captcha-recognition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Captcha Recognition With K-Nearest-Neighbours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                  # used for creating numpy arrays for model creation\n",
    "import cv2                          # used for image processing\n",
    "import os                           # used to read files and directory structure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define global variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "RESIZED_IMAGE_WIDTH = 20                    # width of resized image\n",
    "RESIZED_IMAGE_HEIGHT = 30                   # height of resized image\n",
    "\n",
    "DATASET_PATH = os.getcwd() + '/dataset/'    # Dataset path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## TRAIN MODEL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Open training characters image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = []              # flattened images\n",
    "y = []              # labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "for char in os.listdir(DATASET_PATH):               # for each character folder\n",
    "    # print(char)                                     # print the character name\n",
    "    if (len(char) > 1):                             # if the folder name is more than 1 character, skip it\n",
    "        continue\n",
    "    for img in os.listdir(DATASET_PATH + char):     # for each image in the folder\n",
    "        if img[-3:] != 'png':                       # if the image is not a png file, skip it\n",
    "            continue\n",
    "        # read the image\n",
    "        image = cv2.imread(DATASET_PATH + char + '/' + img, cv2.IMREAD_GRAYSCALE)\n",
    "        # flatten the image\n",
    "        flattenedImage = image.reshape(RESIZED_IMAGE_WIDTH * RESIZED_IMAGE_HEIGHT)\n",
    "        X.append(flattenedImage)                    # append the image to the X array\n",
    "        y.append(ord(char))                         # append the class number to the y array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to numpy arrays\n",
    "X = np.array(X, dtype=np.float32)\n",
    "y = np.array(y, dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 600)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalize The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X /= 255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns                   # used for plotting graphs\n",
    "import matplotlib.pyplot as plt         # used for plotting graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# count the number of times each class occurs\n",
    "plot = np.unique(y, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 3000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (30, 7))                                       # set the size of the plot\n",
    "sns.barplot(x = plot[0], y = plot[1], palette='Blues_d')            # plot the graph\n",
    "plt.show()                                                          # show the graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split dataset in training and testing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier       # KNN classifier\n",
    "from sklearn.model_selection import train_test_split     # used for splitting the dataset into training and testing sets\n",
    "from sklearn.metrics import classification_report        # used for evaluating the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "# count the number of times each class occurs in training set\n",
    "plot_train = np.unique(y_train, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (30, 7))                                           # set the size of the plot\n",
    "sns.barplot(x = plot_train[0], y = plot_train[1], palette='Blues_d')    # plot the graph\n",
    "plt.show()                                                              # show the graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "# count the number of times each class occurs in testing set\n",
    "plot_test = np.unique(y_test, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 3000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (30, 7))                                           # set the size of the plot\n",
    "sns.barplot(x = plot_test[0], y = plot_test[1], palette = 'Blues_d')    # plot the graph\n",
    "plt.show()                                                              # show the graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create K-Nearest-Neighbours Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create KNN object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-30 {color: black;background-color: white;}#sk-container-id-30 pre{padding: 0;}#sk-container-id-30 div.sk-toggleable {background-color: white;}#sk-container-id-30 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-30 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-30 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-30 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-30 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-30 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-30 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-30 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-30 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-30 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-30 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-30 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-30 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-30 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-30 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-30 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-30 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-30 div.sk-item {position: relative;z-index: 1;}#sk-container-id-30 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-30 div.sk-item::before, #sk-container-id-30 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-30 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-30 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-30 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-30 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-30 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-30 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-30 div.sk-label-container {text-align: center;}#sk-container-id-30 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-30 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-30\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-58\" type=\"checkbox\" checked><label for=\"sk-estimator-id-58\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kNearest = KNeighborsClassifier(n_neighbors = 3)    # create the KNN classifier, distance metric is minkowski, p = 2\n",
    "kNearest.fit(X_train, y_train)                      # train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.91125 0.92375 0.9075  0.92375 0.935   0.905   0.91875 0.92    0.9225\n",
      " 0.92875]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rohit/.local/share/virtualenvs/OCR-XFjxz9Cn/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/rohit/.local/share/virtualenvs/OCR-XFjxz9Cn/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/rohit/.local/share/virtualenvs/OCR-XFjxz9Cn/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        49.0       0.86      0.91      0.88        67\n",
      "        50.0       0.98      1.00      0.99        51\n",
      "        51.0       0.95      0.99      0.97        83\n",
      "        52.0       1.00      1.00      1.00        53\n",
      "        53.0       0.98      0.96      0.97        54\n",
      "        54.0       0.88      1.00      0.94        46\n",
      "        55.0       0.98      1.00      0.99        63\n",
      "        56.0       0.80      0.93      0.86        59\n",
      "        57.0       0.98      0.95      0.97        65\n",
      "        65.0       1.00      1.00      1.00        42\n",
      "        66.0       0.82      0.79      0.81        63\n",
      "        67.0       0.87      0.95      0.91        77\n",
      "        68.0       0.94      0.94      0.94        62\n",
      "        69.0       0.89      0.84      0.87        58\n",
      "        70.0       0.91      0.92      0.92        77\n",
      "        71.0       0.98      0.84      0.91        57\n",
      "        72.0       0.97      0.95      0.96        40\n",
      "        73.0       0.82      0.85      0.84        54\n",
      "        74.0       1.00      0.97      0.99        71\n",
      "        75.0       0.95      0.95      0.95        61\n",
      "        76.0       0.00      0.00      0.00         1\n",
      "        77.0       1.00      0.96      0.98        51\n",
      "        78.0       0.97      0.97      0.97        61\n",
      "        80.0       0.91      0.91      0.91        55\n",
      "        81.0       1.00      0.95      0.97        61\n",
      "        82.0       0.97      0.94      0.96        72\n",
      "        83.0       0.93      0.91      0.92        74\n",
      "        84.0       1.00      0.94      0.97        72\n",
      "        85.0       0.93      0.94      0.94        54\n",
      "        86.0       1.00      0.98      0.99        51\n",
      "        87.0       0.98      0.98      0.98        63\n",
      "        88.0       0.99      1.00      0.99        78\n",
      "        89.0       1.00      1.00      1.00        47\n",
      "        90.0       1.00      0.95      0.97        57\n",
      "\n",
      "    accuracy                           0.94      2000\n",
      "   macro avg       0.92      0.92      0.92      2000\n",
      "weighted avg       0.95      0.94      0.94      2000\n",
      "\n",
      "0.944\n"
     ]
    }
   ],
   "source": [
    "# do k fold cross validation\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "kFold = KFold(n_splits = 10, random_state = 45, shuffle=True)         # create the k fold object\n",
    "\n",
    "# get the scores for each fold\n",
    "scores = cross_val_score(kNearest, X_train, y_train, cv = kFold, scoring = 'accuracy')\n",
    "\n",
    "# print the scores\n",
    "print(scores.max())\n",
    "\n",
    "# print the classification report\n",
    "print(classification_report(y_test, kNearest.predict(X_test)))\n",
    "\n",
    "# print the accuracy of the model\n",
    "print(kNearest.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print Model Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd                             # used for creating dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "# classification report\n",
    "test_score = classification_report(y_test, kNearest.predict(X_test), output_dict=True, zero_division=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "accuracy       0.944000  0.944000  0.944000     0.944\n",
      "macro avg      0.948628  0.917548  0.917740  2000.000\n",
      "weighted avg   0.945892  0.944000  0.944053  2000.000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot accuracy for each class\n",
    "df = pd.DataFrame(test_score).transpose()       # convert the dictionary to a dataframe\n",
    "print(df[-3:])\n",
    "df = df[:-3]                                    # remove accuracy, macro avg, weighted avg\n",
    "df['class'] = df.index                          # add a column for class name\n",
    "# convert class number to character\n",
    "df['class'] = df['class'].apply(lambda x: chr(int(float(x))))\n",
    "\n",
    "plt.figure(figsize = (10, 5))\n",
    "plt.plot(df['class'], df['precision'], label = 'precision', color = 'red', marker = 'o')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Precision')\n",
    "plt.show()\n",
    "plt.figure(figsize = (10, 5))\n",
    "plt.plot(df['class'], df['recall'], label = 'recall', color = 'green', marker = 'o')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Recall')\n",
    "plt.show()\n",
    "plt.figure(figsize = (10, 5))\n",
    "plt.plot(df['class'], df['f1-score'], label = 'f1-score', color = 'blue', marker = 'o')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('F1-score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV            # used for hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_range = list(range(1, 11))                # range of k values\n",
    "\n",
    "# create a parameter grid: map the parameter names to the values that should be searched\n",
