language-detection-using-rf-nb-algorithms.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/yassin01/language-detection-using-rf-nb-algorithms?scriptVersionId=113038234\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown","outputs":[],"execution_count":0},{"cell_type":"markdown","id":"0854b234","metadata":{"papermill":{"duration":0.011376,"end_time":"2022-12-05T21:14:19.388782","exception":false,"start_time":"2022-12-05T21:14:19.377406","status":"completed"},"tags":[]},"source":["## ***1- Importing Basic Libraries and Read the Dataset***\n"]},{"cell_type":"code","execution_count":1,"id":"80c1a717","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2022-12-05T21:14:19.411418Z","iopub.status.busy":"2022-12-05T21:14:19.410322Z","iopub.status.idle":"2022-12-05T21:14:19.43443Z","shell.execute_reply":"2022-12-05T21:14:19.4324Z"},"papermill":{"duration":0.039145,"end_time":"2022-12-05T21:14:19.437748","exception":false,"start_time":"2022-12-05T21:14:19.398603","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["/kaggle/input/language-detection/Language Detection.csv\n"]}],"source":["import os\n","for dirname, _, filenames in os.walk('/kaggle/input'):\n","    for filename in filenames:\n","        print(os.path.join(dirname, filename))"]},{"cell_type":"code","execution_count":2,"id":"44b6d82e","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:14:19.461674Z","iopub.status.busy":"2022-12-05T21:14:19.460172Z","iopub.status.idle":"2022-12-05T21:14:19.467246Z","shell.execute_reply":"2022-12-05T21:14:19.465703Z"},"papermill":{"duration":0.022232,"end_time":"2022-12-05T21:14:19.47011","exception":false,"start_time":"2022-12-05T21:14:19.447878","status":"completed"},"tags":[]},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import re \n","import warnings \n","warnings.filterwarnings(\"ignore\")"]},{"cell_type":"code","execution_count":3,"id":"b1a9d796","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:14:19.496043Z","iopub.status.busy":"2022-12-05T21:14:19.495502Z","iopub.status.idle":"2022-12-05T21:14:19.861178Z","shell.execute_reply":"2022-12-05T21:14:19.859389Z"},"papermill":{"duration":0.381852,"end_time":"2022-12-05T21:14:19.864854","exception":false,"start_time":"2022-12-05T21:14:19.483002","status":"completed"},"tags":[]},"outputs":[],"source":["url = 'https://raw.githubusercontent.com/basil-b2s/Language-Detector/master/language_detection.csv'\n","data = pd.read_csv(url)"]},{"cell_type":"code","execution_count":4,"id":"302f5213","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:14:19.887957Z","iopub.status.busy":"2022-12-05T21:14:19.887412Z","iopub.status.idle":"2022-12-05T21:14:19.921409Z","shell.execute_reply":"2022-12-05T21:14:19.919926Z"},"papermill":{"duration":0.048716,"end_time":"2022-12-05T21:14:19.924663","exception":false,"start_time":"2022-12-05T21:14:19.875947","status":"completed"},"tags":[]},"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>Text</th>\n","      <th>Language</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Nature, in the broadest sense, is the natural...</td>\n","      <td>English</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>\"Nature\" can refer to the phenomena of the phy...</td>\n","      <td>English</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>The study of nature is a large, if not the onl...</td>\n","      <td>English</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Although humans are part of nature, human acti...</td>\n","      <td>English</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>[1] The word nature is borrowed from the Old F...</td>\n","      <td>English</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>[2] In ancient philosophy, natura is mostly us...</td>\n","      <td>English</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>[3][4] \\nThe concept of nature as a whole, the...</td>\n","      <td>English</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>During the advent of modern scientific method ...</td>\n","      <td>English</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>[5][6] With the Industrial revolution, nature ...</td>\n","      <td>English</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>However, a vitalist vision of nature, closer t...</td>\n","      <td>English</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                Text Language\n","0   Nature, in the broadest sense, is the natural...  English\n","1  \"Nature\" can refer to the phenomena of the phy...  English\n","2  The study of nature is a large, if not the onl...  English\n","3  Although humans are part of nature, human acti...  English\n","4  [1] The word nature is borrowed from the Old F...  English\n","5  [2] In ancient philosophy, natura is mostly us...  English\n","6  [3][4] \\nThe concept of nature as a whole, the...  English\n","7  During the advent of modern scientific method ...  English\n","8  [5][6] With the Industrial revolution, nature ...  English\n","9  However, a vitalist vision of nature, closer t...  English"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["data.head(10)"]},{"cell_type":"markdown","id":"a642d0ef","metadata":{"papermill":{"duration":0.010103,"end_time":"2022-12-05T21:14:19.945283","exception":false,"start_time":"2022-12-05T21:14:19.93518","status":"completed"},"tags":[]},"source":["## ***2- Understanding the Dataset***"]},{"cell_type":"code","execution_count":5,"id":"51f74a87","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:14:19.968801Z","iopub.status.busy":"2022-12-05T21:14:19.96713Z","iopub.status.idle":"2022-12-05T21:14:19.976503Z","shell.execute_reply":"2022-12-05T21:14:19.975136Z"},"papermill":{"duration":0.024128,"end_time":"2022-12-05T21:14:19.979421","exception":false,"start_time":"2022-12-05T21:14:19.955293","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["(10337, 2)"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["data.shape"]},{"cell_type":"code","execution_count":6,"id":"5709397c","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:14:20.004025Z","iopub.status.busy":"2022-12-05T21:14:20.003489Z","iopub.status.idle":"2022-12-05T21:14:20.032394Z","shell.execute_reply":"2022-12-05T21:14:20.030912Z"},"papermill":{"duration":0.045301,"end_time":"2022-12-05T21:14:20.036205","exception":false,"start_time":"2022-12-05T21:14:19.990904","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 10337 entries, 0 to 10336\n","Data columns (total 2 columns):\n"," #   Column    Non-Null Count  Dtype \n","---  ------    --------------  ----- \n"," 0   Text      10337 non-null  object\n"," 1   Language  10337 non-null  object\n","dtypes: object(2)\n","memory usage: 161.6+ KB\n"]}],"source":["data.info()"]},{"cell_type":"code","execution_count":7,"id":"8bbd1d67","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:14:20.062758Z","iopub.status.busy":"2022-12-05T21:14:20.0623Z","iopub.status.idle":"2022-12-05T21:14:20.075212Z","shell.execute_reply":"2022-12-05T21:14:20.073947Z"},"papermill":{"duration":0.029885,"end_time":"2022-12-05T21:14:20.078191","exception":false,"start_time":"2022-12-05T21:14:20.048306","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["Text        0\n","Language    0\n","dtype: int64"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["data.isnull().sum()"]},{"cell_type":"markdown","id":"856ce93d","metadata":{"papermill":{"duration":0.009947,"end_time":"2022-12-05T21:14:20.098377","exception":false,"start_time":"2022-12-05T21:14:20.08843","status":"completed"},"tags":[]},"source":["#### checking for duplicates"]},{"cell_type":"code","execution_count":8,"id":"6f9617ef","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:14:20.122373Z","iopub.status.busy":"2022-12-05T21:14:20.121399Z","iopub.status.idle":"2022-12-05T21:14:20.14709Z","shell.execute_reply":"2022-12-05T21:14:20.14433Z"},"papermill":{"duration":0.042264,"end_time":"2022-12-05T21:14:20.151181","exception":false,"start_time":"2022-12-05T21:14:20.108917","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["66"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["len(data[data.duplicated()])"]},{"cell_type":"markdown","id":"16d95ae9","metadata":{"papermill":{"duration":0.010887,"end_time":"2022-12-05T21:14:20.172296","exception":false,"start_time":"2022-12-05T21:14:20.161409","status":"completed"},"tags":[]},"source":["There are 66 duplicate rows, let's drop them...\n"]},{"cell_type":"code","execution_count":9,"id":"05736912","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:14:20.197421Z","iopub.status.busy":"2022-12-05T21:14:20.196936Z","iopub.status.idle":"2022-12-05T21:14:20.212592Z","shell.execute_reply":"2022-12-05T21:14:20.211108Z"},"papermill":{"duration":0.032337,"end_time":"2022-12-05T21:14:20.21577","exception":false,"start_time":"2022-12-05T21:14:20.183433","status":"completed"},"tags":[]},"outputs":[],"source":["data.drop_duplicates(inplace=True)"]},{"cell_type":"code","execution_count":10,"id":"fec61c57","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:14:20.239012Z","iopub.status.busy":"2022-12-05T21:14:20.238275Z","iopub.status.idle":"2022-12-05T21:14:20.247415Z","shell.execute_reply":"2022-12-05T21:14:20.245721Z"},"papermill":{"duration":0.023557,"end_time":"2022-12-05T21:14:20.25009","exception":false,"start_time":"2022-12-05T21:14:20.226533","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["(10271, 2)"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["data.shape"]},{"cell_type":"markdown","id":"ef006e47","metadata":{"papermill":{"duration":0.010855,"end_time":"2022-12-05T21:14:20.271483","exception":false,"start_time":"2022-12-05T21:14:20.260628","status":"completed"},"tags":[]},"source":["Let's dive in the dataset!"]},{"cell_type":"code","execution_count":11,"id":"22e88ae7","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:14:20.296512Z","iopub.status.busy":"2022-12-05T21:14:20.295661Z","iopub.status.idle":"2022-12-05T21:14:20.305828Z","shell.execute_reply":"2022-12-05T21:14:20.303944Z"},"papermill":{"duration":0.02764,"end_time":"2022-12-05T21:14:20.309969","exception":false,"start_time":"2022-12-05T21:14:20.282329","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["17"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["data[\"Language\"].nunique()"]},{"cell_type":"markdown","id":"39feb365","metadata":{"papermill":{"duration":0.014262,"end_time":"2022-12-05T21:14:20.342505","exception":false,"start_time":"2022-12-05T21:14:20.328243","status":"completed"},"tags":[]},"source":["The dataset contains texts from 17 different languages"]},{"cell_type":"code","execution_count":12,"id":"e8fa3630","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:14:20.378172Z","iopub.status.busy":"2022-12-05T21:14:20.376408Z","iopub.status.idle":"2022-12-05T21:14:20.393614Z","shell.execute_reply":"2022-12-05T21:14:20.39074Z"},"papermill":{"duration":0.043628,"end_time":"2022-12-05T21:14:20.397784","exception":false,"start_time":"2022-12-05T21:14:20.354156","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["English       1382\n","French        1007\n","Spanish        816\n","Portugeese     736\n","Italian        694\n","Russian        688\n","Sweedish       673\n","Malayalam      591\n","Dutch          542\n","Arabic         532\n","Turkish        471\n","German         465\n","Tamil          464\n","Danish         424\n","Kannada        366\n","Greek          358\n","Hindi           62\n","Name: Language, dtype: int64"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["data[\"Language\"].value_counts()"]},{"cell_type":"code","execution_count":13,"id":"c33ea912","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:14:20.428702Z","iopub.status.busy":"2022-12-05T21:14:20.427327Z","iopub.status.idle":"2022-12-05T21:14:20.815766Z","shell.execute_reply":"2022-12-05T21:14:20.814026Z"},"papermill":{"duration":0.408076,"end_time":"2022-12-05T21:14:20.819605","exception":false,"start_time":"2022-12-05T21:14:20.411529","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAloAAAIuCAYAAABn6KY9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOzddXhb5/UH8O+5Issks+OQIWSHmalMKa9NmdZ1KWztSku7rXWZuekK69Z2+5W5TSlpGmYmh+OgIWYSXHh/f1wlcRyzJV1JPp/n8eNIuve9R44tHb1wXhJCgDHGGGOM+Z5kdACMMcYYY+GKEy3GGGOMMT/hRIsxxhhjzE840WKMMcYY8xNOtBhjjDHG/IQTLcYYY4wxP+FEizHGGGPMTzjRYowxxhjzE060GGOMMcb8hBMtxhhjjDE/4USLMcYYY8xPONFijDHGGPMTTrQYY4wxxvyEEy3GGGOMMT/hRIsxxhhjzE840WKMMcYY8xNOtBhjjDHG/IQTLcYYY4wxP+FEizHGGGPMTzjRYowxxhjzE060GGOMMcb8hBMtxhhjjDE/4USLMcYYY8xPONFijDHGGPMTTrQYY4wxxvyEEy3GGGOMMT/hRIsxxhhjzE840WKMMcYY8xNOtBhjjDHG/IQTLcYYY4wxP+FEizHGGGPMTzjRYowxxhjzE060GGOMMcb8hBMtxhhjjDE/4USLMT8hohuIaA0RVRNRORGtI6IXDY7pPSJa3Ybjc4moxJ8xMcZYOONEizE/IKIHAPwLwM8ALgFwHYBvAFxgZFwAHgNwg8ExMMZYp0FCCKNjYCzsENEhAF8LIW5vcD+JEPqjI6JcAHcIIZKMjoUxxkIR92gx5h9xAAob3lk/ySKiDCISRHQVEf3XO8RYTEQP1z+HiLKJ6GMiOkBEdUS0hYjuIiKp3jFTvW1NJaLPiKiGiPYQ0W0N2jph6JCI4ojoX0R0mIhcRLSfiN5pGDcRDSOi5d7rryOiSR378TDGWOfAiRZj/rEWwJ+I6HoiSmzh2OcA1AH4HYB3ADxMRPV7wroB2A7gNgDneo95BMBfG2nrHQAbAFwMYD6AWUQ0uplrvwhgIoC/ADgLwIMAGva4RQJ4H8BbAC4F4AbwJRFFtvC8GGOs0zMbHQBjYep2AF8DeA+AIKI8AF8AeF4IUdXg2C1CiD96//0zEaUAeJCI/imE0IQQvwL4FdCHHgEshp78/AHAUw3a+kgI8bj32PkAzoc+R2xlE3GOBjBLCPFJvfv+1+AYO4C7hBDzvO0WAFgHYDKAn5r7ITDGWGfHPVqM+YEQYiOAHOiT398AQAD+AWA1EUU3OPyrBre/BNAVQHcAIKIIInqEiHZB702SATwBIJOIGn5Y+qVeDDKAnUfbacJ6APcR0W1E1LeJYzzQe8eO2ur93ly7jDHGwIkWY34jhHALIb4TQtwhhOgP4GYAfQD8vsGhxU3cTvN+fwbAvQDehj50OArA497HIhqcW9HgtqeRY+q7A3rP20MAthPRTiK6osEx1UIIrd7z8jRxbcYYYw1wosVYgAgh3gVQBiC7wUMpTdwu8H6/DMBrQohnhRBzhRCrASg+iqlCCPFnIUQXAEMArADwf0TU3xftM8ZYZ8eJFmN+4J1n1fC+ZAAOAEUNHrq4we1LoCdZB7237dCHDI+2YwLQsNepw7zDnfdBf11omAwyxhhrB54Mz5h/bCKib6DPmSoGkA59+K8O+gq++gYQ0VvQJ8tPhj60eGe94bo5AG73ztEqgz7R3uaLIIloMfQ5Ypuhrzb8A4BaND15njHGWBtwosWYfzwK4EIArwJIgF5TaymA6UKIvQ2OvR/ANOiJlgt69fbX6z3+JwBvApgFwAk9UfsK+pytjloGvVJ8BgAV+mrCc4QQB5s5hzHGWCtxZXjGDEJEGQD2AjhfCPG9weEwxhjzA56jxRhjjDHmJ5xoMcYYY4z5CQ8dMsYYY4z5CfdoMcYYY4z5CSdajDHGGGN+wokWY4wxnyCiXCISjXzNDYLYMryxTDM6Fta5cB0txhhjvlQJ4OxG7mOsU+JEizHGmC8pQojlLR1ERHYhhDMQATFmJB46ZIwx5lf1hu2uJqIPiKgCwHfexxKI6G0iKiIiFxEtJaIxDc4XRHQnET1JREeIqJiIZhGRrcFx6UT0ERGVEFEdEW0koqsahBNJRG8RUSURHSSiR4iI3wuZ33CPFmOMMZ8ioobvLeT9/jyALwFcBkD1JkpzAcRB39C8GMCtAOYSUR8hRGG9Nu4BMA/ANQAGA3gKwD4Az3qvmQJ9S6k66PuKHgAwEECPBrE8C327q98BOA3AQwC2APi0I8+ZsaZwosUYY8yXEgHIDe77g/f7ciHE7UfvJKLfQ0+GBgghdnrvmwtgO/TE6r56beQLIW7w/vtnIpoA4BJ4Ey0AfwHgADBCCFHgve/XRuJbKIS4x/vvOUR0trcdTrSYX3CixRhjzJcqAZze4D639/vsBvefDmANgL0NesEWABjZ4NhfGtze2uCYUwH8VC/Jakpj7fRs4RzG2o0TLcYYY76kCCFW17/Du4E6ABQ1ODYJwFic3AMGALsb3K5ocNsDIKLe7UQAq1oRX0vtMOZTnGgxxoLGoPcHEYB46G/ACQDs0F+n2vKlAagFUNPc16brNymBel7smIZ7vpUBWA19XlZD7kbua04pgLT2BMWYP3GixRjzG2/i1AVAdwDJ0BOo5r4SAJgCFJsbxxOvagBHABQAOOz9XgDg8Fk1tQeeP1J6CLmVbX3jZy37FcCZAPYLIYp90NafiShVCNGw54wxw3CixRjrkEHvD7IC6A2gL4AsAJn1vmdA75UKRjbvV2JzB5mB+QCmINdRBCAf+kq3ffX+nQ9gF3IrPf4LNWx9AGAGgPlE9DyAPdD/P0YDKBRCvNSGtl4CcB2ARUT0BPRVhzkAooQQzzZ7JmN+xIkWY6xVBr0/KBrAMAD9AfSr95WBAPVCGSHTI1uglyfo4v0a28hhMnIdWwGsB7DO+309ciu5InozhBAuIjoFwKMAHgGQCr3Ew0oA37axrSPelYjPAngZehK9E3oZCMYMQ0I0HDJnjHV2g94fZAMwFPqqrlHer2x0wiLHLxYdWXdGnXNYO0/PR/3ES0++9vsmMsZYKOBEi7FObtD7g8zQaxnVT6oGArAYGVew+PJgwd4+spzpwyZLAWzA8eRrHYBtyK3kyfmMhSFOtBjrZAa9PygGwCnQ6w6NATAEwTuPynAr8g/URgoR5efL1ECfCzYHwBzkVub5+XqMsQDhRIuxMDfo/UEm6L1UZ0Bf4TUWPD+zdYSo3JR/wGHAlQ9C35pmDoC5yK3s6Io8xphBONFiLAwNen9QFvSk6gzoPVdxhgYUomyatnP1voN9DA5DANiIo71dwCLkVjqNDYkx1lqcaDEWBga9P8gBfYPco71WWcZGFB6SFWX1vAOHG24FYzQ3gMU4nnitQ24lv5AzFqQ40WIsRA16f1AcgIsBTIeeZPFwoI8NcLsXfXy4aJLRcbSgBPr+fR8D+Am5lY1tZ8MYMwgnWoyFEO9E9guhJ1dnArAaG1F4O7umdv5zR0qnGh1HG5QC+BzA/wFYzD1djBmPEy3Ggtyg9wdFAZgGPbk6B7wBbsDcWl65+LaKyolGx9FO+6D3cv0fcis3GR0MY50VJ1qMBaFB7w+KAHAu9ORqGoBIYyPqnJ4rLll7dm3dcKPj8IHN0Hu5PuSCqYwFFidajAWRQe8PmgLg9wAuAhBjbDTs00MFu3M8ci+j4/AhAX0i/YcAPkVuZZnB8TAW9jjR8gMiygXwcL27CgAsA3C/EGJ3B9u+HECkEOK9jrTDgseg9wclArgBwM3Qt7lhQWJp/oHqGCHCNeGVAfwEPen6hktGMOYfnGj5gTfRugvA2d67sgA8Bn3j3QFCiNoOtP05gCQhxNSORcmMNv6fAydVR9KtAC6BvgEuCyZCVG/KPxCuSVZD5QDeBTALuZX5BsfCWFjpdBvEBpAihFju/foQwPUA0qHPu2kzIuItUsJAXnZOdF52zm152Tlbpi/UHgNwJTjJCkpWgc5UjT0ewL0AdiPX8TVyHacaHRBj4YITrcBZ4/2eQURJRPQ+EZUSUR0RzSeiE4oiElE+Eb1ARP8gooMAqojoPQCXAphCRML7lVvv+OcbtHGD95joevcNJqKlROQioi1EdC4Rrfa2Xf/cSUS0wBtfKRG9Q0QxDY7pSUQfE1GZ97ifiahfg2MeIKJd3usVEdFPRNSl3uMJRPS29zGXN7Yx7fwZB6287Jx+edk5rwI4BGAWgP6nbBDp4C7loBWjaZVGx2AACXr5kF+R69iMXMcfkevghRiMdQAXOAycDO/3QgBfA+gN/RNkCYD7APxGRMOEELvqnXMVgC0AboP+f7UBQE/o26nc5j3mYGsDIKJIAD97Y7gSepmAl6B/mt1c77gJ0PdZ+xrA7wAkAnjae9zvvMckQJ9UWwpgBoA6ADMBzCWivkIIJxFdB+BBAH/1Po9E6NvBRHnbsHmvE+f9GRQDuNXbRh8hRGFrn1uwysvOmQLgAeg1r6j+YxYNGWO3ibXLcygcVrWFnWRVrTM6BoMNAPAmgKeR63gLwCvIrSwwOCbGQg4nWn5EREd/vlkA3gBQDUABMAHAVCHEAu9x8wDkQ082/tigmWlCCFe9NssASEKI5e0I6Uboyc5IIcQhb3u7AaxocNzTAJYKIabXu+4hAL8S0UAhxGYAf4GeMA0VQpR5j1nifR43Qe+1GQ3gFyHEG/Xa/rLev68BMBD6vLWd3jbmAtgO4B7oP4+QlJedcy70JHNCc8ddPV/zLM/hjuVg1F1RVKNjCBJx0D8s/QW5jv8BeA65lduMDYmx0MGv8P6TCH1Vjww9cciCXhOpD4Dio0kWAHgnx38PoGFhxF/rJ1k+MArAmqNJlvfaKwEUHb3t7fUaB+BTIjIf/YLeeyUDGOE99HTo+6xV1TumGvoQ6dFh0PUAziWiR4hoNBGZGsRzuvf4vfXaAIAF9doIGXnZOVJeds5ledk56wDMRgtJFgCkVGBkYqXgXoIglCnL/Pp4Iiv0D1Fbkev4FrmOUC3kylhA8QuJ/1RCT2xGAugOIEMI8SOANKDRSbZFABIauc+XugA40sj99e+Lh7468g0cTxRl6BvZWgD08B6XBD1xlBt8nVLvmH9D79W5HHqvWRERPV4v4UoCMLaRNm6s10bQy8vOMedl59wAfXj0UwBDW3suAebr5mnb/RQa64Asj8wLUBpHAM4HsAi5jqXIdVxodECMBTMeOvQfRQixupH7CwCkNHJ/KoCGxQPbMlHahZP3vYtvcLsQQD+cLLnevyu8180F8EMjxx72fi8D8C30shUNVQOAEEKDPgfsJSLqAeBqAE9An1f2preN1dDnZTXkbuS+oJKXnRMBvbjofdBXlLbL6O0iW9KEoknEf49BJENWHEbHEALGAfgauY4VAO5HbuVCowNiLNjwC3vgrQDwCBFNFkIsBI4N150H4KtWnO9B43vdHQSQ0+C+MxvcXgXgKiLqVm+O1mjoSR4AfRiTiJYD6CeEeLSZOH6F3lO1RQjRYqFDIcQBAE8T0Y0A+tdr40wA+4UQIbOU3ptg3Q59MUOXFg5vkUmgy2nrxfI5w2lsh4NjPtNDURr7QMQaNwbAAuQ6fgAwk/dWZOw4TrQCTAjxMxEtBfAJEc2EvmrvXgB2AM+1ooltAC4koougJ1eHhRCHoSdprxHRg9ATqkuhrxqq7z8A/g7geyJ6xHvNR6APHWr1jrsf+sR3DcDn0HuoekJPBv8mhNgB4EXok9nnEdFr0MsWpAKYAmCxEOIjInoLeq/VcuhDqadAn6P2V+91PoC+YnG+tzTFHuhz20YDKBRCvNSKn0fA5GXnEPTn/Dj0n4fPXLZIs84ZziP5QUOIWoemcY9W250L4GzvpPl/8L6KjPEcLaNcBH0i+csAPoM+5+HUBqUdmvIGgF+gz39aBeAW7/1ve9v7M/R5Qm7oCcExQog66NXqnQA+gT48eD/04cKqesctBjAZ+pDifwF85z3uALzzxoQQJdDnV22DPjz4C4BnATgAbPQ2tczbzn+gD0NeDOAPQoivvW24oCdfc6AnfL8AeAV6MrayFT+LgMnLzjkN+sT9D+DjJAsAHHUY1v2IyPd1u6x9LI3Po2StIwG4DsAO5DpeQK6j4dxTxjoV3oKnkyOiTAA7ANwihPiP0fEEm7zsnEHQE8izWzq2ozal04LHrjJN8fd1WMviVXXdwv2HhhkdR5ioBPAMgJd5P0XWGXGi1ckQ0QPQJ7Tvg94z8wD0XqhsIURVc+d2JnnZOd2gT/S/HgHq+RVAxbX3mmweC2+3ZLQ+Hs+SLw8Vtlieg7XJYei96P9GbiXXKGOdBg8ddj4CwMMAfgLwGoDd0IctOckCkJedE5uXnfME9F6+GxHAvxEC4s5fIda0fCTzt26yohgdQxjqCn2Kw2bkOi42OhjGAoV7tBjDsYnuN0MvP5HcwuF+U2fFlhvuMTdcxMAC7MaKqoV3l1dMNjqOMLcMwD3IrVxmdCCM+RP3aLFOLy87ZwCARdA/bRuWZAFApAcD+h0QeUbGwIAsmYuVBsA4AIuR63gNuY7oFo9mLERxosU6rbzsHHteds6TANahFdvlBMoNc9VSo2Po7DJlmUs7BIYE4A7o2/qca3QwjPkDJ1qsU8rLzjkTwCboiwEsBodzgqxCDI9yikqj4+jMeshKktExdDI9AMxGruND5DoM7VVmzNc40WKdSl52Tmpeds6HAH4G0MvoeBpDQOTli7T1RsfRaQnhStA0rv1kjCsB5CHXca3RgTDmK5xosU4hLzuH8rJzbgGQB/3FPKidtl50NzqGzsrMxUqNlgjgA+Q6fkSuo917iDIWLDjRYmEvLzunP4CFAN7CyRttByWril4jd3CvlhGiNa3c6BgYAL1I8GbkOu5EroPfq1jI4l9eFrbysnOkvOyc+6FPdp9odDxtdc08jatoGyBRVWuNjoEdEw19a7ElyHVw2RMWkjjRYmEpLzsnA8B86Ft/WA0Npp3SyjEyvlrwMFaAdVVU2egY2EnGAliLXMcjyHWE5N8z67w40WJhJy875yboG1tPMjqWjiDAcu08bavRcXQ2mbJMRsfAGmUF8BCAdch1jDE6GMZaixMtFjbysnMS87JzvgbwLoAYg8PxiXF5oq+kCd4XLoCyPHKE0TGwZvUHsAi5jnuMDoSx1uBEi4WFvOyc06D3Yl1odCy+ZBLoOmUT738YSBmyEmt0DKxFFgDPI9fxFXIdcUYHw1hzONFiIS0vO8eSl53zDIBfoG9aG3amL9T47zSAeioyFysNHRdBn7s1wuhAGGsKv4CzkJWXndMbwBIA9yOMf5fjazAirVTsNzqOTkEIT5KqJRodBmuTTOirEm83OhDGGhO2b04svOVl51wIYA2AUUbH4m8E0A1ztb1Gx9EZmIBiAngyfOixAXgduY6PkesIi/mZLHxwosVCirc21mMAvgLQaebSDNkjBpoV4TY6jnAXpWllRsfAOmQ6gNXIdQw2OhDGjuJEi4WMvOycOADfAfg7OlmvgwQknrdKrDY6jnCXoGo1RsfAOqwvgOXIddxsdCCMAZxosRCRl50zEMAqAOcaHYtRLlqmdZoePKN0VRQuVhoe7ADeQa7jfeQ6Io0OhnVunGixoJeXnXMZgOUAehsdi5Gi3BjU67DYYXQc4SxdVjpVT2kncB2Alch1ZBsdCOu8ONFiQSsvO8eUl53zLIBPAUQZHU8wuGmOWmR0DOGslyzz9i7hZwD0eVtXGx0I65w40WJBKS87JxHATwDuMzqWYNL7MIbZ3aLa6DjCVaYs8/BseIoC8D/kOp5AroN7LVlAcaLFgk5eds4gAKsBnG50LMGGgOjfLdbWGR1HuOopK1xDK7w9COD/kOuwGR0I6zw40WJBJS875xQAiwFkGBxK0DpzrUgzOoawJISSoqrJRofB/O5KAHOQ6+CkmgUEJ1osaORl51wBfbiQh2+aYVPQZ+hubaPRcYQbE1As8WtiZzEJwDLkOjr1AhsWGPyiwoJCXnbOPQA+BMCTkVvhul81nqflY5Ga4GKlnUsf6MnWeKMDYeGNEy1mqLzsHMrLznkJwPPoZEVIO6JbKUY5akWJ0XGEk3hN5eS180lyCus7WTO/vcToQFj44kSLGSYvO8cG4GMAdxkcSsghwHr1b9pmo+MIJ2mK6jE6BhZYqqDCMzzPxWkwfZYxc/ZtRsfDwhMnWswQ3u10fgZwucGhhKyJW0RvEkIzOo5wkS5zUfjORAhUXeJ5pOqgSO4K/b1wVsbM2Y8ZHRcLP5xosYDLy87pDmARgClGxxLKzBq6T9os1hgdR7jI4mKlnYYQkG+V79y1QfTu2+Chv2fMnP1OxszZJkMCY2GJEy0WUHnZOf0BLAMw0OhYwsEVCzRhdAzhIsujxBgdA/M/ISCeVq5c+ZM2ZngTh9wM4NOMmbMtgYyLhS9OtFjAeAuRzgfQ3eBQwkZiNUamlouDRscRDnoqMtdV6gQ+UycveEs9f0ILh10C4LOMmbO5l5N1GCdaLCC8SdavALggpA8RIF0/V9ttdBwhTwg1VeFipeFuldZ34f3KjKmtPPxCAF9kzJzNVeRZh3CixfzOm2TNAydZfjF8t+hvVgWvmOsACThiBsxGx8H8Z7+WvPxyz0MT23jaNABfcbLFOoITLeZXedk5g6EnWUlGxxKuJIHks9aI1UbHEcrsQpQaHQPzn0oRuelMz7NDBaT2vOedA+CbjJmzI3wdF+scONFifuNNsn4FJ1l+d8kSLcroGEJZvMrFSsOVW5j3TnG/1N0FW0cSpbMAfJcxc7bdV3GxzoMTLeYXedk5Q8A9WQET48KQzEKxy+g4QlUXLlYallRBxWd6nrVUICbeB82dDuD7jJmzI33QFutEONFiPudNsn4FwKu4AuiGOepho2MIVT0VhctkhBkhUDPd81DpPtHFl6ucTwXwQ8bM2dyDzFqNEy3mU3nZOUPBSZYhsg9iqM0jao2OIxRleWSumRRGhIDyZ/mObatFvxw/ND8FwGyes8VaixMt5jN52TkDAMwFJ1mGICD2kqXaWqPjCEVZshxtdAzMd15QLlv+nTZ+pB8vMQXAh1xBnrUGJ1rMJ/Kyc9Kh713ISZaBzlktUoyOIRSlywr/3oaJb9Tx819XL25rGYf2uBjAmwG4DgtxnGixDsvLzkkG8AuAbkbH0tlFyOg3aK+22eg4QooQIk1ROEENA+u1XovulO+YGsBL3pwxc/YTAbweC0GcaLEOycvOiQbwA4CGm7Myg1z3q1ZhdAyhRAJKLADP0Qpxh0Tiyks8j4w34NIPZsycfacB12UhghMt1m5fXfCoGcBXAPw5F4K1Uc8jGBlTJ8qMjiNURAhRYnQMrGOqhX3L6e7nB2qQjJoz9VLGzNlXGXRtFuQ40WLtMmvGPDrcdeL7a4bdbRUgzeh42HEERFw5X9tkdByhIk7VuFhpCPMI076p7hdTnbAZWd+KALyXMXP2WQbGwIIUJ1qsvZ4EcFWlo9fk5WMeXqmR2W10QOy4qZtEBoTg2lCtkKoqLqNjYO2jCTpytucZlMIRDIWRLdA3oR5jdCAsuHCixdps1ox5twKYefS20548dvH4J7fJZnulgWGxeswa0sfnCS710Ao9ZS5WGoqEQO2Vnr8d2SO6phsdSz1R0GtsZRsdCAsenGixNpk1Y975AF5reL9iiRqyZNyTxU5bQoEBYbFGXDVfU4yOIRRkygpPhA8xQkC9V56xdYXo39/oWBqRCH1fRF9s+8PCACdarNVmzZg3FMDHABqdcKqZrH2Wjc1FdXT33QENjDUquRIjkyoFJ74t6CXLvHddiHldvWjpF9rkUUbH0YzeAD7lgqYM4ESLtdKsGfOSAHwNoPk3JTKlrRoxM7E0of/GQMTFmkaA6fq52naj4wh26bKcYHQMrPV+UEfPf0G5fJLRcbTC6QBeMDoIZjxOtFiLZs2YZwbwCYDWzYUgitsw6La+h9ImLvdrYKxFo3aKHEkTPITYjG6ykmp0DKx1NmsZi26T75pqdBxtcGfGzNk3GR0EMxYnWqw1noe+a33rEUVs73vF6F1ZFy30T0isNSSB1DPWilVGxxGsSIhSK2AzOg7WskIRv/pCz2PjjI6jHf6ZMXO2EYVUWZAgXgHOmjNrxrzrALzfkTaSSjbMH7z57am+iYi1VWUk1v3hTvMwX7a556k9qNte1+hjWX/PQmTvxkeYXYdcKPiwAHU76yBZJThGOZA6PRWmiONTWarWVKHg4wJoLg2JpyUi5aITd8cp/qYYzr1OpN/V8cVmdk3bvnLfwX4dboj5Va2IyBvlfqNnHSKijI6lnYoAjMp/+rwDRgfCAo97tFiTZs2YNxLAWx1tpyRpyNSVI2Yu0kjiISwDOOowrEex2OvLNrte1xVZf8864StqQBRMMSbYM+2NnqPWqdj7zF4IWaDHrT3QZXoXVK6uxMG3Dh47RqlWcPDtg0iYmoCu13ZFyc8lqN58vJ6oXCaj5OcSdLmyi0+eh0PTqnzSEPMbWZgOnOJ+ISmEkywASAXwTcbM2bzwohPiRIs1ataMeSnQt9eJ8EV7NTE9Ji0b+9g6VbI23g3C/OrGOZpPP0lHdItAZO/IY18RGRFw5bvgGOkAmajRc8rmlUF4BNLvTEfssFjET4pHt5u6oXpdNZx7nQCAut11sCRakHxeMhyjHYgbG4fazbXH2ij8rBAJkxNgS/XNaF+KonKx0iCmCSo9z/OkUoz4ZKNj8YFhAP5jdBAs8DjRYieZNWOeBcDnALr7sl23LW7U4vFP7vVYokt92S5r2YD9YohVFn5Lcms21UCtVeEY62jyGOd+J+yZdpiijg8TRg+IBgio3qD3WglFgKzHEzWyEoSqT2+o21WH2i21SL7Qd++5PRSFt48KUkLAea38QMEO0SPT6Fh86PKMmbMfNDoIFlicaLHGvALAL8unVbN9wJJxT1TV2ZN5rkIAEeC4cLm2xl/tV66ohDnBjMi+TY+MCFmc1NtFJgIIcB/Wd3Cy97TDfdCNmrwaeI54ULW6ChEZERBCoODDAqRcmgKT3XeliTI9stlnjTGfEQLqA8rNG5doAwcaHYsfPJoxc3YolKdgPsKJFjvBrBnzrgVwqz+vISRz5vLRD9kqYzO5xlMATVspEv3RrubWUL2uGo5RDhA1PmwIANYUK1wHXBD1drxx5jsBDVBqlWPHJJ+fjPxn8rHjvh2wdbMhbmwcKpZWQCgC8ZN8W2ybi5UGp3fUc5d8rJ4arnsGmgB8mDFzNtdv6yQ40WLHzJoxrxeAWQG5GEkpa4bd07U4eRjvxxcgdg/65+wXW33dbvX6amhuDXFj45o9LmFKApRqBYf/dxhyhQzXIRcOf3AYkHBCgpZyYQqyX8tG3+f7IuPuDGiyhqLPipB2dRqEJnDovUPI+1Medv59J2p31DZzxZZlyApvkxJkflWHzX9SuWay0XH4WXcA7xkdBAsMTrQYgGPzsj4CEBOwixLFbO7/+0H7epy+JGDX7OSun6v6fH5cxYoKWFOtTa42PMrW1YZuN3RD5YpKbL9rO3b9fRcisyIR0TMCZseJI3jmGDOsSVYAQMn3JYjsE4moflEo/60crgMu9H26L5KnJePAGwegye2fZtVNUVJaPooFynat+5Lfy/dOMTqOADk/Y+bsO40OgvkfJ1rsqEcBBH7vMCLL7qyLxm/re8WCgF+7E8oswsgop6j0VXtqnYqajTVwjGl6Enx98ZPjkf1KNno/1hvZL2cj7do0eIo8iOzV+Aie54gHpfNK0eVyvZxDTV4N4sbFwRRlQtzYOAhFwFPoaVfsJESFXQgeOgwSJSJ27XmeJ0cDzYw/h59nM2bOHm50EMy/ONFimDVj3qkA7jcsACI63HXSlHWD/7RAAFxB148IsE9fqK33VXtVa6ogFNHsasOGJKuEiB56L1bF0gpAALGjYxs9tvCTQiSelghrsvXYfZpH78ESmjhhvldbWYU40u6TmU/VCev2qe4XeyswW4yOJcCsAD7OmDk72uhAmP9wotXJzZoxLxHAfxEEvwvlCdlTVoz6xzKNTO3romCtctoG0dNXbVWuqEREjwhEdD253NqO+3fg4LvHi5GqThWFnxaien01qjdVo/DTQhz6zyGkXZ0Gc/TJi/9qt9WibncdkqcdL+cQ1S8KZb+WoWp9FQo/KYQUIcHaxXrSua3h0DSf9ew19PU2GYP/WQPb41XIfKUaLy5zt+q81YdVnPnfWiQ8U4WEZ6pw+ge1WHHwxDq/X2+TkfVKNZKfq8Yj809u99EFblzwUeiUq1OEdOhU94txNYhsPNsOf30A/NPoIJj/GP7m6g9ElEtEJd5/9/XejmtHOxlEJIhoWr378onoeR+Ga7R3AXQ1Ooij6qK6jF8y7onNislW3fLRrD0sKjJHb9fWdbQdpVpBTV7Tw4ZCFUC96VMkEZz7nDj4zkHsf3U/avNq0fO2no2uJBSaXs4h9XepkGzHX6YSTk1AVP8oHHzrIGo21qDHjB6QLO17GUtRVWe7TmzBkv0KLvnEidHdTPjuykjcNNSKv8514+XlzSdbByo1nP5BLRQN+O/Fdvz3YjsUDTjjv3XYV6H/IEvqNFzzpRN/HGHFrHMj8OJyN37ZfTwRO1il4aXlbrx4lk/qDPudJlBxvucJVyESOvvG3tdkzJx9g9FBMP8Iy70OiSgXwB1CiCRvkvQdgEwhRH4b28kAsBfA+UKI7733DQNQKoTY79OgDTBrxrxbAbxhdByNkVT39nErHom3eSp5srIfFMZh2Z9vNYfiBr0+c3ZN7YLnjpT6fOL1Wf+rRZ0MLLrx+I4x9/zswn/We1B4bwysTVTOf3O1B7f/4ELZ/TFwROjHlDsFkp6rxuvnRODWUVZ8v0PGX+e6seU2faTp1u+diLYSnjtTT6yu+dKJtOjjt4OZEHDfJN+37Tdt2BCjYwkStQBG5D99Hpe9CTNh2aPlT0KIdWGSZA0A8ILRcTRFM9n6LR37mKcmMs2ne/QxXWoFRiVUiSKj4zBSpqz4rvJpPesLNZyRdeJQ6Jm9zCh3AcsOqE2eJ6uAWQKi6o2ERlv1+45+HPaogL1e05EWgsfb5PKDCubuUfCPKb7ZnsifhID2sHL9Wk6yThAF4P2MmbP5fTnMhPV/KBFNhd6bBQB7vcOA+d7H0ojo30S0h4icRLSDiB4nomYnfDQcOiSicUT0LREVEFEtEa0noqsbnHOD99qDiGiO97htRHSJT59wK82aMc8K4EMAza/HN5iQTN1Xjnowtiyu7xajYwk3BJivnaflGR2HkbL8VKzUpQhYG6RwR2/nlTRdiuLS/mZEWoB7fnajuFZDca2Gv/zsQnwE4bL+enY1tIsJm4o1/LZXwd5yDV/kyRjZVYIQAnf+5MLjp9oQawv+RXsfqGcu+kA9q1P3qDZhDIC/GB0E862wTrQArAVwr/fflwAYB+Bi7+0kAGUA7gZwNoDnANwI4LU2XiMdwBIAvwdwPoAvAPyHiK5s5NgPAXzrjWEngI+JyKf7CbbSgwAGG3DdtiMpcf2QP2cWpI5ZZXQo4WbsNtFP0kTTXSxhLkOWW79Usg16J0hYdfjEH+vKQ/rtMmfTUzW6xkj47foofJEnI/X5GqQ+X4Mv8xT8fE0kkqP0l+qseAl/m2TDqR/UIevVGgxIMeHKQRb8d6MMjwrcNCz4F+0tUgcteFi5obPUymqPxzJmzu5jdBDMd8I60RJCVAE4Ot69TgixXAixzvvYJiHEvUKIr4UQC6Dvqn4fgOtb6tVqcI2PhRDPCCF+APAbgKe8bf2hkcNfEkK8JoT4BcAN0H/+0xo5zm+8Q4YPBPKaHUYUmZd97bA9GecuMjqUcGISSDtlg1htdBxG6SErfpmAPWOEFV9vU/DOGg/KnQI/71Lw4nJ9Ia3UTGdTQbWGyz5zYkRXE368OhI/Xh2JEV1NOO/DOuyvPN4T9tAUG47cF438O6Mx+6pIuBTggV/deOXsCCgaMON7J5Kfq8agf9Zg8X6l6QsaYLeWtvRaeWa4V33vKDuAdzNmzg7+rknWKmGdaDWHdHcR0VYicgKQAfwfABuAVi9/J6J4InqViPZ525AB3AKgbyOH/3L0H0KIUgDF0LdiCIhZM+ZJAP4FvXZLaCEy52ecN2lLzg3zjQ4lnFy+SOucmyoLURUlhF9qF900zIJbR1pw62wXEp6txiWf1uEfk/V5U12im37vfG6pB7Iq8Plldpzd24yze5vxxeV2mCTg+aUnVjxJipSQHqe/fD+1yI0JPUyYnG7Gm6s92FCkYccd0fjbJBumf+6EuwO1xnypTMSsP9vzzMhOVpC0vSYBuN3oIJhvdNpEC8BdAJ4H8BWACwGMxvFf7LYs2XkPwHToQ49nQq+u/u8m2qhocNvTxmt11B0Axgbwej5XlDpq6uph9ywUoPbvu8KOiavF8G4lYp/RcQSaVcBvxUpNEuH1c+04cl8MNs6IQtG9MRjbXZ+kdfR7Y7aVaBiQYoKl3qpEq4kwINmE3eWN/7rnV2h4Y7UHz56hv4z8lq/i6kEWxNsJVwy0wK0AO0qN/1NxCcvOKe4XM2WYQ+9DnnGeypg5O8PoIFjHdeZE6zIAnwsh/iaE+EUIsQr68tpWI6II6EN/DwshXhdCzBNCrEYQ/lxnzZiXDuBJo+PwhSpH1uRlY3JXqpLZZXQsoY4AumGulm90HIEWq2kV/r5GvJ0wKNWEaCvhjVUejO9hQnZS04lWuoOwuViFRz3eA+VWBDYXq8hwNN4JdO8vLtw+yoqMuOMvOXWyfr6qCbhVYfhWC4qQCk5zPx9djSi/zIkLY9EA3jE6CNZxQZcQ+MHRPveGPUd2AA0rCF6NtrFB/xkea4eIYgBc0MZ2AuFN6MuHw4LLnjR2ybgnd8jmyAqjYwl1g/eKwRZFdKqkNdlPxUoBvczC80vdmLtHwZd5Mi77rA6fb5Xxz/NOfAnq/Wo1fv/N8TBuHm7F4WqBiz9xYvYOGd/vkHHRJ3UoqBG4ZcTJHUEL8hUsP6jigYnHyzlMSTfhjVUefL9Dxn1z3IixEvolGvcyLwQqL/Y8WnMIyWmGBRHaTs+YObux+b4shHSGROvoZPg/EtEYIhrkvT0HwHQiuo2IziKiDwD0bkvDQohKAKsAPERElxLRxQDmAvDb1h7tMWvGvGugr6wMK4olavCScU+WOiMSDhsdSygjIH7ais41Kb67ovhttaVFInyyRcZFH9fhhq+dqJOBJTdFYXDqib1ZigbU67zCiK4m/HR1JKrdAtd+5cR1X+nnzrk2EkO6nHiuJgTu+tmFp06zIcp6vLfr1pFWnJZpxjVfOvHjLgUfXWqHzWzMlCgh4LlFvnvvJpHFK+g65vmMmbONWJ3OfCTsJ8IKIfYR0b0A/gzgTwAOAsgA8CiAZACPew/90nvMd40005yrALwF4AMApQBeBxAJfT6U4WbNmJcM4GWj4/AXzWTptWxMbsHINc/tjK05wC/o7XTBCi3uqwmd4XOXLkOW/fZkR3Q1YdUfWp5nn39XzEn3nZZlxmlZLb8sS0RY98eTr2EzE9690I53LzS2RJ4QEI8r16yeo40cb2gg4SEW+g4ewThSwlohLLfgYcfNmjHvQwCN1fQKL0JUDt78Zn5S6WbDK01fv38fVjkbH5n6sGc6htpPfhPc5HTio4oKrHHW4YiioIvZgvNiY3FzQgJs0vGcYJ2zDg8XFqJYUXBebCweSEmFud4irm8qK/F+eRk+T8+A1MbFXX+7zrR9Zzfq16aTQtRTxSWrp9XWjTQ6jnD1kXLKggeUP3CtLN86N//p8340OgjWdpxohbFZM+adCeBno+MIGCHcfXd+uq774YWGrqzc5XajVjtxpddrJUeQ53ZjQa/eJyRGRz1bXIxNLicuiHUg3WrBdrcbr5WUYFxkJF7ppo8ayELgrD27cVZMDMZERuHRokLMSEzC5XFxAIA6TcO5e/fg+bSuGBnZ9qLnu9Kw6MEbzJPa/oxDz8eHCncO8Hi4B9QPlms5C67w/IOTLN/bAWBg/tPnyUYHwtom7IcOO6tZM+aZEcZDho0isu3oc/lopz1pYZ/dXxpWFLG37cS95jxCYLPLhXNiYhtNsgDgDwkJiDcf/3McHRkFG0nILSrEIVlGN4sF+R4PqjUN9yanwESEXe54LK2tPZZovVNaiqER9nYlWQDQqwDD7S5R5Yyg2HY1EEK6K7JfipV2dvu0lOVXev7WKZJ1A/QFcCf0skQshHSeSRmdzx0AcowOIuCIpAM9Tpu8YeCM+UaHctTi2hpUaRrOi206f6mfZB2V403Yjih6dW9ZCFiIYPImaxESQfYu3j8ke/BRRTnuS0lud5wERF2+WFvX7gZChRA1Dk2EfTIZaBUiauOZnmeHCkj8vuI/D2XMnN3F6CBY2/AfRBjyToDPNToOI5UmDZq6cuQDizWSDN+D5MeqanQxmzGikblZzVnvckIC0MOi71+XbrVCFgLfVlbiiKLg26oqDLTpJQOeKz6CK+Pi0c3SsXqQZ6wVYb+6yQL/FSvtrNzCsnuK+6WeblgDWYC5M4qBvs0bCyGcaIWnJwB0+uKANdHdJy4d+9h6VbK2qRCtLzk1DfNqqnFWTAyoDZPTjygK3iotxQWxsUj09nZFSRJmpqTg74UFmLJ7FwjANfHxWF1Xh/UuJ/6QmNjheK0qeg3fqW3ocENBLCYAxUo7E1VQ0RmeZ22ViI4zOpZO4vqMmbNHGx0Eaz1OtMLMrBnzhgL4vdFxBAuPLW7k4vFP7fNYYkqMuP78mho4hWh22LAhjxC4+/AhREoS/ppy4lSiSx1xWNK7D37MzMLHPdMRJUl4srgIf0lKRqQk4ZUjRzB5106ctWc35lZXtyvma+dphiWmgZCkqHVGxxAuhED1pZ5HKvaL1LDvCQ0iBOA13nQ6dHCiFX5eAP+/nkA1R/RfMu7x2jp7yv5AX/uH6ir0tFgwMKJ1w4ZCCDxQcBi73G682a07HKaTt2yJMZmQbrWCiPBFZSUsRLggNhbzaqrxQ3UVPs/IwJNd0vBAYQFKlLaPnHYtw8i4GhG2w2vdlHb8UNhJhIB8u3znzvWid6coCRJkRgO43uggWOvwG3IYmTVj3jQApxodRzASkjl9+eh/2Ctis/ICdc1qVcWi2to29WY9VVyMeTU1eL1bd2Q1WL3YUI2q4tWSI3gwJRVEhJV1dTgtOgYpZgtGREYiw2LFJlfbd5ohwHrNPG1Lm08MEZmywq97PvCcMn3lD9qY4UbH0Yk9lTFz9slVb/2MiHKJSDTxdY0frpdPRM/Xu/0eEbV6JwtvvIaMaBzFLzhhwlvO4Tmj4whqJCWvHXZ3j6LkEWsCcbm5NdXwCIFzY1qXaL1dWooPK8rxTFoaRrSiRMMbpaUYFxmFIfUm2bvE8fpdTqG1e0PhCXmiNwmhtXxk6MmUZZ6w3UFfqJPmv6FeOMHoODq5LgAeNOjalQDGNfL1UwCu/RiAGwJwHZ/hRCt83AIg2+gggh5R9Jb+Nw7e1+OMJf6+1I9V1ehns6FXIz1TZ+3Zjb8XFhy7/X1VJV4uOYILYh1INVuwwek89lXWyEhXvseDLysrcE/y8XIOI+2R+KGqCj9VV+Hd0lIclmUMbuWQZUMmDd2nbArP/Q+zZJlLO3TAGq3PwnvkW6caHQcDAPw5Y+ZsI2rCKUKI5Y18+b3nSAixWwix2d/X8SVOtMLArBnzogA8bHQcIYPIsjvrwvF5/a6a769LlCsKltfVNtmbpQoBrd6uDEtr9fnnX1dV4sr9+074WlBbc9L5zxUX4/r4BKR6Sz8AwOkxMbg8Lg6PFhXho4pyPJWWhqRG6nO11vQFWlhOtu0hKylGxxCqDoqkFb/zPDzR6DjYMZEAHjA6iPqIKMM7jHg5Eb1FRJVEdJCIHiEiqcGxlxHRTiJyEtFvRDTMe+4NzbR/wtAhEcUR0b+I6DARuYhoPxG908h5w4hoORHVEdE6IgpYYV2uDB8ebgfAbx5tQUQFaROmOiOSFgzb8Opk0lfy+Ey82YyN/ZruYJzbq/cJt59M64on07q2uv1Z3Rtf5HV3cgruTvbNr0JCDUaklomDRQkUPivKhHDGa1q80WGEoioRuel093ODuSBp0JmRMXP2c/lPn3cokBclopPyByFE/e73ZwF8AeB3AE4D8BCALQA+9Z4/EsDHAD4H8CfoBbY/aUcoLwIYD+AvAAoB9ADQcGeQSADvA3jJe8zDAL4konQhhN9XIfMfTIibNWNeNID7jI4jVFXE95uyfPRDyzQyeYyOJdgQIN04V9ttdBy+ZAGKjY4hFHmEee8U94vdXLC1byya+ZMNwN8DfM1EAHLDLyLKqHfMQiHEPUKIOUKImQA2ALik3uN/BZAH4AohxE9CiJcA/KsdsYwGMEsI8YkQYoEQ4n9CiFsaHGMHcJcQ4j9CiB8BzACQhJMTMr/gRCv03QH9F4a1kzMydfyScU9sUUwRVUbHEmyG7hYDzKoImyQ0StPKjY4h1KiCjpzpecZcjtgEo2NhTfp9xszZGQG8XiWAUY18Ha53zC8NztkKoH7v+CgA3wkh6q/Z+bYdsawHcB8R3UZEfZs4xgNgfoNY0CAev+FEK4R5e7PuNTqOcCBbY4YtHv9kgcsaV2R0LMFEApLOWSVWGR2HrySpalgXY/U1IVBzhecfJfkirYfRsbBmWRDYebqKEGJ1I1/1P5RVNDjHA6D+it8uOHk7rPbU77sDwNfQhya3e+d8XdHgmGpRbxV1vTgDsgKZE63Q9ifoXbjMBzSTrd+ysY/KNVFd9xgdSzC5eJkW8Fo9/tJVUblYaSsJAeUu+fZtq0R259ucPjRdmzFzdlM9OsGoEEByg/sa3m6REKJCCPFnIUQXAEMArADwf0TU3wcx+gQnWiFq1ox5MQDuMTqOcCMkU/eVIx+MK4vP3mR0LMEi2oXBWQVip9Fx+EKGLIflSkp/eFm5dPk32oSRRsfBWs0E4BGjg2iDVQDOpxM3gb2gIw0KITZCn7MsIYjKHXGiFbq4N8tfiBLWD76j9+EuY1caHUqwuHGOWtDyUcEvy8PFSlvjO3Xs/FfUS7mMQ+iZnjFz9sAAXMdMRGMb+erWhjaegb7S8GMiOpuI/gLgD97HWl0smYgWE9E9RHQWEZ0J4FUAtQCC5vWbE60QxL1ZAUBk39bvmhF7MqYtMjqUYND3EIZFeMTJBb1CTIascLHSFmzQshb9Sf7zVKPjYO1CCEyvlgPAska+bmxtA0KI1QCuBDAC+hyrSwHc6n24LQuTlkGvFP859NIRSQDOEUIcbEMbfkUnTvhnoWDWjHl/A/C40XF0FinFa+YP3PrvqUbHYbRvxtLC/zvFFJDl0P7y2/6DR5JUrc3zQDqLApGwaoL71eEapJN3M2ehQgPQP//p87YbHUhbefdK/C+ALCHEXqPj8RXu0Qox3pWGdxsdR2dSnDJi6qrh9y0SINXoWIx09mrRxegYOkQId6KqcSmUJtSIiK2nuZ/vz0lWyJMQIqvRieifRHQJEZ1CRPcAeA3A7HBKsgBOtELRTQC4nk2AVcdmTFo29pHVqmRxGh2LUWwK+g7eo4XsIgEzUOzrHQDChSxM+6a4X0qpQ0SU0bEwn7g2Y+bsUPhglAjgDeg1t+6DXhn+KkMj8gNOtELIrBnzJOiT4JkBXBGJY5aMe3KXbI7qtEUvr/9VC9mirlystHGaoJKzPU+jFA7u7QsfNgB3Gh1ES4QQlwshugghLN7vM4QQIfsa0xROtELLNAC9WzyK+Y1iiRy0ePwT5c6IxIDuKxYsupdgZGytKDU6jvZIULWQn8zva0Kg7ir5waLdolu60bEwn5uRMXN22NTAC2WcaIWWu4wOgAFCsmQtG5NrqopJ32F0LIFGgO2q+aE5fNhVUWSjYwgmQkC9T/nj5uXagAFGx8L8Ig5tWAXI/IcTrRAxa8a8wQBOMToO5kVSl9XD70s9kjh4vdGhBNrkzSILIbhcOV1WeH5WPW+oFyz9XJ0y2ug4mF/9OWPmbH6fNxj/B4SOu4wOgDVA5Ng08JacA92mLDM6lEAya+g5catYY3QcbZUlyzajYwgWv6gj5j+nXDHJ6DiY3/UCcL7RQXR2nGiFgFkz5iUjDFdihAUi287el43Z0fuyBUaHEkhXztdaXbk5WGTKMs9XAZCn9Vx8i3zPVKPjYAHzF6MD6Ow40QoNt0JfRcKCEZF0sPvUKesH3bZAACE3pNYeSVUYkVIhQmpBQLqsdPotq4qFY835nsfHGB0HC6gpGTNnDzM6iM6ME60gN2vGPCuOb0vAglhZ4oApK0f+balGUthPuibAdP1cLXQ2mhZCTlbVTl0Rvk7Ytp3ifrGfArPF6FhYwN1mdACdGSdawe8KAKFQeI4BqI3uOmHp2Mc3KCZb2JcSGLFLDDCpIiSSShNQLHXi1ztZmA6e4n4hoRb2aKNjYYa4ImPmbP6/N0infeEJIfxJJMR4bI6RS8Y9ecBtiTlidCz+JAkkn7lWrDY6jtaI1ESZ0TEYRRMoP9/zhKcICSlGx8IMEw3gcqOD6Kw40Qpis2bMywHA8ylCkGqOyFk67vG6WnvqPqNj8adLl2h2o2NojQRNDfsexsYIAdf18syD20TPLKNjYYb7vdEBdFacaAW3G4wOgLWfkMzpK0b/PbLc0Xur0bH4S6wTQ9OLxG6j42hJmqJ4jI4h0ISA9jflpvWLtMGDjI6FBYXxGTNnZxsdRGfEiVaQmjVjngnANUbHwTqIpOR1Q+9KL0oZERJDbO1x4xw16FcfpsuK0SEE3L/VcxZ9qJ4+1ug4WFDhXi0DcKIVvM4E0NXoIJgPEEVtyblxSH7PsxYbHYo/5BzAEJtH1BodR3MyO1mx0t/UIfMfU66dYnQcLOhclzFzNq86DTBOtILXDUYHwHyIyLIn64KJW7OvnW90KL5GgOOiZdpao+NoTpZH6TQrrnZqXZfcKN/PSRZrTAq4UnzAcaIVhGbNmBcP4EKj42C+V9hl7NS1Q+9aIEAhV1m9OeetEklGx9CcnorcKYqVloqYded6nh4FEO/ryJrCw4cBxolWcLoSXAk+bFXE9ZmyfPRDKzQyu42OxVciZOQM2KdtMTqORgmhdlHUsC9t4BTWHVPcL/WSYbYaHQsLamdlzJzdzeggOhNOtILTDUYHwPzLGZkybsn4J/Jkk73S6Fh85fq5WrnRMTRGAo6YAJPRcfiTIqTDp7mfj6lBZKzRsbCgZwK/xwQUJ1pBZtaMef0BjDI6DuZ/siV66JLxTxa7bHGFRsfiC+nFGBFdJ4Iu2YoUotToGPxJCFRe4HnceRhJaUbHwkLGlUYH0JmYjQ6AneQGowMIFaqm4tcNn2LZth9RXlOMaLsDw7Km4NLxzRfT33dkO75b+S72H9kBAOiR1Afnj7oJGak5x47ZsHcxvlz2JtxyHSYPuAjnjrzuhDZ+XPMB9h3ZgRlnP96h56CZrH2WjXn00Mg1z+yOqT3Uq0ONGYwA+xULtZX/OtsUVBOx41W12ugY/EUIuH8v35u/VWQMMToWFlIGZMycnZ3/9HnbjA6kM+AerSAya8Y8gr63IWuF//32DOZv/gqnDbkMt5/3DC4YfTMspuanp5TXFOP17++Dpqm47pSZuO6UmVA1Fa/Pvh9l1UUAgBpnJd6f9xQm9j8Pl0/8M+Zt+hx5B1bXa+MI5m38ApeO881e30IydVs18oGE0vicTT5p0ECnbBDpEEIYHUd9XRQ1LIuVCgHxqHLt2nnacE6yWHv8zugAOgvu0QouowH0MDqIULB1/0qs2TMfD/zubaTFZ7T6vM37V8AlO/GHMx+B3aav+M/qMgB/ff8SbNm/ApMGXIC9xVuREJOKM4bqves7Dq/HtoNrkNNjJADg2xXvYHz2uUh2+HA+KVH8hsG3R2Tv+HBF14KlIbvtkkVDxthtYu3yHBpudCxH9ZSVoEr8fOV/6ukL/6OeE1S9hyyk/A5Ax7rkWatwj1ZwudToAELFsu0/oW/XYW1KsgBA1RSYyASr5fgWfTazHSYyQUB/P1ZVBRbT8UWfVrMNqiYDAPYWbcW2Q2tx9gg/FO0nsm/re9XIXVkXLvR944Fz9XwtqHqQsmQ57FbhLVEHLPiHchMnWawjhmTMnN3b6CA6A060ggsnWq2UX5yHFEd3fLr4Vdz77/Pxl3fPxTs/P4yK2pJmzxuaOQkWsw1fLfsnqp3lqHaW44tlb8Bui8awLP19q3tSLxSU7cWOQ+tQUlWA9XsWoWdyPwgh8PnSWTh/1I2wW6P888SITPt7njl504CbF/jnAv6XUoGRiZWiwOg4jsqU5bAqVrpH67LsavnByUbHwcICDx8GAAXZdIpOa9aMecMABHV17WBy1ztnw2Qyo1tiL5w17Cq4PHX4esXbiI1MwL0XvQ5qpl7jwZJdePOnvx1LymIjE3HbuU+he+Lxueg/rvkAs1e/DwAY0HMMbjnrMaze9St+2/gF7r/0n5DI/59RYqr3LRq55rnxBBFypQmWZdP8ly42TTU6DgCYfeDwoZ6KEhZ1g8pF9Pox7lk5Hli4zh7zhTX5T5830uggwh0nWkHi5Wv/erdkzb5BMvccSM1lCQwAcOc7Z8EkmfHo1R8iOsIBANh1eCNe/u4v+NN5z6Ff98anCFXWluLlb/+CLvHpmNRf34li4ZZvcLB0F+6+8FUkxKQeO7bGWQmP4kJCTCrcshOPfnw9bjzt78hIzcFnS17Hhr2LEBOZgCsm3oleaYP88jxtrvKVY1c+OtCkeSL9cgE/UQmFV99vStIkMnYeqBDa2vwDqgUI+f3dXMKya7R7VnIVoh1Gx8LCSmb+0+flGx1EOOOhwyCherbcKNd8Mchd8XKxu+qjhapn2xohVNnouIJVpC0aXRMyjyVZAJCVNhBmyYKCin1Nnjd3wydQNQU3n/Ew+vccjf49R+PmM3NBJOHXjZ+dcGy03XEs8fp53YfI6jIQvbsOxuKt3+FQ6W48dMX7OGvYVfj33Mcg+2lhmzsifvTi8U/u8ViiyvxyAT8xCXQ5bb1Y3fKR/iUBJeGQZKlCKjjd83wkJ1nMD3j40M840QoCL0yflglgoH5LpAq1YLJc+8MId8Urde7K95YorvXLhfDUGhpkkEmNS2/0fgEBQtMdgkUVB5CWkAGT6XhHi9lkQVp8BkqqDjd6Tml1IRZt+RYXjb0FgL4KcVSf0xFpi8HI3qdC0WQUVxzswLNpnmq2D1wy7olKZ0SS/y7iB5ct0gyfhB4hRPOT9kKAEKi6xJNbfVAkdzU6FhaWONHyM060gkNTG0g7hFY2QXHOG+uueN3kqnxnpeJctlhodSHVu+EPA9PH4nDZXtQ4j+9gs6tgI1RNOWGuVUMJMak4XLYXSr3OQln1oKA8HwnRqY2e89WyNzF5wIVIjOly7D6P4gIAaJrqbcu/Q/BCsmQuG/OwpTImY7tfL+RDjjoM635E5BsZQ5yqhXSxUiHgmSHftXuD6N3X6FhY2BqdMXN2d6ODCGecaAWHC1pxTAS06tGKa9lEd+WbDlfFP9fLdb8t0NTKxrthwtyEnPMQFRGLN3/6GzblL8Wqnb/ig3lPo1+34SfMl8r96Fr83/znjt0en30uKutK8c4vD2PzvuXYtG8Z3vn5IVTWlWJC/2knXWfn4Q3IL87DmcOO71jRJ20IFm35Fpv2LcNXy9+CzRKJlLgAlD8jKXXN8HvTipOGrPP/xTqOALpxjtb0OG4AdFGVkN24WwiIp5SrVv2sjR5mdCwsrBGAs40OIpxxwVKDvTB9WhyASW08zQThHKq610F1rwNgyZOsvYrMtpE9JHNKSG/j0lp2axT+PO15fLbkdfzn1ydgkswYnDH+pO13NE2FJrRjt3sm98Xt5zyNH9Z8gA9+exoA0DUhE3ec9+xJPWGa0PDF0jdwweibYatXd2vigPNxuGwPPpj3FGIjE3HjaX9rsSK9zxDFbh7whwG9d3+xtOfB38YH5qLtN3CfGGKVhdNjIXvLR/teKBcr/VSdsuBtddpUo+NoLbn8MKpWfAn34W2QS/bD1r0/ulz19LHHhSqj5LsX4CncCbW2HGSJgLVLH8RNvha2Ls2XcyqZ/RJqN/960v1db/4nLIn6hxzNXYfSH1+Fc+9aWBJ7IGna3bAkHF9sqrpqcPidPyLld7mwpfXx0bMOG2cA+JfRQYQrXnVosBemT/sdgM9aPLDVpL2SOX2fKWJEsmTu0Z9XMIYpIUS3wwsX9tv5adAXrfxkkrT4i4nSRCOu/Zey8qU3VVYHfULa0Eqt34LLPQ8H/f9tfXU7l6Nszpuwde0Hz5F9MEXFnZBoabIbpT+8jIj0ITDHp0G461C1+ht4ivYg7cZXYYnr0mTbJbNfgvvwdiSde9cJ91tTs0Bm/UNO+fz34Ny7BvFTbkDN5l+hVhajy7XPHzu2bO5b0DzOk9pgAIBSACn5T5+ntXgkazPu0TLeqb5tTsvUlL2ZWs1eAFRAprSd5ojh0ZKl92Aiif+/wwURHeo2ZYrTnrxgyMZZkwnNrAAw2PkrtPgvJhozSyHLo/ipsqz/7NdSlk/3/KOtvdyGs/ceje59xgIAjnz1JFRn1QmPSxYbki/86wn3RWQMxYFXr4RzxzJYRl/cbPuSNQK2btlNPu7atx6OcdNhzxoBa2oWDr5+LTSPC5I1AnLJAdRu+Q1df//Pdj67sJcIYBiANUYHEo54jpbxTvNf0yJNqIcny7XfD3dXvFzjrnx/ieLesEIIuc5/12SBVJbQf8qKUX9fqpEUtKVAIj0Y0O+AyDPi2umKHG/EddurUkRuPMPz7FABKeRem6kdRXzJEgEyWSE0pcPXF6pyrHeLzHo916Ptls37F2LHXApTdEj9OgTamUYHEK5C7o85nLwwfVo3AIFaTRQntNIJSt2vY9wVr5Gr8l8rFOfyxUJzlgfo+sxP6qLSJiwd98RGxWQL2hV2N8xVSwN+USFEN1lpfClpEHIL854p7pd6uGGNMDoWfxJCQGgq1JpyVMz/D0iSEJXT8iipXHIA+1+6DPuevwiF/7sfrv2bTnjcmtobNRt+huqsQtWab2GO6wJTRDTqdq+CUn4IsSMv8tMzChtnGB1AuOKhJGP5eNiw1ezQqsYorqVQXEsVkH2dyZpTZbYN70um2DSDYmId4LHGjlgy/qm8sStyk2yeqmSj42koqxDDo5yistZOASu4SUCpFUgK1PU6QhVUfKbnWWsFYsK+y6VqxeeoWKBvbyVFOpDyu1yYHSnNnmNNzYKtaz9YEntAratE1aqvUPTJP9Dl6mdg69oPABA38UoUffx3HHz1KpDVjuSLHoBQFZTPexfxp9wEMod83Vp/m5Axc3Zk/tPn8YiHj3GiZSyjEq36zBDOYap7LVT3WgFYtkrWPsVm24iekjk5y+jgWOupJlvO0rGP7R+9+qn8qLrCDKPjqY+AyMsXaQv+c6YpYBO8vcVKgz7REgLVl3keLtsnujQ9ASmMRA88HRHpQ6HWlqF67Q8o/uJRpF71NKxJPZs8J3bkiaUG7b1G4vC7t6Ny+WdIueTvAACzIxVd//AmlIpCmGISIVkiULXyK5hjEhDZdzxcBzajbM6bUGvKENlvAhJOvwVk4uSrHiuAyQB+MjqQcMNDh8YKhkSrPgLk/ppn61RP9X+zXOWv7PHUfD1fkw9sFbw8NSQIydxzxai/RZfH9dlidCwNnbZeBLQookPTqlo+ylhCQP6T/Kcda0XfTpFkAYApOh62tD6I7D0GKb97CJI9BlXL27bwWrJEwJ41Ep7C3SfcT5IJloRukCwRUOsqUbn8M8SfdguEIqPkm2fgGDcd3W55G56i3ahez/lEI3ielh9womWQF6ZP6w2g6Y9wQUHN0uQ9Uz01n/V3V7xc6K76eKHq2bFW+GLmKvMfkpLWDbkzozB1lOF7DdZnVdFr5A5tfaCul6qorkBdq71eUC5b8b02boTRcRiFJBOsSelQKorafi6h2bW2FYv+i8h+E2BNzoBcdgBCUxGVMwlSRDSiBpwCd4M5XgwAz9PyC060jBNsvVktOGEFY7W76oPFinvjCiFkp9GRsUYQRW3Nvn7o3vRzFhsdSn3XzNMC9vsS7MVKv1InzH9dvdiQ+mLBQigeeIp2wxzXtjULmuxG3e7VsDZR6NRTvAd125cibtI1x6+lyhCaqv9bdkP4edusEDUwY+ZsnqfrYzxHyzh+LOvgd/FCLZmo1M2FUje3jqTY5SbrINVkGzyAJHuc0cExLyLz3sxpE+vsKQsGbHs/KIpfppVjZHy1KC6PoeZnP/tApiwH7evbOq3Xwr/It081Og5f0mQXnLv1TlSlphTC7UTtNj3Pt/caCefOFXDuWQ171giYohOg1pSjet1sqLXliB110bF2ajb/itIfXkG3P/4LZkcKNHctij9/BFEDToE5Lg2aswpVq76BWlMKx4UzG42l7Nd34Bg3HaZIfe2FJaE7yGJD+fz/ICJ9CKrXzkZsC3W7OrHxAL4wOohwErQvROHshenTCMBUo+PwkUihVY1VXEuguJYooMi1JmtOjTlieF+SYpou9cwCpqjL6Ckue+LC4etemkgQhvZiE2C5dp629dULTYFItIKyWOkhkbjyUs8jE4yOw9e0ukqUfPP0Cfcdvd1txruwJHZH7ZbfUDbvX9BcNTBFJcDWtR+6XHc7rMnpx08SAhAajm7UTiYLpEgHKpd+ArWuAmSywtYtG12ufLrRrXTqti+FWlOOmOHnHbuPzFYkX3A/Sn9+AzUb5yCq3wTEDDvH9z+E8DAGnGj5FG/BY4AXpk/rA2CH0XH4mQCsW03W3iWmiBE9JVNyptEBdXZ255Hlo1c9PtSkKYbWaVIJh6++35SqSWTy53W+OliQ31uWM/x5jbaqFvYto91vZDphizQ6FsaasDD/6fOCogc8XPAcLWOMMjqAACDAM0D1bJ3iqfpvpqv8ld36CsaDW40OrLNy2pPHLhn35HbZbK80Mg6TQNcpm4Tft/ropih+7zVrC48w509xv9iFkywW5EZkzJzt1w9BnQ0nWsboDIlWA2ovfQXjp/1d5S8ddld/skD17Fonjs5OZQGhWKKGLBn3ZLHTllBgZBzTF2p+fe0hIcrtQgRNQqMJOnKW52mpDI5Eo2NhrAVRAAYaHUQ44UTLGCONDsBYoqtQDk2Ra78d5q54udJd9d/FinvTSiGUoF+OHw40k7XPsrG5qI7uscuoGOJrMCKtVOz3V/s2IY74q+22EgK1V3j+fmSv6Brk5VwYO2aM0QGEE060AuyF6dMk6LukM12CUI9MVOrmjHZXvKq6K99drrhWLhGay9DhrbBHprRVI/6aVJIwYIMhlwfohrnaXn+1H6tpQfH7IwSUu+Vbt64UOf2NjoWxNuBEy4c40Qq8HOhds+xkUUKrHKs4F09wV74R6ap4c41ct3Ch0KrbXs2QtYwobuOgW/sdSpu43IjLD9kjBpoV4fZH2ylBUqz0dfWiZV9pkzrhVAEW4jjR8iFOtAKvkw8btpoFom6E6l492V35Toqr4vXNcu3P8zW1NN/owMIKUcT2vleM3pV10cJAX1oCEs9bJfxSvb6Homj+aLctflBHz39BuXyS0XEw1g45GTNnxxgdRLjgRCvw+NNt2xGEZ6Dq2TLVU/V+hqv8lV2emm/na8qhPKMDCwtE0v6eZ0zeOPCW+YG+9EXLtFh/tJspy4aumtqsZSy6Tb5rqpExMNYBEvi9ymc40Qo87tHqMLW3Ju+a6qn+JMdV/tJBT/WnC1TPrg28grFjSpKGTF05YuYijaSA7WUZ5cagXoeFz2vKZcmKYSsOC0X86gs9j40z6vqM+QgPH/oIJ1oB9ML0aWYAQ4yOI7yI7ppycIpc++0Qd8XLFfoKxs28grGdamJ6TFo29rF1qmStC9Q1b5qj+nwOXrosx/m6zdaoFRF5p7pfyFFh4l03WKgbanQA4YITrcAaCMDQqtxhLlFfwfjLaHfFq4q78t1limsVr2BsI7ctbtTi8U/u9ViiSwNxvd6HMczuFtW+bLOHHPhipbIwHTjF/UJSHSJ4sQsLB9lGBxAuONEKrKFGB9CJRAutcpziXORdwfjWarlu0UKh1RQbHVgoUM32AUvGPVFVZ08+4O9rERD9u8XaOp81KERllBDRPmuvFTRBped5nlSKEZ8cyOsy5kd9M2bO5hzBB/iHGFj9jA6gk7JA1I5U3asmuyvfTnJVzNok185ZoKll+4wOLJgJyZy5fPRDtorYzG3+vtaZa0War9oKdLFSIeC8Vn6gYIfowft5snASAYB/p32AE63A6mt0AAwShHuQ6tk0xVP1Xrqr/NWd+grGw35PJkISSSlrh93TrTh52Fp/XsamoM/Q3dpGX7QVo4mADRULAfUB5eaNS7SBvGUJC0c5RgcQDjjRCixOtIKO0kdfwfhxtqv85YOe6s8WqJ49G4QQhtdhChpEMZv7/37Qvh6nL/HnZa77VfPJPK0UVXH6op3WeFs9b8nH6qm8OouFK060fIBXxgSId+ud3kbHwZqjddeUA9015QAAlJApJc9sGxYhWfsNJjLbjI7OUESW3VkXjXfakxZk7/h4ij8u0a0Uoxy1oqQyipI60k53RQ1ImY+56rD5TylXTw3EtRgzCCdaPsA9WoHTE7ziMJQkCbV4klz38yh3xased+W/lymu1UuFcFcZHZhhiOhw10lT1g3+0wIBCJ83D1iv/k3b3NF2Mjz+L1a6Xeu+5Gb5vqn+vg5jBuNEywc40QocHjYMXTFCqxinOBeOd1fMinBVvLVadi5eJLSagE66DhblCdlTVoz6xzKNTB5ftz1xi+hNHRy2zZJlu6/iacwR4VhznufJ0f68BmNBgks8+AAnWoHDiVZ4sELUjlRdKye5K99OdFXM2uhdwbjf6MACqS6qy/gl457YrJhsPq1/ZdbQfdJmsaYjbWTIisNX8TRUJ2zbp7pf7KvAbPHXNRgLInEZM2d3MTqIUNemRIuIcolI1PsqJKLviWiwvwJsIo73iMgvm9H6EZd2CD8ShHuwdwVjT1f5qzs8Nd/N15SC7UYHFgiyNWb44vFPHXZbHT6tTXbFAq1Dw5LdFdkvxUoVIR081f1CXC3svNku60x4+LCD2tOjVQlgnPfrLug9NXOIKMGHcbXkMQA3BPB6vsA9WmFP6avJO6d6qj/q5yp/+YCn+vMFqrx3YzivYNRMtn5Lxz7mqYlM2+urNhOrMTK1XBxs18lC1Dg04fONqjWBivM9T7gLkZDq67YZC3L83tVB7Um0FCHEcu/XxwCuA5AC4GzfhtY0IcRuIUSHJ80GGP+ydipaD03ZP0Wu+Wqwu+KlUnfV/y1S3VtXCaH6fF6T0YRk6r5y1IOxZXF9t/iiPQKk6+dqu9pzrhXweeV/IeC6Sb5/f55I7+XrthkLAd2NDiDU+WKO1gbv9x4A4B1SvKP+Ad4hx5J6t+OI6F9EdJiIXES0n4jeqfd4dyL6lIiKichJRLuJ6LF6j58wdEhEaUT0byLa4z1+BxE9TkTWesdkeGO7nIjeIqJKIjpIRI8QkV/nqr0wfZoV+qpD1jklC7Voklz30yh3xStud+V/liquNUuFcPt0fpOhSEpcP+TPmQWpY1b5ornhu8UAsyranJTGaFqFL65/lBDQHlJuWD9fGxrQ6RGMBZFuRgcQ6nxRR+toAtGWoYMXAYwH8BcAhdCTtMn1Hv8AgB3ALQAqAGSh+dUPSQDKANwNoBx671EugGQAf2xw7LMAvgDwOwCnAXgIwBYAn7Yh/rbqCl54wHQxQisfrzgXQHEucIOiV5lsA1xm29BskqJCe588osi87GuHOe2Ji7Lyf5jUkaYkgeSz1oils0fT+Lacl6SodR25bkPvqWct+q96pl/qhjEWIjjR6qB2JVpEdPS8dACvA1gP4Js2NDEawCwhxCf17vtfg8evFEJ85709v7nGhBCbANxbL74lAGoB/JuI/iTECZ+MFwoh7vH+ew4RnQ3gEvg30eJfVNYYG0TNKNW1AqprhQaK2GCy9qsw2Yb3kkzxodldT2TOzzhvktOeMn9A3ntTO9LUJUu0qNmj2/b5pLui+KxY6UJ10IJHlOs5yWKdXWi+FgWR9iRaiQDkerdLAYwSQrjb0MZ6APcRkQpgrhBiRyOPP0VEiQDmCSGaXTpPRATgTug9YJk4sTBoTwD153v80uD0rfD/sB4nWqwlEoRriOreANW9AYB5u2TJKjBHjOgmmdP6GB1cWxWljprqjEhaOGLdCxMJol29uTEuDMksFLv2dqFW76iQIcs+6TnepXVdep08c3LLRzIW9vj9q4Pau+pwFICx0IflrAA+bOM8pzsAfA192G47Ee0koivqPT4dwGoALwHYR0Triei0Ztq7C8DzAL4CcCH0HrHbvY81rMZe0eC2p5FjfK2rn9tnYUfpp8k7pnqqP+rjKn95XyiuYKxyZE5eNiZ3pSqZXe1t44Y56uG2HJ8lKx3+Wy4VMevO8Tw9EiDqaFuMhQFHxszZUUYHEcrau+pwtRBihRDibegJzVgAl3kfd0NPvuqLr39DCFEhhPizEKILgCEAVgD4PyLq7338kBDiBui9Z+Ogz+P61tvD1ZjLAHwuhPibEOIXIcQq6EOHwYI/EbAO0NKPr2B8udRd9eEi1Z23OhRWMLrsSWOXjHtyh2yOrGjP+dkHMdTmEa3+W86Q5Q4VK3UK686p7hezZJgbvoYx1pnxe1gH+KKb/X/QJ5P/1Xv7IOoVOPP2dDXZGyWE2AjgPm8s2Q0e04QQywE8AiAS+pywxtihJ3j1Xd36p+B3aUYHwMKFSBZq4SS57seR7opXXO7K95YqrnXLhPDUGB1ZUxRL1OAl454sdUYktKl3CgAIiL1kqba2tcf3kJV2LyhQhFRwuvu56GpE+a2yPGMhiudpdUCHVx0KIQQRPQm9R+o06MN3txPROgB7ANwM4IQCgkS02HvcZuib0/4Beg/USiJyAPgZ+srDHQBsAO6B3quV10QYcwD8mYhWANgNPclq9byOAOAih41QNQ0Ltu/Byr0HUF7nQrTNisHd03DhsP5NnvPz5h2Ys3Vno4+dM6gfTsvR/9s3HyrEt+u3wq2omNA7HWcOOLGM2S9bduJgeQVumjjKd08o8GKFVjZecf4GxfmbS1/BONBltg3JISkqyejg6tNMll7LxjxSMHLNsztjaw60ac7ZOatFykdTW3GgEM54TYtv+cDGTkXlxZ5Haw4hOeTmwzEWANyj1QG+KO8AAJ9AL6dwP4BLoRcwfRz6/KfXofd43V7v+GXQK7tnAFABrANwjhDiIBHZAGyCPrm9B4A6AMsBnCmEcDZx/Uehl3J43Hv7SwB/BvBdE8cHGidajfhk5QbsKi7FGQP6ICUmGhVOJ4oqm++YGZPVA9lpJ3ZabD5UhN+27UZ2F/3+WrcHH65Yj9Nz+iAh2o7PV29CemI8+nkfr6hzYtGOPbjz9In+eWLGiNBXMC6H6lquHl/BOKKXZIoLjk+jJKWtHvHXysGb39yQVLp5SGtPi5DRb9BebfOmTGlgc8dZgCLoryltIgQ8t8h3790ksoa29VzGOglOtDqgTYmWECIXekLV8H4VJ1Y+v76R0x+ud/x90IcLG7uGG3oPV3Nx3NDgdg2AGxs5lOodk1//dlNt+Ylf9l4LZdsKirH+QAHuPnMSujhav3VcXKQdcZH2E+6bs3UnUmKi0C1eH/HZV1qO+Eg7Ts3RC3nvLi7FjqKSY4nWDxu3YXRWTyTFhO38TtOJKxgt2yRLVqE5YmQ3yZxqbI8NkWPjwBnZfXd9uqz7oYXjWnvadb9qFffd3PxMh+h2FCsVAuIx5drVc7SRbarXxVgnw4lWB3ARTT97Yfo0CXpBVVbPyr0H0TslsU1JVmNq3R7sLCrB0J7HXwdUTYPFZDp222IyQdX0xXr7Ssuxs7gUZ/QPppFlf5OzNXn7VE/1/3lXMH45X5X3bRJCdGjz5nYjsu3offmYnb0uXdDaU3oewciYOlHW3DFJqtrmBTAfqacu/Ld6DidZjDWvXUPyTOeroUPWtAQAphaP6mT2l1VgQNcUfLl2M9bkH4ImNPTrkoyLhw+Ew976FfqbDhZC1QSG9TxeQaNrnAMFldXYVVyC+MhIbDpYiLMH9oUQAl+v24qzB/ZFhMXij6cVArR0TclP12ryAVARmbpsN0cMi5IsfQYTmQL3QyGSDvQ4dUqdPXn+kM1vTm3xcCDiyvnairfPNTVZQLSroihtCWGZmrPgQeVmLkjKWMt8vlF7Z8I9Wv4XZ3QAwaja5cbq/IM4XFGFa8YNw/RRQ3CwvArvLVmDtnS0rDtwGN3iY5FcbxgwMToSp+X0xpvzV+CpH35DqiMaQ3t2xZp9h6BqGkZl9vDHUwpBIlWoBZPl2h9GuCteqXNXvrfEu4IxYKVRSpMGTV058oHFGkktJklTN4mM5n45MmSl1XWv8rXUZVfJf+vQNkGMdSKcaHUA92j5X9hOBOoYAQHCjRNGIsqmlyyKsdvwz9+WY1dxKfqktjzaWuV0Yc+RUpw36ORtMM8c0AcTeqfDrShIiIqEW1bww6ZtuHrsMGiawJfrNmPToULERNhw6fCByExO8PkzDDEOoZVNOL6CMWal2TbQY7IN6U9SpF9/ODXR3ScuHfvY6nErHskxaZ4m/17MGtLH54k1S/vTiMYez/LIreoKrRBRG870PDtcQOIPmoy1TsfmeHRy/ELjf5xoNcJusSDNEXMsyQKAzKQEmCQJRVXVrWpjw4ECQABDezZeeD/KZkVCVCQAYN62XchITECv5EQs270PBZVV+Os5U3FaTm/8d/laKKrPtsgLBxEQ1aMV17KJ7so3Ha6KN9bLdfMWaGrlIX9d0GOLG7l4/FP7PJaYkuaOu2q+1mTPV6Yst/ip2yUsu6e4X0r3wGJrT5yMdVLco9UBnGj5X7TRAQSjlNhoND4IJEAnLw5t1PoDh5GRlHDSKsSGymrrsHTXPkwbovd87T5SimE9uyHSasGwnl2hqBqOVAfTRgJBxQThGqq610/xVL3bzVX+Wp6n9of5mlK829cXUs0R/ZeMe7y2zp7S5N6myZUYmVQpChp7rIeiNNsNqgoqPMPzXEQlouM6GCpjnQ33aHUAJ1r+xz1ajeifloKCymrUuo/vIrPnSBlUTaBrXMsfnspq67CvtOKESfBN+W5DHsb3zjjWuwUAsrcHS9MEVE2DMcvvQpGco3m2TfVU/6+Xq/zlvZ7qLxeo8r7NvlrBKCRz+vLR/7BXxGY1WpyYANP1c7XtJ58o3Imq1tQWXRACVZd6Hqk8IFJ4mTpjbcc9Wh3AiZb/caLViLG9eiLKZsG/F6/ClsNFWLvvED5asR59UpNOmC/11A+/4dNVG046f/3+w5CIMKRH87sb7S4uxf7SimM1tQAgKzkRS3ftw9bDRfh+Yx5sZjNSwremlh9pmZqSP0Wu+WKgu+LlInfVRwtVz/Y1Qqhyh5olKXntsLt7FCWPWNPYw6N2ihxJEycMIZqBImqkTh4ACAH5dvnOXetF734diouxzsueMXM2z+luJ/7B+R+/gzciwmLBjClj8fW6LfjfsnUwS4QB3brggqEnbr+jaQJaI30l6w8UoE9q0glzvBrShMA367fi3EH9YDMf/1Uf36snCiur8OGK9YiNsOHqscNgNnEFjo4RXYRa0EWunQ0AlSQlbjZFDDWZrP0HE1kiWzr7JETRW/rfONi1J2FJ+oE5E+o/JAmknrFWLPt5JB0reBqlaeUAep4UlYB4Rrli5Q/amAkNH2OMtUkMgHKjgwhFZFTNws7ihenT7gbwgtFxMGYQJ6TYjWbrQNlkGzKAJHvbCh8KIdIKly7I2f7h1Pp3V0Zi3R/uNA87ejvLIy/95lDBSYVHP1cnz79XnjG14f2MsTbLyH/6vH1GBxGKuEfL/7hHi3VmdmhVYxTXUiiupQrIvs5kza4y20b0JVNs8+O+AEBEBWkTpjojkhcM2/DK5KPDg446DOtRLPYeSKFMAOiqKCcNV67W+i7kJIsxn+EJ8e3Ec7T8jxMtxnRmCOcw1b1uirvqX2mu8te2emp/bNUKxor4vlOWj35omUamY6snbpyjHTj67wxZPmF+1gEtacVlnofCatdwxgzW9ikADAD3aAUCJ1qMNUrur3ny+ns8eQBMeyVLz30m28hkydy9PxGdNLHdGZk6fsm4J9aNW5Hby6y6YgfsF0OssqjzWCgyU1aO1cWqEpGbzvA8N5gLkoYWT8l+lM99E+5D2yFFRCF68JlwTLgSJLU8f7Ju+1JULv8Mcsk+kNkGa1ofJF/0ICSrXsO2bscylM/7FzSPEzHDpyFu4lUnnF+x5CN4Cnci5dKH/PLcwgRPZG0nfiHyv9Zv3MdYp6VmavLeqXLNZwPcFS8Xuqs+Xqh6dqwV4sQCpbI1Ztji8U8VumxxhQQ4LlyurQGATFmOAQCPMO+d4n6xmwu25oursaCiumpQ/MnfARCSL/k7HOOvQNWqr1Cx+MMWz63e8DOOfPcc7FkjkHJZLhLP+RMs8V0BoZdwUesqUfL9C4geeg4SzrgVVau+hnPv2mPnK1UlqF71NeJPvdlfTy9ccL7QTtyj5X+a0QEwFlpEmlAPp8m1hwGgnKSkraaIIcdWMGoma99lYx49OGrN03umrTyc+NkkoKesJKmCjpzpecZcjthOv59SqKlZ9wOE7EbyxX+DZIsEMAyapw6Viz+CY8yl3vtOptZVonzev5Bw+h8RM/TsY/dH9j2+LsJ9eDvMsSlwjP0dAMC1fyNc+ethzxwOAKhY8B6ih5ylJ2esOX7r0SKiXAB3CCFOKjpMRO8BGCiEGElENwD4D4AYIURNB68ZDaAawI1CiPe89+UD+FwIcW9H2m6IM1T/40SLsfaLF1rJBKXu17HuitfgrvzXCsW5YrEGT/TKkQ/GOaOy1f77tE1Jihox3fNQSb5I4x3DQ5BzzxpEZA4/IaGKypkMobjhOrCpyfPqti0GAEQPOq3pxlUFZDleBoYsNhwt9eY+tA3OfevhGH9FB59BpxAM+cJsAOMA1Pmp/YsBvOrrRrlHy/840WLMNyKFVjVGcS2B4lqigOwbV/XuX3HqdlPZ3ekXuleLfiONDpC1j1x2EBHpg0+4zxybArLYIJceBHqPafQ8d8F2WBK6oWbjHFQu/QRqXQWsqb0Qf+ofENE9BwBgSc2C50g+XPs2wuRIQd32pYibdA2EECj79W3ETbq2yR4zdgLDEy0hxBEAR/zY/jp/tMuJlv/xbsWMdYCJLLXR5riiWGtihcOSVBtrTdSizfEmuzk66oCpjGb3tLoHLdu8Z5BGRapUESWJ2i4mMpssktVjlqyyRbIpFrIpFsmqWiSbZpasmkWywUwWYZGsZCarZJLMZCKL2URmk4lMFolMFoJklUiyAWQlkB2AnYiarpDL2i3TVYO7bIm4ucFOL6NssbjUJWNmEzvAXF1TjTVlh2BZ+ilemnor4uwOvLniQ2z87GH8eMuHSI5KAOJi8fK46/DCxw8CAE7NGot3+0/D11vm4l+qwA+DL4VkfA4RClq3Ca0/A2gwdEhEGQD2ApgO4DQAV0AfDnwXwCNCCK3euZcCeApADwCrANzdSPv58MPQISda/sc9Wow1gUBKpNlRFGNJKHVYk2piLYmeGEuCFGmOjbBJ9jgTmZOJKB5AVv3zquEs+M66fm981hLzvIjrE/oM+owiFm2zdVHOMe3rfkGkqh2qUt1bSjT3vjgI12AAPkmQCKSayOw0S1aXmSxu/btVtkhWj0WyyWbJqlgkm2ohm2aRrKpZssIiWWEmK8ySlcxkkUxkNpkks8lEZrMEk0UiyUokWQmSjUAR0BfQ2BtbeclOJCBQ63Hinxc+ilOy9F6vkd0GYtw/L8N7a77EfZP1Ce53TbgB1w27CHWyC90dXVDrqcPTC97G6xc8BEVT8fDcF/HD9gVIjkrAk2fdg9HdBzd32c7K79XNiaixnKQ1fwfPAvgCwO+gJ1wPAdgC4FNvu8MBfALgKwB3Ahh49LFA4ETL/7hHi3VaNimyJMaSUBJrTax0WJLcMZZEEW1xWG1SZLRZsiYTKIWIugFo1WbPClTXQkve8j1S0ej4hEPR9h4F8YUlaSU9qtMq/3nBAdvp8773nLb4x+RdvS/JP9T1nAEgKVkIuU6Td29U3VtrNOVQKiD3QzuHQQSESRFytKLK0e05vy1MZHaZyOI0SxaXmaxui2T1eL8rFsmmWCSbYvb20lnIKsyS9WgPHZklC5n0pM5sIrNZIpNZIskiwWQjIhuBbNCTukgiMnzZviMiBtXu2pPur3RXIy6i6TqZjogYEAjjeg49dl+MLQqDuvTDztL8E45NiIzD0VUSry//P4zqNghjewzFv1d/jq3Fu7Hglg+xYM9K3PZNLpb88WPYzNx52YC/Ow0SATS1T2qj+57Ws1AIcY/333OI6GwAl+B4MjUTwA4Alwt9O5wfvb3Tj3cw5lbhRMv/uEeLhSUzWWqiLfFFsZbEilhLUp0+pBdnspujoyxSRLwEqQsRJQE4aSVRe2wxHVi+3LyzuyAx1WarOTxg4Ly0d+i2rdAQ85k6JeFfhS+mnn5WV3tJrLb2ygWfju+9+2v3tr5XLihKHTXUZM0eabJmAwCE5ixXPdu3q548j1CLegJahi/i8zVVKBGqUCI8mtOv1yFIslmyOM1kcR3tqfP2zskW8g69Srbjw65675z+RRbJLFkkE1lMJjKZJDJbJDJZJEgWIslGIJt32DWCiGxNxdAroSd2le0/4b7DVUVwyi70SjxpC8tj+iSmQ0Cg4U5yAgISNZ5LH6gswH/XfoUfb3wXALB0/zpc3P90xEXE4ML+p+Efc1/GnvIDyEnu1ej5nZjS8iEdUgng9EbufxhAS7tI/NLg9lacuPfpaAAfixP3HPwSnGiFDe7RYiGHIMlR5tiiGEtimcOaVB1rSVRiLAkUaY6xWyW7w0TmVCJyAPB7z84Rqtr5s3V9jYvksQAgSYpzxMjvKomQsxSTukGVyxdpgwalKKr7iuqatR+Pj5lcFoNVt3/vGTBg2/tT+u76rGJr9nXzSxMHjgJRFEn2eHPE0LHmiKEAAE2tPKR5tu5VPdtJaOV9AJHi7+cUTAQ0i6y5LTLcsX5+tdLMZHHqvXRWt0Wyus1k9ZglqyctPiLpl22LUhcfnr0q3h6nWCSb9u+VP3W3mMwZPRJilh5xHdDMZCETWUwmydtLB5N5fM+hsS8tQcb8vctLz+4zGQDsVe4a+6bCHXTL6OmNBvH4vDdw3fCL0cNx/L3bqbgBAKqmwqN6AjBIFpKa6m3yFUUIsbrhnURUipYTrYoGtz04sYZlFwDFDY5peNtvONHyP+7RYsFGRJiiSmMs8cWxlqRqhzXJFWNJRJQ51mozRcWayZLoHdLrDqC7UUG64Cn/xbpxUzFVTgAdr+EzbPjsdSaTMn4H+m2TyZptUj0rFJgtBUhYN7O0bMLX0VHbFw6SRpVHY9PfP9Z6WJS6uCGb35zqtsYe2dL/ptUVjt5jUa93RTI5ukn2cd3M9nEAAE0p2q16thxU5d12aNXZQBMzsVlbSYqQoxQhR7m1E1fnj+gVh3k7CQ/98u7YU7J7oaymDt9u3IrJfTOxofLHiajUj3vqh9/QKzkBl48acuzcAV1Tcc+Pjyf+eigbUVYL5m/fA1V4kJhUXPnd/n/q8+kki9tCNnnz4UO2JftXpV87duy6TeWLFItkU1NjI7u9ufLD7laLunPNwW1xFsmckhAdsaZGrpCO99JJFgmS1dtLF+EddrU3MacoXHlaPiRoFQJo+AEqYB+oOtMviVG4R4sFlJms1dGW+OJYS2K5w5rkjLUkqtGWOEuEKTrSKtkSCVKqL4f0fE2DUFeady7ebDowGITJ9R/r03fpgsjIqikA8BmuLAKQDU0vAf6zOsp5o/ln08vFJZ4ZqcliU6Y0aOaNtOvJ91SnSSDN5qlKHr7+5SnOiMRDmwfcvKc6usd4NDI/STKn9pLMqb0sOBVCaKqmHNiierYc0eR9DghnfwBNDoGx9om0WvDHKWPw1bot+PfiVbBbLJjcJxNnDuh7wnGaJqA16G26asxQfL8xD9+t3wqPqiIzMR4zpo6BxaI56tQqx9FXYE0I/Hf1Ypw7uA8OuTaPOeTS7++fQdhWFoun5/9nYGyEDVeOHYT5Rf8b15q4CZJilix1JrK4LPriCLdFsnnMktVjIeuJ8+gkmzg6l+7owggzWejYwgg9oTNLMB1N6Oqvdg2G3zm30QF0wCoAFxDRA/WGDy8J1MU50fI/TrSYz+hDeo7CWEtCWaw1qcZhSVKiLfEUaY61W6WIOO+QXiyApmcQB7F8qXjdb5Yt0SppUxo+lpyyZ3Vq6u5JAKBCUvIwoD8AQNVfOD9Tp3S/0fwzJjhdg3I88uI8m3Xi3i7U+84ZpkMvva3utajIBAC7q7TbqDXPdKuJTNu7eeAfCuvsKWPRxAo/IslksqQPMFnSAQBCyE5N3rNGdW+t1pSDKYCcjSCoLxQOujhicOvUsc0e87dpp550n81ixqUjBuHSEYOaPVciwt1nTjrpfrPJhMtHDTmhl6y1BDSzrLljZbhjXW0+u000EzWcR3esfMnRuXRH59GpFskKvYSJFWbJAhNZTEdXvEpkspi8vXRNrHZt6vfZz0/Rr54BsALAp0T0LvRVh78P1MU50fK/k5fSMNY4EWGKLomxxB9xWJKqYq1J7lhLAiLNDmuEKTLWTJYkgJKJqAf0WjBho5LqDv5kWXewWnI1+k4bGVm+t1+/JX2I9KRmKSatEySNAgDS9Fo5W0VGL1VIBSbS0t4qLO4/pWe3MkGUUBxH3W693VT62pvqVrsH/Y+2GV1XkDl25aOZlTEZ2zcP+H2VOyJhVEtxElnsJmu/ESZrPwCA0JwVqrxjm+rO8wi1qAegZvrgx8FYQ5Iq5Ci1kWFXn1+ITG6zntQ5zXoy57aQ1SORufYqnJyohgIhxGoiugJ6Ha2vAayGXntrZSCuT6Lhcg3mUy9Mn/YHAG8bHQcznoWsVd4hvQqHNdkZY0lQoy3xFrspKsoi2RK8Q3rBMEQQMDKU2vmWLav2SSVjQY1vwG4yeSrHjvusTJK0Y0nMPXhtWSF1HQcA5m0VC8z7aqcAwI/WmYtzpP0TAeDVeMeid+Icx94ZIjyi5tU31e1xtRjR2HXK4vpt3tL/Rlm2xgxr7/MRalWB6tm6W59YX9YLEF3a2xZjQSbmnk++79D+gp0V92j5X4XRATD/kyB5osyOwhhrYrnDklQTa01SYszxZDfHRFqlCEe9IT2eWO21wbRv6WrzrkxBmNr0UUIbMfLbHZKkHettqkVkZSHSjidD6vEPi1+rEyhH0ssE/Km8cuJHsTGbayRpIAC4rBR92+2mQc//S13atQzHdx32SqjYPnDS0pkoThq6Ni/7Gptqtg9o63MiU2ya2T42zWzXO+Y0pXiP6tl6QJN3RQitKhuAo61tMhYEBHh0pt24R8vPXpg+7QycXOODhRZhN0UfibEkHNFX6SW6vav0bLYTh/S4kncrFFJF3i/WDYqHlOYn1QAYMGDegoTEQyfM1/oMVyz6mi471lNl2VQ233TYORUAUlB+ZGXE7clHH1tvs267Ni21T/1J7ySElvs/dVHOQZw0D6y+w13GrdzR5/J4zWTt04an1yR9Yv2h7apnS7Em7431TqxvtBePsSBTe88n3/u9lEu44h4t/6swOgDWPItkq4w2xxd799JzHV+lFxVlOb5KLwUBXA4cjurgPvKzdcO2UqqeAGp5Anm3bluXNkyyAGAOzj6xV6hej1Yx4pOdwrrTTp4+ADDU7cke4XIvXGOPOLZ6URBJD19rnnLn1+qCCXmiyWSra+Gy0WmFy7UD3U9dujvrgq5CMme06ok2QZ9Y36O/ydKjPwAIobg0ee861bOlUpMPJgOebACGV2kPtA0HCrBwxx4cqa6FR1ERH2XHiPRumNqvF8ymxn9Nymrr8OTs3066f2iPNFwzbvix25sPFeLb9VvhVlRM6J1+0irGX7bsxMHyCtw0scXpeZ0dDxl2ACda/ldhdACdmQSTO8oSVxRrSSiNtSTVOqyJarQlXoo0xditUkScROYUb+FNHtLxEw2avNS8fek20+FhoNbNpo2NLc7LzFpz0lypAqQdqEX0iT1h2ol7oa3R+h6eaNp8rBfqtaIjQyemdy/W9GT5mFcuMk0pcagLL1guJlITKwcJQup58Nfx3Q/9puSnn7soP/3MXiBT19Y8h5YQmSNM1j7DjnaYCc1Vqco7tquePKdQCrsDaqcoTV7n8aB3ShKm9usFu9WM/aWV+GXrDlS53Lhk+MBmz502JAeZSfHHbkdZj2+bU+v24MMV63F6Th8kRNvx+epNSE+MR78ueodnRZ0Ti3bswZ2nT/TPEwsvnGh1ACda/ldhdABhTLObYor1DYkTq2ItyZ5YSzwizY4ImykyxkzmFIASiagnTtyOgQXILqlg9UJLXqJGTfccNWSxOI8MHvKLgwj2ho99jiv2QF91eZx64vSHz9XJURNNm4/djhEi9q7yis0vJsSf1CP5f6eYJpfEaMtumqONoGY2npaEZs7K/35Sxr6f3Dt7X7LwUNdJOSApuanj24OkCIfZNni02aZvaCy06kLVnbdblbcJoZb2AkRL1bFD0rhe6Sfc7p2SBLciY8mufbh42ICmKm8AAFJiopCeGN/oY/tKyxEfacepOXq+uru4FDuKSo4lWj9s3IbRWT2RFBPlo2cS1iqMDiCUcaLlfxVGBxCqrFJEZbQ5vsi7IbEz1pooosxx5ghTVIxFsh5dpdcF+vYKLIiUU03+T9b1R2rJ3aYxGSJNHjHy2wIiMbixx1dhTMZJ52jihHfin7VR/YWAh+h44nRjZfX4/zhi15WbTCf1kv08UhpXHoN193yp9aYW6o9JQrH12/np5N67v649uo8i9B5RnyMppovZPrqL2T4aAKCpJXtV95YDmrzLKrTKbABx/rhuMIi0WqFqHdtUQ9U0WEzHR2ItJtOxNveVlmNncSn+ek6r8//OrtDoAEIZJ1p+ds8n38svTJ9WByDS6FiCiUQmV7Q57uheenWxlkQ5xhJvsutDevESmVJ5SC/0eKBU/WrZtO6QVDYOhIy2nj9kyE/LLRZPo8OLmzB4k0qWkyfQN0i0nLBFliNmfQKqh9a//52CYsfvunXxgOiknquV/aRhD11DeY/8n5okCbTYU2XSPFHH91G8dkFp4qCRIPJr14hkSsqUIqdkAlMghNCEcihP9WwpUuW9MRB1/YGTewBDiaYJKJqGQ+WVWLwzH+N6pTfbmwUAn6zaiDqPB9E2G4b17IpzBvaDxawnV13jHCiorMau4hLER0Zi08FCnD2wL4QQ+HrdVpw9sC8iLJZAPLVwwIlWB3CiFRgV6FyJlhZpiimKsSSWxloTqx2WJE+MJQGR5tgI7yq9ZO8WMOneLxbiBIRYZ9q7ZK15bz9Q86v5mpKZuWZhTGzp5KYe/xxXVDT6QINECwDma0MqLjEtPuG+frKcNdnpWrAw0t5ofNt7UM69vzflP/tv1W3WWrfHo76P4ltT9H0Ub1xT4egzBgGohUZEElm650iW7jkWAEIobk3OX696tlRoyoEkCE8OQmxi/YNf/gTF2+M0Ir0bpg3JafJYsyRhfO909EtNhs1ixu7iUvy2fTdKa+pw48SRAIDE6EicltMbb85fAQDITkvG0J5dsWbfIaiahlGZYVXz19+KjA4glHF5hwB4Yfq0rQCaftUIMVYpoiLaEl/krV7ujLUkiiizo/6QXpdOttlqp3ZQKt30q2WTSSa1f8tHNy4h4cD6/gPmDyBCo10MCsye6/FxHYjiGj5mXVS0TKpTTtibbqy0ZevH1idOisdJVDcuvXuZqm+Y3XgsVaLolbfUKpuCNpd1cEYkHN484A97qqN7jGtsH8VAEcJdpXp2bNMn1hd0A9TeRsXSWgfLKyErKvaXVWDO1p0Y1rNri9vq1Ld01z58uXYz7j5zErrGHS9XV+v2wK0oSIiKhFtW8MxP83H12GFIT4jH1+u2YNOhQsRE2HDp8IHITE7wx1MLB3++55PvXzM6iFDFb4aBcQQhkmjpQ3rxhbGWBO+QXpISbYk3203RdqspIl6CKZX0N7s4g0NlBquBq/Bn6/rd5VQ7HoR21xCLiKg+2H/A/O5NJVkA8BtOWwuixjfCEyf3aK3QcrI1gQqJTvw9tQsR+Y+Sss25yYlNJlplsZQ64w5TxGtvqhujXWh0rlhT7K6yrqPWPNNV30fx5oI6e+q4Fse//IDIFmu2DRpttumJitBqilVP3i7Vs00TakkmILoFOqaWdI/XZwlkJicgymbFxys3YEq/LCRFt25EdnD3Lvhy7WYcLKs8IdGKslkRZdNHi+dt24WMxAT0Sk7Eoh17UVBZhb+eMxXbC4/gv8vX4sFzT4HZFFIdgYHCQ4cdwIlWYBw2OgAAIJBqN3tX6em9UXKMOQFR5li71WSPMZMlhYgSAWR4vxg7iQrNvciSt3yXVDgChAkdaUuSlLrhI76rJWp+qO47XNxk3S3SxEnvjAKSdECkbEun4pOSs0trake/Ge9YWWg2j26qzVo7OWbcYerz8tvqyqQqNHlcU/R9FB87uo9ipTsioc1t+BJJ0SnmiFEp5gh9bYKmlu5T3Vv2a/JOs9Aq+wEIqq6cbt6kq6zW2epE61g+20RaW1Zbh6W79uEv3o2ldx8pxbCe3RBptWBYz674au1mHKmuRVocb97QCE60OoATrcA4FIiLWCV7eYwlvjjWklTpsCa6YiyJItocZ4kwRcWYJWsCgVKJKA1AWC4TZ/63zXRoxVLz9rS2lGtozvDh328wmdRxzR1ThdjSUiQ1vf+gaHwu0g/aGM+t0neNnvJuQXHX87qnOUHU5ARyj4Xsd9xqGv70f9TFGcVoV7ElR3V+vwnL/+GTfRR9STIlpkuRk9OByRBCCKEe3qa6txSq8t5oiNr+MHhOaX5JGQAgIar18/s3HCgAcLxnrKHvNuRhfO8MJEQdf2qyqgLQJ+KrmgaeSNMkTrQ6gBOtwOhwomUiszPaHFcYY0ks967S8w7pxditJluCd0gvHkDjRWUY64ASqt79k3VdpYvkMb5qs1+/xQvskdUtJmzf4JLNIGr6OHFyjxYAfKZOybjV3Hii1VNRup9bW7fgh+ioZq+vSWS+//fmiQ9+rM4fuldMbSnWptTbR3FdXvY11vbso+gvRERk7pYtmbtl6xPrVY8m79ugejZXaMqBBAh3Dvz4XvHOwpXok5KIVEcMJCLkl5RjwY49GNoj7Vhv1lM//IZeyQm4fNQQAMDPm3fArSjITIqHzWLBniOlmL99DwZ163LCsOFRu4tLsb+0AleMHnLsvizv8GFqbDR2FZfCZjYjhWtqNYUnw3cAJ1qB0WyiRSA10hxbFGNJKIm1JNY4rEmyd5We3SbZY036kF4CgEzvF2MB4YJcMde6cUMhVUwA+e71IjV118rklL1NrjCsbz5Oa77cgtZ4XHtE156yMB2wkNro8rLHjpSOmxMVuVcmavFv6skrTFNv+UFdcNoGMZmaHJxqWUrJ+mEpi9f7fB9FXyIyWU3WrCEmaxYAQAh3terZtU3zbK3TlIKugNIbHfgZNNQj3oFV+QdRXueERITEqEicO6jfCYVMNU1Aq9fdlBIbjQXb92Dl3gOQVRVxkXZM7ZeF03NOnvOvCYFv1m/FuYP6wWY+/qsyvldPFFZW4cMV6xEbYcPVY4fx/KzG1d3zyfdVRgcRynjVYQC8fuXlY2Is8e/FWhIrHdZkd4wlQYsyO2wRpqhos2RNIlAKGbhCibGGBIS2yrx78UbTvgEgJPqy7aiost3Dhs9OJkKLk2H2I33PA/RiVnPH2OYe3k6q6NfYY19b/7FoqLS7yW1/fom0r70nNXl4U483dMkSbfH0hdpY8sGHVAGIA91PXbY768IO76MYSEKrPaJ68naqnm2qPrFea1UpDBay9t7zyffN/g2y5nGPVgBclP6nwwCyjY6DsdbYJx1Z/5tls10hrVU9Tm1hNrsrhw770dSaJAsAPsWVBwA0/yIvRJOvY1+pEy1Dpd1NnnpmnXN4hkdelm+1NDtP7KgvJ0gTy2Kw8tbZ2iDqYIFQAqjnwXnjux+ar+Snn7s4P/3MLF/to+hPJEUlmyNGJpsj9HpVmlq2X/Vs3ad5dpiFVtEX8G1izgzH87M6iHu0AuDgzEUSABfQ9PJ1xoxWRc5DP1nW7a+SnK1KOtpOU8eM+XKd1eYc2ZqjBSCuw6cFWgvJh+2XQ/tJNL6XZRyqy9fZ/ugganzTaAAoNJkKz+jRNQpEzW6/U9+Q3drGBz/V0smHOxdoZHbv6nXxioPdJvt8H8VA0SfWF+5Q3ZsLVXlPFERtDgCe+BTaPr3nk++nGx1EKGvyxYf5TvenJ2kADhgdB2ONUaA6f7Vsmv+pdWmC/5IsYOCgeYtbm2QBwGqM2dBSkgUAEE1/gKlATHwtIrY1d3oXVe0yvbpmbWvjAoANvaTBD9xgKlbJd5/2JaHY+u76bPKURfdEphaumA8hKn3VdqAQEUnmtH6WqDOmRMT9caQt7k6rJfrijZKl7wKQbSMA2egYWZvtMjqAUMeJVuDkGx0AYw1tNu1f+r5tfvleU/FUkP/2yuveY/OS+PiCNpWE+BKX1bTy0GZ7ipdrOcUtNfBAafnECE3b3srrAQD2pFGfu/5oUmQT9rblvJbo+yh+MHXSkvuQVLJxPoSo9WX7gURkspgsmYOt0dOmRMTdPtgWd4fbEnn2asnccwFg3g5wRYUQ0PTYu48Q0UVE9AsRlRKRh4gOEdHnRHS2v69dL4b3iGi1P9rmRCtw8o0OgLGjiqhy+39tCzYst+wcLwh+nRcU6yjampGxbkRbznHDVrcfGUNaPhJAC4nWZ+rUuJYaMAGml4tLPGjjXIqieOp+2+2mGKcFeW05rzUsitMxePNbUycsfaAurmLHQgjh9vU1Ao3IGm2y9R9pjfndlIj4P/ezOf5YarZPXUam1EWAtN/o+Fij/NqjRUQvAfgC+ur8mwGcDmAm9DmQPxJRL39ePxB4Mnzg7DE6AMac8JT+Yt2w5QhVTUQz85Z8xWqtKx48eE48ESLact4vOHs9iMa38vBmN3H+TRuaIwSc1EKP3QSna1COR16cZ7O2qThpZRQlzfiTyfbqm+paRx1avYKxtWxydfLw9a8k19tHcSzCZC9RkqKSzBHDk8wR+o9NU8sPqp6t+ZpnhyS08j4AQnKuWpjxW48WEV0I4C4ANwoh3mvw8H+J6HwAzibOtQshGn0s2HCPVuD4/BMvY62lQVOWmrcv+D/bIvMRqWpyIJIsItUzYuS3RUSizTsR/Ijzm02eGrA296AHFtsRxG1pTUNvFRb3JyHK2nBtAIDTRjG33mEaWBCPZW09t7W8+yhOHLPq8YORtYVL29r7FgokU3x3i33CRJvjxvG2uL8kWWOu2mmyDVkAil4FoLVDycx3nPDvFnJ3AVjVSJIFABBCfCeEOAwARCSI6G4iepmIjgDY5L0/goieJaIDROQmog1EdG7DtojoZiLa4j1mHxHd31xgRGQloi+JaD8RdWhTdk60AqdVL/SM+doeqWjN+7YF+7aaD04B+W6VXEuGDv1phdksD2rreaVILKxE3NBWHawJhVrxOjZXHd6qeU7xmpZwc2VVu/5WFRNZ7/qjacz2bljYnvNbK6quMGPsqsfGj1z73E6bq2ylP69lJH1ifZc+lsjTpkTE3TLKFndXhCX6kk2Spd8CUMQGAB6jY+wEdt7zyfd+SehJ75UdB+CXNpx2H/Qt5K4F8GfvfZ8DuAHAkwDOB7AKwLdENLTete4D8E8AXwOY5v33Y0R0RxOxRQD4CsAQAJOEEB0aPg2L7ucQsQuAGy0MczDmKxVUu+9H6/qiWnIFfEPjrKxVC6NjytpVh+srXLYdRF1adbAmPGjF69in6tS0q8zzWtXkn8orJ34YG7OlVpLavE2OIJL+cZ158l++UheM2+ab/SCbElu9r6++j2LfLVv63+QJln0U/YVIMpssGYNMlgwAgBBynSbv3qi6t9RqyuFUQO4HH1asZwD8OxKTCP398IQV+aTvDl6/gLcqjvfeFgghptc79jQA5wGYKoRY4L37FyLqC+BvAC4jolgADwN4XAjxiPeYOUQUCeDvRPRPIYRar81IAN8C6A5gshCiw1vocY9WgHR/epIKoE2rmjorRVMwa/n/MOntK9Hr+dMwatalyP31tVad++P2BTjv/VvQ+4XTMeiVabjm03tR5zk+jP/TjkWY8OZ0DHn1fLy0+D8nnf/ykvdw4xczffZcjCBDqfnZsn7+59blXYxIshIT96/r2m1ba+dXnWQJJrd+cr7Wul6N9aJXH03QkdYcSwC9WVhsQr0X37Z66WLTlO9H0UIBaO1to7USKnYMmLR05rCBm99eZ1Kcm/19vWBBZIk0WbNHWmMunRIR/6dsm2NGudl+yjIydVkESPuMji9MNFsaxUca9pjdA70MyNGv2+s99kODY0+HXlB1CRGZj34B+BXA0VIy46DXcvuswTHzAKRCT6iOigLwE4AUAFN8kWQB3KMVaFsADDY6iGB39+ynsGT/Wvxlwg3olZCOgupi7CjJb/G8jzZ8j3/MeRkzxlyJv59yKypc1Vi6by0U7/tlWV0F7vz+cdw5/jr0cKThrz89h+HdBmBKpp6LFFQV41+rPsV317/tz6fnNwJCbDDlL1lj3tNHEKYaEUOEvepATv8F6dTOfRF3ou8OD9n6tvoEvUerFYh2i7QdfehwqyZXD3V7ske43AvX2CPaXR3/g9NNk484tGU3zNX+v737Do+qzB44/j0zk05I6KGD1NC7dDCoa1vLWnF1seza0J+6rrtun921sOqurusqdsV1Ldg7KjU0pROBUEKRACGF9Dblvr8/7gBJSEibmTuTvJ/nyQO5c+feMyEkZ95yzlipZx2ZP3TO3TK688otHE6a+N2uAVcnGvbIhn8dWwCxxbZ3RI+e5Ig2B/YMb+Ehw7V9n9e106aM/P6gOlscYjgK5IhWHuYsT80WTq8Dy3x/X1fjsZrNrTsCSdRen81b5Ryoe/lOT+B4Yt4NGAA8pJTyWyNtnWgFl16nVY+le7/lk/QlLLrxFQZ27NPg5x0rK+AvS/7NX8++m2tH/fjE8fMHnvw9ufHwdnq07cIdE38KwOofNpG6f/2JROuR5c9xzciL6Nsu/Fq3HbYd2/Z1xFblFm+jdsz5k83mLh0z5tMKEWpt4twQC5l9BGhwgiCGanABzE+8k9Qvbe81OJZ/H80ZNbV3j2xDpMm/oL8Yb5uU34aN935oDBRo09TrNEa3rLUTumatVQd7pKwOtz6K/mSzJ3S3xUzq7ogxa/AanqN7vK5th72ujGhU8WBoWBuoVi5gI1pKKY+IrAHOBf5U5fhRfAmVOYtY/Wk1Pj+GWRbi0tPc6vjmlos4NVGD6jNNu4F/Aa+KSJZS6tnTv4qG0YlWcG23OoBQ987Wz5nca0yjkiyAT9KXAnDF8Lrr27m9bqIiTi6Ri4mIwu01f09vPLSNlQc2sPwXbzQ+aAuVUpm9KHLzrmNSMgWxcn2KUmPGfppmt3snNvUKBjbvdoYNadyTGp5oveed3u+XEQ1PtOKVantPfkHaP9u3a9ZIyNpk2xhnnGz/8xveLrYg9QEM1z6KgWRzdOlvc3TpHxGbglKGx/Ac/N7r2pZnuPcnoCqS0etnazKAXQG+x5PAhyJyvVLq9SY8fzHmVGOJUqqupHAN5u7Jbkqpz+q7oFLqdRFpAzwtIsVKqf82Ia5qdKIVXHpEqx6bjmznnP5T+MPXT/De94vwGF5mnjGBv519L0nxHet83uYj2zmjfS/e2voZ/179OrllxxjWZSB/TrmTcT3MjW9DugxgZ85eVh/YSI+EJL7YuYJfTbsZpRR/XvwU90/7OfFR4dGWzYvhWuVIX73LfmQsgmWjWMcNHpy6IiampFmLv1cxdZMSW4Nb9ADgbXiidYhOXSuVY2+UeE7fpLqKGwuLp7yS0HZTvt3erIXmO3rJkPtvtu979BVvud04ZaokYGzKcJyx/9OpfQ58Wbmn32UrwrmPoj+ZC+t7D7NH9AZAKXe54d67wVu5rcTwHOrsW1jf2tcw77zv7U8DWqdKKfWRiDyJOYJ0FvAJkIv5huRc32mnK+vxNbAIc3H73zF/x7YFRgHRSqnfKqUKRMQJ/EtEegMrMP9tBwJnKaUuqyWuZ33J1isiUqKU+rA5r7O1fyMFWwbmnLRWh5zSYyz8/gu2H93Dfy7+M/+44AHSsnbxiw9+f9qyQdklx9h77AeeWr2A3828lZcvn0dsRDTXL7yfnFJz5Lh3YjfumnQ9V791D1Oeu4aBHftwyZBZvLdtEW6vh6tHnFJ6JSTtsh/+7rWoZYd3OY7MRGhwI+RASUra9W3HTgeavJbpuI+4ovHb9Y3GLVjfrPo3uufoC0eyE1ANT+jqcrCz9L3zNru90sHu5l6rsar2UUzK+nZ5OPZRDCSRiBh75KCxkfE/mRHd7q7kqITbCx2xs9aKvesKsPm1xVIYqbk+KiCUUvcCV2CulXoJc5H6M5gL1S+oq8aW77kK+AnwMmZNrkXAc5gL4FdWOe9R4BbgfOAj4E3gp0Dqaa79GPAI8JaInNPU1wcgLbDmXUjLfCB1M2ZtDq0WZzyWQoQ9grW3v0O7GLPk09qDm7nyf//Hm1c/wdQ+tXdyufbtX5K6fz0LrnyMs844E4DiylImPXslc8b8hPun//zEucfKCihzV9AjIYlSVxkzXriOpy/+E2O6DeXP3/yLz3cup1Ncex7+0X1M6BE6exfypHjvosjNx8rE1bhRnwBq0yZv96jRn3cVad76ozJii37BgghEGtVv0ZZbkRa5Ia/Btbqusi/97tGIFxq9E3Nul07LV8TG+KVcQ5tyVfDvZ70H4yppdI0xf3E7Ygp3DP7Z5twOw8chEvBh3E17l7Nk67scLTiIy1NB+zZdmDDwHM4eeTUO+2k7KLF5bypfbX6TI8f2EeGIonenwfz83D8TFWF+q2zZt5L318yn0l3G9KGXcsG4n1V7/hcbFnAgZxe3nfdgk+NX3qIjXtf2DK9rpyjjWH9QXZp8sfDxf/e9/WnDtntrp6WnDoMvDZ1o1SkhOp5eid1OJFkAE3qMINIewe68/XUmWgnR8QjCpF6jThyLj4pjeNIgduftr3Zu+9hE2vv+/vTaNxjffTgTe47i5fXvsj07g+W3/I/le7/jjo+crLr1LaIcAd8wdlqVuAsXR6RtPmzLn4zQ4GmvQHM4KvJHjvoiqrlJFsDn/HgrIo2fAjVUo8onfO49c/DfHS94Grsr8vHs3PGTevfI9Io0e9qvJEYSb73LHvWv57zrOhQzvrnXawpfH8UZlRHxOduG3LS+IHHAREQCtkaptKKIgd1Gc/bIq4mJjONATjqfr19AUdkxrpr6f3U+b/WOz3hn1b85e+TVXDrxFsorS9h5aBOGYQ5klpQX8tqSRzh/7HV0iO/K/1b8k75dhpDc03wvkl+Sw5Kt7/HrnzzTrPjF3rarI2ZiV0eMuQTR8GRneF3bM73uPTEYRYMgeIWAgyggDZZbI51oBd+3wHVWBxGq+nfoTaXn1BkkpVRtO1BOGNChNwpFzQFahcImtc+QHyw8wusbP+CLG18CzF2Ilw05m8ToeC4ZMos/fvMke/MPktzJmp6mCmVscOxdtdm+fzBCQItfNp7hHTvuk302m/JLb7+vOb9pU6Be5WnM6SXEti0idmsCZY0aqoxRKvYPucfS/tKpg1/WV7kiJGbuHfbRj77sXdUrhyn+uGZTRLmLO43Z8q8ZZh/Fn2cUt+k1KRB9FKcO+XG1zwd2H025q4zUbR9x5ZS7av2/XVJeyHtrnuXKKXcxJfnCE8dH9j2Zj+/L3k77+C6cM2o2ALsObyY9c8OJROvjb19g8uAL6JTQ3a+vx+bo3M/m6NwvgpkoZXgNz6FtXte2HMO9LwFVngyN6+0ZgjzAZquDaCn0Gq3gC1gvtJZgVr9JpOfu5VhZwYlj3x7cgtvwMKRz3e2mZvUz62Ou+WHjiWNFlSWkZe0iuXPtidKDS57hZ2Muo2fCyVZ85R5zCZ3X8OLyuk7dTBwkB225W1+LWr5zs2P/NCT0GusOH/HNysjICr8kWVkkZZbQpklztOJtfFHRVGN4o3sZAlxRUnpmF4/Hby1vDJs4fnWzffKWPrK8/rMDy+yj+Oi0YPZRjItui8eoO0/euHcZAGcOPLfOc7xeDxH2kwNxkY4ovIa5nG7f0e2kH9rIeWMD+75WxGa3R/QcGhl33szoxNtHRyX+HxFxF220RfRdDhHbCULR2gDYFuiF8K2JHtEKvi2YW00btRaltfjpqIt5ZcN73Pjeb7lz0nWUuMp4ZNl8pvUeV2291NTnZjOx50gev8Cs4j6y62DOHTCV+7/4Ow/MuJX2sYk8++3/cNjszBnzk1Pus+aHzWw8vJ0nLvzdiWMTe47kpfXvMqBDH1Yd2EhcZCxntG9ySagmKab8yJeRm/cV2sqaXFk90Hr22royMfGo30bY3uWaDJo6JdfIqUOAd7wzO1xk/7ZJt3vpSHbXi3p0LW/sWrI6ichDs+0zbvvMuzxla2Bb9jSEr49in6L43rvShv68oDK6vV87CxiGF4/XzcHc3SxP+4BpQ35c50j1/uwddE7oyZr0L1i06Q2KyvPp2XEAl0+6gzOSzO5IPTr248ixfew6tIn28Uls3pvKReNvRCnFu6v/w4/H30hMZHB3Eos4ou2RA8ccrxerjIpCr3tXute1vVJ5jvYAb8hM/59GUBbCtxY60QqyHvOmeTIfSF0PTLM6llAUHxXHW7Of5M/f/Iu5H/+FSJuDcwZM5c+z7qp2ntfw4q3xO/api/7Ag0uf5a9L/kO5p4Lx3Yfz1uwnSYyuPitlKIO/LP43D8y4hdjIk78vrx99Kek5e7n70wfpHNeep3/856Ctz/LgrVgRsWPtXtvRCQghm2QlJB7Z1rv3Fr+uK1rHxN5NfnITRrRWGcOSlaJIpPEFK3t7PD0vKC1b/nmbOL8mRfMvtM/IbWusvHKlMUmq93mzRKD6KP7y5Qvx+GrXTRh4DpdOvLXOc4vK8skuPMiXG//LpRNvIS6qLd9seZtnPn+AP13zGm1j29OxbTd+NOZanvr0VwAM7XUmY/un8N3ur/F6PUwcfL4/wm4WsUUnOKJGnOmIMt8oKqPoiLdyx16veyfKm3cGqK71XMIKen2WH+ldhxbIfCD178CvrY5DCw3b7Zlr1zh29VCiQrokfWRk6dEJZ36AiP92XH3P8G2PiLPRzZuPs+8pWhmRUdzoRfSrou76rrvkNWm0xgWuiX16HnKL9G3K80/nrC3Gd7d9boyQEFvjk91x5KYdg6+P8DpihjXnOgdzduHyVLI/J50vN7zOuP6zuHra3bWe+/RnvyY9cwN3nP8IQ3qZ/1TlrlL+9Ma1zBh2KReNv/HEuSXlheZuxvguVLrL+etbc7hx1h/o0yWZhaueZsu+VOJj23PN1Lvp19WyzZ61Mrw5+7yV2w8a7t1RylxYn2h1TMC4+97+dIPVQbQUekTLGmutDkCzXo4U7V4UubmkQtxNrqYeLCLeyrHjPskVUU1OimrzLtfkNef50oSpQ4BF3vEVNzm+bNI9IyFyXnZu/n1dOvk90Vo60jahII4tDyw0+kgI7WSr0kdx3a4BVyc0tY9iz07m0/p1HU6b6AReX/p3UkZcSaeEU4vWx0aaO4kHdBt14lhMZBy9Og3kSH71ntFtYhI4/uVatOl/nJE0jP7dRrAs7X0O5WXwp2teY/vBdbz8zd9wXvtfIuzW7iSuymbv1NcWO6MvzEApZSjPoe1e1/c5Xvf+eFRZMsFfZlIJbA3yPVs0vRjeGnpBfCtWgSv/48j1Kz6KXHdGhbj9MiUTaKNHf77O4XD7Ncny4HDtZlDzhhe8TRuSf8c7s1mjh+eWlY/p7Xavbs416rKpv23k7+bYjxpSa182S3XLWjt+Ruq9A/rveW+NGJ79zblWz44DAMgrPlLr413a9TJ3EtfYkaKUwlbHuq684ixSt33MpRNvAcxdiOMHnE1sVDzj+qfgMdxkF2Q2J+yAEhGbLaLHkIi482ZEJ942Jirx/2wRcRdvskX0WwaR2zjZKDmQtt739qfNLtCrnaQTLQv0mDctC9hvdRxacBko71rHrhX/jUol21Y4HbF+LU5D9Ov/7fK4NgV+b/OzjJRNiLRr1kW8TVv6kK56neFRttp/wzfQi0eyz0Cp4uZcoy4Z3WTgPbfYXR4bB+o/O7h8fRQnzUi9t0ef/Z+niuE91JTr7M36HoAO8bUvURrW2xzo3X1484lj5ZUlHMzdRfcOte8k/mDNfKYPvYQO8Uknjrk8FcDJhfiWbSVuAhFHlD2y/+jINpfMjG5359CohDtKHLHnfCeO7ivAnhGg2/ptZ61m0lOH1lkL9LE6CC049tuyNy2N2NbGK0azW9UEU8eOBzZ27borIL0UP+GUFmONJkbTF5nuVD0zhsqBJi9ETvJ6k64uLln+dtv4gOwWzGovPW+fa8/593zvjmg3yYG4R3OYfRQ/m9bnwKLKPf0uW57ZffqQuvoo/uezBxjUYwxd2/XGJnb2Zn3P4q0LGdNv5olpQ+eb1zOg6wh+OvN+AHp3GsSIPpN5Y/njXDzh57SJTuCbLW9jtzmYPvSSU+6x+/AW9mfv4PqzfnPi2ICuI1ma9h5J7Xqz69AmoiJi6ZwY3J3E/mQurB8+wRFlDgQro/io15W+x+tKV8qbewYofzQOX+aHa2hV6ETLOmuAa6wOQgusQinL/DJiU2axrSLk12HVFBNTeGBw8oq+EoCRtyLij+XSqfnTpk0c0QL4yDvFNtTWvAGj3+blT/2oTdzOCpttULMuVIfCNtLp1rvs0f9+1ru5bTmjAnGP5vL1UZzRb+9HpTsHXrMsq8uEUYgkVj2nd+dBfLtzEXnFWdhtdjq07crFZ/6cacknC5kahveUah1zUn7HB2uf4/01z+L2VHJG0jDuuuhxYqNO3Un83upnuHjCz0+05gGYOvTHHD62lwVLHqFtbAdunPX7kFqf1Vxii+/iiB7fxRFtbgQ2vHn7vZXbfjDcuyOVUTgIaOyIsUInWn6ndx1aJPOB1DGA3tXRQrnxlC6L2Lb+gC33TCS0dpA1hN3uLj5z4sKjdru37iqxzfBf5qz4Qi5u9uhexIbc5fbcyiaNKHWkIGdd1B0dRai75UADrIqJTrutS6dhp21d0EwOj6r85wveTUkFhHzCHuw+ilrtlFKG8h7e6a3cdtTr3nt8YX1sPU/7/r63Pw2tbZktgB7Rss5mIBfoaHEcmp9ttR9Ytc6x5wwVcm1zGkqpMWM/2W63e88M1B2WcXYHv1zIaPobxVwSO5UTuTMWV7NGo6aUVwxPdrlTd0RFBqw2nschUXffZp/w4ALvigGHCenp5xp9FDcUJA44M5B9FLXaiYhNHN2TbY7uyRGAUl6X4d6/2evaVmB4DnZEVQ7m1BxgqQWhtnh6MbxFesybZgDfWB2H5j9ZUrBjQdTyrd9F7JmihFAsQtggyckrVkRHlwYsyTpIr33lEuuXHYxiNG80ar0xKMsfcczPyh4iSuX741p1USK2389xTP92oCwL5H38xddHcfqkb/+UF190IBXVuL6Umn+J2CPtkf1GRba5eGZ04txhUYlzyx2xP/rO5ui5HBy7fafpRCsAdKJlrUVWB6A1XxmVOR9Efpf6aeSGQS7xNKlnX6jo2nXn2g4dfwjoiMk7zP7BbxczVLMSrYXeGW38EUZ7w+hwc2HR9/64Vn3+cbl95mfjZbkKk+1zMRXHuo3f+Oi0M9f97VCw+ihq9ROJindEDZ0QGX/ljOh2/zcgKuGWLHvUOJ1oBYBOtKz1ldUBaE1nYLhXOtKX/y9qZWSerXgaEt7/n9rE5+7q1/+74c1ds3Q6CtRmxta+N78pDNWsr/nXxtghSlHpj1D+L79wapxhbPPHterz2tn2GQtm2dYoCJt6R3FlR3tPXPe3yeM2PrY7quKYLiEQYsTW5od7FjgLrI6jJQrrXwzhrse8aYeBoLwL1vxrj+3I+lejlmWmOw7NQEKngndTORwVx0aO/DJWhIAuXt7I+K2G2P3XaqiZiVYFUTHHiN/uj1AEZH5Wth3V+P6LTfHZBNvkf11i26qgNBj38xdfH8UJozb/a1uEq3ij1fFoJ3xtdQAtlU60rKdHtcJIvpTsfzNq5bplkdvHGaL83oLFGoZn3LiPD9hsge+1+B5X+bXApxjN/xm2zBhV5I9YAEZVugaPrahc6a/r1Wf1ENvYv15r229As1oZWaF9wa6h01Y/MGb4989tcnjK0qyOR9O/iwJFJ1rW0+u0woALT/GXEZuWvRf5bbdSqRxvdTz+NGLk16sjIisD3gqoksjyA/Qd6deLKtXsGl9ve2bWWmSzqZ7KzhllUyrbn9c8nW29bUN/fbO90Cs0qUK71Trlbh09feX9wwenv77O5nXtsjqeVqoY3RouYHSiZb0VQIXVQWi1Uyi10b535YKo5RWZ9mMzEVpOtUOgd+/NqQkJ2UEpF/A1521CJL7+MxvBaH4x1XVq0GBD4bcdg20NlXB3fsEef12vIX7oLGfcdbvd5rITqLYsAVejj+I+q+NpZZbNnZ8SNuv9wo1OtCzWY960CsxkSwsxh2zHvl8QtXzHxoh9UxH8OuoRChLbHU7r2SstYGUcavqCH/u/lpIfRrQUNtsB1SXdH+Ecd1Nh8eR2Xu8mf16zPrkJ0vW2O+3tS6PCd93n8T6KM1fc06vP/s9XNrWPotZon1kdQEumE63QoKcPQ0gJFVnvRa5d9UXEpqFu8Q6xOp5AiIoqOTJs2OIuEqQRunzaZRfQbpTfL6z8U3T5c2Oi32s8vXAkOwGlgjpKUBIr7W69y37GsTasD+Z9/U1Q9jP2fzZ1RuovO/bIXLoCZeRYHVMLZgAfWh1ES6YTrdCg302EAC9G5bKIbcveilrVJt9WOoUAljmwktg8FWPGfpIvQudg3fN9rtqBiN97Jvor0Vrond7HH9epapDbfcb08orV/r5ufVwREnvHXPuogx1ZFex7+5vZR/Hd6TNS74tNylq7HKUKrI6pBVo5d37KUauDaMl0ohUCesybthPwyxZzrWnS7Ye+ey1qWdYee9ZMBL8UsQxVo0d/vsHh8AR1pG4lMwJTKV+pCH9cZr/q2tOt7M3rMF2Lx7Nzx9uVyvT3detj2MRx38/tk7/vLcuDfe9AsBuuuCHpr8+Ytup+6Zi7ZRlKlVgdUwvyvtUBtHQ60Qod+pvdArlSnPHfqBUbV0akTzBE9bY6nkDrP2DN8ri4winBvOceBuxySdTAAF3eL4kWQJrq6/dEK0ap2D/kHTvs7+s2iIj89Vr7jOXDWkayBcf7KD4/c8rq35Yn5u9cjlJ+KTbbiin0756A04lW6HjP6gBak0rchZ9Gblj+YeR3vSvEPcbqeIKhU6d965OS9gSs8XFdFjL7SMAu7qepQ4APvNMCsl7tiuLSCV08Hssqof/nx/YZ706RVAVBKaQaDGYfxadm6D6KzbZu7vyUg1YH0dLpRCtE9Jg3bTOw1+o4WjqFMr5z7FnxetQKT5atYAbiv1/UoSw2tmD/oMErB0iQ2wQZ2LzbGJ4cwFv4LTn62DtpsFIY/rpeVS8dye6KUuWBuHZDvDPdPu25823rlZ9KyRx1uxm7aydDdqZTatT9JXMpxWPZ2Vz3wwFG+86vzTfFxZy7N4Mpe3bzn9zcUx5/JjeXuZmnzsDqPorNpt/gB0GDfuiKyA0iskFEikUkX0Q2icg/Ax1cc4hIrog4q3y+TETebcTzXxWRYO/c0d/0AfSDLXfLa1HLdm91HJiO0MHqeILFbncVjR7zmVcsaBW0hsmbldgCuejeb1OHhbRJLCFmh7+uV1Vvj6fnBaVllvb3WzLKdubfr7DtVFDY3Gs9npNDrK3+Xx8VhsF7hQXEiI1RMTG1npPv8fCbI4e5KiGRP3Tuwmv5x1hVerKrUJbbzYL8Y/y6c93fRif7KD66J6oiT/dRbDj9OycI6v2fIiK/BV7ELEHwE+BnwEfAxYENze/uAH5rdRD1eMfqAFqiIik/9E7k6jVfRW4Z6RFjkNXxBJcyxo79JN1mM/zXyLkRPuTKQK+h8WttrrXGkFOHU/zkbzl5kyKUsrQQ58YBtpG//5k9yxCaXLl+fVkZqaUl3Ni+fb3ntrXbWdN/AC/07MnZbWqvVbulooJuERH8vEMHzm/blgvj27K6SqL1z5wcLk9IpHdk/YOXbYt/GDBl7Z98fRSLdB/F09syd35K2Ba4DScNGdG6E3hOKfU7pdTXSqlPlFJOYEBgQ/MvpdR2pdRuq+M4nR7zpq2H8K3sHGo8eMsXR6QteydydfsiW/kkq+OxwpChy1KjossmWHHvcmKKD9M9oK19xI9ThwDveGcEbNQvEiIfzsk7FqjrN9Se7jLo3l/YKzw2Gr3436sUD2Uf5Y4OHWlnb1i1DpHTV0lxK0W0nPxVFG0T3L4ZwC3l5awpK+X2Do0bgDb7KP5W91E8Pb0IPkgakmglAlk1D6oqc+EislxEnq/y+Y9ERFWdXhSRy0XEJSKxVY79XES2iUiliBwQkV/XvI+ITPNdv0xE8kTkBanRxkNEpovIFhGp8E1xTq7lOtWmDkWkh4i8IyLZIlIuIhki8rdanneOiGwVkVIRWSkiQ+v7gjXT2wG+fqvwvf2HNa9FLT+2z549E6H2OYsWrlu3HWs6dMicYdX9P+fHWxAJ3NfeUC5/X3KZMWqIUpTWf2bTnFdaNra32215T7kjHaTXHXPtsRUR7GzM894uKMClFLPbtfNbLIOjotjlquTbslIyXS6+Li5mWHQ0Sikezj7K3R070aaBSV1Nx/soJqe//p3NW9mo19oK6GnDIGlIorURuEtE5ohIXW8rUoGqu5mmYy66rHlso1KqDEBE7geexaxIe5Hv738TkTuPP0FEpgDfYCZ6VwD3ABcAr1Q5pxvwBXDMd85zwBvAiYSuDguAnsAtwPnAQ5w6DdELeMz32GygM/C21PcWrXl0otUMR6Vw5+tRy7esjdg9SYnqbnU8VomPz9l5Rr/1/m3g3Ehfcb5/+xrWFIBEy40j8ijtArJO67gXj2T3RaniQN6jIQraSKdb77R3LY5hc4PO93p5KjeHX3fqTIQffwT2jIzk1vYduPHgQc7dt5f+UVFc0LYtHxcV4VaKnyQ0f5Cxa9baCTNSfzmw/553V+s+igDsnDs/ZZvVQbQWDdlxNRczGXoVUCKyAzMTflwpVeQ7JxX4vYh0UkrlYCZYLwG3iUgbZRaXmwYsBhCRtsCfgQeVUn/xXeNr32jXH0TkWaWUF5gHrFZKXX08GBE5BCwWkWFKqe8xk68K4MIqSVwp8N96XtcEYLZS6hPf58tqOac9MOX4lKOI2IAPgEGAX3ujHddj3rStmQ+k7gACuVOrxSnHlfdV5JZtOVI0lSDvrAs1ERHluSNHLWojUu+bjYA5SpfMEuJHBPQmAUi0AL7xjim9zrE4EJcGIMnrTbq6uGT5223jLRttPK48Wtreeqc9+YnnvWu7FDLxdOc+mZPDyJgYZrTxfz3fOzp2ZHZiImVK0T0iglLD4IncHB7v2g0P8HBWFl+XFNPR7uBPXbowNrbx39pmH8Wlk3tmLvPu63N+6oFe552hbPbW+mbsdasDaE3q/YWklNqK+Uv/YuAZQIA/AutF5Pj/uNWYNVqmikgUZhLzIpAHTPIlViMxEzKASUAcsFBEHMc/gCVAF6CHL+maBLxT45yVgBsY67vWBODr40mWzwcNeO2bgUd8Oyp71XHO/hrruo5Xb+/RgOs3x2sBvn6LYWB4Vjt2Ln8jKtWRYyua3tqTLBHDPXbcx4fE4tG897g6o97FOc0VoETrHe/MboG4blW/zcufGm0YuwJ9n4bwOCTq7tvs4zOSTvx8PsXuykreLyzgjg4dKfJ6KfJ6qTDM1SMlXi8Vpynx0FDtHA66R5ibSF/Iy2N0TAzjYmN5uyCfnZUVfN73DG7t0IH7jhzG1Yz7mX0UP582I/XeTq20j6KB/h0TVA36paSUqvQtgr9TKTUE+DnmYvibfY8XYyYu0zATn3JgKyenFKdgJmgrfZfs6PtzG2bSdPxjqe94T6AdYMdM7qqeU4m5pbun79wkqL6Dxpd01dei4WpgPfAEcEBENovIrBrnFNT4/PgP9uh6rt1crwK6AF899tqObngtavmB7Y7MGVhQuiAUjRi5aG1EhMvSKUOAb5lc15sXvxFvYP6PbFX9BniVNHlXXkPYwf5Edm5FqNR9Mmxi/+2NjmnrBtReRf6Ay4UHmP3DASbu2c3EPbv5W7bZHu+svRk8lO2/VnmH3C7eKsjnV506AfBdWRkXtU0gwW7ngrZtcSnFfnfzc2yb8ka20j6Ki+fOTwl6W6jWrEnFGpVSL4nIo8DgKoePJ1V5wCqllCEiqcClmInRdqXU8R03x/+8CKjtf+hOzKxbAU7g81rOOd7WIguqN8f1jYaddnxbKXUIuME3HTjBd5+PRaSXUirvdM8NtB7zph3NfCD1U8yvnVZDgZQe+DJy89ESqbBkN12o6tNn44q2bXOnWx3HNoZt80hEoDeNgKHcgbr0HtV99yDJDGjT7anlFSMGu9wr06MipwbyPo3x2BX2GTd+5V1+3gY1XTjZVH1sTAyv9uxZ7dyVpaW8eOwY87v3oGek38qZ8Vh2DrMT29E94uSG0uMjZl6lcBkKf6anx/soDtj9buGOwdcvz+04YiwnZ2taoletDqC1qTfREpHOSqnsGsc6AQlUT5JWAHdhjvp8VOXYI5jThFWHpddgjnp1U0p9dpp7rwUGKaX+epoQ1wE3iUhslenDy+p7XccppQxgrYj8BXMKtDdmsmi1F9GJVjVuPCVLIr5ff9CWNwmhyX0JN2/ezEcffXTK8QsvvJBx48bV+bzDhw+zePFiDh82c/yuXbuSkpJCjx4nZ5LT09NZtGgRLpeL8ePHM3PmzGrXWL58OYcPH2b27NlNDb9W7dod2tqj57aQKGHxLrOD8//HG7hE6xPvJAbZFgbq8ic8l5WdPLNX93wl4r9tfM30yrn2GbltjVXXLTUmiK8gbDuHgwmO6r8uDrnNL//Y2FjifMVLf7Q3g/GxsTyYdLKH+IqSEsqVQXqlWZR+UbG5tHdYdMyJqcLj1pWVsaWinIe7nnz++JhYFuQfo19UFN+WlRJns9G3ATW1GivCW54wYtvzM1wR8bnbhty4IT9x4ETMpTAtSSENW1qj+VFDRrTSROQj4CvMKbrewK+AMqrP867EnOqbDNznO7YFc7pvPPDk8ROVUgW+qu3/EpHemAmZDRgInKWUOp4o/Rpz4bsBvAsUY+4EvBD4vVJql++6c4FPfeUkumEWJq2z3YWIJGAWYF0A7MLcbXgf5uhYQHccNcKXQCaBXw8W8hRKbbHvX73BsbefEmb667o/+9nPiKjyg77dabasFxYWsmDBArp27cpll5nfnqtXr+b111/n9ttvJzExkbKyMt5//32mT59Ou3bt+OSTT+jZsyf9+pm1QouKili7di2/+MUv/PUSAIiOLj40dNiSriL+q5LeVB4crt0MHBaUmxmB62/3nnd6v19FBD7Ram8YHW4uLEp9MTEh6D0oT+eTibYp+fGsv+tjI1nMN8oN4lUKo8Zw01+PZnHYc/Kf6l7fG5WHkpK4LCHxxHFDKR7JPsq9HTtVqzp/TWIiu1yV/ObIYTo5HDzerRuRDahK31SR7uKOo7c8NaM8qv2R74fevKc4vvckzPXBLcFbc+enWNYKqrVqyDfPX4FLgKcwd+FlYY78XK2qVDlWSuWISDpmIrTBd8wQkdXAeZxcn3X8/EdF5DBwL2aSU4GZ9Lxd5ZyVIjId+AvmLgk7cAAzCTnqO+eQiFzgi+89zETpOk6OqtWmAkgD7sZc61UGrAXOVRb2I6uqx7xp3swHUl8F/mB1LFY6Ivnbv4rcYrjFO8Xf1+7evTuRDXxnvHv3blwuF1dffTXR0eYSvZ49e/LYY4+xe/duxo8fT2ZmJomJiUydas4E7du3j4yMjBOJ1jfffMPo0aNp34CK2g1ls3nKx4z9tFik2jS+ZVZw1iYltjODcS8JYKJ1hA5JFSoiI1rcAa+o/3/5hVPfbBu/rdRmC/x0ayOsHGobVxDHtj+8aXS1mT/7q7ksIbFaogTwTb/+p1yntmO1sYnwfp++pxyPtNl4MKlrtVGyYIipPNZ1/MbHupbGdjmQNvQXmWWxSZMQCffNNi8E4qK+gZM7lVIdqxyzYf7evhy4RCm1KBD3DgQRyQWe9hVnb7Z6Ey2l1H+A/zTkYkqpU0oSKKXOP835/6WeMgxKqW8xE7XTnbMMqLmVvGONc2ZW+XslcNphBaXUDbUc20+VdQtB8BLw+yDfMySUUpm9KHLzrmNSMgWx/vV7vV5sNlu1xCwyMhJblXfWXq8XR5XplYiICLxeLwCZmZns3buXu+66y69xjR7z2Sa73XNKgV6rfNzwWfvm86qANIA+brPqf2ii7Ah4oiUg87Oy7dd37eJFpGmVOQPk+z62ob+5STLmveKtsCsCvhszFJl9FB/sXRTfa/f3Q39+rCK6Q1DeSATAxrnzUzYE40a+WpMvAFcCPwmnJCsQwj07b9F6zJu2H1/tsdbCi+Fa4di+/M2olTHHbCVTA5lkPfXUU/z1r3/l6aefZv360/cPHzJkCBERESxatIjS0lJKS0tZtGgR0dHRDBkyBICkpCSys7PZt28f+fn57Nixg27duqGU4ssvvyQlJYWoKP8t+Rg4cNXy2NiikEmyionPz6FzQFvuVOMN3IgWwLve6YHeXXzCqErX4DGVlauCdb/GONBF+v3fbXbcdvZaHYuV2hb/MGDy2j+dOXrzk+HaR/H5+k/xm6cx+yLPVkp9GsT7hiSdaIW+F60OIFh22Q9/91rUssO7HEdmIASsqnibNm0466yzuOyyy5g9ezY9evTgs88+Y82aujujxMfHM2fOHHbs2MHjjz/O448/zo4dO7juuuuIizOXsLRr145p06axYMECnnrqKTp37szw4cPZunUrXq+X0aP9l4N07pyxrnOXvSG1rudjLktDxP+rlOsghvIG8vpfeCcMUYqALbiv6d9Hc0baamw8ChU5idLttjvtiWWRtPpq4u0KdodjH8VS4H/BuJFvrfRtwM+UUu9VOT5PRNJEpEREMkXkDRFJqvHc/SLyuIjc6zsnX0TeEpHEKufM9LX4mykiC33X2ysid9S41iQR+VhEjvha6G0WkZ/WEm9DWvhdKCJf+1r2FYnIWhE5t6Ffk5aywK8l+wBzF2TjuqqGkWNSsu/LyE15ZeIKSrmG/v3707//yXUjAwYMwOPxkJqaysSJE2uts1lcXMzChQvp1q3biZ2J69at43//+x8333wzCb42ITNmzGD8+PG4XC4SExNxuVwsXryYyy+/HMMw+OKLL9ixYwdt2rThwgsvpFevxpebiovL3ztw0OpBEmLFWZdyTnC/RwM8dVhKTJtC4rYkUhqUumRtDZVwd37B6ifatwtoWYmmKo6V9rfeZY96ar53fbtS6t6e20p0yt06utPK+zmSdOa6nQOubmvYowZZHdNpvDV3fkrA2z6JyEOY3VpuVkq9WePhzsDDmKWZOmGuzV7i6/JS9f/yVZh1OG/B3Az2T9/z7qh+OV7A3JD3PGaLvP+IyHql1He+x3sDq4D5mOuypwCviIhxPLYqLfy+w2zh143aW/j1BT4BHscsPXU+8IWITFdK1TsSHVI/qLVT9Zg3zQW8bHUcgVCJu/DziI3L34/8tkeZuCz9wZ2cnEx5eTkFBQW1Pr569Wq8Xi9XXnnliUTtqquuwmazsXr16mrnxsbGkpiYCEBqaio9e/akd+/erF+/nqNHj3LnnXcybdo03n33XTyexs1+2e2uwlGjPxcR2jbldQbKQXruK5fY4C7mDnCiBbDCGFEQ6HtUdVNh8eR2Xu+mYN6zMSojJe6OufaRhzqwuv6zW4euWd+On5H6y4ED9ry7JoT7KD4dhHt0AH4HPKmUeqXmg0qpm5RSbyqllmMOIFyB2XWmZh05N3CpUupzpdTzmLM6P6nlfm8qpR5USn0N3ArkVj1PKfWWUurvSqnPMYuhP4LZJ7nq+ux7ONnC71Pf/f4AxNSI/Wml1FO+tWaLMSsifIOvaHt9dKIVHp6C4E1hBJpCGesdGamvR61wHbbnzyAEyhLU1y0mNzeXzp07Y7efXKtst9vp1KkT+fn5tT6noKCA9evXc8455wCwf/9+hg8fTkxMDMOGDcPj8ZCX15iSU8oYO+7j3TabcerWLIu9y+wfgn1PqVlHIADe8c703xbRBno+K7stKnA1wprLa5eIe39hn7S9J7VWkW+NBKRn5tJJM1fc06vP/s9WiuE9ZHVMVSyeOz9lcxDuUwR8C9wsIqNqPigi54vIahEpxOx8crw6/cAapy5Vqtr6y+1AZxGp+Xviq+N/Ueb/l91UKYckIu1E5CkROcDJzjK31Lhfg1r4iUgPEXnN12vZ47vWubXEXiudaIWBHvOmZQLvWB2HPxy05W19LWr5zs2O/dMQOlkdz3Hbt2+vNhJVU0JCAtnZ2Sd2EQJ4PB6ys7NPTBvW9NVXXzF+/Phq13T7ijwahlHtWg0xdNiSFVFR5SE3ZaNAbWRswHfnnSIII1qrjaFDlKIw0PeparDL3W9aeUVojxiJiPM6x4zUobLM6lBCia+P4lRfH8XlIdJH8R9Buo8bs8blYcxptTOOPyAi44GPMZOr6zH7GB9vYl5z00lBjc9dmDvva+4kqu28qtd6FbPV3mOYSdF4zNmhqufU28LPV6biY8waoX8CzvJd64taYq+VXqMVPv4BnLKQL1wUU37ky8jN+wptZZbvknvnnXfo1q0bXbp0QSnFtm3b2LZtG+edd96Jka2nnnqK3r17c8kllwAwZswYNm3axNtvv11tjVZJSQljx4495R779+8nMzOTSy+99MSx3r17s3btWjp16sS+ffuIjIykQ4eGLWvq3mPbqvbtD89s3isPjE2M22qII/j9Fb2BH9EysNkzVcf0npIb1C39/8jOHT+pd49DXhFLm4PX598X22fmJHhTL1utJotZ51DjRB/FGf32fly2c+DVy7K6nDmKKgu6g2jb3PkpXwTrZkqpPBH5EWatzUUiMsXXWeYyIAez/qYC8BUrDwgRicZs8TdXKTW/yvGag0sNaeHXHxgNnK+U+rLKedWmF09Hj2iFiR7zpm0CllgdR2N58FYsifh++dtRqxNCIckC6NChA5s3b+add95h4cKF5OTkcOmll3LmmSd/lxqGUa3fb7du3fjpT39KZWUlH3zwAR988AFut5vrr7+epKRqG2dQSrFo0SJmzZpVre7WuHHj6Nu3L++//z579uzh8ssvr1Z3qy5t2x7d0bfvxjF+eOkB8R5XB3yRba2M4PRj/tKYUBmUG1URo1TsH/KOhdL0U53emmGf9uKPbOsUBP3rFOrshit2SPrrM6etvF865mxehlIl9T/Lr4I1mnWCUuoH4EeYa7a+EJF4zDVPbqWqvTkK5MBBFGZ+c+J70hfHxTXOWwec40uujqtZDPB4QlX1Wr0xF9c3iIRI83itATIfSL0AqLM3ZKjZbs9cu8axq4cS1erbCDVVRGRZzplnvu8WUSFZLLKSyPKb+J8bkaAvzo/YcmyZPat8ZqDvM1AO7vsq6jeWrIs7u2e37446HGHRPH3cLmPz/e8ZZwihtVEjlPj6KG4LUh/FI0CfufNTXAG+T12V4SdjLhhfjbnO+CPgX5i79yZjdnAZCNyllHra95z9wLtKqV9Vuc4NmIvY45VSJSIyE3Nx+3Cl1PdVzlsG5CqlrvB9/h3m7sZfYe4UfMD3edvjcYo5YrwbszNM1RZ+XYBHlVJOMf+d9mCOyP0RiMfsVhMJ5Cml6l3OoUe0wssXmAsDQ1qOFO3+b9SKTasjdk7USVbTiXhdY8d+ciRUkyyAxfxokxVJFhC0Ea1dqmdfj7JZMrr00pHsroRIW7D6rB9oG/XH6+2HDSEU1iWFpON9FCet/dOx+KL9qaiAFt39dzCSrLoopVZjlmqYgTl69RvMdjwf+45dFOAQrgX2YvY0/hdmi74FNWI8BFyA2UnmPcwSEtdhtuU7fk4l5m5GD2bP5b9h7mBs8GYQPaIVZjIfSL2ZEC1iWoEr/+vIrWlHpXAKotdrNNeo0Z+nxsfnhVRR0pru5Pn1+dLBkgX6Eetzl9vzKmcE416fRv5u5TDb/prb0IPi1506LP+iTVxQXqc/dMtTBx5/0SsOg8YXiWtlfH0UD5XFJk30cx/FUqDn3PkptW+J1oJKj2iFn//ia6gdKgyUd61j14r/RqVy1FY4XSdZzdf3jPUrQj3JyicxJ5/2wWu5U4MEaUQL4APvVMt+Vj6YkzcxQqlQrc90isMdpPfcO+zRlQ52Wh1LqPP1UZw8bsPfM6LL877146Vf1klW6NAjWmEo84HUPwJ/tToOgP227E1LI7a18YoxwOpYWor27Q9uHjJ02TCR0N4V/DK3LF8sP7JspCVybXaqrdAdlGS0A4W566Nu7yAWNTj/Mi52w/2dO566vbUO7nw3ux/YjVFpkDw/GXt07e99XDkudt2/65TjCRMS6HlHzxOfF20o4shbRzAqDDrM6kDnS6sXr8/+KJvyfeX0vufkRrLYClX472e9++MrCP6O1DCVnzhg2/dDbqp0R7ZtzuYXLzBg7vyUsEnOW7qQ/kGu1ekZzMq0beo7MVAKpSzzy4hNmcW2ion1n601VHR0ceaQoct6hnqSBZDKjK6WBmAEL+nJI6FjOVHpsVQODtY9qzqvtGzs0273mgMREZMacn7W21nYomwYlQ0rNZZ0dRKxA05uvLLHn0zMPMUeMp/PpNPFnYjsFMmhVw4R0z+G+GFmO1L3MTe5i3Lp9+fqpdTKoiXhtrvsg558zvttpyKCWh4jXPn6KJLTYcTmHYOvt3siYoc34TLv6yQrtOipwzDUY960PMxdHEHnxlv2dcTWZQsj13TUSZZ/2Wzu0jFjPykVCf2+lhn03+WS6AZVRQ4YQwV1dOk7Y5ClU/YvHsnui1L1ltIo3VlKSVoJHc/vWN+pJ0R2jSS2f+yJj6guJzfElWWUEdEhgk4XdiJhQgKJExMp/b70xONZC7NoP719tecc53ZI9F2328ft7UJqg4PR6JS3ddT0VfcPT96xYJ3NW9nYKdh5AQlKazKdaIWvxyG4Fau32g+sWhC1rPCAPWcm0rCKuFrDjRn72Va73RvKjWlPWMjsI1bHgKGCuhZwoXemZSPIAEleb9LVxSUbT3eOMhRH/nuEThd3wt7GP18e5VFI5MmcViIF5TWXnJTtKaN0WymdLqm7yYNhE/sDNzmmbewnumVPI3U92ug+ih/MnZ9y2u8RLfh0ohWmesyblo9Z9yPgsqRgx4Ko5Vu/i9gzRQnWThe1UIMGpy6PiSlu0LSQ1Qxs3u8ZYckUWlWigvvz6xtjzFClqAjmPWt6IC9/SrRhnLqoyufYkmMYbnMdVWMceukQ39/4Pel3p3PkzSMYrpNTjjG9YqjMrKRkRwmuHBdF64uI7hONUooj/ztC58s7Y4+pP6mbd5V9xqLRslyBXhjcCI3oo6iAPwczNq1hQn4diHZaTwD/B4GZaiqjMueryC07c6V4MqKT8kDpkrT7u06d9k+3Oo6GWsvkzUpsDV6YHTBBHtGqJDI6j7YbO1JkWZV+BzieyM6tuL1LJ1WzE7qnxEP2B9n0uKUH4mjYrKpECO1ntafNsDbYom2UppeS+3kurmwXve82F7ZHdo6k0487sf/v+wFoM6INiRMTKVhdgPIo2k1r1+D4XzrPPiM3wVh17TLjTNG/fxrleB/FPgcWufb0u3RFZveZyYit6lDiwrnzU9IsC1Crk/5GD2M95k0rznwg9VHg7/68roHhXu3YtTrdfmgUgiW1g1qLuLhjGQMGrE22ajdbU3zIFaHRakUFv4zIEu/o4qsc1s6ATS2vGDHY5V6ZHhVZ7f/m0XePEtMvhviR8Q2+VkRiBN2uP1kPt01yGxwJDo4sOEL5D+XE9DK7j3S+pDPtU9pjVBpEdozEW+Hl6MKj9Ly9J8pQHF5wmKINRTgSHHT7WTfiBsbVec+PJtmm5Ldh3dxPjaECsXWeqNXK7KP43vR+ez8p2zng6uVZSWeO9BUNdlodm1Y7PUoR/p7Gj3W19tiy1r8atexguuPQDIQEf11XO5XDUVkwavQXDhEa/pvRYuXEFB+ixyir4wBABXdEC+Ad78zO9Z8VeM9lZSeLUifqJFUcqqAgtYDOF3fGW+rFW+o9Mf1nlBvVpgLrkzDO/G9fsb/6LKkj3kFkR7N3Z+6nucQOiCVuUBz5S/OpOFjBwHkD6XRRJw4+cxDDffr7rRhuG//gNbYMBQUNDkyrxm64YofsfH3GtJX3S9cjqx+fOz9lh9UxabXTiVaY6zFvWhlmO4BmyZeS/W9GrVy3LHLbOEPUGX4ITTstwzt23McZNpsRsA72gfAFF22hegNW6ygign3L9WrgYENJXrDvW1N7w+hwc2HRiT5vriwXyqvY++BedszdwY65OzjyurlfYee9Ozny30bsXZAaf9bgynGRtySPpKvMZuolO0pInJSIPc5O4sRElEfhyqq/80taX9vwB26053oF6zdWhLEIb3ls8s43nrM6Dq1ueuqwZZiP2Tiz0X0FXXiKl0Skbci0HZuM0MfvkWm1GjZ88crIyIqwaaty3Fecb+nOu2osGNECkf2qy64zJMvyjQt35RdOebNt/LZSm21o7MBY+vymT7XHS9JKyP08l96/7E1kp8gGX7doXREAMX1ian086+0sOszqUO2ax0fMlKFQnoavdd+XJP3vvs1+6InnvfsivFjSuLsFeC45fcdeq4PQ6qZHtFqAHvOmVQIPNeY5CqU22veuXBC1vCLTfmwmQsN/EmvN0qNn2qp27bLCLsnKpvOhYtqGTpVvC0a0AD4zJgayEXCD2cD2TFa2DaW8jngHbZLbVPuI6mrWtYodGHvi77t+vYvMlzJPXOPoB0c58uYRCtcXUrKthKPvm5+3HduW6J6nVnApTS+lLKOMThedXIMdNyiOY4uPUbS5yCyUGm0jMqnhP06yE6X77XPtbcsj2d7Ur0UrVoLZ5FgLYTrRajleAvY05MRDtmPfL4havmNjxL6pCHUXwNH8LiEha3ufPput37HXBO9x9Z6aO90sZkmitdA7I2RGXsZUupLHVFauauj5yqugyvKpqK5RlO4s5dBLhzjwzwMUri2k4/kd6XHbqYPjyjDLOXS5ogu2qJO/OtqntCduSByZz2VSsrWEnrf1xBbRuF8tRXHS4da77L0K4tjQqCdq/0hO35FtdRDa6elehy1I5gOplwIf1PV4KRVHv4zcvCdfSicTRrvcWorIyLKjE858X4moJKtjaYo5vLnXI5Ehs34vatGhbAFLFqfvivrZ/kjx9LHi3jUV2aRwWq8elYZISCzUbw6HV7kee9G7vvsxJlsdSxjIAfolp++ot1uAZi09otWC9Jg37UNgSc3jXozKZRHblr0ZtSou31Y6RSdZwSfirRw77uOcqknWoi+LOXvW3lM+PvmkqM7r7N/v4oEHjnD1VQc4/7y9zJ59gH88nkNeXvXZrFUrS7nuuh+4/Cf7WbAg/5TrvL4gnz/+IavB8W9n6PZQSrJ8LJvuTlN9f7Dq3jW1NVTC3fkFu62Owx88don85S32iTt6oKvI1+9BnWSFB70YvuW5F9iEL4lOtx/6brVjZxdD1ExLo2rlRo3+Yp3D4a61Jtnjj3clMupk7tu1a90zYqWlBklJDs45J54OHexkZXl4fUE+u3ZX8swz3bHbhcJCL488ks1117UjqauDf/4zhyFDohg3ztwsmJPj4b33CvnPM90bHP+7XJPT4JODx5KpQ4D3vNMix9pCJ7e5qbB4yisJbTcX2O2jrI6luZSI7c/XO2bc/aF3+ZQdKuzWMgbJ98AzVgehNYxOtFqYHvOmbc18IPWlXClO+TJyU2GFuCdYHVNrd0a/75a3aZNf5y+MQYOjiIlp2ODy0KHRDB1afZFyp452fvObLPZmuBgwMIod2yvo0sXBNbMTAdi8uZwNG8pPJFovvnCM8y+Ip3v3huUpHuzuXQwe1qCTg+vULsZB8ol3UvJDjpe9IsEvmlqXF7Ky46/sluRGxLIE1J/+dal9Rm6Cd8XFa9VU0bMvNd2RnL4jJDZlaPXT37wt0HZ75u8/jPwusULclrUK0UwdOh7Y2K3bzimBvEfbtubverdvW73bA1FVRsiio2x43OZj27dXsHFjOddd1/C2KanM3KTEFpA2T02mlGFlC5di4hKKiQmpApGDXe5+08orVlsdhz+9cZZ9+svn2L5VEBrdCELDguT0HalWB6E1nE60WqBzH5qdg/Cg1XG0djExhT8kJ6/oK3L6hOD66w5y7jl7uWHOQT49zfqsqgxD4XYrDh508eKLxxg0KIrBg80Bnn79Itm3z8XmTeUcOeImNbWUgYOiUErxzH/yuPHGdsTFNfy//sf8pOFlxYPFoP6KmAG22hhqeeHSmv6RnTverlRdTYfD0qJxtkn/+IltuwK9HsmspH+/1UFojaOnDluup4GfA0OtDqQ1stvdJaPHfFYpQq+6zmnfwc4NN7Zj8OAoDC8sXVrCk0/mUlFpcMUViae9/u9+l8X6deUADBwYyUMPd8VmM0exunWL4Npr2/GrX5kFtyecGUNKShu+/roEt0dx3vkN7/hTQpuCbLqE3siooVzAqYWegugd78zE8+zrrQzhFDFKxf4h79j3f+nYoeEL8MLAd4Nso/90nez4yxvejjbVqkvS/F6Xcwg/urxDC+Z0Os+ill2IWqApNX78h99Gx5RMbOwz//a3o2zcUM577/c+kTjVJjPTTXGxl0OZbt54o4DoaOFfT3UjMvLkSFVhoZeKCoMuXSIoLze4Yc5Bfv+HziQnR/Ofp3NJTS2lXXs7d9/dieHDa89Z/sf1qZ/JpdMa+zoCrtKbG70sq6OVITjwuHdH/axShFqr5R8qMhj0dAmlbij+bTxtImv/99xfYND3XyWnHL96qIO3rjjZ7ejDdDe/XFRBsQvuHB/Jn2dWX6L21+WVrD/s5ePZsZzds9u6ow7H+Oa8vlDUI0ftf/Rlr8NhNL4LRguwAZiQnL4j9EaYtdPSU4ctmNPpXAq8bXUcrc3g5NQVTUmyAKZPj6O42CAr6/TrXHv0iCA5OZqzz4ln3t+T2LPHxeLF1X9ZJyTY6dLFXBf95v8KGDosmhEjYvj0kyIyMly8+lpPfnptOx7821FcrtrfcC3lnMSmvI6AM5Tb6hA8OCKyaFfnOq37v66oM7mqzePnRLHm5tgTHw+mnEx+c8sMrnu/nFvHRvKfC6L559pKvso4+T2SWWTwxNpK/vkj8zkvHclOQqnypryuUJbZSfrcebs9otJB6Gz5DA4DcwG8TrLCkE60Wr67gWNWB9FaJHXdtbZjxwPTm/r8E/18G1HprEuXCOLjbRw5UntylpXl5uOPi7jllvaAuQtx1tltiI+3c1ZKG9xuRWbmqXnLIbofKJO44Y19DcEgXusTLYCvvePKaju+4oCHL/d4+NXkhpf6GtTRxsQejhMf/duf/PG8NtNL70Qbv5kaxVVDI7h2WARfV0m0Hvimkp+PjjzxnN4eT8/zS8u+a+rrCmXH2kqX2+60dy6JZqvVsQTRC8npO1rkv2droBOtFs7pdB4F7rM6jtagTZu83f37fztMmlEQdsWKUhISbHTp0vDlkwcPuigqMuiaVPtznpt/jIsvaUtS0sld/5UV5giW12suqqeWJQQLuXZ/46IPohAY0QJ42zvzlLVQXkNx1xcV/GlGFB1j/VMb2OWFmCr/vLERgstr/n1tpodv9nr444zqU4kP5uRNjFBqn18CCDGlMZJw2532AbltaQ3JRw7wO6uD0JpOJ1qtgNPpfBX4xuo4WjKHoyJ/5Kgvoupar1MbpzOLt94s4Ltvy1i7ppR5j2SzbFkp113f7sT6rJ9d/wOPP3ayVuhz8/N44YU8Vq4sZfOmcj76qJAHfpNFt24OZp516q23bClnx44KZvtqagGMGBnDxx8XsXZNKc8/d4zYWBs9elYfeVGgNjIu1CrBn2QQEjWEtqm+/b1KqpXYn7/eTaUH5o5vXOH6Gz+qwP7XIrr+o5hfLqqg3H0y+R2VZCct22DpPg/78g3e2+FmXDcbSinu/rKCB1OiaBtVPamLhKiHc/Ja7Gi2K0Ji7rzdPmZ/Z1ZaHUuA/SY5fUeL/XdsDfSuw9bjFsxqwrH1nag1luEZO+6TfTabatTuvJ49Ivnyy2JycjwoBb17R/CbBzpxzjkndwV6vWYph+MGDoziww8L+fyzYlwuRefODqZNi2P2tYmnFD01DMUzz+Rx88/bV3vsxz9uy769Lh55JIcOHez87vediayxlmgzY9O84hjRqC9DEIlXhUSiBbBb9cgYLAeTAPLKDP64tIL//iSGCHvDRrOi7DB3fATn9nPQNkpYtt/D31e5yMg3+Oga87/rGe1s/H5aFCkLzJnKCwY4mD08gte3unF54abRtdcoPa+0bOzTbveaAxERk/zxWkONYRPHr292TP3t297lo/e2yCryq4BXrQ5Cax6967AVcTqdvwT+YXUcLc3wEV+tSEw82uR1WaHoD/w9dZ/0D73dhj62nIotkRvzRlodB8Ad9o9W/Tri7SkAt31azg+Fis9/aiZIr252ceNHFafddVibZ9e5uOPzCjbfGsfIpJPF53PLDEpd0DvRRolLMejpEt68PIaJPez83xcVvLfDQ1Ib4dkLo5nay3wfnWW3Z53Ts1scIg2v6xGGbvncu3zWFjVdaDG9XD3A2OT0Ha1pLVqLpKcOW5d/AeusDqIl6dVry8qWlmS5iKjYR7+QHc0CwKtCZvfV+95p/QG2ZXt5eZObP82IpKBCUVChKPOtJCusUNWmAutzxRAzSdpwxFvteMdYG70TzR/bj6RWMqWnnem9Hcxf72LLUYNdd7bh99OiuPrdcip9nQKSvN6kq4pLNjb7hYa45y+wz3h7um2VIjSmlf3gEZ1ktQw60WpFnE6nF7OIaUgsJA53iYlHvu/Ve2uLq1W0mHM3IZJgdRynZYTO1GEW7btUqIjdu48ZuA2Y9FIZ7f5eTLu/FzP38woAejxRwl1fVDT4msd3ndY1NLO/wOCZ9S4ePccs57B0v5efDo+gXYxwzbAIKj2wK+9kLvrbvPwp0Yaxq0kvMIy8P8U29dkLbRsVhHtpi43A36wOQvMPnWi1Mk6ncyvwd6vjCHeRkaVZw4Yv7iRiXWPjQPmMSxwA5V9+zNGU0ad8lH28sM7nutO3Ufj3P5N73cUcPX8SuT+7lJLXnkO5qreqc32/mdybriD74ukU/esRlLd63lT+1Sfk3XINyqh94Eq8ylvrAxbZaAw4PLWXnaVzYqt9/GaKuSD+82tjuL8RpR7e3W5+PcZ2q71n9a++qmDu+Ej6JJ78EV7mGzHzGopKr6Lq+JkDHE9k51bQCtaKLBthm/DwVbbdymxXE44qgZ8lp+/Qb4hbCL0YvnX6K3ABEHqtVcKA2DwVY8d9fExEDbE6Fn/LJzEnn/ajqx5r94/nIepkPunoWndR7oqli/AePkjsNTfg6NELz95dlLzyLJ69u0j8i7k8UHncFD74W6KnzyJy9ASKnnwYR7+BxF50ufl4eTklL/ybhD88gtjqeC/oPTVh8BTncviF21DuCnreuxBbZMxpX2vZztUUrl2IO/cA4ogisusAOl36O2yR5ihR2a415C95EcNVTvyYi0icem215xesehNX1m46X/4nFnpnxD4Ru52ZfarHu7/ATBSn9XacWKPV/6liZvR28NIlZnzOZRUUV8KUXnbaRgkrDnh4bLWLnyQ7GNHl1ERr+X4PazO9vHbpydc3o7edJ9e6GNLJxpJ9XuIjhUEdqscytbxixGCXe2V6VOTU035hWoAt/WwjfnuD7H7oNW+FXZFkdTyN9Mfk9B3brA5C8x+daLVCTqfT7XQ6r8Ucnta7EBtp9OgvNjgcnilWxxEIH3LldkSq7d5yDB6KLaZh3yZx196ILaHdic8jR42DyCiK//kg3qzD2JO64T14AFVSTJtb70XsdmL3Z+Bav/ZEolX6v5eJGDqSyJFj675RLSNa+UtfQSKjUe76p+iKtyzi2NfzSTjzctqddSNGRQkVB7aC77LeskJyP/0HCZOvwZHQhbwv/01U98HE9DXfm3iKcile9yFJc54AYJExfohSz7pEqHfYymOAt0qaOLijncdXV/LiJhflbuiVINw/OZLfTzt1sNRQinsWVfDIrCjiqiyuv31cJGlHzerxXeNtvHl5DFGOUycen8vKTp7Zq3u+Eml3yoMtzN6uMuCeW+2Z/3zBuy/CS1+r42mgVPSGpRZHJ1qtlNPp3Ol0Ou8DnrU6lnDSv/+3y+PiClriNnIAUpnZpTnPr5pkHRfRfzAA3rwc7EndUG43OCIQuzlaI1HR4DFnSbxZhyn76G06PP/Wae8jRvURrYqD31OxbwNtJ15FwbKXT/tcb1kh+UtepP3ZtxI/6rwTx2MHTj7x98rDO3G07UzCxCvM6/+wlYr9m08kWgXLX6XNyB8R0a4bAGVExxXQZks7SqrthLxhVCQ3jKqee+2/p/rmv2uGRXDNsNrLM9RkE2HTrafWS4tyCC9dEnNilKwu7Q2jw02FRakvJSaE7I5SfzraTnrcMdee+9Sz3h0xbpKtjqceJcANus1Oy6PXaLViTqdzPvCJ1XGEi44d929I6rqrxU677OWM3ZUSPbjm8bzrfszRs8eR+7NLKfvk3UZf1719C9hsOLr1BMDeszd43JR//RneY7mUf/0pjkHmLGzx/CeIveRq7EndTn/RKlOHyvCao1OTZ2OPbVtvPGXpZn3LNsNnneb6HiTiZIIkEVEor5kMVh5Kp/zAZhImX1PtKSuMEfn13jwE/F9+4ZQ4w2g1U1OFcdLxtrvsPQpjCfWdl3clp+/Ya3UQmv/pREu7GThqdRChLiam8MDg5NR+ItS+OrkFWMi1h6t+buvQkbgb76DtAw+S+NCTRAwZTvETD1G68L8Nvqb3WC4l/32R6HMuxNbO7LVoi4klfu6vKHrUSe4V5wBC7GWzcW3ZgHvbFuKuvakBFz5ZxbVk8xcor4f4MRc2KKbKIzuJaN+dkq1fk/mfORx47BKOLPglFZkn+0NHdDkDV85+Kg5sxV2QRdnO1UQmDUApxbHFz5M47XpsUdWnU9/2zuzUoAAsZgPbM1nZNlRobSgIpPIoib/9TvuwI+1YY3UsdXgnOX3Hq1YHoQWGTrRaOafTmQPcALT43UhNZbe7i8eM/dQjQqLVsQSKgRhpjBhU9VjU+Mm0uf4XRI2fRNSZU0l44G9EzTyX0jderHM3YFXK7abwL782E6s7flXtsZjzL6XTB0vo8PpHtH/mdSQ2juL/PEabX9yFxMRQ8tJ/yLniHHKvu5iK1CWnXFt8t/eWF1GQ+l/ap9yM2Bu2EsJbko/72CEKV79F4swb6Hz5n5CIaLIX/glvqTkoFZGYRMKkqzn61u84/NzPiezYi7ghMyjdtgTl9dBmxDmnXHetMWSwUhQ2KAiLjal0JY+prFxldRzB5LFL5D232s/c2Z0VVsdSww/ArVYHoQWOTrQ0nE7nl8DTVscRmpQaM/aTHTab0c/qSALpWyZvVmKvd3dW9PSzUUWFeLMOn/Y8pRSF8/6A58BeEh/5N7b4U6f0bG3icXTvhYhQ/vmH4Igg+pyLqFi1jIolX9J+/hu0/c1fKPr7n/Aey6v+ZN/UYcGKBUR2G0RMv8aVM1Oucjqcfzdthp5FzBlj6fyTPyBio3jDpyfOSZwymx53vUH3216m85VOlMdFwfLXaH/2LWB4yVv0NAefupbDL82lInMbBjb7QdUpvVGBWOjfR3NG2pTKtjqOYFIitj/+zDF9zWBZbnUsPgZwXXL6jgKrA9ECRyda2nG/xuyFqFWRPGT5iujo0glWxxFoH3BFw6ppHi+kKafvclL8n8eoXLWcxL89gaPX6Td8GaUllL7yDPF33o+I4N68nqipZ2Hv2JnI4aOx9+iFJ73Gt6ahcOUcoGTrNyROno1RUYJRUYJym/W6jMpSDHdlLXcDW3QbQIjuNfzksahYIpP64847WO1ce2wCjoTOABSuXUhU9yFE9xxG8aYvcGXvo9stz5Mw6SpyP/o7yuPmC2NC7TcNQW0NlfB/+YV7rI7DCk9cZp/xyQRZocxEx0oPJ6fvSLU4Bi3AdKKlAeB0OiuAK4Biq2MJFV27pa/p0OFgi2qvU5tyoksO0XNUQ86tWPENkpCIrUvXOs8p/d9LlH/4Ngm/e5DI4aPrPO/E+QueJ3LsmUQOOdn1R1WezPtURS11Ng0lnvzDYHjI+u+vOPivazj4r2s49rW5ifbQMzeQ/81ztd4vokMPQNVSu1OdLMleg6fwKCUbP6PdWTcC5i7EuCEzsUe3IW7IDJTXgzv/EO94Z/au9wWHkJsLiyYner2brY7DCq/Psk9/9WzbtwpcFoWwCvhLoC4uIk4RUb4PQ0TyRWSdiDwkIgGpLSYiM333G9bA8/v4zr8oEPGECl3eQTvBV/LhRqDxW8tamPj4nJ39+q0bIdJiGtTWaREXbkHklLpgBX++j4jBw3CcMQAMg4qlX1G59Cvi7/z1iUKiudddTMTIMSTc7wSgfPEXlLz4NNE/uhhbx864tp9s1ebo1gNbYvtq9/BkHqD8y4/o8OLJavMRI8dS9OifiRw1Hm/WIbxHjxCRPLza88SriOoxhC6zH652vHzvRoq+fZfOVzhxJNb+uySm3wQKV71J5Q9bT0w5GpWlVGZl0HbCZbU+J3/JS7QZcxGOhJPVL5THHLxShtfckagUGap7b7eyZ0aIt+6qriHmhazs+Cu7JbkRaViNiRbki/G2Sflt2Hjvh8ZAgVPrZgTOIeCK5PQdgW4lVQgcr2GSgFmk+nbgFhE5Tym1wc/32whMAjL8fN2wphMtrRqn0/me0+l8HPhVvSe3UBERFXkjRi6KEyHO6liCYREX1lqN1N6zD+VffIQ35ygohaP3GbR94G/EnHvyzafyeqDKwnjXenNTV8Wij6lY9HG167X99V+IOe/iaseKn/0nsVf8FHunzieORU89C/f2rRQ9+TASE0PCA3/F3r5D9eAMZbPHJmDvVb33tafQXHIU1XPoicrwh577BVE9h9HxgrvNx7oOIGbARPK+eIrEGTdgi21L0bfvITY78WNOfWNd8UMalYd30uHCX56MsedwitZ/RESHXlQc2IItMoaI9mZutUP12jdC9oVNojXY5e43rbxieWpsTIutD3c6a5NtY5xxsv3Pb3i72KBD/c9otkrg8uT0HVlBuJdHKbW2yueLRORZYAXwlogMVn7cfaqUKgLW1ntiKyOtoPWV1khOp9MOfAPMtDgUCxieiZPe/T4ionKU1ZEEQw6dDt/Ds0mIhNUygshVR1faSjyn1DQrSfuGvM+frNaCJ/PZm4juNZyOF9574jzDVU7+0pcpS1+J8lQS1T2ZdrN+QWSnPtWup5TBkVfvoe34S2kzLOXkcY+bvK+eoWzXauxx7ejwo7kn1nzdaP9izZ8jXp8UiNcdKOUiZZN698j3inS3Ohar9MhR+x572RthNwh0kvzz5PQdLwX4HoiIE7hTKdWxlsfOA74AzldKfSki84ALgb6YPSKXA/cppbKqPGc/5mzHIeA+IA5YBNymlCrwnTMTWAoMV0p97zt2s+/8vkApsA24Qym1TUT6APuAq4FZwDWYy1deAv6ilLJ6DZ1f6ERLq5XT6eyMOQzcqn7wjhj55YqEhJwWvy7ruPncuTxVzgq7kYzI1KOrbWWeyfWfGXztKDq2Meq2RJHwWgP7Tnybb//Wsf2ZVsdhpQ5FKuvJ57zFUR4GBOgWzyan77gjQNeupp5EKxozoXlIKeUUkZeBr4HDQCfMxCgBGHY82fElWjZgK/AM0AP4J7BAKXWH75yZVEm0RGQ6sBj4E7AGaIs5tfiJUmp1lUTrAPAe8CVmwvUb4Gql1Dt+/aJYJKx+EGjB43Q6s4ErgVbTQb53n02prSnJAljLlLCZ4qpGqZAtHJtP2/ZlRO2yOo7Guqq45MwuHs86q+OwUl5bSbr9Tnun0ijSAnD5VODuAFy30ZRSFUAu0MX3+U1KqTeVUsuBDzA3RiUDNUeN3cClSqnPlVLPAy8CPznNrSYAW5VSjyillimlPlZK/VYptbrGeSuUUvcppb5WSj0AbKnnumFFJ1panZxO5xrgl/We2AK0a3doa8+e37eqd/PpJO9wS2RY1gcTQ4X0z65vjeRgrL/xu5eOZCehVLnVcVipJEYSb73L3j8vHn8mnZnAlcnpO0LpjeuJjT4icr6IrBaRQsCDGS/AwBrPWaqUqrqAfzvQWereSLEZGC0iT4jIdBGpq+n6VzU+3w4Bn8INmpD+YaVZz+l0Pg28bnUcgRQVVXJk6LAlXUWo64dAi7SQ2TlWx9BkKrRbIb3jnVF/08UQ1Nvj6Xl+adl3VsdhNVeExMy9wz76QCdW+uFyxxe/h0yrM9/UYQfgqIiMBz7GTK6ux5zam+g7NbrGUwtqfO7CTNiiaruPUuob4EZgOrAMyBWR/4hIzY1GtV235r3Dlk60tIb4BVBzqLdFsNk85WPHfVIgQlj0qfMXD3b3TpKHWB1Hk4Xw1CHAEmPMUKUIy5GhB3PyJkYotd/qOKxm2MRx/832KVv6NLuK/O3J6TtCLXk9C7PqwBrgMiAHc03Ux75din4bkVVKvaaUGos5TXk/ZuL1R39dPxzoREurl9PprMT8z3jA6lj8bfTozzfa7Z5kq+MItpXM2KTEdsoi2bChQrs0jYuIqFwSdtR/ZuiJhKiHc/Ly6j+zFRCRh2bbZywZ0eRk6z/J6Tte8WtMzSQiicDfgT2Yu8tjALeqvjPup/6+r1IqRyn1HOZatfB9k9cEOtHSGsS3OP4iWlDl+AEDVy+PjSs8pVBna/ARl4f3tukQT7QAFntHF1kdQ1OdV1o2tpfbvcbqOELF/AvtM96ZalupoDE1p1KBe+s9K7AcIjLR93GOiBxfaN4VuMZXQ+troJeIPCkis0Tkj8Acf9xcRP4iIv8WkctFZIaI3APMAE7tFN+C6URLazCn0/k9Zr0TvxW4s0qnznvXd+mSMc3qOKxQSlxhNl3q740TypQK+UTrbe9ZdfcpCgMvHcnui1It5o1Vc707zTZ1/gW2DYoGTQnvJzQWvydgTg+uBhZi7ib8L2b5hQ0ASqnPMcspXI65VmsG5ptqf1iHOXo1H7Pm1u2AE/iXn64fFnQdLa3RnE7nrZj/ccJSbGzBvjFjP2kvQoLVsVjhLX6a+on8JKyTzKivDh0QRYj3FVRqb9R1uTZRYbv+728d2i1/p2182NVZC6TRe4wtDyw0+gh1/vzIAyYnp+8IuxIfWmDoES2t0ZxO53PAo1bH0RR2u6to9JjPVGtNsgAWc26i1TH4QRjsEBXZq5J2Wx1Fc/w2L39KtGHohKGKTf1tI383x37UEGrbRVgGXKSTLK0qnWhpTfUA8LbVQTSOMsaO+2SnzWacYXUkVjlMtwNl0mZ4/WeGuDBYowXwqTEprKfZHeB4Iju3Aj31UU1GNxl4zy12l9vG/iqHvcA1yek7dK8/rRqdaGlN4nQ6FeaCycVWx9JQQ4YuTY2KKhtvdRxWWsi1+62OwU9qrdsTat71zgjLgrBVTS2vGDHI5V5ldRyhJqu99Lxjrj2uIoLju0tvS07f8YmlQWkhSSdaWpP5yj5cShh0a+/effvqDh0Oteq1JgrUBsa3lNG8uipRh5RM1ambSzn2WR1Hcz2flZ0sSuVbHUeoKWwjnW69y95jf2fuS07f8aLV8WihSSdaWrM4nc4S4AIISG8wv4hvm53e94wNo6yOw2pbGJ3mFUdPq+Pwk7AY0QLYos44aHUMzdXeMDrcVFj0vdVxhKLyKHnl/BU7/ml1HFroCqtES0ScIqJEpNYFpiKy2/e4sxHXvMH3nDZ+C7QRRGSZiLxrxb39xel05gPnAhlWx1JTRER5zsiRX7UVIdbqWKz2HlcXWh2DXxjKK2H0s+s97/SwSQpP5//yC6fEGcY2q+MIMa8B91gdhBbawuaHVRUVQF8RGVf1oK9fUx/f41qQOZ3OLOBs4JDVsRwnYrjHjvv4sIjqZnUsVnMRUbGX/iOsjsMvlKq0OoTG+NQ7MVkpPPWfGdpsYHsmK9uGUuFd7NZ/PgBuTpuTpjcKaKcVjolWKWZV2WtqHL/Gd7w06BFpADidzv3AOUCuxaEAMGLkorUREa6RVscRCpZwziZEWkZJCy9WF4FslBJi2xYTm251HP4wptKVPLqy0h+NlsPdYmB22py0sN5VqgVHOCZaAG8BV4mIAPj+vMp3/AQRmSQiH4vIEREpFZHNIlJvDycRmSciaSJSIiKZIvKGiCRVefxREdl7/P5Vjt8gIi4R6eT7/D4RWScihSJyVEQ+EZH+9dx7sIi8JSIHRaRMRLaJyD0iYqtyzkzfdOcsEfnI99p2i8i5ImIXkcdEJFdEDonILxvw9fQbp9O5AzgPsLT9SJ++G1Pbts0N66Kc/vQZl4RFOYQGUcpldQiNtdIYFhJvPvzh6aM5I0WpHKvjsNC3wKVpc9LCamRVs064JlrvY3YCn+r7fBrQyXe8qt7AKuBm4MfAe8ArIjK7nut3Bh4GLsScfz8DWFIl2XkZ6IvZqqCqG4FP1MkfQj2Ap4FLgF8AdmC1nH5koTuwE7gDc5H5C8BfMFsk1PQcsJKTDZ/f9d0vHrjW9/k/ROTMel6vXzmdzg2YsVvSvqN9+8wtPXpsm2jFvUNRAYk5x+gQ3i13qhBv+CVa73hntrc6Bn9pa6iEu/MLw7oQazOsB85Pm5NWYnUgWvgIy3e5SqkCEfkSc7ow1ffnl0qpwqqDTEqpEyNcvtGnFZjJzy+AN09z/ZuqPM+O2SsqEzOxW6GUSheRVZiJ1TLfeWdgJnwXV7nOvTWu8zWQjZl4Lajj3ovx1abyxbwSiPXF/EiN019XSj3mOzcT2AYMUkql+I59g9mb8CeY78KCxul0rnI6nedg9rcK2pRVdHTxoSFDl3YXCY/t/8HwEZfvQGS61XH4jaHCbr1TqjF8iFIUixBvdSz+cHNh0eRXE+I3F9jto6yOJYhSgYvS5qSFbbNwzRrhOqIF5jThFSIShdko862aJ4hIOxF5SkQOAG7fxy3AwNNdWETOF5HVIlIIeDCTLGo87yXg8iq7FW8AjgJfVrnORBH5WkTyfNcpA9qc7v4iEu3reL4HqPTF/BDmBoCaiXHVYqF7fH+e6IquzEWrezFHyYLO6XR+C8wCglJ/x2bzlI0Z+2mxCB2Dcb9wsZyzOlsdg18ZKqzWaAF4sTuO0KFFrNM67oWs7HhU+P1bNNFXwHk6ydKaIpwTrY8xk5aHgDigtoq8r2KO6DyGWX5gPOa0X3RdF/XtXvwYM7m6HpgEHJ+Gqvq8dwCDk2vF5gALlDLfbYtIL8z/nALcCkzx3T/7dPcH/g78Cngec/ptPPBgLfcHKDj+F3Vy3UpBjXNc9dwvoHzTiCkEYYH86DGfbbbbPYMDfZ9wso++eyolpmV9Tbzh+ct9kXdcmdUx+NNgl7vf1PKK1VbHEQQfAD9Om5PWov79tOAJ20RLKVUKfArci7kuqtpuQxGJBi4C/qyUeloptUQptZ76X/NlQA5wtVLqY6XUWiCrjvu/hTmSlQL0Al6pcsp5mFN+lyil3lVKrQY2A/Wt1bgS+LdS6lGl1De+mMNuqqQqp9O5GTgLM8kMiIGDVi2LjS2aHKjrh6uFXBsy5Tb8RbwqLHd6veOd2cPqGPztH9m54+xKtbjvsSreAK5Km5MWdusCtdARtomWz7OYI1nza3ksCvP1ndgZIiLxVFlDVYcYwK2qN1Gta6fiS5jrspzAWqVU1amBGMwRr6pJ0lXUvy4upkbMdk4tZRF2nE7n98BM4Ii/r925y57vOnfe23LWIPmJgRhpjDztNHlYCsM1WgA7VO9+XmXz+/e/lWKVivtdXn5m/WeGpeeAn6XNSQvL7zctdIR1oqWUWqaUulQptayWxwqBdcCfRORyEbkM+Aaorzr210AvEXnSVz7hj5jTgrXd/1vMBehTqT6aBeZaKTvmLsdZIvJ/wDxOndqr7f5zReR6EbkQM5FsEZWlfaUfZnByzVuzxcXl7x04cM1gkfD+Xg6EdUzcbIi9q9Vx+J03fAtm7lI9Qq57QnNdVVxyZhePZ53VcfjZ42lz0m5Lm5MWtt9rWuho6b+crsVcDL4A+BdmeYdad/sdp5T6HLOUwuWYa7VmYE5B1uVDoJwai/GVUmmY04pnYk5xXos5LVhfoncX5u6W/2CuJ/ueU3cbhi2n07kbMzFt9sJgh6OycNToz0WEts2PrOV5nyvLrY4hECRMR7QAPvROkfrPCj8vHclOQqmW8v3mTJuTdr/VQWgth1SfIdMaS0S+A3Yqpa63OpZw4nQ622OO1jVxXZUyJpz53saoqPJx9Z/b+lQQVXozb4BInNWx+Jv9h5K1ETsKw7JOWmfyc76NmttRhBaXcP26U4dlX7SJm2l1HM10X9qcNN0gWvOrlj6iFTAiMk5Efo25K/Apq+MJN06n8xhmb8QPm/L8YcMWp+okq26LuHBzS0yygLCeOsymXacKIvfUf2b4eTAnb1KEUvutjqOJDOBWnWRpgaATraZbB/wW+K1SqqWtTwgKp9NZjjlF+2xjntejx/er2rU/UrMqv1bFl1wYa3UMAWOEb6IFsMEYeNjqGAIhEqIezsnLszqOJvBgLnp/3upAtJZJJ1pNpJQSpVQ7pdQ8q2MJZ06n03A6nXcAv2/I+W3bHt3Rp++msQEOK6zl0OlIEQkttpm2eMN7vcNC74yWOdIInFdaNraX273G6jgaoRC4IG1O2htWB6K1XDrR0kKC0+l8GHPzQJ0LnSMiy3JGjPw6UcS6Aqzh4H2u2kmVJuQtThhPHQJ8ZYwbohQttiHxS0ey+6KUJX1OGykDmJQ2J+1rqwPRWraW+8NYCztOp/M1zB2ep7S5EPG6xo37OEtEtbxyBX62hqktrjBmNUZYD2hRTlRsPvE7rI4jUJK83qQri0s2Wh1HPVYAZ6bNSWux/w5a6NCJlhZSnE7nIsy2R9XqDY0c9eW3Dod7uDVRhY90ktPdEtnf6jgCSbzhnWgBLDNGFlgdQyD9Li9/SpRh7LY6jjq8CpyTNictHNeTaWFIJ1payHE6nduBCcBSgDPOWL8iPv7YNGujCg/vck3A2hyFDCO812gBvO2Z2bIafdfgAMeT2bnlhFb9IAX8Nm1O2o26pY4WTDrR0kKSr/zDue3aHXqwW/cduodhA3ixedIZMsTqOAKuBYxofacGDzZUvV0iwtrU8ooRg1zuVVbH4VMEXJw2J01vXtKCTidaWshyOp2eu+9+4Y8i3AJUWB1PqFvJjI1KbB2tjiPgDBX2xT4VNttB1bnZ3RFC3fNZ2cmiVL7FYaQDE9LmpH1qcRxaK6UTLS3kzUrJeAWzefcPVscSyj7iJ16rYwgGMVpGVfXPjTNb/PRVe8PocGNhUZqFIXyCueh9p4UxaK2cTrS0sDArJWM9MA6zWbdWQylxhUfpOtrqOILCUC3i59Y73hl9rI4hGO7OL5waZxjbgnxbBfwVuCRtTtopu5g1LZhaxA8srXWYlZKRA5wLPG51LKHmUy7ZikjrqC/WAqYOAfapbr3cyn7Q6jgCzQa2Z7Kybaig1T8rAi5Pm5P257Q5aeG/oE8LezrR0sLKrJQM76yUjPuBq4FSq+MJFYv5UaLVMQRNCxnRAtim+uy3OoZgGFPpSh5dWbkyCLdaBYxMm5P2QRDupWkN0mJ+YGmty6yUjHcwS0BstjgUyx2h2w+l0qbV1BgTA7vVMfjL+96pDqtjCJanj+aMFKVyAnR5D/AnYEbanLT9AbqHpjWJTrS0sDUrJeN4va0HgVaxELw2C7lmr9UxBJVqOSNaH3snD1aKsG4p1FBtDZVwd35hIIqYZgBT0+ak/S1tTlqr/Tmgha4W8wNLa51mpWS4Z6Vk/BGYDLTKnUXrObOv1TEElWo5I1oFxLcrJbrFl3k47ubCosmJXu9mP17yFWBU2py0b/14TU3zK51oaS3CrJSM74DRwL8wdxy1ClsYleYVR2+r4wgqQ7WYRAtgrZHc8qv5V/F8VnY8SrmbeZl84Mq0OWk3pc1JK/FHXKcjIqoBHzMDfP87q3y+TETeDdT9NP/SiZbWYsxKySiflZJxDzALOGBxOEHxHtcUWB1D0Cla1Lqmd7wzE6yOIZiSXe5+U8srVjfjEkuBEWlz0oKZaEyq8pHiO/ZgjeOBbKQ9CVgYwOtrAaQTLa3FmZWSsRQYgTmt0GK5cVRm0H+E1XEEXQtLtJYZo4YoRZnVcQTTP7Jzx9mVOtTIp7mA3wBnp81JywxAWHVSSq09/gGs8x3OqHpcKRWwel2+6x8N1PW1wNKJltYizUrJKJqVknETcDHQIn9ALeGcTYi0qtEQk2pRiZaLiKgcErdbHUcwxSoV97u8/MYkS+nApLQ5aY+mzUkLuc0DIvIzEVkpIsdEJF9ElorIuBrnvCoi60XkQhHZLiJlIvKZiLQXkf6+55T6zhlR47nVpg618KITLa1Fm5WS8QkwDGhx6xk+45LW+f9XEWF1CP72jXdMq6sJd1VxyZmdPZ519ZymgGeAsWlz0gI5NddcfYAFwJXAtcBBIFVEzqhxXi/MivV/AG7B3MTzPPCW7+MKwAG8JSItojCvRssagte02sxKycgFrly8pN+1wNNAO4tDarZCEnLz6Ng6Wu7U1AITrbe9M7te62h93aVeOpLd5cc9ulbU0dVgG3BL2py05qznCgql1F+P/11EbMDXmKVnrsNMrI5rD0xSSmX4zh0B3A/MUUot8B0T4DNgMLAjKC9AC6jW+Y5Ya5VmpWT8DxgOLLI6lub6iMu3I9LiEo4GanGve4vqN8CrJFDFPENWH4+n13mlZTVLM5QDvwNGh0OSBSAiySLygYgcxazp5wYGAQNrnLr/eJLls8f355JajnUPSLBa0OlES2tVZqVkHJqVknEe5rD9MavjaarlpHSyOgYLRVodgP+J7FVdd1kdhRUeysmbGKHUft+ni4BhaXPSHkmbk9bcEhBBISLxwFdAT+CXwDRgPLAFqDlSV1Djc1ctx48fax29S1sBnWhprdKslIwXgAHAs4RZVfn99M2okJhkq+OwUAtMtOAT7+RWU/+tqkiIeignLwO4Nm1O2nlpc9LCrdPBJKAHcJ1S6g2l1Eql1HqgFW5U0WqjEy2t1ZqVknFsVkrGHcA4INXqeBpqIdcctDoGi7XIROtd7/R+VsdgAQ/wxPmlZT9Jm5P2ptXBNFGM78/K4wdEZDLmAnlN04mWps1Kydg8KyVjOuZuocbW9gkqAzG2Mrrmuo/Ww1BugRa5G+swHbtWKke4jeY0x2JgJM7CX+IsDFgNqiBYC5QAL4jIuSJyE+YOwpD+WaIFj060NM1nVkrGm5gLWB+hyrvTULKeM7cYYu9mdRyWMVRI/rv4y2bVvzWMVv4AXImz8GychWFfP8xXSPRKIAn4CLgHuI2Ti9q1Vk6UapXLAjTttBYv6dcXeBi4mhAaQXmAf6w6KH2mWB2HZVze/OilWWFfnqMuV9qXffdYxPMTrI4jQMqAfwKP4CxsVZXwtdZNJ1qadhqLl/SbADwGTLc6lgqiSm/mDYVIG6tjsUyF92j08qwuVocRKHGUF38fdXOMSIuqcVgJPAc8jLOwRXZp0LTT0VOHmnYas1IyvpuVkjEDuASzDYhlvuKCza06yQLEq8Jiy39TlRITX0Rs2E+n+bgxE6z+OAvv1kmW1lrpREvTGmBWSsbHmMVOb8ei3olfcpGuq2O07EQLINUYHrb13Xy8wGvAIJyFt+EsDGoDaE0LNTrR0rQGmpWS4ZmVkjEf6I/Zqyxolbzz6JBVSELrbLlTlaE8VocQaG97z+pgdQxNpIC3gaE4C2/AWbjP6oA0LRToREvTGmlWSkbJrJSMh4DewB1AwLfkv8fVOzF7qLVuLXzqEGC1MTRZKcKt3MFHmKUarsFZuNPqYDQtlOgf3JrWRLNSMspnpWQ8i9nP7BpgY6DutZppuu8ZIIYKqyr+TeHF7jhMB0vXAzbCl8B4nIWX4ixMszoYTQtFOtHStGaalZLhnZWS8faslIyxwDnAN/68/k4Gp7slsr8/rxm2vC1/6hDgS++EcqtjqMcSYCrOwvNxFq63OhhNC2U60dI0P5qVkvHNrJSMc4CxmOtVmj0Cs5DZ2c0OrKXwKsPqEILhHe+MXlbHUAs38DowBmfhLJyFq6wOSNPCQUuq1aJpIWNWSsZG4JrFS/qdAdwH3MjJnmgN5sXmSWdIa24gXU1rmDoE2Kl69fUo22GHGKHQBSAPmA/8B2fhEauD0bRwo0e0NC2AZqVk7J2VkjEXc+H8g0Cjtu6vZtomJbZOAQkuHHlbR6IFsFP1tLrvYTpmK5meOAv/oJMsTWsanWhpzSIiThFRvg9DRPJFZJ2IPCQiSU243lUickMT48ht7POCZVZKRs6slIw/Ar2AezH7vdXrQ65oFWuSGszbelpZfOidasXPZw/wPnA2zsJknIXP4SwM9fVimhbSdKKl+UMhMAmYjLn77n3geiBNRMY28lpXATf4NboQMislo3RWSsaTwBnAhcB7gKu2c0uJLcyiq66dVUVrmToE+MA7ZYBSBCuxPAL8FeiDs/BynIWLg3RfTWvx9BotzR88Sqm1VT5fJCLPAiuAt0RksFKt5xdkQ8xKyfACnwOfL17SryNwHXATZvV5AD7jkq2ITLMoxNDkbTUDWuSS2KmcyJ2xuAYF6BYKWA48C3yAs7DF1yjTNCvoES0tIJRSBcCvMauonyMiM33Ti8Oqniciy0TkXd/fXwUuB2ZUmY50Vjn3MhH5TkTKRSRPRD4Xkd41rjdaRNaKSJmIbJIwSFRmpWTkzkrJeHJWSsYIYDzmL76Cb/hRW4tDCz1G69h1eNx6Y1BWAC67EfP/Zh+chWfhLHxHJ1maFjh6REsLpGWYaz4m+v5en79hrmFKxKy4DpAJICLXAwuAt3znCZACdAIO+M6Nxeyx9gSQBfwZeF9Eeiulypr7YoJhVkrGemD94iX9fllG3EXAT4HzgShrIwsRrWiNFsA73pltptv9Ugc0HfP/zps4C3f544KapjWMTrS0gFFKVfgWqHdp4PkZInIMsFWdihSz9cw84AOl1OwqT/m4xiVigHuUUkt8zzsCbAKmY1awDhuzUjIqjsC7wLtJSzcnYo70XQvMpBWPRItXidUxBNM3xpghSlEp0qRE+wBmLbc3cRZu9m9kmqY1lE60tEDzxy/GQUA34JV6znNRfeRsu+/PHn6IwTJZZ40qAF4CXkpaurkbcDVm0jXOyrgsYbSuEa0KomKOEb+pA8UN3RRxFFgIvAmswVnYqr5emhaKdKKlBYyIRAMdMH/4N0cH35/11fEpVurkGh6llEtEAKKbef+QkXXWqMOYU6NPJC3d3Bf4ke8jBWj5a7pa0WL445YaowuvsK843Sn5wAeYydVSnIV644mmhRCdaGmBdBbm99gaoMJ3LLLGOe2A+upf5fn+7Oq/0MJf1lmj9mFW7J6ftHSzA7PExvHEawwtcIpRDNXiXlN93vbM7FIj0VLABszp8C+BtTq50rTQpRMtLSBEJBH4O7AHs8ny8SQpGXPXEyLSExgM7K7yVBenjkDtBA4Bc4BPAhZ0GMs6a5QHSPV9/CFp6eaOwNmYSde5mFOv4c9ofSNa69XAQR5l2+UQ4zvMxOornIU5VselaVrD6ERL8weHiEz0/T0es6Hy7Zi7AM/z1dDKFJH1wN9EpAxztOV3nNqSJh24REQuxdxxeFgpdVhEfg28ISJvYE6RKMzpsjeVUusD+/LCT9ZZo3Ixd5m9BZC0dPNwTiZd0wjX6VSj5Y3S1aEQs8bVUoVtSf/K/6btn3dh68syNa0F0ImW5g8JmNODCijCHMX6L/BvpVTVOkCzgRd9j2Vi1vK5t8a1ngFGAy9jTiv+BXAqpf4nIhXA7zF345UCawH9zr4Bss4alQakAY8nLd0cA8zATLxmAUMJl2lGo8XuOiwBVgJLgSXApv3zLtTTgZrWAohqXZt4NE2rIWnp5jjMNV3jMAumjsMsNBtySU3kiqw1tnLvJKvjaKZjmGVHNvr+3ATs2j/vwrAvxuobib4Dc1Q7HvON0BrgRaVUWJVY0TR/0YmWpmmn8NXuGouZeI3GbA00ELBbGBZRy7O+kwrvBCtjaKRDVE+oNu6fd2GDGoqHGxF5Avg/zMLCH2NuYumN2f/0AqC/UirDugg1zRo60dI0rUGSlm6OAoZgJl1VP4K20D5q6ZH14jJCsX6YB9hHlYQKc/qvVUxti8glwIfAjUqpV2t5/MfABqXU4SZePwIwdM9ULRzpREvTtGZJWrq5A9AHszBsd9+fVf/eHYjzx72ilhzZLG5jlD+u1UgKswzJPmBvLX8e3D/vQo8FcYUEEVkKxCml6h1t9HV6+DXwc6AnZgX7h5RSr1U5Zxnm1/sr4DeY3199gNd9xz8D/oTZgusD37VGAk9jvhlYD/xMKfVDlWvOAy4E+gIFmJsN7qu6jlRE9mOuAT0E3If5fbsIuM3Xv1XTGk0vhtc0rVmyzhqVhzlNtKGuc3xTkXUlYcePta/3Zkr5a+rSwNxQUVLjIwtzo8Yh38fxvx/eP+/CSj/du0URkeM13B5v4FP+jVmq5a+YI3/nAC+LSJ5S6tMq500B+mEmWmWYOzHB7J3aEbgLszfqE0A5cCbwKOa/61PA88B5Va7XGXgYOIyZoN0HLBGRYVULHQNXAVuBWzC/L//pe94daFoT6BEtTdNCQtLSzRGYIwixdX1Eph4VW5knDnOtmMP3Ya/yp5fqiVNtyVTJ/nkXlgfvlbVsItIFM0G9TSn1XJXjQvU1fV7MxGkX5hRj1RGsBUCyUmq87/NlmIlTH6XU0SrnLQNGAb2VUoW+Y+8AVwIzlFIrfMfuAP6DOcp2SkN5EbEDSZiJdNXn7ffFOUgp5fEdexK4RimV1KQvkNbq6REtTdNCQtZZo9yYUzoFdZ50VpCC0Zqi5rv2+4DHqnx+F+DGHE38wDcSdtxiYLaI2Kusw9pQNcmqYv3xJMtnD2ah45U1joG5fnAPgIicD/wRs5xJ1XZVA4GqpfeXHk+yfLYDnUUkQinlriUeTTstnWhpmqZpzZEHVHJq8/bXOdnkfZ3vz46Yo1yF1K4r5igT1N0jtaDG5y5q9Dn1HQNfYV4Ry1bz3QAAB/JJREFUGY+5E/IDYB6QjZkYruXU4r21XV+AKMxEUdMaRSdamqZpWpMppTwisgaz68Cfqhw/ii9Z8jV3B7OGmAdz/VVtdcOyq17aj2FehlnT62rlWy8jIr39eH1Nq5NOtDRN07TmehL4UESuV0q9fprzlmCOaCUopb4OSmSmGMCtqi9K/mkQ76+1YjrR0jRN05pFKfWRb9H4qyJyFmbz91ygA+ZIF0CJUmqniMwH3hKRRzHLMERjrpsaqJT6eYBC/Bq4xxfjJ8Bk4LoA3UvTqtGJlqZpmtZsSql7RWQFZhmEl6jegucCpdQXvlPnYu48/AVmiYcizAXnLwUwts9F5DeYC/J/4YvpIl8cmhZQuryDpmmapmlagNisDkDTNE3TNK2l0omWpmmapmlagOhES9MCTET2iYgSkf5+vu5+ETlt2xMRmem79zB/3lvTNE1rGJ1oaVoAicgkzGa4ALMtCGEjZh+6DAvurWma1urpREvTAms2Zr+9b2lAoiUiNatUN4tSqkgptVYppXv7aZqmWUAnWpoWIL7GtVdhtv54GUgWkZFVHr/BN603QUSWiUg5cL/vsXkikiYiJSKSKSJviEitTW1F5I8ikuU79w0RSajy2ClThyJiF5HfisguEan0Xf/VwHwVWibf17S+j5nNuP5pp4VFpI/vHhc1MuY7mxqTpmlNo+toaVrgnAV0Ad7CbHj7NOao1pYa570JPAP8hZN91joDDwOHgU6YDXqXiMiwGj3dZmM2zf0FZp+4R4EXgStPE9dzwM985y4H2gOXN+UFtmKTqvw9BrPi+YPAZ1WObw/g/Y/4YkgP4D00TfMDnWhpWuDMxkycvlRKuUTkK+AaEfltjVYgTyml/lX1iUqpm47/3Tcytgaz2e5UYEWVU2OAC5VSJb5zS4HXRSRZKbWjZkAiMhi4GbhbKfVUlYfebsbrbHWUUmuP/11E2vj+mlH1eFOISExDpnmVUpWYDZE1TQtxeupQ0wJARCKBnwAfKKVcvsNvAb2pPhoC1UdBjj//fBFZLSKFmE14M30PDaxx6tfHkyyfDwABxtcR2lm+P19tyOvQmsY3FfxujWPVpnGrTP/9VEQWiEgBZnuY2q7XXUTSReQbEYmtbepQRC4WkQ0iUioi+SLyrYjMqHEpu4g8LCI5IpItIv8RkSg/v3xN06rQiZamBcb5QCLwuYgkikgisAyo5NRF8UerfiIi4zHXdWUC12MmZhN9D9dcLJ9d9ROlVBlQgjmNWJsOQKlSqqjhL0ULsMeBYszp3odrPigifTBHMTOAi3z/xjXP6Qe8izmF+WPMhsmfYk4LV3Uf0A2zz99jwK3A3X56HZqm1UJPHWpaYBxPphbW8tiVInJPlc9r9sG6DLNH3NXHpxhFpHcd9+lc9RMRiQXaYK7hqU0eECcibXWyFTLWKqXm1vaAr/baEmAdMLvK6GhNo4FipdT9VY59Xst5+5VSN/j+vkhEpmCOvD7apMg1TauXHtHSND8TkTjMUYU3Mafqqn78EnOBfMppLhEDuGus4/ppHeeeU2WNEJhJmgLW13H+Et+fPzvda9CC6pSpY59BmCNZKzGT7rqSLIA0IEFEXhORc33fg7X5qsbn24EejYpW07RG0SNamuZ/lwCxwL+UUt9WfUBEVgG/xxzxWlHLcwG+Bu4RkScx1+xMxpzqqU058JmIPIY5XfgY5rqwWne8KaV2isjzwD9EpLMvhkTgCqXUNQ1+hZo/Ha3j+GTMqb8XlVKe013A9+96CfAA5kiWW0Q+wNz0kFPl1IIaT3Vx6nS0pml+pEe0NM3/ZgO7ayZZAEopN/AO5nRNrYuQlVKfA7/BLLnwMTADqKte0lvAUuAl4EngC8xdhadzB2Ypieswfyk/CZyy7kdrlgogssaxdnWcW3Pq+LhXgBeAD0VkQn03VEp9ppSahrkO72bgbODfDQtX07RA0SNamuZnSqkf1/P4HZjJDpg1rWo751FOXTcjNc7pU+VTZx3XWVbL87yYi65PWXit+U0mML3GsXObcJ3bMNfcfSEiM5VSafU9QSlVCPzPt+Ow5g5XTdOCTCdamqZp/vcBcLOIPIG5Buss4LzGXkQpZYjIz4D3gK9EZJpSak/N80TkVsyk6kvMIrcDMHcxLmj6S9A0zR/01KGmaZqfKaU+A34HXIGZdPWmiWUUfOuzrsJc8L5YRHrWctpWzA4C/8Rc8P4HzGnH3zTlnpqm+Y9U39ikaZqmaZqm+Yse0dI0TdM0TQsQnWhpmqZpmqYFiE60NE3TNE3TAkQnWpqmaZqmaQGiEy1N0zRN07QA0YmWpmmapmlagOhES9M0TdM0LUB0oqVpmqZpmhYgOtHSNE3TNE0LEJ1oaZqmaZqmBYhOtDRN0zRN0wJEJ1qapmmapmkBohMtTdM0TdO0ANGJlqZpmqZpWoDoREvTNE3TNC1AdKKlaZqmaZoWIDrR0jRN0zRNCxCdaGmapmmapgWITrQ0TdM0TdMCRCdamqZpmqZpAaITLU3TNE3TtADRiZamaZqmaVqA6ERL0zRN0zQtQHSipWmapmmaFiA60dI0TdM0TQsQnWhpmqZpmqYFiE60NE3TNE3TAkQnWpqmaZqmaQGiEy1N0zRN07QA0YmWpmmapmlagOhES9M0TdM0LUB0oqVpmqZpmhYgOtHSNE3TNE0LEJ1oaZqmaZqmBYhOtDRN0zRN0wJEJ1qapmmapmkB8v+iqi3gheDUlAAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["language = data[\"Language\"].value_counts().reset_index()\n","\n","plt.figure(figsize=(10,10))\n","labels= language['index']\n","\n","plt.pie(language[\"Language\"], labels= labels, autopct='%.1f%%', textprops={'fontsize': 15})\n","plt.show()"]},{"cell_type":"markdown","id":"dd190b7f","metadata":{"papermill":{"duration":0.013095,"end_time":"2022-12-05T21:14:20.84528","exception":false,"start_time":"2022-12-05T21:14:20.832185","status":"completed"},"tags":[]},"source":["## ***3- Text Preprocessing***"]},{"cell_type":"markdown","id":"dc888dc1","metadata":{"papermill":{"duration":0.011938,"end_time":"2022-12-05T21:14:20.869469","exception":false,"start_time":"2022-12-05T21:14:20.857531","status":"completed"},"tags":[]},"source":["first we should create a new column for cleaned text:"]},{"cell_type":"code","execution_count":14,"id":"cf798b98","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:14:20.896705Z","iopub.status.busy":"2022-12-05T21:14:20.895937Z","iopub.status.idle":"2022-12-05T21:14:20.91403Z","shell.execute_reply":"2022-12-05T21:14:20.912552Z"},"papermill":{"duration":0.035542,"end_time":"2022-12-05T21:14:20.917168","exception":false,"start_time":"2022-12-05T21:14:20.881626","status":"completed"},"tags":[]},"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>Text</th>\n","      <th>Language</th>\n","      <th>cleaned_Text</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Nature, in the broadest sense, is the natural...</td>\n","      <td>English</td>\n","      <td></td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>\"Nature\" can refer to the phenomena of the phy...</td>\n","      <td>English</td>\n","      <td></td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>The study of nature is a large, if not the onl...</td>\n","      <td>English</td>\n","      <td></td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Although humans are part of nature, human acti...</td>\n","      <td>English</td>\n","      <td></td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>[1] The word nature is borrowed from the Old F...</td>\n","      <td>English</td>\n","      <td></td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>10332</th>\n","      <td>ನಿಮ್ಮ ತಪ್ಪು ಏನು ಬಂದಿದೆಯೆಂದರೆ ಆ ದಿನದಿಂದ ನಿಮಗೆ ಒ...</td>\n","      <td>Kannada</td>\n","      <td></td>\n","    </tr>\n","    <tr>\n","      <th>10333</th>\n","      <td>ನಾರ್ಸಿಸಾ ತಾನು ಮೊದಲಿಗೆ ಹೆಣಗಾಡುತ್ತಿದ್ದ ಮಾರ್ಗಗಳನ್...</td>\n","      <td>Kannada</td>\n","      <td></td>\n","    </tr>\n","    <tr>\n","      <th>10334</th>\n","      <td>ಹೇಗೆ ' ನಾರ್ಸಿಸಿಸಮ್ ಈಗ ಮರಿಯನ್ ಅವರಿಗೆ ಸಂಭವಿಸಿದ ಎ...</td>\n","      <td>Kannada</td>\n","      <td></td>\n","    </tr>\n","    <tr>\n","      <th>10335</th>\n","      <td>ಅವಳು ಈಗ ಹೆಚ್ಚು ಚಿನ್ನದ ಬ್ರೆಡ್ ಬಯಸುವುದಿಲ್ಲ ಎಂದು ...</td>\n","      <td>Kannada</td>\n","      <td></td>\n","    </tr>\n","    <tr>\n","      <th>10336</th>\n","      <td>ಟೆರ್ರಿ ನೀವು ನಿಜವಾಗಿಯೂ ಆ ದೇವದೂತನಂತೆ ಸ್ವಲ್ಪ ಕಾಣು...</td>\n","      <td>Kannada</td>\n","      <td></td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>10271 rows × 3 columns</p>\n","</div>"],"text/plain":["                                                    Text Language cleaned_Text\n","0       Nature, in the broadest sense, is the natural...  English             \n","1      \"Nature\" can refer to the phenomena of the phy...  English             \n","2      The study of nature is a large, if not the onl...  English             \n","3      Although humans are part of nature, human acti...  English             \n","4      [1] The word nature is borrowed from the Old F...  English             \n","...                                                  ...      ...          ...\n","10332  ನಿಮ್ಮ ತಪ್ಪು ಏನು ಬಂದಿದೆಯೆಂದರೆ ಆ ದಿನದಿಂದ ನಿಮಗೆ ಒ...  Kannada             \n","10333  ನಾರ್ಸಿಸಾ ತಾನು ಮೊದಲಿಗೆ ಹೆಣಗಾಡುತ್ತಿದ್ದ ಮಾರ್ಗಗಳನ್...  Kannada             \n","10334  ಹೇಗೆ ' ನಾರ್ಸಿಸಿಸಮ್ ಈಗ ಮರಿಯನ್ ಅವರಿಗೆ ಸಂಭವಿಸಿದ ಎ...  Kannada             \n","10335  ಅವಳು ಈಗ ಹೆಚ್ಚು ಚಿನ್ನದ ಬ್ರೆಡ್ ಬಯಸುವುದಿಲ್ಲ ಎಂದು ...  Kannada             \n","10336  ಟೆರ್ರಿ ನೀವು ನಿಜವಾಗಿಯೂ ಆ ದೇವದೂತನಂತೆ ಸ್ವಲ್ಪ ಕಾಣು...  Kannada             \n","\n","[10271 rows x 3 columns]"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["data1 = data.copy()\n","data1[\"cleaned_Text\"] = \"\"\n","data1"]},{"cell_type":"code","execution_count":15,"id":"6317302e","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:14:20.945797Z","iopub.status.busy":"2022-12-05T21:14:20.945345Z","iopub.status.idle":"2022-12-05T21:14:20.953329Z","shell.execute_reply":"2022-12-05T21:14:20.951424Z"},"papermill":{"duration":0.025645,"end_time":"2022-12-05T21:14:20.956007","exception":false,"start_time":"2022-12-05T21:14:20.930362","status":"completed"},"tags":[]},"outputs":[],"source":["def clean_func(Text):\n","  Text = re.sub(r'[\\([{})\\]!