Churn Prediction.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Churn Rate EDA, Balancing, Pre-processing, and ML**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import randint\n",
    "\n",
    "# Data Visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Statistical Analysis and Testing\n",
    "from scipy import stats\n",
    "from scipy.stats import normaltest\n",
    "\n",
    "# Data Sampling and Balancing\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "# Data Preprocessing and Transformation\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.compose import make_column_transformer\n",
    "\n",
    "# Model Selection and Validation\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Machine Learning Models and Algorithms\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Model Evaluation and Metrics\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
    "\n",
    "# Pipeline and Workflow\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``1. Importing Data``"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the [kaggle dataset link](https://www.kaggle.com/datasets/radheshyamkollipara/bank-customer-churn) as well as the data dictionary information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('Customer-Churn-Records.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "      <th>Complain</th>\n",
       "      <th>Satisfaction Score</th>\n",
       "      <th>Card Type</th>\n",
       "      <th>Point Earned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DIAMOND</td>\n",
       "      <td>464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DIAMOND</td>\n",
       "      <td>456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DIAMOND</td>\n",
       "      <td>377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>GOLD</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>GOLD</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave          619    France  Female   42   \n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "3          4    15701354      Boni          699    France  Female   39   \n",
       "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "3       1       0.00              2          0               0   \n",
       "4       2  125510.82              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  Complain  Satisfaction Score Card Type  \\\n",
       "0        101348.88       1         1                   2   DIAMOND   \n",
       "1        112542.58       0         1                   3   DIAMOND   \n",
       "2        113931.57       1         1                   3   DIAMOND   \n",
       "3         93826.63       0         0                   5      GOLD   \n",
       "4         79084.10       0         0                   5      GOLD   \n",
       "\n",
       "   Point Earned  \n",
       "0           464  \n",
       "1           456  \n",
       "2           377  \n",
       "3           350  \n",
       "4           425  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data has 18 columns and 10000 rows\n"
     ]
    }
   ],
   "source": [
    "print(f'The data has {data.shape[1]} columns and {data.shape[0]} rows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RowNumber', 'CustomerId', 'Surname', 'CreditScore', 'Geography',\n",
       "       'Gender', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard',\n",
       "       'IsActiveMember', 'EstimatedSalary', 'Exited', 'Complain',\n",
       "       'Satisfaction Score', 'Card Type', 'Point Earned'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "      <th>Complain</th>\n",
       "      <th>Satisfaction Score</th>\n",
       "      <th>Card Type</th>\n",
       "      <th>Point Earned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15634602</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DIAMOND</td>\n",
       "      <td>464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15647311</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DIAMOND</td>\n",
       "      <td>456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15619304</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DIAMOND</td>\n",
       "      <td>377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15701354</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>GOLD</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15737888</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>GOLD</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerId  CreditScore Geography  Gender  Age  Tenure    Balance  \\\n",
       "0    15634602          619    France  Female   42       2       0.00   \n",
       "1    15647311          608     Spain  Female   41       1   83807.86   \n",
       "2    15619304          502    France  Female   42       8  159660.80   \n",
       "3    15701354          699    France  Female   39       1       0.00   \n",
       "4    15737888          850     Spain  Female   43       2  125510.82   \n",
       "\n",
       "   NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  Exited  \\\n",
       "0              1          1               1        101348.88       1   \n",
       "1              1          0               1        112542.58       0   \n",
       "2              3          1               0        113931.57       1   \n",
       "3              2          0               0         93826.63       0   \n",
       "4              1          1               1         79084.10       0   \n",
       "\n",
       "   Complain  Satisfaction Score Card Type  Point Earned  \n",
       "0         1                   2   DIAMOND           464  \n",
       "1         1                   3   DIAMOND           456  \n",
       "2         1                   3   DIAMOND           377  \n",
       "3         0                   5      GOLD           350  \n",
       "4         0                   5      GOLD           425  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_columns = ['RowNumber', 'Surname']\n",
    "data.drop(columns=drop_columns, inplace = True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exploring the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 16 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   CustomerId          10000 non-null  int64  \n",
      " 1   CreditScore         10000 non-null  int64  \n",
      " 2   Geography           10000 non-null  object \n",
      " 3   Gender              10000 non-null  object \n",
      " 4   Age                 10000 non-null  int64  \n",
      " 5   Tenure              10000 non-null  int64  \n",
      " 6   Balance             10000 non-null  float64\n",
      " 7   NumOfProducts       10000 non-null  int64  \n",
      " 8   HasCrCard           10000 non-null  int64  \n",
      " 9   IsActiveMember      10000 non-null  int64  \n",
      " 10  EstimatedSalary     10000 non-null  float64\n",
      " 11  Exited              10000 non-null  int64  \n",
      " 12  Complain            10000 non-null  int64  \n",
      " 13  Satisfaction Score  10000 non-null  int64  \n",
      " 14  Card Type           10000 non-null  object \n",
      " 15  Point Earned        10000 non-null  int64  \n",
      "dtypes: float64(2), int64(11), object(3)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerId            0\n",
       "CreditScore           0\n",
       "Geography             0\n",
       "Gender                0\n",
       "Age                   0\n",
       "Tenure                0\n",
       "Balance               0\n",
       "NumOfProducts         0\n",
       "HasCrCard             0\n",
       "IsActiveMember        0\n",
       "EstimatedSalary       0\n",
       "Exited                0\n",
       "Complain              0\n",
       "Satisfaction Score    0\n",
       "Card Type             0\n",
       "Point Earned          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerId            0\n",
       "CreditScore           0\n",
       "Geography             0\n",
       "Gender                0\n",
       "Age                   0\n",
       "Tenure                0\n",
       "Balance               0\n",
       "NumOfProducts         0\n",
       "HasCrCard             0\n",
       "IsActiveMember        0\n",
       "EstimatedSalary       0\n",
       "Exited                0\n",
       "Complain              0\n",
       "Satisfaction Score    0\n",
       "Card Type             0\n",
       "Point Earned          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CustomerId</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>1.569094e+07</td>\n",
       "      <td>71936.186123</td>\n",
       "      <td>15565701.00</td>\n",
       "      <td>15628528.25</td>\n",
       "      <td>1.569074e+07</td>\n",
       "      <td>1.575323e+07</td>\n",
       "      <td>15815690.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CreditScore</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.505288e+02</td>\n",
       "      <td>96.653299</td>\n",
       "      <td>350.00</td>\n",
       "      <td>584.00</td>\n",
       "      <td>6.520000e+02</td>\n",
       "      <td>7.180000e+02</td>\n",
       "      <td>850.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>3.892180e+01</td>\n",
       "      <td>10.487806</td>\n",
       "      <td>18.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>3.700000e+01</td>\n",
       "      <td>4.400000e+01</td>\n",
       "      <td>92.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tenure</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>5.012800e+00</td>\n",
       "      <td>2.892174</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Balance</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>7.648589e+04</td>\n",
       "      <td>62397.405202</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.719854e+04</td>\n",
       "      <td>1.276442e+05</td>\n",
       "      <td>250898.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumOfProducts</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>1.530200e+00</td>\n",
       "      <td>0.581654</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HasCrCard</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>7.055000e-01</td>\n",
       "      <td>0.455840</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IsActiveMember</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>5.151000e-01</td>\n",
       "      <td>0.499797</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>1.000902e+05</td>\n",
       "      <td>57510.492818</td>\n",
       "      <td>11.58</td>\n",
       "      <td>51002.11</td>\n",
       "      <td>1.001939e+05</td>\n",
       "      <td>1.493882e+05</td>\n",
       "      <td>199992.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exited</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>2.038000e-01</td>\n",
       "      <td>0.402842</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Complain</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>2.044000e-01</td>\n",
       "      <td>0.403283</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Satisfaction Score</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>3.013800e+00</td>\n",
       "      <td>1.405919</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Point Earned</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.065151e+02</td>\n",
       "      <td>225.924839</td>\n",
       "      <td>119.00</td>\n",
       "      <td>410.00</td>\n",
       "      <td>6.050000e+02</td>\n",
       "      <td>8.010000e+02</td>\n",
       "      <td>1000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count          mean           std          min  \\\n",
       "CustomerId          10000.0  1.569094e+07  71936.186123  15565701.00   \n",
       "CreditScore         10000.0  6.505288e+02     96.653299       350.00   \n",
       "Age                 10000.0  3.892180e+01     10.487806        18.00   \n",
       "Tenure              10000.0  5.012800e+00      2.892174         0.00   \n",
       "Balance             10000.0  7.648589e+04  62397.405202         0.00   \n",
       "NumOfProducts       10000.0  1.530200e+00      0.581654         1.00   \n",
       "HasCrCard           10000.0  7.055000e-01      0.455840         0.00   \n",
       "IsActiveMember      10000.0  5.151000e-01      0.499797         0.00   \n",
       "EstimatedSalary     10000.0  1.000902e+05  57510.492818        11.58   \n",
       "Exited              10000.0  2.038000e-01      0.402842         0.00   \n",
       "Complain            10000.0  2.044000e-01      0.403283         0.00   \n",
       "Satisfaction Score  10000.0  3.013800e+00      1.405919         1.00   \n",
       "Point Earned        10000.0  6.065151e+02    225.924839       119.00   \n",
       "\n",
       "                            25%           50%           75%          max  \n",
       "CustomerId          15628528.25  1.569074e+07  1.575323e+07  15815690.00  \n",
       "CreditScore              584.00  6.520000e+02  7.180000e+02       850.00  \n",
       "Age                       32.00  3.700000e+01  4.400000e+01        92.00  \n",
       "Tenure                     3.00  5.000000e+00  7.000000e+00        10.00  \n",
       "Balance                    0.00  9.719854e+04  1.276442e+05    250898.09  \n",
       "NumOfProducts              1.00  1.000000e+00  2.000000e+00         4.00  \n",
       "HasCrCard                  0.00  1.000000e+00  1.000000e+00         1.00  \n",
       "IsActiveMember             0.00  1.000000e+00  1.000000e+00         1.00  \n",
       "EstimatedSalary        51002.11  1.001939e+05  1.493882e+05    199992.48  \n",
       "Exited                     0.00  0.000000e+00  0.000000e+00         1.00  \n",
       "Complain                   0.00  0.000000e+00  0.000000e+00         1.00  \n",
       "Satisfaction Score         2.00  3.000000e+00  4.000000e+00         5.00  \n",
       "Point Earned             410.00  6.050000e+02  8.010000e+02      1000.00  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().T"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``2. Exploratory Data Analysis``"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation between variables:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "We can see that **Complain has a perfect positive correlation with Costumer Chrun**\n",
    "\n",
    "So let's remove it, since \"Complain\" doesn't provide any additional information or predictive power because it perfectly aligns with the target variabl and can introduce overfitting.\n",
    "\n",
    "**Age has the highest correlation with Costumer Chrun (Positve)**\n",
    "\n",
    "Which makes sense, since older people are less susceptible to change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = data.corr().round(2)\n",
    "\n",
    "plt.figure(figsize=(20,9))\n",
    "sns.set_style(\"white\")\n",
    "sns.heatmap(corr, annot = True, cmap = 'YlOrBr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns=['Complain'], inplace = True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Target Variable"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The Churn variable is imbalanced**. We will need to **perform a oversampling** to balance it.\n",
    "\n",
    "Undersampling would not be a good approach, because it would result in losing a significant portion of the already limited data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style='white', rc={'figure.figsize': (8, 4)})\n",
    "\n",
    "sns.countplot(data = data, x = 'Exited', palette = ['#D3D3D3', 'orange'])\n",
    "plt.title('Number of Churn costumers', \n",
    "          fontweight=\"bold\", \n",
    "          size = 24, \n",
    "          loc = 'left', \n",
    "          pad = 10)\n",
    "\n",
    "plt.ylabel('Quantity', size =12)\n",
    "plt.xlabel('Churn', size= 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CustomerId', 'CreditScore', 'Geography', 'Gender', 'Age', 'Tenure',\n",
       "       'Balance', 'NumOfProducts', 'HasCrCard', 'IsActiveMember',\n",
       "       'EstimatedSalary', 'Exited', 'Satisfaction Score', 'Card Type',\n",
       "       'Point Earned'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical Variables\n",
    "\n",
    "- **Costumers are mostly from france**\n",
    "- **Most costumers have card**\n",
    "- **Costumer have 1 to 2 products**\n",
    "\n",
    "The rest of the variables are well distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categorical_cols = ['Geography', 'Gender', 'Card Type', 'HasCrCard', 'IsActiveMember', 'Satisfaction Score', 'Tenure', 'NumOfProducts']\n",
    "\n",
    "plt.figure(figsize=(20, 10))\n",
    "plt.subplots_adjust(hspace=0.6)\n",
    "plt.suptitle(\"Categorical distribution variables\", fontsize=28,fontweight ='bold', y=1., x = 0.276)\n",
    "\n",
    "for n, col in enumerate(categorical_cols):\n",
    "    # add a new subplot iteratively\n",
    "    ax = plt.subplot(3, 3, n + 1)\n",
    "\n",
    "    sns.countplot(data = data, x = col,  palette = ['#D3D3D3'])\n",
    "\n",
    "    # chart formatting\n",
    "    ax.set_title(f'{col.upper()}', fontdict = {'fontsize' : 18}, loc = 'left', pad = 15)\n",
    "    ax.set_xlabel(col)\n",
    "    ax.set_ylabel('Quantity')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical variables X Churn:\n",
    "\n",
    "- **German costumer have the highest churn rate**\n",
    "\n",
    "- Gender seems to play a role in exiting the bank, **female costumer are more likely to exited the bank**.\n",
    "\n",
    "- **Card type seems to not have much affect on churn rate**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reference to subplotting: https://engineeringfordatascience.com/posts/matplotlib_subplots/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categorical_cols = data.dtypes[data.dtypes == 'object'].index.to_list()\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.subplots_adjust(hspace=1)\n",
    "plt.suptitle(\"understanding Churn in categorical variables?\", fontsize=28,fontweight ='bold', y=1.08, x = 0.323)\n",
    "\n",
    "for n, col in enumerate(categorical_cols):\n",
    "    # add a new subplot iteratively\n",
    "    ax = plt.subplot(1, 3, n + 1)\n",
    "\n",
    "    sns.histplot(data = data, x = col,  hue = 'Exited', palette = ['#D3D3D3', 'orange'], stat=\"probability\", multiple=\"fill\", shrink = 0.6)\n",
    "\n",
    "    # chart formatting\n",
    "    ax.legend(title = 'Churn', loc='upper left', labels = ['1', '0'])\n",
    "    ax.set_title(f'{col.upper()}', fontdict = {'fontsize' : 18}, loc = 'left', pad = 15)\n",
    "    ax.set_xlabel(col)\n",