    "param_grid = dict(n_neighbors = k_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rohit/.local/share/virtualenvs/OCR-XFjxz9Cn/lib/python3.10/site-packages/sklearn/model_selection/_split.py:684: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-28 {color: black;background-color: white;}#sk-container-id-28 pre{padding: 0;}#sk-container-id-28 div.sk-toggleable {background-color: white;}#sk-container-id-28 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-28 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-28 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-28 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-28 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-28 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-28 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-28 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-28 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-28 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-28 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-28 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-28 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-28 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-28 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-28 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-28 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-28 div.sk-item {position: relative;z-index: 1;}#sk-container-id-28 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-28 div.sk-item::before, #sk-container-id-28 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-28 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-28 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-28 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-28 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-28 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-28 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-28 div.sk-label-container {text-align: center;}#sk-container-id-28 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-28 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-28\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=KNeighborsClassifier(n_neighbors=3),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-54\" type=\"checkbox\" ><label for=\"sk-estimator-id-54\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=KNeighborsClassifier(n_neighbors=3),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-55\" type=\"checkbox\" ><label for=\"sk-estimator-id-55\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-56\" type=\"checkbox\" ><label for=\"sk-estimator-id-56\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=KNeighborsClassifier(n_neighbors=3),\n",
       "             param_grid={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid = GridSearchCV(kNearest, param_grid,           # instantiate the grid\n",
    "                    scoring='accuracy')\n",
    "grid.fit(X, y)                                      # fit the grid with data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cross-Validated Accuracy')"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# examine the best model\n",
    "grid_mean_scores = grid.cv_results_['mean_test_score']\n",
    "\n",
    "# plot the value of K for KNN (x-axis) versus the cross-validated accuracy (y-axis)\n",
    "plt.plot(k_range, grid_mean_scores, marker = 'o')\n",
    "plt.xlabel('Value of K for KNN')\n",
    "plt.ylabel('Cross-Validated Accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9324\n",
      "{'n_neighbors': 9}\n"
     ]
    }
   ],
   "source": [
    "print(grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create New KNN Model With Best Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-29 {color: black;background-color: white;}#sk-container-id-29 pre{padding: 0;}#sk-container-id-29 div.sk-toggleable {background-color: white;}#sk-container-id-29 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-29 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-29 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-29 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-29 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-29 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-29 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-29 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-29 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-29 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-29 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-29 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-29 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-29 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-29 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-29 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-29 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-29 div.sk-item {position: relative;z-index: 1;}#sk-container-id-29 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-29 div.sk-item::before, #sk-container-id-29 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-29 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-29 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-29 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-29 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-29 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-29 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-29 div.sk-label-container {text-align: center;}#sk-container-id-29 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-29 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-29\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=9)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-57\" type=\"checkbox\" checked><label for=\"sk-estimator-id-57\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=9)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=9)"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create the KNN classifier\n",
    "kNearest = KNeighborsClassifier(n_neighbors = grid.best_params_['n_neighbors'])\n",
    "# train the model\n",
    "kNearest.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print Model Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "# classification report\n",
    "test_score = classification_report(y_test, kNearest.predict(X_test), output_dict=True, zero_division=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "accuracy       0.932000  0.932000  0.932000     0.932\n",
      "macro avg      0.938844  0.905216  0.905551  2000.000\n",
      "weighted avg   0.936921  0.932000  0.932374  2000.000\n"
     ]
    },
    {
     "data": {
      "image/png": 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tTJ99JubcvDlw880V314uDfzhB/X+D6rVgr08EREiCFKiPfv27cDzz4vjWbPYdt0DDK5IPfK+VoMGiRIGpYWFiUWn7dsD584Bt98uylu0vJYpNbX8AEKSgBMnlK2r9pactbJafdeamU0tiNQhd527/37g+utVnQo3Ey7Hvn3AggXi+PXXPSvdbNRI/J+UJFFe72uSZC8JfPRR1+bcqZO4iHf2rDoZzCNHxIVaq1VctNWSDh28b8+ekwMMHmxfViGvuSK3MLgidezdKzJLAPDcc767n4gIsfC0ZUtRMtGxIxAXp921TGlpyt7OHxybWfhqLQYzV0T+98svYm1LUJA21lw4BldsblPcpEniQuFddwFdung+jlyat3AhkJenzNzKsmkTsHu3uBD64IOufU1QkChPBdTpGihnrTp3BqpX9//9V8SxPfsDD7jfnp1t1xXB4IrU8eqr4qpV//7Addf59r6qVxcvEKKjxULfkoGJltYyRUcrezt/8GUzCxkzV0T+JUn2cu3/+z/7eic1tWolXohfvCg2kyXht9+AlStFif2rr3o31l13ia69GRnAN98oMr0yyVmr++5z7/+HXI6nxror+T7V7hJYFsf27Js3u9eenW3XFcPgivzv8GFRGwzY63p9rVatsj+npbVMXbqIQKKsq0UWi8i8eXNlUmm+bGYhkzNXDK6I/OPrr4GtW4HwcNHQQguCgoB27cQxSwMFSRJtsgFg5EixZ5U3goLEOIBvG1ucPw98+aU4dqWRhaNevUQg+ddfwLFjys+tLJcuiaUEgLbWW5XkSXt2tl1XFIMr8r/XXxdBzB132P9R+lpqavmldFpZy2S1ArNnO/+cHHDNmiVupxW+3ONKJmeuWBZI5HsFBfYLX089JTr1aQU3Ey5u5UrxswgLEw0tlPDww+L/TXIycOCAMmOW9PHHQG6uWMfXvr17X1u9uijLA/zbNv7nn0WpZMOG2m/y4E57dptN7C92/jxwww3aKAHWOQZX5F8nTwKLFonjyZP9d796WsuUmCieDEuKjRXr1DzpAuVL/igLZOaKyH8WLhTrLmrVEsGVlrBjoF1+vr1086mngJgYZcatV09sZQIAH3ygzJiOJMmeWXG1kUVJapQGOnYJ1MNaJFfbs7/1lgikK1UClixh23UFMLgi/3rzTXHlp0sX/5a26WktkyTZ9+Vq21a8b9lSdCnSWmAF+Lcs8NIl0VqfiHzj8mV7BmTyZNEUSEvkzNXff/O5YMECEQTXrGkvDVSKXKq3aBFw9aqyY69fL9bMhYeLxlKekIOrX34RHe58zWazZ8m0XBLoyJX27Gy77hMMrsh/zpyxX63y11ormZ7WMm3fDuzZA4SG2q9KSpK2SgEd+SNzFR5uD96YvSLyndmzRQa/fn3318L4Q506Ym6SZO7NhLOz7UHw1KlAZKSy4/fuLf4nnjsn1t8pSW5kMWSI58H7tdcCDRqI0sKff1ZubmX54w/R5CMiQnTj04uS7dn377dvRfPjj6KZCNuuK47BFfnP228DV66IdVZ33OHf+3Zcy1RWgKWVtUwffyze9+8vXkQA5ddLq80fmSuApYFEvnb+PPDaa+J42jQgJETd+ZSFpYGilCsjQ5R9yZv/Kslqtb/YVrKxxenT9s683gTvFot/SwPl+0hI0F/ZnGN79lat7FvR9O4t1tRVr8626wpjcEX+ceECMGeOOH7+eXUexImJYs2S/CJdVqmSdtYy5eWJmmdALDCVs0EXLqg2pQr5o6EFwKYWRL42Y4bIRLdq5Xm5lj+YfTPhjAzgjTfE8Suv+O7F/siRIshKTRVlmEpYtEhkStq3B9q08W4suR3699/7ft8zx/VWemO12h/PzvYuO39elGqSYhhckX/MmSPWy1x3ndhHQy2JicDRo8C6dcB//yvO5ecDXbuqNydHP/wgyjCio4HbbweqVRPnc3Lc3wzQX/xRFggwc0XkSydOAO++K45nzBCtrrXKMbgqa5G+kb30kvifcOONwMCBvrufunXt/6+VaGxhs9nHefRR78e75RZRMp6WBuzY4f14ZTl5UoxvsYg28HpTWGh/veOMxaKNrWgMRMPPnmQY2dmi5A4AnntO/X/aVivQrZvIoN1wgwhaPv1U3TnJ5JLABx4Q83QstdNqaaC/ywKZuSJS3osvivUrXbtq/wVk69ZiTer586Khg5n88489QHnjDd9Xgcilex9/LJqdeOPnn8U+l1WqAIMGeT+3kBD7EgNflgbKjSw6dNDWtgSuSk0t//+mVraiMRAGV+R7778v/gk2aiT2XtCShx8W7z/4QP0roGfP2p/Ehw4V7wMD7Qt+tR5c+asskJkrImXt2QMsXiyOX31V+2svgoPtnVTNVhr43HMiw3Dnnf6puLj9dtE4IjNTdJzzhtzIYuhQUY6vBLlM77vvlBnPGXlsPZYEAvraisYgGFyRb129KtqvA8CkSdpoGOHo/vvFk/zevcDGjerOZelSkUW74QZRPinT+rorf5cFMnNFpKznnxclWwMG2JtFaJ0ZNxPetEl07gsIEEGwPwQE2BtmeNPY4tQp4NtvxbGSXSh79xYXA7ZtE/ehtMuX7d0I5TVeeqOnrWgMgsEV+dbChUB6umjp6mxjXLVFRgKDB4tjX2yW6A65JHDYsOLn5XVXWs9c+boskJkrIuVt2gSsXCleRE+frvZsXGe2joGSBDz7rDh+6CGgRQv/3ffw4UBQELB5M7Bzp2djLFggMm4336zs3KOiRHMMQKxZVtq6deIicVyc2G9Sj/S0FY1BMLgi38nPt7f1/c9/tNu+VC4N/PJL9bJDe/aIfTQCA+3BnkzLmau8PNFeH/Bf5ur0aecdj4jIPZIknpsB8QL62mvVnY875MzV7t2iWZLRffstsGEDEBZm37fIX6Ki7N10PcleFRaKVt+AMo0sSvJlS3bHLoFaL5ctS3lb0cgfa2UrGoNgcEW+89lnwPHj4ol5xAi1Z1O29u1F6+GrV8Vu5mqQs1Z9+gC1ahX/nJYzV3JJIKD8JpYl1axpD9B9Uf5BZDY//CAWsYeG2jek1YuYGOCaa0Q549atas/GNwoLxYavn30GPP64ODd+vD2L709yKd9nn7kfzP74o2iYUKMGcPfdys9NDq7WrhX/x5UiSfpuwe6orK1oYmO1sxWNgTC4It8oLBTtfAHgqafE1TatsljsNeVqNLYoLBT/sAB7IwtHWs5cyQFfRITvr3pZLGzHTuQt+QX755/bX7A//rg6L9i9ZeTSwKQksYl89+7Agw+K4CQgQL3sYrduQJMmovuvvBejq+RGFg89JAJ5pbVuLf43XL4s/raV8uefYo1vWJj4Peid41Y0S5aI90eOMLDyAQZX5BtffWXf+dsXZQBKGzJEPIH+9ZeoK/en5GSRialeXWSuStJD5srXJYEyNrUg8pzjC/YHHhAvrCwW/67fUZJRNxNOSgLuuaf085zNJtbkJiX5f04Wiz179f77rl+EPHbMvhZKvojpi7n5omugPNbtt2v7ArE75K1oBg8W71kK6BMMrkh5Npt9YfS4cfZW4lpWtaq9TbxcG+4vckng4MFi346S9JC58nUzCxmbWhB5pqwX7JIk1lup8YLdW0bcTLiwUPzfLO/7UWvD12HDxP+oHTtcL8X86CPxvdx6q8h8+YrcyW/VKuX+FoxSEkh+x+CKlPfdd2KRcUSEvexED+SrakuXFl9L5EtZWcCKFeK4ZJdAGTNXdsxcEblPyy/YvXH99WId5tmzwMGDas9GGVre8LVGDftFSFcaW+Tniy6BgO8rWG69VWSXjh8Xrz+8lZEBbNkijnv39n48MhUGV6QsSbJnrcaMsQcGetCxI9C8uajbdrem3FNffSW67V17LdCunfPbMHNlx8wVkfu0/ILdGyEhxttMWOsbvsqlgV98UfEFv+++E/OsXRvo18+38woLA267TRwr0TXwxx/F4+KGG0o3gSCqAIMrUoa8SPr550W5QGgo8OSTas/KPWo0tpBLAocOLbvNq5YzV/Kc/J25YnBF5LoTJ1y7nVov2L1htM2Etb7ha6dOYpP7K1eATz8t/7ZyI4uRI/2zFYuSLdlZEkheYHBF3nNcJC13CAwMFHty6M2DD4qroTt3ih3ffenwYXGl2GIpf4NlLWeu/F0WKGeuWBZoDvJFmy++EO/1Vramtvx8sZG7vPlsRdR6we4No3UM1PqGr642tjh0SLRGt1jse0n6mtwQatMm4MwZz8fJzQXWrBHHDK7IAwyuyDtlLZLOyRHn9bZIunp1MW9AZK98Sb7q16NH+W2Q9ZC58ldZoJy5OnVKNE4h43K8aHP//eJ9/fr6e05RgxxUNW0qsgbp6aKNd1nUfsHuDTlz9eef4v+O3jlu+FqSVjZ8ffBBoFIl4O+/gd9+c34b+f9nQgLQoIF/5hUbK9bhSZIo6/PUr7+KlvN16tjLToncwOCKPFfeImn5nB4XSculgV984f5mia6SJOCTT8RxWY0sZI6ZK611xPJ3WWB0tHiBkZ/v3ZVJ0rayLtqcPKnPizb+UjKoOnJErHd56y2xl57FUjojopUX7J6KjRVvRtpMODHRedtyrWz4WqWK6G4LOG9skZsLLFokjv29FYtj10BPyV/bp0/5FyWIysC/GvKcURdJd+kiXpxkZ4vOgb6wYYMoC4yIAAYMKP+2cuaqsFB7V2b9XRYYFARERYljrrsyJqNetPEluSubs6DqyBFgwgTxYnj58tKL87Xygt0bRisNBOzP9UOHanPDV7k08KuvRLdGRytWiItfdes637vRl+QyvjVrgLw8979ekuz7W7EkkDzE4Io8p/WuRp5yrBH3VWmg3Mhi4EBRXlGeSpXEGjZAe+uu/F0WCLCphdEZ9aKNL8hBVZMmwKhRzoMqx+eXxETg6FHxQl2LL9g9ZcTNhH/9Vbx/8EFtbvjarp3opJeba/9/JpMbWYwaZf/f5c951a4ttjnxZN33vn3iMREcLEr2iTzA4Io8p/WuRt4YNkw8uf7xh9gwUUmXLwNffimOhw6t+PYWi3bXXfk7cwWwqYXRGfWijSfKauhRMqg6erT8oMqR1SpeqGvxBbunHDsGaq102hPHjon9mgID7d+b1pTV2GLfPmD9elFON2qU/+cVEGDPlnlSGih/TffuQHi4cvMiU2FwRZ6r6J+ynhdJ16xpL9f78ENlx165Uqzlql/f9Z+NVjsGMnNFSjPyRRt3OGvoUa8e8NhjxYOqqChg5syKgyoja9NGlAyfOSPKrfVOzsrecANQubK6cynP4MGitP3AAZEFBexrsO68s/xGTb4kl/N99537wTZLAkkBDK7IM7t3A3fdZf/YaIukAfuC4s8+U3atk9zIYuhQ1xfLajVz5e+GFgAzV0an9VbU/lBeQ49584oHVYcPiz0FzRhUyUJDRSACGKM0UA6utP43HhFh30Zk2jRRHvjRR+JjfzeycHT77aLy5OBB4J9/XP+68+ft3Q8ZXJEXGFyR+44dE+1VL14UJQtLlhhzkXS3bkCjRiLLJJfxeevUKbH3B+BaSaBMi5krm03UtQP+Da6YuTI2uRW1syvORrhoU5HyGnrIqlYV2QKzB1WOjLSZsLze6pZb1J2HKxo1Eu9TUoCHHhKNoKxWdZsvRUSI/9+Ae6WBq1eL/2vXXSeyxkQeYnBF7jlzBrjjDhEkNG8unrgGDzbmImnHmnGlGlt89pl48r75ZqBhQ9e/TouZq0uX7C8A1SgLZObKM3rYmDcxEejfv/R5I1y0qUhFDT0A8Tzg603O9cYoHQNPnxbrlgCgc2d151KRpCTg6adLny8sBO69V90tE+TMkzvBlXxbZq3ISwyuyHXZ2WKh6D//iLKcNWvEpruAMRdJA+JKXGCgKDX56y/vxpIke1cld7JWgDYzV3Izi5AQUZbjL3JZIDNX7tPTxrznzon38t/WQw8Z46JNRdjQwzNy5mrXLtE0SK/kDnfXXQfUqKHuXMrjSoZVzS0T5KYWqamuXZQsKLBvPMzgirzE4Ipck5cnXtRs3Sqe8H/6Sb3Fqv4UFWW/gu5tY4tt24A9e8SLxXvvde9rtZi5UqOZBWDPXF26ZC9LpIrpaWPevDz7hrAPPijey+VGRseGHp6JiwNiYsSL+T/+UHs2ntPLeiutb5kQHy+qawoLxYXgimzcKP6n1ahhz4ISeYjBFVXMZhOZlrVrReeiH34AmjVTe1b+I+959emn3l0RlbNW/fu7H5BoMXOlRjMLQLTHlX9+zF65Rm8b8+7aBVy9KjLjd98tznmbOdYLNvTwjMVijNJAvay30kOG1bFrYEXk2/TubY6LOORTDK6ofJIkXpQtWyZa3SYlAe3bqz0r/+rRQ5ROXbwo1nt4Ii9PrHEBxB5a7tJi5kqNPa5kbGrhHq1fZS5p40bxvmNHoHVrcXzgAHDlinpz8he5oYczZmjo4Q29byaclQXs3CmOtR486yHD2reveP/jj6Lsrzxcb0UKYnBF5Zs+HZgzR/xT//hj0czCbBwbW3haGvjDD2INSXS0aBPrLjmA0VJwpVZZIMCmFu7Sw1VmR3Jw1amTKM2tWVNk0PfsUXde/pKYKC7klNyqwQwNPbyh982EN20Sf+fx8aU78GqNHjKsN90kst/nz5cfcB88KJqIBAaa8zUOKU714Gru3LmoX78+QkND0aFDB2zZsqXM2+bn5+Pll19Gw4YNERoaitatW2P16tXFbvPiiy/CYrEUe2tmphI2Jb3/PjBlijiePVs0qzCr4cPFleINGzx7gSeXBD7wgGdXnOXMFcsCBTa1cI8erjI7ksu6OnUSL9JathQfm6U0EBCbmMvB1bvvGqcLqy/dcIN4gZyRITrY6o1cEqj1rBVQPMOq1X0uAwOBXr3EcXldA7//Xrzv0kWd/2dkOKoGV8uWLcOECRMwdepUbN++Ha1bt0ZCQgJOnz7t9PaTJ0/G+++/j3fffRd79uzBo48+igEDBmDHjh3FbteiRQukpaUVvW2Qu++Q677+GnjsMXE8eTLw+OPqzkdtMTH2EgN3s1dnz9qfvN3tEijTYuZKC2WBzFy5pkuX8q+Ea+Eqs+zECfFmtQI33ijOmTG4ysy0lzKNGmWsLqy+EhYGtGkjjvVYGiiX5Wp9vZVMzrBqeZ9LV1qysySQFKZqcDVz5kw8/PDDGD58OJo3b4758+ejUqVKWLhwodPbf/rpp3juuefQu3dvxMfHY/To0ejduzfeeuutYrcLDAxEnTp1it5q1qzpj2/HONatE22abTbgkUeAl19We0baIDe2+OQTsdjeVV98AeTni6uq113n2X1rOXOlRlkgM1fusVrLLkfVylVmmZy1at1aNNAB7MHVn3+qMyc1nDkj3kdE+HerA73T62bCV68CmzeLYy1c5HBVYqK297lMSBDPa3//LeZVUlYWsH69OGZwRQpRLbjKy8vDtm3b0KNHD/tkAgLQo0cPbCrjSTE3NxehJf7JhIWFlcpMHThwADExMYiPj8eQIUNw/PjxcueSm5uLrKysYm+mtWMH0K+fvfX6e++VXVNtNgkJ4ur++fPuta2WSwI9aWQhk7ND2dkVL8z1Fy1krhhcuebSJfvVWTlQl0VHa+cqM1B8vZWsVSvx3kyZK7mCo1YtdeehN3rtGLh1q/i/W6cO0KiR2rNxj5b3uaxWzR6sOste/fSTuPjZpIl4I1KAasHV2bNnUVhYiKioqGLno6KikJ6e7vRrEhISMHPmTBw4cAA2mw1r165FUlIS0hwWYXfo0AGLFy/G6tWrMW/ePBw5cgRdunTBpUuXypzLjBkzUKVKlaK3uLg4Zb5JvTl4EOjZU7wQ69YN+PxzbT1Jqs1qdb+xxd9/i/2tAgO9W7PmGMBopTRQC5krlgW65u23RXlqkyaiacW6dUC9euJzb7yhncAKKL7eStaihbjIk5Fhz+gYnRxc1a6t7jz0Rs5c7dypr+6SjuuteEFTWeWVBrIkkHxA9YYW7pg9ezYaN26MZs2aITg4GGPHjsXw4cMR4NBRqVevXhg4cCBatWqFhIQE/PDDD7h48SK+/PLLMsedNGkSMjMzi95OnDjhj29HW9LSRJec06eB668HvvmGpSjOjBghFpmnpAD//FPx7T/5RLzv08e7K9CBgWJ/J0B7wZWamavTp8XVXirb2bPAm2+K42nTgJAQcfFE3hxbK+3XAfFiePt2cSy/SAZEeWB8vDg2S/ZKDiKZuXJPvXoi+1NQIC5s6YXe1lvpiRw4paSIi8eywkLRydfxNkQKUC24qlmzJqxWKzIyMoqdz8jIQJ06dZx+Ta1atbBy5Urk5OTg2LFj2LdvH8LDwxEv/9N1omrVqmjSpAkOHjxY5m1CQkIQGRlZ7M1ULl4UHXWOHAEaNgRWrwbM9jNwVWys2GQQqDh7VVgIfPaZOPa0kYUjra27UrMssGZNIDhYHJ865f/715NXXxUvKNq0Ae65x36+a1fxXl5voAV//CFeFEdH2zNrMrM1tWDmyjN63Ey4oAD47TdxrKf1VnrRpIkotczLA37+2X5+61ZxESMyErj5ZvXmR4ajWnAVHByMtm3bIjk5ueiczWZDcnIyOjpesXQiNDQUdevWRUFBAb7++mv069evzNtmZ2fj0KFDiNZKi2EtKCwUV3C++AJYswa46y5g1y6xn8xPP4n3VLZHHhHvFy8GcnPLvt3PP4sX/tWri8yVt7TWMVDNskCLheuuXPHvv2KfOkDsWee4b5J8hXzvXvsLebU5rrcqWRolr7syS1MLZq48p7fNhHftEutpq1TxvOkRlc1icV4aKB/37AkEBfl/XmRYqpYFTpgwAR9++CE+/vhj7N27F6NHj0ZOTg6GDx8OABg6dCgmTZpUdPvNmzcjKSkJhw8fRmpqKnr27AmbzYZnn3226DZPP/001q9fj6NHj2Ljxo0YMGAArFYrBpt5jyZHSUlA/fpA9+6iI2DPnqIcISxMZKzKyQLS//TqJVqznz0ryifLIjeyGDxYlGJ5i5mr4hhcVezll8UFgC5dxGPdUY0a9myQVrJXztZbyZi5IlfpbTNheb3VzTdznbOvyMHV99+LTsgA8N134r28zQqRQlQNrgYNGoQ333wTL7zwAq6//nrs3LkTq1evLmpycfz48WLNKq5evYrJkyejefPmGDBgAOrWrYsNGzagqsOLu3///ReDBw9G06ZNce+996JGjRr4/fffUYtX/0Rgdc89zpsAXLkCHD7s/znpUWAgMHKkOP7gA+e3ycwEVqwQx950CXSkpcyVJKm75gpgU4uKHDgAyNtazJjhfJG8lkoDJcmeuXJWvSAHV3//bX9xZGTMXHmubVvxPJ2WBlTQLVgT5PVWLAn0nS5dRPlfRoYoPz5+XGTBAwJKX3gi8lKg2hMYO3Ysxo4d6/RzKSkpxT7u2rUr9uzZU+54S5cuVWpqxlJYCIwbV/ZVPIsFGD9etGHnlbOKjRwJ/Pe/QHIycOiQWKvmaPlysW/JtdcC7dopc59aylxdvWpvJKFGWSDAzFVFXnhBPO779AE6d3Z+m27dRNmgFoKrQ4dEQBEcLPaEK6lRI9Fk5/JlcSFIb+2q3cXMlecqVRL7pG3bJkoDS67f0xJJYjMLfwgOFtupfPWVKAeUl4p07CjW8BIpSFfdAskLqanlX+GXJODECW11DtOyevXEEzUAfPRR6c/LJYFDhyrXVldLmSu5JDAgwN7F0N+YuSrbzp2AfKFp+vSybye/mNu9W5S5qkkuCWzXznkZrdUKNG8ujs2w7oqZK+/oZTPhffvEYy8sTGTcyHfk0sAvvrBXncgNqogUxODKLBzKKxW5HdkbWyxaJDYhlB0+LIJUiwV44AHl7k9LmSs5wIuMLN4kwZ+YuSrb88+L9/fdJ67gl6VWLXvAIq/7UEt5JYEys2wmbLPZgytmrjyjl46B8uPuppvsHVDJNwoLxfuDB8UFKAB45x2xZIJIQQyuzMLVbonsqui6O+8UnRUzMuwLYwH73lY9etizK0rQUuZK7fVWgD24YuaquA0bxN4tVqtoaFGRbt3Ee7VLAx07BZbFLE0tLl60vxBkyZJn5CB9xw5RxqxVXG/lH0lJ9rXSjk6fFmvRGWCRghhcmUWXLuKFflklahYLEBfHJ3h3BAWJTYUBe4mBzWYPrpRqZCHTUuZK7U6BgD1wPXXKHA0OXCFJgNxhdeRIoHHjir9GbmpRYo2rX2Vl2QOm8jJXZgmu5PVWVaoo02nUjBo0EFm//Hz7xtRaJGeuuN7Kd8pbcy6fGz/efkGDyEsMrszCagVmz3b+OTngmjWLzSzcNWqUeP/TT8DRoyJrcOQIEBEBDBig7H1pMXOlVjMLQGRZLRbx4knt9UJasXq1+BsMDRUNLVwhv6j76y/g/Hnfza08W7aIFzkNGpSfPZeDq4MHRWMLo2JJoPf0sJnwsWNirXNgoH2upDyuOSc/Y3BlJomJolNOyexVbKzobpeYqM689Cw+XpT/SRIwZQowdao4f/fdomOVkpi5Ki4oyL7hNUsDRfbuuefE8dix9rLJitSpAzRtWrxrmb+5st4KEL/vWrXE91pB51hdkzNXbGbhHa1vJiw/3tq2BSpXVncuRsY15+RnDK7M5rbb7GnwxYuBdetEpoWBlefkhgGffWYvrfr+e+VruJm5Ko1NLey++kos0o6IAP7zH/e+Vu11V66stwLEhSEzlAYyc6UMrXcMlEsCWY7vW1xzTn7G4MpsTpwQ72vUEGuCunVjKaA3kpKAmTNLnz97VvlFso6Zq7L2K/MXLTS0ANiOXZafLzKnAPD00+43QVBz3ZXNZs8sVBRcAeYIrpi5Uka7duL/28mT9v99WsL9rfyDa87JzxhcmY38IlTJLnZm5e9FsnIgU1Cg/noTLZQFAsxcyT7+GDhwQLwYf/JJ979eDq527vR/ZnTvXvH3VLmyPXAqjxmCK2aulFG5sr19v9ZKA0+fFntcAWVv8k3KcFxzXjLA4ppz8gEGV2YjB1dxcerOwwj8vUi2cmWx8BlQf92V1soCzZy5unoVeOklcfzcc6Is0F0xMaKzoCSJhhj+JJcEtm9v//sujxmCK2aulKPV0kD5cXbddUD16urOxQwSE8Xa8pJrUbnmnHyAwZXZyKURzFx5z9+LZC0W7ay70krmSv47NnPm6r33RHAZFwc8+qjn46hVGujqeitZixbisZCRYQ9CjEb+vpi58p5WOwayBbv/JSaKrr7r1gFLlnDNOfkMgyuzYVmgctRYJKuVjoFaWXNl9rLArCzglVfE8dSpogW7p+Tgyt9NLeQXva4GV5UrAw0bimOjZq/kskBmrrwnZ662bwdyc9WdiyNuHqwOq1WsNR88mGvOyWcYXJkNywKVo8YiWa1krrRSFmj2hhYzZwLnzolW6t5uWi0HV9u3i6DNH86eBfbvF8fu7PNj9NJAZq6U07ChaPCSlwfs2KH2bISsLLG+EWBwRWRADK7MhmWBylFjkaxWMldaKQuUM1eXLvkvINCKs2eBt94Sx9OmubZeqTxxcWLfNpvNf+uu5CYDzZq5t+7EyMFVYaEImAFmrpRgsWhv3dXGjeJxFh/v+n50RKQbDK7MRG6wADC4Uoq/F8kyc1VceDgQGSmOzVYaOGMGkJ0N3HCD2LRaCf4uDXR3vZVMDq7+/FPZ+WjB+fPihTfgfkt9ck7OimqlYyDXWxEZGoMrM8nKAnJyxDGDK+X4c5GsFjJXBQXiRT2gfuYKMGdTixMngLlzxfH06UCAQk/l/t5M2N31VjK5vfbffyu31YFWyOutqlUDgoLUnYtRaC1zxfVWRIbmZR0J6YqctapeHahUSd25GI28SNbXtJC5ciy/UztzBYis4Z495gquXn5ZLM6/5RYgIUG5ceXM1R9/iAA6PFy5sUvKzwe2bBHH8otfVzVsCISFAVeuAIcPizbyRsH1Vsq78UZxAeLECfE8oWYp3tWr9r97Zq6IDImZKzNhp0D900LmSg7sKlXSxpV1szW1+OcfYNEicTxjRtkNVTxRr554KywEfvtNuXGd+fNPsRl21apizZU7rFageXNxbLR1V+wUqLzwcHspqdqlgVu2iOYaderYu14SkaEwuDITdgrUPy1krrTSzEJmtnbsL7wggp8773S/nM4V/lp3Ja+36tjRs7JGoza1YObKN7RSGui43krJCyNEpBkMrsyEmSv901LmSivBlZkyVzt2AMuWiePp031zH/5adyW/yHW3JFAmr7syWlMLZq58QyvBFddbERkegyszYadA/dNC5kornQJlZspcPf+8eD94sD24UJqcudqyxd4Axxc87RQoY+aK3CF3DNy2TZTlqaGgwP53z/VWRIbF4MpMWBaof1rIXGm1LNDomavUVODHH8V+Vi+/7Lv7adBAXIApKPDdVf5Tp4Bjx0Q5YPv2no0hB1cHD4q1W0YhZ64YXCmrcWOgRg3RCEbewNffdu4UjWKqVgWuu06dORCRzzG4MhOWBeofM1elyX/Pp0+rd0Xa1yQJmDRJHI8cCTRq5Lv7slh8XxooB22tWgEREZ6NERUlSuckSXSLNAo5c8WyQGVZLPbslVqlgXJJ4M03K7d9AhFpDh/dZsKyQP2TM1eXLonMghq0tuaqZk0gOFgcp6WpOxclFRYCKSnAF18Ar70muveFhgJTpvj+vuXSwJQU34zv2MzCG0bcTJiZK99RezNhuZkF11sRGRr3uTKLrCzxghxgcKVnjtmizExR5uJvWisLtFhEaeCRIyI7W6+e2jPyXlISMG5c6VLHhAT/7NHjuO7qyhWxp5SSvF1vJWvVCvjlF2Otu2LmynfUbGohSWxmQWQSzFyZhfwirVo1oHJldedCngsKsm/sqta6K62VBQLGamqRlATcc4/zNWTffis+72uNGgExMaLMUumr/FeviqYCgPfBldGaWhQUAOfOiWNmrpQnr+87dgx47z2RmS0s9M99790rfrdhYUDbtv65TyJSBYMrs2BJoHGove5Ka5krwDjt2AsLRcZKksq+zfjxvn9BaLH4rjRw+3YgP1+smWrQwLuxjBZcyYGVxaJOVtro1q61b3w+ZgzQvTtQv75/LljIWaubbrKXMRORITG4Mgt2CjQOtTsGMnPlO6mp5QeIkiQulMgv1HzJV5sJO6638nYT1RYtxBinTwMZGd7PTW1ySWCNGoDVqu5cjEbOCOfnFz9/8qQ47+sAy3HzYCIyNAZXZsFOgcahduZKaw0tAOO0Y3e1IYc/GnfIHQN//12U8ilFqfVWAFCpkr1zohGyV9xA2DfKywjL53yZEZYkNrMgMhEGV2bBskDjUDtzpeWyQL1nrqKjlb2dN5o0EaV7ubmisYUSJEnZ4AowVmkgNxD2DbUzwseOifsPDLR3LCQiw2JwZRYsCzQOrWSuWBaovC5dRKBYVrmcxSIew/64+u2LdVdHj4ryvaAg5Rb1Gym4YubKN9TOCMtBW9u2bChFZAIMrsyCZYHGoWbmSpK0n7my2dSdizesVmDWLOflS3LANWuW/9bjKL3uSs5a3XCD2LNLCUYKrpi58g21M8Jcb0VkKgyuzIJlgcahZuYqJ8e+LkFLwVV0tAg+8vOBs2fVno1vxMYCy5cDiYn+u0953dWmTaI80FtKlwQC9uBq927/tdX2FWaufEPtjDD3tyIyFQZXZpCVJd4ABldGoGbmSg7oAgOV31jWG0FB9qv9em5qcfkyMGGCOJ48GVi3DliyRLw/csS/gRUAXHuteKF/5Qqwdav348mbtyoZXDVsKP4Wr14FDh1Sblw1MHPlG1YrMHu2OHYWYEkS8NZbvskIZ2QA+/eL+735ZuXHJyLNYXBlBvI6lKpV7RvQkn6pmblyLAn0to220ozQ1OL114Hjx8VV9EmTROZo8GDxXo3W3BaLvZTJ29LA7Gxg1y5x3LGjd2M5slpFS3ZA/6WBzFz5TmKiyPzK6zNl8vOYrwLzDRvE++uus18YIyJDY3BlBiwJNBYtZK601MxCpvd27EePAq+9Jo7feku0GdcCuTTQ2+BqyxaxHu6aa0q/wPWWUdZdMXPlW4mJ4nHmmBFesEB8bupUUVqqNLkkkOutiEwjUO0JkB+wU6CxqJm50uIeVzK9Z66eekqUtnXvLjY11Qq5qcVvv4k1bUFBno3ji/VWMqMEV8xc+Z7Var9gAIi/7xUrgO++A4YNE/u6efo37gz3tyIyHWauzICdAo1FzcyVFjsFyvTcjv3nn4GkJPHC7513tFVy2aIFUL26WA/2xx+ej+OL9VYyObj680/lx/aX/Hz7Y5qZK/+xWID33xfPq9u3A6++qtzYmZn2UlgGV0SmweDKDFgWaCyOmStnLbt9SctlgfLft97KAvPzgSeeEMdjxoi1GVoSEOB9S3abzR5cKbneStaqlXh/6JDoaKlHcpfLgAARzJL/REcDc+aI45dfBnbuVGbcjRvF337DhkBMjDJjEpHmMbgyA5YFGoscXOXni2yCPzFzpby5c4G9e4GaNYEXX1R7Ns55G1zt3y+yMmFhQOvWys1LVru2eJMkYM8e5cf3B3m9Vc2aIsAi/xo8WKzJKigQ5YF5ed6PyRbsRKbEZ3AzYFmgsYSH2zvH+XvdlZYzV3psaHH6tFhIDwCvvKLdbmJycLVhg3jx6S55vVX79squZ3Gk93VX8norlgSqw2IB5s0Twe2ffwLTpnk/JjcPJjIlBldmwLJAY7FY7Jkjf6+70nJDCzm4unRJvOnBpEliD7q2bYERI9SeTdlatRKBX3a2WJfiLl+WBMr0HlzJmSs2s1BP7drAe++J4xkzvFtj6Lg3HDNXRKbC4MroLl2yl3IxuDIOOcPh78yVlssCIyKAyEhxrIfSwC1bgIULxfE776izj5WrAgLsLxA9KQ30ZadAmbzuSq9NLdiGXRsGDgQGDQIKC0V54NWrno2zZYsoLYyOFmuuiMg0GFwZnfwis0oV8eKTjEHtzJUWywIB/TS1sNnsTSwefNC3QYdS5NLAlBT3vu78ebGmDGDmqjxsw64dc+cCUVFi/Z6n6yAd11tpqfsnEfkcgyujY0mgMTFz5Zxemlp88gmwebNYPydvHKx1juuuCgtd/7rNm8X7Jk3EehZfad5cvIg9cwbIyPDd/fgKM1faUaOGaM8OAG+8Ifa+chfXWxGZFoMro2OnQGNSO3Ol1eBKD5mrzExg4kRx/MILomxID66/XpRdZmW516paLgn0ZdYKACpVAho1Esd6zF4xc6Ut/fqJrLLNJsoDr1xx/WsLCux/91xvRWQ6DK6Mjp0CjUmtzJXWywL1kLmaNk1kVpo0AcaNU3s2rrNa7S8U3SkN9Md6K5meNxNm5kp7Zs8W+1P98w/w/POuf92OHWK/tapVtbdvHRH5HIMro2NZoDGplbnSS1mgVjNXe/eKF2yAeB8crO583OXuflcFBfayQH8EV3JTC2auSAnVqgEffiiOZ82yr6OqiHy7m2/mnmVEJsRHvdGxLNCY1Mhc5ebaS2O0mrmSLyJoMXMlScD48SLg6NsX6NlT7Rm5r1s38T411bV1V7t3iyv4kZFiTZSv6bmpBTNX2tS7t9gmQZKA4cPF33NFuN6KyNRUD67mzp2L+vXrIzQ0FB06dMCWLVvKvG1+fj5efvllNGzYEKGhoWjdujVWr17t1ZiGx7JAY1IjcyVnrQB7y3Ot0XJZ4DffAD/9JLJVb7+t9mw806aN6Dp68aJrAYxcEnjTTf65gi8HV3//7V7TDbXl5oq1bAAzV1o0c6a4QHnokH29ZFlsNtH0BeB6KyKTUjW4WrZsGSZMmICpU6di+/btaN26NRISEnBavoJXwuTJk/H+++/j3XffxZ49e/Doo49iwIAB2LFjh8djGh7LAo1JjcyVHFxFRmp3Tyb57zwjQ+wxoxVXrgBPPimOn35av/veBAYCnTuLY1fWXflzvRUAxMcDYWFib6JDh/xzn0qQSwIDA7VbcmtmVaoACxaI4zlzgF9+Kfu2e/cC586JBis33OCf+RGRpqgaXM2cORMPP/wwhg8fjubNm2P+/PmoVKkSFsoba5bw6aef4rnnnkPv3r0RHx+P0aNHo3fv3njrrbc8HtPQsrPtL74ZXBmLGpkrrTezAESrb3kdU1qaunNx9NZbwNGjIrM2aZLas/GOXBroyrorfwdXVqu9gYCemlrIwVXNmlyjo1W33w48+qg4HjECuHTJ+e3k9VY33aS/NZVEpAjVnsXz8vKwbds29OjRwz6ZgAD06NEDmzZtcvo1ubm5CA0NLXYuLCwMG/6XgvdkTHncrKysYm+GIJdGRUZqt4yLPKNm5krLV9YtFu01tTh+HHjlFXH85ptibys9k5ta/PqrKIEqS3o6cOSI+J106OCfuQH6XHfF9Vb68PrrQP36wLFjIgPtjBxccb0VkWmpFlydPXsWhYWFiIqKKnY+KioK6enpTr8mISEBM2fOxIEDB2Cz2bB27VokJSUh7X9XqD0ZEwBmzJiBKlWqFL3FGaX5A0sCjUvNzJWWgytAe+uunnlGlAXecgswaJDas/Fe27ZA5crA+fOiYUVZ5Ata113n34s7egyu2ClQHyIiALkK5oMPxBpKR5Jkb2bB9VZEpqWr+oPZs2ejcePGaNasGYKDgzF27FgMHz4cAV6WUUyaNAmZmZlFbyfkoETv2MzCuOTM1aVLovucP+ihLBDQVuYqJQX48ktR6vXOOyKLo3dBQfZ1V+WVBvq7JFCmx+CKmSv96N4dePxxcTxyZPHqgWPHxPNOYKAoCyQiU1ItuKpZsyasVisyMjKKnc/IyECdOnWcfk2tWrWwcuVK5OTk4NixY9i3bx/Cw8MRHx/v8ZgAEBISgsjIyGJvhsA27MblmD1y7OLnS3ooCwS00469oAB44glx/OijQOvW6s5HSa7sdyVnrtQKrg4dcq1tthbImSsGV/owYwbQqJH4Hzthgv28nLVq1040tCAiU1ItuAoODkbbtm2RnJxcdM5msyE5ORkdO3Ys92tDQ0NRt25dFBQU4Ouvv0a/fv28HtOQWBZoXEFBojQL8N+6K71lrtQOrubPF9mT6tWBl19Wdy5KcwyuJKn053NzgT/+EMf+fu6tXRuIihLz+vtv/963p+TMFcsC9aFyZWDRIpGJXrRIbLOQkgIsXiw+L2d2iciUVC0LnDBhAj788EN8/PHH2Lt3L0aPHo2cnBwMHz4cADB06FBMcuistXnzZiQlJeHw4cNITU1Fz549YbPZ8Oyzz7o8pqmwLNDY/L3uSm+ZKzXLAs+cAaZMEcfTpwM1aqg3F1+48UbR8vzsWWDPntKf37FDBFg1a4or/P6mt9JAlgXqz80327dXSEwU5YLr1omPFy8GkpJUmxoRqStQzTsfNGgQzpw5gxdeeAHp6em4/vrrsXr16qKGFMePHy+2nurq1auYPHkyDh8+jPDwcPTu3Ruffvopqjq82KtoTFNhWaCxVasmsjP+zlxpPbjSQuZq8mTx82rdGnj4YfXm4SvBwaLcLzlZZK9atCj+ecf1VmqsM2vZEvj5Z/0EV2xooU/t2on3Jbtmnj8P3HMPsHy5CLyIyFRUDa4AYOzYsRg7dqzTz6WU2KSya9eu2OPsKqkbY5oKywKNzd+ZKz2WBdps/tk3qLBQtGBOSxNNRj74QJx/913tbrjsra5d7cHVY48V/5y83kqtcmxmrsjXCgsBh6qZYiRJXFQYPx7o18+4zwFE5JTqwRX5SE6O/UU3gytj8vdeV3opC4yOFi9s8vNF2ZqvX7AmJQHjxpUuQ+zSxdjtmOV1Vykp9heTgDhWq1OgrFUr8f7PP4vPTauYudKf1NTyS48lSVzgTE21b7xNRKagq1bs5Aa5JCoiQvuZBvIMM1fOBQfbAypflwYmJYnyH2cvsjZsMPa6i/btgdBQkXXZv99+/vhx4NQp0Y5aLpvyt+bNRcby7FmgRPdYzblyBcjOFsfMXOnH//bXVOx2RGQYDK6MiiWBxufvzJVe1lwB/mlqUVgoMlbOuuXJxo8XtzOi0FD7Xj6OLdnlrFWbNuq1ow4LszfS0HppoJy1Cgry72bL5J3oaGVvR0SGweDKqNgp0PjYLbBs/mhq4U5ZkFE5lgbK1F5vJdPLuivH9VZaL18kuy5dxP/Xsn5nFotoJmXk0mAicorBlVGxU6Dx+TNzZbMBWVniWOtlgYB/MlcsC7KvJXHc70rt9VYyObj6809151ERrrfSJ6sVmD1bHJcMsOSPZ81iMwsiE2JwZVQsCzQ+f2auLl2yv3jWQ3Dlj8wVy4KADh3EGre0NODgQdFIZ+dO8Tm1gyu5qYWeMlekL4mJot26/Hwji41lG3YiE2O3QKNiWaDx+TNzJd9HaKh40zr5xY4vM1dyWdDJk87XXVks4vNGLgsKCxMBVmqqKA1s0kSsMYuNVT9rLmeu9uwRc9JqBoGZK31LTBTt1uWtGKKjxWNeq39vRORzDK6MimWBxufPzJVeOgXK5IsKvsxcyWVB99xT+nNmKgvq1k28sFy/XnTnA9RfbwUA8fGiocblyyKr1rSp2jNyjpkr/bNa2W6diIqwLNCoWBZofP7MXOmpmQXgn7JAQFy1njq19HkzlQXJTS3Wrwd++00cq10SCIhW7C1aiGMtlwYyc0VEZCgMrozo8mXg/HlxzODKuBwzV+W1A1eC3jJXcnCVlSXWi/mS/Fjr2RNYsgRYtw44csQcgRUgslSBgSJbvmaNONehg7pzkumhqQUzV0REhsLgyojkq/Xh4fp5MUzukzNX+fliI1Jf0tMeV4DYPFveM8iX2StJAlauFMejRwODB4vyIKOXAjpavVpkiQCgoEC8HzhQGxso66GpBTNXRESGwuDKiBxLArlvinGFh9tfxPt63ZXeygIB/7Rj37EDOH5crO25/Xbf3Y9WJSWJNWd5ecXPnzolzqsdYOlhrytmroiIDIXBlRGxU6A5WCz2YMfX6670VhYI+GfdlZy16tlTdM4zk8JCYNw45yWp8rnx48Xt1CIHV4cPizbxWiRnrhhcEREZAoMrI2KnQPPwV8dAPWau/NGOfcUK8b5/f9/dh1alppb/s5UkkUVPTfXfnEqqVQuIihJz+ftv9eZRlpwcsUYWYFkgEZFBMLgyInYKNA9/dQzUY+bK1+3YDx4Edu8WpZl33umb+9CytDRlb+cr8rorLTa1kEsCQ0NFmS8REekegysjYlmgefgrc6W3hhaA78sCv/lGvO/WzR7kmkl0tLK38xUtr7tybGbB9bFERIbA4MqIWBZoHv7KXOmxLNDXDS3MXBIIAF26lN80x2IRz0Fduvh3XiVpObhiMwsiIsNhcGVELAs0D39nrvRUFujLzFVGBrBxozju10/58fXAagVmzxbHJQMs+eNZs9RvS++415Wv94NzF9uwExEZDoMro7lyBTh3ThwzuDI+Zq7KJv/9Z2SUbhXure++Ey/U27Uzd4Y4MRFYvtweyMpiY8V5LWyk3Ly52Ifr3DkgPV3t2RTHzBURkeEEqj0BUph8lb5yZX29ECbPcM1V2WrWBIKDRWCVlgbUq6fc2HILdrOWBDpKTBTZu9RU8XOOjhalgGpnrGRhYUDjxsD+/aI0UO01YI6YuSIiMhxmroyGGwibiz8yV5Kkz7JAiwWIiRHHSpYGXroErF0rjhlcCVaraOwxeLB4r5XASqbVdVfMXBERGY5HmavCwkIsXrwYycnJOH36NGw2W7HP//LLL4pMjjzAToHm4o/M1dWrQH5+8fvTi9hY4OhRZZtarF4tsmGNG4uSM9K+li1FmaLWgitmroiIDMej4GrcuHFYvHgx+vTpg+uuuw4WZki0g50CzcUfmSt57IAA/e3F44umFo4lgXzu0wdmroiIyE88Cq6WLl2KL7/8Er1791Z6PuQtZq7MxR+ZK8eSQL0FE0q3Y8/LA77/XhyzJFA/5I2E//4bKCgAAjWy3JiZKyIiw/FozVVwcDAaNWqk9FxICWzDbi7+yFzpsVOgTOnMVUqK+HlERQE33aTMmOR7DRqIJj+5ucDBg2rPRpAkZq6IiAzIo+DqqaeewuzZsyFpbc8QYlmg2cgBT1YWUFjom/vQYzMLmRxcKZW5kksC+/UTZZKkDwEBQIsW4lgrpYHZ2SLYA5i5IiIyEI9qIzZs2IB169bhxx9/RIsWLRAUFFTs80lJSYpMjjzAskBzccwmZWYC1asrfx96zlzJjwMlMlc2G1uw61nLlsCWLSK4GjhQ7dnYs1aVKomsGhERGYJHwVXVqlUxYMAApedC3rp61V7Dz+DKHIKDxYuzy5fFuitfBFd63ONKJmeuTp0SwZE32aatW8U+ThERwK23KjM/8h+5qcWff6o7DxnXWxERGZJHwdWiRYuUngcpQb46X6mSfS0OGV+1aiK48tW6Kz2XBUZHiyYceXnA2bPerW2Rs1a9ewMhIYpMj/xIbmqhlbJArrciIjIkrxYNnDlzBhs2bMCGDRtwRr4KR+pxLAnUW1c38pyvOwbquSwwONj+4tXb0sAVK8R7lgTqk5y5OnxYrHdSG4MrIiJD8ii4ysnJwYgRIxAdHY1bbrkFt9xyC2JiYjBy5EhcvnxZ6TmSq9gp0Jx83TFQz5krQJl27Pv2Afv3A0FBInNF+lOzJlCnjjj++2915wKwLJCIyKA8Cq4mTJiA9evX47vvvsPFixdx8eJFfPPNN1i/fj2eeuoppedIrmKnQHNi5qp8SrRjl0sCb7sNiIz0ekqkEi1tJszMFRGRIXkUXH399ddYsGABevXqhcjISERGRqJ379748MMPsXz5cqXnSK5ip0Bz8lfmSu/BlTeZK3YJNAYtNbVg5oqIyJA8Cq4uX76MqKioUudr167NskA1sSzQnHyduTJKWaCnmauTJ4HNm8U6xn79lJsX+Z+Wmlowc0VEZEgeBVcdO3bE1KlTcfXq1aJzV65cwUsvvYSOHTsqNjlyEzNX5uTrzJXZywK//Va8v+km+5od0ifHskBJUncuzFwRERmSR63YZ8+ejYSEBMTGxqJ169YAgF27diE0NBRr1qxRdILkBq65MidmrsrnbUMLlgQax7XXigzkuXPAvHlA8+ZAly6A1er/uTBzRURkSB4FV9dddx0OHDiAzz//HPv27QMADB48GEOGDEFYWJiiEyQX5eba/1kzc2UuXHNVPm8yVxcvAr/8Io65cbr+/fijCKQKCoAxY8S52Fhg9mwgMdF/85AkZq6IiAzKo+AKACpVqoSHH35YybmQN+QXjqGhQPXq6s6F/MuXmauCAiAnp/j96I0cXGVlAZcuARERrn/tDz+In0Hz5kDjxr6ZH/lHUhJwzz2lywFPnhTnly/3X4CVmQnk54tjBldERIbicnD17bffolevXggKCsK38hqEMtx1111eT4zc5FgSyA2EzcWXmSt5vRWg3xbkERFi7llZ4oV0s2aufy1LAo2hsBAYN875OitJEs+Z48eLhiX+KBGUs1bh4QCrPYiIDMXl4Kp///5IT09H7dq10b+cFxoWiwWFhYVKzI3cwU6B5uXLzJUcXFWuLDbQ1au6dd0Prq5eFWVkAEsC9S41tfw1d5IknkNTU4Fu3Xw/H663IiIyLJeDK5vN5vSYNIKdAs3LMXMlX4VXit7XW8liY4G9e91rapGcDGRni8CsbVvfzY18Ly1N2dt5i+utiIgMy6NW7M5c9NVienINOwWalxz45OUBV64oO7beOwXKPGlq4VgSyFJbfYuOVvZ23mLmiojIsDwKrl577TUsW7as6OOBAweievXqqFu3Lnbt2qXY5MgNLAs0r4gIIOB/D2WlL3LofY8rmbvt2AsL7ftbcb2V/nXpIv4GygqSLRZxYapLF//Mh5krIiLD8ii4mj9/PuL+lyFZu3Ytfv75Z6xevRq9evXCM888o+gEyUUsCzQvi8V3667MmrnatElkF6pWBbp29dm0yE+sVtFuHSgdYMkfz5rlv/2umLkiIjIsj4Kr9PT0ouBq1apVuPfee3HHHXfg2WefxdatWxWdILmIZYHm5quOgWbNXMklgXfeqe9GHmSXmCjarcuBtiw21r9t2AFmroiIDMyj4KpatWo48b8ytNWrV6NHjx4AAEmS2ClQDbm5QEaGOGbmypx8nbnSe3DlTuZKktiC3agSE4GjR4FJk8THrVsDR474N7ACmLkiIjIwj4KrxMRE3H///bj99ttx7tw59OrVCwCwY8cONGrUSNEJkgtOnRLvQ0OBGjXUnQupw1eZK6OVBZ4+LRp/lGf3buDQIfF46tnT93Mj/7Ja7a31T5/2XymgIwZXRESG5XIrdkdvv/026tevjxMnTuD1119HeHg4ACAtLQ2PPfaYohMkFziut2JXM3PyVebKKGWBNWsCwcEisEpLA+rVK/u2ctbq9tvF/l5kPE2bivdpaeJv3N8XD1gWSERkWB5lroKCgvD0009j9uzZaNOmTdH5J598EqNGjXJrrLlz56J+/foIDQ1Fhw4dsGXLlnJvP2vWLDRt2hRhYWGIi4vDk08+iatXrxZ9/sUXX4TFYin21szVTUP1ip0CydeZK70HVwEBQEyMOK6oNJAlgcYXGWlvu75/v3/v22azB1fMXBERGY7Lmatvv/0WvXr1QlBQEL6VWxSX4a677nJpzGXLlmHChAmYP38+OnTogFmzZiEhIQH79+9HbSf/dJYsWYKJEydi4cKF6NSpE/755x889NBDsFgsmDlzZtHtWrRogZ9//rno48BAjxJ0+sFOgcRugRWLjRXrbcpranHsGLB9uwjG+vb129RIBc2aiczVvn1A+/b+u9+LF0Wrf0BkVImIyFBcjjr69++P9PR01K5dG/3LuaJrsVhcbmoxc+ZMPPzwwxg+fDgA0eL9+++/x8KFCzFx4sRSt9+4cSM6d+6M+++/HwBQv359DB48GJs3by52u8DAQNSpU8fF78wA2CmQ2C2wYq40tfjmG/H+5ptZsmV0zZoB69b5P3Mlr7eqUgUICfHvfRMRkc+5XBZos9mKskk2m63MN1cDq7y8PGzbtq2o0yAABAQEoEePHti0aZPTr+nUqRO2bdtWVDp4+PBh/PDDD+jdu3ex2x04cAAxMTGIj4/HkCFDcPz4cVe/TX1iWSAxc1UxObgqL3PFkkDzkNdd7dvn3/vleisiIkNTrV7u7NmzKCwsRFRUVLHzUVFR2FfGP7v7778fZ8+exc033wxJklBQUIBHH30Uzz33XNFtOnTogMWLF6Np06ZIS0vDSy+9hC5dumD37t2IiIhwOm5ubi5yc3OLPs7KylLgO/QjlgUSM1cVkx8fZWWuzp0Dfv1VHDO4Mj55La6/gyt2CiQiMjSPGlo88cQTeOedd0qdnzNnDsaPH+/tnMqUkpKCV155Be+99x62b9+OpKQkfP/995g2bVrRbXr16oWBAweiVatWSEhIwA8//ICLFy/iyy+/LHPcGTNmoEqVKkVvcXorr2NZIPkicyVJxmloAVRcFrhqlVgL07o10KCB/+ZF6pCDqwMHgIIC/90vM1dERIbmUXD19ddfo3PnzqXOd+rUCcuXL3dpjJo1a8JqtSJD3vz2fzIyMspcLzVlyhQ8+OCDGDVqFFq2bIkBAwbglVdewYwZM2Cz2Zx+TdWqVdGkSRMcPHiwzLlMmjQJmZmZRW/yBsm6kJfHDYTJN5mr7GzR2QwwRlmg/PgoqyyQJYHmEhcHhIUB+fmi0Ym/MHNFRGRoHgVX586dQxUnL7YiIyNx9uxZl8YIDg5G27ZtkZycXHTOZrMhOTkZHTt2dPo1ly9fRkBA8Slb/7cBpCRJTr8mOzsbhw4dQrTcdteJkJAQREZGFnvTjVOnRIYhJISdp8xMziwpGVzJJYFBQeJFqN7JmatTp+xBo+zyZWDNGnEsbzBLxhYQADRpIo79WRrIzBURkaF5FFw1atQIq1evLnX+xx9/RHx8vMvjTJgwAR9++CE+/vhj7N27F6NHj0ZOTk5R98ChQ4di0qRJRbfv27cv5s2bh6VLl+LIkSNYu3YtpkyZgr59+xYFWU8//TTWr1+Po0ePYuPGjRgwYACsVisGDx7sybeqfdxAmAB75ioz097m2VuOzSyM8LcVHS2+j7w8oORFoJ9+Aq5cAerXB1q1UmV6pAI11l0xc0VEZGgeNbSYMGECxo4dizNnzuDWW28FACQnJ+Ott97CrFmzXB5n0KBBOHPmDF544QWkp6fj+uuvx+rVq4uaXBw/frxYpmry5MmwWCyYPHkyTp48iVq1aqFv376YPn160W3+/fdfDB48GOfOnUOtWrVw88034/fff0cto14lZKdAAoqvicrKsgdb3jBSMwsACA4WL2gzMsS6K8cXt44lgUYIJMk1agRXzFwRERmaR8HViBEjkJubi+nTpxc1k6hfvz7mzZuHoUOHujXW2LFjMXbsWKefS0lJKT7ZwEBMnToVU6dOLXO8pUuXunX/usdOgQSIwKFSJVHeduGCMsGVkZpZyOrWtQdXbdqIcwUFwHffiWOWBJqL3I7dn3tdMXNFRGRoHrdiHz16NEaPHo0zZ84gLCwM4eHhSs6LXMVOgSSrWlUEV0qtuzLSHley2Fhg+/biTS1SU4Hz58WaxU6d1Jsb+R8zV0REpDCP1lwBQEFBAX7++WckJSUVNZM4deoUsrOzFZscuYBlgSSTs1VKtWM3Wlkg4Lwdu1wS2LcvEKja1n+kBrmhxdmzpdfh+UJhof1+mLkiIjIkj15JHDt2DD179sTx48eRm5uL22+/HREREXjttdeQm5uL+fPnKz1PKgvLAkmmdMdAI5YFlmzHLkn24IolgeZTuTJwzTXA8eOiNNDXHVfPn7d3qmR3VyIiQ/IoczVu3Di0a9cOFy5cQJhDi+YBAwYUa61OfsCyQJIpnbkyYllgyczVjh3ihXWlSkCPHurNi9Tjz3VXcklgtWpiiwMiIjIcjzJXqamp2LhxI4KDg4udr1+/Pk46ltuQb+XlAenp4piZK1I6c2XEssCSmSs5a9WzpzH28iL3NWsGrF3rn3VXbGZBRGR4HmWubDYbCp3spfPvv/8iIiLC60mRi9LSRFlTcDBLTIiZK1eUzFw5tmAnc/JnUws2syAiMjyPgqs77rij2H5WFosF2dnZmDp1Knr37q3U3KgijuutAjzuTUJGwcxVxeTgKisL2LUL+OsvwGoF7rxT3XmReuTgyh9lgcxcEREZnkdlgW+++SZ69uyJ5s2b4+rVq7j//vtx4MAB1KxZE1988YXSc6SysFMgOfJV5spIwVVEBBAZKYKruXPFuW7dlNkXjPRJXnN16JAotS5R7q4oZq6IiAzPo+AqLi4Ou3btwrJly7Br1y5kZ2dj5MiRGDJkSLEGF+Rj7BRIjnzVLdBIZYGAyF5lZQGffSY+ZkmgucXEAOHhQHa2CLCuvdZ398XMFRGR4bkdXOXn56NZs2ZYtWoVhgwZgiFDhvhiXuQKdgokR9znyjV16wJ79wJXroiPWRJobhaLKA384w+x7sqXwRUzV0REhuf2Qp2goCBcvXrVF3Mhd7EskBwxc1WxpCRg48bi57p0EefJvPy17oqZKyIiw/OoC8KYMWPw2muvoaCgQOn5kDtYFkiOlMxc5eYC8kUUo2SukpKAe+4BLl8ufv7kSXGeAZZ5yeuufN0xkJkrIiLD82jN1datW5GcnIyffvoJLVu2ROXKlYt9PokvUvyDZYHkSMnMlVwSaLGIBhB6V1gIjBsnti4oSZLE9zl+PNCvn+geSObir3bszFwRERmeR8FV1apVcffddys9F3JHfr7Y5wpg5ooEOXOVmyvWE3nTXEYO0CIijNHmPzXVfjHCGUkSZbapqaJ7IJmLY3AlB9tKKygAzp8Xx8xcEREZllvBlc1mwxtvvIF//vkHeXl5uPXWW/Hiiy+yQ6Aa5A2Eg4L4j5qE8HARCNlsIjjy5nFptGYW8oUIpW5HxtKokXjsZGaK7FJUlPL3ce6cPXCrUUP58YmISBPcuiQ9ffp0PPfccwgPD0fdunXxzjvvYMyYMb6aG5WHGwhTSQEB9mDI23VXRtvjKjpa2duRsYSGAvXri2NflQbK662qVwcCPSoaISIiHXDrVfknn3yC9957D2vWrMHKlSvx3Xff4fPPP4fNZvPV/Kgs7BRIzii17sponQK7dBGPlbLKvSwWsXaxSxf/zou0w9frrrjeiojIFNwKro4fP47evXsXfdyjRw9YLBacOnVK8YlRBdgpkJxRqmOg0coCrVZg9mxxXDLAkj+eNYvNLMzM18EVOwUSEZmCW8FVQUEBQkNDi50LCgpCfn6+opMiF7BTIDnDzFXZEhOB5cvFJsKOYmPF+cREdeZF2iC3Y/fVXlfMXBERmYJbhd+SJOGhhx5CSEhI0bmrV6/i0UcfLdaOna3Y/YBlgeQMM1flS0wU7dZTU0XziuhoUQrIjBX5qyyQmSsiIkNzK7gaNmxYqXMPPPCAYpMhN7AskJxROnNltOAKEIEU261TSXJwdfSo91sZOCOXBTJzRURkaG4FV4sWLfLVPMhdDK7IGaUyV0YsCyQqT61a4vFz4QJw4ADQqpWy47MskIjIFNjDW48KCuz78XDNFTlSKnNl1LJAorJYLL5dd8WGFkREpsDgSo/S0sRGsYGBvApKxSmduWJwRWbiy3VXzFwREZkCgys9kksC69blBsJUHLsFEnnOl8EVM1dERKbAV+Z6JHcKZEkglcRugUSe81VwlZ9vf0wyc0VEZGgMrvSIzSyoLMxcEXnOcc2VJCk37tmz4n1AAFC9unLjEhGR5jC40iMGV1QWJTJXNhtw6ZI4ZuaKzKRhQ7GWNScHOHlSuXHl9VY1a7KUm4jI4Pgsr0dycMWyQCpJDoayskSQ5ImsLPtVe2auyEyCgkSABShbGsj1VkREpsHgSo/kNVfMXFFJcnAlSfZ1U+6SSwJDQ4GQECVmRaQfvlh3xU6BRESmweBKj1gWSGUJCQHCwsSxp+uu2MyCzMwXe10xc0VEZBoMrvSGGwhTRbxdd8VmFmRmzFwREZEXGFzpTXo6UFjIDYSpbN52DGTmiszMF8EVM1dERKbB4Epv5JLAmBjAalV3LqRNSmWuGFyRGcllgf/+C2RnKzMmM1dERKbB4Epv2CmQKuJt5oplgWRm1avbM0z//KPMmMxcERGZBoMrvWGnQKqIt5krlgWS2SldGsjMFRGRaTC40ht2CqSKKJW5YnBFZuWr4IqZKyIiw2NwpTcsC6SKsFsgkXeUDK5yc8XG3AAzV0REJsDgSm9YFkgVYbdAIu8oudeVvN4qMJCPKSIiE2BwpTcsC6SKMHNF5B05c/XPP2LrC2/IwVXNmkAA/+USERkdn+n1pLAQOHVKHLMskMrCzBWRd+rXB4KDgatXgePHvRuLzSyIiEyFwZWeyBsIW61AVJTasyGt4j5XRN6xWoHGjcWxt6WBbMNORGQqDK70hBsIkyu4zxWR95RqasHMFRGRqTC40hN2CiRXeJO5kiSWBRIBygVXzFwREZkKgys9YadAcoUcFOXmijUj7rhyBcjPF8fMXJGZMXNFREQeYHClJ+wUSK6IiLB3JXM3eyVnrQICgPBwZedFpCdKtWNn5oqIyFQYXOkJywLJFQEB9qyTu+uuHJtZWCwKTopIZ+TgKj3d8/WLADNXREQmw+BKT1gWSK7ydN0Vm1kQCZGRonkQ4F32ipkrIiJTYXClJywLJFd52jGQzSyI7JRYd8XMFRGRqageXM2dOxf169dHaGgoOnTogC1btpR7+1mzZqFp06YICwtDXFwcnnzySVwtsWjf3TF1gRsIkzu8zVwxuCLyft3VlStAdrY4ZuaKiMgUVA2uli1bhgkTJmDq1KnYvn07WrdujYSEBJyWr/SVsGTJEkycOBFTp07F3r17sWDBAixbtgzPPfecx2PqRkYGUFAg9reqU0ft2ZDWeZq5YlkgkZ23mSu5JDAoiI8pIiKTUDW4mjlzJh5++GEMHz4czZs3x/z581GpUiUsXLjQ6e03btyIzp074/7770f9+vVxxx13YPDgwcUyU+6OqRtySWB0NDcQpop5mrliWSCRnbfBlXxRr1YtNoghIjIJ1YKrvLw8bNu2DT169LBPJiAAPXr0wKZNm5x+TadOnbBt27aiYOrw4cP44Ycf0Lt3b4/HBIDc3FxkZWUVe9McdgokdzBzReQ9Obg6eFBUDrhLzlxxvRURkWmoFlydPXsWhYWFiIqKKnY+KioK6enpTr/m/vvvx8svv4ybb74ZQUFBaNiwIbp161ZUFujJmAAwY8YMVKlSpegtTosBDDsFkjuYuSLyXmwsEBYmNtY+csT9r3fMXBERkSmo3tDCHSkpKXjllVfw3nvvYfv27UhKSsL333+PadOmeTXupEmTkJmZWfR2Qg5ktISdAskd3mauGFwRiT3j5KYWnpQGMnNFRGQ6gWrdcc2aNWG1WpGRkVHsfEZGBuqU0bBhypQpePDBBzFq1CgAQMuWLZGTk4NHHnkEzz//vEdjAkBISAhCQkK8/I58jGWB5A7uc0WkjGbNgJ07RXDVt697X8s27EREpqNa5io4OBht27ZFcnJy0TmbzYbk5GR07NjR6ddcvnwZAQHFp2z9X3MHSZI8GlM3WBZI7uA+V0TK8KapBTcQJiIyHdUyVwAwYcIEDBs2DO3atUP79u0xa9Ys5OTkYPjw4QCAoUOHom7dupgxYwYAoG/fvpg5cybatGmDDh064ODBg5gyZQr69u1bFGRVNKZusSyQ3MF9roiU4c1eV8xcERGZjqrB1aBBg3DmzBm88MILSE9Px/XXX4/Vq1cXNaQ4fvx4sUzV5MmTYbFYMHnyZJw8eRK1atVC3759MX36dJfH1CWbDTh5UhyzLJBcwW6BRMpg5oqIiNxgkSRJUnsSWpOVlYUqVaogMzMTkZGRak8HSEsDYmLE4urcXCBQ1ZiY9CAjQ2w2bbGIFtIBLlYAh4cDOTnAoUNAfLxv50ikB5cvA5Uri+MzZ4CaNV3/2vr1gWPHgE2bgJtu8sn0iIjI99yJDXTVLdC0HDcQZmBFrpAzV5IEuLpvW36+CKwAZq6IZJUqAfXqiWN3s1fMXBERmQ6DKz1gp0ByV0iI2J8HcH3dlWMQxuCKyM6TdVc5OSLrBXDNFRGRiTC40gN2CiRPuLvuSr5deDgzpESOPFl3JWetQkLEY4qIiEyBwZUesFMgecLdjoFsZkHknCfBlWOnQItF+TkREZEmMbjSAwZX5Al3M1fc44rIObks0JPMFddbERGZCoMrPZDLArnmitzBzBWRMuTM1ZEjomOrK7jHFRGRKTG40gNmrsgTzFwRKSM6GoiIAAoLxTYFrmDmiojIlBhcaZ3jBsIMrsgdnmauGFwRFWexuL/uipkrIiJTYnCldadPi/2HAgLE1VMiV3naLZBlgUSlubvuSg6umLkiIjIVBldaJ5cE1qkDBAWpOxfSF3czVywLJCqbnLlyda8ruSyQmSsiIlNhcKVlhYXA6tXiODJSfEzkKk8zVwyuiErztCyQmSsiIlNhcKVVSUlA/frAlCni4337xMdJSWrOivSE3QKJlOMYXElSxbdn5oqIyJQYXGlRUhJwzz32kkDZyZPiPAMscgW7BRIpp1EjsfY1KwvIyCj/tpLEhhZERCbF4EprCguBceOcXxmVz40fzxJBqhgzV0TKCQkBGjQQxxWVBmZn2/fDYlkgEZGpMLjSmtTU0hkrR5IkNhVOTfXfnEifmLkiUpar667krFWlSkDlyr6dExERaQqDK61JS1P2dmRecubq6lXxVhE2tCAqn6vBFTcQJiIyLQZXWuPqXlbc84oqEhEhNj8FKs5eSZI9c8WyQCLn5L2uKmrHzvVWRESmxeBKa7p0AWJj7S+KS7JYgLg4cTui8gQE2LNQFa27ys4GbDZxzMwVkXPMXBERUQUYXGmN1QrMni2OSwZY8sezZonbEVXE1XVX8ueDg4HQUB9OiEjH5ODq2DHg8uWyb8fMFRGRaTG40qLERGD5cqBu3eLnY2PF+cREdeZF+uNq5sqxJLCsrCmR2dWsCVSvLspoDxwo+3bMXBERmVag2hOgMiQmAv36ia6AaWlijVWXLsxYkXvkphauZq5YEkhUNotFrLvatEmsu2rd2vntmLkiIjItBldaZrUC3bqpPQvSM1czV9zjisg1zZqJ4Kq8dVfMXBERmRbLAomMzNXMFfe4InKNK00tmLkiIjItBldERuZu5orBFVH55HbsrgRXzFwREZkOgysiI3N3zRXLAonKJ2eu9u+3b1/gSJLsZYHMXBERmQ6DKyIjc7dbIDNXROWLjwcCA0Ur9pMnS38+MxPIzxfHzFwREZkOgysiI2PmikhZQUFAo0bi2FlpoJy1Cg8HwsL8Ny8iItIEBldERsbMFZHyylt3xfVWRESmxuCKyMi4zxWR8hzXXZXE9VZERKbG4IrIyLjPFZHyymvHzjbsRESmxuCKyMjkzFVmpvPOZjKWBRK5rrzgihsIExGZGoMrIiOTgyVJArKyyr4dywKJXCevuTp5Erh0qfjnmLkiIjI1BldERhYaKt6A8tddyZkrlgUSVaxaNXvw9M8/xT/HzBURkakxuCIyuorWXV29Kt4cb0tE5SurNJCZKyIiU2NwRWR0FXUMlLNWFgsQEeGXKRHpXlnBFTNXRESmxuCKyOgqylzJwVVkJBDApwQil5S11xUzV0REpsZXUkRGV1Hmis0siNznbK8rmw04e1YcM3NFRGRKDK6IjK6izBX3uCJynxxc/fMPUFgoji9eBAoKxDGDKyIiU2JwRWR0rq65YuaKyHX16gEhIUBuLnDsmDgnr7eKjBSfIyIi02FwRWR0zFwRKc9qBRo3FsfyuiuutyIiMj0GV0RGx8wVkW+UXHclB1csCSQiMi0GV0RG52rmisEVkXtKtmOXywKZuSIiMi0GV0RG52q3QJYFErmnZHDFzBURkekxuCIyOlf3uWLmisg9Jfe6YuaKiMj0GFwRGR33uSLyDTm4On1aXLxg5oqIyPQYXBEZHbsFEvlGRARQt6443r+fmSsiImJwRWR4cubq6lXxVhLLAok851gayFbsRESmx+CKyOgiIwGLRRw7Kw1k5orIc47t2OXMFcsCiYhMi8EVkdEFBNgDJ2fBFTNXRJ6Tg6s9e4CzZ8UxM1dERKalieBq7ty5qF+/PkJDQ9GhQwds2bKlzNt269YNFoul1FufPn2KbvPQQw+V+nzPnj398a0QaVNZ664KC4GsrOK3ISLXycHVxo2AzSaOa9ZUbz5ERKSqQLUnsGzZMkyYMAHz589Hhw4dMGvWLCQkJGD//v2o7eTqX1JSEvLy8oo+PnfuHFq3bo2BAwcWu13Pnj2xaNGioo9DQkJ8900QaV21asDRo6UzV3JgBbAskMgT8porOWtVrRoQFKTefIiISFWqZ65mzpyJhx9+GMOHD0fz5s0xf/58VKpUCQsXLnR6++rVq6NOnTpFb2vXrkWlSpVKBVchISHFbldNXtRPZEZlZa7kksCwMCA42K9TIjKE2FigUiX7x1xvRURkaqoGV3l5edi2bRt69OhRdC4gIAA9evTApk2bXBpjwYIFuO+++1C5cuVi51NSUlC7dm00bdoUo0ePxrlz58ocIzc3F1lZWcXeiAylrL2uuMcVkXcCAuzZK4DrrYiITE7V4Ors2bMoLCxEVFRUsfNRUVFIT0+v8Ou3bNmC3bt3Y9SoUcXO9+zZE5988gmSk5Px2muvYf369ejVqxcKCwudjjNjxgxUqVKl6C0uLs7zb4pIiyrKXLEkkMhz8rorgJkrIiKTU33NlTcWLFiAli1bon379sXO33fffUXHLVu2RKtWrdCwYUOkpKTgtttuKzXOpEmTMGHChKKPs7KyGGCRsTBzReQ7jRvbjwsKRKMYq1W9+RARkWpUzVzVrFkTVqsVGRkZxc5nZGSgTp065X5tTk4Oli5dipEjR1Z4P/Hx8ahZsyYOHjzo9PMhISGIjIws9kZkKGVlrrjHFZF3kpKAOXPsH3/3HVC/vjhPRESmo2pwFRwcjLZt2yI5ObnonM1mQ3JyMjp27Fju13711VfIzc3FAw88UOH9/Pvvvzh37hyio6O9njORLpWVueIeV0SeS0oC7rkHOH+++PmTJ8V5BlhERKajerfACRMm4MMPP8THH3+MvXv3YvTo0cjJycHw4cMBAEOHDsWkSZNKfd2CBQvQv39/1KhRo9j57OxsPPPMM/j9999x9OhRJCcno1+/fmjUqBESEhL88j0RaU5FmSsGV0TuKSwExo0DJKn05+Rz48eL2xERkWmovuZq0KBBOHPmDF544QWkp6fj+uuvx+rVq4uaXBw/fhwBAcVjwP3792PDhg346aefSo1ntVrx559/4uOPP8bFixcRExODO+64A9OmTeNeV2ReFa25YlkgkXtSU4F//y3785IEnDghbtetm9+mRURE6lI9uAKAsWPHYuzYsU4/l5KSUupc06ZNITm7WgggLCwMa9asUXJ6RPpXUbdAZq6I3JOWpuztiIjIEFQvCyQiP2DmikhZrq7h5VpfIiJTYXBFZAZyZiozE7DZ7OeZuSLyTJcuQGwsYLE4/7zFAsTFidsREZFpMLgiMgM5c2WzAZcu2c+zoQWRZ6xWYPZscVwywJI/njWL+10REZkMgysiMwgNBeSGLo6lgSwLJPJcYiKwfDlQt27x87Gx4nxiojrzIiIi1WiioQUR+UG1akB6umhqUa+eOMeyQCLvJCYC/fqJroBpaWKNVZcuzFgREZkUgysis6haVQRXcrZKklgWSKQEq5Xt1omICADLAonMQ153Jbdjv3IFKCgQxywLJCIiIvIagysis5CzU3K2Sn5vtQKVK6swISIiIiJjYXBFZBYlM1eOzSzKaidNRERERC5jcEVkFiUzV2xmQURERKQoBldEZlFW5orBFREREZEiGFwRmUVZa67YzIKIiIhIEQyuiMyiZOaKZYFEREREimJwRWQWZWWuGFwRERERKYLBFZFZlJW5YlkgERERkSIYXBGZBTNXRERERD7F4IrILMrb54qIiIiIvMbgisgs5AzVlStAbi4bWhAREREpjMEVkVlUqQJYLOL44kWWBRIREREpjMEVkVkEBACRkeL44kU2tCAiIiJSGIMrIjNxXHfFzBURERGRohhcEZmJY8dANrQgIiIiUhSDKyIzkTNXZ84Aly+LY2auiIiIiBTB4IrITORA6uhR+zl5HRYREREReYXBFZGZyJkrObgKDwcCA1WbDhEREZGRMLgiMpOSmSuWBBIREREphsEVkZmUzFwxuCIiIiJSDIMrIjORg6njx8V7dgokIiIiUgyDKyIzkTNXBQXiPTNXRERERIphcEVkJiWDKWauiIiIiBTD4IrITOTMlYyZKyIiIiLFMLgiMpOSwRSDKyIiIiLFMLgiMpOSmSuWBRIREREphsEVkZkwc0VERETkMwyuiMwkLAwICbF/zMwVERERkWIYXBGZjWO2ipkrIiIiIsUwuCIyG8d1VwyuiIiIiBTD4IrIbBwDKpYFEhERESmGwRWR2TBzRUREROQTDK6IzCYy0n78559AYaF6cyEiIiIyEAZXRGaSlASsWmX/OCEBqF9fnCciIiIirzC4IjKLpCTgnnuAnJzi50+eFOcZYBERERF5hcEVkRkUFgLjxgGSVPpz8rnx41kiSEREROQFBldEZpCaCvz7b9mflyTgxAlxOyIiIiLyCIMrIjNIS1P2dkRERERUCoMrIjOIjlb2dkRERERUCoMrIjPo0gWIjQUsFueft1iAuDhxOyIiIiLyCIMrIjOwWoHZs8VxyQBL/njWLHE7IiIiIvIIgysis0hMBJYvB+rWLX4+NlacT0xUZ15EREREBqGJ4Gru3LmoX78+QkND0aFDB2zZsqXM23br1g0Wi6XUW58+fYpuI0kSXnjhBURHRyMsLAw9evTAgQMH/PGtEGlbYiJw9Ciwbh2wZIl4f+QIAysiIiIiBageXC1btgwTJkzA1KlTsX37drRu3RoJCQk4ffq009snJSUhLS2t6G337t2wWq0YOHBg0W1ef/11vPPOO5g/fz42b96MypUrIyEhAVevXvXXt0WkXVYr0K0bMHiweM9SQCIiIiJFWCTJ2a6i/tOhQwfceOONmDNnDgDAZrMhLi4Ojz/+OCZOnFjh18+aNQsvvPAC0tLSULlyZUiShJiYGDz11FN4+umnAQCZmZmIiorC4sWLcd9991U4ZlZWFqpUqYLMzExERkZ69w0SEREREZFuuRMbqJq5ysvLw7Zt29CjR4+icwEBAejRowc2bdrk0hgLFizAfffdh8qVKwMAjhw5gvT09GJjVqlSBR06dChzzNzcXGRlZRV7IyIiIiIicoeqwdXZs2dRWFiIqKioYuejoqKQnp5e4ddv