30_intermittent_capstone.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "20bb6ab7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\statsforecast\\core.py:21: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  from tqdm.autonotebook import tqdm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from statsforecast import StatsForecast\n",
    "from statsforecast.models import CrostonOptimized, ADIDA, TSB\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "667b07b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (9,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2d67ee62",
   "metadata": {},
   "outputs": [],
   "source": [
    "def errors(y_true, y_pred):\n",
    "    \n",
    "    # CFE\n",
    "    cfe_all = np.cumsum(y_true - y_pred)\n",
    "    cfe = cfe_all.iloc[-1]\n",
    "    \n",
    "    cfe_max = np.max(cfe_all)\n",
    "    cfe_min = np.min(cfe_all)\n",
    "    \n",
    "    # PIS\n",
    "    pis_all = -np.cumsum(cfe_all)\n",
    "    pis = pis_all.iloc[-1]\n",
    "    \n",
    "    # NOS\n",
    "    nos = len(cfe_all[cfe_all > 0])\n",
    "\n",
    "    errors = {\n",
    "        \"CFE\": cfe,\n",
    "        \"CFE_max\": cfe_max,\n",
    "        \"CFE_min\": cfe_min,\n",
    "        \"PIS\": pis,\n",
    "        \"NOS\": nos\n",
    "    }\n",
    "    \n",
    "    return errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a380cf6e",
   "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>id</th>\n",
       "      <th>parts_id</th>\n",
       "      <th>date</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2674</td>\n",
       "      <td>1998-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2674</td>\n",
       "      <td>1998-02-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2674</td>\n",
       "      <td>1998-03-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2674</td>\n",
       "      <td>1998-04-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2674</td>\n",
       "      <td>1998-05-01</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  parts_id       date  volume\n",
       "0   1      2674 1998-01-01       0\n",
       "1   2      2674 1998-02-01       0\n",
       "2   3      2674 1998-03-01       0\n",
       "3   4      2674 1998-04-01       0\n",
       "4   5      2674 1998-05-01       2"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/car_parts_monthly_sales.csv')\n",
    "\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3878f69a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2674, 2673, 2672, 2671, 2670, 2669, 2668], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['parts_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ce6df3b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parts_ids = df['parts_id'].unique()\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12,9))\n",
    "\n",
    "for i, ax in enumerate(axes.flatten()):\n",
    "    \n",
    "    ax.bar(\n",
    "        x=df[df['parts_id'] == parts_ids[i]]['date'],\n",
    "        height=df[df['parts_id'] == parts_ids[i]]['volume'],\n",
    "        color='lightgray'\n",
    "    )\n",
    "\n",
    "    ax.set_xlabel('Date')\n",
    "    ax.set_ylabel('Volume')\n",
    "    \n",
    "    ax.xaxis.set_major_locator(plt.MaxNLocator(5))\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ad928ae",
   "metadata": {},
   "source": [
    "## Forecasting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dfe545fc",
   "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>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2674</td>\n",
       "      <td>1998-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2674</td>\n",
       "      <td>1998-02-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2674</td>\n",
       "      <td>1998-03-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2674</td>\n",
       "      <td>1998-04-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2674</td>\n",
       "      <td>1998-05-01</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>2668</td>\n",
       "      <td>2001-11-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>2668</td>\n",
       "      <td>2001-12-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>2668</td>\n",
       "      <td>2002-01-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>2668</td>\n",
       "      <td>2002-02-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>2668</td>\n",
       "      <td>2002-03-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>357 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     unique_id         ds  y\n",
       "0         2674 1998-01-01  0\n",
       "1         2674 1998-02-01  0\n",
       "2         2674 1998-03-01  0\n",
       "3         2674 1998-04-01  0\n",
       "4         2674 1998-05-01  2\n",
       "..         ...        ... ..\n",
       "352       2668 2001-11-01  0\n",
       "353       2668 2001-12-01  0\n",
       "354       2668 2002-01-01  1\n",
       "355       2668 2002-02-01  1\n",
       "356       2668 2002-03-01  1\n",
       "\n",
       "[357 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_df = df.drop(['id'], axis=1)\n",
    "model_df.rename({\"parts_id\": \"unique_id\", \"date\": \"ds\",\n",
    "                \"volume\": \"y\"}, axis=1, inplace=True)\n",
    "\n",
    "model_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bf965ec1",
   "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>ds</th>\n",
       "      <th>cutoff</th>\n",
       "      <th>y</th>\n",
       "      <th>TSB</th>\n",
       "      <th>ADIDA</th>\n",
       "      <th>CrostonOptimized</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2668</th>\n",
       "      <td>2001-04-01</td>\n",
       "      <td>2001-03-01</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.215414</td>\n",
       "      <td>0.707330</td>\n",
       "      <td>0.781058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2668</th>\n",
       "      <td>2001-05-01</td>\n",
       "      <td>2001-03-01</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.215414</td>\n",
       "      <td>0.707330</td>\n",
       "      <td>0.781058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2668</th>\n",
       "      <td>2001-06-01</td>\n",
       "      <td>2001-03-01</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.215414</td>\n",
       "      <td>0.707330</td>\n",
       "      <td>0.781058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2668</th>\n",
       "      <td>2001-07-01</td>\n",
       "      <td>2001-03-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.215414</td>\n",
       "      <td>0.707330</td>\n",
       "      <td>0.781058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2668</th>\n",
       "      <td>2001-08-01</td>\n",
       "      <td>2001-07-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.543623</td>\n",
       "      <td>1.877855</td>\n",
       "      <td>2.074792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2674</th>\n",
       "      <td>2001-11-01</td>\n",
       "      <td>2001-07-01</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.204351</td>\n",
       "      <td>0.642959</td>\n",
       "      <td>0.986814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2674</th>\n",
       "      <td>2001-12-01</td>\n",
       "      <td>2001-11-01</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.084467</td>\n",
       "      <td>0.857275</td>\n",
       "      <td>0.751457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2674</th>\n",
       "      <td>2002-01-01</td>\n",
       "      <td>2001-11-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.084467</td>\n",
       "      <td>0.857275</td>\n",
       "      <td>0.751457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2674</th>\n",
       "      <td>2002-02-01</td>\n",
       "      <td>2001-11-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.084467</td>\n",
       "      <td>0.857275</td>\n",
       "      <td>0.751457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2674</th>\n",
       "      <td>2002-03-01</td>\n",
       "      <td>2001-11-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.084467</td>\n",
       "      <td>0.857275</td>\n",
       "      <td>0.751457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ds     cutoff    y       TSB     ADIDA  CrostonOptimized\n",
       "unique_id                                                                 \n",
       "2668      2001-04-01 2001-03-01  2.0  1.215414  0.707330          0.781058\n",
       "2668      2001-05-01 2001-03-01  2.0  1.215414  0.707330          0.781058\n",
