{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4a219f35",
   "metadata": {},
   "source": [
    "# Project Data Science: Bitcoin Price Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "457847d5",
   "metadata": {},
   "source": [
    "## 1. Introduction\n",
    "In this day AI have a significant impact almost in our life and work place, and for this field we will leveraging ai in sequential data development. In this Data Science Project we will do some research about BTCUSD consolidation price with Deep Learning Neural Network LSTM method using PyTorch library\n",
    "### 1. 1 Project Objective\n",
    "The main objective is to predict BTC price until the next months with LSTM (Long Short Term Memory) method\n",
    "### 1. 2 Dataset Description\n",
    "`BTC-USD.csv` is the name of the dataset, which was taken from the Kaggle Dataset and has seven columns: `Date, Open,High, Low, Close, Adj Close, Volume`. The data is a compilation of the price of Bitcoin from 2014 until early 2024, which is a changes every minute. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e581fdc",
   "metadata": {},
   "source": [
    "## 2. Data Preparation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21f28801",
   "metadata": {},
   "source": [
    "### 2.1 Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5b6aad70",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import root_mean_squared_error\n",
    "from pycoingecko import CoinGeckoAPI\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52dc06e6",
   "metadata": {},
   "source": [
    "### 2.2 Loading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3028d126",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Dataset/BTC-USD.csv', parse_dates=['Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0331bf0",
   "metadata": {},
   "source": [
    "### 2.3 Initial Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e46ec19b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-09-18</td>\n",
       "      <td>456.859985</td>\n",
       "      <td>456.859985</td>\n",
       "      <td>413.104004</td>\n",
       "      <td>424.440002</td>\n",
       "      <td>424.440002</td>\n",
       "      <td>34483200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-09-19</td>\n",
       "      <td>424.102997</td>\n",
       "      <td>427.834991</td>\n",
       "      <td>384.532013</td>\n",
       "      <td>394.795990</td>\n",
       "      <td>394.795990</td>\n",
       "      <td>37919700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-09-20</td>\n",
       "      <td>394.673004</td>\n",
       "      <td>423.295990</td>\n",
       "      <td>389.882996</td>\n",
       "      <td>408.903992</td>\n",
       "      <td>408.903992</td>\n",
       "      <td>36863600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-09-21</td>\n",
       "      <td>408.084991</td>\n",
       "      <td>412.425995</td>\n",
       "      <td>393.181000</td>\n",
       "      <td>398.821014</td>\n",
       "      <td>398.821014</td>\n",
       "      <td>26580100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-09-22</td>\n",
       "      <td>399.100006</td>\n",
       "      <td>406.915985</td>\n",
       "      <td>397.130005</td>\n",
       "      <td>402.152008</td>\n",
       "      <td>402.152008</td>\n",
       "      <td>24127600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date        Open        High         Low       Close   Adj Close  \\\n",
       "0 2014-09-18  456.859985  456.859985  413.104004  424.440002  424.440002   \n",
       "1 2014-09-19  424.102997  427.834991  384.532013  394.795990  394.795990   \n",
       "2 2014-09-20  394.673004  423.295990  389.882996  408.903992  408.903992   \n",
       "3 2014-09-21  408.084991  412.425995  393.181000  398.821014  398.821014   \n",
       "4 2014-09-22  399.100006  406.915985  397.130005  402.152008  402.152008   \n",
       "\n",
       "       Volume  \n",
       "0  34483200.0  \n",
       "1  37919700.0  \n",
       "2  36863600.0  \n",
       "3  26580100.0  \n",
       "4  24127600.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get 5 first data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e9b2bf86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3413 entries, 0 to 3412\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype         \n",
      "---  ------     --------------  -----         \n",
      " 0   Date       3413 non-null   datetime64[ns]\n",
      " 1   Open       3412 non-null   float64       \n",
      " 2   High       3412 non-null   float64       \n",
      " 3   Low        3412 non-null   float64       \n",
      " 4   Close      3412 non-null   float64       \n",
      " 5   Adj Close  3412 non-null   float64       \n",
      " 6   Volume     3412 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(6)\n",
      "memory usage: 186.8 KB\n"
     ]
    }
   ],
   "source": [
    "# get data information\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "24f8493e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3413</td>\n",
       "      <td>3412.000000</td>\n",
       "      <td>3412.000000</td>\n",
       "      <td>3412.000000</td>\n",
       "      <td>3412.000000</td>\n",
       "      <td>3412.000000</td>\n",
       "      <td>3.412000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2019-05-21 00:00:00.000000256</td>\n",
       "      <td>14747.360368</td>\n",
       "      <td>15091.809098</td>\n",
       "      <td>14376.126435</td>\n",
       "      <td>14758.111980</td>\n",
       "      <td>14758.111980</td>\n",
       "      <td>1.663026e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2014-09-18 00:00:00</td>\n",
       "      <td>176.897003</td>\n",
       "      <td>211.731003</td>\n",
       "      <td>171.509995</td>\n",
       "      <td>178.102997</td>\n",
       "      <td>178.102997</td>\n",
       "      <td>5.914570e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2017-01-18 00:00:00</td>\n",
       "      <td>921.790009</td>\n",
       "      <td>935.210266</td>\n",
       "      <td>908.876495</td>\n",
       "      <td>921.739258</td>\n",
       "      <td>921.739258</td>\n",
       "      <td>1.685530e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2019-05-21 00:00:00</td>\n",
       "      <td>8288.819824</td>\n",
       "      <td>8464.720703</td>\n",
       "      <td>8108.011475</td>\n",
       "      <td>8285.438965</td>\n",
       "      <td>8285.438965</td>\n",
       "      <td>1.176004e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2021-09-20 00:00:00</td>\n",
       "      <td>24345.831543</td>\n",
       "      <td>24986.300293</td>\n",
       "      <td>23907.724610</td>\n",
       "      <td>24382.675293</td>\n",
       "      <td>24382.675293</td>\n",
       "      <td>2.697648e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2024-01-21 00:00:00</td>\n",
       "      <td>67549.734375</td>\n",
       "      <td>68789.625000</td>\n",
       "      <td>66382.062500</td>\n",
       "      <td>67566.828125</td>\n",
       "      <td>67566.828125</td>\n",
       "      <td>3.509679e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16293.633702</td>\n",
       "      <td>16683.948248</td>\n",
       "      <td>15855.901350</td>\n",
       "      <td>16295.374063</td>\n",
       "      <td>16295.374063</td>\n",
       "      <td>1.907607e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Date          Open          High  \\\n",
       "count                           3413   3412.000000   3412.000000   \n",
       "mean   2019-05-21 00:00:00.000000256  14747.360368  15091.809098   \n",
       "min              2014-09-18 00:00:00    176.897003    211.731003   \n",
       "25%              2017-01-18 00:00:00    921.790009    935.210266   \n",
       "50%              2019-05-21 00:00:00   8288.819824   8464.720703   \n",
       "75%              2021-09-20 00:00:00  24345.831543  24986.300293   \n",
       "max              2024-01-21 00:00:00  67549.734375  68789.625000   \n",
       "std                              NaN  16293.633702  16683.948248   \n",
       "\n",
       "                Low         Close     Adj Close        Volume  \n",
       "count   3412.000000   3412.000000   3412.000000  3.412000e+03  \n",
       "mean   14376.126435  14758.111980  14758.111980  1.663026e+10  \n",
       "min      171.509995    178.102997    178.102997  5.914570e+06  \n",
       "25%      908.876495    921.739258    921.739258  1.685530e+08  \n",
       "50%     8108.011475   8285.438965   8285.438965  1.176004e+10  \n",
       "75%    23907.724610  24382.675293  24382.675293  2.697648e+10  \n",
       "max    66382.062500  67566.828125  67566.828125  3.509679e+11  \n",
       "std    15855.901350  16295.374063  16295.374063  1.907607e+10  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to know mean, max, min, std, 25%, 50%, 75% of each column\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56909303",
   "metadata": {},
   "source": [
    "## 3. Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71957b4e",
   "metadata": {},
   "source": [
    "### 3.1 Handling Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "df619ac2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         0\n",
       "Open         1\n",
       "High         1\n",
       "Low          1\n",
       "Close        1\n",
       "Adj Close    1\n",
       "Volume       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find missing valuee\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "41f21639",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         0\n",
       "Open         0\n",
       "High         0\n",
       "Low          0\n",
       "Close        0\n",
       "Adj Close    0\n",
       "Volume       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop all missing values\n",
    "df.dropna(inplace=True)\n",
    "# check again\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a9fa263",
   "metadata": {},
   "source": [
    "### 3.2 Removing Duplicate Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1142ed84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check duplicate values without Timestamp column\n",
    "df.duplicated().sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd3767cc",
   "metadata": {},
   "source": [
    "## 4. Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7621579e",
   "metadata": {},
   "source": [
    "### 4.1 Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "80b1fc91",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3412</td>\n",
       "      <td>3412.000000</td>\n",
       "      <td>3412.000000</td>\n",
       "      <td>3412.000000</td>\n",
       "      <td>3412.000000</td>\n",
       "      <td>3412.000000</td>\n",
       "      <td>3.412000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2019-05-20 12:00:25.322391808</td>\n",
       "      <td>14747.360368</td>\n",
       "      <td>15091.809098</td>\n",
       "      <td>14376.126435</td>\n",
       "      <td>14758.111980</td>\n",
       "      <td>14758.111980</td>\n",
       "      <td>1.663026e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2014-09-18 00:00:00</td>\n",
       "      <td>176.897003</td>\n",
       "      <td>211.731003</td>\n",
       "      <td>171.509995</td>\n",
       "      <td>178.102997</td>\n",
       "      <td>178.102997</td>\n",
       "      <td>5.914570e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2017-01-17 18:00:00</td>\n",
       "      <td>921.790009</td>\n",
       "      <td>935.210266</td>\n",
       "      <td>908.876495</td>\n",
       "      <td>921.739258</td>\n",
       "      <td>921.739258</td>\n",
       "      <td>1.685530e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2019-05-20 12:00:00</td>\n",
       "      <td>8288.819824</td>\n",
       "      <td>8464.720703</td>\n",
       "      <td>8108.011475</td>\n",
       "      <td>8285.438965</td>\n",
       "      <td>8285.438965</td>\n",
       "      <td>1.176004e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2021-09-19 06:00:00</td>\n",
       "      <td>24345.831543</td>\n",
       "      <td>24986.300293</td>\n",
       "      <td>23907.724610</td>\n",
       "      <td>24382.675293</td>\n",
       "      <td>24382.675293</td>\n",
       "      <td>2.697648e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2024-01-21 00:00:00</td>\n",
       "      <td>67549.734375</td>\n",
       "      <td>68789.625000</td>\n",
       "      <td>66382.062500</td>\n",
       "      <td>67566.828125</td>\n",
       "      <td>67566.828125</td>\n",
       "      <td>3.509679e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16293.633702</td>\n",
       "      <td>16683.948248</td>\n",
       "      <td>15855.901350</td>\n",
       "      <td>16295.374063</td>\n",
       "      <td>16295.374063</td>\n",
       "      <td>1.907607e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Date          Open          High  \\\n",
       "count                           3412   3412.000000   3412.000000   \n",
       "mean   2019-05-20 12:00:25.322391808  14747.360368  15091.809098   \n",
       "min              2014-09-18 00:00:00    176.897003    211.731003   \n",
       "25%              2017-01-17 18:00:00    921.790009    935.210266   \n",
       "50%              2019-05-20 12:00:00   8288.819824   8464.720703   \n",
       "75%              2021-09-19 06:00:00  24345.831543  24986.300293   \n",
       "max              2024-01-21 00:00:00  67549.734375  68789.625000   \n",
       "std                              NaN  16293.633702  16683.948248   \n",
       "\n",
       "                Low         Close     Adj Close        Volume  \n",
       "count   3412.000000   3412.000000   3412.000000  3.412000e+03  \n",
       "mean   14376.126435  14758.111980  14758.111980  1.663026e+10  \n",
       "min      171.509995    178.102997    178.102997  5.914570e+06  \n",
       "25%      908.876495    921.739258    921.739258  1.685530e+08  \n",
       "50%     8108.011475   8285.438965   8285.438965  1.176004e+10  \n",
       "75%    23907.724610  24382.675293  24382.675293  2.697648e+10  \n",
       "max    66382.062500  67566.828125  67566.828125  3.509679e+11  \n",
       "std    15855.901350  16295.374063  16295.374063  1.907607e+10  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptive statistics\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "747ab111",
   "metadata": {},
   "source": [
    "### 4.2 Correlation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e065b547",
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.768140</td>\n",
       "      <td>0.765571</td>\n",
       "      <td>0.772179</td>\n",
       "      <td>0.768385</td>\n",
       "      <td>0.768385</td>\n",
       "      <td>0.627426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Open</th>\n",
       "      <td>0.768140</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999497</td>\n",
       "      <td>0.999122</td>\n",
       "      <td>0.998831</td>\n",
       "      <td>0.998831</td>\n",
       "      <td>0.678427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>0.765571</td>\n",
       "      <td>0.999497</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998970</td>\n",
       "      <td>0.999472</td>\n",
       "      <td>0.999472</td>\n",
       "      <td>0.683762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>0.772179</td>\n",
       "      <td>0.999122</td>\n",
       "      <td>0.998970</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999377</td>\n",
       "      <td>0.999377</td>\n",
       "      <td>0.668720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Close</th>\n",
       "      <td>0.768385</td>\n",
       "      <td>0.998831</td>\n",
       "      <td>0.999472</td>\n",
       "      <td>0.999377</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.677126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adj Close</th>\n",
       "      <td>0.768385</td>\n",
       "      <td>0.998831</td>\n",
       "      <td>0.999472</td>\n",
       "      <td>0.999377</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.677126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Volume</th>\n",
       "      <td>0.627426</td>\n",
       "      <td>0.678427</td>\n",
       "      <td>0.683762</td>\n",
       "      <td>0.668720</td>\n",
       "      <td>0.677126</td>\n",
       "      <td>0.677126</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Date      Open      High       Low     Close  Adj Close  \\\n",
