002_modelagem_preditiva.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelagem preditiva\n",
    "_Machine Learning_\n",
    "\n",
    "---\n",
    "\n",
    "## Sumário\n",
    "\n",
    "1. **Importação de bibliotecas**\n",
    "2. **Carregamento das bases**\n",
    "3. **Análise dos dataframes**\n",
    "4. **Modelagem preditiva**\n",
    "    - 4.1. Preparação dos dados\n",
    "    - 4.2. Treinamento dos modelos com todo o histórico de dados\n",
    "    - 4.3. Treinamento dos modelos com histórico de dados a partir de 01-01-2014\n",
    "    - 4.4. Treinamento dos modelos com histórico de dados a partir de 01-01-2015\n",
    "    - 4.5. Comparativo dos resultados\n",
    "5. **Tunagem dos hiperparâmetros do modelo com Optuna**\n",
    "6. **Salvando e testando o desempenho do modelo**\n",
    "    - 6.1. Salvando o modelo (.pkl)\n",
    "    - 6.2. Comparativo de resultados\n",
    "7. **Testando o algoritmo XGBoost**\n",
    "    - 7.1. Tunagem do modelo com Optuna\n",
    "    - 7.2. Salvando e testando o desempenho do modelo\n",
    "    - 7.3. Comparativo de resultados\n",
    "8. **Testando o algoritmo CatBoost**\n",
    "    - 8.1. Tunagem do modelo com Optuna\n",
    "    - 8.2. Salvando e testando o desempenho do modelo\n",
    "    - 8.3. Comparativo de resultados\n",
    "9. **Teste de aplicação do Voting Ensemble**\n",
    "10. **Análise da distribuição dos erros**\n",
    "11. **Comparação: valores reais x valores preditos**\n",
    "\n",
    "<br>\n",
    "\n",
    "---\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Importação de bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importação de pacotes e definição de parâmetros globais\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import gc\n",
    "import time\n",
    "import optuna\n",
    "import joblib\n",
    "import lightgbm as lgb\n",
    "import catboost as cb\n",
    "\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.linear_model import LinearRegression, ElasticNet\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from optuna.samplers import TPESampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configurações para exibição de dados no Jupyter Notebook\n",
    "\n",
    "# Configurar opção para exibir todas as linhas do Dataframe\n",
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "# Configurar para exibir o conteúdo completo das colunas\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "\n",
    "# Configurar a supressão de mensagens de aviso durante a execução\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Suprimir logs do Optuna\n",
    "optuna.logging.set_verbosity(optuna.logging.WARNING)\n",
    "\n",
    "# Configurar estilo dos gráficos do Seaborn\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Carregamento das bases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Quantidade de objetos removidos da memória: 0\n"
     ]
    }
   ],
   "source": [
    "# Efetuando a limpeza da memória antes do carregamento dos dados\n",
    "\n",
    "print(f'\\nQuantidade de objetos removidos da memória: {gc.collect()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DATAFRAME: df_train\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>StateHoliday</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "      <th>StoreType</th>\n",
       "      <th>Assortment</th>\n",
       "      <th>CompetitionDistance</th>\n",
       "      <th>...</th>\n",
       "      <th>PromoRollingSum_21</th>\n",
       "      <th>PromoRollingSum_28</th>\n",
       "      <th>PromoRollingSum_30</th>\n",
       "      <th>PromoRollingSum_60</th>\n",
       "      <th>PromoRollingSum_90</th>\n",
       "      <th>PromoRollingSum_120</th>\n",
       "      <th>PromoRollingSum_150</th>\n",
       "      <th>PromoRollingSum_180</th>\n",
       "      <th>PromoRollingSum_360</th>\n",
       "      <th>Target</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>2015-06-30</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1270.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
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       "      <td>149389.0</td>\n",
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       "      <td>a</td>\n",
       "      <td>570.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>170586.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>14130.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>243725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>c</td>\n",
       "      <td>c</td>\n",
       "      <td>620.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>334601.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>29910.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>162182.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
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       "   Store  DayOfWeek        Date  Open  Promo StateHoliday  SchoolHoliday  \\\n",
       "0      1          1  2015-06-30     1      1            0              0   \n",
       "1      2          1  2015-06-30     1      1            0              0   \n",
       "2      3          1  2015-06-30     1      1            0              1   \n",
       "3      4          1  2015-06-30     1      1            0              0   \n",
       "4      5          1  2015-06-30     1      1            0              0   \n",
       "\n",
       "  StoreType Assortment  CompetitionDistance  ...  PromoRollingSum_21  \\\n",
       "0         c          a               1270.0  ...                 9.0   \n",
       "1         a          a                570.0  ...                 9.0   \n",
       "2         a          a              14130.0  ...                 9.0   \n",
       "3         c          c                620.0  ...                 9.0   \n",
       "4         a          a              29910.0  ...                 9.0   \n",
       "\n",
       "   PromoRollingSum_28  PromoRollingSum_30  PromoRollingSum_60  \\\n",
       "0                10.0                12.0                 0.0   \n",
       "1                10.0                12.0                 0.0   \n",
       "2                10.0                12.0                 0.0   \n",
       "3                10.0                12.0                 0.0   \n",
       "4                10.0                12.0                 0.0   \n",
       "\n",
       "   PromoRollingSum_90 PromoRollingSum_120  PromoRollingSum_150  \\\n",
       "0                 0.0                 0.0                  0.0   \n",
       "1                 0.0                 0.0                  0.0   \n",
       "2                 0.0                 0.0                  0.0   \n",
       "3                 0.0                 0.0                  0.0   \n",
       "4                 0.0                 0.0                  0.0   \n",
       "\n",
       "   PromoRollingSum_180  PromoRollingSum_360    Target  \n",
       "0                  0.0                  0.0  149389.0  \n",
       "1                  0.0                  0.0  170586.0  \n",
       "2                  0.0                  0.0  243725.0  \n",
       "3                  0.0                  0.0  334601.0  \n",
       "4                  0.0                  0.0  162182.0  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Criando um dataframe a partir do arquivo train_001.csv\n",
    "\n",
    "df_train = pd.read_csv('dados/train_001.csv', sep=',')\n",
    "print('\\nDATAFRAME: df_train')\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DATAFRAME: df_validation\n"
     ]
    },
    {
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       "      <td>1</td>\n",
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       "      <td>a</td>\n",
       "      <td>14130.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>245876.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>4</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>620.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>29910.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>162083.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 38 columns</p>\n",
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      ],
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       "   Store  DayOfWeek        Date  Open  Promo  StateHoliday  SchoolHoliday  \\\n",
       "0      1          4  2015-07-31     1      1             0              1   \n",
       "1      2          4  2015-07-31     1      1             0              1   \n",
       "2      3          4  2015-07-31     1      1             0              1   \n",
       "3      4          4  2015-07-31     1      1             0              1   \n",
       "4      5          4  2015-07-31     1      1             0              1   \n",
       "\n",
       "  StoreType Assortment  CompetitionDistance  ...  PromoRollingSum_21  \\\n",
       "0         c          a               1270.0  ...                 0.0   \n",
       "1         a          a                570.0  ...                 0.0   \n",
       "2         a          a              14130.0  ...                 0.0   \n",
       "3         c          c                620.0  ...                 0.0   \n",
       "4         a          a              29910.0  ...                 0.0   \n",
       "\n",
       "   PromoRollingSum_28  PromoRollingSum_30  PromoRollingSum_60  \\\n",
       "0                 0.0                 0.0                 0.0   \n",
       "1                 0.0                 0.0                 0.0   \n",
       "2                 0.0                 0.0                 0.0   \n",
       "3                 0.0                 0.0                 0.0   \n",
       "4                 0.0                 0.0                 0.0   \n",
       "\n",
       "   PromoRollingSum_90  PromoRollingSum_120  PromoRollingSum_150  \\\n",
       "0                 0.0                  0.0                  0.0   \n",
       "1                 0.0                  0.0                  0.0   \n",
       "2                 0.0                  0.0                  0.0   \n",
       "3                 0.0                  0.0                  0.0   \n",
       "4                 0.0                  0.0                  0.0   \n",
       "\n",
       "   PromoRollingSum_180  PromoRollingSum_360    Target  \n",
       "0                  0.0                  0.0  157057.0  \n",
       "1                  0.0                  0.0  176872.0  \n",
       "2                  0.0                  0.0  245876.0  \n",
       "3                  0.0                  0.0  355383.0  \n",
       "4                  0.0                  0.0  162083.0  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Criando um dataframe a partir do arquivo validation_001.csv\n",
    "\n",
    "df_validation = pd.read_csv('dados/validation_001.csv', sep=',')\n",
    "print('\\nDATAFRAME: df_validation')\n",
    "df_validation.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Análise dos dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "VOLUMETRIA\n",
      "\n",
      "df_train\n",
      "---------------------------------------------\n",
      "Quantidade de linhas (registros):  943795\n",
      "Quantidade de colunas (variáveis): 38\n",
      "\n",
      "df_validation\n",
      "---------------------------------------------\n",
      "Quantidade de linhas (registros):  34565\n",
      "Quantidade de colunas (variáveis): 38\n"
     ]
    }
   ],
   "source": [
    "# Exibindo a quantidade de linhas e colunas dos dataframes\n",
    "\n",
    "# Criação de um dicionário com os dataframes e seus respectivos nomes\n",
    "dfs = {\n",
    "    'df_train': df_train,\n",
    "    'df_validation': df_validation\n",
    "}\n",
    "\n",
    "# Iteração sobre o dicionário para exibir o nome e as dimensões dos dataframes\n",
    "print(f'\\nVOLUMETRIA')\n",
    "for nome, df in dfs.items():\n",
    "    print(f'\\n{nome}')\n",
    "    print(f'-'*45)\n",
    "    print(f'Quantidade de linhas (registros):  {df.shape[0]}')\n",
    "    print(f'Quantidade de colunas (variáveis): {df.shape[1]}')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Função para geração de um dataframe de metadados\n",
    "\n",
    "def gerar_metadados(dataframe):\n",
    "    '''\n",
    "    Gera um dataframe contendo metadados das colunas do dataframe fornecido.\n",
    "\n",
    "    :param dataframe: Dataframe\n",
    "        DataFrame para o qual os metadados serão gerados.\n",
    "    :return: DataFrame\n",
    "        DataFrame contendo os metadados.\n",
    "    '''\n",
    "    metadados = pd.DataFrame({\n",
    "        'Variável': dataframe.columns,\n",
    "        'Tipo': dataframe.dtypes,\n",
    "        'Qtde de nulos': dataframe.isnull().sum(),\n",
    "        '% de nulos': round((dataframe.isnull().sum()/len(dataframe))*100, 2),\n",
    "        'Cardinalidade': dataframe.nunique(),\n",
    "    })\n",
    "    metadados = metadados.sort_values(by='Qtde de nulos', ascending=False)\n",
    "    metadados = metadados.reset_index(drop=True)\n",
    "    return metadados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Variável</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Qtde de nulos</th>\n",
       "      <th>% de nulos</th>\n",
       "      <th>Cardinalidade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Store</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PromoRollingSum_21</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DayOfYear</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MonthsSinceTheCompetitionOpened</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>YearsSinceTheCompetitionOpened</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PromoRollingSum_3</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PromoRollingSum_5</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PromoRollingSum_7</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PromoRollingSum_14</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PromoRollingSum_28</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DayOfWeek</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>PromoRollingSum_30</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PromoRollingSum_60</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PromoRollingSum_90</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>PromoRollingSum_120</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>PromoRollingSum_150</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PromoRollingSum_180</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PromoRollingSum_360</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>IsWeekend</td>\n",
       "      <td>bool</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Quarter</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Day</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Month</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Date</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Open</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Promo</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>StateHoliday</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SchoolHoliday</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>StoreType</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Assortment</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>CompetitionDistance</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>CompetitionOpenSinceMonth</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>CompetitionOpenSinceYear</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Promo2</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Promo2SinceWeek</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Promo2SinceYear</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>PromoInterval</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Year</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Target</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>278695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Variável     Tipo  Qtde de nulos  % de nulos  \\\n",
       "0                             Store    int64              0         0.0   \n",
       "1                PromoRollingSum_21  float64              0         0.0   \n",
       "2                         DayOfYear    int64              0         0.0   \n",
       "3   MonthsSinceTheCompetitionOpened  float64              0         0.0   \n",
       "4    YearsSinceTheCompetitionOpened  float64              0         0.0   \n",
       "5                 PromoRollingSum_3  float64              0         0.0   \n",
       "6                 PromoRollingSum_5  float64              0         0.0   \n",
       "7                 PromoRollingSum_7  float64              0         0.0   \n",
       "8                PromoRollingSum_14  float64              0         0.0   \n",
       "9                PromoRollingSum_28  float64              0         0.0   \n",
       "10                        DayOfWeek    int64              0         0.0   \n",
       "11               PromoRollingSum_30  float64              0         0.0   \n",
       "12               PromoRollingSum_60  float64              0         0.0   \n",
       "13               PromoRollingSum_90  float64              0         0.0   \n",
       "14              PromoRollingSum_120  float64              0         0.0   \n",
       "15              PromoRollingSum_150  float64              0         0.0   \n",
       "16              PromoRollingSum_180  float64              0         0.0   \n",
       "17              PromoRollingSum_360  float64              0         0.0   \n",
       "18                        IsWeekend     bool              0         0.0   \n",
       "19                          Quarter    int64              0         0.0   \n",
       "20                              Day    int64              0         0.0   \n",
       "21                            Month    int64              0         0.0   \n",
       "22                             Date   object              0         0.0   \n",
       "23                             Open    int64              0         0.0   \n",
       "24                            Promo    int64              0         0.0   \n",
       "25                     StateHoliday   object              0         0.0   \n",
       "26                    SchoolHoliday    int64              0         0.0   \n",
       "27                        StoreType   object              0         0.0   \n",
       "28                       Assortment   object              0         0.0   \n",
       "29              CompetitionDistance  float64              0         0.0   \n",
       "30        CompetitionOpenSinceMonth  float64              0         0.0   \n",
       "31         CompetitionOpenSinceYear  float64              0         0.0   \n",
       "32                           Promo2    int64              0         0.0   \n",
       "33                  Promo2SinceWeek  float64              0         0.0   \n",
       "34                  Promo2SinceYear  float64              0         0.0   \n",
       "35                    PromoInterval   object              0         0.0   \n",
       "36                             Year    int64              0         0.0   \n",
       "37                           Target  float64              0         0.0   \n",
       "\n",
       "    Cardinalidade  \n",
       "0            1115  \n",
       "1               7  \n",
       "2             365  \n",
       "3             336  \n",
       "4             346  \n",
       "5               4  \n",
       "6               6  \n",
       "7               6  \n",
       "8              11  \n",
       "9              11  \n",
       "10              7  \n",
       "11             13  \n",
       "12             14  \n",
       "13             13  \n",
       "14             13  \n",
       "15             17  \n",
       "16             17  \n",
       "17             21  \n",
       "18              2  \n",
       "19              4  \n",
       "20             31  \n",
       "21             12  \n",
       "22            911  \n",
       "23              2  \n",
       "24              2  \n",
       "25              5  \n",
       "26              2  \n",
       "27              4  \n",
       "28              3  \n",
       "29            655  \n",
       "30             13  \n",
       "31             24  \n",
       "32              2  \n",
       "33             25  \n",
       "34              8  \n",
       "35              4  \n",
       "36              3  \n",
       "37         278695  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gerar_metadados(df_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Modelagem preditiva"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1. Preparação dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separando as variáveis preditivas e a variável preditora (alvo)\n",
    "\n",
    "features = df_train.columns.drop('Target')\n",
    "target = 'Target'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separando as variáveis numéricas e categóricas\n",
    "\n",
    "numerical_features = df_train[features].select_dtypes(exclude=object).columns\n",
    "categorical_features = df_train[features].select_dtypes(include=object).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converter todas as colunas categóricas para string\n",
    "\n",
    "df_train[categorical_features] = df_train[categorical_features].astype(str)\n",
    "df_validation[categorical_features] = df_validation[categorical_features].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separando os dataframes com as variáveis preditivas e a variável preditora\n",
    "\n",
    "X_train = df_train[features]\n",
    "y_train = df_train[target]\n",
    "X_test = df_validation[features]\n",
    "y_test = df_validation[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pré-processamento, transformação das features numéricas e categóricas\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "\ttransformers=[\n",
    "\t\t('num', StandardScaler(), numerical_features),\n",
    "\t\t('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: X_train: (943795, 37), y_train: (943795,)\n",
      "Shape: X_test:  (34565, 37),  y_test: (34565,)\n"
     ]
    }
   ],
   "source": [
    "# Verificando os shapes\n",
    "\n",
    "print(f'Shape: X_train: {X_train.shape}, y_train: {y_train.shape}')\n",
    "print(f'Shape: X_test:  {X_test.shape},  y_test: {y_test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Verificando transformação de dados\n",
    "\n",
    "X_train_transformed = preprocessor.fit_transform(X_train)\n",
    "X_test_transformed = preprocessor.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape de X_train após transformação: (943795, 958)\n",
      "Shape de X_test após transformação:  (34565, 958)\n"
     ]
    }
   ],
   "source": [
    "# Verificando os shapes após transformação\n",
    "\n",
    "print(f'Shape de X_train após transformação: {X_train_transformed.shape}')\n",
    "print(f'Shape de X_test após transformação:  {X_test_transformed.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2. Treinamento dos modelos com todo o histórico de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definindo os modelos a serem testados\n",
    "\n",
    "models = {\n",
    "    'LinearRegression': LinearRegression(),\n",
    "    'ElasticNet': ElasticNet(),\n",
    "    'LightGBM': lgb.LGBMRegressor(n_estimators=100, random_state=42, verbose=-1),\n",
    "    'CatBoost': cb.CatBoostRegressor(iterations=100, depth=6, learning_rate=0.1, loss_function='MAE', verbose=0),\n",
    "    'GradientBoosting': GradientBoostingRegressor(n_estimators=100, random_state=42),\n",
    "    'XGBoost': XGBRegressor(n_estimators=100, random_state=42, verbosity=0),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def models_evaluation(models, X_train, y_train, X_test, y_test, preprocessor):\n",
    "    '''\n",
    "    Avalia modelos de aprendizado de máquina, calculando métricas de desempenho \n",
    "    no conjunto de treino e teste.\n",
    "\n",
    "    :param models: dict\n",
    "        Dicionário contendo os modelos a serem avaliados.\n",
    "    :param X_train: DataFrame\n",
    "        Conjunto de dados de treino com as variáveis independentes.\n",
    "    :param y_train: Series\n",
    "        Variável dependente para o conjunto de treino.\n",
    "    :param X_test: DataFrame\n",
    "        Conjunto de dados de teste com as variáveis independentes.\n",
    "    :param y_test: Series\n",
    "        Variável dependente para o conjunto de teste.\n",
    "    :param preprocessor: ColumnTransformer\n",
    "        Objeto de pré-processamento que será aplicado aos dados antes do treinamento do modelo.\n",
    "\n",
    "    :return: list\n",
    "        Lista contendo os resultados de avaliação de cada modelo, RMSE e MAE para treino \n",
    "        e teste, além do tempo de execução.\n",
    "    '''\n",
    "    results = []\n",
    "\n",
    "    for model_name, model in models.items():\n",
    "        # Cria um pipeline que combina o pré-processamento e o modelo\n",
    "        pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])\n",
    "\n",
    "        # Registra o tempo de início\n",
    "        start_time = time.time()\n",
    "        \n",
    "        # Treina o modelo usando o pipeline\n",
    "        pipeline.fit(X_train, y_train)\n",
    "        \n",
    "        # Registra o tempo de término e calcula o tempo de execução\n",
    "        end_time = time.time()\n",
    "        elapsed_time = end_time - start_time\n",
    "\n",
    "        # Previsões e cálculo das métricas no conjunto de treino\n",
    "        train_predictions = pipeline.predict(X_train)\n",
    "        train_rmse = np.sqrt(mean_squared_error(y_train, train_predictions))\n",
    "        train_mae = mean_absolute_error(y_train, train_predictions)\n",
    "\n",
    "        # Previsões e cálculo das métricas no conjunto de teste\n",
    "        test_predictions = pipeline.predict(X_test)\n",
    "        test_rmse = np.sqrt(mean_squared_error(y_test, test_predictions))\n",
    "        test_mae = mean_absolute_error(y_test, test_predictions)\n",
    "\n",
    "        # Adiciona os resultados à lista\n",
    "        results.append((\n",
    "            model_name, train_rmse, test_rmse, train_mae, test_mae, elapsed_time\n",
    "        ))\n",
    "\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Modelo</th>\n",
       "      <th>RMSE em treino</th>\n",
       "      <th>RMSE em teste</th>\n",
       "      <th>MAE em treino</th>\n",
       "      <th>MAE em teste</th>\n",
       "      <th>Tempo decorrido (s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinearRegression</td>\n",
       "      <td>74600.379064</td>\n",
       "      <td>309293.409817</td>\n",
       "      <td>51738.388898</td>\n",
       "      <td>271276.664045</td>\n",
       "      <td>28.248643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ElasticNet</td>\n",
       "      <td>84935.244853</td>\n",
       "      <td>87519.132897</td>\n",
       "      <td>61460.551956</td>\n",
       "      <td>63554.317987</td>\n",
       "      <td>55.603750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>38586.776903</td>\n",
       "      <td>44161.800849</td>\n",
       "      <td>27936.856770</td>\n",
       "      <td>32487.902792</td>\n",
       "      <td>11.839526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CatBoost</td>\n",
       "      <td>67985.801128</td>\n",
       "      <td>73214.229602</td>\n",
       "      <td>40463.759302</td>\n",
       "      <td>45533.101445</td>\n",
       "      <td>20.887834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GradientBoosting</td>\n",
       "      <td>65562.258359</td>\n",
       "      <td>70611.741017</td>\n",
       "      <td>45250.375402</td>\n",
       "      <td>48747.665342</td>\n",
       "      <td>335.386967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>21462.326807</td>\n",
       "      <td>28607.617056</td>\n",
       "      <td>15610.326833</td>\n",
       "      <td>21221.732968</td>\n",
       "      <td>9.811405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Modelo  RMSE em treino  RMSE em teste  MAE em treino  \\\n",
       "0  LinearRegression    74600.379064  309293.409817   51738.388898   \n",
       "1        ElasticNet    84935.244853   87519.132897   61460.551956   \n",
