GMM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **GMM**\n",
    "\n",
    "**Elaborated By: Sirine MAAROUFI**\n",
    "\n",
    "---\n",
    "## **Introduction**\n",
    "---\n",
    "\n",
    "In this exercise, we aim to perform clustering using the Gaussian Mixture Model (GMM) on a dataset of wholesale customer spending. The dataset includes various categories of spending, such as on fresh products, milk, grocery items, etc. The goal is to group the customers into clusters based on their purchasing behavior.\n",
    "\n",
    "Clustering is an unsupervised learning technique that aims to find natural groupings in the data. Unlike k-means clustering, GMM allows for soft clustering, where each data point can belong to multiple clusters with different probabilities. This makes GMM a powerful method for dealing with complex datasets where the clusters may overlap.\n",
    "\n",
    "In this exercise, we will:\n",
    "\n",
    "**1. Load and preprocess the data:** Clean the dataset and normalize the features for better performance.\n",
    "\n",
    "**2. Determine the optimal number of clusters:** Use statistical criteria such as BIC (Bayesian Information Criterion), AIC (Akaike Information Criterion), and the Likelihood Ratio Test (LRT) to select the optimal number of clusters.\n",
    "\n",
    "**3. Fit the GMM and make predictions:** Apply the GMM to cluster the data and predict cluster memberships for each data point.\n",
    "\n",
    "**4. Visualize the results:** Create scatter plots to visualize the clusters and understand the segmentation patterns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## **Step 1: Importing the necessary libraries**\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from scipy.stats import chi2\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.mixture import GaussianMixture\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Configure plotting style\n",
    "sns.set(style=\"whitegrid\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: openpyxl in c:\\users\\sirin\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (3.1.5)\n",
      "Requirement already satisfied: et-xmlfile in c:\\users\\sirin\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.12_qbz5n2kfra8p0\\localcache\\local-packages\\python312\\site-packages (from openpyxl) (1.1.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install openpyxl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## **Step 2: Loading the Data**\n",
    "---\n",
    "The dataset is loaded from an Excel file, and we start by displaying its structure to understand the different features. The 'Channel' column, which is not relevant for clustering, is removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data\n",
    "data = pd.read_excel('Wholesale customers data.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Channel  Region  Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "0        2       3  12669  9656     7561     214              2674        1338\n",
       "1        2       3   7057  9810     9568    1762              3293        1776\n",
       "2        2       3   6353  8808     7684    2405              3516        7844\n",
       "3        1       3  13265  1196     4221    6404               507        1788\n",
       "4        2       3  22615  5410     7198    3915              1777        5185"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first few rows of the dataset\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the 'Channel' column as it's not needed for clustering\n",
    "data = data.drop(columns='Channel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Region  Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "0       3  12669  9656     7561     214              2674        1338\n",
       "1       3   7057  9810     9568    1762              3293        1776\n",
       "2       3   6353  8808     7684    2405              3516        7844\n",
       "3       3  13265  1196     4221    6404               507        1788\n",
       "4       3  22615  5410     7198    3915              1777        5185"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the dataset again to confirm the column has been removed\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## **Step 3: Data Normalization**\n",
    "---\n",
    "To ensure that all features contribute equally to the clustering process, we normalize the data using `StandardScaler`, which transforms each feature to have zero mean and unit variance. This step is crucial because the features have different scales of measurement.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply StandardScaler normalization\n",
    "scaler = StandardScaler()\n",
    "data_normalized = scaler.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.59066829,  0.05293319,  0.52356777, ..., -0.58936716,\n",
       "        -0.04356873, -0.06633906],\n",
       "       [ 0.59066829, -0.39130197,  0.54445767, ..., -0.27013618,\n",
       "         0.08640684,  0.08915105],\n",
       "       [ 0.59066829, -0.44702926,  0.40853771, ..., -0.13753572,\n",
       "         0.13323164,  2.24329255],\n",
       "       ...,\n",
       "       [ 0.59066829,  0.20032554,  1.31467078, ..., -0.54337975,\n",
       "         2.51121768,  0.12145607],\n",
       "       [ 0.59066829, -0.13538389, -0.51753572, ..., -0.41944059,\n",
       "        -0.56977032,  0.21304614],\n",
       "       [ 0.59066829, -0.72930698, -0.5559243 , ..., -0.62009417,\n",
       "        -0.50488752, -0.52286938]])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the normalized data\n",
    "data_normalized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## **Step 4: Finding the Optimal Number of Clusters**\n",
    "---\n",
    "### **Method 1: BIC (Bayesian Information Criterion) and AIC (Akaike Information Criterion)**\n",
    "BIC and AIC are statistical criteria used to evaluate model quality while penalizing for complexity. We compute these metrics for different numbers of clusters (from 1 to 10) to find the optimal number that balances model fit and complexity.\n",
    "\n",
    "The results are plotted to visualize the BIC and AIC scores as a function of the number of clusters, helping to identify the cluster count that minimizes these scores.\n",
    "\n",
    "The optimal number of clusters according to BIC and AIC is then used to fit the GMM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA/kAAAImCAYAAAAIWkpCAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAADDMklEQVR4nOzdd1yV5f/H8dc5hwOHjaCCe6HiwL1wa2ZmVq522bLs297jq9nelpU2NJv+KkeOSrPtKiflHikKbkDZG874/UGcr8gQUQ7r/Xw8eAD39bkvPhwulM99Xfd1GxwOhwMRERERERERqfaMlZ2AiIiIiIiIiFwYKvJFREREREREaggV+SIiIiIiIiI1hIp8ERERERERkRpCRb6IiIiIiIhIDaEiX0RERERERKSGUJEvIiIiIiIiUkOoyBcRERERERGpIVTki4iISJXlcDgqOwWpYjQmRERKpyJfRKSGuemmm2jbtm2htx49ejBhwgQ2bdpUKHbx4sW0bduWo0ePFjput9tZuHAhN9xwA71796Zbt26MGTOGuXPnkpub68pvp4gnn3ySoUOHlin22muvpW3btvz000/Fts+YMYO2bdsWOZ6Tk8Nnn33GuHHj6N69O7169eLaa69l6dKlZy0w0tPTueuuu+jcuTM9e/YkJiamTLmWVUHOp7916tSJSy65hNdee43k5ORC8We+XrGxsdxwww2Eh4cTERFBVlYWb7zxBr169aJLly4sXbr0guZbXrGxsdx5550cO3asxJijR4/Stm1bxo4di9VqLdK+ceNG2rZty8aNGysyVae2bdsyY8YMl3ytC+lcxmxqaiozZ87k8ssvp2vXrkRERHDzzTfz+++/F4o7l9/TskpNTeXxxx8nMjLygvYrIlLTuFV2AiIicuG1b9+eZ555BgCbzUZSUhJff/01t99+O4sXL6Z169YlnpuVlcVdd93Ftm3buO6665g4cSJms5kNGzbw+uuvs2bNGt577z3c3d1d9e2Uy8GDB9myZQtt2rRh3rx5XHLJJWU679SpU0ycOJETJ05w00030alTJ+x2OytXruTJJ58kMjKSF154AYPBUOz5S5cuZeXKlUydOpXWrVvTuHHjC/ltOc2fPx/In9XMzMxkx44dfPTRR/z+++98/fXXBAYGAnD33XczYcIE53mff/45W7du5Y033iA4OJgjR44wZ84crr76aq688kpatmxZIfmeq3Xr1rF69eoyxe7atYuPPvqI//znPxWcVc1U1jF74MAB7rjjDux2OxMmTCAsLIzMzEy+//57/vOf//DAAw9w9913V1iee/bs4dtvv2XcuHEV9jVERGoCFfkiIjWQj48PXbp0KXSsb9++REREsHjxYp544okSz33llVf4+++/mTt3bqE++vfvT1hYGI888gjz5s0rVDhWRYsXL6ZRo0ZMmjSJRx99lEOHDtGsWbOznvfEE08QGxvL/Pnzad68ufP44MGDadiwIW+99RZDhgzhoosuKvb8gpn066+/vsQLARfCmT/ffv360bdvX66//nreeustXnzxRQCaNm1aJL/69eszcuRIAOfqjssuu4wePXpUWL4Vyc/Pj/fee49hw4aVegFLileWMZuXl8eDDz6I2Wzmq6++IigoyNk2bNgwnn76ad555x2GDh1KWFiYK9IWEZESaLm+iEgt4enpiYeHR6mFZ2JiIosWLWLcuHFFikiAUaNGcdtttxEcHFzq11q4cCFjx46lS5cudOrUiSuvvJIVK1Y42xcvXkz79u3Ztm0b11xzDeHh4QwZMoSPP/64UD8pKSk89dRT9OrVi549e/LGG29gt9vP+r3abDaWLl3KkCFDGDZsGF5eXs6Z79Ls2bOHP/74g9tvv71QgV/glltu4YYbbsDLy6vY82+66Sbncu2wsDCefPJJANLS0njllVcYNmwY4eHhjBo1im+++abQuUOHDuXll1/m5ptvplOnTkyePPms+Z6pU6dODB8+nKVLl5KVlQUUXjY9dOhQFi9ezPHjx51Ly2+66SYAbr755kLLqxcuXMhll11Gx44dGTx4MDNmzMBmsznbn3zySW6++WaeeeYZunXrxsiRI7HZbNjtdmbPns3FF19Mx44dueSSS5g7d26R12ny5MnMnj2bwYMHEx4ezrXXXsv27duB/PHx1FNPAXDRRRc5X8eSTJo0CR8fH5588slCOZ6ppNtThg4dWuhrtG3blq+//ponn3zSebvGiy++SHZ2Nq+99hp9+vShd+/eTJ48mZycnEJ9paen8+ijjzqXsr/44ovOn0WBX3/9lbFjxxIeHk6/fv148cUXyczMdLbPmDGDiy++mJkzZ9KrVy/69+9PSkoKO3fu5Oabb6Z79+507dqVW265ha1bt5b62uTk5PDee+8xYsQIwsPDGT58OLNnz3b+HpU0Zs+0evVq9u3bxwMPPFCowC9w//33c+ONNxZ720TBa3rmrQxn3i6TmJjII488Qr9+/QgPD+fKK6903j6yceNG54XFCRMmOMetq19PEZHqQEW+iEgN5HA4sFqtWK1W8vLyOHnyJG+++Sa5ubmlLnVdv349VquVIUOGlBjzxBNPlLr0/csvv2Tq1KkMGzaMWbNmMW3aNNzd3Xn00UeJjY11xtntdh588EFGjhzJ7Nmz6datG6+//jpr1651tk+cOJHVq1fzxBNP8Oqrr/L333/zww8/nPX7X7NmDSdPnmT06NFYLBYuvfRSlixZctb9BAq+dkn3Ent4eDB16lQiIiKKbX/mmWcYP348kL+c/u677yY7O5vrr7+e77//nokTJ/L+++/TvXt3Jk+ezIcffljktQsPD+f999939nOu+vXrR15eHjt27CjSNnPmTAYNGkS9evWYP38+V111FVOnTgVg6tSpzJw5E4BZs2bx9NNPExERwYcffsgNN9zARx99xNNPP12ov8jISE6cOMF7773HI488gslk4tlnn+Xdd9/liiuu4MMPP2TEiBG8/PLLvPfee4XO/emnn/jtt9+YMmUKb731FqdOneK+++7DZrMxePBg59L7mTNnnnUJeGBgIFOnTmXnzp3MmTOnXK/bmd544w3c3d2ZOXMmo0ePZu7cuYwePZoTJ04wbdo0brrpJr755psiFzDmzp1LRkYGb7/9NpMmTWLhwoU8+uijzvbvv/+ee+65h5YtW/Lee+9x77338t1333H33XcX2u/h+PHjrF69munTp/PUU09hMpmYOHEiderUYcaMGUyfPp2srCxuv/120tLSiv0eHA4Hd911F3PmzOGqq65y/jzefvtt5+08xY3Z4qxZswaTycSgQYOKba9Xrx5PP/00HTt2LPuLfIbHHnuMAwcO8Nxzz/HRRx/Rvn17nnjiCTZs2ECHDh0KjdWC/F35eoqIVBdari8iUgNt3ryZDh06FDn+8MMP06pVqxLPO3HiBMB53Ud+5MgRbr/99kLFQqNGjRg7dix//fUXl112GZBfgNx9991cddVVAHTv3p1ffvmFVatWMWDAANasWcP27dv56KOPGDhwIAARERFl2sxr8eLFtGnThvDwcADGjh3LN998w08//cTll19e4nnn+/2HhoYSEhIC/G85/VdffcW+ffuYN28eXbt2BWDAgAFYrVbef/99rr32WgICAgBo2LBhoYKwPOrWrQvk7y1wpvbt2xMYGIi7u7szv9DQUOf79u3bk5aWxvvvv88111zDlClTgPxbNQICApgyZQq33nqrc0m81Wrl+eefd37P0dHRLFiwgIcffpg777zTea7BYGDWrFlcf/311KlTx3nuxx9/jI+PDwAZGRk88cQT7Nmzh44dOzpvM2jXrl2Zfh4jR45kxYoVzJw5k6FDh573sv3Q0FCef/55AHr16sXChQvJy8tj2rRpuLm50b9/f3766Sf+/vvvQue1atWK9957D6PRyKBBgzAYDLz88svs27eP1q1bM23aNAYMGMC0adOc5zRv3pxbbrmF1atXM3jwYOfr88QTTzhvodi6dStJSUlMmDCBbt26AdCyZUvmz59PRkYGvr6+Rb6HNWvWsG7dOt566y3n712/fv2wWCy88847TJgwgdatWxcZs8WJjY2lTp06eHt7l+8FLYNNmzZxzz33MGzYMCD/dQ8ICMDd3R0fH59CYzU0NBSHw+HS11NEpLrQTL6ISA3UoUMHvvnmG7755hsWLlzIxx9/zM0338z06dOZPn16iee5ueVf+y3LkviSPPnkkzz66KOkpqaydetWvv32W7788kuAIjPpBUUvgLu7O4GBgc5ltpGRkZjNZgYMGOCM8fLyKnEmsUBiYiIrV67kkksuITU1ldTUVFq3bk2jRo3OumTfZDIBlLrk+1xt2rSJRo0aFfpeAa644gpycnLYtm2b81i7du0u2Nctry1btpCdnc3QoUOdq0GsVqvz4sqff/7pjA0ICHAWiAAbNmzA4XAUe25OTg5//fWXMzY0NNRZ4APOW0DOXNp+Lp599lm8vLx46qmnzvtnePrPy2QyUadOHTp06OD8HYH87//MWd8RI0ZgNP7vz6vhw4cD+RfeDh48SGxsbJHXp2fPnvj4+BR6baHweGjdujWBgYHcddddTJ06lV9++YW6devy2GOPFfoZnG7Tpk24ubkxYsSIQsevuOIKZ3tZmUymC/p7UZzevXszY8YM7r//fhYuXMipU6d44oknnEX4mVz9eoqIVBeayRcRqYG8vb2ds9gF+vfvT2ZmJnPmzGHChAnF3lfbsGFDIH9pa0kzofHx8QQGBhYqdk53+PBhpk6dyvr16zGbzbRs2dK5EdeZj5+zWCyFPjcajc6YlJQUAgICiuwhUK9evZK+bQC+++478vLymDFjRpF7gI8dO8aBAwdKXM3QqFEjIP/7L5g1PFNcXBz169cv86Z6KSkpxeZcMOOemprqPFbSvf7nouCWiPIWKgWbsBXMxJ8pPj7e+fGZs7oF5xbMGp8pLi7O+bGnp2ehtoLC+HwuMAUFBfH000/zyCOP8PHHH9O5c+dy93X6BYgCZfn5nPmzLvg9S01Ndb4+zz33HM8991yRc09/baHw6+vt7c2XX37JBx98wIoVK5g/fz4Wi4Urr7ySKVOmFPu0i5SUFOrUqeO8eHVmjueyLL1Ro0asWrWKjIyMEmfzY2Njz6tAnj59Oh9++CErVqzgp59+wmg00rdvX55//nnn7+bpXP16iohUFyryRURqkY4dO7Jw4UKOHj1abJHfp08fzGYzq1evLnHG/I477gDg22+/LdJmt9u58847MZvNfPPNN7Rr1w43NzeioqKKjS9NnTp1SEpKwmazFSpSznwO/JkWLVpE165deeihhwodz8zM5O677+brr792LkM/U//+/YH8TcaKK/KtVitXXnkl3bp14/333y/T9+Hv78+hQ4eKHD958iSAc/n6hbJu3Tq8vLyKvV2jLPz8/ACYNm1asZsPFlycKO3czz//vNhCsOAiUkUaNWoUK1asYMaMGc7N+woUXJg580JCRkbGBfv6Z47Pgp9zUFCQ8/V5/PHH6dWrV5Fz/f39S+27ZcuWvPHGG9hsNrZv3863337L119/TdOmTZk4cWKx/RX3O1RQ/J7L2Ovfvz9z585l7dq1RVYGQP4Kmosuuojrr7++xE0jz1wJcPrmeAC+vr489thjPPbYYxw8eJDffvuN999/n+eee47Zs2cX6c/Vr6eISHWh5foiIrXI9u3bMZlMNGnSpNh2Pz8/xo8fz4IFC9i5c2eR9qVLl7J3717nct8zJSUlER0dzfjx4wkPD3fO9q9ZswY4t1naiIgIrFYrv/76q/NYbm5ukSW4p9uxYwf79u1j7Nix9O7du9DbkCFD6NOnD99++y3Z2dnFnt+6dWsGDhzIRx99xJEjR4q0z5o1i6SkpBK//+L07NmTY8eOsWXLlkLHv/vuO8xmM506dSpzX2ezZ88efvvtN8aNG4eHh0e5+ujcuTNms5m4uDjCw8Odb25ubrz11ltFdqY/XcH9zklJSYXOTUxM5J133jnrBZrTnb7k/Vw999xzeHl58dZbbxU6XjA7f/oGkAcOHDinvM6mYKwXWL58OQaDgV69etGyZUuCgoI4evRoodcnODiYN998k927d5fY748//kifPn04efIkJpOJrl278uyzz+Ln58fx48eLPadXr15YrVZ+/PHHQse/++47IH8fjLLq378/bdq0Yfr06SQlJRVpf/PNN7FarSXueeHj41NoJQdQaD+DY8eOMWjQIGeuLVu25I477qBv377O7+/MFQmufj1FRKoLzeSLiNRA6enphR4FlZuby++//86iRYu45pprCAwMLPHchx9+mB07dnDTTTdx4403OguFNWvWsGDBAoYMGcLNN99c7LlBQUE0atSIL7/8kpCQEPz8/Fi7di1ffPEFcG73W0dERNC/f3+mTJlCQkICjRo14osvviAxMbHYVQiQP4tvNpud90Gf6corr2TdunX88MMPjB07ttiY5557jptvvpmrr76aCRMm0LlzZzIyMvjxxx9Zvnw51157bbEzmSUZO3YsX331Fffccw/3338/jRs3dv4s7r33Xuds5Lkq+Pk6HA4yMjLYsWMHn332Gc2bN+eBBx4oV5+QP7s7ceJE3nnnHdLT0+nduzdxcXG88847GAyGUp+B3rZtW6644gqefvppjh07RseOHYmOjmb69Ok0bty42JUBJSl4XX755RcGDhxY6oaRZ6pbty6TJ0/mscceK3S8d+/eWCwWXn31VR544AEyMjJ49913nRsfXgg7duxg8uTJjBo1ih07dvDuu+8yfvx45/f+0EMPMXXqVEwmE0OGDCE1NZX333+fuLi4UldfdOvWDbvdzj333MOdd96Jt7c3K1asIC0trcTxPnDgQHr37s2UKVOIi4sjLCyMTZs28dFHHzFmzJgSb0kpjpubG6+//jq33XYb48aNY8KECYSFhZGYmMjixYtZu3YtjzzySIkXrQYPHszy5cvp3LkzzZo1Y/HixYVWuDRq1IiQkBBefPFF0tPTadq0KTt37mT16tVMmjQJwLkZ3qpVq/D39ycsLMylr6eISHWhIl9EpAbavXs311xzjfNzDw8PmjZtykMPPcTtt99e6rl+fn7MnTuX//u//+OHH37g66+/xuFw0Lx5c6ZMmcL48eNLvB8f4P333+ell17iySefxN3dndDQUD744ANefvllIiMjCz3f+mxmzpzJtGnTePfdd8nJyWHkyJFcffXV/Pbbb0Vic3JyWL58Of369SuxaBs+fDjPPfcc8+bNK7HIb9iwIfPnz+fzzz9n2bJlzJ49G3d3d1q2bMmbb77JyJEjy5w/5N97PnfuXN58801n4dyyZUteeumlcj8mDyj087VYLDRp0oTrrruOiRMnFns/+bl48MEHqVevHl999RVz5szB39+fiIgIHn744bPuOv7KK68wa9Ys5s2bR2xsLEFBQYwcOZIHH3ywyExsaXr37k3fvn158803Wb9+fbHLtUtzxRVXsGLFCn7//XfnMT8/P2bMmMGbb77JPffcQ6NGjbj33nudz2K/EO655x527tzJXXfdha+vLxMnTuTee+91tl911VV4e3szZ84c5s+fj5eXF926dWPatGklrrABqF+/PnPmzOGdd95h8uTJZGVl0bp1a2bMmEGfPn2KPafgqQbvvvsun332GYmJiTRu3JiHH36YW2+99Zy/t3bt2vHNN9/w6aef8vXXXxMXF4eXlxdt27Zlzpw5hTbJPNNTTz2F1Wrltddew83NjZEjR/LII48UunVm5syZvPXWW7zzzjskJSXRoEED7r33Xuf+EK1bt2bUqFF8+eWXrF27lmXLlrn09RQRqS4MjjN3QRIRERERERGRakn35IuIiIiIiIjUECryRURERERERGoIFfkiIiIiIiIiNYSKfBEREREREZEaQkW+iIiIiIiISA2hIl9ERERERESkhij5QcdSoi1btuBwODCbzZWdioiIiIiIiNQCeXl5GAwGunbtWmqcZvLLweFw4HA4KjsNqaIcDge5ubkaI1IraLxLbaLxLrWJxrvUJtVlvJe1DtVMfjkUzOCHh4dXciZSFWVmZrJnzx5CQ0Px8vKq7HREKpTGu9QmGu9Sm2i8S21SXcb7jh07yhSnmXwRERERERGRGkJFvoiIiIiIiEgNoSJfREREREREpIZQkS8iIiIiIiJSQ6jIFxEREREREakhtLu+iIiIiIiIXFA2m428vLzKTqNMcnJynO+NxsqZBzebzZhMpgvSl4p8ERERERERuSAcDgexsbEkJydXdiplZrfbcXNz4/jx45VW5AMEBAQQEhKCwWA4r35U5IuIiIiIiMgFUVDg169fHy8vr/MuWF3BZrORk5ODh4fHBZtNPxcOh4PMzEzi4+MBaNCgwXn1pyJfREREREREzpvNZnMW+EFBQZWdTpnZbDYALBZLpRT5AJ6engDEx8dTv37988pDG++JiIiIiIjIeSu4B9/Ly6uSM6meCl63893LQEW+iIiIiIiIXDDVYYl+VXShXjcV+SIiIiIiIiI1hIp8ERERERERkdMMHTqUtm3bOt86duzIJZdcwpw5c5wxixcvpm3btkXOjYyM5J577qFfv3506dKFUaNGMWfOHHJzc12SuzbeExERERERkSrHZnew+2ACianZBPpZaN8yCJPRdbcC3Hbbbdx2220AZGdns337dqZMmYKnpyc33HBDsefMnTuXV199lQkTJnD33Xfj5+fH33//zWuvvcbmzZv54IMPKvwxfSrya6jK/oUQEREREREpr3XbjzN76Q4SUrKdx4L8Ldw5Opy+nRq6JAcvLy/q1avn/LxJkyZs3LiRRYsWFVvk7927l1dffZXHH3+cm2++udB5DRs25MYbb+SHH35g1KhRFZq3ivwaqCr8QoiIiIiIiJTHuu3HeeXzzUWOJ6Rk88rnm3nq5p6VVtdYLJYS2xYuXIivr2+xFwB69uzJZ599RocOHSoyPUBFfo1TlX8hRERERESk9nE4HOTk2soUa7M7mL1kR6kxs5fuoHPremVeqezhbrogO9dv376dZcuWcd999xXbvnPnTjp16oSbW/FldkRExHnnUBYq8msQm93B7KWl/0J89O1OendsoKX7IiIiIiJS4RwOB0/M/IM9MYkXrM+ElGyunfJDmePbNQ/ktXv7n3OhP2vWLD755BMg/9n1eXl5dO7cmcsvv7zY+OTkZJo0aXJOX6MiaHf9GmT3wYRCS/SLcyo5i90HE1yUkYiIiIiISPV07bXXsnTpUpYuXcq3337LBx98QFZWFjfccEOxO+UHBgaSnJzs+kTPoJn8GiQxtfQC/1zjREREREREzofBYOC1e/uXebn+roMJPDtnw1njnp3Yhw4tg8rUZ3mX6/v7+9OsWTPn561atcLf35/rr7+edevWFYnv2rUr33zzDTabDZPJVKT90UcfpVu3blx//fXnnMu50Ex+DRLoV/ImEOWJExEREREROV8GgwGLh1uZ3rq0rU+Qf+n1St0AT7q0rV/mPi/E/fgFHA4HAHa7vUjbuHHjyMjI4P/+7/+KtG3cuJHvv/8eHx+fC5ZLSTSTX4O0bxlEkL+l1CX7df3zH6cnIiIiIiJS1ZiMBu4cHV7sZuIF7riyo0v2GMvMzOTkyZNAfnF/+PBhXn75ZerXr09ERAQrVqwoFN+qVSseeOABXn31VeLi4rjiiivw8PBg/fr1vP3221x88cVcdtllFZ63ivwa5PRfCAN2WrnF42fMItXuyQFrfRwYaVDXG225JyIiIiIiVVXfTg156uaeRR4LXjfAkzuu7Oiyp4V98sknzo33jEYjAQEB9OjRg2nTpuHp6VnsOXfeeSctW7Zk7ty5LF68mOzsbJo0acLdd9/N9ddfX+wy/gtNRX4N07dTQ54dbsa4eR7+hgzn8RSHN4syerDtAMz5bid3XNnxgi5bERERERERuVD6dmpI744N2H0wgcTUbAL98lcku+opYb///vtZY8aOHcvYsWOLHB82bBjDhg2riLTKREV+DZOxdwN1Ij/mzOl6f0MGt/qs5pO0QXy/FrwtZm4YEVY5SYqIiIiIiJyFyWggPLRuZadR7WjjvRrEYbdx6udPSmw3ABPqbsOAnXm//MPS1VGuS05EREREREQqnIr8GiT7yB5saQmlxphzkrmrX/5ulR9/t4ufNx5yRWoiIiIiIiLiAiryaxBbelKZ4ga08WHM4FAA3lu4lT+2HavItERERERERMRFVOTXICafOmWKc/Otw62j2nNJn2bYHfDml38RuSeugrMTERERERGRiqYivwaxNGmHyTeo1BiTXxCWJu0wGAz8Z1xnBnRphNXm4JXPN7PrYOlL/UVERERERKRqU5FfgxiMJuoOv63UGN9OQzEY85/NaDIaeOi6bvRoF0xuno3nP95A1NFkF2QqIiIiIiIiFUFFfg3jHdaH4HGPFZnRN7h5AJCyeTm58Yedx81uRp6Y0IMOLYPIzLbyzOz1HIlLc2nOIiIiIiIicmGoyK+BvMP60PTeD2hw43PUH/0gDW58jmYPfYqlaXscOZmcmP8S1rREZ7zF3Y2pt/cmtLE/qRm5PD1rHXGJmZX4HYiIiIiIiEh5qMivoQxGE57NOuLTYQCezTpidPcgePzjmIMaYks9ReyCV7DnZjnjvSxmnr0jgibBPiSkZPP0h+tITM2uxO9AREREREREzpWK/FrE5OlLyDWTMXr5kRt7kPgl03HYbc52fx8PXpjUl/qBXpxIyGDqrHWkZeZWYsYiIiIiIlJbOew2sg7tJH3XWrIO7SxUu7hSeno6nTt3pm/fvuTl5RVqmzFjBkOHDi1yzm+//cZtt91G79696dq1K2PHjmXBggU4HI4Kz1dFfi1jrhNCyNVPYXBzJzPqLxJ+/qTQQAvy9+TFSX0J9PPgUGwaz360nszsvFJ6FBERERERubAy9m7g8Mz/cOL/niF+6duc+L9nODzzP2Ts3eDyXJYvX05QUBBpaWn88ssvZ41/7bXXePjhh+nVqxeff/45S5YsYdy4cbz88ss8++yzFZ6vivxayNKoDfWuvB8wkPrXj6Rs+r5Qe4O63jx/Z198vczsO5zMS59uIjevcq6aiYiIiIhI7ZKxdwNxi97Allb4Ed+2tATiFr3h8kJ/0aJFDBgwgD59+jBv3rxSY1evXs0nn3zCm2++yV133UVYWBjNmzfnhhtu4Nlnn2XevHls2bKlQvNVkV9L+YRFEDhsAgCJv35B+t71hdqbNfDj2Tsi8PQwsT3qFK/PjcRqs1dGqiIiIiIiUo05HA7sudllerNlZ3Lqp49L7e/Uzx9jy84sc5/ns0T+wIEDbNu2jX79+jF8+HA2btxIdHR0ifFff/01YWFhDBs2rEjbqFGj+Oyzz2jbtm258ykLtwrtXao0/16XY02KI/WvHzn57bu4+QZhadTG2d6maR2evq0Pz3y0no27Ynln3hYeuq4bRqOhErMWEREREZHqwuFwcPyLyeQc/eeC9WlLS+TQmzeVOd6jcRgNJ7yIwXDudcw333yDl5cXAwcOJDs7m+eee4558+bx1FNPFRu/c+dOLr744mLb3NzciIiIOOcczpVm8msxg8FA0PDb8ArtjsOaS+yCV8hLii0UEx5alydv7onJaGDV30eZtWS7SzaLEBERERGRmqJ6ThJarVa+++47hg4disViISAggP79+7N06VJycnKKPSc5ORk/Pz8XZ1pYpc7kb9y4kQkTJhTb1rhxY3777TeOHj3KCy+8wObNm/Hy8mL8+PHcd999mEwmZ+yXX37JJ598wsmTJ+nYsSNTpkyhffv2zvay9FFbGYwm6o95iONfPE1uXDSx81+i4c0vY/L0dcb0ah/CQ9d1482v/uKHdTF4e5qZMLJ9Kb2KiIiIiIjkTyw2nPAijrzii+IzZR3eTdz8l84aF3zNZDyblq0mMZg9yjWLv3r1ak6dOsVll13mPHbZZZexcuVKVqxYwejRo4ucExgYSHJy8jl/rQupUov8rl278scffxQ6tnXrVu677z7uvvtu8vLyuP3222nevDnz5s3j8OHDTJ48GaPRyP333w/AkiVLeP3113nhhRdo3749s2fP5tZbb2XFihUEBgaWqY/azujuScg1/+XYZ0+Rl3CcuG9ep8F1UzG4mZ0xg7o1JjM7j/cXbWfhb/vxtpgZN7R1JWYtIiIiIiLVgcFgwOBuKVOsV8vOmHyDimy6dzqTXxBeLTtjMFbspO3ixYsBuPfee4u0zZs3r9giv2vXrvz999/F9mez2Zg0aRLjx49nxIgRFzTX01Xqcn13d3fq1avnfPP29uaVV15hzJgxjBs3jp9++onjx4/z+uuv06ZNG4YNG8bDDz/M559/Tm5u/vPbP/zwQ2688UauuOIKQkNDefnll/H09GThwoUAZepDwM03kAbX/BeDhxfZh3dzctl7RZblX9q3BTdfln+17LPlu1mxPqYSMhURERERkZrKYDRRd/htpcbUvfi2Ci/wExISWL16NWPHjmXp0qWF3saNG8eWLVvYt29fkfOuvvpq9u3bx6+//lqk7bvvvmPt2rXUq1evQnOvUvfkf/jhh2RlZfHEE08AEBkZSYcOHfD393fG9OnTh/T0dPbs2UNCQgIxMTGFNi9wc3OjR48ebN68uUx9yP+4129G8LhHwWgifddaktYUfTzE+KGtueqi/Bn8DxZtY/XfR12dpoiIiIiI1GDeYX0IHvcYJt+gQsdNfkEEj3sM77A+FZ7Dd999h9Vq5Y477qBNmzaF3u666y6MRmOxj9Pr168f1157LQ8//DCzZs0iKiqKqKgoZs2axTPPPMNNN91E9+7dKzT3KrO7fmJiIp999hmPPPIIAQEBAMTGxhISElIorn79+gCcOHECN7f89Bs0aFAkZu/evWXqo3PnzuXK1+FwkJmZWa5zq7Tg1vhddCupv8wh+Y9vsHvVwavDwEIh4wY1Izkti182HWX6139jNNjo3rZir0ZVJ1lZWYXei9RkGu9Sm2i8S22i8S7lkZOTg91ux2azYbPZzqsvS+ueNGrVnZwje7BlJGHyroNHk3YYjMbz7vtMBSuYHQ6Hs+9FixYRERFBs2bNiny9Ro0acdFFF/Hdd98xfvz4QucBTJ06lfDwcBYuXMicOXOw2Wy0bNmSqVOnMnr06BLzt9ls2O12srKysNuLPr7c4XCUaW+BKlPkf/XVV/j6+nLNNdc4j2VnZxfZmdDDwwPIH0AF/+i4u7sXiSnY7fBsfZRXXl5ezV0JYKqPpWVfPA+uI+WXjzmWmI61botCIRGtHMTGe7EjJpM3v9rKjUPq0iK4bPfZ1BYxMTGVnYKIy2i8S22i8S61ica7nCs3N7fzqrOKCG5FwcL8nAq+3fr0vOfPnw/k15PFee2115wf33fffUXiLr30Ui699NJSv0ZxbVarlYMHD5YYc2btW5wqU+QvXbqU0aNHY7H8r1C0WCxF7psveFG8vLycscXFeHp6lqmP8jKbzYSGhpb7/KrOERZGyo8Osveux2/7UgKveRpz3SaFYtq2tTN93nYi955k/toknr61O6GN/UvosfbIysoiJiaG5s2bO8ehSE2l8S61ica71CYa71IeOTk5HD9+HA8Pj0J1XVXncDjIycnBw6N8u/BfSG5ubjRt2tQ5MX26qKiosvVxoZMqj71793LkyBEuv/zyQsdDQkKKbGYQHx8PQHBwsHOZfnx8PK1atSoUExwcXKY+ystgMJzXRYLqwOvKBziRkUz2kT0kf/sWjW55FTffOoVinrqlN8/N2cD2qFO8OncLr9zTn2YhlftcyKrC09Ozxo8RkQIa71KbaLxLbaLxLufCaDRiNBoxmUzV6nHlBcvnDQZDpeZtMpkwGo14enoWe5GkrBcgqsTGe5GRkQQFBREWFlboeM+ePdm9ezfp6enOYxs2bMDb25uwsDCCgoJo0aIFGzdudLZbrVYiIyPp2bNnmfqQkhnczARf9QTmoIbYUk8Ru+Bl7LmF78tyN5uYfGsv2jQNIC0zj6mz1hGbkFFJGYuIiIiIiNRuVaLI3717N23bti1yfNiwYdSrV48HH3yQvXv38uuvv/LWW29x2223Oe9FuO222/j0009ZsmQJUVFR/Pe//yU7O5vx48eXuQ8pmcnTl5BrJmP08iM39iDxS6bjsBfeKMLLYubZOyJoFuJLYmoOUz5cR0KKNmkRERERERFxtSpR5J88edK5o/7pPDw8mDNnDna7nauvvprnnnuO66+/nrvvvtsZc/XVV3P//ffz9ttvM27cOI4dO8ann35KYGBgmfuQ0pnrhBBy1ZMY3NzJjPqLhJ8/ce5AWcDXy53nJ/WlQZA3cYmZPD1rPSnpF3DDDRERERERETkrg+PMak3OaseOHQCEh4dXciaulb53PfGL3gQcBA67hYDelxeJiUvM5ImZa0lIySa0sT8v/acfXhaz65OtRJmZmezZs4d27drpHjap8TTepTbReJfaRONdyiM7O5vo6Ohqt2GjzWYjOzsbi8VSqffkF2x42aJFi2LvyS9rHVolZvKlevAJiyDwogkAJP76ORl7NxaJCQ704oVJffHzdifqaArPf7yRnLwL+xxLERERERGpeszm/Mm9zMzMSs6keip43Qpex/KqErvrS/Xh3/tyrMlxpP71I/Hfvk0D3+ewNGpTKKZJsC/P3RnB5A/+ZNfBBF79fDP/vaUXZjddUxIRERERqalMJhMBAQHOp5l5eXlV+iPpysJmszkfs14ZM/kOh4PMzEzi4+MJCAg47xxU5Ms5MRgMBA2/jbzkeLIO/E3sgldodMsrmOuEFIoLbRzA1Nv7MHXWOiL3xDH967955IbumIxV/5dcRERERETKJyQkvy4oKPSrA7vdjtVqxc3NDaOx8iYmAwICnK/f+VCRL+fMYDQRPPZhjn/xNLlx0cTOf4mGN7+MydO3UFyHlkE8dUsvXvp0I2u3HsPL4sY94ztXi6t5IiIiIiJy7gwGAw0aNKB+/frk5eVVdjplkpWVxcGDB2natGml7SVgNpsv2CoCFflSLkZ3T0Ku+S/HPn2SvITjxH3zBg2uexqDW+H7R3q0C+aRG7rzxtxIftpwCC+LmVtHtVehLyIiIiJSg5lMpkrdxO5c2O12IP/JbMVteFfd6CZpKTc330AaXDsZg7sn2Yd3cXL5+0UerQfQv3Mj7rmqCwBLVkWx8Lf9Ls5URERERESkdlCRL+fFvX4zgsc9BkYT6TvXkLRmXrFxw3s34/YrOgAwd8Uelv1x0JVpioiIiIiI1Aoq8uW8ebXsTN1L7wQg+Y9vSNv2e7FxoweFcs3F+Tvxz1qyg98jD7ssRxERERERkdpARb5cEH5dhhHQdywAJ3/4kKzo7cXG3XBJGJcPaAnAO/O3sn7HCZflKCIiIiIiUtOpyJcLps7g6/Du0B/sNmIXvUFufNGZeoPBwMQrOjK0RxPsdgevz41k276TlZCtiIiIiIhIzaMiXy4Yg8FI/VH3YmnSDkdOJrHzX8KallQkzmg0cP/VXYgIb4DVZufFTzeyNyaxEjIWERERERGpWVTkywVlcDMTPP4JzIENsaaeInbBK9hzs4vEmUxGHruxO13a1CM718azczYQfTylEjIWERERERGpOVTkywVn8vIl5NrJGL38yI09QPzS6TjstiJxZjcTk2/pRbvmgWRk5TF19nqOn0yvhIxFRERERERqBhX5UiHMdUIIuepJDCYzmfsjSfjlUxwOR5E4i4cbUyf2oUVDP5LTcpgyax0nk7IqIWMREREREZHqT0W+VBhL47bUu/IBAFIjV5C6eXmxcT6eZp67M4JG9bw5mZTF07PWkZyW48pURUREREREagQV+VKhfNpFEHjRBAASfvmMjL0bi42r42vh+Ul9qRvgybGT6Twzez3pWXmuTFVERERERKTaU5EvFc6/9xX4dbsEcBD/7dtkH9tfbFz9Ol68eFdf/H3cOXg8hefnbCA71+raZEVERERERKoxFflS4QwGA0GX3I5nq244rLnELXyFvOS4YmMb1fPh+Tv74m1xY09MIq98tpk8a9FN+0RERERERKQoFfniEgajieAxD+Me3AJbRgqx817CllX8TvotG/nzzMQIPNxN/P1PPNO+/Aubze7ijEVERERERKofFfniMkYPT0KufgqTbxB5CceIW/Q6Dmvx9923axHI5Ft64WYysm77CWYu3IbdXnR3fhEREREREfkfFfniUm5+QYRc818M7p5kH9rFyeXvF/toPYCubevz2I3dMRrg182H+fj7nSXGioiIiIiIiIp8qQQewc0JHvcoGIyk71xD0pr5Jcb27dSQ+6/pCsB3aw4y7+d/XJWmiIiIiIhItaMiXyqFV8su1L10EgDJfywkbfvKEmMv6tmUO0Z3BOCrn//h2zUHXJKjiIiIiIhIdaMiXyqNX9dhBPQdC8DJ5R+QFb29xNgrBrTihhFhAMz5die/bjrkkhxFRERERESqExX5UqnqDL4O7/b9wG4jbtEb5J48XGLsNcPaMHpQKwBmLNjKn9uOuypNERERERGRakFFvlQqg8FIvcvvxdKkHfacTGLnvYQ1PamEWAO3Xd6Bi3s1xe6AaV9G8vfeeBdnLCIiIiIiUnWpyJdKZ3RzJ3j8E5gDG2BNPUXs/Few52YXG2swGLjnqi7069wQq83BS59tYtfBBBdnLCIiIiIiUjWpyJcqweTlS8g1kzF6+ZEbe4D4pdNx2G3FxxoNPHJ9d7qH1Sc3z8bzH2/gwNFk1yYsIiIiIiJSBanIlyrDHNiAkKuewGAyk7k/koRfPys51s3Ikzf3pEPLIDKzrTzz0XqOxKW5LlkREREREZEqSEW+VCmWxmHUu/J+AFI3/0DKpmUlx7q78fRtvWnV2J+U9FymzlpHfGKmq1IVERERERGpclTkS5Xj064vgUNvAiDhl8/I+GdjibHenmaeuyOCxvV9OJWSzZRZ60hKK/5+fhERERERkZpORb5USf59rsS323DAQfzSt8k+tr/kWB8PXpjUl/p1PDlxKoOps9aTnpnrumRFRERERESqCBX5UiUZDAbqXjIRz1ZdcVhziVv4CnnJcSXG1w3w5IW7+lLH14OYE6k8O2cDWTlWF2YsIiIiIiJS+VTkS5VlMJoIHvMI7vWbY8tIIXb+y9iy0kuMb1jXh+cn9cXH08w/h5J46dON5OYVv0O/iIiIiIhITaQiX6o0o4cnIdf8F5NvIHmnjhK36HUctrwS45s38OPZO/pgcTexbf8p3vi/SGw2uwszFhERERERqTwq8qXKc/MLIuSayRjcPck+tIuTyz/A4XCUGN+2WSBP394bs5uRDTtjeWf+Fuz2kuNFRERERERqChX5Ui14BDcneOwjYDCSvmM1SWsXlBrfKbQeT9zUA6PRwMq/jvLR0h2lXhgQERERERGpCVTkS7Xh1aordS+9E4DktQtI276y1PjeHRvw0LVdMRhg2Z/RfPnjXlekKSIiIiIiUmlU5Eu14tf1YgL6jgHg5PIPyIrZUWr84O5NmDSmEwDzf93H4pVRFZ6jiIiIiIhIZVGRL9VOncHX492+H9htxH3zOrknj5Qaf1m/FkwY2Q6AT5ft4qcNMS7IUkRERERExPVU5Eu1YzAYqXf5vXg0DsOek0ns/JewpieVes5VF7Vh3JBQAN77ZhtrtxxzRaoiIiIiIiIupSJfqiWjmzshVz2BObAB1pSTxC14BXtudqnn3HxZey6NaI7DAW9+9ReRe+JclK2IiIiIiIhrqMiXasvk5UfINZMxevqSc+IA8UvfxmG3lRhvMBi4a2wnBnVtjM3u4JXPNrHjwCkXZiwiIiIiIlKxVORLtWYObEDIVU9iMJnJ3L+ZhF8/LzXeaDTw4HVd6dk+mFyrnRc+3sj+I6Uv9RcREREREakuVORLtWdpEka9K+4DIHXzclI2LSs13s1k5IkJPekUWpesHCvPzN7A4dhUV6QqIiIiIiJSoapEkb906VJGjhxJeHg4l112GStWrHC2HT16lEmTJtGtWzf69+/P22+/jc1WeEn2l19+yUUXXUSnTp24/vrr2b17d6H2svQh1ZtP+34EDr0JgIRfPiPjn02lxnuYTUy+tRetmwSQlpnL07PWE5uQ4YpURUREREREKkylF/nffvstkydP5oYbbmD58uWMGjWKhx9+mC1btpCXl8ftt98OwLx583j22Wf5+uuvee+995znL1myhNdff50HHniAxYsX07hxY2699VYSExMBytSH1Az+fa7Et+twwEH80ulkH48qNd7LYubZOyJoGuJLYmo2T89aR0JKlmuSFRERERERqQCVWuQ7HA7eeecdJkyYwA033EDTpk35z3/+Q9++fdm0aRM//fQTx48f5/XXX6dNmzYMGzaMhx9+mM8//5zc3FwAPvzwQ2688UauuOIKQkNDefnll/H09GThwoUAZepDagaDwUDdERPxbNkVhzWXuAUvk5dc+g76ft7uPH9nBCFBXsQmZDJ19npSMzQuRERERESkeqrUIj86Oppjx45x+eWXFzr+8ccfM2nSJCIjI+nQoQP+/v7Otj59+pCens6ePXtISEggJiaGiIgIZ7ubmxs9evRg8+bNAGftQ2oWg9FE8NhHcK/fHFtGCrHzX8aWlV7qOUH+nrwwqS+BfhYOx6bx7EfryczOc1HGIiIiIiIiF45bZX7x6OhoADIzM7n99tvZvXs3jRs35j//+Q9Dhw4lNjaWkJCQQufUr18fgBMnTuDmlp9+gwYNisTs3bsX4Kx9dO7cuVy5OxwOMjMzy3WuVDz/Kx4iYd6z5J06yomFr1JnzOMYTCUPdz9PA5Nv7sqzH0ey/0gyz81Zz1M3dcXdbDrnr52VlVXovUhNpvEutYnGu9QmGu9Sm1SX8e5wODAYDGeNq9QiPz09f4b1iSee4N577+XRRx/lp59+4u677+bTTz8lOzsbPz+/Qud4eHgAkJOT4/whuLu7F4nJyckBOGsf5ZWXl6eVAFWcqdNYfDfOJffIHg5/8xaZ4aPgLL8U1w6ow+e/nWR3dBIvzFnHNQODMBnP/otUnJiYmHKdJ1IdabxLbaLxLrWJxrvUJtVhvJ9Z+xanUot8s9kMwO23386YMWMAaNeuHbt37+bTTz/FYrEUuW++oDD38vLCYrEAFBvj6ekJcNY+zif30NDQcp8vrtCOnOBAkpa+icfxHQQ1DcUnYuxZzoCGjZN4+fO/2Xc8m9932bh3fEeM51DoZ2VlERMTQ/PmzZ3jUKSm0niX2kTjXWoTjXepTarLeI+KKn1j8QKVWuQHBwcD0KZNm0LHQ0NDWbVqFb169WLfvn2F2uLj453nFizTj4+Pp1WrVoViCvoOCQkptY/yMhgM53WRQFzDq30fTNl3cGrFLNI3LMGzXiN8Ow0p9Zwe7b347y1uvPjJRv7cEYufj4X/jOtUpqUxp/P09NQYkVpD411qE413qU003qU2qerjvaz1SKVuvNehQwe8vb3Ztm1boeP79u2jadOm9OzZk927dzuX9QNs2LABb29vwsLCCAoKokWLFmzcuNHZbrVaiYyMpGfPngBn7UNqPr9uw/GPGA3AyeUfkhWz46zn9GgXzCPXd8dggBXrY/h8+e4KzlJEREREROT8VWqRb7FYmDhxIu+99x7Lli3j8OHDfPDBB/z555/ceuutDBs2jHr16vHggw+yd+9efv31V9566y1uu+02570It912G59++ilLliwhKiqK//73v2RnZzN+/HiAMvUhNV/gkBvwbtcX7Fbivnmd3JNHznrOgK6NuGd8/saMi1ZGsfC3fWc5Q0REREREpHJV6nJ9gLvvvhtPT0+mT59OXFwcrVq1YsaMGfTu3RuAOXPm8Nxzz3H11Vfj7+/P9ddfz9133+08/+qrryYtLY23336b5ORkOnbsyKeffkpgYCCQv8ne2fqQms9gMFLvivuwpiWQc/QfYue/RMNbXsHNp06p513SpzkZWVY+XbaLL37Yg7enmZF9W7goaxERERERkXNjcDgcjspOorrZsSN/uXd4eHglZyLnypaZyrHPnsKaFItHg1Y0uPF5jO6Ws573fyv2MP/XfRgM8NB13RjSvUmJsZmZmezZs4d27dpV6Xt6RC4EjXepTTTepTbReJfapLqM97LWoZW6XF/E1UxefjS4djJGT19yThwg/tu3cdhtZz3vhhFhjOrXAocD3p63hY07T7ggWxERERERkXOjIl9qHXNgQ0KuehKDyUzmvs0k/Pr5Wc8xGAzcMTqcoT2aYLc7eG1uJNv2n3RBtiIiIiIiImWnIl9qJUuTMOpdcR8AqZuXk7J5+VnPMRoN3H91F/p0DCHPaufFTzbyz6HEik5VRERERESkzFTkS63l074fgUNuBCDh50/J+GfTWc8xmYw8flMPurSuR3aujWc/2kDMidSKTlVERERERKRMVORLreYfMRrfrhcDDuKXTif7eNRZzzG7mfjvrb0Ia1aH9Kw8ps5ax/FT6RWfrIiIiIiIyFmoyJdazWAwUHfEHXi27ILDmkvcglfIS44/63meHm48M7EPzRv4kZSWw9MfruNUchY2u4Nd0YnsiMlkV3QiNrseXiEiIiIiIq7jVtkJiFQ2g9FE8NhHOP7FFHLjDxE7/yUa3vwyJot3qef5eLnz/KQInpj5BydOZfDoO2tw4CAxNQeAResSCfLfxZ2jw+nbqaErvhUREREREanlNJMvAhg9vAi5ZjImn0DyTh0lbtEbOGx5Zz2vjq+FFyf1xdfLTEJqtrPAL5CQks0rn29m3fbjFZW6iIiIiIiIk4p8kX+5+QURcs1/MbhbyI7ZwckfPsThOPty+6AAT0ym0n+VPvp2p5bui4iIiIhIhVORL3Iaj5AWBI95BAxG0revIvmPhWc9Z/fBBJLTckqNOZWcxe6DCRcqTRERERERkWKpyBc5g1doN+qOuAOApDXzSdu+qtT4xNTsMvVb1jgREREREZHyUpEvUgy/bsPxjxgNwMnlH5AVs6PE2EA/S5n6LGuciIiIiIhIeanIFylB4JAb8G4XAXYrcd+8Tu6po8XGtW8ZRJB/6QV83QBP2rcMqog0RUREREREnFTki5TAYDBS7/L78GjcFntOJrHzXsKanlwkzmQ0cOfo8FL7uuPKjpiMhgrKVEREREREJJ+KfJFSGM0ehFz1JG51QrCmxBO34BXseUU32evbqSFP3dyz2Bn9O0d3pG+nhq5IV0REREREajkV+SJnYfLyo8G1kzF6+pBzIor4pW/jsNuKxPXt1JCPpwxn6m3dGdc3kDZN/AE4FJvm6pRFRERERKSWUpEvUgbmwIaEXPUkmNzI3LeJhN++KDbOZDTQoUUg4c29uOGS1gD8tvkISdpZX0REREREXEBFvkgZWZq0o/7l9wGQumkZKZt/KDW+bdMA2jarg9Vm5/s/DroiRRERERERqeVU5IucA58O/QkccgMACb98Ssa+zSXGGgwGxg0JBeCHdTFkZue5JEcREREREam9VOSLnCP/iDH4dhkGDjvxS6eTczyqxNheHRrQsK43GVl5/LLpsAuzFBERERGR2khFvsg5MhgM1B1xB54tO+PIyyF2wSvkpcQXG2syGhgzOH82/9s1B7Da7K5MVUREREREahkV+SLlYDC5ETz2UdzrN8WWkUzsvJewZWcUGzu0RxMCfDw4mZTFH1uPuThTERERERGpTVTki5ST0cOLkGsmY/IJJO/UUeIWvYE9L4ecI3swH99FzpE9OOw23M0mRvVvAcDiVVE4HI5KzlxERERERGoqt8pOQKQ6c/OrS8g1/+X43Clkx+zg0PRbceTl4AMkbf+WVN8g6g6/jZH9urHw9/1EH09ly76TdGtbv7JTFxERERGRGkgz+SLnySOkBf49LwPAkZdTqM2WlkDcojcwHv6b4b2bAbBkZckb9YmIiIiIiJwPFfki58lht5G2fVWpMad++YQr+rfAaDSwdf9JDhxNdkluIiIiIiJSu6jIFzlP2Uf2YEtLKDXGlpqAf0YM/Ts3BPLvzRcREREREbnQVOSLnCdbelKZ48b++zi9P7YdJy4xsyLTEhERERGRWkhFvsh5MvnUKXNcq8YBdGldD7vdwbdrDlRwZiIiIiIiUtuoyBc5T5Ym7TD5BpUaY/ILwtKkHQBjhuTP5v+88RBpmbkVnp+IiIiIiNQeKvJFzpPBaKLu8NtKjal78W0YjCYAurapR4uGfuTk2vhhXbQrUhQRERERkVpCRb7IBeAd1ofgcY8VO6PvHzEG77A+zs8NBoPz3vxla6PJzbO5LE8REREREanZVOSLXCDeYX1oeu8H1Bn/X9I7XYmlTX5hn3N8X5HY/l0aUTfAk+T0HH6PPOLqVEVEREREpIZSkS9yARmMJjyatCOvYQd8B14LBiPZh3aRE1t4Wb6bycjoQa0AWLIqCpvdURnpioiIiIhIDaMiX6SCmHyD8G4XAUDK5mVF2of3boa3p5njpzLYtOuEq9MTEREREZEaSEW+SAXy7zUKgPRdf2BNTyrU5unhxsi+zQFYtDIKh0Oz+SIiIiIicn5U5ItUIEujNng0ags2K6l//Vik/fL+LXEzGfnnUBK7oxMrIUMREREREalJVOSLVDD/3vmz+al//4w9L6dQWx0/C0N7NAHy780XERERERE5HyryRSqYd9veuPnVxZ6ZSvqutUXaxwxuhcEAG3fFciQurRIyFBERERGRmkJFvkgFMxhN+PUcCUDKpmVF7r1vXN+XXu1DAM3mi4iIiIjI+VGRL+ICvl2GYTBbyDt5hKzo7UXaxw4JBWDlX0dJTM12dXoiIiIiIlJDqMgXcQGTxRvfzkMBSNn0fZH29i2CaNc8EKvNzvdrD7o6PRERERERqSFU5Iu4iH+vywADWQe2kHvqaJH2MYPzZ/NXrIsmMzvPxdmJiIiIiEhNoCJfxEXMdULwatMDgJTNy4u09+4QQqN63mRkW/l54yFXpyciIiIiIjVApRf5cXFxtG3btsjb4sWLAdizZw833ngjXbp0YejQoXzxxReFzrfb7bz77rsMGDCALl26cMcdd3DkyJFCMWfrQ8RV/HvlP04vffsqbJmFd9I3Gg3O2fxv1xzEarO7PD8REREREaneKr3I37t3Lx4eHqxdu5Y//vjD+TZy5EiSkpK49dZbadq0KYsWLeKee+5h2rRpLFq0yHn++++/z1dffcULL7zAvHnzsNvtTJw4kdzcXIAy9SHiKpamHXAPboHDmkvqll+KtA/p3oQAXw9OJWexduuxSshQRERERESqs0ov8vft20fz5s2pX78+9erVc75ZLBYWLFiA2Wzm+eefp1WrVowbN45bbrmF2bNnA5Cbm8snn3zC/fffz+DBgwkLC2P69OnExsby888/A5y1DxFXMhgMztn81MgVOGyF7713N5u4vH9LABavjCryuD0REREREZHSVHqR/88//9CqVati2yIjI+nVqxdubm7OY3369CEmJoZTp06xd+9eMjIyiIiIcLb7+fnRvn17Nm/eXKY+RFzNp0M/TN4B2NITSd+zvkj7yL7NsbibiDmRypZ/TlZChiIiIiIiUl1VepG/b98+EhMTueGGG+jbty/XXXcda9asASA2NpaQkJBC8fXr1wfgxIkTxMbGAtCgQYMiMQVtZ+tDxNUMJjN+PS4FIHXTsiKz9T