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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuration du chemin d'accès\n",
    "import sys\n",
    "import os\n",
    "sys.path.append(os.path.abspath(os.path.join('..', 'src')))\n",
    "\n",
    "# Configuration du chemin d'accès\n",
    "import sys\n",
    "import os\n",
    "sys.path.append(os.path.abspath(os.path.join('..', 'src')))\n",
    "\n",
    "# PANDAS\n",
    "import pandas as pd \n",
    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None) \n",
    "\n",
    "# WARNINGS\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# NUMPY\n",
    "import numpy as np\n",
    "\n",
    "# STATS\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import norm, skew\n",
    "import scipy as sp\n",
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "# MATPLOTLIB\n",
    "import matplotlib as mlp\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight') \n",
    "%matplotlib inline\n",
    "\n",
    "# PANDAS\n",
    "import pandas as pd \n",
    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None) \n",
    "\n",
    "# SEABORN\n",
    "import seaborn as sns\n",
    "\n",
    "# SCIKIT-LEARN: MODELES\n",
    "from sklearn.linear_model import LogisticRegression # Régression logistique\n",
    "from sklearn.svm import SVC # Support Vector Classifier\n",
    "from sklearn.ensemble import RandomForestClassifier # Random Forest\n",
    "from sklearn.ensemble import GradientBoostingClassifier # Gradient Boosting\n",
    "from sklearn.ensemble import AdaBoostClassifier # AdaBoost\n",
    "from sklearn.ensemble import BaggingClassifier # Bagging\n",
    "\n",
    "\n",
    "# SCIKIT-LEARN: VALIDATION CROISEE + OPTIMISATION\n",
    "from sklearn.model_selection import train_test_split # Séparer en données train et test\n",
    "from sklearn.model_selection import cross_val_score # Validation croisée pour comparison entre modèles\n",
    "from sklearn.model_selection import validation_curve # Courbe de validation : visulaisr des scores lors du choix d'un hyper-paramètre\n",
    "from sklearn.model_selection import GridSearchCV # Tester plusieurs hyper-paramètres\n",
    "from sklearn.model_selection import learning_curve # Courbe d'apprentissage : visualisation des scores du train et du validation sets en fonction des quanitiés des données\n",
    " \n",
    " ## YellowBrick\n",
    "from yellowbrick.model_selection import LearningCurve\n",
    "from yellowbrick.model_selection import ValidationCurve\n",
    "\n",
    "## EVALUATION\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# SCHIKIT-LEARN: PIPELINE AND TRANSFORMATEURll\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.compose import make_column_transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('../data/smoking_driking_dataset_Ver01.csv', nrows=100000)\n",
    "df_smoking_drinking = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Affichage des valeurs uniques de la variable SMK_stat_type_cd sous forme de camembert\n",
    "plt.figure(figsize=(10, 5))\n",
    "df_smoking_drinking['SMK_stat_type_cd'].value_counts().plot.pie(autopct='%1.1f%%')\n",
    "plt.title('Distribution of SMK_stat_type_cd')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La répartition des labels est bien similaire au jeu de données réel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (69999, 23)\n",
      "X_val shape: (10000, 23)\n",
      "X_test shape: (20001, 23)\n"
     ]
    }
   ],
   "source": [
    "from preprocessing import split_data\n",
    "X_train, X_val, X_test, y_train, y_val, y_test = split_data(df_smoking_drinking, 'SMK_stat_type_cd')\n",
    "\n",
    "print('X_train shape:', X_train.shape)\n",
    "print('X_val shape:', X_val.shape)\n",
    "print('X_test shape:', X_test.shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Séparation variables continue/catégorielles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalisation des variables continues\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "cont_features = df_smoking_drinking.select_dtypes('float64').columns\n",
    "cont_features = cont_features.drop('SMK_stat_type_cd')\n",
    "\n",
    "cat_features = df_smoking_drinking.select_dtypes(include = ['int64', 'object']).columns\n",
    "cat_features = cat_features.drop(['DRK_YN'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM - Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_svc_pipeline(cat_features, cont_features, kernel='linear', C=1.0, gamma='scale', degree=3, coef0=0.0):\n",
    "    # Créer les pipelines pour les caractéristiques catégorielles et numériques\n",
    "    categoricalPipeline = make_pipeline(\n",
    "        SimpleImputer(strategy='most_frequent'),\n",
    "        OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1)  # Gérer les catégories inconnues\n",
    "    )\n",
    "\n",
    "    numericPipeline = make_pipeline(\n",
    "        SimpleImputer(),\n",
    "        StandardScaler()\n",
    "    )\n",
    "\n",
    "    # Créer le préprocesseur\n",
    "    preprocessor_robust = make_column_transformer(\n",
    "        (categoricalPipeline, cat_features),\n",
    "        (numericPipeline, cont_features)\n",
    "    )\n",
    "\n",
    "    # Créer le pipeline SVM avec les paramètres spécifiés\n",
    "    svr_pipe = make_pipeline(preprocessor_robust, \n",
    "                             SVC(kernel=kernel, C=C, gamma=gamma, degree=degree, coef0=coef0))\n",
    "    return svr_pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "svc_pipe_linear = create_svc_pipeline(cat_features, cont_features, kernel='linear')\n",
    "svc_pipe_rbf = create_svc_pipeline(cat_features, cont_features, kernel='rbf', C=1.0, gamma='scale')\n",
    "svc_pipe_poly = create_svc_pipeline(cat_features, cont_features, kernel='poly', coef0=1)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM - Entraînement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_and_evaluate_model(model, X_train, y_train, X_test, y_test, model_name):\n",
    "    print('\\n\\n')\n",
    "    print(f\"--- Evaluation du modèle : {model_name} ---\")\n",
    "\n",
    "    # Entraînement du modèle\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    # Prédiction sur le jeu de test\n",
    "    y_test_pred = model.predict(X_test)\n",
    "\n",
    "    # Évaluation du modèle\n",
    "    accuracy = accuracy_score(y_test, y_test_pred)\n",
    "    f1 = f1_score(y_test, y_test_pred, average='weighted')\n",
    "    print('Accuracy:', accuracy)\n",
    "    print('F1:', f1)\n",
    "\n",
    "    # Matrice de confusion\n",
    "    cm = confusion_matrix(y_test, y_test_pred)\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "    plt.title(f\"Confusion Matrix for {model_name}\")  # Add model name to the title\n",
    "    disp.plot()\n",
    "\n",
    "\n",
    "    # Rapport de classification\n",
    "    print(classification_report(y_test, y_test_pred))\n",
    "\n",
    "    # Score du modèle\n",
    "    score = model.score(X_test, y_test)\n",
    "    print('Score :', score)\n",
    "    \n",
    "    return accuracy, f1, score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "--- Evaluation du modèle : SVC linear kernel ---\n",
      "Accuracy: 0.680165991700415\n",
      "F1: 0.6936209396628715\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         1.0       0.90      0.75      0.82     12112\n",
      "         2.0       0.44      0.43      0.43      3551\n",
      "         3.0       0.47      0.70      0.56      4338\n",
      "\n",
      "    accuracy                           0.68     20001\n",
      "   macro avg       0.60      0.63      0.60     20001\n",
      "weighted avg       0.73      0.68      0.69     20001\n",
      "\n",
      "Score : 0.680165991700415\n",
      "\n",
      "\n",
      "\n",
      "--- Evaluation du modèle : SVC RBF kernel ---\n",
      "Accuracy: 0.6938653067346633\n",
      "F1: 0.7004263168149192\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         1.0       0.87      0.79      0.83     12112\n",
      "         2.0       0.45      0.41      0.43      3551\n",
      "         3.0       0.49      0.65      0.56      4338\n",
      "\n",
      "    accuracy                           0.69     20001\n",
      "   macro avg       0.60      0.62      0.61     20001\n",
      "weighted avg       0.71      0.69      0.70     20001\n",
      "\n",
      "Score : 0.6938653067346633\n",
      "\n",
      "\n",
      "\n",
      "--- Evaluation du modèle : SVC polynomial kernel ---\n",
      "Accuracy: 0.6943652817359132\n",
      "F1: 0.7020458932053381\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         1.0       0.88      0.79      0.83     12112\n",
      "         2.0       0.45      0.42      0.43      3551\n",
      "         3.0       0.49      0.66      0.57      4338\n",
      "\n",
      "    accuracy                           0.69     20001\n",
      "   macro avg       0.61      0.62      0.61     20001\n",
      "weighted avg       0.72      0.69      0.70     20001\n",
      "\n",
      "Score : 0.6943652817359132\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.6943652817359132, 0.7020458932053381, 0.6943652817359132)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zThj2YgHLZ33FnEcrP5RcvRox4PEbOJr5A0WmUK4Z3YWe95XS+opLAAhuA/e0zuHV2xfy8dtXcMvjsD/nCYtjFpYmAJByNNJi+6jXj5D4wiqeHXIUAP8Wvtz06NV8NGUl2aX3kHK0g61P126iXrN3BPbh6fIrsJYhj/Ulv+SyWrU19VqKn7sD2U7PE/WaK37u3wM/ct+U/pSUB1b123e8E1f2/oCefVcCkFd8OdmnhtKsUSL3ThlMudnzApyhXEiV75nVDBndo+o9U/l+cSTX9VmiZrvi5+4KbOS+Z6+rer88GO1Du4A0UrKmM2ZmK1wcjwLLuHFEZ8IGXgeAk8NJOjb5hCMn7+PO6HAAvHxX0vHK5kTNvtkOZyv2lJuby9GjR6moqMD838vqHRwccHNzY82aNfj4+BAZWfk7rnfv3gwaNIiEhAS6dOlCYmIiAwcOpFu3yiHtkSNH8tFHH7Fq1SqGDRtGYmIi0dHRBAcHAzBx4kT69u1Leno6LVq0qFV8dkuwCgoKcHOznNNz+ufCwsJaJVjOjtm09XvZJvHVKYHQ/X0oyDVxPMtE05YVODgu4dM53rRoupEb+n57xn0SQ7w48UcScAOtfObj6ny4qtndufIXW4fA5yx263Ad3PgtZGeYOFVkIrh1Drs2pQOedGi5mA6BDXeO3BN397Z3CHbRoVMm46bBp29sIGV36jnbuvQs5bHoz3h3wc0kff8lAL2v38eosfDh9NUcO+qBueLPb7Ym0wACg/IpKXEk57g7g2/fya3DTLz+1NcW/Rqki6yCBdCh4xHGTYRP3/6RlOR0WrTK5vkpX/Da3JvYk7wWgN599zLqYfhw7nfgHMydT3TH3/E1Vn56GSs//RX4Ff+AfGa9Av9ZupNNGwqpqHDg0cfXsveEO/NeKsNsrnzvzXy5iD3Jh4h/50sqKkw0pMv8/+qecdcQGFw3pmqYMVFhk5Xca//v5+vry8iRI5kzZw4vvfQS5eXl3HjjjYwcOZLZs2fTvn17i/4hISEsXboUgLS0NIYNG1atPTk5mby8PDIyMiz2DwgIwNvbm5SUlLqfYLm7u1NUZDnR+vTPHh61GzYwUY67yz5rh1bnfPepDy3bF9P2smIC/1sc2LPDjYpyHzpecZgNy51oEVJc7QrCslMd8fGvvFLH1fmwxWvl6FC5Btb/bjt53JEt3zSmx40nadHyzyt8DvwWiIODB5d1/QN3l7Nf+VPfHd7b/tydGiB/n8qLGo4dOsnhvU7nbBt8+2EcTGU8HPUFD0dZHmvWv77gl62+PPdwd1q0ySe000n+s7IZh9JO9yiiadNMft/jyaHUE7Y8rTrB5OF+7k4NTIBP5fIv2Rn5HPnjBDfcsAdn5wqiJ6yp1nfWK5+Q8lsQDs5++PmdZPBtOxh82w6LPqMe3siohzcysG8E3XvsB+Ct+A8snzNwL32u2ctzj1/Nzu0BNjoz+ysvqzh3p4tIRUUFrq6uTJo0ieHDh7N//34ee+wx5s+ff8YijqurK4WFlb8na2ovKKgcnnZ3d6/WfrqtNuyWYIWGhpKTk8OxY8cICKj8D7F3716aNm2Kl5cWtvxf//daEG06FjMhbn/Vtk/eDsTTu4wrrs7n0X4daXJJCa8sr/otRuovbhze14jBo2u/RkyFGV55uiWPzz7IwHsr58kUFTjwVYI/Xfrk4+ndcJMrqb3vvw6h/fV3kfjKOo4dzAGgxzXHiHz0d6Y+2ZVD+ys/lFq2KyB6+m727PLm4L7KL00t2uZzZe9s/r2wrb3Clwvsq89a8ePGIIttPa7OJPKBFKY925OSimbc/nQbpk++lWOH/ryC2S/gFFPmbCFhUYeq/Z988Npqx588ZwtpKT7836IOHDqgIecLqdzO1cKvv/6a1atX89VXXwGVeUVUVBQzZsxg0KBB5OXlWfQvLi6uKuC4ublRXFxcrd3X17cq8fprEeh/968NuyVYrVu3plu3bsycOZMXXniBEydOsGDBAoYPH26vkOqsIQ8eY/6zl9CqQzGXXVXA95/5sHa5H4/PTsfDq4J7nsng1X+25OWnWnLDbcfJPOjC4rnBtLm0iH7DS0irZaHAx7+c6yJyeH9OMC6NKvAJLGPJ/CCOZTjz3IL95z6AXBRyT7hRVNqO/Xt3cXhv5byHVu0qv9XtS/Uk60jlh9PWDQEcPuDG+Jk7WbygHW4e5Tz41B4yDrnxaUJLu8UvF9bxbDeOZ1tWClq3rfzFt29vYxzdvKkwu7H/jwCO/PHnFYhNcisrDVlH3ElLqZzAfvrP/1VW6kBerssZ26RhO3LkSNUVg6c5OTnh7OxM+/bt2bhxo0VbWloaoaGVS4SEhoaSmpparf3aa6/F29uboKAg0tLSqoYJjx49Sk5OTrVhx5rYdQLE/PnzKSsr48Ybb+T222/nmmuuYezYsfYMqU4acE82j0w7xJqP/Zh8Xxv2/OzOc//ax633VVaZbr7rOBMW7OOP31yZ9kAb4mcH06t/LrM/3oujwRT6yZfSuWZQDotmNmPmo61p5FrBS0vTCO2i1ZLFmFPFjkx67EqOH23EuJm7GB2dws9b/Hn2oe4UFWpxSJH6rsLsYPWHEX379uXo0aO8+eablJeXk56eTlxcHIMGDSI8PJxjx44RHx9PaWkpmzdvZsWKFVXzroYPH86KFSvYvHkzpaWlxMfHk52dTXh45cUTQ4cOJS4ujvT0dPLz85k5cyY9evSgZcvafzk0mc3merlw6s6dO6E8nU5Nn7R3KHVaYUlrUo7OpkPgcxfFfLW/Y0BYf3uHUC80a+dP1GsR/OvJzzi8N9ve4dRpF+McLKOC2/gSNftm/vXcao780fDn5f0dz8y/laYt/Ojc2b53Qdi5cyc5pRlscJ1n9WP3LX4aH+emtT7HH374gdjYWH7//Xe8vLwYPHgwUVFRuLi4sHPnTmbMmMGePXvw8/Nj7NixDB06tGrfzz77jLi4ODIzMwkJCSEmJoYrrrgCgNLSUl577TU+//xzCgoK6NmzJ9OnT8ffv/bL+ehrpIiIiNRLffr0oU+fPmds69y5M0uWLDnrvhEREURERJyxzdnZmejoaKKjo887NiVYIiIiYpgtlmloSPTqiIiIiFiZKlgiIiJijNlEuS0qWOdxP8K6SgmWiIiIGGIGKmywDla9vOruLDREKCIiImJlqmCJiIiIYTYZImxA9OqIiIiIWJkqWCIiImJYRQOakG4LqmCJiIiIWJkqWCIiImKIGSi3QY2mIV1FqARLREREDDLZaIiw4Qw7aohQRERExMpUwRIRERHDKlSjqZFeHRERERErUwVLREREDDED5TaYg9WQJrmrgiUiIiJiZapgiYiIiGFaaLRmSrBERETEsArdi7BGenVERERErEwVLBERETHEjIlyGywKatZCoyIiIiJyNqpgiYiIiGGa5F4zVbBERERErEwVLBERETHGrFvlnIsSLBERETGsogFNSLcFpZ8iIiIiVqYKloiIiBhis3sRNqCimCpYIiIiIlamCpaIiIgYZpNb5aiCJSIiIiJnowqWiIiIGGSy0UKjDaeEpQRLREREDDFjm2UazFY/ov1oiFBERETEylTBEhEREcN0L8KaqYIlIiIiYmWqYImIiIhhNlmmoQHRqyMiIiJiZapgiYiIiGGag1UzJVgiIiJiiJZpODcNEYqIiIhYmRIsERERMcZcuZK7tR8YGHb8/PPPCQsLs3hcfvnlXH755QDs2LGDESNGEBYWRr9+/UhMTLTYf/ny5YSHh9O1a1eGDh3K9u3bq9rKy8uZM2cOffr0ISwsjDFjxpCVlWXoJVKCJSIiIvXO4MGD2b59e9Xjq6++wsfHhxkzZpCbm8vo0aMZMmQISUlJzJgxg1mzZvHLL78AsGXLFqZPn87s2bNJSkpi8ODBjBkzhqKiIgDi4uLYuHEjy5YtY/369bi6uhITE2MoPiVYIiIiYphNKljnyWw2M27cOK6//noiIiJYs2YNPj4+REZG4uTkRO/evRk0aBAJCQkAJCYmMnDgQLp164azszMjR47E19eXVatWVbU//PDDBAcH4+npycSJE1m3bh3p6em1jkkJloiIiNRrn332GWlpaTz33HMApKam0r59e4s+ISEhJCcnA5CWlnbW9ry8PDIyMizaAwIC8Pb2JiUlpdYx6SpCERERMayuLNNQUVFBXFwcjz76KJ6engAUFBTg5uZm0c/V1ZXCwsJzthcUFADg7u5erf10W20owRIRERFDzNgmwTqfZRq2bNlCVlYWw4cPr9rm5uZGXl6eRb/i4mI8PDyq2ouLi6u1+/r6ViVep+djnWn/2tAQoYiIiNRbq1evJjw83KLi1L59e1JTUy36paWlERoaCkBoaOhZ2729vQkKCiItLa2q7ejRo+Tk5FQbVqyJEiwRERExrAKT1R/nY9u2bVx11VUW28LDwzl27Bjx8fGUlpayefNmVqxYwbBhwwAYPnw4K1asYPPmzZSWlhIfH092djbh4eEADB06lLi4ONLT08nPz2fmzJn06NGDli1b1jouDRGKiIhIvXXw4EGaNGlisc3X15dFixYxY8YM5s+fj5+fHzExMfTq1QuA3r17M2XKFKZOnUpmZiYhISEsXLgQHx8fAKKioigrKyMyMpKCggJ69uxJbGysobiUYImIiIhhdWWS+/8uEPq/OnfuzJIlS866X0REBBEREWdsc3Z2Jjo6mujo6POOSwmWiIiIGFKXJrnXVZqDJSIiImJlqmCJiIiIYXVliLCuUgVLRERExMpUwRIRERGD/t69A2s6bkOhCpaIiIiIlamCJSIiIsaYwWyLClYDuoxQCZaIiIgYdr4rr18sNEQoIiIiYmWqYImIiIghWmj03FTBEhEREbEyVbBERETEMJtMcm9AVMESERERsTJVsERERMQw3SqnZkqwRERExDANEdZMQ4QiIiIiVlavK1hHDjTimSu72juMOq1ZhyCe+BCibu7A4RQfe4dTpzmGeNg7hPrB3e3PP730mtXEbNI3/HNyMFX9aXbUd/4a1am3k+5FeC56N4uIiIhYWb2uYImIiMiFZwbMNlgVVAuNioiIiMhZqYIlIiIihulmzzVTgiUiIiLGmG20TEMDGiPUEKGIiIiIlamCJSIiIoZpJfeaqYIlIiIiYmWqYImIiIhhtlimoSFRBUtERETEylTBEhEREUMqFxq1/hyshlQUU4IlIiIihtlkmYYGREOEIiIiIlamCpaIiIgYpmUaaqYKloiIiIiVqYIlIiIihmmZhpqpgiUiIiJiZapgiYiIiGG6irBmSrBERETEGLPJNglWA0raNEQoIiIiYmWqYImIiIhhmuNeM1WwRERERKxMCZaIiIgYcvpehFZ/GIwjJyeH8ePH07NnT6666irGjh1LVlYWADt27GDEiBGEhYXRr18/EhMTLfZdvnw54eHhdO3alaFDh7J9+/aqtvLycubMmUOfPn0ICwtjzJgxVcetLSVYIiIiUi89/vjjFBYW8vXXX7N27VocHR2ZNGkSubm5jB49miFDhpCUlMSMGTOYNWsWv/zyCwBbtmxh+vTpzJ49m6SkJAYPHsyYMWMoKioCIC4ujo0bN7Js2TLWr1+Pq6srMTExhmJTgiUiIiLGmW3wMGDXrl3s2LGD2bNn07hxYzw9PZk+fTrR0dGsWbMGHx8fIiMjcXJyonfv3gwaNIiEhAQAEhMTGThwIN26dcPZ2ZmRI0fi6+vLqlWrqtoffvhhgoOD8fT0ZOLEiaxbt4709PRax6cES0RERAyzxRChEb/88gshISF8/PHHhIeH07dvX+bMmUNgYCCpqam0b9/eon9ISAjJyckApKWlnbU9Ly+PjIwMi/aAgAC8vb1JSUmpdXxKsERERKTeyc3NJSUlhX379rF8+XI+/fRTMjMzefbZZykoKMDNzc2iv6urK4WFhQA1thcUFADg7u5erf10W20owRIRERHDzGbrP4xwcXEBYOLEiXh6ehIQEMBTTz3F999/j9lspri42KJ/cXExHh4eALi5uZ21/XTidXo+1pn2rw0lWCIiIlLvhISEUFFRQWlpadW2iooKAC699FJSU1Mt+qelpREaGgpAaGjoWdu9vb0JCgoiLS2tqu3o0aPk5ORUG1asiRIsERERMczec7D69OlDixYteP755ykoKOD48ePMmzePm266iVtvvZVjx44RHx9PaWkpmzdvZsWKFQwbNgyA4cOHs2LFCjZv3kxpaSnx8fFkZ2cTHh4OwNChQ4mLiyM9PZ38/HxmzpxJjx49aNmyZa3j00ruIiIiYpyd7xvo7OzM4sWLmT17NjfffDOnTp2iX79+TJw4kcaNG7No0SJmzJjB/Pnz8fPzIyYmhl69egHQu3dvpkyZwtSpU8nMzCQkJISFCxfi4+MDQFRUFGVlZURGRlJQUEDPnj2JjY01FJ8SLBEREamXgoKCmDdv3hnbOnfuzJIlS866b0REBBEREWdsc3Z2Jjo6mujo6POOTQmWiIiIGHMek9Jre9yGQnOwRERERKxMFSwRERExrgFVm2xBFSwRERERK1MFS0RERAwzuqzCxUYJloiIiBinIcIaaYhQRERExMpUwRIRERHDNERYM1WwRERERKxMFSwRERExTnOwaqQKloiIiIiV1aqC9cYbb9T6gI899th5ByMiIiL1heZg1aRWCdYnn3xSq4OZTCYlWCIiIhcDDRHWqFYJ1n/+8x9bxyEiIiLSYJz3HKykpCSWLFlCfn4+aWlplJaWWjMuERERqcvMNng0IIavIszPz+fBBx9kx44dmEwmrr76al5++WX27dtHfHw8TZs2tUWcIiIiIvWG4QrWq6++islk4uuvv8bV1RWA8ePH4+7uzksvvWT1AEVERKSOMZts92ggDCdYa9euZfz48bRo0aJqW9u2bZkyZQqbNm2yanAiIiIi9ZHhIcLjx48TGBhYbbunpydFRUVWCUpERETqLjNgtsGcqYY0DctwBatz586sWrWq2vYPPviAyy67zCpBiYiISB2nSe41MlzBeuaZZxg1ahTbt2+nrKyMuLg40tLS+PXXX3n33XdtEaOIiIhIvWK4gnXllVfy0Ucf4eXlRatWrfj5558JDg4mISGBnj172iJGERERqWs0wb1G53Wz544dOzJ37lxrxyIiIiLSIJxXgvXNN9/w3nvvkZqaiouLC+3bt2fs2LF0797d2vGJiIhIHWMCTDaYM9WQaliGhwhXrFjBk08+SXBwMI8//jgPPfQQHh4e3HfffXz55Ze2iFFERESkXjFcwXr99deZMGEC99xzT9W2kSNH8vbbbzN//nxuueUWqwYoIiIidVADu+rP2gxXsDIyMrjmmmuqbQ8PD+fQoUNWCUpERETqOE1yr5HhBKt3796sXr262vbvvvuOsLAwqwQlIiIiUp/VaojwjTfeqPp7UFAQsbGx7Nq1iyuvvBJHR0d2797NypUrefDBB20WqIiIiNQRtloYtAENO9Yqwfrkk08sfm7atCm7du1i165dVduaNGnCypUrefrpp60boYiIiEg9U6sE6z//+Y+t4xAREZH6pAFVm2zB8ByssykpKWHr1q3WOpyIiIhIvWV4mYZff/2VmJgYUlJSqKioqNb+22+/WSUwERERqcNUwaqR4QrWrFmzcHJyYsqUKTg7OzNp0iTuv/9+nJycePXVV20Ro4iIiNQ1WqahRoYrWLt27eL999+nS5cuLFu2jPbt23P33XfTtGlTPv74Yy00KiIiIhc9wwlWRUUFgYGBALRp04Y9e/bQvXt3brzxRt566y2rByjVNXKrYPmenTg6Wm4vKTYxqG0XAGJXpHJpt8L/tqzhvbWVf3vy1hCSf/IAwNmlgnueyaTfsBN4+5Vx6PdG/Ht+EOtW+FyYExG7C2hSyIL3/sP0iT3Z+XPgGftEDN/LI0/sZOTt4WRleFRtD26Ww8OPbKJzWDblZSZ++TmA+Lcu41C61xmP0/PqI0yZtYUB1w6xxalIHREQ+N/3VIzle+rVBd/RsdOJav1nvjCw6u9ubqU8OGYXvfoewd29jN92+/Hm/C6k7298xudycyslLv5bdmwPZN7sbtY/GamRLe5F2JAYTrDatm1LUlISgwcPplWrVuzcuROAvLw8SkpKrB6gVNfm0iIcHWHmmJZkprtUbTdXVJZXTSYzbS4t5uMFgaTsasPtU2/l46krOXbgOPuSXav6j3/9AN2uz2PRjGAO/dGIG4edYELcfgrzHNj63Zk/0KThaBJUyPSXf8DTq+ysfZpdks/9o3+ttt3FMYvnYr7g5Ekn4mK7kHO8Ef0H7OfVuHU88fD1ZB7xsOh/xZVHGT9JF8E0dE2CCpk+dyOeXqUW200mM63bnmTpv0PZuK6ZRVtJuW/V35+dnET7S0+w6M3LKSxw4u6Rycyat4FH77+J/DwX/mr0Yztp0rTINicj8jcZTrDuueceJk6cCED//v2JiIjA1dWVn376ia5du1o7PjmDdp2KKDllYsMXPpSXVR+zvqTdKVzdK/jxm8Zkn/ChsDSU33/z4XDKqao+l/fM59pBucTc04ak/1QmU9vXe9KszSm698tTgtWAmUxmbvrHAR4cu6vGfg4OZp55/ifyTrrg6mr5SyzQ80tcXMqZ+PR1ZPw3mdr2YxNejVvHfQ/9xtzp3YHKCsMd9+5h2J1pFBQ44Ua5bU5K7KrqPTVm5xnbm7fIx9WtnB83NSXlVz+LtmatnQFo2y6LHn0ymTy+N1u3NAVg1y8BvLdkNbcO+Z0lizta7Ne9ZwbX3HCI/DxnG5yR1IoqWDUyPMl92LBhzJs3j2bNmtGuXTvmzJnDtm3baNq0KdOmTTuvII4fP054eDhbtmw5r/0vNu06FXMgtdEZkyuAtp0qfxn+/qvbWY/Rd2Auh/e5VCVXlUw8ExHKm5ObWzNcqWPatMsl6pkdfPtVS16ecfZhlaF3puLrW0xiQmi1NlenQxw+5FOVXFUysesXf67qlVG1pf+t++k/cD8LYruw4pO21jwNqUPatMsl6umf+XZ1S16e0b1ae9uQXAD+2Hv2L26Xdz5EUaEjP21tUrXtZG4jdu4IoHuvTIu+np4lPDFuO4vevJyCfCVYUjcZrmAB3HTTTVV/HzhwIAMHDqyhd822bdvGc889x4EDB877GBebtp2KMFeYmLVkL5d1L6T0lIl1K71Z+EIzigocadepiPxcBx6ddog+/0jBvfFanp7lzWvj/Dm4t3KIsF2nIvYlu3LDbSe4+6lMmrc5xeF9jYif3ZQNq3zse4JiU1mZ7jx4dzjZR93o3PXoGfu0bH2SyFHJTIruQ9PggmrtZRWN8fZNw9GxgvLyP7+nBTcvwNOrDE+vEvLzXNiyMZivPm/NqVNORI7SEi4NVVamOw9G9j/re6ptSA75ec6MfmwnPfscwdW1nB3bA3n7jc6Y8QEq5/RlHPGgotzye/+RQ55cf1O6xbZHn/yF9P1erPq8NSPu3mOz8xL5Owzfi/BcHnvssVr3Xb58OfPnz2fcuHG6xU4tnZ5fVV4Oi2YEkzAviA5dC4l8JpNW7U8RPbQd7ToV4+ldQe5xJ+ZP6srdU68i6JJ4Xlmew5jw9hzPdMbbv4zmbU4R2qWI+NlNOZ7lzK33H2Pi2/uZfJ/DXypb0pDk57mQn3f2dgfHCv75/E+sXtmKXTsCzphgHS+8lpCAjfxz4jY+WHgZBQXO9Lv5AN16ZAHg6lZGfp4LGYc9qu0rDc+53lNtQ3Lx9ColN8eF6TG9aBJUyN0jk5n7+jqmT70NADf3EgoLqv9KKip0wt3jzzldva85TK++Rxg7qh/QsC7rr2/qwiT3VatWER0dTaNGjaq23XTTTcydO5cdO3bw4osvkpaWhq+vL2PGjGHEiBFV/ZYvX86CBQs4evQobdu2ZdKkSYSFhQFQXl7Oyy+/zGeffUZRURG9evVi2rRpNGnSpFoMZ3Ne9yI8G5PJZCjB6tu3L4MGDcLJyem8EixHRweadQgyvF99ZnIwE/u8C7nHG5GRXvnLa9N3YHYK4JGJO+l/jyOfJ3Ri9SflpO3yJbC1PyeKrmHJ/HT++eJK7h1fSOLb7XH12IN/01NMHd2L/amVydS7L7ejVccfGDnhOIcOVR8WaugcW/ieu1MDE9C0+L9/etGsdeX5DxqyHW/fcr768mqatXbGJ+AYAEGXeOPk6kVAMy/yTnVhyZL+DBzwPdff9DUAv+4O5suVXbht+HZ8AwNwcW9k8VxePpVD1qef56JwEf7+D/jvpPOApp40a+0DwBcre/HN12WkpVZ+Xp84Ca/HtmH6rE8YNDQNgEaujpgrnKv2Oc3LpxGYHWjW2gdPr2KeHLeDZR9fhZNrc5q1BkcnB9w9Xart1xA5Olnt5isNxs6dO4mIiGDWrFkW23Nzcxk9ejRPPPEEd9xxB0lJSURFRdGhQwe6dOnCli1bmD59OgsXLqRLly4kJCQwZswY1q5di5ubG3FxcWzcuJFly5bh5eXFpEmTiImJ4e233651bHa9F+Hp5R7Ol3eQF098OMpK0dRvjqYC4CHufL4DmfkR1dpvfvJuSvmFvrc1JvjaUXgFplBankvElCct+rl4O9LM41u9rhcJT5dfga8Y8vBV5JdchpvzH7QPXMze7Gd5eNqlAPi5OwMbuG98X0rKAzk9dbPDNaNIy7ufRoWZVJgbUerjR6/+iZjNPzMyZiB/neLZ1Osk8DNRs/pfyFOUC6zqPfVQ5Xvqf938l76l5iSu7FnK3mwIuKQZrk5HiJpp+f5o7n0EHPcRNbM/rf3mYXJoy+XhY7k8vDJ79fJdQcduwUS1vZHK99xFmNXaSx1YGHTnzp1nXH9zzZo1+Pj4EBkZCUDv3r0ZNGgQCQkJdOnShcTERAYOHEi3bpXzUEeOHMlHH33EqlWrGDZsGImJiURHRxMcHAzAxIkT6du3L+np6bRo0aJWsZ3XHKy6Ijczjw+il9k7jAvKJ6CYLj2PsXNLACeO/bnkgrffKWKXweqFv1BetpMjBzz4/TcfAlv7c9eLg/l3zOc8Pe0o+1JKeHvme4yZUsylYQXMv2cR//uBdM+Tv9Gzn5n597xnh7OzL8cWF9/k/g4djzDuefh0YRIpyQcZfNt2Ot5WRmjAjGp9OzV9ipTfmvLe+7dz39Mt2fX9atasvMSiz+PP/EiJjy//mvxNtf0H37aXwbfBvyassdn51Dn2//1zwXXoeIRxE+DTdyrfU46OFfTsvZeMI978vtdyeOXF2Sc4kuEKTWD7pgp69z7CgomrMf/PL+4nnt7F8Ubu/Gv2Gt55/0cAwprfa3GcRk7r8Xdfz9xZ/yAlOdj2J2kn90T3JbBp9eUqLlYVFRXs3r0bNzc33nnnHcrLy7nuuuuIjo4mNTWV9u3bW/QPCQlh6dKlAKSlpTFs2LBq7cnJyeTl5ZGRkWGxf0BAAN7e3qSkpFwcCVZ5eQWHUzLP3bEBqSg6xajEZBLmBfHB3KZV23s+dJTyctjwWTkz//07WQddiB4aUtXu7vwHTZoXsmS+H4dTMln3qQs9ri+ladO9/PR95cKQTs4VXNo1k52b3C+61xXAsdzd3iFccP4+lRNnjmXkcXifKx9/EMS3q66z6NOjTwaRo1KY+lxPDqV7YnbMw83pILff/jXffH4jBw9Uvn9atDpJp8sP8e/4DhzeV31BybycyqGjM7U1WKaLL8Py98kH4FhGPof35QAwfeY2sjLdGf/EtVX92oXmENjkJKu/7EyXmyFpcwD9+pUS3CSlapmGxt6nCG1/hI8+7MDhfTk8Ofr6as83eeZm0vb48H/xHTl4wImiohxbn6LdlJdVv/+v3ZixzTINBo55/PhxLrvsMm6++Wbmz5/PiRMnePbZZxk3bhyBgYG4uVleSe/q6kphYeUC3AUFBWdtLyionHfq7u5erf10W23U6wTrYpRxoBHfJPoyYmwWpadM/PaTO516FHDn41msfL/yKsEPXw3in68e5J/zDrAjyQl/9295atZP/PGbK2s+qlyD5j+f+DJ41DGefWM/780K5thhZ4Y8dIyA4FJmPNLKzmcp9nI8243j2ZYfOq3anARg3++NycrwoFlryD3VlaxML8ZP3sridy7Fzb2MB8fuJuOwB58mtrND5FKXJbzfkaef3c7Tz23ju29a0KRpAfc+8Bv7fvdm4/oQutwMqSlN2bE9gHExW1n05uWcPOlC5MjfKMh3ZtVnbQBITak+f6+s1IGTuS5nbBMbs/Mk94CAABISEqp+dnNzY9y4cdx+++0MHTqU4uJii/7FxcV4eHhU9T1Tu6+vb1XiVVRUdNb9a0MJVj0UO/4SDv3RiJtGHOfupzI5luHM4leCWLqgsvy+Zok/p4ocGDHmKNcN3o6DSyobVzXhjWe9qSiv/EZdXmZiwp1tGfVcBiOfzcDNs5y0nW5MuKMtaTsvvkqOGGM2NyJ2bn+GDN3AuElbKSlxZNuWIOLfvoyiIq1LJJa+XtWaU8VODLszlUkvbqa42JEf1jcj/u1ONPb/c67ejJiePPzYTh4YswsHk5lfd/kza2oP8vM1LCbVJScns3LlSv75z39i+m+1uKSkBAcHB7p06cL7779v0T8tLY3Q0MoLuEJDQ0lNTa3Wfu211+Lt7U1QUBBpaWlVw4RHjx4lJyen2rBjTc47wSopKeHgwYO0bNkSs9mMs/Pf+1BNSUn5W/tfTEpPOfB/sUH8X+zZr6D8/jNfvv/Ml2Ydgnjiw1G8/+p75OVYDvvl5zrx+oRLeH3CJWc5ijR0O38OPOe9Ab/5qhXffFW9qpmV1Zipz/Wu9XMlvHcpCe9dajREqWd2/hzIgOtuq7Z93X8uYd1/qn/WNPb/8+/5+S6G7yk46s6/Tp2XC8XeyzT4+PiQkJCAt7c3o0aNIisri7lz53Lbbbdx880388orrxAfH09kZCTbtm1jxYoVLFiwAIDhw4cTFRXFLbfcQrdu3UhISCA7O5vw8HAAhg4dSlxcHJ07d8bX15eZM2fSo0cPWrZsWev4DCdYZrOZV155hcWLF1NaWsrq1auZN28ejRo14oUXXvjbiZaIiIjIuTRt2pS33nqLV199lbi4OBo1asTAgQMZN24cjRo1YtGiRcyYMYP58+fj5+dHTEwMvXr1AiqvKpwyZQpTp04lMzOTkJAQFi5ciI+PDwBRUVGUlZURGRlJQUEBPXv2JDY21lB8hhOsxYsX89lnnzFlyhReeOEFoHJRr2nTpuHv7090dLTRQ4qIiEh9UwcWGu3RowdLliw5Y1vnzp3P2gYQERFBRET1ZY0AnJ2diY6O/ls5jeFVyz766CMmT57M0KFDq8Y8BwwYwIwZM/jiiy/OOxARERGRhsJwBevgwYNcemn1eRQdOnTg2LFjVglKRERE6rg6UMGqywxXsJo3b84vv/xSbfv3339f68W3REREpH4zma3/aEgMV7AefPBBpk2bRmZmJmazmU2bNrFkyRIWL17MhAkTbBGjiIiISL1iOMEaNmwYZWVlxMXFUVxczOTJk/H39+fpp5/mrrvuskWMIiIiUtfUgXsR1mXntQ7WHXfcwR133MHx48cxm834+/ufeycRERGRi4ThBCspKanatt9//73q71ddddXfi0hERETqtjpwL8K6znCCde+992IymTCb/3wVTCYTJpMJBwcHdu3aZdUARUREROobwwnWt99+a/FzWVkZ+/btIzY2lvHjx1stMBEREam7GtpVf9ZmOMFq3rx5tW2tWrXC3d2dF198kc8++8wqgYmIiEgdpgSrRobXwTqboKAg/vjjD2sdTkRERKTeMlzBOnz4sMXPZrOZvLw84uLiaNWqldUCExERkbpLQ4Q1M5xg9evXr+oehKeZzWY8PDx45ZVXrBaYiIiISH1lOMH64IMPqm1zdnamffv2eHh4WCUoERERqeNUwaqR4QTrvffeIzo6mnbt2tkiHhEREZF6z3CCtXXrVho1amSLWERERKS+UAWrRoavIrztttt4+eWXSU1NpaSkxBYxiYiISB1monKSu9Uf9j4xKzJcwfrmm284fPgwq1evPmP7b7/99reDEhEREanPDCdYjz/+uC3iEBEREWkwapVgXXrppWzYsAF/f39uu+02W8ckIiIiUq/VKsH63xs7i4iIiGiSe82sdqscEREREalU6zlYX375JZ6enufsN2TIkL8Tj4iIiNR1ZhvdKqcBVcVqnWC9+OKL5+xjMpmUYImIiFwMGlAyZAu1TrA2btyIv7+/LWMRERERaRBqlWD99ebOIiIicpFTBatGtZrkrqsIRURERGqvVhWs2267TfcfFBERkSo2meTegNQqwZo1a5at4xARERFpMAzfKkdEREREc7BqpgRLREREDNMQYc20kruIiIiIlamCJSIiIsapglUjVbBERERErEwVLBERETFOFawaKcESERERY3Sz53PSEKGIiIiIlamCJSIiIsY1oGqTLaiCJSIiImJlqmCJiIiIcapg1UgVLBEREam3ysvLuffee3nuueeqtu3YsYMRI0YQFhZGv379SExMtNhn+fLlhIeH07VrV4YOHcr27dstjjdnzhz69OlDWFgYY8aMISsry3BcSrBERETEMJPZ+o/z8cYbb7B169aqn3Nzcxk9ejRDhgwhKSmJGTNmMGvWLH755RcAtmzZwvTp05k9ezZJSUkMHjyYMWPGUFRUBEBcXBwbN25k2bJlrF+/HldXV2JiYgzHpQRLREREjDPb4GHQpk2bWLNmDf3796/atmbNGnx8fIiMjMTJyYnevXszaNAgEhISAEhMTGTgwIF069YNZ2dnRo4cia+vL6tWrapqf/jhhwkODsbT05OJEyeybt060tPTDcWmBEtERETqnezsbCZOnMgrr7yCm5tb1fbU1FTat29v0TckJITk5GQA0tLSztqel5dHRkaGRXtAQADe3t6kpKQYik+T3EVERMQwmyw0WksVFRWMGzeOUaNG0bFjR4u2goICi4QLwNXVlcLCwnO2FxQUAODu7l6t/XRbbamCJSIiIvXKW2+9hYuLC/fee2+1Njc3N4qLiy22FRcX4+Hhcc7204nX6flYZ9q/tlTBEhEREePsWMH67LPPyMrKonv37gBVCdM333zD+PHj2bhxo0X/tLQ0QkNDAQgNDSU1NbVa+7XXXou3tzdBQUEWw4hHjx4lJyen2rDiuaiCJSIiIvXKV199xU8//cTWrVvZunUrt956K7feeitbt24lPDycY8eOER8fT2lpKZs3b2bFihUMGzYMgOHDh7NixQo2b95MaWkp8fHxZGdnEx4eDsDQoUOJi4sjPT2d/Px8Zs6cSY8ePWjZsqWhGFXBEhEREWPO86q/Wh33b/L19WXRokXMmDGD+fPn4+fnR0xMDL169QKgd+/eTJkyhalTp5KZmUlISAgLFy7Ex8cHgKioKMrKyoiMjKSgoICePXsSGxtrOA4lWCIiImKYyd4B/I/Zs2db/Ny5c2eWLFly1v4RERFEREScsc3Z2Zno6Giio6P/VkwaIhQRERGxMlWwRERExDjdi7BGqmCJiIiIWJkqWCIiImKICdssNFqX5nX9XapgiYiIiFiZKlgiIiJinOZg1UgJloiIiBinBKtGGiIUERERsTJVsERERMQwW0xyb0hUwRIRERGxMlWwRERExDhVsGqkCpaIiIiIlamCJSIiIoZpDlbNlGCJiIiIMWZsM0TYgJI2DRGKiIiIWJkqWCIiImKYhghrVq8TLJPJhKlRI3uHUaeZXFyq/tRrVbOK/YfsHUK9UNGotPLPI1lU7M+yczR1W/7gMHuHUOc1DvIEID/Ul5ONne0cTd1W4eJo7xDEgHqdYImIiIidqIJVI83BEhEREbEyVbBERETEOFWwaqQES0RERAzTJPeaaYhQRERExMpUwRIRERHjVMGqkSpYIiIiIlamCpaIiIgYZjKrhFUTVbBERERErEwVLBERETFGN3s+JyVYIiIiYpiWaaiZhghFRERErEwVLBERETFOFawaqYIlIiIiYmWqYImIiIghJmwzB8tk/UPajRIsERERMU5DhDXSEKGIiIiIlamCJSIiIoZpmYaaqYIlIiIiYmWqYImIiIhxqmDVSBUsEREREStTBUtEREQM0xysminBEhEREWPMgNkGGVYDSto0RCgiIiL10qZNmxgxYgRXXnklV199NdOnT6e4uBiAHTt2MGLECMLCwujXrx+JiYkW+y5fvpzw8HC6du3K0KFD2b59e1VbeXk5c+bMoU+fPoSFhTFmzBiysrIMxaYES0RERAwzma3/MOL48eM88sgj3HXXXWzdupXly5fz448/8vbbb5Obm8vo0aMZMmQISUlJzJgxg1mzZvHLL78AsGXLFqZPn87s2bNJSkpi8ODBjBkzhqKiIgDi4uLYuHEjy5YtY/369bi6uhITE2MoPiVYIiIiUu/4+fnxww8/MHToUEwmEzk5OZw6dQo/Pz/WrFmDj48PkZGRODk50bt3bwYNGkRCQgIAiYmJDBw4kG7duuHs7MzIkSPx9fVl1apVVe0PP/wwwcHBeHp6MnHiRNatW0d6enqt41OCJSIiIsaZbfAwyNPTE4DrrruOQYMGERgYyNChQ0lNTaV9+/YWfUNCQkhOTgYgLS3trO15eXlkZGRYtAcEBODt7U1KSkqtY1OCJSIiIvXamjVrWLduHQ4ODjzxxBMUFBTg5uZm0cfV1ZXCwkKAGtsLCgoAcHd3r9Z+uq02lGCJiIiIYaYK6z/Ol6urK0FBQYwbN47169fj5uZWNdn9tOLiYjw8PABqbD+deJ2ej3Wm/WtDCZaIiIgYZ+chwp9++ol//OMflJSUVG0rKSnB2dmZkJAQUlNTLfqnpaURGhoKQGho6Fnbvb29CQoKIi0trart6NGj5OTkVBtWrIkSLBEREal3OnToQHFxMa+88golJSUcOnSIOXPmMHz4cG6++WaOHTtGfHw8paWlbN68mRUrVjBs2DAAhg8fzooVK9i8eTOlpaXEx8eTnZ1NeHg4AEOHDiUuLo709HTy8/OZOXMmPXr0oGXLlrWOTwuNioiIiGH2Xsndw8ODd955h5kzZ3L11Vfj5eXFoEGDiIqKwsXFhUWLFjFjxgzmz5+Pn58fMTEx9OrVC4DevXszZcoUpk6dSmZmJiEhISxcuBAfHx8AoqKiKCsrIzIykoKCAnr27ElsbKyh+JRgiYiISL0UEhLCokWLztjWuXNnlixZctZ9IyIiiIiIOGObs7Mz0dHRREdHn3dsSrBERETEGN0q55w0B0tERETEylTBEhEREcPsPQerrlOCJSIiIsYpwaqRhghFRERErEwVLBERETFMQ4Q1UwVLRERExMpUwRIRERHjbLFMQwOiCpaIiIiIlamCJSIiIoaYsM0cLJP1D2k3SrBERETEOI0Q1khDhCIiIiJWpgqWiIiIGKZlGmqmCpaIiIiIlamCJSIiIsaYgQoblLAaUFVMFSwRERERK1MFS0RERIxrQNUmW1CCJSIiIoZpknvNNEQoIiIiYmWqYImIiIhBZhvdi7DhlMVUwRIRERGxMlWwRERExDDNwaqZEiwRERExTglWjTREKCIiImJlqmCJiIiIMWYw2WKSewOqiqmCJSIiImJlqmCJiIiIcRX2DqBuUwVLRERExMpUwRIRERHDbDIHqwFRgiUiIiLGKb+qkYYIRURERKxMFSwRERExTkOENVIFS0RERMTKVMESERERw3QvwpopwapnuvQ8yUtLks/avnhecxLmN8c/qIQHn0unR7/tuHl+S/RsD/4VE8TeXz2q+jZtUcyo8Qe5/Ko8XN0q2LfHjcXzmvPzD94X4lSkjukYls+oZw/S4YoCigoc2Pq9N+/MbEFutjMAPfrlEPnkYdpetg2z4w/cNdabBRP9KCpwrDpGk+aneOj5dLr0ysPkALuTPFn4YguOHHC112mJzZkZ3CeZodfuplnASXLy3NiwsxXvrOpOYbELAF3aHeGRQUmENMsmv8iFdb+04e2V3Sk6VdkeEvACS2J+O+sz9H18NACuLqU8cMs2bgj7HW+PU/x+xJeFK69i257mtj9NEYOUYNUzabs9eOq2y6ptv/+fB2nfpYDvVvjh5lHO3I9+o6zUxPuvdeYfY2/E1W0hMxenMOYfl3P8qAue3mXMXZJM/klH3pzeksI8R/5xx1FmfJDCc5Ed2bmlsR3OTuwl5PIC5ixJ5ueNjXlhdAj+QaWMevYgzduk8szQy+hz8wli3kzjl81exL14Jbc+2Zf2nRcx599ZPHXbZVSUm2jkWs7MD1NwdIIFU1pScsqB+/95iJc+SubRmy+n4KQ+bhqiu2/cwehBSfz72yvYtqcZzQNO8tDArbRtdoKn3hhAm+ATzItaxc7fmzL5vZsI9ClgbMQWmvmf5Nm3/wHAwZwHSFi1jaPHi6qO2yzgJDH3fsfnGztWbRt353qu7bKPt1dcxR8ZvkRc/RuvjF1FVOxgdu8LuuDnftHTHKwa6ROvninMdyT5Z0+Lbb3CTxDW9yQvjg3h0B9u3P34Ibx9y3j4ps64+jajzwNX8v7U7sTEfk+XXnl8t8Kf/sOP4hNQylNDLyM7s/Jb5E/rvVmwahfDHz6iBOsi89DEdH7/1Z1pD4VSUWECoDDfgUenHCCoxSnueeoQB1LdiLmvPU3aBHHNIz2Jn7CL2fH/of+IY3y1JJBOV+VzSdtTPHd3B37eWPn+Ofi7K+/8Zxe9w3P4ZlmAPU9RbMBkMnNv/5/5fOOlvLWiBwBbU+BkgSvTH/yGDi2OcV3XP8AME97uT1FJZTXU0aGCcXduIMg3D/CkuOwS0g4d4WBmflX7U8N/IO2QH68t6wOAi3MZN165lw+/7kri950B2J7ajI+n/pvBV/+mBOtCM4PJFiu5N6Ccza6T3JOTkxk1ahQ9evTg6quvZvz48Rw/ftyeIdU7Lo0qGDtlP1u+9WbDl34A9L3lOBu+9OX4UZeqfidPuHJP7zC+W+EPwNEMFz55t2lVcgVgNps4vN+V4FanLuxJiF15+ZTRpVceKz9oUpVcAWz8yo97e3clM70RLUKK2bauMWWlf35k5OU0Ij3NlZ435gDg7FL5yViY92efk8crv8M19i27AGciF5qHawmrk0L5emuIxfb0o5XTDJoHnsTFqZyycgeKS//8Pp9bUDlk7O1x5s+aiL6/0r7FMV7+6BrKyiuHoJ0dK3AwmSko/vMzq7zCgYIiF7w9iq16XiLWYLcEq7i4mIceeoiwsDA2bNjAypUrycnJ4fnnn7dXSPXSbQ9m4B9UwpvTWwHg6FRBy5Bi0n93475nDvLq/31N12b38OzcH2jdobBqv/Vf+PPeSy0sjuXpXUaXXifZv8ftgp6D2FebSwtxcICcbGfGv7aXT3ZvY/mv2xgfuxfPxpWJUe5xJ4IuKbHYz9GxgsBmJTRtUflL8qcNjfkj2Y0Hnz9I0xbF+AaWMvaFAxTmO/DDap8LfVpyAeQXNSJ26dXs/KOpxfZrr/gDgD8O+7JyU0fMwOO3baKxezFtmh5n1C3bSDvkR9ohv2rHdHMp5cEB21idFMpv+5tUbS8odmHl5o6MuG4XnVpn4ul2ijv7/ULbZidYkxRq0/OUszCbrf9oQOw2RHj48GE6duxIVFQUjo6OuLi4cMcddzB+/PhaH8PB0YFm7Zucu2MD5ehUwdAHd/Dj980xNWpJs/bg5X0KJ2czw0dncfSIO8sT+nBdZFe8/Rfx8scpTH70WnKyqydQJgczUTFbcfOoYO2qTjRr73PhT8jeKhrWf+7aatOpMomKnrefnUlN+NcLoQQ1L2DYA8m07PAHs57qw6ZvWzEoMo0HJ+Xx267WODse49HJKXg0rqC01FT1//D/FjTiielJxG/YCUBpiQOvTeqOg1sgzdrb7RTtpjDI89ydGpj2lxzmnvAd/JjcjlJzSwA+Wnsto/7xH26/YRcAR3MaM/WDO2jWxIsgf3eAqj9vueonvNxK+Hrb1Vzyl9dv1Y/XcnmbT3nrn59Vbfv4uz7sOXwFl1wEI4ROjqZzd7rIJCcnM2fOHHbv3o2zszNXX301zz33HH5+fuzYsYMXX3yRtLQ0fH19GTNmDCNGjKjad/ny5SxYsICjR4/Stm1bJk2aRFhYGADl5eW8/PLLfPbZZxQVFdGrVy+mTZtGkya1zzlMZnPdSRnHjx9PRkYGH3zwwTn77ty5k1OnTmEyXbxvOF+3DbT2+xfJmbMpKqusYDk7ZHN58GOUlvvwa+Y8KsyVpXhnx2NcFvQ0WfkDOHLyrr8cqYzWvgvwdd/EgRMPkF0YfoHPROzJ1209rf0WkFt0Jb8fH1e13cftB9r4vU7asefIO3U5zRp/TKDnFziYyqkwO5Jd0A8nh3xcnQ+SnPUSni6/0i5gNgWn2pOVPwAzDgR4fINXo13szX6OgpKONUQhDYGHSzJt/edSWu5L6tGplJs9CfL8lGbeH3E0vz85RVfh5JhHU69PMJnKSD06hbIKH4tjXBr0NMWll/DH8X9abHdyyKVD4ETMOHLk5HBKy/1o7PozTTxXcejk3RzNH3gBz9R+XFxc6Ny5s11j2LlzJ0cO5zB32vdWP/a4KdcR3MynVudYXFzMTTfdxO23386jjz5KQUEBzz77LA4ODsyZM4f+/fvzxBNPcMcdd5CUlERUVBTx8fF06dKFLVu2MGbMGBYuXEiXLl1ISEjgzTffZO3atbi5ufHGG2+wZs0a3nrrLby8vJg0aRIFBQW8/fbbtT6XOjHJ3Ww2Exsby9q1a/nwww9rvV9uVj6LJ3xuw8jqtscmJ+HU3IuXHtkAbADAzb2Ufy2HbevceXv2MgJb+nLn1AEsnvQjUc+5k3t8E6/H/Dkz0d2zhKjJ2/AOzubDNy7nPyuygAT7nJC9XaQVrK69M3jiBfj0zVK+X/Xn/z83j1L+9Sls//RTvvyosiLl5BxOh27OhI+JIGHy9zzwxBeYzfB69Ic8M3sLx0qdiXmoBWWlvwJgMgUx8bW9uDjGMjuqrz1Oz64Kr+tg7xAumN6XJTNm8GoOHfVl5v/dSm7BbziYKlg0bhnrd3bkX59dDhQBTnh7DOS1qEXs3/cOX/98CyMHdyL+8900ctzPS6MziPu0O1uSkyyOP+TqLXQOzuapf40i44QrUAi0J/LGI/zjqn8z9R0v8osa9vSGR4Z3pom/y7k7XiRqGglbs2YNPj4+REZGAtC7d28GDRpEQkICXbp0ITExkYEDB9KtWzcARo4cyUcffcSqVasYNmwYiYmJREdHExwcDMDEiRPp27cv6enptGjR4qwx/S+7J1j5+flMmDCB3bt38+GHH9KhQ+0/kCrKKzi8J8uG0dVdjk4VXBZ2lI/fDK72Gpw45kRZcZHF9qMHTmAuL+VktmPV9oDgU0z9VwpNW5xi9hPtWPeFO3Bxvp7ARZtgOZkr51AV5ORavGe8/UoBOJZeiL9vGi6NzGxb501ZaRP6PepBdvoxmrXK5evEAA7vycLHJ5/ftrlxYHe2xfF/Wt+IQfdlXZT/V/M7XhzrM9114w7GDN7Cjr3BPPf2zRQUm4F8/BsX4upSxpZfA6quEAQ4COzL8CbAO4vM7Mq5oZnZhfS74jeKTjmx4ocmlJTmWzyHm3M2x0+6sTXZGfizbf2OAAb1LsfBfISDmQ17ykhZed35jDIBJhsMgBkZk2rbti3vvPOOxbbVq1fTqVMnUlNTad/ecl5CSEgIS5cuBSAtLY1hw4ZVa09OTiYvL4+MjAyL/QMCAvD29iYlJaXWCZZdryI8cOAAw4YNIz8/n6VLlxpKri52bToU4epewa/bqs/x2PqdD2FXn6Sxb2nVtqaX5HNJ2yJ2JXkB4O5ZzuwPU/BvUsrz93Vg3Rf+Fyx2qVsOpLqSke7CdYMsr+DtFZ4DwK4kT64ZeIInZ+/D0enP6uc1/ziIl3c5G7/yBSB9rysduhbg7PK/126bufTKAjLSG9n6NMROIq7+laghW1j7c1ue/tcAi6v8TuS5klvQiCvaHbHYx9ujmBaBuRzJ9rLYflnrLPYcDKCktPp3//1ZPvh4FtGiSY7F9s5tMymvMJFx/OKb72Z3dWiSu9lsZt68eaxdu5aJEydSUFCAm5tlRdPV1ZXCwsqEvqb2goICANzd3au1n26rDbtVsHJzc7n//vvp1asXM2bMwMFBt0U04vQVgQdSq5fEE+Y3o3f/E8z8IIUvlzri47qZJ1/4kaNHXPjqo0AA7nnqIJe0LWbxvOaUlTjQseuf3whLS0wWK75LQ2finZkteP5fe5nwRhpfLQmkRUgxI8cdZP0qX/bu9uCLD038486jRL/6B1s3OhHosYrIx3bx3Wd+7P5v0v5/85vxytLfmP7+Hj59N4jychP9bz/GpVfmM2NsOzufo9iCn1chjw/dxJFsT5Z9fzkdWhyzaD90rDHvrurGMyN+oKDYhbXb2+LtWcy94T9TYTbx7/90weHPGwHQttkJkn47c9Vv5aaODLtmN6+M+ZJFX3YjK8eDqzoc4q4bd/DJuss4nud+xv2k4TvTSJibmxt5eXkW/YqLi/HwqPzd5ubmRnFxcbV2X1/fqsSrqKjorPvXht0SrE8++YTDhw/z5Zdf8tVXX1m0bd++3U5R1R8+gZVXfuXlVv8nzEh35Zlhl/HAc+k8PH47Tq6/sn2DN689G1R1W5O+/zgBwL1PH+Lepw9Z7J950IX7r+lq2xOQOmXDKj+mPuhA5JOHmfZuKnm5TqxKaML7L1f+stu/x50pD4Qy6tmD9Lk5CZz+YGVCCAtf+HNB2tSdHoy7oyP3/fMQz87/nbJSE7//5s6zd3bQwrUNVO9OB3B1KSfYP58FT1efDzvjw+v4ZN3l5Bc24s5+vzCgZwq5Ba7s2NuUCQv7k3Hcy+LqPz+vQvKKzlztLCx2YWxsBI8O3kLUkM24upRx8Kg3r3zclxU/6AIKu7DFQqMGHThwgIcffphmzZqxdOlS/Pwql/5o3749GzdutOiblpZGaGjlkh6hoaGkpqZWa7/22mvx9vYmKCiItLS0qmHCo0ePkpOTU23YsSZ16ipCI3bu3EnGvqPMvX2RvUOp05q1b8LjiyJ5/YGEi3IOjCEX6Rwso5q1b8Lj8ffw+sgP9Z46h/zBYfYOoc67JMiTZ0ddxZz3kizmaUl1Ux7tRbMm3nXiKsKMwznMnbTW6sceN/0GmtbyKsLc3FyGDBlyxpGwEydO0L9/f6KiooiMjGTbtm2MHTuWBQsW0KtXLzZt2kRUVBQLFiygW7duJCQksGDBgqrJ8bGxsXz77bcsWLAAX19fJk2axLFjx1i8eHGtz8Xuk9xFRESknjGbbTLJ3cg8rHONhC1atIgZM2Ywf/58/Pz8iImJoVevXkDlVYVTpkxh6tSpZGZmEhISwsKFC/Hx8QEgKiqKsrIyIiMjKSgooGfPnsTGxho6FSVYIiIiUu+MGjWKUaNGnbW9c+fOLFmy5KztERERREREnLHN2dmZ6OhooqOjzzs+JVgiIiJiXP2cYXTBKMESERER45Rg1UhrI4iIiIhYmSpYIiIiYlwdWKahLlMFS0RERMTKVMESERERw2yyTEMDogqWiIiIiJWpgiUiIiLGqYJVIyVYIiIiYowZ2yRYDShn0xChiIiIiJWpgiUiIiLGaYiwRqpgiYiIiFiZKlgiIiJinBYarZEqWCIiIiJWpgqWiIiIGKaFRmumBEtEREQMMttoknvDSdo0RCgiIiJiZapgiYiIiHEVDafaZAuqYImIiIhYmSpYIiIiYoxulXNOSrBERETEOF1FWCMNEYqIiIhYmSpYIiIiYpwqWDVSBUtERETEylTBEhEREeO0TEONVMESERERsTJVsERERMQgM5grbHPcBkIJloiIiBinSe410hChiIiIiJWpgiUiIiLGmLHNJPcGVBRTBUtERETEylTBEhEREeM0B6tGqmCJiIiIWJkqWCIiImKcKlg1UoIlIiIixinBqpGGCEVERESsTBUsERERMa7CFiu5NxyqYImIiIhYmSpYIiIiYpDZRnOwGs68LlWwRERERKxMCZaIiIgYY6aygmX1x/mFc/z4ccLDw9myZUvVth07djBixAjCwsLo168fiYmJFvssX76c8PBwunbtytChQ9m+fXtVW3l5OXPmzKFPnz6EhYUxZswYsrKyDMWkBEtERESMqzBb/3Eetm3bxh133MGBAweqtuXm5jJ69GiGDBlCUlISM2bMYNasWfzyyy8AbNmyhenTpzN79mySkpIYPHgwY8aMoaioCIC4uDg2btzIsmXLWL9+Pa6ursTExBiKSwmWiIiI1EvLly8nOjqap59+2mL7mjVr8PHxITIyEicnJ3r37s2gQYNISEgAIDExkYEDB9KtWzecnZ0ZOXIkvr6+rFq1qqr94YcfJjg4GE9PTyZOnMi6detIT0+vdWxKsERERMQws7nC6g+j+vbty9dff82AAQMstqemptK+fXuLbSEhISQnJwOQlpZ21va8vDwyMjIs2gMCAvD29iYlJaXWsekqQhEREamXAgMDz7i9oKAANzc3i22urq4UFhaes72goAAAd3f3au2n22pDCZaIiIgYd55zpi4ENzc38vLyLLYVFxfj4eFR1V5cXFyt3dfXtyrxOj0f60z714aGCEVERKRBad++PampqRbb0tLSCA0NBSA0NPSs7d7e3gQFBZGWllbVdvToUXJycqoNK9ZECZaIiIgYZ4tlGqwkPDycY8eOER8fT2lpKZs3b2bFihUMGzYMgOHDh7NixQo2b95MaWkp8fHxZGdnEx4eDsDQoUOJi4sjPT2d/Px8Zs6cSY8ePWjZsmWtY9AQoYiIiBhjNtvmXoRWSrJ8fX1ZtGgRM2bMYP78+fj5+RETE0OvXr0A6N27N1OmTGHq1KlkZmYSEhLCwoUL8fHxASAqKoqysjIiIyMpKCigZ8+exMbGGopBCZaIiIjUe3+9wq9z584sWbLkrP0jIiKIiIg4Y5uzszPR0dFER0efdzxKsERERMQ4m9yLsOHQHCwRERERK1MFS0RERAwz22IOVgOiCpaIiIiIlamCJSIiIsZpDlaNlGCJiIiIcXV4Jfe6QEOEIiIiIlamCpaIiIgYYzaDue4uNFoXqIIlIiIiYmWqYImIiIhhZs3BqpEqWCIiIiJWpgqWiIiIGGeLOVgNiBIsERERMcSMbYYIG9Kgo4YIRURERKxMFSwRERExTkOENTKZzfVz0YmffvqJstJycrPy7B1Knebo7IB3oBe5R/MoL9V/hhrVz/8KF5yjsyPeTbzIzcqjvLTc3uHUaRUejewdQp3n5GjCx8uVnLxiysr1f7Amvl6NcHJy5Morr7RrHDt37qSosJiczJNWP7ZPUGPc3F3p3Lmz1Y99odXbCpbJZMLJ2ZGmrQPtHUq9ENjc394hSAMTeIneU2I9Tfxd7B1CnVdaWorJZLJ3GLi4VP5bubVxtenx67t6W8ESERERqas0yV1ERETEypRgiYiIiFiZEiwRERERK1OCJSIiImJlSrBERERErEwJloiIiIiVKcESERERsTIlWCIiIiJWpgSrAcvOzmbs2LF0796dnj17MmPGDMrKyuwdltRzx48fJzw8nC1bttg7FKnnkpOTGTVqFD169ODqq69m/PjxHD9+3N5hiViFEqwG7KmnnsLd3Z3169ezdOlSNm3aRHx8vL3Dknps27Zt3HHHHRw4cMDeoUg9V1xczEMPPURYWBgbNmxg5cqV5OTk8Pzzz9s7NBGrUILVQO3fv58ff/yRcePG4ebmRosWLRg7diwJCQn2Dk3qqeXLlxMdHc3TTz9t71CkATh8+DAdO3YkKioKFxcXfH19ueOOO0hKSrJ3aCJWoQSrgUpNTcXHx4egoKCqbe3atePw4cOcPGn9O6BLw9e3b1++/vprBgwYYO9QpAFo27Yt77zzDo6OjlXbVq9eTadOnewYlYj1ONk7ALGNgoIC3NzcLLad/rmwsJDGjRvbIyypxwIDA+0dgjRQZrOZ2NhY1q5dy4cffmjvcESsQglWA+Xu7k5RUZHFttM/e3h42CMkEZFq8vPzmTBhArt37+bDDz+kQ4cO9g5JxCo0RNhAhYaGkpOTw7Fjx6q27d27l6ZNm+Ll5WXHyEREKh04cIBhw4aRn5/P0qVLlVxJg6IEq4Fq3bo13bp1Y+bMmeTn55Oens6CBQsYPny4vUMTESE3N5f777+fK6+8knfffRc/Pz97hyRiVRoibMDmz5/PCy+8wI033oiDgwNDhgxh7Nix9g5LRIRPPvmEw4cP8+WXX/LVV19ZtG3fvt1OUYlYj8lsNpvtHYSIiIhIQ6IhQhERERErU4IlIiIiYmVKsERERESsTAmWiIiIiJUpwRIRERGxMiVYIiIiIlamBEtERETEypRgiYiIiFiZEiyRC6hfv3506NCh6nHppZfSvXt37r33XrZu3Wr159uyZQsdOnTg4MGDANx7770899xztdq3sLCQhISEv/X8Bw8epEOHDmzZsuWM7Z988omh+88Z7W+rY4iInItulSNygT3wwAM88MADAJjNZnJycnj11Vd56KGH+Oqrr2jatKnNnvv111/H0dGxVn0XLVrEJ598QmRkpM3iERFpqFTBErnA3N3dCQwMJDAwkCZNmtC+fXumTZtGUVERa9asselz+/j44OXlVau+uouWiMj5U4IlUgc4OVUWk11cXIDKocSZM2cyYMAAevbsyebNmzGbzSxcuJAbb7yRK664goiICD7//HOL42zdupURI0bQpUsXhgwZQkpKikX7X4cId+3axahRowgLC6NPnz5MnjyZwsJCXn/9dd544w0OHTpkMcS4bNkybrnlFrp06cItt9zC+++/T0VFRdXx9uzZw3333UfXrl25+eab2bx5s6HXISMjg+joaPr06UOnTp247rrrmDdvnsVzACQmJnLttdfStWtXnnjiCY4fP17VVlJSwty5c7nmmmsICwvj9ttvZ8OGDYbiEBH5uzREKGJnmZmZzJw5E3d3d6699tqq7f/+979566238PLyokOHDsybN48VK1YwefJk2rVrR1JSElOnTiUvL4/IyEjS09N54IEHGDJkCLNnzyYtLY3Jkyef9XkPHjzIvffeS79+/fjoo4/Iz89nwoQJTJ48mWnTplFYWMiqVatYunQpfn5+fPTRR7zyyitMnjyZK664gl9//ZXp06eTmZnJ+PHjycvLY+TIkXTt2pXExESysrKYNGmSodfikUcewd/fn3fffRdPT0++++47XnzxRTp37sxNN91U1e+DDz4gNjYWFxcXpk+fzgMPPMDy5csxmUxMmDCB1NRU5s6dS9OmTVm7di2PPvoob7zxBtdff73hfx8RkfOhBEvkAnvrrbdYtGgRAGVlZZSUlNCuXTtiY2Np1qxZVb/rrruOPn36AJUTzuPj43nppZe44YYbAGjZsiWHDh3i3XffJTIyko8//piAgACmTJmCo6Mj7dq148iRI8yaNeuMcXz88cd4e3sze/ZsnJ2dAXjxxRf58ccf8fDwwN3dHUdHRwIDAwFYsGABjzzyCLfeeisALVq0ID8/n2nTpvHkk0/yxRdfUFRUxJw5c/Dy8iI0NJTnn3+eqKioWr0uxcXFREREcPPNN9O8eXOgsuL29ttvk5KSYpFgzZ07l44dOwIwZ84cbr75ZjZt2kTz5s1ZuXIlS5cupXPnzgCMGjWK5ORk3n33XSVYInLBKMESucDuvPNO7r33XgAcHBzOOi+qVatWVX9PS0vj1KlTPPvss0yYMKFq++kErbi4mD179nDZZZdZTGK/8sorzxpHSkoKnTp1qkquAK666iquuuqqan2PHz9ORkYGr732Gm+88UbV9oqKCk6dOsXBgwfZs2cPrVu3tjiXsLCwc70cVVxdXbnnnnv46quveP/999m/fz/JyclkZWVZDBF6eHhUJVcArVu3xtvbmz179pCbmwvAfffdZ3Hs0tJSGjduXOtYRET+LiVYIheYt7e3RfJ0Nq6urlV/Pz3hPDY2lrZt21bre3ru1l8npp+e23UmTk5OmEymWsV8OsGZMGFCVVXtfwUHBxt+/r8qKioiMjKSoqIibrnlFiIiIpg0aVK1qxjPdBVkRUUFLi4uVc+fkJCAh4eHRR8HB005FZELR584IvVA27ZtcXJy4vDhw7Rq1arq8f333/Puu+/i4ODApZdeys6dOykpKanab+fOnWc9ZkhICL/++ivl5eVV277++muuvfZaioqKLJIvf39//P39OXDggMXz7969m9jYWAAuvfRS/vjjD4sJ5zU9/1+tX7+e3bt3s3jxYp544gkGDBiAp6cn2dnZFonbyZMnOXDgQNXPKSkp5OXl0b59e0JDQwHIysqyiPOTTz5h2bJltY5FROTvUoIlUg94eXlx5513Ehsby6effkp6ejrLly9n7ty5BAQEAHDXXXdRVFTE888/z969e1m7dq3FcN5f3X333Zw4cYIpU6awd+9etm7dyssvv8zVV1+Nm5sb7u7u5Obm8scff1BWVsZDDz3E4sWLWbx4MQcOHOCbb75h2rRpuLi44OLiwsCBA/H39+ef//wnycnJ/Pjjj8ycObPW53h6/a/PP/+cQ4cOsXXrVsaOHUtpaalF0ujg4MBTTz3Fzz//zM8//8z48ePp0aMH3bt3JzQ0lBtuuIEpU6bw7bffkp6ezrvvvstbb71FixYtzvPVFxExTkOEIvXEhAkT8PPzY/78+WRlZdG0aVMee+wxRo8eDUBQUBDvv/8+M2fO5LbbbiM4OJgxY8Ywbdq0Mx4vKCiIRYsW8fLLL3PbbbfRuHFjBgwYwDPPPANA//79+fjjjxk8eDAffvghDzzwAI0aNWLx4sXMmTMHf39/hg4dytNPPw1Uru/1wQcf8MILL3DXXXfh7e3Nk08+WeuV47t06cKECROIj48nNjaWoKAgBgwYQHBwMDt27Kjq5+fnR0REBGPHjqWoqIgbbriBmJiYqvZ58+Yxb948pkyZQm5uLi1atGD69OkMGzbsvF53EZHzYTJrNUERERERq9IQoYiIiIiVKcESERERsTIlWCIiIiJWpgRLRERExMqUYImIiIhYmRIsEREREStTgiUiIiJiZUqwRERERKxMCZaIiIiIlSnBEhEREbEyJVgiIiIiVvb/+Q5xmXvogRoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_and_evaluate_model(svc_pipe_linear, X_train, y_train, X_test, y_test, model_name='SVC linear kernel')\n",
    "train_and_evaluate_model(svc_pipe_rbf, X_train, y_train, X_test, y_test, model_name='SVC RBF kernel')\n",
    "train_and_evaluate_model(svc_pipe_poly, X_train, y_train, X_test, y_test, model_name='SVC polynomial kernel')\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sans optimisation, le <b><font color='red'>score du SVC linéaire est à 68.2% </font></b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_learning_curve(pipeline, X_train, y_train, model_name=\"SVC\", cv=3, scoring='accuracy'):\n",
    "    # Extract the SVC estimator from the pipeline\n",
    "    svc = pipeline.named_steps['svc']\n",
    "    \n",
    "    plt.figure()\n",
    "    visualizer = LearningCurve(\n",
    "        svc,  # Use the SVC estimator directly\n",
    "        cv=cv,\n",
    "        scoring=scoring,\n",
    "        train_sizes=np.linspace(0.1, 1.0, 10),\n",
    "        n_jobs=-1\n",
    "    )\n",
    "    visualizer.fit(pipeline[:-1].transform(X_train), y_train)  # Apply the pipeline except the last step\n",
    "    visualizer.finalize()  # Finalize the visualizer to ensure it draws correctly\n",
    "    visualizer.show(title=f\"Learning Curve for {model_name}\")  # Set the title directly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QEkFkGARBEIS4srIyfvvb33LFFVcwffp0QqEQf//73/H5fJx99tmJHp4gCIKQACLDIAiCILTwxhtv8Prrr1NcXIzVamXChAlcc801rXbfEQRBEHo+ETAIgiAIgiAIgtAm0VZVEARBEARBEIQ2iYBBEARBEARBEIQ2iYBBEARBEARBEIQ2iS5J7bBy5UpM08RqtSZ6KIIgCIIgCIKw31RVRZKkdu3RIjIM7WCaJmJtePdlmibRaFQ8h0KHE68toTOI15XQWcRrS2hub+a3IsPQDk2ZBdFSsHsKBoOsW7eO4cOH73GnWkHYG+K1JXQG8boSOot4bQnNrVmzpt3nFRkGQRAEQRAEQRDaJAIGQRAEQRAEQRDaJAIGQRAEQRAEQRDaJAIGQRAEQRAEQRDaJBY9C4IgCILQa6iqiqZpiR5GQkQikfhPSZISPBqhMymKgtVq7bDnWWQYBEEQBEHoFXw+H8FgMNHDSBibzcaQIUOw2WyJHorQyaLRKDU1NR0WHIsMgyAIgiAIPZ5hGKiqSlZWVqKHkjC6rgPgcDhQFCXBoxE6W0pKCjU1NeTk5Ox3pkFkGARBEARB6PFUVcVutyd6GILQZSRJwul0oqrqfl+XCBgEQRAEQejxDMNAlsW0R+hdFEWJZ5b2h3jnCIIgCIIgCILQJhEwCIIgCIIgCILQJrHoWRAEQRAEoZ1M0+SrrVWUeYMUpLk4fGgf0aJU6PFEwCAIgiAIgtAO764p4oYlK9hS64ufNiw7lb+cMpnTxg3s8Nu77bbbWLJkCQCapqGqKk6nM/735557jqlTp+7VdV555ZVMmzaNhQsX7vG8s2fP5rLLLmPOnDl7N/B22LRpEw8//DDLly8nGo2Sm5vLiSeeyBVXXCHaviYhyTRNM9GDSHZr1qwBYNy4cQkeibAvgsEg69at48ADD8TlciV6OEIPIl5bQmcQr6vOEQqFAFpMuPfGu2uKOPuVLzFamTbJksRbC47olKChyTvvvMPjjz/Op59+us/Xoes64XA44W1V/X4/xxxzDL/5zW+YP38+DoeDzZs3c9111zF+/HjuvvvuhI2tp9nd635v5rciwyAIgiAIQq/kCUVZX+XZ4/lM0+Sad5e1GiwAGKbJte8uo2+qY4/lSaP6pJPu7Jgj6CUlJcyaNYsLL7yQ//u//+Pkk0/mpptu4qGHHuLzzz+noqICh8PBSSedxC233ALAJZdcwkEHHcQ111zDjTfeiM1mo6qqiu+//56srCwWLFjA+eefD8DRRx/NlVdeyemnn878+fOZOHEiK1asYO3ateTn53PVVVdx0kknxcdy++23s3LlSvr06cM555zDvffey4YNG3YZ99atW6mvr2fu3LnxieyIESO4+eab+eKLL+Ln++WXX7jvvvv4+eefSUlJ4ayzzuLqq69GkiSWL1/OQw89xIYNG0hLS2POnDksXLgQm83GY489xsqVK/F4PBQXF/PEE09w4IEH8uCDD/K///2PaDTKwQcfzM0330xOTk6HPBc9nQgYBEEQBEHodTyhKEPvfpeGULRDrq/EE+Swxz7a4/kynDa23nxahwUNAIFAgKVLlxIOh3nllVf46quveOWVV+jTpw8rV67k17/+NccccwzTp0/f5bLvvPMOzzzzDI8//jj/+Mc/uPPOOzn++OPJy8vb5bxvvfUWL730EsOHD+eJJ57gtttuY9asWVgsFi677DLGjx/P119/TX19PVdccUWb4x01ahTDhg3jV7/6FSeffDJTpkxh/PjxHHzwwRx88MEANDQ0xDMQL7zwAhUVFcyfP5+8vDymT5/OhRdeyO9+9zteeuklysvLueqqq/D7/fHA6Ntvv+XFF19k/Pjx2O12rrvuOgKBAO+88w4Oh4P77ruPK6+8kjfeeEOsQWkH0SVJEARBEAShG5s7dy42m420tDTOPvtsXn75ZXJzc6mqqiIcDpOSkkJlZWWrlz3ooIM47LDDsFgsnHHGGei6TlFRUavnPf744xk9ejQ2m43TTjsNn89HbW0tq1atorCwkFtvvRWXy0W/fv347W9/2+Z4bTYbb731FvPmzeP7779n4cKFHHzwwZx77rn89NNPAHz22WfY7fb4moaBAwfy0ksvMXPmTJYsWcLIkSNZsGABNpuNQYMGcf311/P2229jGAYAAwYM4JBDDiElJQWPx8NHH33EzTffTHZ2NikpKfzxj39kzZo1/PLLL/v56PcOIsMgCIIgCEKvk954pL89JUkrS+u44v9+2OP5njxjOhP7Ze32PB1ZktSkT58+8d9DoRB33nkny5YtIz8/n9GjR2OaZnwivbPc3Nz471arFaBd57VYLPHzVlRUkJmZ2WLNTf/+/Xc7ZrfbzaWXXsqll15KNBrl559/5rnnnuPCCy/k008/pbq6mr59+7Y4+j906FAAamtrGTBgQIvr69+/P+FwmNra2l0ek9LSUgDOPvvsFpdRFIWSkhLGjh2727EKImAQBEEQhDZphoosKciSSMj3ROlOGwcNyt3j+aYPzOHBz9e16I60s+E5qVx6yAEJKW9pfpu33HIL6enpfP3119jtdgzDYNq0aZ16+wUFBdTV1REKheJrEsrKyto8/0MPPcQ333zD22+/DcQyDpMnT2bx4sVMmTKFoqIi8vPzKS8vxzTN+P375JNP8Pv99OvXj48//rjFdRYVFWGz2UhPTwdaPiZN5VUffvhhi6Bn8+bNuwQeQuvEJ6AgCIIgNKMbGt5gDRWerVQ0bKG8YRM1vlLCajDRQxMSRJIk/nLKZOQ2ggFZkrjv5MlJUQvv9/ux2+3Isozf72fRokX4/X5UVe2025wwYQLDhw/nvvvuIxQKUVlZyaOPPtrm+U888UQ2bNjAww8/TGlpKaZpUlNTwxNPPMGgQYMYOXIkM2fORNM0nn76aaLRKEVFRdxzzz1EIhFmz57Nli1beOWVV+J/e/DBBznllFNabcmal5fHzJkzufvuu6mvr0dVVZ566inOPPNMvF5vpz0uPYkIGARBEIRezzAN/OF6qrzbKW/Ygi9Sj2maKLIVWbKg6mFqfMWUe7bgDdZgGHqihyx0sdPGDeStBUcwPCe1xenDc1I7vaXq3rjllltYv34906dP54QTTsDv93P44YezcePGTrtNWZZ59NFHKSws5JBDDmHBggVMmzYtXuK0s1GjRvG3v/2NjRs3cuaZZzJhwgTmzp1LQ0MDf/3rX+PrMV544QW+/fZbZsyYwfz58znnnHOYN28e/fv35/nnn+ejjz7i0EMP5dxzz+Wwww7jtttua3OMixYtIi0tjblz53LwwQfzxRdf8Pzzz7fIOAhtE/swtIPYh6F76409zesCEaoDYfLdTtKc1qQ46tUT9cbXVk9imibhqB9/pIGoHgRTQpb33JveMA1MU8duScFtz8Bhc3foe0y8rjrH/u7D0KRpp+dyb4iCdCczhnSfnZ47ax+GcDjMypUrmT59evx6P/30U26//Xa++uqrDrsdYe+JfRgEQWiVaZqsKa9HN0221vpRJIkUu4VUm4U+qU6yU+xYFZFcFHqvqBrCF6knogYwMFAkC7JkgXbO+WRJBklGM6LUBstQQgoOq5tURxYWRexQ29NJksQRw3ZtOdqbWa1Wrr32Wq699lrmzZtHfX09L774IkcddVSihyZ0EBEwCEIPU9QQIKIb2BQZpzV2pEfVDepCUcp9IQzTxGm1kOqwkOmwkZ/mwmUTHwVCz6bpUXyROkLRAIahIUsKkiSj7GdlriLF3juhqB9/xIPNYifFnkGKLQ1JLJQWeglFUXjiiSdYtGgR999/P3a7neOPP57f//73iR6a0EHELEEQehDDMNlS7cPWRgbBbtmRgvaFNRqCUdZXebEpMql2K6kOC3mpTjKcNhRZTHaE7s0wdPyReoJRH5oeQZYsSJKEInf8V58kSVgkC4ah4wlW4QlU4bCmkOrMwmbZvxIYIfmZpolh6himHu/qIyEjywoSUrcpWdofU6dO5a233kr0MIROIgIGQehBttT40E0TuZ21FYos47LFAoOQphP0aRQ1BJGAFJsFt81CVoqDvFRHi2BDEJKVaRoEol6CES9RLYjU2BJVkVtffNkZZEkBCSJaiKB3O1bFhsuWjtuREfub0CMYpoFh6JimgUnL5aCmaWKiY+gaNAYMvS2AEHoWETAIQg+h6QaF9f79Wp8gSRKOxsBAM0wawirVgQg/VzTgsMik2i2kO+zkpzpIdYjF1EJyME2TsBYgEG5obH1qosiWLg0SWhPLOlgxTRNfuA5vuAaHkoLbmYHD6k7o2IR9YxgGhqk1BgTt6RkT+4zcEUDEumtJkoQsyfGAVhCSnQgYBKGHWF/labNH+P6wKjKNSyEIRHV8kQBbar0okkyK3UK6w0qu20G2y45FLKYWulBUC+EP1xNWAxim0RgkJOcR/NikUEY1ItT6SpFkC05bKmmOrE4pkRI6hmmamKbRWG5kACbtXh2/h+vVTR1oykDIyJIUX1sjCMlGfEoJQg8QVjVKPSHsls7/opElCac19tGh6gY1gQilniCmCU6rQqrDSqbTRl6qUyymFjqcZqj4wvVEov74LsySJKN0o0mW3BgghCIeAuF6bBYnKfZ0XLY0kbVLAjvWIxiYprHTXzv6+WnKQBjoJugtAgi5MQvRfV7bQs8lvs0FoQdYW+nBpiRuotG0vsEEvGGV+mCE9VUerIpMqs1KqtNKvttBhtOOLIsJkbB3DFPHH24gpPqIahEUSem0xctdqSnQ0Q2VhmAlnmAVDqsbxRSLpLvazouWE6d5AGHQtD2gCCCEROven7aCIOAPq1T6wvEWqslAkWWccrPF1F6NovpAi8XUOW4HfdwObGIxtdAK0zQIRn0EIx4iWig+YbJ08yChLU2LocNqgFC4ioBejT/SF4fTIWrcO0nLRcsG7c0emKZJtW87wagPly2V3NRBnZ4Z2hFAxEqidgQQishKtcHn86GqKllZWYkeSo8gPoUEoZv7uaIhqYKF1jQtprZblPhi6rWVHj7ZVMGnm8pZXlzDpiovvnA0wUf3hEQyTZOw6qfGV0pZ/WYagpVohooiW3rNpDmWObFiYuKP1FLesIkaX2njYm5hfxmGgaZHUbUImh6NZRT2Yl1Ccd06lqx6lE/Wvsw3m/+PT9a+zJJVj1Jct65Tx11YWMjNN93CrKOP46Bph3DC8Sdy//334/HWEdXCqHoU3dC6/PPz1ltv5fTTT2/1b0uXLmXs2LHU1NTs9jpGjhzJ999/D8Ds2bN57733Wj1fSUkJI0eOpKSkpF1jO/bYY9m0aRMA7733HrNnz27X5faWYRg899xznHTSSUyaNIlp06Zx0UUXsXLlyk65vUTpHZ/AgtBD1frDNISjiR7GPrEpMi6rgixJ+CMaxZ4AX22r4r8byvlmWxU/l9dT5Quh6TvXEAs9TVSLUOcvp8KzhRpfCaoeRpaVXt+CVJYUZMmCqoep8RVT7tmCJ1iNYeh7vrAAxILQkOqn1l9KlbcI3VRjaxPa1eGopeK6dXy98S38kfoWp/sj9Xy98a1OCxpWrVzF2WfOo6BfAW//402+X/YtTz39BD/9tIZLLvl/6HosS6IbGqoeJqqF0boogJg/fz6//PILP//88y5/e+ONN5g9ezY5OTntvr4PPviAOXPmdMjY6ut3PE9z5szhgw8+6JDr3dmzzz7Lu+++y6OPPsqKFSv48ssvOfjgg1mwYAHbt2/vlNtMhJ6Z2xWEXmJtlSfeBrW7kyUJV+Ni6mjjYuqShthR1abF1FlOG3lpzviia6H70g0NX7iOkOpD01WU+KZqiW2FmqwU2QIm+CMN+MK12C0puO0ZOGxuUZKyE8M0CEV9BKNeomoIUzLjr6+dRbUw3tDuj4BDLPBYXvhhm4GGicmPhR/isOz5+Uhz5mCzONp3Z4A7/nQnc+bM4YorF8ZPGzx4MIvvX8Sdd9xFSUkJgwYNYtyYCZx73q/49wcfMmHCeB5/8jE+/t9HPPv0c2zfXkRubi7nnHMOZ555JgCbNm3iT3/6Exs3bsTtdjN9+nRuvfVW3G43y5Yt495776WoqIjMzExmzpzJDTfcgMXS8rP3gAMOYPr06bz11luMHTs2fnplZSWfffYZb775JpWVldx777389NNP1NbWkpOTw+WXXx4fR3NHH300V155Jaeffjp+v5+77rqLTz75BJfLxTnnnNPivCtWrODhhx9m69ateDweRowYwW233cbEiRM5/vjjAbjkkku46qqryM7O5vHHH+fTTz8FYPny5Tz00ENs2LCBtLQ05syZw8KFC7HZbDz22GNs2rQJm83G559/jsvl4tRTT+X6669v9fn58ccfmTp1KsOHDwfA6XRyySWXUFlZSU1NDYMGDULTNJ544gneeecdfD4fBx54ILfeeiujRo0iHA7z6KOP8sEHHxAMBhk1ahS///3vGT9+PBDLwMyfP58lS5YwadIknn76ab755hsefPBBCgsLycvL47LLLuuwQKst4ltXELqpMk+QYFTr0RuqOawtF1PXBSKxBd6W2M7UaQ4r+W4n6U6bWEzdDRimTiDsIah6UbVwvMORRQQJ7SZLMkgymhGlNliGElJwWN2kOrKwKLZEDy9hdEMjEPEQVv1E9TCSKcWyVLtpsxvVwvxr5cOoerhDxhCMevnv2hf2eD6r4uDUSde2K2goLipm8+Yt3HLbLbv8LScnm0cfe7jl+YtL+PiT/6CqKj98/wO/u+4P3PuXezjmmFls3LiJq6+8hkg0zG9+8xv+9Kc/ccghh/C3v/2N+vp6FixYwNtvv82FF17IH/7wB66++mpOO+00SkpK+NWvfsXUqVPjE/Hm5s+fz4033siNN96Iy+UC4M0332T8+PGMHTuWSy65hIyMDD744ANsNhuvvvoqd911FyeeeCIpKSlt3vc777yToqIiPv74Y2RZbjFhD4fDXH755Vx99dX86le/IhwO88c//pFFixbx+uuv89FHHzFy5Eiee+45DjroIN555534Zbdu3cqFF17I7373O1566SXKy8u56qqr8Pv93HJL7HH++OOPue+++/jLX/7C119/zWWXXcasWbOYOHHiLuOcPXs2f/zjH2loaODwww9n/PjxjBgxIn5dAE899RTvv/8+L7zwAkOGDOHxxx/nsssu49NPP+VPf/oTa9eu5dVXX6Vv37688cYbXHDBBbz//vsUFBQAUFRUxOeff46qqqxfv57LL7+cxYsXM2vWLFavXs3ChQvJzMzk8MMPb/Px3F+iJEkQuiHTNNlQ7enRwUJrLIqMy2bBIsuEVJ0Kb4jvimr478Yyvt5ayerSOkobAkQ1UbKRLEzTIBjxUu0rpqxhM95QDYaho8hW0e1lPymSBZAIRf2Ue7ZR6S3EH2lopRVoz6RqUeqDVVR4tlHesBlfuA7d0FAky24Dhe6krrGsJicnu13nP2n2iTidTtLS0vjnP//F0bOO5oQTjsdisTB69IFcdMlveOeddzExsNttfPHlF3zw7/cxTJ13332HCy64AAC73c6HH37IZ599RkZGBl988UWrwQLArFmzSE9P59///jcAmqbx9ttvc/755wPw5z//mdtvvx2r1UpZWRkpKSmEw2E8Hk+b9yMajfLhhx/GswOZmZn84Q9/iP/darXy5ptvcu655xKNRiktLSUjI4PKyso9PkZLlixh5MiRLFiwAJvNxqBBg7j++ut5++23MYzYe2fw4MHMnTsXRVE48sgjyc3NpbCwsNXrmzt3Lq+++ioOh4PHH3+cOXPmcMghh/Dggw+iaRoA7777LhdffDHDhw9HURQuv/xyHnnkEcLhMO+//z7XX389gwYNwmazsWDBAoYOHcr7778fv42TTz45/rz+/e9/Z9asWRx33HEoisLkyZM5++yzee211/Z43/eHyDAIQjdUWOtH002sCWylmgwkSYov+NYMk/pQlEp/GL28HofVQqrdQobDRl6qA7dd7EzdlcJqkECkPr7zsiwpsQmueAo6XGxHaQuGoeMJVuEJVOGwuUl1ZGKz9JwWraZpEtVCBCIeIlowtiB+P0rZbJbYkf72lCTVBcpZXrjnGvhpg08mMyV/t+fZm5Kk3NxY/X91day0ZWc1NbUtgok+ubnx32trahl14KgW5+/Xrx/lZeUALH5gEU8+8TSPPPwovy/5AxMnTeTW225mxPARvPDiczz5xNPccccdVFdXc/jhh/OnP/2J/Pxd75uiKPzqV7/i7bff5swzz+STTz5BURSOPfZYAIqLi1m0aBGFhYUMHjw4fj+aJuetqa+vJxqN0rdv3/hpAwYMaHGb33//PZdccgnBYJDhw4djsVjatWajtra2xXUB9O/fn3A4TG1tLQC5zR5HiAUouxvv1KlTmTp1KhArx/r8889ZvHgxsixz7bXXUl1dHc8WANhsNiZOnEhVVRWqqtK/f/9dxtN8cXefPn3iv5eWlvLdd9/Fbw9A13UGDhy4x/u+P0TAIAjdjG4YbK3zYRW7KrfKpsjQ+Nj4IxqekMrGGi9WWSbFZiHVbsVtt5DlsuGyWcXj2IFULRLbeVkLxI70drPuRqpuEIxq+CIaYU0nqhlE9Vi2SpElLJKMokhYZBlFBqssY7fIOKwKViW2p4KS4NI4WVJAgogaJBj1YlVsuGxpuB2Z3XIRuWkahKMBAqqXqBrEMHXkxiChI0rZbBYHOan993i+bHc/1pd/s8uC5+bcjiyG503p0AMTBQUFjDhgBP/58COmTp3S4m+1tbUcf+wJ3PXnOzlp9okALW67oF8BxcUtOwqVFJeQk5ONYRisX7eehVf8P2648fdUlFewaNH93PLH23jlry+xafNmbr71JiwWC9sLt3P7bX/innvu5pFHHm31/p111lk88cQTbNq0KX7k32KxoKoql112Gddddx3nnnsukiTx888/t9kJqUlmZiZ2u53i4mKGDh0KQEVFRfzvq1ev5q677uLvf/97fO3Eiy++yLZt2/b4mPbr14+PP/64xWlFRUXYbDbS09P3ePnmAoEAhx12GA899BBHHXUUAHl5ecybN49t27axbt06TNOkb9++lJWVxnYLN02iapQH7n+ACy68ALvdTlFREcOGDWsxnqOPPjr+/+aPeX5+Pqeddhp33nln/LSqqqpOX+DefT7JBUEAYFO1D9F5tP0UObaY2qrIRHWD2mCErbU+vims4ZONZXyyoYyl2yr5sbiWtRX1FNUH8EdU9N0cTRJ20A0NT7CaisaSmJDqB0jaTdUM0yQU1ajyhyms87OhysOa8np+LK5lRWkdG6q9VAfC+CNqPFgA0A2TiK4TjGp4w1Hqg1EqfGG21fr5ubyBlSV1/FhSww9FNfxYUsvq0jp+rmhgQ6WHzdU+ttX5Kar3U+EL0RCKElJ1VN3otPdy04TaNE184XrKGjZT4y0h3Pj8JDPD1PFHGqj2FVNav5m6YDmqFo5tdCcnJlMoSRKTBh2H1EaKTEJi0sBjO2VsN/3xRv757j956smnaWhowDRN1q9bz1VXXM2Bow/k2OOOafVyp50+l88+/YyPPvoYXddZt24dL77wEnNOnYMsy9xzz3089ujjRCIRMrMysdttZGRmIEkSN/z+Rl55+VU0TSM7JxuLxUJ6RjqqHkbVI2i62riHRewFnJmZycknn8yzzz7LqlWrOOusswBQVZVwOIzD4UCSJMrKyli8eHH8b22x2WzMnTuXRx55hIqKCnw+X/xyENtjQZZlHI5YpmbVqlW8+uqrRKPRFtfh8/l2ue7Zs2ezZcsWXnnlFaLRKEVFRTz44IOccsop2Gy7rgMyTTP+r2nvDt3Q0A0Nu8PGUUfPZNGiv7D026V4fPUEg36+/+E7/vOf/3D0MUeh6mFOnXsKL7zwApu3bCQcDfHMM8/wySf/IyMznbmnncpDDz3E9u3biUajvPLKK2zevLnNNrBnnnkm77//Pl9//TWGYVBYWMivf/1rXnzxxTYfz46QnJ/ogiC0StUNttf7e93ahY4W21hux/9V3UTVVXwRKPWEUHUDWZKwNR49dlgUnFaFDIeNdKcNh0Xp1YusDdMgGPEQiHpRtRCSpCBLclIFCdF4tkAlohlENZ2IbqDqOqZJY5Zgx3OoyBLKXtZLyRLIitzqF6kJaLpBrIK5ZeChmyaGaSIBkgQyTZkLCUWSMHSNKn8E6gOkOHVsiozDomCzyLuMu33jlAEZ1YhQ6ytFki04bamkObKS5jmLLVpuIKQGYsEBsUXLShKtRRiQdSAzDjiblUX/xR+ui5/udmQxaeCxDMg6sFNud9q0qbz8yos8++zznDrnNELBEDk5ORx3/LFccunFWK2tZ1rGjx/Pgw89wFNPPc1tt9xORkYGZ887i1/PPw+ABx68n3vuvpejZs7CNEymTJnMn/50W6xT0OOPsHjxAzz/3AvIsszhR8zg2t9eA0ixyTOxXbEhFkxJyJx33rmcdtrpnHXWWWRmZgLgcrm45557eOSRR/jzn/9MdnY2Z599Nps3b2bjxo0MGTKkzft98803c++993LKKadgsVg4//zz+eyzzwA47LDDOPfccznvvPMwDIP+/fszf/58HnjgAWpqasjJyWHevHlcf/31LFiwgEGDBsUn/AUFfXn2uWd46KGHeeyxx7Db7cyefRJXXXMVqh5BNzQM0yCqNS2GNwET3VDR9EjjaTveg3f9+U5eevFl7rn7HkpLSgEYOnQoV11zJaeccjIAF/7mQjRN5/9dejkej5cxY8fw1NNPYLVauf53v+XpJ5/jggsuoKGhgZEjR8YXR7dmwoQJPPjggzz44INcc801OJ1OTj75ZK677ro9vZT2i2SKXZL2aM2aNQCMGzcuwSMR9kUwGGTdunUceOCB8Q4O3dXq0jpqgxFkUYvf5UzTRNUNdCM2yXNYFdBUyku2M/qAEfTLSsNtt2G3yD1yrYRpmoSjfvyRBqJ6EBq70CSSYZqEVR1vRCWk6kRVnahhEFFju+JKkoRVkbvdsglVVamsrCQvLw+L1YpuxCY6phl77UEssFBkCYsioSBjaVYqZVMUbBYFh0XGoshYZGmXzwzTNDBMHZvFRYo9HZctrctft6oWwR9tIKIGUfVI474TnVf4oEZ1Mlx94kel91XTTs+hqB+nLZXc1IHd5j1vmibRaBSbzdYJY27ahToWQMiSjCTt/+fhjmlqY1Nb04y3t439zYzfOphNvzRrgdvaNDeZni8Ti2xHljvntR8KhYBYu9ed7c38NjkOLQiCsEehqEa5NxRvNSp0LUmSsO2U2YnoBr6oQZk3RHlAwzBNLLKMwyrjsFhwWhVSbAqZTjtuR/dcLxFVQ/gi9UTUAAZGrANNFy9ebsoWeMNRolqsNCii6WhGrExg56PuFkXCQs94n0iARZagjfuj6SYaOpFmjcEMM7bWyQAwzcYJnIQix65LkWUskoSiyFjkCLJUi92ikOZII8uVg8vuxCLLHZ5F292i5e7UWleSJPqkDU70MJJQ7PWyIwOhsXMAgSQlYMKfTMFB9yUCBkHoJn6uaMBu6X4Tzt5AliTszQI5w4SgqhFUNar8Jht0H2BilWMlTk6rBYdVJtVuI9NpxWWzoHTS0aV9oWpR/NE6wtEAuqE2LjKVUTpx2ZtumIQ1DV9Yi2ULNL0xMIgdXZcbswVNZEnC1su7hLWlqVSqNYYJhm6gAmjNS6VUNCOIaZYgyXYscgo2SxpWJbag2yLLWJXYP4siYZdl8tOcpNh3P9HfddGy0bj/RvcKEoR90VoA0fJve3M9QmKJgEEQugFPKEKNP4zTJt6y3Y0iSzible5ohokvElsvUe4JEdWbJsMSTpsFh0XBYVXIcFjJcNq7bL2EYej4w/UEVR+aHol3ounonZejuo4/ouOPRIloBhE9tr5AM2LHE607lc/EWgf3jGxBMlNkqXG9QONmiaYPTfdiGE4MIw1NcRLWWq7F2Fzr58C8NAZmultcl2HqBCNeQqqfiBqKrdNo3KRP6UZds4SOJib+3ZmYfQhCN/BLhUcECz2QRYnVmDeJagZRzcATilJSH4ivl7Bb5MashILLaiHTaSXVsf/rJZoWLwejXqItFi/vX5CgGyYhVcMfiWULIlrsX1Q3MDGRENmCZNe0qZ5pRomolUiaBYvswmJJR5KUxgBDYn2llxp/hHF90wipnqRetCwIwr4TMxBBSHJVvhC+iCbKkXqR1tZLhDWdsKZTF4ywtdbANE1kWcbZbL2Ey6qQ5bKTYrfscvkmpmkQivoJRDwtFi/vS5AQ0XT8ERV/VCPamC2IaHpjS1ppl8W23XENhwCSpAAmqu5DNTzIkgOLkoYkWTFNL+WeEOUejVG5mbjs1qTpvCQIQscR72pBSGKmabK+yiOCBSFOlqQWC99brJcwTDbV+uILgZ2NLWEdVhm7RcMmBZClKLIsNXak2fPiZd2I7VvgjaiENYOIGltboOoGhmmiSFKLLIkiSSiKOKrcE0mN7VlNUyOqVRPbREJpDAot/FLpZWCmi7zU5NxdWpJ2v7uwIPREuq63ur/E3hIBgyAksZKGIGHVwCYCBqEddtShx0TUMIGIB0MPoxkaRmN9ukWOZTDsiozNEtsBO8VqJaCqBKJabN8CXY+3J41lC2SaL6UQ2YLeTULZJdi0KhJF9QE8YZXhOalJ1/5ZUWRCoWC3b68tCO1lmiahUIiUlJT9vi4RMAhCkjIMk001PhEsCHsltrmQB8MIYZoa0NiRRmlZcqTpBppuEIhCtT+CZhhYZLmxhWdMbDMzkS0Q2s+qyPgjGqtL6xnZJw1XEq29kmSJqBHC4/Fgt9sTPZyEMM3YPh+GYZJk8VyvpkhGh+/DoOs6oVCIzMzMDtlzI3neyYKwlzRDJaqGcVhdCd9AqjNsqfGhG0ab7REFoYlp6qhaLEgwzChNQYIkte8j3iJLWHrge0hIjKaY85eKBgakp5CfnjwlSjanTETzEgr2zj1ro9EoZWVlFBQUdEiZirD/dF0jy12ATe7YINZms5GSktJhG/SJgEHoFgxTJ6KFCKt+NC2Kqkfj29ID2C1OXPZ0XLbUeHeP7kzTDQrr/aLsQ2iTaRpouhddD2KYEfY2SBCEzmZVZEq8ATyRKCNy05KmREmxyL02b2aYMiY6FquM1dZbH4XkIukGDocduzV5AuvWiG8WIemYpomqRwirfqJaBFUPoxsqSBJyfFIkoTSbGGmGSkOwkoZgFQ6ri1R7FrYkf/PtzsZqD13Qel/oZkzTRDf8aLo/FiSYEpIkiyBBSFoWWSYQ1VldVs8Buamk2MRmbYLQHSX08GVtbS0LFy5k6tSpHHTQQdx9991omtbqeX/44QfOOussJk2axJFHHskzzzwT/1s4HOa2227jsMMOY9q0aSxYsID169fH/15SUsKVV17JwQcfzEEHHcTChQspLi7u9PsntI9uaAQiHur8ZVR5Cymt30SVtxB/uB5VDwOgyFaUxo2k2iI39pCPamGqfNspb9hCfbAK3Wj9NZWsoppOUUMwqXb+FRLHNE00PUA4Wkkoup2oWotpakgoPSKbJvR8shRbH/1LhYdyTyjRwxEEYR8k9Nvm2muvxeVy8dVXX/GPf/yDb7/9lpdffnmX823ZsoVLL72Uc889lxUrVvDMM8/w4osv8p///AeAxx57jMLCQj744AOWLl3KqFGjuPLKK+OXv+KKK0hPT+fTTz/l008/JSMjg4ULF3bV3RSaMUyDsBqkIVhFja+YsvotlDVspiFQSUQLoRs6SmNP+P2ZDDX1lA9FvFT5thHQa/BHGjDM5G+p93NFAzZRitTr6XqISLSScKSYqFaNaUYbgwRRRiB0TzZFptQTZH2lB93onWsIBKG7Slgee/v27fzwww98+eWXOJ1OBgwYwMKFC1m8eDEXX3xxi/O+/vrrzJo1i9NOOw2AUaNG8fe//x23O7Yd/ZYtWzBNE9OMfQDJsozTGStH8Xg85OTkcM0118RbqZ1//vmceuqpeDwe0tPTu+ou7xfdMLrdEedY+YRKKOojoofR9AiargKxbECstAgsUuelqCVJQpGtmBj4I7WoDX5sFiduWwYOm7vDFgN1FH9YpdIXxmkVk8LeSDeiOzocYTS2rpRiPwWhB7AoEiFNZ3VZHQfkpuG2ixIlQegOEhYwbNq0iYyMDPLy8uKnDRs2jLKyMrxeL2lpafHTf/rpJw499FCuu+46li5dSlZWFhdccAHz5s0D4De/+Q1XXXUVBx98MIqikJmZyauvvgpAeno6L7zwQovb/uijj+jXr99eBQumaRIMBvfnLu+XL7dWcdjgXJQkLmyPLUwOEtGCaHoUTY9iYMRLhVrSW72OzhCJRABQoxqypKBGffj89UiSjN2SQootA6slOVrs/Vhci6TrRLpZGVVv1fTaavq5L0xTQ9N9GGYIExVMpVkgm/wZMaHjqara4mdPtbq4hoJ0JwVp3Xe9WXfTEZ9ZQsfSDJVQKIiudn3WzTTNdh84TVjAEAgE4lmAJk3/DwaDLQIGj8fDq6++ykMPPcSiRYtYuXIll112Genp6Zxwwgnous7xxx/PFVdcQUpKCosWLWLhwoW89957u/RafuONN3jxxRd56qmn9mq8qqqybt26fby3+29LuZ+ashIOzE6OD1bTNNFNFY0wBhqGqWGggxnLHiSjioqKXU4zTROzMaixSA5skruV4KZreCIav9SGsItypG6ntdfW7hkghUCKIEkaserQ5D0YICRGXV1doofQ6aprDDYqCgPTbChJlvHtyfb+M0voLIapUysHsciJaXPb3va6CQsYXC4XoVDLxU9N/995RzqbzcasWbOYOXMmANOmTePUU0/lww8/ZNasWVxzzTU8++yz8WzFrbfeyrRp01i6dClHH300EOs9fO+99/Lvf/+bZ555hoMPPnivxmu1Whk+fPi+3NUOUW6tRDdM3H3SGJDR9btU6oZGSPWj6uFY9sCIgmlFltOSrqxnZ5FIhIqKCvLz83e7WY9pxna1tSl2XLY0nNauvW/fbK/hgHRR19udtPe1BY2vLyOAbgQwzQiQiiR1j5JIoWupqkpdXR1ZWVlYrT2/ZMc0TbwmDM12k+bo+fc3kfbmM6sjecMqJZ4QOSk2+rgdXXa73YFmqOSk9Mdm6foDwps3b273eRMWMIwYMYKGhgZqamrIyckBYmsR8vPzSU1NbXHeYcOGEY1GW5ym63q8TMjj8bT4u6LEUvpNH7R1dXVcfvnlRKNR/vGPfzBgwIC9Hq8kSQndTt5ht2NRZAq9EQbkpOPqxNZ0re15oJuxch7ZImOzWLHR/T7U7XZ7uz8gI4aPSNSH3erCbc/EbnF1avBQ5gmiSwp2e3JmZ4Tda+u1FVvHE4i1QSWMJEuNe2skR6ZQSG5Wq7VXBAxNtnnC9DNl+iXgoFhvszffh/sjFNXZVufHH1WxKQqVQRWfCgfkpmIR2XQAFF3G6XQlZB+GvZnXJOzZGjx4MFOmTOGee+7B7/dTXFzMk08+yZlnnrnLec855xz+97//8a9//QvTNFm2bBlLlizh1FNPJT09nSlTpnD//fdTW1tLJBJh8eLFZGZmMmXKFFRV5eKLL8btdvPGG2/sU7CQTGyKzLKi2vgC7/1lmiZRLYw3VEONr5Tyhi2U1m+i1ldKKOJHM1QkScIiWxNWqpMIsqzE1jtoEWp8xVR4tlAfqEDVonu+8F4yTZMN1R7sFhEs9ASmaaLrIcLRSsKRIlStFtNURRtUQdgDmyJT7guxtqJBdFHq5qK6zsZqL2sq6onqerzzn0WWieo6q0rrqfKLFrvdSUJ3+3n00Ue58847mTVrFrIsM3fu3Hi700mTJnHHHXcwZ84cDjnkEJ588kkeffRR7rjjDrKysrjhhhuYNWtW/HoWLVrEnDlz0DSNCRMm8MILL+Byufj444/55ZdfsNvtHHLIIS1u/4MPPqCgoKDL7/f+kCSJqG7wS0UDY/tm7vXldUMjrAaIaAHUxuwBjTX8TZMZi9x7jmi1R7xFa9RPIOLBothx2dy47ZnI8v5P8rfX+dF0E6uS3KVdwu7pegTN8GDoIUzJbOxwJAIEQdgbFlkiohusKq1jRE4qac7E1HUL+0Y3TIrqA1QHwtgUuc0W4RZFYntdgNpAlBE5ItvQHUhmRx2q7sHWrFkDwLhx4xI2hk82lLV4Q0VUnYn9ssjbTXcJwzSIaiFCqh9djxLVouimiozcIRPd7iISibB9+3YGDRrUYSlYwzQwTQO7xYnLno7LlrpPR48Nw+TTTeXiw7KbCoV8FJdsIL9vFhaLBChJv6ZHSH6qqlJZWUleXl6vKknamaob5Kc5GZCRsuczC+3SGd+HAKYJpZ4Alb4wkiTR3oaOJqDrJoOyUsjtpWsbNF2lT9qghJQk7c38NqEZBmHf2a0KP5XXc6TLhs2iJMWeB72JLMkgyWiGSkOwkoZgFQ6ri1R7Fra9eNNvrPZ24iiFzmCaGqrmxTCCRPUgkhwrU5Mk8XEqCB3JqshU+cJ4wyojc9PEgZUkVeENUeYLYRrmXrd+l4hlGwrr/NQGIozITUvq9vG9mfiG66ZMUwczyNIt6xmdn4KmRzExkSQ5vtZAkcXT2xWa2shGtTBV0e0oshWHLZVUR+Zuy7tU3aCoPoDNIr4Ek51pGmiaF90IopuRxvUIEhIWRDtUQeg8iiyh6gary+oZnpNKuihRShp1gQhFDQH0pkBhPyb6VkUmpOmsKq1jUKabHHdy7I0k7CBmlN2EbkRiu7+aUQxTxTQ1QCJsyhTXG6KrRJKIr3eIePGH67FZHLisqaQ4MnZZNL6uokEcSUlipmmgGQF03Y9hRsCUGgNy8bEpCF1NkSU2VnvJS3UwMNOd6OH0ar6wSmG9n7CqY1XkDvsek4g9z4V1fmqDYYbniGxDMhHffN2EqpYjy5Z4fXRT+YNFglJvkHSnFbddlBslC0mSsEgWDEPDG6rBG67BZnHitmXgsLkJqzpl3hAOa+9ZS9IdxEr7grE2qGYITJAkpXEBc6JHJwi9m1WRqQ5E8YbrGdknDasiPj+7UssWqXJji+iOZ1Ekgmos2zA4y012isg2JAMRMHQTpiS3uZjSpshsqvExoSATWSy4TDpNC8w1PUptsAw5KLOlVsUipwDiCy8Z6HoITfdiGGGQTEAECYKQjBQJNMNkdVk9w7LTyHSJEqXOFtV1CusCNISiu+181JGasg1ba/3UBiMMy04V2YYEEwFDD2GasLnGxwG5aYkeirAbimQhGNWoDXixKV5UzYIiu7Ba0pEkETx0JtM0WVPeQE0gQk6KndF5LnTDGyv1wxBtUAWhG7HIMptrvPRxOxiUJUqUOkNTi9SaQASrInVJoLAzqyIRiGqsKqtjSJabLJfINiSKCBh6CFkCTyhKpS9EXqrYRTaZbasLNNukzUTT/Wi6F1m2oyhuLLJbtObsQKZp8vW2Sp79dhNl3nD89L6pVi6clsuhg9NiwYIgCN2KVZGpCUbxRmIlSjZRotQhmlqkVnjDyLKU8D2CJECRJLbU+KlximxDooiAoQexKjJF9UHSHTZRG5+kPKEogcb6zyax4ECJtetUa1GlWmTJiVVJQ5YdInhog2mamOiYhoaBimmomBhg6rEuYsT2yvhmu4f7Pqtg541jy30q931Wxo1HSRw6ODUh90EQhP2jSLEj4T+VNjA0RxyB3l+VvhClnhCmaWJJss1ERbYhsUTA0MNYFYn1VR4mFGQh5pnJp7Dev9u0blNZkmlGiWgVSCjIsguLkh7vwNTTmaaOaRqN3cBUDFONHfIiFgiYmLGgQDLAjG38IyED0i7BlQm8tKx2l2ChiWHCS8urOGSQyOoIQndmUSS21PjwuFWGiBKlvbZLi9Qk/TzckW3wiWxDFxMBQw+kGyaFdX6GZIsPzWRS7Q+jau0/aiM1vj11PYiu+5AlG4qcgsWS2u3WO5iNE37D0DEbswEGOpgG8UCgMWMQm+Y3kVvfQVuSY0GCtPt1ycuKA5T71N2Ordyr8ktliLH5ojWxIHRnVkWmLhDBF44yKi9dlCi1gy+ssr3eT6iDW6R2Nqsi449orCqtY2h2qlj83gVEwNADKbJEdSBMutMqUnZJwjShxBPcpxRv7Mi3BRMDVfegGg3Ikh2LkooipyT0yLhpGo1lQToG0WZlQUaLsiBT0ptSAWBKxAKBncYtsd9rCUzTpLA+wrLiAMuL/ayrCrXrcnVBbb9uVxCE5KDIEoaJKFHag1BUY2tDGF+kc1ukdiZZAiSJTTVespw2huakik6RnUgEDD2UTZHZVusn1W4RvaqTQLk3iG6YWPbz6E3T0XbT1FC1WlSzFll2YFEyUJSO+WKMZQNiE37DbMoGaI3ZAKOxLKgpIGiZDWitLIimHZGbTu7gz/OwZrC6LMCy4gA/lvipDuz95D/LJT4KBaEniZcopagMznIna4VNl4tqOkXeCNWKhxSHIyGdjzqaTZHxRjRWldYzLNstdgPvJOJbsgeL7YzpY0x+RqKH0qsZpkm5N7TfwcKuYiU5hhklopYjaQqK7MJiSW91N+Lmi4RNVAxDi5UA7bRIOLY2oCkQaCMbAAkti6r0RVlWHGBZiZ+fyoOoestFCrIEI3OdTO3v4sMNHmp2E0S4bTIH9nF09pAFQehiVkWmLhjBG45yYF46NkvvPXjW1CK1rMFHSDNI72EHEpu+XjdUe8ly2Rma7RbZhg4mAoYeLqzqlDQE6J+Rkuih9FrF9YFOP7rVNHnX9ECsRatkR5LkxpIhY6dFwmbjIuHWyoKkpNywTDNM1lWGWFbiZ3mxn6KG6C7nSbHJTOmXwrSBbib3SyHdEft4G5Dh4L7PSttc+OyPGiz6vJzfHtEXh6X7H20TBGEHRZYwgZ/K6hmclUqOu3eVKO3cItWmtL0JbE9gU2S8YZXVpfUMFdmGDiUChh5OkSXKvCHSHTZSHb2jy04y0XSDKn+4y+pDd6x3aMoaNP2hfYuEk4knpLG8JMDyEj8rSgMEosYu5xmUaWda/xSmDnBzYB9nqwv2Dh2cyo1H9eOl5VWUe3csgM5zW1FkKPOqLC30UeVXue2Y/mSK8iRB6HEsisy2Oj+ecJSh2am9okSptRap+h4u0xM0fQ1sbMw2DBHZhg4hvhl7AZsis6nGy4SCrG7TAaGnKKzzY+kBNaJdwTRNttRG4lmEjdVhdk4K2BWJ8QUupvV3M3WAmz7u9gXBhw5O5ZBBbn6pDFEX1MhyWRiT5ySqmzz0ZTlfF/rYVBPmuiWF3H5sfwZniRIlQehprIpEQyjK6rI6RvVJ77H7FTW1SNWa1s310smyVZHxhKOsKq1jWHaqyDbsJxEw9BoSm2u8jOyTnuiB9BoRTacuFO0Ri8o6S1DVWVUaZHmJn+XFAepCu6416OO2xAOE8X1d2PexbEiSpF1ap9otEn84qoCCH2t466daqgMav/+giBuPKmBKf9GWWBB6mqaDZmvK6xmc5SbX3XMODuzcIrXj1811P02ZBZFt2H8iYOglZAl8EZVKX4i8VGeih9MrbKvb/SZtvVWpJ8ryEj/Liv38XBFE26nSSJZgdJ6Tqf3dTBvgZmCGrVNrbmVJ4vypuRSkW3l8aQUh1eCO/5Zw6UF5nDw6s9NuVxCExLEqMoV1ATwhlWE53btEKRTVKaz3d+sWqZ2tKduwuqyeYVlu0kS2Ya+JgKEXscgyRfVB0uw2nLaemYpNFv6IijesioABUHWTXyqCLGsMEsq8u26kluZQYguWB8QWLLvtXf/6PGZEBnluG3f/rwR/1ODp7yop9Ua5eHofUconCD2QVZHwRmIlSiNz07vd92JU19leF6C+MZMtvm92rymzsKHaS06KQ7Tb3UsiYOhlrIrEhmoPEwqyxBulE/X27EJdUItnEVaVBgntnEYAhmXbmdpYanRAjiMpJuXj+rp44JTB/Om/xZR7VZasrafCF+X3Mwtw9dB6Z0HozZomkT9XNDAoy0Ufd/Jn4HXDpKghQI0/glWRevV3zb6wKjL1oQgNZVGGZ6eKhjDtJAKGXkg3TLbW+hiWk5roofRIdcEI4cYa0t7CME021YRZXuxnWXGAzbXhXc7jsEhMLIh1NJraP4WclOT8kO6XbuOBkwdzz6cl/FwRYllxgBs+KOK2Y/qT285F1oIgdC9WRWJ7sxKlZKxzN00o8wQp94aQZQmrknxj7C6ant/1VR5y3A4GZ4psw56IgKEXUmSJumCEzKCNLFfv6kndFYrqA70iWAhEdVaWBlhW7OfHkgAN4V0b9vVNtTJ1QGwtwrh8Z7d5XNIcCncdP4DHllbw6WYv2+oiXL9kO7cd25/hOT1nkaQgCDtYFRlfRGV1WT2jkqxEqdIforShZYtUYf9ZFZm6QARPKJZtcItsQ5tEwNBLWRWZrTV+3P0s2HrYjo+JVOkLoRtmUpTXdDTTNCnxRFnWmEVYWxlkpw2WUSQYk+9i2gA30wak0C+tcxcsdyarIvPbw/vSL83GX1fUUBfSuOGD7Vx/ZAGHDhbZOUHoiZqOPK+pqGdQZkrCm4TUBSMU1YsWqZ2p6ft6bZWHPm4Hg0S2oVUiYOjFLIrExiovY/uKTjAdIbajZqhHBQtRzWBNRTAeJFT6d12wnOFUGjsapTCpIAVXEh2V21+SJDFvYg4FaTYe/KqciG5y76elXDAtl9PHZiV6eIIgdBKbIlNUH6AhFGVEblqXlyiJFqldz6bI1AYiNISiDM9JxW0X2YbmRMDQy0V0k6L6AAMzUxI9lG6vxBPANM1ufwSoJqDGA4TV5QEi2s7bp8GIHEc8izAs25GU9b4d6fChaeS6rdz1SQmesM5Ly6op9US5ZFp2oocmCEInsSoygajO6rJ6DshNJcXW+RNI0SI1seLZhkoPeW4HA0W2IU4EDL2cIsXKaDKdNtEpYD/ohkmlN9wta0t1w2RDdWxx77JiP4X1kV3O47TKTOqXwvQBKUzp7ybT2fs+Okb1cfLAKYO4878lFDVE+XijhwpvlAsOFF/ogtBTNR3Y/6XCw8D0FPLTO6dESdMNttX5RYvUJGFTZGoCEepDUUZ0UbCY7Hrft76wC6sis6nGx4SCzB5VTtOViuoDyEnw2JmmyS+VIWqDGtkuC2PynK2uIfBFdH4siWURVpT68UV2bXvaP93G1P6xvRFG57lERw4gP9XG4pMHcd+npawsC/JTRYhFPonb01UGZIkvFEHoqWyKTLEngCcSZXhOWod9VzZvkWoRLVKTStNz/EuFh7zU2NqG3kwEDAIQq6LZVO1lVF56oofS7UR1nepAOOEf9N8U+nhpWRXlvh3rDPqmWrlwWh8OGeRme30klkUo8bO+KoSxU6WRRZYY19fFtMYgoW+a2AmzNSk2hduPG8DT31bynw0NVARMbviwhFuO6c/oPFeihycIQidpKlH6qXz/S5REi9Tuw6bI1Pgj1Ad7d7ZBBAwCABLgi6hUeEKdlnLtqQrrAkkRLNz3WekuQUC5T+WeT0tJs8t4W8kiZLks8QBhQkEKTqs4utUeFlniikPz6OtWePnHWrwRgz9+WMy1h+czc5gIugWhp2pKLKyt8NAv3UVB+t4fJBAtUruf5tmG/FRnr1z3KQIGIc6qyBR5AqQ7bUnVfzqZhaI6DY01p4limiYvLavaJVhorilYkICRfZzxIGFIlr3btj1NNEmSmDM6A4ce4MU1ESK6yf1flFPmVfnVxGzxuApCD2ZVZMo8IbxhlRG57StREi1Suz+bIlPtD1MfinBATnLt1dHZRMAgtGBTZDZUe5hQkCU+y9phW50/4dmFXypDLcqQ2jJvQjZzxmSS7hBv+440oY+Fu4/P4Z7PK6gLary+soYyb5RrZuSLDieC0INZFImQprOqrI6ROWltbvrlj6gU1okWqT2FIkuYZmyvjt6UbRDfZsIuDNNkS60v0cNIep5QFH90zxP1zlYb1Np1vkGZdhEsdJJh2XYePGUQw7JjO6d/vsXLzf8pxhNu33MjCEL3JAGKJLGuykNpQ7DF30JRnXWVHtZWetAMUxxA6GFsikx1IMJPZfWEonqih9PpxKtX2IUsSdQFI9T4d22vKeywvT7xaxcAsl3tCwKy2nk+Yd/kpFi576RBHDQw1kljbWWI65dsp7hBvI8EoaezKjIVvhBrKxoIRXU2VXtZU1FPRNOT4ntC6ByKFDvIuqainuKGQKKH06nEq1holU2RKazzE9V7ftS8L6r9YSJacjw2Y/Kc5KfuvmtD3zQrY/LEYvbO5rTK/PHofswdE9s9vcKn8rv3t7O6rGd/kQiCECtViegGq8vr8Ec1ESj0IjZFpsrfs7MN4tWc5EzT5MstlXy11cfPFcHYTsJdxKJIbKjydtntdSclnmDSpJclSWJ4YylMa2QJLpzaRyzC7SKKLHHxQXksPDQPWYJA1OC2j4r5eGNDoocmCEInkwCHRUF82vY+zbMNJT0w2yBqFJLYu2uKuGHJihbrCZr66h86OLVLxhDVTbbX+3v9hiXNlXmCGGbswyEZFDdE+K7ID4BNkYjqO4LKvmlWLpzada8XYYeTRmWSn2rjvk9LCaoGj35dQaknyoKpucgieBMEQeiRbIpMpT+2S/Tw7LQe00lJBAxJ6t01RZz9ypcYO2UUyn0q931Wyo1H9euSSaAiQZUvTIbDRrpTbORlmCbl3lDS7IhtmCZPfFOBZsT2Bnj01ME0hHXqghpZu9npWegak/ulsPjkQdz53xIq/Sr/t6aOcm+U644swGFJjgyVIAhCZzJNk18qQ9QGNbJ7yfeSIsV28V5TUU+/NBf9Mrr/pp4iYEhCpmlyw5IVuwQLTQwTXlpexSGD3F3yprMqMptrfEzsl5U0E+VEKW4IJFW72U82efi5IgTE2qb2z7DTP8FjEloalGnn/lMG8edPSthQHeab7X6q/13Ercf0FwvRBUHo0b4p9PHSsqoWrb+7ulIikWyNi+HrQhFG5KThsHbfbIM4xJWEvtpatce2puVelV8qQ100IpBliY3VvXs9g6YbVPnCSVNO0hDSePGHKgD6p9s4c3xWgkcktCXTaeGeEwdy+JDYF+SmmjDXLylkW104wSMTBEHoHN8U+rjvs9Jd9glqqpT4prB3tG9XZCmWbShvoMwT3PMFkpQIGJJQmbd9L6i6dvbf7wgSEIholHu6LkhJNoX1ASxJstAZ4PkfqvBHYzs4X3mY2CQs2dktMr+fWcC8CdkAVAc0/vB+EcuK/QkemSAIQscyTZOXllVhtNGnpalSoisbuSSaVZEo94ZYU16fNF0W94aYYSShgrT21bp1dTmDRZEo9gQIRnvfZlQRTacuGE6azhcrSwN8viWW8TnugHTG5nf/+sjeQJYk5k/J5beH98UiQ0gzuOuTEpasrUv00ARBEDrML5WhXTILO+vqSolk0JRt+Kms+2UbRMCQhA4f2odh2buv7UtUX32bIrOh2tvm+oqealudH5uSHLWHEc3gyW8qAMhwKFw4rU+CRyTsrVkj0vnzCQNJtcsYJjzzXRVPf1uB3tbhOEEQhG6k2r/7YKFJV1ZKJBOrIlHmDfFzeX232e9KBAxJSJIk/nLK5N3Wyieyr75pmmyp6R21hwD+iIo3HE30MOL+vqo2fuTm4oP6kGpPjkBG2Dtj813cf/JgCtJim+69v66Buz4pIah2jy8PQRCEnRmmyVdbvby8vKpd5//3unoKe+laLossoRlmbLM3NfkDJxEwJKnTxg3krQVHMDyn9UxDIo/wy5JEQ0il2t873uSF9YGkyS4U1kd4Z00tAJP6pXDk0LQEj0jYH/3SbTxwymDG5seyhctLAvzh/SKq2nl0ThAEIVmsKg1w3Xvb+cvnZdQG23fg4+fKEFf+s5C/fFZKUX2kk0eYnCQJot1gTYMIGJLYaeMGsv7GU/ls4XH87sh87jiuPzmu2MT1yW8rqQ8lLiK1KhKFdf5u8SLfH3XBCKEkWbNhmCZPLK1AN2MbtC08JK/H97LuDVLtCncdP5BZw2PBX2F9hOuXFLKxunfV9gqC0D1tqglx84dF3PJRMZtrYwcS89xW5ozJpK1O7JIEBw10Y2/cAfWrbT6ueHcbiz8vo8TTOwOHZCeagCc5SZI4YlgenmAqFlnh2iMKuOU/xXjDOk8sreDmWf0SNmm0KjLrqzyML+i57TyL6wNJ033oPxsaWFcVm0T+amIOfdPERnp7K6obKJKUdPuJWBWJaw/vS790O6/+WE19SOemfxdx/ZEFvaJXuSAI3U+JJ8Jff6xhabP2qBkOhXkTczhhZAZWRWJsnouXlldR7m22D0OalQunxvZhqA9q/GNNLR+ubyCqm3yx1ctX27wcOTSNX03KoUB8zyUNETB0MxMLUph9YAYfrGvguyI/n2/xctTw9ISNRzVge52fQVnuhI2hs1T6Q6iGiSUJJpd1QY1XllcDsY3AThvXc4O0zqIbJmPy04loBltq/FiVxD+vzUmSxNkTsilIs/Lgl+VEdJN7Pi3lgqm5nDEuS2STBEFICjUBlTdW1vDfTZ5421SnVeb0cVnMHZOF07rjINuhg1M5ZJCbXypD1AU1snba6TnTZeGSg/I4fVwW//ipjg/XN6AZJp9t8fLFVi9HD0/nnAnZ5IvAIeFEwNANXTi1DytKApT7VJ7+rpJxfV3kpFgTMhZFik2sM5w20p095w1tmlDaEEqKYAHgue8rCUQNJGJ7LiTLuLoD3TCxKRJjCzKxKDIpNqh2hAmqetK0yW1uxpA0clOs3PVJCQ1hnZeXV1PmjXL5IflJF+QIgtB7+CM6b/9Uy5K19UT1WKRgkSVmH5jB2eOzSXe2PqWUJGmPrb+zXVYuOziPM8Zl8dbqWj7e2IBmwCebPHy22cMxI9KZNzGHPu7EzHUEsYahW3JYZX57RN/YZmpRg0e/rkjo5ic2RWFzra9HtYQs8wSTZkOZZcV+vtoWS/meOCqDA/t0fTvd7krVDdIdNsb2zWqx6d7wnDR0PTme39aM7OPkgVMGMTAjFoR/vNHD7R8X44/07DVDgiAkn7Bm8PbqWi56ewv/t6aOqG4iS3DMiHSePXMolxyU12awsLdyUqwsPDSfZ88cxvEHpKNIoJvw0UYPl/5jC098U9Hulq1CxxIBQzc1Os8VL0tZURrgo42ehI5HliQ2VCV2DB1FN0zKvaGkqHMPqwZPfRvbcyHTqbBgam6CR9R9RHWD/ukpDM9NZedqHkWWGJDlQtWNxAyuHfJSbSw+eRCT+6UA8FN5kN+9v51yb/K0+BUEoefSDJMP19dz6dtbeeXHagLR2OflwQPdPDZ3CNce3rfTjvj3cVu5akZfnjlzKMeOSEeWQDPgw/UNXPKPrTz9bQW1QRE4dCURMHRjv56UEz8C+cL3VVT4EjeRkICQqne7nQtbU1QfQE6CYAHg9ZU1VPljXZouOziPFFtytHdNdppuMjI3jb7pbWdj8txOXLbkrspMsSncfmx/ThqVAUCJJ8r1S7bzS0X3f58JgpCcmvZSWPjOVp74ppK6xo6MY/KcLJ49kFuO6c+gTHuXjCU/1cY1h/fl6TOGcvTwtMbAweT9dQ1c8vZWnvu+kvpeuvlbVxMBQzdms8RKk2QJQprBw1+VJ3R/BkWWKPUECUS7b9Qf1XWqA+E2W8F1pa21Yf75Sx0A0wakcJjolrNHTa//8QUZ7VpTMyInFTWJS5Mg9r66/JA8LjmoDxLgjejc/J9iPtvSMzJ6giAkj+Z7KZQ1djYanGnn9mP7c99JAzkwb/drETpLQZqN644o4MnTh3Dk0DQkIKqb/OuXei5+ewsv/lCFJ4Gt5nsDETB0cyNynMybkA3AzxUh3l9bn9DxWBWZjVW+hAYu+6OwLoAtCdqo6obJY0srMEywWyQuPyRfdMnZA90wSbFZGF+Qic3SvkyMzaJQkOZM+vU3kiRx6pgsbjmmHw5LbHfQB74o57UV1Umz1kYQhO6rrb0Urj+yL4/OHcy0Ae6k+A7qn27n9zMLeOK0IRw+JHYQLaKbvPNzHRe9vYWXl1fhDYu1Xp0h8TMjYb+dPSGHoVmx9ODLy6sTvumJCWyu8e3xfMkmFNVpCCVHffi/19ezqSb2oX3eJNEZYk9U3SDX7WBkn3TkvfxS65fhSpq9NvbkoIGpLJo9iGxXrJTqjVW13P9FOVEteddiCIKQvEo8Ee79tJTfvred1eWxUscMh8JlB+fx9BlDOWrY3n+mdoWBmXZuOKofj88dzKGDYm3dw5rJP36q46K3tvDXH6vxiSYRHap7fEsKu2VVJK47oi8WOZaie+jL8oQeMZUl8ISiVPvDCRvDvthW50+K7EJNQOXV5TUADMu2c+oYsefC7qi6wbCcVAZmpuzzdYzISSOaxAugmxua7eDBOYMYlh07