       "2          LightGBM    38586.776903   44161.800849   27936.856770   \n",
       "3          CatBoost    67985.801128   73214.229602   40463.759302   \n",
       "4  GradientBoosting    65562.258359   70611.741017   45250.375402   \n",
       "5           XGBoost    21462.326807   28607.617056   15610.326833   \n",
       "\n",
       "    MAE em teste  Tempo decorrido (s)  \n",
       "0  271276.664045            28.248643  \n",
       "1   63554.317987            55.603750  \n",
       "2   32487.902792            11.839526  \n",
       "3   45533.101445            20.887834  \n",
       "4   48747.665342           335.386967  \n",
       "5   21221.732968             9.811405  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Avaliando os modelos\n",
    "results = models_evaluation(models, X_train, y_train, X_test, y_test, preprocessor)\n",
    "\n",
    "# Criando DataFrame de resultados\n",
    "results_df = pd.DataFrame(results, columns=[\n",
    "                            'Modelo', 'RMSE em treino', 'RMSE em teste', \n",
    "                            'MAE em treino', 'MAE em teste', 'Tempo decorrido (s)'])\n",
    "\n",
    "# Exibindo os resultados\n",
    "results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3. Treinamento dos modelos com histórico de dados a partir de 01-01-2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Modelo</th>\n",
       "      <th>RMSE em treino</th>\n",
       "      <th>RMSE em teste</th>\n",
       "      <th>MAE em treino</th>\n",
       "      <th>MAE em teste</th>\n",
       "      <th>Tempo decorrido (s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinearRegression</td>\n",
       "      <td>74409.362859</td>\n",
       "      <td>276823.117601</td>\n",
       "      <td>51872.836322</td>\n",
       "      <td>240122.218335</td>\n",
       "      <td>17.610223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ElasticNet</td>\n",
       "      <td>85217.698738</td>\n",
       "      <td>87332.278344</td>\n",
       "      <td>61768.541665</td>\n",
       "      <td>62443.225486</td>\n",
       "      <td>18.393986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>37508.232887</td>\n",
       "      <td>42825.956651</td>\n",
       "      <td>27228.816279</td>\n",
       "      <td>31749.433281</td>\n",
       "      <td>6.738651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CatBoost</td>\n",
       "      <td>66946.740114</td>\n",
       "      <td>72510.606671</td>\n",
       "      <td>40045.071795</td>\n",
       "      <td>45477.246849</td>\n",
       "      <td>11.919119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GradientBoosting</td>\n",
       "      <td>64445.219168</td>\n",
       "      <td>69331.655819</td>\n",
       "      <td>44933.129775</td>\n",
       "      <td>48135.930325</td>\n",
       "      <td>201.910770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>19679.021450</td>\n",
       "      <td>25890.517653</td>\n",
       "      <td>14269.371007</td>\n",
       "      <td>19121.840148</td>\n",
       "      <td>5.315342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Modelo  RMSE em treino  RMSE em teste  MAE em treino  \\\n",
       "0  LinearRegression    74409.362859  276823.117601   51872.836322   \n",
       "1        ElasticNet    85217.698738   87332.278344   61768.541665   \n",
       "2          LightGBM    37508.232887   42825.956651   27228.816279   \n",
       "3          CatBoost    66946.740114   72510.606671   40045.071795   \n",
       "4  GradientBoosting    64445.219168   69331.655819   44933.129775   \n",
       "5           XGBoost    19679.021450   25890.517653   14269.371007   \n",
       "\n",
       "    MAE em teste  Tempo decorrido (s)  \n",
       "0  240122.218335            17.610223  \n",
       "1   62443.225486            18.393986  \n",
       "2   31749.433281             6.738651  \n",
       "3   45477.246849            11.919119  \n",
       "4   48135.930325           201.910770  \n",
       "5   19121.840148             5.315342  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecionando dados a partir de 01-01-2014\n",
    "df_train_00 = df_train[df_train['Date'] >= '2014-01-01'].copy()\n",
    "\n",
    "# Converter todas as colunas categóricas para string\n",
    "df_train_00[categorical_features] = df_train_00[categorical_features].astype(str)\n",
    "\n",
    "# Separando os dataframes com as variáveis preditivas e a variável preditora\n",
    "X_train_00 = df_train_00[features]\n",
    "y_train_00 = df_train_00[target]\n",
    "\n",
    "# Pré-processamento, transformação das features numéricas e categóricas\n",
    "preprocessor = ColumnTransformer(\n",
    "\ttransformers=[\n",
    "\t\t('num', StandardScaler(), numerical_features),\n",
    "\t\t('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features)\n",
    "])\n",
    "\n",
    "# Avaliando os modelos\n",
    "results = models_evaluation(models, X_train_00, y_train_00, X_test, y_test, preprocessor)\n",
    "\n",
    "# Criando DataFrame de resultados\n",
    "results_df_00 = pd.DataFrame(results, columns=[\n",
    "                            'Modelo', 'RMSE em treino', 'RMSE em teste', \n",
    "                            'MAE em treino', 'MAE em teste', 'Tempo decorrido (s)'])\n",
    "\n",
    "# Exibindo os resultados\n",
    "results_df_00"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4. Treinamento dos modelos com histórico de dados a partir de 01-01-2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Modelo</th>\n",
       "      <th>RMSE em treino</th>\n",
       "      <th>RMSE em teste</th>\n",
       "      <th>MAE em treino</th>\n",
       "      <th>MAE em teste</th>\n",
       "      <th>Tempo decorrido (s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinearRegression</td>\n",
       "      <td>73699.366946</td>\n",
       "      <td>86771.077084</td>\n",
       "      <td>51404.782369</td>\n",
       "      <td>60645.653521</td>\n",
       "      <td>2.232295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ElasticNet</td>\n",
       "      <td>84534.496771</td>\n",
       "      <td>92563.807514</td>\n",
       "      <td>61196.068350</td>\n",
       "      <td>61898.764900</td>\n",
       "      <td>4.950615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>35155.499255</td>\n",
       "      <td>40311.140946</td>\n",
       "      <td>25778.097047</td>\n",
       "      <td>29902.591028</td>\n",
       "      <td>2.588247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CatBoost</td>\n",
       "      <td>67510.325688</td>\n",
       "      <td>74015.997350</td>\n",
       "      <td>40817.270692</td>\n",
       "      <td>46240.349720</td>\n",
       "      <td>4.108876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GradientBoosting</td>\n",
       "      <td>63451.163306</td>\n",
       "      <td>69028.536458</td>\n",
       "      <td>44110.979695</td>\n",
       "      <td>47900.685039</td>\n",
       "      <td>55.316699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>18390.785339</td>\n",
       "      <td>24604.677229</td>\n",
       "      <td>13493.532465</td>\n",
       "      <td>18316.927099</td>\n",
       "      <td>1.759362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Modelo  RMSE em treino  RMSE em teste  MAE em treino  \\\n",
       "0  LinearRegression    73699.366946   86771.077084   51404.782369   \n",
       "1        ElasticNet    84534.496771   92563.807514   61196.068350   \n",
       "2          LightGBM    35155.499255   40311.140946   25778.097047   \n",
       "3          CatBoost    67510.325688   74015.997350   40817.270692   \n",
       "4  GradientBoosting    63451.163306   69028.536458   44110.979695   \n",
       "5           XGBoost    18390.785339   24604.677229   13493.532465   \n",
       "\n",
       "   MAE em teste  Tempo decorrido (s)  \n",
       "0  60645.653521             2.232295  \n",
       "1  61898.764900             4.950615  \n",
       "2  29902.591028             2.588247  \n",
       "3  46240.349720             4.108876  \n",
       "4  47900.685039            55.316699  \n",
       "5  18316.927099             1.759362  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecionando dados a partir de 01-01-2015\n",
    "df_train_01 = df_train[df_train['Date'] >= '2015-01-01'].copy()\n",
    "\n",
    "# Converter todas as colunas categóricas para string\n",
    "df_train_01[categorical_features] = df_train_01[categorical_features].astype(str)\n",
    "\n",
    "# Separando os dataframes com as variáveis preditivas e a variável preditora\n",
    "X_train_01 = df_train_01[features]\n",
    "y_train_01 = df_train_01[target]\n",
    "\n",
    "# Pré-processamento, transformação das features numéricas e categóricas\n",
    "preprocessor = ColumnTransformer(\n",
    "\ttransformers=[\n",
    "\t\t('num', StandardScaler(), numerical_features),\n",
    "\t\t('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features)\n",
    "])\n",
    "\n",
    "# Avaliando os modelos\n",
    "results = models_evaluation(models, X_train_01, y_train_01, X_test, y_test, preprocessor)\n",
    "\n",
    "# Criando DataFrame de resultados\n",
    "results_df_01 = pd.DataFrame(results, columns=[\n",
    "                            'Modelo', 'RMSE em treino', 'RMSE em teste', \n",
    "                            'MAE em treino', 'MAE em teste', 'Tempo decorrido (s)'])\n",
    "\n",
    "# Exibindo os resultados\n",
    "results_df_01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5. Comparativo dos resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Modelo</th>\n",
       "      <th>RMSE em treino</th>\n",
       "      <th>RMSE em teste</th>\n",
       "      <th>MAE em treino</th>\n",
       "      <th>MAE em teste</th>\n",
       "      <th>Tempo decorrido (s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinearRegression</td>\n",
       "      <td>74600.379064</td>\n",
       "      <td>309293.409817</td>\n",
       "      <td>51738.388898</td>\n",
       "      <td>271276.664045</td>\n",
       "      <td>28.248643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ElasticNet</td>\n",
       "      <td>84935.244853</td>\n",
       "      <td>87519.132897</td>\n",
       "      <td>61460.551956</td>\n",
       "      <td>63554.317987</td>\n",
       "      <td>55.603750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>38586.776903</td>\n",
       "      <td>44161.800849</td>\n",
       "      <td>27936.856770</td>\n",
       "      <td>32487.902792</td>\n",
       "      <td>11.839526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CatBoost</td>\n",
       "      <td>67985.801128</td>\n",
       "      <td>73214.229602</td>\n",
       "      <td>40463.759302</td>\n",
       "      <td>45533.101445</td>\n",
       "      <td>20.887834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GradientBoosting</td>\n",
       "      <td>65562.258359</td>\n",
       "      <td>70611.741017</td>\n",
       "      <td>45250.375402</td>\n",
       "      <td>48747.665342</td>\n",
       "      <td>335.386967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>21462.326807</td>\n",
       "      <td>28607.617056</td>\n",
       "      <td>15610.326833</td>\n",
       "      <td>21221.732968</td>\n",
       "      <td>9.811405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Modelo  RMSE em treino  RMSE em teste  MAE em treino  \\\n",
       "0  LinearRegression    74600.379064  309293.409817   51738.388898   \n",
       "1        ElasticNet    84935.244853   87519.132897   61460.551956   \n",
       "2          LightGBM    38586.776903   44161.800849   27936.856770   \n",
       "3          CatBoost    67985.801128   73214.229602   40463.759302   \n",
       "4  GradientBoosting    65562.258359   70611.741017   45250.375402   \n",
       "5           XGBoost    21462.326807   28607.617056   15610.326833   \n",
       "\n",
       "    MAE em teste  Tempo decorrido (s)  \n",
       "0  271276.664045            28.248643  \n",
       "1   63554.317987            55.603750  \n",
       "2   32487.902792            11.839526  \n",
       "3   45533.101445            20.887834  \n",
       "4   48747.665342           335.386967  \n",
       "5   21221.732968             9.811405  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Utilizando todo o histórico de dados\n",
    "# Exibindo os resultados\n",
    "results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os valores elevados de **RMSE** e **MAE** podem ser atribuídos à grande variação na variável dependente _'Target'_, que inclui dias com vendas zero e dias com vendas muito elevadas. Esse cenário reflete a realidade de que drogarias podem estar fechadas ou operar em horários reduzidos, o que influencia os dados. O foco, no entanto, deve ser nas diferenças entre as métricas de treino e teste, o que indica a capacidade dos modelos em **generalizar**, ou seja, manter um bom desempenho ao receber dados novos (não vistos).\n",
    "\n",
    "Dentre os modelos analisados, o **LinearRegression** teve o pior desempenho com os maiores valores de erro. O **GradientBoosting** apresentou boas diferenças entre treino e teste, mas os erros ainda foram elevados, além de apresentar o pior tempo de execução. O **CatBoost** se destacou com métricas razoáveis, mas seu tempo de execução foi elevado em relação aos próximos modelos. O **XGBoost** obteve os menores valores de _RMSE_ e _MAE_ em comparação aos demais modelos, porém com uma diferença maior entre treino e teste, indicando possível overfitting. O **LightGBM**, com _RMSE_ e _MAE_ competitivos e o menor tempo de execução, mostrou-se o modelo mais equilibrado, oferecendo a melhor performance geral."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Tunagem dos hiperparâmetros do modelo com Optuna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective_lightgbm(trial):\n",
    "    '''\n",
    "    Função objetivo para otimizar os hiperparâmetros do modelo LGBMRegressor usando o Optuna.\n",
    "\n",
    "    :param trial: optuna.Trial\n",
    "        Objeto que sugere valores para os hiperparâmetros do modelo durante o processo de otimização.\n",
    "\n",
    "    :return: float\n",
    "        O valor do MAE calculado no conjunto de teste para o modelo treinado.\n",
    "    '''      \n",
    "    model = lgb.LGBMRegressor(\n",
    "        # Número de árvores (boosting rounds) no modelo, cada árvore corrige os erros da anterior\n",
    "        n_estimators=trial.suggest_int('n_estimators', 300, 500),\n",
    "        # Taxa de aprendizado para ajustar o impacto de cada árvore\n",
    "        learning_rate=trial.suggest_float('learning_rate', 0.01, 0.04, log=True),\n",
    "        # Limita a profundidade máxima de cada árvore, controlando o crescimento da árvore e prevenindo overfitting\n",
    "        max_depth=trial.suggest_int('max_depth', 3, 7),\n",
    "        # Número máximo de folhas em cada árvore. Controla a complexidade do modelo\n",
    "        num_leaves=trial.suggest_int('num_leaves', 20, 90),\n",
    "        # Proporção de amostras usadas para treinar cada árvore, útil para prevenir overfitting\n",
    "        subsample=trial.suggest_float('subsample', 0.5, 0.9),\n",
    "        # Proporção de colunas (features) usadas para construir cada árvore, ajuda a reduzir correlação entre árvores e evitar overfitting\n",
    "        colsample_bytree=trial.suggest_float('colsample_bytree', 0.7, 1.0),\n",
    "        # Regularização L1 (Lasso), adiciona penalidade proporcional ao valor absoluto dos coeficientes, ajudando a reduzir o overfitting\n",
    "        reg_alpha=trial.suggest_float('reg_alpha', 0.1, 10, log=True),\n",
    "        # Regularização L2 (Ridge), adiciona penalidade proporcional ao quadrado dos coeficientes, ajudando a reduzir o overfitting\n",
    "        reg_lambda=trial.suggest_float('reg_lambda', 0.01, 10, log=True),\n",
    "        # Semente aleatória para garantir reprodutibilidade dos resultados\n",
    "        random_state=42,\n",
    "        # Silenciar a saída de logs durante o treinamento\n",
    "        verbose=-1       \n",
    "    )\n",
    "\n",
    "    # Cria um pipeline que combina o pré-processamento e o modelo\n",
    "    pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])\n",
    "    \n",
    "    # Treina o modelo usando o pipeline\n",
    "    pipeline.fit(X_train, y_train)\n",
    "   \n",
    "    # Previsões e cálculo das métricas no conjunto de teste\n",
    "    test_predictions = pipeline.predict(X_test)\n",
    "    test_mae = mean_absolute_error(y_test, test_predictions)\n",
    "    \n",
    "    return test_mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a196cde83f404f55bf117f432ef960cb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cria o estudo Optuna com o sampler TPE\n",
    "study = optuna.create_study(direction='minimize', sampler=TPESampler(seed=42))\n",
    "\n",
    "# Otimiza o modelo com a função objetivo\n",
    "study.optimize(objective_lightgbm, n_trials=50, n_jobs=-1, show_progress_bar=True, timeout=1800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Melhores hiperparâmetros: {'n_estimators': 467, 'learning_rate': 0.03833820921489225, 'max_depth': 7, 'num_leaves': 70, 'subsample': 0.5805423737123789, 'colsample_bytree': 0.8124503275340907, 'reg_alpha': 0.4510330249910037, 'reg_lambda': 0.02761451908267608}\n",
      "\n",
      "Melhor MAE: 23419.0702\n"
     ]
    }
   ],
   "source": [
    "# Acessa o melhor MAE e os melhores parâmetros após a otimização\n",
    "mae_lightgbm = study.best_value\n",
    "params_lightgbm = study.best_params\n",
    "\n",
    "print(f'Melhores hiperparâmetros: {study.best_params}')\n",
    "print(f'\\nMelhor MAE: {study.best_value:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Salvando e testando o desempenho do modelo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1. Salvando o modelo (.pkl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_and_save_model(best_model, file_name, model_name, X_train, y_train, X_test, y_test):\n",
    "    '''\n",
    "    Treina um modelo com os melhores hiperparâmetros, salva o pipeline treinado \n",
    "    em um arquivo e avalia o desempenho no conjunto de teste.\n",
    "\n",
    "    :param best_model: object\n",
    "        O modelo otimizado com os melhores hiperparâmetros a ser treinado.\n",
    "    :param file_name: str\n",
    "        Nome do arquivo para salvar o pipeline treinado (formato .pkl).\n",
    "    :param model_name: str\n",
    "        Nome do modelo para identificação nos resultados.\n",
    "    :param X_train: array-like ou DataFrame\n",
    "        Conjunto de características para treinamento.\n",
    "    :param y_train: array-like ou Series\n",
    "        Variável alvo correspondente ao conjunto de treinamento.\n",
    "    :param X_test: array-like ou DataFrame\n",
    "        Conjunto de características para teste.\n",
    "    :param y_test: array-like ou Series\n",
    "        Variável alvo correspondente ao conjunto de teste.\n",
    "\n",
    "    :return: list\n",
    "        Lista contendo o nome do modelo, o RMSE e o MAE calculados no conjunto \n",
    "        de teste, para avaliação do desempenho.\n",
    "    '''\n",
    "    results = []\n",
    "    \n",
    "    # Recriando o pipeline com os melhores hiperpârametros\n",
    "    pipeline_best_model = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                                          ('model', best_model)])\n",
    "    \n",
    "    # Registra o tempo de início\n",
    "    start_time = time.time()\n",
    "    \n",
    "    # Treinando o pipeline final no conjunto de treinamento\n",
    "    pipeline_best_model.fit(X_train, y_train)\n",
    "    \n",
    "    # Registra o tempo de término e calcula o tempo de execução\n",
    "    end_time = time.time()\n",
    "    elapsed_time = end_time - start_time\n",
    "    \n",
    "    try:\n",
    "        # Salvando o pipeline em um arquivo .pkl\n",
    "        joblib.dump(pipeline_best_model, f'modelos/{file_name}.pkl')\n",
    "        print(f'Arquivo {file_name}.pkl gerado com sucesso!')\n",
    "    except Exception as e:\n",
    "        print(f'Erro ao salvar o pipeline: {e}')\n",
    "        \n",
    "    # Previsões e cálculo das métricas no conjunto de treino\n",
    "    train_predictions = pipeline_best_model.predict(X_train)\n",
    "    train_rmse = np.sqrt(mean_squared_error(y_train, train_predictions))\n",
    "    train_mae = mean_absolute_error(y_train, train_predictions)\n",
    "    \n",
    "    # Previsões e cálculo das métricas no conjunto de teste\n",
    "    test_predictions = pipeline_best_model.predict(X_test)\n",
    "    test_rmse = np.sqrt(mean_squared_error(y_test, test_predictions))\n",
    "    test_mae = mean_absolute_error(y_test, test_predictions)\n",
    "    \n",
    "    # Adiciona previsões em teste a lista\n",
    "    results.append((\n",
    "        model_name, train_rmse, test_rmse, train_mae, test_mae, elapsed_time\n",
    "    ))\n",
    "    \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arquivo pipeline_best_model_lightgbm.pkl gerado com sucesso!\n"
     ]
    }
   ],
   "source": [
    "# Parâmetros otimizados para o LightGBM\n",
    "best_params = params_lightgbm\n",
    "\n",
    "# Cria o modelo com os melhores hiperparâmetros\n",
    "best_model = lgb.LGBMRegressor(**best_params, verbose=-1)\n",
    "\n",
    "# Treina o modelo e salva o pipeline treinado em um arquivo .pkl\n",
    "results_lightgbm = train_and_save_model(best_model, 'pipeline_best_model_lightgbm', 'LightGBM',\n",
    "                                        X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2. Comparativo de resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definindo as colunas para o DataFrame\n",
    "columns = ['Modelo', 'RMSE em treino', 'RMSE em teste', 'MAE em treino', \n",
    "           'MAE em teste', 'Tempo decorrido (s)']\n",
    "\n",
    "# Criando DataFrame com os resultados do modelo LightGBM\n",
    "df_results_lightgbm = pd.DataFrame(results_lightgbm, columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Resultado do modelo SEM a tunagem de hiperparâmetros\n"
     ]
    },
    {
     "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>Modelo</th>\n",
       "      <th>RMSE em treino</th>\n",
       "      <th>RMSE em teste</th>\n",
       "      <th>MAE em treino</th>\n",
       "      <th>MAE em teste</th>\n",
       "      <th>Tempo decorrido (s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>38586.776903</td>\n",
       "      <td>44161.800849</td>\n",
       "      <td>27936.85677</td>\n",
       "      <td>32487.902792</td>\n",
       "      <td>11.839526</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Modelo  RMSE em treino  RMSE em teste  MAE em treino  MAE em teste  \\\n",
       "2  LightGBM    38586.776903   44161.800849    27936.85677  32487.902792   \n",
       "\n",
       "   Tempo decorrido (s)  \n",
       "2            11.839526  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('\\nResultado do modelo SEM a tunagem de hiperparâmetros')\n",
    "results_df.iloc[[2], 0:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Resultado do modelo COM a tunagem de hiperparâmetros\n"
     ]
    },
    {
     "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>Modelo</th>\n",
       "      <th>RMSE em treino</th>\n",
       "      <th>RMSE em teste</th>\n",
       "      <th>MAE em treino</th>\n",
       "      <th>MAE em teste</th>\n",
       "      <th>Tempo decorrido (s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>26568.716275</td>\n",
       "      <td>32165.355624</td>\n",
       "      <td>19251.483054</td>\n",
       "      <td>23595.982089</td>\n",
       "      <td>26.046391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Modelo  RMSE em treino  RMSE em teste  MAE em treino  MAE em teste  \\\n",
       "0  LightGBM    26568.716275   32165.355624   19251.483054  23595.982089   \n",
       "\n",
       "   Tempo decorrido (s)  \n",
       "0            26.046391  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('\\nResultado do modelo COM a tunagem de hiperparâmetros')\n",
    "df_results_lightgbm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A tunagem de hiperparâmetros trouxe uma melhoria na redução da diferença entre os valores das métricas **RMSE** e **MAE** nos conjuntos de dados de **treino** e **teste**, o que indica que os ajustes de hiperparâmetros ajudaram os modelos a capturar melhor os padrões dos dados.\n",
    "\n",
    "O modelo **LightGBM** apresentou uma melhoria nas métricas de erro **RMSE** e **MAE** após a tunagem, indicando que o modelo agora está prevendo com erros menores e com uma melhor capacidade de **generalização**. Houve uma redução tanto nos valores das métricas quanto nas diferenças entre os resultados de treino e teste. O **LightGBM** continua se destacando como o modelo mais equilibrado, com redução das métricas de **RMSE** e **MAE**, aliado à melhor capacidade de generalização."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Testando o algoritmo XGBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.1. Tunagem do modelo com Optuna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective_xgboost(trial):\n",
    "    '''\n",
    "    Função objetivo para otimizar os hiperparâmetros do modelo XGBRegressor usando o Optuna.\n",
    "\n",
    "    :param trial: optuna.Trial\n",
    "        Objeto que sugere valores para os hiperparâmetros do modelo durante o processo de otimização.\n",
    "\n",
    "    :return: float\n",
    "        O valor do MAE calculado no conjunto de teste para o modelo treinado.\n",
    "    '''   \n",
    "    model = XGBRegressor(\n",
    "        # Número de árvores (boosting rounds) no modelo, cada árvore corrige os erros da anterior\n",
    "        n_estimators=trial.suggest_int('n_estimators', 300, 500),        \n",
    "        # Taxa de aprendizado para ajustar o impacto de cada árvore\n",
    "        learning_rate=trial.suggest_float('learning_rate', 0.01, 0.04, log=True),        \n",
    "        # Limita a profundidade máxima de cada árvore, controlando o crescimento da árvore e prevenindo overfitting\n",
    "        max_depth=trial.suggest_int('max_depth', 3, 7),        \n",
    "        # Número máximo de folhas em cada árvore. Controla a complexidade do modelo\n",
    "        max_leaves=trial.suggest_int('max_leaves', 20, 90),        \n",
    "        # Proporção de amostras usadas para treinar cada árvore, útil para prevenir overfitting\n",
    "        subsample=trial.suggest_float('subsample', 0.5, 0.9),       \n",
    "        # Proporção de colunas (features) usadas para construir cada árvore, ajuda a reduzir correlação entre árvores e evitar overfitting\n",
    "        colsample_bytree=trial.suggest_float('colsample_bytree', 0.7, 1.0),        \n",
    "        # Regularização L1 (Lasso), adiciona penalidade proporcional ao valor absoluto dos coeficientes, ajudando a reduzir o overfitting\n",
    "        reg_alpha=trial.suggest_float('reg_alpha', 0.1, 10, log=True),\n",
    "        # Regularização L2 (Ridge), adiciona penalidade proporcional ao quadrado dos coeficientes, ajudando a reduzir o overfitting\n",
    "        reg_lambda=trial.suggest_float('reg_lambda', 0.01, 10, log=True),\n",
    "        # Semente aleatória para garantir reprodutibilidade dos resultados\n",
    "        random_state=42,\n",
    "        # Silenciar a saída de logs durante o treinamento\n",
    "        verbosity=0,\n",
    "    )\n",
    "\n",
    "    # Cria um pipeline que combina o pré-processamento e o modelo\n",
    "    pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])\n",
    "    \n",
    "    # Treina o modelo usando o pipeline\n",
    "    pipeline.fit(X_train, y_train)\n",
    "   \n",
    "    # Faz previsões no conjunto de teste e calcula as métricas\n",
    "    test_predictions = pipeline.predict(X_test)\n",
    "    test_mae = mean_absolute_error(y_test, test_predictions)\n",
    "    \n",
    "    return test_mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "384a1998caab4fae893e6e4aedaee5dd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cria o estudo Optuna com o sampler TPE\n",
    "study = optuna.create_study(direction='minimize', sampler=TPESampler(seed=42))\n",
    "\n",
    "# Otimiza o modelo com a função objetivo\n",
