hotel_booking.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# !pip install pandas\n",
    "# !pip install numpy\n",
    "# !pip install seaborn\n",
    "# !pip install matplotlib\n",
    "# !pip install scikit-learn\n",
    "# !pip install tensorflow\n",
    "# !pip install kmodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import re\n",
    "import pickle\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier, RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "from kmodes.kprototypes import KPrototypes\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.models import load_model\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "data": {
      "text/plain": "   IsCanceled  LeadTime  ArrivalDateYear ArrivalDateMonth  \\\n0           0       342             2015             July   \n1           0       737             2015             July   \n2           0         7             2015             July   \n3           0        13             2015             July   \n4           0        14             2015             July   \n\n   ArrivalDateWeekNumber  ArrivalDateDayOfMonth  StaysInWeekendNights  \\\n0                     27                      1                     0   \n1                     27                      1                     0   \n2                     27                      1                     0   \n3                     27                      1                     0   \n4                     27                      1                     0   \n\n   StaysInWeekNights  Adults  Children  ...        Agent      Company  \\\n0                  0       2       0.0  ...         NULL         NULL   \n1                  0       2       0.0  ...         NULL         NULL   \n2                  1       1       0.0  ...         NULL         NULL   \n3                  1       1       0.0  ...          304         NULL   \n4                  2       2       0.0  ...          240         NULL   \n\n  DaysInWaitingList CustomerType   ADR  RequiredCarParkingSpaces  \\\n0                 0    Transient   0.0                         0   \n1                 0    Transient   0.0                         0   \n2                 0    Transient  75.0                         0   \n3                 0    Transient  75.0                         0   \n4                 0    Transient  98.0                         0   \n\n   TotalOfSpecialRequests  ReservationStatus ReservationStatusDate is_h1  \n0                       0          Check-Out            2015-07-01     1  \n1                       0          Check-Out            2015-07-01     1  \n2                       0          Check-Out            2015-07-02     1  \n3                       0          Check-Out            2015-07-02     1  \n4                       1          Check-Out            2015-07-03     1  \n\n[5 rows x 32 columns]",
      "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>IsCanceled</th>\n      <th>LeadTime</th>\n      <th>ArrivalDateYear</th>\n      <th>ArrivalDateMonth</th>\n      <th>ArrivalDateWeekNumber</th>\n      <th>ArrivalDateDayOfMonth</th>\n      <th>StaysInWeekendNights</th>\n      <th>StaysInWeekNights</th>\n      <th>Adults</th>\n      <th>Children</th>\n      <th>...</th>\n      <th>Agent</th>\n      <th>Company</th>\n      <th>DaysInWaitingList</th>\n      <th>CustomerType</th>\n      <th>ADR</th>\n      <th>RequiredCarParkingSpaces</th>\n      <th>TotalOfSpecialRequests</th>\n      <th>ReservationStatus</th>\n      <th>ReservationStatusDate</th>\n      <th>is_h1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>342</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>NULL</td>\n      <td>NULL</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-01</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>737</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>NULL</td>\n      <td>NULL</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-01</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>7</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>NULL</td>\n      <td>NULL</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>75.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-02</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>13</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>304</td>\n      <td>NULL</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>75.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-02</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>14</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>240</td>\n      <td>NULL</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>98.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Check-Out</td>\n      <td>2015-07-03</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 32 columns</p>\n</div>"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotel_booking = pd.read_csv('DATA/hotel_booking.csv')\n",
    "hotel_booking.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "data": {
      "text/plain": "IsCanceled                       0\nLeadTime                         0\nArrivalDateYear                  0\nArrivalDateMonth                 0\nArrivalDateWeekNumber            0\nArrivalDateDayOfMonth            0\nStaysInWeekendNights             0\nStaysInWeekNights                0\nAdults                           0\nChildren                         4\nBabies                           0\nMeal                             0\nCountry                        488\nMarketSegment                    0\nDistributionChannel              0\nIsRepeatedGuest                  0\nPreviousCancellations            0\nPreviousBookingsNotCanceled      0\nReservedRoomType                 0\nAssignedRoomType                 0\nBookingChanges                   0\nDepositType                      0\nAgent                            0\nCompany                          0\nDaysInWaitingList                0\nCustomerType                     0\nADR                              0\nRequiredCarParkingSpaces         0\nTotalOfSpecialRequests           0\nReservationStatus                0\nReservationStatusDate            0\nis_h1                            0\ndtype: int64"
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking Missing Data\n",
    "hotel_booking.isna().sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAJiCAYAAADtzjmPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAD5xUlEQVR4nOzdd1QU5/s28GvpHQQVRJFix16iRo2iYu8msRt7NGLBbjR2xRZRExNbVFBjj8YkJvaKxth7771GwQoC9/sHL/Nj2b6LuvF7fc6Zc2B2np0ZmJ2952m3SkQERERERFbE5n0fABEREVFmDFCIiIjI6jBAISIiIqvDAIWIiIisDgMUIiIisjoMUIiIiMjqMEAhIiIiq8MAhYiIiKyO3fs+AHOkpqbizp07cHd3h0qlet+HQ0REREYQETx79gz+/v6wsdFfR/KfDFDu3LmDgICA930YREREZIabN28iT548erf5TwYo7u7uANJO0MPD4z0fDRERERkjISEBAQEByve4Pv/JACW9WcfDw4MBChER0X+MMd0z2EmWiIiIrI5JAcro0aOhUqnUFj8/P+V1EcHo0aPh7+8PZ2dnhIWF4fTp02rvkZiYiN69eyN79uxwdXVF48aNcevWraw5GyIiIvogmFyDUrRoUdy9e1dZTp48qbw2ZcoUREdHY9asWTh48CD8/PxQq1YtPHv2TNkmMjIS69atw4oVKxAXF4fnz5+jYcOGSElJyZozIiIiov88k/ug2NnZqdWapBMRzJgxA8OHD0fz5s0BALGxsfD19cWyZcvQvXt3xMfHY8GCBViyZAnCw8MBAEuXLkVAQAC2bt2KOnXqWHg6RERE9CEwuQbl4sWL8Pf3R3BwMFq1aoUrV64AAK5evYp79+6hdu3ayraOjo6oVq0a9u3bBwA4fPgw3rx5o7aNv78/ihUrpmxDREREZFINSoUKFbB48WIULFgQ9+/fx/jx41GpUiWcPn0a9+7dAwD4+vqqlfH19cX169cBAPfu3YODgwOyZcumsU16eW0SExORmJio/J6QkGDKYRMREdF/jEkBSr169ZSfixcvjo8//hj58uVDbGwsKlasCEBz6JCIGBxOZGibiRMnYsyYMaYcKhEREf2HWTTM2NXVFcWLF8fFixeVfimZa0IePHig1Kr4+fkhKSkJT5480bmNNl9//TXi4+OV5ebNm5YcNhEREVk5iwKUxMREnD17Frly5UJwcDD8/PywZcsW5fWkpCTs2rULlSpVAgCULVsW9vb2atvcvXsXp06dUrbRxtHRUZmUjZOzERERffhMauIZOHAgGjVqhLx58+LBgwcYP348EhIS0KFDB6hUKkRGRiIqKgoFChRAgQIFEBUVBRcXF7Rp0wYA4OnpiS5dumDAgAHw8fGBt7c3Bg4ciOLFiyujeoiIiIhMClBu3bqF1q1b49GjR8iRIwcqVqyI/fv3IzAwEAAwePBgvHr1Cj179sSTJ09QoUIFbN68WW3O/enTp8POzg4tWrTAq1evULNmTcTExMDW1jZrz4yIiIj+s1QiIu/7IEyVkJAAT09PxMfHs7mHiIjoP8KU72/m4iEiIiKrwwCFiIiIrI7JU90T0fsTNHSD3tevTWrwjo6EiOjtYg0KERERWR0GKERERGR1GKAQERGR1WGAQkRERFaHAQoRERFZHQYoREREZHUYoBAREZHVYYBCREREVocBChEREVkdBihERERkdRigEBERkdVhgEJERERWhwEKERERWR0GKERERGR1GKAQERGR1WGAQkRERFaHAQoRERFZHQYoREREZHUYoBAREZHVYYBCREREVocBChEREVkdBihERERkdRigEBERkdVhgEJERERWhwEKERERWR0GKERERGR1GKAQERGR1WGAQkRERFaHAQoRERFZHQYoREREZHUYoBAREZHVYYBCREREVocBChEREVkdBihERERkdRigEBERkdVhgEJERERWhwEKERERWR0GKERERGR1GKAQERGR1WGAQkRERFaHAQoRERFZHQYoREREZHUYoBAREZHVYYBCREREVocBChEREVkdBihERERkdRigEBERkdVhgEJERERWhwEKERERWR0GKERERGR1GKAQERGR1WGAQkRERFaHAQoRERFZHQYoREREZHUYoBAREZHVYYBCREREVocBChEREVkdBihERERkdRigEBERkdWxKECZOHEiVCoVIiMjlXUigtGjR8Pf3x/Ozs4ICwvD6dOn1colJiaid+/eyJ49O1xdXdG4cWPcunXLkkMhIiKiD4jZAcrBgwcxb948lChRQm39lClTEB0djVmzZuHgwYPw8/NDrVq18OzZM2WbyMhIrFu3DitWrEBcXByeP3+Ohg0bIiUlxfwzISIiog+GWQHK8+fP0bZtW8yfPx/ZsmVT1osIZsyYgeHDh6N58+YoVqwYYmNj8fLlSyxbtgwAEB8fjwULFmDatGkIDw9H6dKlsXTpUpw8eRJbt27NmrMiIiKi/zSzApSIiAg0aNAA4eHhauuvXr2Ke/fuoXbt2so6R0dHVKtWDfv27QMAHD58GG/evFHbxt/fH8WKFVO2ySwxMREJCQlqCxEREX247EwtsGLFChw+fBiHDh3SeO3evXsAAF9fX7X1vr6+uH79urKNg4ODWs1L+jbp5TObOHEixowZY+qhEhER0X+USTUoN2/eRN++ffHzzz/DyclJ53YqlUrtdxHRWJeZvm2+/vprxMfHK8vNmzdNOWwiIiL6jzEpQDl8+DAePHiAsmXLws7ODnZ2dti1axe+++472NnZKTUnmWtCHjx4oLzm5+eHpKQkPHnyROc2mTk6OsLDw0NtISIiog+XSQFKzZo1cfLkSRw7dkxZypUrh7Zt2+LYsWMICQmBn58ftmzZopRJSkrCrl27UKlSJQBA2bJlYW9vr7bN3bt3cerUKWUbIiIi+t9mUh8Ud3d3FCtWTG2dq6srfHx8lPWRkZGIiopCgQIFUKBAAURFRcHFxQVt2rQBAHh6eqJLly4YMGAAfHx84O3tjYEDB6J48eIanW6JiIjof5PJnWQNGTx4MF69eoWePXviyZMnqFChAjZv3gx3d3dlm+nTp8POzg4tWrTAq1evULNmTcTExMDW1jarD4eIiIj+g1QiIu/7IEyVkJAAT09PxMfHsz8K/U8JGrpB7+vXJjV4R0dCRGQ6U76/mYuHiIiIrA4DFCIiIrI6DFCIiIjI6jBAISIiIqvDAIWIiIisDgMUIiIisjoMUIiIiMjqMEAhIiIiq8MAhYiIiKwOAxQiIiKyOgxQiIiIyOowQCEiIiKrwwCFiIiIrA4DFCIiIrI6DFCIiIjI6jBAISIiIqvDAIWIiIisDgMUIiIisjoMUIiIiMjqMEAhIiIiq8MAhYiIiKwOAxQiIiKyOgxQiIiIyOowQCEiIiKrwwCFiIiIrA4DFCIiIrI6DFCIiIjI6jBAISIiIqvDAIWIiIisDgMUIiIisjoMUIiIiMjqMEAhIiIiq8MAhYiIiKwOAxQiIiKyOgxQiIiIyOowQCEiIiKrwwCFiIiIrA4DFCIiIrI6DFCIiIjI6jBAISIiIqvDAIWIiIisDgMUIiIisjoMUIiIiMjqMEAhIiIiq8MAhYiIiKwOAxQiIiKyOgxQiIiIyOowQCEiIiKrwwCFiIiIrA4DFCIiIrI6DFCIiIjI6jBAISIiIqvDAIWIiIisDgMUIiIisjoMUIiIiMjqMEAhIiIiq8MAhYiIiKwOAxQiIiKyOgxQiIiIyOowQCEiIiKrwwCFiIiIrI5JAcrs2bNRokQJeHh4wMPDAx9//DH++usv5XURwejRo+Hv7w9nZ2eEhYXh9OnTau+RmJiI3r17I3v27HB1dUXjxo1x69atrDkbIiIi+iCYFKDkyZMHkyZNwqFDh3Do0CHUqFEDTZo0UYKQKVOmIDo6GrNmzcLBgwfh5+eHWrVq4dmzZ8p7REZGYt26dVixYgXi4uLw/PlzNGzYECkpKVl7ZkRERPSfpRIRseQNvL29MXXqVHTu3Bn+/v6IjIzEkCFDAKTVlvj6+mLy5Mno3r074uPjkSNHDixZsgQtW7YEANy5cwcBAQH4888/UadOHaP2mZCQAE9PT8THx8PDw8OSwyf6TwkaukHv69cmNXhHR0JEZDpTvr/N7oOSkpKCFStW4MWLF/j4449x9epV3Lt3D7Vr11a2cXR0RLVq1bBv3z4AwOHDh/HmzRu1bfz9/VGsWDFlG20SExORkJCgthAREdGHy+QA5eTJk3Bzc4OjoyN69OiBdevWITQ0FPfu3QMA+Pr6qm3v6+urvHbv3j04ODggW7ZsOrfRZuLEifD09FSWgIAAUw+biIiI/kNMDlAKFSqEY8eOYf/+/fjqq6/QoUMHnDlzRnldpVKpbS8iGusyM7TN119/jfj4eGW5efOmqYdNRERE/yEmBygODg7Inz8/ypUrh4kTJ6JkyZKYOXMm/Pz8AECjJuTBgwdKrYqfnx+SkpLw5MkTndto4+joqIwcSl+IiIjow2XxPCgigsTERAQHB8PPzw9btmxRXktKSsKuXbtQqVIlAEDZsmVhb2+vts3du3dx6tQpZRsiIiIiO1M2HjZsGOrVq4eAgAA8e/YMK1aswM6dO7Fx40aoVCpERkYiKioKBQoUQIECBRAVFQUXFxe0adMGAODp6YkuXbpgwIAB8PHxgbe3NwYOHIjixYsjPDz8rZwgERER/feYFKDcv38f7du3x927d+Hp6YkSJUpg48aNqFWrFgBg8ODBePXqFXr27IknT56gQoUK2Lx5M9zd3ZX3mD59Ouzs7NCiRQu8evUKNWvWRExMDGxtbbP2zIiIiOg/y+J5UN4HzoNC/6s4DwoR/Ze9k3lQiIiIiN4WBihERERkdRigEBERkdVhgEJERERWhwEKERERWR0GKERERGR1GKAQERGR1WGAQkRERFaHAQoRERFZHQYoREREZHUYoBAREZHVYYBCREREVocBChEREVkdBihERERkdRigEBERkdVhgEJERERWhwEKERERWR0GKERERGR1GKAQERGR1WGAQkRERFaHAQoRERFZHQYoREREZHUYoBAREZHVYYBCREREVocBChEREVkdBihERERkdRigEBERkdVhgEJERERWhwEKERERWR0GKERERGR1GKAQERGR1WGAQkRERFaHAQoRERFZHQYoREREZHUYoBAREZHVYYBCREREVocBChEREVkdBihERERkdRigEBERkdVhgEJERERWhwEKERERWR0GKERERGR1GKAQERGR1WGAQkRERFaHAQoRERFZHQYoREREZHUYoBAREZHVYYBCREREVocBChEREVkdBihERERkdRigEBERkdVhgEJERERWhwEKERERWR0GKERERGR1GKAQERGR1WGAQkRERFaHAQoRERFZHQYoREREZHUYoBAREZHVYYBCREREVocBChEREVkdkwKUiRMn4qOPPoK7uzty5syJpk2b4vz582rbiAhGjx4Nf39/ODs7IywsDKdPn1bbJjExEb1790b27Nnh6uqKxo0b49atW5afDREREX0QTApQdu3ahYiICOzfvx9btmxBcnIyateujRcvXijbTJkyBdHR0Zg1axYOHjwIPz8/1KpVC8+ePVO2iYyMxLp167BixQrExcXh+fPnaNiwIVJSUrLuzIiIiOg/SyUiYm7hhw8fImfOnNi1axeqVq0KEYG/vz8iIyMxZMgQAGm1Jb6+vpg8eTK6d++O+Ph45MiRA0uWLEHLli0BAHfu3EFAQAD+/PNP1KlTx+B+ExIS4Onpifj4eHh4eJh7+ET/OUFDN+h9/dqkBu/oSIiITGfK97dFfVDi4+MBAN7e3gCAq1ev4t69e6hdu7ayjaOjI6pVq4Z9+/YBAA4fPow3b96obePv749ixYop22SWmJiIhIQEtYWIiIg+XGYHKCKC/v37o0qVKihWrBgA4N69ewAAX19ftW19fX2V1+7duwcHBwdky5ZN5zaZTZw4EZ6ensoSEBBg7mETERHRf4DZAUqvXr1w4sQJLF++XOM1lUql9ruIaKzLTN82X3/9NeLj45Xl5s2b5h42ERER/QeYFaD07t0bv/32G3bs2IE8efIo6/38/ABAoybkwYMHSq2Kn58fkpKS8OTJE53bZObo6AgPDw+1hYiIiD5cJgUoIoJevXph7dq12L59O4KDg9VeDw4Ohp+fH7Zs2aKsS0pKwq5du1CpUiUAQNmyZWFvb6+2zd27d3Hq1CllGyIiIvrfZmfKxhEREVi2bBnWr18Pd3d3pabE09MTzs7OUKlUiIyMRFRUFAoUKIACBQogKioKLi4uaNOmjbJtly5dMGDAAPj4+MDb2xsDBw5E8eLFER4envVnSERERP85JgUos2fPBgCEhYWprV+0aBE6duwIABg8eDBevXqFnj174smTJ6hQoQI2b94Md3d3Zfvp06fDzs4OLVq0wKtXr1CzZk3ExMTA1tbWsrMhIiKiD4JF86C8L5wHhf5XcR4UIvove2fzoBARERG9DQxQiIiIyOowQCEiIiKrwwCFiIiIrA4DFCIiIrI6DFCIiIjI6jBAISIiIqvDAIWIiIisDgMUIiIisjoMUIiIiMjqMEAhIiIiq8MAhYiIiKwOAxQiIiKyOgxQiIiIyOowQCEiIiKrwwCFiIiIrA4DFCIiIrI6DFCIiIjI6jBAISIiIqvDAIWIiIisDgMUIiIisjoMUIiIiMjqMEAhIiIiq8MAhYiIiKwOAxQiIiKyOgxQiIiIyOowQCEiIiKrwwCFiIiIrA4DFCIiIrI6DFCIiIjI6jBAISIiIqvDAIWIiIisDgMUIiIisjoMUIiIiMjqMEAhIiIiq8MAhYiIiKwOAxQiIiKyOgxQiIiIyOowQCEiIiKrwwCFiIiIrA4DFCIiIrI6DFCIiIjI6jBAISIiIqvDAIWIiIisDgMUIiIisjoMUIiIiMjqMEAhIiIiq8MAhYiIiKwOAxQiIiKyOgxQiIiIyOowQCEiIiKrwwCFiIiIrA4DFCIiIrI6DFCIiIjI6jBAISIiIqvDAIWIiIisDgMUIiIisjoMUIiIiMjqMEAhIiIiq8MAhYiIiKwOAxQiIiKyOiYHKLt370ajRo3g7+8PlUqFX3/9Ve11EcHo0aPh7+8PZ2dnhIWF4fTp02rbJCYmonfv3siePTtcXV3RuHFj3Lp1y6ITISIiog+HyQHKixcvULJkScyaNUvr61OmTEF0dDRmzZqFgwcPws/PD7Vq1cKzZ8+UbSIjI7Fu3TqsWLECcXFxeP78ORo2bIiUlBTzz4SIiIg+GHamFqhXrx7q1aun9TURwYwZMzB8+HA0b94cABAbGwtfX18sW7YM3bt3R3x8PBYsWIAlS5YgPDwcALB06VIEBARg69atqFOnjgWnQ0RERB+CLO2DcvXqVdy7dw+1a9dW1jk6OqJatWrYt28fAODw4cN48+aN2jb+/v4oVqyYsk1miYmJSEhIUFuIiIjow5WlAcq9e/cAAL6+vmrrfX19ldfu3bsHBwcHZMuWTec2mU2cOBGenp7KEhAQkJWHTURERFbmrYziUalUar+LiMa6zPRt8/XXXyM+Pl5Zbt68mWXHSkRERNYnSwMUPz8/ANCoCXnw4IFSq+Ln54ekpCQ8efJE5zaZOTo6wsPDQ20hIiKiD1eWBijBwcHw8/PDli1blHVJSUnYtWsXKlWqBAAoW7Ys7O3t1ba5e/cuTp06pWxDRERE/9tMHsXz/PlzXLp0Sfn96tWrOHbsGLy9vZE3b15ERkYiKioKBQoUQIECBRAVFQUXFxe0adMGAODp6YkuXbpgwIAB8PHxgbe3NwYOHIjixYsro3qIiIjof5vJAcqhQ4dQvXp15ff+/fsDADp06ICYmBgMHjwYr169Qs+ePfHkyRNUqFABmzdvhru7u1Jm+vTpsLOzQ4sWLfDq1SvUrFkTMTExsLW1zYJTIiIiov86lYjI+z4IUyUkJMDT0xPx8fHsj0L/U4KGbtD7+rVJDd7RkRARmc6U72/m4iEiIiKrwwCFiIiIrA4DFCIiIrI6DFCIiIjI6jBAISIiIqvDAIWIiIisDgMUIiIisjoMUIiIiMjqMEAhIiIiq8MAhYiIiKwOAxQiIiKyOgxQiIiIyOowQCEiIiKrwwCFiIiIrA4DFCIiIrI6DFCIiIjI6jBAISIiIqvDAIWIiIisDgMUIiIisjoMUIiIiMjqMEAhIiIiq8MAhYiIiKwOAxQiIiKyOgxQiIiIyOowQCEiIiKrwwCFiIiIrA4DFCIiIrI6DFCIiIjI6jBAISIiIqvDAIWIiIisDgMUIiIisjoMUIiIiMjqMEAhIiIiq8MAhYiIiKwOAxQiIiKyOgxQiIiIyOowQCEiIiKrwwCFiIiIrA4DFCIiIrI6DFCIiIjI6jBAISIiIqvDAIWIiIisDgMUIiIisjoMUIiIiMjqMEAhIiIiq8MAhYiIiKwOAxQiIiKyOgxQiIiIyOowQCEiIiKrwwCFiIiIrA4DFCIiIrI6DFCIiIjI6jBAISIiIqvDAIWIiIisDgMUIiIisjoMUIiIiMjqMEAhIiIiq8MAhYiIiKwOAxQiIiKyOgxQiIiIyOowQCEiIiKr814DlB9//BHBwcFwcnJC2bJlsWfPnvd5OERERGQl7N7XjleuXInIyEj8+OOPqFy5MubOnYt69erhzJkzyJs37/s6LPofETR0g87Xrk1q8A6PhIiItHlvNSjR0dHo0qULunbtiiJFimDGjBkICAjA7Nmz39chERERkZV4LzUoSUlJOHz4MIYOHaq2vnbt2ti3b5/G9omJiUhMTFR+j4+PBwAkJCS83QOlD1Zq4kudr1nzdaXvuAHrPnYiovR7lIgY3Pa9BCiPHj1CSkoKfH191db7+vri3r17GttPnDgRY8aM0VgfEBDw1o6R/nd5znjfR2C+//KxE9H/jmfPnsHT01PvNu+tDwoAqFQqtd9FRGMdAHz99dfo37+/8ntqair+/fdf+Pj4aN0+ISEBAQEBuHnzJjw8PEw+LkvKc9/cN/fNfXPf3Df3rb28iODZs2fw9/c3+D7vJUDJnj07bG1tNWpLHjx4oFGrAgCOjo5wdHRUW+fl5WVwPx4eHmb9cbOiPPfNfXPf3Df3zX1z35rlDdWcpHsvnWQdHBxQtmxZbNmyRW39li1bUKlSpfdxSERERGRF3lsTT//+/dG+fXuUK1cOH3/8MebNm4cbN26gR48e7+uQiIiIyEq8twClZcuWePz4McaOHYu7d++iWLFi+PPPPxEYGGjxezs6OmLUqFEazULvojz3zX1z39w39819c9/ml0+nEmPG+hARERG9Q8zFQ0RERFaHAQoRERFZHQYoREREZHUYoBAREZHVYYBiARHB9evX8erVq/d9KERERB8UBigWEBEUKFAAt27deuf7Tk5OxpgxY3Dz5s13vu90r169wsuX/5e87vr165gxYwY2b9783o6JiCxXo0YNPH36VGN9QkICatSo8e4PyAqsWbPmrb7/zZs31b5LDhw4gMjISMybN++t7teaMUCxgI2NDQoUKIDHjx9b/F5JSUm4desWbty4obboYmdnh6lTpyIlJcWi/W7btg3Dhg1D165d0blzZ7XFkCZNmmDx4sUAgKdPn6JChQqYNm0amjRpgtmzZ+stu3HjRsTFxSm///DDDyhVqhTatGmDJ0+e6C175MgRnDx5Uvl9/fr1aNq0KYYNG4akpCSDx52SkoIFCxagTZs2CA8PR40aNdQWa9axY0fs3r37fR/GO/dfDoYvX76Mb775Bq1bt8aDBw8ApF3/p0+ffs9HptvOnTu1fpZev36NPXv2GCxv6XVqaYC0Z88etGvXDh9//DFu374NAFiyZInaPSez5ORknD59GhcuXFBbv379epQsWRJt27Y17SRM1KZNG+zYsQMAcO/ePdSqVQsHDhzAsGHDMHbsWLPfV9vfUZfk5GRs3boVc+fOxbNnzwAAd+7cwfPnz83evyXea7JAS/32229Gb9u4cWOD2yxZsgRz5szB1atX8ffffyMwMBAzZsxAcHAwmjRporXMlClTMGjQIMyePRvFihUz+njSXbx4EZ07d8a+ffvU1qcnTtQXgISHh2Pnzp3o2LGjyfsFgDFjxmDs2LEoV64ccuXKpTXxoj5HjhzB9OnTAaQ9Xfj6+uLo0aP45ZdfMHLkSHz11Vc6yw4aNAiTJ08GAJw8eRIDBgxA//79sX37dvTv3x+LFi3SWbZ79+4YOnQoihcvjitXrqBVq1Zo1qwZVq9ejZcvX2LGjBl6j7tv376IiYlBgwYNUKxYMZPPOzY2FtmzZ0eDBg0AAIMHD8a8efMQGhqK5cuXa51ssHnz5ka//9q1a3W+9uzZM9SuXRsBAQHo1KkTOnTogNy5cxv93ra2trh79y5y5syptv7x48fImTOnxvVWunRpo/8+R44c0ft6SEgIDh48CB8fH7X1T58+RZkyZXDlyhWdZZs0aYLmzZujR48eSjBsb2+PR48eITo6Wuu1llV/cyAtyFi0aBEuX76MmTNnImfOnNi4cSMCAgJQtGhRneV27dqFevXqoXLlyti9ezcmTJiAnDlz4sSJE/jpp58MPpWbc61l9vTpU6xZswaXL1/GoEGD4O3tjSNHjsDX11fj2jlx4oTy85kzZ9TypaWkpGDjxo1GXW+WXqeWBEi//PIL2rdvj7Zt2+Lo0aNITExUjikqKgp//vmnRpkzZ86gYcOGuH79OgAoD1ktWrTA8ePH0bVrV/zxxx9GHfvGjRvh5uaGKlWqAEh7+Jo/fz5CQ0Pxww8/IFu2bFrLnTp1CuXLlwcArFq1CsWKFcPevXuxefNm9OjRAyNHjjS478mTJyMoKAgtW7YEALRo0QK//PIL/Pz88Oeff6JkyZI6y16/fh1169bFjRs3kJiYiFq1asHd3R1TpkzB69evMWfOHKPOP7MnT57g999/xxdffGF6YfkPU6lUaouNjY3G7+mLIT/++KNkz55dxo8fL87OznL58mUREVm0aJGEhYXpLOfl5SUODg5iY2MjTk5Oki1bNrXFkEqVKknVqlXlzz//lKNHj8qxY8fUFn3mzJkjfn5+MmDAAFm2bJmsX79ebTHEz89PFi9ebHA7XZydneX69esiIvL555/L6NGjRUTkxo0b4uzsrLesq6urXL16VURERo0aJZ9++qmIiBw+fFh8fX31lvXw8JBLly6JiMikSZOkdu3aIiISFxcnefLkMXjcPj4+smHDBoPb6VKwYEHZtm2biIjs27dPnJ2dZe7cudKoUSNp1qyZ1jIdO3Y0ejHk0aNHMmPGDClVqpTY2dlJ3bp1ZfXq1ZKUlGSwrEqlkvv372usv337tjg5OWmsHz16tNGLufu+d++eODg46C3r4+Mjp06dEhGR+fPnS4kSJSQlJUVWrVolhQsX1lom49+0Q4cO4uHhIQEBAdKsWTNp1qyZ5M2bVzw8PAz+zXfu3CnOzs4SHh4uDg4Oyr1h8uTJynWrS8WKFWXatGkiIuLm5qaUPXDggPj7++stK2LetZbR8ePHJUeOHJI/f36xs7NT9v/NN99I+/btNbbPeN/MfH9VqVTi4uIiCxYsMLhfEfOu0+PHj8vx48dFpVLJjh07lN+PHz8uR44ckaioKAkMDNS731KlSklsbKyIqP/Njx49qvPe0qhRI6lRo4b8/vvv0qpVK1GpVFKgQAEZM2aMJCQkGHW+6YoVK6bcX06cOCGOjo7y9ddfS4UKFfReaxnviY0aNZJJkyaJiMj169e1fja1CQ4Olr1794qIyObNm8XLy0s2bdokXbp0kVq1aukt26RJE2nXrp0kJiaq/d127twp+fPnN2r/2hw7dsyo72Bt/tMBSkZbtmyRMmXKyMaNGyU+Pl4SEhJk48aNUq5cOdm8ebPB8kWKFJF169aJiPpFffLkSfHx8dFZLiYmRu9iiIuLi5w9e9a4k8xE2w0kY3BmiLe3t/JFb47ixYvLzJkz5caNG+Lh4SH79u0TEZFDhw4ZDDKyZcsmp0+fFhGRypUry9y5c0VE5OrVqwaDG3d3d7lw4YKIiISHh8uMGTNExPgPcq5cueT8+fMGt9MlY2A2ePBg5UZ/6tQpyZ49u9nva44jR45Ir169xMnJSbJnzy6RkZHK3yajmTNnysyZM8XGxkYmTJig/D5z5kyJjo6Wpk2bSqlSpd7KMaYHzCqVShYvXqwWRK9du1YiIiKkYMGCet/DkmBYJO3/1LVrV0lOTlbWJScny5dffikDBw7UW9aSIMPV1VWuXLmiUfbq1avi6Oho8LgtvdZq1qwpgwYN0tj/3r17tX7RX7t2Ta5evSoqlUoOHjwo165dU5Y7d+6o/f1MYex1mhUBkrOzs/JFn/GcL1++rPNv7uvrK4cPHxYRkSdPnohKpZJ58+aZda7mPnyVL19ehgwZIrt37xYnJyflAfXvv/+W3LlzG7VvJycnuXHjhoiI9OnTR7788ksRETl//rx4eXnpLevj4yPnzp0TEc1rVd9nLD4+Xu+yZ88eBihFixaVPXv2aKzfvXu3ziesjJycnOTatWsiov7PuXDhgtHRqznKlSun9bjfhcGDB8vYsWPNLr969Wqxt7cXGxsbteg8KipK6tatq7dso0aNpE6dOjJ27Fixt7eXW7duiYjIpk2bpECBAnrLVq9eXb744gtZvHix2Nvby8WLF0UkLdI39HQlIvLtt99Kz549JTU11eC22uTIkUOOHDkiIupPa5cuXRJXV1ez3tMcd+7ckUmTJknBggXF1dVVvvjiC6lVq5bY2dlJdHS02rZBQUESFBQkKpVKAgIClN+DgoKkYMGCUrt2bdm/f7/BfT558kTmz58vQ4cOlcePH4tI2o03/f+nja4aTpVKJQ4ODlKwYEH5/fff9e7XkmBYRCR79uzKzTejc+fOibe3t96ylgQZuXPnVp5oM5Zdu3athISEGDxuS6+1jLWNGfd/7do1owKkrGDKdZoVAVJISIhs2bJFRNTPOTY2VooUKaK1jEqlknv37im/u7q6mv0QY+7D144dO8TLy0tsbGykU6dOyvqvv/7aqNoykbSHr/TrrWDBgrJq1SoRSbvO3d3djT7ujH+3PXv2SM6cOXWWy9xakXkx9oFZm/90H5SMLl++DE9PT431np6euHbtmsHywcHBOHbsmEab7l9//YXQ0FCjjuHVq1d48+aN2joPDw+N7RISEpSfJ0+ejMGDByMqKgrFixeHvb29wfLavH79Gk5OTga369+/v/Jzamoq5s2bh61bt6JEiRIa+46Ojtb7Xp999hmqVKmCu3fvqrVt1qxZ02D7/6xZs9CzZ0+sWbMGs2fPVtqn//rrL9StW1dv2enTp6Ndu3b49ddfMXz4cOTPnx9AWj+YSpUq6S0LAHFxcdixYwf++usvFC1aVOO8DfVHqFWrFrp27YrSpUvjwoULSv+A06dPIygoyOD+04911apVuHHjhkZbu76+HG/evMFvv/2GRYsWYfPmzShRogT69euHtm3bwt3dHQCwYsUKfPXVV+jXr59S7urVqwCA6tWrY+3atTrbwfU5ceIEwsPDlc9Ut27d4O3tjXXr1uH69etKh+nMUlNTAaR9xg4ePIjs2bObvO+RI0eiTZs26NevH2rWrImPP/4YALB582aULl3aYPnk5GScPXsWhQoVUlt/9uxZ5fh08fLywt27dxEcHKy2/ujRowb7VbRp0wZDhgzB6tWroVKpkJqair1792LgwIFGtclbeq05OTmp3W/SnT9/Hjly5NBb9sKFC9i5cycePHig8Tcy1B/C3Os0/f5r6H+iT/fu3dG3b18sXLgQKpUKd+7cwd9//42BAwfqPG6VSgUbm/8bM2JjY6NxXzBWlSpV0L9/f1SuXBkHDhzAypUrAaT9PfPkyaOzXFhYGB49eoSEhAS1z+eXX34JV1dXo/bdvHlztGnTRhm8Ua9ePQDAsWPHlPukLrVq1cKMGTOUUUMqlQrPnz/HqFGjUL9+fZ3l3N3dMXz4cFSoUEHr6xcvXkT37t2NOn4NZoU1VuiTTz6RGjVqyJ07d5R1d+/elfDwcKlatarB8gsXLpTcuXPLihUrxNXVVZYvXy7jx49Xftbl+fPnEhERITly5NAaPWqTOeLUFoEaE3UmJyfL2LFjxd/fX2xtbdXal3/66SetZcLCwoxeDOnUqZPW9tnnz5+rPQG8K69evZI3b94Y3M7SPiBPnjyRiIgIady4sfz111/K+pEjR8r48eMNlp85c6a4ublJRESEODg4SPfu3SU8PFw8PT1l2LBhesv6+PhItmzZpGfPnnL06FGt2/z7778SFBRk8DhMZWpzgTGePHli9LZ3796VI0eOSEpKirLun3/+MaqJtF+/fpItWzaZOnWq7NmzR/bs2SNTp04VHx8f6devn96ygwYNkipVqsjdu3fF3d1dLl68KHFxcRISEmKw701SUpK0adNG+Uyn1zi2a9fOqNoAS6+1bt26SdOmTSUpKUnc3NzkypUrcv36dSldurT07dtXZ7l58+aJra2t+Pr6SsmSJaVUqVLKUrp0aYP7tfQ6jYmJkT/++EP5fdCgQeLp6Skff/yxUtOtz7Bhw8TZ2VmpqXNycpJvvvlG5/YqlUq8vLyUvoMqlUo8PT1N7lMoktbU3KBBAylRooTafTgyMlJ69+6ts1z16tW1fh7i4+OlevXqRu07KSlJpk6dKn369FFq3kREpk+fLvPnz9db9vbt21KwYEEpUqSI2NnZScWKFcXHx0cKFSqkte9YurCwMJk8ebLO148dOyYqlcqo48/sgwlQLl68KMWKFRN7e3vJly+f5MuXT+zt7aVo0aJKE4Ah8+bNk7x58yoXdZ48eXR+0afr2bOnFClSRFavXi3Ozs6ycOFCGTdunOTJk0eWLl2qtczOnTuNXvQZM2aMhISEyNKlS9U69q5cuVIqVqxo1DlbwsbGRuuF+/DhQ7G1tTWr7KNHjwwGZsHBwfLo0SON9U+ePJHg4GADR/3+FSpUSJYtWyYi6l/0I0aMkIiICL1lY2Nj5dWrV2bvOzk5WX766Sdp3bq11KxZU6pXr6626GNpc8GkSZNkxYoVyu+fffaZqFQq8ff3N9gh3FIpKSkyefJk8ff3Vz7f/v7+MnnyZIOBgqVBhkhak8zq1atl5cqVWvtevC3x8fFSuXJl8fLyEltbWwkICBB7e3upWrWqPH/+XGe5vHnzKp00zbF48WKLrlNLOweLiLx48UIOHjwo//zzjzx79kzvtob6Ehrbp9ASujqR379/X+zs7N7qvtO9fPlSFi5cKBEREfLVV1/J/Pnz5eXLl3rLzJs3T2bOnKnz9Xv37hnViV6bDyZAERFJTU2VTZs2ycyZM2XGjBmyefNms/oZPHz4UG/EmFFAQIDs2LFDRER5uhJJ+4DWq1fPYPnr169rPcbU1FSlc5wu+fLlk61bt4qI+hfG2bNnDXaIEjG/BiQ+Pl6ePn0qKpVKLl26pNYh6t9//5XY2FjJlSuX3n2bOprEmLL37t0Te3t7vWXTvXnzRrZs2SJz5sxR/ga3b982eCNLt3v3bmnbtq18/PHHSv+LxYsXG9WfyNnZWXkKzJEjh/LlfOHCBb39Id68eSO2trZy8uRJo45Rm4iICHF1dZUWLVpI3759JTIyUm3RJ2fOnMpTWcbrbdOmTUaNnrJkhMHz58/lm2++kY8//ljy5csnwcHBaosp0q9VU1kaZKSmppp1P7LkWku3bds2mTp1qkyePFnpn6GPu7u78v99H7KqI/qNGzfk5s2bb+UY9bl06ZIMHz5cWrVqpdyr/vrrL2UkWkZZMXIpXWxsrN5Fn127dmmtgX7z5o3s2rXLqP1ntQ8qQEn36tUrsztAmsrV1VX5ssmdO7f8888/IiJy5coVozqxWVKToKtj7+nTpy3at6EaEEOdomxtbXVWP1symiQrRoOIpD3xFy5cWFxcXNSaxvr27Svdu3c3WH7NmjXi7OwsXbt2FUdHR6X8Dz/8YFRQGhwcrIwYKFeunMyZM0dE0r7oDVUjh4SEWFTbYMkQa3ObC9JZMsKgVatWkitXLhk8eLBMnz5dZsyYobYYw9KgVMS8IOOnn36SokWLioODgzg4OEjRokUNVrens/RaM1fnzp1l9uzZZpe3NKC0pHPwmzdv5JtvvhEPDw/lnuTh4SHDhw83OBQ/NTVVDh48KKtXr5Y1a9bI4cOHTf5/mzosPSuHdnt5eaktrq6uolKpxNHR0eC9xZLvorflgwlQUlJSTO6PkdGjR4+U5pr09lNj2h6LFy+uNMXUqlVLBgwYICJpX8TGDA1TqVTy4MEDjfXXrl0TFxcXvWXLli0rS5YsERH1AGX06NFSpUoVneUsrQHZuXOn7NixQ1Qqlaxdu1atSWrfvn1y+/ZtnWUtGU2SFaNBRCwf72/OPAsZdenSRanynD17tnIz8/Lyks6dO+stu3DhQqlXr54ygsZUlgyxNre5IOO+zR1h4OnpKXFxcWYdt4jlQam5QcY333wjrq6uMnToUCWYHjp0qLi5ucnw4cMNlrf0WhMR2bp1qzRo0EBCQkIkX7580qBBA4O1KFFRUZI9e3bp0KGDfPvtt2oPEvqq89NZGlC2adNGypQpI126dBEXFxelSXf9+vVStGhRvWW7d+8uOXPmlDlz5ig1EelzRun7X2/fvl2Cg4PV7i82NjaSL18+k2oQTB2W/raGdqe7cOGC1KxZUzZu3Kh3O13fRefPnzf4+Uy3detW+frrr6VLly7SqVMntcUcH0yAYml/jLp160qBAgVk0qRJsmjRIqPbHqOjo5UP7Pbt28XZ2VmZuE3fB7Ffv37Sr18/sbGxke7duyu/9+vXT/r06SMVKlSQSpUq6T3m3377TTw9PWXSpEni4uIiU6dOla5du4qDg4PeuV8sqQHJ6Nq1a2odFk0RFhYm//77r1llg4KC5OHDh2aVFTF/vH86c+ZZyCglJUWtKnXlypXSu3dvmTlzpiQmJuotW6pUKXFzcxNHR0cpWLCglC5dWm0xxNIh1iKmNxeki4iIkMDAQAkPDxcfHx+l5mLFihUGjz0oKEjOnDlj9jFbEpRaEmT4+Pgo/Y0yWrZsmd75ldJZeq19//33YmdnJ61atVKCi9atW4u9vb18//33OstlfHDIvBhTA2JpQGlJ52APDw/5888/Ndb/+eef4uHhobXMxYsXxcXFRapXry6//vqrnDt3Ts6ePSu//PKLVKtWTVxdXY1u8rJ07pu34eDBg1KoUCGtr6VPXGhjYyP169dXfm/WrJk0btxYgoKCpE6dOgb3MXr0aLGxsZHy5ctLkyZNpGnTpmqLOT6YYcaLFy/GvHnzULNmTfTo0UNZX6JECZw7d85g+bi4OMTFxemdClibjEPkqlevjnPnzuHQoUPIly+f3vc6evQogLQp7U+ePAkHBwflNQcHB5QsWRIDBw7Uu+9GjRph5cqViIqKgkqlwsiRI1GmTBn8/vvvqFWrls5yO3bsgIigRo0a+OWXX+Dt7a2278DAQPj7+xs898DAQDx9+hQHDhzQOhRR3zDK9JwT5kgfMmuu1NRUrSkEbt26pQyB1CdXrly4dOmSxjDPuLg4hISEGCxvY2OjNqSxRYsWaNGiheEDR9oU3KZOzZ/5GC0ZYg3A7JxF06dPR1BQEG7evIkpU6bAzc0NAHD37l307NlTb9lx48Zh5MiRiI2NhYuLi8n7jouLw969e9U+Z0DaNZyeq0WX2bNnY/78+WjdurWyrnHjxihRogR69+6N8ePH6yybkpKCcuXKaawvW7YskpOTDR63pdfaxIkTMX36dPTq1UtZ16dPH1SuXBkTJkxQW5+RpZ+xbNmyqd1XTOXl5YVZs2ZprB8zZozBsk5OTlqHYAcFBWn8/9PNmDEDFStWxLZt29TWFy5cGM2aNUN4eDimT5+O77//3qhjN2dYelakNdDF1tYWd+7c0fpa+vQcIgJ3d3c4Ozsrrzk4OKBixYro1q2bwX3MmTMHMTExaN++vdnHmdkHE6Dcvn1b6zjv1NRUjblJtClcuDBevXpl0TG8fv0aefPmRd68eQ1um/4F3alTJ8ycOdPo+U4yq1OnDurUqWNSmWrVqgFIuwkFBASofVma4vfff0fbtm3x4sULuLu7q31xqlQqvQFKSkoKYmJisG3bNq3Bzfbt29V+/+6774w+rj59+uh93dzx/unMmWchsz179mDu3Lm4fPky1qxZg9y5c2PJkiUIDg5WcnhoM3r0aKPeXxcvLy80a9bM7PLbtm3T+T9buHCh3rL29vZag+7IyEiD+502bRouX74MX19fBAUFaQRWhvIAWRKUWhJktGvXDrNnz9aYU2jevHlGJZ+z9FpLSEjQOq9Q7dq1MWTIEIPlk5KScPXqVeTLlw92dsZ/XZgTUJ44cQLFihWDjY2NWk4gbUqUKKHztYiICIwbNw6LFi2Co6MjACAxMVFvQLZz505MnDhR62sqlQqRkZH4+uuvjToPc+e+iYqKUpKs/v3335g1axZmzJiBP/74A/369TPq4SFzfjoRwd27dzFr1ixUrlxZa5n0vGdBQUEYOHCg0XOuZJaUlGTUPFSmUImIZOk7viflypVDZGQk2rVrB3d3dxw/fhwhISEYM2YMtm7dajDB1MGDBzF06FCMHDkSxYoVM3rCtJSUFERFRWHOnDm4f/8+Lly4gJCQEIwYMQJBQUHo0qVLlp2jLocOHcLZs2ehUqlQpEgRlC1b1uiy5taAAEDBggVRv359REVFmfxU26tXLyVhn7ZEhelJCNNlfhrRRaVS6U06B6QFszVq1ICtrS0uXryIcuXK4eLFi8iePTt2796tkUhPm+HDh2P69Ol4/fo1AMDR0REDBw7EuHHjDJbNmMxsyZIlOHPmDEJCQvDjjz/ijz/+0JrMLJ0lCfcsZSi55Lp16wy+x5IlSzB37lxcuXLF6ISc6fvWZ9SoUXpfb9myJTw9PTFv3jy4u7vjxIkTyJEjB5o0aYK8efPqTU7Zu3dv2NvbawQZAwcOxKtXr/DDDz/oLbt48WIEBASgYsWKAID9+/fj5s2b+OKLL9TuM7omRrTkWmvbti1KlSqFQYMGqa3/9ttvcfjwYSxfvlxruZcvX6J3796IjY0FAOW+1qdPH/j7+2Po0KF691u6dGlcvnwZImJ0QGljY4N79+4hZ86csLGxgUqlQsavp/TfDSVRbdasGbZt2wZHR0elFvv48eNISkpCzZo11bZN/9L38PDAiRMndE5+d/XqVZQoUULJ8KvPmzdv0LFjR6xYsQIiAjs7O6SkpKBNmzaIiYmBra2t1nIuLi44d+4c8ubNiyFDhuDu3btYvHgxTp8+jbCwMDx8+NDgvjM/bKpUKuTIkQM1atTAtGnTkCtXLoPvYa4hQ4bAzc0NI0aMyLL3/GAClN9//x3t27fH119/jbFjx2LMmDE4f/48Fi9ejD/++ENvkweQNttd69atlaaXdIY+EGPHjkVsbCzGjh2Lbt264dSpUwgJCcGqVaswffp0/P3333r3++LFC0yaNEnnU6m+L5xbt26hdevW2Lt3L7y8vACkfVFVqlQJy5cvR0BAgN59G6oB+ffff/WWd3V1xcmTJ42qas4se/bsWLx4sVE1Fm/Dq1evsGLFChw+fBipqakoU6YM2rZtq1a9acjLly9x5swZpKamIjQ0VGmyMKR06dLo168fvvjiC7Vg+tixY6hbt65aBtnMMt7EM7p//z4CAgK0ZoDNLDk5GTt37sTly5fRpk0buLu7486dO/Dw8NB7Drly5cKUKVPMrsKdPXs2Ro4cicjISEyYMEH5rMTExCA2NtaiZj9D7ty5g+rVq5sVlFoSZFSvXt2o41OpVBq1hhmZe62NHz8e3377LSpXrqzMvrt//37s3bsXAwYMUHvwyljz2LdvX+zduxczZsxA3bp1ceLECYSEhOC3337DqFGjNO6TmZkTUF6/fh158+aFSqVSsgrroq+5o1OnTnrLZpQemOr6XKW7f/8+/P399QZGmV25cgVHjhxBamoqSpcujQIFCujdPmfOnNi0aRNKly6tdo+4fPkySpYsiefPnxu9b3OZOsN15pnJY2NjUaJECbNmJtfmgwlQAGDTpk2IiopS+9IZOXIkateubbBs+fLlYWdnh759+8LX11fj6TC9WSSz/PnzY+7cuahZs6bal825c+fw8ccf48mTJ3r327p1a+zatQvt27fX+lTat29fnWVr166NhIQExMbGKlN4nz9/Hp07d4arqys2b96sd9+W1IAAadMqt2rVyuj+Exn5+/tj586dKFiwoMllLfHmzRsUKlQIf/zxh9EpDLKai4sLzpw5g6CgILVr5sqVKwgNDVWelDNKr7pt2rQpYmNj1dI6pKSkYNu2bdiyZQvOnz+vd9+ZU6qnPxlHRkYaTKnu4+ODAwcOIF++fGadd2hoKKKiotC0aVO18z516pQyzbchhw8fVmoLQ0NDjZrmPt2rV6+wfPly5UvD2KA0q4KM98HcmsfAwECsXLkSFStWVPtfXbp0CWXKlNE6ff67cP/+fcydO9foplRj2djYYPv27Tr7zTx69Ai1atUyKUAxVdu2bXHu3DmULl0ay5cvx40bN+Dj44PffvsNw4YNw6lTpwy+x9ixYzFw4ECN+/mrV68wdepUvX+37777DsOHD0eHDh0wf/58dOrUCZcvX8bBgwcRERGBCRMmaJR525+NDypAsYSLiwuOHj2qkavDEGdnZ5w7dw6BgYFqH+QzZ86gfPnyBqNeLy8vbNiwQWf7oKF979u3T+MmfeTIEVSuXNlgnxpzakAytnE+fPgQY8eORadOnbTmEWrcuLHO95k2bRquXLmCWbNmmdzps3PnznpfN9QXInfu3Ni6dSuKFCli9D6bN2+OmJgYeHh4GMwzZKitOF++fJg7dy7Cw8PVrpnFixdj0qRJOHPmjEaZ9KrbzNXeQFrfjqCgIEybNg0NGzbUu+/04GDBggXw8fFR9r1r1y507doVFy9e1FnW0ipcXZ+VixcvokSJEnqv1wcPHqBVq1bYuXMnvLy8ICKIj49H9erVsWLFCoN5Zd6XmJgYtGzZ0qSauYyaNWum9fOhUqng5OSE/Pnzo02bNibftwxxcXFRargy/q+OHz+OqlWrIj4+Pkv3Z6zjx4+jTJkyegOF0aNHo1OnTiZ1KtXWpJSZoaaldJ999hnKlSun0Qw2depUHDhwAKtXr9Za7unTp/jmm29w8+ZNfPXVV0rfoVGjRsHBwQHDhw83uG9bW1vcvXtXoybo8ePHyJkzp97jL1y4MEaNGoXWrVur/c9HjhyJf//9V2unZXPcunUL/v7+RvV9/GA6yVqqXLlyuHnzpskf9KJFi2LPnj0aH4bVq1cb9XRnSW/3vHnzau0AnJycbDCJGZDWwfbQoUMmBShNmzbVWDd27FiNddo+zJm/2Ldv327WaJLMtVJv3rzBqVOn8PTpU6NGl/Tu3RuTJ0/GTz/9ZHTHP09PT+WLQltSSlOY0/ExKxLuAZaNZnn9+rVFySUtScjZu3dvJCQk4PTp00pgeebMGXTo0AF9+vTR2pcic4dBffQF05YEGV9//TX69OmDzz//HF26dDG5E6Gnpyd+/fVXeHl5oWzZshARHD16FE+fPkXt2rWxcuVKTJ48Gdu2bdP6kLNz506EhYWZfNwfffQRNmzYgN69ewOAcu3Pnz9faSrSJyUlBdOnT9fZXGCo+dgSv//+O8aPH49q1aqhS5cuaN68ucFEqsaMWjJUG55u165dWpuw6tati2+//VZnOUtGLqVL75KQ2fHjxw1+z9y4cUO5Pp2dnZX+Nu3bt0fFihWzLEAJDQ3FsWPHjPre+U8HKNmyZTP66dvQB6J3797o27cvBg0apLU2IHOv8dTUVNjY2GDUqFFo3749bt++jdTUVKxdu1at74shlgyfnDJlCnr37o0ffvgBZcuWhUqlwqFDh9C3b1+9H4R0DRo0wKBBg3DmzBmja0AsyTKa+Yvd3NEk2jpjpqamomfPnkZd9P/88w+2bduGzZs3o3jx4hq91rUFRxk7UerrUGmMwYMHK0//r1+/RtWqVZWOj7pGGaR7n0OsT5w4gVKlSgGARnWzMZ/DQYMGISIiAq9fv4aI4MCBA1i+fDkmTpyIn376SW/ZjRs3atR6hYaG4ocfftDZhKstmNbG0JOxJUHGrVu3sGHDBsTExKB69eoIDg5Gp06d0KFDB/j5+Rks7+fnhzZt2mDWrFnKE2dqair69u0Ld3d3rFixAj169MCQIUMQFxenUb5u3brInTu3sk9D/dLSTZw4EXXr1sWZM2eQnJyMmTNn4vTp0/j777+xa9cug+XHjBmDn376Cf3798eIESMwfPhwXLt2Db/++muWN89kdvjwYZw4cQKLFi1Cv379EBERgVatWqFz58746KOPtJbRVdsSHx+Pn3/+GQsWLMCxY8eMqkF5/vy51uHM9vb2BpvG0kf3XblyBatXrzZ6dF/6d6FKpULBggXVPo8pKSl4/vy52vQb2vj5+eHx48cIDAxEYGAg9u/fj5IlS+Lq1at6a5ZMZcp7/aebeNJ7mBujQ4cOel/XVt2kr9d4hQoVsGTJEhQsWNCivi+m9nbPHJS9ePECycnJSk1A+s+urq4GgzJ9VWzGVmdak/PnzyMsLAx3797Vu52hTnTGBiAPHjzA+fPnlZuCMaN/MjK346MlQ30tGc2SFebPn4/x48fj5s2bANKa20aPHm1wtJu7uzv27NmjBEjpjh49imrVqr3VPhEpKSlKkLFhwwaTg4x0Dx48wNKlSxETE4Nz586hbt266NKlCxo1aqTzs5gjRw7s3btXo6/WhQsXUKlSJTx69AgnT57EJ598gqdPn2qU//fff5V9njhxAjVr1kSXLl3QtGlTnXOCpDt58qQy2if9vjZkyBAUL17c4Lnmy5cP3333HRo0aAB3d3ccO3ZMWbd//34sW7bM4HtoY0wTT0bJycn4/fffsWjRImzcuBGFChVC165d0bFjR701odu3b8fChQuxdu1aBAYG4tNPP8Wnn35qVK34Rx99hEaNGmkEYqNHj8bvv/+Ow4cPay1nyei+2NhYiAg6d+6MGTNmqJ2bg4MDgoKCDNZ8de3aFQEBARg1ahTmzJmD/v37o3Llyjh06BCaN2+OBQsWGDx3Y2RsPjLkPx2gZCVTe423aNECGzZswKRJk5RqUHOY2ts9K4MyS+mamyRj+3jVqlV1DqvLan/++Sc6dOhg1HA8SyQkJCAiIgIrVqxQbpS2trZo2bIlfvjhB503PkN9Z9LpCzIsHepryWiWrPTo0SOkpqYavb8mTZrg6dOnWL58uTKJ4O3bt9G2bVtky5bNqCHO6V6/fm2wyl8Xc4KMjP755x8sXLgQsbGxyJUrF54+fQovLy8sWrRIa1NMtmzZEBsbq1Gb+dtvv6FDhw548uQJLl68iPLlyxtsgjh27BgWLlyI5cuXIzU1FW3btkWXLl1MnpzSGK6urjh79izy5s2LXLlyYcOGDcow+NKlS+vsw5JxVIg2Dx8+xLJly4wOUJKSkrBu3TosXLgQ27dvR6VKlXD//n3cuXMH8+fPR8uWLZVtb926hZiYGCxcuBAvXrxAixYtMGfOHBw/ftykDvW//fYbPv30U7Rp00Zpct62bRuWL1+O1atX66zZs2R0X7pdu3ahUqVKGg+6xkhNTUVqaqrysLtq1SrExcUhf/786NGjh8GA1limBCgfzFT3IqZlkMwKq1evlpw5c0p4ePh7yZj5vgUFBSnJqLy9vSVbtmyiUqnE1dVVfH19RaVSSb58+ZQEcRmVKlVKY5r20qVLS5kyZaRSpUryxRdfyPbt27XuN2NagH79+klkZKS0bNlS3NzcJCIi4m2ftnz++edSoEAB2bhxo8THx0tCQoJs3LhRChUqJJ9//rnOciqVSoKCgqRZs2Ya00AbOyW0n5+fLF682KLjf/nypSxYsMCklOoiWZtR2FQ3btyQ0qVLi729vZJTxt7eXsqUKWPUZy85OdmiXF0Z7d+/X7788ktxdHSUoKAg8fLykqCgICWreWb37t2TqVOnSmhoqDg5OUmrVq2UFAEvX76U/v37S968ebWW7d27t2TPnl2io6Nlz549EhcXJ9HR0ZI9e3bp06ePiIjMnz9fKleubNSx3759W0aNGiWOjo7i6uoqtra2UqVKFY17ZMb8XBmXhIQEg+kYRNJyLaXn1KpSpYpMnDhRRNLSGuTIkUNnubCwMKMWQw4dOiQRERHi7e0tuXLlkiFDhiiZ5kXSUj7kzJlT+b1evXri7u4urVu3lj/++EPJf2NnZyenT582uL/M/vjjD6lUqZK4uLiIj4+PVK9eXcnZpoulaQ0ye/nypcb/zxpkPDdDPpgAxdQMkiJpiafSs1tmzIyrbdHlwYMH8vnnn4uXl5f07t1b48vTWIcOHZIlS5bI0qVLlSyexrp//76cPHlSLU338ePHjSq7c+dOadiwoeTLl0/y588vjRo1kt27dxtVdtmyZRIWFiaXLl1S1l28eFFq1KghK1askJs3b0rlypW1/v2HDh0qnp6eUqVKFenfv7/069dPPvnkE/H09JS+fftKrVq1xMbGRn799VeNsplvVjVq1JCWLVvK3LlztaYLF9EdEGlbDHFxcdGa6n737t16Ezx+9dVXki1bNilZsqTMnDnTrIR/3t7ean/vd8nSBHDpeVx0LcbYvHmzfPfddzJz5kyT8gBZmqvL1CAjODhYHj16JA0bNhR7e3spWrSoTJ8+Xev//Pbt26JSqbTuNzk5WcaPHy9+fn5KAjs/Pz+ZMGGC8iV6/fp1vUFaUlKSrF69WurVqyd2dnZSsWJFmT9/vjx//lxu3LghrVu3liJFiqiVMZSvK2/evDJy5EidubiGDBkiEyZMEJG0Bzk7OzvJnz+/ODg4yJAhQ/T8pc2Xno23ePHiYmdnJ/Xr15d169ZpTbb34MEDtb+5ra2t9OvXTy5cuKC2nbkBijlCQkKUayrjl3hsbKzG/0eXFy9eSEREhOTIkUPr/02fCxcuyNSpUyUiIkJ69eol0dHRSk6hrOTu7v6/F6CYmkFSJO1DmF7Toi3NdcaMlrokJyfLyJEjxc7OTqpUqaL2xVm9enWDx33//n2pXr26qFQqyZYtm3h5eYlKpZIaNWpozSyZ0aFDh6Ro0aJas/sakx57yZIlYmdnJy1atJCZM2fKjBkzpEWLFmJvby8///yzwfIhISFy9OhRjfVHjhxRvnD27t0rfn5+Gtt07dpVxo4dq7F+3Lhx0rVrVxFJSwxWtmxZg8dhjNGjRyvL0KFDxcPDQypWrKgEkh9//LF4eHjI0KFDDb5XQECAnDhxQmP98ePHDWawfv36tSxbtkzCw8PFxcVFPv/8c9m4caPRyfsGDx6s9e9milu3bsnKlSvl+++/NylLraUJ4DIHNFOnTpU2bdqIt7e38oT9tuTLl0+2bt0qIur3h7Nnz4qXl5fWMpYEGelflp07d5Z9+/bpPbbU1FS5du2awXMw5Sm4evXq8uTJE+nVq5f4+PiIj4+P9O3bV06ePKmx7fXr1zUCpNjYWMmTJ49888038ttvv8n69evlm2++kYCAAJk7d66MHz9evLy8lCDEkL///lumTZum92HPUun387Fjx8qtW7dMKrtv3z7p2rWreHh4SPny5eX777+XBw8evNMAZfLkyRIaGir79+8Xd3d32bNnjyxdulRy5MihN7FjRj179pQiRYrI6tWrxdnZWRYuXCjjxo2TPHnyyNKlS3WWi4qKEjs7O7GxsRE/Pz/x9fUVGxsbsbe3l6lTp2bVKYrI/2gNirkZJK9fv252ZtdTp05J6dKlJSgoSGdzhCEtWrSQsmXLqmVqPX36tJQrV05atWqlt2zx4sWlWbNmsn//frl69apamm5jbniFCxeW6OhojfXTpk2TwoULGyzv7OwsBw8e1Fh/4MABJSvw1atXxdXVVWMbDw8PtSrXdBcvXlQyjp49e1bc3NwMHoepunTpIt98843G+pEjRxqVFnzu3LkSHh4ud+7cUdbdvXtXateuLXPmzDH6OK5duyajR4+WkJAQCQgIULL76tOnTx/x8vKSqlWrSq9evUyusVu4cKE4ODiIm5ubBAYGmpSl1tKMwrrMmjVLOnbsqLF+5syZ8urVK+VnfYshTk5Oymci4/3h9OnTWq9PEcuCjIwPP+9D+rHXqFFDli1bprdZ5s2bNxrNDzVq1JCVK1dqbLty5UqpUaOGiIgsXrxYZ4ZcS2W+rtOX/v37y7Bhw2ThwoUagWJW/M1fvHghCxYskMqVK4u9vb2SlT4hIcHo90hOTpapU6fKRx99JL6+vpItWza1RZ9hw4aJs7Oz8qDp5OSk9V6lS0BAgNLU6O7urtxjFy9eLPXq1dNaZvv27WJjYyOjRo1SyzD/+PFjGTFihNja2squXbuMPoZ08fHxsm7dOo17xo0bN7TWamnzwQQouXPnlr1794qI+g1o7dq1EhISorNc+gfZVBMnThRHR0fp1KmTSRdvZh4eHnLgwAGN9f/88494enrqLevm5qb1S95YDg4OOoMEY9o869evL2XKlFFrkjpy5IiULVtWGjRoICIiv/32mxQrVkyjbM6cOSU2NlZjfWxsrNI2fPr0aSUlfVY20Xh4eGhU5YqkVXHqSseeef9ubm5ib28v+fLlU/pDuLm5GbX/dNevX5cxY8ZIcHCw5M6d26gARV+7vDE1dnny5JHx48frrJrXZ8mSJfLZZ5/JixcvTC6rz+XLl8Xd3V1jfVBQkDx69Ej5WddiTPNQ2bJlZcmSJSKifn8YPXq0VKlSRWsZS77wVCqV7NixQ6PZ1dRm2Hv37km7du0kV65cYmtra3SVvaVf1s7Ozjo/I+kPH1euXFF+1ubcuXMSEREhNWrUkJo1a0pERIScO3fOqP2HhYWJh4eHuLq6SpkyZZTPnKenp1SoUEG8vLwkW7ZsarUbKpVKFi9ebHZzvbbjHzRokPj5+YmTk5M0atTIqHIjRoyQXLlyydSpU8XJyUnGjRsnXbp0ER8fH6OC6RcvXsjBgwfln3/+MeqekJGrq6sSKOfOnVv++ecfEUn7X+kKxFu0aCFffvmlzvfs1q2bwYdlkbS+eek1PS9fvpQCBQqIvb292NnZyZo1a0w6j3T/6XlQMjI3g6SYOYhp5syZWL16NRo1amTuIQNI6zmtrce1vb29wTlHatasiePHj2vN4myMgIAAbNu2TaP8tm3bjJovYcGCBWjfvj3Kli2rnENycjJq1qypDElzc3PDtGnTNMr27t0bPXr0wOHDh/HRRx9BpVLhwIED+OmnnzBs2DAAUPJSAOpzWrx+/Ro//vgjQkND1fKLnD59Gj179jR43M7OzoiLi9PIjREXF6dzdIexc2oYkpiYiLVr12LhwoWIi4tDw4YNMWvWLNStW9eoUSCW5qt5+fIlWrVqZVYGa0szCuuyZs0arZNIZZzzxdL5X8ydr+jMmTMGR0/oyqxbs2ZNrfcXY5PeAUDHjh1x48YNjBgxQuuoLX2ePXtmcLSSriSoefLkwYIFCzBp0iS19QsWLFDuDY8fP0a2bNm0ll+zZg1at26NcuXKqX1GixUrhmXLluHzzz/Xe1xNmjSBt7c3Fi1apBxjQkICunTpgipVqqBbt25o06YN+vXrh02bNinlDI1cNGX6hEKFCmHKlCmYOHEifv/9d4ND+NP9/PPPmD9/Pho0aIAxY8agdevWyJcvH0qUKIH9+/cbzLbu4uKiNXu2MUJCQnDt2jUEBgYiNDQUq1atQvny5fH7778r+doyO3DgAJYsWaLzPdu3b28wcSwA7N69W5ntdt26dRARPH36FLGxsRg/fjw+/fRT00/IrLDGCiUlJUmbNm2U/hjp1XPt2rXTW51k7pNG+pNdRunV0aZo3LixVK1aVW7fvq2su3XrllSrVs3giI6HDx9K/fr1ZfTo0bJmzRqTnxR+/PFHcXBwkB49esjixYtlyZIl0r17d3F0dDSpqeLs2bOyfv16+fXXX41+QhIRWbp0qVSsWFGp+qxYsaJa35eXL19q/Zta2kSTXvsVEREhS5YskSVLlkhERIQ4Ozu/1b4QGTvJzpgxQ+s19LYNGjTI7HPM2I9H22JI5lqoUqVKiZ+fn9ja2srcuXP1lh0zZozWmpuXL1/KmDFjjDr+jRs3StWqVcXV1VWcnZ2lcuXKsmnTJp3bp/fl0tUvTV9fL5VKJQcPHtRodjW1GdbNzU1rPy9DDHVyNdRPbf369eLg4CAlSpSQLl26SNeuXaVkyZLi4OAgv//+u4ik3T90NSsGBwfLiBEjNNaPHDnSqBovf39/rX0/Tp06pfQpPHz4sFLDmn7O77NZLZ2Li4tcv35dRNJG3R0+fFhE0moKddXQiohSC6prMUZ0dLRSS7N9+3ZxdnYWBwcHpalKG2dnZ72drG/evClOTk4G9+3k5KSM2Gzfvr3SGfr69es6a28M+eDmQTE1g6SNjQ3Gjx9vcJIsXVFvSkoKoqKiMGfOHNy/f19JvjZixAgEBQUZnIDq5s2baNKkCU6dOoWAgACoVCrcuHEDxYsXx/r165EnTx6dZX/77Te0b99eawpwY58U1q1bh2nTpuHs2bMAgCJFimDQoEFo0qSJwbLvi6enJw4dOqTxv02f18OYPCGrVq3CzJkz1c67b9++ZiU+NJaNjQ3y5s2L0qVL630S1jfNf/Xq1fWWNZSQKyUlBQ0bNsSrV6+0zh5sTsZRY2We88fGxgY5cuRAWFgYChcurLesJTlGzGVjY4MDBw4YzPOjbRZSQ9lxjRUaGoqff/7ZpKSI6fv/5ZdfDE5vrisJKpA2N9Ts2bNx4cIFiAgKFy6M7t274+nTpxoT5mXm4uKCEydOaNTOXrx4ESVLlsTLly/1lndzc8Mff/yhMTfMzp070ahRIzx79gxXrlxBqVKllEn6dF0j71qhQoWwePFiVKhQAZ988gkaNGiAoUOHYuXKlejduzcePHigtVy/fv3Ufn/z5g2OHTuGU6dOoUOHDpg5c6bJx3Ljxg0cOnQI+fLl0znfTVZlci5YsCDGjx+PBg0aIDg4GCtWrECNGjVw/Phx1KxZ06hkoJl9ME086UJCQkzKLQMAc+bM0TuZmEql0hmgTJgwAbGxsZgyZQq6deumrC9evDimT59uMEAJCAjAkSNHsGXLFpw7dw4igtDQUISHhxs87j59+qB9+/YYMWIEfH19DW6vTbNmzUyacr5///4YN24cXF1dDU6q9La+7MxposmsRYsWZgcj5uYZ+eKLL0xOjJhZ5i+GzDcxQ6KiorBp0yYl51TG47H02AzRlp/EWGJBjhEAOHjwIFJTU1GhQgW19f/88w9sbW11VqnnzZv3vX7hzZgxA0OHDsXcuXMRFBRkUtnKlStbdOyBgYFKE8/Tp0/x888/49NPPzVqyvewsDDs2bNHI0CJi4vDJ598YnDfTZo0QefOnTFt2jS1JuCBAwcqza0HDhxQm2HXWp61mzVrhm3btqFChQro27cvWrdujQULFuDGjRsaQUhG06dP17p+9OjRBpPOplu8eDFatmwJR0dHAGnXb968eZGUlITFixfrbKr56aefdD6ka3sA1iYyMhJt27aFm5sb8ubNqwSXu3fvNmr2Ya3MqnexQp9++qnWquspU6bIZ599prOcpdWC5gxfzCpubm7vfE6MsLAwefLkifKzKR02s2XLJg8fPhQRUTq56Vr00dVE4+Tk9NaHq4pY3gnubRg1apQMGDDA4HZeXl6yaNEis/ZhyeiEjE6dOqXWUVTfRIrp14mNjY3GNePh4SE2NjbSs2dPg/v86KOPZPXq1Rrrf/nlFylfvrzWMpbcGzJ+Tizh5eWlVNG7ubkZ/TfPquaObdu2Sdu2bcXZ2VkKFy4sw4cPN2qeptmzZ0uOHDk0PqM5c+aU2bNnG2yKfvbsmXTt2lU5dxsbG3FwcJBu3brJ8+fPRUTk6NGjas1fHTt2lISEBImJiZG7d+9afO5ZxdIh1hcvXjT686Vr0MejR490NullHs2nazHGoUOHZO3atcr/SCRt0rr0ASym+mCaeHLkyIHt27drRGonT55EeHg47t+/r7WcpdWCulLInzlzBuXLl9cZ+S5evNio99fXOalDhw745JNP0LVrV5OO2dgapitXrpj0vobExsaiVatWcHR0NDhlv6HagMxNNKGhoejbty8KFCigtfo5KxNLvq08I5a4dOkSypcvb/DY/fz8sGfPHoNNn9qMHDlSbwI4XbWMe/bsQf/+/XHw4EEAaVNdv3z5UnniValU2LRpk9Zaw6zIMQKkNRmcOHFC49q/evUqSpQoofUpsXr16li3bp3OzoWmev78uUbHd12dVNOZ+zkJDg7GoUOH4OPjY9pBImumfDe2E7ahpujnz5/jypUrEBHky5fPqHxVLi4uOHv2rM4EgP81S5YswZAhQ3Dnzh2D29rY2OD+/fsazZLHjx9H9erVszyL9NuuUf9gmnjMzSBpaXxWtGhR7NmzR+PDsHr1ar3txh07doSbmxvs7Ox0HoNKpdIboBQsWBBff/014uLitPYn0PWFkd7Lu02bNu+0+jrjzdTSPEEZm2jSq58nTpyI48ePa73hzZgxw6L9ZXTv3j0lEHZzc1P6vDRs2BAjRozIsv2Y4u+//zaqeatv3774/vvvdeZR0sfc0Qk//vgj2rdvr7Zux44dCAwMhIjgu+++w+zZs7UGKOnXSXBwsNk5RgDA0dER9+/f1whQ7t69q+QeyUzbiClTg4yrV6+iV69e2LlzJ16/fq2sFyNH8Zj7OUkf9VSjRg2sXbtWI8hKSEhA06ZNNfos1a9fXxld9v3336Nu3bqwtbXFnDlzTNq/JVnPM3Jzc9M5SkqXChUq4NixY+81QDl//jy+//57nD17FiqVCoULF0bv3r2VplVtmjdvrva7iODu3bs4dOiQwftKer82lUqFmjVrql3TKSkpuHr1KurWrWvyeaTnitLl6NGjePPmjfKzLuY2H38wAUqxYsWwcuVKjQySK1as0Bv1jxo1yugssrrKmzN8sUiRIrh//z7atWuHzp07m/whBP6v3XDXrl0aKdD19ZtZsWIFFi1ahOjoaNSrVw+dO3dG/fr1jX7qMSbpnUqlyrLsl7poyziqa59ZmTgxT548uHv3LvLmzYv8+fNj8+bNKFOmDA4ePKi0/b4tltzEgLR2++3bt+OPP/5A0aJFNb7w9XXQNTcwO3jwIPr27au2Lk+ePMoXSPv27dGgQQO9x52xM+erV6+Um2I6QzURtWrVwtdff43169crtTBPnz7FsGHDUKtWLb1lLQky2rZtCyAtAaSvr69F/XzMOe+dO3dq9JEC0obq79mzR2P95s2b0adPH3z11Vdm1bJllRcvXmDSpEk6s3brq93t2bMn+vfvj5s3b6Js2bJwdXVVe92ce60pzB1inTnJqI2NDQoVKoSxY8eidu3aeveZ3i/n2LFjqFOnjtp3WnpNo6FhvpMnT0ZQUJCSQPHzzz/HL7/8gly5cuHPP//U2sk2YxBv6RQI2nwwTTzmZJA8ceKE0e+v76LetGkToqKi1NKSjxw50uBFlZ7ZdOXKlcifPz+6dOmCtm3bGrzpZJXbt28jJiYGMTExePHiBb744gt06dLF4I1JX6falJQUbN26FYmJiVpv3DY2NgZv0iqVCsnJyVpfM7f6OSEhQW0+BX0M/f2HDh0KDw8PDBs2TLkZBQUFKZ3gMs8dkZU6deqk9nv6SJgaNWoYvN60lc9s0aJFOl8zd3RCxmZQIC0Iqlu3LlxcXACkjRYpWLAgEhMTde775cuXGDx4MFatWoXHjx9rvG6oJuL27duoWrUqHj9+rNRsHjt2DL6+vtiyZYveeX8qVaoEIK32SVuQoW8kjJubGw4fPqz3yVmfFy9eYMiQISafd/q9rVSpUti+fbtaR+KUlBRs3LgRc+fOxbVr19TK/f3331i4cCFWrVqFwoULo3379mjZsiX8/f1Nzup74MAB7Ny5U2uAYai6v3Xr1ti1axfat2+vdf6XzAFvRtoetEyZe8ZSISEhaNeuHcaOHau2ftSoUViyZEmWN51nFBsbi5YtW5qVrTskJARLly5FpUqVsGXLFrRo0QIrV65UBgNs3rz5LRyxAWb1XLFSpmaQzDyfgb7lbXr58qXExsZKWFiYuLi4SJs2beT169dvdZ+Z7dy5U8LCwsTGxkZtumNT/PrrrxIaGipeXl46O6v++uuvOpfBgweLs7OzzjH3lmQczdh5TNf/29gcRpnt37//recZsQbmJoDLkSOHzky/IiI7duyQ7Nmz6923uTlGMnr+/LnMnTtXevbsKQMGDJDY2FglWag+rq6uJs3vk1FYWJhJSQ0zM/e8M17j2uZxcXFxkQULFugsb+mU7xMmTBCVSiWFCxeWatWqmTzjsSV5nyyde8ZSzs7OWmfozjgL77vw6tUriYmJkR9++EHrrMCZZZzHpE+fPsrssufPn3/rAz50+aACFFNlvGjXrVsn+fLlkzlz5iijC+bMmSMFChSQdevW6XyP9IRimT158sTkFPS7du0yKUjo1KmT3sUYr169kiVLlkj16tXF2dlZWrZsaXJwFBcXJ5UrVxYXFxcZPHiwyQHO2bNnpWnTpmJraytffPGFMslRZpZkHN25c6eS6Xjnzp16F0My/r9v3LghI0aMkIEDB5qVr8JclmS/zirGjk5o2LCh3uuxQ4cOSmoEXczJMZJVLAkyLl26JOHh4RITEyOHDh0yeap7c8/72rVrcvXqVa0Txt25c8foXCgi5k35njNnTrNHi4m8vbxP70K9evVk4cKFGusXLlwotWvX1lnO0MhGfaO3Bg4cKH369FF+T0xMlJIlS4q9vb14enqKq6urwXxSuXLlUkbbFCxYUFatWiUiaf9/bako3oUPLkBJTEyUmzdvyvXr19UWQz766CPZsGGDxvoNGzZImTJldJbTNZzv3r174uDgYHC/t27dkgkTJkj+/PklV65cMmjQIDl79qzBciIiTZs2VVsaNGgggYGB4unpKc2aNdNbdv/+/dKtWzfx8PCQ0qVLy/fff29yYHHq1Clp2LCh2NnZSefOnfXORqjN7du3pWvXrmJvby8NGzbUmiE4o/edcfTEiRMSGBgoNjY2UqhQITl69Kj4+vqKm5ubeHh4iK2trd5gNitYkv1aRHdOozJlykilSpXkiy++MDvxpS7pycgGDhyo9lm5f/++9O/fX2xtbWXbtm1638OcHCOZnT9/XubOnSvjxo2TMWPGqC36WBJk/P333xIcHGzSLLRZfd5ZJTk5WdatW2dUgOLn52fUU7suluZ9Wrx4sVSqVEly5cql/P2mT58uv/76q9nHZCxzh1hPmzZNsmXLJq1atVKSYLZq1UqyZcsm0dHREhMToyyZFS1aVO39Fi5cKNmyZZNr165JamqqdOzYUerXr6/3uCMiIiQwMFDCw8PFx8dHyQO0YsUKk3KMZaUPJkC5cOGCVKlSxexqeycnJ60R+5kzZ7Q2OaRfYNoSVK1du1YiIiKkYMGCOve3cuVKqVu3rjg7O0vTpk1l/fr1Jj3V6JKSkiLdu3eXyZMn69wmNDRUsmfPLn369DHqKS6zGzduSMeOHcXOzk6aNm1qcnDw9OlTpTnn448/lt27d5tUPisyjj558kQ2bdokS5YskdjYWLVFl7p160rDhg1lz5490r17d8mdO7d06tRJUlJSJCUlRXr27CkVKlQw6VxMZUn2axGRoUOHiqenp1SpUkX69+8v/fr1k08++UQ8PT2lb9++UqtWLbGxsdF5Izf3S/6HH35Q5rTIOLeJg4ODUankixcvrtRu1apVS5nzZebMmZI7d26D5efNmye2trbi6+srJUuWlFKlSimLoZuvJUFGkSJFpHnz5mZnHDfnvNevX680XWVV4jxTTZ48Wfr27Wt2+VKlSom7u7u4ublJsWLFTEoI+uOPP0r27Nll/Pjx4uzsrMxNtWjRIgkLCzP7mIylrUlNV7qEjJo3b671s/D9999LkyZN9O4zY+2aiEirVq2kW7duyu9Hjx6VXLly6X2PpKQkmTp1qvTp00etVnb69Okyf/58vWXflg+mk2zlypVhZ2eHoUOHau1UpWua33RlypRBkSJFsGDBAqWDUWJiIjp37oyzZ89qJEJL74iV3vkqI3t7ewQFBWHatGlo2LCh1v2lT3vetm1bvbPAGkospc358+cRFhaGu3fv6ty3q6sr7Ozs9HZY1TVm3sXFBSqVCr1791Y6EGrTuHFjjXVTpkzB5MmT4efnh6ioKIun1D9//jwWLFiAJUuW4OnTp6hVqxZ+++03vWV+//13tG3bFi9evIC7u7vGbKq6zjt79uzYvn07SpQogefPn8PDwwMHDhxQZiE9d+4cKlasiKdPn1p0Tvp4enpi69at+Oijj9TWHzhwALVr1za4727duiFv3rwao27Gjx+P69evY/78+Rg1ahQ2bNiAQ4cOqW0zf/58fPXVV8iePTv8/Pw0/m6GkgXevHkTa9aswcWLFwEABQoUwGeffWZUYsrp06fD1tYWffr0wY4dO9CgQQOkpKQgOTkZ0dHRejtNAmmzovbs2RNDhgwxuK/MQkNDUaRIEQwePFhrJ1l9w1ldXV0tSuhpznlnnLpc38i8t9lhNDU1FQ0aNMCFCxcQGhpq0mgxQDMtQmb6ZiUODQ1FVFQUmjZtqjY31alTpxAWFmbWlOvvgpubG44dO6Y1PUDp0qX1zibr5eWFgwcPKgMcgoODMWLECGXE5bVr11CkSBG8evXq7Z3AW/DBBCiurq44fPiwwZweuhw4cACNGjVCamqqEswcP34cKpUKf/zxB8qXL6+1XHBwMA4ePIjs2bObtL+goCCjRrOY0+P7zz//RIcOHfDw4UOtrxua/CmdrqG5xgxH1nXzs7GxgbOzM8LDw/WmFzB0A8ssJSVFyThqKEApWLAg6tevj6ioKGUkiTEy56zIePMDjM9ZYQl3d3fs2bNHYzK6o0ePolq1agZHKHl6euLw4cMaN8FLly6hbNmyiI+Px7lz5/DRRx9pTF5myZd8VjMmx0hGHh4eOHbsmMlpMADLgoxGjRqhY8eO5mVy1cLU835fIiIisGDBAlSvXl1rUKdvtJildE2eefHiRZQoUcJqv6QDAwPRq1cvDBo0SG391KlTMWvWLFy/fl1n2YoVK6JFixbo378/Tp8+jRIlSuDSpUsIDg4GAOzatQsdOnTQGLWVkaHJQ43JaJzVPph5UEJDQy2KjMuXL4+rV69i6dKlSk6cli1bok2bNhrj6DMyNw28vgvFWJln7pP/PyfGhg0bdAYX3333Hb788ks4OTnhxo0bSoJCU1gyCVNW5KPRxtbWFk2bNtU6nDyz27dvo0+fPiYFJ+kyH/vbzl+TWY0aNdC3b18sX74c/v7+ANLOp1+/fqhZs6bB8k5OTti3b5/Gl+2+ffuUmsPU1FSt87k8efJE5xwOuhgKFjPSVuOmS3qOEWN9/vnn2Lx5M3r06GF0mXTpCc/MDVD69euHkydPap1M0ZRzBkw/b20MTb6VFRYvXoxffvnF4Pw2b0NwcLDWidr++usvk4ZJm+qff/7Bv//+i3r16inrFi9ejFGjRuHFixdo2rQpvv/+e51zJY0ZMwZdunTBzp071eZP2bhxI3766Se9+x40aBBat26NDRs24PTp06hfv74SnABpD626HrLTZa6Ne/PmDV6+fAkHBwe4uLgwQLHE5MmTMXjwYERFRWm9ERgzt4iLiwu+/PJLk/f94sUL7Nq1S2viOFOaaF6/fm3S+PXMM/elz4kxbdo0nZOp9e/fH61atYKTkxOCg4PfefbPmJiYd7YvXerUqYNDhw6Z9TTdsWNH5Qbz+vVr9OjRQwlg9c3jkVVmzZqFJk2aICgoSCP79dKlSw2W7927N3r06IHDhw+rJWH76aefMGzYMABp8/pomwXZnC/5zAFj5ibRjAFe5ponU2a7NfQ5y58/P0aMGIH9+/ebNOsyYFmQkf63yjwnBmB8E8u2bdt0Tli2cOFCvWXNmXwrK3h7eyNfvnwml7lw4QKyZ89uMDWFvinbBw0ahIiICLx+/RoiggMHDmD58uWYOHGiwS96S4wePRphYWFKgHLy5El06dIFHTt2RJEiRTB16lT4+/tj9OjRWsunb/fdd99h7dq1SuLYvXv3aiS5zOzTTz/Fn3/+iQ0bNqB27dro3bu32usuLi7o2bOn3vd48uSJxrqLFy/iq6++0qjVeVc+mCaejH1CMhITJ+c5c+aM1kBD103o6NGjqF+/Pl6+fIkXL17A29sbjx49gouLC3LmzGmwiSYlJQVRUVGYM2cO7t+/jwsXLiAkJAQjRoxAUFCQwWzIpsqbNy++/vprJcI+dOiQzuYpbU9qWfVEvG3bNp1P/LNmzUKvXr2M3o8xMh73w4cPMXbsWHTq1MmkLxxDk5yle5vV1+nMyX6d7ueff8asWbNw/vx5AGkTsPXu3Rtt2rQBkDZjqUqlgpOTk1qQ8OLFC0RHR6NBgwYmf8kDwNatWzFkyBBERUXh448/hkqlwr59+/DNN98gKipKY0bXjE+A+hjTFKrvvQyVf1/9OIC0p+qxY8eiXLlyWvvWrVu3Tm/59zX51qJFi7Bx40YsWrTI6JrKjLm6YmJi9AYohmaGnj9/PsaPH4+bN28CAHLnzo3Ro0dn+f00o1y5cuH3339X+qQNHz4cu3btQlxcHIC09CejRo3CmTNn3toxvA2HDh1Cu3btcO7cuXe/8/fRM/dtsHRui8uXL0uJEiXUeuhnnOxIl2rVqkm3bt0kOTlZyWZ848YNqVq1qvzyyy8G9ztmzBgJCQmRpUuXqvU4X7lypVSsWNH4P4CR5s6dq5Yh1NQJy7T1RM/8uzGT23l6esqBAwc01k+fPv2tjLk3t2e9tdi2bZsUKVJE4uPjNV57+vSphIaGmjwayhjGZDkNCgoyas6fokWLyp49ezTW7969WwoXLpzlx/4h8PPzk8WLF5td/n1NvmXJKJys9PDhwyzJ6mwMR0dH5W8tIlK5cmUZN26c8vvVq1fFzc1N73tcunRJhg8fLq1bt1aO+6+//tKb8Tszc0Yo6nPkyJH3Ng/KB9PEo2+6aWP07dsXwcHB2Lp1K0JCQnDgwAE8fvwYAwYMwLfffquz3LFjxzB37lzY2trC1tYWiYmJCAkJwZQpU9ChQweN3CmZLV68GPPmzUPNmjXVqs5LlCihM2KtXr26UR1st23bprH+yy+/ROvWrXH9+nWUKFECW7duNSnjacYqZkNPxPpMnz4d9evXx65du5R24W+//Rbjxo3Dhg0bjD4ec477v2jGjBno1q2b1qZKT09PdO/eHdHR0fjkk08MvtfTp0+xZs0aXLlyBQMHDoS3tzeOHDkCX19f5M6dW21bc/tYaXP58mWNfCPpx58VfbKs1a5du/Dtt98qieOKFCmCQYMGGfW/SkpK0jtSzpBs2bLh5s2bCAgIwMaNGzF+/HgAaTXLb7Pmx5i+YProyjL/+PFj5MyZ0+hjN3XwgiV8fX1x9epVBAQEICkpCUeOHFEbjfTs2TO9yS537dqFevXqoXLlyti9ezfGjx+PnDlz4sSJE/jpp5+wZs0ag8dgaISivn4kmWvH5f/3aZw1axYqV65scN9vwwcToKR7+fKl1iYaQwmi/v77b2zfvh05cuSAjY0NbGxsUKVKFUycOBF9+vTRmanR3t5euQh8fX1x48YNFClSBJ6enrhx44bB4719+7bWznepqakaicHSZR7BkVFCQgKWL1+utz+Eu7s7ihUrhkWLFqFy5cpmJ7iLjIzEnDlzUKVKFWVdnTp1lL48Z8+e1Vm2U6dOePz4MWrXro24uDisXLkSUVFR+Ouvvyy6Ieuzfft29OrVC/v379f4oo+Pj0elSpUwZ84co7443rXjx49j8uTJOl+vXbu23kA63YkTJxAeHq4EBV27doW3tzfWrVuH69ev6+zJn5CQADc3N43mjtTUVGXItSEfffQRIiMjsXTpUuTKlQtAWgLCAQMGaO3AZyh9e0bGpHK/desWfvvtN633B0PlzQ0yli5dik6dOqF58+bo06cPRAT79u1DzZo1ERMTozSr6dK1a1csW7bM7CzZzZs3R5s2bVCgQAE8fvxY6R+hbThrVtI3DNgYoqPnQWJiotas9Rk9fvwYI0eOxI4dO7T229HXf8USdevWxdChQzF58mT8+uuvcHFxUbs+Tpw4obdfztChQzF+/Hj0798f7u7uyvrq1atj5syZRh3DgAED0LlzZ5NHKALa+4ul5/maNm2aSe+VVT6YAOXhw4fo1KkT/vrrL62vG4q4U1JSlAyQ2bNnx507d1CoUCEEBgYqbfXalC5dGocOHULBggVRvXp1jBw5Eo8ePcKSJUuUzK/6FC1aFHv27NHocb569WqtHRWBtNqHzJKTk/HDDz9gwoQJyJ07N8aNG2dw3y1atMCmTZtw4cIFqFQqFChQALVq1YKzs7PBsoDlT8QDBw7E48ePUa5cOaSkpGDz5s0GO4NZIitrId61+/fv6336srOz0zmsPKP+/fujY8eOmDJlitpNsF69ejq/LNetW4chQ4bg2LFjGje9169f46OPPsK3336LRo0a6d33woUL0axZMwQGBir9m27cuIGCBQvi119/1dheX/r2jIwZSbVt2zY0btwYwcHBOH/+PIoVK4Zr165BRFCmTBm9ZS0JMiZMmIApU6agX79+yrq+ffsiOjoa48aN01o2Y2CWmpqKefPmYevWrShRooTGNWAosJo+fTqCgoJw8+ZNTJkyRbnH3b1712Cnyaxw+PBhJagLDQ3VeU9Ll97nSaVSKdna06WkpGD37t0Gp5Jo164dLl++jC5dulicQdoU48ePR/PmzVGtWjW4ubkhNjZWLZhauHCh3oSeJ0+exLJlyzTW58iRQ2uiSG0sGaFolbXM76Vh6S1o06aNVKpUSQ4cOCCurq6yefNmWbJkiRQqVEj++OMPg+WrVKmiTFPeunVrqVu3rsTFxckXX3whRYsW1Vnu4MGDytTgDx48UBLalS5dWo4dO2Zwv7/99pt4enrKpEmTxMXFRaZOnSpdu3YVBwcH2bx5s1HnvnTpUgkJCRE/Pz+ZNWuWknNGn/Xr10uOHDk0+mDkyJFDfvvtN6P2+8knn0iNGjXkzp07yrq7d+9KeHi4VK1aVWP79OmbMy8BAQHStm1btXVvQ968efXm9zh79qwEBAS8lX1bKiQkRNauXavz9V9++cWofiAeHh5y6dIlERGlz5RIWu4WR0dHrWVq1aqldybJBQsW6M0xklFqaqps2rRJZs6cKTNmzJDNmzdLamqqUWUt8dFHH8mIESNE5P/O+9mzZ9K4cWP58ccf9ZYtXLiwREdHa6yfNm2awb4zDg4OWhPHXbx4UeffO2NiPX2LMUn33hdzUzKk92lSqVQSEBCg1s+pYMGCUrt2bdm/f7/efbu5uRl1731bnj59qnVW8MePH0tiYqLOcrlz51Zy4WT8bK5du1ZCQkKM2nezZs1k5cqVZhx1Wn9IbakFXr58aXCm6LflgwlQ/Pz8lDwV7u7ucv78eRFJ+yKuXLmywfIbN25UOrVevnxZihQpIiqVSrJnz24wT4ilNm7cKFWrVhVXV1dxdnaWypUry6ZNmwyW++uvv6RkyZLi4eEhY8eOlefPnxu1v71794q9vb18+umnsm/fPnny5Ik8efJE9u7dK82bNxcHBweDiaVE0m6yxYoVE3t7e8mXL5/ky5dP7O3tpWjRolpvylnZ4dIcjo6OWo8r4/noyqT8vvXq1UuKFSsmr1690njt5cuXUqxYMendu7fB98mZM6cyjXXGm+CmTZskT548WsvkypXL4N/N0DTamb169cqswOTixYuyceNGefnypYiI0e/h5uamBGZeXl5Kp8Njx45JYGCg3rLmBBnp0hOQZjZnzhzJnz+/UcduqUuXLkmvXr2kZs2aEh4eLr1791b+72+LpSkZwsLCzM6qXq5cOfn777/NKvs+DRo0SKpUqSJ3795Vpq6Pi4uTkJAQGT16tFHv8dNPP0nevHll1KhRsmbNGpNSG2TM+J7Ro0eP3tvggQ8mQHF3d5erV6+KiEhgYKCSqvvKlStmp7h+/PixUTfAhw8fysGDB+XQoUNaMxtntX/++UfCwsLEyclJIiMj5eHDhyaVr1evntKbX5svv/zS6Ayx7+uJ2BxZVQvxPty7d0/8/f0lICBAJk+eLL/++qusX79eJk2aJAEBAeLv7y/37t0z+D7dunWTpk2bSlJSkri5ucmVK1fk+vXrUrp0aZ25U5ycnPQmsNSVryqzlJQUGTt2rPj7+4utra3yJfnNN9/ITz/9pLfso0ePpEaNGspIq/SynTt3lv79+xvct6+vr5IzKjQ0VLlZHzt2zGDSPUuCjB9//FEcHBykR48esnjxYlmyZIl0795dHB0dtb5nuuTkZDl+/LgSiGX04sULOX78uKSkpOjdt0jaw4+Dg4OUL19e+vXrJ5GRkVK+fHlxdHQ0uobWHB4eHlpH6f3zzz/i6en51vYrInLgwAGpUaOG7Ny5Ux49eiTx8fFqy9vWtGlTadasmcbSvHlzadOmjYwcOVLOnTunUS4pKUnatGmjjIxMzzPWrl07o2rFRfSPVjQUZKhUKq21W9u2bZPs2bMbd/JZ7IMJUMqVKycbN24UEZEmTZpI+/bt5datWzJ48GCjq8dETHtCO3XqlHzyyScaw3SrV69udEbi4OBgrUHNkydPdH5ZqlQqcXFxkX79+ulsNtHXTOLl5aU3c/Dx48ff6hDEjBITE+XcuXNGfwAtkVW1EO/LtWvXpF69ehrD4OvVq6cE54bEx8dL5cqVxcvLS2xtbSUgIEDs7e2latWqOmvgChcuLEuWLNH5nosXL5ZChQoZ3LclQ+rbt28vderUkZs3b2rU/ISGhhrcd5MmTWTevHkikvakmj9/fhk/fryUKVNGatasqbesuUFGurVr10rlypXF29tbvL29pXLlygaz6i5atEjKli2rtakgOTlZypYtq/d/kq5UqVIyZMgQjfVDhgx5q8N93dzc5OjRoxrr9Q1Z7devn3IN9uvXT++iz4ULF6Rs2bJmJ461VIcOHcTT01MCAwOlefPm0qxZMwkKChIvLy9p0aKFFCpUSBwdHZWH6MwuXbokq1evlpUrV1qUEdpYGZN3pv+cvnh4eIiNjY307NnzrR+HNh/MRG0///wz3rx5g44dO+Lo0aOoU6cOHj9+DAcHB8TExCgzKery+PFjtGjRAjt27IBKpcLFixcREhKCLl26wMvLS6MX871791CsWDHkyJEDPXr0QOHChSEiOHPmDObPn4/Hjx/j1KlTBmdpzZzfJd39+/eRN29eraNxLM3jkzFXhTbXr19HkSJF8PLlS737AMyf5fLly5fo3bu3khcofYK6Pn36wN/fH0OHDjW4b1Pdv38fZcqUga2tLXr16oVChQpBpVLh7Nmz+OGHH5CSkqIMt7VmT548waVLlyAiKFCgALJly2bye2zfvh1HjhxBamoqypQpo3eit+HDh2Pp0qU4cOCAxt/m3r17qFChAtq1a4cJEybo3Wf+/Pkxd+5c1KxZUy1Hyrlz5/Dxxx9rnckynZ+fHzZt2oSSJUuqlb169SqKFy+uN5EaAFy5cgXPnz9HiRIl8PLlSwwcOBBxcXHInz8/pk+frjfhH5DWUXjatGnKyLT0UTyWJrvU5ZNPPkFERARatWql9fVVq1Zh1qxZ2L17t973cXJywsmTJ5UkcukuXLiAEiVK4PXr11l2zBk1adIET58+1UjJ0LZtW2TLlk3rBHPVq1fHunXr4OXlherVq+t8b5VKhe3bt+t8vXz58rCzs0Pfvn21dpK1dEoKQ4YOHYqEhATMmjVLGfWWmpqKvn37wt3dHRMmTECPHj1w+vRpZRI3fdauXYvRo0fjxIkTb+V4Y2NjISLo3LkzZsyYoTbwwcHBAUFBQcrU++/cewmL3oEXL17I4cOHjW7+MPUJbfDgwVKmTBmdT+NlypSRoUOH6txfepugSqWSxYsXq7UTrl27ViIiIqRgwYJGnq1pSpQoIQsXLtT5+oIFC6R48eIG32f06NFiY2Mj5cuXlyZNmkjTpk3VFn369OkjZcuWlT179oirq6vy916/fr2UKlXKtBMyQVbUQvyvSUhIkKJFi4q7u7t89dVXMmPGDJk5c6b06NFD3N3dJTQ0VBISEgy+j5OTk1y7dk1E1Pu/nD592mAzi5ubm/I0mbHsgQMHxNvb25LTeycOHTokS5YskaVLl6qlstclR44ceq/HK1euGFXtnidPHlm1apXG+pUrV77VDuE3btyQ0qVLi729vYSEhCj908qUKSM3b958a/sVEXF2dtbahPKuZM+eXekDmdH58+fFx8dHREROnDih1tQ1b948+eyzz6R169ZKJ+Bt27ZJqVKlxNnZWW+TfGY7d+6Uhg0bSr58+SR//vzSqFEjoyZx3LlzpyQlJRm9n3fhgwhQ4uPjtbbHpqSkGN3m6Ovrq/T8zngDvHLlitabZ+nSpfX2ll6+fLneKlRdM7GqVCpxcHCQggULyu+//673mDPOWpiZvk5i0dHR4u3tLRs2bNB47Y8//hAfHx+toxYys2SWy7x58yrHmPHvffHixXcya+G///4rBw4ckH/++cfsznj/Jfv375c///xTbV1sbKwEBQVJjhw5pFu3bvL69Wud5Z8+fSpfffWVeHt7K9ept7e3fPXVV/LkyROjjiFjs0TG//no0aOlSpUqesvWr19fvvnmG6XslStXJCUlRT7//HP59NNPdZb7999/5bvvvtM5A6+u17QxNcgQMX80i4uLixw/flzn68ePHxcXFxeD+x8zZox4eXnJpEmTZPfu3bJnzx6ZOHGieHl5qc1y+rZs3rxZvvvuO5k5c6Zs2bLlre9PJG104bvalzZeXl5aO6SuX79eaTq/cOGC8vPUqVPF3t5eypYtKy4uLuLi4iITJkwQHx8fGT16tEl9DJcsWSJ2dnbSokULpV9gixYtxN7eXn7++Wej3+fly5fvvO+ONv/5AGXt2rVSoEABrcOjXrx4IQULFjRq2KypT2ienp4GRzYY0xksKCjI5E6u6QoVKqS1/0pcXJzefaekpMhnn30mKpVKChcurHTiKlSokNjY2Ejz5s2N6oDn7e2tjIwwVcY+CBn/3seOHRMPDw+z3pN0q1u3rkyaNEn5/cSJE2JnZyddu3aVadOmiZ+fn4waNcrg+6SmpsqDBw/k/v37JneGtmRI/enTpyVHjhxSt25dcXBwkM8++0yKFCkivr6+eq/BsWPHymeffabz9c8//1zGjx+vd9/mBhki5o9mKVmypMyePVvn6z/88IOULFlS775F0v5f0dHRkjt3biWwzJ07t8yYMeOtdGbPypQMBw4ckEGDBknLli01Opzqs2rVKgkNDZVFixbJoUOH5Pjx42rL29a7d2/Jnj27REdHy549eyQuLk6io6Mle/bs0qdPHxERmT9/vjK6tHDhwrJgwQIREdmxY4eoVCqpWbOm0YF/RpYMiX/x4oVERERIjhw5tKZAeR/+8wFKVs3RoO8JrXnz5hrb6xqSle7evXtia2trxBmYr2vXrlKmTBm16vVdu3aJh4eHUTUgK1askCZNmkiRIkWkSJEi0qRJE1m+fLnR+x88eLCMHTvWrGOvWrWqfPfddyLyf39vEZGIiAipU6eOWe9Juvn5+cnBgweV34cNG6Y2/H7VqlVSpEiRt34c5g6pF0mbY2fkyJHSoEEDqVevngwfPlxtDh5tSpYsKVu3btX5+tatWw02KVoyZNbc0SyTJ08WHx8frV+ox44dEx8fH5k8ebLefWeWkJBgVFOcJRo1aqT33jNz5kyDzb8iaTXQ9vb20qBBA3FwcJCGDRtKoUKFxNPTUzp27Ki3rK4RLO+qk2xycrKMHz9e/Pz8lP37+fnJhAkTlE7P169fV5q6nJ2d5fr160p5BwcHg3O96GLJkPiePXtKkSJFZPXq1eLs7CwLFy6UcePGSZ48eWTp0qVmHY+l/vMBSlbN0WDqE5qNjY1cunRJoxosfblw4YLRH4bnz5/Lhg0bZPbs2UaPxBFJezr69NNP5ZNPPpFXr17J9u3bxc3NTWbMmGHUfi3Vp08f8fLykqpVq0qvXr1M6mm/d+9ecXd3lx49eoiTk5P07dtXwsPDxdXVVQ4dOvROjv9/SVYkMrt37560a9dOcuXKJba2tlbxhGWIm5ub2s0/s+vXrxtsUrRkyKw5o1lE0oachoWFiZ2dndStW1ciIyOlX79+UrduXbGzs5Nq1aqZ1F/g/v37ShOPoVofS2TVZIjFixeXWbNmicj/1bCmpqZKt27dZOTIkXrLXrt2Te/yLhnTPKJSqdQedjPWKJvKkiHxAQEBsmPHDhERZR4WkbRResZOO5HV/vNT3T958gTJyck6X3/z5o3e0QHpQkNDceLECcyePRu2trZ48eIFmjdvju7du2PUqFEaI1JEBAULFtT5fiJi1BTLR48eRf369fHy5Uu8ePEC3t7eePToEVxcXJAzZ069KexVKhWWL1+OBg0aoGbNmjhx4gQmTpyIXr16GdwvAISFhaFz5874/PPPjZ7ePqMTJ04oeYFOnTqlcWz6VKpUCXv37sW3336LfPnyYfPmzShTpgz+/vtvo1IEkGksTWQGAB07dsSNGzcwYsQI5MqVy+QpxENCQnDw4EGN5JRPnz5FmTJldI46A4BFixbBzc0Nn3/+udr61atX4+XLl+jQoYPWcra2trhz544ytX5md+7c0cgvlFlqaqrWv429vb3B6cFr1KiBvn37aoxm6devH2rWrKmznL29PTZv3ozp06dj2bJl2L17t3LPmTBhAiIjIw3+v4C0HEoRERFYvny5cqy2trZo2bIlfvjhB62pKiyRVSkZLl++jAYNGgAAHB0d8eLFC6hUKvTr1w81atRQu3YzMzQi610yJkcVALVp/ZOTkxETE6OR6FDfd0G6AQMGoE+fPjh27BgqVaoElUqFuLg4xMTEGMzn8++//yI4OFg57vScRVWqVMFXX31l1HlkufcSFmWhrJqjQZdjx45pfTrcuXOnUYsh1apVk27duklycrISOd+4cUOqVq2qzGybUeb21OPHj0tcXJwEBARIjx49TGpr7d+/v/j6+oqHh4d07dr1Pzn7Ihnnyy+/lI8//lh2794t/fv3Fx8fH7Vpt5cuXSrlypXT+x66agOMlflJMd29e/fEwcFBb9mCBQsqKSUy2rlzp97RbmFhYVrnAUk3ePBgCQsL07vvxo0bS9WqVeX27dvKulu3bkm1atUMNlfoG82ir5N7Vvn888+lQIECsnHjRomPj5eEhATZuHGjFCpUSD7//PMs319WTYaYJ08eZa6mEiVKyLJly0REZN++fUb1UdM2e665/eVMZWpNY2BgYJbOrm3OvDsiabVW6d9ZtWrVkgEDBohIWrNc7ty5jd5/VvrPByjDhg2TvHnzap1F8+7du5I3b14ZNmyY2e+vK0DJKp6ensqQOE9PT6V6dP/+/VoDK20jfzIPmTWlrTU5OVl+/fVXadKkidjb20uRIkVk6tSpRs1Kms7c6ccvXbokw4cPl9atWytfXH/99ZcyDTllnQcPHkiVKlVEpVKJu7u7xpdIjRo1DH5OihQpYvTolYyyYki9o6Oj1mG3V69e1TuL7Zo1a8TOzk6+//57tUnPkpOT5bvvvhN7e3tZvXq13n1nRZDxPkaziKSNBtqzZ4/G+t27dxs1CshUWTUZYuvWrWXatGkiIjJ+/HjJkSOHdO3aVQIDAw12kn1fs+emq1u3roSGhsqPP/4o69atk19//VVtsVbR0dFKt4Lt27eLs7OzODg4iI2NzTvrNpDZfz5Ayao5GnTRFqDo6neibTEk45j5ggULKrPhnj17VusU/YbaVy1pa33w4IGMGzdOnJycxN7eXpo0aaI3D5El04/v3LlTnJ2dJTw8XBwcHJSykydP1jtslCyjL5GZoT4NmzZtktq1a5s8X0xWDKkPCAjQOnTz119/Nfh0N2zYMFGpVOLh4SGlSpWS0qVLKzNk6qtdySwrg4zDhw9LgwYNDG6XeWbP9MXb21v8/f2latWqeuc0CggI0Dpr9PHjx9/KU3FWpWR4/PixUmOVkpIikydPlkaNGkm/fv0MTgvwvmbPTWdpTaO1uH79uvzyyy/vNfHifz5AEcmaORp00RagpN9s9S3G1mLUqlVLGZ/evXt3KV++vCxdulTq1Kkj5cuXt+jYTfHPP/9Ijx49xNPTU/LmzSsjR46Ubt26iYuLi1LVl5kl049XrFhReULKPKzb398/C8+MMurUqZPWgP358+fSqVMnvWW9vLyUJyo3NzeNL01DLBlSP2jQIAkMDJTt27dLcnKyJCcny7Zt2yQwMFDn9ZnRP//8I3369JH69etLvXr1pG/fvkpyUXMZCjI2b94sAwcOlK+//lq5vs+ePStNmjQRGxsbo0arRUdHi4+Pj7Rr104Jjtq1ayfZs2eXCRMmSNeuXcXR0VGZxj+zuXPnSnh4uEbG8dq1axs1Tb85LJkMsW3btrJgwQKLkhk6OjpqnSL+/PnzBkeyZAVTahr1pSoxdsBEtmzZlM+VroDWmM9obGys1rmQEhMTJTY21qjzyWofzFT3QFrH1EePHkFEkCNHDqM68TVv3lzv60+fPsWuXbuQkpKirNu1a5fRx2RoWuVDhw7h2bNnqF69Oh4+fIgOHTooU3AvWrQIJUuW1Ft+yZIlmDNnDq5evYq///4bgYGBmDFjBoKDgw1Ow/3gwQMsWbIEixYtwsWLF9GoUSN07doVderUUf52W7duRdOmTbVOJW7J9ONubm44efIkgoOD1cpeu3YNhQsXfmtTcP+vs7W1xd27dzVSKzx69Ah+fn56O5ynpyXQRVdH1ayQlJSE9u3bY/Xq1bCzS+vbn5qaii+++AJz5syBg4OD3vI3btxAnjx5tHaIvXHjhs5OtFu2bMHmzZthb2+Prl27KlPzDx06FL///jtq1aqFjRs3apSLjY1Fp06d4O3tjX///RfZs2dHdHQ0evbsiU8//RQDBgxAsWLFDJ73p59+ilq1aqFHjx5q6+fOnYvNmzfjl19+wffff4958+bh5MmTGuVLly6NS5cuITExUTnHGzduwNHRUWP6+yNHjhg8HlOYk5KhZs2a2L9/P16/fo08efKgevXqqFGjBqpXr46AgACj9hsQEIDo6GiNDtWrVq3CwIEDcePGDbPOx1ibN2/GtGnTMHfuXAQFBendNr1TqiH6UpfExsaiVatWcHR0RExMjN7vPX2fUV33hsePHyNnzpxq34Hvyn9+FE+6V69eKYEJkJZPZt26dShSpAjq1Kmjs5yhXuyenp744osv1NZlZS6HcuXKKT/nyJEDf/75p9FlZ8+ejZEjRyIyMhITJkxQLiAvLy/MmDHDYICSJ08e5MuXD507d0bHjh2Vv11G5cuXx0cffaS1/IsXL+Di4qKx/tGjR3B0dNS7by8vL9y9e1fjA3r06FHkzp1bb1kyXUJCAiStxhTPnj2Dk5OT8lpKSgr+/PNPg3mjsiIAefHiBXbt2oUbN24gKSlJ7TV9oxQcHBywcuVKjBs3DsePH4ezszOKFy9u9IiN4OBgnTff4OBgrTffzEHGTz/9pBZkHD9+XGeQMX36dERFRWHo0KFYtWoVWrVqhenTp+Po0aPIly+fUccMAJs2bcLkyZM11tesWRMDBgwAANSvX19n7qqmTZsava+sli1bNp33Dl22bduGN2/eYP/+/di5cyd27tyJr776Cq9fv0ZwcLASsLRu3Vrne3Tr1g1ffvklrly5ojaSZfLkycrf7G1q2bIlXr58iXz58sHFxUVjVFP66BgAuHr1qsX7y/i57Nixo9nvIzpGnt66dSvLR3sZ7b3U27wFtWrVUmZefPLkieTMmVPy5MkjTk5O8uOPP771/b948ULOnj1r1qyFDx8+lIMHD8qhQ4e0zgyrS5EiRWTdunUiot5McvLkSSXngz7Gzuioi7nTj4ukVdlXqVJF7t69q4y5j4uLk5CQEBk9erRFx0WaDDVL2traGpxRVSStc+maNWtk3LhxMn78eFm7dq3WPi3aHDlyRPz8/MTDw0NsbW0lR44colKpxNXV1aRRCubQNYLo2rVrOjuLlixZUiZOnCgiablrVCqVlClTxqjRIBknH0xJSRE7OzujRvVlFhAQoHXis+joaGU+kePHj4uvr6/J7/1fkZiYKLt27ZLBgwcrfYf0edez52YWExOjd3mbdE0g+ujRI51/t/R+WTY2NlK8eHEpXbq0spQoUULc3d3fyogvY3wwNShHjhzB9OnTAQBr1qyBn58fjh49il9++QUjR458a+O4Hz58iE6dOuGvv/7S+rq+arHTp0/jq6++wt69e9XWV6tWDT/++CMKFy6sd99Xr15F6dKlNdanzxtgyCeffGJwG32mTp2KsLAwHDp0CElJSRg8eDBOnz6Nf//9V+OcMpswYQI6duyI3LlzQ0QQGhqKlJQUtGnTBt98841Fx0WaduzYARFBjRo18Msvv8Db21t5zcHBAYGBgco8HbpcunQJ9evXx+3bt1GoUCGICC5cuICAgABs2LDBYM1Av3790KhRI8yePRteXl7Yv38/7O3t0a5dO/Tt21dv2ZSUFMTExOjMnK0ru23//v0BpFWRjxw5Uq3GLyUlBf/8848yl09mly9fVrKgf/bZZ7C1tUV0dLRRNSAvXryAq6srgLSM5U5OTkY3UWQ0YsQIfPXVV9ixYwfKly8PlUqFAwcO4M8//8ScOXMApDVD6avVffr0KdasWYPLly9j0KBB8Pb2VrJ2W3Nt5evXr7F3717s3LkTO3bswMGDBxEYGIgWLVroLZc+X0q/fv3w7NkzAIC7u/u7OGQAltU03rp1C7/99pvWGsbo6GiD5UVHj43ExESdzaDptWzHjh1DnTp1lPlYgP/LZvzpp58aeQZZ64MJUF6+fKlchJs3b0bz5s1hY2ODihUr4vr1629tv5GRkXjy5An279+vpAu/f/8+xo8fj2nTpuksd+/ePVSrVg05cuRAdHQ0ChcuDBHBmTNnMH/+fFStWhWnTp3SW+0eHByMY8eOaVRz//XXXwgNDTXq+NesWYNVq1Zp/UAYapPWNbldREQEcuXKpbXMpUuXkD9/ftjb2+Pnn3/G2LFjcfToUaSmpqJ06dIa7eKUNdK/wK5evYq8efOaPMkakNYEky9fPuzfv18JcB4/fox27dqhT58+2LBhg97yx44dw9y5c2FrawtbW1skJiYiJCQEU6ZMQYcOHfT2B+vbty9iYmLQoEEDFCtWzOjjP3r0KIC0G/fJkyfVbtIODg4oWbIkBg4cqLWspUHGpk2blKrx1NRUbNu2TWNCw8aNG+t9j27duiE0NBSzZs3C2rVrISIoXLgwdu3ahUqVKgGA3maLEydOIDw8HJ6enrh27Rq6desGb29vrFu3DtevX8fixYuNPp93YceOHcpy8OBBhISEoFq1aujVqxeqVaum876SUY0aNbB27Vp4eXmpBSYJCQlo2rSpzmDWEgkJCcqkbAkJCXq31TV527Zt29C4cWMEBwfj/PnzKFasGK5duwYRQZkyZfS+53fffQcgLTjLOOkbkBaI7969W+cD76hRowAAQUFBaNmypVrz73v3Xupt3oLixYvLzJkz5caNG+Lh4SH79u0TkbQMpG+z+tPPz08ZDeDu7q4MGV6/fr1arpPMBg8eLGXKlNE5X0CZMmVk6NCheve9cOFCyZ07t6xYsUJcXV1l+fLlMn78eOVnQ2bOnClubm4SEREhDg4O0r17dwkPDxdPT0+j5o7Rl+Pk+++/17pepVJJnjx5pH379rJo0aJ3PvU0pTXttW3bVj7++GO5deuWiKRNaKhtvoyMXFxctA5ZPXbsmNaM35mZOqQ+Ix8fH63Zt43VsWNHkzOyZp63xcXFRebNm6c2j4u2oc/pZQ0t7yI9QM2aNWXQoEEiot4MvHfvXgkMDHzr+zeVSqWSwMBAmT17ttlT8utqzrt//77Y2dlZeohaZWxa0dWcauh//tFHH8mIESNE5P/+V8+ePZPGjRsb7KaQPqGbSqWSgIAAtUneChYsKLVr1zYpv8+rV68kJiZGfvjhB60jot6VDyZAWb16tdjb24uNjY2Eh4cr66OioqRu3bpvbb/u7u7K0LnAwECJi4sTEZErV67ovemWLl1aVq5cqfP15cuXGzVmf97/a+/M42rav///Oqc5jRoMaUQphbrmXBRSQhluKC6ZPiJcQ4Z7TZH5Ete9ly4SonTJPCtTmacyNBlSKJfIUKJh/f7o1/526kxNjvJ+Ph7n8ejsvdd+r805+6z9fq/1Wv/8Q0ZGRtxNr0mTJrRlyxapfLewsOAUGkvfvObPn0+TJk2SaK+pqSm0R0lgYKDIPiMXLlygJUuWUI8ePUhVVZX4fD6ZmJjQ6NGjaefOndwPJqNm2Lt3L6moqHDlqSX/53/99ZfEfhva2toUGxtbbntMTIxUZcZVKalv1KgRF9xUlfT0dKk+Z99KkFFYWEhJSUl08eJFOn/+vMBLEhoaGlzOTOnveGpq6lcpua0os2bNog4dOpCioiLZ2NiQr68v7d27V6pgpSTvj8fj0dmzZwVyAW/dukXLli2rsaDs3LlzlJ+fT0TFHYkroy6upqbG/V9paWlxgpV37tyR2u/u3btL1Ikpy8yZM7kuy0TFOT+tW7cmBQUF0tTUpHr16nEP/F+bOhOgEBXX99+6dUsgEerq1aucUmtN0LZtW+5J0M3NjUaMGEHPnj2jWbNmkZmZmUg7TU1NiU0OJTUiK82rV6/EdlcWhoqKCjeDoaenxwnyJCcnU/369SXaBwcHk66uLt2/f5/btnr1atLQ0JAqAffLly90/vx58vf3JwcHB1JRUSE+ny9RVZRRedq0acNpGpT+wbp9+7bEmcYRI0ZQy5Yt6cqVK1RUVERFRUV0+fJlsra2ppEjR0oc+/r165xc/X///UcuLi6krq5Otra2EsWgfv/9d5o4cWKlkxwLCwvJ39+fS7Lk8/mkqalJixcvpsLCwkqd82tw+fJlMjU1FSpyJ01wpK+vz2lylNUqatKkSY36XhU+fPhAx44do1mzZlH79u1JQUGBWrZsSRMnThSp/Ft65kJYMKmqqkpbt26tcd9Lt5AoizgdoAYNGnD3UisrK252TtoZysrSsmVLgZnA4OBg0tbWptTUVCoqKqJRo0ZRnz59amx8cdT6HBRJOiYlREZG1sj4v/zyCzIyMgAUr+X17t0bu3btgqKiIkJCQkTaffjwQWwjKXV1dbE6IiUUFBTg3LlzePToETw9PQEUN0DT0NAQWIcURsOGDZGVlQVjY2MYGxvjypUraN26NZ48eSIy2ao03t7eyMrKgpOTE2JiYrBnzx4sW7YMx48f59bHxaGgoICuXbuiXbt26NSpE06ePInNmzfj4cOHEm0ZlSMpKQldu3Ytt11DQwPZ2dlibf/44w+MHDkSnTp14konCwoK0L9/f4mNyICqldTHxMTg7NmzOH78OFq2bFmudFPS9/u3337D1q1bsWLFCtjb24OIEBsbi0WLFiEvLw9Lly6V2hdJHDp0SOpjJeWgTJgwAW3btsXRo0cr1aDRzc0NixcvRkREBIDiHIW0tDTMmTNHZomP0qCmpgYXFxe4uLgAKC7NXbt2LTZs2IBNmzYJLT4ouW+ZmZnh2rVrArIJioqK0NfXh5ycXI377uHhgcjIyHKaOy9fvkSPHj3K5SGV0LFjR8TGxsLKygqurq6YMWMG7t69i8jISHTs2FHq8SuaaJuWliaQs3jq1CkMHjyYy22cOnUq+vTpI/X41UmtD1BkVp/9//Hy8uL+trW1RWpqKhITE2FkZFSuG2VZyupRlKZEt0IcT58+hbOzM9LS0vD582f06tUL6urqWLVqFfLy8rgsf1E4Ojri8OHDsLOzw5gxYzBt2jTs3bsXN27ckDrwmzlzJrKystC2bVsUFhbi1KlT6NChg1ibvLw8XLp0CWfPnsW5c+dw/fp1mJqaolu3bti4cWO16swwBGnUqBEePnxYTkAqJiYGZmZmYm21tLRw8OBBpKSkIDExkau+atasmdTjlw2o1dXVpQqotbS0MGDAAKnHKcv27duxZcsWgYCgdevWMDAwwMSJE8sFKFUJMspqj/B4PIHvcukgQ5L4VUpKCvbu3Vuhf+PS/P777+jTpw/09fXx6dMndOvWDZmZmejUqVO1BmXVTVFREa5fv85pocTGxuLjx48wMjISeW8q+UGV1GG6psnIyMCYMWOwbds2gW2Ojo5o2bKlSLu1a9dyD6WLFi3Cx48fsWfPHjRr1oyrUJVEZRJt+Xy+wOfzypUrmD9/PvdeS0sLb9++lWr8akcm8zZ1iMpoGxBJ1qWQZgrXzc2Nhg8fTp8/fxaYvj137hw1a9ZMog+FhYXcuilRsdbD5MmTaf369SKnKUXJMBsaGpKXl5dEaeauXbuSiooKWVtb08SJE2nPnj0VakzIqBorV64kKysrunLlCqmrq9PFixcpNDSU9PT0RCY2VxepqanUokULUlVVJTk5Oe7zOnXqVPrf//5Xo2MrKSkJzWFJTEwU2mxQ2HJK2feiutOW5vTp02RnZ1eum3Dbtm2lalzn4OBAx48fl/5CRRAdHU2rV6+mlStXftVmhRVl1apV5OLiQhoaGlxO3fDhw2nr1q2crowkQkJC6MiRI9x7Pz8/0tTUpE6dOn2VpPzXr1+TlZUV/fLLL0RU3Pna3NycfvrppxpfTqxMom2HDh24tiP37t0jPp8v8G997tw5mSVU1ympe1mgqKiIhg0bwtPTE8OHD5dKvhqQXi5f3GyCrq4uYmNjYWFhUU4u3srKCrm5uVKNURGqKs2soKCARo0awd3dHd27d0fXrl0lzjQxqpfffvsNgYGBXDsBJSUlzJw5E0uWLCl37PTp07FkyRLUq1eP0xQRhSSdBnd3d6irq2Pr1q3Q0dHhPq/nz5/H2LFjkZKSUvmLkkCHDh3QoUMHrhyzhMmTJ+P69eu4cuWKSNszZ85g9uzZWLZsGTp16gQej4dLly5h3rx5WLZsGXr16iXS1traGps2bUKXLl0Etl+8eBHjx49HQkKCWL/379+PefPmwc/PDzY2NuWWtlq1aiXStqioCCEhIYiMjERqaip4PB5MTU0xePBgjBgxolKl5jVN48aN0b17dzg4OMDBwaFSM0cWFhbYuHEjHB0dcfnyZfTo0QPr1q3DkSNHIC8vX2PL/aV59uwZunTpggEDBuDo0aOws7PDrl27anyJSV1dHXfu3EHTpk2hra2NmJgYtGzZEnFxcXBzc0Nqamo5m3379mHYsGH48ccfcf/+fbRr1w6HDx/m9s+ePRtPnjzhlgm/JrV+iUfWvHjxAuHh4QgLC8OqVatgbW2N4cOHw9PTE02aNBFpVx3LGEVFRUKniJ89eyaVMFFKSgoOHjzI3bzMzMzg7u4uNgipqjRzdnY2Ll68iHPnzmHlypUYNmwYzM3N0a1bN3Tv3p3ThmHUDF++fMHSpUvx22+/4cGDBygqKoKVlRXU1NTw+vXrcsHi7du3kZ+fz/1dFWJiYhAbG1tOMMrY2BjPnz+XaF8VzZ5Vq1bB1dUVZ86cEQgy0tPTJebC/PLLL+WCjN69e0NVVVVikPHo0SOhy9AluiSSKMkTGT16NLetZMmIx+OJXCIiIvTv3x/Hjh1D69atYWNjAyJCQkICRo0ahcjISBw4cEDi+F+bFy9eVPkc6enpXGBz4MABDB48GOPHj4e9vT26d+9e5fNLQ5MmTXD69Gl06dIFvXr1ws6dO4UGhPXr10dycjJ0dXWhra0tNmgsLZEvinr16uHz588AioO9R48ecctKr1+/FmozaNAgHDt2DEePHoWTkxMmT54ssF9VVRUTJ06UOHZNwGZQqpEnT55g9+7dCAsLQ2JiIrp27SpUFEiSkE9pxCXSDhkyBJqamvjnn3+grq6O+Ph46Onpwc3NDUZGRgJroGVZvnw5FixYgKKiIujr64OI8OrVK8jJyWHZsmUixauE8eXLFzx58gRNmzblGrlJy4cPH7gEyHPnziEuLg7NmzcXmUjGqBru7u6VSuCrDurXr4+YmBhYWVkJzPjFxMRg0KBBePnypUjbP/74A7/99htGjhyJzZs3w9vbG48ePcL169cxadIkqfIpXrx4gb/++ksgf2bixIkSFXRVVFRw7do12NjYCGyPj49Hhw4d8OnTJ5G2Xbt2hYKCAkJDQzmRsczMTIwYMQJfvnyROJMqSWRSVC+ibdu2YerUqTh48CAcHBwE9kVHR8Pd3R1//vlnuT5j3xLZ2dm4du2aUOVgcX7r6+vj5MmTsLW1ha2tLaZNm4aff/4Zjx49QuvWraUqPqgoooKL3NxcKCkpCcyclA40qqvRXwnu7u5wdXXFuHHjMGvWLOzfv58LSLW1tXHmzJkKXpmMkcnCUh2moKCADh8+TG3atBG5Pi0p/0TaHJTnz5+Tubk5WVpakry8PHXs2JF0dHTIwsJCbMlxdHQ08fl8WrhwoUDNfFZWFs2fP5/k5OSk0ljIycmh0aNHk5ycnEBOweTJk7keJpIoLCykK1eu0PLly8nJyYnTRmHUDO3bt6dRo0YJbHvx4gW1aNFCYv8kb29vev/+fbntHz9+JG9vb4lje3h40Lhx44jo/3rVfPjwgRwdHcv5VJaqavZUhR9//JEcHR3pxYsX3LaMjAzq2bMnde3aVaxtSkoKWVtbk4KCAjVt2pSaNm3KlcyKkxmoKr169RL7HVy6dCk5OTnV2PhV5dChQ6Surs6Vg2tpaXEvSZo7np6eZGdnR2PGjCFVVVWuv9nBgwepZcuWNeKvpP47X6sXz6NHj7gecDk5OeTj40M2NjY0YMAAqfJv3r59SydPnqSdO3fS9u3bBV6ygM2gVBOxsbHYtWsX9u7di7y8PPTv3x9eXl5cmVxppM0/ASQvBX369AlhYWG4desWioqKYGdnBy8vL6ioqIi0GTJkCLS0tBAUFCR0//jx4/HhwweEhYWJHXvq1KmIjY3FunXr4OzsjPj4eJiZmeHQoUNYuHCh0CWBoqIi3Lhxg+uvERsbi5ycHBgYGHDrzg4ODlJ3qWVUjKysLHTt2hVOTk4IDAzE8+fP4ejoiNatWyM8PLzczEppRLVjf/36NRo2bIiCggKxY7948QIODg6Qk5NDSkoK2rZti5SUFOjq6uLChQti2zqoqqoiISEBxsbG0NfXx+nTp9G6dWukpKSgY8eOyMrKEmqXlpYm1qcSjIyMRO57+PAhBgwYgKSkJO64tLQ0mJub48CBAxLzJIgIp0+fFpi56dmzp8in5UOHDsHFxQUKCgoSq4lElSk3bNgQJ06cENln6Pbt23BxcUFmZqbY88sKc3Nz9OnTB8uWLRPaMV0c2dnZmDdvHtLT0+Hj4wNnZ2cAxTIQioqK+O2332rCZQDFVWq7du1C79690bBhwwrZHjt2DHJycujdu7fA9lOnTqGwsFDob0l1cvjwYXh5eSEnJwfq6uoCn08ejyfVElO1I5OwqA4xd+5cMjExIUVFRerTpw/t2rWLcnJyanTMxo0bc5ntJSq2FcHExESsrPmFCxfIxMRE4nmMjIzo8uXLRCT4VJuSkiJSSbbkqcjAwIC8vLxo8+bNUnWHZVQf6enpZGxsTL/88gs1b96chgwZIrYj8bt37yg7O5t4PB49fPiQ3r17x73evHlD27dvp0aNGkk1dm5uLm3dupUmTZpEPj4+tHnzZsrNzZVoZ2pqSjdv3iSiYnHETZs2EVGx4Ji4J2pJsuMlnZwlUVRURCdPnqT169fTunXr6NSpUxUWjfv06ZNUNqWl2iurYqugoCAw41OW58+fk6KiYoX8/5qoqqpy95PaRmkBzIpgY2MjtJ3D8ePHqVWrVlKdY9SoUXTmzJlKCRo2b96cpk6dWuO/XxWBJclWkXPnzmHmzJkYMmRIlapRcnNzhSb/CcvSnzBhAs6fP4/JkycjLy8PhoaGcHR0hKOjI7p37y42ORcozjcoq4NRGlNTU6merF69eiX0qTcnJ0fk0+Hq1avh4OAAc3Nziedn1AzSJvCVoKWlBR6PBx6PJ/T/jcfjwd/fX6qxVVRUMHr0aIGkT2morGaPqMReIkJ4eDj++OMPiYKGQPE1Ojk5oWvXrlBSUpK6AqaoqAhLly7Fpk2b8PLlSyQnJ8PMzAzz58+HiYkJxowZI9RG2N8VobCwUGw+mJycnMQZL1nSu3dv3LhxQ6I2jzAuXLggdr8wocLqpEOHDrh9+3aFZ4FTUlKENnlt0aKF1OKVWVlZcHV1hY6ODoYOHYoRI0aInEUry/PnzzFlypQKz1jVJGyJR8a8evUK3t7eOH78uND94oSc8vPzcfnyZZw7dw7nz5/H5cuXuQ6xjo6OIpdw+Hw+MjMzRU6pv3z5Eo0bN5YoItWtWzcMHjwYkydP5pJ0TU1N4evri4cPH+LEiRNi7Rlfh8om8JVw/vx5EBEcHR2xb98+rpMxUFxmb2xsLDHRtISkpCRs2LABCQkJ4PF4aNGiBXx9fUV2Wi2hqKgIRUVF3I9uREQEYmJi0KxZM0yYMEFkK3lhnDlzBnPmzEFycjKmT5+OmTNnig1SKhNklLB48WJs374dixcvxrhx43Dv3j2YmZkhIiICgYGBuHz5slhf09PTRXZQvnLlikiFUT6fDxcXFygpKQnd//nzZ5w4cULid1xWbN26FYsXL4a3t7fQ8mpxCrzClikrIo5XVf7991/MmTMH06ZNww8//MB1xC5BVGl4w4YNsXv3bjg6OgpsP3PmDDw9PfHff/9JNX52djYiIiKwe/duXLx4ERYWFlxlqbgH04EDB2Lo0KHw8PCQapyvAQtQKkF1rBGX4OXlhdTUVKxbtw4ODg7Yv38/Xr58iYCAAKxZswaurq5S+/X27VusWbMGGzZswMePH0V+Efl8PgICAkTelD98+IAFCxZI/CJfunQJzs7O8PLyQkhICP73v//h/v37uHz5Ms6fP48ffvhBpG1OTg5WrFiBqKgooVn6wjRUGJVj+/btUh8rrlLg6dOnMDIyqrR+xt69ezFs2DC0bdsWnTp1AlD8I3v9+nXs3r0bP/30U6XOKy03b97EnDlzcPHiRYwdOxYLFiwQm/dSQlWCjGbNmiEoKAg9evQQqFxKTExEp06dJCp0tmjRArGxsdDR0RHYHhsbC1dXV5HtCby9vSVeFwCxlX6yRFwulLjyagB49+6dwPv8/Hzcvn0b8+fPx9KlS9GjR49q81MYogIkklAaPn78eFy5cgX79+9H06ZNARTnPw0aNAjt2rXDli1bKuzLs2fPEBYWhuDgYKSkpIidNatKUFhjyHB5qdZSHWvEJTRs2JCuXr1KRMX5GSVqlwcPHiR7e3uxtp8+faIzZ87QvHnzqEuXLqSkpEQWFhY0btw4Cg0NFWlnbGws0I5b1Esa4uPj6eeff6aWLVuSpaUleXl5UXx8vES7oUOHUqNGjWjWrFkUGBhI69atE3gxqp/8/HwKCQmhjIyMStmX7aRb0c66pqamnMplaRYsWECmpqZCbZKTk2no0KH07t27cvuys7Np2LBhEnMVUlJSyMPDg+Tk5KQ6vixNmzalM2fOEJFgrlVCQgJpaWmJtVVWVubyEUrb3r9/X6oGcGPHjiU7OzuB6qnz58+ThoYGrV27tkLX8b1z/vx5srOzq/FxUlNTxb5EkZ2dTR07diR5eXnuHiwvL08ODg709u3bCvvx5csX2r9/Pw0aNIiUlZWpcePGYo+XddduYbAARcaoq6tzia7GxsYUExNDRESPHz8mFRUVoTYLFiygH3/8kZSVlcnKyop8fHwoPDy80j88skBTU5O7VsbXo7IJfETCb2DSSr6XjC2stDY5OVnkZ33cuHHk5+cn8pyzZs2iCRMmiNzv4+NDioqK1Lt3b7p9+7ZEH4VRlSDjhx9+oJ07d5azXbRoEXXp0kXi2EVFRTRo0CD68ccf6dOnTxQdHU1qamosiK8EDx48qNGuwNVBSTL2qlWraMOGDVIF/mWJjo6msWPHkra2NmloaNCoUaPo9OnT33TXblGwJNkqkJ+fDycnJwQFBVU66dPCwgJJSUkwMTFBmzZtEBQUBBMTE2zatIkTdirLkiVLYGRkhMDAQPz000/lpn8rQ15ensjGheJ49OgRtm3bhsePH2PdunXQ19fHiRMnYGhoKLYxlra2tkAuA+PrUNkEPgDlliPKTp1Lonv37rh48WK5styYmBj8+OOPQm0uXLiAnTt3ijynh4cH18VbGJs2bYKysjL+++8/sYm54pRoW7ZsiYsXL5b7N/v3339ha2sr0g4oLm0dMWIEnj9/jqKiIkRGRiIpKQk7duzAkSNHxNoCxUsDYWFhcHV1RY8ePRAfH4/ly5fD19dXom1tpGwrAlFMmTJF5L74+HiB90SEjIwMrFixAq1bt66SfxXhwYMHQgsfxC2VVDYZu4QmTZogKysLvXv3RlBQEPr161ep+/q3AstBqSJ6enq4dOkSmjdvXin7Xbt2IT8/H6NGjcLt27fRu3dvZGVlQVFRESEhIRgyZEg5mxMnTnBdPm/fvg1zc3NOJr4iUvGFhYVYtmxZpZL/gOLkSRcXF9jb2+PChQtISEiAmZkZVq1ahWvXrmHv3r0ibUNDQ3Hw4EFs3779m8oar+tUNoFPHBcuXMC0adNw8+ZNscdt2rQJCxYsgIeHB5fceeXKFfz777/w9/cXSLQtuYmrqKggMTFRZED19OlTWFpaiuw7JW110cKFC0XuO3z4MEaMGIG5c+di8eLF8Pf3FwgyxPXiAYCTJ09i2bJluHnzJqdVtGDBAjg5OQk9vuwPLFCcFzZs2DC4urrCx8eH216Z/69vGWl6fYnq81UCn88v10EaADp27Ijg4GCJCdlV5fHjxxgwYADu3r0r4EdJsCEqB6Uqydgl/PPPPxg8eHClH/7Onz+P33//nUtit7S0hJ+fn8gHiBpHpvM3dYDp06fT7Nmzq+18OTk5dPPmTXr16pVUx79//56OHj1Ks2bNonbt2pGioiJZWVlJpa7p7+9PZmZmFBoaSioqKtz08549e6hjx44S7Tt27Mh1wSw9fX3t2jWJ651t2rQhdXV1UlNTI2tra7K1tRV4MWoGUcs0VVlnlnbqXNwat6j17gYNGlBUVJTIc545c4YaNGhQKb8rwokTJ6hr165Ur149UlFRIXt7ezp58mSNjCWqe3J1/X/VdcrmfKSlpdGnT5++2vh9+/YlNzc3+u+//0hNTY0ePHhAFy9epPbt29OFCxdE2lX1fpyfn09ycnJ09+7dSvm9c+dOkpeXJw8PD07vx8PDgxQUFGjXrl2VOmdVYTMoVWTy5MnYsWMHmjVrhrZt25Z7IpXU4fX8+fPV0jiwsLAQ165dw6FDh/D333+LreIpoaoVBmpqarh79y5MTU3LdVNu0aIF1y1XGJKebMU90TIqT2V7uwDip87z8/MRGxtbLT6WxsPDA/n5+di/f7/Q/W5ublBUVMS///4r9jyfPn0CEXGzdU+fPsX+/fthZWUlciajOvny5YvQajVhCraS/o9K8z0pLmdnZ0NLS0vk/ujoaPj6+uLKlSvlepi9e/cOnTt3xqZNm2p8NkBXVxfR0dFo1aoVNDU1ce3aNVhYWCA6OhozZswQqc1T1fsxADRt2hSRkZGVWsqytLTE+PHjMW3aNIHta9euxebNmyV23q4RZBIW1SG6d+8u8uXg4CDRXkFBgQwNDWn27NkVinwLCwvp6tWrtGLFCnJ2duYUWo2MjGjkyJFS9XyoaoWBgYEBxcbGlrOPjIwkMzMzqa+FUTsQ9mTP4/GoU6dOlJCQUKFzSftEe+vWLVJSUqJBgwbR1atXKTs7m7Kzs+nKlSs0cOBAUlJS4hRmxdGrVy/auHEjERX3G9HX16cmTZqQsrIy/f3332Jtq6LOmZycTF26dKlUr63vmRUrVlB4eDj3fvDgwcTj8ahx48Z0584doTb9+vUTW9m0fv16cnd3r3Zfy6KlpcXdC83MzCg6OpqIiB4+fCgyGZyo6vdjIqLg4GBycXGhrKysCvutqKgoNIk9JSWFlJSUKny+6oAFKFWgoKCAzp07V6kPQwmvXr2iDRs2UOfOnYnH45GNjQ2tXLmS0tPTRdq4uLiQhoYG8Xg8TjJ+y5YtFS6frGqFgZ+fH3Xp0oUyMjJIXV2dUlJSKCYmhszMzGjRokUV8oXxdbl//z4dP36cDh48KPASR1WnzgsKCmjx4sXUuHFjgeaS8+bNoy1btoi0O3z4MOnp6ZX7kdfT05Pocwk6Ojp07949IiLavHkztWrVigoLCykiIoJatGgh1rZfv36kpKREjRs3punTp9OtW7ekvGKizp07U9euXenYsWN0+/ZtunPnjsBLGh4+fEi+vr7Uo0cP6tmzJ02ePLnOt4cwNTXlHn5OnTpFWlpadPLkSRozZgz16tVLqI2RkRE9ePBA5DkTEhLI0NCwRvwtTZcuXWj//v1ERDRs2DBydnammJgYTo5BFFW9HxMVL52rqamRkpISmZubV2jpvGnTplwLidJs2rSJmjVrJtX41Q2r4qkCJY2dEhISKp2UpKurC19fX/j6+uLJkyfYvXs3duzYgV9//RVdu3ZFdHR0ORtNTU1OMr6yyblA1SsMli5dilGjRsHAwIBrglZYWAhPT0/MmzdPqI0oVdOyyKQx1XdAZRP4gKovJyxduhTbt2/HqlWrMG7cOG67jY0NAgMDRSYB9u3bF0+fPsWJEyfw8OFDEBHMzc3h5OQkdYJ1bm4u1NXVARQ3Xxs4cCD4fD46duwocUnl0KFDAuqc69atk1qd886dO7h582alEzNPnjyJ/v37o02bNrC3twcR4dKlS2jZsiUOHz4sMUG3tpKRkcEp6B45cgQeHh5wcnKCiYkJOnToINTm5cuX5cTFSiMvL49Xr17ViL+lmTdvHnJycgAAAQEB6Nu3L3788Ufo6Ohgz549Iu2qej8GAHd390r7PWPGDEyZMgV37txB586dwePxEBMTg5CQEKxfv77S560SMgmL6hBt27blRJyqg4KCAjp8+DC1adOmQlPAlU0Cq0zyX9lpwIcPH9K///5Le/bsoeTkZLG230JL8u+ZyibwlXDmzBlydXUlMzMzatq0Kbm6utLp06elGrsqgmfCqIh4lY2NDa1fv57S0tJIQ0ODLl26REREN27cqHCSbXp6Oq1atYpatGghsdFg27ZtxTbmlESbNm2EJuHPnj27TieTN2rUiJtBMTc3p4iICCIiSkxMFNmI1MzMjCIjI0Wec9++fSIFAauTvLw8+vjxo8C2rKwsqZYIv2YytjAiIyPJ3t6e6tevT/Xr1yd7e3s6cODAVxu/LCxAqSInT56kNm3a0OHDh+nFixcCnV6FqV+KIiYmhnx8fEhPT4/U1dXJy8uLjh07JtamsLCwUlPmVYXH41GTJk1oxIgRtG3btkoLfzG+Pjo6OhQXF0dERBoaGpSYmEhERFFRUdSmTRuxths2bCB5eXkaOnQorV+/ntavX0/Dhg0jBQUF2rBhg8Sxq7LGXjYn4aeffpKYk1Caf//9lxQUFIjP5wssESxbtoycnZ0l2pdQUXXOqKgo6tSpE509e5Zev35d4fuDkpKS0KA/KSlJZnkBX4NJkyaRsbEx9ezZk3R0dOjDhw9ERBQeHi4yMPP19SVra2uhD2u5ublkbW1NkydPrjGfX716RX369CF5eXni8/nUqVMnmXRkfvv2LW3evJnmzJnDpR/cvHmTnj179tV9qSosQKkiolQ1pU2Cmzt3LpmYmJCioiL16dOHdu3aJXW766qWpZmamtLr16/LbX/79q3YJ40LFy7QkiVLqEePHqSqqkp8Pp9MTExo9OjRtHPnTqm+CGlpaQJ5NlevXqWpU6dSUFCQRFtG5alsAh8RUePGjYUGIn/++Sc1atRI4thVWWOvTE5CWTIyMujWrVsCippXr16VKsG3suqcwu4NFbk/NGnShJs9KM2ePXu+Sj6FrPjy5QutXr2apkyZIpDzExgYSJs3bxZqk5mZSY0bNyZDQ0NauXIlHThwgA4ePEgrVqwgQ0NDaty4MWVmZtaYz2PHjqUGDRrQ0qVLac2aNdS8eXPq2bNnhc9z/fp12rFjB+3cuZNu3LhRIdu4uDjS09OjZs2akby8vMBD64gRIyrsi6xhAUoVOXfunMhXYGCgRPtOnTrRn3/+KbXuSWmqOmVeuqdQaTIzM0lRUVEqH758+ULnz58nf39/cnBwIBUVFeLz+WRubi7WrkuXLrRjxw4iIi7JtlOnTqSjo0P+/v5Sjc2oOJVN4CMq/oyJkqqXpsrg0KFDpKmpSStWrCBVVVVavXo1jR07lhQVFenUqVNibZWVlSktLY2IiKZMmULjx48nouKZhMosD1UEAwMDUlZWJjc3N4qIiKjQcqq4+8O5c+ck2vv7+5OWlhatWLGCLly4QBcvXqTly5eTlpYWLVmypCqX9U1TdolEWlJTU8nFxaWcboyLiwvXUqSmMDQ0pKNHj3LvExISSE5Ojr58+SKVfXp6OnXp0oV4PB5pa2uTtrY28Xg8sre35z77kujRowfXGqL0b0JsbCwZGxuXO15bW5v77dHS0uLGFfaSBSxJtoqU1TB59+4ddu3ahS1btiAuLg6//PKLWPtLly5Veuznz5+Xkw0HihUJ8/PzRdqV7sB88uRJaGpqcu8LCwsRFRUlNvGvNAoKCujatSvatWuHTp064eTJk9i8eTMePnwo1u7evXto3749ACAiIgI2NjaIjY3FqVOnMGHCBCxYsECq8RkVQ1wCX3h4uFjb/v37Y//+/fDz8xPYfvDgQfTr10/i2P369cOePXuwbNky8Hg8LFiwAHZ2dlIle2prayM9PR2GhoY4ceIEAgICABRrsUjS+wGAAQMGCE3O5vF4UFZWRrNmzeDp6QkLC4tyxyxYsKDS6pxV1TiaP38+1NXVsWbNGsydOxcA0LhxYyxatEis3Http0GDBvDw8MDo0aPRpUsXqe2MjY1x7NgxvH37lkuobt68ObS1tWvQ22JevHgh0PqgRYsWUFRUxIsXL6RKMB89ejTy8/ORkJDAfQ6TkpIwevRojBkzBqdOnZJ4juvXryMoKKjcdgMDA2RmZpbbHhgYyCWPBwYGVrpTeU3BApRqIjo6GsHBwYiMjISxsTEGDRqErVu3Cj320KFDcHFxgYKCgkCwIAxxfRsq2yOkJNObx+Nh5MiRAvsUFBRgYmKCNWvWiPUrLy8Ply5dwtmzZ3Hu3Dlcv34dpqam6NatGzZu3Cjxxpyfnw8lJSUAwJkzZ7jrbNGiBTIyMsTaMipP7969ub/NzMzw4MEDvHnzRmR1VeneKJaWlli6dCnOnTuHTp06ASiWqo+NjcWMGTOkHr+0DyVcv34d7dq1E2k3cOBAeHp6onnz5sjKyoKLiwuA4ioZYUF6WTQ1NXHgwAFoaWnhhx9+ABHh9u3byM7OhpOTE/bs2YOVK1ciKioK9vb2nF1BQQEmTpyIzp07VypAESZbD/xfYGRkZMR9D0QdN23aNEybNg0fPnwAAO4HpS4TFhaGkJAQ9OjRA8bGxhg9ejR+/vlngXYI4tDW1hb7eaoJiAjy8oI/qfLy8uXE+URx8eJFXLp0SSBItrCwwIYNGwQ+k+JQVlbG+/fvy21PSkoS2gKl9P1/1KhRUo3xNWEBShV49uwZQkJCEBwcjJycHE71ct++fbCyshJp5+7ujszMTOjr64stC+PxeGKfDitbllbyhTE1NcX169ehq6sr+WJL0a1bN1y/fh1NmzZF165dMXnyZHTr1g0NGjSQ+hwtW7bEpk2b4OrqitOnT2PJkiUAip9CqqP5IUMQcY3yShMcHCzwPjAwUOC9trY2Hjx4gAcPHnDbtLS0EBwcLLK0vISPHz9CTk4OKioq3LY7d+5g/vz5OHbsmNjPemBgIExMTJCeno5Vq1ZBTU0NQHE56sSJEyVeV8OGDeHp6Yk///wTfD4fQPH3YOrUqVBXV0d4eDgmTJiA2bNnIyYmhrOTl5eHsbGxVLM0wmjTpo3Yp1IFBQUMGTIEQUFBYpu6vXr1CklJSeDxeLCwsKjwd7a20a9fP/Tr1w9ZWVnYsWMHQkJCMH/+fPTu3RujR49G//79ywUDsoaI0KNHDwG/cnNz0a9fPygqKnLbRDWmNDIyEjrzXVBQAAMDA6l8cHNzw+LFixEREQGg+DckLS0Nc+bMwaBBg8TaysnJISMjA/r6+gLbs7KyoK+vX+nvQJWQycJSHcDFxYXU1dVp2LBhdOTIESooKCAiInl5ebp///5X80MWZWny8vJkaGhIkydPpn379lUqf+bs2bOkpaVFfD6fvL29ue1z586lAQMGVKe7DCrONzIxMaEBAwaQu7u7yFdNkJ6eTp07dyY+n08KCgo0bdo0ysnJoREjRpC8vDwNGjSIK/utKXR1dSkpKanc9qSkJNLR0SEiovj4eNLU1Cx3TFXUOQ8cOEAWFha0ZcsWio+Pp7i4ONqyZQtZWlpSeHg4hYaGUpMmTWjGjBlC7T9+/Eje3t4kJyfH5VTIy8vT6NGjpU6mryv88ccfpKSkRDwej/T09Gj+/Pnf1L/BokWLpHqJ4sCBA9S+fXu6fv06V5J8/fp16tixI5c3Jol3796Rvb09aWlpkZycHBkaGpKCggJ17dpVYl6PqJzE58+fk7KyslTjVzesF08lkZeXx5QpU+Dj4yMglqagoIC4uDixMygl5Ofnw8nJCUFBQTA3N69Jd0WSk5OD8+fPC20LLmqNOycnBxcvXsS5c+dw9uxZ3LlzB+bm5ujWrRvXVVmajsqFhYV4//69wPpwamoqVFVVy0XxjKoxceJEhIeHw8jICKNHj8bw4cMrLS5YUYYPH467d+9i3Lhx2LdvHy5cuIA2bdqgdevWmD9/vlQdbHfs2CF2/88//yx2v7a2NrZv315uyfTQoUMYOXIk3r59i5SUFLRv375czxNbW1s8fPgQ+fn5MDY2LtdvS9QTMQC0b98eS5YsKbesdfLkScyfPx/Xrl3DgQMHMGPGDDx69Kic/f/+9z+cOXMGf/75JzfNHxMTgylTpqBXr17YuHGj2Ouu7WRmZmLHjh3Ytm0b0tLSMGDAAIwZMwYvXrzAihUr0KhRI6lyM74WRIS0tDTo6elVuEu7trY2cnNzUVBQwM3ClPxd9jMnScgyOjoat27d4rpn9+zZU+SxJcu406ZNw5IlS7jZSaD4Hn3hwgWkpqaK7CFUk7AApZJcvnwZwcHBiIiIQIsWLTBixAgMGTIEjRs3ljpAAQA9PT1cunSpUoqwZmZmuH79erklkezsbNjZ2YltSQ4At2/fRp8+fZCbm4ucnBzUr18fr1+/5gIESfYlfPjwATExMVw+SlxcHJo3b4579+6JtFm0aBG8vb2/q2Znsubz58+IjIxEcHAwLl26BFdXV4wZMwZOTk4ilyGmT5+OJUuWoF69epg+fbrY84tqjGlgYICIiAjY29sjMzMTjRs3xrJlyzBnzhypfS+b5Jifn4/c3FwoKipCVVVV4g17ypQpCAsLw6+//op27dqBx+Ph2rVrWLZsGTw9PbF+/Xps2bIFISEhAks8QNUaW6qoqOD27dvllGQTExNha2uLT58+ITU1FVZWVsjNzS1nr6uri71796J79+4C28+ePQsPD4+voowqCyIjI7Ft2zacPHkSVlZWGDt2LIYPHy7QLPD+/fuwtbUt92AlS4qKiqCsrIz79+9X+J6+fft2qY8tmzsoDklNFkseEJ4+fYomTZpATk6O26eoqAgTExMsXrxYpIJvjSKTeZs6RE5ODm3dupXs7e05Iah169bR+/fvpbKfPn26UKVIaahqmXC3bt1o3LhxVFBQwJWkpaWlUdeuXWnfvn1S+1FYWEhXrlyh5cuXk5OTE6eNIg47OzuSk5MjR0dH2rVr11dth84oLsdctGgRmZmZkaGhISeEVZbu3btziq3dunWrVGNMPp9PGRkZ3HtVVVWxPVOkJTk5mXr06EEnTpyQeGxBQQEFBARQw4YNuaWShg0b0tKlS7nl2adPn4rtgVUZ2rRpQyNHjqTPnz9z2758+UIjR47khPFiYmLIxMREqL2KiorQf6t79+6Rqqpqtfr6LaGhoUHjx4+na9euiTwmNzf3m+z5ZWVlRZcvX5bJ2MIEDfl8vlSCht27d6c3b97UtIsVgs2gVCNJSUnYunUrdu7ciezsbPTq1Utilc7kyZOxY8cONGvWDG3bti03lSfsqbTknO7u7ti+fbvQMuHTp08jKSlJ7NhaWlq4evUqLCwsoKWlhcuXL8PS0hJXr17FyJEjkZiYKNSuqKgIN27c4JZ4YmNjkZOTAwMDAzg4OHAvSbMj8fHx2LZtG3bv3o0vX75g6NChGD169FfPvv8eSUtLQ0hICEJCQvDlyxckJiYKTO1WJ3JycsjMzOSW/dTV1REfHy/V0o4kbty4geHDh4v8rAqjpMpBQ0NDapvs7Gzs3bsXjx49gp+fH+rXr49bt26hQYMGYhMYL126hP79+4PP56NVq1bg8XiIj49HYWEhjhw5go4dO2Lnzp3IzMwsV74NAD169ICOjg527NjBJdF++vQJI0eOxJs3b3DmzBmpr6E2kZubW+Elkm+Fo0ePYsWKFdi4cSOsra0lHl9UVISioiKB5NqXL19i06ZNyMnJQf/+/aUutTYzM0NoaCg6d+6M06dPw8PDA3v27EFERATS0tK+qeUwaWABSg1QWFiIw4cPIzg4WGKA4uDgIHIfj8cT2iywpAqhdLO3EkqXCfft21fs2Hp6eoiNjYW5uTksLCzwxx9/oHfv3khMTISdnZ3QKWeg+Maek5ODRo0aoXv37ujevTscHBzQtGlTseOJoqCgAIcPH8a2bdtw4sQJWFhYYOzYsRg1apRA8MWoGqWXeGJiYtC3b194e3vD2dmZ+0yJoqCgAMrKyrhz545UN93S8Pl8WFtbczfg+Ph4TiOiNOJyOURx+/ZtdOvWTWhpZXURHx+Pnj17QlNTE6mpqUhKSoKZmRnmz5+Pp0+fSsyP+fjxI0JDQ5GcnAwiQosWLeDp6SlVufC9e/fg7OyMvLw8tG7dGjweD3fu3IGSkhJOnTqFli1bVtdlfrN8+vSpXHVLRYLLr03pXBJFRUWBqjWgfP6It7c3FBQU8M8//wAoXjJv2bIl8vLy0KhRIzx48AAHDx5Enz59JI6toqKC5ORkGBoaYurUqcjLy0NQUBCSk5PRoUOHcvlVZXn27BkOHTokNCdR1BJuTfJt1WnVEeTk5ODu7i6xs2RhYSEWLVoEGxubCiUsVrVMuARbW1vcuHED5ubmcHBwwIIFC/D69Wvs3LkTNjY2Iu1KOilXV2JvUVERvnz5gs+fP4OIUL9+fWzcuBHz58/H5s2bMWTIkGoZ53umdJKst7c3wsPDK1TOXZVy27I5Gm5ubhU+R9lAn4iQkZEhkDwqjpcvX2LmzJmIiorCf//9Vy6wF3dd06dPx6hRo7Bq1SqBoMLFxQWenp4Sx1ZTU8OECRMkHicMa2trpKSkIDQ0FImJiSAiDB06FF5eXuV++OoSOTk5mD17NiIiIpCVlVVuv0xKXqVk3bp1FTo+NjYWf/75J/d+x44dKCgoQEpKCjQ1NTF79mysXr1aqgClKoKGUVFR6N+/P0xNTZGUlARra2ukpqaCiGBnZ1eha6ou2AyKjFFWVkZCQkK1THdXlBs3buDDhw9wcHDAq1evMHLkSMTExKBZs2bYtm0bWrduXaPj37x5E9u2bUNYWBiUlJTw888/Y+zYsZzw1po1a7Bq1Sq8fPmyRv34HuDz+TAyMoKtra1YXY7IyEiR+7Zt24Z///0XoaGhX60CqISyMzw8Hg96enpwdHTEmjVr0KhRI7H2Li4uSEtLg6+vLxo1alTu30Bc0KSpqYlbt26hadOmUFdXR1xcHMzMzPD06VNYWFggLy9P4PjqEmIEijUoSgLJtLQ0bNmyBZ8+fUL//v3x448/irWtzUyaNAlnz57F4sWL8fPPP+Ovv/7C8+fPERQUhBUrVsDLy0vWLlYb9erVw71797jfgIEDB8LAwAAbNmwAADx48ADdu3fHf//9J/Fcvr6+OHLkCJo3b47bt28jNTUVampqnBChpIozZ2dnLF68mPuc6+vrw8vLC87OzvDx8ameC64AbAZFxtjY2ODx48eVDlAqUyZcQtu2bbm/9fT0cOzYsQqPvWLFCu6ptKxiorgqoFatWiEhIQFOTk7YunUr+vXrJ5A9DhSXjgpbl2dUnJ9//rnKMtZ//PEHHj58iMaNG1e43BYonqonIi634OnTp9i/fz+srKzg5OQk1lZaNU5RxMTE4OLFi2jTpk2FbSuqzlkdQox3795Fv379kJ6ejubNmyM8PBzOzs7IyckBn89HYGAg9u7dK3GWtrZy+PBh7NixA927d8fo0aPx448/olmzZjA2NsauXbtqTYAizfKUsrIyPn36xL2/cuUKVq9eLbD/48ePUo1XFUHDhIQEhIWFASieMf306RPU1NSwePFiuLm5sQDle2Tp0qWYOXMmlixZgh9++KHcTV/cWqukMmFpenW8fv0aqamp4PF4MDExqdC0/9ixY3H+/HmMGDFC6FOpOH766SeMHj1abIKhnp5elX+YGMWEhIRU+Rxubm5VCnLc3NwwcOBATJgwAdnZ2Wjfvj0UFRXx+vVrrF27VuobYMmkb0V8MTQ0LLesUxG/K6LOWfozW9nP76xZs2BjY4PQ0FCEhoaib9++6NOnD7Zs2QKgOLl+xYoVdTZAefPmDffQpqGhweVtdOnSRSY/lBWhostTrVu3xs6dO7F8+XJcvHgRL1++hKOjI7f/0aNHUkv8KygoYObMmeW2S+oJBxTP5Hz+/BlAcb+nR48ecTlOr1+/lmr8auer1w0xBCgpeSzbkl2aduxVKRO+d+8e/fjjj+XawDs4OEjVfp6ISFNTk2JiYqS+Vsb3jY6ODt27d4+IiDZv3kytWrWiwsJCioiIoBYtWki03759O1lbW5OSkhIpKSmRjY0N1xFbEidPniQnJ6dKdbStijqnMErKtsWho6NDcXFxRET04cMH4vF4dP36dW5/QkKCUNXbuoKNjQ3X7blXr16c0u769evJwMBAlq5JZOLEiWRpaUn//vsvqaioUHBwMC1ZsoSaNGlCoaGh5Y6Pjo4mZWVlMjMzIxUVFRo9erTAfh8fH/r555+lHn/Hjh1kb29PjRo1otTUVCIiCgwMpAMHDoi1c3Nzo3/++YeIiPz8/KhZs2YUEBBAdnZ21KNHD6nHr07YDIqMOXv2rMh9kpT77ty5g6CgIMjJyUFOTg6fP3+GmZkZVq1ahZEjR2LgwIFC7TIzMzm117Vr16JFixYgIjx48ACbN29G165dce/ePYlqrtra2lXKRfjWMsYZ4qmqMGBubi6XZHrq1CkMHDgQfD4fHTt2xNOnT8Xarl27FvPnz4evry/s7e1BRIiNjcWECRPw+vVrTJs2Taz9kCFDkJubi6ZNm0JVVRUKCgoC+8UJvWloaCAmJqZC6pwlrFy5EiYmJlyi908//YR9+/ahUaNGOHbsmMg8rzdv3qBhw4YAipNs69WrJ/Bd09bW5poH1kW8vb0RFxeHbt26Ye7cuXB1dcWGDRtQUFDwzd8bKro85eDggJs3b+L06dNo2LAhfvrpJ4H9bdq04Tq/S2Ljxo1YsGABfvnlFyxdupSbrdHS0sK6devE5lqtXbuWW0patGgRPn78iD179qBZs2blenJ9NWQSFjFEkp2dTX/99RfZ2tpKnEEp3V/E3NycE6xKSEggFRUVkXazZs0iOzs7oeJoubm5ZGdnR3PmzJHo686dO2nw4MGV6odx5swZUlVVpZYtW5K8vDy1adOGtLS0SFNTU6zoF0N2iBMGVFBQkGhvY2ND69evp7S0NNLQ0OD679y4cYMaNGgg1tbExIS2b99ebntISIhIkbOyx4l7VRRpZkGIiExNTSk2NpaIiE6dOkVaWlp08