    "    ax.set_ylabel('Percentage')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Continuos variables"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**No column follows a normal distribution, therefore we are going to use normalization as the scaling technique**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAB+AAAAR/CAYAAAAraf/nAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeVxV1f7/8TejCOIAIjhkGopoOZUYmYpjmmNEXm+KpZVmg+YQzn7V0vTmbF6zFMvKEk1T03I2zcJ5ygFxpBRRQxRBZvj94Y9zPZ4DHPAAaq/n48HjAWutvfZnM/jY7s9en2WTlZWVJQAAAAAAAAAAAAAAcE9sizsAAAAAAAAAAAAAAAAeBiTgAQAAAAAAAAAAAACwAhLwAAAAAAAAAAAAAABYAQl4AAAAAAAAAAAAAACsgAQ8AAAAAAAAAAAAAABWQAIeAAAAAAAAAAAAAAArIAEPAAAAAAAAAAAAAIAVkIAHAAAAAAAAAAAAAMAKSMADAAAAAAAAAAAAAGAFJOBhJCEhobhDAAAAAAAAAAAAAIAHkn1xB3C/uHDhglq3bm3S/u6772rAgAEWzfHJJ59o7ty5Ju1btmxRlSpVLDrfV199paefftrCqK0nKytLa9as0dSpU7Vz584iPz8eDu3atdP58+dN2gcPHqz+/fsXfUDFYPfu3XrllVdM2s39O/AgeVivCwAAAAAAAAAAwJpYAQ8dP35cL7/8soYNG6arV68Wdzh4QO3bt89s8l2Sli9frqysrKINCAAAAAAAAAAAAChiJOD/wa5fv65x48YpKChIBw8eLO5w8ID7/vvvc+y7cOGCfvvttyKMBgAAAAAAAAAAACh6JOD/wbZu3aqlS5cqMzOzuEPBAy4hIUHr16/PdcyyZcuKKBoAAAAAAAAAAACgeLAHfDEpW7asxo4da9JerVq1og8GuEfr1q1TUlJSrmO2bt2qv//+W+XLly+iqGBN1apVM/tvVtmyZYs+GAAAAAAAAAAAgPsUCfhiUqpUKQUHBxd3GIBV5FZ+PltaWppWrlypfv36FUFEsDZPT0/+zQIAAAAAAAAAAMgDJegB3JPIyEgdOXLEpL1MmTImbcuXL1dWVlZRhAUAAAAAAAAAAAAUOVbAF5MLFy6odevWJu1fffWVnn76abPHpKWlafv27dqyZYtOnjyp6OhoJSYmKisrSy4uLvLy8pKPj49atmypNm3ayNHR0ew8rVq10sWLF3OMrVatWobPAwMDNWXKlBzHpqWlaevWrdqyZYuOHTumS5cuKTU1VWXLllWFChXk5+enVq1a5XhNuYmLi9Py5cu1detWRUVFKTExUV5eXmrYsKH+/e9/q2HDhpKkTz75RHPnzjU69t1339WAAQOM2nIbt3fvXn3++ec6fPiwkpOTVa5cOdWqVUu9evVSs2bNjI5JSUnRhg0b9MsvvygyMlJXrlxRYmKibGxs5OzsLE9PT/n4+CggIEDt27fP8ecgmf9ZhIeHy83NTampqfrpp5+0evVqnTp1SvHx8fL09NSTTz6pnj17ql69ekbH3bp1S6tWrdKPP/6oc+fOKSUlRZ6enqpXr566d++up556yrJvfD4tX77cpM3Dw0NvvfWWPvjgA6P2P//8U+Hh4WrSpIlFc48YMUI//PCDUdvkyZP14osvSpLOnj2r5cuX67ffflN0dLQyMjLk5eWlZ599Vt26dTP6Xc5LeHi41q9fr2PHjunChQtKTExUZmamSpQoIQ8PD1WvXl3+/v7q0qWL3NzcLJ7XnMDAQB0/ftyorXr16lq/fn2ux2VmZurZZ5/VtWvXjNp79eqlMWPGmIz/66+/9OOPP+rgwYM6c+aMrl+/ruTkZJUoUUJlypRR9erV9eSTT6pDhw7y9vbO9dy7d+/WK6+8YtK+ZcsWValSxewxycnJ2rRpk7Zv366TJ08a/a24uLiocuXKql27tlq3bq2AgADZ2vJOGAAAAAAAAAAAeLCRgH9AHDx4UCNHjtS5c+fM9l+/fl3Xr19XRESE1qxZoypVqmjy5Mlq3LhxocW0du1aTZs2TZcuXTLpu3r1qq5evapjx47pyy+/1JNPPqlRo0apbt26Fs29Zs0aTZo0SdevXzdqj4qKUlRUlFatWqWePXtqxIgRVrmOYcOGKSMjw9AWExOjmJgYk5Lbe/bs0YgRI3J8geHGjRu6ceOGIiMjtXbtWs2ePVvTpk0zvCxgqVOnTmno0KE6efKkUfuff/6pP//8U6tXr9bAgQP19ttvS5KOHDmi999/X1FRUUbjz507p3Pnzmn16tV65ZVXNGLECNnZ2eUrltykpqZqzZo1Ju2dO3dWp06dNGXKFKWmphr1hYWFWZyAz0lGRobmz5+vefPmKT093ajv7NmzOnv2rJYsWaLevXtr2LBhsrGxyXGumJgYDRkyRPv37zfbn56ersTERJ0/f17btm3TnDlzNGLECP3rX/8qcPxdu3Y1ScCfO3dOp06dUs2aNXM8bv/+/SbJd0nq1KmT0depqamaPn26vv76a6Pf62y3bt3SrVu3dOnSJf3++++aN2+eunXrpjFjxuT6wkh+bNu2Tf/3f/+nK1eumO1PTU1VXFycjh49quXLl6tWrVqaOnVqvl6aAAAAAAAAAAAAuN+w3PABcODAAfXp0yfH5Ls5Fy5c0Guvvabw8HCrx5Oenq5Ro0Zp6NChZpPv5hw4cEA9evTQqlWr8hz75ZdfKiQkxCT5frclS5bo/fffv6eS5kePHtWIESPMJinLlStnlCjet2+fXn/99VyrB9ztwoUL6tOnjyIiIiw+5uTJk+rRo4dJ8v1OWVlZmj17tpYtW6Z9+/bplVdeMUm+3+2rr74yqQBwrzZv3mz259SlSxeVKVNGLVq0MOnbsmWLYmNjC3zOrKwsjRgxQnPmzDFJvt8pMzNTixYt0n//+98cx1y7dk0vv/xyjsl3cxITEzV27FgtW7YsX3HfqVOnTrK3N33/acOGDbket2nTJpO2Rx55RA0aNDB8nZWVpcGDB+vLL780+3ttTmZmpsLCwvTWW29ZfExuNmzYoHfeeSfH5Ls5J0+eVM+ePRUZGXnP5wcAAAAAAAAAACguJODvc+np6Ro+fLiSkpLyfWxaWpqGDh2qhIQEq8Y0YcIErVixIt/Hpaamavjw4frpp59yHLNnzx795z//sXjODRs2aOnSpfmOJdsvv/yitLQ0s33t2rUzJEmTk5M1dOhQk9XclkhKStLMmTMtHj9gwADFx8dbNHby5Mnq37+/xb8fCxYs0F9//WVxLHkx93tQs2ZN1a5dW9Ltld53S0tL08qVKwt8zkWLFplddZ+Tzz77TH///bfZvvHjxys6OrpAcUyfPr1Avw+SVL58ebNVADZu3JjrcZs3bzZpu3v1+w8//GB2nCV27typ0NDQAh2b7fr16xo7dmyBEvk3b97UkCFDrPISAAAAAAAAAAAAQHGgBH0e5s6da/VVw/mxa9cu/fnnnybtvr6+atKkiSpUqKD09HRFR0drw4YNJiuLY2Nj9c0336h///6GtoEDByohIUGHDx82m8gcO3as4fO794XeuHFjjit/a9SooZYtW8rd3V2XLl3Spk2bzCY3R40apVq1apnMnZWVpbFjxyozM9PkGCcnJ3Xo0EE1a9bUjRs3tHXrVsNK2XtZTZ2tRIkS6tq1qx555BFFRkZq/fr1RonNZcuWKSYmxuS4OnXqqHnz5ipXrpwSExN17Ngxbdu2zeQadu3apfT0dLOrnu928+ZNSZKrq6s6deqkRx55RFFRUfrhhx9MEr63bt0yfO7g4KCOHTuqZs2aunjxolasWKGUlBSj8Wlpadq0aZNee+21vL8peYiOjtbvv/9u0t6lSxfD5wEBASpXrpzi4uKMxixfvlxvvPFGrqXhc3L69GnD5w0bNpS/v7+cnJz022+/ac+ePSbjU1NTtWnTJr388stG7REREWZXnHt6eqp9+/aqWLGi0tLSdO7cOW3YsEGJiYlG465fv66jR4/qySefzPc1SNILL7ygHTt2GLWdPHlSUVFRevTRR03GHz9+3Gz1hc6dOxt9be7vs2TJkmrdurVq1KghZ2dnXb9+XYcOHVJ4eLhJBYkFCxaoV69eKlmyZEEuSxs2bNCNGzdM2hs2bKjGjRvLzc1NKSkpioqK0saNGw2/79lOnTqln376yeS6AAAAAAAAAAAAHgQk4O9z5soxBwQE6LPPPjNJXr7//vvq27evUTltW1tbHTp0yGjcCy+8IElydnY2m4C/e9/zbBkZGZo6dapJu42NjUJCQtSnTx/Z2v6vqML777+vKVOmaMmSJUbjk5KSNGPGDJPS4Dt27ND58+dN5q9SpYoWLVpklJR87733NG/ePH3yySdmY80PBwcHffvtt3riiScMbYMHD1alSpUMX69bt87kuC5duujjjz82+Tl8+eWXmjx5slFbcnKyLl++rMqVK1sUU61atfTFF1/I3d3d0Pbkk09q+PDhZse7ublp8eLF8vHxMbS1bdtWffr0MRl76tQpi2LIy8qVK01eNLCxsTFKnGa/FPDNN98YjYuKitKuXbv0zDPPFOjc9vb2mjJlitG5+vfvr//85z9atGiRyfgTJ06YtJn7mdasWVPffvutSpcubdTeo0cPde/e3WRldlRUVIET8K1bt1apUqVMKlRs2LBB/fr1Mxlvrvx87dq1TV5kMbd1wVdffaV69eqZnXPAgAEmSfgjR47o6aeftug67mbu36yXX35Z48ePN2kfPHiwXnnlFZ09e9bQ5uDgoAMHDpCABwAAAAAAAAAADyQS8Pe5OxPa2ezs7MyOdXFx0QcffKA1a9aoRo0aqlmzph577DGVKFHCKrH88ssvZlfjv/nmm3r99ddN2h0dHfV///d/unLliknycPPmzTp37pyqV69uaDO3GtnGxkazZs0yWRFsa2urd999V5GRkXnum52Xzp07GyXfJZkkyr/66itFRkYqIiJCJ0+eVGRkpEaMGGF2BXfnzp1NEvDS7f3GLUnA29jY6OOPPzZKvku3E/4ffPCByUpsSRo+fLhR8l2SmjRpoqpVq5r8zK5du5ZnDHnJzMw0W0bez89PFStWNGrr2rWrSQJeur1Su6AJ+L59+5pN0A4cOFBhYWEm36OrV6+ajB08eLBeeOEFnTx5UidOnFBERIReeeUVk+S7JNWtW1fVqlXTmTNnjNrv5Xvp5OSk5557zuT7uHHjRrMJeHNl5c19D8z9m2GuTbr9ksbw4cNlY2Nj+DfD09PT0kswy9zfRE7/Znl4eOjDDz/U77//bjh/tWrV5ODgcE8xAAAAAAAAAAAAFBcS8Pe5WrVqmbRt3bpVXbp0UefOndW0aVPVrl3bkPSqUaOGhgwZUiixmFuBW6pUKbPJwjsNGTJEmzdvNlllu337dqME/JEjR0yObdSokerWrZvj3H379r3nBHybNm3yHFOiRAnVrVs311ik2+XOjx49arbv7nLwOalTp458fX1N2m1tbVW5cmWTFcbOzs4m+4Bne/TRR00S8MnJyRbFkZvff//dbDl0c3u+16tXT97e3ibJ602bNunatWtyc3PL9/l79uxptr1kyZLy8fHRwYMHjdqTkpJMxtra2srb21ve3t7q0KFDjufKysrSsWPHlJ6ebtJn6c80Jy+88IJJAv6PP/5QdHS0UQWGqKgok5+7jY2NOnbsaDKnr6+v9u3bZ9TWo0cPtW/fXq1bt5a/v7/KlClj6DNXJeFemPvd/eabb3Ts2DE9//zzatq0qdGq/UaNGqlRo0ZWjQEAAAAAAAAAAKC4kIDPQ/PmzRUQEGDR2O3bt5vs6Xyv/P395ePjY5J8i4yM1PTp0zV9+nSVKVNGDRo0UKNGjdS4cWPVrVs3xxWn9+Lw4cMmbU8//bRcXFxyPe6xxx5TtWrVdO7cOaP2AwcOqHfv3pJuJznvLEOdLa/E3BNPPGG2jHd+3L1yPD8uX76sY8eO6dixYzp06JAOHDhgtC/7ne4uX56TGjVq5Nhn7ntds2bNHPeWvzPRmu3usvEF8f3335u0lShRQu3btzc7vmvXrpoxY4ZRW1pamn744Qez1RNy4+7uLg8Pj1z772bp916SEhISdPToUR0/flyHDx/Wvn379Pfff5sdm595zWncuLEqVaqk6Ohoo/aNGzca/jYk8y+/+Pn5ycvLy6S9V69eJgn4lJQUrV69WqtXr5atra1q1KihJ598Uo0bN9bTTz+t8uXL39N13Kl9+/aaNWuWSdWBgwcPGl6MKF++vJ588kk99dRTevrpp+Xr62t25TwAAAAAAAAAAMCDhgR8HurVq5fjnuh3i4uLs3oCPrsEe+/evXXlyhWzY27cuKHt27dr+/btkm7vB/7cc88pODhYNWvWtFosly5dMmm7e//pnDz22GMmCfg7k5oJCQlmk5l5lWy3sbHRI488YnaPb0tVqFDB4rEZGRnavn27Nm7cqF27dpn9ntwrcwnkbOYS7bklowujlHdcXJy2bNli0t6yZUuVKlXK7DFdunTRrFmzTJL/y5Yty3cCPq9ksZOTU77mk25XX/jpp5/022+/6dSpUybVGgqLjY2NOnfurM8++8yo/e4EvLny8zlVPWjfvr369OmjL774wmx/ZmamIiMjFRkZqaVLl8rGxkYNGzbUiy++qK5du8rR0bHgF6TbVTFmzZql/v376+bNm2bH/P3339q4caM2btwoSapYsaKef/55BQcHW7RNAwAAAAAAAAAAwP3K/MbAuK94e3tr5cqV6ty5c44rne907do1LV26VJ07d9bYsWPvuUx2trS0NJM2Z2dni441N+7O5Jy5fc0lWbR/fcmSJS2KwRwnJyeLziHdXsHbqVMnvfXWW/rhhx9yTL7fvV99QWLKiblVwpb+DKxlzZo1Sk1NNWlfv369atWqZfajRYsWZlfenz9/Xrt3787X+fO6Xkv+RrJdvnxZ/fr1U7du3fTFF18oMjLSbPK9XLlycnV1zVecljJXtv/gwYOGFeRXrlzRoUOHjPodHBzUrl27HOccMWKEpk6dqqpVq+Z5/qysLB04cEBjxoxRu3bttGvXrvxdgBmNGjXS8uXL1bJlS4tWtl+6dEmLFi3Sc889p5kzZ1qlSgMAAAAAAAAAAEBxIAH/gPDw8NC0adO0Y8cOjRs3Ts2aNctzpW9WVpaWLVumoUOHWiUGc4nPnMqt381cgr1s2bKGz3NKgluyX7m5/b0tZelq6YMHD+qVV14xKZNvb2+vp556Sv369dMnn3yiHTt2GFb1FpStbf7+LAtju4HcmCs/fy+WLVuWr/F5JdgtLWUeFxennj17GipH3KlmzZrq0aOHpkyZop9++knh4eGqXbt2vuK0lLe3t5544gmjtszMTEPZ+S1btpi8FNCsWTOjvx9zunTpoo0bN+rrr79Wr169LHoxJDo6Wn379tWRI0fydxFmVK9eXfPnz9eWLVs0fPhwNW7cOM+KDOnp6Zo/f77+85//3PP5AQAAAAAAAAAAigMl6B8w7u7u6tGjh3r06KHU1FQdPnxY+/fv18GDB3XgwAHFx8ebHLNp0ybt2bNHjRs3vqdzV6xY0WT+M2fOWHSsuXF3ln53dXWVra2tycrXixcv5jpvVlaW/vrrL4tiMMeScttZWVkaMWKEyarvdu3aady4cSYl481VCnhYHDlyRJGRkVadc+PGjYqLi1O5cuUsGm+tvcKnTZtm8rtTo0YNzZgxQ7Vq1TIZX5g/165du+ro0aNGbRs3blSPHj3Mlp/v3LmzRfPa2NiocePGhr/96Oho7d27VwcOHNDBgwfNrvhPTU3V1KlT9fXXXxfwaoxVrlxZr732ml577TXdunVLBw8e1P79+3Xo0CEdPHjQ7Es8X331lYKDg/XII49YJQYAAAAAAAAAAICiwgr4B0hSUpKOHz9uKN3u6OgoPz8/9e/fX5999pl27dql//73v2aTyr/99ptJW34Tmb6+viZtu3fvzrF8fLYzZ84oKirKpL1Ro0aGz+3t7VW9enWTMXv37s117sOHDyshISHXMbmxZPX47t27df78eaM2Z2dnffzxx2b3a4+LiytwPPc7a69+l24nfFetWmX1eXOTlJSkH3/80aR9woQJZpPvUuH+XDt16mSysn/v3r3666+/TEr0Ozs7q1WrVnnOmZWVpUuXLunkyZOGtkqVKqlr166aMGGC1qxZo+3bt6tly5Ymx+7fv98qW1ckJCToyJEjhpdXnJ2d9eyzz2rgwIFatGiRdu/erQ8++MDkuMzMTIWHh9/z+QEAAAAAAAAAAIoaK+DvYzdv3tScOXN09uxZnTt3TtHR0crKytJ7772nt99+22S8nZ2d2rRpo5o1a+rYsWNGfeaShzkl4NPT082W+W7WrJlWr15t1JaQkKDPP/9cgwcPzvE6Zs6cadJma2ur5s2bG7U1bNjQZKX8gQMHdPToUZMS3dnmzZuX43ktYUm591OnTpm0ZWVl5VhO+15L0N+vkpKStG7dOpP2OnXqKCgoyKI50tPT9fHHHysjI8OofdmyZerTp49V4rTEn3/+aTbBnFN5+4iICLMvkViLm5ubmjVrpm3bthna0tPTNXbsWJOV923bts1x64SNGzdq48aNhn8zbt26JVdXV23fvl0uLi4m4z09PfWvf/3L6LySlJGRoRs3bhhVqbDExYsXtXDhQp09e1Znz57VlStXJEkff/yx2b3uHR0d1b17d82YMUPXr1836nuYX2QBAAAAAAAAAAAPLxLw9zFXV1dt2bLFpAz7559/roYNG+qZZ54xOebIkSNmE8bly5c3acup/PrVq1dVsWJFk/a2bduqXLlyJomxzz77TKVLl1afPn2MEtqpqamaMmWKYS/rO3Xq1EmVK1c2anv++edNVlhnZWVp8ODBWrRokVE56uxErrn9u63N3D70SUlJCg0NVb9+/YzaN2zYoGnTppmd5+6k84Nm/fr1ZqsN9OjRQ926dbN4np07d+rXX381ajt79qz27t0rPz+/e47TEjmt7p4/f77mzp1rlIg/efKkBgwYYFKqXbr9e2gtXbp0MUmEm1sF3qlTpxznuHnzpsnK/ps3b2rYsGGaMWOGSpQoYdSXkZFh8lKNdPtlHku3BLhTuXLltHLlSpO/mSlTpsjX19dsdYEtW7aYJN8l8/9mAQAAAAAAAAAA3O9IwN/nXn31VX300UdGbUlJSerdu7eeeeYZ1atXT25ubrp165aOHj2qHTt2mN2r+u7V5pJUunRps+d877331K5dO50/f14lS5bUqFGjJElOTk565513NHHiRKPxWVlZ+vjjj7Vy5Uq1atVKbm5uunTpkjZt2qTo6GiT+V1dXTVw4ECT9iZNmsjX11cRERFG7X/++ae6dOmijh07ytvbW9euXdPGjRtNysIXlqpVq5ptnz59unbv3i0/Pz8lJiYqPDxcf/zxR47zmNvr+kFirvy8g4OD2rVrl695XnjhBZMEvCSFhYUVWQK+SpUqsrGxMUmqb9u2TS+88ILatm0re3t7/fHHH9q+fbsyMzPNzpOUlGS1mFq3bi1XV1fDFhPmuLm5qUmTJjn2d+jQQbNmzTKsPM+2efNmtWjRQm3btlWVKlXk4OCg6Oho/fLLL/rzzz9N5nnmmWdyrPCQG2dnZ3Xv3l2LFy82ar927ZoCAwMVEBCgWrVqqVy5crpx44YOHTqk33//3WQeW1tbNW3aNN/nBwAAAAAAAAAAKG4k4O9zL7/8stauXasjR46Y9IWHh1u0T3JAQIAaNGhg0l6jRg2z4w8fPqzDhw9Lkp588kmjvp49e+q3334zWakrSadPn9bp06dzjcXW1lbTp083Ws1+Z9+YMWP06quvmqwWv3XrlpYvX272mBIlSlg1EXq3Z599NsfE6M6dO7Vz506L5rl27Zq1Qysy58+f1759+0zamzdvnuOLHDlp06aNSpUqZbKafuPGjbp+/brKli17L6FaxM3NTY0bNzbZX126veWAuSoS5sTGxlotphIlSqh9+/Zmf8+zdejQIccy+ZJUsmRJjRs3Tu+++67JywXXrl1TWFhYnnHY2tpqwIABlgd+l3feeUdbtmzRhQsXjNozMjK0detWbd26Nc85/v3vf8vT07PAMQAAAAAAAAAAABSXvDfARrFydHTU3LlzVbNmzQId7+PjoylTppjt8/LyUu3atXM9/vLly0Zf29raatasWXruuefyHYuzs7PmzJmjgICAHMf4+flp5MiRFs85evRoeXl5mbTntL99QZQqVUpDhgzJ1zHOzs4mbZGRkdYKqciZW/0u5V4OPSdOTk5q3769SXtKSopWrVqV7/kKavjw4SYl2XNTFD9Tc/uk38mS73ebNm00YsSIAv0N2NjYaOzYsWZf2LFUmTJl9NlnnxU4gf70009r2LBhBT4/AAAAAAAAAABAcSIB/wDw9PTUd999p+7duxvtsZ4bGxsbvfjii1qyZInc3NxyHDdu3DizicVsV65cMVlJ6+TkpDlz5mjy5Mlm94o3x9/fX0uXLlXbtm3zHNurVy9NmjRJJUuWzHGMk5OTPvjgAwUHB5vtt/T7ZKkePXpo8ODBec5rZ2enAQMG6MMPPzTpM1d2/UGQnp5uNjHu7Oysli1bFmjOF154wWz7smXLCjRfQTz++OOaO3euypQpk+fYNm3aaMWKFSZJ7cjISJOXVO5Fo0aNVLlyZbN9VapUUcOGDS2ap3fv3vr888/16KOPWnzuihUrav78+erRo4fFx+SkRo0a+v77782+aJETe3t7vfHGG1qwYEGuf/sAAAAAAAAAAAD3M0rQPyBcXV31wQcf6M0339S6deu0f/9+nTp1SnFxcUpJSZGTk5PKli2rGjVqqEGDBurcubPZMu93a9iwodauXavQ0FDt3LlTMTExcnBwUPny5VWrVi21bNlSWVlZJonH7AR/ly5d9Msvv2jr1q36448/dPHiRaWkpKhMmTLy8vJS48aN1aZNGzVq1Chf1/vSSy/p2Wef1fLly7Vt2zZFR0crNTVVFStWVEBAgHr06JHr9Tk6OubrfJbo37+/WrVqpW+//Va7d+9WTEyM0tLS5OrqqkcffVSNGjXSv//9b1WpUkXx8fFycHBQWlqa4fjz589r7969RbbPubVs375dV69eNWlv3bp1gROljRo1UpUqVUzKlJ85c0b79u3L9+9LQTVv3lw///yzvvvuO23fvl3nzp1TUlKSnJ2dVbFiRdWrV08vvviiYSuGJ554Qn/88YfRHMuXL9e7775rlXhsbGzUpUsXffrppyZ9+a020Lx5c/3000/auXOntmzZomPHjik6OtpQ+r9UqVKqVKmSateurZYtWyogIKBA+77npEKFCpo9e7bOnDmjdevW6dChQzp9+rTi4+OVmpoqZ2dnlStXTj4+PmrUqJE6deokDw8Pq50fAAAAAAAAAACgONhk3b28GXjAtG7d2iSRO3HiRHXr1q2YIgIKbv78+Zo5c6ZJ+48//igfH59iiAgAAAAAAAAAAACWYgU87hs7duzQb7/9pkqVKqly5cqqVKmSKlWqpLJly+Z4TGZmpq5du2bSXqFChUKMFCgcmZmZWr16tUm7j48PyXcAAAAAAAAAAIAHAAl43DcuX76sL7/80qS9cePG+vrrr80es3nzZt26dcukvWbNmtYODyh0S5cu1dmzZ03aO3bsWAzRAAAAAAAAAAAAIL9sizsAIFv16tXNtu/Zs0cLFizQzZs3DW2xsbH67rvvNGLECLPzVKpUqdDiBKzh77//VlJSkiQpKSlJy5Yt05QpU0zG2dvbKzAwsKjDAwAAAAAAAAAAQAGwBzzuGxkZGWrVqpViYmJyHOPi4qKsrCyzq96zjR49Wq+88kphhAhYzciRI7Vy5Uq5uLgoKSlJmZmZZsd16tRJ06dPL+LoAAAAAAAAAAAAUBCsgMd9w87OTqNGjcp1TGJiYq7J99q1a6t79+7WDg2wOi8vL0m3f6dzSr47OTlp0KBBRRgVAAAAAAAAAAAA7gUJeNxX2rVrp9GjR8ve3j7fx9auXVv//e9/VaJEiUKIDLAuT0/PXPvt7Oz00Ucf6ZFHHimiiAAAAAAAAAAAAHCvSMDjvvPKK6/o+++/V4cOHeTk5JTn+GrVqmn06NFatmyZKleuXAQRAvcuewW8OY8//ri++OILdezYsQgjAgAAAAAAAAAAwL1iD3jc11JSUnTq1CmdOXNG169fV1JSkmxtbeXq6ip3d3fVrVtXFStWLO4wgXy7deuW9uzZo7/++ksJCQmyt7eXh4eH6tatK29v7+IODwAAAAAAAAAAAAVAAh4AAAAAAAAAAAAAACugBD0AAAAAAAAAAAAAAFZAAh4AAAAAAAAAAAAAACsgAQ8AAAAAAAAAAAAAgBWQgAcAAAAAAAAAAAAAwApIwAMAAAAAAAAAAAAAYAUk4AEAAAAAAAAAAAAAsAIS8AAAAAAAAAAAAAAAWAEJeAAAAAAAAAAAAAAArIAEPAAAAAAAAAAAAAAAVkACHgAAAAAAAAAAAAAAKyABDwAAAAAAAAAAAACAFZCABwAAAAAAAAAAAADACkjAAwAAAAAAAAAAAABgBSTgAQAAAAAAAAAAAACwAhLwAAAAAAAAAAAAAABYAQl4AAAAAAAAAAAAAACsgAQ8AAAAAAAAAAAAAABWQAIeAAAAAAAAAAAAAAArIAEPAAAAAAAAAAAAAIAVkIAHAAAAAAAAAAAAAMAKSMADAAAAAAAAAAAAAGAFJOABAAAAAAAAAAAAALACEvAAAAAAAAAAAAAAAFgBCXgAAAAAAAAAAAAAAKyABDwAAAAAAAAAAAAAAFZAAh4AAAAAAAAAAAAAACsgAQ8AAAAAAAAAAAAAgBWQgAcAAAAAAAAAAAAAwApIwAMAAAAAAAAAAAAAYAUk4AEAAAAAAAAAAAAAsAIS8AAAAAAAAAAAAAAAWAEJeAAAAAAAAAAAAAAArIAEPAAAAAAAAAAAAAAAVkACHgAAAAAAAAAAAAAAKyABDwAAAAAAAAAAAACAFdgXdwAPioSEBK1atUpbt27VyZMndf36dTk6OuqRRx7RM888o3//+9+qXr26yXGtWrXSxYsXc5zXzs5Ozs7OqlSpkho3bqy+ffvK09PTZFyvXr20Z8+efMW8atUq1a5dW5J04cIFtW7dOscYSpQoIQ8PD9WpU0ddunRRq1atcpz3k08+0dy5c1W5cmVt3bpVkrR792698sor+YpPktEc2RISEhQWFqZffvlFZ8+e1Y0bN1SqVClVqlRJ/v7+eumll/TYY4/lOXdqaqrWrl2rjRs36vjx47p27Zrs7e1VsWJF+fn5qXv37nr88cctivOXX37R+vXrdeDAAV29elXp6ekqX7686tatq+eff17PPfec7OzszB5ryffGwcFBZcuW1WOPPaYOHTqoW7duJvPl9jPMia+vr1avXp2vYwAAwINt7dq1Gjp0qCTp3//+tyZMmGDxsdeuXdPq1av166+/KjIyUtevX5eDg4Pc3d1Vv359tWnTRu3atZOtrfn3ePO69zVn7969Kl26dL6OAQAAwIMht+dZNjY2KlOmjCpWrKjmzZurd+/ecnNzy3PO//u//1NYWJgkaebMmerQoUOu47OfqwYGBmrKlCn5v4j/LyUlRc8++6xu3rypMmXKaMeOHXJycspx/J3X/sILL+g///lPrvPf+Qzx5MmTOY4r6HNqift1AACKCgl4C2zbtk0jR45UXFycJKls2bLy8fHRjRs3dPr0aZ08eVJLlizRO++8o7feesvsHBUrVlTFihVN2tPT0xUdHa2TJ0/q5MmT+uGHH/TNN98YEud3c3d316OPPmpR3M7OzmbbfXx8VKpUKcPXmZmZunnzpv766y9FRUXp559/VtOmTTVr1iy5urpadC5XV1c9+eSTJu3Xrl3T+fPnJclsv4eHh9HXhw8f1jvvvKOrV6/K3t5ejzzyiKpUqaJbt24pMjJSx44d0+LFizVgwAD1798/x3gOHTqkIUOGGG4oXV1d5e3traSkJP311186e/asli1bpp49e2rkyJGytzf/p/DXX3/p/fff16FDhyRJJUuW1COPPCLp9k30hg0btGHDBvn4+GjOnDk53txme+KJJ+To6GjSnpiYqPPnz2v37t3avXu3fv75Zy1cuFAODg5m57n7Z5iTatWq5TkGAAA8XFasWGH4fM2aNQoJCcnzviErK0uhoaGaO3eukpKSJElubm6qUaOGpNv3PWvXrtXatWvl4+OjuXPn5npPmtO9rzk5vcQIAACAh8vdz7MyMjJ048YNnTp1SidOnNCyZcu0ePFi1apVK8c5kpOTtW7dOsPX3333XZ4JeGvZsGGDbt68KUm6ceOGfvrpJ7344osWHbtq1Sq1b99eLVu2vKcYrPGcWuJ+HQCAwkYCPg+LFi0yvJ34/PPP65133lHNmjUN/VeuXNGnn36qb7/9VrNmzVJKSooGDRpkMk9QUJAGDBiQ43m2b9+u999/X/Hx8Ro0aJB+/vlnsyuLmjdvfk9vakrSmDFj9PTTT5u0p6amavXq1Zo8ebJ27typt99+W6GhoWYTxnerU6eOvvvuO5P2lStXauTIkZJktv9O165dU79+/XT9+nV1795dQ4YMUdmyZQ39CQkJWrhwoT799FPNnDlTHh4eCgoKMpnnp59+UkhIiNLT09WkSRMNGDDAKPkfHx+vL774Qp9//rm++eYbxcfHa+rUqSbzREREqHfv3oqLi1PlypU1ZMgQPffcc4bvR0ZGhjZu3Kg5c+YoMjJS3bp106effio/P78cr3H27NmqUqWK2b7ExER98skn+uKLL7Rr1y4tWLBAb7/9ttmxOf0MAQDAP1t0dLR27dqlsmXLqlq1ajp06JB+/PFHvfzyy7keN3bsWC1fvlyS1LFjR/Xr10++vr6G/vT0dP3666/6+OOPFRkZqR49emjNmjVyd3c3O19e974AAAD458npeVZcXJxGjBihX375RQMHDszxuagkrV+/XgkJCXrmmWe0b98+7dmzR2fOnJG3t3dhh2940TUgIEDbt2/X0qVLLU7AS7fvudetW6cyZcoU6PzWek4tcb8OAEBhYw/4XOzfv1/Tpk2TJL399tuaNWuW0U2NJFWoUEHjxo0zJEo/++wzHT16NN/nCggI0PDhwyVJ58+fV3h4+D1Gn3+Ojo7q1q2bPvvsM9nZ2WnPnj1avHhxkZ1/6dKlun79uho0aKAJEyYYJd8lqVSpUho0aJDhxvaTTz4xmeP8+fMaPXq00tPT9eKLLyo0NNRk5X3p0qX13nvvGcqxrlmzRps2bTIak5CQoAEDBiguLk7169fXypUr1alTJ6OXEezs7PT8889r2bJl8vPz082bNzVkyBBdv369QNfv4uKi4cOHGxL4eb2wAAAAcLeVK1cqMzNTDRs2NGwptHTp0lyPCQsLMyTfR40apRkzZhgl3yXJ3t5eLVu21NKlS1WtWjX9/fffhvtkAAAA4F6UK1dOU6ZMkaOjo86fP6+dO3fmODY7Cd68eXNDMj+v+11ruHDhgnbv3i0bGxsNGTJENjY2Onz4sE6cOGHR8TY2Nrp69aomTpxYoPMX5XNqAABw70jA5yArK0tjx45VRkaG6tevr/feey/X8W+99ZYqVqyozMxMffHFFwU65537rp86dapAc1hD9v7okrRw4UIlJycXyXn/+OMPSVLdunVlY2OT47h//etfkqRLly7p8uXLRn0TJ07UrVu3VKlSJU2YMCHHt2Ul6aWXXlL9+vUlSaGhoUZ9ixcv1p9//ilnZ2d98sknJi8D3MnV1VXTp09XqVKldOXKFX388ce5XmdubGxs1KJFC0m331q9ceNGgecCAAD/LFlZWfrhhx8k3X4g+fzzz0u6XdUnezuduyUlJWnGjBmSpE6dOunVV1/N9RxlypTRsGHDJEnr1q1TQkKClaIHAADAP1m5cuUMCeWcnov+9ddf2rt3r6Tbi5nat28vSVq9enWhP7/84YcflJWVpbp168rX19ew4MfSBTQ9e/aUdHsh0JYtW/J17uJ4Tg0AAO4NCfgc7N+/X2fOnJEk9evXL8/xjo6O+uijj/TFF1/oww8/LNA570w6Z2VlFWgOa8lOwF+/fl379u0rknNm73ceHh6u1NTUHMfVq1dPK1eu1LZt21S+fHlDe3R0tOEN2VdffdWi0vmjRo3S/Pnz9dlnnxnasrKytGTJEknSCy+8IE9Pzzzn8fT0NLqRvpfE+Z0vDRT37wEAAHhw7Nq1SxcuXJCtra1at26tqlWrql69epJyfjC4bt06Q/WenLa+uVuLFi3UvXt3TZ48Wfb27GgFAAAA60hPT5d0u0qkOStXrlRWVpYee+wxeXt7q127dnJwcNCNGzeM9oW3tjtfdG3btq2k2y+vStKPP/5o0UupvXr1UqNGjSRJ48aNy1cFzeJ4Tg0AAO4NCfgc/P7775Julxn39/e36JgmTZqoSZMmcnZ2LtA5f/rpJ8PnDRo0KNAc1uLr66tSpUpJkvbs2VMk52zWrJkk6fTp0+rWrZt++OEHs4lsOzs7Pf7446pUqZLs7OwM7eHh4YaE9bPPPmvRORs0aKCWLVsa7b108uRJxcbGSrq9esxS2TfgaWlpht+f/MrKytL69eslSdWqVct15T0AAMCdsstxNm7c2PACYfaDwZ9//tnsfdUvv/wi6fZ9h6X7ZtrZ2emDDz5Qx44d5eTkZIXIAQAA8E/3559/6tSpU7K1tTU8I7xTZmamVq1aJel/97ilS5dWQECApMItQx8eHq6LFy/KxsbGcO727dvLwcFBt27d0po1a/Kcw8bGRpMnT1bJkiV19erVfCXGi+M5NQAAuDck4HNw9uxZSVLlypUNiejCkp6erh9//FFTp06VdPsGqWHDhoV6TktUrlxZ0u2V5UXhhRdeMOzdFBERoREjRuiZZ57Riy++qI8++kibN2/WzZs3czw++2fm4OCgGjVqFDiOO/duunv/09zUrl3bUMXg5MmT+T7vtWvXNGrUKB0+fFiS5avQAAAAbt68qU2bNkmSunTpYmjv1KmT7O3tlZKSYli1c6fjx49LkmFbHgAAAKCoZGRkKC4uTlu3blXfvn2VmZmpfv36GZ5J3un33383PKO88343+/MjR44Y7m2tLftF10aNGqlSpUqSJDc3NzVt2lSS5cn/qlWrasiQIZKktWvXavPmzRYdV5TPqQEAgHVQMzIH2SuE3NzcrDLfihUrTFZFZ2VlKTExURcuXNCtW7ckSf7+/oZ9OM354YcfzD48vdu7776rAQMG3FPM2eWe8lMS6V44ODjo888/1+zZs/XNN98oNTVVGRkZOnbsmI4dO6bFixfLwcFBTZs21dChQw37QmXL/pmVLVs21z3k8xIXF2f4PD8r0O3t7VWmTBldv35d165dMzvmvffeMymNn5GRoRs3bigqKkpZWVlycHDQe++9p65du+Z4rldeecWimLZs2aIqVapYfA0AAODBtHbtWiUnJ6tEiRJq166dod3d3V3PPPOMfv31Vy1dulS9e/c2Ou7q1auGcdY0d+5czZ07N89xkydP1osvvmjVcwMAAOD+lNfzrL59+2rQoEFm+7KT4A0bNtQjjzxiaG/ZsqVKly6t+Ph4LV26VB988IHV4pWk+Ph4sy+6Zn+9bds2nTx5UgcPHrRoQVWvXr20ceNG7d27V+PGjdNTTz2lcuXK5XqMtZ9TS9yvAwBQ2EjA56BkyZKSbpcTt4ZLly7p0qVLZvs8PT3VqVMntWvXzvDmZE7c3d316KOP5nm+ihUrFijOO2Vf+70ks/PLyclJw4cPV79+/bR582b9+uuv2r17t+ElgLS0NG3btk2//vqrJk2apBdeeMFwrLV+ZikpKYbPs/elt1Re+6AePXo0x76GDRuqadOm6tq1q9F/JMzx8fGx6I3XEiVK5DkGAAA8+FauXClJatWqlck9QpcuXfTrr7/q3Llz2rVrl1HZyuzte3JTp04dZWRkmO3r37+/Bg8ebNJesWJFi+5HrZ34BwAAwP3r7udZmZmZSkxMVFRUlFJTU/Xll18qKSlJo0aNMtp28saNG4bV4ncnwR0dHdWuXTstX75cP/74o4YNG2bVVeJr165VSkqKHB0d1b59e6O+1q1bq1SpUkpISNDSpUstSsDb2Njoo48+UpcuXfT333/rww8/zHUxlmT959QS9+sAABQ2EvA58PDwkGS91d93rkjPysrS1atXtWTJEi1YsEDXrl2Tm5ubRfuWN2/eXFOmTLFKTHnJLvdeunTpIjnfncqVK6du3bqpW7duysrK0qlTpxQeHq6ffvpJhw4dUnp6ukaNGqXHH3/csBI++2cWHx+vzMxM2doWbIeFO1e937hxw+IbzczMTMMbqTm9uXrnivS0tDQdO3ZMM2bM0O7du3X58mU1btw4z+S7JI0ZM8ZQrh8AAPyznT59WkeOHJFk+kBSktq2bStnZ2fdunVL3333nVECvmzZsrp69apiY2NznP+pp55Senq6UVtkZKQSEhJyPCYoKOieqzEBAADg4ZLT86zU1FT98MMP+vDDD/XNN98oIyND48ePN/SvXbtWqampcnBw0PPPP29yfNeuXbV8+XLdunVLq1evVs+ePa0Wc/bK+xYtWpg8Iy1RooSee+45rVy5Uj///LNGjhxpUTXN7FL0kyZN0rp169S+fXs999xzOY639nNqift1AAAKG3vA56B69eqSpJiYmFz3Hb/TtWvXdOHChTzH2djYqEKFCho8eLAmTpyotLQ0zZ8/Xx9++OE9xWxNqampunjxoiTJ29u7WGOxsbGRj4+PXn31VYWFhWnWrFmyt7dXRkaGwsLCDOMee+wxSbcT4adPn7Zo7sTERJ05c8ao7c593yMiIiyO89SpU4Y3UWvVqpXneAcHBzVo0ECLFi1SkyZNFB0drb59+2rfvn0WnxMAAOD77783fP7WW2+pVq1aRh8NGjQwbHe0ZcsW/f3334bx2fcsud07ff311/ruu++MPurUqVNIVwMAAIB/GkdHR3Xv3l39+/eXJC1btsyokmh2EjwtLU3+/v4m97vBwcGGsZbux26JyMhIQzXLjRs3mpy3Vq1ahkpUKSkpFm0bmq1Xr17y8/OTJI0fPz7H7Sylwn1ODQAACgcJ+By0bt1a0u39uXft2mXRMcuXL1fr1q3Vrl07paamWnTMiy++qG7dukmSlixZYrihLG5HjhwxJJOffPLJQj/fL7/8os6dOxuVlM/J888/byj5dO7cOUP7s88+KycnJ0nS77//btF5t23bpg4dOqhp06a6cuWKJKlevXqGPZWyy1tZInusg4ODmjRpYvFx9vb2mj59ujw8PJScnKyBAwca9mMFAADITVpamtasWSPpdtUiT09Psx8VKlQwjL8zYR8QECBJOnbsmP7666+ivwAAAADg/7vzeezx48cl3V4cc+zYMUlS+fLlc7zfzV55HhkZqQMHDlglnuz7ZgcHhxzP6+npadgCMj/J/+xS9CVLllRsbGyue9cX1XNqAABgPSTgc/DII4+ofv36kqTQ0NA898dMTU3VsmXLJN1eie3o6GjxuUaOHKnKlStLkiZOnGhYeV6cli9fLkmqUKGC4W3MwuTk5KTIyEidOHFCp06dynN8dumlO0u9lyxZUq1atZIkffXVV0Z7uefk22+/lSS5ubkZHkzb2dkZSlX98MMPFj2MjouL05IlSyRJnTp1sqjc1J3c3NwMFRBiY2M1duzYfB0PAAD+mbZv324oH79o0SLt2LHD7Mevv/4qHx8fSbdXFGVmZkqSXnjhBcMemXPmzCmeiwAAAAB0OymdLftZbPZipbJly2rbtm053u/++OOPsre/vduqNVbBp6Wl6ccff5Qk/etf/8rxvDt27NB7770nSTp//rzCw8MtPkfVqlU1dOhQSdLPP/+sDRs2mB1XlM+pAQCAdZCAz8WoUaNkY2OjgwcP6tNPP8117PTp03XhwgXZ2trq7bffztd5XFxcNHHiREnSrVu3NG7cuALHbA179uwxrKTq16+f7OzsCv2cTz31lOElhI8++siw+t6c5ORkbdmyRdLt/ZfuNHToUDk5OenixYuaOHGi4eGyOYsXL9b+/fslSe+++65RX79+/VSrVi0lJSVp4MCBiouLy3GepKQkDRkyRLGxsfLw8NCwYcNyvdactGzZUp07d5Z0e2X+unXrCjQPAAD458helePj46O6devmOvbf//63JOnixYvasWOHpNur5ocMGSJJWrNmjUJDQ/M85/Hjx3X27Nl7CRsAAAAwsX37dkm3E/GPP/64UlNTDUnwzp0755pIrlChgmGl+Pr163N9lmeJbdu2GcrCBwUF5Tr2xRdfNKyC/+677/J1nuDgYDVu3FjS/xYKmVNUz6kBAIB1kIDPRYMGDfTmm29KkmbPnq2hQ4earM6+cOGC3n//fX355ZeSpHfeeSfPh5/mNGnSRF27dpUk/frrr1q9evW9BV8AiYmJWrJkid58801lZmbqmWee0csvv1wk53ZwcNCoUaNka2ur33//XT179tSuXbuUkZFhNO7IkSPq06eP/vzzTzVs2FDPP/+8UX+VKlU0cuRI2djYaNmyZerbt68OHjxoNCY2NlYfffSRJk+eLOn2TfJzzz1nNMbR0VEzZsyQp6enjh8/rqCgIP30009GJZsyMjK0Y8cO/etf/9Lvv/8uZ2dnTZs2zVC+viBGjRplWD0/adIkXb9+vcBzAQCAh9vVq1f166+/Ssr7oaAkde3aVc7OzpKMVwX17NlTvXr1kiR9/PHH6tevn8LDw41eZMzKytKhQ4c0cuRIdevWTX///bfs7e312GOPWfOSAAAA8A+UlZWljRs3av78+ZKkdu3aqWLFitq6dashkW7J/W72c8yc9mNPTU3VtWvXcv3IrqiZvfLe19dXjz/+eK7nLVeunGG7zK1bt+Zra8nsUvTOzs65rmwvyufUAADg3tkXdwD3u8GDB6ts2bKaOnWq1q5dq7Vr18rDw0NeXl6Kj49XVFSUpNsJ5Pfee099+/Yt8LlGjBihHTt2KC4uTh999JGaNWtmkszdsWOHxUnxgIAA9e/f36R94sSJhlKjkpSenq74+Hj99ddfhoR3q1atNHXqVEPppqLQpk0bTZs2TR988IEOHz6sV199Va6urqpUqZLs7e0VExNjKLH69NNPa/bs2WZX5//73/9W6dKlNXLkSO3cuVM7d+5UuXLlVKlSJSUlJen8+fPKzMyUra2tXnnlFQ0fPtxsPDVq1NDKlSs1ePBg7dmzR4MHD5azs7OqVq0qW1tbXbhwQfHx8ZJu34zPnDnznh9Cu7m5afjw4Ro5cqRiY2M1efJk/ec//zEZd/fPMDdjx45VnTp17ikuAABw/1m1apXS09Pl4OCgLl265Dm+VKlS6ty5s8LCwrR9+3ZFR0erUqVKkqQxY8aoVq1amjZtmrZv367t27fLxcVFlStXlp2dnaKjo3Xjxg1Jtx8SNm/eXCEhIYay9ndbsWKFfv/9d4uuIygoSC+99JKFVw0AAIAHlbnnWenp6bp48aLhmd/jjz+u8ePHS/pfErxOnTqqXbt2nvM/88wzql69us6dO6ewsDD16dPHqKz9unXr8qw4OXLkSHXo0CFfL7pKt5P/q1evVlpamr7//nu99dZbFh0n3S4xP3ToUMP2lDmx5nNq7tcBAChcJOAt0KdPH7Vs2VLLli3Tnj17FBUVpePHj8vJyUm1a9c2rBSvWrXqPZ3Hzc1NI0aM0PDhw3X9+nVNmjRJ06dPNxoTGxtruCHNy6OPPmq2PTIy0uhrW1tblSxZUo899pjq1q2rLl266JlnninYRdyjjh076tlnn9WqVau0c+dOnT171vBiQPny5fX888+rY8eOatu2ba7zdOjQQU899ZSWL1+u3377TWfPntXJkyfl6Oioxx57TI0bN1b37t3l6+ub6zzly5fX119/re3bt2vt2rU6dOiQzp49q6ysLFWoUEFNmjTR888/r7Zt21qtVP+LL76oNWvWKDw8XKtWrVLnzp3VtGlTozF3/wxzc/PmTavEBQAA7i/Zq3patWplcQWel19+WWFhYcrMzNSyZcs0aNAgQ1+3bt3UsWNHrVu3Ttu2bVNERITOnz8v6faqnjp16sjPz08dO3ZUtWrVcj3PpUuXdOnSJYtiatKkiUXjAAAA8GAz9zyrRIkScnNzU8uWLfXcc8+pS5cusre31+XLl/Xbb79JsjwJLt1emDN58mSdP39eu3btKtAzzlWrVikjI0MODg6G7SLz0rBhQ/n6+ioiIkLLly83rFa3VM+ePbVhwwbt2bMn13HWek7N/ToAAIXLJiu32jYAAAAAAAAAAAAAAMAi7AEPAAAAAAAAAAAAAIAVkIAHAAAAAAAAAAAAAMAKSMADAAAAAAAAAAAAAGAFJOABAAAAAAAAAAAAALACEvAAAAAAAAAAAAAAAFiBfXEHcL9p1KiRUlNT5eHhUdyhAAAAWNXVq1fl6Oioffv2FXcouAfcrwIAgIcV96sPB+5XAQDAw8rS+1US8HdJSUlRRkZGcYcBAABgdenp6crKyiruMHCPuF8FAAAPK+5XHw7crwIAgIeVpferJODvUqFCBUnSli1bijkSAAAA62rdunVxhwAr4H4VAAA8rLhffThwvwoAAB5Wlt6vsgc8AAAAAAAAAAAAAABWQAIeAAAAAAAAAAAAAAArIAEPAAAAAAAAAAAAAIAVkIAHAAAAAAAAAAAAAMAKSMADAAAAAAAAAAAAAGAFJOABAAAAAAAAAAAAALACEvAAAAAAAAAAAAAAAFgBCXgAAAAAAAAAAAAAAKyABDwAAAAAAAAAAAAAAFZAAh4AAAAAAAAAAAAAACsgAQ8AAAAAAAAAAAAAgBWQgAcAAAAAAAAAAAAAwApIwAMAAAAAAAAAAAAAYAUk4AEAAAAAAAAAAAAAsAIS8AAAAAAAAAAAAAAAWAEJeAAAAAAAAAAAAAAArIAEPAAAAAAAAAAAAAAAVkACHgAAAAAAAAAAAAAAKyABDwAAAAAAAAAAAACAFdgXdwAAgIdfZmambG3z985XQY4BAAD3p+TkZGVkZOTYb2dnJycnpyKMCAAAPAwK89kBzyUAAEBBkYAHABQ6W1tbrV+/XomJiRaNd3FxUfv27Qs5KgAAUFQyMjL0/fff59j/0ksvFWE0AADgYWFra6sdO3boxo0bVp23TJkyat68uVXnBAAA/xwk4AEARSIxMVEJCQnFHQYAAAAAAHiI3LhxQ9euXSvuMAAAAAyooQMAAAAAAAAAAAAAgBWQgAcAAAAAAAAAAAAAwApIwAMAAAAAAAAAAAAAYAUk4AEAAAAAAAAAAAAAsAIS8AAAAAAAAAAAAAAAWAEJeAAAAAAAAAC4D8XGxiokJET+/v5q2LCh+vXrp9OnTxv6R44cqVq1ahl9NG/e3NCfmZmpOXPmqFmzZqpfv75ee+01RUVFGZ3jxIkTCg4OVoMGDdSiRQuFhoYW2fUBAAA8jEjAAwAAAAAAAMB96K233tJff/2lBQsW6Pvvv5eTk5N69+6tpKQkSdLJkyfVv39/7dy50/CxatUqw/Hz5s3T0qVLNXHiRIWFhcnGxkZ9+/ZVamqqJCkuLk59+vRRtWrVtGLFCg0YMECzZ8/WihUriuNyAQAAHgok4AEAAAAAAADgPhMXF6cqVaroww8/VN26deXt7a23335bV69e1alTp5SRkaHTp0+rbt268vDwMHy4ublJklJTU7Vo0SINGDBAAQEB8vX11cyZM3X58mVt2rRJkrRs2TI5Ojpq/Pjx8vb2VlBQkHr37q0FCxYU56UDAAA80Io9AX/x4kWTMkm1atXS8uXLJeVdAsmSMkoAAAAAAAAA8CApV66cZsyYoZo1a0qS/v77b4WGhsrLy0s1atTQ+fPnlZKSIm9vb7PHR0REKDExUf7+/oa20qVLq06dOtq7d68kad++ffLz85O9vb1hjL+/v86dO6fY2NhCvDoAAICHl33eQwrXyZMnVaJECW3evFk2NjaGdldXV0MJpDZt2mjChAk6dOiQJkyYoLJlyyooKEjS/8ooTZ48WZ6enpo6dar69u2rtWvXytHRsbguCwAAAAAAAACsYuzYsYbV6p9++qmcnZ0VGRkpGxsbLV68WDt27JCtra0CAgI0aNAgubq6KiYmRpJUsWJFo7kqVKigS5cuSZJiYmLk4+Nj0i9J0dHRcnd3L4KrAwAAeLgU+wr4yMhIVa9eXRUqVDAqleTk5JRnCSRLyigBAAAAAAAAwIPs1Vdf1YoVK9SlSxe98847OnbsmE6dOiVbW1tVrlxZ8+fP1/Dhw7V9+3a9/fbbyszMNOwTf/cipRIlSiglJUWSlJycbLZfkmEMAAAA8ue+WAFfo0YNs305lUD67LPPFBsbq4sXL+ZaRqljx46FHj8AAAAAAAAAFKbs56cffvihDh06pG+++UYfffSRevfurdKlS0uSfHx85OHhoe7du+uPP/6Qk5OTpNuLmLI/l24n1kuWLClJcnJyUmpqqtG5shPvzs7OhX5dAAAAD6P7YgV8bGysevTooSZNmujll1/Wr7/+Kul2CSQvLy+j8XeWQLKkjBIAAAAAAAAAPGhiY2O1du1aZWRkGNpsbW3l7e2tK1euyMbGxpB8z5ZdTj4mJsbwzPTKlStGY65cuWJ45url5WW2X5I8PT2te0EAAAD/EMWagE9NTdX58+eVkJCgQYMG6fPPP1fdunXVt29fhYeH51kCyZIySgAAAAAAAADwoLly5YqGDh2qPXv2GNrS0tJ0/PhxeXt7a+jQoXr99deNjvnjjz8k3V4x7+vrq1KlSmn37t2G/vj4eB0/flyNGjWSJPn5+Wn//v1GSf7w8HBVr16d/d8BAAAKqFhL0Ds6Omrv3r2yt7c3JNGfeOIJnTlzRqGhoXmWQLKkjBIAAAAAAAAAPGh8fX3VtGlTTZgwQRMnTlTp0qU1f/58xcfHq3fv3jp58qTeeustffrpp+rYsaPOnTunDz74QJ06dZK3t7ckKTg4WNOmTZObm5sqV66sqVOnysvLS23btpUkBQUFaeHChRo9erTeeOMNHTlyRIsXL9aECROK89IBAAAeaMW+B7y5vYR8fHy0c+fOPEsgpaenG9qqVq1qNMbX17cQowYAAAAAAACAwmNjY6NZs2Zp+vTpGjRokG7evKlGjRppyZIlqlSpkipVqqTZs2dr/vz5mj9/vlxdXdW5c2cNGjTIMMfAgQOVnp6uMWPGKDk5WX5+fgoNDTUshnJ3d9fChQs1adIkBQYGysPDQ8OGDVNgYGAxXTUAAMCDr1gT8BEREXr55Ze1YMECQ9kjSTp69Khq1Kih2rVra+nSpcrIyJCdnZ0k4xJIrq6uhjJK2Qn47DJKwcHBxXJNAAAAAAAAAGANrq6uGj9+vMaPH2+2v127dmrXrl2Ox9vZ2SkkJEQhISE5jqlXr57CwsLuNVQAAAD8f8W6B7yPj49q1qypCRMmaN++fTpz5owmT56sQ4cOqX///goKClJCQoJGjx6t06dPa+XKlVq8eLHefPNNSbdL2GeXUdqyZYsiIiI0ePBgozJKAAAAAAAAAAAAAAAUhWJNwNva2mr+/PmqW7euBg0apMDAQB0+fFhffPGFatWqZSiBdO7cOQUGBmru3LkmJZAGDhyol156SWPGjNHLL78sOzs7ozJKAAAAwL1IS0vTzJkz1aJFCzVs2FA9evTQgQMHDP0nTpxQcHCwGjRooBYtWig0NNTo+MzMTM2ZM0fNmjVT/fr19dprrykqKqqoLwMAAAAAAABAESj2PeDd3Nz00Ucf5difVwkkS8ooAQAAAAX16aefasWKFZoyZYoeeeQRLViwQH379tVPP/0kR0dH9enTR23atNGECRN06NAhTZgwQWXLllVQUJAkad68eVq6dKkmT54sT09PTZ06VX379tXatWt5aRQAAAAAAAB4yBTrCngAAADgfrdlyxZ16tRJTZs21aOPPqoRI0YoISFBhw4d0rJly+To6Kjx48fL29tbQUFB6t27txYsWCBJSk1N1aJFizRgwAAFBATI19dXM2fO1OXLl7Vp06ZivjIAAAAAAAAA1kYCHgAAAMhF2bJltW3bNl24cEEZGRkKCwuTo6OjateurX379snPz0/29v8rLOXv769z584pNjZWERERSkxMlL+/v6G/dOnSqlOnjvbu3VsclwMAAAAAAACgEBV7CXoAAADgfjZ69GgNHjxYrVu3lp2dnWxtbTV79mxVrVpVMTEx8vHxMRpfoUIFSVJ0dLRiYmIkSRUrVjQZc+nSpaK5AAAAAAAAAABFhgQ8AAAAkIszZ86odOnS+u9//ytPT08tX75cw4cP1zfffKPk5GSTfdxLlCghSUpJSVFSUpIkmR1z48aNorkAAAAAAAAAAEWGBDwAAACQg4sXLyokJERffvmlGjVqJEmqW7euTp8+rU8++UROTk5KTU01OiYlJUWS5OzsLCcnJ0m394LP/jx7TMmSJYvoKgAAAAAAAAAUFfaABwAAAHJw5MgRpaWlqW7dukbt9evX1/nz5+Xl5aUrV64Y9WV/7enpaSg9b26Ml5dXIUYOAAAAAAAAoDiQgAcAAABykJ1AP3nypFF7ZGSkHn30Ufn5+Wn//v3KyMgw9IWHh6t69epyd3eXr6+vSpUqpd27dxv64+Pjdfz4ccOKegAAAAAAAAAPDxLwAAAAQA7q1aunRo0aafjw4dq1a5fOnz+vWbNmKTw8XP369VNQUJASEhI0evRonT59WitXrtTixYv15ptvSrq993twcLCmTZumLVu2KCIiQoMHD5aXl5fatm1bzFcHAAAAAAAAwNrYAx4AAADIga2trebNm6dZs2Zp5MiRunHjhnx8fPTll1+qQYMGkqSFCxdq0qRJCgwMlIeHh4YNG6bAwEDDHAMHDlR6errGjBmj5ORk+fn5KTQ0VI6OjsV0VQAAAAAAAAAKCwl4AAAAIBdlypTRuHHjNG7cOLP99erVU1hYWI7H29nZKSQkRCEhIYUVIgAAAAAAAID7BCXoAeAfLjMzs1DHF5WCxHW/XgsAAAAAAAAAAHgwsQIeAP7hbG1ttX79eiUmJuY5tnz58goICLB4/J3HFLb8XIckubi4qH379oUcFQAAAAAAAAAA+CchAQ8AUGJiohISEvIc5+Likq/xdx5TFPITFwAAAAAAAAAAgLVRgh4AAAAAAAAAAAAAACsgAQ8AAAAAAAAAAAAAgBWQgAcAAAAAAAAAAAAAwApIwAMAAAAAAAAAAAAAYAUk4AEAAAAAAAAAAAAAsAIS8AAAAAAAAAAAAAAAWAEJeAAAAAAAAAAAAAAArIAEPAAAAAAAAAAAAAAAVkACHgBw33F0dFRmZmZxhwEAAAAAAAAAAJAv9sUdAAAAd3NwcJCtra3Wr1+vxMTEPMeXL19eAQEBRRAZAAAAAAAAAABAzkjAAwDuW4mJiUpISMhznIuLSxFEAwAAAAAAAAAAkDtK0AMAAAAAAAAAAAAAYAUk4AEAAAAAAAAAAAAAsAIS8AAAAAAAAAAAAAAAWAEJeAAAAAAAAAAAAAAArIAEPAAA+ZCZmVmo4wEAAAAAAAAAwIPLvrgDAADgQWJra6v169crMTExz7EuLi5q3759EUQFAAAAAAAAAADuByTgAQDIp8TERCUkJBR3GAAAAAAAAAAA4D5DCXoAKCKULgcAAAAAAAAAAHi4sQIeAIoIpcsBAAAAAAAAAAAebiTgAaAIFXbp8szMTNnaUtwEAAAAAAAAAACgOJCAB4CHSH5W2UtS+fLlFRAQUMhRAQAAAAAAAAAA/DOQgAeAh0x+Vtm7uLgUcjQAAAAAAAAAAAD/HNQpBgAAAAAAAAAAAADACkjAAwAAAAAAAAAAAABgBSTgAQAAAAAAAAAAAACwAhLwAAAAAAAAAAAAAABYAQl4AAAAAAAAAAAAAACsgAQ8AAAAAAAAANyHYmNjFRISIn9/fzVs2FD9+vXT6dOnDf0nTpxQcHCwGjRooBYtWig0NNTo+MzMTM2ZM0fNmjVT/fr19dprrykqKspoTF5zAAAAIH9IwAMAAAAAAADAfeitt97SX3/9pQULFuj777+Xk5OTevfuraSkJMXFxalPnz6qVq2aVqxYoQEDBmj27NlasWKF4fh58+Zp6dKlmjhxosLCwmRjY6O+ffsqNTVVkiyaAwAAAPljX9wBAAAAAAAAAACMxcXFqUqVKnrrrbdUs2ZNSdLbb7+trl276tSpUwoPD5ejo6PGjx8ve3t7eXt7KyoqSgsWLFBQUJBSU1O1aNEihYSEKCAgQJI0c+ZMNWvWTJs2bVLHjh21bNmyXOcAAABA/rECHgAAAAAAAADuM+XKldOMGTMMyfe///5boaGh8vLyUo0aNbRv3z75+fnJ3v5/a6z8/f117tw5xcbGKiIiQomJifL39zf0ly5dWnXq1NHevXslKc85AAAAkH+sgAcAAAAAAACA+9jYsWMNq9U//fRTOTs7KyYmRj4+PkbjKlSoIEmKjo5WTEyMJKlixYomYy5duiRJec7h7u5eKNcDAADwMGMFPAAAAJCD3bt3q1atWmY/WrduLUk6ceKEgoOD1aBBA7Vo0UKhoaFGc2RmZmrOnDlq1qyZ6tevr9dee01RUVHFcTkAAAB4QL366qtasWKFunTponfeeUfHjh1TcnKyHB0djcaVKFFCkpSSkqKkpCRJMjsmJSVFkvKcAwAAAPlHAh4AAADIQcOGDbVz506jj0WLFsne3l79+/dXXFyc+vTpo2rVqmnFihUaMGCAZs+erRUrVhjmmDdvnpYuXaqJEycqLCxMNjY26tu3r1JTU4vxygAAAPAgqVGjhp544gl9+OGHqlKlir755hs5OTmZ3FNmJ82dnZ3l5OQkSWbHlCxZUpLynAMAAAD5RwIeAAAAyIGjo6M8PDwMH2XLltXkyZP13HPPqVu3boYyoOPHj5e3t7eCgoLUu3dvLViwQNLth52LFi3SgAEDFBAQIF9fX82cOVOXL1/Wpk2bivnqAAAAcD+LjY3V2rVrlZGRYWiztbWVt7e3rly5Ii8vL125csXomOyvPT09DaXnzY3x8vKSpDznAAAAQP6RgAcAAAAstGTJEl26dEkjR46UJO3bt09+fn6yt7c3jPH399e5c+cUGxuriIgIJSYmyt/f39BfunRp1alTR3v37i3y+AEAAPDguHLlioYOHao9e/YY2tLS0nT8+HF5e3vLz89P+/fvN0rQh4eHq3r16nJ3d5evr69KlSql3bt3G/rj4+N1/PhxNWrUSJLynAMAAAD5RwIeAAAAsEBKSormz5+vV199VRUqVJAkxcTEGFYPZcvui46OVkxMjCQZVh/dOebSpUtFEDUAAAAeVL6+vmratKkmTJigffv2KTIyUsOHD1d8fLx69+6toKAgJSQkaPTo0Tp9+rRWrlypxYsX680335R0u5pTcHCwpk2bpi1btigiIkKDBw+Wl5eX2rZtK0l5zgEAAID8s897CAAAAIDVq1crJSVFvXr1MrQlJyfL0dHRaFyJEiUk3U7YJyUlSZLZMTdu3CjkiAEAAPAgs7Gx0axZszR9+nQNGjRIN2/eVKNGjbRkyRJVqlRJkrRw4UJNmjRJgYGB8vDw0LBhwxQYGGiYY+DAgUpPT9eYMWOUnJwsPz8/hYaGGu5P3d3d85wDAAAA+UMCHgAAALDAqlWr9Nxzz6lcuXKGNicnJ6WmphqNS0lJkSQ5OzvLyclJ0u294LM/zx5TsmTJIogaAAAADzJXV1eNHz9e48ePN9tfr149hYWF5Xi8nZ2dQkJCFBISkuOYvOYAAABA/lCCHgDwj+To6KjMzMziDgPAA+LatWs6ePCgOnToYNTu5eWlK1euGLVlf+3p6WkoPW9uzN2l6wEAAAAAAAA8+FgBDwD4R3JwcJCtra3Wr1+vxMREi44pX768AgICCjkyAPejAwcOyMbGRo0bNzZq9/Pz09KlS5WRkSE7OztJUnh4uKpXry53d3e5urqqVKlS2r17t6pWrSpJio+P1/HjxxUcHFzk1wEAAAAAAACgcJGABwD8oyUmJiohIcGisS4uLoUcDYD7VUREhB555BGTsvFBQUFauHChRo8erTfeeENHjhzR4sWLNWHCBEm3q20EBwdr2rRpcnNzU+XKlTV16lR5eXmpbdu2xXEpAAAAAAAAAAoRCXgAAAAgD3///bfKli1r0u7u7q6FCxdq0qRJCgwMlIeHh4YNG6bAwEDDmIEDByo9PV1jxoxRcnKy/Pz8FBoaKkdHxyK8AgAAAAAAAABFgQQ8AAAAkIfx48fn2FevXj2FhYXl2G9nZ6eQkBCFhIQUQmQAAAAAAAAA7ie2xR0AAMCUo6OjMjMzizsMAAAAAAAAAAAA5AMr4AHgPuTg4CBbW1utX79eiYmJFh1Tvnx5BQQEFHJkAAAAAAAAAAAAyAkJeAC4jyUmJiohIcGisS4uLoUcDQAAAAAAAAAAAHJDCXoAAAAAAAAAAAAAAKyABDwAAAAAAAAAAAAAAFZAAh4AAAAAAAAAAAAAACsgAQ8AAAAAAAAAAAAAgBWQgAcAAAAAAAAAAAAAwAruqwT8uXPn1LBhQ61cudLQduLECQUHB6tBgwZq0aKFQkNDjY7JzMzUnDlz1KxZM9WvX1+vvfaaoqKiijp0AAAAAAAAAAAAAMA/3H2TgE9LS9P777+vW7duGdri4uLUp08fVatWTStWrNCAAQM0e/ZsrVixwjBm3rx5Wrp0qSZOnKiwsDDZ2Niob9++Sk1NLY7LAAAAAAAAAAAAAAD8Q903CfhPPvlELi4uRm3Lli2To6Ojxo8fL29vbwUFBal3795asGCBJCk1NVWLFi3SgAEDFBAQIF9fX82cOVOXL1/Wpk2biuMyAPxDZGZmFncIAAAAAAAAAAAAuM/YF3cAkrR3716FhYVp1apVatGihaF937598vPzk739/8L09/fXZ599ptjYWF28eFGJiYny9/c39JcuXVp16tTR3r171bFjx6K8DAD/ILa2tlq/fr0SExMtGl++fHkFBAQUclQAAAAAAAAAAAAoTsWegI+Pj9ewYcM0ZswYVaxY0agvJiZGPj4+Rm0VKlSQJEVHRysmJkaSTI6rUKGCLl26VIhRA4CUmJiohIQEi8beXeEDAAAAAAAAAAAAD59iL0E/fvx4NWjQQJ07dzbpS05OlqOjo1FbiRIlJEkpKSlKSkqSJLNjUlJSCiliAAAAAAAAAAAAAABMFesK+FWrVmnfvn368ccfzfY7OTkpNTXVqC07se7s7CwnJydJt/eCz/48e0zJkiULKWoAAAAAAAAAAAAAAEwVawJ+xYoVio2NNdr3XZLGjRun0NBQVapUSVeuXDHqy/7a09NT6enphraqVasajfH19S3c4AEAAAAAAAAAAAAAuEOxJuCnTZum5ORko7bnnntOAwcOVIcOHbRu3TotXbpUGRkZsrOzkySFh4erevXqcnd3l6urq0qVKqXdu3cbEvDx8fE6fvy4goODi/x6AAAAAAAAAAAAAAD/XMWagPf09DTb7u7ursqVKysoKEgLFy7U6NGj9cYbb+jIkSNavHixJkyYIOn23u/BwcGaNm2a3NzcVLlyZU2dOlVeXl5q27ZtUV4KAAAAAAAAAAAAAOAfrlgT8Hlxd3fXwoULNWnSJAUGBsrDw0PDhg1TYGCgYczAgQOVnp6uMWPGKDk5WX5+fgoNDZWjo2MxRg4AAAAAAAAAAAAA+Ke57xLwJ0+eNPq6Xr16CgsLy3G8nZ2dQkJCFBISUtihAQAAAAAAAAAAAACQI9viDgAAAAAAAAAAAAAAgIcBCXgAAAAAAAAAAAAAAKyABDwAAAAAAAAAAAAAAFZAAh4AAAAAAAAAAAAAACsgAQ8AAAAAAAAAAAAAgBWQgAcAAAAAAAAAAAAAwApIwAMAAAAAAAAAAAAAYAUk4AEAAAAAAAAAAAAAsAIS8AAAFBJHR0dlZmbm+7iCHAMAAAAAAAAAAIqffXEHAADAw8rBwUG2trZav369EhMTLTrGxcVF7du3L+TIAAAAAAAAAABAYSABDwBAIUtMTFRCQkJxhwEAsJLk5GRlZGSY7bOzs5OTk1MRRwQAAAAAAID7BQl4AAAAAMiHjIwMff/992b7XnrppSKOBgAAAAAAAPcT9oAHAAAAAAAAAAAAAMAKSMADAAAAAAAAAAAAAGAFJOABAAAAAAAAAAAAALACEvAAAABAHlatWqUOHTqobt266tixo37++WdD34kTJxQcHKwGDRqoRYsWCg0NNTo2MzNTc+bMUbNmzVS/fn299tprioqKKupLAAAAAAAAAFAESMADAAAAuVi9erVGjRql7t27a+3aterQoYOGDBmigwcPKi4uTn369FG1atW0YsUKDRgwQLNnz9aKFSsMx8+bN09Lly7VxIkTFRYWJhsbG/Xt21epqanFeFUAAAAAAAAACoN9cQcAAAAA3K+ysrI0e/Zsvfrqq3r11VclSe+8844OHDigPXv2aM+ePXJ0dNT48eNlb28vb29vRUVFacGCBQoKClJqaqoWLVqkkJAQBQQESJJmzpypZs2aadOmTerYsWNxXh4AAAAAAAAAK2MFPAAAAJCDs2fP6uLFi+rcubNRe2hoqN58803t27dPfn5+srf/33ut/v7+OnfunGJjYxUREaHExET5+/sb+kuXLq06depo7969RXYdAAAAAAAAAIoGCXgAAAAgB+fPn5ck3bp1S6+//rqeeeYZdevWTVu3bpUkxcTEyMvLy+iYChUqSJKio6MVExMjSapYsaLJmEuXLhVy9AAAAAAAAACKGgl4AAAAIAcJCQmSpOHDh6tTp05atGiRnn32Wb399tsKDw9XcnKyHB0djY4pUaKEJCklJUVJSUmSZHZMSkpKEVwBAAAAHmTXr1/X//3f/6l58+Z68skn9fLLL2vfvn2G/pEjR6pWrVpGH82bNzf0Z2Zmas6cOWrWrJnq16+v1157TVFRUUbnOHHihIKDg9WgQQO1aNFCoaGhRXZ9AAAADyP2gAcAAABy4ODgIEl6/fXXFRgYKEmqXbu2jh8/ri+++EJOTk5KTU01OiY7se7s7CwnJydJUmpqquHz7DElS5YsiksAAADAA2zIkCGKjY3VjBkz5Obmpm+//Vavv/66Vq5cKW9vb508eVL9+/dXcHCw4Rg7OzvD5/PmzdPSpUs1efJkeXp6aurUqerbt6/Wrl0rR0dHxcXFqU+fPmrTpo0mTJigQ4cOacKECSpbtqyCgoKK45IBAAAeeKyABwAAAHKQXV7ex8fHqL1GjRq6cOGCvLy8dOXKFaO+7K89PT0NpefNjbm7dD0AAABwp6ioKP32228aN26cGjVqpMcee0yjR4+Wp6en1q5dq4yMDJ0+fVp169aVh4eH4cPNzU3S7ZdAFy1apAEDBiggIEC+vr6aOXOmLl++rE2bNkmSli1bJkdHR40fP17e3t4KCgpS7969tWDBguK8dAAAgAcaCXgAAAAgB3Xq1JGLi4sOHz5s1B4ZGamqVavKz89P+/fvV0ZGhqEvPDxc1atXl7u7u3x9fVWqVCnt3r3b0B8fH6/jx4+rUaNGRXYdAAAAePCUK1dOn3/+uZ544glDm42NjbKysnTjxg2dP39eKSkp8vb2Nnt8RESEEhMT5e/vb2grXbq06tSpo71790qS9u3bJz8/P9nb/69Qqr+/v86dO6fY2NhCujIAAICHGyXoAQAAgBw4OTnpjTfe0H//+195enqqXr16WrdunX777Td9+eWXqlGjhhYuXKjRo0frjTfe0JEjR7R48WJNmDBB0u2934ODgzVt2jS5ubmpcuXKmjp1qry8vNS2bdtivjoAAADcz0qXLq2AgACjtp9//ll//vmnmjZtqsjISNnY2Gjx4sXasWOHbG1tFRAQoEGDBsnV1VUxMTGSZKjKlK1ChQq6dOmSJCkmJsak2lOFChUkSdHR0XJ3dy+sywMAAHhokYAHAAAAcvH222+rZMmShnKd3t7e+uSTT/T0009LkhYuXKhJkyYpMDBQHh4eGjZsmGG/eEkaOHCg0tPTNWbMGCUnJ8vPz0+hoaFydHQsrksCAADAA2j//v0aNWqUWrdurVatWmnOnDmytbVV5cqVNX/+fEVFRek///mPIiMjtXjxYiUlJUmSyX1niRIldOPGDUlScnKy2X5JSklJKYKrAgAAePiQgAcAAADy0KdPH/Xp08dsX7169RQWFpbjsXZ2dgoJCVFISEhhhQcAAICH3ObNm/X++++rfv36mjFjhiRpwIAB6t27t0qXLi1J8vHxkYeHh7p3764//vhDTk5Okm7vBZ/9uXQ7sV6yZElJtys+paamGp0rO/Hu7Oxc6NcFAADwMGIPeAAAAAAAAAC4T33zzTcaMGCAmjdvrgULFhiS6TY2Nobke7bscvIxMTGG0vNXrlwxGnPlyhV5eXlJkry8vMz2S5Knp6f1LwYAAOAfgAQ8AAAAAAAAANyHvv32W3344Yfq2bOnZs2aZVQufujQoXr99deNxv/xxx+SpBo1asjX11elSpXS7t27Df3x8fE6fvy4GjVqJEny8/PT/v37lZGRYRgTHh6u6tWrs/87AABAAZGABwAAAAAAAID7zLlz5/TRRx+pbdu2evPNNxUbG6urV6/q6tWrunnzpjp16qTffvtNn376qf78809t375do0aNUqdOneTt7S1HR0cFBwdr2rRp2rJliyIiIjR48GB5eXmpbdu2kqSgoCAlJCRo9OjROn36tFauXKnFixfrzTffLOarBwAAeHCxBzwAAAAAAAAA3Gc2bNigtLQ0bdq0SZs2bTLqCwwM1JQpUzR79mzNnz9f8+fPl6urqzp37qxBgwYZxg0cOFDp6ekaM2aMkpOT5efnp9DQUMNKend3dy1cuFCTJk1SYGCgPDw8NGzYMAUGBhblpQIAADxUSMADAAAAAAAAwH2mf//+6t+/f65j2rVrp3bt2uXYb2dnp5CQEIWEhOQ4pl69egoLCytwnAAAADBGCXoAAAAAAAAAAAAAAKyABDwAAAAAAAAAAAAAAFZAAh4AAAAAAAAAAAAAACsgAQ8AAAAAAAAAAAAAgBWQgAcAAAAAAAAAAAAAwApIwAMAAAAAAAAAAAAAYAUk4AEAAAAAAAAAAAAAsAIS8AAAAAAAAAAAAAAAWAEJeAAAAAAAAAAAAAAArIAEPAAAAAAAAAAAAAAAVkACHgAkZWZmFncIAAAAAAAAAAAAeMDZF3cAAHA/sLW11fr165WYmJjn2PLlyysgIKAIogIAAAAAAAAAAMCDhAQ8APx/iYmJSkhIyHOci4tLEUQDAAAAAAAAAACABw0l6AEAAAAAAAAAAAAAsAIS8AAAAAAAAAAAAAAAWAEJeAAAAAAAAAAAAAAArIAEPAAAAAAAAAAAAAAAVkACHgAAAAAAAAAAAAAAKyABDwAAAAAAAAAAAACAFZCABwAAAAAAAAAAAADACkjAAwAAAAAAAAAAAABgBSTgAQAAAAAAAAAAAACwAhLwAADcRxwdHZWZmZmvY/I7HgAAAAAAAAAAFA774g4AAAD8j4ODg2xtbbV+/XolJibmOd7FxUXt27cvgsgAAAAAAAAAAEBeSMADAHAfSkxMVEJCQnGHAQAAAAAAAAAA8oES9AAAAAAAAAAAAAAAWAEJeAAAAAAAAAAAAAAArIAEPAAAAAAAAAAAAAAAVkACHgAAAAAAAAAAAAAAKyABDwDAA8zR0VGZmZn5Pq4gxwAAAAAAAAAAgNzZF3cAAACg4BwcHGRra6v169crMTHRomNcXFzUvn37Qo4MAAAAAAAAAIB/HlbAAwDwEEhMTFRCQoJFH5Ym6gHcdvHiRdWqVcvkY/ny5ZKkEydOKDg4WA0aNFCLFi0UGhpqdHxmZqbmzJmjZs2aqX79+nrttdcUFRVVHJcCAAAAAAAAoJCxAh4AAADIxcmTJ1WiRAlt3rxZNjY2hnZXV1fFxcWpT58+atOmjSZMmKBDhw5pwoQJKlu2rIKCgiRJ8+bN09KlSzV58mR5enpq6tSp6tu3r9auXStHR8fiuiwAAAAAAAAAhYAEPAAAAJCLyMhIVa9eXRUqVDDpW7x4sRwdHTV+/HjZ29vL29tbUVFRWrBggYKCgpSamqpFixYpJCREAQEBkqSZM2eqWbNm2rRpkzp27FjUlwMAAAAAAACgEBV7CfrY2FiFhITI399fDRs2VL9+/XT69GlDPyU9AQDAgygzM7NQx6PonDx5UjVq1DDbt2/fPvn5+cne/n/vtfr7++vcuXOKjY1VRESEEhMT5e/vb+gvXbq06tSpo7179xZ67AAAAAAAAACKVrGvgH/rrbdka2urBQsWyNnZWbNnz1bv3r21adMmJScnU9ITAAA8kGxtbbV+/XolJibmOdbFxUXt27cvgqhQEJGRkfLw8FCPHj10/vx5Pfroo3r77bfVrFkzxcTEyMfHx2h89kr56OhoxcTESJIqVqxoMubSpUtFcwEAAAAAAAAAikyxJuDj4uJUpUoVvfXWW6pZs6Yk6e2331bXrl116tQphYeHU9ITAAA8sBITE5WQkFDcYeAepKam6vz58ypZsqSGDRsmZ2dnrVmzRn379tUXX3yh5ORkk5c+S5QoIUlKSUlRUlKSJJkdc+PGjaK5CAAAAAAAAABFplgT8OXKldOMGTMMX//9998KDQ2Vl5eXatSooU8++cRsSc/PPvtMsbGxunjxYq4lPUnAAwAA4F44Ojpq7969sre3NyTRn3jiCZ05c0ahoaFycnJSamqq0TEpKSmSJGdnZzk5OUm6ncjP/jx7TMmSJYvoKgAAAAAAAAAUlWLfAz7b2LFj9eyzz2r9+vWaNGmSnJ2dFRMTIy8vL6NxlPQEAABAUXJ2djZZwe7j46PLly/Ly8tLV65cMerL/trT09Nwn2puzN33uQAAAAAAAAAefPdNAv7VV1/VihUr1KVLF73zzjs6duzYPZX0zF55BAAAABRURESEGjZsqH379hm1Hz16VDVq1JCfn5/279+vjIwMQ194eLiqV68ud3d3+fr6qlSpUtq9e7ehPz4+XsePH1ejRo2K7DoAAAAAAAAAFI0CJeD37t2rxMREs33x8fFat25dvuesUaOGnnjiCX344YeqUqWKvvnmm3yV9Lx7DCU9AQAA/rmsdb/q4+OjmjVrasKECdq3b5/OnDmjyZMn69ChQ+rfv7+CgoKUkJCg0aNH6/Tp01q5cqUWL16sN998U9LtF0WDg4M1bdo0bdmyRRERERo8eLC8vLzUtm1bq10vAAAA7h+F8ewUAAAAD44CJeBfeeUVnTlzxmzf8ePHNXLkSIvmiY2N1dq1a41WDNna2srb29tQlpOSngAAAMgva92v2traav78+apbt64GDRqkwMBAHT58WF988YVq1aold3d3LVy4UOfOnVNgYKDmzp2rYcOGKTAw0DDHwIED9dJLL2nMmDF6+eWXZWdnp9DQUJMqTgAAAHg4WOteFAAAAA8me0sHDh8+3LCvelZWlsaPH69SpUqZjDt//rzKly9v0ZxXrlzR0KFD5e7urmeeeUaSlJaWpuPHj6tVq1YqX768li5dqoyMDNnZ2UkyLunp6upqKOlZtWpVSf8r6RkcHGzppQG4j2VmZsrWNn/vChXkGADAg68w7lclyc3NTR999FGO/fXq1VNYWFiO/XZ2dgoJCVFISIjF5wQAAMCDpbDuRQEAAPDgsTgB365dO33xxRdGbVlZWUZf29nZqUGDBurZs6dFc/r6+qpp06aaMGGCJk6cqNKlS2v+/PmKj49X7969VaJECS1cuFCjR4/WG2+8oSNHjmjx4sWaMGGCJOOSnm5ubqpcubKmTp1KSU/gIWJra6v169fnWLrtbi4uLmrfvn0hRwUAuB8Vxv0qAAAAYAnuRQEAAJDN4gR8q1at1KpVK0lSr169NH78eHl7e9/TyW1sbDRr1ixNnz5dgwYN0s2bN9WoUSMtWbJElSpVkiQtXLhQkyZNUmBgoDw8PMyW9ExPT9eYMWOUnJwsPz8/SnoCD5nExEQlJCQUdxgAgPtcYdyvAgAAAJYorHvR69eva8aMGfrll1+UkJCgWrVqaejQoWrUqJEk6cSJE5o0aZKOHj2qsmXLqlevXnr99dcNx2dmZmru3Llavny54uPj9dRTT2ncuHF69NFHDWPymgMAAAD5Y3EC/k5ff/211QJwdXXV+PHjNX78eLP9lPQEkB+Ojo6UoAcAWPV+FQAAAMgPa96LDhkyRLGxsZoxY4bc3Nz07bff6vXXX9fKlSvl5uamPn36qE2bNpowYYIOHTqkCRMmqGzZsgoKCpIkzZs3T0uXLtXkyZPl6empqVOnqm/fvlq7dq0cHR0VFxeX5xwAAADInwIl4JOSkjR//nxt27ZNSUlJyszMNOq3sbHR5s2brRIgAOSHg4NDvsvWly9fXgEBAYUcGQCgKHG/ClhXcnKyMjIycuy3s7OTk5NTEUYEAMD9y1r3olFRUfrtt9/03Xff6cknn5QkjR49Wjt27NDatWvl5OQkR0dHjR8/Xvb29vL29lZUVJQWLFigoKAgpaamatGiRQoJCTE895g5c6aaNWumTZs2qWPHjlq2bFmucwAAACD/CpSAnzRpklasWKHGjRurdu3arDQFcN/JT9l6FxeXQo4GAFDUuF8FrCsjI0Pff/99jv3dunXL9eXHu/fABQDgYWate9Fy5crp888/1xNPPGFos7GxUVZWlm7cuKGjR4/Kz89P9vb/e8Tr7++vzz77TLGxsbp48aISExPl7+9v6C9durTq1KmjvXv3qmPHjtq3b1+uc7i7uxcodgAAgH+yAiXgN27cqMGDB6tfv37WjgcAAAC4Z9yvAqYKcxV7RkaGVq5cmWP/iy++WKB5AQB4EFnrXrR06dImFft+/vln/fnnn2ratKlmzpwpHx8fo/4KFSpIkqKjoxUTEyNJqlixosmYS5cuSZJiYmJynYMEPAAAQP4VKAGfnp6uevXqWTsWAP8A7M8OACgK3K8CpvJaxf7SSy8VYTQAADy8CutedP/+/Ro1apRat26tVq1aafLkyXJ0dDQaU6JECUlSSkqKkpKSJMnsmBs3bki6/YJebnMAAAAg/wqUgG/atKl27NhhVL4IACyRn/3Z2ZsdAFBQ3K8CAACguBTGvejmzZv1/vvvq379+poxY4YkycnJSampqUbjspPmzs7Ohso2qampRlVuUlJSVLJkSYvmAAAAQP4VKAHfoUMHjRs3TteuXVP9+vUNN2x3euGFF+41NgAPKUv3Z2dvdgBAQXG/CgAAgOJi7XvRb775RpMmTVLbtm01bdo0w4p1Ly8vXblyxWhs9teenp5KT083tFWtWtVojK+vr0VzAAAAIP8KlIAfNGiQJGnVqlVatWqVSb+NjQ0PNAEAAFBsuF8FAABAcbHmvei3336rDz/8UL169dKoUaOMtvXz8/PT0qVLlZGRITs7O0lSeHi4qlevLnd3d7m6uqpUqVLavXu3IQEfHx+v48ePKzg42KI5AAAAkH8FSsBv2bLF2nEAAAAAVsP9KgAAAIqLte5Fz507p48++kht27bVm2++qdjYWEOfk5OTgoKCtHDhQo0ePVpvvPGGjhw5osWLF2vChAmSbu/9HhwcrGnTpsnNzU2VK1fW1KlT5eXlpbZt20pSnnMAAAAg/wqUgK9cubK14wAAAACshvtVAAAAFBdr3Ytu2LBBaWlp2rRpkzZt2mTUFxgYqClTpmjhwoWaNGmSAgMD5eHhoWHDhikwMNAwbuDAgUpPT9eYMWOUnJwsPz8/hYaGGsrYu7u75zkHAAAA8qdACfi5c+fmOebdd98tyNQAAADAPeN+FQAAAMXFWvei/fv3V//+/XMdU69ePYWFheXYb2dnp5CQEIWEhBR4DgAAAOSP1RPwpUqVUoUKFXigCQAAgGLD/SoAAACKC/eiAAAA/2wFSsBHRESYtN26dUv79+/X+PHjNXbs2HsODAAAACgo7leB/LOxsVFiYqLZvqysrCKOBgCABxf3ogAAAP9sBUrAm+Ps7KxmzZrpnXfe0ccff6wffvjBWlMDAAArcnR0VGZmpmxtbfN1XEGOAe4n3K8CucvIyNDKlSvN9r344otFHA0AAA8X7kUBAAD+OayWgM9WsWJFnTlzxtrTAgAAK3FwcJCtra3Wr1+f40rHu7m4uKh9+/aFHBlQNLhfBQAAQHHhXhQAAODhZ7UEfFZWli5duqQFCxaocuXK1poWAAAUksTERCUkJBR3GECR4X4VAAAAxYV7UQAAgH+OAiXgfX19ZWNjY7YvKytLH3/88T0FBQAAANwL7lfxT5ScnKyMjIwc+9nHHQD+H3v3H19z/f9//L6z7dgY82tMyo+mkfzOMtVMQ5FSjLd+TIWk9KGokXgXId5v8pt+MKWkjVmSSiG/Kq1R+uFHIiSMDFsb27Gz1/cP3523035vZzvb2e16uexysefz+Xqex+u8HJ7n9Xg9n0+gbDAWBQAAqNyKlYB/+umncx1E+vj4qGvXrmrSpElJ4wIAAACKjfEqKiOr1arY2Ng869nHHQCAssFYFAAAoHIrVgJ+5MiRjo4DAAAAcBjGq3BFzHAHAKBiYCwKAABQuRV7D3iLxaK4uDjFx8crJSVFtWrVUseOHdW3b19VqVLFkTECAAAARcZ4Fa6GGe4AAFQcjEUBAAAqr2Il4FNSUvTII4/owIEDuuaaa+Tn56cjR45o/fr1ev/997Vy5UpVr17d0bECAAAAhcJ4FQAAAM7CWBQAAKByMxXnoNdee02JiYlasWKFvvzyS8XExOjLL7/UihUrlJSUpHnz5jk6TgAAAKDQGK8CAADAWRiLAgAAVG7FSsBv3rxZzz77rDp27GhX3rFjR40aNUpffPGFQ4IDAAAAioPxKgAAAJyFsSgAAEDlVqwEfFpamq677rpc66677jpduHChJDEBAAAAJcJ4FQAAAM7CWBQAAKByK1YC/vrrr9eWLVtyrdu8ebMaN25coqAAAACAkmC8CgAAAGdhLAoAAFC5eRTnoKFDh2rMmDGyWCy69957VbduXZ09e1Yff/yxVq9erUmTJjk4TAAAAKDwGK8CAADAWRiLAgAAVG7FSsDffffdOnr0qN544w2tXr3aVu7p6amnn35aAwcOdFiAAAAAQFExXgUAAICzMBYFAACo3IqVgL948aJGjBihiIgI7dmzR8nJyTp16pQGDhwoX19fR8cIAAAAFAnjVQAAADgLY1EAAIDKrUh7wO/fv1/333+/3nnnHUlSjRo11KVLF3Xp0kVz587VQw89pMOHD5dGnAAAAECBGK8CAADAWRiLAgAAQCpCAv748eN67LHHlJycrGbNmtnVmc1mvfjii0pLS9NDDz2kxMREhwcKAAAA5IfxKgAAAJyFsSgAAACyFToB/9Zbb6lWrVr68MMPdeedd9rVeXt7KyIiQmvWrFHVqlX1xhtvODxQAAAAID+MV+EK0tPTlZaWluuPYRjODg8AAOSBsSgAAACyFXoP+J07d+rJJ59UzZo182xTp04dDR48WO+//74jYgMAAAAKjfEqXIHValVsbGyudf369SvjaAAAQGExFgUAAEC2Qs+A/+uvv9S4ceMC2wUGBrKMEgAAAMoc41UAAAA4C2NRAAAAZCt0Ar527do6c+ZMge3OnTuX75OeAAAAQGlgvAoAAABnYSwKAACAbIVOwAcFBSkuLq7AdmvXrtWNN95YoqAAAACAoiqL8eqRI0fUvn17u9fZv3+/IiIi1K5dO3Xt2lVRUVF2x2RlZWn+/PkKCQlR27ZtNWTIEB07dqxYrw8AAIDyiXunAAAAyFboBPygQYMUHx+vGTNmKCMjI0e9xWLRf/7zH+3YsUMPP/ywQ4MEAADOZTablZWVVaRjitoeKKnSHq9evnxZzz//vC5evGgrO3/+vAYPHqwmTZpozZo1GjlypObNm6c1a9bY2ixevFjR0dGaOnWqYmJi5ObmpmHDhslisRTvRAEAAFDucO8UAAAA2TwK27B169YaP368Xn31VX300Ufq3Lmzrr32WlmtVp08eVLx8fE6f/68nnnmGYWEhJRmzAAAoIx5enrKZDJpw4YNSktLK7B9tWrV1LNnzzKIDPif0h6vLliwQNWqVbMrW7VqlcxmsyZNmiQPDw8FBATo2LFjWrJkicLDw2WxWLRs2TJFRkYqNDRUkjRnzhyFhIRo48aN6t27t0POHQAAAM7FvVMAAABkK3QCXpIefvhhtWjRQlFRUdq8ebPtac5q1arp9ttv15AhQ9S2bdtSCRQAADhfWlqaUlNTnR0GkKfSGq8mJCQoJiZGa9euVdeuXW3lu3btUlBQkDw8/jesDg4O1ptvvqmkpCSdOHFCaWlpCg4OttXXqFFDLVu2VEJCAgn4Sig9PV1WqzXPesMwyjAaAADgSNw7BQAAgFTEBLwk3Xzzzbr55pslXVly02QyydfX1+GBAQAAAMXh6PFqSkqKxo4dq4kTJ6pBgwZ2dYmJiQoMDLQrq1evniTp5MmTSkxMlKQcx9WrV0+nTp0qdkyouKxWq2JjY/Os79evXxlGAwAAHI17pwAAAChyAv5qtWrVclQcACqgrKwsmUwmZ4cBAECeHDFenTRpktq1a6d77703R116errMZrNdWZUqVSRJGRkZunTpkiTl2iY5ObnEsQEAAKD84t4pAABA5VSiBDyAyq0o+0FLUt26dW373wIAUBGsXbtWu3bt0scff5xrvZeXlywWi11Z9lKjVatWlZeXlyTJYrHY/pzdxtvbu5SiBgAAAAAAAOAsJOABlEhR9oOuVq1aKUcDAIBjrVmzRklJSXb7vkvSyy+/rKioKF1zzTU6c+aMXV327/Xr11dmZqatrFGjRnZtWrRoUbrBAwAAAAAAAChzJOABAACAPMyaNUvp6el2ZXfeeadGjRqlu+++W5988omio6NltVrl7u4uSdq5c6eaNm2qOnXqqHr16vLx8VF8fLwtAZ+SkqJ9+/YpIiKizM8HAAAAAAAAQOkiAQ8AAADkoX79+rmW16lTRw0bNlR4eLiWLl2qCRMm6PHHH9dPP/2k5cuXa/LkyZKu7P0eERGhWbNmqXbt2mrYsKFmzpwpf39/9ejRoyxPBQAAAAAAAEAZIAEPAAAAFFOdOnW0dOlSTZs2TX379pWfn5/Gjh2rvn372tqMGjVKmZmZmjhxotLT0xUUFKSoqCiZzWYnRg4AAAAAAACgNJCABwAAAIrg119/tfu9TZs2iomJybO9u7u7IiMjFRkZWdqhAQAAAAAAAHAyk7MDAAAAAAAAAAAAAADAFZCABwAAAAAAAAAAAADAAUjAAwAAAAAAAAAAAADgACTgAQAAAAAAAAAAAABwABLwAAAAAAAAAAAAAAA4AAl4AAAAAAAAAAAAAAAcgAQ8AAAAAAAAAAAAAAAOQAIeAAAAAAAAAAAAAAAHIAEPAAAAAAAAAAAAAIADkIAHAAAAADiVm