       "2668      2001-06-01 2001-03-01  6.0  1.215414  0.707330          0.781058\n",
       "2668      2001-07-01 2001-03-01  0.0  1.215414  0.707330          0.781058\n",
       "2668      2001-08-01 2001-07-01  1.0  1.543623  1.877855          2.074792\n",
       "...              ...        ...  ...       ...       ...               ...\n",
       "2674      2001-11-01 2001-07-01  2.0  1.204351  0.642959          0.986814\n",
       "2674      2001-12-01 2001-11-01  2.0  1.084467  0.857275          0.751457\n",
       "2674      2002-01-01 2001-11-01  0.0  1.084467  0.857275          0.751457\n",
       "2674      2002-02-01 2001-11-01  1.0  1.084467  0.857275          0.751457\n",
       "2674      2002-03-01 2001-11-01  1.0  1.084467  0.857275          0.751457\n",
       "\n",
       "[84 rows x 6 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = [TSB(0.1, 0.1), ADIDA(), CrostonOptimized()]\n",
    "\n",
    "sf = StatsForecast(\n",
    "    df=model_df,\n",
    "    models=models,\n",
    "    freq='MS',\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "cv_df = sf.cross_validation(\n",
    "    df=model_df,\n",
    "    h=4,\n",
    "    step_size=4,\n",
    "    n_windows=3\n",
    ")\n",
    "\n",
    "cv_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df17fab6",
   "metadata": {},
   "source": [
    "## Evaluation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3f05bc59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABKUAAAN5CAYAAADZ5oiRAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC9FklEQVR4nOzdd3gU9drG8XuzSTaFFAKEJCRA6BCKCFKVIkVQEGwoNuwiKCpW9BxFVMJRwYKK4IsgNvSAKDYQpImAQCBKO4gaIEhiBCGBQPq8f6wsLClsIJndTb6f65rrZH/7m5lndiJ5zr2zsxbDMAwBAAAAAAAAJvJxdwEAAAAAAACofgilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJjO190FVLaioiLt379fISEhslgs7i4HAAB4OcMwdOTIEcXExMjHp2q8v0e/BAAAKpKr/VKVD6X279+vuLg4d5cBAACqmNTUVMXGxrq7jApBvwQAACrDmfqlKh9KhYSESLK/EKGhoW6uBgAAeLusrCzFxcU5eoyqgH4JAABUJFf7pSofSp24BD00NJQmCwAAVJiq9DE3+iUAAFAZztQvVY0bIQAAAAAAAMCrEEoBAAAAAADAdIRSAAAAAAAAMF2Vv6cUAACerrCwUPn5+e4uA//w8/OT1Wp1dxkAAOAU9EuepaL6JUIpAADcxDAMpaen6/Dhw+4uBacJDw9XVFRUlbqZOQAA3oh+yXNVRL9EKAUAgJucaLAiIyMVFBREAOIBDMPQsWPHlJGRIUmKjo52c0UAAFRv9EuepyL7JUIpAADcoLCw0NFg1apVy93l4BSBgYGSpIyMDEVGRvJRPgAA3IR+yXNVVL/Ejc4BAHCDE/dECAoKcnMlKMmJ88K9KwAAcB/6Jc9WEf0SoRQAAG7EJeieifMCAIDn4O+yZ6qI80IoBQAAAAAAANMRSgEAAAAAAMB0hFIAAOCspKen67777lOjRo1ks9kUFxenwYMH67vvvqu0fTZs2FCvvPJKpW0fAACgItEvlc3jQ6k//vhDN954o2rVqqWgoCCdd955SkpKcndZAABUa7t371aHDh20bNkyvfDCC9qyZYsWLVqk3r17a/To0SWuw03DK8/48eNlsViclqioKHeXBQBAtUa/dGYeHUodOnRI3bt3l5+fn7755htt375dkydPVnh4uLtLAwCgWhs1apQsFovWr1+vq6++Ws2aNVNCQoLGjh2rdevWSbLf/PKtt97SkCFDFBwcrOeee06SNG3aNDVu3Fj+/v5q3ry53nvvPadtjx8/XvXr15fNZlNMTIzGjBkjSerVq5f27NmjBx980BG8nDB//nwlJCTIZrOpYcOGmjx5stM2GzZsqIkTJ+q2225TSEiI6tevrxkzZlTmS2S6hIQEpaWlOZYtW7a4uyQAAKo1+iUXGB7sscceMy688MJyrZOTk2NkZmY6ltTUVEOSkZmZWUlVAgBQfsePHze2b99uHD9+vNhz2bn5pS7H8woqfG55HTx40LBYLMbEiRPLnCfJiIyMNGbOnGn89ttvxu7du41PP/3U8PPzM9544w1j586dxuTJkw2r1WosW7bMMAzD+O9//2uEhoYaX3/9tbFnzx7jxx9/NGbMmOHYb2xsrDFhwgQjLS3NSEtLMwzDMDZu3Gj4+PgYEyZMMHbu3GnMmjXLCAwMNGbNmuWopUGDBkZERITxxhtvGLt27TISExMNHx8fY8eOHSXWXtb5yczM9Lje4umnnzbatWvn8nz6JQCAN6Bfqvr9km/lRl7nZuHChbrkkkt0zTXXaOXKlapXr55GjRqlO++8s9R1EhMT9cwzz5hYJQAAFavVU4tLfa538zqadWsnx+MOzy7V8fzCEud2jo/Qx3d3dTy+8D/L9Xd2XrF5uyddVq76fv31VxmGoRYtWpxx7vXXX6/bbrvN6fEtt9yiUaNGSZLjncKXXnpJvXv31t69exUVFaW+ffvKz89P9evXV6dO9uONiIiQ1WpVSEiI00fTpkyZoj59+ujf//63JKlZs2bavn27XnzxRd1yyy2OeZdeeqljv4899phefvllrVixwqXj8Aa7du1STEyMbDabOnfurIkTJ6pRo0YlzqVfAgB4O/qlqtEvefTH937//XdNmzZNTZs21eLFizVy5EiNGTNGc+bMKXWdcePGKTMz07GkpqaaWDEAAFWf/U09OV0OXpqOHTs6Pd6xY4e6d+/uNNa9e3ft2LFDknTNNdfo+PHjatSoke68804tWLBABQUFZe6jtG3u2rVLhYUnG9C2bds6fj5xz6WMjIwzHoM36Ny5s+bMmaPFixfr7bffVnp6urp166aDBw+WOJ9+CQCAykW/5BqPvlKqqKhIHTt21MSJEyVJ7du317Zt2zRt2jTdfPPNJa5js9lks9nMLBMAgAq1fcIlpT7nc1pjk/Tvvi7PXf1Y73Mr7B9NmzaVxWLRjh07NHTo0DLnBgcHFxs7vTkzDMMxFhcXp507d2rJkiVaunSpRo0apRdffFErV66Un59fifs4df1Tx053+voWi0VFRUVl1u8tBg4c6Pi5TZs26tq1qxo3bqx3331XY8eOLTaffgkA4O3ol6pGv+TRV0pFR0erVatWTmMtW7bU3r173VQRAACVL8jft9QlwM9a4XPLKyIiQpdcconeeOMNZWdnF3v+8OHDpa7bsmVLrV692mlszZo1atmypeNxYGCgLr/8cr322mtasWKF1q5d67hpt7+/v9O7eZLUqlWrErfZrFkzWa3Or0F1ERwcrDZt2mjXrl3uLgUAgEpBv1Q1+iWPvlKqe/fu2rlzp9PYL7/8ogYNGripIgAAIElvvvmmunXrpk6dOmnChAlq27atCgoKtGTJEk2bNs1xefnpHnnkEQ0bNkznn3+++vTpoy+++EKffvqpli5dKkmaPXu2CgsL1blzZwUFBem9995TYGCg429/w4YNtWrVKl133XWy2WyqXbu2HnroIV1wwQV69tlnde2112rt2rV6/fXX9eabb5r2enia3Nxc7dixQxdddJG7SwEAoNqiXzozj75S6sEHH9S6des0ceJE/frrr/rwww81Y8YMjR492t2lAQBQrcXHx2vTpk3q3bu3HnroIbVu3Vr9+vXTd999p2nTppW63tChQ/Xqq6/qxRdfVEJCgqZPn65Zs2apV69ekqTw8HC9/fbb6t69u9q2bavvvvtOX3zxhWrVqiVJmjBhgnbv3q3GjRurTp06kqTzzz9fn3zyiebOnavWrVvrqaee0oQJE5xu2lnVPfzww1q5cqVSUlL0448/6uqrr1ZWVpZGjBjh7tIAAKi26JfOzGKU9CFCD/Lll19q3Lhx2rVrl+Lj4zV27Ngyv33vdFlZWQoLC1NmZqZCQ0MrsVIAAFyXk5OjlJQUxcfHKyAgwN3l4DRlnR9P7C2uu+46rVq1SgcOHFCdOnXUpUsXPfvss8Vug1AaTzwmAADolzxbRfRLHv3xPUkaNGiQBg0a5O4yAAAAPNbcuXPdXQIAAEC5efTH9wAAAAAAAFA1EUoBAAAAAADAdIRSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADTEUoBAAAAAADAdIRSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADTEUoBAICzsmbNGlmtVg0YMMBpfPfu3bJYLI4lJCRECQkJGj16tHbt2uU0d/bs2QoPD3d6fGI9q9WqmjVrqnPnzpowYYIyMzNLrGPixImyWq2aNGlShR8jAADAuaBfKhuhFAAAOCvvvPOO7rvvPq1evVp79+4t9vzSpUuVlpamn376SRMnTtSOHTvUrl07fffdd2VuNzQ0VGlpadq3b5/WrFmju+66S3PmzNF5552n/fv3F5s/a9YsPfroo3rnnXcq7NgAAAAqAv1S2QilAABAuWVnZ+uTTz7RPffco0GDBmn27NnF5tSqVUtRUVFq1KiRhgwZoqVLl6pz5866/fbbVVhYWOq2LRaLoqKiFB0drZYtW+r222/XmjVrdPToUT366KNOc1euXKnjx49rwoQJys7O1qpVqyr6UAEAAM4K/dKZEUoBAOBp8rLti2GcHCvIs48V5JY8t6jo5Fhhvn0sP8e1uWfh448/VvPmzdW8eXPdeOONmjVrloxT6y2Bj4+P7r//fu3Zs0dJSUnl2l9kZKRuuOEGLVy40KlBmzlzpoYPHy4/Pz8NHz5cM2fOPKvjAQAAXoZ+qRhv7JcIpQAA8DQTY+zLsYMnx9a8ah/7+mHnuS82sY9npp4cW/+2fWzhvc5zX2ljHz+w8+RY8gdnVeLMmTN14403SpIGDBigo0ePnvEyc0lq0aKFJPt9FMqrRYsWOnLkiA4etL8uWVlZmj9/vqOOG2+8UfPmzVNWVla5tw0AALwM/VKp63pTv0QoBQAAymXnzp1av369rrvuOkmSr6+vrr32WpfuUXDi3UGLxVLu/Z6+7ocffqhGjRqpXbt2kqTzzjtPjRo10ty5c8u9bQAAgIpEv+QaX3cXAAAATvPEPzen9As6OdbtfqnLKMnntD/dj/xq/1/fwJNjne6UOoyQLFbnuQ9sKT73vBvKXd7MmTNVUFCgevXqOcYMw5Cfn58OHTpU5ro7duyQJMXHx5d7vzt27FBoaKhq1aolyX7j0G3btsnX9+RrUlRUpJkzZ+quu+4q9/YBAIAXoV8qdV1v6pcIpQAA8DT+wcXHfP0l+bs21+pnX1ydWw4FBQWaM2eOJk+erP79+zs9d9VVV+mDDz7QoEGDSly3qKhIr732muLj49W+ffty7TcjI0Mffvihhg4dKh8fH23ZskUbN27UihUrFBER4Zh3+PBh9ejRQ1u3blXr1q3LtQ8AAOBF6JeK8cZ+iVAKAAC47Msvv9ShQ4d0++23KywszOm5q6++WjNnznQ0WQcPHlR6erqOHTumrVu36pVXXtH69ev11VdfyWq1lrR5SfZ3EdPT02UYhg4fPqy1a9dq4sSJCgsL06RJkyTZ333s1KmTevToUWz9rl27aubMmXr55Zcr8MgBAABcQ7/kOu4pBQAAXDZz5kz17du3WIMl2d/5S05O1t9//y1J6tu3r6Kjo9WmTRs9/vjjatmypX7++Wf17t27zH1kZWUpOjpa9erVU9euXTV9+nSNGDFCmzdvVnR0tPLy8vT+++/rqquuKnH9q666Su+//77y8vLO/YABAADKiX7JdRbjTN9H6OWysrIUFhamzMxMhYaGurscAAAkSTk5OUpJSVF8fLwCAgLcXQ5OU9b5qYq9RVU8JgCA96Nf8mwV0S9xpRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAl1ksljKXW265RZK0fPly9e7dWxEREQoKClLTpk01YsQIFRQUSJJWrFjhtF5gYKASEhI0Y8YMNx4dAADAuaNfcp2vuwsAAADeIy0tzfHzxx9/rKeeeko7d+50jAUGBmrbtm0aOHCgxowZo6lTpyowMFC7du3SvHnzVFRU5LS9nTt3KjQ0VMePH9cXX3yhe+65R40bN1afPn1MOyYAAICKRL/kOkIpAADgsqioKMfPYWFhslgsTmOSNGvWLEVHR+uFF15wjDVu3FgDBgwotr3IyEiFh4dLksaMGaNXX31VmzZtqhJNFgAAqJ7ol1xHKAUAgIc5ln+s3Ov4W/3l62P/s15QVKC8wjz5WHwU4Btwxu0G+QWdXaGliIqKUlpamlatWqUePXq4tI5hGFq8eLFSU1PVuXPnCq0HAABUPfRLVaNfIpQCAMDDdP6w/E3GSz1f0iUNL5Ekfbf3Oz288mF1rNtRswbMcswZMH+ADuUeKrbulhFbzr7YElxzzTVavHixevbsqaioKHXp0kV9+vTRzTffrNDQUKe5sbGxkqTc3FwVFRVpwoQJLjdmAACg+qJfqhr9Ejc6BwAAFcpqtWrWrFnat2+fXnjhBcXExOj5559XQkKC0z0WJOn7779XcnKykpOT9X//93+aOHGipk2b5qbKAQAAzEG/ZGcxDMNwdxGVKSsrS2FhYcrMzCyWNgIA4C45OTlKSUlRfHy8AgICnJ7zlsvRZ8+erQceeECHDx8+49xDhw6pWbNmGjVqlJ555hmtWLFCvXv31qFDhxz3SJCkkSNH6ssvv9S+ffvOqqaKUtb5qYq9RVU8JgCA96Nfqvr9Eh/fAwDAw5zrPQt8fXwdDVdFbvdc1KxZU9HR0crOzi5zntVq1fHjx02qCgAAeCv6parRLxFKAQCACjV9+nQlJyfriiuuUOPGjZWTk6M5c+Zo27Ztmjp1qtPcjIwM5eTkKDc3V+vXr9d7772nq6++2k2VAwAAmIN+yY5QCgAAVKhOnTpp9erVGjlypPbv368aNWooISFBn332mXr27Ok0t3nz5pIkX19fxcXF6e6779b48ePdUDUAAIB56JfsuKcUAABuUNZn8OF+3FMKAAD3o1/ybBXRL/HtewAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAuFEV/74Rr8V5AQDAc/B32TNVxHkhlAIAwA38/PwkSceOHXNzJSjJifNy4jwBAADz0S95torol3wrqhgAAOA6q9Wq8PBwZWRkSJKCgoJksVjcXBUMw9CxY8eUkZGh8PBwWa1Wd5cEAEC1Rb/kmSqyXyKUAgDATaKioiTJ0WjBc4SHhzvODwAAcB/6Jc9VEf0SoRQAAG5isVgUHR2tyMhI5efnu7sc/MPPz48rpAAA8BD0S56povoljw6lxo8fr2eeecZprG7dukpPT3dTRQAAVDyr1UoIAgAAUAb6parJo0MpSUpISNDSpUsdj/klBAAAAAAA8H4eH0r5+vqW6zOKubm5ys3NdTzOysqqjLIAAAAAAABwDnzcXcCZ7Nq1SzExMYqPj9d1112n33//vcz5iYmJCgsLcyxxcXEmVQoAAAAAAABXeXQo1blzZ82ZM0eLFy/W22+/rfT0dHXr1k0HDx4sdZ1x48YpMzPTsaSmpppYMQAAAAAAAFzh0R/fGzhwoOPnNm3aqGvXrmrcuLHeffddjR07tsR1bDabbDabWSUCAAAAAADgLHj0lVKnCw4OVps2bbRr1y53lwIAAAAAAIBz4FWhVG5urnbs2KHo6Gh3lwIAAAAAAIBz4NGh1MMPP6yVK1cqJSVFP/74o66++mplZWVpxIgR7i4NAAAAAAAA58Cj7ym1b98+DR8+XAcOHFCdOnXUpUsXrVu3Tg0