uRJGjNmDPXq1UukHY/Ho//++497r6amRo8fP+beZ2ZmSrw/1CWePn1K+/btkyg29i1Qr149bubCwMCArl69SkREjx8/pnr16gkca2try4mj+fv7V7m3kKWlJde3p2RWnYjo7t27XM+p2gSbQflGiI6ORnBwMCIjI2FsbIxBgwZh69atYm0qWyZ8+vRpzJkzR2j3VBUVFfj5+WHVqlVYvny52PHXrFmDR48eoUGDBjAxMSn3VCouaXLu3LmYMWMGlzG+b98+gYxxxrdD6UqUkydPChUGlCbJe8GCBfD09MS0adPg6OiITp06ASieTbG1tRVrm5GRgc6dO5fb3rlzZ2RkZEgcuyLS4GUpOwvi4eGBffv2oWHDhmJnQUr8NjQ0BAAcOXIEHh4ecHJygomJiUTp8FGjRkFJSQkAkJeXhwkTJnA5aiW5AnWRoqIihISEIDIyksuPMzU1xeDBg9GqVStZuycRMzMzpKamwtjYGFZWVoiIiED79u1x+PDhcpLzCQkJyMnJgba2Nvz9/eHj41MlgbonT54I/S4pKSkhJydHrK23tzeGDx8OR0fHKifUVxcsQJEhz549Q0hICIKDg5GTkwMPDw/k5+dj3759UvXyWbZsGTfNu2TJEowcORI+Pj5cmbAoHj9+LLauvW3btlL14alKgt63mDHOEE7J/zOPxyv3Q19aGFASgwcPRpcuXZCRkSFQTdOjRw+Ry5ElNGvWDBEREfj1118Ftu/Zs0dkz5P3799zSeaShNzEJaMHBQUhNDQUQHFwf/r0aRw/fhwRERHw8/MTq85ZWV2Ksv/Ow4cPL3eMpAaJtREiQv/+/bnAz8bGBkSEhIQEjBo1CpGRkThw4ICs3RRLRZan2rRpA29vb3Tp0gVEhNWrV4tUdF6wYIHEsU1NTXHnzp1yKt7Hjx+HpaWlWNusrCy4urpCR0cHQ4cOxYgRIypV9VatyHYC5/vFxcWF1NXVadiwYXTkyBGuH4i8vDzdv3+/Rsfm8/lCp+pLyMzMJDk5uRr1oUGDBtx1WllZ0cGDB4mI6M6dO+WmQRnfBiYmJvTq1asK2w0YMECqlzj27t1LcnJy1Lt3b1q8eDEtWbKEevfuTfLy8hQZGSnUpvTnvGwSekWS0ZWVlSktLY2IiKZMmULjx48nIqKkpCTS0tISaztp0iQyNjamnj17ko6ODtfzKDw8nGxtbcXafo8EBweTuro6RUdHl9sXFRVF6urqQpf6vmXELU8lJibSkCFDqG3btsTn88na2pratGlT7iXtZyU4OJgMDAwoPDyc6tWrR2FhYRQQEECqqqoUFhYm0f7t27cUFBRE3bp1Iz6fT5aWlrR06dJKJZdXB2wGRUacOnUKU6ZMgY+PT6U6GZdQ2TLhDx8+CF3iAYqfNqmG9fs6duyI2NhYWFlZwdXVFTNmzMDdu3cRGRmJjh071ujYjMrx5MmTctskdUoFUC3tCgYNGoSrV68iMDAQBw4cABHBysoK165dE7k8FB0dzSWWRkdHV3rauirqnFXRpfgeKek4LawFiKOjI+bMmYNdu3bVqtkjIyMjGBkZCd1nYWGB8PBwAMVihFFRURKLE8Th7e2NgoICzJo1C7m5ufD09ORE36QR9tPS0sL48eMxfvx4PHv2DGFhYQgODsaCBQtQUFBQab8qjUzCIgZdunSJxo4dSxoaGtS+fXvasGED/ffff1LPoFSlTFjU06S0T5VaWlqkra0t8SWOR48ecWWUOTk55OPjQzY2NjRgwAAuwYzxbVG2U+rgwYOJx+NJ1SlV1pTuJlwWSbNCbBbk69GgQQO6ffu2yP23bt2SmFAtK6KiosjS0pLevXtXbl92djZZWVnRhQsXvpo/r169opcvX9KLFy/I19eXlJWVpbb98uUL7d+/nwYNGkTKysrUuHHjGvRUNEzqXsbk5uYiPDwcwcHBuHbtGgoLC7F27VqMHj1aZDOxzMxMWFtbQ09PDxMmTChXJpyVlSW2TPj8+fNS+datWzeh27dv3y6VfVUSExnfHl+7U2p15ZAAxXk0kZGR5STzX758iR49euDevXsibfPz87F+/Xqkp6dj1KhR3IzNunXroKamhrFjx4q03b59O3R1deHq6gqgWIDtn3/+gZWVFcLCwiR2/P7eUFRUxNOnT0W2Lnjx4gVMTU2/ySTh/v37w8HBQWTJ+x9//IGzZ89i//79Qvf/+++/CAsLQ3JyMng8Hpo3bw5PT08MHjxY4tjZ2dmYNGkSTp06BQUFBcyZMwe+vr7w9/fH77//DisrK0yfPh3Dhg0Te56zZ89i9+7d2LdvHwoLCzFw4EB4eXnB0dFRYkPRGkEmYRFDKImJieTn50cNGzYkZWVl6tevn9DjqqtMWJaYmprS69evy21/+/YtmZqaysAjhiSqkotRGaorh4SIqH379jRq1CiBbS9evKAWLVrQoEGDqt33EszNzSkqKoqIimdNVVRUKCgoiPr16ycx7+Z7hM/nC5RXl+VbLq82MjKiBw8eiNyfkJBAhoaG5bYXFhaSh4cH8Xg8srCwIDc3N+rfvz+Zm5sTn8+nIUOGUFFRkdixfXx8qEmTJjRjxgxq2bIl8fl8cnFxIQcHB07wThIGBgakrKxMbm5uFBERIfT35WvDApRvkIKCAtq/f7/IAMXW1pb27Nkj0j4sLEzk1PO7d++kfkkiLS2N0tPTufdXr16lqVOnUlBQkERbcZoaioqKEu0ZX59GjRpxmh7m5uYUERFBRMWBtbq6erWPd+7cOcrPzyciorNnz9K5c+dEviTx+vVrsrKyol9++YWIiJ49e0bm5ub0008/UWFhoUT7yqpzqqio0NOnT4mo+MFixIgRRFS8RKurqytx3O8NHo9Hffr0EZlI3adPn282QFFSUqKUlBSR+1NSUoQus6xZs4bq169Phw8fLrfv4MGDVL9+fQoMDBQ7tpGREZ0+fZqIipfPeTweTZ06tUL+BwUFUVZWVoVsahqWJPsNIicnB3d3d5FlvFUpE9bS0pKYLEhEYhuZleDp6Ynx48djxIgRyMzMRM+ePWFtbY3Q0FBkZmYKLYuTRlPDxMRE7LgM2TBw4EB4enqiefPmyMrKgouLC4BiReOSDtTVSeklxu7du1fpXDo6Ojh58iS6dOkCADh69Cjs7Oywa9cuiVPXVVHnVFNTQ1ZWFoyMjHDq1Clu+r9sgzhGMdIsC3+rCbIGBga4e/euyO9CfHy80KWrkJAQrF69Gn379i23r3///li1ahXWrVsntp/OixcvOGkKMzMzKCsri116LEtBQQEmTpyIzp07f/VO5WKRdYTEqDhVKRMW9xRa0adSLS0tSkxMJKLiHhmdO3cmouK+J6KWaUr3HSrdh4jH45GioiKZm5sLfZJgyJ4vX77Q6tWracqUKXTr1i1ue2BgIG3evLlGxzY1NaV58+Zxn7fKkpycTPr6+uTl5SVx2ryEqqhzenp6kp2dHY0ZM4ZUVVW5Zc2DBw+SlZVV5S+E8c3h6+tL1tbWIpfera2tafLkyeX2KSsrc7NswkhNTZWY4Fp2aays8rA0mJmZfXPJ7mwGpZZS2TJhUYmvlSE/P59Tujxz5gz69+8PAGjRooVIdc+S7q6mpqa4fv06dHV1q80fRs1SlU6pVcXX1xdhYWFYunQpbG1tMWLECAwZMkRkMiVQXB4sbLYwNzcXhw8fFijJF9eLpyrqnH/99RfmzZuH9PR07Nu3jxvz5s2bEhMWGbWLefPmITIyEubm5vD19YWFhQV4PB4SEhLw119/obCwEL/99ls5OxUVFWRnZ4ssRX7//j1UVFTEjk1EYpWHS4iMjBTr/9y5cxEaGvrNzKKwKp5aCJ/PF7tMQ1Iu0ZSQm5srtGGfJFnpDh06wMHBAa6urnBycsKVK1fQunVrXLlyBYMHD8azZ8/K2Vy9ehVv3rzhlgcAYMeOHVi4cCFycnLg7u6ODRs2cF80hmw5dOgQXFxcoKCgILA8J4ySALUmSU5Oxq5duxAeHo7Hjx/DwcEBw4cPFzrtL221GSB+acHKygrLly+Hm5sb1NXVERcXBzMzM/zxxx8ICQkR29KhLO/evcOuXbuwZcsWxMXFSf0dZdQOnj59Ch8fH5w8eZJ7SOTxeOjduzf+/vtvocvXrq6uMDIywsaNG4Wec8KECUhPT8fRo0dFjuvt7S2Vf+IUxm1tbfHw4UPk5+fD2Ni4XHBTkc95dcEClFpIVcuES3j16hW8vb1x/Phxofsl3TzPnTuHAQMG4P379xg5ciSCg4MBAL/++isSExOFRuvOzs5wcHDA7NmzAQB3796FnZ0dRo0aBUtLS6xevRr/+9//sGjRIimukFHT8Pl8ZGZmQl9fX2yuRkUC4uriypUr8PHxQXx8fI2OvW3bNsyfPx9r1qzBmDFjsGXLFjx69AjLli3D1q1bMXToUInnENZra9CgQRJ7EDFqJ2/fvsXDhw9BRGjevDm0tbVFHnvp0iV0794d7u7umDlzJicbkZCQgDVr1uDgwYM4e/Ys7O3ta9Rnf39/sfsXLlxYo+MLRVZrSwzZ4+npSZ07d6Zr165RvXr16NSpU7Rz506ysLCgI0eOSHWOgoICrhtnCU+ePBGZI9OwYUO6fv069/7XX38le3t77n1ERARZWlpW4moY3wsl1WINGzYkFRUV8vDwkMquoKCA9u7dS0uWLKGAgACKjIzkWkxI4p9//iEjIyMuX6pJkya0detWevbsmUib9PR0WrJkCZmampK+vj75+vp+lVYWjNpHZGQk6erqliuj19HRob1798raPZnBZlBqGZIEq0ojSbyqUaNGOHjwINq3bw8NDQ2uM/KhQ4ewatUqxMTEiLVftGgRvL29KyQ2paysjJSUFK7Da5cuXeDs7Ix58+YBAFJTU2FjY8M1QWR8GwjrMGtmZoZBgwZhxIgRNd79tGRpZ/fu3UhNTYWDgwO8vLwwcOBAkYKGpXn48CH69OmD58+fw8LCAkSE5ORkGBoa4ujRo2jatKlUfrx+/RpFRUUoLCzEsmXLsGXLFqHVOH369EFMTAz69u3LdeiWk5ODgoIC4uLipGoGyqg9SGp2WRpReSC5ubk4efIkUlJSAADm5uZwcnKqUnfjipKdnY29e/fi0aNH8PPzQ/369XHr1i00aNAABgYGX80PDhkHSIwKIkmmviLiVerq6lwTKGNjY4qJiSEiosePH5OKiopEezs7O5KTkyNHR0fatWuXVMI+RkZGdP78eSIqlh9XUVGhM2fOcPvj4+MlyuQzvi5FRUXk6upKPB6P2rRpQ0OHDqUhQ4ZQq1atiMfjkZubW437wOPxqF27dhQYGEgZGRkVtndxcSFnZ2cBnYfXr1+Ts7Mz9enTR6jN27dvydPTk3R1dalRo0a0fv16KiwspAULFpCqqiq1bduWdu/eLdRWTk6Opk2bRsnJyQLb2QxK3WTUqFHca+TIkaShoUGGhoacfouRkRFpaGiUEwv8loiLiyM9PT1q1qwZycvLc9Vq8+bN4/R7vjasiqeWcfbs2Wo7l4WFBZKSkmBiYoI2bdogKCgIJiYm2LRpk9jqiBJu3ryJ+Ph4bNu2DdOmTcOkSZMwdOhQjB49Gu3atRNq4+zsjDlz5mDlypU4cOAAVFVVBZpYxcfHS/00y/g6hISE4MKFC4iKiirXxC06Ohru7u7YsWNHjepTJCYmwtzcvNL258+fx5UrVwSqE3R0dLBixQqRa/u//vorLly4gJEjR+LEiROYNm0aTpw4gby8PBw7dkxsjtfFixcRHByMtm3bokWLFlzVEaNuUjr5dPbs2fDw8MCmTZsgJycHoDifb+LEieVmtf/44w+px5gyZUr1OCuC6dOnY9SoUVi1apXArKSLiws8PT1rdGyRyCQsYnwThIaG0rZt24iouAmXnp4e8fl8UlZWFmgKJw35+fkUGRlJ/fr1IwUFBbK2tqZ169ZRdna2wHH//fcfdenShXg8Hqmrq1NkZKTAfkdHR/r111+rdF2M6qVXr160fPlykfuXLl1KTk5ONe7H27dvafPmzTRnzhxuJuTmzZti80BK0NbW5lRwSxMTEyNyxq461DlzcnJo69atZG9vTwoKCsTn82ndunX0/v37Cp2HUXvQ1dUVqteTmJhI9evXF9hmYmIi1etrtP/Q0NCghw8fEpGg3k9qaiopKSnV+PjCYAFKHSAnJ4cSEhIoLi5O4FWZ89y8eVNid1dhfP78mcLDw8nJyYnk5eWpa9euZGFhQerq6kKDnezsbKEJillZWWI7zzK+Pt9Ch9m4uDjS1dWt9PTziBEjqGXLlnTlyhUqKiqioqIiunz5MllbW9PIkSOF2sjLy9Pz58+59yoqKnT37t1KX4O0vbYYtRstLS1O2K80+/fvr5GeVdWFvr4+J8BYOkA5efIkNWnSRCY+sQClFvPff/+Rq6uryFwUSUjbREocN27coEmTJlH9+vWpUaNGNHv2bIF+FL///jvp6+tXeRyG7FBQUKAXL16I3P/8+fMa75/k6OhIfn5+RCR484yNjSVjY2OJ9m/fvqX+/ftzisWKiorE5/PJ3d293CxfCdWhzikMSb22GLWbadOmkba2Nq1evZouXrxIFy9epNWrV5OOjg5NmzZN1u6JZNy4ceTu7k5fvnzhPutPnz4lW1vbCs8cVhesiqcW4+XlhdTUVKxbtw4ODg7Yv38/Xr58iYCAAKxZs4Zr8S4KRUVFNGzYEJ6enhg+fDisra0rNH6rVq2QkJAAJycnjBs3Dv369ePWXEt49eoVGjRowCnIMmofcnJyyMzMhJ6entD9L1++ROPGjWtUi0RTUxO3bt1C06ZNBcTSnj59CgsLC+Tl5Ul1npSUFCQkJAAoFmAT10OIz+fDxcWFEw08fPgwHB0dK6TOyfj+KCoqwu+//47169dzitqNGjXC1KlTMWPGjHL3yNI8e/YMhw4dEiqcuXbt2hr1+/379+jTpw/u37+PDx8+oHHjxsjMzESnTp1w7Nixcp/7rwFLkq3FREdH4+DBg2jXrh34fD6MjY3Rq1cvaGhoYPny5RIDlBcvXiA8PBxhYWFYtWoVrK2tMXz4cHh6eqJJkyYSx//pp58wevRoseVnenp6LDip5VAZGe2yfP78ucZ9UFZWFlpin5SUJDJwEkbz5s25oERSaXRZddnhw4dLPQ7j+4XP52PWrFmYNWsW95mVJPkAAFFRUejfvz9MTU2RlJQEa2trpKamgojENoetLjQ0NBATE4Po6GjcunULRUVFsLOzQ8+ePWt8bFGwGZRajIaGBuLj42FiYgITExPs2rUL9vb2ePLkCVq2bInc3Fypz/XkyRPs3r0bYWFhSExMRNeuXREdHV2D3jNqC9Uho11Vxo8fj1evXiEiIgL169dHfHw81/W7a9euWLduncRz7NixA6tXrxbQmfDz88OIESNqzG/G90lBQQHOnTuHR48ewdPTE+rq6njx4gU0NDSgpqYm1KZ9+/ZwdnbG4sWLuVlCfX19TkfHx8fnK19FsS6KlpbWVx+3BBag1GLatWuHgIAA9O7dG+7u7tzMyR9//MGJ7VSEwsJCHD9+HPPnz5daPlyWU5KM74eqTj+vXbsW8+fPh6+vL+zt7UFEiI2NxV9//YWAgABMmzbtK10Jo67z9OlTODs7Iy0tDZ8/f0ZycjLMzMzwyy+/IC8vD5s2bRJqp66ujjt37qBp06bQ1tZGTEwMWrZsibi4OLi5uSE1NbVG/V65ciVMTEy4cngPDw/s27cPDRs2xLFjx9C6desaHV8oMsl8YVQL1VUmHBMTQz4+PqSnp0fq6urk5eVFx44dk2h35swZUlVVpZYtW5K8vDy1adOGtLS0SFNTkxwcHCp7WQyGSKKiomj16tW0cuVKrgRYGkxMTGj79u3ltoeEhJCJiUl1usj4znFzc6Phw4fT58+fBRK6z507R82aNRNp16BBA07Ez8rKig4ePEhERHfu3KF69erVuN+mpqZcKf6pU6dIS0uLTp48SWPGjKFevXrV+PjCYAFKHaKiZcJz584lExMTUlRUpD59+tCuXbsoJydH6vHatWtH8+fPJ6L/q6z48OED9e/fn/7+++9KXQODUZb8/HySk5OrUomvkpKSQHVZCcnJyTLTeGDUTXR0dDgdlNIBypMnT8QqdLu5udE///xDRER+fn7UrFkzCggIIDs7O+rRo0eN+62srExpaWlERDRlyhQaP348ERElJSXJrDxadHtSxjdP2a7GqqqqsLOzg66urlT2586dw8yZM/H8+XMcPXoUnp6eFer7kJCQwCUSysvL49OnT1BTU8PixYuxcuVK6S+EwRCDvLw8jI2Nq1Ql1KxZM0RERJTbvmfPHjRv3rwq7jEYApT0airLs2fPxPaNWrt2LTp06ACguM9Zr169sGfPHhgbG2Pr1q015m8J2traSE9PBwCcOHGCS44loq/eqbwEVsVTi+nVq1eVyoQvXbpUpfHr1avHVXA0btwYjx49QsuWLQEUN1VjMKqLefPmYe7cuQgNDRWQq5cWf39/DBkyBBcuXIC9vT14PB5iYmIQFRUlNHBhMCpLr169sG7dOvzzzz8AiqvFPn78iIULF6JPnz4i7czMzLi/VVVV8ffff9e4r6UZOHAgPD090bx5c2RlZcHFxQUAcOfOHbHl+DUJS5Ktxbx+/ZorE758+bJUZcKHDh2Ci4sLFBQUcOjQIbHn79+/v9j97u7ucHV1xbhx4zBr1izs378fo0aNQmRkJLS1tXHmzJlKXxuDURpbW1s8fPgQ+fn5MDY2LpcUe+vWLYnnuHnzJgIDA5GQkAAigpWVFWbMmAFbW9uacpvxHfL8+XM4OjpCTk4OKSkpaNu2LVJSUqCrq4sLFy5AX19fqJ2ZmRmuX78OHR0dge3Z2dmws7PD48ePa9Tv/Px8rF+/Hunp6Rg1ahT3vVi3bh3U1NQwduzYGh1fGCxAqSNIWybM5/ORmZkJfX198PmiV/h4PJ7Eab3Hjx/j48ePaNWqFXJzczFz5kzExMSgWbNmCAwMhLGxcZWvi8EAimdAeDweRN2uFi5c+JU9YjBE8+nTJ4SHh+PmzZucnoiXlxdUVFRE2pS+N5fm5cuXMDIy+ip6Q98aLECpQ1SmTJjB+JbJzc2Fn58fDhw4gPz8fPTo0QMbNmyQOs9KGESEs2fP4tOnT+jcuTO0tbWr0WPG90x+fj4sLCxw5MgRWFlZSWVTMpPt7u6O7du3Q1NTk9tXWFiIqKgonD59GklJSTXic2l27tyJoKAgPH78GJcvX4axsTHWrVsHU1NTuLm51fj45ZBJai6jWqlMmfCXL1+oe/fulJSUVOlxTU1N6fXr1+W2v3379qt032TUfWbOnEmqqqo0btw4mjJlCunq6tLgwYOltn/79i39/PPPZG1tTWPHjqV3796Rvb098Xg84vF4pK+vX6nGmgyGKBo3bkwPHjyQ+viSzyKfz+f+LnkpKiqSubk5HT58uAY9Lubvv/8mXV1dCggIIBUVFa76aNu2bdS9e/caH18YLECpxVS1TFhXV5eSk5MrPT6Px6OXL1+W256ZmVnjzeMY3wdmZmYUFhbGvb969SrJy8sL7YQtjDFjxlDz5s1pyZIl1KFDB+rUqRN17NiRrly5QteuXaPu3btT3759a8p9xnfI8uXLaeTIkZSfn18hOxMTk0p1kq8uLC0tuS7Mpcuj7969Szo6OjLxiS3x1GI6d+4MLy8vDBkypFJT3jNmzICCggJWrFhRIbtvaUqSUbdRVFTEkydPBPo9qaioIDk5GYaGhhLtDQwMsHv3bnTr1g3Pnz+HoaEhoqOj0b17dwDAtWvX0L9/f2RmZtbUJTC+MwYMGICoqCioqanBxsam1jSXVFFRQWJiIoyNjQUacqakpKBVq1b49OnTV/eJlRnXYqpaJvzlyxds2bIFp0+fRtu2bct9kURJ1bu7uwMoTqQt21BNQUEBJiYmWLNmTZV8YzCA4oBXUVFRYJu8vDwKCgqksn/58iXMzc0BFAcrysrKAoGNkZERXr16VX0OM757tLS0MGjQoErZnj9/Hr///jsSEhLA4/FgaWkJPz8//Pjjj9XsZXlMTU1x586dcsUNx48fh6WlZY2PLwwWoNQyqrNM+N69e1yXzOTkZIF94jq9lnQnNjU1xfXr16uUsMhgiIOEdFLOy8vDhAkTBAJqUU+lRUVFAu3t5eTkBD7bkjoaMxgVpbJNM0NDQ+Ht7Y2BAwdiypQpICJcunQJPXr0QEhICDw9PavZU0H8/PwwadIk5OXlgYhw7do1hIWFYdmyZV9FKE4YbImnllFdZcKFhYWIiYmBjY1NhYWvrl69ijdv3nBCPkBxp9iFCxciJycH7u7u2LBhg8CPCoNRGaraSZnP5yMgIIDrIDt79mz4+flxQfWHDx+wYMECVvHGkDmWlpYYP358ucaVa9euxebNm5GQkFDjPmzevBkBAQGcoqyBgQH8/f3Ru3dvgWXWrwULUL5jlJWVkZCQAFNT0wrZOTs7w8HBAbNnzwYA3L17F3Z2dhg1ahQsLS2xevVq/O9//8OiRYtqwGsGQ3pMTEykmiV58uTJV/CGUVexs7NDVFQUtLW1YWtrK/YzJ0pUUElJCffv3y+n2vrw4UNYW1sjLy+vWn0Wx+vXrznJ/mXLlmHLli0sB4UhPfn5+XByckJQUBC3xl5RbGxs8Pjx4woHKHFxcQgICODeh4eHo0OHDti8eTMAwNDQEAsXLmQBCkPm1HSLegYDANzc3LgZ45IcvYpiaGiIqKiocgFKVFSUVAnhlSU7OxuTJk3CqVOnoKCggDlz5sDX1xf+/v74/fffYWVlheDg4BobXxwsQKmlKCgo4N69e1VaQ1+6dClmzpyJJUuW4IcffiiXJKuhoSHU7u3bt2jQoAH3/vz583B2dubet2vXjpsiZDC+VbKzs6GlpSVrNxh1gNJKxhVVNR49ejTWr1+PGTNmYMqUKbhz5w46d+7M9YsKCQnB+vXrq9tljl9//RUXLlzAyJEjceLECUybNg0nTpxAXl4ejh07hm7dutXY2BKRSXEzo1qYPn06zZ49u9L2pQWB+Hw+9yp5LwojIyM6f/48ERF9/vyZVFRU6MyZM9z++Ph40tbWrrRfDEZ1s2LFCgoPD+feDx48mHg8HjVu3Jju3LkjQ88Y3zt8Pp/Tk4qMjCR7e3uqX78+1a9fn+zt7enAgQM1Or6RkRGdPn2aiIgePXpEPB6Ppk6dWqNjSgubQanFVLZMuISzZ8+K3Hf79m2R+5ydnTFnzhysXLkSBw4cgKqqqkAZXHx8PJo2bSrlVTAYNU9QUBBCQ0MBAKdPn8aZM2dw4sQJREREwM/PD6dOnZKxh4y6Ap/PFzuzXTYhm0qlgQ4YMAADBgyoMd+E8eLFC06W38zMDMrKyjJpDCgMFqDUYipbJlxC2am7d+/eYdeuXdiyZQvi4uLwyy+/CLULCAjAwIED0a1bN6ipqWH79u0CWhXBwcFwcnKq4NUwGDVHRkYGt45/5MgReHh4wMnJCSYmJujQoYOMvWPUJfbv3y/wPj8/H7dv38b27dvh7+8v1EaW5e5FRUVQUFDg3svJyZV72JUVrIqnllKVMuGyREdHIzg4GJGRkTA2NsagQYMwaNAgiW3o3717BzU1NQGdCQB48+YN1NTUyglsMRiyonHjxti7dy86d+4MCwsLBAQE4KeffkJSUhLatWuH9+/fy9pFRh1n9+7d2LNnDw4ePCiwnc/nQ1NTU2KQ8ubNmxrxi8/nw8XFhUvyPXz4MBwdHb8JBVw2g1JLkZOTQ+/evZGQkFCpAOXZs2cICQlBcHAwcnJy4OHhgfz8fOzbt0/qLpylJe5LU9WAicGobgYOHAhPT080b94cWVlZnIbPnTt3ylVNMBg1QYcOHTBu3Dih+/z9/UXeT2uasmrgw4cPl4kfwmABSi2msmXCffr0QUxMDPr27YsNGzbA2dkZcnJy2LRpUw15ymDIlsDAQJiYmCA9PR2rVq3ihNsyMjIwceJEGXvHqOt8+vQJGzZsQJMmTYTuHzp0KPT19b+yV8VUVvn2a8CWeGoxp06dwuzZsytcJiwvL48pU6bAx8cHzZs357YrKCggLi5O6hkUBoPBYAiira0tsFxDRPjw4QNUVVURGhpargWJnJwcMjIyZBagfMuwGZRaTIn2SP/+/ct9IcRJ3V+8eBHBwcFo27YtWrRogREjRmDIkCFfxWcGQxbs2LFD7P6ff/75K3nCqOsEBgYK3I/5fD709PTQoUMHaGtrlzuezRGIhs2g1GLOnz8vct/t27dFVuGUkJubi/DwcAQHB+PatWsoLCzE2rVrMXr0aKirq1eztwyG7Cj7w5Cfn4/c3FwoKipCVVW1xhIQGQxG5WEBSh2ibJlwRRqgJSUlYevWrdi5cyeys7PRq1cvid2SGYzaTEpKCnx8fODn54fevXvL2h1GHSE+Pl7qY1u1alWDntR+WIBSB6hsmbAwCgsLcfjwYQQHB7MAhVHnuXHjBoYPH47ExERZu8KoI0gSagMkL8MzimE5KLWU6igTFoacnBzc3d0r3fCKwahNyMnJ4cWLF7J2g1GHiIyMxMyZM+Hn54dOnToBAC5fvow1a9Zg1apVlXpw/F5hMyi1kNJlwl5eXlyZMKvCYTCEU3Y2kIiQkZGBP//8E4aGhjh+/LiMPGPUNdq3b49FixahT58+AtuPHTuG+fPn4+bNmzLyrPbBZlBqIadOnRJaJsxgMIRTdkaQx+NBT08Pjo6OWLNmjWycYtRJ7t69K1SbytTUFA8ePJCBR7UXvqwdYFScixcv4sOHD2jbti06dOiAP//8E69evZK1WwzGN0tRUZHAq7CwEJmZmdi9ezcaNWoka/cYdQhLS0sEBAQgLy+P2/b582cEBATA0tJShp7VPtgSTy2GlQkzGBWn5JYnywZtjLrLtWvX0K9fPxQVFaF169YAgLi4OPB4PBw5cgTt27eXsYe1Bxag1BFYmTCDIZ4dO3Zg9erVSElJAQCYm5vDz88PI0aMkLFnjLpGbm4uQkNDkZiYCCKClZUVPD09v5kuwbUFFqDUMViZMINRnrVr12L+/Pnw9fWFvb09iAixsbH466+/EBAQgGnTpsnaRQaDUQYWoDAYjDqPqakp/P39y0nab9++HYsWLcKTJ09k5BmjLrJz504EBQXh8ePHuHz5MoyNjREYGAgzMzO4ubnJ2r1aA0uSZTAYdZ6MjAx07ty53PbOnTsjIyNDBh4x6iobN27E9OnT4eLigrdv33JibNra2li3bp1snatlsACFwWDUeZo1a4aIiIhy2/fs2cNK9RnVyoYNG7B582b89ttvkJf/PyWPtm3b4u7duzL0rPbBdFAYDEadx9/fH0OGDMGFCxdgb28PHo+HmJgYREVFCQ1cGIzK8uTJE6FqsUpKSsjJyZGBR7UXNoPCYDDqPIMGDcLVq1ehq6uLAwcOIDIyErq6urh27RoGDBgga/cYdQhTU1PcuXOn3Pbjx48zHZQKwmZQGAzGd8EPP/yA0NBQWbvBqOP4+flh0qRJyMvLAxHh2rVrCAsLw7Jly7B161ZZu1erYFU8DAajTvL+/XtoaGhwf4uj5DgGozrYvHkzAgICkJ6eDgAwMDCAv78/evfuDQMDAxl7V3tgAQqDwaiTyMnJISMjA/r6+uDz+UKVY1nbe0ZN8vr1a661wrJly7BlyxZ8+vRJ1m7VGtgSD4PBqJNER0ejfv363N9M2p5Rk2RnZ2PSpEk4deoUFBQUMGfOHPj6+sLf3x+///47rKysEBwcLGs3axVsBoXBYDAYjCoyceJEHD58GEOGDMGJEyeQkJCA3r17Iy8vDwsXLkS3bt1k7WKtg1XxMBiMOo+ZmRnmz5+PpKQkWbvCqKMcPXoU27Ztw++//45Dhw6BiGBubo7o6GgWnFQSFqAwGIw6j6+vL06cOAFLS0v88MMPWLduHVOQZVQrL168gJWVFYDigFhZWRljx46VsVe1GxagMBiMOs/06dNx/fp1JCYmom/fvti4cSOMjIzg5OSEHTt2yNo9Rh2gqKgICgoK3Hs5OTnWvbiKsBwUBoPxXXLlyhX4+PggPj6eVfEwqgyfz4eLiwuUlJQAAIcPH4ajo2O5ICUyMlIW7tVKWBUPg8H4rrh27Rp2796NPXv24N27dxg8eLCsXWLUAUaOHCnwfvjw4TLypO7AZlAYDEadJzk5Gbt27cLu3buRmpoKBwcHeHl5YeDAgVBXV5e1ewwGQwgsQGEwGHUePp+Ptm3bwtPTE0OHDkXDhg1l7RKDwZAAC1AYDEadJzk5Gebm5rJ2g8FgVABWxcNgMOo85ubmyM7OxpYtWzB37ly8efMGAHDr1i08f/5cxt4xGAxhsBkUBoNR54mPj0ePHj2gpaWF1NRUJCUlceJtT58+ZaXGDMY3CJtBYTAYdZ5p06bB29sbKSkpUFZW5ra7uLjgwoULMvSMwWCIgpUZMxiMOs+NGzfwzz//lNtuYGCAzMxMGXjEYDAkwWZQGAxGnUdZWRnv378vtz0pKQl6enoy8IjBYEiCBSgMBqPO4+bmhsWLFyM/Px8AwOPxkJaWhjlz5mDQoEEy9o7BYAiDJckyGIw6z/v379GnTx/cv38fHz58QOPGjZGZmYlOnTrh2LFjrGcKg/ENwgIUBoPx3RAdHY1bt26hqKgIdnZ26Nmzp6xdYjAYImABCoPBqNMUFBRAWVkZd+7cgbW1tazdYTAYUsJyUBgMRp1GXl4exsbGrGMxg1HLYAEKg8Go88ybN09AQZbBYHz7sCUeBoNR57G1tcXDhw+Rn58PY2Pjckmxt27dkpFnDAZDFEyojcFg1Hnc3d3B4/HAnscYjNoDC1AYDEadJTc3F35+fjhw4ADy8/PRo0cPbNiwAbq6urJ2jcFgSIDloDAYjDrLwoULERISAldXVwwbNgxnzpyBj4+PrN1iMBhSwHJQGAxGnaVp06ZYunQphg4dCgC4du0a7O3tkZeXBzk5ORl7x2AwxMECFAaDUWdRVFTEkydPYGBgwG1TUVFBcnIyDA0NZegZg8GQBFviYTAYdZbCwkIoKioKbJOXl0dBQYGMPGIwGNLCkmQZDEadhYgwatQoKCkpcdvy8vIwYcIEgVLjyMhIWbjHYDDEwAIUBoNRZxk5cmS5bcOHD5eBJwwGo6KwHBQGg8FgMBjfHCwHhcFgMBgMxjcHC1AYDAaDwWB8c7AAhcFgMBgMxjcHC1AYDAaDwWB8c7AAhcFgMBgMxjcHC1AYDAaDwWB8c7AAhcFgMBgMxjcHC1AYDAaDwWB8c/w/+fQa7LGXSTEAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hotel_booking.isna().sum().plot(kind='bar');"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Data Preprocessing"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [],
   "source": [
    "# Creating a function to reformat the feature names:\n",
    "def camel_to_snake(name):\n",
    "    name = re.sub('(.)([A-Z][a-z]+)', r'\\1_\\2', name)\n",
    "    return re.sub('([a-z0-9])([A-Z])', r'\\1_\\2', name).lower()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [],
   "source": [
    "# Reformating the feature names:\n",
    "hotel_booking.columns = hotel_booking.columns.map(camel_to_snake)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [],
   "source": [
    "# Removing spaces:\n",
    "features = ['meal', 'reserved_room_type', 'assigned_room_type', 'deposit_type', 'agent', 'company']\n",
    "for feature in features:\n",
    "    hotel_booking[feature] = hotel_booking[feature].str.strip()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Fill missing company and agent values\n",
    "\n",
    "* missing `company` values says that booking did not came via any company\n",
    "* missing `agent` values says that booking did not came from a agent\n",
    "\n",
    "----\n",
    "\n",
    "Therefore fill missing `company` and `agent` values as follows:\n",
    "* missing `company` values as **No Company**\n",
    "* missing `agent` values as **No Agent**"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [
    {
     "data": {
      "text/plain": "   is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n0            0        342               2015               July   \n1            0        737               2015               July   \n2            0          7               2015               July   \n3            0         13               2015               July   \n4            0         14               2015               July   \n\n   arrival_date_week_number  arrival_date_day_of_month  \\\n0                        27                          1   \n1                        27                          1   \n2                        27                          1   \n3                        27                          1   \n4                        27                          1   \n\n   stays_in_weekend_nights  stays_in_week_nights  adults  children  ...  \\\n0                        0                     0       2       0.0  ...   \n1                        0                     0       2       0.0  ...   \n2                        0                     1       1       0.0  ...   \n3                        0                     1       1       0.0  ...   \n4                        0                     2       2       0.0  ...   \n\n      agent     company days_in_waiting_list customer_type   adr  \\\n0  No Agent  No Company                    0     Transient   0.0   \n1  No Agent  No Company                    0     Transient   0.0   \n2  No Agent  No Company                    0     Transient  75.0   \n3       304  No Company                    0     Transient  75.0   \n4       240  No Company                    0     Transient  98.0   \n\n   required_car_parking_spaces  total_of_special_requests  reservation_status  \\\n0                            0                          0           Check-Out   \n1                            0                          0           Check-Out   \n2                            0                          0           Check-Out   \n3                            0                          0           Check-Out   \n4                            0                          1           Check-Out   \n\n  reservation_status_date is_h1  \n0              2015-07-01     1  \n1              2015-07-01     1  \n2              2015-07-02     1  \n3              2015-07-02     1  \n4              2015-07-03     1  \n\n[5 rows x 32 columns]",
      "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>is_canceled</th>\n      <th>lead_time</th>\n      <th>arrival_date_year</th>\n      <th>arrival_date_month</th>\n      <th>arrival_date_week_number</th>\n      <th>arrival_date_day_of_month</th>\n      <th>stays_in_weekend_nights</th>\n      <th>stays_in_week_nights</th>\n      <th>adults</th>\n      <th>children</th>\n      <th>...</th>\n      <th>agent</th>\n      <th>company</th>\n      <th>days_in_waiting_list</th>\n      <th>customer_type</th>\n      <th>adr</th>\n      <th>required_car_parking_spaces</th>\n      <th>total_of_special_requests</th>\n      <th>reservation_status</th>\n      <th>reservation_status_date</th>\n      <th>is_h1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>342</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>No Agent</td>\n      <td>No Company</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-01</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>737</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>No Agent</td>\n      <td>No Company</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-01</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>7</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>No Agent</td>\n      <td>No Company</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>75.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-02</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>13</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>304</td>\n      <td>No Company</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>75.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-02</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>14</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>240</td>\n      <td>No Company</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>98.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Check-Out</td>\n      <td>2015-07-03</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 32 columns</p>\n</div>"
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotel_booking['agent'].replace(to_replace='NULL', value='No Agent', inplace=True)\n",
    "hotel_booking['company'].replace(to_replace='NULL', value='No Company', inplace=True)\n",
    "\n",
    "hotel_booking.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [],
   "source": [
    "hotel_booking['country'].fillna(\"unknown\", inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Drop missing children values"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [],
   "source": [
    "hotel_booking.dropna(subset=['children'], inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [
    {
     "data": {
      "text/plain": "is_canceled                       0\nlead_time                         0\narrival_date_year                 0\narrival_date_month                0\narrival_date_week_number          0\narrival_date_day_of_month         0\nstays_in_weekend_nights           0\nstays_in_week_nights              0\nadults                            0\nchildren                          0\nbabies                            0\nmeal                              0\ncountry                           0\nmarket_segment                    0\ndistribution_channel              0\nis_repeated_guest                 0\nprevious_cancellations            0\nprevious_bookings_not_canceled    0\nreserved_room_type                0\nassigned_room_type                0\nbooking_changes                   0\ndeposit_type                      0\nagent                             0\ncompany                           0\ndays_in_waiting_list              0\ncustomer_type                     0\nadr                               0\nrequired_car_parking_spaces       0\ntotal_of_special_requests         0\nreservation_status                0\nreservation_status_date           0\nis_h1                             0\ndtype: int64"
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotel_booking.isna().sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [
    {
     "data": {
      "text/plain": "is_canceled                         int64\nlead_time                           int64\narrival_date_year                   int64\narrival_date_month                 object\narrival_date_week_number            int64\narrival_date_day_of_month           int64\nstays_in_weekend_nights             int64\nstays_in_week_nights                int64\nadults                              int64\nchildren                          float64\nbabies                              int64\nmeal                               object\ncountry                            object\nmarket_segment                     object\ndistribution_channel               object\nis_repeated_guest                   int64\nprevious_cancellations              int64\nprevious_bookings_not_canceled      int64\nreserved_room_type                 object\nassigned_room_type                 object\nbooking_changes                     int64\ndeposit_type                       object\nagent                              object\ncompany                            object\ndays_in_waiting_list                int64\ncustomer_type                      object\nadr                               float64\nrequired_car_parking_spaces         int64\ntotal_of_special_requests           int64\nreservation_status                 object\nreservation_status_date            object\nis_h1                               int64\ndtype: object"
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check data type of each column\n",
    "hotel_booking.dtypes"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Fix data types of `children` and `reservation_satatus_date`\n",
    "\n",
    "* There were 2 mismatched data types can be found in dataset.\n",
    "* `children` column data type should be fixed as integer\n",
    "* `reservation_status_date` column data type should be fixed as datetime"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [],
   "source": [
    "hotel_booking['children'] = hotel_booking['children'].astype(int)\n",
    "hotel_booking['reservation_status_date'] = pd.to_datetime(hotel_booking['reservation_status_date'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [
    {
     "data": {
      "text/plain": "is_canceled                                int64\nlead_time                                  int64\narrival_date_year                          int64\narrival_date_month                        object\narrival_date_week_number                   int64\narrival_date_day_of_month                  int64\nstays_in_weekend_nights                    int64\nstays_in_week_nights                       int64\nadults                                     int64\nchildren                                   int32\nbabies                                     int64\nmeal                                      object\ncountry                                   object\nmarket_segment                            object\ndistribution_channel                      object\nis_repeated_guest                          int64\nprevious_cancellations                     int64\nprevious_bookings_not_canceled             int64\nreserved_room_type                        object\nassigned_room_type                        object\nbooking_changes                            int64\ndeposit_type                              object\nagent                                     object\ncompany                                   object\ndays_in_waiting_list                       int64\ncustomer_type                             object\nadr                                      float64\nrequired_car_parking_spaces                int64\ntotal_of_special_requests                  int64\nreservation_status                        object\nreservation_status_date           datetime64[ns]\nis_h1                                      int64\ndtype: object"
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotel_booking.dtypes"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Check for the duplications\n",
    "\n",
    "* We does not remove duplications since there can be more than one bookings with the same features on same day"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "data": {
      "text/plain": "True"
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotel_booking.duplicated().any()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [
    {
     "data": {
      "text/plain": "        is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n5                 0         14               2015               July   \n22                0         72               2015               July   \n43                0         70               2015               July   \n138               1          5               2015               July   \n200               0          0               2015               July   \n...             ...        ...                ...                ...   \n119349            0        186               2017             August   \n119352            0         63               2017             August   \n119353            0         63               2017             August   \n119354            0         63               2017             August   \n119373            0        175               2017             August   \n\n        arrival_date_week_number  arrival_date_day_of_month  \\\n5                             27                          1   \n22                            27                          1   \n43                            27                          2   \n138                           28                          5   \n200                           28                          7   \n...                          ...                        ...   \n119349                        35                         31   \n119352                        35                         31   \n119353                        35                         31   \n119354                        35                         31   \n119373                        35                         31   \n\n        stays_in_weekend_nights  stays_in_week_nights  adults  children  ...  \\\n5                             0                     2       2         0  ...   \n22                            2                     4       2         0  ...   \n43                            2                     3       2         0  ...   \n138                           1                     0       2         0  ...   \n200                           0                     1       1         0  ...   \n...                         ...                   ...     ...       ...  ...   \n119349                        0                     3       2         0  ...   \n119352                        0                     3       3         0  ...   \n119353                        0                     3       3         0  ...   \n119354                        0                     3       3         0  ...   \n119373                        1                     3       1         0  ...   \n\n        agent     company days_in_waiting_list    customer_type     adr  \\\n5         240  No Company                    0        Transient   98.00   \n22        250  No Company                    0        Transient   84.67   \n43        250  No Company                    0        Transient  137.00   \n138       240  No Company                    0        Transient   97.00   \n200       240  No Company                    0        Transient  109.80   \n...       ...         ...                  ...              ...     ...   \n119349      9  No Company                    0        Transient  126.00   \n119352      9  No Company                    0  Transient-Party  195.33   \n119353      9  No Company                    0  Transient-Party  195.33   \n119354      9  No Company                    0  Transient-Party  195.33   \n119373     42  No Company                    0        Transient   82.35   \n\n        required_car_parking_spaces  total_of_special_requests  \\\n5                                 0                          1   \n22                                0                          1   \n43                                0                          1   \n138                               0                          0   \n200                               0                          3   \n...                             ...                        ...   \n119349                            0                          2   \n119352                            0                          2   \n119353                            0                          2   \n119354                            0                          2   \n119373                            0                          1   \n\n        reservation_status reservation_status_date is_h1  \n5                Check-Out              2015-07-03     1  \n22               Check-Out              2015-07-07     1  \n43               Check-Out              2015-07-07     1  \n138               Canceled              2015-07-01     1  \n200              Check-Out              2015-07-08     1  \n...                    ...                     ...   ...  \n119349           Check-Out              2017-09-03     0  \n119352           Check-Out              2017-09-03     0  \n119353           Check-Out              2017-09-03     0  \n119354           Check-Out              2017-09-03     0  \n119373           Check-Out              2017-09-04     0  \n\n[31994 rows x 32 columns]",
      "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>is_canceled</th>\n      <th>lead_time</th>\n      <th>arrival_date_year</th>\n      <th>arrival_date_month</th>\n      <th>arrival_date_week_number</th>\n      <th>arrival_date_day_of_month</th>\n      <th>stays_in_weekend_nights</th>\n      <th>stays_in_week_nights</th>\n      <th>adults</th>\n      <th>children</th>\n      <th>...</th>\n      <th>agent</th>\n      <th>company</th>\n      <th>days_in_waiting_list</th>\n      <th>customer_type</th>\n      <th>adr</th>\n      <th>required_car_parking_spaces</th>\n      <th>total_of_special_requests</th>\n      <th>reservation_status</th>\n      <th>reservation_status_date</th>\n      <th>is_h1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5</th>\n      <td>0</td>\n      <td>14</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>...</td>\n      <td>240</td>\n      <td>No Company</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>98.00</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Check-Out</td>\n      <td>2015-07-03</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>0</td>\n      <td>72</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>2</td>\n      <td>4</td>\n      <td>2</td>\n      <td>0</td>\n      <td>...</td>\n      <td>250</td>\n      <td>No Company</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>84.67</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Check-Out</td>\n      <td>2015-07-07</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>0</td>\n      <td>70</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>2</td>\n      <td>2</td>\n      <td>3</td>\n      <td>2</td>\n      <td>0</td>\n      <td>...</td>\n      <td>250</td>\n      <td>No Company</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>137.00</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Check-Out</td>\n      <td>2015-07-07</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>138</th>\n      <td>1</td>\n      <td>5</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>28</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>...</td>\n      <td>240</td>\n      <td>No Company</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>97.00</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Canceled</td>\n      <td>2015-07-01</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>200</th>\n      <td>0</td>\n      <td>0</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>28</td>\n      <td>7</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>240</td>\n      <td>No Company</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>109.80</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Check-Out</td>\n      <td>2015-07-08</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>119349</th>\n      <td>0</td>\n      <td>186</td>\n      <td>2017</td>\n      <td>August</td>\n      <td>35</td>\n      <td>31</td>\n      <td>0</td>\n      <td>3</td>\n      <td>2</td>\n      <td>0</td>\n      <td>...</td>\n      <td>9</td>\n      <td>No Company</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>126.00</td>\n      <td>0</td>\n      <td>2</td>\n      <td>Check-Out</td>\n      <td>2017-09-03</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>119352</th>\n      <td>0</td>\n      <td>63</td>\n      <td>2017</td>\n      <td>August</td>\n      <td>35</td>\n      <td>31</td>\n      <td>0</td>\n      <td>3</td>\n      <td>3</td>\n      <td>0</td>\n      <td>...</td>\n      <td>9</td>\n      <td>No Company</td>\n      <td>0</td>\n      <td>Transient-Party</td>\n      <td>195.33</td>\n      <td>0</td>\n      <td>2</td>\n      <td>Check-Out</td>\n      <td>2017-09-03</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>119353</th>\n      <td>0</td>\n      <td>63</td>\n      <td>2017</td>\n      <td>August</td>\n      <td>35</td>\n      <td>31</td>\n      <td>0</td>\n      <td>3</td>\n      <td>3</td>\n      <td>0</td>\n      <td>...</td>\n      <td>9</td>\n      <td>No Company</td>\n      <td>0</td>\n      <td>Transient-Party</td>\n      <td>195.33</td>\n      <td>0</td>\n      <td>2</td>\n      <td>Check-Out</td>\n      <td>2017-09-03</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>119354</th>\n      <td>0</td>\n      <td>63</td>\n      <td>2017</td>\n      <td>August</td>\n      <td>35</td>\n      <td>31</td>\n      <td>0</td>\n      <td>3</td>\n      <td>3</td>\n      <td>0</td>\n      <td>...</td>\n      <td>9</td>\n      <td>No Company</td>\n      <td>0</td>\n      <td>Transient-Party</td>\n      <td>195.33</td>\n      <td>0</td>\n      <td>2</td>\n      <td>Check-Out</td>\n      <td>2017-09-03</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>119373</th>\n      <td>0</td>\n      <td>175</td>\n      <td>2017</td>\n      <td>August</td>\n      <td>35</td>\n      <td>31</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>42</td>\n      <td>No Company</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>82.35</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Check-Out</td>\n      <td>2017-09-04</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>31994 rows × 32 columns</p>\n</div>"
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotel_booking[hotel_booking.duplicated(keep='first')]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Feature Engineering"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Creating new feature called** `arrival_date_full` **using** `arrival_date_year`**,** `arrival_date_month` **and** `arrival_date_day_of_month`"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [],