@#$,\"%^*?:;~`0-9]', ' ', Text)   # removing the symbols and numbers\n","  Text = Text.lower()   # converting the text to lower case\n","  Text = re.sub('#\\S+', '', Text)  # remove hashtags\n","\n","  return Text "]},{"cell_type":"code","execution_count":16,"id":"05d54745","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:14:20.98374Z","iopub.status.busy":"2022-12-05T21:14:20.98328Z","iopub.status.idle":"2022-12-05T21:14:21.060764Z","shell.execute_reply":"2022-12-05T21:14:21.05907Z"},"papermill":{"duration":0.094566,"end_time":"2022-12-05T21:14:21.063531","exception":false,"start_time":"2022-12-05T21:14:20.968965","status":"completed"},"tags":[]},"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>Text</th>\n","      <th>Language</th>\n","      <th>cleaned_Text</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Nature, in the broadest sense, is the natural...</td>\n","      <td>English</td>\n","      <td>nature  in the broadest sense  is the natural...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>\"Nature\" can refer to the phenomena of the phy...</td>\n","      <td>English</td>\n","      <td>nature  can refer to the phenomena of the phy...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>The study of nature is a large, if not the onl...</td>\n","      <td>English</td>\n","      <td>the study of nature is a large  if not the onl...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Although humans are part of nature, human acti...</td>\n","      <td>English</td>\n","      <td>although humans are part of nature  human acti...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>[1] The word nature is borrowed from the Old F...</td>\n","      <td>English</td>\n","      <td>the word nature is borrowed from the old f...</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>10332</th>\n","      <td>ನಿಮ್ಮ ತಪ್ಪು ಏನು ಬಂದಿದೆಯೆಂದರೆ ಆ ದಿನದಿಂದ ನಿಮಗೆ ಒ...</td>\n","      <td>Kannada</td>\n","      <td>ನಿಮ್ಮ ತಪ್ಪು ಏನು ಬಂದಿದೆಯೆಂದರೆ ಆ ದಿನದಿಂದ ನಿಮಗೆ ಒ...</td>\n","    </tr>\n","    <tr>\n","      <th>10333</th>\n","      <td>ನಾರ್ಸಿಸಾ ತಾನು ಮೊದಲಿಗೆ ಹೆಣಗಾಡುತ್ತಿದ್ದ ಮಾರ್ಗಗಳನ್...</td>\n","      <td>Kannada</td>\n","      <td>ನಾರ್ಸಿಸಾ ತಾನು ಮೊದಲಿಗೆ ಹೆಣಗಾಡುತ್ತಿದ್ದ ಮಾರ್ಗಗಳನ್...</td>\n","    </tr>\n","    <tr>\n","      <th>10334</th>\n","      <td>ಹೇಗೆ ' ನಾರ್ಸಿಸಿಸಮ್ ಈಗ ಮರಿಯನ್ ಅವರಿಗೆ ಸಂಭವಿಸಿದ ಎ...</td>\n","      <td>Kannada</td>\n","      <td>ಹೇಗೆ ' ನಾರ್ಸಿಸಿಸಮ್ ಈಗ ಮರಿಯನ್ ಅವರಿಗೆ ಸಂಭವಿಸಿದ ಎ...</td>\n","    </tr>\n","    <tr>\n","      <th>10335</th>\n","      <td>ಅವಳು ಈಗ ಹೆಚ್ಚು ಚಿನ್ನದ ಬ್ರೆಡ್ ಬಯಸುವುದಿಲ್ಲ ಎಂದು ...</td>\n","      <td>Kannada</td>\n","      <td>ಅವಳು ಈಗ ಹೆಚ್ಚು ಚಿನ್ನದ ಬ್ರೆಡ್ ಬಯಸುವುದಿಲ್ಲ ಎಂದು ...</td>\n","    </tr>\n","    <tr>\n","      <th>10336</th>\n","      <td>ಟೆರ್ರಿ ನೀವು ನಿಜವಾಗಿಯೂ ಆ ದೇವದೂತನಂತೆ ಸ್ವಲ್ಪ ಕಾಣು...</td>\n","      <td>Kannada</td>\n","      <td>ಟೆರ್ರಿ ನೀವು ನಿಜವಾಗಿಯೂ ಆ ದೇವದೂತನಂತೆ ಸ್ವಲ್ಪ ಕಾಣು...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>10271 rows × 3 columns</p>\n","</div>"],"text/plain":["                                                    Text Language  \\\n","0       Nature, in the broadest sense, is the natural...  English   \n","1      \"Nature\" can refer to the phenomena of the phy...  English   \n","2      The study of nature is a large, if not the onl...  English   \n","3      Although humans are part of nature, human acti...  English   \n","4      [1] The word nature is borrowed from the Old F...  English   \n","...                                                  ...      ...   \n","10332  ನಿಮ್ಮ ತಪ್ಪು ಏನು ಬಂದಿದೆಯೆಂದರೆ ಆ ದಿನದಿಂದ ನಿಮಗೆ ಒ...  Kannada   \n","10333  ನಾರ್ಸಿಸಾ ತಾನು ಮೊದಲಿಗೆ ಹೆಣಗಾಡುತ್ತಿದ್ದ ಮಾರ್ಗಗಳನ್...  Kannada   \n","10334  ಹೇಗೆ ' ನಾರ್ಸಿಸಿಸಮ್ ಈಗ ಮರಿಯನ್ ಅವರಿಗೆ ಸಂಭವಿಸಿದ ಎ...  Kannada   \n","10335  ಅವಳು ಈಗ ಹೆಚ್ಚು ಚಿನ್ನದ ಬ್ರೆಡ್ ಬಯಸುವುದಿಲ್ಲ ಎಂದು ...  Kannada   \n","10336  ಟೆರ್ರಿ ನೀವು ನಿಜವಾಗಿಯೂ ಆ ದೇವದೂತನಂತೆ ಸ್ವಲ್ಪ ಕಾಣು...  Kannada   \n","\n","                                            cleaned_Text  \n","0       nature  in the broadest sense  is the natural...  \n","1       nature  can refer to the phenomena of the phy...  \n","2      the study of nature is a large  if not the onl...  \n","3      although humans are part of nature  human acti...  \n","4          the word nature is borrowed from the old f...  \n","...                                                  ...  \n","10332  ನಿಮ್ಮ ತಪ್ಪು ಏನು ಬಂದಿದೆಯೆಂದರೆ ಆ ದಿನದಿಂದ ನಿಮಗೆ ಒ...  \n","10333  ನಾರ್ಸಿಸಾ ತಾನು ಮೊದಲಿಗೆ ಹೆಣಗಾಡುತ್ತಿದ್ದ ಮಾರ್ಗಗಳನ್...  \n","10334  ಹೇಗೆ ' ನಾರ್ಸಿಸಿಸಮ್ ಈಗ ಮರಿಯನ್ ಅವರಿಗೆ ಸಂಭವಿಸಿದ ಎ...  \n","10335  ಅವಳು ಈಗ ಹೆಚ್ಚು ಚಿನ್ನದ ಬ್ರೆಡ್ ಬಯಸುವುದಿಲ್ಲ ಎಂದು ...  \n","10336  ಟೆರ್ರಿ ನೀವು ನಿಜವಾಗಿಯೂ ಆ ದೇವದೂತನಂತೆ ಸ್ವಲ್ಪ ಕಾಣು...  \n","\n","[10271 rows x 3 columns]"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["data1[\"cleaned_Text\"] = data1[\"Text\"].apply(lambda x:clean_func(x))\n","data1"]},{"cell_type":"markdown","id":"0b370a5c","metadata":{"papermill":{"duration":0.013101,"end_time":"2022-12-05T21:14:21.090022","exception":false,"start_time":"2022-12-05T21:14:21.076921","status":"completed"},"tags":[]},"source":["### *Separating Input and Output features*"]},{"cell_type":"code","execution_count":17,"id":"576b60db","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:14:21.118602Z","iopub.status.busy":"2022-12-05T21:14:21.118103Z","iopub.status.idle":"2022-12-05T21:14:21.12422Z","shell.execute_reply":"2022-12-05T21:14:21.122942Z"},"papermill":{"duration":0.023354,"end_time":"2022-12-05T21:14:21.126721","exception":false,"start_time":"2022-12-05T21:14:21.103367","status":"completed"},"tags":[]},"outputs":[],"source":["X = data1[\"cleaned_Text\"]\n","y = data1[\"Language\"]"]},{"cell_type":"markdown","id":"e899ba7a","metadata":{"papermill":{"duration":0.013087,"end_time":"2022-12-05T21:14:21.153476","exception":false,"start_time":"2022-12-05T21:14:21.140389","status":"completed"},"tags":[]},"source":["### *Lable Encoding*"]},{"cell_type":"code","execution_count":18,"id":"645431a9","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:14:21.184383Z","iopub.status.busy":"2022-12-05T21:14:21.183982Z","iopub.status.idle":"2022-12-05T21:14:22.278186Z","shell.execute_reply":"2022-12-05T21:14:22.276691Z"},"papermill":{"duration":1.113037,"end_time":"2022-12-05T21:14:22.281217","exception":false,"start_time":"2022-12-05T21:14:21.16818","status":"completed"},"tags":[]},"outputs":[],"source":["from sklearn.preprocessing import LabelEncoder\n","encoder = LabelEncoder()\n","y = encoder.fit_transform(y)"]},{"cell_type":"markdown","id":"56475a37","metadata":{"papermill":{"duration":0.01291,"end_time":"2022-12-05T21:14:22.307759","exception":false,"start_time":"2022-12-05T21:14:22.294849","status":"completed"},"tags":[]},"source":["### *Bag of Words*\n","Both the input and the output features must be in numerical form.Therefore, we will use CountVectorizer to build a Bag of Words model"]},{"cell_type":"code","execution_count":19,"id":"29174493","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:14:22.335845Z","iopub.status.busy":"2022-12-05T21:14:22.335343Z","iopub.status.idle":"2022-12-05T21:14:23.155936Z","shell.execute_reply":"2022-12-05T21:14:23.154815Z"},"papermill":{"duration":0.839491,"end_time":"2022-12-05T21:14:23.160291","exception":false,"start_time":"2022-12-05T21:14:22.3208","status":"completed"},"tags":[]},"outputs":[],"source":["from sklearn.feature_extraction.text import CountVectorizer\n","CV = CountVectorizer()\n","X = CV.fit_transform(X).toarray()"]},{"cell_type":"code","execution_count":20,"id":"b1128086","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:14:23.19198Z","iopub.status.busy":"2022-12-05T21:14:23.191394Z","iopub.status.idle":"2022-12-05T21:14:23.200536Z","shell.execute_reply":"2022-12-05T21:14:23.199033Z"},"papermill":{"duration":0.028342,"end_time":"2022-12-05T21:14:23.20325","exception":false,"start_time":"2022-12-05T21:14:23.174908","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["(10271, 39404)"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["X.shape"]},{"cell_type":"markdown","id":"d71979b3","metadata":{"papermill":{"duration":0.012693,"end_time":"2022-12-05T21:14:23.230648","exception":false,"start_time":"2022-12-05T21:14:23.217955","status":"completed"},"tags":[]},"source":["### *Splitting Training and Testing Data*\n"]},{"cell_type":"code","execution_count":21,"id":"b47437b1","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:14:23.261275Z","iopub.status.busy":"2022-12-05T21:14:23.260314Z","iopub.status.idle":"2022-12-05T21:14:26.297396Z","shell.execute_reply":"2022-12-05T21:14:26.296072Z"},"papermill":{"duration":3.05617,"end_time":"2022-12-05T21:14:26.300924","exception":false,"start_time":"2022-12-05T21:14:23.244754","status":"completed"},"tags":[]},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"]},{"cell_type":"markdown","id":"c0200785","metadata":{"papermill":{"duration":0.01408,"end_time":"2022-12-05T21:14:26.328607","exception":false,"start_time":"2022-12-05T21:14:26.314527","status":"completed"},"tags":[]},"source":["## ***4- Building and Training Model***\n"]},{"cell_type":"code","execution_count":22,"id":"854b89c0","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:14:26.358166Z","iopub.status.busy":"2022-12-05T21:14:26.357665Z","iopub.status.idle":"2022-12-05T21:14:26.633031Z","shell.execute_reply":"2022-12-05T21:14:26.631454Z"},"papermill":{"duration":0.293979,"end_time":"2022-12-05T21:14:26.636836","exception":false,"start_time":"2022-12-05T21:14:26.342857","status":"completed"},"tags":[]},"outputs":[],"source":[" from sklearn.naive_bayes import MultinomialNB\n"," from sklearn.ensemble import RandomForestClassifier\n","\n"," models = {\"M_Naive_Bayes\" : MultinomialNB(), \"Random_Forest\" : RandomForestClassifier()}"]},{"cell_type":"code","execution_count":23,"id":"a0bf6354","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:14:26.665723Z","iopub.status.busy":"2022-12-05T21:14:26.665212Z","iopub.status.idle":"2022-12-05T21:16:58.511946Z","shell.execute_reply":"2022-12-05T21:16:58.510514Z"},"papermill":{"duration":151.877891,"end_time":"2022-12-05T21:16:58.528699","exception":false,"start_time":"2022-12-05T21:14:26.650808","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["M_Naive_Bayes training started...\n","M_Naive_Bayes trained.\n","Random_Forest training started...\n","Random_Forest trained.\n"]}],"source":["for name, model in models.items():\n","    print( f'{name} training started...')\n","    model.fit(X_train, y_train)\n","    print(f'{name} trained.')"]},{"cell_type":"markdown","id":"cc5e0472","metadata":{"papermill":{"duration":0.013095,"end_time":"2022-12-05T21:16:58.555515","exception":false,"start_time":"2022-12-05T21:16:58.54242","status":"completed"},"tags":[]},"source":["## ***5- Model Evaluation***"]},{"cell_type":"code","execution_count":24,"id":"a9e02b2b","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:16:58.583761Z","iopub.status.busy":"2022-12-05T21:16:58.583093Z","iopub.status.idle":"2022-12-05T21:16:58.587656Z","shell.execute_reply":"2022-12-05T21:16:58.586812Z"},"papermill":{"duration":0.02163,"end_time":"2022-12-05T21:16:58.590044","exception":false,"start_time":"2022-12-05T21:16:58.568414","status":"completed"},"tags":[]},"outputs":[],"source":["from sklearn.metrics import accuracy_score, confusion_matrix"]},{"cell_type":"code","execution_count":25,"id":"66fdc6f3","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:16:58.618312Z","iopub.status.busy":"2022-12-05T21:16:58.617591Z","iopub.status.idle":"2022-12-05T21:17:00.346347Z","shell.execute_reply":"2022-12-05T21:17:00.344663Z"},"papermill":{"duration":1.745662,"end_time":"2022-12-05T21:17:00.348788","exception":false,"start_time":"2022-12-05T21:16:58.603126","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["M_Naive_Bayes accuracy score :  0.9805295950155763\n","Random_Forest accuracy score :  0.926791277258567\n"]}],"source":["for name in models:\n","  acc_score = accuracy_score(y_test, models.get(name).predict(X_test))\n","  print(f'{name} accuracy score :  {acc_score}')"]},{"cell_type":"markdown","id":"a53a1980","metadata":{"papermill":{"duration":0.012548,"end_time":"2022-12-05T21:17:00.374314","exception":false,"start_time":"2022-12-05T21:17:00.361766","status":"completed"},"tags":[]},"source":["*The accuracy of the \"Multinomial Naive Bayes\" model is 0.98 which is very good and better a bit than \"Random Forest\"*"]},{"cell_type":"markdown","id":"5eb30caf","metadata":{"papermill":{"duration":0.012976,"end_time":"2022-12-05T21:17:00.400196","exception":false,"start_time":"2022-12-05T21:17:00.38722","status":"completed"},"tags":[]},"source":["## ***6- Model Prediction***"]},{"cell_type":"code","execution_count":26,"id":"554d52cb","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:17:00.429093Z","iopub.status.busy":"2022-12-05T21:17:00.42867Z","iopub.status.idle":"2022-12-05T21:17:00.435047Z","shell.execute_reply":"2022-12-05T21:17:00.433768Z"},"papermill":{"duration":0.023701,"end_time":"2022-12-05T21:17:00.437736","exception":false,"start_time":"2022-12-05T21:17:00.414035","status":"completed"},"tags":[]},"outputs":[],"source":["def prediction(text):\n","  x = CV.transform([text]).toarray()  # converting text to bag of words model (Vector)\n","  lang = model.predict(x) # predicting the language\n","  lang = encoder.inverse_transform(lang) # finding the language corresponding to the predicted value\n","  print(\"The langauge is in\",lang[0])  # printing the language"]},{"cell_type":"code","execution_count":27,"id":"3ab5bfac","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:17:00.466467Z","iopub.status.busy":"2022-12-05T21:17:00.466036Z","iopub.status.idle":"2022-12-05T21:17:00.486298Z","shell.execute_reply":"2022-12-05T21:17:00.484348Z"},"papermill":{"duration":0.037899,"end_time":"2022-12-05T21:17:00.4891","exception":false,"start_time":"2022-12-05T21:17:00.451201","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["The langauge is in Arabic\n"]}],"source":["prediction( \"أنا أحب زكا وفريقها الرائع\")"]},{"cell_type":"code","execution_count":28,"id":"8f4691e2","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:17:00.517451Z","iopub.status.busy":"2022-12-05T21:17:00.51705Z","iopub.status.idle":"2022-12-05T21:17:00.53818Z","shell.execute_reply":"2022-12-05T21:17:00.53662Z"},"papermill":{"duration":0.038281,"end_time":"2022-12-05T21:17:00.540754","exception":false,"start_time":"2022-12-05T21:17:00.502473","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["The langauge is in English\n"]}],"source":["prediction(\"I Love Zaka and its Nice Team\")"]},{"cell_type":"code","execution_count":29,"id":"d8ff4039","metadata":{"execution":{"iopub.execute_input":"2022-12-05T21:17:00.570042Z","iopub.status.busy":"2022-12-05T21:17:00.569608Z","iopub.status.idle":"2022-12-05T21:17:00.587395Z","shell.execute_reply":"2022-12-05T21:17:00.586007Z"},"papermill":{"duration":0.035518,"end_time":"2022-12-05T21:17:00.589984","exception":false,"start_time":"2022-12-05T21:17:00.554466","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["The langauge is in German\n"]}],"source":["prediction(\"Ich liebe Zaka und ihre nettes Team\")"]}],"metadata":{"kernelspec":{"display_name":"Python 3","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":"3.7.12"},"papermill":{"default_parameters":{},"duration":171.965701,"end_time":"2022-12-05T21:17:01.426324","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2022-12-05T21:14:09.460623","version":"2.3.4"}},"nbformat":4,"nbformat_minor":5}