       "Date       1.000000  0.768140  0.765571  0.772179  0.768385   0.768385   \n",
       "Open       0.768140  1.000000  0.999497  0.999122  0.998831   0.998831   \n",
       "High       0.765571  0.999497  1.000000  0.998970  0.999472   0.999472   \n",
       "Low        0.772179  0.999122  0.998970  1.000000  0.999377   0.999377   \n",
       "Close      0.768385  0.998831  0.999472  0.999377  1.000000   1.000000   \n",
       "Adj Close  0.768385  0.998831  0.999472  0.999377  1.000000   1.000000   \n",
       "Volume     0.627426  0.678427  0.683762  0.668720  0.677126   0.677126   \n",
       "\n",
       "             Volume  \n",
       "Date       0.627426  \n",
       "Open       0.678427  \n",
       "High       0.683762  \n",
       "Low        0.668720  \n",
       "Close      0.677126  \n",
       "Adj Close  0.677126  \n",
       "Volume     1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the correlation between columns\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6458efea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the correlation matrix\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(df.corr(), annot=True, cmap='coolwarm', fmt='.2f')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "923fc36f",
   "metadata": {},
   "source": [
    "### 4.3 Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c229a404",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vyV8Fkz96eLFWWmkVpx7S6k2rL62JXrQiTivntOJQK5/ttIpPX7NWqmnVWzD0UHFvun218lRpVa0/Ohu7jp1FK6zOOuss47xO4GPRqlIdR21RoYdg2+m2jGQFlVZoagVyd1+3vld1Uh8dz0suucTnOXRytFDp2HlX6mmrCe/tp5WMWqWmFeNWFae9Gs5eZR4MPaT/+OOPN9oP6GRFdjpxmVZAajW7VfWnE5XpZ++BBx7wqKLTatUbb7zRo0o3UrRa1V6FqFXmOhZaJaqH1NurV/VQc62G1EPCW1tb26+3ZqbXqnDvalet5NfPnk5q1Rmt+H3++eeNNgJ33XWXR6W9VhBqWwFdRr+DwkU/Q/q51pM+p1YcazW+fqa0fUV36eRbOkGXVsJr9bH396u+VutzGur3djB0bOzfn/re0s+VVlgG29ZGq0ZVKBOg6WRaenSC/j7zfi1Wy5dA30/W9aG22PBWX19vtEjQFib6nRoJWiGtVej6+dXPcaRep7Yo0SMc9LOkFdvedB30vaEV3IHapvjTr18/jzEGACDSaJUAAICIEZxaIYx1eKWdHoKrAYkup0GSHsKsh+W/8cYbxh+GGlzqffVwb+/ZpjUEtUJOf71ktf2A0n6i3sFuIPoHrtV7UA811cOtNTjTIM/f4ep77LGHBEsPA1V6yHBnPf+s16br4++16YziKlCvVG966P+9995rHCquh99792xcvXq13/t5h7BKe5AqDdTsh3Qrf+GmhgI6lvb+ieHU0RiE8rqt96oehu2v5YW2rQj1NQS7/bQVgdI2Df7eG7pO/vo7d0QDYv1saYh57rnnBmyToL1TtR2EtpLQNgn+PqP27RMJGrL6C9m1j6cGUnoYtTddX+0ZWl5ebpy3Pjf6PaOH8+vJm4at+tnR94V9J5A3DRK1VYSGw/od4G+bWIe9h4sGXRouKw2jNTzVkF0//xrqLl682AhXu0rbc2gQvN9++/n0ge3u93Zn9DtAd0oE81noiNW3VXdMBEP74Wrwra9Xd3IFe79w08BYv390DPxtT2/aF1Zb5Hh//9h7GnuH8vp7St/bjz76qPE9Egka1GtLDg1ZdXt6vxa9TtvP6DrozpVQWJ8zbUcCAEA0ENwCABKWVjNpIBgozAuFVekTqELKut5e6aNVYVph9+KLL7b3PtRKVK0g1Eoxqwej9Ue8BlMd0arDYGkvSe8/mIOpEgqG9RqtkKkj1mvTClA9dee16fNqj04NwDTk1KBF/0jWyiy9TXsR6sRU/vjr0WlVZNorHa1xtsamO9spVIEeO9TXHYnXEK7tF+j6jmjopkGsVvbqRGVatfn+++8bOzQ0CNYJqbr6GQ23QJWA1rbyd7t1m1YP2z83GuZ2NvGTfm46Cm5jvU10x4F+F2l1po6XVgnrJIg64VNXhfL9E+7XH6jXr7/PQkesil092sD76AdvGuJrmKk7Qt566y2/O3is91WgyRat67vTq1h39miQqQG8TkIZDA1u/e0k8hfcamirQbrubNDvNJ2gLRKvU3sS6xEEWimtvYG9g1mtstU+3nqEgfZu70pVsupsXAEg0elcDLpjUfu+6/9HtFe/vx3ngej/c3QOBT1CUnfYaf9y76PzdKe8Hjmjz6Hfz1oAE+jotGRGqwQAQMKyJn559913u/1Y1h+MWhXXUVsGezCjf7jpH7kaWOhhk1rZpeukPzW89X5srbjV9gqBTjpRT6SEciio9UdxMIG49dr0D/GOXptW6XVGKyw1vNQQXCd40ometFpQt7H3xE5dZa2v96z0lkDjH8kxCPV1x/I1WJNlBXruQNd3xqqqtXZuWD+tdg1d/YwGYlUL6x8V3iIZ/Fp0HbWqsqPPjJ40FO3sccK1TbrLmlzO3lqjK7ry/RMPr99OQ0P7jq1AdKItPbJBvxv0D1etnPZHJ2izH53hTScrVPpHcVdpVbK+5/R7SNfHfrImUtTKVb1sVd7Pnz/f5z3r78gLHQcNc7UdkIbD2sIjEq9Tq9dPOOEEYweSBsrW49np72qtltX/N+j3gP11auW40gkI9bI1iZ+dNabWGAOAU+nRP/p9GurEsxY9ekZ3fOoOyUBHN+gOUT1SUY+yiWTxRKKj4hYAkLC0h6n2WNTDHvUPwo7aDGjFYkezY++6667Gzw8//NAIcwLN6j169Gi/99dDaU855RSjp6L+saiPYx3mPHbsWGMd9Y/0nXbaSeKdrq/VV1X75XbULsFaVl9boD/Gg6WHwSttO+EtXO0LrPHTx/Pue6jVXFYgEU2hvm77e1X/w+vdLkHDlEjRVhJWlaD+h9z7vaHr1BW60+L66683dnroT+twcQ1hLAUFBUabAj2UWwMc71ngO/uM2lmHomvPXu/ez3qof6Tp5+bNN9+UpUuXGv1Eu0q/a/QPHt0ppIGzdyViKNuku6w2Avq+6Iz1nvVXwap/4On7SquutWVIR+0Suvu9HSn6Pa9HICxbtsxv6wWlvaK13Y7+XtLvWq26D+SAAw4wfmq46/2502qljz76yHgfWN/HXaH91q0+6d706BKtttLvTA00O6oC96atNLTSVr/nnnjiCY+dMd708639ijW41Z1Z3p9NrUi2t0Wx02pv/R7RSm1/lbYWXfdAr1Pfc/rdohXQ2v5Et4k3HVP7dyEAOJX+P8v7/1p2+ntXf5dpH389wkR3aOlcB4MHDzZuz8zMlIkTJ7b/f0uX8ab/b9Hv3Ei3ukp0VNwCABKW/sdAq3t0j/ARRxwRMHDRCUqs/xQEohPCaJWNth/wrrLRKkhth6BhzzHHHGNcp4f86GHd3jRo0D9wNUDQ/7BYAbM1UZe/ajT9QzySYVuotE+nTuyjIaa2gvCmgbT1ny/tjaqHumtPQZ04yh/dTnqYbGes/+h5bwv9j5wG9OGgkwzpOOp4er9f9L0U6BDdSAr1dVvvVQ02dAIeO+0xGqkevUpDFa2c0xBGJ1Ly/pyF2t/WHqboZ0vDvxNPPNE4HPnUU09tnwTPosGRVvVdddVVHqGfVtDddttt7ct0xjoc3bt9iVbh6WRbkXb55Ze3Vxrr4YH+vkesXtMd0e8Y3WGk4Z01OZvl559/Niba0ipJnXAqknTcrKr6QP1N7azgz98ET7179zYOpdQqzSuvvNInCNbvV+tzGur3drRY2yDQGGoAq3/M6lEb+p7rKLS1As1DDjnEeJ1asWqnFbL6ftEx7qwncEf0j22t/vd3ssZLP/N62er52xmt1NUqKn0v6u+HjkJbpaGwtjGw+u3ax16/23QHoe6g9e5RrpWy2mJGv580fO2ob62ue6DXqb/31BVXXGFc9jfZoo6pTtDpL9QFgGSi8zLozjktPNDvbv1+1h3wnR1tgtBRcQsASGhanWf1itQ/fvUPLw0TdXIcPWzbqqDxN/mSN60G0kNVNRTSP6z1PrqHWA+/1AonDSa06s86jFervbQfp1ZX6R+DVVVVRi8nPWxXq0+tZfWP3tdff90ID7QiSnvraZWd/pGqj6/Vi/YwNB7of7w0fLCqH/W8Bma6LXXbaNWRFThqOKIVUFrFpEGRHjKtQbX+Z+7rr7829sTra+zs0FL9w1sn6NJJZbRiSvfy6/PpNj322GONqq/u0vfFk08+abQg0MBZf2ofTK3Y0/XUkEHfM9HUlddtTeyj99Hx0J6UGqZOmzatfdK8SNHn1s+J9qjU/7Tr+1+rYPV9osG4BiydTWrnj4Y6L7/8shHOWJe9aZCnVXf6HPqa9fA9nZxLP6O6c0DDHquFSkd0Z4pucw3GtVpV/9jQKj99bP2c6muJJP0OuPvuu41esDre+jq0ulBDSQ27NHzX16FheGf0cXSbaYivk0ppdaYG2a+++qoR6Or13pWL3aE9RK3+2tbkZPp+0+8w/Q62greO6Drqe0Rfv37urAroG264wfip66zX63ey7tDQdgIaUuvOCq1O/d///tcejobyvR0t+n2o34G6rtr2xE57vOrnRL/vddz1vawnb94tB7SFiv5+098tGvZq72cNp/U7Q1sH3HHHHT6PoeGjVQVvVfbrWOmYKe1VeO2110qk6Bjpe0V3BupPf20UdKI763eJFZrqd5/+ztTfJfpZ0YBfx1OrijUAtn+/6OvXnTUa8ur7yl9bHh0L/a7sLh07XRf9bgql/RAAOI11lJruiLZ+x+rvKP1do9frdzfCyAUAgAN8++23rksuucS14447ugoKClwZGRmufv36uQ477DDXU0895WpoaGhfdvny5S79FXjGGWf4PM5vv/3muvDCC11bb7218Rg9e/Z0HXXUUa6FCxd6LFdRUeG65ZZbXAcccICrf//+rszMTOP59t9/f9eLL77oamtr83lsfd6LL77Ytd1227mysrKM9Rw2bJjr1FNPdU2bNi2o12mt+6BBg4JaXl+jLq/382fevHnG7ZMmTfK5bcOGDa6rr77atf322xvrW1RU5Np5551d119/vau2ttZj2aqqKtcdd9zhGj16tCsvL8+VnZ3tGjx4sOt3v/ud6x//+IerpqYmqPVdunSp68gjj3T17t3blZubazzeP//5z4Bj1tHr6+i1zZo1yzVu3DhXTk6Oq7i42PX73//e9d1333W6vTqiz+Pv+aZOnWpcrz/D9brVjz/+6DruuOOMcdH7jB071jVjxoyAz6fvGe/3TWfrprfpe9qbbqtjjjnG57nvvfde4z7Bvp+9DR061Lj/XnvtFXCZ+vp6472mn3V9n+Xn5xtjqZ87bx1tvyVLlrgOP/xw4/76ntXXOX/+/JC2n0XvG+i/1R29pz744APXCSec4CotLTW+b3r16mV8xi6//HLXokWLXMHS7yP9rOp3i34X6bgcdNBBrnfeecfv8h29lkCs1+F90u+xMWPGuO655x5jbIJ9D/3rX/8yXquOofVYdvqdcfvtt7tGjRplfE51nIYPH+669NJLXWvXru3S97bqyvhan239TgnWZZddZtxHfzf5+17q7OTPqlWrXGeeeabxu0Zfp75e3R6bNm0Kacy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      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the closing price over time and volume\n",
    "# make plot with 2 Y axis\n",
    "fig, ax1 = plt.subplots(figsize=(14, 6))\n",
    "\n",
    "# Plot Close price in left side\n",
    "ax1.plot(df['Date'], df['Close'], color='blue', label='Close Price')\n",
    "ax1.set_xlabel('Date')\n",
    "ax1.set_ylabel('BTC Price (USD)', color='blue')\n",
    "ax1.tick_params(axis='y', labelcolor='blue')\n",
    "\n",
    "# make second Y axis for volume\n",
    "# Plot Volume in right side\n",
    "ax2 = ax1.twinx()\n",
    "ax2.bar(df['Date'], df['Volume'], color='gray', alpha=0.3, label='Volume')\n",
    "ax2.set_ylabel('Trading Volume', color='gray')\n",
    "ax2.tick_params(axis='y', labelcolor='gray')\n",
    "\n",
    "# Tambahkan judul dan legend\n",
    "plt.title('Close Price and Trading Volume of Bitcoin (2014-2024)')\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d1e4f38",
   "metadata": {},
   "source": [
    "### 4.4 Distribution Analysis\n",
    "- Close Price data is rigth skewed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d238c2a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distibution of the closing price\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(df['Close'], bins=50, kde=True, color='blue')\n",
    "plt.title('Distribution of Bitcoin Closing Price')\n",
    "plt.xlabel('Price (USD)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91d6f9e9",
   "metadata": {},
   "source": [
    "## 5. Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b9c80455",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# chech CUDA\n",
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d8dd639",
   "metadata": {},
   "source": [
    "### 5.1 Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "648e00a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3382, 30), (3382,))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make function for make sequence data xs and ys\n",
    "def create_sequences(data, seq_length):\n",
    "    xs, ys = [], []\n",
    "    for i in range(len(data) - seq_length):\n",
    "        x = data.iloc[i:(i + seq_length), 4]\n",
    "        y = data.iloc[i + seq_length, 4]\n",
    "        xs.append(x)\n",
    "        ys.append(y)\n",
    "    return np.array(xs), np.array(ys)\n",
    "\n",
    "# make sequence data with 30 days\n",
    "X, y = create_sequences(df, 30)\n",
    "\n",
    "# check the shape of X and y\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "07ea5fe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[424.440002, 394.79599 , 408.903992, 398.821014, 402.152008,\n",
       "         435.790985, 423.204987, 411.574005, 404.424988, 399.519989,\n",
       "         377.181   , 375.46701 , 386.944   , 383.61499 , 375.071991,\n",
       "         359.511993, 328.865997, 320.51001 , 330.07901 , 336.187012,\n",
       "         352.940002, 365.026001, 361.562012, 362.299011, 378.549011,\n",
       "         390.414001, 400.869995, 394.77301 , 382.556   , 383.757996],\n",
       "        [394.79599 , 408.903992, 398.821014, 402.152008, 435.790985,\n",
       "         423.204987, 411.574005, 404.424988, 399.519989, 377.181   ,\n",
       "         375.46701 , 386.944   , 383.61499 , 375.071991, 359.511993,\n",
       "         328.865997, 320.51001 , 330.07901 , 336.187012, 352.940002,\n",
       "         365.026001, 361.562012, 362.299011, 378.549011, 390.414001,\n",
       "         400.869995, 394.77301 , 382.556   , 383.757996, 391.441986],\n",
       "        [408.903992, 398.821014, 402.152008, 435.790985, 423.204987,\n",
       "         411.574005, 404.424988, 399.519989, 377.181   , 375.46701 ,\n",
       "         386.944   , 383.61499 , 375.071991, 359.511993, 328.865997,\n",
       "         320.51001 , 330.07901 , 336.187012, 352.940002, 365.026001,\n",
       "         361.562012, 362.299011, 378.549011, 390.414001, 400.869995,\n",
       "         394.77301 , 382.556   , 383.757996, 391.441986, 389.54599 ],\n",
       "        [398.821014, 402.152008, 435.790985, 423.204987, 411.574005,\n",
       "         404.424988, 399.519989, 377.181   , 375.46701 , 386.944   ,\n",
       "         383.61499 , 375.071991, 359.511993, 328.865997, 320.51001 ,\n",
       "         330.07901 , 336.187012, 352.940002, 365.026001, 361.562012,\n",
       "         362.299011, 378.549011, 390.414001, 400.869995, 394.77301 ,\n",
       "         382.556   , 383.757996, 391.441986, 389.54599 , 382.845001],\n",
       "        [402.152008, 435.790985, 423.204987, 411.574005, 404.424988,\n",
       "         399.519989, 377.181   , 375.46701 , 386.944   , 383.61499 ,\n",
       "         375.071991, 359.511993, 328.865997, 320.51001 , 330.07901 ,\n",
       "         336.187012, 352.940002, 365.026001, 361.562012, 362.299011,\n",
       "         378.549011, 390.414001, 400.869995, 394.77301 , 382.556   ,\n",
       "         383.757996, 391.441986, 389.54599 , 382.845001, 386.475006]]),\n",
       " array([391.441986, 389.54599 , 382.845001, 386.475006, 383.15799 ]))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the first 5 data of X and y\n",
    "X[:5], y[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2ec79f6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2705, 30), (677, 30), (2705,), (677,))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# divide the dataset into train 80% and test\n",
    "train_size = int(len(X) * 0.8)\n",
    "\n",
    "# divide the dataset into train and test\n",
    "X_train = X[:train_size]\n",
    "X_test = X[train_size:]\n",
    "\n",
    "y_train = y[:train_size]\n",
    "y_test = y[train_size:]\n",
    "\n",
    "# check the shape of X_train, X_test, y_train, y_test\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7c91fcd",
   "metadata": {},
   "source": [
    "### 5.2 Feature Transformation with MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0e9eadff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# MinMaxScaler\n",
    "scaler_X = MinMaxScaler(feature_range=(-1, 1))\n",
    "scaler_y = MinMaxScaler(feature_range=(-1, 1))\n",
    "\n",
    "# fit the scaler to the data\n",
    "X_train_scaled = scaler_X.fit_transform(X_train)\n",
    "X_test_scaled = scaler_X.transform(X_test)\n",
    "\n",
    "y_train_scaled = scaler_y.fit_transform(y_train.reshape(-1, 1))\n",
    "y_test_scaled = scaler_y.transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "37830c26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([2705, 30]),\n",
       " torch.Size([2705, 1]),\n",
       " torch.Size([677, 30]),\n",
       " torch.Size([677, 1]))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change to tensor\n",
    "X_train_tensor = torch.tensor(X_train_scaled).float()\n",
    "y_train_tensor = torch.tensor(y_train_scaled).float()\n",
    "X_test_tensor = torch.tensor(X_test_scaled).float()\n",
    "y_test_tensor = torch.tensor(y_test_scaled).float()\n",
    "\n",
    "# check the shape of X_train_tensor, y_train_tensor, X_test_tensor, y_test_tensor\n",
    "X_train_tensor.shape, y_train_tensor.shape, X_test_tensor.shape, y_test_tensor.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8dad42a",
   "metadata": {},
   "source": [
    "### 5.3 Make Dataset and Dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0dd79003",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make train andt test dataset\n",
    "train_dataset = TensorDataset(X_train_tensor, y_train_tensor)\n",
    "test_dataset = TensorDataset(X_test_tensor, y_test_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6eb9f365",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make dataloader\n",
    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1f1ff1f",
   "metadata": {},
   "source": [
    "## 6. Modelling using LSTM / GRU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "44c5d58d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make class for LSTM\n",
    "class LSTM(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, num_layers):\n",
    "        super().__init__()\n",
    "        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_size, 1)\n",
    "    \n",
    "    def forward(self, x, num_layers, hidden_size):\n",
    "        h0 = torch.zeros(num_layers, x.size(0), hidden_size)\n",
    "        c0 = torch.zeros(num_layers, x.size(0), hidden_size)\n",
    "        out, _ = self.lstm(x, (h0, c0))\n",
    "        out = self.fc(out[:, -1, :])\n",
    "        return out\n",
    "\n",
    "class GRU(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, num_layers):\n",
    "        super().__init__()\n",
    "        self.gru = nn.GRU(input_size, hidden_size, num_layers, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_size, 1)\n",
    "    \n",
    "    def forward(self, x, num_layers, hidden_size):\n",
    "        h0 = torch.zeros(num_layers, x.size(0), hidden_size) \n",
    "        out, _ = self.gru(x, h0)\n",
    "        out = self.fc(out[:, -1, :])\n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc727011",
   "metadata": {},
   "source": [
    "### 6.1 Model Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3b75a55d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100, Loss: 0.8863\n",
      "Epoch 1/100, Loss: 0.8166\n",
      "Epoch 1/100, Loss: 0.7500\n",
      "Epoch 1/100, Loss: 0.7476\n",
      "Epoch 1/100, Loss: 0.7373\n",
      "Epoch 1/100, Loss: 0.5893\n",
      "Epoch 1/100, Loss: 0.5790\n",
      "Epoch 1/100, Loss: 0.5175\n",
      "Epoch 1/100, Loss: 0.5271\n",
      "Epoch 1/100, Loss: 0.4083\n",
      "Epoch 1/100, Loss: 0.4502\n",
      "Epoch 1/100, Loss: 0.3968\n",
      "Epoch 1/100, Loss: 0.3531\n",
      "Epoch 1/100, Loss: 0.2785\n",
      "Epoch 1/100, Loss: 0.2457\n",
      "Epoch 1/100, Loss: 0.1444\n",
      "Epoch 1/100, Loss: 0.0979\n",
      "Epoch 1/100, Loss: 0.1295\n",
      "Epoch 1/100, Loss: 0.2504\n",
      "Epoch 1/100, Loss: 0.2572\n",
      "Epoch 1/100, Loss: 0.1317\n",