5ub0tLScv1JT093dngAAJQLixcv1qBBg+zKxo8fr+bNm9v9dOnSxVaflZWl+fPnKyQkRG3bttWQIUN07Ngxuz7279+viIgItWvXTl27dlVUVFSZnA8AAICr8nB2AAAAAACAys1qtSouLi7Xuv79+5dxNAAAlD/vvPOO5s+fr6CgILvyX3/9VU8++aQiIiJsZe7u7rY/L168WNHR0Zo+fbrq16+vmTNnatiwYVq/fr3MZrPOnz+vwYMHq3v37po8ebL27NmjyZMnq2bNmgoPDy+z8wMAAHAlJOABAAAAAAAAoBw6ffq0JkyYoN27d6tp06Z2dVarVYcOHdKIESPk5+eX41iLxaJly5YpMjJSoaGhkqQ5c+YoJCREGzduVO/evbVq1SqZzWZNmjRJHh4eCggI0LFjx7RkyRIS8AAAAMXEEvQAAAAAAAAAUA7t3btXvr6+Wrdundq2bWtXd/ToUWVkZCggICDXYw8cOKC0tDQFBwfbymrUqKGWLVsqISFBkrRr1y4FBQXJw+N/87SCg4N15MgRJSUllcIZAQAAuD5mwAMAAAAAAABAORQWFqawsLBc6w4ePCg3NzctX75c27dvl8lkUmhoqJ599llVr15diYmJkqQGDRrYHVevXj2dOnVKkpSYmKjAwMAc9ZJ08uRJ1alTx9GnBAAA4PKYAQ8AAAAAAAAAFcxvv/0mk8mkhg0b6o033tC4ceO0bds2jRgxQllZWbp06ZIkyWw22x1XpUoVZWRkSJLS09NzrZdkawMAAICiYQY8AAAAAAAAAFQwI0eO1GOPPaYaNWpIkgIDA+Xn56eBAwfq559/lpeXl6Qre8Fn/1m6klj39vaWJHl5eclisdj1m514r1q1almcBgAAgMthBjwAAAAAAAAAVDBubm625Hu27OXkExMTbUvPnzlzxq7NmTNn5O/vL0ny9/fPtV6S6tevXypxAwAAuDoS8AAAAAAAAABQwTz33HMaOnSoXdnPP/8sSWrWrJlatGghHx8fxcfH2+pTUlK0b98+dezYUZIUFBSk3bt3y2q12trs3LlTTZs2Zf93AACAYiIBDwAAAAAAAAAVzD333KOvv/5ar7/+uv744w9t27ZNL774ou655x4FBATIbDYrIiJCs2bN0ubNm3XgwAGNHj1a/v7+6tGjhyQpPDxcqampmjBhgg4dOqS4uDgtX75cw4cPd/LZAQAAVFzsAQ8AABzObDYrKytLJlPRnvUrzjEAAAAAUBndcccdmjdvnt544w298cYbql69uu699149++yztjajRo1SZmamJk6cqPT0dAUFBSkqKkpms1mSVKdOHS1dulTTpk1T37595efnp7Fjx6pv375OOisAAICKjwQ8AEkkvQA4lqenp0wmkzZs2KC0tLRCHVOtWjX17NmzlCMDAAAAgIppxowZOcruuusu3XXXXXke4+7ursjISEVGRubZpk2bNoqJiXFIjAAAACABD+D/K2qirG7dugoNDS3lqABUdGlpaUpNTXV2GAAAAAAAAAAAlAmnT3e9cOGCXnrpJXXp0kUdOnTQgw8+qF27dtnq9+/fr4iICLVr105du3ZVVFSU3fFZWVmaP3++QkJC1LZtWw0ZMkTHjh0r69MAXEJ2oqwwP5cuXXJ2uAAAAAAAAAAAAEC54vQE/JgxY/Tjjz9q9uzZio2N1U033aShQ4fq8OHDOn/+vAYPHqwmTZpozZo1GjlypObNm6c1a9bYjl+8eLGio6M1depUxcTEyM3NTcOGDZPFYnHiWQEAAAAAAAAAAAAAKhunLkF/7Ngxff311/rggw/UoUMHSdKECRO0fft2rV+/Xl5eXjKbzZo0aZI8PDwUEBCgY8eOacmSJQoPD5fFYtGyZcsUGRlpWwp7zpw5CgkJ0caNG9W7d29nnh4AAAAAAAAAAAAAoBJx6gz4WrVq6a233lKrVq1sZW5ubjIMQ8nJydq1a5eCgoLk4fG/5wSCg4N15MgRJSUl6cCBA0pLS1NwcLCtvkaNGmrZsqUSEhLK9FwAAAAAAAAAAAAAAJWbUxPwNWrUUGhoqMxms63ss88+0x9//KHbb79diYmJ8vf3tzumXr16kqSTJ08qMTFRktSgQYMcbU6dOlXK0QMAAAAAAAAAAAAA8D9O3wP+art379aLL76obt26KSwsTOnp6XbJeUmqUqWKJCkjI0OXLl2SpFzbZGRklE3QAAAAAPD/ubm5KS0tLc8fwzCcHSIAAAAAAABKkVP3gL/apk2b9Pzzz6tt27aaPXu2JMnLy0sWi8WuXXZivWrVqvLy8pIkWSwW25+z23h7e5dR5AAAAABwhdVqVVxcXJ71/fr1K8NoAAAAAAAAUNbKxQz4FStWaOTIkerSpYuWLFliS6b7+/vrzJkzdm2zf69fv75t6fnc2vxz6XoAAAAAAAAAAAAAAEqT0xPwK1eu1JQpU/Twww9r7ty5dsvJBwUFaffu3bJarbaynTt3qmnTpqpTp45atGghHx8fxcfH2+pTUlK0b98+dezYsUzPAwAAAADgeAUt65+enu7sEAEAAAAAAGycugT9kSNH9Oqrr6pHjx4aPny4kpKSbHVeXl4KDw/X0qVLNWHCBD3++OP66aeftHz5ck2ePFnSlb3fIyIiNGvWLNWuXVsNGzbUzJkz5e/vrx49ejjrtAAAAAAADlLQsv79+/cvw2gAAAAAAADy59QE/Oeff67Lly9r48aN2rhxo11d3759NWPGDC1dulTTpk1T37595efnp7Fjx6pv3762dqNGjVJmZqYmTpyo9PR0BQUFKSoqym4mPQAAAAAAAAAAAAAApc2pCfgnn3xSTz75ZL5t2rRpo5iYmDzr3d3dFRkZqcjISEeHBwAAAAAAAAAAAABAoTl9D3gAAAAAAAAAAAAAAFwBCXgAAAAgH0lJSYqMjFRwcLDat2+vJ554QocOHbLV79+/XxEREWrXrp26du2qqKgou+OzsrI0f/58hYSEqG3bthoyZIiOHTtW1qcBAAAAAAAAoAyQgAcAAADy8dRTT+n48eNasmSJYmNj5eXlpccee0yXLl3S+fPnNXjwYDVp0kRr1qzRyJEjNW/ePK1Zs8Z2/OLFixUdHa2pU6cqJiZGbm5uGjZsmCwWixPPCgAAAAAAAEBpcOoe8AAAAEB5dv78eV177bV66qmndMMNN0iSRowYofvuu0+//fabdu7cKbPZrEmTJsnDw0MBAQE6duyYlixZovDwcFksFi1btkyRkZEKDQ2VJM2ZM0chISHauHGjevfu7czTAwAAAAAAAOBgzIAHAAAA8lCrVi3Nnj3blnw/e/asoqKi5O/vr2bNmmnXrl0KCgqSh8f/nmsNDg7WkSNHlJSUpAMHDigtLU3BwcG2+ho1aqhly5ZKSEgo8/MBAAAAAAAAULqYAQ8AAAAUwr///W+tWrVKZrNZr7/+uqpWrarExEQFBgbatatXr54k6eTJk0pMTJQkNWjQIEebU6dOlU3gAAAAAAAAAMoMM+ABF5WVleXsEAAAcCmPPvqo1qxZoz59+ujpp5/W3r17lZ6eLrPZbNeuSpUqkqSMjAxdunRJknJtk5GRUTaBAwAAAAAAACgzzIAHXJTJZNKGDRuUlpZWYNu6deva9qUFAAC5a9asmSRpypQp2rNnj1asWCEvLy9ZLBa7dtmJ9apVq8rLy0uSZLFYbH/ObuPt7V1GkQMAAAAAAAAoKyTgAReWlpam1NTUAttVq1atDKIBAKDiSUpK0s6dO9WrVy+5u7tLuvKQW0BAgM6cOSN/f3+dOXPG7pjs3+vXr6/MzExbWaNGjezatGjRoozOAnBtbm5u+T506u7ubvcADAAAAAAAQGkiAQ8AAADk4cyZM3ruuedUp04dde7cWZJ0+fJl7du3T2FhYapbt66io6NltVptCfqdO3eqadOmqlOnjqpXry4fHx/Fx8fbEvApKSnat2+fIiIinHZegCuxWq2Ki4vLs75///5lGA0AAAAAAKjs2AMeAAAAyEOLFi10++23a/Lkydq1a5cOHjyocePGKSUlRY899pjCw8OVmpqqCRMm6NChQ4qLi9Py5cs1fPhwSVf2fo+IiNCsWbO0efNmHThwQKNHj5a/v7969Ojh5LMDAAAAAAAA4GjMgAcAAOWC2WxWVlaWTKaiPR9YnGOAwnJzc9PcuXP12muv6dlnn9Xff/+tjh076v3339c111wjSVq6dKmmTZumvn37ys/PT2PHjlXfvn1tfYwaNUqZmZmaOHGi0tPTFRQUpKioKJnNZmedFgAAAAAAAIBSQgIeAACUC56enjKZTNqwYUO+e/lerVq1aurZs2cpR4bKrnr16po0aZImTZqUa32bNm0UExOT5/Hu7u6KjIxUZGRkKUUIAAAAAAAAoLwgAQ8AAMqVtLQ0paamOjsMAAAAAAAAAACKjPVaAQAAAAAAAAAAAABwAGbAAwAAAABQxtLT02W1WvOsd3d3l5eXVxlGBAAAAAAAHIEEPAAAAAAAZcxqtSo2NjbP+v79+5dhNAAAAAAAwFFYgh4AAAAAAAAAAAAAAAcgAQ8AAAAAAAAAAAAAgAOQgAcAAAAAAAAAAAAAwAFIwAMAAAAAAAAAAAAA4AAk4AEAAAAAAAAAAAAAcAAS8AAAAAAAAAAAAAAAOAAJeAAAAAAAAAAAAAAAHIAEPAAAAAAAAAAAAAAADkACHgAAAAAAAAAAAAAAByABDwAAAAAAAAAAAACAA3g4OwAAAAAAAEqLm5ub0tLS8qx3d3eXl5dXGUYEAAAAAABcGQl4AAAAAIDLslqtiouLy7O+f//+ZRgNAAAAAABwdSxBDwAAAAAAAAAAAACAA5CABwAAAAAAAAAAAADAAUjAAwAAAAAAAAAAAADgACTgAQBAhWU2m5WVlVWkY4raHgAAAADKg8WLF2vQoEF2Zfv371dERITatWunrl27Kioqyq4+KytL8+fPV0hIiNq2bashQ4bo2LFjReoDAAAARePh7AAAAACKy9PTUyaTSRs2bFBaWlqB7atVq6aePXuWQWQAAAAA4DjvvPOO5s+fr6CgIFvZ+fPnNXjwYHXv3l2TJ0/Wnj17NHnyZNWsWVPh4eGSriTto6OjNX36dNWvX18zZ87UsGHDtH79epnN5kL1AQAAgKIhAQ8AACq8tLQ0paamOjsMAAAAAHCo06dPa8KECdq9e7eaNm1qV7dq1SqZzWZNmjRJHh4eCggI0LFjx7RkyRKFh4fLYrFo2bJlioyMVGhoqCRpzpw5CgkJ0caNG9W7d+8C+wAAAEDRsQQ9AAAAAAAAAJRDe/fula+vr9atW6e2bdva1e3atUtBQUHy8PjfHKvg4GAdOXJESUlJOnDggNLS0hQcHGyrr1Gjhlq2bKmEhIRC9QEAAICiYwY8AAAAAAAAAJRDYWFhCgsLy7UuMTFRgYGBdmX16tWTJJ08eVKJiYmSpAYNGuRoc+rUqUL1UadOnZKfBAAAQCXDDHgAAAAAAAAAqGDS09NlNpvtyqpUqSJJysjI0KVLlyQp1zYZGRmF6gMAAABFRwIeAAAAAAAAACoYLy8vWSwWu7LspHnVqlXl5eUlSbm28fb2LlQfAOAIWVlZFbp/ACgqlqAHKoCsrCyZTDwvAwAAAAAAgCv8/f115swZu7Ls3+vXr6/MzExbWaNGjezatGjRolB9AIAjmEwmbd++XcnJyQ7v29fXV126dHF4vwBQEiTggQrAZDJpw4YNSktLK1T7unXrKjQ0tJSjAgAAACo+Nze3PMfZ7u7uttmDAACUN0FBQYqOjpbVapW7u7skaefOnWratKnq1Kmj6tWry8fHR/Hx8bYEfEpKivbt26eIiIhC9QEAjpKcnKxz5845OwwAKBMk4IEKIi0tTampqYVqW61atVKOBgAAAHANVqtVcXFxudYNGDAg34dgSdADAJwpPDxcS5cu1YQJE/T444/rp59+0vLlyzV58mRJV/Z+j4iI0KxZs1S7dm01bNhQM2fOlL+/v3r06FGoPgAAAFB0JOABAAAAAMhFfsl5Serfv38ZRgMAgL06depo6dKlmjZtmvr27Ss/Pz+NHTtWffv2tbUZNWqUMjMzNXHiRKWnpysoKEhRUVEym82F7gMAAABFQwIeAAAAAAAAAMq5GTNm5Chr06aNYmJi8jzG3d1dkZGRioyMzLNNQX0AqByysrJkMpmcHQYAuAQS8AAAAAAAAAAAAJWYyWTS9u3blZyc7NB+GzZsqA4dOji0TwAo70jAAwAAAAAAAAAAVHLJyck6d+6cQ/v09fV1aH8AUBGwnggAAAAAAAAAAAAAAA5AAh4AAAAAAAAAAAAVjre3t7Kyskqt/9LsG4DrYgl6AAAAAAAAAAAAVDhms7nU9q/39fVVly5dHNongMqBBDwAAACQjwsXLmj27NnaunWrUlNT1bx5cz333HPq2LGjJGn//v2aNm2afvnlF9WsWVODBg3S0KFDbcdnZWVp4cKFWr16tVJSUnTzzTfr5ZdfVuPGjZ11SihAenq6rFZrnvWGYZRhNAAAAACAgpTG/vUAUFwk4AEAAIB8jBkzRklJSZo9e7Zq166tlStXaujQoYqLi1Pt2rU1ePBgde/eXZMnT9aePXs0efJk1axZU+Hh4ZKkxYsXKzo6WtOnT1f9+vU1c+ZMDRs2TOvXr5fZbHby2SE3VqtVsbGxedb369evDKMBAAAAAABARUICHgAAAMjDsWPH9PXXX+uDDz5Qhw4dJEkTJkzQ9u3btX79enl5eclsNmvSpEny8PBQQECAjh07piVLlig8PFwWi0XLli1TZGSkQkNDJUlz5sxRSEiINm7cqN69ezvz9AAAAAAAAAA4mMnZAQAAAADlVa1atfTWW2+pVatWtjI3NzcZhqHk5GTt2rVLQUFB8vD433OtwcHBOnLkiJKSknTgwAGlpaUpODjYVl+jRg21bNlSCQkJZXouAAAAAAAAAEofCXgAAAAgDzVq1FBoaKjdUvGfffaZ/vjjD91+++1KTEyUv7+/3TH16tWTJJ08eVKJiYmSpAYNGuRoc+rUqVKOHgAAAAAAAEBZIwEPAAAAFNLu3bv14osvqlu3bgoLC1N6enqOfdyrVKkiScrIyNClS5ckKdc2GRkZZRM0ckhPT1daWlqeP4ZhODtEAAAAAAAAVFDsAQ8AAAAUwqZNm/T888+rbdu2mj17tiTJy8tLFovFrl12Yr1q1ary8vKSJFksFtufs9t4e3uXUeT4J6vVqtjY2Dzr+/XrV4bRAAAAAAAAwJUwAx5wgqysLGeHAAAAimDFihUaOXKkunTpoiVLltiS6f7+/jpz5oxd2+zf69evb1t6Prc2/1y6HgAAAAAAAEDFxwx4wAlMJpM2bNigtLS0AtvWrVtXoaGhZRAVAADIzcqVKzVlyhQNGjRIL774okym/z3DGhQUpOjoaFmtVrm7u0uSdu7cqaZNm6pOnTqqXr26fHx8FB8fr0aNGkmSUlJStG/fPkVERDjlfAAAAAAAAACUHhLwgJOkpaUpNTW1wHbVqlUrg2gAAEBujhw5oldffVU9evTQ8OHDlZSUZKvz8vJSeHi4li5dqgkTJujxxx/XTz/9pOXLl2vy5MmSruz9HhERoVmzZql27dpq2LChZs6cKX9/f/Xo0cNZpwUAAAAAAACglJCABwAAAPLw+eef6/Lly9q4caM2btxoV9e3b1/NmDFDS5cu1bRp09S3b1/5+flp7Nix6tu3r63dqFGjlJmZqYkTJyo9PV1BQUGKioqS2Wwu69MBAAAAAAAAUMpIwAMAAAB5ePLJJ/Xkk0/m26ZNmzaKiYnJs97d3V2RkZGKjIx0dHgAAAAAAAAAyhlTwU0AAAAAAAAAAAAAAEBBSMADAAAAAAAAAAAAAOAAJOABAAAAAAAAAAAAAHAAEvAAAAAAAAAAAAAAADgACXgAAAAAAAAAAAAAABygXCXgFy9erEGDBtmV7d+/XxEREWrXrp26du2qqKgou/qsrCzNnz9fISEhatu2rYYMGaJjx46VZdgAAAAAgErIzc1NaWlpef6kp6c7O0QAAAAAAFDGPJwdQLZ33nlH8+fPV1BQkK3s/PnzGjx4sLp3767Jkydrz549mjx5smrWrKnw8HBJV5L20dHRmj59uurXr6+ZM2dq2LBhWr9+vcxms7NOBwAAAADg4qxWq+Li4vKs79+/fxlGAwAAAMCRvL29lZWVJZOp9Oaylnb/AJzD6Qn406dPa8KECdq9e7eaNm1qV7dq1SqZzWZNmjRJHh4eCggI0LFjx7RkyRKFh4fLYrFo2bJlioyMVGhoqCRpzpw5CgkJ0caNG9W7d29nnBIAAAAAAAAAAIDDkKgte2azWSaTSdu3b1dycrLD+/f19VWXLl0c3i8A53N6An7v3r3y9fXVunXrtGjRIp04ccJWt2vXLgUFBcnD439hBgcH680331RSUpJOnDihtLQ0BQcH2+pr1Kihli1bKiEhgQQ8AAAAAMBpspeoz41hGGUcDQAAACqy0kwEN2zYUB06dHB4v64iOTlZ586dc3YYACoQpyfgw8LCFBYWlmtdYmKiAgMD7crq1asnSTp58qQSExMlSQ0aNMjR5tSpU6UQLQAAAAAAhZPfEvX9+vUr42gAAABQ0ZVWItjX19fhfQJAZVau1ytJT0/PsY97lSpVJEkZGRm6dOmSJOXaJiMjo2yCBAAAAAAAAAAAAABA5TwB7+XlJYvFYleWnVivWrWqvLy8JCnXNt7e3mUTJAAAAAAAAAAAAAAAKucJeH9/f505c8auLPv3+vXr25aez62Nv79/2QQJAAAAAAAAAAAAAIDKeQI+KChIu3fvltVqtZXt3LlTTZs2VZ06ddSiRQv5+PgoPj7eVp+SkqJ9+/apY8eOzggZAAAAAAAAAAAAAFBJlesEfHh4uFJTUzVhwgQdOnRIcXFxWr58uYYPHy7pyt7vERERmjVrljZv3qwDBw5o9OjR8vf3V48ePZwcPQAAAAAAAAAAAACgMvFwdgD5qVOnjpYuXapp06apb9++8vPz09ixY9W3b19bm1GjRikzM1MTJ05Uenq6goKCFBUVJbPZ7MTIAQAAADhLenq63Spa/2QYRhlGAwAAAAAAgMqkXCXgZ8yYkaOsTZs2iomJyfMYd3d3RUZGKjIysjRDAwAALsBsNisrK0smU9EWASrOMQCcx2q1KjY2Ns/6fv36lWE0AAAAAAAAqEzKVQIeAACgNHl6espkMmnDhg1KS0sr1DHVqlVTz549SzkyAAAAAAAAAIArIAEPAAAqnbS0NKWmphaqbXFnzQMAAAAAAAAAKh8S8AAAAPkozqz5unXrKjQ0tJQjAwAAAAAAAACUNyTgAQAACqEos+arVatWytEAAAAAAAAAAMoj1lIFAAAAAAAAAAAAAMABSMADJZSVleXsEAAAAAAAAAAAAACUAyxBD5QQ+wIDAAAAAAAAAAAAkEjAAw7BvsAAAAAAAAAAAAAASMADAAAAqHDS09NltVpzrTMMo4yjAQAAAAAAAK4gAQ8AAACgwrFarYqNjc21rl+/fmUcDQAAAAAAAHCFydkBAAAAAAAAAACK58SJE2revHmOn9WrV0uS9u/fr4iICLVr105du3ZVVFSU3fFZWVmaP3++QkJC1LZtWw0ZMkTHjh1zxqkAAAC4BGbAAwAAAAAAAEAF9euvv6pKlSratGmT3NzcbOXVq1fX+fPnNXjwYHXv3l2TJ0/Wnj17NHnyZNWsWVPh4eGSpMWLFys6OlrTp09X/fr1NXPmTA0bNkzr16+X2Wx21mkBAABUWCTgAQAAAAAAAKCCOnjwoJo2bap69erlqFu+fLnMZrMmTZokDw8PBQQE6NixY1qyZInCw8NlsVi0bNkyRUZGKjQ0VJI0Z84chYSEaOPGjerdu3dZnw4AAECFxxL0AAAAAAAAAFBB/frrr2rWrFmudbt27VJQUJA8PP43Dys4OFhHjhxRUlKSDhw4oLS0NAUHB9vqa9SooZYtWyohIaHUYwcAAHBFzIAHAAAAAKCccXNzU1paWp717u7u8vLyKsOIAADl1cGDB+Xn56eHHnpIR48eVePGjTVixAiFhIQoMTFRgYGBdu2zZ8qfPHlSiYmJkqQGDRrkaHPq1KmyOQEAAAAXQwIeAAAAAIByxmq1Ki4uLs/6/v37l2E0AIDyymKx6OjRo/L29tbYsWNVtWpVrVu3TsOGDdPbb7+t9PT0HPu4V6lSRZKUkZGhS5cuSVKubZKTk8vmJAAAAFwMCXgAAAAAAFAhpKeny2q15lnPygAAKhuz2ayEhAR5eHjYkuitWrXS4cOHFRUVJS8vL1ksFrtjMjIyJElVq1a1/ZtpsVjs/v3MyMiQt7d3GZ0FAACAayEBDwAAAAAAKgSr1arY2Ng861kZAEBlVLVq1RxlgYGB+uqrr+Tv768zZ87Y1WX/Xr9+fWVmZtrKGjVqZNemRYsWpRg1AACA6zI5OwAAAAAAAAAAQNEdOHBA7du3165du+zKf/nlFzVr1kxBQUHavXu33eohO3fuVNOmTVWnTh21aNFCPj4+io+Pt9WnpKRo37596tixY5mdB+AqsrKynB0CKhBvb+9S/TvD30fAeZgBDwAAABTS4sWLtXPnTr333nu2sv3792vatGn65ZdfVLNmTQ0aNEhDhw611WdlZWnhwoVavXq1UlJSdPPNN+vll19W48aNnXEKAAAAcCGBgYG64YYbNHnyZL388suqVauWVq1apT179ig2NlZ169bV0qVLNWHCBD3++OP66aeftHz5ck2ePFnSlSXsIyIiNGvWLNWuXVsNGzbUzJkz5e/vrx49ejj57ICKx2Qyafv27UpOTnZovw0bNlSHDh0c2iecz2w2l9rfGV9fX3Xp0sWhfQIoPBLwwFWysrJkMrEwBAAAyOmdd97R/PnzFRQUZCs7f/68Bg8erO7du2vy5Mnas2ePJk+erJo1ayo8PFzSlaR9dHS0pk+frvr162vmzJkaNmyY1q9fb9unEwAAACgOk8mkN954Q7NmzdKzzz6rlJQUtWzZUm+//baaN28uSVq6dKmmTZumvn37ys/PT2PHjlXfvn1tfYwaNUqZmZmaOHGi0tPTFRQUpKioKMaqQDElJyfr3LlzDu3T19fXof2hfCmNvzMAnIsEPHAVk8mkDRs2KC0trVDt69atq9DQ0FKOCgAAONPp06c1YcIE7d69W02bNrWrW7VqlcxmsyZNmiQPDw8FBATo2LFjWrJkicLDw2WxWLRs2TJFRkbaxgxz5sxRSEiINm7cqN69ezvjlAAAAOBCateurVdffTXP+jZt2igmJibPend3d0VGRioyMrI0wgMAAKh0mOoL/ENaWppSU1ML9XPp0iVnhwsAAErZ3r175evrq3Xr1qlt27Z2dbt27VJQUJA8PP73XGtwcLCOHDmipKQkHThwQGlpaQoODrbV16hRQy1btlRCQkKZnQMA1+Pm5qa0tLQ8f9LT050dIgAAAAAAlRIz4AEAAIB8hIWFKSwsLNe6xMREBQYG2pXVq1dPknTy5EklJiZKkho0aJCjzalTp0ohWgCVhdVqVVxcXJ71/fv3L8NoAAAAAABANhLwAAAAQDGlp6fn2BuzSpUqkqSMjAzbajm5tUlOTi6bICuo9PR0Wa3WPOsNwyjDaAAAAAAAAIDCIQEPl5aVlSWTiZ0WAABA6fDy8pLFYrEry8jIkCRVrVpVXl5ekiSLxWL7c3Ybb2/vsgu0ArJarYqNjc2zvl+/fmUYDQAAAAAAAFA4JODh0kwmkzZs2KC0tLQC29atW1ehoaFlEBUAAHAV/v7+OnPmjF1Z9u/169dXZmamraxRo0Z2bVq0aFF2gQIAAAAAAAAoEyTg4fLS0tKUmppaYLtq1aqVQTQAAMCVBAUFKTo6WlarVe7u7pKknTt3qmnTpqpTp46qV68uHx8fxcfH2xLwKSkp2rdvnyIiIpwZOgAAAAAAAIBSwNrcAAAAQDGFh4crNTVVEyZM0KFDhxQXF6fly5dr+PDhkq7s/R4REaFZs2Zp8+bNOnDggEaPHi1/f3/16NHDydEDAAAAAAAAcDRmwAMAAADFVKdOHS1dulTTpk1T37595efnp7Fjx6pv3762NqNGjVJmZqYmTpyo9PR0BQUFKSoqSmaz2YmRAwAAAAAAACgNJOBRYWRlZclkYtEGAADgPDNmzMhR1qZNG8XExOR5jLu7uyIjIxUZGVmaoQEAAAAAAAAoB0jAo8IwmUzasGGD0tLSCtW+bt26Cg0NLeWoAAAAUBzp6emyWq151huGUYbRAJVLQZ8/d3d3eXl5lWFEAAAAAAC4DhLwqFDS0tKUmppaqLbVqlUr5WgAAABQXFarVbGxsXnW9+vXrwyjASqXgj5/AwYMyPfBZxL0AAAAAADkjQQ8AAAAAACwsVqtiouLy7O+f//+ZRgNAAAAAAAVCxtqAwAAAAAAAAAAAADgAMyABwAAAAAADpPfHvMsXw8AAAAAcHUk4AEAAAAAcDFubm557uNuGEapvnZ+e8yzfD0AAAAAwNWRgAcAAAAAwMXkt497v379yjgaAAAAAAAqDxLwAAAAAACg0PKbXS+V/gx7AACA4srKypLJZHJ2GECp8/b2LvW/73yegLyRgAcAAAAAAIWW3+x6Kf8Z9gUl79kjHgAAlCaTyaTt27crOTnZ4X03bNhQHTp0cHi/QHGYzeZS/fvu6+urLl26OLxfwFWQgAcAAAAAAGWioOQ9e8QDAIDSlpycrHPnzjm8X19fX4f3CZRUaf19B5A/1oYAAAAAAAAAAAAAAMABmAEPAAAAAADKBfaXBwAAAABUdCTg4RRZWVkymViAAQAAAADwPyXZXx4AAAAAgPKABDycwmQyacOGDfnObLha3bp1FRoaWspRAQAAAAAAAAAAAEDxkYCH06SlpSk1NbVQbatVq1bK0QAAAAAAAAAAAABAyZCABwAAAOBw6enpslqtedazjzOA0pDfHvLu7u7y8vIq44gAAAAAAJUNCXgAAAAADme1WhUbG5tnPfs4AygN+e0h379//zKOBgAAAABQGZGABwAAAJCrgmaxM5sUAAAAAAAAsEcCHgAAAECuCprFzmxSAAAAAAAAwB4JeAAAAAAAgAqK1UoAAAAAoHwhAQ8AAAAAAFBBsVoJAAAAAJQvJOABAAAAFIubm5vS0tJyrTMMo4yjAYD85fdvlsRMcQAAAACAY5CABwAAAFAsVqtVcXFxudb169evjKMBgPzl92+WJA0YMKDUEvQsEw8AQOFlZWXJZDI5OwwA+fD29i71zyr/FqAiIwEPAAAAAAAqvYIS9Pkt5V5Qgt0wDK1Zs6ZYfQMAUNmYTCZt375dycnJDu23YcOG6tChg0P7BCors9lcap9VSfL19VWXLl0c3i9QVkjAAwAAAAAAlEBB+7CzKggAAEWTnJysc+fOObRPX19fh/YHoHQ+q4ArYO0GAAAAAAAAAAAAAAAcgAQ8AAAAAAAAAAAAAAAOQAIeAAAAAAAAAAAAAAAHIAEPAAAAAAAAAAAAAIADkIAHAAAAAAAAAAAAAMABPJwdAAAAAAAAAPKWnp4uq9Waa51hGPke6+bmprS0tDzr3d3d5eXlVaL4AAAAAEfy9vZWVlaWTKbSmUdcmn0DEgl4OAj/WAEAAAAAXFl+ieyCkuAl6Tu7/zVr1uRa169fv3z7tlqtiouLy7O+f//++R6fX/Kf5D0AAABKg9lslslk0vbt25WcnOzQvn19fdWlSxeH9gn8Ewl4OITJZNKGDRvyvWGQrW7dugoNDS2DqAAAAAAAcIz8EtkFJcFL0rcj+i8Jq9Wq2NjYXOsKSt4DAAAAJZGcnKxz5845OwygyEjAw2HS0tKUmppaYLtq1aqVQTQAAAAAAAAAAEdjNVQAAPJHAt6JijNQYXADAAAAAAAcpTDL3wMAcLXSWhZakho2bKgOHTo4vF8AAMqSSyTgs7KytHDhQq1evVopKSm6+eab9fLLL6tx48bODi1fRVm2Xboyc7xnz56lHBUAAAAcraKOVwEArq80l7/Pb/946UryP78EP3vMA2WH8SqKqrSWhfb19XV4nwAAlDWXSMAvXrxY0dHRmj59uurXr6+ZM2dq2LBhWr9+vcxms7PDy1dhl20vLmbZAwAAOF9FHq8CAJCXwsyeX7NmTZ71/fr1yzf5zx7zQNlhvOpauL8LAIBzVfgEvMVi0bJlyxQZGanQ0FBJ0pw5cxQSEqKNGzeqd+/eTo7Qccxmc5EHT8yyBwAAcK7KNF4FAFQupTl7HkDZYbzqelgiHgDy5u3tXeoPKvEgFCp8Av7AgQNKS0tTcHCwraxGjRpq2bKlEhISXGqA6OnpWaSEet26dRUaGlrqs+wBAACQt8o0XgUAwJHym2Ff0uXr81sen6XvUdkwXnVNLBEPALkzm82l+qBSvXr1FBQU5PB+s5HcrxjcjPy+rVQAX3zxhUaOHKkff/zR7svRM888o/T0dL355ptF6q9169ayWq1q0KCBo0PN1aVLl5SVlVWottlfAAt7TFHbS1eejvT29i5U238qzbjK4pjKHFdlPvfyGldlPnfiKn+vQVylf0xJ/v8tilOnTsnd3V0///xzqb8W/qcij1cNw8j3wc9q1arlWZ9fXWnXu+pru+p5VdbXdtXzqqyv7arnVdFfuyD59e3m5pbncYW5lZbf8eVVeTgvxqvOUZHHq9KVB2oK+12tsEwmU4V/EKc03hdJ8vDwkNlsrpD9E7tz+id25/RfkWMv7f7LKnaLxVKo8VVRuLm5sTWMkxV2vFrhZ8BfunRJknL8hatSpUqxnlypUqWKLBaLQ2IrjOLcbC/qMWVxQ784r1MW516cYypzXJX53ItzjKu8RnGOIa7yF1dlPvfiHFNe4ypt2V8AULYq8njVzc1NPj4++bbJr74kx5a03lVf21XPq7K+tqueV2V9bVc9r4r82gUp7vEVMbleGOXhvBivOkdFHq9KqvCJ8tJS2u9LRe6f2J3TP7E7p/+KHHtp91/asTOmcU2FHa9W+AR89gfEYrHYfVgyMjKKddN7165dDosNAAAAYLwKAACA8ozxKgAAgGNV+E0CspcyOnPmjF35mTNn5O/v74yQAAAAABvGqwAAACjPGK8CAAA4VoVPwLdo0UI+Pj6Kj4+3laWkpGjfvn3q2LGjEyMDAAAAGK8CAACgfGO8CgAA4FgVfgl6s9msiIgIzZo1S7Vr11bDhg01c+ZM+fv7q0ePHs4ODwAAAJUc41UAAACUZ4xXAQAAHKvCJ+AladSoUcrMzNTEiROVnp6uoKAgRUVFyWw2Ozs0AAAAgPEqAAAAyjXGqwAAAI7jZhiG4ewgAAAAAAAAAAAAAACo6Cr8HvAAAAAAAAAAAAAAAJQHJOABAAAAAAAAAAAAAHAAEvAAAAAAAAAAAAAAADgACXgAAAAAAAAAAAAAAByABDwAAAAAAAAAAAAAAA5AAh4AAAAAAAAAAAAAAAcgAQ8AAAAAAAAAAAAAgAOQgEehHDlyRO3bt1dcXJytbP/+/YqIiFC7du3UtWtXRUVF2R2TlZWl+fPnKyQkRG3bttWQIUN07Nixsg7dZZ04cULNmzfP8bN69WpJXJ/yYO3atbr77rvVunVr9e7dW5999pmtjuvjPPHx8bl+dpo3b65u3bpJ4vo40+XLlzVnzhx17dpV7du310MPPaTvv//eVs+1AcqPCxcu6KWXXlKXLl3UoUMHPfjgg9q1a5etvqDPq6tKSkpSZGSkgoOD1b59ez3xxBM6dOiQrb6yvi9XK853C1dV0jG9KyvJWNoVOWIM66pKOn50VWlpaZoyZYpCQ0N18803a8SIEfrjjz9s9ZX1fYFr4Htf6SiLe418py+6xYsXa9CgQXZl5eVa8H9J7tdn/PjxOT5HXbp0sdVzfUpPSe9TcG1KT0HXxiU/NwZQAIvFYvTr188IDAw01qxZYxiGYZw7d87o1KmTMWHCBOPQoUNGbGys0bp1ayM2NtZ23IIFC4zOnTsbW7duNfbv328MGTLE6NGjh5GRkeGsU3EpmzdvNlq3bm2cPn3aOHPmjO3n0qVLXJ9yYO3atcaNN95ovPPOO8bRo0eNhQsXGi1atDC+//57ro+TZWRk2H1mzpw5Y3z11VdGy5YtjVWrVnF9nGzevHnGbbfdZuzYscM4evSoMWHCBKNDhw5GYmIi1wYoZwYPHmz06dPHSEhIMA4fPmxMmTLFaNOmjXHo0KFCfV5d1YABA4yBAwcaP/30k3Ho0CFj5MiRxm233WZcvHixUr8v2Yr73cJVlXRM76pKOpZ2RY4Yw7qqko4fXdXjjz9uhISEGF9++aVx6NAhY+LEicatt95qnDt3rlK/L3ANfO8rHaV9r5Hv9EX39ttvG82bNzciIiJsZeXlWvB/Se7XxzAMo2/fvsbs2bPtPkdJSUm2eq5P6SnpfQquTenJ79oYhmt+bkjAo0CvvfaaMWjQILubZG+88YYREhJiXL582a7dXXfdZRjGlZsD7du3N1auXGmrT05ONtq0aWOsX7++bE/ARb3++utGnz59cq3j+jhXVlaWcccddxgzZsywKx8yZIjxxhtvcH3KGYvFYvTu3dt49tlnDcPg8+Nsffr0MaZPn277/e+//zYCAwONDRs2cG2AcuTo0aNGYGCgsXv3bltZVlaW0aNHD2Pu3LkFfl5d1blz54zRo0cbBw8etJXt37/fCAwMNH788cdK+75crTjfLVxZScb0rqqkY+nKoqhjWFdWkvGjq8r+v2fr1q22MqvVatx5553GwoULK+37AtfA977SU9r3GvlOX3iJiYnG0KFDjXbt2hk9e/a0S/CWl2tRmf8vye/6ZGZmGq1btzY2btyY67Fcn9JT0vsUXJvSU9C1cdXPDUvQI18JCQmKiYnRf/7zH7vyXbt2KSgoSB4eHray4OBgHTlyRElJSTpw4IDS0tIUHBxsq69Ro4ZatmyphISEMovflf36669q1qxZrnVcH+f6/fffdeLECd1777125VFRURo+fDjXp5x5//33derUKY0fP14Snx9nq1mzprZs2aI///xTVqtVMTExMpvNuvHGG7k2QDlSq1YtvfXWW2rVqpWtzM3NTYZhKDk5ucDPq6uqVauWZs+erRtuuEGSdPbsWUVFRcnf31/NmjWrtO9LtuJ+t3BlJRnTu6qSjqUri6KOYV1ZScaPrurIkSOSpI4dO9rKTCaTWrRooYSEhEr7vsA18L2v9JT2vUa+0xfe3r175evrq3Xr1qlt27Z2deXlWlTm/0vyuz5Hjx5VRkaGAgICcj2W61N6SnqfgmtTegq6Nq76uSEBjzylpKRo7Nixmjhxoho0aGBXl5iYKH9/f7uyevXqSZJOnjypxMREScpxXL169XTq1KlSjLryOHjwoJKSkvTQQw/p1ltv1YMPPqgdO3ZI4vo429GjRyVJFy9e1NChQ9W5c2cNGDBAX375pSSuT3mSkZGhN954Q48++qjtGnB9nGvChAny8PBQt27d1Lp1a82ZM0dz585Vo0aNuDZAOVKjRg2FhobKbDbbyj777DP98ccfuv322wv8vFYG//73v3Xbbbdpw4YNmjZtmqpWrVqp35eSfLdwZSUZ07uqko6lK4PijGFdWUnGj67Kz89Pkmzj42wnTpxQUlJSpX1f4Br43ld6SvteI9/pCy8sLEyvvfaarrvuuhx15eVaVOb/S/K7PgcPHpSbm5uWL1+usLAwde/eXVOmTNHff/8tqXD/hnF9iqek9ym4NqWnoGvjqp8bEvDI06RJk9SuXbscMw8kKT093e7DIklVqlSRdOVmwKVLlyQp1zYZGRmlFHHlYbFYdPToUaWmpurZZ5/VW2+9pdatW2vYsGHauXMn18fJUlNTJUnjxo3TPffco2XLlum2227TiBEjuD7lzEcffaSMjAwNGjTIVsb1ca7Dhw+rRo0aWrRokWJiYtSvXz+NGzdOBw4c4NoA5dju3bv14osvqlu3bgoLCyvw81oZPProo1qzZo369Omjp59+Wnv37q3U70tJvlu4qpKO6V1VScfSlUFxxrCurCTjR1fVtm1bBQQE6OWXX9apU6dksVj0zjvvaP/+/bJYLJX2fYFr4Htf6SiLe418p3eM8nIt+L8kd7/99ptMJpMaNmyoN954Q+PGjdO2bds0YsQIZWVlcX3KUFHvU3Btys4/r42rfm48Cm6Cymjt2rXatWuXPv7441zrvby8ZLFY7Mqy/wJWrVpVXl5ekq4M3rL/nN3G29u7lKKuPMxmsxISEuTh4WH7B6FVq1Y6fPiwoqKiuD5O5unpKUkaOnSo+vbtK0m68cYbtW/fPr399ttcn3Jk7dq1uvPOO1WrVi1bGdfHeU6cOKHIyEi98847tuUyW7durUOHDmnBggVcG6Cc2rRpk55//nm1bdtWs2fPllTwv6WVQfbynVOmTNGePXu0YsWKSvu+lPS7hasq6ZjeVZV0LF0ZFGcM66pKOn50VZ6enlq0aJFeeOEFde3aVR4eHuratav69++vX375RRaLpVK+L3ANfO8rHWVxr5Hv9I5RXq5FZf0/tiAjR47UY489pho1akiSAgMD5efnp4EDB+rnn3/m+pSR4tyn4NqUjdyujat+bpgBj1ytWbNGSUlJ6tq1q9q3b6/27dtLkl5++WX17t1b/v7+OnPmjN0x2b/Xr1/ftoxDbm3+uYQDiqdq1ao5nsYJDAzU6dOnuT5Olv0eBgYG2pU3a9ZMf/75J9ennDh37px++OEH3X333XblXB/n+emnn3T58mW1bt3arrxt27Y6evQo1wYoh1asWKGRI0eqS5cuWrJkie1LTEGfV1eVlJSk9evXy2q12spMJpMCAgJs/xZVxvelpN8tXFlJxvSuqqRjaVdX3DGsqyrp+NGVNW3aVDExMfruu++0c+dOLVq0SBcuXFCTJk0q9fuCio/vfaWntO818p3eMcrLteD/kty5ubnZkojZsse1iYmJXJ8yUNz7FFyb0pfXtXHVzw0JeORq1qxZ+vTTT7V27VrbjySNGjVKb731loKCgrR79267m4s7d+5U06ZNVadOHbVo0UI+Pj6Kj4+31aekpGjfvn22p9JRfAcOHFD79u21a9cuu/JffvlFzZo14/o4WcuWLVWtWjX9+OOPduUHDx5Uo0aNuD7lxPfffy83NzfdcsstduVcH+fJHgj9+uuvduUHDx5U48aNuTZAObNy5UpNmTJFDz/8sObOnWt3s66gz6urOnPmjJ577jl99913trLLly9r3759CggIqLTvS0m/W7iqko7pXVVJx9KurrhjWFdV0vGjq0pNTVVERIR++eUX+fr6qkaNGvr777/1zTffKCQkpNK+L3ANfO8rHWVxr5Hv9I5RXq4F/5fk7rnnntPQoUPtyn7++WdJVx4o5fqUrpLcp+DalK78ro3Lfm4MoJACAwONNWvWGIZhGGfPnjWCgoKMcePGGb/99puxZs0ao3Xr1kZcXJyt/ezZs41bbrnF2LRpk7F//35jyJAhxp133mlkZGQ46xRchtVqNQYMGGDcc889RkJCgnHo0CHj1VdfNVq1amUcOHCA61MOLFq0yGjfvr3x8ccfG8eOHTMWL15stGjRwvj222+5PuXEggULjDvvvDNHOdfHeaxWq/HQQw8ZPXv2NHbu3GkcOXLEmDNnjnHjjTcaP/zwA9cGKEd+//1346abbjKefvpp48yZM3Y/KSkphfq8uqKsrCxjyJAhxl133WUkJCQYv/76qzF69GgjKCjIOHHiRKV9X3JT1O8WrsgRY3pXVdKxtCsryRjWFTli/OiqIiIijAcffNA4cOCAsX//fuOhhx4y+vTpY1y+fLlSvy9wDXzvc7yyuNfId/riGTdunBEREWH7vbxcC/4vueKf1+fLL780mjdvbixevNg4duyYsXXrViMsLMwYM2aMrQ3Xp3Q44j4F16Z0FHRtXPVzQwIehXb1TTLDMIwff/zR+Ne//mW0atXKuOOOO4z33nvPrn1mZqbx3//+1wgODjbatWtnDBs2zDh+/HhZh+2ykpKSjPHjxxu33Xab0bp1a2PgwIFGQkKCrZ7r43zLli0zwsLCjJtuusno06ePsXHjRlsd18f5Xn75ZeNf//pXrnVcH+e5cOGCMWnSJKNr165G+/btjYEDBxrx8fG2eq4NUD68/vrrRmBgYK4/48aNMwyj4M+rq0pJSTFefvll47bbbjPatGljDBkyxDh48KCtvrK+L/9U1O8WrqqkY3pXVpKxtCsryRjWVZV0/OiqTp8+bYwcOdLo2LGjccsttxjjxo0zkpKSbPWV9X2Ba+B7X+koi3uNfKcvun8meA2j/FwL/i/J/fps2LDBuP/++402bdoYt912mzFjxgwjPT3dVs/1KR2OuE/BtSkdhbk2rvi5cTMMwyj6vHkAAAAAAAAAAAAAAHA19oAHAAAAAAAAAAAAAMABSMADAAAAAAAAAAAAAOAAJOABAAAAAAAAAAAAAHAAEvAAAAAAAAAAAAAAADgACXgAAAAAAAAAAAAAAByABDwAAAAAAAAAAAAAAA5AAh4AAAAAAAAAAAAAAAcgAQ8ATmIYhrNDAAAAAAAAAAAAgAORgAfg8n7++WdFRkaqa9euatOmjbp166aJEyfq+PHjpfaacXFxat68uf78809J0oIFC9S8eXNb/e7duzV8+PAcx3355Zd69NFH1bFjR7Vu3Vo9evTQ1KlTdfbs2VKLFQAAAJXT2LFj1bx5c7311lvODgUAAAAuYtCgQWrevLndT8eOHfXII4/ou+++K1Jf/7ynCgAVBQl4AC7t/fff1wMPPKCkpCQ999xzWrJkiZ588kklJCQoPDxce/fuLZM4BgwYoJiYGNvvq1ev1qFDh+zafPjhh3rqqafUuHFjzZw5U0uWLNGjjz6qL774QgMHDtSFCxfKJFYAAAC4vtTUVH3xxRcKDAzUqlWrWJ0JAAAADtOyZUvFxMQoJiZGK1eu1IwZM+Tp6amhQ4fqt99+c3Z4AFDqPJwdAACUlt27d2vatGl6+OGHNWHCBFt5p06d1K1bN/Xr10/jx4/XunXrSj0Wf39/+fv759tm0aJFuueee/TKK6/YyoKDg9WxY0fdd999io2N1eOPP17aoQIAAKAS+OSTT2S1WjVx4kQ98sgj+uqrrxQSEuLssAAAAOACfHx81K5dO7uyW2+9VZ07d1ZcXJzGjRvnnMAAoIwwAx6Ay4qKilL16tU1ZsyYHHW1a9fWCy+8oDvvvFOpqakaNGiQnn/+eY0aNUodOnTQE088IUnKyMjQf//7X4WGhqpVq1a699579emnn9r1lZWVpcWLF6tr165q27atRowYoeTkZLs2Vy+X9MILL+jDDz/UiRMn1Lx5c8XFxUmSzp49m+vMoxYtWmj8+PFq1aqVrezy5ctatGiRunfvrjZt2qh3795as2aN3XGffvqp+vXrp/bt2+u2227TSy+9ZBfXggUL1KNHDy1cuFCdOnVS9+7ddf78eUlXZuj37t1brVq1UteuXbVgwQJlZmYW+r0HAABA+bZmzRp16tRJnTp1UtOmTRUdHZ2jTVRUlLp166Y2bdrogQce0JdffqnmzZsrPj7e1ubgwYMaPny4OnTooA4dOujpp58u1a2eAAAAUDF5e3urSpUqcnNzkyRZrVa99dZbuueee9SmTRu1a9dODzzwgHbu3JlnH4U5Jvue59atW3XvvfeqVatWuuuuu/Thhx/a9ZWUlKQXX3xRt956q9q3b6+HH35Yu3fvttVnZWXprbfeUo8ePWx9vPfeew5+VwC4KmbAA3BJhmHoq6++UlhYmLy9vXNt07NnT7vfP/vsM/Xs2VOLFi2S1WqVYRh6+umn9f3332vUqFEKCAjQxo0bNXr0aFksFt1///2SpJkzZ+rdd9/Vk08+qXbt2mnDhg167bXX8oxtxIgROnfunPbt26eFCxeqUaNGkqSuXbvqk08+UUZGhnr16qWgoCDVr19fkvTYY4/Z9TFu3Dht3rxZTz31lNq2basdO3boxRdflLu7u+6//34tXrxY8+bN00MPPaTRo0fr+PHjmjdvnvbs2aNVq1bJy8tLknTy5Elt3LhRs2fP1vnz51WrVi29+eabmjNnjiIiIjR+/Hjt379fCxYs0KlTp/Tqq68W53IAAACgHDl8+LB+/PFHzZ49W5LUr18/zZ07V6dPn7aNPxcuXKhFixZp6NChCg4O1o4dOzR69Gi7fo4cOaIHHnhA119/vWbMmCGr1arXX39dDz74oD766CPVqVOnzM8NAAAAzmcYhm0yj2EYunDhgt59911ZLBaFh4dLkmbNmqWVK1fq+eefV/PmzZWYmKhFixbpmWee0datW1W1atUc/Rb2mL/++kuvvPKKnnrqKTVs2FBRUVF64YUX1KZNGwUEBOjixYt64IEHdPnyZT333HPy9/fX8uXL9fjjjys2NlYBAQGaNGmS4uLiNHz4cLVv314JCQl69dVXlZKSoqeffrrs3kwAFRIJeAAu6fz588rIyNC1115b6GNMJpOmTJliG6h9/fXX2rFjh+bMmaO7775bkhQSEqJLly5p1qxZuueee3Tx4kW99957euSRRzRy5Ehbm9OnT2vHjh25vk6jRo1Uu3Ztmc1mu6WYpkyZoqysLH3xxRfatGmTrW1YWJgGDx5sW8L+t99+0yeffKIJEybokUcekSR17txZJ0+eVHx8vO644w69/vrrGjBggF5++WVb/4GBgXr44YcVFxenhx56SJKUmZmpcePG6dZbb5Uk/f3333r99dc1cOBATZw4UZJ0++23q2bNmpo4caIGDx6sG264odDvKQAAAMqf2NhY1ahRQ927d5ck3X///Zo7d65Wr16t//u//9PFixe1ZMkSPfzww3r++eclXRkTXrp0STExMbZ+Fi5cKC8vL73zzjvy8fGRdGVc2r17dy1dupSlRQEAACqphIQE3XTTTTnKx4wZo4CAAEnSmTNnNHr0aA0aNMhW7+XlpZEjR+rXX39V+/btcxxf2GMuXbqkadOmqXPnzpKkJk2a6I477tC2bdsUEBCgDz/8UMePH9fatWvVokULSVLHjh11//33KyEhQSaTSatWrdKYMWNsK6XefvvtcnNz05tvvqmHHnpItWrVctC7BcAVkYAH4JJMpis7bFit1kIfc+2119o9Wblz5065ubkpNDTUbvn1sLAwrVu3Tr/99pv++usvXb58Wd26dbPrq1evXnkm4PNSvXp1zZ8/X3/++ae2bdum+Ph4xcfH65133tGqVasUFRWlDh06aNeuXZKkHj162B0/d+5cSdK2bdtksVh077332tV37NhRDRs2VHx8vC0BL11JzGf74YcfdOnSJYWFheU4Z+nKQwkk4AEAACquzMxMrVu3Tt27d1dGRoYyMjLk5eWlTp06afXq1Xrqqae0Z88epaen51gx6p577rFLwH/77bfq1KmTvLy8bGNHHx8fdezYUd98802ZnhcAAADKj5tuukmTJ0+WdGUGfEpKirZv3645c+bo4sWLGj16tG0F0XPnzunYsWM6cuSIvvzyS0lXtt/MTVGOuXriU/bEposXL0qSdu3apWuvvdaWfJekKlWq6LPPPpMkffDBBzIMI9d7pK+//rp2795te5gVAHJDAh6AS6pZs6aqVaumkydP5tnm4sWLslgsqlmzpiSpbt26dvUXLlyQYRjq0KFDrsefOXNGKSkpkq7sKX81Pz+/Ysd+7bXX6uGHH9bDDz+srKwsbdq0SePHj9fUqVMVFxenCxcuSFKeS3pm7/P+z/PJLvv7779zlGXL7jv7yc5/OnPmTFFPBwAAAOXI1q1bdfbsWcXFxSkuLi5H/ZYtW5Seni4p5xg3t/Hyp59+qk8//TRHP/88FgAAAJVHtWrV1Lp1a7uy22+/XRcvXtTSpUv1yCOP6OTJk5o8ebJ+/vlneXl5qVmzZmrYsKGkK0n73Pz888+FPubqbUmzJ2tlt7lw4UK+2yVl3yPt3bt3rvWnT5/O81gAkEjAA3Bht99+u+Lj45WRkaEqVarkqI+Li9O0adO0cuXKXI+vXr26qlatqnfffTfX+saNG+unn36SJCUlJen666+31WUP0grr888/18svv6wPPvhATZs2tZWbTCbdeeedSkhI0KpVqyRJNWrUkHTlSc/spzcl6ffff9e5c+fk6+srSTp79qxtSadsf/31l6677ro848jue9asWWrSpEmO+tyS+gAAAKg4YmNj1bBhQ02fPj1H3ahRoxQdHa0nn3xS0pXx5tVj3HPnztm1r169um699VYNHjw4R18eHtxuAAAAgL0bb7xRq1ev1uHDhzVy5Eg1b95c69evV0BAgEwmk7Zt26bPP/8812NTU1P1+OOPF+mYvFSvXl1//vlnjvIffvhBPj4+tnuky5cvV7Vq1XK0u+aaa4r0egAqH5OzAwCA0jJkyBBduHBBc+bMyVGXlJSkpUuXqnHjxnbLEV3tlltu0cWLF2UYhlq3bm37+e2337Ro0SJlZmaqffv28vLy0oYNG+yO3bJlS76xZT91me2GG27QhQsXtHz58lzbHz161LZU/M033yxJtn3is82ZM0dTpkxR27ZtZTab9fHHH9vV79q1SydPnsxzRr8ktW3bVp6enjp9+rTdOXt6euq1117LdWAKAACAiuHs2bPasWOHevfurU6dOuX4ufvuu/X111+revXqql69ur744gu74/95Y/OWW27RoUOHdOONN9rGja1atdI777yjjRs3luWpAQAAoAL44Ycf5O7uripVqujChQt65JFHdMMNN9julW7fvl2SlJWVlePY33//vcjH5KVjx446fvy4fv31V1uZxWLRyJEjtWrVKgUFBUmSzp8/b3eP9MKFC5o7d26RJ18BqHx4JB2Ay2rXrp2eeeYZzZ07V4cPH1bfvn1Vq1Yt/fbbb1q2bJnS0tL01ltvyc3NLdfjQ0NDFRQUpBEjRmjEiBEKCAjQTz/9pAULFuj222+3Las5YsQIzZ07V97e3goODta2bdsKTMDXqFFDZ8+e1bZt23TjjTfq+uuv1xNPPKE333xTJ0+eVJ8+feTv76+kpCR99NFH2rlzp95++21JUosWLdSzZ0/NmjVL6enpuummm/TVV19p48aNmjt3rmrWrKknnnhCCxculKenp7p166Y///xT8+bNU7NmzdSvX78846pVq5Yef/xxzZs3T6mpqerUqZNOnz6tefPmyc3NzW5fJAAAAFQsH374oTIzM/NcSrNv375auXKl1q9fr8cff1zz58+Xt7e3brnlFn333Xf64IMPJP3vYdIRI0bogQce0PDhw/Xggw+qSpUqiomJ0aZNmzR//vwyOy8AAACUL6mpqdqzZ4/t98uXL2vz5s36+OOPNXDgQF1//fXy8fHRG2+8IQ8PD3l4eOjzzz9XbGysJOnSpUs5+mzatGmRj8lLv3799N577+mpp57SM888o9q1a+v9999Xenq6Bg0apEaNGqlPnz7697//rRMnTqhVq1Y6cuSI5syZo2uvvTbXlUMB4Gok4AG4tKeeekotW7bU+++/r+nTp+vChQvy9/dXly5d9OSTT+a7XJDJZNJbb72lefPm6c0331RSUpLq16+vxx57TE8//bSt3fDhw1W1alUtX75cy5cvV/v27TVu3DhNmjQpz7779eunbdu26emnn9aoUaP0xBNPaMyYMbZlmKZOnarU1FTVqFFDHTt2VGxsrF3ye+bMmVq4cKHee+89nT9/Xk2bNtXcuXPVs2dPSdLIkSNVt25drVixQqtXr1bNmjXVs2dPPfvss3b7H+Xm2WeflZ+fn1auXKmlS5fK19dXnTt31pgxY1S9evVCvvMAAAAobz788EPdcMMNeT5U2aZNG11//fVas2aNtm7dqqysLMXExCgqKkpt27bV888/r+nTp6tq1aqSrjwY+v7772vOnDkaO3asDMNQYGCgFi1apG7dupXlqQEAAKAc2bdvnwYOHGj7vUqVKmrUqJFGjx6toUOHytPTU4sXL9Z///tfPfPMM6pWrZpuvPFGrVixQsOGDdOuXbsUFhZm12f16tWLfExefHx8tGLFCv33v//VtGnTlJmZqbZt2+q9995To0aNJEnTp0/Xm2++qejoaCUmJqpOnTq6++679eyzz8rd3d1xbxYAl+RmGIbh7CAAAAAAAED5kJmZqfXr16tTp05q0KCBrfz999/X1KlTFR8fb9sXEwAAAAAA2CMBDwAAAAAA7PTu3Vtms1lPPfWUatWqpQMHDmjevHnq0aOHpk+f7uzwAAAAAAAot0jAAwAAAAAAO8ePH9fs2bMVHx+vlJQUXXPNNerTp4+GDx8uT09PZ4cHAAAAAEC5RQIeAAAAAAAAAAAAAAAHMDk7AAAAAAAAAAAAAAAAXAEJeAAAAAAAAAAAAAAAHIAEPAAAAAAAAAAAAAAADkACHgAAAAAAAAAAAAAAByABDwAAAAAAAAAAAACAA5CABwAAAAAAAAAAAADAAUjAAwAAAAAAAAAAAADgACTgAQAAAAAAAAAAAABwABLwAAAAAAAAAAAAAAA4AAl4AAAAAAAAAAAAAAAcgAQ8AAAAAAAAAAAAAAAOQAIeAAAAAAAAAAAAAAAHIAEPAAAAAAAAAAAAAIADkIAHAAAAAAAAAAAAAMABSMADAAAAAAAAAAAAAOAAJOABAAAAAAAAAAAAAHAAEvAAAAAAAAAAAAAAADgACXgAAAAAAAAAAAAAAByABDwAAAAAAAAAAAAAAA5AAh4AAAAAAAAAAAAAAAcgAQ8AAAAAAAAAAAAAgAOQgAcAAAAAAAAAAAAAwAFIwAMAAAAAAAAAAAAA4AAk4AEAAAAAAAAAAAAAcAAS8AAAAAAAAAAAAAAAOAAJeAAAAAAAAAAAAAAAHIAEPAAAAAAAAAAAAAAADkACHgAAAAAAAAAAAAAAByABDwAAAAAAAAAAAACAA5CABwAAAAAAAAAAAADAAUjAAwAAAAAAAAAAAADgACTgAQAAAAAAAAAAAABwABLwAAAAAAAAAAAAAAA4AAl4AAAAAAAAAAAAAAAcgAQ8AAAAAAAAAAAAAAAOQAIeAAAAAAAAAAAAAAAHIAEPAAAAAAAAAAAAAIADkIAHAAAAAAAAAAAAAMABSMADAAAAAAAAAAAAAOAAJOABAAAAAAAAAAAAAHAAEvAAAAAAAAAAAAAAADgACXgAAAAAAAAAAAAAAByABDwAAAAAAAAAAAAAAA5AAh4AAAAAAAAAAAAAAAcgAQ8AAAAAAAAAAAAAgAOQgAcAAAAAAAAAAAAAwAFIwAMAAAAAAAAAAAAA4AAk4AEAAAAAAAAAAAAAcAAS8AAAAAAAAAAAAAAAOAAJeAAAAAAAAAAAAAAAHIAEPAAAAAAAAAAAAAAADkACHgAAAAAAAAAAAAAAByABDwAAAAAAAAAAAACAA3g4O4DyrHnz5kVqX716de3atSvXui1btmj9+vXas2ePzp49K5PJpDp16qht27a66667dOedd+Y4JiwsTCdOnChy3O+++646depk18f//d//aeTIkbY2cXFxGj9+vO33BQsW5BrDPw0fPlxbt26VJN17772aNWtWvu179uypI0eOyN3dXVu2bFH9+vXt6v/8809169atsKdm59dff7XFvnDhwiIdO378eD322GO23/N6r00mk6pUqSJfX18FBATozjvv1P333y8vL698+09NTVVMTIy2bt2q33//XcnJyfLx8dE111yj4OBg9e/fX9dff32hYv3hhx/0wAMPSJJCQkK0dOnSfNtffW2z36PieumllxQTEyNJmjNnju6+++5827/wwgv68MMP86x3c3OTl5eX6tWrp9atW+vRRx9VmzZtbPWvvvqqli9fLklavHhxof5uTJs2Te+++648PT21atUqtWzZsjCnBgBAhVfQOMrT01M+Pj5q0qSJunbtqoiICPn4+OTb5/HjxxUbG6tvvvlGf/75p1JSUuTj46OAgAB16dJFAwYMUJ06dfI8Pntc1rBhQ3355Zd2ddlj61tuuUXvvvuu3NzcCnVumzdv1rXXXqv4+Hg98sgj+cafm9xiye/1CqtFixb66KOP8m0zdOhQffXVV5KkVatWqW3btvm2HzRokL777rs8600mk7y8vNSgQQO1b99ejz/+uJo2bZpnP7Vq1dInn3yS7zWT/jcWnj59uvr162crL2hsl5tFixape/futt/z+k6VfS61atVSYGCgevfurbvvvlvu7u5Fej0AAAAAAIDyhgR8ITRp0kS1a9cusF21atVylGVkZGjkyJHatm2bJMnPz0/NmjWTdOVG3/r167V+/XrdfPPNev311+Xr62s7tlWrVjkS1pL0/fff5xtX9erVC3diV9mwYUOBCfjk5GR9/fXXhe5z9+7dOnLkiCTJarVq1apVdg8BSFKVKlXUoUOHHMempqbq4MGDkq68D2azucDXM5vNatWqVaFiy+19laQGDRqoQYMGtt8Nw9DFixd16tQpff311/r666/19ttva968eWrRokWuffz44496+umn9ddff8nDw0PXXXedrr32Wl28eFEHDx7U3r17tXz5co0cOVJPPvlkgbGuWbPG9uevvvpKx48f13XXXVeo8yyJ9PR0ffLJJ7bfP/jggwIT8Nl8fHwUGBiYo9wwDJ09e1bHjh3TsWPH9Omnn+qVV17RgAEDJEljxozR1q1bdezYMU2aNEm33HJLvn+ff/jhB61YsUKS9PTTT5N8BwBUWoGBgTmS65cvX9a5c+f0ww8/6IcfflBMTIzeeecdNW7cOMfxVqtVr732mt59911dvnxZbm5uuu6663TNNdcoMTFRu3fv1u7du/XGG29o4sSJ6t+/f7Fj/e677/T+++8rIiKiSMdVr14913HjuXPndPToUUnKtd7Pz69Ir5Pbe5mbJk2a5Ft/6tQpffPNN7bfP/jggwIT8Nnq1KmT53U6ffq0Dh8+rMOHD+ujjz7SokWLFBoamms/58+f1+TJkzV//vxCvW5e8hrb5aZmzZq5lv/zu0tWVpYuXryoP//8U1u2bNGWLVv0zjvvaOHChXbjcQAAAAAAgIqGBHwhDB8+3G4mSFG89NJL2rZtm66//nr997//VevWrW11hmHoq6++0gsvvKDdu3fr6aeftiUTJeV5oyx7FklJ4srm4eGhzMxMbdmyRRkZGapSpUqebTdu3KjLly8Xuu/sxHFoaKi2bdum1atXa8SIEXazWvz8/PTBBx/kOPbqGU7z5s3TtddeW+Dr5dVXUYSHh+d4SEC6cq2+/fZbTZo0SUePHtXjjz+uDz74IEci/Ny5c3riiSd04cIFDRw4UGPGjLG7CZmamqqlS5fq9ddf15w5c+Tn56fw8PA847l06ZI+++wzubm56fbbb9eOHTsUExOj559/vkTnWRgbNmxQamqqOnfurF27dum7777T4cOHFRAQUOCxLVu21HvvvZdn/eHDhzVmzBgdOHBAkydPVnBwsK677jp5eXnp1VdfVUREhM6cOaMZM2Zo2rRpufZhsVj04osvKisrS+3atdMTTzxR7HMFAKCimzhxom0FpH+Kj4/XiBEjdPLkSY0bN07R0dF29ZmZmXrqqae0fft2eXh4aOjQoXr00UftHlg8dOiQXn/9da1fv14TJkzQwYMH9eKLLxY73tdee02hoaFFeqiwZcuWuY71rl79p6RjQSn/97Io4uLilJWVZRsLf/bZZxo/frzdA7d56dKli2bMmJFn/Z49ezR69GidPHlSzz//vLZs2ZLnQwOff/65Pv3000I/SJmbgsZ2hZHXdxer1aovv/xSkyZN0i+//KKhQ4dq5cqVeSbyAQAAAAAAyjv2gC9FJ06c0Lp16yRJCxcutEu+S1eW4w4JCdGcOXMkSQkJCdq5c2eZxlinTh01a9ZMFy9e1Pbt2/Nt+9lnn0lSoWYZX7x40db+qaeeUrVq1XT69Glt2bKl5EE7gZubmzp37qz3339f/v7++uuvv/TKK6/kaBcdHa0LFy6oXbt2mjx5co4bhz4+Pnr22WdtNx8XLFiQ7+tmJ8FvuOEG3X///ZKu3My1WCwOOa/8ZD9A0aVLF9tN6H/esC+ugIAAzZs3T+7u7rp8+bJiY2NtdR07drTNiIuNjc3zM7FgwQL9/vvv8vb21n/+8x+WKwUAIA+dOnXSmDFjJF1ZPeaXX36xq3/ttde0fft2mc1mLV26VGPHjs2xWlCzZs302muv2RLdy5cv16efflqseNzc3HTx4kWNHz9ehmEUq4/yzjAM29Lt4eHhatKkidLT04u8nHte2rVrp1dffVWSlJKSkue1yF7mf/LkyTp79qxDXtvR3N3d1aNHD7377ruqVq2aDh8+rLlz5zo7LAAAAAAAgGIjAV+K9u3bp6ysLNu+mXm55ZZbbEtY/vjjj2UU3f/06tVL0pVkb17OnTunb7/9VgEBAXnu43i1DRs26OLFi/Lz81O7du1se2o6YlaSM9WtW1cvvPCCJGn79u366aef7Op//vlnSVLr1q3z3df0X//6l6QrS5OePn06z3ZxcXGSriTB77jjDnl5eSkpKUkbN24s0XkU5Pjx40pISJB0ZQWDnj17SpI++ugjpaenO+Q1mjRpYtuzNHu7gWzPPfecGjVqJOnKLLSLFy/a1e/du1fLli2TJEVGRha4BCwAAJVdjx49bH++erx5/Phxvfvuu5Ku/J/auXPnfPt57LHHdNddd0mSJk2apNTU1CLHkv2gXUJCQolnVZdX3333nY4fPy5PT0/deuuttvF2TEyMw14jODhYVatWlZRzLJXtvvvuk4+Pjy5cuKBJkyY57LVLQ0BAgEaMGCHpykOYiYmJTo4IAAAAAACgeEjAlyJPT09JV5Yd37VrV75t33rrLX3++ecaNGhQWYRmJ/uGYPYy9Ln54osvlJmZqd69exeqz+zZ0926dZObm5vuueceSdLXX3+t48ePOyBq57nrrrtUq1YtSdKmTZvs6rKv+c6dO/Odpd6mTRvFxcVpy5Ytqlu3bq5t/vjjD1sS/M4771S1atV0xx13SCr9Bxni4uJkGIauv/56BQQE6K677pKnp6eSk5Pt9oUvKZPpyj9B/5z95u3trVdffVVubm76888/NW/ePFtdZmamXnzxRWVmZur222/XQw895LB4AABwVdWrV7f9OS0tzfbnlStXKjMzUzVr1tTAgQML1dfo0aMlScnJyfroo4+KHEtYWJjuvfdeSdLs2bN17NixIvdR3mWPhYODg1W9enXb+f7+++/69ttvHfIabm5u+T7wKUn+/v62h0c3btyo9evXO+S1S8uAAQNsKyRt27bN2eEAAAAAAAAUCwn4UtShQwfbrJQnnnhCc+bM0aFDh3Jt27hxYzVp0kTVqlUryxAlXZltEhgYqLS0NO3YsSPXNtnLWhYmAX/s2DHbAwfZNxtvu+021a5dW4ZhOGwZc2cxmUxq166dpCuzm64WEhIi6co+qQMGDNCHH36o5OTkHH24u7vrpptu0jXXXJPn0ulr1qyRYRhq1KiR2rZtK0m2BxkSEhJ0+PBhR52SnaysLK1du9bu9WrUqKHQ0FBJjluG/rffftNvv/0mSWrfvn2O+qCgID388MOSpBUrVthmdi1btkwHDhyQr6+vLUkPAADyd3WS29/f3/bn7K1eOnXqpCpVqhSqr6ZNm+qGG26QpGInSSdOnCg/Pz9dunRJL774orKysorVT3mUmpqqL774QtL/xlIBAQG68cYbJTluLLVt2zbbwxTZY9PcDBgwQLfffrskacqUKfrrr78c8vqlwdfX17Zy2D/H2QAAAAAAABUFCfhSVKNGDds+mWlpaXrjjTfUu3dvdenSRc8995w++OADHT161LlB/n/ZS4xn79t+tb/++ksJCQm66aabCrXUd/aMn4YNG+rmm2+WJHl4eOjuu++WVHZ7mJema6+9VpJ08uRJu/L777/ftl/6gQMH9MILL6hz587q16+fXn31VW3atEl///13gf1nZWXZZpRlP8QgXVkOPntf+dKaBf/NN9/YzqtPnz628uw///TTT9q3b1+JXuOHH37Q//3f/8kwDNWtW9e2JP8/Pffcc7ruuuuUmZmpKVOm6M8//9SiRYskSf/+979z7E8LAABy9/bbb0uSbUl06cp4I/sBt8JsMXS17GTyr7/+Wqx4atasqcmTJ0uSdu3aZVsG3xV88sknunTpkry9ve2W/s8eS23atKlE+7FnZWVp+/bttu8Z2asV5Wfq1Km2pehffvnlYr92WchrnA0AAAAAAFBReDg7gIpg/Pjxthtc+Xn33Xdtydds//rXv1S3bl1NnTpVJ06ckCSdPn1a69evty0BGRAQoMcff1x9+/Z12mzeu+++W/Pnz7ctQ3/1DKgNGzYoKyvLlkDPj9Vqtc2evvfee+3Op0+fPlqxYoXOnTunL774wjYjyFFOnDhRqJvHt9xyS4n3G81eqeDChQt25Z6ennrrrbc0b948rVixQhaLRVarVXv37tXevXu1fPlyeXp66vbbb9dzzz1nmz32T1999ZVOnTolyT4J7unpqZ49eyo6OlofffSRnn/+eXl5eZXoXP4p+wGK9u3b67rrrrOV33HHHapRo4ZSUlIUHR2tV155Jc8+9u3bpwcffDBHeXp6uk6fPq2kpCRJVx7SWLhwoW1J/3+qWrWqpk2bpkcffVTfffedHn30UaWnp6tnz552DyYAAICc0tPT9fvvvysmJsY2Pnvsscds298kJyfLarVKku0Bv8LK7uPcuXPFjq9bt27q06eP1q1bpzlz5qhr166FetiztD3yyCOFard582ZbsvhqV2/FdPXqVvfcc49mzpypy5cva82aNRo+fHiefW/fvj3XsdTFixd16tQp2wpLLVq00KJFi2zbIOWlQYMGeuGFFzRx4kRt3rxZ69atsxtjFuS7774r1Di7b9++mjFjRqH7zU1e42wAAAAAAICKggR8ITRp0kS1a9cusN3Ve2teLSwsTKGhofr222+1ZcsW7dy5024p+sOHD2v8+PH64osvNH/+fJnNZofFXlhNmzZVixYtdODAAe3YsUPdu3e31X366adyc3MrVAL+q6++0unTpyUpx029tm3bqkmTJjp69Kiio6MdnoA3m81q1apVge0CAwNL/FqXL1+WpFwfmPDy8tK4ceP0xBNPaNOmTdqxY4fi4+NtNxEvX76sLVu2aMeOHZo2bZruv//+HH3ExcVJurJX/D9vRPfp00fR0dFKSUnRJ598ovDw8BKfT7bk5GTbvvb/vH5ms1l33XWXVq9erY8//lhjx46Vj49Prv2kpqbq+++/z7WuatWq6tWrl0JDQ3X33XcXuNxtp06d9OCDD2rlypX6888/5efnp0mTJhX95AAAcGGFSRoPGDBAzzzzjO33q1ckKur4M3sLHcMwinTcP02cOFE7d+7UX3/9pfHjx+v999+XyeTcRboCAwPzHONcLbcxzOHDh/Xjjz9KyjmWqlevnoKDg/XNN98oJiZGw4YNy/Nck5KSbA8s/lPNmjV13333qVu3burevXue2xn904ABA/T555/bxqDBwcGqV69eoY718fEp1BjaEQ9Q5DfOBgAAAAAAqAhIwBfC8OHD1a9fvxL14e7urttuu0233XabpCuzheLj47V161Z9+umnslgs2rJlixYsWKDnnnvOEWEXWa9evXTgwAFt2LDBloBPTEzUDz/8oHbt2umaa64psI/sGT833XSTbf/Gq917771asGCBEhISdOjQITVr1sxh8fv5+ZXasuz/lJqaKunKNgN5qVWrlgYMGKABAwbIMAz99ttv2rlzpz799FPt2bNHmZmZevHFF3XTTTfZzYS/cOGCNm/eLCnnjVtJuvnmm3Xdddfp+PHj+uCDDxyagF+/fr0sFos8PT3Vq1evHPX33XefVq9erYsXL+qjjz6y7dH+T1evMmAYhtLS0vT555/rv//9ry5cuCCLxaKwsLBC7zX7/PPPa+XKlZKkJ598Ms8Z8wAAVFb/TBq7ubmpSpUqqlmzppo3b67u3bvnGHf5+vrKzc1NhmEUebbx+fPnJalQD6nmx9fXV6+88oqeeuopff/991q+fLkGDx5coj5LauLEiTlWtSqs2NhYSVKdOnVs4/6r3Xffffrmm2904sQJ7dixQ6Ghobn2c/VMcsMwlJycrA8//FBz585VcnKyzGaz7rjjjkIn37NNnTpV99xzjy5cuKCXXnpJb7zxRqGOa9myZYlXkCqswoyzAQAAAAAAyjMS8E5Su3Zt9erVS7169dKzzz6rJ554QgcPHtTKlSs1ZswYp8z46NWrl+bMmaMtW7bIYrHIbDbrs88+k2EYhZqtfv78eX355ZeSpL179xa4TGV0dLQmTpzokNjL2uHDhyUp14cMcuPm5qbAwEAFBgbq0Ucf1Weffabnn39emZmZiomJsXsfPv74Y9uMtKlTp2rq1Kl59vvzzz9r7969uummm0pwNv+T/QDF5cuXFRwcnG/b6OjoPBPwV3Nzc5OPj4/Cw8PVqlUrPfjgg9q8ebOGDRumd955R1WrVi2wj6uXby1MewAAKpviJI29vLzUuHFjHT16VAcOHCjSsfv375fkmJWFwsLCbEvRz507V127di1wSfXyKDMzU+vWrZN0ZQZ7QeOzDz74IM8E/NXc3NxUs2ZNDR48WM2bN9ewYcO0evVqJSUlaeHChUVKwvv7+2vcuHGaOHGitmzZorVr1+a6GpMzFXWcDQAAAAAAUN44d31HF2a1WjVgwADdcccdio+Pz7dtgwYNNG7cOElXZnz89ddfZRFiDo0bN1bLli2VmpqqHTt2SLqy/Ly7u7t69uxZ4PHr1q3T5cuXZTKZVL9+/Tx/shOoa9eu1aVLl0r1nEpDRkaG9u3bJ0nq0KGDrXzr1q269957C3UTs1evXrb39MiRI3Z12UnwqlWr5vs+Zj+kER0d7YjT0oEDB7R3715JV/Z1zet1s/eIPXjwYJ7LzOelefPmtuXjf/zxR02ePNkhsQMAgOK54447JF3ZRig9Pb1Qx/z555+2hH3Xrl0dEsfEiRPl5+en9PR0jR8/vsRL2zvD1q1bdfbsWUlXlpvPayyVvW3V9u3bderUqSK9xq233qr/+7//kyR9+eWXWrRoUZHjHDBggEJCQiRJr776qm37qPLg1KlTtvfk6nE2AAAAAABARcIM+FLi7u6us2fP6uTJk9q6dWuBM5L8/PwkSSaTyZbgdIZevXpp37592rBhgwIDA/XTTz+pc+fOqlu3boHHZu9b3qVLF7355pt5ttuwYYOeeeYZ/f333/rkk0/Uv39/h8VfFtavX6+LFy9Kknr37m0r9/Ly0sGDByVJv/32m92y8rnJvuZXL6d+4MAB24yy6dOn5/vgw/Dhw7V161atX79e48aNK9RepfnJTvzXrFlTW7ZsyXMv2DNnzuiOO+5QZmamoqOji3xztE+fPtq0aZM+//xzrV27VmFhYbrrrrtKFDsAACieBx98UCtWrNCFCxf07rvv6oknnijwmMWLF8tqtap69eq67777HBLH1UvR//DDD3r77bcd0m9Zyh4L33DDDVq/fn2e7X7++Wf1799fVqtVq1at0jPPPFOk13niiSe0ZcsW/fjjj3r99dfVtWtXtWnTpkh9ZC9Fn5ycrJdeeqlIx5am1atXS5LMZrN69Ojh5GgAAAAAAACKhxnwpSh7/+7o6Gj9+uuv+bb95JNPJF2Z1ZJX4rMsZO/7vWXLFtuNw6uTzHnZu3evbSZUQXuSd+/e3ZZ8Lqs92x0lKSlJc+bMkSTdeeeddktj3nzzzWrYsKGkK7OJLl++nGc/6enptn3er545lr1vaK1atRQWFpZvLA8++KAk2fZjLwmLxaKPP/5YknTvvffm+3ewXr166tatm6QrD1Nk7wFbFC+//LLtQZMpU6YoJSWl6EEDAIASa9y4sUaOHClJmj9/vrZv355v+5iYGNtDey+99JJtNrcjhIWF2RL6K1eudFi/ZSEpKUnbtm2TVPBYuHXr1mrVqpWkKwnnzMzMIr2Wu7u7Xn31VXl6eiorK0sTJkzId9yZG39/f73wwguSrszcP3nyZJGOLw2HDx+2PXjx8MMPswc8AAAAAACosEjAl6LBgwerSZMmunjxogYNGqT33ntPycnJdm3OnTun1157TW+99Za8vLw0ZswYJ0V7xXXXXaebbrpJf//9t5YsWSJPT89CzT7JvhFbu3Zt21KmefHw8NCAAQMkSb/88ot++eWXkgdeyjIzM7V161Y9+OCD+uuvv1SvXr0c+9d7enrqxRdflMlk0jfffKOHH35Y3377raxWq127n376SYMHD9Yff/yh9u3b2x56KEoSXLqy0kB2wj+vZejPnTuX70924vvLL7+0JdILumks/S/5n5GRoQ8//LDA9v9Up04dRUZGSpL++usv/ec//ylyHwAAwDGGDRumu+66S5cvX9ZTTz2lWbNm5dgS6fjx45owYYJttvRDDz1ke9jUkSZMmCA/P78KtwT92rVrlZmZKU9Pz0KtCpA9lvrrr79sD2UWRbNmzTRs2DBJV7YFWrJkSZH76N+/v7p06SJJTn2/MzIy9PHHH+uRRx7RxYsXFRgYaFtmHwAAAAAAoCJiCfpCePPNN23LIRbkySefVGhoqKQrS3m//fbbGj16tPbs2aOpU6dqxowZuvbaa1WjRg2lpKTo2LFjMgxDderU0YwZM3TTTTeV5qkUSq9evbR3716lpaWpa9euBS6Jb7FYbLPl+/TpI09PzwJfY+DAgXrzzTdltVoVHR2tqVOnljjuv/76y3YzsyB+fn6aP39+jvI1a9bom2++sf2elZWltLQ0HT9+3LYv6vXXX69Fixapfv36OY7v3r27Zs2apVdeeUU//vijHn30UVWvXl3XXHONPDw8lJiYqKSkJElSp06dNG/ePLm7u0uSNm3apAsXLkgqXBLcZDJp4MCBmj17tg4ePKjdu3fr5ptvtmvTuXPnfPto0aKFPvroI9sDFC1bttSNN95Y4Gt37txZTZs21ZEjRxQTE6PBgwfb9qQvrP79+2vdunWKj49XbGys7r33XgUHBxepDwAAUHImk0nz5s3T66+/rkWLFmnJkiVaunSpGjdurBo1aigpKUknTpyQJHl7e2vChAm2hykdzdfXV1OmTNGTTz5ZKv0XxtSpUwu9tc+///1vtWzZ0rb8fNeuXVW7du0Cj+vdu7f+85//KCUlRdHR0cXajuepp57SZ599piNHjuj111/XXXfdZbc6U2FMmTJF99xzj/7+++982+3bt6/Q4+yWLVvq3//+d47yf36nslqt+vvvv3X8+HHbDP727dtrwYIFJd5aCQAAAAAAwJlIwBfC0aNHdfTo0UK1zU6uZrvmmmsUHR2tzZs3a/PmzdqzZ4/OnTunP//8U76+vmrfvr3CwsL0r3/9S76+vqUQfdH16tVLs2bNkiTdfffdBbbftGmTbWZ/YRLH0pVlL7t27arNmzfrk08+0QsvvFDiG20Wi0Xff/99odpmzxz/p1OnTunUqVN2Zd7e3qpbt65atGihHj16qHfv3vk+ZNC7d2/ddtttWrt2rb766iv9/vvvOn78uKxWq+rWratevXqpd+/eOVYWyL5xe9NNN6lFixaFOo/+/ftrwYIFunz5sqKjo3Mk4Avj9OnT+vrrryUV/vpJ0gMPPKDp06fr6NGj/6+9fw/Tqq73x//nII1gKioKGJa6dQNyhqDwwEHMw9atWyS1FFIxsvSLWyskQwW3mpYHPG00Ec2duUXFKN07/SAdrB0RUHYSMgzYpiCKCorCCHP//vDHbCdQBlwz98zweFwX18Wste73vNa673vmNfdzrffKr371qy2G/Zvzb//2bznhhBOybt26XHrppXnkkUfSqlWrrR4HAPhgKioqcu655+b444/Pgw8+mP/5n//JkiVL8txzz2WXXXZJv379Mnjw4AwfPjxt27at11oOP/zwnHjiiZkxY0a9fp/38swzz9R529dffz2/+93vsmjRoiR176Vat26dE088Mf/xH/+R2bNnZ8mSJdlvv/22qs7KyspcccUVGTlyZKqqqnLppZfme9/73ladFNmhQ4dcfPHF+frXv/6+273xxht17rNbttz8n5h//zdVRUVFWrdunY4dO6Z79+75p3/6pxxxxBFbfVInAAAAQGNTUWpq8zsCAAAAAAAAQCPkHvAAAAAAAAAAUAABPAAAAAAAAAAUQAAPAAAAAAAAAAUQwAMAAAAAAABAAQTwAAAAAAAAAFCAluUuoLHp169fqqqqstdee5W7FACAQr300kuprKzMvHnzyl0KH4B+FQBorvSrAAA0BwL4v7Nu3bps2LCh3GUAABRu/fr1KZVK5S6DD0i/CgA0V/pVAACaAwH832nXrl2SZNasWWWuBACgWEcccUS5S6AA+lUAoLnSrwIA0By4BzwAAAAAAAAAFEAADwAAAAAAAAAFEMADAAAAAAAAQAEE8AAAAAAAAABQgLIH8M8//3w6d+68yb8HH3wwSbJgwYKMGDEivXv3zpAhQzJ16tRaj6+urs7NN9+cgQMHplevXhk1alSWLl1ajl0BAAAAAAAAYDtW9gD+z3/+c3bcccf8/Oc/zy9+8Yuaf8cff3xeffXVnHXWWdlvv/0yffr0jBkzJjfddFOmT59e8/jJkyfn/vvvz5VXXplp06aloqIio0ePTlVVVRn3CgCA5sIJowAAAABAXbUsdwHPPPNM9t9//7Rr126Tdffcc08qKyszceLEtGzZMgcccECWLl2aKVOmZPjw4amqqspdd92VsWPHZvDgwUmSSZMmZeDAgZk5c2aOO+64ht4dAACamY0njD7xxBOpqKioWb7LLrvUnDD6qU99KpdffnmeeuqpXH755dltt90yfPjwJP93wujVV1+d9u3b59prr83o0aPz6KOPprKysly7BQAAAADUg0ZxBfyBBx642XXz5s1L//7907Ll/50nMGDAgCxevDgrV67MwoULs2bNmgwYMKBm/a677pquXbtm7ty59V47AADN37tPGN1rr71q/rVq1SoPPPBAzQmjBxxwQIYPH54zzzwzU6ZMSZKaE0bHjBmTwYMHp0uXLpk0aVJefPHFzJw5s8x7BgAAAAAUrewB/DPPPJOVK1fmtNNOyyGHHJLPfvaz+fnPf54kWb58eTp06FBr+41Xyr/wwgtZvnx5kmTvvffeZJtly5Y1QPUAADR3ThgFAAAAAOqqrAF8VVVVlixZkjfeeCMXXHBB7rjjjvTo0SOjR4/O7Nmzs3bt2k2m5dxxxx2TJOvWrctbb72VJJvdZt26dQ2zEwAANGtOGAUAAAAA6qqs94CvrKzM3Llz07Jly5oQvXv37nn22WczderUtGrVKlVVVbUeszFY32mnndKqVask7wT5G/+/cZvWrVs30F4AANBcbTxhtHXr1rnooouy00475Yc//GFGjx6du++++wOdMLpq1aqG2QkAAAAAoMGUNYBP3gnS/16nTp3yi1/8Ih06dMiKFStqrdv4dfv27bN+/fqaZR/72MdqbdOlS5d6rBoAgO2BE0YBAAAAgK1R1inoFy5cmD59+mTevHm1lv/xj3/MgQcemP79+2f+/PnZsGFDzbrZs2dn//33T9u2bdOlS5fsvPPOmTNnTs361atX5+mnn06/fv0abD8AAGi+dtppp02uYO/UqVNefPHFLZ4wunHq+c1t8/dT1wMAAAAATV9ZA/hOnTrlH//xH3P55Zdn3rx5efbZZ3P11Vfnqaeeyhe/+MUMHz48b7zxRsaPH59Fixbl4Ycfzj333JNzzjknyTtXJI0YMSLXXXddZs2alYULF+bCCy9Mhw4dcuSRR5Zz1wAAaAacMAoAAAAAbI2yTkHfokWL3H777bnuuutywQUXZPXq1enatWvuvvvudO7cOUly55135qqrrsqwYcOy11575aKLLsqwYcNqxjj//POzfv36XHLJJVm7dm369++fqVOnbnKVEgAAbK13nzA6YcKE7L777nnggQfy1FNP5aGHHsqee+6ZO++8M+PHj8/nP//5/P73v88999yTyy+/PEntE0b32GOPdOzYMddee60TRgEAAACgmaoolUqlchfRmBxxxBFJklmzZpW5EgCAYulzts0rr7yS6667Lk8++WTNCaNf/epXa65g//3vf5+rrroqTz/9dPbaa6+MGjUqI0aMqHn8hg0bcsMNN+Thhx+uOWH0sssuyz777LNN9XgeAYDmSp8DAEBzUNYr4AEAoLHbY4898o1vfOM91/fs2TPTpk17z/U77LBDxo4dm7Fjx9ZHeQAAAABAI1LWe8ADAAAAAAAAQHMhgAcAAAAAAACAAgjgAcqsurq6SY8PAJRH0b/j9QwAAAAAH5x7wAOUWYsWLfLkk09m1apVhY/dpk2bDBo0qPBxAYDyK7KH0DM0ftXV1WnRothz6OtjTAAAANjeCeABGoFVq1bllVdeKXcZAEATo4fYfhR90qaTLgAAAKB+COABAACgCXDCBQAAADR+5poDAAAAAAAAgAII4IEGU11d3STHBgC2L/XRV+hVAAAAALYPpqAHGkzR963cyP0rAYAiudc2AAAAANtKAA80KPetBACaAj0LAAAAANvCFPQAAAAAAAAAUAABPADbrL7vZ+t+uQAAAAAAQFNiCnoAtlnR98h9N/fLBQAAAAAAmhoBPAAfiHvkAgA0Pa1bt051dXVatChuYryixwMAAICmSAAPAAAA25nKyspCZzMyexEAAAC8QwAPAAAA2ymzGQEAAECxzA0HTUh1dXWTHh8AAAAAAACaM1fAQxNS5BSRf8+UkQAAAAAAAPDBCOChiTFFJNDUVVdXp0WL+pmEpz7HBgAAAACALRHAAwANqr5m82iImTycPAAAAAAAwPsRwAMADa6pzubRlE8eAAAAAACg/gngAQC2QlM9eQAAAAAAgPpnnlMAtkvV1dVNenwAACi3ontePTQAANAcuAIeCuYevtA01NdU4onpxAEA2D4U2VProQEAgOZCAA8Fq69Qr2PHjunbt2+hYzYXrVu3rvcTH5xY0TyZShwAAD4YPTUAAEBtAnioB/XxAUSbNm0KHa85qaysdDUzAAAAAAAAZSeAB5oNV14AAAAAAABQTuZTBgAAoMmorq5u1OMBAAAA2zdXwNMo1ef9tt3LGwAAmq4ibz3kVkPbp/r4m9DfmQAAAGwkgKdRqq/7efuADQCAhta6dWvhXMHceqjxqa/XeX2MWfTfm/7OpDFqKu9HAABojgTwNFo+VAMoDx+sARSrsrKy8MCvY8eO6du3byFjQRHq43Ven8G2vzdp7prS+xEAAJobATwAUEt9zUKSCIyA7VuRgV+bNm0KGQeKJtiGxsP7EQAAykMAX0b1fYWhKxgB2Fb19WGdwAgAAAAAgOZMAF9G9XmFoanBoDju2woAAAAAAEBdCODLzHRg0PjVx/0sNzIdNwAAQP2pj5OpnaANAAC8HwE8QB3VxwkzpuMGAACoP0WfTG3GQQAAYEsE8ADNmOnzAYC6qK+eQR/CB6GXpShmHwQAABqSAB6gGTN9PgBQF/XRM7hKlA+qPl6XelgAAADqmwAeYDtg+vyGVd9Xa7kSDID64ipRGqMiX5d6WAAAAOqbAL6ZEv6wtUzvCMWpz5kHXE0IAMC28PceAABAwxDAN1MNMe10fYz97vFpWPX5mkk8r2yfXEUIAND81cfJzPURlpvOvxj1dfK6EyQAAKD5EMA3c/U57XR9BUumBCwvzysAAEDdFX0yc33OeGQ6/w+uPk5eN8sVAAA0LwJ4AAAAgA/I7EfbF883AADwXsxtBQAAwHZp41TSRauPMQEAAICmwRXwAAAAbJdMJQ0AAAAUTQAPAADAds1U0gAAAEBRTEEPQKNUX1PCAgBAY6YPBgAAaNpcAQ9Ao1QfU8Ju1LFjx/Tt27fQMRvKxg9kW7RwDh0AQHNUH31wU+5/AQAAmhoBPACNWn1MCdumTZtCx2tI9XliQuLDWQCAxqLIPrgp978AAABNjQAeAJqg+rpXbVP+cNbsAAAANEX6WAAAaF4E8ABAs2B2AAAAmqKi+1h9KwAAlJcAHgBoVswOAABAU1RUH6tvBQCA8jK3FQAAAAAAAAAUQAAPAAAABdl4L2cAAABg+2QKerYrGz8Ma9HCuScAAEDxir6Xc+J+zgAAANCUCODZrtTHh2Hv5oMxAAAgKe5ezon7OQMAAEBTIoBnu1Tkh2Hv5oMxAAAAAAAA2H6ZhxsAAAAAAAAACiCABwAAAKCsWrdunerq6nKXAQAA8IGZgh4AAACAsqqsrEyLFi3y5JNPZtWqVYWM2bFjx/Tt27eQsQAAAOpKAA8AAABAo7Bq1aq88sorhYzVpk2bQsYBAADYGqagBwAAAAAAAIACCOABAAAAAAAAoAACeAAAAAAAAAAogAAeAAAAAAAAAAoggAcAAAAAAACAAgjgAQAAAAAAAKAAAngAAAAAAAAAKIAAHgAAgMK1bt061dXV5S4DAAAAoEG1LHcBAAAAND+VlZVp0aJFnnzyyaxataqQMTt27Ji+ffsWMhYAAABAfWhUAfzixYtz0kkn5dJLL81JJ52UJFmwYEGuuuqq/PGPf8xuu+2WkSNH5uyzz655THV1dW699dY8+OCDWb16dT7+8Y9nwoQJ2Xfffcu1GwAAAPz/rVq1Kq+88kohY7Vp06aQcQAAAADqS6OZgv7tt9/OV7/61bz55ps1y1599dWcddZZ2W+//TJ9+vSMGTMmN910U6ZPn16zzeTJk3P//ffnyiuvzLRp01JRUZHRo0enqqqqHLsBAEAztnjx4vTp0ycPP/xwzbIFCxZkxIgR6d27d4YMGZKpU6fWekx1dXVuvvnmDBw4ML169cqoUaOydOnShi4dAAAAAGgAjSaAv+WWW/LhD3+41rIHHngglZWVmThxYg444IAMHz48Z555ZqZMmZIkqaqqyl133ZUxY8Zk8ODB6dKlSyZNmpQXX3wxM2fOLMduAADQTDlhFAAAAADYkkYRwM+dOzfTpk3LN7/5zVrL582bl/79+6dly/+bKX/AgAFZvHhxVq5cmYULF2bNmjUZMGBAzfpdd901Xbt2zdy5cxusfgAAmj8njAIAAAAAW1L2AH716tW56KKLcskll2TvvfeutW758uXp0KFDrWXt2rVLkrzwwgtZvnx5kmzyuHbt2mXZsmX1WDUAANsTJ4wCAAAAAHVR9gB+4sSJ6d27d44//vhN1q1duzaVlZW1lu24445JknXr1uWtt95Kks1us27dunqqGACA7YkTRgEAAACAumq55U3qz4wZMzJv3rw88sgjm13fqlWrTe6NuTFY32mnndKqVask70ztufH/G7dp3bp1PVUNAMD2pL5OGF21alU9VQwAAAAAlEtZA/jp06dn5cqVGTJkSK3lEyZMyNSpU/ORj3wkK1asqLVu49ft27fP+vXra5Z97GMfq7VNly5d6rd4AACaPSeMAgAAAABbo6wB/HXXXZe1a9fWWnbUUUfl/PPPz7HHHpv/+q//yv33358NGzZkhx12SJLMnj07+++/f9q2bZtddtklO++8c+bMmVMTwK9evTpPP/10RowY0eD7AwBA8+KEUQAAAABga5Q1gG/fvv1ml7dt2zYdO3bM8OHDc+edd2b8+PH5/Oc/n9///ve55557cvnllyd5ZyrPESNG5Lrrrssee+yRjh075tprr02HDh1y5JFHNuSuAADQDDlhFAAAAADYGmUN4Lekbdu2ufPOO3PVVVdl2LBh2WuvvXLRRRdl2LBhNducf/75Wb9+fS655JKsXbs2/fv3z9SpUze5zyYAAGwtJ4wCAAAAAFuj0QXwf/7zn2t93bNnz0ybNu09t99hhx0yduzYjB07tr5LAwCAWpwwCgAAAAC8W6ML4AEAoDFzwigAAAAA8F5alLsAAAAAAAAAAGgOBPAAAAAAAAAAUAABPAAAAAAAAAAUQAAPAAAAAAAAAAUQwAMAAAAAAABAAQTwAAAAAAAAAFAAATwAAAAAAAAAFEAADwAAAAAAAAAFEMADAAAAAAAAQAEE8AAAAAAAAABQAAE8AAAAAAAAABRAAA8AAAAAAAAABRDAAwAAAAAAAEABBPAAAAAAAAAAUAABg4J6hgAAPg9JREFUPAAAAAAAAAAUQAAPAAAAAAAAAAUQwAMAAAAAAABAAQTwAAAAAAAAAFAAATwAAAAAAAAAFEAADwAAAAAAAAAFEMADAAAAAAAAQAEE8AAAAAAAAABQAAE8AAAAAAAAABRAAA8AAAAAAAAABRDAAwAAAAAAAEABBPAAAAAAAAAAUAABPAAAAAAAAAAUQAAPAAAAAAAAAAUQwAMAAAAAAABAAQTwAAAAAAAAAFAAATwAAAAAAAAAFEAADwAAAAAAAAAFEMADAAAAAAAAQAEE8AAAAAAAAABQAAE8AAAAAAAAABRAAA8AAAAAAAAABRDAAwAAAAAAAEABBPAAAAAAAAAAUAABPAAAAAAAAAAUQAAPAAAAAAAAAAUQwAMAAAAAAABAAQTwAAAAAAAAAFAAATwAAAAAAAAAFEAADwAAAAAAAAAFEMADAAAAAAAAQAEE8AAAAAAAAABQAAE8AAAAAAAAABRAAA8AAAAAAAAABRDAAwAAAAAAAEABBPAAAAAAAAAAUAABPAAAAAAAAAAUQAAPAAAAAAAAAAUQwAMAAAAAAABAAQTwAAAAAAAAAFAAATwAAAAAAAAAFEAADwAAAAAAAAAFEMADAAAAAAAAQAEE8AAAAAAAAABQAAE8AAAAAAAAABRAAA8AAAAAAAAABRDAAwAAAAAAAEABBPAAAAAAAAAAUAABPAAAAAAAAAAUQAAPAAAAAAAAAAUQwAMAAAAAAABAAQTwAAAAAAAAAFAAATwAAAAAAAAAFEAADwAAAAAAAAAFEMADAAAAAAAAQAEE8AAAAAAAAABQAAE8AAAAAAAAABSg7AH8ypUrM3bs2AwYMCB9+vTJF77whSxatKhm/YIFCzJixIj07t07Q4YMydSpU2s9vrq6OjfffHMGDhyYXr16ZdSoUVm6dGlD7wYAAM2UfhUAAAAAqKuyB/Bf+tKX8txzz2XKlCl56KGH0qpVq5x55pl566238uqrr+ass87Kfvvtl+nTp2fMmDG56aabMn369JrHT548Offff3+uvPLKTJs2LRUVFRk9enSqqqrKuFcAADQX+lUAAAAAoK5alvObv/rqq9lnn33ypS99Kf/4j/+YJDn33HPzL//yL/nLX/6S2bNnp7KyMhMnTkzLli1zwAEHZOnSpZkyZUqGDx+eqqqq3HXXXRk7dmwGDx6cJJk0aVIGDhyYmTNn5rjjjivn7gEA0MTpVwEAAACArVHWK+B333333HDDDTUfZr788suZOnVqOnTokAMPPDDz5s1L//7907Ll/50nMGDAgCxevDgrV67MwoULs2bNmgwYMKBm/a677pquXbtm7ty5Db4/AAA0L/pVAAAAAGBrlPUK+He79NJL88ADD6SysjK33XZbdtpppyxfvjydOnWqtV27du2SJC+88EKWL1+eJNl777032WbZsmUNUzgAANsF/SoAAAAAsCVlvwf8RmeccUamT5+eE044Ieedd17+9Kc/Ze3atamsrKy13Y477pgkWbduXd56660k2ew269ata5jCAQDYLuhXAQAAAIAtaTRXwB944IFJkiuuuCJPPfVU7r333rRq1SpVVVW1ttv4QeVOO+2UVq1aJUmqqqpq/r9xm9atWzdQ5QAAbA/0qwAAAADAlpT1CviVK1fm0UcfzYYNG2qWtWjRIgcccEBWrFiRDh06ZMWKFbUes/Hr9u3b10zlubltOnToUM/VAwDQ3OlXAQAAAICtUdYAfsWKFfnKV76SX//61zXL3n777Tz99NM54IAD0r9//8yfP7/WB56zZ8/O/vvvn7Zt26ZLly7ZeeedM2fOnJr1q1evztNPP51+/fo16L4AAND86FcBAAAAgK1R1gC+S5cuOeyww3L55Zdn3rx5eeaZZzJu3LisXr06Z555ZoYPH5433ngj48ePz6JFi/Lwww/nnnvuyTnnnJPknXtpjhgxItddd11mzZqVhQsX5sILL0yHDh1y5JFHlnPXAABoBvSrAAAAAMDWKOs94CsqKnLjjTfm+uuvzwUXXJDXX389/fr1y/e+97185CMfSZLceeedueqqqzJs2LDstddeueiiizJs2LCaMc4///ysX78+l1xySdauXZv+/ftn6tSpqaysLNduAQDQTOhXAQAAAICtUdYAPkl22WWXTJw4MRMnTtzs+p49e2batGnv+fgddtghY8eOzdixY+upQgAAtmf6VQAAAACgrso6BT0AAAAAAAAANBcCeAAAAAAAAAAogAAeAAAAAAAAAAoggAcAAAAAAACAAgjgAQAAAAAAAKAAAngAAAAAAAAAKIAAHgAAAAAAAAAKIIAHAAAAAAAAgAII4AEAAAAAAACgANsUwM+dOzdr1qzZ7LrVq1fnv/7rvz5QUQAA8EHoVwEAAACActimAP5zn/tcnn322c2ue/rpp3PxxRd/oKIAAOCD0K8CAAAAAOXQsq4bjhs3LsuWLUuSlEqlTJw4MTvvvPMm2y1ZsiR77rlncRUCAEAd6FcBAAAAgHKr8xXwRx99dEqlUkqlUs2yjV9v/NeiRYv07t07V199db0UCwAA70W/CgAAAACUW52vgB86dGiGDh2aJBk5cmQmTpyYAw44oN4KAwCAraFfBQAAAADKrc4B/Lt997vfLboOAAAojH4VAAAAACiHbQrg33rrrdx+++35yU9+krfeeivV1dW11ldUVOSJJ54opEAAANha+lUAAAAAoBy2KYC/6qqrMn369HziE5/IQQcdlBYt6nwreQAAqHf6VQAAAACgHLYpgP9//+//5cILL8wXvvCFousBAIAPTL8KAAAAAJTDNl0KtH79+vTs2bPoWgAAoBD6VQAAAACgHLYpgD/ssMPy5JNPFl0LAAAUQr8KAAAAAJTDNk1Bf+yxx2bChAl55ZVX0qtXr7Ru3XqTbU488cQPWhsAAGwT/SoAAAAAUA7bFMBfcMEFSZIZM2ZkxowZm6yvqKjwgSYAAGWjXwUAAAAAymGbAvhZs2YVXQcAABRGvwoAAAAAlMM2BfAdO3Ysug4AACiMfhUAAAAAKIdtCuBvvfXWLW7z//1//9+2DA0AAB+YfhUAAAAAKIfCA/idd9457dq184EmAABlo18FAAAAAMphmwL4hQsXbrLszTffzPz58zNx4sRceumlH7gwAADYVvpVAAAAAKAcWhQ10E477ZSBAwfmvPPOy7e+9a2ihgUAgELoVwEAAACA+lZYAL/R3nvvnWeffbboYQEAoBD6VQAAAACgvmzTFPSbUyqVsmzZskyZMiUdO3YsalgAACiEfhUAAAAAqG/bFMB36dIlFRUVm11XKpVM6QkAQFnpVwEAAACActimAP68887b7AeaO++8c4YMGZL99tvvg9YFAADbTL8KAAAAAJTDNgXwY8aMKboOAAAojH4VAAAAACiHbb4HfFVVVR5++OHMmTMnq1evzu67755+/fpl2LBh2XHHHYusEQAAtpp+FQAAAABoaNsUwK9evTqf+9znsnDhwnzkIx/JXnvtlcWLF+fRRx/N9773vdx3333ZZZddiq4VAADqRL8KAAAAAJRDi2150PXXX5/ly5fn3nvvzY9//ONMmzYtP/7xj3Pvvfdm5cqVuemmm4quEwAA6ky/CgAAAACUwzYF8LNmzcoFF1yQfv361Vrer1+/nH/++fl//+//FVIcAABsC/0qAAAAAFAO2xTAr1mzJh/96Ec3u+6jH/1oXnvttQ9SEwAAfCD6VQAAAACgHLYpgP+Hf/iH/OQnP9nsulmzZmXffff9QEUBAMAHoV8FAAAAAMqh5bY86Oyzz86Xv/zlVFVV5fjjj8+ee+6Zl19+OY888kgefPDBTJw4seAyAQCg7vSrAAAAAEA5bFMAf+yxx2bJkiW5/fbb8+CDD9Ys/9CHPpTzzjsvp556amEFAgDA1tKvAgAAAADlsE0B/Jtvvplzzz03I0aMyFNPPZVVq1Zl2bJlOfXUU9OmTZuiawQAgK2iXwUAAAAAymGr7gG/YMGCnHjiifnOd76TJNl1110zaNCgDBo0KDfeeGNOO+20PPvss/VRJwAAbJF+FQAAAAAopzoH8M8991zOPPPMrFq1KgceeGCtdZWVlfn617+eNWvW5LTTTsvy5csLLxQAAN6PfhUAAAAAKLc6B/B33HFHdt9993z/+9/PUUcdVWtd69atM2LEiEyfPj077bRTbr/99sILBQCA96NfBQAAAADKrc4B/OzZs/P5z38+u+2223tu07Zt25x11lmZPXt2EbUBAECd6VcBAAAAgHKrcwD/0ksvZd99993idp06dTKlJwAADU6/CgAAAACUW50D+D322CMrVqzY4navvPLK+151BAAA9UG/CgAAAACUW50D+P79++fhhx/e4nYzZszIQQcd9IGKAgCAraVfBQAAAADKrc4B/MiRIzNnzpxcc801Wbdu3Sbrq6qq8s1vfjM///nPc/rppxdaJAAAbIl+FQAAAAAot5Z13bBHjx65+OKL841vfCM/+MEPcvDBB2efffbJhg0b8sILL2TOnDl59dVX86//+q8ZOHBgfdYMAACb0K8CAAAAAOVW5wA+SU4//fR06dIlU6dOzaxZs2quLPrwhz+cww47LKNGjUqvXr3qpVAAANgS/SoAAAAAUE5bFcAnycc//vF8/OMfT5K8+uqradGiRdq0aVN4YQAAsC30qwAAAABAuWx1AP9uu+++e1F1AABA4fSrAAAAAEBDalHuAgAAAAAAAACgORDAAwAAAAAAAEABBPAAAAAAAAAAUAABPAAAAAAAAAAUQAAPAAAAAAAAAAUQwAMAAAAAAABAAQTwAAAAAAAAAFAAATwAAAAAAAAAFEAADwAAAAAAAAAFEMADAAAAAAAAQAEE8AAAAAAAAABQAAE8AAAAAAAAABRAAA8AAAAAAAAABRDAAwAAAAAAAEABBPAAAAAAAAAAUAABPAAAAAAAAAAUQAAPAAAAAAAAAAUoewD/2muv5bLLLsugQYPSt2/ffPazn828efNq1i9YsCAjRoxI7969M2TIkEydOrXW46urq3PzzTdn4MCB6dWrV0aNGpWlS5c29G4AAAAAAAAAsJ0rewD/5S9/Ob/73e9yww035KGHHkq3bt1y9tln59lnn82rr76as846K/vtt1+mT5+eMWPG5Kabbsr06dNrHj958uTcf//9ufLKKzNt2rRUVFRk9OjRqaqqKuNeAQDQXDhhFAAAAACoq7IG8EuXLs3//M//ZMKECenXr1/+4R/+IePHj0/79u3z6KOP5oEHHkhlZWUmTpyYAw44IMOHD8+ZZ56ZKVOmJEmqqqpy1113ZcyYMRk8eHC6dOmSSZMm5cUXX8zMmTPLuWsAADQTThgFAAAAAOqqrAH87rvvnjvuuCPdu3evWVZRUZFSqZRVq1Zl3rx56d+/f1q2bFmzfsCAAVm8eHFWrlyZhQsXZs2aNRkwYEDN+l133TVdu3bN3LlzG3RfAABofpwwCgAAAABsjbIG8LvuumsGDx6cysrKmmU/+tGP8r//+7857LDDsnz58nTo0KHWY9q1a5ckeeGFF7J8+fIkyd57773JNsuWLavn6gEAaO6cMAoAAAAAbI2y3wP+3ebPn5+vf/3rOeKIIzJ06NCsXbu2VjifJDvuuGOSZN26dXnrrbeSZLPbrFu3rmGKBgCg2XLCKAAAAACwNRpNAP/EE0/k7LPPTs+ePXPDDTckSVq1arXJvTE3Bus77bRTWrVqlSSb3aZ169YNUDUAANsTJ4wCAAAAAO+nUQTw9957b8aMGZNBgwZlypQpNcF6hw4dsmLFilrbbvy6ffv2NVcSbW6bv78SCQAAPggnjAIAAAAAW1L2AP6+++7LFVdckdNPPz033nhjrauD+vfvn/nz52fDhg01y2bPnp39998/bdu2TZcuXbLzzjtnzpw5NetXr16dp59+Ov369WvQ/QAAoPlywigAAAAAUBdlDeAXL16cb3zjGznyyCNzzjnnZOXKlXnppZfy0ksv5fXXX8/w4cPzxhtvZPz48Vm0aFEefvjh3HPPPTnnnHOSvDOV54gRI3Lddddl1qxZWbhwYS688MJ06NAhRx55ZDl3DQCAZsIJowAAAABAXbUs5zd//PHH8/bbb2fmzJmZOXNmrXXDhg3LNddckzvvvDNXXXVVhg0blr322isXXXRRhg0bVrPd+eefn/Xr1+eSSy7J2rVr079//0ydOnWT+2wCAMDW2twJoxu1atUqw4cPz5133pnx48fn85//fH7/+9/nnnvuyeWXX56k9gmje+yxRzp27Jhrr73WCaMAAAAA0EyVNYD/4he/mC9+8Yvvu03Pnj0zbdq091y/ww47ZOzYsRk7dmzR5QEAsJ1zwigAAAAAsDXKGsADAEBj5oRRAAAAAGBrlPUe8AAAAAAAAADQXAjgAQAAAAAAAKAAAngAAAAAAAAAKIAAHgAAAAAAAAAKIIAHAAAAAAAAgAII4AEAAAAAAACgAAJ4AAAAAAAAACiAAB4AAAAAAAAACiCABwAAAAAAAIACCOABAAAAAAAAoAACeAAAAAAAAAAogAAeAAAAAAAAAAoggAcAAAAAAACAAgjgAQAAAAAAAKAAAngAAAAAAAAAKIAAHgAAAAAAAAAKIIAHAAAAAAAAgAII4AEAAAAAAACgAAJ4AAAAAAAAACiAAB4AAAAAAAAACiCABwAAAAAAAIACCOABAAAAAAAAoAACeAAAAAAAAAAogAAeAAAAAAAAAAoggAcAAAAAAACAAgjgAQAAAAAAAKAAAngAAAAAAAAAKIAAHgAAAAAAAAAKIIAHAAAAAAAAgAII4AEAAAAAAACgAAJ4AAAAAAAAACiAAB4AAAAAAAAACiCABwAAAAAAAIACCOABAAAAAAAAoAACeAAAAAAAAAAogAAeAAAAAAAAAAoggAcAAAAAAACAAgjgAQAAAAAAAKAAAngAAAAAAAAAKIAAHgAAAAAAAAAKIIAHAAAAAAAAgAII4AEAAAAAAACgAAJ4AAAAAAAAACiAAB4AAAAAAAAACiCABwAAAAAAAIACCOABAAAAAAAAoAACeAAAAAAAAAAogAAeAAAAAAAAAAoggAcAAAAAAACAAgjgAQAAAAAAAKAAAngAAAAAAAAAKIAAHgAAAAAAAAAKIIAHAAAAAAAAgAII4AEAAAAAAACgAAJ4AAAAAAAAACiAAB4AAAAAAAAACiCABwAAAAAAAIACCOABAAAAAAAAoAACeAAAAAAAAAAogAAeAAAAAAAAAAoggAcAAAAAAACAAgjgAQAAAAAAAKAAAngAAAAAAAAAKIAAHgAAAAAAAAAKIIAHAAAAAAAAgAII4AEAAAAAAACgAAJ4AAAAAAAAACiAAB4AAAAAAAAACiCABwAAAAAAAIACCOABAAAAAAAAoAACeAAAAAAAAAAoQKMK4CdPnpyRI0fWWrZgwYKMGDEivXv3zpAhQzJ16tRa66urq3PzzTdn4MCB6dWrV0aNGpWlS5c2ZNkAAGwn9KsAAAAAwPtpNAH8d77zndx88821lr366qs566yzst9++2X69OkZM2ZMbrrppkyfPr1mm8mTJ+f+++/PlVdemWnTpqWioiKjR49OVVVVQ+8CAADNmH4VAAAAANiSluUu4MUXX8z48eMzf/787L///rXWPfDAA6msrMzEiRPTsmXLHHDAAVm6dGmmTJmS4cOHp6qqKnfddVfGjh2bwYMHJ0kmTZqUgQMHZubMmTnuuOPKsUsAADQj+lUAAAAAoK7KfgX8n/70p7Rp0yY//OEP06tXr1rr5s2bl/79+6dly/87T2DAgAFZvHhxVq5cmYULF2bNmjUZMGBAzfpdd901Xbt2zdy5cxtsHwAAaL70qwAAAABAXZX9CvihQ4dm6NChm123fPnydOrUqdaydu3aJUleeOGFLF++PEmy9957b7LNsmXL6qFaAAC2N/pVAAAAAKCuyn4F/PtZu3ZtKisray3bcccdkyTr1q3LW2+9lSSb3WbdunUNUyQAANst/SoAAAAA8G6NOoBv1apVqqqqai3b+EHlTjvtlFatWiXJZrdp3bp1wxQJAMB2S78KAAAAALxbow7gO3TokBUrVtRatvHr9u3b10zlubltOnTo0DBFAgCw3dKvAgAAAADv1qgD+P79+2f+/PnZsGFDzbLZs2dn//33T9u2bdOlS5fsvPPOmTNnTs361atX5+mnn06/fv3KUTIAANsR/SoAAAAA8G6NOoAfPnx43njjjYwfPz6LFi3Kww8/nHvuuSfnnHNOknfupTlixIhcd911mTVrVhYuXJgLL7wwHTp0yJFHHlnm6gEAaO70qwAAAADAu7UsdwHvp23btrnzzjtz1VVXZdiwYdlrr71y0UUXZdiwYTXbnH/++Vm/fn0uueSSrF27Nv3798/UqVNTWVlZxsoBANge6FcBAAAAgHdrVAH8Nddcs8mynj17Ztq0ae/5mB122CFjx47N2LFj67M0AADQrwIAAAAA76tRT0EPAAAAAAAAAE2FAB4AAAAAAAAACiCABwAAAAAAAIACCOABAAAAAAAAoAACeAAAAAAAAAAogAAeAAAAAAAAAAoggAcAAAAAAACAAgjgAQAAAAAAAKAAAngAAAAAAAAAKIAAHgAAAAAAAAAKIIAHAAAAAAAAgAII4AEAAAAAAACgAAJ4AAAAAAAAACiAAB4AAAAAAAAACiCABwAAAAAAAIACCOABAAAAAAAAoAACeAAAAAAAAAAogAAeAAAAAAAAAAoggAcAAJqs6urqcpcAAAAAADValrsAAACAbdWiRYs8+eSTWbVqVSHjdezYMX379i1kLAAAAAC2PwJ4AACgSVu1alVeeeWVQsZq06ZNIeMAAAAAsH0yBT0AAAAAAAAAFEAADwAAAAAAAAAFEMADAAAAAAAAQAEE8AAAAAAAAABQAAE8AAAAAAAAABRAAA8AAAAAAAAABRDAAwAAAAAAAEABBPAAAAAAAAAAUAABPAAAAAAAAAAUQAAPAAAAAAAAAAUQwAMAAAAAAABAAQTwAAAAAAAAAFAAATwAAAAAAAAAFEAADwAAAAAAAAAFEMADAAAAAAAAQAEE8AAAAAAAAABQAAE8AAAAAAAAABRAAA8AAAAAAAAABRDAAwAAAAAAAEABBPAAAAAAAAAAUAABPAAAAAAAAAAUQAAPAAAAAAAAAAUQwAMAAAAAAABAAQTwAAAAAAAAAFAAATwAAAAAAAAAFEAADwAAAAAAAAAFEMADAAAAAAAAQAEE8AAAAAAAAABQAAE8AAAAAAAAABRAAA8AAAAAAAAABRDAAwAAAAAAAEABBPAAAAAAAAAAUAABPAAAAAAAAAAUQAAPAAAAAAAAAAUQwAMAAAAAAABAAQTwAAAAAAAAAFAAATwAAAAAAAAAFEAADwAAAAAAAAAFEMADAAAAAAAAQAEE8AAAAAAAAABQAAE8AAAAAAAAABRAAA8AAAAAAAAABRDAAwAAAAAAAEABBPAAAAAAAAAAUAABPAAAAAAAAAAUQAAPAAAAAAAAAAUQwAMAAAAAAABAAQTwAAAAAAAAAFAAATwAAAAAAAAAFEAADwAAAAAAAAAFaBYBfHV1dW6++eYMHDgwvXr1yqhRo7J06dJylwUAAEn0qwAAAACwvWgWAfzkyZNz//3358orr8y0adNSUVGR0aNHp6qqqtylAQCAfhUAAAAAthNNPoCvqqrKXXfdlTFjxmTw4MHp0qVLJk2alBdffDEzZ84sd3kAAGzn9KsAAAAAsP1o8gH8woULs2bNmgwYMKBm2a677pquXbtm7ty5ZawMAAD0qwAAAACwPWlZ7gI+qOXLlydJ9t5771rL27Vrl2XLlm31eCtWrMiGDRtyxBFHFFLflqxduzbV1dWFj9uyZctUVlbWy/j1OXZ9j6/25jm+2sszvtrLM35Trr2+x2/Ktbdo0SI33nhjoWNuzrJly7LDDjvU+/ehtqberzYFRb4v6+O93hTGbAo11seYTaHGpjJmU6ixqYzZFGqsjzGbQo1NZcz6qFG/CgAAddfkA/i33norSVJZWVlr+Y477phVq1Zt9Xg77rhjg96Ls1WrVk12fLWXZ/ymXHt9j6/28oyv9vKM35Rrr+/xm3Lt9W3jh7E0rKberzYF9fG+3F7HbAo11seYTaHGpjJmU6ixqYzZFGqsjzGbQo1NZcym2LfqVwEAaA6afAC/8Y+JqqqqWn9YrFu3Lq1bt97q8ebNm1dYbQAAoF8FAAAAgO1Hk78H/MapPFesWFFr+YoVK9KhQ4dylAQAADX0qwAAAACw/WjyAXyXLl2y8847Z86cOTXLVq9enaeffjr9+vUrY2UAAKBfBQAAAIDtSZOfgr6ysjIjRozIddddlz322CMdO3bMtddemw4dOuTII48sd3kAAGzn9KsAAAAAsP1o8gF8kpx//vlZv359Lrnkkqxduzb9+/fP1KlTU1lZWe7SAABAvwoAAAAA24mKUqlUKncRAAAAAAAAANDUNfl7wAMAAAAAAABAYyCABwAAAAAAAIACCOABAAAAAAAAoAACeAAAAAAAAAAogAAeAAAAAAAAAAoggAcAAAAAAACAAgjgAQAAAAAAAKAAAvgGVl1dnZtvvjkDBw5Mr169MmrUqCxdurTcZdXZa6+9lssuuyyDBg1K375989nPfjbz5s2rWX/xxRenc+fOtf4NGjSoZn1d9n/BggUZMWJEevfunSFDhmTq1Km11pf7GD7//POb7GPnzp3z4IMPFlZ/Yz4Gc+bM2ez+d+7cOUcccUSS5v06mDx5ckaOHFl4rQ0xRlE2dwx+/OMfZ/jw4enTp0+GDh2ab37zm1m7dm3N+i29b+pSf2M/Bo3ldV+uYzBy5Mj3/NkwY8aMJE3/dbCl34Hb288CKIeGeB9SXosXL06fPn3y8MMP1yzzvDZdM2bMyLHHHpsePXrkuOOOy49+9KOadZ7Xpuftt9/OpEmTMmTIkPTp0yennXZafvOb39Ss95w2PeX6+xYAAJq9Eg3qlltuKR188MGln/70p6UFCxaURo0aVTryyCNL69atK3dpdXLWWWeVTjjhhNLcuXNLzz77bOmKK64o9ezZs7Ro0aJSqVQqDRs2rHTDDTeUVqxYUfNv5cqVNY/f0v6/8sorpU9+8pOl8ePHlxYtWlR66KGHSj169Cg99NBDdR6jvs2aNavUo0eP0osvvlhrP996661C6m/sx2DdunW19nvFihWlX/ziF6WuXbuWHnjggVKp1HxfB3fffXepc+fOpREjRtQsayzPeV3GqK9jMHfu3NJBBx1U+va3v11asmRJ6Wc/+1lp8ODBpa997Ws127zf+6Y5HINSqXG87st5DF599dVNfjZ84QtfKB1zzDGl119/vVQqNf3Xwfv9DtzefhZAudT3+5DyqqqqKp100kmlTp06laZPn14qlRpvX8iWzZgxo3TQQQeVvvOd75SWLFlSuvXWW0tdunQp/eY3v/G8NlE33XRT6dBDDy39/Oc/Ly1ZsqQ0fvz4Ut++fUvLly/3nDZB5fz7FgAAmjsBfANat25dqU+fPqX77ruvZtmqVatKPXv2LD366KNlrKxulixZUurUqVNp/vz5Ncuqq6tLRx55ZOnGG28srV+/vtSjR4/SzJkzN/v4uuz/7bffXho4cGDp7bffrtnm+uuvLx199NF1HqO+3XbbbaUTTjhhs+uKqL8pHIN3q6qqKh133HGlCy64oFQqlZrl62D58uWls88+u9S7d+/SMcccU+sDisbynG9pjPo8Bl/5yldKZ511Vq3tZ8yYUeratWvNhzDv976pS/2N/Rg0ltd9OY/B33vkkUdKXbt2LS1cuLBmWVN+HWzpd+D28rMAyqkh3oeU1/XXX18aOXJkrQDe89o0VVdXlw4//PDSNddcU2v5qFGjSrfffrvntYk64YQTSldffXXN16+//nqpU6dOpccee8xz2oSU++9bAADYHpiCvgEtXLgwa9asyYABA2qW7brrrunatWvmzp1bxsrqZvfdd88dd9yR7t271yyrqKhIqVTKqlWrsmTJkqxbty4HHHDAZh9fl/2fN29e+vfvn5YtW9ZsM2DAgCxevDgrV65sFMfwz3/+cw488MDNriui/qZwDN7te9/7XpYtW5aLL744SZrl6+BPf/pT2rRpkx/+8Ifp1atXrXWN5Tnf0hj1eQxGjRqViy66aJPHrF+/Pm+88UaS93/f1KX+xn4MGsvrvpzH4N3efPPNfOtb38oZZ5yRzp071yxvyq+DLf0O3F5+FkA5NcT7kPKZO3dupk2blm9+85u1lntem6a//vWvef7553P88cfXWj516tScc845ntcmarfddstPfvKT/O1vf8uGDRsybdq0VFZW5qCDDvKcNiHl/vsWAAC2BwL4BrR8+fIkyd57711rebt27bJs2bJylLRVdt111wwePDiVlZU1y370ox/lf//3f3PYYYflmWeeSUVFRe65554MHTo0n/rUp3LFFVfk9ddfT1K3/V++fHk6dOiwyfokeeGFFxrFMXzmmWeycuXKnHbaaTnkkEPy2c9+Nj//+c+TFFN/UzgGG61bty633357zjjjjJoam+PrYOjQobn++uvz0Y9+dJN1jeU539IYH9T7HYOuXbumS5cuNV9XVVXl7rvvTrdu3bLHHnskef/3TV3qb+zHoLG87st5DN7t/vvvz5o1a/KlL32p1vKm/DrY0u/A7eVnAZRTQ7wPKY/Vq1fnoosuyiWXXLLJ8+N5bZqWLFmS5J2T8s4+++wcfPDBOfnkk/PjH/84iee1qRo/fnxatmyZI444Ij169MikSZNy44035mMf+5jntAkp99+3AACwPRDAN6C33norSWp9aJgkO+64Y9atW1eOkj6Q+fPn5+tf/3qOOOKIDB06NH/5y1/SokWLdOzYMbfffnvGjRuXn/3sZzn33HNTXV1dp/1fu3btZtcn74S95T6GVVVVWbJkSd54441ccMEFueOOO9KjR4+MHj06s2fPLqT+xn4M3u0HP/hB1q1bl5EjR9Ys2x5eB+/WWJ7zLY3RUNavX5+LLrooixYtyoQJE5Js+X1Tl/ob+zFoLK/7xvA62LBhQ7773e/mtNNOyy677FKzvLm9Dv7+d6CfBdDw6uN9SHlMnDgxvXv33uRq6aRp9YX8n42zII0bNy7//M//nLvuuiuHHnpozj333ML+bqLhPfvss9l1113z7//+75k2bVpOOumkjBs3LgsXLvScNhP6UQAAKEbLLW9CUVq1apXknRBi4/+Td/4Aad26dbnK2iZPPPFEvvrVr6ZXr1654YYbkiRjxozJmWeemV133TVJ0qlTp+y111459dRT84c//KFO+9+qVatUVVXV+l4b/0Dbaaedyn4MKysrM3fu3LRs2bLmD8ru3bvn2WefzdSpUwupv7Efg3ebMWNGjjrqqOy+++41y7aH18G7NZbnfEtjNISNweqcOXNy880310xnuKX3zcEHH9zkj0Fjed03htfBr3/967zwwgs55ZRTai1vTq+Dzf0O9LMAGlZ9vQ9peDNmzMi8efPyyCOPbHa957Vp+tCHPpQkOfvsszNs2LAkyUEHHZSnn346d999t+e1CXr++eczduzYfOc730m/fv2SJD169MiiRYtyyy23eE6bCf0oAAAUwxXwDWjjFF0rVqyotXzFihWbTM/VmN17770ZM2ZMBg0alClTptT80VVRUVETPm3UqVOnJO9MQVaX/e/QocNm1ydJ+/btG8Ux3GmnnTY5m7tTp0558cUXC6m/KRyDJHnllVfy29/+Nscee2yt5dvL62CjxvKcb2mM+rZixYqcfvrp+e1vf5spU6Zk6NChtda/3/smafrHoLG87sv9OkjeCcV69uy52Sktm8Pr4L1+B/pZAA2nPt+HNLzp06dn5cqVGTJkSPr06ZM+ffokSSZMmJDjjjvO89pEbTz2G/uhjQ488MD87W9/87w2Qb///e/z9ttvp0ePHrWW9+rVK0uWLPGcNhP6UQAAKIYAvgF16dIlO++8c+bMmVOzbPXq1Xn66adrziBv7O67775cccUVOf3003PjjTfWClK+8pWv5Oyzz661/R/+8Ick73zQUpf979+/f+bPn58NGzbUbDN79uzsv//+adu2bdmP4cKFC9OnT5/Mmzev1vI//vGPOfDAAwupv7Efg41+85vfpKKiIp/4xCdqLd8eXgfv1lie8y2NUZ9WrVqVM844I6+88kruu+++DBgwoNb6Lb1v6lJ/Yz8GjeV1X85jsNH8+fM3eQ0kzeN18H6/A/0sgIZR3+9DGt51112X//7v/86MGTNq/iXJ+eefnzvuuMPz2kR17do1H/7wh/O73/2u1vJnnnkmH/vYxzyvTdDG4PXPf/5zreXPPPNM9t13X89pM6EfBQCAgpRoUDfccEPpE5/4ROmJJ54oLViwoDRq1KjSUUcdVVq3bl25S9uiv/71r6Vu3bqVzjvvvNKKFStq/Vu9enXpxz/+calz586lyZMnl5YuXVr66U9/Who6dGjpy1/+cs0YW9r/l19+udS/f//SuHHjSn/5y19K06dPL/Xo0aP08MMP13mM+rRhw4bSySefXPrnf/7n0ty5c0uLFi0qfeMb3yh17969tHDhwkLqb+zHYKNbbrmldNRRR22yvLm/DsaNG1caMWJEzdeN5Tmvyxj1dQzGjRtX6tatW2n27Nmb/GxYv379Ft83zeEYNJbXfTmPQalUKq1fv77UrVu30g9/+MNNtm/qr4Mt/Q7cHn8WQENriPchjUOnTp1K06dPL5VKjbsv5P39+7//e6lPnz6lRx55pLR06dLS5MmTS126dCn96le/8rw2QRs2bCiddtpppWOOOaY0e/bs0uLFi0uTJk0qHXTQQaXf/va3ntMmqlx/3wIAQHMngG9g69evL33rW98qDRgwoNS7d+/S6NGjS88991y5y6qT2267rdSpU6fN/hs3blypVCqVHnvssdKJJ55Y6tmzZ+nQQw8tXXPNNaW1a9fWjFGX/f/d735XOuWUU0rdu3cvHX744aXvfve7tdaX+xiuXLmydPHFF5cOPfTQUo8ePUqnnnpqae7cuYXW39iPQalUKk2YMKF0yimnbHZdc34dbC50bCzP+ZbGKMq7j8GGDRtKPXr0eM+fDRtr3NL7pikfg40ay+u+nMfg5ZdfLnXq1Kn05JNPbvYxTfl1UJffgdvbzwJoaA31PqT83h3Al0qe16bsrrvuKg0dOrTUrVu30gknnFCaOXNmzTrPa9Pz2muvlSZOnFgaMmRIqU+fPqVTTz21NGfOnJr1ntOmp1x/3wIAQHNXUSqVSuW+Ch8AAAAAAAAAmjr3gAcAAAAAAACAAgjgAQAAAAAAAKAAAngAAAAAAAAAKIAAHgAAAAAAAAAKIIAHAAAAAAAAgAII4AEAAAAAAACgAAJ4AAAAAAAAACiAAB7YLpRKpe3yewMAwN/TnwIAAED9EcADjdLXvva1dO7c+T3/ffKTn6zTOMuXL88555yT559/vmbZ0KFD87Wvfa2+Sq9l1qxZGTduXCFj3XLLLencuXOtZa+++mquvvrqfOpTn0r37t3ziU98ImeccUYef/zxrR5/zpw56dy5c+bMmVNIvQAA/J+RI0du0tN27949Q4YMyeWXX55Vq1Zt1Xib6w23ZHO98ea8Xx/euXPnfPOb39yq71sOelsAAADKpWW5CwB4L3vttVduvfXWza5r2bJuP75++ctf5qc//WkuvfTSmmW33nprdt5550Jq3JLvfOc79Tb22rVrc/rpp2f9+vUZPXp09ttvv7z++uv50Y9+lPPPPz8XX3xxzjzzzHr7/gAAbJ2uXbtmwoQJNV+//fbb+dOf/pQbbrghCxYsyH/+53+moqKiTmOdfPLJGThw4FZ9/831xu/l05/+dE4++eTNrmvXrt1WfV8AAADYngjggUarsrIyvXv3Lnzcrl27Fj5mOTz22GN59tln89hjj2X//fevWf6pT30qa9euzS233JKRI0dmhx12KGOVAABstPPOO2/S3/bv3z9r1qzJzTffnN/97nd17n87dOiQDh06FF/ku8avj14cAAAAmjtT0ANN2nPPPZcvfelL+eQnP5levXrl1FNPzc9+9rMkycMPP5yLL744SXLEEUfUTDv/7ino//a3v6Vz5855/PHHc+6556Z379455JBDMnny5Lzxxhv5+te/no9//OM55JBDcu2119a6X+bf/va3XHTRRTnssMPSrVu3HHzwwbnooovy6quvJnlnmtFf//rX+fWvf11r+svXXnstl112WQ455JD06NEjp5xySmbPnl1rv9atW5err746hx56aPr06ZOLL74469atq7XNyy+/nGTz9/A855xzcu6556aqqqpm2RNPPJHTTjstffr0Sffu3XPMMcfk3nvvfd/ju6XHbJza8/7778/hhx+eQw45JE888UQ6d+6cX/ziF7XGeuqpp9K5c+f8+te/ft/vCQCwvenevXuS5IUXXqhZ9t///d856aST0qdPnxx66KG57LLLak1T//dT0I8cOTLjx4/PHXfckSFDhqRHjx75zGc+k9/97ndJ3rs3/qC21BMn7/Tf3/jGN3LGGWekb9++ueyyy2r6yNmzZ2fUqFHp1atXDjnkkHzzm9/M+vXrax5bXV2dO+64I0ceeWS6d++eo48+Ot/97nc3qeP+++/P0UcfnZ49e2bEiBG1jiUAAAA0JAE80KitX79+s/9KpVKqq6tzzjnn5M0338y3vvWtTJ48ObvttlvOPffcLF26NEOGDMmXvvSlJO9MO3/uuee+5/cZP358OnXqlNtuuy0DBgzITTfdlE9/+tNp1apVbrrppgwdOjR33nlnHnvssSTJW2+9lc997nN59tlnM2HChEydOjUjRozIo48+mhtuuCFJMmHChHTt2jVdu3bNtGnT0q1bt6xbty5nnHFGZs2alQsvvDC33nprOnTokM9//vO1QvixY8dm2rRpGT16dG688casWrVqk+nsBw4cmJYtW+aMM87Irbfemqeeeipvv/12kqRnz545++yz07p16yTJT3/605x33nnp1q1bJk+enFtuuSUdO3bMFVdckd/85jebPSZb85hJkyZl3LhxGTduXD75yU+mffv2+cEPflBrm+9///v56Ec/mv79+2/paQcA2K4sXrw4SfLRj340STJ58uRceOGF6dWrV26++eacd955efzxxzNy5MisXbv2Pcd5/PHHM2vWrFxyySW54YYb8vLLL+f888/Phg0btqo3Tt4Jvt+rF9+oLj3xRt/73vfSuXPn3HLLLfmXf/mXmuVf/epX8/GPfzy33357jj/++Nx111156KGHatZPnDgxN998c0444YTcfvvtOeaYY/KNb3wj//7v/16zzb333psJEyZk4MCBmTx5cnr16lWnafYBAACgPpiCHmi0nn/++XTr1m2z6/71X/81J598cp599tl88YtfzODBg5O8EzzfeuutWbduXfbdd9987GMfS5IcdNBB2Weffd7zew0cODAXXHBBkuTAAw/Mf/3Xf6Vt27a57LLLkiSHHnpofvSjH+U3v/lN/umf/ilLlixJhw4dcs0119R8jwEDBuQPf/hDzRXeBx54YM295jdO3/nAAw9k4cKFeeCBB9KrV68kyaBBgzJy5Mhcd911mT59ev7yl7/k8ccfz2WXXZbTTz+9pr7jjz8+ixYtqqm5c+fOmTRpUi6//PLccsstueWWW9KqVav069cvw4cPz7HHHluz7aJFi3LiiSdm/PjxNcv69OmTT37yk5k7d2769u27yTHZmsd85jOfyTHHHFPz9Yknnpjvfve7WbNmTT784Q+nqqoqP/rRj3LGGWfU+b6mAADNTalUqhVgr1q1Kr/+9a9z2223pXfv3unevXtWrVqV2267LSeffHKt+8V36tQpp59+eh5++OGcdtppmx1//fr1mTp1ak0PumbNmowbNy4LFixI9+7d69wbJ++cBDB58uTNrvvZz36WDh061Kkn3qhdu3b52te+lhYt3rkOYOPsUCeffHLOO++8JMnBBx+cJ554Ij/96U/zmc98JosXL84DDzyQL3/5y/nCF76QJDnssMNSUVGRb3/72znttNOy2267ZfLkyTn66KNzySWX1Gzzxhtv5P7773/ffQQAAID6IIAHGq299tort91222bXtW/fPnvuuWcOPPDAXHrppfnlL3+ZQYMG5bDDDquZWnNr9OnTp9b3TVITkCdJRUVF2rRpk9dffz3JOx9a3nfffamurs5zzz2XJUuW5C9/+Uv++te/1vpQ9e/Nnj07e+21V7p161Zru8MPPzzf+ta3smrVqsybNy/JO1ODbtSiRYscffTRtQL4JDnqqKNy+OGH51e/+lV++ctfZs6cOfnlL3+ZX/ziF3nsscdy0003paKiIp///OeTJG+++Wb+93//N4sXL84f/vCHJKm5av7vbc1j3j39aZIMHz48d9xxR2bOnJkTTzwxTzzxRFavXp0TTzzxPY8NAEBzN3fu3E1OMG3RokUOPvjgXHHFFamoqMhTTz2VqqqqHH/88bW269evXzp27Jg5c+a8ZwD/7hNAk3d65uSdK9W31imnnJJTTjlls+vatm2bZOt64gMOOKAmfH+3d/fhyTv3nn/zzTeTJL/61a9SKpUydOjQWuMNHTo0t912W+bPn5/9998/K1eurNU7J8k//dM/CeABAAAoCwE80GhVVlamR48e77vNXXfdldtuuy0zZ87M97///XzoQx/Kpz71qUycODG77bZbnb/Xuz+o3Gjj9O3v5e677863v/3tvPrqq9lzzz3TrVu3tG7duiak35zXXnstL7300nte2f/SSy/V3Ntzjz32qLVu44kBf+9DH/pQBg4cmIEDByZJVqxYkSuvvDKPP/54fvrTn+bwww/PK6+8kgkTJuSJJ55IRUVF9t1333z84x9Psvl7yCfZqsds/BB2o3333Tf9+/fPjBkzcuKJJ2bGjBkZMGBAOnbs+J7HBgCguevWrVsuv/zyJO+c4Lnjjjtm7733rtWLbuwF99xzz00ev+eee75vr/n3/evGwLu6unqra23Xrt0We/Gk7j3x5vYnSVq1arVJzRt7zddeey1Jctxxx232sS+++GJNz1zX3hkAAADqmwAeaNLat2+fiRMnZsKECVm4cGEee+yxTJkyJW3atKn5cLM+PPLII7nmmmvyla98JZ/+9KdrPvD713/915qrxDdnl112yX777Zfrrrtus+v32Wef7L777kmSl19+OR/5yEdq1m38AHKjz3zmM9l///1z9dVX11rerl27mgB+0aJFOfzww/PVr341zz77bO6+++707ds3lZWVeeutt/Lggw++Z63b8ph3Gz58eC6++OIsXrw4//M//7NJnQAA25sPf/jDWwy127Rpk+SdXvCAAw6ote6ll16quU98Y7CtPXFd7brrrkmSe+65Jx/+8Ic3Wf+Rj3wkq1evTpKsXLmy1rq/750BAACgoWw6/xtAE/Hb3/42hxxySH7/+9+noqIiBx10UC688MJ06tQpy5cvT5LNTnNZhPnz52eXXXbJF77whZoPGtesWZP58+fXusLo77//Jz7xiSxbtixt27ZNjx49av7Nnj07d955Z3bYYYcMGDAgSfLYY4/VeuxPfvKTWl937Ngxjz32WJ577rlN6lu8eHGSd+4VurHeo48+OgMGDEhlZWWS5Mknn0zy3ldEbctj3u3oo4/OTjvtlMsuuyytWrXKUUcdtcXHAABs73r16pXKyso88sgjtZbPmzcvL7zwQvr27bvNYxfdG9e1J95W/fv3T5K8+uqrtXrn1157LTfeeGNee+217Lffftl777232DsDAABAQ3EFPNBoVVVV5amnnnrP9Z07d06rVq1y0UUXZcyYMdlzzz3zy1/+MgsWLMjnPve5JP931czMmTMzaNCgTa4i2lY9e/bMf/7nf+aaa67J4YcfnhUrVmTq1Kl5+eWXa65a2vj9f/vb32b27Nnp2rVrTjrppNx7770566yz8sUvfjF77713fvnLX2bKlCkZMWJEPvShD2XffffNqaeemkmTJmX9+vU56KCD8oMf/CB//vOfa9Vw4YUXZs6cOfn0pz+dz33uc+nTp09atGiRP/zhD7nrrrsyaNCgDBo0qKbeRx55JN26dUuHDh3y29/+Nt/+9rdTUVHxnvcE3ZbHvFvr1q1z3HHHZdq0aTnllFM2mV4UAIBN7bbbbvnCF76QW2+9NR/60IdyxBFH5G9/+1tuuummHHjggTnppJO2eeyt6Y2XL1/+nr14q1at0qVLlzr3xNuqU6dOOeGEE3LppZfm+eefT/fu3bN48eJMmjQp++yzT/bbb79UVFTkq1/9ar7yla/kkksuyTHHHJOnnnoq//mf//mBvz8AAABsCwE80Gi99NJLOfXUU99z/UMPPZS77ror119/fa666qqsXr06++23X/7t3/6t5oPJT37ykznkkENy/fXXZ/bs2bnjjjsKqW3YsGH529/+lunTp+e+++5L+/btM3jw4Jx22mm59NJLs2jRohx44IE5/fTT88c//jGjR4/O1VdfneOPPz7f+973cv311+faa6/N66+/no4dO+YrX/lKRo0aVTP+hAkTsueee+bee+/NqlWrMnDgwHzxi1/MjTfeWLPNPvvsk+9///v59re/nUceeSRTpkxJqVTKvvvum7PPPjuf+9znUlFRkSS55pprcsUVV+SKK65Ikuy33365/PLL88Mf/jDz5s3b7D5uy2P+3uGHH55p06Z9oA+KAQC2NxtPLr333nvz4IMPZrfddssxxxyTCy64YJP7vG+NremNH3rooTz00EObXfeP//iPefTRR+vcE38QV199db797W/n/vvvz/Lly9O2bdsce+yxueCCC7LDDjskSf75n/85LVq0yOTJk/ODH/wgnTp1yr/927/ly1/+8gf63gAAALAtKkqlUqncRQDQPE2cODHz58/fZApVAAAAAACA5sgV8AAU7j/+4z/y17/+NdOmTcvVV19d7nIAAAAAAAAahAAegMLNmzcvP//5zzNy5MiceOKJ5S4HAAAAAACgQZiCHgAAAAAAAAAK0KLcBQAAAAAAAABAcyCABwAAAAAAAIACCOABAAAAAAAAoAACeAAAAAAAAAAogAAeAAAAAAAAAAoggAcAAAAAAACAAgjgAQAAAAAAAKAAAngAAAAAAAAAKMD/DyFVdcx/o/unAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2500x2000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist_cols = ['CreditScore', 'Age', 'Balance', 'EstimatedSalary', 'Point Earned']\n",
    "\n",
    "plt.figure(figsize=(25, 20))\n",
    "plt.subplots_adjust(hspace=0.3)\n",
    "plt.suptitle(\"Histogram Analysis\", fontsize=28,fontweight ='bold', y=0.93, x = 0.195)\n",
    "\n",
    "for n, col in enumerate(hist_cols):\n",
    "    # add a new subplot iteratively\n",
    "    ax = plt.subplot(3, 3, n + 1)\n",
    "\n",
    "    sns.histplot(data = data, x = col,  color = 'grey', ax = ax)\n",
    "\n",
    "    # chart formatting\n",
    "    ax.set_title(f'{col.upper()}', fontdict = {'fontsize' : 18}, loc = 'left')\n",
    "    ax.set_xlabel(col)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing if Balance and CreditScore are normal distributions:\n",
    "\n",
    "*null hypothesis: x comes from a normal distribution*\n",
    "\n",
    "[normaltest](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.normaltest.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Balance: The null hypothesis can be rejected\n",
      "CreditScore: The null hypothesis can be rejected\n"
     ]
    }
   ],
   "source": [
    "distribution_test_col = ['Balance', 'CreditScore']\n",
    "\n",
    "for col in distribution_test_col:\n",
    "    alpha = 0.05\n",
    "    k2, p = normaltest(data[col])\n",
    "\n",
    "    if p < alpha: # null hypothesis: x comes from a normal distribution\n",
    "        print(f\"{col}: The null hypothesis can be rejected\")\n",
    "    else:\n",
    "        print(f\"{col}: The null hypothesis cannot be rejected\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Credit Score and Age have a few outliers, but we will leave them for now**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2500x2000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist_cols = ['CreditScore', 'Age', 'Balance', 'EstimatedSalary', 'Point Earned']\n",
    "\n",
    "plt.figure(figsize=(25, 20))\n",
    "plt.subplots_adjust(hspace=0.3)\n",
    "plt.suptitle(\"Outliers in continues variables\", fontsize=28,fontweight ='bold', y=0.93, x = 0.235)\n",
    "\n",
    "for n, col in enumerate(hist_cols):\n",
    "    # add a new subplot iteratively\n",
    "    ax = plt.subplot(3, 3, n + 1)\n",
    "\n",
    "    sns.boxplot(data = data, x = col,  color = 'grey', ax = ax)\n",
    "\n",
    "    # chart formatting\n",
    "    ax.set_title(f'{col.upper()}', fontdict = {'fontsize' : 18}, loc = 'left')\n",
    "    ax.set_xlabel(col)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``3. Pre-Processing``\n",
    "\n",
    "The pre-processing will consist in 3 steps:\n",
    "\n",
    "- Enconding\n",
    "- Balancing\n",
    "- Scaling"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Enconding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = data.drop(columns=['CustomerId', 'Exited'])\n",
    "labels = data['Exited']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Card Type_DIAMOND</th>\n",
       "      <th>Card Type_GOLD</th>\n",
       "      <th>Card Type_PLATINUM</th>\n",
       "      <th>Card Type_SILVER</th>\n",
       "      <th>Geography_France</th>\n",
       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender_Male  Card Type_DIAMOND  Card Type_GOLD  Card Type_PLATINUM  \\\n",
       "0            0                  1               0                   0   \n",
       "1            0                  1               0                   0   \n",
       "\n",
       "   Card Type_SILVER  Geography_France  Geography_Germany  Geography_Spain  \n",
       "0                 0                 1                  0                0  \n",
       "1                 0                 0                  0                1  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_col = ['Gender', 'Card Type', 'Geography']\n",
    "one_hot_enconder = OneHotEncoder(dtype=int, drop='if_binary')\n",
    "\n",
    "encoded_features = one_hot_enconder.fit_transform(features[categorical_col]).toarray()\n",
    "encoded_features = pd.DataFrame(encoded_features, columns = one_hot_enconder.get_feature_names_out(input_features=categorical_col))\n",
    "encoded_features.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Satisfaction Score</th>\n",
       "      <th>Point Earned</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Card Type_DIAMOND</th>\n",
       "      <th>Card Type_GOLD</th>\n",
       "      <th>Card Type_PLATINUM</th>\n",
       "      <th>Card Type_SILVER</th>\n",
       "      <th>Geography_France</th>\n",
       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>2</td>\n",
       "      <td>464</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>3</td>\n",
       "      <td>456</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>3</td>\n",
       "      <td>377</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
       "0          619   42       2       0.00              1          1   \n",
       "1          608   41       1   83807.86              1          0   \n",
       "2          502   42       8  159660.80              3          1   \n",
       "\n",
       "   IsActiveMember  EstimatedSalary  Satisfaction Score  Point Earned  \\\n",
       "0               1        101348.88                   2           464   \n",
       "1               1        112542.58                   3           456   \n",
       "2               0        113931.57                   3           377   \n",
       "\n",
       "   Gender_Male  Card Type_DIAMOND  Card Type_GOLD  Card Type_PLATINUM  \\\n",
       "0            0                  1               0                   0   \n",
       "1            0                  1               0                   0   \n",
       "2            0                  1               0                   0   \n",
       "\n",
       "   Card Type_SILVER  Geography_France  Geography_Germany  Geography_Spain  \n",
       "0                 0                 1                  0                0  \n",
       "1                 0                 0                  0                1  \n",
       "2                 0                 1                  0                0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_features = pd.concat([features.drop(columns=categorical_col), encoded_features], axis = 1)\n",
    "encoded_features.head(3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Balancing: Over Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    7962\n",
       "0    7962\n",
       "Name: Exited, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SEED = 50\n",
    "np.random.seed(SEED)\n",
    "sm = SMOTE(random_state=SEED)\n",
    "\n",
    "res_features, res_labels = sm.fit_resample(encoded_features, labels)\n",
    "res_labels.value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling\n",
    "\n",
    "Scaling address the issue of wide-raging data by scaling features to similar scales, which:\n",
    "\n",
    "- **Prevents features from dominating the learning process based solely on its scale**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CreditScore</th>\n",
       "      <td>15924.0</td>\n",
       "      <td>0.599191</td>\n",
       "      <td>0.183287</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.476000</td>\n",
       "      <td>0.602000</td>\n",
       "      <td>0.726000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>15924.0</td>\n",
       "      <td>0.309894</td>\n",
       "      <td>0.135235</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.216216</td>\n",
       "      <td>0.297297</td>\n",
       "      <td>0.391892</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tenure</th>\n",
       "      <td>15924.0</td>\n",
       "      <td>4.820522</td>\n",
       "      <td>2.733478</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Balance</th>\n",
       "      <td>15924.0</td>\n",
       "      <td>0.325882</td>\n",
       "      <td>0.244695</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.410515</td>\n",
       "      <td>0.514235</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumOfProducts</th>\n",
       "      <td>15924.0</td>\n",
       "      <td>1.421628</td>\n",
       "      <td>0.573614</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HasCrCard</th>\n",
       "      <td>15924.0</td>\n",
       "      <td>0.630306</td>\n",
       "      <td>0.482737</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IsActiveMember</th>\n",
       "      <td>15924.0</td>\n",
       "      <td>0.366930</td>\n",
       "      <td>0.481982</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <td>15924.0</td>\n",
       "      <td>0.499960</td>\n",
       "      <td>0.288708</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.251791</td>\n",
       "      <td>0.500319</td>\n",
       "      <td>0.748095</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Satisfaction Score</th>\n",
       "      <td>15924.0</td>\n",
       "      <td>2.859709</td>\n",
       "      <td>1.324236</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Point Earned</th>\n",
       "      <td>15924.0</td>\n",
       "      <td>0.552433</td>\n",
       "      <td>0.239864</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.353008</td>\n",
       "      <td>0.552781</td>\n",
       "      <td>0.749432</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender_Male</th>\n",
       "      <td>15924.0</td>\n",
       "      <td>0.414783</td>\n",
       "      <td>0.492700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Card Type_DIAMOND</th>\n",
       "      <td>15924.0</td>\n",
       "      <td>0.183936</td>\n",
       "      <td>0.387444</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Card Type_GOLD</th>\n",
       "      <td>15924.0</td>\n",
       "      <td>0.179540</td>\n",
       "      <td>0.383816</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Card Type_PLATINUM</th>\n",
       "      <td>15924.0</td>\n",
       "      <td>0.178284</td>\n",
       "      <td>0.382764</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Card Type_SILVER</th>\n",
       "      <td>15924.0</td>\n",
       "      <td>0.179164</td>\n",
       "      <td>0.383501</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geography_France</th>\n",
       "      <td>15924.0</td>\n",
       "      <td>0.387151</td>\n",
       "      <td>0.487114</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geography_Germany</th>\n",
       "      <td>15924.0</td>\n",
       "      <td>0.247865</td>\n",
       "      <td>0.431786</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geography_Spain</th>\n",
       "      <td>15924.0</td>\n",
       "      <td>0.176086</td>\n",
       "      <td>0.380906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count      mean       std  min       25%       50%  \\\n",
       "CreditScore         15924.0  0.599191  0.183287  0.0  0.476000  0.602000   \n",
       "Age                 15924.0  0.309894  0.135235  0.0  0.216216  0.297297   \n",
       "Tenure              15924.0  4.820522  2.733478  0.0  3.000000  5.000000   \n",
       "Balance             15924.0  0.325882  0.244695  0.0  0.000000  0.410515   \n",
       "NumOfProducts       15924.0  1.421628  0.573614  1.0  1.000000  1.000000   \n",
       "HasCrCard           15924.0  0.630306  0.482737  0.0  0.000000  1.000000   \n",
       "IsActiveMember      15924.0  0.366930  0.481982  0.0  0.000000  0.000000   \n",
       "EstimatedSalary     15924.0  0.499960  0.288708  0.0  0.251791  0.500319   \n",
       "Satisfaction Score  15924.0  2.859709  1.324236  1.0  2.000000  3.000000   \n",
       "Point Earned        15924.0  0.552433  0.239864  0.0  0.353008  0.552781   \n",
       "Gender_Male         15924.0  0.414783  0.492700  0.0  0.000000  0.000000   \n",
       "Card Type_DIAMOND   15924.0  0.183936  0.387444  0.0  0.000000  0.000000   \n",
       "Card Type_GOLD      15924.0  0.179540  0.383816  0.0  0.000000  0.000000   \n",
       "Card Type_PLATINUM  15924.0  0.178284  0.382764  0.0  0.000000  0.000000   \n",
       "Card Type_SILVER    15924.0  0.179164  0.383501  0.0  0.000000  0.000000   \n",
       "Geography_France    15924.0  0.387151  0.487114  0.0  0.000000  0.000000   \n",
       "Geography_Germany   15924.0  0.247865  0.431786  0.0  0.000000  0.000000   \n",
       "Geography_Spain     15924.0  0.176086  0.380906  0.0  0.000000  0.000000   \n",
       "\n",
       "                         75%   max  \n",
       "CreditScore         0.726000   1.0  \n",
       "Age                 0.391892   1.0  \n",
       "Tenure              7.000000  10.0  \n",
       "Balance             0.514235   1.0  \n",
       "NumOfProducts       2.000000   4.0  \n",
       "HasCrCard           1.000000   1.0  \n",
       "IsActiveMember      1.000000   1.0  \n",
       "EstimatedSalary     0.748095   1.0  \n",
       "Satisfaction Score  4.000000   5.0  \n",
       "Point Earned        0.749432   1.0  \n",
       "Gender_Male         1.000000   1.0  \n",
       "Card Type_DIAMOND   0.000000   1.0  \n",
       "Card Type_GOLD      0.000000   1.0  \n",
       "Card Type_PLATINUM  0.000000   1.0  \n",
       "Card Type_SILVER    0.000000   1.0  \n",
       "Geography_France    1.000000   1.0  \n",
       "Geography_Germany   0.000000   1.0  \n",
       "Geography_Spain     0.000000   1.0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scal_col = ['CreditScore', 'Age', 'Balance', 'EstimatedSalary', 'Point Earned', 'Tenure', 'Satisfaction Score']\n",
    "\n",
    "# Scaling specific columns using Normalization\n",
    "min_max_scaler = MinMaxScaler()\n",
    "\n",
    "res_features[hist_cols] = min_max_scaler.fit_transform(res_features[hist_cols])\n",
    "res_features.describe().T"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``4. Simple Model Selection``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(res_features, res_labels, train_size = 0.8, stratify = res_labels)\n",
    "\n",
    "def fit_model(model):\n",
    "    model.fit(x_train,y_train)\n",
    "    y_pred = model.predict(x_test)\n",
    "    f1 = f1_score(y_test, y_pred)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    precision = precision_score(y_test, y_pred)\n",
    "    recall = recall_score(y_test, y_pred)\n",
    "    print(f\"{type(model).__name__}\")\n",
    "    print(f\"Accuracy: {accuracy}\\n F1-Score: {f1}\\n Precision: {precision}\\n Recal: {recall}\\n----------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier\n",
      "Accuracy: 0.8285714285714286\n",
      " F1-Score: 0.8326180257510729\n",
      " Precision: 0.8131736526946107\n",
      " Recal: 0.8530150753768844\n",
      "----------------------------\n",
      "LogisticRegression\n",
      "Accuracy: 0.862480376766091\n",
      " F1-Score: 0.8524258760107817\n",
      " Precision: 0.9193313953488372\n",
      " Recal: 0.7945979899497487\n",
      "----------------------------\n",
      "RandomForestClassifier\n",
      "Accuracy: 0.9001569858712716\n",
      " F1-Score: 0.8987905792488862\n",
      " Precision: 0.9109677419354839\n",
      " Recal: 0.8869346733668342\n",
      "----------------------------\n",
      "KNeighborsClassifier\n",
      "Accuracy: 0.847723704866562\n",
      " F1-Score: 0.8376297288249079\n",
      " Precision: 0.896774193548387\n",
      " Recal: 0.7858040201005025\n",
      "----------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ferna\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier(random_state=SEED)\n",
    "lr = LogisticRegression(random_state=SEED, max_iter = 1000)\n",
    "rfc = RandomForestClassifier(random_state=SEED)\n",
    "knnc = KNeighborsClassifier(n_neighbors=6)\n",
    "\n",
    "models = [dt, lr, rfc, knnc]\n",
    "\n",
    "for model in models:\n",
    "    fit_model(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best model: RANDOM FOREST CLASSIFIER"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "**But can we trust it?**\n",
    "\n",
    "In our fit_model function, we performed a single division of our data into training and testing sets, which means we might just have obtained a relatively easy test sample.\n",
    "\n",
    "\n",
    "To address this issue and avoid relying solely on one split, we can utilize **cross-validation (CV)**. By partitioning our data into k-folds and performing training and testing on multiple folds, we can obtain a more reliable estimation of our model's performance **(reducing Potential Sampling Variation)**. \n",
    "\n",
    "We can use CV both to tune Hyperparameters and Model Selection in an **Nested CV loop**."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **``5. Nested CV``**\n",
    "\n",
    "Nested Cross validation consits of two CV process: \n",
    "\n",
    "- **Inner CV** (Model Selection)\n",
    "- **Outer CV** (Hyperparamter Tuning)\n",
    "\n",
    "In the Outer CV, our dataset is divided into k-folds. During each iteration of the Outer CV, the Inner CV is performed, which further divides the training data from the Inner CV into k-folds for each hyperparameter setting (as illustrated in the image below). The hyperparameter models are trained on the k-folds of the inner loop, and their results are averaged. The model with the best average performance is chosen to be trained and tested in the Outer loop. This process is repeated for each fold in the Outer CV.\n",
    "\n",
    "\n",
    "[My notes about Nested CV](https://lacerdash.notion.site/CV-and-Nested-CV-38ed644da89c4faca445dc17e496e204?pvs=4)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Image Description](Nested_CV.png)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Selection with CV"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repeting our Simple Model Selection but now using **cross_val_score**, we can see that the overall ranking ramained the same:\n",