   "source": [
    "hotel_booking['arrival_date_full'] = hotel_booking['arrival_date_year'].astype(str) + \"-\" + hotel_booking['arrival_date_month'].map({'January':1, 'February':2, 'March':3, 'April':4, 'May':5, 'June':6, 'July':7, 'August':8, 'September':9, 'October':10, 'November':11, 'December':12}).astype(str) + \"-\" + hotel_booking['arrival_date_day_of_month'].astype(str)\n",
    "\n",
    "hotel_booking['arrival_date_full'] = pd.to_datetime(hotel_booking['arrival_date_full'], format=\"%Y-%m-%d\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [
    {
     "data": {
      "text/plain": "   is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n0            0        342               2015               July   \n1            0        737               2015               July   \n2            0          7               2015               July   \n3            0         13               2015               July   \n4            0         14               2015               July   \n\n   arrival_date_week_number  arrival_date_day_of_month  \\\n0                        27                          1   \n1                        27                          1   \n2                        27                          1   \n3                        27                          1   \n4                        27                          1   \n\n   stays_in_weekend_nights  stays_in_week_nights  adults  children  ...  \\\n0                        0                     0       2         0  ...   \n1                        0                     0       2         0  ...   \n2                        0                     1       1         0  ...   \n3                        0                     1       1         0  ...   \n4                        0                     2       2         0  ...   \n\n      company days_in_waiting_list customer_type   adr  \\\n0  No Company                    0     Transient   0.0   \n1  No Company                    0     Transient   0.0   \n2  No Company                    0     Transient  75.0   \n3  No Company                    0     Transient  75.0   \n4  No Company                    0     Transient  98.0   \n\n  required_car_parking_spaces  total_of_special_requests  reservation_status  \\\n0                           0                          0           Check-Out   \n1                           0                          0           Check-Out   \n2                           0                          0           Check-Out   \n3                           0                          0           Check-Out   \n4                           0                          1           Check-Out   \n\n   reservation_status_date is_h1 arrival_date_full  \n0               2015-07-01     1        2015-07-01  \n1               2015-07-01     1        2015-07-01  \n2               2015-07-02     1        2015-07-01  \n3               2015-07-02     1        2015-07-01  \n4               2015-07-03     1        2015-07-01  \n\n[5 rows x 33 columns]",
      "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>is_canceled</th>\n      <th>lead_time</th>\n      <th>arrival_date_year</th>\n      <th>arrival_date_month</th>\n      <th>arrival_date_week_number</th>\n      <th>arrival_date_day_of_month</th>\n      <th>stays_in_weekend_nights</th>\n      <th>stays_in_week_nights</th>\n      <th>adults</th>\n      <th>children</th>\n      <th>...</th>\n      <th>company</th>\n      <th>days_in_waiting_list</th>\n      <th>customer_type</th>\n      <th>adr</th>\n      <th>required_car_parking_spaces</th>\n      <th>total_of_special_requests</th>\n      <th>reservation_status</th>\n      <th>reservation_status_date</th>\n      <th>is_h1</th>\n      <th>arrival_date_full</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>342</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>...</td>\n      <td>No Company</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-01</td>\n      <td>1</td>\n      <td>2015-07-01</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>737</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>...</td>\n      <td>No Company</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-01</td>\n      <td>1</td>\n      <td>2015-07-01</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>7</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>No Company</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>75.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-02</td>\n      <td>1</td>\n      <td>2015-07-01</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>13</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>No Company</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>75.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-02</td>\n      <td>1</td>\n      <td>2015-07-01</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>14</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>...</td>\n      <td>No Company</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>98.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Check-Out</td>\n      <td>2015-07-03</td>\n      <td>1</td>\n      <td>2015-07-01</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 33 columns</p>\n</div>"
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotel_booking.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Creating a new feature called** `status_minus_arrival_date` **representing length of stay or how many days before arrival did the customer cancel**"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [],
   "source": [
    "hotel_booking['status_minus_arrival_date'] = np.abs(hotel_booking['arrival_date_full'] - hotel_booking['reservation_status_date']).astype(str)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [],
   "source": [
    "def format_lenght(date):\n",
    "    return date[0]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "outputs": [],
   "source": [
    "hotel_booking['status_minus_arrival_date'] = hotel_booking['status_minus_arrival_date'].map(format_lenght).astype(int)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "outputs": [
    {
     "data": {
      "text/plain": "   is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n0            0        342               2015               July   \n1            0        737               2015               July   \n2            0          7               2015               July   \n3            0         13               2015               July   \n4            0         14               2015               July   \n\n   arrival_date_week_number  arrival_date_day_of_month  \\\n0                        27                          1   \n1                        27                          1   \n2                        27                          1   \n3                        27                          1   \n4                        27                          1   \n\n   stays_in_weekend_nights  stays_in_week_nights  adults  children  ...  \\\n0                        0                     0       2         0  ...   \n1                        0                     0       2         0  ...   \n2                        0                     1       1         0  ...   \n3                        0                     1       1         0  ...   \n4                        0                     2       2         0  ...   \n\n   days_in_waiting_list customer_type   adr required_car_parking_spaces  \\\n0                     0     Transient   0.0                           0   \n1                     0     Transient   0.0                           0   \n2                     0     Transient  75.0                           0   \n3                     0     Transient  75.0                           0   \n4                     0     Transient  98.0                           0   \n\n  total_of_special_requests  reservation_status  reservation_status_date  \\\n0                         0           Check-Out               2015-07-01   \n1                         0           Check-Out               2015-07-01   \n2                         0           Check-Out               2015-07-02   \n3                         0           Check-Out               2015-07-02   \n4                         1           Check-Out               2015-07-03   \n\n   is_h1 arrival_date_full status_minus_arrival_date  \n0      1        2015-07-01                         0  \n1      1        2015-07-01                         0  \n2      1        2015-07-01                         1  \n3      1        2015-07-01                         1  \n4      1        2015-07-01                         2  \n\n[5 rows x 34 columns]",
      "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>is_canceled</th>\n      <th>lead_time</th>\n      <th>arrival_date_year</th>\n      <th>arrival_date_month</th>\n      <th>arrival_date_week_number</th>\n      <th>arrival_date_day_of_month</th>\n      <th>stays_in_weekend_nights</th>\n      <th>stays_in_week_nights</th>\n      <th>adults</th>\n      <th>children</th>\n      <th>...</th>\n      <th>days_in_waiting_list</th>\n      <th>customer_type</th>\n      <th>adr</th>\n      <th>required_car_parking_spaces</th>\n      <th>total_of_special_requests</th>\n      <th>reservation_status</th>\n      <th>reservation_status_date</th>\n      <th>is_h1</th>\n      <th>arrival_date_full</th>\n      <th>status_minus_arrival_date</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>342</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-01</td>\n      <td>1</td>\n      <td>2015-07-01</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>737</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-01</td>\n      <td>1</td>\n      <td>2015-07-01</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>7</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>75.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-02</td>\n      <td>1</td>\n      <td>2015-07-01</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>13</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>75.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-02</td>\n      <td>1</td>\n      <td>2015-07-01</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>14</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>98.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Check-Out</td>\n      <td>2015-07-03</td>\n      <td>1</td>\n      <td>2015-07-01</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 34 columns</p>\n</div>"
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotel_booking.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Exploratory Data Analysis\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Canceled Vs Not Canceled\n",
    "\n",
    "plt.title(\"Percentage of Bookings for Status\", fontsize = 20, pad = 10)\n",
    "sns.barplot(x=hotel_booking['is_canceled'].unique(), y=hotel_booking['is_canceled'].value_counts(normalize=True))\n",
    "plt.xlabel(\"Booking Status\", fontsize = 12, labelpad = 5)\n",
    "plt.ylabel(\"Percentage of Bookings\", fontsize = 12, labelpad = 5)\n",
    "plt.xticks(ticks=[0, 1], labels=['Not Canceled', 'Canceled']);"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 500x1000 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAw8AAANLCAYAAAAHFaGsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzddVgV6d8G8PvQ3SUoYaGoiB0YKDaoa6xrJ7ZrJwZgrIJdWKviqhi7subagon1s9ZObFBBBEmBef/gPbMcD+AQCu7en+s6lzLzzBMzz5kz35lnZmSCIAggIiIiIiL6ApWirgAREREREX0fGDwQEREREZEkDB6IiIiIiEgSBg9ERERERCQJgwciIiIiIpKEwQMREREREUnC4IGIiIiIiCRh8EBERERERJIweCAiIiIiIkkYPBBRnrm5uUEmk8HX1/c/Wf5/3caNG1GvXj0YGBhAJpNBJpNhyZIl4vz09HQsWrQI1apVg66urphm9+7dAL7O9pOXERYWVmh5FjcxMTEYOXIkypQpA01NTbHNsbGxRV01KqC+fftCJpOhb9++eZr3LRR1+VT8qBV1BYi+N+np6di1axf279+P8+fP482bN0hMTISRkRHKly+Phg0bokePHqhcuXJRV/W7s3v3bly7dg0uLi744Ycfiro6X9X32o8WLlyI8ePHAwDU1NRgYWEBmUwGXV1dMc3o0aOxYsUKAICGhgYsLS0BAFpaWt++wkUsKCgIERERcHNzg5ubW77zSU9Ph7u7O65duwYA0NPTg7GxMQBARaX4ngc8e/YsduzYgVOnTuHly5eIjY2Fnp4ebG1tUbduXXTp0gVNmzaFTCYr6qr+54SFhSEsLAz29vb/6sDgyJEjCAoKwsWLF/H69WtkZGTA0tISVlZWqF69Oho2bIhmzZrB3NxcYbnC+u7m5luU8VUIRCRZeHi4UL58eQGA+FFXVxdMTEwEFRUVhekdO3YUUlJSirrKX0Xjxo0FAIKPj0+h5tunTx8BgNCnT59c0/Xq1UtwdHQUli9fXqjlfyvfcz+ysrISAAgjR44UUlNTlebHxcUJ6urqAgAhICBAyMjIUErzNbafo6Oj4OjoKFy4cKHQ8iwMhfVdOXjwoNhPTp8+XTiV+4pev34ttGzZUqEvq6ioCMbGxmL/kH+qVasmPHjwoKirXKRy2/dJ3S/mlY+PjwBAaNy4ca7pJk+eLDg6OgqTJ08u1PK/tuTkZOHHH39U6oMmJiZKfTC77+fX+p371mV8DbzyQCTRvn378OOPPyIlJQWmpqYYP348OnXqhHLlygHIPDN49epV7Nq1C4GBgQgJCUFiYiI0NDSKuOb/Pr/99ltRVyHfvud+9PbtW0RGRgIABg4cCHV1daU0d+/exadPnwAAQ4cOzfaM8tfYfnfv3i30PIuTv//+GwDg7OyMBg0aFHFtcvfkyRM0bNgQL1++hLq6Ory8vNC3b1/UqFEDqqqqEAQBT548wf79+7FkyRJcvXoVN2/eRNmyZYu66pSNuXPnYu7cuUVdjTybMGECfv/9dwBA//79MXLkSFSqVAlqamrIyMjAgwcPcPToUezYsYNXvvKIwQORBA8ePEDPnj2RkpICJycnHD58GCVLllRIo6qqipo1a6JmzZqYMGEC+vfvX0S1peLqe+9HiYmJ4v/19PTynYbyTr5ei/s6TUlJQceOHfHy5Uvo6+tj3759aNy4sUIamUyG0qVLY+TIkRg2bBhmz54NVVXVIqox/RvFx8dj7dq1AIDBgwdj9erVCvNVVFTg6OgIR0dHjBgxAklJSUVRze9XUV/6IPoedOnSRQAgaGlpCffu3ZO8XHZDNl6/fi2MHz9ecHJyEnR1dQUdHR3ByclJmDBhghAZGZltPk+ePBEvrz558kR4+PChMHDgQMHe3l7Q0NAQ7OzsBEEQhNDQUDGdIAjClStXhO7duws2NjaCmpqa0uXptLQ0YePGjUKLFi0ECwsLQV1dXTAzMxNatGghbNu2Ldv6C0Lul1qjoqKE9evXCx06dBAqVKggGBgYCFpaWkKZMmWEAQMGCDdv3lRaJmu9c/qEhoZKKl9u165dgoeHh9guCwsLwcPDQwgJCclxmc+HB/z+++9C48aNBWNjY0FbW1uoWrWqsGTJEiE9PT3HPHJT1P0oq9DQUKFr165CqVKlBE1NTcHAwECoVauW4O/vL3z8+FEpbW7bxs7OTti4cWOuabL2PSnb7/bt28KwYcOEihUrCnp6eoKurq5Qvnx54aeffhL++OMPpW2QXT8pSJvl8tsnvrQ+5N/lL5GXn9Pn83WYlJQkLF68WKhXr55gZGQkaGpqCra2tkKvXr2Eq1ev5liOnZ2dAEDYuHGjEB8fL0yfPl2oXLmyoKenJ7mugiAIgYGBYt1+++03ScsIgqC0/u7evSsEBAQI7u7uQunSpQUtLS1BX19fcHFxEaZOnSq8fftWUltSUlKEgIAAwdnZWdDR0REMDAyEJk2aCAcPHvxinc6fPy/07dtXKFOmjKCjoyPo6+sLFStWFPr16yccPnw4x+X+/PNPoX379kKJEiUEdXV1wcjISGjYsKGwatWqbIf6CUL+hy3FxsYK27ZtE7p37y5UrlxZMDY2Frd5t27dhPDwcKVlsv6e5PTZuHGjpPLlQkNDhc6dOwvW1taChoaGYGpqKjRt2lTYsGGDkJaWlu0ynw+bOnbsmNCmTRvBzMxM0NTUFCpUqCD4+voKSUlJOZabk4sXL4pt+euvv/K0bF6/u0+fPhVWrFghtGnTRihXrpygo6Mj6OrqChUrVhRGjRolPH36tEBlfP77n5Os/f5zMTExwvTp04Vq1aoJ+vr6grq6umBpaSlUqVJFGDx4sHDs2LE8rSMGD0RfEBkZKY5DHzBgQIHyCgsLE4yMjMQdgXwnI//b2Ng42/HMWXceW7duFX/Q5ctnFzz88ccf4rhO+QF81gO4yMhIoU6dOgo7K0NDQ4W/27Vrl+14+9wO/j4/2DEwMBDU1NTEvzU1NYU//vhDYZmzZ88KlpaWgpaWlnhwbWlpqfA5e/aspPJTUlKEn376SWGMq7GxscK9BN26dcv2Rzzrj+Tw4cPF5bNuMwBC7969c9jCOSsO/UgQBOHTp0+Cl5eXQnv09PQEVVVV8W9HR0chIiJCXEa+fczMzMQ0ZmZm4rapWbOmsH37dsHS0lIwNjYW02Tdfh06dBDz+1LwMG/ePIXtJT9wzFrn9+/fKyyTW/CQnzbL5bdPyNeH/Duoq6ur1KefPXuWwxb+x8iRIwVLS0tx+8p/9OWf+fPni2lfvHghVK5cWayTurq6wndaRUVFWLZsWbblyA88FixYIN6Po6GhIbZTavDg5OQkABDKli2b48kHKeT1ASDIZDLByMhIkMlk4jQbGxvh7t27uS67fPlycR+nrq4u7jflea5fvz7b5dPS0oSRI0cqbF95gJ51X/m5+Ph4wdPTU2n/l7Xe9erVE2JiYpSWzW/wID8Az9qvNTU1Fdq5dOlShWWePXuWa5+ytLQUtm/fLql8QRCEMWPGKG2rrN+tpk2bCnFxcTnWvXHjxkJAQIAgk8my3dZNmjTJMQDJSdbgYe3atXlaNq/fXfn+LGvfyLr/MjQ0VNof56WMggYPz58/F2xtbZV+E7Nuoy/d9/I5Bg9EX7Bt2zbxC7Z///585/Ps2TPxh9jJyUk4c+aMOO/UqVOCo6OjAEAwMTERXrx4obBs1p2Hnp6eUKdOHeHSpUvifPlZ7KzBg56entCmTRvhzp07Yrr79+8LgpB5gF2rVi0BgFC9enXhwIEDQkJCgiAIgvDx40dh06ZNgoWFhQBAGD16tFJbcjv48/X1FaZNmyZcvXpVPJubnp4u3Lx5U+jRo4e4o3z58qXSslJvDMyt/HHjxok/YtOnTxcPMmNiYgRvb29x/UyaNCnH8o2NjQUNDQ1h0aJFwocPHwRBEIR3794pHIAeP3481zp+rjj0I0EQhFGjRokH9oGBgUJ0dLQgCIKQmpoqhIaGCtWqVRP7xedng6X8iH1+9Ss7uW2/rGeu27Vrp3C2PDo6Wjhy5Ijw008/idtFLrfgoSBtLmifKKwbIr90c2taWpp4oGxoaChs2bJFDPwfPXqkcFCb3ZlY+YGHnp6eYGVlJYSEhIgB9vPnz8X9Q25ev34tljF+/Pj8N1YQhJ9++klYvny58PDhQ7EdKSkpwrFjx4TatWuL2ys78rYYGxsLNjY2wu7du8W23L17V6hbt67Y1tjYWKXlJ06cKLajf//+ClcJo6KihN27dws//fST0nI//PCDGDgFBweLB8xJSUnCnj17hNKlSwsAhB9++EFp2fwGD6tWrRLGjBkjnD9/XtzXZWRkCI8fPxZGjRolyGQyQVVVVbhy5YrSslJvmM6t/OXLl4vratCgQcLr168FQcj8HVm8eLF44ii79SUv38jISFBRURGmTJkiXlH68OGDMGPGDDHvnAK9nCQmJorBno2NjXDx4sU8LS8I0r+7w4cPF+bNmyfcvn1bSExMFAQh84TFhQsXhFatWgkABGtra3FeXssoaPAwYMAAAYBgb28vHDt2TAzE0tLShIiICGHVqlXZ/h7mhsED0RdMmzZN/OJmd8Ar1ZAhQ8QfNPkONqvnz58LBgYGAgBh+PDhCvOy7jzs7OyE+Pj4bMvIeuBWu3btHM/WrFixQgAgVKpUKdszQoIgCJcvXxZkMpmgoaEhREVFKcwryAGRh4eHAECYNWuW0ryCBg8vXrwQf6ymTJmS7bJjx44Vz7a9evUq2/Kz2wHL1ahRQwAgeHl55VrHzxWHfvT3338LMplM0NHREW7cuJFt/nFxcULJkiUFAMKff/6pMO9rBw8xMTHiFYauXbvm6cx1TsFDQdtc0D7xrYKH7du3i/U8dOiQ0vxPnz6JwUXlypWV5ssPPHI60JTi2LFjYh22bt2arzykiI+PFywtLQUA2V5hk7dFU1NT4eSJ3Js3b8SrnFu2bFGYd+/ePfGs8cSJEyXXaf/+/QIAwcrKKtugXRAyv5vys/2fDyH7Wk9bkl8ty+5qZ0GDh8TERMHExEQAMq/mZmfZsmVin8h6witr+bl9Pzp27CgAEJo1a5ZrHbMzZ84cMX8AQoUKFYQBAwYIq1atEi5fvix8+vQp1+UL47ublpYmODs7CwCEzZs356uMggYPFStWFAAIwcHB+WyFsuL7cGiiYiI6Olr8v4mJSb7yEAQBO3fuBAAMGTIEVlZWSmlKliyJIUOGAAC2b9+eY14jRoyQdNPkhAkTcrwJ8ddffwUADBs2DPr6+tmmqVGjBipVqoTU1FSEhoZ+sTypPDw8AABnzpwptDzldu3ahbS0NGhpaWHy5MnZppk2bRo0NTXx6dMn/PHHH9mmKVWqFHr37p3tvHbt2gEAbty4kae6FYd+tH79egiCAA8PD1SpUiXbMvT19cV3bBw+fDhf9cyvP/74A/Hx8VBXV8eiRYsK5QkohdXmr9EnCtOOHTsAAPXq1UPLli2V5qupqcHHxwcAcPPmTfHpTZ9r1aoVqlWrlq86FEYfl0JPT0+8CTu3/Ujnzp1RoUIFpenm5uaoV68eAOVttmnTJmRkZMDU1BR+fn6S6yTfp/bq1Qs2NjbZpilZsiSaNGkC4Nt9t77m/vbo0aOIiYkBgBxf+Dhs2DCUKFECALBt27Zs02hqaorvjvlc+/btAeTvu+Xt7Y1ly5aJffHu3btYv349hg4dipo1a8LMzAxeXl54/PhxnvOWSlVVFa1atQLwdbaBFEZGRgCA169fF1qefNoS0RcIglDgPJ48eSLuZJs1a5ZjuubNmyMgIADR0dF48uQJHBwclNK4urpKKjOndPHx8eKOePr06Zg5c2aOecjr/PTpU0llyl2/fh1r1qzBmTNnEBERgY8fPyqtxxcvXuQpTykuX74MAKhVqxYMDAyyTWNsbIyaNWvi7NmzYvrP1apVK8cXb1lbWwP4Z91IVRz6kfzH6+DBg9kGHnIfP34EkPftXlDnzp0DkBm4yg84Cqqw2vw1+kRhkvfl3PpFkyZNoKqqivT0dFy+fDnbYErq/iU7Wft4YQR++/fvx+bNm3Hp0iVERUUpPMlLLrf9SJ06dXKcl9M2k/fB5s2b5+mlhvJ+tnbt2lwfRfzhwwcAhfvdevz4MQIDAxEaGopHjx4hPj4eGRkZCmm+5v62VKlSKF++fLZpVFVV0bRpU2zdujXH/W2lSpVyPCFW0O/Wzz//DC8vL/z1118IDQ3FxYsXcfPmTSQlJeHDhw9Yv349tm3bhh07dsDT0zNfZQDA6dOnsX79epw/fx4vXrxAQkKCUpqvsQ2k8PT0RHh4OCZPnoy7d++iY8eOqF+/fo6/kVIweCD6AjMzM/H/MTEx4s4sL968eSP+P6ezUgAUHtv55s2bbIMHCwsLSWXmlC4yMlL8YZG6Q87uRzsnK1aswKhRo8QyZDIZDA0NoampCQBISkpCXFxctjvXgpKv59zWMfDPes66XbLK6WoMkHkGF4D4LgOpikM/evXqFYDMA2X5wXJu8rLdC4P8HRJ2dnaFlmdhtflr9InCJKXva2lpwczMDFFRUTn2fan7l+xk7eNZr0LkVUZGBnr27KlwplpNTQ3Gxsbi+04+fPiA5OTkXPcj+dlm+emDnz59wrt378R6yQOE3BTWd+vPP/9Et27dkJKSIk4zMDCAlpYWZDIZUlNT8f79++9+f5uWlpafKgIAtLW10alTJ3Tq1EnM6+LFi1i7di02bdqExMREdO3aFQ8fPsz1BENOJk2ahICAAPFvVVVVhb768eNHJCQkfJVtIMWECRNw/fp17Ny5E+vWrcO6desgk8lQqVIltGrVCgMHDswx+MsJhy0RfUGlSpXE/1+9erXA+Uk9I5dTOqnPQ88pXXp6uvj/8+fPQ8i89ynXT06XpD93584djB49GhkZGfjxxx9x8eJFJCcn4/3794iMjERkZCQWLVoEoHDOxOekoOv4aygO/Ui+7efNmydpu4eFhRW4nvlRmNvle2lzYflW+5fsFFYfl58NVlVVxYwZM/DgwQOkpKQgJiZG3I907twZwNfbj+SlD2bdp27fvl1SPwsKCipwHaOjo9G3b1+kpKSgadOmCAsLQ2JiIj58+ICoqChERkaKL0n7morj/jY3ampqqF+/PoKCgjBjxgwAQEJCQq7DhXNy9OhRMXAYNmwY/v77b6W+OmbMGABf9zcvN+rq6tixYweuXbuGGTNmoGnTptDR0cHNmzexYMECODk5YeHChXnKk8ED0Rc0adJEHK7w559/5iuPrGfznj9/nmO6rJc1zc3N81XWl1haWor/z2ncc3798ccfSE9PR8WKFbF9+3bUqlVL6c3I8jN7X4N8Pee2joF/1vPXWsfZKQ79SH5WrbC3e2GRD1WKiIgotDyLe5sLi5S+n5ycLF4R+Bp938rKCk5OTgCAPXv25PtgSX4Q5+XlBT8/P5QtW1ZpyNjX2o/kpw9qaWnB0NAQwLftZ3/99Rfi4uJgbGwsvoxPW1tbIc1/dX8r1eDBg8X/37t3L8/Ly/tqy5YtsXLlSlSuXFkpAC/oNpBffQEyv8M5+dIVr6pVq8LPzw/Hjx9HbGwsjh07hkaNGiE9PV28OiEVgweiL7C0tBQvdwYHB+P+/fuSl5X/eDo4OIg3bR0/fjzH9MeOHQMAmJqaZjtkqTAYGxuLP/D5OdOSG/mPSNWqVXMcHy5vY3bky+T3oKNmzZoAMsfi5rQjjY2NVbg34lspDv1IPp79wIEDkobwfGv169cHkLn9CuvmvqJuc0H7tFTyvp9bvwgLCxOHf3ytvj98+HAAmW9T37Jli+Tlso7Rl+9Hcrpx++PHj7hw4UIBapkzeR88evRorgdqn5P3s99//13pfoOvRb6eHB0doaOjk22ab7G/ffHiRY77s/T0dPGBG99yfytV1nst5ENr5aSsny/1VUEQcOLEiRyXl1KGsbGxUnmfu3//PmJjY3PM43Nqampwd3fHgQMHoKmpCUEQcu0rn2PwQCTB7Nmzoaenh6SkJHTs2BEvX77MNf379+/RqVMn8QBWJpPhp59+AgCsWbMm2zMRr169wpo1awAA3bp1K+QWKBo0aBCAzAONLwUQeblRLevZt+x2hgcPHsx1WIj8Bq687ASz6tSpE9TU1JCcnAx/f/9s0/zyyy9ISUmBurq6eDD/rRR1Pxo4cCBkMhliY2MxYcKEXMv+9OnTNz/Y/vHHH2FgYIC0tDSMGTOmUA64i7rNBe3TUnXt2hUAEB4ejiNHjijNT0tLEx+OULlyZVSuXPmr1GPAgAHijdjDhw/HqVOnck2fnp6OmTNn4sCBA+I0+X4kpzOhs2bNQnx8fCHVWFHfvn2hqqqK6Oho8elUUsj3qffv38f8+fNzTZuQkIDU1NQC1RP4Zz3dv38/20Dn2rVrCA4OznH5gvbN5s2bw9TUFEDOT1tas2aNeN/R1/5dy+rdu3f43//+98V0mzZtEv9fvXp1hXlS1s+X+urq1atzfZqTlDJ0dXVRpkwZAJlPFMzOnDlzclw+6/0wn9PU1BSvlORlyCKDByIJypcvj82bN0NDQwO3bt2Ci4sL/P398fDhQzFNeno6rl69ihkzZqB06dIICQlRyMPb2xtGRkaIiYlBs2bNxKd6AMDZs2fRrFkzxMbGwsTEJMfHjBaWIUOGiE8i6dWrF6ZNm6ZwRiMxMRFhYWEYMWKEuNOSQv5Iulu3bmH48OFi4JGQkIA1a9agc+fO4o9NduQHNKdPn8bdu3fz3C4bGxuMGjUKQOYYdx8fH3GnHBsbi+nTp4s/7GPHji20J/pIVdT9yMXFBaNHjwaQ+aP2448/4tq1a+JBenp6Oq5fv45Zs2ahTJkyuHbt2tdZETkwNDQUxw/v2LEDHTp0UKjD+/fvceDAAbRv3x5xcXGS8izqNsv79F9//fXFYLEgOnXqJH6nu3TpguDgYPFm4CdPnqBTp04IDw8HAIWbOwubpqYm/vzzT5QoUQLx8fFo1qwZhg8fjkuXLincGxAREYHAwEBUqFABPj4+CvPk+5F169Zh7dq14oG2fPx4QEBArvuRgihbtqwYZAYEBMDLywsPHjwQ5799+1bsm1m1b99enDZ58mQMHTpU4Wx8amoqLly4gEmTJsHOzi7Hm4fzokWLFlBRUUFMTAx69Ogh9q/U1FTs3LkTLVq0yPVmZHnfvHXrlsJ+RCptbW0xaNi2bRuGDBmCqKgoAJm/IcuXLxe/ez/99BNq1KiR5zLyKzIyEjVr1kTjxo2xevVq3Lt3T+E7f+/ePYwZM0a8H8HOzk7pZJKU7668rx48eBCzZs0Sb4qOjY3FL7/8gp9//lnSb96X9g/ywGvDhg0IDAxEUlISgMwrEV5eXtixY0eOV5/s7OwwZcoUnD9/XiGQePjwIXr06IHExESoqKhk+4jnHBXGyyKI/ivOnDkjlC1bVuHFMxoaGoKJiYnC6+hlMpnQrVs38a2mcmFhYYKhoaGYTldXV3xpEP7/TZunTp1SKlfqS2KkvKBL7u3bt0LTpk0V2mJgYCAYGRkJMplMnKampqa0bG4vtunatatCnkZGRoKqqqoAQKhRo4b4RlI7OzulZWNiYgRzc3NxWTMzM8HOzk6ws7MTwsPDJZWfkpIidOnSRcxDRUVFMDY2Vtg+2W0bQZD2MqaNGzfmWH+piqofCULmS4tGjx6tULaWlpZgamoqvmBP/sn69mpB+DZvmBYEQfjll18U1oO2trb48jj5R/42XTn59OzeMF2QNhe0T9y/f198IZmKiopgaWkp9unnz5/nmOfnpLzQ68WLF0KlSpUU+pT8beTy8pcuXZrtsjm9YCq/Xr58Kbi7uyusWxUVFcHExETQ0NBQmF6nTh3h0aNH4rLv378XKlSooLBc1v3S4MGDc90uUtqS2/JpaWniy9XkHz09PfGNxUDmW7w/l5CQoLT/09XVVdr/AFB6kVx+XxI3adIkhXwNDQ0FdXV1AYDg4OAgbN26Ncfv46dPn8Q30gOZL56U983ff/9dUvmCIAhjxoxR2GcZGxsrfK+aNGmS7ctIpfTpvPymZXXnzh2F3zH5b5mJiYn4eyT/lC5dWrh165ZSHlK+u6mpqULDhg2V2i/f3h4eHuILQrNrp9T9Q3x8vODk5KT0nQAyX3i6bdu2HPv9599BY2NjsUx5nRcvXpyn9csrD0R54Orqirt372Lbtm3o0aMHypYtCy0tLcTHx8PExAQNGjTA1KlTcefOHQQHB0NdXV1h+caNG+Pu3bsYN24cKlasiIyMDAiCgIoVK2L8+PG4c+cOGjZs+E3aYmZmhmPHjmHPnj3o3LkzSpUqhZSUFCQlJcHGxgatW7fGihUr8nzz6tatW7FkyRI4OztDU1MT6enpqFKlCubOnYuzZ8/m+oI7Y2NjnDp1Cl27doWNjQ0+fPiAp0+f4unTp5LHH2toaGDHjh3YtWsXWrduDVNTU8THx8PU1BStW7dGSEhIttvmWyrKfqSqqorFixfjypUrGDRoEBwdHaGqqooPHz7A2NgYrq6u8PX1xbVr1wr0zP+CmDJlCq5fv46BAweibNmyAABBEODo6Ihu3bohJCQkT88oL8o2lytXDqGhoWjXrh3Mzc0RHR0t9umCPH4yOzY2Nrh8+TIWLVqEunXrQltbG4mJiShVqhR69eqF//3vfxg5cmShlpkTa2trHDt2DKdOncLw4cPh7OwMIyMjxMXFQVtbG1WrVsWQIUMQFhaG8+fPo3Tp0uKyRkZGOHfuHEaPHg17e3uoqqpCTU0Nbm5u2LZtG1avXv1V666qqooVK1bgzJkz6NGjB2xtbfHp0ydoaGigUqVKGDBgQLbDR3R0dLBt2zaEhoaiV69eKF26NDIyMvDx40dYWFigadOmCAgIwIMHD774eFOp5s2bh99++w21a9eGtrY2Pn36hLJly8Lb2xtXr17N9ZHQampqOH78OLy8vGBvb4+EhASxb+Zl+N6iRYtw4sQJdOrUCZaWlvj48SP09fXRpEkTbNiwAUePHs31CsjXUKFCBTx//hxr1qxBz5494ezsDF1dXXz48AGampqwt7dHu3bt8Ouvv+L27dvifYBZSfnuqqur48iRI/Dx8UH58uWhrq4OQRBQu3ZtrFq1Cnv37s11OJDU/YOenh7OnDmDsWPHwsHBAWpqauLQ2/DwcHHYYnaOHDmCKVOmoGHDhihVqpR41aJs2bLo168fLl26JF4hkkr2/1EJERERERFRrnjlgYiIiIiIJGHwQEREREREkjB4ICIiIiIiSRg8EBERERGRJAweiIiIiIhIEgYPREREREQkCYMHIiIiIiKSRK2oK0BElNWRO2+LugpERFQMtKhoXtRVoGzwygMREREREUnC4IGIiIiIiCRh8EBERERERJIweCAiIiIiIkkYPBARERERkSQMHoiIiIiISBIGD0REREREJAmDByIiIiIikoTBAxERERERScLggYiIiIiIJGHwQEREREREkjB4ICIiIiIiSRg8EEkUFBQEmUyGiIiIb1puWFgYZDIZwsLCJC9z+/Zt+Pr6ZlvXvn37wt7evtDqR0RERP8dakVdASIqfLdv34afnx/c3NyUAoXp06dj1KhRRVMxov+IlKRE7N+6DlfOnkDix3hY2tiieaeeqNGwWa7LXQs/iavnTuDZg7v4EPMW+oYmKF2xClp37Q8L61IKadM+fcLh3zfhYthhfIh5CwNjU9Rs1BytuvSDhqbm12weEf2HMXgg+o8pU6ZMUVeB6F/v13lT8fThHbTrPQQW1ra4fOooghb6QsjIQM3GLXJc7ljIFhgYm6Llj71hammN2HdvcOSPzQgY2x/jAtaghG1pMW3QQl/cvhKOVl36wbZcBUTcvYXDv2/C62dPMHiq/7doJhH9B3HYElEBHDt2DO7u7jAwMICOjg5cXV1x/PhxhTQPHz5Ev379UK5cOejo6MDGxgZt27bF33//rZTf3bt30apVK+jo6MDMzAxDhgxBfHx8nuoUFBSEH3/8EQDQpEkTyGQyyGQyBAUFAch+2JJMJsOIESOwceNGODo6QltbGzVr1sT58+chCALmz58PBwcH6OnpoWnTpnj48GG+1gXRf8Gty+G4e/0SugwehwYtf0D5KtXRffgkVKhaC7s3BSIjPT3HZQdPDcAg73mo6+6BcpWroZZbS4yYuQRpaZ8QunenmO7JvZu4fv4kPHsMQovOvVChai20+qkvWnftj5uXzuLutUvfoqlE9B/E4IEon7Zs2YIWLVrAwMAAmzZtws6dO2FiYoKWLVsqHDS/evUKpqammDdvHg4dOoSVK1dCTU0NderUwb1798R0UVFRaNy4MW7evInAwEBs3rwZHz9+xIgRI/JULw8PD/zyyy8AgJUrVyI8PBzh4eHw8PDIdbn9+/fj119/xbx587Bt2zbEx8fDw8MD48aNw9mzZ7FixQqsXbsWt2/fRqdOnSAIQp7XBdF/wfULp6CppY1qrk0Uptdxb4MPMe8Q8eB2jsvqGxkrTTM0MYORqTnev3sjTnt8J/Pkg1ONegppK9esDwC4Fh6W3+oTEeWKw5aI8iExMRGjRo2Cp6cn/vzzT3F6mzZtUL16dXh7e+PChQsAgEaNGqFRo0ZimvT0dHh4eKBSpUpYs2YNFi1aBABYvHgx3r59i6tXr6Jq1aoAgNatW6NFixZ49uyZ5LqZm5ujXLlyAAAnJyfUrVtX0nIpKSk4cuQIdHV1AWRejfjhhx8QGhqKK1euQCaTAQDevn2L0aNH4+bNm6hSpUqe1gXRf8HrZ49hWcoeqqqKP7E29plDBl8/fYzSFapIzu9d5EvEvI2Cc50s+5G0NACAmrq6Qlr5368iHuWr7kREX8IrD0T5cO7cOcTExKBPnz5IS0sTPxkZGWjVqhUuXbqEhIQEAEBaWhp++eUXODk5QUNDA2pqatDQ0MCDBw9w584dMc/Q0FBUqlRJDBzkunfv/k3a1KRJEzFwAICKFSsCyAxg5IFD1ulPnz4FkLd18bmUlBTExcUpfFJTU75WE4m+iYT4D9DV01earqNn8P/z4yTnlZ6ehuAV86CppY0mbbuI061K2QMAntxRHP746M4NsQ5ERF8DrzwQ5UNUVBQAoHPnzjmmiYmJga6uLsaOHYuVK1di0qRJaNy4MYyNjaGiogIvLy8kJSWJ6aOjo+Hg4KCUj5WVVeE3IBsmJiYKf2toaOQ6PTk5GUDe1sXn5s6dCz8/P4VpPYeNR68RE/NYe6JiJkvArTxPWhaCICB4xTw8un0DAybNhrG5pTjPqXpdmJcoiT2/rYK+kQlsy1ZAxP1b2LdlLVRUVCFT4blBIvo6GDwQ5YOZmRkAYPny5TkOC7K0zPyh37JlC3r37i3ehyD37t07GBkZiX+bmpoiMjJSKZ/sphUneVkXn5syZQrGjh2rMO3UE+lnZYmKI119w2yvLiR+zJym+/9XIHIjDxwunTyCXiOnwrlOQ4X5aurqGDpjAX5bMgsrfccAADS0tNG25yAc3rkJhiZmhdASIiJlDB6I8sHV1RVGRka4ffv2F29olslk0PzsmesHDhzAy5cvUbZsWXFakyZNEBAQgOvXrysMXQoODs5z/eTlZb2y8bXkZV18TlNTU2ndaGhw2BJ936xtS+N/p48hPT1N4b6HV08fAwBK2JXOaVEA/wQOF078he4jJqOWW8ts05mXKIlx/msQG/0WifFxMCthg6SEj9j161KUreRSaO0hIsqKwQNRPujp6WH58uXo06cPYmJi0LlzZ1hYWODt27e4fv063r59i1WrVgEAPD09ERQUhAoVKsDZ2Rn/+9//MH/+fJQsWVIhz9GjR2PDhg3w8PDA7NmzYWlpia1bt+Lu3bt5rl/lypUBAGvXroW+vj60tLTg4OAAU1PTgjf+M3lZF0T/Bc51G+Hc0X24Fn4SNRq4i9MvhB6EoYkZ7Ms55bisIAjYttIfF078hZ+GTkBd99yfkgYARqbmMDI1BwDs37oOGlraqNfMs+ANISLKBoMHonzq2bMnbG1tERAQgMGDByM+Ph4WFhZwcXFB3759xXRLly6Furo65s6di48fP6J69eoICQnBtGnTFPKzsrLCyZMnMWrUKAwdOhQ6Ojro0KEDVqxYgfbt2+epbg4ODliyZAmWLl0KNzc3pKenY+PGjQr1KkxS1wXRf0GlGvVQoWot7Fy9AMmJCTAvURL/O3UUd65cQO8xM6CiqgoA2Lp8Li6GHoLP6h0wsci8t+mPdUsQfmw/6rp7wNquNJ7cuynmq6augVKly4t/HwvZCn1jE5iYWSLuw3tcPXMCNy6eRu9R08RggoiosMmErA9rJyIqYkfuvC3qKhAVWEpSIvZtXYurZ0ORGB8Hi5K2aNGpF2o0bCam2bx0Di6GHoTvmt9halkCAOAzsDNi3mZ/n5OJuRX81v0h/n1wx0ZcDD2E2Oi3UNfQgL1jJbTo1BtlK1XNdnmi702LigyCiyMGD0RUrDB4ICIigMFDccVhS0TfEUEQkJ6enmsaVVVVhfcyEBERERUWPgia6DuyadMmqKur5/o5efJkUVeTiIiI/qV45YHoO9K2bVtcunQp1zSOjo7fqDZERET0X8Pggeg7Ympq+lUet0pEREQkBYctERERERGRJAweiIiIiIhIEgYPREREREQkCYMHIiIiIiKShMEDERERERFJwuCBiIiIiIgkYfBARERERESSMHggIiIiIiJJGDwQEREREZEkDB6IiIiIiEgStaKuABFRVsaaGkVdBSIiIsoBrzwQEREREZEkDB6IiIiIiEgSBg9ERERERCQJgwciIiIiIpKEwQMREREREUnC4IGIiIiIiCRh8EBERERERJIweCAiIiIiIkkYPBARERERkSQMHoiIiIiISBIGD0REREREJAmDByIiIiIikkStqCvwPTl37hyOHDmC0aNHw8jIKM/LBwcH482bNxg9enS+6+Dm5gYACAsLy3ceuVm+fDmWLl2KZ8+e4dOnT3j//n2+2lpc+Pr6ws/PD4IgfNNl/80Kox8T/dslJyXi902rceH0MSTEx6FEKTu0/bEP6rm1yHW5S2dDceH0cTy+fxvvo9/C0MgE5Z2c0bHnQFjZ2Cqk3RkUiOuXzuHdm0ikpiTDyNQMlV1qo33XfjCzLPE1m0dE/2EMHvLg3Llz8PPzQ9++ffMdPNy8ebPYHnRdu3YNI0eOhJeXF/r06QM1NTXo6+sXdbUKxMvLC61atSrqavyrFPd+TFQcLJk1CY8f3MZP/YajhI0tzoUdxkr/aRCEDNRvkvM+af/vv8HQ2BTtu/aDhZUNot9FYe/2IEz7uTd8F69HSbsyYtrEhI+o59YC1qUcoKWjg5fPnmDPtg24cuEU5q3eDn0Do2/QUiL6r2HwQKJbt24BAAYOHIjatWsXcW0KR8mSJVGyZMkirUNiYiJ0dHSKtA5E9O1cu3gWN69ewLBJs1DfrSUAwKlqTbyLisS29ctRt1FzqKiqZrvsWN+FMDQyUZhWqWpNjO7bHgf/3IaBo6eJ0/sOn6iQzsm5BiwsrTF/xmhcCT+Fxi3bFXLLiIh4z4Nkvr6+mDBhAgDAwcEBMpkMMpkMYWFhyMjIQEBAACpUqABNTU1YWFigd+/eePHihbi8m5sbDhw4gKdPn4rLymQycb6fnx/q1KkDExMTGBgYoHr16li/fn2hDZnZsGEDqlatCi0tLZiYmKBDhw64c+eOQv169uwJAKhTpw5kMhn69u0rKe/ExESMHz8eDg4OYv41a9bEtm3bxDR9+/aFnp4ebt26BXd3d+jq6sLc3BwjRoxAYmKiQn6CICAwMBAuLi7Q1taGsbExOnfujMePHyuVfejQIbi7u8PQ0BA6OjqoWLEi5s6dK8739fVVWM8AsGPHDrRo0QIlSpSAtrY2KlasiMmTJyMhIUFSe3MjL+/KlSvo3LkzjI2NUaZMmTy1SxAEBAQEwM7ODlpaWqhevToOHjwINzc3cdgaAAQFBUEmkyEiIkJh+bCwMLFvZnXs2DG4u7vDwMAAOjo6cHV1xfHjxxXSvH37FoMGDUKpUqWgqakJc3NzuLq64tixYwC+3I9XrVqFqlWrQk9PD/r6+qhQoQK8vb0LuFaJvi+Xw8Ogpa2DOg3dFaY3auGJ99Fv8fDerRyX/TxwAABjU3OYmFkg5m3UF8vWNzQCgByDEyKiguKVB4m8vLwQExOD5cuXIyQkBCVKZI4ndXJywtChQ7F27VqMGDECnp6eiIiIwPTp0xEWFoYrV67AzMwMgYGBGDRoEB49eoQ///xTKf+IiAgMHjwYtraZY1rPnz+Pn3/+GS9fvsSMGTMKVPe5c+fC29sb3bp1w9y5cxEdHQ1fX1/Uq1cPly5dQrly5RAYGIht27Zh9uzZ2LhxIypUqABzc3NJ+Y8dOxabN2/G7NmzUa1aNSQkJODmzZuIjo5WSPfp0ye0adMGgwcPxuTJk3Hu3DnMnj0bT58+xb59+8R0gwcPRlBQEEaOHAl/f3/ExMRg5syZqF+/Pq5fvw5LS0sAwPr16zFw4EA0btwYq1evhoWFBe7fv4+bN2/mWt8HDx6gTZs2GD16NHR1dXH37l34+/vj4sWLOHHiRB7XbvY6duyIrl27YsiQIWJQIrVdfn5+8PPzw4ABA9C5c2c8f/4cAwcORHp6OhwdHfNVny1btqB3795o3749Nm3aBHV1daxZswYtW7bE4cOH4e6eeZDTq1cvXLlyBXPmzEH58uURGxuLK1euiNsyt368fft2DBs2DD///DMWLFgAFRUVPHz4ELdv387vaiT6Lr2IeATrUvZQVVX8ibV1KCvOL+/kLDm/N69f4t2bSNSo1zjb+enpaUhPS8Or50+xZc1iWNnYopZrk/w3gIgoFwweJCpZsqR4YF+tWjXY29sDAO7evYu1a9di2LBhWL58uZi+WrVqqFOnDhYvXow5c+bAyckJRkZG0NTURN26dZXy37hxo/j/jIwMuLm5QRAELF26FNOnT1c6ey5VbGwsZs2ahTZt2iA4OFic7ubmhnLlysHX1xdbt26Fk5OTeIa8cuXKqFmzpuQyzp49ixYtWmDMmDHiNA8PD6V0qampGDduHEaOHAkAaN68OdTV1TF16lScPXsWrq6uOH/+PNatW4eFCxdi7Nix4rINGzZE+fLlsWjRIvj7++Pjx48YO3YsXF1dceLECXH9yA+CczNt2j+X/QVBgKurKypWrIjGjRvjxo0bcHaW/qOekz59+sDPz0/8W2q7YmNj4e/vjw4dOuDXX38V01WqVAmurq75Ch4SExMxatQoeHp6Khzwt2nTBtWrV4e3tzcuXLgAIHNbenl5YeDAgWK69u3bi//PrR+fPXsWRkZGWLZsmThNyvYg+rf5GP8B5lY2StN19Q3F+VKlp6dh3ZLZ0NLSRusO3ZXmx8a8w4gebcS/yzhWxlT/VdDS5lBJIvo6OGypgEJDQwFAaYhP7dq1UbFiRaVhITk5ceIEmjVrBkNDQ6iqqkJdXR0zZsxAdHQ03rx5k+/6hYeHIykpSal+pUqVQtOmTSXXLze1a9fGwYMHMXnyZISFhSEpKSnHtD169FD4u3v3zB9D+Xrcv38/ZDIZevbsibS0NPFjZWWFqlWrikNxzp07h7i4OAwbNizPgdXjx4/RvXt3WFlZieu6cePMM3pZh3IVRKdOnRT+ltqu8PBwJCcnK62n+vXrw87OLl91OXfuHGJiYtCnTx+FsjMyMtCqVStcunRJvDpSu3ZtBAUFYfbs2Th//jw+ffokuZzatWsjNjYW3bp1w549e/Du3bsvLpOSkoK4uDiFT2pKSr7aSVSc5LpfkrjPEgQB6xbPxr2b1zBkvC9MzS2V0ugbGmHm0iDMWLAOA0Z5I+HjB8yZNBTvY778/SMiyg8GDwUkH84hH8aUlbW1tdLQnexcvHgRLVpkPr5v3bp1OHv2LC5duoSpU6cCQK4H49+ifl+ybNkyTJo0Cbt370aTJk1gYmKCH374AQ8ePFBIp6amBlNTU4VpVlZWCvWMioqCIAiwtLSEurq6wuf8+fPiAenbt28BIM83Q3/8+BENGzbEhQsXMHv2bISFheHSpUsICQkBULB1ndXn61tqu+TrQb5esspumhRRUZnjpDt37qxUtr+/PwRBQExMDIDM+0H69OmDX3/9FfXq1YOJiQl69+6NyMjIL5bTq1cvbNiwAU+fPkWnTp1gYWGBOnXq4OjRozkuM3fuXBgaGip8glYvylc7iYoLPX1DfIxTvrqQ8P9XHPT0DL6YhyAI+HXJbJwNPYRB42bkOGRJVVUNpcs7oXylqmjS6gdMmRuIt5EvsW/npoI1gogoBxy2VEDyg+HXr18rHci+evUKZmZmX8xj+/btUFdXx/79+6GlpSVO3717d6HW73NS6/clurq64jj9qKgo8SpE27ZtcffuXTFdWloaoqOjFQII+UGpfJqZmRlkMhlOnz4NTU1NpbLk0+T3Y2S9KV2KEydO4NWrVwgLCxOvNgCZw7sK0+dnHaW2S74esjtYj4yMFIfLARD7SspnZ+o/P+Mv38bLly/PdsgcAPF+CzMzMyxZsgRLlizBs2fPsHfvXkyePBlv3rzBoUOHcmyvXL9+/dCvXz8kJCTg1KlT8PHxgaenJ+7fv5/tlZMpU6YoDOMCgL9fJn+xHKLirJR9WYSfPIL09DSF+x6eP3kEAChpXyanRQH8EzicOrofXqOnoUHT1pLLNjW3hJGpOSJfPstf5YmIvoBXHvJAfoCX9ex006ZNAWTekJrVpUuXcOfOHYUx35qamtme2ZbJZFBTU4NqlqdjJCUlYfPmzQWuc7169aCtra1UvxcvXuDEiROFPibd0tISffv2Rbdu3XDv3j2lJylt3bpV4W/5fRjypwh5enpCEAS8fPkSNWvWVPpUqVIFQOYwHkNDQ6xevTpPT6SSH9R/fgC/Zs2aPLUzr6S2q27dutDS0lJaT+fOncPTp08VpskDiRs3bihM37t3r8Lfrq6uMDIywu3bt7Mtu2bNmtDQ0FCqs62tLUaMGIHmzZvjypUr4vSc+nFWurq6aN26NaZOnYrU1FTxMcCf09TUhIGBgcJHI5vgiuh7UrO+G5KTEnHpTKjC9NPHD8DY1BxlHSvluKwgCPh16RycOrof/X+egsYt2uap7MhXzxHz7g0sSxTtI6qJ6N+LVx7yQH6At3TpUvTp0wfq6upwdHTEoEGDsHz5cqioqKB169bi05ZKlSqlcBNxlSpVEBISglWrVqFGjRpQUVFBzZo14eHhgUWLFqF79+4YNGgQoqOjsWDBgmzPUOeVkZERpk+fDm9vb/Tu3RvdunVDdHQ0/Pz8oKWlBR8fnwKXUadOHXh6esLZ2RnGxsa4c+cONm/ejHr16im830BDQwMLFy7Ex48fUatWLfFpS61bt0aDBg0AZB7oDho0CP369cPly5fRqFEj6Orq4vXr1zhz5gyqVKmCoUOHQk9PDwsXLoSXlxeaNWuGgQMHwtLSEg8fPsT169exYsWKbOtav359GBsbY8iQIfDx8YG6ujq2bt2K69evF3g95EZqu4yNjTF+/HjMnj0bXl5e+PHHH/H8+XP4+voqDVuqVasWHB0dMX78eKSlpcHY2Bh//vknzpw5o5BOT08Py5cvR58+fRATE4POnTvDwsICb9++xfXr1/H27VusWrUKHz58QJMmTdC9e3dUqFAB+vr6uHTpEg4dOoSOHTuK+eXUjwcOHAhtbW24urqiRIkSiIyMFIcl1apV66uuX6LipGqt+qhcrQ42rvBHUmICLK1LIjzsCG5cDsfQCTPFx6iuWzwLp4/9hUUbQsQ3Qv+2agFOHt6Lxi3aopR9GTy887eYr5q6BuzLZj404dmTB9iyZjFqN2gKixI2kMlU8DziIQ79uQ36+obw6NTz2zeciP4TGDzkgZubG6ZMmYJNmzZh3bp1yMjIQGhoKFatWoUyZcpg/fr1WLlyJQwNDdGqVSvMnTtXYYjOqFGjcOvWLXh7e+PDhw8QBAGCIKBp06bYsGED/P390bZtW9jY2GDgwIGwsLDAgAEDClzvKVOmwMLCAsuWLcOOHTugra0NNzc3/PLLLyhXrlyB82/atCn27t2LxYsXIzExETY2Nujdu7d4z4acfGjWyJEjMXv2bGhra2PgwIGYP3++Qro1a9agbt26WLNmDQIDA5GRkQFra2u4uroqvLxuwIABsLa2hr+/P7y8vCAIAuzt7dGnT58c62pqaooDBw5g3Lhx6NmzJ3R1ddG+fXvs2LED1atXL/C6yI3Uds2cORO6uroIDAzE5s2bUaFCBaxevRoLFixQyE9VVRX79u3DiBEjMGTIEGhqaqJr165YsWKF0tOuevbsCVtbWwQEBGDw4MGIj4+HhYUFXFxcxJvptbS0UKdOHWzevBkRERH49OkTbG1tMWnSJEyc+M/LqHLqxw0bNkRQUBB27tyJ9+/fw8zMDA0aNMBvv/0m+bG/RP8Wo6f7Y+emVfhj8xokxMfBupQdhk+ajXpuLcQ0GRkZyMhIh4B/rp5evZAZ/J88sg8nj+xTyNPMogSWbNoDIPN9EMam5vgrJBgfYt4hPT0dJmYWqFa7Adp17ZftzdVERIVBJhTWW8iIctG3b1/88ccf+PjxY1FX5bslH9r1+cvf/m0uPZb+GEsiIvr3qlXasKirQNngPQ9ERERERCQJhy19x9LT03O9WVgmkynchJ1XgiAgPT091zSqqqr5foHd9yRzeEFGrmnU1Ph1IiIion83Xnn4jrm7uys9tz/rR/7G6PzatGlTrvmrq6vj5MmTkvIKCgr6rocszZw584vrIiIi4qvWISws7F8/ZImIiIiKN97z8B27d+8e4uPjc5yvqakpPiEqP6Kjo/HkyZNc0zg6OkJfXz/fZXwvXr16hVevXuWaxtnZOdtHnlLe8J4HIiICeM9DccXggYiKFQYPREQEMHgorjhsiYiIiIiIJGHwQEREREREkjB4ICIiIiIiSRg8EBERERGRJAweiIiIiIhIEgYPREREREQkCYMHIiIiIiKShMEDERERERFJwuCBiIiIiIgkUSvqChARZaWprlrUVSAiIqIc8MoDERERERFJwuCBiIiIiIgkYfBARERERESSMHggIiIiIiJJGDwQEREREZEkDB6IiIiIiEgSBg9ERERERCQJgwciIiIiIpKEwQMREREREUnC4IGIiIiIiCRh8EBERERERJIweCAiIiIiIkkYPBRQ3759YW9v/03KCgsLg0wmQ1hY2Dcp73snk8kwYsSIXNNERERAJpMhKCjo21SKiIiI6DumVtQV+N5Nnz4do0aNKupqUD6VKFEC4eHhKFOmTFFXhYj+5ZKSErF9YyDCTx7Fx7g4WNvao0PXvnBt0jLX5S6cPoHwU8fw8N4txLx7CyNjEzhWqoouvQejREnbb1R7IqJM333wkJiYCB0dnSIrX8pBpyAISE5Ohra29jeo0bdVHNuWlJQkuT6ampqoW7fuV64RERGwwHc8Ht27jR5eP6NESVucOX4IS+Z4IyMjAw3dW+e43O4dQTAyNkOn7gNgUcIG0W+jEBK8AROH9sAvy4NQyp4nP4jo2/muhi35+vpCJpPhypUr6Ny5M4yNjVGmTBkIgoDAwEC4uLhAW1sbxsbG6Ny5Mx4/fqywvCAICAgIgJ2dHbS0tFC9enUcPHgQbm5ucHNzE9MFBQVBJpMhIiJCYfnshg1lN2xJPlxm9erVqFixIjQ1NbFp0yYAwIMHD9C9e3dYWFhAU1MTFStWxMqVK5XaevfuXbRq1Qo6OjowMzPDkCFDEB8fn6/19vLlSwwaNAilSpWChoYGrK2t0blzZ0RFRQEAkpOTMW7cOLi4uMDQ0BAmJiaoV68e9uzZo5RXbm37Ejc3N1SuXBmnT59G3bp1oa2tDRsbG0yfPh3p6ekKaf38/FCnTh2YmJjAwMAA1atXx/r16yEIgkI6e3t7eHp6IiQkBNWqVYOWlhb8/PyyLV8QBHh7e0NdXR3r1q0DkP2wJXk/u3XrFrp16wZDQ0NYWlqif//++PDhg0KesbGxGDBgAExMTKCnpwcPDw88fvwYMpkMvr6+ktaL3KpVq1C1alXo6elBX18fFSpUgLe3tzhf3i+PHj2Kfv36wcTEBLq6umjbtq1SXz969Cjat2+PkiVLQktLC2XLlsXgwYPx7t07pXLv3r2Lbt26wdLSEpqamrC1tUXv3r2RkpIipomMjMTgwYNRsmRJaGhowMHBAX5+fkhLS8tTG4j+q65cOIMb/7sAr5GT0dyzEyq71MKQcdPhXKMONq9dqrQPzGryrCWYNGsRmrRqh0pVa6BRszaYEbAKnz6lYv+urd+wFURE3+mVh44dO6Jr164YMmQIEhISMHjwYAQFBWHkyJHw9/dHTEwMZs6cifr16+P69euwtLQEkHlA6ufnhwEDBqBz5854/vw5Bg4ciPT0dDg6OhZqHXfv3o3Tp09jxowZsLKygoWFBW7fvo369evD1tYWCxcuhJWVFQ4fPoyRI0fi3bt38PHxAQBERUWhcePGUFdXR2BgICwtLbF169Yvjt/PzsuXL1GrVi18+vQJ3t7ecHZ2RnR0NA4fPoz379/D0tISKSkpiImJwfjx42FjY4PU1FQcO3YMHTt2xMaNG9G7d+8vtk2qyMhIdO3aFZMnT8bMmTNx4MABzJ49G+/fv8eKFSvEdBERERg8eDBsbTMvyZ8/fx4///wzXr58iRkzZijkeeXKFdy5cwfTpk2Dg4MDdHV1lcpNSUlB3759ceDAAezbtw+tWrX6Yl07deqEn376CQMGDMDff/+NKVOmAAA2bNgAAMjIyEDbtm1x+fJl+Pr6onr16ggPD5eU9+e2b9+OYcOG4eeff8aCBQugoqKChw8f4vbt20ppBwwYgObNmyM4OBjPnz/HtGnT4Obmhhs3bsDIyAgA8OjRI9SrVw9eXl4wNDREREQEFi1ahAYNGuDvv/+Guro6AOD69eto0KABzMzMMHPmTJQrVw6vX7/G3r17kZqaCk1NTURGRqJ27dpQUVHBjBkzUKZMGYSHh2P27NmIiIjAxo0b89wGov+ai2dCoaWtg3qNmylMb9KyHZb+MhUP796EY6Wq2S5raGyiNM3EzBymZpaIfhP1VepLRJST7zJ46NOnj3h2+fz581i3bh0WLlyIsWPHimkaNmyI8uXLY9GiRfD390dsbCz8/f3RoUMH/Prrr2K6SpUqwdXVtdCDh48fP+Lvv/+GsbGxOK1Vq1bQ19fHmTNnYGBgAABo3rw5UlJSMG/ePIwcORLGxsZYvHgx3r59i6tXr6Jq1cwfk9atW6NFixZ49uxZnuoxY8YMvHv3DtevX0fFihXF6V26dBH/b2hoKB4AAkB6ejrc3d3x/v17LFmyRCl4yK5tUkVHR2PPnj1o164dAKBFixZISkrCqlWrMHHiRDFYyFqfjIwMuLm5QRAELF26FNOnT4dMJhPnv3nzBrdv30b58uWzLTMmJgbt27fHkydPcPr0aXGdfsmAAQMwYcIEAECzZs3w8OFDbNiwAevXr4dMJsOhQ4dw5swZrFq1CkOGDAGQuT01NDTEQEOqs2fPwsjICMuWLROnubu7Z5u2Zs2aWL9+vfi3vA+vXLkSU6dOBQCxPkDmFZf69evDzc0NdnZ2OHjwoLj+x44dCzU1NVy8eBHm5ubiMj169BD/7+vri/fv3+PWrVvi9nF3d4e2tjbGjx+PCRMmwMnJKU9tIPqveRbxCDa2DlBVVfzZtStdLnP+k0c5Bg/ZiXr1Am/fvEYtV7fCrCYR0Rd9V8OW5Dp16iT+f//+/ZDJZOjZsyfS0tLEj5WVFapWrSoOMQoPD0dycrLCQREA1K9fH3Z2doVex6ZNmyocXCcnJ+P48ePo0KEDdHR0FOrapk0bJCcn4/z58wCA0NBQVKpUSekgt3v37nmux8GDB9GkSROFwCE7v//+O1xdXaGnpwc1NTWoq6tj/fr1uHPnzhfblhf6+vrigatc9+7dkZGRgVOnTonTTpw4gWbNmsHQ0BCqqqpQV1fHjBkzEB0djTdv3igs7+zsnGPg8OTJE9SrVw8fPnzA+fPnJQcOAJTq6ezsjOTkZLH8kydPAlAMxACgW7duksuQq127NmJjY9GtWzfs2bMn2+FFcjn14dDQUHHamzdvMGTIEJQqVUrcnvJ+Lt+miYmJOHnyJLp06aIQOHxu//79aNKkCaytrRX6bevWmWO05eshL22QS0lJQVxcnMInNctwKaJ/i49xH6Cnb6A0XT7tY1ys5LzS09OwauFMaGlpw7NT3n8XiIgK4rsMHkqUKCH+PyoqCoIgwNLSEurq6gqf8+fPiwcw0dHRAAArKyul/LKbVph1lJeflpaG5cuXK9WzTZs2AKBQ18Kq59u3b1GyZMlc04SEhKBLly6wsbHBli1bEB4ejkuXLqF///5ITk7+YtvyQj6ELCt5u+Tb6OLFi2jRogUAYN26dTh79iwuXboknlVPSkqSXJ+LFy/i/v376Nq16xfXw+dMTU0V/tbU1FQoPzo6GmpqajAxURxSkF0bv6RXr17YsGEDnj59ik6dOsHCwgJ16tTB0aNHldLm1Dfk6y8jIwMtWrRASEgIJk6ciOPHj+PixYticCqv//v375Genv7F9RIVFYV9+/Yp9dtKlSoB+Kff5qUNcnPnzoWhoaHCZ/3KhRLWGNH3J+sV02xmSspDEASsWjATd/6+hp8nzYSZReH/fhER5ea7HLaUdQdsZmYGmUyG06dPiwd3WcmnyQ8EIyMjldJERkYq3PSspaUFAAo3jAKQdCY1uzoCgLGxMVRVVdGrVy8MHz4822UcHBzEuuZUz7wyNzfHixcvck2zZcsWODg4YMeOHQr1/rz9crn+AH6B/CbtrOTtkm+j7du3Q11dHfv37xe3BZB5r0Ve6/PTTz/BysoKU6dORUZGBqZNm5bvun/O1NQUaWlpiImJUQgg8rOdAKBfv37o168fEhIScOrUKfj4+MDT0xP3799XuDqWU98oW7YsAODmzZu4fv06goKC0KdPHzHNw4cPFZYxMTGBqqrqF/uHmZkZnJ2dMWfOnGznW1tb57kNclOmTFEYbggA9998yrU+RN8jPQNDxMd9UJr+MT4uc76+4RfzEAQBqxbOwqnjBzFioh+HLBFRkfgurzxk5enpCUEQ8PLlS9SsWVPpU6VKFQBA3bp1oaWlha1bFZ9Mce7cOTx9+lRhmjyQuHHjhsL0vXv35rueOjo6aNKkCa5evQpnZ+ds6yo/eG7SpAlu3bqF69evK+QRHByc53Jbt26N0NBQ3Lt3L8c0MpkMGhoaCgfhkZGR2T5tqaDi4+OV1mNwcDBUVFTQqFEjsT5qampQVVUV0yQlJWHz5s35KnPatGlYsmQJZsyYked7EXLTuHFjAMCOHTsUpm/fvr1A+erq6qJ169aYOnUqUlNTcevWLYX5OfVh+RPD5Nvx82B6zZo1Cn9ra2ujcePG+P3333MNjD09PXHz5k2UKVMm236bNXiQ2gY5TU1NGBgYKHw0sjkJQPS9s3Uoi5fPniA9XfEJZc+ePPz/+bk/blUeOIQd3ouhY6ejUbM2X62uRES5+S6vPGTl6uqKQYMGoV+/frh8+TIaNWoEXV1dvH79GmfOnEGVKlUwdOhQGBsbY/z48Zg9eza8vLzw448/4vnz5/D19VUaBlKrVi04Ojpi/PjxSEtLg7GxMf7880+cOXOmQHVdunQpGjRogIYNG2Lo0KGwt7dHfHw8Hj58iH379uHEiRMAgNGjR2PDhg3w8PDA7Nmzxact3b17N89lzpw5EwcPHkSjRo3g7e2NKlWqIDY2FocOHcLYsWNRoUIF8VGnw4YNE59CNWvWLJQoUQIPHjwoUJs/Z2pqiqFDh+LZs2coX748/vrrL6xbtw5Dhw4Vb8b18PDAokWL0L17dwwaNAjR0dFYsGBBtleWpBo1ahT09PQwaNAgfPz4EcuWLSvQFRQg8wZ4V1dXjBs3DnFxcahRowbCw8Px22+/AQBUVKTH5gMHDoS2tjZcXV1RokQJREZGikN6atWqpZD28uXLCn146tSpsLGxwbBhwwAAFSpUQJkyZTB58mQIggATExPs27cv2+FD8icw1alTB5MnT0bZsmURFRWFvXv3Ys2aNdDX18fMmTNx9OhR1K9fHyNHjoSjoyOSk5MRERGBv/76C6tXr0bJkiXz1Aai/5o6DZrg+F9/4vypE3Bt0kKcHnZkP4xNzVG2QuUclxUEAasXZQYOg0Z7o0mrdjmmJSL62r774AHIPKNat25drFmzBoGBgcjIyIC1tTVcXV1Ru3ZtMd3MmTOhq6uLwMBAbN68GRUqVMDq1auxYMEChfxUVVWxb98+jBgxAkOGDIGmpia6du2KFStWwMPDI9/1dHJywpUrVzBr1ixMmzYNb968gZGREcqVKyfe9wBkjl8/efIkRo0ahaFDh0JHRwcdOnTAihUr0L59+zyVaWNjg4sXL8LHxwfz5s1DdHQ0zM3N0aBBA3GoTb9+/fDmzRusXr0aGzZsQOnSpTF58mS8ePEix3cm5JeVlRVWrlyJ8ePH4++//4aJiQm8vb0VymnatCk2bNgAf39/tG3bFjY2Nhg4cCAsLCwwYMCAfJc9YMAA6OrqolevXkhISFB46lZ+qKioYN++fRg3bhzmzZuH1NRUuLq6YsuWLahbt6742FQpGjZsiKCgIOzcuRPv37+HmZkZGjRogN9++03pZub169dj8+bN6Nq1K1JSUtCkSRMsXbpU3J7q6urYt28fRo0ahcGDB0NNTQ3NmjXDsWPHxABNrmrVqmL/mDJlCuLj42FlZYWmTZtCQ0MDQOY9JZcvX8asWbMwf/58vHjxAvr6+nBwcECrVq3Em+fz0gai/5pqtV3hXKMO1i2bi6TEj7CyKYUzJw7j2qVzGDlllnilNXDBTJw8sh8rNu+BuWXm/VwbVszHiYN70KRVe9g6lMP923+L+aqrq8OhXIUiaRMR/TfJhM/fuvUfJB/ukfXlb1T43Nzc8O7dO9y8ebOoq/JVBQcHo0ePHjh79izq169faPkGBQWhX79+uHTpEmrWrFlo+RY3N55/LOoqEH0VSUmJ2LZhJcJPHsXH+DjYlLJHh2794NqkpZhmRYAPTh7Zj5Vb9sHCKnNI4LAenngb9TrbPM0tSyBw6/5vUn+ib825lF5RV4Gy8a+48kBUVLZt24aXL1+iSpUqUFFRwfnz5zF//nw0atSoUAMHIvr+aWvroP/wCeg/fEKOaUZM9MOIiYpXfBkcEFFxwuDhOyUIAtLT03NNo6qqWuBx/VKkp6cjtwtYMplM4ebnfxN9fX1s374ds2fPRkJCAkqUKIG+ffti9uzZYpq0tLRccsgc/pSX+yOIiIiIigqHLX2n5ENYchMaGioOyfqa3NzcxBeFZcfOzg4RERFfvR7FUUREhPgI3pz4+PjA19f321ToO8BhS0REBHDYUnHF4OE7FR0djSdPnuSaxtHREfr6+l+9Lvfu3UN8fHyO8zU1NcVH5v7XpKamKj3y93PW1tbZPu70v4rBAxERAQweiisGD0RUrDB4ICIigMFDccWB1kREREREJAmDByIiIiIikoTBAxERERERScLggYiIiIiIJGHwQEREREREkjB4ICIiIiIiSRg8EBERERGRJAweiIiIiIhIEgYPREREREQkCYMHIiIiIiKSRK2oK0BElJWOpmpRV4GIiIhywCsPREREREQkCYMHIiIiIiKShMEDERERERFJwuCBiIiIiIgkYfBARERERESSMHggIiIiIiJJGDwQEREREZEkDB6IiIiIiEgSBg9ERERERCQJgwciIiIiIpKEwQMREREREUnC4IGIiIiIiCRh8EB55uvrC5lMhnfv3n3Tcvv27Qs9Pb0vpnNzc4Obm9vXr1AhkNomIvq+JCUmYu2yAPT6oTl+cK+NEf264OSxQ19c7t2bKKxdFoBJIwagS+sG8GjogqN/7fnicikpyRjYrR08Grpg17ZNhdEEIqJsqRV1BYgKW2BgYFFXgYj+4+ZMG4v7d26h35BRsC5lh5NH/0KA32QIQgbcmrfJcblXL58h9MhBlC5XHjXrNpAUcADA5l8DkZKcVFjVJyLKEYMH+tdxcnIq6ioQ0X/YpfDTuHrpPCb4zIVbs9YAgKrVa+FN1GtsCFyMhk1bQlVVNdtlK1etgW37QwEAD+7ekhQ83Lv9N/bt2oYJ03/B3BkTCq8hRETZ4LAlyrfnz5+jY8eOMDAwgKGhIXr27Im3b9+K8zMyMhAQEIAKFSpAU1MTFhYW6N27N168eKGU14YNG1C1alVoaWnBxMQEHTp0wJ07d75Yh7Nnz8LMzAyenp5ISEgAoDxsKSIiAjKZDAsWLMCiRYvg4OAAPT091KtXD+fPn1fKc926dShfvjw0NTXh5OSE4OBg9O3bF/b29nleR4cOHYK7uzsMDQ2ho6ODihUrYu7cuUrpHj58iDZt2kBPTw+lSpXCuHHjkJKSopDGz88PderUgYmJCQwMDFC9enWsX78egiAopLO3t4enpycOHTqE6tWrQ1tbGxUqVMCGDRuUyj1z5gzq1asHLS0t2NjYYPr06fj1118hk8kQERGhkHbHjh2oV68edHV1oaenh5YtW+Lq1asKaR4/foyuXbvC2toampqasLS0hLu7O65du5bndUf0vQo/dQLa2jpo6NZcYXqzNu0R/e4t7t3+O8dlVVTy9rP86dMnLJ3nC88OP6FcBZ44IaKvj8ED5VuHDh1QtmxZ/PHHH/D19cXu3bvRsmVLfPr0CQAwdOhQTJo0Cc2bN8fevXsxa9YsHDp0CPXr11e4X2Lu3LkYMGAAKlWqhJCQECxduhQ3btxAvXr18ODBgxzL37lzJ9zd3dGlSxfs2bMHurq6udZ35cqVOHr0KJYsWYKtW7ciISEBbdq0wYcPH8Q0a9euxaBBg+Ds7IyQkBBMmzYNfn5+CAsLy/P6Wb9+Pdq0aYOMjAysXr0a+/btw8iRI5WCp0+fPqFdu3Zwd3fHnj170L9/fyxevBj+/v4K6SIiIjB48GDs3LkTISEh6NixI37++WfMmjVLqezr169j3LhxGDNmDPbs2QNnZ2cMGDAAp06dEtPcuHEDzZs3R2JiIjZt2oTVq1fjypUrmDNnjlJ+v/zyC7p16wYnJyfs3LkTmzdvRnx8PBo2bIjbt2+L6dq0aYP//e9/CAgIwNGjR7Fq1SpUq1YNsbGxeV5/RN+rp08eoZS9A1TVFC/uO5Qp9//zHxZaWduC1iA5OQm9vIYXWp5ERLnhsCXKt44dOyIgIAAA0KJFC1haWqJHjx7YuXMnatSogbVr12LYsGFYvny5uEy1atVQp04dLF68GHPmzEFsbCxmzZqFNm3aIDg4WEzn5uaGcuXKwdfXF1u3blUq29/fH1OnTsUvv/yCiRMnSqqvvr4+9u/fLw4XsLa2Ru3atXHw4EF07doVGRkZ8PHxQZ06dfDHH3+IyzVo0ABly5aFtbW15HXz8eNHjB07Fq6urjhx4gRkMhkAwN3dXSltamoq/Pz88OOPP4ppLl++jODgYMyYMUNMt3HjRvH/GRkZcHNzgyAIWLp0KaZPny6WAQDv3r3D2bNnYWtrCwBo1KgRjh8/juDgYDRq1AgAMHv2bKiqquL48eMwMzMDAHh4eKBKlSoK9Xv+/Dl8fHwwYsQILFu2TJzevHlzlCtXDn5+ftixYweio6Nx7949LFmyBD179hTTdezYUfJ6I/o3iPsQCyvrkkrT9fUNAQDxWU5YFMSjB3exKzgIM+Ytg5a2Nj7EFkq2RES54pUHyrcePXoo/N2lSxeoqakhNDQUoaGZY3b79u2rkKZ27dqoWLEijh8/DgAIDw9HUlKSUrpSpUqhadOmYjo5QRAwePBg+Pj4IDg4WHLgAGQeGGcdZ+zs7AwAePr0KQDg3r17iIyMRJcuXRSWs7W1haurq+RyAODcuXOIi4vDsGHDFA7qsyOTydC2bVuFac7OzmK95E6cOIFmzZrB0NAQqqqqUFdXx4wZMxAdHY03b94opHVxcREDBwDQ0tJC+fLlFfI8efIkmjZtKgYOQOaQic/bf/jwYaSlpaF3795IS0sTP1paWmjcuLF4VcbExARlypTB/PnzsWjRIly9ehUZGRm5tj0lJQVxcXEKn8+HaxF9j3L93n9hnyBFeloals71RcOmLVGjTv0C50dEJBWDB8o3Kysrhb/V1NRgamqK6OhoREdHAwBKlCihtJy1tbU4X2o6udTUVOzYsQOVKlVC69at81RfU1NThb81NTUBAElJSQp1sbS0VFo2u2m5kd/7UbKk8tnHz+no6EBLS0upbsnJyeLfFy9eRIsWLQBk3pNx9uxZXLp0CVOnTlVog9znbZXnmTVddHS0pLZGRUUBAGrVqgV1dXWFz44dO8QhaDKZDMePH0fLli0REBCA6tWrw9zcHCNHjkR8fHy2bZ87dy4MDQ0VPmuWzc9+RRF9JwwMjRAfF6s0PT4+84qDvoFBgcvY/ftWRL5+ge79BuNjfBw+xsch8f/v+/qUmoqP8XFIT08vcDlERJ/jsCXKt8jISNjY2Ih/p6WlITo6GqampuLB6+vXr5UOoF+9eiWe7c6a7nNZ08lpamoiNDQULVu2RLNmzXDo0CEYGxsXSnvkdZEfLGcVGRmZp7zMzc0BINubw/Nj+/btUFdXx/79+xUCjd27d+c7T1NTU0ltlW+DP/74A3Z2drnmaWdnh/Xr1wMA7t+/j507d8LX1xepqalYvXq1UvopU6Zg7NixCtOef8j9agVRcWdXuixOHTuE9LQ0hfseIh5l3utg51C2wGU8ffwQCR8/YmC3dkrzNv+6Ept/XYllG7ajTLkKBS6LiCgrXnmgfPv8XoSdO3ciLS0Nbm5uaNq0KQBgy5YtCmkuXbqEO3fuiGP/69WrB21tbaV0L168wIkTJ7K9R6BatWo4efIkXrx4ATc3N6UhO/nl6OgIKysr7Ny5U2H6s2fPcO7cuTzlVb9+fRgaGmL16tVKT0PKD5lMBjU1NYVhV0lJSdi8eXO+82zcuDFOnDihcPN6RkYGfv/9d4V0LVu2hJqaGh49eoSaNWtm+8lO+fLlMW3aNFSpUgVXrlzJNo2mpiYMDAwUPvIrQkTfq/qNmiIpKRFnTyoOuzx+aB9Mzczh6FQlhyWl+7Fnf8xdtk7hM9FnHgCgTfsfMXfZOljb2H4hFyKivOOVB8q3kJAQqKmpoXnz5rh16xamT5+OqlWrokuXLtDQ0MCgQYOwfPlyqKiooHXr1oiIiMD06dNRqlQpjBkzBgBgZGSE6dOnw9vbG71790a3bt0QHR0NPz8/aGlpwcfHJ9uyK1asiNOnT6NZs2Zo1KgRjh07JmmIUG5UVFTg5+eHwYMHo3Pnzujfvz9iY2Ph5+eHEiVK5OkRinp6eli4cCG8vLzQrFkzDBw4EJaWlnj48CGuX7+OFStW5KluHh4eWLRoEbp3745BgwYhOjoaCxYsKNCB9tSpU7Fv3z64u7tj6tSp0NbWxurVq8VH3srba29vj5kzZ2Lq1Kl4/PgxWrVqBWNjY0RFReHixYvQ1dWFn58fbty4gREjRuDHH39EuXLloKGhgRMnTuDGjRuYPHlyvutJ9L2pWbcBqtWqi5UL5yAx4SNKlLTFyWMH8b8LZzF++hzxJMCSeb44fmgf1m/fBwurfx7IcCb0KAAg8lXmlcuH925DW1sHANCgSebjX0vZOaCUnYNCuVGvXwIArGxKwrlara/bSCL6z2LwQPkWEhICX19frFq1Srzpd8mSJdDQ0AAArFq1CmXKlMH69euxcuVKGBoaolWrVpg7d67CmPwpU6bAwsICy5Ytw44dO6CtrQ03Nzf88ssvKFeuXI7lly5dWgwgGjZsiOPHj6N06dIFatOgQYMgk8kQEBCADh06wN7eHpMnT8aePXvw7NmzPOU1YMAAWFtbw9/fH15eXhAEAfb29ujTp0+e69W0aVNs2LAB/v7+aNu2LWxsbDBw4EBYWFhgwIABec4PAKpWrYqjR49i/Pjx6N27N4yNjdGrVy80btwYkyZNgqGhoZh2ypQpcHJywtKlS7Ft2zakpKTAysoKtWrVwpAhQwBk3gNTpkwZBAYG4vnz55DJZChdujQWLlyIn3/+OV91JPpeTZ29CL+tW44t61chPv4DStk6YKLPPDRu1kpMk5Gejoz0dKWrk5+/6G1/yA7sD9kBADhw+tpXrzsRUW5kQmGMqSD6F4uNjUX58uXxww8/YO3atUVdna+uRYsWiIiIwP3794uk/Idvkr6ciIiI/vXKWmgXdRUoG7zyQJRFZGQk5syZgyZNmsDU1BRPnz7F4sWLER8fj1GjRhV19Qrd2LFjUa1aNZQqVQoxMTHYunUrjh49Kt70TERERJQVgweiLDQ1NREREYFhw4YhJiYGOjo6qFu3LlavXo1KlSoBANKzGWaQlUwmU7ixuThLT0/HjBkzEBkZCZlMBicnJ2zevFnhJW9EREREchy2RJRHbm5uOHnyZI7z7ezsEBER8e0q9C/DYUtERARw2FJxxSsPRHm0Zs2aHF96BoCPGiUiIqJ/LQYPRHnk6OhY1FUgIiIiKhJ8SRwREREREUnC4IGIiIiIiCRh8EBERERERJIweCAiIiIiIkkYPBARERERkSQMHoiIiIiISBIGD0REREREJAmDByIiIiIikoTBAxERERERScI3TBNRsSKTFXUNiIiIKCe88kBERERERJIweCAiIiIiIkkYPBARERERkSQMHoiIiIiISBIGD0REREREJAmDByIiIiIikoTBAxERERERScLggYiIiIiIJGHwQEREREREkjB4ICIiIiIiSRg8EBERERGRJAweiIiIiIhIEgYPRP8CQUFBkMlkiIiIKLQ8+/btCz09PUlpf/vtN3Tt2hWOjo5QUVGBvb19odWDiIiIig+1oq4AERWch4cHwsPDUaJEiSIpf/PmzYiMjETt2rWRkZGBT58+FUk9iIqLpMRE/LZuBU6fOIr4+A8oZWuPH3v2R+NmrXJd7t2bKOzatgmPHtzDk4f3kPDxI8Z4+6F5m/a5LpeSkozhfbrg1YtnGDBsDDp171OYzSEiEjF4IPoXMDc3h7m5eZGVf/jwYaioZF7I9PT0xM2bN4usLkTFweypY/Hgzi30HTIKNrZ2CDv6F/x9JyMjIwNNWrTJcblXL54h9OhBlC5bHjXrNsDJY4cklbd5XSBSkpMKq/pERDnisCWif4HPhy1dvXoVnp6esLCwgKamJqytreHh4YEXL17kOe+HDx+iTZs20NPTQ6lSpTBu3DikpKQopJEHDkQEXAo/jauXzmPYOG+0+aEzqlavhVGTfFCtVl1sCFyM9PT0HJet7FID2/eH4pcla9Chay9J5d27/Tf27tqGwaMmFlYTiIhyxF98on+ZhIQENG/eHFFRUVi5ciWOHj2KJUuWwNbWFvHx8XnK69OnT2jXrh3c3d2xZ88e9O/fH4sXL4a/v/9Xqj3R9+/cqRPQ1tZBwybNFaY3b9Me0e/e4t7tv3NcNq+B+KdPn7Bkri88O/6EchWc8lVfIqK84LAlon+Zu3fvIjo6GuvXr0f79v+Mk+7SpUue80pNTYWfnx9+/PFHAIC7uzsuX76M4OBgzJgxo9DqTPRv8vTxI5Syd4CqmuJPrEOZcv8//yGcqrgUSlnBG9cgOTkJvb2G40NsTKHkSUSUG155IPqXKVu2LIyNjTFp0iSsXr0at2/fzndeMpkMbdu2VZjm7OyMp0+fFrSaAICUlBTExcUpfD4fEkX0vYn7EAt9fUOl6foGmdPi4j4USjmPHtzFruAgjBg/DVra2oWSJxHRlzB4IPqXMTQ0xMmTJ+Hi4gJvb29UqlQJ1tbW8PHxyfNTkHR0dKClpaUwTVNTE8nJyYVS17lz58LQ0FDhs3rp/ELJm6hIyWQ5z0LO86RKT0vDkrm+aOjeEjXq1C9wfkREUnHYEtG/UJUqVbB9+3YIgoAbN24gKCgIM2fOhLa2NiZPnlzU1RNNmTIFY8eOVZj2Ii6jiGpDVDgMDI0QHxerND3+/6846BsYFLiM3Tu3IvLVC0yZGYCP8XEAgMSEBACZww0/xsdBW0cXqqqqBS6LiCgrBg9E/2IymQxVq1bF4sWLERQUhCtXrhR1lRRoampCU1NTcVoKHzdJ3zf70mVx8tghpKelKdz3EPH4IQDArnTZApfx9MlDJHz8CK+u7ZTmbf51JTb/uhLLN25HmXIVClwWEVFWDB6I/mX279+PwMBA/PDDDyhdujQEQUBISAhiY2PRvHnzL2eQD7dv3xbvrYiMjERiYiL++OMPAICTkxOcnPgUGPrvqNeoKQ7tC8GZk8fR2L2lOP3YwX0wNTOHo1OVApfxY8/+aNZaMXB4HxMNf9/JaPPDj2jUtAWsbWwLXA4R0ecYPBD9y5QrVw5GRkYICAjAq1evoKGhAUdHRwQFBaFPn6/z1tmdO3fCz89PYZr8CU0+Pj7w9fX9KuUSFUe16jVAtVp1sXLBHCQlfESJkrY4efQg/nfhLCbMmCMOJVoy1xfHDu3D+h37YGllLS5/JvQoAOD1q8z3sjy4exva2joAgAb///jXUnYOKGXnoFBu1OuXAIAS1iXhXL3W120kEf1nyQRBEIq6EkREco/ectgSff+SEhOxae1ynA49ivi4Dyhl64AuvfqjcbNWYppFc6bj2MF92Pj7AViWsBGnt2ngkmO+f525luO8qNcv0e9HDwwYNgadun+dEwVE31IZcz5FrDhi8EBExQqDByIiAhg8FFcctkT0H5ORkYGMjNyfaKSmxl0DERERKeN7Hoj+Y/r37w91dfVcP0RERETZ4bAlov+YiIgIvHv3Ltc0NWvW/Ea1UcZhS0REBHDYUnHF4IGIihUGD0REBDB4KK44bImIiIiIiCRh8EBERERERJIweCAiIiIiIkkYPBARERERkSQMHoiIiIiISBIGD0REREREJAmDByIiIiIikoTBAxERERERScLggYiIiIiIJGHwQEREREREkqgVdQWIiLLSUOU5DSIiouKKv9JERERERCQJgwciIiIiIpKEwQMREREREUnC4IGIiIiIiCRh8EBERERERJIweCAiIiIiIkkYPBARERERkSQMHoiIiIiISBIGD0REREREJAmDByIiIiIikoTBAxERERERScLggYiIiIiIJGHwQAoiIiIgk8kQFBRU1FX5TwgKCoJMJkNERIQ4rW/fvrC3t89XfsHBwViyZEm282QyGXx9ffOVLxHlTVJiIgIX++Ontu5o3bgmBvf+EaFHD35xubdvIhG42B9jh/ZD++auaFbPGYcP7Mk27fkzJzHPzxtePTqiZYPqaFbPubCbQUSkRK2oK0DFS4kSJRAeHo4yZcoUdVUoH4KDg3Hz5k2MHj1aaV54eDhKliz57StF9B/kO2UM7t25Ca+ho1HS1g7Hj/yFOTMmISMjA+4tPXJc7uWL5zh+5C+UKeeI2vUa5BpwnDl5Andu/Y2y5StAQ0MD9+/e/hpNISJSwODhO5aUlARtbe1CzVNTUxN169Yt1DypeOB2Jfo2Lpw7jf9dDIe33zw0bdEGAOBSozbeRL7G2hWL4dasFVRVVbNd1tmlBnYdPAkAuHfnVq7Bw9gpPlBRyRxAsHzBLwweiOib4LClIubr6wuZTIarV6+iY8eOMDAwgKGhIXr27Im3b9+K6ezt7eHp6YmQkBBUq1YNWlpa8PPzAwBERkZi8ODBKFmyJDQ0NODg4AA/Pz+kpaUBAD59+gQLCwv06tVLqfzY2Fhoa2tj7NixAHIetnTmzBm4u7tDX18fOjo6qF+/Pg4cOJBtWz6X3dCcEydOwM3NDaamptDW1oatrS06deqExMTEPK2/Q4cOwd3dHYaGhtDR0UHFihUxd+5ccf7ly5fRtWtX2NvbQ1tbG/b29ujWrRuePn2abR1DQ0MxdOhQmJmZwdTUFB07dsSrV6+Uyg0ODka9evWgp6cHPT09uLi4YP369Qppjh07Bnd3dxgYGEBHRweurq44fvx4ntont3LlSjRq1AgWFhbQ1dVFlSpVEBAQgE+fPolp3NzccODAATx9+hQymUz8yGU3bOnmzZto3749jI2NoaWlBRcXF2zatEkhTVhYGGQyGbZt24apU6fC2toaBgYGaNasGe7du6eQ9urVq/D09ISFhQU0NTVhbW0NDw8PvHjxIl/tJvoenTl5HNo6OmjctIXC9JYe7RH97g3u3vo7x2XlwYAUeUlLRFRYuOcpJjp06ICyZcvijz/+gK+vL3bv3o2WLVsqHBxeuXIFEyZMwMiRI3Ho0CF06tQJkZGRqF27Ng4fPowZM2bg4MGDGDBgAObOnYuBAwcCANTV1dGzZ0/s2rULcXFxCuVu27YNycnJ6NevX451O3nyJJo2bYoPHz5g/fr12LZtG/T19dG2bVvs2LEjz22NiIiAh4cHNDQ0sGHDBhw6dAjz5s2Drq4uUlNTJeezfv16tGnTBhkZGVi9ejX27duHkSNHKhyoRkREwNHREUuWLMHhw4fh7++P169fo1atWnj37p1Snl5eXlBXV0dwcDACAgIQFhaGnj17KqSZMWMGevToAWtrawQFBeHPP/9Enz59FAKSLVu2oEWLFjAwMMCmTZuwc+dOmJiYoGXLlvkKIB49eoTu3btj8+bN2L9/PwYMGID58+dj8ODBYprAwEC4urrCysoK4eHh4icn9+7dQ/369XHr1i0sW7YMISEhcHJyQt++fREQEKCU3tvbG0+fPsWvv/6KtWvX4sGDB2jbti3S09MBAAkJCWjevDmioqKwcuVKHD16FEuWLIGtrS3i4+Pz3Gai71XE44ewtSsNVTXFi/uly5YHADx5/LAoqkVEVCg4bKmY6Nixo3jA1qJFC1haWqJHjx7YuXMnevToAQB48+YNbt++jfLly4vLDRkyBO/fv8etW7dga2sLAHB3d4e2tjbGjx+PCRMmwMnJCf369cPixYuxY8cOMagAMs+416hRA1WqVMmxbpMnT4axsTHCwsKgp6cHAPD09ISLiwvGjx+PLl26ZHvFISf/+9//kJycjPnz56Nq1ari9O7du0vO4+PHjxg7dixcXV1x4sQJsXx3d3eFdJ07d0bnzp3Fv9PT0+Hp6QlLS0sEBwdj5MiRCulbtWqFZcuWiX/HxMRg4sSJiIyMhJWVFZ48eYJffvkFPXr0wJYtW8R0zZs3F/+fmJiIUaNGwdPTE3/++ac4vU2bNqhevTq8vb1x4cIFyW0FgEWLFon/z8jIQMOGDWFqaop+/fph4cKFMDY2hpOTE4yMjCQPPfP19UVqaipCQ0NRqlQpsY6xsbHw8/PD4MGDYWhoKKZ3cnJSaLOqqiq6dOmCS5cuoW7durh79y6io6Oxfv16tG/fXkzXpUuXPLWV6HsX9+EDSljbKE3XNzD8//mx37hGRESFh1ceigl5gCDXpUsXqKmpITQ0VJzm7OysEDgAwP79+9GkSRNYW1sjLS1N/LRu3RpA5lUDAKhSpQpq1KiBjRs3isveuXMHFy9eRP/+/XOsV0JCAi5cuIDOnTuLgQOQeeDYq1cvvHjxQmnoype4uLhAQ0MDgwYNwqZNm/D48eM8LQ8A586dQ1xcHIYNG5Zr4PLx40dMmjQJZcuWhZqaGtTU1KCnp4eEhATcuXNHKX27du0U/nZ2znx6ifyqwtGjR5Geno7hw4fnWreYmBj06dNHYZtkZGSgVatWuHTpEhISEvLU3qtXr6Jdu3YwNTWFqqoq1NXV0bt3b6Snp+P+/ft5ykvuxIkTcHd3FwMHub59+yIxMVHpqsWX1k3ZsmVhbGyMSZMmYfXq1bh9+8vjr1NSUhAXF6fwSUlJyVd7iIqT3PZLeTnZQkRU3DB4KCasrKwU/lZTU4OpqSmio6PFaSVKlFBaLioqCvv27YO6urrCp1KlSgCgMDSnf//+CA8Px927dwEAGzduhKamJrp165Zjvd6/fw9BELIt29raGgAU6ihFmTJlcOzYMVhYWGD48OEoU6YMypQpg6VLl0rOQ34/yJeeHtS9e3esWLECXl5eOHz4MC5evIhLly7B3NwcSUlJSulNTU0V/tbU1AQAMa2UcqOiogBkXvX4fLv4+/tDEATExMRIbCnw7NkzNGzYEC9fvsTSpUtx+vRpXLp0CStXrlSoW15FR0fnabt+ad0YGhri5MmTcHFxgbe3NypVqgRra2v4+PgoDL/Lau7cuTA0NFT4rFyiPGSK6HtiYGiIuA8flKbHx2VOk1+BICL6HnHYUjERGRkJG5t/LnOnpaUhOjpa4YAtu7NVZmZmcHZ2xpw5c7LNV34gCADdunXD2LFjERQUhDlz5mDz5s344YcfYGxsnGO9jI2NoaKigtevXyvNk99IbGZmBgDQ0tICkHk2WX5gCSDbewsaNmyIhg0bIj09HZcvX8by5csxevRoWFpaomvXrjnWR87c3BwAcr0R98OHD9i/fz98fHwwefJkcXpKSkqeDt5zKvfzM/Zy8vWxfPnyHIcPWVpaSi5z9+7dSEhIQEhICOzs7MTp165dk5xHdkxNTSVt17yoUqUKtm/fDkEQcOPGDQQFBWHmzJnQ1tZW2AZyU6ZMEW/Wl3uTt4syRMWOQ5lyCD16EOlpaQr3PTx59CBzfumyRVU1IqIC45WHYmLr1q0Kf+/cuRNpaWlwc3PLdTlPT0/cvHkTZcqUQc2aNZU+WYMHY2Nj/PDDD/jtt9+wf/9+REZG5jpkCQB0dXVRp04dhISEKJzhzsjIwJYtW1CyZElxKJX8xWY3btxQyGPfvn055q+qqoo6deqIZ9GvXLmSa33k6tevD0NDQ6xevRqCIGSbRiaTQRAEhUAGAH799VfxJt+8atGiBVRVVbFq1aoc07i6usLIyAi3b9/OdpvUrFkTGhoaksuUB41Z2yEIAtatW6eUVlNTU/KVCHd3d5w4cULpaVK//fYbdHR0CvRoV5lMhqpVq2Lx4sUwMjLKcbtqamrCwMBA4fP59iL63jRo3BRJiYk4FXZMYfqRv/bC1MwCFSrlfI8ZEVFxxysPxURISAjU1NTQvHlz3Lp1C9OnT0fVqlW/eLPpzJkzcfToUdSvXx8jR46Eo6MjkpOTERERgb/++gurV69WGGLTv39/7NixAyNGjEDJkiXRrFmzL9Zt7ty5aN68OZo0aYLx48dDQ0MDgYGBuHnzJrZt2yYe3LZp0wYmJiYYMGAAZs6cCTU1NQQFBeH58+cK+a1evRonTpyAh4cHbG1tkZycjA0bNgCApPoAgJ6eHhYuXAgvLy80a9YMAwcOhKWlJR4+fIjr169jxYoVMDAwQKNGjTB//nyYmZnB3t4eJ0+exPr162FkZCSpnM/Z29vD29sbs2bNQlJSErp16wZDQ0Pcvn0b7969g5+fH/T09LB8+XL06dMHMTEx6Ny5MywsLPD27Vtcv34db9++zTX4+Fzz5s2hoaGBbt26YeLEiUhOTsaqVavw/v17pbRVqlRBSEgIVq1ahRo1akBFRQU1a9bMNl8fHx/xnpkZM2bAxMQEW7duxYEDBxAQEKBws7QU+/fvR2BgIH744QeULl0agiAgJCQEsbGxCjeUE/3b1a7XEDVq18OygNlITPgIm5K2OHH0IC6dP4spvnPFdzwsmOODIwf3YvPvB2BZ4p8TPadOHAEAvH6VeWX1/p1b4jt9GmV5/GvU61e4d+cmAODVy+cKy1qWsIFjxUpfuaVE9F/E4KGYCAkJga+vL1atWgWZTIa2bdtiyZIlXzxDXaJECVy+fBmzZs3C/Pnz8eLFC+jr68PBwQGtWrVSGpLUrFkzlCpVCs+fP8fUqVMlPSe8cePGOHHiBHx8fNC3b19kZGSgatWq2Lt3Lzw9PcV0BgYGOHToEEaPHo2ePXvCyMgIXl5eaN26Nby8vMR0Li4uOHLkCHx8fBAZGQk9PT1UrlwZe/fuRYsWLbKrQrYGDBgAa2tr+Pv7w8vLC4IgwN7eHn369BHTBAcHY9SoUZg4cSLS0tLg6uqKo0ePwsMj5ze8fsnMmTNRrlw5LF++HD169ICamhrKlSun8OSmnj17wtbWFgEBARg8eDDi4+NhYWEBFxcX9O3bN0/lVahQAbt27cK0adPQsWNHmJqaonv37hg7dqx4Y7zcqFGjcOvWLXh7e+PDhw8QBCHHKzOOjo44d+4cvL29MXz4cCQlJaFixYrYuHFjnusIAOXKlYORkRECAgLw6tUraGhowNHREUFBQQrbhOi/wHfuYmxYswyb1gUiPu4DStk5YOpMfzRp/s93NiMjHRnp6RCg+B2dOXW8wt97dm3Hnl3bAQDHwv+5snvtyiXMnz0922VbtGmHidNnF2qbiIgAQCbkdGRB34Svry/8/Pzw9u3bfI0xJ/q3eR7Dpy0RERFQyoTDWIsj3vNARERERESScNgSFTvp6ek5DrUBMm/GlY8ZJiIiIqJvh8OWqNhxc3MTX26XHTs7O0RERHy7CtE3xWFLREQEcNhSccUrD1TsrFmzBvHx8TnO56M8iYiIiIoGrzwQUbHCKw9ERATwykNxxRumiYiIiIhIEgYPREREREQkCYMHIiIiIiKShMEDERERERFJwuCBiIiIiIgkYfBARERERESSMHggIiIiIiJJGDwQEREREZEkDB6IiIiIiEgStaKuABFRVuqqPKdBRERUXPFXmoiIiIiIJGHwQEREREREkjB4ICIiIiIiSRg8EBERERGRJAweiIiIiIhIEgYPREREREQkCYMHIiIiIiKShMEDERERERFJwuCBiIiIiIgkYfBARERERESSMHggIiIiIiJJGDwQEREREZEkDB5ISVBQEGQyGSIiIoq6KsVGcHAwlixZ8lXytre3R9++fb9K3kXll19+we7du4u6GkRERFTIZIIgCEVdCSpe3r59i0ePHqFatWrQ1NQs6uoUC56enrh58+ZXCajs7e3h5uaGoKCgQs+7qOjp6aFz5875alPkh0+FXyGiryQxMRHrVy9D6LHDiI/7AFs7B3TvMwDuLdp8cdn3MdFYvXwRws+cRHJyMsqUc4TXkJ9Ro3ZdhXSpqanYtWMLDh/Yi9evXkJbRxvlHJ3QZ8BgVHauJqZ7/eoluv7QMtuyZswOkFQnouLEylC9qKtA2VAr6gpQ8WNubg5zc/OvXk5iYiJ0dHS+ejlERF/L9EmjcPf2LQwePholbe1x7PABzJw2ERkZApq38shxudTUVIwZ7oWP8XH4edxkGBmbYPfv2zFh1BAsWrkOLtVriWnnz/HBscMH0KOPF6rXrIO4uA8I3vQrRg7uh5W/bkbFSlUU8u7YpTuatVQsu2Qpu8JtOBH9Z3HYEin5fNjS1atX4enpCQsLC2hqasLa2hoeHh548eKF5Dz79u0LPT09/P3332jRogX09fXh7u4OIPNHdPbs2ahQoQI0NTVhbm6Ofv364e3btwp52Nvbw9PTE3/++SecnZ2hpaWF0qVLY9myZUrlxcXFYfz48XBwcICGhgZsbGwwevRoJCQkKKRbuXIlGjVqBAsLC+jq6qJKlSoICAjAp0//nP12c3PDgQMH8PTpU8hkMvEjJ7X+nz59wsSJE2FlZQUdHR00aNAAFy9elLwOs4qNjcWAAQNgYmICPT09eHh44PHjx5DJZPD19VVY7/b29krL+/r6KrQBAARBQGBgIFxcXKCtrQ1jY2N07twZjx8/Vkj3pf4gk8mQkJCATZs2ievKzc0tX+0kKs7Onz2FyxfCMWbiNLTr2AXVa9bGxKl+qFmnHlYvX4j09PQclz2wNwRPHj2A7y8L0byVJ2rVqQ+/eYtQytYOq5cvEtOlpqbi+JG/4N6yDbyGjkT1WnXg5t4CcxetRHp6Go4e2q+Ut6VVCVSqUlXhY2hk9DVWARH9B/HKA+UqISEBzZs3h4ODA1auXAlLS0tERkYiNDQU8fHxecorNTUV7dq1w+DBgzF58mSkpaUhIyMD7du3x+nTpzFx4kTUr18fT58+hY+PD9zc3HD58mVoa2uLeVy7dg2jR4+Gr68vrKyssHXrVowaNQqpqakYP348gMwrGo0bN8aLFy/g7e0NZ2dn3Lp1CzNmzMDff/+NY8eOiQfOjx49Qvfu3cUg4/r165gzZw7u3r2LDRs2AAACAwMxaNAgPHr0CH/++adCm/JS/4EDB+K3337D+PHj0bx5c9y8eRMdO3bM83rMyMhA27ZtcfnyZfj6+qJ69eoIDw9Hq1at8pTP5wYPHoygoCCMHDkS/v7+iImJwcyZM1G/fn1cv34dlpaWkvpDeHg4mjZtiiZNmmD69OkAAAMDgwLVjag4OhV2HNo6OnBzb6EwvbVnB8yaPhF3bt1QGFaU1emwY7C1c0BlZxdxmpqaGpq39sS6wKV4+yYK5haWUFHJDMD19PQVltfR1YWKigo0NDi0lIi+LQYPlKu7d+8iOjoa69evR/v27cXpXbp0yXNenz59wowZM9CvXz9x2vbt23Ho0CHs2rULHTt2FKdXrVoVtWrVQlBQEIYOHSpOf/XqFa5evYqqVasCAFq3bo03b95g1qxZGDZsGHR0dLBs2TLcuHEDFy5cQM2aNQEA7u7usLGxQefOnXHo0CG0bt0aALBo0T9n+DIyMtCwYUOYmpqiX79+WLhwIYyNjeHk5AQjIyNoamqibl3Fscg7d+6UVP+7d+9i06ZNGDNmDAICAgAAzZs3h6WlJXr06JGn9Xjo0CGcOXMGq1atwpAhQ8S8NDQ0MGXKlDzlJXf+/HmsW7cOCxcuxNixY8XpDRs2RPny5bFo0SL4+/tL6g9169aFiooKzM3NldYX0b/Jk0cPYWdfGmpqij+lZcqWBwA8fvQwx+DhyaOHcHapoTRdvmzE44cwt7CEmpo6fujcFQf2hqBGrbrisKV1gUuhq6cHzx86K+URvGk91gUuhaqqGspXqIhuvfrDtVGTgjaXiAgAhy3RF5QtWxbGxsaYNGkSVq9ejdu3bxcov06dOin8vX//fhgZGaFt27ZIS0sTPy4uLrCyskJYWJhC+kqVKomBg1z37t0RFxeHK1euiHlWrlwZLi4uCnm2bNkSMplMIc+rV6+iXbt2MDU1haqqKtTV1dG7d2+kp6fj/v37X2yP1PqHhoYCgFKg0KVLF6UDjy85efKkuGxW3bp1y1M+We3fvx8ymQw9e/ZUaIeVlRWqVq0qtqOw+0NKSgri4uIUPikpKQXKk+hbifsQCwMDQ6XpBoaG4vzcl1W+Iqf///l9+PBBnDZizCR06d4bMyaPQZumddH1h5a4eeMqFgduQMlStmI6DQ0NeP7QGWMmTcfiwA2YMNUP6ekZ8B7/M/bv/iO/zSQiUsDggXJlaGiIkydPwsXFBd7e3qhUqRKsra3h4+OjcF+AFDo6Oko/llFRUYiNjYWGhgbU1dUVPpGRkXj37p1CeisrK6V85dOio6PFPG/cuKGUn76+PgRBEPN89uwZGjZsiJcvX2Lp0qU4ffo0Ll26hJUrVwIAkpKSvtgmqfWX1+3z+qupqcHU1PSL5WQVHR0NNTU1mJiYKEy3tLTMUz6ft0MQBFhaWiq14/z582I7CrM/AMDcuXNhaGio8Fm+yD/f7SD65j67d0hxVs7zvrzsP//fvGEtdmzZhL4Dh2HJqg34ZcFylLKzx7gRA3H/3h0xnamZOSZ4+6JJs5ZwdqmO5q08sHxtEMo5VsTalUuQlpYmuVlERDnhsCX6oipVqmD79u0QBAE3btxAUFAQZs6cCW1tbUyePFlyPtn9kJqZmcHU1BSHDh3Kdhl9fcVxvpGRkUpp5NPkB+FmZmbQ1tYW71nIrkwA2L17NxISEhASEgI7u3+eRHLt2rUvNyaP9ZfXLTIyEjY2NuL8tLQ0MbCQytTUFGlpaYiJiVEIILJbN1paWtmeyf88KDMzM4NMJsPp06ezfTxv1mmF1R8AYMqUKQrDpADgfTLPadD3wcDQKNurC3H/f9VAP5urEorLflCaHh+XOU1+RSPiySNsWLsCQ34ei649/xnyWad+A/T+qT1WLgnA0lUbcyxHTU0dTZu1wpqVi/Hi+VPYO5SR1DYiopwweCDJZDIZqlatisWLFyMoKEgcJlQQnp6e2L59O9LT01GnTp0vpr916xauX7+uMHQpODgY+vr6qF69upjnL7/8AlNTUzg4OOTaHkDxwFgQBKxbt04praamZrZXIqTWX/60oa1bt6JGjX/GOe/cuTPPZwMbN26MgIAA7NixQ+F+kO3btyultbe3x5s3bxAVFSVemUhNTcXhw4eV2jFv3jy8fPlS8v0sufWHnNbX5zQ1NZWClUSB73mg70PpsuVw/MhfSEtLUxh++PhR5pDH0mXK5rqsPF1Wjx89AAA4lCkHAHj04B4EQUAFp8oK6dTU1FG2nCOuXbn8xXoKyHydk4oKA3MiKjgGD5Sr/fv3IzAwED/88ANKly4NQRAQEhKC2NhYNG/evMD5d+3aFVu3bkWbNm0watQo1K5dG+rq6njx4gVCQ0PRvn17dOjQQUxvbW2Ndu3awdfXFyVKlMCWLVtw9OhR+Pv7i++MGD16NHbt2oVGjRphzJgxcHZ2RkZGBp49e4YjR45g3LhxqFOnjniTcbdu3TBx4kQkJydj1apVeP/+vVI9q1SpgpCQEKxatQo1atSAiooKatasKbn+FStWRM+ePbFkyRKoq6ujWbNmuHnzJhYsWJDnJxG1atUKrq6uGDduHOLi4lCjRg2Eh4fjt99+A6B4gPDTTz9hxowZ6Nq1KyZMmIDk5GQsW7ZM6RGSrq6uGDRoEPr164fLly+jUaNG0NXVxevXr3HmzBlUqVIFQ4cOldwfqlSpgrCwMOzbtw8lSpSAvr4+HB0d89ROouKuoZs79u/+A6dCj6Jp89bi9EMH9sLM3AIVKznnvGxjdywOmI3bN2/AqXJmurS0NBw9uB9OlZ1hZm4BADAzy/z39t83FN79kJqaivt378DcIvfhimlpn3Di6CEYGhnDpqRtrmmJiKRg8EC5KleuHIyMjBAQEIBXr15BQ0MDjo6OCAoKQp8+fQqcv6qqKvbu3YulS5di8+bNmDt3LtTU1FCyZEk0btwYVaoovvzIxcUF/fr1g4+PDx48eABra2ssWrQIY8aMEdPo6uri9OnTmDdvHtauXYsnT55AW1sbtra2aNasmfjegwoVKmDXrl2YNm0aOnbsCFNTU3Tv3h1jx44Vn8YkN2rUKNy6dQve3t748OEDBEGAIAh5qv/69ethaWmJoKAgLFu2DC4uLti1axe6du2ap3WmoqKCffv2Ydy4cZg3bx5SU1Ph6uqKLVu2oG7dujDK8jx3BwcH7NmzB97e3ujcuTNKlCiBsWPH4u3bt/Dz81PId82aNahbty7WrFmDwMBAZGRkwNraGq6urqhduzYA6f1h6dKlGD58OLp27So+Ovfzm9+Jvnd16zdEzTr1sMh/FhISEmBT0hbHj/yFi+FnMG3mPKiqqgIA/GdNx+G/9iI45CCsSlgDANq064jdf2yHz5SxGDR8DIxNTLD7j+149jQCi1b+c/Wzikt1VHCqjI2/BiI5JQlVXWoiISEeu3YG4/WrF5jqN1dMu2JxANLS0lClqgtMTM3wJioSITuD8fD+XUyeMVusDxFRQcgEQRCKuhJEUtjb26Ny5crYv1/5pUiUOXyrR48eOHv2LOrXr1/U1cm3yA8ctkTfj8TERPy6ailCjx1GfNwH2No5oEdfL7i3aCOmmes3FYcO7MH23YdRwvqfe55iot9h9fJFCD9zEskpyShbrgIGDBmBmrXrKZTx8WM8tm/eiFNhxxD1+jW0dbRh51AG3Xr1R936DcV0B/aGYN+fv+Pl82dISEiAjq4OKjpVQZfuvVGrruvXXxlEhczKUL2oq0DZYPBA3w0GD//Ytm0bXr58iSpVqkBFRQXnz5/H/PnzUa1aNfFRrt8rBg9ERAQweCiuOGyJCiQjI+P/2Lvv+BrP/4/jr0MGMRIiMUIkYn3tHaMIsSN2UWqvFkWNolZixV41olZUa1ZKbWK1tUr51ahqoo3ZGCFBIsj4/aE5dWQ4VkX7fj4e59Ge676u6/5cd07kfO7ruu+b+Pj4VOs873MM/suedfF0unTpSJcuHVmyZGH16tWMHz+eqKgocufOTefOnRk/fvw/FKmIiIj8F2nmQV5K586dWb58eap19BEz37PuC9+pUycCAgL+mWDeEM08iIgIaOYhrVLyIC8lNDQ0yTMDnlahQoV/KJq337Fjqd92MUeOHMYLvv+tlDyIiAgoeUirlDyISJqi5EFEREDJQ1qlJ8aIiIiIiIhZlDyIiIiIiIhZlDyIiIiIiIhZlDyIiIiIiIhZlDyIiIiIiIhZlDyIiIiIiIhZlDyIiIiIiIhZlDyIiIiIiIhZlDyIiIiIiIhZlDyIiIiIiIhZLN50ACIiT7K0MLzpEERERCQFmnkQERERERGzKHkQERERERGzKHkQERERERGzKHkQERERERGzKHkQERERERGzKHkQERERERGzKHkQERERERGzKHkQERERERGzKHkQERERERGzKHkQERERERGzKHkQERERERGzKHkQERERERGzKHkQ+Zfbt28fBoOBffv2PXfb0NBQDAYDAQEBxrKDBw/i4+NDRETEK4tR5N8mOjqKWVP9aFLPA4/KZenUtgW7dmw1q+2tW+GMH/MpDWtXo1bV8vTo1I5jRw4nW/f+/WgWLfiMNs0aUdO9DA1qVaVvz85cunjhVQ5HRMTI4k0HICJvl4MHD+Lr60vnzp2xs7N70+GIpEnDBw/g1zOn+fCjj8mXPz87t29hzPAhJMTHU69h4xTbPXz4kH4fdOPe3bsMGDycbNmzs37tKj7+qBdzFiymbPmKxrrR0VH07dmFmzdu0KFLdwoWKsy9e3c59fP/ERNz/58Ypoj8Byl5EBEReYUO/vAdRw8fxGfiFOo18AKgfEV3wv78k7mzpuNZryHp06dPtu2mDev5PSSYhcu+omTpMgCUq1CJTm1bMG/2dBZ/sdpY9/N5c7jwx+98seYbnPLmM5ZXr1n79Q1ORP7ztGxJJI0LCQmhS5cuFCpUCBsbG5ycnPD29ubUqVNJ6v766680aNAAGxsbcuTIwQcffMDdu3eT1HNxcaFz585Jyj08PPDw8EgxFh8fH4YMGQKAq6srBoPBZEnUnj178PDwwN7enowZM+Ls7EzLli2Jjo5+obGLvI327w3CxsaG2nXqm5R7NWnGzRvX+eX0yRTbfrd3N84ursbEAcDCwoL6jbz55fQpbly/BkDM/fts2rCeWnXqmyQOIiKvm2YeRNK4q1evYm9vz6RJk3BwcODWrVssX74cd3d3Tpw4QZEiRQC4du0aNWvWxNLSkvnz55MzZ06++uor+vbt+8pi6d69O7du3eKzzz4jMDCQ3LlzA1CsWDFCQ0Px8vKievXqLF26FDs7O65cucL27dt5+PAhNjY2rywOkbTs95AQ8rsWwMLC9E9swUKPf1fPhwRTsnTZ5NueD6Z02fJJyt0KFf5rewgOjjn59ewv3L9/n3zO+Zk6cSxBO7Zx/340BQsVodsHfahWveYrHpWIyGNKHkTSuBo1alCjRg3j+7i4OLy8vChevDgLFy5kxowZAMycOZMbN25w4sQJSpcuDUDDhg2pV68eFy9efCWx5M2bF2dnZwDKli2Li4uLcduuXbuIiYlh6tSpxv0DtGvX7pXsW+RtcScygjxOeZOUZ81q+9f2yBTbRkZEGOsl1zYyMgKAGzcez0B8uXwJbgULMWrsRAzp0rFqRQCfDOjD9M/8qVz1nZcdiohIElq2JJLGxcbGMnHiRIoVK4aVlRUWFhZYWVkRHBzM2bNnjfX27t1L8eLFTb64wz/35b1MmTJYWVnRs2dPli9fzu+///7MNg8ePODOnTsmrwcPHvwD0Yq8XgaDIZWNL942cVtCfDwAlhaWTP9sIe/UrEW16jWZNns+9jkcCFjs/9wxi4iYQ8mDSBo3cOBARo0aRbNmzdi0aRNHjhzh6NGjlC5dmvv3/76jSnh4OLly5UrSPrmy18HNzY2goCAcHR3p06cPbm5uuLm5MXv27BTb+Pn5YWtra/KaNW3yPxKvyOuS1dbOOEPwpDt3Hs84JDezkMjWzry2WW3tAChRugyZMmUy1suQMSNly1fg3K9nk/QhIvIqaNmSSBr35Zdf0rFjRyZOnGhSfvPmTZNbpdrb2xMWFpakfXJlGTJkSPYM/82bN8mRI8cLx1q9enWqV69OXFwcx44d47PPPmPAgAHkzJmTtm3bJqk/fPhwBg4caFJ2Lzb5u9CIvC3cChYiaMdWYmNjTa57OB/ym3F7SgoULMz54OAk5YltC/zVNvH6ieQkJCSQLrWZDxGRl6CZB5E0zmAwYG1tbVK2ZcsWrly5YlJWq1Ytzpw5w88//2xSvnLlyiR9uri4cPKk6R1ffvvtN86dO/fMeBJjeXLW42np06fH3d2defPmAXD8+PEU+8qaNavJ6+mxirxtatauQ3R0NPt27zIp37ZpIzkcHClWolTKbWt5ciH0d86c+vv3MzY2lh1bN1O8RCkcHBwByOHgQIlSZTj18wmi7t0z1o25f5//++kYxUuWTtK3iMiroJkHkTSucePGBAQEULRoUUqVKsVPP/3E1KlTyZvX9ILMAQMGsHTpUry8vBg/frzxbku//vprkj47dOjA+++/T+/evWnZsiUXLlxgypQpODg4PDOekiVLAjB79mw6deqEpaUlRYoU4auvvmLPnj14eXnh7OxMTEwMS5cuBaBOnTqv4EiIvB2qVKtOxcpVmeo3lqioe+TN58yu7Vs5fPAHxoyfbHzGw0TfUWzbvJG1G7eTO08eABo3bcH6tasYOfRjPvzoY7Jltydw7WouXghlzoLFJvv56OPB9O3ZhQF9evJ+524YDAZWrQggIuI2PT786B8ft4j8Nyh5EEnjZs+ejaWlJX5+fty7d49y5coRGBjIyJEjTerlypWL/fv3079/fz788ENsbGxo3rw5c+fOpWnTpiZ127Vrx9WrV/H392fZsmWUKFGCBQsW4Ovr+8x4PDw8GD58OMuXL2fRokXEx8ezd+9eypQpw86dOxkzZgxhYWFkzpyZEiVK8O2331KvXr1XekxE0jq/abNYOG8Oi/3ncicykvwurvj6TaVu/UbGOvHxccTFxQEJxjIrKys+81/CvNnTmTFlIjExMRQqXJQZn/mbPF0aoGTpsszxX8Ln8z/Dd8RQAIqXLMW8RQEmz4kQEXmVDAkJCQnPriYi8s8Ij4p90yGIiEgaYJ9J57jTIl3zICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZnnrk4fQ0FAMBgMBAQFvOpRn8vDwoESJEm9k3y4uLjRu3PiZ9QwGAz4+Pq8/oDRo69at/9mxA+zbtw+DwcC+ffteWZ8BAQEYDAZCQ0NfWZ8iIiLy5li86QBeVu7cuTl06BBubm5vOpR/hUOHDpE3b943HcYbsXXrVubNm/efTiBE5PlER0fx+bw57Nm1gzt3Isnv4sr7XbpTt36jZ7a9dSuc+bOnc+D7/cTExFCwUBF69e5HBffKJvX69OjMiZ+OJmnvXqUaM+d9bny/2H8eSz+fn+L+fP2mmhWXiEhq/tHk4f79+2TMmPGV9mltbU3lypWfXVHMomMpImK+4YMH8OuZ03z40cfky5+fndu3MGb4EBLi46nXMOXZ3ocPH9Lvg27cu3uXAYOHky17dtavXcXHH/VizoLFlC1f0aR+nrz58Bk/2aQsc5YsJu+bNG9J5arvJNnXpPFjuHL5UrLbRESe13MvW/Lx8cFgMHDixAlatGhB1qxZsbW15f333+fGjRvGeonLZAIDAylbtiwZMmTA19cXgLCwMHr16kXevHmxsrLC1dUVX19fYmNjAXj06BGOjo506NAhyf4jIiLImDEjAwcOBFJetvTDDz/g6elJlixZsLGxoWrVqmzZsiXZsTwtuaUWe/bswcPDA3t7ezJmzIizszMtW7YkOjr6eQ8h33//PZUrVyZjxow4OTkxatQo4uLiTOrcunWL3r174+TkhJWVFQUKFGDEiBE8ePDApF5MTAzDhw/H1dUVKysrnJyc6NOnDxEREc+MY/78+VhYWDBmzBhj2dPLlhKPxd69e/nwww/JkSMH9vb2tGjRgqtXr5r09+DBAwYNGkSuXLmwsbGhRo0a/PTTT7i4uNC5c2djvejoaAYPHoyrqysZMmQge/bsVKhQgVWrVpl9DBOX2KxatYoRI0aQJ08esmbNSp06dTh37lyS+kuXLqV06dLG/TVv3pyzZ88at3fu3Jl58+YZj0Hi63mW2xw5cgRvb2/s7e3JkCEDbm5uDBgwwLg9JCSELl26UKhQIWxsbHBycsLb25tTp0691Ni2b9+Op6cntra22NjY8L///Q8/Pz+TOseOHaNJkyZkz56dDBkyULZsWdauXWvWuMxte/jwYapVq0aGDBnIkycPw4cP59GjR2btQ+RtdPCH7zh6+CCDho+kWavWlK/ozvBRY6lYuSpzZ01P8u/6kzZtWM/vIcGMmzSd+o0aU6lyVSZMmYmzc37mzZ6epL61tTUlSpU2ebm4FjCp45gzV5I69jly8Mf5EDxq1yVLlqyv/BiIyH/PC1/z0Lx5cwoWLMjXX3+Nj48PGzZsoH79+iZfFo4fP86QIUPo168f27dvp2XLloSFhVGpUiV27NjB6NGj2bZtG926dcPPz48ePXoAYGlpyfvvv8/69eu5c+eOyX5XrVpFTEwMXbp0STG2/fv3U7t2bSIjI1myZAmrVq0iS5YseHt7s2bNmucea2hoKF5eXlhZWbF06VK2b9/OpEmTyJQpEw8fPnyuvsLCwmjbti3t27dn48aNtGrVivHjx9O/f39jnZiYGGrVqsUXX3zBwIED2bJlC++//z5TpkyhRYsWxnoJCQk0a9aMadOm0aFDB7Zs2cLAgQNZvnw5tWvXTpJoPNlu8ODBDBgwgMWLFxuTutR0794dS0tLVq5cyZQpU9i3bx/vv/++SZ0uXbowa9YsunTpwsaNG2nZsiXNmzdPksgMHDiQBQsWGD8XK1as4N133yU8PPw5juRjn376KRcuXGDx4sV8/vnnBAcH4+3tbfJH28/Pj27dulG8eHECAwOZPXs2J0+epEqVKgQHBwMwatQoWrVqBTxeupX4yp07t1lx7Nixg+rVq3Px4kVmzJjBtm3bGDlyJNeuXTPWuXr1Kvb29kyaNInt27czb948LCwscHd3TzYpMGdsS5YsoVGjRsTHx+Pv78+mTZvo168fly9fNtbZu3cv1apVIyIiAn9/fzZu3EiZMmVo06bNM68VMrftL7/8gqenJxEREQQEBODv78+JEycYP368WcdP5G20f28QNjY21K5T36Tcq0kzbt64zi+nT6bY9ru9u3F2caVk6TLGMgsLC+o38uaX06e4cf1aim2fx+aNgSQkJNCkectX0p+IyAsvW2rRogVTpkwBoF69euTMmZP27duzdu1a2rdvD8D169f55ZdfKFy4sLHdBx98wO3btzlz5gzOzs4AeHp6kjFjRgYPHsyQIUMoVqwYXbp0YebMmaxZs8aYVMDjM+Hly5enZMmSKcY2bNgwsmXLxr59+8icOTMAjRs3pkyZMgwePJjWrVsnO+OQkp9++omYmBimTp1K6dKljeXt2rUzu49E4eHhbNy4kSZNmgCPj939+/dZsGABn3zyCc7OzixfvpyTJ0+ydu1a3n33XQDq1q1L5syZGTp0KLt27aJu3brs3LmTHTt2MGXKFIYMGWKsly9fPtq0acMXX3xhcuzg8dKxDh06EBQUxLZt2/D09DQr7gYNGjBnzhzj+1u3bvHJJ58QFhZGrly5+OWXX1i1ahVDhw41nvWuW7cuOXPm5L333jPp68CBA9SrV4+PP/7YWObl5fWcR/KxYsWK8eWXXxrfp0+fntatW3P06FEqV65MREQE48aNo1GjRqxcudJYz8PDg0KFCuHj48NXX32Fm5sbOXPmBF5s6VafPn1wdnbmyJEjZMiQwVj+ZJJbo0YNatSoYXwfFxeHl5cXxYsXZ+HChcyYMeO5xnbv3j0GDhxItWrV2LNnj/Ez/fTPtHfv3hQvXpw9e/ZgYfH4V75+/frcvHmTTz/9lI4dO5IuXfLnEcxtO3bsWBISEtizZ4/xOHp5eb2xGwSI/BN+Dwkhv2sB4+9GooKFigBwPiSYkqXLJt/2fDCly5ZPUu5WqPBf20NwcMxpLL9y+RL1PaoQHRVFztx5qFOvIV2698L6iX9vnhYfH8/WTRvIm885yTIoEZEX9cIzD4kJQqLWrVtjYWHB3r17jWWlSpUySRwANm/eTK1atciTJw+xsbHGV8OGDYHHswYAJUuWpHz58ixbtszY9uzZs/z444907do1xbiioqI4cuQIrVq1MiYO8PiLV4cOHbh8+XKyZ3lTU6ZMGaysrOjZsyfLly/n999/f672T8qSJYsxcUjUrl074uPj+e6774DHS6QyZcpkPBOeKHHpz+7du431nixP9O6775IpUyZjvUTh4eHUrl2bH3/80bisy1xPx1yqVCkALly4APz9c2vdurVJvVatWiX5w1qpUiW2bdvGsGHD2LdvH/fv3zc7jueN69ChQ9y/fz/JMcqXLx+1a9dOcoxexG+//cb58+fp1q2bSeLwtNjYWCZOnEixYsWwsrLCwsICKysrgoODTZZQJXrW2A4ePMidO3fo3bt3islwSEgIv/76q/H39cnfuUaNGvHnn3+m+PvwPG337t2Lp6enMXGAx79zbdq0SfF4iLzt7kRGkDWrbZLyxLI7kZEpto2MSL1tZGSEsaxUmbL0G/gJE6fOYvLMuVStVp2vvljKx317ER8fn+I+fjx0gGthYTRu1iLFOiIiz+uFk4dcuXKZvLewsMDe3t5k6UlySz6uXbvGpk2bsLS0NHkVL14cgJs3bxrrdu3alUOHDvHrr78CsGzZMqytrZOcyX7S7du3SUhISHbfefLkAXju5TFubm4EBQXh6OhInz59cHNzw83NjdmzZz9XP4DJl6tEiccyMa7w8HBy5cqV5Auho6MjFhYWJvUsLCxwcHAwqWcwGMiVK1eScf72228cOXKEhg0bPvcZYXt7e5P31tbWAMYv/on7enp8iZ+LJ82ZM4ehQ4eyYcMGatWqRfbs2WnWrJlxCdHriCulz8OLLJV6WuK1Ps+6S9XAgQMZNWoUzZo1Y9OmTRw5coSjR49SunTpZBOoZ43NnP0mLpsaPHhwkt+53r17A6a/cy/aNvEz+7Tkyp704MED7ty5Y/JKabmdSFqU6iz2Mya4U2v75LZeffrT4t22lK/oTtV3ajBw6Ag+/Ohj/u/4Mb7ftyfFPjZtDCS9hQVe3s1SD0RE5Dm88LKlsLAwnJycjO9jY2MJDw83+cKT3D+MOXLkoFSpUkyYMCHZfhO/4AO89957DBw4kICAACZMmMCKFSto1qwZ2bJlSzGubNmykS5dOv78888k2xIv8M2RIweA8SzxgwcPjF/MIPkvU9WrV6d69erExcVx7NgxPvvsMwYMGEDOnDlp27ZtivE87ck18InCwsKAv78s2tvbc+TIERISEkyO4fXr14mNjTXGb29vT2xsLDdu3DBJIBISEggLC6NiRdNp6ipVqvDuu+/SrVs3ABYsWJDicpXnlRj7tWvXkv1cPClTpkz4+vri6+vLtWvXjLMQ3t7exkTxVUmMK6XPQ+KxfBmJx/7J6wyS8+WXX9KxY0cmTpxoUn7z5k3s7Oxey34Txzd8+HCT62WeVKRIkZdua29vb/wcPym5sif5+fklueZmyPBRDB0xOtV2ImlBVls7kxmCRHfuPJ5xSG5mIZGt3Yu3BWjQqDFzZ07l9KmfqVm7TpLtEbdv88P+vVR9pwb2ORyS6UFE5MW88DfHr776yuT92rVriY2NxcPDI9V2jRs35vTp07i5uVGhQoUkryeTh2zZstGsWTO++OILNm/eTFhYWKpLluDxF1N3d3cCAwNNzubGx8fz5ZdfkjdvXuNSKhcXFwBOnjS9qG3Tpk0p9p8+fXrc3d2Nd+Y5fvx4qvE87e7du3z77bcmZStXriRdunTG9fCenp7cu3ePDRs2mNT74osvjNuf/O+T6+IB1q9fT1RUVLLLkjp16sTq1atZtmwZHTt2TPVuIM8jMfanL0j/+uuvjXfRSk7OnDnp3Lkz7733HufOnXuhu1elpkqVKmTMmDHJMbp8+TJ79uwxOUZPn9k3V+HChXFzc2Pp0qWpnjU3GAwmSSrAli1buHLlynPtL1HVqlWxtbXF39+fhISEZOsUKVKEQoUK8fPPPyf7+1ahQgWyPHW7xxdpW6tWLXbv3m2SHMfFxT3zBgXDhw8nMjLS5DVg8NAXOh4i/zS3goW48MfvSf6NOx/ym3F7SgoULMz5ZGZbE9sWSKXtk1I6AbR9y7c8evSIJs10obSIvFovPPMQGBiIhYUFdevW5cyZM4waNYrSpUsnWfP+tLFjx7Jr1y6qVq1Kv379KFKkCDExMYSGhrJ161b8/f1NlmF07dqVNWvW0LdvX/LmzUudOknPsDzNz8+PunXrUqtWLQYPHoyVlRXz58/n9OnTrFq1yng2v1GjRmTPnp1u3boxduxYLCwsCAgI4NKlSyb9+fv7s2fPHry8vHB2diYmJoalS5cCmBXPk+zt7fnwww+5ePEihQsXZuvWrSxatIgPP/zQeAF5x44dmTdvHp06dSI0NJSSJUvyww8/MHHiRBo1amTcZ926dalfvz5Dhw7lzp07VKtWjZMnTzJmzBjKli2b7K1u4fF1CDY2NrRq1Yr79++zatUqrKysnmscTytevDjvvfce06dPJ3369NSuXZszZ84wffp0bG1tTf7Aubu707hxY0qVKkW2bNk4e/YsK1asoEqVKtjY2LxUHE+zs7Nj1KhRxot733vvPcLDw/H19SVDhgwmt6lNvAh/8uTJNGzYkPTp01OqVCmzjs28efPw9vamcuXKfPzxxzg7O3Px4kV27NhhTLQbN25MQEAARYsWpVSpUvz0009MnTr1hR/KlzlzZqZPn0737t2pU6cOPXr0IGfOnISEhPDzzz8zd+5cABYuXEjDhg2pX78+nTt3xsnJiVu3bnH27FmOHz/OunXrUtyHuW1HjhzJt99+S+3atRk9ejQ2NjbMmzePqKioVMdgbW2dJKF6FJVysimSltSsXYdvv/mafbt3Uad+Q2P5tk0byeHgSLESpVJuW8uTaX7jOHPqJMVLPq4XGxvLjq2bKV6iFA4Ojqnue+umjQAUL1k62e2bNgaSw8GRytWqP++wRERS9VLJg4+PDwsWLMBgMODt7c2sWbOe+UUrd+7cHDt2jHHjxjF16lQuX75MlixZcHV1pUGDBkmWJNWpU4d8+fJx6dIlRowYYdYym5o1a7Jnzx7GjBlD586diY+Pp3Tp0nz77bc0bvz3Q3uyZs3K9u3bGTBgAO+//z52dnZ0796dhg0b0r17d2O9MmXKsHPnTsaMGUNYWBiZM2emRIkSfPvtt9SrV++5jluuXLmYN28egwcP5tSpU2TPnp1PP/3UZOlGhgwZ2Lt3LyNGjGDq1KncuHEDJycnBg8enOSZDBs2bMDHx4dly5YxYcIEcuTIQYcOHZg4cWKSL2VPatSoEVu3bsXb25umTZsSGBj40g/wW7ZsGblz52bJkiXMnDmTMmXKsHbtWho0aGCyLKd27dp8++23zJw5k+joaJycnOjYsSMjRox4qf2nZPjw4Tg6OjJnzhzWrFlDxowZ8fDwYOLEiRQq9PfZvXbt2nHgwAHmz59vvHvQH3/8YZyhSk39+vX57rvvGDt2LP369SMmJoa8efOaXPQ8e/ZsLC0t8fPz4969e5QrV47AwEBGjhz5wmPr1q0befLkYfLkyXTv3p2EhARcXFzo1KmTsU6tWrX48ccfmTBhAgMGDOD27dvY29tTrFixZyb75rYtUaIEQUFBDBo0iE6dOpEtWzY6dOhAy5Yt6dmz5wuPTyQtq1KtOhUrV2Wq31iiou6RN58zu7Zv5fDBHxgzfjLp06cHYKLvKLZt3sjajdvJ/dfseuOmLVi/dhUjh37Mhx99TLbs9gSuXc3FC6HMWbDYuI//O/4Ty5cspGatOuTJm5eHDx5w6OD3fBv4NeUruvNODY8kcZ05dZI/zofQsWtPYwwiIq+KISGl9Q4p8PHxwdfXlxs3bryS9eLy73bw4EGqVavGV1999UK3tpX/nnDNPMhbJDo6ioXz5rBn13buREaS38WVDl17ULd+I2Od8WM+ZeumjazfvJPcef6+JuxW+E3mzZ7Oge/3ExMTQ6HCRenZ+yMqulcx1rl88QIzp00i5LdzREbcBoOBfPnyU6d+Q97r0DnZE3aTxo1h04b1rN24Dae8+V7vARB5jewzvfA5bnmNlDzIK7Nr1y4OHTpE+fLlyZgxIz///DOTJk3C1taWkydPpnobU5FESh5ERASUPKRV+qm8AnFxcSlesAqPlxf9F6aOs2bNys6dO5k1axZ3794lR44cNGzYED8/P7MTh4SEhGdexJ0+ffrnesjfy4iPj0/1PupAkudYiIiIiPxbPffMgyTl4eFhfEhacvLnz09oaOg/F9BbbN++fdSqVSvVOsuWLUvy0LfXpXPnzixfvjzVOvoVerU08yAiIqCZh7RKycMrcO7cOe7evZvidmtra+OdfCR1d+/efeYTwF1dXZM8QO11CQ0NTfEhaokqVKjwj8TyX6HkQUREQMlDWqXkQUTSFCUPIiICSh7SqlfzeGEREREREfnXU/IgIiIiIiJmUfIgIiIiIiJmUfIgIiIiIiJmUfIgIiIiIiJmUfIgIiIiIiJmUfIgIiIiIiJmUfIgIiIiIiJmUfIgIiIiIiJmUfIgIiIiIiJm0XO/RSRNMWB40yGIiIhICjTzICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHyICIiIiIiZlHy8B/l4+ODwWB402GkqnPnzri4uLzpMF5IaGgoBoOBgIAAY9nBgwfx8fEhIiIiSX0PDw88PDz+sfhSktwxd3FxoXPnzs/VT2pjFfk3iY6OYuZUP7zr1aRm5TJ0bNucXTu2mtX21q1wxo35lAa1q+JRtRw9Or3H0SOHktTr3aMTVcoVS/Ia0Kdnqv3/eOSgsW7E7dsvND4RkadZvOkARFIyatQo+vfv/6bDeCG5c+fm0KFDuLm5GcsOHjyIr68vnTt3xs7OzqT+/Pnz/+EIzffNN9+QNWvW52qT2lhF/k2GD+7P2TOn+fCjj3HO78LO7VsYPXww8fHx1G/YOMV2Dx8+5KMPunLv7l0+HjycbNntWb92JR9/1Is5C5ZQrnxFk/pOefPhM36KSVnmLFlS7D86OopJ48aQw8GRmzeuv9wgRUSeoORB0qwnv3i/baytralcubLZ9YsVK/Yao3k5ZcuWfdMhiKRJB3/Yz4+HD+I7cSr1GngBUL6iO2F/XmXurGnUqdeQ9OnTJ9t204b1/B4SzOfLVlKydBkAylWoRMe2zZk3expLvlhjUt/a2poSpUqbHdv8OTPJkjUrVd+pScBi/xcboIhIMrRs6T9gy5YtlClTBmtra1xdXZk2bVqSOvPmzaNGjRo4OjqSKVMmSpYsyZQpU3j06JGxzrhx47CwsODSpUtJ2nft2hV7e3tiYmIA2LNnDx4eHtjb25MxY0acnZ1p2bIl0dHRZsed3BIag8FA3759WbFiBf/73/+wsbGhdOnSbN682ex+ASpWrIiXl5dJWcmSJTEYDBw9etRYFhgYiMFg4NSpUwCEhITQpUsXChUqhI2NDU5OTnh7exu3J3p62ZKPjw9DhgwBwNXVFYPBgMFgYN++fUDSZUuJ7adNm8aMGTNwdXUlc+bMVKlShcOHDycZz6JFiyhcuDDW1tYUK1aMlStXvrJlX08vW4qPj2f8+PEUKVKEjBkzYmdnR6lSpZg9e7ZZYxX5t9i/dzc2NjbUrlPfpNyrSXNu3rjOmdMnU2kbhLOLqzFxALCwsKB+I29+OX2K69evvXBc/3f8GBsD1/LpqHGkT6c/8yLyamnm4V9u9+7dNG3alCpVqrB69Wri4uKYMmUK166Z/mE6f/487dq1w9XVFSsrK37++WcmTJjAr7/+ytKlSwHo1asXEyZMYOHChYwfP97Y9tatW6xevZq+ffuSIUMGQkND8fLyonr16ixduhQ7OzuuXLnC9u3befjwITY2Ni81pi1btnD06FHGjh1L5syZmTJlCs2bN+fcuXMUKFDArD7q1KnD3LlzefToEZaWlly7do3Tp0+TMWNGdu3aRcWKj5cMBAUFkTNnTkqWLAnA1atXsbe3Z9KkSTg4OHDr1i2WL1+Ou7s7J06coEiRIsnur3v37ty6dYvPPvuMwMBAcufODTx7xmHevHkULVqUWbNmAY+XcjVq1Ig//vgDW1tbAD7//HN69epFy5YtmTlzJpGRkfj6+vLgwQOzjsXzmjJlCj4+PowcOZIaNWrw6NEjfv31V+P1DS86VpG3ze8hweR3dcPCwvRPacFChY3bS5VOfubu9/PBlC5bPkl5wUKP/w3543wIjo45jeWXL1+inkdloqOiyJU7D3XqNaRz9w/IkCGDSfuYmBgmjh1Fm3YdKfK/Yny/f89LjVFE5GlKHv7lRowYQc6cOdm1a5fxj0z9+vWTnJGeMWOG8f/j4+OpXr069vb2dOnShenTp5MtWzYcHR1p27YtixYtYvTo0VhZWQGwePFiHjx4QO/evQH46aefiImJYerUqZQu/fc0e7t27V7JmO7fv09QUBBZ/lrvW65cOfLkycPatWsZNmyYWX3UqVOHSZMmcfjwYapXr27sr0OHDgQFBfHpp58Cj5MHT09PY7saNWpQo0YN4/u4uDi8vLwoXrw4CxcuNDmOT8qbNy/Ozs7A42VA5s4IZMmShc2bNxuXPuTJk4dKlSqxbds22rZtS3x8PGPGjMHd3Z2vv/7a2O6dd96hYMGC5MmTx6z9PI8DBw5QsmRJfHx8jGX16/995vVFxyrytomMjCCPU74k5Vmz2hm3p9g2IoKsWW2TaWubpG3pMuWoU68h+V1cefAghkMHvufLL5by8/8dZ97nAaR7Ynbh8wVziI+Pp/sHfV9sUCIiz6D5zH+xqKgojh49SosWLUzOTmXJkgVvb2+TuidOnKBJkybY29uTPn16LC0t6dixI3Fxcfz222/Gev379+f69eusW7cOeJxoLFiwAC8vL+OXxDJlymBlZUXPnj1Zvnw5v//++ysdV61atYyJA0DOnDlxdHTkwoULZvdRrVo1MmTIQFBQEAC7du3Cw8ODBg0acPDgQaKjo7l06RLBwcHUqVPH2C42NpaJEydSrFgxrKyssLCwwMrKiuDgYM6ePfvqBvkXLy8vkzXTpUqVAjCO9dy5c4SFhdG6dWuTds7OzlSrVu2VxwNQqVIlfv75Z3r37s2OHTu4c+fOC/f14MED7ty5Y/J6XTMmIq9Dajete9Yd7VLb/uS2Xn360+LdtpSv6E7Vd2oyaOhIen/0Mf93/Bjf7ft7ZuHM6ZOsXbmCT0aMSTIjISLyqih5+Be7ffs28fHx5MqVK8m2J8suXrxI9erVuXLlCrNnz+b777/n6NGjzJs3D3h8pj9R2bJlqV69unHb5s2bCQ0NpW/fv89yubm5ERQUhKOjI3369MHNzQ03NzfjmviXZW9vn6TM2traJM5nyZAhA9WqVTMmD7t376Zu3bp4eHgQFxfH999/z65duwBMkoeBAwcyatQomjVrxqZNmzhy5AhHjx6ldOnSz7V/cz09Vmtra+Dvn0l4eDjwOIF6WnJlr8Lw4cOZNm0ahw8fpmHDhtjb2+Pp6cmxY8eeuy8/Pz9sbW1NXrOmTXoNUYu8era2dkRGRiYpv3MnAiDZmQVjWzu7ZGcm7tyJfGZbgAaNHp8AOnPqZ2PZRN+R1Kxdh/8VK8Hdu3e4e/cODx8+BCAq6h5RUVGp9ikiYg4tW/oXy5YtGwaDgbCwsCTbnizbsGEDUVFRBAYGkj9/fmP5//3f/yXbb79+/Xj33Xc5fvw4c+fOpXDhwtStW9ekTvXq1alevTpxcXEcO3aMzz77jAEDBpAzZ07atm37agb4kjw9PRk9ejQ//vgjly9fpm7dumTJkoWKFSuya9curl69SuHChcmX7+9lCV9++SUdO3Zk4sSJJn3dvHnzjdySNDG5ePoaFiDZn/urYGFhwcCBAxk4cCARERHGZV7169fn0qVLz3VNy/Dhwxk4cKBJWVSs/lmSt4NbwcLs2rGF2NhYk+sezocEA1CgYKFU254PDk5Sfj7kt2e2fZIh3d8zFL+fD+H38yHs2bUjSb1WTepTqHARvlj9jVn9ioikRDMP/2KZMmWiUqVKBAYGGu+CBHD37l02bdpkfJ84PZ54VhsgISGBRYsWJdtv8+bNcXZ2ZtCgQQQFBdG7d+8Up9/Tp0+Pu7u7cabi+PHjLz2uV6VOnTrExsYyatQo8ubNS9GiRY3lQUFB7Nmzx2TWAR4fqyePEzy+gPvKlSvP3N/TswavQpEiRciVKxdr1641Kb948SIHDx58ZftJiZ2dHa1ataJPnz7cunWL0NBQwPyxWltbkzVrVpPX08dXJK2qWduT6Oho9u3eZVK+ddMGcjg4UrxEqZTb1qrDhdDfTWYOYmNj2bF1E8VLlMLBwTHVfW/dtBGAEiX/vq5s3ucBSV6NvJsBMHnGZwwfPe55hygikoRO8f3LjRs3jgYNGlC3bl0GDRpEXFwckydPJlOmTNy6dQuAunXrYmVlxXvvvccnn3xCTEwMCxYs4HYKTyRNnz49ffr0YejQoWTKlCnJ04f9/f3Zs2cPXl5eODs7ExMTY7xj09Nfxt+k8uXLky1bNnbu3EmXLl2M5XXq1GHcuHHG/39S48aNCQgIoGjRopQqVYqffvqJqVOnkjdv3mfuL/GOTbNnz6ZTp05YWlpSpEgRk+s3nle6dOnw9fWlV69etGrViq5duxIREYGvry+5c+c2uZDyVfH29qZEiRJUqFABBwcHLly4wKxZs8ifPz+FCj0+W/o6xiqS1lSpVoNKlasyxc+XqKh75M3nzK7tWzl88Ad8xk82Xq80wXck2zZvZN3G7eTO4wRA46YtWL92JSOGfsyHHw0kW/bsBK5dzYULocxZsMS4j/87foyAJZ9Ts5YnTnnz8eDBAw4f/J6NgesoX9Gdd2rUMtYtV6FSkhiPH/sRgFKly2GXLdvrPBwi8h+h5OFfrm7dumzYsIGRI0fSpk0bcuXKRe/evbl//z6+vr4AFC1alPXr1zNy5EhatGiBvb097dq1Y+DAgTRs2DDZftu0acPQoUPp0KGD8ZahicqUKcPOnTsZM2YMYWFhZM6cmRIlSvDtt99Sr1691z5mc6VLlw4PDw+++eYbkyShSpUqZMqUifv371OrVi2TNrNnz8bS0hI/Pz/u3btHuXLlCAwMZOTIkc/cn4eHB8OHD2f58uUsWrSI+Ph49u7da/J8hxfRs2dPDAaD8Za1Li4uDBs2jI0bN3Lx4sWX6js5tWrVYv369SxevJg7d+6QK1cu6taty6hRo7C0tARe31hF0hq/abNZOG82i/w/405kJPldCjDWbxp16zcy1omPjycuLs6knZWVFZ/5L2Xu7OnMmDKBmJgYChcuyozP/E2eLm2fw4H06dOxbLE/kRG3wWAgX778dP+gL+06dHktJwhERFJjSEhISHjTQcjb57PPPqNfv36cPn2a4sWLv+lw5CkREREULlyYZs2a8fnnn7/pcJ7Lrai4Z1cSEZF/veyZkn9Cu7xZmnmQ53LixAn++OMPxo4dS9OmTZU4pAFhYWFMmDCBWrVqYW9vz4ULF5g5cyZ3796lf//+bzo8ERER+RdR8iDPpXnz5oSFhVG9enX8/f1fqI+4uDhSm/AyGAwmzzZ4XrGxsaluT5cu3b9qqt/a2prQ0FB69+7NrVu3sLGxoXLlyvj7+xuTu9d9zEVEROS/QcuW5B/n4eHB/v37U9yeP39+4117nldoaCiurq6p1hkzZozJ05H/C17nMX/VtGxJRERAy5bSKiUP8o87d+4cd+/eTXG7tbW18W49z+vhw4ecPHky1Tp58uQhT548L9T/2+p1HvNXTcmDiIiAkoe0SsmDiKQpSh5ERASUPKRV/56F3yIiIiIi8lopeRAREREREbMoeRAREREREbMoeRAREREREbMoeRAREREREbMoeRAREREREbMoeRAREREREbMoeRAREREREbMoeRAREREREbNYvOkARESeZDC86QhEREQkJZp5EBERERERsyh5EBERERERsyh5EBERERERsyh5EBERERERsyh5EBERERERsyh5EBERERERsyh5EBERERERsyh5EBERERERsyh5EBERERERsyh5EBERERERsyh5EBERERERsyh5EBERERERsyh5EBERERERsyh5EJGXsm/fPgwGA/v27XvToYj846Kjo5g51Y/GdWtSw70MHdo0Z9f2rWa1vXUrnLGjP6V+rarUrFKO7h3f4+iRQ6m2iYmJ4d2mDalcthhffbE0yfZLFy/gM3IoTRvWpmblsrT0rs+saZOJjIh4keGJiCRh8aYDEBEReVsNG9Sfs2dO07vfxzjnd2HHti2MGj6Y+IR46jdsnGK7hw8f8lGvrty9e5ePhwwnW3Z71q9ZyYC+vfhswRLKVaiYbLvP588h5v79ZLfdvnWL7p3eI1OmzPT8sB+5cufm3K9nWew/l+PHjhCw8mvSpdM5QxF5OUoeROQfER0djY2NzZsOQ+SVOfj9fn48fJCxE6dSr6EXAOUruhP251XmzpxGnXoNSZ8+fbJtv92wnvMhwSwKWEnJ0mUet61QiQ5tmjN39jSWrliTpM2Z0ydZt/orfCdM4dNPPk6y/bt9e4iMiGD8pOlUdK9ijOfRw4csmDuL4N9+pUjRYq9o9CLyX6VTECL/cSEhIXTp0oVChQphY2ODk5MT3t7enDp1KkndX3/9lQYNGmBjY0OOHDn44IMPuHv3bpJ6Hh4elChRgu+++46qVatiY2ND165d/4nhiPxj9u3djY2NDbXr1jcpb9ykOTduXOfMqZMptt2/J4j8Lq7GxAHAwsKCBo28+eX0Ka5fv2ZS/9Gjh0zwGUnL1u0oWrxEsn1aWDw+H5g5cxaT8sxZsgJgZWVt9thERFKi5EHkP+7q1avY29szadIktm/fzrx587CwsMDd3Z1z584Z6127do2aNWty+vRp5s+fz4oVK7h37x59+/ZNtt8///yT999/n3bt2rF161Z69+79Tw1J5B/xe0gwLq5uxi/tiQoWLvx4+/nglNueD6ZgocJJygsWLgLAH+dDTMqXfL6A+/fv06vPRyn2WbOWJ7ly5WbOjCn8fj6Y6OgoTvx0jBXLFvFOjVq4FnAze2wiIinRsiWR/7gaNWpQo0YN4/u4uDi8vLwoXrw4CxcuZMaMGQDMnDmTGzducOLECUqXLg1Aw4YNqVevHhcvXkzS761bt1i3bh21a9f+ZwYi8g+LjIzAKW++JOVZs9o93p7KRcqRERFkzWqbTFvbJG1/O3eWLwOWMG32fDJmtOH27dvJ9pk5SxYWf7Ga4YP7065VU2O5Z936jBk/2YwRiYg8m2YeRP7jYmNjmThxIsWKFcPKygoLCwusrKwIDg7m7Nmzxnp79+6lePHixsQhUbt27ZLtN1u2bM9MHB48eMCdO3dMXg8ePHj5QYmkAQaD4VkVntk2NjaW8T4jqVO/IZWrvpNqd3fuRPLJx32JirqH78Qp+C/5giHDR/PzieMMGdCH2NjY5x6DiMjTlDyI/McNHDiQUaNG0axZMzZt2sSRI0c4evQopUuX5v4Td3UJDw8nV65cSdonVwaQO3fuZ+7bz88PW1tbk9fMaZNefDAi/yBbWzvuREYmKb9zJwKArLZJZxaMbe3suBMZkUzbSJO2a1Z+wdXLl+nWszd3797h7t07RN27B8CDBw+5e/cOcXFxAKxYtoTfzp1lzoIl1G/YmDLlKtCydVt8J07hyKED7Ni6+WWGKyICaNmSyH/el19+SceOHZk4caJJ+c2bN7GzszO+t7e3JywsLEn75MrAjLOuwPDhwxk4cKBJWXSc/lmSt4NbocLs2r6F2NhYk+sezgc/vtahgFuhlNsWLExISNJrIs4H/2bS9nxICPfu3eXdpg2T1P18/hw+nz+HL1avp3CR/xH82684OOYkh4ODSb3//XWBdWrXYIiImEszDyL/cQaDAWtr07uwbNmyhStXrpiU1apVizNnzvDzzz+blK9cufKF921tbU3WrFlNXk/HIpJW1azlSXR0NHt37zIp37JpAw4OjhQvWSqVtnW48MfvnD719+9TbGws27duonjJUjg4OgLQsUt35i0KMHmN85sGQPNWbZi3KIC8+ZwByOHgwPXr15LcqenUycf7cMyZ/CyhiMjz0Ck+kf+4xo0bExAQQNGiRSlVqhQ//fQTU6dOJW/evCb1BgwYwNKlS/Hy8mL8+PHkzJmTr776il9//fUNRS7yZlV9pwaVKldl6kRfoqLukS+fMzu3b+XwwR/wmTDZ+IyHCT4j2bp5I19/u53ceZwA8G7WgvVrVzLik4/p3W8g2bJlZ/261Vy4EMpnC5YY9+HiWgAX1wIm+7169XFinzdfPspXqGQsb9W6HTu2bqbfB93o2KUHOXPl4nxIMMsWLyS7vX2qD60TETGXkgeR/7jZs2djaWmJn58f9+7do1y5cgQGBjJy5EiTerly5WL//v3079+fDz/8EBsbG5o3b87cuXNp2rRpCr2L/LtNmj4b/7mzWbTgM+5ERpLfpQDj/KZRt0EjY524+Hji4uJIeKKdlZUVny1cyrxZ05k+eQIPYmIoVLgoMz/zT/Hp0s9StFhxFn+xiqWL/PGfN5uI27dwcMxJ9Zq16NbzQ+yyZXvJ0YqIgCEhISHh2dVERP4Zt6Pj3nQIIiKSBmSzSf4J7fJm6ZoHERERERExi5IHERERERExi5IHERERERExi5IHERERERExi5IHERERERExi5IHERERERExi5IHERERERExi5IHERERERExi5IHERERERExi5IHERERERExi5IHERERERExi5IHERERERExi5IHERERERExi5IHERERERExi8WbDkBE5EkW6QxvOgQRERFJgWYeRERERETELEoeRERERETELEoeRERERETELEoeRERERETELEoeRERERETELEoeRERERETELEoeRERERETELEoeRERERETELEoeRERERETELEoeRERERETELEoeRERERETELEoeRERERETELEoeRNIgHx8fDAYDN2/efCX9de7cmcyZM5tV18XFhc6dO7+S/Yr820VHRzF9ykQa1KlB1Yqlade6OTu2bTGr7a3wcHxGDcezZhWquZelS4e2/HjkUJJ68z6bRbvWLahdvTJVK5amaaO6TBg7mj+vXjGpd/aXM0yeOJY2LZtQo0p56tV6h949u3D0yOFXMlYREQCLNx2AiKQt33zzDVmzZn3TYYi8FYZ83I9fzpymb/+B5M/vwvZtmxkxbDAJCQk0aNQ4xXYPHz7kw55duHf3LoM/+ZRs2bOzbs0qPurdk/kLl1C+QiVj3Xt371C/YSNcXd2wyZSJP34PYckif77bt5c1gd9iZ5cNgB3btnDm9CmaNGtB4cJFuX//Puu/Xs2HPbvgM96Pxt7NXvfhEJH/AENCQkLCmw5CREz5+Pjg6+vLjRs3yJEjx0v317lzZ77++mvu3bv3CqJ7ve7GxL/pEETM8sP3+xnQ9wPGT5pGg4ZexvI+vbrx+/kQNu/YQ/r06ZNtu27NSiZPHMfSL1ZSqnRZAGJjY2n3bjMy2mRi+VdrUt33gR++o3+fXozyGU/T5i2BxzMZ2e3tTerFxcXxftuW3L9/nw2bd7zMcEX+cVkyaIFMWqSfikgadunSJVq0aEHWrFmxtbXl/fff58aNG8bta9asoV69euTOnZuMGTPyv//9j2HDhhEVFZVsf2fOnMHT05NMmTLh4OBA3759iY6ONqmT3LKlO3fuMHjwYFxdXbGyssLJyYkBAwYk2c+6detwd3fH1tYWGxsbChQoQNeuXV/NwRBJY/btCcLGxoY6deublHs3a8GNG9c5fepkim337gkiv4urMXEAsLCwoGHjJpw5fZLr166luu9s2bIDkN7i7+Tk6cQBIH369BT9X3Guhf1p1phERJ5FyYNIGta8eXMKFizI119/jY+PDxs2bKB+/fo8evQIgODgYBo1asSSJUvYvn07AwYMYO3atXh7eyfp69GjRzRq1AhPT082bNhA3759WbhwIW3atEk1hujoaGrWrMny5cvp168f27ZtY+jQoQQEBNCkSRMSJy8PHTpEmzZtKFCgAKtXr2bLli2MHj2a2NjYV39gRNKA8yHBuLi6YWFhugK4UKHCxu2ptS1UuEiS8sS2v58PSbItNjaWmJgYfj37C9On+OGc34XannVTjTE2Npb/O3GMAm4FnzkeERFz6JoHkTSsRYsWTJkyBYB69eqRM2dO2rdvz9q1a2nfvj0jR4401k1ISKBatWr873//o2bNmpw8eZJSpUoZtz98+JBBgwbRr18/AOrWrYulpSUjRozgwIEDVKtWLdkY5syZw8mTJzly5AgVKlQAwNPTEycnJ1q1asX27dtp2LAhBw8eJCEhAX9/f2xtbY3tdfG1/FtFRkTglDdfkvLEz39kZEQqbSPJmtU2SXnWv9pGPNX25s0bNPCsYXxfomQpFi5ejo1NplRj/Nx/LpcuXmTarLmp1hMRMZdmHkTSsPbt25u8b926NRYWFuzduxeA33//nXbt2pErVy7Sp0+PpaUlNWvWBODs2bPP7K9du3YAxv6Ss3nzZkqUKEGZMmWIjY01vurXr4/BYGDfvn0AVKxY0Rjj2rVruXLlSop9Jnrw4AF37twxeT148OCZ7UTSCoPBkPI2Ut72uG0q2556b2eXjS9WrmNxwFeMHDOWO5GR9OreiZs3rqfYx4bAdSxdtJD3O3bBo5ZnqrGIiJhLyYNIGpYrVy6T9xYWFtjb2xMeHs69e/eoXr06R44cYfz48ezbt4+jR48SGBgIwP3795Ntm1z/4eHhKcZw7do1Tp48iaWlpckrS5YsJCQkGG8nW6NGDTZs2EBsbCwdO3Ykb968lChRglWrVqXYt5+fH7a2tiav6VMnmX+ARN4gWzs7IiIikpRHRkYCf88iJN/W1ljvSXf+KrO1tTMpt7CwoFjxEpQpW45mLd5lweIArly5TMDSxcn2/+2GQCaO86F5y9b0HzjEzBGJiDybli2JpGFhYWE4OTkZ38fGxhIeHo69vT179uzh6tWr7Nu3zzjbACT7Zebptk/2DyRJKp6UI0cOMmbMyNKlS1Pcnqhp06Y0bdqUBw8ecPjwYfz8/GjXrh0uLi5UqVIlSdvhw4czcOBAk7KHCZYpxiKSlhQsVJgd27YQGxtrct1DSPBvALgVLJRy24KFjfWeFBIc/My2ADlz5sLBwYGLF0KTbPt2QyDjfUfh5d2MT0f5pDo7IiLyvDTzIJKGffXVVybv165dS2xsLB4eHsYvBNbW1iZ1Fi5caHZ/K1euBMDDwyPFNo0bN+b8+fPY29tToUKFJC8XF5ckbaytralZsyaTJ08G4MSJE8n2bW1tTdasWU1eT49HJK3yqF2H6Oho9gTtNCnfsmkjDg6OlChZKoWWUMuzDqF//M7pkz8by2JjY9m65VtKlCyFg6Njqvu+dPEC169dI28+Z5PyTRu/YbzvKBp6eTPKZ5wSBxF55TTzIJKGBQYGYmFhQd26dTlz5gyjRo2idOnStG7dmrt375ItWzY++OADxowZg6WlJV999RU///xzsn1ZWVkxffp07t27R8WKFTl48CDjx4+nYcOGvPPOOynGMGDAANavX0+NGjX4+OOPKVWqFPHx8Vy8eJGdO3cyaNAg3N3dGT16NJcvX8bT05O8efMSERHB7NmzTa7DEPk3qfZODdwrV2XShLFERd0jX7787Ni+hYMHvmfcxCnGZzyMHTOCLZs2smHzDnLneTyT2KRZS9auXsnQIR/zUf+BZMuena/XrOLChVDmL1xi3Efwb+eYMXUSnnXr4eSUD0M6AyHBwaz6cjm2tnZ06NTFWDdo53bG+YykcJGitGjVhjOnTW8VW6RoMaysrP6BIyMi/2ZKHkTSsMDAQHx8fFiwYAEGgwFvb29mzZqFlZUV9vb2bNmyhUGDBvH++++TKVMmmjZtypo1ayhXrlySviwtLdm8eTP9+vVj/PjxZMyYkR49ejB16tRUY8iUKRPff/89kyZN4vPPP+ePP/4gY8aMODs7U6dOHePMg7u7O8eOHWPo0KHcuHEDOzs7KlSowJ49eyhevPjrODwib9zUmXOY/9ls/Od/xp3ISFxcCzBh0jTqP/HQuPj4eOLi4njykaxWVlYsWLSMOTOnMXXSeGJiYihcpChz5i00ebp0dnt7cjg48OUXAdy8cYO4uFgcc+binRo16dK9F7ly5TbW/eH7/cTHx/Pr2V/o1qldkli/3RpEnieWQYqIvAg9YVpE0hQ9YVpEREBPmE6r9FMRERERERGzKHkQERERERGzKHkQERERERGzKHkQERERERGzKHkQERERERGzKHkQERERERGzKHkQERERERGzKHkQERERERGzKHkQERERERGzKHkQERERERGzKHkQERERERGzKHkQERERERGzKHkQERERERGzKHkQERERERGzWLzpAEREnmQwGN50CCIiIpICzTyIiIiIiIhZlDyIiIiIiIhZlDyIiIiIiIhZlDyIiIiIiIhZlDyIiIiIiIhZlDyIiIiIiIhZlDyIiIiIiIhZlDyIiIiIiIhZlDyIiIiIiIhZlDyIiIiIiIhZlDyIiIiIiIhZlDyIiIiIiIhZlDz8C6xcuZJZs2a96TDM5uHhgYeHx5sO47UKDQ3FYDAwbdq0f2yfAQEBGAwGQkNDn7vtvn37MBgMfP3118+s+7Z93kREROTVsXjTAcjLW7lyJadPn2bAgAFvOhSzzJ8//02H8K/k5eXFoUOHyJ0792vdz9v2eRN5naKjo5j/2Wx27dzGnchIXFwL0LlrD+o39Hpm21vh4cyeOZXvv9tHTEwMhQsXoXffAVSqXMVY5969e6xZ+SVHDh8g9I8/iI6OJo+TE40aN+G99h2xtrY21r165TLeDesku6+Jk6ebFZOIyLMoeZB/XLFixd50CP9KDg4OODg4vOkwRP5TBn/8Eb+cPs1HAwbinN+F7Vu38OnQQcTHx9PQyzvFdg8fPuSDHp25d/cug4eOIHv27KxdvZK+vXuw4POllK9QCYCwP6+y8qvleDVuQvsOXchoY8P/HT/G5wvmcuTQQeZ/vhSDwWDSd5t279OwYWOTsnz587/6wYvIf5KWLb0Fbty4Qc+ePcmXLx/W1tY4ODhQrVo1goKC8PDwYMuWLVy4cAGDwWB8JfL19cXd3Z3s2bOTNWtWypUrx5IlS0hISDDW6datG9mzZyc6OjrJvmvXrk3x4sWN79etW4e7uzu2trbY2NhQoEABunbt+lzjeXrZ0pNLfGbMmIGrqyuZM2emSpUqHD582Ox+ExISyJkzJ3369DGWxcXFkS1bNtKlS8e1a9eM5TNmzMDCwoKIiAhj2bFjx2jSpAnZs2cnQ4YMlC1blrVr1ybZT1hYGL169SJv3rxYWVnh6uqKr68vsbGxqcb36NEjOnXqRObMmdm8ebPZ4zIYDPTt25cVK1bwv//9DxsbG0qXLp2kj+SWLSUkJDBx4kTy589PhgwZqFChArt27Upx6dijR48YMWIEefLkIWvWrNSpU4dz584Ztz/r87ZgwQJKly5N5syZyZIlC0WLFuXTTz81e6wib5Mfvt/PkUMHGTZiNC3fbUvFSpUZ5TMO9ypVmT1zKnFxcSm23RD4NedDgpk0bSaNvLypXKUaU6bPJn9+F2bP+Hu5o5NTXjZv283AIcOpWas2ldwr0/PDvnzQpx8/HjnE/504nqTvXLlyU7J0GZOXnV2213IMROS/R8nDW6BDhw5s2LCB0aNHs3PnThYvXkydOnUIDw9n/vz5VKtWjVy5cnHo0CHjK1FoaCi9evVi7dq1BAYG0qJFCz766CPGjRtnrNO/f39u377NypUrTfb7yy+/sHfvXuOX8UOHDtGmTRsKFCjA6tWr2bJlC6NHj37ml2ZzzZs3j127djFr1iy++uoroqKiaNSoEZGRkWa1NxgM1K5dm6CgIGPZsWPHiIiIIEOGDOzevdtYHhQURPny5bGzswNg7969VKtWjYiICPz9/dm4cSNlypShTZs2BAQEGNuFhYVRqVIlduzYwejRo9m2bRvdunXDz8+PHj16pBhbREQE9evXZ+fOnezfv5/GjRunWDc5W7ZsYe7cuYwdO5b169eTPXt2mjdvzu+//55quxEjRjBixAgaNGjAxo0b+eCDD+jevTu//fZbsvU//fRTLly4wOLFi/n8888JDg7G29vb+CUotc/b6tWr6d27NzVr1uSbb75hw4YNfPzxx0RFRT3XWEXeFnt3B2FjY0Odeg1Myps0bcGN69c5fernFNvu2xNEfhdXSpUuayyzsLCgoZc3Z06f5PpfJzsy2tiQ0cYmSfsSJUoBcC3sz1cxFBERs2nZ0lvgwIEDdO/e3eTLadOmTY3/b2dnh7W1NZUrV07SdtmyZcb/j4+Px8PDg4SEBGbPns2oUaMwGAyUKlWKmjVrMm/ePLp3726sP3fuXLJmzUrHjh0BOHjwIAkJCfj7+2Nra2us17lz51cyzixZsrB582bSp08PQJ48eahUqRLbtm2jbdu2ZvVRp04dVq9ezaVLl8iXLx9BQUEULVqUwoULExQURLt27Xj06BHfffcd/fv3N7br3bs3xYsXZ8+ePVhYPP61qF+/Pjdv3uTTTz+lY8eOpEuXDh8fH27fvs2ZM2dwdnYGwNPTk4wZMzJ48GCGDBmSZFlWaGgoXl6P1xofPnyY/C+wfOD+/fsEBQWRJUsWAMqVK0eePHlYu3Ytw4YNS7bN7du3mTFjBm3atGHhwoXG8hIlSlClShUKFy6cpE2xYsX48ssvje/Tp09P69atOXr0KJUrV6ZYsWIpft4OHDiAnZ0dc+bMMZZ5eno+91hF3hbnQ37DtYCb8d+MRIUKF/lrezCly5RLtm1ISDBly5VPUm5sez4Yx5w5U9z30R8fz8q6FSyUZFvA0kXMmzOL9BbpKfq/YnTq3J2atWqbNygRkWfQzMNboFKlSgQEBDB+/HgOHz7Mo0ePzG67Z88e6tSpg62tLenTp8fS0pLRo0cTHh7O9evXjfX69+/P//3f/3HgwAEA7ty5w4oVK4zLbAAqVqwIQOvWrVm7di1Xrlx5haN8fMFvYuIAUKrU4zNrFy5cMLuPOnUeXyyYOPuwa9cu6tatS506ddi1axfweAYlKirKWDckJIRff/2V9u3bAxAbG2t8NWrUiD///NO4dGfz5s3UqlWLPHnymNRr2LAhAPv37zeJ5/jx41SuXJmcOXNy4MCBF0ocAGrVqmVMHABy5syJo6Njqsfm8OHDPHjwgNatW5uUV65cGRcXl2TbNGnSxOT98/wMKlWqREREBO+99x4bN27k5s2bz2wj8jaLjIwga1bbJOVZ/zq58uSyyCRtIyJMTsIkSiyLTKVt8G/nWB6whFqedY3JBoCllRXNW77L8JE++C8OYNSYccTHxTGwf2++Wb/OzFGJiKROycNbYM2aNXTq1InFixdTpUoVsmfPTseOHQkLC0u13Y8//ki9evUAWLRoEQcOHODo0aOMGDECeHw2O1HTpk1xcXFh3rx5wOP181FRUSbXD9SoUYMNGzYQGxtLx44dyZs3LyVKlGDVqlWvZJz29vYm7xPvIvJknM+SP39+3NzcCAoKIjo6mkOHDhmTh8uXL3Pu3DmCgoLImDEjVatWBTBeCzF48GAsLS1NXr179wYwfhG+du0amzZtSlIv8bqQp78w79q1i2vXrtG9e3fjEqkX8fSxgcfHJ7VjEx4eDjxONJ6WXFly+3men0GHDh1YunQpFy5coGXLljg6OuLu7m5M2pLz4MED7ty5Y/J68ODBM/clklY8fbGyudv+qvHcba9euUz/vh+QK2cuRvmMM9nm4ODIyDHjqFuvAWXLlaehlzeLln1JkaLF+Gz29Fe2xFRE/tuUPLwFcuTIwaxZswgNDeXChQv4+fkRGBj4zOVCq1evxtLSks2bN9O6dWuqVq1KhQoVkq2bLl06+vTpw9dff82ff/7J/Pnz8fT0pEiRIib1mjZtyu7du4mMjGTfvn3kzZuXdu3amVxn8aZ5enqye/du9u/fb1yq9b///Y88efKwa9cugoKCqF69uvGLcY4cOQAYPnw4R48eTfZVpkwZY9169eqlWK9bt24msQwZMoQePXrQsWNHvvjii3/0OCQmAk9eKJ7oWYnni+rSpQsHDx4kMjKSLVu2kJCQQOPGjVOcufDz88PW1tbkNX2K32uJTeRVs7W1IzIyIkn5nb+u00puZsHY1i75tonXeGVNpu2fV6/Qq1snLNKnZ8GiAGxt7Z4Zo6WlJfUaNCQyIoJLF82fxRURSYmueXjLODs707dvX3bv3m1cYpTSGWiDwYCFhYXJUqD79++zYsWKZPvu3r07Pj4+tG/fnnPnzjF58uQU47C2tqZmzZrY2dmxY8cOTpw4QZUqVVKs/0+qU6cOn3/+ObNmzaJy5crG5T6enp588803HD16lIkTJxrrFylShEKFCvHzzz+blCencePGbN26FTc3N7Jle/bdS9KlS8fnn39OlixZ6Ny5M1FRUXz44YcvN0Azubu7Y21tzZo1a2jRooWx/PDhw1y4cCHFpUvP8qwZD4BMmTLRsGFDHj58SLNmzThz5kyyS7aGDx/OwIEDTcoeYfVCcYn80woWKsyObVuIjY01ue4hJPjxDQmSux7h77aFjPWelFhW8Km2f169Qs+uHUkggc+XfEHOXLnMjjPx7nrPngkREXk2JQ9pXGRkJLVq1aJdu3YULVqULFmycPToUbZv3278QliyZEkCAwNZsGAB5cuXJ126dFSoUAEvLy9mzJhBu3bt6NmzJ+Hh4UybNs3koUJPsrOzo2PHjixYsID8+fPj7W16j/LRo0dz+fJlPD09yZs3LxEREcyePRtLS0tq1qz52o+FuWrXro3BYGDnzp34+voay+vUqUOnTp2M//+khQsX0rBhQ+rXr0/nzp1xcnLi1q1bnD17luPHj7Nu3eP1wmPHjmXXrl1UrVqVfv36UaRIEWJiYggNDWXr1q34+/uTN2/eJDFNnz6dLFmy0Lt3b+7du8eQIUNe4xF4LHv27AwcOBA/Pz+yZctG8+bNuXz5Mr6+vuTOnZt06V5s4jGlz1uPHj3ImDEj1apVI3fu3ISFhRlnFhKvl3matbV1ks/jvQcJydYVSWtq1a7LN+vXsSdoJ/UaNDKWb/52Aw6OjpQoWTrVtpMm+HLq5M+ULPW4XmxsLNs2b6JEydI4OP69tPDPP6/Ss2tH4uLj+XzpF+TO42R2jI8ePWLXjm3YZctGPmc960FEXp6ShzQuQ4YMuLu7s2LFCkJDQ3n06BHOzs4MHTqUTz75BHh8sfOZM2f49NNPiYyMJCEhgYSEBGrXrs3SpUuZPHky3t7eODk50aNHDxwdHZMsr0nUpk0bFixYwIcffpjky6W7uzvHjh1j6NCh3LhxAzs7OypUqMCePXtMngXxptnb21OmTBlOnDhhkiQk/n/i9ifVqlWLH3/8kQkTJjBgwABu376Nvb09xYoVM7ngOHfu3Bw7doxx48YxdepULl++TJYsWXB1daVBgwapzkb4+PiQOXNmhgwZwr1790wSm9dlwoQJZMqUCX9/f5YtW0bRokVZsGABI0aMeOFrMFL6vFWvXp2AgADWrl3L7du3yZEjB++88w5ffPGFHl4n/0rVqtfAvUpV/Mb7cu/ePfI5O7Nj2xYOHviecX5TjbO+Y8eMYPO3G9i4Zafxi3/T5i1Zt3olQwcP4KP+A8lub8+6NasIvfAHCz5fatzHrfBwenXrxM2bNxjlO4Hb4eHc/ut6JgDHnLmMsxAzpvoRGxtL6TLlsM+Rg2thYaxZ9SXnfj3LmHETTWahRURelCHhyaeFyX/eoEGDWLBgAZcuXUr2Il15+/3xxx8ULVqUMWPGpMkHuGnmQd4m0dFRzJszi107t3MnMgIX1wJ06daT+g29jHXGjBzG5m83sGlbEHmc/p6ZDA+/yewZU/nhu33ExMRQuEhRPuzbH/fKVY11jh09Qq9unVLcf88P+tCr90cAbPxmPevXreHypQvci4oik00mipcoSfuOnalS9Z3XMHqR1yuztZbapUVKHgR4vA7+t99+o1evXvTq1YtZs2a96ZDkFfj5559ZtWoVVatWJWvWrJw7d44pU6Zw584dTp8+neJdl94kJQ8iIgJKHtIqJQ8CPL6QzsbGhkaNGrFs2TLjsx2eR1xcHKl9nAwGwwtPmyckJBifcpyS9OnTv1UXBD7rtonp0qV74esSEoWEhPDBBx/w888/E/HXfeU9PDyYMGFCkjtppRVKHkREBJQ8pFVKHuSVcXFxSfVhYjVr1mTfvn0v1Pe+ffuoVatWqnWWLVv2yp52/U94VqLTqVMnAgIC/plg0hAlDyIiAkoe0ipdMC2vzKZNm1J9wNeTT0h+XuXLl+fo0aOp1nF1dX3h/t+EZ40n8fkTIiIiImmFZh5EJE3RzIOIiIBmHtIqPWFaRERERETMouRBRERERETMouRBRERERETMouRBRERERETMouRBRERERETMouRBRERERETMouRBRERERETMouRBRERERETMouRBRERERETMouRBRERERETMYvGmAxAReVI6w5uOQERERFKimQcRERERETGLkgcRERERETGLkgcRERERETGLkgcRERERETGLkgcRERERETGLkgcRERERETGLkgcRERERETGLkgcRERERETGLkgcRERERETGLkgcRERERETGLkgcRERERETGLkgcRERERETGLxZsOQORJnTt3Zt++fYSGhr62fXh4eACwb9++N9rXxIkTKVasGM2aNXvpOETkzYiOjmLeZ7PZuWMbdyIjcXEtQJduPWjQ0OuZbW+FhzNrxlS+/24fMTExFCpchD4fDcC9chWTet/t38vOHds5d/YXQkP/IDY2lhOnfk3Sn//8z1i4YF6K+/ObMt2suEREUqPkQdKUUaNG0b9//zcdxj9i4sSJtGrVSsmDyFts0ICPOHP6NP0GDCS/iwvbtm5h+CeDSIiPp6GXd4rtHj58SK/unbl79y5Dho4ge/bsrFm9kr4f9mDB50upULGSse6e3UGcOvl/FC1aDEsrK87+cibZPpu3eJeq1aonKR/nM4rLly9RLZltIiLPS8mD/GMePXqEwWDAwiLpxy46OhobGxvc3NzeQGTyT7h//z4ZMmTAYDC86VBEXonvv9vP4UMHmTh5Gg0bNQagYqXK/Hn1CjNnTKVeg0akT58+2bYbAr8mJCSYgBWrKF2mLAAVKrnTplUzZs+cxoqVa411R/uMI126x6uMJ00Ym2LykDNXLnLmymVSdvXKZc6fD6GRlzdZsmZ96TGLiOiaB3kuISEhdOnShUKFCmFjY4OTkxPe3t6cOnXKpN6+ffswGAysWLGCQYMG4eTkhLW1NSEhIXTu3JnMmTNz6tQp6tWrR5YsWfD09AQeL1tycXEx9lO2bFmqV096tiwuLg4nJydatGhhLPP19cXd3Z3s2bOTNWtWypUrx5IlS0hISHjpcSckJDBlyhTy589PhgwZKFeuHNu2bUtSLyYmhkGDBlGmTBlsbW3Jnj07VapUYePGjSb1DAYDUVFRLF++HIPBgMFgMC6BAggLC6NXr17kzZsXKysrXF1d8fX1JTY21uyYx40bh4WFBZcuXUqyrWvXrtjb2xMTE2MsW7NmDVWqVCFTpkxkzpyZ+vXrc+LECZN2x44do23btri4uJAxY0ZcXFx47733uHDhgkm9gIAADAYDO3fupGvXrjg4OGBjY8ODBw/Mjl8krdu7JwgbGxvq1mtgUt6kWQtuXL/OqVM/p9h2z+4gXFxcjYkDgIWFBY0ae3P61EmuX7tmLE9MHF7Ehm8CSUhIoFmLVi/ch4jIk5Q8yHO5evUq9vb2TJo0ie3btzNv3jwsLCxwd3fn3LlzSeoPHz6cixcv4u/vz6ZNm3B0dAQeT9k3adKE2rVrs3HjRnx9fZPdX5cuXfjhhx8IDg42Kd+5cydXr16lS5cuxrLQ0FB69erF2rVrCQwMpEWLFnz00UeMGzfupcft6+vL0KFDqVu3Lhs2bODDDz+kR48eScb84MEDbt26xeDBg9mwYQOrVq3inXfeoUWLFnzxxRfGeocOHSJjxow0atSIQ4cOcejQIebPnw88ThwqVarEjh07GD16NNu2baNbt274+fnRo0cPs2Pu1asXFhYWLFy40KT81q1brF69mm7dupEhQwbg8RKq9957j2LFirF27VpWrFjB3bt3qV69Or/88ouxbWhoKEWKFGHWrFns2LGDyZMn8+eff1KxYkVu3ryZJIauXbtiaWnJihUr+Prrr7G0tDQ7fpG0LiT4N1wLuCWZTS1cuAgA55/6d+tJ50OCKfRXvWTbnk+5rbni4+PZtPEb8jnnN1kGJSLyMrRsSZ5LjRo1qFGjhvF9XFwcXl5eFC9enIULFzJjxgyT+m5ubqxbty5JP48ePWL06NEmX/6T0759e4YMGUJAQAATJkwwlgcEBJAzZ04aNmxoLFu2bJnx/+Pj4/Hw8CAhIYHZs2czatSoF14uExERweTJk2nevDmLFy82lhcvXpxq1apRpMjfXwBsbW1N4oiLi8PT05Pbt28za9YsOnbsCEDlypVJly4dDg4OVK5c2WR/Pj4+3L59mzNnzuDs7AyAp6cnGTNmZPDgwQwZMoRixYo9M25HR0fatm3LokWLGD16NFZWVgAsXryYBw8e0Lt3bwAuXbrEmDFj6Nu3L3PmzDG2r1u3LoUKFcLX15c1a9YA0KpVK1q1+vsMZlxcHI0bNyZnzpysXLmSfv36mcTg6emZJHkR+beIjIwgb958Scqz2toCEBEZkWLbiIgIYz2Ttlltjdtf1qGDBwgL+5OP+g986b5ERBJp5kGeS2xsrPEuQVZWVlhYWGBlZUVwcDBnz55NUr9ly5Yp9pXatkT29vZ4e3uzfPly4uPjAbh9+zYbN26kY8eOJmf89uzZQ506dbC1tSV9+vRYWloyevRowsPDuX79+guM9rFDhw4RExND+/btTcqrVq1K/vz5k9Rft24d1apVI3PmzFhYWGBpacmSJUuSPT7J2bx5M7Vq1SJPnjzExsYaX4mJ0v79+82OvX///ly/ft2YwMXHx7NgwQK8vLyMy8N27NhBbGwsHTt2NNlfhgwZqFmzpsmdpO7du8fQoUMpWLAgFhYWWFhYkDlzZqKiop775w+PZ2ru3Llj8tLSJnm7pHxSwpDKNiDVExqv4tqgDYFfY2FhQZNmzV+6LxGRREoe5LkMHDiQUaNG0axZMzZt2sSRI0c4evQopUuX5v79+0nq586dO9l+bGxsyGrmxXtdu3blypUr7Nq1C4BVq1bx4MEDOnfubKzz448/Uq9ePQAWLVrEgQMHOHr0KCNGjABINjZzhYeHA5DrqQsRkysLDAykdevWODk58eWXX3Lo0CGOHj1K165dTa4vSM21a9fYtGkTlpaWJq/ixYsDJLs8KCWJ14zMm/f49o2bN28mNDSUvn37muwPoGLFikn2uWbNGpP9tWvXjrlz59K9e3d27NjBjz/+yNGjR3FwcHiun38iPz8/bG1tTV7TpviZPT6RN8nW1o7IZGYX7kRG/rU96cxCIjs7OyKTmV24c+fZbc1x+/Zt9u/bwzvVa5Ijh8NL9SUi8iQtW5Ln8uWXX9KxY0cmTpxoUn7z5k3s7OyS1E/p7NnznFWrX78+efLkYdmyZdSvX59ly5bh7u5usnRn9erVWFpasnnzZuM6foANGzaYvZ+U2NvbA4+vRXhaWFiYyQXeX375Ja6urqxZs8ZkjM9zNj1HjhyUKlXKZJnWk/LkyWN2XwD9+vXj3Xff5fjx48ydO5fChQtTt25dk/0BfP3118nOpCSKjIxk8+bNjBkzhmHDhhnLE6/zSM6zfs7Dhw9n4EDTJRVxBqtnjkkkLShUqDDbt20hNjbWZBY0OPg3ANwKFUqxbcFChQj5q96Tgn/7q23BlNuaY8umjTx69IjmLXWhtIi8Wkoe5LkYDAasra1NyrZs2cKVK1coWLDga9ln+vTp6dChA7NmzeL777/n2LFjSdbRJ94C9snbIt6/f58VK1a89P4rV65MhgwZ+Oqrr0yW4Rw8eJALFy6YJA8GgwErKyuTL81hYWFJ7rYEYG1tnezZ+saNG7N161bc3NzIli3bS8ffvHlznJ2dGTRoEPv372fmzJkm8dWvXx8LCwvOnz+f6jIjg8FAQkJCkp//4sWLiYuLe6HYrK2tk/QX/fDl744l8k+o5VmXwPXr2B20k/oNGhnLN23cgIOjIyVLlk61rd94X06d/JmSpR7Xi42NZeuWTZQsVRpHx5wvFduGb9bj4OhItXdqPLuyiMhzUPIgz6Vx48YEBARQtGhRSpUqxU8//cTUqVPJmzfva91v165dmTx5Mu3atSNjxoy0adPGZLuXlxczZsygXbt29OzZk/DwcKZNm5bki+mLyJYtG4MHD2b8+PF0796dd999l0uXLuHj45Nk2VLjxo0JDAykd+/etGrVikuXLjFu3Dhy586d5I5RJUuWZN++fWzatIncuXOTJUsWihQpwtixY9m1axdVq1alX79+FClShJiYGEJDQ9m6dSv+/v7PdbzTp09Pnz59GDp0KJkyZTJZ7gXg4uLC2LFjGTFiBL///jsNGjQgW7ZsXLt2jR9//JFMmTLh6+tL1qxZqVGjBlOnTiVHjhy4uLiwf/9+lixZkuysk8i/3TvVa1C5SlUmjvMl6t498jk7s33rFg4e+J4JflONJzN8Ro9g87cb+HbrTvLkcQKgWfOWrF29kk8GDaDfgIFky27PujWruBD6Bws+X2qyn6tXr3Dm9OPbYSfeennXzu0A5HFyonjxkib1T538mfMhwXTr0SvF50yIiLwoJQ/yXGbPno2lpSV+fn7cu3ePcuXKERgYyMiRI1/rfgsXLkzVqlU5ePAg7du3T7IeuHbt2ixdupTJkyfj7e2Nk5MTPXr0wNHRkW7dur30/seOHUumTJmYP38+K1asoGjRovj7+zNt2jSTel26dOH69ev4+/uzdOlSChQowLBhw7h8+XKS29HOnj2bPn360LZtW6Kjo40XJ+fOnZtjx44xbtw4pk6dyuXLl8mSJQuurq7GL/bPq02bNgwdOpQOHToku5Z6+PDhFCtWjNmzZxuvKcmVKxcVK1bkgw8+MNZbuXIl/fv355NPPiE2NpZq1aqxa9cuvLy8njsmkX+D6bM+Y+6cWSyY9xmRkRG4uBbAb8p0GjT8+3ciPi7u8ezcE8+csbKyYuGiZcyaOZXJfuOJiYmhcJGifDb/8yS3VT324xHGjPrUpOyTQQMA8G7SjLETJpls2xD4NQaDgWbNtWRJRF49Q8KreIKWiKRpn332Gf369eP06dPGC6/TKi1bEhERABurl7/rmLx6mnkQ+Rc7ceIEf/zxB2PHjqVp06ZpPnEQERGRtE0zD/KfFhcXR2q/AgaDIU2uGTY3bhcXF8LCwqhevTorVqxI9nazaY1mHkREBDTzkFYpeZD/NA8Pj1QfupY/f35CQ0P/uYDM9LbGbQ4lDyIiAkoe0iolD/Kfdu7cOe7evZvidmtra0qWLJni9jflbY3bHEoeREQElDykVUoeRCRNUfIgIiKg5CGtSvemAxARERERkbeDkgcRERERETGLkgcRERERETGLkgcRERERETGLkgcRERERETGLkgcRERERETGLkgcRERERETGLkgcRERERETGLkgcRERERETGLxZsOQETEhB4oKiIikmZp5kFERERERMyi5EFERERERMyi5EFERERERMyi5EFERERERMyi5EFERERERMyi5EFERERERMyi5EFERERERMyi5EFERERERMyi5EFERERERMyi5EFERERERMyi5EFERERERMyi5EFERERERMyi5OE5rFy5klmzZr1UH/PnzycgIOCVxPMm7du3D4PBwL59+950KG9c586dcXFxea378PDwwMPD4433NXHiRDZs2PBK4hAREZG3jyEhISHhTQfxtmjcuDGnT58mNDT0hfsoUaIEOXLkeOu/dN+5c4dffvmFYsWKkTVr1jcdzht1/vx57ty5Q9myZV/bPhK/7L+Kz83L9JU5c2ZatWr1WhPg6Ef6J0neHtHRUcybM5udO7ZxJzISF9cCdOnWgwaNvJ7Z9lZ4OLNmTOX7/fuIiYmhUJEi9PloAO6Vq5jU+27fXnbu2M65X38h9I8/iI2N5cTpX5Ptc96cWZz79Sy//nqWG9ev4920GWMnTHolYxX5p9lYGt50CJIMzTzIC8maNSuVK1f+1ycOjx49IjY2Ntlt0dHRALi5ub3WxEFE0q5B/T9i07cb6PVhH+b6f07xEiUZ/skgtm3ZlGq7hw8f0qt7Z348fJghw0Ywc8487O1z0PeDHhw7+qNJ3T27gzh18v8oUKAghQoXSbXfL1csJyIygpoetbG0tHzp8YmIPE3JwxNu3LhBz549yZcvH9bW1jg4OFCtWjWCgoLw8PBgy5YtXLhwAYPBYHwl8vX1xd3dnezZs5M1a1bKlSvHkiVLeHJix8XFhTNnzrB//35j+8TlLgEBARgMhiSzGsktDzpx4gSNGzfG0dERa2tr8uTJg5eXF5cvXzZ7rJ07dyZz5sz8+uuv1K9fn0yZMpE7d24mTXp8hurw4cO88847ZMqUicKFC7N8+fJnxpXYZ0hICI0aNSJz5szky5ePQYMG8eDBg1TbAoSGhmIwGEzOav/++++0bduWPHnyYG1tTc6cOfH09OT//u//zB5rSEgIXbp0oVChQtjY2ODk5IS3tzenTp1KdkwrVqxg0KBBODk5YW1tTUhIiHFsp06dol69emTJkgVPT0/juJ9ctlS2bFmqV6+eJI64uDicnJxo0aKFscycz82LSkhIYMqUKeTPn58MGTJQrlw5tm3blqReTEwMgwYNokyZMtja2pI9e3aqVKnCxo0bTeoZDAaioqJYvny58fP75PKnsLAwevXqRd68ebGyssLV1RVfX98Uky+Rt9333+3n8KGDDB85mlat21KxUmVG+46jcpWqzJw+lbi4uBTbbgj8mpDgYCZPn0mjxt5UrlqNqTNm45zfhdkzppnUHe07jo1bdjB5+kxKlS6dakwHjvzEF1+tYcRoHywslDyIyKtn8aYDSEs6dOjA8ePHmTBhAoULFyYiIoLjx48THh7O/Pnz6dmzJ+fPn+ebb75J0jY0NJRevXrh7OwMPP7y/dFHH3HlyhVGjx4NwDfffEOrVq2wtbVl/vz5AFhbWz9XjFFRUdStWxdXV1fmzZtHzpw5CQsLY+/evdy9e/e5+nr06BEtWrTggw8+YMiQIaxcuZLhw4dz584d1q9fz9ChQ8mbNy+fffYZnTt3pkSJEpQvX/6ZfTZp0oRu3boxaNAgvvvuO8aNG4etra3xODyPRo0aERcXx5QpU3B2dubmzZscPHiQiIgIs/u4evUq9vb2TJo0CQcHB27dusXy5ctxd3fnxIkTFClieiZv+PDhVKlSBX9/f9KlS4ejoyPw+ExhkyZN6NWrF8OGDUvxS3GXLl3o378/wcHBFCpUyFi+c+dOrl69SpcuXYxl5nxuXpSvry++vr5069aNVq1acenSJXr06EFcXJzJmB88eMCtW7cYPHgwTk5OPHz4kKCgIFq0aMGyZcvo2LEjAIcOHaJ27drUqlWLUaNGARhnnsLCwqhUqRLp0qVj9OjRuLm5cejQIcaPH09oaCjLli17qbGIpEV7dwdhY2ND3XoNTMqbNG/Bp58M5tTJnylTtlyybfcEBeHi6krpMn/PWlpYWNCosTdzZ8/k+rVrOObMCUC6dOaf53ueuiIiL0LJwxMOHDhA9+7d6dGjh7GsadOmxv+3s7PD2tqaypUrJ2n75Jej+Ph4PDw8SEhIYPbs2YwaNQqDwUDZsmXJmDGjccnPi/j1118JDw9nyZIlJrG1bt36uft6+PAh48ePN54J9/DwYPPmzfj5+XH8+HHjUpwKFSrg6OjIypUrn5k8PHz4EF9fX959910APD09OXbsGCtXrnzuL8Ph4eGcO3eOWbNm8f777xvLnzxzb44aNWpQo0YN4/u4uDi8vLwoXrw4CxcuZMaMGSb13dzcWLduXZJ+Hj16xOjRo02+/Cenffv2DBkyhICAACZMmGAsDwgIIGfOnDRs2NBYZs7n5kVEREQwefJkmjdvzuLFi43lxYsXp1q1aibJg62trUkccXFxeHp6cvv2bWbNmmVMHipXrky6dOlwcHBI8vn18fHh9u3bnDlzxpgIeXp6kjFjRgYPHsyQIUMoVqzYC41FJK0KCfkN1wJuWFiY/ikt/NfSovMhwSkmD+dDgimbzL+nT7ZNTB5ERNISnaJ4QqVKlQgICGD8+PEcPnyYR48emd12z5491KlTB1tbW9KnT4+lpSWjR48mPDyc69evv7IYCxYsSLZs2Rg6dCj+/v788ssvL9yXwWCgUaNGxvcWFhYULFiQ3Llzm6zhz549O46Ojly4cMGsPr29vU3KSpUqZVbbp2XPnh03NzemTp3KjBkzOHHiBPHx8c/dT2xsLBMnTqRYsWJYWVlhYWGBlZUVwcHBnD17Nkn9li1bpthXatsS2dvb4+3tzfLly43x3r59m40bN9KxY0eTLxqv63Nz6NAhYmJiaN++vUl51apVyZ8/f5L669ato1q1amTOnBkLCwssLS1ZsmRJsscnOZs3b6ZWrVrkyZOH2NhY4ysxUdq/f/8Lj0UkrYqMiMDW1jZJeda/ylKbIY2IiCBr1lTaRqbcVkTkTVLy8IQ1a9bQqVMnFi9eTJUqVciePTsdO3YkLCws1XY//vgj9erVA2DRokUcOHCAo0ePMmLECADu37//ymK0tbVl//79lClThk8//ZTixYuTJ08exowZ81zJDoCNjQ0ZMmQwKbOysiJ79uxJ6lpZWRETE/NCfVpbW5vV9mkGg4Hdu3dTv359pkyZQrly5XBwcKBfv37PtURr4MCBjBo1imbNmrFp0yaOHDnC0aNHKV26dLI/m9y5cyfbj42NjdkXiHft2pUrV66wa9cuAFatWsWDBw/o3Lmzsc7r/NyEh4cDkCtXriTbni4LDAykdevWODk58eWXX3Lo0CGOHj1K165dzf65Xbt2jU2bNmFpaWnyKl68OAA3b95Mtt2DBw+4c+eOyevJ62NE0rxUZgefNXOY2nYDusuMiKRNWrb0hBw5cjBr1ixmzZrFxYsX+fbbbxk2bBjXr19n+/btKbZbvXo1lpaWbN682eSL8/PcDz+x3dNfnJL70lWyZElWr15NQkICJ0+eJCAggLFjx5IxY0aGDRtm9j7flOcZa/78+VmyZAkAv/32G2vXrsXHx4eHDx/i7+9v1v6+/PJLOnbsyMSJE5Psz87OLkn9lP6gP88Sovr165MnTx6WLVtG/fr1WbZsGe7u7iZLd17F5yYl9vb2AMkmvmFhYSYXeH/55Ze4urqyZs0akzE+z5f4HDlyUKpUKZNlWk/KkydPsuV+fn74+vqalH06cjQjRvuYvW+RN8XWzo7IZGYX7kRGPt6ezKxEIjs7OyKTmV0wp62IyJukmYcUODs707dvX+rWrcvx48eBx2fQkzsbbDAYsLCwIH369May+/fvs2LFiiR1U+oj8cvcyZMnTcq//fbbFGM0GAyULl2amTNnYmdnZ4wzrXuRsQIULlyYkSNHUrJkyecaq8FgSHJh+pYtW7hy5YrZfTyv9OnT06FDBzZs2MD333/PsWPH6Nq1a5K4zP3cPK/KlSuTIUMGvvrqK5PygwcPJllCZjAYsLKyMkkcwsLCktxtCVL+/CY+A8XNzY0KFSokeaWUPAwfPpzIyEiT1+Chw19kyCL/uEKFCvPH7+eT3Dwh+LffAHArWCi5ZgAULFSIkL/qmbQN/qttoZTbioi8SZp5+EtkZCS1atWiXbt2FC1alCxZsnD06FG2b99uvEC3ZMmSBAYGsmDBAsqXL0+6dOmoUKECXl5ezJgxg3bt2tGzZ0/Cw8OZNm1asndSSpw1WLNmDQUKFCBDhgyULFmSihUrUqRIEQYPHkxsbCzZsmXjm2++4YcffjBpv3nzZubPn0+zZs0oUKAACQkJBAYGEhERQd26df+RY/WycuXKRZ06dfDz8yNbtmzkz5+f3bt3ExgYaFLv5MmT9O3bl3fffZdChQphZWXFnj17OHny5HPNsDRu3JiAgACKFi1KqVKl+Omnn5g6dSp58+Z91UMz0bVrVyZPnky7du3ImDEjbdq0Mdn+PJ+b55UtWzYGDx7M+PHj6d69O++++y6XLl3Cx8cnybKlxo0bExgYSO/evY13ZRo3bhy5c+cmODjYpG7JkiXZt28fmzZtInfu3GTJkoUiRYowduxYdu3aRdWqVenXrx9FihQhJiaG0NBQtm7dir+/f7LH29raOsl49ZA4eVvU8qxL4Nfr2L1rJ/Ub/n392KZvN+Dg6EjJUinfVrWWZ138xvty6uTPxnqxsbFs3byJkqVK4+ioi6VFJG1S8vCXDBky4O7uzooVKwgNDeXRo0c4OzszdOhQPvnkEwD69+/PmTNn+PTTT4mMjCQhIYGEhARq167N0qVLmTx5Mt7e3jg5OdGjRw8cHR3p1q2byX58fX35888/6dGjB3fv3iV//vyEhoaSPn16Nm3aRN++ffnggw+wtrambdu2zJ07Fy+vv59UWqhQIezs7JgyZQpXr17FysqKIkWKEBAQQKdOnf7RY/YyVqxYwUcffcTQoUOJi4vD29ubVatWUaFCBWOdXLly4ebmxvz587l06RIGg4ECBQowffp0PvroI7P3NXv2bCwtLfHz8+PevXuUK1eOwMBARo4c+TqGZlS4cGGqVq3KwYMHad++fZJlCM/zuXkRY8eOJVOmTMyfP58VK1ZQtGhR/P39mTbN9B7yXbp04fr16/j7+7N06VIKFCjAsGHDuHz5cpIlRbNnz6ZPnz60bduW6Ohoatasyb59+8idOzfHjh1j3LhxTJ06lcuXL5MlSxZcXV1p0KAB2bJle+nxiKQ171SvQeUqVZk4zpeoqHvkc3Zm+9YtHPzheyZMmmqcVfQZNYLN327g2207yZPHCYBmLVqydvVKPhk4gH4fDyRbdnvWrV7FhdA/WLBoqcl+rl69wpnTj59Lc+nSJQB27Xy8lDZPHieKlyhprHvs6I/cvn0LgPj4OP68etVYt3yFSsle0yYi8jwMCa/iaVQiIq+IZh7kbRIdHcXc2bPYtWM7kZERuLgWoGv3njRo9PdJn9EjhrFp4wa27Agij9PfM3DhN28ya8ZUvt+/j5iYGAoXLUrvvv2pXKWqyT6+3RDImJGfJrt/76bNGDthkvF9984d+OnY0WTrLlq6nAqV3F9muCL/KBtL3TggLVLyICJpipIHEREBJQ9plZYt/cvEx8c/81kITz/Q6G31Xxrrk+Li4kgt5zcYDCYXYYuIiIi8Krrb0r9M165dk9xr/+nXv8XYsWOfOdbQ0NA3HeYr5+npmeqY3dzc3nSIIiIi8i+lZUv/MqGhoSk+kCvRkxclv82uXr3K1atXU61TqlQprKys/qGI/hnnzp1L9SF51tbWlCxZMsXtaZ2WLYmICGjZUlql5EFE0hQlDyIiAkoe0iotWxIREREREbMoeRAREREREbMoeRAREREREbMoeRAREREREbMoeRAREREREbMoeRAREREREbMoeRAREREREbMoeRAREREREbMoeRAREREREbMoeRAREREREbNYvOkARESeZMDwpkMQERGRFGjmQUREREREzKLkQUREREREzKLkQUREREREzKLkQUREREREzKLkQUREREREzKLkQUREREREzKLkQUREREREzKLkQUREREREzKLkQUREREREzKLkQUREREREzKLkQUREREREzKLkQUREREREzKLk4RXr3LkzLi4ur3UfHh4eeHh4vPG+Jk6cyIYNG15JHGld586dyZw585sO47XYt28fBoOBr7/++k2HIpKmRUdHMWXSBOrWeodK5UrSumVTtm/dYlbbW+HhjBoxDI933KlcoTQd27fhyOFDydY9fOggHdu3oXKF0ni8486oEcO4FR6epN7FixcYMWwIDep44F6+FI0b1GHaFD8iIm6b1AsJCWbCOJ/HfVYsQ5kSRTj645HnPwAiIih5eOVGjRrFN99886bD+Ef8l5IHEZGB/T9i07cb6PVhX+b5L6J4iZIM+2QgW7dsSrXdw4cP6dm9Mz8ePsSQYSOYOWc+2e3t6fNBd44d/dGk7rGjP9L3wx5kt7dn5pz5DBk2giOHD9Kze2cePnxorHfr1i06tmvN//3fcXp/1J+5Cz6nzXvtCfx6Lb26dyE+Pt5Y95czp9m3J4isWW1xd6/8ag+KiPznWLzpAN4mjx49wmAwYGGR9LBFR0djY2ODm5vbG4hMJO27f/8+GTJkwGAwvOlQRJ7b99/t5/ChA/hNmU7DRo0BqFipMn9evcrM6VOo36AR6dOnT7btN4HrCAn+jeVfrqZ0mbJ/tXWndcumzJoxlS9XrTPWnTl9Cs75XZg2Y47xb42TU146d3iPDYFf07ptOwD27dlNREQEk6fNwr1yFWM8Dx8+5LPZM/jt3K8U/V8xABp7N6VJ0+YA7Nq5nf379r6GIyQi/xX/uZmHkJAQunTpQqFChbCxscHJyQlvb29OnTplUi9xKceKFSsYNGgQTk5OWFtbExISYlzCcurUKerVq0eWLFnw9PQEki5bKlu2LNWrV08SR1xcHE5OTrRo0cJY5uvri7u7O9mzZydr1qyUK1eOJUuWkJCQ8NLjTkhIYMqUKeTPn58MGTJQrlw5tm3blqReTEwMgwYNokyZMtja2pI9e3aqVKnCxo0bTeoZDAaioqJYvnw5HekTdAAASVVJREFUBoMBg8FgsvwpLCyMXr16kTdvXqysrHB1dcXX15fY2FizY543bx7p0qXj+vXrxrLp06djMBjo06ePsSw+Pp5s2bIxaNAgY9nDhw8ZP348RYsWxdraGgcHB7p06cKNGzeS7GfNmjVUqVKFTJkykTlzZurXr8+JEyeeGd+BAwfIkSMHjRs3Jioqyqwx+fj4YDAYOHPmDO+99x62trbkzJmTrl27EhkZaawXGhqKwWAgICAgSR8GgwEfH58kfZ48eZJ3333X+HMbOHAgsbGxnDt3jgYNGpAlSxZcXFyYMmVKsrHFxMQwcOBAcuXKRcaMGalZs2ayx+HYsWM0adKE7NmzkyFDBsqWLcvatWtN6gQEBGAwGNi5cyddu3bFwcEBGxsbHjx4YNZxEklr9uzehY2NDXXrNTApb9q8BTeuX+fUyZ9TbhsUhIurqzFxALCwsMCrcRNOnzrJtWvXALh27RpnTp+isXdTk5NUZcqWI7+LC3t2B/3d3vLx9qeXU2bJkgUAKytrY1m6dP+5P/Ui8hr95/5FuXr1Kvb29kyaNInt27czb948LCwscHd359y5c0nqDx8+nIsXL+Lv78+mTZtwdHQEHn85bdKkCbVr12bjxo34+vomu78uXbrwww8/EBwcbFK+c+dOrl69SpcuXYxloaGh9OrVi7Vr1xIYGEiLFi346KOPGDdu3EuP29fXl6FDh1K3bl02bNjAhx9+SI8ePZKM+cGDB9y6dYvBgwezYcMGVq1axTvvvEOLFi344osvjPUOHTpExowZadSoEYcOHeLQoUPMnz8feJw4VKpUiR07djB69Gi2bdtGt27d8PPzo0ePHmbHXKdOHRISEti9e7exLCgoiIwZM7Jr1y5j2bFjx4iIiKBOnTrA42SiadOmTJo0iXbt2rFlyxYmTZrErl278PDw4P79+8a2EydO5L333qNYsWKsXbuWFStWcPfuXapXr84vv/ySYmxr167F09OT1q1bs3HjRjJlymT2uABatmxJ4cKFWb9+PcOGDWPlypV8/PHHz9XH01q3bk3p0qVZv349PXr0YObMmXz88cc0a9YMLy8vvvnmG2rXrs3QoUMJDAxM0v7TTz/l999/Z/HixSxevJirV6/i4eHB77//bqyzd+9eqlWrRkREBP7+/mzcuJEyZcrQpk2bZBOdrl27YmlpyYoVK/j666+xtLR8qTGKvCnnQ4JxLeCWZOa5UOEiwOPrClJrm1gvubbn/2p7PuQ3k/Kn6yZuB6hVuw65c+dhxrTJhIQEEx0dxU/HjrJsySJqetSigGbBReQ1+c8tW6pRowY1atQwvo+Li8PLy4vixYuzcOFCZsyYYVLfzc2NdevWPd0Njx49YvTo0SZf/pPTvn17hgwZQkBAABMmTDCWBwQEkDNnTho2bGgsW7ZsmfH/4+Pj8fDwICEhgdmzZzNq1KgXXu4RERHB5MmTad68OYsXLzaWFy9enGrVqlGkyN9/qGxtbU3iiIuLw9PTk9u3bzNr1iw6duwIQOXKlUmXLh0ODg5Urmy6htbHx4fbt29z5swZnJ2dAfD09CRjxowMHjyYIUOGUKxYsWfGXaRIEfLmzUtQUBDvvfceDx8+5Pvvv6dfv35MnjyZixcv4uzsTFBQEJaWlsaf69q1a9m+fTvr1683mdkpXbo0FStWJCAggA8//JBLly4xZswY+vbty5w5c4z16tatS6FChfD19WXNmjVJ4po8eTIjRoxg4sSJfPLJJ88cR3K6devGkCFDgMdJUkhICEuXLmXJkiUv/HPu2bMnAwcONPa5c+dO5s6dS2BgIM2bP16y4OHhwebNm/nqq69Mjg2Ag4MD33zzjXH/77zzDoUKFcLPz49FixYB0Lt3b4oXL86ePXuMX6Lq16/PzZs3+fTTT+nYsaPJWU5PT08WLlz4QuMRSUsiIiLImzdvknJbW1sAIiMiUm1rm9U25baREcZ6T5ab1rUzbofHMwxfrFzDoAH9aNWssbG8bv0GTPCb+qzhiIi8sP/czENsbCwTJ06kWLFiWFlZYWFhgZWVFcHBwZw9ezZJ/ZYtW6bYV2rbEtnb2+Pt7c3y5cuNF7Ddvn2bjRs30rFjR5OzWHv27KFOnTrY2tqSPn16LC0tGT16NOHh4SZLd57XoUOHiImJoX379iblVatWJX/+/Enqr1u3jmrVqpE5c2YsLCywtLRkyZIlyR6f5GzevJlatWqRJ08eYmNjja/ERGn//v1mx+7p6UlQ0OOp+oMHDxIdHc3AgQPJkeP/27vzuJry/w/gr0s77SmVobTaylr2ViJlG4MJ2ZkxMyLLyC5LiOzJMMrPFjNjjLKMUoyhphrGYIzRjJhQKNlKWs7vj6b7ddwbJ1vh9Xw8zoP7OZ/tnNut+z6fz+ccI/noQ3x8vHzaUXn7enp68PX1FbXfvHlz1K1bF0ePHgUA/PjjjyguLoa/v78on4aGBlxcXOT5ygmCgLFjx2LOnDnYsWPHCwcOANCzZ0/RawcHBzx69Oil3mcfHx/R60aNGkEmk4kCVBUVFVhbW+PKlSsK5f38/ESBS4MGDdC+fXskJpbNj05PT8eff/4p/zl68px5e3vjxo0bCiNZz/uMFBYW4t69e6KNU5uounpWYP+8oP+ZZSHeV1HeJ9Pv3b2LCV+Mw8OHD7BoyTJs3rId02fOwW+nfkXA559WaoooEVFlvHfBQ2BgIGbNmoXevXsjJiYGv/zyC1JTU+Ho6CiazlLO1NRUaT1aWlrQ0dGR1OaIESNw7do1+ZfdnTt3orCwEMOGDZPnSUlJQdeuXQEAGzduxIkTJ5CamooZM2YAgNK+SZXz3y3+6tatq7Dv6bQ9e/agf//+MDc3x7Zt25CUlITU1FSMGDECjx49ktRednY2YmJioKqqKtqaNGkCALh9+7bkvnt6euLq1au4dOkS4uPj0aJFCxgbG8Pd3R3x8fEoKCjAyZMn5VOWytvPy8uDmpqaQh+ysrLk7ZfPM27Tpo1Cvl27din08/Hjx9i1axeaNGki+kL+IgwNDUWv1dXL5ie/zPtsYGAgeq2mpgYtLS1oaGgopCt7Lyv6+Sj/+Sk/X5MnT1Y4X+PGjQOg+N5W9PkpFxISAl1dXdEWuiTkOUdK9Obp6Ymv/JcrX6uko2S0QFT27vPL6unpAUAF7eSJRiQiN2/ExT//RMTGzfDu4YuWrVqj/0A/LFqyDEknf37uHaCIiF7Uezdtadu2bfD398eiRYtE6bdv35b/4n6SlCtAz+Pl5QUzMzNERkbCy8sLkZGRcHZ2Fk3diY6OhqqqKmJjY0Vf9l7FrVDLv6hmZWUp7MvKyhIt8N62bRssLS2xa9cu0TFW5mqwkZERHBwcRNO0nmRmZia5rvKF6PHx8YiLi0OXLl3k6TNnzsRPP/2EwsJCUfBgZGQEQ0NDHDp0SGmd5QsKjYyMAADffvut0hGYp6mrqyMxMRFeXl7w9PTEoUOHoK+vL/lYKqP8Z+Dp856j5F7vr0pFPx/lPz/l5ysoKEhhylO5J6fAAc//nAQFBcmnWpUrraFeQW6iqmNtY4tDB2JRXFwsGjFO/6tsHYK1tc0zy5bne1L6pf/K2pSVtbK2/S/9Ijp1dlHIW74fAC7+eQHGJsaoU8dYlK9J02aiuomIXrX3buRBJpPJr/KW279/P65du/ba2qxZsyaGDBmCvXv34vjx40hLS8OIESMU+qWioiK61V9BQQG2bt360u23bdsWGhoa2L59uyj95MmTCtNXZDIZ1NTURF/6srKyFO62BJR9mVZ2pdzHxwfnzp2DlZUVWrdurbBVJngwNTVF48aN8d133+HXX3+VBw9dunTBrVu3EBYWBh0dHbRp00bUfk5ODkpKSpS2X/4F18vLCyoqKvj777+V5mvdurVCf1q0aIFjx44hMzMTrq6uLzXN6FlMTEygoaGB33//XZSu7H14VXbu3Cm6s9eVK1dw8uRJ+V207OzsYGNjgzNnzlR4vsoDM6nU1dWho6Mj2p7+fBJVB+4ensjPz8eRuMOi9Jh936OOsTGaOTg+s+zly/+I7shUXFyM/bH70MzBEcbGJgDKPvdNmzlgf2wMSkpK5Hl/P/MbMi5fhodnF3lanTrGuJmdLR8RLHfmt9/+q0txJJGI6FV470YefHx8EBUVBXt7ezg4OODXX39FaGio0oVwr9KIESOwZMkS+Pn5QVNTEwMGDBDt79GjB8LCwuDn54cxY8YgJycHy5YteyVfpPT19TF58mQsWLAAo0aNwkcffYR///0Xc+fOVZiq4uPjgz179mDcuHHo168f/v33X8yfPx+mpqYKd4xq1qwZjh49ipiYGJiamkJbWxt2dnYIDg5GXFwc2rdvj/Hjx8POzg6PHj1CRkYGDhw4gIiIiEqdbw8PD6xZswaampro0KEDAMDS0hKWlpY4fPgwevbsKboSOHDgQGzfvh3e3t4ICAiAk5MTVFVVkZmZicTERPTq1Qt9+vSBhYUFgoODMWPGDPzzzz/o1q0b9PX1kZ2djZSUFNSqVUvpXbQaNWqE48ePw9PTE507d0Z8fPwr//mRyWQYPHgwNm/eDCsrKzg6OiIlJQU7dux4pe086ebNm+jTpw9Gjx6Nu3fvYs6cOdDQ0EBQUJA8z4YNG9C9e3d4eXlh2LBhMDc3R25uLi5cuIBTp04pvbkA0bugYycXtG3XAQvnz8WDhw9Qv359HDywHyd+Po6Fi0PlF37mzpqOmH17EXMwDmZm5gCA3n37YVf0DkwJDMD4iZNgYGCI3dE7cCXjMiI2RoraCZg4GZ+OGYEpgQHoP9APubk5WL1iOaxtbNGrz//WEA34eBAO7I/Bp6OHY/jIMahrWhfply5h44b1MDQ0grePrzxvQUEBfj5ettbs7JmyAObXtFTk5d2BpqYmOnYSj3IQET3Lexc8rFq1CqqqqggJCcGDBw/QsmVL7NmzBzNnznyt7dra2qJ9+/Y4efIkBg0apHA3DXd3d2zevBlLliyBr68vzM3NMXr0aBgbG2PkyJEv3X5wcDBq1aqF8PBwbN26Ffb29oiIiMCyZctE+YYPH46bN28iIiICmzdvRsOGDTFt2jRkZmYqfJFetWoVPvvsMwwcOBD5+fnyRcampqZIS0vD/PnzERoaiszMTGhra8PS0lL+Bb0yPD09sWbNGnTs2FE0pcvT0xMbN24UTVkCykZ69u3bh1WrVmHr1q0ICQmBiooK6tWrBxcXFzRr1kyeNygoCI0bN8aqVavka1Hq1q2LNm3a4JNPPqmwTw0bNpQHEJ06dcKRI0fQsGHDSh3X8yxfvhwAsHTpUjx48ADu7u6IjY0VTTN7lRYtWoTU1FQMHz4c9+7dg5OTE6Kjo0UPPnRzc0NKSgoWLlyICRMm4M6dOzA0NETjxo3Rv3//19IvouoibNUarFm1AuvXrsbdu3mwtGyIxUvD0M27hzxPSWkpSkpKRKN4ampq+GpTFFaEhWLJogV49KgAdvaNsHb9RrRu4yRqo42TM9aEf4X161Yj4PNPoKGhiU4urgicNBVqamryfI2bNMXWHbvxVUQ41q5egTt3cmFsbAIXN3eM/eQz6Ov/bw1Ubm4OpgQGiNqJCF8DADA1M8fBwwmv9DwR0btNJryKJ5AREb0iBUVV3QMiIqoONPlooGrpvVvzQEREREREL+a9m7b0rnl6ePxpMplMtAi7unhb+12R0tJS+XM8KvL0k2mJiIiI3jYceXjLeXh4KNxz/8ntyfnq1cnb2u+KBAcHP/N4VFVVkZGRUdXdJCIiInopXPPwlrt48SLu379f4X51dXXRAuHq4m3td0WuX7+O69evPzOPg4ODaMEjKcc1D0REBHDNQ3XF4IGIqhUGD0REBDB4qK44bYmIiIiIiCRh8EBERERERJIweCAiIiIiIkkYPBARERERkSQMHoiIiIiISBIGD0REREREJAmDByIiIiIikoTBAxERERERScLggYiIiIiIJGHwQEREREREkjB4ICIiIiIiSRg8EBERERGRJAweiIiIiIhIEgYPREREREQkCYMHIiIiIiKShMEDERERERFJwuCBiIiIiIgkYfBARERERESSMHggIiIiIiJJGDwQEREREZEkDB6IiIiIiEgSBg9ERERERCQJgwciIiIiIpKEwcMrNmzYMFhYWLzWNlxdXeHq6lrldS1atAh79+59Jf2orFd5Dt6ENWvWwNraGmpqapDJZMjLy6vqLr2QkydPYu7cuUr7b2FhAR8fnzffKaI3JD//IZYuXogubh3h1LIZ+n/YC4cO7JdUNjcnB7NmTINrR2e0be0I/0ED8EtyktK8yUkn4T9oANq2doRrR2fMmjENuTk5SvOmX/oLkwPHw61TW7Rp0RTdu7pj4fy54jzpl7Bw/tyyOts0R/OmdkhN+aVSx05EVI7Bwys2a9YsfP/991XdjTeiKoOHt8lvv/2G8ePHw83NDQkJCUhKSoK2tnZVd+uFnDx5EvPmzXtrgx+ilxEY8AVi9u3F2E8/x7qIjWjStBmmTQ3Egf0xzyz3+PFjjBk1DCnJSZgybQZWrA6HgaEhPvtkFNJSU0R501JT8Pmno2FgaIgVq8MxZdoM/JJ8EmNGDcPjx49FeVNTkjH444/w8MFDzJg9D+u/2ozPPh8PdXV1Ub4/zp/D0YR46Ojowtm57as5GUT03lKp6g68TYqKiiCTyaCionja8vPzoaWlBSsrqyroGVVn58+fBwCMHj0aTk5OVdwbInoRx386huSkEwhZuhzdvctG2No4tcWN69exYvlSeHXzRs2aNZWW/X7PN0i/9Be2bIuGY/MW/5V1Rv8Pe2FlWCi27fxGnnfF8qWo38ACy8JWy//WmJvXw7AhH2Pvnm/Rf6AfAKCgoADTv5yMNk5tsXpdBGQymbwOn569Re37+PZCz159AABxhw/h2NHEV3NSiOi99N6NPKSnp2P48OGwsbGBlpYWzM3N4evri7Nnz4ryHT16FDKZDFu3bsWkSZNgbm4OdXV1pKenY9iwYahduzbOnj2Lrl27QltbGx4eHgAUpy21aNECnTp1UuhHSUkJzM3N0bdvX3navHnz4OzsDAMDA+jo6KBly5b4+uuvIQjCSx+3IAhYunQpGjRoAA0NDbRs2RIHDx5UyPfo0SNMmjQJzZs3h66uLgwMDNCuXTv88MMPonwymQwPHz7Eli1bIJPJIJPJRNOIsrKyMHbsWNSrVw9qamqwtLTEvHnzUFxcXKX99vDwgL29vcI5FQQB1tbW6NGjR6X6t3nzZjg6OkJDQwMGBgbo06cPLly4IN/v6uqKwYMHAwCcnZ0hk8kwbNgwSXVHRUVBJpMhISEBo0ePhqGhIXR0dODv74+HDx8iKysL/fv3h56eHkxNTTF58mQUFRWJ6sjNzcW4ceNgbm4ONTU1NGzYEDNmzEBhYaEon0wmw+eff46tW7eiUaNG0NLSgqOjI2JjY+V55s6diylTpgAALC0t5e/70aNHRXUdOnQILVu2hKamJuzt7bF582app5Oo2ko4EgctLS106dpNlN6rT1/cunkTZ38/U3HZ+HhYWFrKAwcAUFFRQQ+fnjh39ndkZ2cDALKzs3H+3Fn4+PYSXaRq3qIlGlhYIOFIvDwt7vAh3Lp1C0OHjxQFDsrUqPHe/aknotfovRt5uH79OgwNDbF48WLUqVMHubm52LJlC5ydnXH69GnY2dmJ8gcFBaFdu3aIiIhAjRo1YGxsDKBsGLpnz54YO3Yspk2bVuGX4uHDhyMgIACXLl2CjY2NPP3w4cO4fv06hg8fLk/LyMjA2LFjUb9+fQBAcnIyvvjiC1y7dg2zZ89+qeOeN28e5s2bh5EjR6Jfv374999/MXr0aJSUlIiOubCwELm5uZg8eTLMzc3x+PFjxMfHo2/fvoiMjIS/vz8AICkpCe7u7nBzc8OsWbMAADo6OgDKAgcnJyfUqFEDs2fPhpWVFZKSkrBgwQJkZGQgMjKyyvodEBCAXr164ciRI/D09JSXP3jwIP7++2+sXr1act9CQkIwffp0fPzxxwgJCUFOTg7mzp2Ldu3aITU1FTY2NggPD8fOnTuxYMECREZGwt7eHnXq1JHcBgCMGjUKffv2RXR0NE6fPo3p06ejuLgYFy9eRN++fTFmzBjEx8djyZIlMDMzQ2BgIICygMrNzQ1///035s2bBwcHBxw/fhwhISH47bffsH+/eK72/v37kZqaiuDgYNSuXRtLly5Fnz59cPHiRTRs2BCjRo1Cbm4u1qxZgz179sDU1BQA0LhxY3kdZ86cwaRJkzBt2jSYmJhg06ZNGDlyJKytrdG5c+dKHTdRdfJ3+iVYNrRSGHm2sS37PZSefgnNW7SssGyLVq0U0svL/p1+CSYmJvg7/S9R+tN5z5w+JX99Ki0VAFBaWoJhQz7GubNnoamliQ4dOiFwypcwNjZ5gaMkIpJAeM8VFxcLjx8/FmxsbISJEyfK0xMTEwUAQufOnRXKDB06VAAgbN68Wem+Bg0ayF/fvn1bUFNTE6ZPny7K179/f8HExEQoKipS2q+SkhKhqKhICA4OFgwNDYXS0lL5PhcXF8HFxUXyMd65c0fQ0NAQ+vTpI0o/ceKEAOCZdRUXFwtFRUXCyJEjhRYtWoj21apVSxg6dKhCmbFjxwq1a9cWrly5IkpftmyZAEA4f/58lfW7pKREaNiwodCrVy9R/u7duwtWVlai8/y8vmlqagre3t6i9KtXrwrq6uqCn5+fPC0yMlIAIKSmpkqq++lyX3zxhSi9d+/eAgAhLCxMlN68eXOhZcuW8tcRERECAGH37t2ifEuWLBEACIcPH5anARBMTEyEe/fuydOysrKEGjVqCCEhIfK00NBQAYBw+fJlhf42aNBA0NDQEL3vBQUFgoGBgTB27FjJx53/mBu36rd5dukqDBs+QiH9yrVswdbWVlizLqLCso0bNxGmz5ilkJ6UckqwtbUVvtsbI+Q/FoRvv98n2NraCsmppxXyBs2YJTRp0kT+etjwEYKtra3QqnVrYdHipcKxn5OE/9u2U3BychI8PLsIOXfzlfblh9iDgq2trXDs5+QqP6fcuD1vo+rpvRvLLC4uxqJFi9C4cWOoqalBRUUFampquHTpkmi6SbkPP/ywwrqeta+coaEhfH19sWXLFpSWlgIA7ty5gx9++AH+/v6iq1gJCQnw9PSErq4uatasCVVVVcyePRs5OTm4efPmCxxtmaSkJDx69AiDBg0Spbdv3x4NGjRQyP/NN9+gQ4cOqF27NlRUVKCqqoqvv/5a6flRJjY2Fm5ubjAzM0NxcbF86969OwDg2LFjVdbvGjVq4PPPP0dsbCyuXr0KAPj7779x6NAhjBs37rnD/0/2raCgQGEK0gcffAB3d3ccOXJEUj1SPH0Ho0aNGgGAwhSrRo0a4cqVK/LXCQkJqFWrFvr16yfKV97np/vo5uYmWshtYmICY2NjUZ3P07x5c/nIGQBoaGjA1ta2wjoKCwtx79490fb0lCqi6uJZvx+e97vjmWUh3ldR3ifTS0vLpl56eXXHhMApaOPUFv36D8Sc4IX49+oVHNwfq7QOIqKX9d4FD4GBgZg1axZ69+6NmJgY/PLLL0hNTYWjoyMKCgoU8pdPzXialpaWfJrO84wYMQLXrl1DXFwcAGDnzp0oLCwUffFMSUlB165dAQAbN27EiRMnkJqaihkzZgCA0r5JlfPfLf7q1q2rsO/ptD179qB///4wNzfHtm3bkJSUhNTUVIwYMQKPHj2S1F52djZiYmKgqqoq2po0aQIAuH37dpX2e8SIEdDU1ERERAQAYN26ddDU1MSIESMk9evJvin7+TAzM5PvfxUMDAxEr9XU1CpMf/JYc3JyULduXYUvIsbGxlBRUVHoo6GhoULb6urqlfrZq2wdISEh0NXVFW2hS0Ikt0f0pujp6Sm9y9jdu3cBADq6us8ue/f5ZfX09ACggnbyoPtEG+V523cQr6lr36ETZDIZLlw4X2F/iIhexnu35mHbtm3w9/fHokWLROm3b9+W/zJ+kpQrQM/j5eUFMzMzREZGwsvLC5GRkXB2dhbNFY+OjoaqqipiY2OhoaEhT38Vt0It/0KXlZWlsC8rK0u0wHvbtm2wtLTErl27RMdYmavBRkZGcHBwwMKFC5XuNzMzq9J+6+rqYujQodi0aRMmT56MyMhI+Pn5KX3/n9e3GzduKOy7fv06jIyMJNf1uhgaGuKXX36BIAiic3Lz5k0UFxdXiz4GBQXJ12iUK62hXkFuoqpjbWOLQwdiUVxcLBoxTv+rbJ2CtbVNRUVhbWMrz/ek9Ev/lf1vPZyVte1/6RfRqbOLQt7y/UDZGohDByt+xgQXSRPR6/Le/XaRyWQK98Dev38/rl279trarFmzJoYMGYK9e/fi+PHjSEtLU7jKXX4L2Cdv9VdQUICtW7e+dPtt27aFhoYGtm/fLko/efKkwnQSmUwmf5BZuaysLIW7FgEVX1H28fHBuXPnYGVlhdatWytsUoOH19VvABg/fjxu376Nfv36IS8vD59//rmkPpVr164dNDU1sW3bNlF6ZmYmEhIS5HffqkoeHh548OCBQgD6f//3f/L9lVX+2XmZkbCn69PR0RFtT38+iaoDdw9P5Ofn40jcYVF6zL7vUcfYGM0cHJ9Z9vLlf0R3ZCouLsb+2H1o5uAoX9xsYmKCps0csD82BiUlJfK8v5/5DRmXL8PDs4uoTplMhp9//knU1onjP0EQBDg8oz9ERC/jvQsefHx8EBUVhZUrVyIhIQGhoaEYPnw46tWr91rbHTFiBAoLC+Hn5wdNTU0MGDBAtL9Hjx548OAB/Pz8EBcXh+joaHTq1OmVfJHS19fH5MmT8f3332PUqFH48ccfsWnTJvTv319h+o+Pjw8uXryIcePGISEhAVu2bEHHjh2VTs9p1qwZjh49ipiYGKSlpeHixYsAgODgYKiqqqJ9+/ZYv349EhIScODAAYSHh8PHxweZmZlV2m8AsLW1Rbdu3ZCYmIiOHTvC0bFyf2j19PQwa9Ys7Nu3D/7+/jh48CC2bdsGNzc3aGhoYM6cOZWq73Xw9/eHg4MDhg4dihUrViA+Ph5z587F9OnT4e3tLbrblFTNmjUDAKxatQpJSUlIS0vD/fv3X3XXiaqdjp1c0LZdByycPxfffbsbqSnJCJ47Cyd+Po4JgVPkF37mzpqOVo6Ncf36/y5I9e7bD1bWNpgSGIAD+2OQnHQSUydNwJWMywiYOFnUTsDEyci4/A+mBAYgOekkDuyPwdRJE2BtY4teff63zs6yoRUGfDwIe/d8i+Whi5GcdBK7ordj7uwZsG/UGF27dZfnLSgoQNzhQ4g7fAhnz5QFML+mpSLu8CH8fFzaGjQionLv3bSlVatWQVVVFSEhIXjw4AFatmyJPXv2YObMma+1XVtbW7Rv3x4nT57EoEGDRHNXAcDd3R2bN2/GkiVL4OvrC3Nzc4wePRrGxsYYOXLkS7cfHByMWrVqITw8HFu3boW9vT0iIiKwbNkyUb7hw4fj5s2biIiIwObNm9GwYUNMmzYNmZmZmDdvnijvqlWr8Nlnn2HgwIHIz8+Hi4sLjh49ClNTU6SlpWH+/PkIDQ1FZmYmtLW1YWlpiW7dukFfX79K+11uwIABOHjwYKVHHcoFBQXB2NgYq1evxq5du6CpqQlXV1csWrRIdFveqqKhoYHExETMmDEDoaGhuHXrFszNzTF58uQXDm5cXV0RFBSELVu2YOPGjSgtLUViYqLoGR9E76qwVWuwZtUKrF+7Gnfv5sHSsiEWLw1DN+//3bygpLQUJSUlomfJqKmp4atNUVgRFoolixbg0aMC2Nk3wtr1G9G6jfjBkW2cnLEm/CusX7caAZ9/Ag0NTXRycUXgpKny9U7lpnw5HSYmJtjz3bfYuX0b9PX14NXdG18EBEJV9X95c3NzMCUwQFQ2InwNAMDUzBwHDye8snNERO8+mSC8gieQEb2FPvzwQyQnJyMjIwOqqqpV3R36T0HR8/MQEdG7T5N/mqul927kgd5vhYWFOHXqFFJSUvD9998jLCyMgQMRERGRRBx5eMs9PTz+NJlMJlqEXV1UVb8zMjJgaWkJHR0d+Pn5Ye3atQrtvO6+CYIgWgypTM2aNSt1R693CUceiIgI4MhDdfXeLZh+13h4eCg8T+HJzcrKqqq7qFRV9dvCwgKCIODu3btYv3690iDgdfdty5Ytz6xfVVVV8oP0iIiIiN4kjjy85S5evPjMu92oq6vL75BTnVTnfr/uvuXk5ODy5cvPzGNnZyd62vP7hCMPREQEcOShumLwQETVCoMHIiICGDxUV5y2REREREREkjB4ICIiIiIiSRg8EBERERGRJAweiIiIiIhIEgYPREREREQkCYMHIiIiIiKShMEDERERERFJwuCBiIiIiIgkYfBARERERESSqFR1B4iIniSTVXUPiIiIqCIceSAiIiIiIkkYPBARERERkSQMHoiIiIiISBIGD0REREREJAmDByIiIiIikoTBAxERERERScLggYiIiIiIJGHwQEREREREkjB4ICIiIiIiSRg8EBERERGRJAweiIiIiIhIEgYPREREREQkCYMHomoiKioKMpkMaWlpz83r6uoKV1dXUZpMJsPcuXMlt5ORkfFiHSUiufyHD7E0ZCE8XTuiTYtm6N+3Fw4e2C+pbE5ODmZNnwaXDs5wbuWIIX4D8EtyktK8yUknMcRvAJxbOcKlgzNmTZ+GnJwcUZ5r1zLh2MRO6Sa1T0REz6NS1R0gosoLDw+v6i4QEYCJE77A+XNnETBxEho0sMDB/bGYNiUQQmkpvH18Kyz3+PFjjBk5DPfv38PUoBkwMDDErp3bMW7sKGzYFInWbZzkedNSU/DZJ6PRqbMLVq4JR25uDlaGLcOYkcOwc/d3UFNTE9X98aAh8O7hI0qr36DBqz1wInpvMXggegs1btz4tbdRUFAATU3N194O0dvq+E/HkHzyBBYvXY7u/31Zd3Jui+s3riNs+VJ4dfdGzZo1lZb9/rtvkH7pL/zf9mg4Nm8BAGjj5IyP+vbCiuWh2B79jTxv2LKlqN/AAstWrIaKStmfbXPzehg6+GPs3fMt+g/0E9VtamoKB8fmr+GIiYg4bYnojfrzzz/x8ccfw8TEBOrq6qhfvz78/f1RWFgoz3P//n18+umnMDIygqGhIfr27Yvr16+L6lE2bUmZ5ORkdOjQARoaGjAzM0NQUBCKiooU8llYWMDHxwd79uxBixYtoKGhgXnz5gEAsrKyMHbsWNSrVw9qamqwtLTEvHnzUFxcLC+fkZEBmUyGZcuWISwsDJaWlqhduzbatWuH5OTkFzxbRNVbQnwctLS00MWrmyi9d+++uHXzJs7+fqbiskfiYWFpKQ8cAEBFRQU+vj1x7uzvyM7OBgBkZ2fj/Lmz8PHtJQ8cAKB5i5ZoYGGBI0fiX/FRERE9G0ceiN6QM2fOoGPHjjAyMkJwcDBsbGxw48YN7Nu3D48fP5bnGzVqFHr06IEdO3bg33//xZQpUzB48GAkJCRUqr0//vgDHh4esLCwQFRUFLS0tBAeHo4dO3YozX/q1ClcuHABM2fOhKWlJWrVqoWsrCw4OTmhRo0amD17NqysrJCUlIQFCxYgIyMDkZGRojrWrVsHe3t7rFy5EgAwa9YseHt74/Lly9DV1a3cCSOq5tLTL8GyoZXoSz0A2NjZle2/dAnNW7RUXvbSJbRs1Uoh3ca2rOzf6ZdgYmKC9Et/AQBs/6vzSba2dvjt9CmF9M2bvsLqlSugolIT9o0aY/iIUXB196jcwRERVYDBA9EbEhgYCBUVFaSkpKBOnTry9EGDBonydevWDatXr5a/zs3NxdSpU5GVlYW6detKbi84OBiCICAhIQEmJiYAgB49eqBp06ZK89+8eRN//PEHbG1t5WmffPIJ7ty5g/Pnz6N+/foAAA8PD2hqamLy5MmYMmWKaAqVtrY2YmNj5VM1zMzM4OTkhIMHD2LgwIGS+070NsjLy0O9evUU0ssD5bt3855ZVkdJQC0vm5cn+ldZ8K2jq4e8vP+1oaamhg/79Ufb9u1hZFQHWTduYOeObQj4YhzmzFuAvv0+knhkREQV47QlojcgPz8fx44dQ//+/UWBgzI9e/YUvXZwcAAAXLlypVJtJiYmwsPDQx44AEDNmjUxYMAApfkdHBxEgQMAxMbGws3NDWZmZiguLpZv3bt3BwAcO3ZMlL9Hjx6iOd7P63thYSHu3bsn2p6cwkVU3clksmftffGyT++rIO+TddSpY4zZ8+ajq1d3tGzVGt4+vti8ZRvsGzXGqhXLRFMNiYheFIMHojfgzp07KCkpUXqV8mmGhoai1+rq6gDKFjBXRk5OjtKRiopGL0xNTRXSsrOzERMTA1VVVdHWpEkTAMDt27dfqu8hISHQ1dUVbaFLQp5/cETVgJ6e+Mp/ubt37wJQPlrwZNm7Esrq6umVpSvJe+9u3nOnA6qqqsKrW3fk5eXhaiUvQBARKcNpS0RvgIGBAWrWrInMzMw31qahoSGysrIU0pWlAcqvghoZGcHBwQELFy5UWsbMzOyl+hgUFITAwEBRmlBT/aXqJHpTbGxscfBALIqLi0XrHi79VbZOwdrGpsKy1ra2uPTfeoYnPV3W2sb2v/SL6NTZRZz30l+wshGPFiojCGX/1qjx7JEQIiIpOPJA9AZoamrCxcUF33zzjcLV+tfFzc0NR44ckd+1BQBKSkqwa9cuyXX4+Pjg3LlzsLKyQuvWrRW2lw0e1NXVoaOjI9rKRyuIqjt3T0/k5+cjPu6wKD3mh+9Rx9gYzRwcKyzr4eGJy//8g9+fuCNTcXEx9sfuQzMHRxgbl003NDExQdNmDtgfG4OSkhJ53t/P/IaMy5fh4dnlmX0sKirCj4cOQF9fHx/U57MeiOjlceSB6A0JCwtDx44d4ezsjGnTpsHa2hrZ2dnYt28fNmzY8MrbmzlzJvbt2wd3d3fMnj0bWlpaWLduHR4+fCi5juDgYMTFxaF9+/YYP3487Ozs8OjRI2RkZODAgQOIiIiQNBWL6F3UsZML2rbvgIXBc/HwwQN8UL8+Dh3YjxM/H8eiJaHy9T9zZk1HzA97EXsoDmZm5gCA3n37IXrnDkyZGICAiZNgYGiIXdE7cCXjMjZsEt/FbELgZHwyegQmBwZgwEA/5ObkYNWK5bC2sUXvPh/K84UuCUFxcTGat2gJIyMjZGXdwM7t23DxzwsIXhBS4TMniIgqg8ED0Rvi6OiIlJQUzJkzB0FBQbh//z7q1q0Ld3d3hSfEvgpNmzZFfHw8Jk2ahKFDh0JfXx9DhgzBhx9+iDFjxkiqw9TUFGlpaZg/fz5CQ0ORmZkJbW1tWFpaolu3btDX13/l/SZ6m6xYuQZrVq9A+NrVuHs3D5aWDbE4NAzdvXvI85SWlKKkpARC+fwhlN0ZaePXUVixPBSLFy3Ao0cFsLNvhHURG0VPlwbKHh63dv1XCF+7GuM/+wQaGpro7OKKiZOnin53WNvY4Nvdu3BwfywePnwALa1aaNqsGdZ/9TXad+j4+k8GEb0XZMKTv82IiKrYI94QhoiIAGjwEne1xDUPREREREQkCYMHIiIiIiKShMEDERERERFJwuCBiIiIiIgkYfBARERERESSMHggIiIiIiJJGDwQEREREZEkDB6IiIiIiEgSBg9ERERERCQJgwciIiIiIpKEwQMREREREUnC4IGIiIiIiCRh8EBERERERJIweCAiIiIiIkkYPBARERERkSQMHoiIiIiISBIGD0REREREJAmDByIiIiIikoTBAxERERERScLggYiIiIiIJGHwQEREREREkjB4ICIiIiIiSRg8EBERERGRJAweiIiIiIhIEgYPREREREQkCYMHIiIiIiKShMEDERERERFJwuCBiIiIiIgkYfDwCu3YsQMrV66s6m5I5urqCldX16ruxmuVkZEBmUyGZcuWVXVXAABHjx6FTCbD0aNHX0v9L/OeDhs2DLVr135uvvz8fMydO/e1HQNRdZX/8CGWhiyEp2tHtGnRDP379sLBA/sllc3JycGs6dPg0sEZzq0cMcRvAH5JTlKaNznpJIb4DYBzK0e4dHDGrOnTkJOTo5Bv7aoV+HzcWHi6dYJjEzvMmj6twvbjD/+IoYMGolM7J3Rs2xp+A/ohZt9eSX0nInqSSlV34F2yY8cOnDt3DhMmTKjqrkgSHh5e1V2gV+xNvKf5+fmYN28eALzzwSfRkyZO+ALnz51FwMRJaNDAAgf3x2LalEAIpaXw9vGtsNzjx48xZuQw3L9/D1ODZsDAwBC7dm7HuLGjsGFTJFq3cZLnTUtNwWefjEanzi5YuSYcubk5WBm2DGNGDsPO3d9BTU1Nnnfr1i2wtbWDq5s79u75rsL2v9/zLebOmgHPLl4Y/cmnkEGGfT/sxcygL5F3Jw9Dhg57JeeHiN4PDB7eY40bN67qLtArxveU6PU4/tMxJJ88gcVLl6N7Dx8AgJNzW1y/cR1hy5fCq7s3atasqbTs9999g/RLf+H/tkfDsXkLAEAbJ2d81LcXViwPxfbob+R5w5YtRf0GFli2YjVUVMr+RJub18PQwR9j755v0X+gnzxvUsop1KhRNoEgdt++Cvu+d893MDMzR2jYSnn+9h074eLFC9i3dw+DByKqFE5bqoRbt25hzJgx+OCDD6Curo46deqgQ4cOiI+Ph6urK/bv348rV65AJpPJt3Lz5s2Ds7MzDAwMoKOjg5YtW+Lrr7+GIAjyPCNHjoSBgQHy8/MV2nZ3d0eTJk3kr7/55hs4OztDV1cXWlpaaNiwIUaMGFGp43l6isuTU3zCwsJgaWmJ2rVro127dkhOTpZcryAIMDExwWeffSZPKykpgb6+PmrUqIHs7Gx5elhYGFRUVJCXlydPS0tLQ8+ePWFgYAANDQ20aNECu3fvVmgnKysLY8eORb169aCmpgZLS0vMmzcPxcXFz+xfUVERhg4ditq1ayM2Nlbe5/DwcDRv3hyamprQ19dHv3798M8//yics6ZNmyI1NRWdOnWSn/vFixejtLRUlPfPP/9Et27doKWlBSMjI3zyySe4f/++5PNYLioqCjKZDImJifj0009hZGQEQ0ND9O3bF9evX1fo39OjAZmZmejXrx+0tbWhp6eHQYMGITU1FTKZDFFRUQrtpaenw9vbG7Vr18YHH3yASZMmobCwEEDZz0idOnUAlP1Ml/+cDxs2DMCzPyNEb7OE+DhoaWmhi1c3UXrv3n1x6+ZNnP39TMVlj8TDwtJSHjgAgIqKCnx8e+Lc2d/lvxOzs7Nx/txZ+Pj2kgcOANC8RUs0sLDAkSPiz1F5IPA8Kioq0NTSEuWXyWSoVas21NTVJdVBRFSOwUMlDBkyBHv37sXs2bNx+PBhbNq0CZ6ensjJyUF4eDg6dOiAunXrIikpSb6Vy8jIwNixY7F7927s2bMHffv2xRdffIH58+fL8wQEBODOnTvYsWOHqN0//vgDiYmJ8i/jSUlJGDBgABo2bIjo6Gjs378fs2fPfu6XZqnWrVuHuLg4rFy5Etu3b8fDhw/h7e2Nu3fvSiovk8ng7u4u+sKYlpaGvLw8aGho4MiRI/L0+Ph4tGrVCnp6egCAxMREdOjQAXl5eYiIiMAPP/yA5s2bY8CAAaIvullZWXBycsKPP/6I2bNn4+DBgxg5ciRCQkIwevToCvuWl5cHLy8vHD58GMeOHYOPT9kVxLFjx2LChAnw9PTE3r17ER4ejvPnz6N9+/aiYKe87UGDBmHw4MHYt28funfvjqCgIGzbtk2eJzs7Gy4uLjh37hzCw8OxdetWPHjwAJ9//rmkc6jMqFGjoKqqih07dmDp0qU4evQoBg8e/MwyDx8+hJubGxITE7FkyRLs3r0bJiYmGDBggNL8RUVF6NmzJzw8PPDDDz9gxIgRWLFiBZYsWQIAMDU1xaFDhwCUBbvlP+ezZs0C8OzPCNHbLD39EiwbWom+1AOAjZ1d2f5Llyoue+kSbG3tFNJt/kv7O/3Sf/n+AgDY2inmtbW1w9//7a+sjwcNweV//sbGDeuRm5uLO3dysSXya1z44zyGDq/cRSciIk5bqoQTJ05g1KhRoi+nvXr1kv9fT08P6urqaNu2rULZyMhI+f9LS0vh6uoKQRCwatUqzJo1CzKZDA4ODnBxccG6deswatQoef61a9dCR0cH/v7+AICTJ09CEARERERAV1dXnq/86u/L0tbWRmxsrHwI3szMDE5OTjh48CAGDhwoqQ5PT09ER0fj33//xQcffID4+HjY29vD1tYW8fHx8PPzQ1FREX766ScEBATIy40bNw5NmjRBQkKC/I+0l5cXbt++jenTp8Pf3x81atTA3LlzcefOHZw/fx7169cHAHh4eEBTUxOTJ0/GlClTFKbwZGRkoEePHgCA5ORkNGjQQP7/jRs3Yvny5QgMDJTn79SpE2xtbREWFib/8gyULXw8cOAAnJyc5Md69OhR7NixQ/4erVixArdu3cLp06fh6OgIAOjevTu6du2Kq1evSnwnxLp164bVq1fLX+fm5mLq1KnIyspC3bp1lZbZsmUL0tPTcfDgQXTrVnbFtGvXrsjPz8eGDRsU8j9+/Bjz5s3DRx99BKDsnKalpWHHjh2YPXs21NXV0apVKwBAvXr1FH7Wn/cZIXpb5eXloV69egrp5b+D797Ne2ZZnSd+VyuU/W/ktfxfXSV5dXT1RCO0leHZpSvCVq3BrOnTsHb1SgCAhoYG5i9ajK5e3V+oTiJ6f3HkoRKcnJwQFRWFBQsWIDk5GUVFRZLLJiQkwNPTE7q6uqhZsyZUVVUxe/Zs5OTk4ObNm/J8AQEB+O2333DixAkAwL1797B161b5NBsAaNOmDQCgf//+2L17N65du/YKjxLo0aOHaO6ug4MDAODKlSuS6/D09AQA+ehDXFwcunTpAk9PT8TFxQEoG0F5+PChPG96ejr+/PNPDBo0CABQXFws37y9vXHjxg1cvHgRABAbGws3NzeYmZmJ8nXvXvaH8NixY6L+nDp1Cm3btoWJiQlOnDghDxzK65LJZBg8eLCorrp168LR0VHhrkJ169aVBw5PnqMnz09iYiKaNGkiDxzK+fn54UX17NlToU3g2e/LsWPHoK2tLQ8cyn388cdK88tkMvj6ihd+Pn1sz1LZz0hhYSHu3bsn2sqnSBFVN09ORVWy98XLPr2vgrzPbr9iJ47/hOlfToG7ZxeER2zEhk2R6PPhR5g9Iwh7v694oTURkTIMHiph165dGDp0KDZt2oR27drBwMAA/v7+yMrKema5lJQUdO3aFQCwceNGnDhxAqmpqZgxYwYAoKCgQJ63V69esLCwwLp16wCUzXd/+PChaP1A586dsXfvXhQXF8Pf3x/16tVD06ZNsXPnzldynIaGhqLX6v/NiX2yn8/ToEEDWFlZIT4+Hvn5+UhKSpIHD5mZmbh48SLi4+OhqamJ9u3bA4B8etDkyZOhqqoq2saNGwcAuH37tjxvTEyMQr7ydSHl+crFxcUhOzsbo0aNkk+RKpednS1fp/F0fcnJyQp1PX1+ys/Rk+cnJydH6WhARSMEUrzI+5KTkwMTExOFdGVpAKClpQUNDQ2Fdh49eiSpj5X9jISEhEBXV1e0hS4JkdQW0Zukp6f8yn/5dE5lowVPlr0roazuf7+blOW9dzfvmW1URBAEzJk1Ha1at0HwghB06NQZbdu1x7TpM9Hd2weLFy5Qus6OiKginLZUCUZGRli5ciVWrlyJq1evYt++fZg2bRpu3rwpnweuTHR0NFRVVREbGyv6YrZ3716FvDVq1MBnn32G6dOnY/ny5QgPD4eHhwfsnpoD26tXL/Tq1QuFhYVITk5GSEgI/Pz8YGFhgXbt2r2yY34Z5fPmjx07Jp+qpa2tDTMzM8TFxSE+Ph6dOnWSfwk2MjICAAQFBaFv375K6yw/D0ZGRnBwcMDChQuV5jMzMxO9njJlCtLT0+Hv7y8PusoZGRlBJpPh+PHj8r48SVna8xgaGir9wvy8QPNVMzQ0REpKyhvrR2U/I0FBQaKpYgAg1OQCTqp+bGxscfBALIqLi0XrHi79VbYOwdrGpsKy1ra2uKRkvcLTZa1tbP9Lv4hOnV3EeS/9Bav/9ldGzu3buHXrFvr1d1DY16RpM8Ts24vr16/B2rri/hMRPYkjDy+ofv36+Pzzz9GlSxecOnUKgOLV53IymQwqKiqiqUAFBQXYunWr0rpHjRoFNTU1DBo0CBcvXnzmIlt1dXW4uLjI5+SfPn36ZQ7rlfL09ER2djZWrlyJtm3bQltbG0BZUPH9998jNTVVPmUJKAsMbGxscObMGbRu3VrpVl6Hj48Pzp07BysrK6X5ng4eatSoga+++goBAQEYNmwY1q9fL9/n4+MDQRBw7do1pXU1a9as0sfu5uaG8+fP48wZ8R1Ynl4M/7q5uLjg/v37OHjwoCg9Ojr6heuUOhKl7DOirC4dHR3R9iLBGtHr5u7pifz8fMTHHRalx/zwPeoYG6OZg2MFJQEPD09c/ucf/P7EHZmKi4uxP3Yfmjk4wti4bCTQxMQETZs5YH9sDEpKSuR5fz/zGzIuX4aHZ5dK91tHVxfq6ur4/cxvCvt+P3MaNWrUQB2jOpWul4jeXxx5kOju3btwc3ODn58f7O3toa2tjdTUVBw6dEh+lbxZs2bYs2cP1q9fj1atWqFGjRpo3bo1evTogbCwMPj5+WHMmDHIycnBsmXLKvySpKenB39/f6xfvx4NGjRQmIM+e/ZsZGZmwsPDA/Xq1UNeXh5WrVoFVVVVuLi4KK2zKri7u0Mmk+Hw4cPyh4oBZUHF0KFD5f9/0oYNG9C9e3d4eXlh2LBhMDc3R25uLi5cuIBTp07hm2/K7oceHByMuLg4tG/fHuPHj4ednR0ePXqEjIwMHDhwABEREUoXNy5fvhza2toYN24cHjx4gClTpqBDhw4YM2YMhg8fjrS0NHTu3Bm1atXCjRs38PPPP6NZs2b49NNPK3XsEyZMwObNm9GjRw8sWLAAJiYm2L59O/7888/KnsaXMnToUKxYsQKDBw/GggULYG1tjYMHD+LHH38EIP1Wj0/S1tZGgwYN8MMPP8DDwwMGBgYwMjKCvr7+cz8jRG+rjp1c0LZ9BywMnouHDx7gg/r1cejAfpz4+TgWLQmVXxyaM2s6Yn7Yi9hDcTAzMwcA9O7bD9E7d2DKxAAETJwEA0ND7IregSsZl7FhU6SonQmBk/HJ6BGYHBiAAQP9kJuTg1UrlsPaxha9+3woypuWmoI7ubkAgNLSEly/fg1xP5aN8LVq4wQDAwOoqamh/0A/bN0SiRlBU+HVzRs1a9RAQkI8DuyPRZ8P+8mnSxERScHgQSINDQ04Oztj69atyMjIQFFREerXr48vv/wSU6dOBVC22Pn8+fOYPn067t69C0EQIAgC3N3dsXnzZixZsgS+vr4wNzfH6NGjYWxsjJEjRyptb8CAAVi/fj0+/fRThS94zs7OSEtLw5dffolbt25BT08PrVu3RkJCguhZEFXN0NAQzZs3x+nTp0VBQvn/y/c/yc3NDSkpKVi4cCEmTJiAO3fuwNDQEI0bN0b//v3l+UxNTZGWlob58+cjNDQUmZmZ0NbWhqWlJbp16wZ9ff0K+zV37lzUrl0bU6ZMwYMHDzBv3jxs2LABbdu2xYYNGxAeHo7S0lKYmZmhQ4cOCoujpahbty6OHTuGgIAAfPrpp9DS0kKfPn2wdu3aN3r3oVq1aiEhIQETJkzA1KlTIZPJ0LVrV4SHh8Pb21th/YdUX3/9NaZMmYKePXuisLAQQ4cOxYYNG577GSF6m61YuQZrVq9A+NrVuHs3D5aWDbE4NAzdvXvI85SWlKKkpET0DB81NTVs/DoKK5aHYvGiBXj0qAB29o2wLmKj6OnSQNnD49au/wrha1dj/GefQENDE51dXDFx8lTR06UBYP26NUhL/d+0xLTUFPnrTZH/BwMnZwBA4OSpaNjQCt/ujsb0aVMglJai3gf1ETRjNj78qD+IiCpDJjz5G46qjUmTJmH9+vX4999/lS7QJXoZixYtwsyZM3H16lWlIzRV6dGreVwJERG95TR4ibta4ttSzSQnJ+Ovv/5CeHg4xo4dy8CBXtratWsBAPb29igqKkJCQgJWr16NwYMHV7vAgYiIiKo3Bg/VTLt27aClpQUfHx8sWLDghep4esj8aTKZTLR4uzIEQRAt5FOmZs2aL3w/8vfJmzqXWlpaWLFiBTIyMlBYWCifSjRz5syXqpeIiIjeP5y29A6ysLB45kO9XFxcFB58JtXRo0fh5ub2zDyRkZGv7GnX77KoqCgMHz78mXkSExPh6ur6ZjpUTXDaEhERAZy2VF0xeHgHnT179plP6dXW1lZ4boRU9+/flz/luSKWlpacbiVBTk4OLl++/Mw8dnZ28tvTvi8YPBAREcDgobpi8EBE1QqDByIiAhg8VFd8SBwREREREUnC4IGIiIiIiCRh8EBERERERJIweCAiIiIiIkkYPBARERERkSQMHoiIiIiISBIGD0REREREJAmDByIiIiIikoTBAxERERERScInTBMREVUjhYWFCAkJQVBQENTV1au6O0REIgweiIiIqpF79+5BV1cXd+/ehY6OTlV3h4hIhNOWiIiIiIhIEgYPREREREQkCYMHIiIiIiKShMEDERFRNaKuro45c+ZwsTQRVUtcME1ERERERJJw5IGIiIiIiCRh8EBERERERJIweCAiIiIiIkkYPBARERERkSQMHoiI6J0UFRUFmUyGjIyMqu5KtXL06FHIZDIcPXr0ldXJc030/mDwQERE76QePXogKSkJpqamVd0VIqJ3hkpVd4CIiOh1qFOnDurUqVPV3SAieqdw5IGIiN5JT0+lOX36NHx8fGBsbAx1dXWYmZmhR48eyMzMrFS9v/zyC3x9fWFoaAgNDQ1YWVlhwoQJ8v3p6ekYPnw4bGxsoKWlBXNzc/j6+uLs2bOiesqnD+3cuRMzZsyAmZkZdHR04OnpiYsXLyq0e+jQIXh4eEBXVxdaWlpo1KgRQkJCRHnS0tLQs2dPGBgYQENDAy1atMDu3bslHZfUssnJyejQoQM0NDRgZmaGoKAgFBUVSWqDiN5+HHkgIqJ33sOHD9GlSxdYWlpi3bp1MDExQVZWFhITE3H//n3J9fz444/w9fVFo0aNEBYWhvr16yMjIwOHDx+W57l+/ToMDQ2xePFi1KlTB7m5udiyZQucnZ1x+vRp2NnZieqcPn06OnTogE2bNuHevXv48ssv4evriwsXLqBmzZoAgK+//hqjR4+Gi4sLIiIiYGxsjL/++gvnzp2T15OYmIhu3brB2dkZERER0NXVRXR0NAYMGID8/HwMGzaswuOSWvaPP/6Ah4cHLCwsEBUVBS0tLYSHh2PHjh2SzyERveUEIiKid1BkZKQAQLh8+bKQlpYmABD27t37UnVaWVkJVlZWQkFBgeQyxcXFwuPHjwUbGxth4sSJ8vTExEQBgODt7S3Kv3v3bgGAkJSUJAiCINy/f1/Q0dEROnbsKJSWllbYjr29vdCiRQuhqKhIlO7j4yOYmpoKJSUlonYTExMrXXbAgAGCpqamkJWVJTo+e3t7+bkmoncbpy0REdE7z9raGvr6+vjyyy8RERGBP/74o9J1/PXXX/j7778xcuRIaGhoVJivuLgYixYtQuPGjaGmpgYVFRWoqanh0qVLuHDhgkL+nj17il47ODgAAK5cuQIAOHnyJO7du4dx48ZBJpMpbTM9PR1//vknBg0aJO9D+ebt7Y0bN24onQpV2bKJiYnw8PCAiYmJvHzNmjUxYMCACs8HEb1bGDwQEdE7T1dXF8eOHUPz5s0xffp0NGnSBGZmZpgzZ47k+fq3bt0CANSrV++Z+QIDAzFr1iz07t0bMTEx+OWXX5CamgpHR0cUFBQo5Dc0NBS9VldXBwB5XintZmdnAwAmT54MVVVV0TZu3DgAwO3bt1+6bE5ODurWratQh7I0Ino3cc0DERG9F5o1a4bo6GgIgoDff/8dUVFRCA4OhqamJqZNm/bc8uV3bnreAutt27bB398fixYtEqXfvn0benp6le63lHaNjIwAAEFBQejbt6/SPE+vtXiRsoaGhsjKylLYryyNiN5NDB6IiOi9IpPJ4OjoiBUrViAqKgqnTp2SVM7W1hZWVlbYvHkzAgMD5SMEyup/et/+/ftx7do1WFtbV7q/7du3h66uLiIiIjBw4EClU5fs7OxgY2ODM2fOKAQtz1OZsm5ubti3bx+ys7PlU5dKSkqwa9euSrVJRG8vBg9ERPTOi42NRXh4OHr37o2GDRtCEATs2bMHeXl56NKli+R61q1bB19fX7Rt2xYTJ05E/fr1cfXqVfz444/Yvn07AMDHxwdRUVGwt7eHg4MDfv31V4SGhj53ulNFateujeXLl2PUqFHw9PTE6NGjYWJigvT0dJw5cwZr164FAGzYsAHdu3eHl5cXhg0bBnNzc+Tm5uLChQs4deoUvvnmmwrbkFp25syZ2LdvH9zd3TF79mxoaWlh3bp1ePjw4QsdGxG9fRg8EBHRO8/GxgZ6enpYunQprl+/DjU1NdjZ2SEqKgpDhw6VXI+Xlxd++uknBAcHY/z48Xj06BHq1asnWvS8atUqqKqqIiQkBA8ePEDLli2xZ88ezJw584X7P3LkSJiZmWHJkiUYNWoUBEGAhYWFqO9ubm5ISUnBwoULMWHCBNy5cweGhoZo3Lgx+vfv/8z6pZZt2rQp4uPjMWnSJAwdOhT6+voYMmQIPvzwQ4wZM+aFj4+I3h4yQRCEqu4EERERERFVf7zbEhERERERScJpS0RE9N4rLS1FaWnpM/OoqPBPJhERRx6IiOi9N2LECIVnHDy9ERER1zwQEREhIyOjwoeolWvduvUb6g0RUfXF4IGIiIiIiCThtCUiIiIiIpKEwQMREREREUnC4IGIiIiIiCRh8EBERERERJIweCAiIiIiIkkYPBARERERkSQMHoiIiIiISJL/B6vo5LDlKZkgAAAAAElFTkSuQmCC\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation between is_canceled and other features\n",
    "\n",
    "fig = plt.figure(figsize=(5,10))\n",
    "ax = sns.heatmap(hotel_booking.corr()[['is_canceled']].abs().sort_values('is_canceled', ascending=False), annot = True, annot_kws = {\"size\":12}, cmap='Blues', cbar=False)\n",
    "ax.set_title('Correlation Coefficient for Cancellation Status', fontsize=18)\n",
    "ax.tick_params(axis = \"both\", labelsize = 12)\n",
    "y_min, y_max = ax.get_ylim()\n",
    "ax.set_ylim(top=y_max+1);"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Canceled Vs Not Canceled By average lead time\n",
    "\n",
    "plt.title(\"Average Lead Time of Bookings per Status\", fontsize = 20, pad = 10)\n",
    "sns.barplot(x=hotel_booking['is_canceled'], y=hotel_booking['lead_time'])\n",
    "plt.xlabel(\"Booking Status\", fontsize = 12, labelpad = 5)\n",
    "plt.ylabel(\"Average Lead Time (days)\", fontsize = 12, labelpad = 5)\n",
    "plt.xticks(ticks=[0, 1], labels=['Not Canceled', 'Canceled']);"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Canceled Vs Not Canceled By average total number of special requests\n",
    "\n",
    "plt.title(\"Average Total Number of Special Requests of Bookings per Status\", fontsize = 20, pad = 10)\n",
    "sns.barplot(x=hotel_booking['is_canceled'], y=hotel_booking['total_of_special_requests'])\n",
    "plt.xlabel(\"Booking Status\", fontsize = 12, labelpad = 5)\n",
    "plt.ylabel(\"Average Total Number of Special Requests\", fontsize = 12, labelpad = 5)\n",
    "plt.xticks(ticks=[0, 1], labels=['Not Canceled', 'Canceled']);"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Canceled Vs Not Canceled By average number of car parking spaces required\n",
    "\n",
    "plt.title(\"Average Number of Car Parking Spaces Required per Status\", fontsize = 20, pad = 10)\n",
    "sns.barplot(x=hotel_booking['is_canceled'], y=hotel_booking['required_car_parking_spaces'])\n",
    "plt.xlabel(\"Booking Status\", fontsize = 12, labelpad = 5)\n",
    "plt.ylabel(\"Average Number of Car Parking Spaces Required\", fontsize = 12, labelpad = 5)\n",
    "plt.xticks(ticks=[0, 1], labels=['Not Canceled', 'Canceled']);"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Canceled Vs Not Canceled By average number of booking changes\n",
    "\n",
    "plt.title(\"Average Number of Booking changes per Status\", fontsize = 20, pad = 10)\n",
    "sns.barplot(x=hotel_booking['is_canceled'], y=hotel_booking['booking_changes'])\n",
    "plt.xlabel(\"Booking Status\", fontsize = 12, labelpad = 5)\n",
    "plt.ylabel(\"Average Number of Booking changes\", fontsize = 12, labelpad = 5)\n",
    "plt.xticks(ticks=[0, 1], labels=['Not Canceled', 'Canceled']);"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Canceled Vs Not Canceled By average number of previous cancellations\n",
    "\n",
    "plt.title(\"Average Number of previous cancellations per Status\", fontsize = 20, pad = 10)\n",
    "sns.barplot(x=hotel_booking['is_canceled'], y=hotel_booking['previous_cancellations'])\n",
    "plt.xlabel(\"Booking Status\", fontsize = 12, labelpad = 5)\n",
    "plt.ylabel(\"Average Number of previous cancellations\", fontsize = 12, labelpad = 5)\n",
    "plt.xticks(ticks=[0, 1], labels=['Not Canceled', 'Canceled']);"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Percentage of booking cancelled for room types\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.title(\"Percentage of Bookings Canceled for Each Reserved Room Type\", fontsize = 20, pad = 10)\n",
    "sns.barplot(x=hotel_booking['reserved_room_type'], y=hotel_booking['is_canceled'])\n",
    "plt.xlabel(\"Room Type\", fontsize = 12, labelpad = 5)\n",
    "plt.ylabel(\"Percentage of Bookings Canceled\", fontsize = 12, labelpad = 5);"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Percentage of booking cancelled for deposit types\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.title(\"Percentage of Bookings Canceled for Each Deposit Type\", fontsize = 20, pad = 10)\n",
    "sns.barplot(x=hotel_booking['deposit_type'], y=hotel_booking['is_canceled'])\n",
    "plt.xlabel(\"Deposit Type\", fontsize = 12, labelpad = 5)\n",
    "plt.ylabel(\"Percentage of Bookings Canceled\", fontsize = 12, labelpad = 5);"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Clustering"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Create the clusters to customer segmentation and improve the features of the dataset**"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "outputs": [],
   "source": [
    "X = hotel_booking.drop(columns=['is_canceled', 'reservation_status', 'agent', 'company', 'country', 'reservation_status_date', 'arrival_date_full'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "outputs": [
    {
     "data": {
      "text/plain": "   lead_time  arrival_date_year arrival_date_month  arrival_date_week_number  \\\n0        342               2015               July                        27   \n1        737               2015               July                        27   \n2          7               2015               July                        27   \n3         13               2015               July                        27   \n4         14               2015               July                        27   \n\n   arrival_date_day_of_month  stays_in_weekend_nights  stays_in_week_nights  \\\n0                          1                        0                     0   \n1                          1                        0                     0   \n2                          1                        0                     1   \n3                          1                        0                     1   \n4                          1                        0                     2   \n\n   adults  children  babies  ... assigned_room_type booking_changes  \\\n0       2         0       0  ...                  C               3   \n1       2         0       0  ...                  C               4   \n2       1         0       0  ...                  C               0   \n3       1         0       0  ...                  A               0   \n4       2         0       0  ...                  A               0   \n\n  deposit_type  days_in_waiting_list  customer_type   adr  \\\n0   No Deposit                     0      Transient   0.0   \n1   No Deposit                     0      Transient   0.0   \n2   No Deposit                     0      Transient  75.0   \n3   No Deposit                     0      Transient  75.0   \n4   No Deposit                     0      Transient  98.0   \n\n  required_car_parking_spaces total_of_special_requests  is_h1  \\\n0                           0                         0      1   \n1                           0                         0      1   \n2                           0                         0      1   \n3                           0                         0      1   \n4                           0                         1      1   \n\n  status_minus_arrival_date  \n0                         0  \n1                         0  \n2                         1  \n3                         1  \n4                         2  \n\n[5 rows x 27 columns]",
      "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>lead_time</th>\n      <th>arrival_date_year</th>\n      <th>arrival_date_month</th>\n      <th>arrival_date_week_number</th>\n      <th>arrival_date_day_of_month</th>\n      <th>stays_in_weekend_nights</th>\n      <th>stays_in_week_nights</th>\n      <th>adults</th>\n      <th>children</th>\n      <th>babies</th>\n      <th>...</th>\n      <th>assigned_room_type</th>\n      <th>booking_changes</th>\n      <th>deposit_type</th>\n      <th>days_in_waiting_list</th>\n      <th>customer_type</th>\n      <th>adr</th>\n      <th>required_car_parking_spaces</th>\n      <th>total_of_special_requests</th>\n      <th>is_h1</th>\n      <th>status_minus_arrival_date</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>342</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>C</td>\n      <td>3</td>\n      <td>No Deposit</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>737</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>C</td>\n      <td>4</td>\n      <td>No Deposit</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>C</td>\n      <td>0</td>\n      <td>No Deposit</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>75.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>13</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>A</td>\n      <td>0</td>\n      <td>No Deposit</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>75.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>14</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>A</td>\n      <td>0</td>\n      <td>No Deposit</td>\n      <td>0</td>\n      <td>Transient</td>\n      <td>98.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 27 columns</p>\n</div>"
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### K-Prototypes Clusterning\n",
    "\n",
    "* Since our dataset have both numerical and categorical features, K-Prototype clustering is the most appropriate model to create clusters"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "outputs": [],
   "source": [
    "# Creating a loop to try our different number of clusters:\n",
    "# kproto_costs = []\n",
    "# for n in range(2, 8):\n",
    "#     #do the clustering\n",
    "#     kproto = KPrototypes(n_clusters=n, init='Huang', verbose=2, n_jobs=3, random_state=42, n_jobs=3)\n",
    "#     clusters = kproto.fit_predict(X, categorical=[2, 10, 11, 12, 13, 16, 17, 19, 21, 25])\n",
    "#     print(f'cost: {kproto.cost_}')\n",
    "#     print(f'iter: {kproto.n_iter_}')\n",
    "#\n",
    "#     # get cost\n",
    "#     cost = kproto.cost_\n",
    "#\n",
    "#     #gather them\n",
    "#     kproto_costs.append((n, cost))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "outputs": [],
   "source": [
    "# costs_df = pd.DataFrame(kproto_costs, columns=['n', 'cost'])\n",
    "# costs_df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "outputs": [],
   "source": [
    "# plt.figure(figsize=(10,5))\n",
    "# plt.plot(costs_df['n'], costs_df['cost'])\n",
    "# plt.xticks([2,3,4,5,6,7])\n",
    "# plt.xlabel('Number of Clusters', fontsize=12)\n",
    "# plt.ylabel('Cost', size=12)\n",
    "# plt.title('Cost per Number of K-Prototype Cluster', size=20);"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "outputs": [],
   "source": [
    "# Creating the 4 customer clusters:\n",
    "# kproto = KPrototypes(n_clusters=4, init='Huang', verbose=0, random_state=42, n_jobs=3)\n",
    "# clusters = kproto.fit_predict(X, categorical=[2, 10, 11, 12, 13, 16, 17, 19, 21, 25])\n",
    "# print(f'cost: {kproto.cost_}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "outputs": [],
   "source": [
    "# pickle.dump(kproto, open('Models/kproto.pkl', 'wb'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "outputs": [],
   "source": [
    "kproto_loded = pickle.load(open('Models/kproto.pkl', 'rb'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cost: 436604819.63317287\n"
     ]
    }
   ],
   "source": [
    "clusters = kproto_loded.fit_predict(X, categorical=[2, 10, 11, 12, 13, 16, 17, 19, 21, 25])\n",
    "print(f'cost: {kproto_loded.cost_}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "outputs": [],
   "source": [
    "# Creating a \"clusters\" feature in our dataset:\n",
    "hotel_booking['clusters'] = clusters"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "outputs": [],
   "source": [
    "# Renaming the clusters:\n",
    "hotel_booking['clusters'] = hotel_booking['clusters'].map({0:'Type 1', 1:'Type 2', 2:'Type 3', 3:'Type 4'})"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "outputs": [
    {
     "data": {
      "text/plain": "   is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n0            0        342               2015               July   \n1            0        737               2015               July   \n2            0          7               2015               July   \n3            0         13               2015               July   \n4            0         14               2015               July   \n\n   arrival_date_week_number  arrival_date_day_of_month  \\\n0                        27                          1   \n1                        27                          1   \n2                        27                          1   \n3                        27                          1   \n4                        27                          1   \n\n   stays_in_weekend_nights  stays_in_week_nights  adults  children  ...  \\\n0                        0                     0       2         0  ...   \n1                        0                     0       2         0  ...   \n2                        0                     1       1         0  ...   \n3                        0                     1       1         0  ...   \n4                        0                     2       2         0  ...   \n\n   customer_type   adr required_car_parking_spaces total_of_special_requests  \\\n0      Transient   0.0                           0                         0   \n1      Transient   0.0                           0                         0   \n2      Transient  75.0                           0                         0   \n3      Transient  75.0                           0                         0   \n4      Transient  98.0                           0                         1   \n\n  reservation_status  reservation_status_date  is_h1  arrival_date_full  \\\n0          Check-Out               2015-07-01      1         2015-07-01   \n1          Check-Out               2015-07-01      1         2015-07-01   \n2          Check-Out               2015-07-02      1         2015-07-01   \n3          Check-Out               2015-07-02      1         2015-07-01   \n4          Check-Out               2015-07-03      1         2015-07-01   \n\n  status_minus_arrival_date clusters  \n0                         0   Type 1  \n1                         0   Type 1  \n2                         1   Type 3  \n3                         1   Type 3  \n4                         2   Type 3  \n\n[5 rows x 35 columns]",
      "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>is_canceled</th>\n      <th>lead_time</th>\n      <th>arrival_date_year</th>\n      <th>arrival_date_month</th>\n      <th>arrival_date_week_number</th>\n      <th>arrival_date_day_of_month</th>\n      <th>stays_in_weekend_nights</th>\n      <th>stays_in_week_nights</th>\n      <th>adults</th>\n      <th>children</th>\n      <th>...</th>\n      <th>customer_type</th>\n      <th>adr</th>\n      <th>required_car_parking_spaces</th>\n      <th>total_of_special_requests</th>\n      <th>reservation_status</th>\n      <th>reservation_status_date</th>\n      <th>is_h1</th>\n      <th>arrival_date_full</th>\n      <th>status_minus_arrival_date</th>\n      <th>clusters</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>342</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>...</td>\n      <td>Transient</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-01</td>\n      <td>1</td>\n      <td>2015-07-01</td>\n      <td>0</td>\n      <td>Type 1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>737</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>...</td>\n      <td>Transient</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-01</td>\n      <td>1</td>\n      <td>2015-07-01</td>\n      <td>0</td>\n      <td>Type 1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>7</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>Transient</td>\n      <td>75.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-02</td>\n      <td>1</td>\n      <td>2015-07-01</td>\n      <td>1</td>\n      <td>Type 3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>13</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>Transient</td>\n      <td>75.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-02</td>\n      <td>1</td>\n      <td>2015-07-01</td>\n      <td>1</td>\n      <td>Type 3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>14</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>...</td>\n      <td>Transient</td>\n      <td>98.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Check-Out</td>\n      <td>2015-07-03</td>\n      <td>1</td>\n      <td>2015-07-01</td>\n      <td>2</td>\n      <td>Type 3</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 35 columns</p>\n</div>"
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotel_booking.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "outputs": [],
   "source": [
    "# Save the dataframe to csv for the future usages\n",
    "# hotel_booking.to_csv('DATA/hotel_booking_with_cluster.csv', index=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "hotel_booking = pd.read_csv('DATA/hotel_booking_with_cluster.csv')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "   is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n0            0        342               2015               July   \n1            0        737               2015               July   \n2            0          7               2015               July   \n3            0         13               2015               July   \n4            0         14               2015               July   \n\n   arrival_date_week_number  arrival_date_day_of_month  \\\n0                        27                          1   \n1                        27                          1   \n2                        27                          1   \n3                        27                          1   \n4                        27                          1   \n\n   stays_in_weekend_nights  stays_in_week_nights  adults  children  ...  \\\n0                        0                     0       2         0  ...   \n1                        0                     0       2         0  ...   \n2                        0                     1       1         0  ...   \n3                        0                     1       1         0  ...   \n4                        0                     2       2         0  ...   \n\n   customer_type   adr required_car_parking_spaces total_of_special_requests  \\\n0      Transient   0.0                           0                         0   \n1      Transient   0.0                           0                         0   \n2      Transient  75.0                           0                         0   \n3      Transient  75.0                           0                         0   \n4      Transient  98.0                           0                         1   \n\n  reservation_status  reservation_status_date  is_h1  arrival_date_full  \\\n0          Check-Out               2015-07-01      1         2015-07-01   \n1          Check-Out               2015-07-01      1         2015-07-01   \n2          Check-Out               2015-07-02      1         2015-07-01   \n3          Check-Out               2015-07-02      1         2015-07-01   \n4          Check-Out               2015-07-03      1         2015-07-01   \n\n  status_minus_arrival_date clusters  \n0                         0   Type 1  \n1                         0   Type 1  \n2                         1   Type 3  \n3                         1   Type 3  \n4                         2   Type 3  \n\n[5 rows x 35 columns]",
      "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>is_canceled</th>\n      <th>lead_time</th>\n      <th>arrival_date_year</th>\n      <th>arrival_date_month</th>\n      <th>arrival_date_week_number</th>\n      <th>arrival_date_day_of_month</th>\n      <th>stays_in_weekend_nights</th>\n      <th>stays_in_week_nights</th>\n      <th>adults</th>\n      <th>children</th>\n      <th>...</th>\n      <th>customer_type</th>\n      <th>adr</th>\n      <th>required_car_parking_spaces</th>\n      <th>total_of_special_requests</th>\n      <th>reservation_status</th>\n      <th>reservation_status_date</th>\n      <th>is_h1</th>\n      <th>arrival_date_full</th>\n      <th>status_minus_arrival_date</th>\n      <th>clusters</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>342</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>...</td>\n      <td>Transient</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-01</td>\n      <td>1</td>\n      <td>2015-07-01</td>\n      <td>0</td>\n      <td>Type 1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>737</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>...</td>\n      <td>Transient</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-01</td>\n      <td>1</td>\n      <td>2015-07-01</td>\n      <td>0</td>\n      <td>Type 1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>7</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>Transient</td>\n      <td>75.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-02</td>\n      <td>1</td>\n      <td>2015-07-01</td>\n      <td>1</td>\n      <td>Type 3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>13</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>Transient</td>\n      <td>75.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Check-Out</td>\n      <td>2015-07-02</td>\n      <td>1</td>\n      <td>2015-07-01</td>\n      <td>1</td>\n      <td>Type 3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>14</td>\n      <td>2015</td>\n      <td>July</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>...</td>\n      <td>Transient</td>\n      <td>98.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Check-Out</td>\n      <td>2015-07-03</td>\n      <td>1</td>\n      <td>2015-07-01</td>\n      <td>2</td>\n      <td>Type 3</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 35 columns</p>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotel_booking.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "         is_canceled                                                          \\\n               count unique top freq      mean       std  min  25%  50%  75%   \nclusters                                                                       \nType 1       14390.0    NaN NaN  NaN  0.644128  0.478793  0.0  0.0  1.0  1.0   \nType 2       32053.0    NaN NaN  NaN  0.451377  0.497638  0.0  0.0  0.0  1.0   \nType 3       49625.0    NaN NaN  NaN  0.249390  0.432665  0.0  0.0  0.0  0.0   \nType 4       23318.0    NaN NaN  NaN  0.347671  0.476241  0.0  0.0  0.0  1.0   \n\n          ... status_minus_arrival_date                                    \\\n          ...                    unique top freq      mean       std  min   \nclusters  ...                                                               \nType 1    ...                       NaN NaN  NaN  3.037526  1.962887  0.0   \nType 2    ...                       NaN NaN  NaN  3.364896  2.370746  0.0   \nType 3    ...                       NaN NaN  NaN  2.825310  1.982018  0.0   \nType 4    ...                       NaN NaN  NaN  3.242645  2.147328  0.0   \n\n                              \n          25%  50%  75%  max  \nclusters                      \nType 1    2.0  2.0  4.0  9.0  \nType 2    1.0  3.0  5.0  9.0  \nType 3    1.0  2.0  4.0  9.0  \nType 4    1.0  3.0  4.0  9.0  \n\n[4 rows x 374 columns]",
      "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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"10\" halign=\"left\">is_canceled</th>\n      <th>...</th>\n      <th colspan=\"10\" halign=\"left\">status_minus_arrival_date</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>count</th>\n      <th>unique</th>\n      <th>top</th>\n      <th>freq</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>...</th>\n      <th>unique</th>\n      <th>top</th>\n      <th>freq</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n    <tr>\n      <th>clusters</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Type 1</th>\n      <td>14390.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.644128</td>\n      <td>0.478793</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.037526</td>\n      <td>1.962887</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>Type 2</th>\n      <td>32053.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.451377</td>\n      <td>0.497638</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.364896</td>\n      <td>2.370746</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>5.0</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>Type 3</th>\n      <td>49625.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.249390</td>\n      <td>0.432665</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.825310</td>\n      <td>1.982018</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>Type 4</th>\n      <td>23318.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.347671</td>\n      <td>0.476241</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.242645</td>\n      <td>2.147328</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>9.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>4 rows × 374 columns</p>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_analysis_df = hotel_booking.groupby(hotel_booking['clusters']).describe(include='all')\n",
    "cluster_analysis_df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "0    75166\n1    44220\nName: is_canceled, dtype: int64"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotel_booking['is_canceled'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "clusters  is_canceled\nType 1    1               9269\n          0               5121\nType 2    0              17585\n          1              14468\nType 3    0              37249\n          1              12376\nType 4    0              15211\n          1               8107\nName: is_canceled, dtype: int64"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotel_booking.groupby(hotel_booking['clusters'])['is_canceled'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "* **Type 1** Cluster has the lowest cancellation percentage (20.96%)\n",
    "* **Type 2** Cluster has the most cancellation percentage (32.71%)\n",
    "* **Type 1** Cluster has the lowest not cancelled percentage (6.81%)\n",
    "* **Type 3** Cluster has the most not cancelled percentage (49.55%)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "clusters  arrival_date_month\nType 1    September             2569\n          October               2214\n          July                  2196\n          August                1771\n          June                  1639\n          May                   1393\n          April                  725\n          November               689\n          December               479\n          March                  426\n          January                162\n          February               127\nType 2    July                  4798\n          August                4699\n          June                  4333\n          May                   4255\n          April                 3706\n          October               2519\n          September             2250\n          March                 1880\n          December              1137\n          February              1104\n          November               789\n          January                583\nType 3    March                 6726\n          February              6422\n          October               4999\n          January               4987\n          November              4867\n          April                 4635\n          December              4525\n          September             3182\n          May                   3023\n          August                2279\n          June                  2113\n          July                  1867\nType 4    August                5124\n          July                  3800\n          May                   3120\n          June                  2854\n          September             2507\n          April                 2023\n          October               1428\n          March                  762\n          December               639\n          November               449\n          February               415\n          January                197\nName: arrival_date_month, dtype: int64"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotel_booking.groupby(hotel_booking['clusters'])['arrival_date_month'].value_counts().T"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "* **September** is most common traveling month for **Type 1** Cluster\n",
    "* **July** is most common traveling month for **Type 2** Cluster\n",
    "* **March** is most common traveling month for **Type 3** Cluster\n",
    "* **August** is most common traveling month for **Type 4** Cluster"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Model Preparation"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "hotel_booking = pd.get_dummies(hotel_booking, columns=['arrival_date_month', 'meal', 'market_segment', 'distribution_channel', 'reserved_room_type', 'assigned_room_type', 'deposit_type', 'customer_type', 'clusters'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "outputs": [
    {
     "data": {