5e7gzv0wyARSv3uzw/ERERERGpvtzOHlJxrFYrBw8eJDQ0lCeffBIfHx+WL1/OnXfeyaeffkp2djbu7u6FzvHw8AAgJyeHrKwsgGJjUlJSAM7aR3k5HA4yMzPLfb7UXAXjsuB9ccxh/eGPb8g5cYCUqK24N2pbqH14z4Ys+yOa7VGn2BkVS8uGfhWas0h5lWW8i9QUGu9Sm2i8S21SXca7w+HAYDCcNa5Si3w3Nzc2btyIyWTCYrEA0LFjR/bv38/HH3+MxWJxbqBXoKAw9/Lycp6Tm5vr/LggxtPTE+CsfZRXXl4ee/bsKff5UvPFxMSU2u7VoAMeR7cSu3ohGV3HFWnv0MTCjkNZfPnDdsb3C6qgLEUujLONd5GaRONdahONd6lNqsN4P3MCuziVWuQDeHt7FznWunVr/vjjD0JCQoiPjy/UVvB5cHAwVqvVeaxp06aFYtq2zZ8ZPVsf5WU2mwkNDS33+VJzZWVlERMTQ/PmzZ0Xm4qTV8+XhLlbcY/fR8OGQbj51y/UfmNAGk+8v4Fdh7OYNK459euU3JdIZSnreBepCTTepTbReJfapLqM96ioqDLFVWqRv3//fq655ho++OADevfu7Ty+c+dOQkNDadeuHfPmzcNms2EymQDYsGEDLVq0ICgoCF9fX3x8fNi4caOzyE9NTWX37t3ceOONAPTs2bPUPsrLYDCc10oAqfk8PT1LHyNN25DZsjNZB7eRu3MlfhffWqi5fSsvurSpx9Z9J/lp0zEmjelUwRmLlN9Zx7tIDaLxLrWJxrvUJlV9vJdlqT5U8sZ7rVq1omXLljz//PNERkZy4MABXnnlFbZu3cp//vMfxo0bR3p6OpMnTyYqKorFixfz2WefMWnSJCB/qcKNN97ItGnT+O2339i7dy8PPfQQISEhDB8+HOCsfYhUJv9elwOQtvU37DlF93gYNyR/tcgvmw6TmpFbpF1EREREROR0lVrkG41GPvzwQzp16sSDDz7ImDFj2LZtG59++ilt2rQhKCiIOXPmEB0dzZgxY5g5cyaPP/44Y8aMcfZx//33M378eKZMmcJ1112HyWTi448/xmw2A5SpD5HK4tmyM+agRjhys0jb9nuR9s6t69GyoT85uTZ+WBddCRmKiIiIiEh1Uun35NetW5dXXnmlxPZOnToxf/78EttNJhOPPfYYjz32WLn7EKksBoMR/16jOLViFimbl+PX41IMRtNp7QbGDAnlzS//YtkfBxkzOBQPs6mUHkVEREREpDar1Jl8EQGf8EEYPX2wJseTuW9zkfb+nRtSv44nKem5/L75cCVkKCIiIiIi1YWKfJFKZjR74Nc1fw+JlE3LirS7mYxcObAVAEtWH8Bmd7g0PxERERERqT5U5ItUAX49LgWjG9lH9pBzvOijMS7u3QwfTzMnTmWwYeeJSshQRERERESqAxX5IlWAm28gPu37ApCyeXmRdk8PN0b2awHAkpVROByazRcRERERkaJU5ItUEf69RgGQvvtPrKkJRdpH9W+B2c3IP4eT2B2d6Or0RERERESkGlCRL1JFeDRohaVJO7DbSP3rxyLtdXwtDO3RBIBFK/e7Oj0REREREakGVOSLVCH+vS4HIHXLz9jzcoq0jxkcisEAm3fHcTg21dXpiYiIiIhIFaciX6QK8WrTA7eAYOxZ6aTvWF2kvVE9H/p0bADAklUHXJ2eiIiIiIhUcSryRaoQg9GEf8+RQP7j9BwOe5GYsYNDAVj19xESUrJcmp+IiIiIiFRtKvJFqhjfzkMxuHuSl3CMrANbi7SHNQ+kXfNArDYH36896PoERURERESkylKRL1LFGD288OtyEZA/m1+ccUPyZ/NXrI8hMzvPZbmJiIiIiEjVpiJfpAry63kZGIxkRW8jN/5wkfae7UNoXN+HzGwrP204VAkZioiIiIhIVaQiX6QKMgfUx7ttL6D42Xyj0cCYf+/N/27NAfKsRe/dFxERERGR2kdFvkgVVfA4vfSda7BlpBRpH9K9MXV8PTiVks3arUddnZ6IiIiIiFRBKvJFqiiPxm3xaNAKhy2P1L9/LtJudjNx+YCWACxeGYXD4XB1iiIiIiIiUsWoyBepogwGg3M2P/WvH3FYi26wd2lEczw9TByKTeOvvfGuTlFERERERKoYFfkiVZh3uwhMvoHYMpJJ3/1HkXYfL3eG924OwJJVUS7OTkREREREqhoV+SJVmMHkhn+PSwFI2bis2CX5VwxsidFoYHvUKfYfSXJ1iiIiIiIiUoWoyBep4ny7XozBzZ3c+BiyD+8q0l6/jhcDuzYC8u/NFxERERGR2ktFvkgVZ/L0xafTYCB/Nr84Y/99nN667ceJTchwVWoiIiIiIlLFqMgXqQb8e40CIHN/JHmJJ4q0t2joT7e29bE7YOnqA65OT0REREREqggV+SLVgHtQIzxbdQMcpGxeXmxMwWz+L5sOk5Ke48LsRERERESkqlCRL1JN+PfOn81P27YSW3bRJfmdWtelZSN/cvNs/LAuxsXZiYiIiIhIVaAiX6Sa8GzeCXO9pjjysknb+muRdoPBwLgh+bP5y/44SHau1dUpioiIiIhIJVORL1JNGAwG/HtdBkDq5h9w2G1FYvp1akj9QC9SM3L5PfKIq1MUEREREZFKpiJfpBrx6TgQo5cf1tRTZOzdUKTdZDIyemArAJauOoDN7nB1iiIiIiIiUolU5ItUI0Y3d/y6XQJAyqbiH6d3ca+m+HqZOZGQwYYdRXfiFxERERGRmktFvkg149d9BJjcyDm2j+xj+4q0WzzcGNm3BQCLVu7H4dBsvoiIiIhIbaEiX6SacfMJwKfDAKDk2fxR/VtidjOy/0gyOw8muDI9ERERERGpRCryRaoh/175j9PL2LMea8rJIu0Bvh5c1LMpAItXRrk0NxERERERqTwq8kWqIY/g5liadQSHnZTIFcXGjBnUCoMBIvfEcSg21cUZioiIiIhIZVCRL1JNFczmp235BXtuVpH2hvV86NOxAQBLVmk2X0RERESkNlCRL1JNebXujjmwAfacTNK2rSw2ZuyQUABW/32UhJSiFwJERERERKRmUZEvUk0ZDEb8el4GQMrm5Tgc9iIxYc0C6dAyCKvNwXdrDro6RRERERERcTEV+SLVmG+nwRgt3liTYsnc/1exMWMH58/m/7ghhoysPFemJyIiIiIiLqYiX6QaM7p74ttlGFDy4/R6tAumSbAPmdlWftoQ48LsRERERETE1VTki1Rz/j1HgsFI9qGd5MRGF2k3Gg2MGZQ/m//d2oPkWYsu6xcRERERkZpBRb5INefmVxfvdhEApGwufjZ/cPfGBPp5kJCSzZotR12ZnoiIiIiIuJCKfJEaoOBxeum7/sCanlSk3exm4vIBrQBYvCoKh8Ph0vxERERERMQ1VOSL1ACWRm3waNQWbFZS//qp2JgREc3x9DBxODaNv/bGuzhDERERERFxBRX5IjWEf6/8x+ml/v0TdmtukXYfTzOX9GkOwKKV+12ZmoiIiIiIuIiKfJEawjusD25+dbFnppK+c02xMVcMaIXJaGDngQT2HS66rF9ERERERKo3FfkiNYTBaMKv50gg/3F6xd13X6+OJwO7NgLy780XEREREZGaRUW+SA3i22UYBrOFvJNHyIrZXmzM2CGtAVi//TgnTmW4Mj0REREREalgKvJFahCTxRvfzkMBSNlY/OP0mjfwo1tYfewOWLpas/kiIiIiIjVJuYv83NxcvvrqK+69916uueYaDhw4wNdff8327cXPHpZFdHQ0Xbt2ZfHixc5je/bs4cYbb6RLly4MHTqUL774otA5drudd999lwEDBtClSxfuuOMOjhw5UijmbH2I1CT+PUcCBrIO/E3uqaPFxowbEgrAr5sOk5Ke48LsRERERESkIpWryE9MTGTcuHG89NJLHDp0iO3bt5Odnc2qVau46aab2LJlyzn3mZeXx6OPPkpmZqbzWFJSErfeeitNmzZl0aJF3HPPPUybNo1FixY5Y95//32++uorXnjhBebNm4fdbmfixInk5uaWuQ+RmsQc2ACv1j0ASNm8vNiY8FZ1CW3sT67VzvI/o12ZnoiIiIiIVKByFfmvv/46GRkZ/PDDDyxZssS5wde7775LeHg477777jn3OWPGDHx8fAodW7BgAWazmeeff55WrVoxbtw4brnlFmbPng3kryb45JNPuP/++xk8eDBhYWFMnz6d2NhYfv755zL1IVIT+fceBUD69lXYMtOKtBsMBsYOzr83f9kf0WTnWl2an4iIiIiIVIxyFfkrV67kgQceoFmzZhgMBudxDw8PbrvtNnbt2nVO/W3evJn58+fz6quvFjoeGRlJr169cHNzcx7r06cPMTExnDp1ir1795KRkUFERISz3c/Pj/bt27N58+Yy9SFSE1madsA9uAUOay5pW38pNqZvpwYEB3qRlpnLb5sOuzhDERERERGpCG5nDykqJyeHgICAYttMJhN5eXll7is1NZXHH3+cKVOm0KBBg0JtsbGxtGnTptCx+vXrA3DixAliY2MBipxXv359Z9vZ+qhbt26Zcz2dw+EodGuBSIGsrKxC7yuLZ5fh5P40i+TNP2AOH4bBVPTXfWREEz5d/g9LVkUxsHN9TCbtxSnnpqqMdxFX0HiX2kTjXWqT6jLeHQ5HoUn2kpSryA8PD+err75i0KBBRdq+//57OnbsWOa+nn32Wbp27crll19epC07Oxt3d/dCxzw8PID8Cw0FP4TiYlJSUsrUR3nl5eWxZ8+ecp8vNV9MTEzlJuDwx9/dG9KTOLBqKXkNOxQJaeBtx9PdSFxSFot/2ULHZl6VkKjUBJU+3kVcSONdahONd6lNqsN4P7O2LU65ivwHHniAW265hSuvvJJBgwZhMBhYtmwZM2bM4I8//mDOnDll6mfp0qVERkby/fffF9tusVicG+gVKCjMvby8sFgsQP69+QUfF8R4enqWqY/yMpvNhIaGlvt8qbmysrKIiYmhefPmznFYWdLTRpC+fhEBcdsJGjqu2Ct/I+MtLFp1kL9jrIy/JKxMVwdFClSl8S5S0TTepTbReJfapLqM96iosj3+ulxFfo8ePfj000958803mTNnDg6Hg88++4z27dsza9Ys+vTpU6Z+Fi1aREJCAoMHDy50/JlnnuGHH34gJCSE+Pj4Qm0FnwcHB2O1Wp3HmjZtWiimbdu2AGfto7wMBsN5XSSQms/T07PSx4hH78vI2PQd1rhoTImHsTRpVyRmzJA2fP9HDAePpXLwRBbhoeW7hUVqt6ow3kVcReNdahONd6lNqvp4L+tkXLmK/PXr19O1a1fmzZtHdnY2KSkp+Pj44O3tfU79TJs2jezs7ELHhg8fzv33388VV1zBt99+y7x587DZbJhMJgA2bNhAixYtCAoKwtfXFx8fHzZu3Ogs8lNTU9m9ezc33ngjAD179iy1D5GazOTtj0/4INK2/krKpmXFFvn+Ph5c1KspK9bFsGjlfhX5IiIiIiLVWLl22brvvvucj6izWCwEBwefc4EP+TPpzZo1K/QGEBQURHBwMOPGjSM9PZ3JkycTFRXF4sWL+eyzz5g0aRKQfz/CjTfeyLRp0/jtt9/Yu3cvDz30ECEhIQwfPhzgrH2I1HT+vS4DIOOfTeQlxxUbM3pQKwwG+GtvPDEnUl2ZnoiIiIiIXEDlKvL9/PwK3QNfUYKCgpgzZw7R0dGMGTOGmTNn8vjjjzNmzBhnzP3338/48eOZMmUK1113HSaTiY8//hiz2VzmPkRqMvd6TfFs0RkcdlI3/1BsTMO6PkSE5z+lYsmqst3rIyIiIiIiVU+5lutPmjSJF198kejoaMLCwoq9b6Fnz57lSuiff/4p9HmnTp2YP39+ifEmk4nHHnuMxx57rMSYs/UhUtP59xpFVvQ2Urf+Rp2B12D0KPo7O3ZwKOu2n2D130e56dJ21A2oupuOiIiIiIhI8cpV5D/zzDMATJ8+HSi8AUDBs/v0eDmRqsOzVRfMQY3ISzhG2rbf8e81qkhM22aBdGgZxK6DCXy39iC3XV70kXsiIiIiIlK1lavI/+KLLy50HiJSgQwGI/69RnFqxSxSNi/Hr8elGIymInHjhoSy62ACP66P4ZphbfD2NFdCtiIiIiIiUl7lKvJ79ep1ofMQkQrmEz6IxFVfYk2OJ3NfJN5hvYvEdA8LpkmwL0fi0vhxfQzjhrauhExFRERERKS8yrXxHkB0dDQPPfQQ/fr1Izw8nIEDB/Lwww9z4MCBC5mfiFwgRrMHfl3znzqRsun74mOMBsYObgXAd2sPkGe1uSw/ERERERE5f+Uq8qOiorjqqqtYt24dAwcO5JZbbqFv3778+eefXHXVVSr0Raoov+4jwGgi+8geck4U/3s6qFtjAv0sJKbmsPrvoy7OUEREREREzke5ivxp06bRuHFjfv31V1555RUeeeQRXn31VX799VeaN2/u3JBPRKoWN78gfNr3AyBl07JiY8xuJq4Y0BKAxasOYLc7XJafiIiIiIicn3IV+Zs3b+auu+7C19e30HFfX1/uvPNONm/efEGSE5ELr2Bn/fTdf2JNSyw2ZkREczw93DgSl0bk3jhXpiciIiIiIuehXEW+m5sbHh4exba5u7uTm5t7XkmJSMXxaNAKS5N2YLeRGrmi2BhvTzMjIpoDsHhllAuzExERERGR81GuIj88PJyvvvoKh6PwMl6Hw8GXX35Jx44dL0hyIlIx/HtdDkDqlp+x5+UUG3PFgJaYjAZ2HUzgn0PFz/iLiIiIiEjVUq5H6D3wwANcd911XHHFFYwYMYJ69epx8uRJfvzxR6Kjo/n0008vdJ4icgF5temBW0B9rMnxpO9YjV+34UVi6gZ4MqhbY36PPMLiVVE8dbMenSkiIiIiUtWVeyZ/zpw5WCwWZs6cydSpU5k5cyYWi4WPPvqInj17Xug8ReQCMhhN+Pe8DMjfgM/hsBcbN3ZwKADrd5zg+Kl0l+UnIiIiIiLlU66ZfIA+ffowb948cnNzSU1Nxc/PD6vVWmQzPhGpmnw7DyVx9TzyEo6RdWArXqHdisQ0a+BHj3bBRO6JY+mqA9w9vnMlZCoiIiIiImVVrpn8vLw8nnnmGa6++mo8PT0JDg5my5YtRERE8Nprr2G3Fz8rKCJVh9HDC78uFwGQsrn4x+nB/2bzf9t8mOS04u/fFxERERGRqqFcRf6MGTP47rvvuOyyy5zH2rdvz6OPPsqCBQuYM2fOBUtQRCqOX8+RYDCSdXAbuScPFxvTsVUQoU0CyLXaWfbnQRdnKCIiIiIi56JcRf7333/PE088wW233eY8FhAQwC233MJDDz3EN998c8ESFJGKYw4IxqtN/h4aKZuWFxtjMBgYNyR/Nv+HP6PJzrG6LD8RERERETk35Sryk5KSaNKkSbFtLVu2JDY29rySEhHXCeid/zi99B2rsWWkFBsTEd6QkCAv0jLz+HVz8TP+IiIiIiJS+cpV5Lds2ZKffvqp2Lbff/+dZs2anVdSIuI6Ho3D8GjQCoctj9S/fy42xmQ0MHpQ/mz+ktUHsNm074aIiIiISFVUriJ/woQJLFiwgPvvv5/vvvuOP//8k++//56HH36YL7/8kttvv/1C5ykiFcRgMODfK382P/WvH3FY84qNu6hnE/y83YlPzGTd9hOuTFFERERERMqoXI/QGz16NBkZGbz//vv8/PP/Zv7q1KnD008/zejRoy9UfiLiAt7tIjD99gW29ETSd/+Jb6fBRWIs7m5c1q8FX//8D4tW7ad/l4YYDAbXJysiIiIiIiUqV5EPcMMNN3D99dcTHR1NcnIydrud1q1b4+/vfyHzExEXMJjc8OtxKUmrviRl0zJ8wgcVW8Bf1q8Fi1ZGceBoCtujTtG5db1KyFZEREREREpyTsv1t2/fzl133cXSpUuB/GW+69at49Zbb+Wmm25i0KBBfPzxxxWRp4hUML9uF2Nwcyc3Lprsw7uKjfH38WBYz/xNNxevinJleiIiIiIiUgZlLvL37t3LTTfdxJ49e/Dy8gJgx44dvPTSSzRp0oQZM2Zw9913M336dH799dcKS1hEKobJ0xeff5fpp2xcVmLc6EGhGA3w9954oo8Xvxu/iIiIiIhUjjIX+bNmzSIsLIwff/yR4cOHA/DFF18AMG3aNIYNG8add97J9ddfz9y5cysmWxGpUP69RgGQuT+SvMTiN9drUNebiE4NAVii2XwRERERkSqlzEX+5s2buemmm/D09HQe++OPP2jSpAlhYWHOY/3792f37t0XNksRcQn3oEZ4tuoGOEjZ/EOJcWMH5z9Ob82WY5xMynJRdiIiIiIicjZlLvKTk5MJCQlxfn7gwAGSkpLo3bt3oThPT09yc3MvXIYi4lIFs/lp237Hlp1RbEybpnUIb1UXm93Bd2sPuDI9EREREREpRZmL/ICAABISEpyfb9iwAYPBQERERKG4AwcOEBgYeOEyFBGX8mzRCXO9pjjysknbWvL+GmOH5M/m/7QhhvSsPFelJyIiIiIipShzkd+rVy8WLFiAw+HAarWyaNEiPDw8GDBggDMmNzeXL7/8km7dulVIsiJS8QwGA/69LgMgdfMPOOy2YuO6h9WnaYgvWTk2VqyLdmWKIiIiIiJSgjIX+f/5z3/YsmULw4YNY/jw4ezevZvbb78dX19fABYtWsS1115LdHQ0EydOrLCERaTi+XQciNHLD2vqKTL+2VhsjMFgcN6b//3ag+RZi78YICIiIiIirlPmIr9169YsWLCA3r1707p1a5555hnuu+8+Z/vbb79NUlIS7733Hu3atauQZEXENYxu7vh1uwQo/XF6A7s2JsjfQlJaDiv/Ouqq9EREREREpARu5xIcGhrKyy+/XGzbN998Q7169TAay3zdQESqML/ul5C8fgk5x/4h+9g+LI3aFIkxuxm5YkArPl22iyWrohjWsylGo6ESshURERERETiHmfyzCQ4OVoEvUoO4+dTBp0N/AFI2lTybPyKiGV4WN47GpxO5J85V6YmIiIiISDFUlYtIifx7XQ5Axp71WFNOFhvjZTEzok9zABat3O+q1EREREREpBgq8kWkRB7BzbE06wgOOyl//Vhi3BUDW+JmMrA7OpG9MYkuzFBERERERE6nIl9ESuXfaxQAaVt+wZ6bVWxMkL8ng7o1BmDxqiiX5SYiIiIiIoWpyBeRUnm17o5bnRDs2RmkbV9VYtyYfx+nt2HnCY6dTHdRdiIiIiIicjoV+SJSKoPBiH/PywBI3bwch8NebFyzED96tAvG4YClqw+4MkUREREREfmXinwROSvfzkMwWrzJSzxB5v6/SowbOyR/Nv+3zYdJSst2VXoiIiIiIvIvFfkiclZGd098uwwDIGXz8hLjOrYMok3TAPKsdpb/Ee2q9ERERERE5F8q8kWkTPx7XAoGI9kxO8iJiyk2xmAwMHZwawCW/xlNVo7VhRmKiIiIiIiKfBEpEzf/eniH9QEgZdOyEuP6hDegQZA36Vl5/LLpkKvSExERERERVOSLyDnw7305AOm71mJNTyo2xmQ0MHpwKwC+XX0Am634jfpEREREROTCU5EvImVmadQGj0ZtwGYl9a+fSoy7qGdT/LzdiU/K4o9tx12YoYiIiIhI7aYiX0TOiX+vUQCk/v0TdmtusTEeZhOj+rcEYPGqKBwOh8vyExERERGpzVTki8g58Q7rg5tfXeyZqaTvXFti3Mi+zXE3mzh4LIXt+0+5MEMRERERkdpLRb6InBOD0YRfz5EApG5eVuIsvb+PB8N7NQVg0cr9LstPRERERKQ2q/QiPyEhgccee4w+ffrQtWtX7rzzTg4cOOBs37NnDzfeeCNdunRh6NChfPHFF4XOt9vtvPvuuwwYMIAuXbpwxx13cOTIkUIxZ+tDRM6Nb5dhGMwWcuMPkxWzvcS4Kwe1wmiALftOEn08xYUZioiIiIjUTpVe5N9zzz0cOnSI2bNn880332CxWLjlllvIysoiKSmJW2+9laZNm7Jo0SLuuecepk2bxqJFi5znv//++3z11Ve88MILzJs3D7vdzsSJE8nNzb9XuCx9iMi5MVm88e08BICUjSU/Ti8kyJu+nRoCsHhllEtyExERERGpzSq1yE9JSaFRo0a8+OKLdOrUiVatWnH33XcTHx/P/v37WbBgAWazmeeff55WrVoxbtw4brnlFmbPng1Abm4un3zyCffffz+DBw8mLCyM6dOnExsby88//wxw1j5EpHz8e14GGMg68De5CcdKjBs7JBSANVuPEZ+U6aLsRERERERqp0ot8v39/XnzzTdp06YNAImJiXz22WeEhIQQGhpKZGQkvXr1ws3NzXlOnz59iImJ4dSpU+zdu5eMjAwiIiKc7X5+frRv357NmzcDnLUPESkfc2ADvFr3ACB10/IS41o3qUOn0LrY7Q6+W3PQVemJiIiIiNRKbmcPcY2nn36aBQsW4O7uzgcffICXlxexsbHOCwAF6tevD8CJEyeIjY0FoEGDBkViCtrO1kfdunXLla/D4SAzU7OSUlRWVlah9zWZR+eLydy/mbTtK7H0Ho3R4lNs3GV9m7A96hQ/bojhiv5N8PE0uzhTqSi1abyLaLxLbaLxLrVJdRnvDocDg8Fw1rgqU+TffPPNXHPNNXz55Zfcc889fPXVV2RnZ+Pu7l4ozsPDA4CcnBznD6G4mJSU/E2+ztZHeeXl5bFnz55yny81X0xMTGWnUPEcDnx96+OWFk/Mb/PJadm32DB3h4P6AWbik/P4avnfDOjg5+JEpaLVivEu8i+Nd6lNNN6lNqkO4/3M2rY4VabIDw3Nv2/3pZdeYtu2bfzf//0fFovFuYFegYLC3MvLC4vFAuTfm1/wcUGMp6cnwFn7KC+z2ezMWeR0WVlZxMTE0Lx5c+c4rMmyHKNJ+Xk2Pse30WLEzRhMxf+zMj4ngPcX7yLyQDa3XNkDd7PJxZlKRaht411qN413qU003qU2qS7jPSqqbBtZV2qRn5iYyPr167nkkkuc98wbjUZCQ0OJj48nJCSE+Pj4QucUfB4cHIzVanUea9q0aaGYtm3bApy1j/IyGAzndZFAaj5PT89aMUY8uw4l/c8F2NKTcBzehneHAcXGDevdkgW/HeBUSjYb9yRySZ9mLs5UKlJtGe8ioPEutYvGu9QmVX28l2WpPlTyxnunTp3i4YcfZv369c5jeXl57N69m1atWtGzZ0/++usvbDabs33Dhg20aNGCoKAgwsLC8PHxYePGjc721NRUdu/eTc+ePQHO2oeInB+Dmxm/7iOA/MfpORyOYuPMbkauGNgKgCWrorDbi48TEREREZHyq9Qiv02bNgwcOJAXX3yRzZs3s2/fPp588klSU1O55ZZbGDduHOnp6UyePJmoqCgWL17MZ599xqRJk4D8+xFuvPFGpk2bxm+//cbevXt56KGHCAkJYfjw4QBn7UNEzp9ft+EYTGZyTkSRc/SfEuMu6dMMb4sbx06ms2l3rAszFBERERGpHSq1yAd46623iIiI4KGHHuKqq64iOTmZL7/8koYNGxIUFMScOXOIjo5mzJgxzJw5k8cff5wxY8Y4z7///vsZP348U6ZM4brrrsNkMvHxxx9jNufv3l2WPkTk/Ji8/fHpOBCAlE3flxjnZTEzIqI5AItXlu2eIhERERERKTuDo6S1tVKiHTt2ABAeHl7JmUhVlJmZyZ49e2jXrl2VvqfnQsuNP8zRjx4Cg5Emd8/EHFD8nhcJKVlMfOkXrDYHr987gHYtAl2cqVxItXW8S+2k8S61ica71CbVZbyXtQ6t9Jl8EakZ3Os3xbNFZ3DYSY1cUWJckL8nQ7o3AWDxqv2uSk9EREREpFZQkS8iF4x/r1EApG79DXtOVolxYwbnP35y465YjsanuSQ3EREREZHaQEW+iFwwnq26YA5qhCMnk7Rtv5UY1yTYl57tg3E4YOnqAy7MUERERESkZlORLyIXjMFgxL/nZQCkbF6Ow24rMXbckNYA/B55hKTUbJfkJyIiIiJS06nIF5ELyqfTYIyePliT48ncF1liXPsWgbRtWoc8q53v/zjowgxFRERERGouFfkickEZzR74dR0OQMrmZSXGGQwGxg7Jvzd/xboYsnKsLslPRERERKQmU5EvIhecX/cRYDSRfXg3OSdKnqXv3bEBDep6k56Vxy8bD7kwQxERERGRmklFvohccG5+Qfi07wdAyqbvS4wzGQ2MGdQKgKVrDmC12V2Sn4iIiIhITaUiX0QqRMEGfOm7/8Sallhi3NCeTfH3cedkUhZ/bDvuqvRERERERGokFfkiUiE8GoZiadIO7DZSI1eUHGc2Map/SwAWr9yPw+FwVYoiIiIiIjWOinwRqTD+vUYBkLrlF+x5OSXGjezbAg93E9HHU9m676Sr0hMRERERqXFU5ItIhfFq0xO3gPrYs9JI37G6xDg/b3cu7tUUgMWrolyVnoiIiIhIjaMiX0QqjMFoct6bn7J5ealL8a8c2AqjAbbuO8mBo8kuylBEREREpGZRkS8iFcq381AM7p7knTpK1sGtJcaFBHnTv3MjAJasOuCi7EREREREahYV+SJSoYweXvh2uQgo/XF6AGOGhAKwdtsx4hMzKzw3EREREZGaRkW+iFQ4/54jwWAk6+A2ck8eLjEutHEAnVvXxW538O0azeaLiIiIiJwrFfkiUuHMAcF4tekJQMqm5aXGjh3cGoCfNx4iLTO3wnMTEREREalJVOSLiEsE9L4cgPSda7BlppYY17VtPZo38CM718YP66JdlZ6IiIiISI2gIl9EXMKjcRgeDVrhsOaS+vfPJcYZDAbG/ntv/rK10eTm2VyVooiIiIhItaciX0RcwmAw4NdrFACpkStwWPNKjB3QpRF1AzxJTs/h98gjrkpRRERERKTaU5EvIi7j0y4Ck08gtoxk0nf/WWKcm8nIlQNbAbB0dRR2u8NVKYqIiIiIVGsq8kXEZQwmM349LgUgZdMyHI6Si/fhvZvibXHj2MkMNu6KdVWKIiIiIiLVmop8EXEpv24XY3BzJzcumuzDu0uM87KYubRvCwAWr9zvqvRERERERKo1Ffki4lImT198Og0GIGXT96XGXj6gJW4mI3sPJbE7OsEF2YmIiIiIVG8q8kXE5fx7XgZA5r5I8hJPlBgX6GdhSPfGACxeGeWS3EREREREqjMV+SLicu51G+PZqivgIGXzD6XGjhmc/zi9jbtiORKX5oLsRERERESqLxX5IlIp/HtdDkDatt+xZWeUGNck2JfeHUIAWLr6gEtyExERERGprlTki0il8GzRCXO9pjjysknb+lupsQWz+b9HHiExNdsV6YmIiIiIVEsq8kWkUhgMBvx75d+bn7p5OQ67rcTY9i0CCWtWB6vNzrI/DroqRRERERGRakdFvohUGp8OAzB6+WFNPUXGPxtLjDMYDIwdkj+b/8Of0WRm57kqRRERERGRakVFvohUGqPZA79uwwFI2bis1NheHRrQqJ43GdlWft542BXpiYiIiIhUOyryRaRS+XUfASY3co79Q/axfSXGmYwGRg/Kn83/ds0BrDa7q1IUEREREak2VOSLSKVy86mDT4f+AKRsKn02f2iPJgT4eHAqOYu1W4+5Ij0RERERkWpFRb6IVDr/nqMAyNizHmvqqRLj3M0mRg1oAcDilVE4HA6X5CciIiIiUl2oyBeRSucR0gJLsw7gsJMSuaLU2JF9W2BxNxFzIpUt/5x0UYYiIiIiItWDinwRqRL8e10OQNqWX7DnZpUY5+vlzsW9mwGweNV+l+QmIiIiIlJdqMgXkSrBq3V33OqEYM/OIG376lJjrxzYCqPRwLb9p4g6muyaBEVEREREqgEV+SJSJRgMRvx7XgZA6uZlOBwl754fHOhF/84NAViyMsol+YmIiIiIVAcq8kWkyvDtPASjhxd5iSfIjPq71Nixg/Mfp/fH9uPEJmS4Ij0RERERkSpPRb6IVBlGd098uw4Dzv44vVaNA+jSuh52u4Nv1xxwRXoiIiIiIlWeinwRqVL8e4wEg5HsmB3kxMWUGjtmSP5s/s8bD7Fh5wlW/32UHVGnsNn1aD0RERERqZ3cKjsBEZHTufnXwzusDxl71pGyaTn1L7+nxNiubepRv44n8UlZvPTpJufxIH8Ld44Op2+nhq5IWURERESkytBMvohUOf698x+nl75rDdb05BLj1u84QXxS0cftJaRk88rnm1m3/XhFpSgiIlJj2ewOdkUnsiMmk13RiVohJ1LNaCZfRKocS6M2eDRqQ86xfaT+/ROBA68pEmOzO5i9dEep/Xz07U56d2yAyWioqFRFRERqlHXbjzN76Q4SUrIBWLQukSD/XVohJ1KNVPpMfnJyMlOnTmXgwIF069aN6667jsjISGf7+vXrGTt2LJ07d2bEiBEsX7680Pk5OTk899xzRERE0LVrVx555BESExMLxZytDxGpevx7jQIg9a8fsVtzi7TvPpjg/AOkJKeSs/h8+S427Ypl3+Ek4hIzycmzVUi+IiIi1d267cd55fPNRf5/1Qo5keql0mfyH374YU6ePMlbb71FUFAQc+fO5fbbb2fJkiU4HA4mTZrErbfeyhtvvMGqVat4/PHHCQwMJCIiAoBnn32WyMhIZsyYgbu7O8888wz3338///d//wfAgQMHztqHiFQ93mF9MPnVxZZ6ivSda/HrclGh9sTU0gv8AktWHWDJqsK773t6mAjwseDv406Arwf+Ph4E+HoQ8O97f5//fezjacZg0EoAERGp2bRCTqTmqNQi/9ChQ/z555989dVXdO/eHYCnn36atWvX8v3335OQkEDbtm156KGHAGjVqhW7d+9mzpw5REREEBcXx9KlS/nwww/p0aMHAG+99RYjRoxgy5YtdO3alc8//7zUPkSkajIYTfj3uJTE3+eSunkZvp2HFiq2A/0sZeqnTdM62B0OktNySE7LwWqzk5VjIysngxMJGWc932Q0FCr68y8MWAg4/QLBaRcH3EyVvkBKRETknO04cKpMK+R2H0wgPLSui7ISkfKo1CK/Tp06zJ49m/DwcOcxg8GAwWAgNTWVyMhIhg0bVuicPn368NJLL+FwOPjrr7+cxwq0aNGC4OBgNm/eTNeuXc/ah2boRKou364Xk7R2Ibnxh8mO2YFni07OtvYtgwjyt5T6B0ndAE9ev2+Ac8bB4XCQmW0lJT2HpLQcUtJzSE7PISUth6T0fz9P+9/7jGwrNruDxNTsMq8c8PE0l7464LSLBZ4ebvo3SEREKoXd7uBwXBrbo06yI+oUW/45Wabzyvr/oYhUnkot8v38/Bg0aFChYz/99BOHDh3iv//9L0uWLCEkJKRQe/369cnKyiIpKYm4uDjq1KmDh4dHkZjY2FgAYmNjS+0jMDCwAr4zEbkQTBZvfDsPITVyBSmblhUq8k1GA3eODueVzzeXeP4dV3YstKTQYDDg7WnG29NMw3o+Z/36eVYbyWm5zosByWn/XhQ47eOCiwIpGbnY7Q7Ss/JIz8rjaHz6Wft3N5tKXBEQcMbnvt7uVW55ZMHuy7tiMrFbEunWzrPK5SgiIvkcDgdH49PZHnWKHVGn2HHgFKkZRfe8OZvv1h7E0+JG97Bg/ZsvUkVV+j35p/v777956qmnGD58OIMHDyY7Oxt3d/dCMQWf5+bmkpWVVaQdwMPDg5ycHICz9lFeDoeDzMzMcp8vNVdWVlah93J+3DsOhcgfyYz6i9SjB3ALbOBs6xIawMPXduKzH/4hMTXHeTzI34ObL21Ll9CA8/499fYAbw93Gga5A74lxhUU+CnpuaRk5JKSnkvqv+/zP88hJSPPeUEgN89Obp6N+KSsYh8DeCaDAfy83PH3ccfPO/+9/xnv/bzdCfj3vbvZdF7f99ls3BVX6HVftC6RQL+d3DKyLb07BFfo1xapLPr3XaoTh8NBXGJW/sXYg0nsik4kOb3w374eZiNtm9WhY4s6tGteh+nztxf6/7Q4+w4n8cLHG6nrb+GiHo0Y2r0RAb4epZ4jUtVVl3/fy7oSvcoU+b/++iuPPvoo3bp1Y9q0aUB+sX5mIV7wuaenJxaLpdhCPScnB09PzzL1UV55eXns2bOn3OdLzRcTE1PZKdQY3vVCcT+5n6O/zyOrw4hCbX5GuHdkXQ6dzCE9y46Pp5Fm9TwwGhPZsyexhB4rlgkINENgABAAYAAs/77lXyjIybOTkWMnI9tGRnb++/Rsu/PjjGw76dk2MnLsZOXYcTjIv1hQxlkXD7MBbw8T3p5GvD2M+FhMeFuMeP/7/vTPLWbDOd02sPtIFgvWJhQ5npiaw1vztnP1gCDaNyn/v68iVZHd7nD+OxMdt/fff2c0iylVS3KGlei4HGLicoiOyyE1s/ATZUxGaFrPg+bBHrQI9qBhoDtuJgOQjS3jBMM6+7BgbclF/qXd/UnOsLH1YCanUrKZ/9sBFv5+gLAmnvQI9aZFsIduQ5NqrTr8/V7cJPeZqkSR/3//93+89NJLjBgxgtdee82ZeIMGDYiPjy8UGx8fj5eXF76+voSEhJCcnExubm6hbzY+Pp7g4OAy9VFeZrOZ0NDQcp8vNVdWVhYxMTE0b978vC4kyf/k+Iwn6ZtX8Dyxk6aj7sBoKbrUvkMl5OUqVpudtMy8M1YFFLdaIP+9zeYgJ89BTp6VxLPfNYCbyYC/tzt+Z6wK+N8qAQ/ncW+LG+8u+7PU/n7bns6YYV1VAEmNcebKFYBAPw+tXJFKl5iaza7oJHYdTGRXdFKRlWEmk4HWjf3p0CKQDi3r0Lqxf6krvdq1g8aNio73ghVyBeM9N8/Ghl1x/LL5KPsOp7D7cBa7D2fRIMiLi3s1ZlCXhvh4mSvmmxapANXl7/eoqKgyxVV6kf/VV1/xwgsvcNNNNzF58uRCV/969OjBpk2bCsVv2LCBbt26YTQa6d69O3a7nb/++su5U350dDRxcXH07NmzTH2Ul8FgwMvLq9znS83n6empMXKBeLbpTkb95uTGx2Dd+ycBfcdUdkou5+cLjcpQSzgcDjKy8k7bLyCX5LRsktNzi91PICvHitXmICE1h4SzLNEsq4SUHJasPUzbZnXwdHfD08MNi4cJT49/P3Z30wUAqTbWbT/OW/O2FzlesHLlqZt70rdTw0rITGqjpLRsdkYlsP3AKXZEneTYycJPiTEaDbRuEkCn0Lp0Cq1LWPNALO7n9uf+kJ4tGNi9OX/vOcauvdF0CGtBt3aNCt1/7wWM6OvLiL6hRB9PYcX6GFb9dYQTCZl8sWIf836Jon+XRozs25w2Tetodl+qjar+93tZf5cMDofDUcG5lCg6OprLL7+cwYMH88wzzxRqs1gsxMbGMmbMGG655RbGjBnD6tWrefPNNws9/u6RRx5h69atvPzyy3h6evLMM8/g4+PD3LlzAdi/f/9Z+zhXO3bkP0P09KcCiBTIzMxkz549tGvXrkr/I1HdpG1fycnvZ2LyDaTpPR9gMFX6NcoaISfPRkpB0f/vkwaS0wtvKlhwsSA1Iwf7Bfofw8P936Lf/X8XACz/XgTwdHfD0+KGxf1/FwbO1m52M9aaPyJtdge7DyaQmJpNoJ+F9i2DtPnVBeJwOLDaHORZbeTk2cjOsfH4zLUkp5V8AaxugCdzJl+sn4FUiNSMXHYeyN8ob/uBUxyOTSvUbjRAy0b+dAqtR3hoXdq3CMTLcmFm0M/175nM7DxWbznGinXRRB9PdR5v2cifSyOaM6hbYzw99H+3VE3V5e/3stahlVrkf/jhh0yfPr3YtjFjxvDqq6+yZs0a3njjDWJiYmjcuDH33XcfI0eOdMZlZmby8ssv89NPPwEwcOBApkyZQp06dZwxZ+vjXKnIl9JUl38kqhuHNY/DM+/ClpFM/dEP4tNhQGWnVOvY7A427TzBy6U80aBAi4Z+mExGsnOsZOVYne8v1EWCM5mMhvyLAO6mfy8AuJVwgcDk/Nji4YbXaasMLO5ueP17rsXDrUoWbeu2H2f20h2FHh0Z5G/hztHhNW42uaDgzs2zkZuXX3TnWe3k/Pt5/pudXGtBu73w8dPOOz2uoM3Zj/X082zlGqM3XBLGoG6NCQ700ioVOS8ZWXnsOpjg3AE/+kQKZ/6l3qKhH+GhdenUqi4dWtXFx7NilsWX9+8Zh8PBP4eSWLE+hrVbj5FntQPg6eHGkO6NGdm3Bc0a+FVIziLlVV3+fq8WRX51pSJfSlNd/pGojpLWLiBpzXw8GoTS8NZXa83MbVViszu4/cWfCxWZZyppZtPhcJBrtZOVbSU7N7/oz3JeBLCRlZNHVo6thLbTPne25xdlFcXdbHJeBCh60eCMVQaltFv+XXngfp6rDdZtP17qIyMrctl4fsFtL1og59n/Lb7/93HuGcVzzhlFd0HBXRCbd8Z5Of+255Wz4L6QTEYDtnNIwsPdRJNgX5qF+NIsxC//rYEvgX4W/XslxcrKsbI7OoHt+/Nn6g8eTS4y7psE+9IptC7hoXXp2DIIfx/X7GR/If6eSc3I5ffIw6xYF8PxU/+7taBd80BG9m1Ov84NMbtV7NNgRMqiuvz9XtY6VGtmRKTa8Ot2Ccl/LibnRBQ5x/7B0jisslOqdUxGA3eODi+12Lzjyo7FzoIbDAY8zCY8zCbgwvyRarM7yP638M887eJBdo6NzNNWEGTnWMnKtRVaVVDookH2/9rt//6FXVB0UobNC8vCaDQUv5KghH0LPC3/rkzwcMPdbOL9RdtK7f+DRdvx9XL/txgvZZbbeuYs95mz2f8W7qcV3Ll5tiKzia7mbjbhYTbibjbh7mbCveDjf8eU2c2Ix7+fn97mbj7tuNu/sf+2e5we61Y41uxmZOeBBP77QekbTQI0qOvNqeQscnJtRB1JJupIcqF2H08zzRr40fTf4r/5vx/7ep19h2SpWbJzreyNSXTO1O8/klzkQlLDut75M/WhdQlvVZc6fpZKyvb8+Xm7M3pQKFcMaMWOqFP8sD6aDTtj2ROTyJ6YRD76difDejZlRERzGtT1rux0RWoMFfkiUm2YvP3x6TiQtG2/kbJxmYr8StK3U0OeurlnkWXjdQM8uePKji5dNm4yGvD2NOPtaSbI//z7czgc5Fntp10AsP17AcBawgWC/7Vnn7ny4N9zcnLzVxvY7Q4ysq1kZFvPP9FiJKfnlKkgPV8GA/8WxYWLbHezEbNb4cK5oPj+X8xpRfgZse5nnOc85mastD0X2rcMIsjfctaVKx88cREAsQkZHDqRyqHYtH/fp3L8VAbp/y7B3nWw8KMnA/0s+bP+Dfyc75vU98Wi+5ZrjDyrjb2HkvLvqY86xT+HkrDa7IVi6gd60fnfmfrwVnWpG1B1d/YuL6PRQOc29ejcph4JKVn8sukwP62P4VRKNotXRbF4VRRd29Tj0r4t6NU+GJOp/Jtji4iKfBGpZvx7jSJt229k/LORvOR4zAH1KzulWqlvp4b07tig1N2XqyODweAsLi/Uklib3UFO7hm3HxS5aHDGKoN/Vxdk5+bHn0zOIjG15EKzQICvBwE+HmeZ5f5fkV1odryEwt39tPM8zEbcTLVnk8NzXbnSqJ4Pjer50LfT/9pz82wcO5nOoROpxPx7AeBwbCrxSfk/08TUbLbsO+mMNxggJNA7f9b/tOK/UT0f3FT4VHlWm539h5PZfuAk2/efYm9MIrnWwkV9XX/L/2bqQ+sRHFh1lwZXhCB/T669uC1XDW1N5J44VqyP4e9/4tmy7yRb9p0k6P/bu+/wqMq8feD3mT7JZFJmUiGNmgSSABKqQEQEFSzI6rovFkR0BV3fa9fK2lB/+O4uuupSFAUWd1dXVBREBdHVtdAR6aFJGukzKZMyfc7vj0kmGZJAIGUyk/tzXbmSOXPOmefEI3Cf53m+T6gKM8YmYvq4ROhCA++BB1FPYMgnIr+iiEqAOjkT5txDMO3/Arpp83zdpD5LKhEwLDkCEksZUpMj/D7gdxepRECQSt6pitdHzhg61Ev/+B2jkT5If9mfQ611duSKQi5FclwokuO8h5o0WOwoKKtFfkkt8ktNyC8xoaC0FtV1VpQY61FirMeeY6We/WVSAf0iNUiM0SIhtnnYf1Q4i/35ktPpwi9FNZ7h98dzjbDYvGuFhIUokTFQ7wn2sfrgPvOg7EKkUgnGDo/F2OGxKDXWY9uuPHy9rwDGGgve234S7399CmOHxeDa8UkYMTiS9znRJWDIJyK/EzpmpjvkH/wPwif9GhIln/RTYOvosPG0AboebFXf0R0jV4JUcqQkRiAlMcJre3WtFQVl7l7/As+w/1qYrQ73NIDSWuBg8/5KhRQJ0SGeIn/u71qEhygZJLuByyUit7gGR35xD78/dtaIhvOm4IQEKZA+SIeMgXpkDI5E/ygN/1tcRIwuGPNmDcPca1Ow83AJtu7Kw7GzRuw6UoJdR0oQqwvGteMTcXVWQo8VHiTyZwz5ROR31ANHQq6Lg91YjNrD3yA0a6avm0TUrTpT8JC6Rk+NXAkLUSIsJBIZgyI920RRREW1uXm+f6kJBSW1KCyvhdXmxOnCapw+r9hfSJAcCTHaFnP+3T9rWOzvkoiiiILSWndPfeN69XVmu9c+wSoZhg/UeyrgJ8Zo2et8meQyKaaM6o8po/ojv9SEbbvy8M3+QpQY6/H3z47jn1tP4MrMOFw3IQmpSRF8eELUDoZ8IvI7giBBaNYsGLa9hZq9n0N7xbUQJFyChwJbbyp4SD1LEAREhQchKjwIWWkxnu1OpwslxvrmIf+lJuSX1KLEUIfahraL/elCVe4h/y0q/feP1kCl4D8JAXeoL6qow5EzBhw6Y8DRXwyoqbN57aNWSjFsgLtIXsYgPZL7hfIBWzdIjNHit7MzcPf1afj+YBG27szFmXM1+O+Bc/jvgXNIitXi2vFJuOqK/p2aDkUUiPgnOhH5JU1GNiq/ew+O6jI0nN6P4KFjfd0kom7XNGz8+FkjKk0WRGhVSBugY8Doo6RSCfpHhaB/VAgmZjY/5LHZnSgsq/UU+Wvq/a+oMsNYY4GxxoIDJ8s9+wuCe7h0YkzzcP/EmBDE9YFif6IootTY4JlTf+SXClSarF77KORSpCVHeHrqB/UPC/jfS2+iUsowfWwipo9NxOnCKmzdmYfvfi5CXokJb358GOs/O4Ypo/rj+gnJGNCvC5ZZIQoADPlE5JckciW0I6ejeufHqNn7GUM+9RlSicDienRBCrkUA/uHYWD/MK/t9Wa7e55/Y69/QWkt8kpMMNXbUGKoR4mhHruPehf76x8V4tXrnxAT4vfF/sqrGjxL2h35xYCKKrPX+3KZBCmJEcgY7O6tH5IQDrmMob43GBwfjsG/Dsf8G4fjm/0F2LYrD4Vldfhydz6+3J2PoQnhuG5CEq4c0Q9KOUf4Ud/FkE9Efkt7xbWo3r0ZloLjMB34ChKlClJNOFTxqRy+T0R0nmC1HKnJEUhN9i72V1VrQYFnyH/jnP9SE8xWJ/Ial/4Dijz7qxRST/BPiNEiqbHgX1gvLfZXabLg8BkDDp+uwJFfDCg1Nni9L5UIGJIQjozB7uH3KYkRUDAg9moatRw3ThqIG64cgKNnjdi6Mw+7jhTjZEEVThZUYc3mo7g6KwHXjk9E/6gQXzeXqMcx5BOR35JpdVD1GwJLYQ4MW9/0bJeG6KCfPh/BKeN82DoiIv8QHqJCeIgKmUOai/25XI3F/los75dfakJhWR0sNidOFVTjVEG113lCghTNFf4bC/4lxGihUV/+fGmnS7zk6SnVtVZPkbzDZwwoqqjzel8iETC4fxjSG4ffpyVFQKXkP4n9kSAISB/oHnFRVWvB13vdvfvlVWZs/v4XbP7+F2QM0uP6CckYOzyG0yyoz+CfaETkt+pP7IalMKfVdmetEWUblyF6zmMM+kREl0EiERAdEYToiCCMaVHsz+F0ocRQ7yny1/QQoMRYj9oGG47+YsTRX7yL/elDVUhoUeE/MVaL+OiQiw6n3nm4uFWhSV2oCvffnO5VaNL9uQbPvPr80lqv8wgCMKBfqKdQ3rABOhZqC0DhISrcevUQ3HLVYPx8shxbd+Zhf06pexTHGQPCQ5Tuuf3jEhEVHuTr5lIv4nSJOJZbiWN5DXCpKjEqVe33tW4Y8onIL4kuJwzb111wH8NX6xA0JItD94mIuohMKkF8dAjio0NwZWbzdmtjsb+CxvCfV2pCQYkJhhqL5+vAieZif5KmYn+N8/yTGh8CxOqDIZNKsPNwcZtLRhprLPi/d/bhV1cPhsPhwuHTBuSW1EAUvfdLitUifZA71A8foOPSgX2IVCJgdGo0RqdGo7yqAdt352P7nnxU1Vqx4etT+PA/pzA6NQbXTUjCyKFRfh/mqHPOf5i4cWcldKHHWj1M9DcM+UTklyyFOXDWGi+4j9NkRF3OLmjSJvbKeaJERIFCKZdiUP8wDDqv2F+d2e6p8F9QYkJeY89/bYMdxYZ6FBvqsetIiWd/mVSCfpHBKDlv3vz5PvrPaa/X8dGaxp76SAwfqEOoRtll10b+Kyo8CHdcl4rbpw/FnqOl+GJnLg6fMWDv8VLsPV6KqIggXDsuEdPGJCA8ROXr5lIPu9jDxMV3Z/lt0GfIJyK/5Kyr6tB+FZteheGLN6GITIAiKgGKqET398hESNWabm4lEVHfplHLkZasQ1qyzrNNFEVU11qbC/2VNFf7t9icrYbbtycrNRpTRvVH+iA9IrQMaNQ+mVSCiZlxmJgZh3Pltdi2Kx//2VeA8soG/OOLHLz35QmMT4/DdROSMHyAjh0DfYDTJeKtTUcuuM/bm49i7PBYvxztwZBPRH5Jqgnv2I6CBKLNDGvRSViLTnqfIyQCishEKKITGx8CJEKh6wdBxrmaRETdRRAEhGtVCNeqMGJIlGe7yyWivKoBX+zIxSff/XLR80wZ1R9TRvXvzqZSAOofFYIFNw3Hnden4seDRdi6Mw8nC6rww8Ei/HCwCPHRGlw7PglTRyd0qmgk9R51ZjuKK+rco4cq6lBcUY/T56q86n20xVBtxvGzRr9ctpYhn4j8kio+FdIQ3QWH7Eu1OsQ/sAKOqlLYKvJhKy+ArTwftvJ8OEwGOGsrYa6thPnsz80HSaSQ6+KaQ3/jd1loJJ/sExF1I4lEQIwuGFlpMR0K+ey9p85QyqW4OisBV2cl4Jdz1di6Kw/fHTiHwrI6vL3pKN75PAdTRvbDdROSMDi+gx0L5DMWmwMlhnoUV9Sj2FCHosYwX2yoQ02d7bLPW2m68IOA3oohn4j8kiCRQj99Pso2Lmt3H/018yGRKxqH6ScAw5rfc1nqYaso9IR+W4X7AYDL2gB7RSHsFYWoP76j+fOUQVBExrt7/j3D/hMhVQV352USEfU5aQN00IWqLtjLpg9TI22Art33iS7FwP5heOjWEZh/wzB8+9M5bN2Zi/zSWny1twBf7S3AoP6huG5CMiaP6MflFn3I7nCh1FiPEkO9O8R7eubrYLhIr3yEVolYvQb9IjWI0wfD7nDh3S9PXPQz/fVhIu9SIvJbwSnjED3nMRi2r/Pq0ZdqddBfM/+Cy+dJVMFQxadAFZ/i2SaKIpy1xubgX17gHgFgKIZobYD13ElYz50/5F/nFfoVkQlQ6PtBkHKIHxHR5ZBKBNx/c3qbBbGa3HfTcL+cJ0u9W5BKjpkTk3H9hCTk5FVi6648/HiwGGfO1WD5Bwex7tOjuGp0PK4bn4SEGK2vmxuQnC4RFVUN3j3yjWG+vLIBLrH9Y0OC5IhrDPHuMK9BXGQwYvXBrZbNdLpEbNudF7APExnyicivBaeMQ9CQLHe1/boqSDXhUMWnXtayeYIgQKbVQ6bVI2jQFZ7totMOu7G4OfQ3Dvt3D/k3wlxrhPmX9ob8J3lGEsi0HPJPRNQREzLisPjuLK+lrQD3P7rvu2m431a8Jv8gCIKnYOSCG4fjP/sKsG1XPkqM9fjsx1x89mMuhg3Q4brxSZiQEQu5jEv1XgpRFFFpsqC44rweeUMdSgwNcDhd7R6rVkq9euTjIt1BPk6vgTa440tlBvrDRIZ8IvJ7gkQKdeLw7ju/VO7pqQcmebY7LfWwVxS06PUvgLU8H+IFh/wneKr7N40A4JB/IqLWJmTEYezwWBw/a0SlyYIIrQppA3R++49u8k+hGiVuuWowbp4yCAdPV2DbrjzsOVaKY2eNOHbWiNDNClwzJhEzxiUiRse/z5uIoghTva3NHvkSQz0sNme7x8plEsTqgz098u5Q7w704SHKLuswCeSHiQz5RESXSaoKhjQ+Far4VM82URThNBm8ev2t5fmwG4sah/yfgPWc9xwwqVbvvcQfh/wTEQFw97b5Y2VrCjwSiYBRQ6MwamgUjDVmbN+djy/35MNYY8FH35zGxm9PY9TQKFw3Pgmj02L6zMOoBou9zR75oop61Jvt7R4nkQiIjgjy7pFvDPW6MHWP/f6aHiYeyCnCsRO5GJaSjFGp/fz+vx9DPhFRFxIEAbLQSMhCIxE0uP0h/9Yyd7E/p8kAp8kAs8kA8y8Hmk8kkUKu6+fp9VdGuXv+pVo9h/wTERH5kC5Ujd/MSMFt04Zg7/EybN2Zi59PVeCnE+X46UQ59GFqzBiXiOljE/22cFtLVruzsXK9u0e+ZeG76lrrBY+NDFe3CPHNPfLREUGQSSU9dAUXJpUIGJYcAYmlDKnJEX4f8AGGfCKiHnGxIf/u0N887N895L8A9ooC1ONHz/4SZRDkjb3+yhbF/iQc8k9ERNSjpFIJxqfHYnx6LEoM9di2Kw9f7S2AodqMd7edwPvbT2Ls8BhcPz4Z6YP0kPTi8OhwulBW2dC89FyLHnlDtfmCx4aFKNvskY/RB0MpZ70CX2DIJyLyoQsN+be2rPBfXgC7sQiuFkP+a1ueR6v39PY3zfeX6+I45J+IiKgHxOqDcc8NwzD32hTsPFyMrbvycDy3EjsPl2Dn4RLE6YNx3YQkXJ2VgJCgtgvEOV1it9agcLpEGKrNnmXnilv0yJdVNsB1gdL1wWq5pxfe0yPfWL3+/Mr15HsM+UREvUzLIf/Bg0d7totOO2yGItjOK/bXNOS/wWRAw5mfmk8kkUGhj2su8hfJIf9ERETdSSGXIvuKeGRfEY+8EhO27szFtz+dQ7GhHms/PYZ/fJGDSSP64boJSRiaEO75+3jn4eJWBeB0oSrcf3P6JRWAE0URVbXWNnvkS431sDvar1yvVEjRT69BbGSwp2feXfguGNpgBf/t4EcY8omI/IQglUMZnQRldJLXdqe5rjH4N/X658NWUQjR2tC43F8BcKx5f4kyqLnAX4tif5c65F90OWEtzIG8+BisGkA9eMRlLV1IREQUiJJitVg4JxPzZg3DdwfOYevOPJwtrsE3+wvxzf5CJMdpcd2EZKgUUvz1vQOtjjfWWPB/7+zD4ruzWgV9U70NxYbGHvkWhe9KDHUwW9uvXC+TShCrD2rshffukY/QqhjkAwRDPhGRn5OqNVAnpEGdkObZJooiHKYKT8i3lefBVlEAu7EYLmsDLIU5sBTmeJ1HptU31g1oCv6JjUP+W/9VUX9iNwzb18FZa4QGQNXhzTCF6KCfPh/BKeO6+5KJiIj8hlopw7XjkzBjXCJOFVRh6648/PBzEXKLTVj10aGLHr/iw4PIKzWh1FDvWZKutuEClesFIDoi2KtHvmmufGR4UEAUlqMLY8gnIgpAgiBAHhoFeWiU95B/hx02Y8sh/+5h/85aIxwmAxztDfmPSvL0/DtMRhi2rm71mc5aI8o2LkP0nMcY9ImIiM4jCAKGJkZgaGIE7r1xOL7ZX4hN/z0DQ4sh+m2pbbDj31+ebLVdH6pyh/fzeuSjI4Ihl/WOyvXkGwz5RER9iCC70JD//Oae/8afRZu5ech/Bxm+WoegIVkcuk9ERNSOkCAFbpo8EKEaBV55t/VQ/fMNH6DDqJQoT498rD4YKgWjHLWNdwYRETUO+R8GdcIwzzZRFOGoqWic4+/u+bcUnYKzpuKC53KajCha9wSUcYMgD4+BPDwGsvAYyMOjIVGou/tSiIiI/IZO27G/F/9nRgrSB+m7uTUUKBjyiYioTYIgQB4WBXlYFIKHZAEA6o79gPJNr130WFtZLmxlua22S4NDIQuLgTwiBvKwGMjCoz0PAiRBWhb8ISKiPiVtgA66UJVXVf3z6cPUSBug68FWkb9jyCciog6TasI7tF/o+JshSGSwV5fCUVUGe1UpXOZaOOtr4KyvgbWo9dxCQaFuDPzR7p7/sGjII2IhC4+GLETH4f9ERBRwpBIB99+cjv97Z1+7+9x303AWy6NLwpBPREQdpopPhTREB2etsd19pFodIrL/p1Uod1nqYa8qg72qBPaqMjiqSmGvLoW9qgxOk9E9/7+dEQCQyiAPjfLq+Zc1fQ+LgkSm6OpLJSIi6hETMuKw+O4svLXpiFePvj5MjftuGt5q+Tyii2HIJyKiDhMkUuinz0fZxmXt7qO/Zn6bve4SVTCUsQOgjB3Q6j2XwwZHdTnsVaXu8N/Y++9+EFAOOB2wVxbDXlkMc+tWQRoS0Wr+f9PPUlVw5y6aiIiom03IiMPY4bE4ftaISpMFEVoV0gbo2INPl4Uhn4iILklwyjhEz3kMhu3rvHr0pVod9NfMv6zl8yQyBRT6/lDo+7d6T3Q54ag1eob9n/8gQLSZ4aw1wllrhKXgWOtzq0MaA3805GGNDwAiYiELi4FUE8Y6AERE1CtIJQKL61GXYMgnIqJLFpwyDkFDslB9+iAKTx1D/JBhCBs8olvmzQsSKeShUZCHRkGdlO71niiKcDWYYK8ug72yxP0goLrpQUAZnPXVcJlrYTXXwlp8uvW55UrIwqI9Pf9e0wBCI1kHgIiIiPwOQz4REV0WQSKFMj4V9jpAGZ/qk0AsCAKkwaGQBodC1W9Iq/ddVjPs1Y3z/xt7/x3VpbBXlsJhMkC0W2GvKIC9oqCNk0sgC41ssRJAi2kAYdGQKFQ9cIVEREREl4Yhn4iIApZEqYYyOgnK6KRW74lOOxw1Fd7z/5umA1SXQ3TY4Kgug6O6DGYcanW8VBN+3jSA5lEAErWm26YBiC4nLIU5cNZVQaoJh8pHD1iIiIiod2LIJyKiPkmQyiGPiIM8onXVYlF0wVlb5R76X9lUALDMUwvAZamDs64KzroqoDCn1fESZVCLnv9Yr1UBpCEREATJZbW5/sTu1rUQQnTQT7+8WghEREQUeBjyiYiIziMIEsi0Osi0OqgThrV632mubV4G8LxpAM66SrisDbCVnoWt9Gzrc0vlkIVFefX8y8Oj3T+HRUGQyttsU/2J3W2uauCsNaJs4zJEz3mMQZ+IiIgY8omIiC6VVB0CqToEiBvU6j2X3QpHdVnb0wBqKiA67bAbi2A3FrU+cdPDhfAYyMMa5/9HxECmjYLhy7UXbJPhq3UIGpLFoftERER9HEM+ERFRF5LIlVBEJkARmdDqPdHlhMNkcAf+ytLGlQCapwGIdgscNRVw1FTAgiOX9LlOkxHVez6DOiEVEmUQJAo1JMogCArlZU8PICIiIv/DkE9ERNRDBInU3UMfFg0kZ3q9J4oinPU15xUAdI8GsFUUQrSZL3r+qm/+garWnwpBqYZEofIK/xKlGoLC/d39df57zT9LGvcTpPxnAxERUW/Hv62JiIh6AUEQINOEQaYJgyo+xes9c/5RlPzruYueQxYWDYgiXLYGuKxmwOUEIEK0NsBpbYCztrJzbZQp2n0AIFGo3Q8TzntY0PRaaPFakCm6bfWB7iK6nLAW5kBefAxWDaAePIJTI4iIqFfqVSF/9erV+PHHH/HPf/7Tsy0nJwdLly7F0aNHERERgXnz5uGuu+7yvO9yubBixQp8+OGHqK2tRVZWFp599lnEx8d3+BxERES9mSo+FdIQnVdV/fNJtTrEL1zuCZ6iKEJ02CDaLHBZ3aG/Kfw3vRZbvra13G722i7are5zOmxwOmxAfU3nLkiQNIZ/VXP4V7R4KND4Wmg1yqDFqIKm43sgaLdc1UADoOrwZpi4qgEREfVSvSbkv/vuu3jttdcwevRoz7aqqircc889mDp1Kp5//nkcPHgQzz//PIKDgzFnzhwAwKpVq/Dee+/hT3/6E2JiYrBs2TIsWLAAW7ZsgUKh6NA5iIiIejNBIoV++vw2q+s30V8z3yvwCoIAQa4E5EpIg0M79fmiy+l5SCBazW0+GHDZzBBbPUxo/rnpPUAERBdcljq4LHWdahcACHJV85SDlg8Aml43TVPwPBhoPcrAPbqAqxoQEVFg8HnILysrw3PPPYc9e/YgKSnJ670PPvgAcrkcL7zwAmQyGQYOHIj8/Hy89dZbmDNnDmw2G9atW4dHH30U2dnZAIBXX30VkyZNwvbt2zFr1qyLnoOIiMgfBKeMQ/Scxzw9yk2kWh3013Rvj7IgkUKq1kCq1nTqPKIoQrRbWjwksLTxYKD54YHo9brFyAKrGaLT7j6n3QKn3QJnXetqBJdEKvOMLvA8LFCoYck/esHDuKoBERH1Nj4P+ceOHYNcLsenn36KlStXoqioeUmh/fv3Y8yYMZDJmps5btw4rF69GgaDAcXFxaivr8f48eM972u1WqSlpWHfvn2YNWvWRc+h1+t75kKJiIg6KThlHIKGZMFSmANnXRWkmnCo4lP9JmAKguCez69QAyERnTqX6LCfN5KgacTAeaMMWjwg8JqG0DQywWZxn9DpgKvBBFeD6ZLa4TQZ0XD2EIIHjerU9RAREXUVn4f8qVOnYurUqW2+V1paiiFDhnhti4qKAgCUlJSgtLQUABAbG9tqn6b3LnaOyw35oiiioaHhso6lwGY2m72+EwUy3u8+EjkAkkhABGC2WH3dGh+SAUqt+wuA0Ph1KQsGii4XRLvF8wBAtJndr61mWPOPwnz024ueo+yDlyCPGwJlfBoUCcMgjxnIlQjI7/HPd+pL/OV+F0WxQ4Vre/XfQBaLBQqFwmubUqkEAFitVs9/hLb2qamp6dA5LpfdbkdOTs5lH0+BLy8vz9dNIOoxvN8psMgAhECmjkNIR3YXRdiLTsJedBLY/QlEqRyO8HjYdUlw6JLgDIkG/Gw1AaIm/POd+hJ/uN/Pz7Zt6dUhX6VSwWazeW1rCuZBQUFQqVQAAJvN5vm5aR+1Wt2hc1wuuVyOQYMGXfbxFLjMZjPy8vKQlJTkuQ+JAhXvdwpkomsoKnK2wlXX/tKDEk0Ewucshr0oB7aCY7AVHofLXAu54SzkhrMAAEGlgSI+Fcr4YVAkDIM0LNrvlhCkvod/vlNf4i/3+5kzZzq0X68O+TExMSgvL/fa1vQ6OjoaDofDsy0hIcFrn6FDh3boHJdLEIROPSSgwKdWq3mPUJ/B+50C1ox7L7iqQeSMexHcfwDQfwAwdiZE0QVbeQHMeUdgyTsCc8ExiJY6WE/vg/X0PgCAVKuHOikd6qThUCdlQNbJ+gRE3Yl/vlNf0tvv944+IO7VIT8rKwvvv/8+nE4npFJ3UaHdu3cjOTkZOp0OISEh0Gg02LNnjyfkm0wmHD9+HHfccUeHzkFERETUnktd1UAQJFBGJ0EZnQSMvQGi0wFryRmYc4/AnH8ElnMn4TQZUHf4W9Qdds/3l+v6NYb+DKgSh3V6FQOizhJdTlgLcyAvPgarBlAPHuE3BT6JqJeH/Dlz5mDNmjV46qmnsGDBAhw+fBjr16/H888/D8A9H+GOO+7Ayy+/jIiICPTr1w/Lli1DTEwMpk+f3qFzEBEREV1I06oG1acPovDUMcQPGYawDoYeQSqDqn8KVP1TED7pVrjsVlgKT8CcdxiWvCOwlpyF3VgEu7EIpp+2AYIEyphkqJpCf3wKJHJlD1wlkVv9id2eh1oaAFWHN8MUooN+evcu1UlEXadXh3ydToc1a9Zg6dKlmD17NiIjI/H4449j9uzZnn0efvhhOBwOPP3007BYLMjKysLatWshl8s7fA4iIiKiCxEkUijjU2GvA5SdWLZQIlciaEAmggZkAgCc5jpY8o/BnHcY5rwjsBuLYC35BdaSX1CzaxMglUHVb6i7pz85HcrYQazcT92m/sTuNqenOGuNKNu4DNFzHmPQJ/IDgiiKoq8b4W+OHDkCAEhPT/dxS6g3amhoQE5ODlJTU3v1nB6irsD7nfqSnrjfHSYjzPlHYM47CnPeEThNBq/3BYUK6oRhjT396VBEJUAQLmXRQKK2iS4nClYs9JqWcj6pVoeEB9/g0H0KOP7y75mO5lA+CiYiIiLqJWRaHULSsxGSng1RFOGoKnHP589zz+l3mevQcOYnNJz5CQAgCdK6e/kTh0OdnAEZK/dTB4guJxzVZbBVnIPNUAi74RwsRacuGPABwGkywlKYA3Xi8B5qKRFdDoZ8IiIiol5IEATII+Igj4iD9ooZ7sr9ZXnuwJ97BJbC43A1mFB/fAfqj+8AAMhCI5uL+CUNh0wT7uOrIF8SXU7Yq8pgryj0hHlbRSHsxiKITvtlndNZV9XFrSSirsaQT0REROQHBEECZcwAKGMGIGzcTRCddliKTnuW67MUnYKjpgK1h75B7aFvAADyyHhP6FcnpEGiCvbxVVB3cIf50sYwfw62igLYDedgNxa3G+YFmQJyfX8o9P0h18cDogtV3/37op9VtWsTpJoIqBOHdfVlEFEXYcgnIiIi8kOCVA51QhrUCWnA5F/DZTN7Kvebc4/AVpYHe0Uh7BWFMO37wl25P3ZgY+hPhzI+BRKZwteXQZdAdDpgryp198o3Bnq7oRA2YzHgdLR5jCBXQq7rD0Vkfyj08e5gHxkPWWik19x60eWE6cD2iw7Zt5floeRfz0KVMAzhk2/j0H2iXoghn4iIiCgASBRqBA0ciaCBIwEAzoZamPOPepbrs1eWwFp8Gtbi06je+TEEqRzK+JTm0B87kAXVegnR6YC9ssQd4huH2tsMhbAbSwBX+2G+qVdeEXlemO9AcUZBIoV++vw2q+s30V/3W9jK8mA69B9YCo6h5F/PQZWQhvBJt0GVOJz1IIh6CYZ8IiIiogAkDQqBJnU8NKnjAQAOkwHmXPdSfea8I3DWVbmH+ecdQRUAQRkEdcIwqJPToU5MhzwynqGtm4lOuzvMN/XKN82drywBXM42jxHkKneYj4yHQt/YOx8ZD1movtMrLQSnjEP0nMdg2L7Oq0dfqtVBf818z/J5YRNvQfXOT2A6+DUsBcdR8u4SqOJT3WE/KZ33DZGPMeQTERER9QEyrR4hmVMRkjkVoijCbizyBH5L/lG4LPVoOL0PDaf3AQCkwWFQJQ33zOmXh0X5+Ar8l+hoDPOGQnfhO0NjqL9QmFeoGofXxzcPtY/sD5m282H+QoJTxiFoSBaqTx9E4aljiB8yDGGDR3iN8pBp9dBfex/CJtyC6l2foPbnr2EpzEHJe89D2T8F4ZNugzo5g2GfyEcY8omIiIj6GEEQGnuB+yN09HUQXU7YSnObQ39hDpz11ag/9iPqj/0IAJCFRbsDf3IG1InDIQ0O9fFV9D6iww6bschTxd5d0b4Q9spSQHS1eYygUEPR2CvfNNReoe8PqVbvs5AsSKRQxqfCXgco41PbncYh0+qgn7EAYeNno3rXJtT+/BWs506g9N8vQNlvqHvOfnImwz5RD2PIJyIiIurjBIkUyrhBUMYNQtiE2RAddliKTsKcewTm/COwFp2Go7oMtQfLUHvwawCAIioRqsb5/OqEYZAo1T6+ip7jcthgNxa7e+RbDLW3V10gzCuDoNDHew+1j0yANCTC70OwO+zfi7AJLcJ+0UmU/vtFKPsNcffsDxjh99dJ5C8Y8omIiIjIiyCTQ504vLFy+m/gspphKTjurtyfdwS28nzPl2nvZ+7K/XGDG3v606HqNxSCTO7ry+g0l90Ku7G4RTV791rz9qqydsO8RBnUGOJbFMDTxwdEmL8YWUgE9NPnu3v2d29C7YHtsBadQun7/w/KuMHusD9wZMD/Hoh8jSGfiIiIiC5IolQjaPAVCBp8BQDAWV/jrtzfWMjPUV0Ga9FJWItOonrHRxBkCqjiUz2V+xUxyb26cr/LboXdUOQJ8U1D7R3V5e2HeVWw1/D6pmAv1YT3+RArCwmH/pp7EDb+ZtTs3gzTT1/CWnwapRuWQhk7yB32B43q878nou7CkE9EREREl0QaHApN2kRo0iYCAOzV5Z5efkveUTjrq2HOPQRz7iEA7kCsShjmmdMv1/XrUMATXU53fYC6Kkg14VBdYH54R7jD/LkWBfDcgd5RXQ5AbPMYiUrT2CPfogCePh5STRhD6kXINOHQTZuH0HFNYX8brCVnUPrBS1DGDkTYpNsQNOgK/h6JuhhDPhERERF1ijwsCvIR06AdMc1dud9Q6C7il3sE5oJj7sr9p/ai4dReAIBUEwF1U+X+5AzItPpW56w/sbv1Um4hOuinNy/l1h6XzezpmW+5NJ2jugLthnl1iNfw+qZgLw0OZQjtJJkmDLppdyNs/M2o9oT9X1D2wf9BETMQ4ZNuRdDg0fw9E3URhnwiIiIi6jKCIEARmQBFZAJCs2ZCdDlhLfnFU7nfWngCzrpK1B39HnVHvwcAyCNim4v4JQ6HpeA4yjYua3VuZ60RZRuXIXrOYwhOGQeXzQyboQj2igLYGnvl7YZzcNSUt9s+SZC2zQJ4kiAtQ2Y3kwaHQnf1XQgbdxOq93wK0/5tsJX+grIP/wRFdDLCJ92GoCFZ/O9A1EkM+URERETUbQSJFKp+Q6DqNwThE+fAZbfCeu5kc+gv+QX2yhLYK0tQe2C7+6CLDMkv2/QapMGhcJoM7e4jDQ719Mq3HGrPpf98TxocCt3UOxE27ibU7PkUNfu3wlaWi7KP/uwO+1feiqChWRAEia+bSuSXGPKJiIiIqMdI5EqokzOgTs4AALgs9TC3qNxvrygEXM4Ln8Rp9wR8aXCYO8yfXwAvSNvdl0KdJA3SIuKqOxA69kbU7NmCmv1fuMP+xr9AEZXo7tkfOoZhn+gSMeQTERERkc9IVMEIHpKF4CFZAICan7bBuO3tix4XduWtCM2aCWlQSHc3kbqZO+zPdYf9vVtQs+8L2MrzUbZxGRRRCQi78jYEp4xl2CfqIIZ8IiIiIuo1FPr+HdpPnTScAT/ASINCEJH9PwgdewNq9nzm7tkvL0D5xy9DHpmA8Em3IjhlHMM+0UXw/xAiIiIi6jVU8amQhuguuI9Uq4MqPrWHWkQ9TaoOQUT2b5Dw4BsIu/JWCMog2CsKUP7xKzj39h9Qd3wHxItN6SDqwxjyiYiIiKjXECRS6KfPv+A++mvmQ7hIcT7yf1K1BhFTbneH/Um3QaIMgr2iEOWf/NUd9o/9yLBP1AaGfCIiIiLqVYJTxiF6zmOtevSlWp1n+TzqO6RqDSIm/xrxD72J8Mm/hkQVDLvhHMo3vdoY9n9g2CdqgXPyiYiIiKjXCU4Zh6AhWbAU5sBZVwWpJhyq+FT24PdhUlUwwifdhtCsmajZ9wVq9m5pDPuvQf7Dhwi78lfQpE3kPUJ9HkM+EREREfVKgkQKdeJwXzeDehmJKhjhk25FaNb1qNm/FTV7tsBuLELF5tdR3RT2h13JsE99FofrExERERGR35GoghF+5a+Q8NAbCM/+H0jUGtgri1Hx6d9wbvX/ovbwfzmMn/okhnwiIiIiIvJbEmUQwifOQcKDbyI8ey4k6hDYK0tQsWU5Ct98GLWHv2XYpz6FIZ+IiIiIiPyeRKlG+MRbkPDgG4i46g5IgrRwVJWiYssKd9g/9A1Ep8PXzSTqdgz5REREREQUMCRKNcImzEbCg6sQMfXO5rD/2UoUvvkwTAf/w7BPAY0hn4iIiIiIAo5EoUbY+JvdPftX3wVpcCgc1WUwfL4KhW/+Dqafv2bYp4DEkE9ERERERAFLolAhbNxNiF+0ChFX390Y9sth+OINFL7xEEw/fwXRafd1M4m6DEM+EREREREFPHfYvxHxD76BiGnzIA0Og6OmAoYv3kThqodgOrCdYZ8CAkM+ERERERH1GRK5EmFjb0D8g6ugu+Yed9g3GWDYuhoFqx6C6acvIToY9sl/MeQTEREREVGfI5ErETpmljvsT58PqSYcTpMBhm1voWDVg6jZv41hn/wSQz4REREREfVZErkSoVkzG8P+vZBqIuCsNcL45dsoWLUINfu3wuWw+bqZRB3GkE9ERERERH2eRKZAaNb1iH9wJXQzFkAaEgFnbSWMX65B4coHUbPvC4Z98gsM+URERERERI0kMgVCR1+HhEWroJtxH6QhOjjrKmHcvrYx7H8Ol93q62YStUvm6wYQERERERH1NoJMjtDR10I74mrUHvoGVTs/htNkgHH7OlTv+BhhE2YjZOQ1kMiVvm4qkReGfCIiIiIionYIMjm0V8xASOZU1B7+FtU7NsJhMsD41d9RvfMThI6/GdpR0xn2qdfgcH0iIiIiIqKLEGRyaEdNR/yiFdBf/wBkoZFw1lej8uv1KFy5CNV7PuUwfj8kupywFuZAXnwM1sIciC6nr5vUaezJJyIiIiIi6iBBKod25DUIychG7eHv3D37NeWo/Pod1OzahNBxN0E7agYkCpWvm0oXUX9iNwzb18FZa4QGQNXhzTCF6KCfPh/BKeN83bzLxp58IiIiIiKiS+QO+9MQv3A59DMXQhYWBWd9DSr/8w8UrFyI6l2b4LJZfN1Makf9id0o27gMzlqj13ZnrRFlG5eh/sRuH7Ws8xjyiYiIiIiILpMglUE7YhriH1iOyFkPQhYWDVeDCZXf/NMd9nd+ApfN7OtmUgsupwOG7WsvuI/hq3V+O3Sfw/WJiIiIiIg6SZDKEJI5FZrhk1F39HtU7dgIR1UpKr/9F6p3b0bYuBuhveI6SJRqXzfVr4miC6LVDJe1AS7P93rv15am1y33adrPDJelHhBdF/wcp8kIS2EO1InDe+jKug5DPhERERERURfxhP30Kag7+gOqfvywMey/i+rdmxE69kaEjr4OEmWQ13GiywlLYQ6cdVWQasKhik+FIJH66Cq6h+h0tAjcjV+WBrhsjd/Pf8/ryx3WRasZgNgj7XXWVfXI53Q1hnwiIiIiIqIuJkikCMnIhmb4JNQd+wHVP26EvbIYVf99DzV7PkXomBsQmnU9JMogrwJwTaS9qACcKIoQHTbvYG5tO5yLFwjwosPWdY2SyCBRBUGiDIJEoW7+WRnc+F3d+D0IElWw1z62inMo/+SVi36EVBPede3tQQz5RERERERE3USQSBGSng3NsEmoO74D1T9+CLuxGFXf/Rs1e7ZAPSAT9cd3tDquqQBc9JzHOhX0RdEF0WbxDudNw9Y9YbweLpv5vH28v9CF89MFubI5gJ//pQqCRBHUIrQHQVCqW4R3d4AXZAoIgnBZny/X9YM0RNeq6F5LUq0OqvjUy71En+ozId/lcmHFihX48MMPUVtbi6ysLDz77LOIj4/3ddOIiIiIiCjACRIpQoZPhiZtIuqP70TVjx/CbixqM+C3ZPhyLeSRCRDtlhbD1utb9KabG39uex561w5vF5p7yFVBEBQtgnl7oV153j4KNQSpb2OoIJFCP30+yjYua3cf/TXz/Xa6RJ8J+atWrcJ7772HP/3pT4iJicGyZcuwYMECbNmyBQqFwtfNIyIiIiKiPkCQSKEZPgnBaRNQ9f0HqN7x0QX3d9ZV4tybv+v8B7cc3t6iR/z8HnLP8HavIe/u14JCCUEIjAXaglPGIXrOY62nSWh10F/TO6ZJXK4+EfJtNhvWrVuHRx99FNnZ2QCAV199FZMmTcL27dsxa9Ys3zaQiIiIiIj6FEEihSKyf8d2lsogVYdcuIf8vC/hvH0Eqfyyh7cHquCUcQgakoXq0wdReOoY4ocMQ9jgEX7bg9+kT4T8EydOoL6+HuPHj/ds02q1SEtLw759+xjyiYiIiIiox3W0sFvsb57xy6Xc/IEgkUIZnwp7HaAMkBUN+kTILy0tBQDExsZ6bY+KivK8d6lEUURDQ0On20aBx2w2e30nCmS836kv4f1OfQnv954h6pIg0UTAVVfZ7j4STQRcuiRmj27kL/e7KIodGo3RJ0J+03+s8+feK5VK1NTUXNY57XY7cnJyOt02Clx5eXm+bgJRj+H9Tn0J73fqS3i/dz/5oGwEH/wYANAyvjWVyjMNyobx5Mkeb1df5A/3e0fqyfWJkK9SqQC45+Y3/QwAVqsVarX6ss4pl8sxaNCgLmkfBRaz2Yy8vDwkJSVd9v1F5C94v1Nfwvud+hLe7z0oNRWW/v1h+u+/vHr0pZoIaLPvQOzgLB82rm/wl/v9zJkzHdqvT4T8pmH65eXlSEhI8GwvLy/H0KFDL+ucgiAgKCioS9pHgUmtVvMeoT6D9zv1JbzfqS/h/d4zgjKnIDz9SlgKc+Csq4JUEw5VgMwP9ye9/X7vaOHEwFj/4CJSUlKg0WiwZ88ezzaTyYTjx48jK4tPxoiIiIiIyLcEiRTqxOHQDJsEdeJwBny6bH2iJ1+hUOCOO+7Ayy+/jIiICPTr1w/Lli1DTEwMpk+f7uvmEREREREREXWJPhHyAeDhhx+Gw+HA008/DYvFgqysLKxduxZyudzXTSMiIiIiIiLqEn0m5EulUjz22GN47LHHfN0UIiIiIiIiom7RJ+bkExEREREREfUFDPlEREREREREAYIhn4iIiIiIiChAMOQTERERERERBQiGfCIiIiIiIqIAwZBPREREREREFCAY8omIiIiIiIgCBEM+ERERERERUYBgyCciIiIiIiIKEAz5RERERERERAFCEEVR9HUj/M2BAwcgiiIUCoWvm0K9kCiKsNvtkMvlEATB180h6la836kv4f1OfQnvd+pL/OV+t9lsEAQBo0aNuuB+sh5qT0Dpzf/hyfcEQeADIOozeL9TX8L7nfoS3u/Ul/jL/S4IQoeyKHvyiYiIiIiIiAIE5+QTERERERERBQiGfCIiIiIiIqIAwZBPREREREREFCAY8omIiIiIiIgCBEM+ERERERERUYBgyCciIiIiIiIKEAz5RERERERERAGCIZ+IiIiIiIgoQDDkExEREREREQUIhnwiIiIiIiKiAMGQT0RERERERBQgGPKJiIiIiIiIAgRDPlEXqa6uxrPPPovJkydj1KhR+M1vfoP9+/f7ullE3S43NxcjR47Exx9/7OumEHWbTZs24frrr0d6ejpmzpyJrVu3+rpJRN3C4XDg9ddfx1VXXYWRI0di7ty5OHjwoK+bRdTlVq9ejTvvvNNrW05ODu644w6MGDECU6dOxT/+8Q8fta5zGPKJusgf/vAH/Pzzz/jrX/+KjRs3IjU1Fffeey/Onj3r66YRdRu73Y5HH30UDQ0Nvm4KUbfZvHkznnrqKcydOxeff/45Zs2a5fkznyjQvPHGG/jwww/x4osvYtOmTUhOTsaCBQtQXl7u66YRdZl3330Xr732mte2qqoq3HPPPUhISMDGjRvx4IMP4uWXX8bGjRt908hOYMgn6gL5+fnYsWMHlixZgtGjRyM5ORnPPPMMoqKisGXLFl83j6jbLF++HBqNxtfNIOo2oiji9ddfx1133YW5c+ciISEBCxcuxIQJE7B3715fN4+oy3399deYNWsWrrzySiQmJuLJJ59EbW0te/MpIJSVleGBBx7Ayy+/jKSkJK/3PvjgA8jlcrzwwgsYOHAg5syZg3nz5uGtt97yTWM7gSGfqAuEh4fjrbfeQnp6umebIAgQBAEmk8mHLSPqPvv27cOGDRvwpz/9yddNIeo2ubm5KCoqwg033OC1fe3atfjtb3/ro1YRdR+dTodvv/0W586dg9PpxIYNG6BQKJCSkuLrphF12rFjxyCXy/Hpp58iMzPT6739+/djzJgxkMlknm3jxo1DXl4eDAZDTze1UxjyibqAVqvFlClToFAoPNu+/PJL5OfnY9KkST5sGVH3MJlMePzxx/H0008jNjbW180h6ja5ubkAgIaGBtx7770YP348br31VnzzzTc+bhlR93jqqacgl8tx9dVXIz09Ha+++ir+9re/ISEhwddNI+q0qVOnYvny5YiPj2/1XmlpKWJiYry2RUVFAQBKSkp6pH1dhSGfqBscOHAAixcvxvTp05Gdne3r5hB1uSVLlmDkyJGtejeJAk1dXR0A4IknnsCsWbOwbt06TJw4EYsWLcKuXbt83DqirnfmzBmEhIRg5cqV2LBhA2655RY8+uijyMnJ8XXTiLqVxWLx6rADAKVSCQCwWq2+aNJlk118FyK6FF9//TUeffRRjBo1Ci+//LKvm0PU5TZt2oT9+/ez3gT1CXK5HABw7733Yvbs2QCA1NRUHD9+HH//+98xfvx4XzaPqEuVlJTgkUcewfr16zF69GgAQHp6Os6cOYPly5dj1apVPm4hUfdRqVSw2Wxe25rCfVBQkC+adNnYk0/Uhf71r3/hd7/7Ha666iq8+eabnqd/RIFk48aNMBqNyM7OxsiRIzFy5EgAwHPPPYcFCxb4uHVEXSs6OhoAMGTIEK/tgwYNwrlz53zRJKJuc+jQIdjtdq8aQwCQmZmJ/Px8H7WKqGfExMS0WkWi6XXT3wX+gj35RF3kvffew4svvog777wTTz31FARB8HWTiLrFyy+/DIvF4rVt+vTpePjhh3HjjTf6qFVE3WPYsGEIDg7GoUOHPD2bAHDq1CnOUaaA0zQf+eTJk8jIyPBsP3XqVKtK5ESBJisrC++//z6cTiekUikAYPfu3UhOToZOp/Nx6y4NQz5RF8jNzcVLL72Ea665Br/97W+9KnCqVCqEhIT4sHVEXau9p9k6nc7vnnQTXYxKpcKCBQuwcuVKREdHIyMjA59//jl27NiB9evX+7p5RF0qIyMDV1xxBZ544gk899xziImJwaZNm7Br1y78+9//9nXziLrVnDlzsGbNGjz11FNYsGABDh8+jPXr1+P555/3ddMuGUM+URf48ssvYbfb8dVXX+Grr77yem/27NlcYoyIyI8tWrQIarUar776KsrKyjBw4EAsX74cY8eO9XXTiLqURCLBG2+8gddeew2LFy9GTU0NhgwZgvXr17dabowo0Oh0OqxZswZLly7F7NmzERkZiccff9xTj8WfCKIoir5uBBERERERERF1HgvvEREREREREQUIhnwiIiIiIiKiAMGQT0RERERERBQgGPKJiIiIiIiIAgRDPhEREREREVGAYMgnIiIiIiIiChAM+UREREQdwFWHiYjIHzDkExERdYM777wTaWlpOHLkSJvvT506FU8++WSPtOXJJ5/E1KlTe+SzLoXD4cCTTz6JkSNHYtSoUdi9e3e7+1qtVqxfvx5z5szBFVdcgTFjxuD222/Hpk2bvML38uXLMXTo0C5tp81mw0svvYQtW7Z06XmJiIi6A0M+ERFRN3E6nVi8eDFsNpuvm9Ir/fDDD/jkk08wb948rF69Gunp6W3uZzAY8Otf/xpvvPEGrrrqKrz66qv4y1/+gqFDh+LJJ5/EM88806297OXl5XjnnXfgcDi67TOIiIi6iszXDSAiIgpUISEhOH36NFauXInf//73vm5Or1NdXQ0AuOWWWxAfH9/ufk888QRKS0uxYcMGJCUlebZnZ2cjLi4Of/3rX3HVVVfh6quv7uYWExER9X7sySciIuomqampuPnmm7FmzRocPXr0gvsOHToUy5cv99p2/tDzJ598Evfeey82bNiAadOmISMjA7fffjtyc3Px7bff4oYbbkBmZiZuvfVW5OTktPqMDRs2IDs7GxkZGbj77rtx/Phxr/eLi4vxhz/8AWPGjEFmZmarfc6dO4ehQ4fi73//O6699lpkZmZi48aNbV6P0+nEu+++ixtuuAEZGRnIzs7Gyy+/DKvV6rmWpukK06ZNw5133tnmeXJycvDjjz/i3nvv9Qr4TebNm4e5c+ciKCiozePbmhbx8ccfY+jQoTh37hwAwGKxYMmSJZg8eTKGDx+Oa6+9FmvXrvVcc9PDg8WLF3tNe9i/fz/uuOMOZGZmYsyYMXjiiSdQWVnp9TlpaWn48MMPMXHiRIwZMwZnzpxBQUEBHnjgAYwdOxaZmZn49a9/