SPDFVi83/6dYpOOFLmOaJj9XBPliq5efK5Kno5zQfrVBjce+LmfhO9viG685rTLnTc7hubOGccrozG7RxnlwloM/zurPI6cO5qCBscAhpBm8ubqWi97awmsrqkV3uQ6S3Cv+hHYbnOXgvEm5vPJjNRuqw7yzpo6zGkuVEsGqyBTW+Ul3WNtdHpJI3lAUf1RNioDhme8qCWkGshTbcyEZujUlI5PYfhlj8zNx7udicKdNIS/VQY0/0i0e72yXlb+cNIj7vyjjuyI/66pCXLdkO386rj8DMrpmMaLQO31T6OOlZVWU+5rt3Jtq5cJpfcSu5N2AP6LzzsYInxcX7bqXwoRs0h3dc1o4LNvBrcf0Z1NNiNdX1rCsOEBQNXhjVS3vra3ntLFZzBmdiUs0DtlniZ8dCR3m9HFZjMx1APDayhoK6xJ7hN+qyKyr7B4LM7c3JMfahe+2+/h2ux+A2QdmMiJH7LnQGt0EmyIzoWD/g4UmAzPc3SJYaOKwytx0dD9OHxvLQFX6VX73/nZWlQUSPDKhp/qm0Md9n5W2CBYAyn0q931WyjeF3a8Utbdo2kvh//2ziI8L1db3UuimwUJzI3Kc3H7sAB5o1pI6EDX424oaLnp7C2+vriWkdo9scrJJ/AxJ6DCKLPHbI/piU2KLIh/6qjzhHWA0w2RbnT+hY9iTmkCYsJr4lGVQ1Xn6u0oAclIszJ+Sk+ARJSdVN8ly2RiTn9GhE3xJgmHZqUT1xL8W2kuRJX4zvQ9XHpqPLMW+GG//qJj/bGhI9NCEHkI3TPwRnUpflGe/r6StalfDhJeWV4nypCTT1l4K0/u7On0vhUQa2cfJnccPYNHsgUzoG+vs5IsYvPJjNRe9vYV31tQSFmu/9kr3DyeFFvqn21kwNZfnvq9iS22Et1bXcN7kxG32pcgSNf4wGQ4bma49t7lMhOKGYFIsev3bjzXUBGJ16P/v4DxcVpE63VlUNxiUmUJeaudkXlIdVrJcDrxhNSla67bXCaMyyEu1ct9npQSiBo8vraDMG+WCqblJuWCxOzBNk18qQ9QGNbJdFsbkOZOiS0x76IZJWDMIRg1CqkFQbfqpx342Oz3+t+hO52k8PaK1PwAo96r8UhlibH5iWm8KOximydJtPv66ojreHhVgdB8HJw+WOGRkX6zWnhco7Gx0nou7TxzImvIgr62s5ueKEN6wzovLqnnn5zrOHJfNiaMysFsSPwdIdiJg6IFOGZ3Jt9t9/FwR4s3VtRw0MJXhOY6EjceqyGyt9THBnoklCSbmzVV4QhgmJHpt16aaEO+vi7XEPXigm4MHiVrgnemGyeg+6bgdnfslNzTbzcqSOnbZHjrJTeqXwuLZg7jjvyVU+lXeWVNHmSfK744swGFNrvddsktEnb5hmoQbJ+reUJRSj06FESRqyM0m8HorQcCuE/7wXkzyO9qT31Qwa0Q6U/qlMCjT3m2CrJ5kVWmAl5dXx9ujAgzJsrNgSi7j82xUVVUlcHSJMa6vi3vzB/JTeZDXVtSwtipEQ0jn+R+qGtd8ZnH8ARnYRODQJskU+cM9WrNmDQDjxo1L2BiWrPkOi9z+I84VvihXvruNsGYyMMPGw3MGJ/yNYFVkxuRndPntRiIRtm/fzqBBg7DbdywINUyTlaV1KAn+QtMNk9++V8jWughOq8xTpw8hJ6XnH/lpL90wsSoSB+alY1W6JutSF4ywpca/xy4hqqpSWVlJXl5e0hytawhp/Pl/payvCgEwPNvBrcf2I9uVHONLdk11+q2V3sgS3HhUv3jQYJomYc1s9ch90/93d3S/+c+urqt2WCRcVgWnVcZlk2M/rTv9jJ+uUO1X+dvKmr26jSyXhcn9UpjcL4VJ/VJItYusaWfaVBPi5WXV8faoENtLYf6UHI4YmoYsSUn5mdXVTNNkZVmQ11bEmsQ0yXZZmDchm2MPSO/SqoOIFuHQIWPpk5bRZbfZZG/mtwnNMNTW1nLrrbfyww8/oCgKc+bM4YYbbsBi2XVYP/zwA4sXL2bz5s2kpaVx7rnnctlllwEQDoe55557+N///kc0GmX06NHcdNNNjBo1CoBgMMhdd93Fp59+iqZpzJo1i9tvv52UlH1vw5js8lNtXDw9j8e/qaCoIcrfVtbwm2l9EjqmkKpT2hCkX0ZypKtLGoIkw7Gv99bWs7UutnfG+VNyRLDQjKobZDhtDM9J69ID/lkuO9XOMMGonhSvkb2R4bRwzwkDePircr7c5mNzbZjrl2zntmP6MzQ7cZnGZKfqBrUBlWe+232d/qLPS0mzK4Q1k5Bq0JVH3OwWaaeJ/a4T/tYm+zsHAQ6LvNfrf0zT5H+bPbsseG7ObZPJT7WypTaCCdQFNT7Z5OGTTR5kCUbkOGIBRH83B+Q4ulWTgWRW4onw1x9r4u1RIbaXwryJOZwwMqNbtEftSpIkxYLYAhfLSwK8vrKGTTVhaoMaT35byds/1TJvYg7HjEjHIl6jcQkNGK699lry8vL46quvqKmp4fLLL+fll1/m4osvbnG+LVu2cOmll3L77bczd+5cNmzYwIIFCxg0aBAnnHACjz32GIWFhXzwwQe4XC4eeOABrrzySj755BMA7rrrLsrLy/noo4/QdZ1rr72W+++/n9tvvz0Rd7vLHD8ynW+2+1hRGuDdNXUcMtCdsG3dIda6rdQbJN1pxW1P7KRYN0wqfaGEr12o8qv8bUU1EPsyPWlUZkLHk0yiukG/dBf90hPzmh2encbKkjos3fDL1maR+f3MAvql1/DGqlpqAho3fFDEH44qYNoAd6KH16UM08QT0qkNatQFNWqDauNPrdlpWrt3h9UMqAu1f2G8TZFik3Xbjkl7W5P9pp822STsa6BfXg6pTlv8b4mcYEuSxIXT+uw2+3L1jL4cOjgVT1hjVWmQH0v9rCgN0BDSMUzYUB1mQ3WYN1bVkmKTmVSQwuT+sQyEOFCy92oCKm+srOG/mzzx58RplTl9XBZzx2ThFKWIuyVJEtMGuJnaP4Ufiv28tqKGrXURqgMajy+t4O3VtZwzMZujh6eL4JYEBgzbt2/nhx9+4Msvv8TpdDJgwAAWLlzI4sWLdwkYXn/9dWbNmsVpp50GwKhRo/j73/+O2x374tuyZQumaca7M8iyjNMZWxQZCoVYsmQJr776KhkZGQD87ne/4/zzz+cPf/hD/Hw9kSRJXD0jnyve3UYgavDgV+U8duqQhNYz2xSZTTU+JhRkJnQx5rY6f8LXU5imyVPfVhDRYu3trhJ7LsRpuskBuWlkOBO3UF6RJQZmudheF0h4YLkvJEnivMm59E2z8ejXFYQ0g7s+KeHi6X04ZXRmt68tN02TQNSgrtmkv7ZZQNB0Wn1Qo6ObxU0bkMIBOc7dlPIo8cBgX45QxspGfORl2JKqbOTQwanceFQ/XlpeRXmzhbR906xcOHXH+o50h4Ujh6Vx5LA0DNOksC7CitIAK0oDrK0Mohmxjl5fF/r4uvGo+MAMG5P7pTClv5sxec6El9AmM39E5+2falmytr5H7aWQKJIkcdDAVKYNcPPddj+vr6yhsD5CpV/lka8reGt1Lb+alMORQ9N69Xd0wl5VmzZtIiMjg7y8vPhpw4YNo6ysDK/XS1paWvz0n376iUMPPZTrrruOpUuXkpWVxQUXXMC8efMA+M1vfsNVV13FwQcfjKIoZGZm8uqrrwKxwERVVQ444IAWtxMOhyksLOTAAw9s13hN0yQYDO75jJ1EjaqYyt7XuKbb4OJpOTyyNPYB/+IPFVwyPXFdkyB2xG9tWS0jcrpmYW8kEmnxM6zqVHoSv6vzN9v9LCuO9cw/5cB0BqQpqGrb6f7ewDBNJGBkbhoO2Yw/Z4mSbpWxYBCNaq1OsJuer2R+3g4f5CLb0Zf7vqjAFzF49vsqihvCXDQ1J2m//KK6QV1Qpy6kxX4GtR2/Nzstso+RgMsqk+VSyHJa4j+zXRb8UZ03Vtfv8fJzD0xndN6eDjYZmLrBvnRsTubX1bR+DqYWDGBtVZj6kE6WU+HAPg6kxvr41gxIUxiQlsapB6YRUg1+rgixoizIqvIgFb5YZ7iihihFDVH++Us9NkVibJ6TiQVOJhe4KEizdvsAtyNENIP313t495eGeHtUWYKZQ1M5Z0ImuSlWwNzt6yaZX1vJYFo/B1MK+vFtUYA3V9dR7FEp96k8+GU5f19Vw7zxmRw2qGP37ImqGsFQmKCl6+eYpmm2+72VsIAhEAjscnS/6f/BYLBFwODxeHj11Vd56KGHWLRoEStXruSyyy4jPT2dE044AV3XOf7447niiitISUlh0aJFLFy4kPfeew+/P7YHgMvl2uV2AoH2b3Ckqirr1q3b5/u7vyqrKvdq0XNzo1wmE/oorK7S+fcGLwe4o4zKTuwRiHLDwF9jI8vZdeOoqKgAoNATIaIbCf0CCqkmz34f+3DIckgcna9RWVmZsPEkA90wcVhkBqXZqCzzJno4cVbdoLAhjFVuO8Csq6vrwhHtvWzg99McPLEiRGXQ5MMNXopqAlw8wYFDgc31Bg0Rgwy7zPBMudPeG4Zp4o2YeCIm9Y0/PRGDhrBJQyT2zxMxCOzjXMYiQ4ZdIt0ukWGXG39KZDhanuaw7Hz/NEDDNE0+cUpUh9oORHJdElmmh8rKzn+NJvPrKgdo2leyqmrvHouBNhg4GOYOdlAVNFhbo7G2VmdDnU5Eh6husqIsyIqyIC9SS5ZDYkyOwuhsC6OyFZy7PH89m26YLC3V+GBrFE9kx2tzQq7CqSNsFLhNDH8dlXux5VEyv7aSwXAH3DTdxo8VMu9viVIZNCnzqjz0dRVvrKzm5GE2JudZOqRSIqqrbIko1DoTU35rs7Uvk5+wWaPL5SIUCrU4ren/Oy9GttlszJo1i5kzZwIwbdo0Tj31VD788ENmzZrFNddcw7PPPhvPVtx6661MmzaNpUuXkp+fH7/uputtup2mkqb2sFqtDB8+fO/vaAfZThDLfnSIuTZd4+olxfgiBq+t13j45L64bIk9wq7qBnl56Tg6eb+BSCRCRUUF+fn5qMhUyT4yE5zufub76vgH/8JD8xjYr+cuwG+PqGaQ67YzMMOVlEcSU7whKrzhXdYzqKpKXV0dWVlZSVU60po8YHGBzqIvKvi5MswvtTp3fxdBkqC6cf8PgPxUCwsmZ3PwwPZ/Ppqmib+pPCikt/zZ7PeGsN7mguLdkYAMZ7OMgMtClrPpp4Vsl0Kmy0Kqbf+Dnd9M97P4y9YXPssS/GZaH/LzO3cdSHd6Xe2vPGDckNjvqm6yvjrMyrIgK8uCFNZHAagLm3xVovFViYYiwchcBxMLXEwucDEky9Zj9xoxTJNvtwd4bVVdi8Xmo/s4mD85m1G5e9/EoDe9tjrCyflw4niTrwr9vPVTPeU+lYqAyfM/RRiYYXLOhEwOGpCyX6/BsBpl2MAR9ElN2/OZO9jmzZvbfd6EBQwjRoygoaGBmpoacnJiO9pu2bKF/Px8UlNblqoMGzaMaDTa4jRd1+NlQh6Pp8XfFUVBkiSsVitDhgzBarWyefNmJkyYEL8dq9XK4MGD2z1eSZJaZCm6mtVm3ecMA0Cu1coVh+Zz32dlVAc0XllZx9Uz+nbgCPee1QrbPGEmFGR1SQccu91OcX0It9O+5zN3onVVIT7aGDsid/iQVA4enJHQ8SSaqpuMzHeT407s87I7Q3Lt+LV69DZmu1artVt8+WZZrdx1wiCe+KaCTzZ5qAlqu5ynwqex+MtKbjzKwqGDUwlrsUCgNrBj0XDzxcJNQUF0H8uD3DaZLFesJCg7xRoLAFIssWCg8fRMp6XLyqcOH5aJolj2WKffFbrL66qjWK0weYCNyQNiE6e6oMbKxrUPK0sDeCM6uglrq8KsrQrz+qo60h0Kk5q1bs3swqx1Z9rdXgpT+qfsd2Dc215b+8MKHDsyi6NHZPLZFg9vrKyl0q9S1BBl0ReVDM2yc97kHKYPcO/T82JIBi6nIyFzzL0Zb8LeWYMHD2bKlCncc8893HnnndTX1/Pkk09y5pln7nLec845h4svvph//etfzJkzh+XLl7NkyRLuv/9+0tPTmTJlCvfffz9PPfUUbrebhx9+mMzMTKZMmYLT6eTEE0/k/vvv55FHHgHg/vvv5+STT8bh6F0tBmcMSePI7X6+2Orl440eDhmUmvCOKbphUljvZ0hW54+jIRQlENUSunZBM0weX1qBCaTYZC45KG+Pl+mpTGJH0Mbkp+OyJf+X/PDsNNZU1Cd87cv+sioSVx+Wx/dFPnyR1tdFNbUPtSkSQXXfAgGbIpHdbNIfDwBSrLHgwGUh02XBkYSLWw8dnMohg9z8UhmiLqiR1c12eu4pslwWZo1IZ9aIdHTDZEttmB9LA6wsCbC+Orbppies8/kWL59viR2EGZZtZ1K/FKb0czOqj7PbtRRtz14KQmIossQxIzKYOSyd/23y8PdVNVQHNLbWRbjrk1KGZzs4b3IOUzsgoEtGCd24raamhjvvvJPvv/8eWZaZO3cuv/vd71AUhUmTJnHHHXcwZ84cAL744gseffRRtm3bRlZWFhdffDHnnHNO/HoWLVrE0qVL0TSNCRMmcNNNNzFkSCzP6ff7+ctf/sKnn36KqqrMmjWLW2+9td3RXHfcuK0tvojOwne2Ni5Ws/DE6UMSvplOVDcYkZNGpqtzOuJEIhEKCwvx2tOxWhPXdQfgHz/V8vLyWBvVhYfm9do2qrphYrfIHJiXkbQLb1uzvd5PjT8SH3N33QTp54ogN/67aJ8uK0uQ6dwRCMSDghQLWa4dwUBKB5QH9Vbd9XXVlfwRndXlAVaUxDIQzcvqmjgtMhMKXPHN4/LTEvv5vztdtZeCeG11HFU3+e+mBt5aHWtd3eSAnFjgMLlf+wKH7rJxm9jpuR16UsAA8EORnzs/KQHgyKFp/H5mQYdc7/7QDZMJBZmd0uo0EomwYv0WcGfidCSu7KXCG+WKd7cR0U1G9XGyaPbAXnm0SNUNslx2hnVRl6yOZJqwqqwuvplbd/3y/WKrl8Wfl+3xfIcMdDOpf0qzTIGVdIfSrYK87qi7vq4SxTRNSjxRfmwMHn6uCLZaIleQZmVKfzeT+6UwLt+V0BbjTbp6LwXx2up4Uc3go40e3l5dS11oR+BwYB8n503OYULf3a/N6y4BQ/LXAQgdbvpAN8eOSOe/mzx8sdXLIYPczBjS9YttmlNkiQ3VXsbkZ3T4dRuGSXVIo2964r4cTNPkiW8riegmigRXHpbfK4OFqG4wMCOF/LTuuf+JJMHw7FTWVTVg248mBImW7WrfR/+pY7MYm58cO7MLQlskSWJAhp0BGXbmjs0iohn8XBGM7/1Q3BBb41jmVSlbW8+StfVYZIkxeU4m909hSr8UBmXauzQjJvZS6DlsFplTRmdy3AHp/GdDA2//VEtDSGddVYhb/lPM2Hwn503KZVzf7v1ZKl6RvdQlB/VhVVksjfvkt5WMzXeRkeDFYmFVp6QhQP+Mju0YVO4LYZLYRNqXW32sLI218T19XDaDM5N3gW9n0Q2TA/ukk+ro3ke1Uh1WslwOvOHu28d8TJ6TvqnWFp1XdtY3zcqYPe41IAjJx26RmdLfzZT+sbVxVX41HjysLgsQiBpohsnq8iCry4O8tKyaLJclXro0sSCFNEfnHBAIawZLfqnnH2tqW+ylcPTwdM6dlEMfd/f+fOzN7BaZU8dkcfzIDP69rp5/rKnDG9b5uSLETR8WMb6vi/Mm5TCm2UEY0zT5pTJEVaCUUXkRDh/aJ2lLOUXA0Eu5bArXHN6XW/5TjDes8/jSCm6e1S+hL1RFlijzhkh32DpsUqkbJpW+MEoC75cvovPc97E9FvJTrZwzMTthY0kEwzRRJIlxBRnd+qh8c0Oz3awq3fMGX8lKkiQunNaH+z4rbbN96IVTk/eLSxD2Rh+3lRNGZnDCyAx0w2RDdYgVpQF+LAmwuSaMSawj0yebPHyyyYMEHJDriAUQ/d0ckONoVxle0+SvNqiRvdNCec0w+e/GBt5Y2bJs5eCBbuZPyWVQLzyI1FM5LDKnj8vmxFGZfLCunv9bU4svYvBTeZCfyouY1C+F8yblUBfUeGlZVeOBm9g+UcOyU/nLKZM5bdzAxN6JVog1DO3Q09YwNPfUtxV8sK4BgOuP6MtRw9M7/Db2lmGaTCjI6pA66W11fqo8fqqrqhJWs/no1+V8vNEDwF3HD2BSL9pzQdNNUh0WDshN75LWuV2pLhhhXXkd9TXV3bYe+JtCX1K0DxVaEnXmXccT1lhVGmRFqZ8VpQHqQ7tuzZ1ik5lYkMKU/rEMRE7Krs/JN4W+ZpO/mL6pVi6YlothwF9XVFPW7H02Nt/Jgql9OLBP12bxxGur6wWjOkvW1vPOz3XxrNLuyJLEWwuO6JKgQaxhENrtwql9WFESoNyn8vR3lYzr62r1w7BrSWyu8TKyz/4FL6puUOOPJHStwM8VwXiwMHNYWq8KFlTdoG+as8NLzJJFlstOmt1CXTc+5iLahwq9XbrDwpHD0jhyWBqmabKtLhIvX1pbGUQzIBA1WFroi3cwGphhY3K/FKb0dzMmz8nykkCr2bpyn8q9n7ZsLtCReykI3YPLFut2dfLoTP71Sz3vrqklpLX9vWGYJje+v4K5Ywck1WtEBAy9nMMq89sj+nLDB0UEogaPfV3Bn47rn9AXqSyBL6JS6QuRl7rvR18K6/xYFQl1zwF9p1B1gye+iaUZ3TaZi6f3ScxAEkDVDYZ3YqvcZDE0282WkkSPYv9IkiQWNgsCsffC0GwHQ7MdnDk+m5BqsKY8yI+N2YemTFxRQ5Sihij//KUemxy73J52MO+TYuH8qbliL4VeLMWmcO6kHEZkO7jjk91/cWyu8fH1tioOH5o8ezWJgEFgdJ6L08Zm8c7PdfxYGuCjjR5OGJmR0DFZZJmi+iBpdhtO296XYoWiOvWhaEI32frHmrp4d47fTO+T8EXlXcEwQQLG5mfu0/PW3VhkmXynFVU3ENl9QehZnFaZ6QPdTB8YWzxd7o3G1z78VB4grJnEKkz2nGW85vC+TCjomdlWYe8EtfYdxSzzhDp5JHun589ghHb59eQclpX4KW6I8sL3VUwqcJGXmtijw1ZFYkO1hwkFWXtd/15Y709osFDqifLW6logVqt67IjErw3pbLoZ2yhpZJ/0XtWnP8tpIWAVH6WC0NP1TbMxO83G7AMzUXWTdVVB/vVzHd8XB/Z4WU9417URQu/U3rbWBenJ1aUu8buWCEnBZpG57oi+yBKENIOHv6rASILabN0w2Vbn36vLeENRfJHEtbw0TZMnvqlA1U0sMlxxaH5S1SF2BlU3yHbZGJ3fvXZu7ijDslJQW9koSui+TNMkqlYTjhYTVasR/UGE5qyKxPi+KZw2rn1d77LaOUkUer6mtta7MzwnlRlDkquMWQQMQtyIHCdnT4h9+K2pCPL+2sS3jVRkidpAmLpgpN2X2d4QSGh24dPNXn4qDwJw1vhsBmT07HZ5Ud1gcJabwVnuRA8lYexWhYJ0J/qeCpmFbiEcLaXW+xH1/i/xBH6g3v8ltd6PCEdLEz00Icm0Z/In9jQRmmtqa93WsTVZkrjv5MlJd6BRBAxCC/Mm5DA0KzbBfXl5NSWe9k/UO4tVkdla4yeq7zmlWxuIEFYTl/r1hDVe+KEKgH5pNs4a37P3XNANkzH56eS6HYkeSsL1S3dhs4iP1O4uHC3FE/gO3WhZZqIbATyB70TQILSw58mf2NNE2NWhg1O58ah+9E1rGWwOz0ntspaqe0vkyIQWrIrEdUf05dr3ConqJg99Wc6i2YMSXmZiUSQ2VnkZ2zdzt+crbghgTWB24cUfqvBGYgHLFYfl9dgJpG6Y2CwyY/MzsCTw8U42w7PTWFNRn9AMl7DvTNPEH1qz2/P4Q2uwWwvEBFCIa5r8iT1NhL3R1NZ6ZZmHPu4CRuXnMGNI8gaXImAQdjE4y8G5k3J59cdqNlSHeffnOs5MgiPlEd2kqD7AwMzWO01UeEPohpmw4GZ1WYD/bfYCcMyIdMb37ZkdMVTdINNpZ1hOao/bjG1/OW0KeakOavyRhAfZwp6Zpo5uBNB1P5oRIKrV7JJZ2JluBPAGV2C35qHIbhQlBVkSLbJ6O7GnibAvJEliTJ6TQ4f0o09aRqKHs1siYBBadca4LL4v8rGhOszfVtQwtX8Kg7MSW3aiSFDpC5HptJHqaPkFbZgmZd5gwiZpUW3HngtpDoXfTEuuxUodJaobDExPIT/Jujckk4EZbuqC0UQPQ2hkGFF0w49uBND0QPx3XQ9gmPvWtjAcLSQcLYz/X5bsKIobRXZjkd3x3xXFjSyJr9neQuxpIvRk4pOsu+jiebAiS/z2iL5c/c/G0qSvynnglMFYEnzU1KrIbKrxMaEgs0VwUNIQxDTp8sepyVs/1VLWmIq+eHof0hw9bw8CTTcZmZtGurNnb8a2vyQJhmensq6qAZvS814HycY0TQwzhK43BgWNwUBTYGCa7e+YJmHDpD3BnkTz3vuGGcHQIqjU7nJOWXLsCCaaAgnZjUVJQRLBhCAI3YT4tOom0uwW/BGzzYVVnaF/up0FU3N57vsqttRGeHNVDedNzu26AbRBkmBTtZdRebG9DXTDpMoXxqIkJlooqo/wj59iE4UJfV0cNSwtIePoLIZpIksS4wsysFnEBLg9Uh1WslwOvGG1S9+zPdWO0qFYINAyKAgC7d3OXUaRU1CUFBQ5pTEbEPtdkVMAmVrvR7stS1LkFLJSj8M0Q2iGPzYGvWlM/sbL7hiPYYYxtDAqNbuOpjGY2DkrYZo9u7OaIAjdjwgYuomhWamsLvfQ1YfQTxmdybfbffxcEeLN1bUcNDCV4TmJLU2SAF9EpcIbIj/NSWGdHyVBwYJhmjz+TQWaEVswvrCH7bmgGyZuu4URuWnIPeh+dYWh2W5WlSa+NXF3ESsdagwC9MZMQePve1M6JEnWxgAgFgxYmv0uS3uuKXc7x+EJfLfbv8uyDMQCD8hr8XfTNDGMYCyY0P2NAY6/WTDRPDPRdjBhcdrwhtKwRt3NMhSpKHIKkiQCd6F7MUzQDQOTWKWA+DbpfkTA0E3IsszAjBQK6/xd2gVIliSuPbwvV767jbBm8uCXZTw8Z3DCu/9YFZmihgBOq0JtMJKwrjT/3ehhbWVsMnPOhGz6pfecch1VN8hLdba5yFzYPVmSGJyVwpYaP9YEBbTJZEfpUCAeGGjNft+b0iFZcjbLDMTKe5p+l+X9ew86bP2Ag/GH1rTINChyCm7nuMa/t02SpNjYlBSw7hxMGOhGsFlWwt+ifKp5MCHJUTS9Bk1vLTPh3FHe1KLcSQQTQnIxAU036ON20D8jBd0wqPCFaQhFCWuaKNvsRkTA0I3kuh1U+yPt2o+gI+Wn2rhoeh+e+KaSooYor62s4cIkWNRrU2TWVnhw2hLzgVMf0nhxWWzPhYEZNk5v546f3YGqGwzLSSXLJUoj9keWy061I0xQ1ZP2iJppmqhaDYYZRpYcWC05+5wli5UOBeOZgdiagh2/713pkKvVLEFXTIodtn7YrQXNHhcnVkv2fmcPJUnGosQm9+zUWKl5MBGNevAFqnE4TAwz0Fh21TwzESKqhYDqXW5Dll2xEic5ZZdyJ0nqmAMrHfmaEXquqG6Q5bIzODMl3n5bkRUGZqYwMDOFsKpT4QvREIoS1Q3RjjrJiYChmxme62Z1aX2X7zVwwsgMvtnuZ2VpgHfW1HHwQDcH5iW+G0SiggWA57+vIhCNTYCuODS/RxxFNgHThLH5mQl9bHuS4TlprCypS9gam90JR0v3+kj6jtKhHe1Im/5vGMF23/aO0qGUxgltY2CguNtVOtTZJEnCZu26NVvNgwmZbDz1KaRm5mG1WhuDicBuMhM7GEaQaBvPQzyY2GURdkq7g4l9ec0IvUtUN8hw2hidmYLNomCaJnWBciJqELvVRaYrVrrrsCoMznIDEIxqVHhDeCIqqggekpIIGLoZm6LQL91FuTfUpS1EJUnimhn5XPHuNgJRgwe/KuexU4fgsPbON/WPJX6+2Brbc+GEkRmM6QGt9HQTHBaZUX3SxR4CHUiRJQZmudhel9hNBXfWtKPxzpp2NNb00Siys7EdqT8eFJhm+1vGtl06lIIk2RIeFHQXsWAiFYuS2kZmoqnEy98iqNg5gIsHE1rVzrewU0an2SLsZsHEnl4zcHCvDhp6e+Ylqhuk2q2MzE2PH3Cq9G5jQ8UPhKLe+PmctjRG5k8nL21I/DSXzcLQnNgGd/7GNYreiIpumEn1udmbiYChGypId1EbjKAb5p7P3IFyUqxcelAeD31VTrlX5eXlVfy/Q/K7dAzJIKwZPPlNJQAZToULpia+c9T+UnWTHLedIY1He4SO1cftpNofRe3icsK2tGdH40B4bTuuSWozSyDq6btG82DCvksw0XxjOn+L31sGE2Y8IETb5RZQZBeylIKm1+12LL15F+zenHlRdROXTWF4TiruZi/CSu82VhX9j+bldAChqJdVRf9j4sBZLYKGJm67leG5sevxhKJU+cN4wyqGSY/I5HdXImDopoZnp7Gmor7L03ZHD0/j2+0+vivy8/66Bg4ZlMqEgt61KPbvq2qo9McWaF5yUB5ue/eeFEV1g0GZKeSlis3YOtOIHDeryxoS+oVnmia64SMY3rrHHY2bSFjiAcCOuvjY77Ls6pWTw+5CkhQsShoWJY2dVyM1BRPNsxJNWYqWXakagwn2/HrRjQBVDf9CkixIyI2ZCQVJkpFQQJIbT1eg8aeE3Hi60nj+9pyu7HQ9za9fAaQufV1218yLaZqAgYkBZuNPTEzTiJ1uNv6/xd93nK4ZOlYZMl1W7BaJGp9BlVdvzHrpFNetZedgodmts7ZsKbJkwWZxYJXtWBQbVsXWojwu3WmL7/1TH4pS7Q/hC2uYphlfFyF0DREwdFNOm0Ke20FNINLlpUlXHJbP2spteCM6D39VzhOnDcHVS+rdC+vCvLsmdpRtSr8UjhiSmuAR7R/dMBndJx33TjtnCx3PZlHol+7s8nJCw4gQ1aqIqFVE1cq9alGa6pyM0z5YBAU9UPNgYmc7gglffAF7VKtBN3ztuGYd09Rj08SuTYLvpJUAo0WwsadgpuXpbQUpJhK+4OrdjsQXXI0sOUAyd0y8TQOTxgl70wSdPf29jdNbndi3PvE3TQOLS6fO33FPTkP7jj3sIqqFWLH9P7ucrshWrEpjACHbsSo2LMqOnzlOG2FNJqhKhFUJqfE8smQl9px2n88r0zTRtFrKGtZimPnkpQ1J2vGLgKEbG5jppi7U/nrijpLptLDw0Dzu+6yM6oDG8z9UcfWMvl0+jq5mmCaPLa1AN8GuSFx+aF7SvrH3JFYXKjGmIAOraGvXZbqinNA0DVStlqhWSUStQtP3fS8Ii5LabV/jwr5rLZiIqtXU+7/c42UdtkEosgPTNDDR4xNY09Rpmry2frrebMKrs//RRtNtdcBV7e9IzBD1/s8TO4hmOvMtLUkysqQ0tlHepb6tXXRDRTdUaH+n5WZkZMmKJFmRJRtS0++yrfE0K5Jk2+ln8/N2bWaqqYxtVVHstFRHNlMHn8ignLFdNo72EgFDNyZJMDTLzYZqb5eXJs0YksYRhT6+3Obj440eDhmUyrQBPbv+/T/rG9hQHQbgV5NyyE/tnnsuqI0dLIbnpHXqF4fQuo4uJ4yVGfmJqpVEtEpUtbpxwtWSLNmxWfOwWfKwWnJp8H+xxx2NrZae0ypY2D9WSw6KnLLH10yaa0qHTLri5TLxQEKPHyGPZTF2Cjziv+vNjqy387JtBC1N5+96EiA1K8tq+ilBPDuy0+lN50NufPxbv5xhmPj9QdzuNCyKZafLybHbbXH9EqYZu3yO20We24Uiy/HAIPazKeOyoxSsLlDOsm3v7/Geju13JC5bGqoeQdOjqEZkx+/NfqpGFE2PoOrRWDDRJgPDjIAZ2adnLh5gtBFQxE+XW/t7+w++tVXG5gvX8vn615g56rykCxpEwNDNpTttZDhtBKJal/d5/3+H5LOmIkh9SOexryt44vQhpHbzev621AZVXl4e63k+ONPO/2/vzuOjKu/9gX/OOvuSyU4gBBMWi1YRFBEsVkpd6lqxerVavXXFStVqqddWK/dqVeSn4hXLbbVetNaFahFb7GZvtW6IgmBVBCo7gezJZDLLOef5/THJyBgCWSY5M5PP2xdCzjmZeQZOZs7nPN/nec45ImRzi/onblqoCLhREcj9WZ1ylUtXUOpzoj7c/3LCZJlRHWKJvYgb+3qYzlSGrhalQoKq+NMu5HqzojF7F6iLJElDes4kH0fJioHz3QPI54EiYTSirWPtIR/D6zoauhpC2gX9Fy7M0y/4B0cikUBrYi9cenLK3oOxBGBZAiV+J0YG3ZD70K4Cdxlcuj9tdqQvcut+jAiO7fPrtYSVHiisWCpMdG2LGzG0xSKIGcmvgQQsEe9cILLnLichEhAi0esVY9LJ3YNGqmfj88ABaAh39FzGJiCwZusqVBZOzKr3YAaGPFBd6MO6XY1DPhWm36ng+unlWPCXnWjsMPDzt/bilpNGDGkbhsov3t6HSMKCBOD66WVQc3DaUcMUGFfsR9CVmz0j+aQy6EVTpPflhJ+XGe1DLLG3xzIjVQlAV0s6Q0LRQS+2BrqiMQ0/w/Wc+XzQdfdLJlUJIBL79JA9L27HYVl18XcoCVOgyONAZYGnX9cWkiRhfNlxB5wlqfMIjCs7rl9/J7IkQ1ed0FVnr45PmBb2tHagpSOO9ngCmgpAxGGJBITV+btIQIjP/9wVLtKPiR+w9/ZzA+vd2F9btAH7WreiNNB9Fim7MDDkAUWWUFngwbamMFR5aEuTjqv04mtjA/jLphb8/V+tOKHKi+lV3QfR5bJ3d4Txj63JwX6nHx7E+JLcmk3IEoAsAUeOCMKh2n+3jpLlhNWFPny8rxn6AcaQpJcZ7essM+peD/x5mVEJdK0Eity3c3OwVjSm/MVzJt1Q97wMtrhpocClY3TIc8D3pr4o9Y/B0ZWz8GntakT262lw636M+8I6DINJU2RUFniAAg9ihona1g40d6iImSZ0rW+vUQire6DYP2hY8YPu78uAmshBemfswMCQJ4q9TtSFY4jbMM/7lVNL8MHudtS1G3jkzb2YWOpG0JUfp1ZHwsKSN2sBACG3iksn59aaC6Yl4NZVjC/x96k7mQafz6mh0O1ESzQBWUquoBw39qVCwsHLjEo6y4wCA74QGeoVjSn38ZxJlw89L3HDRMCl4/DSQEZvLJX6x6DEV4WmSC1iRgRO1Y1g50rPdnCoCkaHvBgNoCNuojac7HmIm2avApIkyZAkB+RuExUfmhACAgbiib1oaX/nkMe79ey6+ZofV3UEAKixaZ53j67g+zPK8eM/7kBr1MR/v1GL22ZV5MwdlYN5em096tqTd3avPr4EnhyaPjZuWij1OTG6IL8Ho+cqS1gIOsPY3bQZceNgZUZ+6GppZ0goOmBZBBHZK1d7XuKmBa+uYWyxDx59cKbXliQJIU/2zaTo0pXUYqXtMQO1bR1oiSVgmNagTCQjSRIkaHBoFYecQMDnLESJvyrjbRgIfvLkEV1VMMLvQm04iqFeG+roCg++MSGI33/SjLe3h/F/W1rx1ZrA0DYiw7Y0RLHin8k1F44b5cUJo3NnzQXDtHBYyIcib9/vgtDgEEIgEm9BfXgXGsK70Ni++4CzfSTLjEpSIaGvZUZEZI9c6nlJmAJOh4QJIT/8HNcGj0NFtSP5Gd8WjWNvWxQtsQRMS2Q8PByqjE2ChClVp2Vd2GRgyDMVQTfqI1EIG+advuzYEry/qx172hL4+dt7cWS5G0We3FwQzLQE/vuNWlgCcKoSrpmWO2suGKZAud/NsJAF4kYUje270RDehfrwTkQT4W7HyJICXSuEopTAkaEyIyKiAzHM5CJuh4U8KCvInZtgQ8nn1OFzJkNUS0cce7tWlwYyNuFJT2VsXIeBhlRNkQ//rG0Z8rUZXJqMG04sx4/+sB3tcQsP/6MWP/36yJy8+Pn9x03YVJ9cc+HbxxSjxJsbwUcAcDtUVAQ5baodLGGhJbIP9eGdaAjvQktHHQ40yM3rKEChdySKvBUo8JQDUGyZ6YyIhgezc/KLUUE3QoYTBW72KvRGwKUj4NIhBNAUiaEuEkVbhwFAQB3gNVZXGVsktgc1xSUoD5ShxF+VtddMDAx5yKNrKPY60RSJDflA14llbpx7RAgvfNiI93a144+ftuDU8cEhbcNA1YUTePK9egBAdaEDZ36pwOYW9Z4lBMYXZ9dAqXwmhEBCRLCr+RM0R/f2WGakK04UeitQ6B2JQm8FnJqn2zGjCtzY1tgObYiDPhHlL9MSkCRghM+F8oAbsVgMkYbsvCDNZpIEhDwOhDwOWEKgoT2GhvYY2mIJyJLU75s9kiRBVQsxIng4SvzBzDY6wxgY8tToAg+a+zDPeyZ9+5givLszjB3NcTz2zj5MGuFGaQ6tirz