      "text/plain": "   is_canceled  lead_time  arrival_date_year  arrival_date_week_number  \\\n0            0        342               2015                        27   \n1            0        737               2015                        27   \n2            0          7               2015                        27   \n3            0         13               2015                        27   \n4            0         14               2015                        27   \n\n   arrival_date_day_of_month  stays_in_weekend_nights  stays_in_week_nights  \\\n0                          1                        0                     0   \n1                          1                        0                     0   \n2                          1                        0                     1   \n3                          1                        0                     1   \n4                          1                        0                     2   \n\n   adults  children  babies  ... deposit_type_Non Refund  \\\n0       2         0       0  ...                       0   \n1       2         0       0  ...                       0   \n2       1         0       0  ...                       0   \n3       1         0       0  ...                       0   \n4       2         0       0  ...                       0   \n\n   deposit_type_Refundable  customer_type_Contract  customer_type_Group  \\\n0                        0                       0                    0   \n1                        0                       0                    0   \n2                        0                       0                    0   \n3                        0                       0                    0   \n4                        0                       0                    0   \n\n   customer_type_Transient customer_type_Transient-Party clusters_Type 1  \\\n0                        1                             0               1   \n1                        1                             0               1   \n2                        1                             0               0   \n3                        1                             0               0   \n4                        1                             0               0   \n\n   clusters_Type 2  clusters_Type 3  clusters_Type 4  \n0                0                0                0  \n1                0                0                0  \n2                0                1                0  \n3                0                1                0  \n4                0                1                0  \n\n[5 rows x 88 columns]",
      "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>is_canceled</th>\n      <th>lead_time</th>\n      <th>arrival_date_year</th>\n      <th>arrival_date_week_number</th>\n      <th>arrival_date_day_of_month</th>\n      <th>stays_in_weekend_nights</th>\n      <th>stays_in_week_nights</th>\n      <th>adults</th>\n      <th>children</th>\n      <th>babies</th>\n      <th>...</th>\n      <th>deposit_type_Non Refund</th>\n      <th>deposit_type_Refundable</th>\n      <th>customer_type_Contract</th>\n      <th>customer_type_Group</th>\n      <th>customer_type_Transient</th>\n      <th>customer_type_Transient-Party</th>\n      <th>clusters_Type 1</th>\n      <th>clusters_Type 2</th>\n      <th>clusters_Type 3</th>\n      <th>clusters_Type 4</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>342</td>\n      <td>2015</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>737</td>\n      <td>2015</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>7</td>\n      <td>2015</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>13</td>\n      <td>2015</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>14</td>\n      <td>2015</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 88 columns</p>\n</div>"
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotel_booking.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "X = hotel_booking.drop(columns=['is_canceled','reservation_status', 'agent', 'company', 'country',\n",
    "                     'reservation_status_date', 'arrival_date_full'])\n",
    "y = hotel_booking['is_canceled']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, stratify=y, test_size=0.15)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Modeling"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Baseline Model"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "dummy = DummyClassifier(strategy=\"most_frequent\")\n",
    "dummy.fit(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 94,
   "outputs": [
    {
     "data": {
      "text/plain": "DummyClassifier(strategy='most_frequent')",
      "text/html": "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DummyClassifier(strategy=&#x27;most_frequent&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyClassifier</label><div class=\"sk-toggleable__content\"><pre>DummyClassifier(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div></div></div>"
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "outputs": [],
   "source": [
    "dummy_training_score = dummy.score(X_train, y_train)\n",
    "dummy_testing_score = dummy.score(X_test, y_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score (Baseline): 0.6296044462839236\n",
      "Testing Score (Baseline): 0.6296068796068796\n"
     ]
    }
   ],
   "source": [
    "print(f'Training Score (Baseline): {dummy_training_score}')\n",
    "print(f'Testing Score (Baseline): {dummy_testing_score}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Logistic Regression Model"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "outputs": [],
   "source": [
    "# lg = LogisticRegression(random_state=42)\n",
    "# lg.get_params()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Baseline Logistic Regression**"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "outputs": [],
   "source": [
    "# lg.fit(X_train, y_train)\n",
    "#\n",
    "# lg_training_accuaracy = lg.score(X_train, y_train)\n",
    "# lg_testing_accuaracy = lg.score(X_test, y_test)\n",
    "# lg_cross_val_score = cross_val_score(lg, X, y, cv=StratifiedKFold(shuffle=True)).mean()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "outputs": [],
   "source": [
    "# print(f'Baseline Logistic Regression Training Accuracy: {lg_training_accuaracy}')\n",
    "# print(f'Baseline Logistic Regression Testing Accuracy: {lg_testing_accuaracy}')\n",
    "# print(f'Baseline Logistic Regression Cross-val-score: {lg_cross_val_score}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "outputs": [],
   "source": [
    "# pickle.dump(lg, open('Models/logistic_regression_baseline.pkl', 'wb'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "outputs": [],
   "source": [
    "lg_baseline_loded = pickle.load(open('Models/logistic_regression_baseline.pkl', 'rb'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\conda\\env\\FDM\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "E:\\conda\\env\\FDM\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "E:\\conda\\env\\FDM\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "E:\\conda\\env\\FDM\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "E:\\conda\\env\\FDM\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "lg_training_accuaracy = lg_baseline_loded.score(X_train, y_train)\n",
    "lg_testing_accuaracy = lg_baseline_loded.score(X_test, y_test)\n",
    "lg_cross_val_score = cross_val_score(lg_baseline_loded, X, y, cv=StratifiedKFold(shuffle=True)).mean()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline Logistic Regression Training Accuracy: 0.7824947279213228\n",
      "Baseline Logistic Regression Testing Accuracy: 0.7824436006254188\n",
      "Baseline Logistic Regression Cross-val-score: 0.7685406968327779\n"
     ]
    }
   ],
   "source": [
    "print(f'Baseline Logistic Regression Training Accuracy: {lg_training_accuaracy}')\n",
    "print(f'Baseline Logistic Regression Testing Accuracy: {lg_testing_accuaracy}')\n",
    "print(f'Baseline Logistic Regression Cross-val-score: {lg_cross_val_score}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Hyperparameter Tuned (Grid Searched) Logistic Regression**"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "outputs": [],
   "source": [
    "# lg_gs_grid = {\n",
    "#     'penalty': ['none', 'l1', 'l2'],\n",
    "#     'C': [3.0, 3.5, 4.0],\n",
    "#     'max_iter': [100, 200, 300],\n",
    "#     'solver': ['liblinear', 'sag', 'saga']\n",
    "# }\n",
    "#\n",
    "# lg_gs = GridSearchCV(estimator=LogisticRegression(random_state=42),\n",
    "#                      param_grid=lg_gs_grid,\n",
    "#                      scoring='accuracy',\n",
    "#                      cv=5,\n",
    "#                      n_jobs=3,\n",
    "#                      verbose=2)\n",
    "#\n",
    "# lg_gs.fit(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "outputs": [],
   "source": [
    "# lg_gs_training_accuaracy = lg_gs.score(X_train, y_train)\n",
    "# lg_gs_testing_accuaracy = lg_gs.score(X_test, y_test)\n",
    "# lg_gs_cross_val_score = cross_val_score(lg_gs.best_estimator_, X, y, cv=StratifiedKFold(shuffle=True)).mean()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "outputs": [],
   "source": [
    "# print(f'Best Logistic Regression Training Accuracy: {lg_gs_training_accuaracy}')\n",
    "# print(f'Best Logistic Regression Testing Accuracy: {lg_gs_testing_accuaracy}')\n",
    "# print(f'Best Logistic Regression Cross-val-score: {lg_gs_cross_val_score}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "outputs": [],
   "source": [
    "# pickle.dump(lg_gs, open('Models/logistic_regression_gs.pkl', 'wb'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "outputs": [],
   "source": [
    "lg_gs_loded = pickle.load(open('Models/logistic_regression_gs.pkl', 'rb'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "outputs": [],
   "source": [
    "lg_gs_training_accuaracy = lg_gs_loded.score(X_train, y_train)\n",
    "lg_gs_testing_accuaracy = lg_gs_loded.score(X_test, y_test)\n",
    "lg_gs_cross_val_score = cross_val_score(lg_gs_loded.best_estimator_, X, y, cv=StratifiedKFold(shuffle=True)).mean()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Logistic Regression Training Accuracy: 0.8135556475295138\n",
      "Best Logistic Regression Testing Accuracy: 0.8123743578289033\n",
      "Best Logistic Regression Cross-val-score: 0.8130350273563101\n"
     ]
    }
   ],
   "source": [
    "print(f'Best Logistic Regression Training Accuracy: {lg_gs_training_accuaracy}')\n",
    "print(f'Best Logistic Regression Testing Accuracy: {lg_gs_testing_accuaracy}')\n",
    "print(f'Best Logistic Regression Cross-val-score: {lg_gs_cross_val_score}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Decision Tree Classification Model"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Baseline Decision Tree Classification**"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "outputs": [],
   "source": [
    "# dt = DecisionTreeClassifier(random_state=42)\n",
    "# dt.get_params()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "outputs": [],
   "source": [
    "# dt.fit(X_train, y_train)\n",
    "#\n",
    "# dt_training_accuaracy = dt.score(X_train, y_train)\n",
    "# dt_testing_accuaracy = dt.score(X_test, y_test)\n",
    "# dt_cross_val_score = cross_val_score(dt, X, y, cv=StratifiedKFold(shuffle=True)).mean()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "outputs": [],
   "source": [
    "# print(f'Baseline Decision Tree Classifire Training Accuracy: {dt_training_accuaracy}')\n",
    "# print(f'Baseline Decision Tree Classifire Testing Accuracy: {dt_testing_accuaracy}')\n",
    "# print(f'Baseline Decision Tree Classifire Cross-val-score: {dt_cross_val_score}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "outputs": [],
   "source": [
    "# pickle.dump(dt, open('Models/decision_tree_classifire_baseline.pkl', 'wb'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "outputs": [],
   "source": [
    "dt_baseline_loded = pickle.load(open('Models/decision_tree_classifire_baseline.pkl', 'rb'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "outputs": [],
   "source": [
    "dt_training_accuaracy = dt_baseline_loded.score(X_train, y_train)\n",
    "dt_testing_accuaracy = dt_baseline_loded.score(X_test, y_test)\n",
    "dt_cross_val_score = cross_val_score(dt_baseline_loded, X, y, cv=StratifiedKFold(shuffle=True)).mean()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline Decision Tree Classifire Training Accuracy: 0.9988174776798912\n",
      "Baseline Decision Tree Classifire Testing Accuracy: 0.9395242349787805\n",
      "Baseline Decision Tree Classifire Cross-val-score: 0.9363660718633369\n"
     ]
    }
   ],
   "source": [
    "print(f'Baseline Decision Tree Classifire Training Accuracy: {dt_training_accuaracy}')\n",
    "print(f'Baseline Decision Tree Classifire Testing Accuracy: {dt_testing_accuaracy}')\n",
    "print(f'Baseline Decision Tree Classifire Cross-val-score: {dt_cross_val_score}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Hyperparameter Tuned (Grid Searched) Decision Tree Classification**"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "outputs": [],
   "source": [
    "# dt_gs_grid = {\n",
    "#     'max_depth':[None, 2, 5, 10],\n",
    "#     'max_features' : [0.5, 0.7, 0.9],\n",
    "#     'min_samples_split': [2, 5, 10, 25, 30],\n",
    "#     'min_samples_leaf': [1, 2, 3]\n",
    "# }\n",
    "#\n",
    "# dt_gs = GridSearchCV(estimator=DecisionTreeClassifier(random_state=42),\n",
    "#                      param_grid=dt_gs_grid,\n",
    "#                      scoring='accuracy',\n",
    "#                      cv=5,\n",
    "#                      n_jobs=3,\n",
    "#                      verbose=2)\n",
    "#\n",
    "# dt_gs.fit(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "outputs": [],
   "source": [
    "# dt_gs_training_accuaracy = dt_gs.score(X_train, y_train)\n",
    "# dt_gs_testing_accuaracy = dt_gs.score(X_test, y_test)\n",
    "# dt_gs_cross_val_score = cross_val_score(dt_gs.best_estimator_, X, y, cv=StratifiedKFold(shuffle=True)).mean()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "outputs": [],
   "source": [
    "# print(f'Best Decision Tree Classifire Training Accuracy: {dt_gs_training_accuaracy}')\n",
    "# print(f'Best Decision Tree Classifire Testing Accuracy: {dt_gs_testing_accuaracy}')\n",
    "# print(f'Best Decision Tree Classifire Cross-val-score: {dt_gs_cross_val_score}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "outputs": [],
   "source": [
    "# pickle.dump(dt_gs, open('Models/decision_tree_classifire_gs.pkl', 'wb'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "outputs": [],
   "source": [
    "dt_gs_loded = pickle.load(open('Models/decision_tree_classifire_gs.pkl', 'rb'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "outputs": [],
   "source": [
    "dt_gs_training_accuaracy = dt_gs_loded.score(X_train, y_train)\n",
    "dt_gs_testing_accuaracy = dt_gs_loded.score(X_test, y_test)\n",
    "dt_gs_cross_val_score = cross_val_score(dt_gs_loded.best_estimator_, X, y, cv=StratifiedKFold(shuffle=True)).mean()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Decision Tree Classifire Training Accuracy: 0.9608388024990638\n",
      "Best Decision Tree Classifire Testing Accuracy: 0.9343310252401161\n",
      "Best Decision Tree Classifire Cross-val-score: 0.9396579640998223\n"
     ]
    }
   ],
   "source": [
    "print(f'Best Decision Tree Classifire Training Accuracy: {dt_gs_training_accuaracy}')\n",
    "print(f'Best Decision Tree Classifire Testing Accuracy: {dt_gs_testing_accuaracy}')\n",
    "print(f'Best Decision Tree Classifire Cross-val-score: {dt_gs_cross_val_score}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Bagging Classification"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Baseline Bagging Classification**"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "outputs": [],
   "source": [
    "# bc = BaggingClassifier(random_state=42)\n",
    "# bc.get_params()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "outputs": [],
   "source": [
    "# bc.fit(X_train, y_train)\n",
    "#\n",
    "# bc_training_accuaracy = bc.score(X_train, y_train)\n",
    "# bc_testing_accuaracy = bc.score(X_test, y_test)\n",
    "# bc_cross_val_score = cross_val_score(bc, X, y, cv=StratifiedKFold(shuffle=True)).mean()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "outputs": [],
   "source": [
    "# print(f'Baseline Bagging Classifire Training Accuracy: {bc_training_accuaracy}')\n",
    "# print(f'Baseline Bagging Classifire Testing Accuracy: {bc_testing_accuaracy}')\n",
    "# print(f'Baseline Bagging Classifire Cross-val-score: {bc_cross_val_score}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "outputs": [],
   "source": [
    "# pickle.dump(bc, open('Models/bagging_classifire_baseline.pkl', 'wb'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "outputs": [],
   "source": [
    "bc_baseline_loded = pickle.load(open('Models/bagging_classifire_baseline.pkl', 'rb'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "outputs": [],
   "source": [
    "bc_training_accuaracy = bc_baseline_loded.score(X_train, y_train)\n",
    "bc_testing_accuaracy = bc_baseline_loded.score(X_test, y_test)\n",
    "bc_cross_val_score = cross_val_score(bc_baseline_loded, X, y, cv=StratifiedKFold(shuffle=True)).mean()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline Bagging Classifire Training Accuracy: 0.9944519994481562\n",
      "Baseline Bagging Classifire Testing Accuracy: 0.9585660040205495\n",
      "Baseline Bagging Classifire Cross-val-score: 0.9572311607893095\n"
     ]
    }
   ],
   "source": [
    "print(f'Baseline Bagging Classifire Training Accuracy: {bc_training_accuaracy}')\n",
    "print(f'Baseline Bagging Classifire Testing Accuracy: {bc_testing_accuaracy}')\n",
    "print(f'Baseline Bagging Classifire Cross-val-score: {bc_cross_val_score}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Hyperparameter Tuned (Grid Searched) Bagging Classification**"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "outputs": [],
   "source": [
    "# bc_gs_grid = {\n",
    "#     'n_estimators': [20, 30, 45, 50],\n",
    "#     'max_features': [0.5, 0.8, 1, 1.5]\n",
    "# }\n",
    "#\n",
    "# bc_gs = GridSearchCV(estimator=BaggingClassifier(random_state=42),\n",
    "#                      param_grid=bc_gs_grid,\n",
    "#                      scoring='accuracy',\n",
    "#                      cv=5,\n",
    "#                      n_jobs=3,\n",
    "#                      verbose=2)\n",
    "#\n",
    "# bc_gs.fit(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "outputs": [],
   "source": [
    "# bc_gs_training_accuaracy = bc_gs.score(X_train, y_train)\n",
    "# bc_gs_testing_accuaracy = bc_gs.score(X_test, y_test)\n",
    "# bc_gs_cross_val_score = cross_val_score(bc_gs.best_estimator_, X, y, cv=StratifiedKFold(shuffle=True)).mean()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "outputs": [],
   "source": [
    "# print(f'Best Bagging Classifire Training Accuracy: {bc_gs_training_accuaracy}')\n",
    "# print(f'Best Bagging Classifire Testing Accuracy: {bc_gs_testing_accuaracy}')\n",
    "# print(f'Best Bagging Classifire Cross-val-score: {bc_gs_cross_val_score}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "outputs": [],
   "source": [
    "# pickle.dump(bc_gs, open('Models/bagging_classifire_gs.pkl', 'wb'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "outputs": [],
   "source": [
    "bc_gs_loded = pickle.load(open('Models/bagging_classifire_gs.pkl', 'rb'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "outputs": [],
   "source": [
    "bc_gs_training_accuaracy = bc_gs_loded.score(X_train, y_train)\n",
    "bc_gs_testing_accuaracy = bc_gs_loded.score(X_test, y_test)\n",
    "bc_gs_cross_val_score = cross_val_score(bc_gs_loded.best_estimator_, X, y, cv=StratifiedKFold(shuffle=True)).mean()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Bagging Classifire Training Accuracy: 0.9987287885058831\n",
      "Best Bagging Classifire Testing Accuracy: 0.9640384185838732\n",
      "Best Bagging Classifire Cross-val-score: 0.9616621842721932\n"
     ]
    }
   ],
   "source": [
    "print(f'Best Bagging Classifire Training Accuracy: {bc_gs_training_accuaracy}')\n",
    "print(f'Best Bagging Classifire Testing Accuracy: {bc_gs_testing_accuaracy}')\n",
    "print(f'Best Bagging Classifire Cross-val-score: {bc_gs_cross_val_score}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Random Forest Classification"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Baseline random Forest Classification**"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "outputs": [],
   "source": [
    "# rf = RandomForestClassifier(random_state=42)\n",
    "# rf.get_params()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "outputs": [],
   "source": [
    "# rf.fit(X_train, y_train)\n",
    "#\n",
    "# rf_training_accuaracy = rf.score(X_train, y_train)\n",
    "# rf_testing_accuaracy = rf.score(X_test, y_test)\n",
    "# rf_cross_val_score = cross_val_score(rf, X, y, cv=StratifiedKFold(shuffle=True)).mean()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "outputs": [],
   "source": [
    "# print(f'Baseline Random Forest Classifire Training Accuracy: {rf_training_accuaracy}')\n",
    "# print(f'Baseline Random Forest Classifire Classifire Testing Accuracy: {rf_testing_accuaracy}')\n",
    "# print(f'Baseline Random Forest Classifire Classifire Cross-val-score: {rf_cross_val_score}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "outputs": [],
   "source": [
    "# pickle.dump(rf, open('Models/random_forest_classifire_baseline.pkl', 'wb'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "outputs": [],
   "source": [
    "rf_baseline_loded = pickle.load(open('Models/random_forest_classifire_baseline.pkl', 'rb'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "outputs": [],
   "source": [
    "rf_training_accuaracy = rf_baseline_loded.score(X_train, y_train)\n",
    "rf_testing_accuaracy = rf_baseline_loded.score(X_test, y_test)\n",
    "rf_cross_val_score = cross_val_score(rf_baseline_loded, X, y, cv=StratifiedKFold(shuffle=True)).mean()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline Random Forest Classifire Training Accuracy: 0.9988076233272236\n",
      "Baseline Random Forest Classifire Testing Accuracy: 0.9407527362072816\n",
      "Baseline Random Forest Classifire Cross-val-score: 0.9360812835267301\n"
     ]
    }
   ],
   "source": [
    "print(f'Baseline Random Forest Classifire Training Accuracy: {rf_training_accuaracy}')\n",
    "print(f'Baseline Random Forest Classifire Testing Accuracy: {rf_testing_accuaracy}')\n",
    "print(f'Baseline Random Forest Classifire Cross-val-score: {rf_cross_val_score}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Hyperparameter Tuned (Grid Searched) Random Forest Classification**"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "outputs": [],
   "source": [
    "# rf_gs_grid = {\n",
    "#     'n_estimators': [100, 150, 200],\n",
    "#     'max_features': [40, 45, 50, 55],\n",
    "#     'max_depth': [None, 8, 13, 15]\n",
    "# }\n",
    "#\n",
    "# rf_gs = GridSearchCV(estimator=RandomForestClassifier(random_state=42),\n",
    "#                      param_grid=rf_gs_grid,\n",
    "#                      scoring='accuracy',\n",
    "#                      cv=5,\n",
    "#                      n_jobs=3,\n",
    "#                      verbose=2)\n",
    "#\n",
    "# rf_gs.fit(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "outputs": [],
   "source": [
    "# rf_gs_training_accuaracy = rf_gs.score(X_train, y_train)\n",
    "# rf_gs_testing_accuaracy = rf_gs.score(X_test, y_test)\n",
    "# rf_gs_cross_val_score = cross_val_score(rf_gs.best_estimator_, X, y, cv=StratifiedKFold(shuffle=True)).mean()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "outputs": [],
   "source": [
    "# print(f'Best Random Forest Classifire Training Accuracy: {rf_gs_training_accuaracy}')\n",
    "# print(f'Best Random Forest Classifire Testing Accuracy: {rf_gs_testing_accuaracy}')\n",
    "# print(f'Best Random Forest Classifire Cross-val-score: {rf_gs_cross_val_score}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "outputs": [],
   "source": [
    "# pickle.dump(rf_gs, open('Models/random_forest_classifire_gs.pkl', 'wb'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "rf_gs_loded = pickle.load(open('Models/random_forest_classifire_gs.pkl', 'rb'))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "outputs": [],
   "source": [
    "rf_gs_training_accuaracy = rf_gs_loded.score(X_train, y_train)\n",
    "rf_gs_testing_accuaracy = rf_gs_loded.score(X_test, y_test)\n",
    "rf_gs_cross_val_score = cross_val_score(rf_gs_loded.best_estimator_, X, y, cv=StratifiedKFold(shuffle=True)).mean()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Random Forest Classifire Training Accuracy: 0.9224166814481957\n",
      "Best Random Forest Classifire Testing Accuracy: 0.9185838731293277\n",
      "Best Random Forest Classifire Cross-val-score: 0.9182316168812831\n"
     ]
    }
   ],
   "source": [
    "print(f'Best Random Forest Classifire Training Accuracy: {rf_gs_training_accuaracy}')\n",
    "print(f'Best Random Forest Classifire Testing Accuracy: {rf_gs_testing_accuaracy}')\n",
    "print(f'Best Random Forest Classifire Cross-val-score: {rf_gs_cross_val_score}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Neural Network - Sequential Model"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "outputs": [],
   "source": [
    "# X.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "outputs": [],
   "source": [
    "ss = StandardScaler()\n",
    "X_train_sc = ss.fit_transform(X_train)\n",
    "X_test_sc = ss.transform(X_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "outputs": [],
   "source": [
    "# model = Sequential()\n",
    "# model.add(Dense(64, activation='relu', input_shape=(81,)))\n",
    "# model.add(Dropout(0.18))\n",
    "# model.add(Dense(32, activation='relu'))\n",
    "# model.add(Dropout(0.15))\n",
    "# model.add(Dense(1, activation='sigmoid'))\n",
    "# es = EarlyStopping(monitor='val_loss', patience=5)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "outputs": [],
   "source": [
    "# model.compile(loss='binary_crossentropy',\n",
    "#               optimizer='adam',\n",
    "#               metrics=['binary_accuracy'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "outputs": [],
   "source": [
    "# history = model.fit(X_train_sc,\n",
    "#                     y_train,\n",
    "#                     batch_size = 256,\n",
    "#                     validation_data =(X_test_sc, y_test),\n",
    "#                     epochs = 500,\n",
    "#                     verbose = 2,\n",
    "#                     callbacks=[es])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "outputs": [],
   "source": [
    "# train_loss = history.history['loss']\n",
    "# test_loss = history.history['val_loss']\n",
    "#\n",
    "# # Training and Testing loss by epoch\n",
    "#\n",
    "# plt.figure(figsize=(10, 5))\n",
    "# plt.plot(train_loss, label='Training Loss', color='#185fad')\n",
    "# plt.plot(test_loss, label='Testing Loss', color='orange')\n",
    "# plt.title('Training and Testing Loss by Epoch', fontsize = 20)\n",
    "# plt.xlabel('Epoch', fontsize = 11)\n",
    "# plt.ylabel('Binary Crossentropy', fontsize = 11)\n",
    "# plt.legend(fontsize = 11);"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "outputs": [],
   "source": [
    "# # Training and Testing accuracy by epoch\n",
    "#\n",
    "# plt.figure(figsize=(10, 5))\n",
    "# plt.plot(history.history['binary_accuracy'], label='Training accuracy')\n",
    "# plt.plot(history.history['val_binary_accuracy'], label='Testing accuracy')\n",
    "# plt.title('Training and Testing Accurary by Epoch', fontsize = 20)\n",
    "# plt.xlabel('Epoch', fontsize = 12)\n",
    "# plt.ylabel('Binary Accuracy', fontsize = 12)\n",
    "# plt.legend(fontsize = 12);"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "outputs": [],
   "source": [
    "# train_score = model.evaluate(X_train_sc,\n",
    "#                              y_train,\n",
    "#                              verbose=2)\n",
    "# test_score = model.evaluate(X_test_sc,\n",
    "#                             y_test,\n",
    "#                             verbose=2)\n",
    "# labels = model.metrics_names\n",
    "#\n",
    "# print('=' * 50)\n",
    "# print(f'Sequential Model Training Accuracy: {train_score[1]}')\n",
    "# print(f'Sequential Model Testing Accuracy: {test_score[1]}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "outputs": [],
   "source": [
    "# model.save('Models/sequential.h5')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "outputs": [],
   "source": [
    "loded_sequential_model = load_model('Models/sequential.h5')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_3 (Dense)             (None, 64)                5248      \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 32)                0         \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 7,361\n",
      "Trainable params: 7,361\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "loded_sequential_model.summary()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3172/3172 - 29s - loss: 0.0807 - binary_accuracy: 0.9733 - 29s/epoch - 9ms/step\n",
      "560/560 - 1s - loss: 0.0872 - binary_accuracy: 0.9720 - 1s/epoch - 3ms/step\n",
      "==================================================\n",
      "Sequential Model Training Accuracy: 0.9732552766799927\n",
      "Sequential Model Testing Accuracy: 0.9720236659049988\n"
     ]
    }
   ],
   "source": [
    "sq_nn_train_score = loded_sequential_model.evaluate(X_train_sc, y_train, verbose=2)\n",
    "sq_nn_test_score = loded_sequential_model.evaluate(X_test_sc, y_test, verbose=2)\n",
    "\n",
    "print('=' * 50)\n",
    "print(f'Sequential Model Training Accuracy: {sq_nn_train_score[1]}')\n",
    "print(f'Sequential Model Testing Accuracy: {sq_nn_test_score[1]}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Model Comparison\n",
    "\n",
    "**For convenience, only Hyperparameter Tuned (Grid Searched) models are used for comparison**"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "outputs": [
    {
     "data": {
      "text/plain": "                 model  training_accuracy  testing_accuracy\n0             Baseline           0.629604          0.629607\n1  Logistic Regression           0.813556          0.812374\n2        Decision Tree           0.960839          0.934331\n3   Bagging Classifier           0.998729          0.964038\n4        Random Forest           0.922417          0.918584\n5       Neural Network           0.973255          0.972024",
      "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>model</th>\n      <th>training_accuracy</th>\n      <th>testing_accuracy</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Baseline</td>\n      <td>0.629604</td>\n      <td>0.629607</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Logistic Regression</td>\n      <td>0.813556</td>\n      <td>0.812374</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Decision Tree</td>\n      <td>0.960839</td>\n      <td>0.934331</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Bagging Classifier</td>\n      <td>0.998729</td>\n      <td>0.964038</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Random Forest</td>\n      <td>0.922417</td>\n      <td>0.918584</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Neural Network</td>\n      <td>0.973255</td>\n      <td>0.972024</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison_df = pd.DataFrame(data=[('Baseline',dummy_training_score, dummy_testing_score),\n",
    "                                             ('Logistic Regression',lg_gs_training_accuaracy, lg_gs_testing_accuaracy),\n",
    "                                             ('Decision Tree', dt_gs_training_accuaracy, dt_gs_testing_accuaracy),\n",
    "                                             ('Bagging Classifier', bc_gs_training_accuaracy, bc_gs_testing_accuaracy),\n",
    "                                             ('Random Forest', rf_gs_training_accuaracy, rf_gs_testing_accuaracy),\n",
    "                                             ('Neural Network', sq_nn_train_score[1], sq_nn_test_score[1])],\n",
    "                                       columns=['model', 'training_accuracy', 'testing_accuracy'])\n",
    "comparison_df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Baseline**\n",
    "- If we predict 0 - Not cancelled (most frequent value) as the prediction. It will give around 63% accuracy\n",
    "\n",
    "**Logistic Regression**\n",
    "- Model is not overfitting or underfitting (training score and testing score both close together).\n",
    "- Model testing accuracy is low when comparing with other models.\n",
    "\n",
    "**Decision Tree**\n",
    "- Model is slightly overfitting (training score is comparatively higher than testing score).\n",
    "- Model testing accuracy is good, but can not use this model because of overfitting\n",
    "\n",
    "**Bagging Classification**\n",
    "- Model is slightly overfitting (training score is comparatively higher than testing score).\n",
    "- Model testing accuracy is good, but can not use this model because of overfitting\n",
    "\n",
    "**Random Forest Classification**\n",
    "- Model is not overfitting or underfitting (training score and testing score both close together).\n",
    "- Model testing accuracy is good, we can use this model for classification\n",
    "\n",
    "**Neural Network - Sequential Model**\n",
    "- Model is not overfitting or underfitting (training score and testing score both close together).\n",
    "- Model testing accuracy is very high, we can use this model for classification.\n",
    "- Since neural network models are like `Black boxes` it is hard to interpret."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Model Evaluation"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Neural Network - Sequential Model**"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "560/560 [==============================] - 1s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "# Making predictions\n",
    "preds = loded_sequential_model.predict(X_test_sc)\n",
    "preds = preds >= 0.5\n",
    "\n",
    "conf_mat = confusion_matrix(y_test, preds)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 300x300 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(3, 3))\n",
    "ax = sns.heatmap(conf_mat,\n",
    "                 annot=True,\n",
    "                 cmap='Blues',\n",
    "                 cbar=False,\n",
    "                 fmt='d')\n",
    "\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.xlabel(\"Predicted Label\");"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "* True Positive (Predicted cancelled and actually cancelled) - 6181\n",
    "* False Positive (Predicted cancelled and actually not cancelled) - 49\n",
    "* True Negative (Predicted not cancelled and actually not cancelled) - 11226\n",
    "* False Negative (Predicted not cancelled and actually cancelled) - 452"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "outputs": [
    {
     "data": {
      "text/plain": "0    75166\n1    44220\nName: is_canceled, dtype: int64"
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotel_booking['is_canceled'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98     11275\n",
      "           1       0.99      0.93      0.96      6633\n",
      "\n",
      "    accuracy                           0.97     17908\n",
      "   macro avg       0.98      0.96      0.97     17908\n",
      "weighted avg       0.97      0.97      0.97     17908\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, preds))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "* In our dataset we have slightly unbalance classes (Not cancelled - 75116, Cancelled - 44220)\n",
    "* Therefore we can not use `accuracy` as good measure\n",
    "* We have to focus on `recall` and `precision`\n",
    "* In our model `false negatives` are **worse than** `false positives` we should aim for higher `recall`"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Random Forest Classification**"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "preds = rf_gs_loded.predict(X_test)\n",
    "\n",
    "conf_mat = confusion_matrix(y_test, preds)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 300x300 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(3, 3))\n",
    "ax = sns.heatmap(conf_mat,\n",
    "                 annot=True,\n",
    "                 cmap='Blues',\n",
    "                 cbar=False,\n",
    "                 fmt='d')\n",
    "\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.xlabel(\"Predicted Label\");"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "* True Positive (Predicted cancelled and actually cancelled) - 5441\n",
    "* False Positive (Predicted cancelled and actually not cancelled) - 266\n",
    "* True Negative (Predicted not cancelled and actually not cancelled) - 11009\n",
    "* False Negative (Predicted not cancelled and actually cancelled) - 1192"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.98      0.94     11275\n",
      "           1       0.95      0.82      0.88      6633\n",
      "\n",
      "    accuracy                           0.92     17908\n",
      "   macro avg       0.93      0.90      0.91     17908\n",
      "weighted avg       0.92      0.92      0.92     17908\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, preds))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "* In our dataset we have slightly unbalance classes (Not cancelled - 75116, Cancelled - 44220)\n",
    "* Therefore we can not use `accuracy` as good measure\n",
    "* We have to focus on `recall` and `precision`\n",
    "* In our model `false negatives` are **worse than** `false positives` we should aim for higher `recall`"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}