Spam Email Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cc1c8862",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings \n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "91d47f14",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn.ensemble import StackingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cc348497",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Message</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5567</th>\n",
       "      <td>spam</td>\n",
       "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5568</th>\n",
       "      <td>ham</td>\n",
       "      <td>Will Ì_ b going to esplanade fr home?</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5569</th>\n",
       "      <td>ham</td>\n",
       "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5570</th>\n",
       "      <td>ham</td>\n",
       "      <td>The guy did some bitching but I acted like i'd...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5571</th>\n",
       "      <td>ham</td>\n",
       "      <td>Rofl. Its true to its name</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5572 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Category                                            Message Unnamed: 2  \\\n",
       "0         ham  Go until jurong point, crazy.. Available only ...        NaN   \n",
       "1         ham                      Ok lar... Joking wif u oni...        NaN   \n",
       "2        spam  Free entry in 2 a wkly comp to win FA Cup fina...        NaN   \n",
       "3         ham  U dun say so early hor... U c already then say...        NaN   \n",
       "4         ham  Nah I don't think he goes to usf, he lives aro...        NaN   \n",
       "...       ...                                                ...        ...   \n",
       "5567     spam  This is the 2nd time we have tried 2 contact u...        NaN   \n",
       "5568      ham              Will Ì_ b going to esplanade fr home?        NaN   \n",
       "5569      ham  Pity, * was in mood for that. So...any other s...        NaN   \n",
       "5570      ham  The guy did some bitching but I acted like i'd...        NaN   \n",
       "5571      ham                         Rofl. Its true to its name        NaN   \n",
       "\n",
       "     Unnamed: 3 Unnamed: 4  \n",
       "0           NaN        NaN  \n",
       "1           NaN        NaN  \n",
       "2           NaN        NaN  \n",
       "3           NaN        NaN  \n",
       "4           NaN        NaN  \n",
       "...         ...        ...  \n",
       "5567        NaN        NaN  \n",
       "5568        NaN        NaN  \n",
       "5569        NaN        NaN  \n",
       "5570        NaN        NaN  \n",
       "5571        NaN        NaN  \n",
       "\n",
       "[5572 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df =  pd.read_csv(r\"C:\\Users\\KALYAN\\Desktop\\Machine Learning\\SPAM.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "74567d76",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df.drop(labels=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dd6c7cb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category    0\n",
       "Message     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2e2f82d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category    object\n",
       "Message     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5a48466b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[data['Category'] == 'spam', 'Category',] = 0\n",
    "data.loc[data['Category'] == 'ham', 'Category',] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0c1bc1f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    4825\n",
       "0     747\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e0c4d4f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data['Message']\n",
    "\n",
    "Y = data['Category']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2e966f37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Go until jurong point, crazy.. Available only ...\n",
       "1                           Ok lar... Joking wif u oni...\n",
       "2       Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3       U dun say so early hor... U c already then say...\n",
       "4       Nah I don't think he goes to usf, he lives aro...\n",
       "                              ...                        \n",
       "5567    This is the 2nd time we have tried 2 contact u...\n",
       "5568                Will Ì_ b going to esplanade fr home?\n",
       "5569    Pity, * was in mood for that. So...any other s...\n",
       "5570    The guy did some bitching but I acted like i'd...\n",
       "5571                           Rofl. Its true to its name\n",
       "Name: Message, Length: 5572, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "53ede04d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       1\n",
       "2       0\n",
       "3       1\n",
       "4       1\n",
       "       ..\n",
       "5567    0\n",
       "5568    1\n",
       "5569    1\n",
       "5570    1\n",
       "5571    1\n",
       "Name: Category, Length: 5572, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e5fcf412",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "711f5006",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5572,)\n",
      "(4457,)\n",
      "(1115,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a8fdbc37",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_extraction = TfidfVectorizer(min_df = 1, stop_words='english', lowercase = 'True')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "796fdced",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_features = feature_extraction.fit_transform(X_train)\n",
    "X_test_features = feature_extraction.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "124c387b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train = Y_train.astype('int')\n",
    "Y_test = Y_test.astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74683297",
   "metadata": {},
   "source": [
    "# LOGISTIC REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fdb86511",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression:\n",
      "\n",
      "Training Data Accuracy: 0.9661207089970832\n",
      "Testing Data Accuracy : 0.9623318385650225\n",
      "Precision             : 0.959\n",
      "Recall                : 0.9989583333333333\n",
      "F1 Score              : 0.9785714285714285\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "lr.fit(X_train_features, Y_train)\n",
    "\n",
    "\n",
    "lr_train = lr.predict(X_train_features)\n",
    "lr_test = lr.predict(X_test_features)\n",
    "\n",
    "        \n",
    "lr_train_acc = accuracy_score(Y_train, lr_train)\n",
    "lr_test_acc = accuracy_score(Y_test, lr_test)\n",
    "\n",
    "\n",
    "lr_precision = precision_score(Y_test, lr_test)\n",
    "lr_recall = recall_score(Y_test, lr_test)\n",
    "lr_f1 = f1_score(Y_test, lr_test)\n",
    "\n",
    "\n",
    "print(\"Logistic Regression:\\n\")\n",
    "print(\"Training Data Accuracy:\", lr_train_acc)\n",
    "print(\"Testing Data Accuracy :\", lr_test_acc)\n",
    "\n",
    "print(\"Precision             :\", lr_precision)\n",
    "print(\"Recall                :\", lr_recall)\n",
    "print(\"F1 Score              :\", lr_f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3398ca05",
   "metadata": {},
   "source": [
    "# DECISION TREES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "669b602f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tress:\n",
      "\n",
      "Training Data Accuracy: 1.0\n",
      "Testing Data Accuracy : 0.9659192825112107\n",
      "Precision             : 0.973305954825462\n",
      "Recall                : 0.9875\n",
      "F1 Score              : 0.9803516028955533\n"
     ]
    }
   ],
   "source": [
    "dtrees = DecisionTreeClassifier()\n",
    "dtrees.fit(X_train_features, Y_train)\n",
    "\n",
    "\n",
    "dt_train = dtrees.predict(X_train_features)\n",
    "dt_test = dtrees.predict(X_test_features)\n",
    "\n",
    "\n",
    "dt_train_acc = accuracy_score(Y_train, dt_train)\n",
    "dt_test_acc = accuracy_score(Y_test, dt_test)\n",
    "\n",
    "\n",
    "dt_precision = precision_score(Y_test, dt_test)\n",
    "dt_recall = recall_score(Y_test, dt_test)\n",
    "dt_f1 = f1_score(Y_test, dt_test)\n",
    "\n",
    "\n",
    "print(\"Decision Tress:\\n\")\n",
    "print(\"Training Data Accuracy:\", dt_train_acc)\n",
    "print(\"Testing Data Accuracy :\", dt_test_acc)\n",
    "\n",
    "print(\"Precision             :\", dt_precision)\n",
    "print(\"Recall                :\", dt_recall)\n",
    "print(\"F1 Score              :\", dt_f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90ea91a3",
   "metadata": {},
   "source": [
    "# K NEAREST NEIGHBORS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "47523a35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K Nearest Neighbors:\n",
      "\n",
      "Training Data Accuracy: 0.9199012788871438\n",
      "Testing Data Accuracy : 0.905829596412556\n",
      "Precision             : 0.9014084507042254\n",
      "Recall                : 1.0\n",
      "F1 Score              : 0.9481481481481481\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train_features, Y_train)\n",
    "\n",
    "\n",
    "knn_train = knn.predict(X_train_features)\n",
    "knn_test = knn.predict(X_test_features)\n",
    "\n",
    "\n",
    "knn_train_acc = accuracy_score(Y_train, knn_train)\n",
    "knn_test_acc = accuracy_score(Y_test, knn_test)\n",
    "\n",
    "\n",
    "knn_precision = precision_score(Y_test, knn_test)\n",
    "knn_recall = recall_score(Y_test, knn_test)\n",
    "knn_f1 = f1_score(Y_test, knn_test)\n",
    "\n",
    "\n",
    "print(\"K Nearest Neighbors:\\n\")\n",
    "print(\"Training Data Accuracy:\", knn_train_acc)\n",
    "print(\"Testing Data Accuracy :\", knn_test_acc)\n",
    "\n",
    "print(\"Precision             :\", knn_precision)\n",
    "print(\"Recall                :\", knn_recall)\n",
    "print(\"F1 Score              :\", knn_f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43f66f5c",
   "metadata": {},
   "source": [
    "# RANDOM FOREST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bf28b066",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest:\n",
      "\n",
      "Training Data Accuracy: 1.0\n",
      "Testing Data Accuracy : 0.9811659192825112\n",
      "Precision             : 0.9785932721712538\n",
      "Recall                : 1.0\n",
      "F1 Score              : 0.9891808346213292\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train_features, Y_train)\n",
    "\n",
    "\n",
    "rf_train = rf.predict(X_train_features)\n",
    "rf_test = rf.predict(X_test_features)\n",
    "\n",
    "\n",
    "rf_train_acc = accuracy_score(Y_train, rf_train)\n",
    "rf_test_acc = accuracy_score(Y_test, rf_test)\n",
    "\n",