    "\n",
    "1. Random Forest Classifier\n",
    "2. Logistic Regression\n",
    "3. KNeighbors Classifier\n",
    "4. Decision Tree Classifier\n",
    "\n",
    "But we now have 5 results per model, which gives us a better performance estimation of our model with real world data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier(random_state=50)\n",
      "Mean accuracy:0.834\n",
      "Scores: [0.828 0.84  0.835 0.833 0.833]\n",
      "\n",
      "LogisticRegression(max_iter=1000, random_state=50)\n",
      "Mean accuracy:0.861\n",
      "Scores: [0.857 0.851 0.867 0.872 0.856]\n",
      "\n",
      "RandomForestClassifier(random_state=50)\n",
      "Mean accuracy:0.899\n",
      "Scores: [0.904 0.904 0.897 0.891 0.901]\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ferna\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "c:\\Users\\ferna\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "c:\\Users\\ferna\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "c:\\Users\\ferna\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier(n_neighbors=6)\n",
      "Mean accuracy:0.845\n",
      "Scores: [0.843 0.852 0.851 0.836 0.843]\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ferna\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    }
   ],
   "source": [
    "outer_cv = KFold(n_splits = 5, shuffle=True)\n",
    "for model in models:\n",
    "    scores = cross_val_score(model, res_features, res_labels, cv = outer_cv, scoring='accuracy')\n",
    "    print(model)\n",
    "    print(f'Mean accuracy:{scores.mean().round(3)}')\n",
    "    print(f'Scores: {scores.round(3)}\\n')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cross Validation Illustration Image"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"CV.png\" alt=\"Cross Validation Ilustration\" width=\"500\" height=\"300\" />"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning with CV"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will use **RandomizedSearchCV** to explore a grid of hyperparameters for two models: *RandomForest* and *KNeighborsClassifier*.\n",
    "\n",
    "- *RandomForest:* Our best model from the hyperparamter tuning had an 0.877 accuracy, while the default setting of the model in our last CV had an 0.899.\n",
    "\n",
    "- *KNeighborsClassifier:* Found a slighty better hyperparameter setting incresing from 0.845 to 0.854 our accuracy.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RandomForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_criterion</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_min_samples_leaf</th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.944918</td>\n",
       "      <td>0.029941</td>\n",
       "      <td>0.029136</td>\n",
       "      <td>0.000327</td>\n",
       "      <td>entropy</td>\n",
       "      <td>None</td>\n",
       "      <td>8</td>\n",
       "      <td>67</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': None, 'm...</td>\n",
       "      <td>0.877551</td>\n",
       "      <td>0.886342</td>\n",
       "      <td>0.871272</td>\n",
       "      <td>0.870958</td>\n",
       "      <td>0.879397</td>\n",
       "      <td>0.877104</td>\n",
       "      <td>0.005703</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.034543</td>\n",
       "      <td>0.290840</td>\n",
       "      <td>0.043909</td>\n",
       "      <td>0.011349</td>\n",
       "      <td>gini</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 10, 'min_sa...</td>\n",
       "      <td>0.881005</td>\n",
       "      <td>0.884458</td>\n",
       "      <td>0.872214</td>\n",
       "      <td>0.865620</td>\n",
       "      <td>0.878455</td>\n",
       "      <td>0.876350</td>\n",
       "      <td>0.006695</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.216220</td>\n",
       "      <td>0.029935</td>\n",
       "      <td>0.051599</td>\n",
       "      <td>0.004124</td>\n",
       "      <td>gini</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 10, 'min_sa...</td>\n",
       "      <td>0.864678</td>\n",
       "      <td>0.870644</td>\n",
       "      <td>0.860911</td>\n",
       "      <td>0.857771</td>\n",
       "      <td>0.871231</td>\n",
       "      <td>0.865047</td>\n",
       "      <td>0.005287</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.649137</td>\n",
       "      <td>0.006017</td>\n",
       "      <td>0.026168</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>gini</td>\n",
       "      <td>10</td>\n",
       "      <td>28</td>\n",
       "      <td>92</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 10, 'min_sa...</td>\n",
       "      <td>0.862166</td>\n",
       "      <td>0.873783</td>\n",
       "      <td>0.859969</td>\n",
       "      <td>0.856829</td>\n",
       "      <td>0.870917</td>\n",
       "      <td>0.864733</td>\n",
       "      <td>0.006510</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.647106</td>\n",
       "      <td>0.003563</td>\n",
       "      <td>0.026470</td>\n",
       "      <td>0.000376</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>45</td>\n",
       "      <td>33</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': None, 'min_...</td>\n",
       "      <td>0.860597</td>\n",
       "      <td>0.868132</td>\n",
       "      <td>0.856515</td>\n",
       "      <td>0.852747</td>\n",
       "      <td>0.863379</td>\n",
       "      <td>0.860274</td>\n",
       "      <td>0.005334</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "19       0.944918      0.029941         0.029136        0.000327   \n",
       "7        1.034543      0.290840         0.043909        0.011349   \n",
       "13       1.216220      0.029935         0.051599        0.004124   \n",
       "4        0.649137      0.006017         0.026168        0.000200   \n",
       "5        0.647106      0.003563         0.026470        0.000376   \n",
       "\n",
       "   param_criterion param_max_depth param_min_samples_leaf  \\\n",
       "19         entropy            None                      8   \n",
       "7             gini              10                      8   \n",
       "13            gini              10                     30   \n",
       "4             gini              10                     28   \n",
       "5             gini            None                     45   \n",
       "\n",
       "   param_min_samples_split                                             params  \\\n",
       "19                      67  {'criterion': 'entropy', 'max_depth': None, 'm...   \n",
       "7                       21  {'criterion': 'gini', 'max_depth': 10, 'min_sa...   \n",
       "13                      26  {'criterion': 'gini', 'max_depth': 10, 'min_sa...   \n",
       "4                       92  {'criterion': 'gini', 'max_depth': 10, 'min_sa...   \n",
       "5                       33  {'criterion': 'gini', 'max_depth': None, 'min_...   \n",
       "\n",
       "    split0_test_score  split1_test_score  split2_test_score  \\\n",
       "19           0.877551           0.886342           0.871272   \n",
       "7            0.881005           0.884458           0.872214   \n",
       "13           0.864678           0.870644           0.860911   \n",
       "4            0.862166           0.873783           0.859969   \n",
       "5            0.860597           0.868132           0.856515   \n",
       "\n",
       "    split3_test_score  split4_test_score  mean_test_score  std_test_score  \\\n",
       "19           0.870958           0.879397         0.877104        0.005703   \n",
       "7            0.865620           0.878455         0.876350        0.006695   \n",
       "13           0.857771           0.871231         0.865047        0.005287   \n",
       "4            0.856829           0.870917         0.864733        0.006510   \n",
       "5            0.852747           0.863379         0.860274        0.005334   \n",
       "\n",
       "    rank_test_score  \n",
       "19                1  \n",
       "7                 2  \n",
       "13                3  \n",
       "4                 4  \n",
       "5                 5  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'max_depth' : [3, 5, 7, 10, None],\n",
    "              'min_samples_leaf' : randint(2,120),\n",
    "              'criterion': ['gini', 'entropy'],\n",
    "              'min_samples_split': randint(2,120)\n",
    "              }\n",
    "\n",
    "inner_cv = KFold(n_splits = 5, shuffle=True)\n",
    "\n",
    "hp_search = RandomizedSearchCV(RandomForestClassifier(), \n",
    "                               param_distributions=param_grid, \n",
    "                               n_iter= 20, \n",
    "                               cv = inner_cv,\n",
    "                               random_state=SEED)\n",
    "\n",
    "hp_search.fit(res_features, res_labels)\n",
    "results = pd.DataFrame(hp_search.cv_results_)\n",
    "results.sort_values(by='mean_test_score', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(criterion='entropy', min_samples_leaf=8,\n",
      "                       min_samples_split=67)\n",
      "Best model accuracy : 0.877\n"
     ]
    }
   ],
   "source": [
    "best_estimator = hp_search.best_estimator_\n",
    "print(best_estimator)\n",
    "print(f'Best model accuracy : {hp_search.best_score_.round(3)}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'n_neighbors': randint(5, 10),\n",
    "            'weights': ['uniform','distance'],\n",
    "            'algorithm': ['auto','ball_tree', 'kd_tree', 'brute'],\n",
    "            'metric' : ['euclidean', 'minkowski', 'chebyshev', 'braycurtis']\n",
    "            }\n",
    "\n",
    "inner_cv = KFold(n_splits = 5, shuffle=True)\n",
    "\n",
    "hp_search = RandomizedSearchCV(KNeighborsClassifier(), \n",
    "                               param_distributions=param_grid, \n",
    "                               n_iter= 50, \n",
    "                               cv = inner_cv,\n",
    "                               random_state=SEED)\n",
    "\n",
    "hp_search.fit(res_features, res_labels)\n",
    "results = pd.DataFrame(hp_search.cv_results_)\n",
    "results.sort_values(by='mean_test_score', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier(algorithm='ball_tree', metric='braycurtis', n_neighbors=7,\n",
      "                     weights='distance')\n",
      "Best model accuracy : 0.854\n"
     ]
    }
   ],
   "source": [
    "best_estimator = hp_search.best_estimator_\n",
    "print(best_estimator)\n",
    "print(f'Best model accuracy : {hp_search.best_score_.round(3)}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Nested CV loop**\n",
    "\n",
    "Now it is time to combine both techniques, **RandomizedSearchCV** and **cross_val_score**, in a Nested CV loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Score: 0.878\n",
      "Scores ---> [0.885 0.871 0.865 0.886 0.883]\n"
     ]
    }
   ],
   "source": [
    "inner_cv = KFold(n_splits = 5, shuffle=True)\n",
    "outer_cv = KFold(n_splits = 5, shuffle=True)\n",
    "\n",
    "param_grid = {'max_depth' : [3, 5, 7, 10, None],\n",
    "              'min_samples_leaf' : randint(2,120),\n",
    "              'criterion': ['gini', 'entropy'],\n",
    "              'min_samples_split': randint(2,120)\n",
    "              }\n",
    "\n",
    "hp_search = RandomizedSearchCV(RandomForestClassifier(), \n",
    "                               param_distributions=param_grid, \n",
    "                               n_iter= 20, \n",
    "                               cv = inner_cv,\n",
    "                               random_state=SEED)\n",
    "\n",
    "scores = cross_val_score(hp_search, res_features, res_labels, cv = outer_cv, scoring='accuracy')\n",
    "print(f'Mean Score: {scores.mean().round(3)}')\n",
    "print(f'Scores ---> {scores.round(3)}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most important features for the Random Forest were: \n",
    "- Age\n",
    "- IsActiveMember\n",
    "- NumOfProducts\n",
    "- Gender_Male\n",
    "- Geography_France\n",
    "\n",
    "Age and IsActiveMember were also the 2 most correlated features with the target variable Churn in the HeatMap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_27f6b_row0_col0 {\n",
       "  background-color: #662506;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_27f6b_row1_col0 {\n",
       "  background-color: #d15205;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_27f6b_row2_col0 {\n",
       "  background-color: #fd9728;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_27f6b_row3_col0 {\n",
       "  background-color: #fec652;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_27f6b_row4_col0 {\n",
       "  background-color: #fec859;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_27f6b_row5_col0 {\n",
       "  background-color: #fee18c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_27f6b_row6_col0 {\n",
       "  background-color: #feeba2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_27f6b_row7_col0 {\n",
       "  background-color: #fff1ae;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_27f6b_row8_col0, #T_27f6b_row9_col0 {\n",
       "  background-color: #fff1b0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_27f6b_row10_col0 {\n",
       "  background-color: #fff2b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_27f6b_row11_col0 {\n",
       "  background-color: #fff4b5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_27f6b_row12_col0 {\n",
       "  background-color: #ffface;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_27f6b_row13_col0 {\n",
       "  background-color: #fffbd0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_27f6b_row14_col0 {\n",
       "  background-color: #fffbd3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_27f6b_row15_col0 {\n",
       "  background-color: #fffcd4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_27f6b_row16_col0 {\n",
       "  background-color: #fffcd8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_27f6b_row17_col0 {\n",
       "  background-color: #ffffe5;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_27f6b\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_27f6b_level0_col0\" class=\"col_heading level0 col0\" >Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_27f6b_level0_row0\" class=\"row_heading level0 row0\" >Age</th>\n",
       "      <td id=\"T_27f6b_row0_col0\" class=\"data row0 col0\" >0.204420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_27f6b_level0_row1\" class=\"row_heading level0 row1\" >IsActiveMember</th>\n",
       "      <td id=\"T_27f6b_row1_col0\" class=\"data row1 col0\" >0.150743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_27f6b_level0_row2\" class=\"row_heading level0 row2\" >NumOfProducts</th>\n",
       "      <td id=\"T_27f6b_row2_col0\" class=\"data row2 col0\" >0.106046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_27f6b_level0_row3\" class=\"row_heading level0 row3\" >Gender_Male</th>\n",
       "      <td id=\"T_27f6b_row3_col0\" class=\"data row3 col0\" >0.079290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_27f6b_level0_row4\" class=\"row_heading level0 row4\" >Geography_France</th>\n",
       "      <td id=\"T_27f6b_row4_col0\" class=\"data row4 col0\" >0.077045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_27f6b_level0_row5\" class=\"row_heading level0 row5\" >Geography_Spain</th>\n",
       "      <td id=\"T_27f6b_row5_col0\" class=\"data row5 col0\" >0.057357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_27f6b_level0_row6\" class=\"row_heading level0 row6\" >Balance</th>\n",
       "      <td id=\"T_27f6b_row6_col0\" class=\"data row6 col0\" >0.045491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_27f6b_level0_row7\" class=\"row_heading level0 row7\" >Card Type_GOLD</th>\n",
       "      <td id=\"T_27f6b_row7_col0\" class=\"data row7 col0\" >0.038922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_27f6b_level0_row8\" class=\"row_heading level0 row8\" >Card Type_PLATINUM</th>\n",
       "      <td id=\"T_27f6b_row8_col0\" class=\"data row8 col0\" >0.038305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_27f6b_level0_row9\" class=\"row_heading level0 row9\" >Geography_Germany</th>\n",
       "      <td id=\"T_27f6b_row9_col0\" class=\"data row9 col0\" >0.038099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_27f6b_level0_row10\" class=\"row_heading level0 row10\" >Card Type_DIAMOND</th>\n",
       "      <td id=\"T_27f6b_row10_col0\" class=\"data row10 col0\" >0.037254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_27f6b_level0_row11\" class=\"row_heading level0 row11\" >Card Type_SILVER</th>\n",
       "      <td id=\"T_27f6b_row11_col0\" class=\"data row11 col0\" >0.035212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_27f6b_level0_row12\" class=\"row_heading level0 row12\" >Point Earned</th>\n",
       "      <td id=\"T_27f6b_row12_col0\" class=\"data row12 col0\" >0.019941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_27f6b_level0_row13\" class=\"row_heading level0 row13\" >EstimatedSalary</th>\n",
       "      <td id=\"T_27f6b_row13_col0\" class=\"data row13 col0\" >0.018525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_27f6b_level0_row14\" class=\"row_heading level0 row14\" >Satisfaction Score</th>\n",
       "      <td id=\"T_27f6b_row14_col0\" class=\"data row14 col0\" >0.017105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_27f6b_level0_row15\" class=\"row_heading level0 row15\" >CreditScore</th>\n",
       "      <td id=\"T_27f6b_row15_col0\" class=\"data row15 col0\" >0.016335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_27f6b_level0_row16\" class=\"row_heading level0 row16\" >Tenure</th>\n",
       "      <td id=\"T_27f6b_row16_col0\" class=\"data row16 col0\" >0.013999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_27f6b_level0_row17\" class=\"row_heading level0 row17\" >HasCrCard</th>\n",
       "      <td id=\"T_27f6b_row17_col0\" class=\"data row17 col0\" >0.005912</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2252fa31ac0>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_importance = best_estimator.feature_importances_\n",
    "features_importance = pd.DataFrame(features_importance, index = res_features.columns, columns =['Importance'])\n",
    "features_importance.sort_values(by='Importance', ascending = False).style.background_gradient(cmap ='YlOrBr')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Extra`\n",
    "\n",
    "Plotting confunsion matrix based on the cross validation process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1442.4,  150. ],\n",
       "       [ 238.8, 1353.6]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_features_array = res_features.values\n",
    "res_labels_array = res_labels.values\n",
    "\n",
    "from sklearn.model_selection import cross_val_score, KFold\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "conf_matrix_list_of_arrays = []\n",
    "inner_cv = KFold(n_splits=5, shuffle=True)\n",
    "outer_cv = KFold(n_splits=5, shuffle=True)\n",
    "\n",
    "param_grid = {\n",
    "    'max_depth': [3, 5, 7, 10, None],\n",
    "    'min_samples_leaf': randint(2, 120),\n",
    "    'criterion': ['gini', 'entropy'],\n",
    "    'min_samples_split': randint(2, 120)\n",
    "}\n",
    "\n",
    "hp_search = RandomizedSearchCV(\n",
    "    RandomForestClassifier(),\n",
    "    param_distributions=param_grid,\n",
    "    n_iter=20,\n",
    "    cv=inner_cv,\n",
    "    random_state=SEED\n",
    ")\n",
    "\n",
    "# Performing nested cross-validation and compute confusion matrices without using cross_val_score\n",
    "for train_index, test_index in outer_cv.split(res_features):\n",
    "    X_train, X_test = res_features.iloc[train_index], res_features.iloc[test_index]\n",
    "    y_train, y_test = res_labels.iloc[train_index], res_labels.iloc[test_index]\n",
    "\n",
    "    hp_search.fit(X_train, y_train)\n",
    "    y_pred = hp_search.predict(X_test)\n",
    "    conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "    conf_matrix_list_of_arrays.append(conf_matrix)\n",
    "\n",
    "# Compute average confusion matrix\n",
    "average_cm = np.mean(conf_matrix_list_of_arrays, axis=0)\n",
    "average_cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(average_cm, annot=True, cmap='YlOrBr', fmt='g')\n",
    "plt.title('Average Confusion Matrix')\n",
    "plt.xlabel('Predicted Labels')\n",
    "plt.ylabel('True Labels')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}