aNHB3aQAAAAAAADgHHh1KzZ07190lAAAAAAAAoBJ49Mf3AAAAAAAAUDURSgEAAFQhiYmJslgseuCBB9xdCgAAQJkIpQAAAKqIDRs2aMaMGWrbtq27SwEAADgjQikAAIAq4OjRo7rhhhv09ttvq2bNmmXOzc3NVVZWltMCAABgNkIpAACAKmD06NG67LLL1Ldv3zPOTUxMVFhYmGOJi4szoUIAAABnhFIAAABebu7cudq0aZMSExNdmj9u3DhlZmY6ltTU1EquEAAAoDhfdxcAAACAs5eamqr7779f3377rQICAlxax2azyWazVXJlAAAAZSOUAgAA8GJJSUnKyMhQhw4dHGOFhYVatWqVXn/9deXm5spqtbqxQgAAgJIRSgEAAHixPn36aMuWLU5jt956q1q0aKHHHnuMQAoAAHgsQikAAAAvFhISotatWzuNBQcHq1atWsXGAQAAPAk3OgcAAAAAAIDpuFIKAACgilmxYoW7SwAAADgjrpQCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDqvCqUSExNlsVj0wAMPuLsUAAAAAAAAnAOvCaU2bNigGTNmqG3btu4uBQAAAAAAAOfIK0Kpo0eP6oYbbtDbb7+tmjVrljk3NzdXWVlZTgsAAAAAAAA8i1eEUqNHj9Zll12mvn37nnFuYmKiwsLCHEtcXJwJFQIAAAAAAKA8PD6Umjt3rjZt2qTExESX5o8bN06ZmZmOJTU1tZIrBAAAAAAAQHn5uruAsqSmpur+++/Xt99+q4CAAJfWsdlsstlslVwZAAAAAAAAzoVHh1JJSUnKyMhQhw4dHGOFhYVatWqVXn/9deXm5spqtbqxQgAAAAAAAJwNjw6l+vTpoy1btjiN3XrrrWrRooUee+wxAikAAAAAAAAv5dGhVEhIiFq3bu00FhwcrFq1ahUbBwAAAAAAgPfw+BudAwAAAAAAoOrx6CulSrJixQp3lwAAAAAAAIBzxJVSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADTEUoBAAAAAADAdIRSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADTEUoBAAAAAADAdIRSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADTEUoBAAAAAADAdIRSAAAAAAAAMB2hFAAAgJebNm2a2rZtq9DQUIWGhqpr16765ptv3F0WAABAmQilAAAAvFxsbKwmTZqkjRs3auPGjbr44os1ZMgQbdu2zd2lAQAAlMrX3QUAAADg3AwePNjp8fPPP69p06Zp3bp1SkhIcFNVAAAAZSOUAgAAqEIKCwv13//+V9nZ2eratWuJc3Jzc5Wbm+t4nJWVZVZ5AAAADnx8DwAAoArYsmWLatSoIZvNppEjR2rBggVq1apViXMTExMVFhbmWOLi4kyuFnC/rVu3ursEVDDOKeA6T/nvhVAKAACgCmjevLmSk5O1bt063XPPPRoxYoS2b99e4txx48YpMzPTsaSmpppcLQAAAB/fAwAAqBL8/f3VpEkTSVLHjh21YcMGvfrqq5o+fXqxuTabTTabzewSAQAAnHClFAAAQBVkGIbTfaMAAAA8DVdKAQAAeLknnnhCAwcOVFxcnI4cOaK5c+dqxYoVWrRokbtLAwAAKFWlXSn1/fff68Ybb1TXrl31xx9/SJLee+89rV69urJ2CQAA4FUqql/6888/ddNNN6l58+bq06ePfvzxRy1atEj9+vWrjLIBAAAqRKWEUvPnz9cll1yiwMBAbd682XHp+JEjRzRx4sTK2CUAAIBXqch+aebMmdq9e7dyc3OVkZGhpUuXEkgBAACPVymh1HPPPae33npLb7/9tvz8/Bzj3bp106ZNmypjlwAAAF6FfgkAAFR3lRJK7dy5Uz169Cg2HhoaqsOHD1fGLgEAALwK/RIAAKjuKiWUio6O1q+//lpsfPXq1WrUqFFl7BIAAMCr0C8BAIDqrlJCqbvvvlv333+/fvzxR1ksFu3fv18ffPCBHn74YY0aNaoydgkAAOBV6JcAAEB151sZG3300UeVmZmp3r17KycnRz169JDNZtPDDz+se++9tzJ2CQAA4FXolwAAQHVXKaGUJD3//PN68skntX37dhUVFalVq1aqUaNGZe0OAADA69AvAQCA6qzSQilJCgoKUseOHStzFwAAAF6NfgkAAFRXlRJK5eTkaOrUqVq+fLkyMjJUVFTk9DxfcwwAAKo7+iUAAFDdVUooddttt2nJkiW6+uqr1alTJ1kslsrYDQAAgNeiXwIAANVdpYRSX331lb7++mt17969MjYPAADg9eiXAABAdedTGRutV6+eQkJCKmPTAAAAVQL9EgAAqO4qJZSaPHmyHnvsMe3Zs6cyNg8AAOD16JcAAEB1Vykf3+vYsaNycnLUqFEjBQUFyc/Pz+n5v//+uzJ2CwAA4DXolwAAQHVXKaHU8OHD9ccff2jixImqW7cuN+4EAAA4Df0SAACo7iollFqzZo3Wrl2rdu3aVcbmAQAAvB79EgAAqO4q5Z5SLVq00PHjxytj0wAAAFUC/RIAAKjuKiWUmjRpkh566CGtWLFCBw8eVFZWltMCAABQ3dEvAQCA6q5SPr43YMAASVKfPn2cxg3DkMViUWFhYWXsFgAAwGvQLwEAgOquUkKp5cuXV8ZmAQAAqgz6JQAAUN1VSijVs2fPytgsAABAlUG/BAAAqrtKCaVWrVpV5vM9evSojN0CAAB4DfolAABQ3VVKKNWrV69iYxaLxfEz90gAAADVHf0SAACo7irl2/cOHTrktGRkZGjRokW64IIL9O2331bGLgEAALwK/RIAAKjuKuVKqbCwsGJj/fr1k81m04MPPqikpKTK2C0AAIDXoF8CAADVXaVcKVWaOnXqaOfOnWbuEgAAwKvQLwEAgOqiUq6U+vnnn50eG4ahtLQ0TZo0Se3atauMXQIAAHgV+iUAAFDdVUoodd5558liscgwDKfxLl266J133nF5O9OmTdO0adO0e/duSVJCQoKeeuopDRw4sCLLBQAAMF1F9UsAAADeqlJCqZSUFKfHPj4+qlOnjgICAsq1ndjYWE2aNElNmjSRJL377rsaMmSINm/erISEhAqrFwAAwGwV1S8BAAB4q0oJpRo0aFAh2xk8eLDT4+eff17Tpk3TunXrSg2lcnNzlZub63iclZVVIbUAAABUpIrqlwAAALxVhYVSr732mstzx4wZU+7tFxYW6r///a+ys7PVtWvXUuclJibqmWeeKff2AW+wdetWtW7d2t1lnJOqcAwAcLYqu18CAADwJhUWSr388ssuzbNYLOVqsrZs2aKuXbsqJydHNWrU0IIFC9SqVatS548bN05jx451PM7KylJcXJzL+wMAAKgsldUvAQAAeKMKC6VOvy9CRWnevLmSk5N1+PBhzZ8/XyNGjNDKlStLDaZsNptsNlul1AIAAHAuKqtfAgAA8EaVck+pU534RhmLxXJW6/v7+ztudN6xY0dt2LBBr776qqZPn15hNQIAALjTufZLAAAA3sinsjY8Z84ctWnTRoGBgQoMDFTbtm313nvvnfN2DcNwupE5AACAt6qsfgkAAMAbVMqVUlOmTNG///1v3XvvverevbsMw9APP/ygkSNH6sCBA3rwwQdd2s4TTzyhgQMHKi4uTkeOHNHcuXO1YsUKLVq0qDLKBgAAME1F9UsAAADeqlJCqalTp2ratGm6+eabHWNDhgxRQkKCxo8f73KT9eeff+qmm25SWlqawsLC1LZtWy1atEj9+vWrjLIBAABMU1H9EgAAgLeqlFAqLS1N3bp1KzberVs3paWlubydmTNnVmRZAAAAHqOi+iUAAABvVSn3lGrSpIk++eSTYuMff/yxmjZtWhm7BAAA8Cr0SwAAoLqr0CulkpOTdd5552nChAkaNmyYVq1ape7du8tisWj16tX67rvvSmy+AAAAqgv6JQAAALsKvVLq/PPPV4cOHfTnn39q/fr1ql27tj777DN9+umnql27ttavX68rrriiIncJAADgVeiXAAAA7Cr0SqkffvhB77zzjh5//HHl5+fryiuv1IsvvqiLL764IncDAADgteiXAAAA7Cr0SqmuXbvq7bffVnp6uqZNm6Z9+/apX79+aty4sZ5//nnt27evIncHAADgdeiXAAAA7CrlRueBgYEaMWKEVqxYoV9++UXDhw/X9OnTFR8fr0svvbQydgkAAOBV6JcAAEB1Vymh1KkaN26sxx9/XE8++aRCQ0O1ePHiyt4lAACAV6FfAgAA1VGF3lPqdCtXrtQ777yj+fPny2q1atiwYbr99tsrc5cAAABehX4JAABUVxUeSqWmpmr27NmaPXu2UlJS1K1bN02dOlXDhg1TcHBwRe8OAADA69AvAQAAVHAo1a9fPy1fvlx16tTRzTffrNtuu03NmzevyF0AAAB4NfolAAAAuwoNpQIDAzV//nwNGjRIVqu1IjcNAABQJdAvAQAA2FVoKLVw4cKK3BwAAECVQ78EAABgV+nfvgcAAAAAAACcjlAKAAAAAAAApiOUAgAAAAAAgOkIpQAAAAAAAGA6QikAAAAAAACYjlAKAAAAAAAApiOUAgAAAAAAgOkIpQAAAAAAAGA6QikAAAAAAACYjlAKAAAAAAAApiOUAgAAAAAAgOkIpQAAAAAAAGA6QikAAAAAAACYjlAKAADAiyUmJuqCCy5QSEiIIiMjNXToUO3cudPdZQEAAJwRoRQAAIAXW7lypUaPHq1169ZpyZIlKigoUP/+/ZWdne3u0gAAAMrk6+4CAAAAcPYWLVrk9HjWrFmKjIxUUlKSevTo4aaqAAAAzoxQCgAAoArJzMyUJEVERJQ6Jzc3V7m5uY7HWVlZlV4XAADA6fj4HgAAQBVhGIbGjh2rCy+8UK1bty51XmJiosLCwhxLXFyciVVWbVu3bnV3CQA8EP82ACUjlAIAAKgi7r33Xv3888/66KOPypw3btw4ZWZmOpbU1FSTKgQAADiJj+8BAABUAffdd58WLlyoVatWKTY2tsy5NptNNpvNpMoAAABKRigFAADgxQzD0H333acFCxZoxYoVio+Pd3dJAAAALiGUAgAA8GKjR4/Whx9+qM8//1whISFKT0+XJIWFhSkwMNDN1QEAAJSOe0oBAAB4sWnTpikzM1O9evVSdHS0Y/n444/dXRoAAECZuFIKAADAixmG4e4SAAAAzgpXSgEAAAAAAMB0hFIAAAAAAAAwHaEUAAAAAAAATEcoBQAAAAAAANMRSgEAAAAAAMB0hFIAAAAAAAAwHaEUAAAAAAAATEcoBQAAAAAAANMRSgEAAAAAAMB0hFIAAAAAAAAwHaEUAAAAAAAATEcoBQAAAAAAANMRSgEAAAAAAMB0hFIAAAAAAAAwnUeHUomJibrgggsUEhKiyMhIDR06VDt37nR3WQAAAAAAADhHHh1KrVy5UqNHj9a6deu0ZMkSFRQUqH///srOznZ3aQAAAAAAADgHvu4uoCyLFi1yejxr1ixFRkYqKSlJPXr0KHGd3Nxc5ebmOh5nZWVVao0AAAAAAAAoP4++Uup0mZmZkqSIiIhS5yQmJiosLMyxxMXFmVUe4DG2bt3q7hKAYvi9BAAAAHAqrwmlDMPQ2LFjdeGFF6p169alzhs3bpwyMzMdS2pqqolVAgAAAAAAwBUe/fG9U9177736+eeftXr16jLn2Ww22Ww2k6oCAAAAAADA2fCKUOq+++7TwoULtWrVKsXGxrq7HAAAAAAAAJwjjw6lDMPQfffdpwULFmjFihWKj493d0kAAAAAAACoAB4dSo0ePVoffvihPv/8c4WEhCg9PV2SFBYWpsDAQDdXBwAAAAAAgLPl0Tc6nzZtmjIzM9WrVy9FR0c7lo8//tjdpQEAAAAAAOAcePSVUoZhuLsEAAAAAAAAVAKPvlIKAAAAAAAAVROhFAAAAAAAAExHKAUAAAAAAADTEUoBAAAAAADAdIRSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADTEUoBAAAAAADAdIRSAAAAAAAAMB2hFAAAAAAAAExHKAUAAAAAAADT+bq7AAAAAAAA4P0yczO17eC2cq/XsW5H+Vv9JUm/Z/6u9Ox0xdaIVf3Q+pKkY/nHlPxXcqnrt4hooYiAiLOqGe5FKAUAAAAAAM7Zr4d/1d1L7i73esuHLVftwNqSpLn/m6uP/veR7mp7l+5rf58kKf1YepnbrVejnuZfPl/BfsFnV7ib5RXm6XjB8VKf9/XxdTq2zNxMSVKIf4h8LPYPwB3LP6b8onyX95lTmHOW1VYsQikAAAAAAHDOAn0D1bxm83KvZ7VYHT9HBkWqec3mjpBKkvx9/Evd7v6j+/XH0T/02qbXNK7zuPIX7WY7/96pEYtGKDs/u9Q5/Rr005ReUxyPL5p7kQwZTmHey0kva+7OuS7vd2DkQHVs1/HsC68ghFIAAAAAAOCctarVSvMun3dO27ijzR26o80dTmOxIbGlbnft/rW6a8ld+uh/H2lA/AC1j2x/Tvs3U15hnsatHldmIOUka7907G/nsaJCKWNH8fEzMYrKN7+SEEoBAAAAAACv1DWmq4Y2GarPfv1MT/3wlOZdPk82q83dZblk2k/TtOvQLtUsLNT8+OtVs+fj9ieOZEgvt5RkkZ46IIss9vHVr0jrp2vTRQ9JvZ+wX2GWd1R6q7sel/TYuD8kvwD73KXPSGtekzrfI13ynH2sqEh6ro4kaWfCEDMPtVR8+x4AAAAAAPBaD3d8WLUDa2t31m5N/2m6u8txSXJGst7Z+o4k6akDf6vOru/k6+NrX6x+8g2OlG+NSPn6+Mrq88/HG201pOBI+dpC5OvjK4vFIskiBUfKGhx5cn0fX/naQu3bCAh1Hg+OlG9wpHx8rKUXZyKulAIAAAAAAF4rzBamJzs/qQdXPKivU77WXW3vUoBvgLvLKlV+Yb7+9cO/VGQUaVDDgerb+0rplPtqqUYd6ZFdxVfs85R9OZWtRslzez5iX07l4+OYW7h16zkeRcUglAIAAAAAAF6tb4O+eqrrUxrYcKBHB1KS5Gf10/3n368ZP8/Q412elGxh7i7JbQilAAAAAACA17um2TXuLsFl/Rr0U9/6ff/5CF71RSgFAAAAAACqDMMwtODXBTo/8nw1DGvo7nIcjuYdVU5hjmoH1pYkWX5ZpGOpP2udXyf94R9f4jqD2saoZrC/JOmn1MP6ed/hUrc/oHW06oTYb/K+bX+mNu05VOrcOJ+CszyKikUoBQAAAAAAzllRkaGCIqPU560+Fll9LC7N9bFIvlb7d7MZhqH8wtLn+lktTlccTd08VW9veVsd6nbQO5e8Ix+LZ3zH24sbX9Syvcv0/IXPq0dsD+mnjxS0/XMl5Q/TG4VDS1zngvgIRyi1YudfennpL6Vuv3W9MEcotebXg3r+6x2lzn2+b+TZH0gFIpQCAAAAAADnbOOeQxo2fW2pzz86oLlG9WoiSdq2P0uDX19d6tz7Lm6ih/o3lyT99le2+k5ZWercazrE6sVr2kmSCosMxVh7Kcx/nnrG9pJhGJKbPyFnGIaSUv/UipRNOpyfqYxMSbGSmg1QWmaO0o501qV1o0pct4btZGzTJLKGLm1T8jxJCg/yd/wcXzu4zLlhNs/42CChFAAALkg9kqrle5eXe73rW14vXx/7n9vVf6zW74d/V4eoDkqolSBJ+uvYX/om5ZsS17VYLOper7sahTU6+8IBAACquAa1ghw/7/37mB7+KFWyPKj/bA/QvDpr1CSyhprUqaHQQD+dX7+m2sTabyx+4Giuvvo5rdTtto0NU/v6NSVJh4/l6fPk/aXObRUTqgsaRkiSjuYWaH7SPklSyoFsfbstXfszcyTdIWvwb9oXGyUlSDrvekW1G64pLt5X6rK20bqsbbRLc/u2qqu+reqW+vxWvn0PAADv8dvh3/TixhfLvd61La6V7z9/br/6/St9+fuXerjjw45Qan/2/jK3W3trbS0culAh/iFnVzgAAIBJzq8frp/H9y/1eX/ryY/RtYoJdXlufO3gUucaRZLVejLUOZpToBZRIfr9r2zl5Bdp2/4sbUv7WzKskix6bEALRyi1//BxPb1wW6k1jLm4iSOUOnA0t8y5d14U7wilMo/nF5sb6GdVz2ZR6p/QQRe3OPnROW50DgAAzigyKFKXNbqs3Ov56GRDdV6d82TIUHzYyRtZ1rTVLHW7G9M36s9jf+r1za9rXOdx5S8aAADARL5WH4VaXbt/k9XHotAAvwqf2yY2TIse6KGCwiKlHjquxb+u1fu/vab6lqGqaXRWozrBjrlhgX66rE3pVx41rXvyTcFgm2+Zc1tGhzp+DvSzOuZm+yWpds1DGt9jjGrYAk6ukL5VCq8vBYSevqlqhVAKAAAXtKrVSpMumnRO27i2xbW6tsW1TmP1Q+uXut21+9fqriV3ae7OuRrSZIha1Wp1TvsHAACoLnytPoqvHSzLH78ps2C/Um0f6vWh1yoiIMIxp0GtYL1xw/kubS86LNDluRHB/nrjhvN14PgBDf18jJL2Z6pDSpyGtxhun2AY0ofDpCNp0m2LpbhO5T6+qoJQCgAAF6RlHtf6lL9Lfb5tbLjia9vfecs4kqO1vx0sdW5CTKiaRNrfefs7O0/f7/qrlJn11Tu2v5bv+1bPrn1W71/6vqw+1rM+BgAAgOrmtta36ds93+qXQ7+o18e9zvhNfIG+gVp7/cmbtY/+brR++OMHTeg+QZc3vlyStGrfKo1ZNuaM+y4yimTIUMuIlrq66dUnn8g5LFn9JIuPVDfhrI6rqiCUAgDABdv+yNL9c5NLff65oa0dodSuP4+WOffJS1s6QqndB7PLnNs0updq1FmjrQe3av6u+RrWfNjZlA8AAFAt+Vn9NKH7BN266FYdLziuQqOwzPmnP19kFKnQKLR/i98/DMM443ZOCPYL1nMXPic/6ykfPwysKd3/k3T0L8k/uPSVqwFCKQAAXFAz2F/dm9Qq9fmo0JP3CAgL9Ctzbkx4oOPnEJtviXNz84vkY7Fo4pWt9ePBLE1aP0nr0tYRSgEAAJRTQq0ErRi2Qtn52Wece/qNxyddNEl5hXmq4V/DMdYlpouWXbPMpX3X8K+hQN/AUp6s49I2qjJCKQAAXNChQU19cEcXl+a2rhfm8tymdUPOODe+9nWKCY5Rr7heLm0TAAAAzoL8ghTkF1Tu9cJsYcXGbFab6gQRKFUE126LDwAA3MbqY1Xv+r2r/VcGAwAAeL2sNOn1C6QvH5SKitxdjdsRSgEA4MGS9hzSfR9t1icbUyVJWXlZmvbTNOUX5ru5MgAAAJTb3jXSgV+kfRskHyIZPr4HAIAH27z3kL74ab/2HMzWNR1iddui27Tz0E75+fjpjjZ3uLs8AAAAlEeTvtLwuZLBVVISV0oBAODRrmhfT35Wi37el6ntaVkakTBCDUMbqm3ttu4uDQAAAOUVECY1Hyi1uMzdlXgEQikAADxYrRo29W8VJUn6ZEOqBjUapE8v/1Sdoju5uTIAAADg3BBKAQDg4a69IE6StGDzH8otKJKf1c/xXGFRobvKAgAAQHn8kSRtmCkd/M3dlXgMQikAADzchU1qq154oLJyCrRoa7okKb8oX7O3ztaVC6/Usfxjbq4QAAAAZ7RlnvTVWGnt6+6uxGMQSgEA4OF8fCwa1tF+tdTcDXslSQVFBZq7c65+z/xdb/38ljvLgwdYtWqVBg8erJiYGFksFn322WfuLgkAAJyuTgspvofUqJe7K/EYfPseAABe4JqOsfpqy371bVlXhmEo0DdQ4zqN073L7tV7295Tq4hWCrWFlrmN9pHtFegbKEnak7VHfxz9Q9HB0YoPi5ck5RbmKunPJJdriq0Rq/qh9c/+oFBhsrOz1a5dO91666266qqr3F0OAAAoSYcR9gUOhFIAALji4G/S/76UakRJ7a49Of7Tx9LRdKnFIKlWY/vY4b3StgVSUC2p/Y0n526dL2Xuk5oNkOo0t49lpUlbPpFsoVLHW0/O3b5QOrT7n7nNFBMeqG8f7OlUUs+4nro47mItS12mR1Y9csZD+OqKrxwh0me/fqb/2/J/urHljXqs02OSpEM5h3T3krtdfkkssuie8+7R3W3vlo+Fi6/daeDAgRo4cKDL83Nzc5Wbm+t4nJWVVRllAQAAlIlQCgAAVxz4RVrylFSvo3MotX6G9MdGqXazk6HU37/b50a2cg6lkmZLKauk0HonQ6nMffa5NRs6h1Kb35N2fSttnSfdvarUsp7o/ISyC7J1OOfwGQ/Bz+fkDdJrB9ZW85rNFRkU6Rjz9fFV85rNz7gdyf7xwd8yf9P0n6br4riL1TzCtfXgGRITE/XMM8+4uwygUmzdulWtW7d2dxmmqE7HWl1wTquwrP1ScB3plC+sqSze9HtEKAUAgCtCY6R2w6Wa8c7jTftLtZvanz+hRl373FPHJKnxxfZAKrzBybGgCPvc4NrOc2MvkOomSD0fcxo+nleob7amKbZmkDrFR6hucF39X///K/fh3NDyBt3Q8gansdqBtTXv8nkub+PzXz/X0fyjBFJeaNy4cRo7dqzjcVZWluLi4txYEQAAVdwnI6Q/t0nXzpGa9HV3NR6DUAoAAFdEt5OuKOGG4r0eKz4W2bLkuRc+WHysVuOS5/Z8tMQyXl++S28s/00Xt4hUp/iIMxRduYY0GeL0+NdDv2pt2lrd2PJGWSwWN1UFV9hsNtlsNneXAQBA9VCYb78tQ362FNHY3dV4FEIpAAC8yFXnx+qN5b9pxc4MpWUeV3RYoLtLkiTlFORo7MqxSslMUU5Bju5se6e7SwIAAPAMVj/poZ3SX/+z37IBDtyVFAAAT1VUJP04XXr/Kun4YUlSozo11Ck+QkWG9N+N+9xb3ylsVpuGtxiu2BqxuqoZ3/4GAADgxMdHqttK4mpyJ4RSAAB4Kh8facNM6del0m/LHMPXXWC/98/HG1JVVGS4qzonFotFw1sM1+dDP1dEwMmPFW5M3yjD8Iwaq7KjR48qOTlZycnJkqSUlBQlJydr79697i0MAACgDHx8DwAAT9blHin3iP3G5/+4tE20nl64TX8cPq4ffjugi5rWcWOBzvyt/o6fv/jtCz2x+gkFWANcvsdUw9CG+mTwJ47H1315nX7P/F1v9HlDF0TZX4N5v8zTCxteKHUbd7a5s9p9fHDjxo3q3bu34/GJm5iPGDFCs2fPdlNVAABABbn2q95jO9q/wMbPM2694CkIpQAA8GQdby02FOBn1RXt62nO2j2auyHVo0KpUx3OPSxfi69yCnNcXuf0ubmFuTpecFxFRpFjrLCoUMcLjpe6jYKigvIX6+V69erFFWkAAHiiPzZJu7+XMnZIfZ52dzUeh1AKAAAvdO0FcZqzdo+O5BSoqMiQj4/n3Z/gplY3aWiTocrKy3J5HV+Lc2vyVt+3VGAUqFZALcfYZY0u04WxF5a6jRD/kPIXCwAAUBlqN5OGvCnlH+N+UiXw+FBq1apVevHFF5WUlKS0tDQtWLBAQ4cOdXdZAACYpyBP2rvGfvl3s0skSQkxYVr9WG/F1gxyc3FlC/EPOaeQqG5w3WJjNfxrqIZ/jXMpCwAAwBzBtaT2N7i7Co/l8Tc6z87OVrt27fT666+7uxQAANxj++fSnCHS0mechj09kAIAAADK4vFXSg0cOFADBw50eX5ubq5yc3Mdj7OyXP/IAAAAHqlJHykkWoppLxXmS1Y/p6czjuQor6BI9cJLvnHmiZuMu3LPobOZCwAAgBIc2i3t3yzV7yaFFL/6G14QSpVXYmKinnnmmTNPBODRtm7dqtatW7u7jErn6nF68uvhybVVGUER0tgdJd6H4N01u/X0wm2lrjrjpg7qnxAlSfo8eb8e+Di51LmvXneehpxXT5K0eNufGvl+UqlzE69so+Gd6rt4ABXHld83ficBAIBH2PGF9O2/pGYDpOs/dnc1HsnjP75XXuPGjVNmZqZjSU1NdXdJAACcu1KuSurYsKZsvlXuzzkAAEDl+fQu6d3LpYz/nRxL+d4+tvhJ57kL77OP708+OZa63j721cPOc79+xD6+90f7Y1uoVLeN1LD0L2ip7qrclVI2m002m83dZQAAUDmy0qTAmpJfgCT7Dc9/erq/snMLSpxeI+Dkn/oBraOU1LRvqZsOtp2c27tFHSX968xzi4oMpRzMVuM63HgcAAB4oD1rpd3fSz0fPTmW+qP9o3W5p9zuJztDSlkpGUXO6+9LkjK2STmHT44dO2ifm3fUee7+zdK+DVLnkfbHHUbYFxdui1BdVblQCgCAKmvuDdL/vpSu+0hqcaljOMDPqgA/6xlXd3WeJNl8rbLVKHvuwaO5GvXBJv0v/YiWPNhDkaEBLm0bAADAFIf3Su8OlorypchWUstB9vFLEqW8bCmi0cm5sRdIV/6fVKOO8zb6jpdyMqU6LU+ORbW1zw2q6Ty31zjp2N9SdDvnce7DWSpCKQAAvEVojCSL/d26U0Ipt5UT6KfsvAJlHs/XEwu26u2bO3DzcwAA4DnC60vdx0h/p0iNe58cL6mPCq9vX07XrH/xsbB6Uttrio836XP2tVZTHh9KHT16VL/++qvjcUpKipKTkxUREaH69c2/wSoAAG7T/QGpxyNSjUh3VyJJ8rP66KVr2mnw1NVauuNPfZb8h65oH+vusgAAQHWWlSbZaki2EPvj3v+yX6nEG2ceyePvjLpx40a1b99e7du3lySNHTtW7du311NPPeXmygAAMFlYPY8JpE5oERWq+/s0lSSNX7hdGVk5bq4IAABUW6nrpRk9pQUjpaJ/7g3l40Mg5cE8PpTq1auXDMMotsyePdvdpQEAAEkjezZWm3ph/3yMb4uM02/mmXvU/q7l8UPO40fS7eOFp9ykPS/bPnbs79Pm/mkfLzp17rES51oKCMYAAKieLPZ+4+8U5xuTw2N5fCgFAABO8XeK9Ond0gcl3MfATXz/+Rifv9VHS3dkaMHmP5wnbJwpTWkhLRrnPP5GJ/v4od0nx5I/tI99+YDz3Bk9pSktFJD5+8mx7Z/Z5356l9PU0P3fn+shAQAAbxR3gXT9J9Lt30pBEe6uBi4glAIAwJv4BUo/z5V2fSsdzXB3NQ7No0J0f9+mahEVomZ1Q5yftPhIPr6S5bRv8/PxtS9nO1eWf8ZPm8sl+gAAuEfaT9KMXtLHNzmPf/2offyXb0+O/bXTPnb6G23f/ts+vv3zk2OH9tjH3r3cee6y56U3u0qZp7wh1ri3/Z5S8Aoef6NzAABwipAoqf/zUsx5UqBnvQN4d49GuvOiRvL3kbT1U6nVUPt9HLrdZ19O9+jvxccuuN2+nO7BrZKknK1bT46dN9y+nCYzrq/izu4QAADAucjLlvZvtn90/1QHf7WPHz/lI/f5x+xjofWc5x5KsY9nHzg5VpBrHwus6Tz38B4pY7u08D7ppk8r9lhgCkIpAAC8Tbd73V1BiXyt/1yA/cNr0pJ/q6D5fPle9z5XLgEAUF3UaWH/+JxfkPN47yelzndLdVufHItoZJ/ra3Oee9FDUvubpMiWJ8dCY+xzT79quuu9UuurpHodKvY4YBpCKQAAUKEKbaEyfGyatKue7jySq7qhAe4uCQAAmCEoQmp2SfHx2BJCo4CwkufGtC8+ZqtR8tzotvYFXot7SgEA4I3++kVa/bK0L8ndlRRjtL9ZI8Pe0v8d66Fxn5bwbXwAAACAuFIKAADvtOY1afN7UueRJb/7aDbDkIoKJauvfK0+euy6flr12mot+1+Gbn93o2rY7C1HbM1APTqghWO1Z77YpoNH80rcZGSITf8a1MrxOPGbHfrfnr8UtmVzsbnhQX6aMKR1sXEAAAB4LkIpAAC8UcvB0tE/pbhO7q7EbtMcadO70lUzpYh4Na0bogf6NdULi3Zq2f9OfktgQkyoUyj13Y4M7f37WImbbFQ72CmUWrnzL/0v/Zik4vOjwwIIpQAAALwMoRQAAN6o2SUl31vBHfJzpBWJ0pE06X9fOr5pb2SPxooJC9Tf2SevhKpVw99p1fsubqIjOQUlbjY00M/p8d09G2n7b3sVHRVdbG6wzXquRwEAAACTEUoBAIBz4xcg3f6ttGGm1GW0Y9jHx6Kh7euVsaJ0Tcc4l3dzRftYNfU7rNat48+6VAAAAHgObnQOAIA3yzsm7Vnr7iqk8PpSv2ckH1oLAAAAuIbOEQAAb5V9QHohXpp9mXT8kPn7/+VbKe0n8/cLAACAKoGP7wEA4K2Ca0s146W8bOnQbikgXCrItT/na5MsFvvPhQVSUYHkY5Wsp9ynKT+n9LkWH8nXv/hcq7/9aqhDu6X5d0gFx6URX0r1O1figQIAAKAq4kopAAC82S1fSQ/8LMW0tz9+vq59OXbw5JwfXraPffWQ87r/aWgfz0w9ObZ+un3s81HOc19uZR8/8Iv9sS1Uathdij5Pqnd+RR8VAAAAqgGulAIAwJsF13LPfoMipOs+lHIyna++AgAAAFxEKAUAQFUybp/9f/2CT451GyN1Hin5nBYePbKr+NwL7pTOv1nyOa1FuP9nSYbkF3RyzGKRAsMrqnIAAABUM4RSAABUFRaLZAspPu5rsy+nK3Guv/O9pBxza5x7fQAAAMApuKcUAAAAAAAATEcoBQAAAAAAANMRSgEAAAAAAMB0hFIAAAAAAAAwHaEUAAAAAAAATEcoBQAAAAAAANMRSgEAAAAAAMB0hFIAAAAAAAAwHaEUAAAAAAAATEcoBQAAAAAAANMRSgEAAAAAAMB0hFIAAAAAAAAwHaEUAAAAAAAATEcoBQAAAAAAANMRSgEAAAAAAMB0hFIAAAAAAAAwHaEUAAAAAAAATEcoBQAAAAAAANMRSgEAAAAAAMB0hFIAAAAAAAAwHaEUAAAAAAAATEcoBQAAAAAAANMRSgEAAAAAAMB0hFIAAAAAAAAwHaEUAAAAAAAATEcoBQAAAAAAANMRSgEAAAAAAMB0hFIAAAAAAAAwHaEUAAAAAAAATEcoBQAAAAAAANMRSgEAAAAAAMB0hFIAAAAAAAAwHaEUAAAAAAAATOcVodSbb76p+Ph4BQQEqEOHDvr+++/dXRIAAIBHoV8CAADexuNDqY8//lgPPPCAnnzySW3evFkXXXSRBg4cqL1797q7NAAAAI9AvwQAALyRr7sLOJMpU6bo9ttv1x133CFJeuWVV7R48WJNmzZNiYmJxebn5uYqNzfX8TgzM1OSlJWVZU7BQCU6evSoS7/Lrs5zh6pwDBWpKrweVeEY4D1c+T2q7N+1E9s2DKPS9lFe9Eueg3/r3K86/V2qCsdQkTz59ahOv5dwP2/qlzw6lMrLy1NSUpIef/xxp/H+/ftrzZo1Ja6TmJioZ555pth4XFxcpdQIAACqpyNHjigsLMzdZdAvAQAAj3WmfsmjQ6kDBw6osLBQdevWdRqvW7eu0tPTS1xn3LhxGjt2rONxUVGR/v77b9WqVUsWi6VS662KsrKyFBcXp9TUVIWGhrq7HJiM81+9cf6rL8592QzD0JEjRxQTE+PuUiTRL3kC/pup3jj/1Rvnv3rj/JfO1X7Jo0OpE05vjgzDKLVhstlsstlsTmPh4eGVVVq1ERoayn9k1Rjnv3rj/FdfnPvSecIVUqejX3I//pup3jj/1Rvnv3rj/JfMlX7Jo290Xrt2bVmt1mLv8mVkZBR7NxAAAKA6ol8CAADeyqNDKX9/f3Xo0EFLlixxGl+yZIm6devmpqoAAAA8B/0SAADwVh7/8b2xY8fqpptuUseOHdW1a1fNmDFDe/fu1ciRI91dWrVgs9n09NNPF7vEH9UD57964/xXX5x770O/5F78N1O9cf6rN85/9cb5P3cWw5O+z7gUb775pl544QWlpaWpdevWevnll9WjRw93lwUAAOAx6JcAAIC38YpQCgAAAAAAAFWLR99TCgAAAAAAAFUToRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUh5i1apVGjx4sGJiYmSxWPTZZ585Pf/nn3/qlltuUUxMjIKCgjRgwADt2rXLac5vv/2mK664QnXq1FFoaKiGDRumP//802nOL7/8oiFDhqh27doKDQ1V9+7dtXz58jPWt2XLFvXs2VOBgYGqV6+eJkyYoNPvkZ+bm6snn3xSDRo0kM1mU+PGjfXOO++Uud3ExERdcMEFCgkJUWRkpIYOHaqdO3c6zRk/frxatGih4OBg1axZU3379tWPP/54xpq9iVnnf9OmTerXr5/Cw8NVq1Yt3XXXXTp69OgZ6zvT+b/llltksViKLQkJCWVu15Xzf/ToUd17772KjY1VYGCgWrZsqWnTpp2xZm/hymtgGIbGjx+vmJgYBQYGqlevXtq2bZvTnNzcXN13332qXbu2goODdfnll2vfvn1Ocw4dOqSbbrpJYWFhCgsL00033aTDhw+fsUZX/vv/4IMP1K5dOwUFBSk6Olq33nqrDh48WOZ2p02bprZt2yo0NFShoaHq2rWrvvnmG6c5n376qS655BLVrl1bFotFycnJZ6zXm5h5/p9//nl169ZNQUFBCg8Pd6m+nJwc3XLLLWrTpo18fX01dOjQEuetXLlSHTp0UEBAgBo1aqS33nrLpe2/+eabio+PV0BAgDp06KDvv//e6fmS/m3p0qWLS9tG1US/RL9Ev0S/RL9Ev0S/VLX6JUIpD5Gdna127drp9ddfL/acYRgaOnSofv/9d33++efavHmzGjRooL59+yo7O9uxfv/+/WWxWLRs2TL98MMPysvL0+DBg1VUVOTY1mWXXaaCggItW7ZMSUlJOu+88zRo0CClp6eXWltWVpb69eunmJgYbdiwQVOnTtVLL72kKVOmOM0bNmyYvvvuO82cOVM7d+7URx99pBYtWpR53CtXrtTo0aO1bt06LVmyRAUFBerfv7/juCSpWbNmev3117VlyxatXr1aDRs2VP/+/fXXX3+59Np6AzPO//79+9W3b181adJEP/74oxYtWqRt27bplltuKbM2V87/q6++qrS0NMeSmpqqiIgIXXPNNWVu25Xz/+CDD2rRokV6//33tWPHDj344IO677779Pnnn7v68no0V16DF154QVOmTNHrr7+uDRs2KCoqSv369dORI0cccx544AEtWLBAc+fO1erVq3X06FENGjRIhYWFjjnXX3+9kpOTtWjRIi1atEjJycm66aabyqzPlfO/evVq3Xzzzbr99tu1bds2/fe//9WGDRt0xx13lLnt2NhYTZo0SRs3btTGjRt18cUXa8iQIU4NRHZ2trp3765Jkya5/Jp6EzPPf15enq655hrdc889LtdXWFiowMBAjRkzRn379i1xTkpKii699FJddNFF2rx5s5544gmNGTNG8+fPL3PbH3/8sR544AE9+eST2rx5sy666CINHDhQe/fudZo3YMAAp39fvv76a5frR9VDv0S/RL9Ev0S/RL9Ev1TF+iUDHkeSsWDBAsfjnTt3GpKMrVu3OsYKCgqMiIgI4+233zYMwzAWL15s+Pj4GJmZmY45f//9tyHJWLJkiWEYhvHXX38ZkoxVq1Y55mRlZRmSjKVLl5Zaz5tvvmmEhYUZOTk5jrHExEQjJibGKCoqMgzDML755hsjLCzMOHjw4Dkde0ZGhiHJWLlyZalzMjMzz1izN6us8z99+nQjMjLSKCwsdMzZvHmzIcnYtWtXqfW4cv5Pt2DBAsNisRi7d+8u17GXdP4TEhKMCRMmOM07//zzjX/961/l2ra3OP01KCoqMqKiooxJkyY55uTk5BhhYWHGW2+9ZRiGYRw+fNjw8/Mz5s6d65jzxx9/GD4+PsaiRYsMwzCM7du3G5KMdevWOeasXbvWkGT873//K7UeV87/iy++aDRq1Mhpvddee82IjY0t9/HXrFnT+L//+79i4ykpKYYkY/PmzeXepjeprPN/qlmzZhlhYWHlrm3EiBHGkCFDio0/+uijRosWLZzG7r77bqNLly5lbq9Tp07GyJEjncZatGhhPP7442fcJ2AY9Ev0S/RL9Ev0S6ejX6Jf8kZcKeUFcnNzJUkBAQGOMavVKn9/f61evdoxx2KxyGazOeYEBATIx8fHMadWrVpq2bKl5syZo+zsbBUUFGj69OmqW7euOnToUOr+165dq549ezpt+5JLLtH+/fu1e/duSdLChQvVsWNHvfDCC6pXr56aNWumhx9+WMePHy/XsWZmZkqSIiIiSnw+Ly9PM2bMUFhYmNq1a1eubXurijr/ubm58vf3l4/Pyf/sAwMDJckxpySunP/TzZw5U3379lWDBg3Kdawlnf8LL7xQCxcu1B9//CHDMLR8+XL98ssvuuSSS8q1bW9x+muQkpKi9PR09e/f3zHHZrOpZ8+eWrNmjSQpKSlJ+fn5TnNiYmLUunVrx5y1a9cqLCxMnTt3dszp0qWLwsLCHHNK4sr579atm/bt26evv/5ahmHozz//1Lx583TZZZe5fNyFhYWaO3eusrOz1bVrV5fXq2oq6/xXprVr1zrtW7L/jmzcuFH5+fklrpOXl6ekpKRi6/Xv379YzStWrFBkZKSaNWumO++8UxkZGRV7AKgy6JdOol+yo1+iX6Jfqprol6pWv0Qo5QVatGihBg0aaNy4cTp06JDy8vI0adIkpaenKy0tTZL9H8vg4GA99thjOnbsmLKzs/XII4+oqKjIMcdisWjJkiXavHmzQkJCFBAQoJdfflmLFi0q8/Oy6enpqlu3rtPYiccnLmP//ffftXr1am3dulULFizQK6+8onnz5mn06NEuH6dhGBo7dqwuvPBCtW7d2um5L7/8UjVq1HDUvGTJEtWuXdvlbXuzijr/F198sdLT0/Xiiy8qLy9Phw4d0hNPPCFJjjklceX8nyotLU3ffPPNGS9FPl1p5/+1115Tq1atFBsbK39/fw0YMEBvvvmmLrzwwnJt3xuU9BqceI1LOgcnnktPT5e/v79q1qxZ5pzIyMhi+4yMjCzz4yiunP9u3brpgw8+0LXXXit/f39FRUUpPDxcU6dOPeMxb9myRTVq1JDNZtPIkSO1YMECtWrV6ozrVUWVef4rU2m/IwUFBTpw4ECJ6xw4cECFhYVlHpckDRw4UB988IGWLVumyZMna8OGDbr44osd/+cTOBX9Ev0S/RL9Ev1S1Ue/VPX6JUIpL+Dn56f58+frl19+UUREhIKCgrRixQoNHDhQVqtVklSnTh3997//1RdffKEaNWooLCxMmZmZOv/88x1zDMPQqFGjFBkZqe+//17r16/XkCFDNGjQIMcf2YSEBNWoUUM1atTQwIEDHTVYLBanmox/btp3YryoqEgWi0UffPCBOnXqpEsvvVRTpkzR7Nmzdfz4cX3//feO7daoUUMffPBBseO899579fPPP+ujjz4q9lzv3r2VnJysNWvWaMCAARo2bJhXpb/noqLOf0JCgt59911NnjxZQUFBioqKUqNGjVS3bl2nOWdz/k81e/ZshYeHO93g71zO/2uvvaZ169Zp4cKFSkpK0uTJkzVq1CgtXbr0LF5Nz1bWfwMlnYOSXv+y5pQ0/9Q5Z3v+t2/frjFjxuipp55SUlKSFi1apJSUFI0cOVJS2ee/efPmSk5O1rp163TPPfdoxIgR2r59e5nHVVVV9vk/k9LOvyvK+h0p6/yf6biuvfZaXXbZZWrdurUGDx6sb775Rr/88ou++uqrctWH6oF+iX6Jfol+iX6p6qNfqnr9kq+7C4BrOnTooOTkZGVmZiovL0916tRR586d1bFjR8ec/v3767ffftOBAwfk6+ur8PBwRUVFKT4+XpK0bNkyffnllzp06JBCQ0Ml2e/kv2TJEr377rt6/PHH9fXXXzsuHzxxqXJUVFSx9PhEg3MitY2Ojla9evUUFhbmmNOyZUsZhqF9+/apY8eOTt8CcXrae99992nhwoVatWqVYmNjix1/cHCwmjRpoiZNmqhLly5q2rSpZs6cqXHjxp3V6+ltKuL8S/YbN15//fX6888/FRwcLIvFoilTpjjmnO35P8EwDL3zzju66aab5O/v7xg/2/N//PhxPfHEE1qwYIHj0ua2bdsqOTlZL730Uqk3EvRGpb0GUVFRkuzvrkRHRzvGMzIyHK9jVFSU493cU9/9ycjIULdu3RxzTv92IUn666+/HNs52/OfmJio7t2765FHHpFkP0fBwcG66KKL9Nxzz5V5/v39/dWkSRNJ9t+TDRs26NVXX9X06dNde+GqiMo+/64o6fy7orTfEV9fX9WqVUthYWHFzr/NZpPVai1xvdP/fThVdHS0GjRoUOzbtIAT6Jfol+iX6Jfol6ou+qXix1USb+uXuFLKy4SFhalOnTratWuXNm7cqCFDhhSbU7t2bYWHh2vZsmXKyMjQ5ZdfLkk6duyYJDl9Rv7E4xPfONKgQQNHM1OvXj1JUteuXbVq1Srl5eU51vn2228VExOjhg0bSpK6d++u/fv3O31d7i+//CIfHx/HV9Oe2G6TJk0UEhIiyf5H+d5779Wnn36qZcuWOTUEZTEMw2suR6xI53L+T1W3bl3VqFFDH3/8sQICAtSvXz9JZ3/+T1i5cqV+/fVX3X777U7jZ3v+8/PzlZ+fX+x31mq1On1Lkjc702sQHx+vqKgoLVmyxDGWl5enlStXOv6AdujQQX5+fk5z0tLStHXrVsecrl27KjMzU+vXr3fM+fHHH5WZmemYc7bn/9ixYyWeoxPHV9r5L+31qE7/bZt1/l1R0vl3RdeuXZ32Ldl/Rzp27Cg/P78Sz7+/v786dOhQbL0lS5aUWfPBgweVmprq1HACJaFfkmO96vRv6gn0S3b0S/RLVQX9UhXvlyr2vuk4W0eOHDE2b97s+HaPKVOmGJs3bzb27NljGIZhfPLJJ8by5cuN3377zfjss8+MBg0aGFdeeaXTNt555x1j7dq1xq+//mq89957RkREhDF27FjH83/99ZdRq1Yt48orrzSSk5ONnTt3Gg8//LDh5+dnJCcnl1rb4cOHjbp16xrDhw83tmzZYnz66adGaGio8dJLLznVHxsba1x99dXGtm3bjJUrVxpNmzY17rjjjjKP+5577jHCwsKMFStWGGlpaY7l2LFjhmEYxtGjR41x48YZa9euNXbv3m0kJSUZt99+u2Gz2Zy+XcXbmXH+DcMwpk6daiQlJRk7d+40Xn/9dSMwMNB49dVXy6zNlfN/wo033mh07tzZ5eM+0/k3DMPo2bOnkZCQYCxfvtz4/fffjVmzZhkBAQHGm2++6fJ+PJkrr8GkSZOMsLAw49NPPzW2bNliDB8+3IiOjjaysrIcc0aOHGnExsYaS5cuNTZt2mRcfPHFRrt27YyCggLHnAEDBhht27Y11q5da6xdu9Zo06aNMWjQoDLrc+X8z5o1y/D19TXefPNN47fffjNWr15tdOzY0ejUqVOZ2x43bpyxatUqIyUlxfj555+NJ554wvDx8TG+/fZbx5yDBw8amzdvNr766itDkjF37lxj8+bNRlpamsuvsScz8/zv2bPH2Lx5s/HMM88YNWrUcPybc+TIkTJr3LZtm7F582Zj8ODBRq9evRzrnfD7778bQUFBxoMPPmhs377dmDlzpuHn52fMmzevzO3OnTvX8PPzM2bOnGls377deOCBB4zg4GDHt1AdOXLEeOihh4w1a9YYKSkpxvLly42uXbsa9erVczp2VC/0S/RL9Ev0S/RL9Ev0S1WrXyKU8hDLly83JBVbRowYYRiGYbz66qtGbGys4efnZ9SvX9/417/+ZeTm5jpt47HHHjPq1q1r+Pn5GU2bNjUmT55c7CtoN2zYYPTv39+IiIgwQkJCjC5duhhff/31Gev7+eefjYsuusiw2WxGVFSUMX78+GLb3rFjh9G3b18jMDDQiI2NNcaOHev0D0VJSjpmScasWbMMwzCM48ePG1dccYURExNj+Pv7G9HR0cbll19urF+//ow1exOzzv9NN91kREREGP7+/kbbtm2NOXPmuFSfK+f/8OHDRmBgoDFjxgyXj/tM598wDCMtLc245ZZbjJiYGCMgIMBo3rx5icfmrVx5DYqKioynn37aiIqKMmw2m9GjRw9jy5YtTts5fvy4ce+99xoRERFGYGCgMWjQIGPv3r1Ocw4ePGjccMMNRkhIiBESEmLccMMNxqFDh85Yoyvn/7XXXjNatWplBAYGGtHR0cYNN9xg7Nu3r8zt3nbbbUaDBg0Mf39/o06dOkafPn2cGizDsDdwJb0+Tz/99Bnr9gZmnv8RI0aUuK/ly5eXWWODBg1KXO9UK1asMNq3b2/4+/sbDRs2NKZNm+bS8b/xxhuO34Hzzz/f6evNjx07ZvTv39+oU6eO49++ESNGFDsuVC/0S/RL9Ev0S/RL9Ev0S1WrX7IYxj931wIAAAAAAABMwj2lAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAFQpt9xyiywWiywWi/z8/FS3bl3169dP77zzjoqKilzezuzZsxUeHl55hQIAALgJ/RIAT0EoBaDKGTBggNLS0rR7925988036t27t+6//34NGjRIBQUF7i4PAADA7eiXAHgCQikAVY7NZlNUVJTq1aun888/X0888YQ+//xzffPNN5o9e7YkacqUKWrTpo2Cg4MVFxenUaNG6ejRo5KkFStW6NZbb1VmZqbjXcTx48dLkvLy8vToo4+qXr16Cg4OVufOnbVixQr3HCgAAMBZol8C4AkIpQBUCxdffLHatWunTz/9VJLk4+Oj1157TVu3btW7776rZcuW6dFHH5UkdevWTa+88opCQ0OVlpamtLQ0Pfzww5KkW2+9VT/88IPmzp2rn3/+Wddcc40GDBigXbt2ue3YAAAAKgL9EgCzWQzDMNxdBABUlFtuuUWHDx/WZ599Vuy56667Tj///LO2b99e7Ln//ve/uueee3TgwAFJ9nskPPDAAzp8+LBjzm+//aamTZtq3759iomJcYz37dtXnTp10sSJEyv8eAAAACoa/RIAT+Hr7gIAwCyGYchisUiSli9frokTJ2r79u3KyspSQUGBcnJylJ2dreDg4BLX37RpkwzDULNmzZzGc3NzVatWrUqvHwAAoLLRLwEwE6EUgGpjx44dio+P1549e3TppZdq5MiRevbZZxUREaHVq1fr9ttvV35+fqnrFxUVyWq1KikpSVar1em5GjVqVHb5AAAAlY5+CYCZCKUAVAvLli3Tli1b9OCDD2rjxo0qKCjQ5MmT5eNjv7XeJ5984jTf399fhYWFTmPt27dXYWGhMjIydNFFF5lWOwAAgBnolwCYjVAKQJWTm5ur9PR0FRYW6s8//9SiRYuUmJioQYMG6eabb9aWLVtUUFCgqVOnavDgwfrhhx/01ltvOW2jYcOGOnr0qL777ju1a9dOQUFBatasmW644QbdfPPNmjx5stq3b68DBw5o2bJlatOmjS699FI3HTEAAED50C8B8AR8+x6AKmfRokWKjo5Ww4YNNWDAAC1fvlyvvfaaPv/8c1mtVp133nmaMmWK/vOf/6h169b64IMPlJiY6LSNbt26aeTIkbr22mtVp04dvfDCC5KkWbNm6eabb9ZDDz2k5s2b6/LLL9ePP/6ouLg4dxwqAADAWaFfAuAJ+PY9AAAAAAAAmI4rpQAAAAAAAGA6QikAAAAAAACYjlAKAAAAAAAApiOUAgAAAAAAgOkIpQAAAAAAAGA6QikAAAAAAACYjlAKAAAAAAAApiOUAgAAAAAAgOkIpQAAAAAAAGA6QikAAAAAAACYjlAKAAAAAAAApiOUAgAAAAAAgOkIpQAAAAAAAGA6QikAAAAAAACYjlAKAAAAAAAApvN1dwGVzTAMHTlyxN1lAACAKiYkJEQWi8XdZVSIoqIi7d+/v0odEwAAcJ8TWUxMTIx8fEq/HqrKh1JZWVkKDw93dxkAAKCKOXz4sMLCwtxdRoXYv3+/4uLi3F0GAACoYlJTUxUbG1vq81U+lDohNTVVoaGh7i4DAAB4uaysrCoX4ISEhEiiXwIAABXjRL90oscoTZUPpU5cgh4aGkqTBQAAKkxV+pgb/RIAAKgMZ+qXuNE5AAAAAAAATEcoBQAAAAAAANMRSgEAAAAAAMB0Vf6eUgAAeLrCwkLl5+e7uwz8w8/PT1ar1d1lAACAU9AveZaK6pcIpQAAcBPDMJSenq7Dhw+7uxScJjw8XFFRUVXqZuYAAHgj+iXPVRH9EqEUAABucqLBioyMVFBQEAGIBzAMQ8eOHVNGRoYkKTo62s0VAQBQvdEveZ6K7JcIpQAAcIPCwkJHg1WrVi13l4NTBAYGSpIyMjIUGRnJR/kAAHAT+iXPVVH9Ejc6BwDADU7cEyEoKMjNlaAkJ84L964AAMB96Jc8W0X0S4RSAAC4EZegeybOCwAAnoO/y56pIs4LoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHRuDaVWrVqlwYMHKyYmRhaLRZ999pnjufz8fD322GNq06aNgoODFRMTo5tvvln79+93X8EAAMAhPT1d9913nxo1aiSbzaa4uDgNHjxY3333XaXts2HDhnrllVcqbfveqmHDhrJYLMWW0aNHu7s0AACqNfqlsvm6c+fZ2dlq166dbr31Vl111VVOzx07dkybNm3Sv//9b7Vr106HDh3SAw88oMsvv1wbN250U8UAAECSdu/ere7duys8PFwvvPCC2rZtq/z8fC1evFijR4/W//73v2Lr5Ofny8/Pzw3VVn0bNmxQYWGh4/HWrVvVr18/XXPNNW6sCgCA6o1+6czceqXUwIED9dxzz+nKK68s9lxYWJiWLFmiYcOGqXnz5urSpYumTp2qpKQk7d271w3VAgBgjmN5BaUuOfmFFT73bIwaNUoWi0Xr16/X1VdfrWbNmikhIUFjx47VunXrJNm/keWtt97SkCFDFBwcrOeee06SNG3aNDVu3Fj+/v5q3ry53nvvPadtjx8/XvXr15fNZlNMTIzGjBkjSerVq5f27NmjBx980HEl0Anz589XQkKCbDabGjZsqMmTJztts2HDhpo4caJuu+02hYSEqH79+poxY8ZZHbsnqlOnjqKiohzLl19+qcaNG6tnz57uLg0AgEpBv1Q1+iW3XilVXpmZmbJYLAoPDy91Tm5urnJzcx2Ps7KyTKgMAICK0+qpxaU+17t5Hc26tZPjcYdnl+r4ac3UCZ3jI/Tx3V0djy/8z3L9nZ1XbN7uSZeVq76///5bixYt0vPPP6/g4OBiz5/6d/rpp59WYmKiXn75ZVmtVi1YsED333+/XnnlFfXt21dffvmlbr31VsXGxqp3796aN2+eXn75Zc2dO1cJCQlKT0/XTz/9JEn69NNP1a5dO91111268847HftISkrSsGHDNH78eF177bVas2aNRo0apVq1aumWW25xzJs8ebKeffZZPfHEE5o3b57uuece9ejRQy1atCjX8Xu6vLw8vf/++xo7dmypX9VMvwQA8Hb0S1WjX/KaUConJ0ePP/64rr/+eoWGhpY6LzExUc8884yJlQEAUL38+uuvMgzDpebk+uuv12233eb0+JZbbtGoUaMkyfFO4UsvvaTevXtr7969ioqKUt++feXn56f69eurUyd7UxkRESGr1aqQkBBFRUU5tjllyhT16dNH//73vyVJzZo10/bt2/Xiiy86NVmXXnqpY7+PPfaYXn75Za1YsaLKhVKfffaZDh8+7HTsp6NfAgCgctEvucYrQqn8/Hxdd911Kioq0ptvvlnm3HHjxmns2LGOx1lZWYqLi6vsEgEAqDDbJ1xS6nM+p135kvTvvi7PXf1Y73Mr7B+GYUhSqVfhnKpjx45Oj3fs2KG77rrLaax79+569dVXJUnXXHONXnnlFTVq1EgDBgzQpZdeqsGDB8vXt/SWZceOHRoyZEixbb7yyisqLCyU1WqVJLVt29bxvMViUVRUlDIyMs54DN5m5syZGjhwoGJiYkqdQ78EAPB29EtVo19y6z2lXJGfn69hw4YpJSVFS5YsKfMqKUmy2WwKDQ11WgAA8CZB/r6lLgF+1gqfW15NmzaVxWLRjh07zji3pMvVT2/ODMNwjMXFxWnnzp164403FBgYqFGjRqlHjx7Kz88vdR+nrn/q2OlOv2moxWJRUVHRGY/Bm+zZs0dLly7VHXfcUeY8+iUAgLejX6oa/ZJHh1InAqldu3Zp6dKlqlWrlrtLAgCg2ouIiNAll1yiN954Q9nZ2cWeP3z4cKnrtmzZUqtXr3YaW7NmjVq2bOl4HBgYqMsvv1yvvfaaVqxYobVr12rLli2SJH9/f6dvmZOkVq1albjNZs2aOd71qy5mzZqlyMhIXXZZ+e57AQAAKhb9kmvc+vG9o0eP6tdff3U8TklJUXJysiIiIhQTE6Orr75amzZt0pdffqnCwkKlp6dLsp9cf39/d5UNAEC19+abb6pbt27q1KmTJkyYoLZt26qgoEBLlizRtGnTSn1X8JFHHtGwYcN0/vnnq0+fPvriiy/06aefaunSpZKk2bNnq7CwUJ07d1ZQUJDee+89BQYGqkGDBpLs3wqzatUqXXfddbLZbKpdu7YeeughXXDBBXr22Wd17bXXau3atXr99dfP+JH/qqaoqEizZs3SiBEjyrx8HwAAmIN+yQWGGy1fvtyQVGwZMWKEkZKSUuJzkozly5e7vI/MzExDkpGZmVl5BwIAQDkdP37c2L59u3H8+HF3l3LW9u/fb4wePdpo0KCB4e/vb9SrV8+4/PLLHX+nJRkLFiwott6bb75pNGrUyPDz8zOaNWtmzJkzx/HcggULjM6dOxuhoaFGcHCw0aVLF2Pp0qWO59euXWu0bdvWsNlsxqltzLx584xWrVoZfn5+Rv369Y0XX3zRaZ8NGjQwXn75Zaexdu3aGU8//XSJx1bW+fHU3mLx4sWGJGPnzp3lXtdTjwkAUL3RL1X9fsnyz4tQZWVlZSksLEyZmZncLwEA4DFycnKUkpKi+Ph4BQQEuLscnKas81MVe4uqeEwAAO9Hv+TZKqJf8uh7SgEAAAAAAKBqIpQCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAADAWVmzZo2sVqsGDBjgNL57925ZLBbHEhISooSEBI0ePVq7du1ymjt79myFh4c7PT6xntVqVc2aNdW5c2dNmDBBmZmZJdYxceJEWa1WTZo0qcKPEQAA4FzQL5WNUAoAAJyVd955R/fdd59Wr16tvXv3Fnt+6dKlSktL008//aSJEydqx44dateunb777rsytxsaGqq0tDTt27dPa9as0V133aU5c+bovPPO0/79+4vNnzVrlh599FG98847FXZsAAAAFYF+qWyEUgAAeJq8bPtiGCfHCvLsYwW5Jc8tKjo5VphvH8vPcW3uWcjOztYnn3yie+65R4MGDdLs2bOLzalVq5aioqLUqFEjDRkyREuXLlXnzp11++23q7CwsNRtWywWRUVFKTo6Wi1bttTtt9+uNWvW6OjRo3r00Ued5q5cuVLHjx/XhAkTlJ2drVWrVp3V8QAAAC9Dv1Ql+iVCKQAAPM3EGPty7ODJsTWv2se+fth57otN7OOZqSfH1r9tH1t4r/PcV9rYxw/sPDmW/MFZlfjxxx+refPmat68uW688UbNmjVLxqlNYQl8fHx0//33a8+ePUpKSirX/iIjI3XDDTdo4cKFTg3azJkzNXz4cPn5+Wn48OGaOXPmWR0PAADwMvRLxXhjv0QoBQAAym3mzJm68cYbJUkDBgzQ0aNHz3iZuSS1aNFCkv0+CuXVokULHTlyRAcP2pvPrKwszZ8/31HHjTfeqHnz5ikrK6vc2wYAAKho9Etn5uvuAgAAwGme+Oc+AH5BJ8e63S91GSX5nPan+5Ff7f/rG3hyrNOdUocRksXqPPeBLcXnnndDucvbuXOn1q9fr08//dS+OV9fXXvttXrnnXfUt2/fMtc98e6gxWIp935PX/fDDz9Uo0aN1K5dO0nSeeedp0aNGmnu3Lm66667yr19AADgReiXXFrX0/slQikAADyNf3DxMV9/Sf6uzbX62RdX55bTzJkzVVBQoHr16jnGDMOQn5+fDh06VOa6O3bskCTFx8eXe787duxQaGioatWqJcl+49Bt27bJ1/dkO1NUVKSZM2d6RJMFAAAqEf1Sqet6U79EKAUAAFxWUFCgOXPmaPLkyerfv7/Tc1dddZU++OADDRo0qMR1i4qK9Nprryk+Pl7t27cv134zMjL04YcfaujQofLx8dGWLVu0ceNGrVixQhEREY55hw8fVo8ePbR161a1bt26/AcIAABwjuiXXEcoBQAAXPbll1/q0KFDuv322xUWFub03NVXX62ZM2c6mqyDBw8qPT1dx44d09atW/XKK69o/fr1+uqrr2S1WkvavCT7u4jp6ekyDEOHDx/W2rVrNXHiRIWFhWnSpEmS7O8+durUST169Ci2fteuXTVz5ky9/PLLFXjkAAAArqFfch03OgcAAC6bOXOm+vbtW6zBkuzv/CUnJ+vvv/+WJPXt21fR0dFq06aNHn/8cbVs2VI///yzevfuXeY+srKyFB0drXr16qlr166aPn26RowYoc2bNys6Olp5eXl6//33ddVVV5W4/lVXXaX3339feXl5537AAAAA5US/5DqLcabvI/RyWVlZCgsLU2ZmpkJDQ91dDgAAkqScnBylpKQoPj5eAQEB7i4Hpynr/FTF3qIqHhMAwPvRL3m2iuiXuFIKAAAAAAAApiOUAgAAAAAAgOkIpSrA1q1b3V0CAAAAAACAVyGUAgAAAAAAgOkIpQAAAAAAAGA6QikAAAAAAACYjlAKAAAAAAAApiOUAgAAAAAAgOkIpQAAAAAAAGA6QikAAAAAAACYjlAKAAC4zGKxlLnccsstkqTly5erd+/eioiIUFBQkJo2baoRI0aooKBAkrRixQqn9QIDA5WQkKAZM2a48egAAADOHf2S63zdXQAAAPAeaWlpjp8//vhjPfXUU9q5c6djLDAwUNu2bdPAgQM1ZswYTZ06VYGBgdq1a5fmzZunoqIip+3t3LlToaGhOn78uL744gvdc889aty4sfr06WPaMQEAAFQk+iXXEUoBAOBhjuUfK/c6/lZ/+frY/6wXFBUorzBPPhYfBfgGnHG7QX5BLu8nKirK8XNYWJgsFovTmCTNmjVL0dHReuGFFxxjjRs31oABA4ptLzIyUuHh4ZKkMWPG6NVXX9WmTZuqRJMFAAAqD/1S1eiXCKUAAPAwnT/sXO51Xur5ki5peIkk6bu93+nhlQ+rY92OmjVglmPOgPkDdCj3ULF1t4zYcvbFliAqKkppaWlatWqVevTo4dI6hmFo8eLFSk1NVefO5T9+AABQvdAvVY1+iVAKAABUqGuuuUaLFy9Wz549FRUVpS5duqhPnz66+eabFRoa6jQ3NjZWkpSbm6uioiJNmDDB5cYMAADAW9Ev2RFKAQDgYX68/sdyr+Nv9Xf83Kd+H/14/Y/ysTh/n8miqxadc22usFqtmjVrlp577jktW7ZM69at0/PPP6///Oc/Wr9+vaKjox1zv//+e4WEhCg3N1fr16/Xvffeq4iICN1zzz2m1AoAALwT/VLV6Jf49j0AADxMkF9QuZcT90eQJF8fXwX5BTndH6Gs7VaWevXq6aabbtIbb7yh7du3KycnR2+99ZbTnPj4eDVp0kQJCQm69dZbddNNN+n555+vtJoAAEDVQL9UNfolQikAAFDpatasqejoaGVnZ5c5z2q16vjx4yZVBQAA4DmqY7/Ex/cAAECFmj59upKTk3XFFVeocePGysnJ0Zw5c7Rt2zZNnTrVaW5GRoZycnIcl6O/9957uvrqq91UOQAAgDnol+wIpQAAQIXq1KmTVq9erZEjR2r//v2qUaOGEhIS9Nlnn6lnz55Oc5s3by5J8vX1VVxcnO6++26NHz/eDVUDAACYh37JzmIYhuHuIipTVlaWwsLClJmZWewO9hVl69atat26daVsGwBQNeXk5CglJUXx8fEKCAg48wowVVnnx4zewmxV8ZgAAN6PfsmzVUS/xD2lAAAAvNwff/yhG2+8UbVq1VJQUJDOO+88JSUlubssAACAMvHxPQAAAC926NAhde/eXb1799Y333yjyMhI/fbbbwoPD3d3aQAAAGUilAIAAPBi//nPfxQXF6dZs2Y5xho2bFjmOrm5ucrNzXU8zsrKqqzyAAAASsXH9wAAALzYwoUL1bFjR11zzTWKjIxU+/bt9fbbb5e5TmJiosLCwhxLXFxcpde5devWSt8HAADwLoRSAAC4UVFRkbtLQAm86bz8/vvvmjZtmpo2barFixdr5MiRGjNmjObMmVPqOuPGjVNmZqZjSU1NNbFiAADKx5v+LlcnFXFe+PgeAABu4O/vLx8fH+3fv1916tSRv7+/LBaLu8uq9gzDUF5env766y/5+PjI39/f3SWdUVFRkTp27KiJEydKktq3b69t27Zp2rRpuvnmm0tcx2azyWazmVkmAADlRr/kmSqyXyKUAgDADXx8fBQfH6+0tDTt37/f3eXgNEFBQapfv758fDz/ovLo6Gi1atXKaaxly5aaP3++myoCAKBi0C95torolwilAABwE39/f9WvX18FBQUqLCx0dzn4h9Vqla+vr9e8E9u9e3ft3LnTaeyXX35RgwYN3FQRAAAVh37JM1VUv+TWUGrVqlV68cUXlZSUpLS0NC1YsEBDhw51PG8Yhp555hnNmDFDhw4dUufOnfXGG28oISHBfUUDAFCBLBaL/Pz85Ofn5+5S4KUefPBBdevWTRMnTtSwYcO0fv16zZgxQzNmzHB3aQAAVAj6parLrdekZ2dnq127dnr99ddLfP6FF17QlClT9Prrr2vDhg2KiopSv379dOTIEZMrBQAA8EwXXHCBFixYoI8++kitW7fWs88+q1deeUU33HCDu0sDAAAok1uvlBo4cKAGDhxY4nOGYeiVV17Rk08+qSuvvFKS9O6776pu3br68MMPdffdd5tZKgAAgMcaNGiQBg0a5O4yAAAAysVj796ZkpKi9PR09e/f3zFms9nUs2dPrVmzptT1cnNzlZWV5bQAAAAAAADAs3hsKJWeni5Jqlu3rtN43bp1Hc+VJDExUWFhYY4lLi6uUusEAAAAAABA+XlsKHXC6XdyNwyjzLu7jxs3TpmZmY4lNTW1sksEAAAAAABAObn1nlJliYqKkmS/Yio6OtoxnpGRUezqqVPZbDbZbLZKrw8AAAAAAABnz2OvlIqPj1dUVJSWLFniGMvLy9PKlSvVrVs3N1YGAAAAAACAc+XWK6WOHj2qX3/91fE4JSVFycnJioiIUP369fXAAw9o4sSJatq0qZo2baqJEycqKChI119/vRurBgAAAAAAwLlyayi1ceNG9e7d2/F47NixkqQRI0Zo9uzZevTRR3X8+HGNGjVKhw4dUufOnfXtt98qJCTEXSUDAAAAAACgArg1lOrVq5cMwyj1eYvFovHjx2v8+PHmFQUAAAAAAIBK57H3lAIAAAAAAEDVRSgFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAADg5caPHy+LxeK0REVFubssAACAMvm6uwAAAACcu4SEBC1dutTx2Gq1urEaAACAMyOUAgAAqAJ8fX1dvjoqNzdXubm5jsdZWVmVVRYAAECp+PgeAABAFbBr1y7FxMQoPj5e1113nX7//fdS5yYmJiosLMyxxMXFmVhp2bZu3VohcwAAgOcjlAIAAPBynTt31pw5c7R48WK9/fbbSk9PV7du3XTw4MES548bN06ZmZmOJTU11eSKAQAA+PgeAACA1xs4cKDj5zZt2qhr165q3Lix3n33XY0dO7bYfJvNJpvNZmaJAAAAxXClFAAAQBUTHBysNm3aaNeuXe4uBQAAoFSEUgAAAFVMbm6uduzYoejoaHeXAgAAUCpCKQAAAC/38MMPa+XKlUpJSdGPP/6oq6++WllZWRoxYoS7SwMAACgV95QCAADwcvv27dPw4cN14MAB1alTR126dNG6devUoEEDd5cGAABQKkIpAAAALzd37lx3lwAAAFBufHwPAAAAAAAApiOUAgAAAAAAgOkIpQAAAAAAAGA6QikAAAAAAACYjlAKAAAAAAAApiOUAgAAAAAAgOk8OpQqKCjQv/71L8XHxyswMFCNGjXShAkTVFRU5O7SAAAAAAAAcA583V1AWf7zn//orbfe0rvvvquEhARt3LhRt956q8LCwnT//fe7uzwAAAAAAACcJY8OpdauXashQ4bosssukyQ1bNhQH330kTZu3OjmygAAAAAAAHAuPPrjexdeeKG+++47/fLLL5Kkn376SatXr9all15a6jq5ubnKyspyWgAAAAAAAOBZPPpKqccee0yZmZlq0aKFrFarCgsL9fzzz2v48OGlrpOYmKhnnnnGxCoBAAAAAABQXh59pdTHH3+s999/Xx9++KE2bdqkd999Vy+99JLefffdUtcZN26cMjMzHUtqaqqJFQMAAAAAAMAVHn2l1COPPKLHH39c1113nSSpTZs22rNnjxITEzVixIgS17HZbLLZbGaWCQAAAAAAgHLy6Culjh07Jh8f5xKtVquKiorcVBEAAAAAAAAqgkdfKTV48GA9//zzql+/vhISErR582ZNmTJFt912m7tLAwAAAAAAwDnw6FBq6tSp+ve//61Ro0YpIyNDMTExuvvuu/XUU0+5uzQAAAAAAACcA48Opf6/vTsPj6o8/z/+mWyTfSAhe2LY17DIvigisiqKS8UqZXGpRQEFalWw/VX9VkNVqIoVxSpai+KKokIqSgBR1gCyKbsQMBDWBAJJSPL8/pgyYchCAsnMJHm/rutcZp65z5n7zJOE2ztnnhMSEqIXX3xRL774ortTAQAAAAAAQBXy6DWlAAAAAAAAUDvRlAIAAAAAAIDL0ZQCAAAAAACAy9GUAgAAAAAAgMvRlAIAAAAAAIDL0ZQCAAAAAACAy9GUAgAAAAAAgMvRlAIAAAAAAIDL0ZQCAAAAAACAy9GUAgAAAAAAgMvRlAIAAAAAAIDL0ZQCAAAAAACAy9GUAgAAAAAAgMvRlAIAAAAAAIDL0ZQCAAAAAACAy9GUAgAAAAAAgMvRlAIAAAAAAIDL0ZQCAAAAAACAy9GUAgAAAAAAgMvRlAIAAAAAAIDL0ZQCAAAAAACAy9GUAgAAAAAAgMtdUlPqu+++0+9+9zv16NFDBw4ckCS9++67Wr58eZUmBwAAUJtRUwEAgLqs0k2pTz75RAMHDlRAQIDWr1+vvLw8SdLJkyf17LPPVnmCAAAAtRE1FQAAqOsq3ZT629/+ptdee01vvPGGfH19HeM9e/bUunXrqjQ5AACA2qq6aqrk5GRZLBZNmDChCrIEAACoPpVuSm3btk29e/cuMR4aGqoTJ05URU4AAAC1XnXUVGvWrNGsWbPUrl27y8wOAACg+lW6KRUTE6OdO3eWGF++fLkaN25cJUkBAADUdlVdU506dUrDhw/XG2+8ofr161dFigAAANWq0k2pP/zhD3r44Ye1atUqWSwW/frrr5ozZ44eeeQRPfjgg9WRIwAAQK1T1TXV2LFjdcMNN6hfv34Xjc3Ly1N2drbTBgAA4Go+ld3h0UcfVVZWlq699lrl5uaqd+/eslqteuSRRzRu3LjqyBEAAKDWqcqaau7cuVq3bp3WrFlTofjk5GQ99dRTl5I2AABAlal0U0qSnnnmGT3xxBPaunWrioqK1Lp1awUHB1d1bgAAALVaVdRU6enpevjhh/X111/L39+/QvtMnjxZkyZNcjzOzs5WQkJCpV4XAADgcl1SU0qSAgMD1blz56rMBQAAoM653JoqLS1NmZmZ6tSpk2OssLBQy5Yt0yuvvKK8vDx5e3s77WO1WmW1Wi/5NQEAAKpCpZtSubm5mjFjhlJTU5WZmamioiKn5y/nFsYAAAB1RVXVVNddd502bdrkNHb33XerZcuWeuyxx0o0pAAAADxFpZtS99xzjxYtWqTf/OY36tq1qywWS3XkBQAAUKtVVU0VEhKipKQkp7GgoCCFh4eXGAcAAPAklW5KffXVV1qwYIF69epVHfkAAADUCdRUAACgrqt0UyouLk4hISHVkQsAAECdUZ011ZIlS6rluAAAAFXJq7I7TJs2TY899pj27t1bHfkAAADUCdRUAACgrqv0lVKdO3dWbm6uGjdurMDAQPn6+jo9f+zYsSpLDgAAoLaipgIAAHVdpZtSd955pw4cOKBnn31WUVFRLHQOAABwCaipAABAXVfpptQPP/ygFStWqH379tWRDwAAQJ1ATQUAAOq6Sq8p1bJlS505c6Y6cgEAAKgzqKkAAEBdV+mm1NSpU/XHP/5RS5Ys0dGjR5Wdne20AQAA4OKoqQAAQF1X6Y/vDRo0SJJ03XXXOY0bY2SxWFRYWFg1mQEAANRi1FQAAKCuq3RTKjU1