    "\n",
    "rf_precision = precision_score(Y_test, rf_test)\n",
    "rf_recall = recall_score(Y_test, rf_test)\n",
    "rf_f1 = f1_score(Y_test, rf_test)\n",
    "\n",
    "\n",
    "print(\"Random Forest:\\n\")\n",
    "print(\"Training Data Accuracy:\", rf_train_acc)\n",
    "print(\"Testing Data Accuracy :\", rf_test_acc)   \n",
    "           \n",
    "print(\"Precision             :\", rf_precision)\n",
    "print(\"Recall                :\", rf_recall)\n",
    "print(\"F1 Score              :\", rf_f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c144d9c",
   "metadata": {},
   "source": [
    "# STACKING MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "df960558",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stacking Classifier:\n",
      "\n",
      "Training Data Accuracy: 0.9997756338344178\n",
      "Testing Data Accuracy : 0.9865470852017937\n",
      "Precision             : 0.9856115107913669\n",
      "Recall                : 0.9989583333333333\n",
      "F1 Score              : 0.992240041386446\n"
     ]
    }
   ],
   "source": [
    "estimators = [ ('lr', lr), ('dtree', dtrees), ('knn', knn), ('rf', rf) ]\n",
    "stack = StackingClassifier(estimators, final_estimator = SVC(kernel='linear'))\n",
    "stack.fit(X_train_features, Y_train)\n",
    "\n",
    "\n",
    "stack_train = stack.predict(X_train_features)\n",
    "stack_test = stack.predict(X_test_features)\n",
    "\n",
    "\n",
    "stack_train_acc = accuracy_score(Y_train, stack_train)\n",
    "stack_test_acc = accuracy_score(Y_test, stack_test)\n",
    "\n",
    "\n",
    "stack_precision = precision_score(Y_test, stack_test)\n",
    "stack_recall = recall_score(Y_test, stack_test)\n",
    "stack_f1 = f1_score(Y_test, stack_test)\n",
    "\n",
    "\n",
    "print(\"Stacking Classifier:\\n\")\n",
    "print(\"Training Data Accuracy:\", stack_train_acc)\n",
    "print(\"Testing Data Accuracy :\", stack_test_acc)   \n",
    "           \n",
    "print(\"Precision             :\", stack_precision)\n",
    "print(\"Recall                :\", stack_recall)\n",
    "print(\"F1 Score              :\", stack_f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae56053e",
   "metadata": {},
   "source": [
    "# Metrics Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c82afbcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_acc_list = {\"LR\":lr_train_acc,\n",
    "                  \"DT\":dt_train_acc,\n",
    "                  \"KNN\":knn_train_acc,\n",
    "                  \"RF\":rf_train_acc,\n",
    "                  \"STACK\":stack_train_acc}\n",
    "\n",
    "test_acc_list = {\"LR\":lr_test_acc,\n",
    "                  \"DT\":dt_test_acc,\n",
    "                  \"KNN\":knn_test_acc,\n",
    "                  \"RF\":rf_test_acc,\n",
    "                  \"STACK\":stack_test_acc}\n",
    "\n",
    "precision_list = {\"LR\":lr_precision,\n",
    "                  \"DT\":dt_precision,\n",
    "                  \"KNN\":knn_precision,\n",
    "                  \"RF\":rf_precision,\n",
    "                  \"STACK\":stack_precision}\n",
    "\n",
    "recall_list = {\"LR\":lr_recall,\n",
    "               \"DT\":dt_recall,\n",
    "               \"KNN\":knn_recall,\n",
    "               \"RF\":rf_recall,\n",
    "               \"STACK\":stack_recall}\n",
    "\n",
    "f1_list = {\"LR\":lr_f1,\n",
    "               \"DT\":dt_f1,\n",
    "               \"KNN\":knn_f1,\n",
    "               \"RF\":rf_f1,\n",
    "               \"STACK\":stack_f1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f23ebde4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Traning Accuracy</th>\n",
       "      <th>Testing Accuracy</th>\n",
       "      <th>Precision Score</th>\n",
       "      <th>Recall Score</th>\n",
       "      <th>F1 Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LR</th>\n",
       "      <td>0.966121</td>\n",
       "      <td>0.962332</td>\n",
       "      <td>0.959000</td>\n",
       "      <td>0.998958</td>\n",
       "      <td>0.978571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DT</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.965919</td>\n",
       "      <td>0.973306</td>\n",
       "      <td>0.987500</td>\n",
       "      <td>0.980352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.919901</td>\n",
       "      <td>0.905830</td>\n",
       "      <td>0.901408</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.948148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>0.978593</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.989181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STACK</th>\n",
       "      <td>0.999776</td>\n",
       "      <td>0.986547</td>\n",
       "      <td>0.985612</td>\n",
       "      <td>0.998958</td>\n",
       "      <td>0.992240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Traning Accuracy  Testing Accuracy  Precision Score  Recall Score  \\\n",
       "LR             0.966121          0.962332         0.959000      0.998958   \n",
       "DT             1.000000          0.965919         0.973306      0.987500   \n",
       "KNN            0.919901          0.905830         0.901408      1.000000   \n",
       "RF             1.000000          0.981166         0.978593      1.000000   \n",
       "STACK          0.999776          0.986547         0.985612      0.998958   \n",
       "\n",
       "       F1 Score  \n",
       "LR     0.978571  \n",
       "DT     0.980352  \n",
       "KNN    0.948148  \n",
       "RF     0.989181  \n",
       "STACK  0.992240  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1 =  pd.DataFrame.from_dict(train_acc_list, orient = 'index', columns = [\"Traning Accuracy\"])\n",
    "a2 =  pd.DataFrame.from_dict(test_acc_list, orient = 'index', columns = [\"Testing Accuracy\"])\n",
    "a3 =  pd.DataFrame.from_dict(precision_list, orient = 'index', columns = [\"Precision Score\"])\n",
    "a4 =  pd.DataFrame.from_dict(recall_list, orient = 'index', columns = [\"Recall Score\"])\n",
    "a5 =  pd.DataFrame.from_dict(f1_list, orient = 'index', columns = [\"F1 Score\"])\n",
    "\n",
    "org = pd.concat([a1, a2, a3, a4, a5], axis = 1)\n",
    "org"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c2bf5803",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Kqnbt2r7HyjIzM+tcOK7cCy+8IAHSqVOnar3uq6++KgHS8ePHJUmquXBcua+//rpKwb6a7r9+/Xpp9uzZUqdOnSRnZ2dJpVJJfn5+0rx586S4uLgq16vt96SwsFD64IMPpJ49e0qOjo6So6Oj1L17d+m9996TCgsLq51fW5vj4uIkhUIhCscJjYJMkixYCEIQBEEQBMEKxBwWQRAEQRAaPRGwCIIgCILQ6ImARRAEQRCERk8ELIIgCIIgNHoiYBEEQRAEodETAYsgCIIgCI1ekykcZzKZuHbtGi4uLrUu6iUIgiAIQuMiSRIFBQW0bNkSubz2fpQmE7Bcu3at1sW8BEEQBEFo3FJSUmjdunWtx5tMwOLi4gKYv2FXV1cbt0YQBEEQhLrQaDS0adOm4nW8Nk0mYCkfBnJ1dRUBiyAIgiDcZW41nUNMuhUEQRAEodETAYsgCIIgCI2eCFgEQRAEQWj0RMAiCIIgCEKjJwIWQRAEQRAaPRGwCIIgCILQ6ImARRAEQRCERk8ELIIgCIIgNHoiYBEEQRAEodETAYsgCIIgCI1evQOWP/74g0mTJtGyZUtkMhmbNm265WMOHDhAnz59sLe3p3379nz77bfVzvn1118JDAzEzs6OwMBAwsPD69s0QRAEQRCaqHoHLEVFRfTo0YOvvvqqTudfuXKFCRMmMGTIEE6ePMk///lPnnvuOX799deKc44cOcKMGTOYN28ep0+fZt68eUyfPp2oqKj6Nk8QBEEQhCZIJkmSdNsPlskIDw9n6tSptZ7zj3/8g82bN3Pu3LmKfU899RSnT5/myJEjAMyYMQONRsP27dsrzhk3bhzNmjVj7dq1NV5Xq9Wi1Worvi5f7TE/P18sfihUselkKnZKOeODfW3dFOE2lF6Ip2DnToyFBbZuinCbirQG4q4XYFAoMSnVSGo1JqUKk0qNSWX+WlKpMKnskFQqJJUaSaUGOztM6rJttRpUdqBSIpPLkctALpMhlwFln+UyGTLKPstu/lkuM7+GlZ8vl4OMysfN23/9utrjajhPXva1rPLX5deWV35s2bX+2qZKX8vLFgSsfLz8c1Oh0Whwc3O75eu31VdrPnLkCGPGjKmyb+zYsSxZsgS9Xo9KpeLIkSO8+OKL1c754osvar3uhx9+yDvvvGONJgtNyP/2XuSz3fHIZLD/5WG09XSydZOEOjDm5ZG/dRv5GzdSGhdn6+YIFtDRgtfSypXoFCr0ciU6hRKdXIW+7LNOoaRUrkSvUKErO09X6Zi+fF+Vcypfo6bjNz4bZXJzNNII/DWAKQ+MKgKdvwQ+skrnlQd3srJg7a/BXpXzyo/J4ZMHexDga5tOAasHLGlpafj4+FTZ5+Pjg8FgICsrC19f31rPSUtLq/W6r732Gi+99FLF1+U9LIJQ7pvfL/PZ7ngAJAnWRCfzzwkBNm6VUBvJYKDozz/JC99E4d69SHq9+YBKhcuw+1C372DbBgq3paBUz8ojSSBJ9PJ1QmXUI9frkRt0yPU687Zeh8Jg3r7x2bxt/qxHVmkwwM5kwM5ksMn3Y5LJ0CtUZR9K9GWBjl5RHhCVBUjl22WBjrYsMNLKzYGWtuxDJ68tSKoeiOnkSkxyxY22SIAkYQTgtgdL6qVUb2yQ+9TE6gELVO+6Kh+Fqry/pnNu1uVlZ2eHnZ2dBVtZM316OgoXF+SOjla/l2A5Px5M4OMd5wEYFeDNnnMZ/HwshZdGd8ZepbjFo4WGpE1IID88nPzfNmPIyKjYbxcQgHtYGK6T7kfZrJkNWyjciZW7LrA8/xIDOzTn7cdDb+sakiSBXo9Jp0PSapG0WkxaLVLZ16bSUiStDklXfqzsPF3ZebV8bdJV2taW3jhPq61yL0mnq2iLXJKwM+iwM+hu0mIrUiiR2alBbYdMrYaybdTmYTOZ2g5JrYayYbTybUltB2oVktr+xrBb2XGTSl22z65smE6FSaUClR1GpflrY9lj2ns52+b7pgEClhYtWlTrKcnIyECpVNK8efObnvPXXhdbyPj4Ywr2/47LiBG4TpyA0+DByNVqWzdLuIkVRxJ5b5t5ztTzIzvx3MhODP33flLzSth25joP9Glt4xYKxoICNBHbyd+4kZLTpyv2K9zdcZ08CfewMOwDRG/Y3U5nMLE2OgWAuaH+t30dmUwGajUKtRqcG/4FUzKZkPT6SsHSjeDoRoBUetPgyPx15YBIWzVA0mprDJZMOh2U9zYCGA1IxQYoLr6tPhVZ2cftUq1ZDb1738EVbp/VA5YBAwawZcuWKvt27dpF3759UalUFefs3r27yjyWXbt2MXDgQGs376YkkwntpctIJSVotm1Ds20bcldXXMaMxm3CBBxDQpApxLv1xmRNVDL/+u0sAE8P68ALozohk8mYHeLHJzsvsDIySQQsNiKZTBRHRpIXvomC3buRSkvNBxQKnIcMwW1aGC7DhpnfNQpNws6zaWQVavF2sWN0oO3fgN4umVyOzM4OGqBXvyaS0XijN6lKT5K2omepei9TpWN1DqRK/xJU6cx/pybTjedCbZvnAG4jYCksLOTSpUsVX1+5coVTp07h4eGBn58fr732GqmpqaxYsQIwZwR99dVXvPTSSzz++OMcOXKEJUuWVMn+ef755xk6dCgff/wxU6ZM4bfffmPPnj0cOnTIAt/i7ZPJ5bT7bROlMTHmgCViO4bMTPI3/Er+hl9ReHriOm4crhMm4NCrZ5OatX03+vlYCv8MjwHg8SHteGVsl4qfyYx+bfhiTzynUvKITc2nWys3Wzb1nqJLSSE/PJy8TZswXLtesV/dsQPuYdNwmzwJpZeXDVsoWMuqyCQAZvX3Q6UQdUpvl0yhQObgAA4O2OItsmQwVAQwChv0cJWrd1rz77//zvDhw6vtf+SRR1i+fDnz588nMTGR33//veLYgQMHePHFFzl79iwtW7bkH//4B0899VSVx2/YsIE33niDhIQEOnTowPvvv8+0adPq3K66pkXdCclopPjYcTTbtpnTLPPzK46pWrbEdeIEXCdMwK5rVxG8NLDwk1d56efTSBLMH9iWtyYFVvsZPL/uJL+dusbMfm346IHuNmrpvcFUVIRm5y7yN26k+Nixiv1yV1dcJ04wD/kEB4u/kyYsPr2AMZ//gUIu489/jKCFm72tmyQ0UnV9/b6jOiyNSUMELJVJOh1FR46Qv20bhXv2Yiourjimbt/+RvDSrp3V23Kv23rmGs+tPYlJgjkhfrw3tVuNL4RHE3N46Nsj2KvkRP1zFG4OKhu0tumSJImSY8fI2xiOZudOpPK/CZkMp0GDcAubisuoUcht1K0uNKx//RbLiiNJjAtqwbfz+ti6OUIjJgKWBmQqKaHwwB9otm2j8MCBKjPK7QMDcZ04EdcJ41H5isJllrYjNo1n1pzAaJKY3rc1H03rjlxe87t2SZIY/9+DnE8r4F/3B7JgsAgmLUF/7Rr5v/1GXvgm9MnJFftV/n7mIZ+pU1C1aGHDFgoNrUhrIOSDvRRqDax+LIRBHT1t3SShERMBi40YCwoo2LsXzbYIig4fBuONnHWHPn3MPS/jxqH08LBZG5uKvefSeWrVcfRGiWm9WvHJQz1Q1BKslFsVmcQbm2Jp7+XE3pfuE0MSt8lUWkrB7j3kh2+k6EikudANIHd0xGXCeNynTcOhVy/x/N6jVkcl8Xq4+DsT6kYELI2AISeHgl270GzdVmUcH4UCp9BQXCdOxGX0KBQuLrZr5F3qQHwmj/90DJ3RxKQeLfliRs9bBisAhVoDIe/voUhnZM1jIQwU7/zqTJIkSk+fNg/5RERgKiysOOYYEoJb2FRcx4wRNYvucZV7Mt+8P5CFoidTuAURsDQy+rQ0NBHb0UREUBobW7FfplLhdN9Q3CZOxHnYMOQODjZs5d3hz0tZLFh+FK3BxPhuLfjfrF71ykB4c1MsKyOTGN+tBd/MFWPrt6JPzyB/82/kh29Cl5BQsV/VsiVuYWG4hU1F3VqkigtmxxJzeLB8rthro3BzFHPFhJtrNGsJCWaqFi1ovuBRmi94FF1iIvkREWi2RaC7fJnCPXsp3LMXmaNjRYE650GDRD2KGkQmZLPwJ3OwMirAh//OrF+wAuYCVisjk9gVl066phQfV5G98FcmnY7CffvJC99I0cFDFXUYZPb2uI4dg1vYNBz790MmF6mqQlXlqcxTerQSwYpgUaKHxYYkSUIbH49m6zY0ERHoU1Mrjsnd3HAdMxrXiRNx7NdPFKjD/M7t4aXRFOuMDOvixXfz+mCnvL3nZfp3R4i+ksMLozrxwqjOFm7p3UmSJErj4sjfGI5m69YqafsOvXrhNi0M1/HjbVqHQWjcsgq1DPxwHzqjiS3PDia4tah3JNya6GG5C8hkMuy7dMG+Sxe8XnqR0tOnyd8WgWbHdoyZWeT9soG8Xzag8PLEddx43CZOwL5Hj3tyAtuplDzmLztKsc7IkE6efDv39oMVMPeyRF/JYW10Ms8M73hPF7Uy5OSg2bKFvI3haC9cqNiv9PHBbcoU3MKmivR8oU5+PpaCzmiiRxt3EawIFicClkZCJpPh0LMnDj174vPqPyg+etRcXXfXboyZWeSuXEnuypWoWrXCdcIEXO+fiF3nzvdE8BKbms+8JVEUag2Etvfg+3l973gBw3FBLfB0VpOu0bInLp3xwfdWyrmk11N48CB5GzdS+PsBMJhXvpWp1biMGolb2DScBg4QPXtCnRlNEqsjzWnt8+5g3SBBqI0YEmrkJJ2Owj//RLMtgoJ9+24U48Jc2tx1wgTcJk5E7d80/0HEXdMw+8dI8or19GvbjOWP9sfJzjJx9n92XuCr/ZcY1LE5qx+7vVVk7zal8fHkbwwnf8sWjNnZFfvtg4NxC5uK28SJKNzEO2Oh/vadT2fB8mO4O6qIfG2kWBVdqDORJdQEmUpKKPz9d/K3baPowB9IlVbwtO/WzdzzMmF8kynSFZ9ewMzvI8kp0tHLz52VC0NwtlCwApCaV8KQj/dhkmDPS/fR0btpzs0w5uWRv20b+eGbqmSoKZo3x23yZNzCpmLfWczjEe7Mo8ui2X8hk8eHtOP1iYG2bo5wFxEBSxNnLCigYPceNNu2URQZeaNAnUyGY1mBOpexY+/aAnWXMgqZ+X0kWYVaurd2Y+XCEKuU0n/sp2PsOZfOo4Pa8takIItf31Yko5Giw4fNQz579t4IbpVKXIYPwy1sGs5DBiNTiSwO4c6l5BQz9JP9SBL8/n/DaOvpZOsmCXcREbDcQwzZ2Wh27kSzLYKS48dvHFAocBo4ENcJE8wF6u6S7I4rWUXM+O4IGQVaAn1dWfN4CO6O1knxPhCfySNLo3GxVxL1z5E4qu/uaV3ahCvkh4eT/9tvGDIyKvbbde2K+7QwXO+//64NYoXG66Pt5/n2wGWGdvZixYL+tm6OcJcRAcs9Sn/tGprtO9Bs20ZpXFzFfplajfN995lrvAwbhty+cdYeSckpZvp3R7ieX0oXHxfWPhGKh5P16tGYTBLDP/2dpOxiPn4gmBn9/Kx2L2sxFhaiiYggf2M4JadOVexXuLnhOmkS7tPCsA8UXfSCdZTqjQz8aB85RTp+eLgvowN9bN0k4S4jAhYB7ZUraMoL1FWqUCp3dMR51EhcJ5QVqGskwwKpeSVM//YIqXkldPByYt0TA/Bysf7Kvt//cZkPIs4T1NKVrX8bfFdkXkkmE8VRUeSFh1OwazdSaan5gFyO85AhuE2bhvPwYchF8UHBysJPXuXF9adp6WbPwX+MqNMSGYJQmQhYhAqSJKE9f74ieNFfu1ZxTOHmhsvYsbhOmIBjv742S2NNyy9l+ndHSM4ppp2nE+ufCMW7gSrQ5hbpCPlwLzqDifCnB9LLr1mD3Pd26K5eNWf5bNpU5eeo7tDBPOQzaRIqb28btlC410xb/CcnkvP4vzGdeXZEJ1s3R7gLicJxQgWZTIZ9QAD2AQF4vfQSJSdPmYOXHTswZmWR9/PP5P38M0ovL1wnjMd1wgTsu3dvsJ6GDE0ps3+IJDmnGD8PR9Y8HtJgwQpAMyc1k7q35NcTV1kVmdzoAhZTcTGanbvIDw+nODq6Yr/cxQXXCRPMQz4N+PMShHJnr+VzIjkPlULG9H5tbN0coYkTPSz3MMlgoDg6mvyICAp27cak0VQcU7VpY06TnjjBqimvWYVaZn4fyaWMQlq5O7D+yVBaN2v41X5PJucStvgwaqWcqNdG0syK82bqQpIkSk6cIG/jRgq278BUXn9HJsNpwADcpk3DZdTIRjsXSbg3vLYxhrXRydzf3ZevZve2dXOEu5QYEhLqxaTTUXTo0I0CdSUlFcfsOnXEdeJEXCdMQO1nuUmpuUU6Zv0Qyfm0Alq42vPzkwPwa97wwQqYA4RJXx0iNlXDPyd05YmhHWzSDv316+T/9ht54eHok5Ir9qv8/XAPC8Nt8mRULVvapG2CUJmmVE/I+3sp0RtZ/0QoIe2b27pJgpVIkoSuxECxRodrcwcUKssuZSICFuG2mYqLKdi/H822CAoPHoTKBeqCg3GdOAHX8eNR+dx+NkB+sZ7ZP0Zy9poGbxc71j85gHY2rt2w/mgy//g1Bv/mjux/eRjyBpo8aCotpWDPXvLDwyk6fBjK/iRljo64jhuH+7QwHPr0EUM+QqPy0+FE3tp8ls4+zux8Yaj4/bwL6UrNQUiJRkdxrR9aijU6TAbz/6UZb/THs7VlS2SIOSzCbZM7OuI2cSJuEydizM+nYE95gbooSmNiKI2JIePjf+PYty+uEyfiMnYMymZ1n/ehKdXz8NIozl7T4OmsZs3jITYPVgAm92jFe9vOkZRdzMFLWdzX2ctq95IkidIzZ8gLD0ezLQJTQUHFMcd+/XCbNg3XMaORO9n+eRGEv5IkiZWRSYB53SARrDQeBr2xItioEojk6yguKPus0VJcoMegNdbr2moHJboSg5Vafmuih0WoM0NWFpodO9Fs20bJyZM3DiiVOA0cgNvEiTiPHIXCufYX2UKtgYeXRHEiOY9mjirWPTGALi1cGqD1dfPOlrMs+zORUQE+/PhIX4tf35CZSf7mzeSFh6O7dLliv7KlL+5Tw3ALm4q6jZi8KDRuRy5nM+uHSBzVCqL+ORIX+8ZRGqGpMhpNlGj0lBToKMo393iUVAQfVT/qG1Ao1XIcXdU4utrh6KbG0UVt/uxq/nBwvbGttNL6UKKHRbA4pacnHnPn4DF3DvrUVDTbt5O/LQLtuXMU/XGQoj8OIrOzKytQNxHn+4ZWmRRarDOwYNlRTiTn4eagYtVjIY0qWAGYG+rPsj8T2Xc+ndS8Elq5O9zxNSWdjoL9v5O/cSOFhw5VLKMgs7fHZcxo3MPCcAwJQSa37LiwIFjLqrLelbBerUSwcptMJonSQn2lnhAtRTX1jGh0lBbqb33BSuRK2Y0gpFLAUfmjPBBR2989YYDoYRHumDYhAc22CDTbtqFLTKzYL3dywmXUSFwnTkTepz8LV5/iSEI2LvZK1jwWSnDrxrkq8JwfI/nzUjbPDu/I/43tctvXKY2LIy98E5otWzDm5VXsd+jZE7dpYbiOH4/CpXEFbIJwKxmaUgZ+tA+DSWL780MI8BX/b8tJkoS22FBl/keJRm8egikfkikbnikp1COZ6v7yK5PLcHBRVQpEVNUDkrKeEbWD8q4aphOTboUGJ0kS2nPnyN+2DU3EdgzXr1ccK3ZwZr9PN6Lb9eW1f8ykV9vGm1GwPeY6i1afwNNZzeFXR6JW1r3nw5CTg2brVvI2hqM9f75iv9LbG7cpU3ALC8OufTtrNFsQGsT/9l7ks93x9PVvxoZFA23dHKuTJAm91viXOSDlwchfhmQKbkxOrRMZODircHCpHHDYVRuWcXRVY++kQtZEqwiLISGhwclkMuwDA7EPDMT75ZcpOXWK3C1buf7bVpyKC5iYGMnExEiUcb+QPn68ucZLt26N7p3AqEAffFztSNdo2XE2jck9bp5GLOn1FB48RH74Rgp+P1CRVSVTqXAeNRL3sDCcBg2yWRVhQbAUg9HEmihzuv28Af42bs2dMeiMNWTE6KoM0ZR/bdCZ6nVtO0dljXNA/jpMY++iQqEQQ8F1JQIWwSpkcjmqHj15O9bE3lE96ZeXwGt2KThEHsSQnk7O8uXkLF+Oyt8P1wkTcJswAbtOjaOst0ohZ1Z/P77Yc5FVR5JqDVi0Fy+StzGc/C1bMGZlVey379YNt7CpuE2ciMLdvYFaLQjWt+dcBmmaUpo7qRnXrYWtm1ON0WAyT0a9SXZMSYGe4nwtutL6Zcio7BQ4uKpxqikQcbvRK+LgorLa5NR7nQhYBKswGE08v+4ku+PSUatV/O2VufTs5GkuUPfHH2giIijYtx99UjLZ33xL9jffYte5c1mBuvE2z5SZ1d+PL/ddIjoxhwtpBRWTg435+WgiIsjbGE5pTEzF+YrmzXGbNAm3sDDsu1ivMrAg2NLqKPNk2xn92mCnbJgX5RuTU7U1DMtU7RUpLarf5FSFUl4xFONQeRjGpdLwjKt5yOZumpzaVIk5LILFGU0SL64/xebT11Ar5Hz/cB+Gdam+IJ+pqIiCffvRRESYs2cqF6jr0R23iRNxGTfOZov5LVp1nO2xaTzcvzV/9yowD/ns2Yuk05lPUCpxHnYf7mFhOA8d2mhWvRYEa0jILGTEpweQyeCPV4bTxuP2q1JLkoS2yEBRTfNA/pohU6CjPq9S8vLJqW52tQzJ3OgVUdsrGt2Q9L1IzGERbMJkknhlw2k2n76GUi5j8ZzeNQYrYM4icpt0P26T7seYl4dm9240EREUR0VTevoMpafPkP7hRzj274/rxAm4jB5drwJ1d+qRNjK8f45g9M7jpJTkV+y369wZt2lhuE2ahLJ54508LAiWtLps7sqILt71DlYyUwo4FpFIQXZpxZCNyVj/yal/zY5xqJwdU9YrYu/YdCen3utED4tgMSaTxD/DY1h3NAWFXMbXs3sxrptvva9jyMxEs30HmogISk6dunFAqcR50CBc75+I8/ARNy1Qd7uMhYUU7NhB3sZwSk6cuNEmJxe8pk7GbVoY9oGB4l2ZcE8p0RkJ/XAv+SV6lj3aj+G1vAmpSWFuKT9/eIwSja7asYrJqeUBR1nxMoe/Fi9zViEXk1ObLNHDIjQoSZL41+ZY1h1NQS6Dz2f0vK1gBUDp5YXHw/PweHgeuqupaCIi0EREoD1/nsIDByg8cACZvT3Ow4bhOnECzkOHIrezu/22m0wURx8lP3wjml27byz8KJeTG9SHxfZdye0ZwtaXRohARbgnbTlzjfwSPW08HLivU92XrDDojWz/NoYSjY7mrZwIndLhRq+Ii9rii+gJTZsIWIQ7JkkS726NY1VkMjIZ/OehHrdMBa4rdetWeD7xOJ5PPI720iU0ERHkb9uGPimZgh07KNixA7mzMy6jRuE6cSJOoSF1nkuiu3qV/PBN5G/ahD419cY927UzD/lMnkKxazOOfbCH0sxSjiXl0q+th0W+L0G4m5RXtp0T4l/nRUElSeLA6gtkJBVg56Rk/FPdcfO688rRwr1LBCzCHZEkiQ+3n2fZn4kAfDytO9N6t7bKvew6dsTruefw/NvfKD0bh2bbNjTbt2NISyN/kznwUDRrhsu4sbhNnIhD797Vyt2biovR7NpFfvgmiqOiKvbLnZ1xnTAB92lh2PfoUdGT4gZM7dmKdUdTWHkkSQQswj3ndEoeZ67mo1bKmd637tl7Z/Zd5XxkGjIZjH2smwhWhDsmAhbhtkmSxH92XeD7PxIAeD+sG9P7WT8dWSaT4dAtCIduQXi/8n+UnDhB/rZtFOzYiTE3l7y168hbuw5lixa4jh+P68SJSDoteRs3UrB9B6aiovIL4TQgFLewabiMHlVl3aPK5ob6s+5oCttjr5NVGIin8+0PPwnC3aa8d+X+YF88nNR1eszV8zn8+eslAAY+0JE2ASLQF+6cmHQr3Lb/7rnI53viAXh7UiDzB9m25Lyk11MUGYVm2zYK9uzBVFhY43kqPz/cw6biNmUKqpZ1G7qa+vWfnErJ45WxXXhmeEdLNlsQGq28Yh0hH+xFazDx66KB9PG/dZaeJquEXz48RmmRns4hPoyaLyapCzcnJt0KVrX490sVwcobEwNsHqxAWSn8IYNxHjIYk/ZtCv/4A822CAr37weFAtexY3GfFoZD3771/gc6L9SfUyl5rIlK5qn7OqAQaZPCPWDD8atoDSYCfV3p7ed+y/P1WiMR38ZQWqTHy8+F4XO6imBFsBgRsAj19uPBBP694wIAfx/XhceGtLdxi6qT29nhOno0rqNHY9LpkAEydd26s2sysbsv/29bHKl5Jfx+IYORAT6Wa6wgNEImk1RRe2XeAP9bBh6SJLFv5Tmyrxbi4KJi/FPBKNWiRL1gOSKnTKiXnw4n8t62cwC8OKozTw9r/MMjcrX6joIVAHuVomLC4cqyMX1BaMr+vJzFlawiXOyUTOl566HTk7uSuXQsA7lcxrgngnHxqHlOmHCXMBkhJwEu7IBDX0D4IvhhBBSk26xJoodFqLPVUUm8tfksAM8O78hzIxt/sGJJc0L8+P6PBA7EZ5KUXYR/c8sXrhOExmLlEXNg/kCf1jiqb/5SkXQ2myObLgMwZEYnWnZyt3bzBEsx6s2BSeaFso/zkHUBsi6CobT6+ZnnwcU2PcwiYBHq5OejKbweHgvAk0Pb8/KYzvfc2LR/cyfu6+zFgfhM1kQl89qEAFs3SRCs4lpeCXvOmd9Jzw31u+m5eenF7F5yFiQIHORL0NBWDdFEob70pZB9sWpgknkBci6DyVDzYxR24NkJvLqAZxfzZ59uDdvuSkTAItxS+Mmr/GPjGQAeHdSWV8ffuxPp5oX6cyA+k5+PpfDi6M7Yi2XkhSZoXXQyJgkGtG9OR2+XWs/TlRqI+DYGbbGBFu1dGTqzyz37v6HR0BZAVnylwKQsOMlLAslU82NUTuDVGby6Vg1OmrVFL5mIz4snNjOWmKxj/LPzGG5/2cs7IwIW4aa2nL7Gyz+fRpLM77T+df+9naI4vKs3rdwdSM0rISLmutWK5AmCregMJtYeTQHMNYhqI5kk9iyLI/d6EY5uasY9GSxK7Tek4pyywOQ8ZMbf6DHRXK39MfZuN4ISr643AhPXViCXI0kSVwuucibrDLGXNxCTFcO57HPoTDfWgZrWaRq9fXo3wDdYnQhYhFrtiL3OC+tPYZJgZr82vDu52z0drAAo5DJmh/jxyc4LrIxMEgGL0OTsiksjs0CLl4sdY4Jqn6twbHsiV05nIVfKGP9UME5uoqCixUkSFGaY55RUHsbJvABFGbU/zsm7LCgpD0zKek+cvaHS//Cc0hxis2KJTdxiDlKyYsnX5le7nKvalWDPYLp5dsPLoe5rSVmaCFiEGu2JS+fZNScxmiQe6N2aD8KC67yGSFM3vW8bvtgTz8nkPGJT8+nWys3WTRIEiymvbDurvx+qWlZITjiVSfSWKwAMm92FFu3E38AdkSTIv/qXwKSs16Q0r/bHubauFJhUCk4cq1cWLjWUci7zFDGZMcRmxRKTFcPVwuq9MSq5igCPALp5diPYK5hgz2D8XPwaxZtVEbAI1ey/kMHTq09gMElM6dmSfz/YXQQrlXi52DGumy9bTl9jdVQSH07rbusmCYJFXEwvIDIhB4Vcxqz+NS+zkXOtiD3L4gAIHtaagIGWWej0nmAyQm5ipaGcsgAlKx50NVfmBhk0a1tpKKfsw7Mz2NU8v8hoMnIl/woxWTHEZJkDlPjceIySsdq5bV3b0t2ruzlA8QymS7MuqBR1W0C2oYmARaji0MUsnlx5HJ3RxITgFnz6UA9R1bUG80L92XL6GptOXuPV8QG4OTTOP3BBqI/y3pVRAd74ulVfrFBbrCfi2zPotUZadnJn0EP3VmmDOjPoylKFz1edZ5IVD0ZtzY+RK8Gjw42ekvLApHlHUN184cj0ovQqwcnZ7LMU6YuqndfcvjnBXsF09zQHKEGeQbiq756lbETAIlQ4cjmbx1YcRWcwMTrQh//O7IWyli7he12/ts3o4uPChfQCNp64yqONYGkCQbgTRVoDv55IBWBeaNtqx00miV1L4sjPKMHZw45xT3RDca//f9CXmOuVZF4oG84pTxVOqD1VWGkPzTtVCkzK5pd4tIc69GwU6go5m33WHKCUDe9klFSfz+KgdCCoeVDF3JPuXt3xcfRpFEM7t0sELAIARxNzWPjTUUr1JoZ38eKr2b1qHb8WzCtGzx3gz5ubYlkVmcT8gW3v6n8EgvDbqWsUag2093RiYIfm1Y5HbU4g+Ww2CpWcCU91x8HlzqpH31W0BTfmlFSeZ5KbBNSyfrDauWqKcHlw4u4P8rqVQ9Cb9FzMvUhM5o3ek4T8BKS/3FMuk9PJvVNFYNLNsxvt3dqjlDetl/im9d0It+VEci6PLjtKsc7IkE6efDO3D3ZKUV/kVsJ6teKjiHNcziziSEI2Azt42rpJgnBbJElixZFEAGaH+FWbs3bxWDondpiHi0bM64qXX+21We5qxTmVqr1WThVOrf0x9u7gHXAjE8erUqpwPd7ElKcUlw/txGTFcD7nPNoahpBaOrWsmBDbzbMbAR4BOKpsVR2l4YiA5R535moejyyNplBrYED75nw/r68ohlZHznZKwnq3YlVkMqsik0TAIty1TiTncj6tAHuVnIf6VJ1sm3W1gH0rzOuH9RztR+f+LWzRRMuRJChM/0sp+rLgpCiz9sc5+9TQY9IFnLzqFZiUyy3NrcjWKe89ydPmVTvPRe1yY1jHsztBnkF4Otyb/2tEwHIPO3stn3lLoikoNdC/rQdL5vfFQayuWi9zQ/1ZFZnMrrPppGtK8XEVC74Jd59VkeZVmSf3aImb4415FKWFeiK+icGgM9EmoBkDpja+ldlrZTKZi6hVFFWrFJiUVq81UsGtTfX6JV6dwaHZbTel1FDK+ZzzFfNObpZS3NWja0WAEuwZjL/rrVfKvleIgOUedSGtgLk/RpFfoqe3nztLH+13ywXOhOq6tnClX9tmHE3MZV10Cs+P6mTrJglCvWQXatl25jpQdbKtyWhixw+xFGSX4uppz5jHuiFvjPPaylOFKxdWy7pgDlRqyJQBQCavmirsWTlV2PnOmiOZuJJ/hTOZZyp6UC7mXsQgVZ+E29a1bZVJsZ2bdUatuIfmBtWTeIW6B13KKGTOj5HkFuvp3tqN5Qv642wnfhVu19xQf44m5rImOomnh3cQk5WFu8rPx66iM5ro0dqN4NY3CsAd3niZ1Au5KO0UTFjUHXsnG6fuG3Tmhfr+Woo++9JNUoVV5rTg8kyc8h6T5h1BZZne0PSi9CpDO7dKKS4PULp5drurUoobA/EqdY+5klXE7B8iySrUEejrysoFIbjaixoid2JctxZ4OqtJ12jZey6dcd18bd0kQagTo0lidZR5Mm3ldYMuRF7n9F7zekKjHgmgeas763WoF11xLasKJ0ANhc8Ac6qwZ+e/VHztAh7t6pQqXFeFukLisuMqytjHZMWQUVxzSnFg80CCPYMrPlo4tRBDO3dIBCz3kOTsYmb/EElGgZauLVxY9VhIlfFq4fbYKRXM6NeGr/dfZlVksghYhLvGH/GZXM0twc1BxaQe5oq1GUka9q+6AEDfCW3p0NvbOjcv1VSv+Jp5HvKSqT1V2KXmUvTufnVOFa6r8pTiit6TzJhaU4o7unesCEy6eXajg3uHJpdS3BiIZ/QecTW3mFk/RHI9v5SO3s6seiwEDycxVmops/r7sfj3yxy6lMXlzEI6eDXgO1JBuE0ryyrbPtSnNfYqBcUaHdu/jcFoMNE2uDn977dQQURtAcRuhIxzNwKUgmu1n+/gUb0UvVdXcPG9rYycW5EkiauFV4nNiq2Ye3Iu51ytKcXlE2KDvYLvmZTixkAELPeA6/klzP4hitS8Etp7OrHmsRA8ncXKqpbUupkjI7t6s+dcBqsjk/nXpEBbN0kQbiolp5j9F8zDGXNC/TEaTOz4PobCXC3uPo6MWhCEzFLLcux4DU6urL7fxbdqJo5XV/OHk3XTdstTimOzYiuGd2pMKVa5VFkEsJtnt3s2pbgxuK2AZfHixXzyySdcv36doKAgvvjiC4YMGVLr+V9//TVfffUViYmJ+Pn58frrr/Pwww9XOeeLL77gm2++ITk5GU9PTx588EE+/PBD7O1FmuidyNCUMvuHKJJzivFv7siax0PxFqm3VjE31J895zLYcDyFV8Z2ESniQqO2JjoZSYIhnTxp5+nEgTUXuH4pH7W9ggmLgrFzsND72cIMOLPevN3vMfDteSMjx8HdMve4ifKU4srBSUpBSrXzylOKK3pPPIPxc/VDLhOT6BuLev9Grl+/nhdeeIHFixczaNAgvvvuO8aPH09cXBx+fn7Vzv/mm2947bXX+OGHH+jXrx/R0dE8/vjjNGvWjEmTJgGwevVqXn31VZYuXcrAgQOJj49n/vz5AHz++ed39h3ewzILtMz6IZIrWUW0bubAmsdDaeHWNIIVSZIw6EzoSg3oS43oSg3oSgzoSo3oS82fzfvMn33autIl1LqT3oZ28sLPw5HknGK2nL7G9H41r3YrCLamNRhZf9T8oj0v1J+zB1OJ/SMVZDB6QRDNWjhZ7mbHloJRB636wsRPLXfdGpSnFJcXYovJiiE+J/6WKcXBnsF08egiUoobuXoHLJ999hkLFy7kscceA8w9Izt37uSbb77hww8/rHb+ypUrefLJJ5kxYwYA7du3JzIyko8//rgiYDly5AiDBg1i9uzZALRt25ZZs2YRHR1929/YvS6nSMfcH6O4nFmEr5s9ax8PpZX7zVf8bAhGowl9WRChqxRoVAQdZZ8rn2MOQCodKzWiKzEg1TIvryaxB1LJulrIoAc7Wi1okctlzAnx48Pt51kRmchDfVuLrAChUdoek0ZOkQ5fN3sC1XZsWXcKgJBJ7Wnb3YJDHgYtHP3RvD3gactdt0xGcUaVRQBjs2NrTCn2sPeoWKE42CuYoOZBuNm51XBFoTGrV8Ci0+k4fvw4r776apX9Y8aM4fDhwzU+RqvVVhvWcXBwIDo6Gr1ej0qlYvDgwaxatYro6Gj69+9PQkICERERPPLII7W2RavVotXemBCl0Wjq8600aXnF5mDlQnoB3i52rH08lDYetz8pTDJJ6HVGdCVG9NobvRblPRhV91Xq4SgxVNtn1Jss+J0CMlDbK1HbK1CVfVY7lH22V6KyV2DUmzh78Bqn96ZgMpgYMqOz5cbm/+Khvm34dHc8sakaTl/Np2cbd6vcRxDuRPlk29nBLdn9w1lMRokOvbzoM97/Fo+sp5gN5nL3rq0gYPIdXapIX8TZrLMVvSdnss7UmlIc4BFQsQhgsGcwvk6+4s1DE1CvgCUrKwuj0YiPj0+V/T4+PqSlpdX4mLFjx/Ljjz8ydepUevfuzfHjx1m6dCl6vZ6srCx8fX2ZOXMmmZmZDB482NzVbzCwaNGiaoFRZR9++CHvvPNOfZp/T8gv0TNvSTRx1zV4O9mxfHYfmkkyMlMKzEHDX3o39JUCiyq9HCUG9NqyY1pjrVmGt0upkqOqFFhUBBwOf/m6fJ+dElWlY2oHJSo7BSo7RZ3+EXn5ufD7mgvEHEjFaJQYNruLVYIWDyc193f3ZeOJVFZFJomARWh04q5pOJ6Ui51MhufpAnI0OjxaOjHikQDLvqhLEkR+Y97u/3i96qHoTXou5V6qss7O5bzLN00pLg9OREpx03VbP9W//lJLklTrL/qbb75JWloaoaGhSJKEj48P8+fP59///jcKhXlS4u+//87777/P4sWLCQkJ4dKlSzz//PP4+vry5ptv1njd1157jZdeeqnia41GQ5s2d/+cAZNJqggUbgQQNwKNakMnlYZVSosNpGYVMVQvMVqyR5EnY//HJy3WNplcdiPAcLjRg1ERYDgoUduV93CUHau0r/K5DV3iO2hIKxRKOftWnCPu0DVMBhPDHw6otiqtJcwN9WfjiVS2nL7G6xMCaCbSx4VGZFVUEkgwz86VnJRC7ByVTFgUjNrewi/yiQchPQZUjtC79t7yyinF5cHJuexzlBpLq53r6+RbsQhgN89uBDYPFCnF95B6/YZ6enqiUCiq9aZkZGRU63Up5+DgwNKlS/nuu+9IT0/H19eX77//HhcXFzw9zWOlb775JvPmzauYFxMcHExRURFPPPEEr7/+OnJ59Rc3Ozs77OwaR2quJEkY9abqPRM1DpFUDzQq79Nra6nkWEfmP92qL8JKO0WVnozy3onywMLcm1EeiFQdTqm8T6GS39Xdql0H+CJXytiz7BznI9MwGiVGzQ+wePDUq407QS1dOXtNw4bjV3l86F20YJzQpGlK9Ww6mUovnYJm+TpkMhjzWBBuXlZ40S/vXekxCxw9KnbnleYRmx1bsQhgbFYsudrcag8vTykuX2dHpBQL9QpY1Go1ffr0Yffu3YSFhVXs3717N1OmTLnpY1UqFa1btwZg3bp13H///RWBSHFxcbWgRKFQIEkSUn1mVlpB7IGr5GWWoC8bGtGVVJoEWmJEpzVPEDWZLNtOuUJW0ZNRZW6GXVlPhv2NwAKljB8iE4nNKEBhp+CDh7oT6O9eEZhYoxfhbtW5XwsUCjm7fjzLxaPpmIwmRi8MQmHBoEUmkzEv1J9XN8awKiqJhYPbiZ+B0CiEn0ileZHEiBLzm70BYR3xC2xu+RtlX4YL283boYtI0iTx7elvOZ15usaUYqVcSddmXavUO/F39RcpxUIV9e4DfOmll5g3bx59+/ZlwIABfP/99yQnJ/PUU08B5qGa1NRUVqxYAUB8fDzR0dGEhISQm5vLZ599RmxsLD/99FPFNSdNmsRnn31Gr169KoaE3nzzTSZPnlwxbGQr5yPTSL9Sxwm9MswBRS2TP8t7KyoPjVQLSsrmbShUdftDLdUbeeynYxzKzcfZWcnKhf3o5Xf7y6DfCzr09mbckzJ2/BDL5ROZGA2xjHu8W52f87qY3LMl70ecIym7mEOXshja2cti1xaE2yFJEr8eTGRysRo50KmfDz1HW2kYPeo7QIJOY8CzEx/ueYo/U/+sONzWte2N3hPP7iKlWKiTegcsM2bMIDs7m3fffZfr16/TrVs3IiIi8Pc3zy6/fv06ycnJFecbjUY+/fRTLly4gEqlYvjw4Rw+fJi2bdtWnPPGG28gk8l44403SE1NxcvLi0mTJvH+++/f+Xd4hzr186FlJ/cbgYVd5YmhVfep1AqrZZ/URGsw8tSq4xy6lIWjWsHyR0WwUlftengxYVF3tn8TQ+KZLCK+jWH8k91QWqjYm6NayQO9W7P8cCIrI5NEwCLY3JH4LHomG3CU5Hi0dmL4vK7WGeItyYOTq8zboYu4kn+FP1P/RIaMz4d9Tt8WfUVKsXBbZJKtx1wsRKPR4ObmRn5+Pq6uTX/Jbp3BxNOrj7PnXAb2KjnLH+1PaHsrdO02cSnnc4j4+gwGvYnWXZsx4enuqCwUtFzKKGTUZweQy+DgP0Y0ijo4wr1JkiQ+/Nch3DL1GFUy5r8dimtzK/0+Hv4Sdr0BXgHw9BE+jP6INefXMKz1ML4c+aV17inc1er6+i0GCO9CeqOJ59aeZM+5DOyUcpY80k8EK7epTVcP7v9bD5R2Cq6ez2Xrl6fRlVavink7Ono7M7BDc0wSrItOvvUDBMFKDm1NwC1TjwmJPrM6Wy9YMRog6nvzdugiCvVFbLq0CYBZAbOsc0/hniEClruM0STx0s+n2XE2DbVCznfz+jCoo5g5fydadW7G5Od6orZXcO1iHlv+dxptiWWClrmh5qHStdEp6AwWLponCHWQHJfN6W3mQnEXW6kYNLCV9W52YRvkJ4Njc+g+nd8u/0axoZj2bu0Z4DvAevcV7gkiYLmLGE0Sr/xymi2nr6FSyPhmbm+GdfG2dbOaBN8Obkx+vhd2jkrSEvLZ/MVJSov0d3zd0YE+eLvYkVWoZefZmosrCoK15GcWs+vHs8iAM2oDwydaOcX+yGLz574LMCntWHt+LQCzu86+q0siCI2DCFjuEiaTxD83xrDxZCoKuYwvZ/VmZEDNtW+E2+PTzpUpL/TCzklJRlIBv31xkpJC3R1dU6WQM6u/eVHQ8nLogtAQdKUGIr6JQVts4JrCxAlPGRO6+1rvhqnHISUS5Cro9xh/pv5JkiYJF5ULkzpMst59hXuGCFjuApIk8eZvsaw/loJcBv+d2ZNx3VrYullNkpefC2Ev9cbBRUVWSiG/fX6SYs2dBS2z+vuhkMuIvpJDfHqBhVoqCLWTJIm9P50j51oROpWMTU5aHuzfBjulFctElBeK6/YAuLRg9fnVAEztNFVUoxUsQgQsjZwkSbyzJY7VUcnIZPDZ9J7c372lrZvVpDVv5czUF3vj6KomO7WITZ+doChfe+sH1qKFmz2jy3rDVoleFqEBHN+eSMLJTGQKGb/YlVKsgNllPX1WobkGZ8PN239JZZ7VRUy2FSxDBCyNmCRJfBBxjuWHEwH49wPdmdrLihPmhAoeLZ0Ie7k3Tu525KYVE/7pCQpzq69tUlfzBpgn3248kUqR1jITegWhJlfOZBG15QoAhUEuXFOaGN7F+45WbL+loz+CyQD+g6BlT9adXwfAfa3vo43r3b/Gm9A4iIClkZIkiU92XuCHg+Z/PB+EBfNQX/GH35DcfRwJe7k3Lh725GeUEP7pCTRZJbd1rYEdmtPey4lCrYFNp1It3FJBMMtNK2LP0rMgQdfBLVmdmQPAvLJsNavQFcOxpebt0EUU6gpFKrNgFSJgaaT+u/cii3+/DMC7U4KYHWLF7lyhVm5eDkx9uReunvZoskoJ/+wE+ZnF9b6OTCZjToj5RWPlkSSbr5ElND3aEvMkW12pEd+ObmR3ciS/RE/rZg7WrbR8Zj2U5IK7P3SZIFKZBasRAUsj9PX+S3yx5yIAb0wM4OEBbW3boHuca3MHwl7ug7uPI4U5WsL/c4LctKJ6X+fB3q2xV8k5n1bA8aTqq9MKwu2STBK7l54lL70Y52Z2jHsimNVHzcUK54T4o7DWkiGSdGOybchTmGQykcosWI0IWBqZ7/+4zCc7LwDw6viuPDbEynUThDpxbmbH1Jd60czXiaJ8HeGfnSTnWv2CFjdHFVN6mOcgiRRnwZKitiSQFJONQiVn/FPBXNIUc/pqPmqFnOl9W1vvxpf3QtYFULtAr7kilVmwKhGwNCLL/rzCBxHnAXh5dGeeuq+DjVskVObkZkfYS71o3sqZEo2OTZ+fIOtqYb2uUV75dntMGlmFt595JAjlLh3P4Ph2cwA8fE4XvP1dK7LRJnb3pbmznfVuXt670nse2LuKVGbBqkTA0kisjEzinS1xADw3oiN/G9nJxi0SauLgombqi73w8nOhpEDPps9PkJlc99oqwa3d6NHGHZ3RxM/HUqzYUuFekJ1ayN4V5wDoMbINXUJ9yS/W89upawDMDbXi3LfMC3BpDyCD/k+IVGbB6kTA0gisP5rMm5tiAXjqvg68OLqzjVsk3Iy9s4opL/TEp50r2iIDmz4/SdqV/Do/vjxjY3VkMkaTmHwr3J7SIj0R35zBoDXSumszBk4z98j+cjwFrcFEgK8rvf2aWa8B5b0rXSeCRzuRyixYnQhYbOzX41d5dWMMAAsGteMf47qIiWoWlFuay8q4lexI3GHR69o5qpj8XE98O7qhKzGw+b+nuHYpr06Pvb+7L+6OKlLzSjgQn2HRdgn3BpPRxK4fY9FkleLS3J6xj3VDrpBjMkmsjjJPtp0X6m+9/yXFOXDaHKAQ+rRIZRYahAhYbOi3U6m8suE0kmT+5/Lm/QEiWLGQ+Nx43jr8FqM3jObfR//NKwde4VLuJYveQ+2g5P5ne9Cqizv6UiNbvjxN6oVbZ//YqxQ81Mc8EXLlETH5Vqi/I5sSSDmXi1ItZ8Ki7tg7qwA4fDmbK1lFuNgpmdLTihWxjy8DQwm06A7+A0Uqs9AgRMBiIxEx13np59OYJJjVvw3vTA4SwcodMpqM7E3ey8KdC3lg8wNsvLgRrVGLg9IBgOVnl1v8nmp7JROf6UGbgGYYtEa2fnWalLicWz6uvCbL7/GZJGfXv66LcO+6EJXGqd3mXpSRjwTi2dq54tjKyEQApvVuhZOd0joNMOoh+gfz9oBnMCGJVGahQYiAxQZ2nU3jubUnMZokHuzTmvenBiO3Vp2Ee4BGp+Gnsz8xMXwiL+x/gei0aBQyBWP8x/DTuJ/4ccyPAGy7so20ojSL31+lVjDh6e74BzfHoDexbfEZEmOybvqYtp5ODO3shSTB6mjRyyLUTWZyAftXmTMJe4/zp2Mf74pj1/NL2B2XDtzIRrOKs5ug4Do4+0BQmEhlFhqMCFga2P7zGTyz5gQGk8SUni35+IHuIli5TVfyr/B+5PuM+mUU/zn2H1ILU3Gzc2NBtwVsn7adT4d9Sm+f3nT36k4fnz4YTAZWn1ttlbYoVQrGPxlMux6eGA0mtn8bQ8KpzJs+Zm5Z9eJfjl2lVG+0SruEpqNYoyPimzMY9Sb8uzUnZHLVGk1ro1MwSRDa3oNOPi7WaYQkQeTX5u1+j4HSTqQyCw1GBCwN6ODFTJ5cdRy9UWJisC+fPtTDehUomyiTZOJQ6iGe2vMUkzdNZt2FdZQYSujo3pG3BrzF7gd382KfF/F19q3yuAXdFgDwS/wvaHQaq7RNoZQz9oludOzjjckosfP7WC4dr31S7Yiu3rR0syenSMf22OtWaZPQNBiNJnb+EEthrhY3bwdGLwis8kZHbzSxNto8TGTV3pWUKLh2EhR20HeBSGUWGpSVBjmFvzp8OYvHfjqGzmBiTKAPX8zsiVIh4sW6KtYX89vl31hzbg2JmkQAZMi4r819zA2YS/8W/W86dj641WA6unfkUt4lfrnwCwuDF1qlnQqF3PxiopQRH5XOrh9jMRoC6RLSotq5SoWc2SF+/GdXPCuPJBHWy4oVSYW72p+/XOLaxTxU9gomLOqOnaOqyvFdZ9PJLNDi5WLHmMDqv2sWE7nY/Ln7dHDyZF2UeS6LSGUWGoJ4xWwA0VdyWLj8GFqDiRFdvflqdm9UIlipk5SCFD45+gmjfhnFB1EfkKhJxFnlzNyAuWwL28aXI74kxDfklhP95DI5jwQ9AsDqc6vRGXVWa7NcIWfkI4F0HeiLJMGe5XGcO3ytxnOn92uDSiHjRHIeZ6/VvZaLcO+I+/MaMb9fBWD0o4F4+DpVO6e8su2sfm1QK630vyUvGc5tMW+LVGbBBsSrppUdT8rl0WXRlOiNDO3sxeI5va33D6WJkCSJ6OvRPLfvOSZunMiKuBUU6Avwd/Xntf6vseehPfyj/z/q/Y5uYruJeDt4k1mSybaEbVZqvZlcLmPE3K4EDW0FEuxbcZ7YP1KrneftYs/YIPM74lWRyVZtk3D3SUvI58Ba89pi/Se1o12P6qsuX8oo4EhCNgq5jFnWXNU96juQTNB+GPgEilRmocGJV04rOnM1j/lLoynSGRnYoTnfz+uDvUph62Y1WqWGUn6N/5UHtjzAwl0L2Z+yHwmJQS0HsXjkYjZP3czsgNk4qaq/w6wLlULFvMB5ACw7uwyTZLJk86uRyWXcN6sz3Yebh3oOrLnAmf3Vy/GXV77ddDIVTaneqm0S7h5F+Vq2fxeDySDRrocnfce3rfG88kB3ZFdvfN0crNMYbQGcWGneDn0Gk2QSqcxCgxNzWKwkNjWfuT9GUaA10L+dBz8+0lcEK7VIK0pj/YX1bIjfQJ42DwAHpQOTO0xmdtfZtHe33IrVD3Z+kO/OfMeV/CscSDnAcL/hFrt2TWQyGYOnd0KulHNqdzIH11/EqJfoNebGO+H+7Tzo7ONMfHohG49fZf6gdlZtk9D4GfXmTLPifB3NfJ0Y9Wggshom6BdpDfx63DxcNG+AFSfbnloD2nxo3hE6jhKpzIJNiIDFCs6naZi3JApNqYE+/s1YOr8fjmrxVFcmSRKnM0+z6twq9iTtwSiZ03pbObdiVtdZhHUKw1XtavH7Oqudmd5lOktjl7L87HKrByxgDloGTuuAQinj+PYkDm+8hNFoqnjHLJPJmBvqz79+O8uqqGQeGdhWvGO9h0mSxIF1F0i/osHOUcmERcGo7Wv+/7H59DUKtAbaeToxqIOndRpkMt1YNyjkKZDLRSqzYBPiVdTCLqYXMOeHKHKL9fRo486yR/vhbK2Kk3chnVHHzsSdrDq3irjsuIr9/Vr0Y07AHIa1HoZCbt2eqDkBc1gRt4ITGSc4lXGKnt49rXo/MAcloVM6oFDKid5yhajfEjAZTPS7vx0ymYywXq34aPt5LmUUEpmQw4AOza3eJqFxOvtHKuf+vI5MBmMWBuHuXXNAIElSxdIOc0L8rFfP6eJOyL0C9m7Qc7ZIZRZsRrySWlBCZiGzf4wiu0hHt1aurHi0P672qls/8B6QVZLFzxd+5ucLP5Ndmg2AWq7m/g73M7vrbLp4dGmwtng7ejOp/STCL4WzLHYZ/x3x3wa7d7+J7VAo5RwJv8zRbYkYDRKhU9vjYq8irFcrVkclsyoySQQs96hrF3M5uP4iAKFTO+AXVPvvwYnkPOKua7BTynmwjxVT4o+UFYrrMx/UTqw7KVZlFmxDBCwWkpRdxOwfosgs0NK1hQsrF4Tg5iiClbPZZ1kdt5rtidsxmAyAOWCY2WUmD3Z+kGb2zWzSrvlB8wm/FM7+lP0k5CfQ3s1y82RupfdYf+QKGX9uuMSJnUkYDSYGPdiRuaH+rI5KZufZNDI0pXi72jdYmwTbK8gpZcf3sZhMEp36eleZ51ST8lTmyT1a4u6otk6j0mIg8SDIFND/CZHKLNiUCFgsICWnmNk/RJGmKaWTtzOrHwuhmZOV/oHcBQwmA3uS97A6bjWnMk9V7O/h1YO5AXMZ6T8Sldy2wVx79/YMazOM31N+Z8XZFbw98O0GvX/PUX4olHL+WBfP6b0pmAwmhszoTF//ZhxLymXd0RSeG9mpQdsk2I5BZ2T7tzGUFOjxbOPM8IdvvnJ7TpGObWfM1ZGtOtm2fO5K4BRwa81v51aLVGbBZkTAcoeu5ZUw+8dIUvNKaO/lxOrHQ2jubGfrZtlEXmkeGy5uYN35daQXmxdhU8qVjG07lrkBc+nm2c3GLazq0aBH+T3ldzZf3syzvZ7F08FKkxZrETysNQqlnP2rzxNzIBWjwcTcUD+OJeWyJiqZp4d1ENWQ7wGSJLF/9Xkykwuwd1Ix/slgVOqbz+P6+VgKOqOJ7q3d6N7a3ToNK8yAmF/M26FPi1RmweZEwHIH0jWlzP4hkpScEvybO7LmsVC8Xe69bvz43HjWnFvD1oStaI1aADzsPZjeZTrTO0/Hy7F6savGoJd3L3p49eB05mlWn1vN872fb/A2BA5uiVwpY99P54j78zqdDCY8HdWkaUrZcy6Dcd2sWGZdaBRO700hPiodmVzG2Ce64ep581oqJpPE6ijzcJBV1w06thSMOmjdD9r048+rB0Uqs2BT4u3bbcos0DLrh0gSs4tp3cyBNY+H0sLt3glWjCYj+5L38djOx3hg8wP8evFXtEYtAR4BvDfoPXY/uJtnej7TaIMVMGfuPNrtUQDWn19Pkb7IJu3oGurL6AVByOQyLkalM1fujEyi4kVJaLpSzuVw+NdLAAx6sCOtu9x6TteBi5mk5JTg5qBiUveW1mmYvhSO/mjeDl0EIFKZBZsTPSy3IbtQy5wfI0nILKKlmz1rHw+llbuVKkw2MgW6AsIvhrP2/FquFpoLVilkCkb4jWBuwFx6efe6q7qKh7cZTlvXtiRqEtkQv6FivaGG1qmfD3KFjF0/nkV1tYTJKjVb4rNIyCykvZezTdokWFd+Zgk7f4xFkqBraIuKisi3sqoslfnBPq1xuMXQ0W2L/RWKMsG1NQRMEanMQqMgeljqKbdIx5wfo4hPL8TH1Y41j4fSxqPpv9u4kn+F9yPfZ+QvI/nk2CdcLbyKq9qVBd0WsH3adj4b9hm9fXrfVcEKmBdFnB80H4CVcSvRm2xXGr9Db2/GPRWMXCmjs17BlCI1qw+LXpamSK81sv3bM2iLDHj7u3DfnC51+ttJySlm34UMwFx7xSok6caqzP0fB4WSdefNqcxDWw8VqcyCzYgelnrIL9Ezb2kU59MK8HQ2ByttPW9vXZu7gUkycfjaYVafW82h1EMV+zu6d2ROwBwmtp+Ig/Lu71m6v8P9fHnyS9KL09lxZYdNx+fbdfdkwqLubFt8ho4GBcl7r1MwshMuzvdu1llTI0kSe386R3ZqEQ6uasY/FYyyjst2rIlORpJgSCdP6/W8JR6E9FhQOUKfR6qkMs8OmG2dewpCHYgeljoqKNXzyNJoYlM1NHdSs/bxEDo00a76Yn0x686vY8qmKSzas4hDqYeQIWNY62H8MOYHNk7eyIOdH2wSwQqAncKOuYFzAVgauxRJkmzaHv+g5tz/THcMMvDTyVn72XH0WqNN2yRYzomdSVw+kYFcIWP8E91wbla3uW9ag5Gfj5oXz7TqZNvyVOaes8GhmViVWWg0RA9LHRRpDTy67CinUvJwd1Sx6rEQOvm42LpZFne14Cprz68l/GI4BfoCAJxVzkztOJXZXWc36a7ghzo/xA9nfuBS3iUOpR5iSOshNm2PX2Bz1CN8KN2bBtdK2PrVaSY+073WNWWEu0NiTBaRvyUAMHRmZ3w7utf5sTti08gu0uHrZs/Irt7WaWD2Zbiw3bwd8pRIZRYaFfHf7xZKdEYWLD/KsaRcXO2VrFoYQoCv5RflsxVJkjiadpRV51bxe8rvSJh7F/xd/ZnddTZTOk7BSdV0h73Kudm58WDnB1kRt4LlZ5fbPGABmDGhE1OPpTA1X8W1i3ls+d8p7v9bT+wcxJ/t3SgvvZjdS+NAgqAhLQka0qpejy9fN2hWfz/r1eeJ+g6QoNNY8OwkUpmFRkX857uJUr2Rx1ccI+pKDi52SlYuDKFbKzdbN8siSg2lRFyJYPW51cTnxlfsH9hyIHMC5jC41WDksrt4xFBbCJprUHDN/NmzC7Tuc9OHzAucx5pza4hOiyY2K9bmhe48nNT06d2Cn49eY3apA2kJGjZ/cZJJz/XE3kks+3A30ZUYiPjmDLoSA74d3Bgyo3O9Hn/uuoZjSbko5TJm9rNST2dJHpxcZd4WqcxCIyQClptQymV4udjhpFawfEE/erRxt3WT7lhaURrrL6xnQ/wG8rR5ADgoHZjcYTKzu86mvXvDralzWyQJSnLLgpHroEk1b1d8vm7+rM2v+ji5Cp6JguYdar10C6cWjG83ni0JW1gWu4xPh31q5W/m1uaG+hN+MpX1zloe1TuSkVTAb1+cZPLzPXEQE3HvCpJJYveyOHLTinFyt2PsE91QKOv3ZqB83aCxQS2st8bUyZWgLwLvQGg/jMT8RJHKLDQqImC5CaVCzn8e6sGV4R3o6H33zlmRJKmimuvupN0YJfMEzpZOLZnVdRZhncJws2sEPUcmk7n2Q3nwUSUgqfRhKKnb9excwbUl6IogPwX2vgvTf7rpQ+Z3m8+WhC3sSd5DsiYZP1crpY7WUW8/dwJ9XYm7rsE0zBuHPzLJSilk02cnmfJCLxxdRdDS2EVvu0LimSwUSjnjnwrGya1+S3cUlOoJP5kKWHGyrdEAUd+bt0MXgUxWMXdFpDILjYUIWG5BIZfdtcGK3qhnR+IOVp9bzdnssxX7+/r0ZW7AXIa1GYZCbqXCU39l1ENB2o3ekILrf+kZKQtQylZ0viXH5uZgxLWV+bNLy7Kvy/f5gl3Zzy39LHw7GOI2QcpRaNOv1st2btaZwa0Gcyj1ECviVvBG6Bt3/r3fAZlMxrwB/ry2MYY156/z64v92PzfU+RcK2LTZyeY8mKver8ACg0n4WQmx7YlAjBsThd82tZ//lv4yVSKdUY6ejsT2t7Dwi0sc34r5Ceb/66CHxKpzEKjJAKWJiirJItfLvzCz/E/k1WSBYBarmZi+4nMDphNV4+ulr2hvuQvvSA1BCSFGUAd0oVlcnD2qRp8uPjeCExcW5q/VtWjW9wnyJyieXIV7HoDFuyAm2Q7LOi2gEOph9h0aRNP93waD3srvUjU0ZSeLflg2zkSs4uJKyoh7KXe/PbFSXLTign/9ARTX+xV59RYoeFkXytkz/I4ALqPaE3XAb71voYkSRXDQfNC/a2XpVOeytx3IagcxKrMQqMkApYm5Gz2WdacW8P2K9srKrZ6O3gzo+sMHuz8YP1feCUJtJrqQzJ/DUhKcut2PbnK3PNRJfhoWbWnxNkHFFb4tRz+OsT8CimR5neTAbVnPPT16UtQ8yDOZp9l7fm1PNPzGcu3px4c1Uoe6NOa5YcTWXkkie8f7kvYy73Z9PlJ8jNKCP/0BFNe6HXLRfOEhlNapCfimxj0WiOturgz8IGOt3Wd6Cs5xKcX4qBSENa7fllFdZZ63Px3IVdBv4UilVlotETAcpczmAzsTd7L6nOrOZlxsmJ/d6/uzA2Yyyj/UajkNWSUSBIUZ9c8R6RyQKIrrFtDVI5/GZKpISBxbA5yG2UeubaEgc/CH5/A7reg8zhQ1JxpU74o4v8d+D/Wnl/Lo0GP2jxDYm6oH8sPJ7LnXDrX8kpo6elQEbRoMksI/8zc0+LmJTI5bM1kkti95CyazBJcPOwZ+3g3FLeZhryyrHdlaq9WuNpbKTOsvHel2wPg0kKkMguNlghY7lJ5pXlsuLiBdefXkV6cDoBSrmRs27HM6TKTYHtvc8BxPqIsc6byXJGybBqjtm43s3e/MS+ktnkj9m43HWZpFAY9D8eXQ85l8+f+j9d66ii/UbR2bs3VwquEXwpnTsCcBmtmTTp6uxDa3oPIhBzWRSfz0pguuHjYVwwP5aUXE/4f85yWZi2aft2cxixy02WS43JQquSMXxR829lcGQWl7DybBpgDVqvQXIOz4eZtkcosNHIiYLnLXMw6y+rYZWxL2Udp2bCPh0zNQ/JmzCgx4BX9G+z7HqQ6lnJ38q7UM1LTvBFfUDeRF0A7Fxj2Kmx7GX7/CLrPAPuaJ0Eq5ArmB83nvaj3WBm3khldZqCU2/bPZV5oWyITclh7NIVnR3RCrZTj3MyOqS/14rcvTpF7vYjwz04y5YWeNG/ZNJeNaOwuHk3n5K5kAEY8EoBXm9ufsP/z0RT0Ronefu4EtbRSFl/0D+aJ7v6DoGVPkcosNGoiYGlMtIU11Ba5jjE/lT+KkliNhii7G1k9AVodczQFjCsqwu6v81llirKgo2X1eSPlAYmLLyjvsbTY3o+Yu8CzL8GfX8DIf9V66pSOU1h8ejGphansTtrN+HbjG66dNRgT5IOXix2ZBVp2xaVxf/eWADi52RH2Ui9+++8psq+Wpzz3xLP13ZnddrfKTClg34pzAPQe60envj63fS2D0cSaKHPgM2+AlVKZdcVwfJl5O/RpAJHKLDRqImBpCDctdnb9xlDNX4qdFchkhLs4s9bVmat2KkCBXJIYWaJlrtGRXk4dkLVrVT0gcWkJzt7QUCnLdxOFCka9A+vnwJGvzVkRbjVPZrRX2jOz60wWn1rMsthljGs7zqYTEFUKObP6+/G/vRdZeSSpImABcHBRM/XFXmz+7ykykwvY9Jm5uJy3f9NZRqIxKynQsf2bGAx6E36BHoRMqb1AYV3sO5/BtfxSPJzUjO9W/+yiOjmzzvx/yd0fuowXqcxCoycCljtl6WJnahcS3VqwxtmO3yiiGPPQjqvCgQfajGJW4Fx8PQMa/3yRxqzrRPAbAMlHYP/7MHVxrafO6jKLZbHLOJdzjsjrkQxoadsUz1n92/D1/ktEXcnhYnpBlUU47Z1UTHmhJ1u+PE36FQ2/fXGKSX/rQYv2jaAoYBNmNJrY+WMsBTmluHk5MHphEHL5nf19rirrXZnetw32Kiu88TCZbky2DXkK5AqxKrPQ6ImA5VbyUyH/qmWKnTl4/GVo5saH5NKSw0UprLq8kUOphwDzhNiO7h2ZHTCb+9vfj4NSpK1ahEwGY96DH0fCqTXm7vAWNa8b5G7vTljHMNacX8Oy2GU2D1h83RwYFeDNzrPprIpM4p0pVdtt56hi8vM92frVaa5fymfzf09x/9960LIeqwIL9XP410ukXshDZadg/KLgO17nKTGriD/iM5HJYE6IlSbbJuyDrHhQu0CvuSKVWbgriIDlVlY/BBlnb3GSrHqxs2rDNL6gqh5wFOuL2XJ5C6sPf8GV/CtlV5NxX+v7mBM4h5AWIeKfhzW07gtBYeYMid3/gnkbaz314aCHWX9hPUeuH+F8znnLF96rp3mhbdl5Np1fT6Ty93FdcbKr+mestlcy6W892bb4NKkXzKs8T3ymB627NLNRi5uu80euc2bfVQBGPRpokcnOq6PMqczDOnvRxsNKWTpHynoVe88De1eRyizcFUTAcivN2oK2oHoWTeWAxNmn1poetUktTGXtubVsvLiRAn0BAE4qJ8I6hjGr6yybr2FzTxj5FpzbCpf3wuV90GFEjae1cm7FGP8xbE/czrLYZXw89OMGbmhVAzs0p52nE1eyivjt1DVm1/AuXGWnYOIzPdj+bQwpcTls/eo0ExYF4xfY3AYtbprSr2j4ffUFAPpNbEv7nl53fM1SvZFfjpsDIKtNts04b/6dl8kh5ElApDILdwcRsNzKrDUWu5QkSRxLP8aquFX8fvV3TJIJAD8XP2YHzGZKhyk4q0U6aoPxaGeuxRK5GHb9C568r9aJyvO7zWd74nZ2Ju7kud7P0crZSlVH60AulzEnxI/3tp1jxZFEZvVvU2MvnEqtYMKiYHZ8H0tSTDbbFp9h/JPBtA32tEGrm5aifC3bv4vBaDDRtrsn/Sa2s8h1t565Tl6xntbNHLivs7dFrllNVNnclS4ToFlbkcos3DVsVHb03lJqKGXjxY08uOVBFuxcwL6UfZgkEwNbDuTrkV+zJWwLcwLmiGDFFoa+AnZukB4DZ9bXelpg80BCfUMxSkZWxq1swAbW7KE+bbBXyTmfVsCJ5NqXRlCqFIx/Mpj2Pb0wGSS2fxtDwsnMBmxp02M0mNjxXSxFeVqatXBk9KOByO5wkm258sq2s0P8UFjomlUU58DpdeZtkcos3GVEwGJF6UXp/O/E/xi9YTRvHX6L+Nx4HJQOTO88nU1TNvHd6O8Y2noocpn4MdiMowcMfdm8ve8980KOtXi026MAbLy4kbzSvAZoXO3cHFVM7mFOa155JOmm5yqUcsY8HkTHvt6YjBI7fojl4rH0hmhmk3RwfTxpCfmoHZRMWNQdtYNlOqpjruZzOiUPtULO9L5WChyOLwNDKfj2AP+BIpVZuKvc1ivl4sWLadeuHfb29vTp04eDBw/e9Pyvv/6agIAAHBwc6NKlCytWrKh2Tl5eHs888wy+vr7Y29sTEBBARETE7TTPpiRJ4lTGKV458Arjfh3HDzE/kKfNo6VTS17u8zK7H9zNmwPepIP7ndVpECyo/5Pg5mfO/oqsPcV5gO8Aunp0pcRQwvoLtffGNJS5oeY5DhExaWQX3nyZBYVCzuhHA+kS0gKpbK2bC1FpDdHMJiX2j1TOHrwGMhi9IBB3H8vN9yhflXlCcAs8ne0sdt0KBp25si2Ye1dkMpHKLNxV6h2wrF+/nhdeeIHXX3+dkydPMmTIEMaPH09ycnKN53/zzTe89tprvP3225w9e5Z33nmHZ555hi1btlSco9PpGD16NImJiWzYsIELFy7www8/0KqV7eYJ1JfeqGfL5S3M3jabedvnsSNxBwbJQF+fvnw+7HO2TdvG/G7zcbMTNTEaHZU9jHzTvH3wcyjKqvE0mUzG/KD5AKw5v4ZSQ2kDNbBm3Vu706O1GzqjiZ+PXb3l+XKFnBGPBBAw0BdJgj3L4zh3+FoDtLRpuHYpj4Pr4wEIndLeonOB8ov1/HY6FbgRiFpc3G/mMgzOPhA0TaQyC3edegcsn332GQsXLuSxxx4jICCAL774gjZt2vDNN9/UeP7KlSt58sknmTFjBu3bt2fmzJksXLiQjz++kWmxdOlScnJy2LRpE4MGDcLf35/BgwfTo0eP2//OGkhWSRbfnP6GMb+O4Z+H/klsdixquZqpHafyy6RfWDZuGaP8R9l8HRrhFro9aO4m1xXAgdqzgMa0HUNLp5bklOaw+fLmBmxgzcpf3FZHJWE0/XV9hurkchnD53al29BWIMG+FeeJ/SPV2s286xXmlrLj+1hMRokOvb3pPdayQcWGE1cp1Zvo2sKFPv5WSD+XJIj82rzd73FQqvkz9U+RyizcVeoVsOh0Oo4fP86YMWOq7B8zZgyHDx+u8TFarRZ7e/sq+xwcHIiOjkavNy/et3nzZgYMGMAzzzyDj48P3bp144MPPsBorH0BP61Wi0ajqfLRkOKy43j90OuM2TCGxacWk1WShbeDN3/r9Td2P7Sb/zfo/9m8XodQD3I5jP5/5u1jSyHrUo2nqeQqHg56GIDlZ5djNNVxkUkrmdSjJW4OKq7mlvBHfN0m08rkMobO6kz3Ea0BOLDmAmf2p1izmXc1g97I9m9jKNHoaN7KmZGPBFi0N0KSJFaXDQfNG+BvnZ6OlCi4dhIUdtDXPBdLpDILd5t6BSxZWVkYjUZ8fKou6uXj40NaWs3j4WPHjuXHH3/k+PHj5rTeY8dYunQper2erCxz13tCQgIbNmzAaDQSERHBG2+8waeffsr7779fa1s+/PBD3NzcKj7atLH+7HaDycDOxJ08sv0RZmydwebLm9Gb9HT36s7HQz5mx4M7eKL7E3jYe1i9LYIVtL8POo0xVy7e+3atp4V1DMPNzo2UghT2pexruPbVwF6l4KE+5sCjPMOkLmQyGYMf6kSvMeYaLgfXX6xYZVi4QZIkDqy+QEZSAXZOSiYsCkZlZ9lS+YcvZ5OQVYSznZKpPa00DF4+N6vHDHDyFKnMwl3ptibd/vUdgCRJtb4rePPNNxk/fjyhoaGoVCqmTJnC/PnzAVAozH/4JpMJb29vvv/+e/r06cPMmTN5/fXXax1mAnjttdfIz8+v+EhJsd47xHxtPktiljB+43j+78D/cSLjBEqZkgntJrBmwhpWT1jNhPYTUMnvrCS30AiMftdcUOvcFkiOrPEUR5UjM7rMAGBpzFIk6dZDMdY0p2xYaP+FDFJyiuv8OJlMxoCwDvSd0BaAwxsvcSwi0QotvHud2X+V85FpyGQw9vFuuHpafnmM8iyvab1bVatabBG5SebfZ4CQRYBIZRbuTvUKWDw9PVEoFNV6UzIyMqr1upRzcHBg6dKlFBcXk5iYSHJyMm3btsXFxQVPT/OkNV9fXzp37lwRwAAEBASQlpaGTqer8bp2dna4urpW+bA0g8nAO0feYdQvo/jixBekFaXhYe/Bk92fZOeDO/l46McEewVb/L6CDXkHQK+55u1db5rH/mswu+ts1HI1sdmxHEs/1oANrK6dpxNDOnkiSbA6qn69JDKZjJDJ7QmZbC58FrU5gagtCTYPwhqDq+dz+HODeWhw4AMdadPV8j2nafml7D5nTjG32mTb6O9BMkH74eATKFKZhbtWvQIWtVpNnz592L17d5X9u3fvZuDAgTd9rEqlonXr1igUCtatW8f999+PXG6+/aBBg7h06RImk6ni/Pj4eHx9fVGr1fVpokUp5UqSNcmUGksJ8AjgvUHvsevBXTzb61m8Ha1UhVKwveGvg8oRrkbDuZon1jZ3aM7UjlMBWBa7rAEbV7PyF7ufj6WgNdR/Xk3fCe0YMM2can9sWyKRmy7f00GLJquEnT+cRTJJdAlpQY+R1umFWBudjNEkEdLOg86VVt62GG0BnCgrI1FWKE6kMgt3q3oPCb300kv8+OOPLF26lHPnzvHiiy+SnJzMU089BZiHah5++OGK8+Pj41m1ahUXL14kOjqamTNnEhsbywcffFBxzqJFi8jOzub5558nPj6ebdu28cEHH/DMM89Y4Fu8M8/3fp7l45az/v71TOk4BTuFFeojCI2LSwsY+Dfz9p63zfUravBI0CPIkHEw9SAXcy82XPtqMLKrN75u9uQU6dgec3v1VXqP8WfwQ50AOLEzmT9/uXRPBi16rZGIb2MoLdLj5efCsDldrDIRVm80sTba3CNmtd6VU2tAq4HmnaDjqCqpzLO6zhKpzMJdpd4By4wZM/jiiy9499136dmzJ3/88QcRERH4+5v/4K5fv16lJovRaOTTTz+lR48ejB49mtLSUg4fPkzbtm0rzmnTpg27du3i6NGjdO/eneeee47nn3+eV1999c6/wzvU3as7fXz6iD/se83A58DJG3ISzNVBa+Dn6sco/1GAOWPIlpQKObP7myfQ1mfy7V/1GNmG+2Z3AeD0vhT+WBePVId06aZCkiT2rTxH9tVCHFxUjH8qGKXaspNsy+2OSyejQIunsx1jg1pY/gYmE0SWzQMMfQrk8opUZmeVM5M7TLb8PQXBimRSE3kLpdFocHNzIz8/3yrzWYR70LGlsPVFcPCA50+BffWifzGZMcyOmI1SpmT7A9tp4WSFF546yigoZeCH+zCYJCKeG0Jgy9v/O4j78xr7V50HCQIH+TJsTleLrZfTmJ3YmcSR8MvI5TKmvNiLlp3crXavWd9HciQhm7+N6MjLY7pY/gbnI2DdLLB3h5fiQO3EU3ue4s/UP5kXOI+/9/u75e8pCLehrq/fYhEbQahNr4fBswuU5MChz2s8JdgrmL4+fTFIBlbFrWrgBlbl7WLP2G7mgGlV1O33sgAEDmrJqPmByGQQ9+d19q44h6mJ97Qknc3myKbLAAyZ0cmqwcqljAKOJGQjl8Gssp4xiytPZe4zH9ROIpVZuOuJgEUQaqNQwuh3zNuR30B+zeXvyxdF/CX+FzS6hi1g+FfzyuZCbDqZiqZUf0fX6hLSgtELg5DJZVyITGPP0rMYjaZbP/AulJdezO4lZyt6lIKGWndZkFWR5mHzkQE+tHS3fKo0189A4kGQKaD/44BIZRbufiJgEYSb6TwO/AebV7jd916NpwxpNYSO7h0pNhTzy4VfGriBVYW086CTtzPFOiPhJ+685H6nvj6MfTwIuULGxWMZ7PrxLEZD0wpadKUGIr6NQVtsoEV7V4bOtM4k23LFOgO/HjcHv/OsNdk26lvz58Ap4NZapDILTYIIWAThZmQyGPOuefv0OvM712qn3FgUcdW5VeiMNWcVNQSZTFaRcbIqMskiWT4denkz/slg5EoZCScz2fF9LEZ90whaJJPEnmVx5F4vwslNzbgng1GorPtvcfOpaxRoDbRt7sjgjpZbQLFCYQbElAXOA8yZliKVWWgKRMAiCLfSqg90ewCQYHfNxeQmtJuAt6M3WSVZbE3Y2vBtrCSsdysc1QouZhQSdSXHItds292TiYu6o1DJSTyTRcQ3ZzDobLuOkiUc257IldNZyJUyxj0VjJObdcsWSJLEirLKtnNC/JFbYyLz0SVg1EHrftC6r0hlFpoMEbAIQl2M/Bco1JDwO1zaW+2wSqHi4UBz/aFlscswSbbrgXC1VzG1l3kOxp2kOP+VX1Bz7n+mO0q1nOS4HLZ+fQa99u4NWq6cziR6yxUAhs3uQot21bPALO1kSh5x1zXYKeU8WLYGlEXpS+HYEvN2WaE4kcosNBUiYBGEumjWFvo/Yd7e/SbUsErzA50ewEXlQqImkQMpBxq2fX8xN8Q8LLQzNo0MTanFrtu6qweT/tYTlZ2C1Au5bPnyFLpSg8Wu31Byrhexe1kcAMHDWhMwsGWD3HdVWe/KpB4taeZkhSresRugKBNcW0OAOTgpX5U5rFOYWJVZuKuJgEUQ6mrIy+ZaLBlx5gqif+GsdmZ6l+kALDtr23L9gS1d6ePfDINJYv1Ryy4M2rKTO5Of74naXsH1S/ls+d8ptCV3T9CiLdYT8c0Z9KVGWnZyZ9BDHRvkvjlFOrbGXAesNNlWkm4Uiuv/OCiUIpVZaFJEwCIIdeXoAUNfMW/vfx901VdGnhMwB5VcxcmMk5zMONnADayq/EVxTXQyBgunI7do78aUF3th56gkLUHD5i9OUlp0Z2nUDcFkkti1JI78jBKcPewY90Q3FIqG+Tf4y7EUdAYTwa3c6NHG3fI3SDwI6bHmdbD6PAKIVGahaREBiyDUR/8nwN0PCq5D5NfVDns5ejGpwyTA9osijg9ugYeTmuv5pew9n2Hx63v7uzLlxV7YO6vISCrgty9OUlJouwypuojanEDy2WyUKjkTnuqOg0vDLK5qMkkVK2lbLZX5SFmhuJ6zwaGZSGUWmhwRsAhCfSjtYORb5u1D/4XCzGqnPBJkfne7P2U/CfkJDdm6KuyUCqb3Nb+rXmXBybeVebVxYeqLvXBwVZOVUsimz05SrGmcQcvFY+mc2GF+HobP64qXnxVWR67FHxczSc4pxtVeyaQeVpgvk30Z4neYt0MWASKVWWh6RMAiCPUVNA1a9gJdARz4qNrh9m7tGd5mOAArzq5o6NZVMSfED5kMDl7M4kpWkVXu0byVM2Ev9cLRTU3OtSI2fXaCojytVe51u7KuFrBvxTkAeo72o3P/hl3zqTxgfLBPGxyssZhi1LeABJ3GgmdHkcosNEkiYBGE+pLLYUxZ1dtjyyDrYrVTysv1b768mczi6r0wDaWNhyPDu3gDsNpKvSwAzVo4EfZyb5yb2ZGbVkz4pycoyLFcdtKdKC3UE/FNDAadiTaBHgwI69Cg90/JKa4YkpsTaoV1g0ry4KQ5E4hQc++KSGUWmiIRsAjC7Wg7GDqPB8kIe96udriXdy96evVEb9Kz+tzqhm9fJXPLXiR/OX6VUr316qa4ezsS9nJvXJrbk59ZwqbPTqDJKrHa/erCZDSx88dYCrJLcfW0Z8zCIOsUa7uJtdHJSBIM7uhJBy9ny9/g5ErQF4F3ILQfBohUZqFpEgGLINyu0e+ATA7nt0LS4WqHy3tZfr7wM0V66wzH1MV9nb1p3cyB/BI9W05fs+q9XD0dCHu5N25eDmiySgn/9AR5GdWzqRrK4Y2XuXo+F6WdggmLumPvpGrQ+2sNRn4+Zk4rn2uNybZGA0R9Z94OXQQymUhlFposEbAIwu3y6gK9zdVt2VW9ZP+wNsNo69qWAn0BG+I32KCBZgq5jDkhN9YXsjYXD3vCXu5NsxaOFOZq2fTpCXLTGj5guxB5ndN7zcHCqPkBNG9lhd6NW9gRm0ZWoY4WrvaMCvC2/A3Ob4X8FHBsDsHmGkAilVloqkTAIgh3Ytg/QeUEqcfgbHiVQ3KZvKKXZWXcSvRG29Upmd63NWqFnNNX8zlzNc/q93Nyt2PqS73xaOlEUb6O8M9Okp1aaPX7lstI0rB/1QUA+k5oS4deVggW6qA8QJzV3w+lNeq9RJalMvddCCp7kcosNGkiYBGEO+HiA4OeM2/vfQcMVbNj7m9/P54OnqQXp7M9cbsNGmjW3NmOCcHmzJiG6GUBcHRVM/WlXni2caZEo2PT5yfJulpg9fsWa3Rs/zYGo8FE2+Dm9L+/ndXvWZPzaRqOJuailMuY2d8KPR1Xj0NKFMhV0G8hIFKZhaZNBCyCcKcGPAvOPpCbaF4ptxK1Qs2cgDmAuZCcVMNKzw1l3gDzsNBvp66RX9wwvT0OzmqmvNALb38XSgv1bPrsJBlJGqvdz2gwseP7GApztbj7ODJqQRCyBp5kW648MBwT5IOPq73lbxBVVoY/+EFwaSFSmYUmTwQsgnCn7Jxh+D/N23/825xmWsn0LtNxVDpyKe8Sh1IPNXz7yvT2a0aArytag4lfjlt2faGbsXdSMfmFXrRo74q22MBvn58kLSHfKvc69MtFrl/KR22vYMKiYOwclFa5z60UlOoJP5EKWGmyrebajSFIkcos3CNEwCIIltBzLnh1hZJcOPhplUOualce6vwQYNtFEWUyWUWK8+qoZEymhuvtsXNQMum5nrTs5I6u1Mjm/57i2sU8i94j7tA1Yg+kggxGLwyiWQsni16/PjadTKVIZ6SDlxMD2je3/A2ifwCTAfwHg28PQKQyC02fCFgEwRIUShj9rnk76jvIS65yeG7gXJQyJUfTjhKTGWODBppN7dkKZzslV7KKOHw5u0HvrbZXcv+zPWjVpRl6rZEtX57i6vkci1z7+uV8Dqw1T7INmdSetsGeFrnu7ZAkiVWRN9YNsvjQjK4YjpcFvmW9KyKVWbgXiIBFECyl0xhoOwSMWtj3XpVDLZxaMKH9BMC2vSxOdkoe6N0KgJWRiQ1+f5Wdgvuf6Y5foAcGnYmtX58h+eydBU6FuVp2fBeDySjRoZcXfcZbaXHBOjqamMuF9AIcVAqm9Wlt+RucWWfuyWvWFrqMB0Qqs3BvEAGLIFiKTAZj/p95+8x6uHaqyuHyRRH3JO0hWZOMrZTPqdgdl871/IavRKtUKxi/KJi2wc0x6k1s++YMiWeybutaBr2R7d/FUKzR4dHSiRGPBNh8sunKssm2U3u1xNXewoXqTCaILJtsG/IUyBUilVm4Z4iARRAsqWWvigJe7K5aTK5zs84MaTUECYkVcbZbFLGTjwsh7TwwSbA2uuEm31amVCkY92Qw7Xt5YTJIbP8uhoST9VtzSZIk/lgbT0aiBjtHJRMWBaO2t80k23KZBVp2xF4HrDTZ9vI+yIoHtQv0NGefiVRm4V4hAhZBsLSRb4LCDq78ARd3VzlUXkhu06VNZJc07BySyspTnNdGJ6M3mmzSBoVSzpjHgujU1xuTUWLHD7FcPJZe58fH/J7KucPXkclg7GPdcPOy/UTTn4+loDdK9PZzJ6ilm+VvUF4orvfDYO8qUpmFe4oIWATB0tz9IORJ8/buf5nXeynT16cv3Zp3Q2vUVrzQ2MKYwBZ4udiRWaBl19m6BwmWplDIGbUgiC4hLZBMEruXnOVCVNotH5d6IZdDv5hXyR4wrSNtAj2s3dRbMpqkihWxrdK7knEeLu81r18V8gQgUpmFe4sIWATBGoa8BPbukHkOTt1YrVkmk1X0sqy7sI5ivW0WBlQr5czsZ56caYvJt5XJ5TJGPBJAwCBfJAn2LI8j7s/aF2ksyCllxw+xSCaJzv196DmqcUwy3Xc+g2v5pTRzVDEh2NfyNygvFNdlgnnCLSKVWbi3iIBFEKzBoRnc93fz9v4PQHdj8b+RfiNp49KGfG0+4ZfCa7mA9c3q74dcBpEJOVzKsH7J/JuRy2UMn9OVbkNbgQT7V54n9o/UaufpdUYivjlDaaEeLz8Xhs/t2miGQcor207v1wZ7lcKyFy/OgdPrzNsDngFEKrNw7xEBiyBYS7/HwN0fCtPg8FcVuxVyBfOD5gOw4uwKDCZDLRewrpbuDowK8AGoqBtiSzK5jKGzOtNjhLnH5MCaC5zed2NSsCRJ7F95nqyUQhxcVIx/Khil2sKBwW1Kyi7iQHwmMhnM6W+F4aBjS8