZPCxGrVqqFChQrQ0ysyf8qJiIiIiIqEInOEn3W+On19fZibm2uoGiIi0hVmZmZwd3eHu7u7SntGRgaeP3+OBw8e4MGDB3j48CEePHjw2SFeUFAQgoKCcPjwYanN0NAQbm5uUniXGeTZ29t/8fsiIiIiIiLNKDJhXXbz1QmCoKFqiIhI1+np6aFixYqoWLEiunTpIrW/H+JlBngPHz7E48ePPznES01NhY+PD3x8fFTabW1tpeAuM8irUqUKjIyM8uS9ERERERFR/inSYR0REVFB+1iI936Ad+/ePTx+/BgZGRmf9BqRkZE4ffo0Tp8+LbXJ5XK4ubmhVq1a0q1mzZqcC4+IiIiISMswrCMiItIC74d4Xl5eUntaWhr8/f1x9+5d6Xbv3j0EBwd/0vMrFArcu3cP9+7dw6ZNm6R2V1dXKbxzd3dHrVq14ODgkGfvi4iIiIiIPk2RCeuyzllna2uroUqIiIhyz8DAQFoRtnfv3lJ7dHQ07t27J4V3d+/exf3795GUlPRJzx8QEICAgAB4e3tLbQ4ODio98GrVqgVXV1dOH0FEREREVACKTFjHnnVERKRLrK2t0bRpUzRt2lRqUygUCAgIkMK7zCDv+fPnEEUx188dFhaGY8eO4dixY1Kbubk5atasCQ8PD9SuXRt16tRBuXLlGOAREREREeUxhnVEREQ6Qi6Xo3z58ihfvrzKfHjx8fG4e/cu7ty5Iy1I8eDBA6Snp+f6ud++fYsLFy7gwoULUpulpSU8PDxQp04dKcBzdnZmgEdERERE9AWKbFjHYbBERFRUFCtWDA0bNkTDhg2ltrS0NDx48EAK73x8fODr64vExMRcP++bN2/UFrKws7OTgrvMr/b29nn6foiIiIiIdFmRCeuyzlnHnnVERFSUGRgYSPPRZVIqlXj27JlKDzwfHx+1C14fEhERgaNHj+Lo0aNSm52dHZycnODo6Ah7e3u0bt0a3bt3z9P3Q0RERESkK4pMWMdhsERERB8mk8lQoUIFVKhQAd9++y0AQBRFBAcHw8fHB3fu3MGtW7dw8+ZNhIeH5/p5IyIiEBERgTt37gAANm3ahG7dunG4LBERERFRNopEWKdQKBATE6PSxrCOiIjo4wRBgJOTE5ycnNCxY0cA/xfg3bx5Uwrvbt26hdjY2Fw9p5WVFYM6IiIiIqIcFImw7s2bN1AqlSptnLOOiIjo87wf4Hl5eQF4F+AFBASoBHi3b9/Odg48MzOzgi6ZiIiIiKjQKBJhXdb56gDA2tpaA5UQERHpJkEQULZsWZQtW1YaQqtQKODv74958+Zh27Zt0r7GxsaaKpOIiIiISOvJNF1AQcg6X52ZmRmMjIw0VA0REVHRIJfL4ebmhubNm6u0828wEREREVHOimRYx/nqiIiICo6pqanK/eyGxhIRERER0TtFMqzjfHVEREQFJ+scdQkJCRqqhIiIiIhI+xWJsC7rnHXsWUdERFRwGNYREREREeVekQjrOAyWiIhIc7KGdRwGS0RERESUM4Z1RERElK+yhnWpqalIT0/XUDVERERERNqtSIZ1nLOOiIio4GQN6wD2riMiIiIiykmRCOs4Zx0REZHmZBfWcd46IiIiIqLsFYmwjsNgiYiINMfU1FStjWEdEREREVH2GNYRERFRvtLX14eBgYFKG8M6IiIiIqLs6XxYl5qaivj4eJU2zllHRERUsLIOhWVYR0RERESUPZ0P67L2qgPYs46IiKigZQ3ruMAEEREREVH2ilxYJwgCihcvrqFqiIiIiib2rCMiIiIiyp0iF9ZZWVlBLpdrqBoiIqKiiWEdEREREVHuFLmwjvPVERERFTyGdUREREREuaPzYV1kZKTKfc5XR0REVPBMTU1V7mdd/ImIiIiIiN7R+bAua886hnVEREQFjwtMEBERERHlDsM6IiIiyndZe9YxrCMiIiIiyl6RC+s4Zx0REVHBY886IiIiIqLc0fmwjnPWERERaV7WnnVcYIKIiIiIKHs6H9ZxGCwREZHmcRgsEREREVHuMKwjIiKifMdhsEREREREuaPTYZ0oipyzjoiISAtwGCwRERERUe7odFgXHx+PtLQ0lTb2rCMiIip4HAZLRERERJQ7Oh3WZe1VBzCsIyIi0gQOgyUiIiIiyp0iFdYZGBionSwQERFR/uMwWCIiIiKi3ClSYZ2trS0EQdBQNUREREUXh8ESEREREeWOTod1kZGRKvc5BJaIiEgzsvZsT01NRUZGhoaqISIiIiLSXjod1mXtWcewjoiISDOy9qwD2LuOiIiIiCg7DOuIiIgo3zGsIyIiIiLKHZ0O66Kjo1XuW1tba6gSIiKioi27BZ64yAQRERERkTqdDuvYs46IiEg7GBgYQC6Xq7SxZx0RERERkTqdDuvYs46IiEg7CILAFWGJiIiIiHKBYR0REREViKxDYTkMloiIiIhIHcM6IiIiKhDsWUdERERE9HE6G9aJosiwjoiISItk7VnHsI6IiIiISJ3OhnUJCQlIT09XaeMCE0RERJqTtWcdh8ESEREREanT03QB+SXrSrAAe9YRERFpEofBEhHRl1AoFEhJSUFSUhKSkpKQmpoKpVIJhUIBURQhiiIAQCaTQRAEyGQyyOVyGBkZwdjYGCYmJjAyMoJMprN9VohIR+hsWJd1CKyenh6KFSumoWqIiIiIw2CJiCiTKIpIT09HVFQUIiIiVG6RkZGIjY1FQkICEhMTpVtKSgoUCoUU0CmVSimgy/yaSRAE6WtmaJf51cTEBCYmJjA1NYWpqSnMzMxgZWUFOzs72NnZwd7eHra2trCzs4O1tTXkcrn0fEREBaHIhHXW1tb8gCUiItIgDoMlIip64uLi8OLFCzx//hwBAQEICAjAq1evEB4ejujoaGRkZKjcFAqF9DUzjFMqlSrB3JfKDPDev8nlcsjlcujp6UFPT0/63sDAADY2NrC3t4eLiwvKlCkDV1dXlCtXDi4uLmp/24iI8kKRCuuIiIhIczgMlohIN4miiISEBPj7++P+/fvw9/fHkydP8OTJE4SHhyM9PV0lhMvaK04T9WbWkRsBAQEqgV7mzcDAAI6OjqhQoQIqVKiAihUronr16ihXrhyMjY3z+V0QkS5jWEdEREQFgsNgiYgKv8ygKyAgALdu3cLNmzdx8+ZNPH78GAkJCWq94fKTvr4+9PX1pd5xmSOp3n/9tLQ0ZGRkfNHrZD5Xds8TGRkJPz8/lXnyLC0tUbVqVdSuXRt16tRB7dq14eTkpFIjEdGHFJmwjivBEhERaRaHwRIRFQ7v93gTRRFxcXG4fPkyLl++jFu3bsHHxwexsbE5zheXG+/PKWdsbIySJUvC0dFRutnb28PS0hLFihXL9mZmZgZ9ff1cvVZqaioSEhIQHx+P+Ph46fu3b98iPj4esbGxCAsLQ0hICF6/fo3g4GCEhoYiNTU1259J1p9VZjiYKTw8HOHh4Thz5oz0Hu3s7ODh4YHatWujUaNGqFevHkxMTFTCOwZ5RJRJZ8O6rKvBsmcdERGRZnEYLBGRdsoMnDJDp4SEBPj6+uL06dM4c+YMbt26hfT09Fw9lyAIajd9fX2UKVNGGiparlw5ODs7o2TJkihZsiQsLCw+a4XW3IZbxsbGMDY2hq2tba6fW6lUIiYmBiEhIQgJCcGrV6+kob1PnjxBUFCQyiq0798yvR9mhoWF4ciRIzhy5AgAwMjICF999RVatGiB5s2bo2rVqtJKtZk/C4Z3REWXzoZ1HAZL9GEKhQKRkZEIDQ1FTEwMoqOjVb7GxsYiKSkJiYmJKl+zrsKV+X3mgZi+vj4MDAykr8bGxjA3N0exYsVUvlpZWcHW1lZaacvW1hbW1tbQ09PZjyWiIo/DYImItEdmMJeWlobk5GQ8f/4c58+fx9mzZ3H16lXExsZ+9DkEQVBZjMHU1BRly5ZFlSpVpGCuQoUKKF26NAwMDDQWPn3O68rlculYtUaNGirbRFFEcnIyAgIC8OTJE/j7+0vz9QUFBSE5OVlljr7seuWlpKTg7NmzOHv2rNTzztPTE82bN0ejRo1QqlQpGBoaSsN8GdwRFS06e1bMsI6Kuvj4eGnFrYCAAAQGBiI4OFi6OhgaGprrSXULiiAIcHBwgLOzM0qVKqVyK1u2LMqVKwcTExNNl0lEn4nDYImINEsURSmcS0hIwMOHD3Hs2DH8999/ePz48UfnmDM0NISBgQEMDAxgZGSEEiVKoFatWnB3d0etWrVQqVIlmJub63ywJAgCTExMULVqVVStWlVqF0UR0dHRePDgAXx8fODj4wNfX19ERkYiNTUVaWlp0u19oigiPDwc3t7e8Pb2hr6+PqpXr462bduibdu2KFu2LExMTGBiYgI9PT2d//kSEcM6okJNqVTi5cuXePjwIR49eoSHDx/i4cOHeP78OSIjIzVd3icTRRGhoaEIDQ3FjRs3st2nVKlSKituVa1aFTVq1ODvOFEhwGGwREQFTxRFJCYm4u3bt3jz5g18fHxw5swZnD17FoGBgR98rJGREczMzGBqagpLS0vUrFkT7u7uqFq1Ktzc3GBvb8/g6D2CIMDGxgZNmjRBkyZNALw7Xg8ODsaDBw9w7949+Pj44O7du9LceYmJiSpz4wFAeno6bt++jdu3b2PevHmoXLkymjdvjhYtWsDNzQ3m5uYwNzdXm/OOiHQHwzqiQiIlJQX379/HnTt3cOfOHfj4+OD+/ftISkrSdGkF6uXLl3j58iVOnTql0u7o6IgaNWqgevXqqFmzJurWrYvSpUvzAIZIi2QdBsuedURE+UMURSQlJSEyMhIhISG4du0aLly4gEuXLqmdJ73PyMgIFhYWKF68OKytrVG5cmV4eHjAw8MD1apVg6GhIY+tPpFMJoOzszOcnZ3Rtm1biKKIhIQE+Pn54fbt27hz5w78/f0RGxuL2NhYxMXFqfW8e/ToER49eoQVK1agZMmS8PT0RJMmTVCnTh04ODjA1tYWRkZGGnqHRJQfdDasy7rABFeDpcJEFEU8ffoUV65cwZUrV3Djxg08ePDgi5edz45MJoO1tTWsrKxUvlpaWkpXUk1MTKSvRkZGkMvl0k0mk0Eul0OpVCI9PV26ZXbxT0pKUlltKz4+HnFxcYiOjkZERAQiIyMRExPzxe8jODgYwcHBOHr0qNRma2uL+vXro169eqhXrx7q1q0Lc3PzL34tIvo87FlHRJS/UlNTER4ejhcvXuDu3bs4d+4czp07l2NAJwgCTE1NpYUeypcvjxo1aqBmzZqoUqUKzM3NP2vhB8qZIAgoVqwYPD094enpCYVCgejoaNy/fx++vr64e/cunj9/Lk1bk5ycrPL4kJAQ7Nq1C7t370bJkiXRrFkzNGnSBFWqVEHp0qVhY2OT61VyiUh76WRYl5qaqnYCwJ51pM0yMjJw69YtnDt3TgroPnTV81M4OTnB1dUVZcqUgYuLi3Qw5ujoiJIlS8LW1hZyuTxPXutzpaenIyoqCuHh4Xj9+rXUe+7Vq1d4+fKlNN/ep4qMjMShQ4dw6NAhAO+CSXd3dzRt2hRNmzaFp6cnLCws8vrtEFEOsltgQhRF9tIgIvoCmWHPkydPcP/+fdy8eROXLl1CYGBgjiu4Fi9eHGXKlEGZMmVQqVIlac45JycnBj0FTC6Xw87ODs2bN0fz5s2RmpqKwMBA+Pj44M6dO3jy5AkCAwPx4sULxMfHS48TRRHBwcHYsmULdu/ejUqVKqFhw4aoXbs2qlSpgvLly3/2KrtEpHk6GdZlF3IwrCNtolQqcffuXZw5cwZnzpzBhQsXVP74fioLCwu4ublJt4oVK6Js2bIoXbp0oegSr6+vjxIlSqBEiRKoWbNmtvskJibi2bNnePLkibTq1sOHD3H//n21eT5yolQqcevWLdy6dQuLFy+GTCaDh4cHmjdvjnbt2qFBgwY8QCXKR1l71imVSqSkpMDY2FhDFRERFU6iKCIjIwNPnz7FjRs3cOPGDdy+fRuPHj3K8ZiyZMmSqFSpEipWrIhKlSqhSpUqqFy5MhwcHBjoaBFDQ0NUqlQJlSpVQo8ePfDq1Ss8evQIDx48kFadffz4scr81KmpqfDz84Ofnx+KFy+OatWqwcPDA3Xr1kXdunXh4uKi8YvzRPRpikxYV7x4cQ1UQvR/oqOj8d9//+Ho0aM4fvy42lDt3KpQoQLc3d3h7u6OmjVromrVqnBwcND5nimmpqaoUaMGatSoodKekZGBJ0+ewM/PD3fv3oWvry9u3bqVq5+vUqnEzZs3cfPmTSxcuBDm5uZo1aoV2rVrh7Zt28LR0TG/3g5RkZQ1rAPeBfEM64iIcidzLrrr16/jwoULuHHjBu7evYuQkBCIoqi2f/HixeHu7o46deqgRo0aKF++PFxdXXluVEjI5XKULl0apUuXRtu2bREVFYXnz59Lx77Xr1+Hn5+fyhywsbGx0vyELi4uqFGjBurVq4fGjRvDw8MDBgYGOn/eQKQLikRYZ2lpCT09nXyrpMVEUcS9e/dw6NAhHD16FNeuXYNSqfyk53BwcEDDhg3x1VdfoW7duqhRowbnXMtCT09P6lHYq1cvAO9+9oGBgbh27RquX7+O69evw8fHR22y3qzevn0Lb29veHt7AwDc3d3RtWtXdO3aFRUrVsz390Kk67IOgwXehXWcV5aI6ONevXqFkydP4syZM3j48CGePn2a7UI9RkZGqFq1qrQAQYUKFeDq6gpzc3OGNIWYIAiwtbWFra0t6tWrh6+//hoBAQHw9/eXFhDx9/eXhj4rlUoEBgYiMDAQ58+fx/79+1GlShW0bNkSLVq0gJ2dnYbfERF9iE4mWFnDOp4EUEERRRE3b96Et7c39u7di2fPnn3S46tWrYrGjRujYcOGaNCgAVxcXHhQ9RkEQYCrqytcXV3x3XffAXi3mu61a9ekiZavXr360fAuc+XdadOmoUqVKujatSu6dOmC6tWr89+F6DOYmJiotXFFWCKinCkUCty4cQP79+/H5cuXERQUhJCQELULwDKZDKVKlULLli3RtGlTVK5cGc7OzrCysoJMJuNxi44RBAE2NjawtrZGrVq10LJlS7x8+RL379/H6dOnce7cOYSFhUm9LWNjY3H9+nXcvn0bZ86cQenSpdGsWTN88803PK4l0lI6GdZlHf7G+eooP4miiOvXr2P79u3Yu3cvXr9+nevHli1bVppMtlmzZrC3t8/HSos2IyMjaWEJAEhOTsa1a9dw6tQpHDt2DD4+Ph98/IMHD/DgwQPMmTMHbm5u6NOnD3r37o1SpUoVQPVEukEul8PIyAgpKSlSG1eEJSJSl56ejosXL2LDhg24ceMGQkJCsp2LzsDAADVr1kTPnj3RpEkTODo6onjx4hzqWEQIgiDN/ezg4IDq1aujTZs2ePnyJU6dOoUdO3bgyZMnUCgUAN5NH/PixQu8ePECvr6+2L17N5o0aYL+/fvD3d2d89oRaRGdDOuy9qxjWEf54fHjx9i2bRu2bduG58+f5+oxxsbGaNGiBb7++mu0a9cOpUuXzt8iKUfGxsZo1qwZmjVrhnnz5iE0NBTHjx/HsWPH8N9//yEuLi7Hxz58+BBTp07F1KlT0aRJE/Tt2xddu3aFpaVlwb0BokLKzMyMYR0RUQ5SU1Nx8eJFrFq1CteuXUNkZGS2C2kZGRnh66+/Rp8+fVC3bl1YWlrC2NiYC0UUYYIgwNjYGE5OTihRogSqVauG/v374+LFi9i0aRPOnDkjhXYA8ObNG7x58wbPnz/H0aNH0bx5cwwZMgQeHh6cQopIC+jkbyHDOsov0dHR2LJlCzZt2oQ7d+7k6jGlSpVCp06d0L59ezRp0oQTqWupEiVKYODAgRg4cCDS09Nx4cIFeHt7Y9++fQgLC8vxcefPn8f58+cxcuRI9OjRAz/88AMaNGjAq9lEOTA1NVXpAc9hsERE76bruHLlCpYvX46LFy/izZs3yMjIUNvPzs4OAwcORO/eveHi4gJjY2Po6enxuINUyOVyFCtWDGZmZujRowc6duwIf39/bNy4EVu2bMHbt2+lfZOTkxEYGIjNmzfj6NGjaNOmDUaMGIEaNWpAX19fg++CqGhjWEf0EUqlEqdPn8a6deuwb9++j85zBrxbsTVzfjMPDw8eQBUy+vr6aNGiBVq0aIHly5fj6tWr0sITL1++zPYxKSkp2LRpEzZt2oQqVarghx9+QN++fbnaGlEWWVeEZc86IirK0tLScOPGDSxevBinT59GUlJStguS1ahRA2PHjoWXl5cU0LEXHX2MIAgwMDCAgYEBateujZo1a2LWrFnYvn07/v77b5XRQWlpaQgNDcXmzZtx4MABdOrUCT/99BPc3NzY045IA3TyE55hHeWF0NBQ/Prrryhbtixat26NnTt3fjCoK1++PGbOnIn79+/j8ePHmD9/PmrXrs2grpCTyWRo2LAh/vjjDwQGBuLChQsYMmQILCwscnzMgwcPMGbMGJQsWRIDBw6En59fAVZMpN2yrgjLsI6IihJRFCGKIhQKBZ4+fYrvv/8eLVu2xIEDB5CQkKAW1NWvXx/e3t64du0a+vXrB3NzcxgYGDCoo08mk8lgYGAAGxsbjBw5Er6+vli7di3c3NxU9lMoFIiNjcWmTZvg6emJCRMmSIuaZP7/JaL8p5Of8lwNlj6XKIq4fPkyevXqhVKlSmHGjBl48eJFjvs7ODhg7NixuHnzJvz9/TFr1ixUqVKFAZ2OkslkaNSoEVavXo2wsDB4e3vDy8srxyECKSkp2LBhA2rWrInmzZvj0KFD2V4tJypKsvas4zBYIioqRFGEUqlEZGQkpkyZglq1amHz5s0qc9IJggA9PT00atQI+/btw5kzZ+Dl5QVDQ0Ou6kp5QhAEyGQymJqaYtCgQbhy5QrWrVuHmjVrqiwwIYoi4uPj8ddff6F69er4/fff8fbtWym0I6L8pZNhHVeDpU+VnJyMf//9Fx4eHvD09MSOHTuynScEAAwNDdGrVy+cOHECr169wp9//skedEWQkZERunTpIq0AvGjRIpQrVy7H/c+ePYtOnTqhYsWKWL58OXsTUZHFYbBEVNSIooi0tDTExMTgjz/+QJUqVfD777+rfP5lhieNGzfGjh07cPToUXTu3BnGxsYQBIHHmZTnMv9fWVhYYNCgQTh9+jRWrVoFDw8PGBkZqfyfi46OxuTJk1G9enWsX78e8fHxSE9PZ2hHlI90MqzjMFjKrejoaPz6669wcXHB4MGD4ePjk+O+1atXx99//42QkBBs27YNrVu35vwNBODdZM8TJ06Ev78/Tp8+jR49euTY2+7Zs2cYNWoUSpcujQULFnxw1VkiXZR1GCx71hGRLktLS0NkZCQ2bNiAOnXqYNKkSSodCwRBgLm5ORo2bIh///0Xhw4dQteuXdU+K4nym5WVFQYPHowTJ07gzz//RM2aNWFqaqoS2r18+RJDhgxBo0aNsHfvXsTExOTYwYGIvozOhXWZY+zfx7COsnrx4gVGjx4tDXWNjIzMdj9jY2N8//33uHnzJnx9fTFq1ChYWVkVcLVUWMhkMjRv3hw7d+7E69evMXv2bNjb22e7b1RUFKZOnQoXFxdMnz5drUcwka4yMTFRuZ+cnKyhSoiI8o9CoUBERAQOHz6MDh06YPjw4QgMDJS2Z/ZoqlWrFubNm4d9+/ahR48eKFasmAarJnp37jxs2DAcOnQIkydPRpUqVdR6xd+9exd9+vRBr169cPbsWcTExHCqF6I8pnNh3Zs3b9S64zKso0wPHz5E7969Ua5cOSxbtgxJSUnZ7lemTBksXrwYr1+/xpo1azjMlT6ZnZ0dZsyYgaCgIKxfvx7Vq1fPdr+4uDjMnTsXpUuXxsSJExnakc7LGtbl9DlMRFQYiaKI2NhYXL16FT///DP69euHmzdvqgQZ5ubmqFWrFn766Sfs27cPI0eO5PkKaR1HR0f88ssv2L17N4YOHYqqVauq/A3PyMjAyZMn8e2332Lu3Lnw8fFBfHw8h8YS5RGdC+uyDoEFGNYRcP/+ffTs2RNVq1bFtm3boFAost2vVatWOHToEJ4+fYrx48ezFx19MUNDQwwYMAC+vr44c+YMvv7662z3S0xMxOLFi+Hq6orZs2fj7du3BVwpUcFgWEdEuio5ORn379/H//73PwwcOBCbNm1SmZfOzMwMdevWxY8//oitW7dixowZKFWqlAYrJvq4SpUqYfHixVi/fj0GDx6MmjVrwsjISNoeExODZcuWYciQIdiwYQOePn2KtLQ0DVZMpBt0bsKtrGGdiYkJjI2NNVQNadq9e/cwZ84c7NmzJ8d95HI5vv32W0ycOBE1atQowOqoKBEEAc2aNUOzZs1w584dzJ8/H3v37lW7+hgfH49Zs2Zh2bJlmDp1KoYPH87PMNIpDOuISNcoFAoEBwfj9OnT2L59Oy5fvqzy2aanp4datWqhRYsW6NGjB2rWrMkRG1SoCIKA2rVrw93dHVeuXMHu3btx9uxZPHjwAEqlEhkZGfDx8UFAQADOnTuHnj17okmTJrC1tYVMpnP9g4gKhM6FdVwJloB3k/hPnz4dO3bsyHEfU1NTDBkyBGPHjoWLi0sBVkdFnbu7O/bs2YNHjx5hwYIF2fb2jI6Oxvjx4/HHH39g7ty56NevHw92SCcwrCMiXSGKIhITE3Hp0iXs2bMHx44dQ0hIiMo+5cuXR5s2bdCtWzc0bNgQcrmcQR0VWjKZDA0bNkSdOnVw6tQpeHt74+TJk3j9+jWAd9O77N27F76+vujQoQO6d++O2rVrq/TEI6Lc0bmwjivBFm1hYWGYM2cO1qxZk+PKRBYWFhgzZgzGjBnDYa6kUZUrV8amTZswc+ZMzJo1C1u3blXraRccHIyBAwdi+fLlWLp0KTw9PTVULVHeYFhHRLpAqVTi5cuX2LJlC/bu3Yu7d++qXHizsrJCmzZt0KVLF7Ro0QKWlpYM6UgnCIIAQ0NDtGvXDu7u7mjSpAm8vb1x5swZadh3QEAA/vnnH9y+fRs9evTAt99+C1tbW/4OEH0ChnWkE96+fYtFixbhzz//zPHEz9LSEmPHjsWYMWNgaWlZsAUSfUDZsmWxefNm/Pzzz/jll19w4MABtX1u376NRo0aoUePHli4cCFKly5d8IUS5QGGdURUmImiiIyMDBw/fhxr167FpUuXEBMTI23X19dH/fr18d1336FVq1ZwcXGBnp7OnXIRQSaToUSJEujVqxc8PDxw7NgxbN++HX5+flAqlUhLS8Ply5cREBCAa9euYdiwYfD09ORIEaJc0rm/HAzrihaFQoH169dj2rRpiIiIyHYfCwsLjB8/HqNHj4aFhUUBV0iUe1WrVsX+/ftx/fp1TJ06FWfOnFHbZ9euXThw4AAmTJiAqVOnqgUfRNqOYR0RFVaiKCIsLAxLlizB4cOH8fTpU5VVXl1cXNCnTx907twZlStXhomJCXsSkc4zMDBAlSpV4OzsjPr162Pnzp3YvXu3dG4WGhqKPXv24P79++jRowdGjhwJCwsL/m4QfYTOxdpZwzobGxsNVUL57fz586hduzaGDBmSbVBnaGiIiRMnIiAgANOnT2dQR4VGvXr1cPr0aRw9ehSVKlVS256amop58+ahatWqOHr0qAYqJPp8DOuIqDBSKpXYt28fvLy8sHr1avj7+0tBnYGBAVq0aIEVK1Zg3LhxcHd3h6mpKcMIKjIEQYCFhQUaNGiA6dOnY+nSpahduzbkcjkAID09Hffu3cMff/yBrl274ty5c5otmKgQ0Pmwjj3rdE9QUBC6deuGpk2bwtfXV227TCbD4MGD8ezZMyxatIjz0lGh1a5dO9y9exfLli3L9v9xYGAg2rdvj+7duyM4OFgDFRJ9OoZ1RFTYhIWFYejQofjxxx9x48YNxMfHS9usrKwwYcIErFmzBq1atYK1tTWH+VGRJZfLYWdnBy8vL2zatAmDBw+GmZmZtD02Nhbnz59H3759MXnyZLx9+1aD1RJpN537S8LVYHVXWloafvvtN1SuXBne3t7Z7tOxY0fcv38fa9euhZOTUwFXSJT39PX1MXLkSDx9+hRjxozJdt6bPXv2oFKlSli6dKnaqrJE2sbU1FTlPsM6ItJmFy9eRPv27bF582aEh4dLC0HJZDLUqFED27Ztw9SpU1G6dGkYGBhouFoizRMEAUZGRqhYsSIWL16MNWvWoHTp0lJPU4VCgeDgYCxbtgxeXl64f/++hism0k46F9axZ51uOnfuHGrWrIkpU6YgOTlZbXuVKlXw33//4eDBg6hcubIGKiTKX1ZWVli6dCn8/PzQqFEjte0JCQkYN24cPD098fjxYw1USJQ72fWsy7oKMhGRpqWnp2PZsmXo0aMHfHx8kJqaKm3T09PDjz/+iJMnT6Jly5Yc8kqUDZlMBjMzM3Tr1g3nzp1D9+7dVXqdJiUl4dy5c+jcuTN27typMv8jETGsIy0XGRmJfv36oVmzZnj06JHadmtra6xcuRK+vr5o1aqVBiokKlhubm44f/481q9fn+3n27Vr11CzZk0sXryYvexIK2UN6zJXjCMi0gaiKCI8PBwjRozA1KlTERYWpnJBwcXFBUeOHMHSpUthY2MjzclFROoEQYCenh5KlSqFrVu3YsuWLSrHr0qlEs+fP8eIESMwc+ZMvH37lhfwiP4/nQrrRFHkAhM6QhRF7NixA25ubti8ebPadkEQMGLECDx9+hTDhw/Pdmggka4SBAEDBgyAv78/Bg8erLY9NTUVEydOZC870krZrWDMobBEpGmiKEKpVOL27dvo06cP/v33XyQkJEjb5XI5evbsCR8fH7Rq1QpyuZy96YhySRAEyOVyfPvtt/Dz80OzZs1UetlFR0fj999/x/Dhw6VVlhnaUVGnU2FdYmKi2tV59qwrfEJCQtC5c2f06tVLbQ5CAPDw8MCNGzewfPlyFC9eXAMVEmkHa2trrF27FhcuXEC5cuXUtmf2svvrr794wENag2EdEWkbURSRnJwMb29vDBgwAKdOnZJ6pwuCAEtLSyxbtgxbt26FpaUlQzqizyAIAgRBQMmSJXHs2DFMnz4dJiYm0u9Tamoqtm3bhoEDB+Ls2bNIT0/n8SsVaToV1mUX7DCsKzxEUcT69evh5uaGgwcPqm03NzfH8uXLcf36ddSuXVsDFRJpp0aNGsHPzw9jx45VO4FITU3F2LFj8fXXXyM8PFxDFRL9n+zCusTERA1UQkT0bhheZGQkVqxYgREjRuDBgwfSNkNDQ3h4eODo0aP44Ycf2JuOKA8IggBDQ0NMmzYNO3fuRIUKFaCvry9tv3LlCr7//nts3boVcXFxDOyoyNKpsC7rEFg9PT2Ym5trqBr6FOHh4ejUqRMGDRqEuLg4te1du3bF48ePMWLECM4NQpQNExMT/Pnnnzn2sjt+/DiqV6+Oo0ePaqA6ov+jr6+v9jnOnnVEVNBEUYRCoUBAQAAWLVqEGTNmIDIyEsC7MMHKygp9+vTB3r17Ub9+fR5/EuUxfX19tGvXDt7e3ujQoQMsLCykbS9evMBPP/2E//3vfwgJCeHiE1Qk6XRYZ2VlxatfhcChQ4dQrVo1HD58WG2bnZ0ddu/ejT179qBEiRIaqI6ocPH09JR62WUVERGB9u3bY+zYsUhJSSn44ojw7iQ4uxVhiYgKiiiKSE9Px+3bt/HLL7/gjz/+kP4u6uvro0KFCpg1axYWL14MZ2dnnk8Q5RO5XA43NzesXLkSY8aMgYuLixSMv3nzBjNnzsT8+fPx6NEjKBQK9rKjIkWnwzoOgdVuCQkJ+OGHH9CpUyfpSub7+vTpg4cPH6Jbt24aqI6o8MrsZXfy5MlsQ+6//voLX331FQICAjRQHZH6UFiGdURUUERRRFJSEv777z+MGzcOO3fulAIAU1NTNG7cGH/88QeGDBkCS0tLzRZLVAQIggAHBwdMmDAB8+bNQ+3atWFoaAgASE9Px8qVK/Hzzz/j0qVLnMeOihSGdaQRd+7cQa1atbBmzRq1bfb29jh06BA2b97Mf0OiL9CyZUv4+fmhY8eOatt8fX3h7u6OQ4cOaaAyKuoY1hGRJoiiiKioKGzYsAGjRo3ClStXpG3FixeHl5cX5s+fjzZt2sDIyEiDlRIVPcWKFUP37t0xd+5ctGnTBsWKFZO2HTlyBKNGjcLOnTsRHx/PwI6KBJ0K62JiYlTuM+jRPqIoYvny5fjqq6/w7Nkzte3ffPMN7t27hw4dOmigOiLdY2triwMHDmDlypVqJx5xcXHo1KkTpk6dioyMDA1VSEURwzoi0oQnT55gwYIFmDp1Kl68eAEA0uqUQ4YMwS+//II6depwfjoiDTEwMEDz5s0xc+ZMfPfddyrn8/fu3cOECRPwxx9/4PXr1xqskqhg6HRYZ2VlpaFKKDtv3rxBt27dMGrUKKSlpalsMzU1xdq1a7Fv3z7Y2tpqqEIi3SQIAoYPH45bt26hcuXKatsXLFiANm3aICIiQgPVUVHEsI6ICtrVq1cxefJkLFu2DG/fvgUAyGQylC9fHpMnT8a4ceNQsWJFzk9HpGEymQy1atXCzz//jLFjx8LJyUn6vYyIiMDChQvxyy+/qKzcTKSLGNZRgbh58ybc3d2xd+9etW316tWDn58fBg8ezAMkonxUpUoV3LhxA99++63atjNnzsDDwwN37tzRQGVU1DCsI6KCIooiDh8+jIkTJ+LAgQNST3K5XI569eph4cKFGDBgABwcHDRcKRFlEgQBZcqUwY8//oj58+fDzc0NMtm76CIlJQVbt27FhAkTVIayE+kahnWU79atWwdPT08EBgaqbZs4cSIuXryIsmXLaqAyoqLHzMwM27Ztw99//w09PT2Vba9fv4anpyf27NmjoeqoqGBYR0QFQaFQYOPGjZg6dSquXLkizXMll8vRuXNn/PXXX2jfvr3K3FhEpD2srKzQvXt3rFixAk2aNJECO4VCgZMnT2LChAk4cuQI57AjncSwjvJNamoqhg0bhu+//15t2KuVlRUOHz6MRYsWQV9fX0MVEhVNgiBg1KhRuHDhAhwdHVW2JScno3v37pgzZw4PfCjfMKwjovyWlpaGZcuWYe7cubh//770N00mk2H48OFYvHgxPDw8eBxKpOWMjIzg6emJf/75B127dpXaFQoFbty4galTp2Lr1q1QKpUarJIo7zGso3wREhKCZs2aYdWqVWrbGjZsCF9fX7Rv314DlRFRpq+++gp37txB48aN1bbNnDkT3377LUMUyhcM64goPyUlJWHhwoVYvHgxAgICVIK6OXPmYPbs2XBxcZF66RCRdpPL5ShfvjyWLVuGkSNHSu0KhQL379/HnDlzsGbNGqSnp2uwSqK8pVN/oRjWaYdr167Bw8MDV69eVdv2008/4ezZs3B2dtZAZUSUlZ2dHU6ePInvv/9ebduuXbvQpEkThIeHa6Ay0mUM64gov8TFxWHevHlYtmwZQkJCpKDOwMAAq1evxrhx42BlZcV5kokKGUEQYGdnh/nz52Pu3LlSu1KpxLNnzzB//nysXLkSKSkpGqySKO/oTFgniiLDOi2wY8cONG3aFGFhYSrtxsbG2Lp1K5YsWcLhBkRaJvMEZunSpWq9DG7duoUGDRrg6dOnGqqOdBHDOiLKD2/evMGiRYuwcuVKREZGSkGdmZkZdu7cib59+8LY2FjDVRLR5xIEAWZmZhg/fjz++ecf6bxSFEW8evUKCxcuxKpVq5CcnKzhSom+nM6EdfHx8VAoFCptDOsKjiiKmDNnDnr16oXU1FSVbaVLl8aVK1fw3Xffaag6IvoYQRAwZswYHD16FBYWFirbAgIC0KBBA9y4cUND1ZGuMTU1VbnPsI6IvoQoinj79i2WL1+O5cuX482bN9I2Ozs7HDhwAB06dIC+vj571BEVcoIgwNDQEAMHDsTmzZulYwpRFBEaGorffvsNW7ZsQUpKCudfpkJNZ8K6rL3qAIZ1BSU1NRX9+vXDzJkz1ba1bNkSt27dQs2aNQu+MCL6ZG3atMG1a9dQpkwZlfaoqCg0a9YMhw8f1lBlpEvYs46I8oooioiPj8fatWvx+++/4+3bt9K2MmXKYN++fWjSpAnkcjmDOiIdIQgC9PX14eXlha1bt8LGxkbaFhYWhhkzZmD37t1ITU1lYEeFls6GdXK5nMuwF4DY2Fi0atUKW7ZsUds2fPhwHDt2DNbW1hqojIg+V6VKlXDlyhW4u7urtCclJeGbb77B2rVrNVQZ6QqGdUSUF0RRREJCArZu3YrZs2dLQZ0gCKhUqRK2bNmCevXqMagj0kGCIMDAwADt2rXDunXr4OjoKG0LCwvDpEmTcPjwYQZ2VGjpTFgXGxurcp8Tx+a/169fo1GjRrh48aJKu0wmw9KlS7FixQro6elpqDoi+hIODg44d+4cWrdurdKuVCoxZMgQ/PnnnxqqjHQBwzoi+lKiKCIxMRHe3t6YMmWKFNTJZDJUqVIFy5cvR926dSGXyzVcKRHlJwMDA7Ru3Rp//PEHXFxcpAwgLCwMY8aMwalTpxjYUaGkM2EdF5coWI8ePUKDBg3w4MEDlXYzMzMcPHgQY8aMYVhKVMgVK1YMhw8fRr9+/dS2/fTTT5g7dy4PfOizMKwjoi+RGdTt378fP/30E+Li4gC8G1lTrVo1LFq0CI0bN+ZFY6IiwsjICB07dsSvv/4KV1dXacG0kJAQ/Pjjjzh79izS0tJ43EqFCsM6+mRXr16Fp6cnXr16pdJesmRJXLp0Ce3bt9dQZUSU1/T19bFhwwZMnTpVbdv06dMxZcoUHvjQJ8u6GmNiYqKGKiGiwkYURSQnJ2P//v0YO3asNLpGT08PNWrUwOzZs9GyZUtplUgiKhqMjY3RrVs3TJ06FWXLlpUCu1evXmH48OE4e/YsMjIyeNxKhQbDOvokJ06cQIsWLdR+3hUrVsSVK1dQo0YNDVVGRPlFEATMmzcPCxcuVNu2cOFCjBo1CkqlUgOVUWGVNaxLSUnRUCVEVJiIooi0tDQcOnQI48aNQ3R0NIB3QV21atUwefJktGvXjkEdURFlbGyMb7/9Fj/99JNKYBcUFIQff/wR586dg1KpZGBHhQLDOsq1AwcOoFOnTkhOTlZpr1+/Pi5fvgwXFxcNVUZEBWHSpElYsWKFWvuKFSvwww8/MLCjXDMyMlK5z7COiHJDoVDg0KFDGD9+PKKiogC8G/papUoVTJw4EZ06dYKBgYGGqyQiTTIxMUGfPn0wZswYlClTRgrsAgMDMXbsWJw7d06zBRLlEsM6ypWdO3eiW7duSEtLU2lv3749Tp06xRVfiYqIH3/8ERs2bJAOfDKtW7cOP/74I69UUq6wZx0RfSqlUomDBw/i559/RnBwMIB3i0lUrFgRkyZNQpcuXWBoaKjhKolIG5iZmaF///4YO3YsHB0dpbnUHz58iIkTJ+L8+fM8ZiWtx7COPmrjxo347rvvkJGRodLeu3dv7Nu3D6amphqqjIg0oX///ti+fbvaxN2rVq3C6NGjefBDH5W1Z13WHttERO8TRRGHDx/GtGnTEBAQAODdFA3Ozs6YOHEigzoiUmNmZoZ+/fph7NixKh1LfHx8MGXKFFy+fFmD1RF9HMM6+qC1a9diwIABasPbvv/+e2zcuJFzghAVUT169IC3t7daYLd8+XKMHz+egR19UNawTqFQqF0QIiLK9N9//2H69Ol4/Pix1GZjY4OpU6eie/fuap8pREQAYG5ujoEDB2LcuHEqHUyuX7+OqVOn4ubNmxqsjujDGNZRjjZu3IgffvhBrX3UqFFYtWoV5HK5BqoiIm3RqVMn7Ny5U+2z4M8//8TkyZMZ2FGOsg6DBTgUloiyd/HiRUybNg337t2T2kxNTTFjxgz06tWLIzyI6IOKFy+OoUOHYty4cdJFZlEUceXKFUyePBn379/XcIVE2WNYR9navn07Bg0apHayPWnSJPz1119q81URUdHUpUsXbN26Ve0zYdGiRfj11181VBVpu+x6wXAoLBFldffuXUyePBm+vr7SMamenh5mzpyJfv36oVixYhqukIgKA2tra4wZMwajR4+W5q9TKBS4ePEiJk+ejKCgIA1XSKROZxIXhnV5x9vbG3379lUb+vrLL7/gt99+kz7giIgAoGfPnti0aZPaZ8PMmTOxbNky9rAjNdmFdexZR0TvCw0NxS+//IKbN29CoVBI7VOmTMGwYcNgbm6uweqIqDCJjIxEYGAgBg0ahIEDB0rt6enpOHv2LObMmYO3b99qsEIidToR1iUnJ6sd5DOs+zyHDx/Gt99+q3JQBLzrUTdnzhwGdUSUrd69e2P9+vVqnxGjR49Gq1atNFQVaSsOgyWiD0lKSsLcuXNx9uxZpKenS+0jR47Ezz//zB51RJRrJ06cQPPmzdGuXTu0bNkS7u7u8PLykrYnJSXhwIED+PPPPzl/LmkVnQjrsvaqAxjWfY6LFy+ie/fuah9SY8aMYY86Ivqo/v37Y8WKFWrtp0+fRq9evTRQEWkrfX19tb8pHAZLRMC7oWkrV66Et7c3EhISpPZOnTrht99+g4mJiQarI6LCJCoqCjNmzMCDBw8QHR2NsLAwTJ48GbNmzULdunWl/aKjo7Fp0ybs2LGDI0JIa+hkWCcIAiwsLDRUTeF09+5ddOzYUa1nw/Dhw/Hnn38yqCOiXBk+fDimTJmi1r5jxw6MGjVKAxWRNhIEQW0oLHvWEZEoijh8+DD+/fdfhIeHS+3VqlXDmjVrYGJiwmNSIsoVURSxf/9+vHz5UiWAS0xMRGxsLDZv3gxHR0epPSAgAP/73/9w9epVBnakFXQyrLO0tORKpZ8gMDAQbdq0QVxcnEr7gAEDsHz5ch4UEdEnefPmTbbt+/btU/ucoaIr61BYhnVERZsoivDz88Py5cvx6NEjqd3W1habN2+GjY2NBqsjosJEFEU8e/YMa9euRVhYmMo2PT09lC1bFmXLlsWGDRtUVpS+cuUKli1bhhcvXjCwI43TybCOQ2BzLzw8HK1bt1b7EOvUqRPWrFnDVV+J6JNs374d//vf/7LdFhwcnO3iNVQ0Ze1Zx2GwREWXKIoICQnBypUrcebMGandxMQEq1atgpubG2QyGS8gE9FHiaKIS5cuoXfv3rh+/bra9vHjx8PR0RFyuRwNGzbEwoULoaenJ23ft28f/v33X7x584aBHWmUTiQxDOs+T1JSEjp27Ihnz56ptDdq1Ag7duxQ+dAiIvqY27dvY/DgwR/c59ChQ5g9e3YBVUTajD3riAh4d2IdHx+PLVu2YOvWrdIFHUNDQ0ybNg0tW7aEvr6+hqskosIiJiYGa9euxc2bN9W2mZubY8SIEdJ9Y2Nj9OzZE0OHDpXOfVNTU/HPP/9g//79SElJYWBHGsOwrohSKpXo16+f2odY9erVcfDgwWxX6iMiysnr16/RsWNHtd5RixcvVhu6NGfOHBw4cKAgyyMtxDnriAgA0tPTcebMGaxcuRJJSUkAAAMDA/Tu3RsDBgzgyq9ElGsZGRm4ePEibt26pbbNyckJK1euRMmSJVV66VpbW2PMmDFo06aNFNhFRUXhr7/+ws2bN6FQKAqsfqL3MawroqZMmQJvb2+VttKlS+P48eOwtLTUTFFEVCglJCSgY8eOCA0NVWkfMmQIxo8fj127dqnNI9q3b1/4+/sXZJmkZTgMlogUCgUePHiAZcuW4eXLlwAAuVyOpk2bYuzYsShRooSGKySiwiIlJQU+Pj7YvHkzHj58qLLNzs4OO3fuRK9evdSmeRIEAa6urpg8eTJq1qwpBXl+fn5YuXIlXrx4wSlcSCMY1hVBa9aswaJFi1TaLCwscPToUR4UEdEnSUtLQ8+ePeHr66vS3qxZMyxfvlz6fsmSJSrb4+Pj4eXlhfj4+IIqlbQMh8ESFW2iKCI8PBxr165VmafOzc0N48ePR+XKlTlHHRHlSnJyMs6ePYuffvoJe/fuVds+bNgw1KpVK8f52OVyOWrXro1x48bBxcVFavf29sa2bdvw9u1bDoelAsewrog5c+YMhg8frtKmp6cHb29vVK5cWUNVEVFhlJ6ejp49e+Lo0aMq7RUqVMCePXtgYGAgtY0ePRq9e/dW2e/Ro0cYNGgQD36KKA6DJSq6RFFEUlISdu/ejQ0bNkjtJUqUwNChQ/HVV19x7mQiypWMjAz4+flh3rx5uHTpktr26tWro0ePHjA0NPzg8xgZGaF169bo168fihcvLj33smXLcOzYMaSnp+dL/UQ5YVhXhLx48QI9evRQG3f/zz//oEWLFhqqiogKo4yMDPTp0wf79+9XabeyssLhw4fVPocFQcDq1atRs2ZNlfY9e/bgjz/+yOdqSRtxGCxR0aVQKHDu3Dn88ccf0jx1xYoVQ7du3fDNN99wnjoiypW0tDRcuXIFS5YsweXLl9W2N2/eHHPnzkXZsmVz7FX3PhsbG/Tq1Qtt2rSRjlOioqKwYMEC3Llzh8NhqUDpZFiXmYTT/0lKSoKXlxeio6NV2n/++eePrt5IRPS+1NRU9OnTB7t27VJpNzMzw5EjR1C+fPlsH2diYgJvb2+1z+iff/4ZFy9ezLd6STtxGCxR0SSKIvz9/fHrr79K89Tp6emhYcOGGDBgABwdHTVcIREVBkqlEo8fP8Yvv/yCPXv2qG2vUaMGpkyZohK85UaFChUwZMgQVK9eXRqKf+/ePfz6668ICQnhiBAqMDoZ1rFnnSpRFDFkyBC1OaU6deqE+fPna6YoIiqU4uPj0aFDB+zcuVOl3cTEBEePHkX9+vU/+HhXV1ds3bpVZR4ihUKBHj16ICwsLF9qJu3EYbBERY8oioiLi8Ovv/6K69evS+3ly5fH0KFDUa1aNc5TR0QfpVAocPjwYUycODHbC741a9bEpEmTUK9ePZVpWXJDJpOhQYMG+P777+Hk5CS1nzhxAgsXLkRaWhoDOyoQDOuKgD///BPbtm1TaatYsSI2b96cq+7AREQAEB4ejqZNm+LUqVMq7UZGRjh48CAaNWqUq+dp164dZsyYodIWFhaGXr16ISMjI8/qJe3GYbBERdPKlStx4MAB6b6trS369++Pli1bQl9fX4OVEVFhEBUVhf/973/4+eef8d9//6ltb9myJX777Td07Njxs4fUGxkZoWvXrujWrRvMzc0BvAsIN27ciO3bt39R/US5VeiTmrS0NCQkJKi0Maz7PxcvXsSkSZNU2ooVK4b9+/dLHzxERB/j4+ODevXq4c6dOyrtpqamOHjw4CfPezl9+nS0adNGpe3cuXOYOXPmF9dKhQOHwRIVPZcuXcLSpUul33cjIyO0adMGAwcOhJmZmYarIyJtd+zYMXTo0AFz587F48eP1bbXqlULY8aMQbNmzb547ksrKyuMHj0a9erVkxa8iY+Px6xZs/DkyZMvem6i3Cj0YV1sbKxaG8O6dyIjI9GrVy+1BSW2bNmCSpUqaagqIipsNm/ejAYNGiAoKEil3dbWFmfPnkWrVq0++Tnlcjm2bNkCZ2dnlfb58+fj+PHjX1QvFQ4cBktUtMTFxWHcuHGIjIwE8G7hoXLlymHKlCmwtbXVcHVEpO0ePXqEkSNH4vr16wgPD1fbXrduXSxbtgwtWrT45KGvOSlVqhRmzJgBBwcHqS0oKAjjxo1DWlpanrwGUU50MqzjAhPvJtzs168fgoODVdpnzpyJTp06aagqIipMUlNTMXr0aPTr108tSClTpgwuX76MOnXqfPbz29jYYNeuXWrDnvr06YPXr19/9vNS4cBhsERFy+zZs1XmT7awsMCkSZNQsWJFzlNHRDmKjIzE0qVL0a5dOwQGBma7T6lSpaRecFl77n8JmUyGevXq4ccff4SJiYnUfubMGaxYsSLPXocoO4U+rMs6X52ZmVmeJemF2e+//67WO6Vly5aYPn26hioiosLk3r17qFOnDpYtW6a2rWHDhrhy5UqOq75+ivr162PRokUqbdHR0fjuu+84f52O4zBYoqLj1KlTWLNmjTTaQ09PD19//TV69uwJuVyu4eqISFvt27cPjRo1wuTJkxEUFJTtwg4ODg6YNWsWevToIQ1XzUv6+voYPXo0ateuLV1YSE1NxYIFC3D//v08fz2iTDoX1nEILHD58mVMmzZNpc3BwQFbtmzhARERfZBSqcTSpUtRp04d3Lt3T237iBEjcObMGZXhAF9qzJgx6Ny5s0rbxYsXMXv27Dx7DdI+7FlHVDS8efMG48aNQ2JiotRmb2+PhQsXckEJIsqWKIo4fPgwpkyZAn9/f6SmpqrtY2RkhOnTp+PGjRvo169fvn6emJiY4O+//4apqanUFh0djbFjx3I4LOUbhnU65u3bt+jdu7fKPHWCIGDr1q2wt7fXYGVEpO3u3bsHT09PjBs3Tu2gyNDQEOvXr8fy5cvzvPeyIAj4999/4eLiotI+b948nD17Nk9fi7QHe9YR6T5RFDFnzhw8ffpU6hGjr6+PRYsWwdHRkcNfiUiFKIpQKpV48eIF/v77b/j7+2e7n7GxMS5fvozZs2fDyckp3zukCIKA6tWrY9q0aZDJ3kUoSqUS169fx4oVK7Lt8Uf0pRjW6ZgxY8aoTQI/c+ZMNG/eXEMVEZG2S0pKwpQpU+Du7o6rV6+qba9evTpu3bqFAQMG5FsNxYsXx44dO1SGL4iiiD59+iAqKirfXpc0hz3riHSbKIo4c+YMtm3bJl0AEgQBXl5e6NWrF4M6IpKIogiFQoGYmBjs2LEDXl5eOHnypNp+enp6KFasGFatWgV3d3cIglBgnyWCIGD8+PGoV6+e1JaQkIBly5bh/v37DOwozzGs0yF79uzBhg0bVNoaN26MX375RTMFEZFWUyqV2LZtGypXrozffvst2zniJkyYgBs3bqBq1ar5Xk/9+vUxf/58lbaQkBAMGjSIB0A6yNDQUOU+h5EQ6QalUon09HS8efMGv/32m8oFl5IlS2Lp0qUM6ogIwLuQLiMjA1FRUTh48CC6dOmC3r17w8/PT21fExMTjBw5Eo8ePUKfPn00UO27nsGrV69GsWLFpLaQkBD8/vvvPI6hPMewTkecP38e3bt3V2krVqwYNm3axHnqiEjNxYsXUb9+ffTu3RsvX75U216uXDmcOXMGv//+u1qokp/Gjx+PNm3aqLQdOnQIK1euLLAaqGBk/X+V3Xw0RFS4pKam4unTpzhy5Ah+/fVX3LlzR5qaxcjICH/++WeeznlKRIWTKIpIT09HeHg4jh07hgEDBqBLly64cOFCtvs7ODhg3rx5+P333zU+hL5KlSqYPXu2NBokNTUV58+fx8GDB6FUKjVWF+mevF8upYAxrAPS09PRunVrtfbly5erzQFFREXbjRs3MHv2bBw9ejTb7fr6+vj5558xdepUtTnFCoJMJsPGjRtRvXp1RERESO0TJkxAs2bN4ObmVuA1Uf7IOvchr0gTFW4KhQKXLl3C1KlTcePGDbXtXbt2RZcuXTRQGRFpk7S0NISFheH27dvYvHkz9u3b98H9S5Ysic2bN6Nx48b5strr5xg2bBgOHjyIc+fOAQCCg4Oxfv16fPXVV3ByctJscaQz2LNOB9SoUUPtJMfCwgI9e/bUUEVEpG2uX7+O9u3bo169ejkGdU2bNoWvry9+/fVXjQR1mezt7bFx40aVtpSUFPTu3Zu9r3QIe9YR6Zb4+HgcPHgw26DOwsICEydOhEwm4xBYoiIqJSUFT548we7duzF27Fh06dLlg0Gdra0t6tWrh/nz56N+/fpaE9QJggBDQ0PMmTMHNjY2AN5drLh9+za2b9/OBbMoz+hcWFe8eHENVaIZL1++xOPHj9Xa4+Li0LZtW0RGRmqgKiLSBgqFAt7e3vD09ET9+vVzDOkqVKiAAwcO4MyZM1rTc61t27YYM2aMSpuvry9mzpypoYoor7FnHZFuSUtLyzF0t7CwwJkzZ/D8+XPOQUpURKSmpiIqKgpRUVG4e/cuNmzYgAkTJqBv374fDOlsbGzQtGlTjB8/Hps2bULv3r1hYmJSgJV/nEwmQ926dTF48GBpddiIiAjs378fvr6+/JyjPKEd8fQXiI2NVblflHrWiaKIkSNH5vhhcO7cObi7u2PXrl346quvCrg6ItKUt2/fYt26dfj777/x4sWLHPezs7PD9OnTMXToUOjr6xdcgbn022+/4fTp07h//77UtmjRInz99ddo3LixBiujvJA1rGPPOqLCzcLCAmXLloW5uTnevn2rsu3ly5f46aefcOPGDSxduhT29vYaqpKI8ltqaioCAgJw7tw5PHnyBADw6NEjnDlzBunp6Tk+rnjx4qhbty6aN2+O9u3bo3z58mrHCtrEwMAA33//Pc6cOYObN28CAPz8/LB7926UK1dO6nVH9Ll0LqyztLTUTCEasHv3bhw6dEilTSaTqUxs+fr1azRq1AjTp0/HtGnTtKb7MBHlPR8fH6xbtw6bNm1CfHx8jvvZ2dlh0qRJGDZsGExNTQuwwk9jZGSErVu3ok6dOlKvK1EU0b9/f9y9e1dlJS4qfLgaLJFuEUURMpnsgwub7dy5E2ZmZhg0aBAvJBPpmPT0dAQEBODEiRM4ffo0Ll++jOjo6I8+ztTUFE2bNkXz5s3RqlUrVKhQoUAXN/tcgiDA2dkZY8eOxfDhw/H27VskJibiyJEjaNiwITp16sRzb/oihXoYrCiKePPmjUpbURkG++bNG4wePVqlzd7eHqdOnYKjo6NKu0KhwKxZs9C4cWM8e/asIMskonz25s0brFy5Eh4eHnB3d8eKFStyDOocHR2xePFiBAQEYPz48Vod1GWqXr065s6dq9L24sUL/PTTTxqqiPJK1qvl6enpXEWNqBB7/PgxTp06pXZs/j5RFLFu3Tr8/PPP2LNnj1oPPCIqfDIyMvD8+XOsWLECY8eOxfz583Hw4MGPBnUGBgbo1KkTFi1ahN9++w0//vgjqlWrViiCukwGBgZo0aIFunbtKrU9e/YM+/fvx8uXLzVYGemCQh31JiQkSMvBZyoqYd2UKVMQHh6u0rZs2TI0a9YMt2/fxrfffiutTpPp6tWrqFatGqZOnYpJkyYVqg9CIvo/GRkZOH36NDZv3gxvb++PTmRbu3ZtjBs3Dt27d9fK4a4f89NPP+HQoUO4ePGi1LZ27Vp06dIF7dq102Bl9CWy+xuUnp7Ov01EhVBCQoLUkyZzepZatWrB0dERZ8+eRWJiorSvKIq4fPkywsLCsGfPHvTu3RtNmjSBubm5psonok8giiKioqKkc9FTp05h//798Pf3R1hY2EcfLwgCOnfujE6dOqF+/fooXbo0jIyM8rvsfCEIAqytrdG/f39cvnwZT548gUKhwIkTJ9C6dWs4OTlp9VBe0m6FOqzLOgQWKBrDYG/cuIFVq1aptHXo0AHdunUD8K6H3cmTJzF//nzMmTNHJdBMSUnBjBkzsGXLFvz1119o06YNV+UiKgSUSiWuXLmC7du3Y/fu3R9dPEYmk6Fz584YN24cGjZsWKh/z+VyOTZs2IAaNWogISFBav/+++9x//79InORRtdkd/CalpbGsI6oEPL398e+ffuknt1GRkYYOnQomjVrhmvXrmHkyJEqvb6VSiWePn2KFy9e4NatW6hfvz7GjBmDOnXqaOotEFEuREZGYvfu3di8eTOSkpIAAOHh4WqdSLIjCAK6du2Knj17wt3dHY6OjjrxN19PTw81atRAnz59MHPmTIiiiIiICOzbtw/169dHuXLlNF0iFVI6FdYJgqDzV+UUCgV+/PFHlUUlTExMsHz5cpWTcT09PcyYMQOtW7dGnz598Pz5c5XnefLkCdq1a4dmzZphwYIFqFevXoG9ByLKHaVSiVu3bmHPnj3YuXNnrrrTOzk5YcCAARg0aBDKlClTAFUWDFdXVyxZsgRDhw6V2kJCQjB+/Hj8+++/GqyMPld2YV1qairnIiQqZOLi4nD69GncunVLamvZsiWaNGmC8uXLw9nZGc+ePcOCBQuQkZGh8tj09HQ8f/4cr1+/RmhoKH744QeUL18elpaWOnMiT1RYvXnzBkFBQRAEAampqXj8+DEWLVqEsLAwREVFffTxgiCgdevW6NKlC/T19eHo6IiqVavCxsZG53qbmZubo3Xr1jhy5AiuX78OADhx4gS8vLzg5ORUaHsOkmYV6rAu65wYlpaW0tLJumrdunW4ffu2StuMGTPg4uKS7f7169eHj48PfvnlFyxfvlxtPqCzZ8+ifv366NChAyZNmgRPT89C3QOHqLBLT0/HuXPnsH//fhw4cADBwcEffYy+vj46deqEwYMHo3Xr1h+c3LswGzJkCPbs2YOTJ09KbevXr0f37t05HLYQyu4knItMEBUuoigiMDAQW7ZskVZ0LlasGLp16wZXV1cIggBjY2NMnDgRgiBg3rx5alPYAO+C+gsXLsDHxwf6+vqQy+Xo2LEjRowYATc3N07STlSA4uPjceLECfz999/Saq6iKCItLe2Dc1K+z9PTEyNHjkSjRo1gZWUF4F1nErlcrpPnmjKZDFWqVEGXLl1w+/ZtZGRkIDExERs3bkSDBg3g6uqq6RKpECrUyVbWnnW6PhQqNjYWU6dOVWmrVKkSxo0b98HHFStWDH/99Rdu3ryJunXrZrvP4cOH0bhxY3z11VfYvXs3T5iIClBiYiK8vb3Rt29f2NnZoXXr1li5cuVHg7ratWvjjz/+QHBwMPbs2YN27drpbFAHvLtCu27dOrWeV0OHDv3g6reknXLqWUdEhcfbt29x5MgRPHjwQGpr3bo16tevrzJHarFixTBlyhT4+vpi+PDh2Z6sZ2RkIDY2FhEREQgNDcX69evRqFEjzJs3TxpuR0R5Ly0tDc+ePcPVq1exe/dudOnSRZqDLXOIa0RERK6CuurVq2PDhg3S85QoUQJGRkYwMjKCnp6eTgZ1mTJXtW3QoIHUdvbsWVy8ePGj80sTZUenwjpdn69uzpw5aqvqLF++PNfdiN3d3XHlyhWsW7cOTk5O2e5z/fp19OjRA05OTpg4cSIeP378xXUTkbqoqCisX78e33zzDWxsbNCtWzds2bLlowdClStXxpw5c/DkyRPcvHkT48aNg62tbcEUrQWcnZ2xZMkSlbZXr15hypQpGqqIPhd71hEVbqIo4vXr11i9erU0csPKygpff/01ypUrp3ZSbmRkhCpVquCvv/7C/fv30atXrw8+f3p6Ot6+fYtff/0Vo0aNwrVr13D+/HkEBwdDFEXpRkS58/7vjSiKUCgUePjwIYYNG4aqVauiSZMm+O6773D69GkkJSV90grtLi4uWLFiBU6ePIk+ffrA3t4e+vr6Oh3OZSUIAmrUqIF27dpJw14VCgWWL1+OiIgIfl7RJyvUYV3Wk1pd7ln35MkTLF++XKWtS5cuaNGixSc9j1wux6BBg/D06VMsWbIE1tbW2e4XGRmJxYsXo3LlyqhVqxYWLFiAZ8+efXb9RAQEBQXhr7/+QtOmTWFvb49Bgwbh4MGDH73aVqFCBUyePBm+vr548OABpk+fjvLlyxdQ1drn+++/V/vsW7lyJa5cuaKhiuhzZDcUhj3riAoHURSRkJCAHTt2qMyn2rx5czRu3DjHaWkEQYCenh4qV66Mf/75B+PHj/9oj3CFQoF///0XDRo0QLNmzeDq6opZs2bhwoULCA0NhVKpZHhH9AGiKEKpVOL169c4e/YsXr58iYCAAIwePRrVqlXD+vXrkZqaivT0dGRkZHzw90gQBMhkMhgZGWHatGm4evUqLl++jBs3bmD48OGwtbXV2aGuuWFgYIAWLVqgfv36UtutW7dw4sQJpKWl8TOKPkmhngCiKA2DnTRpksqkvAYGBvj9998/+/mMjIzw008/4YcffsC6devw559/IigoKNt9fX194evri6lTp6JixYpo06YN2rRpg6ZNm8LExOSzayDSdaIo4tGjR9i7dy/27duHO3fu5PqxderUgZeXFzp37ozKlSvnY5WFjyAIWL16NapWrYrk5GQA737WQ4YMgY+Pj85NWqyrBEGAgYGBSkDHnnVEhUdoaCj++ecf6b69vT1at26NsmXLfvBEPXObubk5FixYgJ49e2Lx4sU4dOgQlEollEpltoFB5v20tDTMmTMHc+bMgbW1NcaMGQNPT08YGBigfPnysLKykl5DEATpRlRUvB9eK5VKBAcHY+3atViyZMknD8eUy+VS+Kanp4eZM2eiXr16KFu2LBwdHfPpHRRegiCgVq1aaN68OW7duoWEhAQAwB9//IFOnTrBzs5OwxVSYaJTYZ2uDoO9ePEiDhw4oNI2bty4PJmo0szMDGPGjMGIESOwe/durFy5EpcuXcpxf39/f/j7++Pvv/+GoaEhGjZsiIYNG6JBgwaoV6+eTgemRLmhVCpx8+ZN7Nu3D/v27ZMm5v0YuVyOpk2bwsvLC998802OQ9XpHVdXV/z666+YMGGC1Pbw4UP8/vvvmDZtmgYro09haGioEtaxZx2R9hNFEampqdiyZYu0IqQgCGjYsCFat279ScGYvr4+6tSpgw0bNiAoKAhxcXG4d+8eli9fjvv372e7GMX7oqOjMWPGDOl+3bp10bdvX5QtWxYZGRlwcHBA+fLlYWJiAkEQIIqiFD4Q6RpRFJGeno7w8HA8efIEaWlpuHr1KlauXKk2ldLH6Ovrw9TUFN26dUOfPn2gr68PZ2dnODk5Mfz+CD09PbRv3x6nTp3ChQsXAACPHz/G4cOH0a9fP5X5PIk+RKfCOl0MikRRxKRJk1TabGxs8nx+Jj09PfTq1Qu9evXC48ePsXbtWmzevBkRERE5PiY1NRVnzpzBmTNnpDY3NzfUqVMH1apVQ9WqVVGtWjWUKFGCH+qk09LT03HhwgXs27cP+/fvz9UKrgBgbGyMtm3bwsvLC+3bt5dWy6LcGTNmDLZt26bSY3Hu3Lno2bMnypUrp8HKKLey9oJkzzqiwiEiIgJr166V7pcoUQKtW7eGi4vLZz2fsbExKlWqBACoV68eXF1dMW/ePPj5+SE2NjbXc2fduHEDN27ckO4LgoDvvvsOLVu2hIWFBZKSklCmTBlUqFBBmvBeFEWp913mapVEhUlmgB4aGopTp05h5cqV8PX1/aTn0NPTg4WFBQwNDaULyD/++CPc3d05YuEz1KxZE82aNYOfnx/i4uIAAMuWLYOXlxeKFy/Oc2PKlUId1hWFOesOHDiAa9euqbTNnDkTFhYW+faalSpVwuLFi7Fw4UJcuHABu3btgre3NyIjIz/62IcPH+Lhw4cqbVZWVqhWrZpKgFe1alWYm5vn11sgyncKhQLnzp3Djh07sHfvXsTExOTqcVZWVujUqRM6d+6MVq1acSj5F9DT08OaNWtQp04d6UQuJSUFI0eOxLFjx3ggVAhkXWSCYR2RdsuclH7z5s0IDQ0FAMhkMlSvXh2tW7fOs9dp3rw5atWqhUOHDmH16tWIiIhAfHw8wsLCPrnerVu3YuvWrVKbXC5Hly5dUKdOHdjb2yMtLQ36+vrQ09NDpUqVULp0aemzSV9fHwYGBvx7QlolswddZm/0sLAwXLp0Cf/+++8HR0hlRy6Xw8HBAVWrVsWAAQNQuXJlmJiYwNHRkceoX0Amk6Fjx444evQobt26BVEU4efnh2PHjqFXr178TKFcKdRhna4Pg1UoFGrDucqWLYsffvihQF5fLpejWbNmaNasGVasWIE7d+7gxIkTOH78OK5evfrRoQmZYmJicP78eZw/f16lvVSpUlKIl3mrVKkSuwaT1hJFEdevX8f27duxa9euXJ80ODk5oXPnzujSpQsaNWoEPb1C/dGrVdzd3TFq1Cj89ddfUtuJEyfg7e2Nbt26abAyyo2sV+s5DJZI+4WHh2PNmjXSfWtra7Rs2RKlS5fO09cpXrw4+vXrhw4dOiA4OBi3b9/G+vXrkZCQAJlMhtDQ0Fz3ZH+fQqHA7t27sXv3brVtbm5uaN++PZycnKBQKGBtbQ0PDw84ODhAT08PCoUCJiYm0kqPRAUtJSUFL1++xO3btxESEgJRFHHixAmcOnUqV493dnaGi4sLBEFAUlIS7OzsMGDAALRu3VrnzqU1rWbNmvD09MSjR4+kueuWLl2K9u3bw8LCgoEdfVShPmPU9WGw27dvV+ulNnfuXI10RZbJZKhduzZq166NadOmIS4uDufOncPly5dx9epV3Lx585NPsl6+fImXL1/iyJEjUpuBgQGqVq2KmjVrSrfq1avna09Coo8JCgrC+vXrsWnTJgQGBubqMRUrVoSXlxe6dOmC2rVr8w9yPpozZw52796NkJAQqW3cuHFo164dTE1NNVgZfQyHwRIVLqIoYuPGjdKiZDKZDBUrVkSnTp3y7e+clZWVNEqjY8eOCA8Ph1wux4ULF3Dw4EG8efMGQUFBePXq1Re/VtYRIoIgoEWLFqhduzbMzMyQmpqKEiVKwNPTExYWFpDL5bC2tmZ4R/kqOTkZCQkJiImJwZkzZ3DkyBEcPXr0k1YWLVu2LCpXrozu3bujffv2EEURkZGRKFGiBEO6fCKXy9GjRw+cOHFC+ly5desWjh49il69emm4OioMCnVYp8vDYDMyMjB79myVturVq6NHjx4aqkiVhYUFvvnmG3zzzTcA3p1g+fr64tq1a7h37x7u3buH+/fvIzEx8ZOeNy0tDXfu3FFbNdPNzQ2enp5o2LAhPD09UaZMGYYflK9SU1Oxf/9+rFu3DqdOncrVAZGHhwe6dOkCLy8vruBagMzNzfHnn3+iZ8+eUtvr168xb948zJ8/X4OV0cdkHQbLnnVE2i04OFilV525uTnatm1bYPOEWltbw9raGgBQvnx5eHl5ISQkBNeuXcOFCxeklS4jIyNV5or6XKIo4tSpUyq9lmQyGVq3bo0SJUpAX18fVapUQaNGjaTedxYWFpzji/JEcnIyHj9+jLNnz0qdHA4cOJCrORxLlSqFqlWrwsjICGZmZmjbti1atmwJGxsb6RzKxsYmv99Ckefh4YHGjRsjMDAQycnJAN6tDJvZu47oQwp1WKfLw2C3bt2KZ8+eqbT9+uuvkMlkGqrowwwMDFC3bl3UrVtXalMqlXjx4gXu378vBXj37t2Dv79/rofQZsq80rl69WoAgKOjI77++mu0b98eLVu2ZO8ZyjNPnjzBypUrsXnz5lzNQ1ezZk306tULPXv2/OyJtenLde/eHatXr8bp06eltiVLlmDw4MEoW7asBiujD2HPOqLCZePGjXjx4gWAd73OypQpgx49emjkAqpMJoONjQ1sbGxQuXJleHl5ISUlBaIoIiwsDOfOnZOOpTMyMqCvr48HDx7g8uXLX/S6SqUSx48fl+6bmZnhq6++gpOTE/T19eHu7o769evD0dERlpaWnPqCci0tLQ2xsbHSYhGnT5+WRjIlJSXl6jlKliyJJk2aoHXr1qhfvz6MjY1hbm4OCwsLrT2P1GX6+vro168fjh8/Ln123rlzBwcPHkTfvn01WxxpvUL71yMlJUW6epZJV3rWKRQKzJs3T6Wtdu3a6Nixo4Yq+jwymQyurq5wdXVFp06dpPbU1FT4+/urBHj37t37pOELmVd216xZA0NDQzRt2hTdunVD9+7deZWCPpkoijh79iz+/PNPHDly5KO96CpUqIBevXrh22+/lVavI80SBAF///03atSogYyMDADvDnrHjx+P/fv3a7Y4yhF71hEVHqGhoSorwJqYmKBjx45acUFEX18ftra20v3MeZHj4uIgCAIUCgX09PTw/PlznDt3DgEBAZDJZLC0tERcXBxu3bqF27dvf9ZrJyQk4OTJk9J9R0dHVKpUCba2tmjdujVq166NkiVLwsrKiqNCKFsZGRl48eIFDh8+jKtXryItLQ1RUVG4e/cu3r59m6vnsLGxQdu2bdGmTRs0bNhQCo9J8zw8PNCiRQts3rwZaWlpEEURf/75J7y8vGBmZqbp8kiLFdqwLusQWEB3wrpdu3bh6dOnKm2zZs3SmT/whoaGqF69OqpXr67SHhsbCz8/P/j6+kq3Bw8eSCfeOUlNTcWJEydw4sQJjBo1Cp06dcKQIUPQokULnfmZUf5ITU3F9u3bsXTpUvj5+X1wXwsLC3z33XcYNGgQPDw8+H9LC7m5uWH06NH4448/pLYDBw7g9OnTaNGihQYro5ywZx1R4bF79268fPlSul+yZEn069dPa3vrGBsbw9jYWKXN1tYWNWrUQExMDARBgLGxMVJSUvD8+XPcuXMH8fHxAIAXL17gyJEjn7z6LPDugnLmwhdXr16VetgNHDgQXbp00dqfFxU8pVKJ169fY+/evThw4AAeP36c6/9zenp6+Oabb+Dh4QFRFFGuXDnUqVMHjo6OHIatZQwMDPDDDz/g4MGDiIyMBADcv38fJ0+ehJeXl4arI21WaMO6rENgAd0YBiuKIhYsWKDS5uHhga+//lpDFRWc4sWLo2nTpmjatKnUlpqaCj8/P1y6dEm6ZX7IZSclJQW7du3Crl27UKVKFYwePRr9+vXjxL+kIjk5GatWrcKiRYsQGhr6wX2bNm2KwYMHo0uXLlzCvhCYPn06Nm/erPI5MW7cOPj4+EAul2uwMspO1p51DOuItFNSUhJWr14t9Tw3MDBAp06d8nwF2PwmCAJMTEzU/p7b29vD3d1dukCckJCAHj164NmzZxAEAampqfDx8cHWrVs/aSqXoKAgaTGOhw8fIjAwEP3794ednV3evSkqdERRRGhoKHbu3Int27cjKCgIERERH32cTCbD4MGD4ebmBmdnZ7i7u8Pa2hqiKMLY2JghnRarWbMmmjdvjj179kChUCAjIwP/+9//8M033zDApxzpTFhnamqqE119jxw5gnv37qm0TZs2rcj24jE0NJTmwvvpp58giiIeP36Mo0eP4siRI7h48WKOPe8ePHiAoUOHYvbs2Zg2bRoGDx6sdmJIRUtmSLdw4cIPXrm0tLTEkCFDMHToUK0Y3kO5Z2lpiblz52Lo0KFS271797BhwwYMHjxYg5VRdrKeWHAYLJF2On78OJ4/fy7dNzY2xoABA3TmIohcLleZ/9jCwgL29vZo2LAhBEGAUqlEQkIC+vbti6CgIBgZGeHly5c4f/48/vvvv1wtQvXixQv89ttv2LhxI6ZMmYLevXvn51siLRUeHo7Nmzdj3bp1iIyMRHR09EcfIwgCunXrhgEDBsDDwwOmpqYwNDTUiXPfosLAwACDBw/GgQMHoFAoIIoibty4gZs3b6JevXqaLo+0lM6EdboyBHbhwoUq993c3KQVV+ndH6vKlSujcuXKGD9+POLi4nDixAls374dR44cQXp6utpjQkJCMGLECCxcuBCLFy9Gt27dimz4WVQpFAps3rwZ06ZNQ0hISI77lS9fHmPGjEH//v05h0QhNnjwYCxfvlzlwsf06dPRs2dP/rtqGQ6DJdJ+oihizZo10u+nTCZD8+bNdX7OVj09PZXFIczMzGBjY4OMjAwIgoD09HQMGDAAT548wYMHD7Bv3z6VVWOzExMTg5iYGIwbNw5+fn5o1aoVatWqxVU5iwhvb2+MHz8esbGxuZqLThAEdOzYEcOHD4e7uzuKFy8OPT09nscUUo0aNULVqlVx69YtAEBiYiI2btzIsI5yVGj7XGads04Xwrrr16/j0qVLKm2TJk1i19gPsLCwQI8ePbBv3z6EhoZi+fLlOR48vnz5Ej169EDbtm3V5gQk3XX69Gl4eHhg4MCBOQZ1zZo1w6FDh/D48WOMGDGCgU4hJ5fLsWTJEpW20NBQLF26VDMFUY6yrpL4sTlKiajg+fr64vr161AqlQDefcYOGTKkSK5yqqenByMjIxgaGsLMzAwODg7w9PTE4MGDsWXLFly8eBF//PEHqlat+sHniYyMxF9//YUuXbqgWbNmOHjwYAG9A9KUsLAwjBw5EkFBQR8M6mrVqoWVK1fiyJEjuHr1KtavX4+WLVvC1tYW+vr6DOoKMUNDQwwePFg6t8/IyMDBgwc/2JGAirZCmwJl7VmnC/PVvT8pOvBuNalevXppqJrCx9raGiNGjMCDBw9w4sSJHCeU/++//1CtWjWsWLEiV8MWqHB6/fo1unbtipYtW+a4eETr1q1x+fJlnDlzBh06dGAwrkNatWqFNm3aqLQtWrTog3NeUsHLOoSHYR2R9tm8eTMSExOl++XKlUOrVq00WJH2EAQBcrkchoaGsLOzQ4MGDTBq1CicPHkSFy9exLx583LsNZeWloaEhATcv38fY8eOxT///IO4uLgCfgdUEMLDwzFr1ixERUXluE+ZMmXwv//9D8ePH8eQIUPQtm1b1KlTh73pdIggCPjuu+9gYWEhtcXFxWHXrl0arIq0WaE9M9W1YbCvXr2Ct7e3Stvo0aM5UehnkMlkaN26NU6dOoVz586hUaNGavukpqZi5MiR6NSpE0/edYxCocDff/+NypUrY+/evdnu06JFC1y+fBknTpxAgwYNCrhCKigLFy5UObiNj49XW8CHNIs964i0W1RUFA4cOCANgRUEAUOHDtWZuerykiAIkMlk0NPTk+a7mzJlCvz8/DBhwoQPHtMHBgbixx9/RM+ePeHr68uLyYWcKIoQRRERERFYsmQJqlatilWrVmX7N87Gxga///47rl27hqFDh8LW1hZ6enqQyWSQyWQM6XRMsWLF0L9/f+l+UlISduzYwWlAKFuFNqzTtWGwK1euVFldytTUFD/88IMGK9INTZo0wfnz57Fr1y6ULFlSbfvhw4fh4eGBu3fvaqA6ymuPHj1C/fr1MWbMGCQkJKhtr1KlCo4dO4aTJ08ypCsCatSogT59+qi0rVy5Eq9evdJQRZRV1rAuu3lHiUgzRFHE3r17ERMTI7WZmpqiX79+DBA+QhAE6VaiRAksWrQIS5YsgZmZWY5BpyiK+O+//7B9+3bEx8czsCuERFGEQqFAZGQkVq9ejfr162PChAnZ9qjT09PD0KFDce/ePYwfPx62trbS/xnSXYIgYNiwYdJoHqVSiaCgoI/Od0lFU6EN63RpGGxKSgrWrl2r0jZgwIBC/Z60iSAI6N69Ox4/foxRo0apbX/16hUaNmyII0eOaKA6yguiKGL58uVwd3eXJm19n42NDVatWgVfX1+0bduWB0JFyOzZs1WGWqampmLu3LkarIjex551RNpLoVBg3759iI+Pl9r69OnD49NPlBnAjBw5Ev7+/pg6dSosLS1haGiotq8oijh06BD27t2L6OholQv5pN0yQ7rt27ejbdu2GDZsGAIDA3Pc38bGBrNmzYKDgwNDuiKmQoUKaN26tXQ/Li4O+/fvl+YFJcqkM2FdYe5Zt2fPHrUrLiNHjtRQNbqrWLFi+Pvvv3H06FHY2dmpbEtISECnTp3w77//aqg6+lxhYWH4+uuvMWrUKKSkpKhtHzRoEB4/fowffvihSE6GXdSVKVNGrZfyv//+i4CAAA1VRO/jnHVE2svX1xdPnz6VAiM9PT2O+vhCJUuWxOzZs+Hn54cRI0agZMmSMDIyUtnn0aNHGDhwIIYNG4aHDx8ysCsElEolnj59ipEjR6Jv377w8fHJcV9zc3OUKVMGy5cvh4ODQwFWSdpk+PDhUkCbnJyMGzdu4PXr1xquirRNoQ3rdGkY7OrVq1XuN2/ePMcVTenLtWvXDnfv3lUbBqlUKjF48GC1Xo6kva5evQp3d3ccP35cbVvFihVx/vx5rFu3DtbW1hqojrTFtGnTVE6GMjIy8P/Yu++wKK7vf+Dv2UIH6YqAiKKAFbFj70ZjicZurFHUWGI39th7j2L5aOxdY+8VKyr2DhZQKYIgvezu/P7w536dnTWxwN4t5/U8eeI9s5Jj3J2dOXPuvTNmzGCYEfmIOusI0U88z+PYsWNITExUxypWrIhy5coxzMo4cByHIkWKYO7cuTh16hR++uknra/bvXs3xo4dSwU7PadQKBAREYE1a9Zg165dn32dk5MTSpcujYkTJ+Ly5cto27atDrMk+qZBgwbw9PRUj9++fYtTp04xzIjoI4Mt1hnLNNjHjx8jNDRUEOvXrx+jbExHwYIFcerUKdF6VgDQp08frF69mkFW5EvxPI+VK1eiTp06iImJER0fNGgQbt68idq1azPIjugbNzc3DBgwQBBbv379v05PIbpBa9YRop+Sk5Nx/vx5pKSkqGPdunWjBe/zkEQiQYkSJdC/f3/BTfunDh48iKlTpyIhIYHWsNNDSqUSd+7cwejRozF//nytf0cFCxZE1apVMX78eJw4cQLDhg1DwYIFGWRL9AXHcbCwsECHDh3UscTERJw9e1brLCFiuoymWGeonXXr1q0TjF1cXNCqVStG2ZgWCwsLbNiwAX/88YfoWL9+/XDgwAEGWZH/kpubi379+qFfv36iG3s3NzccPXoUS5YsgaWlJaMMiT4aOXKk4D2hUCgwe/ZshhkRgKbBEqKvrl69ihcvXqjXUHJwcEDz5s0ZZ2V8pFIpypcvj8mTJ6NWrVpaZwLs2rULR48epU0n9AzP84iOjsaff/6Jf/75R3S8UKFCqFevHsaMGYPt27dj8ODBcHNzo2I3AfChWP/zzz+rZ35kZ2fj/v37ePjwIePMiD6hYh1DSqUSGzduFMS6dev2r1u7k7zFcRymT5+O8ePHC+IqlQqdOnX61zUniO6lp6ejdevWoqnjwId28tu3b6NJkyYMMiP6rlChQqKu5XXr1uH169eMMiIATYMlRB8pFAqcO3cOsbGx6liTJk2o0JBP7Ozs8Msvv2DFihXo3r27aB0znucxb948LFy4EA8ePKCCHWM8zyM1NRWhoaGYN28e9u/fL3pN2bJlMXz4cKxatQoDBw6El5eXevdPQj4qXbo0KlWqpB6/evUK58+fp884UTPIs4ZCoRDsTAUY5jTY06dP482bN4JYjx492CRjwjiOw5QpUzB27FhBPD09HT/++KPo74iw8fbtW9SvXx+HDx8WHRs5ciSOHj0KFxcXBpkRQzFixAjBw5CcnBwsXLiQYUaEinWE6J9Xr14hPDxcfa3NcRw6d+4MqVTKODPjJZfLUbp0aQwZMgRdu3aFs7Oz4Pi9e/cwefJkjB07VjA1mejerVu3MGnSJIwaNQp//fWX6LiXlxcGDx6M/v37w8fHhzY3I1pxHAczMzN06tRJHUtISEBYWBjevXvHMDOiTwyyWPf+/XtRzBA76zZt2iQYBwYGokyZMoyyMW0cx2HatGn45ZdfBPE3b96ga9eutLAvY69evULNmjURFhYmiFtYWGD79u2YM2cOXQyR/1S4cGH06tVLEFu5cqWoU5voDhXrCNE/V69eRWRkpLq7w9fXF1WqVKHOIB0oUqQIevbsiYCAAK3HDxw4oLWTi+hGcnIyRo8ejYULF+Lq1aui40WKFMGAAQPQokULWFtbM8iQGBKZTIYGDRqo1zBUKpV49OgR7ty5wzgzoi8M8ltX242VoRXrsrKysHfvXkFM22YHRHc4jsPq1atFmxKcOXMGM2fOZJQViYqKQp06dfDkyRNB3NHREadOnUL79u0ZZUYM0ciRIwU3nGlpaQgJCWGYkWnTXLOONpgghK3s7Gxcu3ZNsERA69atYWdnR1NgdaREiRLo0qULvL29Rcd4nsesWbOo84aBpKQkrF69+rM7dvr5+WHGjBno0aMHbSBBvgjHcShUqBAaNmyojkVGRuLatWvq9UKJaTPIYl1ycrJgLJfLDW4x+SNHjgim8nIch44dOzLMiACAubk59uzZI9qVa9KkSbhw4QKjrEzXixcvUKdOHTx79kwQL1KkCC5cuICgoCBGmRFDVaxYMVGBd8mSJcjOzmaUkWmjzjpC9MuLFy/w6NEj9Y6EZmZmaNmyJa2nrENyuVy9Pm/Hjh1hZWUlOP7o0SPRBnUkf507dw4dO3bEggULtBZR3N3d8fvvv6NNmzZwdXVlkCExVFZWVvjpp5/U45SUFNy/fx9v375lmBXRFwZZrNO2uYShPe3buXOnYFynTh24ubkxyoZ8ysnJCVu2bBF036hUKvTs2ZO209ah6Oho1KlTBy9evBDE/f39cenSJfj7+7NJjBi8ESNGCMaxsbHYsWMHo2xMGxXrCNEvjx49QmRkpHocEBBAi+MzYG9vj7p162LcuHGiDSdUKhXmz5+Pbdu2McrOtLx79w7jx4/HyZMnBZuuAB+K2R06dMD27dvRqVMng2seIezJZDKULVsWRYsWBfChe/bp06eIiIhgmxjRCwb5zWvoO8FmZ2fj4MGDgli7du0YZUO0qVmzJv78809BLCIiAjNmzGCUkWl5+/YtGjVqhKioKEG8dOnSOHPmDNzd3RllRoxBxYoVUbduXUFs4cKFtPsWA5rTYKlYRwg7SqUSDx48EDwkq1+/PmxsbAzuobgxkMlk8Pf3R9euXUXddTExMZgwYQLi4+MZZWc69u7di4cPH2rtqCtQoAAmT56MatWqwc7OjkF2xNBxHAd7e3vUrFlTHXvy5AkeP35M16XEMIt1mtNgDa1Yd+rUKdEU2E/bX4l++OOPP1CtWjVBbNasWXj48CGjjExDSkoKmjZtisePHwvi5cqVw5kzZ2gdEJInfv/9d8H45s2buHTpEptkTJhmZx2tWUcIO2/evBFMgQWAevXqiQpFRHekUil69eqltRD05s0b0WZ1JG9FRERgxYoVWtcItLKywowZM1CyZEnaKZl8F1tbW8FD5Hfv3uHRo0e08zMxzGKdZmedvb09m0S+keYuTtWrV6cpsHpIKpVi1apVgpvJ3NxcDBgwgJ505JOcnBy0bt0a4eHhgnipUqVw6tQpuLi4MMqMGJsff/wRxYoVE8SWLl3KKBvTRdNgCdEfz549EzyQLFWqFLy8vKgQwViRIkUwduxYWFhYCOIZGRlYsWIFddflk2PHjqFt27YIDw8XXfe3a9cOd+7cQY8ePWiKOPluFhYWKFeuHJycnAB8mAp7//59REdHM86MsGaQZxdDngbL8zwOHDggiLVq1YpRNuS/lC1bFsOHDxfEzp49iyNHjjDKyHjxPI9+/frhzJkzgriXlxeOHz8OZ2dnRpkRYySVSjFgwABBbM+ePYiLi2OUkWmiYh0h+oHneTx79gyPHj1Sx2rUqAF7e3uaAssYx3EYOHAggoODRYWht2/fYuvWrfQQOQ/xPI8zZ85g3LhxuHPnjuj/rVQqxfz581GsWDHRdxgh38rJyUkwo+v+/ft4+fIlfbZNnEEW6wx5Guzt27fx5s0bQaxly5aMsiFfYuLEiShSpIgg9scff9CW2nls7ty5ot3NXF1dceLECVqjjuSLXr16CToVcnNz8b///Y9hRqaH1qwjRD+8e/cODx8+FCzTUqNGDRQoUIBhVuQjjuMwatQoUXfd+/fvERISgvv379N1aR5QqVSIjo7G6tWrcePGDdFxc3NzrFy5Eh4eHlTEJnmG4zg4OjqievXq6tjLly/x9OlTZGdnM8yMsGYUxTpDupA4fPiwYFysWDH4+voyyoZ8CSsrK0ydOlUQu3PnDrZs2cIoI+Ozb98+jBkzRhCzsrLCkSNHUKJECUZZEWPn4OCAjh07CmJr1qyhGx4dojXrCNEPr1+/xu3bt9VjDw8PlCxZEubm5gyzIp8qXLiwaDdz4MMOvu3atcPx48fp++s78DyPBw8eYNCgQdi6davouKWlJU6dOoVevXpRoY7kOTs7O1SoUEGwRujNmzdpxoeJM4pinSGtWXf8+HHB+IcffqATvgHo0qULypQpI4hNnDiRukDywJMnT/DLL78I2rw5jsPmzZsRGBjIMDNiCvr16ycYP3/+HKdOnWKUjemhabCEsMfzPF6/fo179+6pY1WqVIGrqytdo+qZfv36aV0W5NGjR1i+fDmtX/cdUlNTsXfvXtHa4sCHQsrUqVNRpUoV+kyQfCGRSFC4cGEEBASoY7du3UJcXBxNhTVhBlmse//+vWBsKMW6tLQ00W6DTZs2ZZQN+RpSqRQzZ84UxJ4/f45du3Yxysg4ZGRk4OeffxZMuwGA2bNno3Xr1mySIialSpUqKFu2rCCmOR2b5B8q1hHCXnZ2Nl6+fClYpqVixYq0VqweKlSoEEJCQuDj4yM6du/ePZw4cQJKpZJBZoYtNzcX165dw759+0THPD09MWnSJAwYMEC0dAMhealgwYKoUKGCevz06VPExMRQx6wJM4pinaFMgw0NDRVM8ZHJZKhTpw7DjMjXaN68OapWrSqIzZ07l552fCOe59G/f3/cvXtXEO/atavWaR6E5AeO4/Drr78KYnv27BF1cJP8QWvWEcJeUlKSoKvOxsYGvr6+sLW1ZZgV0YbjOLRp0wY7d+4Uref7sTNc8z6J/DulUomHDx9i7ty5onXqXFxcsHTpUgwcOBCWlpaMMiSmwtHREaVKlVI/yMzMzMTDhw+Rnp7OODPCikEW6wx1zbrTp08LxtWqVaMLIQPCcRxGjhwpiIWHh4t2LyVfZsuWLdiwYYMgVqZMGaxcuZKmGBCd6ty5s6BolJ2djZ07dzLMyHRIpVLBmNasI0T33r17J1ivrkSJEnBzcxPtPEr0A8dxKF26NIYOHSo6lpCQQFNhv4JCocC9e/ewYsUKHDt2THS8Q4cOqF69OszMzBhkR0yNubk5vLy8ULhwYXXs5s2b9ADZhBnkt7ChToM9e/asYFyvXj02iZBv1rp1a9HUg7lz5zLKxnC9fPkSv/32myBma2uL3bt3CxZWJUQXnJ2d0aJFC0Fs48aNjLIxLZrFOupUJkS3VCoV4uPj8fDhQ3XM398fhQoVYpgV+S8ymQxt27YVTJkDgMePH+PmzZv04OML8DyP2NhYTJw4ESEhIaLjPj4+aNmyJRwdHRlkR0xV4cKFBZvr3bp1C8nJyXR9ZKIMrliXm5uLjIwMQcwQOutSU1Nx8+ZNQYyKdYZHKpVi2LBhgtjRo0fx/PlzRhkZHqVSie7du4uK7qtXr0bJkiUZZUVM3S+//CIYh4aGIioqilE2pkOzc4fWZSFEt7KysvD06VMkJiYC+NC15evrC1dXV8aZkX/DcRwKFSqErl27CuIRERE4ePAgXr16xSgzw6FQKHD06FGtG0r4+PhgyJAhqFixomhtVULyU+HChQX3Qy9evMCrV69oLUoTZXDFOm3rMBhCse7KlSuCD5lcLhetf0YMQ/fu3eHg4CCIrV27llE2hmfp0qU4d+6cIPbLL7+gQ4cOjDIi5MPO3Jpd2tu2bWOTjAmhYh0hbKWlpSE8PFw9dnJyQrFixajL3QDIZDL4+/uL4mfPnsXFixeRk5PDICvDwPM84uPjsWTJEtGxKlWqYPLkyejUqRN11RGdc3R0hI+Pj3qNxJycHNy8eVPUrERMg1EU6wxhGqzmLrAVK1akCyEDZWVlJXqSuW7dOloY/Qu8fPkS48ePF8S8vLywdOlSRhkR8oG5uTl+/vlnQWzHjh2MsjEdVKwjhK2UlBRcv35dPfb29kaRIkVovToDIJVK4efnh5o1awrib968wYMHD2hR+n+hUqmwefNm0SZnnp6e6NGjB9q0aQMnJydG2RFTJpfL4e3tLVi3LiwsjIp1Jsrgvok1F1iUyWQGsTuPZrGuRo0ajDIheUFz98jXr19rXZiW/J+Pu79qXjyuX7/eILpjifHT7O68ceMGIiMjGWVjGjQ3k6FiHSG6o1Kp8PbtWzx48EAdK168ODw9PRlmRb4Ux3Fwc3NDz549RcdevnyJpKQkBlnpP57nsWPHDixevFh0rHz58vjhhx8M4t6SGC8vLy8ULVpUPb527RrS0tJo3ToTZHDFOm2bS+j7zpE8zyMsLEwQq1atGqNsSF4oV64cKlWqJIj973//Y5SNYdi5cyeOHDkiiAUHB6NOnTqMMiJEqG7dunB2dhbE9u7dyygb00CddYSwk5OTg0ePHiEzMxPAh8+jt7c33NzcGGdGvpS5uTnq1q2L1q1bC+JHjhxBWFgYTYXVIj4+HiNHjsSbN28EcQ8PD7Ro0YKK1YQ5Ly8veHl5qcdxcXGIioqidetMkMEX6wyhIyciIkLUEUjr1Rk+ze66w4cPIyUlhVE2+i09PR3Dhw8XxNzc3DBr1ixGGREiJpPJRDc8u3fvZpOMiaBiHSHsZGdn4969e+qxo6MjihQpAnNzc4ZZka/BcRycnJxED5CTkpIQGRmpLsSS/7NmzRq8fv1aFC9RogSaN28u2qWcEF1zcHCAh4cHLCwsAHy4Nrp37x7t8myCDK5Yp1n0MoRi3adrgQBAoUKF4OHhwSgbklfat28PuVyuHmdnZ+PAgQMMM9Jf8+bNE+1MtmTJEoNYb5KYlrZt2wrGV69eRWxsLKNsjB8V6whhJzs7W7BmV8GCBeHu7q73M1aIkEwmg62trdY4/V0KxcTEYPbs2aK4p6cn+vTpQ12lRC9IJBJ4enoKNji5c+cOFetMkMEV67RNg9V3N27cEIwrVqxIX55GwMHBAY0aNRLEdu7cySgb/RUdHS26MGrYsKGoKEKIPqhXrx5sbGzUY57ncejQIYYZGTcq1hHCTnZ2Nu7fv68eu7q6wt3dnWFG5FsolUpkZ2eL4leuXEFCQgKDjPTXihUrkJaWJojZ29ujf//+aN++PW2sQvSGp6enYGmWu3fvUrHOBBncGckQp8HeunVLMK5QoQKbREiea9++vWB85MgRrTsWm7KJEycKpmFIJBIsWLCACtZEL5mbm6Np06aCGBXr8g8V6whhQ6VSISEhQbBul6urq2AHQmIYLCwstHZE7t+/H+Hh4XSD//+9fv0aCxYsEC3SX7BgQfTq1YumvxK9olmsu3//PrKzs2mTCRNjcMU6zWmw+t5Zx/M8bt++LYgFBASwSYbkuVatWgmmwubk5NCusJ94/PgxNmzYIIgFBwejbNmyjDIi5L/9+OOPgvGJEydoke58olms43meLkQJ0QGFQoE7d+6ox1KpFAULFoSrqyvDrMi3kMvlqFSpEpo3by6Iq1Qq7NixA2/fvjX58yrP8wgJCRF1INrY2KBv3770vid6x8PDQ1CsS09PR0REBD3UNDEGV6wztM66uLg4UQt6+fLlGWVD8pq9vT0aNGggiB0+fJhRNvpn8uTJgi8Va2trTJ48mV1ChHyBH374QTBOS0vDpUuXGGVj3LRNOTL1m0pCdEGzWGdvbw8vLy+aBmiAOI5DkSJF0KxZM8hkMsGxXbt24d69eya/i2RUVBRWrFgBhUIhiDs7O6Nr164024PoHTs7O7i7u6s3mQCA27dvm/xn2dQY3DeyoRXrPl0LBACsrKxQrFgxRtmQ/KD5JPPIkSP01APAvXv3sH37dkFsyJAh9PSS6D1XV1cEBgYKYtQxmz+0FQbo/ElI/lMoFIKZH05OTvDy8mKYEfkeFhYWqFmzJho2bCiI8zyPmJgYk77BVyqVWL16NdLT0wVxc3NzDBo0iK5Lid7y9vYWzCK8efMmXSOZGIMr1hnaNFjNYp2/vz89tTQyzZo1E4zj4+MRHh7OKBv9MXv2bEGHjJ2dHYYPH84wI0K+XJMmTQTjkydPMsrEuFGxjhDd43keOTk5uHfvnjrm6OhIxToDV7x4cVSrVk0Q4zgO7u7uJr0e29OnT7FhwwZkZWUJ4oULF8Yvv/zCKCtC/luxYsUEtY5bt25BoVDQDAQTYnBVI0PrrHv06JFgXKpUKUaZkPxSrFgx+Pr6CmKmviB9dHQ0tm3bJogNHTpUsAU5IfpMszvhxo0bSEpKYpSN8aJiHSFsxMfHIy4uTj12dHREkSJFGGZEvpdCoRBtJsHzPOLj4022s+7t27dYvnw5EhMTBXGpVIoRI0bAxcWFUWaE/Ddvb29BrePx48dad34mxsvgi3X63ln3+PFjwdjPz49RJiQ/aXbXmXqxbtGiRYJ1QSwtLTFw4ECGGRHydYKCggTrhPA8j9DQUIYZGScq1hGieyqVCk+fPlWPpVIpHB0d4eTkxDAr8r1yc3ORmZkpiq9Zswbx8fEm142Tk5ODdevWYcuWLcjIyBAc8/X1RYcOHRhlRsiXcXd3h52dnXqckZGBV69eMcyI6JrBFes0p8Hqe2fdpxdDAFCyZElGmZD8pFmsu379uui9aipSU1OxevVqQaxXr16CHY0I0XcWFhaoXr26IHbu3DlG2RgvKtYRons8zwseJltZWZn8VEljYGtrCz8/P9EabGfOnMGZM2dEmysYu4cPH2LHjh2irjoAGDNmDBWnid6ztbWFs7OzYOOYJ0+emFzh3ZQZVLGO53mDmgabmZmJ6OhoQaxEiRKMsiH5qWbNmjA3N1ePTbkLZ8uWLUhNTVWPJRIJhg0bxjAjQr5NnTp1BOMLFy4wysR40W6whOgez/N48uSJevyxWEcMm5mZGerUqYMaNWqIjv311194+/atyZxfs7KysHv3bjx79kx0rH79+mjZsiWDrAj5OhKJBJ6enrC0tFTHHj9+bDKfY2JgxbrMzEzRWgz6PA32xYsXoljx4sV1nwjJdxYWFqJFfU2xC4fneYSEhAhiLVq0oB2QiUGqVauWYBweHi7aTY58H+qsI0T3VCqVoFhnbW0NDw8PhhmRvFK0aFE0atQIhQsXFsTDwsIQERFhMjf5CQkJWtearVq1KmbNmiWYWkiIPtMs1j169MhkPsfEwIp1ml11gH531j1//lwwdnFxgY2NDaNsSH7T7MIxxWLdtWvXcOvWLUEsODiYTTKEfKeqVasKpoUpFApcv36dYUbGh+M4UYyKdYTkH57ntRbrqLPOOJiZmaFWrVpaN7TT3A3VmOXm5mqd9tuvXz9UrFhR63cPIfqIOutMGxXr8tHLly8FY29vb0aZEF3QLNaFh4cjJSWFUTZsrFu3TjD28vJC48aNGWVDyPextrZGuXLlBLHLly8zysY4UWcdIbqXlJSEhIQE9ZiKdcbF2tpacHP/kSmtWffixQvBe/yjwoULU6GOGBTNYt3Tp0+hVCqpYGciDKpYp7lgv5WVFeRyOZtkvkBUVJRg7OXlxSgTogvVqlUTvB9VKpVJrXGVm5uLnTt3CmK9e/emBauJQdOc3n7t2jVGmRgnKtYRonsfb/aAD92tdnZ2cHFxYZwVySt2dnZaZ/I8ePDAJM6v6enpOHz4MB48eCCISyQSuLm5McqKkG/j6ekJKysr9TglJQWxsbEMMyK6ZFDFOkPaXAIQF+s8PT0ZZUJ0wcrKClWrVhXEzp8/zygb3Ttx4oRox62uXbsyyoaQvFG5cmXBmIp1eYumwRKie5/uBGtmZgZ3d3e9fvhNvo69vT3atm2LkiVLCuKzZ8/G69evjb4j59atW7h06ZJo2m+nTp1oDWVicBwdHWFvb69+uKm5mzcxbgZdrNPnzSUA4M2bN4IxLd5r/GrXri0YX716lVEmurdlyxbBuHr16jT1mxg8zWJddHS01qk1JO/QFCVC8g/P84JlWszNzeHu7k6fOyMilUrh7+8v6pZMSEhAREQEo6x0Iz09HceOHUNYWJggznEcBg4cCEtLS3qvE4MikUhED1Q0l9oixsuginWa02D1vbNOs1hHrdfGr0qVKoLxjRs3TKJLJDc3FwcOHBDEOnfuzCgbQvKOn58fLCwsBLHw8HBG2RgfbR0edCNFSP769PrUzMyMpsAaoc8tFWRubs4gG91JSUlBTEyMaH2+bt26oVSpUvT9QgwOx3FwdXWFTCZTxzRrDMR4GVSxztCmwWrOJ6dinfHTLNalpqaaRKtyaGioaDONNm3aMMqGkLwjk8lEm0zcuXOHUTaEEPL9Pr3Rk8vlcHJyYpgNyQ/Z2dnqdQk/tWfPHgbZ6I5MJtNapGzZsiV11RGD5ezsTMU6E2XQxTp9ngablZUlKl4ULFiQUTZEV9zc3EQ7qmm24hsjza66SpUqoXDhwoyyISRvlS9fXjC+ffs2o0yMD3XWEaJ7msU6Z2dnhtmQ/ODq6ooCBQqIzqeLFi1CdHQ0o6zyF8/ziIyM1PpALSsry+jX6iPGy8XFhYp1JsqginWGNA327du3opirqyuDTIiumeKC9AcPHhSMW7RowSgTQvJe2bJlBeP79+8zyoQQQr4Pz/OCmR/UWWecHBwc0KlTJ9HmdjzP4/nz54yyyl+ZmZk4f/48Ll68KIibm5vD29sbUqmUUWaEfB/qrDNdBlWsM6TOOs0FyCUSiV7nS/KO5lRYYy/WvXz5UrRgcfPmzRllQ0jeK126tGD88OFDrdOLyNejzjpCdCs7O1twjUrFOuPVpEkT0RI8HMcZ7eZf6enpWpslunbtCl9fXyrWEYOl2VkXExPDMBuiSwZdrNPnzrp3794Jxo6Ojuotl4lx0+ysu3XrFnJychhlk//OnTsnGDs6OqJChQqMsiEk75UqVUowzsrKQlRUFKNsCCHk22mup0zTYI1Xbm6u6IEIz/NISkpilFH+4nle66ZuNWrUgJWVFYOMCMkbLi4ugrUY4+Pjjfrekvwfg6oeGdI0WM0vQkdHR0aZEF2rWLGiYJyTk4OnT58yyib/nT17VjCuU6cOFaaJUSlYsKDo+8YUNo7RBeqsI0S3Xr9+rf41x3GwtraGjY0Nw4xIfnn//j1yc3NF8Tlz5iA7O5tBRvlHpVIhMjISFy5cYJ0KIXlOcxqsSqVCfHw8w4yIrhjUHbUhTYM1pMIiyVsODg6iTSbu3bvHKJv8d/78ecG4Xr16jDIhJH9wHAdfX19BzJgL8IQQ4/VpsU4qlcLV1ZUK5EbKzc0NPj4+MDMzE8S3b9+Oly9fMsoqf2RlZeHGjRuiTd3c3d3h4+OjdYdYQgyFnZ2dqDv003M5MV4GVawzpAKY5k6w+pwryXuaC9Iba7Hu3bt3iIyMFMRq1arFKBtC8o+Pj49grPm+J9+GOusI0a1P1/SSSqW0Xp0Rs7OzQ8+ePeHi4iKI8zyP9PR0Rlnlj9zcXNG9FwC0bt2a1qsjBk8qlYqW1NK2PiMxPgZVrDOkNetSU1MFY1tbW0aZEBbKlCkjGBtrse7GjRuCsYWFhWgxfkKMARXrCCHG4NOChkQioetTI1exYkXR3zHHcXB1dWWUUf7IyspCXFycKF6sWDFYWloyyIiQvGVnZyco1mnWRYhxMphinUqlEj0x0edpsJpPrGg9ENOiWay7e/cuo0zy1/Xr1wXjgIAAmmpAjJLm7nkvXrxgk4iRoc46QnRLs1hH16fG7f3791AoFIIYz/OCHYENnVKpxJMnT3Do0CFB/OOajNRVR4yBjY2N4PpIszGIGCeDKdalpqaKLur1ubMuMzNTMKZdiEyLZrHu2bNnRjflAADCw8MF40qVKjHKhJD8VbRoUcH4xYsXWgtNhBCizz69wZNIJLC2tmaYDclvcrlctOkXx3EwNzdnlFHey8nJwaNHj0Qd7+XKlUPZsmWN6s9KTJe1tbXgs0zFOtNgMMU6besQ2NnZMcjky2RkZAjG1IJtWkqVKiV4+sHzPB48eMAwo/zx6NEjwVhzrT5CjIWnp6dgnJaWRlMQ8gB11hGiW5/e4H3sPCLGy8rKSlSsA4xreR6e50XdgwBQv359+Pj4UGcdMQqanXXaaiPE+Bh0sU6fv2g0t0SnpzqmxdLSEsWKFRPEjG33SKVSKfoz+fv7M8qGkPzl4eEhir169YpBJoQQ8u00p8FSsc64xcfHIycnRxBTqVTYuXMno4zy3ueKdVZWVpDJZAwyIiTvUbHONBlssc7Gxkavn5RofjFSsc70GPuC9C9fvhQVpf38/BhlQ0j+Mjc3h7OzsyD25s0bRtkYD+qsI0S3qFhnWhwdHWFmZiaI8TyPuXPnat2QwRBxHCf6MwLapwATYqg0p8GmpaUxzIboisGcwTSLdfo8BRaA6AkPPdkxPcWLFxeMja1YFxERIRg7ODiIihmEGJPChQsLxlSsI4QYmk9v8KhYZ/wKFSqELl26iDYSSUxMxLNnzxhllbd4nhc9PAaA3NxcqFQqBhkRkvesra2ps84EUbEun2gW6/S5C5DkD81psMZWrNOcAujl5UUdMcSoFSpUSDCOj49nlInxoM46QnSL1qwzLRKJBD169BAtHSSVSkVrsRqqz22YYWZmRp11xGhoFutogwnTYDBnMEMr1mnegFCxzvQYe2edZrFO25pehBiTggULCsbGMoWIEGI6Pt0ATSKR0DItJkAmk4mKVjzPG8WDEZ7nkZKSgrNnz4qOZWdnU2cdMRqWlpaCz6zmZpbEOFGxjpB8olmsi4mJMaoTq2axzt3dnVEmhOiGi4uLYJyQkMAoE+NBnXWE6JZm8YKWaTF+0dHRyM3NFcRyc3MREhKi9RxsSJRKJe7du4dt27YJ4lKpFG5ubrCysmKUGSF5SyqVCq6PDP2zS74MFet0hD5QpkdzGiwAo1kfBBB3Fbm5uTHKhBDdcHJyEoypWJc/qFhHSP5RKpWCMX3ejJ+Xl5dourNCocCKFSsMfu1VhUKBqKgoUbxu3bqoUaMGLCwsGGRFSN7T7I7VPJcT40TFunyiOe2VPlCmx9raWrTG1fPnzxllk/eSk5MFY0dHRzaJEKIjmu/xpKQkRpkYD3qQRYhufdpZx3EcrellAlxdXTFw4EBR4UqhUBh8sY7jOFhaWoriZcuWNZo1+QgBxMU6muJtGgzmG9rQi3WaG04Q06C5jltMTAyjTPLe+/fvBWN7e3s2iRCiIw4ODoIxFeu+n7aLTSoeEJJ/ND9z1FlnGjp16iRan1Amkxn8esM8zyMrK0sUz83NpUYJYlQ0z9VUrDMNBnNFbGjFOjMzM8FYc60IYho0p4YaU7FOs7OOinXE2BUoUEAw1vxeIl9P280UbchECCF5S1tBS6VSITs7m0E2eedzO8HKZDL6LiFGRbNYRzMTTAMV6/KJZrHO0L8Mybcx5mKd5mYZmuuhEGJsNL93qFj3/ahYR4huadsVlBi/9+/fizpxeJ4XzZIwRNoaIpRKJXUeEaOi+X6mayXTQMW6fKL5lEfbEy1i/AoXLiwYG/raIJ/SnNotl8sZZUKIbtja2grGaWlpdKP7nbQtEUHTYAnJP5/e4PE8TwUNE1GwYEHRuVUqlcLV1ZVRRnlHW9FCIpHQFG9iVDTP1XStZBoM5m/Z0Ip1moudZmZmMsqEsGTMnXWaN9kymYxRJoTohpWVlShG5/bvo9lZJ5VK6QaLkHxEOwqapvfv34seLimVSqPoENdWcOZ5nh6mEaOiea6mYp1pMJi/ZUMr1mlOCdScMkhMgykV66gdmxg7bVO96dz+fbQV6wgh+efTLnie52kDNBORlpamdRpsWloao4wIIV9DoVAICtA0o8k0ULEun9jY2AjG9GVomjSLdbGxsUYz5UTzppqezhNjZ2FhIYrReqTfh4p1hOjWpw8deJ6n7mAT8blrT2O4JtXWYcRxHHVpE6OSkZEhKNbRWuGmwSCKdTzPG3yxzhjazMnX01wLxFimHADiwgWty0iMnbYd5+h9/32oWEeIbn269qZKpaKHySbCyclJdH5VKBSIiIhglFHe0fawWKVS0TRYYlQ0u2P1vRZC8oZBFOsyMjJET340F/rWNwUKFBCMjWG3JfL1NN8HgPG8FzSLddRhRIydtikH2nahI1+OinWE6NanN3gqlYqm8psIT09PBAcHC86xmZmZWLZsGd69e8cws++n7btZKpXSml7EqKSnpwsK0PpeCyF5wyDOYto6kfS9mmxvby8YJycnM8mDsGVraytqwzfWYh1d8BNjp62QRMW670PFOkJ069MbPJ7nkZ6ezjAboitmZmZo1KiRoIClUqnw4sULREVFMczs+/A8r/VhsUKhoOVZiFFJS0sTFOv0vRZC8obBFuv0vZrs4OAgGCclJTHKhLAkkUhE71VjKdZpFqQN/cksIf+F4zjRrsfGsN4PS1SsI0S3NKfBUrHOdJQsWVLUbSaTyURLthgSjuO0ricrl8vp+4QYFeqsM00GWawzNzfXunaQPnFychKM379/TztumSjNqbDGsmads7OzYJyYmMgoE0J0R7NTlop130fze1GzGEoIyVtUrDNdnyteGXJR63ObpOTm5lJnHTEq6enpgmtOzfXxiXEyyGKdIbR9ahYyACpmmCpjXb9QsyCdkJDAKBNCdIeKdXmLOusI0a1Pb/BoGqxpefbsmeg7Kzc3F9HR0Ywy+n4SiQRWVlaiOHXWEWOjOQ2WOutMAxXr8omTk5Popi4uLo5RNoQlYy3WaRak6f1NTIHm7nKa53nydahYR4hu0QYTpsva2loU4zgOlpaWDLLJG597D9+8eRMvX76kHWGJ0cjIyKA160yQQRTrUlNTBWNDeHPKZDJR5xEVM0yT5vtV8/1sqIoUKSIYv3z5klEmhOiOZlcCFeu+DxXrCNEtzWmwxnJNQv6btqIWz/PIyspikE3ekMvl8Pb2Fu0Ie/78eVy8eNGg/2yEfMTzPFJSUgTXoNRZZxoMolhniJ11AFCoUCHBODY2llEmhCXN9RVzcnIYZZK3ihYtKhi/ePGCSR6E6JJmcYnWWPs+VKwjRLc+3UxAqVQiISGBuo9MhLe3t2iDCTMzM3h4eDDK6PtJpVKUKlUKnTp1EsR5nkd8fLzW9ewIMTQqlQqJiYmCYp0hbwxDvhwV6/KRm5ubYPz69WtGmRCWNJ/2GWux7tWrV8jNzWWTDCE6oG19OioufR8q1hGiW+7u7upffyzW0dqbpkHbNZpKpTL4DfBsbW0RFBQkisvlclFxkhBD9P79e1GX6KfncmK8DOIMZqjFOs0nVVSsM01mZmaCsbEUtLy9vQVjlUqFZ8+eMcqGkPyn7bOr7zuT6zsq1hGiW5/e4H3cYCItLY1hRkRX4uPjRYVZpVJp8Bvg8Tyv9UF4bm4uFaKJUYiPjxcU1WUymdbNLInxoWJdPtKseBvybkvk22l21hlLsc7BwUE01fvu3buMsiEk/2VnZ4timp9v8nU0OzqoWEdI/nJ1dRVM38/NzaXd3E2Eu7u7qNNMLpejYMGCjDLKGxzHaV2SIiMjw+C7BgkBgLdv3wreywULFqTrTxNBxbp8pLkAf1RUFKNMCEuaFxDGdOFQpkwZwfjevXuMMiEk/2lbqNrCwoJBJsaD1gAkRLfMzc0FHRlUrDMdaWlpovUJlUol0tPTGWWUNz5XrDt9+jSePn0q+p4hxNBoFutoCqzpoGJdPvLy8hKMabdM06TZgm9MnSNly5YVjO/cucMoE0Lyn7aFqi0tLRlkYjxoGiwhuvfpjR4V60xHUlKS1mKd5n2WoTEzM4Ofnx98fHwE8du3b+Pu3btau+IJMSTx8fGCmVlUrDMdVKzLR5oL8L979w7v379nkwxhxpineZUrV04wvnbtGqNMCMl/2op11Fn3fTTXGdJc45MQkvcKFy6s/nVOTg7evn3LMBtCvo9UKkXJkiXRrFkz0bGMjAzqrCMGjzrrTBcV6/KRl5cXOI4TxGgBftNjzMW6qlWrCsavXr3Cq1evGGVDSP7SnCpkZmZGa4Z8JyrWEaJ7mp11VKwzDfb29qL7EqlUajD3Vf/GwsICrq6uovizZ8+QkZHBICNC8g4V60wXFevykbm5OTw9PQWxiIgIRtkQVox5mpevry/s7e0FsStXrrBJhpB8prljorW1NaNMjAcV6wjRLY7j4OHhoR7n5OQgNjaWYUZEV2xtbUXFOolEYhTfZXK5HAUKFBDF9+7diydPnlB3HTFob968EUyD/fQcTowbFevyWYkSJQTjp0+fMsqEsKJ5M2pMnTgSiUTUXRcaGsooG0Lyl+YyBtpuDMjXoWIdIbpXvHhx9a+zs7OpI95EvHnzRrSOskKhQFxcHKOM8o6FhQUqVqyIKlWqCOJv3rzB06dPBYUOQgxJVlYWYmJiBO9hzfUZifEyyGKdra0to0y+nmax7tGjR4wyIaykpqYKxob0/v0SNWvWFIxPnjzJKBNC8hcV6/Ke5g0UFesIyX++vr7qX+fm5iI+Pt7gdwQl/02zq+6/4oZEIpGgePHiomtSwDj+fMR0xcbGimZ2aNYXiPHS+2Jddna2aBcfQ+qs8/PzE4ypWGd6jL1Y16hRI8H4wYMH9JSeGKWkpCTB2MHBgVEmxoM66wjRPW9vb8hkMvU4LS0NMTExDDMiupCVlSWK8TwvOg8bKo7jIJGIb20vXLhAxWhisF69eiXY4Mzd3d3o7iXJ5+l9sU6z0AEYVrHO399fMH748KFo23Ri3Iy9WFepUiXRunUnTpxgkwwh+ejdu3eCsaOjI6NMjIcxLxNAiL6ysLCAl5eXepyeno7Xr18zzIjogpeXl6iYJZfLBbsDGzJra2u4uLiI4ps2baJ164jB0izWlSxZEhKJhDpGTYTeF+s0p8AChlWsK1WqlGCclpaGqKgoRtkQFoy9WCeVStGgQQNB7MCBA4yyIST/JCYmCsZUrPt+1FlHiG597D76dBpVRkYG3rx5wzArogvaus7+LW5oLC0tUbt2bdSoUUMQz8nJwfPnz6lYRwzSq1evBF2xNAXWtOj92VmzWCeRSGBlZcUom6/n7u4uWtfo7t27jLIhusbzvOgG3xinzjVv3lwwPnr0KE05IEYnPj5eMNb2BJ98HSrWEaJ7HMcJFiinYp3x43keK1asgEKhEMRzc3MRHR3NKKu8xXEcihcvjnLlyomOmZubUycSMTg8z4s660qUKEHvZROi98U6bV1JhvQG5TgOZcuWFcTu3LnDKBuia2lpacjIyBDEChUqxCib/NOyZUtIpVL1ODMzE0eOHGGYESF5j4p1eY+KdYToHsdxWjvraJkW4/XmzRssW7ZM1F1mbW2NIkWKMMoq7ykUCq07vx44cACZmZn0HicGhed5vH79mop1Jswgi3WGpnz58oLxrVu32CRCdC42NlYUK1iwIINM8peTkxPq1asniO3cuZNRNoTkD80F2I2x8K5rtBssIbonkUhEnXUxMTFQqVQMsyL56dWrV6JCnaWlJQYMGABXV1dGWeU9Ozs7FC5cWLT+6YYNG/DgwQMq1hGDkpycjKSkJMG5uWTJklSsMyF6X6zT3KrYxsaGUSbfTrNYd/PmTUaZEF3TLNbZ2trC2tqaUTb5q23btoLxvn37kJyczCYZQvKBZrHOzc2NUSbGgzaYIET3Pk6D/bhWGc/zSEpKwtu3bxlnRvJLkSJFBDMggA/Fuo4dOzLKKH9YW1ujcePGqFy5siDO8zyWLVtG3XXEoLx69UrQuGRtbW00G8KQL6P3xTpj6KwLDAwUjCMiIvD+/XtG2RBd0txdzRi76j5q166d4EY7Ozsb27ZtY5gRIXknNTVVtIaqu7s7o2yMB02DJYQNZ2dnwTXJu3fv8PLlS4YZkfyUnZ0tivE8LzoHG4OAgAAEBQXBwsJCEN+yZQuePXvGKCtCvt7z588FjQ+lS5eGTCajzjoTovfFOmPorCtTpoyoW+DGjRuMsiG6FBkZKRgXLVqUTSI64OTkhJYtWwpif//9N5tkCMljr169EsWoWPf9qFhHiO5xHAe5XI6AgAB1LDExES9evGCWE8lfcXFxommwKpVKtBarMbC2tkazZs1QqlQpQZzneVGHPCH67NmzZ4JiXWBgoKhDlhg3vS/WGUNnnbm5uWgq7LVr1xhlQ3Tp6dOngrGxb7fdo0cPwfjq1as07ZsYhaioKMHYycnJoHYm11dUrCOEDZlMJirWPX/+nKYIGqmEhAStf7fGeuNftGhRODs7i+Lx8fH0HicGQalUiop1FSpUMNrPLNFO74t1xtBZBwBVqlQRjK9cucIoE6JLERERgrGxF+uaNm0qWnR/6dKljLIhJO9odpwYc5esLlGxjhA2ZDIZKlSooB6npqYiKipK63RJYtjS09Oxfv160d+tRCIRTRU1FnK5HDKZTBRfvXo1UlJSqGBH9F5SUhJev36tvk7iOA4BAQFUrDMxel+sM4bOOgCoWrWqYHz58mX6ojABmp11n+6+ZoxkMhn69esniG3ZsoUWrSYGT3OdGyrW5Q3aDZYQNqRSKfz8/GBubg7gQxdHbGws4uLiGGdG8lp0dDRCQ0OhUCjUMYlEgho1aqBIkSIMM8s/zs7OqFSpEhwcHATx8+fP4/Dhw4yyIuTLRUVFISEhQT12dXWFh4cHrVdnYvS+WGcsnXXVq1cXjOPi4miRUyP35s0b0Vogfn5+jLLRnX79+gluuLOzs7FixQqGGRHy/TTXnyxevDijTIwL7QZLCBscx8HR0VHw4CEuLk60MRYxfNoWpJfL5Rg6dKjB3lf9FwsLC7Rt2xbFihUTHZs9e7ZgaiEh+igqKkrQ7FCmTBlYWFhQsc7E6H2xzlg663x8fODi4iKIXbhwgVE2RBc0NxGxtbU1iRv8ggULomPHjoLY4sWLRZ9lQgyJZpesKXyWdYGmwRLCBsdxMDc3R9myZdWxuLg4rZvpEMNmY2MDiUR4yyeRSIz+AbKfnx+aN28Oe3t7Qfzu3bvYsWMHm6QI+ULR0dGCzrry5cvTNZIJ0vtinbF01nEch5o1awpioaGhjLIhunD9+nXBODAwUHSxZKyGDRsmGL97946664jBUqlUomKdr68vo2yMC3XWEcKOtmJddHQ0LdNiZOLi4gRTYIEP32tv3rxhlJFumJmZoXPnzihcuLAgzvM8Zs+eTUu0EL2Vk5ODqKgoJCUlqWPly5enayQTpPeVA81inaF21gFA7dq1BeOzZ8+ySYTohGaxrlKlSowy0b3y5cujZcuWgti8efOQnp7OKCNCvl1UVBQyMzMFMSrW5Q3N/6+WlpaMMiHE9FhYWKBcuXLqcXp6Ol6+fIn3798zzIrktc9NmzOF6XTFixdH+/btRfePUVFR2LhxI6OsCPl3sbGxiI6OhlKpBPBhjdHSpUtTsc4E6X2xTnPqnKF21gFA3bp1BePIyEhER0ezSYbkq9zcXFHnpCkV6wBgwoQJgvHbt2+xePFiRtkQ8u0ePHggGBcoUAAFCxZklI1xycrKEoypWEeI7shkMhQrVgzOzs4APnQcPX/+nNatMzKhoaGiByMATGLnX5lMhu7du4s2mlAqlZg/fz5tqEL0UlRUFF6+fKke+/n5wcnJySQK7ERI74t1xtRZV65cOdGXxalTpxhlQ/LT1atXRYXmevXqMcqGjUqVKqFZs2aC2KxZs0SbbhCi7+7fvy8Yly5dmi6Y8ojmDaSFhQWjTAgxPRzHoUCBAihfvrw69uzZM1q3zogkJCRg1apVSElJEcRlMpnR7gSrqUiRIujdu7foYVBCQgKWL1/OKCtCPu/ly5eCYl2lSpVgbW1N154mSO+LdcbUWSeRSEQFm5MnTzLKhuSnEydOCMblypUzyU6c6dOnC75YUlNT8eeffzLMiJCvd+fOHcG4dOnSjDIxPjQNlhC2bGxsEBgYqB6/ePGC1q0zEjzPY/fu3aK16TiOw2+//QZ3d3dGmemWRCJB7969YWVlJYjn5ORgyZIl+Pvvv+n9TvRGTk4OXrx4gdjYWHWsUqVKovcvMQ16X6wzps46AGjYsKFgfOLECahUKkbZkPxy5MgRwbhx48aMMmErICAAv/zyiyC2cuVKUacSIfrs9u3bgvGnXSjk2ymVStEGE1SsI0S3bG1tERAQoB5nZGTg2bNntG6dEUhMTMTff/8t2FES+NBVN2jQIJPq0ilcuDAGDRoEmUwmiCcnJ2PmzJk064Pojbi4OERGRqoLyHK5HOXKlaPrIxOl18W63Nxc0XoKhtxZB4iLNvHx8aIbQWLYIiIicO3aNUHMVIt1ADBt2jTB1DalUokBAwbQU0xiELKyskRr1n26IDv5dprr1QFUrCNE18zMzFC0aFFB9//jx49pLS8jEBsbi4yMDFF8+PDh8PDwYJAROxzHoW/fvrC2thYdS0hIwLZt2+i6lOiFmJgYPH36VD329/eHs7MzJBK9LtuQfKLXf+uaXXWA4XfWFS9eHD4+PoLY4cOHGWVD8sPWrVsFY2dnZ9HmIqbE09MTw4cPF8TOnz+PDRs2MMqIkC93584d9W5cwIcL/k+7UMi301asozXrCNE9R0dHlClTRj1+8uQJ4uPjqXhh4MzNzSGVSkXx1q1b6z4ZPeDm5obZs2eLphO+e/cOGzduRExMDKPMCPmA53nExMQgMjJSHStfvjwKFCjAMCvCkl4X6zTXqwMMv7MOAH744QfB+NChQ4wyIXmN53ls3rxZEOvQoYPJb7U9duxYFC1aVBAbMWKEaGoGIfrm+vXrgnGJEiUM/qGRvtC2OyF11hGie46Ojihbtqx6HBkZiTdv3tAyLQYuLS0Nubm5ori2mKkIDg7GkCFDRPFXr15h9+7dgodzhOhadnY2Xr58KSgcBwQEwM7OjmFWhCW9LtZp66wzhmKd5g6ZV65cobUSjMSNGzfw+PFjQaxLly6MstEfVlZW+OuvvwSxhIQEmg5L9F5YWJhgXKVKFUaZGB8q1hGiHwoUKIDSpUur1/PKzMzEw4cPRTuIEsOhUChw48YNJCUlCeKenp6iGT6mZsiQIaJN3+Li4rB7925ER0czyoqQD/dG9+7dU49tbGzg6+urdfo2MQ16XazT7KyztLTU2s5taOrWrSv40PE8j4MHDzLMiOSVFStWCMbe3t6oVq0ao2z0S7NmzdC2bVtBbOfOndi2bRujjAj5b1euXBGMqViXdzSLdRzHwczMjFE2hJguc3NzFC1aVLA76LVr1/D27VuGWZHv8fLlS2zevBmvX78WxEePHi0qVJkaV1dXjBkzRhR/8uQJ9u3bp3WJBkLyG8/ziIuLQ3h4uDrm7++PQoUK0Xp1Jkyv/+aNbSfYjywsLNC0aVNBbO/evYyyIXklMTERW7ZsEcR69OhhUrtt/ZelS5fC0dFREPvtt99EF5OE6IOEhARRp2z16tUZZWN8NG+ILCws6HxJCCPu7u6Cna7Dw8NpKqyBysnJwYEDB/Do0SPRsQoVKjDISP906tQJvr6+glhMTAw2bNiA/fv3IzExkVFmxFQpFAq8ePECDx8+VMcCAwNRqFAhhlkR1vS6WKfZWWcMU2A/0lzc9fjx41rX6COGY+3atYKbT7lcjr59+zLMSP+4ubmJug+TkpLQpUsXKBQKRlkRot3ly5cFYysrK8HNLPk+mp11NAWWEHYKFy6M8uXLqzs4YmNjcffuXa3T1Yl+S0pKwqVLlxAbGyuIBwQEmPwUWOBDF7ezszNGjBghOhYeHo5x48Zh69atVLAjOpWSkoLr16+rd3C2sLBAQEAAnJ2dGWdGWNLrYp2xdtYBwI8//ijYdODjUzBimJRKJZYvXy6ItWvXjp6GaNG+fXt06tRJEDt37hwmT57MJiFCPuP8+fOCcZUqVUx+s5i8RMU6QvSHra0tSpUqJZgiGRoaiuTkZHZJkW8SExOjdQOvYcOGwcnJiTqYAUgkErRo0UK06R8AREREYPHixbhw4YJJb8ZBdCsxMREXLlxQj4sVK4YSJUrA3NycYVaENYMq1hlTZ529vT0aNmwoiO3YsYNRNuR77d27Fy9evBDEBg4cyCYZA7Bs2TJ4enoKYtOnT8eRI0cYZUSI2Llz5wTjOnXqMMrEOFGxjhD9IZFI4OPjA39/f3Xs0qVLSExMpI2gDEhWVhbOnj2L+/fvC+LNmjVDkyZNaO2r/+9jd920adO0zoKJiIjAoUOHEBcXxyA7YmqUSiWio6Nx+/Ztdaxs2bIoWrQou6SIXtDrM7bmtFBj6qwDPnQYferIkSP0BNMAqVQq/Pnnn4JYhQoVaGOJf+Ho6IgdO3aod577qGvXroiIiGCUFSH/Jzk5GTdu3BDEateuzSgb46RZrLOwsGCUCSEE+NDJ8WmxLiYmBrdv30ZOTg7DrMjXePLkCY4dO4b4+HhBvHXr1rC3t6euuk9IpVIEBATg999/19pht3//foSGhtKGEyTfZWRkICwsTF37kMvlKFOmDAoXLsw4M8KaXhfrjLmzDvjwxak5FXbPnj0MMyLfYs+ePYJttgHgjz/+oAui/1CtWjXMnTtXEHv37h1atGhBRWvC3Llz5wQLq5ubmyMoKIhhRsZH8waIOusIYcve3h6lSpWCk5MTgA8PI8+cOYP09HTGmZEvkZWVhTNnzuDSpUuCOMdx8PX1hVQqZZSZ/vrYUTp69GhUrlxZcCwuLg4TJ07EihUraF1xkq9SU1Nx9uxZ9djDwwN+fn70EJPod7HO2Dvr7O3t0axZM0Fs06ZNjLIh30JbV13p0qXRtm1bRhkZliFDhqBNmzaC2KNHj9ChQwfacIIwdfz4ccE4KCiILpryGE2DJUS/SCQSlCpVCt7e3urYqVOnkJ6eTlNhDUBkZCROnjyJlJQUQbxly5bw9fWlKbCfIZfLUbFiRdF6ysCH6bDTp09HSEgIbbZC8oVKpUJCQoJgU7MSJUqgZMmS1PhB9LtYZ+yddQDQpUsXwfjs2bOIiopilA35Wlu3bhV11U2aNIkuiL4Qx3FYv349ypUrJ4gfP34cv/32G90cEGaOHTsmGDdu3JhRJsaLinWE6B9/f38UL15cPX716hWuX79OD9D0XGpqKo4cOYKTJ08K4hzHoWfPnnB0dKQb/39hbW2N5s2bo127dqJjiYmJmD59OiIiIgQd94TkhezsbJw5c0bdpMRxHEqUKCE4DxPTpdcVBWPvrAOAFi1aoECBAuoxz/PYuHEjw4zIl0pLS8OoUaMEMeqq+3o2NjbYv38/XF1dBfFVq1ZhwoQJjLIipuzp06eIjIwUxKhYl/dozTpC9I+LiwtKly4NOzs7AB+6Pvbs2YPs7GzGmZHPSUxMxMyZMzFx4kTR8gKtW7dGxYoVRWsEEyGO4+Dj44N58+ahT58+ouPv37/H2rVraXdYkucyMzPxzz//qMeFChVCuXLlYGVlxS4pojf0ulhnCp11FhYW6NixoyC2du1aenJjAKZNm4Y3b94IYtOnT6euum/g5eWFf/75R7Q9+fTp07Fo0SI2SRGTdejQIcG4UKFCCAgIYJOMEaM16wjRPxKJBFWqVIGXl5c6duDAAaSmplK3ux5SKBQICwvD3LlzRQ9AOI5Dx44d4erqSl11X0AikcDT0xM9e/ZErVq1RMeXLl2KsLAw8DxPnwWSJ1QqFV69eoXQ0FB1rHjx4qhUqRJ9ZgkAPS/WmUJnHQD07NlTMH727JlgkUmif548eYIFCxYIYk2aNEHLli0ZZWT4qlevji1btoiKnUOHDsWaNWsYZUVM0YEDBwTjpk2bUhE+H2RkZAjG9BSZEP2gWax7//49jh07Rg+S9QzP80hOTsaWLVtE05QlEgkGDBiAWrVqCTazI/+O4zhUq1YNgwcPRsmSJQXHlEol6tatixEjRiA6Opo+D+S7KRQK7N27F0qlEsCHHYpLliyJMmXKMM6M6Au9vvswhc464MNFUenSpQWxlStXMsqG/BelUolevXoJWuHlcjkWL15MT0G+U5s2bRASEiKK9+nThz4TRCeSk5Nx/vx5QaxFixaMsjFupvIdT4ihcXBwQJUqVeDg4KCObd++HQqFgjqK9IhKpcKDBw9Em9NJpVKMHTsWU6dOhZubG12bfiWO49CoUSMEBgaK/t+pVCosWLAAxYoVw9q1a+nzQL4Zz/PIzs7Grl271DE3NzdUr14dZmZmDDMj+kSvi3Wm0lnHcZxofYS9e/ciLi6OUUbk38ydOxcXL14UxH7//Xf4+voyysi49OnTBzNnzhTF+/Xrh7/++otBRsSUHDx4UNChYG5uTuvV5RMq1hGiv+rUqQM3Nzf1+NSpU4iJiWGYEfkUz/NIT0/H6tWrRcdq1aqFli1bCoqt5OsUKFAAgwYNQlBQEKRSqei4UqnEsGHDEBkZSR125JvduXNHsFGhh4cHatSowTAjom/0ulhnShfy3bp1EyyunZubq/ULmLB169YtTJw4URArVqwYbYSQx0aPHo3x48eL4gMHDsTs2bPpSSbJN7t37xaMGzZsaNTfPSxpfsdbW1szyoQQoqlSpUooWrSoulCRm5uL7du30/evHuB5Hrm5uQgPDxd11Zmbm6Nu3bqiGTvk6wUFBWHjxo2fLdhlZGRg3LhxCA8PR3p6OoMMiaHieR4qlQqbN29Wx8zNzVGqVClq/iACel2sM5XOOuDDlINOnToJYiEhIbTrkB55//49OnbsKPg7kUgk2LBhg1G/N1ngOA5TpkzBpEmTRMfGjBmDoUOH0pNMkudSU1Nx9OhRQYx2d84/mjc3VBQlRH9YWVmhfv36sLe3V8e2bduGnJwcdkkR8DyPxMREHDt2DJMnTxYdr1y5Mho3bkxrgOYRb29vTJ06FbVr14aTk5PgmFKpxI4dO/DDDz9g06ZNok2TCPk3KSkp2Lt3r3rs4uKChg0b0hrJREBv3w08z5tUZx3woWvoU69fvxZ1eRA2lEolOnfujMePHwvio0ePpnblfMJxHCZPnowpU6aIji1evBidO3dGdnY2g8yIsTpw4IDgYlsmk9GmMfnI1L7jCTE0jRo1gouLi3p869Yt3Lhxg7rrGMrOzsbMmTPRsmVLnDt3TnDM2toaNWvWRPny5RllZ5zq1KmDXbt2YdiwYVo360hISMCcOXNw8OBBJCQkMMiQGKLDhw8jNjYWwId7Hg8PD9SuXZtxVkTf6G2xLiMjQ3QxYOzdS4GBgaLCz4IFC+iiSA+MGzcOhw8fFsQCAwO1PtUkeWvChAmYP3++KL59+3Y0bNgQ8fHxDLIixmjr1q2CcaNGjURP0kneoWIdIfqtVKlSKFu2rKBA8ddff1FnO0OhoaFYtGiR1mOlS5dGy5YtqasuHzg6OqJ///6oXr261g07nj17ht9//x1r166l61Lyn7Kzs7Fq1Sr12MrKCtWrV0fhwoUZZkX0kd4W6zQv4gHTuJD//fffBeNr164hNDSUTTIEwIcL09mzZwtiLi4u2Lt3L+3WoyPDhg3Dpk2bRE80L1y4gEqVKuHmzZuMMiPGIiEhQTQFtn379oyyMQ2a02BpzTpC9ItMJkO7du1gZ2enjh06dAhPnz5lmJXpun37NqZNm6a1WOrk5IRmzZpRV10+cnBwwJw5c9CrVy8EBASIjr9+/RrLli3Drl27aA078q+uXbsm2KzQyckJbdq0oZ2biYjeFuu0neRM4UK+devWKFq0qCA2Z84cNskQrFu3TjQ9WSaTYffu3ShSpAijrExTly5dcOjQIVHRPjo6GjVq1MC2bdsYZUaMwY4dOwS7wFpYWKBNmzYMMzJ+1FlHiP5r0KABSpYsqV5HKSUlBWvXrmWclemJiYnBhAkTRA/w5XI5goKCMHbsWHTv3p266vJZ1apVsWDBAsyePRs9e/YUHY+OjkZISAj+97//ISIigtYeJyIqlQrLli2DUqkE8OG+MjAwEJUqVWKcGdFHBlOs4zgOlpaWjLLRHZlMhmHDhglihw4dwt27dxllZLo2b96MX3/9VRRftmwZatWqxSAj0qhRI4SGhooKpZmZmejUqRMGDBhAC/ySb7J+/XrBuEWLFoJuEpL3qFhHiP5zdHTEzz//DHNzc3Vsx44diImJYZiVabl06RLGjBmDo0ePipbGcXR0xMSJE9G/f3/Rw36SP+zs7NCoUSNMnjwZzZo1Ex2/e/cuZs2ahaFDh2Lnzp2iDROJaXv48CGOHz+uHltbW6NLly6wsLBgmBXRVwZTrLOysjKZ1tBevXrB0dFREJs5cyajbEzTggUL0LVrV9FUg6lTpyI4OJhRVgQAAgICcO3aNa0F0xUrVqBatWqijUAI+TcPHz5EWFiYINa9e3dG2ZiG3Nxc0a6SVKwjRD+1a9dOsNEEbYCmOydPnsT48eOxdetWUZeWubk5unTpgjp16phEQ4M++bghwJQpU7RuRBUTE4ODBw9i0qRJuHDhAu2iTNT+/vtvvH//HsCH91GJEiXQoEEDxlkRfWUwxTpTmAL7kbW1NYYMGSKIbd++HU+ePGGUkelQKpUYNmwYhg8fLjo2ZswYjBs3jkFWRJOrqytOnjyJvn37io7dvn0bFStWxP/+9z/anIV8kf/973+CccGCBdGkSRNG2ZgGU13qghBD5O7ujtatW0MqlQL4cK20du1aZGRkMM7MeKWmpmLHjh2YMGECLly4ICrUyWQyDBgwAMOHD6eOHEYkEgkCAgIwa9YsjBo1SutrIiIiMGzYMCxYsABRUVG0OYuJi4uLw/bt29X3J3K5HB07doS9vT3bxIjeomKdnho4cKCgy0ClUmH69OkMMzJ+cXFxaNKkCRYuXCg6NnToUMyYMcNkujsNgZmZGUJCQrB27VrRE+X09HT8+uuvaN68OV6/fs0oQ2IIsrOzRVNgu3fvDplMxigj02Cqm0gRYogkEgn69Omj3uSJ53lERETgxIkTjDMzTuHh4ejbty9+//13XL9+XVSok0gk2LRpE8aOHUu7RzImlUrh6+uLPn36oHPnzlrvEx49eoQ5c+agcePGmDNnDmJiYuhhsonatWsX4uLi1GMHB4fPvm8IAahYp7ccHR0xaNAgQWzTpk3UXZdPzp49i4CAAJw6dUp0bPbs2Zg/fz6dSPUQx3Ho2bMnrl+/jtKlS4uOHzlyBGXKlMGGDRvowohotXfvXiQkJAhivXr1YpSN6dBWrDO173lCDImfnx8aNmyoHmdkZOB///sfdQrloczMTBw+fBgjR47Enj17EBMTI9j46KMxY8agdevWcHZ2ZpAl0SSRSODt7Y158+Zh/fr1qFq1qug1SUlJePz4MWbMmIEqVapg586dDDIlLGVlZWHdunXq4jvHcfj5559RsGBBxpkRfUbFOj02bNgwwZ9bpVJh8uTJ7BIyQikpKRgwYADq1auH2NhYwTG5XI5NmzZh1KhRVKjTc6VKlUJYWJjWDUGSk5PRvXt3NGjQAPfv32eQHdFnISEhgnGdOnXg6+vLKBvTofkdb25uru7aIYToH5lMhv79+6uvh5RKJa5du4aLFy8yzsw4vHz5EoMGDUKHDh1w7ty5z65xNmTIEIwfP16w4QdhTyqVolChQujYsSM2btyI9u3ba31damoqXr16heDgYNy+fVvHWRKW9u3bh4iICHXzgFQqRXBwsHqnbUK00dt3BxXrAGdnZ1F33bZt22hn2DzA8zx27tyJUqVKYcWKFaLj7u7uOHPmDLp06cIgO/ItrKyssHr1ahw4cABubm6i42fOnEFAQABGjBhBO3MRAMC9e/dw7tw5Qaxfv36MsjEtmp11pvgdT4ihadSokaCLPSkpCWvWrKHO9e+gUChw/fp1DB48GOvWrUNaWhqUSqXodRzHYcKECZg1axatUaenOI6DXC6Hj48PhgwZgqCgoM++Njk5GbVq1cLUqVNFzQLE+CgUCqxatUpw7dO4cWOts4II+ZTBFOusrKwYZcLWiBEjYGtrqx7zPE+bHHynCxcuoHr16mjfvr3W9cwaN26MmzdvokaNGgyyI9/rxx9/xL1797QWWhUKBebPn48SJUpgyZIlyM7OZpAh0RfLli0TjF1dXfHTTz8xysa0aBbraL06QvSfTCYTLKSfnZ2NCxcu4ObNmwyzMiw8z6v/SUtLw/jx41G1alXs37//s1OKO3TogLt372LSpEkwNzen2R56juM4VK9eHfv378eyZcvg4eGh9e8sNTUVkyZNQpkyZTBv3jzEx8er3xvEePA8j6NHj+LBgweCQvyYMWOoq478J719h2juMGWqT92dnJwwYsQIQezAgQMIDQ1llJFh4nkep06dQuPGjVGrVi1cvXpV9BorKyssWLAAR44cgYuLC4MsSV5xdHTEpk2b8M8//6BIkSKi43FxcRgyZAhKlCiBNWvWiBZvJsbv3bt32LBhgyDWt29fmlqkI1SsI8TwcByHDh06oFixYupYfHw8Nm3aRGvXfQGFQoFnz57h+PHjOHfuHIKDgzF79myt/+8kEgnMzc0xePBg/PXXXyhVqhSkUikV6gwEx3FwdHTEgAEDcOPGDcyaNQuOjo6i1/E8j8TERIwcORJVq1bFypUr8fbtWyiVSiraGQmlUom///4biYmJ6ljt2rVRs2ZNhlkRQ6G3xTqaBvt/hg4dKioejRw5kk7iXyA9PR3r1q1DlSpV0LBhw8/uXNaoUSPcu3cPQ4cOpaccRqRVq1Z4+PAhxo0bBzMzM9Hx6Oho9OnTBz4+PliwYAHev3/PIEvCwqpVq5CZmakey2QymgKrQ5pT0alYR4hhkMvlGDlypLpolJaWhjNnzuDx48eMM9NPKpUKOTk5SEtLw9y5c+Hn54emTZuiXr162LJli9bfI5fL0bRpUxw/fhxz5syBk5MTFekMEMdx4DgOrq6uGDVqFM6ePQtXV9fPvv7Fixfo378/mjZtii1btiAuLg65ubl0v2fAeJ7H+fPncfPmTXVjgFQqxZgxYxhnRgyF3lYlqFj3f2xtbTFx4kRB7OrVq9i+fTujjPSbUqnE+fPnMWDAABQuXBi9evXC9evXtb62ePHi2LFjB44dOwZvb28dZ0p0wcrKCtOmTcO9e/fQvHlzra+JiorC8OHD4enpiaFDh9JNh5HLycnBkiVLBLH27dvD3d2dUUamJyUlRTAuUKAAo0wIIV+rU6dOKFq0qHocFRWFXbt2aV1rzdSoVCpkZ2cjKysL79+/x507d7Bhwwb07t0bEydO1Lq760cymQzOzs7o1KkT5s2bh9q1a1O3txEpW7Ysli9fDm9vb9jZ2X32dTdv3kS3bt3QsWNH7N27F2/evEFmZiYyMzOhUCioeGdAcnNzsX37dsTExKhjVatWFeysTci/kbFO4HOoWCcUHByMJUuW4OnTp+rY6NGj0apVK1haWjLMTD8oFAqEhoZi165d2LNnz38u1urq6orx48cjODhYa8cVMT4lSpTAwYMHcfHiRYwbN060sQDwodtn0aJFWLRoEapXr46ePXuiffv2VEgwMlu2bBFcOAHA77//ziYZE6XZxfpvNy6EEP3BcRysra0xZMgQDBs2DCqVCu/evcOpU6fQsWNHlChRgnWKOvexey4zMxPR0dF4+PAhsrKycPfuXWzYsAFv3779z59hbm6O6tWrY+jQoWjQoIHJ3/cYq7Zt26JmzZrYtGkT1q1bh9jYWMH0yE+dO3cO586dQ9OmTdG6dWtIpVK4u7ujQoUKsLa2hrm5Od3D6Lnr16/jypUr6pkccrkcw4cPpynt5ItRsc5AyOVyzJ49G23atFHHoqKiMH/+fIwfP55hZuxERkbixIkTOHHiBE6fPo3k5OT//D3FixfHyJEj0b17d9pNy0TVqFEDZ86cwalTpzBx4kRcvnxZ6+suX76My5cvY/DgwWjcuDF++ukntGjRAk5OTjrOmOQllUqFOXPmCGJ16tRB5cqVGWVkmjSLdVQQJ8RwSKVStG/fHn/99Zf6IfKjR49w6NAh/Pbbb5DL5YwzzD88zyM7OxuZmZmQSCTIyMhATEwM7t69iydPnuDo0aMIDw//4p8nk8lQtGhRVKlSBYMGDUK1atXyMXuiDwoWLIhhw4ahc+fO2L59O7Zv347nz58jLi5O6+uPHj2Ko0ePAvhQLO/duzf8/PxQpEgRBAYGwtnZGQBgaWlJxTs9kpmZif379+P58+fqWOXKldGoUSMq1JEvRsU6A9K6dWvUqVNH0BE0c+ZMdO/eHZ6engwzy38KhQJ3795VF1AuXrwoOPn9G47j8MMPP6BPnz5o0aIFpFJpPmdL9B3HcWjYsCEaNmyIy5cvY+HChdi9e7fWRZ6zsrKwf/9+7N+/H1KpFDVr1kTDhg3RoEEDVK5cGTKZ3p5GiRb79+/Hw4cPBTHNTXxI/qNiHSGG6+Pi+cHBwRg1ahRUKhXi4uJw7NgxNGnSBP7+/qxTzHOZmZlITExEXFwcrl27htjYWMjlcrx58wbXr19HWFjYF/8sjuPg5+eHggULolChQujUqRPq1q1LHcYmhOM4uLm5YfDgwWjTpg327t2Lffv24eHDh/86O4jneaxZswbAh0Jvy5YtUbFiRfA8Dx8fH1SsWBH29vaQyWSwsbGha1RGeJ7HjRs3cObMGfUavWZmZhg0aBAsLS2pWEe+mN5+gqlYJ8ZxHBYtWoSKFSuqiwoZGRkYPnw4duzYwTi7vJWQkKAuzF2+fBnXrl0TvSf+S9myZdGuXTt069YNXl5e+ZQpMXTVq1dH9erV8fz5cyxfvhwbNmxAfHy81tcqlUr1tIQJEybA1tYWNWrUQNWqVVG1alVUrlxZ/YST6B+e5zF9+nRBrEyZMp9dy5DkH5oGS4hhMzMzQ6tWrfD333/j3r17AD5M+Tp8+DC8vLxgZWXFOMNvl52djYSEBGRmZsLa2hqvXr3CuXPn8PjxYzx+/BgXLlz45nXDypUrh8DAQLRp0wb+/v5wcXGhhxUmTCKRoEiRIvjtt9/w448/4uDBgzh27Bhu3Ljx2WvRjxQKBfbs2YM9e/YAAJycnNCgQQN4enrCwsICFSpUQGBgIMzNzSGVSqFQKGBnZwcbGxsqFuWz9+/f48CBA3jw4IE6FhQUhPr161PTCPkqVKwzMAEBAejbty9CQkLUsZ07d+L48eNo3Lgxw8y+nVKpxP3793Hp0iVcunQJly9fRkRExDf9rPLly6Ndu3b4+eef4evrm8eZEmPm7e2NuXPnYsaMGThy5AjWrVuHgwcP/uti0KmpqYLpCQDg4eGB0qVLq/8pXrw4ihYtCnd3d3rCydjRo0dFm82MGTOGLloZoA0mCDFsHMehcOHCCA4OxrBhw5Cbm4uEhAQcOXIEdevWRcWKFVmn+EWUSiXevXuH+Ph4mJmZITMzE+fPn8f169eRnp4OW1tbPH/+HJcuXUJOTs43/TdKly6NkiVLokiRImjatCmqV69O5zwiIJPJULx4cfTv3x9NmjTBiRMncOrUKYSGhuLdu3df9DMSExMFzRvFixdH5cqVYWFhAZlMBoVCAQcHBzRp0gRlypRBTk4OzMzMkJubC5lMBqVSCYlEAplMBkdHR9rc5BvxPI+wsDCcOHECaWlpAD5sFvnbb7/B3t6erjnJV9HbO0fNYp0hP6HLa9OmTcOOHTsEJ+/ffvsNd+/eNYh12NLS0nD16lVcvHhRXZzTvHH7Uq6urmjYsCEaNWqEhg0bwsPDI4+zJaZGLpejZcuWaNmyJd69e4eDBw9i7969OHbsmHqB2H/z6tUrvHr1CseOHRPEPy4M7OXlhSJFisDV1RXOzs5wdnaGk5MTnJ2dYW9vr14wWPPfZmZmkEj0dgNvvcfzPKZMmSKIFS9eHB06dGCUkWmjabCEGD5LS0s0adIENWrUwNmzZwEA165dw4kTJ1CiRAm97ZjleR7v3r1DZGQk7ty5gzNnziAhIQFmZmbIysrCnTt3/rOr6b+UKVMGVapUgaenJ6pUqYLixYvDzc0Ntra2dLNOPsvMzAx+fn7w9vZGw4YNce7cOURERIDjOOTm5iIqKgr79+//op2XIyMjERkZKYqfPn0aJUqUQG5uLuRyORQKBaRSKVQqFTiOg0wmQ2BgIBo2bIgCBQqou/LkcjlycnLg4uICAHj79q3WYt/HrlOO46BSqUS/38zMTPTvjz9DIpHAxcXFoO/74+PjcfToUUFXXbNmzVCzZk2jXs+T5A+DKdZRZ93/cXJywuzZs9GnTx91LCIiAjNmzBDdjOoDhUKBsLAwHD9+HCdOnMDVq1e/6EtGm+LFi6unLdaoUQNly5alAgbJN46OjujWrRu6deuGjIwMnDx5EqdOncKpU6dw//79r/pZSqUSUVFRiIqK+uZ8ZDIZzM3NP1vQ+7d/W1pawsrKClZWVrC2thb82traWl04/Dglx9huJo4ePYorV64IYmPHjqVuR0aoWEeI4eM4Dh4eHujRo4d6uZKUlBTs27cP9erVQ5UqVfTmuyQpKQkxMTEwMzPDpUuXsHPnTsTHxyMmJgbR0dHf/fNdXV3RvHlzeHt7w9LSEgEBAfD19YW9vT2sra3pWpV8FXNzc/j7+6NYsWLq7iylUomkpCQ0adIEBw4cwOHDh79pOvbt27dx+/btf33N6dOn8c8//8DS0hISiQRKpRJSqRRKpVI9jTY1NVUd+7TYx/O8+nPP87zo93/s8vv4709/BsdxKF++PJo1awZ3d3etxT5zc3NkZ2ejUKFCerfpm0qlQnh4OA4dOoTs7GwAH84N3bt3h5OTk96cD4nh0Nu7FCrW/btevXph7dq1gp0sZ82ahQ4dOqB06dIMM/sgLS0NBw8exK5du3Dy5EnRjdmXsLKyQuXKldXFuWrVqsHV1TUfsiXkv1lZWak77gAgNjYW58+fx9WrVxEWFoYbN258Uefd91AoFFAoFF+9fuPXkslkcHJygru7O7y9vVGsWDF4e3vD29sbPj4+8Pb2Nqg1N3iex4QJEwSxYsWK4ZdffmGUEaE16wgxDhYWFqhRowYaNWqEf/75BwBw8+ZNHD9+HCVLloSDgwOz3N6/f49Xr17h1q1bWL9+PRISEiCVShEXF/ddBTqJRIL27dujRo0aiI+PVxfnypQpA1tbW8hkMlhaWhrU9yTRTx8f0H7k6uoKLy8v1K9fH127dkVMTAxUKhWOHj2KU6dOffNaipoSEhKQkJCQJz/ra129ehWHDh2CtbW1uvCnrehnZ2eHX375BUFBQcjJyYFcLodEIoG7uztsbW2Z5B4bG4tDhw7h2bNn6ljbtm1RoUIFejhMvolevmt4nqdi3X+QSCRYuXIlAgMD1Wtq5ebm4tdff8WFCxeYXCCkp6fj4MGD2LFjBw4fPoysrKyv+v3e3t4ICgpSF+fKlStHJzaitwoVKoT27dujffv2AD4U0h4+fIj79++r/3n06BFevnyJjIwMxtl+HYVCgbi4OMTFxSE8PFx03MrKCmXLlkX58uVRqVIl1KxZE35+fnr7xHDv3r24ceOGIDZhwgSajsAQrVlHiHHgOA5FihRB+/btcf78ebx79w7Z2dnYunUrmjRpgsqVK+v0uyE9PR1PnjzB1atXsWfPHrx48QJpaWmIi4vTuuP7l+I4Di1btkT16tUREBCAsmXLokCBAsjJyYFUKoWVlRVdsxKdsLCwgI+PD7y8vJCTkwOe59GqVSvcv38f7969Q0xMDLZt24a7d++yTvWbpKam4tGjR1/02kePHsHOzk5d1OM4Dv7+/mjZsiWqVq2KYsWKwdLSMp8z/kCpVOL27dvYs2eP+t68aNGi+Omnn+Ds7Ky318hEv+nlt0p2drboC5WKdWJly5bFiBEjMGvWLHXsypUrWLp0KX7//Xed5XH//n2EhIRgw4YNX7z2nEwmQ4UKFVCjRg3UqFEDQUFBKFy4cD5nSkj+kclkKFu2LMqWLSuI8zyPxMREvHz5Ei9evMDLly/x+vVrJCYmIiEhQf3vhIQEpKamfvMUcV3KyMjA1atXcfXqVaxatQrAh+n5NWvWRNOmTdGsWTMUKVKEcZYfKBQKjB8/XhArWbIkunbtyigjkpOTI3qYQ8U6QgyXXC5H1apV8cMPP2Dz5s0AgCdPnmDPnj0oXry4TqaqZWVlISwsDEuXLsXFixeRmZn5Xd+p1atXR7NmzeDr6wuVSgV3d3eULFkS1tbWMDc3Vxfm6P6EsMBxnHo9YwCwsbGBl5cXlEolFAoF2rZti6dPnyInJ0c9hfTMmTNYtWqVenqmMfj4YPlTz58/x/nz52FpaYnGjRuja9eusLGxQeHCheHu7p5vU9I/FkljY2PVsbZt2yIgIIC6bMk308tinbYuFPoy1G7ixInYvXs3nj59qo6NHTsWzZs3R4kSJfLtv8vzPE6fPo0ZM2bg9OnTX/R7ypQpg8aNG6Nx48aoWbMm/Z0Sk8BxnHo9uC/ZHU+pVCInJwfZ2dmif2uLfclrsrOzkZWVhfT0dGRkZCAjI0Pw6/fv3yMxMfG7OgATExOxb98+7Nu3D8CHhwk//fQTunbtmq/nov+yYcMGPHz4UBCbMmUKdUAwpG1ZBCrWEWK4OI6Dl5cXmjdvjlOnTiE2NhZKpRJr1qxB8+bNERQUlC83q0qlEtHR0bh//z52796N3bt3IyMj4193cdfGxcUFXbt2RdWqVWFnZwcbGxuUKFEC9vb26rwlEgkkEgl1xxC9xHEc5HK5esZAiRIlULx4ccG02CZNmiA4OBiRkZGwtLREZmYmLC0tkZWVpd7kISEhAWvXrkVoaOi//rfyarptfsjNzUVSUhKSkpKwefNm7N69W71xRu/evTFw4EB4enrm6Wc5JycHV69exc6dO9X/b/z9/dG4cWNaq458F728W9G2HhMVdrSztLTEmjVrUKdOHXUsMzMTPXr0wPnz5/Pl4ujYsWOYNGkSrl69+q+vk8lkaNiwIX7++Wf88MMP1DlHyBeQSqWwtLTUWdv+pzIyMtRdfnFxcXj58iWeP3+u/ufZs2eCXaj/zd27d3H37l1MmTIFVatWxS+//IJu3brpdB2RjIwMTJw4URALCAhAu3btdJYDEdNWrKM16wgxbBKJBLVq1ULjxo2xYcMGAB8e4ixZsgR+fn7qHSTzSnZ2NmbNmoXZs2dDqVSq//lS5ubm6NWrFxo0aIDKlSvDzc1N3XHzcTod3WATQ/WxuPwpuVyOUqVKqZct0VZw43keLVq0QEREBDIyMtQbQuTk5MDCwgKZmZnw9vYGx3F49uyZuthnbm6OnJwcyGQy9UYTHMdp/f1WVlbIyMhQ/9vS0hLx8fHYtWsXduzYkaeFwNzcXOTm5qrHCxYswLJly9CrVy/Ur18f1apV++77U57nERsbi/nz56vXrpZIJPjxxx9RuXJl2lyGfBcq1hmB2rVrY+DAgVi2bJk6dunSJcybNw+jR4/Os//OgwcPMHz4cBw9evSzr+E4Dg0bNkSHDh3QunVrvdulhxDyeVZWVihSpMi/TmGNi4vD7du3cefOHVy/fh0XLlzA69ev//XnfpwyO3bsWPTu3RuDBg2Ct7d3XqcvsmjRIlFus2fPpgsnxjSLdR8XYyeEGC6O4+Du7o6WLVvi8uXL6hkfu3fvRqdOndCiRQvIZLLvLoDxPI8XL15g6tSp+Pvvv7/oxv7jf1MikWDkyJFo3LgxfHx84O7urj5GhTliCjiO+89GDkdHR1SpUuU/f5aPj09epQUAaN68OSZOnIjExET151GlUmkt9u3cuROLFi0SLZv1X+cDpVKJzMxM/PXXX1i+fDksLS0xatQo9O3bF4UKFQLwdecCnueRlZWFvXv3CjZ9rFSpEpo3bw57e/sv/lmEaMPxetjHeuPGDVSqVEk9NjMzM6r59fkhPT0d5cuXR2RkpDoml8sRFhaGgICA7/rZmZmZmDBhAhYtWvTZp5aurq749ddf0adPHxQtWvS7/nuEEMPB8zyioqJw/vx5HD16FEePHv3P7juJRIJOnTrhzz//RPHixfMlr7i4OPj4+CAtLU0da9CgAU6cOEE3ZYydOXMG9evXV4+dnJyY7TpHyEdZWVmYPXs2Jk+erI4dOnQIjRs3pmnzXyE1NRV//PEHVq1ape5oKV26NE6dOgVXV9dvPv/yPA+FQoH58+dj7Nix/3pT/rEgIZVK0alTJ3Tv3h0SiQTe3t7w8PAQvI4Qoj++pizx6tUrPH/+XN3dl56ejv/973/Yv38/VCoVlErlF/88JycnjBo1Cl27dkXBggW/eMo7z/OIiIhArVq11GvnWVtbY9SoURgzZox6TUFiGHJycrBlyxb07NlTHVuxYgV69erF7O9SL9sLaCfYr2dtbY2///5bcGLJzc1F586dv2sdqkuXLiEgIADz58/XWqjz9vZGSEgIXr58ienTp1OhjhAT83Gtol9++QWbN29GfHw8Lly4gMGDB3922pNKpcLmzZvh5+eH/v37482bN3me1/jx4wWFOo7jMG/ePLo50wOanXU0BZYQ42Fra4t27dqhfPny6tj9+/exevVq5ObmftMUt+zsbDx69AgDBgz4z0KdTCZDzZo1sXjxYoSHh2PlypWoW7cuateurV6niqa4EqKfPv18/tc/np6eqF27NipXroxatWqhadOm2LBhA65evYr58+ejevXqsLOzg62tLSwtLf/1M5+YmIjRo0ejVq1aCAkJwZs3b/5z7Uue55GRkYG5c+eqC3UcxyEoKAht2rShQh3JEwZRrLOysmKUiWGpWbMmRo0aJYg9fPgQI0aM+OqfpVKpMG3aNNSsWRNPnjwRHS9UqBDWrFmDJ0+eIDg4GBYWFt+cNyHEeEilUtSoUQOLFy/G69evcejQIbRq1UrrRZJCoUBISAh8fX2xaNGir14U/HNu3bqF//3vf4JY9+7dv7vLmOQNzWIdbS5BiHGpUaMGmjZtKvhsz507V7AZ2pdKTU3FkiVLULlyZaxZs0ZroU4qlcLBwQFubm5o06YNli5digEDBqBUqVJ0w0yICbG0tERAQAAGDx6Mw4cPIzQ0FGfPnsWcOXNQqlSp/7zeePbsGQYOHIj27dvjzJkzyMzM/OzafjzP48qVK1i7dq06XqhQIbRq1QqlS5fO8z8bMU0GUayjzrovN2XKFAQGBgpiK1aswN69e7/4ZyQnJ6N169aYMGGC6ARlbm6O8ePH4+nTp+jduzdNDSGEfJZcLkezZs3wzz//ICIiAkOHDoWNjY3odWlpaRg6dCiqVq2K69evf9d/k+d5DBo0SHDusra2xvTp07/r55K8Q8U6QoybTCZD9+7dERgYqF4fKyUlBePHj9e6LrU2CoUC8fHxmD9/PqZOnar190kkEri6uqJOnTpYsmQJTp48iTVr1gi6+gghpqlAgQIoV64cAgMDMXDgQJw9exbTp09H2bJlUaRIETg6On729166dAkjR47EunXr8PTpU2RkZIjuiRMTEzF27Fj1zDO5XI6goCC0b9+eOndJnqFinZExMzPD5s2bRYt19+7dGy9fvvzP3//ixQtUrVoVBw4cEB2rWrUqbt68ialTp2q94SaEkM8pVqwYFixYgOfPn2PEiBFau3HDw8NRrVo1TJ8+/at29fvU1q1bceHCBUHsjz/+oN2o9Yjmmob/dsFMCDFMPj4+6NKlC1xdXdWxf/75Bzt27PjP83tKSgouXLiAiRMnYvHixUhNTRW9RiKRoEyZMpgxYwZ27dqFrl27olSpUjrdcZwQYjicnZ3x22+/4dSpU1i/fj3GjBmDatWqwc3NTevrb9++jd9++w19+vTBpk2bEBERoT535ebmIiQkBGFhYQA+TH/19vZGjx498nzna2LaqFhnhPz8/LBkyRJBLCkpCR06dEBOTs5nf9/9+/dRo0YN0bRXiUSCGTNm4OLFi/D398+XnAkhpsHZ2Rlz585FRESEYAHXj5RKJcaPH49GjRp99Vp2KSkpomn/3t7eGD58+HflTPIWFesIMQ0///wzateuLZiKOnHiRNy+ffuzvyc3NxeHDh1C3759sXLlSiQnJ4teU7x4cbRq1Qpz5sxB79694eDgkB/pE0KMkIuLC+rWrYsRI0Zg7969mDRpEho2bPjZot358+cRHByMUaNG4c6dO1CpVDh79izmzp2rfo2VlRV++OEHNGrUSFd/DGIiqFhnpHr37o0OHToIYlevXsXo0aO1vv7atWuoXbu26ObY2dkZJ06cwB9//PGfW30TQsiXcnd3x9q1a3H+/HmtDwHOnDmD8uXLIzQ09It/5uTJkxETEyOILVy4kNbU1DNUrCPENBQoUACDBw9GiRIl1NPCXr9+jQkTJiA+Pl7r73n+/Dm2bt362fXtAgMDMW3aNKxduxZNmjTJt9wJIcaN4zgUKlQIffr0wdq1azFs2DD4+vp+9vX//PMPxo8fj2vXrmHcuHHqjl+pVIqAgAAEBwfD3NxcV+kTE0HFOiPFcRxWrlyJ4sWLC+KLFi3Cjh07BLEHDx6gSZMmohuosmXL4saNG6hfv36+50sIMU21atXCrVu3MGnSJEgkwq+khIQENGzYEJs2bfrPn3Pr1i1RR/EPP/yAli1b5mm+5PtRsY4Q01GtWjUEBwcLlk85fvw4QkJCRJsKxcXFYevWreqpZZrq1q2LmTNnokOHDrC3t8/PtAkhJkIikcDT0xN9+vTBn3/+iU6dOqFIkSJaX3v48GGMHTsWN27cUMecnZ0xePBg+Pn56SplYkKoWGfEChQogJ07d4qq/L169cL9+/cBANHR0WjSpAmSkpIEr6levTrOnTv32ZMVIYTkFTMzM0yePBlnz56Fh4eH4FhOTg5++eUXTJo0SeuOXMCHqbPBwcGCdZDMzc2xdOlSWuRXD1GxjhDTIZFI0LVrV7Rs2VL9QEahUGDlypU4fvy4+nUZGRk4cuQI1q1bh7i4OMHPKFWqFMaNG4fZs2ejUaNGdF4nhOS5AgUKoG3btpg5cyZmzJiBzp07a11/7vTp0+rrUblcjl9++QUtWrSg8xLJF3pZrMvIyBCMqVj37SpUqIBly5YJYunp6WjdujWePXuGxo0b49WrV4LjjRo1wokTJ2gNEEKITtWqVQu3b99Gs2bNRMemTJmCgQMHQqVSiY59usjvR2PHjhV1FhP9QMU6QkyLg4MDxo4dK5hiFhsbi1mzZuHly5dITk7Gxo0bMXfuXK2boQ0ZMgQjRoxA5cqV6YaYEJJvZDIZvLy80L59e0ydOhWDBg367Fp2AFCuXDkMHjxYtLEjIXlFL4t11FmXt3799Vf06tVLEIuIiECZMmXw6NEjQbxatWrYu3cv/T8nhDDh6OiI/fv3Y8iQIaJjy5cvx4ABAwQFu1evXuGPP/4QvK5kyZKfXZ+TsEfFOkJMj7+/P6ZOnarebEKlUuH69euYO3curly5gunTp+PBgwei39e6dWs0a9YMBQoUoEIdIUQn5HI5ihUrhn79+mHhwoWfnWn25s0b3Lp1S7fJEZOil8U6zc46KysrRpkYj7/++guVKlUSxDIzMwVjf39/HDx4kAp1hBCmpFIpFi1ahGXLlonWsVu5ciWCg4OhUqnA8zwGDBigXuT3o5CQEFrkV0+pVCrRsgtUrCPE+HEch2bNmqFfv37qWGZmJnbv3o3Vq1cjOjpa9HtatWqFiRMnolChQlSoI4TonIuLC1q2bImAgADI5XLR8djYWIwePRr79u1jkB0xBVSsMxEWFhbYs2fPZ2+KPDw8cOzYMTg5Oek4M0II0e63337D9u3bRTtRr1mzBqNHj8a2bdtw4MABwbFevXqhXr16ukyTfIXU1FTRVGYq1hFiGiwtLTF+/HiUKVNGHYuLi8Phw4dFrx06dCgWLFiAsmXLQiaT6TJNQghRu3TpEh49eoTc3FzRMZ7n8fjxY/z555+IiIhgkB0xdnpZrNPs+KJ54HnD09MTEydO1Hrsjz/+gKenp44zIoSQf/fzzz9jx44dopu1efPm4ddffxXEChYsiLlz5+oyPfKVNKfAAlSsI8SUODs7Y/ny5erpsDzPIycnB+bm5uA4DhzHoU2bNujduzeKFi1KhTpCCDPR0dGYO3cuIiMjP/salUqF27dvo0OHDjh79qzukiMmQS+LdZqddVSsyztDhgxBt27dRPFx48aJ1q8jhBB90KZNG+zcuVN006b5XbF8+XIq/Og5zWKdXC6npRcIMTHVqlXDlClT1GOVSgVLS0v06dMHly5dwurVq+Hv7y9aBoEQQnQlOzsbixYtwsWLF6FUKtXxTp06iablq1Qq3Lx5E1OnTsWLFy90nCkxZnr5LajZWUfTYPPW+vXrERgYKIglJyejWbNmiI2NZZQVIYR8XuvWrbF69erPHm/bti3atGmjw4zIt9C2uQStRUWI6eA4DjKZDAMGDEDz5s3V8eTkZISFhSElJQUODg5UqCOEMMPzPHbu3Im9e/ciLS1NHe/Xrx/Wrl2L2bNni85RPM/j9OnT6NatG8LCwsDzvK7TJkZIL78JaRps/rt8+TJatGghiD1//hw//PADUlJSGGVFCCGf16NHD8Hi5J/6XJzoF9oJlhDCcRxsbGywYMECeHl5qeO3bt3C2rVr8eLFC7rRJYQwwfM8bt26hfXr1+P58+fqeGBgICZPngxzc3OMGDECFy5c0DpN/8KFC1i/fr1o8zNCvoVeFutoGmz+MzMzw9atW1GxYkVB/NatW/jpp5+QnZ3NKDNCCNFOpVIhPDxcFC9XrhxtKmEgqFhHCAE+FOyKFi2K+fPnC2bQ7N+/H1u3bkVaWhoV7AghOsXzPBISEvD333/jzJkz6riTkxMWL14MZ2dn9dqaVapUwV9//QVbW1vRz0hKSqLmF5In9LJYR9NgdcPa2hqHDh1CsWLFBPGPLbyaO/YRQghLS5cuRVhYmCBWoEAB3Lp1S7RjLNFPVKwjhHxkZmaGevXqYeDAgZDL5QA+3AOsWbMGJ0+ehEKhYJwhIcRU8DyPrKws7N27F1u2bFGvU2dhYYGJEyeiQoUKgmtNqVSK3r17Y+/evShQoIA6bm5uDg8PD9jb2+v6j0CMkEEU66izLv8ULFgQx44dg6urqyC+Y8cODB06lJ5qEkL0wv379zF69GhBzM7ODmFhYbTmmQGhYh0h5FMODg7o3bs3GjRooF4D6vnz51iyZAnCw8OpYEcI0Ync3FycO3cOS5cuRUJCAoAPm2B16NABbdu21boZllQqRVBQEGbPng1fX18UL14cP//8M7p16wYbGxtd/xGIEdK7Yh3P8zQNVsd8fHxw+PBh0UllyZIlmDhxIqOsCCHkg5ycHHTt2lU0PX/Pnj0oWbIko6zIt0hKShKMqVhHiGnjOA7FihXDsGHD4Ovrq46fPXsWS5cuxfPnz2mmByEkXykUCty9excLFy7EvXv3AHw4N1WsWBEDBw6Em5vbZ3+vpaUlevfujZMnT+LAgQNYvnw5ypQpo6vUiZHTu2KdtrXSaBps/qtYsSL27Nmjnobw0bRp0zB9+nRGWRFCCDBhwgTcunVLEBs6dCgaNGjAJiHyzT4+rf7IwcGBUSaEEH0hk8lQrVo1DBo0CIULF1bHt23bhtWrVyMhIYFmehBC8oVKpUJ0dDSWLVuG48ePq+M+Pj4YNmwYypQp85+7U8tkMnh4eMDf3x92dnb5nTIxIXpXrNOcAgtQZ52uNGrUCOvXrxdNKRs/fjwWLFjAKCtCiCk7evQo5syZI4iVLl0aM2bMYJQR+R5vaGtC4QAAOxZJREFU374VjF1cXBhlQgjRJ7a2tmjdujW6deum7rhVKpVYsWIFNm7ciIyMDCrYEULy1MfNIFauXInNmzer44ULF0a/fv3QsGFDWFhYMMyQmDq9K9ZpToEFqLNOlzp16oSVK1eK4sOHD8fy5csZZEQIMVVv3rxBt27dBDG5XI5NmzbRxZOB0izWaa6XSggxXW5ubujRowd+/PFH9fpQaWlpmDdvHnbt2gWlUkkFO0JInvi4ocTff/+NkJAQ5ObmAvjQ8d+xY0e0b9+euv8Jc3pXrKPOOvb69OmDpUuXiuK//fYbVq1axSAjQoipUSqV6Nq1q6i4M3fuXAQEBLBJinw36qwjhPybEiVK4LfffkNQUJB6aZbY2FhMnz4dJ0+eZJwdIcRYqFQq7Nq1CwsWLMD79+8BfKg5NGnSBL/++ivc3d0ZZ0iIARTrOI6Dubk5o2xM18CBA0VTzwAgODhYayGPEELy0vTp03HmzBlBrGXLlhg8eDCjjMj3ys7ORkpKiiBGxTpCyKckEgkCAwMxdOhQ+Pv7q5dmefr0KcaOHYvr169Tdx0h5LvwPI8TJ05g6tSpePPmDYAPO7tWrlwZQ4YMga+vr2hZKEJY0LtinbadYOnDwsbIkSMxZcoUUXzw4MGYO3cug4wIIabg3Llz+PPPPwUxT09PrFu3jr4PDJhmVx1A02AJIWIymQz16tXD8OHDBbsw3rx5E4MGDcKDBw8YZkcIMXQXLlzAsGHD8PTpU3XMx8cHo0aNQsWKFf9zQwlCdEXv3omanXU0BZat8ePHY+LEiaL4qFGjMHXqVAYZEUKMWUxMDDp16gSVSqWOSaVSbN26Vb3oODFMmsU6qVQKe3t7NskQQvSahYUF2rRpg/79+wt2VwwLC0OfPn3w/PlzhtkRQgxVeHg4goOD8fDhQ3XMxcUFI0eORIMGDdTT7wnRB1SsI/+K4zj8+eefmD59uujYxIkTMW7cOJqOQAjJEzk5OWjXrh1iYmIE8SlTpqBGjRqMsiJ5JT4+XjB2dnamp9eEkM+ysbHBoEGD0KVLF/WSODzPIywsDD169EBsbCzjDAkhhuTx48fo0aMHHj16pI5ZW1tjyJAh6NSpE21eRvSO3l0la06DpZ1g9cPYsWOxYMECUXzGjBno378/lEolg6wIIcZkxIgRuHjxoiDWqFEjjBkzhlFGJC/R5hKEkK9lZ2eHmTNn4scff1QX95VKJS5fvozevXurF4YnhJB/8/r1a3Tr1g33799XN5rI5XL8+uuvGDp0KNUciF7Su2Idddbpr6FDh+Kvv/4SxVeuXIn27dsjKyuLQVaEEGOwceNG0eY1Xl5e2LJlC3VfGQnNYh2tV0cI+S8cx8HOzg5r1qxBrVq11PHc3FycPn0aAwcOFN07EELIp5KSktCrVy+Eh4erl1mRSCT4+eefMXPmTKo3EL2ld3dAVKzTbwMGDMD//vc/0c3znj170KRJEyQnJ7NJjBBisG7evIm+ffsKYhYWFtizZw+cnZ0ZZUXymuY0WOqsI4R8CY7jUKBAAezatQtlypRRx7OysvDPP/9gwoQJUCgUtCwLIUSA53lkZGRg0KBBOHfuHBQKhfpYvXr1sGbNGlhYWNDmZURv6V2xjqbB6r9evXph586dMDMzE8TPnz+POnXqiNabIoSQz0lISECbNm1EnbkhISEIDAxklBXJDzQNlhDyrTiOg5OTEw4fPgwvLy91PC0tDevXr8fChQupYEcIUeN5HllZWRg/fjz279+P7Oxs9bHAwEDs3r0blpaWVKgjek3vinXUWWcY2rRpg2PHjgl26AKAO3fuoFq1arh79y6jzAghhiI7Oxs//fQTXrx4IYgPGDAA3bt3Z5MUyTc0DZYQ8j04joOHhwcOHDiAwoULq+MJCQlYvHgxVq9ejdzcXCrYEWLiPnbUzZo1C+vXr0dqaiqAD+cQf39/7N+/H3Z2dlSoI3qPinXkm9WtWxfnz59HoUKFBPGoqCjUqFEDR44cYZQZIUTf8TyPX3/9FRcuXBDEq1evjoULFzLKiuQn6qwjhHwvjuNQunRpbN26VXD9+fr1a8yfPx+bN29GVlYWFewIMVE8zyMtLQ3Lli3DypUr8e7dOwAfzh3FixfHrl27ULhwYSrUEYOgd8U6mgZrWMqXL4+LFy/Cx8dHEE9NTcWPP/6odUMKQgiZNm0aNm3aJIi5u7tj165doin2xDjQmnWEkLwgkUgQFBSElStXwsPDQx1/9uwZ5syZgz179iAzM5MKdoSYGJ7nkZKSgrVr12Lp0qWIi4sD8KFQV7JkSWzYsAF+fn5UqCMGQ++KddRZZ3iKFSuGS5cuISgoSBBXqVQYOHAghgwZAqVSySg7Qoi+2bZtGyZOnCiIWVtb4+DBg4KpTcS4UGcdISSvyGQyNGnSBAsXLkSxYsXU8UePHmHmzJnYt28f0tPTqWBHiIngeR7JycnYuHEjFixYgNevXwP4UNwvU6YMQkJCULlyZdEmiYToM717t1KxzjC5uLjg1KlT6NSpk+jYkiVL0KxZM3UbMiHEdF2+fBk9evQQxCQSCbZt24aAgAAmOZH8l52djZSUFEGM1qwjhHwPc3NzNG/eHNOmTYOvr686fv/+fUyfPp0KdoSYCJ7nkZSUhM2bN2Pu3LmIiooC8OH6skKFCpg/fz6CgoIgk8kYZ0rI19G7Yh1NgzVcFhYW2Lx5MyZNmiQ6dvz4cVSqVAl37txhkBkhRB88fPgQLVq0EOzIBQALFizAjz/+yCgroguaXXUAddYRQr6fpaUlWrRogfHjx6N06dLq6W0fC3a7du1CWloaFewIMVI8zyMxMRHr16/HnDlzBIW6ypUrY8qUKahVqxYtsUIMkt4V66izzrBxHIfJkydj06ZNopPi8+fPUb16dWzbto1RdoQQVqKiotC4cWMkJiYK4v3798fgwYMZZUV0RXO9OqlUCgcHB0bZEEKMiY2NDVq1aoUxY8agXLly6oLdw4cPMW3aNKxduxYpKSlUsCPEyPA8j/j4ePz111+YO3cuoqOjAXy4xggKCsKECRNQr149WFhYMM6UkG9DxTqSL7p06YIzZ86IdorNyMhAp06dMHLkSCgUCkbZEUJ06e3bt2jcuDFevXoliDdp0gRLliyhhX5NQGxsrGBcsGBBWjeGEJJnbG1t0bJlS4waNQrly5dXf69ERkZi1qxZmDdvHt69e0cFO0KMBM/zePXqFaZNm4ZFixYhJiYGwP8V6saOHYv69etTLYEYNL27UqZpsMYjKCgIN27cQLVq1UTH5s2bh4YNG+LNmzcMMiOE6Epqaip++OEHPH78WBCvUqUKdu3aReuHmIiPF9EfaT7IIYSQ72VnZ4cWLVrgjz/+QEBAgPqBQGxsLJYuXYo//vgDsbGxVLAjxMDxPI+nT59ixIgRWLt2LZKTkwF82HimVq1aGD9+POrWrUuFOmLw9K5YR511xqVw4cI4e/Ys+vTpIzp27tw5BAQE4Pjx4wwyI4Tkt6ysLLRu3Ro3btwQxP39/XH48GHY2Ngwyozommaxzs3NjVEmhBBjZmtrq950olKlSpBKpQCA9+/fY9OmTQgODkZkZCTjLAkh3yM8PBx9+/bF3r171Y0+ZmZmqF+/PqZOnYratWtTDYEYBb0v1lFnneEzNzfHqlWrsHLlSsjlcsGxt2/fomnTppgwYQJNiyXEiOTk5KB9+/Y4ffq0IF6kSBEcP34cTk5OjDIjLGhOg6XOOkJIfrG2tkaDBg2wYMECVK1aVV2wy8zMxJEjR9C1a1fRQyRCiGE4ceIEunfvjtDQUOTm5gL4cK/ZpEkTzJ07F1WrVqU16ojR0LtineY0WKqKG4++ffvi/Pnz8PT0FMR5nse0adPQoEEDvH79mlF2hJC8kpOTg3bt2uHAgQOCuIuLC06cOAEPDw9GmRFWqLOOEKJL5ubmqFq1KlatWoVatWqpC3YKhQLXrl1Dly5daGYHIQaE53ls3rwZvXv3xv3796FSqQB86Khr1aoVlixZgtKlS4saQwgxZHpXrKNpsMatWrVquHXrFlq0aCE6dv78eZQrVw67d+9mkBkhJC987Kjbv3+/IG5ra4ujR4+iZMmSjDIjLFGxjhCiazKZDP7+/ti4cSOaNGmiXsNOpVLhyZMn6NmzJ0JCQqBUKhlnSgj5Nzk5OZgyZQp+//139Y6vACCXy9G1a1csX74cXl5e6qI8IcZC74p12dnZgjG1sRofR0dH7Nu3D/PnzxctLv/u3Tv8/PPP6NmzJ1JSUhhlSAj5FtnZ2ejQoQP27dsniFtbW+Pw4cMIDAxklBlhjabBEkJYkEgkcHd3x5YtW9C+fXv1LrE8z+PNmzcYN24cxo0bJ5rZQwjRD+/evUO/fv0wb948JCQkqONSqRSDBg3CsmXL4OjoqP5sE2JM9K5Yl5WVJRhTsc44cRyHYcOGITQ0FEWKFBEd//vvv1G+fHlcuHCBQXaEkK+Vnp6Oli1b4p9//hHEra2tcfToUdSsWZNNYoQ5nueps44QwgzHcbCzs8P69esxcOBAwbF3795h6dKlGDBgAOLi4minWEL0BM/ziIyMRPfu3bF582akpaWpj3EchxkzZmD27NmwsLCgQh0xWnpVrON5XlSsMzc3Z5QN0YWP02Lbt28vOvbixQvUqVMHY8eOFXVcEkL0R3JyMpo0aSJa/8fa2hpHjhyhQp2Je//+vei7nYp1hBBd4jgOcrkcixcvxvLlywX3FxkZGdiyZQuCg4Px+PFjqFQqKtoRwgjP81CpVAgLC0PPnj1x+PBh5OTkqI/b2dlhz549GDlyJKRSKRXqiFHTq2Ldxx1dPkWddcbPwcEB27Ztw8aNG2FnZyc4plKpMHPmTFSoUAFXr15llCEh5HPi4uJQv359XLx4URC3sbHB4cOHUatWLUaZEX2hOQUWoGmwhBDd4zgOHMchODgYBw4cgIeHh3odu9zcXOzfvx/9+/fHpUuXoFQqqWBHiI7xPI/c3FwcPnwY/fr1Q2hoqHojCalUCj8/P5w9exatWrVSf54JMWZ6VazTfPIOULHOVHAch65du+L27dtab+4fPnyIoKAgjBw5UrQJCSGEjSdPnqB69eq4efOmIO7o6IjTp0+jdu3ajDIj+kRzCqy9vT19txNCmJFIJGjQoAEOHz6MKlWqwMzMDMCHQsHZs2cxcOBAHDhwAOnp6VSwI0RHVCoVkpOTsWHDBgwZMgS3bt1SH7OwsEDjxo1x7NgxBAQEUJGOmAy9L9bRNFjTUrRoUZw5cwYzZ84Ubb2tUqkwb948WsuOED1w6dIlBAUF4fnz54J4oUKFcO7cOVSuXJlRZkTf0OYShBB9I5FIULp0aWzduhVt27YVzOy4ffs2+vfvj5CQELx+/Zq67AjJRzzPQ6FQIDIyEnPmzMGIESPw7Nkz9XFHR0f06dMH69evh6enJxXqiEnR+2IdPX03PVKpFGPGjMH169dRsWJF0fGnT5+iVq1aCA4Oxrt37xhkSIhp2717Nxo0aIDExERBvGjRoggNDUWZMmUYZUb0EW0uQQjRRxKJBF5eXli4cCF+++03uLm5qQsBcXFxGDt2LCZNmoSbN28iJyeHCnaE5DGe55GZmYmLFy9i5MiRmD17Nt6/fw/gw+ezaNGiGDduHKZMmQIXFxcq1BGTo1fFOm2bCFCxznSVK1cOV65cwcyZM9VTFD61atUq+Pn5YePGjXQBRYgOqFQqTJ48GT///LPo4UrFihVx+fJl+Pj4MMqO6Csq1hFC9BXHcShYsCBGjBiB8ePHo3Tp0uprztzcXKxdu1Y9LTYpKYmuNwnJIzzPIy4uDlu3bkW/fv2wb98+9efL0tISlStXxsyZM9G3b1/Y29uzTZYQRvSqWKd58/dx5yZiumQyGcaMGYNbt26hWrVqouNv375Ft27dUL9+fTx69IhBhoSYhrS0NLRr1w5//vmn6FizZs1w9uxZmt5ItKJpsIQQfefo6IgePXpg1qxZaNy4sWBa7NWrVzFgwAAsXrwYDx8+pIIdId9JpVLhxo0bmDVrFoYOHSq4h3NycsJPP/2EuXPnok2bNrCxsWGYKSFs6XWxztzcnNpdCQDA398fFy5cwIIFC2BtbS06fvbsWZQrVw5//PEHUlNTGWRIiPGKjIxEjRo1sGfPHtGxPn36YN++fXQxRT7rzZs3gjF11hFC9JGVlRWaNm2KadOmoXfv3nB3d1ffh7x9+xbTp0/H2LFj8c8//yA9PZ1xtoQYpuTkZGzduhWjR4/G4sWL1fdtEokEPj4+GDRoECZPnoyaNWtqnVlFiCnRq2Kd5jRYmgJLPiWVSjF06FA8fPgQP/30k+h4bm4uZs2ahZIlS2L9+vXqrb4JId9uz549CAwMxJ07dwRxiUSChQsXYuXKlZDJZIyyI4bg1atXgrGHhwejTAgh5N9JpVKUL18eI0eOxIQJExAQEKD+jlMqldi3bx9Gjx6NadOm4e7du9RlR8gX4nkeV65cwYQJEzBmzBicPn1afczc3By1a9fGlClTMGjQIJQoUYIadgiBnhXrNDvrqFhHtPH09MSePXuwf/9+eHl5iY7HxsaiR48eqFatGi5fvswgQ0IMX05ODn7//Xe0bdsWKSkpgmMODg44evQofv/9d7qYIv+K53kq1hFCDI6bmxu6deuG+fPno23btoIOn6dPn2Lx4sUYNmwY/v77b6SlpTHMlBD9l5iYiCVLlmD48OEICQkRXBfY2NigZ8+emD17Ntq2bQtHR0eGmRKiX/SqHULbNFhCPqdFixaoX78+pk6divnz50OhUAiOX7t2DUFBQejcuTNmzJihtbBHCBGLiIhAly5dEBYWJjrm7++P/fv300YS5IskJycjIyNDEKNiHSHEEFhaWqJOnTrw9PREQEAA5s+fj4SEBABAZmYmTp8+jadPn6rXtCtbtiw9wCLkEzzP4+LFi1i+fDnOnDmDuLg4QTeql5cXxowZg+bNm8PDw4M+P4Ro0KvOOpoGS76WtbU1Zs2ahbt37+KHH37Q+potW7agZMmSGD58OBITE3WcISGGg+d5hISEoHz58loLdZ07d0ZYWBgV6sgX0+yqA4DChQszyIQQQr7ex3W0+vfvj507d6Jx48bqYyqVCi9fvsTGjRsxYMAAbNiwgdayI+T/S0pKwuLFizF48GDs2bMHsbGxgkJdx44dsX37dvzyyy/w9PSkQh0hWuhVsY6mwZJv5efnh8OHD+PQoUMoWbKk6HhOTg4WLFiAYsWKYebMmaJOD0JMXUxMDJo3b47+/fuLPh/m5uZYuXIlNm3aRBtJkK+iWawrWLAgLRhNCDE4BQoUQK1atbBu3TosWrQI9vb26mMZGRm4cuUK/vjjD4wcORKPHz+mteyIyVKpVLh+/ToGDBiAadOm4fbt24KGHHd3d6xfvx6LFy9G5cqVtW4cSAj5QK+LdTQNlnytZs2a4e7du1iwYAEKFCggOp6SkoKxY8eiRIkSWLVqFXJychhkSYj+4HkeO3bsQJkyZXDkyBHRcR8fH1y5cgV9+/alp57kq9F6dYQQYyGVSuHm5oa+ffvi7NmzaNKkifqYUqlETEwM1q5diw4dOmDDhg2i9V4JMXYJCQlYuHAhOnfujN27dyMxMVGw4V/nzp1x9uxZdOjQAa6urpBI9KoUQYje0atPCE2DJXnBzMwMQ4cOxdOnTzF48GDI5XLRa968eYPg4GD4+vpizZo1yM3NZZApIWxFR0ejdevW6NChA969eyc63q9fP9y6dQsBAQG6T44YBSrWEUKMCcdxsLCwQNmyZbF161bMmzdP8HA4Ozsbd+7cQb9+/dCrVy/cvXsXSqWSYcaE5L/c3FyEhoaiQ4cOGDt2LCIiIgT3Vm5ubli/fj1CQkJQvHhx6rAn5AvpVbGOpsGSvOTi4oLFixfj0aNH6Ny5s9bXvHjxAn369IGvry/Wrl1LRTtiEpRKJZYsWYJSpUph//79ouOFChXC4cOHsWLFCpqeQL4LFesIIcaG4zhIJBLY29tj0KBBOHLkCGrWrKk+zvM8srKysGfPHtSrVw/z589HWloaeJ6n6bHEaHx8PyckJGDs2LFo1qwZzpw5g5ycHMH7vGXLljh16hQ6d+4MGxsbcBxHMzUI+UJ6XayjabAkLxQrVgybN2/GjRs30KhRI62vef78OXr37g0/Pz+sXr1a1OVJiLG4ffs2qlevjiFDhiAtLU10vF27drh3795nN2wh5GtoFuvc3d0ZZUIIIXmL4zjI5XJUq1YNu3fvxp9//glXV1d1IYLneSQmJmL06NGoXbs2QkNDoVQqqWBHDB7P88jNzcU///yDatWqYd68eeqCNPDhs+Hl5YXly5djw4YN8PPzg1QqpSIdIV9Jr4p1NA2W5KfAwEAcP34cJ0+eRFBQkNbXPHv2DH379oW3tzfmzZuH1NRUHWdJSP54+/Yt+vfvj8DAQFy7dk103NXVFVu3bsX27dvh5OTEIENijKizjhBizD52Cbm6umLcuHHYu3cvfvjhB1haWgoKEzdv3kSdOnXQq1cvREREIDs7m4p2xOCoVCpkZWXh5s2b+Pnnn9GmTRtERkaqj3McBzs7O3Tq1AkHDx5Ev379UKBAAeqmI+Qb6VWxjqbBEl1o0KABLly4gGPHjqFatWpaXxMTE4ORI0eiSJEiGD9+POLj43WcJSF54+NOyCVKlEBISIhgod+Pfv31Vzx8+BAdO3akiymSp16/fi0YU7GOEGKspFIpgoKCsHnzZsyaNQt+fn6itbk2btyI8uXLY8yYMYiMjERGRgYV7YjeU6lUSEtLw/3799G/f39UqVIFBw4cELzGwsICgYGBWL58OVavXo0yZcrQNSUh30mvi3U0DZbkF47j0LhxY1y6dAlHjx5F1apVtb4uOTkZ06dPh5eXF/r27Yv79+/rOFNCvg3P8zh48CDKli2L4cOH4/3796LX+Pr64ty5c1i9ejUcHR0ZZEmMWWpqquh9R8U6Qoixs7e3x+DBg7Fz50507twZ7u7ukEql6uOZmZlYtGgRatSogXnz5uHp06eCKYSE6Aue55GSkoL79+9j4sSJqFmzJv7++2/BpilyuRze3t4YMGAAdu3ahS5dusDKyoph1oQYD70q1tE0WKJrHMehSZMmuHz5Mo4dO4Z69eppfV1WVpb6KVGjRo1w8OBBrR1KhOiDc+fOoVatWmjRogWePHkiOm5paYk///wTt2/fRu3atRlkSEyBZlcdQGvWEUJMR+nSpbFq1SosWbIEdevWRaFChQSdRvHx8fjzzz/RqlUrrFmzBk+ePEF6ejoV7QhzH4t0d+/exaJFi9C8eXMsWrQIKSkp6tdIJBJ4eHigWbNmWL16NebOnYuiRYuyS5oQIyRjncCnaBosYeVjp13jxo1x9epVzJ49G3v37tX62pMnT+LkyZMoUaIEBg0ahG7duqFAgQI6zpgQsbCwMIwfPx4nTpz47Gs6d+6MWbNmwdPTU4eZEVOkuV6do6MjPW0nhJgUuVyONm3aoF69etixYwd27tyJ+/fvIzY2FsCH6YWPHj3CmDFjsGfPHnTp0gVBQUHw9vaGtbU1TSMkOqVSqZCSkoKIiAicPXsWmzZtwv3796FQKASv8/T0RNmyZdGlSxe0atUK1tbWjDImxLhRsY4QDVWrVsWePXvw8OFDzJ07F5s2bUJubq7odU+fPsXgwYMxZswYdOzYEcHBwahcuTJdWBGdu3HjBqZOnYp9+/Z99jVVqlTBokWLUL16dR1mRkwZbS5BCCEfODg4IDg4GK1atcKuXbtw6NAhhIeHq9dEzs7ORmhoKMLDw1G5cmW0aNECQUFBKF26NGxsbOjakuQrlUqF5ORk3L17F+fPn8eBAwdw584d0aw3T09PVKxYES1btkTr1q3h4ODAKGNCTINeFes0Twi0Zh1hyd/fH2vXrsX/a+/eg6Mq7z+Ofzab+5WQC7kSoCXhokCQCkKAoLRGQVDEFlEq7bR0aum0jrba6h8d2+nY/tHpTL3VdmSQohUdCsULiJjAIIRLIQIJhIRLCCRAYkJCrmZ3z+8PfrvDbgISctkn4f2aObN7Tk423yAuZz/n+zzPH/7wB7322mt6/fXXVVtb2+m8lpYWvfnmm3rzzTc1adIkrVixQo899piio6P9UDVuFZZlKT8/Xy+99NJ1O+lGjBihF198UY899pgCAoya+QCDXEVFhdc+YR2AW11SUpJ+9rOfaf78+dq0aZO2bNmi3bt3q66uTpLU3NysgoIC7dmzR5MnT9bcuXM1a9Ysfetb3yK0Q6+zLEt1dXUqLCzUjh079Omnn+rw4cOdmhSSkpKUk5Oj++67T/fff7+GDRvG30WgHxgV1tFZBxOlpKTo97//vZ5//nm98847+utf/6pDhw51eW5RUZGefPJJPfPMM3r44Yf1xBNPaM6cOYQk6DUul0sbN27USy+9pL17917zvOTkZL3wwgv60Y9+1Gk1OqA/+IZ1zGUDAFemXhkxYoSefPJJzZ07V1u3btXHH3+s3bt3exblaW1t1eeff679+/frww8/1PTp03XPPfdo1qxZiomJIShBj1iWpYsXLyo/P1/5+fnatWuXjh071mm4a0JCgmbNmqW8vDzNnTtXw4cP5zMN0I8I64AbFBoaqh/84Adavny5tm/frpdfflkbN27s9A+bdKXbbs2aNVqzZo3S09O1bNkyPfHEE8rMzPRD5RgMLl26pNWrV+uVV15RWVnZNc+Li4vTc889pyeffJL5weBXp0+f9trPyMjwTyEAYCC73a4xY8Zo5MiRys3N1a5du/TRRx9p586dqq+vl3Rl1NH+/ftVVFSkTz/9VLfffrtmzpype+65R1lZWYR26Ban06nDhw9r27Zt+vzzz3Xo0CGdPHmy06Imw4YN0z333KN7771XU6dO1ahRoxQUFOSnqoFbl1FhHcNgMRDYbDbl5uYqNzdX1dXVWrVqlf7xj390+mDqVllZqT/+8Y/64x//qGnTpmnJkiV65JFHlJKS0r+FY0A6fPiwXnnlFa1Zs0YtLS3XPC8xMVG/+MUvtHLlSoZgwwi+nXWEdQDgzWazKTQ0VLfffrtGjx6tOXPmaP/