      "Epoch 1/100, Loss: 0.1326\n",
      "Epoch 1/100, Loss: 0.1173\n",
      "Epoch 1/100, Loss: 0.0924\n",
      "Epoch 1/100, Loss: 0.0725\n",
      "Epoch 1/100, Loss: 0.0912\n",
      "Epoch 1/100, Loss: 0.0510\n",
      "Epoch 1/100, Loss: 0.1432\n",
      "Epoch 1/100, Loss: 0.1268\n",
      "Epoch 1/100, Loss: 0.0831\n",
      "Epoch 1/100, Loss: 0.1152\n",
      "Epoch 1/100, Loss: 0.0741\n",
      "Epoch 1/100, Loss: 0.0702\n",
      "Epoch 1/100, Loss: 0.0531\n",
      "Epoch 1/100, Loss: 0.0927\n",
      "Epoch 1/100, Loss: 0.0465\n",
      "Epoch 1/100, Loss: 0.0405\n",
      "Epoch 1/100, Loss: 0.0498\n",
      "Epoch 1/100, Loss: 0.0488\n",
      "Epoch 1/100, Loss: 0.0234\n",
      "Epoch 1/100, Loss: 0.0569\n",
      "Epoch 1/100, Loss: 0.0283\n",
      "Epoch 1/100, Loss: 0.0027\n",
      "Epoch 2/100, Loss: 0.0198\n",
      "Epoch 2/100, Loss: 0.0162\n",
      "Epoch 2/100, Loss: 0.0232\n",
      "Epoch 2/100, Loss: 0.0158\n",
      "Epoch 2/100, Loss: 0.0143\n",
      "Epoch 2/100, Loss: 0.0168\n",
      "Epoch 2/100, Loss: 0.0101\n",
      "Epoch 2/100, Loss: 0.0066\n",
      "Epoch 2/100, Loss: 0.0031\n",
      "Epoch 2/100, Loss: 0.0054\n",
      "Epoch 2/100, Loss: 0.0080\n",
      "Epoch 2/100, Loss: 0.0058\n",
      "Epoch 2/100, Loss: 0.0057\n",
      "Epoch 2/100, Loss: 0.0060\n",
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      "Epoch 2/100, Loss: 0.0058\n",
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      "Epoch 2/100, Loss: 0.0065\n",
      "Epoch 2/100, Loss: 0.0069\n",
      "Epoch 2/100, Loss: 0.0090\n",
      "Epoch 2/100, Loss: 0.0025\n",
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      "Epoch 2/100, Loss: 0.0068\n",
      "Epoch 2/100, Loss: 0.0026\n",
      "Epoch 2/100, Loss: 0.0038\n",
      "Epoch 2/100, Loss: 0.0031\n",
      "Epoch 2/100, Loss: 0.0082\n",
      "Epoch 2/100, Loss: 0.0030\n",
      "Epoch 2/100, Loss: 0.0045\n",
      "Epoch 2/100, Loss: 0.0068\n",
      "Epoch 2/100, Loss: 0.0018\n",
      "Epoch 2/100, Loss: 0.0046\n",
      "Epoch 2/100, Loss: 0.0034\n",
      "Epoch 2/100, Loss: 0.0025\n",
      "Epoch 2/100, Loss: 0.0064\n",
      "Epoch 2/100, Loss: 0.0011\n",
      "Epoch 2/100, Loss: 0.0052\n",
      "Epoch 2/100, Loss: 0.0028\n",
      "Epoch 2/100, Loss: 0.0040\n",
      "Epoch 2/100, Loss: 0.0031\n",
      "Epoch 2/100, Loss: 0.0082\n",
      "Epoch 3/100, Loss: 0.0039\n",
      "Epoch 3/100, Loss: 0.0025\n",
      "Epoch 3/100, Loss: 0.0031\n",
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      "Epoch 6/100, Loss: 0.0010\n",
      "Epoch 7/100, Loss: 0.0016\n",
      "Epoch 7/100, Loss: 0.0007\n",
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      "Epoch 7/100, Loss: 0.0015\n",
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      "Epoch 7/100, Loss: 0.0035\n",
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      "Epoch 100/100, Loss: 0.0002\n"
     ]
    }
   ],
   "source": [
    "# make LSTM model with input size 1, hidden size 64, and num layers 2\n",
    "lstm = LSTM(input_size=1, hidden_size=64, num_layers=2)\n",
    "# make GRU model with input size 1, hidden size 64, and num layers 2\n",
    "gru = GRU(input_size=1, hidden_size=64, num_layers=2)\n",
    "# use MSE loss function and Adam optimizer\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(lstm.parameters(), lr=0.001)\n",
    "\n",
    "# make training iteration\n",
    "num_epochs = 100\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    # for epoch in range(num_epochs):\n",
    "    for seqs, labels in train_loader:\n",
    "        # reshape the data\n",
    "        seqs = seqs.view(seqs.size(0), seqs.size(1), 1)\n",
    "        # make prediction\n",
    "        y_pred = lstm(seqs, num_layers=2, hidden_size=64)\n",
    "        # calculate the loss\n",
    "        loss = criterion(y_pred, labels.view(-1, 1))\n",
    "        # backpropagation\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        print(f'Epoch {epoch+1}/{num_epochs}, Loss: {loss.item():.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10ea69ef",
   "metadata": {},
   "source": [
    "### 6.2 Model Evaluation Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dacc2481",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 1388.8043212890625\n"
     ]
    }
   ],
   "source": [
    "# mse = torchmetrics.MeanSquaredError()\n",
    "\n",
    "# set the model to evaluation mode\n",
    "lstm.eval()\n",
    "# make prediction\n",
    "with torch.no_grad():\n",
    "    for seqs, labels in test_loader:\n",
    "        seqs = seqs.view(seqs.size(0), seqs.size(1), 1)\n",
    "        y_pred_log = lstm(seqs, num_layers=2, hidden_size=64)\n",
    "        # invers log transformation\n",
    "        y_pred = scaler_y.inverse_transform(y_pred_log.detach().cpu().numpy())\n",
    "        y_true = scaler_y.inverse_transform(labels.view(-1, 1).cpu().numpy())\n",
    "        rmse = root_mean_squared_error(y_pred, y_true)\n",
    "\n",
    "#rmse = sklearn.sqrt(mse.nump)\n",
    "print(f\"Test RMSE: {rmse}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98abc8bf",
   "metadata": {},
   "source": [
    "LSTM Test RMSE: 1118.783447265625  \n",
    "GRU Test RMSE: 21445.294921875"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e11cce4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'True Price vs Predicted Price')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eJ5nuVR8jR45U08bkQF0CJsGSq6rOPvtstaaFCxeqwJRkv0gQ5qyzzsK1116rfq6pSVBHStjeeust1Th52rRpqimxBNUk6CcZY5I9VpVkRUnDbXkOJaAjgQgJQtx3330qwFJXBx98sBrxLn2WpERKnpeUlBR06tRpt0y2W265Rb1GErCRYJVksUnwTYI4QoIfEiB55JFH1MQ3CRJK8KlVq1Yq2CXT76qSfVgyvGRimLz+kkEjgRZ5LiQzyWiBISGliZLF9+CDD6rAmQS0JBgnJYG7ZmvV9/kgIiKKFM1fn86NREREREREREQUM9hjiIiIiIiIiIgoTjEwREREREREREQUpxgYIiIiIiIiIiKKUwwMERERERERERHFKQaGiIiIiIiIiIjiFANDRERERERERERxioEhIiIiIiIiIqI4xcAQEREREREREVGcsui9ACPIz8+Hx+NBtMrOzsa2bdv0XgYZGPcRqg33EaoN9xGqDfcRqg33EaoN9xGqDfeR+rFYLMjIyKj9evW83ZgkQSG3241opGla6DH4/X69l0MGxH2EasN9hGrDfYRqw32EasN9hGrDfYRqw30kclhKRkREREREREQUpxgYIiIiIiIiIiKKUwwMERERERERERHFKQaGiIiIiIiIiIjiFANDRERERERERERxioEhIiIiIiIiIqI4xcAQEREREREREVGcYmCIiIiIiIiIiChOMTBERERERERERBSnGBgiIiIiIiIiIopTDAwREREREREREcUpBoaIiIiIiIiIiOIUA0NERERERERERHGKgSEiIiIiIiIiojjFwBARERERERERUZxiYIiIiIiIiIiIKE4xMEREREREREREFKcYGCIiIiIiosjx+5F6223AY4/pvRIiIqqBpaYziYiIiIiIwsG6bBmSXnpJbdtatkR53756L4mIiKpgxhAREREREUWMec2a0Hba9ddDKynRdT1ERFQdA0NERERERBQxln//3bm9cSNSHnxQ1/UQEVF1DAwREREREVHkA0MDB6qTpFdfhfXnn/VdFBERhTAwREREREREkQ8MTZiAsjPOgOb3I/266wCXS++lERERA0NERERERBRJ5mBgaL/9UHT77fBmZ8O6ejVSnnpK76UREREDQ0REREREFClaYSHMeXmBbzp1gj8jA4X33KO+TX72WVj+/FPfBRIREQNDREREREQU2TIyb04OkJystl0jRsB53HHQPJ5ASZnHo/MqiYjiGwNDREREREQUEZa1a9WpZ599dp6paSpryJeaCtuSJUh66SVEM8uKFdBKSvReBhFRgzEwREREREREEe0v5K0aGALga9ECRbfdprZTHn54Zx+iKJP83HPIGTQIOX37ImHePL2XQ0TUIAwMERERERFRRFjWrNk9Y6hS2Vlnofyoo2ByuZB+ww2A349okjBrFlLuu09tm7dsQdaoUUibNAlaaaneSyMiqhcGhoiIiIiIKLI9hjp23P1CTUPBww/DZ7cjYeFCJL77LqKFNM3O+N//oPn9KB01CiXjxqnzk958E9mDB8P20096L5GIqM4YGCIiIiIioogGhmrKGBLe9u1RLNlCAFLvugumzZthdKZt25A5ZgxMZWUoP/poFN57L4ruugvb338fntatYVm3DlkjRyJVpq+5XHovl4ioVgwMERERERFR2Gn5+TAVFKhtb4cOe7xe6YUXoqJXL5iKi5F2883GLikrL0fmhRfCsnGjCnblTZkCWK3qooqjj8a2OXNQduaZKpMoefJkZA8fDusff+i9aiKivWJgiIiIiIiIIjaRzNuiBfyJiXu+otmsSsr8FgscM2fCPm0aDMnvV72QbL/8Al9aGna89hr8GRnVr5KaioLHHsOOV1+Ft1kzWP/+G82OPx7Jjz8OeDy6LZ2IaG8YGCIiIiIioiYvI6vK07UrSi6/XG2n3XILtLw8GHECWeJHH8FvNiN/yhR4O3Xa43XLhwzBtq+/hnP4cGgeD1IfeQTNTjoJllWrmnTNRER1wcAQERERERGFXX0CQ6L48svh7twZ5u3bkXbnnTDcBLL771fbhXfdhfJjj631Z3xZWch/4QXkP/20yjCy/f47socNQ9KLLwI+XxOsmoiobhgYIiIiIiKisDMHJ5LVMTCEhIRASZmmqcychHnzYLgJZKNHo2zMmLr/sKbBOXIkts6dC1e/ftBcLqTdcQeyzjgD5vXrI7lsIqI6Y2CIiIiIiIh0zxgS7oMPRunYsWo77YYboJWWwlATyO66q0G342vZEnlvvYWC+++Hz+FAwqJFyB44EInvvmvsZttEFBcYGCIiIiIiovDy+xsUGBLFEyfC07atmvyV8uCD0I3LVX0C2fPPhyaQNYimoWz0aDW5rPyQQ2AqLUX6ddch8/zzYcrNDefKiYjqhYEhIiIiIiIKK5OMqi8sVNve9u3r9bP+pCQUVgaEkl55Bdaff47IGus9gSw9PSw37e3QATs+/hiFt9wCv80G+9y5yBkwAPbPPw/L7RMR1RcDQ0REREREFJn+Qi1bwu9w1Pvny/v2RdkZZ6i+PunXXw+Ul6PJJ5B9/LGaQJZXywSyBjGbUXrJJdg2fToqDjwQpoICZF5yCdIvvdSQE9mIKLYxMERERERERGHV0DKyqgpvuw3e7GxYV61CylNPoanYZ86sNoGsog4TyBrKs//+2P7FFyi+6ioVhEr87DPkDByIhLlzI3afRES7YmCIiIiIiIgMFxjyZ2Sg8J571HbyM8+o6WCRZlm+HOmXXRaYQHb++fWbQNZQNhuKr78e2z/7DO5994V561ZkjR4daL5dUhL5+yeiuMfAEBERERERRaSUzNOxY6NuxzViBJzHHQfN41GNmuHxIKITyC64YOcEsjvvRFNy9+qFbTNnouTCC9X3SW+/jexBg2BbtKhJ10FE8YeBISIiIiIiikjGkLcRGUOKpqHw3nvhS02FbckSJL30EiI2gWzcuMAEso4dGz+BrKEcDhTdeSe2f/ghPG3awLJ+PbJOPx2pEqRyuZp+PUQUFxgYIiIiIiKiyIyq79Ch0Tfna94cRbfdprZTHn44lI0U9glkv/4a9glkDVVx5JFqrH3pWWepsrbkF15A9rBhsC5Zouu6iCg2MTBERERERERhY9qxA6biYvg1DZ56jqrfk7KzzkL5UUfB5HKpII4EcyI2gWzffWEE/pQUFD76KHa8+mqoCXezE05A0uTJei+NiGIMA0NERERERBT+UfWtWgF2e3huVNNQ8PDD8NntSFi4EInvvht1E8gaqnzIEGz7+ms4jz8emteLtHvugXnjRr2XRUQxhIEhIiIiIiIyXn+hXXjbt0exZAsBSL37bpi2bIm+CWQN5MvMRP6UKaFm3uY1a/ReEhHFEAaGiIiIiIjIUKPq96T0wgtR0asXTEVFSLvppgaXlFWbQHbMMU0+gaxBpDSvXTu1yYwhIgonBoaIiIiIiCgqAkMwm1HwyCPwW61wzJwJ+7RpjZ9ANmWKPhPIGsDburU6tWzYoPdSiCiGMDBERERERERh7zEUkcCQ3O7++6PkssvUdtott0DLy4vqCWT14W3bVp2a16/XeylEFEMYGCIiIiIiorCPqg93j6Gqii+/HO7OnWHevh1pd91V559LfvZZQ04gqytvmzbqlKVkRBRODAwREREREVFYmLZvh6m0FH6TKdQPJyISEgIlZZqGxA8/RMK8eXWbQPbAA4aeQFbnwBAzhogojBgYIiIiIiKisAhlC0kvnISEiN6Xu08flI4dq7bTbrgBWmlp3SaQjRlj6Alke+MJBoY2bwY8Hr2XQ0QxwtKYH/7000/xzjvvYPjw4RgzZgy2bt2KyyrrfXd19dVX44gjjlDb27dvx4svvojly5fDbrejb9++OOecc2A2m0PXl8veeOMNrF+/HllZWTj11FPRr1+/arc5Y8YMfPHFFygoKED79u0xduxYdOrUqTEPiYiIiIiIDNpfaFfFEyfCPmsWLOvXI+XBB1FUQ1mZmkA2Zkx0TSDbA1/z5qrxtuZ2w5ybG2pGTUSkS2Bo9erVmD17tgrIBDVr1gwvvPBCtevNmTMHn3/+OXr16qW+9/l8uP/++5Geno577rkH+fn5eOaZZ1RQSIJDQgJMDzzwAAYPHozLL78cy5Ytw5QpU9TPHHTQQeo6CxcuVIGj8ePHY7/99sOXX36Je++9F0888QTS0tIa+rCIiIiIiKiBLGvWRLy/UFX+pCQUPvQQss4+G0mvvALnCSfAfcghu08g27Rp5wQyS6M+G9eXyQRvq1awrFunyskYGCIi3UrJXC4Xnn76aUyYMAFJSUk7b8xkUsGbql8//fSTyhSSzCCxZMkSbNiwQQV8OnTooAJGZ555JmbOnAlPZTrkrFmzkJOTg9GjR6NNmzYYNmwYDj/8cBX8CZo2bRoGDhyI/v37q+tIgMhms+Gbb75p/LNCRERERETGGlW/B+XHHouyM85QZWLp118PlJcHLqj8PlonkNXaZ4gj64koTBoULn/ppZdUQKdHjx6YOnXqHq+3Zs0arF27FuPGjQudt3LlSrRr104FjYIkC0huU8rG9tlnH6xatQrdu3evdls9e/bEa6+9prYlgCS3ffLJJ1cLSsnPyO3vidvtVl9BmqbB4XCEtqNRcN3Run6KPO4jVBvuI1Qb7iNUG+4jFFR1IlnV/SHS+0jR7bcj4ZtvYF21CilPPYWSG25A0jPPIHHqVDWBLP/55+Hr1AmxsIcGA0OWjRtj6neOf0eoNtxHDBQY+v777/Hvv/+qcrDafP3112jdujW6dOkSOk/6AVUNColg6ZdcFjzdtRxMvnc6naioqEBJSYkqSdv1duT7TZs27XE9n3zyCT766KPQ9xKEevDBB5GdnY1o16JFC72XQAbHfYRqw32EasN9hGrDfSTO+f3AunVqM/Oww4CWLZtuH5H7mjwZOO00pDzzDFLk/X3lBDLt6aeRdeaZiBldu6qTlB07kFLDcxzt+HeEasN9ROfAkDSNlqydW265RZVt7Y0EcBYsWKCaRhvFKaecguOPPz70fTDSuG3btlAZW7SRxyC/GFu2bIFf/hkT7YL7CNWG+wjVhvsI1Yb7SPTZsMGEl19OwvjxpWjVyheW2zTl5qJ55aj6LdJGQiZnNeU+cuSRyDjuONinTwduvlmdJRPIiqTKoMpaop1DWnZICd3KlciLocfFvyNUG+4j9WexWOqUCFOvwJCUbxUWFmLixImh8yRzZ8WKFWpCmEwok5Iu8cMPP6C8vFxNHNs1q0caV1cltxm8LHgaPK/qdaTsSwJSqamp6n6CGUZ7y0aqymq1qq+aRPuOJeuP9sdAkcV9hGrDfYRqw32EasN9JHo8/3wSXn45GatWWfDmm3lhuU1zsPF027ZqcpbKIGrifaTg3nuR8/33MBUV7ZxAFmP7pKey4bT0GIrF3zf+HaHacB8Jv3oFhqSHzyOPPFLtvMmTJ6NVq1Y46aSTQkGhYBnZwQcfrII4VXXu3Fn1JZJAT7BcbOnSpSroI02khUwZW7x4cbWfk+vIz6pFWyzo2LGjmlZ26KGHhgJU8r00qiYiIiIioj1bsyZwGPD113YsXmxFr147+3BGU+Ppmsa55734Iuxz5qD46qujewLZHkjgTZg3bpSDIDWpjIioMer1V0SCN9I4uupXQkICUlJS1HaQpHZJFpFMDduVNJGWAJCMqJfG1L///jvee+89DB06NJTNM2TIEDWy/q233sLGjRvVxLJFixZhxIgRoduRkrC5c+di3rx5asqZNK+WDKV+/fo16gkhIiIiIop1a9fuDJg89lhKWG7TbIDAkKg4+mgU3XEH/Lv0LI0V3hYtVLmeVlEB07Ztei+HiGJARELoki2UmZmpppbtSrKKJk2apAI50qtIAktSbiYj64NkVL1c5/XXX8dXX32FrKwsXHzxxWp6WdCRRx6JoqIifPDBB6qErEOHDrjpppv2WkpGRERERBTvvF7pMWQOfS9ZQ7//bsVBB7nDM5GsQ4dGr5H2wmpVwSHLpk0wr1+vsqSIiHQNDN1xxx27nXfOOeeorz2R5kc33njjXm+3W7dueOihh/Z6HSkbY+kYEREREVHdbdpkhtutwWbz4/jjnZg6NVFlDb3xRl7Ul5LFCyknU4GhjRvhPvhgvZdDRFGOBalERERERHFk7dpAtlDbth5cfXUxTCY/5s61Y8mSmoe01InPZ5hSsnjgrWxAbdmwQe+lEFEMYGCIiIiIiCiOrFsXKBpo396Ljh29GDnS2eheQzKq3uRywW82h5ojU+R4K4f2SCkZEVFjMTBERERERBRH1q0LZAx16OBRp1deGcgamjPHjqVLrY3rLyRBocqBMtREk8mIiBqJgSEiIiIiojicSCYZQ0Kyhk45pXFZQ6H+Qh07hm2dVIeMIZaSEVEYMDBERERERBSXpWSBjKGqWUOzZ9vxxx/1z/hh4+mm5ansMaRKyfx+vZdDRFGOgSEiIiIiojghMYSdpWSBjCGx775enHxyMGsoud63G2w8zVH1Tdt82uR0wpSfr/dyiCjKMTBERERERBQn8vJMKCkxQdP8aipZVcGsoVmzHFi2LJBVVFfMGGpidju8OTlqk+VkRNRYDAwREREREcXZqPoWLXwSW6imU6eqWUP16DXk88Gybp3aZGCo6XAyGRGFCwNDRERERERx1l8oOJFsV1deWaKyhmbOrHvWkGnzZmgyqt5iCQUrKPLYgJqIwoWBISIiIiKiOBHsL1S18XRVnTp5cNJJgayhxx9Pqd+o+nbtAEv9StCo4TwMDBFRmDAwREREREQUp6Pqa3LVVSWqB9GMGXXLGmJ/IX0wY4iIwoWBISIiIiKiOFFbxtCuWUNPPJFS98AQJ5LpEhiyMDBERI3EwBARERERUdz1GNpzxlDVrKHp0x1YvtxSp1H1no4dw7hSqg0zhogoXBgYIiIiIiKKA2VlGrZurT1jSOy3nwcnnli3rKFQjyGWkukSGDIVFUErKtJ7OUQUxRgYIiIiIiKKozKy9HQf0tP9tV4/mDX01VcO/PnnHrKGvF6OqteJPykJ3owMtc2sISJqDAaGiIiIiIjiqIystmyhoM6dPTjhBNdeJ5SZZVR9RQX8Nhu8rVuHcbVUFywnI6JwYGCIiIiIiCgOrF0bLCPbe3+hqq66qnivWUPmNWvUqUdG1ZsDt09Nx9u2rTplA2oiagwGhoiIiIiI4kB9M4ZEly4eHH+8a4+9hkL9hQwykczvB+67LwWHH56Dt95KhM+HmBbM0mLGEBE1BgNDRERERERx1GOoQ4e6B4aqZg19+aUDK1ZYah5Vb5D+Qi++mIRnn03B+vUWTJyYjlNOaYa//tr7VLVYyBgyr1+v91KIKIoxMEREREREFFcZQ3UvJRP777/nrCEjBYZmzUrAXXelqu0TTnAiKcmHX36xYejQbDzwQAqcgSFrsdljaONGvZdCRFGMgSEiIiIiohjn8QAbNtRtVP2esobEtGmOahk4ZoMEhpYts+B//8uA36/h3HNLMXlyPr75ZiuGDnXC49Hw9NMpGDgwB/PnJyCWeFhKRkRhwMAQEREREVGM27jRrAIkCQl+tGhR/8Y7gawhZ/UJZTKq/r//ApsdO0IvubkmjBmThbIyE4491oV77imEpgGtW/vwyiv5ePnlPLRo4VUZU2efnYXLLkvH9u2m2Col27EDWlmZ3sshoigVG38RiYiIiIio1jKydu08MDXwCCCYNfTll3aVNSTlS5rbDX9CArytWkEPZWUaxozJxObNZuy3nxtTpuTDaq1+nWHDXJg3byvGjStRvZI++SQRffvm4J13or85tT8tDb6UQKCO5WRE1FAMDBERERERxbiGjKrfVdeuHowY4VTlWtJrKNRfqH17NDja1AgS1LnyynQsXWpDZqYXr7+eh7Q0f43XTUnx4667ijBt2nYceGAFCgpMuP76dJx2WhZWrrTERp8hNqAmogZiYIiIiIiIKMY1ZFR9Ta6+OthryI4dP6xT2x6dRtVLQ+mvvnLAZvOrkrG6BL0OOsiNL7/cjttuK4TD4cOPPyZgyJBsPPRQClyB/trRGxhinyEiaiAGhoiIiIiI4mZUfcMzhoJZQ8OHB7KG/poWCER4dWg8/f77DjWWXjz6aAEOOaSizj9rsQATJpRi3rxtGDTIBbdbw5NPBppTf/edDdHGw8lkRNRIDAwREREREcW4tWvDkzFUNWvIvEafiWQLF9pwww3pob5HI0c2bA59mzZevPZaHl54IQ/Nm3vVc3TWWc1wxRXp2LEjeg6TWEpGRI0VPX/xiIiIiIio3vz+nRlD4QgMHXBAIGuoE1Y3eWDon3/MGD8+U01YO/FEJ667LhCkaiiZXjZiRKA59QUXBJpTf/xxIo49NkdlJclzFy2BIQtLyYiogRgYIiIiIiKKYTKaXUa5S9CjbdvGlZIFXX15Pjpijdpere2HppCfr+H887NU4+jevSvw2GP5KrATDqmpftxzTxE+/3w7unZ1q/u45poMnH56FlavtkRHxhBLyYiogRgYIiIiIiKKg4lkrVp5kZAQntvsnroWVnjghB0PvR35wFBFBVSm0L//WtCmjQevvJIHhyP899O7txvTp2/DLbcUwm73YdGiBAwenI1HHzVuc2pv27bq1LxlC1BervdyiCgKMTBERERERBQXE8nCky0kgqPq/8G++PTzpIhm1Ug516RJ6SpIk5zsU2Pps7N9Ebs/qxW45JJAc+oBA1yoqNDw2GMpGDw4B99/b7zm1L7MTPjsdrVt3rRJ7+UQURRiYIiIiIiIKA4CQx06NL6/0K6BoaKcjmpC2RNPJCNSnnsuGe+/nwiTyY8pU/Kx//7hexx7I2V3b7yRh8mT85CT48WaNRaccUYzXHVVOvLyDHQYpWkcWU9EjWKgv2hERERERBSpUrJwZgyZKwNDLfu2U6effuqISNbQl1/acd99qWr77rsL0b9/05ZKSQ+jE08MNKcePbpU9Wn68ENpTp2NDz4wTnPqYDkZG1ATUUMwMEREREREFBelZOHPGMo4pD2GDnWqrKEnnwxv1tCSJVY1Ol6MHVuCMWPKoJe0ND/uv78Qn366Hfvv70Z+vhlXX52BM87IUpPS9OZt3VqdMmOIiBqCgSEiIiIiohgWHFXfoUP4ewzJqPqrry6pkjUUniDJxo0mjBmTCZfLpPr83H57EYzg4IPdmDFjG268sQh2ux8LFyZg0KAczJgR6PGjl1Ap2fr1uq6DiKITA0NERERERDGqpETD9u3m8GYMud2hAIQEhrp3d2PIECd8PskaSmn0zZeWahgzJgtbt5pVds5zz+XDYqCJ8dKc+rLLSvD111tx1FHlqjn1lClJxphMxpH1RNQADAwREREREcV4tlBGhhepqeFpiCNBIc3rVZOwfM2bq/OuuWZn1lBjSqu8XuDSSzPw559WZGd71QSylBSDNPLZhfRsmjQpkMm0caO+5WQelpIRUSMwMEREREREFPMTycJfRubdZx/AFDickKyhwYNdjc4auvvuVMyZY1dlWq+8koc2bcK37kho3Tqwvi1bzJJIpX/G0ObNgKdpprYRUexgYIiIiIiIKMYzhiLReFrKyKq65ppidfrJJw3LGnrzzUS8+GKggfXjj+ejd28dIy11lJ3tg9XqVwGx3Fz9soZ8OTnw22wqk8u8ZYtu6yCi6MTAEBERERFRjFq7NjiRLDKNp6vq0cONQYMCWUNPPVW/rKH58xNw881pavuGG4rUiPhoIAlTrVp59S8nM5ngbdVKbbKcjIjqi4EhIiIiIqIYFYlR9eaqpWS7CGYNTZ3qwJo1dQuUrFplwYQJGfB6NZx2WhmuuCLQryhaBMvJ9O4zxMlkRNRQDAwREREREcWoiIyqX7u2xowh0bOnGwMH1j1raMcOE0aPzkRRkQmHHVaOhx4qgKYhqhglMOQJBoaYMURE9cTAEBERERFRDJJmyMFgRdgyhioqqo2qr0nVrKF//91zsMTlAsaOzcR//1nQoYMHL72Uj4QERB3DZQxxZD0R1RMDQ0REREREMWjDBrMqz7LbfWje3BeW2zT/9x80nw++xETV8LgmBx3kxoABLnXfe5pQ5vcD112Xjl9+sSEtzafG0mdmhmeN8R4YsrCUjIjqiYEhIiIiIqKY7i/kDVt5VmhUfYcO2NuN1pY19MQTyfjkk0RYLH48/3weOnWK3hHrRgsMsZSMiOqLgSEiIiIiohi0dm3TjarfVa9eO7OGdu019NlndjzySKravu++QhxzTAWiWTAwJBlakgmle2Bo0ybAF53ZV0SkDwaGiIiIiIhiPGOoqQND4uqrA1lDH3/sCAWpfvnFiquvzlDbEyaUYNSoMkS74Lj60lITior065ztbdkSfrMZWkUFTFu36rYOIoo+DAwREREREcX0RLIwjqoPTiTr2LHW6/bu7Ub//sFeQ8mQH73gggyUl2sYMsSJm28uQixITPQjM9MA5WQWC7wtWqhNlpMRUX0wMEREREREFIMimTHkrUPGUNWsoY8+cmDoUBlPb0a3bm4880wBzPq25InNPkNt26pTCwNDRFQPDAwREREREcUY6XUTzBgKW4+h8vLQKHSPNJ+ugz593OjXL5A1tHIl0KKFF6+9tgNJSTo244nlwFDr1uqUGUNEVB8MDBERERERxZitW01wOk0wmfxo0yY8GUOW4Kj6pCT4srPr/HPBCWWJicBrr+WhVavYa4wcDAxt2sTJZEQUfQL5pUREREREFHNlZBKwsNnCc5vmqo2n9zKqvqasoQ8/3IEuXbLQrJlH18ldkW5ALZPJjFBKxsAQEdUHM4aIiIiIiGJ2VH0Y+wutWVOv/kJVHXVUBbp3R8zaWUqm7+fuHpaSEVEDMDBERERERBSzjac9uoyqjzeG6TFUtZQsFlOziCgiGBgiIiIiIorZUfVhzBgKjqpnYGiPgaHcXBPcbv2bT5ucTpjy8vRbCBFFlUblOn766ad45513MHz4cIwZMyZ0/sqVK/Huu+9i9erVMJlM6NChA26++WbYKgucS0pK8Morr+DXX3+Fpmk47LDDcMEFF8But4duY926dXj55Zfxzz//IDU1FcOGDcNJJ51U7f4XLVqE999/H9u2bUOLFi0watQo9O7