    "study.optimize(objective_xgboost, n_trials=50, n_jobs=-1, show_progress_bar=True, timeout=1800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Melhores hiperparâmetros: {'n_estimators': 497, 'learning_rate': 0.02984997380796835, 'max_depth': 7, 'max_leaves': 90, 'subsample': 0.7371908729645792, 'colsample_bytree': 0.8685283603826094, 'reg_alpha': 0.2684936674225752, 'reg_lambda': 1.119848476606604}\n",
      "\n",
      "Melhor MAE: 25876.6605\n"
     ]
    }
   ],
   "source": [
    "# Acessa o melhor MAE e os melhores parâmetros após a otimização\n",
    "mae_xgboost = study.best_value\n",
    "params_xgboost = study.best_params\n",
    "\n",
    "print(f'Melhores hiperparâmetros: {study.best_params}')\n",
    "print(f'\\nMelhor MAE: {study.best_value:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.2. Salvando e testando o desempenho do modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arquivo pipeline_best_model_xgboost.pkl gerado com sucesso!\n"
     ]
    }
   ],
   "source": [
    "# Parâmetros otimizados para o XGBoost\n",
    "best_params = params_xgboost\n",
    "\n",
    "# Cria o modelo com os melhores hiperparâmetros\n",
    "best_model = XGBRegressor(**best_params, verbosity=0)\n",
    "\n",
    "# Treina o modelo e salva o pipeline treinado em um arquivo .pkl\n",
    "results_xgboost = train_and_save_model(best_model, 'pipeline_best_model_xgboost', 'XGBoost',\n",
    "                                       X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.3. Comparativo de resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definindo as colunas para o DataFrame\n",
    "columns = ['Modelo', 'RMSE em treino', 'RMSE em teste', 'MAE em treino', \n",
    "           'MAE em teste', 'Tempo decorrido (s)']\n",
    "\n",
    "# Criando DataFrame com os resultados do modelo XGBoost\n",
    "df_results_xgboost = pd.DataFrame(results_xgboost, columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Resultado do modelo SEM a tunagem de hiperparâmetros\n"
     ]
    },
    {
     "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>Modelo</th>\n",
       "      <th>RMSE em treino</th>\n",
       "      <th>RMSE em teste</th>\n",
       "      <th>MAE em treino</th>\n",
       "      <th>MAE em teste</th>\n",
       "      <th>Tempo decorrido (s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>21462.326807</td>\n",
       "      <td>28607.617056</td>\n",
       "      <td>15610.326833</td>\n",
       "      <td>21221.732968</td>\n",
       "      <td>9.811405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Modelo  RMSE em treino  RMSE em teste  MAE em treino  MAE em teste  \\\n",
       "5  XGBoost    21462.326807   28607.617056   15610.326833  21221.732968   \n",
       "\n",
       "   Tempo decorrido (s)  \n",
       "5             9.811405  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('\\nResultado do modelo SEM a tunagem de hiperparâmetros')\n",
    "results_df.iloc[[5], 0:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Resultado do modelo COM a tunagem de hiperparâmetros\n"
     ]
    },
    {
     "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>Modelo</th>\n",
       "      <th>RMSE em treino</th>\n",
       "      <th>RMSE em teste</th>\n",
       "      <th>MAE em treino</th>\n",
       "      <th>MAE em teste</th>\n",
       "      <th>Tempo decorrido (s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>30645.068833</td>\n",
       "      <td>36256.118494</td>\n",
       "      <td>21968.653544</td>\n",
       "      <td>26354.57338</td>\n",
       "      <td>34.039545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Modelo  RMSE em treino  RMSE em teste  MAE em treino  MAE em teste  \\\n",
       "0  XGBoost    30645.068833   36256.118494   21968.653544   26354.57338   \n",
       "\n",
       "   Tempo decorrido (s)  \n",
       "0            34.039545  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('\\nResultado do modelo COM a tunagem de hiperparâmetros')\n",
    "df_results_xgboost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Testando o algoritmo CatBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.1. Tunagem do modelo com Optuna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective_catboost(trial):\n",
    "    '''\n",
    "    Função objetivo para otimizar os hiperparâmetros do modelo CatBoost usando o Optuna.\n",
    "\n",
    "    :param trial: optuna.Trial\n",
    "        Objeto que sugere valores para os hiperparâmetros do modelo durante o processo de otimização.\n",
    "\n",
    "    :return: float\n",
    "        O valor do MAE calculado no conjunto de teste para o modelo treinado.\n",
    "    '''\n",
    "    model = cb.CatBoostRegressor(\n",
    "        # Número de árvores (boosting rounds) no modelo, cada árvore corrige os erros da anterior\n",
    "        n_estimators=trial.suggest_int('n_estimators', 300, 500),        \n",
    "        # Taxa de aprendizado para ajustar o impacto de cada árvore\n",
    "        learning_rate=trial.suggest_float('learning_rate', 0.01, 0.04, log=True),        \n",
    "        # Limita a profundidade máxima de cada árvore, controlando o crescimento da árvore e prevenindo overfitting\n",
    "        max_depth=trial.suggest_int('max_depth', 3, 7),        \n",
    "        # Regularização L2 (substitui reg_alpha e reg_lambda)\n",
    "        l2_leaf_reg=trial.suggest_float('l2_leaf_reg', 0.1, 10, log=True),      \n",
    "        # Proporção de amostras usadas para treinar cada árvore, útil para prevenir overfitting\n",
    "        subsample=trial.suggest_float('subsample', 0.5, 0.9),       \n",
    "        # Subamostragem de features\n",
    "        colsample_bylevel=trial.suggest_float('colsample_bylevel', 0.7, 1.0),  \n",
    "        # Semente aleatória para garantir reprodutibilidade dos resultados\n",
    "        random_state=42,\n",
    "        # Silenciar a saída de logs durante o treinamento\n",
    "        verbose=0,\n",
    "    ) \n",
    "\n",
    "    # Cria o pipeline com o pré-processamento e o modelo\n",
    "    pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])\n",
    "\n",
    "    # Treina o modelo\n",
    "    pipeline.fit(X_train, y_train)\n",
    "\n",
    "    # Previsões e cálculo do MAE no conjunto de teste\n",
    "    test_predictions = pipeline.predict(X_test)\n",
    "    test_mae = mean_absolute_error(y_test, test_predictions)\n",
    "    \n",
    "    return test_mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d747a834897e4f9fa52552cf4a943cc6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cria o estudo Optuna com o sampler TPE\n",
    "study = optuna.create_study(direction='minimize', sampler=TPESampler(seed=42))\n",
    "\n",
    "# Otimiza o modelo com a função objetivo\n",
    "study.optimize(objective_catboost, n_trials=50, n_jobs=-1, show_progress_bar=True, timeout=1800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Melhores hiperparâmetros: {'n_estimators': 482, 'learning_rate': 0.03986548425355291, 'max_depth': 7, 'l2_leaf_reg': 0.3210988448273863, 'subsample': 0.7966195933821545, 'colsample_bylevel': 0.9619976182479291}\n",
      "\n",
      "Melhor MAE: 34536.5655\n"
     ]
    }
   ],
   "source": [
    "# Acessa o melhor MAE e os melhores parâmetros após a otimização\n",
    "mae_catboost = study.best_value\n",
    "params_catboost = study.best_params\n",
    "\n",
    "print(f'Melhores hiperparâmetros: {study.best_params}')\n",
    "print(f'\\nMelhor MAE: {study.best_value:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.2. Salvando e testando o desempenho do modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arquivo pipeline_best_model_catboost.pkl gerado com sucesso!\n"
     ]
    }
   ],
   "source": [
    "# Parâmetros otimizados para o BaggingRegressor\n",
    "best_params = params_catboost\n",
    "\n",
    "# Cria o modelo com os melhores hiperparâmetros\n",
    "best_model = cb.CatBoostRegressor(**best_params, verbose=0)\n",
    "\n",
    "# Treina o modelo e salva o pipeline treinado em um arquivo .pkl\n",
    "results_catboost = train_and_save_model(best_model, 'pipeline_best_model_catboost', 'CatBoost',\n",
    "                                       X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.3. Comparativo de resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definindo as colunas para o DataFrame\n",
    "columns = ['Modelo', 'RMSE em treino', 'RMSE em teste', 'MAE em treino', \n",
    "           'MAE em teste', 'Tempo decorrido (s)']\n",
    "\n",
    "# Criando DataFrame com os resultados do modelo BaggingRegressor\n",
    "df_results_catboost = pd.DataFrame(results_catboost, columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Resultado do modelo SEM a tunagem de hiperparâmetros\n"
     ]
    },
    {
     "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>Modelo</th>\n",
       "      <th>RMSE em treino</th>\n",
       "      <th>RMSE em teste</th>\n",
       "      <th>MAE em treino</th>\n",
       "      <th>MAE em teste</th>\n",
       "      <th>Tempo decorrido (s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CatBoost</td>\n",
       "      <td>67985.801128</td>\n",
       "      <td>73214.229602</td>\n",
       "      <td>40463.759302</td>\n",
       "      <td>45533.101445</td>\n",
       "      <td>20.887834</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Modelo  RMSE em treino  RMSE em teste  MAE em treino  MAE em teste  \\\n",
       "3  CatBoost    67985.801128   73214.229602   40463.759302  45533.101445   \n",
       "\n",
       "   Tempo decorrido (s)  \n",
       "3            20.887834  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('\\nResultado do modelo SEM a tunagem de hiperparâmetros')\n",
    "results_df.iloc[[3], 0:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Resultado do modelo COM a tunagem de hiperparâmetros\n"
     ]
    },
    {
     "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>Modelo</th>\n",
       "      <th>RMSE em treino</th>\n",
       "      <th>RMSE em teste</th>\n",
       "      <th>MAE em treino</th>\n",
       "      <th>MAE em teste</th>\n",
       "      <th>Tempo decorrido (s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CatBoost</td>\n",
       "      <td>41463.821476</td>\n",
       "      <td>48335.6479</td>\n",
       "      <td>30317.066545</td>\n",
       "      <td>35073.572313</td>\n",
       "      <td>49.555953</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Modelo  RMSE em treino  RMSE em teste  MAE em treino  MAE em teste  \\\n",
       "0  CatBoost    41463.821476     48335.6479   30317.066545  35073.572313   \n",
       "\n",
       "   Tempo decorrido (s)  \n",
       "0            49.555953  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('\\nResultado do modelo COM a tunagem de hiperparâmetros')\n",
    "df_results_catboost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Teste de aplicação do Voting Ensemble"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A técnica de **voting ensemble** consiste em combinar previsões de diversos modelos para melhorar a precisão, robustez e generalização, nesse método entende-se que a combinação de vários modelos pode superar a performance de um único modelo ao reduzir os seus erros. Se esse método tem as vantagens de melhor generalização, melhor robustez como redução de erros e risco de overfitting há também as desvantagens maior custo computacional, dependência de vários modelos e dificuldade na interpretabilidade devido à complexidade da combinação de diferentes algoritmos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Carregando os modelos salvos\n",
    "pipeline_best_model_lightgbm = joblib.load('modelos/pipeline_best_model_lightgbm.pkl')\n",
    "pipeline_best_model_xgboost = joblib.load('modelos/pipeline_best_model_xgboost.pkl')\n",
    "pipeline_best_model_catboost = joblib.load('modelos/pipeline_best_model_catboost.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fazendo previsões com o conjunto de dados de teste\n",
    "predictions_lightgbm = pipeline_best_model_lightgbm.predict(X_test)\n",
    "predictions_xgboost = pipeline_best_model_xgboost.predict(X_test)\n",
    "predictions_catboost = pipeline_best_model_catboost.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Aplicando o Voting Ensemble (média das previsões)\n",
    "predictions_voting_ensemble = np.mean([\n",
    "    predictions_lightgbm,\n",
    "\tpredictions_xgboost,\n",
    "\tpredictions_catboost,\n",
    "], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_models(y_true, models, metrics=None):\n",
    "    '''\n",
    "    Avalia o desempenho de múltiplos modelos com base em métricas especificadas.\n",
    "\n",
    "    :param y_true: array-like\n",
    "        Valores reais do conjunto de teste.\n",
    "    :param models: dict\n",
    "        Dicionário contendo os nomes e previsões dos modelos.\n",
    "    :param metrics: dict\n",
    "        Dicionário com métricas a serem avaliadas. \n",
    "        Se None, usa métricas padrão de regressão.\n",
    "        \n",
    "    :return: DataFrame\n",
    "        DataFrame com os resultados de avaliação.\n",
    "    '''\n",
    "    if metrics is None:\n",
    "        metrics = {\n",
    "            'MSE': mean_squared_error,\n",
    "            'RMSE': lambda y, pred: np.sqrt(mean_squared_error(y, pred)),\n",
    "            'MAE': mean_absolute_error,\n",
    "            'R²': r2_score\n",
    "        }\n",
    "\n",
    "    results = []\n",
    "    for model_name, predictions in models.items():\n",
    "        row = {'Modelo': model_name}\n",
    "        for metric_name, metric_func in metrics.items():\n",
    "            row[metric_name] = metric_func(y_true, predictions)\n",
    "        results.append(row)\n",
    "    \n",
    "    return pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dicionários de modelos\n",
    "\n",
    "models = {\n",
    "\t'LightGBM' : predictions_lightgbm,\n",
    "\t'XGBoost' : predictions_xgboost,\n",
    "\t'CatBoost' : predictions_catboost,\n",
    "}\n",
    "\n",
    "model_ve = {\n",
    "    'Voting Ensemble' : predictions_voting_ensemble\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Avaliando os modelos base e o voting ensemble\n",
    "results_mdls = evaluate_models(y_test, models)\n",
    "results_ve = evaluate_models(y_test, model_ve)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Modelos Base:\n"
     ]
    },
    {
     "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>Modelo</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>R²</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>1.034610e+09</td>\n",
       "      <td>32165.355624</td>\n",
       "      <td>23595.982089</td>\n",
       "      <td>0.927434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>1.314506e+09</td>\n",
       "      <td>36256.118494</td>\n",
       "      <td>26354.573380</td>\n",
       "      <td>0.907803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CatBoost</td>\n",
       "      <td>2.336335e+09</td>\n",
       "      <td>48335.647900</td>\n",
       "      <td>35073.572313</td>\n",
       "      <td>0.836133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Modelo           MSE          RMSE           MAE        R²\n",
       "0  LightGBM  1.034610e+09  32165.355624  23595.982089  0.927434\n",
       "1   XGBoost  1.314506e+09  36256.118494  26354.573380  0.907803\n",
       "2  CatBoost  2.336335e+09  48335.647900  35073.572313  0.836133"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Voting Ensemble:\n"
     ]
    },
    {
     "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>Modelo</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>R²</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Voting Ensemble</td>\n",
       "      <td>1.453718e+09</td>\n",
       "      <td>38127.657768</td>\n",
       "      <td>27869.29287</td>\n",
       "      <td>0.898038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Modelo           MSE          RMSE          MAE        R²\n",
       "0  Voting Ensemble  1.453718e+09  38127.657768  27869.29287  0.898038"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exibindo os resultados\n",
    "\n",
    "print(\"\\nModelos Base:\")\n",
    "display(results_mdls)\n",
    "\n",
    "print(\"\\nVoting Ensemble:\")\n",
    "display(results_ve)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. Análise da distribuição dos erros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcular os erros (resíduos)\n",
    "errors_lightgbm = predictions_lightgbm - y_test\n",
    "errors_xgboost = predictions_xgboost - y_test \n",
    "errors_catboost = predictions_catboost - y_test\n",
    "errors_voting_ensemble = predictions_voting_ensemble - y_test\n",
    "\n",
    "# Criar um DataFrame para os erros\n",
    "errors_df = pd.DataFrame({\n",
    "\t'Erro' : np.concatenate([\n",
    "                errors_lightgbm, errors_xgboost, errors_catboost, errors_voting_ensemble\n",
    "                ]),\n",
    "\t'Modelo' : ['LightGBM'] * len(errors_lightgbm) + ['XGBoost'] * len(errors_xgboost) + \n",
    "                ['CatBoost'] * len(errors_catboost) + ['Voting Ensemble'] * len(errors_voting_ensemble) \n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FLBZj3LhxhSa6Dh48KMxIa926tcX3SS6XY/bs2UIy7ty5c0hOToajoyPq1auHu3fvIjo6Gj179sQLL7yA1q1bo3Xr1vjpp58KjbEweUkuAOjatatFXa+ePXti9uzZ0Ol0OHfuHPR6PeRyucX1Go0GL7zwwmPHeOaZZyySXADwzz//CLNxatWqhXHjxgnnxGIxpk+fjj179iAzMxORkZG4ceMGmjRpgiZNmuDEiRPIyclBjx49EBQUhDZt2qB169YWSyiLc+3aNWEpr4ODA0aMGCGcs7e3x2uvvYYvvviiwHX53zMjRoywmLnp5uaGiRMn4qOPPgIAHDp0CJMnT4a/vz+kUikMBgN+++03REVFoVOnTmjVqhWCgoIs3rOlIZFI0K9fP+F1/jpuTk5OQpILgMVSTp1OV677KeuzO3XqlPD10KFDhf9GisVi9O/fX3if//vvv8UmuvLH/f7771v8YaFNmzZ4+eWXhRlyhw4dwjPPPGOR+F2wYAGOHTuGDh06oFWrVpg6dWqRP9tERFQ9MdFFRERUyfKSNAAKLeT8qJEjR+LUqVP4559/kJ6ejr179wqJEVdXV/Ts2RNjxowpUc2lR6lUKjg5OZX6OiD3F2Y7OzuLY/mXLyYkJJSon7xkTknFxsYKX+evJ5Qnb7bWo+7evYtt27bh1KlTuHPnToFx89e0yr9EKW/5VH75fzHWarVISUkp8jmmpKRYJDTr1Kljcb6wYvKPxlDYDKzatWvDxsYG6enpMJvNePjwIRwdHfHFF1/gzTffREpKCsLCwhAWFoa1a9dCJBKhWbNmGDRoEAYNGlSgGPej8teE++WXX/DLL78U2i4nJwcPHz4sUOjd09Oz2ARBYd+/Bw8eCF/7+/sXmH1lY2OD2rVr4+bNm0L7Jk2a4KOPPsIbb7whzDLavHmzMCOuQYMG6NevH4YPHw6VSvXYmBITE4WvfXx8Coxfku9XYe+Z/Mfy2rq7u2Pu3LlYsGABDAYDzp8/j/PnzwPITWZ27NgRw4cPR+fOnR8b86McHBygUCiE1/mTkI8+8/yF7kvzM1DY/ZT12UVHRwtfDxs2rNA2AArUCntUUlKSRV2uwn5umjZtKiS68uLu2bMnjh8/jt27dyM7OxuHDx/G4cOHAeT+N7pbt24YO3ZsqTYzICKiqsM/TxAREVWysLAw4etHkx6FUSgU+Omnn7BmzRr0798frq6uwrn4+Hhs2LABffv2tUgQlFT+2Q6llTdzI7/8S3uKSqQ8mmAqrJ+SerTgelH++OMP9OvXD+vXr0dYWBg6duyId999F+vXry+0fXFJoJKOC6BAzbNHlz8Vdf/FxfBoHHnjtGzZEocOHcJHH32E9u3bC0kOs9mMK1eu4JNPPsE777xT7D3k/z6p1Wq4uLgU+S8zM7PA9SV5bxXWpqz37evri/3792PhwoV44YUXLArf3759G0uXLsWwYcOKrfOU//tV2FK1omZhluY9k3+MIUOG4MCBA3j77bfRpEkT4Zxer8exY8cwbty4Us3EA1Bgl8b845VkB0egbPdT1meXv62jo2OR77PiYipJwf6i4l60aBE2b96MIUOGWCQDU1NT8csvv6Bfv34FNqYgIqLqiTO6iIiIKpHZbLYo0l6SHfuA3F/EmjZtKhSZv3//Pi5cuIB169bh1q1b0Gq1+OmnnzB//vxSxVOandwelZaWhqioKKHYN2A5G8fNzU34Ov/MnvxLBIHc3Q1LI/8SuvDwcJjNZotfsI8ePYrw8HDUr18fjRs3hoODAz777DMhobRx40YEBgYCQKEJGgAW93T9+vUC5/MvRbS3txeKcxfG0dERcrlcKJQeFhZmsXtcUbNU8sfw6G5wQO69Z2RkAMh9vj4+PsI5tVqNl19+GSNGjIBer8eVK1dw6tQprFq1Cjk5OTh69ChCQkIQEBBQZNz5v399+vQp8N563HJNoGTvrcLa5E8y3Lx5s8A46enpFjWV8ieL5XI5unTpgldeeQUGgwGhoaE4e/Ysvv/+e2i1Wly7dg2HDx+22KXyUfnfXxEREcjJybGIs6gNJLy8vITv0/Xr1/Hiiy9anM//Psq/CQKQOzNz7NixmDp1KrRaLS5duoQDBw7g119/BQCsWLECb7zxhlWW0JV0s4my3E9Zn527u7tQr+z7778Xfj4BFFtTLT9bW1vY2toKs7quX79uUVC+qLjz1KtXDx999BFkMhmioqJw8eJFbNmyBRcuXEBOTg5WrlxZ6qQjERFVPs7oIiIiqkRr1qxBZGQkgNxfyl977bXHtjcajXjjjTfQrl07PPvsszh27BiA3F/uX3nlFYtf2GNiYoSv8/9C/LgZUyX9pbco3377rfC1Xq/H2rVrhdetW7cWvs6/RPP27dsWfeTfOa8kmjVrBo1GAyB3qdK+ffuEcwaDAV999RUWLlyIMWPG4NSpU0hMTBQKtAO5y7ry7Nmzx6LvvFlMQUFBwrF9+/bhwoULwuv09HQsX75ceN2tW7fHPkepVGqRUFq7dq0wq8RgMOC7774r9Lr8MZw/f16o0wbkPuv8BfDbtWsHBwcHnDt3Dr1790ZAQABeffVVpKenQy6Xo3Xr1pg0aZJFUfP8y8UK88wzzwhfHzx40OL99ddffyEwMBADBgzAJ598UujssJK8twpr06FDB2F5YWRkpEViwWw2Y9myZUKCsnbt2vD398e9e/fQv39/BAYGonv37oiNjYVUKkWzZs0wZswYi8RicffdtGlTYXytVovt27cL55KSkiw2CMgv//dr48aNFjM34+PjsWrVKuF1XtJo3bp1CAoKQmBgIGbPng0AsLOzw/PPP4/33ntPaJ+ZmYmUlJTHxm1tZbmfsj67du3aCV9v2LDBYjbhu+++i44dO2LkyJEWPwMlifurr76yWCp+4cIFi/9e5NUqmz59Ojp27Ij27dsLMXt5eaF3794YOnSo0D7/zwAREVVfnNFFRERUAVJTU7F06VIAub+cZ2Rk4PLly8IuZkBuUfL8s2YKI5FI4OvrK8wCmzx5MoKCguDh4YGoqCihSDNgmVjKvyTsypUr+Pjjj6FSqTBr1iyr3F+eX375Bffu3UOzZs1w5swZ3Lp1C0BuAiP/L4j+/v7C13///Tc++eQTNGnSBH/99ZewO2RJKRQKjBgxQvhFe8aMGThx4gRq166NkydPCrWbXFxc8OKLL8JsNkOhUAhL1kaPHo1u3brh/v37OHnypEXfWVlZUKvVeOaZZ9CmTRucP38eBoMBo0aNQlBQEBwcHPD3338L9avs7OwwadKkYmMeOXKkUKR///79iIyMRIsWLXDu3DmEhoYWek3t2rXRp08f/P777wByd4PbvXs3vLy8cObMGWH3OplMhvfffx9AbhIwPj4eOTk5iIiIQN++fdG5c2fIZDJcvHhRSFZIpVK0bNnysTG3adMGzZo1w9WrV5GSkoI+ffoISb0DBw4gKysL169fR8eOHcudMM1Po9Fg1KhRwvd36dKlOHHiBBo0aIDg4GCLn6GZM2dCJBLBz88POTk5wmzB/v37IygoCGq1WpjVlSf/z0lhVCoVBg0ahI0bNwLILU5+6tQpODs748iRI4iLiyv0ul69euGHH37A/fv3kZaWhoEDByIoKAhyuRzHjh1DUlISgNwZa6NGjQIABAYGCsXZDx48iFdffRUBAQHIzs62+Lnw8/Mrcy29sirL/ZT12Y0aNQp79uyB0WjE/v37ERYWhjZt2uDOnTtCofrk5GTMnTu32LjHjh2LP/74A9nZ2QgJCUGfPn3QuXNnpKSk4MiRI8IyyW7dugmbTDRs2FCo2/X555/j77//hq+vLxISEnDkyBGh7+LeO0REVD0w0UVERFQB0tPTH7vT29ChQ/HWW2+VqK8ZM2bg9u3bOH/+PPR6Pf74448CbVq3bi3sAgjk1mmSyWTIycmBwWDAtm3b4OXlZdVEV6dOnRAdHY1Lly7h0qVLFufeeecdtGjRQnjdvXt3rFixQlietHXrVuHc66+/ji1btpRq7HfeeQc3btzA8ePHYTQaC8zMUqlU+O6774T6VMOHDxdmBsXExGDTpk0AchNyGo1GmPXx4MEDNG7cGGKxGF999RXeeustYSe5R2ee2dvbY+XKlYUWVH/Uiy++iCFDhgj3HRISgpCQEABA27ZtkZ6eXujyxHnz5iE5ORknT56E2WwukBRUKBRYtGiR8KxVKhW+//57jB07FjqdDpGRkRbPOu+eP/zwQ4ulkYURiUT46quvMGzYMMTFxUGr1WLXrl0Wbdq3b4/JkycXe/+lNWXKFERHRwvf17Nnz1okq8RiMT788EN069ZNiPXbb7/FiBEjEB8fj6SkJOzcubNAv2PGjLFYFleUadOm4dKlS7h69SrMZjMOHToknBs5ciQ2bNhQ4Bq1Wo2VK1di4sSJCA8Ph06nK7CbppeXF1avXi0kolu2bIk5c+bg888/B5C7U+mju5Wq1epCdyqsaGW5H6Bsz65Ro0b46KOPMH/+fJjNZty4ccPi50EkEuHjjz8ucqOJ/Pz9/bFkyRLMmjULGRkZiIqKwrZt2yzatGvXDosWLRJejxkzBteuXcOBAwdgNBot/oCQp27dunj33XeLHZ+IiKoeE11ERESVQKVSwcXFBYGBgXjttdeEmQQloVar8d///he///47fv31V4SHhyMxMREqlQr169dHr169MHToUItaOK6urli+fDm++eYb3L9/HzY2NoXunFYednZ2+Prrr/HNN9/gwIED0Gq1aNCgAcaOHYtevXoVuIctW7Zg6dKl+Pvvv5GVlYWGDRti5MiRePHFF0ud6JLJZPj++++xa9cu7Nq1SygS7erqik6dOmH8+PEWO6R98MEH8PHxwc8//4wHDx5AJpOhSZMmGDduHC5evIjvv/8eQO6smsaNGwPIrVG1bds2/PLLL9i3bx9u3boFnU4HT09PdOnSBWPHjrWoSVScTz/9FE2bNsXGjRtx//59uLq6ol+/fpg4cSKGDx9e6DUajQY//vgj9u3bh927d+PGjRtITU2Fq6srOnbsiHHjxlnsdAkArVq1wv79+7FhwwacPHkSMTExyMzMhJOTEwICAjBy5MgSv/9q166N33//HT/++COOHj2KyMhISKVS1K5dGwMHDsTgwYMtdvSzFolEgsWLF+Pll1/Gzp07ERISgqSkJDg4OKBdu3YYM2YMmjVrZnGNn58ffv/9d2zevBlHjhxBVFQU0tLS4ODggGbNmmHw4MHo2rVricZXq9XYsGEDVq1ahb179yIhIQH16tXD6NGj0bNnz0KTNQBQv3597NmzB1u3bsWBAwdw//596PV6+Pj44MUXX8Qbb7xRYKfSkSNHIiAgAOvXr8fVq1cRGxsLk8kET09PdOjQAWPHjrWov1aZynI/ZX12r7/+Oho3box169bhwoULSElJgbOzM5o0aYKxY8eW6r+ZL730Epo2bYrNmzfj+PHjiIqKglgshr+/PwYMGICBAwdaFLaXSCRYvnw5evXqhW3btiEsLAxxcXGQy+WoU6cOunbtitGjRwtLpomIqHoTmUuzbRARERFRNTNv3jwhUWZjY4Np06Zh2LBhVRwVEREREVUFJrqIiIjoiRYeHg57e3tcvnwZ48aNg1gsxrlz5yyWUxERERHR04FLF4mIiOiJdfnyZUilUuh0OqGmj4eHB9RqdRVHRkRERERVgTO6iIiI6Ik1aNAghIaGQiQSwc3NDR07dsSECRPg7e1d1aERERERURVgoouIiIiIiIiIiGoEcVUHQEREREREREREZA1MdBERERERERERUY3ARBcREREREREREdUITHQREREREREREVGNwEQXERERERERERHVCEx0ERERERERERFRjcBEFxERERERERER1QhMdBERERERERERUY3ARBcREREREREREdUITHQREREREREREVGNwEQXERERERERERHVCEx0ERERERERERFRjcBEFxERERERERER1QhMdBERERERERERUY3ARBcREREREREREdUITHQREREREREREVGNwEQXERERERERERHVCEx0ERERERERERFRjcBEFxERERERERER1QhMdBERERERERERUY3ARBcREREREREREdUITHQREREREREREVGNwEQXERERERERERHVCEx0ERERERERERFRjVBjEl3Z2dmYPXs22rRpg06dOmHt2rVFtr1+/TpeffVVtGzZEq+88gquXr1qcX7v3r3o1q0bWrZsiUmTJiEpKUk4ZzabsXTpUrRv3x7t2rXD4sWLYTKZhPPJycmYPHkyAgMDERQUhD179hQaw/nz59G1a9cCx//44w+89NJLCAgIwJgxYxAZGVnaR0FERERERERE9FSSVnUA1rJ48WJcvXoV69evR1RUFGbOnAkvLy/06NHDop1Op8Obb76JPn364Msvv8TPP/+MCRMm4NChQ1Cr1bh8+TLmzJmDefPmoVGjRvj8888xa9YsrF69GgDw3//+F3v37sW3334Lg8GA6dOnw9nZGWPHjgUAzJo1C1lZWdi2bRtCQkLw0Ucfwc/PDy1atBBiuHnzJqZOnQqFQmER28WLF/H+++9j7ty5QhJt2rRp2LZtW4megclkgsFggFgshkgkKs/jJCIiIiIiIiKqNsxmM0wmE6RSKcTioudticxms7kS46oQOp0O7du3xw8//IBnnnkGAPDdd9/h1KlT2Lhxo0XbnTt34vvvv8fhw4chEolgNpvx0ksv4a233sLAgQMxY8YMiMVifPnllwCA6OhovPDCCzh06BB8fHzQpUsXTJkyBQMHDgQA7NmzB//5z39w5MgRhIeH48UXX8Rff/0Fb29vAMCcOXNgNBqF/rZu3YpFixbBx8cH6enpOHLkiBDbO++8A1tbW3zxxRcAgIcPH2LUqFHYuXMnnJycin0Oer0eV65cKefTJCIiIiIiIiKqnpo3bw65XF7k+Roxoys0NBQGgwGBgYHCsdatW2PVqlUwmUwWmb6QkBC0bt1amPEkEonQqlUrBAcHY+DAgQgJCcH48eOF9p6envDy8kJISAjkcjmio6PRtm1bi3EiIyMRFxeHkJAQeHp6CkmuvPN5s8EA4MSJE1i0aBHS09Px7bffWtzH2bNnhYQYAPj4+FgkwoqTd5/NmzeHRCIp8XVEREREpRUcHIznn38ex48fR0BAQFWHQ0RERDWc0WjElStXHjubC6ghia74+Hg4OjpaZPRcXFyQnZ2NlJQUi9lQ8fHxqF+/vsX1zs7OuH37NgAgLi4Obm5uBc7HxMQgPj4eACzOu7i4AIBwvrBrY2NjhdffffcdAGDXrl0W7bRaLVJTU2E0GjF27FiEhoaiRYsW+PTTT+Hu7l6i55CXvJNIJEx0ERERUYVq2rQpgoOD4e3tzc8dREREVGmKK9VUI4rRZ2ZmFpi2lvdar9eXqG1eu6ysrCLPZ2VlWfT96DjF9f04Op0OAPDZZ5+hT58++P7776HX6zFhwgSLYvdERERE1YFSqUT9+vWhVCqrOhQiIiIiQY1IdCkUigLJpLzXj374KqptXruizqtUqkKTZ3lfq1SqYvt+nLy/hL766qvo378/WrRogaVLl+LWrVsIDg4u9noiIiKiyhQWFobhw4cjLCysqkMhIiIiEtSIRJe7uzuSk5NhMBiEY/Hx8VAqlbCzsyvQNiEhweJYQkKCsOSwqPOurq7CEsK8JYz5v847X9S1xXF0dIRMJkPdunUtjjk4OCAmJqbY64mIiIgqU3JyMjZv3ozk5OSqDoWIiIhIUCNqdDVu3BhSqRTBwcFo06YNAODChQto3rx5gSJlLVu2xA8//ACz2Szsunjx4kW89dZbwvkLFy4IuypGR0cjOjoaLVu2hLu7O7y8vHDhwgWh4PyFCxfg5eUFNzc3BAQEIDIyEjExMfDw8BDOl6RAq1QqRdOmTREaGopevXoBAJKSkpCcnIxatWpZ5TkRERERERERlYfRaEROTk5Vh0E1kEwms0rdzxqR6FKpVOjfvz8+/fRTLFy4EHFxcVi7di2++OILALmzrmxtbaFUKtGjRw8sW7YMn3/+OYYMGYKtW7ciMzMTPXv2BAAMHToUI0aMQEBAAJo3b47PP/8cXbp0gY+Pj3B+6dKlQiJr2bJlGDNmDIDcXRI7deqE6dOnY86cObhy5Qr27t2LTZs2leg+3njjDcyaNQuNGzdGw4YNsWTJEjRu3BgtWrSw9iMjIiIiIiIiKjGz2YyYmBikpKRUdShUgzk4OMDDw6PYgvOPUyMSXQAwa9YsfPrppxg1ahRsbGwwefJkdO/eHQDQqVMnfPHFFxg4cCBsbGywevVqfPLJJ9i+fTv8/f2xZs0aqNVqAEBgYCDmz5+Pb775BqmpqXj22WexYMECYZyxY8ciMTER77zzDiQSCQYNGoTRo0cL5xcvXow5c+Zg8ODBcHV1xcKFC0ucqOrRowe0Wi2WLFmCxMREtGvXDt999125vsFERERERERE5ZWX5HJzc4NarebvqWRVZrMZOp0OcXFxAABPT88y9yUym81mawVGVctoNCI4OBgBAQHc5puIiIgqVHR0NFavXo0JEyaU68MoERFVf0ajEbdu3YKbmxucnZ2rOhyqwRITExEXF4eGDRsWyGuUNOdRY2Z0EREREVHl8fT0xKefflrVYRARUSXIq8mVtxKKqKLkvcdycnLKPIGnRuy6SERERESVS6vV4uDBg9BqtVUdChERVRIuV6SKZo33GBNdRERERFRqd+7cQY8ePXDnzp2qDoWIiIhIwKWLRERERERERFQmURlxSM5OrbTxHBX28NK4lbi9v78/NmzYgGeeeabAuRUrVuDs2bPYuHFjsf18+OGHAIAvv/yy0POJiYk4e/YsevbsKRwzGAxYv3499uzZg/DwcEilUgQEBGDixIlo3bo1ACAiIgJdu3YVrhGLxXB2dka/fv3w3nvvQSrNTdsEBQUhMjISmzZtQtu2bS3GPnHiBMaPH48BAwYUGd/ThIkuIiIiIiIiIiq1qIw4vLx/LLKM2ZU2plKiwL5eP5Uq2VWUMWPGYMSIEVaICli6dCnMZrOQ6DKZTJgwYQJu3LiBmTNnolWrVtDpdNizZw9Gjx6NDRs2IDAwULh+x44d8PT0hNFoRFhYGD788EPY29vjzTffFNrIZDIcOXKkQKLr8OHDXFaaDxNdRERERERERFRqydmpyDJm4xW/l+CidKrw8RKykvBL2EEkZ6daJdGl0WisEFUus9ls8frnn3/GhQsX8Pvvv8PHx0c4PmPGDKSmpmL16tVYtWqVcNzJyQmurq4AAA8PDwwbNgwHDhywSHS1adMGR44cwcyZMy3GPXLkCAICAqx2L086JrqIiIiIqNQUCgXq1asHhUJR1aEQEVEVc1E6WSXxVNkeXbp48uRJLFq0CA8ePEC7du3g6+uLjIwMYTlgeno63nvvPRw5cgSOjo54//330adPH6xYsQK//vorAODs2bM4cuQIfvnlFwwcONAiyZXn/fffh1wuf2xsKpWqwLEuXbpgyZIluHv3LurVqwcACA4Ohr29PerUqVOeR1GjsBg9EREREZVa06ZNcefOHTRt2rSqQyEiIiq3hw8fYuLEiejZsyd2796N5s2bY/PmzRZtDh06hKZNm2Lv3r3o2bMnZs+ejbS0NIwZMwY9e/ZEz549sXPnTuj1ely/fh1t2rQpdCwnJyfY2NgUGUt0dDR27NiBvn37Why3s7ND69atceTIEYuYunXrVo47r3mY6CIiIiIiIiKip9qOHTvQokULvP3226hbty6mTp2Kli1bWrQJDAzEuHHj4OPjg7fffht6vR737t2DRqOBUqmEUqmEk5MTUlJSYDabYW9vL1wbFhaGwMBAi3/59e7dG4GBgWjZsiW6dOmCrKws9OvXr0CcXbt2tUh0/fXXX0x0PYKJLiIiIiIqtcuXL8PV1RWXL1+u6lCIiIjK7ebNm2jevLnFsUfrXuVfhmhrawsAyM4uWIg/L8Gl1WqFY97e3ti9ezd2796NefPmQafTWVyzZs0a4fymTZvg7e2N119/HXq93qJd165dERISgqSkJNy5cwfZ2dkF4n7asUYXEREREZWawWBAQkICDAZDVYdCRERUbhKJpEBB+UdfSySSAtc92gbIrWPp7++PS5cuCbswymQy+Pr6AgBiYmIKXOPl5QVvb28AgJ+fH3x9fdG5c2f8888/eOGFF4R23t7eqF+/Po4dO4a4uDjO5ioEZ3QRERERERER0VOtQYMGuHbtmsWxR18/jkgksnj92muvYdeuXYiOji7QNjY2ttj+8hJoRqOxwLmuXbvi2LFjXLZYBM7oIiIiIiIiIqIa6/LlywWWGLZt29bi9eDBg/HTTz9hzZo1ePHFF3Hw4EGcP38etWvXLtEYKpUKt2/fRmxsLNzd3TF06FD8+++/GDJkCN599120atUKmZmZ+P3337F+/Xq0bt3a4vqkpCRhJ+OUlBR8/fXXcHR0RPv27QuM1bVrV6xbtw4KhaLAfRATXURERERERERUDglZSdV6nKVLlxY49ueff1q8rlWrFr755hssWrQI33zzDZ599ll07doVMpmsRGP069cPkyZNQt++fXH69GmIxWJ8++232L59O7Zs2YL58+dDJBKhcePGWLBgQYEdFV999VXhaxsbG7Ru3Rpr164tdHfGZs2awc7ODh06dCh0OeXTTmQubEEpPZGMRiOCg4MREBDANzsRERFVqPT0dFy5cgXNmzd/7BbpRET05MvKykJYWBj8/PygVCqF41EZcXh5/1hkGQsWZK8oSokC+3r9BC+Nm1X7vXXrFgwGA5o0aSIce/PNN9G8eXNMnjzZqmNR0Yp6rwElz3lwRhcRERHRU0JvzEF8VhJidQmIyYzP/V9dPGIzExCXmYROHq0xptGrUEoVxfZlY2ODDh06VELURERUXXlp3LCv109Izk6ttDEdFfZWT3IBQHh4OObMmYPly5ejTp06+Pfff3Hq1ClMmzbN6mNRxWKii4iIiKiGi89MwpST8xCSGAoz/jeZXyGWw15hC1uZBgqJHN9d24wd9w5gZsAEvOTTuUBh3fwiIiKwfPlyTJs2TdglioiInj5eGrcKSTxVtm7duuH27duYM2cOEhMT4efnh6+++gqNGjWq6tColJjoIiIiIqrBHqZHY8zRmdDmpKO37wtwkNvBTm4DO5kNFBK5RTIrMSsZf0acxHv/foZWLk0xp/UkNHGsX2i/cXFx+OqrrzB8+HAmuoiIqEaYOHEiJk6cWNVhUDkx0UVERERUQ91MuYexRz+ESCTCGP9X4aiwe2x7Z6UjhtbvgzupD3Aw4m8MOjgJg+r2wNQWo+GsdKykqImIiIjKjokuIiIiohrofNwVTDwxF3ZyWwxr0Bc2MnWJr61v7ws/W2+ci7+CvQ+OYn/4MUxqNgLDG/aHTMyPj0RERFR9ias6ACIiIiKyrqORpzD22IdwUztjlP+AUiW58kjEErR3D8DkZiPRxLEBlgT/gHf/WYAck6ECIiYiIiKyDia6iIiIiGqQ3WF/4p2T81Df3hev1+8LpaT4HRQfRyNTobfvC3i9fh8cjzqL9/9diByTAS4uLnj77bfh4uJipciJiIiIyo+JLiIiIqIa4r+hOzHrzFIEujTBq3V7WnWZYUMHP7xWrxeORJ7CjFNfwsu7FlauXInatWtbbQwiIiKi8mKii4iIiKgG+O7qJiwOXoPOHm3Rp3YQxCLrf8zzd6iLV+v2wKGIk5h2bAHOnT8HnU5n9XGIiIiIyorVRImIiIiecHdTw7Hy2iY859kOXWt1qNCxGjvWxyt+PbDxr+1YMXsezp47i7Zt2lbomEREVH2lpqZW6h891Go17O3tS9Q2NDQUr7zyCj7++GO89tprwvGsrCwMGDAAzz33HGbNmgUA2LFjB7Zv3467d+/CbDajSZMmGDt2LIKCgoTr/P39Lfp3dHREt27dMGvWLGg0GivcXdHMZjO2bNmCYcOGVeg4NQETXURERERPuKUhP8BBbofnPSsn4dTUqQG61uqAH3EK313bhJ9at66QGWRERFS9paamYuXKlcjJyam0MWUyGSZNmlSiZFejRo0wbtw4LFmyBF26dIG7uzsAYOnSpTCZTHjvvfcAAHPmzMH+/fvxwQcfoFOnTjAajTh8+DCmTp2KJUuWoEePHkKfK1asQGBgIEwmE6Kjo/Hxxx9j8eLFmDdvXsXc8P87d+4c5s+fz0RXCTDRRURERPQEOxMbjGNRZ/Bq3Z6QWrEmV3Hq2efW5joWdRafnPsa89q+y2QXEdFTRqfTIScnB+3bt4ednV2Fj6fVanH69GnodLoSz+qaNGkSDh48iPnz52PlypU4deoUfv75Z2zatAlKpRLHjx/HL7/8gp9//hmBgYHCdW+++SYMBgNWrlxpkeiyt7eHq6srAMDd3R0TJkzAvHnzKjzRZTabK7T/moSJLiIiIqInlMlswqJLq+Gj8URTxwZVEsMLXs/gl3sHIRFJ8EmbKRCJRFUSBxERVR07Ozs4OTlVdRiFksvl+OyzzzB8+HDs378fX331FUaNGiUktXbu3Innn3/eIsmVZ+TIkRgyZMhj+1epVBavs7Oz8c0332Dv3r1ITU1F+/bt8cknn8DT0xMAEBMTgy+++AKnTp2CSCRCnz59MGPGDMjlcuTk5GDevHk4dOgQ9Ho9nnnmGcybNw85OTkYOXIkgNzlkxs2bMAzzzxjjcdTI/HPbkRERERPqN/vH8GNlLvo7t2p0hNMYpEYKo0KjRzroV+drth+dz+WX15bqTEQERGVRJs2bTBkyBBMnz4dMpkM7777rnAuODgYrVu3LvQ6GxubxybwkpKSsHHjRvTt21c49sknn+DQoUNYtGgRtm7dCoPBgLfffhsmkwl6vR6jRo1CZmYmNm7ciK+//hrHjh3D4sWLAQCbN2/GuXPnsHbtWuzcuRMZGRlYuHAhPD09sWLFCgDAyZMnC03K0f9wRhcRERHREyjLkI3ll39CE8f6qG3rVenj12lSF+uCd+R+DSDLqMePN7bBS+2GoQ36VHo8REREj/P888/j559/RvPmzSGXy4XjycnJcHBwEF7nzaTKb9++ffDyyv3/2vHjx0MikcBsNiMzMxMODg749NNPAeTWLNuzZw9++OEHtG/fHkBuPbAuXbrgn3/+gV6vR2xsLLZv3y4svfz4448xceJEvPfee4iIiIBCoUCtWrXg4OCAL7/8EikpKZBIJEL7vGWTVDQmuoiIiIieQBtu7UJiVgqG1Otd1aEAADq4ByIlW4vPLnwLd7ULgip490ciIqKSysjIwIIFC9CuXTvs3r0bAwYMEBJR9vb20Gq1QluZTIbdu3cDAGJjYzFixAiYTCbh/GeffYaWLVvCbDYjOTkZmzZtwtChQ/H7778jIiICJpMJLVu2FNo7ODjAz88Pd+/ehV6vR506dSzqi7Vq1QoGgwHh4eF47bXXsG/fPnTq1Ant2rVDt27dMHDgwAp+OjUPly4SERERPWESs5Kx+vpWtHVtDmelQ5XEEHE7HO/3fBsRt8OFYy/5dEYjx3qY9u/nuJwYWiVxERERPWrRokUAgFWrVqFbt26YO3cuMjMzAQAtWrTApUuXhLYikQi+vr7w9fUVZnHl5+7uDl9fX9SpUweBgYH44osvkJmZiQMHDkChUBQ6vtFohMlkKvS80WgU/rdBgwY4cuQIlixZAldXVyxfvhxjxoxhIfpSYqKLiIiI6Anz7dWNMJvNeN6rXZXFoNfrEXEnHHq9XjgmFokx0O8luKtc8NbxjxCeFlVl8REREQHAv//+i+3bt2P+/PnQaDT4+OOPkZSUhP/85z8AgCFDhuDYsWO4du1agWtjY2OL7V8sFsNsNsNoNMLHxwdSqRTBwcHC+eTkZDx48AB+fn7w8/PD/fv3kZKSIpwPDg6GVCpF7dq1sXv3bhw9ehQ9e/bEokWL8OOPP+LChQtITEzkZi+lwEQXERER0RPkbmo4tt/dj86ebaGWqoq/oJLJxFIMqdcbErEE44/PRnJ2alWHRERET6n09HTMmTMHAwYMQKdOnQDkzsh6//33sWHDBly+fBnPP/88hg4dijfeeAMbN27EvXv3cPfuXaxevRrjx49H/fr1LWp4paamIj4+HvHx8bh//z7mz58Po9GIoKAgaDQavPrqq1iwYAHOnDmD0NBQTJ8+HR4eHnj22Wfx7LPPwsfHBzNmzMDNmzdx+vRpLFiwAL1794adnR3S0tLw+eef49SpU3j48CF+//13eHh4wNHRUdjd8erVq8jOzq6Kx/nEYI0uIiIioifI0pAfYC+3xTNuLao6lCJpZCoMq98XP93cgYkn5mLdC0uglBa+nIOIiJ58+WtcVadxFi1ahJycHMyaNcvieF5NrTlz5mDXrl346KOP0Lp1a2zZsgXffPMNcnJyUL9+fbz77rt47bXXLJYcTp48WfhapVKhWbNm+OGHH+Dj4wMAmDlzJhYtWoQpU6ZAr9ejY8eOWLdunVAA/7vvvsOCBQswePBgaDQa9OnTB9OmTQMADBs2DDExMZg+fTpSU1PRrFkzfP/995BIJPD398ezzz6LIUOGYPny5ejevXuZnuHTQGTmYs8aw2g0Ijg4GAEBAZBIJFUdDhEREVnZmdhgjD46A6/W7YlmTg2rNJZ71+5gVv938cXur1G3af1C20Skx2DdrV143qsdvu74ESRifj4hInoSZWVlISwsDH5+flAqlcLx1NRUrFy5Ejk5OZUWi0wmw6RJkywKulPNUdR7DSh5zoMzuoiIiIieACazCYsurYaPxhNNHRtUdThw9/HA9FVz4e7jUWQbbxsPDKrbA1vv7MXi4DWY1WpiJUZIREQVzd7eHpMmTYJOp6u0MdVqNZNc9FhMdBERERE9AX6/fwQ3Uu5irP+r1aIgrcbOBm26PlNsu0YOddGr9vPYcOtXuKicML7xa5UQHRERVRZ7e3smnqhaYTF6IiIiomrObDbjxxvb0MihLmrbFtzqvCqkxCfj11XbkRKfXGzbdm4t8bxnOywP+Qnb7uythOiIiIjoacVEFxEREVE1F5pyD3e0D9DapVlVhyJIikvE1mUbkBSXWKL2L3i1xzNuAZh3fgX2PThawdERERHR04pLF4mIiIiqud/uH4aNTI16drUrfWyz2YycDD2yUzKRnayDVCOHvZ9zqfsRiUTo4fMcso3ZmHF6EdRSJV6o1aECIiYiIqKnGRNdRERERNWY0WTE7/f/QlPHhhW+a6HZbEZmfDqykjNzE1spOmSnZMJkMAEARFIxzAYTMqK1MNuUfuNusUiEvnW6QW/KwdR/PsOPzy9EO/eW1r4NIiIieoox0UVERERUjZ2OvYTE7BS84uxf4WPFnAtHyu14QATINHLI1ArY+jhCZiOHTKOAWC5BZlw6km/FISGtZEsWHyURifGK30vYcmcv3joxF+uDlqB5JdwbERERPR1Yo4uIiIioGttz/y+4Kp3gpXav0HFS7yci5XY8HOq7oFanenBvXRtOjd1hW9sRSicNJAopRCIR1O62cA2oBSmkaNq8KcT60o8lFUsxpN7LcFU5YdyxWbiVEmb9GyIiIqKnEhNdRERERNVURk4mDkWcRHMnf4hEogobJ1ubhegzD6Bys4HGyx4i8ePHktsq0ahbCwwfMxKZN1KRfDu+1GPKJTK8Xr8vNDIVxh77EOFpUWUNn4iIiEjApYtERERE1dRfkf8gy5iNFs6NKmwMk8GEyL/vQiKXwLGhW4kTamYRIPZUQqEQI+bsA2Ql6+DR2gciScn/jqqSKjC8QT/89+YvGHN0JjZ1Ww4PtWtZb4WIiKpAamoqdDpdpY2nVqthb29f6utSU1Px/fff488//0RiYiK8vLzw2muvYeTIkRCLi///rlOnTsHNzQ316tXDmTNnMHLkSOGcRCKBh4cHXn/9dYwbN67UsZVWeno6Dh8+jP79+1f4WE8iJrqIiIiIqqk9YYdRx7YWHBV2FTZG7IVw6LVZcA30hrgUSarIsIdYMGEu5q5eAJeGrki+HY/slEx4d64HqUpW4n5sZBqMaNAf/735C0YdmY6NXZfBTVX6XR2JiKjypaamYuXKlcjJyam0MWUyGSZNmlSqZFdycjJee+01uLm54fPPP4e3tzeuXLmCBQsW4OHDh5g7d26xfYwePRobNmxAvXr1hGMnT54EAOTk5OD69euYMWMGvLy80KtXr9LfWCmsW7cOZ86cYaKrCEx0EREREVVDcZmJOB17CS/7BlXYGKlhiUi5kwDHhm6Q2yjK3I/G0x5StRyJ12Pw8Pgd1OneqNjlj/k5KOwwyn8g1uVLdrkoHcscDxERVQ6dToecnBy0b98ednYV90eZPFqtFqdPn4ZOpytVomvZsmWQy+X46aefoFDk/v+dj48PlEol3n77bQwfPhx+fn6ljsfV9X+zkL28vNC7d2/s37+/whNdZnPpdz5+mrBGFxEREVE1tP/BUYhFYjR1bFAh/WenZiL6zAOo3W2h9rAtd38KexWcm3ggKzEDiTdiSn29k8IeoxoOQFJWCkYd+QBJWSnljomIiCqHnZ0dnJycKvxfWZJper0e+/btw7Bhw4QkV54XXngB69atQ61atXDnzh2MHTsWgYGBaN68OV5//XXcvXsXABAUlPtHp5EjR2LFihVFjqVWqy1ex8TEYOrUqWjXrh2eeeYZfPbZZ9Dr/7eLy6VLlzB06FAEBAQgKCgIP//8s3AuKioKY8aMQWBgIDp06IAFCxYgJycHu3btwrfffouzZ8/C35+7FheGiS4iIiKiamj3/cNo6OAHlbTsM62KYjIYEfH3XUgUUjg0cLVaoXuFvQo2Pg6ID4lCVnLp67U4Kx0xsuEAxGcm4Y2jM5CcrbVKXERE9PQKDw+HTqdD8+bNC5wTiURo3749pFIp3nrrLdSqVQt79uzB1q1bYTQasWTJEgDAzp07AQArVqzAmDFjCh3nzp072LdvH/r27QsgN8E2atQoZGZmYuPGjfj6669x7NgxLF68GABw9+5djBo1Cm3btsWuXbswefJkLFq0CIcOHQIALFiwAGq1Grt378bKlStx8OBBbN++Hb169RISYHlLJ8kSly4SERERVTO3UsJwM+UehtbrXSH9x5wLR05aNtxala4uV0nY13FGdpIOUf+Gwa9H41IVpwcAV5UTRjYcgPW3dmHM0ZlYF7QY9vLyzzgjIqKnk1ab+0cTW9ui/78kKysLQ4YMweuvvy7MyhowYAB+/PFHAICTkxMAwN7eHhqNRrguMDAQAGAwGKDX6xEYGIhOnToBAP7++2/ExsZi+/btwjLLjz/+GBMnTsR7772H7du3o0mTJpg2bRoAoG7durh79y5+/PFHvPjii4iMjETTpk3h5eUFX19frFmzBnZ2dlAqlVCr1ZDJZBZLJ+l/OKOLiIiIqJr5/cERqKUq1LevY/W+U+4lIPVeIhwauEKmKftsMZ/6vvj+4H/hU9/X4rhILIKjvzuyUzMRfyWqTH27qZwxokF/hKdHYezRD6HVp5c5TiIiero5ODgAyC2cXxS1Wo2hQ4di9+7dmD17NoYMGYKFCxfCZDI9tu/du3dj9+7d2LNnD3766Sfo9XpMmDABQO6MrTp16ljUEmvVqhUMBgPCw8Nx9+5dtGjRwqK/wMBAYbnkuHHj8Pvvv6NDhw6YNm0aoqKi4O3tXZZH8NRhoouIiIioGjGZTfjt/mE0dawPqVhi1b716dmIORsOtbstNB7lKxosFoshk8sK3ZJdbquAna8TEq/FIDOhbEkqD7UrRjbsj3vahxh3bBbSczLKFS8RET2dateuDVtbW1y7dq3Q8xMnTsTRo0cxaNAg7N27F3Xr1sWUKVMwY8aMYvv29fWFr68v6tati06dOmHu3Lk4e/Ysbt26VaAeGAAYjUbhfws7bzKZhDZ9+/bF0aNH8f777yMjIwNTpkzBV199VZpbf2ox0UVERERUjZyLu4y4zES0dG5s9b6Tb8VBJAIcGpR/qUPMw2gsfvczxDyMLvS8bW1HyG0ViPw3DCaDsUxjeKrdMKJhf9xJvY83j89BpiGrPCETEdFTSCqVolevXti8ebNFIXgAOHLkCI4cOYKHDx8iLi4OGzZswLhx49CxY0dERUWVenfDvPYmkwl+fn64f/8+UlJShPPBwcGQSqWoXbs2/Pz8EBISYnH9pUuXhN0fv/rqKyQmJmLo0KFYvXo13n33Xfz5558AYLXamjUVE11ERERE1chv9/+Cs8IB3hoPq/ZrMpiQcjcBag87q9Tlys7Mwq2QUGRnFp58EolEcGzkDkOGHnHBkWUep5bGHcMa9MO1pNuYfupLGE1lS5oREdHTa/LkyUhPT8fYsWNx9uxZhIeHY8eOHfjwww8xcuRING/eHDqdDocPH0ZERAR27NhRIDGmVqtx+/ZtpKWlCcfi4+OFf1evXsWSJUtQt25dNGzYEM8++yx8fHwwY8YM3Lx5E6dPn8aCBQvQu3dv2NnZ4fXXX8eNGzewfPlyhIWF4ddff8WWLVswbNgwAMC9e/cwf/58hIaG4vbt2zh+/DiaNGkCAFCpVIiLi0NERETlPsgnRI1JdGVnZ2P27Nlo06YNOnXqhLVr1xbZ9vr163j11VfRsmVLvPLKK7h69arF+b1796Jbt25o2bIlJk2ahKSkJOGc2WzG0qVL0b59e7Rr1w6LFy+2WLebnJyMyZMnIzAwEEFBQdizZ0+hMZw/fx5du3YtMsYDBw5wq1AiIqKnTKYhC388PIHmTv5W/2utNjwJJr0RNl7lW7JYGjK1HHZ+zki+GYeMmLLvoOhj44lBdXvgSOQpLApeY8UIiYjIGrRaLZKSkir8X15h+dJydXXFzz//DB8fH3zwwQfo3bs31q9fjylTpuDDDz9EYGAgJk2ahHnz5qFv377YtWsXPv74YyQmJiI2NhYAMGLECCxevBgrVqwQ+u3UqRM6deqEzp07Y8yYMfDw8MCaNWsgFoshkUjw3XffAQAGDx6MadOmoWvXrpg/fz4AwMvLC6tXr8bff/+NPn364Pvvv8eHH36IV155BQDw6aefwsXFBSNGjMDgwYPh5uaGOXPmAABefPFFmEwmvPzyy0hMTCzz962mEplLOxevmlqwYAHOnTuHL774AlFRUZg5cyYWLlyIHj16WLTT6XTo3r07+vTpg0GDBuHnn3/GgQMHcOjQIajValy+fBkjRozAvHnz0KhRI3z++edQq9VYvXo1AGDt2rXYsGEDli5dCoPBgOnTp2P06NEYO3YsAOCtt95CVlYWZs+ejZCQEMyfPx+bN2+2KDJ38+ZNjBkzBgqFAkeOHClwL1qtFr169UJ8fDxu3rxZ4mdgNBoRHByMgIAASCTWrelBREREFW9/+DG8/+9CTGk2Cs5KB6v2HfbHdcBkhkuLWlbp78GtMCyYMBdzVy+Ab0O/ItuZzWYkXI6CUW9A3ZebQiIv+6bfZ+NCsC/8GGYFTsRI/wFl7oeIiEonKysLYWFh8PPzg1KpFI6npqZi5cqVyMnJqbRYZDIZJk2aZFHknWqOot5rQMlzHmX/pFGN6HQ67NixAz/88AOaNm2Kpk2b4vbt29i8eXOBRNf+/fuhUCgwY8YMiEQizJkzBydOnMAff/yBgQMHYtOmTejZsyf69+8PAFi8eDFeeOEFPHz4ED4+PtiwYQOmTJmCNm3aAAA++OAD/Oc//8HYsWMRHh6Oo0eP4q+//oK3tzcaNmyI4OBgbNmyRUh0bd26FYsWLYKPjw/S0wsvzrp48WL4+PggPj6+4h4aERERVTt7wg6jto2X1ZNcmUkZyErUwbmpdZdDloRIJIKjvxtizz9E7IWH8OpQdFKsOO3cWiI5W4svL61CLY07unp3tGKkRERUWvb29pg0aRJ0Ol2ljalWq5nkoseqEYmu0NBQGAwGBAYGCsdat26NVatWwWQyWewGFBISgtatWwvLAUQiEVq1aoXg4GAMHDgQISEhGD9+vNDe09MTXl5eCAkJgVwuR3R0NNq2bWsxTmRkJOLi4hASEgJPT0+LLT9bt24tzAYDgBMnTmDRokVIT0/Ht99+W+Bezp49i7Nnz2LOnDl48803rfOAiIiIqNpLzErGyZjz6OnznNX7TrkVD4lCCqWzxmp9Orm7YOQHY+Hk7lJsW6lSBod6Lki+FQfb2o6wreVQ5nFf9O6EFL0W759aiA1BS9HCuVGZ+yIiovKzt7dn4omqlRpRoys+Ph6Ojo6Qy+XCMRcXF2RnZ1vscJDX1s3NzeKYs7MzYmJiAABxcXFFns+bYZX/vItL7oe7vPOFXZu3phcAvvvuO3Tv3r3Q+9Dr9Zg7dy4+/vjjAlP0iIiIqGY7EH4cIojQzKmhVfs16g1IvZ8IjaedVet+2drb4rmXX4CtvW2J2qs9bKFwVCH2/EOYjKbiLyiCWCTCQL+X4K5ywVsn5iIivfBdH4mIiOjpVCMSXZmZmRZJLgDC60e3Dy2qbV67rKysIs9nZWVZ9P3oOMX1XZyVK1eiadOm6NSpU4naExERUc3x58OTqGdXG2qpyqr9pt5LhNlkhsbTukXo01LTcGLfUaSlphXfGLmz6B3quSInIxvJobHFX/AYMrEUQ+q9DDFEGH98DlL1JYuBiIiIar4akehSKBQFkkl5rx+dGVVU27x2RZ1XqVSFJs/yvlapVMX2/Ti3bt3C9u3bMXv27GLbEhERUc2iM2TiUsI11LOrbdV+zWYzkm/FQeViU64i8IVJik3AhqU/ISk2ocTXyDRy2HjZI/5qNHIyS/aHwKJoZGq83qAv4jIT8c7fn0JvLF9/REREVDPUiESXu7s7kpOTYTAYhGPx8fFQKpWws7Mr0DYhwfIDWUJCgrDksKjzrq6ucHd3F/rOPw4A4XxR1xbnzz//RGpqKl588UUEBgYKdcICAwPx22+/FXs9ERERPbnOx12BwWy0eqJLF5sGfVo2bLyqT+0UO18niEQixAdHlrsvF6UjhtbrjeCEG5hzdjlqyGbiREREVA41ItHVuHFjSKVSBAcHC8cuXLiA5s2bWxSiB4CWLVvi0qVLwgchs9mMixcvomXLlsL5CxcuCO2jo6MRHR2Nli1bwt3dHV5eXhbnL1y4AC8vL7i5uSEgIACRkZFCva+88wEBAcXew/Dhw3HgwAHs3r0bu3fvxmeffQYA2L17N4KCgkr9TIiIiOjJ8W/sRTjIbeGidLRqv8m34iDVyCG3rz61P8UyCezqOCH1XiIyEzPK3V9tWy/09+uGvQ+OYHfYIStESERERE+yGpHoUqlU6N+/Pz799FNcvnwZhw8fxtq1azFy5EgAubOu8upr9ejRA1qtFp9//jnu3LmDzz//HJmZmejZsycAYOjQodizZw927NiB0NBQzJgxA126dIGPj49wfunSpThz5gzOnDmDZcuWCeP4+PigU6dOmD59OkJDQ7Fjxw7s3bsXw4YNK/YeHBwc4OvrK/zLmz3m6+sLGxsbqz8zIiIiqj5ORl+An62PVYvF5+j0SItIgY2Vi9Bbg8bTDjKNHDHnwq0yC6u5kz8CnBvj84srEaOLL/4CIiIiqrFqRKILAGbNmoWmTZti1KhRmDdvHiZPnizsbtipUyfs378fAGBjY4PVq1fjwoULGDhwIEJCQrBmzRqo1WoAuUsF58+fj5UrV2Lo0KGwt7fHF198IYwzduxY9OrVC++88w6mTp2Kfv36YfTo0cL5xYsXQ6PRYPDgwVi1ahUWLlyIFi1aVN6DICIioidKXGYi7mofWH3ZYsrdBIjEIqjdS7YrYmkpVEo0bNkIClXpZ4uJRCLY13dBVmIGtPeTrBJPD5/nIRFL8BGXMBIRET3VRGZ+EqgxjEYjgoODERAQAIlEUtXhEBERUQn8dv8wZp5ejOktx8NGprZKn2aTCXd2X4HCQQXHhm5W6bMiJF6PRk66HvX6NINYVv7PLrdSwrD5zm+Y3/Y9vFqvpxUiJCIiAMjKykJYWBj8/PwKbLaWmpoKnU5XabGo1WrY25es9uTrr78OT09PLFu2rMC53377DQsWLMA///wjbDxXmMTERJw9e1ZYBebv748NGzbgmWeeKdsNFOHMmTPCarFH1apVC0eOHLHqeOWxa9cufPvtt0XG9OGHHwIAvvzyy1L3/bj3WklzHtbdfoeIiIiISuXfmIvwVLtZLckFAGkRqTBk5sCpiUeZrjfqDUi+GQdjtgEKBxUUDmooHJQQS//3odJkMsFoMEIilRSoiVpS9nVdEHsuHAnXouEW4F2mPvJr6OCHQJem+PLSKnT0aIVaGvdy90lEREVLTU3FypUrkZOTU2ljymQyTJo0qUTJrpdffhlfffUV9Hp9gWTWgQMH0L1798cmuQBg6dKlMJvNQqLr5MmTJU60lcXJkycLHONEltJhoouIiIioipjNZvwTcwH+9nWt2m/yrTjI7ZSQ2yhKfW12aiYSr8fAbDJDplFAF5eO9MhUAIDMRvH/iS8VYuJi8fnbH2Pu6gXwbehXpjilShlsvB2QdCMWDvVcIbctfbyP6uHdGfe04ZhzZhn++8KialefjIioJtHpdMjJyUH79u1hZ2dX4eNptVqcPn0aOp2uRMmmnj17YuHChTh16hSef/554Xh6ejpOnjyJNWvWFNvHo4vgXF1dSx94KVR0/0+DGlOji4iIiOhJc0f7AAlZyahn52O1PrO1WdDFpsHGq3R/bTabzUiLSEF8SCTEUgkc6rrA1tsBDg1c4dDAFRove4gkIuhitEi8Go344Ij/v6588drWdoRYJkHspYfl6+j/KaUK9PXtijNxwdh2d59V+iQiosezs7ODk5NThf8rbTLNyckJHTp0wJ9//mlx/PDhw3BwcBCWH+7atQs9e/ZEixYtMHDgQJw7dw4AsGLFCvz666/49ddfERQUBCB36eKZM2cAAEFBQdi8eTMGDx6M5s2bo1+/frh69aowzsOHDzF69Gi0bNkSffr0wU8//ST0UxZnzpxBUFAQtmzZgs6dOyMgIADTp0+HXq8HkJsInDx5Mtq0aYO2bdvigw8+QHp6unD91q1bERQUhMDAQIwYMQI3b94UzgUFBWHnzp145ZVX0KJFC4wZMwaRkZGYPHkyWrZsiX79+uH27dsW8SxfvhytWrVC586dsXHjxiLjPnToEHr16oWWLVti0KBBOHv2bJmfQUkw0UVERERURf6NuQipSIratrWs1mfK7XiIZRKoXEu+a7PJaELSjVik3k2A0kkDuzpOQs0skUgEiVwKpaMatt6OcGjoBof6rlA6awAA6RHJ5Sr+LpaIYV/XGekPU5ARoy1zP/nVt/dFa5dmWHxpNSLSo63SJxERPZl69+6Nv/76C0ajUTj2xx9/oFevXhCLxdi1axcWLFiACRMmYPfu3ejYsSPefPNNxMbGYsyYMejZsyd69uyJnTt3Ftr/ihUr8Oabb+K3336Dra0tPvvsMwCAwWDAhAkTYGdnh19++QVvvvkmvv3223LfT1xcHA4ePIgff/wRK1aswJ9//ondu3cDAL755hvEx8fj559/xoYNGxAaGorvvvsOAHDkyBF8++23mDt3Ln799Ve0bt0aI0eORGpqqtD3119/jffffx9btmzB9evXMWDAAHTs2BE7d+6ESqXC8uXLhbaRkZG4efMmtm3bhmnTpmHRokVCAjC/0NBQzJw5ExMnTsRvv/2Gvn37Yvz48Xjw4EG5n0VRmOgiIiIiqiL/xFyAr60XZGLrVJMwGU1IuZsAjYctROKSLdnL0ekRd+EhshIzYOPtAI2H3WOX+4lEIkgUUigdcmuK6eLSkfYwuVxxq1xtILdTIuZ8OMwm6+yT9JJPZyilSsw+swwms8kqfRIR0ZOnW7du0Ol0wiyttLQ0nDx5En369AEAbNy4ESNGjED//v1Rt25dfPDBB2jYsCE2bdoEjUYDpVIJpVIJJyenQvsfMGAAunXrBj8/P7zxxhvCjK7Tp08jOjoaCxcuRP369dGnTx8MHz682HgDAwML/Fu1apVwPicnBx999BH8/f3RuXNndO7cGVeuXAGQm3zSaDTw9vZG48aN8Z///AevvPIKAODHH3/EhAkT8MILL6BOnTp49913UatWLfz2229C3wMHDkTHjh3RrFkztG/fHg0aNMDQoUPRoEED9O3bF/fu3RPaKhQKfPnll2jQoAEGDBiAPn36YOvWrQXu56effsLgwYPRp08f+Pr6YuTIkXjuuefw888/F/ssyoo1uoiIiIiqgN6Yg/Nxl/GsRxur9amL0cKUY4Ta3bZk7ePTkRwaC7FMAvu6LpAoSv/RUOmohjYsCRKZFBrPstVnEYlEcKjvgriLEUi+HQcn//IXkVdI5OjrG4T1t37Fz7d/x7CG/crdJxERPXlsbGzQpUsX/Pnnn2jfvj0OHz4Mb29vNGvWDABw9+5dTJo0yeKagIAA3L17t0T916lTx2KsvML8N2/ehJ+fH2xs/jfDOiAgAPv2PX5Zfd7srPwerUfm6+trMabBYAAAjBw5Em+//TY6dOiADh064KWXXhISenfv3sWSJUssZmVlZ2fj/v37wmsfn/+VUlAqlahVq5bF6/ybDvj4+MDR0VF43aRJE+zYsaNA7Hfv3sWBAwewbds24VhOTg46depU5DMoLya6iIiIiKpAcOINZBqzUc+uttX6TItIgVQlg1T9+B2kACD1fiLSHiRDbq+Ejac9RJLSTfT39PHC/FVfwsbOBtnxOiTfistdMumiKVPsclslNJ52iA+JhF1tJ0hVsjL1k19du9po69oCS0N+RGfPtqht61XuPomI6MnTp08fLFiwAHPnzsWBAwfQu3dv4ZxCUXAjFKPRCJOpZLOBZbLC//9KIpEUWNpfkqX++ZNYRXl0p8i8fjt06IDjx4/jr7/+wrFjx/Dxxx/j5MmTWLp0KYxGI2bPno0OHTpYXJs/Effo7o6P21X50XMmk6nQZ2E0GjF+/Hj079/f4rhSqSz6BsuJSxeJiIiIqsC/MRegkarhobbO7kp5xeSVzppidxrUa7OQ9iAZKlcb2NRyKHWSCwCkMikcnR0hk8mg8bSD3E6JxOsxyE7NLOstwM7PGQAQ9/+F7q3hRe9noZYqMfvs0nLVEiMioifX888/D51Oh9OnT+PUqVMWiS4/Pz+EhIRYtA8JCYGfX+6OwmXdvbdBgwa4f/++RTH4a9eulamvklq3bh2uXbuGAQMG4D//+Q+++OILoRC/n58fYmJi4OvrK/xbtWoVgoODyzTWw4cPkZn5v//Pv3z5MurWLbiLtJ+fHyIiIizG3bZtG06cOFGmcUuCiS4iIiKiKvBPzAX42XlDXMYP0I/KSsyAMcsAlfPjZ1SZzWak3I2HRCmFytWmzB/gE2Lj8dOy1UiIjYdIJIJNLQdIVTIkXIlGTkZ2mfqUyCSwq+OM1HuJ0MWnF39BCSgkcrxc+wVciL+KE9HnrNInERE9WeRyOV588UUsWrQIDRs2tFhuOHr0aGzatAm7d+9GWFgYli5ditDQUAwaNAgAoFKpEBkZidjY2FKN2aFDB3h6emLu3Lm4e/cu/vjjD2zYsKHY6+Lj4wv9V5IZZjExMZg/fz6Cg4Nx//59HDx4EE2aNAEAvPHGG1i/fj12796N8PBwLFmyBAcOHEC9evVKdV95srOzMXPmTNy+fRtbt27FwYMHMWrUqALtRo8ejf3792PDhg0IDw/HunXrsG7dOovvgbVx6SIRERFRJUvVp+F60m309i37FuOPSotIgVgmhtz+8UsBMuPSoddmw87XqcxJLgDIzMhE8OmL6D6gJwBAJBbBtrYjtPcTEX85Cm6B3pAqS7/8UONph4wYLWLOPoBfzyYlLqr/OPXsasPXphZWXt2I5zzbluu+iYioIK3WOrvmVuQ4vXv3xq5duzBr1iyL47169UJCQoKwY2Hjxo2xdu1aIQHUr18/TJo0CX379sXp06dLPJ5YLMaKFSswd+5c9OvXD3Xr1sXAgQOLnclUVO2q48ePFzvm1KlTkZaWhokTJ0Kn06Ft27ZYsmRJgftMSEhA/fr18f3335c54dS4cWO4u7tj8ODBcHR0xMKFC4W6Z/kFBARg8eLFWLFiBRYvXozatWtj2bJlaNu2bZnGLQmRmXO4awyj0Yjg4GAEBAQUWFtLRERE1cefD//G1H8W4L3mb8BBUbYC7o+6u/cqpEoZnBoVXcjdZDQh5uwDSJUy2Po4FtmuJB7eC8fimZ9jxqI58Kn7vzpjphwjUsMSIZaK4RroDYms9J9J9NosxF2KgHub2nDydytXnHnupD7Axtu78ePzC/Gsp/U2ACAiehpkZWUhLCwMfn5+FrWVUlNTsXLlSosi5RVNJpNh0qRJBQq0VzeJiYm4fv06OnfuLBz78ccfcfz4cWzcuLEKI6veinqvASXPeXBGFxEREVEl+zfmIlyVTlZLcunTsqBPzYJtLYfHtksLT87dldG38C3SrUEsk8DO1wmp9xOReC0ari1rlXoGldxOCY3H/xem93Us08ywR9Wzqw0fjSe+vboRHT1ac1YXEZEV2NvbY9KkSdDpdJU2plqtrvZJrjwTJ07E7Nmz8fzzz+PBgwdYv3493nrrraoOq8ZjoouIiIiokp2MOQ8/W2+r9ZcWkQKIRVA4qYtsY8jKQdrDFKicNZDIK/YjoEQhha23A7T3k5ARrYWNV+l/IbGr64zMhHTEXYqAVwe/csckEonwnGdbbL7zG87EBaO9e2C5+yQiotxk15OSeKpMzs7O+Prrr4Wi8C4uLhg+fDhef/31qg6txmOii4iIiKgSPUyPRmRGLJ7zsF5tirSIFCgdVBA/ZvfE1LsJEEtEULnYFNmmNOwd7dHn9f6wdyz8lxuZRgGFowqp9xJyk2uK0n3slMgksPNzRsrteDg2cLVK3A3s66CWxh0rr25ioouIiCpct27d0K1bt6oO46nDXReJiIiIKtG/MRcghhh1rDSjy5BtQGZ8OpSP2W0xOyUTmQkZULvbQvSYZFhp2Dnao/uAnrArItEFAGp3O0AkQvLtOJSlLKzG0w4yGwWizz6A2VT+srK5s7ra4Xz8FZyNu1zu/oiIiKj6YaKLiIiIqBL9G3MR3jYeUEoVVukvIzIVMAOqIhJdZrMZKXfiIVXJILdXWWVMANBl6HDlXAh0GUXXZRFLxNB42CErUYfMhIxSjyESieBQ3wXZyZlIuRNfnnAF/vZ+8FS74rurm6zSHxHR04R72VFFs8Z7jIkuIiIiokpiNBlxKvYS6tr6WK3PtIhkyO2URS4NzIjWIidDD7WHnVULsCfGJmDN4u+QGJvw2HZyOyVktgqk3I6HKcdY6nEU9iqoPWwRFxwJQ1b5d/XKm9V1Ji4YF+Ovlbs/IqKngUyWuylIZRadp6dT3nss7z1XFqzRRURERFRJriXfRlpOBurZ1bZKfyajCelRWtj6OBR+3mCENiwRCgcVZGq5VcYsLZFIBI2nPVLvxCM1LBGODd1K3Ye9nwuyEh4gPjgSnu3rlDumRg714K5ywXfXNuHHLl+Uuz8ioppOIpHAwcEBcXFxAHJ3PuTutWRNZrMZOp0OcXFxcHBwgEQiKXNfTHQRERERVZJ/Yy5CKVGglsbdKv3pYtJgNpqgcil82aL2fhJMJjPUbrZWGa+sJDIJ1O62yIjWQuVmA6VD0btDFnq9XAK7Os5IuRMPOz9naNzLdz/i/9+Bcce9A7icGIoWzo3K1R8R0dPAw8MDAIRkF1FFcHBwEN5rZcVEFxEREVEl+SfmAurY1oJEXPa/UuaXFpECqUoGaSGztXJ0eqRHpULtaguxzDrjlYfCUY3s1Ewk34qHR2ufUhfF13jZITM+DdGnwlD35ablvqcmjvXhqnTCd1c3YdXzn5WrLyKip4FIJIKnpyfc3NyQk1P+peREj5LJZOWayZWHiS4iIiKiSpCRk4nghOvo7tPZKv2ZzWakRSRD5awpdPlIyt0EiGWSx+7GWB5SmRQe3p6Qykr2cVIkEsHGywEpd+OhfZAM+7rOpRpPJBLB0d8NsecfIu5SBDza+ZYlbIFYJMZznm3xS9hBXEu6jaZODcrVHxHR00IikVglGUFUUViMnoiIiKgSnI+/AoPZaLX6XFlJOhizDFC52BQ4l6PTIztJB7WrDUTiiqmh4unjhTlffQpPH68SXyNRSKFysUHaw2To07NLPaZUJYd9XWck345HRoy21Nc/qqlTQ7goHfH9tc3l7ouIiIiqBya6iIiIiCrB6dhLcJDbwVnhYJX+0h6mQCwTQ26vLHAuIyoVIokYcjuVVcayJpWLDSQKKZJvxpVpC3GNlz0UDipEnQqDsQy7OOYnEYnR2aMN/or8F6HJd8vVFxEREVUPTHQRERERVYIL8VdR28bTartUpUUkQ+lUcNmiyWhCRkwaFA6qCpvNBQARYQ8xfeRURIQ9LNV1IrEIGi975KRnIz0ipdTj5i1hNGYbEXexdGMXprmTP5wUDviOs7qIiIhqBCa6iIiIiCpYliEbN5LvwMfG0yr96dOyoU/NKrT+VmZ8OsxGE5ROpdvZsLTMZjOyMrPKNCtLppZD6aSG9n4SDFmlL2gsVcpgX9cZKXcSkB6VWurr85OIJejoHoi/Iv5BXGZiufoiIiKiqsdEFxEREVEFu5p0CwazET42Ja9n9TjpkSmAWFRoMis9KhUyGwUk8uq955DKzRYisQgpd+LLdL3G0w4KRxWiT9+HUW8oVyzNnPwhFknw+/2/ytUPERERVT0muoiIiIgq2KXE61BI5HBTlW6nwaKkPUyG0kEFscTyo5w+LQs5adlQOlbsbC5rEEvEUHvYIStRh8yE9FJfLxKJ4NjQDcYcI2IvlG8Jo0qqgL+DH34N+7NMM9SIiIio+mCii4iIiKiCXYq/Bm+NBySi8n/0MmYboItPL3TZYkaUFmKZGDJbRbnHqQxyOyVkNgqk3ImHyWgq9fVSpQwOdV2Qei8RaWWo95VfgHNj3NWG40YKi9ITERE9yZjoIiIiIqpAZrMZlxJyE13WkB6VCpgB1SOJLpPBCF1cGhQOaqsVvH8c91oemLFoDtxrlf2+RCIRNJ52MOqN0N5PKlMfag9bKJ3UiD5zH8bssi9hrGfvCxuZBrvDDpW5DyIiIqp6THQRERERVaAH6VFI0aehtpXqc6U9TIbcVgGJwrIGly42DWaTudKWLcoVcvjUrQ25Ql6ufiRyKdSutkiPSIE+PbvU1+ctYTQbTIg5F172OERiNHdqiN/v/4UcU/lqfhEREVHVYaKLiIiIqAIFJ1yDCCLUssKMLrPJhIxobYFli2azGelRqZDbKSGWSco9TkkkxSdh+49bkBRftplY+SmdNZAopEi+FVemGlkShRQO9V2hfZCE1LCy75zY0rkxUvRanIw+X+Y+iIiIqGox0UVERERUgS7GX4O7yhkqafnrZmUmZMBkMBWYtaVPzYJBlwNFJRahz0hLx98HjyMjrfSF5B8lEucuYcxJy0ZGtLZMfajdbaF2s0HM2QfQp5V+ZhgAeKpd4aF2xZ77XL5IRET0pGKii4iIiKgCXUi4ZpXZXACQEaOFWFqw2Hx6VCrEcglkmvItI6xKMo0CCgcVUu8lwqgv29JBhwZuEEsliPznLsym0he3B4AWTo1wJPIUUvVpZbqeiIiIqhYTXUREREQVRKtPxz1tOGrbeFqlv4xoLRQOKoti80a9AZkJ6VA6Vk4R+oqkdrcDAKTcTSjT9WKpGI6N3ZGVpEP85egy9dHCyR8GkxF/hB8v0/VERERUtZjoIiIiIqogIYk3AAA+VihEb9QbkJmYUWB5YkZM7swjhUPlLVusKGKpGGp3W2TGpSMrWVemPhR2Stj5OiHxWjQyYku/DNJWrkF9e1/uvkhERPSEYqKLiIiIqIJcSrgOG5kaTgr7cveli00DzLCoz2U2m5ERlQqFvQpiaeV+rLOxt8ULL3eFjb2tVftVOKggVcuRcjseZlPpC9MDgG1tRygcVIj6JwzG7NIvg2zp3AjBiTfwIC2yTOMTERFR1WGii4iIiKiCXIy/Bm+Np1WWFGZEayFVySBVyYRj2ck6GLMNlVqEPo+jsyMGjh4MR2dHq/YrEuUWpjdk5iAtPLnMfTg2cofJYETU6ful3smxkUM9KCUK7Ll/uEzjExERUdVhoouIiIioAhhMRlxODIWPlQrRp0droXBUWR6LSoVEKbVIflWW7MwshN28i+zMLKv3LVXKoHLRQBuehJz0su2gKFVI4djQDekRKUi5U7qaXzKxFE0c62NP2CGYzGUrak9ERERVg4kuIiIiogpwKzUMmcYs+FihEL0+PRs56dkWyxYNWTnIStRVWRH6uOg4LP9oMeKi4yqkf5WrLSRyKZJCY8u8hFHlYgONpx1iL4QjOzWzVNe2dG6MKF0cLsRfLdPYREREVDWY6CIiIiKqAJcSrkMiksBL417uvjJicouqKxz+N6MrI1oLkVgEhb2qqMueaCKxCDa1HJCToYf2QVKZ+7Gv5wKJUobIk/dgMpZ8dlZtGy84KRy4fJGIiOgJw0QXERERUQUITrgGL7UbZGJpufvKiNZCbqeEWCoB8P9F6GO0kNurIJLU3I9zUpUMKjcbpIUnl3pGVh6xRAynRu7I1mYhPrjkxeXFIhGaOzXEgfDjyDRYf3kmERERVYya+8mIiIiIqApdiL8Gb5vy1+cym8zIeKQ+V3ZKJkx6o8UMr5pK5WIDqUqGpNDYUs3Iyk9uo4B9HSckhcZCF5dW4utaOjeGzpCJI5GnyjQuERERVT4muoiIiIisLC4zEdG6ONS2Qn2urGQdTDlGi/pcmXHpEMslVVKEPo9YIoaNrQ3EFTyjTCTKXcJo1BuRei+xzP3YeDtAbqdE1On7MBlKljBzVjqgto0XdocdKvO4REREVLmY6CIiIiKysuCE6wAAb035E10Z0VqIJGLIbZUAcmd46RLSobBTVkkR+jy1fL3xxdplqOXrXeFjSRRSaNxtkRGViqykjDL1IRKJ4OjvhpwMPeIvl3wJY0vnRvg35gLiMsueZCMiIqLKw0QXERERkZVdTLgGJ4U97OQ25e4rIzoVCgclROLcpFZWsg5mgwnyGlqEvigKRzVkGjmSbsbBlGMsUx8ytTx3CeONWOji00t0TVPHBhCLJNj74EiZxiQiIqLKxUQXERERkZVdjL+GWpry1+cy5Rihi8+wWLaoi0uDRCGFRFH+IvflEf0wCvPe+QjRD6MqZTyRSARNLQeYjSYk344vcz95SxijT4eVqOaXSqqEv4Mf9oRx90UiIqInARNdRERERFaUZcjGjeQ78LFCfS5dXBpgNkPx/4kus9GErIQMyKt42SIAGHIMSIiNhyHHUGljSmQSaDzskRmfXqqi8vmJRCI4NnSDPk2PhMslS9I1cayPW6lhiMyILdOYREREVHmY6CIiIiKyomvJt2EwG61SiD4jRguJQioUnc9M0sFsMkPxlC1bzE9ur4TcTonk2/EwZpctySbTyGFXxxGJ12OQmVD8Esb6dr6QiMQ4Fnm6TOMRERFR5Sn1nPfk5GQcPnwYp06dQkREBNLS0uDo6AgvLy8899xz6NKlC+zs7CoiViIiIqJq71LCdSgkcripXMrdV3qUFgpHlTB7SxeXBomy6pctViWRSASNpz1S7sYj6WYcXJp7lml2m62PIzITMhB16j78ejV57O6RSqkCvrbeOBJ5CsMa9itP+ERERFTBSvwpKSkpCd9//z127twJo9GIevXqoVatWvD19YVWq8Xt27exf/9+yOVyDBkyBOPHj4ezs3NFxk5ERERU7VxKyK3PJRGVb+J8jk4PvTYLNrXsAQAmgwlZiTqo3cpf4P5JJ5aKYeNlj7TwZKRHpMDWx7HUfYhEIjj5uyH2wkMkXImCW8Djd49saF8Hf0X+i4wcHTQy9WPbEhERUdUpUaLrwIEDWLBgAVq0aIHPPvsMQUFBUKkKTplPT0/HiRMnsH37drz88sv4+OOP0atXL6sHTURERFQdmc1mXIy/hpbOjcrdV0aMFgCE+lyZiRmA2Qy5XfVYtuji4Yq350yBi4drlYwvt1VC6axB6r1EyO2UZVrOKdMoYOfrhMTrMbD1cYTKWVNkW3+Huvjj4Qn8E3MB3X06lyd0IiIiqkAl+lPjli1b8NNPP2HVqlV4+eWXC01yAYCNjQ169eqFdevW4YcffsDmzZutGiwRERFRdfYgPQopeq1VCtFnRGshs1FAIpMAyF22KFXLIJFLyt23NajUKjQOaAqVuuoSb2p3W0jVMiRej4Exx1imPmx9HCHTKBB1Kgzmx+zC6KSwh7vKGceizpQ1XCIiIqoEJUp0bdy4EY0bNy5Vx82bN2eii4iIiJ4qwQnXAADemvIlusxmMzKitVA65iaRTDlGZCfrqs1sLgBITU7F/u2/IzU5tcpiEIlEsPF2hNloRtKNGJjN5tL3IRbB0d8Nem0WEm/EPLZtA/s6OBp5GkZT2ZJqREREVPGssuvi1atX8eeff0Kr1VqjOyIiIqIn0qWE63BXuUAlVZSrn+yUTBizDf9btpiQDpgBhZ3SGmFahTY5FQd27IW2ChNdACCRSWDj7YDs5EykPUwuUx9yGwVsvOyReC0GhqycItv529dFil6Ly0k3yxouERERVbBSJ7ri4uIwYsQIfPfddwCATZs24dVXX8WUKVPQvXt33L592+pBEhERET0JLsRfg7fGo9z9ZERrIRKLoLDPTWzp4tIh08ghllWPZYvVjdxGAZWLDbRhSchOySxTH7a+TjADSLgaXWQbbxsPaKRqHIs8XcZIiYiIqKKVOtG1ZMkShIWFoXnz5jCZTFi1ahU6duyI3bt3o379+li2bFlFxFms7OxszJ49G23atEGnTp2wdu3aIttev34dr776Klq2bIlXXnkFV69etTi/d+9edOvWDS1btsSkSZOQlJQknDObzVi6dCnat2+Pdu3aYfHixTCZ/lfPITk5GZMnT0ZgYCCCgoKwZ8+eQmM4f/48unbtanHMbDZjzZo1CAoKQqtWrTBq1CjcuXOnLI+DiIiIKplWn4572nDUtlJ9Lrm9CiKxGEa9AdkpmZCXodj600TlZgOpRo7EGzEw6g2lvl4ik8DWxwHJt+KhT8sutI1YJEZ9e18ciTxV3nCJiIiogpQ60XXy5EnMnDkTnTt3xsWLF5GQkICRI0eiUaNGGDduHM6fP18RcRZr8eLFuHr1KtavX49PPvkE3377Lf74448C7XQ6Hd588020adMGu3btQmBgICZMmACdTgcAuHz5MubMmYN33nkH27Ztg1arxaxZs4Tr//vf/2Lv3r349ttv8c033+D333/Hf//7X+H8rFmzkJaWhm3btmHixIn46KOPcPnyZYsYbt68ialTpxaoI7F161asXbsWc+fOxS+//AJvb2+MHz8emZll+8skERERVZ7LiaEww1zuQvQmowm6uDShPldmfDogyt1lkIomEolgW8sBZpMZSTdiy1Svy6aWAyRyMeJDIops42/vhzvaB4hMf3w9LyIiIqoapU506XQ6eHjkTsk/ceIE5HI52rdvDwCQy+Vl+lBRXjqdDjt27MCcOXPQtGlTvPjiixg3blyhxfD3798PhUKBGTNmoF69epgzZw40Go2QFNu0aRN69uyJ/v37o1GjRli8eDGOHz+Ohw8fAgA2bNiAKVOmoE2bNmjfvj0++OADYZzw8HAcPXoUn332GRo2bIhXX30Vffv2xZYtW4Txt27diiFDhsDZ2blAbL/++ivGjBmDF154AX5+fvj000+RkpKCixcvVsRjIyIiIiu6lHAdNlI1nBQO5eonMy4dZpNZqM+li0uHzEYBsdQqpVWtRqVRo03ndlBp1FUdikAsk8C2lgOyUzKR9qD09brEEjHsfJ2gfZCMzMSMQtvUs68NiUiCo1FcvkhERFQdlfoTU506dXD+/Hnk5OTg4MGDaNeuHRSK3IKrv/32G+rUqWPtGIsVGhoKg8GAwMBA4Vjr1q0REhJisawQAEJCQtC6dWuIRCIAuX/9a9WqFYKDg4Xzbdq0Edp7enrCy8sLISEhiI2NRXR0NNq2bWsxTmRkJOLi4hASEgJPT094e3tbnL906ZLw+sSJE1i0aBFGjx5d4D5mzJiBvn37Cq9FIhHMZjPS0tLK9mCIiIio0gQnXIeXxl34jFFWGTFaiOUSyDRyGLJyoNdmQVGNdlvM4+LuglFTxsLF3aWqQ7Egs1FA5WoD7YMkZCXrSn292sMOUrUccZciCv0DrlKiQB1bby5fJCIiqqZKnegaP348vv32W3To0AEPHz7EG2+8AQAYNGgQfvvtN4wdO9bqQRYnPj4ejo6OkMvlwjEXFxdkZ2cjJSWlQFs3NzeLY87OzoiJyZ1+HhcXV+T5+Ph4ALA47+KS++Eu73xh18bGxgqvv/vuO3Tv3r3Q+2jTpo0wWw4AduzYAYPBgNatWz/2/omIiKhqmcwmXE66aZVC9OnRqVA6qCASifItWyzfLo4VIUefg/joOOToi96lsKqoXG0g08iRdCO21PW6RCIR7P2coYtNQ0ZM4TuKN7Svg3Nxl5GeU/isLyIiIqo6pU509e7dGxs2bMCbb76JLVu24NlnnwUAtG3bFqtWrUKvXr2sHmRxMjMzLZJcAITXer2+RG3z2mVlZRV5Pisry6LvR8cpru/SCAkJwaJFizB27Fi4urqW+noiIiKqPPfTIpCekwFvjXu5+jFk5SA7OTPfssU0yG2VEEmq17JFAIiJiMb8KXMRE1H0LoVVRSQSwcbbAWazGUmhpa/XpXRWQ26nRNzFwmd1+TvUhcFsxD8xF6wVMhEREVmJtCwXtW7dWphllJ2dDblcjpkzZ1o1sNJQKBQFkkl5r5VKZYna5rUr6rxKpbJIauUt18xrq1Kpiu27pC5duoTx48fjueeew9SpU0t1LREREVW+y4mhEEGEWuWc0aWLzS1XoHBUI0enR066HjY+DlaI8OkjlkpgU8sBaQ+SkB6ZCltvhxJfKxKJYF/XGfHBkdDeT4K9n2VtVUeFHTxULjgaeRov+Txn5ciJiIioPMr058F79+7h3XffRbt27RAYGIjr169j3rx52Lhxo7XjKxF3d3ckJyfDYPjf1PT4+HgolUrY2dkVaJuQkGBxLCEhQVhyWNR5V1dXuLu7C33nHweAcL6oa0vqzJkzGDNmDNq3b49ly5ZBLK5+f8ElIiIiS8GJoXBVOUEpLd8Sw4wYLaRqOaQKKTLj0iESiyC34W6LZSW3UUDprEHqvQTo07JLda3CXgWliwZxwZEwGU0Fzjewr4PjUWdgNBmtFS4RERFZQamzKDdu3MCgQYNw7do19OnTR5jOLZFIsHDhQvz6669WD7I4jRs3hlQqFQrKA8CFCxfQvHnzAomili1b4tKlS0LcZrMZFy9eRMuWLYXzFy78bxp6dHQ0oqOj0bJlS7i7u8PLy8vi/IULF+Dl5QU3NzcEBAQgMjJSqPeVdz4gIKBE93Hr1i1MnDgRnTt3xtdffw2ZTFbaR0FERERVICThOmqpy7dsEQAyorVQOKhgNpuhi0uDzFYJkbh8xe2fdmo3W0gUUiTdiCk0YfU49n7OMGTqkXwrrsA5f4e6SNGnISQx1FqhEhERkRWUOtG1aNEiNGvWDAcOHMCsWbOEhNFHH32EQYMGYcOGDVYPsjgqlQr9+/fHp59+isuXL+Pw4cNYu3YtRo4cCSB31lVefa0ePXpAq9Xi888/x507d/D5558jMzMTPXv2BAAMHToUe/bswY4dOxAaGooZM2agS5cu8PHxEc4vXboUZ86cwZkzZ7Bs2TJhHB8fH3Tq1AnTp09HaGgoduzYgb1792LYsGEluo+PP/4Ynp6emDVrFpKTkxEfH28ROxEREVU/mYYs3Eq9j1o25Vu2qE/LRk6GHkpHFQy6HBgyc6Cw52yu8hKJRbD1doQhy4DUuwnFX5CPTC2HxsMOCVejCxS1r6Vxh41MjaNRp60ZLhEREZVTqRNdwcHBGD16NKRSaYHts3v16oX79+9bK7ZSmTVrFpo2bYpRo0Zh3rx5mDx5srC7YadOnbB//34AgI2NDVavXo0LFy5g4MCBCAkJwZo1a6BW5xZ9DQwMxPz587Fy5UoMHToU9vb2+OKLL4Rxxo4di169euGdd97B1KlT0a9fP4wePVo4v3jxYmg0GgwePBirVq3CwoUL0aJFi2Ljj4+Px6VLl3Dnzh106dIFnTp1Ev7lxU5ERETVz7Wk2zCZTfApZ32ujNjcHf4UDipkJuQuW5Rpqt9ui3l86tbGih2r4VO3dlWHUiyJQgqNhx0yorW5O1mWgp2vE8wGExKvx1gcF4vEqG/niyOR/1ozVCIiIiqnUhejVygURc4wSklJKbDrYGVRqVRYtGgRFi1aVODczZs3LV63aNHisUssBw4ciIEDBxZ6TiKRYNasWZg1a1ah552dnbFq1api4310DFdX1wJxEhERUfV3OSkUcrEMrirn4hs/Rka0FnI7JcRSCTLj0yGzVXDZohUpHFXIychG0q04uNspIVWU7GOwRCGFjbcDkkJj4djQDTL1/z7r+jvUxba7+/AwPRo+Np4VFToRERGVQqlndD377LP45ptvLOpQiUQiZGRkYO3atejYsaNVAyQiIiKqzkISbsBL4w6JqOwbyJjNZmTE5NbnMmTlICdDD7lt9V62GBsZg2Wzv0RsZEzxjasBkUgEjac9RCIg6UaMUH6jJGx9HAGRCEmhsRbH69nVhlQkwdFILl8kIiKqLkr9iWz69OnQ6XTo0aMHhg0bBpFIhC+//BI9evRAdHQ0pk2bVhFxEhEREVVLwYk3UEtTvkL02cmZMOmNUDqqkZmQAYhydwyszvTZety/HQZ9tr6qQykxsVQMm1oO0KdmIe1hcqmu03jaIeVOPIw5/9tlUSGRo46tN45EnqqIcImIiKgMSp3o8vT0xJ49ezBq1CiYzWbUrl0bOp0OvXv3xq5du4Si7UREREQ1XawuAXGZifAub32uGC1EYhHkdsrcZYs2CogkZZ8hRkWTaRRQuWigDUtCtrbkG/7Y1HKAyWAqUNC+oYMfzsdfQZo+w9qhEhERURmUukYXADg6OuK9996zdixERERET5SQxFAAKH+iK1oLuYMKJoMRem0WNF721giPiqBys0VOhh5JN2Lg3qY2xCVIKkoVUqhcbYRaXXn10/zt/bA//BhOxpxHz9rPV3ToREREVIwyJbqSkpKwdu1anD17FlqtFo6OjmjTpg1Gjx4NZ+fyFWIlIiIielJcTgyFg9wWdnKbMvdhMpqgi0+Dna9T7rJFoNrX53qUISsHmQnpEEvEkChlkCilkCqkEImr56w0kUgEm1oOSLkbj7QHSbCv61Ki62y9HRB3MQJpESmwq+0IAHBQ2MFT7YqjkaeZ6CIiIqoGSvTp4+DBg8LXMTExGDhwINavXw+FQoEmTZpAKpXiv//9L/r374/Y2NjH9ERERERUcwQnXodXOetzZcanw2w0Q+GoRmZCOqQaOcTS6pkgys/J1RkjJr8BtUSJ1LsJyMnQI1ubhYyoVGjvJSLpRiySb8Yi9X4iMmK0yErRwZCphxklLwJfkSQKKVQuNkiLSEGOrmR1xuS2SijsVUi6YVmAv75dHfwdfRYms6kiQiUiIqJSKNGnqPfffx9JSUkAgCVLlkAikWD//v3YuHEjli9fjo0bN+LAgQNQKpX46quvKjRgIiIiourAYDLiWtJtq9TnEsskkCgkyE7JfGJmc6lUSvjXbgCkGyGzVUDjYQcbL3vYeDtA7W4LpZMaEpUMZpMJ2amZyIhMReq9RGjvJ8Fsqh4JIZWLDcQyCVJux5d4F0Ybb3tkJmQgMyFdOFbf3hcp+jTcSL5TUaESERFRCZUo0WU2m5GYmAgAOHnyJKZMmVKg6LyPjw8mTZqEEydOWD9KIiIiomrmdup9ZBmzrVKfS+GgQnZSJmAG5HbVP9GlT8tEZPB9/HvsJIxKQOmoFmpWicQiSBRSyGwUUDqqoXa1zU2A1XKAykUDg04PbXgyzNVg9pNILILGww7ZKZnIjE8v/gIASmcNpCoZEm/8bxWDj8YDSokCJ6MvVFSoREREVEIlSnSZTCbo9blTuo1GIxwdHQtt5+TkhPT0kn1IICIiInqSXU68ATHE8FS7lbkPY7YBWUk6KB3V0CWkQ6qSQSKTWDFK6zKbTEiPSkFaeArSMtOxf99+pGeW7LOfSCKCVC2HysUGhoy8ZFfVL2OU2yohs1Ug5W4CTIbik2+59b3skfYwGTnp2QAAiViCOrbeOBF9tqLDJSIiomKUKNEll8shEv3/zjL+/vj9998Lbbdnzx40bNjQetERERERVVOXE2/CQ+0CuURW5j4y4tIAAHI7BbKTdNV62aIhMwcpdxOQnZwJhaMKCgdVmfqRqmRQudrAkK5H2sOkapHs0njYwZRjgvZBUonaqz3sIJaIkXQrTjhW3642QhJDkZ6TUVFhEhERUQmUaNfF119/HSpV7oeZt99+G2PHjkVqaip69eoFV1dXxMfHY9++fTh58iS++eabCg2YiIiIqDq4lHANtcpZiF4XrYVUJYMhMwdmk7laLls0w4yshAzoYtMglomh9rSFRCaFKElU5j6lKhlULhpkJmQg7WEybH0chT+qVgWJXAqVqwbpkSnQeNhCplE8tr1YIobG0w4pt+Ph0twLEpkE9e19YQw34nRsMLp5P1tJkRMREdGjSpTomjlzpvD1s88+iy+//BJLly61qMfl4uKChQsX4sUXX7R+lERERETViFafjrC0CAQ4Ny5XP+kxufW5MhMyIFFIIVGU6KNZpcqISkV2ciZktgoo7FVCLa7yyl3GCGQmZCA9IgU2Pg4QoeqSXSpnG2SnZCL5djxcW9YqNvFmU8sBaREpSLkTD+fGHnBU2MNF6YiT0eeZ6CIiIqpCZfo01b9/f/Tr1w/37t1DamoqbGxsuGSRiIiInhpXkm4CAGqVoxB9TkY2ctKyYePtgJRb8VA6qa0VntXo0zKFpYqPLqtUKBVo2LQRFMrHz356HKlaDqULkJWX7PKuumSXSCyCxtMeaQ+SkBmfDrWb7WPbSxRSqF1tkRQaByd/d4jEItSzq40T0edgNpurdIYaERHR06xENboe9dNPP2HixImoV68eWrVqhZSUFHTu3BmbN2+2dnxERERE1c7lxFCoJEo4KwvfoKckMmJy63OJRCKYjaZqV5/LZDAiPSIVElXuDoqPcnV3w4T3J8LVvezF+AFAppZD6ayBPjUL6ZGpMKPqanbJbRSQ2ylLXJjextseBp0eaQ+TAQD17XwRrYvD/bTIig6ViIiIilDqRNfatWuxbNky1K5dWzjm7e2N7t2744svvsCOHTusGiAREZVcdSjq/KTis6PSCEkMRS2NO8TlmLWTEaOFzFaB7GQdxHIJJMrqs2zRDDPSIlJgBqB0sil0dpLJZEJWZhZMpuITQsWRaeRQOmmgT8ms8mSX2t0OJoMJ2vuJxbaV2yqhcFAh8UYsAKCOrTekIglOxpyr6DCJiOj/2LvvOCvK6/Hjn5nb697tld4tFEFsqKiIWFCxlxjFGPklauwFjYn6tURijcQWY8GKBUUQKyCKNFmaNOlt2d5u7/P7Y+XGdRfYu+zdpZz365VX2Jln5jk7uwnDuec5jxC7kXSia/Lkydxyyy3ce++9iWMFBQXcf//93Hjjjbz++uvMnz+fESNGMGPGjDYNVgixfxk+fDiKonD88cfvdsxll12Goihcc801Sd97+PDh+xbgIeaVV17hjjvuaLf5vv/+e4qKilBVFb1ej16vZ+TIke02f1sJhULceuutvPPOOx0dSqts2bIFRVF4/fXX9/leXq+XG264gby8POx2O2eddRY///zzvgd5kNE0jeVVq/epEb2mafhK3ZjSzASqfBgd5v1qqVuw2kfUF8aSaUPVNR/Xzu0l3HfD3ezc3jbVSwa7EVOGlXBdAH+pu03u2Ro6ow5rlh1vST0Rb2iv4+2FLoLVPvyVXow6A50dBXxfurgdIhVCCCFEc5JOdJWVlXHkkUc2e27AgAFs3LiRRx55hF69evHUU0/tc4BCiP2bqqosWLCAHTt2NDnn8/mYNm1aq+77/PPP8/zzz+9reIeUhx9+