2bIFu3fvxqhRo4rOyV/nzpgzZsxAlSpVit7i4uLc/VaIiIiIiMjkVF9z5Y0FCxagZcuWaN++vVfjTJo0CZmZmUVvJ06cUGiGRERERERkFqoGVzVr1oTVakVGRkax8xkZGahTp065X5uTk4OlS5di5MiRxc7LX+fOmCEhIYiMjCz2RkRERERE5A5Vg6vg4GC0bdsWycnJRedsNhuSk5PRsWPHcr/2q6++Qm5uLh544IFi5xs0aIA6deoUGzMrKwubN2+ucEwiIiIiIiJPBao9gQkTJmDYsGFo164d2rdvj1mzZiEnJwfDhw8HAAwdOhR169bFjBkzin3dggUL0L9/f9SoUaPYeYvFgvHjx+O///0vGjdujAYNGmDKlCmIiYlB//79/fVtERERERGRyageXA0aNAhnzpzBCy+8gPT0dFx//fVYvXp1UUOK48ePIyCgeIJt//792LBhA3766SenYz777LPIycnBI488gosXL+Lmm2/G6tWrERoa6vPvh4iIiIiIzEn1fa60iPtcERERERERoKN9roiIiIiIiIxC9bJALZKTedxMmIiIiIjI3OSYwJWCPwZXTly6dAkAuJkwEREREREBEDFClSpVyr0N11w5YbPZcOrUKURERMBisag6l6ysLMTFxeHEiROKrv/yxbh6GdNX4+plTF+Nq5cxfTWuXsb01bhmnyu/f37//P75/fP71/737ylJknDp0iXExMSUarRXEjNXTgQEBCA2NlbtaRTjq82NfTGuXsb01bh6GdNX4+plTF+Nq5cxfTWu2efK75/fP79/fv9K08tc9fT9e6KijJWMDS2IiIiIiIgUwOCKiIiIiIhIAQyuNC4kJARTp05FSEiI5sfVy5i+GlcvY/pqXL2M6atx9TKmr8Y1+1z5/fP75/fP75/fv/a/f39gQwsiIiIiIiIFMHNFRERERESkAAZXRERERERECmBwRUREREREpAAGV0RERERERApgcKVRv/76K/r27YuYmBhYLBasXLnS6zFnzJiBG2+8EREREahduzb69++P/fv3ez3uvHnz0KpVq6JN3jp27Igff/zR63Edvfrqq7BYLBg/frzHY7z44ouwWCzF3po1a6bI/E6ePIkHHngANWrUQFhYGFq2bIk//vjD4/Hq169faq4WiwVjxozxeMzCwkJMmTIFDRo0QFhYGBo2bIhp06bB2542ly5dwvjx41GvXj2EhYWhU6dO2Lp1q1djyjZt2gSr1Yo+ffp4PdZDDz1U7GdZo0YN9OzZE3/++afXY6enp+Pxxx9HfHw8QkJCEBcXh759+yI5OdmreQYFBSEqKgq33347Fi5cCJvN5tU8S/4M5LeePXv6ZNyDBw96PGZ6ejrGjRuHRo0aITQ0FFFRUejcuTPmzZuHy5cvezTH/v37lzqfkpICi8WCixcvejzXiu5DK2M6G2v58uUIDQ3FW2+9pei43oxlsVjw6KOPlvrcmDFjYLFY8NBDD3k87quvvlrs/MqVK2GxWDydbrG//eDgYDRq1Agvv/wyCgoKPB4TAE6cOIERI0YgJiYGwcHBqFevHsaNG4dz584pMtegoCA0aNAAzz77LK5everVXM+cOYPRo0fjmmuuQUhICOrUqYOEhAT89ttvbo/l7HnE8e3FF1/0aI7dunVz+vph8eLFqFq1qkdj9u3bt8znztTUVFgsFpf/v8yfPx8RERHF/m6ys7MRFBSEbt26Fbut/Jx16NAhl+daWFiITp06ITExsdj5zMxMxMXF4fnnn3d5LJkkSejRowcSEhJKfe69995D1apV8e+//7o9rvz9lfXWvXt3t8f0NwZXGpWTk4PWrVtj7ty5io25fv16jBkzBr///jvWrl2L/Px83HHHHcjJyfFq3NjYWLz66qvYtm0b/vjjD9x6663o168f/v77b0XmvXXrVrz//vto1aqV12O1aNECaWlpRW8bNmzweswLFy6gc+fOCAoKwo8//og9e/bgrbfeQrVq1Twec+vWrcXmuXbtWgDAwIEDPR7ztddew7x58zBnzhzs3bsXr732Gl5//XW8++67Ho8JAKNGjcLatWvx6aef4q+//sIdd9yBHj164OTJk16NCwALFizA448/jl9//RWnTp3yeryePXsW/UyTk5MRGBiIO++806sxjx49irZt2+KXX37BG2+8gb/++gurV69G9+7dPQ6G5XkePXoUP/74I7p3745x48bhzjvv9PpFm+PPQH774osvvBqzrHEbNGjg0ViHDx9GmzZt8NNPP+GVV17Bjh07sGnTJjz77LNYtWoVfv75Z6/nS8BHH32EIUOGYN68eXjqqafUnk6RuLg4LF26FFeuXCk6d/XqVSxZsgTXXHONx+OGhobitddew4ULF5SYZhH5b//AgQN46qmn8OKLL+KNN97weLzDhw+jXbt2OHDgAL744gscPHgQ8+fPR3JyMjp27Ijz5897PdfDhw/j7bffxvvvv4+pU6d6PB4A3H333dixYwc+/vhj/PPPP/j222/RrVs3jwJBx+ePWbNmITIysti5p59+2qu5KmnkyJFYu3at0wBi0aJFaNeuncuvW7p3747s7OxiF2VTU1NRp04dbN68uVgAvG7dOlxzzTVo2LChy3O1Wq1YvHgxVq9ejc8//7zo/OOPP47q1at79DdgsViwaNEibN68Ge+//37R+SNHjuDZZ5/Fu+++i9jYWLfH7dSpU6n/JWlpaXj//fdhsVjw2GOPuT2m30mkeQCkFStWKD7u6dOnJQDS+vXrFR+7WrVq0kcffeT1OJcuXZIaN24srV27Vuratas0btw4j8eaOnWq1Lp1a6/nVNJ//vMf6eabb1Z8XEfjxo2TGjZsKNlsNo/H6NOnjzRixIhi5xITE6UhQ4Z4PObly5clq9UqrVq1qtj5G264QXr++ec9HleSxO8+PDxc2rdvnzRo0CBp+vTpXo03bNgwqV+/fsXOpaamSgCk06dPezxur169pLp160rZ2dmlPnfhwgW3x3M2T0mSpOTkZAmA9OGHH3owy/LH9pbS4yYkJEixsbFOf6aSJHn0OChrjuvWrZMAePS7cvU+tDKm41ivvfaaFBoaKiUlJSk6rlJjXXfdddJnn31WdP7zzz+XWrVqJfXr108aNmyYR+PeeeedUrNmzaRnnnmm6PyKFSskb14KOfveb7/9dummm27yeMyePXtKsbGx0uXLl4udT0tLkypVqiQ9+uijHo3rbK6JiYlSmzZtPJ2qdOHCBQmAlJKS4vEYZVm0aJFUpUoVRcYq6/WDN/eRn58vRUVFSdOmTSt2Xv7fNW/ePLfGi46OlmbMmFH08bPPPiuNGTNGuvbaa6V169YVnb/llls8egxIkiTNnj1bqlatmnTq1Clp5cqVUlBQkLRz506PxpItXrxYCg8Plw4fPizZbDape/fu0oABA7was6Q9e/ZIERERXr+u8BdmrkwsMzMTAFC9enXFxiwsLMTSpUuRk5ODjh07ej3emDFj0KdPH/To0UOB2QEHDhxATEwM4uPjMWTIEBw/ftzrMb/99lu0a9cOAwcORO3atdGmTRt8+OGHCsxWyMvLw2effYYRI0Z4Vb7SqVMnJCcn459//gEA7Nq1Cxs2bECvXr08HrOgoACFhYUIDQ0tdj4sLMzrrOCXX36JZs2aoWnTpnjggQewcOFCr0sYHWVnZ+Ozzz5Do0aNUKNGDY/GOH/+PFavXo0xY8agcuXKpT7vabmJM7feeitat26NpKQkxcbUonPnzuGnn34q82cKwKvHAQH/+c9/MG3aNKxatQoDBgxQezpOjRgxAosWLSr6eOHChRg+fLhXY1qtVrzyyit49913PSpXclVYWBjy8vI8+trz589jzZo1eOyxxxAWFlbsc3Xq1MGQIUOwbNkyRZ4Ld+/ejY0bNyI4ONjjMcLDwxEeHo6VK1ciNzfX6znpSWBgIIYOHYrFixcX+3189dVXKCwsxODBg90ar3v37li3bl3Rx+vWrUO3bt3QtWvXovNXrlzB5s2bPS6Ne/zxx9G6dWs8+OCDeOSRR/DCCy+gdevWHo0lGzZsGG677TaMGDECc+bMwe7du4tlsrx18eJF9OvXD926dcO0adMUG9eXGFyZlM1mw/jx49G5c2dcd911Xo/3119/ITw8HCEhIXj00UexYsUKNG/e3Ksxly5diu3bt2PGjBlezw8AOnToUJQWnzdvHo4cOYIuXbrg0qVLXo17+PBhzJs3D40bN8aaNWswevRoPPHEE/j4448VmffKlStx8eJFj9YZOJo4cSLuu+8+NGvWDEFBQWjTpg3Gjx+PIUOGeDxmREQEOnbsiGnTpuHUqVMoLCzEZ599hk2bNiEtLc2r+S5YsAAPPPAAAFHKkpmZifXr13s15qpVq4peDERERODbb7/FsmXLEBDg2VPhwYMHIUmSYmv3KtKsWTMcPXrUqzEcfwby2yuvvOL13EqO62kJq/wzbdq0abHzNWvWLBr7P//5jyJzDA8P9+righ79+OOPeP311/HNN9/gtttuU3s6ZXrggQewYcMGHDt2DMeOHcNvv/1W9HzgjQEDBuD666/3uhTOGUmS8PPPP2PNmjW49dZbPRrjwIEDkCQJ1157rdPPX3vttbhw4QLOnDnj0fjyYyA0NBQtW7bE6dOn8cwzz3g0FiACjMWLF+Pjjz9G1apV0blzZzz33HOKrGXVgxEjRuDQoUPF/jctWrQId999N6pUqeLWWN27d8dvv/2GgoICXLp0CTt27EDXrl1xyy23ICUlBYBYh5ybm+txcGWxWDBv3jwkJycjKioKEydO9Gickj744APs3r0b48ePxwcffIBatWopMq7NZsP999+PwMBAfP7557q5sBao9gRIHWPGjMHu3bsVWXMEAE2bNsXOnTuRmZmJ5cuXY9iwYVi/fr3HAdaJEycwbtw4rF27tlRWxFOOL6JatWqFDh06oF69evjyyy8xcuRIj8e12Wxo165d0QvUNm3aYPfu3Zg/fz6GDRvm9bwXLFiAXr16ISYmxqtxvvzyS3z++edYsmQJWrRogZ07d2L8+PGIiYnxap6ffvopRowYgbp168JqteKGG27A4MGDsW3bNo/H3L9/P7Zs2YIVK1YAEP/ABw0ahAULFpRa3OuO7t27Y968eQDEWrn33nsPvXr1wpYtW1CvXj23x1Myk+bq/Xn7z8XxZyBTIntdctyysk6e2rJlC2w2G4YMGeLxFXJn3/vmzZsVedGuF61atcLZs2cxdepUtG/fHuHh4WpPyalatWqhT58+RVmBPn36oGbNmoqM/dprr+HWW29VbP2OHLDk5+cXvRj0tPGCrKLnFk+zTfJjICcnB2+//TYCAwNx9913ezSW7O6770afPn2QmpqK33//vSiA/+ijj7y+KKh1zZo1Q6dOnbBw4UJ069YNBw8eRGpqKl5++WW3x+rWrRtycnKwdetWXLhwAU2aNEGtWrXQtWtXDB8+HFevXkVKSgri4+O9Wnu4cOFCVKpUCUeOHMG///6L+vXrezyWrHbt2vi///s/rFy5UtGmPs899xw2bdqELVu2ICIiQrFxfY2ZKxMaO3YsVq1ahXXr1nm02NAZuUtS27ZtMWPGDLRu3RqzZ8/2eLxt27bh9OnTuOGGGxAYGIjAwECsX78e77zzDgIDA1FYWOj1nKtWrYomTZp41dEMAKKjo0sFkddee60iJYfHjh3Dzz//jFGjRnk91jPPPFOUvWrZsiUefPBBPPnkk15nBhs2bIj169cjOzsbJ06cwJYtW5Cfn4/4+HiPx1ywYAEKCgoQExNT9PufN28evv7666JyVk9UrlwZjRo1QqNGjXDjjTfio48+Qk5OjsdlnI0bN4bFYsG+ffs8npM79u7d63GTCJnjz0B+UyK4KjludHS0R+M0atQIFoulVCfT+Ph4NGrUqFSplDdzbNSoEerWrevxeHpUt25dpKSk4OTJk+jZs6fXmXtfGjFiRFFWZMSIEYqNe8sttyAhIQGTJk1SZLzu3btj586dOHDgAK5cuYKPP/7Y44sL8t//3r17nX5+7969qFWrlsclx/JjoHXr1li4cCE2b96MBQsWeDSWo9DQUNx+++2YMmUKNm7ciIceesgn2UFPRUZGOv3fcfHiRbczTCWNHDkSX3/9NS5duoRFixahYcOG6Nq1q9vjNGrUCLGxsVi3bh3WrVtXNEZMTAzi4uKwceNGrFu3zuOsKABs3LgRb7/9NlatWoX27dtj5MiRil0klP9XK2Xp0qV48803sXTpUjRu3Fixcf2BwZWJSJKEsWPHYsWKFfjll1+8fpFWHpvN5lX99W233Ya//voLO3fuLHpr164dhgwZgp07d8JqtXo9x+zsbBw6dMjjF4Gyzp07l3oh+M8//3iUCSlp0aJFqF27tiKtyC9fvlyq/M1qtXrd3ltWuXJlREdH48KFC1izZg369evn0TgFBQX45JNP8NZbbxX7/e/atQsxMTGKdLaTWSwWBAQEFOtK5o7q1asjISEBc+fOddp1U4n23rJffvkFf/31l9dXmbWuRo0auP322zFnzhyvO5mSc/Xq1cP69euRnp6u6QCrZ8+eyMvLQ35+vtN2z9549dVX8d1332HTpk1ejyUHLNdcc43XLy7lv//33nuv1PNSeno6Pv/8c8WyQQEBAXjuuecwefJkj58Dy9K8eXNNPX6bNm2K7du3lzq/fft2NGnSxKux7733XgQEBGDJkiX45JNPvFof3b17d6SkpCAlJaVYlcYtt9yCH3/8EVu2bPG4JPDy5ct46KGHMHr0aHTv3h0LFizAli1bMH/+fI/G86WdO3di5MiRePXVVxV/7PsDywI1Kjs7u1hG5ciRI9i5cyeqV6/ucTp4zJgxWLJkCb755htEREQgPT0dAFClShWvrgZPmjQJvXr1wjXXXINLly5hyZIlSElJwZo1azweMyIiotRasMqVK6NGjRoerxF7+umn0bdvX9SrVw+nTp3C1KlTYbVa3V50WtKTTz6JTp064ZVXXsG9996LLVu24IMPPsAHH3zg1bg2mw2LFi3CsGHDFLka1LdvX0yfPh3XXHMNWrRogR07dmDmzJleXxFes2ZN0RqZgwcP4plnnkGzZs08Xny+atUqXLhwASNHjix1RfHuu+/GggULnO6B44rc3Nyiv/sLFy5gzpw5yM7ORt++fT0aDwDmzp2Lzp07o3379nj55ZfRqlUrFBQUYO3atZg3b16ZV6BdmWdhYSEyMjKwevVqzJgxA3feeSeGDh3q8Vwdx3YUGBioWMmVEt577z107twZ7dq1w4svvohWrVohICAAW7duxb59+9C2bVu1p6h7cXFxSElJQffu3ZGQkIDVq1cjMjLS4/EyMzOxc+fOYudq1KiBuLg4j8e0Wq1Fjx8lLqg5atmyJYYMGYJ33nlH0XGVMGfOHHTq1AkJCQn473//iwYNGuDvv//GM888gyZNmuCFF15Q7L4GDhyIZ555BnPnzvWoTPLcuXMYOHAgRowYgVatWiEiIgJ//PEHXn/9dY8vsPnC6NGjMWfOHDzxxBMYNWoUQkJC8P333+OLL77Ad99959XY4eHhGDRoECZNmoSsrCyvgl95C4/8/Pxi2a+uXbti7NixyMvL8zi4mjRpEiRJKtrrrX79+njzzTfx9NNPo1evXoqUByrh7Nmz6N+/P7p164YHHnig1P8rq9Wq2Joun1GhQyG5QG4PXPLN0/abkiQ5HQ+AtGjRIq/mOmLECKlevXpScHCwVKtWLem2226TfvrpJ6/GdMbbVuyDBg2SoqOjpeDgYKlu3brSoEGDpIMHDyoyt++++0667rrrpJCQEKlZs2bSBx984PWYa9askQBI+/fvV2CGkpSVlSWNGzdOuuaaa6TQ0FApPj5eev7556Xc3Fyvxl22bJkUHx8vBQcHS3Xq1JHGjBkjXbx40ePx7rzzTql3795OP7d582YJgLRr1y63xx02bFixv/uIiAjpxhtvlJYvX+7xXGWnTp2SxowZU/Q4qFu3rnTXXXcVa5/ryTwDAwOlWrVqST169JAWLlwoFRYWejXPkj8D+a1p06Zej6t0C/JTp05JY8eOlRo0aCAFBQVJ4eHhUvv27aU33nhDysnJUWyOWm/F/uCDD0p33323ImM5m9+///4rNW7cWLrpppukzMxMj8d19nc1cuRIReboyJtW7CXHPXLkiBQcHKx4K3YlHDlyRBo2bJgUFRUlWSwWCYCUmJjo0d++rKy5zpgxQ6pVq1aZWx+U5+rVq9LEiROlG264QapSpYpUqVIlqWnTptLkyZNLtZJ3l5Kt2CVJkrZs2SLdfvvtUq1ataQqVapIHTp0UGybm40bN0oAyvzf5aojR45IAKRmzZoVO3/06FGvnqtTUlIkq9UqpaamlvrcHXfcId16661ebfUiScptd7N48eIyX68CkOrVq+f1ffiaRZL8vCKbiIiIXNKzZ080atQIc+bMUXsqpKKpU6di5syZWLt2LW666Sa1p0NE5WBZIBERkcZcuHABv/32G1JSUjwugyXjeOmll1C/fn38/vvvaN++vcfbRxCR7zFzRUREpDEDBgzA1q1bMWzYMPz3v//Vzf4uRERmx+CKiIiIiIhIAcwrExERERERKYDBFRERERERkQIYXBERERERESmAwRUREREREZECGFwREZGpWSwWrFy5Uu1pEBGRATC4IiIiQ0tPT8fjjz+O+Ph4hISEIC4uDn379kVycrLaUyMiIoPhJsJERGRYR48eRefOnVG1alW88cYbaNmyJfLz87FmzRqMGTMG+/btU3uKRERkIMxcERGRYT322GOwWCzYsmUL7r77bjRp0gQtWrTAhAkT8Pvvvzv9mv/85z9o0qQJKlWqhPj4eEyZMgX5+flFn9+1axe6d++OiIgIREZGom3btvjjjz8AAMeOHUPfvn1RrVo1VK5cGS1atMAPP/zgl++ViIjUx8wVEREZ0vnz57F69WpMnz4dlStXLvX5qlWrOv26iIgILF68GDExMfjrr7/w8MMPIyIiAs8++ywAYMiQIWjTpg3mzZsHq9WKnTt3IigoCAAwZswY5OXl4ddff0XlypWxZ88ehIeH++x7JCIibWFwRUREhnTw4EFIkoRmzZq59XWTJ08uOq5fvz6efvppLF26tCi4On78OJ555pmicRs3blx0++PHj+Puu+9Gy5YtAQDx8fHefhtERKQjLAskIiJDkiTJo69btmwZOnfujDp16iA8PByTJ0/G8ePHiz4/YcIEjBo1Cj169MCrr76KQ4cOFX3uiSeewH//+1907twZU6dOxZ9//un190FERPrB4IqIiAypcePGsFgsbjWt2LRpE4YMGYLevXtj1apV2LFjB55//nnk5eUV3ebFF1/E33//jT59+uCXX35B8+bNsWLFCgDAqFGjcPjwYTz44IP466+/0K5dO7z77ruKf29ERKRNFsnTS3tEREQa16tXL/z111/Yv39/qXVXFy9eRNWqVWGxWLBixQr0798fb731Ft57771i2ahRo0Zh+fLluHjxotP7GDx4MHJycvDtt9+W+tykSZPw/fffM4NFRGQSzFwREZFhzZ07F4WFhWjfvj2+/vprHDhwAHv37sU777yDjh07lrp948aNcfz4cSxduhSHDh3CO++8U5SVAoArV65g7NixSElJwbFjx/Dbb79h69atuPbaawEA48ePx5o1a3DkyBFs374d69atK/ocEREZHxtaEBGRYcXHx2P79u2YPn06nnrqKaSlpaFWrVpo27Yt5s2bV+r2d911F5588kmMHTsWubm56NOnD6ZMmYIXX3wRAGC1WnHu3DkMHToUGRkZqFmzJhITE/HSSy8BAAoLCzFmzBj8+++/iIyMRM+ePfH222/781smIiIVsSyQiIiIiIhIASwLJCIiIiIiUgCDKyIiIiIiIgUwuCIiIiIiIlIAgysiIiIiIiIFMLgiIiIiIiJSAIMrIiIiIiIiBTC4IiIiIiIiUgCDKyIiIiIiIgUwuCIiIiIiIlIAgysiIiIiIiIFMLgiIiIiIiJSAIMrIiIiIiIiBfw/rpbHd4E38VkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sXnE//5te2mSzF7jGZMzEwdIbDlh6i2djW3thLYZuHlrsfpNaT8KckDlm/yDOnr3797v47uh3aFixIU69egpH7hzJ93uVnpWO9/a8h/kn5wMAmvo2xdqBa1G3fF1ZYnj09BGCVwbjwv0L8Pf0R8ToCL1u/WEqsUmxaP1La8SnxqNL9S7YOWwnnJRO5g6rRJg4FcFWEqdzCecQtCIIKZkpuuYLJZkephW1mH9iPj7Y+4Fs1aflZ5Zj3LZxECFifIvxmN9zvkl7+pf0ZJSbq6MrqnhUQRXPKjn/z/1nzyrwdfNFhxUdEJcSV+gYzg7OiHorCr4evoa+BALwyf5P8EXEFxhUfxA2DNpg7nB0LP1i1Jg2R27GsM3DkJGdke+xsGFhCK1VshuPUw5jJg7WUMWy94pLST/kA4AyzmUwpOEQjGo6Ci0qt2C3URO69eQW6i2oh0xNJnYN24XutboXuf/2a9sx+s/RePj0IVwcXDCv+zyMe26cQf9mSRlJ6LKqC07Fn0Il90o4NPoQapWtpfd4pnIm/gyCVgQhLStNt1zDGn52mTgVwRYSpzvJd9DmlzaIS4lD54DOCBsWVupPpuSqPi09tRSvbX8NADCx5UT82ONHk/+SlPRkHOAdgKSMpDwld0PZ6gneHI7fOY42y9rAU+WJh1Mflup+X8ZiDRejxqQVtSg7pyyS1En4PPhzdKjaAZsub8L8k/NR3bs6Lr5xsdjGNPSfkr5XvdPmHYTUDIG/pz/8vfyLXQRuLdPfSvohlxz3h7M0oihiyu4pmHt8bqH7CBDg7uQODycP3E29q9tev0J9jGwyEsMbD7fIaVq25uWNL2P9pfXoWqMrdg/fXaJrmviUeIzYOgJ7b+0FkLNGeGmfpXqtRUrNTEXob6E4EnsE5V3L4+Cog6hfoX6pxzGXndd3ou/avtCIGnza+VNM7zTd3CEVi4lTEaw9cUpWJyNoRRDO3zuPeuXr4Z8x/6CMSxm9xtKKWiw4sQAf7PsAT7Oews3RDV93/Rr/a/G/ElWMFv+7GON35PTtn9R6En4I/cEsnyyUdj3G06ynuJtyF3HJcYhLifvv/7n+fCf5Tok6ENriCd5ctKIWvt/64sHTB9g/Yj+Cqxd/gWlM1nIxakznEs6h6c9N4e7kjifvP4GDwgEp6hTUX1gfd5Lv4P327+OrkK/MHabV0Kc6DgCeKk9dEuXvmfNV1asq/L38Udm9MkJWhxRaHbek6W8lTRyb+DTBjE4z0LdOX7PHLAetqMUbO97Az6d+1m0TIOR5b8n9vtKvTj/si9qHledWYnPkZl3FVyEoEFozFCObjES/uv3y3b/Hnqvjcjl25xjaLmsLAQLOvH4GTXyblPi5WlGL749+jw/3fYgsbRb8PP3w2wu/oVNApxKPkZ6Vjl5reuFA9AF4O3vjwMgDaOrbVI9XYl4///sz/rfjfwBgFdPvmTgVwZoTpyxNFnqt6YU9t/bAx80Hx8YdQ4B3gMHj3nx8E2O2jcGh24cA5K8+FfRmvPjfxZi4ayKAnEWR33b71qzlWOkiF0ChJ6PSXOTuj9qPLqu6FLsfK07yGrV1FFaeW4kpbabgu9DvzBaHva/FkPx0/Ce8FfYWQmuGImx4mG77tqvb0G9dPygFJU6/fhqNfRqbMUrrUdLEoXWV1nia9RSxybFIzEiU5diW8F5V3IdczwrwDsCElhMwttlYvT8gNLdsbTZG/zkav53/DQpBgaV9lsLb2TtfJdvf0x9zu8/Nd55KykjCH5f/wK9nf8U/sf/otns7e2Nwg8EY1XQUWldpjS1Xtth1dVwOoigiaEUQ/on9B6Objsbyfsv1GufU3VMYsmkIrj++DgECPgz6EDM6zSh2FoU6W43+6/sj7EYYPJw8sHfEXrSq0kqvGCzBB3s/wJx/5sBR4Yiw4WF4vnrJpqmaAxOnIlhr4iSKIsZtG4flZ5fD1dEVB0cdRIvKLWQbv7DqU0W3inh799t53oy9Vd5IVCcCAKa2m4o5IXMsYg5rQdOqCjsZFYddxcxj4+WNGPTHINQuVxtXJ141Wxz2vhZD8uKGF7EpchO+fP5LfBj0YZ7HBm4YiM2Rm9GqSiscGXOEvwcloM/7SmpmKmKTYhGbHJv//8mxuPXkFjI1mcUe21Kq48VVchf3XozoxGgsObUEj9IfAchZkzqyyUi82epN1KtQz+Qx60udrcaQTUOw5coWOCgc8NsLv2Fww8EA9KsOXX90HavOrcKq86vytHyu5F4J8an576lmT9VxOWyO3IyBGwbCxcEF19+8nu9WHKWRmpmKyWGTsezMMgA5H4asGbgGNcrUAJD/379NlTYYunkotlzZAhcHF+wevhtB1Qy/wbw5aUUthm4aivWX1sNL5YUjY49Y7JRDJk5FsNbE6fODn2N6+HQoBAX+fPlP9K7d2yjHebb6VJQX6r6ATS9tsoikSSLnVAW5q1hUvGR1Msp9XQ7Z2mxcm3gNgeUCzRJHSadUBQcE4+WGL6Nl5ZZoWLFhqdZlWfq0GlEU4fOtDx48fYDDow+jfdX2eR6PS45DvQX1kJKZgp96/ISJrSaaKVLrIl2cPUvf95WSNhywpCR/c+RmjP5zNJLVybptz37IlZ6VjjUX1mDe8Xm4cP+Cbr9uNbvhrVZvoUdgj3xTyi3pd+pp1lMM3DAQYTfC4KR0wh+D/kDfOn1lGVsrahEeHY6V51bij0t/ID07vdB9+SFfyWRqMtFgYQPceHwDn3T8BJ8FfybLuH9c+gOvbX8NiRmJ8HDywMJeC+Hq6JrvQ14XBxekZ6dDpVRh+9DtupvCW7uM7AyErArBP7H/oJpXNRwbdwy+7pbXUIuJUxGsMXFafW41RmzNmR+6sOdCjG853qjHkzrvTQ6bXOR0Cn9Pf5t/M5azikUl02VVF+yP2o8fQn/A5DaTzRJDSStOuTk7OKOpb1O0qNQCLau0RMvKLVGnfJ0C1wtaQ9OJyAeRqL+wPpwdnJH4fmKBDWgWnlyICTsnwMPJA5cnXIafp58ZIrU+7Za1w9E7R/Nss7fq+JTdU/DDsR/QO7A33mn3TqFJjiiKCI8Ox48nfsSfV/7UvcbAsoF4s9WbGNV0FDxUHhb1O5WiTkGftX1w8PZBuDq64s+X/zTahfDOazvRa22vYvezpMTZEknTkn3cfHD9zevwUHnINnZMUgyGbx6ua8ZVlGkdpmFWl1myHdsSPHr6CG2XtcX1x9fRvFJzHBx1EG5ObuYOKw8mTkWwtsRpf9R+dP+tO7K0WZjabiq+7