duzEPiYiIiIgo6q1dG/6MoWo9hqia7GwfbDY/Kio05Oaaw9bwu94SEuBt3hzm3FyVNeTLytJnHUQUHxlDEvSZPXs22rdvX+18CQrde++96NmzJ+677z7cf//9GDp0qAoABT311FNYv349brnlFkyaNAkrVqzA888/H7q8rKwM99xzD5o1a4YHHngA5557Lj788EPMmTMndJ2///4bTz75JAYMGIAHH3wQhxxyCB5++GH8999/DX1IREREREQxIeyj6l0umDdtanCPoVhnMu1sQK17OVmwz9D69bqug4hiPDDkcrnw9NNPY8KECUhKSqp22euvv47jjjsOJ598Mtq2bYtWrVrhyCOPhNVqVZdv2LABv//+Oy6++GLst99+2H///TF27FgsXLgQeZXpjgsWLIDH48Gll16qbuOoo45Stzlt2rTQ/Xz11Vc46KCDcOKJJ6JNmzY466yz0LFjR8yYMaNxzwgRERERURQrLtaQlxfeUjLLunXQ/H74UlKYhbIHRgkMeTiZjIiaIjD00ksvoVevXujRo0e18wsLC7Fq1SqkpaWpbKDx48fj9ttvx19//VUto0iCSfvuu2/ovO7du6uMIslCCl6na9eusFh2VrpJBtKmTZtUGVrwOvJzVcl15P6JiIiIiOI9Wygry4vkZH/4G0/XY1R9PPYZ0n1kfbAB9caNuq6DiGK4x9D333+Pf//9V5WI7So3N1edStnXeeedp3oLffvtt7jrrrvw6KOPomXLligoKFA9g6oym81ITk5Wlwk5zcnJqXad9PT00GXB60oAqir5PngbNXG73eorSIJRDocjtB2NguuO1vVT5HEfodpwH6HacB+h2nAfMZZ166yhbKFwvSbBwJCUkTXkNuNhHwkGhjZtMuv6OH2VGUOW9euj6vmOh32EGof7iEECQ9u3b8drr72msoGCjaSr8leORBw0aBD69++vtvfZZx8sW7YM33zzDc455xzo6ZNPPsFHH30U+l7WJv2JsrOzEe2k+TbR3nAfodpwH6HacB+h2nAfMYbgMKr997epD2bDovIDYEePHnA04jZjeR/p1i1wun17Elq2rN5uo0lVVnXYc3PD9/o3oVjeRyg8uI/oHBhas2aNKhebOHFi6Dyfz6eaR0tvnyeeeEKdJz1/qmrdurUKKgUzf4qKiqpd7vV6VYlYMCtITnfN/Al+X/U6spaq5Pvg5TU55ZRTcPzxx4e+D0YaZaqZ9DSKRvIY5Bdjy5YtocAcUVXcR6g23EeoNtxHqDbcR4zljz8kqz4ROTnF2Lw50IahsTL//BMJ8p48OxvOzZvr/fPxsI8kJckH51lYs8aNzZsDxz56sCQmQj729q1di9wGvFZ6iYd9hBqH+0j9SXueuiTC1CswJD19HnnkkWrnTZ48WTWYllHyzZs3R0ZGhuoFVNXmzZtVo2jRuXNnlJaWqiCTNIsWklEkL2ynTp1C15Fx9xKsCfYZWrp0qbofKSMLXuePP/7AiBEjQvcj15GG1nsiDbCDTbB3Fe07lqw/2h8DRRb3EaoN9xGqDfcRqg33EWNYu3bnRLJwvR6WNWvUqbt9+0bdZizvI61be0I9hnw+v26tmDyVU8lM8mF8QQH8u7TfMLpY3kcoPLiP6Nx8WvrxtGvXrtpXQkICUlJS1LZE8GRK2PTp0/HDDz+oSN57772HjRs3qrHywWwiCRLJeHppNi2NqV955RU1uSwzM1Nd5+ijj1YBoSlTpqix9jKxTG6zarbP8OHDsWTJEnzxxRfq9j/44AP8888/GDZsWLifIyIiIiKiqGs+Ha6JZJrTCXNl5om38oNd2l3r1j51WlpqQlGRfj1Q/ImJ8FYeV3EyGRFFpPl0bSSDRxo8y9h6KQ9r3749br311mp1gFdccQVefvll1ZRagkmHHXaYGlkflJiYqPoYyXUmTZqkAk+nnnqq6l0U1KVLF3U7EniS7CKpn73++utVgIqIiIiIKB5VVASaHwczhsLBvHatOvWlpcGXkRGW24xFDocfmZle5OWZ1cj6tDSPrpPJzHl5ajKZJ9j8iIgoUoGhO+64Y7fzTj75ZPW1J1IOduWVV+71diWgJIGjvTniiCPUFxERERERAevXSxmThsREH7KzAxksjcVR9fWbTCaBISknO+AAfQNDWLpUTSYr120VRBSTpWRERERERGRc69YFPvdt315G1SP8gSGq88h6PanAEEvJiKiOGBgiIiIiIoqx/kLhKiOrWkrmZWCozoEhKSXTk7dtW3XKwBAR1QUDQ0REREREMWLt2p0ZQ+ESyhjq0CFstxmrWrUyRmDIw4whIqoHBoaIiIiIiGKulCx8GUMsJau7Nm2CgaGwz/ipF2/lyHoGhoioLhgYIiIiIiKKEWEfVV9WBvOWLWqbgaEoKiULZgzl5anXkIhobxgYIiIiIiKKAT4f8N9/4c0YMldmC/nS0+HnqPo6B4Zyc01wu/Vbhz8tDb7UVLXNrCEiqg0DQ0REREREMUCCES6XBrPZHwpQNBbLyOqnWTMfbDY/fD4NW7bonDXEcjIiqiMGhoiIiIiIYqi/kPS5sVrDc5sMDNWPyWSgBtTByWTr1+u6DiIyPgaGiIiIiIhiQCRH1Xs6dgzbbcY6owSGQn2GNm7UdR1EZHwMDBERERERxYBIjqr3clR99DWgZikZEdURA0NERERERDE1kYyj6o0xsl7nwFBlKZmFpWREVAsGhoiIiIhiTXk57NOmAU6n3ishHXoMhStjSCspgXnrVrXNwFD9M4Y2bWIpGRFFBwaGiIiIiGJM2u23I3PCBCRPnqz3UkiXUjJPWPsLeTMz1fhzirJSsmDz6dxcwOXSdS1EZGwMDBERERHFENPWrUj84AO1nbBwod7LoSZSWKihoMAU1owhy5o16tTLbKF6adUqEJjbsMEMv1+/dfgyMuBzONS2edMm/RZCRIbHwBARERFRDEl65RVo5eVq27p0KeANXyNiMn4ZWXa2F0lJ4YlGsL9Qw7Ru7VOnpaUmFbDTjabtLCdjA2oi2gsGhoiIiIhihPSESXrjjdD3ptJSWFat0nVN1DTWrjWHfyJZcFQ9J5LVi8PhR2amscrJLAwMEdFeMDBEREREFCMS33kHpsJCeDp2RPnhh6vzrL//rveyqEkbT4dvIpk5mDHUsWPYbjNeGKbPEEfWE1EdMDBEREREFAvcbiS/8ILaLLnkElT07q22bYsX67wwivZR9ewx1PCR9YaZTMbAEBHtBQNDRERERDHA8dlnMG/eDG9ODspGjoT7oIPU+QwMxdtEsjCNqi8uhnn7drXNHkP116qVMTKGPMHJZAwMEdFeMDBEREREFO38/tBo+tJx4wC7HRW9eqnvLX/9Bc3p1HmB1FQZQ+EqJQtlCzVrBn9KSlhuM56wlIyIogkDQ0RERERRLuGbb2D96y/4kpJQet556jxfy5bwNm8OzeuFddkyvZdIESRD6DZvDpaSecPbX4jZQo0MDAUyuXQvJduyBfCEr8yQiGILA0NEREREUS75uefUadm558KflhY4U9NQUVlOZmU5WUxbv94Cv19DUpIPWVmBUelhyxjiRLJGBYY2bNA3Y8iXkwO/zaYCxFJqSkRUEwaGiIiIiKKYBH0SFi2C32JBiZSRVcE+Q/E3ql7TENbAEDOGGhcYys01SV94/ZhM8LZqpTZZTkZEe8LAEBEREVEUC/YWcp58MnyV/USCgn2GOLI+PkbVR2IiGQNDDdOsmQ82m19lcm3ZonOfoWAD6vXrdV0HERkXA0NEREREUUr6wNinT1fbJRdfvNvl7p491anlv/9g2rGjyddHTd14Ojz9har1GOrYMWy3GU9MJgNNJgv2Gdq4Udd1EJFxMTBEREREFKWSX3gBms8H14AB8HTtutvl/tRUuDt1UtvsMxQPo+rDkzGkFRbCnJenttljqOE4mYyIogUDQ0RERERRyLR9OxI/+EBtl1x66R6vF+ozxHKymBWxUfXSuDg5OSy3GY8MExiqLCWzsJSMiPaAgSEiIiKiKJT06qvQXC7VR6ji8MP3eD32GYptPl9gKlk4R9Wzv1CMBYZYSkZEtWBgiIiIiCjKaKWlSHrtNbVdcsklajT9nrgrA0NqMpnf32RrpKaxebMJ5eUaLBZ/qKdNY5nXrlWnLCOLwcCQRBKJiHbBwBARERFRlEl87z2YCgrg6dABrmHD9npdd9eu8Nts6vrBA36KvYlkbdp4YQlsNpplzRp1yoyhGAkMtWgBv9kMze2GKTdX17UQkTExMEREREQUTdxuJD3//M5JZOZaDjptNri7dQtsspws5nBUvXG1auUJBYZ0TdazWOBt2VJtsgE1EdWEgSEiIiKiKOKYNg2WjRvhbdYMZaedVqefqejdW51yMlnsWbs2/KPqGRgKj9atA2VbpaUmFBbuudyzSRtQs88QEdWAgSEiIiKiaOH3I/m559Rm6dixgMNRpx/jZLLYzxgK26j6/HxVdii8DAw1isPhR1aWwUbWczIZEdWAgSEiIiKiKJEwfz6sf/4JX2IiSkePrvPPVVQGhqzLlgEVFRFcIek1qj7cE8lUX5rExLDcZjwzXANqlpIRUQ0YGCIiIiKKEsFsobJzzoE/I6POPyeZH770dGjl5bD+9VcEV0jRnjEUKiPjRLKwBoY2bdI3MOSpLCVjYIiIasLAEBEREVEUsC5dioQFC9R0odKLLqrfD2vazqwh9hmKGfn5GgoLTWHtMWSpnFzH/kLh0apV4HXZsCFMI+MaW0rGwBAR1YCBISIiIqIokDx5sjp1nnRS6CCvPthnKHazhZo396p+NuFgDpaSMTAUu6Vkuo5IIyIjYmCIiIiIyODM69bBPm3azhH1DcCModjtLxSuMjLBiWQxGhhq1Qp+TYPJ5YJpxw5d10JExsPAEBEREZHBJb34IjSfD65+/eDp1q1Bt+Hu1UudWlavhlZUFOYVkh7WrrWEd1S938/AUIwGhpCQAF/z5mqT5WREtCsGhoiIiIgMzJSXh8R331XbJZdc0uDb8TVrphrQan6/6ldE0S/cjadNMqq+sFBte9l8OizatAkEhnJzTXC79V0L+wwR0Z4wMERERERkYImvvabKPyp69EDFUUc16rbYZyi2hHtUvXnNGnXqbdkSfocjLLcZ77KyfEhI8MPv17BlCyeTEZExMTBEREREZFCa04mkV17ZmS2kaY26PfYZitVSMk94J5IxWyhsTCagZUsDNqAmIqqCgSEiIiIig3K8/z7M+fnwtG8P1/Dhjb69YJ8hZgxFP6cToQyUcGUMhfoLdewYltuj6n2GNmwwRmDIwsAQEe2CgSEiIiIiI/J4kPz882qz5KKLAEsgO6Qx3N27w282w7xlC0ybN4dhkaSX9esD+0NKig8ZGb6wjqpn4+nYbEDNjCEi2hMGhoiIiIgMyP7ll7D89x+8mZlwnnlmWG7Tn5gIT5cuaptZQ9Ft7dqdo+obWWG4W8aQl4GhsGJgiIiMjoEhIiIiIqPx+5E8ebLaLB07NqyNgCsqy8nYZyhWJpJxVH20BIY2bTJGYMhUXAytcvocEZFgYIiIiIjIYGwLFsD2xx/wORwoPf/8sN52aDIZA0MxMpEsTKPqd+xQAQO/pqmeVhQ+rVt7DJExJAFmb1aW2javX6/rWojIWBgYIiIiIjKYYLZQ2dlnw5+ZGdbbDmUMLV0KeMOUbUJRnzEUKiNr2RKw28Nym7R7KZnfD2M0oN64Ud+FEJGhMDBEREREZCCWZctg//Zb1SS6VJpOh5mnc2f4EhNhKimBZfXqsN8+Reeo+mDjafYXCr9WrQKBodJSEwoLw9QQqrF9hpgxRERVMDBEREREZCDJU6aoU+fxx8Pbtm3478BshrtHD7VpZQPqqCSJXuvXR2hUPQNDYSctwrKyjDWyng2oiagqBoaIiIiIDEI+xXd8/rnaLr3kkojdD/sMRbfNm81wuzVYrX60bMnAUDQwWgNqM0vJiKgKBoaIiIiIDCLpxReheb0oP+YYuLt3j9j9hPoMMWMoqkfVt23rlQSwsJaSeTp2DM8NkiFH1ntYSkZENWBgiIiIiMgAtLw8JL7zjtouufTSiN6XOxgYWrECcDojel8UucbT4ZpIVnVUPXsMRbbP0MaNgddOLywlI6KaMDBEREREZABJb7wBk9MJd7duKmMokrytWsGbnQ3N44F12bKI3hdFblR9uBpPm7Ztg6m0NDCqPhJ9rQht2hgjYygUGMrPh1ZaqutaiMg4GBgiIiIi0pvTiaRXXtmZLaRFeHKRpoXKyWwsJ4viiWRhHlXfujVH1cd4KZk/NRW+tDS1zawhIgpqVC7jp59+infeeQfDhw/HmDFj1Hl33HEH/vzzz2rXGzRoEC6qMm51+/btePHFF7F8+XLY7Xb07dsX55xzDsxViqTlsjfeeAPr169HVlYWTj31VPTr16/a7c6YMQNffPEFCgoK0L59e4wdOxadOnVqzEMiIiIianKJH34I844dqv+HTCNrCtKA2jFrFvsMRaFwZwyZ165Vpywji/3AUDAAaCosVIEhT5cuei+HiKI5MLR69WrMnj1bBWR2NXDgQJx55pmh7202W2jb5/Ph/vvvR3p6Ou655x7k5+fjmWeeUUEhCQ6JrVu34oEHHsDgwYNx+eWXY9myZZgyZYr6mYMqp2gsXLhQBY7Gjx+P/fbbD19++SXuvfdePPHEE0irjIITERERGZ7Xi+Tnn1ebpRMmAJam6UES7DPEyWTRxe+v2mMoTBlDa9aoU04ki3xgKDfXBLcbsFr1W4uUC1r//JMZQ0TUuFIyl8uFp59+GhMmTEBSUtJulyckJKggTvArMTExdNmSJUuwYcMGFfDp0KEDevXqpYJIM2fOhMcT+NRj1qxZyMnJwejRo9GmTRsMGzYMhx9+uAr+BE2bNk0FoPr376+uIwEiCUB98803DXlIRERERLqwT58Oy9q18KWno+yss5rsfit69lSnlnXrYMrLa7L7pcbJzzehuDjwFr5t2/BkDHFUfeRlZfmQkOCH369h82aD9BliYIiIGhMYeumll1RAp0ePHjVe/t1332HcuHG49tprValZeXl56LKVK1eiXbt2KmAUJFlATqdTlY2JVatWofsuI1p79uypflZIAGnNmjXVrmMymdT3wesQERERGZ7fj+TJk9Vm6QUXwF/lw7SI33VaGtz77qu2WU4WfaPqW7TwwuEIz20yMBR5JhPQsqUxyslULyl53RkYIqJK9c5V/v777/Hvv/+qcrCaHH300WjWrBkyMzOxbt06vP3229i0aROuu+46dbn0A6oaFBLB0i+5LHi6azmYfC/Bo4qKCpSUlKiStF1vR76X+9oTt9utvoI0TYOj8j+qbEej4Lqjdf0UedxHqDbcR6g23Ecix7ZokWr+7LfbUTZ2bJM/x1JOZv3nH7WGioEDG3w73Ef0GVUflufb79/ZY6hjx4i9htxHApPJpHH4pk0WaNrOY5Km5mvXLpQxZKTXg/sI1Yb7iEECQ9I0+rXXXsMtt9xSrW/Qro2mgyQzKCMjA3fddRe2bNmCFi1aQE+ffPIJPvroo9D3++yzDx588EFkZ2cj2un93JLxcR+h2nAfodpwH4mAl19WJ9rYsWh+4IFNf/99+wIffYSUP/9ESsuWjb457iORF6z669o1AS3D8JpBPlQtK1MpLTmHHSbNQRFJ8byP7LcfsGABUFSUjpYtq3/A3aQqe7baNm0Kzz4UZvG8j1DdcB/ROTAk5VuFhYWYOHFi6DzJ3FmxYoWaECZlY1LSVVVwSlgwMCRZPdK4uiq5TRHMAJLT4HlVryPZPRKQSk1NVfcTzDAKqikbqapTTjkFx1eZ9BGMNG7bti3U3yjayGOQ51WeX790IyTaBfcRqg33EaoN95HwWLnSgvx8DYcdFsgUsPz5J7KnT4ffZMK2c8+Fd/PmJl+TtWNHNJP3cz/8gFwJEDTwU1juI01n2TLJqk9ETk4xNm8uafTt2X78EVlSRtamDbbt2IFI4T4CZGQkA0jBX3+VYvPmIt3WoSUkQB1Wb9mCzVJGaLfDCLiPUG24j9SfxWKpUyJMvQJD0sPnkUceqXbe5MmT0apVK5x00km7BYXE2srUVMkcEp07d8bUqVNVoCdYLrZ06VIV9JEm0kKmjC3eZUKGXEd+NvjgOnbsqKaVHXrooaEAlXwvjar3xGq1qq+aRPuOJeuP9sdAkcV9hGrDfYRqw32k4fLyTDjhhCw4nRoWLtyqJhQlVfYWco0YAY9MedXhua3o2hV+mw2m/HyY1q2Dt4Zps0bbRxxTpyLp5ZdV+Z0/JQW+lJSdp6mpO0+Tk3f/PiUl0OwlBnoMtW/vDstzba4ykawpfr/j+e9I69aeUI8hPZ8Df0YGfImJMJWVwbRhA7yVvcaMIp73Eaob7iPhV6/AkARvpDxs1wlkKSkp6nyJ3C1YsAC9e/dGcnIy/vvvP7z++uvo2rVraKy9NJGWAJCMqB81apTK8nnvvfcwdOjQUNBmyJAhakrZW2+9paaOScBn0aJFmDRpUuh+JfPn2WefVQEiyUr66quvVJPrfv36heeZISIiIgqTZ59NDk2S+u03K9rhPzg++0x9X3LJJfotLCEB7m7d1Mh6aUDd2MBQxHm9SL37bpi3bm3wTQQDRL7UVPiTkwOnewswpaTA3b17IKhkoB5D7duHZ1S9ubLxtJeNpyOuVStjNJ+WzECZTGZauRKWjRsNFxgioihoPr3XG7NY8Mcff4SCNFlZWTjssMMwcuTI0HUkq0gCPDLZTHoVSWCpb9++amR9kIyql+tIUEluS27n4osvVtPLgo488kgUFRXhgw8+UMGlDh064KabbtprKRkRERFRU9u82YTXXksKfb9smRWjfn0RmseD8iOPhLtybLxeKg46SAWGbL/9BtdJJ8HozbolKORLT0fB/ffDVFwMrbh452lR0c7TkpKd38vlFRXqNkwlJUBJCcz1KN3zJyTA1a8fXCecANfgwSqgpAfJOMvNDWYMcVR9tJFMQbFhg2QMNbhyMywkMGRduZIj64koPIGhO+64I7Qt08juvPPOWn9GatxuvPHGvV6nW7dueOihh/Z6HSkb21vpGBEREZHennwyBS6XBpvNj4oKDWt+LUHikrfVZSWXXqr38uAONqKNgpH1jk8/VafO4cPhOvHE+v2wy1U9kFRUVKfAkmnbNjXW2zFzpvpSQaL+/eE6/vgmDxKtWxcICqWl+ZCREZ4yCgaGmj5jqKxMeqVqYXsNGxoYEub163VbAxHFaMYQEREREVXvB/Puu4lq+8Ybi3DnnWk4bPErMLnK4O7aFeUGKIGv6NVLnVqXLQPcbmnKCEMqL4fjq6/UprMhmU12O3zSZDc7G/UqwvL7YfnrLzi++EJ9WdasgWPGDPWlgkQDBgSCRIMGRTxItLOMLExDU3y+UCmZp0OH8Nwm7ZHDAWRlebFjh1mVk2VkePQPDDFjiIgkm1bvBRARERHFqkcfTYHHo2HAABdGjy5FktmJi1xP7+wtpGctSSXpLeNLS4PmcsHy998wqoRvv4WpsBDe5s1RccQRTXfHmgZP164ovuEGbJ0/H1tnz0bxlVfC07EjNAlWTZ+OjP/9Dy169kTGhRfC/tln0EpLI9x4Ojz9hUxbtsDkcsFvNsO7Sx9Riow2bQKv3aZN+vYZkil0wrxxo67rICJjYGCIiIiIKAL++suCTz5xqO0bbihWE6GvzXkdLZCLkszWcNa3FCpSTCZUVPY5kj5Dhi8jO+EEwKzTQbUEiQ44YGeQaNYsFF9xhSrDksCaBIkyL70ULXr0QMb48WEPEoU7Y8hSOT3Y27atcTPFYrTPkN4NqFlKRkRVMTBEREREFAEPP5wCv1/D8cc70b27W03UmlDymLps9oGXGepA3Oh9hrSyMthnzVLbzpNPhiFIkKhbNxRPnIit332HrTNnovjyy1VJlgoSffWVChI1DwaJPv9cPY5w9Bjq0CE8GUPsL6TnZDKLMQJDW7YESkiJKK6xxxARERFRmC1ebMWMGQ6YTH5cf32xOk8CG5nFq5CHDLyEC3EYAlOyjCDUZ8iggSF57kxOpwq6BINYhiJBogMPRLF8TZwIy/LlgZ5E06aprBwJEsmX9DgqHzhQZT3JqT8x0H+qrtauDXPGEANDcZsx5MvOVj2ypBxSJvSxlJAovjEwRERUC8uyZUj88EPVpLP04ovhbd1a7yURkcE98ECqOj39dCc6dQocxCdPmaJOn8Ol+OWvDAC5MIpgsMWycqWaxqXXOPY9cXzyiTpV5XcG6MtU5yDRpEmqqbd92rRA4+p16+D48kv15XM4qgeJpDPxXng8gTHn4QwMBRtPS58pavqR9boymeBt1UoFB6UBNQNDRPGNgSEiohpoBQXqQCTp3XdhXb48dH7iu++i5MorUXLRRUBCgq5rJCJj+u47GxYsSIDV6sc11wSyhbS8PNh++UVtT9EuwdatZuTmmtC8uQ9G4MvJgad1a1g2boR1yRJUHHUUjELLz1eNp4XzlFMQVTQN7u7d1VcoSBScbvbffyqjSL5UkGjQIDiPPx7lAwbUmEkkzYqlkXlCgh8tW/rCmzHEiWRNHhjSu/m08LRtGwoMEVF8Y2CIiCjI54NtwQIkvveeGkMs6dXCb7PBNXQoTFu3IuHHH5H6wANIfP99FN57L8r79tV71URkIH4/8OCDgWyh884rDU0gsv36qzp177svksw5wErgjz+saN488HfGKFlDEhiSPkNGCgxJCZbmdsPdtSs8nTsjalUNEt14I6x//LEzSLR+faD07Isv1FW9LVrA0769yuIInhbnd0IOeiO1TYYkezSez6cymARLyZo+MCSB4YoKwGbTby0cWU9EQQwMEVHck1GtjvffV8EeS5U3R3IQUnb22Sg75RT4MzPVEZ9j6lSk3n23+oQt65xz4Bw+HEV33MHyMiJSZs9OwOLFNjgcPlx+eUno/GC2UMUhh+DACjdWrrSqwNCgQcYJDFX07q1KnIzWZyg0jcwoTafDFSTq0UN9Fd90E6xLl6qgkJScSZBIGgKrpsA//hj6kcGVxYfOfxNhGdgOnnbt4G3fPhQ4UttyoC/j7+pA+spIk2y/xRKYSkZNolkzn8r6Ki/XsGWLGe3ahaeReEME37tUfe9DRPGJgSEiik/l5bDPnKmygxLmz4cmH/PLB6ipqergo+yss9Qb9mq9LDQNzlNPhWvwYKQ88giSXn1VfZKd8M03KLnqqkB5mZ4f/RGRrny+ndlC48aVIifHt1tgyH3wwehe7MbUqYGMISMJTSYz0Mh605YtsC1apLadJ52EmCRBop491VfRzTer0jnJ5DH/99/O07VrUfLHBmSUbIDDVwb89Resf/212035NQ2+qtlGEjDq0CEURPJlZYX+r4X6C0lQyMJDgqYiT79MJvv3X4tqQK1rYKgyIMiMISLifwEiiiuWP/9UwaDEjz+GqaAgdH75kUeq7CDXccfV2gDUn5qKorvuUsGjtJtvRsJPPyH1/vsD5WX33MPyMqI49dlnDvz1lxWpqT5ccsnObCGpFwmOgZeMoR7bA6OhDRcY6t4dfpNJZaqYNm+Gr2VLvZeksmgkcF/Rp098ZLVomspQdctX5aS4oPHjMzDnKxMeu3I5zjpkBczr1qk+RaEA0rp1MJWWqkwg+cIPP+x2877ExFCWkVxXsIxMn3KyYGBITywlI6IgBoaIKOZphYWqFEECQralS0PnSw+HsjPPRNkZZ6hPVOvLc8AB2DF1Khwff4zUe+6BZc2aQHnZiBEovP12+FheRhQ33G7gkUdS1LYEhdLTA1mIQhoOS8mOLz0dnn33RbfmgcDQpk0W7NhhQlaWMRpQ+5OS4OnSBdYVK2BbsgQuIwSGYrGMrIFkVH0FrEjp3R7l/VvsfgW/H6a8vJ0Bo7VrQ4EjOU+CRaayMphWrFCvcZDskxSfI+tDgaFNmwIpj2FpXkVE0YiBISKK3UbSixYFGklL41KXS53tt1rhGjJEZfuozB5zI9+USXnZaaep2wyVl335JRK+/prlZURx5P33E9WBe7NmXlVGVpXt55/VacXBB6u/GSkpfuyzj0dlDCxbZkXfvgbqM9SrlwoaWBcvhmvYMF3XIqVOkmklWUwy0j2eSbXzunWB/1cdOuyh9EjKyLKy1Je7d+/dL3e5VGZI1SwjraQEpePGRXj1ZNjAUPPmqseUNHc35eYaIkuQiPTBwBARxRTTpk1I/OAD9RWctiLcXbqoYJD0CFI9FsJsT+Vljg8+QJGUlx17bNjvk4iMwekEHn88kC10xRUlSEramS20a+PpoO7d3SowtHSpsQJDqs/QO+/Atnix3kuB47PP1Gn50UfDl52NeCaZZaWlJmiaH23behp2I3Y7vJ06qS/SV+vWHkMEhqS3lLdlS9XwXBpQVzAwRBS3mC9IRNGvokJNcsk87zw0P+wwpD78sAoK+ZKTUTpqFLZNm4Ztc+ei9KKLIhIUqqm8LP/JJ+Ft1gzWf/5B1tlnI+Oii2DauDGi901E+njjjSQ1XahVKw/OPbd0t1SPmgJDPXpUGLLPUEVlA2rrkiWB0hK9yBTIysAQy8ikjCwQQGjZ0ouEBL1XQ40lzacNERhinyEiqsSMISKKWpaVK5H4zjuqx485Ly90fvnhh6vMHdfxx9faSDoiguVlMr3s0Uerl5ddfTVKxo9neRlRjCgu1vD008lq+9pri3c7aFf9XbZuVWWsFTLpsNKBBwb6DEkpmZFIjyGfwwFTSQks//wDz3776bIOi5SzrVwJv82me0mbEaxbF3jL3r69fhOsKDKlZFImWHUAqm6BofXr9VsEEemOGUNEFHU0pxOpt92GnP79kfziiyooJHXyxZddhtzvvsOOjz+G8/TT9QkKVeFPS1PlZdtmzED5IYfAJOu+7z5kDxoE2/z5uq6NiMLjpZeSkJ9vRseOHpx2mnO3y0Nj6g88EKjyNykYGJID/oICHY8Kd2WxwF0ZwJI+Q3oJZgu5BgxQf0vj3c7+Qg0sIyNDZgyVlZl0//1nxhARCQaGiCiqWH//Hc2GDkXyyy+r751Dh2LH668j96efUHzjjfB27Aij8XTrhh2ffIL8J54IlZc1k/KyCRNUTyQiik55eRqmTAlkC11/fZHEVHYTajxdpYxMZGTs7BVjtKwh1WdI1q5XYEjKyILTyE46SZ81GIw0NhfMGIoNEiOWRvVGKCfztG2rTs0sdyeKawwMEVF0cLtVWVazE09UgRXJENrx1lvIf+UVlA8apD7lNjQpLzv9dGydPx8l48apKTuOadOQ07cvkp97TvVJIqLo8txzKSgpMaFbNzeOPz4w+XBXof5CMpFsF9KA2oiBoVCfod9/1+X+rb/8ohrh+pKSUD54sC5rMG4pGTOGYq2cbNMmnSeTtW6tTllKRhTfGBgiIsOzrFqlAkIpjz0GzeuF88QTsXXuXJT3749os1t5WVkZUu+9F9mDB7O8jCiKbNliwquvJqntiROLYKrhHZVWVATLX3/VmDFUNTBktAbU7l691Kn1zz/ViHPdysiGDtW9JNgoah1VT1EbGNqwwWKMUjLJGJKGR0QUlxgYIiLj8vmQ9NJLyB42DLalS+FLT0fec88hf/Jk+DMyEM12Ky9bvTpQXnbxxSwvI4oCTz6ZApdLwyGHlGPAgJrHzdt++w2a3w9P+/bw5eRETWBIDhTl75Lm8cC6fHnT3rnHA8cXX6hNTiMLKC3VsG1bIDDEjKHYYZTJZN5WreDXNJhcLph27NB1LUSkHwaGiMiQ5JOrrDPPRNrtt0NzueDq1w9b58yBK5b6TVQtLxs7NlBe9sUXO8vLPDwAIDJq9sY77ySq7UmTivc4USjUX6hPnxovDzagXrPGgpISAzWg1jTd+gwlLFwI8/bt8GZkoPzYY5v0vo2eLZSe7kNaGjM6YnEyma5sNviaN1ebLCcjil8MDBGRsUjT0Q8/RPbAgeoAQcYmF9x/P/Leegu+li0Ri1R52d13Y9v06dXKy2SCGREZz6OPpsDj0dCvnwuHH77n/mCh/kI1lJGJ7GwfWrTwwu/XsHy5sbKG9OozFGw67RoxArAa6znRu78QJ5LFFsMEhjiZjIgYGCIiI5EU5ozx45Fx1VUwFRejondvbJs1C2WjR6tPsGOd58ADsWPqVBRUBoSSXn5Z9VciIuP4+28Lpk4N9L2ZOLF4z1eUMqzffttrYCga+gw1acaQywX79Olqk2Vku2cMsYwstrRpY4zm04KTyYiIgSEiMoSEWbOQPWAAHNOnw2+1omjSJGz/5BNDjp+PKJMJZeefD+fQoaq/R+rtt7MZJJGBPPxwisrwGT7ciR49AkGdmlhXrFDZf76UFHi6dNnj9Xr0qDBkYKiiZ091alm7Flp+fpPcp/2bb2AqKoK3RQtUHHZYk9xnNOCo+tjOGMrNNek+mDQ4mczCUjKiuMXAEBHpSisuRtq11yLrggtUXwn3/vtj27RpKLn8cuOPoI+gottug99mg/3bb5Ewe7beyyEiAL//bsX06Q6YTH5cf31xrSPXQ/2FahpZtkufIaONrJcG/5599lHbtiYqJwuWkTmll9xenrP4nUjGjKFYkpXlQ0KCXwWat2zRuQE1S8mI4h7/6xKRbmyLFqkx7UnvvacmYpRcfDG2ffmlKqmKd94OHVBy0UVqO+3OO4HymqceEVHTefDBFHV66qlOdO6894P0UOPpgw/e6/WCpWQrV1rgdBqrZLYiOLa+CQJDWkkJ7HPmqG2WkdXcY4gZQ7FFKuSDk8k2bNA5MBQsJWNgiChuMTBERE3P5ULqXXch6/TTVdqy1Lbv+OgjFN16K2C36706wyi54gpVUiGlHMkvvqj3coji2vff2zB/vh1Wqx/XXLP3bKFqgaG99BcSLVr40KyZFz6fhj//NFaWZFNOJrPPnKkmUEqWkrt794jfX7Rwu3cGDdhjKPYYpQF1tYwhlq8TxSUGhoioSVmWLUP28OFIfv55aH4/Ss85B9vmzEHF4YfrvTTD8Scloeimm9R28pNPwrRli95LIopLcpz04IOpanvUqDK0a7f3zA3Txo2wbNoEv9kcauK8t6yBYK8iw/UZqpoxFOGDxVAZmWQLxcGwgbqSgIHXq8Fu96N5c5/ey6EYDQx5KnsMmUpKoBUW6roWItIHA0NE1DQ8HhXcyB4xAta//4Y3Oxs7XnsNhQ8/DH9yst6rMyznyJGqR4kaYc/x9US6mDMnAb/+aoPd7sMVV9QhW6iyv5D7gANUgLc2Ru0zpNZvtcK8YwfMEWxKq+XlIWH+fLXNMrKay8jatfOw7VIMB4Z0n0zmcMDbrJnaZDkZUXzivxgiijjzmjVodsopSH3oITVpyzl8OLbNnYvywYP1XprxaRoK77pLbSZ+/DGsv/6q94qI4orPtzNbaNy40jplbQQDQ7WVkRl9ZL2U9kpwSFgjWE7m+PJL9b+h4sAD4enUKWL3E43Wrg2WkbG/UCxq08ZjiIyhquVkFgaGiOISA0NEFDl+PxJfew3ZQ4bA9ttv8KWmIv+pp5D/wgvwZWXpvbqoIX0+ys48U22n3XZb4EiViJrEF1/YsWKFFSkpPlxySUmdfiYUGKql8fSugaG//7Yars98qM9QBBtQVysjoz00nmZ/oVgUbD5tpMAQM4aI4hMDQ0QUEabNm5E5ahTSb74ZJqcT5Ucfja1z5sB56qnsH9EARZMmwZecrA7OHB9+qPdyiOKm8e9DDwWyhS6+uAQZGbX32dFKS2FdvrxegaE2bbxIT/fB7dZUcMiQfYYilDFk2rQJth9/VNuuE0+MyH1EM46qj58eQ3r3fA4FhiJYNkpExsXAEBFF5NPfnIEDYf/2W/jtdlUKtePdd+GrbG5I9efLyUHx1Ver7dT774dWXHufEyJqnA8/TMTatRZkZXlx4YWldfoZCaBoXi88rVrV+W+exMqNWk4WbJ5t/eOPQKQszByff64GEZQfeii8/B+xG46qj20tWwZe17IyE/Lz9f3QzBMMDG3cqOs6iEgfDAwRUdhoBQXIuOQSZPzvfzAVFqKiZ09smzkTpePGgV0zG6907Fh4OnaEeds2pDzxhN7LIYppLhfw2GMpavvyy0uQnFy3j/NDjafrmC0U1L17hSEDQ/I3R8qATS4XLH//Hfbbd3z2mTp1nnRS2G872kkGSTBjiKVkscnhAJo1M0YDapaSEcU3HqkRUdik33CD+vRXRjQXXXcdtn/2GRuJhpPNhsI77lCbSS+/DPM//yCq+P1IveMONO/ZE5aVK/VeDdFevflmEjZvNqtP9M87r27ZQg1pPL3rZDKjBYYkqO/u2TMifYZkMIFt6VL1P8N1wglhve1YsG2bSWWSmEx+tG3LjKFYtbOcLJAdpnvzaZaSEcUlBoaIKCyktMk+e7ba3vHeeyiRsierwQ5wYkD5wIFwDRgAze1GWmWQKFokvv46kl98Eebt25E8ZYreyyHao5ISDU8/nay2r7mmWIZz1Y3PB1vl5MD6BoaCpWTS6DoCFVuNUlHZgDrcfYaC2ULlxx7LgQR7KSOTBsU2m96roaboM2SEwJCpoABaSd0a7RNR7GBgiIjCIuGbb6BVVKiyg4ojjtB7OTFNsob8VivsX3+NhLlzEQ1sCxcGJqpVsn/2GbTCQl3XRLQnL72UhB07zNhnHw9OP72szj8npVamoiL4EhPh7tq1XvfZoYNXTT4rL9ewapW+mQN76jMU1owhvx+OTz5RmywjqxlH1ccHowSG/Ckp8KWnq22WkxHFHwaGiCgs7DNnqlPnsGGcOhZh3n33DfRtkvH1kjVUEehNYlQy4SRjwgTVkLds5Ei4999f9StJ/PhjvZdGtBtpADtlSiBb6Prri+qV+BjqLySBFEv9gjvShs2o5WTBjCEJfIUrk8CyfDms//yjBhS45P8G7TFjiBPJYptRAkMi2ACegSGi+MPAEBE1XkUF7JWZK3yD3zSKr7oK3uxsWNasQdIrr8CotLIyZI4dC3NeHip69EDBQw+h9Lzz1GWJb74Z6K5KZCCTJyejuNiErl3dOOEEV71+1vbzzw0qIwsKBoaWLTNWYMjXvLmasibTw9R0sjBI/PRTdSqlsZKpQLvb2XiaGUOxzEiBIU/btuqUgSGi+MPAEBE1WsLChTAVF8ObkxMqOaDIkgOpohtvVNspjz8O09atMBy/H+nXXgvrn3/C26wZ8l56SY1gcY4cCZ/DAevKlaEDaSIjyM014eWXk9T2xIlF9R6mGGo8Xc+JZLv2GVq61HgNZdyVWUO2cPQZ8vlUOalwnnxy428vRq1dGxxVz4yheAgM6T2VrGrGkIWBIaK4w8AQETWafcYMdeoaMoRj6ZuQ8/TTVYmHqaQEqQ88AKNJfvbZwJQ6qxX5L74IX+UbTn9qauhgUGUNERnEk0+mwOUyoU+fCgwaVF6vn5XgrGXdOvg1DRV9+jQqMLR8uQVegyWJBIP+4WhALQE0y6ZN8CUnq4wh2nvGEEvJ4iMwJIFpvSvDObKeKH7xCI6IGv/J76xZapNlZE3MZELhXXepzcT334c1zKOkGyNhzhykVAarCu+5BxWHHlrt8rLKcjLHtGkw5eXpskaiXQ/C3347UW1PmlRU71ZpwWwhz/77q+BnQ+y7rwcOhw9Opwlr1liMOZksDH9nHMEysuOOU1mEVPNkPGmALlhKFtuysnxISPDD79ewebPOk8lYSkYUtxgYIqJGkYMEc24ufCkpKD/qKL2XE3fcffqg7NRT1XbarbeqQJ3ezKtXI+Oyy1Q/EuknVHbuubtdx92zJyq6d1eT7BwffKDLOomqeuyxFHg8Go491oUjj6z/x/ah/kINzBYSZjPQrZvHkA2o5XfWbzKpTB9Tbm4jbsgN+xdfqE2WkdU+kSwzU6bVsRdbLJMgdKtWxugzxIwhovjFwBARhaeMTMoBbMbrixEPim66SY3Htv32GxxTp+q6Fq2oSDWblp5T5YceGspoqkkwayjprbcMEdCi+PX33xZ8/HEgc2XixOIG3Uaov1ADG08Hde8eCEotXWqswJA/KQmezp0bPbY+YcEC1Yzem5WF8qOPDuMKY3MiGbOF4kObNsYIDHmCU8m2bQNc9Wu+T0TRjYEhIgpPYGjoUL2XErd8LVqg5Mor1XbqffeFbZx0vXm9KlNIRlDLBKP8F17Ya7BQsgWkx4jl339h+/77Jl0qUVWPPJKiyjiOO86Jgw4K9PmpF6czNK2r8YEhY04mq1ZO1og+Q6EysuOPByzGKpczEo6qjy+tW3sMERjyZ2TAlxRowG/euFHXtRBR02JgiIgazLJ6tQoC+G02lLOBqK5Kxo+Hp0MHVdaX/PTTuqwh5aGHYJ87F367HfkvvwxfdnatGQgyoSyUNUSkg99/t+KrrxzQND+uv76B2UJLl0Jzu+HNzoa3XbuwBYaMlkgXmkzW0IwhpzP0YQLLyOpWSsaMofhgmMlkmhYqJ+NkMqL4wo9qiKjB7NOnq1MpB5Dx6aSjhAQU3n47si64AMkvvICys86Cd599muzuZfR0yjPPqO2CRx6Bu0ePOv1c6bnnIumNN9TBokx18uXkRHilRNU99FDgb9fIkU506eJpfBlZfbtW72K//TyqEW1xsUk1xN5nH+MEBiqCk8mWLAmUf1ZOoXQ6gdGjM7FmDWC3Z8Ph8KuvxMTgqU9tH537DS4tKUF+als881tfOP7Wqlxn5/WlAXfV8yXxsJFPaxSXkjFjKJ4CQ3pnDAkJDFn//pt9hojiDANDRNRg9pkz1SnLyIyhfPBguPr2hf3bb5F6113If/XVJrlfy7JlSL/mGrVdcsklcJ5ySp1/1tOtm2rWa/v1VyS+9x5KrrgC0Sr52WdhXrdOTWFjv63o8OuvVnz7rR0Wix/XXtuwbKFqjacPPrjRa7Jaga5d3fj9d5tqQG2kwJCnSxf47HaYiopgWbMGnk6d1Pnz5tmxcGFCrW8tR+ITdfpC0dm48+6MOt+v2bx78Cg724tTT3Xi+OOdsNsRw6PqjfP6U+QEm09v2GCMwJAwr1+v91KIqAkxMEREDWLavBm2xYvh1zS4hgzRezkkNA1Fd96JhEGD4Jg1C2Xffovyvn0jepemHTuQOW4cTC4XXP37o+jGG+t9G5I1pAJD77yDkv/9LzCaKcpIcEz6OwUPnkvHjdN7SVQH8+cHghnHHedqeMmO3w9rMGMoDIEhceCBOwNDJ55ooAawVivc3bsj4eefVZ+hYGBo5sxAZGbMGOCUU7ajrEyD06mp0+C2N78YJz09DfAChcedjFPsZVWuYwpty2lw2+0OpAl5vZoa3161fdqKFVbMn2/HnXem4pxzynDeeWWhBr7RrqJiZ+YIM4biL2PI79c3Qy4UGGKPIaK4wsAQETWIfdas0Lh0lv8Yh2e//VA6ZgySX3oJqbffjm2zZwdSECLB7UbGhAmqD4H0N8qXUrIGBHWcJ5yAtDvugGX9eiRIMCsK+1WlPProzu3HHkPZyJGqiScZ22+/BTK7Dj20/uPpg8z//ANzfr7qrSVBk3AI9hn64w/jZZ5JnyEJDEmfIefpp8PjAWbP3hkY6tzZDb8c2e7C8cFHsHrL4e7UCZe92A7QCmq/Lzd2CzAFTk2qN9SbbyZi0yYLnnkmBc89l4zBg10YM6YMxxxTHtWlZ5I14vNpqqQuJ8dgjaYoohlDsm/n52vIzNz9d6ipeDiynigusfk0ETVIqIHosGF6L4V2UXzNNWoUtHXVKiS99lrE7idVspMWLVKTxfJefRX+9PSG3ZDDgbLTT1ebiW++iWhjXbpUZWj5TSZ42rWDqaAAKU8+qfeyqBYSuwgGhnr1anhgKNRfqGfPsJUQ9ugRDAxZ1TqNJNRnqLIB9c8/21BQYEJGhg9HHbXnn3N89tnOptN1jNpITDs11Y8WLXzo2NGLbt08OOQQN449thxXXFGCRYu24qWX8nD00eUqkDJzpgNnn52Fvn2z8fLLSSgq0qJ+VH00B7io7qQcUsojjdCAOtR8mqVkRHGFgSEiqjetsBAJCxeqbfYXMh5/WhqKJ04MZa9IuVe4Jb77LpIrexjlP/00PJ07N+r2ys49V53a58yBadMmRGO2kBzwFj7wgNqWgJxZOvGSYf37r1kFNKTRc7duDRhRv2t/oUaOqa+qSxe36nsk6zNCM9qq3MHA0PLlgMuFGTMC2UKSrbOn6fPyNyjhu+/UtvOkk8K2Frk/KQN8//0dmDdvKy64oATJyT78848Vt92Whj59mmPixDSsWGGJyv5CLCOL13IyiyECQ6bc3EDaHhHFBQaGiKje7F9/Dc3jgbtzZ3g7dtR7OVQDmUpWceCBqklsyoMPhvW2rT//jLTKXkJF112H8jD0mJISuPIjjoDm8yHp3XcRLWQ6kwSzJFuo+KqrVE8n14ABanR5sOcQGdPixbZQP5/GJPqEMob69AnnkMHQhLSlSyNUCtpA3rZt4c3MVPu4Zfmfof5CQ4fuuReS/YsvoHm9KqsqUv8zZJrbPfcU4ddfc3HffQWqpE16F731VhIGDcrBqadm4fPP7VFxnLt27c6MIYq/cjK9g8G+7GxVGiv/j82bN+u6FiJqOgwMEVGDy8hcLCMzLrMZRXffrTalqbP1jz/C1nQ886KL1EGhc/hwlFx5JcKl9LzzQutVjUuiKVto5Eh4991XbRfdeiv8ZjMc06fDtmiRziukPQlHGZmWlwfr6tVhbTwd1L17RaiczFA0TfUZEvkzl2L9egvsdh/69i2vvYwsjNlCe5Kc7Mf555fh66+34cMPt2PECKeaavbDDwm45JJMHHZYczz2WDJyc437FpgZQ/HJMCPrNQ3eVq3UJieTEcUP4/5XJCJjcrmQ8M03gU0Ghgyt4tBDUXbyydD8fqTeemugqUpjuFzIvPBCmLduhbtrVxQ88QRgCt+/EdmfJBPBvGUL7HPnwuhkKpOsU4JAxVUCZFJWV3bOOaE+TPCxeawR/fZbIODSu3fj+wtJQ2V/ZibCKdiAetkyq/H+tvTurU5LvlmqTqXnT2JizdeVyUYJP/2kJlg6TzyxydYovXmOPLICL7yQjx9+yMVVVxWrHi65uWY8+mgqDj20OS65JAM//mgzXB+nYI8hjqqPz8CQEUbWswE1Ufxp1Dv6Tz/9FGeccQZeq6G5qUykuO+++9TlP/30U7XLtm/fjvvvvx/nnnsuLrzwQrz55pvweqv/81u+fDkmTpyIc845B5dffjnmzZu3233MmDED//vf/zBq1CjcdNNNWF35qR0RRU7CggUwlZbC26IF3D166L0cqkXRzTfD53CoKULBT+0bxO9H+qRJahKRLz0deS+/DH9SUjiXqupnpAQuWppQS/+mULbQLuUxxdddp5py2/74A46PP9ZphbQnTifw55+BgEufPo3oL/TrrxHJFqoaGJJSMqMFLoIZQ1mrA49/2LA9l5EF/+5UHH44fC1bQg+tWvlw/fXF+OmnXDz3XB4OOaQcHo+Gzz93YOTIZhg8OFtNOCst1b/Ts7zWzBiKT4bJGKosGRUcWU8UPxocGJIgzOzZs9G+ffsaL//yyy+h1TBKwefzqaCQxyO14PeowI4Efd5///3QdbZu3YoHHngA3bp1w0MPPYQRI0ZgypQp+L1yAoZYuHAh3njjDZx22ml48MEH1TruvfdeFBYWNvQhEVEd2GfO3JktxHEphudr1Qoll12mtlPvvhtaWVmDbifppZeQ+OGHKjsmb8oUePfwt7+xykaNUqcJ8+bB/N9/MCrrr7+qXlsqW+iqq3a73NesGUquuEJtpz7wQIOfd4qMZctsKjAgGSTBgzGjNJ4OOuAAD0wmP7ZvNxuu7ElNYJOMlorVyNJ2YNCgvZSRffppk5WR1UZ6SZ10kguffroDM2duxahRpaoMbsUKKyZNSsfBBzfHbbel4p9/9Dswl9fa5TKp8rc2bZgxFE+Cf4v0nkomvK1bq1NOJiOKHw16p+FyufD0009jwoQJSKrhE+O1a9di2rRpuOSSS3a7bMmSJdiwYYPKAurQoQN69eqFM888EzNnzlTBIjFr1izk5ORg9OjRaNOmDYYNG4bDDz9cBZuC5PYHDhyI/v37q+uMHz8eNpsN31SWuBBRBHi9ocAQx9RHj5IJE+Bp21aVaCU/80y9f942f74KKomi225DxTHHIFK8HTrAdeyxqvwt8e23YfhsodNOU2uuScm4cSodX573pOefb+IV0t78+uvOMrIGx7crKmBbsiRiGUMOh181VDZiA2opm8vL3Edtn9N5EbKyai6XtKxeraaX+S0WOEeMgJEceKAHDz1UqJpV3357ITp08KCoyISXX07Gscc2x9lnZ2LWrAT5t6dLGZkECazGetmpiQJDUu5YvudYa9NmDLGUjChuNGge4ksvvaQCOj169MDUqVOrXVZeXo4nn3wS48aNQ3p6+m4/u3LlSrRr167aZQcddJC6zfXr12OfffbBqlWr0L1792o/17Nnz1DJmgSQ1qxZg5NPPjl0uclkUj8jt78nbrdbfQVJRpPD4QhtR6PguqN1/RRd+4hkSZh37IAvLQ1umSClw34nb9J//tmGmTMT1Okhh1TgmmtKkJJisFoLI0lMRPHttyPjwguRPGUKnGefDW+7dnXaR8zr1iHz0kvVRKGyM85A2YUXRvx1d44eDfv8+Uh87z2UXHdd4GN+A7H+8gvs8+apg92Sq67a8/PhcKD45puRccklSH72WTjPOQe+Fi0QjWLtf01wIlnv3u4GPyYJeGguF3wZGfB16hSR50bKyf7+26oynIYObXgvpEj4xXwohuBfnNzyB2hanxr3kWC2UHm/fkBWFoy492RkABMmlGH8+DJ8+20CXn01EXPnJmD+fLv6atPGg/POK8NRR1WgZUsvcnJ80ts/4oEhmUgWK79vsfp3JNyaNfPDbvfD5dKQm2vRdSpdcGS9BIaa8vXiPkK14T5ioMDQ999/j3///VeVg9Xk9ddfR5cuXXDIHtKqCwoKdgsYpaWlhS4LngbPq3odp9OJiooKlJSUqJK0XW9Hvt+0adMe1/7JJ5/go48+Cn0vQSgpQ8vOzka0axGlBxsUZfvIggXqxHTCCWhZJbAQaVKFM3s2IK0qvvhC+pRVnyz0xRfJePpp4JRTWN22R2PHAu+8A+3rr5Hz0ENADX1vdttHSkqA8eOB/Hzg0EOR+PrrSLQHRlNH1OjRwK23qjG5LaVH3emnw1Aqs660889HzuGH7/26EybIP0aYfvgBzeXnXn4Z0SxW/tdUJvpgyJBUtGyZ2rAb+ftvdWI6+mi0rJzgE25HHw3I25ZVq1LQsmUKjEL+JMzYcQSG4H0cpi1BUpXeQaF9RJrlTJumNu1jxqClTv2F6kN6xsvXv/8CkycHfl03bLDg/vt37iMSFJKXWxIq5Ni5ptPmzQPXa4gdOwKnBxyQEBXPWTz/HYkEeWsln3G7XDnQ9eXv00edWDZtQsucnIbv0A3EfYRqw31E58CQNI2WrJ1bbrlFlW3t6pdffsGyZctUXyAjOuWUU3D88ceHvg9GGrdt2xYqY4s28hjkF2PLli2q4TdRxPYRvx/ZH3+s/mjk9+0L1+bNiKQdOzTMmWPHzJl2zJuXoD5BC0pP92HQIJf6tP+FF5Kwdq0Fp54KDB7swr33FqJNG06BqonlllvQ7NtvoU2dih0ffogKOerc0z4iwfeLLoJj2TJ4c3KwfcoU+ORosIkkn3kmUp54AuVPPYW8ynUagfXnn9Fs1iyVLbRt/Hh46/B7YL35ZjQ74QT4X30V2886C54DD0S0iaX/NZs3m7B+fXPVv6dNm1xs3tywx5M+dy4k57ioe3eURujvYfv2UkvUDD//7MXmzVthFFOn2rHId6jatv/xAzZv2gTNZKq2j1iWLkX2qlXw2+3IPeww+CP8PyOcJP599dXAxRdDNaj+6CMH/v3Xovr/eL0apO3K3lqvWCx+NG/uRcuWPrRqJaeBXlZVv8/OrjnzaNky+dDTgezsImzeXIpYEkt/RyKlefNMrFyZgKVLC9Cli1O/hchrZbFA83iQK0MnIhT83v1uuY/Q3nEfqT+LxVKnRJh6BYakfEuaO8u0sCDJ3FmxYoWaEDZkyBDk5uZizJgx1X7u0UcfRdeuXXHHHXeorJ5dp4cFG0YHM4DkdNcm0vK9lH1JQCo1NVWVjgUzjPaWjVSV1WpVXzWJ9h1L1h/tj4GMvY9YVqyAZe1a9Sbf1a9fRPY3mcQigSD5+uknG3y+ncGg1q09avLN0KEuHHpoRaj3whlnlOLpp1Pw3HPJmD3bju+/t+G664oxblwpLA0qlo1d7i5dUDp6NJJffRWpt92GbdIvqsqTVHUfSX7iCTi++gp+mw15L74Ir3wE3oR/Y2Tce/JTTwWm4K1eDe+++8IIUh55RJ1KWZ30barLcyKjvctOOgmJn32mxtfvkGELUZraFgv/a4Jj6rt08SAx0dew3drv39l4+uCDI/acHHBAoPx982Yztm3T0KyZMYLe06fb8TsOgtdkgXn7dpg2bICvsidJcB9xVLYacA0eDJ/Mso/C/UYCRGecUaa+gqXMW7eaVHNgeU1qOpXgkTQ237jRgr0NdAoGjwKBop0Bo2A/KZlIFu2/a7H8dyRS5L0OkID16036PkcmE7ytWsHy338wr18PbxOnL3EfodpwHwm/eh02SQ+fRyrfFAdNnjwZrVq1wkknnaQCNoMGDap2+XXXXYfzzz8fB1c2ZuzcubPqSySBnmC52NKlS1XQR5pIi/322w+LFy+udjtyHflZtWiLBR07dlTZSYceemgoQCXfS6NqIgo/+4wZ6rT8mGPglzf5YSB/z5cts2LGjEAwSCbDVNWtm1sFgoYOdaJbN0+Nx9LSJuyGG4px8slOTJyYhp9+SsBdd6Xh448T8dBDBTjooIaPoo5Fxddeq/p+WP/6S42EL7vggt2uIw3GUyv/1hfcfz/cEWisW5eJKOUDBsA+Zw6S3n5bNb3Wm+2nn5Dw3XeB3kKVE8fqqvjGG+GYMQMJ33+PhNmzUT5kSMTWSXu3ePHOxtMNJRPzzNu2wW+1oqJHD0RKcrIfHTtKX0UL/vjDiv79de5IqwaQIJDFCRNKOnZD2uolsP72G8orA0OKzwfH55+rTWeVfpDRTjJ8JIgjX0DN/1skAV2CR3sKHMlp9eBRzW/FOao+PhltMpkKDEkD6srjLSKKXfUKDEnwRhpHV5WQkICUlJTQ+TVl7DRr1kxNGQs2kZYA0DPPPINRo0apLJ/33nsPQ4cODWXzSOaRTCl766231NQxCfgsWrQIkyZNCt2mlIQ9++yzKkDUqVMnfPXVV6rxdT9pcEhEYReuaWTS/33RIhtmzQoEgzZt2vlnSMbzHnZYRWUwyIW2beveeLFzZw8+/ngH3n8/Effck4rly604/vhmGDOmFBMnFrM5dSV/RgaKr78e6TfdpII/aoR0VlbocsvffyP98svVdskFF8B51lm6rbX03HNVYCjx/fdRdMMNgY/vjZAtdOaZoYktdSXXLxk/HinPPIO0u+/G1v79JY01QiulvZG+ZI0NDAWzhdwyKKNyiEWk9OhRYajA0PffJ6C0VMrGvDAf2RNYvQS2339HeZVx9LYff1TT+HypqXDJvh5HJAmzVSvJAPKhT5+9B49qChzJlwQDu3ZlYCieA0MbNxogMCT/5xYt4mQyojjR5IUWUgImAR6ZQia9iiSw1LdvXzWyPkiCSHIdaWQtAZ+srCxcfPHFanpZ0JFHHomioiJ88MEHKrjUoUMH3HTTTXstJSOihjFv3AjbH3/AbzKhfPDgev98SYmmPmGWQNDcuXYUFppClzkcPnWwM2SICwMHupCZ2fAAjskEnH12meo1dOedqZg6NRGvvpqM6dMduPPOQowY4YrWCp6wKjv3XCS9+SasK1Yg9eGHUfTAA+p8raAAmWPHwlRaivIjjkDR7bfruk7JGPJIKvumTXB8+SWc0khKJ7YfflDZPpIhUnLllQ26jZLLLkPiu+/CsmaNev5LpSE4NSk5IF+yJJgx1PBswqplZJEmk8lkuJcEhoxA/o4LCd67ex4EvPEGrL//Xu06wWlkzuHD5RNEXdYZLcGjPWUeUXySkkLDBIaqTCYjotjX6MCQ9A3aGwnc7EqaH9144417/blu3brV2sRaysZYOkbUdGVkFYceCl+V7JK9kU9DpeePlIktWJCAioqdEZmsLK8KBMnXMceUh/0Dd+nD8fTTBTj99DLceGO6ak49YUKmCjxJc+r6ZCLFJLMZhXfdhWann47Et95C2XnnyR9mNVZd+kh52rRB/gsv6J/RYjajbNQoFbySdeoZGAplC511lkqvbwh/SgqKr7sO6TfeiJRHH0XZyJHw88OMJvXXXxY4nSakpPjQqVPDMzJsv/6qTiv2MIE1nA48MBA4kLJbvfl8MiGySmCoZS+1bV26NBB1E2437F9+qTZVRiIR1VmbNjsDQ1Jur+eHWfJeQDAwRBQfdn5sT0RUS2DINXToXq+3ZYtJNYE+8cRm6N27OW64IR1ff21XQaEOHTy4+OISfPrpdixenItHHinEkCHhDwpVdeyxFZg7dyuuuqoYVqtfZSv175+NKVOSVElbPKs48kg4R4yA5vOpRtS48UYkfPstfA4H8l5+Gb7MTBiBBGL8ZjMSfvoJlr/+0mUNtoULkbBokcoWKq4ss2tMU213584wFRQg5cknw7ZGql8ZWa9eFSrDsCG0wsLQvtgUGUPBwNB//1mQn6/p3p9p61azCqwdcUQ5PJ06wZecDJPTqcpQRcL8+TDn58ObnY2Ko47Sdb1E0UYakAsJYOv9+86MIaL4wsAQEe2Vlpen+kUI1x4y9PLyNNXX56ijmuPee1Px6682+P0aDjqoAhMnFuGbb7ZiwYKtuPXWIhxySEWNI3ojRdrSXH99MWbP3obDDitXb7buvjsNw4dnh6YTxStp6CxT5iTogWCz6cceM9Q4dV+LFqGApGQN6SHlscfUadnZZ8PXwGyhEIsl1Eg76dVXYV67NhxLpHoHhhpRRvbbb9D8fnjat4evsn9iJKWn+9GunccQWUPBMrIBA1ywyVNpMsHds6c6L1hOZg+WkZ1wQqBbMxHV6z1LdrYxGlAHA0MWGa/H6U9EMY+BISLaK2n+q3m9cB9wALy7NJ+X3kGPP56MI45ojsmTk+Fyaaqh6333FeCXX7bgyy+344orSlRjaL17++y3nwcffbQDjz6aj/R0H/7806oym266KQ1FRfHZeEje9JVccknoe5m05TrxRBiNKnWTwNDHH0MrC4yNbio2mSIm2UI2W6OzhYLK+/eHq18/aG43Uu+9Nyy3SXUTDAY3qvH0L780WbZQ1T5DRggMSWlwsIwsqKJXoJzMJtNky8pgnz5dfc8yMqLGNaDesKHJW8FWIyPqpbek5nLBtH27rmshoshjYIiI6jSNrGq2kIwrfuGFJBxxRA4eeSQVJSUmNVr+jTd24PPPt+P888sqx/kai5SOnHWWE/Pnb8Vpp5WprKbXX09Cv345+OILe1x+IFbyv//BNWgQMH68mlZmROVHH62yM0xFRbBXjsBuEn6/6gUULAHztWoVtpsuuvVW9Ybb8dVXoYw8iqyCAg3//BPGxtNN0F9o18CQng2oV6+2qOdPynIHDNg5Hc1dORhEZQxNmwZTWRk8bdvC3aePbmslimaGaUBts8HXvLnaNK9fr+9aiCjiGBgioj3SnE4kzJuntp1Dh6q+PG+/nYijj26OO+9MQ15eYKzu5Ml5mDFjGwYOLNc9M6gusrJ8ePLJArz//nbss48HublmXHxxJkaPzsT69fFV+uB3OJD/xhsS6TNu2YfJpCapCZnm1VRsCxYg4ccf4U9IQPFll4X1tj3776+CTSL1zjsDXX0pon7/PVBGJv3OMjMb+Hx7PLBKZoxOGUNLlwYeg55lZEcdVY6UFP9uGUOq79JLL+3MFoqGfwZEBmSkkfVsQB2dkp99FmkTJ8K0bZveS6EowsAQEe2RNBE1uVzqjcFHqw5WmTXSUHrzZjNatfKosizpH3Tiia4GN3LV09FHV2DOnK245ppi2Gx+1Si7X79sTJ7M5tRGU3bmmar5s+3332H9448mzRYqHTUKvpYtw34XMqFMGvfaliyB45NPwn77FP4yMuuKFSojxpeaCk+XLmjqwNC//1pQXKzpPqZ+1z5g3hYtVCN7zJ6tznOefLIuaySKBUYKDHnbtlWnSW+9pd4TwhvnU12jQMKsWUi97z71mmUPGoSEuXP1XhJFiSg8lCOippJQ2Svi7bKR+N9lmWrsu4yav/POQnz33VZVlmXRtwQ+LI0er7020Jxapuy4XCbcc08ajjsuG7/8Et/NqY3El5UF5/DhajuxCbKGEr77Dgk//6yac0u5XST4srNRUtm3KPX++1WGHkXO4sW2xvcXCpaRSZlUE0bDJctRgvFi+fKm/7u0daspFFgbMqR6YKhq1pBwd+kCT9euTbo+olgdWa+3YGZkwvffI+vss9H84IORevvtgdLReKy/NzitpATpN92ktn0pKTBv346s0aORdvPNMupO7+WRwTEwREQ1WvSdCe6pgU8ZXskbidRUH264oQiLFm3FhReWqoBKLOnUyYMPP9yBxx7LR0aGFytWWHHyyc1w441pKCxkSYSRmlBLdo1WXBzZbKHKKW0qW6hFi4jdVcmFF6qMPPPmzUh6/vmI3U+8k+OXsEwkqxoYamJ69hmaPVt6sGno1asCLVrsXoYX7DMkXMwWIgpLxpDeU8lE2ejR2P7ppyg97zz40tNh3roVyS+9hOwRI5Bz7LFIfuwxmNes0XuZVCnloYfU+wlPu3bI/eEHlIwfr85Peu01ZA8fDsvy5XovkQyMgaEop/4Yz5ql9zIohvz+uxVnn52Jp8/6C+nePGxHFnpe0gMLF+biyitLkJQUu58QSUuMM8+U5tTbcMYZgebUb7wRaE792Wfx2ZzaSCoOPxzuTp1UKU8kS68kXd72668RzRYKsdtRfOONoZ4AptzcyN5fnFqzxoyCAhMSEvw44ICGB4aswYlkTdh42giBoeA0spqyhXbNGOI0MqLwBIak/2H5zj7v+tA09feu8IEHsGXxYux47TWUnXQSfHY7LGvWIPXRR9H8mGPQbMQIJL30Ekxbt+q84Phl/e03JL3yitoufPBB+NPTUXTHHdjx9tvw5uTAunIlso8/PvAhFPsaUg0YGIpitu++Q87RRwMXXAA2RKHGWrnSggsvzMCIEdmYP9+OkabAgbd2wiBMusWJjIz4iYpIY9rHHy/ABx9sV821t24149JLMzF2bAYqGl6FQo2ladWbUEciUlc1W+jcc0MTWSJJDqTlwFoCXikPPxzx+4vnMjIJrtga2L/ZtHEjLJs2wW82w10lENJUDjxQn8BQSYmG779PUNvDhu0hMHTwwXANHAhcfjm8HTo06fqIYvE9iN0e+P8mPR0Nw2ZD+eDBKHjuOeQuXYr8p56Cq39/9TdR+v+l3X47mvfpg8yzz4bjgw8im9lL1bndSL/hBmh+P8pOPRXlxx4buqi8Xz9smzMHziFDoFVUIO2uu5A5ahRMW7boumQyHgaGoljFoYfC26yZ5JrCXtkLhqi+/vvPjCuvTMeAAdmYPt0Bk8mP008rxYScjwNXGLlzTH28OeqoQHPqa68tUs2pZ81yqPH2pJ+y005TmTzWP/9Un46Fm0zhs/32m/o0NOLZQkGahsLbb1ebie+9x1TvCAiWkTWqv1BltpD7gAPgT0rSLWNIxsaXlTVdeeu8eQkoL9fUNLf99gv0OdpNQgLyJVj71FNNti6iWM5eNszI+j2Qv4HOU09F3ltvIfe331Bwzz2o6N1bNaG3z5+PjKuvRouDDkLGhAmwz5wJ/VOfYlvylClqOII3IwNFle8ndu3TmP/KKyh44AH1/kZeI2lMbZ8xQ5f1kjExMBTNEhJ2fnpemTpIVFe5uSbcdFMajj02Bx99lKjKpoYPd2Lu3G14Zvz3SNiyET6HA+XHHIN4lpAAXHNNCe65p1B9//jjKcjL459OvfgzMuA84QS1LRM3IjWJTPoq+HJy0FTchxyiHpd82ief5rFuMbyCjZOlR05jA0N6lJEJ6e2Tk+OFz6fhzz8tTT6NTLKFOIGeKP4mk9XG16wZyi64ANu/+AK5Cxei6PrrVdm35nLBMW0aMseORYvevZF2ww2wLVrEMqYItBVJefxxtS2lYxIE2mPW9XnnYfvMmag48ECY8/OROW6cel20srKmXTQZEo9uopwcvMhYKNtPP8GybJney6EokJen4d57U3DkkTkq+8Xt1tC3rwtffbUNL76Yj86dPXBUfoIg6adwOPResiGcdVaZ6k1SWGjCI4+k6L2cuCYlXsLx+efQCgrCdrsJX38N2+LFgWyhSy9FUyu66Sb4bTYkLFjA8bJh5HRKICUQGOrTJwyNpyun9OghWE62bFnTlJNJlfrcuTsDQ0TUNFq39kRNYKgqb/v2KLnqKmybNw/bZsxAyYQJ8LZoAVNBAZLefhvNTjsNzQ89FKn33BM4buGHII3j9yN94kRo5eVwHXusyuKqjadTJxXEK770Uvg1Tb0u2UOHwrp0aZMsmYyLgaEop6blnHaa2k5m1hDV0ifiiSeSceSRzfHccylqLPvBB1fgww+345138tCz584DpmBqqWtY/JaR7cpsBu68M5A19Oabifjrr6b7xJ6qc/fpA3fXrurTyMSPPgp/ttCYMWqUfFPztmunppSJ1LvvZu+4MJGePF6vhubNvaHyjPrSSktV+aKeGUNVy8mWLm1go6R6+uEHmwqGZ2V5G1WGR0QNG1lvhMlkDaJpcHfvjqLbbkPuTz9h+/vvo/Tss+FLTVVTs5InT0bO0KHIHjAAyU89BfN//+m94qgkvZwSFi5UH2hJg/A6p3XabCi++WbseO89FbiTRuLNTjhBDcGAt2H/Jyn6MTAUCy6/XJ04Pv0Uprw8vVdDBuNyAU88ARxxRDYefjgVxcUmlfny+us78Omn23HkkdXf7JvXrlV1ytJMUDUTpRB5rqTcTko57rwzlR906UXT1OhckSjlZGF4IRLmzIFtyRJVPllyySXQS4k0783MhHX1aiS+/bZu64jVMrKGlkJZFy+G5vXC06oVfK1aQS9NPZls1qyd08gkOE5ETcPoPYbqxWxGxdFHo/CRR9Rks7wXX4Rz+HCVISuTslIffBDNjzgCzU46CYmvvQYUBj6Eo70zbd8eKD0HUHzddSpbq77kddkqjamHD4fm8SD1vvuQdeaZatgCxR8GhmLBEUegokcPlUaY+M47eq+GDFZCMWJEM1x9NbBjhxn77OPBc8/lYebMbRg0qLzGgyTVJLByNLj0c6Hqbrkl0IhaJrfNmROY1ENNzzlyJHyJibCuWgXbjz82Plvoscd2ZgtJU3+d+FNT1Rs8IdPRNL5BbrRffw02ng5DGZmO2UJVA0MyRVKC/pEk8dbaxtQTUWREU4+herHb4Ro+HPkvvogtv/+O/EcfRflRR6mSJunjlnbTTUD//uxDVAept9+uSvTc3bqhdPz4Bt+OvNfPf+EF5D/2mHpflbBoEXIGD4b988/Dul4yPgaGYoE0Exs7Vm0mvv464NnD1BCKO99+m4AVK6yQ+M7DDxfgm2+24qSTXDDt5Tc/GBhiGVnN2rf3Yvz4ErV9551pHF+vE39KCpynnKK2E2UaUiMkzJ4N29Kl6g2RntlCQWWjRsG9336qMWQKpzyFbVR9OCaS6R0YkoPFjAwvPB4Nf/8d2ayh5cst2LTJAofDh2OO4UQhIr0CQ7GanexPS4PzrLOw44MPkPvLLyi87Tb4ZOLj4sWwLVyo9/IMTXoiJn76KfwmEwoeflj1m20UTYPzzDOxbdYsVPTqBVNhITIvuQTpV10FrSTwnpdiHwNDMcJ54omq/MAio+srD+yJZs8OZLRIj/JRo5ywWmtPS5VG5sI5dGhTLDEqXX55CZo18+Lffy147bXoH1//yy9W/PUXok5wKqPjq69g2rGj0dlCpRdcsOdpHk3JYlF9GYITJ6W8kxpm0yYTNm82w2TyV+ujVi8+H2y//qo23To2nhaS5dlU5WQzZgQGD/TrV84ZBERNrGXLQGDI6TQhP98UFz1TSydMCDVPTnzvPb2XZFjS8y7txhvVdum4cXD37Bm22/busw+2f/IJiq+8UgWdEj/8ENlDhsBa+T+QYlvs/6WJF3a7+pRZJL36qt6rIQOQLNzgNJnK6d61ss+ercZlS2mir3XryC4wiqWk+DFxYnFofP2OHaaozio78cRmOOaYQD+qaOLu0QMVPXtCq6hQDRgbwj5rFmx//KE+pSy9+GIYRXn//mrCiDw2qfmnxmUL7b+/B4mJDfvY3fL33zAVF6uMMml6rredDagjHRgK/P8YOjTK/jAQxQC7HcjOjtFysr2QrBVh/+orllLvgZSZWzZsgKdNGxRff33478BqRfENN2DHRx/B07o1LOvWodkppyD58cdZlRLjovdohnZTOnq0ahgstaGWyukpFL+WLLFi2zYzUlKkDKBuPxOaRsZsoVqdeWYZunVzo6goesfX5+druOaadLW9ffvOA8FoUlbZhDpJmlDXtydBlUlkKlsoMxOGoWkqa0g+sXN8+WUok490KCOr7C/k7t278en6UTKy/r//zKoM2Wz2Y+BABoaI9BCzfYb2wn3QQUC3bmrqqOOzz/RejuFYlyxB0ksvqe3C+++HX0rvIqTisMOwbfZslJ18shq+kPrII2h26qmcIBfDGBiKITIpxXXccWqbWUM0e7Y9VAZgs9UtNTXhu+/UNvsL1W98/VtvJWLFCv0PGOvr5pvTsGWLGZoWyKR4//1ERBvnSSfBl5ICy9q1sC1YUK+flUCodflylS1UMmECjMbTtSvKzj5bbafK5BE242zwRLJwBIYqdC4jC+rRIxAYksCNu+H9tPdq5szA/49DD61AZmaMNjghMrh4DAypetlg39QGZgLHLI8H6ddfD83nU8Ga8gEDmqQPVMGzzyL/6afhS05W/fayBw+G4+OPI37f1PQYGIoxpZV/TB1Tp0LLz9d7OWSAwNDgwXVrGprwzTdqsp2nQwd4unSJ8OpiwxFH7Bxff8cdaVHVIPLTTx347LNElRHw3HMF6rz5823YuDG6/i34