je+++67N9hMREV0q9uQTERF1oz/+8Y/YsWMHFi9ejI0bN0KhUHTqfD///DPKy8vx5JNPwmq1YsmSJbj//vshCAIefvhhqNVqPPfcc3j00Ufx+eefe44rLS3FihUr8Mgjj0Cj0WDFihW48847sWXLFsTFxaGyshK333471Go1nnnmGajVarzzzjuYO3cuPvroIwwcONBzruXLl+Opp56CRqNBZmZmm+189tlnsXnzZtx3330YPXo0jh8/jpUrVyInJwdr1qzBokWLEBMTgzfeeAMrVqxAcnJym+f54YcfAKDdmgJKpRLPPvvs5f46AQAvvfQSfvzxRzzxxBPQ6/X4/vvv8Ze//AVhYWG44YYbsGLFCjz00ENYuHAhpk+fDgDYt28f7rnnHowbNw6vvfYaampq8Prrr+Ouu+7CRx99BJVKBcD9sGPdunVYunQpqqqqkJycjFmzZiEqKgp/+ctfIJPJ8I9//AMLFy7E1q1bkZiY2KlrISIiYsgnIiLqRqGhoXjhhRewcOHCLhm2X19fj9dee80Tuvfu3Yv3338f69evx/jx4wEA+fn5+POf/wyTyQStVgvAHTZXrlyJjIwMAEBmZiamTZuGf/7zn3jiiSfwzjvvoLq6Gv/+97/Rr18/AMDkyZNx/fXX4/XXX8ff/vY3Txuuu+46zJkzp902njlzBh999BEeeeQR3H///QCAiRMnIioqCo8//ji+//57TJkyBQkJCQDcIx769+/f5rlKSkoAoN33u8LevXsxceJEzJw5EwAwduxYBAUFQafTQaFQIDU1FQCQkJCAtLQ0AMArr7yC5ORkrF69GlKpFID7dzpz5kxs3LgRc+fO9Zz/gQceQHZ2NgCgoqICZ8+exaJFizBlyhQAQEZGBlasWMHaDURE1CU4XJ+IiKibTZ06FTfeeCPWrFmDY8eOdepcoaGhXr3qer0eALx61MPCwgAAJpPJsy0+Pt4T8AEgMjISI0aMwL59+wAAu3btQmpqKqKjo+FwOOBwOCCRSDB58mTs3LnTqw1Nobc9e/fuBQBPaG4yc+ZMSKVS7Nmzp6OX6wnQTqezw8dcqrFjx+KDDz7Afffdh3/9618oLCzEgw8+6Anm5zObzTh06BCmTJkCURQ9v6/4+HgMHDgQO3bs8Nq/5e9Lr9dj0KBBeOaZZ/DEE09gy5YtcLlcWLx4MQYPHtxt10hERH0He/KJiIh6wNNPP41du3Z5hu1fLo1G0+b29uakN2l6GNCSTqfz9JRXV1cjPz8fw4YNa/N4s9nc4c+qqakB4H6Q0JJMJkN4eDhqa2sveHxLTaMKiouLMWjQoDb3KSsrQ1RUFARB6PB5W3rqqacQExODTz/9FC+++CJefPFFjBw5EkuWLEFKSkqr/U0mE1wuF95++228/fbbrd5XKpVer1v+vgRBwLp16/DGG2/gq6++wqZNmyCXyzFt2jQ8//zzCA0NvaxrICIiasKQT0RE1ANCQ0OxZMkSPPjgg1i1alWb+5zfW93Q0NBln98UvFuqqKhAREQEAPdKAGPGjMHjjz/e5vGXUkugKahWVFR4QjoA2O12VFVVITw8vMPnuvLKKwEA3333XZsh3+Fw4KabbsKoUaMu+/eqUCiwcOFCLFy4EMXFxfj222+xatUqPPLII151DZoEBwdDEATMmzev1WgFAFCr1Re8pujoaCxZsgTPPfccTpw4gW3btuHtt99GeHg4nnvuuQseS0REdDEcrk9ERNRDpk2bhlmzZuGtt97yqsIOuHvoy8rKvLYdOHCgyz47NzcXBQUFntclJSX4+eefMXbsWADAmDFjkJubi+TkZKSnp3u+Nm/ejI8++sgzbL4jxowZAwCtAvLnn38Op9OJK664osPnGjx4MCZPnoy3334bhYWFrd5fvXo1qqqqcOONN7Z5vEajQWlpqde2n376yfOzxWLBjBkzsG7dOgBAXFwc5s6di5kzZ6K4uBgAWl27RqNBWloazp496/W7Gjx4MJYvX37B6Qg///wzJkyYgMOHD0MQBKSmpuL3v/89hgwZ4vk8IiKizmBPPhERUQ965plnsHv3bhgMBq/t2dnZ+Pzzz5GZmYnExER8/PHHyM/P77LPVSqVWLhwIX7/+9/D6XTi9ddfR1hYGO6++24A7qXoNm/ejHnz5mH+/PkIDw/HF198gQ8++ACLFy++pM8aNGgQZs+ejb/97W8wm83IyspCTk4OVqxYgbFjx2LSpEmXdL7nn38ed999N2677TbcddddyMzMRH19PbZt24bPP/8ct99+O6699to2j73qqquwevVqrF69GpmZmfjmm2+we/duz/sqlQrDhg3DihUrIJfLMXToUOTm5uKTTz7BjBkzALhHOQDuugUDBw5EZmYm/vCHP+D+++/HI488ghtvvNFTRf/QoUNYtGhRu9eSlpYGlUqFxx9/HL/73e+g1+uxc+dO5OTk4K677rqk3wsREVFbGPKJiIh6UFhYGJYsWYKHHnrIa/vixYvhcDjw5z//GTKZDNdffz0eeeQRPP30013yuWlpaZgxYwaWLFmC2tpajB8/Hn/84x89w/Wjo6Px/vvv45VXXsGSJUtgtVqRlJSEpUuX4le/+tUlf97SpUuRmJiIjRs34u2330ZUVBTuuusuLFq0CBLJpQ0kjIuLw4YNG/DOO+/gs88+w1tvvQWFQoEBAwbglVdewfXXX9/usb/97W9RWVmJtWvXwm63Izs7G0uXLsXChQs9+7zwwgt47bXXsG7dOlRUVECn0+FXv/oV/vd//xeAu+f+nnvuwYYNG/Ddd99hx44duPLKK7F27VqsWLECDz/8MORyOYYNG4a///3vGDFiRLvtUSqVWLduHV555RUsXboUJpMJSUlJeOGFF3DLLbdc0u+FiIioLYIoiqKvG0FEREREREREncc5+UREREREREQBgiGfiIiIiIiIKEAw5BMREREREREFCIZ8IiIiIiIiogDBkE9EREREREQUIBjyiYiIiIiIiAIEQz4RERERERFRgGDIJyIiIiIiIgoQDPlEREREREREAYIhn4iIiIiIiChAMOQTERERERERBQiGfCIiIiIiIqIA8f8BdyhbiCzUXlcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of clusters according to BIC: 9\n",
      "Optimal number of clusters according to AIC: 9\n"
     ]
    }
   ],
   "source": [
    "# Define the range of cluster numbers to try (from 1 to 10)\n",
    "n_clusters_range = range(1, 11)\n",
    "\n",
    "# Lists to store BIC and AIC scores for each number of clusters\n",
    "bic_scores = []\n",
    "aic_scores = []\n",
    "\n",
    "# Compute BIC and AIC for each number of clusters\n",
    "for n_clusters in n_clusters_range:\n",
    "    # Fit a Gaussian Mixture Model with the current number of clusters\n",
    "    gmm = GaussianMixture(n_components=n_clusters, random_state=42)\n",
    "    gmm.fit(data_normalized)\n",
    "    \n",
    "    # Append the BIC and AIC scores to their respective lists\n",
    "    bic_scores.append(gmm.bic(data_normalized))\n",
    "    aic_scores.append(gmm.aic(data_normalized))\n",
    "\n",
    "\n",
    "# Create a plot to visualize how BIC and AIC change with the number of clusters\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(n_clusters_range, bic_scores, label='BIC', marker='o')\n",
    "plt.plot(n_clusters_range, aic_scores, label='AIC', marker='o')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Score')\n",
    "plt.title('BIC and AIC for Different Numbers of Clusters')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Find the number of clusters corresponding to the minimum BIC and AIC scores\n",
    "optimal_clusters_bic = n_clusters_range[np.argmin(bic_scores)]\n",
    "optimal_clusters_aic = n_clusters_range[np.argmin(aic_scores)]\n",
    "\n",
    "# Print the optimal number of clusters based on BIC and AIC\n",
    "print(f'Optimal number of clusters according to BIC: {optimal_clusters_bic}')\n",
    "print(f'Optimal number of clusters according to AIC: {optimal_clusters_aic}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Method 2: Likelihood Ratio Test (LRT)**\n",
    "LRT is another method to determine the optimal number of clusters. It compares models with consecutive numbers of clusters to check whether adding an additional cluster significantly improves the fit. The test results in a p-value, which is used to assess the significance of the improvement. We choose the largest number of clusters where the p-value is below the significance threshold (0.05)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of clusters according to LRT: 9\n"
     ]
    }
   ],
   "source": [
    "# Set the maximum number of clusters to test\n",
    "max_clusters = 10\n",
    "lrt_p_values = []\n",
    "\n",
    "# Perform LRT for models with different numbers of clusters\n",
    "for k in range(1, max_clusters):\n",
    "    # Fit GMM with k and k+1 components\n",
    "    gmm_k = GaussianMixture(n_components=k, random_state=42)\n",
    "    gmm_k_next = GaussianMixture(n_components=k + 1, random_state=42)\n",
    "\n",
    "    gmm_k.fit(data_normalized)\n",
    "    gmm_k_next.fit(data_normalized)\n",
    "\n",
    "    # Calculate the log-likelihoods for both models\n",
    "    log_likelihood_k = gmm_k.score(data_normalized) * data_normalized.shape[0]\n",
    "    log_likelihood_k_next = gmm_k_next.score(data_normalized) * data_normalized.shape[0]\n",
    "\n",
    "    # Perform LRT: Calculate the test statistic\n",
    "    lrt_stat = 2 * (log_likelihood_k_next - log_likelihood_k)\n",
    "    # Degrees of freedom = difference in the number of parameters\n",
    "    d = data_normalized.shape[1]\n",
    "    df = (k + 1) * (d + 1) - k * (d + 1)\n",
    "    # Calculate the p-value for the test statistic\n",
    "    p_value = 1 - chi2.cdf(lrt_stat, df)\n",
    "    lrt_p_values.append(p_value)\n",
    "\n",
    "# Find the largest number of clusters where the LRT is significant (p-value < 0.05)\n",
    "significance_level = 0.05\n",
    "optimal_clusters = None\n",
    "\n",
    "for k, p_value in enumerate(lrt_p_values, start=1):\n",
    "    if p_value < significance_level:\n",
    "        optimal_clusters = k + 1  # Adding 1 because LRT compares k vs. k+1 clusters\n",
    "\n",
    "# Print the optimal number of clusters according to LRT\n",
    "print(f'Optimal number of clusters according to LRT: {optimal_clusters}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## **Step 5: Fitting the GMM and Making Predictions**\n",
    "---\n",
    "We fit the GMM using the optimal number of clusters obtained from BIC, AIC, or LRT. The model predicts the cluster assignments for each data point, which are then added to the original dataset. This step allows us to analyze the clustering patterns more effectively.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the GMM model again with the optimal number of clusters determined by LRT\n",
    "n_clusters = 9\n",
    "gmm = GaussianMixture(n_components=n_clusters, random_state=42)\n",
    "\n",
    "# Fit the model on the normalized data\n",
    "gmm.fit(data_normalized)\n",
    "\n",
    "# Predict the clusters for each data point\n",
    "clusters = gmm.predict(data_normalized)\n",
    "\n",
    "# Update the original data with the new cluster labels\n",
    "data['Cluster'] = clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## **Step 6: Visualizing the Clustering Results**\n",
    "---\n",
    "Scatter plots are used to visualize the clusters in two-dimensional space, focusing on spending in categories like fresh products and milk. The colors in the plots represent the different clusters, allowing for easy identification of customer segments with distinct spending behaviors.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot to visualize the final clustering results\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x=data_normalized[:, 1], y=data_normalized[:, 2], hue=clusters, palette='viridis')\n",
    "plt.title('GMM Clustering on Normalized Spending')\n",
    "plt.xlabel('Spending on Fresh Products (normalized)')\n",
    "plt.ylabel('Spending on Milk (normalized)')\n",
    "plt.legend(title='Cluster')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## **Conclusion**\n",
    "---\n",
    "In this exercise, we successfully applied the Gaussian Mixture Model for clustering wholesale customer data. By utilizing statistical methods such as BIC, AIC, and LRT, we determined the optimal number of clusters, providing a robust approach for customer segmentation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## **Refrences**\n",
    "--- \n",
    "* [In Depth: Gaussian Mixture Models](https://jakevdp.github.io/PythonDataScienceHandbook/05.12-gaussian-mixtures.html) \n",
    "* [Understanding Gaussian Mixture Models: A Comprehensive Guide](https://medium.com/@juanc.olamendy/understanding-gaussian-mixture-models-a-comprehensive-guide-df30af59ced7)"
   ]
  }
 ],
 "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": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}