+/dqwYYO2bt3q6bRzOBwqKSlRSUmJduzYobfffltTpkzR/PnzlZOTo7CwMD//JjBZY2Oj8vPz9cEHH3gCuq6m9klMTNS8efM0f/58ZWdnKzU1ldEZgB8ZFdbRWYeBJjk5Wb/97W/17LPPauvWrfr73/+uTZs2yel0dnl+YWGhCgsL9dRTT2nmzJn63ve+p8WLFysxMbGfK4fJWlpatH79ev3zn//U9u3br3vuyJEj9atf/UrLly/nYh3GcDqdqqys9DrGMFgA6JrNZlNYWJiysrKUkZGhnJwcFRUV6d1339WmTZvU1NTkObe6ulrV1dUqKirSRx99pDFjxmjRokXKy8tTcnKyH38LmMSyLJ0+fVqbNm3Spk2bVF5ervPnz3f6vC1dGZWxePFiPfzwwxo3bpwSEhII6QADENYBvcButysvL095eXk6f/681q5dq9WrV+vw4cNdnm9Zlnbs2KEdO3bo5z//uebMmaOHHnpICxYsUHp6ej9XDxNYlqVdu3Zp1apVWrdunS5fvnzd8ydMmKDnnntOjzzyiAIDjXorB1RdXd1pgmo66wDg64WGhiojI0PJycmaNm2aVqxYobfeekvvv/++17VBS0uLysvLdfr0aRUWFurVV19VXl6eJ3AhbLk1tba26sCBA1q3bp3y8/N17tw5NTQ0dNlIkJCQoKVLl2rp0qUaNWqUhgwZwjUlYBCj/m/0DesYBouBKCkpSU8//bSefvppFRUVafXq1Vq7dq1qamq6PN/lcmnbtm3atm2bVq5cqcmTJ2vBggVauHChJk6cyHwkg1xpaanWrVunNWvWXHcuOulKKPzQQw9p5cqVmjVrFn83YCzfIbChoaEaNmyYn6oBgIEnODhYw4YNU1xcnLKzs/Wb3/xG7733nlatWqXy8nLPeQ6HQ7W1taqrq1NJSYneeOMNZWdna/HixXrggQeUkJDAogCDnNPp1NmzZ7V+/Xr95z//UUlJiZqbm9Xe3t5pPjpJmjRpkn74wx9qwYIFGjp0qMLDw2W32/1QOYDrMSqs852zjs46DHSTJk3SpEmT9Oc//1mffPKJ1q1bpw0bNqixsfGa33PgwAEdOHBAv/vd75SRkaH77rtPeXl5uvvuuxUVFdWP1aOvlJaW6r333tN77713zZWFr5aYmKif/OQnWrFihdLS0vqhQqBnfMO64cOHEy4DwE0IDAxUTEyMoqOj9cwzz2jlypX67LPP9MYbb2jLli1yuVySrtz8bWlpUUtLi7Zu3aqCggK98MILysvL02OPPabZs2crKCiI9+JBwrIstba2avPmzXr77beVn5+v5uZmdXR0eP5OXC04OFgPPvigVqxYoalTpyo4OJi/D4DhjArr6KzDYBUUFKR58+Zp3rx5amtr05YtW/Tuu+/qv//9r5qbm6/5fRUVFXr99df1+uuvKzAwUNOnT9e9996re++9V9nZ2dwpHSBcLpf279+vDz/8UBs2bLihgC4gIEDf+c53tHz5cj344IO8H2JAYSVYAOhdNpvNE7AsWLBA999/v0pLS/Xaa6/p3XffVV1dnedcl8ul9vZ2XbhwQW+99Zb+9a9/KT09XUuWLNGiRYs0efJkr2tIAhuzXd0d53A4tGvXLr3//vt6//33VVNTI5fL1WUHnSSlpqZq2bJl+vGPf6z09HTZ7XbZbDb+mwMDgFFh3VdffeW1z4dTDEahoaFauHChFi5cqNbWVn388cfasGGDPvjgA9XX11/z+xwOh2eeu+eff15Dhw7VrFmzlJubq9mzZ2vChAmEdwZpaGjQJ598og8//FAff/yxLl68eEPfl5WVpeXLl2vZsmVKTU3t4yqBvuHbWcfiEgDQO9xBS1BQkG677Ta9/PLLev7557Vu3TqtXbtWhw4dksPh8IQ3lmXJ6XTq9OnTeumll/SnP/1JqampnilXZsyYodDQUM/rEuKYwbIsz3b58mUVFBR4Pi98+eWX1/w+m82mkJAQTZ06Vd///ve1cOFCDR061OvrAAYGo8M6JkbFYBcWFqZFixZp0aJFcjgc2rlzpzZu3KgNGzZ06kzxVVdXpw0bNmjDhg2SpNjYWM2aNUszZ87UtGnTNHnyZFYH7UctLS3atWuX8vPzlZ+fr3379snhcNzQ9yYlJWnx4sVaunSppk2bxoUUBjzfsI7OOgDoXe5rBZvNptTUVD311FP66U9/qgMHDmjt2rXatGmTLly4IIfD4TUs0rIsnT17Vq+++qpee+01xcfH6/7779fChQs1ffp0xcTEyG6304HVz9zBnNPplNPpVE1NjQoKCrRx40Zt3br1ulPo2O12BQUFKT09XQ8//LCWLFmicePGKSgoqB9/AwC9zZiwzrIswjrc0gIDA5Wbm6vc3Fz95S9/0eHDh7V582Zt3rxZO3fu7LSyoq/6+npt3LhRGzdulHRl6O2kSZM0bdo0TZs2TVOnTtWoUaO46OoldXV12rt3r3bv3q2CggIVFhZ2eg+7HndA98gjj2jGjBlM7ItBhWGwAND/QkNDNX36dE2fPl0vvviitmzZonfeeUf79u3T5cuX1d7e7rUqqGVZqqmp0erVq7V69WolJCRo5syZmjNnjnJycpSamqrQ0FCv+c24juwd7nDuq6++0ldffaXW1ladPn1aO3bs0GeffabCwkJdunTpmt8fGBiokJAQxcTEaPbs2VqyZInmzJnD/NbAIGJMWNdVBwphHW5VNptNEyZM0IQJE/TrX/9aTU1NKigo0JYtW7R582avVcCupaOjQ/v27dO+ffv0t7/9TZIUHR3tWfQiOztb2dnZ3Hm7Aa2trSouLtbevXtVWFioPXv26Pjx491+naysLM2fP18PPPCAcnJyCOgwKFmWpTNnzngdYxgsAPSvuLg4LV26VI8++qhOnDihTZs2acuWLTp69Kiam5vV3Nzcab7wmpoarV+/XuvXr5fdbtfo0aN11113acaMGZoyZYri4uIUFhamsLAwhYSEcB3TTQ6HQ+3t7WptbVVLS4suXryoPXv26PPPP9fu3btVUVFxzbnnpCsjciIiIhQZGalJkyYpLy9P8+bNY/ExYJAyJqzrqiOFsA64IjIyUvPnz9f8+fMlSWfOnNH27dtVUFCg7du368SJEzf0Oo2NjZ5579yCgoKUmZmpsWPHauzYsRo3bpzGjh2rzMzMW24YrcvlUmVlpQ4fPqxDhw7p0KFD+uKLL3T8+PEuV9b6OsHBwcrNzfUsLvKNb3yjD6oGzFJTU6PW1lavY3TWAYB/2Gw2ffOb39RTTz2lX/7yl6qoqND27du1Y8cOHThwQHV1daqvr1dTU5NXUOR0OnXs2DEdO3ZMq1atUlBQkMaPH68pU6Zo4sSJGjNmjBITEz3hUUREhMLCwgjw/p/D4VBra6uampo84WhVVZWOHj2qoqIi7d+/X6Wlpde9vgwICFBUVJSGDh2quLg43XnnnZo9e7ZmzZqlpKSkfvxtAPgDYR0wAA0fPlzLli3TsmXLJEmVlZWeC689e/boyJEjNxwudXR0qLi4WMXFxV7HbTab0tPTNWrUKI0aNUojR470PM/IyFBiYuKAvCBrb2/XuXPndObMGZWXl6usrExlZWU6fvy4Tpw40ekuc3fY7XZNmTJFc+bM0Zw5czRjxgxFRET0YvWA+XyHwAYGBiolJcU/xQAAPGw2m0aMGKERI0Zo2bJl+vLLL7V7927t3LlTRUVFqqqqUm1trerr6zt9Nuvo6FBRUZGKioo8r5WcnKysrCxlZWVpzJgxGjVqlBISEhQdHa2oqChPiOceQjsYuYeyukO5xsZGNTQ06OLFiyovL9exY8dUWlqq0tJS1dbWfu3rhYaGKi4uTvHx8UpLS9Mdd9yhnJwc3XnnnYqOjh60f44AOiOsAwaB9PR0Pf7443r88cclSZcvX9a+fftUWFjo2Wpqarr1mu6hbGfOnFFBQUGnrwcEBCgpKUmpqalKSUlRamqqkpKSFBcX57W57waGh4f3yWq1lmWpra1NjY2Nqq2tVW1trWpqajyPNTU1Onv2rCorK1VZWakLFy702s8ODQ3VHXfcobvuusszv0t0dHSvvT4wEPkuLpGWljYgg30AGMwCAgKUkJCgBQsWaP78+aqvr9eRI0d0+PBhFRcXq6KiQtXV1aqurlZNTU2nm8CWZamqqkpVVVXKz8+XJIWEhCgtLU0jR45Uenq6UlNTlZycrPj4eMXExCg6OtrzGB0drfDwcAUGGvNx9Josy5LD4fCEce6toaFBly5dUm1trefP4syZMzp58qSqq6u/dr5p6coNrcTERCUnJyslJUUjR47Ubbfdpttvv13jxo1TVFQUAR1wizLm3ZGwDug9UVFRuvvuu3X33XdLunKRce7cOR08eFBFRUU6ePCgDh48+LUrzl6Py+XyXJjcqPDwcM9dVvfmnvMkMDDQ61G6MoTA4XCoo6PD87y9vV1NTU1qamrS5cuX1dTUdFNDVLvLZrNp9OjRmjp1qmfBjgkTJjDfH+DDd1j+qFGj/FQJAOBGBAQEKC4uTrNnz9bs2bPV0dGhqqoqz8iDsrIyzw3cc+fO6eLFi14LVbi1t7frxIkTnf4diIyMVEJCghITEz1bQkKChgwZosjISE8Xnntz74eHhyskJEQhISEKCgryrFDbG9wrr7oXeGhra1Nzc3Ona8yrn9fX16umpkYXL15UTU2NLly40OXUD9cTFBSkYcOGKS0tTcOHD9fw4cOVmZmpzMxMjR49WomJiQMiwATQ94x5JyCsA/qOzWZTWlqa0tLS9MADD3iO19fXq7i4WEePHtXRo0dVUlKio0ePdpocvre0tLSopaWlT167N0VHR2vixImeRT4mTpyo8ePHKzIy0t+lAcbz/ZDGXI0AMLAEBQUpIyNDGRkZmjt3rmcKkYqKCp09e1ZVVVU6d+6cKisrPaMXamtruwzwJHlCr1OnTnX6WmhoqCeci4qK8trcN3VDQkIUHBys4OBgz/7VxwMCAjwr1bpHcbhcLs+Kqy6XS+3t7Z02d1DnDuvcwdzVm/tYV59Vv05gYKCGDRum9PR0paWlKT09XSkpKUpJSVF6eroyMjKUnJzMjV8AXTI6rOONC+hbsbGxysnJUU5OjtfxpqYmlZWV6eTJk17bqVOndPr06Rtq6zddZGSkRowYodGjR3vuZrqfDxs2jCEHwE0irAOAwSUkJMQzb7E7/Lp06ZKnw6ympkZVVVWqqKjQqVOnVFlZqaqqKl28ePFrrxnb2trU1tZ2Q9O1BAQEKCgoqNPmDurcmyRPUOeut6Ojo9N2vZVXuyskJERJSUlKTk7W8OHDPXM8JyUlKSEhwdNZ6J53jutMAF/HmLDO943cbrczxw3gJ5GRkcrOzlZ2dnanrzmdTs9F2dWbe1jEl19+6dnq6uqueZe1r8TGxio+Pl4JCQmKj49XfHy8UlJSPHc03VtMTAwXSkAfYBgsAAxeNptNdrvdMzfx2LFjZVmWOjo61NDQoPr6ejU0NOjy5ctqaGjQ+fPndfbsWZ07d87zeO7cOV2+fLnbP/vqDjl/iY2N9czV7B61kpaWpsTERMXExCgqKkpDhgxRbGysoqOje3XoLoBbizFhnW9nHUNgATPZ7XYlJSUpKSlJkydPvu65lmV5LtyuXrr+6s09H53T6fR6tCzLc8c0MDBQgYGBnn3fOU3cj7GxsXTkAn7U3t6uyspKr2N01gHA4Gaz2RQcHOzpIHNzh2stLS1qbW1Va2ur53lDQ4PXYmBdbQ0NDf0yL7F05fo2NjbW8zt0tcXFxSk6OlphYWEKDw/3egwJCSGUA9CrCOsA9BmbzaYhQ4ZoyJAh/i4FQD+oqKjoNKyIsA4Abk0BAQEKCwtTWFiY13H30NSrFxG7+vHq5+6A71o3fVtbW+VyueR0Oj2v6/7Z7s1utys8PFzh4eGKiIjwLHZ29cJnoaGhXjeG3c99HxnCCqC/ENYBAIBe4TsENi4uTjExMX6qBgBgIvdQWrvdrpCQkGue5zvv3NWPVz93n9vVz7n6uXshCt9H3/nuAMAEhHUAAKBXsLgEAKC3XB2gMZc5gFtNgL8LcCOsAwBgYCOsAwAAAHqOsA4AAPQKVoIFAAAAeo6wDgAA9Ao66wAAAICeI6wDAAA9ZlmWTp486XWMsA4AAADoPsI6AADQY9XV1Wpra/M6RlgHAAAAdJ+xYV1QUJCfKgEAAN3lOwQ2NDRUycnJfqoGAAAAGLiMDevorAMAYODoanGJgABjLjMAAACAAcOYq2jCOgAABq7y8nKvfVaCBQAAAG4OYR0AAOix0tJSr/3MzEw/VQIAAAAMbIR1AACgx44fP+61n5WV5adKAAAAgIGNsA4AAPSIy+VSWVmZ1zHCOgAAAOD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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_side_by_side('isolated.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "id": "CjfTtFEOz0GW",
        "outputId": "899e99b8-ae08-4308-c02c-8953da3c8326"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Circle detected\n",
            "Star detected\n",
            "Rectangle detected\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 2 Axes>"
            ],
            "image/png": 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4yOrv7p6VmT5m7Yxp06Zh3Lhx8PPzw5QpU+Ds7AwzMzMMHjxYb72RzMjD9T2IiIiI9LVu3RqFChXC7Nmz0bt3b/j4+ODy5cuYNGkSAgICEBkZCXd3d5QpUwbjx4+X3i9//vw4fvw4Bg4ciGnTpsHNzQ0//PADbG1tMXDgQFhZWUnX9u7dG8HBwfD398eBAwdQvXp1HD58GHXr1v1gNnNzc+zevRsDBw7E9OnTYWVlhVatWuHHH3/E119//dlfc3h4OOLj49GnTx+9cytXrkTBggVhY2ODkydPYtq0adiyZQtWr14NBwcHfPXVV5g0aRIcHR0BvJldsWvXLgwePBhr166FIAj45ptvMGfOHJQpU+azM36J7t27IywsDMuWLcPBgwfh4+ODtWvXYsuWLThx4oR0nUqlwt69ezFo0CCsWrUKZmZmaNWqFSZMmICqVavK/h9+7OuCiDIOm3VEBqxZs2b4888/cebMGWlH13edPn0ajx49Qt++fT/r43t4eECn0yE4OBhFihSR6umtN/Iltm7ditq1a0vrvr0VExPzn09VswrX9yAiIqLsqHv37ujevXu658zMzNK9h1m1atV/ftzSpUvrTW8dPHiw3owGMzMzzJgxAzNmzJBd++jRI9lxrVq19DZFK1asWLpr1KW9LiAgAAEBAenmTFtP+3nfx87ODtOmTcO0adM+eJ2zszNWr179nxnTk97XPHHiREycOFHv2vd9je824IA3I+JGjx6N0aNHy+rpbSDn6uqqN3V5586dAKC36cbHvi6IKGNwzToiAzZ8+HBYW1ujb9++elM2o6Ki0K9fP9jY2GD48OGf9fHfjvj6/fffZfWMXmdDpVLp3Yhs2bLFoNZs4/oeRERERB8vMTFRdhwZGYk1a9agWrVq753VQIYl7f/Dt+vtOTg4oGzZsgqlIiKAI+uIDFqRIkWwatUqdOrUCSVLlkTPnj3h5eWFR48ewd/fHxEREdiwYQMKFSr0WR+/XLlyaNOmDebPn4/IyEhUqlQJJ0+exL179wDI16r4Es2aNcPkyZPRo0cPVKlSBTdu3MC6deuk0WeGgOt7EBEREX28ypUro1atWihWrBhevHgBf39/vH79+r1r9ZLhGTBgABITE1G5cmUkJydj+/btOHfuHKZNm5ZlS+oQUfrYrCMycO3atYO3tzemT58uNehcXFxQu3ZtjBkzBiVKlPiij7969WrkypULGzZswI4dO1CvXj1s2rQJRYsWla1V8SXGjBmD+Ph4rF+/Hps2bULZsmWxd+9ejBo1KkM+fkbg+h5EREREH69JkybYunUr/vjjDwiCgLJly8Lf3x81atRQOhp9pDp16mDOnDnYs2cPkpKSULhwYSxcuBA//vij0tGITJ4gfsxkeiIyKf/88w/KlCmDtWvXolOnTkrHMQo7d+5Eq1atcObMGVStWlXpOERERERERGSkuGYdkYlLu1YFAMyfPx9mZmZ8MvoeXN+DiIiIiIiIMgunwRKZuF9//RVXrlxB7dq1oVarsX//fuzfvx99+vRB/vz5lY5nkLi+BxEREREREWUWToMlMnGHDx/GpEmTcPv2bcTFxaFAgQLo0qULxo4dC7Wa/fz0rF+/HnPmzMH9+/el9T369+/P9T2IiIiIiIjoi7FZR0REREREREREZCC4Zh0REREREREREZGBYLOOiIiIiIiIiIjIQLBZR0REREREREREZCDYrCMiIiIiIiIiIjIQbNYREREREREREREZCDbriIiIiIiIiIiIDASbdURERERERERERAaCzToiIiIiIiIiIiIDwWYdERERERERERGRgWCzjoiIiIiIiIiIyECwWUdERERERERERGQg2KwjIiIiIiIiIiIyEGzWERERERERERERGQg264iIiIiIiIiIiAwEm3VEREREREREREQGgs06IiIiIiIiIiIiA8FmHRERERERERERkYFgs46IiIiIiIiIiMhAsFlHRERERERERERkINRKByAiIiIiIiLKSqIoAgB0Oh20Wi10Oh10Op3s3Nv/piUIguy/ZmZmUKlUMDMzg5mZmewcEdHnYLOOiIiIiIiIsg1RFKHT6aDRaJCamiq9vXuckpKChIQEhIeH48WLF3jx4gWio6Oh0Wj03rRaLQRBgEqlglqthkqlgrm5OVQqFSwtLeHi4oKcOXMiV65ccHZ2hrW1NczNzWFubg61Wi39+d2aIAhs6BHRe7FZR0REREREREZJFEWIooj4+HjExMQgJiYG0dHRiIqKQmRkJCIiImRvb2vh4eF4/fq1NJouo6jVajg5OcHV1RUuLi6y/7q5ucHV1RWurq5wcnJCjhw5pDdra2s274hIIojvG9tLREREREREZEBEUZRGxIWHh+Ply5d48eIFnjx5ggcPHkhvkZGR0Gq1SsdNl7m5OfLkyYOCBQuiUKFC8PLyQv78+eHm5gZ3d3fkzJkTLi4usLS0ZAOPyESxWUdEREREREQGSaPRICIiAo8ePcKTJ0/w5MkThISEyN7CwsKQnJz82Z/D0tIS9vb2sLOzg1qtlq1B9/a/b6fWpl3jLiUlBbGxsYiNjYVGo/nsDNbW1siTJw/y5s2LfPnyIV++fMifP7/05unpCScnJzbviEwEm3VERERERERkMERRRHBwMC5duoRLly7h7t27ePHihTSaLiEh4b2bP6RlY2MDJycnODk5wdnZWfrvu28ODg6wtbWFlZWVbKMIQRCkP7+dbvu2SffuuniJiYmIi4vDq1evEBUVJb1FR0dLU3Lf/vljm4pmZmZwcHCQps7mzJkTxYsXR4UKFVCuXDnkzZuXjTuibIzNOiLKdKmpqdLivVFRUdLTx9evXyMhIUG2gK9Op4NarYaFhQXMzc1hYWEBBweHdG+wrKyslP7SiIiIiCgDaDQa3LlzB6dOncKpU6dw//59hIeHIyIiAomJif/5/iqVCrly5YKPjw9KlCiBYsWKIW/evLC3t4elpeUH395u+PAlRFFEamoqkpOT3/uWlJSEmJgYPH78GLdv38atW7dw+/ZtREVFfdTnsLOzk6bK+vj4oFatWqhevToKFCgAlUr1RfmJyLCwWUdEXywlJQX//vsvgoKCEBwcjEePHklvoaGhH30D8qkcHBxQoEABeHh4yN4KFSqEYsWKwdbWNlM+LxERERF9uZSUFNy5cwcHDhzAwYMH8fDhQ7x+/fqjppQ6ODigRIkS+Prrr1GqVCmUKFECuXPnho2NDaysrGBtbQ0LCwuD2nVVFEVotVqkpKQgMTFRenv69Clu3LiB69ev4/r16wgKCkJCQsIHP9a7D7RLliyJRo0aoWHDhsibNy8bd0TZAJt1RPRJXrx4gStXruDKlSu4du0abt++jfv37xvcAr6CIMDT0xMlSpRA8eLFUbx4cXz99dfw8fHhDQwRERGRQrRaLW7duoW//voLe/fuRVBQEJKTk5GSkvLenVnVajW8vLxQqVIlVKhQAeXLl0fhwoWlUXFv3wypMfex3p1O+/YtNjYWd+7ckaYB//3333jx4sV7p/6qVCpYWFjA2toaZcuWRbNmzdCsWTMUKlQoi78aIsoobNYR0XtptVrcuHEDp0+fxunTp3H+/Hk8e/ZM6VhfxM7ODuXLl0elSpVQsWJFVKxYEblz51Y6FhEREVG29LYZlZiYiO3bt+P333/H5cuXpXpabxtuarUaxYoVQ+vWrfHNN9+gWLFi6TbkjK0591/e/fX87Tp5KSkpuHTpEnbt2oWdO3fi8ePH0tp56Xm7MUbt2rXxww8/oF69erCysjLKZiaRqWKzjohkHj58KE1FOHnyJF69epVhH1ulUkmL+Nrb28Pe3h62trYwNzeHWq2GSqWCSqWCRqNBSkoKUlJSkJycjNevX0sL82Zknrc8PT1Rp04d1K1bF7Vr12bzjoiIiOgLvG3EJSUl4enTp9iwYQOWL1+O58+fp3v925FhlpaWKFu2LJo2bYpmzZqhSJEisutMtdH07q/sOp0OgYGB2LVrF/bt24egoCDpvjm95p1KpYKPjw/69++Ppk2bws3NDZaWlmzcERk4NuuITJxWq8WZM2ewfft27Nu3D/fv3//sj5UnTx4UKVIEXl5e8PT0hKenJ/Lnz4+cOXPC3d0dLi4uMDMz+6K8Go0GMTExiIqKwrNnz/DkyRM8fvxYenv06BGCg4M/eoew9Pj4+KBOnTqoX78+6tWrBxsbmy/KTERERGQK3o4Ci4yMxK1bt7Bx40bs2LED0dHRetdaWFjA3t5eWnOtYcOGaNiwIQoUKPDF94umIjk5GUFBQdi/fz8OHTr0n2v+FSxYEB06dECrVq3g4eEBJycnqFQqNu2IDBCbdUQmSKvV4siRI9iyZQv++usvREREfNL729jYoGzZsihXrhxKlSoFHx8fFCtWDI6OjpmU+NMkJCQgKCgIt27dwq1bt3Dz5k3cuHEDT548+eSPZWVlhbp166J58+Zo1qwZ8ubNmwmJiYiIiIyXKIqIiYnBkydPcOXKFWzduhWnT59GXFyc3rXOzs7w8PBAqVKlUKdOHe5mmkFSU1Ol3XRPnDiBoKAgPHv2LN1ZKS4uLmjcuDFatmwJHx8f5MuXD3Z2dmzaERkQNuuITMjNmzexevVqrF27FqGhoR/9foUKFUL16tVRvXp1VKpUCUWLFjXKG6oXL17gwoULuHDhAv7++29cunQJsbGxn/QxypYti1atWuHbb7/Vm5pBREREZEpEUcTTp09x/fp1nDt3DocOHcL169f1Nh57u0GEj48Pqlevjvr166N48eJGeT9pDDQaDa5cuYJDhw7h7NmzuHPnDp49e6Y3TVatVqNSpUpo0KABKlWqhFKlSsHd3Z1NOyIDwGYdUTYXGRmJdevWYdWqVbh69epHvY+7u7s0FaF27drIkydPJqdUxtvdyI4dO4Zjx47h5MmTeP369Ue/f5kyZdC+fXu0b98eBQsWzMSkRERERIYlNDQUx44dw/79+3Hs2LF0HwQ7ODjg66+/RuXKlVGlShVUrlwZbm5ubAZlEZ1Oh6dPn+Ls2bM4c+YMzp07h7t37yIpKUl2nZmZGQoWLIj69eujWbNmqFy5MpycnBRKTUQAm3VE2VZgYCB+++03rFu3Tu8HcnpKly6NVq1aoXnz5vj6669Ncq2Qt08hjx07hoMHD+LMmTN6T4bfp0KFCujcuTM6duwIV1fXTE5KREREpIyYmBgcOXIEO3bswKlTpxASEqK3VrC7uztq166N2rVro1y5cihWrBhsbGzYpFOITqdDVFQUbt26hYsXL+LIkSM4c+YMEhISZNeZm5ujcOHCqFu3Ltq3b48KFSrAyspKodREpo3NOqJsRKPRYPfu3ViwYAFOnjz5n9eXL19eWmSWI8P0RUVF4cCBA9i9ezf279//UTvRmpubo1mzZujevTsaN24Mc3PzLEhKRERElLmSkpJw/vx5rFy5EqdPn8azZ89kmxgIggBPT0+0bdsW9evXR9GiRZEzZ05YWFiwSWcgRFFEQkICQkJCcOfOHezbtw87d+7Ey5cvZddZW1vDy8sLDRo0QO/eveHt7W2SD/KJlMRmHVE2kJSUBH9/f8yaNQuPHz/+4LV58uRB586d0bVrVxQvXjyLEhq/1NRUnDlzBtu2bcPWrVvx4sWL/3wfNzc3dO7cGX369IG3t3cWpCQiIiLKWDqdDv/++y/mzZuHffv24eXLl0hOTpZd4+XlhaFDh6Jhw4Zwc3ODvb0916MzcBqNBjExMXj58iW2b9+OJUuW4Pnz57JrbGxskDt3bnz77bcYOHAgcubMqVBaItPDZh2REUtISMAff/yBX3/99YMbRpibm6NNmzbo0aMH6taty5unL6TVanHy5Els3rwZ27Zt+6jddGvVqoX+/fujZcuWsLCwyIKURERERF8mKioKc+fOhb+/P6KiopCSkiI77+rqisGDB6Nnz55wcnLiKDoj87YVkJSUhCdPnmDhwoVYs2aN3hrOVlZWcHNzw+DBg9G7d2/Y29srEZfIpLBZR2SE4uLisGTJEsyePVtv2Pq7cubMiX79+qFv377InTt3FiY0HampqTh27BhWr16N7du3/+f6gDlz5kTPnj3Rp08feHh4ZFFKIiIioo/zdqqkv78/pk2bhvDwcL1dRO3s7NC5c2eMHDkS+fLlg0qlYpPOiL1tCaSkpODq1auYOnUqjh49qjeCUq1Wo0iRIhg3bhxatWoFS0tL/n8nyiRs1hEZEY1Ggz///BMTJ078YJOufPnyGDRoENq1awdLS8ssTGjaXr16hc2bNyMgIADnzp374LVmZmZo06YNhg4dikqVKmVRQiIiIqL0iaKIlJQUnD17Fj/99BMCAwNlTTpBEGBra4uaNWtizJgxqFChAtRqtXSOjN+7I+3++usvzJw5E0FBQXoPoy0tLdG0aVNMmDABxYoVg1qt5muAKIOxWUdkBERRxO7duzFy5EgEBQW997q6deti3LhxqFGjBn9gKuzu3btYsWIFVqxY8Z/TZCtVqoShQ4eiVatW0k0vERERUVbRaDQICwvDihUrsGjRIoSHh8vOOzo6olixYhgwYAC++eYb2NnZKZSUslJUVBRWrFiBP//8EyEhIYiPj5ed9/HxwZgxY9CoUSM4OTlxEwqiDMRmHZGBu3TpEoYNG4ZTp06995pGjRph3LhxqFKlShYmo4+RnJwsLdp7+vTpD17r4eGBgQMHomfPnnB0dMyihERERGSqRFFEXFwcLly4gEWLFuHAgQOyqY+Ojo746quv0KFDB3Tp0gVubm4KpiWlPHv2DMuWLcPOnTvx6NEjxMXFSefs7e3Ro0cPdO/eHcWLF4e5uTkHDRBlADbriAxUREQERo0aBX9///de06RJE0yYMAG+vr5ZmIw+161bt7B06VIEBATIbnLSsre3xw8//IChQ4fyppiIiIgyhVarRXBwMHbs2IHly5fj3r170jm1Wo1SpUqhVatW6NKlC9fZJYiiiDt37iAgIAC7du3Cv//+K5sm7evri/79+6NBgwbIkyePgkmJsgc264gMjE6nw8qVKzFy5EhERkame025cuUwe/Zs1KpVK2vDUYaIiYmBv78/fvvtNzx58uS919nY2KBv374YPnw4NwghIiKiDCGKIpKSkrB//36sW7cOBw4cQEJCgnQ+V65caN68Obp164bKlStDEASOlCIAb147iYmJOHLkCAICAnDs2DG8evVKOm9vb4+2bdviu+++Q82aNWFhYaFgWiLjxmYdkQEJDAxE//7937s5gYeHB6ZNm4YOHTpwTYhsQKPRYPv27Zg7dy4uXLjw3ussLS3Rs2dPjBgxgk+2iYiI6IvcuXMHf/75J3bt2oUHDx5IdZVKhcqVK6NHjx5o1qwZ3Nzc2KSjdGk0Gjx8+BDbtm1DQEAA/v33X7zbVihZsiTatm2Lrl27wtPTU7mgREaMzToiA5CcnIyJEydi1qxZ0Gq1euft7Owwfvx4DBgwAFZWVgokpMx2/vx5zJ07F9u3b5dNKXiXWq1G165dMW7cON74EBER0SdJTEzExo0b4e/vjytXrsh2+HR2dkaHDh3QvXt3lCxZEpaWlmzU0QeJoohXr17h4sWLWLRoEQ4dOqS33mGNGjXQv39/1K1bl6PsiD4Rm3VECrty5Qq6deuGW7dupXu+Xbt2mDdvHvLmzZvFyUgJ9+7dw/Tp07FmzZp0G7cAYG5ujv79+2Ps2LFwd3fP4oRERERkbEJDQzF16lTs2LEDYWFh0igoQRDg7e2NUaNGoXHjxnB2doZKpVI4LRkTjUaD0NBQbNy4ETNmzEBUVJR0Tq1Wo2DBgujfvz969OjBDdSIPgGbdUQKSUlJwdSpUzFt2rR0mzKFChXC4sWL0bBhQwXSkdKCg4Mxc+ZMrFy5EikpKeleY2tri59++gk//fQTHBwcsjghERERGYOgoCCMHTsW+/fvR2JiolQ3NzdHq1atMHHiRBQsWBAWFhYcTUef5e06iEFBQRg8eDBOnTolnRMEAY6Ojvjhhx8wePBguLq6KpiUyHiwWUekgBs3bqBLly64fv263jlzc3OMGTMGo0aN4pRXwrNnzzBr1iz88ccfsukq73JxccHYsWPRv39/vmaIiIhIGjl35coVjBgxAidPnpQts+Hk5IQ5c+agY8eObNJRhnm7AcWiRYswefJkxMfHS+dUKhW6deuGadOmSTND+Lojej8264iykCiKWLp0KYYMGSJb0+GtMmXKYNWqVShZsqQC6ciQvXjxAjNmzMDvv//+3pF2+fPnx7Rp09CxY0duQEJERGSiRFFEamoqzp49i8GDByMwMFA6p1arUbVqVQQEBEibVrFhQhlJFEXodDpcvnwZ33//Pf755x9Zo7hOnTpYvnw5PDw8eL9K9AFs1hFlkejoaPTu3Rvbtm3TO6dWq/Hzzz9jzJgxMDc3VyAdGYvHjx9j4sSJWL169Xs3oqhYsSIWLFiAihUrZnE6IiIiUpIoioiLi8P+/fvx008/4dmzZ9I5e3t7dOzYETNnzoSDgwObdJSpRFFEUFAQRo8ejUOHDsmmYBcpUgQrV65EuXLlOCuE6D3YrCPKAufPn8d3332Hx48f650rWbIkAgICULZsWQWSkbG6desWfv75Z+zcufO913Tu3BnTp09Hvnz5si4YERERKUIURYSFhWHt2rWYPHky4uLiAABmZmbImzcvBgwYgL59+8Le3p6NOsoSoigiODgYc+bMwcaNG2WbT7ydit28eXOuY0eUDjbriDKRKIqYO3cuRo4cme4mEkOGDMH06dNhaWmpQDrKDv7++2+MHj0aJ06cSPe8jY0NxowZg2HDhvF1RkRElE3pdDrcuHEDCxcuxMqVK6XR9+bm5ihZsiSGDh2KFi1awM7OTuGkZIrCwsLw559/wt/fH0+fPpVen9bW1vjxxx/Ru3dvFCpUiNNiid7BZh1RJomPj0evXr2wceNGvXMuLi4ICAhAs2bNFEhG2Y0oitizZw9++ukn/Pvvv+le4+3tjd9//x21a9fO4nRERESUmZKSknDo0CHMmzdP9vDO2toaNWrUwJAhQ1CzZk1ONyRFxcbGYvPmzVi0aBECAwOlhp2VlRW++eYbDBw4EL6+vlwSiOh/2KwjygTBwcFo2bKlbEHft2rUqIF169ZxaiJluJSUFGn3rVevXqV7TadOnTBnzhzkzJkzi9MRERFRRouNjcXq1auxaNEi3L17V9oF1tbWFh06dED//v1RunRpqFQqhZMSAampqThx4gQWLFiAffv2Sa9XS0tLVKxYEcOGDUP9+vXZWCYCm3VEGe7w4cPo0KGDbE0G4M1OW+PHj8e4ceN4w0SZKjw8HOPGjcOff/6Z7iYUjo6OmDlzJnr37s3pBkREREbq1atXWLZsGRYtWoRnz55JjQ9HR0cMGzYMXbp0QYECBbg+HRkUURRx9+5dLFu2DIsXL0ZqaiqANxvulSxZEuPGjUOTJk24fAuZPDbriDLQb7/9hiFDhug1SBwdHbFu3To0bdpUoWRkiq5cuYJ+/frh8uXL6Z6vVasWli9fjkKFCmVxMiIiIvoSr1+/xtKlSzF//nyEhYVJjbrcuXNj/vz5aNiwIRwdHRVOSfR+ERER2LZtG0aPHo3o6GgAbzZDKVq0KH799Vc0aNAAFhYWCqckUg6bdUQZQKvVYsiQIVi4cKHeOR8fH+zcuRNFihRRIBmZOq1Wi2XLlmHMmDHpTo21trbGL7/8goEDB3LEJxERkRGIi4vD8uXLMW3aNISHh0t1b29vrF+/HsWLF2eTgwyeKIpISkrCiRMn0LdvXzx9+hTAm9lIBQoUwB9//IE6depArVYrnJRIGWzWEX2huLg4dOzYEbt379Y716pVK6xatQr29vYKJCP6f2FhYRg+fDjWrl2b7vlKlSph5cqV8Pb2zuJkRERE9DFEUURiYiLWrFmDESNG4PXr19K5EiVKYPv27dxRk4yKKIrQaDQ4cuQI+vfvj8ePH0vn3N3dsXHjRtSsWZOvaTJJfNUTfYHnz5+jZs2a6TbqJk2ahK1bt7JRRwYhV65cWLNmDQ4ePIgCBQronf/7779RpkwZLFq0CHyGQ0REZFhEUURycjK2bNmCIUOGSI06QRBQtmxZbNmyBYULF+b6dGRUBEGAWq1GvXr1MG/ePHh5eUnnXr58iRYtWuDUqVPprsFMlN1xZB3RZwoKCkKDBg2kIdtvWVhYYOXKlejYsaNCyYg+LDY2FqNHj8bixYvTPd+oUSOsWLECuXPnzuJkRERElJYoikhJScFff/2FHj16ICEhAcCbBfkrVKiARYsWoXTp0hx9REYtNTUVmzdvxsSJE/Hw4UOpQefk5ITt27ejWrVqnBJLJoXNOqLPcOXKFTRq1AgRERGyurOzM3bu3Inq1asrlIzo4506dQo9e/bE/fv39c65uLjgjz/+QOvWrRVIRkRERG8lJSVh165d6NWrF2JjYwEA5ubmqFy5MqZPn45KlSqxUUfZgkajwdq1azFt2jQ8ePBAatjlyZMHAQEBqFmzJtdjJJPBf9WJPtGJEydQu3ZtvUZdoUKFcP78eTbqyGjUqFED169fx4ABA/TORUZGok2bNujTpw8SExMVSEdEREQJCQnYsmUL+vbtq9eomzhxIht1lK2o1Wp07twZo0aNQsGCBaVp3aGhofjhhx9w+PBhJCcnK5ySKGvwX3aiT7Br1y40atRIull6y9fXF+fPn8dXX32lUDKiz2NjY4PffvsNBw4cSHfa659//omKFSvi7t27CqQjIiIyXYmJiVi7di2GDRuGmJgYAP8/9XXs2LGoUaMGG3WU7ajVanTp0gWjR4+W1rATRREPHjzAmDFjcOjQIaSkpCickijz8V93oo+0bt06tG7dWu9pTt26dXH06FG4ubkplIzoyzVs2BA3btxAmzZt9M7duHED5cqVw7p16xRIRkREZHo0Gg02bdqEqVOn4uXLlwAAMzMzlClTBuPHj0fdunWhUqkUTkmUOczNzdGlSxdMmTIFHh4eAACdTodbt25h+vTpOHv2LDedoGyPzTqij7B27Vp06dIFWq1WVm/dujX27t0LOzs7hZIRZRwXFxds2bIFAQEBsLW1lZ2Lj49H586d0bt3b06LJSIiykSiKOLQoUOYN28eQkJCpHrx4sUxc+ZMNurIJJibm6Ndu3aYN28e8ufPDwDQarW4cuUKFixYgFu3boHL71N2xmYd0X9Yu3YtunbtqvfDoEePHti0aRMsLS0VSkaU8QRBQLdu3XD58mWUKFFC7/zy5ctRo0YNvV2QiYiIKGNcu3YNCxYswO3bt6XRQx4eHli2bBlq1KjBHTHJZJibm6NZs2ZYunSpNIspJSUFhw8fxrJlyxAWFqZwQqLMw2Yd0Qe8r1E3ePBg+Pv782aJsi1vb29cuHABvXr10jt3+fJllC9fHqdOnVIgGRERUfYVEhKCxYsX48SJE9BoNADerC+7YsUK+Pr6ckQdmRy1Wo26detizpw50iCJhIQEBAQEYNOmTYiLi1M4IVHmYLOO6D3e16j76aefMHfuXGl3IqLsysbGBn/++SfWrFmjNy325cuXqFu3LhYvXswpCERERF9IFEXExcVh3bp12Lhxo2wB/cWLF6NGjRps1JFJEgQBFhYWaN68OUaOHCnV4+PjMXnyZJw9exYajYb3o5TtsFlHlI6tW7eiW7du6TbqZs2axUYdmZTOnTvj4sWLKFy4sKyu0Wjw448/onfv3tyVi4iI6DOJogiNRoOjR49i9uzZSEhIAPBmQ4mRI0eiQ4cObNSRSRMEAY6OjvDz80P79u2l38Wio6PRt29fPHz4UOGERBmPzTqiNA4dOoSOHTvq7TDERh2ZMh8fH1y6dAmNGzfWO+fv74/GjRsjJiYm64MREREZOVEUcePGDfz8888IDw8HAKhUKnzzzTcYOnQoLC0tef9JJk8QBBQoUABDhgxB+fLlpb8Tjx8/hp+fHyIiIji6jrIVNuuI3nH+/Hm0atUKqampsjobdURAjhw5sHv3bowePVrv3LFjx1ClShU8evQo64MREREZKVEU8eTJE4wfPx43b94E8GZEXfny5TFp0iS4ubnx/pPofwRBQPny5TFkyBB4eXlJfzfOnj2LYcOG8cExZSts1hH9T2BgIJo0aSJNPXirX79+bNQR/Y9KpcK0adOwefNmWFtby87duXMHFStWxMWLFxVKR0REZDxEUURERASmT5+OvXv3SvWvvvoKY8eORbFixXj/SZSGWq3GN998gz59+sDV1VWqr1mzBnPnzsXr168VTEeUcdisIwIQHByMhg0b6j2N6dChAxYtWsQbJaI02rVrh1OnTiFnzpyy+suXL1GrVi3s2rVLoWRERETGISEhAb/99huWL18u1fLmzYuBAweidu3aMDc3VzAdkeGytbVFjx498O2330qboImiiHnz5iEgIABJSUkKJyT6cmzWkcmLjo5GkyZNEBYWJqs3adIEq1ev5oK+RO9Rvnx5XLhwAcWLF5fVExMT0bp1a6xevVqhZERERIZNFEWsXbsW8+bNk9ZJdnJyQpcuXdCmTRvY2dkpnJDIsLm7u2PIkCFo1KgR1Go1gDc7xM6ZMwf79u3j+nVk9NisI5OWkpKC1q1bIygoSFavVq0atmzZwieaRP/Bw8MDZ8+eRf369WV1rVaLbt26YcGCBQolIyIiMlwXLlzAr7/+ivj4eACAlZUVmjRpgr59+8Ld3V3hdETGoWDBgpgwYQIqVKggzYQKCQnBokWLcPfuXYXTEX0ZNuvIZImiiD59+uDEiROyeokSJbB7927Y2NgoE4zIyDg6OmLv3r3o0aOH3rnBgwdj/PjxfLpJRET0P9HR0Zg0aZK0KZMgCChevDhGjx4NDw8PZcMRGZkSJUpg7ty5cHNzA/DmgfGlS5fg7+8vNcOJjBGbdWSypk6dilWrVslquXLlwt69e5EjRw5lQhEZKXNzc/j7+2PYsGF656ZMmYKBAweyYUdERCZPFEUsXrwYZ8+elaa/2tvbc0MJos8kCAIqVKiAKVOmSLW4uDjs2LED+/fv5/0nGS0268gkbdq0CePHj5fVbGxssGfPHhQoUEChVETGTRAEzJo1CzNmzNA7t2jRIvz444+8YSIiIpMliiLOnDmD9evXIzY2FgBgZmaG7777Ds2aNYOZGX81I/ocKpUKXbt2RYsWLaTaw4cPsX79ejx48ID3n2SU+BOBTM6NGzfg5+cnqwmCgA0bNqBcuXIKpSLKPkaOHIlly5bpjQ74/fff2bAjIiKTJIoiYmJisGjRIty7d0+qe3p6Ytq0aVwnmegLWVpaYu7cudKaj6IoYv/+/di1axeSkpJ4/0lGh806MinR0dFo1aoVEhISZPX58+fjm2++USgVUfbTp08fbNy4UW835d9//x0//PADb5iIiMikaLVarF+/HidOnIBWqwXwprmwZMkSODs7K5yOyPgJgoD8+fNj+vTpUvM7KSkJy5cvx5UrV3jvSUaHzToyGTqdDp07d8aDBw9k9d69e2PgwIEKpSLKvtq3b59uw27JkiUcYUdERCZDp9Ph6tWr2LhxI16+fAngzbS9fv36oV69egqnI8o+zM3N8c0336BNmzbStPI7d+5gxYoVCAsL470nGRU268hkTJo0Cfv27ZPVfH19sXDhQoUSEWV/bdu2fe8IuzFjxiiUioiIKGuIoojIyEhs2LABf//9t1SvWLEiRo8ezQ0liDKYs7MzBg0ahKJFi0q1DRs2YN++fUhJSWHDjowGm3VkEvbv34/JkyfLau7u7ti2bRssLS0VSkVkGt7XsJsxYwZmzZqlUCoiIqLMl5ycjMOHD2PTpk3QaDQAgLx582LixIlwdXVls44og5mZmaFEiRLo37+/NMU8KSkJs2bNQmBgIJt1ZDTYrKNsLzQ0FN26dZPVVCoVNm/ejHz58imUisi0vG3Ypd3pbsSIEfD391coFRERUebR6XR48OABlixZgtDQUACAlZUV+vXrh8qVK+s9xCKijGFnZ4dmzZqhcePGUKvVAIB79+5h2rRpePXqFRt2ZBTYrKNsTafToXv37ggPD5fVZ82ahZo1ayqUisg0tW3bFn/++adevU+fPti2bZsCiYiIiDJPYmIiNmzYgPPnz0u1Ro0aoUOHDrC1tVUwGVH2V6BAAXTr1g3FixeXanv37sWGDRsUTEX08diso2xt3rx5OHTokKzWokULDB48WJlARCbOz88Ps2fPltV0Oh06duyIM2fOKJSKiIgoY4miiHv37mH58uXS7q9FihSBn58fPDw8OP2VKJOpVCpUrVoV3377LVxcXAAAqamp+PXXX6WRrkSGjM06yrauXLmC0aNHy2p58uTB8uXLeYNEpKCffvpJb3OJlJQUtGzZEvfv31coFRERUcZJTU3F/Pnz8eLFCwBvGgctW7ZE9erVYW5urnA6ItNgY2ODTp06oWTJktLvf0+ePMG8efMUTkb039iso2wpISEBHTt2RGpqqlQTBAFr1qyBq6urgsmICACmTp2Kvn37ymqRkZFo2rQpoqKiFEpFRET05URRxNWrV2XT7YoXL46GDRvC0dFRwWREpidfvnxo3769tNmEKIpYsWIFgoKCFE5G9GFs1lG2NGHCBNy7d09WGzlyJOrUqaNQIiJ6lyAIWLRoEZo0aSKr37t3D23atEFKSopCyYiIiL6MKIoYN26c9NDY3NwcNWvWRJUqVTi7gyiLmZmZoWPHjihSpIj09+/169eYOXMmN5ogg8ZmHWU7Fy9exNy5c2W1ChUqYPLkyQolIqL0qNVqbNy4EV9//bWsfuLECfTr1483UEREZHREUcT+/ftx9OhRqVa0aFG0bt0aVlZWCiYjMl0ODg74/vvvYW9vDwDQaDTYv38/zp8/z/tNMlhs1lG2kpycDD8/P+h0OqlmaWmJ1atXc30QIgNkb2+PPXv2IE+ePLL6ypUrsWTJEoVSERERfZ64uDhMmjRJagBYWVmhRo0aqFq1KkfVESlEEAS0b98epUuXlv4eRkdHY/HixUhKSlI4HVH62KyjbGXatGm4deuWrDZhwgR4e3srlIiI/ku+fPmwe/du2NjYyOqDBg3CuXPnFEpFRET0aURRxNq1a3H9+nWp5uXlhW7duvGhMZHCLC0tMWrUKGl0XUpKCs6dO4ejR49ydB0ZJDbrKNsIDAzEtGnTZLUyZcpg2LBhCiUioo9VtmxZrFmzRlbTaDRo27YtwsLCFEpFRET08R4/foylS5dKa9VZWVmhefPmKF++vMLJiAgAGjRoIFsvOSQkBBs2bEBERISCqYjSx2YdZQuiKOKHH36ARqORamq1GitWrOCTTCIj0bp1a4waNUpWCw0NRbt27WQ7OxMRERkajUaDlStX4v79+9IoncKFC6N3794wM+OvXESGQKVSYdSoUXB3dwcApKam4ty5czhy5IhsGSUiQ8CfHJQtbNq0CWfOnJHVRo4cidKlSysTiIg+y9SpU1GvXj1Z7cyZMxg9erRCiYiIiP7b9evX8ddffyExMRHAm4fGffv2RaFChRRORkTvKlmyJPr06SMdP336FLt378bTp08VTEWkj806Mnrx8fF6U129vLzw888/K5SIiD6XSqXChg0bUKBAAVl9zpw5OHTokEKpiIiI3k+j0WDDhg2yUXXly5dHp06duKkEkYERBAG9evWS1jTXarU4fvw4zp8/z9F1ZFDYrCOjN2PGDISEhMhqc+fOhZWVlUKJiOhLuLq6Yvv27bCwsJDVu3btipcvXyqUioiIKH23b9/G+fPnER8fL9XGjRsHJycnBVMR0fvkyZMHQ4cOlY5fvHiBkydP8j6TDAqbdWTUHj58iFmzZslq9erVQ4sWLRRKREQZoVy5cvj1119ltRcvXsDPz487dhERkcEQRRGnTp3C3bt3pVqNGjX0lnQgIsMgCALUajUaNmwoLZkkiiKOHj2Khw8fKhuO6B1s1pFRGzVqFJKTk6VjlUqFBQsWcMoBUTYwcOBANG7cWFbbu3cvFi1apFAiIiIiuRcvXuDChQuIjIyUav379+cGZ0QGTBAEuLi4oF27dlLt/v37uHjxIhISEhRMRvT/2Kwjo3XlyhVs2bJFVvvxxx/h4+OjUCIiykiCICAgIAA5c+aU1YcPHy4bwUBERKQEURTxzz//4PLly1LN09MTTZo0UTAVEX0MGxsb1KtXT9oZVhRF7Nq1Cy9fvuQsDjIIbNaR0Ro7dqzs2MnJCRMmTFAoDRFlBnd3d6xevVpWS05Ohp+fH7RarUKpiIiIgISEBFy8eBH37t2Taj169ICVlRVneRAZOEEQkC9fPtksjtOnTyMoKIgbTZBBYLOOjNLJkydx8OBBWW3kyJFcyJcoG2rQoAEGDx4sq507dw6LFy9WJhAREZk8URQRHByMEydOSL/Yu7i4oE2bNpwCS2Qk3NzcUK9ePdjZ2QF4s7Pzxo0bER8fz9F1pDg268gopR1BlytXLgwYMEChNESU2X755RcUKlRIVhs9ejSCg4MVSkRERKYsNTUVgYGBuHTpklRr0aIF8ubNy1F1REbC3NwcpUqVgq+vr1Tbs2cPHj9+rGAqojfYrCOjc/bsWZw8eVJWGzduHGxsbBRKRESZzcbGBsuXL5fVEhIS0Lt3bz75JCKiLBcVFYX9+/cjLi4OAODg4ICWLVtKI3SIyDgULFgQNWrUgJWVFQAgMjISmzdvhkajUTgZmTo268jo/PLLL7LjvHnzomfPngqlIaKsUqtWLfTr109WO3r0KFauXKlQIiIiMkVarRYPHjzAoUOHpFqVKlVQokQJqFQqBZMR0aeytbVF5cqVUbBgQam2Zs0aREZG8oEwKYrNOjIq165dw/79+2W14cOHw9LSUqFERJSVZs6cifz588tqo0aNQnR0tEKJiIjI1CQmJmLv3r14+fIlgDdT6Zo2bYpcuXJxCiyRkREEAWXLlkXZsmVhZvamPfL48WPs2LFD4WRk6tisI6MyZ84c2bGbmxt69+6tUBoiymoODg5YunSprBYeHs6doImIKEuIoojo6Ghs2LBBqpUoUQLlypWTptERkXFxdnZGjRo1kDNnTqm2ePFipKamKpiKTB2bdWQ0nj9/js2bN8tqgwYN4lp1RCamSZMmaNmypay2ePFiBAYGKhOIiIhMhlarxeHDh2UL0FevXh1FixblqDoiI2VmZoa6devKpsIGBQXh2LFjCqYiU8dmHRmNJUuWyJ5uWFlZ6a1fRUSmYe7cubIRDDqdDoMHD+baIkRElKm0Wi12794t/bzJkycPKlWqBCcnJ4WTEdGX8PT0RJUqVWBrawvgzd/1HTt28N6SFMNmHRmFpKQkLFu2TFbr0qULXFxcFEpEREry8vLCqFGjZLXjx4/jwIEDCiUiIqLsThRFxMfH48iRI1LN29sbxYsX56g6IiNnZmaG2rVrw9nZWart27ePU2FJMWzWkVHYuXMnwsPDZbVBgwYplIaIDMGIESP0NpsYOXIktFqtQomIiCi7O378OOLi4gC8WZi+cOHCKFSokMKpiCgj+Pr6ygaDREZG4vz58womIlPGZh0ZBX9/f9lxrVq1ULx4cYXSEJEhsLa2xpQpU2S1GzduYO3atQolIiKi7O7dHSJdXV1RqlQpadocERk3FxcXlC9fHhYWFgCA1NRU7N27l1NhSRFs1pHBCw4Olk03AMAdYIkIANC5c2eULFlSVps4cSKnLBARUYZ7/fq17J40T548KFOmjIKJiCijNWjQQFoXWavV4tixY0hISFA4FZkiNuvI4K1cuVJ2nCNHDrRq1UqhNERkSFQqFWbOnCmrPXr0iKPriIgow505cwYvX74E8GZ9K09PT/j4+CiciogyUvXq1aV160RRxNOnT3H9+nWFU5EpYrOODJooiti4caOs1rlzZ1hbWyuUiIgMTaNGjVC5cmVZ7ZdffoFGo1EoERERZUfbtm2TpsPZ2dmhTJkycHR0VDgVEWWknDlzwtfXF2Zmb1ol8fHxOHr0qMKpyBSxWUcG7fr16/j3339ltc6dOyuUhogMkSAIGD9+vKz24MEDrF+/XqFERESU3bx69QqHDx+Wjp2cnFCtWjXuAkuUzQiCgBYtWkClUgEAEhMTcfr0aU6FpSzHZh0ZtC1btsiOPTw84Ovrq1AaIjJUDRs2RIUKFWS12bNnc0FgIiLKEGfPnkV4eDiAN7/M58yZE+XLl1c4FRFlhgYNGsDGxgYAoNPp8OTJE9y8eVPhVGRq2KwjgyWKIjZv3iyrtWvXjk8wiUiPIAgYN26crHbjxg0cO3ZMoURERJSd7Nu3D1qtFgBgaWmJypUrw8HBQeFURJQZXFxcULNmTek4MjISf//9t4KJyBSxWUcG659//sH9+/dltfbt2yuUhogMXdOmTfHVV1/JavPnz1cmDBERZRtJSUk4evSotBaqtbU16tatywfIRNnU26mwb0VHR+PixYtITU1VMBWZGjbryGDt2LFDduzp6cnpBkT0XmZmZhg0aJCstmfPHty7d0+hRERElB1cuHABkZGR0rG9vT1q1KihYCIiymxNmzaV1q3TarV49OiR3lrqRJmJzToyWIcOHZIdt27dmk8wieiDunbtihw5cshqy5cvVyYMERFlCxcuXEBiYiKANw+GqlSpwl1gibI5Nzc3VKlSRToOCwvD3bt3FUxEpobNOjJIUVFRuHTpkqzWqFEjhdIQkbGws7NDz549ZbXVq1dz2gIREX0WURRx48YNJCcnAwBUKpVsLSsiyp4EQUDdunWl46ioKDx+/Jibl1GWYbOODNKxY8eg0+mkYysrK1SrVk3BRERkLNI26168eIH9+/crlIaIiIxZYmIi7t+/Lz30MTMzQ7ly5RRORURZwdfXV/rz69ev8fTpUz4ApizDZh0ZpLRTYGvUqAFra2uF0hCRMSlWrBgqV64sq/n7+yuUhoiIjFlwcDBiYmKkY3t7e3h7eysXiIiyTMmSJWFlZQXgzbp1z58/l61fSZSZ2KwjgyOKol6zrkGDBgqlISJj5OfnJzvet28foqOjFUpDRETG6u7du4iLi5OOixUrBnt7ewUTEVFWcXJyQuHChaXj58+fIzQ0VMFEZErYrCOD8+zZMzx+/FhWq1+/vkJpiMgYtW/fHpaWltKxRqPBrl27FExERETGKG2zrmLFigqmIaKsIggCVCoVypYtK9WeP3+OsLAwBVORKWGzjgzOhQsXZMeOjo4oUaKEQmmIyBg5ODjobUqzbds2hdIQEZEx0ul0bNYRmbC0zbrQ0FCEhoZykwnKEmzWkcFJ26yrWLEizMz4UiWiT9OmTRvZ8aFDh/D69WuF0hARkbGJjIxEaGgoNBqNVKtQoYKCiYgoK6lUKpQpU0Y6jo+Px7Nnz5CQkKBgKjIV7ICQwUmvWUdE9KmaN28Oc3Nz6Tg5OVlvPUwiIqL3efr0KaKioqTj3LlzI1++fAomIqKsJAgCChQogBw5cki1x48fyzadIcosbNaRQdFoNLhy5YqsVqlSJYXSEJExy5EjB2rXri2rHTlyRKE0RERkbB4/fizbnKhs2bIQBEHBRESU1aytrVGsWDHpmM06yips1pFBuXXrlt6wYl9fX4XSEJGxS7s5DZt1RET0MURRxOPHj2Uj68qXL89mHZEJEQQBlpaWKF68uFQLDg5GdHQ0162jTMdmHRmUmzdvyo49PT3h6uqqUBoiMnb16tWTHT948ADBwcEKpSEiImOh0Wjw5MkTvHr1SqrxATKR6bG0tJRtdhgSEoLw8HA26yjTsVlHBuXOnTuyYx8fH4WSEFF2UKpUKbi5uclqx44dUygNEREZi8jISISFhUGn0wEALCwsUKpUKYVTEVFWs7CwgLe3N9RqNQAgJSUFjx49QlJSksLJKLtjs44MStpm3bvrAxARfSozMzPUqlVLVrt06ZIyYYiIyGhERUXJpsDmy5cPzs7OnAZLZGLMzMzg5uYGFxcXqfbs2TM26yjTsVlHBoXNOiLKaBUqVJAds1lHRET/JS4uDrGxsdJx/vz52agjMkGCIMDKygo5c+aUahEREUhJSVEwFZkCNuvIYKSmpuLff/+V1disI6IvlbZZFxgYyKehRET0QbGxsYiLi5OOc+fOzWYdkYmysLCAs7OzdBwREYHU1FQFE5EpYLOODEZwcDA0Go2sxmYdEX2pcuXKyX7B0mg0CAwMVDAREREZurQj63Lnzg0zM/7qRGSKLC0tZZseRkZGcmQdZTr+xCGD8fTpU9lxjhw54OTkpFAaIsou7O3tUaRIEVktKChIoTRERGQM4uLiOLKOiADoj6yLjIzkyDrKdGzWkcEICQmRHefLl0+hJESU3Xz11Vey47RT7omIiN4VGxsrG1mXK1cuNuuITJSFhYVsgwmuWUdZgc06MhjPnj2THefNm1ehJESU3aQdWcdmHRERvY9Wq0VcXJxsfdNcuXJxGiyRiUo7si4mJgZJSUkQRVHBVJTd8ScOGYy0zTqOrCOijMJmHRERfaykpCS8evVKVuPIOiLTlbZZB7wZXafVahVKRKaAzToyGGmnwXJkHRFllEKFCsmO066RSURE9FZiYiJiYmKkYysrKzg4OCgXiIgUpVar4ejoCLVaLdVevnzJZh1lKjbryGCEhYXJjvPkyaNQEiLKbtzd3WXHkZGRvMEiIqJ0pR1Z5+7uDnNzc46sIzJRgiDAxsYGjo6OUu3FixfQ6XQKpqLsjs06MhhppxtwJ1giyihpm3U6nQ5RUVEKpSEiIkOWdmRdzpw5oVKplAtERIqztrbWa9bxwS9lJjbryGCkbda9+48hEdGXcHV11auFh4crkISIiAxd2madu7s7m3VEJs7GxgY5cuSQjjkNljIbm3VkMF6/fi07ZrOOiDKKhYWF3npDkZGRCqUhIiJDlpiYqDcN9t21qojI9FhbW7NZR1mKzToyCKmpqUhISJDV2KwjooxkZWUlO05OTlYoCRERGTKNRoOkpCTp2MHBAWZm/LWJyJRZWFjA2tpaOo6Pj+eadZSp+FOHDELaUXUAm3VElLEsLS1lx2zWERFRenQ6nWzEDDeXICIzMzPZdHiNRqNgGjIFbNaRQUhMTNSr2djYKJCEiLIrjqwjIqKPIYqibMQM16sjIkEQ9Jp1oigqmIiyOzbryCCkN4SYN0ZElJEsLCxkxykpKQolISIiQ5Z2ZJ1arebIOiITZ2ZmJpsOz5F1lNnYrCODkN7inGzWEVFGSk1NlR2bm5srlISIiAyZKIp6zToiMm1pR9ZptVqOrKNMxWYdGYT0RtZxIV8iykhpR9KlXcOOiIgISH8aLEfWEZk2jqyjrMZuCBkENuuIKLOlXaMu7bRYIiIiQL9Zx3tSIhIEQfZvAUfWUWbjTx4yCOlNg+WNERFlJDbriIjoY717H5reQ2UiMi0ccUtZjd0QMgjprR2Vdn0pIqLPpdPpEBMTI6s5OjoqE4aIiAxa2uluHEFDRDqdTtas41qWlNnYrCODYGNjo1dLSEhQIAkRZUevX7/WGxnh4uKiUBoiIjJkaReS59pURMSRdZTV2Kwjg2Btba1XS0xMVCAJEWVHkZGRejU264iIKD3pLSTPkXVEpk2n03GXaMpSbNaRQeDIOiLKTGmbdRYWFun+u0NERMSRdUSUVnrNOo6so8zEZh0ZBHNzc70NJTiyjogyyrNnz2THuXLl4g0WERGly8zMTK9Zx5F1RKZNFEWOrKMsxWYdGQRBEPRGucTFxSmUhoiymydPnsiOPTw8FEpCRESGTq1Ww9LSUjpOb91TIjItKSkpsplfNjY2eoNNiDISX11kMJydnWXH6a0xRUT0OdI26woUKKBQEiIiMnTW1tbIkSOHdBweHi4bUUNEpicxMRGvX7+Wjt3d3dmso0zFVxcZDDc3N9lxeHi4QkmIKLt5/Pix7Jgj64iI6H2srKzg6OgoHb98+ZLr1hGZuMTERMTExEjH7u7usunyRBmNzToyGGzWEVFmuXfvnuzY09NTmSBERGTwrK2t9Zp1HFlHZNoSExPx6tUr6ZjNOspsbNaRwWCzjogyg0ajwd27d2W1YsWKKZSGiIgMXXrTYFNTU7nJBJGJEkUR8fHxsmmwOXPmZLOOMhWbdWQw2Kwjoszw8OFDpKamymps1hER0fukbdbFx8cjNjZWuUBEpCiNRoPXr18jJSVFqrFZR5mNzToyGO7u7rLj58+fK5SEiLKTO3fuyI7d3Nzg4uKiUBoiIjJ0VlZWyJEjBwRBkGqhoaEcWUdkolJSUmSbH6pUKri4uLBZR5mKzToyGGkXfE+7IDwR0ee4evWq7NjHx0ehJEREZAzMzMxgb28PKysrqRYaGgqdTqdgKiJSStpmnYODA2xsbBRMRKaAzToyGGkXfH/8+DEX8yWiL3bx4kXZcfny5RVKQkRExkAQBNjZ2cHe3l6qcWQdkelKTk6WNetcXV1hYWEhG31LlNHYrCOD4eXlJTtOTU1FaGioQmmIKDsQRRGXLl2S1Xx9fRVKQ0RExsLOzg52dnbScVhYGJt1RCYqJSUFUVFR0rGLiwvMzc0VTESmgM06Mhju7u6wtraW1YKDgxVKQ0TZQXBwsOxJKABUqFBBoTRERGQs7O3t9Zp1nAZLZJrSToN1cXGBhYWFgonIFLBZRwZDEAS9qbBs1hHRl0g7qs7V1VXv3xkiIqK0bG1tObKOiABwZB0pg806MigFCxaUHd+9e1ehJESUHaRdr65ChQpcX4SIiP5T2mmwz58/h06nY8OOyMSIoojk5GRERERINTbrKCuwWUcGpXjx4rLjmzdvKpSEiLKDkydPyo65Xh0REX2MHDlyIEeOHNLxo0ePEBcXp1wgIlKEKIqIjIzEixcvpFru3Lllu0UTZQY268igpG3W3bp1S6EkRGTsoqKicPXqVVmtZs2aCqUhIiJj4urqijx58sDM7M2vSykpKbh+/brCqYgoq6WkpCAoKAharRYAYG5ujoIFC7JZR5mOzToyKCVKlJAdP3z4EPHx8QqlISJjduLECdl0JSsrK1SuXFnBREREZCwsLS1RoEABODg4SLULFy4omIiIlJCUlCSb7ZU3b164u7tDpVIpmIpMAZt1ZFCKFSsmW09KFEXcuXNHwUREZKyOHj0qO65WrRqfghIR0Ufz9PSEi4uLdHzx4kWuWUdkYpKTk3Hjxg3p2MvLC05OTgomIlPBZh0ZFGtraxQqVEhWCwwMVCgNERmztM26unXrKpSEiIiMkYeHB5ydnaXjy5cvQ6fTKZiIiLKSKIpISEjA7du3pZqXl5fs3wWizMJmHRmcr7/+WnZ8+fJlhZIQkbEKCQnR202azToiIvoUnp6esl/KX758iUePHikXiIiylCiKCA4OxqtXr6Sap6cnR9ZRlmCzjgxO2t0auT4IEX2qffv2yY4dHR1RtmxZhdIQEZExypEjB/Llywdzc3MAgE6nw5UrVxRORURZRavV4p9//pGmv9vb2yNfvnxcVoWyBJt1ZHDSNusCAwORmJioUBoiMkZ//fWX7Lhhw4ZcCJiIiD6JmZkZChcuDFtbW6l28eJFBRMRUVbSarW4du2adJwrVy7kypVLtsY6UWZhs44MTvny5WFm9v8vTY1GI/tHkojoQ+Li4nDkyBFZrUWLFgqlISIiY1akSBHY2dlJx5cvX+YmE0QmQBRFaWTdW7ly5ULOnDmVC0Umhc06Mjh2dnYoXry4rManmET0sQ4dOoTk5GTpWK1Wo3HjxgomIiIiY1WkSBHZyLobN24gJSVFwURElFXi4uIQFBQkHefOnZvNOsoybNaRQUo7Ffbs2bMKJSEiY5N2CmyNGjW4EDAREX2WggULwtHRUTpOuzMkEWVfN27cgEajAQAIgoDcuXPDzc1N4VRkKtisI4NUrVo12fGJEyeg0+kUSkNExkKj0WDv3r2yGqfAEhHR57Kzs0OhQoW4yQSRCXp3o0NHR0cUKFAAFhYWCiYiU8JmHRmk2rVry44jIiJw8+ZNhdIQkbE4evQoIiMjZbVvvvlGoTRERJQdlCpVCpaWlgDeLDh/6tQprltHlM3pdDocP35cOnZxcYGnp6dygcjksFlHBsnDwwNeXl6y2rv/WBIRpWfDhg2y4/Lly/PGioiIvkjlypVhbW0N4M0v8H///TciIiIUTkVEmen58+eykXW5cuWCt7e3gonI1LBZRwarTp06suNjx44plISIjEFiYiK2b98uq3Xs2FGhNERElF2ULVsWefLkkY4jIiJw7tw5BRMRUWY7ePAg4uLiAADm5uYoXLgwChYsqHAqMiVs1pHBSjsV9uTJk9ICn0REae3duxexsbHSsSAI+PbbbxVMRERE2YGdnR1q1aoFtVoN4M0mE4cOHVI4FRFlpm3btkl/zpEjBypUqAArKysFE5GpYbOODFbaZt2rV6/w999/K5SGiAxd2imwtWrVko2EICIi+hyCIKBp06ZSsy45ORkXLlxAdHS0wsmIKDOEhYXJRs+6uLigcuXKCiYiU8RmHRmsPHnyoFSpUrLanj17FEpDRIYsKipKbxdYToElIqKMUq1aNeTIkUM6Dg8Px+XLl5ULRESZ5vDhw0hMTAQAmJmZIV++fChRooTCqcjUsFlHBq158+ay4927dyuUhIgM2bp165CcnCwdm5ubo3Xr1gomIiKi7MTKygqNGjWSjmNiYnD+/HnuCkuUzYiiiH379kGr1QIArK2tUb16dZibmyucjEwNm3Vk0Jo1ayY7vn37Nh4+fKhQGiIyRKIo4s8//5TVWrVqBWdnZ4USERFRdiMIAlq0aAFBEAAAsbGxuHLlijT6hoiyh1evXuH06dPQ6XQAAFtbW9SqVUv6u0+UVdisI4NWoUIFuLm5yWppp7oRkWm7fPkybty4Iav16tVLoTRERJRd1alTB/b29gDePCh69uwZbt68qXAqIspIJ0+elHaBBQB3d3dUqFBBwURkqtisI4OmUqnQpEkTWW3nzp3KhCEig5R2VJ2npyfq1q2rUBoiIsqu7O3t0aBBA+k4JCQE165d41RYomxCFEUcPnwYSUlJAAC1Wo06derA2tpa4WRkitisI4P3zTffyI5PnDiBFy9eKJSGiAxJXFyc3i6wPXv2hJkZf7wREVHGe3c91IiICAQGBiIhIUHBRESUUaKionDhwgWkpKQAeLMGctplmYiyCn+bIYPXqFEj2NraSsc6nQ5bt25VMBERGYo1a9bIpiqYmZmhe/fuygUiIqJsrWbNmnBxcQEAaLVa3L17l+spE2UTly9fxosXL6TRsrlz50bFihUVTkWmis06Mng2NjZ6o+s2bdqkUBoiMhQ6nQ4LFiyQ1Zo0aYJ8+fIplIiIiLIzQRDg4uKCGjVqSLXbt28jMDBQWoyeiIyTVqvF4cOHERkZKdXq168PBwcHBVORKWOzjozCt99+Kzs+c+YMQkJCFEpDRIbg4MGDuHv3rqw2aNAghdIQEZEpUKvVaNu2rXT84sULnDt3DhEREQqmIqIv9e+//+LChQvSDs+CIOC7775TOBWZMjbryCg0atRI9lRDFEVs3rxZwUREpLS0o+qKFy/OjSWIiChTmZmZoXr16vD29gbwZpT3yZMncf/+fYWTEdGXOHbsGO7duydNgS1fvjwqVaqkcCoyZWzWkVGwtLREy5YtZbXVq1crE4aIFHfnzh0cPHhQVhs0aBAEQVAoERERmQJBEODs7Iz27dtLtTt37uDKlSvSiBwiMi4RERE4e/YswsPDpVrfvn1hYWGhYCoydWzWkdHo1KmT7Piff/7BtWvXFEpDREqaN2+e7NjFxQWdO3dWKA0REZkSGxsbNGvWDI6OjgDejK7bt28fwsLCpFE5RGQcRFHE1atX8c8//0h/f93d3WXT3YmUwGYdGY26desif/78strKlSsVSkNESnn27BkCAgJktb59+8La2lqZQEREZHLy5cuHxo0bS8cnTpzAgwcPuNEEkZFJTk7GxYsXce/ePanWo0cP2NjYcMYGKYrNOjIaKpUK3bt3l9XWrl2LpKQkZQIRkSJmz56N1NRU6djS0hI//vijgomIiMiUvN0VtnHjxrC0tAQAJCUlYceOHYiLi+PoOiIjIYoiHj58iDNnzkCj0QAAbG1t0bFjR6jVaoXTkaljs46MStpmXXR0NHbt2qVMGCLKci9fvsQff/whq/n5+SF37twKJSIiIlNkYWGBUqVKoVy5clJt586dCA0NVTAVEX0KrVaLGzdu4MKFC1KtadOm8PDw4Kg6UhybdWRUChYsiNq1a8tqy5YtUygNEWW1+fPnyxbwVqlUGDFihIKJiIjIVHl5eaFOnTowNzcHADx//hy7d++WRugQkWGLiIjA0aNHERMTAwCwtrbGt99+CxsbG2WDEYHNOjJCfn5+suNjx47h9u3bCqUhoqwSHR2NxYsXy2pdunSBp6enMoGIiMikOTg4oHLlyvDy8pJqq1atQkxMDKfCEhk4nU6Hhw8f4uDBg1KtQoUKKFOmDKfAkkFgs46MTtu2beHm5iarLVq0SKE0RJRVZs2ahdevX0vHgiBg1KhRCiYiIiJTJggCypQpA19fX2nK3O3bt3Ho0CGFkxHRf0lISMDRo0fx9OlTAG9ma7Rs2RLu7u6cAksGgc06MjpWVlbo3bu3rLZ69Wq8evVKoURElNlCQ0OxYMECWa19+/YoWrSoQomIiIgAd3d3VK1aFe7u7gDeLFi/aNEi2UZIRGRYRFFEZGQkNm3aJI2C9fb2RsWKFTkFlgwGm3VklPr37w+VSiUdx8fHY+XKlQomIqLMNHXqVCQkJEjHKpUKkyZNUjARERHRm59HtWrVQpEiRaTalStXsGPHDgVTEdGHpKamYvv27QgKCpJqderUQZEiRTiqjgwGm3VklPLly4dWrVrJaosWLYJWq1UoERFllgcPHujtANujRw+OqiMiIoNQpEgRVKlSBba2tgDeNAImTpwoe8hERIbh7ai6OXPmSL875s6dG9WrV4eLi4vC6Yj+H5t1ZLQGDBggO37w4AG2b9+uUBoiyizjx4+X7axnaWmJCRMmKJiIiIjo/6lUKnTp0gUeHh5S7cGDB1i+fDk3miAyMKIoYuHChQgJCZFqtWrVQtWqVWFmxvYIGQ6+GsloVa9eHWXLlpXVZsyYwZsiomzk0qVLWL9+vaw2YMAA5MuXT6FERERE+ooXL45mzZrB2toawJvRdQsWLEB4eLjCyYjoXc+ePZOtg5wvXz40btwYuXPnVjAVkT4268hopbcT5NWrV3HkyBGFEhFRRtLpdBg4cKCs5uDgwB1giYjI4AiCgP79+yNv3rxSLSwsDEuWLOEyLUQGQqPRYMqUKdIUdZVKBV9fXzRq1Ihr1ZHBYbOOjFrr1q1RuHBhWW3GjBkKpSGijLR+/Xr8/fffstrYsWO5nggRERkkT09P9O7dG5aWlgCAhIQEbNy4EXfv3lU4GREBb2ZsrFu3TjrOkycP2rdvDzc3NwVTEaWPzToyaiqVCiNGjJDVjh07hgsXLiiUiIgyQlxcHEaOHCmrFS5cGIMGDVIoERER0X/z8/ODj4+PdPzs2TMEBAQgMTFRwVREFBsbixkzZiApKQkAYG5ujkqVKqFx48YKJyNKH5t1ZPS6du2qt8YAF58nMm7Tpk3D8+fPZbW5c+dKoxWIiIgMkaurK0aOHAkLCwsAbx4+HTx4EFeuXFE4GZFp27dvH06cOCGtb+7u7o4+ffrAwcFB4WRE6WOzjoyepaUlhg4dKqsdPHgQZ8+eVSgREX2JoKAgzJkzR1Zr2LAhmjVrplAiIiKij9e8eXPUr19fOr5//z62bduGmJgY5UIRmbDQ0FD88ccfiIuLAwCo1Wo0adIE1atXVzgZ0fuxWUfZwvfff4+cOXPKauPGjVMoDRF9Lp1Ohz59+iAlJUWqqdVqzJs3jwv/EhGRUbC2tsbPP/8MOzs7AG/Wrjt8+DAuXrwojeohoqwhiiI2b96Mf/75BzqdDsCbUXVDhgzhjA0yaGzWUbZgY2ODMWPGyGrHjx/HsWPHFEpERJ/D398fp0+fltUGDRqEYsWKKZSIiIjo05UuXRpdu3aVju/du4f9+/cjIiJCwVREpuf+/fvYuXOnNLJVEAT07dsXX331lbLBiP6DIPLxDmUTSUlJKFKkCJ49eybVKleujLNnz3JEDpERCA0NRbFixfDq1Sup5unpiZs3b8LW1lbBZERERJ9GFEUEBQWhVq1aePnyJYA3O08uWbIETZs2hUqlUjghUfaXkpKCX375BfPmzUNsbCwAwMPDA9euXYOTk5PC6Yg+jCPrKNuwsrLCzz//LKudP38eO3bsUCgREX2KQYMGyRp1ALB06VI26oiIyCgVKFAA/fr1k46fP3+OpUuX4vHjx5wOS5TJRFHEiRMnsH37dqlRBwA///wzcuTIoVwwoo/EZh1lKz169ICXl5esNmLECNn6V0RkeLZv344tW7bIap06dULDhg0VSkRERPT5BEGAjY0NOnfujNKlS0v1/fv3Y8eOHUhMTGTDjiiTiKKI58+fY/369bh586ZUr127Njp16qRgMqKPx2YdZSsWFhaYPn26rPbgwQMsXrxYoURE9F/CwsLQp08fWc3Z2Rnz5s1TKBEREdGXEwQBHh4eGDZsGBwcHKT6zJkzcevWLTbriDJJSkoK9u/fj+3bt0s1Nzc3zJ07F1ZWVlwiiYwCm3WU7bRv3x6VKlWS1aZMmYKoqCiFEhHR+4iiiJ49eyIyMlJWnz9/Ptzc3BRKRURElDEsLCxQq1YtdOjQAebm5gCA8PBwTJgwAeHh4WzYEWUwnU6HGzduYNmyZdL0V2tra4waNQre3t5s1JHRYLOOsh1BEDB37lxZLTo6GpMmTVIoERG9z59//ol9+/bJam3atEHnzp0VSkRERJSx8uTJg86dO+Prr7+WGgX79+/H8uXLodFoFE5HlH2IoojIyEisWLECly9fBgCYmZmhcePG+Pbbb2FpaalwQqKPx2YdZUuVK1fGt99+K6stXrwY169fVygREaV1//59DB06VFbLmTMnli5dyqeeRESUbQiCgPLly6Nz585wd3eX6nPmzMGxY8cUTEaUvWg0GuzZswdr1qyRakWLFsX333+PnDlz8v6SjAqbdZRtTZ8+HRYWFtKxVqtF3759odVqFUxFRACQnJyMjh07Ij4+Xlb39/eHq6urQqmIiIgyh7W1Ndq0aYN69epJo3uio6Px888/Izg4WOF0RMZPFEXcvHkTM2fORFxcHAAgR44c6NatG8qXLw+1Wq1wQqJPw2YdZVteXl4YPXq0rHbhwgX88ccfCiUioreGDx+OS5cuyWp9+/ZF06ZNFUpERESUufLmzYvvv/8ehQsXlmpXr17FL7/8guTkZAWTERm/+Ph4jBs3Dnfv3gXwZvprvXr10LZtW9kGL0TGgs06ytZGjRqFIkWKyGqjR49GWFiYQomIaMuWLVi4cKGsVqRIEcyePVuhRERERJnv7XTYvn37Ss0DnU6HnTt3Yt26dQqnIzJuv/32Gw4fPiwdFy1aFN26dYOnpyenv5JRYrOOsjUrKyssXbpUVnv16hUGDx6sTCAiE/fvv/+iZ8+espqVlRW2bt0KOzs7hVIRERFlDQsLC/To0QM1a9aESqUCAERFRWH+/PkIDAxUOB2RcTp16hRmz56NlJQUAIC9vT1atmyJevXqSX/PiIwNm3WU7dWpUwddunSR1TZt2oStW7cqlIjINCUmJqJ9+/aIjY2V1RctWoRSpUoplIqIiChr2draYvbs2XB2dgbwZq2tO3fuYMKECXjx4gVEUVQ4IZFxEEURwcHB6N27N6KjowG8GcFaokQJ/PDDD9z9lYwam3VkEmbPng0nJydZrW/fvggNDVUoEZFpEUURPXv2xD///COrd+3aFX5+fsqEIiIiUoAgCChSpAh++eUXadF7jUaDw4cPY86cOUhMTGTDjug/iKKImJgY9OvXDw8ePJDq7u7uGDFiBPLkycPpr2TU2Kwjk+Du7o7ffvtNVouKikLPnj15M0SUBWbMmIENGzbIaj4+Pvj99995I0VERCZHEAT07NkT3bt3lxp28fHxWLduHTZu3IjU1FSFExIZtoSEBEycOBFnzpyBVqsFANjY2KBPnz5o0aIF7y/J6LFZRyajU6dOaNu2ray2f/9+7g5LlMl27dqFsWPHymr29vbYunUrbG1tFUpFRESkLEEQMGfOHNSvX19aV+v58+dYvHgxTp06xYYd0XskJiZi1apV2LJlCxISEgAA5ubmaNOmDcaOHctGHWULgshhRWRCIiMjUaJECdlusDY2Nrh27Rq++uorBZMRZU83btxAlSpVEBcXJ9UEQcCePXvQpEkTBZMREREpTxRF3L9/H927d8f58+elGR9NmzbF9OnT4ePjwwXyid6RkpKCgwcPYuzYsbhx4waAN/eWjRo1wpo1a+Di4qJwQqKMwZF1ZFJcXFywYsUKWS0hIQFt27aVnsoQUcYICwvDN998I2vUAcDMmTPZqCMiIsKbJoOXlxd++eUX2WZL+/fvx/z58xEWFsYlW4j+R6vV4tq1a5g/fz5u3rwp1atXr4758+dLm7YQZQds1pHJady4Mfr16yer3bhxA99//z1vhogyyOvXr9G4cWM8evRIVu/SpQuGDRumTCgiIiIDpFarUbFiRYwYMQIFCxYEAOh0Oqxfvx6//fYb4uPjeY9KJk8URTx+/Bjz58/H6dOnpb8TZcqUwfTp01G4cGFOf6Vshc06Mklz5sxBiRIlZLVVq1bB399foURE2UdycjJat26tt/NrpUqV8Mcff/BGioiIKA1ra2s0adIEffv2haurKwAgKSkJy5Ytw++//85mHZk0URQRHR2NhQsXYvfu3dJ6jl5eXhg/fjwqVKgAMzO2Nih74SuaTJKNjQ22bt0Ke3t7Wf3HH3/EtWvXFEpFZPx0Oh26d++Oo0ePyuoFCxbEzp07YWVlpVAyIiIiw+bo6IguXbqgXbt2sLa2BgC8evUKs2bNwqZNmxROR6Sc1NRUBAQEYOXKlYiPjwcAODk5Yfjw4ahfv760ozJRdsJmHZmsokWL6o2kezsiKDw8XKFURMZLFEX89NNP2Lhxo6zu7u6OgwcPImfOnAolIyIiMnyCICBnzpwYPHgwatasKY0UioiIwNChQ3HkyBGFExJlPZ1Ohx07dmDKlCl49eoVAMDCwgJ9+vRBu3btYGNjw1kblC2xWUcmrV27dhg0aJCs9ujRI7Rs2RJJSUkKpSIyPqIoYuzYsZg/f76sbmtri3379qFw4cLKBCMiIjIiZmZmKFy4MMaNG4ciRYpI9bCwMPj5+cnW6iLK7kRRxJ49e9CrVy/ExMRI9ebNm6NXr15wcXFho46yLTbryOT9+uuvqFy5sqx27tw59OjRgzdDRB9p4sSJmD59uqymVquxfft2lCtXTqFURERExsfMzAyVK1fGzz//LK1fBwBPnz5F7969cfHiRd6jUran0+lw8OBBdOzYEXFxcVK9XLlyGDRoEAoVKsRGHWVrbNaRybOwsMD27dtRoEABWX3jxo2YOHGiMqGIjMjkyZMxefJkWU0QBKxatQoNGjRQKBUREZHxEgQBnTp1wogRI+Dg4CDV7927h4EDB+Lq1avQarUKJiTKPBqNBocPH0aXLl2kNeoEQUDhwoUxYsQIVKtWjY06yvbYrCMCkCtXLuzZs0dvw4nJkydj9erVCqUiMnzTp0/HhAkT9OrLly9Hx44dFUhERESUPQiCgJ9++gkjRoxAjhw5ALyZFnj58mWMHTsWgYGB0Gg0yoYkymDJyck4evQo+vfvj4iICABv/i54eHhg7NixaNeuHRt1ZBLYrCP6n5IlS2Lz5s162377+fnhr7/+UigVkWESRRHjx4/HmDFj9M4tW7YMfn5+CqQiIiLKXszMzDBq1CgMHz5cmhKr0+lw5MgRTJ48GYGBgUhNTVU4JVHGSExMxJEjRzB8+HA8evRIqhcoUABjx45F165d2agjk8FmHdE7GjVqhIULF8pqWq0W7du35w5cRP+j0+kwcOBATJkyRe/c77//jj59+mRpnuTkZAwePBhFixZFnz59kJiYmKWfn4iIKDOZmZlh2LBhGD58OHLlygXgzf3p7t27MWXKFPzzzz8cYUdGLz4+Hvv378f48eNx8+ZNaV1GT09PjBs3Dn5+fnqDKoxdQkICHj58iJCQEDbdSY8gcnVSIj0//fQT5s6dK6vZ2Njg8OHDqFKlikKpiJSXmpqKHj16YN26dXrnfvvtNwwYMCDLM33//fdYsmSJdLx06VL07ds3y3MQERFlFlEUkZCQgGXLlmHu3LkICQkB8KaR16BBA4wfPx6+vr5QqVQKJyX6dPHx8di3bx+mTZuG69evS426woULY+LEiejQoUO2e23HxsZi9+7d2L59O2xsbDBgwACUKVMGarVa6WhkIPhKIErH7Nmz8erVK/j7+0u1hIQENGnSBMePH0eZMmUUTEekjMTERLRr1w579+6V1QVBwJIlSxRpkO3Zs0fWqAOA69evZ3kOIiKizCQIAmxsbNCzZ09YWFhg1qxZePLkibRjZmxsLKZPn86F98noJCUlYffu3Zg6dSpu3bol1UuWLIlJkyahefPm2W5EnVarxZUrVzBjxgzcuHEDgiAgf/788Pb21ltDnUxX9nrVE2UQQRCwbNkyfPvtt7L6q1evUK9ePVy6dEmhZETKePHiBerUqaPXqDM3N8eGDRsUadSFhYWluzYef0khIqLsSBAEODg4oEuXLhg1ahQ8PDwAvBl1d/bsWfTv3x+7du1SOCXRxxFFEcnJyVi7di3Gjh0ra9SVLVsWs2fPRtOmTaFSqbLdvd2LFy/wxx9/4Pbt2wDefC9CQ0O5wzPJsFlH9B4qlQpr1qxBs2bNZPWoqCjUqVMHJ0+eVCgZUda6efMmKlasiL///ltWt7a2xq5du/Sa2llBFEX06NED4eHheufy5MmT5XmIiIiywrsNu5EjRyJ//vzSuVu3bqF///749ddfuf4VGbzk5GSMGzcOo0aNwsOHD6V6uXLlMGvWLNSuXRvm5ubZrlGXlJSEffv2YefOnbLmnJWVVbb7WunLsFlH9AHm5ubYvHkzateuLavHxcWhUaNG2L9/v0LJiLLG/v37UaVKFTx+/FhWz5EjBw4fPoxGjRopkmvRokU4cOBAuuccHR2zOA0REVHWEQQBtra26NGjB0aOHImcOXNK50JDQzF16lQMGDAAr169UjAl0ftFRkaibdu2+O233xAZGSnVy5Qpg+nTp6NGjRpQq9XZrnkliiKePXuGqVOn6m2IZmtrm+2m+9KX4auB6D9YW1tj9+7daNCggayelJSEFi1aYMuWLQolI8o8oihi4cKFaNasGWJjY2XnPDw8cPr0aVStWlWRbDdv3sTw4cPfe57NOiIiyu4EQYClpSX69euHKVOmwNXVVToXGxuLlStXokePHnj8+DG4nyAZCp1Oh5s3b6JOnTrYt28fkpOTpXOlS5fGr7/+irp162bLqa9vp/1OmjRJ7yE48KZZl92+ZvoybNYRfQRbW1vs2rULrVq1ktVTU1PRoUMH/PbbbwolI8p48fHx6NatGwYOHAidTic7V6lSJVy4cAElSpRQJFtSUhI6deoku7lLiwvzEhGRKRAEAWZmZujVqxfWrVsHT09PaWROSkoK/vrrL/Tq1QtXr16FRqNh044UI4oiUlJScODAATRv3hyBgYHS69Hc3Bw1atSAv78/6tatCzMzs2zZtBJFEXv37sXatWvTPc+RdZQWXw1EH8nS0hKbN29Gly5dZHWdTodBgwZh4MCBXBSUjN7du3dRqVIlrFmzRu/cd999h+PHj8um22S1n3/+GYGBgR+8xsbGJovSEBERKUsQBAiCgAYNGmDfvn2oVKkSLC0tAby5Rz1y5Ah++OEHHD16FElJSWzYUZYTRRGvX7/GypUr0bdvXzx69Eg6Z2dnh+bNm2P16tUoW7ZstmzSvRUSEoIhQ4a897ydnR2bdSTDVwPRJ1Cr1QgICED//v31zi1cuBAtW7bE69evFUhG9OW2bt2KChUq4ObNm3rnJk6ciHXr1sHKykqBZG+cPXsWc+fOldVKliyp15xTMiMREZFSvL29sXPnTnTq1Ek2LfbChQsYNGgQtmzZglevXrFhR1lGq9Xi2bNnmDdvHsaPH49nz55J53Lnzo2+fftixYoV0s7G2VVcXBzGjh2Lp0+fAgDMzMxgbW0tu4bTYCktNuuIPpGZmRkWL16MyZMn653bs2cPfH19ERQUpEAyos+TlJSEQYMGoV27dnrr09nb22Pr1q2YMGGCojcQCQkJ6N69u+wXDEtLS6xfv17vWjbriIjIFAmCAFdXV8yfPx/Dhg3DV199JZ27e/cuhg8fjkWLFiEkJIQNO8p0Go0Gt27dwtSpUzFv3jy8fPlSOlemTBlMnjwZM2bMyPZrDWu1WmzatAmbN2+Wam5ubqhZs6bsOk6DpbT4aiD6DIIgYNy4cVi7di0sLCxk5+7evQtfX1/s3LlTmXBEn+Cff/5B+fLl0113sWTJkrh8+TLatGmjQDK5sWPH4v79+7LalClTULx4cSQlJcnqbNYREZGpEgQBdnZ2+PHHHzF58mRUqVIFKpUKAPDy5Uv88ssv+Pnnn3H69Gku30KZQhRFJCUlYf/+/Rg7dixWrlwpzTyysLBAixYtMHfuXPj5+Umvzezs2rVrmDNnjrTesqWlJZo0aYJGjRrJruM0WEqLrwaiL9CpUyccOXIELi4usnpsbCxatWqFIUOGfHAhfCKlaLVazJw5E76+vrh165be+a5du+Lvv/+WPZVXyunTp7FgwQJZrVKlShg6dCg0Go3eJhhs1hERkSkTBAG2trZo0aIFpk2bhmbNmkk/G5OSkrB69WoMHToUs2bNQlhYmMJpKbt58uQJJk2ahFGjRmHPnj1ITU0FAOTIkQN9+/bF9OnTUb169Wy7kcS7IiMjMWfOHOmBsyAI8PHxwYABA6BWq2XXcmQdpaX+70uI6EOqV6+Oy5cvo3Xr1rh27Zrs3Pz583Hy5Els3LjRIJoeRAAQHByMbt264fTp03rnLC0t8dtvv6F3794GcQOVkJAAPz8/vemvK1euhEqlQkJCgt77sFlHRET05udhlSpV4ObmBjc3N2zatAmxsbEQRRFXrlzBw4cPce7cOQwYMAB16tQxiVFOlHk0Gg22bduGxYsX4+rVq4iPj5fO5c2bFwMGDECXLl2QK1cuk2hK6XQ6rFu3DkeOHJEallZWVhg7diyKFCmCI0eOSNeq1WppYxiit7L/3xKiLODp6YmzZ8/q7RQLvBn6XLZsWSxdupTrg5CiNBoN5syZgxIlSqTbqCtdujSuXLmCPn36GESjDgDGjBmT7vRXb29vANCbAgtAb2o6ERGRqTI3N4e3tzemTp2KUaNGwcnJSToXHR2N/fv348cff8Svv/6KiIgIBZOSMXvy5Al++OEHDB06FGfOnJE16nx8fDBjxgz0798fuXPnNolGHQBcvHgR69evR2RkpFTz8/ND06ZNodPpkJiYKNVtbGygUqkM5v6bDINp/E0hygLW1tZYtWoVfv/9d70nI/Hx8ejfvz/q16+Px48fK5SQTNmVK1fg6+uLYcOG6Y1GEwQBo0aNwoULF1C8eHGFEuo7ffq03lp6b6e/vpV2CiwAjgwgIiJ6h5mZGdzd3TFkyBDs3LkTZcqUkc5pNBr8+++/+OWXX9CnTx8EBgam+7OVKD1arRZ79uxB69atERAQgOfPn0uDEywsLNCyZUusX78e7du3h729vck0o169eoW1a9fi2rVr0vejUKFCmDhxIqysrKDVamX349bW1rx/JT1s1hFlIEEQ0L9/f1y8eBHFihXTO3/06FGUKFECCxcu5KK+lCXi4uIwZMgQ+Pr66k3TBt6MCj158iSmT59uUCPSEhMTPzj99a30Rquayo0gERHRxxIEAdbW1qhWrRpOnjyJUaNGSWtmiaKI+Ph4/PXXX2jdujU2b96MxMREiKLIWSGk5+3rIiIiAr/88gv8/Pxw9epVpKSkSNc4ODhgxowZWLduHUqVKgULCwuTuT8TRREHDx7EX3/9JX1PBEHA77//Lq1znrZZ93ZkHdG72KwjygSlSpXCpUuX0KdPH71zcXFxGDhwICpUqIALFy4okI5MgU6nw+rVq+Ht7Y358+en+5S8b9++uH79OqpXr65Awg+bMmWK3vTXqVOnStNfiYiI6NOZmZnBzs4Ov/zyC44ePYqvv/4a5ubmAN7cOzx48ACdO3fGkCFDEBYWxlF2JCOKIlJTU/HPP/+ge/fumDRpEsLDw2Wj6apWrYqzZ89i8ODBsLa2NpkmHfDm+/Pvv/9i/fr1ePbsGYA3jbq+ffuiVq1a0vdCq9XKpsFyZB2lh806okxia2uLZcuW4cCBA8iXL5/e+WvXrqFy5cro1asXnj9/rkBCyq5Onz4NX19fdOvWDSEhIXrnfXx8cObMGSxduhQODg4KJPywwMBAzJo1S1arVKkShgwZonctR9YRERF9GkEQYGZmhurVq+PAgQPo378/XF1dpbXEtFotli1bhlq1amHfvn2IiIiARqNRODUpLTU1FaGhoVi9ejVatGiBvXv3Ss1ctVqNPHnyYOLEiThy5AiKFy8OQRBM6p5MFEUkJCRgz5492Ldvn1T39vbGiBEjpKY4wJF19HHYrCPKZA0bNsTNmzfRs2dPvXOiKMLf3x9FihTB+PHjERsbq0BCyi4ePnyItm3bokaNGrhy5YreeUtLS0ydOhXXrl1D1apVFUj437RaLfr06SP7pcDc3BzLly9P9yaGzToiIqLPIwgCcuXKhWnTpmHhwoWoXLkybGxspPP37t1Dy5Yt0bt3bxw6dAjPnz/nMi4mKDU1FY8ePcLOnTvRuXNn9O7dG0+fPpXOOzs7o1GjRtiyZQtGjBgBKysrk7wX0+l0uHbtGpYtWybt/uro6Iiff/4ZefPmlX1P2Kyjj8FmHVEWcHR0xPLly3HixAn4+PjonU9ISMCUKVNQuHBhzJ49G3FxcQqkJGP1+PFj9O3bF0WLFsW2bdvSvaZBgwa4ceMGxo4da1Br06W1dOlSvenhI0eOfO/GF2zWERERfRlbW1u0b98eixcvRvfu3ZE/f35plJ1Op8POnTvx3XffYcyYMdi3bx9CQ0M5PdYEaDQaPHjwAFu2bMHAgQPRsWNHHD9+XDpvbm6OEiVK4KeffsIff/yBypUrm2zDSRRFhIeHY/Hixbh37x6AN6MNO3TogLp16+rde7NZRx9DELlqKFGWSk1Nxfz58zFp0iTZtubvcnV1xdChQ/HDDz8Y5DRFMgxPnjzBtGnTsGLFCukJXlre3t6YM2cOGjdubPBNrGfPnsHHx0c2wrRIkSIIDAyElZXVe98nf/78slp0dDRy5MiRmVGJiIiypaioKOzevRsbNmzAqVOnZOtqCYKA/Pnzo0mTJmjWrBmqVq0KR0dHg7+/oE+j0+kQEhKCEydOYPfu3Th06BBevXolu8bV1RUNGjRA165dUb16ddmITFOUmpqKJUuWYMSIEUhOTgYAVKhQAQsWLEDFihWl5vdbDx48QPfu3XHmzBkAQOvWrTF//ny9e1oybWzWESnk+fPnmDhxIvz9/d/7dNLe3h5+fn4YMGAAChUqlMUJyVA9fPgQs2fPxvLly9/bpHNxccGkSZPQp08f2RoZhqxVq1bYuXOnrHb8+HHUqlXrve+TXrMuJiYGjo6OmZCQiIgo+9Nqtbh9+zaOHz+OHTt24PTp07Lpr2q1GkWKFEGtWrXQpk0bVK9eHebm5mzaGTlRFBETE4PDhw9j586dOHnypN4mI1ZWVmjQoAFatmyJWrVqwcPDQ68RZWpEUcSFCxfQoUMHPH78GADg7u6OcePGoUePHrC1tdV7n3v37uG7777D1atXAQCdO3fGzJkzkSdPnizNToaNzToihd26dQujRo3Cnj173nuNIAho2rQpevXqhSZNmhhN84UyjiiKOHv2LObNm4edO3e+t8FrZWWFH374AWPHjoWTk1MWp/x8u3fvxjfffCOr+fn5wd/f/4Pv9/TpUxQoUEBWY7OOiIjoy6WkpODevXs4c+YMAgIC9JapsLS0RKFChVC9enX4+fmhQoUKbNgZobc7vB4/fhyrV6/G+fPn8fTpU9n6wYIgoG7duujWrRsqVqwIT09P/j7yP7GxsWjZsiWOHTsG4E0z+9tvv8X06dPfO1Luzp07aN26NYKCggAAffr0weTJk5EzZ84sy02GT610ACJTV7x4cezevRsXL17ElClT0m3aiaKIPXv2YM+ePXBzc0Pnzp3RuXNnlClThjdF2Vxqaiq2bt2KefPm4dKlS++9ztLSEv369cPIkSORO3fuLEz45RITEzFw4EBZzc3NTW9H2PSk9/rnMygiIqIvZ2FhgeLFi6NQoUKoWbMm9u3bhz/++ENakys5ORm3b9/Gw4cPceTIEdSvXx/dunVDhQoVuP6WkdBoNDhx4gQCAgJw+vRphIWFISUlRXZNhQoVMHDgQFSrVg158uQx6LWPlTB37lycOnVKOi5atCi6deuGvHnzvvd90q5ZZ21tzb8zpIfNOiID4evri927d+PKlSuYNm3ae0dPhYeHY968eZg3bx68vLzQunVrtG7dGhUrVuQ/8tnIvXv3sGLFCqxatQphYWHvvc7S0hJ9+vTBqFGjjHbo/IwZM/Do0SNZbe7cuXB2dv7P903vhvF9U4OJiIjo0wiCAGtra3h7e8PT0xONGjXCihUrsHr1akRERAAAkpKS8ODBAzx79gw7duxA2bJl0adPH9SrVw92dnYKfwWUnqioKGzbtg3+/v74999/ERsbq3f/VLBgQQwaNAitW7eGu7s7pzqn4/z581iwYIE0CtHBwQGtWrVCzZo1Pzg9mBtM0MfgNFgiA/Xo0SMsWrQIy5cv11vUNT1OTk6oU6cO6tevj6+++gqFCxdGzpw5+fTLiMTFxWHr1q3w9/eXFpx9HwcHB/Tq1QtDhw794JM7Q/fgwQMUL15cWowXAGrUqIETJ0581A1hTEyM3nTfZ8+eGfX3hIiIyFC9nTJ57do1/PLLLzh06JDsZzgAmJmZSTuF9unTB23atJE9gGPDJ2u9/XVfFEUEBwdj9erVCAgIQGhoKDQajd6MBGdnZ3Tp0gUDBgyAh4cHVCoV/5+lIz4+HvXr18fff/8NURQhCAIqV66M7du3w93d/b3fM1EUce3aNdSoUUPabHDKlCkYOHAgNxYkGTbriAxcXFwcNm7ciJUrV+LcuXOf9TGsrKxQtWpV5MqVS/aWO3duFChQAPnz53/vbpuUuRITE3HgwAFs27YNf/31F+Li4j54vaenJwYNGgQ/P79s8QO9WbNm2Lt3r3SsUqlw7do1lCxZ8qPePyEhQW/h3ocPH8LLyytDcxJR9sbbYaJPI4oidDod9u3bh4kTJ+L27dtITU1Nd1ZI/vz50a1bN/j5+SF37tywsLAw+U0JssLb/0dJSUkICgrCkiVLsHPnTkRGRupdq1KpYGNjg5o1a+Lnn3+WrT/IRp0+nU6H6dOnY/r06VLDzdXVFStWrECzZs0++D0TRRGXL19GxYoVpZ89s2fPRr9+/dLdjIJMF5t1REbk3r17CAgIwMaNGxEcHJyhH9vd3R0FChRAgQIF4OHhIf357bGrqyt/WGeQ+Ph47N+/H1u2bMHevXulH/IfUrVqVQwZMgQtW7bMNsPk09tUYvDgwZg3b95HfwyNRqO3wHFQUBCKFi2aIRmJyDSIoojo6GhpJz8i+nipqak4ePAgNmzYgDt37rz3OisrK7Ru3Rp+fn4oVaoU7OzsYGlpycZdBtPpdEhMTMTr169x4cIFLF++HIcPH9Zbiw4AbG1t4eDggBo1aqBnz56oVasWN474D6Io4uLFi+jYsSMePnwI4M2mEn5+fliyZMl/vp51Oh3Onj2LGjVqSLXff/8dPXr04OAJkmGzjsgIiaKI69evY9u2bdi+fTtu376d6Z/TxsYGnp6e8PLygqenp96fnZ2d2cx7D51Oh+vXr+Pw4cM4dOgQzpw5ozdlJD1ubm7o0qUL/Pz8ULx48SxImnUSExPh4+MjW6suV65cCAoK+qSdXEVRhEqlko2KCQwM/OiReUREwJtmw4YNG9CtWzeloxBleyqVCpUqVULbtm1RsWJF5M6dG87OzrCzs2Pj7jNpNBq8fv0aUVFReP78OU6dOoUtW7YgMDBQ71pzc3O4uroif/78aNKkCTp06MCHnJ8gPDwcAwYMwPbt26V1/kqWLImDBw9+1CZvWq0Whw4dQpMmTaTaypUr0alTJzZKSYYbTBAZIUEQULp0aZQuXRpTpkzB06dPcfToURw5cgRHjhzBixcvMvxzJiQk4Pbt2+9tDNrb26fbxHv75xw5cmR4JkOl0Whw69YtXLhwASdOnMCRI0cQHh7+Ue9rZmaGRo0aoWfPnmjWrFm2XXNw1qxZeptKzJ49+5MadcCbvwsWFhay5md6T46JiIjIMGi1Wpw9exZnz56Fk5MTfH19UaVKFZQuXRoeHh7Imzev9BCYD4LTJ4oitFotXr58iZCQEDx48ACXL1/GuXPncO3aNSQlJem9j62tLby8vFCqVCk0atQIjRo14syZT5SUlITNmzfj1KlTUqPOyckJ06ZN+6hGHfDmIX7aWTXcDZbSw2YdUTaQP39+dO/eHd27d4coirh//z727t2L7du3Izk5GQMHDkRYWJjsLTQ0FCEhIYiJicmQDLGxsbhx4wZu3LiR7nlHR8f3jsrz8vKCvb19huTIalqtFo8ePcL169dx4cL/sXffYVFc79vA71mW3nsVpCOKHcXea+w91sSWqLHHrrHGknztsSQmGo2xJJZYYq/YQYGICAhK7wIinS3z/sHL/JgFFBSYXXw+17WXzNmFfdBFZu95zjmP8OjRIzx+/LhSU1tLiEQidOrUCcOGDcPgwYMr/cteVSUkJGDTpk28sY4dO2L06NEf9PUorCOEEEJUg5aWFi9IyszMxOXLl3H58mUYGxujYcOGaNq0KRo2bAhXV1e4uLjA1tYWYjG9bQWKu4BjYmIQERGBFy9e4OnTpwgKCkJ4eHiF556mpqZo1qwZfHx80KVLF7Rs2RL6+voU0lVRycymI0eOICkpCUDxReOpU6eiT58+lf46crmct0a1WCyGpqYm/XuQMuh/PULqGIZh4Orqijlz5mDOnDnvffzbt28RFxeHmJgYxMbG8m4xMTFISEiATCb76LqysrIQFBSEoKCgcu83MTHhgrt69erB0tKS2wij5GNzc3NBTtakUimSk5ORkJCAmJgYhIaGcrcXL16Ue/XyfdTU1NC1a1cMGzYMgwYNgoWFRQ1UrpyWLl3K265eJBJh586dH3ySoth9SGEdIaQ6jB07Fi4uLjQtj5APVFRUhLNnz/KmYi5fvhzPnz/Ho0ePEBUVxduQIjMzE3fv3sXdu3eho6MDFxcXuLq6wsPDgwvwHBwcoK2t/ckEGyzLIisrC1FRUXj69CmCg4Px4sULREREICoqqsJlVdTU1ODu7o42bdqgXbt2aNmyJVxdXWlNtI+QkpKCI0eOICAggBvr0qULZs2aVaXfEyzL8oJVLS0tqKurfzKvaVJ5FNYR8okzMDBAw4YNK1wTTSqVIjExEVFRUYiOjkZ0dDTv47i4uHJ3/qqqjIwMZGRk8H4BKmIYBmZmZjAzM4OhoSEMDQ1hZGTEfVxyrK2tDQ0NDe6mrq4OdXV1bsqAVCrl/pRKpcjOzkZWVhbevn2LrKwsZGVlITMzE4mJiUhISEBKSkq17BTo4uKCHj16oGfPnujSpUuVp3zWBf7+/jh06BBvbPLkyWjcuPEHf00K6wghNWH48OHo3r07TU0i5APl5eXh5cuXvLBu/PjxYFkWr169QkREBB4+fIh79+7h1atX3LTCks99+vQpnj59Ck1NTdjY2MDGxgYWFhZwdHSEm5sbXF1d4ebmBhsbmzoRqrMsC6lUitjYWLx48QLh4eEIDw9HfHw8N901OTm5wovo2traaNSoEdq2bYuWLVvC0dERjo6OsLKyqhN/P0IqLCzErVu38Pfff3MX6a2srLBixQpYWVlVKWhT7KwrCesIUURhHal1UqkUhYWFZW4FBQXclu9yuZzbbryiY7lcDoZhoKamBrFY/M4/NTQ0oK2tDS0tLWhra1OrcRWIxWJuV9hOnTqVuV8ikSA+Pr5MiFfycUJCQrUEXUDxSUxaWlql138TmqOjI1q3bo0uXbqgR48ecHR0FLokQbEsi7lz5/LG9PX1sXbt2o/6uoph3Yd0OhJCiCJ1dXVoampSWEfIB5JKpWVCIpFIBBsbG9SrVw9t2rRB//79kZ6ejhcvXnDr/IaFhfEuBBcWFiIqKgpRUVFgGAZaWlrQ09ODnp4edHV1YWZmhoYNG6JRo0bw8vKCh4eHSmx8JpfLkZycjJCQEISEhODZs2cICwtDZmYmcnJyuNu7LkJqa2ujadOm6NGjB9q3bw8HBweYmJjAyMgIampqSv93oApYlkV0dDR27drFm/46Z84ceHt7V/nvWLGzTltbm8I6Ui4K60iFWJZFTk4O3rx5g8zMTGRmZuLNmze8Xx45OTnIzs4uM1Zyy83NRUFBAS+Qq44urOpQEtyV/lNPTw8GBgbvvRkbG8PMzAympqYwNjb+pK9Wqaurc1fuunTpUub+oqIixMbGltuVFxUVxf3SU3XGxsZo0aIFWrduzd0+pamtlfH333/j3r17vLHly5d/9N+Tnp4e77j0FFtCCCGEKB+GYaCpqcktd+Lm5oZu3bph8eLFiIyMxJUrV3Dp0iU8efKEd9GXZVnk5+cjPz+fu3jLMAwePnwIDQ0NaGpqQldXF05OTmjSpAnXWWZtbc09l56eXq2FWCXTWEvWi05OTkZiYiJevHiBoKAgJCYmcu+VioqKUFRU9N6L3GKxGG3btsVnn32Grl27wt7eHjo6OtDW1oZIJKKArprl5OTg0KFDePjwITfWs2dPDBs2DDo6OlX++1bcYEJTU5PWZCTlolfFJ6KwsJDrSEpLS0Nqair3cUkQVzqQK/lTKpUKXXqNKSgoqJYOHJFIBBMTE5iamnIBXslUTUtLS9jY2MDa2pr7UzFYqOs0NDTg4uICFxeXcu8vKChAbGwsF+KVBHgpKSncZhhpaWlKEfJqaGjA1taWWz+lQYMG3M3c3JxOjt6hoKAACxcu5I05OTlh9uzZH/21FX+mSk8tIIQQQohyYxiGW7LEwMAAlpaWaN26NRYvXoy4uDicO3cO586dQ1BQELKzs8t8PsuyvPP6tLQ0xMTEwNfXlwuvSv+pr68PGxsb2Nraws7ODra2trC1teWm2WppaUEsFkMsFnN1lXTYliyhIpFIIJFIIJVKkZeXh+TkZMTHxyMhIQEJCQncUiqJiYnIz8/nZgixLMsty1JyXBmmpqbw8fFB//790adPH24dZzU1tU+6aaCmyeVyPH36FDt27ODeF5uZmWH69OmoX7/+B5370zRYUlkU1qkolmWRnZ2NxMREJCUlITExEampqbwQrvTHb9++FbrkOksul+P169d4/fo1wsPD3/t4fX19WFtbcwGejY0NHBwc4ODgwO2OamBgUAuVKwctLS24ubnBzc2twsfIZDK8fv0aycnJSElJQWZmJrKysvDmzRtujbnSH5e+Ollyk0gkYBiGO/kqPU1aX18fBgYGMDQ05P40NDSEpaUl70TO1NSUArkPtG3bNsTExPDGfvzxR2hqan7019bV1eUdU1hHCCGEqC6RSMStOezu7g53d3fMnz8fubm53OYUfn5+8Pf3R1xcHBd8ldyA/1v/rTw5OTlISkrCkydPuLHqOr/70KVfRCIRd9PU1ISLiws3U6NVq1ZwdnbmbUJA56M1r+T99rfffsudWzIMg3HjxqFdu3YfHJKWt8EEddaR8tCrQsmU/KdQEsCVDuNKf5yUlFTh9tyqimEYaGho8H5ZldxKroQpjpVcmVLcNKDkT2XoxlKUnZ2N7OxsvHjxosLHGBsb88I7BwcHuLq6okuXLtDR0anFapWDmpoaLC0tYWlpKXQp5ANkZGRg48aNvLFOnTph8ODB1fL1qbOOEEIIqXtKB1IMw0BfX58LsEqkpKQgKCgIAQEBCAgIwLNnz5Cens5dqC15nyCTyd4ZpFXX+srvIhKJoKamxt3U1dWhoaEBKysrNGnSBM2bN0ezZs3g5eUFExOTGq+HvJtMJsOuXbt4019btWqFYcOGwdTU9IO/LnXWkcqisK6WFRYWIj4+HrGxsYiLi0NsbCzv47i4uHLbu4XGMAy306aBgQG3qKu+vj73cXk3XV1daGlpQVNTk7spHpfcxGJxtV8lKrmqVlRUhIKCAuTn51f4Z35+PnJycvD27Vvulp2dzTsu2S00IyOjRsPSkinJQUFBvHF9fX0MHz4c48ePR4cOHajtnaiEDRs2ICsrize2efPmavt5p7COEEII+TRZWlqiV69e6NWrF4D/24ziv//+Q0REBDcrIzU1FW/evIFEIuGCPMWPS6a1foiS6bIlAZzisYaGBkxMTLiLz7a2tmjQoAEaN24MGxsb2shGybAsi8ePH2P9+vXcmKmpKUaMGAFvb++P+tqKa9ZRZx2pCL0qqllWVhZevXrFrb9VOoSLjY1FSkqKoPWJRCKYmZnB3NwcFhYWMDc3h4mJCYyNjbmbkZER79jY2BgGBgYqGQyVXgNDcarcxyooKEB6ejpev35d7p+pqancIrJJSUnVMhU5Ozsb+/fvx/79++Hg4IAvv/wS8+bNg76+fjV8R4RUv7i4OOzcuZM3NmrUKLRo0aLanoPCOkIIIYQAxYv1e3h4wMPDgzfOsiyKioqQnp7Ona+/fv0aaWlpvHP4rKysMuvLlV5brmR2T+l18NTU1LjN5xRvpqam3IZ0irvXE+WVnJyMb7/9lgvVNDQ00LVrV4wYMeKju+DKmwZLnXWkPBTWVVFhYSFiYmK4QC4qKor3cWZmZq3XZGRkBBsbG1haWnIBXOkwrvTxp75zaXXS0tLiFqStjNzcXN405pI/4+LiEBMTg5iYGCQmJlb6+WNiYrBq1So8evQI//77L61dQZTSypUrUVhYyB2LxWKsXbu2Wp+DwjpCCCGEvEvJ7rMl60VXpPQGECU3uVwOmUzGBXQlmzqUTGctCe9I3VBYWIgdO3Zw018ZhoGzszOmTJkCOzu7j/765U2Dpc46Uh56VShgWRZpaWmIjIxEZGRkmVAuMTGxVtY0AABDQ0PuF0rpzQgUP9bW1q6VesjH0dXVfeeuqEBxt15cXByio6MRExOD6OhoREdHIzIyEn5+fuW+9i5evIjr16+je/fuNVk+IVX2/PlzHDx4kDc2derUd/4MfAgK6wghhBBSHUp3zVG306fpwoULOHDgAGQyGYDi93Djxo1Dp06dquXrU2cdqaxPMqxjWRavX79GREQEIiMjERERwfu4NnZO1dfXh729PerVqwd7e3vex3Z2drCxsfkkNxL41GlpacHV1RWurq5l7ktISMCff/6JQ4cOISQkhHff0qVL0a1bN7qqR5TK0qVLeZu86OrqYsWKFdX+PBTWEUIIIYSQjxUZGYkdO3bg9evXAIqnPXfq1AkTJ06stmnMtMEEqaw6G9axLIv09PQyQVzJx4qLnVcnhmFga2sLR0dHODg4cCFc6WDO0NCwxp6f1E22trZYuHAhFixYgF27dmHmzJncff7+/nj06BF8fHwErJCQ//PgwQOcOXOGNzZv3jxYWVlV+3MphnXKuEkPIYQQQghRXoWFhdi7dy/8/f25rjpTU1OsWrUKlpaW1fY8tMEEqSyVf1Xk5eUhIiIC4eHhvFtERESNBnKmpqZwdHTkbk5OTtzH9vb20NTUrLHnJp82hmHw1Vdf4X//+x9iYmK48eDgYArriNJQ7KAzMzPDt99+WyPPpbjBSm10RxNCCCGEkLrj4sWLOHfuHBekMQyD5cuXo1mzZtX2HCXrIebl5XFj1FlHKqIyYR3Lsnj06BEeP37MC+ViY2Nr5Pk0NDTg5OQEZ2fnMmGco6MjDAwMauR5CakMdXV1NGvWjBfWRUdHC1cQIaXcuXMH169f540tXbq0xv7fNDY25h0LsdEPIYQQQghRPSzLIj4+HocPH0ZERAQ33qVLF3z11VdQU1Or1ufLy8vjrUNOnXWkIkr/qkhISMDBgwdx4MABREZGVuvXVldXh7OzM1xcXLh1wko+rlevXrX/YBJSnerXr887Lh3cESKkVatW8Y6tra3x9ddf19jzmZiY8I4zMjJq7LkIIYQQQkjdIZVKcfr0aVy9epUL0fT19bFr165qW6euBMuyZdZWps46UhGlDetiY2Mxa9YsnDt3jrdAeVWpq6vDycmJF8SVfGxvb0+BHFFZ1tbWvOP09HSBKiHk//j6+uLGjRu8sSVLltTortWKnXV5eXkoKiqq9hMsQgghhBBSd8jlcgQEBODIkSPcMipisRirVq2Ci4tLjWzep7i2MnXWkYoo7ati9OjRuHfvXqUfX69ePbi7u3M3Nzc3uLq6wt7enl78pE5S3KSE1ukiymDlypW8Y1tbW0yZMqVGn1MxrAOKp8JW52LAhBBCCCGk7ijZkPL48eN49OgRN961a1dMmDChRpp6FDvrxGIxNDQ0IBKJqv25iOpTyhQrKiqqwqDOzc0N3t7evGDOxcUFurq6tVwlIcKiRfWJsrl16xZu3brFG1uyZAm0tLRq9HnLC+syMjIorCOEEEIIIeWSSCS4ffs2Dh06xI3Z29tjyZIlMDIyqpGuOpZlee/ZNDQ0aCYIqZBShnX//vsv71hTUxPjxo3DxIkT4ePjUyM/OISoGsXF+hVbqgmpbYpr1dna2mLy5Mk1/rwaGhrQ1dXldu8CaJMJQgghhBBSPrlcjlevXmHbtm3cUkK6urqYOnUqmjVrVqNLZZV+z6apqQlNTc0aey6i2lQirBs5ciT27dsnUDWEKCfFsI4664iQ7ty5g9u3b/PGli5dWmsnIMbGxhTWEUIIIYSQd2JZFgUFBdizZw/u37/PjXft2hXDhg0r8x6rup+7dFhHnXXkXZQurMvNzcXNmzd5Y5999plA1RCivMoL61iWpc5TIoiNGzfyju3s7DBp0qRae35jY2PEx8dzx7QjLCGEEEIqIpPJEBQUhL///hv5+fnw9vZG165dYW1tTefSn4Dz58/j0KFD3O6vLi4umDBhAhwdHWv8318xrKPOOlIRpQvr7ty5g8LCQu5YLBajZ8+eAlZEiHJSDOtkMhny8/Oho6MjUEXkU/X06VNcuHCBN7Zo0aJaPfkwMTHhHVNnHSGEEEIq8vr1a/z555/4+eefIZVKcerUKdjY2KB9+/YYMGAA2rRpQx1PdVRcXByWL1+ON2/eAAB0dHQwaNAgdOvWrcb/zRU3mNDU1KTXGamQ0oV1iYmJvOPGjRvDyMhImGIIUWLltWi/ffuWwjpS6zZt2sQ7NjMzw8SJE2u1BsVNJqizjhBCCCEVKSwsRGpqKvLy8gAA8fHxiI+Px7Nnz3D8+HE4OTlh5MiRGDhwIOzs7ASullQXuVyORYsWITIykhtr1qwZJkyYAENDwxp/fpoGS6pC6fYILvkPs0RNzhknRJUp7gYL0Lp1pPZFRUXh+PHjvLFZs2bVemhsZmbGO05LS6vV5yeEEEKI6rC2tkabNm3KBDR5eXlISEjAvXv3sGDBArRr1w5fffUVHj16hKKiIoGqJdXl6NGjOHPmDDf91dLSEuPHj0eDBg1qbfozTYMllaX0YR11CRFSvvLapimsI7Vt8+bNkMlk3LGuri5mzJhR63VYWlryjlNSUmq9BkIIIYSoBrFYjFGjRmH48OFQV1cvc79cLkd+fj5iY2Oxb98+dOzYET169MChQ4fw5s0byGQyLvAhyo9lWcTHx2PFihXIz88HUPwaaN++PcaNG1eju78q1lF6Gix11pF3obCOEBWm2Hla+koNITUtNTUVv/32G29s6tSpZdaPqw2KYV1qamqt10AIIYQQ1cAwDExMTDBv3jy0bdsWIlHFb4tZlkVRURF8fX0xYcIEuLu7Y968eQgICMDbt29RVFREwZ2Sk0gkWLlyJW8zMltbWyxfvhxaWlq1VgeFdaQqKKwjRIWVtyMsIbVlx44dKCgo4I7V1dUxb948QWqxsLDgHVNYRwghhJB3YRgGDRo0wKJFi+Dm5sabBqmmpgYbGxtYWlqWCXNSU1OxY8cOtGrVCv369cNvv/2G8PBwZGRkQCKRUHCnZGQyGc6cOYOLFy9CIpEAKM4YvvnmGzRt2rTWd/8t3VxBG0yQd1G6DSYorCOk8iisI0LJz8/H3r17eWNjx44VbBFmmgZLCCGEkA/Ru3dvREdHY/Xq1dz5g5qaGnx8fPDZZ5/h0aNHePz4MWJjY5GRkQG5XA6geKrsnTt3cPfuXdSrVw/9+vVDz5494eHhAVtbW+jq6tZ6EETKevXqFXbv3s07N+zcuTOmT59e67WUt8EErVlHKkJhHSEqjMI6IpQjR44gPT2dN/btt98KVE3Zzro3b96gqKiIrlYSQggh5J0YhsHEiRORmJiILVu2IC8vD0VFRbh58ya8vLywdu1apKWl4caNG7h37x6eP3+OiIgIbnYBy7KIjY3F7t278ccff8Db2xvdunVD8+bN4eHhAXt7+3dOsyU1JysrCwcOHEBAQAAXsjo6OmLDhg2C5Qylp8FSZx15FwrrCFFhFNYRIbAsix07dvDGevbsCU9PT4EqKttZBxRPUxGq048QQgghqkNTUxPz589Heno69u7dC5ZlkZmZiQMHDqBevXoYPXo0GjZsiNGjRyMwMBB+fn548uQJAgICEB0dzX2d7Oxs3LhxAzdv3oSjoyNatmwJb29vtGzZEk2aNIGxsbFw3+QnRiqV4tatWzh16hT3HklLSwvLli2Dl5eXIDVRZx2pCgrrCFFhFNYRIfj6+uLp06e8sdmzZwtUTTFjY2OoqanxdqalsI4QQgghlWVkZIRly5YhNTUVJ0+eBADExcVh165dsLOzQ7du3WBqaoru3bujS5cuiI6ORmhoKB4/foybN28iICCA65piWRavXr3Cq1evcO7cOXh4eKBhw4Zo164dOnfuDGdn53J3oSXVJyoqCn/88QciIyO5sdGjR2PEiBGC1MOyLCQSCbcbLUCddeTdlD6s09bWFqgSQpSfvr4+75jCOlIbtm/fzjt2cXFB7969BaqmmEgkgoWFBZKSkrgxWreOEEIIIVVhY2OD77//HsnJybh37x5YlkVwcDC2bNkCGxsbNGrUCAzDQE1NDc7OznByckKnTp0wYsQIPH/+HFevXsWFCxeQkJDAbTSRn5+PwMBA/Pfff7hy5Qrq16+Pli1bYsCAAWjXrh2tbVcDsrOzcfbsWVy5coW7kOvl5YVvv/0Wenp6gv195+bmctNxAeqsI++mdJPnqbOOkMqjzjpS26Kjo3HmzBne2MyZM5ViLRbaEZYQQgghH8vV1RX/+9//4OTkBOD/plPu2LGjzIVAhmGgr6+PBg0aYODAgfj+++9x8eJFbN++HW3btuWdH8nlcqSmpsLPzw8HDhzAxIkT0bdvX2zduhVxcXG0i2w1kcvlCAkJwZ49e7gpp1paWli6dClcXFwErU3xvZqGhgZ11pEKCf/uSgGFdYRUnmJYV3oNBEJqwq5du3hXBPX19fHFF18IV1ApiuvWJScnC1QJIYQQQlQRwzBgGAbNmzfHxo0buVksRUVFOHToEP78888y71dLPk9dXR1mZmZo2LAhpk6din///Rf37t3D9OnTYWZmxnt8fn4+EhMTce/ePaxYsQLNmjXD559/josXL6KoqKhWvte6KiMjAzt37sSrV6+4sXHjxqFLly4Qi8WCdjEqvlejsI68C4V1hKgw6qwjtSk/Px+//fYbb+zLL78s8zoUiq2tLe84ISFBoEoIIYQQoqpKgreePXti4cKFUFNTA1Ac2H333Xfw9fWFTCarsBOOYRhoamrCyMgIrVu3xvbt2xEeHo7ffvsNbdu25QVGcrkceXl5SE9Px19//YVBgwbBy8sLP/zwA6Kjo7nnoa6792NZFlKpFFevXsWxY8e4vzN3d3d88cUXsLCwEHy6cen3amKxGFpaWgJWQ5QdhXWEqDAK60htOnXqFDIzM3lj33zzjUDVlKUY1sXHxwtUCSGEEEJUGcMwMDAwwNixYzFy5EhuOmteXh4mTpyIiIiISn8dsVgMY2NjfPnll7hx4wZu376NL7/8EjY2NtDU1OQCJJZlUVRUhBcvXmDRokVo0aIFJk6cCF9fX2RlZaGoqIiCu/dISkrCokWLuFkgWlpamDx5Mpo2bSpsYf9f6c46sVjM+/cnRBGFdYSoMArrSG369ddfecc9evSAq6urQNWUpbjzK4V1hBBCCPlQDMOgfv36mD59Olq3bs0FdklJSRg/fnyVltsomV6rqamJtm3b4tdff8X9+/exceNGtG7dGlZWVmU2VszIyMChQ4fQrVs39O3bF3v37kVERAQyMjIgkUiq9XutCwoKCrBixQrExcUBKN58rGfPnujXrx90dHSUIhRTDOuos468C4V1hKgwCutIbYmIiMCtW7d4Y5MnTxammApQWEcIIYSQ6ubj44MZM2bA0dGRC3z8/f0xf/58pKWlfdDXZBgGDg4OmDNnDm7duoXDhw9j8uTJaNasGaytrSEWi7nHymQyPHjwALNnz0bbtm0xf/58nD17FqGhocjMzOStJfypksvl+Oeff3Do0CFuzMHBAZ9//jnc3d0FrIyv9Hs1dXV1CuvIO4nf/5DaI5PJUFhYyBujsI6QipUseluCwjpSU/bv3887NjU1xcCBAwWqpnyK02CTk5MhkUigrq4uUEWEEEIIUXVqamoYMmQI4uLi8L///Q/p6ekAgL///hsODg5YtGgRjIyMPvjra2pqolu3bujatSsSEhJw7do13Lx5E8+ePcPLly+RlZXFPTY9PR0HDx7E4cOH0axZM/To0QNt27aFh4cH7OzsPtlplc+fP8fy5cu5KcK6uroYMGAA+vTpo1R/H7RmHakKpQrrCgoKyoxRWEdIxRQ76/Lz8yGVSnlX4wj5WBKJBL///jtvbPz48dDU1BSmoAoodtaxLIvk5GTUq1dPoIoIIYQQUhdoa2tj8uTJSExMxL59+1BQUACpVIq9e/fC0tISX331VZlprFXFMAzs7OwwYcIEDBs2DE+fPsX9+/fh5+eHgIAAbsMJoLjJ5fHjx3jy5AmsrKzQunVrtG7dGt7e3mjWrBmMjY2VKqSqSRkZGVi9ejWioqIAFIerzZo1w6RJk2BoaChwdXylp8Gqq6t/9GuG1G1K9Y6+vG2wKawjpGLl7cKZnZ0NY2NjAaohddWFCxfKrMsyadIkgaqpmKmpKTQ1NXkd2vHx8RTWEUIIIeSjmZmZYf78+UhMTMTp06chl8uRlZWFnTt3wsLCAiNHjuR2jv0YDMNAT08Pbdu2RatWrZCQkICQkBA8efIEd+7cwf3795Gbmwug+MJkUlIS/vnnH1y5cgUuLi5o1KgRfHx80LFjRzRq1KhaalJWcrkcv//+Oy5evMh11ZmYmGD69Olo2LChwNWVRdNgSVVQWEeICisvrHv79i2FdaRaKW4s0aZNG6U8AWIYBra2tnj16hU3RuvWEUIIIaS6ODg4YN26dYiNjYW/vz9YlkVMTAx27twJGxsbdOrUqVo72sRiMRwcHODg4ICOHTti2LBhePnyJW7evIlLly4hNDSUC6ny8vLw9OlTPH36FFevXoWDgwM8PT3Rp08fdO3aFRYWFtVWl7J48OAB9u/fz+UI6urqGD58OPr3789tCKJMaBosqQqlD+uoNZSQiunq6oJhGN4W7rRuHalOqampuHjxIm9M2TaWKM3Ozo7COkIIIYTUGHd3d+zZswf9+/dHUlISZDIZnjx5gl27dsHa2rrGNjTQ09ODh4cH3N3d0bZtW0yePBlPnjzBX3/9hVu3bvGmWKalpSEtLQ3//fcfrly5Ant7e/To0QPDhw+Hp6dnnVjPNyMjAzt37sSLFy+490Kurq5YvHgx9PT0BK6ufIqddZR1kHdRqrhZMazT1NSs0227hHwskUhEm0yQGvXXX39x66MAxd3OI0aMELCid1PcZILCOkIIIYRUJ4Zh0LRpU+zfv59bv1cikeD8+fPYv38/MjIyavS5RSIRTExM4OHhgREjRuDgwYO4fv06Fi5cCFtbW15nn0QiQXJyMvz9/bFlyxb06tULI0aMwOnTp5Gbm8u74K9KWJbFsWPHcPPmTUgkEgDF74s2b95cZg1jZVI6UBWLxUq3/jNRLkod1tEUWELeT3EqLIV1pDodOXKEdzxw4EClvVoJoMz6dDExMQJVQgghhJC6SiQSoWvXrvjxxx+5sYKCAuzduxfnz59HYWFhjQdhDMNAQ0MDRkZGaNGiBdatW4fg4GD88ccf6NKlC28aKMuyyM/PR0pKCs6ePYuRI0eicePGWLVqFaKioiCXy8GyrEqEdyzLIigoCH/99RdSU1O58alTp6JHjx5Ku7EGy7K0Zh2pEgrrCFFxqtJZFxERgadPn0IqlQpdCqmkV69e4cGDB7yxMWPGCFRN5Tg6OvKOS3YGI4QQQgipLgzDQF1dHePHj8eUKVO4YOzt27dYuXIlnjx5wgVgtVGLSCSCuro6jIyMMHr0aFy/fh0hISFYuHAh7O3toampydUol8shkUjw6tUrrFmzBq6urujXrx9OnTqFrKwsFBUV1VrtVcWyLHJycvDXX3/h/v373LiTkxPWrVunlOvUlUZr1pGqUKpXM4V1hFSdKnTW/fjjj3Bzc0OTJk2grq6OSZMm4cKFC7xdO4nyOXr0KO/Y1NQUPXv2FKiayqGwjhBCCCG1gWEYGBgYYOHChejcuTMXFEVHR2PJkiWIjo4WpKaSm4eHBzZu3IinT59i//796NOnD6ytraGtrc3rPpPJZLh48SJGjBiBli1b4vvvv0dgYCAyMjJQVFSkVKGdXC7H7du38ddff3HTX7W1tbFt2zYYGxsrbVddidLTYKmzjrwPhXWEqDjFsK70LwFlwLIsNm7cyBvbv38/PvvsM1hYWGDs2LE4ffp0uRvMEGEphnUjRoxQ+gWJFcO6zMxMZGVlCVQNIYQQQuoyhmHg6OiI5cuXw9PTkwuLfH19sXbtWqSlpQkadjEMA0NDQ4wePRonT57EP//8g2+++QatWrWCjY0NxOL/229SLpfj5cuXWLNmDXr27Imvv/4aJ0+exPPnz5GRkQG5XC7Y9wGA23n30KFD3GZiampq+OKLL9C1a1el76oDqLOOVI1SvaIV36zT7iiEvJ+yd9YxDAN7e/ty73v79i3+/PNPDBkyBObm5hg2bBiOHj2qdN/DpygsLAwhISG8sc8//1ygairPwcGhzFVV6q4jhBBCSE1RU1ODj48P5s6dy1s799ChQ9ixYwdycnIErO7/aGpqolWrVti0aRNOnjyJTZs2YcyYMWjVqhXMzMx4j83IyMCJEycwduxYjB07Fhs2bMC5c+fw/Plz5ObmClJ/Xl4ezp49i3PnznFjzZo1wzfffKMSTT5SqRT5+fncMXXWkfcRv/8htYfCOkKqTtnDOgBYuXIlBg8e/M7H5OXl4eTJkzh58iQ0NDTQo0cPDB06FAMGDICpqWktVUpKnD59mndsbW2Ndu3aCVRN5WlqasLW1pa3C2xUVBSaNm0qXFGEEEIIqdO0tbUxcOBAxMTE4KeffkJGRgZYlsXmzZthbm6OadOmQUNDQ+gyARRfSLe1tcXYsWMxcuRIPH36FPfv38ejR4/w33//ITIyEgUFBQCKu+2CgoIQFBSEffv2wcfHB23atEHLli3RuHFj2NjYQE1NrcZrlslkCAgIwK5du7jaLCwsMHPmTDg5OSn99Feg7Own6qwj76NUYV3ppBmgabCEVIYqhHWDBg3C559/XmZaZUWKiorw77//4t9//4Wamhq6dOmCIUOGYPDgwbCysqrhaglQNqwbNGiQSkwvAIqnwpYO60qmShBCCCGE1BRTU1NMnDgRsbGxOH78OPLz81FQUIANGzbAxMQEY8eOVbpQSV1dHS1atECzZs0watQoPH36FE+ePMHdu3fx4MEDvH79mntsVlYWLl++jOvXr8PR0RFNmzZFq1at0LFjR3h5eUFLS6tGvj+WZZGZmYkff/wRkZGRAIqDriFDhqBHjx4qE3i9ffuWNyWaOuvI+yjVO6+SlLwEddYR8n6qENYBwI4dO2BhYVFmvEGDBu8MgWQyGa5du4bp06fDxsYGHTp0wLZt2xAbG1uT5X7S4uLi4O/vzxsbMmSIQNVUHW0yQQghhBAh2Nvb49tvv0Xr1q25jrOUlBRs2LABV69eFbi6iolEIpibm6Nr166YOXMmNm/ejP3792PhwoXw8vLinatLpVJERETgxIkTWL9+PaZNm4avv/4af/75J1JTU6t9jT6WZbFv3z5cvnyZG2vZsiXGjBlT7nsLZaX4Hk1dXV1pui2JclKqsE6xs46SZkLeT19fn3esrGGdmZkZdu/eXWbcwcEBycnJ+PXXX9GnT593bmDAsizu3r2LuXPnwsHBAd7e3ti4cSMiIiJqsvRPzj///MM7NjY2RqdOnYQp5gNQWEcIIYQQITAMgwYNGmD9+vW8NZvDw8OxZs2aMusBKxuGYaCtrQ1XV1f07dsXixYtwt9//42jR4/i888/5zUJlHS8BQQE4OjRo1iwYAF69eqF+fPn4+7du9xurR/Lz88PmzdvRlFREYDi9xQjRoxAixYtamUKbnUpPQ1WTU0N2traKjNrhQhDqV4dimEdddYR8n6q0lkHAEOHDsWIESN4Y5cuXcKxY8cwadIkXLhwAampqTh8+DAGDx783v8DHj9+jCVLlsDNzQ2NGzfGqlWrEBwcrFRbzKuis2fP8o779++v9LvAlubk5MQ7prCOEEIIIbVFJBLB29sb27Zt485l5XI5/P398f333yMtLU3gCitHTU0NJiYmcHd3x+DBg7Fr1y74+/vjxx9/RLNmzXhTXiUSCZKTkxEUFIQ9e/ZgyJAh6NKlC/bu3YukpKQPriEvLw+zZs1Ceno6gOK/2/bt22PUqFEq19hT+j2ampoaLflF3kupwjrFabCq9gNIiBAUwzrFxUuVza5du2Btbc0bW7BgAZ49ewYAMDIywpgxY3Dq1CmkpaXhxIkT+Pzzz8t0ECoKDg7G6tWr0bhxY7i7u2PJkiXw9/en4K6KcnNz4evryxsbOHCgQNV8GMXOulevXkEmkwlUDSGEEEI+NWKxGJ999hnWrl3LhVolazKXbJKgSueo6urqMDIygqurK2bPno0bN27g0qVLGDJkSJn3IgUFBUhLS8P9+/cxe/ZstGrVCt988w0CAwMhkUjAsix3exeWZfHDDz8gMDCQG3N0dMRXX30FKysrpVv/731Kv0cTiUQU1pH3UqqwjjrrCKk6VeqsA4pb13///XfeWGFhIcaMGYPCwkLeuK6uLoYOHYojR44gNTUV586dw5dffgkTE5N3PkdERAQ2btyIVq1awd7eHrNmzcLNmzchlUqr+9upc27dusVNMwCKTza7desmYEVV5+bmxjsuLCykNQ4JIYQQUqvU1NQwZ84cjB8/nguW3r59iwMHDuD06dOQy+UCV1g1DMOAYRioq6vD0NAQPXr0wN9//w1/f3+sXLkSjo6O0NTU5KZ2siyLoqIixMfHY9euXWjdujX69OmDY8eO4c2bN5BIJBX+HbAsC39/f+zYsYM7f9fR0UH//v3Rs2dPlQvqgLKddZR1kPdRqrCONpggpOpULawDgJ49e2LOnDm8sadPn2Lx4sUVfo6Wlhb69euH/fv3Izk5GVevXsXXX38NS0vLdz5XfHw8du7cia5du8LKygoTJ07E+fPny/x/Q4pdunSJd9y2bVsYGhoKVM2HsbCwKFNzeHi4QNUQQggh5FMlEomwfft2dOjQgQuYYmNj8dNPP8Hf319lO/9LgjuRSAQ3NzesWrUKz549w9GjRzFw4EBYWFhAV1eXtyabRCLB9evXMXr0aDRq1AiLFy/GkydPkJ6eXqbTMCsrC8uWLePe15SsBbhw4UKVXedNcc066qwj76NUr3TaYIKQqisvrFOFtvoNGzbAy8uLN7Zt2zacPn36vZ+rrq6O7t27Y8+ePUhISMCdO3cwZ84c3kK+5UlPT8eBAwfQv39/mJubY+TIkTh27JhKBJy1pfROWwDQq1cvgSr5cAzDwN3dnTdGYR0hhBBCahvDMDAwMMDu3bvRsGFDbvz+/fv46aefEBMTo3IddhXR0dHB4MGDcfLkSdy9exfLli1D27ZtYWdnV+Z9fWJiIrZu3Yq2bdtixIgROHDgAIKDg/H69Wvk5eVh3759ePz4MRdmmpiYYNmyZWWW0lElFNaRqlLqsI466wh5P8WwTi6XIy8vT6BqKk9LSwtHjhyBpqYmb/zLL7/Eq1evKv111NTU0L59e2zduhXR0dHw9/fH4sWLy0yFVJSTk4O//voLn3/+OczNzdGnTx/s2rUL0dHRH/Lt1AmvXr0qs7Nu7969Barm41BYRwghhBBlwDAM3NzcsHHjRt66usePH8evv/6KN2/eqMSF9spiGAaurq5YvHgxzp07h127duHLL79E69atYW5uzuuMk0qluHHjBr755hsMGjQIq1atwr59+7B//368efMGQHF34ujRozFgwACBvqPqQRtMkKpSqrCONpggpOrK23hBVTrFGjVqhG3btvHGsrKyMGLEiDLr11UGwzBo2bIlNmzYgLCwMISEhGDt2rVo3rz5Oz+vqKgIly5dwjfffANHR0c0bNgQixYtgq+v7ye1zt2tW7d4x+bm5mjatKkgtXwsCusIIYQQoizU1dXRsWNHzJs3D1ZWVgCKg6qffvoJhw8fRlFRUZ0K7IDi83IjIyMMGDAA27Ztw759+7By5UqMGTMGTZo04V2wl8vliIqKwq5duzBv3jzeeVurVq2wZMkSqKmpCfFtVJusrCzuYwrrSGUoVVhHnXWEVJ0qh3UA8NVXX+Hzzz/njT158gTz58//qK/LMAw8PT2xfPlyPHnyBFFRUdi6dStvzZCKPH/+HD/88AM6deoEc3NzjBo1CocPH8br168/qiZlp7gLbOfOnVV2XRAK6wghhBCiTPT09DBs2DCMGzeOmxmTnZ2NTZs24dSpUwJXV7M0NDTg5eWF6dOnY8uWLdi6dSvWr1+PESNGwMHBgfdYuVzOBZfm5ub4/vvvuYBTlVFnHakqpXoXRhtMEFJ1mpqaZaaSqlJYxzAMfv755zLhyq5du3DkyJFqe5769etjzpw58PX1RVJSEn755Rf07t0b6urq7/y8N2/e4Pjx4xg3bhwsLCzQokULLFy4EFeuXFGJ6cZVcefOHd5xhw4dBKrk4ym+nhISEpCTkyNQNYQQQgj51DEMA0tLS0yZMgV9+vThzkETExOxYsUK3L59W+AKax7DMDAzM0OXLl0wa9YsbNy4Eb/88gvWrVuHDh068AIshmEwb948lT4fLY3COlJVShXW0QYThHwYxXXrSi9gqgr09fXx999/lwnoJ02ahICAgGp/vpITpYsXLyItLQ3Hjh3DuHHjYGZm9s7PY1kWAQEB+PHHH9GrVy8YGxujS5cuWLduHR4+fAiJRFLttdaWxMTEMmsFqvLJkYuLS5kOyhcvXghUDSGEEEJIcQDl5OSEefPmoWXLlty5ysuXLzFr1qxPaiaAWCyGo6MjunfvjlmzZuG3337Dn3/+ia+++gqdOnXCypUrMWnSJIjF4vfOilEFtMEEqSqlDuuos46QyilvR1hV4+XlhV27dvHGCgoKMGjQIKSmptbY8xoaGmLkyJE4dOgQkpOT8eDBAyxbtqxSa7UVFRXh1q1bWLFiBdq0aQMjIyN06dIFy5cvx8WLF7mFcVWBYledoaFhmd16VYm2tnaZaRVhYWECVUMIIYQQUkxNTQ3NmzfHwoULUa9ePW48JCQEM2bMQEZGhoDV1T6RSAR9fX24uLjgs88+w8aNG3H8+HHMnz8fZmZmdSKoA6izjlSdUoV1tMEEIR+mLoR1QPFOsF9//TVvLC4uDsOHD6+VrjU1NTX4+Phg3bp1CAwMRHx8PH755RcMHDiwUr9Q8/LycOvWLXz//ffo27cvTExM4OXlha+//hoHDx5EcHCw0m5Ycf/+fd5xu3btVH4hXw8PD95xSEiIQJUQQgghhPwfsViMfv36Ydq0adDT0wNQvFbbvXv3sGjRIkgkkjq34cT7MAwDdXV1GBkZwdLSEnp6enUmqAPKdtZRYxJ5H6UK66izjpAPU1fCOgDYvn072rdvzxvz9fXF3Llza70WW1tbTJkyBf/88w8yMzNx+/ZtrouuMkEWy7J49uwZfv75Z3zxxRdo3Lgx9PX10bp1a0ybNg379u3Dw4cPebtDCUVxurGPj49AlVQfxc7A4OBggSohhBBCCOETi8WYP38+BgwYALFYDKC4eeX06dPYvXs35HK5wBWS6sKyLE2DJVUmFrqAEjKZDEVFRbwxCusIqRzFHWFVOazT0NDAiRMn0LJlS8THx3Pju3btgqenJ6ZPny5YXR07dkTHjh2xZs0avH37Frdv38b169dx9epVPH/+vFJfp6CgAH5+fvDz8+ONW1tbw9PTEw0aNICHhwecnJzg6OgIBweHGv+/UC6XIzAwkDfWokWLGn3O2tC4cWPe8dOnTwWqhBBCCCGkLHV1dezatQtRUVF4+PAhWJZFeno69u7dC3d3d/To0UPlZzoQQCqV8mYRUmcdqQylCesKCwvLjNE0WEIqpy511gHFG0CcPn0aHTp04P1imzlzJurXr4++ffsKWF0xAwMD9O/fH/379wcAvH79Gvfv38f9+/dx7949+Pv7l/v/WkWSkpKQlJSE69evl7nP0tIS9evXh729PSwtLWFpaQkLCwvuZmpqCj09Pejp6UFXV5e7OlsZLMsiNDQUubm5vPHmzZtX+msoK8WwLiYmBllZWTA0NBSoIkIIIYQQPiMjI/z+++/o168fIiIiABSvs7tlyxbY2NigUaNGEImUakIcqaLs7GzetGbqrCOVoTRhneIUWIA66wiprLoW1gFAy5Yt8euvv2Ls2LHcmFwux8iRI3H37l00adJEwOrKMjMzw4ABAzBgwAAAxRcgAgICcO/ePdy/fx8BAQGIiYn5oK+dkpKClJQUPHr0qFKP19LSgp6eHrS0tMAwTJmbXC5HXl4e8vPzkZeXV2aahbW1NaysrD6oVmXi4eEBsVjMWycwODi4zDRrQgghhBAhubq6Yvfu3Zg0aRJiY2MBALdv38auXbuwfPly2NnZ1an12z41OTk5vGMK60hlKE1Yp7i5BEBhHSGVpRjWlV4TQZWNGTMGL168wJo1a7ixnJwcfPbZZ3j06BFsbW0FrO7dNDU10aZNG7Rp04YbS09PR2BgIAICAhAQEICgoCBERkZCJpNV63MXFBSU+39qZdWFrjqgeOpygwYNeGvVPX36lMI6QgghhCgVhmHQpUsXrFq1CkuWLEFKSgqKiorw119/oV69epgxYwaMjIwosFNRip116urq0NDQELAiogqUJqwrr7OOpsESUjl1sbOuxKpVqxAZGYkjR45wYwkJCejXrx9u375d5ntXZqampujevTu6d+/OjRUVFSEyMhLPnz9HaGgonj9/joiICERHRyM9PV2QOkvXp+qaNGlSJqwjhBBCCFE2IpEIw4cPR1JSEjZu3Ijs7Gy8efMGP//8M+zs7DBq1Ch6f6yiSod1IpEIurq6NLWZvJdSh3WampoCVEKI6qnLYR3DMNi/fz9iY2Nx9+5dbjwoKAiDBg3ChQsXVPrERUNDA56envD09CxzX3Z2NmJiYhAdHY2oqCgkJSUhNTUVqampSElJ4f4s7//PDyESiTBgwABMmzatWr6eMqBNJgghhBCiChiGga6uLr788kskJSVh7969kEqliI+Px8aNG+Hg4ICOHTvShhMqqPSsJ4ZhoKenJ2A1RFUoTVinOGWrZK0lQsj71eWwDigO7v/55x+0adOGW3gXAG7evImxY8fi+PHjdfLERV9fH40aNUKjRo3e+biioiLk5uYiJyeHu2VnZ6OoqAgsy3I3uVwOlmUhEomgo6NT5mZgYFDnTh4Uw7rg4GDI5XK6mkkIIYQQpcMwDCwtLfH1118jLi4OZ86cAQCEh4djwYIFOHbsGJydnel9sop5+/Ztmc46Qt5HacI6xc4QWq+OkMrT19fnHde1sA4onkJ64cIFtGvXDqmpqdz4yZMnMWPGDOzZs+eTPXHR0NCAhoYGjI2NhS5F6SiGdTk5OYiMjISbm5tAFRFCCCGEVEwkEsHd3R0zZ85EXFwcAgICAACBgYH46quvcObMmTp3cbWuU+yso7COVIbStBYodtZRWEdI5dX1zroSLi4uuHjxYplw8ueff8Z3330nUFVEmVlZWcHa2po35ufnJ1A1hBBCCCHvJxaL0bFjR0ydOpU7j5HL5bh16xZmzZoFuVwucIWkKnJycniddRS2kspQmrBOsbNOldegIqS2fSphHVC8U+mZM2fK7KC0bt06/PDDDwJVRZQVwzDw9vbmjfn7+wtUDSGEEEJI5YjFYowfPx4jR46Ejo4OgOLA7vDhw9i2bRsFdipEMayjzjpSGUob1lFnHSGVpxjWFRQUQCKRCFRNzevSpQuOHj1aZt2xRYsWYdu2bcIURZQWhXWEEEIIUTUMw0BbWxtLlixB586dIRYXr2AlkUiwZs0aXLx4ETKZTOAqSWWUngYLAOrq6igsLKTAlbyT0oR15W0wQQipHMWwDij7S6GuGTJkCPbs2VNmfO7cueWOk09Xq1ateMeBgYF1OswmhBBCSN1hYWGBDRs2wNPTk1uf+e3bt1i4cCGCgoIo8FEBpTvrioqKcPHiRezduxd+fn6IjY1FRkYGtzEcISVogwlC6oDywrq3b9/CxMREgGpqz9SpU5GXl4e5c+fyxqdPnw4NDQ1MmjRJoMqIMmnZsiXvuKCgAN27d8fnn38Od3d3uLm5wcbG5pPdoIQQQgghyq1x48ZYv349vvnmG0RHR4NlWbx48QIrV67Etm3baIdYJVc6hJNKpXj06BEePXoEHR0deHp6ok2bNmjdujWcnJxgZmYGMzMzGBoalplFRD4tShPW0QYThHw4XV1dMAzD+0VQl9etK23OnDkoLCzE4sWLeeNTpkwBwzCYOHGiQJURZWFiYgJnZ2e8fPmSG/P19YWvry93rKenBzc3N7i6usLe3h52dnaoV68e7OzsYGdnB0tLSzphIoQQQohg+vbti2XLlmHx4sVIT0+HVCqFr68vduzYgSVLlpTZUIsojw4dOuDMmTOIjY3ljefl5eHx48d4/Pgxdu7ciXr16qFRo0Zo3LgxGjRoAHt7e9jb28Pa2hra2toUyH5ilCasow0mCPlwDMNAX1+fF9B9KmEdULxWXUFBAVatWsWNsSyLSZMmoaCgANOnTxeuOKIUOnTowAvrFOXk5CAgIAABAQHl3i8SiWBiYgJTU9Nyb4aGhtDW1q70TUNDg064CCGEEFJpDMNgwoQJSEtLw9q1a5Gfn4/s7Gz8/fffqF+/PiZNmgRDQ0OhyyTl6NevHwoLC/HkyRNEREQgIiICMTExKCws5D0uLi4OcXFxuHjxIrS0tODs7MzNAnFzc+M+NjExoYvInwClDeuos46QqjEwMPhkwzoA+O6771BYWIgNGzbwxmfMmIGCggLMmzdPoMqIMli8eDEuXryIlJSUD/p8uVyO169f4/Xr19VST8mi0e+66ejoQFdXF7q6uryPFY+NjY250NDY2BhqamrVUiMhhBBClItYLMa0adOQnp6OrVu3Qi6XIzk5GXv37oW9vT369+8PTU1NocskCrS1tTFmzBgMGjQI8fHxiI+PR1RUFJ49e4agoCAEBwfjzZs3vFlSBQUFCAkJQUhICMRiMUxNTbnZH+7u7vDy8oKXlxfc3Nzo37yOUpqwjqbBEvJxFNet+9TCOoZh8P333wNAmcBu/vz5yM/Px7Jly4QojSgBd3d3vHz5EteuXcN///2HkJAQvHjxAi9evEBeXl6t18OyLPLy8qr9uRmGgZGRERfeWVhYoF69etzN3t6e+5hCPUIIIUS1MAwDQ0NDzJ49G0lJSThy5AgAIDIyEps2bYKDgwNatGhBXVdKiGEY6OnpwcPDA+7u7pBKpXjz5g1ev36N5ORkPH/+HH5+fnj06BHCw8N5nyuVSpGSkoKUlBT4+/tDR0eHO9eztrZG8+bN4ePjgxYtWtDSLXWI0oR1NA2WkI+jGNbV9d1gy1MS2Glra+O7777j3bd8+XLk5ORg/fr1NP3wE6Wrq4uBAwdi4MCB3JhcLkdiYiLCw8Px4sULvHr1irviGRcXh4SEBEilUgGrrhqWZZGZmYnMzExERkZW+DhNTU24ubmhQYMG8PDwgIeHB7y8vNCgQQMK8QghhBAlxjAMbG1tsWjRIsTExODevXtgWRaBgYH47rvvsH//flhZWdH5rhJjGAbq6uowNzeHubk5PDw84OPjgxEjRiA3NxcJCQm4c+cObt++DT8/P2RkZPA+v+SCb1xcHP777z/cuXMHv/32G4yNjdGwYUN07twZXbp0gZubG8RipYl8SBUpzb8cddYR8nE+9c66EgzDYMWKFdDS0sLChQt5923cuBFpaWnYu3cv/eIiAIrXoivZRKJbt25l7pfL5dyVzPT09Apv2dnZyM/PL/emuB6JMigsLERwcDCCg4N543p6emjZsiVatWqF1q1bo3Xr1rC1tRWoSkIIIYSUh2EYeHp6YtmyZZg2bRpiYmIgk8lw7do1rFu3Dps3b6bmFxVSenkUMzMz2Nvbo0WLFpg+fTrevHmDx48f4/Lly7hy5QpiY2Mhl8u5z2VZFjk5OcjJyUFycjIiIiJw4cIFaGhowMHBAZ07d0b37t3RoUMHWtNQxSjNu1XqrCPk41BYx7dgwQJoaWlh1qxZvPHffvsNaWlpOHbsGF0UIO8lEolgbW39UTusyeVyFBQUVBjm5eXlVTiel5eH3Nxc7qZ4nJOTg4yMjGqbTpuTk4Nbt27h1q1b3Jibmxu6d++O7t27o2vXrnSiRwghhAiMYRioqamhc+fOmDdvHpYvX47s7GxIpVLs3bsXjRo1wldffUXTIVUQwzBgGAZaWlrQ1NSEvr4+7OzsMGDAAEilUjx//hxXr17F5cuXcf/+fUgkErAsy613J5VKIZVKkZeXhzdv3iA4OBg//fQTtLW10aZNG/Tt2xc9evSAi4sLxGIx7zVC3ZjKRWnDOnoTTUjVUFhX1syZM6Gjo4OpU6fyrkCdPXsWPXv2xNmzZ2FsbCxgheRTIBKJoKOjAx0dnRp7joKCAq7LLyMjA+np6Xj9+jWSkpIQGxvL7S4WFxdX5WCvZG2/3bt3QywWo0uXLhg8eDAGDhwIGxubGvqOCCGEEPIuJYHO559/jufPn2P//v2QSCSQy+WYM2cO3Nzc0KVLFwrsVFhJeMYwDEQiEdTU1NCsWTM0a9YMCxcuxNu3b3Hr1i1cuXIFt2/fRmxsLCQSCSQSCWQyGQBw74FycnJw9epVXL16FWKxGK6urujRowd69+6NFi1aQEdHB+rq6lBXV6fXjJJQmrCOpsES8nEorCvfpEmTYGZmhlGjRvH+n7l79y46dOiAf//9Fw4ODgJWSMjH09LSgq2t7XunrLIsi7S0NISFhSEsLAyhoaEICwtDSEgI4uLi3vs8UqmUO9GbPn06OnfujAkTJmDYsGHQ09Orrm+HEEIIIZXAMAzMzc0xY8YMREVF4fr165DJZCgqKsKECRNw4cIFeHl5UcdUHaH472hoaMitx1xQUIDQ0FDcuHEDN2/eRHh4OLKyspCbm4v8/HzeTrNSqRShoaEIDQ3Fjh07YGpqijZt2qBz585o164dbG1toaurC11dXWhoaNDrRyBKE9bRNFhCPo6+vj7vmMK6/zNw4EBcuXIF/fv3R1ZWFjceEhKC1q1b49y5c/D29hawQkJqB8MwsLCwgIWFBTp27Mi7LyUlBX5+ftxOZH5+fryfl/KUTJmdMWMGRowYgZkzZ6J58+Y1+S0QQgghRIGXlxfmzJmDpKQkhISEcBtoTZ8+HQcPHoSzs7PQJZIapqWlxXXdzZ07F4mJifDz88PDhw/x+PFjpKamIiMjA5mZmWUapdLT03H+/HmcP38eampq8PDwQNu2bdGmTRt4eHjA3NwcpqamMDAwgEgkovCulihNWKf4gqGwjpCqoc66d+vQoQPu3LmDXr16ISkpiRtPSUlBp06dcPjwYQwZMkTACgkRlqWlJfr374/+/fsDKJ42ERQUhGvXruHatWvw9fWtcLOMvLw8/P777/j999/Rrl07zJo1C0OGDKGNXAghhJBa0rNnT8THx2PVqlVITEwEy7Lw8/PD6tWrsWHDBtow6hNSegO1wYMHo6ioCKGhoQgKCkJQUBAiIiIQHx+PhIQEZGZm8pYLkslkCAkJQUhICH799VdYWFigSZMmaN68ORo2bIh69erB1tYW1tbW0NXVFfC7rPuU5iy6qKiId6ypqSlQJYSoJsWwLjs7W6BKlJeXlxfu37+PPn36ICwsjBvPz8/H0KFDsWnTJixYsICuFhGC4hO95s2bo3nz5li4cCGys7Nx+fJlnD59GufPn6/wgsC9e/dw7949uLq6YsWKFfj8888ptCOEEEJqmJqaGsaOHYvU1FSsX78eeXl5kEgkOHPmDOzs7PDtt9/CxMRE6DJJLWMYBpqammjatCmaNm0KqVTKLYkSGhqKFy9eICIiAi9fvkRUVBQvl2FZFikpKbhy5QquXLkCLS0tODo6wt3dHe7u7nBxcYGrqytcXFxgaWlJ53vVTGn+NhXDOg0NDYEqIUQ1UWdd5dSvXx8PHjzAsGHDcP36dd59ixYtQlhYGHbv3k3dvYQo0NfXx7BhwzBs2DAUFBTg3LlzOHjwIC5dusQtYlxaREQExo8fjzVr1mD16tUYNWoULVhMCCGE1CBtbW1Mnz4daWlp2LFjB1iWxdu3b3Hw4EHUq1cPX3zxBa0N/4kTi8WwtraGtbU1OnfujNzcXMTHxyM2NhYxMTEICwtDcHAwnj17huTkZN5adyXr4oWGhoJhGBgbG8PBwQEODg5wcXGBl5cXGjduDHd3d2hpaVEDxEeisI6QOoLCusozMjLCxYsXMW3aNPz222+8+w4cOICQkBCcPHkSdnZ2AlVIiHLT0tLC8OHDMXz4cCQnJ+PXX3/F7t27eVPMS0RGRmLMmDHYsWMHtm3bBh8fHwEqJoQQQj4NxsbGWLx4MRISEnDixAkAQFJSEnbv3g07Ozv07dsXampqAldJlAHDMNDT04OHhwfc3d3BsiwyMzORlpaG5ORkREREcGsZh4eHl+m6y8jIQEZGBgIDA6GlpQUzMzNYWFjA2toaTZs2RcuWLdGyZUvY2tpScPcBlCask0gkvGMK6wipGgrrqkZdXR379u2Du7s7Fi5cyLvPz88PLVu2xIkTJ9C+fXuBKiRENVhZWWH58uVYtGgRTp48iR9//BEBAQFlHvfo0SO0adMGo0ePxpYtW2BpaSlAtYQQQkjdZ2lpiR9//BExMTHw9/cHy7IIDQ3F9u3bYWdnh6ZNm1J4QngYhgHDMDA1NYWpqSnc3d3h4+ODQYMG4e3bt4iPj8fDhw9x584d3Lt3D2/evOF9fkFBAeLj4xEfHw+GYXD79m3o6+vD0NAQLi4u6NixIzp16oRGjRpBR0dHmG9SxSjNfBTFzjp1dXWBKiFENZUX1pVuWyZlMQyDBQsW4OTJk2UWSE1JSUGXLl2we/du+nskpBLU1dUxatQoPH78GOfOnUPLli3LfdyRI0fg6emJP/74g362CCGEkBpib2+PvXv3wsbGBkDxxgG3b9/Gnj17kJycLHB1RNkxDAMtLS2Ym5vD2dkZ7du3x6xZs3D48GE8f/4cFy5cwLx589CkSZMya9WxLIucnBwkJSUhLCwMFy9exKpVq9C7d2+0atUK06ZNw+nTp5GRkUHngu+gtGEdddYRUjX6+vq8Y5ZlkZubK1A1qmXIkCF4+PBhmW3tpVIpZsyYgfHjxyMnJ0eg6ghRLQzDoF+/fvDz88M///wDV1fXMo/JyMjA+PHj0bdvXyQkJAhQJSGEEFJ3lXRJeXl5YceOHdw6dVKpFAcOHMDx48eRl5dHQQmpNDU1NWhra8PIyAhWVlbo1asXNm7ciAcPHuDZs2fYuXMn+vTpAwMDgzJdmzKZDHl5ecjMzERISAh++eUXjBw5Eg4ODujRowd++OEHBAUFQSqVQi6Xg2VZem2CwjpC6gzFzjqApsJWRaNGjeDv749evXqVue/w4cPw9vbGs2fPBKiMENXEMAwGDhyIZ8+eYcuWLTA0NCzzmEuXLqFJkyb4999/BaiQEEIIqbsYhoFYLEaPHj2wdOlSrvtJKpXiu+++g6+vL2QyGYUipMoYhoFIJIK6ujq0tLTg5uaGGTNm4N9//0VMTAzOnTuHWbNmoUGDBtDV1YWmpiZvnUS5XA6JRIKcnBxcv34dixYtQvPmzeHo6IgpU6bgxIkTiI+PR15eHoqKirgA71NDYR0hdYRiZx1AYV1VGRsb499//8WSJUvK3BcWFoZWrVph//79n+QvC0I+lIaGBubOnYsXL15g5MiRZe5PT09Hv379MG/ePBQWFgpQISGEEFI3MQwDAwMDjB07FiNGjOACu+zsbEybNg1hYWECV0hUXUkXZ8nNyMgIn332GbZt24Znz57hyZMn2Lp1K4YMGQIXFxeYmZlBR0cHIhE/imJZFvHx8di/fz9GjBgBLy8vDBw4EFu2bIGfnx8SEhKQmZmJgoKCT+a9mNJsMEFhHSEfR0NDA1paWigoKODGsrOzBaxINampqWH9+vVo2bIlvvzyS17gmZ+fj0mTJuHWrVvYtWtXuQEpIaR8FhYWOHbsGIYMGVJuaLd161akpaXhjz/+EKA6QgghpO5ycHDAN998g9jYWDx48AAymQzR0dGYNm0aDh8+DAcHB6FLJHVMSXjn7u4Od3d3fPXVV8jIyIC/vz/u3r0LPz8/JCYmIjMzkwvhSsvKysK1a9dw7do1MAwDNzc3tGnTBj4+PmjcuDHMzMxgbGwMQ0NDiMXiOrlhilKEdXK5HDKZjDdGYR0hVWdgYMD7j4466z7ckCFD0KRJE4wcORJPnjzh3ffHH3/g3r17+OOPP9C2bVuBKiREdaSnp+PChQs4c+YMLl++XOHjDh8+jP3799MmU4QQQkg1YhgG3t7emDVrFtLS0vDixQuwLIu7d+9i6dKl2Lx5M6ysrIQuk9RhIpEIZmZm6NOnD/r06YOCggK8ePECAQEBCAwMREREBJKSkpCUlITXr1/z8iGWZREeHo7w8HD8/vvvMDAwQNOmTdGiRQs0btwYjo6OsLGxgbW1NXR1detMcKcUYZ1EIikzRifqhFSdgYEBUlNTuWMK6z6Os7Mz7t27h2+//RY//fQT775Xr16hQ4cOWLZsGVasWEH/ZxGiID09HX///TeOHz+OO3fulLkoVx5PT0/emiaEEEIIqR5isZjb2Gnjxo1ISUkBABw/fhxWVlZYsWIFjIyMhC2SfDK0tLTQuHFjNG7cGBMmTEB6ejrCwsLw/PlzhIWFITIyEi9fvkR0dDTy8vJ4n/v27Vv4+vrC19cXmpqasLe3h6enJxo0aABXV1c4OTnB0dERtra2ZXaqVSVKUbniFFiAOusI+RCKm0xQWPfxNDU1sXPnTnTs2BGTJ0/m/Z3K5XKsXbsWly5dwuHDh+Hm5iZgpYQILy8vD2fPnsWRI0dw8eJFSKXSSn2enp4eevfujfXr15dZw4QQQggh1UNXVxdjx45FfHw89u7di9zcXMhkMvz666+wsLDAnDlzoKmpKXSZ5BPDMAzMzMzQvn17tGvXDgUFBUhISEBUVBSioqIQGhqK58+f4/nz54iPj+d9bmFhISIiIhAREYEzZ87AxMQE9vb2qF+/PhwdHdGwYUM0atQI7u7uMDQ0VKmuOwrrCKlDFNdQo7Cu+gwfPhwtW7bEuHHjcO/ePd59/v7+aNq0KTZt2oQZM2ZQ2EA+KSzL4uHDh/jll19w4sQJ5OTkvPdzGIZBmzZt0Lt3b3Tr1g3e3t7UnUoIIYTUAjMzM8yZMwcxMTE4ffo0ZDIZ3r59iz179sDKygrjxo2jc1kiGIZhoK2tDRcXF7i4uEAmk+HNmzdITU1FSkoKoqKiEBAQAH9/f4SEhJQ578zIyEBGRgaCgoIgFothbm4OS0tL2NraomHDhmjVqhW8vb1hZ2en9K9zCusIqUOos65mOTo64vbt29i0aRNWrlzJ6xrKz8/HrFmzcO7cORw+fBgWFhYCVkpIzcvMzMQff/yBX375BSEhIe99vLa2Nnr27ImBAwfis88+o58RQgghRCC2trbYsGEDXr58icDAQABAXFwcdu7ciXr16qFr164CV0hIMTU1NZiamsLU1BQeHh5o27Yt+vfvjzdv3iA5ORlPnjzBnTt3cPfuXaSmpvJ2ipVKpdw6eP/99x9u3bqFP//8E4aGhnB2dkbbtm3Rrl07NG/eHLq6ugJ+l+WjsI6QOoTCupqnpqaGpUuXomfPnhgzZgxevHjBu//q1ato2rQpjh07ho4dOwpUJSE1x8/PDz/99BP+/vvvMjt3KdLS0sKAAQPw+eefo2fPntDR0amlKgkhhBBSEYZh4OzsjP3796NHjx54/fo15HI5goKCsH37dtSrVw+urq5Cl0kID8Mw0NDQgJmZGUxNTeHk5IQWLVpg/PjxyM3NRXBwMG7evIkbN24gMDAQcrmc+1yWZZGbm4vc3FwkJCQgPDwcN27cgJaWFoyMjNCmTRt06dIF1tbWMDQ0hJeXV5lZa7VNKcK68jaYoLCOkKpTDOuys7MFqqTua9myJQICArBw4ULs3r2bd19SUhK6dOmCHTt2YMaMGQJVSEj1kUqlOHXqFLZt24YHDx6887EikQjdunXDmDFjMHjw4DL/LxFCCCFEeAzDoHHjxjh06BAGDBgAqVQKmUyGS5cuwd3dHUuXLqUNJ4jSYhiGmzKrra0NIyMj2NjYoHv37pDJZEhLS8P169dx8eJF+Pr6IiUlhdd1J5PJuPAuPT0dUVFROH78OLd+3g8//IChQ4dCS0tLsO9RKSbpltdZR2vXEFJ11FlXu3R1dbFr1y5cu3YNNjY2vPvkcjlmzpyJ+/fvC1QdIR8vIyMDP/zwA5ycnDBy5Mh3BnXOzs7YuHEj4uPjceXKFUyYMIGCOkIIIUSJiUQi9OzZE2vXruXGioqKcOXKlUotcUGIsmAYBiKRCOrq6tDU1ISdnR0mTJiAY8eOISIiAtevX8fcuXPh6uoKNTW1Mp8vl8shkUhQVFSExMRESKVSwde0U4rOOsWwTiwWq9QuHYQoCwrrhNGtWzcEBgZi7NixuHr1KjfOsiwmT56MwMBA2lmLqJSIiAhs3boVBw8eRF5eXoWPU1dXx5AhQzB16lR07txZ8JMaQgghhFQey7IoLCws87teQ0MDYrFSRAWEfBCWZVFQUID8/HxkZWXhzZs3KCwsrFTOpCzns0rxE6gY1tEUWEI+DIV1wrGwsMClS5ewaNEi/O9//+PGQ0NDsX79eqxevVrA6gipnLCwMHz//fc4cuQIb50PRXZ2dvjmm28wceJEmJub12KFhBBCCKkuEokEx44dw/r167kxQ0NDDBgwAA0aNBCwMkKqhmVZLpjLyspCcnIyHj58iLt378Lf3x+pqakVfi7DMNDT04OhoSE0NDRgYmICQ0ND3rRZIVBYR0gdorgIJoV1tUskEmHDhg24efMmnjx5wo1v2LABAwcORPPmzQWsjpCKhYSEYN26dTh+/Pg7T0zatGmDOXPmYPDgwbRcBSGEEKLCStan+/bbbyGTyQAAOjo6GDJkCMaNG0dLWRClJ5VKkZGRgeTkZCQlJSEsLAwBAQF48uQJwsLCuNd1ebS0tGBpaQlLS0vY2NigcePGaNq0KUxNTWFgYAA3NzfBZ0ZRWEdIHUKddcITi8X49ddf0bJlS+4XhEQiQZ8+fXDnzh24ubkJXCEh/+e///7DunXrcOLEiQofIxaLMXz4cMyePRutW7euxeoIIYQQUhNYlsWdO3ewcOFCZGZmAihe2qJ9+/aYOXMmHBwcBK6QkLJKuudiYmIQFRWFly9fIjQ0FCEhIQgJCUFGRkaFF51FIhHMzc3h6OgIR0dHODs7w9PTE56ennB1dYWOjk4tfzfvpxRhneJusHS1npAPQ2GdcmjatCkWLFiAjRs3cmOpqano0aMH7t69i3r16glYHSFAQEAA1q5di3/++afCx+jq6mL69OmYNWsW7Ozsaq84QgghhNSop0+fYvXq1YiIiABQPA3Qzc0NCxYsQNOmTYUtjpBSZDIZ4uPjER4ejtDQUISHhyMqKgqvXr1CXFwc8vPzK/xcDQ0NuLi48EI5Z2dnODk5wdzcnNtRVlkpRVin2J5Y3u4chJD3UwzrsrOzBaqErFq1Cg8ePMDt27e5sdjYWPTo0QO3bt2ClZWVgNWRT5Wfnx/Wrl2L8+fPV/gYfX19zJo1C3PmzIGZmVktVkcIIYSQmhYXF4eNGzfiwYMH3Pq0JiYm+O6779CpUyelDi9I3ceyLHJzcxEaGorHjx8jICAAUVFRSEpKQlJSEtcJWhFra2s0adIEzZs3h5eXF+rVqwdra2tYWlpCR0dHpV7fShHWKS5iTWEdIR9GMawrKChAUVERTS0XgKamJs6ePYuuXbvy1q8LDw9H165dcfPmTVhaWgpYIfmUPHjwAGvWrMGlS5cqfIyhoSFmz56N2bNnw8TEpBarI4QQQkhtePv2LXbu3Il///0XhYWFAIrfe69atQoDBgygGW5EECzLIiUlBffv38fdu3cREBCAxMREvHnzBm/evCkzE7M0LS0tNGjQAB06dEC7du3g6uoKY2NjGBkZQV9fX6WzJaUI6xQ765Rlq1xCVE15C8FmZ2fD1NRUgGqIgYEBLl26hA4dOiAsLIwbDw0N5QI7CwsLASskdd2dO3ewZs0aXLt2rcLHGBsbY968efjmm29gZGRUe8URQgghpNZIpVIcOXIEhw8f5s2+mTlzJiZNmgQtLS0BqyOfmoKCAgQFBeHmzZu4efMmnj9/jvz8fOTn56OgoOCdG54ZGhqiQ4cO6N69O9q1awc7Ozvo6OhAW1sbYrFYpbrn3kUpwzpVTj8JEVJ5Yd3bt28prBOQmZkZrl27hk6dOuHly5fc+PPnz7nAztzcXMAKSV3Dsixu3bqFNWvW4NatWxU+ztTUFN9++y2mT59OO74RQgghdRjLsvD19cXu3buRlJTEjffo0QPr1q2Dtra2gNWRuqp04MayLF6/fg1fX19cuHABN2/eRGpqKqRSKaRSaZnZlqUxDIOGDRuiR48e6NGjB1q1agUdHR2IxWKoqakp/dpzH0opwjrFfxjqrCPkw+jo6EAkEvF+pmiTCeHZ2tri5s2b6Ny5M169esWNh4SEoGvXrrhx4wYFduSjsSyLa9euYc2aNbh7926Fj7OwsMCCBQvw9ddfQ09PrxYrJIQQQkhtY1kWkZGR2L59O4KDg7lxDw8PHDhwQCl3wSSqi2VZyOVyyGQyyGQyRERE4NKlS7h06RL8/f2Rm5v7zq45NTU1iEQiGBsbw8fHB3379kWvXr1gZ2cHkUjEhXJ1MZxTpBRhHXXWEVI9GIaBvr4+srKyuDEK65RDvXr1uMAuKiqKG3/27Bm6deuGGzdu0GL+5IOwLItLly5hzZo1ePjwYYWPs7KywqJFizB16lQ6MSeEEEI+ASzLIj09Hb/++ivOnj3LjVtaWuLnn3+GlZXVJxF6kJpTEs4VFhaioKAAmZmZePjwIa5fv44bN24gJibmnZ+vrq4OTU1NaGlpwcbGBl27dkWPHj3g4+MDY2PjT/r1qRRhHXXWEVJ9DAwMKKxTUvb29lxgFx0dzY0HBwdzgR1NWSaVxbIszp8/jzVr1uDx48cVPs7W1haLFy/GpEmTaJoLIYQQ8olgWRYFBQU4efIkduzYwY0bGRlh7dq18Pb2piYZUmUlXXEFBQXIyspCVlYWYmJicP/+fdy+fRuPHz9GTk5OhZ+vpqYGPT09GBgYwMjICE2aNEG7du3Qvn17NGjQgF6TpShFWEeddYRUH8W1p0ovIEuE5+DggFu3bpUJ7J4+fYru3bvj2rVrFNiRd5LL5Thz5gzWrFmDoKCgCh9nb2+PJUuW4Msvv4SmpmbtFUgIIYQQQZV0O127dg0rV65EQUEBAEBfXx9Tp07FoEGD6AIeqZKioiJkZGQgNTUVqampCAsLg5+fH/z9/REZGQmpVFrh52pra8Pc3ByWlpawt7dHs2bN0Lp1a7Ro0QJGRkafdPfcuyhlWEeddYR8OMWwjjrrlI+DgwNu3ryJTp06ITY2lhsPCgpCjx49cO3aNZiYmAhYIVFGcrkcJ0+exNq1a3lrzihydHTE0qVLMX78eGhoaNRihYQQQghRFg8fPsSiRYuQkpICANDU1MRnn32GSZMm0dIr5L3kcjmys7MRGxuL6OhovHr1Cs+fP8ezZ88QEhLCm8mlSCQSwcrKCvb29nBwcICzszO8vLzg5eUFJycnCoorSSnCOsVpsNRZR8iHo7BONdSvXx+3bt1Cp06dEBcXx40HBgZygZ2xsbGAFRJlIZPJ8Ndff2HdunV4/vx5hY9zcXHBsmXLMGbMGKirq9dihYQQQghRJqGhoVi4cCFCQ0MBFIcnrVu3xqxZs+Di4kKdTKQMlmXBsixiY2MRFhaGsLAwhIeHIzIyEpGRkUhISIBEIqnw8/X19eHi4oIGDRrA3d0dLi4ucHZ2hqOjI8zNzek19wGUIqyjabCEVB8K61SHo6MjF9jFx8dz4wEBAVxgZ2RkJFyBRFBFRUU4fPgwNm7ciIiIiAof5+7ujuXLl2PUqFEQi5Xi1zohhBBCBJKUlITFixfj/v373JizszPmzp2LFi1a0Cw2wvPmzRs8e/YMQUFBCAoKQkxMDBITE5GYmIisrKwKd25lGAbW1tZo3rw5mjVrBk9PTzg4OMDGxgZWVlbQ0NCggO4jKcVZPU2DJaT66Ovr844prFNuTk5OXGCXkJDAjT958gSDBg3CpUuXoKWlJWCFpLbl5+fjt99+ww8//MDrulTk6emJFStWYPjw4XSRixBCCCHIzc3F8uXLcfnyZW7M1NQUs2bNQs+ePWl5DAKpVIq4uDg8ePAA9+7dQ3BwMFJSUpCZmYnMzMx3rj2npaWFpk2bwsfHB61bt4aLiwtMTU1hYmICPT09Oh+tZkoR1tE0WEKqD3XWqR5nZ2du04nSgd3t27cxYcIEHDt2jK5MfQKys7OxZ88ebNmyhVtfpjyNGzfGihUrMGTIELq4RQghhBAAxQ0wmzdvxqlTp1BUVASgeJ26MWPG4IsvvoCOjo7AFRIhsCwLiUQCf39/3Lp1C7dv30Z4eDhyc3ORm5uLwsLCd3bP2djYoEOHDujatSt8fHxgamoKXV1d6OjoQCwW03uUGqQUYR111hFSfSisU00uLi64efMmOnbsiOTkZG78r7/+go+PD+bOnStgdaQmZWRkYOfOndi+fTsyMzMrfFzz5s2xYsUKDBgwgH5PEkIIIYTDsixOnjyJ/fv3482bNwCKg5b27dtjzZo10NXVFbZAUuMUA7fY2FjcvHkTV69exa1bt5CVlQWJRAKpVFqmWao0NTU1NGrUCL169UKvXr3g5eUFXV1dqKurc8utUEBXO5QirKPOOkKqD4V1qsvV1RUXL15Ex44dkZ2dzY0vW7YM48ePh6mpqYDVkeoWExOD7du3Y9++fcjJyanwce3bt8eyZcvQq1cvOjkihBBCCA/LsggICMC2bdsQExPDjdevXx8HDx6EgYEBnT/UQSXhHMuykMvlKCgoQEhICC5cuIALFy4gJCSE65qrqHNOJBJBJBLB2NgYXbp0Qa9evdC9e3fY2tqCYRjudUOvH2EoRVhHnXWEVB/FsK506EOUX9OmTXH8+HH07duXG8vPz+c2nSCqz8/PD5s3b8aJEyfeeWWzZ8+eWLZsGTp27FiL1RFCCCFEVbAsi4SEBGzbtg2PHj3ixo2MjHDs2DHY2toKWB2pbizLQiaToaioCEVFRcjKysKdO3e47rnY2Nh3fr5YLIampia0tLTg6uqKbt26oVevXvD29oampiaFckpGKcM66qwj5MNRZ53q69SpU5kxuoih2mQyGc6cOYMtW7bg3r1773zsoEGDsHTpUnh7e9dSdYQQQghRNSzLIisrCwcOHMDp06e5C4CGhobYuXMnnUfUEXK5HPn5+cjOzsbbt2/x4sUL3L17F3fu3MHjx4+59QnLIxaLoaurC319fRgaGqJ58+bo1KkTOnToACcnJ25aK1FOSvGvo9iWSW9KCflwFNapvtJXRoHi/xNbtWolUDXkY2RmZuL333/Hrl278PLlywofJxKJMHLkSCxduhSNGjWqxQoJIYQQoooKCgpw7tw57N27F7m5uQAAPT09zJkzB8OHD6cuKRXFsiykUilev36N169fIykpCc+ePcPDhw/x6NEjJCQklGl2KsEwDHR1dWFubg5zc3M4ODigZcuW8Pb2RrNmzWBkZFS73wz5KEoR1imqaE41IeT99PX1eccU1qmeO3fu8I6bNWtW5t+VKC+WZeHv7489e/bg2LFjKCgoqPCxenp6mDRpEmbPng1HR8darJIQQgghqkoqleL+/fv43//+h8TERADFO7+OGjUKM2bMgIaGhsAVkqqQy+XIzMxEfHw84uLiEB0djeDgYAQHByM0NJTbNKQ8IpEIVlZWsLe3h4ODA9zd3dGkSRM0adIEDg4OUFNTo+BWRSlFWKf44qGwjpAPV96adXK5nDpWVcjt27d5xx06dBCoElIVWVlZOH78OPbu3YvAwMB3PtbOzg6zZs3ClClT6ConIYQQQiqNZVmEhYVh3bp1ePr0KYDiwKZnz56YN28eTE1NKZxRcizLoqioCHFxcQgPD0dYWBgiIiK4W3x8/DszESMjIzg7O8PNzQ0uLi5wc3ODm5sbnJ2dYWxsTO/76ggK6wipYxTDOpZlkZubS51ZKiI7O7tMZ115a9gR5SCVSnH16lUcPHgQZ86ceWcXHQA0b94c8+fPx/Dhw6Gurl5LVRJCCCGkLmBZFmlpaVi+fDnv4m6rVq0wd+5cuLi4UFCjpErWGAwMDERwcDBCQkLw6tUrxMXFISEhAbm5uRXmIGKxGPb29mjatCmaNm0Kd3d31KtXD7a2trCysqJOyjqKwjpC6hjFsA4ongpLYZ1quHbtGiQSCXesrq6Obt26CVgRUcSyLB4/foy//voLhw8fRnJy8jsfr66ujqFDh2LatGno0KEDXe0mhBBCyAeRSCRYtGgRLly4wL1ndnJywowZM+Dj40MXApVQQUEBFi5ciNDQUOTl5SE9PR3p6enIysrinfMr0tbWRvPmzdGmTRt4e3vDyckJZmZmMDU1hZ6eHp1PfgIorCOkjikvlMvOzhagEvIhLly4wDvu2LEjBa1KQCaT4cGDBzh58iROnTqF2NjY936Og4MDvvrqK0ycOBGWlpa1UCUhhBBC6iqWZbF+/XocP36cC3mMjIwwbtw4DBo0CNra2gJXSMojFotx8eJFREZGvvexTk5OaN++PTp27Ahvb2+YmJhAT08Purq6EIvFFNB9YiisI6SOUVdXh7a2NvLz87kx2mRCNchkMpw/f5431rdvX4GqIenp6bh+/TquXr2K8+fPv7eDDij++evXrx8mTZqE3r17Q01NrRYqJYQQQkhdd/ToUWzbto07x1dXV0evXr0wc+ZM6OrqClwdqYiamhoMDAzAMEyZnENHRwfNmjVD79690bNnTzg7O0NLSwvq6upQV1encO4TR2EdIXWQgYEBhXUqyNfXt0wg1K9fP4Gq+TTdv38f//77L65cuYInT55U+veRt7c3xo8fj1GjRsHMzKyGqySEEELIp4JlWTx58gSLFi1CVlYWgOL3z40aNcL69ethYmJCoY4SYxgGdnZ2CAoKAsuyUFNTQ6tWrTBjxgx06tQJlpaWEIlE3FqD9G9JSlBYR0gdpK+vj5SUFO6YwjrVcOzYMd5x06ZN4ebmJlA1n55p06Zh7969lX68q6srhg0bhrFjx8LT07MGKyOEEELIp4hlWcTFxWHevHlITEzkxq2srLB161Y4OjpSuKMCnJ2duX8nLS0tfPbZZxg9ejR3P/0bkvJQWEdIHaS4yQSFdcpPIpHg5MmTvLGRI0cKVM2nJzU1tVJBXaNGjTB06FAMHToUjRo1opMrQgghhNQIlmWRkZGBdevW4fHjx5DL5QAAQ0NDrF69Gp06dRK4QlJZJd1zMpkMcrkcubm5dA5J3ovCOkLqIArrVM/58+eRnp7OG6OwrvYYGRlBQ0MDRUVFvHFtbW106tQJPXr0QL9+/ajTkRBCCCG1IicnB7/99hvOnDnDLW+jra2N6dOnY9KkSQJXR6qi9GZxLMsiNzdXwGqIqlDKsI4Q8nEorFM9+/bt4x23adMGjo6OAlXz6dHQ0MDYsWOxf/9+bkxHRwfx8fEwNjYWsDJCCCGEfGry8/Nx8eJF7N+/H6mpqQCKdxUdPXo0li9fzq1vRlSDvr4+l3nI5XLk5OQIXBFRBUrxU06ddYRUL8WwLjs7W6BKSGXExsbi0qVLvLEpU6YIVM2na968ebzjvLw8HD9+XKBqCCGEEPIpkkqlePLkCbZv347w8HBufODAgVi1ahW0tbUFrI58iNKddSXTYAl5HwrrCKmDqLNOtezbt4/3/56BgQFGjBghYEWfpoYNG6JPnz68sa1bt3JrxBBCCCGE1CSWZREbG4v//e9/ePToETfesWNHLF++HDY2NjQrTQWV7qxjWZY660ilUFhHSB1EYZ3qyMvLw549e3hjY8aMga6urkAVfdrmz5/PO37x4kWZrkdCCCGEkOrGsizevn2LjRs34tKlS5DJZACABg0aYOnSpWjYsCFNf1VR1FlHPoRS/LRTWEdI9Sr9CwGgsE6ZHTx4sMzGEjNmzBCoGtK1a1d4eXnxxrZv3y5QNYQQQgj5VMhkMuzatQt//vknCgsLAQBmZmaYP38+OnbsCLFYKZabJx9AsbMuNzeXZm6Q91KKsE7xCgG9cAn5ONRZpxpkMhm2bt3KG+vduzcaNmwoUEWEYRjMnj2bN3blyhWEhIQIVBEhhBBC6jqWZXHu3DmsW7cOeXl5AABNTU1MnjwZQ4YMgZaWFk1/VWGlwzqgeF3CkkCWkIooRVinrq7OO5ZIJAJVQkjdQGGdavjrr78QERHBG1Ochklq3+jRo2FmZsYb27Fjh0DVEEIIIaQuY1kWz58/x8SJE5Gfnw+g+OJh7969MXXqVBgZGVFQp+L09PR4xzKZjAtlCakIhXWE1EEU1ik/mUyGNWvW8MaaNm2Kbt26CVQRKaGtrY2vv/6aN3bo0KEy05UJIYQQQj4Gy7JITk7GiBEj8ObNGwDFQV2TJk0wf/581K9fn4K6OkBPT4/37yiXy7lglpCKKEVYpzj/nsI6Qj6OYliXnZ0tUCWkIsePH0dYWBhvbOXKlXRCpiSmTZvG+91UUFCAffv2CVgRIYQQQuqazMxMzJgxA+Hh4dyYjY0NZs6cifbt29N5YR0hFouhra3NHUulUtpkgryXUoR11FlHSPWizjrlVlBQgOXLl/PGmjZtioEDBwpUEVFkY2ODkSNH8sZ++ukn+v1ECCGEkGqRlZWF//3vf7h8+TK386uBgQEmTJiAcePGUVBXx5R+f0addaQyKKwjpA5SDOsKCwtpEVMlsmPHDkRFRfHGVq1aRSdlSmbOnDm844SEBJw8eVKYYgghhBBSZ+Tl5eHYsWM4cOAAt3aZWCzGsGHDsHDhwjLvjz8FJbukhoWF4f79+4iNja1TG0+Wfn9Ga9aRylCK/Z8prCOkeunr65cZy87OhqampgDVkNJSU1Oxbt063lj79u0xYMAAgSoiFWnZsiXatWuHe/fucWPbt2/HqFGjBKyKEEIIIapMIpHg+vXr2LlzJ5KTk7nxvn37YtWqVTA0NBSwutonl8uRmpqKgIAA+Pv7w8/PD/Hx8WjcuDFWrVoFJyenOnFBm8I6UlVKGdZJpVKBKiGkblDsrAOKp8Iq7nBJat/KlSvLrCG4ZcuWOnESUhfNnj2bF9Y9fPgQDx8+hI+Pj4BVEUIIIUQVsSyLgIAAbNu2jbd2cdu2bbFhwwbUq1dPwOpql0QiQVBQEHx9ffHo0SMEBwcjKiqKmw0UHBwMbW1t7Ny5ExoaGip/rly6mYLCOlIZShHW0QYThFQvHR0diEQiXus4rVsnvKdPn+KXX37hjY0bNw7e3t4CVUTeZ/DgwahXrx7i4uK4se3bt1NYRwghhJAqi46Oxq5du3D//n1unToXFxf8+OOPaNCggcDV1Y74+Hhcv34dly9fRlhYGGJjY5Genl7mcSzL4ujRo+jUqRNGjx4tQKXVizrrSFXRmnWE1EEMw9AmE0pGJpNh8uTJvABVW1sb69evF7Aq8j5isRgzZ87kjZ04cYI3bYUQQggh5H3evHmDgwcP4tSpUygoKAAAGBsbY/PmzWjVqpXA1dWswsJC3Lt3D3PmzEH//v2xaNEi/P333wgMDCwT1IlEIq6LLicnB8uXL0dMTIwQZVcrCutIVVFYR0gdpRjWKU69JLXrp59+gr+/P29s4cKFsLOzE6giUlmTJ0+GtrY2dyyVSvHrr78KWBEhhBBCVElRUREuXbqErVu3Ijc3F0DxBcHVq1ejW7duUFNTU/lpnopYlkVGRgYOHjyIfv36YfDgwdi7dy+CgoKQkpLCW/pKJBLB3d0d33//Pc6ePYtmzZpx98XExGD+/PlcJ6KqorCOVBWFdYTUUdRZpzxiYmKwbNky3pi7uzsWL14sUEWkKoyNjfH555/zxn755ReVP2kkhBBCSM2Ty+V4/vw55s6dyzsfnzp1KkaMGAEdHZ06EdSxLAuWZSGXyxEaGooFCxagadOmmDJlCq5fv460tDRuPboS6urq6Nu3L06ePIlHjx5h4cKF6NOnD6ZMmQJTU1Pu616+fBl//vknWJYV4lurFopr1uXn5wtYDVEFShnWyWQylf5BJEQZUFinHFiWxYwZM7irqCX27dsHLS0tgaoiVTVt2jTecVxcHP7991+BqiGEEEKIKmBZFmlpafjiiy+4JTQYhkHXrl3x9ddfw8LCQqWDupJwrqioCJmZmTh8+DC6desGT09PbN68GXFxcZBIJNx7e5FIBE1NTTg5OWHlypUICwvD+fPnMXDgQBgYGEAsFkMkEuHLL79E27ZtoaamBgDIzc3F+vXrERMTo7I5AW0wQapKKTeYAIq76zQ0NASohpC6ofQvBIDCOqEcOHCgTKgzdepUdOjQQaCKyIdo2bIlWrZsicePH3Nje/bswYABAwSsihBCCCHKLDs7G9OnT8d///0HoDioc3d3x+zZs9GwYUOVDepYlkVRURHevn2LFy9e4OTJkzh27BiSkpLKPFYkEkFbWxuGhoZo3749xowZg549e0JTU7PC719TUxPr1q1DYGAg4uPjARTPVNmwYQO2bdvGW55EVdA0WFJVShHWlRfKFRUVUVhHyEegzjrhvXr1CrNnz+aNWVlZYdOmTQJVRD7G9OnTMXHiRO748uXLePXqFZycnASsihBCCCHKKDs7Gxs2bMCpU6e4MQsLC3z11Vfo1asXRCKlmORWaSVddJmZmUhISIC/vz9OnjyJu3fvIicnp8zjNTQ0YGFhAUdHR3Tr1g3Dhg2Dm5tbmVl1FWncuDEWLFiAxYsXIz8/HwUFBbh06RLOnj2LoUOHltvwo8wUwzqaBkveRyle4eUl4/n5+dDT0xOgGkLqBgrrhCWVSjFu3LgyJy8///wzjIyMhCmqFshkMpw9exYpKSkYOXIkjI2NhS6p2owcORLz5s3DmzdvABSftP78888UvhJCCCGEp6CgAIcOHcK2bdu4MT09PYwePRrjx4+HpqamcMV9gJycHERFReHFixfw9fXFlStXEB4eXu6UVAsLCzg7O6NRo0bo1asXOnToAHNz8w/qIpwyZQpu3bqF06dPAwDi4+Px22+/oUmTJnB3d1epzkTqrCNVpdRhHSHkw1FYJ6xNmzbh/v37vLHJkyfX6WmTL1++xPjx47nve+PGjbh7926d2fFWR0cHX3zxBe/E+7fffsPq1atp/UFCCCGEACgOYs6cOYMNGzagoKAAQPEa7b1798bMmTNhYmIicIWVI5VKERsbi6dPn+LJkye4f/8+/Pz8yu2i09HRgYeHBxo3bozWrVujbdu28PT0/OjuN21tbaxbtw4hISF48eIF5HI5/Pz8cPToUcydO1elLoBTWEeqisI6QuooCuuEc//+faxatYo35uzsjK1btwpTUA1jWRa//vor5s6dy9tIIyYmBp999hnu3LlT5vWoqr7++mteWJeeno4TJ05g7NixwhVFCCGEEKXAsixu3bqFTZs2ceu3MQyDVq1aYdmyZXB0dBS4wndjWRbZ2dkIDAzEnTt38PjxYwQFBSE2NrZMFx3DMLC1tUXbtm3Rpk0bNG/eHF5eXjAyMqrWjjcPDw+sWLECX331FfLy8pCVlYW//voL3t7e6N27t8pMhy29nrhcLkd+fj5kMhm3iQYhipTila2urg6RSAS5XM6NUVhHyMdRDEeys7MFquTT8vr1a4wcORJSqZQbE4lE+OOPP+rk1P7k5GRMnToV586dK/f+p0+fYujQobhw4UKl1yhRZu7u7ujatStu3LjBjf38888U1hFCCCEEz549w5YtW/Ds2TPuva2DgwM2bNiAJk2aCFxdxeRyOeLj43H58mXcuHEDISEhiI6OLvf9g4aGBpo1a4bevXujTZs2cHNzg62tLdTV1WtkWirDMBgwYADu37+PPXv2AAAiIiLw+++/w9PTU2XWDlZ8byaRSFBUVKSSm2WQ2qEUYR3DMNDW1uZ1ZFBYR8jHoc662ieXyzF+/Hhu16oSy5cvR5s2bQSqqmbI5XLs378fCxYs4NZwq8i1a9cwZcoUHDhwQKXWFqnItGnTeGHd3bt38eLFC7i5uQlYFSGEEEKElJSUhN27d+PmzZuQSCQAAF1dXfz0009o27atUp4DFRUVwdfXF6dPn8adO3eQkpKCjIwM3kXnEsbGxhg4cCAGDBiARo0awcLCAvr6+jW+UQbDMNDT08OMGTPw8OFDBAYGQiaT4dKlS+jYsSMmTZoEXV3dGq2hOhgYGIBhGK5DUSKRoKCggMI6UiGl2YJG8UVKYR0hH6d0qzVAYV1t+OGHH3Dx4kXeWJcuXfDdd98JVFHNCAsLQ5cuXTBlypRyg7oJEybAwsKCN3bw4EGsXr26liqsWQMGDICZmRlv7PfffxemGEIIIYQILjc3F3/++SeOHj3Kex+7ZcsW9OzZU+mmOsbExGDz5s1o06YNPv/8c/z2228IDg5GamoqL6hjGAZt27bFrl278OTJE2zduhX9+vWDq6srDA0Na21HW5FIBBcXF3z77bdcbpCbm4stW7Zwa9kpO8UZNlKplFvTkJDyUFhHSB1FnXW1686dO1i+fDlvzNLSEkeOHFG6E7QPVVBQgNWrV6NJkybw9fUtc7+FhQXOnTuH33//HefPn4eOjg7v/tWrV2P//v21VW6N0dDQwLhx43hjBw8eLPcqNCGEEELqLpZlIZPJcPv2bWzbtg1ZWVncfXPmzMHYsWMFXVONZVnuVlBQAF9fX4wfPx7e3t5YsmQJAgMD8fr1axQWFvI+z8zMDBMnTsS9e/dw6dIlTJ06FfXr14eRkZFgy5poaGigc+fOGD16NDcWExODH374Abm5ueXuTKtMRCIRL7Ar6awjpCIU1hFSR1FYV3tSUlIwatQoyGQybkwkEuHIkSOwsrISsLLqwbIsTp8+DU9PT6xatQpFRUVlHjN69Gg8e/YM/fr1AwB4e3vj2LFjZa64Tp06FZcvX66VumvSxIkTeceJiYm4cuWKQNUQQgghRChhYWFYuXIlEhISABR3o/Xq1QtLliyBtra2INNfWZaFXC6HRCJBamoqfvnlF3Tq1AndunXDH3/8gbS0NEgkEi7gEolE0NLSQqNGjbBlyxYEBARg37598PHxgZ6eHsRiseDTeBmGgbW1NcaMGQN3d3du/NixY/j3338hl8uVPrArPfOJOuvI+1BYR0gdVd4GE6rQIq5qpFIpRo0ahcTERN74ypUr0bVrV4Gqqj7Pnz9Hz549MWTIEERFRZW5v379+rh06RL+/PNPmJub8+7r378/fvrpJ96YTCbDsGHDEBQUVJNl17hGjRrB29ubN1YXugYJIYQQUjksyyIlJQXr16/H48ePARQHSp6enti4cSPMzMxqPeBiWRb5+flIT0/HgwcPMGfOHDRu3Bhff/01/Pz8ymyApqenBysrK4waNQqnT5+Gv78/5s6di3r16kEkEoFhGMFDutIYhkHr1q0xby4UJAAAMjtJREFUZswY3gyO+fPnl1kzWhlRWEeqQik2mAAorCOkuimGdQCQk5NT7jj5cMuWLcOtW7d4Y927d8eyZcuEKaiapKenY+3atfjpp594HYMl1NTUMG/ePKxcufKdi/pOmzYNMTEx2LRpEzeWk5ODvn374uHDh7C3t6+R+mvDxIkT4e/vzx2fPXsWaWlpZUJLQgghhNQtLMsiJycHe/bswZEjR7hxW1tbrFq1Cp6enrW2nhvLspBIJMjMzERiYiJu3LiBkydP4sGDB+U+XkdHB2ZmZnB2dsaAAQMwZMgQlTof09HRwdChQ/Hw4UNcvnwZMpkMiYmJWLJkCfbt26fUm02Ufh9G02DJ+yhNWKe4thGFdYR8nPJCuezsbArrqtGpU6fwww8/8MZsbW3x559/quw6dfn5+di+fTs2bNhQ4dTpDh06YMeOHWjatGmlvub69esRExODY8eOcWNJSUno378/7t69W2YzFFUxatQozJ07lzvRkkgkOHLkCGbPni1wZYQQQgipSRKJBEePHuVdjDQ1NcWMGTPQvXt3aGho1HgNcrkc2dnZiI6OxrNnz3Dt2jVcunQJKSkpZaaDqqmpwdLSEk5OTmjVqhX69OmDdu3aQUtLS6k65yqrQYMGGDNmDEJCQhATEwMAOH78OLp3744vvvii1oLSqmAYhjrrSJUoTVhHnXWEVK/yApC3b9/C1tZWgGrqnvDwcHzxxRe8MXV1dZw4caLMTqiqQCaT4dChQ/juu+8qnEZga2uL//3vfxg5cmSVTuxEIhF+//13JCUl4fbt29z406dPMWbMGJw+fVolw00jIyMMGTKEd0WdwjpCCCGkbpPL5Thz5gyWLVvGbcygp6eHESNGYPTo0TAyMqqx5y5Zi+7Vq1d49uwZ/P39cfv2bfz333/Izc0t83gdHR14enqiSZMmaNu2LTp06ABHR0dBN72oDgzDoF+/fnjw4AH279+PvLw8yOVyfP/992jSpAlatGghdInlos46UhVK81NKYR0h1UssFkNbW5v3s0SbTFSPnJwcDB06FNnZ2bzxbdu2wcfHR6CqPgzLsrh06RIWLlyIZ8+elfsYDQ0NLFiwAEuWLPngqQWampo4ffo02rZti7CwMG783LlzWLhwITZv3vxBX1do48aN44V1fn5+iIyMhIuLi4BVEUIIIaSm3LhxA8uWLcPr168BFF+s7dSpE6ZNm4Z69erV2PO+fv0ajx8/xoMHDxAQEICnT58iLi6u3E0VnJ2d0aZNG7Rq1QrNmzeHp6cnjI2Na6w2IRgYGGD69Ol48uQJHj16BLlcjpiYGKxfvx4///wzzMzMhC6xDENDQ+5jCuvI+yhNWKc4Dba8KwOEkKoxMDCgsK6asSyLKVOmICQkhDc+btw4TJs2TaCqqo5lWdy6dQurV6/mdbspGj16NNatWwdHR8ePfk5jY2OcP38erVu3Rnp6Oje+ZcsWuLu7Y+rUqR/9HLWte/fuMDc3R1paGjd25MgRfPfddwJWRQghhJCa8N9//+G7775DZGQkgOIOrwYNGmDRokVo2LBhtU8plUgkeP78Oa5evYq7d+/ixYsXiIqKKjfkMTAwQJs2bdCjRw80a9YMLi4usLW1VcnZC5Xl4eGBefPmYfLkycjKyoJMJsP169dx4MABzJ8/X+mmw5burKNpsOR9lCasU5yyp9ixQgipOgMDA6SkpHDHFNZ9vJ07d/LWXgOAxo0bY+/evSqx5gfLsrhx4wZWr16NO3fuVPi4rl274ocffqj2aQTOzs44deoUunfvDolEwo3PmDEDzs7O6NatW7U+X00Ti8UYOXIkb9fbI0eOYMWKFSrxeiCEEEJI5SQmJmLVqlV4/Pgx181mZmaG1atXw8fHp9qCIZZl8fbtW9y8eROnT59GQEAAEhMTkZmZWaaLTiQSwcHBAQMHDkSvXr3g5uYGKysraGtrfxLnIQzD4LPPPsP48eOxa9cuyOVyvH37FocPH4aPjw86dOggdIk8itNgaTYheRelCesUF72nUIGQj0c/V9Xr3r17mD9/Pm/M0NAQJ0+eLNMdrGxYlsW1a9ewevVq3Lt3r8LHeXl54YcffkCvXr1q7CSvY8eO+OWXX/Dll19yY1KpFMOGDcPDhw/h7u5eI89bU0aPHs0L68LDwxEYGIjmzZsLWBUhhBBCqktubi42bdqEq1evchcb1dXVsXz5cvTp0wfq6uof/RwsyyI6OhpHjx7F6dOnERUVhezsbBQVFZV5rFgsRvv27TFmzBh07twZ5ubm0NXVhZqa2icR0pWmra2N5cuX49q1awgNDQXLsggNDcUvv/yChg0bwsTEROgSObTBBKkKpekLpc46QqofhXXVJzk5GcOHD4dUKuWN//HHH0q9PhnLsrh8+TLatWuHnj17VhjU2dnZ4cCBAwgMDETv3r1r/ETviy++wOLFi3ljb968Qb9+/XhTZFWBj49PmWnCpdexI4QQQojqkslkOHjwII4fP85bqmnixIn4+uuvoamp+UFfl2VZsCyLwsJCnDt3DkOHDkWTJk2wevVqPHnyBOnp6WWCOlNTU8yePRuPHj3C+fPnMWHCBDg7O8PQ0BBisfiTC+pKmJub46effuK+f4lEgmvXruHUqVPlruknlNKZB61ZR95HacI6xVCBwjpCPh79XFUPiUSCkSNHIikpiTe+dOlS9O/fX6Cq3q1k44g2bdqgd+/eePDgQbmPq1evHnbv3o3IyEh88cUXtbquyffff48hQ4bwxiIjIzF06NByryIrK4ZhMHr0aN7Y0aNHIZfLBaqIEEIIIdWBZVncvHkTP//8M29pmY4dO2Lr1q3Q0NCo8teTy+WQSCSIjIzE2rVr4e7ujkGDBuH06dNcJ11JwCQSiaCpqYkuXbrg8OHDiI6OxpYtW9CsWTPo6upCXV39kw3oSmMYBp06dcKMGTO4seTkZBw/fhzBwcFKE9jRmnWkKpQmrFPsrKMOIEI+HnXWVY8lS5bA19eXN9a9e3esWbNGoIoqxrIsLly4AB8fH/Tp0wePHj0q93H29vbYu3cvIiIiMG3atA++KvwxRCIRDh06VGZdvNu3b+Prr79WmhOrylAM6xITE+Hn5ydQNYQQQgj5WHK5HOHh4fjpp5/w9OlTbtzV1RWHDx+GlpZWpb5OSQddQUEB0tLScPnyZYwdOxbe3t5YuXIlYmJieBf4RCIRdHV14eTkhOnTp+PevXu4fPkyxowZAz09PYhEIgroyiESibBixQo0aNCAG7t9+zb++ecfZGdnK8V5JXXWkapQmjXraBosIdWPQvCP9/fff2Pz5s28sXr16uHo0aNKtbsWy7L4999/sXr1ajx+/LjCxzk4OGDp0qX44osvqnw1uCbo6uri7NmzaNWqFRISErjxAwcOwMPDAwsXLhSwusrz9PREgwYNEBoayo2dOnUKPj4+AlZFCCGEkA/BsizS0tLw66+/4ty5c9y4ra0t9u3bB1tb20oFZkVFRcjIyEBSUhKuXr2Kv/76C0FBQZDJZGUeq6urC1NTU7i6umL48OH47LPPKv08pLi7zsTEBOvXr8f48eORnZ0NiUSCgwcPwsfHB127doVYLGz8Ud6adSzL0r8xKZfShHU0XY+Q6keddR8nNDQUEydO5I1paGjgxIkTMDMzE6gqPpZlce7cOaxZswZPnjyp8HH169fHsmXLMH78eKUI6UqzsbHB2bNn0aFDB+Tl5XHjixcvhoeHBwYMGCBgdZU3ZMgQfP/999zxqVOnsGnTJjoBI4QQQlQIy7LIy8vD8ePH8fPPP3Ndb+bm5li1ahVat279zp1f5XI50tPTERcXh+DgYFy8eBFXr15FRkZGmcdqaGjA2toa9erVQ7t27dC3b1+0bt1akBkPdYFYLEbnzp3xxRdfYM+ePZBKpXj16hX27dsHd3d32NvbC3peVvq9mUwmQ2FhIYV1pEJKE9ZRBxAh1Y/Cug+XnZ2NoUOHIicnhze+Y8cOtGrVSqCq/g/Lsjhz5gzWrFmDwMDACh/n5OSEZcuWYdy4cdWyU1lNad68Of78808MGTKEm6bAsixGjx6N+/fvo3HjxgJX+H6DBw/mhXUvX77Es2fP4OXlJWBVhBBCCKkslmUhk8lw4cIFbNq0iTsPNDQ0xNSpUzFkyJByp7+yLIv8/Hy8evUKwcHBePDgAW7fvl3uemklHWANGjRAkyZN0KFDB3Ts2BFWVlYU2lQDAwMDTJo0Cf7+/nj48CEA4OzZs2jbti2mTZtW6enLNVVbaUVFRSgqKhK0JqK8lCasU3zhFhQUQCqVCt6qSogqo7Duw7Asi0mTJvGmNALFO5hOnTpVoKqKyeVy/PPPP1izZg3++++/Ch/n7OyM5cuXY8yYMUod0pU2aNAgbNiwgbdLbG5uLvr37w8/Pz9YWloKWN37NW/eHPb29oiNjeXGTp06RWEdIYQQokLu3buHlStXIjExEQCgqamJQYMGYdKkSTA2NuY9Vi6X4/Xr1/Dz88PDhw/h7++PgIAApKenlwnp1NXV4eLiAh8fH7Rq1QqtWrVCgwYNoKWlRSFdNRKJRHBzc8OUKVMQHR2N5ORkFBUVYceOHfD29kbbtm3f2RlZkxTfmxUWFqKwsJDCOlIupUnCFDvrgOLOFsX/EAkhlUdh3YfZtm0b/v77b95Y06ZNsXv3bsFOpuRyOU6fPo01a9bwFjlW5OrqiuXLl2P06NEqebFj4cKFeP78OQ4dOsSNxcbGYtCgQbh586ZSn8wwDIMhQ4Zg27Zt3NipU6ewcuVK4YoihBBCSKWFh4djwYIF3AVbkUiEDh064JtvvuFNoZTJZAgMDMT169dx//59hIeHIyYmptwNA/T19dGpUyd06dIFzZs3h5ubGywsLFTyPE1VaGtro3fv3nj06BEOHDgAiUSC6OhorF69GsePH4exsbEg5/Q6OjoQiUTc1OqSzjpCyqM0/0OUF9a9ffuWwjpCPgKtBVl1vr6+WLBgAW/MyMgIJ0+ehLa2dq3XI5fLcfLkSaxZswbPnj2r8HFubm5YsWIFRo0apdInfwzD4JdffsHLly9x7949bvzhw4eYPHky/vjjD6W++jx48GBeWPf06VO8fPkSzs7OwhVFCCGEkPdKS0vDrFmzeBt1eXh4YM6cOWjcuDHU1NSQnJyMy5cv49y5cwgLC0NiYiIyMzPL/XqNGzdG//790bVrVzg4OMDa2ho6Ojq19e188qysrDBmzBgEBARw/6a3b9/G9u3bsWrVqlqvh2EYqKmpQVdXl3tPRmEdeReleUdXsg116W2rs7KyBKyIENVHnXVVk5SUhJEjR5bZoevw4cNwcnKq1VpkMhlOnDiB/9fenUdFXe9/HH99h2FTVEBCXIrMXBA11NRSc7esrDTJEqXVyttyNe140/q12a3M0rpWmi22SCmZ0clKq2tmi5aIqdGmuSQJqIiGys78/uAw1+/MYCgw8wWej3PmMN/3fGfmLSrMvOazzJo1S+np6ZWe17FjRz344IO69tprLbU7bXUEBgZqxYoV6t27t/bs2eOsJyUlqXPnzpo5c6YPuzu5fv366YwzztCBAwectVWrVunOO+/0YVcAAOBkCgsLNW3aNK1du9Y5fTUyMlJ33HGHBgwYoB9++EFLly7Vp59+qqysLB05ckQlJSVujxMaGqrhw4crISFBcXFxCg8Pd77PhXfZbDb16dNH8fHx2rlzpw4dOqTi4mI999xzuvjii9WvXz+v92QYhkJCQpxhXcU0WMATy/zUsNlsCg0NNdU87ZgDoOrYuKXqSkpKNHbsWGVlZZnq//d//6fLL7/ca32UlpbqnXfeUdeuXXXddddVGtTFxMTo7bffVnp6uhISEupNUFchMjJSH374oUJCQkz1+++/XytWrPBRV3/Pz89Pl156qam2atUqH3UDAAD+jsPh0FNPPaWUlBQVFxdLkoKCgnTJJZcoJCRE8fHxGjFihF544QWlp6crJyfHFNQZhqG2bdvqoYce0ldffaXXXntNI0eOVHR0tJo2bUpQ50MBAQG68cYb1bt3b+ffw5EjRzR58mS3TeS85cT3Z4ysw8lY6idHeHi46ZiwDqgeTzsO8emNZ0888YS+/vprU+2SSy7x2npjpaWlSkpKUpcuXZSQkOC2uUWF2NhYLV26VNu2bdO4cePqXUh3oq5du+qdd95xm/aamJiotLQ0H3X190aMGGE6XrNmjcc1bAAAgG85HA6tWLFCixYtMi0XU1RUpJUrV+rmm2/Wp59+qtzcXFOoYrPZFBgYqIsvvljJycnasmWLHnzwQcXGxqpx48ay2+2WXrajoTAMQ5GRkZo8ebJat27trG/btk1PPfWUaVaft/ohrENVEdYB9ZhrWCcxus6TTZs26dFHHzXVoqOjlZSUVOthWElJiZYsWaLOnTtrwoQJ+uWXXzye16VLFyUnJ2vr1q31asrr3xk5cqTmzJljqh0/flxXXnmlMjMzfdTVyQ0fPtz0Av348eNuQTAAAPCtsrIybd68WbNnz1ZGRobbbbm5uaYwx263q3Hjxmrfvr1mzpypbdu2afXq1YqPj1eTJk1ks9lkGAYhncUYhqFLLrlEo0ePVmBgoKTykGzRokXasGGD2669td3LibNGCOtwMpYO6ypbrBNA1RDW/b38/HwlJiaapjP4+flp2bJlat68ea09b8VIutjYWCUmJuq3337zeF63bt20fPlybdmyRddcc02DnEoxdepU3Xzzzaban3/+qauuukr5+fk+6qpyERER6tWrl6nGVFgAAKzD4XAoIyNDc+bM0ebNmys9z2azqUmTJoqOjtbo0aP1xhtvKC0tTY8++qjat2/vxY5RHYZhaMaMGercubOzlpOTo8cff1w5OTle7cV1ZB2znlAZS73rc935lZF1QPUEBwe7jcAirDO7//773aaczpw5U3369KmV56tYk65Lly6aMGFCpSFdXFycVqxYoc2bN2vMmDENMqSrYBiGFixYoAEDBpjqGzdu1E033eTVT0SrynUqLGEdAADWkZ+fr2XLlumjjz7yuFFEQECAzj77bA0aNEjTpk1TSkqKlixZojFjxigkJITRc3VQVFSUHn30UeeH8SUlJdqwYYNef/11ry1X4joNlg0mcDKWevfHNFigZhmGwY6wJ/HNN99o3rx5plqPHj30wAMP1PhzlZWVKTk5Wd26dVNCQkKl0127d++ulJQUpaWlafTo0Q06pDtRQECA3nvvPbVt29ZUX7ZsmWbNmuWjrirnuslEenq69u7d66NuAADAiXJycrR161bTOnWS1KJFCw0YMEC33nqrnn76aSUlJenBBx9UXFycAgICfNQtaspll12mW265xfn6OicnR2+//ba+//57r334yzRYVJXd1w2ciLAOqHlNmzY1TSl3fVHSUBUVFen222831QIDA/XWW2/V6IuxsrIyvf/++3r44Yf1448/Vnpejx499PDDD2vkyJF8WluJiIgIrVy5UhdeeKEpdH7ooYfUqVMnjR071ofdmfXq1UthYWGm/3tr165VYmKiD7sCAABSeWDSoUMHhYaGqqioSDExMTr//PPVq1cv9erVSzExMfL39/d1m6hhNptNU6dO1Xfffacvv/xSkvTTTz/pzTffVLt27UybUNQGTyPrCOtQGcI6oJ478ReCxMi6Ck8//bTS09NNtccee8y0lkV1OBwOffDBB3rooYe0devWSs+Li4vTI488oiuuuIKQrgo6d+6spUuXauTIkaZFn2+44Qa1bdvWba04X/Hz89PAgQOVkpLirK1bt46wDgAAC2jWrJkmTJigjh07qri4WG3btlWnTp0UFhbG67F6LjIyUrNmzdK1116rzMxMFRYW6qOPPtIFF1yg8ePHKzg4uNae29NusEyDRWUsNb/Kdc06NpgAqo9psO527NjhNnWye/fumjJlSo08/hdffKELLrhAo0ePrjSo69atm1asWKG0tDRdeeWVvDA8BZdeeqnmzp1rqhUUFOiqq67Sn3/+6aOu3Lmusbdu3TofdQIAAE5ks9l09tln65prrlFCQoL69u2r8PBwXo81EL1799a9997r/PvOysrSa6+9pp9//rnWp8O6hnWMrENlLBXWMbIOqHmEdWYOh0N33HGHaSFZm82mRYsWyW6v3mDjzZs3a8SIERoyZIi+//57j+fExsbq3Xff1ebNmzV69GheFJ6mf/7zn7rttttMtczMTF155ZU6fvy4j7oycw3rfvvtN2VmZvqoGwAAcCLDMJwXNByGYSggIEBjx47VyJEjnfXU1FQlJSXVagbBBhM4FZYO67y9jTJQHxHWmX3wwQf67LPPTLW77rpL559//mk/5o4dOzRu3Dj16NFDq1ev9nhOTEyMli1bpq1btyo+Pp6NI6rJMAw9//zzGjRokKmelpamG264wTRF1lfi4uLcpqF/9dVXPuoGAAAAUvnryJYtW2rKlClq0aKFJKm4uFgvv/yyNm7c6HGH4Jp6XjaYQFVZ6t1iRESE6fjo0aNe20YZqK8I6/6nqKhI06dPN9Vat2592ruJZmdn64477lBMTIyWLl3q8Zxzzz1XSUlJ2rZtm8aOHUtIV4P8/f313nvv6dxzzzXVly9frocfftg3TZ3Az89P/fv3N9WYCgsAAOB7NptNPXv21KRJk5yjK/Py8vTQQw/p0KFDtTYd1tM0WG/tRIu6xVLvGiMjI91q+/fv90EnQP1BWPc/L730krZv326qzZkzx+179HcKCgo0e/ZstW/fXgsWLPD46VtUVJQWLFign376SQkJCfLz86tW7/AsPDxcK1euVLNmzUz1WbNm6e233/ZRV//jOhW2YucxAAAA+I5hGGratKlGjRpler32/fffa+HChSouLq7xEM11ZF1paSlhHSplqbAuNDRUAQEBplp2draPugHqB9cgKi8vz0ed+Nbhw4f1yCOPmGq9e/fWddddV+XHcDgcWrFihTp37qz77rvP4/eyWbNmevzxx7Vjxw5NmjRJ/v7+1e4dJ9exY0e9++67boHozTffrO+++85HXZUbOHCg6Tg9Pb1BB+YAAABWYRiGunTpogkTJuiMM85w1mfPnq20tLRaeU7XJVIKCgpUXFxcK8+Fus1SYZ1hGG6j6wjrgOpx/YXQUIOCJ554wm0dzGeeeabKiwpv3rxZgwcP1pgxY7Rr1y6324OCgjR9+nTt3LlTM2bMUOPGjWukb1TN8OHD9Z///MdUKyws1FVXXaU//vjDR12V7zJ84sYlDoej1l78AQAA4NTY7XZdfvnluvzyy50Dh44fP6577rlHBw4cqNHnch1ZJ5WHdbW1Rh7qNkuFdZL7VFjCOqB6mAZbvkuoa5Bz9dVXu60n5kl2drYmTpyonj17VjqF8cYbb9T27ds1e/Zst41y4D133HGH7rzzTlMtOztbl112mXJzc33SU1BQkLp162aqpaam+qQXAAAAuGvZsqWuv/56xcbGOj/I37Bhg+bOnVvju7UGBwebZoMwsg6VsVxYV7EbSwXWrAOqh7CufF26EzersdvtevLJJ096n7KyMi1cuFAdO3bUq6++6nEtif79+ys1NVWLFy9WmzZtarxvnLpnn31Ww4YNM9XS09M1atQon21Y5LrT8MaNG33SBwAAADzr16+fxo8fr7CwMGdt0aJF+vjjj2vsOQzDkN1uV6NGjZw1RtahMpYP6xhZB1RPQw/rsrOztXDhQlPtxhtvVPv27Su9zw8//KC+ffvqH//4h44cOeJ2e3R0tJKTk7Vu3Tr17NmzxnvG6bPb7UpOTlanTp1M9XXr1ikxMVGlpaVe76lXr16mY0bWAQAAWEtAQIDGjx+vwYMHO5cwOXz4sJ566im3Deqqw3UqLCPrUBnLhXVMgwVqVkMP6+bOnav8/Hznsd1u18yZMz2ee/ToUU2dOlU9e/b0uDFB48aN9e9//1s///yzrrnmmiqvdwfvCgsL06pVq9SyZUtTffny5brnnnu8vuOW68i6nTt36tChQ17tAQAAACcXFRWl6dOnq1WrVpLK1xr+4Ycf9Nxzz9XYDA2bzWZa2zo/P5+wDh5ZLqxjZB1Qs1zDuqNHj6qsrMxH3XhXXl6eXnrpJVPt+uuvV9u2bd3OXbNmjbp06aJ58+Z5/P4kJCRo+/btmjlzpoKDg2utZ9SM6OhoffLJJ24brMyfP19PP/20V3uJjY1VUFCQqcboOgAAAOvp0aOH7r33XufouoKCAq1cuVIpKSk18vg2m42RdagSy4d1rFkHVI9rWCeVB3YNweLFi03TWA3D0H333Wc65+jRo7rzzjs1dOhQ7dmzx+0xOnTooM8//1xJSUluI7Vgbeedd57ef/99+fv7m+rTp0/XkiVLvNaHv7+/4uLiTLVNmzZ57fkBAABQNXa7XRMnTtSQIUOctb1792rx4sXauXNntR/fMAzTyDrWrENlLBfWMQ0WqFmuI4ukhjEVtrS0VM8995ypduWVV5rWqvvyyy/VrVs3vfjii273DwwM1COPPKKtW7dq6NChtd4vasfQoUP1+uuvu9Vvuukmffrpp17rwzWsS09P99pzAwAAoOqCgoL0wgsvOEfAlZWV6dtvv9WSJUtUVFRUrcd2nQbLyDpUxnJhnevIupycHP7xAtXQUMO6Tz75xO3Tr3vuuUeSVFxcrOnTp2vQoEHatWuX230HDhyobdu26cEHH1RgYKBX+kXtSUhI0Jw5c0y1kpISjRo1Sl9//bVXeoiNjTUd//TTT155XgAAAJwawzDUrl07PfHEE7LZyiOTo0ePasWKFfrmm2+qtf6x68i6wsJCRtbBI8uFda7TzBwOh7KysnzUDVD3uW4PLjWMsO6VV14xHXfv3l0DBgzQ7t27ddFFF7mFN5LUqFEjzZ8/X2vWrDnpbrGoe6ZNm6bJkyebavn5+br88suVlpZW68/vGtb9/PPPPtmZFgAAAFVz0003afjw4c7jLVu2aOnSpcrKyjrtwM7TyDrCOnhiubAuIiJCAQEBplpGRoaPugHqh4a2I2xmZqZWrlxpqk2aNEkrVqxQXFycx51e+/fvry1btuiuu+5yfoKG+sMwDM2dO1fjxo0z1f/66y9dfPHFtT7SzTWsKygo8DiqEwAAAL5nGIaCg4M1a9YstWnTxll/99139dlnn6mwsPC0H5dpsKgKy70jNQzD9J9BIqwDqquhhXWvv/66adRSQECA1q9fr/j4eNOGExW3PfPMM/ryyy917rnnertVeJHNZtMbb7yhK664wlTPycnR8OHDa2TR4MpERkYqIiLCVGPdOgAAAOuy2WyKjY3V5MmTnQFbbm6uFi5cqF9//VVlZWWn9ZjsBouqsFxYJ4mwDqhhDSmsczgcevPNN021oqIij5sMnHvuuVq/fr2mTp3KaLoGwt/fX8nJyaYdviRp3759GjZsWK3+vuncubPpmHXrAAAArC04OFjx8fEaMWKEs7Zhwwa9+uqrysvLO+XpsEyDRVVZ8t0pYR1Qs1zDury8PB91Uvu2bdumX3755W/PS0hI0KZNm9SjRw8vdAUrCQoK0gcffKALL7zQVN+1a5cGDx5ca79zXKfCMrIOAADA2gzDUOvWrXXLLbeoU6dOksoHB7z11lv6+OOPT3l0nWEYjKxDlRDWAQ1AQxpZt3Tp0pPeHhgYqFdeeUVLlixx+76g4QgJCdHHH3+suLg4U33Hjh0aNGiQ9u7dW+PP6Tqy7tdff63x5wAAAEDN8vf3V9++fTVu3Dhn0Hb48GHNmjVLu3fvPqXRdUyDRVUR1gENQJMmTUzH9TWsczgcJw3rWrZsqXXr1umWW26RYRhe7AxWFBoaqtWrVzs/Ja3w+++/a9CgQfrjjz9q9Plc10RkgwkAAIC6oWnTpho7dqz69+/vrP3yyy+6//77T2l0XWVh3enuLov6i7AOaAAaysi6LVu2VBqA9OzZUxs3blTv3r293BWsLDIyUl988YVbYLdz504NGjRIe/bsqbHnatu2rek4Jyen3v5fBAAAqE8Mw1D79u01YcIERUdHSyofKJCSkqKkpKRTepwT16wrLS1VUVERYR3c1Imwbt++fae10wqAcg0hrHM4HDr//PM93jZ27FitW7dOrVu39nJXqAuioqK0du1axcTEmOq7du2q0cAuOjrabUQno+sAAADqBj8/P1199dUaPHiwAgICJEmFhYW67777lJGRUaXAzXWDCUk6fvy4SktLa6Vn1F11IqwrKSnR/v37fdQNUPc1hLDurbfe8vhLbsqUKXrnnXfUqFEjH3SFuqJFixb64osv3NaV2717ty666KIaWV8uKChIrVq1MtUI6wAAAOqOoKAgTZs2Te3atXPW9u/fr3vvvbdKu7oS1qGqLBnWRUZGym63m2pMhQVOX0MI6/r16+dWGzJkiObOnSubzZI/6mAxFYGd666te/fuVf/+/bVp06ZqP4frVNidO3dW+zEBAADgHYZhqEuXLpo0aZKaNWsmqXwq6+rVq7V8+fIqzQgMDAyUn5+f8zg/P5+wDm4s+Q7Wz8/PbfRBTS/0DTQkrmFdXl6ejzqpPa1bt3bbXGLVqlVsJIFTUrGGXZcuXUz1gwcPavDgwVq7dm21Hv+cc84xHTOyDgAAoO659dZbNXDgQOeggCNHjmju3LnasWPHSafDGoYhu92u4OBgZ42RdfDEkmGdJOeijRV2797tm0aAeqAhjKwLCgrStddeq5ycHL3//vv6/fff5e/v7+u2UAedccYZWrt2rdtmJHl5eRoxYoQ++OCD035sRtYBAADUfcHBwXr00Ud19tlnSypfP/vnn3/Wiy++qCNHjpz0vn5+fqYlegjr4Illwzre0AA1p0mTJqbj+hjWVQgPD9eoUaPcRjABp6J58+b6/PPPNXToUFO9sLBQY8aM0RtvvHFaj+v6u23v3r2n3SMAAAB857zzztOMGTOco+SOHTumDz/8UKtXrz7p+nWEdaiKOhPWMVUIOH2uu1lW7F4EoHJNmjTRRx99pDFjxpjqpaWluvHGGzV79uwq7fp1opYtW5qOMzMzq90nAAAAfCMhIUHXXXed83jPnj166623Tro5GWEdqsKyYR3r+gA154svvjAdX3jhhT7qBKhbAgMDtWzZMk2cONHttvvuu0+TJk2q0s5fFVzDuoMHD6qoqKjafQIAAMD7goODNWPGDHXr1k1S+Ye669at03vvvafDhw97vA9hHarCsmGd68i63bt3n/IIBqAhKykp0R9//KGkpCR98sknptuGDBnio66AusfPz0+LFi3Sv/71L7fbFi1apCuuuKLKm7a4hnWStH///mr3CAAAAN8455xz9PDDDzunw+bl5emNN97Qxo0bPYZwrmEdu8HCE7uvG6iMa1iXn5+v7OxsRUVF+agjwDpKSkqUmZmpjIwM7d27VxkZGW7XMzMzK/2hP3jwYC93DNRthmHoySefVGRkpO69917Th0erVq3SRRddpJUrV6pNmzYnfZzmzZvL399fxcXFzlpmZubf3g8AAADWYxiGbDabBg0apIkTJ2r+/PmSymcGvvTSS4qJiXF7ncfIOlSFZcO6Vq1aKSAgwDQ9aNeuXYR1qPeKi4u1b98+Z+jmKZDLyspSWVnZaT3+mWeeqZiYmBruGmgYpk6dqujoaE2YMEEFBQXO+pYtW9SnTx+lpKSoV69eld7fMAxFRUWZNpZg3TrA2pjZAQA4GcMwFBoaqokTJ2rNmjVKT0+Xw+FQSkqKRowYocTERAUEBMgwDEnlYV3FKDyJkXXwzLJhnc1mU3R0tLZv3+6s7dq1i7W2UGeVlpbqwIEDyszM1L59+0xfK65nZGQoKyur1t4YhISEaP78+c5fFABO3ZgxY9S6dWtdccUVOnjwoLO+b98+XXTRRXrllVc0YcKESu/vGtZlZWXVar8ATs+BAweUkZEhm82yq8YAlpafn6/jx4/7ug3Aazp06KDJkyfr7rvvVmFhoUpLS/XII49owIABat++vfM8m83GyDr8LcuGdVL5VFjXsA6wmtLSUu3fv7/SAK7ia3Z2tk9+CDdq1EiDBw/WmDFjdNVVVyk8PNzrPQD1zQUXXKANGzbosssu02+//easFxYWKjExUVu3btUTTzwhPz8/t/uyIyxQN9xwww2+bgEAUEcYhqGgoCANGzZMo0ePVnJyssrKypSRkaHHHntMCxYsUKNGjWQYBiPrUCWWDutcd4T9/ffffdQJGpqSkhIdPHhQ2dnZys7O1v79+53XTzzOyspSdnb2aU9Jra7AwEC1adNGZ555ptq0aeO8nHnmmWrdurWio6PVvHlzRtIBtaBdu3Zav3694uPj3XZcnjNnjn788Ue9/fbbCg0NNd3mGtYxsg4AAKB+OPvsszV+/HilpaU5P9BdsmSJLr30Ul177bXOsK5x48bO+zCyDp5YOqw7caioJNPoBeBUHT9+XAcOHHAL3zxdz8nJ8fkaNcHBwZUGcRXXCeIA3woPD9fq1as1depUPf/886bbPvnkE/Xu3Vvvvfeeunbt6qw3b97cdN6hQ4e80isAAABql2EYGjhwoOLj4zV//nzl5eXJ4XBoxowZ6t69uzp16sQGE6gSS4d1HTp0MB0T1qFCQUGBDh48qJycHB08eLDSy4m35+fn+7ptSeVrFERFRally5Zq1aqV6Wvr1q2dYVxYWBhBHFAH+Pv7a/78+erWrZvuvPNO006v27dvV58+fbRo0SLnOnZhYWGm++fm5nq1XwDubDab2rVrx9RXoJadGFAA9VWTJk2UkJCg1NRUrVmzRiUlJdqzZ48eeOABvfzyy4R1qJI6FdYdOHBAubm5bm90UHcVFBQoNzfXeTl06JDpODc312Mgd+zYMV+37sbPz6/SEO7Er2eccYbHdawA1G233nqrYmJidPXVV+vAgQPOen5+vhITE/Xtt99q3rx5hHWABdlsNvXp00fdu3f3dStAvRYUFOTrFgCviImJ0S233KKffvpJGRkZkqSVK1dq8eLFuu2220xhXX5+vkpKSuRwOBisASdLh3Vt27aVn5+fKWXevn27evfu7cOucKLS0lLl5eXpyJEj+uuvv3TkyBEdPnzYLXCrLIgrKCjw9R/hpAzDUEREhFq0aKEWLVooMjLS7XqrVq3UqlUrRUREEMIBDVz//v2VmpqqMWPGKDU11XTbggULlJqaqoSEBFOdsA7wPcMwZLfbZbdb+qUxAKCOsNlsuuyyy7R+/XotXLhQBQUFKiws1IIFC9S1a1dTWFdaWqr8/HzCOphY+hWJv7+/zjnnHNOOsL/++ithXTU5HA4VFRXp6NGjOnbsmPLy8vTXX385wzbX655qFdetOMLt7/j7+7sFb5UFcQRwAE7VWWedpa+//lpTpkzRwoULTbdt3LhRGzduNNUI6wAAAOqfkJAQTZkyRV999ZXS0tLkcDi0e/duPfvssxo2bJjp3GPHjvl8zXRYi6XDOql8KuyJYV3FunWlpaXau3ev9uzZo0aNGikqKkotWrRQQECAr1qtUcXFxcrPz1d+fr4KCgqc1ysux44dc4Ztnr6e7LZjx46ppKTE13/EGmGz2RQeHq6IiAiPl+bNm7sdh4aG8okFgFoVGBioBQsWqG/fvrr99ttPumbm4cOHVVZWJpvN5sUOAQAAUNvOOussPfbYYxo1apQKCwtVUlKi9evXm5ZMkcrDurKyMgaKwMlwWDy+nTp1qubNm2eqde3aVb/99psKCwvdzg8PD1dsbKzWrVtX6WOWlZWppKREpaWlzovrcXFxsYqKimrsUlnwVtlxQ1xgMiQkRGFhYQoPD1dYWJjz4hq4nXgJDQ3lDS4AS9u2bZuuvvpq7dixo9JzDh8+rGbNmnmxKwAAAHhDSUmJ7rnnHj3//PPOms1mU1lZmfN48eLFGjdunAIDA33RIiyoToysc7Vt27ZKzz906JC++uorRUREVBrGofY0adJEoaGhprDN9eIaxoWFhSk0NFT+/v6+bh8AalzXrl2VmpqqiRMnavny5R7Pyc3NJawDAACoh+x2ux577DF9/PHH2rlzpySZgjrpfyPrgAqWD+s6dux4WvfLycmp4U7qt5CQEDVt2lRNmzZVs2bNTut6SEgIo9wAwINmzZopOTlZixYt0uTJkz2ODAcAAED91LRpU73wwgvO6bCuWLMOriwf1sXExMgwDP7hqjyRDwoKUnBwsEJCQtS4cWPT16rWXG9r3Lgxc+MBoJYZhqHbb79dffv21dixY/XLL79IkuLi4hQdHe3j7gAAAFBbDMPQkCFDdOutt2rBggVuM/4YWQdXlg/roqKiNH78eC1ZskRS+eiExMRExcfHq1OnThoxYoR++OGHWu0hICCg2peKkC04ONh0/VRqdrvl/7oAAH+ja9eu2rRpk5KTk3X06FGNGzeOTW8AAADqOX9/f02bNk0bNmxQamqq6bajR48S1sHE8htMSOXzuVevXi1JGjhwoBo1auS87b///a/++usv2e12+fn5OS81cRwYGCi73c6bKAAAAAAAUC1FRUVKSUnR3Xffrf379zvrt99+u2bPns0axnCqE0O1bDabLr30Uo+3DR061MvdAAAAAAAAnJqAgAANGTJECQkJeuGFF1RcXCypPMSrA+Oo4EXsBgAAAAAAAOAF4eHhmjBhgvr16yepfD27Dh06yN/f38edwUrqxDRYAAAAAACA+qCwsFDfffedPvroIzVt2lTXXXed2rZtK5uN8VQoR1gHAAAAAADgRcXFxTp+/LhsNpsaN25MUAcTwjoAAAAAAADAIohuAQAAAAAAAIsgrAMAAAAAAAAsgrAOAAAAAAAAsAjCOgAAAAAAAMAiCOsAAAAAAAAAiyCsAwAAAAAAACyCsA4AAAAAAACwCMI6AAAAAAAAwCII6wAAAAAAAACLIKwDAAAAAAAALIKwDgAAAAAAALAIwjoAAAAAAADAIgjrAAAAAAAAAIsgrAMAAAAAAAAsgrAOAAAAAAAAsAjCOgAAAAAAAMAiCOsAAAAAAAAAiyCsAwAAAAAAACyCsA4AAAAAAACwCMI6AAAAAAAAwCII6wAAAAAAAACLIKwDAAAAAAAALIKwDgAAAAAAALAIwjoAAAAAAADAIgjrAAAAAAAAAIsgrAMAAAAAAAAsgrAOAAAAAAAAsAjCOgAAAAAAAMAiCOsAAAAAAAAAiyCsAwAAAAAAACyCsA4AAAAAAACwiP8HjOMwm9VwvAwAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_side_by_side('frag2.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 951
        },
        "id": "9D1Itqdvz3nN",
        "outputId": "38a178a2-620f-4907-ec34-1a7157a54ea2"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Line detected\n",
            "Line detected\n",
            "Line detected\n",
            "Line detected\n",
            "Line detected\n",
            "Line detected\n",
            "Line detected\n",
            "Line detected\n",
            "Line detected\n",
            "Line detected\n",
            "Line detected\n",
            "Line detected\n",
            "Line detected\n",
            "Line detected\n",
            "Line detected\n",
            "Line detected\n",
            "Line detected\n",
            "Line detected\n",
            "Line detected\n",
            "Line detected\n",
            "Line detected\n",
            "Line detected\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Symmetry Identification"
      ],
      "metadata": {
        "id": "zWF0N5EtkhvL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Utility Functions"
      ],
      "metadata": {
        "id": "BkWiR8Qw0UcN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def check_pairwise_vertex_symmetry(approx, tolerance):\n",
        "\n",
        "\n",
        "    num_points = len(approx)\n",
        "    symmetry_lines = []\n",
        "\n",
        "    # Function to check if two points are approximately equal\n",
        "    def points_are_equal(p1, p2):\n",
        "        return np.linalg.norm(np.array(p1) - np.array(p2)) < tolerance\n",
        "\n",
        "    # Function to reflect a point across a line defined by two points (p1, p2)\n",
        "    def reflect_point(p, p1, p2):\n",
        "        # Vector from p1 to p2\n",
        "        v = np.array([p2[0] - p1[0], p2[1] - p1[1]])\n",
        "        # Vector from p1 to point p\n",
        "        u = np.array([p[0] - p1[0], p[1] - p1[1]])\n",
        "        # Project u onto v to find the foot of the perpendicular\n",
        "        proj = np.dot(u, v) / np.dot(v, v) * v\n",
        "        foot = np.array(p1) + proj\n",
        "        # Reflection vector\n",
        "        reflection = 2 * (foot - np.array(p))\n",
        "        return np.array(p) + reflection\n",
        "\n",
        "    # Check for symmetry by drawing lines between each pair of points\n",
        "    for i in range(num_points):\n",
        "        for j in range(i + 1, num_points):\n",
        "            p1 = approx[i][0]\n",
        "            p2 = approx[j][0]\n",
        "\n",
        "            is_symmetrical = True\n",
        "            reflected_points = []\n",
        "            for k in range(num_points):\n",
        "                if k == i or k == j:\n",
        "                    continue\n",
        "                p = approx[k][0]\n",
        "                reflected = reflect_point(p, p1, p2)\n",
        "                if not any(points_are_equal(reflected, approx[m][0]) for m in range(num_points) if m != i and m != j):\n",
        "                    is_symmetrical = False\n",
        "                    break\n",
        "                reflected_points.append(tuple(reflected))\n",
        "\n",
        "             # If all reflected points match the given points, record the line of symmetry\n",
        "            if is_symmetrical:\n",
        "                # Check if this symmetry line is not already in the result list\n",
        "                symmetry_line = (tuple(p1), tuple(p2))\n",
        "                if symmetry_line not in symmetry_lines and (tuple(p2), tuple(p1)) not in symmetry_lines:\n",
        "                    symmetry_lines.append(symmetry_line)\n",
        "\n",
        "    return symmetry_lines"
      ],
      "metadata": {
        "id": "qFgIBDI30Io-"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def check_perpendicular_bisector_symmetry(approx):\n",
        "\n",
        "    num_points = len(approx)\n",
        "\n",
        "    symmetry_lines = []\n",
        "\n",
        "    def points_are_equal(p1, p2, tolerance=5):\n",
        "        return np.linalg.norm(np.array(p1) - np.array(p2)) < tolerance\n",
        "\n",
        "    def is_duplicate_line(new_line, existing_lines, tolerance=2):\n",
        "        new_midpoint, new_slope = new_line\n",
        "        for midpoint, slope in existing_lines:\n",
        "            if abs(midpoint[0] - new_midpoint[0]) < tolerance or abs(midpoint[1] - new_midpoint[1]) < tolerance:\n",
        "                return True\n",
        "        return False\n",
        "\n",
        "    for i in range(num_points):\n",
        "        p1 = approx[i][0]\n",
        "        p2 = approx[(i + 1) % num_points][0]\n",
        "\n",
        "        # Midpoint of the edge\n",
        "        mx = (p1[0] + p2[0]) // 2\n",
        "        my = (p1[1] + p2[1]) // 2\n",
        "\n",
        "        # Slope of the line (p1, p2)\n",
        "        if p1[0] == p2[0]:\n",
        "            slope = np.inf  # vertical line\n",
        "        else:\n",
        "            slope = (p2[1] - p1[1]) / (p2[0] - p1[0])\n",
        "\n",
        "        # Slope of the perpendicular bisector\n",
        "        if slope == 0:\n",
        "            perp_slope = np.inf  # vertical line\n",
        "        elif slope == np.inf:\n",
        "            perp_slope = 0  # horizontal line\n",
        "        else:\n",
        "            perp_slope = -1 / slope\n",
        "\n",
        "        # Equation of the perpendicular bisector line\n",
        "        is_symmetrical = True\n",
        "        for k in range(num_points):\n",
        "            p = approx[k][0]\n",
        "            # print(f\"Point {p}\")\n",
        "            if perp_slope == np.inf:\n",
        "                # Check vertical line symmetry\n",
        "                reflected = (2 * mx - p[0], p[1])\n",
        "            elif perp_slope == 0:\n",
        "                # Check horizontal line symmetry\n",
        "                reflected = (p[0], 2 * my - p[1])\n",
        "            else:\n",
        "                # Check for other slopes\n",
        "                # Line equation: y = perp_slope * (x - mx) + my\n",
        "                c = my - perp_slope * mx\n",
        "                x = (p[0] + perp_slope * (p[1] - c)) / (1 + perp_slope ** 2)\n",
        "                y = perp_slope * x + c\n",
        "                reflected = (2 * x - p[0], 2 * y - p[1])\n",
        "\n",
        "            # print(f\"Reflected Point {reflected}\")\n",
        "\n",
        "            if not any(points_are_equal(reflected, approx[m][0]) for m in range(num_points) if m != k):\n",
        "                is_symmetrical = False\n",
        "                break\n",
        "\n",
        "        if is_symmetrical:\n",
        "            symmetry_line = ((mx, my), perp_slope)\n",
        "            if not is_duplicate_line(symmetry_line, symmetry_lines):\n",
        "                symmetry_lines.append(symmetry_line)\n",
        "\n",
        "    return symmetry_lines"
      ],
      "metadata": {
        "id": "We3-mFtY0Zgx"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "For drawing dashed lines to visualise the symmetry"
      ],
      "metadata": {
        "id": "amlYfSRT0c8D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def draw_dashed_line(img, start_point, end_point, color, thickness=1, dash_length=10):\n",
        "    \"\"\"\n",
        "    Draw a dashed line on the image.\n",
        "    \"\"\"\n",
        "    # Calculate the line length\n",
        "    line_length = int(np.linalg.norm(np.array(end_point) - np.array(start_point)))\n",
        "\n",
        "    # Calculate the number of dashes\n",
        "    num_dashes = line_length // dash_length\n",
        "\n",
        "    # Calculate direction vector of the line\n",
        "    direction = np.array(end_point) - np.array(start_point)\n",
        "    direction = direction / np.linalg.norm(direction)\n",
        "\n",
        "    for i in range(num_dashes):\n",
        "        start_dash = start_point + direction * (i * dash_length)\n",
        "        end_dash = start_dash + direction * (dash_length / 2)\n",
        "        start_dash = tuple(start_dash.astype(int))\n",
        "        end_dash = tuple(end_dash.astype(int))\n",
        "        cv2.line(img, start_dash, end_dash, color, thickness)"
      ],
      "metadata": {
        "id": "KLZXbFhN0bQl"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def check_symmetry(csv_file):\n",
        "\n",
        "    # Regularize curves\n",
        "    regularized_plots = regularize(csv_file, verbose=False)\n",
        "     # Extract the base name of the file without the extension\n",
        "    base_name = os.path.splitext(os.path.basename(csv_file))[0]\n",
        "\n",
        "    # Get the regularized image path\n",
        "    regularized_image_path = get_regularized_image(regularized_plots, filename = f\"{base_name}_regularized.png\", padding=0)\n",
        "\n",
        "    # Read the regularized image\n",
        "    regularized_image = cv2.imread(regularized_image_path)\n",
        "    if regularized_image is None:\n",
        "        print(f\"Error: Unable to load image at path {regularized_image_path}\")\n",
        "        return\n",
        "\n",
        "    # Define colors for specific shapes\n",
        "    colors = {\n",
        "    \"Square\": (0, 100, 0),        # Dark Green\n",
        "    \"Rectangle\": (0, 0, 255),     # Blue\n",
        "    \"Ellipse\": (139, 0, 0),       # Dark Red\n",
        "    \"Star\": (255, 140, 0),        # Dark Orange\n",
        "    \"Circle\": (139, 0, 139),      # Dark Magenta\n",
        "    \"Triangle\": (0, 255, 0),      # Green\n",
        "    \"Irregular\": (85, 107, 47),   # Dark Olive Green\n",
        "    }\n",
        "\n",
        "    # Create a copy of the original image for drawing symmetry lines\n",
        "    symmetry_image = regularized_image.copy()\n",
        "    gray_image = cv2.cvtColor(symmetry_image, cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "    # Apply thresholding to get a binary image\n",
        "    _, thresh_image = cv2.threshold(gray_image, 200, 255, cv2.THRESH_BINARY)\n",
        "\n",
        "    # Find contours in the binary image\n",
        "    contours, _ = cv2.findContours(thresh_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
        "\n",
        "    # List to store shape information\n",