ExNDPQmS6tOE2ufbmS00diz8RsoWqkImlEngyrZ4MT89rScJmkjmZKMnklX2VtC78XTVBvipqT5UVGhqOlkkA0PDhjFbiEgvcRkYEueeC7/Fov7vWaKxAWKEJL34YuDDrPR0FN1xR5NPgpXsIfmAxFRSgowrrkD6ZZex3C/GRNcRANWq4tBD1dhCk8vFxm1xTA7u//zTqhqu9u9ftzf21aaR1TTHnvY6vn7BgoRQs+9oaMh7001pavuqq4rVpLq+faWySsNHH0Vf1lBpZRNq6Rdkys2te7bQihXqEzAjZgsF+XJyUHLZZWo7RXoNOZ16Lylq/PWXFS6XSQVR9t23YX0RTFu3qv4KMkq5QkrJDED+PEsZa6QaUOflafjxx0CaKfsLEeknbgNDOTkoHzxYbSa+/77eqzEE87p1qreQUNPbsrObfA3edu2w/eOPUXTddap1SeInn6jsIduPPzb5WigyGBiKNZqGksqsoaTXXgO8gX8qFF/mzAl82tunTwWysuqQxuJ2wz5njtpkGVn9P72/6KLAKM+77jL++HqpRrrmmgwUFprQq1eFmrAmLrggcPkHHyRGVeZTsOSq/JBDoHk8dQuIS7ZQcBLZuHHwZ2TAyErGj4enVSs1dTL5xRf1Xk7UlZHJfm4yNa6MzLP//vCnpsIodk4mq0OdcD3J0ALJgjzgADfatuV7CCK9xG1gSPoHnnWWOlUlS0Z/YxVpfj/SJk1SH/qXH3kknGecod9aLBaUXH21mlzmad8elo0bkXXaaUh58EE2po4BDAzFIOm54c3IUB3rZcoUxW9gqM5lZIsWwVRUBG+zZob5VDyaSHBFpofI+PpXXzX2+PrXXkvCd98lwG734ckn82X4hHLaaUBSkg9r11rw00/hP9hsqtH1iW+/XWtAXKadqGyhlBSUXHQRDM/hQPFNN6nN5GeeUVksVLvffrM1vozsl18M1V8oKJIj64NlZMwWItJXPAeGZDKnNycH5h07YJ87F/FM2oPY58+HPyEBBRKAMUBWv7tPH2ybNQtlp5+ueh6lPPVUqCk2RS8GhmKRw4Gyc85Rm0mvvKL3aqiJlZVp+P77QEnT4MGu+k8jk67KVC/JyX5MmlSktp94wrjj61etsuDeewNZD7feWoR9990ZQJHBFiec4IreJtQjRqi6e/n0KmHevD1fUd7AyMhVyRa68EL40wNT2aIh4F/RqxdMpaWhdHKqW2BIMoYa3XjaIP2Fdm1AvXy5JayJwVKpOG9e4P/HsGEsWyQyQmBo61Yzyuv2OV/ssFhU0EHEc2sMU14eUiv7CRVfdRW8HTvCKPzJySh44gkUTZqkvnd8/rneS6JGMubRCzVa2fnnqzHHCd9/D8vff+u9HGpCkg1SXq6hXTsP9tuvDmmdPt/O/kIcU99gp5/uxIEHVqjx9Q8/nGLIRrxXXJEOl0tDv34unH9+2W7XOfPMwHlffGFHaan+n0jVOyBemV69tybU9mnTYP3rL/hSU1WJVtQwmVB0++1qM/Hdd2H580+9V2Ro+fka1qyxNC4w5HTCumyZITOG9tnHg8REn+qh9M8/lrD+/3A6TWjd2oNu3VgWQKSnzEyfyu4VmzfH34d2wf/pCV9/DdOWLYhHEhQy5+XB3bUrSi65BEZUduaZ6tS2ZEncvk6xgoGhGOVt3TrUK4ZZQ/El2ABZsoXqkm1qXboU5i1b1OSj8qOOivwCY3p8fSBr6O23E/Hnn8YaXy+ZTEuX2pCe7sOjjxbUuG8ceqhbHXCWlZkwbVqgnCSalI4apU4T5s6FaePG3a/g9VbPFkoLNOCOFpK14jz+eJW2nSbj66OtGVQT+v33QLaQ7M8NHbduW7oUmtutyhmk6abR/t4EG1AvXWqNSBmZAaoViOKa/A7GczmZt1OnQP9Anw+JcTgePWH+fPW4ZfhBwUMPIVT7b8AhGZLRLOK97C/aMTAUw0oru8lK4zatIDCOmmKbNBYO9hcaNKiOZWTTp6vT8gEDAHv0BQOM5PDDKzBiRGB8/Z13Gmd8/a+/WvH008lq+/77C9CiReATyJrehJ5xRlmoCXVUvok88kj1JjLp3XdDpZXyexHKFlq5MpAtdOGFiEZFN90Ev82GhO++U5+iUhOUkUm2kAGjJOHuMyQlabNns78QkZHEc2CoahNqVU5mlDdVTUBzOlXD6eDxnNvg/T9dgwapU/a2jW4MDMWwiiOOUKmHJqeT4x7jhHxyvG2bGcnJPhWkqPeYegrL+PqEhMD4+lmz9A+0SWDkiisy4PVqGDmyDCeeuPcDvtNOK4PJ5McPPyRg7droeyNaet556jTxnXfw24+SVdECEyemVcsWkobT0ZYtFORt3x6llZMnU+++m1NA9mDxYmtoMmNYAkMGFAwMLVsWnsDQr7/asGOHGWlpPhx2WJxPASIyiHgPDLmOPx6+xERY1qwJDQOIB8mPPQbLunXwtmyJ4okTYXSuwYPVqe277wLN6igqMTAUyzQtlDXE0fXxIfhpb9++5bDVYbCUefVqWFetgt9qhUsyhqjR2rXzYvz44Pj6VN0bRt59d6qaNNaypRf33FNY6/VbtfLh2GPLozZrSAKcMl3PnJuLn27/DhUVGt55Jwl5U75U+7ovLU2NqI9mxVdcoSZPyuNJ4vj63UiG2OLFjZxI5vfvnEhmsMbTNQWGgllx4SgjGzjQZdSKBaK4DQxt2hSfgSFpcOw64QS17YiTJtSWZcuQ/PzzarvgvvvUc2B0ngMOgKdVK5hcLiQsWKD3cqiBGBiKcc6RIwOTev77T/XdoPgIDNV1Gplj1ix1KuU3/tTAtCoKz/j6nByvCsjoOb7+668T8MYbgft//PF8pKXVLQ07WE724YeO6Isn22yh1PMj/3hVnZrgRdqTT+zMForyfV2ynYorU8xT778fNr4Jq2bNGjMKCkyw2/3o2rVhgSHLP//AVFAAv90O94EHwohkuIA8xpISE/79t3EHjVKhMWMGy8iIjKZVq8A/4Q0b4jMwJIL/02XqlVZaipjm9SL9+uuheb2qp2D5kCGICpqG8sqsIZaTRS8GhmKcXyb1VP5BTXo1cJBEsWnTJhOWL7dC0/wYMKC8Xv2FWEYW2fH127c3/Z/avDwN114bGMU+blwJjjmm7qUhcmAo5SSbNlnw/feBZubRpOycc+CDhmGYieO6/IWz8S7alv4Nd3J61GcLBZWNGoWyU09Vbx4zLr4Y5v/+03tJhhHMFurevaLBmS+hMrKePVWw0YgsFoQCX40tJ1u1yqIC2VIG269fvM3FJjKueC8lC2ZtevbZB6ayMtUrMJYlvfyyGnwgvRALZchEFAmWk6kG1HHUDyqWMDAUB0rHjFGj6+3z58OyapXey6EICTad7tPHjays2usKTLm5sP32m9p2RcsnElE2vl4OTIuLTXjooaYdXy//jydNSsfWrWbst58bN94YCFLVlfQgP/nkQI34++87EG1yk/bBHC2wTz+5/zN4MDnw5urtFlfCn9K0r0XEyJSSBx9UgQtzfj4yx42DVhbI9Ip3wcbTDS4jk2mNBi8jCzrwwGAD6sYFr4LZQkcdVa4C20RkvMBQ3B5ra1r1JtQxyrx+PVJk+pgMmrjlFviaN0c0KT/iCNUPSiYdW//4Q+/lUAMwMBQHvG3bhqK4zBqKXfUtI7NXlpHJiElfixYRXVs8Mpl2jq9/991ELF/edOPrp0514MsvHbBY/Hj66QI4GhDbOfPMQJBhxgwHCguNN5Fpb15/PQnP+S9W252+mIzWJauwA5m4YvU1WLIkhpqnOBzIe/FF1VPJ+uefSL/2Wn5KpwJDgde4d+/YbTwd1KNHeCaTBfsLDRvGMjIiI5H+gMLlMiE/P34P28pOO019yJ3w008w//MPYo7fj7SbblIDg8oPOwxlZ5+NqGO3o/zYYwObLCeLSvH7FyZeR9d/+CG0ovplD5DxyeSpYMlPnQNDM2aoU5aRRY5M9jn++MD4+jvuaJrx9fKp4s03ByZuXX11cahBbUMOOPff3w2XS8Nnn0VP1pAMw3jttURMw/EoSWuhRteLGQdegWKk4pFHYiRjqJKvdWvkv/AC/BaL6r+Q/NxziGdOp4YVK6yNGlVvysuDtfLAo6JPHxhZ1QbUDf37snmzCb//blNlyHX9/0FETUMyeKVnYbyXk8kHmOX9+6vtxA8+QKyxf/457F9/Db/NhkLJGpJPF6NQMBEhgYGhqBSdex3VW8XRR8PdubOqz43FP6jxbsECG8rLNbRt60HnzrWPr5bgYML336ttJwNDTTK+fuHChNCn8pEiMZCrrkpX5WuSLXHZZYHpaA2haTubUEfTdLKPP05EXp4ZLdsA3jGBT9xkgte+j50Ls9mPr7+249dfrbH19/2ww1Aoo+sBpNx/PxK++QbxaulSK7xeDS1aeNWEvcaUkbk7dYI/MxNG1rmzG1arXzXbXr++YQeNs2bZQ6V3OTlhGG9GRGHFPkMBoXKyDz8EPLW/140WWn4+0m67LTR11NOpE6JV+cCB8GsabH/8AdPmzXovh+qJgaF4HF0v5WThmG1LDeP3w7J8OVDR8DKHvZWRyQF9beTAUXO71YGPN4r/AUWDtm29uOiiktDo+EiOr3/ppSQVgHI4fHjqqXzVnLYxTj3VqcrRpJnvypVNVwrXUPJn7YUXAlPYxo0rRdmE8Sg74wwUPPkk2nVz4PTTA4GuRx+NrawhUXbeeSgdNQqa34+MSy+Fec0axHsZWV3+FtbE6GPqq0pIALp0aVw5WTAwxDIyImNPJov3wJBr0CB4MzNhzs1Fwrx5iBWp99wD8/bt6gP8kv/9D9HMl50N90EH7WxCTVGFgaE44jz1VNXl3rJ2bVx/oqy3pClTkDNkCLKHDAn1sWjswfDcucHAUN2iDg6Wkek2vv6VVyIzvv7vvy144IHAGPbbby/CPvs0fs58s2Y+DBwYOFh8/33jZw19/XUC/vnHipQUH84+u0yNdS94/HH1CZa48soSFej69ls7fvrJmJOmGkzTVNaQ9MQxFRUhc+xYaCUNzxiL9sbTvXq5Y76/UDj6DBUV7SxDHjIk0HCeiIyZMRTPI+sVm00dy4jE999HLLB9/z2SKhtqqxIyg07BbNB0MpaTRR0GhuKIPykJZWeeqbbZhFonPh+SXn9dbVpXrUKzk09G2qRJjer7JAcDublmJCX5cNhhdQgMlZcj4euv1aZr6NAG3y/VXVJS9fH127aF90+vJJ9dfnmGKiccMMCFc88N33SqM88MHCx+/LED7oYfazeJ559PVqejRpUhJWX3hivt2nlDTbVjrdeQkpCAvBdegLdFC/X3Jf2KK+IuO3TnRLIGZmRWVMC2ZElUBYaCk8kaMrL+m28S4HZr6NTJjU6dGh9MJqLwYynZTsHjGBmgYtqxA1HN6UT6DTeozdLRo6MiS7VefYYWLIAmjR8pajAwFI+j6zUN9m++ic2u/gZn++knWNavhy8lBaWVtdJJb76JnH79YP/qqwZNFAqWkfXtW67KCmojvYVMJSXq4DGY7klNM76+R48KlJSY8PDD4Q1KPPZYCpYvtyIjw4tHHilocAlNTSTQlJXlxbZtZnUQaVTLlllUGZ1kBI0du+dMGckakp4skiWxcGH0fzK3Kxlvm/fSS6qBpWPmTCQ/8QTixaZNJmzZYla9pIJZNPUlI3a18nJVruDdd19Eg2ADaumvVN9/IcG+Z0OHsoyMyOiBoU2bGBjydO2Kip49oXk8cEydimiW8uSTqopD3o8X3XgjYuk18rRuDc3lgu277/ReDtUDA0NxxtuhQ6isIpi5Qk3H8dFH6tR5/PEofPRRbP/wQ3g6dlT10pnjxyNj3DiYNm2q123Ont3AaWRDhkTt1INoH1//zjuJKpARDj//bMWzzwYyZR58sBDNm4c3Q8RqBUaOdBq+CXUwW+iEE5xo3dq31zfY55yzs9dQLE53d/fqhYIHHlDbqY8+Gvqdj5dsoa5d3UhM9De+jCycEdYIkscrwbAdO8xqwlh9Mg2lGbtgYIjIuJgxVHPWkConi9J/4pYVK5A8ebLaLrznHvhTA60AYoKmoTxYTjZnjt6roXrgUWEcKh07NvQHNR57UOjG6YTjiy8Cm6edpk4rjjwSW2fPRvGVVwbGTc+ciZz+/ZEopX7e2tP65SBg2bLAmOEBA+pQRubzqfRbwf5CTe/QQytw4olO+P3hGV9fWqrhyisz4PNpOO20MowYEZmDu2D5lWSn7dhhMmSmyOefO9T2RReV1nr9yy4rVpPifvghQU30i0XOM89EybhxaltKyiwrVyLWSZP0RvcXCk4ki5IyMuFwAPvt56l3OdmiRQlqgqH0P2vMc0ZEkdWmTeD94Nat5ogOsIgWzpNPht9uh3XFCliXLkXU8XqRfv31KuvJedxxcB13HGKNq2pgKM5K2qOZ8d7hU8SVH3MM3Pvuq8qJHDLykZqEqocuKYGnbVtUHHpolQvsKL7hBmybORMVffqo66TfcovqPySfKOzNnDk7xwxLo+DaWH/9FeZt21QT8vIjjmj8g6J6u/nmwPh6OSibMaNx4+vvvDMV69ZZ0Lq1B3ffXYhI6drVo8rgPB4NU6cGAjBG8soryWptRxxRXqcSIhljfu65gQDSI4+kRusHjrUquvVW9XtuKi1F5gUXQCsoQLxMJGsQvz+qJpLVVE72xx91D3QG//5ItimTR4mMKyPDB7s98B5v82ZmDclgCQmoiMTKxs3RRCo2bIsXq7YSMjQiFsl7D19SkqqIkBJtig58KxCPTKZQ1hBH1zedxGAZmUxUqOFduGf//bH9009RcN998CUnw/bbb8geNgwpUhKyh+ZtVcfU14VkJAmXlBPGwOSDaP3kb8KExo+vlxLCt99OUtliTzxRgNTUyEY3gllDMp3MSIGUkhINb78dKHG76KK6Z0D+738lsNv9+OUXG7791ri9kxrFakX+88/D06aN6mOQcdlldcpEjEbSGH3p0sY1njavW6cC536rFRU9eiA6A0N1yxiSf/vBMfUsIyMyNqlqZTlZzeVkjk8/3eN7ZCMybdwYeF8vH97ceCN8LVsiJiUkoLxvX7XJ6WTRg4GhOCWlTBJ8sP7zDxLmz9d7OTHPtHUrEubNU9tllaM2a76iCWXnn4+t8+bBOWyYSjNNefpp5AwaBNuCBdWu6nTuHDM8aFAd3tj7/bBPn642OY1MX5ddVoLmzb0q2+fllwO9cepDyrmuuy5dbY8fX4ojj2xghkQ9nHSSEzab///t3QmczPX/B/DXXHvf7lvIkVtIyP1HKLmFn6OcoTsVhS6pdOJHhZAUleOXKykpR5cjJbkid2utvY85/4/3Z8zY1bKHmZ3r9Xw89rEzs7Oz3+Wz3/l+39/3gYMHDUWafuQuH38chpQULapXN6FTp4JH2aQX09Chjqwh/+w1JKwlSiBx4UJYQ0LU0IHIV16BP5J1mZWlQXS0FdWqWW6ov5Cpfn2VyenPgSFpVC2NumWaZatWrE0h8nYcWZ+bsVUrddFDm5KCUF/po2ezIWbKFJXFK33sMv7zH/izrE6d1OdgBoZ8BgNDAcoWEXFldP2iRZ7eHL8nVzQ0VqsqFbNUq5bv8+UKwqWFC9V0IZlWIFf7Sw4YgJhHH4UmMVE95/vvg9SJUMWKZtSube8vcT3SY0RexyZR/PbtXfJ70Y2Pr3/77YhCja+XAMakSdFISNChVi0TnnzS/jruFhtrc2YWrFzpHeVkZjOwcGG4M0BW2HKYBx5IQ2ioVfWm+fprP80akn+nevVUs3sROXcuQtauhb+WkTVubCxyWZSvlpGJunVNKntQgj3x8doCTyNr3z7b12JgRAGJk8nyuJDqaELtI+VkIevXq+wZyUpNevVVvx8AI8OOZBJ20O+/F3qwDnmGf69Iynd0vQj+5hvoTpzw9OYERBlZxuWm0wUlDenit25F+rBhaucqDcNltH3o6tXYkmMaWUGG5zgmE2W3bq0Cg+RZfftmomFD+/j6V18t+Ph6Ccps2hSqRq6/886lYj2pc5STrVoV5hUNMDduDMGpU3rExVlU8+3CKlXKihEj/D9ryNGsM/WBB9RtCTDrf/8d/jiRTPqtFZUvB4Yk2Fy9esEbUHNMPZFvYSnZv2X276+OjYO3b4fu1Cl4M01yMqKfeVbdThs/HuZateDvrCVLwtSkibrN6WS+gYGhACaZK1kdOkBjsyF88WJPb47f0v/xBwwHDsAWFITMu+4q9PfLCMvkGTOQsHo1TLVqQXfxouoVct9nfVEFJwpcPhPsGFPPaWReN75eyqEKMr7+1Ckdpk6NVrcffzwV9erlnynmSm3aZKNsWQuSkrTO/laeIkEcx4j6YcMy1GSmohg3Ll2V00jTXkfPFX+V+tRTyGrXDtqsLMTdfz+0l7MP/SswZCzyQbv+0KEro+p9UEHLyY4f1+HQIQP0eplmycAQkS8oX56BoatZKlaEsXVrdTts5Up4M9PYqdBdiMeZyJpIHDsRgcJRTsY+Q76BgaEAlz5ihDMNU5Oe/5hnKrywzz937hxtsbFFfh1Ts2a4sGkTUp54AlZ9EDoYv8QB1EWXA2/ba2qu59QpBP36q7qy4hghSZ7XrJkRPXtmFGh8vfQMfvjhGJVh1KxZNsaNK3ijZVfR6STT6UoTak+SptFSAiYT3oYPL/q+Ky7Oivvuu5I15Ne9+HU6XJo7F+aqVaE/fRqxY8bYuzb7uMREDY4ftwdWGzUqWmBImv3LRRL5t7GWKgVfVK9ewQJDjmyhFi2MiInx4zQ5Ij8cWc/AUG4ZAweqz6ErVnjtMB3dyjWo/N1nMEOHvqmL8cCj5fzhrbdAHOccwTt2QJNR+MxuKl4MDAW47HbtYL7pJmhTUxF6udyJXMhsRuiqVepmRr9+N/56QUFIe/hhvDpkF75FW4QjAyVmvICSPXpcfxzk5Z4iciXcV096/NWUKalqOpaMr5fSqGt5//1w/PBDMMLCrGoKmQRpPKF/f/sb+7ffBuP8ec+9hbz7rr23UJ8+GShZ8sYOBmVKXESEFX/8Ybju/4E/sMXEIHHRIjVGNnjnTkT5wajcffvs2ULVqplVL6wbaTztq9lChckYcgSGunb1nUk+RIEuZymZP5c9F1Zmly6wRkdDf+bMv4a0eAOdTCF7crK6/UboZOwJaoENG0LxwAOxAREckonL0iRck53tlf8/lBsDQ4FORtdfzhpS5WR8t3EpVfccHw9LXJwKwrnKx3vqoT22YvOAd2CNiUHQb7+hZLduiHr++bwj8jLOk2VkXnuwN3bslfH1WXlUdvzxhx6vvBKlbkv5WdWqnhs5Xr26RWUsWa0afP65Z7KGpBRm06YQZ9PpGyXBBMfrvP66n2cNyYFarVpIevttdTti4UKEenkKvrvLyPwlMOTIGDp9Wq+yqPKSkKBV2Xaic2cvaBRGRAVSrpz9fT8rS4tLl3j65hQaqnroCenD6VWsVuhHPoowYzJ+wG0oN+cBLFiQqCa8BkxwKEelAsvJvB/3LISM/v3V1WPD4cOM5rqYIwtLvWkF2Q/Gb5RkaezfH6QaTped3E+Nts/o2VNNPYt4912Uat8ewVu3Op+vSUqS9A51m2PqvZNMx5LePSdP/nt8vTR5fvDBWBiNGtVo/N57PZ+KO2CAPdNgxYpQj8SSFyyIUOV30h+lZk3X9FkaNSoNUVFW1Xvliy/8O2vI0dg+9ZFH1O2Yp56CYe9e+Kq9e69MJCsSs9n5+/ti42mH6Ggbqla9fgPqLVuCVVC3fn2jMwOBiLxfcDBQujRH1l+3nGzjRvsxr5cImf8eSuzfjnSEYWmn99CxqxUdO2Zj4cJEVQYvwaFx4+T4Dn4t2xEYkgbU/n7lzccxMESwRUYi83KZE0fXu44mNRUhGzeq25mFnEZ2PVu22E9aGzc2qRIaKQ1L+u9/cXHpUpgrVFC9Q0oMGYKYCROgTUhAyNdfqwY1ptq1YbnpJpdtB7l2otDTT18ZX59z3LT0vTl40KAmb732WlKBJtC52113Zaox78eOGbB7d/4TkFzp0iWNCkiJ0aPTXHpS7Xi9N96IVD2d/F3qo48is3NnleIdN3IktPHx8DVyjCm9psSttxbt0qvhjz+gzcyENSoK5po14cscWUO//573hQhOIyPyXZxMljdT/fow1amj3stCL2fIe5r+wAFEzXxF3Z4S9gbGvVHa+bUOHa4EhzZu9P/gUHaLFioBQSooDPv3e3pz6DoYGCLFUU4maX66kyc9vTl+IWTDBjX9x3TzzTA1aODywJBkj+SU3bEjLmzdirRRo2DTahG2ejVKt22L8Dlz1NdZRubdevfOVBkP6elXxtf/+GMQ5s2zZxC99lqyGq/uDSIibOje3b7+Vq4s3nKyZcvCkZmpxS23mNC6tWuPpEaOTEdMjBVHjxqwdm0Rx5z5Eq0WSe+8o/ZRuvPnETt6NHzt6PSvv/RITtYiJMSK2rVNN1ZGduut9nGBPszRZ2j//n8HbDMyNPjuOwaGiHwVJ5Ndg0bjzBryinKyrCyEj5kIvcWItbgb1V/uhxIlch+/tW+fjUWL7MGhTZtCMXasHweHgoOR3batuslyMu92Q0dAa9asQf/+/bE4x6jz9957DxMnTsTgwYNx//3349VXX8WZM2dyfV9CQgJefvllDBkyBCNHjsSHH34Iy1WXZw8cOIAnn3wSgwYNUq/37eVSmJw2bdqE8ePHq581efJkHD169EZ+nYBmrlEDWW3b2kfXL1ni6c3xC2GffnolW8hFaR6ZmcD339uvBHfq9O8De1t4OFKmT0fCunUw1a0LbVISDJdHMDMw5N3kfHT69GR1+5NPwrBrVxAeeihGlUwNGJCBrl2960ROtklIACUzs3jSmOSg6YMPwp0No12dPRUZaXP2e5KsofyG/flLxmjiwoUqWyb4558R/eyz8CWOjLUGDUwwFDF5LeiXX3y+v1BBGlB/910wsrI0qFzZjDp1AmBxE/kZZgxdW2bv3rAZDAjav19l63hS5IyXEX78EM6jDBa3moPeffI+fmvX7kpw6Msv/Ts4xD5Dfh4YkiDMV199hSpVquR6vFq1ahg3bhzefPNNTJkyBTabDS+++CKsl2sK5bMEhcxms3pcAjsS9FmRI8IbHx+PmTNnom7duiqw1L17d8yfPx/79u1zPmfnzp1YunQp+vbti1deeUVtx0svvYTkZPuJFd3A6PqPP+ZIwRukO30awbt2qfHwGb16uex1t2+XA3stKlS4/oG9qWFDXFi/HsnPPANbSIjMJYa5fn2XbQe5R9OmJtxzj318/aBBJXDqlB6VKpnx3HPet1+TUddygpmWpsWGDcXTk2fNmlD8849O9WO6+273TFQaMSJdle3J+PNVqwIgawiApXp1XJozR+2vwpctQ9iHH8JXOMrImjQpegdPZ8aQD/cXujowdOKEHikpuSOnjobtnTtneUVJKhEVDkfWX5s1Lg5ZnTt7PGso+LvvELlwgbr9QPACPP2GvSfotUhw6IMPEtV0WgkOjRnjn8EhqWqQYwzDgQPQXpUwQj4eGMrKysLs2bMxZswYhIfbr946dOrUCbfccgtKly6tgkQDBw7ExYsXVbBH/Prrrzh9+rTKAqpatSoaN26MAQMG4Msvv1TBIrF582b1/UOHDkXFihXRtWtXtGjRAuvXr3f+nHXr1qFjx45o3769es6oUaMQFBSErTma7lLhZHfoAHOVKtAmJztHrHuK7tgxNWHLccDua0I//1x9NrZqBWuFCi573a++cpSRZed/YG8wIH3cOJz/809782meCfiEyZNlfL1VNZvWaGxqNL1ksnhjhpNjdP2KFe4vJ5Mm1++9Zy+ru+++dFf1cs+zTE6agYu33or0/4khOQ7aUp96St2WrKGgn36C308ks1rV76k7dw42nQ6mxo3h6+LirOrCgThw4ErWkBxeOd4/vC37kIgKlzF09iwDQ9dtQi3nMDK5o5hpLl1C5IP2oQ7/xTg0nnKHM5h3PW3bXgkObd4citGj4zyx+W5lLVECJinXdjShJv8JDC1YsEAFdBrk0zdFAkgSqJEgT8mSJdVjhw8fRuXKlRETE+N8XqNGjZCZmYlTp06p+0eOHEH9q7IbGjZsqL5XSADpr7/+yvUcrVar7juekxeTyYSMjAznh/xMB41G47MfLtt+vR4ZjtH1H3wAeeXi/l0kKBX13HMo3aGDmrAVO2YMNFlZXvHvXOAPuVrhmEbWr58L/581+PrrnIGhAn6vjLIIDvb8vws/CvRRsaIVjzxiD0xMnJiO2283ee1+pH9/+du0YceOYDUi253b9/33waoJd1iYFUOGZLj1Zw0fnomSJS34+289PvsszONrorg+0idMQOZdd0FjMql+QxIwudE14s4P6TV18KDe2Xg6v+dr09LsV3PffBNxgwejbN26KHk5o1NKbxEe7vHfyRUf9evbA0O//RbkfOyXX4KRlKRFbKwVzZu7b5/ibWuEH973wTVS9I+cpWSe3hZvXCPGdu1gKVcOukuXELplS/Fut0z4fPIpBF04jz9RCx81noERIwp+rNK2rRGLF9uDQxLEl+CQ/QKh/3w4y8lc8H/D/Yim0P9eBWE/oiqEHTt24Pjx46oc7Fok+2fZsmXIzs5G+fLl8cwzz0Cvt/+opKSkXEEhER0d7fya47PjsZzPkUCO0WhEWlqaKkm7+nXk/tmzZ6+5XatXr8Znl0/YxU033aTK0EqVKgVfV7ZsWde80EMPAa++CsOff6KcBNnatUOxkMuZ770HTJ0KXLxofywoCLp//kG5detkXjd8xo8/SkdUICwMMffdh5iI3OPHi2rPHhlVr85d0Lt3HKRCzCNrhNzupZdkfDpQtWoENBrXrB93rJFy5WS6BiCD7zZuLI1p09y2aXC0srv/fi3q1HH/Wp48GXj0UWD27BhMnBjjtgwlr/Pxx0DLltDt348yY8dKUzPk3Nl4037ku+/sU8kkKbNJkzL/TjE7dkzqzu0fu3ZJpMT+eE6yQ23eHEFTp6KcLGg/0LKllI1JyX8UypWLUo/Jf6O4+24tKlVy7+/pTWuEvBPXSNE4+qjFx+sQG1uu0MeBAbFG5AL3jBmIlelkciBVXJYuBdavgwl6DNctw8IPY1CxYu7z1PwMGACUKCGTX+2DZiZMKAcpQJDru35h8GDg5ZcRsmMHykVF2d9/bwD3I65XqMCQNI2WRtMS6JGyrWu54447VDbRpUuX8MUXX6h+Qy+88MJ1v6c49OrVCz169HDed0TQLly44Cxj8zXyO8gfxvnz51U/J1eI6tsX4UuXIuvVV3GpVi24W9C33yJq+nQYLmd7mWrWVA2UpU9PzKRJsMyYgXjZS/rIO2DUvHmQXV1Gt25ITk0F5MMFli+XAEEk2rTJUn9bnlwj5H6y3CUQWBxuZI306hWCr7+OxcKFZowcecEtQ53+/FOPL78sBa3WhkGDLuDcOffPku/ZE5g5szT+/luHt95Kxn/+Ezh913TvvYeSd94J7S+/IGPoUCS/9RY0Wq3X7Uc2b5Y9bRQaNcrEub/OqTG4Un4szaQNu3dD57jIkIO5cmWYmjZVjaZlCpm5Th3g8oUrnDsHf1C1qpxFxOGnn0w4dy5BxcJWrZILYHq0aZOIc+fcU6PA9xrKD9fIjZF/spCQsqqJ/J498bjpJve/F/raGtF1747SM2bAtmkT4vfsgbUYAv4yzbnEA+MhBX7T8BxaP1wLcXHnivSWIsmrS5YEYdiwOKxfr0GPHll4//1L/hEcio1FqcqVoT95EokrViD7zjuL9DLcjxSeJOgUJBGmUIEhKd+S5s4yLcxBMncOHjyoJoQtX75clXSFhYWpD7n6VrNmTYwYMQI//fQTWrdurbJ6rp4e5mgY7cgAks9XN5GW+6GhoSq4FBUVpX6OI8PIIa9spJwMBoP6yIuvLyzZflf9DunDh6vAUPCXX0J76hQsFSvCHXRHjyL6+ecRIukG0gA1Nhapjz+OjCFD7AfqRiMi3n4b+jNnEPbRR0i/7z54vexshK5dq25m9unj0nW1ZYv9XaFTp8wiva4r1wj5p6KsEelXEhlpVY2yd+40oFUr13dNfO+9MOfPkobXxbGMJTA3cWIann02Gm+9FYF+/dL948CsAMyVKiFx3jyUGDxYTVc01auHjMtXXr1lP6I9exbh6//Em/gFffd+j7K19kNz1QUeW1AQTA0aOINA8mEtc1VmkXqi538fV6pf3/43ePSoHunp0ohah5Mn9apEoU2bbLf//3nLGiHvxTVSdNJD7NgxA06f1qJqVd+8qO3ONWKuWhXZt92G4B9/ROinnyJt4kS4lcWC6IkToUtPw3a0wuqbH8WG8Yk39LbSunU2liy5qIJDkjl0//2xeP99KTODz5NysoiFCxG8ZcsNT0vmfsT1CnVtV3r4zJo1S00Kc3xUr15dBXzktgRrrvWf5sjIkUDRyZMncwV+9u/fr4I+0kRa3HzzzfhNUr5zkOfI9zqiXtLY+vfff88VoJL7judQ0Zlr1UJ269bQWK0Ik9RINzRni5o6FaU7dlRBIZtej7RRoxC/fTsyhg+/cvU2KMi5Q4+YO1eaVsHbhXzzjRoRbylbFtmtWrnsdc+f1+LXX6VfhA0dO/pZRzryaaGhNueEMHc0oY6P12LVqjDniPriNGhQupqAdu6cDh9/7P4G297EeMcdSLk8ul4NAnDUInmCyQTDvn0IX7AAsWPHonSzZijbrBme3vcfPIy3UfHsHhUUspQujUzJ1Jw6FRfWrsW5P/9Ewtq16vfI6tYt76CQHypd2ooyZSxquuGBA5JtZz+bkGzTsDAeRBP5Mo6sz1+G1GRJv89PPnF74D/iv/9F8M8/IwWRGIqleGVWqksuIrVubcSSJRIMsuKbb0IwalScL5wG5Su7U6crDagvTywn71GojCEJ3kjj6JyCg4MRGRmpHv/nn3/UGHlpFC1ZPTKNbM2aNSrLR5pVC/maBIDmzJmDwYMHqyyfTz75BF26dHFm83Tu3NnZp0imjknAZ9euXXjq8sQUISVhc+fOVQGiGjVqYMOGDaqnUbvi6onj5yQ7J3j7doR/9BFSH3lE/vNv/EVNJoQtW4aoWbNU8MQROU5+9lk1LvlaO/eId96B/uxZhH38sbM5trcKvdzDKqN3b0DnujdtR9PpRo1MKFWKO1LyLgMGZOCjj8Kxfn0IXnpJ49IpaosXh6sGjLfeakTTpsU7Ikyuzj34YComT47B7NmR6vd0xa7QV6SPHAnD77+rZvoSkMEtt9iHEsjBnBxsy+fLtzVy3/HYVZ9V0XZej1/je+RxbWKiKgdTZWG//grtVUfEMkVsj6UhdmluR783bgFuv9We3crpi0q9eib8849ONaB2BIa6dPGDswqiAOeYcsXJZNeW1aMHrM8+C/2JE2rypPG229zyc6R8OXLWLHV7Imajw/2l0bRpisteX4JDS5cmYujQOBUcGjkyDgsW+HbmUHaLFrBGREB34YJ6b/eHaaD+pNDNp69HAjt//vmnCtJIg2gp66pTpw5efPFFZzNpySqSAI9MNpNeRRJYatu2rRpZ7yBTzOQ5S5YsUa9VokQJjB07Vk0vc2jZsiVSUlKwcuVKFVyqWrUqJk+efN1SMiq4rE6dVDmB/tQphK1Zg4x7772h1wveulVNGzMcOaLum2rXRsq0achu0+b633g5ayjm6acROWeOfTu8dI8oJzKOsjgpI3OlK2VkPLAn79OkiQk1aphw9KgBX3wRikGDXNOPJzNTg6VLPZMt5DBwYAbmzo3AmTN6FfwaOTIdAUOjQdLMmdAfOYKgX3+VtGF4qtWjNSbGXg52uSxs7dnbMPLhyqhfz4je/RM8tFXeq359k7qgIEEhCQ5Jfy6ZZklEvq18eWYM5ccWHo7Mu+9G+Mcfq6whdwSGNJmZiJkwQWWrfoq++KbCIHzzpOvfi6Q8/8MP7cGhrVv9IDgUFITstm0Run49Qr76ioEhL6OxsThPNZ+WUfa+SBpwSS+nc+fOubzOMnz+fES/8AJMt9yCC5s3F+lKrJxQSBmClFgJS1wcUp94AhmDBl0pGctPdjbKtGqlRicnvfSSvdzMC4UtXoyYKVNgrF8fCTISxkUyM+XqrzQb1GLz5njUrWv2mjVC/sEVa+S//43ASy9FoWlTI9audc3B0ZIlYSpbR/oKbd8e78okvEL56KMwTJoUg1KlLNi1K16VzwUS6edTYuhQGA4edD5mk/cD+ZAS8hyfC/u482tXPz80FMbGjVUQSJpFm6tVs3/tsueei8J770Vg2LB0zJiRuychyVQy6UsR57zfvHk2Vq/+dzNuV+J7DeWHa+TGrVwZikceiVWloR9/nAh/46o1Yvj5Z5S65x5YQ0Pxz759sLloQrBD9OTJCF+yBGdQHg2wH+8sA9q3d1/wfdeuIPznP3HIzNSiXbssLFzou8Eh6f0U+/DD9vPLr74q9PdzP1K05B2XN5+mwCJlXJGvvQbDH38UOhVTk5iIyDffVDtNjcUCm8GgytNSH3oItsvZYwUWHIxUyRqaPBmRs2fbs4a8sAuslFuIzL59Xfq6O3YEq6BQ+fJm3