FQai4S52eeWCtSmYV7jQhYBMFalHYw6i3z9p//hYIbc0Umd5iMh70H14qusStxl40aeGPy7a/Hr1KktU3gVJlMJmPQQx3pPdZckffQzxc5scvcc3FqTwoXj6Yjl8sY+3g3XDyssD7PbVodZQ747uvshV9zCw/NGHRw9EfzdugzIJOJVGbhniQCFkGwpqBp0KoP6Ivg9w8rdtsr7ZnV1dyNv+ys7RZFHNTBk7bNHSnQGth8uvZ5Iw1JJpMROrUDfSe2BeDIxsvsXnaWIxsvATB4eidadW5mwxZWVao38vMxc0/QPGtMto3bBAXXzQtsBoUBIpVZuDeJgEUQrEkmgzFlVW9PrIDMCxWHZnaZiYPSgfM55zly/YhNmieXyyoyWlYeSbLpatKVyWQyQia1J2RyewDio9KRJAgY6Eu3+1rZuHVVbTtznbxiPa3cHRjWxduyF5ekG6nM/R4HpVqkMgv3LBGwCIK1+Q+ELhNBMsKetyt2u9u7M63TNACWxy63TduAB/u0xk4pJ+66hhPJeTZrR036TmjLwGkdAWjR3o2hszo3uhfo8sq2s0P8UMgt3LaUKLh2EpT20NecXSZSmYV7lQhYBKEhjH4HZAq4EAGJf1bsnhc4D4VMwZHrRziXfc4mTXN3VDOpR0vgRqZLY9JrjB+PfDiQsJd7obR09s0dik3N51RKHiqFjBn9rDDx9cjX5s/dp4OTeUFHkcos3KtEwCIIDcGzE/SZb97e9YZ5TRiglXMrxrQdA9h2UcTyuRfbzlwnp0hns3bUxrmZPXJF4/t3VR7gTQj2xdPZzrIXz02C81vN26FPAyKVWbi3Nb7/AILQVA17FdTOcO0EnN1YsfvRIHNX/67EXaQWVq890hB6tHGne2s3dEZTxQRS4ebyS/RsOmX+eVmlsm309yCZoP1w8A4ARCqzcG8TAYsgNBRnbxj0gnl77ztg0AIQ0DyAAb4DMEpGVsattFnzyl90V0clYTQ1jsm3jdmvx69SqjfRtYULff0tnLWkLTBP0oaK3hWRyizc60TAIggNacDT4NwC8pIh+oeK3eXl+jde3EheaZ5Nmjape0tc7ZWk5JTwR3z9Vk6+10iSxKqoG+sGWXwi8Kk1oNVA807QcRQgUpkFQQQsgtCQ1E4w4nXz9h+fQEkuAKG+oXT16EqJoYR1F9bZpGkOagUP9TUPMzTGybeNyZHL2SRkFuFsp2RqLwunWZuMEFm2blDoUyCXi1RmQUAELILQ8HrOAe9AKM2DP/4DlC2KWDaXZc25NZQaSm3StDkh5gqz+y5kkJJjm4UZ7wblqcxhvVrhbKe07MXjd0LuFfPimT3ME2tFKrMgiIBFEBqeXAGj3zVvR38PuYkAjGk7hpZOLcnV5vLbpd9s0rT2Xs4M7uiJJMGaaNuvL9QYpeWXsivOvMyCVSbblheK6zPf3CMHrDm/BhCpzMK9TQQsgmALHUdBu/vAqIO9/w8ApVzJw0EPA/BT3E8YTUabNK38RfjnoyloDbZpQ2O27mgyRpNE/3YedGnhYtmLXz8DiQfNNXv6Pw6YU5kPpR4SqczCPU8ELIJgCzIZjPl/gAxiN0DqCQDCOobhZudGSkEKe5P32qRpowK8aeFqT3aRjh2xaTZpQ2OlN5pYW9bzZJXelahvzZ+DpoJba0CkMgtCORGwCIKt+PaA7jPM27veBEnCUeXIzC4zAVgWa5tFEZUKObPL5rKsPCIm31a2Jy6ddI0WT2c144JaWPbiBekQ84t5W6QyC0I1ImARBFsa8QYo7CDpEMTvAMxZIHYKO2KzYzmWfswmzZrZrw1KuYxjSbmcu66xSRsao/LJtjP7+aFWWvjf57Gl5iHC1v2hdV9ApDILQmUiYBEEW3JvA6GLzNu73wKjgeYOzZnacSpg7mWxBW9Xe8aW9SCIFGezSxmFHL6cjVwGs8p6oCxGXwpHfzRvl/0+iFRmQahKBCyCYGtDXgIHD8i6ACfNlW4fDnwYuUzOwdSDxOfG26RZ5XM0wk+mUlCqt0kbGpPVZYXiRnT1oZW7g2UvHrsBirPAtTUEmNOWRSqzIFQlAhZBsDV7N7jvH+bt/R+AthA/Vz9G+o0E4KezP9mkWaHtPejo7Uyxzkj4SduscdRYFOsMbDh+FYB5Ayw82VaSbhSKC3kCFOa6LiKVWRCqEgGLIDQGfRdAs3ZQlAGHvwRgQbcFAEQkRJBW1PDZOjKZjLllQx+rIpNsMgG4sdhy+hoFpQb8mzsypKOnZS9+5Q9IjwWVI/Q2p7WLVGZBqE4ELILQGCjVMOot8/bh/0FBGt08u9GvRT8MksFmiyJO69MaB5WC+PRCoq/k2KQNtiZJEivKsqXmhPghl1t4Lkl570rPOeBgXkRRpDILQnUiYBGExiJwKrTuB/pi89AQVJTr3xC/AY2u4bN1XO1VFWvlrLxHJ9+eSsnj7DUNaqWch/pYOHjIvlyRHUbIU4BIZRaE2oiARRAaC5kMxrxn3j65EjLOMbjVYDq6d6TYUMzPF362SbPmhpqHhXbEppFRYJs1jmypPFCb1L0lzZzUlr141LeABJ3GgmdHQKQyC0JtRMAiCI2JXyh0vR8kE+x+y7woYjdzL8vqc6vRGrUN3qSglm709nPHYJL4+WhKg9/flnKLdGw9cx2wwmTbkjw4udq8PcBcKE6kMgtC7UTAIgiNzah3QK6Eizvhyh+MbzseH0cfskqy2Hp5q02aVP5ivSYqGYPRZJM22MIvx1PQGUx0a+VKj9Zulr34iRWgLwLvIPO6UohUZkG4GRGwCEJj49kR+ph7Vdj1BiqZgnmB8wBYfnY5JqnhA4bx3Xxp5qjiWn4p+85nNPj9bcFkklgdZV43aF6ov2V7O4wG80rdYC4UV3ZtkcosCLUTAYsgNEbDXgW1C1w/DbEbeLDzg7ioXEjUJPJ7yu8N3hx7lYLp/cwTTleVvYg3dQcvZZGUXYyLvZLJPVpZ9uLnt0B+Cjg2h+CHAJHKLAi3clsBy+LFi2nXrh329vb06dOHgwcP3vT8r7/+moCAABwcHOjSpQsrVqyo9dx169Yhk8mYOnXq7TRNEJoGJ08Y/IJ5e++7OKFgepfpgO3K9c/p749MBn/EZ5KYVWSTNjSk8oUfH+zTGge1wrIXL09l7rsQVPaASGUWhFupd8Cyfv16XnjhBV5//XVOnjzJkCFDGD9+PMnJNb/r+uabb3jttdd4++23OXv2LO+88w7PPPMMW7ZsqXZuUlIS//d//8eQIUPq/50IQlMT+jS4tDS/E4/+njkBc1DJVZzKPMXJjJMN3hy/5o7c19kLuFGmvqm6mlvMvvPpwI0lCix38eOQEgVyFfR7DBCpzIJQF/UOWD777DMWLlzIY489RkBAAF988QVt2rThm2++qfH8lStX8uSTTzJjxgzat2/PzJkzWbhwIR9//HGV84xGI3PmzOGdd96hffv2t/fdCEJTonY0r+YMcPA/eKGomIi5NHapTZo0r+zF++djVynVG23ShoawNjoZkwSDOjang5ezZS8eudj8OfhBcPEBRCqzINRFvQIWnU7H8ePHGTNmTJX9Y8aM4fDhwzU+RqvVYm9vX2Wfg4MD0dHR6PU3FlR799138fLyYuHChXVqi1arRaPRVPkQhCanx0zw6Qal+fDHf3gk6BFkyPg95XcS8hMavDnDunjTyt2B/BJ9RbpvU6MzmFhflr49z9K9K/mpELfJvC1WZRaEeqlXwJKVlYXRaMTHx6fKfh8fH9LSal7rZOzYsfz4448cP34cSZI4duwYS5cuRa/Xk5WVBcCff/7Jkv/f3n3HVV39Dxx/XS5TpoqKA0RFREBwgiMHpuJMc2IO0NLKsmFl+TPce2XWVysVcSXOzFE5cw9cuCBRFHFAqClD2dzfH1euXkUFvOz38/H4PLx8xjnnXvDe9z2f8z5n6VIWL16c47ZMnz4dS0tLzWZrK/d8RQmkp4T2k9SPg3+hRgZ42XoBhbMoolJPwYDHE8mV1Jlv/7oYw93EVCpZGNGubqVXX5AbJxZDZjpUfwMquwOSyixETuVp0O2z3wBUKtULvxX4+/vTqVMnmjZtioGBAd27d8fPzw8ApVJJQkICAwcOZPHixVhb53xRsTFjxhAXF6fZbtwoXRNaiVLE4U2o6QWZabBnkmYiua0RW4l9VPApxn0b22KgVHD2xgPO34wr8Prz26rHg237e9ihr9RhImXqIzj5eMD0494VkFRmIXIqV/8bra2tUSqVz/WmxMbGPtfrksXExISAgAAePXpEZGQkUVFR2NvbY25ujrW1NREREURGRtKtWzf09fXR19dnxYoVbNmyBX19fSIiIrIt18jICAsLC61NiBKrw2RAARc3UT81gwYVG5CWmcbqsNUF3hRrMyM616sMqFdxLkkuxSQQHPkfSj0F/T3sdFv4uSBIfgBl7aFOJ0BSmYXIjVwFLIaGhjRq1Ihdu3Zp7d+1axfNmzd/6bUGBgZUq1YNpVJJUFAQXbt2RU9PDycnJ86fP09ISIhme+utt/Dy8iIkJERu9QgBYFMP3B9/oO38liEufgCsv7SexNTEAm9O1tiO38/eIu5R2ivOLj6yAjBvl0pUsjB+xdm5kJn5JJXZ8wP1rT4klVmI3NDP7QWjRo1i0KBBNG7cmGbNmvHLL78QFRXFBx+oVxodM2YMt27d0sy1Eh4eTnBwMJ6enty/f5958+Zx4cIFli9X3383NjbG1dVVqw4rKyuA5/YLUaq1/RYuboKoI7R++JAaljW4FneNjZc34uviW6BNaVS9LE425vwTk8CG0zd5940aBVp/fkhMSWfT6ZsADPTU8WDbiL1wNxyMLKDBQHV9ksosRK7k+gZtv379mD9/PpMmTaJ+/focOHCAP/74g+rV1f/Bo6OjteZkycjIYO7cubi7u9O+fXuSk5M5cuQI9vb2OnsSQpQKllXVc7MAersn4ldXPV3/itAVpGUUbC+HQqHQzE+y+th1VCpVgdafH347c4uHqRnUrGBKs1rldVv4sf+p/20wCIzMgSepzDUsa0gqsxA5oFCVhHcaID4+HktLS+Li4mQ8iyi5kuNgQQN4dI/UTjPxvr6Wu0l3mdJiCt0duhdoUxJT0mk6bQ+JKemsfs+TFg45HzRf1KhUKjp9f5B/YhIY382ZIS102GMUGwYLm4JCDz45A2XtyVRl8tbmt7gef52xnmPxcfLRXX1CFDM5/fyWtYSEKE6MLaH1NwAYHpjDQEf1OjSBFwMLvJfDzEifng3Va+xkTWNfXJ28fp9/YhIwMVDSs2E13RZ+/Cf1v05d1ANukVRmIfJCAhYhipvGQ6BcLXh4hz53YzA1MOXKgyscvPXyNb3yQ9ZtoV1h/xITl1zg9etKVsDVvX4VLE0MdFfww3twNkj9+PHtPJBUZiHyQgIWIYobpQG0mwCAxbFf6FNdnSJbGIsiOlYyx6NGOTIyVawJLp6rON9NTOHPC+pZe3W+btCpZZCeDJXrg516nIqkMguRNxKwCFEc1e0Gtk0hPYkBsTfRV+hz8t+TnLtzrsCbkpXivCY4irSMzAKv/3WtPXGDtAwVDeyscK1qqbuC01Mh+PHs3U1HwOPJNSWVWYi8kYBFiOJIoXg8mRzYnNtA58otAPVYloLm7WKDtZkRsQkp7Ar9t8Drfx0ZmSp+Pa7uGdJ5KnPoZkiMATMbcHkbkFRmIV6HBCxCFFe2HuDcHVSZ+MWox2Dsvr6bqPiCvTVjqK+HTxN1T0FxG3z79z+x3HqQhFUZA7q4VdZdwSrVk1WZPd4DfUNAUpmFeB0SsAhRnL05HvT0qR1xgFblXFChKpRFEft72qGngKNX73ElNqHA68+rVcfVAVa/xrYYGyh1V3DUMbh9BvSNodFQQHtV5nec3pFVmYXIJQlYhCjOyteCxu8C4Bet/vDdfGUzd5PuFmgzqlqZ8ObjlY1XHSseg2+v33vI/vA7ALzjqeN1g7J6V9z6gal6EjpJZRbi9UjAIkRx1/prMLKg8e1Q6pWpQmpmquabfEHKyrDZeOomj1LTC7z+3Pr1eBQqFbR2rED18qa6K/h+JPyzTf1YVmUWQmckYBGiuDMtD298jgIY8q96LZygf4J4lPaoQJvR0sGa6uXLkJCSzu8htwu07txKTstg3ckbwJMsJ50JXgyqTKjpBRXrApLKLIQuSMAiREnQ9EOwqEbbuzexM7AgPjWe3678VqBN0NNTaDJtVh4t2usL/XE+mvuP0qhqZYKXU0XdFZySAKfVC7/S7CPNbkllFuL1ScAiRElgYAJtv0UJ+N6JAWD5xeWkZRbsooi9G1XDUF+P0Oh4ztx4UKB158bKY+rxPu942qHU0+Hg1zOrISUeyteGWm8C6lTm3yN+V9cnqcxC5JkELEKUFG79wKYebz24RzmFIdEPo9kZubNAm1DW1JBublUAWFVEU5wv3IrjTNQDDJQK+jbWYW9HZsaTdYOafgh66rfX3yN+52HaQ0llFuI1ScAiREmhpwftJ2OsUvHOfXWW0LILywr81sygZurbQtvOR/Pfw9QCrTsnVj9OZe7kWpkK5ka6Kzj8L7h/DYytwF29+rKkMguhOxKwCFGS1PICh3b4xMVjgh6X7l/iaPTRAm2CezVL6lW1JDU9k/WPB7YWFXFJaWw+ox4QrPN1g44tUv/byA8M1VlHksoshO5IwCJESdN+EpYq6BX3ACj4RREVCgUDm6rnNVl1/DqZmUVn8O2m0zdJSsugTiVzmtiX1V3B0ecg8iAolOAxXLNbUpmF0B0JWIQoaSq5QP13GBSfgFIFx6KPEXovtECb8JZ7VcyN9bnxXxL7L98p0LpfRKVSserxYNuBzarr9vZMVu+KSw+wrApIKrMQuiYBixAlkddYqmCI98OHAAReCCzQ6k0MlfRppB7QuvpY0Rh8e/TqPSLuPMTUUMnbDarqruCEf+HCBvXjppLKLER+kYBFiJLIogo0+4ghcfEA7Ly+k5sJNwu0CQMe3xba808sN/4r2EnsspPVu/J2w6qYGenrruCTSyEjFap5QLVGgKQyC5EfJGARoqRq8SlO+pY0f5REhiqDlaErC7T6WhXMaOFQHpUK1gQX7vpC/8Yns+Piv4COB9umJcOJperHT03DL6nMQuieBCxClFTGFtDmG00vy6bLG7mffL9Am5A17f26kzdISc8o0LqfFhR8g4xMFR725XCysdBdwRc2wKO7YFEN6qqzgCSVWYj8IQGLECVZIz88TatRNyWV5IwUgi4FFWj17epWopKFEXcTU/nrQkyB1p0lLSOTX4PVt4OyblPphEoFRx+vyuw5HJTq20ySyixE/pCARYiSTGmAot0kTS/LmtDVJKUnFVj1+ko9+ns8TnEupMG3e8L+5d/4FKzNDOnoaqO7gq8dgNiLYGAKDQdrdksqsxD5QwIWIUo6py60L+9G1bR07qfGseXKlgKtvr+Her2eE5H3+ScmvkDrhifrBvVrYouRvlJ3BR973LtS/x0wUc/pIqnMQuQfHQ6VL/oyMzNJTS16U4WL0s3AwAClUocfpM9SKNDvMJXBa7sz3bocged+obdjb5R6+VjnUypZGOPtUok/zsew6th1pvSoVyD1AkTcSeTwlXvoKdD09OjEvQj1VPwAnh9odksqsxD5p9QELKmpqVy7do3MzMzCbooQz7GyssLGxib/BmhWa0wP27YsSjzFzaRYdkftxtveO3/qysZAz+r8cT6G307f4uuOTpgbGxRIvauPqbOT2jpVpFpZHd6eyVrk0LEjWDsAksosRH4rFQGLSqUiOjoapVKJra0tenpyJ0wUDSqVikePHhEbGwtA5cqV862uMu0m4bPSi5+szFl26ns6VO9QYBkszWqVp2YFU67eecjmM7cY1Mw+3+tMSs1gwyn1WkY6TWVOegBnVqsfSyqzEAWmVAQs6enpPHr0iCpVqlCmjAyCE0WLiYkJALGxsVSsWDH/bg+Vq0H/2j1Z9u8OLibe4GT0cZpUaZo/dT1DoVAwqGl1Jm4NZdWxKAY21fHU+NnYevY28cnp2JUrQ6vaFXRX8OkVkPYQKrpAjdaApDILURBKRVdDRoZ6/gdDQ8NCbokQ2csKpNPS0vK1nnJtvqVHkrqOgGPT87WuZ/VsWA0TAyWX/k3gRGT+zgejUqlYcSwSgAGedujp6SiAyEiH4F/Uj5t+CI8DE0llFiL/lYqAJYt86xFFVYH9bZYph2/dgeipVBxKuMql2HMFUy9gaWJA9/pVgCeZO/nl7M04LtyKx1Bfjz6NdTj49Z+tEHcDylhDvT6a3VmpzD0cekgqsxD5pFQFLEIIsH1jNO0eJ8stPziuQOvOGkvy14Vo7iSk5Fs9K4+qA6KubpUpZ6rDntWsVZmbvAsGxsAzqcxOksosRH6RgEWI0sbAmCH13gXgz4QrxNwJLbCqXata0sDOirQMFetO3siXOu4/TGXbudvAk6UBdOLmKbhxHJSG0Phdze6nU5ntLHSYOi2E0CIBi3iOQqFg8+bNhd0MkY9cPT/FI0OfdIWClX9/XaB1D/RUBxGrj10nI1Ol8/I3nLpJSnomLlUsqG9rpbuCsyaKc+0N5pUASWUWoiBJwFJEKRSKl25+fn75Vnd0dDSdOnXKt/KfNXz4cJRKJUFBBbvOTammp8cQt+EAbHh4lbjokAKruotbZazKGHA7Lpm9/8TqtOzMTBWrjqtvBw3SZSZS3C0I3ax+3PTJRHGSyixEwZGApYiKjo7WbPPnz8fCwkJr3/fff691vi6zS2xsbDAyMtJZeS/z6NEj1q5dy1dffcXSpUsLpM6XKU0zIbdo9AG1MeKRnh7r935VYPUaGyjp93ggrK7XFzp05S7X7z3C3Fiftx4P8NWJE4shMx2qvwGV3QFJZRaioJXKgEWlUvEoNb1QNpUqZ13gNjY2ms3S0hKFQqH5OTk5GSsrK9atW0ebNm0wNjZm1apV3Lt3j/79+1OtWjXKlClDvXr1WLNmjVa5bdq04ZNPPmH06NGUK1cOGxsbJkyYoHXO07eEIiMjUSgUbNq0CS8vL8qUKYO7uztHjx7Vumbx4sXY2tpSpkwZ3n77bebNm4eVldUrn+f69etxdnZmzJgxHD58mMjISK3jKSkpjB49GltbW4yMjKhdu7ZWYHPx4kW6dOmChYUF5ubmtGzZkoiICM1z/eyzz7TK69Gjh1bvlL29PVOmTMHPzw9LS0uGDRsGwNdff42joyNlypShZs2a+Pv7PxcUbtmyhcaNG2NsbIy1tTU9e/YEYNKkSdSr9/z0840aNWLcuIId5PoyCoWCIW7q57sq+RYp1w4WWN3veNqhUMD+8Dtcv/dQZ+VmZR/1blSNMoY6mmYq9RGcXKZ+3GyEZrekMgtRsErFxHHPSkrLwHncjkKpO3SSt87eSL/++mvmzp3LsmXLMDIyIjk5mUaNGvH1119jYWHB9u3bGTRoEDVr1sTT01Nz3fLlyxk1ahTHjx/n6NGj+Pn50aJFC9q3b//CusaOHcucOXOoXbs2Y8eOpX///ly5cgV9fX0OHz7MBx98wMyZM3nrrbfYvXs3/v7+OXoOS5cuZeDAgVhaWtK5c2eWLVvGxIkTNccHDx7M0aNHWbBgAe7u7ly7do27d+8CcOvWLVq1akWbNm3Yu3cvFhYWHD58mPT09Fy9jrNnz8bf359vv/1Ws8/c3JzAwECqVKnC+fPnGTZsGObm5owePRqA7du307NnT8aOHcvKlStJTU1l+/btAAwdOpSJEydy4sQJmjRpAsC5c+c4c+YM69evz1Xb8ltH96EsOL+YGP0Utu39hl5DD2nmFslP1cub0qp2BfaH32H18Sj+r3Pd1y7z1oMk9oT9C8AATx0Otj27BpIfQFl79VT8j0kqsxAFq1QGLCXFZ599pvlWn+XLL7/UPB45ciR//fUX69ev1wpY3NzcGD9+PAC1a9fmxx9/ZM+ePS8NWL788ku6dOkCwMSJE3FxceHKlSs4OTnxww8/0KlTJ03djo6OHDlyhG3btr20/ZcvX+bYsWNs2rQJgIEDB/LJJ58wfvx49PT0CA8PZ926dezatYt27doBULNmTc31//vf/7C0tCQoKAgDAwNN3bnVtm1brdcN0Ape7O3t+eKLL1i7dq0mYJk6dSo+Pj5awZW7u/pWQbVq1fD29mbZsmWagGXZsmW0bt1aq/1FgYGeAYNchzD7/E8EZt7j7Yu/oefa89UX6sCgptXZH36HdSdvMKq9I8YGrzfD75rjUWSqoHmt8jhUNNNNIzMzn6wb5PkhPF4wUlKZhSh4pTJgMTFQEjqp4BZ+e7ZuXWncuLHWzxkZGcyYMYO1a9dy69YtUlJSSElJwdTUVOs8Nzc3rZ8rV66sWcvmRZ6+Jmu9m9jYWJycnLh06RJvv/221vkeHh6vDFiWLl2Kt7c31tbWAHTu3Jl3332X3bt306FDB0JCQlAqlbRu3Trb60NCQmjZsqUmWMmrZ19HgA0bNjB//nyuXLlCYmIi6enpWFhYaNWddfsoO8OGDWPo0KHMmzcPpVLJ6tWrmTt37mu1M7/0qufHTxeWEmkIfx+YyJtOXUE//2eF9nKqSFUrE249SGL7uWh6NaqW57JS0zMJOqFOk9ZpKnPEHrgbDkYW0GCAZrekMgtR8ErlGBaFQkEZQ/1C2XQ5MO/ZQGTu3Ll89913jB49mr179xISEoK3t/dzA0mf/YBXKBSvXMX66WuynkPWNSqV6rnn9aqxOhkZGaxYsYLt27ejr6+Pvr4+ZcqU4b///tOMUclaY+dFXnVcT0/vuXZkNzj52dfx2LFj+Pj40KlTJ7Zt28aZM2cYO3as1uv4qrq7deuGkZERv/32G1u3biUlJYVevXq99JrCYmpgSr/HvQTLlElwMqBA6lXqKXjHU/1h/7oz3+64GMPdxBQqmhvRzrmSLpqnlpXK3GAQGJkDksosRGEplQFLSXXw4EG6d+/OwIEDcXd3p2bNmly+fDnf63