vq1SY7LqUr/saRPMe3BD0d/wJS/p6BL9S7YO2KvWWIoycVoWZeyGNtsLE7Fn8K/d/9Fkjop337uTu5oXqk5WlZuiZZVWqJF5RY4E38Gg/4YZPFNJ6TFvcEBwdg/cn+B+2hFLdovb49jd46hf93+2DJ4i4mjtD7qbDXKf1MeqZmpWNxrMTxVnnZZHR+3bRyWnVlW4DTQwkQ9icKCkwvwy+lfdL9vHk4e6FitI3Ze32kRv1OP0x+jx+89cCLuBDxVntgxdAc6VO1gtOPZc6dCuSRmJKLWj7XwKP0RFvdajNdbvC77MTRaDb449AVmHpxZ5H62+oH0jcc30HZZWzx8+hB9avfBxkEbC7w3lrmUJjdwMFFMpIdL9y9hwPoByNJm4aUGL5m0e1V8Sv750obsZ82UCqXNJ4eWpHft3pjy9xQcun0IyerkYlsxG4NSocS87vN0F6O5SRdjS/os0V2MaUUtbj6+iZN3T+Jk3EmcvHsSp+NPIzUzFQdvH8TB2wd1z1dAUWAyJkKEAAGTwyajX51+Zj9xSjF3rNax0H0UggJLei/Bc0uew9YrW7H1ylb0r9vfRBHmZw0fcuyP2o/UzFRU9qiMV5u/Kss97wbUG4CNL20ssOJiqdVxqemFt7N3iZ9TvUx1fNvtW8zsPBOrzq3Cj8d/xNVHV7Hj+o4C9zf179T9tPvourorzt87j7IuZfH38L/RvHJzox6zkkclWfezR7MjZuNR+iPUK18PY58ba5RjKBXKnO56B4veLzY5FhExETZ3zVGrbC1se3kbnl/1PP669hfKfl0WaVlpusctbcZFUUx3l1IqlfiUePRc0xNJ6iR0qNoBK/uvNOlNZflmTOYSWC4QgWUDkaXNwp6be8wWx4B6A/C/Fv/Lt93P0y/fJ9gKQYHAcoEY2mgofuj+Aw6POYzkack4/7/zWNZ3Gca3GI8WlVtAKSihReFt7kWIuhOnOYmiqFvn2Kla0a10G/k0wtR2UwEAE3dOzLNuxZQ2R25GwLwABK8MxtDNQxG8MhgB8wKwOXKzWeIpzLar2wAAfWv3lfU9fUC9AYieFI0X6r4AABjWcBiiJkVZ7IWIdD+90iROEncnd7zR8g1cnnAZc0LmFLmvqX6n7iTfQccVHXH+3nn4uvvi4KiDRk+aACCoahD8PP10H+gUpLxreQRVte5GA8YSnRiNecfnAQC+6foNHBTGqyfY+wfSbf3bYmLLnLWwuZMmIGfN7IsbXrS49+uCMHGyQKmZqei9tjdikmJQu1xtbB28Nd/9GoytuDdjAQL8Pf35ZkxGITU/2X59u1njOBV/CgDw2nOvYc2ANTgw8kCJL0YdFA5o5NMIY5qNwcJeC3Hy1ZMlbm9r7hPnrSe3EJcSB0eFI1r7tS52/086foKaZWoiLiUOH+//2AQR5iVNVXu2fbylnYy1ohbbruUkTv3q9pN9fKVCiUYVGwEAvJy9LK7alptUcSrjrH+bcYWggL+nf4n2Nebv1K0ntxC0IghXH12Fv6c/Do06hIYVGxrteLlJ1XEAhZ6vkzKS8rQyp/98tP8jqDVqPF/9efQM7GnUY9n7B9IarQbrLq0r8DFpFsbksMnQaDWmDKvUmDhZmGxtNgZvHIzT8adRwbUCdg7diXKu5UweR1FvxtL3c7vPtegTM1kvKXHaeX1niW5EbAzn753HibgTcFA44LPgzzCk0RB0Duhs0M98Va+qJdrP3CdOqdrUqkoruDq6Fru/i6MLFvdeDACYf2I+jt85btT4ctNoNZgUNqnQ6Y+A5ZyMT909hbspd+Hu5I7gAOPc4Fmq4EgVHUulz1S9gpT0d2X24dlYf3F9iVq3l8aVh1cQtCII0YnRqFW2FiJGR5i8G6g0VfPZ9tl+nn5oUbkFsrRZ6LO2D87EnzFpXJbuZNxJrLmwBgIEfNvV+PeitPcPpCNiIgq9NyJgOTMuisPEyYKIooiJOydi5/WdcHFwwV9D/kLNsjXNFk9Rb8aWuNiYbEeHqh3g4eSB+2n38e/df80Sw9JTSwEA/ev2h4+7jyxjWsuJsyTrm54VUiMEwxsPhwgRr21/DVmaLGOFl4c1nYylaXrda3UvsEuhHKRERK4b5xqLXIlTSaaqAcCF+xfw8qaXUfWHqvhk/ydF/syU1NmEs+i4oiPuptxFgwoNcGjUIVTzrmbwuPqQpmoeGHlAVx2PnhSNQ6MOoVO1TkhWJ6P7791x/dF1s8RnaURRxLt73gUAvNLkFTSr1Mzox7T3D6RtZaoiEycL8vU/X+PnUz9DgIA1A9eUaIqMsRX0ZmzJ8+bJNjgpnRBaKxQAsP2a6afrPc16itXnVwMAXn3uVdnGtZYTZ0nXNz3r+27fo6xLWZy/dx4/HPvBGKHlY00n4z+v/gkA6FdH/ml6kjIuOVPfLLnipBW1siVOxf1OCRCwpPcSTO84Hb7uvriXdg9fRHyBgLkBGLB+APbe2gt9mgsfu3MMwSuD8eDpAzxX6TmEjwo3e6VYamSUuzru4uiCP1/+E818m+maV8Qlx5k1Tkuw7eo2HLp9CM4Ozvgi+AuTHdeeP5C2lamKTJzMSKPVIDw6HGsvrMX0A9Pxwb4PAAA/hP5g1s5UzyrozZjI2HoH5kzXK6xjljFtvLwRSeokBHgHyH7/lcJOnL7uvhZx4oxNikVUYhSUghLt/NuV6rkV3Crgu27fAQBmhs/ErSe3jBFiHtZyMo56EoUL9y9AKSiNupbCGipOqZmpuim4UqJniOIuRl9t/io+Df4UMZNjsOHFDegc0BkaUYMtV7ag6+quqLugLuYdm1fo31nuc3V4dDj23dqHkFUhSMxIRHv/9tg/Yj/Ku5Y3+HUYi5ezF8KGhyGwbCBuJ91Gt9+64XH6Y3OHZTZZmiy8t/c9AMCUNlPg71WydXJysdcPpK1lxkVx2I7cTAq6WR+Qc7E4qc0kM0VFZDl6BPaAAAGn408jLjku30WRMS09nTNNb2yzsUbpZjmg3gD0q9MPETERmLBzAi4/uIz32r9nESdOqdr0XKXn9LoJ5MgmI7Hq3CociD6A8TvGI2xYmFHXDmRmZ0IhKApdCyfdANbcJ2Npml5QtSCUdSlrtONIzRaepFtuxUlKUFRKlWyNj3L/ThXWjt5R6YhBDQZhUINBuHT/Ehb9uwirzq3CtUfXMHn3ZHy4/0MMbTgUE1pNQFPfpgAKP1cDOdNTtw7eanE38yxIRbeK2PPKHrRf3h6XH1xGz997Yu+IvXB3cjd3aCa35NQSXHt0DRVcK+D9Du+bJQZ7vM1J7tt8CBAKvOecJcy4KA4rTmZQWAcoIOfTdUvpAEVkThXdKqJVlVYAcppEmErkg0gcjjkMhaDA6KajjXYc6cQ5pukYAEDYjTCjHas09FnflJsgCFjcezFUShX+vvk31l5cK2d4OqIoYs7hOeixpocuaSrsk0xLOBmbYpoeYB0VJ7mm6T2rNLMjGlRsgPk95yNuShwW9lyIBhUa4GnWU/xy5hc0+7kZ2i1rh0m7JhV6rgZyPlixhqRJUs27Gv5+5W+UdSmL43HHMWD9AKiz1WaJ5dkqnqmatyRlJOluQvtp50/Ncp9Ae2YLUxWZOJlYUR2gJJbSAYrI3KTueqacrvfL6V8AAL0Ce5mkyiVN2wqPDsfTrKdGP15xpIqTvokTANQuVxsfd8xpSz45bLLs04KS1ckYuGEgPtj3AbSiFqOajsKaAWvy/Xs5OzhbxMn4cfpj3d9r3zp9jXosKRlRa9TIyM4w6rH0JVXD5E6c9OGh8sD4luNxYfwFHBp1CIMbDIaDwgFH7xzFjyd+LPRcLUDAe3ves7pzdf0K9bFz6E64Obphz609GLF1hMlfgznvuTbnnzl4+PQh6pavi3HPjTP68Sg/a5+qyMTJxKypAxSRufUK7AUA2HNrj0kuAtXZaqw8txKAvE0hilK3fF0EeAdArVHjQNQBkxyzMAmpCbj66CoECAZPbXuv/XuoX6E+Hjx9gPf2vCdThMDlB5fRamkrbLmyBY4KRyzutRjL+y7HkEZDdCfjb7p+AyBnLUOHqh1kO7a+dl3fBY2oQcOKDVGjTA2jHstD5aGbXmqp0/V093CSYX2TXARBQFC1IKx7cR1i347VVYILY83n6tZ+rbFlcM7vz4ZLGzBh5wS9GmTow5z3XItJitE1rfk65Gs4Kh2NdiwqmjWvnWfiZGLW1AGKyNya+jZFZY/KeJr1FOHR4UY/3tYrW/Eo/RGqeFRBj8AeRj8ekHPB1rNWTtXJlFMSCxJxO+cisJFPI4Mvap2UTvi5988AgGVnluFg9EGD4/vj0h9otbQVrj66Cj9PP0SMjsDrLV7XraGSTsbvtnsXLSu3hEbUYN3Fgm+4aEqmmqYH5NwU1kvlBcByp+sZa6qeXHzdfUvcFMZaz9Vda3bF7wN+hwABP5/6GZ8c+MToxzT3Pdc+3v8xMrIz0Dmgs242A1FpMXEyMWvpAEVkCQRB+K+73jXjT9eTmkKMaTYGDgrT9c6RpuvtvLHTZJ/8FkRa31TaNuSF6VC1A1577jUAwOvbX9d7PUW2Nhvv/v0uXtr4EtKy0hAcEIxTr50q8pYNI5qMAABdBdFc1Nlq7LqxC4Dxp+lJLL0luaUnToB9nKsHNRiku3H1lxFf4oejxr2FgDln3JyOP627xYQpbnZLtouJk4nZSjtGIlORPhncfn27UZOKm49vYl/UPggQMLbZWKMdpyDB1YOhUqoQnRiNKw+vmPTYucmxvulZc7rOga+7L64+uorZh2eX+vn3Uu+h6+qu+O5oTpvz99q9h79f+RsV3SoW+byXG74MB4UDTsefxsX7F/WKXQ4Hog8gNTMVldwroUXlFiY5pqU3iJASOm+Vt3kDKYK9nKtfa/4aZj0/CwAw5e8pWHnWeB80lPT3UO4qniiKeOfvdwAAwxoNQ/PKzWUdn+wLEycTs5YbYBJZiuerP69LKi4/uGy040hNIbrV7IZq3tWMdpyCuDq6Irh6MADzTdd79PQRLty/AEDexMnb2Vv3njcrYhYiH0SW+LnH7hxD8yXNER4dDncnd2wctBFzus4pUTWwvGt53Rq51edW6xe8DKQ25H3r9DVKa/uCWHpLcktc4/QsezpXf9DhA7zTNiexGLttrO5nVg4arQbbr21H37V98daut0r0HH1ug1CUHdd3IDw6HCqlCl8+/6WsY5P9YeJkBrbQjpHIVNyc3PB89ecBGK+7XpYmCyvOrgBguqYQz+pRK2dN1c4b5kmcDsccBpDTrKK4ak5pDao/CD0DeyJLm4XXt79e6D2XJKIoYtHJRei4oiPiUuJQt3xdnHz1JAbWH1iq445sMhIA8NuF38zS/UwUxTyJk6lYesXJGqbqAfZzrhYEAd90/Qajmo6CRtTgpT9eMnhN4p3kO/g0/FMEzAtAn7V98Ne1vyBChJPSqdjnjtgyAt8d+Q7pWekGxQDkTPOdumcqAGBym8km/1CMbA8TJzOx9naMRKakm653bbtRxt9+bTvupd1DRbeK6FOnj1GOURxpnVPE7Qgkq5NNfny51zflJggCFvRcAFdHV0TERGD5meWF7puelY7Rf47GGzvfQJY2CwPrDcSJcSdQt3zdUh+3Z2BPlHUpi7spd7E/ar8hL0Evp+JPIS4lDm6O/yX/psDEST72cq4WBAFL+yxFvzr9oNao0WdtH5yJP1OqMXJXl6rNrYaZB2fiTvIdlHUpiyltpiByQiTWDlwL4f//y3P8//++ikcVPMl4gnf3vIva82tj2ellyNZm6/26fjn9C648vIJyLuUwrcM0vcchkjBxMiNrbsdIZErSlKt/Yv+R/Z5AwH9NIUY1GVWiT0SNoVbZWggsG4gsbRb23dpn8uMbY31TbgHeAfg8+HMAwNQ9U3E3+W6+G2DeenIL7Za3w8pzK6EQFPim6zf4Y9Afek/dUTmo8HKDlwGYp0mEVG3qXqs7nB2cTXZc3VQ9C20OoVvjZAWJE2A/52oHhQPWvbgOnap1QkpmCkJ/C8W1R9eKvVltQdUlrahFx2od8fuA3xE3JQ7fhX6HuuXrFlnF2/TSJkRPjsbyvsvh7+mPO8l3MO6vcWi4sCE2Xd5U6jWuyepkzAifAQCY2XkmvJy9DPsLIgJgurZRRER6quZdDQ0rNsTF+xex+8ZuDGk0RLaxY5JiEHYjDADMfkPEnoE9Me/4POy8vhMv1HvBZMdNykjCmYScT5eNlTgBwFut38LvF37H6fjTqPVTLaRn/zcVp7xreaRnpSMtKw0VXCtg/Yvrdeu+DDGiyQgs/HchNkduRoo6Rfb1E0WR2pCbcpoeYD0VJynBI8vh7OCMbUO2IXhlME7Hn0b75e3hqHBEfOp/DRv8PP3wQ7cf4OzojCWnlmDH9R266bdlXcpiVJNReLX5q4VWiQfUG4B+dfohIiYC8SnxqORRCUFVg3QJ6ehmozGk0RAsOrkIX0Z8iauPruLFP15Ei8ot8FWXr9ClRpcSvZav//ka99PuI7BsIF5v/rqBfzNEOVhxIiKrILUl335d3ul6y88shwgRwQHBCCwXKOvYpSVN19t1Y5dJ25IfiT0CrahFjTI14OfpZ7TjOCgcdBWg3EkTADx8+hBpWWkILBuI06+fliVpAoBWVVqhTrk6SM9Ox6bITbKMWRJRT6Jw/t55KAWlrmJqKmxHTobwVHli17BdqOReCQ+fPsyTNAE5FaZBGwcVW10qSnFVPGcHZ7zd9m3cmnQL0ztOh5ujG/69+y9CVoeg6+quOBl3ssjx7yTf0XXi/Lorb3ZL8mHiRERWoVftnIvPXdd3GTTnPTeNVoNlZ5YBMF9TiNw6VusIV0dXxKXE6TrcmYIx1zflptFq8OOJH4vcJz07HZXc5bs3jiAIuns6rTq3SrZxi/PXtb8A5NzLqpxrOZMdF7D8ipPU7Y+Jk+Uq51L8z6wAAZNbT0bkhEgcHHUQQxsNlX1KqqfKE58Gf4pbk27hrVZvwVHhiL239qLVL63w4oYX89y+IfeUwnHbxiEjOwNBVYNMcuNpsh9MnIjIKrTxa4OyLmXxJOMJjt05JsuYYTfCdIuXTTk1rjDODs7oUj1nGoop25Ibe32TpLgbYAI5nxTLfQPM4Y2HA8i5p9LtxNuyjl0Yc03TA/6bAmeJiVO2NhspmSkAmDhZsoiYiHyVpmeJENGvbj+9GreUVkW3ipjXYx6uvXkNI5uMhAABmyI3ocHCBhi3bRyWnFqCgHkBCF4ZjKGbh2L3zd0ActbH8ma3JCcmTkRkFRwUDrqW3XJ115OaQoxoPMKki/eLIk3XM1XilJaZhpN3c6a9GLviVNIbW8p9A8yqXlURHJAz9e+387/JOnZBnqQ/0bVzNsen3VJCYon3ccrdMZKJk+Uy1+9qcQK8A/Br/19xfvx59KvTD1pRi2VnluH17a8X+KHMtH3TsDlys0ljJNvGxImIrIa0VkSOxCk+JV43zqvNzT9NTyIlh0dij5jkwvfYnWPI1mbDz9MPAd4BRj1WJY+STcEr6X6loZuud36V0deP7bqxCxpRgwYVGqBm2ZpGPVZBLHmqnhSTm6Mb151YMHP+rpZEw4oNsfXlrYgYFVFsJ9TJYZPNch83sk1MnIjIaoTWCoVSUOLSg0uITow2aKwVZ1dAI2rQzr8d6leoL0+AMqjmXQ31K9SHRtRgz609Rj9e7vVNxp7SElQ1CH6efvnu4SIRIMDf0x9BVYNkP/bAegPh6uiKa4+u4UTcCdnHz82c0/SA/5pDJGYkFnuzYVPj+ibrYM7f1dLIFrORqcks9HERImKTY2Wf/kv2i4kTEVmNsi5l0b5qewDAjms79B5HK2rxy+lfAFhGU4hn9axluul6plrfBOR00prXfR4AFHoDzLnd5xrlPjkeKg/dTUuN2SRCna3Gruu7AJhnmh7wX1IiQkSKOsUsMRSGHfWsgzl/V0vDUqcUku1i4kREVkU3Xc+AtuT7o/YjKjEKXiovvNTgJblCk03utuTGrBios9W6RhvGXt8kKeoGmBtf2qhLboxhROOc6XprL66FOlttlGMcvH0QKZkp8HX3RcsqLY1yjOI4Ozjr1uxZWkty3T2cXHgPJ0tnzt/VkrL0KYVke3gDXCKyKr1r98b7e9/HgagDSMtMg5uTW6nHkJpCDGs0DK6OrnKHaLD2VdvDw8kD99Pu43T8abSo3MIoxzkRdwJqjRo+bj6oXa62UY5RkOJugGksz1d/HlU8qiAuJQ47ru8wyoXfn1dypun1qd0HCsF8n016O3sjITXB4tY5seJkXcz1u1pS0pTCuOQ4iMi/dlGAAD9PP7NPKSTbwYoTEVmVeuXrobp3dag1auyL2lfq5z9Ie4AtkVsAWFZTiNyclE7oWrMrAOimfRlD7ml6pm7ZW9wNMI11TKk1uTGm64miiG3XtgEw3zQ9iaW2JJcqYEycrIc5fldLylqmFJLtYOJERFZFEASDuuutPLcSWdostKjcAk19m8ocnXx065xuGG+dk9QYwhTrmyzFK41fAQDsuL4DD58+lHXsMwlncCf5DlwdXdGlRhdZxy4tS21Jrqs4qbzNGgfZDmuYUki2g4kTEVmd3rV7A8i5+C1Na2lRFC26KURuPQJz2pIfv3Nc9gt8AMjSZOFI7BEAplvfZAkaVGyA5pWaI1ubjXUX18k6tjRNL7RmqNnvC2apLcm5xomMYUC9AYieFI0DIw9gzYA1ODDyAKImRTFpItkxcSIiq9MpoBPcHN1wN+UuziacLfHzImIicPXRVbg5umFIwyHGC1AGlT0qo4lPE4gQsfvGbtnHPx1/GmlZaSjrUhYNKjaQfXxLJt3TaeW5lbKOK7UhN/c0PeC/xMRSm0Nwqh7JzZKnFJLtYOJERFbH2cEZITVCAJRuup7UFOLlhi/DQ+VhlNjkJHXXM8Z0PWl9U1DVILM2MTCHIQ2HwEHhgH/v/ovLDy7LMmZ0YjTO3TsHhaBAr9q9ZBnTENJUOEurOHGNExFZM/s6WxKRzcg9Xa8knqQ/wcbLGwFY/jQ9iZQ4hd0Ik/3O9/a4vklSwa2C7u929bnVsoz519W/AADt/dujvGt5WcY0hK7iZKlrnJg4EZEVYuJERFZJuvA9EXcC91LvFbv/b+d/Q0Z2Bhr7NEarKq2MHZ4s2vi1gbezNx6nP8aJuBOyjavRanA45jAA+1rflJt0T6ffLvwmS1JqSdP0gFxrnNSJZo3jWbo1Ts5c40RE1oeJExFZpcoelfFcpecgQsSuG0W37BZFEUtOLwGQU20ydettfTkoHBBaMxQAsPO6fNP1zt87jyR1EjycPNDEt4ls41qT3rV7w9vZG3eS7yA8OtygsRIzEnUVvL51+soQneEsvTkEK05EZI2YOBGR1eodmDNdr7h1TsfjjuPi/YtwdnDGsEbDTBGabIyxzkla39Shagc4KOzzPugqBxVebvAyAMObROy6vgvZ2mzUK18PgeUC5QjPYFJFx9Km6knxMHEiImvExImIrJa0zunvm38jU5NZ6H5LT+U0hRhUf5DVtUHuXqs7gJwueAmpCbKMac/rm3Ib2XQkAGBT5CakZqbqPY6lTdMDLLPipM5WIz07HQATJyKyTkyciMhqNa/cHD5uPkjJTEHE7YgC90lWJ2PdpZz79VhLU4jcKrpVRMvKLQHkNIkwlCiKuoqTva5vkrSu0hqBZQPxNOspNkdu1muMTE2mbqpov7qWkzhZYjvyJHUSAECAAC9nLzNHQ0RUekyciMhqKQSFbipbYdP11l5Yi6dZT1G3fF10qNrBlOHJpketnJvhyrHO6fKDy3iU/gguDi5oXrm5weNZM0EQdPd0WnVulV5jHIw+iGR1MnzcfCyq6YglVpykWDxVnnbXAp+IbAPfuYjIqhXXlly6d9O4ZuOspinEs6Tk8O+bfyNLk2XQWFK1qZ1/OzgpnQyOzdoNbzwcALA/aj9ik2JL/Xxpml6f2n0sKhmQ1jg9zXpa5DRWU+L6JiKydpbzLk9EpIeuNbrCUeGI64+v49qja3keOxN/BqfiT8FJ6aRbz2KNWlRugfKu5ZGkTsLRO0cNGovrm/IK8A5Ap2qdIELEb+d/K9VzRVHEtqvbAFjWND0gp6ojsZSqEzvqEZG1Y+JERFbNQ+WBTgE5a3Wena4nVZteqPuCRdyUVF9KhVLXJMKQ6Xpc31SwkU1ykupV51dBFMUSP+9swlnEJsfC1dEVXap3MVZ4elEqlLrkydISJ2tr0EJEJGHiRERWT2pLnnu6Xlpmmq6CYI1NIZ7Vs9b/tyU3IHG68fgG4lPj4aR0sqj1OOY2sP5AuDi44MrDK/j37r8lfp40Ta9bzW5wcXQxVnh6s7SW5Kw4EZG1Y+JERFZPWud06PYhJGXkdO7acGkDUjJTUKNMDQRXDzZneLLoVrMbFIICF+5f0GstDvDf+qbWVVpb5IW+uXiqPPFCvRcAlK5JhCW2Ic/N0hpESB3+mDgRkbUye+K0YMECBAQEwNnZGa1bt8aJEyeK3H/u3LmoU6cOXFxc4O/vj7fffhsZGRkmipaILFHNsjVRp1wdZGuz8ffNvwHkbQphSYv29VXOtRza+LUBAF3769Li+qbCjWic011v7cW1JWqmEJMUg7MJZ6EQFOgV2MvY4enF0lqS6ypOKm+zxkFEpC+zXk2sX78eU6ZMwYwZM3D69Gk0adIEoaGhuH//foH7r1mzBh988AFmzJiByMhILFu2DOvXr8eHH35o4siJyNJIVadlZ5bhm3++wdE7R6GAAqOajjJvYDIydLoe1zcVLqRGCCq5V8Kj9Ecl+vuVmkK082+HCm4VjB2eXiyt4sQ1TkRk7cyaOH3//fd49dVXMXr0aNSvXx+LFy+Gq6srli9fXuD+R44cQfv27TF06FAEBASgW7duGDJkSLFVKiKyfdJC+N03d+O9ve8BAJwcnAzuQmdJegTm3M9pX9Q+qLPVpXru7cTbuJ10G0pBibb+bY0RnlVTKpS61uQlma5n6dP0gP/WOFla4sSpekRkrcyWOGVmZuLUqVMICQn5LxiFAiEhITh6tOALnXbt2uHUqVO6ROnWrVvYuXMnevbsWehx1Go1kpOT83wRkW3ZHLkZM8Nn5tuekZ2BFze8iM2Rm00flBE09W0KX3dfpGam4nDM4VI9V6o2tajcAu5O7sYIz+pJN8Pdfm07Hj19VOh+SRlJCI8OBwD0rdPXFKHpRUpQLKU5BNc4EZG1M1vi9PDhQ2g0Gvj4+OTZ7uPjg4SEhAKfM3ToUHz22Wfo0KEDHB0dUbNmTXTu3LnIqXqzZ8+Gl5eX7svf31/W10FE5qXRajApbBJEFN5GenLYZGi0GhNGZRwKQYEetXKqTqWdrsf1TcVrWLEhmvk2Q5Y2C+svrS90v103diFbm4265euidrnaJoywdCx1qh4TJyKyVla1Yjo8PByzZs3CwoULcfr0aWzevBk7duzA559/Xuhzpk2bhqSkJN1XbKx+3aiIyDJFxETgTvKdQh8XISI2ORYRMREmjMp4egb+/zqnG6VLnLi+qWSkqtPKcysL3ccapukBudqRW1hzCCkuIiJrY7bEqXz58lAqlbh3716e7ffu3YOvr2+Bz/nkk0/wyiuvYNy4cWjUqBFeeOEFzJo1C7Nnz4ZWqy3wOSqVCp6ennm+iMh2xKfEy7qfpetaoyuUghJXHl7BrSe3SvSc+JR4XH98HQIEtK/a3sgRWrehjYZCKShxIu4Erjy8ku/xTE2mrtpnydP0AFaciIjkZrbEycnJCc2bN8e+fft027RaLfbt24e2bQteuPz06VMoFHlDViqVAFCqu70Tke2o5FFJ1v0snZezFzpU7QAA2HW9ZG3JpWpTU9+mvGgtRkW3iromHKvPrc73+KHbh5CsTkZFt4poXaW1qcMrFUtqRy6Kom6tFX8GichamXWq3pQpU7B06VKsXLkSkZGRGD9+PNLS0jB69GgAwIgRIzBt2jTd/n369MGiRYuwbt06REVFYc+ePfjkk0/Qp08fXQJFRPYlqGoQ/Dz9IEAo8HEBAvw9/RFUNcjEkRlPaafrcX1T6Uj3dFp9fjW0Yt7ZDH9eyZmm16d2HygVln3esaSKU3p2OrK0WQCYOBGR9XIw58EHDx6MBw8eYPr06UhISEDTpk0RFhamaxgRExOTp8L08ccfQxAEfPzxx4iLi0OFChXQp08ffPnll+Z6CURkZkqFEvO6z8OLG16EACFPkwgpmZrbfa7FX+SWRs/Annh/7/vYH7Uf6VnpcHF0KXJ/rm8qnT51+sDb2RuxybE4GH0QwdWDAeRUTaT1TZY+TQ+wrHbkUgxKQcmujkRktczeHGLixIm4ffs21Go1jh8/jtat/5v6EB4ejl9//VX3vYODA2bMmIEbN24gPT0dMTExWLBgAby9vU0fOBFZjAH1BmDjSxtRxbNKnu1+nn7Y+NJGDKg3wEyRGUeDCg3g5+mHjOwMXVvswjx8+hCXHlwCAN0UPyqas4MzBjcYDCBvk4hz984hNjkWLg4uCKkRUtjTLUbuduTmns6ee32TIBRcHSYisnRmT5yIiOQwoN4ARE+KxoGRB7BmwBocGHkAUZOibC5pAgBBENCzVs50vV03il7nFHE7p5tg/Qr1UcGtgtFjsxVSd72NlzciLTMNwH/T9LrV7AZXR1ezxVZSUuKkETVIy0ozayxc30REtoCJExHZDKVCic4BnTGk0RB0DuhsU9PzniWtc9pxfUeR1QRpfROn6ZVOW7+2qFmmJtKy0rDlyhYAsKppegDg6ugKR4UjAPPfBJcd9YjIFjBxIiKyQl1qdIGjwhG3ntzC9cfXC91PWt/ExhClIwiCruo099hczD8+H2cSzgAAetfubc7QSkwQBItpEKG7h5ML7+FERNaLiRMRkRVyd3JHp4CcKpJ0X6FnJWYk4mzCWQBMnPRRwTVnauOp+FN4M+xNAICT0gmHYw6bM6xSsZSW5Kw4EZEtYOJERGSlpHVOhSVO/8T8AxEiapWthcoelU0ZmtXbHLkZE3ZOyLc9U5OJFze8iM2Rm80QVelZSsVJSty8Vd5mjYOIyBBMnIiIrJS0zung7YNIzUzN9zjXN+lHo9VgUtikPK3tnzU5bDI0Wo0Jo9KPpSROrDgRkS1g4kREZKVql6uNGmVqIFOTif1R+/M9zvVN+omIicCd5DuFPi5CRGxyLCJiIkwYlX6kezlZSnMIrnEiImvGxImIyEoJgoAetXoAyD9dLzUzFf/e/RcAK06lFZ8SL+t+5sSKExGRfJg4ERFZMWm63s7rO/O0JT8aexQaUYOqXlVRzbuaucKzSpU8Ksm6nznpKk5mbg6hW+PExImIrBgTJyIiK9Y5oDOcHZwRmxyLyw8u67ZzfZP+gqoGwc/TDwKEAh8XIMDf0x9BVYNMHFnpseJERCQfJk5ERFbM1dEVwQHBAPJO1+P6Jv0pFUrM6z4PAPIlT9L3c7vPtYobLFtaO3KpAkZEZI2YOBERWTnddL0bOYlTelY6jscdB8CKk74G1BuAjS9tRBXPKnm2+3n6YeNLGzGg3gAzRVY6rDgREcnHwdwBEBGRYXoG9sSbu97E4ZjDSMpIwtmEs8jUZMLX3Re1ytYyd3hWa0C9AehXpx8iYiIQnxKPSh6VEFQ1yCoqTRJLSJy0opaJExHZBCZORERWrkaZGqhTrg6uPrqKvbf24tKDSwByqk2CUPA6HSoZpUKJzgGdzR2G3iyhHXlqZiq0ohYAEycism6cqkdEZANyd9fj+iaSWELFSTq2SqmCi6OL2eIgIjIUEyciIhsg3c9py5UtusSpg38Hc4ZEFkBqDpGSmYJsbbZZYuA0PSKyFUyciIhswOP0xxAg4EnGE2RpswAAvdb2wubIzWaOjMzJS+Wl+3NSRpJZYpCmCTJxIiJrx8SJiMjKbY7cjCGbhkCEmGd7XHIcXtzwIpMnO+aodIS7kzsA803XY8WJiGwFEyciIium0WowKWxSvqQJgG7b5LDJ0Gg1pg6NLISUsJjrXk66ezi58B5ORGTdmDgREVmxiJgI3Em+U+jjIkTEJsciIibChFGRJTF3gwhWnIjIVjBxIiKyYvEp8bLuR7bH3C3JpUqXt8rbLMcnIpILEyciIitWyaOSrPuR7WHFiYhIHkyciIisWFDVIPh5+kFAwTe6FSDA39MfQVWDTBwZWQppbRHXOBERGYaJExGRFVMqlJjXfR4A5EuepO/ndp8LpUJp8tjIMkhT5FhxIiIyDBMnIiIrN6DeAGx8aSOqeFbJs93P0w8bX9qIAfUGmCkysgTmnqqnW+PExImIrJyDuQMgIiLDDag3AP3q9ENETATiU+JRyaMSgqoGsdJEFjNVj4kTEVk7Jk5ERDZCqVCic0Bnc4dBFsbcFSfdGidnrnEiIuvGqXpEREQ2zOztyNM5VY+IbAMTJyIiIhtmzopTtjYbKZkpeeIgIrJWTJyIiIhsmDnXOCWrk3V/ZuJERNaOiRMREZENy11xEkXRpMeWqlxujm5wVDqa9NhERHJj4kRERGTDpMQpU5OJjOwMkx6b65uIyJYwcSIiIrJhHk4eUAg5p3tTT9djK3IisiVMnIiIiGyYIAhmaxDBxImIbAkTJyIiIhtnrpbkuns4ufAeTkRk/Zg4ERER2ThzVZykqYGsOBGRLWDiREREZOPM1ZJcN1VP5W3S4xIRGQMTJyIiIhvHNU5ERIZj4kRERGTjpIqPuRInrnEiIlvAxImIiMjG6abqmbg5BNc4EZEtYeJERERk4zhVj4jIcEyciIiIbJyuHTlvgEtEpDcmTkRERDbO3BUnKXEjIrJmTJyIiIhsnLnakUtrqlhxIiJbwMSJiIjIxpmj4qTOViM9Oz3P8YmIrBkTJyIiIhtnjsQpSZ2k+7OnytNkxyUiMhYmTkRERDZOWmOUlJEErag1yTGlJM1L5QWlQmmSYxIRGRMTJyIiIhsnVZxEiEhWJ5vkmFzfRES2hokTERGRjVM5qODi4ALAdDfBZStyIrI1TJyIiIjsgKnXOTFxIiJbw8SJiIjIDpgrcZJaoRMRWTsmTkRERHbA1Pdyko7DihMR2QomTkRERHbAbFP1VN4mOR4RkbExcSIiIrIDUktyNocgItIPEyciIiI7wDVORESGYeJERERkB3QVJ65xIiLSCxMnIiIiO8B25EREhmHiREREZAeYOBERGYaJExERkR0wdTty3RonZ65xIiLbYPbEacGCBQgICICzszNat26NEydOFLl/YmIiJkyYgEqVKkGlUqF27drYuXOniaIlIiKyTqasOImiqOvex4oTEdkKB3MefP369ZgyZQoWL16M1q1bY+7cuQgNDcXVq1dRsWLFfPtnZmaia9euqFixIjZu3IgqVarg9u3b8Pb2Nn3wREREVsSU7cjTs9ORpc0CwMSJiGyHWROn77//Hq+++ipGjx4NAFi8eDF27NiB5cuX44MPPsi3//Lly/H48WMcOXIEjo6OAICAgABThkxERGSVTFlxko6hFJRwd3I3+vGIiEzBbFP1MjMzcerUKYSEhPwXjEKBkJAQHD16tMDnbNu2DW3btsWECRPg4+ODhg0bYtasWdBoNIUeR61WIzk5Oc8XERGRvZHWOKVnp0OdrTbqsXI3hhAEwajHIiIyFbMlTg8fPoRGo4GPj0+e7T4+PkhISCjwObdu3cLGjRuh0Wiwc+dOfPLJJ/juu+/wxRdfFHqc2bNnw8vLS/fl7+8v6+sgIiKyBp4qTwjISWKMXXXi+iYiskVmbw5RGlqtFhUrVsSSJUvQvHlzDB48GB999BEWL15c6HOmTZuGpKQk3VdsbKwJIyYiIrIMCkEBT5UnAOMnTmxFTkS2yGxrnMqXLw+lUol79+7l2X7v3j34+voW+JxKlSrB0dERSqVSt61evXpISEhAZmYmnJyc8j1HpVJBpVLJGzwREZEVKuNSBknqJKO3JGfiRES2yGwVJycnJzRv3hz79u3TbdNqtdi3bx/atm1b4HPat2+PGzduQKvV6rZdu3YNlSpVKjBpIiIiov+YqkGE7h5OLryHExHZDrNO1ZsyZQqWLl2KlStXIjIyEuPHj0daWpquy96IESMwbdo03f7jx4/H48ePMWnSJFy7dg07duzArFmzMGHCBHO9BCIiIqthqpbkUkXLW+Vt1OMQEZmSWduRDx48GA8ePMD06dORkJCApk2bIiwsTNcwIiYmBgrFf7mdv78/du/ejbfffhuNGzdGlSpVMGnSJLz//vvmeglERERWw9QVJ07VIyJbYtbECQAmTpyIiRMnFvhYeHh4vm1t27bFsWPHjBwVERGR7dFVnLjGiYio1Kyqqx4RERHpj2uciIj0x8SJiIjITpgqcdKtcWLFiYhsCBMnIiIiOyFVgDhVj4io9Jg4ERER2Qk2hyAi0h8TJyIiIjthqnbkujVOzlzjRES2g4kTERGRnTBFxUkrallxIiKbxMSJiIjITpgicUrNTIVW1OY5HhGRLWDiREREZCek5hCJGYkQRdEox5CSMielE5wdnI1yDCIic2DiREREZCekCpBG1CA1M9Uox8i9vkkQBKMcg4jIHJg4ERER2QkXBxc4KZ0AGK8ludR4gtP0iMjWMHEiIiKyE4IgGH2dExtDEJGtcijpjufPny/xoI0bN9YrGCIiIjKuMs5lcD/tvtFakjNxIiJbVeLEqWnTphAEodDFpNJjgiBAo9HIFiARERHJx1QVJ6kRBRGRrShx4hQVFWXMOIiIiMgEjJ04SWunvFXeRhmfiMhcSpw4VatWzZhxEBERkQlIlSBjNYfgVD0islUlTpy2bdtW4kH79u2rVzBERERkXFIliM0hiIhKp8SJU//+/Uu0H9c4ERERWS5dxcnIzSG4xomIbE2JEyetVmvMOIiIiMgEdGuc1IlGGV+3xokVJyKyMbyPExERkR0p42yaihMTJyKyNSWuOD0rLS0NBw8eRExMDDIzM/M89tZbbxkcGBEREcmPN8AlItKPXonTmTNn0LNnTzx9+hRpaWkoW7YsHj58CFdXV1SsWJGJExERkYUyejvy/69kSZUtIiJboddUvbfffht9+vTBkydP4OLigmPHjuH27dto3rw5vv32W7ljJCIiIpkYsx15tjYbKZkpAFhxIiLbo1fidPbsWbzzzjtQKBRQKpVQq9Xw9/fH119/jQ8//FDuGImIiEgmxqw4JauT8x2HiMhW6JU4OTo6QqHIeWrFihURExMDAPDy8kJsbKx80REREZGspCl0qZmpyNJkyTq2lIy5ObrBUeko69hEROam1xqnZs2a4eTJkwgMDESnTp0wffp0PHz4EKtXr0bDhg3ljpGIiIhk4uXspftzkjoJ5V3Lyza2tL6J1SYiskV6VZxmzZqFSpUqAQC+/PJLlClTBuPHj8eDBw/w888/yxogERERycdB4QAPJw8A8rckZ0c9IrJlelWcWrRooftzxYoVERYWJltAREREZFzezt5IyUyRfZ0TEycismV6VZyioqJw/fr1fNuvX7+O6OhoQ2MiIiIiIzJWgwhpPKlzHxGRLdErcRo1ahSOHDmSb/vx48cxatQoQ2MiIiIiIzJWS3JpPFaciMgW6ZU4nTlzBu3bt8+3vU2bNjh79qyhMREREZERGbvi5K3ylnVcIiJLoFfiJAgCUlJS8m1PSkqCRqMxOCgiIiIyHqklOZtDEBGVnF6JU8eOHTF79uw8SZJGo8Hs2bPRoUMH2YIjIiIi+XGNExFR6enVVW/OnDno2LEj6tSpg6CgIABAREQEkpOTsX//flkDJCIiInkZK3HiGicismV6VZzq16+P8+fP46WXXsL9+/eRkpKCESNG4MqVK7wBLhERkYXTTdWTuTkEp+oRkS3Tq+IEAJUrV8asWbPkjIWIiIhMwOjNIZg4EZEN0qviBORMzRs+fDjatWuHuLg4AMDq1atx+PBh2YIjIiIi+RmrHblujZMz1zgRke3RK3HatGkTQkND4eLigtOnT0OtVgPI6arHKhQREZFlM9oap3SucSIi26VX4vTFF19g8eLFWLp0KRwdHXXb27dvj9OnT8sWHBEREcnPGO3I1dlqpGenA2DiRES2Sa/E6erVq+jYsWO+7V5eXkhMTDQ0JiIiIjKi3BUnURRlGTNJnaT7s6fKU5YxiYgsiV6Jk6+vL27cuJFv++HDh1GjRg2DgyIiIiLjkRKnLG2WrkpkKGnan5fKC0qFUpYxiYgsiV6J06uvvopJkybh+PHjEAQBd+/exe+//4533nkH48ePlztGIiIikpG7kzuUQk5yI9d0Pa5vIiJbp1c78g8++ABarRZdunTB06dP0bFjR6hUKkydOhXjxo2TO0YiIiKSkSAI8Hb2xqP0R0jMSEQVzyoGj8lW5ERk6/SqOAmCgI8++giPHz/GxYsXcezYMTx48ABeXl6oXr263DESERGRzORuSc7EiYhsXakSJ7VajWnTpqFFixZo3749du7cifr16+PSpUuoU6cO5s2bh7fffttYsRIREZFM5G5JrruHkwvv4UREtqlUU/WmT5+On3/+GSEhIThy5AgGDRqE0aNH49ixY/juu+8waNAgKJVcEEpERGTp5G5JLlWuWHEiIltVqsTpjz/+wKpVq9C3b19cvHgRjRs3RnZ2Ns6dOwdBEIwVIxEREcnMWBUnb5W3LOMREVmaUk3Vu3PnDpo3bw4AaNiwIVQqFd5++20mTURERFbGaIkTK05EZKNKlThpNBo4OTnpvndwcIC7u7vsQREREZFx6abqydwcgmuciMhWlWqqniiKGDVqFFQqFQAgIyMD//vf/+Dm5pZnv82bN8sXIREREclO7ooT1zgRka0rVeI0cuTIPN8PHz5c1mCIiIjINNiOnIiodEqVOK1YscJYcRAREZEJcY0TEVHp6HUDXCIiIrJuUoIjVzty3RonZ65xIiLbxMSJiIjIDkkJjhwVJ1EUdQkYK05EZKuYOBEREdkhOafqpWenI0ublWdcIiJbw8SJiIjIDknNIZLUSdBoNQaNJSVfSkEJdyfepoSIbBMTJyIiIjuUuzKUrE42aKzcjSEEQTBoLCIiS8XEiYiIyA45KZ3g6ugKwPCW5FzfRET2gIkTERGRnZJrnRNbkRORPWDiREREZKeYOBERlZxFJE4LFixAQEAAnJ2d0bp1a5w4caJEz1u3bh0EQUD//v2NGyAREZENklqSG3ovJ909nFx4Dycisl1mT5zWr1+PKVOmYMaMGTh9+jSaNGmC0NBQ3L9/v8jnRUdH491330VQUJCJIiUiIrItclWcpDVS3ipvwwIiIrJgZk+cvv/+e7z66qsYPXo06tevj8WLF8PV1RXLly8v9DkajQbDhg3Dp59+iho1apgwWiIiItshVYgMbQ7BqXpEZA/MmjhlZmbi1KlTCAkJ0W1TKBQICQnB0aNHC33eZ599hooVK2Ls2LHFHkOtViM5OTnPFxEREf1XIeIaJyKi4pk1cXr48CE0Gg18fHzybPfx8UFCQkKBzzl8+DCWLVuGpUuXlugYs2fPhpeXl+7L39/f4LiJiIhsga7ixDVORETFMvtUvdJISUnBK6+8gqVLl6J8+fIles60adOQlJSk+4qNjTVylERERNZBt8ZJnWjQOLo1Tqw4EZENczDnwcuXLw+lUol79+7l2X7v3j34+vrm2//mzZuIjo5Gnz59dNu0Wi0AwMHBAVevXkXNmjXzPEelUkGlUhkheiIiIuvGduRERCVn1oqTk5MTmjdvjn379um2abVa7Nu3D23bts23f926dXHhwgWcPXtW99W3b18EBwfj7NmznIZHRERUCnK3I2fiRES2zKwVJwCYMmUKRo4ciRYtWqBVq1aYO3cu0tLSMHr0aADAiBEjUKVKFcyePRvOzs5o2LBhnud7e3sDQL7tREREVDS5K05SIkZEZIvMnjgNHjwYDx48wPTp05GQkICmTZsiLCxM1zAiJiYGCoVVLcUiIiKyCnK0I9eKWlaciMguCKIoiuYOwpSSk5Ph5eWFpKQkeHp6mjscIiIis4lOjEb1edXh7OCM9I/S9RojWZ0Mr6+8AABPP3wKF0cXOUMkIjKq0uQGLOUQERHZKWlqXUZ2BjKyM/QaQ6o2OSmd4OzgLFdoREQWh4kTERGRnfJQeUCAAED/dU651zcJgiBTZEREloeJExERkZ1SCAp4OedMs9M3cZI68nF9ExHZOiZOREREdszQluRsDEFE9oKJExERkR0ztCU5EycishdMnIiIiOyYoS3JdWucXHgPJyKybUyciIiI7JihFScp4fJWecsTEBGRhWLiREREZMekhIdrnIiIisbEiYiIyI5JU+y4xomIqGhMnIiIiOyYXM0huMaJiGwdEyciIiI7pmtHrmdzCN0aJ1aciMjGMXEiIiKyY2xHTkRUMkyciIiI7Jhc7ciZOBGRrWPiREREZMcMbkf+/934pCl/RES2iokTERGRHTMkccrWZiMlMyXPOEREtoqJExERkR2TKkWJGYnQitpSPTdZnaz7s5ezl6xxERFZGiZOREREdkyqFGlFLVIzU0v1XKlK5eroCielk8yRERFZFiZOREREdszF0QUqpQrAf+uVSorrm4jInjBxIiIisnP6rnNiRz0isidMnIiIiOycvi3JmTgRkT1h4kRERGTnWHEiIioeEyciIiI7p2/iJFWopIoVEZEtY+JERERk56TmDqVtDqGrOKm8ZY6IiMjyMHEiIiKyc5yqR0RUPCZOREREdk5XcWJzCCKiQjFxIiIisnNc40REVDwmTkRERHaO7ciJiIrHxImIiMjOcY0TEVHxmDgRERHZOSZORETFY+JERERk5/RtRy7tLz2fiMiWMXEiIiKyc/pUnNTZaqRnp+d5PhGRLWPiREREZOek5hBpWWnI0mSV6DlJ6iTdnz1VnkaJi4jIkjBxIiIisnO5E5+SVp2k/TxVnlAqlEaIiojIsjBxIiIisnMOCgd4OHkAKHlLcq5vIiJ7w8SJiIiIdNP1Sltx4vomIrIXTJyIiIio1A0imDgRkb1h4kRERESlbknOxImI7A0TJyIiIip1xUlaCyVN8SMisnVMnIiIiEiXAJW0OYSu4qTyNlJERESWhYkTERER6RIgrnEiIioYEyciIiLSJUBc40REVDAmTkRERPRfO3J1Yon25xonIrI3TJyIiIiI7ciJiIrBxImIiIjYjpyIqBhMnIiIiIgVJyKiYjBxIiIiolK1IxdFUVeZkipVRES2jokTERER5ak4iaJY5L7p2enI0mbleR4Rka1j4kRERES6BChbm42nWU+L3FeapqcQFHB3cjdyZEREloGJExEREcHN0Q0OCgcAxU/Xy72+SRAEY4dGRGQRmDgRERERBEEocYMIrm8iInvExImIiIgAlLwlOTvqEZE9YuJEREREAErekpyJExHZIyZOREREBKDkLcmZOBGRPWLiRERERABKXnGSEiuucSIie8LEiYiIiAAA3ipvAJyqR0RUECZOREREBCDXVD02hyAiyoeJExEREQHINVVPnVjkfkyciMgeMXEiIiIiACVvR65b4+TCNU5EZD+YOBEREREAtiMnIiqKRSROCxYsQEBAAJydndG6dWucOHGi0H2XLl2KoKAglClTBmXKlEFISEiR+xMREVHJSIkQ25ETEeVn9sRp/fr1mDJlCmbMmIHTp0+jSZMmCA0Nxf379wvcPzw8HEOGDMGBAwdw9OhR+Pv7o1u3boiLizNx5ERERLZFmnrHihMRUX5mT5y+//57vPrqqxg9ejTq16+PxYsXw9XVFcuXLy9w/99//x1vvPEGmjZtirp16+KXX36BVqvFvn37TBw5ERGRbSnJVD2tqNU9zvs4EZE9MWvilJmZiVOnTiEkJES3TaFQICQkBEePHi3RGE+fPkVWVhbKli1b4ONqtRrJycl5voiIiCg/KRFKVidDo9UUuE9qZiq0ohYAK05EZF/Mmjg9fPgQGo0GPj4+ebb7+PggISGhRGO8//77qFy5cp7kK7fZs2fDy8tL9+Xv729w3ERERLYodyKUpE4qcB+p2uSkdIKzg7MJoiIisgxmn6pniK+++grr1q3Dli1b4Oxc8Jv3tGnTkJSUpPuKjY01cZRERETWwVHpCDdHNwCFtyTPvb5JEARThUZEZHYO5jx4+fLloVQqce/evTzb7927B19f3yKf++233+Krr77C3r170bhx40L3U6lUUKlUssRLRERk67ydvZGWlVboOicpoeL6JiKyN2atODk5OaF58+Z5GjtIjR7atm1b6PO+/vprfP755wgLC0OLFi1MESoREZFdKK4lOTvqEZG9MmvFCQCmTJmCkSNHokWLFmjVqhXmzp2LtLQ0jB49GgAwYsQIVKlSBbNnzwYAzJkzB9OnT8eaNWsQEBCgWwvl7u4Od3d3s70OIiIiW1BcS3ImTkRkr8yeOA0ePBgPHjzA9OnTkZCQgKZNmyIsLEzXMCImJgYKxX+FsUWLFiEzMxMvvvhinnFmzJiBmTNnmjJ0IiIim1NcS3ImTkRkr8yeOAHAxIkTMXHixAIfCw8Pz/N9dHS08QMiIiKyU9LapcKaQ0hT+LjGiYjsjVV31SMiIiJ5seJERFQwJk5ERESko6s4sTkEEVEeTJyIiIhIhxUnIqKCMXEiIiIineISJ90aJxeucSIi+8LEiYiIiHSkhIhT9YiI8mLiRERERDqcqkdEVDAmTkRERKRTbDvy/9/OxImI7A0TJyIiItIpquKUrc1GSmYKAN7HiYjsDxMnIiIi0pHWOKk1aqRnped5LFmdrPuzl7OXSeMiIjI3Jk5ERESk4+7kDoWQc3nwbNVJ+t7V0RVOSicTR0ZEZF5MnIiIiEhHISjgpcqpJj2bOHF9ExHZMyZORERElEdhLcmlRIrrm4jIHjFxIiIiojwKaxDBVuREZM+YOBEREVEehbUkZ+JERPaMiRMRERHlUVjFSZq6x8SJiOwREyciIiLKQ0qMuMaJiOg/TJyIiIgoDykx4honIqL/MHEiIiKiPNgcgogoPyZORERElEdh7ci5xomI7BkTJyIiIsqjuIqTlFgREdkTJk5ERESUB9uRExHlx8SJiIiI8uAaJyKi/Jg4ERERUR6FtSOXKlBMnIjIHjFxIiIiojykNUxJGUnQiloAgDpbjfTs9JzHeR8nIrJDTJyIiIgoD6miJEJEijoFAJCkTtI97qnyNEdYRERmxcSJiIiI8nB2cIazgzOA/6brSeubPFWeUCqU5gqNiMhsmDgRERFRPs82iOD6JiKyd0yciIiIKJ9nW5Lr7uHE9U1EZKeYOBEREVE+z1ac2IqciOwdEyciIiLKh4kTEVFeTJyIiIgoH6kludQcQvo/EycisldMnIiIiCgfb5U3gPwVJ65xIiJ7xcSJiIiI8tFVnJ5pDsGKExHZKyZORERElI9ujZM6Mef/TJyIyM4xcSIiIqJ8pARJqjhJa5ykShQRkb1h4kRERET5SGuZ2FWPiCgHEyciIiLKh+3IiYjyYuJERERE+TzbjpyJExHZOyZORERElE/uipMoirq1TmxHTkT2iokTERER5SMlSE+zniJJnYQsbRYAVpyIyH4xcSIiIqJ8PFWeuj9HJ0YDABSCAu5O7maKiIjIvJg4ERERUT5KhVKXPEU9iQKQU20SBMGcYRERmQ0TJyIiIiqQNF0vKjEqz/dERPaIiRMREREVSFrPJE3V4/omIrJnTJyIiIioQFJLcqnixMSJiOwZEyciIiIqECtORET/YeJEREREBdKtcXrCNU5EREyciIiIqEBShSktKy3P90RE9oiJExERERXo2USJiRMR2TMmTkRERFSgZ6fmMXEiInvGxImIiIgK9GyiJHXZIyKyR0yciIiIqEDPJkqsOBGRPWPiRERERAXiGiciov8wcSIiIqICcY0TEdF/mDgRERFRgfKtceJ9nIjIjjFxIiIiogJxqh4R0X+YOBEREVGBXB1d4ahwBAA4KZ3g7OBs5oiIiMyHiRMREREVSBAEXZXJ29kbgiCYNyAiIjNi4kRERESFkhInpaBEeHQ4NFqNeQMiIjITi0icFixYgICAADg7O6N169Y4ceJEkfv/8ccfqFu3LpydndGoUSPs3LnTRJESERHZj82Rm3E78TYAID41HsErgxEwLwCbIzebOTIiItMze+K0fv16TJkyBTNmzMDp06fRpEkThIaG4v79+wXuf+TIEQwZMgRjx47FmTNn0L9/f/Tv3x8XL140ceRERES2a3PkZry44UVkajPzbI9LjsOLG15k8kREdkcQRVE0ZwCtW7dGy5YtMX/+fACAVquFv78/3nzzTXzwwQf59h88eDDS0tKwfft23bY2bdqgadOmWLx4cbHHS05OhpeXF5KSkuDp6SnfCyEiIrIRGq0GAfMCcCf5ToGPCxDg5+mHqElRUCqUJo6OiEg+pckNzFpxyszMxKlTpxASEqLbplAoEBISgqNHjxb4nKNHj+bZHwBCQ0ML3V+tViM5OTnPFxERERUuIiai0KQJAESIiE2ORURMhAmjIiIyL7MmTg8fPoRGo4GPj0+e7T4+PkhISCjwOQkJCaXaf/bs2fDy8tJ9+fv7yxM8ERGRjYpPiZd1PyIiW2D2NU7GNm3aNCQlJem+YmNjzR0SERGRRavkUUnW/YiIbIGDOQ9evnx5KJVK3Lt3L8/2e/fuwdfXt8Dn+Pr6lmp/lUoFlUolT8BERER2IKhqEPw8/RCXHAcR+ZdCS2ucgqoGmSE6IiLzMGvFycnJCc2bN8e+fft027RaLfbt24e2bdsW+Jy2bdvm2R8A9uzZU+j+REREVDpKhRLzus8DkJMk5SZ9P7f7XDaGICK7YvapelOmTMHSpUuxcuVKREZGYvz48UhLS8Po0aMBACNGjMC0adN0+0+aNAlhYWH47rvvcOXKFcycORP//vsvJk6caK6XQEREZHMG1BuAjS9tRBXPKnm2+3n6YeNLGzGg3gAzRUZEZB5mnaoH5LQXf/DgAaZPn46EhAQ0bdoUYWFhugYQMTExUCj+y+/atWuHNWvW4OOPP8aHH36IwMBAbN26FQ0bNjTXSyAiIrJJA+oNQL86/RARE4H4lHhU8qiEoKpBrDQRkV0y+32cTI33cSIiIiIiIsCK7uNERERERERkDZg4ERERERERFYOJExERERERUTGYOBERERERERWDiRMREREREVExmDgREREREREVg4kTERERERFRMZg4ERERERERFYOJExERERERUTGYOBERERERERXDwdwBmJooigCA5ORkM0dCRERERETmJOUEUo5QFLtLnFJSUgAA/v7+Zo6EiIiIiIgsQUpKCry8vIrcRxBLkl7ZEK1Wi7t378LDwwOCIJg7HCQnJ8Pf3x+xsbHw9PS0qzGNNa61jGmsca1lTGONay1jGmtcaxnTWONay5jGGtfeY+Xr5+vn67ff168vURSRkpKCypUrQ6EoehWT3VWcFAoF/Pz8zB1GPp6enrL/4FjLmMYa11rGNNa41jKmsca1ljGNNa61jGmsca1lTGONa++x8vXz9fP12+/r10dxlSYJm0MQEREREREVg4kTERERERFRMZg4mZlKpcKMGTOgUqnsbkxjjWstYxprXGsZ01jjWsuYxhrXWsY01rjWMqaxxrX3WPn6+fr5+u339ZuC3TWHICIiIiIiKi1WnIiIiIiIiIrBxImIiIiIiKgYTJyIiIiIiIiKwcSJiIiIiIioGEyczOTQoUPo06cPKleuDEEQsHXrVoPGmz17Nlq2bAkPDw9UrFgR/fv3x9WrVw2Oc9GiRWjcuLHuBmVt27bFrl27DB43t6+++gqCIGDy5MkGjTNz5kwIgpDnq27dugbHFxcXh+HDh6NcuXJwcXFBo0aN8O+//+o9XkBAQL44BUHAhAkTDIpTo9Hgk08+QfXq1eHi4oKaNWvi888/h6H9X1JSUjB58mRUq1YNLi4uaNeuHU6ePGnQmABw9OhRKJVK9OrVy+CxAGDUqFF5/j7LlSuH7t274/z58waNm5CQgDfffBM1atSASqWCv78/+vTpg3379hkcp6OjI3x8fNC1a1csX74cWq1W7zifff3SV/fu3fUes6hxb9y4ofeYCQkJmDRpEmrVqgVnZ2f4+Pigffv2WLRoEZ4+fapXjP3798+3PTw8HIIgIDExUe9YizuGpYxZ0FgbN26Es7MzvvvuO1nHNWQsQRDwv//9L99jEyZMgCAIGDVqlN7jfvXVV3m2b926FYIg6BtunrEFQYCTkxNq1aqFzz77DNnZ2XqPGRsbizFjxqBy5cpwcnJCtWrVMGnSJDx69EiWOB0dHVG9enW89957yMjI0HtMAHjw4AHGjx+PqlWrQqVSwdfXF6Ghofjnn3/0Gq+g95LcXzNnziz1mJ07dy7w+uHXX3+Ft7e3XnH26dOn0PfOiIgICIJQqnPL4sWL4eHhkefnJjU1FY6OjujcuXOefaX3rZs3b5ZobI1Gg3bt2mHAgAF5ticlJcHf3x8fffRRieOUiKKIkJAQhIaG5nts4cKF8Pb2xp07d0o9rvTaCvsKDg4u9ZimxsTJTNLS0tCkSRMsWLBAlvEOHjyICRMm4NixY9izZw+ysrLQrVs3pKWlGTSun58fvvrqK5w6dQr//vsvnn/+efTr1w+XLl2SJe6TJ0/i559/RuPGjWUZr0GDBoiPj9d9HT582KDxnjx5gvbt28PR0RG7du3C5cuX8d1336FMmTJ6j3ny5Mk8Me7ZswcAMGjQIINinTNnDhYtWoT58+cjMjISc+bMwddff42ffvrJoHHHjRuHPXv2YPXq1bhw4QK6deuGkJAQxMXFGTTusmXL8Oabb+LQoUO4e/