HKL/zYaJN/Wp08GZs6MxC+/BOHoUR1q1LixaSlSVfT++/YDOcnS8VRQSPTvn4E5cyJUA18JVo0dG0BZQ/J/Ub48ErZsQbkyZXDun3/gDYdhe/faS88bN3Z9s3N/akDtwDIyIv/AHkMFIxcUTNWrw3DsGEK/+OKGKx9yCv76a3V+I4ZjMdr1DkX79rkHIrna7bfbM4ckOPTttyG47744FRzyxfL27I4dYdNq1fml7swZWCpU8PQm0WVuGCxM/sIWG+ssiQpfuLBg32QyqeeWueMORCxapIJCmV26IP6bb5AydWrhg0KXZQwcCEu5ctCdP696DXkb/dGjCNq7V/W9yLznHpe+tkwkEJ06ZbN9BnmtMmWsaNfOfrXs009vvFHzV1+F4PhxPaKjraqcy5Ok/d3DD6c6M6PS0wPwD1F2PhKd84KdkNEIVR4lmjRhYCgv5ctbERd3JTjLwBCRf8gZGEpO1qiscvbwzYNGg8yBA680oXYR7cWLiHnsMXX7TTyMPXEd8NxzrusrlF9waNmyRISFWbFtmz04JP//vsYaF6eygUVwETKGyH2YMUTXlT5ihGpAHbJpE7RnzsB6raiuzYbgb76x9xE6dkw9ZKpTB8nTpqnpNjdMsoYmTFClWs5eQ16UNeRoOp3dvj2sJUu67HUlQ9IRGPq//+OBPXk3ac4sfU0++0xKr1JvKMvnvffC1echQ9IRHu75HJU+fTLxzjuROHFCrxpijx/vmZ5HBBw8aEBWlgYxMVZUq3ZjmWn+SuJ30mdo2zYdatUy4aab+O9E5A/KlbP/LUsm+S23lHM+rtPZEBRkU4fGBoP9dlCQHCrbLt+X9i5XHr/ebXm+vI7jdnS0DV27Zvpc6VJGnz6InDlTDTGQC7jmGjVu7AVtNkQ/8YRqnHxAUxdP217GGy+kIC6u+CJzLVrYg0NDhsThu+/swaFFi3wvcyj7//5PTXOT6WTe2iIkEDEwRNdlrlMH2bffjuBduxD+4YdIzTEZzkF/6JC9j9C336r7lhIlkDppkj1448L6D3k9KSWTXkMS/c8YNgxewWpF6KpV6maGi8vIZNSwjMqWqwMtW7JxKHk3CV7Gxlpw/rwO27YFo0OHoq3ZX3814IcfgqHX2zBihHeUbUlLtEceScVDD8WqrKGhQ9NdOn2NilZG5gUJTF6rTZtsdVW5d28fvKRMRHmSgM0992RgzZrcmbkWi2QP2TOI3GHCBD2eftqeOesrrGXKILtDB9XkOHTFCqROmXJDrycVC6FffgmjJgiDbB+hdUegZ8/i37/edlvu4NCIESXwwQcSHPKdYxKZSh01YwaCd+yAJj1dNQwnz2NgiAo2un7XLoTJ6PqHH3ZOBZMpXJGvv46wDz+80kdo5EikPvggbFFRrt8QR68hyRqSXkOSIuoFWUNBP/wAvWRTRUWpHZ0rSTmN4wDf167UUOCRq429emVi0aIIrFgRVuTA0Lvv2g8Q5ICrXDnvyZG/5x7JGorAsWMGLFoUjoceYtaQJ+zezTKygpDeXM2bG9GokW8O1yCivM2dm4Q5c5JUWa3JpIHRqJE5Lde8LZ/lfs7b8r3Z2blv279mf479a8DFizps3x6Mjz8Ow2OPpar3eV8i5woSGAr79FN10VrVhheB7q+/EDV1qro92fYSjoXXx4KXL3js4oQEhz76yB4c+v77YAwfHofFi30nOGS++WaYq1SB/u+/Efzdd8i6805PbxIxMEQFkdW5M8zly0N/9ixC165FZq9equmaNJfWJtunwWTeeSdSpkyB5aab3L6Dd2YNrViBjKFD4TVNp++6yxk0c0d/ISJfKSeTwNDmzSG4dEmD2NjCHaRI34R160I9OqL+ellDjz6ahvHjY/HuuxEqmykqyjcOwvzJnj2OwBADHvmtV/4bEfknCUjItVEpFYMbRwKYzTLVsAz++UeHL78MwV13+VZbg6yOHWEpWVKVfwVv3Yrszp0L/yJmM2IffBDazExs07bHG9ZH8eLkFGe/J0+RwL8EhwYPjlPBu2HD4rBkiY8EhzQaZHXqhIiFC1XgjoEh78Dm05Q/vd5Z/xn5zjso3bEjoqdPV0EhGTWYsHIlLi1Y4PagkBISonoNiYjZs9Uoe0/SZGYiZN06dTuzXz+XvvY//2ixb5/9BKhjR996I6bAVa+eTM8zqSuOa9YUvuh9wYJwlRLfunU26tb1vil8d92ViZo1TUhO1qptpeKVmKhRfZ5Eo0bMGCIicneAWS74iOXLb3ywRLEzGJyDdOSCclFEvPOOGjCTpo/GEOsSNG1mwtChnh2K4dCsmT04FB5uVVOMJTgkJYW+wFFlIVPe2EHdOzAwRAWSfu+9sIWEQH/iBPR//aWi70mvvYYLmzbB2KpVsW6L9BqylC2rMpjCVq6EJ6mm3OnpKh3S2LSpS1/7m29CnH00SpfmDpN8h+MgUsrJCiMlReM88PS2bCEHaZv26KP2PgvvvReBpCTfOADzF3v32oPl1aubEBPjA1dFiYh83L332t/TpZ/N33+7rndocckYMEB9lkbH2gsXCvW9ht27EfnWW+r2SPN8xAdVxKxZydB60Rm0PTh0ERER9uDQ0KFxyMjw/mMT4223wRoZCV1CAgz79nl6c4iBISooW1wc0h54QP0Bp44fj/jt25ExaJBLm0sXWEgI0saPv5I1JMXRHp5GpppOu7jQ+Kuv7P2TmC1Evkaa3cokExkp/scfBa9YlqBQWpoWN99sQrt23ls+2b17FurUMSE1VauCQ1R8WEZGRFS8Kle2oE0b+7Go9BryNeZatWBs3Bgasxmhn39e4O+TpshSQiZ9VD8NuhcrMBAPPZSKGjW8L5u5WTOTMzi0c6ePBIeCgpDdrp26KeVk5HkMDFGBpT72GM7/+SdSJ0+GLTLSo9uSPmgQLGXKqKbPnsoa0p4/rxqmCUeaqqtkZcmVGXtgiGPqydfI6FbHui1o1pD0MVi40F6aNXp0ulddjbuabJs04RRSTiblTVQ89uyxNw5l42kiouIzeLA9a2jlyjD1fu1r1MAaRzmZrWDZplHTp6tKiQuhFTHK+F/Urm3CAw94ZzazaNr0SnBo1y57Q2pvr9CSPkOCgSHv4MWH3kQFzBp65x2PZA2FrlkDjdWK7ObNYalSxaWvLamgmZlalCtn8co+K0T56d/ffhC5alVogf48168PwdmzepQsaUHv3t5Ru389XbtmoV49I9LTtaoRNbmfHOA6+q4xMEREVHw6d85CiRIW1YT66699b0xu5t13wxoSAsPhwzDs3Zvv80O+/BLhy5fDptGgX+aHSNVGY9asJK+fyibBoeXLLyI01F5W5pji6a2yOnSATauF4eBB6E6f9vTmBDwGhshnqayh0qXtWUOfflq8P9xmc/7MTCkjc7Er08iyPDYKk+hGtG+fjdKlLUhMzP8gUi7eOYIrw4enu3q4n1vI36Uja0hG1yck8O3U3Y4d0yMlRYuQECtq12bAnIiouEhApH//THV72TLfKyezRUUhq3t3dTvsk0+u+1xtfDyiH39c3Z4X/hi2oR3uvz8djRv7Rgnzrbea8H//Zy/H//Zbe/WBN7cqcfRoDd6yxdObE/B4JEu+KzTUY1lD+gMHYPjzT9iCg5HZo4dLX1tOkrdsYRkZ+f4kkz59MgtUTvbjj0H49dcghITYvGbSR0HIgVfDhkZkZGgxbx6zhoqrjKxRI5NaX0REVHzuvTfdGWw4c0brs+VkoWvXqqnCebLZEPPYY9AlJuJkXAM8kvYiKlUyY9Ik+4UgX9GuXZZPBIZE9uXpZCwn8zzf+6smyiF98GB71tDp0wi73Ai6ODh+VlbnzrBFR7v0tQ8c0KuSGkkDbdXKexvwEhV0Otk33wTjwoVrv928+669t1DfvhkoUcLLC+Kvyhp6/HH7weLixWGIj+dbanE0nvaVq7ZERP6kenULbr89G1arptBTR72BsUULNUVYm5aGkA0b8nxO2JIlCPnmG1iCQnBn4nIYEYxXXklGWJhvTcFs29Z+/vDrrwYkJmp9Y2z9zp3QpHlvD6dA4N0rhaggWUMPPHAla8hUDCcMMtVg9eor08jcVEZ2xx3ZPlFSQ3QtN99sRuPGRlgsGnz+eWiezzl2TIevvrIv9FGj0nyyZE763WRlaTF3LrOGimciGfsLERF5sgm1TCezWOBbtFpk9O9/zXIy/dGjiH7hBXX75egZ+AN10a9fhjPI4kvKlrWq6ak2m8Y5zMZbmWvUgLlqVWiMRudQH/IMBobI56UPGQJLqVLQnzpVLFlDwdu2QZeQAEvJkshu29ZtgSFHfTCRP2QNySSTvAaBvP9+hDpwkbLJGjV87SjTnjX0xBP2rKEPPwzH+fN8W3WH9HQN/vzTXj/GwBARkWfceWcmYmKsKrPdF8qUrpbRr59qKC3ZKbq//77yBaMRMRMmQJOVhUNVOmDqhYfUMIxp05Lhq9q1840+Q3Igxelk3oFHsORfWUNvv+32rCFH8CnznnsAg73nhatIKcrevfar4h07sr8Q+b6ePTNV76BDhwwqpTknSW/+9FN7JtHo0b6XLeQg2X3Nm2cjO1uDOXOYNeQO+/cbVPlC+fJmdSWUiIiKn2SyS9m3WL7c98rJrBUqILtNG3U7bOVK5+ORb7yBoN9+gykqFp3PLIUNWrzwQjJiY32rhOxafYa8fmy9o5zs66/he6lo/oOBIfILGf/5jzNrKPTzzwv9/dOmRaFfvxI4eVJ33edpkpPVCEv1M/v1g6tJLxYhDW3LlPHyvThRAURF2dQVRnF1T4KlS8NUCVb9+kbcfrvvZoHk7DX00Ufhqk8YuRb7CxEReVc5mZSB//OP751KZgwYoD6HSmDIYkHQjz8iYs4c9djTJebjpLkCOnfOxF13+fYF2mbNjKpf6YULOvzxh3cflxhvuw3WqCjoLl6EYe9eT29OwPK9v2aiPNgka2jcOHU7spBZQ2fParFgQQR27gxGjx4l8csv184CCl2/HprsbJhq1YK5bl24mqPXCqeRkT/p399+ELl2bSiyLi9t+bx4sb3p9Jgx6Sq44statTKqLD+jUYNRo+KQlOTjv5CXTiRjGRkRkWfVrGlG06b2/oG+2IQ6q0sXWGNioD97VjWhjnnwQWhsNuxrPBivH++PiAgrXnop2eePS4KD7ccmYts2L29aajAgu107dZPlZJ7DwBD5jYyhQ1XfH/3JkwhdtarA37dhw5WmuBcv6tC/f0msXZv3DjTUUUYmTadd/I4hJ8rbtnFMPfmf1q2NqFDBjORkLb780v63tWZNqLqKVa6cBT16XGNsrI95661Laqzt33/r8dBDsV6fuu0rpDeVI2Po1luZMURE5GmDB6c7m1D73HtdSAgyevVSN2MffFBNNs6qUAVd/pyrHpsyJQXly/vaL3X9crKtW728z1COcrIQKScjj2BgiBDoWUPr19tPVCdNSlEBGekT8sADcXj7bWmKe+V50qQu+McfVdM6xxuKK+3aFYzMTC3KlrWgbl2zy1+fyFO0WqBfvyvlZPJ39d579l48I0emubpVl8fExdnw/vuXEBxsU03kZR9CN+7sWR3i43XQ622oV48ZQ0REniZlVlFRVpw8qcf27fbAvS/JGDhQfZZJWDatFo+X/gDxmdFo0SIbQ4bYs5z9gaMB9S+/BCEtzbtToLLat4dNp4Ph4EHoTp/29OYEJAaGyP+yhkqUgP7vvwuUNSQThH7+2f6GJs30Fi5MdI7MfvXVKDzySAyyLw8Hc7xe9h13wFqunNvKyDp1yvL59FWia5WTydhUucIozaglXXvQIP85ABP165vw8stJ6vbrr0c6+4ZR0e3ebY8c3nKLSWYNEBGRh4WG2tCrV6azt56vMderB2P9+ur2z//3CObuba8u6rzySpK6mOUvbrrJgipVzDCZNNi507sDeLbYWBibNVO3g1lO5hF+tPSJAFtYmHNCWeQ77wDm62febNoUokZlS9+KChWs0OmA6dNTMGNGEnQ6Gz79NAyDBpVA4sUc08ikjMzV222TwBDLyMh/Valiwe23Z6u/t8mTo9Vj996boZpT+5sBAzIxZEi6+l0nTozNt6k9XZ+jjKxJE5aRERF5WzmZlIhfvOh7p5SX3n0Xp16eg24/zlD3H344FTVq+N9ErCtj60N8p5yMgSGP8L2/YqKCZg2dOJFv1tC6dfbLz9275+5xMmxYBpYsSVQZDT/8EIzpXY6q17OGhSHrzjtdvs0HD+px9qweISFWtGp1OUWJyE+zhuTKlVZrw8iR9oNKf/T888lo3NiIpCQtRo6MQ6Z/tFHyiL17HYEhlpEREXkLaXvQqJFRvad/+qnvpXNaqlTBgz+OwMWkIJWROm6cvWLA3+QcW+/tsjt1Up+Dd+2CJs0//z+8GQND5J9ZQzl7DV0ja+jCBS1+/NF+wtG9+7+zdNq3z8aaNQmqaW6nc8vVY6ea36Ve311lZHfcIaMlXf7yRF6hR48shIdbnX9zFSv635W5nNNA3n03EXFxFhw4YMDTT8fk6llGBWM0Ar/9Zi8lk0AbERF5D0c5uJST+dp73JYtwVizJkxdqJo1K8lv+h1erWVLIwwGmxqMcfy4d2cwm6tXh7lqVdX7KXjbNk9vTsBhYIj8UsawYbDExdmzhlavzvM5GzeGwGrVqKsdlSrlfYJap44Z6z8/jUG6Fer+qO9HueWqCMfUUyAIC7OpK3LSYF1Stv2dlKfOm3dJHXRKWeqHH/reWF9P++MPgxoIEBNjVb0SiIjIe/TsmYmwMCv++kuPH37w7h42OUkW7zPP2MvaR49OR8OG/luqHBFhQ7NmRt/IGtJoWE7mQQwMkV+SrJ50R9bQW2/lmTW0fr2jjOz6wZhKv25GlCUJ8SEVscXSHg8/HIvXXot02ZURyVzat89+maJjRwaGyL898kgadu/+B7VrB8bkvdatjXj6aXsQbOrUaOzZ46eXJN1k716Ds4yMTfmJiLwv6OBoQr18ue9c/JDJqKdO6VGunAWPPeb/F6ocfYa2bvWdPkPBMrbewgtCxYmBIfJb6TmzhtasyfU1aZK3a5f9yka3btdv/uFoOh1y/z0YP8HeE+WttyIxfnwMslwQx5GpRdKktkEDI8qWtZfZEJH/kCwp2c9IH4ZRo+KQkMC33sI3nmYZGRGRN5eTyQXXS5e8P4J/7pwWs2dHqNvPPJOispn9naPPkEwmc0xb9lbG5s1hjYqCLjERhj17PL05AYVHp+S3bOHhSB87Ns+sIZmgYLFoUK+eEVWrXjsarb14EcFbt6rbWf36qiv/r79+CXq9DWvXhqF//5I3fJLHMjIi/yaZLm+8kYTq1U04f16HceNi8xuYSJdxIhkRkXeTMixp3ixlv59/7v1ZQzNmRCEzU4tmzbJVKVwguOUWM0qXtqjf+6efvLzkz2BAVvv26mbIli2e3pqAwsAQ+bX04cNhiY2F/vhxhK5d63x8/fqQApWRyfdozGYYGzWC+eab1WMDB2Zi+fKLiI62YvfuINx1V0kcPqwv0vZJxtG2bY4x9V4ewieiIouMtGHBgkuqF8POncGYOTPK05vk9SSz88QJ+75VesEREZF3XvxwjK6XcjJvbkL9yy8GrFoVBo3GhuefTwmYEmX5Pdu2tZ9nbNvm/eVk2ewz5BEMDFFgZQ1ZLCrNdfv24DzH1F8t9HIZWUbfvrkeb9XKiP/9LwFVqphx8qQePXuWxHffFT4C/8MPwcjI0KpmvPXq8Yo4kT+rWdOsMofEvHkRzgA1Xb+/UI0aJkRHe/GZBhFRgOvdOxMhIVYcOmTA7t3e2UvPagWmTbM3nB4wIAMNGgTWcbejz5DXN6CWC+ft28Om08Fw6BB0J096enMCBgNDFBBZQ9aYGOj/+ktlAG3eHAKzWYM6dUyoXv3aZWT6w4cR9OuvsOn1yOrZ819fr1HDjHXrElQqakqKFkOGlMBHH4UVqYxMmk4HylULokB2111ZGDMmTd1+5JEYHDlStGzDQMAyMiIi3xAVZVPvb47R9d7os89CsW9fECIirHjySf9vOH21Nm2yVabUwYMGnD/v3SEAW0yM6jUkWE5WfLx7VRC5gC0iAmmXs4Yi3noLG74IKli20Oefq89ZHTvCGheX53Pi4qz45JOL6NUrQ/UsmjQpBi++GKWuSuS7XTYJDDnKyNhfiChQTJ6cgttvz0Z6uhYjR8YiLY1R4byw8TQRke9wlJP9738hSEnxrvc1eZ99+WV7CffDD6eidOnAG/Yi5yyNGtkvtDjaWHizrE6d1OdglpMVGwaGKCCkjxihsoYMx46h3Hf2CWU9elwnGGOxIOxyYCjzqjKyq4WEALNnJ+Gxx1KcJSKjR8ciM/P6b4p//qnHmTN6hITY1EhrIgoMer3sJy6pEtKjRw0qc8ibezJ4ggTX9+27MqqeiIi8W9OmJtSsaUJWlharV4fCm8gUsvh4HapWNeO+++wBrEDk6DPkU2Prd+2CJtUzGV6606cR+fLL6rwwEDAwRIGTNTRmjLo92fICat+chZtvvvZYoKCdO6E7d04FkyRjKD9SBvboo2mYPfsSgoJs2LgxFH36lMA//2jzLSNr3ToboaE8KyQKJKVKWfHuu4kwGGzYsCEU777rnan3nnL0qB6pqVqEhlpRqxZHuBEReTs5FnaMrpdyMm+54HHihA7vvWcfTz9tWjKCvT9Zxu1j67//PtjrYx2W6tVhrlYNGpMJwdu2FevP1qSkIHLGDJRu0waRc+Y4e876OwaGKKCyhlL1saiNQ5hcY/l1nxt2eQeQeffdKMw7iDTfW7HiImJjLfj11yB0714KBw7k3UOEY+qJAptcXZ0+PVndfumlKOzY4eUjZIvRnj32bCFJe5cMKyIi8n59+mSoC6QHDhiwf793NKF+4YUoGI0atG2bFfATgBs3NiEqyoqkJK0zK9cXsoaKbTqZyYSwxYtRulUrRM6dC012NrJbtoSpbl0EAgaGKGCkIAqzbI+q270PzLxmWqAmPR0hGzbkOY2sIJo3N6qm1NWrm3DunA69epXE11/nDi4lJGidE3ek8TQRBaZhwzLQt28GrFYNxo2LxdmzfFsW7C9EROR74uJszh6ehR3I4g4yMXjTplDodDZMnx444+mvRS603HGHY2y9D/UZ+uYb95Zz2WwI3rwZpTp2RMyUKdAlJsJUowYuLl6MiytXwlyvHgIBj0ApYGzZEoI3LQ8iSRuL8JNHELJuXZ7PC9m4EdqMDJhvugmmJk2K9LOqVrWocfYtW9obzA4fHocPPrjyBimBIptNg/r1jShXLvAa4BGRnRykzpyZjFtuMeHiRR1Gj45DdmBf0MwVGJKrm0RE5Dsc5WRr1oQiPd1zkRizGZg+3T6efvjwdNSsybLknGPrfaHPkLFZM1ijo1WgJmjPHrf8DMNvv6FEv34oMWKE6kVrKVECSTNm4MLXXyNbMpYCKJrIwBAFjPXrQ5CKKOxoPl7dj3zzzTyjz44yMpUtdAM7g5gYGz766CIGDLBnAzzzTAyefTZK/UgJUolAT2klIqgeYwsWJCI62oq9e4OcB7KBSqbHHDpkrx9r3JgZQ0REvuT224246SazujC6dq3nmlAvWxaGQ4cMqr3Do48G3nj6a5GSOiGlZJcueXnQw2BAVvv2bplOpj1zBjEPPohSXbuqBte24GCkTpiA+B07kDFsmD29KsAwMEQBQa5YOCLjoZOGqeiz4ci/s4a0Z88iaPt2dTuzT58b/rlBQcDrryfh6aftE8sWLYpQ2UOO9E32FyIiUaWKRTWv12hsWLo0HCtXetdEl+L0668GFUyvUMGMsmWZUUlE5EvkmqpjdP3y5Z4pJ0tM1OC11+zj6SdNSlUXa8muQgWrmh4n77PShNrbZbu4z5AmLQ2RM2eiTJs2zgnUGb17I/7775H69NOwRUYiUDEwRAFBSreysjRqTGWt5mFIGzVKPR751lv2uciXha1eDY3Nhuzbb4elUiWXvUFOmJCmJhDJaPpvvglRV1HKlLGgXj2WSRCRXceO2c6rmk8/HYPffw+8q1VCsqZEkybcPxIR+aJ+/TLV1E3Zn19rCIs7vfFGpGqwXKeOCYMH20vb6N/lZD7RZ6hdO9h0OhgOH4bu77+L/kJmM8KWLrU3lp49G5qsLGS3aIELGzYgafZsWCpUQKBjYIgCwvr19qvv0hBPAjXp991nzxo6fPhK1pDN5hxHWJSm0/np0SMLn32WgFKl7OVrnTplQcu/QCLK4eGH09ChQ5YKZI8aFef9ad4u6gMhU8jeeScC/fuXUAf0gmVkRES+qWRJKzp3tmfFL18eXqw/+88/9SrzVjz3XDJ0umL98T6hfXt7YOjbb0Pk9Mer2WJiYGzevOhZQ9JY+uuvUer//g8xTz8NXUICzNWqIXHRIlz87DOYGjZ0/Ub7KJ6WUoEdPqzHihWhbm0K7w6ZmRrnVLDu3e1vUrboaKSNHJkra0iaj0mgyBYSgqzu3d2yLdJIdf36BDz5ZAoef5z1zkSUmwSLpaSsShUzTp7UY+LE2JxJjX5BDkKlh9DCheEYMSIW9eqVxV13lcIrr0Rhx45gZGdr1O/frRtLbYmIfJUjU2fVqlB1LF5c7y/TpkXDYtGgW7dMtGrFCwx5ad48GyEhVpw/r1OBNH8dW6///XeUGDgQJYYOVed4lthYJL/wAuK/+QZZXboEVGPpgvD+lUAeJzvZpUvDVENUo1GDS5e0GDvWXjvsC775JhiZmVpUqmRGgwZXShPS778fEe+/D8OhQwhZvx5BP/+sHs/s0sWt9aUVKljw4INpbnt9IvJt0gvhvfcS0bNnKdUbTTJofD2QfPq0Dtu3B2H79mAV/ImPz30JVxpvyxTH1q3lw4jq1c08XiMi8mEyFl2OvU+d0mPduhBVXuZumzeHqPeZ4GAbnn3W3t+T/i0kBGjZ0qjaW0g5WZ06Zq8PDEU//zyCfvgBmtRUoFy56z5fe+4col59FaGffqpahNiCgtR5X+rEiSo5gPLGwBBdV2qqBk88EYMvvrjSCPW99yIwYkQ6gr2/LNU5jcyRLZTzREN2DOkjRyLyjTfUhDJtQoJ6PNMNZWRERIVRr54ZM2cm4eGHY/Hmm5Fo2NDoU1MML17UYseOK4GgEydyH27IlcrmzY0qCCTBIOm3xnR/IiL/yoC9994MvPpqlGpC7e7AUHa2lI7ZG06PGZOGypV9rMShmLVtm60CQ3IBytsv+FuqVYOpenU1Tj7422+BmjXzfJ4mPR0R8+YhfP58aDPt6y2jZ0+kPvUULJUrF/NW+x4GhuiapPHpmDFx6oBer7epyVoLFkTg3DkdPv00DEOGeH8zN9knOEbDS3+hq0k5WfiCBSprSFhKlUJ2mzbFvp1ERFeTg2hp3LlkSTgeeigWGzZcQNWqFq8dMf/jj/ZAkHz88Ych19d1OhsaNTJdzgjKxq23Gn3m4gIRERXNgAEZeP31SPz0U7BqSVGzpvsyU+Qc5e+/9Shb1qKGvtD1tWsn5drR+OmnIGRkaBAWZvP66WQqMCTlZKNH5/6ixYKwFSsQ+dpr0MXHq4eMTZsiedo0mJo08cwG+yAGhijP0rElS8Lw3HP20rGKFc2YN++SmhAjV3SlpGzevAgMHJgBvZevoO++s08AK1/erPr7XE1lDd1/v8oYEpm9esHrfykiChjTpyfj998N2L07CCNHxuGLLxIQGur5gze5Mrtnz5VA0L59BpjNuWu/ZBpMq1b2QFCLFkZERnp+u4mIqPiULWtVw1a+/DJUZQ1Nn+6e8q5//tHi7bcj1O3Jk1MQHs73m/xUr25R53inT+uxc2cQOnXK9vpysoj58xHy9dcqEOQgGURRL7wAw59/qvvmqlWRMnkysrp1Yw+hQuIZMOWSkmIvHVu3zl461qVLJt54I0n1vHA0kpMdr2QRSYlWz57e3RxUapqFNDG91r5BZQ0tWgRNSgoy+vUr3g0kIrqOoCDg3XcT0bVrKRw8aMCkSdF4552kYj/WkWOw3buB1avDVa8gyQ7Kyso9v0IaRjsCQdLwU6bSEBFRYBs0KEMFhj77LBRPPZWi+tu42ssvR6kLwU2aGNGrl/t7GfkDOY6QsfXLlulVnyFvDwxJBpA1JgbaS5eAXbugNxoR+cILCJHSMkB9LfWhh5A+fLj94IkKjYEhcvrtNwPGjo11lo4980wKRo5Mz3UCImmG99+fjlmzojBnTiTuvvvaARdvuKL91VchzlHx1xuDmLBqFbSJiTDfcksxbiERUf7KlbNi/vxLGDCgBFatClNlWMOHZ7gl+HP2rA7Hj+tUOr68F5w44bitU6W5gL1/gyhZ0uJsFi2fK1XyzjI3IiLy7Gj0cuUsqhXFpk2huOce1wZu9u41qBYX4vnnk1VvIyoYe2AoXPUZAry8Wbdej6wOHRC2ahVw330oeewYNFYrbAaDCgZJUMgWG+vprfRpDAzRdUvH8jJ8eLoqJZMeEjLxq2NH74wwf/99MFJTtarWWE6krsdcu3axbRcRUWHdfrsRU6ak4Pnno9Uo3rp1TWjWLO99dH4B81OndCro4wj42ANAevW4yXTtSL8Ma2zRIsvZJ6hWLU4OIyKi65M2FNKEWiZsfvRRmEsDQ1Yr8Oyz9ilT/fpl5Nk2gq5NsnwlGeD4cTkm0KFKFe++wJPVqZM9MHTkCOTwI7N7d1U2Zqla1dOb5hcYGApw+ZWO5SU21qYaT7/7bgTmzInw2sCQ43fq1i2TVw+IyOeNHp2umlHLlMixY+OwceMFlC7973ItaSKZM9PHfsBnvy0ZQVbrtaM5QUE2VK5sVk2upTTsppvM6kDxppssuO220rhw4RJscjWBiIiogKQv6ZtvRmDnzmD89ZcO1aq5JgCxenWoel8MD7eqITlUOFFRNjRtasQPPwTj22+DMWyYdw8Wyu7UCcZmzRAUFoaERx5Rt8l1GBgKYAUpHbuW0aPT8MEH4WrKgHSzl7HD3sRoBDZvvjKmnojI18m++fXXk/Dnn3ocOWLAuHGxGDo0PVfgR/bn8fHXn/seFmZVgZ+qVSUAlDMIZFEZlnmNjddoNOzLT0RERVKhgkWVlMl49I8/DsOUKak3/Jrp6RrMmGEvb37wwTSUKcO+dkUdW+8rgSFbeDgurl2LcuXKwXTunL3shVyGh3kBqLClY9eaMiApmx99FI7ZsyPw4YeJ8CY7dgQjOVmLUqUsaNbMu4JWRERFJZNWFixIRPfupdSBnHzkJSbGejnbxx74cQSA5LM0hWYJGBERFXcTagkMrVwZhieeSL3h/sBStXD+vJQ/mTFyJMfTF5UE7F55xX7uJBfW2bc5cDEwFGCKUjp2LePGpamov+zkDxzQo25dM7yFTEwTd96ZlefVbyIiX1WjhgX//e8lPPdclArySKnX1dk/RdmnExERuYuMrS9d2qKyWmU4zI1k9J88qVMtLcTUqe6ZdBYopGdhiRIWXLyowy+/BKFlS15QD1TsvBJgpWN33llKBYWkdGz69GQsXHipyCcQUnbgmPY1d6595+wNTCZg40Z74Kt7d46sJCL/I73dvvvuAlatuog330zCQw+loWfPLDRsaGJQiIiIvI7BAPTvby9VWr7cPkWsqF54IQrZ2Ro1CKFLF7aMuBHSh1XKyYSMrafAdUMZQ2vWrMHy5cvRrVs3DB8+HGlpaVi5ciV+/fVXJCQkICoqCs2aNcPAgQMRFnZlByBfe//993HgwAGEhISgbdu2GDRoEHQ5Ujvka0uXLsWpU6dQokQJ9OnTB+3atcv18zdt2oQvvvgCSUlJqFKlCu677z7UqFHjRn4lv+SK0rFrGT8+Ff/7X6hqhippoRIs8rRdu4KQlKRV0e8WLRj1JiIiIiLyNJlONmdOpApAyCTMSpUKf96wY0cQNmwIhVZrw3PPJbM02kVj61etClNj659++sb7P1GAZQwdPXoUX331lQrIOCQmJqqP//znP3j99dcxfvx4FSSaN2+e8zlWqxUvv/wyzGYzXnzxRfWcb7/9FitWrHA+Jz4+HjNnzkTdunXx6quvonv37pg/fz727dvnfM7OnTtV4Khv37545ZVX1Ha89NJLSE5OLuqv5LelY2PGxGLKlBgVFJLSsS+/vOCSoJCoV8+MDh2y1JQbGWHvDRxlcl27ZrFZKhERERGRF5By5zvuyIbNplHtKArLbAamTbOPpx86NAO1a3tPGwtf5sgYOnDAgPh4FhQFqiL9z2dlZWH27NkYM2YMwsPDnY9XrlwZjz/+OJo2bYqyZcuiXr16Klto9+7dsFjsEWEJFJ0+fRoTJ05E1apV0bhxYwwYMABffvmlChaJzZs3o3Tp0hg6dCgqVqyIrl27okWLFli/fr3zZ61btw4dO3ZE+/bt1XNGjRqFoKAgbN269cb/VfyodKxr11JYv941pWPXMmGCveHbp5+G4fx5z+5MZAlt2mQvNHaUuRERERERkecNGpSuPq9YEaaO2wtDStAOHjSoAQuPPcbx9K4i/QobNLBXWbCcLHAVKZ9iwYIFKqDToEEDrFq16rrPzcjIQGhoqLNM7PDhwyqAFBMT43xOo0aN1GtK2dhNN92EI0eOoH79+rlep2HDhli8eLG6LQGkv/76C/fcc4/z61qtVn2PvP61mEwm9ZFz/K5sm+O2L3Jsd87tl9KxxYuldCzKWTo2f37S5Swh1/+eLVqY1OSvn38OwvvvR2DqVM+lIP70U5BqnhYba1XN03z1/9Xda4QoJ64Ryg/XCOWHa4TywzVComvXbMTFWdREMRlg06WLPVslvzWSlKTBq6/ax9NL+4oSJdQzi2/D/Vy7dkbs3x+Eb78NQf/+3ntxnfsRLwoM7dixA8ePH1flYPlJSUnB559/jk6dOjkfk35AOYNCIjo62vk1x2fHYzmfk5mZCaPRqHoZSUna1a8j98+ePXvN7Vm9ejU+++wz530JQkkZWqlSpeDrJENLSCXdyJGA49fs2RP44AM9YmNLuvXnT5smGTrAsmURmDEjArGx8AhHwlivXlpUrlzOMxvh5WuE6Fq4Rig/XCOUH64Ryg/XCN13HzBrFvD553EYPrxga0RGql+6JFO0gEmToqHX5z5XpBvTty/wzjvA99+HonRpSeqAV+N+xMOBIWkaLVk7zzzzjCrbyi9TSPoESZlXv3794A169eqFHhK9uMwRabxw4YKzjM3XyO8gfxjnz5/Hr7/qVD+hv//Wq9KxZ59NwciRGcjKAs6dc+92NG4M3HJLSfzxhwEvv5yKRx6xl5cVJ6lW/Oyz0gB06NAhEefOXbkCEchyrhGbpJMRXYVrhPLDNUL54Rqh/HCNkMPdd+swa1ZpbNhgw+7d8Shf3nrdNXL4sB5z5shFbg2mTr2ICxc4XMbVpG1wREQZXLyoxebNCWjUyDX9aF2N+5HC0+v1BUqEKVRgSMq3pLnzk08+6XxMMncOHjyoJoTJhDIp6ZLMnhkzZqgyLek5JBuTM6tHGlfn5GgY7cgAks9XN5GW+/J6EpCSaWfycxwZRtfLRsrJYDCoj7z48sKSTV+0KDRX6VjOqWPF9atNmJCKBx6Iw4IFYRg9Og1hYcX7byqlbPHxOkRHW9GqVVax/d6+Qta4L69zcj+uEcoP1wjlh2uE8sM1QtWrm9GiRTZ++CEYn3wS+q8LyjnXiHyaNi0SFosGXbtmqhH1XD6uJ6fr0hh848ZQbN0ahIYNvTv4xv2I6xWqU7D08Jk1a5aaFOb4qF69Olq3bq1uS7BGMoVk2pgEgyZNmvSvzKKaNWvi5MmTuQI/+/fvV0EfyS4SN998M3777bdc3yfPke8V8trVqlXD77//nitAJfcdzwmkqWP9+wNTpkS7ZepYYXTvnoWqVc24dEmnmsMVt3Xr7E2nO3fOQj4JbURERERE5CGDBmWozzKd7PKMojxt2RKMbdtCEBRkr4Yg908n+/ZbNqAORIUKDEnwRhpH5/wIDg5GZGSkui1BIRkZn52djbFjx6rMIcnikQ8J3DiaSEsAaM6cOThx4oQaQf/JJ5+gS5cuzmyezp07q5H1y5Ytw5kzZ9TEsl27dqmx9Q5SEvb111+rUfcy5UyaV8vPbdeuHQLFH3/o0aVLSdVPyJ1TxwoTaR43zh7xnz8/AsZiDDTL8tqwwd5IvHv3zOL7wUREREREVCjdumWq6WJnzujx3Xd5ByLkXGL6dHsvIalGkHH35D7t2tkDQ3v2BCE5mc2dA02RppJdizSlloli4sEHH8z1NQkEyQh6ySp66qmnVCBHehVJYKlt27ZqZL2DPE+es2TJEmzYsAElSpRQgSaZXubQsmVL1dx65cqVKvBUtWpVTJ48+bqlZP4mPNyGxEStqgmdO/ciGjf2fMpfv34ZeOONSJw7p8OqVaEYOLB4gjS7dxvUdIOICCvatGFvISIiIiIibyWDofv0ycDChRGq0qB9+38fvy9aFI4TJ/QoXdqCiROLv39poKlUyYLq1U04dsyA7duDVTUIBQ6NjcV5qvl0zjH2vkTGs7dtWxJZWee8ps5y/vxwvPBCNKpVM+Pbb+OLpav99OlReP/9CPTunYHZs3P3ngp00qStXLlyOHfOe9YIeReuEcoP1wjlh2uE8