        "    shapes = []\n",
        "    used_centroids = []\n",
        "    i = 0\n",
        "\n",
        "    # Iterate through each contour to classify and label shapes\n",
        "    for contour in contours:\n",
        "        if i == 0:\n",
        "            i = 1\n",
        "            continue\n",
        "        # Approximate contour\n",
        "        epsilon = 0.02 * cv2.arcLength(contour, True)\n",
        "        approx = cv2.approxPolyDP(contour, epsilon, True)\n",
        "\n",
        "        # Calculate area and perimeter\n",
        "        area = cv2.contourArea(contour)\n",
        "        perimeter = cv2.arcLength(contour, True)\n",
        "\n",
        "        # Filter out small contours\n",
        "        if area < 500:\n",
        "            continue\n",
        "\n",
        "        # Calculate the centroid\n",
        "        M = cv2.moments(contour)\n",
        "        if M[\"m00\"] == 0:  # To prevent division by zero\n",
        "            continue\n",
        "        cx = int(M[\"m10\"] / M[\"m00\"])\n",
        "        cy = int(M[\"m01\"] / M[\"m00\"])\n",
        "        centroid = (cx, cy)\n",
        "\n",
        "        # Filter out contours that are too close to already processed ones\n",
        "        if any(np.linalg.norm(np.array(centroid) - np.array(c)) < 10 for c in used_centroids):\n",
        "            continue\n",
        "        used_centroids.append(centroid)\n",
        "        shape = identify_shapes(contour)\n",
        "        shape_name = shape['shape']\n",
        "        approx = shape['approx']\n",
        "\n",
        "        # Get the color for the shape from the dictionary\n",
        "        color = colors.get(shape_name, (0, 0, 0))  # Default to black if shape not in dictionary\n",
        "\n",
        "        if shape_name == \"Circle\":\n",
        "            # Draw dashed radial lines from the center\n",
        "            cx, cy = int(np.mean([p[0][0] for p in approx])), int(np.mean([p[0][1] for p in approx]))\n",
        "            mean_radius = np.mean([np.sqrt((cx - p[0][0])**2 + (cy - p[0][1])**2) for p in approx])\n",
        "            angles = np.linspace(0, 2 * np.pi, 17)  # Draw 16 radial lines\n",
        "            for angle in angles:\n",
        "                end_x = int(cx + mean_radius * np.cos(angle))\n",
        "                end_y = int(cy + mean_radius * np.sin(angle))\n",
        "                draw_dashed_line(symmetry_image, (cx, cy), (end_x, end_y), color)\n",
        "            print(\"Circle with radial symmetry (few lines shown)\")\n",
        "\n",
        "        else:\n",
        "            if shape_name == \"Irregular\":\n",
        "                pairwise_vertex_symmetry_lines = check_pairwise_vertex_symmetry(approx, tolerance = 20) # Higher tolerance for irregular shapes\n",
        "            else:\n",
        "                pairwise_vertex_symmetry_lines = check_pairwise_vertex_symmetry(approx, tolerance = 5) # Lower tolerance for regular shapes\n",
        "\n",
        "            perpendicular_bisector_symmetry_lines = check_perpendicular_bisector_symmetry(approx)\n",
        "\n",
        "            # Draw symmetry lines joining vertices\n",
        "            for line in pairwise_vertex_symmetry_lines:\n",
        "                draw_dashed_line(symmetry_image, line[0], line[1], color)\n",
        "\n",
        "            # Draw perpendicular bisector symmetry lines within shape\n",
        "            min_x = np.min(approx[:, 0, 0])\n",
        "            max_x = np.max(approx[:, 0, 0])\n",
        "            min_y = np.min(approx[:, 0, 1])\n",
        "            max_y = np.max(approx[:, 0, 1])\n",
        "\n",
        "            for mid, slope in perpendicular_bisector_symmetry_lines:\n",
        "                if slope == np.inf:\n",
        "                    # Vertical line within the bounding box\n",
        "                    start_point = (mid[0], min_y)\n",
        "                    end_point = (mid[0], max_y)\n",
        "                else:\n",
        "                    # Calculate intersection points of the bisector with the bounding box\n",
        "                    x1 = min_x\n",
        "                    y1 = int(slope * (x1 - mid[0]) + mid[1])\n",
        "                    x2 = max_x\n",
        "                    y2 = int(slope * (x2 - mid[0]) + mid[1])\n",
        "\n",
        "                    # Ensure the points are within the bounding box\n",
        "                    if y1 < min_y:\n",
        "                        y1 = min_y\n",
        "                        x1 = int((y1 - mid[1]) / slope + mid[0])\n",
        "                    elif y1 > max_y:\n",
        "                        y1 = max_y\n",
        "                        x1 = int((y1 - mid[1]) / slope + mid[0])\n",
        "\n",
        "                    if y2 < min_y:\n",
        "                        y2 = min_y\n",
        "                        x2 = int((y2 - mid[1]) / slope + mid[0])\n",
        "                    elif y2 > max_y:\n",
        "                        y2 = max_y\n",
        "                        x2 = int((y2 - mid[1]) / slope + mid[0])\n",
        "\n",
        "                    start_point = (x1, y1)\n",
        "                    end_point = (x2, y2)\n",
        "\n",
        "                # Draw the dashed line\n",
        "                draw_dashed_line(symmetry_image, start_point, end_point, color, dash_length=10)\n",
        "\n",
        "            total_symmetry_lines = len(pairwise_vertex_symmetry_lines) + len(perpendicular_bisector_symmetry_lines)\n",
        "            if total_symmetry_lines != 0:\n",
        "                print(f\"{shape_name} shape with {total_symmetry_lines} lines of symmetry\")\n",
        "\n",
        "    # Create a figure with two subplots\n",
        "    plt.figure(figsize=(15, 10))\n",
        "\n",
        "    # Plot the original image\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.imshow(regularized_image)\n",
        "    plt.title(\"Regularized Image\")\n",
        "    plt.axis('off')\n",
        "\n",
        "    # Plot the symmetrical image\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.imshow(symmetry_image)\n",
        "    plt.title(\"Lines of Symmetry\")\n",
        "    plt.axis('off')\n",
        "\n",
        "    # Show the combined plot\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "Xo5Ya9cD0gUq"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Calling Symmetry Identification"
      ],
      "metadata": {
        "id": "HbhMNHLp0lBC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "check_symmetry('frag0.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "swRxNX4z0oim",
        "outputId": "105a0933-29fd-4c91-add8-e6ac8b4b929a"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rectangle shape with 2 lines of symmetry\n",
            "Circle with radial symmetry (few lines shown)\n",
            "Irregular shape with 1 lines of symmetry\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "check_symmetry('isolated.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "id": "HIxlhm1t0wYE",
        "outputId": "5d4c084b-89c2-403e-889e-72e846e16cf8"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Star shape with 5 lines of symmetry\n",
            "Rectangle shape with 2 lines of symmetry\n",
            "Circle with radial symmetry (few lines shown)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Occlusion Solution"
      ],
      "metadata": {
        "id": "LCtHGlNz2jrw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using Model Fitting"
      ],
      "metadata": {
        "id": "-ir6DWaC2r6U"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Importing The libraries"
      ],
      "metadata": {
        "id": "ohHwDOZ52w30"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "from skimage.measure import EllipseModel, CircleModel\n",
        "import scipy.optimize as opt\n",
        "from tqdm import tqdm\n",
        "import csv"
      ],
      "metadata": {
        "id": "ZWyEYURe2piQ"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Utility Functions"
      ],
      "metadata": {
        "id": "0QoAsJuz3EiH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def custom_ransac_ellipse(points, max_trials=1000, residual_threshold=2):\n",
        "\n",
        "    points = np.array(points)  # Convert to NumPy array if not already\n",
        "    best_model = None\n",
        "    best_inliers = []\n",
        "    max_inliers = 0\n",
        "\n",
        "    for _ in range(max_trials):\n",
        "        # Randomly select 5 indices\n",
        "        sample_indices = random.sample(range(len(points)), 5)\n",
        "\n",
        "        # Use these indices to get the corresponding points\n",
        "        sample_points = points[sample_indices]\n",
        "\n",
        "        # Fit an ellipse model to these points\n",
        "        model = EllipseModel()\n",
        "        if not model.estimate(sample_points):\n",
        "            continue\n",
        "\n",
        "        # Calculate the residuals (distances of points to the fitted ellipse)\n",
        "        residuals = np.abs(model.residuals(points))\n",
        "\n",
        "        # Determine inliers\n",
        "        inliers = residuals < residual_threshold\n",
        "        num_inliers = np.sum(inliers)\n",
        "\n",
        "        # Update the best model if it has more inliers\n",
        "        if num_inliers > max_inliers:\n",
        "            best_model = model\n",
        "            best_inliers = inliers\n",
        "            max_inliers = num_inliers\n",
        "\n",
        "    return best_model, best_inliers"
      ],
      "metadata": {
        "id": "Y5CmYO9AkAMU"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def custom_ransac_circle(points, max_trials=1000, residual_threshold=2):\n",
        "\n",
        "    points = np.array(points)\n",
        "    best_model = None\n",
        "    best_inliers = []\n",
        "    max_inliers = 0\n",
        "\n",
        "    for _ in range(max_trials):\n",
        "        # Randomly select 3 indices\n",
        "        sample_indices = random.sample(range(len(points)), 3)\n",
        "        sample_points = points[sample_indices]\n",
        "\n",
        "        # Fit a circle model to these points\n",
        "        model = CircleModel()\n",
        "        if not model.estimate(sample_points):\n",
        "            continue\n",
        "\n",
        "        # Calculate the residuals (distances of points to the fitted circle)\n",
        "        residuals = np.abs(model.residuals(points))\n",
        "\n",
        "        # Determine inliers\n",
        "        inliers = residuals < residual_threshold\n",
        "        num_inliers = np.sum(inliers)\n",
        "\n",
        "        # Update the best model if it has more inliers\n",
        "        if num_inliers > max_inliers:\n",
        "            best_model = model\n",
        "            best_inliers = inliers\n",
        "            max_inliers = num_inliers\n",
        "\n",
        "    return best_model, best_inliers"
      ],
      "metadata": {
        "id": "nnKajYz_j_W5"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a cost function to minimize the geometric distance to the ellipse\n",
        "def cost_function(params, points):\n",
        "    xc, yc, a, b, theta = params\n",
        "    total_distance = 0\n",
        "    for (x, y) in points:\n",
        "        x_rot = (x - xc) * np.cos(theta) + (y - yc) * np.sin(theta)\n",
        "        y_rot = -(x - xc) * np.sin(theta) + (y - yc) * np.cos(theta)\n",
        "        distance = (x_rot / a)**2 + (y_rot / b)**2 - 1\n",
        "        total_distance += np.abs(distance)\n",
        "    return total_distance"
      ],
      "metadata": {
        "id": "ZU8uiMNn5Rsz"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_coincident_points(points, curve_boundary, tolerance):\n",
        "    remaining_points = []\n",
        "\n",
        "    for point in points:\n",
        "        is_coincident = False\n",
        "        for boundary_point in curve_boundary:\n",
        "            if np.linalg.norm(np.array(point) - np.array(boundary_point)) <= tolerance:\n",
        "                is_coincident = True\n",
        "                break\n",
        "        if not is_coincident:\n",
        "            remaining_points.append(point)\n",
        "\n",
        "    return np.array(remaining_points)"
      ],
      "metadata": {
        "id": "aiAtoIUxlAWQ"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot(paths_XYs, title, ax):\n",
        "\n",
        "    colours = ['red', 'green', 'blue', 'yellow', 'purple']\n",
        "    for i, XYs in enumerate(paths_XYs):\n",
        "        c = colours[i % len(colours)]\n",
        "        for XY in XYs:\n",
        "            ax.scatter(XY[:, 0], XY[:, 1], c=c, s=50)\n",
        "    ax.set_aspect('equal')\n",
        "    ax.set_title(title)"
      ],
      "metadata": {
        "id": "X4elBMYy3zlh"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_ellipse_boundary_points(ellipse, count=100):\n",
        "\n",
        "    (x0, y0), (a, b), theta = ellipse\n",
        "    boundary_points = []\n",
        "\n",
        "    for t in np.linspace(0, 2 * np.pi, count):\n",
        "        x = a / 2 * np.cos(t)\n",
        "        y = b / 2 * np.sin(t)\n",
        "        x_rot = x * np.cos(np.radians(theta)) - y * np.sin(np.radians(theta))\n",
        "        y_rot = x * np.sin(np.radians(theta)) + y * np.cos(np.radians(theta))\n",
        "        boundary_points.append([x_rot + x0, y_rot + y0])\n",
        "\n",
        "    return np.array(boundary_points)"
      ],
      "metadata": {
        "id": "iF9Hq8fa3bBH"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_circle_boundary_points(circle, count=100):\n",
        "\n",
        "    (x0, y0), radius = circle\n",
        "    boundary_points = []\n",
        "\n",
        "    for t in np.linspace(0, 2 * np.pi, count):\n",
        "        x = radius * np.cos(t)\n",
        "        y = radius * np.sin(t)\n",
        "        boundary_points.append([x + x0, y + y0])\n",
        "\n",
        "    return np.array(boundary_points)"
      ],
      "metadata": {
        "id": "L_rHWiwZ3cp5"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def process_curves_and_save_to_csv(input_csv_file, closed_curves, output_csv_file):\n",
        "\n",
        "    def calculate_residuals(points, model_boundary_points):\n",
        "        \"\"\" Calculate the sum of residuals between points and model boundary points. \"\"\"\n",
        "        return np.sum([np.min([np.linalg.norm(p - mbp) for mbp in model_boundary_points]) for p in points])\n",
        "\n",
        "    def fit_ellipse(remaining_points):\n",
        "        \"\"\" Fit an ellipse to the remaining points using RANSAC. \"\"\"\n",
        "        model_ellipse, inliers = custom_ransac_ellipse(remaining_points)\n",
        "        if model_ellipse is not None:\n",
        "            xc, yc, a, b, theta = model_ellipse.params\n",
        "            ellipse = ((xc, yc), (2 * a, 2 * b), np.degrees(theta))\n",
        "            initial_guess = (ellipse[0][0], ellipse[0][1], ellipse[1][0] / 2, ellipse[1][1] / 2, np.radians(ellipse[2]))\n",
        "            result = opt.minimize(cost_function, initial_guess, args=(remaining_points,), method='L-BFGS-B')\n",
        "            xc, yc, a, b, theta = result.x\n",
        "            refined_ellipse = ((xc, yc), (2 * a, 2 * b), np.degrees(theta))\n",
        "            return get_ellipse_boundary_points(refined_ellipse, len(remaining_points))\n",
        "        return None\n",
        "\n",
        "    def fit_circle(remaining_points):\n",
        "        \"\"\" Fit a circle to the remaining points using RANSAC. \"\"\"\n",
        "        model_circle, inliers = custom_ransac_circle(remaining_points)\n",
        "        if model_circle is not None:\n",
        "            xc, yc, r = model_circle.params\n",
        "            circle = ((xc, yc), r)\n",
        "            return get_circle_boundary_points(circle, len(remaining_points))\n",
        "        return None\n",
        "\n",
        "    print(f'Total curves to process: {len(closed_curves)}')\n",
        "\n",
        "    with open(output_csv_file, 'w', newline='') as csvfile:\n",
        "        writer = csv.writer(csvfile)\n",
        "        i = 0\n",
        "        for idx, curve in tqdm(enumerate(closed_curves), total=len(closed_curves), desc=f'Processing curves'):\n",
        "            i += 1\n",
        "            points = extract_all_points_from_curve(curve, input_csv_file)\n",
        "\n",
        "            # Remove points that coincide with any other curve\n",
        "            remaining_points = points\n",
        "            for j, other_curve in enumerate(closed_curves):\n",
        "                if j != idx:\n",
        "                    remaining_points = remove_coincident_points(remaining_points, extract_all_points_from_curve(other_curve, input_csv_file), tolerance=1)\n",
        "\n",
        "            if len(remaining_points) < 5:\n",
        "                print(f\"\\nSkipping curve {idx} - Not enough remaining points to process after removing coincident points.\")\n",
        "                continue\n",
        "\n",
        "            # Fit ellipse with progress bar\n",
        "            with tqdm(total=1, desc=f'Fitting Ellipse to Curve {idx}', position=1, leave=False) as pbar:\n",
        "                ellipse_boundary = fit_ellipse(remaining_points)\n",
        "                pbar.update(1)\n",
        "\n",
        "            # Fit circle with progress bar\n",
        "            with tqdm(total=1, desc=f'Fitting Circle to Curve {idx}', position=1, leave=False) as pbar:\n",
        "                circle_boundary = fit_circle(remaining_points)\n",
        "                pbar.update(1)\n",
        "\n",
        "            # Calculate residuals for both models\n",
        "            ellipse_residuals = calculate_residuals(remaining_points, ellipse_boundary) if ellipse_boundary is not None else float('inf')\n",
        "            circle_residuals = calculate_residuals(remaining_points, circle_boundary) if circle_boundary is not None else float('inf')\n",
        "\n",
        "            # Determine the better model\n",
        "            if ellipse_residuals < circle_residuals:\n",
        "                print(\"\\nEllipse provides a better fit.\\n\")\n",
        "                best_boundary = ellipse_boundary\n",
        "                best_model_type = \"ellipse\"\n",
        "            else:\n",
        "                print(\"\\nCircle provides a better fit.\\n\")\n",
        "                best_boundary = circle_boundary\n",
        "                best_model_type = \"circle\"\n",
        "\n",
        "            if best_boundary is not None:\n",
        "                # Check if the remaining points coincide with the best boundary\n",
        "                remaining_points_after_check = remove_coincident_points(remaining_points, best_boundary, tolerance=1.5)\n",
        "                model_coincides = len(remaining_points_after_check) == 0\n",
        "\n",
        "                if model_coincides:\n",
        "                    # Store the boundary points of the best-fitting model in the CSV file\n",
        "                    for point in best_boundary:\n",
        "                        writer.writerow([curve[0][0], curve[0][1], point[0], point[1]])\n",
        "\n",
        "                    # Also write the original points after removing those coinciding with the best boundary\n",
        "                    for point in remove_coincident_points(points, best_boundary, tolerance=1.5):\n",
        "                        writer.writerow([curve[0][0], curve[0][1], point[0], point[1]])\n",
        "                else:\n",
        "                    print(f\"The best model ({best_model_type}) does not fully coincide with the points.\")\n",
        "                    # Store the original points in the CSV file\n",
        "                    for point in points:\n",
        "                        writer.writerow([curve[0][0], curve[0][1], point[0], point[1]])\n",
        "            else:\n",
        "                print(\"\\nNo valid model was found.\")\n",
        "                # Store the original points in the CSV file\n",
        "                for point in points:\n",
        "                    writer.writerow([curve[0][0], curve[0][1], point[0], point[1]])\n",
        "\n",
        "    print(f\"\\nProcessed curves saved to {output_csv_file}\")"
      ],
      "metadata": {
        "id": "jQcodpBU4AHX"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def complete_curves(input_csv, method):\n",
        "  base_name = os.path.splitext(input_csv)[0]\n",
        "  output_csv = f\"{base_name}_{method}_output.csv\"\n",
        "\n",
        "  # Process the curves and save to the new output CSV\n",
        "  segments, closed_curves, open_curves = group_segments_by_curve(input_csv, tolerance=1)\n",
        "  process_curves_and_save_to_csv(input_csv, closed_curves, output_csv)"
      ],
      "metadata": {
        "id": "cbFNO9vNk4cn"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def visualize_completed_curves(input_csv, method):\n",
        "  input_data = read_csv(input_csv)\n",
        "  base_name = os.path.splitext(input_csv)[0]\n",
        "  output_csv = f\"{base_name}_{method}_output.csv\"\n",
        "  output_data = read_csv(output_csv)\n",
        "\n",
        "  # Plot side by side\n",
        "  fig, axs = plt.subplots(1, 2, tight_layout=True, figsize=(16, 8))\n",
        "  plot(input_data, 'Original Data', axs[0])\n",
        "  plot(output_data, 'Processed Data', axs[1])\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "4JtbusgSk634"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Calling Occlusion Model"
      ],
      "metadata": {
        "id": "nloqTR1O3IzT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "complete_curves('occlusion1.csv', 'interpolate')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HTSuj7Q73LZp",
        "outputId": "cb3d1399-892c-4d33-e467-a5d013aeca75"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total curves to process: 3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing curves:   0%|          | 0/3 [00:00<?, ?it/s]\n",
            "Fitting Ellipse to Curve 0:   0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "Fitting Ellipse to Curve 0: 100%|██████████| 1/1 [01:21<00:00, 81.29s/it]\u001b[A\n",
            "                                                                         \u001b[A\n",
            "Fitting Circle to Curve 0:   0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "Fitting Circle to Curve 0: 100%|██████████| 1/1 [00:00<00:00,  2.34it/s]\u001b[A\n",
            "                                                                        \u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Ellipse provides a better fit.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing curves:  33%|███▎      | 1/3 [01:24<02:48, 84.07s/it]\n",
            "Fitting Ellipse to Curve 1:   0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "Fitting Ellipse to Curve 1: 100%|██████████| 1/1 [00:52<00:00, 52.87s/it]\u001b[A\n",
            "                                                                         \u001b[A\n",
            "Fitting Circle to Curve 1:   0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "Fitting Circle to Curve 1: 100%|██████████| 1/1 [00:00<00:00,  2.70it/s]\u001b[A\n",
            "                                                                        \u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Ellipse provides a better fit.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rProcessing curves:  67%|██████▋   | 2/3 [02:19<01:07, 67.12s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The best model (ellipse) does not fully coincide with the points.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "Fitting Ellipse to Curve 2:   0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "Fitting Ellipse to Curve 2: 100%|██████████| 1/1 [00:22<00:00, 22.26s/it]\u001b[A\n",
            "                                                                         \u001b[A\n",
            "Fitting Circle to Curve 2:   0%|          | 0/1 [00:00<?, ?it/s]\u001b[A/usr/local/lib/python3.10/dist-packages/skimage/measure/fit.py:308: UserWarning: Input does not contain enough significant data points.\n",
            "  warn(\"Input does not contain enough significant data points.\")\n",
            "\n",
            "Fitting Circle to Curve 2: 100%|██████████| 1/1 [00:00<00:00,  4.28it/s]\u001b[A\n",
            "Processing curves: 100%|██████████| 3/3 [02:42<00:00, 54.16s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Ellipse provides a better fit.\n",
            "\n",
            "\n",
            "Processed curves saved to occlusion1_interpolate_output.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "visualize_completed_curves('occlusion1.csv', 'interpolate')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "dbEB6kUU6IU4",
        "outputId": "d6ade5ad-6b2c-4888-b57b-68492e791dc5"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "complete_curves('occlusion2.csv', 'interpolate')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-e0SKKhj6z93",
        "outputId": "de1b2792-c2b5-43c3-847c-fa2b7bd29e2a"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total curves to process: 5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing curves:  20%|██        | 1/5 [00:00<00:01,  3.13it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Skipping curve 0 - Not enough remaining points to process after removing coincident points.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rProcessing curves:  40%|████      | 2/5 [00:00<00:01,  2.56it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Skipping curve 1 - Not enough remaining points to process after removing coincident points.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "Fitting Ellipse to Curve 2:   0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "Fitting Ellipse to Curve 2: 100%|██████████| 1/1 [00:58<00:00, 58.32s/it]\u001b[A\n",
            "                                                                         \u001b[A\n",
            "Fitting Circle to Curve 2:   0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "Fitting Circle to Curve 2: 100%|██████████| 1/1 [00:00<00:00,  2.68it/s]\u001b[A\n",
            "                                                                        \u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Ellipse provides a better fit.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing curves:  60%|██████    | 3/5 [01:03<00:57, 28.79s/it]\n",
            "Fitting Ellipse to Curve 3:   0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "Fitting Ellipse to Curve 3: 100%|██████████| 1/1 [00:50<00:00, 50.99s/it]\u001b[A\n",
            "                                                                         \u001b[A\n",
            "Fitting Circle to Curve 3:   0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "Fitting Circle to Curve 3: 100%|██████████| 1/1 [00:00<00:00,  4.22it/s]\u001b[A\n",
            "                                                                        \u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Ellipse provides a better fit.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing curves:  80%|████████  | 4/5 [01:58<00:39, 39.05s/it]\n",
            "Fitting Ellipse to Curve 4:   0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "Fitting Ellipse to Curve 4: 100%|██████████| 1/1 [00:57<00:00, 57.37s/it]\u001b[A\n",
            "                                                                         \u001b[A\n",
            "Fitting Circle to Curve 4:   0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "Fitting Circle to Curve 4: 100%|██████████| 1/1 [00:00<00:00,  4.00it/s]\u001b[A\n",
            "                                                                        \u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Ellipse provides a better fit.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing curves: 100%|██████████| 5/5 [03:00<00:00, 36.03s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Processed curves saved to occlusion2_interpolate_output.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "visualize_completed_curves('occlusion2.csv', 'interpolate')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "3QhbPLdgmwzC",
        "outputId": "dcec8b2e-c06a-40bb-ff34-d91362fc5a5e"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 2 Axes>"
            ],
            "image/png": 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  ]
}