euQWNJunfvrvt73bdvHxwcHNC7d2+9x4uOjkbz5s2xf/9+fPPNN7hw4QLCwsIQHBxsUKIrxRkdHY1du3YhODgYkyZNQu/evQ26IMv9+qWvtWvX6j1eUeNWr15dr7Fu3bqFZs2a4e+//8asWbNw5swZHD16FO+99x62b9+OvXv3GhwvAb/88guGDRuGRYsW4Z133jF3ODr+/v5Yt24d0tPTddsyMjKwZs0aVK1aVe9xnZ2dMWfOHDx58kSOMPOQfv6vX7+Od955BzNnzsQ333yj11i3bt1CixYtcP36daxduxY3btzA4sWLsW/fPrRt2xaPHz82OM5bt27hhx9+wM8//4wZM2boPR4ADBw4EGfOnMHKlStx7do1bNu2DZ07d9Y7ycv9HjJ37lx4enrm2fbuu+8aFK9cxo4diz179hSYHKxYsQItWrQo1XVLcHAwUlNT83zoGhERAV9fXxw/fjxPgnvgwAFUrVoVNWvWLNHYSqUSv/76K8LCwvD777/rtr/55psoW7asXj8DgiBgxYoVOH78OH7++Wfd9qioKLz33nv46aef4OfnV+px27Vrl+9cEh8fj59//hmCIOCNN94o9ZgmJ5LZARC3bNki65j3798XAYgHDx6UdVxRFMUyZcqIv/zyi8HjpKSkiIGBgeKePXvETp06iZMmTTJovBkzZohNmjQxOK7c3n//fbFDhw6yjvmsSZMmiTVr1hS1Wq1B4/Tq1UscM2ZMnm0DBgwQhw0bpveYT58+FZVKpbh9+/Y825977jnxo48+0nvclJQU0d3dXbxy5Yo4ePBg8csvv9R7LMnIkSPFfv365dkWEREhAhDv37+v15g9evQQq1SpIqampuZ77MmTJ3qNWVCcoiiK+/btEwGIS5culXVcQ8k9bmhoqOjn51fg36koinr9HhQW44EDB0QAev9bleQYljJm7rHmzJkjOjs7i5s3b5Z1XLnGatiwofjbb7/ptv/+++9i48aNxX79+okjR47Ua9zevXuLdevWFadOnarbvmXLFtHQy5yCXn/Xrl3FNm3a6DVe9+7dRT8/P/Hp06d5tsfHx4uurq7i//73P9niHDBggNisWTO9xhPFnPc4AGJ4eLjeYxRlxYoVopeXl8HjFHb9YMj4WVlZoo+Pj/j555/n2S6duxYtWlTqMStVqiTOnj1b9/17770nTpgwQaxXr5544MAB3faOHTvq9Xswb948sUyZMuLdu3fFrVu3io6OjuLZs2dLPU5uv/76q+ju7i7eunVL1Gq1YnBwsPjCCy8YNOazLl++LHp4eBh0TWFKrDjZqKSkJABA2bJlZRtTo9Fg3bp1SEtLQ9u2bQ0eb8KECejVqxdCQkJkiC7H9evXUblyZdSoUQPDhg1DTEyMQeNt27YNLVq0wKBBg1CxYkU0a9YMS5culSlaIDMzE7/99hvGjBlj8JSSdu3aYd++fbh27RoA4Ny5czh8+DB69Oih95jZ2dnQaDRwdnbOs93FxcWgat6GDRtQt25d1KlTB8OHD8fy5csNnlL4rNTUVPz222+oVasWypUrV+rnP378GGFhYZgwYQLc3NzyPa7vFJDCPP/882jSpAk2b94s67iW5NGjR/j7778L/TsFYPDvgb17//338fnnn2P79u144YUXzB1OgcaMGYMVK1bovl++fDlGjx5t0JhKpRKzZs3CTz/9pNcUotJwcXFBZmZmqZ/3+PFj7N69G2+88QZcXFzyPObr64thw4Zh/fr1srwXXrx4EUeOHIGTk5PeY7i7u8Pd3R1bt26FWq02OCZr4uDggBEjRuDXX3/N8+/xxx9/QKPRYMiQIaUeMzg4GAcOHNB9f+DAAXTu3BmdOnXSbU9PT8fx48f1mrL25ptvokmTJnjllVfw2muvYfr06WjSpEmpx8lt5MiR6NKlC8aMGYP58+fj4sWLeSpQhkpMTES/fv3QuXNnfP7557KNa0xMnGyQVqvF5MmT0b59ezRs2NDg8S5cuAB3d3eoVCr873//w5YtW1C/fn2Dxly3bh1Onz6N2bNnGxyfpHXr1rpy9aJFixAVFYWgoCCkpKToPeatW7ewaNEiBAYGYvfu3Rg/fjzeeustrFy5UpaYt27disTERL3m9T/rgw8+wMsvv4y6devC0dERzZo1w+TJkzFs2DC9x/Tw8EDbtm3x+eef4+7du9BoNPjtt99w9OhRxMfH6z3usmXLMHz4cAA500uSkpJw8OBBvceTbN++XXey9/DwwLZt27B+/XooFKV/q7tx4wZEUZRlnVxJ1a1bF9HR0Xo/P/frl75mzZplcFzPjqvvtFLp77ROnTp5tpcvX1439vvvvy9LjO7u7gZ9aGCNdu3aha+//hp//vknunTpYu5wCjV8+HAcPnwYt2/fxu3bt/HPP//o3g8M8cILL6Bp06YGT08rjCiK2Lt3L3bv3o3nn3++1M+/fv06RFFEvXr1Cny8Xr16ePLkCR48eKBXfNLvgLOzMxo1aoT79+9j6tSpeo0F5CQPv/76K1auXAlvb2+0b98eH374ocHrRq3FmDFjcPPmzTznphUrVmDgwIHw8vIq9XjBwcH4559/kJ2djZSUFJw5cwadOnVCx44dER4eDiBn7a9ardYrcRIEAYsWLcK+ffvg4+ODDz74oNRjFGTJkiW4ePEiJk+ejCVLlqBChQqyjKvVajF06FA4ODjg999/t5oPzRzMHQDJb8KECbh48aLB63skderUwdmzZ5GUlISNGzdi5MiROHjwoN7JU2xsLCZNmoQ9e/bkq2QYIvdFUuPGjdG6dWtUq1YNGzZswNixY/UaU6vVokWLFrqLz2bNmuHixYtYvHgxRo4caXDMy5YtQ48ePVC5cmWDx9qwYQN+//13rFmzBg0aNMDZs2cxefJkVK5c2aBYV69ejTFjxqBKlSpQKpV47rnnMGTIEJw6dUqv8a5evYoTJ05gy5YtAHJOzoMHD8ayZcvyLZItreDgYCxatAhAzvq0hQsXokePHjhx4gSqVatWqrHkroCV9JiGnDxyv36JHFXnZ8ctrFqkrxMnTkCr1WLYsGF6f7Jd0Gs/fvy4LBfk1qJx48Z4+PAhZsyYgVatWsHd3d3cIRWoQoUK6NWrl+7T/F69eqF8+fKyjD1nzhw8//zzsq6VkRKSrKws3cWePk0MJMW9t+hbJZJ+B9LS0vDDDz/AwcEBAwcO1GssycCBA9GrVy9ERETg2LFjuuT8l19+keUDP0tWt25dtGvXDsuXL0fnzp1x48YNRERE4LPPPtNrvM6dOyMtLQ0nT57EkydPULt2bVSoUAGdOnXC6NGjkZGRgfDwcNSoUUPv9X7Lly+Hq6sroqKicOfOHQQEBOg1Tm4VK1bE66+/jq1bt8raIOfDDz/E0aNHceLECXh4eMg2rrGx4mRjJk6ciO3bt+PAgQN6LdwriNRJqHnz5pg9ezaaNGmCefPm6T3eqVOncP/+fTz33HNwcHCAg4MDDh48iB9//BEODg7QaDSyxO3t7Y3atWsb1P2rUqVK+RLEevXqGTwFEABu376NvXv3Yty4cQaPBQBTp07VVZ0aNWqEV155BW+//bbBVb2aNWvi4MGDSE1NRWxsLE6cOIGsrCzUqFFDr/GWLVuG7OxsVK5cWffvv2jRImzatEk3xVRfbm5uqFWrFmrVqoWWLVvil19+QVpaml7TKwMDAyEIAq5cuWJQTKURGRmpd9MFIO/rl77kSJyeHbdSpUp6jVOrVi0IgpCv42eNGjVQq1atfNOXDImxVq1aqFKlit7jWaMqVaogPDwccXFx6N69u0HVdmMbM2aMrpoxZswY2cbt2LEjQkNDMW3aNNnGDA4OxtmzZ3H9+nWkp6dj5cqVen14IP38R0ZGFvh4ZGQkKlSooPc0YOl3oEmTJli+fDmOHz+OZcuW6TVWbs7OzujatSs++eQTHDlyBKNGjTJaVU8fnp6eBZ47EhMT9aoM5TZ27Fhs2rQJKSkpWLFiBWrWrIlOnTrpNVatWrXg5+eHAwcO4MCBA7pxKleuDH9/fxw5cgQHDhzQq5oJAEeOHMEPP/yA7du3o1WrVhg7dqxsHwBK52q5rFu3Dt9++y3WrVuHwMBA2cY1BSZONkIURUycOBFbtmzB/v37Dbr4Ko5WqzVovnOXLl1w4cIFnD17VvfVokULDBs2DGfPnoVSqZQlztTUVNy8eVPvizwAaN++fb6LvGvXrpW6elGQFStWoGLFirK143769Gm+KWlKpdKgFte5ubm5oVKlSnjy5Al2796Nfv36lXqM7OxsrFq1Ct99912ef/9z586hcuXKsnSAy00QBCgUijwdvEqqbNmyCA0NxYIFCwrsTilHi+vc9u/fjwsXLhj8CbElK1euHLp27Yr58+cb3PGTClatWjUcPHgQCQkJFp08de/eHZmZmcjKyiqw5bEhvvrqK/z11184evSoLONJCUnVqlUNuniUfv4XLlyY7z0pISEBv//+u2xVHIVCgQ8//BAff/yxXu9/Ralfv75F/f7WqVMHp0+fzrf99OnTqF27tkFjv/TSS1AoFFizZg1WrVpl8Hrk4OBghIeHIzw8PM8Mi44dO2LXrl04ceKEXtP0nj59ilGjRmH8+PEIDg7GsmXLcOLECSxevFjvWI3l7NmzGDt2LL766ivZf/dNgVP1zCQ1NTVPJSQqKgpnz55F2bJl9SrRTpgwAWvWrMGff/4JDw8PJCQkAAC8vLwM+hR32rRp6NGjB6pWrYqUlBSsWbMG4eHh2L17t95jenh45Ft75ebmhnLlyhm0Juvdd99Fnz59UK1aNdy9exczZsyAUqnUaxGn5O2330a7du0wa9YsvPTSSzhx4gSWLFmCJUuW6D0mkJN8rlixAiNHjpTtU5w+ffrgyy+/RNWqVdGgQQOcOXMG33//vcGf5u7evVu3LuXGjRuYOnUq6tatq9di7u3bt+PJkycYO3Zsvk8CBw4ciGXLlhV4j5eSUqvVup/9J0+eYP78+UhNTUWfPn30Gm/BggVo3749WrVqhc8++wyNGzdGdnY29uzZg0WLFhX6yXFJ49RoNLh37x7CwsIwe/Zs9O7dGyNGjNBrzNzj5ubg4CDbNCg5LFy4EO3bt0eLFi0wc+ZMNG7cGAqFAidPnsSVK1fQvHlzc4do9fz9/REeHo7g4GCEhoYiLCwMnp6eeo+XlJSEs2fP5tlWrlw5+Pv76z2mUqnU/f7I9WGZpFGjRhg2bBh+/PFHWceVw/z589GuXTuEhobiiy++QPXq1XHp0iVMnToVtWvXxvTp02U71qBBgzB16lQsWLBAr6mLjx49wqBBgzBmzBg0btwYHh4e+Pfff/H111/r9cGZsYwfPx7z58/HW2+9hXHjxkGlUmHHjh1Yu3Yt/vrrL4PGdnd3x+DBgzFt2jQkJycbnNhKt7LIysrKU7nq1KkTJk6ciMzMTL0Sp2nTpkEURd29zAICAvDtt9/i3XffRY8ePWSZsieHhw8fon///ujcuTOGDx+e73ylVCplW0NlNGbo5Efif21yn/3SpwWlKIoFjgVAXLFihUFxjhkzRqxWrZro5OQkVqhQQezSpYv4999/GzRmQeRoRz548GCxUqVKopOTk1ilShVx8ODB4o0bNwyO7a+//hIbNmwoqlQqsW7duuKSJUsMHnP37t0iAPHq1asGjyVJTk4WJ02aJFatWlV0dnYWa9SoIX700UeiWq02aNz169eLNWrUEJ2cnERfX19xwoQJYmJiol5j9e7dW+zZs2eBjx0/flwEIJ47d06vsUeOHJnnZ9/Dw0Ns2bKluHHjRr3Gk9y9e1ecMGGC7vegSpUqYt++ffO0j9U3TgcHB7FChQpiSEiIuHz5clGj0egd57OvX/qqU6eO3mNK48rdhvvu3bvixIkTxerVq4uOjo6iu7u72KpVK/Gbb74R09LSZIvR0tuRv/LKK+LAgQNlGaug+O7cuSMGBgaKbdq0EZOSkvQet6Cfq7Fjx8oSY26GtCN/dtyoqCjRycnJKO3IDRUVFSWOHDlS9PHxEQVBEAGIAwYM0OtnX1JYnLNnzxYrVKhQaPv/omRkZIgffPCB+Nxzz4leXl6iq6urWKdOHfHjjz/O105dH3K1IxdFUTxx4oTYtWtXsUKFCqKXl5fYunVr2W7zcuTIERFAoeeu0oiKihIBiHXr1s2zPTo6Wu/36/DwcFGpVIoRERH5HuvWrZv4/PPPG3y7E7lu9/Lrr78Wer0KQKxWrZrBxzA2QRTNsAKaiIjIznXv3h21atXC/PnzzR0KmdGMGTPw/fffY8+ePWjTpo25wyGiInCqHhERkQk9efIE//zzD8LDww2amkq24dNPP0VAQACOHTuGVq1a6XX7BCIyDVaciIiITOiFF17AyZMnMXLkSHzxxRdWc/8SIiJ7x8SJiIiIiIioGKwHExERERERFYOJExERERERUTGYOBERERERERWDiRMREREREVExmDgREZHNEgQBW7duNXcYRERkA5g4ERGR1UpISMCbb76JGjVqQKVSwd/fH3369MG+ffvMHRoREdkY3gCXiIisUnR0NNq3bw9vb2988803aNSoEbKysrB7925MmDABV65cMXeIRERkQ1hxIiIiq/TGG29AEAScOHECAwcORO3atdGgQQNMmTIFx44dK/A577//PmrXrg1XV1fUqFEDn3zyCbKysnSPnzt3DsHBwfDw8ICnpyeaN2+Of//9FwBw+/Zt9OnTB2XKlIGbmxsaNGiAnTt3muS1EhGR+bHiREREVufx48cICwvDl19+CTc3t3yPe3t7F/g8Dw8P/Prrr6hcuTIuXLiAV199FR4eHnjvvfcAAMOGDUOzZs2waNEiKJVKnD17Fo6OjgCACRMmIDMzE4cOHYKbmxsuX74Md3d3o71GIiKyLEyciIjI6ty4cQOiKKJu3bqlet7HH3+s+3NAQADeffddrFu3Tpc4xcTEYOrUqbpxAwMDdfvHxMRg4MCBaNSoEQCgRo0ahr4MIiKyIpyqR0REVkcURb2et379erRv3x6+vr5wd3fHxx9/jJiYGN3jU6ZMwbhx4xASEoKvvvoKN2/e1D321ltv4YsvvkD79u0xY8YMnD9/3uDXQURE1oOJExERWZ3AwEAIglCqBhBHjx7FsGHD0LNnT2zfvh1nzpzBRx99hMzMTN0+M2fOxKVLl9CrVy/s378f9evXx5YtWwAA48aNw61bt/DKK6/gwoULaNGiBX766SfZXxsREVkmQdT3YzsiIiIz6tGjBy5cuICrV6/mW+eUmJgIb29vCIKALVu2oH///vjuu++wcOHCPFWkcePGYePGjUhMTCzwGEOGDEFaWhq2bduW77Fp06Zhx44drDwREdkJVpyIiMgqLViwABqNBq1atcKmTZtw/fp1REZG4scff0Tbtm3z7R8YGIiYmBisW7cON2/exI8//qirJgFAeno6Jk6ciPDwcNy+fRv//PMPTp48iXr16gEAJk+ejN27dyMqKgqnT5/GgQMHdI8REZHtY3MIIiKySjVq1MDp06fx5Zdf4p133kF8fDwqVKiA5s2bY9GiRfn279u3L95++21MnDgRarUavXr1wieffIKZM2cCAJRKJR49eoQRI0bg3r17KF++PAYMGIBPP/0UAKDRaDBhwgTcuXMHnp6e6N69O3744QdTvmQiIjIjTtUjIiIiIiIqBqfqERERERERFYOJExERERERUTGYOBERERERERWDiRMREREREVExmDgREREREREVg4kTERERERFRMZg4ERERERERFYOJExERERERUTGYOBERERERERWDiRMREREREVExmDgREREREREVg4kTERERERFRMf4PQzPH9qSh/OsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot accuracy for each class\n",
    "df = pd.DataFrame(test_score).transpose()       # convert the dictionary to a dataframe\n",
    "print(df[-3:])\n",
    "df = df[:-3]                                    # remove accuracy, macro avg, weighted avg\n",
    "df['class'] = df.index                          # add a column for class name\n",
    "# convert class number to character\n",
    "df['class'] = df['class'].apply(lambda x: chr(int(float(x))))\n",
    "\n",
    "plt.figure(figsize = (10, 5))\n",
    "plt.plot(df['class'], df['precision'], label = 'precision', color = 'red', marker = 'o')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Precision')\n",
    "plt.show()\n",
    "plt.figure(figsize = (10, 5))\n",
    "plt.plot(df['class'], df['recall'], label = 'recall', color = 'green', marker = 'o')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Recall')\n",
    "plt.show()\n",
    "plt.figure(figsize = (10, 5))\n",
    "plt.plot(df['class'], df['f1-score'], label = 'f1-score', color = 'blue', marker = 'o')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('F1-score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## TEST MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "DATASET = os.listdir(os.getcwd() + '/samples/')    # get the list of images in the samples folder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select 5 random images from dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random                                  # used for generating random numbers\n",
    "\n",
    "images = random.sample(DATASET, 5)             # select 5 random images from the dataset\n",
    "image_list = []                                # create an empty list to store the images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read all images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in images:                               # for each image in the list\n",
    "    img = cv2.imread(os.getcwd() + '/samples/' + i)     # read the image\n",
    "    image_list.append(img)                              # append the image to the list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate Height and Width of Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50 180\n"
     ]
    }
   ],
   "source": [
    "IMAGE_HEIGHT = image_list[0].shape[0]           # get the height of the image\n",
    "IMAGE_WIDTH = image_list[0].shape[1]            # get the width of the image\n",
    "print(IMAGE_HEIGHT, IMAGE_WIDTH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preprocess Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(img):\n",
    "    # convert to grayscale\n",
    "    imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    # get binary image\n",
    "    thresh = cv2.adaptiveThreshold( imgGray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 0)\n",
    "\n",
    "    # remove noise from image\n",
    "    close = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8))\n",
    "\n",
    "    # get dilated image\n",
    "    dilate = cv2.dilate(close, np.ones((2, 2), np.uint8), iterations=1)\n",
    "\n",
    "    # invert image colors\n",
    "    image = cv2.bitwise_not(dilate)\n",
    "\n",
    "    # display image\n",
    "    plt.imshow(image, cmap='gray')\n",
    "    plt.show()\n",
    "\n",
    "    return image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(img):\n",
    "    image = preprocess(img)                  # preprocess the image\n",
    "    pred = []                                # create an empty list to store the predictions\n",
    "    for i in range(0, IMAGE_WIDTH, IMAGE_WIDTH//5):\n",
    "        # get character from captcha\n",
    "        letter = image[10:IMAGE_HEIGHT-10, i:i+IMAGE_WIDTH//5]\n",
    "        # resize image to 20x30\n",
    "        letter = cv2.resize(letter, (RESIZED_IMAGE_WIDTH, RESIZED_IMAGE_HEIGHT))\n",
    "        # dilate image\n",
    "        letter = cv2.dilate(letter, np.ones((2, 2), np.uint8), iterations=1)\n",
    "        # flatten image to 1d numpy array\n",
    "        letter = letter.reshape((1, RESIZED_IMAGE_WIDTH * RESIZED_IMAGE_HEIGHT))\n",
    "        letter = np.float32(letter)         # convert to float32\n",
    "        letter /= 255                       # normalize\n",
    "        \n",
    "        # get prediction\n",
    "        res = kNearest.predict(letter)      # predict the character\n",
    "        res = chr(int(res[0]))              # convert the result to character\n",
    "        pred.append(res)                    # append the prediction to the list\n",
    "    return ''.join(pred)                    # return the prediction as string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict all captchas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CMPC8\n",
      "CMEC8.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FXMUB\n",
      "FXMUB.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SYSHJ\n",
      "SYSHJ.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T799M\n",
      "T799M.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2ZXB8\n",
      "2ZXB8.png\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(image_list)):\n",
    "    print(predict(image_list[i]))\n",
    "    print(images[i])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.6 ('OCR-XFjxz9Cn')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "9c45e1f0cc01eb96fe216d96cd83d654b40d677ab160d09401a2e7ecdca93f19"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}