mOrqvVcgtJXevXvz2WefsWDBAhYvXsyaNWv44osv2m3+tlJaWsozzzxDJBLp6FA63BVXXMEHH3zAP/7xDyZNmkRJSQmnnHIKtbW1HR3afmWHr4y6sIeifejPFaoLEAtF0ZkMxCOx/Wq3xVgogr/Mg8FuRG8xtOvcRrsJk8tCsMZPuAVJplQxZ9rQmfTUrq/ca7WnOdOK3mqgZk0ZAD2cXfixYgXhWLg9QhVCCCHEbySd6CosLGT+/PnNnvvxx4Yy7REjRnDddddRXl6+b9EJIfZ7Rx11FGazudlly9OmTcNms1FYWJj0fQ877DAOO+ywtghRpEhubi4DBgxg6NChDBw4kF69eqGqrWr9KPYD8+fPZ9q0abzxxhtcc801XHDBBXzzzTe43W5JOv/G8qo1wL41oveVuVFUhVgwgqpX2z2htDuapuHeXoeqVzG5OqY5vsFhQjXp8e2sQ9P2fVlkayiqgjXPSdgdJFjl2/NYRWnYgXF7HRFviJ7OzgRjIYorV7VTtEIIIYT4taT/RXLJJZfw3//+l8cff5zi4mK2bNnCkiVLePLJJ3n55Ze5++67ueWWWygqKmLcuHGpiFkIsR+x2WycffbZzSa6Jk+ezEUXXYRe37jvTFVVFTfccANdunTBaDSSkZHBmDFj2LJlS2LMb5cuBoNB/u///o++fftiNpvp1asXjz/+eKPeMMOHD+d3v/sdF110ETabjdNPP323cS9YsICRI0fidDrJzs7m8ssvp6Tkf8tvSktLufbaa+nUqRMWi4WhQ4fy6aefNrqHoig8//zzXHfddWRkZOBwOLjkkkuaJPnffPNNjjrqKKxWK507d2b8+PGEw//7pH/lypWcc845OJ1OnE4nY8aMYdOmTYnz3377LYqiMHPmTEaOHInVaiUvL4+7776bWCwGQNeuXdm6dStvvPEGiqKwZcsWXn/9dfR6Pa+88gp5eXlkZGSwevVqYrEYjz/+OEcccQQWiwWbzcbxxx/P7Nmzmzyj008/fbfPaMWKFVxwwQVkZ2djMBgoLCzkL3/5C4FAIKmfW3OCwSB33XUXnTp1wmQy0b9/fyZPntxoTNeuXXnwwQe59dZbycrKwuFwcOWVV+L1enn88ccpKioiLS2NCy+8cLeVblu2bKFbt24AjB07lq5duybOff3115x44omkpaWRmZnJFVdcwfbt2xPnd/d8hw8fznXXXcfjjz9O586dMZvNnHDCCSxatKjR3IsXL2bUqFFkZmbidDoZPXo0q1bt/R/GU6ZMYcCAAVgsFo466iiWLVvWZExNTQ3jxo0jNzcXs9nMsccey8yZM/d43y+//BKbzdZo+Wl2djYnn3yytCL4jRU1a8kyp2MzWFp9D1+ZG4PTRKDah2E/WrboL/cQD0UxZ9lQ1A7a/VBRMGdYiEfiBCr3nGRKJaPdhN5mxL21Zq9VXdZcB4pOpW5TFbmWLJwGu/TpEkIIITpI0omua665ht///ve8+eab/O53v+PMM8/kyiuv5LXXXuPqq69m7NixQMMn/TfeeGObByyE2P9ceumlTZYvut1uPv/8cy6//PJGYzVN4+yzz+arr77i8ccf56uvvuKBBx5g5syZ/L//9/+avb+maYwePZoJEyZw3XXXMW3aNC6++GLuu+++JtdMnjwZh8PBp59+yl133dXs/ZYuXcrJJ59MMBhk0qRJvPjiiyxevJgzzjiDaDRKeXk5Rx99NN999x2PPvooH330EV27duX8889vsrvsvffeSywW47333uOf//wn06ZN45Zbbkmc//e//83vf/97Bg8ezMcff8z48eP517/+xU033QTAunXrOP7446moqOCNN97gv//9L5s2beKEE06goqKi0VxXXnklJ554ItOnT+eKK65gwoQJvPLKKwB8/PHH5OXlcdZZZzF//nzy8/MBiMViPPnkk/z3v//l6aefpl+/ftxzzz089NBDjBs3ji+++IL//Oc/1NTUcNFFF+H3+xs9o3A43OgZjRo1ilgsRmlpKSeeeCI+n4/XX3+dzz//nMsuu4znnnuOZ599Numf229/3mPGjOHFF1/ktttu49NPP+X444/nsssuY9KkSY3GPvnkk2zbto333nuP++67j3feeYchQ4bw5Zdf8p///IfHHnuMqVOn8re//a3ZufLz85kyZQoAf/3rX/n444+BhuTkyJEj6dSpE++++y5PP/008+fP57jjjmv0c2nu+QJ8+OGHfPLJJzz33HO8++67lJWVceGFFyYSk7Nnz+b4449H0zRee+01XnnlFbZv387xxx/P2rVrd/tspk2bxkUXXUT//v355JNPuOSSS/jd737XaEwwGOTUU09l6tSpPPLII0yZMoWioiJGjRrFrFmzdnvvNWvW0L17d3Q6XaPjPXv2lD5dv7G0ajUF1tbvNKjF4vjLvRisJmLB6H6zbDHsCxGs9mF0WdAZO7Yxvs6gx+gwEaj0Egt33LJia7adiC9MsHrPCTdVp2LNsVO3oQo06O7sxHelkugSQgghOkKr3mLuvvtu/vznP7N06VLq6+txOp3079+f9PTWb7EthDhwnX322dhsNj744ANuvfVWoCHxkpOTw7BhwxqNLS0txWaz8eSTTybODR8+nA0bNvDyyy83e/8vvviCb775hnfffZfLLrsMgNNPPx2r1cr999/PzTffzOGHHw6A0WjkxRdfxGQy7TbeRx55hMzMTL766ivM5oZ/YBYUFHDFFVewcuVK3n33XSorK1m3bh1dunQB4KyzzmLEiBHccccdXH755YklekceeSSvvfZa4t6LFi1KVLfF43Eeeughzj//fP7zn/8kxvh8Pt59910ikQgPPvggVquVb775BqfTCcBpp51G9+7d+ec//8k///nPxHV//OMfuf/++wE49dRT+eSTT5g+fTrjxo1j0KBBmEwmsrOzOfbYYxt9v/fddx9nn3124uudO3fy6KOPJpJtAGazmQsvvJAVK1Zw7LHHJp7Rl19+2eQZrV69mtLSUgYOHMiHH36Iw+EAGpatf/3113z77bfcc889Sf3cfu2bb77hiy++4L333uPSSy8F4IwzzsDn83HPPfdwxRVXJKoEnU4nkydPRq/XM2LECN544w1KSkpYuHAhaWlpAHz++ef88MMPzf4umEwmBg0aBECPHj0YNGgQ8Xicu+66izPOOKNRg/oTTjiBww47jCeeeIIJEybs9vkCRCIRvvzyy8TP1OPxcPXVV7Ns2TIGDx7MPffcQ69evZgxY0YisTRy5Eh69OjB3/72N95///1m433ooYcYOnQob775ZuK5KIrCPffckxjz5ptvsnz5chYsWMAxxxwDwJlnnsnw4cO5++67E20GfmvX3+e/5XA4cLs7rjH4/iYcC/Nz3SZOLxq298G7Eaj2ocXiaPE4ik7BYDW2YYSto8XieHfUoTM1JJhaIr+wgAefeQSLtfWVbXtiTLMQ8Ufw7nTj7JqBQvtXmBlsv1R1banBnGnbY+WdLd+Jr9SNd2c9PdO68OGmL6gIVJNjyWzHiIUQQgjR6mYqDoeDk046idGjR3PyyScnkly/Xm4jhDg0WCwWRo8e3Wj54q4kxW//UVBQUMCsWbM44YQT2LJlC19//TXPPfccP/zwA6FQ842Hv/32W/R6PRdffHGj47sqWebMmZM41q9fvz0muQDmzp3LmWeemUjgABx33HFs3ryZgQMH8u2333L88ccnkly/nq+srKxRxc1xxx3XaExRURE+X8Mn/+vWraOiooILLrig0Zg77riD4uJiDAYDM2fOZPjw4VitVqLRKNFoFKfTyYknnsjXX3/d6Lo9zbUnAwcObPT122+/zc0330xlZSVz587ltdde46233gJI/Az29IyOPPJIRo4cyZw5czCbzaxevZpPP/2URx55hIqKisQ9kvm5/drMmTNRFIWzzz478Uyi0SjnnnsupaWlrFy5MjF26NChjZbG5ubm0qdPn0SSCyAzM5O6urq9Pqddfv75Z8rKyppUI/bo0YPjjjuOb7/9ttHx3z5fgMMPP7xR0qioqAhoSHL6fD5+/PFHLrnkkkbVUy6Xi9GjRze5/y6BQIDi4mJGjx7d6Pgll1zS6OuZM2eSl5fH4MGDE88uFosxevRoFi9evNvG8ntaTiq91/5nTd1GIvEoRfuw46Kv1I2qVwm7gxjs5g5bIriLhoanpA4tHt9rMufXdHoddqcdnV6398GtoKgK5nQLUV+YsDuYkjlaoqVVXUaHGYPdRN2GSno4O6OgMFd2XxRCCCHaXdIVXfX19Tz99NMsWrSIcDic6FmgaRp+v5/6+nrWrFnT5oEKIfZvl156KWPGjGHHjh1YLBa++eYbHn744WbHvv3224wfP57t27eTkZHBoEGDsFp33/S4pqaGrKysJkuq8vIaGkH/Oolht9v3Gmt1dTU5ObtfdlRTU0P37t2bHG9uvt/Grapq4v8Xd/WF2tNc1dXVTJ48uUn/KWjoj/Rrzc21t15X0PSZLF68mD//+c/8+OOPWK1WDj/8cDp37gzQKPY9xR2Px7n33nv597//jdfrpVOnTgwdOhSLxZK4RzI/t1+rrq5G07REpdhv7dy5M5Fcaq4CyWaz7TbulqipqWkU56/l5eWxZMmSRsea+51r7mcFDc+trq4OTdN2e//dPZfa2lo0TSMrK6vR8V3LVHeprq6mrKwMg6H55ualpaXNVmCnpaU1u4mM2+1ulDg81C2vWote0ZNryd774N3wltZjcJoJ1fixd9r7/2elWqguQMQTwpxpQ9W3PKlZVVHF1Pc+5rzLxpCVk7X3C1pBbzWis4Twlbox2k0oHZB0Tbaqq259JXnhLhTacplbupgLup/RjtEKIYQQIulE16OPPspnn33GiSeeyKZNm7BYLHTt2pXi4mLcbjcPPfRQKuIUQuznRo0ahcPh4MMPP8Rms9GtWzcGDx7cZNzcuXP5/e9/z1/+8hfuuOOOxI6Md911F3Pnzm323hkZGVRVVRGLxRolTUpLSwGa/MN/b1wuF5WVlU2Oz5gxg0GDBpGRkUFZWVmT88nO53K5AJrMVV1dzZIlSzj++ONxuVyMGDGC22+/vcn1v23i3xbcbjejRo2if//+rFq1ir59+6KqKjNmzOCjjz5qFHtzz+izzz5j8ODBvPrqqzz11FO89NJLXHDBBYlEyNChQxNjW/tzc7lc2O32Js3xd+nZs2fy33gSMjIyAHb7O5Ds79tvuVwuFEVJ+v4ZGRmoqtokGfXbRvsul4tevXo1Wnb5a7ua7/9Wnz59+PLLL4nH440quDZs2JDoPSZgRfVaCmw56NXWVTFFgxGC1X4s2TZQFIz2li0TTJVYOIpvZz16mxGDLbkllMFAgNXLVnLGeaNSFF0Ds8uKr9SNv8KLLa9pcrs9WLPtuLfUEKz2YcnafXLSmuOgfmMVdRur6JHRmR/KionFY+ha+fsihBBCiOQl/bHY999/z0033cQLL7zApZdeSl5eHs888wxffPEFffr0YcOGDamIUwixnzOZTJx//vl8+OGHvP/++02Wfe0yb9484vE4DzzwQCLJFYvFEsv0mqtQOvnkk4lGo012dty13O63fcD25sQTT+Srr75qtPPh0qVLOfvssykuLubkk09m3rx5bN26tcl8eXl5LU609O3bl6ysLKZNm9bo+KRJkzjrrLMIh8OcfPLJrF69moEDBzJkyBCGDBnC4MGDeeqppxKN0Vvqt5VTzVm7di3V1dXcfPPNHHbYYYmExueffw787/k394yKi4s555xzWLp0KXPnzuXwww9n7NixiSRXSUkJP/30U+Ierf25nXzyyXi9XjRNSzyTIUOG8NNPP/Hggw8SjUaTeSx79dvn1qdPH/Ly8nj33XcbHd+0aRPz589P+vftt2w2G0OGDOH9999PNKeHhorp6dOn7/b+ZrOZ448/no8++qjRDnC//f06+eST2b59Ozk5OY2e31dffcWECRN2m0AdOXIkHo+HL7/8MnGssrKS7777rtFOjIe6fW1E7yvzABANRDHYjR26bFFDw7ujDkWnYnbtvqq2o6kGHcY0C8FqH9FgxzSm/3VV1552YFT1KpZfmtL3cHTGHfGysnZdO0YqhBBCiKQTXW63u1Hj3l29Umw2G9dee+1ue4sIIQ5+l156KfPmzWP27Nm7TXTtqvi58cYbmT17Nh999BGnn346y5cvB2i259SZZ57JKaecwh//+EeefPJJvvnmG+6//34eeughrr76ag477LCk4rz//vupqKjg7LPPZvr06XzwwQdcdtllDB06lJEjR3LbbbeRkZHBaaedxltvvZXYUXDWrFk8+uijLe5XpNPpePDBB3n//fe56aab+Prrr5k4cSJ///vfufHGG0lPT+dvf/sbGzZs4JxzzmHq1Kl8+eWXXHjhhbz33nsMGDAgqe/L5XKxdOlS5syZQyAQaHZMnz59cDqdPPLII3z22Wd89dVXXH/99Tz//PPA/55/c8/oiiuu4JhjjmHEiBEMHTqUFStW8I9//IM5c+bw3//+l5NOOolQKJS4R2t/bmeddRYnnXQS5513Hi+88ALffvstEyZM4E9/+hM6nW6fK6p+a1eibubMmSxcuBBVVXnsscf48ssvueKKK/j888958803Of3008nIyOC2227b5zkfe+wxfv75Z8466yw+/fRTPvzwQ0499VRCodBud4iEhqrq1atXc8EFF/DFF1/w7LPPNhk/duxYunTpwumnn84bb7zB7Nmzuffee7n//vspKCjY7ZLGk046ieHDh3PllVfyyiuv8PHHHzNixAhcLhd/+tOf9vl7PhjUBOvY6S+nkz1/74N3w1daj95iIOINYerg3RbD9UGigQjmDCuKrmP7hO2N0WFC1av4dtajsftEUyq1tFeXLT+NqD9MuseKRWdibmlxO0UohBBCCGhFois9PR2Pp+HTyK5du1JdXZ3oJ5Kbm9tsfw8hxKHh9NNPx+VyccQRR9C3b99mxwwfPpx///vfzJs3jzPPPJPbbruNzp07M2XKFKChavS3FEVJ7C749NNPc/bZZ/PBBx/w2GOP8eqrryYd56BBg/j222+JRqNccskl3HTTTQwbNozPPvsMo9FIXl4e8+bNY/Dgwdx0001cdNFFbNu2jalTpzJ27Nik5vrzn//M66+/zuzZsznnnHN4+umnufvuuxO7Kfbv35/vv/8eRVG46qqruOiiiygtLeWTTz5p0sR+b+644w7Kyso444wzKC5u/h9WaWlpTJ06FU3TuPjii7nqqqvYtm0b3333HQ6HI/H8dz2jSCTC6NGjufrqqxk2bBjTp0/HYDAwfvx4/vSnP/Hss89y5pln8s9//pOrrrqKBx54gFWrVlFXV9fqn9uupZSXXXYZjz76KGeccQYvvvgit912G++9915Sz6QlnE4nt912Gx9//DFnnnkmkUiEa665hg8//JB169Zx/vnnc9ttt3H88cfz448/NttbK1mnnXYa33zzDYFAgMsuu4w//vGPFBUVsXDhQo444ojdXnfiiSfy+eefU1JSwpgxY3jppZeaPEubzcZ3333HsGHDuOuuuzjzzDOZMmUK//jHP3jqqaf2GNeUKVM477zzuPPOO7nmmmsoLCxk5syZsqvyL5ZXN2xEUWhr3e+Apml4d9ajMzVU1RnsHZfo0jQNf5kbnUWP3tx88nN/oqgKpgwr0UCEUG3zifxUa2lVl9FhwmA3Ur+hmm7OTswtbX6nUyGEEEKkhqLt6W/qZtx1111s3ryZZ555hoKCAo477jjGjRvH2LFjefzxx/niiy9221dFpFYsFmPZsmUMHDiwRUuYhBBib1atWsXRRx/Nvffey/jx4+X/W8Qh7ZkVr/HO+k+5vf8fWrwz4a8Fa/1snrEag92Epmmkdc1MQZQtE6jy4i/3YMt3ohpa979rT72bJQuKOerYwTjS2qd3VqDKRywYwdU7B1XX/o3pI74Q7i01ZB6et8deXd6SOuo2VuE/ycT0stnMu+BD0ozNb7AhhBBCiJZpac4j6TeEm2++merqau6++24URWHcuHE8/vjjHHPMMbz++utceOGF+xS4EEKI/UM4HMbj8fDEE09w//3373azACEOFcWVKymy57cqyQXg3VkPSkOyxOjowGquWBx/pRe9zdjqJBeAI83JyWec0m5JLgBTesPOrr4yd7vN+Wstreqy5jpQFIXcagdxNBaUL23HKIUQQohDW9JbehUWFjJjxgy2bNkCNPQCycrKYsmSJfTv358xY8a0dYxCCCE6wJYtWxgxYgQ6nY6rr76a4447rqNDEqLDROJRfqr5meH5x7T6Hr6d9RhsRiLeMMYO7M/lr/SCpmFyWfbtPj4/61f/TK/D+mC1tU8ze1WnYnJZCNUGiKRbMFjbf9fKluzAqOp1WLLtBDe7yemRydzSYs7odFI7RyqEEEIcmpJOdH3zzTcMHz68Uf+d0aNHM3r06DYNTAghRMfq3bs3Xq+3o8MQYr+wpnYDoVi41Y3oY5EY/kofeqsBvcWAbh8qqfZFLBwlWO3D6DTv89K/mqpqJr3wOrf+/Y52S3QBGOwmIr4w3p1uXD2zUGjfRvq/ruoyZ9p2W+Fny3dSuayEw+Jd+L70RzRNa3U1oBBCCCFaLuk3nBtvvJFhw4bx0EMPJXZJE0IIIYQ4mC2pWoVB1ZNvzWnV9f5yD2gasUCkQ5ct+ss9KKrSoRVl+0pRFMzpVuKhKKEaf4fE0JIdGI1OM3qrkfzqNMoDVWx0b2vHCIUQQohDV9IVXR9//DHTp0/n888/591336Vz586ce+65nHvuuXTq1CkVMQohhBAHpHA4zMSJE3E4HPTu3ZtevXqRn9/6Hk+i4yytXE2hLRe92rpKLO/OelSjjng41mFJpmggTNgdxJRhRVEP7N9BnUmP3mrAX+nF5LKgqO3bmL4lVV2KomDLd1K/qQqrzczcssX0TOvSrnEKIYQQh6KkE139+vWjX79+3HnnnRQXF/PZZ5/xzjvvMHHiRAYOHMh5553HZZddlopYhRBCiAOK3+/njTfeYOXKlcTjcQBsNhu9evVK/GdXAqx3795kZmZKEmw/pGkaS6pW0tfVo9XX+3bWo+pUFJOCzpT069c+02ho4K7qVQw2Y7vPnwqmNAu+UjfBGv8ed0BMlf/16vJjybI1PybXQf3magYFe/D9zh+5po9s2iSEEEKk2j69aQ0ePJjBgwdz22238cwzz/Duu++ybNkySXQJIYQQgMvlYvny5YRCITZt2sT69etZv34969atY/369cyfP58dO3Y0Gv/b5NeuhFhaWloHfieHthJfGVXBWjq3sj9XxBMi4guj6BRM6e3Xy+q3MUT9ESxZu+8plSyDwUBh5yIMBkOb3C9ZqkGH3mbEX+nFnGHtmKouqwHPjtrdJrp0Bh2WLBsF1Rqf2Bfijwaw6vdtEwAhhBBC7FmrE13hcJjZs2fz2WefMWfOHOLxOKeccgrnnXdeW8YnhBBCHPBMJlOiIvq3fD4fGzduTCS/diXCvv76ayoqKhLjcnJymq0C69mzJ1ZrxyRPDhVLqlYBtLoRvbe0HgAtpnVIf65ENZdJj87Sdkmp3II8bnvgzja7X2uY0ixEfWEClV6suc52n9+cacO7vY6wJ7jbn609P41AhReXz8qC8mWcWig72AohhBCplHSia86cOXz22WfMnDkTn8/HwIEDueeeezjrrLPk02YhhBAiSTabjf79+9O/f/8m5+rr65tUga1evZpPPvmEurq6xLjCwsJmq8C6d++OyWRqx+/m4LSkajU55sxWV+J4d9ajM+rQNA19GyaaWipU6ycejmHNdRx0S2NVvYrBYSJQ7cOcaUPVt+9ulkaHGdWow7O9jszD8pofk2ZGbzVwmKczc3YulESXEEIIkWJJJ7rGjRtH586dGTt2LOeeey6dO3dORVxCCCHEIS8tLY0hQ4YwZMiQRsc1TaO6urpJFdiiRYt4++238fkadoJTVZUuXbo0uxyyS5cu6PXt3yvqQLSkciVF9uaTGHsTj8XxlXlQFAVjmrndE01aPI6/3Iveamjz3mA7tu7gX488xV/uu42iLkVteu9kGJ0WIt4QgUovtvz2/dBVURTMGTb8ZW6iwQh6c9NEpqIo2PKc5G0O8/22H9GGaAddwlEIIYTYnyT9xnPLLbcwZswYcnNzUxGPEEIIIfZCURSysrLIysri+OOPb3RO0zRKS0sbVYGtX7+eWbNm8Z///IdQKAQ09Ffq3r17kyqw3r17U1hYiNrO/Y72V56wjw31Wzmv62mtuj5Q4YW4hoaGqQOWLQaqfGixOKa0VDRr14hFY4CWgnu3nKpTMDotBGv8mLNs6Aztm8A1uywEKj14S+px9chqdsyupvS2Cj3r6jfTx9W9XWMUQgghDiVJvwm89NJL9OjRg9NPPz0V8QghhBBiHyiKQkFBAQUFBZx88smNzsViMbZv395kOeSnn37K5s2bicViAJjNZnr27Nnscsjc3NxDqhplefUaNDQ62Qtadb23tB5FVUABfTvvdhiPxghUeTE4TKiG9l3S196MDhNhTxB/hRdHoatd51Z0KuZ0K77Sepxd0ptdPqkz6jFnWOlZn8eckoWS6BJCCCFSKOlEV15eHl6vNxWxCCGEECKFdDodXbt2pWvXrk0+sIpEImzZsqXJcsj33nuPbdu2oWkNVTsOh6PZKrBevXqRkZHREd9WSi2pWoVNbyXT5GrV9d6d9aAoGOymdk8Q+is8oDQs7TvYKaqC0WkmXBcglmVDZ2rfXmjmDBuBah++UjeOTunNjrHlp5G20s/89cVcf/jl7RqfEEIIcShJOtF16aWX8sgjj7B06VL69OmDzdZ0O+Xzzz+/LWITQgghRDsxGAyJxNVvBYNBNm7c2KgKbN26dcyZM4fS0tLEuIyMjGarwHr16oXD4WjPb6fNLKlcSSd7XquSVBF/mHB9EACjs32XLcbCEUK1AYwuC6ru0KjAM9pNRDwhfOUenJ3bN+mqGnSY0ix4S+qwF7oaqvh+w5xhRTNAdKuPupAbl6n9d4kUQgghDgWKtusj2hbq27fvnm+oKKxZs2afghKtE4vFWLZsGQMHDkSnO7iXKAghhNg/eDweNmzY0GQ55Lp166iurk6My8vLa7YKrEePHlgs+2fFUTQeY+hH5zMsbwjD8ofs/YLfqNtQSenCraBARt+8ZpMfqeLZXkPEF8GW70zZvJFwmOrKajKzMzEY23dZ5u5EvCGCNX7Sumeit7RvTNFghPqNVWT0y8Wa03xit2pjGe6ddRx7xcmM7jGiXeMTQgghDnQtzXkkXdE1c+bMfQpMCCGEEAcPh8PBoEGDGDRoUJNztbW1TZJfy5Yt44MPPsDtdgMNH5B16tSp2eWQ3bp1w2Bo3yVov/Zz3SYCsRCdW92fy42iKuhtxnZNckWDEcLuEKYMa0rnNRiN5BXmp+z+raG3GVHdQXzlHtK6Zrbv3GYDBrsRz/ZaLNn2ZqsAXQWZBHd4mbdskSS6hBBCiBRJOtFVWFjY6OtQKITRaDykGtMKIYQQYu/S09MZOnQoQ4cObXRc0zQqKiqa7Aw5d+5cXn/9dQKBANDQU6xbt25NqsB69+5Np06dUl69vLRqFXpFR74tJ+lrtbiGd2c9WlzD1M7LFv0VHhS9iiHFze9rqmr4etqXnD76DDKy9o/+bIqiYHJZCFT5CPtCGG2mdp3fnGnHs7WGUH0As8va5LzeYiBsi+PfXEssHkOnSgW+EEII0dZatf/ypk2b+Ne//sW8efPwer188MEHfPjhh3Tv3p2rrrqqrWMUQgghxEFEURRyc3PJzc1l2LBhjc7F43F27tzZqAps/fr1fPXVV7zwwgtEIhEAjEYjPXr0aHY5ZEFBQZt8ALekahUFtlwMavKvS4FqH1o0DoDB3n6JrmggTMQTwpxhTfmHkH6fj0XfL+CEU4ftN4kuAJ3FgGrU4S93Y+iehUL7fRhrsBnRmfV4t9c1m+gCsOY6MG5SWbB5CSf0OLrdYhNCCCEOFUm/ua1Zs4Yrr7ySzMxMRo8ezTvvvAM0fOr66KOPYrfbGTNmTJsHKoQQQoiDn6qqFBUVUVRUxKmnntroXDQaZdu2bU12hpwyZQpbtmwhHm9ILFmt1ibJr11/zsrKanECqLhyJT2dXVr1ffhK64GGxIeqV1t1j1bNW+FB1avoU1zNtT9TFAVTmoVApZeIJ4jR0X494BRFwZxpw1dST8QfxmBt+nPIzs9m+xY33y+aK4kuIYQQIgWSTnQ9/vjjHHHEEbz66qsAvP322wD89a9/JRQKMWnSJEl0CSGEEKLN6fV6unfvTvfu3Rk1alSjc+FwmE2bNjVZDjlp0iR27NiRGJeWltZsFVivXr1wuVyJcaW+CioC1ZxacFyrYvVsrwPA4Gi/aq6IP0TUG8acaTvkW0roLQZ0Jj2+Mg8Gh7ldq7pMTguBcg/eHXWk92667FWn1xNwxtA21xKPx1HV9kuECiGEEIeCpBNdy5Yt46mnnkKv1xOLxRqdO+uss5g+fXqbBSeEEEII0RJGo5G+ffs2uzu03+9vdmfImTNnUl5enhiXnZ2dSH7Fs3W4w+VgChE0BTFbW56wigYjhOoa+owZ27E/l7/C21DNZe24Bv77E5PLgr/cQ6iu+X5ZqaKoCuYMG74yN86uGeiMTV+3LXl29GvDFK9eytFHDG632IQQQohDQdKJLpPJRDAYbPZcXV0dxv1ke2khhBBCCGhYyti/f3/69+/f5Jzb7W5SBbZ69Wp+WruSgNvPA8/eBUBGbiZ5XQvI71pAftfChj93KSC3cz4GU+PEkq+0YUdJncWAztA+zcYjvhBRXxhzVvtVczmcDk49awQOp6Nd5kuWzqRHbzEQKPdgSjOjKO1XOWVKt+Kv8uLdWd/s7o+5mblsNazn+0U/SKJLCCGEaGNJJ7pOOOEE/vWvf3HUUUeRnZ0NNPQj8Pl8vPrqqxx//PFtHqQQQgghRCo4nU4GDx7M4MGNkw1jvvh/KL44R8V7U7plJ6Wbd1K2dScbf9rA3GlzCPkbPvRTVJWsguxfEmAF5HUpQO9VsMZNFPTt3C7fg4aGv9yDalDRW9qvmist3cXZF41ut/law+Sy4Ct1E6z2Y8myt9u8ql7F7LLgK6nH2SkdRdc4yWbRm/CmhXHvqMXr9WK3t19sQgghxMEu6UTXnXfeyaWXXsqoUaPo27cviqLwj3/8g82bN6NpGk899VQq4hRCCCGEaBe+iJ91dZs5u8up9M7uR+9B/Rqd1zSNusrahgTYlhLKfvnvVQt/Ytb7XxEJN+wMqepUsnKyyc7PISc/h+z83F/+nIsr09VmvZki3jDRQARLO1ZzAQQDQXZs3U5Rl06YLe23RDMZqkGH3mbEX+nFnG5tknBKJXOmjWCNH1+5B3tBWpPzlmw7WnWcpcuWcuKwE9stLiGEEOJgl3SiKz8/n6lTp/L666+zYMECOnfujN/v55xzzmHs2LHk5DRtuimEEEIIcaBYXr2WOBqd7fnNnlcUhfScDNJzMjhs6BGNzrl31LJqajF1njp8SojK0goqSytYueQnqitmE4817AxpMBjIyvtfEiznlyRYdn4uTpezxQkrDQ1/hRvVqEPXjtVcAFUVlbwwYSK3/v0Oirp0ate5k2FyWYj6w/grvdjynO02r86ox+g049lehy2/6c+0IC2HzeaNLFi8kGEnDDvkNxAQQggh2krSiS6A9PR0br311raORQghhBCiwy2tWo1VbybLnJH0tb7tdbhcLgr6dGqyVC4WjVFTWU1FaTmVpRWJ/14yr5jaqho0TQPAZDYlKr8akl8Nf87Jz8HmaHzPiCdELBDFkm2XRMluqDoVo8NMsNqHOdOKztCq199WMWfacG+uJljta/L74DKmUevwk1PhY8eOHXTqtP8mC4UQQogDSav+pl+0aBFGo5GBAwdSWlrKgw8+SElJCaNGjeKGG25o6xiFEEIIIdrNksqVFNnyUZNMHGmahnt7HQDGNEuT8zq9LpG4+q1IOEJVeSUVpRVUJhJhFWxcs4H62rrEOKvNSk5BLtl5OWQX5GBTLWRmZFCU1RU9bVPRtWbFKgD69T+8Te63PzA6zYS9IfzlHhxF6e02r8FqRG814tlW2yTRpSgK9gwH4ZoYS5YskUSXEEII0UaSTnR98sknjB8/nmuvvZaBAwdy//33U1xczAknnMCLL76IwWDg+uuvT0WsexQKhXjwwQf56quvMJvNXHvttVx77bXNjl29ejV///vfWbduHT179uTBBx/kiCP+t/Rg+vTpPPPMM1RWVjJs2DD+7//+j4yMhk91NU3jySef5MMPPyQej3PRRRdxxx13JPps1NbW8re//Y25c+eSnp7OzTffzHnnndckhsWLF3P33Xczc+bMRsf3NLcQQgghUisWj7Gseg3H5Q5K+tpgjZ94JIbOpEt6t0WD0UB+pwLyOxU0ORcKBKksq6SyrIKKneUNybCyClYvXYnP60uMczgdZOflkJWTRVZudkMyLDebrJwsDEnsir1h7Qa+/WIWRw87hvMvv2C/7b+VDEVVMKVZCNX6iWaG0Vvab5dwS5YNz7ZaQvUBTL9JgBbactli2czKlSsZNWoUJpOp3eISQgghDlZJJ7pef/11xowZw5133kllZSXz5s3j9ttv5w9/+AOvvvoqkydP7pBE14QJE1i5ciVvvPEGO3fu5O6776agoIBRo0Y1Guf3+7n++usZPXo0//jHP3j33XcZN24cX3/9NVarlRUrVnDffffx4IMP0rdvXx555BHGjx/PSy+9BMBrr73G9OnTmThxItFolDvvvJPMzEz+8Ic/ADB+/HiCwSCTJ09m+fLl/PWvf6Vbt26NtjT/+eefufnmm5u8zOxtbiGEEEKk1vr6Lfijgd3259oT97YaAIwua5vGZLKYKerWiaJu/6v40dCoW19FIBjAHw9QWV5JVXklleUVlO7YyYri5QQDwcR4V4arIfmV+0vyKzeb7NxsMrIz0esbvw6ec/G55OTnMvWdKWxcu4HLr7uS7r17NIlLp9ORlp6GTpdcUq+jGOxGIp4gvjI3zm6ZKLTPUk+D3YTOpMezvbZJoivXms2P1uV09eSwatUqjjrqqHaJSQghhDiYJZ3o2rRpE/feey8Ac+bMQdM0TjvtNACOPPJInnnmmTYNsCX8fj8ffPAB//nPfzj88MM5/PDDWb9+PW+//XaTRNeMGTMwmUzcddddKIrCfffdx3fffccXX3zBBRdcwFtvvcWZZ57J+eefDzQk0E455RS2b99Op06dmDRpEn/5y18YMmQIAHfccQfPPvssf/jDH9i2bRuzZ89m5syZFBUV0bt3b5YtW8Y777yTSHS99957PP7443Tq1Amv19sotr3NLYQQQojUWlK1ClVRKbDmJn2te0tDosvkbLpssa2F64PEw1Fc+RlkmfR07t6l0XlN0/B6vL8kvyqpKq+gsryKLRs2UzzvR8LhMACqqpKelUF2zi/Jr7yG/+7Zpye3PnAH7/33HZ5//DlOPes0Rp53ZqOkWH5RAX978qGUf69tRVEUTC4LgSofEU8Io6N9KtUURcGcacO3s56IP4zB+r9qMoOqJ82ZRtgXZ8mSJZLoEkIIIdpA0okup9OZSNB8//33FBQU0LVrVwC2bdtGenr79T3YZe3atUSjUQYN+t8yg8GDB/Piiy8Sj8cbbd+9fPlyBg8enGjYqigKRx11FMuWLeOCCy5g+fLl/PGPf0yMz8/Pp6CggOXLl2M0GiktLeXoo49uNE9JSQkVFRUsX76c/Px8ioqKGp3/dUXWd999x+OPP47X62XixImNvo89zS2JLiGEECL1llWtpsCai1GXXL+rsDdE1B9p2P3QmNoKp4adFj3ozHr0puZf5RRFweF04HA66Nare+PrNQ13XX3DcshfqsCqyitZv/pn5s/5gVg0BjT0FMvIyiQ7L5tZM2ZSPG8xvxv3e7o1U911oNBZDKhGPb4yDwaHqd2qukxpFgIVHrzb60jv07hHW4Elh02mEowlKuXl5eTmJp9kFUIIIcT/JJ3oOuaYY5g4cSIbNmxg5syZjB07FoAvv/ySZ599lmHDhrV5kHtTWVlJeno6xl/1n8jKyiIUClFXV9eox1VlZSU9e/ZsdH1mZibr168HoKKigpycnCbny8rKqKysBGh0PisrCyBxvrlry8vLE18///zzAEyZMqXJ97GnuYUQQgiReosrV9LVUZj0dbuWLZpcqa/mCtUFiIdjWHMdrbpeURTS0l2kpbvo2a9Xo3PxeJza6tqGxvhl5WzdsJltm7aBAnW1dXzy3sfc+rc7ACjdsZP/PP0if7z1/5Ff1LS32P5IURRM6RYC5R5CdQHMbbzMdLfzqgrmDBu+cjfObhnojP97BS+w5bLUtIojTF1YvHgxZ599drvEJIQQQhys1L0Paey+++4jPT2diRMnctxxxzFu3DgAHnvsMQoKCrj99tvbPMi9CQQCjZJcQOLrXaX5exu7a1wwGNzt+WAw2Ojev51nb/femz3NLYQQQojUqghUU+qvaFV/rvqN1QBNejC1NU3TCFR40Fn06HZTzbUvFEXB5/Wyfs06vv96DksXLSUQCHD0Ccfwh5uv56bxtyTGxmIx6mvricVibR5HKulNevQWA/5yD1o83m7zmjKsKIqCt6S+0XGHwYbdaCOSrrFixQp57xNCCCH2UdJvSBkZGfz3v/9tcvydd96hoKBjPs0zmUxNXgp2fW02m1s0dte43Z23WCyNklq7GsnvGmuxWPZ679Z+HxZL6j8dFkIIIQ51SypXAdDJltz7TCwcJewOohp0jSp1UiFU5yceiWPNtKXk/lPfncL333yHzW7jyMEDuHDIAHr26YVOf2A0nG8pk8uCr9RNoMaHNat1lXHJUnUqpnQr3p31ODqno+oaPm9WFIVCWx6rI1s4PFzEqlWrGrXjEEIIIURyWvU2pmka3333HT/++CNut5uMjAyOPfbYDkt05ebmUltbSzQaTTRJraysxGw243Q6m4ytqqpqdKyqqiqxZHB357OzsxM9EyorKxN9uHYtZ9x1fnfXtvT72JfrhRBCCNF6CyqWkW3OwGFMLonk3l4LgNGV2ubmDdVcXvQWQ8oSaseefAJHDOpPt97dD5jdFFtDNegw2I0EKr2YXTZUfdKLHFrFnGEjWO3DV+rGUeRKHO9sz2dt3Uac2S4WL14siS4hhBBiHyT9t3pNTQ2XXHIJ48aN4/XXX2fWrFm88sorjB07luuuuy6xvK899evXD71ez7JlyxLHiouLOfLIIxs1ogcYMGAAS5cuRdM0oOGlccmSJQwYMCBxvri4ODG+tLSU0tJSBgwYQG5uLgUFBY3OFxcXU1BQQE5ODgMHDqSkpKRRT63i4mIGDhzYou9jT3MLIYQQIrXmlRa3qj9X3fqGD6lS3e8pVOsjHo2nNKGWV5hHz369Duok1y7GNCtoEKj0tNucOqMOY5oZ7466xLsoQKYpHZvegictyM6dO6U/qxBCCLEPkk50TZgwge3bt/Pvf/+bn376iblz57JixQqefPJJli9fzhNPPJGKOPfIYrFw/vnn88ADD7BixQq++eYbXn31VX7/+98DDVVXuxJwo0aNwu1288gjj7BhwwYeeeQRAoEAZ555JgCXX345U6dO5YMPPmDt2rXcddddDB8+PLHr4eWXX84TTzzBwoULWbhwIU8++WRink6dOjFs2DDuvPNO1q5dywcffMD06dO58sorW/R97G1uIYQQQqTGTl8F232ldHd2Tuo6LRYnWO1D1aspXbaoxeP4K3zobUZ0htQuj2yprJxs/nTXjWTlHJiV56pOwei0EKzxEwtH2m1eS5adWChKoNKbOKYoCkW2fJZG1mGxWFi8eHG7xSOEEEIcbJJOdM2cOZM77riD0047DUVp2JJZVVXOOussbr31VqZPn97mQbbE+PHjOfzww7n66qt58MEHuemmmxg5ciQAw4YNY8aMGQDY7XZeeukliouLueCCC1i+fDkvv/wyVmvDp7CDBg3ioYce4t///jeXX345aWlpPPbYY4l5/vCHP3DWWWdx4403cvPNN3PeeedxzTXXJM5PmDABm83GJZdcwosvvsijjz5K//79W/Q97G1uIYQQQqTGgvKlKCh0dRQldZ2ntKGxuNGZ2mWLwRo/WiyOKcXzJMNsMdOzby/Mlv0npmQZHSYUnYKvrP2quvRmAwabEc+22kZVXZ3tBbijXtILM/npp5+kKb0QQgjRSor2679hW2Do0KE8/vjjnHLKKU3OzZ8/nxtuuIElS5a0WYCi5WKxGMuWLWPgwIGHxJIDIYQQoq3cNf8fLKlaxfX9Lkvqum2zfsZX6iGtVxZ6oyElsWnxOLU/V6CzGLCkqAl9a9TX1jF35vcMO+1E0tJdHR1Oq4W9IUI1fhxdMzDaTO02p2drDVn9CzCnN3zYqmkaU7d8w1BXf7SfvJx99tkMHjy4XeIRQgghDgQtzXkkXdF17rnn8sorrxAKhRodj8fjvPnmm5xzzjnJRyuEEEII0UE0TWNe2ZKkq7k0TcNX7kXRqSlLcgEEqn1ocQ2Tc//ahdnj9jBrxjd43O1XDZUKBpsR1ajHV1KHFo+325w6sx7PLxsZwC/LF+15LPWsIT8/v1HfViGEEEK0XIuaPIwfPz7x52g0yrJlyzjttNMYPnw4WVlZ1NfX88MPP1BZWcnll1+esmCFEEIIIdraBvdWqkN19HAk1xMzUOmDuIYhhc3htXicQJUPg92IamifnQEPNYqiYMm04it1E6j0Ys117v2iNpnTjrekjrA3hNHeUEnW2Z7PuvrN2PPTWFe8htLSUvLz81MejxBCCHEwaVGia+HChY2+zs3NBWDevHmNjqenp/Pll19y1113tVF4QgghhBCptaB8KXpFRyd7QVLX1fzcsDOeJSt1ywkDlV6Iaxj3s2qug41qaNgNMVDlw+i0oLekrkJvF2OaGbVCh3trDVmHNySzss0ZWHRmNqulWK1WFi9ezOjRo1MeixBCCHEwaVGia9asWamOQwghhBCiQ8wvW0onez5GXXLJDV+pG0VV0JtSkxSJx+IEqn0YHCZUvVRzpZrRYSbqC+PdWUdajywUlJTOpygKlmw7vp31hD1BjA4ziqJSZMtjafUqLu02kp9++omRI0diMrVP7zAhhBDiYNDqtya32813333HZ599xvz58/F6vXu/SAghhBBiPxKNx1hUsZxuSS5bDNUHiEfiGOypS0AEKhverVK9o2NrWW02hp54LFbb/tMgf18oqoIp00YsGCVY5WuXOU0uCzqTnvotNYljnewF1ITqMeXaiEaj/PTTT+0SixBCCHGwaFFF12+9/PLLPP/88wSDwcQxo9HIuHHjuOGGG9osOCGEEEKIVFpZsw5fNEB3Z3KJrurVvyxbzE5NkicejRGs8WG0m1F1+2c1V0ZWBpeOPbh6s+pNegx2I/4KD0anGZ2xVa/KLbarqsu7o45QfQBTmoUcSwZmnYlVvg0UFBRQXFzM4MGDUZTUVpgJIYQQB4uk35w++ugjnnrqKc455xwmTZrEjBkzeOONNzjnnHOYOHEiH3/8cSriFEIIIYRocwvKl2LWmSiw5SZ1nWdHXcOyRbMxJXEFKr2ggGE/reYCiITDlJWUEgmHOzqUNmVKs6KoKt6d9WhoKZ/P6DSjM+tx/1LVpf6yfHFJ5Uq6d+9OWVkZpaWlKY9DCCGEOFgkneh6/fXXufzyy3n44YcZOnQo3bt355hjjuHRRx/l8ssvZ9KkSamIUwghhBCizc0vX0oXRyE6peWvRCF3gHg4hsGemiRXLBIlWOPH6DCj6vbfKp7y0nL+ef8/KC8t7+hQ2pSiUzCnW4j6woTqgnu/YF/nUxSs2Q5CdQGCtX6gYffF6lAdMaeSaEovhBBCiJZJOtG1detWRowY0ey50047jU2bNu1zUEIIIYQQqRaMhlhatZpujqKkrqv6qaG6xpLjSEVYDdVcqoLRvv9Wcx3s9FYjeqsBf2k98Vg85fMZHCb0FgPuLTVomkaOJQuTzsTSqtV0796dlStXEgqFUh6HEEIIcTBIOtGVm5vLzp07mz23Y8cO7Hb7PgclhBBCCJFqS6pWEYlHku7P5d1Rh6JLzW6LsXCUUG0Ao9OMsh9Xcx0KTOlWNDR8pfUpn0tRFCw5DsLuIMEa/y/LF3MprlxJt27diEajrFixIuVxCCGEEAeDpBNdp556Ks8++2yTv2yXL1/Oc889x6mnntpmwQkhhBBCpMr88qU4DDZyzJktvsZf6SUejWN0pKbayp+o5krdbo6iZVSdisllJVwfJOxNfTWVwdZQRebeUo2maXSy51MVqqEOD4WFhfz4449oWup7hgkhhBAHuqS3krnpppuYN28el156KYWFhWRlZVFVVUVJSQk9evTg9ttvT0WcQgghhBBtal7ZEro6ipLaza5qZUNVuyW77SvYY6EI4boARpcFRT0QqrkUdHodcCDE2joGm5GIL4yvpA5Dr2wUNXU7YCqKgjXHgXtLDcFqH7mZ2RhVI0uqVjO01+HMnj2brVu30rVr15TFIIQQQhwMkv7b2m638+GHH3L//fdz5JFHYrVaOfLII7n//vv58MMPcblcKQhTCCGEEKLt1Ic9rKndkNSyRU3T8Jd5UPQqOmPSnxXula/cg6JTD5hqrqIuRUx4+SmKuiTX4+xAoigKlgwr8Wgcf4U35fMZbCYMNiP1m6tRUSiy5bKkciU5OTmkpaWxcOHClMcghBBCHOha9ZZmMpm44ooruOKKK9o6HiGEEEKIlFtUsRwNje6Olie6PNvr0OIa5nRLm8cTDYSJeEKYMqwHSDXXoUM16DCmWQhW+zClmdFbUrPb5i6WHAfuzdUEKr10thfwbelCSgOV9OzZkyVLllBfX09aWlpKYxBCCCEOZKmrvxZCCCGE2E/NL1tKpsmFy+Rs8TXVq8sAMGe1/bJFX7kHVa9isKU2idKWyneW8dQD/6R8Z1lHh5JyRqcJ1ajDs60WLcW7MBqsRgwOE/VbasixZGJUDSytXEXXrl3R6/UsXrw4pfMLIYQQBzpJdAkhhBDikDOvbAndnC1fchePxgjW+FAN6i99qdpOxBci6gs39OZKol9YR4tEIpRs20EkEunoUFJOURQsmTbi8TieHbVopLYpvDXbQSwQIVThp8CWy5KqVRgMBrp160ZxcTHRaDSl8wshhBAHMkl0CSGEEOKQUu6vYqu3hO6Ozi2+pm5TNWhgyrC2aSwaGv5yD6pRh95iaNN7i7alGnRYMmxEvGECVant16W3GDA6zbi31NDZkkepv4Iyf8PyxUAgwKpVq1I6vxBCCHEgk0SXEEIIIQ4p88uXAtDV0fKKrtqfywGwZNjaNJaIJ0Q0EMGUdmBVcx2q9FYjRoeZQLmXsC+U0rksOXZioShOtxmDqmdJ5SqcTif5+fksXLgQTUttVZkQQghxoGp1oqu+vp6ZM2fy7rvvUlNTw6ZNm+QvXCGEEELs9xaULyXfmoPN0LKm8tFAhLA7hGrSo6ht9xmhhtbQm8uoR2du+10cRWoYXWZ0Jj3e7XXEo7GUzaM3GTClW/BsqaOzqYCFFcvQNI1evXpRWlpKSUlJyuYWQgghDmSteqt64YUXeOmllwgGgyiKQv/+/XnmmWeora3l1VdfxelseWNXIYQQQoj2omkaP5QtoXda1xZfU7OuAgBLGy9bDNcHiYeiWHIdB2Q1V0ZWJr//0zVkZGV2dCjtSlEUzFk2/GVuPNtqcXbLTNnPz5rjJOyuoKgunTmmbWz27KBrXiF2u51FixZRVNTyqkQhhBDiUJH0x5JvvfUWzz33HGPHjuX9999PVHH97ne/Y/v27Tz77LNtHqQQQgghRFvY7NlOVbCG7s5OLb6mfmMVACZXyyrAWkLTNPzlbnQWPXrTgVnNZbVZGXD0IKy2tk0AHghUnYo5y040EMFf7kndPHoVa44DrSpCTszFgvKlqKpKz549WbVqFV5vanuFCSGEEAeipBNdb775Jtdffz0333wzhx9+eOL4ySefzC233MKsWbPaNEAhhBBCiLYyv3wZOkVHF3thi8aH6gNEAxH0FkObLlsM1fqJR+KY0touedbePPVu5nw5G0+9u6ND6RB6kx6jy0Kw2kfYHUjZPKZ0Kzqznp51+Syu+IlILEL37t1RFIUlS5akbF4hhBDiQJX0G9vOnTsZOnRos+e6d+9OVVXVPgclhBBCCJEKC8qW0Mmeh1HXsh0Oa9Y2NKE3ZbZd1ZIWj+Ov9KK3GtAZD8xqLoD6uno+nfwJ9XX1HR1KhzE6TOgtBjwldcTCkZTMoSgKtvw09EEFl8fC8pq1GI1GunTpwo8//kgslro+YUIIIcSBKOlEV35+PkuXLm323MqVK8nPz9/noIQQQggh2losHmNBxfIW77aoaRrurTUAmBzmNosjWONHix7Y1VyigaIomDJsqKqKe1sdWjyeknkMViMml4Ue7jwW7Gh4D+/duzder5e1a9emZE4hhBDiQJV0ouuiiy7ixRdf5L///S9btmwBwO/38+WXX/LSSy8xZsyYto5RCCGEEGKfra7dgDfio4ejc4vG+8s9xCNx9DZjmy1b1OJxApVe9DYjqkHXJvcUHUvVNTSnj4eieEvdaKRmF3JrjgMVlfAOL3UhNy6Xi5ycHBYtWpSS+YQQQogDVdJvbX/84x8ZM2YMTzzxBOeccw4Av//977nlllsYPnw448aNa/MghRBCCCH21bc7F2LRmSm05bZofM3PDbstmttwt8VAlQ8trkk110FGZ9RjzrASrgsQqvWnZA7VoMOSbSPfl868TT8C0LNnT7Zt20Z5eXlK5hRCCCEOREk3hlAUhYceeoixY8eyYMEC6uvrcTgcHH300fTu3TsVMQohhBBC7LNvdvxAz7Qu6NS9V1LFozG8Oxt6TxnbaNliPBYnUO3F4DCh6tuusX1HMVssHDbwCMwWSdoBGOwmoqEovp1uVKMeo83U5nNYMx14qz2U/VxCvE+coqIirFYrixYtYvTo0W0+nxBCCHEganUH1G7dutGtW7e2jEUIIYQQIiVKvGWsq9/MJd3PatF497ZaiGsYHCYURWmTGAKVXtDA6Dw4EkNZOVn84S9/7Ogw9ivmDCuBaBzPtlpc3TPRmVq26UFLKYqCIdeCviTE4p+XMrTfYHr06MGKFSsYMWIEFkk6CiGEEC1LdI0fPz6pmz722GOtCkYIIYQQIhVmlsxHr+jomdalReNr1jQsBWurZYuxaIxgtQ+j04yqa5vEWUeLRWME/AEsVgs6vfQbg4ZElDnLTqDCjXtLDWk9slDb+Nmku1xsrdrCxlXrGdjjSHr06MGqVatYsmQJJ5xwQpvOJYQQQhyIWpToWrhwYaOvKyoqiEajFBQUkJ2dTV1dHdu3b8doNNK3b9+UBCqEEEII0VozS+bRzdkJk86417GhugChugCoCoY2Wn4WqPA03K8Nd2/saKUlO3n6wSe49e93UNSlU0eHs99QdQqWbAf+MjfurTWkdctss80MABRUyDSglcZZsfInhhw1mM6dO7No0SKOO+441DacSwghhDgQtSjRNWvWrMSfp02bxhNPPMFzzz1H//79E8c3bNjAn//8Z84888y2j1IIIYQQopXqQm4WV/7EWZ1ObtH42vUNTeiNzrZZthgLRwjVBjC6LAdNNZfYM1WvYsm246/w4NlRi6NzBgpt97PPcWSxuXYz6rr19Ozegz59+vDll1+yZs0aDj/88DabRwghhDgQJf2Rz9NPP81tt93WKMkFDbu+3HLLLbzyyittFpwQQgghxL76rnQRcS1OH1f3vY6NR2PUbawGwOxqm2WL/nIPik7BaG/75uRi/6Uz6bFk2oh4wvhL3WhobXZvq95CxAkxvcbixYtxuVzk5OSwYMGCNptDCCGEOFAlneiqra3F6XQ2e06v1+P3p2ZLZSGEEEKI1pi5Yz5FtnycRvtex7q31qLF4igGFb1t78sc9yYaCBN2hzCmWVBUqeY61OitRkzpFoI1foLVvja9d641k+2WSqqqqti8eTO9e/dmx44dlJSUtOk8QgghxIEm6UTXwIEDeeGFF6ivr290vKKigueee45jjjmmzYITQgghhNgXoViY70sX0cfVtUXja35uWLZoTre2yVIzX7kHVa9iaIOkmTgwGR1mDA4T/jIPYU+gze6bbc4kYAyjTzOybNkyMjMzsdvtUtUlhBDikNeiHl2/dvfdd3PVVVdxyimnMGjQIFwuF9XV1SxdupS0tDReeOGFVMQphBBCCJG0BeXLCMRC9HX12OvYQI2PUG1DZbrJZdnnucO+EFFfGHOWrU16fe1vCjoV8si/H8dokiTe3phcFrRoHM+2OpzddRgs+/7M9KqeTHMG2yNVdPJlsGLFCnr16sXy5cs5/fTTd7sCQwghhDjYJV3R1bdvX6ZPn86ll16K1+tl5cqVBINBrr32Wj799FOKiopSEacQQgghRNJmlcwj0+Qi25yx17F16ytBAZ3VgM6Q9GeBjWho+MvdqEYdeothn+61v1JVFbPFLLv8tYCiKJgzbahGHZ6tNcTC0Ta5b541k+poHZmF2WzevBmHw4FOp+PHH39sk/sLIYQQB6JWvcXl5uZy9913t3UsQgghhBBtJq7FmbljHr1d3fZaURWLxKjfXA0aWNJt+zx3xBMkFohiybYflNVcAJXlFUx56yMu+N2FZOfmdHQ4+z1FVbBkO/CXu3FvrcHVPQtFt29JQpfRiUlnpNzQ0EN36dKldO3aleLiYk466SQMhoMzySqEEELsiXwEJ4QQQoiD0k81P1MdqqNfC5YtujdXo8U0UMDo3LfdETU0fGUedCb9QVvNBRAKhli3ai2hYKijQzlgqDoFa7adeDSOe2sNmhbfp/spqORaslhXt4VuPbrh9XpRVZVAIMCKFSvaKGohhBDiwCKJLiGEEEIclGbtmI9Nb6WTPX+P4zRNo/aXZYsmlwVlH5fiheoCxMOxNunzJQ4+qkGHJdtONBDBu6MeDW2f7pdrySIcD1MZq6OwsJD169eTl5fHggUL0LR9u7cQQghxIJJElxBCCCEOSl/vmEuvtC6oyp5fd4LVPkJ1AdDAlG7dpzk1LU6g3IPeYkBn2rc+X+LgpTfpMWfZCLuDBMo9+3Qvq95CmtHB6roNdO7cGYPBQDgcpqqqik2bNrVRxEIIIcSBQxJdQgghhDjobPGUsNmzo0W7Ldaur0RRFRSDus9LDYM1fuLRuFRzib0yWI0YXRYCVT6CNb59ule+NZsd3lK8MT89evSgpqYGm83GggUL2ihaIYQQ4sCxT4mujRs3smzZMrZu3dpW8QghhBBC7LNZJfMwqHp6ODvvcVwsFMW9pQYtrmHJsKHQ+sbxWjxOoMKL3mZENehafZ8DhSs9nTFXXoQrPb2jQzlgGR0mDHYjvlI3YU+w1ffJtmRiVA2sqllPZmYmmZmZhMNhNmzYQFVVVRtGLIQQQuz/WpXomj59OieeeCLnnHMOl19+OaNGjeLEE0/kk08+aePwhBBCCCGS982OefRwdsao23OFVv3marR4Qx+jfa3CClT50OIaprRDo5rL7rQz7LQTsTvtHR3KAUtRFEzpVnQWPZ7ttUQD4VbdR0Ul15rFmtqNRONRevToQTweR6fTsXDhwjaOWgghhNi/JZ3omjVrFnfeeSc9e/bk0Ucf5eWXX+bhhx+me/fujB8/nm+//TYFYQohhBBCtEx1sJblVavp4+q+x3ENTegrUPQqBrsRVd/6Kqx4NE6gyovBYULVHxqdIfxeH8Xzf8Tv3bdld4c6RVGwZNpRDTrcW2uJRaKtuk+BJYdwPMxG9zZMJhNdunQhFouxdOlSAoFAG0cthBBC7L+SfhN74YUXGDVqFK+99hpjxozhxBNP5MILL+SNN95g1KhRvPTSS6mIUwghhBCiRb7duRAN6J3WbY/j/BVewu4QWjS+z03oA5UNDcWNzkOjmgugprqGd/7zFjXVNR0dygFPURUs2XZQ+GUpbTzpe5j1ZjJMLlbWrAOgoKAAq9VKLBZj8eLFbR2yEEIIsd9KOtG1bt06xowZ0+y5MWPGsHbt2n0OSgghhBCitWbumEdnewF2w56TV3W/VHOhgtFhbvV8sUiUYI0fo9OMqmt9jy9xaFN1KtZsO/FoDPfWGjRNS/oeBdZsKgLVVAZrUBSFHj0aNmOYN28esVisrUMWQggh9ktJJ7rS09Opr69v9lxdXR1Go3GfgxJCCCGEaI1ANMi88iX0du25misajODeVgdxDZPLiqK0PkHlr/CCqmC0tz5ZJgSAatBhyXIQ9UfwltShkVyyK8Pswqwzsap6PQAul4u0tDSCwSA//fRTKkIWQggh9jtJJ7qOO+44Jk6cSFlZWaPjpaWl/Pvf/+aEE05os+CEEEIIIZIxr2wJoViYfnvpz1W3oQrQ0OIa5n1oQh8LRQjXBTA6zShSzSXagN6sx5xpI1wfJFDhTepaBZU8azbr3ZsJxRoa2/fq1QuAmTNntqpKTAghhDjQ6JO94LbbbuPCCy9k5MiRDBo0iKysLKqqqli6dClpaWncfvvtqYhTCCGEEGKvZpXMJ8eSSaY5fbdjtFic2nUV6IwNr0E6y553ZtwTX7kbRa9itJtafY8DldFopEuPrlLNnwIGm5F4NEag0oveosfoaHkyNt+azTbPTtbVb+bIjD5YLBYyMzOprq5m7dq19OvXL4WRCyGEEB0v6Yqu7OxsPv74Y6666ioCgQArV64kEAhw1VVX8fHHH1NYWJiKOIUQQggh9igWjzGrZN5em9C7t9USDUSIhaKY0i0otK4SK+IPEfGEMTktKOqhV82Vk5/LX+67lZz83I4O5aBkdJrRWwx4ttcRC0Vafp1qJNPS0JR+VwXXrqquL774IiWxCiGEEPuTpCu6ADIzM7nzzjvbOhYhhBBCiFYrrlpFXdhD3z0sW9Q0jZq15egtBqKBCKa01i1b1NDwl3lQDSp6W+srwoTYHUVRMGfY8Fe4cW+twdUjG0XXss+oC6y5LK9ew05/BYW2XAwGA9nZ2VRWVrJ27Vr69u2b4uiFEEKIjtOqRNeKFStYuHAh4XA48UmRpmn4/X6Ki4t5//332zRIIYQQQoi9mb5lJummNIpsebsdE6j0Eqzxo5r0GBwmVL2uVXNFPCGigQiWLNs+NbI/kO3Yup2nH3yCW/9+B0VdOnV0OAclRadgybLjK3fj3l6Ls2tGiyoQ04wObAYrq2rWUWhrqLjr2bMnlZWVzJgxgz59+hyyv7dCCCEOfi1KdL300kuMGzcOgLfffpuHH3642WaWqqoybNiwto1QCCGEEGIvwrEwn2//jkGZh+3xH/DVa8vRmfTEQlFseY5WzaWh4SvzoJr0+9TfS4iWUA06LJl2ApVeAuUerLnOvV6joJBvyWaTezu+aACb3oJer09Udf3000/079+/HaIXQggh2l+L6p//9a9/UV5eDsBbb73FSSedxMKFC7n22mu55JJLWLZsGc8++ywmk4lzzz03pQELIYQQQvzWd6U/4o346J/ZZ7djwt4Q3u11qAYV1ajD0MoG8qG6APFwFJPLIlUxol3oLQaMLguBKh+h+kCLrsm1ZKEoCmtrNyaOde/esKz3iy++IBaLpSRWIYQQoqO1uBl9fX09ADt27OCKK64gLS2NI444guLiYsxmM2eccQbXX389kyZNSlmwQgghhBDNmbZlJgXWHHIsmbsdU/tzBYpeJeINY86wtqoJvabFCZR70FsM6E2t6gAhRKsYHSb0ViPekjqigb03p9erenIsGayqXU/8l5UYRqORrKwsAoEAixYtSnXIQgghRIdoUaIrHo8TDocBMBgMmM1mALp06cLWrVuJRBr+sh08eDBbtmxJTaR7EQqFuPfeexkyZAjDhg3j1Vdf3e3Y1atXc/HFFzNgwAAuvPBCVq5c2ej89OnTGTFiBAMGDOCGG26gpqYmcU7TNJ544gmOPfZYhg4dyoQJE4jH44nztbW13HTTTQwaNIhTTz2VqVOntnhuTdN47rnnOOmkkzj66KO55ZZbGs0thBBCiKY8YR+zdy7kiIzdV3PFIjHqNlRisBlBAXO6tVVzBWv8xKNxTK7WNbEXorUamtNbUQ06PNtqiEfje70m35qDL+Jnq7ckcaxr164AzJ49m0CgZdVhQgghxIGkRYkuq9WKTtfQrLVfv37Mnj0bgG7duhGPx1m+fDkAZWVlKQpz7yZMmMDKlSt54403+Pvf/87EiROb3ULZ7/dz/fXXM2TIEKZMmcKgQYMYN24cfr8faGi0f99993HjjTcyefJk3G4348ePT1z/2muvMX36dCZOnMi//vUvpk2bxmuvvZY4P378eDweD5MnT+ZPf/oTf/3rX1mxYkWL5p48eTIffvghTzzxBG+//TYVFRXcd999qXxsQgghxAHvqx3fE41HOTKj927H1G+sIh6LE/GFGpYcqi0uak/Q4nECFV70NiOqoXVN7A8muQV5jH/sr+QW7L75v2hbitrQnD4e1/Bsr2m2Z+6vOQx2nAYHq2rWJY5ZLBbS09OJRCLMmTMn1SELIYQQ7a5Fb3m33HILDkdDw9axY8fy+uuvc++992K1WjnttNO46667+Mc//sHjjz/O4MGDUxpwc/x+Px988AH33Xcfhx9+OKeffjrXXXcdb7/9dpOxM2bMwGQycdddd9GjRw/uu+8+bDZbIin21ltvceaZZ3L++efTt29fJkyYwJw5c9i+fTsAkyZN4i9/+QtDhgzh2GOP5Y477kjMs23bNmbPns3DDz9M7969ufjiizn33HN55513WjT3nDlzOOussxg6dCi9e/fmuuuuY8GCBe3xCIUQQogD1rQtM+nmLMJptDd7Xotr1Kwtx+gwo0U1zBm2Vs0TqPSixTVMaVLNBQ1V/lm52RgM0pC/Pal6FUuWnag/gr/MvdfxBbZstntLqQ97Esc6dWrYJXPRokVUV1enLFYhhBCiI7Qo0XXVVVdRVFQEwIgRI3jxxRfp0aMHAA899BBdu3blvffeo3v37vztb39LXbS7sXbtWqLRKIMGDUocGzx4MMuXL2+0rBBg+fLlDB48ONE8VlEUjjrqKJYtW5Y4P2TIkMT4/Px8CgoKWL58OeXl5ZSWlnL00Uc3mqekpISKigqWL19Ofn5+4lntOr906dIWze1yufj2228pLy8nGAzy2Wef0a9fv7Z7UEIIIcRBptxfxaKKFfTfw7JFT0kdEV+YeCyO3mpEb04+MROPxghU+zA4TKj65KvBDkbVldW8/fIkqislUdLe9GY9pnQLwRo/wTr/HsdmWzIxqAZW/qqqy+l0YrfbURSFb775JtXhCiGEEO2qVW9qw4cPZ+zYsQCkp6fz/PPPM3fuXN58803y8/PbNMCWqKysJD09HaPRmDiWlZVFKBSirq6uydicnJxGxzIzMxPLLisqKnZ7vrKyEqDR+aysLIDE+eau3bVj5d7mvuGGG9Dr9Zx00kkcddRRLF68mKeeeiqpZyGEEEIcSj7bNhudoqOfq+dux9SsKcdgNxL1NTShbw1/pRcUMDqlmmuXgN/PkgXFBPx7TrSI1DDYTehtRnwl9cSCu29Or6KSb81mde0GQrGGnruKolBUVEQ8Hmft2rWJd1UhhBDiYJB0oisSifDQQw9xySWXJI4tXbqU4447rklj9vYSCAQaJbmAxNe7mujvbeyuccFgcLfng8Fgo3v/dp693Xtv50tKSjCbzbz44ou8+eab5OXlce+997bwKQghhBCHnk+3zKSPqxtmvanZ84EaH4FKL6peh6pXMTrNSc8RC0cI1fgxOi2ouuR3ahQiFRRFwZxuRTWouLfXou3hHbzQlks8HmdN7YbEsaysLIxGI3q9Xnp1CSGEOKgkneh67rnn+Pjjjzn77LMTx/r06cOtt97K5MmTeeWVVwDaNeFlMpmaJLR2fb1rh8i9jd01bnfnLRZLs8mzXX+2WCytvrfZbEbTNO6++27Gjh3LKaecwuDBg3nmmWeYN29eotm/EEIIIf5nQ/1Wfq7bxJF7WLZYs6YcnUlPqD6AKd2aaB+QDH+5F0WnYLQ3n0wToqMoqoI500Y8HMO3h35dRtVIjiWTFdVriWkN7+i7qrqi0Shr1qyRqi4hhBAHjaQTXdOnT08kZHbJyMjg2muv5dZbb+XDDz/k888/p3///kyaNKlNg92d3NxcamtriUajiWOVlZWYzWacTmeTsVVVVY2OVVVVJZYU7u58dnY2ubm5iXv/eh4gcX531+5t7pqaGkpLS+nT538v6/n5+aSnp1NSUoIQQgghGpu+dRZWvZleaV2aPR/xh3FvrcVgN4FGq5YtRgNhwu4gxjQLiirVXGL/ozPqMaVbCdUGCLkDux1XZM/DFw2w0b01cSw3NxedTofBYJCqLiGEEAeNpBNdNTU1iZ1afqt79+5s27aNf/7zn1xwwQX85z//2ecAW6Jfv37o9fpEU3eA4uJijjzySNTfbB8+YMAAli5dmtiOWdM0lixZwoABAxLni4uLE+NLS0spLS1lwIAB5ObmUlBQ0Oh8cXExBQUF5OTkMHDgQEpKShI9t3adHzhw4F7nTktLw2g0snHjxsS1NTU11NXVNWpuL4QQQgiIa3E+3fIN/Vw90av6ZsfUrqtEUSHsC2J0mlH1uqTn8ZV7UPUqBptx74MPMc40JyPPHYUzzbn3wSKlDHYjeosBb0k9sUi02TE2vZUMk4tlVWsS76J6vZ68vDxisZhUdQkhhDhoJJ3o6tatG19++WWz52bNmoXVauX//b//x5gxY6ivr9/nAFvCYrFw/vnn88ADD7BixQq++eYbXn31VX7/+98DDVVXu/prjRo1CrfbzSOPPMKGDRt45JFHCAQCnHnmmQBcfvnlTJ06lQ8++IC1a9dy1113MXz48ERy7/LLL+eJJ55g4cKFLFy4kCeffDIxT6dOnRg2bBh33nkna9eu5YMPPmD69OlceeWVe51br9dzwQUX8Pjjj/Pjjz+ybt067rzzTgYMGMCRRx7ZLs9RCCGEOFAsrVpNqb+S/pl9mz0fj8apXV+ByWUhHoxhzky+mivsCxH1hTG6LK1a8niwc7rSOOP8M3G60jo6lEOeoiiYMmwoioJ3ex0aWrPjiux5VAdr2en/X0JrV1N6o9HId999114hCyGEECmjaLs+0mmhTz75hHvuuYeRI0cyYsQIMjMzqampYfbs2Xz++ec89thjnH/++dTW1jJ79mwuuOCCVMXeSCAQ4IEHHuCrr77Cbrfzhz/8gWuuuQZo6CH22GOPJWJZsWIFf//739m4cSN9+vThwQcf5LDDDkvca8qUKfzrX/+ivr6eE044gf/7v/8jPT0dgFgsxoQJE5gyZQo6nY6LLrqI22+/PfECXF1dzX333ce8efPIzs7m1ltv5Zxzzknce09zh0IhnnnmGT777DNCoRDHH388999/PxkZGS16BrFYjGXLljFw4EB0uuQ/tRZCCCEOFA8s/hdfbvuOm4+8BrWZJFTt+krKFm3FmGYmFoqS1iMLhZYnqzQ06jdUoWka1lyHJLqaEQwE2bJxM117dMNsSb7Jv2h70VCUQLkHS7YNa07TSjsNjSVVq8gwpXF2l1MSx9etW0d1dTXRaJQ//elPTXYJF0IIIfYHLc15JJ3oAnj77bd5/vnnqa6uThxLT0/npptu4oorrmhdxGKfSaJLCCHEoSAci3DiJ5fSP7Mvpxed0OS8pmlsnLYSVa8jVOvHVuDEnG5Lao5gnR9fST2WXAd6U/NLIw91O7Zu5+kHn+DWv99BUZfm21qI9heqCxB2B3F2zcBga7qBQnmgkrV1m7is5zmkmxqq8QKBAIsXL8ZoNNKzZ08uvvji9g5bCCGE2KuW5jySfnPbuHEjV155JVdccQWbN2+mrq4Op9NJ9+7dm/TDEkIIIYRoaz+ULcYd8dI/s/ndFr076oh4Qlhz7KAqmNIsSd1fi8fxl3vQWwyS5BIHHGOamWgwimdHHa6e2ai6xu/n2ZZMNnt2sLxqLcMLjwEa2oBkZWXhdrtZvXo1FRUVUtUlhBDigJV0ZuqKK67gk08+QVEUunfvzlFHHUXPnj0lySWEEEKIdvHplpnkWbPJtWQ1e756dRlGp5lAjR+zy4KS5DtKoMaHFo1jciWXIBNif6AoCpYsG1o8jrekab8uFZVCWy7r6jfjjwYTx4uKigiHw5hMJunVJYQQ4oCWdHbKYDAk+lUJIYQQQrQnb8THrJL5HJneu9nz/goPgSofBocJLRpPugl9PBonUOnFYDeiGqQNgDgwqXoVS4aNiCdEqMbf5Hy+JRsFWFWzPnHM4XDgcrlQVZVVq1ZRWVnZjhELIYQQbSfpRNfNN9/MhAkTmD59Ohs2bGDnzp1N/iOEEEIIkQrf7PiBSDzKkbtZtli9pgy91UCwxofBbkRnNCR1f3+lBzQwpiW/S+OhRq/Xk5mThV4vyzv3R3qrEb3diK/UTSwYaXxONZBrzWJlzVqi8WjieKdOnQgEAphMJubMmdPeIQshhBBtIuk3kwceeIBYLMadd9652zFr1qzZp6CEEEIIIZrz6ZaZdHUUkWZ0NDkXcgfx7qjHVpCGb2c9tm4t27V4l1g4QqjGj9FlQdXJLot7k1eYz73/uL+jwxB7YHZZ8YeiuLfX4uqR1WgZb5Etj52+Cn6u28zhGb0ASEtLw+FwEI/HWbVqFSeffDLZ2dkdFb4QQgjRKkknuh5++OFUxCGEEEIIsUcVgWoWli/jnC6nNnu+Zk0ZqlFHyB1AbzVgsDbdcW5PfGUeFJ2K0Z7cdULsrxRVwZxpw1/mwV/hxZbnTJwz68xkWdJZUb2Gw9J7oigKiqJQVFTEmjVrMJvNfPfdd1x44YUd+B0IIYQQyUs60TVmzJhUxCGEEEIIsUdTN3+NTtVxWHqvJueigQj1m6qx5Njxl3lwdE6un2jEHyLiCWHOsKKoUs3VEju3l/DiP//N/7vzBgo6FXZ0OGI3dEY9RpeFYLUPo9PUKAFcZMtjadVqtnpL6OooAiAzMxOr1Yper2flypWcfPLJZGU1v/GDEEIIsT9q1VaJNTU1PPHEE1xyySWMGjWKyy+/nCeffJLq6uq2jk8IIYQQAk3T+HDTF/RL74lF37TiqubnclAg4o+gM+sxOFpelaWh4StzoxpU9DZjW4Z9UIvH4/i8PuLxeEeHIvbC6DChGnV4S+rRfvXzchocpBkdLKv6X9uRXVVdbrc7UdUlhBBCHEiSTnSVlZUxZswY3njjDUwmE4cddhh6vZ7XXnuN888/n/Ly8lTEKYQQQohD2OLKn9jm3clRmYc3ORePxKhdV4k500bEHcSSZUeh5VVZYXeQWCCKyWVFUaSaSxx8FEXBnGElHo7hr/Q2Oldky6PUX0FF4H8fWOfk5GAymbBYLKxcuZK6urp2jlgIIYRovaQTXf/85z/R6/XMmDGDN998k6eeeoo333yTzz//HLPZzNNPP52KOIUQQghxCPto0xdkmlx0dTRdIle3sYp4NEY8EkM16jCmmVt8X03T8Je50Zn16C3J7dAoxIFEZ9RjTLMQrPIRDYQTxzPNLmx6Cz9W/JQ4pigKhYWF1NbWYjAYmDdvXkeELIQQQrRK0omuuXPn8pe//IVOnTo1Ot6pUyduuOEGKW8WQgghRJvyRnx8sf07Bmb1a1JxpcU1qteUY063EqoNYMmyJVXNFazxEY/EMbksbR22EPsdo7NhCaNnRx2apgGgoNLZXsg2bwnl/qrE2Ly8PPR6PXa7naVLl+Lz+ToqbCGEECIpSSe6YrEY6enNN3jNyMjA6/U2e04IIYQQojVmbP2WcCzCwMzDmpxzb6sh6m+oTlH1KiaXtcX31eJx/BVe9DYjOmPS+/Mc8rJzc7jp3lvIzs3p6FBEC/16CWOgwpM4nm3JwGawsqhyReKYTqdLVHVpmsaiRYs6ImQhhBAiaUknuvr06cO0adOaPTd16lR69+69z0EJIYQQQuzywabP6ZXWFafR3ui4pmlUryrDmGYmWOPHnGVLqseWv9ILmoYpTaq5WsNkNtG1ZzdM5pY3/hcdT2fUY3SaCfxqCaOCQld7ATu8pZT6KhJjCwoKUBQFp9PJokWLCIfDu7utEEIIsd9IOtH15z//mWnTpnH99dfzySef8MMPP/DJJ5/wxz/+kRkzZjBu3LhUxCmEEEKIQ9C6us2srFnHUVlNq7n8ZR5CdQFUvYqiUzGnt7yaKxaJEqzyYXSYUfWt2oT6kFdXU8fU9z6mrqauo0MRSTKmmVENKp4d9YkljJnmDBwGGwsrViSO6fV68vPzcbvdhEIhlixZ0pFhCyGEEC2SdJ3+CSecwD/+8Q+eeOKJRv24srKyePTRRzn99NPbNEAhhBBCHLo+2vQFdoON3mndmpyrXl2G3mYkWO3Hkm1HUVuesPKXeUBVMDpa3rheNOb1ePjuq28ZfNwQXBmujg5HJKFhCaMNf7mHQKUHa44TBYUujkJW1qyjxFdOkT0PgKKiIkpLS3E6ncyfP5+jjz4anU7Xwd+BEEIIsXutakhx/vnnc95557Fp0ybq6+tJS0uje/fusiW3EEIIIdpMOBZm6pZv6J/RB53a+B/WwVo/vjI35kwr0UAES6at5ff1hQi7g5gyrCg6eXcRhyad6ZcljJU+jE4LerOBDJMLp8HBoorlFNpyURQFo9FIYWEhJSUlxONxVq5cyYABAzo6fCGEEGK3Wl2r/8MPP/Dpp58ydepUPvvsM4qLi9syLiGEEEIc4maVLKA+7OGorMObnKteXYbOpCdY68ecbkXRteyVRkPDt9ONatRhsBnbOmQhDihGZ8MSRu8vuzAqKHR1FlAeqGKbd2diXGFhIYqiYLPZ+OGHHxJLG4UQQoj9UdIVXfX19YwbN45ly5ah1+txuVzU1dXxwgsvcNJJJ/Hcc89hNMqLoxBCCCH2zYebPqezvYBsS0aj42FvCPfWGszpVmI1USxZLa/mCtX4iIejWHMdUokuDnmK+qsljFVerNkOXMY0XEYHiypX0Nne0IzeYDBQWFjI9u3b8fl8rF+/XjagEkIIsd9KuqLr0UcfZfPmzUycOJGffvqJuXPnsmLFCp599lmWLVvG008/nYo4hRBCCHEI2emrYF7ZEgZm9mtyrmZNGapeJVQfwJRuQdW3rF9QPBrHV+5FbzOiM7Wqe4P4FZvdxvGnDMNmb3miUex/dCY9RoeJQIWXWDDyS6+uIqoCNWzx7kiMKywsRKfTYTabmTt3bgdGLIQQQuxZ0omub7/9ljvuuIMRI0YkPglVVZWRI0dy6623Mm3atDYPUgghhBCHlk82f4VRNXBERuOqkWgwQt2GKox2M1pMw5Jlb/E9/RVuQMPksrRxtIem9MwMLrzqYtIzM/Y+WOzXjGkWVL2KZ2cdGhouo5N0UxqLyhvvwFhUVEQwGGT79u1s27atg6MWQgghmpd0okvTNLKyspo9l5+fj9/v3+eghBBCCHHoimtxPtr0BYdl9MSka9wOoebnClAg5A5gclnQGVtWmRUNhAnVBjA6Lagt7Ocl9iwcCrNj63bCoXBHhyL2kaIqmDJsxAJRQjUN7/JdHIXUhOrY6P5fQis/Px+9Xo/BYOCHH37oqHCFEEKIPUr6TW/MmDG88MIL+Hy+Rsej0ShvvfUWY8aMabPghBBCCHHoWVi+nJ3+iiZN6GORGLU/V6C3GtHiGtZcR4vup6HhLa1HNagYHaZUhHxIqigr5+kHn6CirLyjQxFtQG/Wo7cZ8ZV7iEdjpBkcZJhc/Fj5U6Oqrs6dOxOJRFi3bh0VFRUdHLUQQgjRVNINKiwWC1u2bOG0007jtNNOIzc3l9raWubMmUNZWRlpaWmMHz8eAEVRePTRR9s8aCGEEEIcvD7a9Dk55gw62fIbHa/bUEk8GiPujWHJtLW4N1eoLkAsEMWSY5cG9ELsgcllJRqox1fqxtEpna6OQpZUrWK9ewu907oBkJeXx/bt24nH48ybN4/zzz+/Y4MWQgghfiPpRNenn36K3d7QD2P+/PmNzuXl5bFkyZLE1/IyKYQQQohk1Ic9fLXjB4YXHNPoPSIei1Ozphy92UAsFMWS3bLeXFo8jr/Mg95qQG82pCpsIQ4Kqk7B7LIQrPET9oZw2O1kmdP5sfwnejq7oCoqOp2Ozp07s3HjRlasWMEpp5xCWlpaR4cuhBBCJCSd6Jo1a1Yq4hBCCCGEYPqWWcS1GAMz+zY67t5SQzQQAcCa60BRW9Z9wV/hRYvHMblatsxRiEOd3mZE9YXx7azD0DObLo5CllSuYnXthsTmEHl5eWzbto1YLMaCBQs444wzOjhqIYQQ4n9a3Y3V7Xbz3Xff8dlnnzF//ny8Xm9bxiWEEEKIQ9AHmz6nd1o37AZb4pimaVSvKkU16lD1KuZMa4vuFQtFCFb7GhrQ66UBfVtTFAWT2SQV/AcZRVGwZFiJR+IEKr3Y9TbyrNksLF9OMBoEGnZc79q1K/F4nMWLF8tmVEIIIfYrSVd0Abz88ss8//zzBIPBxDGj0ci4ceO44YYb2iw4IYQQQhw6VtWs4+e6TVzRc3Sj454ddYQ9IQBshWkoyt6TVg0N6N0oemlAnyqFnYt49PkJHR2GSAHVoMPoNBOo8mFyWejqKKIqWMP88mWcUngsALm5uWzbto1wOMzChQs55ZRTOjhqIYQQokHSH29+9NFHPPXUU5xzzjlMmjSJGTNm8MYbb3DOOecwceJEPv7441TEKYQQQoiD3PsbZ+AyOuiZ1jVxTNM0qleWougUdCY9JpelRfeKeIJEfWHMLguKKhVHQiTL6DSj6FW8O+sxqHq6OopYW7eRMn8V0FD51aVLFzRNY8GCBYRCoQ6OWAghhGiQdKLr9ddf5/LLL+fhhx9m6NChdO/enWOOOYZHH32Uyy+/nEmTJqUiTiGEEEIcxHwRP9O2zGJg1mHoflWx5a/wEKzxo8W0ht5c7D1ppcXj+Ha60Zn16CzSgD5VykrKmPDXxygrKevoUEQKKKqCOd1K1B8hVBcg35qNw2Dj+9If0TQNgJycHMxmM+FwmMWLF3dwxEIIIUSDpBNdW7duZcSIEc2eO+2009i0adM+ByWEEEKIQ8v0rbMIxkIclXV4o+NVK8tAAZ3FgKGFSxADVT7i0TjmdKv0j0qhaDRC+c4yotFIR4ciUkRvMaC3GvCXutFi0CutK9XBWlbVrgcaqrq6d+8OwPfff08kIr8LQgghOl7Sia7c3Fx27tzZ7LkdO3Zgt7dsu28hhBBCiF0mb/iM3q5upBn/tztisMaPv8wNGtjynS2q5oqFIwQqvRgcJlSDLpUhC3FIMLmsaJqGr8yNw2Anz5rFwvLlBH5pTJ+RkYHT6SQUClFcXNzB0QohhBCtSHSdeuqpPPvss6xYsaLR8eXLl/Pcc89x6qmntllwQgghhDj4raxZx5q6jQzJOqLR8apVDR+sGexGDBbjXu+joeHZUYeqVzGltayXlxBiz1S9isllIVwXIOIL0dXRCY04C8qXAg1VXT179gRgzpw5xGKxjgxXCCGESH7XxZtuuol58+Zx6aWXUlhYSFZWFlVVVZSUlNCjRw9uv/32VMQphBBCiIPU+xs+w2V00jOtS+JY2BPEs60OaKjmaolglY9YIIol1yEN6IVoQwa7iYgvjHdnPa6e2b80pt9Ev/Re5FmzsNlsZGdnU1lZyeLFiznmmGM6OmQhhBCHsKQruux2Ox9++CH3338/Rx55JFarlSOPPJL777+fDz/8EJfLlYIwhRBCCHEw8kZ8TN86i0FZh6H+qgl91cpSAIwuMzrj3hvKx8IR/OUeDA4TelPSn+OJVsjMzmTsTdeRmZ3Z0aGIFFMUBXOGlXg4RqDKm2hM/13pokRj+p49e6IoCrNnz04cE0IIITpC0m+Cf/jDH7juuuu44ooruOKKK1IRkxBCCCEOEdO3ziYYCzMo67DEsYgvTP2malDAlrv3ai4NDe+OehRZstiuLFYrRww6sqPDEO1EZ9RjcJgIVHgxpVnoldaVpVWrWVW7niMyeqPX6ykoKKCkpIR58+ZxwgkndHTIQgghDlFJV3QtWbJEdjASQgghxD7TNI33Nkynz2+a0Fcs3Q6AJcuOqt97Q/lgtY9oIII5wyZLFtuRu97NzM++xl3v7uhQRDsxOS0oOhVvaT12g+2XxvTLEo3pu3XrhqqqzJkzh3g83sHRCiGEOFQlneg68cQT+fTTT2X7YCGEEELsk5U16/i5bhODf9WEPuIP495ai6JTsGTvfSfnxJJFuxG9WZYstid3XT0zPpqOu66+o0MR7UTRKZjSLUS9YSLu4C+N6bVGjek7d+5MJBJh1qxZHRytEEKIQ1XSb4Qmk4lPP/2Uzz//nB49emC1WhudVxSFN954o80CFEIIIcTB6f2NM5o0oS9dsAUAW2HaXivINTS8JfUoOhWTy7rHsUKItqG3GNBZ9HhL3aT3+l9j+r6uHuTbcigqKmLHjh0sWLCAk08+GYNh7z32hBBCiLaUdEVXWVkZgwYN4ogjjsBisaBpWqP/SJmyEEIIIfbGG/Hx2W+a0IfcAXylblSjDpNj7722QjU+ov4I5gyrLFkUop0oioLZZUWLxvFXNDSmTzM6+HbnAqLxKIqi0KVLF2KxGDNmzOjocIUQQhyCkq7oevPNN1MRhxBCCCEOIdO3ziYUi3BU1uGJYzt/2AyAvXP6Xq+PhaP4yjzobUb0ZqkYEaI9qQYdxjQLwWofJpeF3mndKK5cSXHlSo7JHUh+fj5bt25l2bJlDB8+nLS0tI4OWQghxCEk6YquXTZv3sz777/Pyy+/zJQpUygpKWnLuIQQQghxkNrVhL63qxtOY0MfLl+5m2CNH73ViMG058RVw5LFOhRVxSxLFjuMxWKh/5CBWCyy0+WhyOgwoepVfDvrsejNdHYUsLRqDZXBGhRFoWvXrgBMmzatYwMVQghxyEm6oiscDnPPPffw+eefo2la4riqqlx66aX87W9/k10ZhRBCCLFbP9X8zM91m7iy57lAQ+Jr57xfqrmKXHu9PlTjJ+qPYMm2o+jknaOjZOZkcfWfx3Z0GKKDKKqCKcNKoMJLqDZA5/R8qoO1zN4xn4t6nEleXh5bt25l48aNbNu2jc6dO3d0yEIIIQ4RSVd0PfHEE8ycOZN77rmHb7/9lhUrVjB79mzuvPNOpkyZwssvv5yKOIUQQghxkHh/4wzSf9WEvm5DJVF/BIPTjM6g2+O1sUgUX5m7YcmiRZYsdqRoNEpdTR3RaLSjQxEdRG82oLca8ZW50WLQK60rNSE3y6pWJ3p1AUyZMkV2bBdCCNFukk50ffbZZ9x6661cffXV5OXlYTQayc/PZ+zYsdx4441Mnjw5FXEKIYQQ4iDgCfv4bOtsBv7ShD4WiVG+ZAcA9jznHq9N7LKoyi6L+4OyklL+746/U1ZS2tGhiA5kSrcAGr4yNw6DnU72PBZX/kRtqJ68vDxMJhP19fV8++23HR2qEEKIQ0TSiS6/30/37t2bPdevXz9qa2v3OSghhBBCHJymb51F+FdN6CuX7kCLxjG6LKh7qeYKVvmI+sKYM6yosmRRiP2CqlMxpVkI1wWI+EN0cRRi0pmYXbIAgG7dugEwb948duzY0ZGhCiGEOEQkneg644wzeOutt4jH403OTZ06lVNOOaVNAhNCCCHEweW3TehDdQFq11cCYM1x7PHaiC+Ev9yDwWGSJYtC7GcMdhOqUYe3xI2iKfRO60pFoJqVNevIysrCbrej0+n45JNPZKmrEEKIlEu6Gf2RRx7Js88+yznnnMPo0aPJycmhtraWmTNnsnz5cq6++momTpwIgKIo3HDDDW0etBBCCCEOPMuqV7OufjO/63UemqZRunAL0LD0aU+9ueLRGJ7tdahGPSaX7PAnxP5GURTM6Vb85R6C1T7SspwU2HJYULGMro5CunbtysqVK6mpqWHOnDmcdtppHR2yEEKIg1jSia7/+7//A8DtdvPss882Of/aa68l/iyJLiGEEELs8va6qWSZ0+nh7EL95moCVT4ALFn23V6jaRqebbWgaViybLKzsxD7KZ1Jj8FuxF/hwZhmppujE9XBOmaXLuKcTsNJS0sjGAwyd+5c+vXrR0FBQUeHLIQQ4iCVdKJr7dq1qYhDCCGEEAexikA1X2z/ntOLTiAeilK2eBsoCqY0Mzrj7l9H/OUeooEIllwHqj7pjgsihQo6FfL4S0+i6uTnIhoY06xE/BF8pW6cnTPo7erKiuqf+bl+M127dmX58uVYrVY++eQTrr/+evT6pP8pIoQQQuxVi95MWttgXhrTCyGEEALgg40z0Cs6BmYeRnnxdohpDVVa2buv5gq5AwSrfRhdFvQm+Qfx/kZVVfQGPaoqiS7RQNU1LGGMeEKEPUHSjS7yrNnMKytGbzWSmZmJpmlUVlby/fffd3S4QgghDlItejO58MILefPNN4lEIi26aSAQ4L///S9jxozZp+CEEEIIceALxyK8u34aR2b2IVYZxL2lBhQwuSy7reaKhSN4S+rQWwwYHaZ2jli0RGVZBc8//hyVZRUdHYrYj+itBnRmPd6d9WjxON0dnQBYUL6ULl26EAgEyM/P5/vvv6e0tLSDoxVCCHEwalGia9KkSXz99decdNJJ/P3vf2f+/Pl4vd5GYzweD99++y0PPvggJ510Et988w1vvvlmSoIWQgghxIHj6x1zqQ7VcXTGEZQt3IrOpEeLa1h2s9OiFo/j3lqLqqqYM6Qv1/4qFAqx8ecNhEKhjg5F7Ed2NabXYnF8ZW4MqoGujiJ+rtuER/GTk5NDTU0NaWlpTJ06lVgs1tEhCyGEOMi0aB1AUVERkyZN4quvvuLll19m8uTJKIqC0+nEYrHgdrsJBAJomsZhhx3GI488wsiRI1MduxBCCCEOAG+t+4Rujk6oG8JE/GFQFMwZtmZ3WtTQ8JbUE4/EsOY6UHSS5BLiQKMadJjSLIRqA5jSLOTZsij1V/B96WLO6Tyc4uJiOnfuzIYNG/j+++8ZPnx4R4cshBDiIJJUw4uRI0cycuRINm/ezIIFC9i+fTter5f09HQKCgo44YQTKCoqSlWsQgghhDjArKpZz7LqNVyaPYrq4jIMdhNRf3i3vblCNT7C7iDmTNsem9QLIfZvBoeJiD+Mt6QOV89seqZ1YWnVajYFdpCfn8+WLVvo3bs333//Pb1795ZdGIUQQrSZVr1BduvWjW7durV1LEIIIYQ4yLyz/lNcBifm1TGiZgMRTwhrrqPZnfoigTC+Ug8GuxGDzdgB0Qoh2oqiKFgybfhK3fgrvThzneRZs1lUsYyLOp9JeXk5AC6Xiw8++IDrr78ei8XSwVELIYQ4GBw02+SEQiHuvfdehgwZwrBhw3j11Vd3O3b16tVcfPHFDBgwgAsvvJCVK1c2Oj99+nRGjBjBgAEDuOGGG6ipqUmc0zSNJ554gmOPPZahQ4cyYcIE4vF44nxtbS033XQTgwYN4tRTT2Xq1KlJzf3FF19wxhlnMHDgQK699lpKSkr25bEIIYQQHaY25Gb61lmcEDqCUI0fnVGHatBhzrA1GRuPxvFsq0U16jC5rB0QrUiWKyOdi6+5DFdGekeHIvZTqkGHMc1CsMpHNBCmm6OIqBZnWf0aCgoKWL9+PUcddRR+v59PPvkETdM6OmQhhBAHgYMm0TVhwgRWrlzJG2+8wd///ncmTpzIF1980WSc3+/n+uuvZ8iQIUyZMoVBgwYxbtw4/H4/ACtWrOC+++7jxhtvZPLkybjdbsaPH5+4/rXXXmP69OlMnDiRf/3rX0ybNo3XXnstcX78+PF4PB4mT57Mn/70J/7617+yYsWKFs29ZMkSbr/9dsaOHcuUKVMwGo3cdtttqXxsQgghRMp8tOkLTGEDzk0q5iwb4fog1hw7itq475amxfFsq2loUJ/V9LzYP9kddo496TjsjuaXoQoBYHSaUA0qnpJ6DIqBLvYCVtWsx5JlR1VVNm/ezLHHHsu6dev44YcfOjpcIYQQB4GDItHl9/v54IMPuO+++zj88MM5/fTTue6663j77bebjJ0xYwYmk4m77rqLHj16cN9992Gz2RJJsbfeeoszzzyT888/n759+zJhwgTmzJnD9u3bgYYdKP/yl78wZMgQjj32WO64447EPNu2bWP27Nk8/PDD9O7dm4svvphzzz2Xd955p0Vzv/rqq5x77rlcdtlldO/enfvuu4/KyspGFWVCCCHEgSAWj/HOuqmcVHM4iqoQD0fRmfUY0xovTdLQ8GyvIxqIYMm2o+oPileTQ4LX42XBd/Pxerx7HywOWcovm0/EQ1ECVV4KbblYDRbmVS6hU6dObNq0CYvFwmGHHcasWbPYsmVLR4cshBDiAHdQvE2uXbuWaDTKoEGDEscGDx7M8uXLGy0rBFi+fDmDBw9ObFWuKApHHXUUy5YtS5wfMmRIYnx+fj4FBQUsX76c8vJySktLOfrooxvNU1JSQkVFBcuXLyc/P79RQ/7BgwezdOnS3kFV/QAAvQJJREFUFs29aNEiTj/99MS1nTp1YtasWWRkZLTBUxJCCCHaz7c7F6KriOOoNWLLcxJ2h7DmOBJ/B0JDkstX6ibiCWHJsqE3SfP5A0ldTS0fvP4edTW1HR2K2M/pTHoMDhOBCi/xUIwezs6U/X/27jw8zvq89/979n2XRvtuW17wbhMDNrsBZ+UkTVOSZimkTXOapKFZWkLaLCeEU35wpScNSZsFEgqEJUAAB0IggDHeMLIl2ZZsS7J2jWZGu2bfnt8frqc4NrZWjyzdr+vyZVvPdo/Ammc+z/d7fyNBwpYEJpOJt99+mxUrVuD1evnNb37D+Ph4rksWQghxEZt20BWPx3M+nz4YDOJyudDr/6dxbV5eHvF4nJGRkTP29Xq9p33N4/HQ398PQCAQeNftwWAQ4LTteXl5ANntZzv2VLPNc117bGyM0dFR0uk0t912G1dccQWf//zns8cKIYQQF5NHmn7LpYFFGNxmooNhtBY9OqvhtH2iAyHiQxEMLjNaszSfF2I+MzhMqLXqk6sw6u3km9zsDdRTWV3J4OAgnZ2dbNq0iUwmw1NPPXXGw2ohhBBioqYUdJ04cYIvf/nLXHrppaxdu5ampia+853v8F//9V8zXd+ERKPR00IuIPv3RCIxoX1P7ReLxd51eywWO+3cf3qd8537XNtP9en63ve+xwc+8AF+8pOfkEgk+NznPidv9EIIIS4qbaNdpJrG0SlajE4TqUgSS8Hpo7liIxGi/hB6uxG9zXCOswkh5gOVWoXBbSEVTREfClNtKyeeTnA80YXX66W+vh61Ws1ll11GV1cXr776aq5LFkIIcZGadNDV3NzMn/3Zn3HkyBE+8IEPZEdzaTQavv/97/PMM8/MeJHnYzAYzgi0Tv3daDROaN9T+73bdpPJdNbw7NSfTSbTlM9tNBrRaDQAfPSjH+Xmm29m1apV3HvvvRw/fjw7tVEIIYS4GDyy9ylqRguxV7gZ7xlB7zCiNf3Pg55EKEa4dxStRY/eYTzHmYQQ84nWqEVr0RPuH0eX0VBmLaRx4CjuEg+ZTIaGhga8Xi+rVq1i165dHDt2LNclCyGEuAhNOuj613/9Vy655BJefPFF7rjjjmzQ9c1vfpM/+7M/46GHHprxIs+noKCA4eFhUqlU9mvBYBCj0Yjdbj9j34GBgdO+NjAwkJ1S+G7b8/PzKSgoyJ77ndcBstvf7djzXdvlcqHT6aiurs5uc7lcOJ3O7LRKIYQQYq4bjowQqguQNCuoMpBJpDHn27LbU9EE413DaExajG7zaaO8xMXFYDBQU7sIg0FG5ImJMzjNqNRqQr2jlFqKMGj17BtspKKighMnTjAwMMDSpUspKSnhmWeeYXhYesAJIYSYnEkHXfX19XzmM59Bq9WecXP63ve+NycrpSxbtgytVnvayKe6ujpWrlyJWn36S1y9ejUHDx7MBnSKonDgwAFWr16d3V5XV5fd3+fz4fP5WL16NQUFBRQXF5+2va6ujuLiYrxeL2vWrKG3t/e0YKquro41a9ac99parZYVK1Zw9OjR7LFDQ0MMDw9TUlIyM98oIYQQYpY9tP1RjEk97pp8xrtHMLrNaP67yXw6kWKscxi1ToPJY5WQ6yKXX+jlf//jF8kv9J5/ZyH+m1qjwugykQonSI7GqbGV0R3ykbGrsVqtvP322yiKwnve8x50Oh1PPPEEyWQy12ULIYS4iEw66DIYDNleVX9qZGTkjB5UF4LJZOLmm2/m29/+No2Njbzyyis88MADfOpTnwJOjro6VfNNN93E2NgYd911F62trdx1111Eo1G2bdsGwC233MKzzz7Lk08+ydGjR/n617/O1VdfTVlZWXb7vffey759+9i3bx/33Xdf9jplZWVs3ryZr33taxw9epQnn3yS7du384lPfGJC1/6rv/or/uu//osXX3yRtrY2vvGNb7Bs2TJWrVp1Qb+fQgghxFT09vUy3BxgLC+OMpQERcGUbwUgk0oz1jEIKjDlW1GpJeS62GUyGVLJlPQSFZOmNetPTmH0jeJU27DpLBwYOMKiRYsYGRmhtbUVvV7PFVdcwcDAAM8880zOF78SQghx8Zh00HXFFVfwwx/+8LRRSyqVinA4zAMPPMDll18+owVO1B133MGKFSv49Kc/zXe+8x2++MUvcsMNNwCwefNmXnjhBQCsViv/+Z//SV1dHR/+8IdpaGjgpz/9KWazGYC1a9fy3e9+l/vvv59bbrkFh8PB3Xffnb3Obbfdxnvf+16+8IUv8Pd///d86EMf4jOf+Ux2+z333IPFYuHP//zP+Y//+A++//3vZ4Oq8137pptu4o477uD/+//+Pz784Q+TTqf58Y9/LE+8hRBCzHnpdJpHn3qMsC6O1+sl1DuKKc+KWqtByWQY6xwik1Yw51tRa6a96LOYA/q6e/nHz32Fvu7eXJciLkKnpjCOd49QZi6iN9xPRBunqKiIxsZGotEoLpeLTZs20dzcLM3phRBCTJhKmeTjEZ/Px8c+9jHGxsZYunQpDQ0NbNy4kfb2dhRF4de//nV29JO4sNLpNPX19axZsybb3F4IIYS4EN58801e+eMrdJaMsHi0gEwqg6M6D4CxziFSkQRmry07jVFc/Ho6u/nBd+7l9m99ldIKufcTk5eOp4j4xzF6zDRrOsk3ubm+6Arq6uooKirKPkA/evQo9fX1fOhDH8q2BBFCCLHwTDTzmPQj1aKiIp599lk+/elPoygK5eXlRCIR3v/+9/P0009LyCWEEEIsMIODg7z2+mv0WIeoVheRDCWwFDlABaGeEVLhBKY8q4RcQojTaAxa9E4TscEI5epCOsZ7GUuHqKqqoqurKzuDpLa2lpqaGp5//vmc9AMWQghxcZnSHafL5eL222+f6VqEEEIIcZFRFIXnn3+ehDpF3JFB6Y1jdJvRmnWEekdJjMUw5lnQmnS5LlUIMQfpbQbSsSSqYBqL08SB4BGuL72C/v5+6urquOmmm9BoNKxfv55QKMTjjz/OZz/7WTweT65LF0IIMUdNKOjav3//pE66cePGKRUjhBBCiIvLgQMH6OzspMndzarxKlQaFSavlYhvjMRIFIPbjM584ReqEUJcHFQqFUaPhbBvjPJwPkeVLjYmVrFo0SIOHjzI0aNHWbFiBWq1miuuuIJXXnmFRx99lNtuuy3b51YIIYR4pwn16Fq6dOlZG6K/89B3bm9ubp6h8sRkSI8uIYQQF9L4+Dj3338/o6Yoft0wFUEPtnIXqWiSaDCE3mnCYDfmukwxS1KpFKGxEFa7Fa1WpqWK6Tn1c2PAMo6zwMU1JZdx4sQJfD4fN910E3a7HTj5c+eVV16hsLCQT37yk3LPK4QQC8hEM48J3ZU89NBD2T/39fXxz//8z3zkIx9h27Zt5OfnMzIywquvvspjjz3Gd7/73elXL4QQQog5TVEUXnjhBTIoNBpa2Ti4BL3dSDqROhlyOSTkmu+0Wi1OtzPXZYh5QmvSobMZ8IxD+0Av494wFRUVDA0NsXfvXq6//nrUajU2m43Nmzfz2muv8fzzz/OhD31IVigXQghxmgkFXZdeemn2z5/85Cf5zGc+w1e+8pXT9lm3bh1Go5EHH3yQ9773vTNbpRBCCCHmlKamJo4ePUq0QkXNQDHqjAqNSUekfxydzYDebsh1iWKWDQYG2P6b53n/n30Ajzcv1+WIecDgNJGOJykLeaj3H2FL6aUsWbKEhoYGmpubWbFiBQD5+flceuml7N27F4/Hw5YtW3JcuRBCiLlk0qsuNjY2ctlll51129q1azl+/Pi0ixJCCCHE3BWJRHjhhRfIL/LSEurAE7aitxuJ+sfRWvQYnCYZYbEARKNRGt+uJxqN5roUMU+oVCpMHivajIbR3mHCyQh2u52ysjIOHz7M8PBwdt/KykpWrFjBq6++yrFjx3JYtRBCiLlm0kFXYWEhO3fuPOu23//+95SXl0+7KCGEEELMXS+99BKpVIo+9whLRotRGzTER6JozXqMbrOEXEKIKVPrNBjcZhwJM/XthwAoLy/HbDazZ88e0ul0dt9LLrmE0tJSnn76aYLBYK5KFkIIMcdMOuj6q7/6Kx588EG+8pWvsH37dnbt2sWzzz7L5z//eX7zm9/wv//3/56NOoUQQggxB7S0tNDY2MjiS2rp7+zDkNKRiafRmLQYPRJyCSGmz2A1kjRkSPpjDI2PoFarqa2tZXx8nMOHD2f3U6lUvOc978FkMvHYY48Ri8VyWLUQQoi5YtJL5PzFX/wFqVSKn/zkJ/zud7/Lfr2oqIh7772Xbdu2zWiBQgghhJgb4vE427dvp7CwkKZYG6UhDypAY9Bi8lgl5BJCzBhbnoNx3zBNTUe4bMMmLBYLFRUVNDc3U1JSQl7eyb5wOp2OzZs38/LLL/PUU09xyy23oFZP+lm+EEKIeWRK7wJ/+Zd/ya5du/jd737Hr3/9a37/+9/z2muv8b73vW+m6xNCCCHEHPHKK68QiUSoXbWU8PGTvXJUOjWmfCsqtYRcC43d6eC9H3k/dqcj16WIeUin0ZKwQzqRovnoURRFobS0FJvNxp49e0ilUtl9bTYbl112GW1tbbz66qs5rFoIIcRcMK3HHTU1Naxdu5bKykrgZHPaN954YybqEkIIIcQc0tnZydtvv82qVat48+AeTCk9Ko0as9cuIdcCZXfYue59W7E77LkuRcxTHquTQWOI4aEhOjs7UalU1NbWEo1Gqa+vP23foqIiVq9eza5du06b3iiEEGLhmfTUxd7eXr797W/z1ltvkUgkzrpPc3PztAsTQgghxNyQTCZ57rnnyMvLI63KkA7GUVRg9dpQayTkWqiikQhtx9qoqa3BZDbnuhwxD+nUOgwWI2NKlO7ubkwmEwUFBVRVVdHa2kpJSQlFRUXZ/WtraxkeHubZZ5/F4/Gctk0IIcTCMekRXXfffTcHDhzgox/9KMuWLWPdunXceuut1NbWolKp+NGPfjQbdQohhBAiR3bs2MHIyAhLly6loa6eDAqGfDNqnSbXpYkcGgwO8uC//5zB4GCuSxHzWJ7RxbA2hN5ioKWlhdHRUYqKinA6nWc8eFepVGzcuBG73c5jjz1GOBzOYeVCCCFyZdJB1/79+7n99tv55je/yYc//GEMBgNf+9rXeOqpp9i4cSN//OMfZ6NOIYQQQuRAX18fu3fvpra2lr1796KgELWmMBqNuS5NCLEAGDR6LHoTQ8YQJpOJpqYm4vE4S5YsIZFIUFdXd9r+Wq2WK664gkQiwZNPPkk6nc5R5UIIIXJl0kFXOBymtrYWgOrqapqamgDQaDR8/OMfZ+/evTNboRBCCCFyIp1O8+yzz+JwOOjp6SGVSjGqj+BxunJdmhBiAXHpHQzGR/AU5qFSqTh8+DAajYaamho6Ozvp6uo6bX+LxcLll19OV1cXr7zySo6qFkIIkSuTDrq8Xi8DAwMAVFRUMDo6SjAYBMDpdDI4KMPXhRBCiPngzTffJBgMYjAYGB8fJ6SJYXAY0al1uS5NCLGA2A02NCo1fbEg5eXlxONxmpubyc/PJy8vj/37958xTdHr9bJ69Wr27t1Ld3d3jioXQgiRC5MOuq666ir+7d/+jYMHD1JSUkJhYSEPPPAAoVCIp556ioKCgtmoUwghhBAXkN/v54033iA/Px+/309GozBmiZJndOe6NDFHaLU6CooL0Wol+BSzS40Kp8FOT9iHTq+ntLSUkZERTpw4waJFi1Cr1ezdu5dMJnPacUuWLMHj8fDcc8+RSqVyVL0QQogLbdJB15e+9CXsdjv/7//9PwBuv/12fvWrX7Fx40aef/55/uqv/mrGixRCCCHEhZNOp3nmmWcwmUwEAgFUahXdhgEKTHmoVZO+dRDzVGFJIV//3h0UlhTmuhSxALgMDhLpJIHoIFarlcLCQnw+H8FgkCVLlhAMBjl69Ohpx6jVajZu3Mjg4CBvvvlmjioXQghxoWkne4DL5eLJJ58kEAgA8MEPfpDi4mLq6+tZtWoVl1566YwXKYQQQogLZ+fOnfj9/uzfR6xRzGojDoMth1UJIRYyo8aAWWuiO9RHoTkPt9tNIpGgra2NSy65hLKyMg4dOkRBQQEejyd7nNPpZNmyZezcuZPly5fj9Xpz+CqEEEJcCFN+LPvON4kNGzbw2c9+VkIuIYQQ4iLX39/PG2+8gVp98hbB5LEwkhmnwJyPClWOqxNzSW9XD9/431+nt6sn16WIBcJlsDMQGyaaigFQUFCAxWKhubkZj8eD1Wplz549JJPJ045bsWIFVquV55577ozpjUIIIeafCY3ouuOOOyZ10rvvvntKxQghhBAid9LpNE8//TQqlYpMJoPT5aQ51YFDb8OiNeW6PDHHKIpCPBZHUZRclyIWCIfeRn8kSHfYxxJHFSqVitLSUjo6OmhqaqK2tpYjR45w4MAB3vOe92SP02g0bNy4kT/+8Y/s37//tG1CCCHmnwkFXfv27Tvt74FAgFQqRXFxMfn5+YyMjNDd3Y1er2fp0qWzUqgQQgghZteOHTuyKylbLBbGTTGSYykqrXk5rkwIIUCtUuPQ2+kJ9bPYXolKpUKj0VBeXk5HRwctLS1UVFTQ3t5OcXExZWVl2WPz8/NZtGgRf/zjH6mtrcXpdObuhQghhJhVE5q6+Oqrr2Z/3X777Xg8Hp544gleffVVHn/8cV566SWee+45vF4v27Ztm+2ahRBCCDHDfD4fO3fuBMBgMJBf4qVjvJc8owu9RlbVE0LMDS6jnXg6QSA2lP2aTqejoqKCVCqF3+/H7Xbz1ltvEYlETjt29erV6HQ6tm/fLiMRhRBiHpt0j64f/OAH/MM//AOrVq067euLFi3iy1/+Mj//+c9nrDghhBBCzL50Os2vf/1r4OQUn8rKSlpGO1CjJt/kznF1QgjxP0waIyatge7xvtO+rtfrKS8vJxaLkUwmUalU7N2797SeXDqdjvXr19PW1sahQ4cudOlCCCEukEkHXcPDw9jt9rNu02q1Zzw5EUIIIcTc9txzzzE+Po5KpaK6uprxdJi+cIACswe1asrr1oh5zltYwO3f+irewoJclyIWGJfBSTA2RCwdO+3rRqOR8vJyQqEQBoOBQCDAsWPHTtunpKSE8vJyfv/73xMOhy9k2UIIIS6QSd+9rlmzhp/85CeMjo6e9vVAIMC///u/S3NHIYQQ4iJy/PhxGhsbAaisrESr1dI81IZJa8BpOPuDLSEA9AY9pRVl6A36XJciFhin3oZGpaYn5D9jm9lspqysjPHxccxmM42NjQwNDZ22z7p168hkMvz+97+/UCULIYS4gCYddP3jP/4j7e3tXHPNNdx222185Stf4TOf+Qxbt25ldHSUb3zjG7NRpxBCCCFmWDQa5bHHHgOgtLQUk8lEfzTISGKMAlM+KlQ5rlDMZcODQzz1X08yPDh0/p2FmEFqlRq73kZX2HfWXltWq5WSkhIikQharZa9e/eSTqez241GI2vWrOHw4cO0tLRcyNKFEEJcAJMOupYuXcr27dv52Mc+RigU4vDhw8RiMW699Vaee+45SktLZ6NOIYQQQswgRVH44Q9/iKIouN1u7HY7yUyK5uET2PUWrDpzrksUc1w4FGb3a28SDsn0L3HhuQ0O4qk4wdjZg1aHw0FhYSHJZJKxsbHsyNVTKisr8Xq9vPrqq9KYXggh5hntVA4qKCjgH//xH2e6FiGEEEJcAIqi8POf/5xYLIbBYKCwsBCAo8OtpDJJKm3FOa5QCCHOzag1nGxKH/bhNXnOuo/b7SadThMMBjl27BjFxcUUFJzsKadSqVixYgWvvfYara2tLF68+EKWL4QQYhZNKOj67W9/y1VXXYXL5eK3v/3tefe/+eabp1mWEEIIIWaDoig89dRT9PX1oVarqa6uBiAQHaQn7KfEUoBOrctxlUIIcW4qVLgMDvrCQWLpBEbN2XvF5eXlkUqlGB4eZteuXbz//e9Hrz+5r9frJS8vjzfeeINFixahUsl0bSGEmA8mFHT90z/9E0888QQul4t/+qd/Oue+KpVKgi4hhBBiDlIUhRdffJEjR44AUFVVhUqlIplOcnjoODadRRrQCyEuGk6djX7VAL0hHzWOirPuo1KpKCwsJJVKMT4+zq5du7jmmmuy25YvX84bb7xBR0cHVVVVF7J8IYQQs2RCQdcf//hH8vPzs38WQgghxMVFURReeeUV9u/fD0BhYSEGgwGAppE2UpkUlbYSaUAvJsxqs3HlDVdjtdlyXYpYoNRqDQ6dle6wj2p7+buOyFKpVJSWltLe3o7f7+fIkSOsWLECgKKiIlwuF2+88YYEXUIIMU9MKOgqKSk5659PSaVShEIhnE7njBUmhBBCiJmhKAqvvfYau3fvBsBiseByuQDwRwfoC/splSmLYpKcbicf+ov/lesyxALnNNhpH+9hJDGGy+B41/1UKhWVlZW0tLRw6NAhPB4PhYWF2VFdu3btoru7m7KysgtYvRBCiNkw6VUXU6kUP/rRj3j++ecB2LdvH1dccQWXXXYZn/70pxkdHZ3xIoUQQggxda+//jo7d+4EQK1WU1paetqURbvegkOmLIpJisfidLS2E4/Fc12KWMDMOhM6tZa+iP+8+6rVaiorKwHYsWMHY2NjAJSWluJwOLI/J4UQQlzcJh10/fCHP+QnP/lJ9o3he9/7Hk6nkzvuuIOuri7uu+++GS9SCCGEEFPz+uuv88Ybb2T/XlpaikajAeDIcAvpTIZic4FMWRSTFvQH+Pfv/xtBfyDXpYgFTIUKp96OLxQkoyjn3d9gMFBcXJydzh2JRFCpVCxbtoyWlhZ8Pt8FqFoIIcRsmnTQ9bvf/Y5/+Id/4BOf+ARtbW20tLTw+c9/nk996lPcfvvtvPrqq7NRpxBCCCEm6fXXX2fHjh2o1Sff7l0uF1arFYD+SBBfJEiR2YtWPaFOBkIIMSc5DDaSSoqB2NCE9nc6ndjtdhKJBH/84x+Jx+OUl5djtVplVJcQQswDkw66AoEAq1evBk7eQKvVaq688krgZGPb8fHxma1QCCGEEJO2Y8cOduzYgcFgQFEU9Ho9hYWFACSyUxatOAzWHFcqhBDTY9QYMGoN9IbPP33xlKKiIrRaLeFwmNdff510Os2yZctobm4mGAzOYrVCCCFm26SDLq/XS09PDwCvvvoqy5Ytw+12A3Dw4MHsTbQQQgghcmPHjh28/vrrOBwO4vE4iqJk+3IpisKR4RYURaHY7JUpi0KIecGptxOIDpJS0hPaX6PRZBfZGhkZYefOnZSVlWE2m3nzzTdns1QhhBCzbNJB1/vf/37uvvtubrvtNurq6vjIRz4CwF133cW///u/84EPfGDGixRCCCHExJwKuQoLC7MLxHi9XoxGIwD90SD9MmVRzAC1Wo3FaslOjRUilxx6Kxklgz8y8dFYFosl+8A+GAyyb98+amtrOXToEMPDw7NVqhBCiFk26TuTL3/5y9x6662oVCq+8pWv8PGPfxyAQ4cOceutt/L5z39+xosUQgghxPmdCrmqqqro7+8/GURYLHg8HgAiqSiHB4/j0NtxGGw5rlZc7IrLSvjuD79PcVlJrksRAp1ah0Vnojc8ucURvF4vWq0Ws9lMb28vQ0ND6PV6GdUlhBAXMZWiTGB5EnFRSKfT1NfXs2bNmuyKWkIIIRaGUyFXbW0tJ06cQFEUMpkMixYtQqvVklYy7PUfJJaOs8hWjlot7xNCiPllKD5KX9jPNSWXYdToJ3xcKBSiq6uLwsJC+vv78Xq9DAwM8Pd///fY7fZZrFgIIcRkTDTzmNKchfHxcfbu3UskEuFsOdnNN988ldMKIYQQYgpOhVwrVqygp6cHRVFIpVKUl5ej1Z58qz820sp4Iky1o0xCLjEj+nt9PPDvP+fWL36WwpKiXJcjBA6dBR8qfBE/VbayCR9ntVpxOBwEg0HKy8vp6upCo9Gwa9cutm3bNosVCyGEmA2TDrp27tzJl770JaLR6Fm3q1QqCbqEEEKIC+RUyLVy5UqGhoYYHx8nk8ngdruxWk+uqOiLBOgc91Fs8WLSGHNcsZgvUqkUg4EBUqlUrksRAgCNWotNb6EvHJhU0AUnV49va2sjHA5TUlJCb28vb7/9Nlu2bMn+LBVCCHFxmHTQdd9991FdXc0dd9xBQUGBNCAVQgghcuSdIVc8Hqe3txedTodOp6OgoACAUDLCocFjOPV2XAZHjisWQojZ5dTb6Ar5CCcjWHTmCR+n0WgoLCykp6eHpUuX4vF4GBwc5LXXXpPFtoQQ4iIz6aCrra2NH//4x2zYsGE26hFCCCHEBLzxxhvZkEun03Ho0CFsNhuhUIjq6mpUKhVpJc3BgSa0ai3FFi8qVLkuWwghZpVVb0WjUtMbCbDEUTmpY202Gzabjba2NtauXcvY2BgHDx5k06ZN5Ofnz07BQgghZtykh2MVFxcTCoVmoxYhhBBCTMDu3bt57bXXWLlyJU6nkwMHDuByuRgfH6ewsBCDwQBA01ALkVSUMmsRapWMwBZCzH9qVNj1VvrC/WftJXwuKpWKwsJC0uk0HR0dLFmyBEVR+NWvfsX4+PgsVSyEEGKmTfqu93Of+xz3338/PT09s1GPEEIIIc6hrq6Ol19+meXLl1NUVMTu3btxOp2Mjo5it9txOp0A9IT66Qn7KTbnY9QYclu0mJfyvPn89T/8LXleGeki5haH3k40FWc0Mflw6tTU70AgAIDBYCAWi/HII48Qj8dnulQhhBCzYNJTF59//nn8fj9bt27F7XZjNJ7e1FalUvHKK6/MWIFCCCGEOOnw4cNs376dxYsXU1NTwx/+8AfMZjPJZBKNRkNRUREqlYpQMkzTcAsugx2n9OUSs8RoMrL0kmW5LkOIM1h0JnRqLX2RAE6DfdLHO51OxsbGaGlpobCwkO7uboaGhnjiiSf4+Mc/fs4l7YUQQuTepIOuwsJCCgsLZ6MWIYQQQryL48eP88wzz1BVVcXKlSuzD5WsVis+n4+qqio0Gg1pJc2BgSPoNDqKzN4cVy3ms7GRUfa8vpvLrr4cu1MCVTF3qFDh0NvoC/tZ5qxBpZpcf0KVSkVRUREnTpwgHo+jKApVVVW0trby3HPPcfPNN0/6nEIIIS6cSQddd99992zUIYQQQoh30dHRwZNPPklxcTHr169n586dRCIRqqqqaGlpwev1YjKZUBSFQ4PHiKXiVNvLpS+XmFVjo2P84bnfs2LtJRJ0iTnHYbAxEBsmGBvCa/JM+ni9Xk9+fj5+vx+bzUYwGOQ973kPe/bswW63c911181C1UIIIWbCpIOuU9ra2ti1axeBQIBPfvKTdHd3s3TpUqxW60zWJ4QQQixovb29PProo+Tl5bFp0yYOHDhAIBBg6dKltLa2YrFY8HhOfojrGO/BFwlSZinCoNHnuHIhhMgdo8aAQWvAFwlMKegCcLvdjI2NkUgkGB8fx263s2bNGt58803sdjsbN26c4aqFEELMhEkHXZlMhn/5l3/hqaeeQlEUVCoV27Zt48c//jFdXV08/PDDMrVRCCGEmAGBQICHH34Yp9PJ5s2bOX78OCdOnGDx4sX09/eTTqcpKSlBpVIxEBvm6MgJ8o1uHAZbrksXQoicUqHCqbfijwyQdqfRqCbfV+vUKozt7e1oNBpOnDjB+vXriUQivPjii9jtdmpra2eheiGEENMx6TkNP/7xj3n++ef53ve+x65du7LL9n7ta18jk8nwgx/8YMaLFEIIIRaaoaEhHnroIUwmE1u2bKG3t5fGxkbKy8tJp9MMDw9TWlqKVqslkopyMNiEVWfBa57ayAUhhJhvHDo7aSWDPzow5XOYTCbcbjeZTIb29nbS6TRr1qyhpKSE3/zmN/T29s5gxUIIIWbCpIOup556ii996Ut85CMfyS5hDrBs2TK+9KUvsWvXrpmsTwghhFhwxsfHeeihh9BoNFx11VX4fD727NmD1+vF7XbT3t6O2+3GarWSVtLUBY+gVqsosxSgQhokiwvDZDazbtN6TGZzrksR4qz0Gh0WrYm+UGBa58nPz0etVpNKpejp6UGtVrNp0yacTiePPvooQ0NDM1SxEEKImTDpoGtgYIBly86+lHRBQQFjY2PTLmoq4vE43/jGN9iwYQObN2/mgQceeNd9m5qa+OhHP8rq1av5yEc+wuHDh0/bvn37dq6//npWr17N3/3d35325qUoCvfeey+bNm3i0ksv5Z577iGTyWS3Dw8P88UvfpG1a9dy7bXX8uyzz07q2qe8+OKLMhRaCCEWoEQiwaOPPkoymeSqq66it7eXPXv2UFBQQE1NDceOHUOv11NQUICiKDQOHiOailJhLUGjnnLrTSEmzZPv4RN/8yk8+TKKUMxdDoONYGyYRDo55XNoNBqKioqAk/fyAFqtli1btqDRaHjkkUeIRCIzUq8QQojpm3TQVVFRwY4dO8667a233qKiomLaRU3FPffcw+HDh/nVr37Ft771LX70ox/x+9///oz9IpEIf/M3f8OGDRt4+umnWbt2LZ/73Oeyb06NjY3ceeedfOELX+Dxxx9nbGyMO+64I3v8gw8+yPbt2/nRj37ED3/4Q55//nkefPDB7PY77riD8fFxHn/8cT7/+c/zzW9+k8bGxgld+5SxsTHuuuuu2fg2CSGEmMMymQy/+c1vGBwc5Morr6S3t5f9+/dTVFTE4sWLOXHiBLFYjNLSUlQqFe3j3fRHgpRYCqT5vLjgkskkA/4gyeTUAwQhZptdZ0UF+KLTG9Vls9kwGAyMjo4yMjICgMFg4MorryQSifDrX/9a/i0IIcQcMemg69Of/jQPPfQQ3/3ud9m9ezcqlYrOzk4eeOABHnjgAT7+8Y/PRp3nFIlEePLJJ7nzzjtZsWIFW7du5bOf/SyPPPLIGfu+8MILGAwGvv71r1NTU8Odd96JxWLJhmIPP/ww27Zt4+abb2bp0qXcc8897Nixg+7ubgAeeughvvSlL7FhwwY2bdrEV7/61ex1urq6eO211/je977HkiVL+OhHP8oHP/hBHn300Qld+5R77rmHsrKy2fyWCSGEmGMUReHFF1+ktbWVyy+/nL6+Pg4cOEBJSQk1NTUMDAzg9/spLCzEYDAwEBvi2Eg7+UY3dr00nxcXnr+vn7vv+B7+vv5clyLEu9KqtVj1ZnqnOX1RpVJRUlICwP79+7Nft9lsbNmyBZ/Px9NPP33aTA8hhBC5Memg66Mf/Shf/vKXefrpp/mbv/kbFEXhH/7hH/jBD37Arbfeyi233DIbdZ7T0aNHSaVSrF27Nvu19evX09DQcMabTUNDA+vXr0elOtnDRKVSsW7dOurr67PbN2zYkN2/qKiI4uJiGhoa8Pv9+Hy+05YSXr9+Pb29vQQCARoaGigqKqK0tPS07QcPHpzQteHkqLi33nqLv/3bv52Zb44QQoiLwp49e3j77bdZv349gUAg23i+qqqKeDzO8ePHsdlsOJ3O/24+34xNms8LIcR5OXR2RhNjRFLRaZ3HaDRiNBoZHBxkeHg4+3WPx8Pll1/OsWPH+MMf/jDdcoUQQkzTlJp5fO5zn+MTn/gEBw8eZGRkBLvdzurVq09rTn8hBYNBXC4Xev3/TNvIy8sjHo8zMjKC2+0+bd9FixaddrzH46GlpQU4uZS71+s9Y3t/fz/BYBDgtO15eXkA2e1nO9bv90/o2olEgn/+53/mX/7lX9DpdJP/RgghhLgoNTU18fLLL7Ns2TLGxsY4fvw4VVVVlJaWoigKR48eRa1WU1xcTIYMdcHDaNRqSi2F0nxeCCHOw663oImo6QsHWOSYXpuVwsJCOjo62LNnD9u2bcs+wC4pKWHdunXs27cPp9PJpk2bZqJ0IYQQUzCloCuTyZBIJFixYgUulyv7Az5XotHoaSEXkP17IpGY0L6n9ovFYu+6PRaLnXbuP73O+c59vu33338/K1asYPPmzezbt2+Cr14IIcTFrLu7m2eeeYby8nJisRjt7e3U1NRQXFwMnJwWPz4+TmVlJWq1mvrBZqKpGNX2cjRqTY6rF0KIuU+tUmPTWekN+6mxl0/rs4vJZEKn0zE2NsaJEyeoqanJblu8eDGRSISXXnoJu93O8uXLZ6J8IYQQkzSpoGv79u089thjNDQ0kEqlgJNDeNetW8ctt9zC9ddfPytFno/BYDgj0Dr1d6PROKF9T+33bttNJtNpoZbBYDjtOiaTacrnNhqNHD9+nCeeeILnn39+ci9eCCHERWtoaIhf//rXuFwuFEWhvb2dJUuWUFBQAJxcnKSrq4u8vDzMZjOto530R4JU2Iql+bwQQkyC02CjY7yXsVQYh8465fOoVCrcbjd+v5/6+npKS0uznwsAVq1aRTgc5umnn8ZqtVJeXj4T5QshhJiECfXoSqfTfPnLX+arX/0q3d3dvO997+O2227j1ltv5dprr+X48eN88Ytf5J/+6Z9mu96zKigoYHh4OBu+wclpgkajEbvdfsa+AwMDp31tYGAgO+Xw3bbn5+dnP3icmsL4zj+f2v5ux57v2n/4wx8YHR1l69atrF27lr/+678GYO3atTz33HOT+4YIIYSY8yKRCI888gharRatVkt3dzdLly7Nvtek02mOHj2KyWQiPz8ff3SQltEOvCYPtml8SBNippRWlHHfA/+P0gpZQEfMfRadGa1KS19o+osnnGrXkk6nT+u1CyeDsPe85z14PB5+/etfZ1uYCCGEuHAmFHQ9+uij/OEPf+DOO+/k9ddf5//+3//LV7/6Vb72ta9x33338frrr/PNb36T559/nt/85jezXfMZli1bhlarPe2Npq6ujpUrV6JWn/4SV69ezcGDB1EUBTi5ytWBAwdYvXp1dntdXV12