07b3oMCzIEnD99PKcuesbNy0cXhLImfbmqoQZQ0N4NxrCO1HfthNRIwzUpR8jSTIK3GUo9FagyDsSPuehBz+WeF2oC8eRsGGmMyLKL12LrpX6nRgZ8CBLb1rnJFmSUOx1otjrhGkJ1LVH0dgeRziegDKA8JDtGBjylCxJqAp5sbm+dcjvWOqqjBtPLMfNL29Dh2Hhwddrcddpo3JiWs+3trXh7e3JOvMzv1SAmqLeLQxjN9MSqPC74XPmRulULukqM+oah3DwMqNkL0KBpwyq3Pd/i7FFXnywu4m9DETULwLJcQrFXidGBfu36Br1niJLKPO5UOZzwbQE9rV1oLFzgThVVpBPf/0MDHmswJ2svWuPGxjqc3ZcsQvfOqoQz6xrwIbaCF7+qAlnTQwNcSv6JhI38fO39gIAij0qvn1Mbsx2IQC4NIXjFg5BCJGcCzwRgUNzo6CHucCTsxm1oiG8Cw3hnWjoocxIU5wocJXB6nBgbOWR8HsGXrqmqwoqAm7sae3gBz0R9UnctBByO1BV4BlwfT31nSJLKA+4UR5wwzAt7G2LoqkjjvaEAV2Rh/w6LNMYGPJcdaEPa3c2Qh3qeVYBXHBUEVZvD+NfjTH875o6TB7pRUUge0uTnny/Hg2R5JoL10wrhUvLjTdcyxIYX57bU9gOtr2tn2Fj7Wp07Ldypkv3Y3znaqMJM4bG8O7UYOWORFu3xzhQmVE8Hse2bdvgUDMX1kYE3GiIxGBaNkx1RkQ5J25aCLg0fKkgAD2Di65R/6mKjIqgGxVBN+KmhdrW5AJxkYQBXVVyMjwwMOQ5RZYwKuTGdhsGU2qKhBu/Uo4bX9qKmCnw/17bjfu+MTor75x+WteBlz9KLpx1wmgvplbmxnRzcdPChGJ/Vv6dZou9rZ9h3fa/4otlRB3xVqzb/he49QAi8dZu+wHA4wiiyDtyQGVG/VFT6MeG2qYhn+mMiHJH3LTgc2gYXxyAK4cWFR1udEVGZYEHKPAgmjBR29aB5o444oYFXc2d93gGhmGg1OtCfTiGhGkN+XOPCTlx0aRiLHuvDhvronjxw0bM+XLhkLfjYLrWXEiW9si46vhSu5vUK4YlUO53cdGdgxBCYGPtahwoDHSJxFtSf9YUR2q60+RsRvasku3SFZT6nKgPxxgGiShN3LTg0VXUFPngdXDcWi5xagqqOleXjsQN1LZ2oN6I58SgdAaGYaKmyIcPdttzx/K8I0N4e1sbPq2P4qn36zFllBdVBdmzqNiKfzbiX40xAMB3JhfnxGJzXeMWRgW7T89Jn2ts35NWhtSTkQUTMDI0AX5nUdbMgV0Z9KLJppnOiCj7JEwLDlXGuGI/grxRlPPcuorDinyoLHDC78yea6Ke5E5fCA2IQ1Uwwu+CaUNZtCInS5N0RYJhCTzw2m4YWVKfvbctjl+vTa65ML7YidMmBO1tUC9ZlsCEEo5b6ElHPIzN+97DBzv+0qvjQ94RCLiKsyYsAMl5v6sLfbb0DBJR9ugazzSm0IsvjwgxLJAt2MMwjIwMelDfHrPluUcFHfjOlGL84p192NIQw7Pr6nGxzbMQCSHw6Ft7ETMEZAn43vSynCj/iJsWxnPcQjeWZWJf2zbsbNqIhvDOPn2vM4ODljPJ59QQcjvQEk3k1fR8RHRoyUXXJFQE3Cjzu+xuDg1zDAzDTE2RDx/tbbGlNOnMLxXgrW1t+LC2A89+0ICplT5b1zn4x2dtWLOzHQBw7hEhjAll/5oLpiVQ5nMhwDtMKeFoI3Y2bcTu5s1ImNG0fSH3CITjTYgbHT1+v1v3I+guG+xm9tuYQi/W7WqyuxlENEQsISAEUOZzYUTAnRP17ZT/GBiGGa9DQ7HHiaaO2JAvpCZLEm6YUY7v/e4zRI3krEkPnlVlyywB4ZiJ/3knueZCqVfDvx1dNORt6CuB5ICpygKOWzDMOGpb/oWdTRvR0rEvbZ9T86AiOB4VBePg0n09zpKUJGFc2XFZVYr0RclFGD3YXN/GWZOI8ljXomslvuSia7mw2CkNHwwMw9DokAdNu+wZTFnm1/Hd40rwyJt7sb05OX7g8mNLhrwd//teHZo6TADA3BNK4cyBNRcsITC+2G93M2wjhEBLxz7sbPwEta3/gmkZqX2SJKPENxoVBeNR5K2AJH3+71nqH4OjK2fh09rVndOnJrl1P8Z1rsOQ7UJuBwKuKCJxMyfn7yaig4ubForcDowOeVluSlmJgWEY6rpjuaU+DM2GBd1OHR/Em9vCWLurHS9saMTxlV4cXjp0NeQf741g1SfNAICvjPFh8kh7ps7si4RpYVyxf1iu3hkzOrCneRN2Nm1Ee6w5bZ/HEcTIgvEYERwLXe25xrfUPwYlvqrkSs9GBE7VjWAPKz1nq5pCP9btauTFBFEeiZsWClw6vhTyQFe4lgJlLwaGYSrkdqDOGUUkMfR3LCVJwvdnlOG6Fz9De9zC/3t9Dx4+e8yQ3OU3LIH/fjNZiuTRZVw5NfvXXDAFUOpzDqtxC0JYqA/vwq6mjdjXuhViv3IiRVZRFqjGyILxCLhKen3RL0kSQp7ywWryoFNkCaMK3NhmwyKMRJRZcdOE36lz0TXKGQwMw1h1YXJtBjvuWBZ5NFw1tRQPvL4He1oT+N/36nD1ECyY9sKGRmxrSs4UdfmUEhS4s/9HwKFIqCzI/l6QTIjEW7Gr6VPsbvoUUaM9bV/QVYKK0ASU+Q+DqmT/WhmDocTrQn04jrhp2t0UIuqHrkXXxhb74NGH5/sY5absv1qiQaMqMkYG3djRFIFqQ2nSyTV+vLWtDW9vD2PlR004vtKLo0YM3oDePW0JPLMuuebCl0pc+Pr47F/HwLQEjigL2t2MQWVaBva1bsOupo1oaN+Vtk9TnBgRHIuRBePhdRbY1MLsUlPkxQe7m9jLQJRDEqYFp6ZgfMg/rHqLKX8wMAxzpT4X6tpjMGxYHEqSJFw3vQwf7f0MrTETD76+B4+cOwbuQeieFUJg6Tt1iJsCqgxcN70s62egyPdxC23RBuxs3IjdLZthmOnrgxR5R6KiYAJKfJWQZXbX709XFVQE3NjT2sHxDERZzrAEVFlCdaEPIU/2r+ZL1BMGBkJNoQ/r9zTZMmVjgUvF3BNKcc/fdqOu3cAvV+/DvBmZrzNfXWvggz3Ji9LzjizE6ILsfuM2BVCSh+MWEmYctS1bsLPpE7R21Kftc2rezgHM4+DSh0cJVn+NCLjREImlVoAlouxiWCI17qjUy0XXKPcxMBCcmoIynwt14agtdyxnjPHjK1vb8NpnbfjTpy2YNtqHY0dl7oKxLWZi+SfJaWTL/Rq+dVRhxh57sDgUCaPzZNyCEALNkVrsbNqI2pZ/wRKf199LkoxS/xiMLBiPkGdETs1aZLeaQj821NoT9ImoO8MSMC0LmqJghN+Fcj8XXaP8wcBAAIDKAg8aI7FDHzhIrplWhg21ETR1mHj4H7V45Jtj4HNkphRl2fsNaEsk78Red0IZHDYsFNcXhmVhYlnu1+vHEhHsav4Uu5o+RSTekrbP6whhZGg8ygM10NXsX2E7G7l0BaU+J+rDMZYmEQ2xuGkBENAUBU5FgVNX4NVVBFwap0elvMTAQCnVhT58sq/FlsGUfqeC700vw3/+ZRcaOwz8/K29uOWkEQN+3A9rI/jL5jYAwEmHeXH0IA6qzoSEaaGmyA8tRz9wLGGhvm0HdjVtRF3b9rTpUFVZ65wOdQL8riL2JmRAZdCLpog9izASDQeWABKmCUmS4FRlODUVTlVB0KXBo2sM6zRsMDBQis+pIeR2oCWagB3vgVMrffja2AD+sqkFf/9XK06o8mJ6Vf9XNk6YFv77jVoAgEcDLptclKmmDgpTACVeJwrcuTduoT3WkpwOtflTxIxI2r4CdxkqCsajNDAGqsxpBDNJkuwN+kT5pKukSJVlODQFTlWBR1MQdOlwairLi2hYY2CgNGMKvVi7sxF2vTNeObUEH+xuR127gUfe3IuJpW4EXf07TZevb8TOluTd1/PGORBwZvdde12RMDqUO+MWTMvA3tbPsLNpI5ra96Tt01VXajpUjyNoTwOHCbuDPlEuinfODKgrMpyqAqemwOtQ4XfqHBdEdAAMDJRGliRUhbz4V0MYmg1rM3h0Bd+fUY4f/3EHWqMmHnmzFv9xckWfy1d2tsTw7AcNAIAjSp2YNiK7w4JhCnypLPvXhQCA1o567GzaiD3Nm2FY+5fDSCj2jcLIgvEo8lVClvihO1TGFHqxbleT3c0gyjqWEEhYFmRIcGoKHKoCl6Yg4GRJEVFfMDBQN4UeB/aFo4gZ9qwme3SFB9+YEMTvP2nGW9vC+L9/teKr1b2/mBZC4JE39qbmv75majGkaPZeTCVMC9VFvqweKJcwY9jTvBk7mzaiLdqQts+l+zunQx0Lp5bdY0TylSxJGBPyYlN9K++O0rBlmBZMIaDKMpxdJUW6ioBTY0kR0QDZGhgaGhrwk5/8BKtXr4aiKDjrrLMwf/58qGr3Zq1evRoLFy7E5s2b4ff7cdFFF+Hqq68GAEyaNCntWMuyEI1GsWjRIpxxxhnYsWMHFixYgA8++ACKouDEE0/Ej3/8Y/j9/a+Pz3dji3xYt6vJlhWgAeCyY0vw/q527GlL4Odv7cWRZW4UeXpX//7XzS3YUJuso7/gqEJUBHTsjQ5ma/vPFECx14mQO/vWhRBCoLF9D3Y1fYK9rVvTpkOVJSU1HWqBp5wDmLNAgVtHwKUhEjfBfw3KZwLJGy1AekmR36HB59Q4nodoENj6U3XDDTfA7Xbj9ddfx/Lly/HWW2/hiSee6Hbcli1bcNVVV+Giiy7C+++/j6VLl+Lxxx/HK6+8AgBYu3Zt2q9TTjkFM2bMwKmnngoAuOmmm1BTU4M33ngDq1atwu7du3HPPfcM5UvNOaoiY2TAbdvCUC5Nxg0nlkMC0B638PA/aiHEodvS0mHgsdX7AAAjAzrmfDk0yC0dGF0GqrJs3EI00Y4t+9bi9U+fxZqtv8eeli2psOB3FuHw8uk4acLF+PKoryLk5doJ2aSm0A+Li7lRHrGEQMw0kTAtKLIEt66iyOPAl0oCmDyyEEdXhDChNICqkBchj4NhgWiQ2NbDsG3bNqxevRqvvfYaXC4XRo0ahblz52LhwoW44oor0o59+umnMWvWLJx77rkAgAkTJuCZZ56B19v9QuuFF17Am2++iZUrV6Z6KrZs2YLJkydDCAEhBCRJgsvFlRcPpSzgQl0kaltomFjmxjlHhPDih414b1c7/vRpC04ZHzzo9zz27j60xZJ3nr43vQyaIiNh2VNadSiGKfCliqDdzQCQnA61rm07djV9grq2nUDadKg6yoM1GFkwHn5Xds80Ndx1rSy7rbGdF06UcwzTgoXkeexSFTg1FW5dQcChw6Vnb8km0XBgW2DYtGkTgsEgSktLU9uqq6uxe/dutLa2ppULrV+/HieccAJuuukmvPHGGwiFQrjssstwwQUXpD1mW1sb7r33Xtxxxx0oKPh84avrr78eixYtwrJly2CaJo4++mjcfPPNg/8i84Ddq8leckwR1uwMY0dzHL98Zx+OHuFGqe/A046u292OVze3AgC+Pi6AI8rcQ9nUPombFmqyYNxCe6wZO5s2YnfzJsSNjrR9IU85KgomoNRfBUXmcKdcUeJ1oT4cR9zMzqBM9MWSIldnSZHPqcPnUBl2ibKQbVcB7e3t3e7yd30diUTSAkNLSwuWLVuGBx54APfddx/Wrl2Lq6++GoFAIFV2BADLli1DRUUFTjvttLTHlSQJ1157LS6//HI0NTXhpptuwu23346FCxf2ur1CCEQikUMfOEhi0RhkeejfRGUAQV1GQyQO1YbZJCQA108rxo9e2YUOw8IDr+3GnbNHQP5CGUzMsPBI55oLfoeMbx9dgEQiAQDdfrebYQmEXDo8ChCLZXZ1bSEEWqL7EDMicKhuBJwl3UqGTCuBfeFtqG3ZjJbovrR9uuJCmb8G5f4auHRfsr0JEwZ48XkgXf9+mf53HKiRXg0baiPQs3xVczqwbHvPGghTCBimBUWSoWsSnEpyliK/1wmPrkJO+1wRsIwEYoZtzc172fqeNZwZVgIdHRGYiaGv5uiquukN2wKD2+1GR0f6Hc2urz2e9JlWdF3HrFmzcNJJJwEAjj32WJx99tlYtWpVKjAIIbB8+XLMmzcv7cV/+OGHeOihh/Duu+9CVVW43W788Ic/xMUXX4w77rjjgGVNB5JIJPDxxx/39+UOWJtZC8mmISdCCNQ3RyHZNJTSD+DUMRr+8K8EPtwbxbPv7sTJo9N7GV7aHMOetuSH63njNESa6/HFeNfY2Dg0DT4IIQQUWUIo4MC2tvqMPna7VY9GYwsMfD7CW4UTIbUabqkQcdGGNqsWYWsfRFoAkOCWQvAp5XBJIUjtEva1NwKw/+8rV9TW1trdhG5Eh4HdkQSnjcxh2fCe1VtCCJhCwBKAKkvQFRm6ArgUBUFdgSYDkpk8Fw0k311y59Xln2x8zxquLGGiQY5Ale1ZtFXXe/e8tgWGsWPHorm5GfX19SgqStZFb9myBWVlZfD5fGnHVldXIx6Pp20zTTNtEOyGDRvQ0NCQ1uMAAHv27IFpmrAsK7VN0zRIkgSlD+Ugmqahpqam18dnWm2Lw5Yehi6haAKf1rfZVpp0WZHAR007sbUpjt9tTuArE0pR4U+e5Dua4/jT1h0AgKPKXTjzqPRZexKJBBobGxEKhaBp9q40bJgCE0v9cGiZLUWqC2/HZ3s+wv5jDwDAQBT7jH/Cobq7rcDs0vwoD9SgzFcNXeWYnv6IxWKora1FWVkZHI7smumqUgj8c28LOAY692TTe9aBCCEQNy1IkgRNluDqXN/A79DgZUlRVsvm96zhyrASKPKMtOVzePPmzb0+1rbAUFVVhcmTJ+Puu+/GggUL0NTUhCVLlmDOnDndjr3wwgtxxRVXYMWKFTjrrLOwZs0arFy5Evfff3/qmPfeew8TJ07sVuY0efJkuFwu3H333bj11lsRDoexaNEizJ49u08DnyVJgtttX028I+awdSGsYocDLXELrTHDltVkNQ34wcwRuPGlrYibAovf2IfLppSgscPA8x80wLCSKyV/b3p5j2lZ0zRbP3wTpoVxxT74PZl9kxZC4F8N7+OLYWF/XWFBkVSUBsZgZMEEBN2lnOEoQxwOR1Z++H6pXLN1DBINjN3vWQBgWgKmsCBDhlOX4VRUuPTkwmduXe1WHkq5IVvfs4YjxZThcrnh0IY+MPTlGsDWkYyLFy/GggULMGvWLMiyjHPOOQdz584FkFxb4c4778RZZ52FadOmYcmSJVi8eDHuvPNOhEIhzJ8/H7NmzUo91o4dO9IGUHcJhUJ47LHHcP/99+PEE0+Ew+HAySefjFtuuWXIXme+GFPow7rdjYBNpUljQk5cNKkIy96rx6f1MfzHKzvS9p8w2odyvz1deodiWgKFHicKMxwWAKApUouOeOshjxtdeCRqSo6BqmTn3xFlnktXUOpzoj4cY2kSHVLctAAIaIoCp6LAqSvw6ioCLs32CRqIyF62BoaioiIsXrz4gPvWrl2b9vXMmTMxc+bMHh/r9ttv73HfEUccccD1HahvFFlCVYEX/2pos63LecRBAsFrn7XihCofTqjy9XiMXVQ5uRJvJnXEw6hr246djb0bWxNwFzMsDEOVQS+aIvFDH0jDhiUEEpYFGRKcneVETlVB0KXBo2sMl0TUzYACQzwex86dO1FZWQkhhO1dpzT4Cj0O7G2L2jJloxAC/7umrsf9lgB+tWYfpo32ZlWpTcK0cNSIAgy0SUIItHbUY1/bNtS1bUNbtG9DBp1q9k4zS4NHkoDqQh8+2dfC2vJhKNG5toEqS8kVkVUFHl1FwKnBqakDfl8iouGhX4FBCIFFixbhySefRCKRwB//+Ec88MADcDgcWLBgAYNDnqsp9mL9riaoQ3zx8c+9HamZkHqypzWBf+7tyJo1GOKmhcNCPuhq/7rzTctAQ3hXZ0jY3m2tBADw6EHEjAgMq+e7yG7dj6C7rF9toNznc2oIuR1oiSZsGYNEg49rGxDRYOpXYHjyySexYsUK3HHHHViwYAEA4Gtf+xruvPNOFBYWclG0PKcrCkYE3NjT2jGkXdcNkd5Nzt3Yy+MGm2kJFLodKPL2bdxCNNGOurbtqGvbjobwLlgivTdHkmSE3OUo9lei2FcJt+7H3tbPsG77X3Hggc8SxpUdl1W9LjT0xhR6sW5Xk93NoAzgQGQiGmr9CgzPPvssbr/9dsyePRv/+Z//CQA4/fTToes67rrrLgaGYWBEwI2GSAzmEM7ZWOju3eka6uVxg02RJRxWeOjxFEIItEUbsK9tO+ratqG1o/v6DJriQJF3FEr8o1HkHdltLEKpfwyOrpyFT2tXI7LfAGi37se4suNQ6h8z8BdEOU2WkuNoNtW3ctakHNE1fSkHIhOR3fp1ZbVz504cfvjh3baPHz8e9fWZXYyKsldNoX9Ip2ycWOpCuU87aFlSuV/DxFL71xQwTAtHHmTcgmkZaGzfg7rOUqNoor3bMR49gGL/aJT4KhFwlx5yWt1S/xiU+KrQFKlFzIjAqboRdJexZ4FSCtw6Ai4Nkbhp01xn1JP9ByLLEuBQJITcDpQEPByITES261dgqKiowPr16zFy5Mi07X//+98xatSojDSMsp9LV1DqdaK+fWimbJQkCZcfW4J7/rbrgItRyRJw+ZQS2y+QE6ZAVcgLxxfGLcSMDtS3bce+tu1oCO+EaaWXTkmQEHSXocRfiWLfaHgcgT4/tyRJCHnKB9R+ym81hX6s29XIC1AbHWogcjwew7ZYC0YF3XA4OLMZEdmvX4Hhu9/9Lu68807s3bsXQgi89dZbeOaZZ/Dkk0/i1ltvzXQbKYtVFnjR2DF0UzaeUOXDj75agV+t2Yc9rZ/3NJT7NVw+pcT2KVVNSyDk1lHsdUIIgXCsKTkeoXUbmjv2dTtelXUU+Uah2FeJIu9I6KrThlbTcKLIEkYVuLGtsZ0DYQcZByITUb7oV2A477zzYBgGHn30UUSjUdx+++0oLCzEjTfeiH/7t3/LdBspi0kScFjIi411Q1cXfUKVD9NGe/HPvR1ojBgIuVVMLHXZ3rMAALIkEHC04uM9G1DXuh0dibZux7g0H0r8o1Hsq0SBp9zWFbxpeCrxulAfjtsyPXK+4kBkIspn/QoML730Ek499VRccMEFaGxshBAChYWFmW4b5YiAS0fQpaM9bgxZXbQkSVkzdaplxRBL7EU0vhumtQ/7mr84xkJC0F2CYl8lSnyj4XEEsyLc0PBWU+TFB7ubeJe7HzgQmYiGm34Fhv/6r//CxIkTEQgEEAqFMt0mykHVhb5hVRdtmG2IJfYgltiDhNF9oL8iayjyjkSxrxLFvlHQVfsHYhPtT1cVVNgwPXK2s4SAZQlYnX+WpeT4ov1XRA44NXgdHIhMRMNHvwJDVVUVNm7ciOrq6ky3h3KUIkuoLPBga2M4L+9YCmEhYTSkQoJphbsd49S8nb0IlQh5RkCWeaeRspsd0yMPBksIWCL5uwAgCUBISPV4KpIEWZYgA1AUqXMmos5tkgRFTs5MpEgyVEWCLsvQFBmqIkORAU1WuCIyEQ1r/QoMY8eOxc0334xf/vKXqKqqgsORvjDVz372s4w0jnJLsdeJunAsb+qiLZFAPFHbGRJqIUT36VxVpQAOrRxfrpgAnzPEUiPKOUM9PXIXSyTXGTCFgBAieXUvkPoZUiQJkoT9Luil/S7897vIl2WosgRVkaDJMnRFgSzvFxL4M0lENGD9Cgzbt2/H5MmTAQB1dXUZbRDltppiLz7Ylbt10YYZRiyxB/HEHsSNenRfOVmBQyuBQyuHrpXDEg4cURaES2dvAuUml66g1OdEffjQ0yMLIFmuIwQEBLqu87v+J0OC1HmxrsgSJEmCAqnzAl7uvIBPToGsyck7+LqSvKuvSPLnwYClPkREWaVfgeHJJ5/MdDsoT+hKbtVFCyGQMBsQi3eVGnWf1UiWnHBo5XDo5dDVEkhSMhwkTAujQ26GBcp5lUEvwlEjOcuPLCfv4EvoXrLTWaqjKTI0WYIqJ0OAwot8IqK81q/AAACRSAQvvfQSNm7cCFVVMXbsWJx++unwer2ZbB/loBEBN+rbY7BEdtZFJ0uN9qXGIwjRfR0JVQkmQ4JWDlXpPquRJQSCLh0lXg5mptwnScDE8qDdzSAioizVr8CwZ88efPvb30ZDQwPGjBkD0zTx3HPP4ec//zmefvpplJWVZbqdlGOqi7z4Z23LoNVFCyGQMOphiShkyQlNLTr4+AEphmj8M4Sj+xA36gBYXzhAhq6WwKGXw6GVQZEPPmWrLEmoLrJ3kTgiIiKiodCvwHDPPfegvLwczz//fGpa1fr6enz/+9/HwoULsWjRoow2knKPR9dQ7HWiKRLL+KDDaHwXwh0bYFrtqW2K7IHXdSScegWAZKAwzCbEEnsQje+G5m5F5AsdCbLkgK4lA4JDK4Uk9e7HIWEKHFEW5GBKIiIiGhb6FRjeeOMN/OpXv0pbg6GoqAjz58/HlVdembHGUW6rKvCiuaN7uc9AROO70NL+drftptWOlva3kTDGQYg4YolaWCLa7ThV8acGLGtK32c1SpgWKgs8HLdAREREw0a/AoOiKHA6nd22OxwOxOOZvUCk3CVJwGEhLzbWtWakNEkIgXDHhoMeE4l9+sVWQFWKEIu4ESoYC6cj0O/nt4RAwKWj1MdxC0RERDR89Osq7phjjsGSJUuQSHw+L30ikcCjjz6KSZMmZaxxlPsCLh1Bl95tctL+SBj1aWVIPVPh1CsR8ExFcfBM+F3TYBmHHpdwKJIkoYbjFoiIiGiY6VcPw80334wLL7wQs2fPxhFHHAFJkrB+/XqEw2FOuUrdVBf6sG5XY7+nXeya1SgS3dyr4/3uSXA5KlNfm+i+4FpfxU0LR5YVcNwCERERDTv9CgzV1dVYsWIFnn76aXz66acQQuCMM87AhRdeiFGjRmW6jZTjFFnCqAI3tjW293pBN8NsQyxRe5AF1A72fAPrSfiihGlhNMctEBER0TDV73UYOjo68PWvfx0333wzAODxxx+HYRgZaxjllxKvC3XhOBKmecD9QliIG/WIJ/YglqiFaYW7HSNBB2BBoOfzTJE90NTCTDUblgD8To3jFoiIiGjY6tcYhtdffx3nnnsuXn311dS2VatW4Zvf/CbWrFmTscZRfhlb5EXC/LynwLSi6IhtRXP4bdQ1r0Rz+HVEYpvTwoKqBOFxHo6Q76soDp4Bv2fKQZ/D6zqyzzMfHYwEYGyxP2OPR0RERJRr+tXD8OCDD+KKK67AvHnzUtuef/55PPDAA7j//vvxzDPPZKyBlD80RUaBM4I9rdsQT9TCMJu6HSNBga6VQtfKOhdQS7+zn1xn4fhDrsOQCXHT4noLRERENOz1KzBs2bIFDz74YLftc+bMwbJlywbaJsojhhlHQ/su1LXtQF3bdsSNjm7HKLKnMyCUQ1eLIEkHHyvg1Cvg0Ebst9KzC5pamNGeBcOyUBnwwK33u2qPiIiIKC/062ooFArho48+6jbAedOmTfD7Wb4x3EVirahr2466tu1ojOyBEFbafgkSFKUQTr28sxfB1+eLfUmSoGvFmWx2iiUAn0NDWYDjFoiIiIj6FRjOPfdc3HnnnWhtbcWXv/xlSJKEDRs24MEHH8S5556b6TZSlrOEhab2WtR3hoT2eEu3YzTFiWLfKBT7RqHQOxI7mxNo6ohlbbkPxy0QERERJfUrMMydOxdNTU1YsGABEokEJEmCoii45JJLcP3112e6jZSFYkYH6jvLjBrCO2FY3dc68DkLUeyrRLFvFAKuYkjS52PsR4d0NO/KvlXB46aFiWWBrA0yREREREOtX4FBURTcfvvt+MEPfoAtW7bgtddew/HHH48pUw4+gw3lLiEE2qINnaVGO9DSsa/bMYqkIuStSPUkODVvj48nSxKqQl5sqm+F3su1GQZbwkyOW/Domt1NISIiIsoafQoMjzzyCJYtW4bnnnsOo0ePxqeffoorr7wS4XAYjzzyCI4//ng8+uijcDqdg9VeGkKGlUBj+PMByzEj0u0Yl+ZFka8Sxb5KhDzlUOTen1IFbh0Bl4ZI3ITd9/O71lvguAUiIiKidL2+unv22WexdOlSXHbZZSgsTC6Mdeutt8LtduPZZ5+F1+vF9ddfj6VLl+L73//+oDWYBlck3prqRWhq3wNLpC+0JkFC0F2aKjXyOAoGNDtRTaEfa3c2QlXsjgwCNUUct0BERET0Rb0ODM8//zx+9KMf4aKLLgIArF+/Hlu3bsXNN9+M6upqAMC1116Le+65h4Ehh1jCQnNkb2pWo/ZYc7djNMWBIu8oFPsqUeQbCU1xZOz5FVnCqAI3djRFbAsNcdPCl0oDUGS7QwsRERFR9ul1YNiyZQtOOOGE1Ndvv/02JEnCzJkzU9tqamqwe/fuzLaQMi5uRJMDlsPbUd+2E4bVffCx1xlCsbcSxf5KBL8wYDnTSn0u1LfHkDCtQx+cYYYpMDLghtfBcQtEREREB9KnMQz7l5689957CIVCGDt2bGpbe3s7XC7WgGeb5IDlxtS0p80HGLAsSwoKOwcsF3kr4dJ7HrA8GGqKfPhgd9OQDoAWADwOFSMC7iF7TiIiIqJc0+vAMH78eLz77rsYPXo0Wltb8c477+CUU05JO2bVqlUYN25cxhtJfWdaBhrCu1DXth31bTsQNdq7HePUvJ0zGlUi5BnRpwHLmeZQFZT7XdgXjmGoKpOEEBjH9RaIiIiIDqrXV4gXX3wxbr/9dmzcuBFr165FPB7HJZdcAgDYt28fVq5cicceewx33XXXoDV2OBJCYG/rZ6ht2QKn5kWBu6zHQcYd8bbUgOXG9t3dBiwDEoLuks4By5XwDnDAcqaNCnrQ0B4bkueKmxa+VMJxC0RERESH0uvAcOaZZyIWi+E3v/kNZFnGgw8+iCOOOAIA8D//8z945plncOWVV+Lss88etMYON9vqP8SaravQFm1IbXPpfowvOw6l/jGwhIWWyN7UtKfhWFO3x1AVB4q8I5MDlr0joavZPeVtTZEPH+1tGdTSJMMUqAi44XVy3AIRERHRofSpBmXOnDmYM2dOt+1XXnklrrvuOhQUFGSsYcPdtvoP8X+f/BoCIm17R7wV67b/BUF3KcKxZhhm9zvyXkdBqhch4C6BPIgDljPN69BQ6HagJRoflNWWBQC3Q0UFxy0QERER9UpGitZLS0sz8TDUSQiBNVtXdQsL+2uO7E39WZYUhDwjUmsjuHTfUDRz0Iwp9GLdru69JZlgCYHxHLdARERE1Gv2jXKlHu1t/SytDKknxd5KjAxNQKG3wtYBy5kmSxKqQh5sqQ9Dy+AI6Lhp4stlIY5bICIiIuqD3KlVGUYi8bZeHVdeUIMS/+i8CgtdQm4HfI7MvS5DWCj3ueDjuAUiIiKiPmFgyELuXpYUOdX8rsOvKfLBMHsuy+otIQScisJxC0RERET9wMCQhUr9Y+BzFh70GLfuR9BdNkQtsoeqyBgZdMO0BhYaLAGM9usZahURERHR8MLAkIUkScKUqtMgoadaewnjyo7LqjUUBkuZ3wWHpvT7++OmhepCL5Rh8HdFRERENBgYGLLU6KIjcNKEi7v1NLh1P46unIVS/xibWjb0qkM+xE2rz99nWALlfhf8HLdARERE1G/5N1o2j4wuOgKVhROxt/Uz7GraBJfmQfAgKz3nK5euoMznQl042usZjgQAl6ZgVNCDWGxoVo8mIiIiykcMDFlOkiSUBQ6DaZk5tQBbpo0KetAY6f2Fv2UJTCgPDGKLiIiIiIaH4XsFSjlFkoDqQh/ipnnIYxOmhZoiH9dbICIiIsoABgbKGT6nhpDbiYNNmmRaAmV+FwIuzopERERElAkMDJRTxoS8SI5Q6E4AcHaOWyAiIiKizGBgoJyiyBJGF3iROMCsSZYQGF/st6FVRERERPmLgYFyTqHHAa8jfarUhGmhptAHVeEpTURERJRJvLqinFRT5IXR2ctgWgKlPo5bICIiIhoMDAyUkzRFwciABwlTwKHKqCzguAUiIiKiwcB1GChnlQVcaI7GUVPks7spRERERHmLgYFy2oRSLs5GRERENJhYkkRERERERD1iYCAiIiIioh4xMBARERERUY8YGIiIiIiIqEe2BoaGhgbMnTsXU6ZMwdSpU3HXXXfBMIwDHrt69Wqcf/75mDRpEmbOnImlS5em9k2aNCnt11FHHYXx48fj5ZdfBgDEYjH813/9F6ZPn47JkyfjO9/5DrZs2TIkr5GIiIiIKJfZGhhuuOEGuN1uvP7661i+fDneeustPPHEE92O27JlC6666ipcdNFFeP/997F06VI8/vjjeOWVVwAAa9euTft1yimnYMaMGTj11FMBAD/96U/xz3/+Ey+++CLeeustVFdX4/vf//5QvlQiIiIiopxk27Sq27Ztw+rVq/Haa6/B5XJh1KhRmDt3LhYuXIgrrrgi7dinn34as2bNwrnnngsAmDBhAp555hl4vd5uj/vCCy/gzTffxMqVK6GqKhoaGrBixQr84Q9/QElJCQDg5ptvxmeffQYhBCRJGvwXS0RElCeEELCECUmSIUusbCYaDmz7Sd+0aROCwSBKS0tT26qrq7F79260tramHbt+/XqMHDkSN910E6ZOnYrTTjsNq1evRnFxcdpxbW1tuPfee/Ef//EfKCgoAAB8+OGH8Pl8WLduHb7xjW9g2rRp+OEPf4iCggKGBSIiJC8ATcuAZZkQQtjdHLJJ13lgWAkYVgKWMCCEgCzJUGUNmuqEU/PA6wwi5CmHzxmCJCkwrQTPG6I8Z1sPQ3t7O1wuV9q2rq8jkQj8fn9qe0tLC5YtW4YHHngA9913H9auXYurr74agUAgVXYEAMuWLUNFRQVOO+20tO9ta2vDn/70Jzz55JPQNA0LFizANddcgxdffBGKovSqvUIIRCKRgbzkAYlFY5Bl3snpj1gslvY7Uabk4rklhIApDEiQoMgqFFmHqmhwKwFYInmxaFkmLGFBCAtCJP9sWRYETAgBSAAgSbzDPEgyfV519Qjs948HWZIhSwpkSYEkJ39XJBUORYMqOzr3yQe+sSYAmIAKFX7NBUOOIxxvRjTRDkuYUGSuCZutcvE9K98ZVgIdHRGYiaEP3X2ptLHtp9rtdqOjoyNtW9fXHo8nbbuu65g1axZOOukkAMCxxx6Ls88+G6tWrUoFBiEEli9fjnnz5qW9eF3XYZom5s+fj1AoBAC49dZbMW3aNHz22WeoqanpVXsTiQQ+/vjjfr3WTGgzayFxUqsBqa2ttbsJlKey9dxK3vW1khf3SF4UylChwglF0iBJCQAdh3qYtMdL/mdBCAMWTFgwk1/DSu1H19cQyYtLAICU/I89u712qPMq+fdtQQIgpGQWSH5OyJAgQ5KSv8tQkr8kLbl9kP4dkmE0hjgiMEUcEIDEQJmVsvU9aziyhIkGOQJV1m15fl3v3fPaFhjGjh2L5uZm1NfXo6ioCEBycHNZWRl8Pl/asdXV1YjH42nbTDO963zDhg1oaGhI63EAkAoE+3+/aZoA0KcuVE3Teh0uBkNti4M9DP0Ui8VQW1uLsrIyOBwOu5tDeSSbzi1LWMm6ckhQZR2KokGTHXCobmiKw5YL9a5eCtMyYIoETDMBU5iwRFcPhgnR2W5LWJ3ZIvl/ueuCdxgGjGi0A3tq96C0rAQO3YG0HgFZ7ezZSfYIaIoORdYP3iNgA0tYiMRbEU20IWFGkz0ZDA+2y6b3LEoyrASKPCOhq65DH5xhmzdv7vWxtgWGqqoqTJ48GXfffTcWLFiApqYmLFmyBHPmzOl27IUXXogrrrgCK1aswFlnnYU1a9Zg5cqVuP/++1PHvPfee5g4cWK3Mqeamhoce+yxuP322/HII4/A4XDgnnvuwcSJEzF27Nhet1eSJLjd7v6/4AFyxBzs+h8gh8PBN0gaFEN9bnWVDMmSDFXRoSo6dMUJh+aBpuhZc9HYV5YwYVlWZx19HIYV73yt+wULy+oMFyYsIZJ314HkBWmW92AkX0Oy96Wrx0WWVCiyDFlSIckyFFmBWzPQvC+KitA4+Dy+nA1OXk9yYhLTMtAWbURHvA2mZXSGh9x7PfnEjs9DTjRzYIopw+Vyw6ENfWDoy7+HrYWGixcvxoIFCzBr1izIsoxzzjkHc+fOBZBcW+HOO+/EWWedhWnTpmHJkiVYvHgx7rzzToRCIcyfPx+zZs1KPdaOHTvSBlDv79FHH8XChQtxzjnnIBwOY+rUqViyZMmQvEYiooGwrGTJjyQp0JTkeANdccGpeaHIal59AMuSAllRoCoaHDj4h6cQItl7IUyYVgKGGe8cqNvZW2FZsITR2fOS7M0AkuEiGSwyN/5i/yAgdRUG7RcEZFmGLCtQJK3z39ABRVIgywceQxdBBJq8F5qi93hMLlFkFUF3CYLuEsQTHWiLNSGaaAcgIEu5//qoZ6aVXFtLU51wqC5YwkTCjMM04zA7e0QZIHODJDi1wSFt2LABAHDkkUfa1oZdTZvYw9BPsVgM27Ztw+jRo9nDQBmV6XPLtAwICCiSClXRoCo6HKobDs0NVdYy0OLhq+ui3hJmMlyYCZjCSIYKS3TOCGTBtJLHJMdhdI4RTkkOFE+WB30eBFRJ6+zpOXgQ6K1IJIKPP/4Yhx9+uK0924NJCAuReBsi8RbEEhFInSVVNLgG+/MwOcA+2Yukqy64dB9cuveAwdC0DMQSHYiZ7TDNBBKdoX+4hQjDTKDEP9qWHoa+XN9yKgMioiGWmrUGAnLqrrMGh+qBQ3NxlplBIEkyFEmGAhWacugLpeTMUGayhEZWMhIE6HOSJMPjCMDjCMCyTIRjTYjE25AwY1Ck/Oo5y3ddvXiaosGhuuFxBKCrrkP+GyqyCrfDBzc+H7dqCRNxI4poor0z2MdhdA6g5zgYe/FTiYhoEO0fDhRZg6Y4oCganJoHDsXFi9AsJUsyZEWGqrBnZ7DJsgK/qwh+VxESRgxt0UZO0Zrlut7TNMUJj+aBxxHMyL+VLClwah44tc9nyxTCQsKII2qEETdjqSABCPZMDSH+JBIRZUhXdzwgQZW11IBkp+aFrjpYr010CJrqQMhbDiEEool2tEebETPbASExXNuo68aHLCnQNRfcmh8u3TMkd/wlSYauOaFrzrT2GFYC0Xg7ElYHDDOOhJmAEGZyKmGeKxnHwEBE1A9CWDDNRGqgq6Y6ocsOODQ3NNXJu15EAyBJEly6Fy7dC0tYaI82I5JoQ9zoYMnSEOmaLEBVHHDqLnj0IHTVeehvHAKSJEFTdGguHUBBarthJRBLRBAzIp3jIhIwxfAbFzEYGBiIiA7h81l2kh9SSuc0ph6lCE1yAsW+0Xk7OJXIbrIkw+cKwecKwbASaIs2IBpvh2kZLFnKMNNKrgKvay44VS88Dn9O3a1XZQ1q59iYLqZlIG50IGpEYJrx5CxNVgJgiOgT/qQREe3HskxYsCBDhqYmF8XSFRecugeqrKV9uEREhB82RENIlTUUuMsANxBNRBCONSEWbwcksOSvH5KrcxtQZQ0O1QW3ww+H6smr9zVFVjtna0ofXJ0wYuhIhGGYCRhWDIYVhxCAwsHVB8TAQETDVnKO8ORc8KkF0FQXnKon79Y4IMo3Ts0Np+ZOTdHaHmtG3Ih2rnjNC76edK2+LsseOFQXPHoAmjq8phyXJQUOLTlldZfPB1e3I2FG9xsXYUGWObiagYGIhg0hLABIzVTkUN1wah6WNRDlsP2naDUtA+FoEzoSbTDMBEtOkL42girrcMh+lPoPS63ETUk9Da42rQSiiXbEzY5kb4QZ75wlShpWnx3D55US0bBmCgNeRwECruJhfwFBlK8UWUXAXYwAihE3OhCOJleVFsNsVemuRQq1zsUfu9ZG6OjowF65ZdjfLe8tSZKgKjq8SvrgatMykiHCSIaIhBmHJRIQQN4OymdgIKK8ZlkmFFlBsW80HFkywwcRDT5ddSHkdSWnaI2HEY43d64qLeflBfMX10ZwOwJcIX6QKLKa6tXqYlkmYmYHYolIZzlTbL+y19wPEQwMRJS3TMuEx+lH0FXCmmaiYUqSJLgcPrgcPljCRDjajEi8DYYZzekLuc9LjdTOtRF8cOrevAxDuUCWFbhkL1za56VelrCQ6Fy5OmHFYZrxzsHVubfoHAMDEeUdSyRnOSr2jUwb1EZEw5ssKfC7CuF3FSJhxBGONSKaCHdO0Zr9d+OzeW0E6k6W5AMPrjbjiCXaETejAJATN7QYGIgor5iWAbfuR4GnNCfehInIHpqqo0AtgxACMaMd4VgzYonsW1XaFAYgJDg0J5yqF26Hf1gNts03kiRDV505F/R4xhFRXhDCAiSgyDsSTt1jd3OIKEdIkgSn5oVTS64qHYm1oD3egoQR7ZxlaWhvPOy/NoLeuTaCU3XzBgjZioGBiHKeKQy4NC8KPOU5VRNKRNlFlmR4nQXwOgtgWgbaoo3oiLcl1y0YxClaLcuEgAVNdSZnNRqGayNQdmNgIKKcJYQAIBDyjIB7v1U8iYgGSpFVBN0lCLpLEE90oC2WnKIVGZiitWvAcrI8xQWXM1lqNJymfqXcwsBARDnJtEw4NDcKveX8kCWiQaVrLhRqrtSq0pF4S+cUrb2f6UZ0ro2gyjocmhtuRwAO1ZWzszTR8MLAQEQ5RQgBIUyEPOVwO/x2N4eIhpH9V5W2LBPhaBMiiVYkzPgBF+wyLQOQBDTFBZfqgdvJtREoNzEwEFHOsCwTuupCoXdEVs1iQkTDjywr8LuL4EcREkYMbdFGRBPtMIUBRVKgay74tSK4dB/HVlHOY2AgoqwnhICAiYC7FF5n0O7mEBGl0VQHQt7y5AxHVgKKrLHUiPIKAwMRZTVLmNAUHSHPaKgKu/KJKHtJkgRV0e1uBlHGMTAQUVYSQsCCiaCrGF5nyO7mEBERDVsMDESUdbpmEinxVvJuHRERkc0YGIgoq1iWAZ+rED5nIWuAiYiIsgADAxFlBSEsyLKCEn8VVzglIiLKIgwMRGQ70zLgdRYg4CpmrwIREVGWYWAgIttYlglFVlDiHw1dddrdHCIiIjoABgYisoVpGfA4gwi6StirQERElMUYGIhoSFnCggwZxb5KODSX3c0hIiKiQ2BgIKIhY1oGPHoAQU8JJEm2uzlERETUCwwMRDToLGFBkiQU+UbCqXnsbg4RERH1AQMDEQ0qUxhwaV4UeMohs1eBiIgo5zAwENGgEMICAIQ8I+DWfTa3hoiIiPqLgYGIMs60TDg1N0LecsiSYndziIiIaAAYGIgoY4QQELAQ8pTB7fDb3RwiIiLKAAYGIsoIyzKhqy4UekdAltmrQERElC8YGIhoQIQQsISFoKcEXkfQ7uYQERFRhjEwEFG/mcKAriR7FRSZbydERET5iJ/wRNRnXWMVgq5ieJ0hu5tDREREg4iBgYj6xBImVFlHoXcEVEW3uzlEREQ0yBgYiKjXTMuA31UIn7MQkiTZ3RwiIiIaAgwMRHRIQliQZRWl/ipoqsPu5hAREdEQYmAgooMyLQM+Zwh+VxF7FYiIiIYhBgYiOiDLMqHICkr8o6GrTrubQ0RERDZhYCCibkzLgMcZRNBVwl4FIiKiYY6BgYhSLGFBlmQU+yrh0Fx2N4eIiIiyAAMDEQHo7FXQAwh6SiBJst3NISIioizBwEA0zFnCgiRJKPKNhFPz2N0cIiIiyjIMDETDmCVMODUvCjxlkNmrQERERAfAwEA0DAlhAZBQ6BkBp+61uzlERESUxRgYiIYZ0zLg1DwIecshS4rdzSEiIqIsx8BANEwIISBgIeQph9vht7s5RERElCMYGIiGAcsyoGtuFHpGQJbZq0BERES9x8BAlMeEELCEhaCnFF5H0O7mEBERUQ5iYCDKU6YwoCsuFHpHQJH5o05ERET9w6sIojxkWQaC7mJ4nSG7m0JEREQ5joGBKM9YlolC70g4dS7CRkRERAPHlZqI8ogpDBT6KhgWiIiIKGPYw0CUJyxhoNg7Cg7NbXdTiIiIKI+wh4EoD5iWiSJfJcMCERERZRx7GIhynGUZKPGPhq467W4KERER5SFbexgaGhowd+5cTJkyBVOnTsVdd90FwzAOeOzq1atx/vnnY9KkSZg5cyaWLl2a2jdp0qS0X0cddRTGjx+Pl19+udvj3HLLLbjkkksG7TURDSUhLIYFIiIiGlS2BoYbbrgBbrcbr7/+OpYvX4633noLTzzxRLfjtmzZgquuugoXXXQR3n//fSxduhSPP/44XnnlFQDA2rVr036dcsopmDFjBk499dS0x1m+fPkBQwRRrkkuyGai2FcJjWGBiIiIBpFtgWHbtm1YvXo1brnlFrhcLowaNQpz587Fr3/9627HPv3005g1axbOPfdcSJKECRMm4JlnnsHkyZO7HfvCCy/gzTffxP333w9V/bziavPmzViyZAnOP//8QX1dRINNCAFIAqX+Kmiqw+7mEBERUZ6zbQzDpk2bEAwGUVpamtpWXV2N3bt3o7W1FX6/P7V9/fr1OOGEE3DTTTfhjTfeQCgUwmWXXYYLLrgg7THb2tpw77334o477kBBQUFqezQaxY033og77rgD69evx2effdbn9gohEIlE+vFKMyMWjUGWOUa9P2KxWNrvuUwIAQAo9IxEPGYgjgOX8NHQ6OjoSPudKBN4XtFg4blF+xNCQJKkXh1rW2Bob2+Hy+VK29b1dSQSSQsMLS0tWLZsGR544AHcd999WLt2La6++moEAoG0sqNly5ahoqICp512WtrjLliwANOnT8fMmTOxfv36frU3kUjg448/7tf3ZkKbWQuJk1oNSG1trd1NGJDkDzbgkorQKG+2uzm0n61bt9rdBMpDPK9osPDcoi66rvfqONsCg9vt7pZwu772eNIXndJ1HbNmzcJJJ50EADj22GNx9tlnY9WqVanAIITA8uXLMW/evLS09NJLL+GTTz7BM888M6D2apqGmpqaAT3GQNS2ONjD0E+xWAy1tbUoKyuDw5GbJTxdPQvFvkrIkmJza6hLR0cHtm7diqqqqm43QIj6i+cVDRaeW7S/zZt7f/PRtsAwduxYNDc3o76+HkVFRQCSg5vLysrg8/nSjq2urkY8Hk/bZppm6iIKADZs2ICGhoZuA51XrFiBzz77DCeccAKA5MWjaZqYMmUKXnrpJYwYMaJX7ZUkCW63fXPcO2IOyBIDw0A4HI6cDAzJngUZpf7RkGWGhWzkcrlsfX+g/MTzigYLzy0C0OtyJMDGQc9VVVWYPHky7r77boTDYezYsQNLlizBnDlzuh174YUX4q9//StWrFgBIQTeffddrFy5EmeffXbqmPfeew8TJ07slpgfe+wxrF27FmvWrMGaNWtw1VVXYfLkyVizZk2vwwKRXYSwIMsKSgMMC0RERGQPW29ZL168GIZhYNasWfjWt76FE088EXPnzgWQXFvhpZdeAgBMmzYNS5YswbJlyzB58mTceuutmD9/PmbNmpV6rB07dqQNoCbKdUJYUGQNJf7RLEMiIiIi29i60nNRUREWL158wH1r165N+3rmzJmYOXNmj491++239+o5r7/++t43kMgmlrCgyhqK/ZUsRSMiIiJb2RoYiKg7S1jQFB3FvlGQGBaIiIjIZgwMRFnEEhZ0xYEi30iGBSIiIsoKDAxEWUIIC7rqQpG3ok8zFxARERENJgYGoizQFRYKGRaIiIgoyzAwENnMEiacmgchzwiGBSIiIso6DAxENkqGBS9CnnKGBSIiIspKDAxENrGECZfmQ8hbbndTiIiIiHrEwEBkA0uYcDv8KHCX2d0UIiIiooNiYCAaYpZlwuMMIugusbspRERERIfEwEA0hEzLhM9ZgIC72O6mEBEREfUKAwPREDGFAb+zEH53kd1NISIiIuo1BgaiIZAMC0XwuwrtbgoRERFRnzAwEA0yyzIRdBfD6wzZ3RQiIiKiPpPtbgBRPjMtAwF3CcMCERER5Sz2MBANEtMyEfSUwusI2t0UIiIion5jYCAaBJYwEPKUw+3w290UIiIiogFhYCDKMFOYCHlGwK377G4KERER0YBxDANRBlmWgUJPOcMCERER5Q32MBBliGkZKPKOhFP32N0UIiIiooxhYCDKAEuYKPKNhFNjWCAiIqL8wsBANECWMFDkrYRDc9ndFCIiIqKM4xgGogEwLRNFPoYFIiIiyl/sYSDqJ0uYKPFXQleddjeFiIiIaNAwMBD1gxAWSnyjoakOu5tCRERENKgYGIj6QAgBAYFS/2ioim53c4iIiIgGHccwEPWSEAJgWCAiIqJhhj0MRL2QDAtAib8KqqLZ3BoiIiKiocPAQHQIXWGhNFAFReaPDBEREQ0vvPohOgghBCRJQqm/CrKs2N0cIiIioiHHwEDUg2RYkFEaGA1ZYlggIiKi4YmBgegAhLAgyypK/JUMC0RERDSsMTAQfYElLKiyhmJ/JWSJE4kRERHR8MbAQLQfS1jQFA3FvkpIDAtEREREDAxEXZJhQUexbxTDAhEREVEnXhURAbAsE7riYFggIiIi+gL2MNCwJ4QFXXOjyFsBSZLsbg4RERFRVmFgoGFNCAu66kIhwwIRERHRATEw0LBlCRNOzYOQZwTDAhEREVEPGBhoWEqGBS8KvSPsbgoRERFRVmNgoGHHEibcmh8F3jK7m0JERESU9RgYaFixLBNuZwAF7lK7m0JERESUEzh/JA0bpjDhcQYZFoiIiIj6gIGBhgUhLHj1IILuErubQkRERJRTGBgo71mWAV32wOcstLspRERERDmHYxgor1nChNdRCIecsLspRERERDmJgYHylmWZCLiLIVsOALV2N4eIiIgoJ7EkifKSaRkIuEvgdRbY3RQiIiKinMYeBso7pmWiwFMGjyNgd1OIiIiIch4DA+UVUxgIecrhdvjtbgoRERFRXmBgoLxhWgZC3hFw6z67m0JERESUNxgYKC9YlokibwWcutfuphARERHlFQ56ppxnCgOFDAtEREREg4I9DJSzLMsEIFDkGwmn5rG7OURERER5iYGBcooQAqYwoKtOeB0F8DiDkCV2lBERERENFgYGygmWZUCWVbgcXvgcIaiKbneTiIiIiIYFBgbKWpZlApKAQ/XA6ymHU3Pb3SQiIiKiYYeBgbLK/iVHPmcIbkeAJUdERERENmJgoKxgpkqOfPA7C6HIPDWJiIiIsgGvysg2lmVCAHBqHhR4ClhyRERERJSFGBhoSAlhwRImdNUFn6sQHt0PiSVHRERERFmLgYGGhGkZUGQVbkcAPmeIJUdEREREOcLWq7aGhgb85Cc/werVq6EoCs466yzMnz8fqtq9WatXr8bChQuxefNm+P1+XHTRRbj66qsBAJMmTUo71rIsRKNRLFq0CGeccQZ27tyJe+65B2vWrIEQApMnT8att96KUaNGDcnrHK4sYQIiWXLk9YTg0Fx2N4mIiIiI+sjWWpAbbrgBbrcbr7/+OpYvX4633noLTzzxRLfjtmzZgquuugoXXXQR3n//fSxduhSPP/44XnnlFQDA2rVr036dcsopmDFjBk499VQAwHXXXYdAIIBXX30Vr776KoLBIObOnTuUL3XYEMJK9SYE3aUYUVCDQl8FwwIRERFRjrKth2Hbtm1YvXo1XnvtNbhcLowaNQpz587FwoULccUVV6Qd+/TTT2PWrFk499xzAQATJkzAM888A6/X2+1xX3jhBbz55ptYuXIlVFVFS0sLioqK8P3vfx9ud3JQ7aWXXoqzzz4bLS0tCAQCg/9i85wQApYwoSgq3DpLjoiIiIjyiW1XdZs2bUIwGERpaWlqW3V1NXbv3o3W1lb4/f7U9vXr1+OEE07ATTfdhDfeeAOhUAiXXXYZLrjggrTHbGtrw7333os77rgDBQUFAIBAIIDHHnss7bg//vGPqKio6FNYEEIgEon056VmRCwagyxn1+Bg0zIgQYZDc8OnF0JTHQCAWDQOIG5v4/bT0dGR9jtRpvDcosHA84oGC88t2p8QApIk9epY2wJDe3s7XK70MpWuryORSFpgaGlpwbJly/DAAw/gvvvuw9q1a3H11VcjEAikyo4AYNmyZaioqMBpp53W4/P+5je/weOPP45HH320T+1NJBL4+OOP+/Q9mdRm1kKyt4IMQPLkEpIFBTo0yQ1NckKSYgCa7G7aIW3dutXuJlCe4rlFg4HnFQ0WnlvURdf1Xh1nW2Bwu93dEm7X1x6PJ227ruuYNWsWTjrpJADAsccei7PPPhurVq1KBQYhBJYvX4558+YdMC3F43H87Gc/wx/+8AcsXboUxx9/fJ/aq2kaampq+vQ9mVTb4rCthyFZcmRAkTW4NB88jiBkSbGlLf3R0dGBrVu3oqqqqltIJRoInls0GHhe0WDhuUX727x5c6+PtS0wjB07Fs3Nzaivr0dRURGA5ODmsrIy+Hy+tGOrq6sRj6eXuJimCSFE6usNGzagoaEhrcehS2NjI6699lrE43EsX768X7MjSZKUGgNhB0fMAXmI1yuwhAkJUnKWI2cBdDW331xcLpet/4aUv3hu0WDgeUWDhecWAeh1ORJg4yxJVVVVmDx5Mu6++26Ew2Hs2LEDS5YswZw5c7ode+GFF+Kvf/0rVqxYASEE3n33XaxcuRJnn3126pj33nsPEydO7JaYE4kErrjiCni9XvzmN7/hVKqHYHXOcqTKGgrcZSgP1iDkHZHzYYGIiIiI+sfWovjFixfDMAzMmjUL3/rWt3DiiSempjudNGkSXnrpJQDAtGnTsGTJEixbtiy1hsL8+fMxa9as1GPt2LEjbQB1l7/97W/45z//iXfffRfTpk3DpEmTUr927949NC80ywkhYFgJSJIEn7MAI4I1KPZXwu3w9yl9EhEREVH+sXXuy6KiIixevPiA+9auXZv29cyZMzFz5sweH+v2228/4Pavf/3r2LhxY/8bmcdMYUCGDKfmhdcZgt45yxERERERURdOlj/MWMKCgAWH4kbQUQKn7mUvAhERERH1iIFhGOhaWE1VdPgcQXiduTXLERERERHZh4Ehj5mWAVlS4NQ88DlDqYXViIiIiIh6i4Ehz1iWCUgCuuJG0FMKp+phyRERERER9RsDQx7oWlhNUx3wOoLwsOSIiIiIiDKEgSGHmVYCiqzCpXvhdYSgqb1b3puIiIiIqLcYGHJMV8mRQ/GgwFMKB0uOiIiIiGgQMTDkCAETsqzB6yjoLDmydc09IiIiIhomGBhyxIjAWMgyxyUQERER0dDibeocwbBARERERHZgYCAiIiIioh4xMBARERERUY8YGIiIiIiIqEcMDERERERE1CMGBiIiIiIi6hEDAxERERER9YiBgYiIiIiIesTAQEREREREPWJgICIiIiKiHjEwEBERERFRjxgYiIiIiIioRwwMRERERETUIwYGIiIiIiLqEQMDERERERH1iIGBiIiIiIh6xMBAREREREQ9YmAgIiIiIqIeSUIIYXcjst37778PIQR0Xbe7KdQPQggkEglomgZJkuxuDuURnls0GHhe0WDhuUX7i8fjkCQJxxxzzCGPVYegPTmPP1S5TZIkhj0aFDy3aDDwvKLBwnOL9idJUq+vcdnDQEREREREPeIYBiIiIiIi6hEDAxERERER9YiBgYiIiIiIesTAQEREREREPWJgICIiIiKiHjEwEBERERFRjxgYiIiIiIioRwwMRERERETUIwYGygmNjY2YPXs23nnnndS2Dz74AOeffz4mTZqEk08+Gc8//3za97z44ouYPXs2jj76aHzzm9/E2rVrU/tM08S9996LE044AZMmTcK1116Lffv2pfY3NDRg7ty5mDJlCqZOnYq77roLhmEM/gulIfPJJ5/g8ssvx3HHHYfp06fjhz/8IRobGwHw3KKBeeutt3D++efjmGOOwfTp0/Gf//mfiEajAHhu0cCZpolLLrkEP/rRj1LbeF7RoBNEWW7NmjXia1/7mhg3bpx4++23hRBCNDc3i+OOO0489dRTIpFIiDfffFNMmjRJfPDBB0IIId5++20xadIksWbNGhGPx8WvfvUrMXXqVBGJRIQQQjz88MPizDPPFLt37xZtbW3ihhtuEFdeeWXqOb/97W+LH/zgByISiYjt27eLb3zjG+IXv/jF0L94GhQdHR1i+vTp4qGHHhKxWEw0NjaKK6+8Ulx99dU8t2hAGhoaxJFHHil++9vfCtM0xd69e8UZZ5whHnroIZ5blBEPPvigmDBhgpg/f74Qgp+HNDQYGCirvfDCC+Kkk04Sv//979MCw3PPPSe+/vWvpx17++23ix/+8IdCCCF+8IMfiB//+Mdp+0899VSxfPlyIYQQX/nKV8RLL72U2ldXVyfGjx8vtm/fLrZu3SrGjRsnamtrU/t///vfi5NOOmlQXiMNvS1btojvfve7wjCM1La//OUv4phjjuG5RQPW1tYmhBDCsiyxceNGMXv2bPHkk0/y3KIBe/PNN8Xpp58u5s2blwoMPK9oKLAkibLajBkz8Oc//xmnn3562vZNmzZh3LhxadtqamrwySefAAA2b97c4/62tjbU1tam7S8qKkIgEMDGjRuxadMmBINBlJaWpvZXV1dj9+7daG1tzfRLJBscdthh+OUvfwlFUVLb/vjHP2LixIk8t2jAvF4vAGDmzJk488wzUVxcjG9+85s8t2hAGhoacNttt2HRokVwuVyp7TyvaCgwMFBWKy4uhqqq3ba3t7envWECgNPpRCQSOeT+9vZ2AIDb7e62v729/YDf2/V11+NT/hBC4IEHHsDf/vY33HbbbTy3KGP+9Kc/4bXXXoMsy5g3bx7PLeo3y7Jwyy234PLLL8eECRPS9vG8oqHAwEA5yeVypQYRdolGo/B4PIfc3/Vm19HRccD9bre7276ur7sen/JDOBzGvHnzsHLlSjz11FMYP348zy3KGKfTidLSUtxyyy14/fXXeW5Rvy1duhS6ruOSSy7pto/nFQ0FBgbKSePGjcOmTZvStm3evBljx44FAIwdO7bH/YFAAKWlpdi8eXNqX11dHZqbmzFu3DiMHTsWzc3NqK+vT+3fsmULysrK4PP5BvFV0VDavn07zjvvPITDYSxfvhzjx48HwHOLBub999/Hqaeeing8ntoWj8ehaRpqamp4blG/rFixAqtXr8aUKVMwZcoUvPzyy3j55ZcxZcoUvmfRkGBgoJw0e/Zs1NfX44knnkAikcDbb7+NlStX4rzzzgMAzJkzBytXrsTbb7+NRCKBJ554Ag0NDZg9ezYA4Jvf/CYeffRR7NixA+FwGHfffTeOO+44VFZWoqqqCpMnT8bdd9+NcDiMHTt2YMmSJZgzZ46dL5kyqKWlBd/5zndwzDHH4LHHHkMoFErt47lFAzF+/HhEo1EsWrQI8Xgcu3btwr333os5c+bglFNO4blF/fLKK6/g/fffx5o1a7BmzRqcccYZOOOMM7BmzRq+Z9HQsHvUNVFv7T9LkhBCrF+/XlxwwQVi0qRJYtasWeK3v/1t2vG/+93vxCmnnCKOPvpoMWfOHLFu3brUvng8LhYuXChOPPFEccwxx4hrr71W1NfXp/bX1dWJ66+/Xhx33HHi+OOPF/fcc0/ajDqU2x5//HExbtw4cdRRR4mjjz467ZcQPLdoYDZt2iQuv/xyMWXKFPHVr35V/L//9/9ELBYTQvDcosyYP39+apYkIXhe0eCThBDC7tBCRERERETZiSVJRERERETUIwYGIiIiIiLqEQMDERERERH1iIGBiIiIiIh6xMBAREREREQ9YmAgIiIiIqIeMTAQEREREVGPGBiIiLLUj370I4wfP/6gv/rrkksuwY9+9KNeH3/yySfj4Ycf7vfz2fEcTU1NeP755zP2eEREwxUXbiMiylJtbW2IRqOpr2fMmIH/+I//wOmnn57aVlxc3K/Hbm5uhqIo8Pl8vTq+sbERDocDHo+nX8/XGyeffDLOPfdcXH/99Rl5vFtvvRU7d+7Ek08+mZHHIyIarlS7G0BERAfm8/m6XdD7fL5+h4T9BYPBPh0fCoUG/JxDjffDiIgygyVJREQ57IUXXsDJJ5+Mu+66C1OmTME111wDAHj11Vdx4YUXYtKkSTjyyCMxZ84cvPnmm6nv278kqesxXnzxRcyePRtHHHEEzjvvPKxduzZ1/P7lQg8//DAuueQS/OIXv8BXvvIVHHnkkbj00kvxr3/9K3V8Y2MjbrzxRkyZMgVTp07FwoULcemll/a65Gjnzp0YP348Vq1ahfPPPx9HHnkkZs2aheXLl6eOaWhowLx58zB16lR8+ctfxoUXXojVq1cDSJZzvfjii1i9enWqdKu1tRV33HEHZs6ciYkTJ2L69Om44447Ur0477zzDsaPH4+///3vOOOMM3DEEUfgG9/4Bv72t7+lte3JJ5/EKaecgi9/+cs4/fTTsWLFitS+vXv3pr3ua665Blu3bu1Vm4mIshUDAxFRjtu1axf27t2LF198ET/4wQ/w4Ycf4rrrrsPXv/51vPTSS3j++edRWFiIm2++GfF4/ICPsW/fPjzzzDNYuHAhnn32WciyjPnz5/d4l37t2rV499138T//8z944oknsHv3btx5550AAMuycPXVV2Pbtm34xS9+gccffxzr16/v14XxPffcg2uuuQa/+93vMG3aNPzkJz/Bjh07AAA//elPEY1G8dRTT2HlypUYM2YM5s6di0gkgttuuw2nnXYaJk2ahH/84x8AgPnz52P9+vVYvHgx/vjHP+LWW2/FCy+8gGeffTbtORcuXIjbbrsNL7zwAkaNGoWbb74Z7e3tAIDHHnsM999/P7773e/i5ZdfxsUXX4xbb70Vb7zxBiKRCC655BKYpomnnnoKTz75JAoKCvCtb30Le/fuPWSbiYiyFUuSiIjywNy5czFq1CgAwMcff4wf//jHuPjii1P7L730Uvz7v/87GhoaUF5e3u37E4kEfvrTn+Lwww8HAFx99dW47rrrUFdXh5KSkm7HG4aB++67L1XadMkll2DhwoUAgNWrV2P9+vVYtWoVDjvsMADAgw8+iK9+9at9fl2XX345Zs2aBSB5wf/888/jgw8+wKhRo7B9+3aMGzcOlZWVcDgcuO2223DmmWdCURS43W44nU5ompYq4Zo+fTqmTJmCCRMmAABGjhyJp556Chs3bkx7zhtuuAHTpk1L/fnss8/Gp59+ikmTJuGJJ57ApZdeim9961sAgIsvvhjRaBSmaeL3v/89mpqasGjRImiaBgC466678M477+C5557D9ddff9A2ExFlKwYGIqI8UFVVlfrz4YcfjkAggF/84hf47LPPsHXrVnz88ccAANM0e3yM6urq1J+7xk4kEokDHltUVJQ2DsLn86WO/eijjxAIBFJhAQAKCwsxZsyYPr+ug7Xpe9/7Hm655Rb8+c9/xpQpUzBjxgycfvrpcDgcB3ysiy66CK+++ipWrFiB7du349NPP8WOHTvS/u4ApLXb6/WmnrOxsRH79u3DUUcdlXb8d7/7XQDAnXfeiXA4jOOOOy5tfywWw5YtW/rVZiKibMDAQESUB5xOZ+rP7777Lv793/8dM2fOxJQpU/CNb3wDHR0duO666w76GLqud9vWU0nSgY7toigKLMvqZcsP7mBtmj17Nl5//XW8/vrrePPNN/HLX/4SDz30EJ577jmMHTu22/dcc8012LhxI84880yccsopuOmmm/CTn/yk18/ZtV2SpAO21bIsjBkzBo8++mi3fW63u89tJiLKFgwMRER55rHHHsPUqVPx3//936ltXVOLDsXMQRMmTEBbWxu2bNmS6iFobm7Gtm3bMvYc8XgcixYtwtlnn43TTz8dp59+Ojo6OjBjxgz83//9H8aOHZt2Yf/RRx/h73//O5577rlUD0EikcD27dtTpVyH4vV6UVJSgg0bNqTKpABg3rx5KCkpwbhx47BixQr4fL7UrFKGYeCmm27Cqaeeiq997WuHbDMRUTbioGciojxTXl6OjRs3Ys2aNdi5cyd++9vf4qGHHgKAHgc9Z9LUqVNx9NFH44c//CHWrVuHTz75BDfffDM6Ojp6vDvfV7qu44MPPsBPfvITrFu3Djt37sQLL7yA9vZ2TJo0CUDyrv6+ffuwY8cOFBUVQVVVrFq1Cjt27MCGDRtwww03oK6urk9/J1dddRX+93//F7/73e+wfft2/PrXv8Zf//pXfO1rX8NZZ52FQCCA733ve1i3bh22bNmCW2+9FX//+98xduzYXrWZiCgbsYeBiCjPzJs3D/X19akpVmtqanD33Xfjlltuwfr169PGBQyWxYsXY8GCBbjsssvgcDhw0UUXYcuWLanBwJnw0EMP4Wc/+xmuvfZatLW14bDDDsOiRYswZcoUAMA555yDP//5zzjjjDPw5z//Gffccw8efvhh/PrXv0ZxcTFOOukkXHbZZfjrX//a656Xb3/724jFYli8eDHq6upQVVWFBx54AMcffzwA4KmnnsJ9992HK664AqZp4vDDD8djjz2W6j04VJuJiLIRV3omIqKMamxsxAcffIAZM2akAkI8HsfUqVNxxx134JxzzrG3gURE1CfsYSAiooxSVRU33ngjLrzwQvzbv/0bEokEHnvsMei6jq985St2N4+IiPqIPQxERJRxb7/9Nh588EFs3LgRkiRh8uTJuPnmm1OrLhMRUe5gYCAiIiIioh5xliQiIiIiIuoRAwMREREREfWIgYGIiIiIiHrEwEBERERERD1iYCAiIiIioh4xMBARERERUY8YGIiIiIiIqEcMDERERERE1CMGBiIiIiIi6tH/B5FX79n83SxfAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1h40\n",
    "plot_learning_curve(svc_pipe_linear, X_train, y_train, model_name=\"SVC avec noyau linéaire\")\n",
    "plot_learning_curve(svc_pipe_rbf, X_train, y_train, model_name=\"SVC avec noyau RBF\")\n",
    "plot_learning_curve(svc_pipe_poly, X_train, y_train, model_name=\"SVC avec noyau polynomial\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_validation_curve(pipeline, X_train, y_train, param_name, param_range, model_name=\"SVC\", cv=3, scoring='accuracy'):\n",
    "    # Extract the SVC estimator from the pipeline\n",
    "    svc = pipeline.named_steps['svc']\n",
    "    \n",
    "    plt.figure()\n",
    "    visualizer = ValidationCurve(\n",
    "        svc, param_name=param_name, param_range=param_range,\n",
    "        cv=cv, scoring=scoring, n_jobs=-1\n",
    "    )\n",
    "    visualizer.fit(pipeline[:-1].transform(X_train), y_train)  # Apply the pipeline except the last step\n",
    "    visualizer.finalize()  # Finalize the visualizer to ensure it draws correctly\n",
    "    plt.show()  # Ensure the plot is displayed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)\n",
      "Cell \u001b[0;32mIn[31], line 3\u001b[0m\n",
      "\u001b[1;32m      1\u001b[0m \u001b[39m# Affichage de la courbe de validation pour le paramètre 'C'\u001b[39;00m\n",
      "\u001b[1;32m      2\u001b[0m param_range_C \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mlogspace(\u001b[39m-\u001b[39m\u001b[39m3\u001b[39m, \u001b[39m2\u001b[39m, \u001b[39m6\u001b[39m)  \u001b[39m# Valeurs pour C\u001b[39;00m\n",
      "\u001b[0;32m----> 3\u001b[0m plot_validation_curve(svc_pipe_linear, X_train, y_train, param_name\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mC\u001b[39m\u001b[39m'\u001b[39m, param_range\u001b[39m=\u001b[39mparam_range_C, model_name\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mSVC avec noyau linéaire\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "\u001b[1;32m      5\u001b[0m \u001b[39m# Affichage de la courbe de validation pour le paramètre 'gamma'\u001b[39;00m\n",