VyciI4OFhr38mTJ196zR9//EFCQgJnzpwhJCREs61fv57Nmzdz79496tWrR2ZmJvv378+2DDc3Nw4ePPjCDKkKFSoQHR2t+TkjI4MLFy688vkcPnyY6tWrM3bsWBo3bkzt2rW5fl37A9XNzY09e/a8sAx9fX18fX1ZtmwZy5Ytw8fHp0gvvDmg3hAMFErOGhtx5shs9YrEBaBvY1sMlApCbjzgwq24PJeTFfD097DDQKmjt7XYMIjYCwo98Byu2S2pzEIUDglYShAHBwd27drFkSNHCAsL4/333ycmJibf6x05ciR//PEH8+bN4/Lly/z888/8+eefL+1NWrp0KV26dMHd3R1XV1fN1qtXLypUqMCqVauwt7fH19eXoUOHsnnzZq5du8a+fftYt24dAB9//DHx8fH4+Phw8uRJLl++zMqVK7l06RKgHpuyfft2tm/fzj///MOIESN48ODBK5+Pg4MDUVFRBAUFERERwYIFC/jtt9+0zhk/fjxr1qxh/PjxhIWFcf78eWbNmqV1znvvvcfevXv5888/GTp0aC5f1YJlbWLNW7W6AxBgDByeXyD1VjA3opOr+hZjXlOcw/9NIPjafyj1FPT30OHtmaxp+J26qAfcIqnMQhQmCVhKEH9/fxo2bIi3tzdt2rTBxsaGHj165Hu9LVq04KeffmLevHm4u7vz119/8fnnn2NsbJzt+f/++y/bt2/P9haJQqGgZ8+emttCixYtonfv3owYMQInJyeGDRvGw4fqNNjy5cuzd+9eEhMTad26NY0aNWLx4sWa21dDhw7F19eXwYMH07p1a2rUqIGXl9crn0/37t35/PPP+fjjj6lfvz5Hjhx5LuupTZs2rF+/ni1btlC/fn3atm3L8ePHtc6pXbs2zZs3p06dOlqDnosqX1c/FCjYZ1qGqyd/gbibBVLvoGbqMSebQ24Rl5T7+YSyAp0OzpWwscz+by7XHt6Dc2vVj5t+pNktqcxCFB6FKqcTgxRx8fHxWFpaEhcXpzU4EiA5OZlr165Ro0aNF36ICt0aNmwY//zzDwcPFtzcHkWNSqXCycmJ999/n1GjRr303KLyN/rZ35+xJ2oPbyckMqmqN7z9U77XqVKp6Dj/IJf+TWBcV2eGvlEjx9cmpqTTdNoeElPSWf2eJy0crHXTqAOzYe8UqFwfhu/TpHp/uPtDDt06xMC6A/nao2CXNBCipHrZ5/fTpIdF6MScOXM4e/YsV65c4YcffmD58uX4+voWdrMKTWxsLPPmzePWrVsMGTKksJuTY34ufgBsNTMl9sI6iD6X73UqFAoGPu5lWXX8eo4nVwTYfOYWiSnp1KxgSvNa5XXToPRUCF6iftx0hCZYkVRmIQqXBCxCJ4KDg2nfvj316tXjp59+YsGCBbz33nuF3axCU6lSJWbMmMEvv/xC2bJlC7s5OVa/Yn0aVmxIukLBagsz2OUPBdAJ+3aDqpgaKrl65yFHI+7l6BqVSqW5HTTQU4frBoVuhsQYMLMBlyfp+pLKLEThKpXzsAjdyxoIK9SK853WIa5DOL33NOsszBl2bT9mV/ZA7Xb5WqeZkT5vN6zKqmNRrDx2neY5uLVz6vp9/olJwNhA77XmcNGiUsHR/6kfe7ynmY9GUpmFKHzSwyKE0NKqWitqWtYkUU+PDeaPe1kyM/K93oGPJ3zbGfovMXHJrzw/K5W5u3tVLE1eb/JAjahjEB0C+sbQ6Elml6QyC1H4JGARQmjRU+hpxrKstLIkLTYUQn7N93qdbCzwsC9HRqaKNcFRLz33bmIKf5xXz7GTlWWkE8ce96649QNT9ZgYSWUWomiQgEUI8ZwuNbtQwaQCsUo9/jAzhb+nQqruVlV+kazBt0EnokjLePHsy+tO3iAtQ0V9Wytcq1rqpvL7kfCPegFLmn6o2X3k9hFJZRaiCJCARQjxHEOlIQOdBwIQWK48mQnRcHRhvtfb0cUGazND/o1PYXfov9mek5GpYvUxdQ/MQF2uGxS8GFSZUKstVHyyevTqsNWArMosRGGTgEUIka0+jn0wNTDlihIOmRirZ79NfPkima/LUF+Pfk1sgRevL7TvUiy3HiRhVcaArm6VdVNxSgKcXqF+3HSEZrekMgtRdEjAIoTIlrmhOX0d+wIQULEKpCbCvhn5Xm9/Dzv0FHAk4h5XYhOfO56Vyty3sS3Gulr9/MxqSIkHa0eo9aZmt6QyC1F0SMAiAAgMDMTKyqqwmyGKmAF1B6Cvp88pvXTOGRnCqUC4m78LalYrW4a2ThUBWH1cu5cl6t4j9oXfAeAdXa0blJkBxx+vG+T5Aeip3xYllVmIokUCliJKoVC8dPPz88tz2fb29syfP19rX79+/QgPD3+9RudCUlISZcuWpVy5ciQlJRVYvSJ3KplWokuNLgAEVq0NqgzYPSHf680am7Lh1E0epaZr9q8Ovo5KBa0cK2BvbaqbysL/Ug+4NbYCdx/NbkllFqJokYCliIqOjtZs8+fPx8LCQmvf999/r9P6TExMqFixok7LfJmNGzfi6uqKs7MzmzZtKrB6s6NSqUhPT3/1iaVUVorzblUi1w0M4Z9tcP1IvtbZqnYF7MqVISE5nS0htwFITstg3YkbAAzS5WDbrFWZGw8BQ3UQJKnMQhQ9pTNgUanUKZqFseVwBlQbGxvNZmlpiUKh0Np34MABGjVqhLGxMTVr1mTixIlaH7oTJkzAzs4OIyMjqlSpwieffAKoVxm+fv06n3/+uaa3Bp6/JTRhwgTq16/PypUrsbe3x9LSEh8fHxISEjTnJCQkMGDAAExNTalcuTLfffcdbdq04bPPPnvl81u6dCkDBw5k4MCBmpWZn3bx4kW6dOmChYUF5ubmtGzZkoiICM3xgIAAXFxcMDIyonLlynz88ccAREZGolAoCAkJ0Zz74MEDFAoF+/btA2Dfvn0oFAp27NhB48aNMTIy4uDBg0RERNC9e3cqVaqEmZkZTZo0Yffu3VrtSklJYfTo0dja2mJkZETt2rVZunQpKpUKBwcH5syZo3X+hQsX0NPT02p7ceNQ1oHW1VqjQsXyWg3VO3fm75T9enoKBjZV3/JZeUy9vtCfF6K5/yiNqlYmmltGry36HEQeBD19aDJMs1tSmYUoevI0Nf/ChQuZPXs20dHRuLi4MH/+fFq2bPnC8//3v//x448/EhkZiZ2dHWPHjmXw4MGa44sXL2bFihVcuHABgEaNGjFt2jQ8PDzy0rxXS3sE06rkT9mv8n+3Nd/i8mrHjh0MHDiQBQsWaD7Ihw8fDsD48ePZsGED3333HUFBQbi4uBATE8PZs2cB2LRpE+7u7gwfPpxhw4a9rBoiIiLYvHkz27Zt4/79+/Tt25cZM2YwdepUAEaNGsXhw4fZsmULlSpVYty4cZw+fZr69eu/styjR4+yadMmVCoVn332GVevXqVmzZoA3Lp1i1atWtGmTRv27t2LhYUFhw8f1gRkixYtYtSoUcyYMYNOnToRFxfH4cOHc/06jh49mjlz5lCzZk2srKy4efMmnTt3ZsqUKRgbG7N8+XK6devGpUuXsLNTf3gOHjyYo0ePsmDBAtzd3bl27Rp3795FoVAwdOhQli1bxpdffqmpIyAggJYtW1KrVq1ct68o8XPxY//N/fyedocRRmZY3zoJF38D1575VmefRrbM2RnOxdvxhNx4wMqj6vEs/T1sUerpqMcjq3fFuQdYVtXsllRmIYqeXAcsa9eu5bPPPmPhwoW0aNGCn3/+mU6dOhEaGqp5U3/aokWLGDNmDIsXL6ZJkyYEBwczbNgwypYtS7du3QD1N97+/fvTvHlzjI2NmTVrFh06dODixYtUrVr1uTJLu6lTp/LNN99oVkOuWbMmkydPZvTo0YwfP56oqChsbGxo164dBgYG2NnZaYK/cuXKoVQqMTc3x8bG5qX1ZGZmEhgYiLm5OQCDBg1iz549TJ06lYSEBJYvX86vv/7Km2+qsyqWLVtGlSqvDgQDAgLo1KmTZlHAjh07EhAQwJQpUwB1gGtpaUlQUBAGBuop1x0dHTXXT5kyhS+++IJPP/1Us69JkyY5eu2eNmnSJNq3b6/5uXz58ri7u2vV89tvv7FlyxY+/vhjwsPDWbduHbt27aJdO/XaOllBFsCQIUMYN24cwcHBeHh4kJaWxqpVq5g9e3au21bUNKrUCDdrN87dPceauq0YGfIH7JkITl1A3yhf6ixrakhXt8psOn2LCVtDOXvjAQZKBX0fpz2/toR/4cIG9WNJZRaiyMt1wDJv3jzeffddzUq88+fPZ8eOHSxatIjp06c/d/7KlSt5//336devH6B+gz927BgzZ87UBCyrV6/Wumbx4sVs2LCBPXv2aPXE6IxBGXVPR2HQwbe1U6dOceLECU1PB0BGRgbJyck8evSIPn36MH/+fGrWrEnHjh3p3Lkz3bp1Q18/d79ue3t7TbACULlyZWJj1fNwXL16lbS0NK1eMEtLS+rUqfPSMjMyMli+fLnWGJyBAwfy+eefM3HiRJRKJSEhIbRs2VITrDwtNjaW27dva4Kk19G4cWOtnx8+fMjEiRPZtm0bt2/fJj09naSkJKKi1JOUhYSEoFQqad26dbblVa5cmS5duhAQEICHhwfbtm0jOTmZPn36vHZbC5tCoWCI6xA+3/c5QUnXedesEmXuR8KJpdBsxCuvz6tBTauz6fQtzt54AEBH18pUNDfWTeEnl0JGKlTzgGqNNLsllVmIoilXY1hSU1M5deoUHTp00NrfoUMHjhzJfhBeSkoKxsbabzAmJiYEBweTlpaW7TWPHj0iLS2NcuXKvbAtKSkpxMfHa205plCob8sUxqaDwXuZmZlMnDiRkJAQzXb+/HkuX76MsbExtra2XLp0if/973+YmJgwYsQIWrVq9cLX+0WeDRgUCgWZmerp0rNWI352MOKrVinesWMHt27dol+/fujr66Ovr4+Pjw83b95k586dgPrv40VedgxA73FK6tPteNHzNjXVvjX31VdfsXHjRqZOncrBgwcJCQmhXr16pKam5qhugPfee4+goCCSkpJYtmwZ/fr1o0yZknFLwcvWi+oW1YlPTWBTvY7qnQdmQdKDfKuzvq0VLlUsND8P9NRRAJGWrA62QCvg0kpldpJUZiGKklwFLHfv3iUjI4NKlSpp7a9UqRIxMTHZXuPt7c2SJUs4deoUKpWKkydPEhAQQFpaGnfv3s32mm+++YaqVatqut2zM336dCwtLTWbra2OuomLgYYNG3Lp0iUcHBye27I+sE1MTHjrrbdYsGAB+/bt4+jRo5w/fx4AQ0NDMjJeb/XdWrVqYWBgQHBwsGZffHw8ly+/fI6OpUuX4uPjoxVshYSEMGDAAM3gWzc3Nw4ePJhtoGFubo69vT179uzJtvwKFSoA6iyrLE8PwH2ZgwcP4ufnx9tvv029evWwsbEhMjJSc7xevXpkZmayf//+F5bRuXNnTE1NWbRoEX/++SdDhw594bnFjVJPia+L+jbkioQw0irUgaT7cHBuvtWpUCjwbWYPgJONOR41XvwlJlfOr4dHd8HSFpy6aXZrpTJXkVRmIYqSPA26ze5b9YvS/vz9/YmJiaFp06aoVCoqVaqEn58fs2bNQql8fpbKWbNmsWbNGvbt2/dcz8zTxowZw6hRozQ/x8fHl5qgZdy4cXTt2hVbW1v69OmDnp4e586d4/z580yZMoXAwEAyMjLw9PSkTJkyrFy5EhMTE6pXV6eC2tvbc+DAAXx8fDAyMsLa2jrXbTA3N8fX15evvvqKcuXKUbFiRcaPH4+ent4L/xbu3LnD1q1b2bJlC66urlrHfH196dKlC3fu3OHjjz/mhx9+wMfHhzFjxmBpacmxY8fw8PCgTp06TJgwgQ8++ICKFSvSqVMnEhISOHz4MCNHjsTExISmTZsyY8YM7O3tuXv3Lt9++22OnpODgwObNm2iW7duKBQK/P39NT1KWa+br68vQ4cO1Qy6vX79OrGxsfTtq54RVqlU4ufnx5gxY3BwcKBZs5L1ofdWrbf48cyPRD+MYUf9d+i6awYc/xk8hoFV/tw+6d2oGko9BY2ql9VNerFK9WSwrcdwUKrfBiWVWYiiLVc9LNbW1iiVyud6U2JjY5/rdcliYmJCQEAAjx49IjIykqioKM3YiGc/KOfMmcO0adPYuXMnbm5uL22LkZERFhYWWltp4e3tzbZt29i1axdNmjShadOmzJs3TxOQWFlZsXjxYlq0aIGbmxt79uxh69atlC9fHlAPNo2MjKRWrVqaHom8mDdvHs2aNaNr1660a9eOFi1aULdu3RcGmitWrMDU1DTb8SdeXl6Ym5uzcuVKypcvz969e0lMTKR169Y0atSIxYsXa25R+fr6Mn/+fBYuXIiLiwtdu3bV6tnJ6sFr3Lgxn376qWYw76t89913lC1blubNm9OtWze8vb1p2LCh1jmLFi2id+/ejBgxAicnJ4YNG8bDh9qrGL/77rukpqaWqN6VLEZKIwbUHQBA4L1TqOzfgIwU2DM53+rU01PQq1E13U0Ud20/xF4EA1NoOEizW1KZhSjiVLnk4eGh+vDDD7X21a1bV/XNN9/kuIxWrVqp+vfvr7Vv1qxZKgsLC9XRo0dz2ySVSqVSxcXFqQBVXFzcc8eSkpJUoaGhqqSkpDyVLXImMTFRZWlpqVqyZElhN6VQHTp0SKWvr6+KiYnJ8TXF6W/0QfIDVZNVTVSuga6qw+eWq1TjLdTbrdOF3bScWd1X3d5tX2jt/mDXByrXQFfVjOMzCqlhQpROL/v8flquJ44bNWoUS5YsISAggLCwMD7//HOioqL44IMPAPWtmqcze8LDw1m1ahWXL18mODgYHx8fLly4wLRp0zTnzJo1i2+//ZaAgADs7e2JiYkhJiaGxMTnFz4TRceZM2dYs2YNERERnD59mgED1N+8u3fvXsgtKxwpKSlcuXIFf39/+vbt+8Jex+LO0siSXrV7ARAQfQDqPc6CyufJ5HTi7hX1VPwATT/U7JZUZiGKvlwHLP369WP+/PlMmjSJ+vXrc+DAAf744w/N7Yjo6GhNGiio01jnzp2Lu7s77du3Jzk5mSNHjmBvb685Z+HChaSmptK7d28qV66s2Z6dNVQUPXPmzMHd3Z127drx8OFDDh48mKcxMSXBmjVrqFOnDnFxccyaNauwm5OvBjkPQqlQcjz6OKEN+4PSUD1j7OVdhd20lzv+k/pfx45Q/slkfpLKLETRp1CpivpXopyJj4/H0tKSuLi458azJCcnc+3aNWrUqPHSgbxCFJbi+Df6zcFv2H51O53sOzErxRCO/AAV6sIHhzQDWYuUpPswz1k90/XgLVBTPZ9OYmoi7Ta042HaQ35u9zPNqzYv5IYKUbq87PP7aaVzLSEhxGsb4jIEgB3Xd3CzQX/1asd3wiBk9csvLCynV6iDlYouUKOVZrekMgtRPEjAIoTIkzrl6tCiSgsyVZmsuPo7tB6tPvD3NPVCn0VJRjoc/0X9uOmHmgkcJZVZiOJDAhYhRJ4NcVX3svx2+Tfu1+sNVtUhMQaO/FjILXtG2BaIvwllrJ8MEkZSmYUoTiRgEULkmYeNB3XL1SU5I5mgK5ug3Xj1gcPfqxcXLCqyJopr8i4YPBkjJKsyC1F8SMAihMgzhULBUFf1BHlr/llDUp1OULURpD2Efc8vhloobp6Em8HqTKbG72p2SyqzEMWLBCxCiNfSrno7qppV5X7KfX6P2AIdHs8sfHoF3LlUuI0DOLZQ/a9rbzB/MjeOpDILUbxIwCKeY29vz/z583V+riiZ9PX0NYsiLr+4nHRbD6jTBVQZsGt84TYu7hZc3Kx+/NREcbIqsxDFjwQsRZifnx8KhQKFQoGBgQE1a9bkyy+/fG7tGl07ceIEw4cP1/m5r+Pnn3/G3d0dU1NTrKysaNCgATNnzsz3ekXO9HDogZWRFTcTb7I7aje0mwAKJYT/CZGHCq9hwb+oAyf7llD5yfpkksosRPEjAUsR17FjR6Kjo7l69SpTpkxh4cKFfPnll9mem5aWppM6K1SoQJkyORuAmJtz82rp0qWMGjWKTz75hLNnz3L48GFGjx6dr0s36Oq1LC1M9E0040CWXViGyro2NFL3urDzW3hq1esCk/oQTgWqHz/VuyKpzEIUT6UyYFGpVDxKe1QoW24nFjYyMsLGxgZbW1veeecdBgwYwObNmwGYMGEC9evXJyAggJo1a2JkZIRKpSIuLo7hw4dTsWJFLCwsaNu2LWfPntUqd8uWLTRu3BhjY2Osra3p2bOn5tizt3kmTJiAnZ0dRkZGVKlShU8++eSF50ZFRdG9e3fMzMywsLCgb9++/Pvvv1pl1a9fn5UrV2Jvb4+lpSU+Pj4kJCS88DXYunUrffv25d1338XBwQEXFxf69+/P5MnaKwQHBATg4uKCkZERlStX5uOPP851u/LyWgo1HycfjJXGhN4L5UTMCWgzBgzN4PYZuLip4Bt0NgiSH0DZGuqp+B+TVGYhiqciOH92/ktKT8LzV89Cqfv4O8dfK33SxMRE69v/lStXWLduHRs3bkSpVALQpUsXypUrxx9//IGlpSU///wzb775JuHh4ZQrV47t27fTs2dPxo4dy8qVK0lNTWX79u3Z1rdhwwa+++47goKCcHFxISYm5oUf2CqVih49emBqasr+/ftJT09nxIgR9OvXj3379mnOi4iIYPPmzWzbto379+/Tt29fZsyYwdSpU7Mt18bGhv3793P9+nXNmlXPWrRoEaNGjWLGjBl06tSJuLg4Dh8+nKt25eW1FE+UMy5HD4ceBF0KIuBiAB7tfoIWn8LfU2HPRKjbDfSNCqYxmZlPUpk9PwA9peaQpDILUTyVyoCluAoODubXX3/lzTff1OxLTU1l5cqVVKhQAYC9e/dy/vx5YmNjMTJSfzjMmTOHzZs3s2HDBoYPH87UqVPx8fFh4sSJmnLc3d2zrTMqKgobGxvatWuHgYEBdnZ2eHh4ZHvu7t27OXfuHNeuXcPW1haAlStX4uLiwokTJ2jSpAkAmZmZBAYGYm5uDsCgQYPYs2fPCwOW8ePH07NnT+zt7XF0dKRZs2Z07tyZ3r17o6en7iScMmUKX3zxBZ9++qnmuqz6ctquvLyWQttgl8GsC1/H4VuHufTfJeo0+whOLIUHURC8GJp//OpCdCFiD9y7DEYW0GCAZrekMgtRfJXKgMVE34Tj7xwvtLpzY9u2bZiZmZGenk5aWhrdu3fnhx9+0ByvXr265gMW4NSpUyQmJlK+fHmtcpKSkoiIiAAgJCSEYcOG5aj+Pn36MH/+fGrWrEnHjh3p3Lkz3bp1Q1//+T+dsLAwbG1tNUEBgLOzM1ZWVoSFhWkCA3t7e02wAlC5cmViY2Nf2IbKlStz9OhRLly4wP79+zly5Ai+vr4sWbKEv/76i7t373L79m2tQC4v7crLaym02Zrb0r56e3ZE7iDwYiDTW06HtmNhy0g4MFsdPJiUzf+GHP2f+t+Gg8Hoyd+apDILUXyVyoBFoVAUm65gLy8vFi1ahIGBAVWqVMHAwEDruKmpqdbPmZmZVK5cWetWRxYrKytAfVspp2xtbbl06RK7du1i9+7djBgxgtmzZ7N///7n2qJSqbIdwPjs/mevUygUZOZgUKarqyuurq589NFHHDp0iJYtW7J//34aN2780uty2q68vJbieUNchrAjcgd/XfuLTxp8QuX6A9S3Z2JD4cAc8M6+J01nYsPg6t+g0AOPJ71gksosRPFWKgfdFiempqY4ODhQvXr15z7os9OwYUNiYmLQ19fHwcFBa7O2tgbAzc2NPXv25LgNJiYmvPXWWyxYsIB9+/Zx9OhRzp8//9x5zs7OREVFcePGDc2+0NBQ4uLiqFu3bo7rywlnZ2cAHj58iLm5Ofb29i98TnltV05eS/E8F2sXPG08SVelszJspXr8SPtJ6oPBv8D9yPxtQNbYFaeuUPbJmCdJZRaieJOApYRp164dzZo1o0ePHuzYsYPIyEiOHDnCt99+y8mTJwH1mJA1a9Ywfvx4wsLCOH/+PLNmzcq2vMDAQJYuXcqFCxe4evUqK1euxMTEJNvBr+3atcPNzY0BAwZw+vRpgoODGTx4MK1bt35lL8jLfPjhh0yePJnDhw9z/fp1jh07xuDBg6lQoQLNmqk/eCZMmMDcuXNZsGABly9f5vTp05pbZ3ltV05eS5G9rEURN4RvIC4lDhzaQY3WkJEKeya/4urX8PAenFurftx0hGa3pDILUfxJwFLCKBQK/vjjD1q1asXQoUNxdHTEx8eHyMhIKlVST0vepk0b1q9fz5YtW6hfvz5t27bl+PHsx/RYWVmxePFiWrRooemZ2bp163PjOrLq3rx5M2XLlqVVq1a0a9eOmjVrsnbt2td6Tu3atePYsWP06dMHR0dHevXqhbGxMXv27NG0w9fXl/nz57Nw4UJcXFzo2rUrly9ffq125eS1FNlrXqU5jmUdSUpPYt2ldaBQQIfJgAIubIBbp/On4lMBkJ4MleuDXVPNbkllFqL4U6hyOzFIERUfH4+lpSVxcXFYWFhoHUtOTubatWvUqFEDY2PjF5QgROEpiX+jWyO28n+H/o/yxuXZ0XsHRkoj2PQ+nAuC6m+A3zZ1IKMr6akwvx4kxkDPxeDWV3Pow90fcujWIQbWHcjXHl/rrk4hxGt72ef306SHRQiRLzrW6IiNqQ33ku+xNWKremfbb0FpBNcPQfhfuq3w4m/qYMXMBpx7aHZLKrMQJYMELEKIfGGgZ8Bg58GAelHEjMwMsLJ9Mk3+rnGQka6bylQqOPY4ldljGOgbag4FXQoCJJVZiOJOAhYhRL7pVbsXFoYWRMZHsu/GPvXONz4Hk3JwNxzOrNBNRVFHIfos6BtDoyGa3YmpiWy+shmQVGYhijsJWIQQ+aaMQRn61ekHQMDFAPVaWiZW0Hq0+