f5/Ph8/nY/Xq1RQUFFBcXHza9rq6OoqLi/F6vaxZs4be3l76+/tP275mzZrzXvsv//IvefHFF/ntb3/Lb3/7W773ve8B8Nvf/pZrr7125r5ZQgghci6VSvHYY48RCoVIp9P4/X6WL1+efTAC0NbWRiKRoKSkhHAqSsNAE3a9lXyT+xxnFkIIcTYqVDgMVvoigey9+FRpNBpsNhsajYb29nYCgcAZ2zdv3ozZbOahhx4640G3EEKI2TWhoOu3v/0tf/EXf8EnP/nJs85p12g0fOITn+DP//zPeeaZZ2a8yPMxmUzcfPPNfPvb36axsZFXXnmFBx54gE996lPAyVFXp/pr3XTTTYyNjXHXXXfR2trKXXfdRTQaZdu2bQDccsstPPvsszz55JMcPXqUr3/961x99dWUlZVlt997773s27ePffv2cd9992WvU1ZWxubNm/na177G0aNHefLJJ9m+fTuf+MQnznttp9NJRUVF9tepJ/oVFRVYrfLkXggh5gtFUXj++efp6ekhkUigVqtZu3YtHs//rJ44ODiI3++nsLAQlUZFXfAQOo1Oms+LOSXg8/PDu35AwCcjVsTFwam3k0gnGYyPTPtcLpeLZDKJ2Wzm7bffPmOld71ez1VXXYVOp+NXv/rVaas0CiGEmF0TCrra29u58sorz7vfli1bOH78+LSLmoo77riDFStW8OlPf5rvfOc7fPGLX+SGG24AYPPmzbzwwgsAWK1W/vM//5O6ujo+/OEP09DQwE9/+lPMZjNwcqrgd7/7Xe6//35uueUWHA4Hd999d/Y6t912G+9973v5whe+wN///d/zoQ99iM985jPZ7ffccw8Wi4U///M/5z/+4z/4/ve/z6pVqyZ0bSGEEPPfa6+9RmNjI4qiUFZWxpo1a057H0gkEhw/fhyr1YrD6eTgYDPxdJJySxFq1YTetoW4IBKJBJ1tHWf0HxVirjJqDRg0evrC0w9nLRYLGo0Gi8WSXS33TxkMBq6++mpUKhW/+tWvGB0dnfZ1hRBCnJ9KmcDY3WXLlvHoo4+ydu3ac+538OBBPvGJT9DU1DRjBYqJO9UnYM2aNWg0shKXEELMJYqi8PLLL7Nnzx40Gg0rVqzA4XCcsc+RI0cYGxujpqaG1vFO2sd7qbSVYNXJQxExt/R0dvOD79zL7d/6qvTpEheNQHSAwdgI15VejkY1vftln89HOBzG4/EQCAR473vfi8ViOWO/cDjMq6++isFg4K/+6q9ktoYQQkzRRDOPCT0aVhRlQsGJWq2e9px3IYQQYr451Xh+z5496PV6Nm7ceEbIBeD3+xkeHqa4uBh/fID28R6KzHkScgkhxAxx6O2klQyB6OD0z+VwkEgkcLvdaDQaDh48eNb9LBYLV199NdFolIceeohIJDLtawshhHh3MgdCCCGEmEVtbW38+Mc/pq2tDb1ez7p169DpdGfsF4vFaGtrw+l0kjEoHB5qwWWw4zY6L3zRQggxTxk0esxa04xMXzSZTOh0OgYGBqiqqqKnp4e+vr6z7muz2bjmmmsYHx/n4YcfzvYPFkIIMfO0E93x29/+9nmH2YZCoWkXJIQQQswHAwMD7Ny5k8bGRvR6PRqNhpUrV5415FIUhebmZrRaLa58N3sCBzFqDBRZCqT5vJiz3B43H//rv8TtkZVAxcXFobfSHxkgmU6i05z5M3miVCoVdrudgYEBFi1ahN/vp66uDq/Xi1Z75scsu93OVVddxWuvvcYjjzzCJz/5SfR6/XReihBCiLOY0IiujRs3YrFYUBTlnL8sFgsbNmyY7ZqFEEKIOcvn8/HEE09w//3309LSQmFhIYlEgqVLl77r4iNdXV2EQiGKioupH2wmo2QotxahlpBLzGFmq4X1l23EbD2zJ5EQc5lDb0MBfNHg9M/lcJBOpxkZGaGmpoZIJEJzc/O77u9yubjqqqvw+/38+te/JplMTrsGIYQQp5vQiK7/+q//mu06hBBCiItaZ2cnO3fupK2tDavVysaNG9FoNOzdu5fKykrc7rOPehkfH6erq4u8vDw64j2MJsapspeiVU940LUQOREaC1G//yBrNq7Fapfm2uLioVVrserM9IX9lFuLp3Uuo9GIXq/H7/ezbNkySktLaW5uprKyEpvNdtZjPB4PW7ZsYceOHTzxxBN87GMfO+sIMCGEEFMjPbqEEEKIKVIUhZaWFh544AF++ctfMjg4yGWXXcZ73/te3G43+/fvJz8/n9LS0rMen8lkOHbsGEajkbg5Tde4j2KLF7PWdIFfiRCTNzI8zDOP/IaR4eFclyLEpDn1NobjY0RT0++V5XA4GBoaIpPJUFZWhk6no66u7pyLdHm9XrZs2UJ7e7s0qBdCiBkmQZcQQggxSYlEgkOHDvHTn/6URx99lEgkwpYtW7jxxhupqKggkUiwc+dOTCYTixcvRqU6+xTEjo4OYrEYdq+DpuEWXAYHLsOZqzEKIYSYWXa9FbVKTV84MO1zORwOMpkMAwMDaDQaampq6O/vp6en55zHFRYWcs011xAMBvnZz37GwMDAtGsRQggxiWb0QgghxEKWTCZpaWnhyJEjtLS0kEwmKSgo4JprrsHr9WbDrEwmw65du0gmk6xZswaNRnPW842OjtLb24s730PjaAtGjZEii/dCviQhhFiw1Co1Np2VvoifanvZuz6QmAi9Xo/JZCIYDOL1evF4PLjdbg4cOEBhYeFZFyE5JS8vj+uvv56dO3fy85//nD//8z+nurp6yrUIIYSQoEsIIYR4V8lkktbWVo4cOcLx48dJJpO4XC6WLVtGWVnZWfuv1NfXMzAwwMqVKzEajWc9bzqd5tixY5hMJjrSfaQzaSodxdJ8XgghLiCnwUbneC/jqTB23fT6zNntdvx+P6lUCq1WS01NDXV1dRw5coQ1a9ac81ir1cr111/P7t27efjhh3nve98rC3wJIcQ0SNAlhBBCvEMymaStrY0jR45w7NixbLi1dOlSysvL37W5MMCJEyc4fvw4NTU1OBzvPgXxxIkTJBIJlDwtI9FxKm2l6NRTX+JeiFwwGA0sWbEUg9GQ61KEmBKrzoxWpaEv7MfunF7Q5XA48Pv9DAwMUFhYiNFopKysjGPHjlFZWYnT6Tzn8Tqdji1btnDw4EF+97vfMTg4yNatW1GrpdOMEEJMlgRdQgghFrxoNEpLSwvNzc20tbWRTCZxOp0sXbqUsrIy7Hb7ec8xODjI22+/TWFhIUVFRe+639DQEP39/RjdFo5FOyi2eLHopPm8uPjkF3j53Fc+n+syhJgyFSocBhu9YT+1juppTV/UarVYLBaCwSCFhYUAlJaWEggE2L9/P9dff/15z69Wq1m/fj02m419+/YxNDTERz7yEfR6/ZTrEkKIhUiCLiGEEAvS6Ogox44d4+jRo3R0dKAoCh6PJ7s8/ETCrVOi0ShvvvkmFouFmpqad/0wk0qlaGlpwWAy0JLowmWwS/N5cdHKZDIk4gn0Br2MOhEXLafBzmBshEBskAJT3rTOZbfb8fl8JBIJ9PqT/y4WLVrEoUOHOHHiBDU1NRM6z5IlS7BarezZs4cHHniAW2655ZyjhIUQQpxOgi4hhBALxtDQEIcPH+bo0aP4fD5UKhUFBQWsW7eOkpISzGbzpM+ZTqfZtWsX6XSalStXnvMDf1tbG8lUkqBhHINGR5GlAJX05RIXqb7uXn7wnXu5/VtfpbSiLNflCDElJo0Rs9ZI97hvxoKuYDBISUkJAE6nE6/XS319PSUlJe/au/FPFRcXc91117Fz505+9rOf8bGPfYyyMvl3JoQQEyFBlxBCiHktnU5z/Phx9u/fT3t7O1qtlqKiIjZt2kRxcfG0p4QcPHiQwcFBVq1ahcHw7r2KBgYGCAQCRK0p4iSosZZL83khhJgDXAYHvWE/0XQMk2ZiQdTZaDQarFYrgUAgG3QBVFdX8/bbb3Pw4EEuu+yyCZ/P6XSydetW3nzzTR544AE2btzItddeO+GwTAghFioJuoQQQsxLo6OjHDhwgAMHDhAKhcjLy+M973kPZWVlaLUz8/bX1tZGa2srixcvPudUx2QySUtLCxjUBBimyirN54UQYq5w6G30R4J0h/pZ4qic3rkcDnp7e4nFYtlASqfTUVVVRUtLC1VVVdkeXhNhNBq59tpraWlp4eDBgzQ3N3PTTTexfPnyafUUE0KI+UyCLiGEEPOGoii0tbXx9ttvc/z4cTQaDZWVldTU1OByuWb0WgMDA7z99tsUFRWd80OLoigcP36cdCZNjyFIoTkfi27yUySFEELMDrVKjV1vpyfkY7G9YloBks1mQ6VSEQgEKC8vz369oKAAv9/P22+/zbZt29BoNBOvT62mtraW0tJSDh48yG9+8xtqamp43/veN+PvbUIIMR9I0CWEEOKil0wm2b9/P/v372dkZASXy8X69eupqKhAp5v5kVORSIQ333wTm81GdXX1OfcNBoMMDQ0xaAphMVjwGJ0zXo8QQojpcRvtDI+OEIwN4TV5pnwetVqNzWY7I+hSqVQsWrSIgwcP0tTUxMqVKyd9bovFwubNm+nt7aWuro4f//jHXHXVVVx22WWTCs6EEGK+k6BLCCHERUtRFJqamnj55ZcZHx+nvLycDRs24PF4Zm1KRzKZ5I033kBRFJYtW3bO5vOxWIyWlhbi+iRJfZoaS7E0nxfzRlFJMd/5t7swmU25LkWIaTNpjJi0BrrG+6YVdMHJ6Yvd3d2Ew2EsFkv26xaLhdLSUpqbm6moqJjU6r7vVFJSgtfr5fDhw7z66qs0Njby/ve//7RgTQghFjJZC1oIIcRFyefz8ctf/pLf/OY3WK1Wtm3bxqZNm8jLy5u1kCuTybBnzx7Gx8dZsWLFORvZK4pCc3MzKTIM6McotxajVskTdzF/aLQarHYrGq38fy3mB5fBSTA2RDQdm9Z5rFYrarWaQCBwxraysjL0ej1vv/02iqJM+Ro6nY61a9dyww03kMlkePDBB3n22WcZGRmZRuVCCDE/SNAlhBDiohIKhXjuuef46U9/yujoKFdffTVbtmzBZrPN+rXr6+vx+XwsW7bstKf0Z9PV1UUoFCJgGKbQUoBBM73VHYWYawYCA/zihz9jIDCQ61KEmBFOvQ2NSk1vyD+t86hUKux2O4FA4IwwS6PRsGjRIgKBAB0dHdO6DoDL5eK6665j/fr1NDc38+///u88++yzDA4OTvvcQghxsZKpi0IIIS4K6XSaffv2sWPHDgDWrVvHokWLzjl1cCYdP36c48ePs2jRovM2/x0dHaWrq4sRfRirxYbTMPshnBAXWiwapan+MDd+6KZclyLEjDjZlN5GV9hHjb18WqODnU4nIyMjjI2N4XA4TtvmcrnIz8+nvr6e4uJiDAbD9OpWq1m8eDFVVVW0trZy7NgxGhoaWL58OVu2bKGgoGBa5xdCiIuNBF1CCCHmtFOrFr700kuMjIywaNEiLrnkkml/MJiM3t5eDhw4QElJCUVFRefcN5VK0Xy0mbg6ScqsUGrOu0BVCiGEmC63wcHw2Oi0m9KbTCa0Wi2BQOCMoAugurqauro6GhoauPTSS6dTcpZWq2Xp0qUsXryY9vZ2mpubOXLkCLW1tWzZsoWSkpIZuY4QQsx1EnQJIYSYs+LxONu3b+fw4cMUFhZy0003nfUDw2waHh5m9+7deDweqqqqzrmvoignm88nEgxbI1RZS6X5vBBCXERM2pNN6btD02tKr1KpcDgcBINBampqzhh9rNfrqaiooK2tjaqqKvLz86dbetap6ZHV1dV0dHTQ3NzMz3/+c6qrq7nyyiupqKiYsWsJIcRcJEGXEEKIOcnn8/Hkk08SCoW47LLLKC+f3jSSqYhEIuzYsQOz2Uxtbe15rx8MBhkYGGDIME6xrQCtWt5mhRDiYuMyOOgLB4il4xg1Ux897HA4GBwcZGhoiLy8M0f3FhUVEQgEeOutt7jpppvQaGZ2YQe1Wk11dTWVlZX09PTQ1NTEL3/5SyorK7n66qsl8BJCzFvSjF4IIcScoigK+/fv5xe/+AUqlYobbriBioqKCx5yJZNJduzYgaIoLF++/LwfQGKxGMeOHyOkjeJwOrDoTBeoUiFyw+F08MGP3YzDeWFHWQox2041pe8J9U/rPEajEYPBcNbVF+HkqK/FixcTCoVoamqa1rXORa1WU15ezo033sjmzZsZHR3ll7/8JQ899BBdXV2zdl0hhMgVedQshBBizojFYjz33HM0NzezePFi1qxZM+NPuCcik8mwe/duQqEQq1evRq8/94qJiqLQ2HSIJCkydg0ug3zwF/OfzWHnqhuvyXUZQsw4tUqDXW+dsab0gUCAVCqFVnvmRy+LxUJpaSlNTU2Ul5fP6vR8lUpFaWkpJSUl9Pb2cvjwYR588EGqq6u5+uqrKSsrm7VrCyHEhSQjuoQQQswJfX19/Od//idtbW1cccUVrF+/Pmch1759++jv72fZsmVYLJbzHtPa3kYsHCVkSVBk9UpfLrEgRMIRGvYfJBKO5LoUIWac2+AknooTjA1N6zwOhwNFURgYGHjXfcrLyzEajbz11lsoijKt603EqcDrxhtv5IorrmBwcJAHHniAhx9+mJ6enlm/vhBCzDYJuoQQQuSUoijs27ePX/ziF2g0Gm644YacPVU+FXJ1dXVRW1uLy+U67zEDQwP4evsY18codhaglpBLLBBDA4M89JNfMjQwmOtShJhxRq3hZFP6sG9a59FqtVgsFvx+/7vuo1arWbx4MYODg7S2tk7repOhUqkoKyvjpptu4vLLL2dgYIBf/OIXPPLII/T3T2/aphBC5JJMXRRCCJEz0WiU5557jqNHj7JkyRJWr16dk1FccDLk2rt3L93d3dTW1k5oBaxwOExTUzNRTQKXxyXN54UQYp5QofrvpvTBGWlK39fXRywWw2g0vus+hYWFNDQ0UFJSgtlsnvL1JkulUlFeXk5paSnd3d0cOXKEn/70p1x55ZVs2bIlZ+/LQggxVTKiSwghRE4MDAzws5/9jBMnTrB582bWrVt3UYVciUSCg431JFRJ9B4TZmk+L4QQ84pTZ0OtUk27Kb3NZkOlUhEMBs+5X1VVFWq1mrq6umldb6rUajUVFRXcdNNNLF++nDfeeIOf//zn79pMXwgh5ioJuoQQQlxw7e3t/PznPyeTyXDDDTdQWlqas1oymQx79uyhu7ubpUuXTijkymQy1B+qJ5lKknSAyyjN54UQYr5RqzU49FY6Q72klfSUz6PRaLDZbPj9/nP24NJqtVRXV9Pb20t3d/eUrzddarWalStXsnXrVqLRKD/96U/ZvXs3mUwmZzUJIcRkSNAlhBDigjpw4AAPP/wwLpeL6667DqvVmrNaToVcPT09LF26lLy8vPMeoygKR5qPEIlECdniFNrOH4wJMR/pdDpKykvR6XS5LkWIWZNndJNIJ+kJv3uPrYlwOp1Eo1HC4fC5r5eXh8fjoa6ujkQiMa1rTpfb7Wbr1q0sWrSIl19+mV/+8pcMDU2vOb8QQlwIEnQJIYS4IBRF4eWXX+b555+nurqaK6+8Er1en7N6MpkMu3fvpqenh2XLlk0o5AJo72hnZGiEMVOEIkeBrLAoFqyC4kL+4dtfo6C4MNelCDFrDBo9Tr2dttFOMsrURzRZLBY0Gs15pwGqVCpqampIJpM0NDRM+XozRavVsnbtWq699lqGh4f5j//4D/bv339BVocUQoipkqBLCCHErEskEjz++OPs3r2btWvXsn79etTq3L0FnQq5ent7WbZsGR6PZ0LH9ff309vTy4g+TL47X5rPCyHEApBvchNPJ+gJT71Xl0qlwuFwnHf6IoDBYKCyspK2trY50x/L6/Vy4403Ul5ezgsvvMDDDz/M2NhYrssSQoizkqBLCCHErBofH+fBBx+kra2NLVu2UFtbi0qVu1FQ0WiU1157bdIh1+joKMdbWghpozjdDkyas6+cJcRC0dPZw9f/5h/o6ezJdSlCzCqDRo9Db6d1rIvMNEYyORwOUqkUIyMj5923qKgIu93O/v37Saen3h9sJul0OjZu3MhVV12Fz+fjxz/+MZ2dnbkuSwghziBBlxBCiFnj8/n42c9+xtjYGNdddx0lJSU5rScQCPD73/+e0dFRVq5cOeGQKxaL0Xi4kZg6gdFtxqa3zXKlQlwMFNKpNCBTmMT85zW5iafi9E5jVJfRaESv109olJZKpWLRokWEQiGampqmfM3ZUFRUxLZt23A6nTz88MMcO3Ys1yUJIcRpJOgSQggxK44dO8aDDz6ITqdj69atuFyunNWiKArNzc289tprGI1G1q5di8MxsZUSY7EYdfV1JJUUKpcWl9E5u8UKIYSYc06O6rLRNtY55f5Up6YvDgwMTGiUlsVioaysjKampgmNAruQ9Ho9V155JYWFhTz++OPU19fnuiQhhMiSoEsIIcSMUhSFPXv28Nhjj1FQUMC1116LyWTKWT2JRII333yThoYGSktLWbly5YSb4I+Pj/P2gTriqQRxh0KexT3L1QohhJir8k1uoqn4tFZgdDqdZDIZBgcHJ7R/WVkZJpOJ/fv3k8lMvRn+bNBoNFx++eVUVVXx7LPPsnv37lyXJIQQgARdQgghZlAmk+GFF17gD3/4A0uXLuWKK65Aq81dw/bh4WFeeukl/H4/y5cvp7KycsL9wYLBIPUN9cSUOGFHkkJ7vqywKIQQC5hRY8Cut05rVJdOp8NkMk24ybxarWbRokUMDg7S2to6pWvOJrVazcaNG1m+fDkvv/wyL7/8sqzIKITIOVkuSgghxIyIx+M8+eSTnDhxgo0bN1JTU5PTetra2qirq8NsNrNmzZoJjypTFIWenh46OjqIaONErEkq7KUScgnxJwqKCvja//knPPkT63UnxHyQb3LTNtpFX8RPiaVwSudwOp34fD4SicSERhg7HA4KCwtpbGykpKQEi8UypevOFpVKxapVqzAYDOzevZtIJMIHPvCBnK6uLIRY2OSnjxBCiGkbGRnhF7/4Bd3d3Vx11VU5DblCoRBvvvkm+/fvx+v1snr16kmFXC0tLXR0dBAyxBmzxCmzFqGWkEuIM+j0egpLitBNcCqwEPOBSWPErrfSOto15ZFLdrsdODlyeKKqqqpQq9XU1dXN2RFTtbW1bNq0iYaGBp544glSqVSuSxJCLFASdAkhhJiW3t5efv7znxOJRLjuuusoLJzaE+7pSiaTNDQ08MILLxAMBlm6dCmLFy+e8BPlVCrF4cOH8fv9hCxxxgwRKm3FaNUy+FmIsxkaGOLxB3/N0MBQrksR4oLKN7qJpKL4ohMPqt5Jo9Fgs9nw+/0TDq20Wi01NTX09fXR3d09peteCJWVlWzZsoXW1lYefvhhYrFYrksSQixAEnQJIYSYsqamJn75y19iMpnYunXrhFcynEmKonDixAm2b9/OsWPHKC0tZf369eTn50/4HLFYjPr6esbGxhizxRlRh6iwlaBT62axciEubpFwmLd27iUSDue6FCEuKJPWiE1noXV06r26XC4X4XCYsbGxCR+Tl5dHXl4edXV1xOPxKV33QiguLubqq6/G5/Pxy1/+klAolOuShBALjARdQgghJk1RFHbt2sWTTz6ZvaE1Go0XvI5AIMBLL73EW2+9hd1uZ/369VRUVKDRaCZ0fDqdprOzk7q6OpLJJIOWEONEqLKXYdQYZrl6IYQQFyuvyUM4GaF/iqO6LBYLer2evr6+SR1XXV1NOp2mvr5+Ste9UPLz87n22msZGxuTkV1CiAtOgi4hhBCTkk6nef7553nllVdYvnw5l1122QVfWXF8fJxdu3bx6quvkk6nWb16NUuXLp1w2KYoCv39/ezfv5+uri4sDhs+8zBRVZwqe6mEXEIIIc7JpDVincaoLpVKhcvlYmBggEQiMeHjDAYDlZWVtLe34/f7J33dC8npdHL11VczPDzMo48+SjKZzHVJQogFQoIuIYQQEzY+Ps5DDz1EQ0MD73nPe1i1ahUq1YVr1D48PMzu3bv53e9+h9/vZ8mSJaxevTrb2Hei5zhw4AAtLS0YjUaKKktoyXSRVmWospVh0EhjbSGEEOfnNbkJJSP4o4NTOt7pdKJSqfD5fJM6rrCwELvdzv79++d8w3eHw8GVV16Jz+fjySefJJ1O57okIcQCIEGXEEKICens7OQ///M/CQaDXHPNNVRVVV2Q6yqKgt/v57XXXuOll14iEAhQU1PDhg0bKCgomHDQFg6HOXToEIcPH0ZRFCorK7EXuqgbOoxKpaLKVopeIz25hJgom93Gte+9HpvdlutShMgJs9aEVWfh2MgJ0srkAxyNRoPT6aSvr29So8JUKhWLFy8mHA5z5MiRSV/3QsvLy+OKK66gra2NZ599ds6uGimEmD9kKSkhhBDnpCgKe/bs4ZVXXiE/P5/LL7/8gvTjymQy9Pb20tzczNDQEBaLhdraWvLz8yccbmUyGYaGhvD7/QwNDaHX6yktLcVmszGSGOPt/kPoNTrKrbK6ohCT5XA5ed+ffSDXZQiRU0XmfFpHO2kb7WKJc/IPgFwuF8PDwwSDQbxe74SPM5vNlJeXc/ToUcrLy3G5XJO+9oVUVFTEpk2b2L17N2azmRtvvPGCjggXQiwsclcvhBDiXcXjcZ599lmam5tZtmwZK1euRK2e3cHA6XSajo4OmpubCYVCOBwOVqxYgcvlmtBNsaIojI2N4ff7CQaDZDIZDAYDhYWF2XMMxIY5EDyMUWugwlqCWiUDnIWYrFg0Rk9nN6UVZRhNF34xCiHmAoNGT77JTdtYN4WWfOw666SONxqNWCwWent7JxV0AZSWljIwMMBbb73F1q1bZ/39ebrKy8uJx+Ps27cPs9nMlVdemeuShBDzlARdQgghzioQCPD4448zPj7O5s2bKS0tndXrxeNxWltbOX78OPF4HI/HQ01NzYT7b4XDYQKBAIFAgEQigU6nw+12Y7fbTxuB1hvq5/DwcSxaM2W2YtTIE2UhpmIgEOQn9/yI27/1VUorynJdjhA5k2dyM5oMcWjwGJcXrJv0SCWXy0VPTw/j4+PYbBOfCqxWq1m0aBENDQ20trayZMmSyZZ+wS1evJhEIsFrr72GyWRi48aNuS5JCDEPSdAlhBDiDIcOHeL555/HYrFwww03TOrGe7LGx8c5duwY7e3tKIpCQUEBJSUlmEymcx6nKAqjo6MMDQ0xODhILBZDrVbjcDgoLi7GZDKd9mEjoygcHWmhc9yHy2CnyFIgIZcQQohpU6Oi1FxA21gXHeM9VNknF/zabDa0Wi19fX3U1tZO6li73U5RURGNjY2UlJRgsVgmdXwuLF++nHg8zgsvvIDJZOKSSy7JdUlCiHlGgi4hhBBZ6XSal156if3791NZWcmGDRvQamfnrWJgYICjR4/S09ODTqejpKSEoqIi9Pp3X/UwmUwyNDSU/ZXJZNBqtVitVvLz87FarWd9kh5LJzgYPMJoYpxiSwFug2NWXpMQQoiFyaQ14jE6aRntoNCcj0k78em8KpUKt9tNMBikuroanW5yC6NUVlYyODhIXV0dW7ZsmfO9r1QqFWvXriWRSPDMM89gNBpZtGhRrssSQswjEnQJIYQAwO/38+yzz+L3+9mwYQM1NTUzfrOsKAo+n4+mpiYGBgYwmUwsWrQIr9eLRqM56zGJRIJgMEgwGGR8fBwAk8mE2+3GZrNhNBrPWedwbJQDg0dQFIUqeylm7blHigkhhBBTUWDKYywR4vDQcTbkr5zUe6jL5SIYDNLf309Z2eRGhGm1Wmpqamhubqanp2fSx+eCSqXi0ksvJZFI8MQTT/CpT31q1lskCCEWDgm6hBBigUulUuzcuZM333wTm83Gddddh8fjmdFrZDIZenp6aGpqYmRkBJvNxvLly3G73Wf9IJBKpRgcHCQQCDAyMgKA1WqlqKgoO8XjfBRFoSvcR/NQG2adkTJLkaysKMQM0mg0OFyOdw2phVho1Co1xZYCOsd78UUCFFsKJnysRqPB4XDQ19dHaWnppB80eTwePB4PdXV1FBQUnHN09FyhVqu5/PLL2bFjB4888gi33nor+fn5uS5LCDEPqBRFUXJdhJgZ6XSa+vp61qxZIzedQogJ6e7u5rnnnmNoaIhly5axfPnyGf35kclk6OjooKmpiVAohNPppKysDIfDccZNvKIoDA8P4/f7GRwcRFEUTCYTTqcTu90+qbrSSpojQy30hv14jE4KzfmopB+XEEKIC6An1E84GeHK4kvRayY+DTEWi3HixAmWLVtGXl7epK8bj8epq6ujqqqKDRs2TPr4XEkkErz66qtkMhluu+22CS9CI4RYeCaaecztNWgnIR6P841vfIMNGzawefNmHnjggXfdt6mpiY9+9KOsXr2aj3zkIxw+fPi07du3b+f6669n9erV/N3f/R1DQ0PZbYqicO+997Jp0yYuvfRS7rnnHjKZTHb78PAwX/ziF1m7di3XXnstzz777ISvrSgKP/3pT7n22mtZt24dn/70p2ltbZ3ut0YIIc6QSCR48cUXeeCBB1AUhRtuuIGVK1fOWMiVSqU4fvw427dv56233sJgMLBmzRpWrlyJ0+k8LeRKpVJ0d3ezd+9ejhw5wvj4OPn5+SxevJiqqipcLtek6golI+zxH8QXCVJqKaTI7JWQSwghxAVTaM5DQaF5ZHL38UajEZPJRG9v75SuazAYqKyspLW1lWAwOKVz5IJer+fKK68klUrx8MMPE41Gc12SEOIiN2+CrnvuuYfDhw/zq1/9im9961v86Ec/4ve///0Z+0UiEf7mb/6GDRs28PTTT7N27Vo+97nPEYlEAGhsbOTOO+/kC1/4Ao8//jhjY2Pccccd2eMffPBBtm/fzo9+9CN++MMf8vzzz/Pggw9mt99xxx2Mj4/z+OOP8/nPf55vfvObNDY2Tujajz32GA888AD//M//zFNPPUVpaSl//dd/LT/shRAzqrW1lfvvv58DBw6wdu1arrvuOpxO54ycO5FI0NTUxPPPP8+BAwewWCysW7eO5cuXn7FyYyKRoL29nX379tHZ2YnNZqO6upqamhry8vIm3YxXURTax7rZ1V9HIp2kyl6K0yBPhYWYLb6ePr77lX/B19OX61KEmFO0ai2F5jz6wgGC0cFJHet2uxkbGyMcDk/p2qem+O/fv590Oj2lc+SC2WzmqquuYmxsjMcee4xkMpnrkoQQF7F5EXRFIhGefPJJ7rzzTlasWMHWrVv57Gc/yyOPPHLGvi+88AIGg4Gvf/3r1NTUcOedd2KxWLKh2MMPP8y2bdu4+eabWbp0Kffccw87duygu7sbgIceeogvfelLbNiwgU2bNvHVr341e52uri5ee+01vve977FkyRI++tGP8sEPfpBHH310Qtd+5plnuPXWW7nmmmuoqqri29/+NiMjIxw4cOBCfBuFEPNcJBLhmWee4ZFHHsFsNnPTTTdRW1uLWj39t4JYLEZDQwPPPfcchw4dwuVysWHDBpYuXXrGUuexWIzW1lbeeustent7cTqdLF68mKKiIozGia9S9U7hZIS9/nqOjpzAbXBQ46jApJnauYQQE5NOpxkdHr2oPkwLcaE4DHasOguHh1tIKxP/N2K329FqtVMe1aVSqVi8eDHj4+McPXp0SufIFYfDwZYtW+jt7eXpp58+bdaMEEJMxrwIuo4ePUoqlWLt2rXZr61fv56GhoYzfkA2NDSwfv367LQZlUrFunXrqK+vz25/55z2oqIiiouLaWhowO/34/P52Lhx42nX6e3tJRAI0NDQQFFR0Wkrhqxfv56DBw9O6Npf//rX+eAHP5g9VqVSoShKdpUxIYSYimQyyd69e7n//vs5evQol156KVdddRVWq3Xa5w6Hw9TV1fHcc89x/PhxCgoK2LhxI4sXL8ZkOn11w0gkwrFjx9i/fz9+vx+Px8OSJUsoLCycUHP5s1EUhfbxbt7sryOajlFlK6PQnI9apioKIYTIIRUqis35JNJJmofbJn6cSoXT6SQQCJBKpaZ0bYvFQklJCUeOHGFsbGxK58iVvLw8Lr/8co4dO8YLL7yAtJMWQkzFvFh+KhgM4nK5TltdJC8vj3g8zsjICG63+7R9Fy1adNrxHo+HlpYWAAKBAF6v94zt/f392bnu79x+qlHkqe1nO9bv90/o2n/aNPLJJ58klUqxfv36CX4nhBDifySTSQ4cOMCbb75JOBymsrKSVatWnRFATcXo6CjNzc10dnai0WgoLS2luLj4rNMNU6kUnZ2d9PX1odVqKSgowOVyTXskWSQVpXHwKMPxMTxGJwWmPNSqefH8RgghxDyg1+gpNOfTHfLh0NspsxZO6Di3283AwAA+n4+ysrIpXbu8vJyBgQHefvttrrnmmkmv4phLJSUlbNy4kbfeegur1crVV1+d65KEEBeZeRF0RaPRM5bQPfX3RCIxoX1P7ReLxd51eywWO+3cf3qd8537fNvfqaGhgX/913/ltttuk2V2hRCTkkqlqKurOy3guuqqq87okTVZmUyGvr4+jh8/TiAQwGAwUFVVRWFh4VmbxSuKgt/vp729nXQ6jdfrxePxTPtmW1EUusJ9HBs+gUatocpWikVnntY5hRBCiNngNjiIpmI0DR/Hrjfj0J+/d6RWq8XpdNLd3U1RUdGURj1rNBoWLVrE4cOHaW9vp7q6eirl50x1dTXRaJQdO3ZgtVovqlUkhRC5Ny+CLoPBcEZYdOrvf9rv5d32PbXfu203mUynhVoGg+G065hMpimf+09rPHjwIH/913/NlVdeyd///d9P4DsghBAnA64DBw6wc+dOwuEwFRUVMxJwnVruvLW1lUgkgs1mo7a2lry8vHcdlTU+Pk5LSwvhcBiHw0FBQcGUpye+03BslCMjLYwnwrgNTgrNMopLiFzJ8+bz+a9/gTyvPJAT4lyKLF7i6QR1A01sLliPXnP+xVa8Xi9jY2N0dHScMSNkolwuF16vl4MHD1JcXDzlPpi5snz5cmKxGC+88AJWq5WlS5fmuiQhxEViXgRdBQUFDA8Pk0qlsh+kgsEgRqMRu91+xr4DAwOnfW1gYCA75fDdtufn51NQUJA996k+XKemM57a/m7HTuTaAPv27eNv//ZvueKKK7jvvvtmpEm0EGJ+S6VSHDx4kJ07dxIKhaioqODKK6884+ffZA0ODtLS0kJXVxdw8ufckiVLzhmcnVpJ8dSIr8rKSszm6Y+2iqVjHB05gS8cxKw1UmMvx6S9uG7YhZhvjCYji5YuznUZQsx5alSUWQtpG+3mwEAT7/GuOu/oZq1WS15eHj6fj+Li4im/l1ZXV1NXV0ddXR1XXHHFlM6RKyqVirVr1xKLxXjqqaf45Cc/SXl5ea7LEkJcBOZFirJs2TK0Wm22qTtAXV0dK1euPCMoWr16NQcPHsw2NlQUhQMHDrB69ers9rq6uuz+Pp8Pn8/H6tWrKSgooLi4+LTtdXV1FBcX4/V6WbNmDb29vfT395+2fc2aNRO69vHjx/n85z/Pli1b+Ld/+7ez9roRQgg4+fOju7ub7du3c9999/Hiiy/idrvZtm0bmzZtmnLIdaph/EsvvcTLL79Mf38/5eXlXHrppecMuRRFoaenh/379zMwMEBRURHV1dXTDrnSSprW0U7e6NtPMDJMqaWAKnuZhFxCzAGjwyP87jfPMzo8kutShJjzdGodZdZCRuKjHB2ZWHN6j8eDVqvlxIkTU7+uTkd1dTXd3d1TXskxl9RqNZs2bcLtdvPoo4+e9jlLCCHezbwY0WUymbj55pv59re/zfe//30CgQAPPPAAd999N3By1JXNZsNoNHLTTTdx3333cdddd/EXf/EXPPbYY0SjUbZt2wbALbfcwic/+UnWrFnDypUrueuuu7j66quzjSBvueUW7r33XgoLTzaTvO+++7j11lsBKCsrY/PmzXzta1/jzjvv5NChQ2zfvp2HH34Y4LzX/pd/+ReKioq44447GB4ezr6+U7ULIcTw8DCNjY00NDQwPDyM2WymqqqK6urqKU9RjMfjdHd309nZSTAYRKVS4Xa7Wb58OW63+7xPnd85TfHUNImz9eyaDEVR8EcHOTrSSiyVwGN04jW5Uaumd14hxMwZHxvn1RdeYfXGNThczlyXI8ScZ9GZKbLk0zHei0Nvo9hScM79VSoVBQUF9Pb2Mjw8jMvlmtJ18/PzCQaD7N+/n/z8/DN6Bs91Go2GzZs38/rrr/OrX/2KT3/609nPYkIIcTYqZZ6s2RqNRvn2t7/NH/7wB6xWK7fddhuf+cxnAKitreXuu+/mwx/+MACNjY1861vfoq2tjdraWr7zne+wfPny7LmefvppfvjDHzI6OsoVV1zB//k//yf7xpJOp7nnnnt4+umn0Wg0/Nmf/Rlf+cpXsh8EBwcHufPOO9m9ezf5+fncfvvtvP/978+e+92uHQwG2bx581lf2ztrP5d0Ok19fT1r1qyZ9odMIcTcEYvFOHLkCA0NDXR3d6PVaikrK6OyshKv1zul5u7JZJLe3l46Ozvp7+9HURScTif5+fnk5eVNqJ9WKpWio6MDn8+H0WikqKhoRlZ0HI6P0jLawWBsBJvOQqE5H4Pm4ropF2Ih6Ons5gffuZfbv/VVSiumtjKcEAuNgkJf2M9oIsSmwrU4dNZz768odHR0oFKpWLdu3ZQXdInFYhw4cICqqqqLtrF7IpHg9ddfJxKJSNglxAI10cxj3gRdQoIuIeYLRVEIBAJ0dnbS3t5OS0sLmUyGwsJCKioqKC0tnVJj91AoRF9fH319fQQCATKZDHa7PRtuTfQJr6IoBINB2trayGQy5OfnT2jk1/mcDLg6GYwNY9AaKDTlYdNZpnVOIcTskaBLiKnJoNA+drL/5RUF69Gdpzl9NBqlvb2dxYsXTyvc6evro62tjWuvvfa0HsEXEwm7hFjYJpp5zIupi0IIcTHLZDL4/X46Ozvp6Oigs7OTWCyGWq3G4/GwcuVKKioqJj1aKpPJMDAwQF9fH729vYyPj6NSqXA4HFRWVuLxeCY9LToWi9HS0sLIyAg2m43CwsJp9xMcjo3SMnZyBJdRa6DcWoRNb0XF9IIzIYQQYi5So6LcWkzbaBcHB5vYmH/u5vQmkwmHw0F7e/uER12fTVFREcFgkLfeeott27ZdlA/G9Xo9V199tUxjFEKckwRdQghxASmKwujoKAMDAwQCATo6Oujq6iIej6PRaPB4PNTU1OD1erNNaCcjEonQ39+Pz+ejv7+fZDKJXq/H5XJRWlqK0+mc0g1yKpWip6eHnp4eNBoN5eXlWK3nnm5xPkOxUVol4BLiomW2WLh0yybMFhl5KcRknWxOX0T7eC/NI20sc9acM+zyer20trbS1dVFdXX1lK6pUqlYvHgxBw4c4PDhw9kFsS42EnYJIc5Hpi7OIzJ1UYi5Ix6PMzAwwODg4Gm/Dw0NkUqlgJPNVfPy8sjPz88GW5P9t5tKpQgGg9lwa2xsDDi5iIXL5cLtdmO1Wqc8rVBRFPr6+ujq6iKdTuPxeMjLyztjRdvJnM8fHaRjvJfh+AgmrQGv0Y1VAi4hhBAL0FB8hL5wgCXOKmrs5efcNxAIMDg4yIYNG6a1UFVXVxddXV3ccMMNU25wPxfINEYhFh7p0bUASdAlxOzJZDLE43FisRjhcJhwOEwoFMr+HolECIVC2a/FYrHssSaTCZvNhs1mw263Z/9ssVgmHRidGhF2KtgKBoNkMhkMBgNOpxOXy4XT6Zz2dEJFURgcHOTEiRPE4/Fso/qpnjeWTtAT8tEV6iOeTmDRmsgzOiXgEuIilkwkGAwO4sn3oLvIVnETYi4JRAcIRIdY6a6l1PruYU0mk6G1tRWHw3HaQlqTlclkqK+vR6/Xs3Xr1ik/vJoLToVd0WiUT33qUxJ2CTHPSY8uIYT4b+8MqU79/s4/v/P3d25/59eSyeRZz200GjEajRgMBgwGAx6Ph5KSEsxmczbUmm7oFIvF8Pv92emIp/p3neq15XQ6MZvN024Gf8ro6CgnTpwgFAphtVopKSmZ0pNjRVEYjo/RGerFHxk42R9Mb6PUWohJM/Un0UKIucHv80szeiFmQL7JQzKT5tDQcfQaHV6T56z7qdVqvF4vfX19jI6O4nA4pnQ9tVrN4sWLqa+v5/jx4yxdunQ65efUO6cxPvTQQxJ2CSEACbqEEBcpRVGIRCIMDw+fNroqHA6fNroqEokQiUTe9TxqtRq9Xo9Opzvtl16vx2KxZP/8zm0GgyEbbs3GU9BTTeRPjdoaHh4GwGKx4PF4cLlcOByOGb22oiiMjY3R09PD0NAQRqORiooKLFPovZNMJ+mLBuka7yOUDGPQ6Ckw5+HS29GoZbSpEEII8U4qVBRbvKSVNPUDTWz0rsZlsJ91X4fDwdDQEG1tbaxdu3bKD7lsNhslJSUcOnSIkpISbDbbdF5CTv1p2PXJT36SoqKiXJclhMghCbqEEHPWqfBlaGiI4eHh7O+Dg4MMDw+TSCRO2/9UAHUqhHK5XBQVFWVHW/1pkKXT6ebMNN9oNIrP5zutibxOp8PpdLJkyRJcLhf6WZgalEqlsqPFotEoer2ekpIS7Hb7pG6eM0qGQHSI3kg/wcgQCmDXW6i0lWDRmWV6ohBCCHEOKlSUWovoHO+hLnCITYVrserMZ+6nUlFYWEhHRwd9fX2UlJRM+ZoVFRUMDg7y1ltvcc0111zUUxjfGXY9+OCDfOQjH6G2tjbXZQkhckSCLiHEnJDJZAgEAvT19dHX10dvby/BYJB0Op3dx2KxYLVasVgsFBYWYrVasVqtmEwm9Hr9RXWDlslkGB4ezr7eU6O2bDYbRUVF024ify6nAsT+/n6CwSCKomCz2SgvL8disUz4moqiMBIfoyfspz8SJKWkMGmNFJrzcOhtaNXyFiOEEEJMlBoV5ZZi2kM9vBVs5PKCtRg1hjP2M5vNuN1uTpw4gcViwel0Tul6Go2GxYsXc+jQIY4dO8ayZcum+QpyS6/Xc+2117J3714ee+wxtm7dymWXXTYr91JCiLlNPoUIIS64U43Oe3t7s0FPf38/qVQKlUqF3W7H7XazatUqbDZbNtyaK6OvpiqdTuP3++np6aG3t5d4PI5Wq8Xlcs3qqC04+T2PxWIMDg7S399PNBpFp9ORn5+P0+lEq53420E4GaE34qcv7CeaiqNTa3EZ7DgNdgwaaUgtxMKhQqPVgIzYFGLGaNQaKqzFnBjrZn+gkU0Fa9Gd5cFRQUEB8XicpqYm1q1bN+VVGJ1OJ6WlpTQ2NlJQUIDb7Z7uS8gprVbLFVdcwaFDh3j55ZcJBoO8//3vv+jvIYUQkyOrLs4jsuqimMvGxsZobW2lra2NEydOZFcltNlsuFwu3G43brcbl8s17ebtc0kqlcLn82XDrVQqhclkwuPx4Ha7Jz1FcDKSySTDw8OMjIycNtXz1Pd8MqO34ukk/dEAPaF+xhIhNCo1dr0Vp96OWWeSqYlCCCHEDIqnE7SPdWPTW9joXYVGdea9fTqdpr29HY1Gw5o1ayb10OqdMpkMDQ0NqFQqbrzxximfZ67p6OjgrbfeorS0lI997GOYzWdOBRVCXFwmmnlI0DWPSNAl5pJkMklnZydtbW20trYyMDAAgMfjobCwkPz8fNxu96yNYMqlVCpFT08PPT09+Hw+0ul0tpF8Xl7ejK6Q+KfXHRsbY2RkhJGREcLhMHByKP87p3lO9OdDWknjjw7SF/ITjA2jAmx6Cw6dDZvBilrCLSGEEGLWRFMx2sd78BicrMtfgVp1ZouGWCxGR0cHTqeT5cuXT/n+IhKJcPDgQWpqali/fv10S58zBgYGePPNNzEajXz84x8nPz8/1yUJIaZBgq4FSIIukWvDw8McPXqU1tZWurq6SKVSmM1mCgoKKCoqoqCgAIPhzF4T84GiKASDQdrb2+nu7iaVSmGz2bLhlslkmvHrhcNhxsfHGR8fZ2xsjGg0Cpwctm82m7Ph1mSezCqKwkhijJ6QD18kSFrJYNGacBhs2HVW6bslhMjy9/XzyE//i0/8zScpKC7MdTlCzEuhZJiuUB8eg4t1+ZegPkuQFQqF6OrqorS0lKqqqilfq6+vj7a2Nq688kqKi4unU/acEg6H2blzJ5FIhI9+9KMsWrQo1yUJIaZoopmHfGIRQkzL6OgoTU1NHD58mL6+PjQaDfn5+VxyySUUFRXN6tS8uSAUCtHe3k57ezuRSASj0UhxcTEFBQVT7pdxNplMJjtaa3R0lFAoRCaTAU6uNmk2m3G5XNnG/JP9nifTSXojfrpDPkLJCHqNDo/RiVNvRy99t4QQZ5FMJunt6iGZTOa6FCHmLavOQrm1mM7xPhoGm1jjOXPUltVqxev10tPTg8Viwev1TulaRUVFDA0NsW/fPrZt2zaj9zG5ZLFYuO6669izZw+PPvooN954I5deeum8vj8VYqGToEsIMWnj4+McOXKEI0eO0NPTg0ajoaioiMsuu4zi4uJ51WPrbJLJJN3d3bS3txMMBtFoNOTl5bFo0aIZC/YURSEUCmX7a42NjaEoCmq1GovFkp0CaTQap7zapKIoDMVH6Qn76I8EUZSTUxMrbSVYdGbpuyWEEELMAVadhXJbMV3jfdTTzBrPsjPuNTweD/F4nOPHj2MymbDZbJO+jkqlYsmSJRw4cIC33nqLLVu2zJswSKfTsXnzZhobG/n9739PIBDgpptumvf3rEIsVBJ0CSEmJBwOZ8Otrq4u1Go1hYWFbNq0iZKSkgVxo3CqoX57ezvJZBKn00ltbS0ej2dGpgsnk0kGBgYYHBxkdHSUTCaDWq3GbDbj9XqxWCwYDIZp33QmMyl6Qj66Qj4iqSgGjR6vKQ+n3iZTE4UQQog5yKazUG4romvcR4OqmdXu08MulUpFcXExiUSCpqYm1q5dO6U+qHq9nsWLF9PU1ERbW9u8muanVqtZs2YNdruduro6Ojs7+fCHPzyvpmkKIU6STzRCiHeVTqdpaWmhvr6elpYWFEWhsLCQSy+9lNLS0nnZSP5PZTIZfD4fx48fx+/3o9Ppsj3HZmJIfyqVYmBggGAwyMjICEC2cb3FYsFkMs3Y09RQMkJnqIfekJ+MouDQ2yiweWTVRCGEEOIiYNNZKbMW0hXqR8UxVrlrzwi7ysrKOHHiBEeOHGHVqlVTehB3auGggwcP4vV6sdvtM/kycq66uhqPx8PevXv5xS9+wVVXXcXmzZunPEJeCDH3SDP6eUSa0YuZ4vf7qa+vp7GxkUgkgtvtprKykoqKinnbTP5PxeNxTpw4QUtLC5FIBJvNRlFREfn5+dO+EUqlUgwODhIMBhkeHgbAbDbjcDiw2Wwzuqy3oigEY0N0jPcyGBtGq9LiNtpxG5wyeksIMS2RcISWpmMsXl6L2WLOdTlCLBgj8XF6wj5KLAWs/JOwCyAajdLR0YHZbGbFihVTundLp9McPHgQo9HI1q1b52UIlE6nOXLkCM3NzZSUlPC//tf/wu1257osIcQ5yKqLC5AEXWI6otEohw4dor6+Hp/Ph8FgoKKigurqapxOZ67Lu2BGRkY4duwYnZ2dAOTl5VFcXDylXhfvpCgKY2Nj9PX1MTg4iKIomEwm7HY7DodjRsMtgFQmTU/YR+d4L5FUDLPWiNvgxG6woZbRW0IIIcRFbSQ+Rk+4n1JLEZe4F58RdsViMbq6ugC45JJLpnQfMz4+TkNDA0uXLmX16tUzUvdcNDAwwN69e4nH49x4442sW7du3vQmE2K+kaBrAZKgS0yWoiicOHGCAwcOcOzYMTKZDMXFxVRVVVFUVLRg/j86NT3x2LFjBAIBDAYDRUVFFBYWTrv3WDKZxO/34/P5iMVi6PV6nE4nDodjVvqahZIRukI99Pz39ES73kae0YlJOz9WThJCzB3jo2Mc2FvHuk3rsTnm19QmIS4GI/FResL+dx3ZlUql6O7uJhaLUVtbS35+/qSv0d3dTUdHB5dddhkVFRUzVfqck0wmqa+vp62tjcWLF/PBD34Qq9Wa67KEEH9iopmHzFsRYgEaGxujvr6eAwcOMDo6isPhYOXKlVRWVs6bpaQnIplM0t7ezrFjxwiHw9hsNpYuXYrH45nWEP2zjd6y2+0UFBRgNptn/Cmhoij4Y4N0jfcyGBtBq9LiMThxGR3o1PN/kQAhRG6Mjozy3OO/pWbpIgm6hMgBp8EBqOgN+0kqKdZ4lqNR/c/9i1arpbKykt7eXo4ePUokEqG8vHxS9yGlpaVEIhH27duXXfV5PtLpdGzcuJHi4mL279/Pj3/8Yz74wQ+ydOnSXJcmhJgCCbqEWCAymQwtLS3U1dXR2tqKRqOhrKyMjRs34vF4FtQQ7fHxcVpbW2lrayOdTpOXl8eiRYum3Wz1bKO3vF4vTqdzVkbHJdNJusM+OkN9xFJxzFoTpZYi7AarTE8UQgghFgCnwY5GraE71MfbgUOs916CVvU/9xwqlYqSkhIMBgNdXV1EIhFqa2sn/EBPpVKxePFiYrEYO3fuZOvWrfN6pFNJSQkej4f9+/fz+OOPs2LFCm688cZpt7AQQlxYEnQJMc8NDw9z4MAB6uvrCYVCuN1u1q9fT3l5+YJYNfEURVHw+Xy0tLTg8/nQarUUFRVRVFQ0rQb7p0Zv+Xw+BgYGsqO3CgsLZ3TFxHdebyA2RF8kQH/k5PWcehsl9gKZniiEEEIsQDadhQprCV2hPvb569mYvwq95n9GdKtUKvLz8zEYDPT29tLQ0MCKFSsmfB+oVqtZvnw59fX1vPHGG1x//fXz+h7SaDSyefNmOjs7qa+v50c/+hHXXHMNl1566bxsyi/EfCRBlxDzUDKZ5OjRoxw8eJD29nZ0Oh0VFRXU1NTgcrlyXd4FFY/HaW9vp6WlhXA4jNVqZfHixeTn509rlFUqlcqO3opGo+j1evLz83E6nTPeWF5RFMZTYXpDfvoifhLpJAatAa/Jg1Nvk9UThRBCiAXOojNTaS+lc6yPvf6DbCxYhUlz+gMwu92OTqeju7ubAwcOsGjRoglPRdTpdKxYsYKGhgZ27drFVVddNa9DH5VKRWVlJcXFxTQ2NvLSSy/R0NDA+973PkpLS3NdnhDiPKQZ/TwizeiFz+fjwIEDHDp0iHg8jtfrpaqqirKyshkPX+a64eFhWlpa6OzsJJPJnLZ64lRHWSmKwsjICIFAgGAwiKIo2Gw2XC4XFotlxkdvxdJx+sJ+esN+QskIWpUWh8GKU2/HqDWgkumJQogcGggM8Oxjz/Chv/hf5HnnZ98eIS42iXSCjlAvKpWaS/NXYdWZz9gnlUrR19eXHem/aNGiCY9uHxkZ4fDhw9TU1LB+/foF0/picHCQuro6hoaGWL9+Pddddx0mkynXZQmx4MiqiwuQBF0LUzQapbGxkYMHD+L3+zGZTFRWVlJdXb3g+gkkk0m6uro4ceIEg4ODGAwGCgsLKSwsnPIQe0VRCIVCBINBAoEAyWQSvV6Pw+HA5XLNaICoKAqhVJhAZIhgdJDhxBhqlQqbzorTYMeqM0u4JYQQQohzSmaSdIT6yGTSbPSuxKE/swfpqdYL/f39KIqSHb00keCqv7+flpYW1q5dS21t7Wy8hDkpk8nQ2trKoUOH0Ol0bN26ldWrVy+YsE+IuUCCrgVIgq6FI5PJ0N7ezsGDBzl69CiZTIaSkhKqqqooKiqa10PJ/5SiKAQCAdrb2+nu7iadTuN2uykoKJhWk/1YLIbf7ycYDBKNRtFoNDgcDhwOB0ajccZuatJKmsHYCMHYIP7oEPFUHI1KjUVnxqaz4NBZUavl37MQYu5Jp9JEI1FMZhMarfycEmIuSWdSdIZ8xNNx1uQtx2vynH2/dBq/38/IyAhWq5UlS5ZgsVjOe/729nZ6enrYsmULJSUlM13+nBaNRqmvr6ezs5Py8nLe97734fV6c12WEAuCBF0LkARd819/fz+NjY0cOnSIUCiE3W6nurqayspKjMaF1Yg8HA7T0dHBiRMnCIfDmEwmCgoK8Hq9U2ourygK4+PjDA8PMzg4SDgcRqVSYbfbcTgcMzY1UVEUoukYwdgwwcggg/ERMkoGvUaHTWfBprNg1pll1UQhxJzX09nND75zL7d/66uUVpTluhwhxJ/IKBm6wz7GE2GWOquptJW+671MJBKhr6+PRCJBaWkpFRUV53xwqigKzc3NjIyMcP311y+4HrBw8r68rq6OUCjExo0bufrqq2U6oxCzbKKZx8Jq2iPERWh8fJzGxkYaGxsJBAIYDAbKy8uprKzE7XYvqOHSiUSC7u5uurq68Pv9qNVq8vPzqampwW63T/p7kUgkGB4eZmhoiOHhYdLpNGq1GovFQklJCTabbUZGx6UyaQbiwwxGhwjGhoim4qhQYdaZ8Jo82HQW9BqdTEsUQgghxIxRq9SUW4sJRAY4OnKCUDLCCvdi1Koz723MZjM1NTUEg0F6enoIBAJUVlbi9XrPen+lUqmora2lsbGRHTt2cNVVVy24sKuwsJCbbrqJ48ePZ3vkXnPNNaxfv35Bza4QYi6SoEuIOSiRSNDc3ExjYyPt7e2o1WqKi4vZsmXLgpuamEwm6e3tpaurC5/Ph6IoOJ1OFi9eTF5e3qR6ZCWTSUZHRxkbG2NkZIRwOAycXEba5XJhtVoxmUzTDg8VRSGciuCPDBCMDTEcHwcUDBo9Vp0Zr8mDRWs+642mEEIIIcRMUaGiwJyPQaOnN+wnnIqwPu8SdBrdmfuqVHi9XhwOB4FAgOPHj9PT00NVVRUul+uM+yONRsOKFSs4cuQIf/zjH9m8eTOFhYUX6qXNCRqNhmXLllFZWUljYyMvvPACb7/9Ntu2baOysjLX5QmxYEnQJcQckUwmaWlp4ciRI7S0tJBMJvF6vWzYsIGysrIpN1O/GKXTaXw+H52dnfT19ZFOp7Hb7VRVVZGfnz+h74WiKMRisWywNTo6SiwWA0Cr1WIymSguLsZqtc5IQ3lFURhLjtMfGaQ/EiSSimZ7bRVb8rFqzeg1C+e/oRBCCCHmDqfBgV6jp2vcxy5/HRvyV2LVnb0Xl8FgoKysjEgkQiAQ4MiRI9hsNmpqas5Y6Eiv17Nq1Sqam5vZsWMHl156KVVVVRfiJc0pJpOJ97znPSxatIiDBw/yq1/9imXLlnHDDTfgdDpzXZ4QC44EXULkUDKZpLW1lSNHjnD8+HGSySQul4tly5ZRXl6O1WrNdYkXTDKZxOfz0d3djc/nI5VKYbVaKSsrIz8//7w9yDKZDOPj44yNjWV/pVIp4OQNm9lsxuPxYDab0Wq1M9Zvayg+ij86QH90gHgqjlalwaa3km9yY5FeW0IIIYSYI8xaE9X2MrrDfezuP3DOJvVwcjpjRUUF4XAYv99PfX09brebqqoqzGZzdj+NRsPy5ctpbW1l3759RKNRli1btqDaa5zi8Xi47rrr6OzspKGhgfvvv5/LL7+cK664YkE9tBYi16QZ/TwizegvDqfCraamJo4dO5YNt0pLSykvLz/jSdl8lkgk6O3tpaenB5/PRyaTwWq14vF4yMvLO+0m6k/F4/HTQq1QKAScHHZvNpsxmUzZ32fy34OiKIwkxvFFAvSF/SQzKXRqLXa9FbvOillnkl5bQogFIZPJkIgn0Bv0C2pKvRAXu4ySoSfcz1giRK2ziipb2XlDKUVRGBsbw+/3k0ql8Hq9lJeXn9Z8XVEUurq66OrqYtGiRaxbt25B/2xIJpM0Nzdz7NgxzGYz1113HatWrVqQAaAQM0VWXVyAJOiau6LRKC0tLRw7diw7LdHpdFJWVkZZWRl2uz3XJV4wsViM3t5euru78fv9KIqCzWYjLy8Pj8fzrqvVpFIpRkZGGBkZYXh4ODsNUafTZUMts9mMwWCYlRuIcDJCbyRAX7ifaCqOTq3Fobfh0Nswag0SbgkhhBDioqGgEIgMEowNUWjO5xL3EnTq80/2URSFoaEhBgcHSaVS5OXlUV5ejsXyP9MgfT4fra2tlJSUcNlll81Ii4iLWSgUoqGhge7uboqKirjxxhupqKjIdVlCXJQk6FqAJOiaW4aGhjh27BjHjh2jq6sLRVFwu90UFxdTXl6+oMKtaDRKT08P3d3dBAIBABwOR3bklsFgOOOYU08OR0ZGGBoayo7Y0uv1mM1mrFZrdhribImnk/gifnrDfsYSITQqNXa9FafeLiO3hBALXtAf4OmHn+LDf/kR8gu8uS5HCDEF44lxesJ+9Boda/MvwaGbWNsMRVEYHh5mYGCAVCqF2+2moqIi23ZjcHCQo0eP4nK5uPLKK896r7fQBINBDh48yNDQEMuWLWPr1q0LbqVKIaZLgq4FSIKu3MpkMvT29mbDrYGBATQaDQUFBRQXF1NcXHzOqXjzTTgczoZbAwMDqFQqHA5HduTW2foUpNNphoeHCQaDDA0NkclkUKvVWCwWrFYrVqsVne7MVYJmUjQVoz8axB8ZYDg+hgoVNr0Fp96GVW+VnltCCPHfejq7+cF37uX2b32V0oqyXJcjhJiiRDpBz3+PWF/mrqHcUjzh0fGKojAyMsLg4CCJRAKXy0VFRQU2m43x8XGOHDmCXq9n48aNC25FxrNRFIWOjg4OHTpEPB7n0ksv5corrzxvL1ohxEkTzTwW9jhSIaZpdHSUtrY22traaG9vJxqNYjQaKSoqYunSpRQWFi6o4drhcJju7m66uroYGhpCpVLhcrlYsmQJbrf7rCFVKpViaGiIYDDI8PAwiqJgMBhwu93YbDaMRuOs9jJQFIVwKkJ/ZID+aJDxRBgVKqw6MyWWAmw6C9oJDOUXQgghhLgY6TV6Ku1l+CNBmoZaGYqNcIm7dkJTGU/d6zmdTsbGxggGg9TX12O32yktLWXVqlW0trby+uuvU1ZWxtq1axfUg98/pVKpqKqqoqysjKNHj7J//37q6+u55pprWL9+/YLuaSbETJJPb0JMQjwep6OjgxMnTtDa2poNc06tQFNUVITH41lQb1LRaJTu7m46OzsZHBxErVbjcrmora3F7XafNehLpVIMDg4SDAYZGRlBURRMJhP5+fnY7fZZXZVGURTimTgj8RDD8VEC0QEiqRgalRqrzkqppQi73oJatXD+GwohhBBiYVOjosjsxao10RP2s6v/bdbmLcehn1irjVMj9+12O+Pj4wwODtLU1ITRaKSkpASv10tnZycvvPACl1xyCUuWLFlQ98t/SqvVcskll1BTU0NjYyMvvPACb731Ftdffz1LliyRhvVCTJMEXUKcQyqVore3Nxtu9fT0ZFcGLCgoYNmyZXi93gXXd+BUz63Ozs7stMTzhVunejn4/X4GBwez4ZbX68Vut8/alMRkJsVoYpyR+BijiTFGEuMk0kkAdGotVp2FfJMHi84s0xKFEEIIsaDZ9DZqNEZ6wj729Nez1FVNhbVkwsGLSqXCbrdjt9uJ/P/t3Xd8FHX+P/DXbN/0SpMq3EZIJ0BQQAQ9QKQIWLGgwAknnP6UJjZAhEOU01NAAUXPr97pIdhAFDm7CGgkQQQEBCEkIb1v3/n8/tjMuJtsSABpm9eTx7C7Uz8zO9mdee/n8/5YrSgtLcWvv/4KrVaLNm3awO12Izs7G4cPH0avXr3QqlXLzu9nNpuRmZkJi8WC7OxsvPXWW+jQoQOuueYadOzY8XwXj+iixRxdQYQ5us6c3W5Xu0U+evQoCgoK4PF4oNfr0apVK7Rp0wZt2rRBWFhYi/ulxeFwqMGtoqIiSJKEqKgoxMfHIzY2ttHgVk1NDQoLC1FcXAy32w2DwYCoqChERkb+IcEtWciwexywue2wuR2/P/fYYXXbYHM7AABaSQOzzgyz1ogQvQlmrYlNEomIzkBNVQ2yv9+FtN7pCItoXgJrIro4yBAotJag1F6OGGMUkmMtCNEF7hm7KS6XC6WlpSgvLwcAREVFweFwwGq1olOnTkhPT2eOKnivm0+cOIHdu3ejvLwcf/rTn3D11VejdevW57toRBcMJqNvgRjoOjVCCFRWVqrBm2PHjqk9AprNZsTFxSE+Ph7x8fGIjIxskdWrnU4n8vLycOzYMZw4cQJCCERFRSEuLg5xcXGNBqrsdjuKiopQVFQEm80GnU6HiIgIREVFwWg0NhkkFELALTxweJxweBxwyE443E44ZBecHmfdeCccslOtnaXQSzrotXroNFoYNHqYtEaYdSYYtHr2kkhERER0Cmpctci3FsEte/CnyM7oEt7+tH/s9Xg8auJ65cdPt9sNSZLQvn17dOnSBa1atWqR19y+hBA4duwY9uzZg+rqaqSkpGDQoEGIioo630UjOu8Y6GqBGOhqnBACVVVVyM/PR0FBAfLz85Gfnw+bzQYAiIiIQGxsrBrYaok1thQulwv5+fk4duwYCgoKIMsyIiIiEB8fj7i4uEbzZ7ndbpSUlKCwsBBVVVVq1fXIyEiEhoaqx1MIAafsht3jrYHl8Dhg89Q9uusCWx4HPEL2W79W0kCr0UEvaaHVaKGTdNBpNNBr9NBrdN5HrZ7ND4mIzhFrTS32/bQX3ZN7ICQs9HwXh4jOElnIKLSVotRejghDGJJjExChP/1anEII1NbWory8HNXV1QAArVYLj8cDk8mEzp07o3Pnzi0+sOPxeHD48GH8/PPPcLlc6NWrFwYMGIDQUH7eUsvFQFcLxECXlyzLqKioQFFRkV9Qy2q1AvDW1oqOjkZMTIw6tPTq0krNrdzcXJw4cQKyLCM8PFwNbjWWgyxg3q0QM8zhIdCYtXDILtg8DtjddrVJod3jgOzzsSNBgl6rg16jg06qe1Sfa9XXTA5PRHRhOX40F88ueAYPzJuJ9p06nO/iENFZZnPbkWcthN3txKURHdAtsiO00pndc7jdblRUVKCsrAxutxtarRZCCMiyjMjISHTp0gWdOnWC2Xx6zSaDgdvtxi+//IL9+/cDAC6//HJkZma26N4rqeVqbsyDCWrooqXU0lKayBUXF6OwsBAlJSVwu90AAJPJhOjoaHTp0kUNarXkL0pfdrsdx48fR25uLoqKiiCEQEREBDp16oS4uLgGwT+X7IbVbYPVZUNFZQWqyqrgrLQBMuDRyLAZXajSWuGSioFaeAf4NyUM0ZkRaQiHQaODTquHXtJBq9GySSERERHRBc6sM6FrREcU28pwpCoXJ6zFSIpJQKwp8rTXqdPpEBcXh9jY2Aa1vGpra5GTk4Ps7GyEhIQgOjoaUVFRiI6ORnR0NEJCQlpECwydTofExER069YNe/fuxbZt27Bt2zb07NkTV1xxBSIjT//4EwUrBrrogudyuVBWVobS0lKUlpairKwMJSUlKCoqgtPpBOD9AlC6NE5OTlabzJnN5hbxBdgcQghUVFSgoKAABQUFKC4uhiRJCIsIR5uObaENN8ApuVDsrsTRikLUumywuq2oddthddmgd2oQ6QxBpDMEeqGDW/LArnfDafRA0kvQa/SI1UR7a2JpdXUBLh2DWERERERBQoKEVuZYRBrCkV9biJ1F2YgzRaNrRCdEGyNO+7pbkiSEhYUhLCwMHo8HNTU1qKqqUoNeSkL7wsJC9QdtpYMjJehlNpthMplgNpthNpsDdpR0MTMajUhPT0ePHj1w4MAB5OTk4IcffkBSUhL69evX4nuwJPIVXH/9dNFyuVwoLy9HeXm5GtRSHquqqtT5DAYDwsPDERYWhu7duwfMAUW/czgcyD+Rj2PHj6G4sBhupxuQALdJoDrSjhJtJRw4ClTDOwDQaXQwag0waPQIcRvQ2h4BXW00tDIADaAJ0UEfakSYUc9jTkRERNQCGbUGdI5oj0pHDUocZdhRlI1oQwQujeyIeFPMGV0jarVaREZGIjIy0i/oVVNTAyGEGszSaDRwuVw4duwYHA4HZNk/v6ter/cLfvk+KoPZbIbBYLiormmNRiOSk5Nx2WWX4fDhw/jll1+we/duWCwW9O/fHx06sCk5EQNddE7Isozq6mo1mFVRUaE+Ly8vR21trTqvVqtFeHg4wsPD0b59e/V5eHj4RfdFdK7ZHXYcyj+MYyeOo7K4HLDKkCDBpnWiRm9HTbgDwgSYdEaYtEa017WBSWuEQauHQWOAXtLCU+uGs9oOZ7UDwi0DGgm6ED30IQZoTayhRUREXgaDAZ26dm60kxIiCm4SJEQZwxFpDEONqxbFtnJkFe9BuD4UXSM7oo05/oyv232DXrIso6amBjU1NaitrVVbdhgMBsTGxiI8PBwmkwkajQZOp9NvKC8vh8vlgtPphMfj8d8PSUJISIj6Y3r94UKtGabX65GQkIBu3brh6NGj2L9/P9auXYuOHTuif//+6Nq1a4vvwZJaLiajDyLnMxm9y+VCZWUlKioqUFlZqQ4VFRWoqKhAdXU1fE+1kJAQhIaGIjQ0FGFhYepjWFgYTCYTg1nNIMsyistLsP/4QRSVFMFRaYPO6f0yc0seOAxuiBAJ+jAjzCYzzFojjFoDJPh/4Xlcbriq7XBUOeCu9V4wSDoNdCYddAxuEREREVEzCAhYXTYU28tR46pFiM6MLhHt0docD6NW/4dvz+12w2q1qoPdbgcAaDQaGI1GtQmjb20uo9EIWZbVoJcy2O12dbDZbH61w0wmEyIjIxEVFaUOERERF1znX0II5OXlYd++fSgtLUV4eDhSU1ORmpqKuLi48108oj8Ee11sgc5GoEsIAZvNhurqalRVValt5ZXnymvliwXw/ipiNpsRGhqKkJAQNagVEhKiBrUutC+GC5nyHijHu7C8GEVlRXBU26GRJQgIOHRuyAYBbYge5tAQhIeEQacJ/OuT7HLDVeuCy+qAq9YJ2en9VUtj1EFn1kNv1kGjvzB/uSIiIiKiC5/NbUexvQxVzhoAQJg+FPHmaMSZYhBtjDjj3hoD8Xg8sNlscDgcagBLCWj50ul00Gg00Gq10Gq1fq81Go3a66Pb7Ybb7YbH41EffQNgGo3Gr8aUEAJCCEiSpP5oX/9Rq9WqzSaNRiOMRqP63GQyqTXLzuReSQiB0tJS/Pbbbzh27BicTifatWuHtLQ0JCUlsWMuuqgx0NUCNfdNd7vdsNlssNlsqK2tRW1trVoFuP5jbW1tg+q9vr+OmM1mNfmjb2CL1WSbz+Px+P2KZLfbYbVa1YBidXW1+h4ICDi0Lji1HsAowRRqRlREJEx6U8BaVwICst1dF9RywWV1epsjwltrS1sX3NKZ9JA0rLVFRETNd/xoLp5d8AwemDcT7TsxJwwRNeSW3ahxWVHrsqLGZYVLuKGRNIgxRiLOFI0YUxRMWhMMGt1Za9EhhIDb7VYDYErASpZlCCHg8XjU4JYsy37BqfqBKgB+yykBMYVOp1MDWAaDAXq9Xr0vU5ZxuVzq4Ha71UdfZrMZERERiIiIQHh4uPp4qj1Nejwe5OXl4bfffkNBQQE0Gg0sFgtSU1PRrVs3Vj6gi05zYx6sthGEvvrqKzgcDrXqrTIoQZT6H6SA99cFpTqvUrU3Ojrar+cSZWgqiOX7haEMyheC8lyZTxnqv/YdF2j+5g6+Zar/3PdLItDzQI++zxsrrzLU/xXI97nL5VLfD5fL1eAY6vV6SAYNrJIDZcYq2DR2QC8hLCQcseYotDH4/xImhAyP0w23ww1P3eC2uyE73UDdrmsMWm9Qy6iDxqiHRsvAFhERERGdPTqNDlHGCEQZI7w/2HqcqKkLeh2oPAK5ou66HBKMOgNMGmNdLlmDN4+sxvtjrPIPkgRN3Y+7Gsl7TyKEDAHvJa8QAgJ19w74fd0SJEArQWOWAGiglXTQAdBptDBoDTBoDNBKp/dDvRACTqfT736roqJCvYcxGo1qq5bw8HCEhoY2SNUihFDvD6xWq3r/lpeXB5vNpt5r+OYsU5pTRkZGNpr6RavVomPHjujYsSPsdjuOHj2K3377Dfv27UNISAgsFgs6d+6MLl26ICIi4rT2n+hCxEBXENq1axc0Gg30ej0MBgMMBgNiYmKg1+thNBrV8b7VZXU6768oSlt3p9Op/uphtVpRUVHhN04J2tSvylu/Su+Fpv4XwNms0KhUh/atCu37GB4ejtjYWPXXHkmnwQlnCY7W5CO39iiEkBGuDUWMIRLttW1hgB7CLUOukmF310D2yJDdMmSnG7LL55hrAI1OC41eC53ZDK1BC61BxxpbRERERHTeSJBg0no7RIozRUOGgMPtgFt2wSU8cHnccMlu1LhqUe6sgtvjgkecu/sKneQNehm1ehjrctuatUZEGMIRYQiDvpG0IJIkqfdVCiX4pVQ8UIJfSisNrVbrl9ZFaRWj1OLyJYSAw+FQc5HV1taiuLgYR48eVe+79Ho9IiMj1Q68lKBaeHg49HpvfjSTyYSEhAQkJCSgvLxcDXplZ2cDAGJiYtClSxd06dIFnTt3Rmho6B99iInOGQa6gtDw4cMDRvQ9Ho/aHNFqtaKsrAw2m0390LTZbAFrFylBM51Opw6+7dmVQQniaDQatfaTUvNJ+RBuqraUsg5JktSaY/VrUjXW7r2x6sWnUr23sVpgShDPt0aWMk557luFWRnqV41W5lXWKQsZbtlTVwNOhgQNQiHhMrTzK5cTtVCzC2gk7y9bGgkarQStUQd9mDewpdHrIGnB5PFEREREdEHTQIJZZwJganQeodTLEgIQAnJdLS2ltpYQgCT5X/tq6mp+QbkHCLCs9zJfwAMZHtkDt+yGW8jeR9mDGlctKpxVcHpckOuCbSadEZGGcETowxBhCEOEIRRGjTHgvYZv8CsyMrKuGL83oVRqfpWVlaGgoEBdTqfTqalhlPQwISEhauWEmJiY34+NELDb7eq9XW1tLUpKSpCbm+vXgkev16s9SvrmB4uPj0eHDh0gSRKqqqpQUlKCgwcPIisrCwDQqlUrdO7cGR07dkRcXBxiY2Mv2B4oierjmRqEioqK/HJvKfm2fBPGA1BrdRkMBoSGhiImJsavPblOp4Ner2/QVFGWZbV2l+9gtVrVJnq+TfVOt9ZU/RpQvgkjfcf7Btx8A16+r4Hfm0H6tsH3fe5yuRo0M1TG1c9TFqisvmWoXx7lOQC4ZBesHgeswgancEJoAJ1eB5PWCKPOCK1GW7ecsnJvQAsaLYNYRERERNRiSMqVrzeahdNqXNjUsidJUyUg4PS4YHPbYfc4UOuyotRWDrfw3hsYtQbEmaIRa4xCjDkKZm3jQTtJkqDX66HX6xEWFqaOV+6tfFvPVFVVobi42K+ljG9PkkrASkk3U79XRd+czEpQrbKyEiUlJWrrnAaHQauFwWBAeHg4AKCmpgY//vgjdu7cqc6j3DPGx8ejTZs2iI+PR1xcHEJDQ89ajjWi08Fk9EFEScz28ccfw+12w2Aw+EXtlecmkwkGgyFgri2lmq3yS4MSxFI+IJUEjr40Go1fUMy3ZpdvgKr+h1+gZoT1A1BKjSjfaYFqS51uc0nfQJRveSVJatAjS2OBt6Y+1F0eF4od5Si2laLYVgaX7IZW0iBMH4ZwfQjC9CGN9pBIRER0oXK5XKgsq0BkTJTaNIaIKNgJCLhld13gy4ZatxU2twMAEKIzIcYYjThzFGKM0TBqT/+zsX4SfaUHSeXR9/5Hr9ergS/fez7lvi/QfZeyHmWdSlJ83wT5vsnzT/bDvyRJ6r1neHg4IiMjER0drTajVAalPIHKRNQc7HWxBVLe9BMnTsBoNDbo7laptaQEr3x/NfAd50tptug7KEEt32aMFwrldK6fwB7wb97oW9Prj95+rduKckcVKhxVKHdWotZlAwCYdUaE6UIRZghFiC5wL4lERERERHRxcctu2Nw21NQNjrrAV5g+BLGmaMSaohBjjGo0z9epUlKmKIEq32BV/RpbSjPKQEEwJVdzc7fpuz2n06m2GlI62ApUU6wxSt5oJY+0EgAzGo1+KXPq33sqr31b+tR/VJ7XH99Up2p04WtxvS46HA4sWLAAW7ZsgclkwsSJEzFx4sSA8+7duxfz5s3DgQMH0K1bNyxYsABJSUnq9I0bN+K5555DcXEx+vfvj4ULF6rtoYUQWLZsGd555x3IsowbbrgBM2fOVP9oysvL8fjjj+Obb75BdHQ07r//fowePfoP2XZz5eXlBYzM149p+tbE0ul0arJCg8Hg9wFyMVGCV+ei3LIQ6hdapdMb2KpwVKlJM806I0xaM6JDIxCqD4Few1+7iYgoeJQWl+Ljdzdh2JjrEBsfe76LQ0R03ug0OoQbwhFu8Db7c8tu1LisqHVbUWAtwtHqPABAhCEMsaYob1NHU5RfL+qnQml9otPpEBIS0mC6UsGhfhCsrKzML2cw4M0L5hsIq98SyDdvcv2k+/UpNcWUZpNKLmglKb/vNpXaXcq9qCRJcDgcqK2t9evorP5wKsG0+nxb5vgGwZQKHUp5Ag2+HbkFerzY7puDXdAEupYuXYo9e/bgX//6F/Lz8zFnzhy0a9cOw4YN85vParXinnvuwciRI7FkyRL85z//wZQpU/Dpp58iJCQEu3fvxiOPPIIFCxbgsssuw6JFizB37lysWrUKAPDqq69i48aNWL58OdxuN2bNmoXY2FhMmjQJADB37lzY7Xa8/fbbyMnJwaOPPoouXbogJSXljLfdXOUVFZAhQ9JooNEBOqMeOo0BkOBtg66VIDSADAEZMmzCAyG8HxgSALgAuAHY6xK7w5svSgsNNJIGWo0WWqnuueR9rpW00Gm00Gq00EELrUYHTRBUR/UID5weF6x1AS2ry1pXRdkGq9sO1CW11Ek6hOhNiDfHwqw1wqwzqV0eExERBSOb1Yoft2dh4NBBABjoIiJS6DQ6RBkjEGX09qDo9LhQ6/beRxyvOYEjVcchQUK4IQQR+ghEGsMQaYxAuC7kD7mH0Gg0jQalfGuD+TaHtNlsqKysbNA5mdIsUgno+AZ3lNpXCqUJo8FgUJPw+25XCXwpyfOVDtEUZrMZkZGRiImJQUREBCIjIxEZGenXPL5+ShvfoFhj4xqbx3e80kzUarX6BdV8OyM7WfNNJXhXvzMB5bXveGUwmUxswnmWBEWgy2q1Yt26dVizZg0SExORmJiIgwcP4s0332wQ6Proo49gNBoxe/ZsSJKERx55BF999RU+/vhjjB07Fm+88QauvfZaXH/99QC8AbRBgwYhNzcXHTp0wOuvv4777rsPvXr1AgDMnDkT//znPzFp0iQcO3YMn3/+Of73v/+hffv2sFgsyM7Oxr///W+kpKSc8bab67ChAC5P3QeUgDdo5UMjSXWJHTWQJHg/TNU/MKHEbtTFvf8LyLK335PmdvOrkaTfA2CSFlpJB53GGyjTSVpooATHfg+YaSSNN6Cm0UCjjIPGW2aNBhIADTR1wTcJGgmQ6jJICrXgvz/WdagCWchqryoeIcMj6h5ljzeYJbvg9LjglJ1wuOsePc56+yrBqNXDoNXDrDMhyhgOo8YAg1YPnUbHpohERERERNSAQauHQRuJaGOkmuDem9vLjlJHGY7XngAg6oJfoYg0hCPSEI4QnRkhOhNM2sC9O54O39pggSi1snyDYC6XC7W1taioqGgQCNNqtWrTQ98miL6vlVzOSq4uXx6PRw14KcGvY8eO+QXATCYTQkND1ZxfYWFhCAsLQ2hoKMxm8zkLFvnmLAs0+KYGKi8vR2FhoTqu/nEDvAFJJQCmHBuz2YzQ0NBGA2XMb9Y8QRHo2r9/P9xuN9LT09VxGRkZeOmllyDLsl9b3JycHGRkZKgnhyRJ6NmzJ7KzszF27Fjk5OTgL3/5izp/27Zt0a5dO+Tk5MBgMKCgoAC9e/f2205eXh6KioqQk5ODtm3bon379n7TlRpZZ7LtUwl0dQlvD4/w/B4gggSNVNcD4B8QjFG7+ZVlb60w4fPoO9SN88gyZHjHuWVvDSkZAkLI8EBAlr09M3qDaN6g2rnkrZmmqQvI6aCXtAjRmRFhCIeuLlBn0Oih1+oZzCIiIiIiotMmQYJRa4BRawDqKlzJELDX9exoc9lRbC9Dbo03+KUsZdYZYNZ6A19mnQlmnRF6jR56jQ56jfeHd71G660UcAaBEN9aWfWDUsDvSfLrB3ncbjeqqqrUafVptdrfc23p9dDr/fNvaXVahEWEITI60ptPS9LA7XLD6XSqecDKysqQn5/vl1daCRY11gmb0kTSt4ni6ebqUpo9mkyN967ZGKUWnZIbO9Cg9IqpdAoXqMM1jUaj1hyrv9+Batz5BhvrD8GcsywoAl3FxcWIjo6GwWBQx8XFxcHhcKCiosIvx1VxcTG6devmt3xsbCwOHjwIACgqKkKrVq0aTD9x4gSKi4sBwG+60pWrMj3QsoWFhWe87eZQcnCJUhdkjwen1w/hmVFbR6od+J5OW2X5pKEu4fP/qagfpJLU/5v6IpDhggMuOE55m0RERMHKUWZDaGgoHGU21Bqrz3dxiIguekZoYUQoohAK5Z5IQEAIQDiU+x8P7KiFHbXnsaRN0AOSqEuB43MXpuSOht1+RquvXxtNCRRVVlae0XpPlfC5JxUB7k+bumN1adwoNdXApa3XBMvkHTRCA72shU72PmplLfRCC+Gyw+l0nvP9PV2+FX3qB2FP9bVWq8XAgQMb5B+vLygCXTabzS/IBUB9Xb8XwcbmVeaz2+2NTrfX/UH6TvfdTlPrPpNtN4cS8R1y9Z+bNT8RERHRmZg44e7zXQQiIiJqYQLVdvMVFIEuo9HYIBikvK5frbCxeZX5GptuNpv9glpKYj9lXrPZfNrrbs62m0On0yE5ORkazZlVVyUiIiIiIiIiupAonRE0lmNOERSBrtatW6O8vBxut1vd4eLiYphMJkRERDSYt6SkxG9cSUmJ2mSwsenx8fFo3bq1um4lD5fSnFGZ3tiyZ7rt5tBoNA1qhBERERERERERtRRBkX2se/fu0Ol0yM7OVsdlZWWptZt8paamYteuXb/nsxICP/74I1JTU9XpWVlZ6vwFBQUoKChAamoqWrdujXbt2vlNz8rKQrt27dCqVSukpaUhLy/PL6dWVlYW0tLSznjbRERERERERER0ckER6DKbzbj++usxf/587N69G1u3bsXatWtx5513AvDWulLyaw0bNgxVVVVYtGgRDh06hEWLFsFms+Haa68FANx66614//33sW7dOuzfvx+zZ8/GVVddpfZ6eOutt+KZZ57Bjh07sGPHDixbtkzdTocOHdC/f3/MmjUL+/fvx7p167Bx40bcdtttf8i2iYiIiIiIiIiocZJoKl39RcJms2H+/PnYsmULwsLCMGnSJNx1110AgISEBPz973/H2LFjAQC7d+/GvHnz8OuvvyIhIQELFixAjx491HVt2LABzz//PCorK9GvXz8sXLgQ0dHRALzdgi5duhQbNmyAVqvFDTfcgBkzZqg5sUpLS/HII49g27ZtiI+PxwMPPIARI0ao6z6TbRMRERERERERUeOCJtBFREREREREREQtW1A0XSQiIiIiIiIiImKgi4iIiIiIiIiIggIDXUREREREREREFBQY6CJqgYQQmDhxIjZs2OA3vry8HH/729+Qnp6OwYMH4/333/ebvnfvXtx4441ITU3FuHHjsGfPHr/pGzduxDXXXIPU1FRMmzYNZWVlftt85pln0LdvX/Tp0wdLly6FLMvN3jbR2bJ3714kJCT4DUrnJQCQm5uLu+66C2lpaRg+fDi++eYbv+W3bduGESNGIDU1FXfeeSdyc3P9pr/22msYMGAA0tPT8fDDD8Nms6nTHA4HHn74YfTq1Qv9+/fH2rVrz+7OEv1BeO5SMPj0008bfP7fd999AHjNQ8HP6XRixIgR2LFjhzrufF7zNLVtolMiiKhF8Xg84oknnhAWi0WsX7/eb9qUKVPEhAkTxC+//CL++9//iqSkJJGTkyOEEKK2tlb069dPLFmyRBw6dEgsXLhQXHHFFaK2tlYIIUROTo5ISUkR7777rti3b5+4/fbbxT333KOu+5VXXhEDBw4U33//vfjuu+9E//79xcsvv9ysbROdTe+//74YPXq0KCoqUoeysjIhhBCyLIuRI0eKGTNmiEOHDomXXnpJpKamiry8PCGEEHl5eSItLU288sor4sCBA+L+++8XI0aMELIsCyGE+Pjjj0VGRob47LPPRE5Ojhg+fLhYsGCBuu0nnnhCjBw5UuzZs0ds2bJFpKeni82bN5/7g0B0injuUjBYuXKlmDJlit/nf2VlJa95KOjZ7XYxbdo0YbFYxPbt24UQ5/eap6ltE50qBrqIWpATJ06I22+/XVx11VWiV69efoGuo0ePCovFInJzc9VxDz/8sJgzZ44QQoh169aJwYMHq19msiyLP//5z+o6Zs2apc4rhBD5+fkiISFBHDt2TAghxMCBA/22995774lBgwY1a9tEZ9M//vEP8eCDDwactm3bNpGWlqbe3AghxIQJE8Tzzz8vhBDiueeeE7fffrs6zWq1ivT0dPWicfz48eq8Qgjx/fffi5SUFGG1WkVtba1ITk5W5xVCiBUrVvitj+hCxHOXgsWMGTPEsmXLGoznNQ8Fs4MHD4pRo0aJkSNH+gW6zuc1T1PbJjpVbLpI1IL8/PPPaNu2LdavX4/w8HC/aTk5OWjbti3at2+vjsvIyMCuXbvU6RkZGZAkCQAgSRJ69uyJ7OxsdXqvXr3UZdu2bYt27dohJycHhYWFKCgoQO/evf3WnZeXh6Kioia3TXQ2/frrr+jcuXPAaTk5OejRowdCQkLUcRkZGY2e92azGYmJicjOzobH48FPP/3kNz0tLQ0ulwv79+/H/v374Xa7kZ6e7rfunJwcvyYuRBcanrsULBr7/Oc1DwWznTt3IjMzE2+//bbf+PN5zdPUtolOle58F4CIzp3Bgwdj8ODBAacVFxejVatWfuNiY2NRWFioTu/WrVuD6QcPHgQAFBUVBVz+xIkTKC4uBgC/6XFxcQCgTj/ZtonOpl9//RWyLGPkyJGorq7GlVdeidmzZyMsLKzRc/PEiRMAGv+7OXHiBKqqquBwOPym63Q6REVF4cSJE9BoNIiOjobBYFCnx8XFweFwoKKiAjExMWdxr4lOX3FxMc9duugJIXDkyBF88803WLVqFTweD4YNG4b77ruP1zwU1MaPHx9w/Pm85mlq20SnioEuoiBit9sbvVCKj4/3+5WkPpvN5vflAwAGgwFOp7NZ0+12e6PT7Xa7+tp3GuBNhNnUuonOxMn+LmJiYpCbm4v27dtj8eLFqKqqwt///nfMmjULL7744hn9XQQ6732nCyECTgPAc58uaI2d9wDPXbp45Ofnq+fyc889h+PHj+PJJ5+E3W7nNQ+1SOfzmod/F/RHY6CLKIjk5OTgzjvvDDhtxYoVuOaaaxpd1mg0NvgycTqdMJlMZzTdbDb7fZEZjUb1OeCt9tzUuonORFN/F9u3b4fRaIRerwcALFmyBOPGjUNhYSGMRiMqKir8lmnOeR8REdHgXPedbjab4fF4Ak4DwHOfLmiNnfcAz126eFxyySXYsWMHIiMjIUkSunfvDlmWMWvWLPTp04fXPNTinM9rnqa2TXSqGOgiCiKZmZn45ZdfTmvZ1q1bo6SkxG9cSUkJ4uPjTzpdqWZ8suVbt24NwFvlWclJoVTtV6afbNtEZ+JU/y66du0KACgsLETr1q1x6NAhv+nNOe+7d++OqKgoGI1GlJSUqOt0u92oqKhAfHw8hBAoLy+H2+2GTuf9Oi4uLobJZEJERMRp7y/R2da6dWueuxQUoqKi/F537doVDocD8fHxvOahFud8XvM0tW2iU8Vk9EQEwJswMi8vz68tfFZWFtLS0gAAqamp2LVrF4QQALy5LX788Uekpqaq07OystRlCwoKUFBQgNTUVLRu3Rrt2rXzm56VlYV27dqhVatWTW6b6Gw5dOgQ0tPTkZubq47bt28fdDodOnXqhNTUVPz8889qlXzAe242dt7bbDbs3bsXqamp0Gg0SE5O9puenZ0NnU6Hyy67DN27d4dOp/NLtJqVlYXk5GRoNPx6pgsXz10KBl9//TUyMzNhs9nUcfv27UNUVJSaHJ7XPNSSnM9rnqa2TXSqeDVCRACADh06oH///pg1axb279+PdevWYePGjbjtttsAAMOGDUNVVRUWLVqEQ4cOYdGiRbDZbLj22msBALfeeivef/99rFu3Dvv378fs2bNx1VVXoUOHDur0Z555Bjt27MCOHTuwbNkytTlZU9smOlsuvfRSdOrUCY899hgOHDiAH374AY899hhuvPFGREZGok+fPmjbti3mzp2LgwcPYvXq1di9ezduuOEGAMC4cePw448/YvXq1Th48CDmzp2L9u3bIzMzE4A34esrr7yCrVu3Yvfu3Zg/fz5uuukmmM1mmM1mXH/99Zg/fz52796NrVu3Yu3atY02syS6UPDcpWCQnp4Oo9GIRx99FIcPH8aXX36JpUuXYvLkybzmoRbpfF7zNLVtolMmiKhFGjRokFi/fr3fuJKSEjFlyhSRnJwsBg8eLD788EO/6Tk5OeL6668XycnJ4oYbbhA///yz3/T169eLgQMHirS0NDFt2jRRVlamTnO73WLx4sWiV69eIjMzUzz99NNCluVmb5vobMnPzxfTpk0TvXr1En369BELFy4UDodDnf7bb7+J2267TSQlJYnrrrtOfPvtt37Lf/HFF2LIkCEiJSVFTJgwQRw7dsxv+qpVq8Tll18uMjIyxNy5c4XdblenWa1WMXv2bJGWlib69+8vXn311bO6r0R/FJ67FAwOHDgg7rrrLpGWlib69esnXnjhBfXahNc81BJYLBaxfft29fX5vOZpattEp0ISoq5OLhERERERERER0UWMTReJiIiIiIiIiCgoMNBFRERERERERERBgYEuIiIiIiIiIiIKCgx0ERERERERERFRUGCgi4iIiIiIiIiIggIDXUREREREREREFBQY6CIiIiIiIiIioqDAQBcREREREREREQUFBrqIiIiIiIiIiCgoMNBFRERERERERERBgYEuIiIiIiIiIiIKCgx0ERERERERERFRUGCgi4iIiIiIiIiIggIDXUREREREREREFBQY6CIiIiIiIiIioqDAQBcREREREREREQUFBrqIiIiIiIiIiCgoMNBFRERERERERERBgYEuIiIiIiIiIiIKCgx0ERERERERERFRUGCgi4iIiIiIiIiIggIDXUREREREREREFBQY6CIiIiIiIiIioqDAQBcREREREREREQUFBrqIiIiIqEUSQpzvIhAREdEfjIEuIiKic+Shhx5CQkJCo0O/fv3OdxFx/PhxJCQkYMOGDee7KGds8ODBeOihh5o9v9PpxIQJE5CYmIju3bujR48eSE9Px5EjR/6wMn3yyScYP378H7Y+ALjjjjtwxx13/KHrDAYbNmxAQkICjh8/DgB44YUXkJCQoE7PysrCPffcc76Kd9bU3++WqP573ZR33nknKM8FIqKWSne+C0BERNSSxMfHY/ny5QGn6fX6c1ya4LZ8+XKEhYWd0jKPPPII7HY7tFotDAYD2rZte8rraExpaSkWLFiANWvW/CHro1Nz4403YsCAAerrdevW4ddffz2PJaKzpf573ZRx48bhzTffxDvvvIMbbrjhLJaMiIjOBQa6iIiIziGDwYC0tLTzXYwWoUePHqc0v8FggMViOUulAV588UWkpKQgMTHxrG2DGtemTRu0adPmfBeDzoFTfa8lScKUKVPwxBNPYMSIETCZTGexdEREdLax6SIREdEF6I477sDMmTNx3333IS0tDXfffbfarPDVV1/FsGHDkJqaivXr1wMAfvrpJ0yaNAmZmZno2bMnpk6dioMHDza5nS1btmDUqFFISUnBmDFjsH///gbzVFRU4PHHH8cVV1yB5ORk3HTTTfjuu+9Oul6l+VROTg7GjBmDlJQUjBw5Eh9//LE6z8n258CBA5gyZQp69uyJnj17Ytq0acjNzQUAOBwOZGRk4KmnnvLbptvtRt++ffHkk08CaNh0cePGjeq+9u3bFzNnzkRhYaE63W63Y9myZRgyZAiSkpLQs2dP3H333di3b5/fdr799luMHz8eGRkZyMzMxIwZM1BQUHDS41FWVoZ33nkHI0aMOKV9aG6ZfDkcDqxYsQLDhg1DcnIyhgwZgtWrV0OWZXWeQOeXsuzSpUsxcOBAJCUlYeTIkfjoo4/81r9nzx5MmDABGRkZSE9Px1133YXs7OyT7n9CQgLeeOMNzJkzB+np6bjiiiuwaNEiOByOP6RMsixj5cqVuOqqq5Camop7770XlZWVfvP4Nmd76KGH8O677yIvL8+vqW51dTX+/ve/45prrkFycjJGjBiBd95554z3vzkee+wx9OvXDx6Px2/8okWLkJmZCZfLBQDYunUrxo8fj/T0dCQlJWHYsGF48803T7rups7ZDRs2oEePHli3bh369euHPn364NChQ+r2xo4di+TkZPTr1w9PPvkkrFaruqzdbsf8+fNx5ZVXquV55ZVXTlqehx56CBMmTMC8efPQs2dPDB8+HB6PB7IsY/Xq1fjzn/+MpKQkDB06FP/3f//nt6zH48Hq1asxYsQIpKSkIC0tDbfccgu2b9+uzlO/6eKxY8cwdepUZGZmIjU1FTfffDO+/PJLv/UOGjQIDodD/QwiIqKLFwNdRERE55jb7Q441E+MvXnzZoSGhuLFF1/E5MmT1fEvvPAC/vKXv2Dp0qXo168ftm/fjltvvRUAsHjxYjz55JMoKCjALbfcctKmWZ999hnuu+8+JCQkYMWKFbj22msxa9Ysv3kcDgcmTJiA//3vf3jggQewfPlytGnTBpMnT24y2AUAU6ZMwdVXX43ly5ejS5cu+H//7/81uMGsvz9HjhzBLbfcgtLSUjz11FNYtGgRcnNzceutt6K0tBRGoxFDhw7F5s2b/Y7Zt99+i/LycowePbpBObKysjB79mwMGTIEa9aswdy5c7F9+3bMmDFDnWf27NlYv3497rnnHqxduxZz587FwYMHMWPGDHU77733HiZOnIi2bdviH//4B+bOnYtdu3bh5ptvRmlpaaPHYcuWLXC73Rg0aBAANHsfmlMmX0IITJ06FS+//DJuvPFGvPTSSxg2bBiee+45zJs3z2/e+ueXEALTpk3DW2+9hbvvvhsvvvgi0tPT8cADD+C9994DANTU1GDy5MmIjo7GCy+8gGeffRY2mw2TJk1CdXV1o/sPAP/85z9RWlqK5557DpMnT8bbb7+NOXPmnHGZAODpp5/GihUrcMMNN2D58uWIiorCsmXLGi3Lvffei4EDByI+Ph5vv/02rrrqKtjtdowfPx4ffvghJk+ejJUrVyIjIwOPPPIIXnrppTPe/6aMHj0aJSUl2LFjhzpOlmVs3rwZ1113HfR6Pb744gtMmzYNiYmJWLlyJV544QV06NABTzzxBHJycgKut7nnrMfjwdq1a7Fo0SLMnTsXXbt2xYcffohp06bh0ksvxYoVKzB9+nR88MEHuPfee9Xzb/Hixfjqq68wZ84cvPLKK7j66quxdOnSJgNGP/zwAwoKCrBixQrMmDEDWq0W8+fPx/PPP49Ro0ap5+7ixYuxYsUKdblnnnkGK1euxM0334yXX34ZCxcuREVFBe6//37YbLYG25FlGVOmTIHNZsPSpUuxcuVKREVF4a9//SuOHj2qzmc0GjFo0CB8+OGHzXvDiIjowiWIiIjonJgzZ46wWCyNDi+//LI67+233y5SU1OFw+FQx+Xm5gqLxSIefvhhv/XecMMNYvjw4cLtdqvjKisrRZ8+fcR9993XaHnGjh0rbrzxRr9xq1atEhaLRaxfv14IIcTbb78tLBaLyM7OVueRZVncdtttYuzYsY2ue/369cJisYjly5f7LTd69Gh1m43tz4MPPiiuuOIKUV1drY4rLy8XGRkZYsmSJUIIIbZv3y4sFov4/vvv1XlmzZolhg0bpr4eNGiQmDNnjrpf6enpfsfziy++EC+88IKQZVk4HA4xceJEsWnTJr+yrF27VlgsFlFUVCQ8Ho/o16+fmDhxot88R48eFYmJieKpp55q9Hjcf//9YtSoUX7jmtqH5pRJCO+5cvvtt6v7ZLFYxMaNG/2WWbFihbBYLOLAgQPqMvXPr2+++UZYLJYG25s5c6bo16+fcLlcYteuXcJisYisrCy//V+6dKkoKChodP8tFosYMmSIcLlc6rhXX31VWCwWcejQoTMqU2VlpUhMTBRPP/203zyTJk0SFotF5ObmCiGEeP7554XFYlGnz5kzRwwaNEh9/eabbwqLxSJ+/PFHv/U8/PDDIjk5WZSXl5/2/jeHLMti0KBBYu7cueq4bdu2+f39rVmzRj2nFeXl5cJisYhVq1YJIX7/28vNzW32Oass89577/mV58orrxSTJk3yW1Yp0+effy6EEGLo0KHi0Ucf9Ztn+fLl6vRAlM9C32N2+PBhkZCQoO6H4tlnnxXJycmirKxMCOH9fHjttdf85vnkk0+ExWIRu3btEkL4v9dFRUXCYrGIDz74QJ2/qqpKLF68WP17ULz22muie/fufp89RER08WGNLiIionMoPj4e77zzTsChfk2kSy+9FAaDocE6unfvrj63Wq346aefcO2110Kr1arjIyIiMGjQIOzcuTNgOex2O37++We1hpHi2muv9Xv93XffIT4+HomJiWrNM4/Hg0GDBmHPnj0NmofVN2bMGPW5JEn485//jN27d8NutwfcHwDYvn07+vTpA5PJpG4zLCwMvXr1wrZt2wAAffr0Qbt27bBp0yYA3ppnW7duDVibCwB69+4Nm82GESNGYNmyZfjhhx/Qv39/TJ8+HZIkwWAw4JVXXsHw4cNRWFiI7du346233sLnn38OwNsj45EjR1BcXKw2P1R07NgR6enpjR5rAMjNzUX79u39xjW1D80pU307d+6ETqfDsGHD/MaPGjVKna6of3599913kCQJAwcO9KtpOHjwYBQXF+PgwYP405/+hJiYGEydOhWPP/44Pv30U8TFxWHWrFlN5kQaOXIkdLrf08MOHToUAPD999+fUZmys7PhcrmaPJebsnPnTlxyySVIT0/3Gz9q1Cg4HA7k5OSc1v7LsuxX9vpNExWSJGHUqFHYunWr+t5u2rQJnTt3RmpqKgBg8uTJWLJkCWpra7Fnzx589NFHWLVqFYDA58OpnrO+f4uHDx/GiRMnMHjwYL/y9+7dG2FhYfj2228BAJmZmfjvf/+Lv/zlL3jjjTeQm5uLadOm4aqrrmrsUAMAoqKi/I7Z9u3bIYRosL3BgwfD4XAgKysLALBs2TJMmDABZWVl+OGHH7B+/Xp88MEHjR6DuLg4dOvWDY899hjmzJmDDz/8ELIsY+7cufjTn/7kN+8ll1wCj8eDEydOnLTsRER0YWMyeiIionPIYDAgOTm5WfOGhoYGHB8SEqI+r66uhhACcXFxDeaLi4trtDlVZWUlhBCIjo72G9+qVSu/1xUVFSguLm40gXpxcTEiIyMb3Yf664uNjYUQAlVVVQH3R9nmRx991CAPEwDExMQA8AYFRo4ciXXr1uHRRx/F559/DqvVipEjRwYsR3p6OlavXo3XXnsNr776KlavXo24uDhMnToVd9xxBwDg66+/xuLFi3H48GGEhobisssuU8smhEBFRQUANHqs9+7d2+hxqKmpgdls9hvXnH1oqkz1VVZWIjo62i/oCXgDrAD8zof651dFRQWEEOjZs2fAfSgqKkL37t3x5ptv4sUXX8TmzZvx9ttvw2QyYfTo0Xj00UcDBmYVrVu39nsdGxurlvlMyqScS/XPZWWfm6uysjLgMsr7XVVVhdDQ0FPe/4cffhjvvvuu+vqSSy7BZ599FrAMo0ePxosvvoivv/4aAwYMwJYtWzBhwgR1ellZGebNm4etW7dCkiR06tQJvXr1AhD4fDjVc9b3b1FZdsGCBViwYEGD5YuKigB4eypt06YNPvjgAyxcuBALFy5Eeno65s+fj8suuyzgfgKB32sAuO666wLOr+TT++mnn7BgwQL89NNPMJvN6NatG9q1awcg8DGQJAlr167Fiy++iE8//RTvvfce9Ho9rrnmGixYsMDv80vZ/zNthkpEROcXA11EREQXsfDwcEiShJKSkgbTiouLERUVFXC5qKgoaDSaBsspN5u+6+/cuTOeeeaZgOupX0upvoqKCr+b7JKSEmi1WkRFRak3yvWFh4fjiiuuUJOR+/KtETR69GisWrUKO3bswEcffYTevXvjkksuabQsAwYMwIABA2Cz2bB9+3a8/vrrePLJJ5GamoqoqChMmzYN11xzDVatWoUOHTpAkiS8+eab+PrrrwFAPZaNHev6gRZf0dHRAW+eT7YPx44da7JM9UVGRqK8vBwej8cv2KUc65OVMTw8HCEhIXj99dcDTu/UqRMAb62rp59+Gh6PB7t378b777+P//znP+jYsaNfLrn6ysvL/V4rx1EJXp5umXbv3g0AKC0txaWXXqpOq38uNyUyMtIvZ5OiuLgYwO/H7lT3f/r06bjtttvU1ycLBnbp0gUpKSnYvHkzNBoNqqqq1Np4ADBz5kwcPnwYr732GtLT02EwGGCz2fDf//434PrO5JyNiIgA4M0T16dPnwbTlQCRwWDAX//6V/z1r39Ffn4+Pv/8c6xcuRIzZsxQays2h7K9f/3rXwGD/O3atVNzpCUkJGDTpk249NJLodFo8OWXX+KTTz5pdN2tW7fG/PnzMW/ePOzfvx8ff/wx1qxZg+joaL/cdUrQ9WTHhYiILnxsukhERHQRCwkJQVJSEjZv3uzXJKq6uhpffPEFMjIyAi5nNBqRnp6OLVu2+NWCqF/TpE+fPigoKEBsbCySk5PV4dtvv8XLL7/coOZQfVu3blWfCyGwZcsWZGRknPRmX+nxrXv37ur2kpKS8Nprr+HTTz9V5+vatSsSExOxadMmfPnll34BgfqeeuopjBs3DkIImM1mDBo0SE2Enp+fjz179sDhcOCee+5Bx44dIUkSAKgBJSEEunTpgvj4eGzcuNFv3bm5ucjOzm601hHgvUkP1DPjyfahOWUKdOzcbrdf75YA1KZdjZ0PyrJWqxVCCL/3+sCBA1ixYoW63r59+6K4uBharVatuRMREYH8/PxG1w00PLc++eQTSJKEvn37nlGZ0tPTYTKZGuyz0sSzMRqN/2Vw7969kZeXh127dvmN/+CDD6DX65GSknJa+9++fXu/svv2BhjI6NGj8fXXX2PTpk3o2bMnOnTooE7LysrCkCFDkJmZqf4NffXVVwDg16um4kzO2UsvvRSxsbE4fvy4X/lbt26NZcuWYe/evbDb7Rg6dCjWrl0LwHue33bbbbjuuuuaPB/qU2qmlZeX+22vrKwM//znP1FRUYHDhw+joqICd955J7p166a+hyc7Brt27cIVV1yB3bt3Q5IkdO/eHQ888AAsFkuDMhYWFkKr1TaofUhERBcX1ugiIiI6h5xOJ7KzsxudnpCQ0KCJW1NmzJiBSZMm4Z577sH48ePhcrmwevVqOJ1OTJs2rdHlHnzwQUyYMAHTp0/HzTffjCNHjqi9yynGjh2LN954A3fffTemTp2Ktm3bYtu2bVizZg1uv/126PX6k5Zt6dKlcDgc6NKlC9atW4dff/0V//rXv066zL333otbbrkFU6ZMwa233gqj0Yi3334bW7duxfPPP+837+jRo/HUU08FzEvlq2/fvnj11Vfx0EMPYdSoUXC5XHj55ZcRFRWFvn37orKyEjqdDk8//TQmTpwIp9OJDRs24IsvvgDgzYWm0Wjw4IMPYu7cuZgxYwZGjRqF8vJyLF++HJGRkQFroCn69euHzZs3o7q6GuHh4c3ah8TExCbLVN+VV16JzMxMPProoygsLMRll12GnTt3Ys2aNRgzZgy6devWaBkHDhyI3r17495778W9996Lrl27Yvfu3Xj++ecxYMAAxMTEoGfPnpBlGdOmTcM999yD0NBQdb+GDBnS6LoBIDs7GzNnzsTo0aOxf/9+vPDCC7jpppv8AjmnUybAe84899xzMJvN6Nu3L7788ssmA10REREoKSnBl19+ie7du2Ps2LH497//jWnTpuG+++5D+/bt8dlnn2H9+vWYPn06IiIizmj/m2v48OFYsmQJPvroowY9ZaakpODDDz9EYmIi2rRpgx9//BGrV6+GJEkBexw8k3NWq9XigQcewOOPPw6tVotBgwahqqoKK1euRGFhIRITE2EymZCYmIjly5dDr9cjISEBR44cwbvvvqvmYGuuhIQEjBo1Co899hjy8vKQlJSEI0eO4Nlnn0X79u3RuXNnWK1WhIWF4aWXXoJOp4NOp8Mnn3yCd955BwACHoMePXrAZDJh9uzZ+Nvf/oa4uDhs27YN+/btw5133uk3b1ZWFnr16nXKn8FERHSBOefp74mIiFqopnpdtFgsYu/evUII/570FEovhUqPiL62b98uxo8fL1JSUkSvXr3E1KlTG/QoFsi3334rxo0bJ5KTk8W1114rPvvsswbbKCkpEXPnzhWXX365SEpKEkOHDhVr1qwRHo+n0fUqvbi9//77Yvjw4SI5OVncfPPNYvv27c3anz179ohJkyaJ9PR0kZaWJm666SaxdevWBvOVlJSIHj16BOxd0rfXRSGE+PDDD8WYMWNEWlqaSE9PF5MnTxb79+9Xp2/evFlcd911Ijk5WfTv319Mnz5d7Ny5UyQkJIg33nhDne/jjz8WY8aMEYmJiSIzM1PMnDlT5Ofnn+QoC1FaWiqSkpIa9B7Y1D40p0z1zxWr1SqWLFkiBgwYIBITE8XQoUPFyy+/7Pd+BTq/hBCitrZWLF68WFx55ZUiMTFRDB48WCxbtkzY7XZ1npycHDFx4kTRp08fkZycLMaOHSu2bNly0v23WCxi8eLF4v777xcpKSli4MCBYsWKFX49hZ5JmYQQ4vXXXxdXX321SEpKEnfccYf497//fdJeF3/55RcxbNgwkZiYqPb0V1paKh5++GHRt29fkZSUJEaNGiXWrVvnt53T2f9TNWXKFJGUlCQqKir8xh8/flxMmTJFZGRkiIyMDDFu3Djx/vvvi0mTJolx48YJIfx7XVQ0dc4GWkaxadMmMWbMGJGUlCT69Okjpk6d6vd3U11dLRYuXCiuuuoqkZiYKK688kqxZMkSYbPZGt2/+j1eKlwul1i+fLm4+uqr1XXNmzdPlJeXq/Ns375djB07VqSkpIjLL79cTJw4Ufzwww8iPT1d7UWy/nt95MgRMX36dHH55ZeLxMREcd1114m33nrLb9t2u1307t1bvPnmm42Wm4iILg6SEAHqvRMRERGdgQ0bNmDu3Ln43//+12Qer5Zi4cKFOHjwYKP5poJZQkICpk+fjr/97W/nuyhEAb333nt45plnsHXrVphMpvNdHCIiOgPM0UVERER0DkydOhX79+9Xk6cT0YVBlmWsXbsW06dPZ5CLiCgIMNBFREREdA7Ex8dj/vz5WLx48fkuChH5WL9+PeLj43HLLbec76IQEdEfgE0XiYiIiIiIiIgoKLBGFxERERERERERBQUGuoiIiIiIiIiIKCgw0EVEREREREREREGBgS4iIiIiIiIiIgoKDHQREREREREREVFQYKCLiIiIiIiIiIiCAgNdREREREREREQUFBjoIiIiIiIiIiKioMBAFxERERERERERBYX/D4xQQ9bjQyreAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "  # Define o tamanho da figura\n",
    "plt.figure(figsize=(14, 7))\n",
    "\n",
    "# Plotagem da densidade dos erros para cada modelo\n",
    "sns.kdeplot(errors_lightgbm, label='LightGBM', fill=True, color='#28a745', alpha=0.4)\n",
    "sns.kdeplot(errors_xgboost, label='XGBoost', fill=True, color='gray', alpha=0.3)\n",
    "sns.kdeplot(errors_catboost, label='CatBoost', fill=True, color='gray', alpha=0.3)\n",
    "sns.kdeplot(errors_voting_ensemble, label='Voting Ensemble', fill=True, color='gray', alpha=0.3)\n",
    "\n",
    "# Linha vertical destacando o erro zero\n",
    "plt.axvline(0, color='black', linestyle='--', linewidth=1)\n",
    "\n",
    "# Ajusta o formato dos rótulos do eixo y para números normais (não em notação científica)\n",
    "plt.ticklabel_format(style='plain', axis='y')\n",
    "\n",
    "# Configuração do título e dos eixos\n",
    "plt.title('Distribuição dos erros dos modelos\\n', fontsize=16, fontweight='bold')\n",
    "plt.xlabel('\\nErro de previsão (valores preditos - valores reais)', fontsize=12)\n",
    "plt.ylabel('Densidade (proporção de observações)\\n', fontsize=12)\n",
    "\n",
    "# Limita os valores do eixo x para focar em erros mais relevantes\n",
    "plt.xlim([-140000, 140000])\n",
    "\n",
    "# Adiciona legenda para identificar os modelos\n",
    "plt.legend()\n",
    "\n",
    "# Adiciona anotação para destacar a concentração em torno de 0\n",
    "plt.annotate(\n",
    "    'Maior concentração em torno de 0',\n",
    "    xy=(0, 1.0e-5),  # Coordenadas do ponto a ser destacado\n",
    "    xytext=(-100000, 1.1e-5),  # Coordenadas do texto\n",
    "    arrowprops=dict(facecolor='black', edgecolor='black', arrowstyle='->'),  # Propriedades da seta\n",
    "    fontsize=12, color='black'  # Configurações do texto\n",
    ")\n",
    "\n",
    "# Remove a grade de fundo para deixar o gráfico mais limpo\n",
    "plt.grid(False)\n",
    "\n",
    "# Exibe o gráfico\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Análise da distribuição dos Erros**\n",
    "\n",
    "**1. Modelo LightGBM:**\n",
    "-  A curva de densidade do LightGBM é a mais **estreita e alta**, indicando que a maioria dos erros está concentrada em torno de zero.\n",
    "- Essa característica sugere um modelo **preciso e consistente**, com previsões próximas aos valores reais.\n",
    "-  A **leve inclinação à esquerda** da curva indica que o modelo apresenta uma tendência a subestimar os valores reais em alguns casos, ou seja, os erros negativos (previsões menores que o real) são ligeiramente mais comuns.\n",
    "\n",
    "**2. Demais Modelos (XGBoost, CatBoost e Voting Ensemble):**\n",
    "- As curvas de densidade dos outros modelos, incluindo o Voting Ensemble, apresentam formato mais **largo e baixo** em comparação ao LightGBM.\n",
    "- Isso implica que os erros desses modelos possuem maior variabilidade e dispersão, o que sugere previsões menos precisas em relação ao LightGBM.\n",
    "- Ainda assim, essas curvas estão centradas em torno de zero, um indicativo de que, mesmo com maior dispersão, as previsões estão relativamente próximas dos valores reais.\n",
    "\n",
    "**3. Amplitude dos Erros:**\n",
    "- A **largura das bases das curvas** revela a presença de erros extremos (outliers) para todos os modelos. Esses outliers indicam observações mais difíceis de prever com precisão, possivelmente devido a características únicas ou não representativas nos dados.\n",
    "\n",
    "**4. Densidade em torno de zero:**\n",
    "- A concentração significativa dos erros em torno de zero, especialmente no LightGBM, reforça que os modelos estão conseguindo gerar previsões **geralmente próximas aos valores reais**, com diferenças pequenas na maioria das observações."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11. Comparação: valores reais x valores preditos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Criar subplots com grade 2x2 para os gráficos\n",
    "fig, axes = plt.subplots(2, 2, figsize=(14, 12))\n",
    "\n",
    "# Lista com os dados do modelo: nome, previsões, cor e R² (ajustado para exibição)\n",
    "models_data = [\n",
    "    ('LightGBM', predictions_lightgbm, '#28a745', round(results_mdls.iloc[0, 4] * 100, 2)),\n",
    "    ('XGBoost', predictions_xgboost, 'gray', round(results_mdls.iloc[1, 4] * 100, 2)),\n",
    "    ('CatBoost', predictions_catboost, 'gray', round(results_mdls.iloc[2, 4] * 100, 2)),\n",
    "    ('Voting Ensemble', predictions_voting_ensemble, 'gray', round(results_ve.iloc[0, 4] * 100, 2))\n",
    "]\n",
    "\n",
    "# Loop para iterar pelos modelos e seus respectivos eixos (subplots)\n",
    "for ax, (model_name, predictions, color, r2) in zip(axes.flatten(), models_data):  \n",
    "    # Gráfico de dispersão dos valores reais x preditos\n",
    "    ax.scatter(y_test, predictions, alpha=0.3, color=color, label=model_name, s=20)  \n",
    "    # Linha de referência (diagonal ideal: predito = real)\n",
    "    ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], '--', color='black', linewidth=1.5)  \n",
    "    \n",
    "    # Títulos e rótulos para cada gráfico individual\n",
    "    ax.set_title(f'Modelo: {model_name}       R²: {r2}', fontsize=12, fontweight='bold')\n",
    "    ax.set_xlabel('\\nValores Reais\\n', fontsize=10)\n",
    "    ax.set_ylabel('\\nValores Preditos\\n', fontsize=10)\n",
    "    ax.grid(True, linestyle='--', alpha=0.6)  # Grade para facilitar a leitura\n",
    "    ax.set_xlim([y_test.min() * 0.9, y_test.max() * 1.1])  # Ajusta os limites do eixo X\n",
    "    ax.set_ylim([y_test.min() * 0.9, y_test.max() * 1.1])  # Ajusta os limites do eixo Y\n",
    "\n",
    "# Título geral da figura (aplicado a todos os subplots)\n",
    "fig.suptitle('Comparação de valores reais e preditos para os modelos', fontsize=16, fontweight='bold')\n",
    "\n",
    "# Ajuste de espaçamentos entre os gráficos para evitar sobreposição\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.96])\n",
    "\n",
    "# Exibir a figura com os subplots\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Comparação: valores reais x valores preditos**\n",
    "\n",
    "**1. LightGBM:**\n",
    "\n",
    "O modelo LightGBM apresentou o melhor desempenho geral, com uma alta concentração de pontos próximos à linha de referência, indicando previsões bastante alinhadas aos valores reais. Observando a dispersão, fica evidente a maior consistência e menor presença de outliers ou grandes erros.\n",
    "\n",
    "**2. XGBoost e Voting Ensemble:**\n",
    "\n",
    "Ambos os modelos tiveram desempenhos razoáveis, mas apresentam maior dispersão em comparação ao LightGBM. O XGBoost mostra uma leve melhoria em relação ao Voting Ensemble, que combina previsões de diferentes algoritmos, mas ainda apresentam alguns desvios consideráveis para valores extremos.\n",
    "\n",
    "**3. CatBoost:**\n",
    "\n",
    "O modelo CatBoost foi o que apresentou maior distanciamento entre os valores preditos e reais. É visível uma maior concentração de erros, especialmente para faixas intermediárias de valores reais, indicando que há necessidade maior afinamento no hiperparâmetros.\n",
    "\n",
    "**4. Métrica R²:**\n",
    "\n",
    "A métrica R² avalia o ajuste do modelo aos dados reais. Valores mais próximos de 100% indicam maior acurácia e menor erro residual. O **LightGBM** lidera com o maior coeficiente de determinação, reforçando seu desempenho superior, já o **CatBoost** em comparação aos demais reflete necessidade de melhor ajuste.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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