sM1Qlf78089OnYsrSoffv75H5QpY3Oukfh4DVq3Lo20NC3efPMS+vdnRUBxmDo1SgXrBg1Kx2uv5e756w24Hyk8qcoqSPPpQpWSkfe57TaTx0bDX8uQIRkqNfSvv/TYuNHe98edZJ+wfr395zCyTURERETk/WrXNuPWW40wmzVYuTJ3f9JXXolUQaFGjYzo25dBoeLiyNySPkOMuwQWBobI5SIibBg+PF3dnjMnwu07lX37DDh7Vo/wcCvatmVgiIiIiIjIFwwenO5sQn25JS3279fjk0/sgaLnnkuGlmesxaZFCyOCg23q3OrIEZd2nSEvxz8zcov7709HaKgVv/0WdM2Gcq6ybp296XSnTlmqXpmIiIiIiLzfXXdlITLSqnoJ7dwZpC4oP/tsNGw2jWoR0bSpb7b78FWhoTa0aMHpZIGIgSFyi7g4q3MM5ezZEW77OSwjIyIiIiLyTWFhNvTqZS8VW7YsDCtWAD//HKQuME+ezPH0nsCx9YGJgSFymzFj0mAw2LBrV7CaGuYOv/1mwKlTevXm0aEDp5EREREREfliOdmmTSF47DH7YzKFrFy5y7Vl5JE+Qz/+GIxMtncKGAwMkdtUqGBF796Zzl5D7uDIFpKgkKQ+EhERERGR76hXz4wGDYwwGjU4e1bGppsxejTH03vKzTebUa6cBVlZGvzwA7OGAgUDQ+RWDzyQCo3Ghs2bQ9VISleXkTn6C3XvznA2EREREZEvcrSgEFOnprJvqAdpNJI1ZG/RwXKywMHAELlVjRoW3Hmnfccyd65rs4b++EOvGtWFhNjQqRPLyIiIiIiIfJFUGTRrZsSwYUC3buwb6mnsMxR4GBgit5MaYbF2bShOntS57HUd2UIS0Q4PZxkZEREREZEvkmP5tWsvYvFie8YKedYdd2RDp7Ph6FEDTp923fkbeS8GhsjtGjQwoU2bLFgsGsyfH+GGMjJeVSAiIiIiInKF6GgbmjQxqtvMGgoMDAxRsZgwwZ41tGJFGC5cuPFld+iQHn/9pUdQkJSRMTBERERERETkKiwnCywMDFGxaNnSiMaNjaq7/YIF4Tf8euvXhzp3WJGRLCMjIiIiIiJy9dj67duDYTJ5emvI3RgYomIhtcKOXkNLloQjJUXjkjH1nEZGRERERETkWvXrmxAba0FqqhZ79gR5enPIzRgYomLzf/+XhZo1TWrnIsGhojpyRI9DhwwwGGzo3JllZERERERERK6k07GcLJAwMETFRqsFxo+3Zw1JOVlmEZN91q0LcXbLl8ZoRERERERE5FoMDAUOBoaoWPXsmYmKFc1ISNCpRtQ30l+oRw+WkREREREREbkzMLR/fxASEhg68Gf836ViZTAA48bZs4bmzYsodCOzY8d0OHjQAL2eZWRERERERETuUqaMFXXr2k/YvvuOWUP+TO/pDaDAM2BABt58MxKnT+uxdm0o+vYteObPhg32bKFWrbIRG8syMiIiIiIiIndp1y4LBw4YsHVrMHr39mzFxr59Bnz1FXDxYigsFsBsBqxWwGLR5LotX7N/2G/L42Zz3rev971WK/Dgg6lo1sz/x7IxMETFLjQUGDkyHTNnRmHu3Ai1g5H+Q4WbRsZsISIiIiIiIndq1y4bc+dGqowhCZQU9LzNlWw24PXXI1VygV1Msf3se+/NAMDAEJFbDBuWroJChw9L1DcEXbrkH+j5+28dfvstCDqdDV27MjBERERERETkTk2bGhEeblU9YiVzSMbYF6fsbODxx2OwapW9P22bNoBen6UCVNJeRD7LBLWct+V80f7537ft33fldn7fW7+Yf19PYWCIPCIqyqaCQ3PmRGL27AjVL0ijKVjT6dtvN6JECWvxbCgREREREVGACgqyt/HYvDlUTScrzkBJYqIGI0fG4ccfg1WQZubMZBUkOnfuEmySRkQuw+bT5DFSThYSYsPevUHYuTOoEGVknEZGRERERETkr2Prjx/X4e67S6mgUGSkFcuWJWLwYJ4HugsDQ+QxpUpZMXCg1GwCc+ZEXPe5p0/rsG9fEDQaG+68k2VkRERERERExaF9e3tg6JdfgpCamk+Zhwv8/HMQ7rqrJI4f16NCBTPWrElAmzb2bSD3YGCIPGrs2DSVFvjddyHYv9+Qb7ZQixZGFVAiIiIiIiIi96tSxYKqVc1qmteOHe7NGlq7NgQDBpTApUs6NGhgxBdfJKB2bbNbfyYxMEQeVqmSBffcY08JlF5D+fUXYhkZERERERFR8Wrf3l61IWPr3UFaBr3zTgQeeCAO2dkadOmSic8/v4gyZZgUUBwYGCKPGz8+TX3euDEER4/+ux/62bNa7N7NMjIiIiIiIiJPja0X27YFqyCOK5lMMnksGq+8EqXujxqVhvffv4SwMDaYLi4MDJHH1aplVhFhm02D//7331lDGzfas4WaNTOibFlGjImIiIiIiIpTy5ZGBAXZcOqUHseO6Vz2usnJGgwZUgKffBIOrdaGl15KwvTpKWpcPBUfBobIK0yYYM8a+vzzUJw5k3tZrlvnmEbGbCEiIiIiIqLiJtk7zZsb1e1t2+znZzfq1CkdevYsie3bgxEWZsUHHyRi+HD7cCIqXgwMkVdo0sSEli2zVUOzd9+9kjV0/rxWdaUXd97J/kJERERERESe0K5dlsvG1u/da0CPHiVx5IgBZctasHp1Ajp14uQxT2FgiLzGxIn2rKHly8OQmGhfmps2hagSsyZNjKhQgWVkREREREREnuwztHNnELJuoJhjw4YQ9O1bAgkJOtxyiwlffHEB9epx8pgnMTBEXuOOO7LVSMLMTC0WLgxXj61bx2lkREREREREniZj4yW7JytLi59+KnzWkDStnj8/HKNHx6rX6NAhS2UKlS/PBABPY2CIvIZGc6XX0AcfhOPECR1+/NFeRsb+QkRERERERJ49X2vbNrtIY+vNZuDpp6PxwgvRqiJk2LB01VMoIoKTx7wBA0PkVWQcffXqJiQnazFyZBysVg0aNTKiUiWLpzeNiIiIiIgooLVtm+UcW19QqakaDB8ehw8/DIdGY8O0acl46aVk6PVu3FAqFAaGyKtotcD48fasoYMHDeozs4WIiIiIiIi8o/2HjJU/dMjwr2nSeZHn9OpVElu3hiAkxIoFCy5h9Oh0lX1E3oOBIfI6vXploly5KxlC3bqxvxAREREREZGnxcXZ0KiRSd3+7rvrj63/7TcD7r67lLrgX6qUBZ9/fhFdu/KivzdiYIi8TlAQMG6cPWtImlFXrcoyMiIiIiIiIm+aTna9PkNffRWM3r1L4Px5HWrVMmHdugRnQIm8D6v6yCsNH56OkBAbbrvNvtMhIiIiIiIiz2vXLgtvvBGJ7duDVVPpq3sFLVoUjmnTolS/WCk9e++9RERFscm0N2PGEHklnQ4YPDgDNWowW4iIiIiIiMhbSOZPTIxVDQzau9feF1ZYLMDUqVF49tloFRS69950fPjhRQaFfAADQ0RERERERERU4Iv4kgkktm2z9xnKyNBg5MhYLFwYoe4//XQKXnstGYYrcSPyYgwMEREREREREVGhysnEt98G459/tKqf0ObNoQgOtmHevERMmJDGyWM+hD2GiIiIiIiIiKjA2ra1Zwzt22dA9+6lcO6cDnFxFnzwQSKaNmWTaV/DjCEiIiIiIiIiKrBy5ayoU8cEm02jgkLVq5vwxRcJDAr5KAaGiIiIiIiIiKhQ7rzTXk52++3ZWLs2AVWrcnCQr2IpGREREREREREVyoQJqaoJdePGRjaZ9nEMDBERERERERFRoQQHA82bGz29GeQCLCUjIiIiIiIiIgpQDAwREREREREREQUoBoaIiIiIiIiIiAIUA0NERERERERERAGKgSEiIiIiIiIiogDFwBARERERERERUYBiYIiIiIiIiIiIKEAxMEREREREREREFKAYGCIiIiIiIiIiClAMDBERERERERERBSj9jXzzmjVrsHz5cnTr1g3Dhw9Xj23ZsgXbt2/H8ePHkZmZiQ8++ADh4eG5vi8tLQ2LFi3C7t27odFocNttt2HEiBEICQlxPufvv//GwoULcezYMURFRaFr167o2bNnrtfZtWsXVqxYgQsXLqBs2bIYPHgwmjRpciO/EhERERERERFRwChyxtDRo0fx1VdfoUqVKrkez87ORqNGjdCrV69rfu8777yDU6dO4ZlnnsFTTz2FgwcP4t1333V+PSMjAy+++CJKliyJmTNnYsiQIfj0009V0Mnh0KFDePvtt9GhQwe88soraNasGV577TWcPHmyqL8SEREREREREVFAKVJgKCsrC7Nnz8aYMWP+lQ3UvXt33HPPPbj55pvz/N7Tp09j3759GDt2rHpO7dq1cd9992Hnzp1ITExUz5GMI7PZjAceeACVKlVCq1atcOedd2LdunXO19mwYYMKQN19992oWLEiBg4ciGrVqmHTpk1F+ZWIiIiIiIiIiAJOkQJDCxYsQOPGjdGgQYNCf+/hw4dVMKl69erOx+rXr69KyiQLyfGcOnXqQK+/UunWsGFDnD17VpWhOZ4j35eTPOfIkSNF+ZWIiIiIiIiIiAJOoXsM7dixQ/UPevnll4v0A5OSklTPoJx0Oh0iIiLU1xzPKV26dK7nxMTEOL/meG50dHSu58h9x2vkxWQyqQ8HCUaFhoY6b/six3b76vaT+3GNUH64Rig/XCOUH64Ryg/XCOWHa4TywzXiJYGhhIQELF68WPUGCgoKgq9ZvXo1PvvsM+f9m266SfUnKlWqFHydNN8muh6uEcoP1wjlh2uE8sM1QvnhGqH8cI1QfrhGPBwY+uuvv5CcnIwnn3zS+ZjValXNo6W3j0wo02qvX50mmT8pKSm5HrNYLKpEzJEVJJ+vzvxx3M/5HNmWnOS+4+t5kYbYPXr0cN53RBplqpn0NPJF8jvIH8b58+dhs9k8vTnkhbhGKD9cI5QfrhHKD9cI5YdrhPLDNUL54RopPGnPU5BEmEIFhqSnz6xZs3I9Nm/ePJQvX16Nks8vKCRq1qyJ9PR0FWSSZtHi999/V/+xNWrUcD7n448/VsEaR5+h/fv3q58jZWSO5/z222+q2bWDPOdaTa+FwWBQH1fL2cvIV0k5HtH1cI1QfrhGKD9cI5QfrhHKD9cI5YdrhPLDNVJwBY11FCoiIv14KleunOux4OBgREZGOh+XzB75kCiekPHx8n0yel6COjJBTKaJyXj6UaNGqeDPokWL0LJlS8TFxanvad26tRpPP3/+fBVwktH2GzduxLBhw5w/t1u3bpg+fTq++MOqbNoAAAjkSURBVOILNGnSRPU+OnbsGEaPHo3Cio2Nha/zh3I4ci+uEcoP1wjlh2uE8sM1QvnhGqH8cI1QfrhGvGQq2fVs3rwZkyZNUoEfMW3aNHX/l19+cT7nwQcfVNk/zz//vGpiXatWLYwZM8b59bCwMNXHKD4+Hk899RSWLl2KPn36oFOnTs7nyPfI62zZsgVPPPEEfvzxR/X56sCVv8vMzFSlffKZKC9cI5QfrhHKD9cI5YdrhPLDNUL54Rqh/HCNuM8N11BJ1k5O/fv3Vx/XI5lDDz300HWfU6VKFRU4up7bb79dfQQyKcGTKXGssaRr4Rqh/HCNUH64Rig/XCOUH64Ryg/XCOWHa8SHMoaIiIiIiIiIiMg3MDBERERERERERBSgGBjycTJlrW/fvnlOWyMSXCOUH64Ryg/XCOWHa4TywzVC+eEaofxwjbiPxsYCPSIiIiIiIiKigMSMISIiIiIiIiKiAMXAEBERERERERFRgGJgiIiIiIiIiIgoQDEwREREREREREQUoPSe3gAquk2bNuGLL75AUlISqlSpgvvuuw81atTw9GaRl1i5ciU+++yzXI+VL18eb731lse2iTzrjz/+wP/+9z8cP34cly5dwuOPP47mzZs7vy6zCGTdfP3110hPT0ft2rUxcuRIlCtXzqPbTd6zRubOnYtt27bl+p6GDRtiypQpHthaKm6rV6/GTz/9hDNnziAoKAg1a9bEkCFD1HuLg9FoxNKlS7Fz506YTCa1PmQ/EhMT49FtJ+9ZI9OnT1f7mpw6deqE0aNHe2CLqbht3rxZfVy4cEHdr1ixopoy1bhxY3Wf+xDKb41wH+IeDAz5KNlZyk5z1KhRuPnmm7F+/Xq89NJL6qQ/Ojra05tHXqJSpUp49tlnnfe1WiYJBrLs7GxUrVoVHTp0wKxZs/719bVr12Ljxo0YP348SpcujRUrVqj9yhtvvKEO8Mn/5bdGRKNGjfDAAw847+v1PJQIFHIg3qVLF1SvXh0WiwUff/wxXnzxRbWPCAkJUc9ZsmQJ9uzZg0cffRRhYWFYuHAhXn/9dbzwwgue3nzykjUiOnbsiAEDBjjv8z0mcMTFxWHQoEHqopNckJKLDa+++qr6kONW7kMovzUiuA9xPR7N+ah169apP4j27dur+xIgkp3o1q1bcc8993h688hLSCCIV1jIQa60OK62XE3eeDds2IDevXujWbNm6rEJEyaofcvPP/+MVq1aFfPWkretkZyBIO5XAtPVmWESRJYr+X/99RduueUWZGRk4JtvvsFDDz2EevXqqedIEPGRRx7B4cOHVfYIBfYacQgODuZ+JEA1bdo01/17771XZYccOXIEJUqU4D6ErrtGHIEh7kNcj4EhH2Q2m9UbbM4AkAQA6tevr3aaRA7nz5/HmDFjYDAY1JupRN9Llizp6c0iLxQfH6/KUhs0aOB8TK7USXmq7FcYGKKcGQFyohceHq4O3AcOHIjIyEhPbxZ5gASCREREhPosxyaSJSLHIw4VKlRQ7zs8qQtMV68Rh++//159yIndrbfeij59+qgTPQosVqsVu3btUtmqsn/gPoTyWyMO3Ie4HgNDPiglJUX9kVwdJZX7Z8+e9dh2kXeREkO5yiJ1/dIrRPoNTZ06VaXjhoaGenrzyMtIUEhcXYoq9x1fI5Iysttuu02VGkrgWcpEZsyYoUoOWaoaWOQ4ZPHixahVqxYqV66sHpN9hWSUSdAwJ+5HAlNea0S0bt1anehLucjff/+Njz76SB2/Sk8zCgwnT55U2WXSQ0hKDOX/XvrInDhxgvsQuu4aEdyHuAcDQ0R+Kmc5iDQndwSKJOou/UOIiAorZ+aYnOjJvmXixIk4cOBAriu85P+k78epU6fw/PPPe3pTyMfWiDSJzbkfiY2NVc+RYHPZsmU9sKVU3OSi5WuvvaYyyn744Qc12OC5557z9GaRD6wRCQ5xH+IevLzng6KiotSV2asj53KftZZ0LXL1RXaystMkuppj35GcnJzrcbnP/QpdS5kyZVQZGfcrgXfCL30Np02bpnqCOMi+QsrdZaphTtyPBJ5rrZG8OCbqcj8SOCQrSE7gq1WrptocyNAD6XPIfQjlt0bywn2IazAw5KN/KPJH8vvvv+dK15X7rL2la8nKylI7TL6xUl6kNEjWxm+//eZ8TK7SHD16lPsVuqaLFy8iLS1NXa0j/ydN6uWEX8aRS2my7DdykmMTnU6Xaz8i6f0JCQncjwSI/NZIXqR8SHA/ErjkPEZKhrgPofzWSF64D3ENlpL5qB49eqiUOtmBSpRUIqjSlKtdu3ae3jTyEkuXLlVd/aUGV3oMrVy5UmWaSV0uBXZwMGfDaXkzlaagsk66deuGVatWqfGgcjD/ySefqDdZx5QyCuw1Ih+ffvqp6jEkQcR//vkHy5YtU1f0GjZs6NHtpuIhJ/zbt2/HpEmTVK86R+ayNKqXUcHyWUqV5f1H1ovcX7RokTqh40ldYMhvjcj+Rb7epEkTtUakj4iMJ69Tp44qTSX/t3z5ctWvTo475D1H1oMMNZB+MtyHUH5rhPsQ99HYJLRPPmnTpk343//+p950Jb1uxIgRqo8MkXjrrbdw8OBBpKamqvLD2rVrq+lBrL0NXNIHJq8a/rZt26qRwvJ2IAHELVu2qGwhWTP333+/KkGkwHC9NTJq1ChV73/8+HGV5i9NH2WK3YABA5iJGCD69++f5+PSv85xYcpoNKqTuh07dqiSEAkayhQ7rpHAkN8akcyP2bNnq95DckFTysyaN2+O3r17qyAA+b958+apKge5aCn/53Iy37NnT+dUVO5D6HprhPsQ92FgiIiIiIiIiIgoQLHHEBERERERERFRgGJgiIiIiIiIiIgoQDEwREREREREREQUoBgYIiIiIiIiIiIKUAwMEREREREREREFKAaGiIiIiIiIiIgCFANDREREREREREQBioEhIiIiIiIiIqIAxcAQEREREREREVGAYmCIiIiIiIiIiChAMTBERERERERERBSgGBgiIiIiIiIiIkJg+n9QgevUENriQQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot y_pred and y_true\n",
    "plt.figure(figsize=(14, 6))\n",
    "plt.plot(y_true, label='True Price', color='blue')\n",
    "plt.plot(y_pred, label='Predicted Price', color='red')\n",
    "plt.title('True Price vs Predicted Price')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8d5949b",
   "metadata": {},
   "source": [
    "## 7. Prediction of Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "534ce0ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a function for prediction\n",
    "def predict(model, input_seq, future_days, num_layers, hidden_size):\n",
    "    # make model to be evaluation mode\n",
    "    model.eval()\n",
    "    preds = []\n",
    "  \n",
    "    input_seq = input_seq.view(-1, 1).clone().detach()\n",
    "\n",
    "    for _ in range(future_days):\n",
    "        # prepare input shape\n",
    "        seq_input = input_seq[-60:].unsqueeze(0)\n",
    "\n",
    "        # make prediction\n",
    "        with torch.no_grad():\n",
    "            y_pred_log = model(seq_input, num_layers, hidden_size)\n",
    "            \n",
    "        # save the prediction\n",
    "        preds.append(y_pred_log.item())\n",
    "\n",
    "        # update the input sequence with the prediction\n",
    "        input_seq = torch.cat((input_seq, y_pred_log), dim=0)\n",
    "\n",
    "    # invers log transformation\n",
    "    #preds_tensor = torch.tensor(preds)\n",
    "    #preds_tensor = torch.expm1(preds_tensor)\n",
    "\n",
    "    preds_array = np.array(preds).reshape(-1, 1)\n",
    "    preds_tensor = scaler_y.inverse_transform(preds_array)\n",
    "    return preds_tensor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8635d385",
   "metadata": {},
   "source": [
    "### 7.1 Predict from Existinc Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "40e9e6b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make prediction for 60 days ahead\n",
    "last_seq = X_test_tensor[-60:]\n",
    "\n",
    "predictions = predict(\n",
    "    model=lstm,\n",
    "    input_seq=last_seq,\n",
    "    future_days=60,\n",
    "    num_layers=2,\n",
    "    hidden_size=64\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dc0ec910",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 1)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the prediction shape\n",
    "predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fcf907be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get future TRUE data from csv\n",
    "btc_future = pd.read_csv('Dataset/btc_future.csv', parse_dates=['Open time'])\n",
    "btc_future = btc_future.rename(columns={'Open time': 'Date'})\n",
    "btc_future = btc_future[(btc_future['Date'] >= '2024-01-21') & (btc_future['Date'] <= '2024-03-21')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c679d146",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the prediction\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(df['Date'].iloc[-1000:], df['Close'].iloc[-1000:], label='Actual Price', color='blue', linewidth=1)\n",
    "plt.plot(pd.date_range(start=df['Date'].iloc[-1] + pd.Timedelta(days=1), periods=60), predictions, label='Predicted Price', color='red', linewidth=2)\n",
    "plt.plot(btc_future['Date'], btc_future['Close'], label='True Price', color='green', linewidth=1, alpha=0.5)\n",
    "plt.title('Bitcoin Price Prediction')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price (USD)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b9e39403",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the model\n",
    "torch.save(lstm.state_dict(), 'lstm_model.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b0e4e69",
   "metadata": {},
   "source": [
    "### 7.2 Predict Recent Price from API Data (Optional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9a96a4a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize the CoinGeckoAPI\n",
    "cg = CoinGeckoAPI()\n",
    "# get the data from CoinGecko API\n",
    "data = cg.get_coin_market_chart_by_id(id='bitcoin', vs_currency='usd', days=180)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "835e2f93",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make dataframe from the data\n",
    "prices_df = pd.DataFrame(data['prices'], columns=['timestamp', 'price'])\n",
    "prices_df['timestamp'] = pd.to_datetime(prices_df['timestamp'], unit='ms')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f629d738",
   "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>timestamp</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-11-13</td>\n",
       "      <td>88264.599746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-11-14</td>\n",
       "      <td>90488.100075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-11-15</td>\n",
       "      <td>87407.512737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-11-16</td>\n",
       "      <td>90947.975094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-11-17</td>\n",
       "      <td>90606.454894</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   timestamp         price\n",
       "0 2024-11-13  88264.599746\n",
       "1 2024-11-14  90488.100075\n",
       "2 2024-11-15  87407.512737\n",
       "3 2024-11-16  90947.975094\n",
       "4 2024-11-17  90606.454894"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make prices daily\n",
    "daily_prices = prices_df.resample('D', on='timestamp').agg({'price': 'last'}).reset_index()\n",
    "daily_prices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "59ab35c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# change daily_price to numpy array\n",
    "daily_prices_array = daily_prices['price'].values.reshape(-1, 1)\n",
    "\n",
    "# scale the data\n",
    "daily_prices_array = scaler_y.transform(daily_prices_array)\n",
    "daily_prices_tensor = torch.tensor(daily_prices_array).float()\n",
    "\n",
    "# check the shape of daily_prices_tensor\n",
    "daily_prices_tensor.shape\n",
    "\n",
    "# make prediction for 60 days ahead\n",
    "predictions = predict(\n",
    "    model=lstm,\n",
    "    input_seq=daily_prices_tensor,\n",
    "    future_days=30,\n",
    "    num_layers=2,\n",
    "    hidden_size=64\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a51e5538",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the prediction\n",
    "plt.figure(figsize=(14, 6))\n",
    "plt.plot(daily_prices['timestamp'], daily_prices['price'], label='CoinGecko Price', color='blue')\n",
    "plt.plot(pd.date_range(start=daily_prices['timestamp'].iloc[-1] + pd.Timedelta(days=1), periods=30), predictions, label='Predicted Price', color='red')\n",
    "plt.title('CoinGecko Bitcoin Price Prediction')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price (USD)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a070ba41",
   "metadata": {},
   "source": [
    "## 7. Interpretation and Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb16b559",
   "metadata": {},
   "source": [
    "### 7.1 Interpretation of Result\n",
    "The LSTM model applied to BTC/USD price data demonstrated strong performance. Using 3,382 daily BTC data points from September 18, 2014, to January 21, 2024, along with the application of various data transformation and normalization techniques, the model was able to produce predictions that closely matched actual values.\n",
    "\n",
    "Evaluation using the Root Mean Squared Error (RMSE) metric showed an error margin of approximately 1,600 USD, which is considered low given the volatility of BTC prices. This indicates that the LSTM model effectively learned patterns from historical data with high accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5732ec2",
   "metadata": {},
   "source": [
    "### 7.2 Conclusions and recommendation\n",
    "\n",
    "the Conclusion from visualization Actual vs predicted price data have accurate result with the spike of BTC price from 21-01-2024 forward make this model resulted as `reliable prediction model`. For the next step may this model will need some improvement with add extra feature or another hyperparameter tuning.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}