oS/p0PKi9eRyrFjC9X/uvUD0ycDwiWVWYiSQwIWIUS+eqfuOxjqGXLuzjnOxJ5R72z8rnpRwoexcOSHlxfwKvcj4Z/Ha2FJKrMQJZYELEKIfGVtYs1bDupU4mUXlql36htCu/Hqx0d+gISYvFdw/BdQZUKttlDRSbNbUpmFKFkkYBFC5DtfZ18UKNh3cx8RDx6vw+TcA6o1gbRH8Pe0vBWcHA+nH4+DafqR1iFZlVmIkkUCFiFEvrO3tKetXVsAAi8GqncqFNBhivrxmZXqNYByK2Q1pCaAtaO6h+UxSWUWouSRgEW8kr29PfPnz9f8nDVzrBC5kTVd/7ar24h99Hh1brum6jV/VJmwa3zuCszMgOM/qR97fgB6T97OJJVZiJJHApYizM/PD4VCgUKhQF9fHzs7Oz788EPu379f2E17qdjYWN5//33s7OwwMjLCxsYGb29vjh49WthNE4XIvYI7DSs2JD0znVVhq54caDcR9PTh8g64diDnBV76Uz3g1tgK3J/0okgqsxAlkwQsRVzHjh2Jjo4mMjKSJUuWsHXrVkaMGPHqCwtRr169OHv2LMuXLyc8PJwtW7bQpk0b/vvvv3yrMzU1Nd/KFroz1HUoAOsvrSch9XE6s7XDk7lTdn4LmZk5KyxrVebGQ8DwyRgVSWUWomSSgKWIy+qhqFatGh06dKBfv37s3LlT65xly5ZRt25djI2NcXJyYuHChVrHb968iY+PD+XKlcPU1JTGjRtrFjuMiIige/fuVKpUCTMzM5o0acLu3bvz3N4HDx5w6NAhZs6ciZeXF9WrV8fDw4MxY8bQpUsXrfOGDx9OpUqVMDY2xtXVlW3btmmOb9y4ERcXF4yMjLC3t2fu3Lla9djb2zNlyhT8/PywtLRk2LBhABw5coRWrVphYmKCra0tn3zyCQ8fPszz8xG61bJaS2pZ1iIxLZEN4RueHGjzDRiaqyd/u7DhxQVkiT6rnt5fTx+aDNPsllRmIUquUhmwqFQqMh89KpTtddaavHr1Kn/99RcGBgaafYsXL2bs2LFMnTqVsLAwpk2bhr+/P8uXLwcgMTGR1q1bc/v2bbZs2cLZs2cZPXo0mY+/xSYmJtK5c2d2797NmTNn8Pb2plu3bkRFReWpjWZmZpiZmbF582ZSUlKyPSczM5NOnTpx5MgRVq1aRWhoKDNmzECpVAJw6tQp+vbti4+PD+fPn2fChAn4+/sTGBioVc7s2bNxdXXl1KlT+Pv7c/78eby9venZsyfnzp1j7dq1HDp0iI8//jhPz0Xonp5CD18XXwBWha4iLSNNfcDUGt74TP14zyRIS355QVm9K849wLKqZrekMgtRcukXdgMKgyopiUsNGxVK3XVOn0JRJucpltu2bcPMzIyMjAySk9Vv4vPmzdMcnzx5MnPnzqVnz54A1KhRg9DQUH7++Wd8fX359ddfuXPnDidOnKBcuXIAODg4aK53d3fH3d1d8/OUKVP47bff2LJlS54+6PX19QkMDGTYsGH89NNPNGzYkNatW+Pj44ObmxsAu3fvJjg4mLCwMBwdHQGoWbOmpox58+bx5ptv4u/vD4CjoyOhoaHMnj0bPz8/zXlt27blyy+/1Pw8ePBg3nnnHT777DMAateuzYIFC2jdujWLFi0qMasgF3ddanbhxzM/EpsUy/Zr2+nh0EN9oOkIOLEU4m5A8M/Q4tPsC0j4F85veHLNUySVWYiSq1T2sBQnXl5ehISEcPz4cUaOHIm3tzcjR44E4M6dO9y4cYN3331X07NhZmbGlClTiIhQz3UREhJCgwYNNMHKsx4+fMjo0aNxdnbGysoKMzMz/vnnnzz3sIB6DEtWj463tzf79u2jYcOGmh6SkJAQqlWrpglWnhUWFkaLFi209rVo0YLLly+TkZGh2de4cWOtc06dOkVgYKDWa+Ht7U1mZibXrl3L8/MRumWoNGSg80AAAi8Ekql6PGbFsAy0Hat+fGAuPHrBmKcTSyAzDWw9odqTLx6SyixEyVYqe1gUJibUOX2q0OrODVNTU02PyIIFC/Dy8mLixIlMnjxZc1tn8eLFeHp6al2XdXvF5BX1ffXVV+zYsYM5c+bg4OCAiYkJvXv3fu1BrMbGxrRv35727dszbtw43nvvPcaPH4+fn98r26RSqZ4be5DdrTRTU1OtnzMzM3n//ff55JNPnjvXzk5SW4uS3o69+eXcL0TERXDw5kFa27ZWH3DvD0cXQuxFODAbOk7XvjAtGU4uVT/OWvX5MUllFqJkK50Bi0KRq9syRcn48ePp1KkTH374IVWqVKFq1apcvXqVAQMGZHu+m5sbS5Ys4b///su2l+XgwYP4+fnx9ttvA+oxLZGRkTpvt7Ozs2buFjc3N27evEl4eHi2vSzOzs4cOnRIa9+RI0dwdHTUBGLZadiwIRcvXtS65SWKJnNDc/o49mHZxWUEXAh4ErDoKaHDJFjVC4IXg8cwKPfkdiHn18Oje2BpC07dNLsllVmIkk9uCRUzbdq0wcXFhWnT1FOZT5gwgenTp/P9998THh7O+fPnWbZsmWacS//+/bGxsaFHjx4cPnyYq1evsnHjRs2cKA4ODmzatImQkBDOnj3LO++8o+m5yYt79+7Rtm1bVq1axblz57h27Rrr169n1qxZdO/eHYDWrVvTqlUrevXqxa5du7h27Rp//vknf/31FwBffPEFe/bsYfLkyYSHh7N8+XJ+/PFHrfEq2fn66685evQoH330ESEhIVy+fJktW7ZobqGJomVA3QHo6+lzOvY0Z++cfXLAoR3U9FLf9tkz+cl+lerJYFuP4aB88n1LUpmFKPkkYCmGRo0axeLFi7lx4wbvvfceS5YsITAwkHr16tG6dWsCAwOpUaMGAIaGhuzcuZOKFSvSuXNn6tWrp5WR891331G2bFmaN29Ot27d8Pb2pmHDhnlum5mZGZ6ennz33Xe0atUKV1dX/P39GTZsGD/++KPmvI0bN9KkSRP69++Ps7Mzo0eP1oxPadiwIevWrSMoKAhXV1fGjRvHpEmTtAbcZsfNzY39+/dz+fJlWrZsSYMGDfD396dy5cp5fj4i/1QyrUTXml0B9VgWLR0mAwq4uAluPr59e22/+laRgSk0HKw5VVKZhSgdFKrXybMtQuLj47G0tCQuLg4LCwutY8nJyVy7do0aNWpIpogokkrr32jEgwh6/N4DBQq29NiCvaX9k4O/fQhnfwW75jDkD/i1n3o2XI/h0Hm25rRDtw7x4e4PMTMwY0+fPZIdJEQx87LP76dJD4sQotDUsqpFm2ptUKFieehy7YNtvwV9Y4g6AkcWqIMVFOp1g54iqcxClA4SsAghCpWfqx8AW65s4W7S3ScHLKs+mWdl1zj1v44doXwtzSmSyixE6SEBixCiUDWs2BC3Cm6kZqbya9iv2gff+AzKlH/ys6QyC1FqScAihChUCoWCoS7qRRGDLgXxKO3Rk4PGltBmjPpxpXpQo5XmkKQyC1G6SMAihCh0bWzbYG9hT0JqAhsvb9Q+2OQ96LMc+v8KT2UASSqzEKWLBCxCiEKn1FMy2EWdqrwidAVpmWlPDioU4NIDrJ7c8pFUZiFKHwlYhBBFwlu13qKccTliHsawI3LHS8+VVZmFKH0kYBFCFAlGSiMG1lUvirjswrJs14/KIqnMQpQ+ErAIIYqMvnX6YqJvQvj9cI7cPpLtOZLKLETpJAGLEKLIsDSypLdjb0Ddy5IdSWUWonSSgKUI8/PzU68s/cx25coVAA4cOEC3bt2oUqUKCoVCsxryy2RkZDB9+nScnJwwMTGhXLlyNG3alGXLsv9wEKKgDao7CKVCyfGY41y8d1HrmKQyC1F6ScBSxHXs2JHo6GitLWthw4cPH+Lu7q61qOCrTJgwgfnz5zN58mRCQ0P5+++/GTZsGPfv38+vp0Bqamq+lS1KnspmlelUoxPw/KKIksosROklAUsRZ2RkhI2NjdaWtdJyp06dmDJlCj179sxxeVu3bmXEiBH06dOHGjVq4O7uzrvvvsuoUaM052RmZjJz5kwcHBwwMjLCzs6OqVOnao6fP3+etm3bYmJiQvny5Rk+fDiJiYma435+fvTo0YPp06dTpUoVHB0dAbh16xb9+vWjbNmylC9fnu7duxMZGfmar5Aoifxc/ADYeX0nNxJuAJLKLERpVyoDFpVKRVpKRqFshb04to2NDXv37uXOnTsvPGfMmDHMnDkTf39/QkND+fXXX6lUqRIAjx49omPHjpQtW5YTJ06wfv16du/ezccff6xVxp49ewgLC2PXrl1s27aNR48e4eXlhZmZGQcOHODQoUOYmZnRsWNH6YERz6lTrg4tqrYgU5XJiosrAEllFqK00y/sBhSG9NRMfvl0f6HUPfz71hgYKXN8/rZt2zAzM9P83KlTJ9avX5/n+ufNm0fv3r2xsbHBxcWF5s2b0717dzp1UnfBJyQk8P333/Pjjz/i6+sLQK1atXjjjTcAWL16NUlJSaxYsQJTU1MAfvzxR7p168bMmTM1gY2pqSlLlizB0NAQgICAAPT09FiyZInmm/GyZcuwsrJi3759dOjQIc/PSZRMQ12GcvjWYTZf2cyI+iMklVmIUq5UBizFiZeXF4sWLdL8nBUk5JWzszMXLlzg1KlTHDp0SDNw18/PjyVLlhAWFkZKSgpvvvlmtteHhYXh7u6u1Y4WLVqQmZnJpUuXNAFLvXr1NMEKwKlTp7hy5Qrm5uZa5SUnJxMREfFaz0mUTE1smuBc3pnQe6HMPDFTUpmFKOVKZcCib6jH8O9bF1rduWFqaoqDg4NO26Cnp0eTJk1o0qQJn3/+OatWrWLQoEGMHTsWExOTl16rUqleOHbg6f3PBlaZmZk0atSI1atXP3ddhQoV8vAsREmnUCgY4jqEr/Z/xfar2wFJZRaiNCuVAYtCocjVbZmSztnZGVBnHdWuXRsTExP27NnDe++9l+25y5cv5+HDh5qg5PDhw+jp6WkG12anYcOGrF27looVK2JhYZE/T0SUOO3s2lHNrBo3E28CksosRGlWKgfdlhSJiYmEhIQQEhICwLVr1wgJCSEqKuqF1/Tu3ZvvvvuO48ePc/36dfbt28dHH32Eo6MjTk5OGBsb8/XXXzN69GhWrFhBREQEx44dY+nSpQAMGDAAY2NjfH19uXDhAn///TcjR45k0KBBmttB2RkwYADW1tZ0796dgwcPcu3aNfbv38+nn37KzZs3dfq6iJJDX08fXxf1WKoaljVoWqVpIbdICFFY8hSwLFy4kBo1amBsbEyjRo04ePDgS8//3//+R926dTExMaFOnTqsWLHiuXM2btyIs7MzRkZGODs789tvv+WlaaXKyZMnadCgAQ0aNABg1KhRNGjQgHHjxr3wGm9vb7Zu3Uq3bt1wdHTE19cXJycndu7cib6+usPN39+fL774gnHjxlG3bl369etHbGwsAGXKlGHHjh38999/NGnShN69e/Pmm2++ci6YMmXKcODAAezs7OjZsyd169Zl6NChJCUlSY+LeKk+jn2Y0GwC33t9j55CvmMJUWqpcikoKEhlYGCgWrx4sSo0NFT16aefqkxNTVXXr1/P9vyFCxeqzM3NVUFBQaqIiAjVmjVrVGZmZqotW7Zozjly5IhKqVSqpk2bpgoLC1NNmzZNpa+vrzp27FiO2xUXF6cCVHFxcc8dS0pKUoWGhqqSkpJy+3SFKBDyNyqEKK1e9vn9NIVKlbuJQTw9PWnYsKFW5krdunU1E4U9q3nz5rRo0YLZs2dr9n322WecPHmSQ4cOAdCvXz/i4+P5888/NedkzfWxZs2aHLUrPj4eS0tL4uLinvvGnpyczLVr1zS9QkIUNfI3KoQorV72+f20XPWvpqamcurUqefmzOjQoQNHjmS/smpKSspzb8AmJiYEBweTlpYGwNGjR58r09vb+4VlZpUbHx+vtQkhhBCiZMpVwHL37l0yMjKeG1xZqVIlYmJisr3G29ubJUuWcOrUKVQqFSdPniQgIIC0tDTu3r0LQExMTK7KBJg+fTqWlpaazdbWNjdPRQghhBDFSJ5GsD07D4fqJXNz+Pv706lTJ5o2bYqBgQHdu3fHz88PQLMmTm7LBPX08XFxcZrtxo0beXkqQgghhCgGchWwWFtbo1Qqn+v5iI2NfWFKq4mJCQEBATx69IjIyEiioqKwt7fH3Nwca2trQL2+TW7KBPWigBYWFlqbEEIIIUqmXAUshoaGNGrUiF27dmnt37VrF82bN3/ptQYGBlSrVg2lUklQUBBdu3ZFT09dfbNmzZ4rc+fOna8sM7dyOb5YiAKTmZlZ2E0QQogiLdcz3Y4aNYpBgwbRuHFjmjVrxi+//EJUVBQffPABoL5Vc+vWLc1cK+Hh4QQHB+Pp6cn9+/eZN28eFy5cYPny5ZoyP/30U1q1asXMmTPp3r07v//+O7t379ZkEb0uAwMDFAoFd+7coUKFCrIsvSgyVCoVqamp3LlzBz09Pa31l4QQQjyR64ClX79+3Lt3j0mTJhEdHY2rqyt//PEH1atXByA6OlprptWMjAzmzp3LpUuXMDAwwMvLiyNHjmBvb685p3nz5gQFBfHtt9/i7+9PrVq1WLt2LZ6enq//DFGPlalWrRo3b94kMjJSJ2UKoUtlypTBzs5O0+sohBBCW67nYSmqcpLHnZGRoUmlFqKoUCqV6OvrS8+fEKJUyuk8LKVq8UOlUqmVmSSEEEKI4kH6n4UQQghR5EnAIoQQQogiTwIWIYQQQhR5JWYMS9bYYVlTSAghhCg+sj63X5UDVGICloSEBABZU0gIIYQohhISErC0tHzh8RKT1pyZmcnt27cxNzfXaXpofHw8tra23LhxQ6b/L6bkd1j8ye+weJPfX/GXn79DlUpFQkICVapUeelcVCWmh0VPT49q1arlW/myXlHxJ7/D4k9+h8Wb/P6Kv/z6Hb6sZyWLDLoVQgghRJEnAYsQQgghijwJWF7ByMiI8ePHY2RkVNhNEXkkv8PiT36HxZv8/oq/ovA7LDGDboUQQghRckkPixBCCCGKPAlYhBBCCFHkScAihBBCiCJPAhYhhBBCFHkSsAghhBCiyJOA5TE/Pz969OiR7TF7e3sUCgUKhQITExOcnJyYPXv2KxdqEgXHz89P8zsyMDCgUqVKtG/fnoCAADIzM9m3b5/m+Iu2wMDAwn4apUJ2/9c2bNiAsbExs2bNYsKECSgUCj744AOtc0JCQlAoFERGRgIQGRmJQqGgYsWKmrXEstSvX58JEybk47MQ2Xn6/6G+vj52dnZ8+OGH3L9/X3PO0++nWVt+zlJemsXGxvL+++9jZ2eHkZERNjY2eHt7M3369By/HyYlJVG2bFnKlStHUlJStvVs3LiRNm3aYGlpiZmZGW5ubkyaNIn//vsPgMDAQKysrLSuCQsLo1q1avTs2ZOUlJQcPR8JWHJo0qRJREdHExYWxpdffsn//d//8csvvxR2s8RTOnbsSHR0NJGRkfz55594eXnx6aef0rVrV5o3b050dLRm69u3r+b8rK1fv36F/RRKpSVLljBgwAB+/PFHRo8eDYCxsTFLly4lPDz8ldcnJCQwZ86c/G6myKGn/x8uWbKErVu3MmLECK1zst5Ps7YzZ84UUmtLtl69enH27FmWL19OeHg4W7ZsoU2bNjg7O+f4/XDjxo24urri7OzMpk2bnqtj7Nix9OvXjyZNmvDnn39y4cIF5s6dy9mzZ1m5cmW27Tpx4gQtW7bE29ub9evX53hulxKzllB+Mzc3x8bGBoD33nuPRYsWsXPnTt5///1CbpnIkvUNAqBq1ao0bNiQpk2b8uabb7JixQree+89zbkmJiakpKRozheFY9asWYwbN45ff/2VXr16afbXqVOHihUr8u2337Ju3bqXljFy5EjmzZvHRx99RMWKFfO7yeIVnv5/WK1aNfr16/dc7+XT76cifzx48IBDhw6xb98+WrduDUD16tXx8PB47tyXvR8uXbqUgQMHolKpWLp0KQMGDNAcCw4OZtq0acyfP59PP/1Us9/e3p727dvz4MGD58rbu3cv3bt354MPPmD27Nm5ek7Sw5JLKpWKffv2ERYWhoGBQWE3R7xC27ZtcXd3z/abgShc33zzDZMnT2bbtm1awUqWGTNmsHHjRk6cOPHScvr374+DgwOTJk3Kr6aKPLp69Sp//fWXvFcWAjMzM8zMzNi8eXOOb7k8KyIigqNHj9K3b1/69u3LkSNHuHr1qub46tWrMTMze64HLcuzt4F+++03unTpwv/93//lOlgBCVhy7Ouvv8bMzAwjIyO8vLxQqVR88sknhd0skQNOTk6acQ+iaPjzzz+ZOXMmv//+O+3atcv2nIYNG9K3b1+++eabl5alUCiYMWMGv/zyCxEREfnRXJEL27Ztw8zMDBMTE2rVqkVoaChff/211jlZ76dZ24IFCwqptSWXvr4+gYGBLF++HCsrK1q0aMH//d//ce7cuRyXERAQQKdOnTRjWDp27EhAQIDm+OXLl6lZs2aOAtLExET69OnDV199xZgxY/L0nCRgyaGvvvqKkJAQ9u/fj5eXF2PHjqV58+aF3SyRAyqVCoVCUdjNEE9xc3PD3t6ecePGPTdg9mlTpkzh4MGD7Ny586XleXt788Ybb+Dv76/rpopc8vLyIiQkhOPHjzNy5Ei8vb0ZOXKk1jlZ76dZ2+DBgwuptSVbr169uH37Nlu2bMHb25t9+/bRsGHDHCUYZGRksHz5cgYOHKjZN3DgQJYvX05GRgaQu/dWExMT2rdvz+LFiwkLC8vT85GAJYesra1xcHCgWbNmbNy4ke+++47du3cXdrNEDoSFhVGjRo3CboZ4StWqVdm/fz/R0dF07NjxhUFLrVq1GDZsGN98880rs/JmzJjB2rVrZQBnITM1NcXBwQE3NzcWLFhASkoKEydO1Don6/00a3v21oHQHWNjY9q3b8+4ceM4cuQIfn5+jB8//pXX7dixg1u3btGvXz/09fXR19fHx8eHmzdvar5AODo6EhERQVpa2ivLUyqVbN68mUaNGuHl5UVoaGiun4sELHlQtmxZRo4cyZdffimpzUXc3r17OX/+fLZjJEThsrOzY//+/cTGxtKhQwfi4+OzPW/cuHGEh4cTFBT00vI8PDzo2bPnK28hiYI1fvx45syZw+3btwu7KQJwdnbm4cOHrzxv6dKl+Pj4aPWEhYSEMGDAAJYuXQrAO++8Q2JiIgsXLsy2jGcH3RoZGbFp0yY8PDzw8vLiwoULuWq7BCxPiYuLe+6XExUVle25H330EZcuXWLjxo0F3ErxIikpKcTExHDr1i1Onz7NtGnT6N69O127dpUu5yKqWrVq7Nu3j3v37tGhQwfi4uKeO6dSpUqMGjUqR+Mcpk6dyt69e7l06VJ+NFfkQZs2bXBxcWHatGmF3ZRS5d69e7Rt25ZVq1Zx7tw5rl27xvr165k1axbdu3d/6bV37txh69at+Pr64urqqrX5+vqyZcsW7ty5g6enJ6NHj+aLL75g9OjRHD16lOvXr7Nnzx769OnD8uXLnyvb0NCQjRs30rx5c9q2bcv58+dz/JwkYHnKvn37aNCggdY2bty4bM+tUKECgwYNYsKECWRmZhZwS0V2/vrrLypXroy9vT0dO3bk77//ZsGCBfz+++8olcrCbp54gazbQw8ePHhhKuRXX32FmZnZK8tydHRk6NChJCcn50NLRV6NGjWKxYsXc+PGjcJuSqlhZmaGp6cn3333Ha1atcLV1RV/f3+GDRvGjz/++NJrV6xYgampKW+++eZzx7y8vDA3N9fMsTJz5kx+/fVXjh8/jre3Ny4uLowaNQo3Nzd8fX2zLd/AwIB169bRqlUr2rZtm+OBwAqV3NMQQgghRBEnPSxCCCGEKPIkYBFCCCFEkScBixBCCCGKPAlYhBBCCFHkScAihBBCiCJPAhYhhBBCFHkSsAghhBCiyJOARQghhBBFngQsQgghhCjyJGARQgghRJEnAYsQQgghirz/BwmryoJEq/PnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alg = ['LR','DT','KNN','RF','STACK']\n",
    "plt.plot(alg,a1)\n",
    "plt.plot(alg,a2)\n",
    "plt.plot(alg,a3)\n",
    "plt.plot(alg,a4)\n",
    "plt.plot(alg,a5)\n",
    "legend = ['Traning Accuracy', 'Testing Accuracy', 'Precision Score', 'Recall Score', 'F1 Score']\n",
    "plt.title(\"METRICS COMPARISION\")\n",
    "plt.legend(legend)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b2b950ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HAM MAIL\n"
     ]
    }
   ],
   "source": [
    "input_mail = [\"Hi this is  kalyan\"]\n",
    "\n",
    "input_mail_features = feature_extraction.transform(input_mail)\n",
    "\n",
    "prediction = stack.predict(input_mail_features)\n",
    "\n",
    "if(prediction == 0):\n",
    "    print(\"SPAM MAIL\")\n",
    "else:\n",
    "    print(\"HAM MAIL\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}