      "\u001b[1;32m      6\u001b[0m param_range_gamma \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mlogspace(\u001b[39m-\u001b[39m\u001b[39m4\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m6\u001b[39m)  \u001b[39m# Valeurs pour gamma\u001b[39;00m\n",
      "\n",
      "Cell \u001b[0;32mIn[30], line 10\u001b[0m, in \u001b[0;36mplot_validation_curve\u001b[0;34m(pipeline, X_train, y_train, param_name, param_range, model_name, cv, scoring)\u001b[0m\n",
      "\u001b[1;32m      5\u001b[0m plt\u001b[39m.\u001b[39mfigure()\n",
      "\u001b[1;32m      6\u001b[0m visualizer \u001b[39m=\u001b[39m ValidationCurve(\n",
      "\u001b[1;32m      7\u001b[0m     svc, param_name\u001b[39m=\u001b[39mparam_name, param_range\u001b[39m=\u001b[39mparam_range,\n",
      "\u001b[1;32m      8\u001b[0m     cv\u001b[39m=\u001b[39mcv, scoring\u001b[39m=\u001b[39mscoring, n_jobs\u001b[39m=\u001b[39m\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m\n",
      "\u001b[1;32m      9\u001b[0m )\n",
      "\u001b[0;32m---> 10\u001b[0m visualizer\u001b[39m.\u001b[39mfit(pipeline[:\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m]\u001b[39m.\u001b[39mtransform(X_train), y_train)  \u001b[39m# Apply the pipeline except the last step\u001b[39;00m\n",
      "\u001b[1;32m     11\u001b[0m visualizer\u001b[39m.\u001b[39mfinalize()  \u001b[39m# Finalize the visualizer to ensure it draws correctly\u001b[39;00m\n",
      "\u001b[1;32m     12\u001b[0m plt\u001b[39m.\u001b[39mshow()\n",
      "\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/yellowbrick/model_selection/validation_curve.py:233\u001b[0m, in \u001b[0;36mValidationCurve.fit\u001b[0;34m(self, X, y)\u001b[0m\n",
      "\u001b[1;32m    219\u001b[0m skvc_kwargs \u001b[39m=\u001b[39m {\n",
      "\u001b[1;32m    220\u001b[0m     key: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_params()[key]\n",
      "\u001b[1;32m    221\u001b[0m     \u001b[39mfor\u001b[39;00m key \u001b[39min\u001b[39;00m (\n",
      "\u001b[0;32m   (...)\u001b[0m\n",
      "\u001b[1;32m    229\u001b[0m     )\n",
      "\u001b[1;32m    230\u001b[0m }\n",
      "\u001b[1;32m    232\u001b[0m \u001b[39m# compute the validation curve and store scores\u001b[39;00m\n",
      "\u001b[0;32m--> 233\u001b[0m curve \u001b[39m=\u001b[39m sk_validation_curve(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mestimator, X, y, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mskvc_kwargs)\n",
      "\u001b[1;32m    234\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrain_scores_, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtest_scores_ \u001b[39m=\u001b[39m curve\n",
      "\u001b[1;32m    236\u001b[0m \u001b[39m# compute the mean and standard deviation of the training data\u001b[39;00m\n",
      "\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:211\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;32m    205\u001b[0m \u001b[39mtry\u001b[39;00m:\n",
      "\u001b[1;32m    206\u001b[0m     \u001b[39mwith\u001b[39;00m config_context(\n",
      "\u001b[1;32m    207\u001b[0m         skip_parameter_validation\u001b[39m=\u001b[39m(\n",
      "\u001b[1;32m    208\u001b[0m             prefer_skip_nested_validation \u001b[39mor\u001b[39;00m global_skip_validation\n",
      "\u001b[1;32m    209\u001b[0m         )\n",
      "\u001b[1;32m    210\u001b[0m     ):\n",
      "\u001b[0;32m--> 211\u001b[0m         \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "\u001b[1;32m    212\u001b[0m \u001b[39mexcept\u001b[39;00m InvalidParameterError \u001b[39mas\u001b[39;00m e:\n",
      "\u001b[1;32m    213\u001b[0m     \u001b[39m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n",
      "\u001b[1;32m    214\u001b[0m     \u001b[39m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n",
      "\u001b[1;32m    215\u001b[0m     \u001b[39m# the name of the estimator by the name of the function in the error\u001b[39;00m\n",
      "\u001b[1;32m    216\u001b[0m     \u001b[39m# message to avoid confusion.\u001b[39;00m\n",
      "\u001b[1;32m    217\u001b[0m     msg \u001b[39m=\u001b[39m re\u001b[39m.\u001b[39msub(\n",
      "\u001b[1;32m    218\u001b[0m         \u001b[39mr\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mparameter of \u001b[39m\u001b[39m\\\u001b[39m\u001b[39mw+ must be\u001b[39m\u001b[39m\"\u001b[39m,\n",
      "\u001b[1;32m    219\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mparameter of \u001b[39m\u001b[39m{\u001b[39;00mfunc\u001b[39m.\u001b[39m\u001b[39m__qualname__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m must be\u001b[39m\u001b[39m\"\u001b[39m,\n",
      "\u001b[1;32m    220\u001b[0m         \u001b[39mstr\u001b[39m(e),\n",
      "\u001b[1;32m    221\u001b[0m     )\n",
      "\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:1985\u001b[0m, in \u001b[0;36mvalidation_curve\u001b[0;34m(estimator, X, y, param_name, param_range, groups, cv, scoring, n_jobs, pre_dispatch, verbose, error_score, fit_params)\u001b[0m\n",
      "\u001b[1;32m   1982\u001b[0m scorer \u001b[39m=\u001b[39m check_scoring(estimator, scoring\u001b[39m=\u001b[39mscoring)\n",
      "\u001b[1;32m   1984\u001b[0m parallel \u001b[39m=\u001b[39m Parallel(n_jobs\u001b[39m=\u001b[39mn_jobs, pre_dispatch\u001b[39m=\u001b[39mpre_dispatch, verbose\u001b[39m=\u001b[39mverbose)\n",
      "\u001b[0;32m-> 1985\u001b[0m results \u001b[39m=\u001b[39m parallel(\n",
      "\u001b[1;32m   1986\u001b[0m     delayed(_fit_and_score)(\n",
      "\u001b[1;32m   1987\u001b[0m         clone(estimator),\n",
      "\u001b[1;32m   1988\u001b[0m         X,\n",
      "\u001b[1;32m   1989\u001b[0m         y,\n",
      "\u001b[1;32m   1990\u001b[0m         scorer,\n",
      "\u001b[1;32m   1991\u001b[0m         train,\n",
      "\u001b[1;32m   1992\u001b[0m         test,\n",
      "\u001b[1;32m   1993\u001b[0m         verbose,\n",
      "\u001b[1;32m   1994\u001b[0m         parameters\u001b[39m=\u001b[39m{param_name: v},\n",
      "\u001b[1;32m   1995\u001b[0m         fit_params\u001b[39m=\u001b[39mfit_params,\n",
      "\u001b[1;32m   1996\u001b[0m         return_train_score\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n",
      "\u001b[1;32m   1997\u001b[0m         error_score\u001b[39m=\u001b[39merror_score,\n",
      "\u001b[1;32m   1998\u001b[0m     )\n",
      "\u001b[1;32m   1999\u001b[0m     \u001b[39m# NOTE do not change order of iteration to allow one time cv splitters\u001b[39;00m\n",
      "\u001b[1;32m   2000\u001b[0m     \u001b[39mfor\u001b[39;00m train, test \u001b[39min\u001b[39;00m cv\u001b[39m.\u001b[39msplit(X, y, groups)\n",
      "\u001b[1;32m   2001\u001b[0m     \u001b[39mfor\u001b[39;00m v \u001b[39min\u001b[39;00m param_range\n",
      "\u001b[1;32m   2002\u001b[0m )\n",
      "\u001b[1;32m   2003\u001b[0m n_params \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(param_range)\n",
      "\u001b[1;32m   2005\u001b[0m results \u001b[39m=\u001b[39m _aggregate_score_dicts(results)\n",
      "\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/utils/parallel.py:65\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n",
      "\u001b[1;32m     60\u001b[0m config \u001b[39m=\u001b[39m get_config()\n",
      "\u001b[1;32m     61\u001b[0m iterable_with_config \u001b[39m=\u001b[39m (\n",
      "\u001b[1;32m     62\u001b[0m     (_with_config(delayed_func, config), args, kwargs)\n",
      "\u001b[1;32m     63\u001b[0m     \u001b[39mfor\u001b[39;00m delayed_func, args, kwargs \u001b[39min\u001b[39;00m iterable\n",
      "\u001b[1;32m     64\u001b[0m )\n",
      "\u001b[0;32m---> 65\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39m\u001b[39m__call__\u001b[39m(iterable_with_config)\n",
      "\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/joblib/parallel.py:1098\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n",
      "\u001b[1;32m   1095\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_iterating \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n",
      "\u001b[1;32m   1097\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend\u001b[39m.\u001b[39mretrieval_context():\n",
      "\u001b[0;32m-> 1098\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mretrieve()\n",
      "\u001b[1;32m   1099\u001b[0m \u001b[39m# Make sure that we get a last message telling us we are done\u001b[39;00m\n",
      "\u001b[1;32m   1100\u001b[0m elapsed_time \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime() \u001b[39m-\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_start_time\n",
      "\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/joblib/parallel.py:975\u001b[0m, in \u001b[0;36mParallel.retrieve\u001b[0;34m(self)\u001b[0m\n",
      "\u001b[1;32m    973\u001b[0m \u001b[39mtry\u001b[39;00m:\n",
      "\u001b[1;32m    974\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend, \u001b[39m'\u001b[39m\u001b[39msupports_timeout\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mFalse\u001b[39;00m):\n",
      "\u001b[0;32m--> 975\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_output\u001b[39m.\u001b[39mextend(job\u001b[39m.\u001b[39mget(timeout\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtimeout))\n",
      "\u001b[1;32m    976\u001b[0m     \u001b[39melse\u001b[39;00m:\n",
      "\u001b[1;32m    977\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_output\u001b[39m.\u001b[39mextend(job\u001b[39m.\u001b[39mget())\n",
      "\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/joblib/_parallel_backends.py:567\u001b[0m, in \u001b[0;36mLokyBackend.wrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n",
      "\u001b[1;32m    564\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Wrapper for Future.result to implement the same behaviour as\u001b[39;00m\n",
      "\u001b[1;32m    565\u001b[0m \u001b[39mAsyncResults.get from multiprocessing.\"\"\"\u001b[39;00m\n",
      "\u001b[1;32m    566\u001b[0m \u001b[39mtry\u001b[39;00m:\n",
      "\u001b[0;32m--> 567\u001b[0m     \u001b[39mreturn\u001b[39;00m future\u001b[39m.\u001b[39mresult(timeout\u001b[39m=\u001b[39mtimeout)\n",
      "\u001b[1;32m    568\u001b[0m \u001b[39mexcept\u001b[39;00m CfTimeoutError \u001b[39mas\u001b[39;00m e:\n",
      "\u001b[1;32m    569\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mTimeoutError\u001b[39;00m \u001b[39mfrom\u001b[39;00m \u001b[39me\u001b[39;00m\n",
      "\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/concurrent/futures/_base.py:451\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n",
      "\u001b[1;32m    448\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_state \u001b[39m==\u001b[39m FINISHED:\n",
      "\u001b[1;32m    449\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__get_result()\n",
      "\u001b[0;32m--> 451\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_condition\u001b[39m.\u001b[39mwait(timeout)\n",
      "\u001b[1;32m    453\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_state \u001b[39min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n",
      "\u001b[1;32m    454\u001b[0m     \u001b[39mraise\u001b[39;00m CancelledError()\n",
      "\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/threading.py:320\u001b[0m, in \u001b[0;36mCondition.wait\u001b[0;34m(self, timeout)\u001b[0m\n",
      "\u001b[1;32m    318\u001b[0m \u001b[39mtry\u001b[39;00m:    \u001b[39m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n",
      "\u001b[1;32m    319\u001b[0m     \u001b[39mif\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "\u001b[0;32m--> 320\u001b[0m         waiter\u001b[39m.\u001b[39macquire()\n",
      "\u001b[1;32m    321\u001b[0m         gotit \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n",
      "\u001b[1;32m    322\u001b[0m     \u001b[39melse\u001b[39;00m:\n",
      "\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Affichage de la courbe de validation pour le paramètre 'C'\n",
    "param_range_C = np.logspace(-3, 2, 6)  # Valeurs pour C\n",
    "plot_validation_curve(svc_pipe_linear, X_train, y_train, param_name='C', param_range=param_range_C, model_name=\"SVC avec noyau linéaire\")\n",
    "\n",
    "# Affichage de la courbe de validation pour le paramètre 'gamma'\n",
    "param_range_gamma = np.logspace(-4, 1, 6)  # Valeurs pour gamma\n",
    "plot_validation_curve(svc_pipe_rbf, X_train, y_train, param_name='gamma', param_range=param_range_gamma, model_name=\"SVC avec noyau RBF\")\n",
    "\n",
    "# Affichage de la courbe de validation pour le paramètre 'degree'\n",
    "param_range_degree = [2, 3, 4, 5, 6]  # Valeurs pour degree\n",
    "plot_validation_curve(svc_pipe_poly, X_train, y_train, param_name='degree', param_range=param_range_degree, model_name=\"SVC avec noyau polynomial\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import optuna\n",
    "\n",
    "def hyperoptimize_svc_model(pipeline, X_train, y_train, X_test, y_test, kernel='linear', n_trials=5, timeout=600):\n",
    "    # Définissez la fonction objective pour Optuna\n",
    "    def objective(trial):\n",
    "        if kernel == 'linear':\n",
    "            C = trial.suggest_loguniform('svc__C', 1e-3, 1e1)\n",
    "            pipeline.set_params(svc__C=C)\n",
    "        elif kernel == 'rbf':\n",
    "            C = trial.suggest_loguniform('svc__C', 1e-3, 1e1)\n",
    "            gamma = trial.suggest_loguniform('svc__gamma', 1e-4, 1e1)\n",
    "            pipeline.set_params(svc__C=C, svc__gamma=gamma)\n",
    "        elif kernel == 'poly':\n",
    "            C = trial.suggest_loguniform('svc__C', 1e-3, 1e1)\n",
    "            degree = trial.suggest_int('svc__degree', 2, 5)\n",
    "            pipeline.set_params(svc__C=C, svc__degree=degree)\n",
    "        else:\n",
    "            raise ValueError(f\"Unsupported kernel type: {kernel}\")\n",
    "\n",
    "        # Apply the preprocessing steps except the last step\n",
    "        X_train_transformed = pipeline[:-1].fit_transform(X_train, y_train)\n",
    "        score = cross_val_score(pipeline.named_steps['svc'], X_train_transformed, y_train, cv=3, scoring='accuracy').mean()\n",
    "        return score\n",
    "\n",
    "    # Créez un objet study et optimisez la fonction objective\n",
    "    study = optuna.create_study(direction='maximize')\n",
    "    study.optimize(objective, n_trials=n_trials, timeout=timeout)\n",
    "\n",
    "    # Affichez les meilleurs hyperparamètres\n",
    "    print('Best parameters:', study.best_params)\n",
    "\n",
    "    # Entraînez le modèle avec les meilleurs hyperparamètres\n",
    "    pipeline.set_params(**study.best_params)\n",
    "    pipeline.fit(X_train, y_train)\n",
    "\n",
    "    # Prédiction sur le jeu de test\n",
    "    y_test_pred = pipeline.predict(X_test)\n",
    "\n",
    "    # Évaluation du modèle\n",
    "    accuracy = accuracy_score(y_test, y_test_pred)\n",
    "    f1 = f1_score(y_test, y_test_pred, average='weighted')\n",
    "    print('Accuracy on test set:', accuracy)\n",
    "    print('F1 score on test set:', f1)\n",
    "\n",
    "    # Matrice de confusion\n",
    "    cm = confusion_matrix(y_test, y_test_pred)\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "    disp.plot()\n",
    "    plt.title(f\"Confusion Matrix for SVC with {kernel} kernel\")\n",
    "    plt.show()\n",
    "\n",
    "    # Rapport de classification\n",
    "    print(classification_report(y_test, y_test_pred))\n",
    "\n",
    "    return study.best_params, accuracy, f1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-06-12 00:21:10,161] A new study created in memory with name: no-name-bb47be44-6c2b-43e3-b14f-436fefb5b796\n"
     ]
    }
   ],
   "source": [
    "# Hyper-optimisation pour un noyau linéaire\n",
    "best_params_linear, accuracy_linear, f1_linear = hyperoptimize_svc_model(\n",
    "    svc_pipe_linear, X_train, y_train, X_test, y_test, kernel='linear', n_trials=5, timeout=600)\n",
    "\n",
    "# Hyper-optimisation pour un noyau RBF\n",
    "best_params_rbf, accuracy_rbf, f1_rbf = hyperoptimize_svc_model(\n",
    "    svc_pipe_rbf, X_train, y_train, X_test, y_test, kernel='rbf', n_trials=5, timeout=600)\n",
    "\n",
    "# Hyper-optimisation pour un noyau polynomial\n",
    "best_params_rbf, accuracy_rbf, f1_rbf =poly, f1_poly = hyperoptimize_svc_model(\n",
    "    svc_pipe_poly, X_train, y_train, X_test, y_test, kernel='poly', n_trials=5, timeout=600)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Après optimisation, le gain est faible avec un <b><font color='red'>F1 score à 69.7% </font></b> pour le noyau linéaire"
   ]
  }
 ],
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