tTryAAAAqFOoqQAAQF1X6abUNddcUx15AAAA1CnUVAAAoK6rdFNq2bJl5T7fu3fvS04GAACgrqCmAgAAdV2lm1J9+vQpMWaxWBxfs/4BAADAxVFTAQCAuq7Sd987fvy405aZmamUlBR16dJFX3/9dXXkCAAAUOtQUwEAgLqu0ldK2Wy2EmP9+/eX1WrVxIkTlZaWViWJAQAA1GbUVAAAoK6r9JVSZYmIiNC2bduq6nAAAAB1EjUVAACoKyp9pdTGjRudHhtjlJGRoalTp6p9+/ZVlhgAAEBtRk0FAADquko3pTp06CCLxSJjjNN49+7d9dZbb1VZYuccOHBAjz32mBYuXKgzZ86oefPmevPNN9WpU6cqfy0AAABXcXVNBQAA4Gkq3ZTas2eP02MvLy9FRETI39+/ypI65/jx4+rVq5euvfZaLVy4UJGRkdq1a5fq1atX5a8FAADgSq6sqQAAADxRpZtSiYmJ1ZFHqf7+978rISFBs2fPdow1bNjQZa8PAABQXVxZUwEAAHiiCjWlXn755Qof8KGHHrrkZC40f/58DRw4ULfffruWLl2quLg4Pfjgg/r9739f5j55eXnKy8tzPM7Ozq6yfAAAAC6Hu2oqAAAAT1ShptQ//vGPCh3MYrFUaQG1e/duzZw5U5MmTdKUKVO0evVqPfTQQ7JarRo5cmSp+yQnJ+upp56qshwAoDpt3rxZSUlJ7k4DgIu4q6YCAADwRBVqSl245oGrFBUVqXPnznr22WclSVdeeaW2bNmimTNnltmUmjx5siZNmuR4nJ2drYSEBJfkCwAAUB531VQAAACeyOtydjbGlLhjTFWKiYlR69atncZatWqlffv2lbmP1WpVaGio0wYAAODJqrumAgAA8ESX1JT697//rbZt2yogIEABAQFq166d3n333arOTb169dK2bducxrZv387CoAAAoFZwVU0FAADgiSp9973p06frL3/5i8aNG6devXrJGKPvv/9eY8aM0ZEjRzRx4sQqS27ixInq2bOnnn32WQ0bNkyrV6/WrFmzNGvWrCp7DQAAAHdwZU0FAADgiSrdlJoxY0aJNZ2GDh2qNm3a6Mknn6zSAqpLly6aN2+eJk+erKefflqNGjXSiy++qOHDh1fZawAAALiDK2sqAAAAT1TpplRGRoZ69uxZYrxnz57KyMiokqTON2TIEA0ZMqTKjwsAAOBOrq6pAAAAPE2l15Rq2rSpPvzwwxLjH3zwgZo1a1YlSQEAANR21FQAAKCuq/CVUhs2bFCHDh309NNPa9iwYVq2bJl69eoli8Wi5cuX69tvvy21sAIAAEAxaioAAAC7Cl8p1bFjR3Xq1EmHDh3S6tWr1aBBA3322Wf69NNP1aBBA61evVq33HJLdeYKAABQ41FTAQAA2FX4Sqnvv/9eb731lh5//HGdPXtWt956q55//nn17du3OvMDAACoVaipAAAA7Cp8pVSPHj30xhtv6ODBg5o5c6b279+v/v37q0mTJnrmmWe0f//+6swTAACgVqCmAgAAsKv0QucBAQEaNWqUlixZou3bt+vOO+/U66+/rkaNGun666+vjhwBAABqHWoqAABQ11W6KXW+Jk2a6PHHH9cTTzyh0NBQ/fe//62qvAAAAOoMaioAAFAXVXhNqQstXbpUb731lj755BN5e3tr2LBhuvfee6syNwAAgFqPmgoAANRVlWpKpaen6+2339bbb7+tPXv2qGfPnpoxY4aGDRumoKCg6soRAACgVqGmAgAAqERTqn///kpNTVVERIRGjhype+65Ry1atKjO3AAAAGodaioAAAC7CjelAgIC9Mknn2jIkCHy9vauzpwAAABqLWoqAAAAuwo3pebPn1+deQAAANQJ1FQAAAB2l3X3PQAAAAAAAOBS0JQCAAAAAACAy9GUAgAAAAAAgMvRlAIAAKjhZs6cqXbt2ik0NFShoaHq0aOHFi5c6O60AAAAykVTCgAAoIaLj4/X1KlTtXbtWq1du1Z9+/bV0KFDtWXLFnenBgAAUKYK330PAAAAnunGG290evzMM89o5syZWrlypdq0aeOmrAAAAMpHUwoAAKAWKSws1EcffaScnBz16NGj1Ji8vDzl5eU5HmdnZ7sqPQAAAAc+vgcAAFALbNq0ScHBwbJarRozZozmzZun1q1blxqbnJwsm83m2BISElycLVB9Nm/e7O4U4CaePPeenBvgTjSlAAAAaoEWLVpow4YNWrlypR544AGNGjVKW7duLTV28uTJysrKcmzp6ekuzhYAAICP7wEAANQKfn5+atq0qSSpc+fOWrNmjV566SW9/vrrJWKtVqusVqurUwQAAHDClVIAAAC1kDHGad0oAAAAT8OVUkANsnnzZiUlJbk7DQCAh5kyZYoGDx6shIQEnTx5UnPnztWSJUuUkpLi7tQAAADKRFMKAACghjt06JBGjBihjIwM2Ww2tWvXTikpKerfv7+7UwMAACgTTSkAAIAa7s0333R3CgAAAJXGmlIAAAAAAABwOZpSAAAAAAAAcDmaUgAAAAAAAHA5mlIAAAAAAABwOZpSAAAAAAAAcDmaUgAAAAAAAHA5mlIAAAAAAABwOZpSAAAAAAAAcDmaUgAAAAAAAHA5mlIAAAAAAABwOZpSAAAAAAAAcDmaUgAAAAAAAHA5mlIAAAAAAABwOZpSAAAAAAAAcDmaUgAAAAAAAHA5mlIAAAAAAABwOZpSAAAAAAAAcDmaUgAAAAAAAHA5mlIAAAAAAABwOZpSAAAAAAAAcDmaUgAAAAAAAHA5mlIAAAAAAABwOZpSAAAAAAAAcDmaUgAAAAAAAHC5GtWUSk5OlsVi0YQJE9ydCgAAAAAAAC5DjWlKrVmzRrNmzVK7du3cnQoAAAAAAAAuU41oSp06dUrDhw/XG2+8ofr167s7HQAAAAAAAFymGtGUGjt2rG644Qb169fvorF5eXnKzs522gAAAAAAAOBZPL4pNXfuXK1bt07JyckVik9OTpbNZnNsCQkJ1ZwhgOqwefNmd6cAAAAAAKhGHt2USk9P18MPP6z//Oc/8vf3r9A+kydPVlZWlmNLT0+v5iwBAAAAAABQWT7uTqA8aWlpyszMVKdOnRxjhYWFWrZsmV555RXl5eXJ29vbaR+r1Sqr1erqVAEAAAAAAFAJHt2Uuu6667Rp0yansbvvvlstW7bUY489VqIhBQAAAAAAgJrBo5tSISEhSkpKchoLCgpSeHh4iXEAAAAAAADUHB69phQAAAAAAABqJ4++Uqo0S5YscXcKAAAAAAAAuExcKQUAAAAAAACXoykFAAAAAAAAl6MpBQAAAAAAAJejKQUAAAAAAACXoykFAAAAAAAAl6MpBQAAAAAAAJejKQUAAAAAAACXoykFAABQgyUnJ6tLly4KCQlRZGSkbr75Zm3bts3daQEAAFwUTSkAAIAabOnSpRo7dqxWrlypRYsWqaCgQAMGDFBOTo67UwMAACiXj7sTAAAAwKVLSUlxejx79mxFRkYqLS1NvXv3LnWfvLw85eXlOR5nZ2dXa44AAACl4UopAACAWiQrK0uSFBYWVmZMcnKybDabY0tISHBVerXe5s2b3Z0CKsiT56qiuXnyOQBARdCUAgAAqCWMMZo0aZKuuuoqJSUllRk3efJkZWVlObb09HQXZgkAAGDHx/cAAABqiXHjxmnjxo1avnx5uXFWq1VWq9VFWQEAAJSOphQAAEAtMH78eM2fP1/Lli1TfHy8u9MBAAC4KJpSAAAANZgxRuPHj9e8efO0ZMkSNWrUyN0pAQAAVAhNKQAAgBps7Nixeu+99/T5558rJCREBw8elCTZbDYFBAS4OTsAAICysdA5AABADTZz5kxlZWWpT58+iomJcWwffPCBu1MDAAAoF1dKAQAA1GDGGHenAAAAcEm4UgoAAAAAAAAuR1MKAAAAAAAALsfH9wAAAAAAwGU7ffa09p/aX+n9GtkaydfLV5KUeTpTJ/JOKMw/TA0CGkiS8grztDd7b5n7J4YmyuptvbSk4VY0pQAAAAAAwGX76dhPGp0yutL7pQ5LdTSg/rXpX3r/5/d1f7v7Nf7K8ZKkA6cO6Lb5t5W5f1J4kt674T1ZLJZLyhvuQ1MKAAAAAABcNh8vH4X7h1d6P4uKm0lBvkEK9w9XoE+gY8zb4l3mcU/kndDmo5uVdihNnaM7Vz5puBVNKQAAAAAAcNnaR7TXkjuWXNYxHu74sB7u+LDTWGJoYpnHffKHJ/XJjk/04bYPaUrVQCx0DgAAAAAAaqQ7WtwhSVq0b5GOnjnq5mxQWTSlAAAAAABAjdQqvJXaNmirgqICzds5z93poJJoSgEAAAAAgBprWIthkqSPt3+swqJCN2eDymBNKQAAAAAAcPlO7JPWz5ECw6Vu9xeP//iBdGy31GqIFN3WPpadIaW9LVlDpJ7jimM3fyId3i41HyjFdbSP5RyVVs+SfP2lqyYWx26dLx3aokFN+mhGYKS6RnfV6YLTCvELqfZTRdWgKQUAAAAAAC5f1n5p6VQpvJlzU2rjB9Kub6WwRsVNqVMH7bGhcc5NqS3zpJ++kIIji5tSp4/aYwPqOzelNn8sbf1c/hveU8rD6+Tr5Vv954gqRVMKAAAAAABcvuAoqct9UlCk83jzQfaGVHjT4rHABvZYf5tzbNN+9uNEtioe87fZY30DnWMb95EiWkq9JpTZkMovKNLqPcf03c7DOplboIf6NlO0zV+StPjnQ/rmp8wyT+eBa5ooIcz+mt/tOKyFmw+WGXtPr0ZqGhksSVq5+6jm//hrmbEjuieqVUyoJGndvuP6OG1/mbG/7ZKgdvH1JEmbD2TpvdX7yoy99co4dW4YJknadvCk3lnxS5mxQ9rFqGeTBmU+7yo0pQAAAAAAwOULbyLdMK3k+PlXTZ1TL6H02E6jS46FRJUe2/meEkNbjm7RtiPpKshuo29/PqTlO44oJ794nalRPRo6mlKb9mfrvVVlN3lu7xTvaEr9lFF+7JC2MY6m1I7MU+XGXtcy0tGU2nM4p9zYHo3DHU2pfcdOlxvbIb6eoyn1a9aZcmNbRIXQlAIAAAAAAKgK3x/4XmO+GaOigmDl7Hhc51oeESFWXdsiQvH1AxUe7OeI7944TJP6Ny/zeDG2AMfXnRLLjz3XvJKk9vG2cmMbNQhyfN06NrTc2BbRxetjNYsMLje2TVxo8WuEB5Ub2yGhXpnPuRJNKQAAAAAAcNl2HDqpaV9vL/P5m6+M1aCkGEnS3qM5Sl7wc5mxg9tGa2iHOElSRtYZPTV/a6lx4bl7dW/Yj2ocH6euXe5WA/9oZRyKVlKCn/q3aKq+LSPVJjZUXl6WEvt2axyubo3DK3RunRLrq1Ni/QrFtouv57i66WJaxYQ6rpq6mGZRIWoWVbFF3Bs2CNJD1zWrUKw70ZQCAAAAAACX7fjps0rZUva6S+0SitePyj5TUG5ss6hgx9c5eYVlxvbz2qzGB16UMtvKt9v9Srn1K2XlFikyxL/yJwCXoykFAAAAAAAuW8PwQP3t5qQynz//I2Ox9fzLjU2KK25gRQRby4wNzG2gI+m71aBZN0mS1ddPkdyEr8agKQUAAAAAAC5bZKi/ftc9sUKx4cHWCsfaAn3LiU2U1K3E6M7jO/VL9i/ql9ivQq8B96ApBQAAAAAAao0NmRs0YuEIhfiFqFdcLwX4BFx8J7iFl7sTAAAAAAAAuCy5WdKJfZKkdhHtFBccp5P5J5WyJ8XNiaE8NKUAAAAAAEDN9eNcaeoV0hcTJEleFi/9pvlvJEkfbvvQjYnhYmhKAQAAAACAmiusif2/Z445hm5peot8vHy0+ehmbTm6xU2J4WJoSgEAAAAAgJortoP02C/S/UscQ+EB4eqf2F+S9NG2j9ySFi6OphQAAAAAAKi5vH2lgPolhu9ocYckacGeBTqZf9LVWaECaEoBAAAAAIBap2NkRzWt11RnCs7oi11fuDsdlIKmFAAAAAAAqNnS10jzHpCWTHUMWSwW3d78dkn2Bc+NMe7KDmWgKQUAAAAAAGq2U4ekH9+Tfv7SafjGJjcqwCdAu7J2Ke1QmpuSQ1l83J0AAAAAAADAZYnrJPWZIsW0dxoO8QvR9Y2u1yc7PtH9i+6X1duqFXetcDw/+bvJSk1P1Z86/0m3Nb9NkrTm4BqNXzy+0imk3Jqiev71JEl/X/13zds5TzFBMXpr4Fuq719yzSvQlAIAAAAAADVdaIzU57FSnxreari+2PWF8ovyVVBU4PRcXmGecs7mOI0XmSLlnM2pdApGxR8PzC/Ml9XbqomdJirUL7TSx6oraEoBAAAAAIBaq1n9Zkq9I1VZuVklnpvSbYomdpzouMJJktpFtNOCWxZU+nXObz6NvXKsRrcZrdjgWHl7eV9S3nUBTSkAAAAAAFDznc2VDv8syUixVzo9FeoXWuoVSw0CGkgBzmMBPgFKCE24rFTC/MMU5h92WceoC1joHAAAAAAA1Hwb5kizrpEWP+PuTByOnDmiGetn6MkfnnR3Kh7Jo5tSycnJ6tKli0JCQhQZGambb75Z27Ztc3daAAAAAADA00QlSQH1JX/PWcPpZP5Jzdo4S/N2zlPGqQx3p+NxPLoptXTpUo0dO1YrV67UokWLVFBQoAEDBignp/ILjgEAAAAAgFosoav06B7pN2+5OxOHRrZG6hrdVUWmSJ/s+MTd6Xgcj15TKiUlxenx7NmzFRkZqbS0NPXu3dtNWQEAAAAAAI9jsbg7g1Ld3uJ2rT64Wp/u+FR/aP8H+Xr5ujslj+HRV0pdKCvLvlJ+WFjZi4Xl5eUpOzvbaQMAAAAAAHCH6xKuU5h/mA6fOayl6UvdnY5HqTFNKWOMJk2apKuuukpJSUllxiUnJ8tmszm2hITLWzG/Km3evNndKQCoofj9AdjxswAAAMq1LUV6c4C04FF3Z+Lg6+2rW5vdKkn6cNuHbs7Gs9SYptS4ceO0ceNGvf/+++XGTZ48WVlZWY4tPT3dRRkCAAAAAAC3KsyT0lfZNw9yW7PbZJFFKzJWaF/2Pnen4zE8ek2pc8aPH6/58+dr2bJlio+PLzfWarXKarW6KDMAQF2x5dcsvfPDL2U+f8uV8erRJFyStOPQSb3x3e4yY29oF6trmkdIkvYezdE/U3eWGduraQMN7RB3aUkDAADUNVf0lG79lxTTzt2ZOIkPiVevuF5afmC5Pt7+sSZ1nuTulDyCRzeljDEaP3685s2bpyVLlqhRo0buTgkAUEdlnMjVh2v3l/l8u/h6jqZU5sm8cmObRYY4mlJHc/LLjY2xBTi+PpVXoDV7jqlHk3D5+3pX9hQAAABqv+AIqd3t7s6iVMOaD9PyA8s1b+c8jbtynPy8/dydktt5dFNq7Nixeu+99/T5558rJCREBw8elCTZbDYFBARcZG8AAKpOk8hg/WlgizKf75BQz/H1FWGB5cZ2bljf8XWsLaDM2LOFRRrSLsbx+Lvth/XAnHWy+nipZ5NwXdsyUte2iFRCWGAlzgQAAADucHX81YoKjNKh04e0aO8i3dD4Bnen5HYe3ZSaOXOmJKlPnz5O47Nnz9bo0aNdnxAAoO7yzZRX/UVlPv3dYft2jtf/+k6/b/d7+XjZ/7n9du+32n58uyz+PST9L8DnRJnHtVqkPK/ektpIks6cLVSszV+/ZuUqddthpW47LGmLmkQEKcYWoH/c0UERIfaPsM9ZtVcLNx0sM9+pt7VVfH17M+vjtP36bP2BMmOfHtpGjSOCJUlf/PirPlhjX6+xcUSQ/t+Q1vLxrjFLVNZay5Yt0/PPP6+0tDRlZGRo3rx5uvnmm92dFgAA7pH9q7RvpeRvk5pe5+5sHHy8fHRb89v06oZX9eG2D2lKycObUsYYd6cAAIAkaW/2Xr3646uV3u+etvfI53//3H6z7xt9uftLBfoGqkNkB0nSodOHyj3u6z++romdJmpk65G6tWO8brkyTtsPnVLqtkyl/pyptXuPa9fhHO06nKO8gkLHfr8cydHynUfKPO6Z/OLYfcdOlxt7Kq/A8fWBE2ccsct3HlH3xuG6vm1MWbvCRXJyctS+fXvdfffduu2229ydDgAA7vXzV9KCR6RmAz2qKSVJtza9Va//+LrWZa7TzuM71bR+U3en5FYe3ZQCAMBTxATF6I4Wd1R6Py9L8VVE3WK6Kcg3SM3rN3eMhfuHl3ncA6cOaPmB5Xph7Qv68fCPerrn0wr2C1aL6BC1iA7RmGuaKOvMWa3ec0yn8wsUFlS8LsHQDnFKirOVmVeUzd/x9eCkaDWJCCozNqF+8ccDr2sZqRibv1J/ztRnG37VnFV7aUp5gMGDB2vw4MEVjs/Ly1NeXp7jcXZ2dnWkBQCAe8R0kOI6S9FJ7s6khKigKF2bcK12ntipY7nH3J2O29GUAgCgAlqEtdCfu//5so5xc9ObdXPTm53G4kPiyzyuMUYfbPtAf1/zdy3au0g7ju/QP/r8w+kvarYAX/VvHVVi36Q4W7lNqfO1iglVq5jQCsU2iwpRs6gQdUqsr89//FXf7zyq3YdPOT7eh5ohOTlZTz31lLvTAABcYPPmzUpK8rxGSo2T0EX6/bfuzqJMT/V6SiG+IbJYLNVy/Jr0fcQiEAAAeCiLxaLftvyt3h70tqICo/RL9i+6a8Fd2nZsm7tTU3z9QPVtESlJem/VPjdng8qaPHmysrKyHFt6erq7UwIAoM4I9QuttoZUTcOVUgAAeLj2Ee314Y0f6tGlj8rH20fN6jdzd0qSpOHdr9C3P2dq9S/HZIwpt7g6ffa0Mk9nyuptVUxw8cf90rPTVWgKy9zvQhm5GUrIS5DNar8KLLcgVwdzDsrHy0fxIfGXfjJ1jNVqldVqdXcaAABUv4J8ycfv4nFukFuQqx9+/UF9r+jr7lTchqYUAAA1QJh/mF7v/7pyC3Md61TlFuQqOz9bkYGRbsnpmuaRevferurVpIEsFouMMdqbvVdrD61V2qE0jWw9Uq3CW0mSVmSs0ITUCeoQ0UHvXv+u4xijUkbp8JnDZb1EqR4NfFQjWo+QJP107CeNXDhSiaGJ+vKWL6vu5AAAQM22fo70zZNS84HS0FfcnU0J+YX5GvzpYB05c0Rzh8xVm/A27k7JLWhKAQBQQ3h7eSvIq3hB8mdXPaul+5dq2jXT1Dm6s8vz8bJIsRHZ+mj7Ykcj6vwGU7P6zRxNKV8vX4X4hSjI13lB9WC/YOUW5lb4NYsKi+TnVfzXTi+LV6nHBQAAdZxfkJSTKR3a4u5MSuXn7aeu0V21IXODjp456u503IamFAAANdCp/FPacnSLTuSd0Nmis47xv3z/F+04vqNSx7q+0fUa2WakJOnImSMa9+04+Xj56D/X/8cR89ya57Tu0Dqn/TJyMkrcNcbXy1dtG7RV5+jO6hHTwzHeO763frjzhxKvPf/m+ZXKdfPmzUpqWbxwZ/uI9qUet645deqUdu7c6Xi8Z88ebdiwQWFhYbriiivcmBkAAG7SqLd032IpsqW7MynTlG5TFOwbLG8vb3en4jY0pQAAqIg9y6Sv/ihFJUm3zy4e/+hu6dBm6YZp9uJHktJXS5+PlcKaSHfNLY797EFp/xppwDNS8wH2sYyN0if3SqFx0sjPimO/nCj9slzqcp/U7Q8l0gn2C9Z/rv+PVmWsUo/Y4ubP7qzd2nK0cn8R7BjV0fF1QVGBthzdIl8vX6eY9Oz0Uo/r7+2v2ICW2rs/Sl1ju2rGrUNl9WatIldbu3atrr32WsfjSZMmSZJGjRqlt99+201ZAQDgRoFh9s2DnVsjU5JS9qSoyBRVav/2ke0VFxwnSTqYc1DrDq1TPWs9har4rsrf7vtWeQV5JfZtaGuo1uGtLzHzqkNTCgCAisjPkY5sl6yhzuMn9tnH83OKx86eto95XfDPbFa6fTwvu3isIM8+VpjvHJv9q308NLZ4LOeI9NMXUssbpOBIBfgEqE9CH6fd/tT5T8rOz1ZlnCtmJKmetZ7+ed0/HetWnTOm/Rjd3uJ2p7FQv1C1CW+jNb9k6a41q7T8qLfyb/SSte7+sc9t+vTpI2OMu9MAAACXaMryKU5Xv1fEs1c966jjfjr6kx777jG1i2inyVdMLo5Z9awyT2eW2Pd3rX5HUwoAgBojoZs0eoFkDXYeHzJdyjslRbYqHotpb4/1DXCOHZgs5WZJDZoXj0W0sMf6+DvHXvdX+xVSCd2Lx37+UvpygrTu39L9qaWm2SGyQ6VP7Xz+Pv7qHd+7xHibBmUvvtmjcbiaRARp1+EcfbbhV43onnhZOQAAAFSJg5uk7f+VwhpLSbe6O5tydY3uqoKigkrt0yCggePr+v711S26mxrXa+wUc2XklTqRe6LEvomhnlGv0ZQCAKAiAsOkhr1Kjse0LzkWUL/02OikkmP+oaXHRrW2b06xNin2SqnVkOKxokLp3VukkGjp1lnF4ytfk375Tmp/Z3H86WPS/PH2K7iGvVMcu3a2tPMbe7GWdJt9LO+UNO9/Hxu8/R3J+38lw4b3pJ+/kloOkTrcKUmyWCwa3i1RT3+5VXNW7tXvul0hi8VS8pwAAABcae8KafH/Sc0He3xT6rX+r13W/h0iO+hfA/8lyb4G5zkvXPPCZR23utGUAgCgpmhzi30rOm+9gfRV0p6l9obV+TI22K+sSuhaPFaQax+7YL0oHdxkH49uWzxWdNY+Jkk672NhmVvt42HOf4X7TatAPfdfL/188KTW7TuuTomevYYDAACoAxK6SG2HSQ2vcncmKANNKQAAahqv89Z7imgp3fovqfCCBSw73GVvSMV1Lh7zt0lD/iFdsF6U2v7GfhVXTIfiMd9Ae6zkHN9qqL0hFXVeA2v3UoW+d4deibpd9+0fpP+s3EdTCgAAuF/sldJtb7g7C5SDphQAADVZYJjU7vaS4416F98N8By/IKnzPSVjE3vat/P5WEuPTehi38639XOp4IzaR1qk/dJXmzL01NA2CvX3Lbk/AAAA8D80pQAAwOW5YZrUfKAaRLfTnyJy1L91lELzMqW0T6Suvy+54DsAAIAr5WZLZ89IIVHuzgQX8Lp4CAAAQDksFqn5QFlCYzT22qZqHhUiLXteWvQX6dP7i+OO7pLmDpfmP+S8/3fT7eM7vy0ey9pvH5s3xik0fPtc+/jPC4oHTx22j300uurPDQAA1GzLX5SmJkipz7g7E5SCK6UAAEDVu6K7zM5vZOn+QPFY7gn7Ium2BOfYA2n28ab9isfyTtnHAsOdQgOO/yylL3L+uGHBGXusD1dkAQCAC9S7wv7fU4fcmwdKRVMKAABUqV2HT+mlrS1kiZqtlxLPW3/KdoV98XS/YOcdOt8tNb1OuqJH8VhIlD3Wx98p9HjDIarX/nop/ry7CgbUt8d6UdYAAIALNB8oPbrHvg4nPA7VGwAAqFKFRUbzf/xV3l4WTc7KVbTtf42l4IjSF08//wqpcwLqlxqbE9VZSkpyHrSGlH5cAAAAvyD7Bo/EmlIAAKBKNY8KUdeGYSosMpq7Zp+70wEAAICHoikFAACq3PDu9vUb5q5OV0FhkZuzAQAAddquVOmzsdLa2e7OBBfg43sAAKDKDUqKVniQnw5m5+rtH37RgNbRuiI8UJKUe7ZQG/dnlblvZIhVDRvYL7M/W1ik9ftOOJ7bnZmr03uOOR6HB/upSYR9jarCIqO0vccVW89f8fUDq+GsAABAjXR4m/Tj+8WLnkvSd9Ok7f+1LwHQ/rf2sdPHpPd/K1m8pHtSimN/eEX6ab7U4S6p02j7WH6O9O4t9q9HfSH5WO1fr/mXtPFDKek2qdsfqv3UajqaUgAAoMpZfbx1e+cEvbZ0l/721U86lJ2rJ25oLUk6cipPw15fUea+w7tdoWduaStJOplbUEps8d1zbu0Yp+nDOkiyN7CGvb5CE/o104R+zav0fAAAQA2W2EPyDZCuebR47NhuKX2V1HxQ8VjhWfuY5YIPlZ3Yax9v1Lt4zBTZxyTJmPNi0+3j8V2Ei6MpBdRCmzdvVtKFCwEDgIvdc1VDrdt7XEdy8hQWZHWM+3h5qXFE2QuONggujvW2WJxi8/Py5Gctfj4yxPnufI0jglQ/0K8q0gcAALVFTHvp7oWSxVI81uX3UvPBUkTL4jF/m3THnJL7dxwlNbpGatCseMzHvzjW27d4vP1v7Q2psEb2x4VnpT3L7HcaRgk0pQAAQLWIDPHXh2N6lBiPtvlr8R/7VOgYtkBfp9jymu7+vt4VPi4AAKhjYto5P47tYN/O5+svtRpSct/oJPt2Pm/f0mMjW9k3yd6QmtFROrFP+n2qFNfxUrOvtVjoHAAAAAAAoKp5+0oJ3aSgCClrv7uz8UhcKQUAAAAAAFAdBk2VrKGSD8sLlIamFAAAAAAAQHUIauDuDDwaH98DAAAAAACobkd2Ot+pDzSlAAAAAAAAqo0x0js3Sa90kg6sc3c2HoWmFAAAAAAAQHWxWKSQaMniLf1KU+p8rCkFAAAAAABQna59Qur/tL05BQeaUgAAAAAAANWpfqK7M/BIfHwPAAAAAADAVXKzWPD8f2hKAQAAAAAAuMKXk6QXWkj717o7E49AUwoAAAAAAMAVzp6WCs5I2xe6OxOPwJpSAAAAAAAArnDVJKnzPVJ8F3dn4hFoSgEAAAAAALhCRHN3Z+BR+PgeAAAAAACAqxUV1fkFz2lKAQAAAAAAuNKKf0ovt5fSV7s7E7eiKQUAAAAAAOBKh7ZIJ/ZJP77n7kzcijWlAAAAAAAAXKn7A1LDq6Q2t7g7E7eiKQUAAAAAAOBK0W3tWx3Hx/cAAAAAAADgcjSlAAAAAAAA3OHnr6R/D5X2rXR3Jm5BUwoAAAAAAMAdti2Qdi+R8nOKx5ZMlV5sK/0wo3gsP8c+9mJb6eyZ4vHvX7KPLX2ueMyY4tjTx4rHV71uH/vmyeo6m0qjKQUAAAAAAOAOXe+XvHykptcVj505br8z35kTxWPG2MdO7LN/7Yg98b/Y487HLS02N9s+dn6jys1Y6BwAAAAAAMAdYtpLD613HusxVmo7TAqJLh7zDZDuW2z/2sdaPN7lXqnlECk4wvkY52L9Q4vHrhwuNekrBYVXXf6XiaYUAAAAAACAu9S7ouTjC8e8vKX4TiX3tcXbt/NZLKXHhsbaNw/Cx/cAAAAAAADgcjWiKfXqq6+qUaNG8vf3V6dOnfTdd9+5OyUAAACPQr0EAABqGo9vSn3wwQeaMGGCnnjiCa1fv15XX321Bg8erH379rk7NQAAAI9AvQQAAGoij29KTZ8+Xffee6/uu+8+tWrVSi+++KISEhI0c+ZMd6cGAADgEaiXAABATeTRC53n5+crLS1Njz/+uNP4gAED9MMPP5S6T15envLy8hyPs7KyJEnZ2dnVluepU6cqdPyKxgFlqUvfa7XhHCqiLs0pUBU84Wfh3Oub82+x7EZ1sV7yhO+DsnhybnVFbfi3tTacgzt48vvBnMKVPOH7qKL1kkc3pY4cOaLCwkJFRUU5jUdFRengwYOl7pOcnKynnnqqxHhCQkK15AgAAOqmkydPymazuTsN6iUAAOCxLlYveXRT6hyLxeL02BhTYuycyZMna9KkSY7HRUVFOnbsmMLDw8vcB2XLzs5WQkKC0tPTFRoa6u504GLMf93G/NddzH35jDE6efKkYmM965bK1Evuw89M3cb8123Mf93G/JetovWSRzelGjRoIG9v7xJ/5cvMzCzx18BzrFarrFar01i9evWqK8U6IzQ0lB+yOoz5r9uY/7qLuS+bJ1whdQ71kufgZ6ZuY/7rNua/bmP+S1eResmjFzr38/NTp06dtGjRIqfxRYsWqWfPnm7KCgAAwHNQLwEAgJrKo6+UkqRJkyZpxIgR6ty5s3r06KFZs2Zp3759GjNmjLtTAwAA8AjUSwAAoCby+KbUHXfcoaNHj+rpp59WRkaGkpKStGDBAiUmJro7tTrBarXqr3/9a4lL/FE3MP91G/NfdzH3NQ/1knvxM1O3Mf91G/NftzH/l89iPOV+xgAAAAAAAKgzPHpNKQAAAAAAANRONKUAAAAAAADgcjSlAAAAAAAA4HI0pQAAAAAAAOByNKU8xLJly3TjjTcqNjZWFotFn332mdPzhw4d0ujRoxUbG6vAwEANGjRIO3bscIrZtWuXbrnlFkVERCg0NFTDhg3ToUOHnGK2b9+uoUOHqkGDBgoNDVWvXr2Umpp60fw2bdqka665RgEBAYqLi9PTTz+tC9fIz8vL0xNPPKHExERZrVY1adJEb731VrnHTU5OVpcuXRQSEqLIyEjdfPPN2rZtm1PMk08+qZYtWyooKEj169dXv379tGrVqovmXJO4av7XrVun/v37q169egoPD9f999+vU6dOXTS/i83/6NGjZbFYSmxt2rQp97gVmf9Tp05p3Lhxio+PV0BAgFq1aqWZM2deNOeaoiLvgTFGTz75pGJjYxUQEKA+ffpoy5YtTjF5eXkaP368GjRooKCgIN10003av3+/U8zx48c1YsQI2Ww22Ww2jRgxQidOnLhojhX5+Z8zZ47at2+vwMBAxcTE6O6779bRo0fLPe7MmTPVrl07hYaGKjQ0VD169NDChQudYj799FMNHDhQDRo0kMVi0YYNGy6ab03iyvl/5pln1LNnTwUGBqpevXoVyi83N1ejR49W27Zt5ePjo5tvvrnUuKVLl6pTp07y9/dX48aN9dprr1Xo+K+++qoaNWokf39/derUSd99953T86X9bunevXuFjo3aiXqJeol6iXqJeol6iXqpdtVLNKU8RE5Ojtq3b69XXnmlxHPGGN18883avXu3Pv/8c61fv16JiYnq16+fcnJyHPsPGDBAFotFixcv1vfff6/8/HzdeOONKioqchzrhhtuUEFBgRYvXqy0tDR16NBBQ4YM0cGDB8vMLTs7W/3791dsbKzWrFmjGTNm6IUXXtD06dOd4oYNG6Zvv/1Wb775prZt26b3339fLVu2LPe8ly5dqrFjx2rlypVatGiRCgoKNGDAAMd5SVLz5s31yiuvaNOmTVq+fLkaNmyoAQMG6PDhwxV6b2sCV8z/r7/+qn79+qlp06ZatWqVUlJStGXLFo0ePbrc3Coy/y+99JIyMjIcW3p6usLCwnT77beXe+yKzP/EiROVkpKi//znP/rpp580ceJEjR8/Xp9//nlF316PVpH34LnnntP06dP1yiuvaM2aNYqOjlb//v118uRJR8yECRM0b948zZ07V8uXL9epU6c0ZMgQFRYWOmLuuusubdiwQSkpKUpJSdGGDRs0YsSIcvOryPwvX75cI0eO1L333qstW7boo48+0po1a3TfffeVe+z4+HhNnTpVa9eu1dq1a9W3b18NHTrUqYDIyclRr169NHXq1Aq/pzWJK+c/Pz9ft99+ux544IEK51dYWKiAgAA99NBD6tevX6kxe/bs0fXXX6+rr75a69ev15QpU/TQQw/pk08+KffYH3zwgSZMmKAnnnhC69ev19VXX63Bgwdr3759TnGDBg1y+v2yYMGCCueP2od6iXqJeol6iXqJeol6qZbVSwYeR5KZN2+e4/G2bduMJLN582bHWEFBgQkLCzNvvPGGMcaY//73v8bLy8tkZWU5Yo4dO2YkmUWLFhljjDl8+LCRZJYtW+aIyc7ONpLMN998U2Y+r776qrHZbCY3N9cxlpycbGJjY01RUZExxpiFCxcam81mjh49elnnnpmZaSSZpUuXlhmTlZV10Zxrsuqa/9dff91ERkaawsJCR8z69euNJLNjx44y86nI/F9o3rx5xmKxmF9++aVS517a/Ldp08Y8/fTTTnEdO3Y0f/7znyt17JriwvegqKjIREdHm6lTpzpicnNzjc1mM6+99poxxpgTJ04YX19fM3fuXEfMgQMHjJeXl0lJSTHGGLN161YjyaxcudIRs2LFCiPJ/Pzzz2XmU5H5f/75503jxo2d9nv55ZdNfHx8pc+/fv365l//+leJ8T179hhJZv369ZU+Zk1SXfN/vtmzZxubzVbp3EaNGmWGDh1aYvzRRx81LVu2dBr7wx/+YLp3717u8bp27WrGjBnjNNayZUvz+OOPX/Q1AWOol6iXqJeol6iXLkS9RL1UE3GlVA2Ql5cnSfL393eMeXt7y8/PT8uXL3fEWCwWWa1WR4y/v7+8vLwcMeHh4WrVqpX+/e9/KycnRwUFBXr99dcVFRWlTp06lfn6K1as0DXXXON07IEDB+rXX3/VL7/8IkmaP3++OnfurOeee05xcXFq3ry5HnnkEZ05c6ZS55qVlSVJCgsLK/X5/Px8zZo1SzabTe3bt6/UsWuqqpr/vLw8+fn5ycur+Mc+ICBAkhwxpanI/F/ozTffVL9+/ZSYmFipcy1t/q+66irNnz9fBw4ckDFGqamp2r59uwYOHFipY9cUF74He/bs0cGDBzVgwABHjNVq1TXXXKMffvhBkpSWlqazZ886xcTGxiopKckRs2LFCtlsNnXr1s0R0717d9lsNkdMaSoy/z179tT+/fu1YMECGWN06NAhffzxx7rhhhsqfN6FhYWaO3eucnJy1KNHjwrvV9tU1/xXpxUrVji9tmT/Hlm7dq3Onj1b6j75+flKS0srsd+AAQNK5LxkyRJFRkaqefPm+v3vf6/MzMyqPQHUGtRLxaiX7KiXqJeol2on6qXaVS/RlKoBWrZsqcTERE2ePFnHjx9Xfn6+pk6dqoMHDyojI0OS/ZdlUFCQHnvsMZ0+fVo5OTn605/+pKKiIkeMxWLRokWLtH79eoWEhMjf31//+Mc/lJKSUu7nZQ8ePKioqCinsXOPz13Gvnv3bi1fvlybN2/WvHnz9OKLL+rjjz/W2LFjK3yexhhNmjRJV111lZKSkpye+/LLLxUcHOzIedGiRWrQoEGFj12TVdX89+3bVwcPHtTzzz+v/Px8HT9+XFOmTJEkR0xpKjL/58vIyNDChQsveinyhcqa/5dfflmtW7dWfHy8/Pz8NGjQIL366qu66qqrKnX8mqC09+Dce1zaHJx77uDBg/Lz81P9+vXLjYmMjCzxmpGRkeV+HKUi89+zZ0/NmTNHd9xxh/z8/BQdHa169eppxowZFz3nTZs2KTg4WFarVWPGjNG8efPUunXri+5XG1Xn/Fensr5HCgoKdOTIkVL3OXLkiAoLC8s9L0kaPHiw5syZo8WLF2vatGlas2aN+vbt6/ifT+B81EvUS9RL1EvUS7Uf9VLtq5doStUAvr6++uSTT7R9+3aFhYUpMDBQS5Ys0eDBg+Xt7S1JioiI0EcffaQvvvhCwcHBstlsysrKUseOHR0xxhg9+OCDioyM1HfffafVq1dr6NChGjJkiOMf2TZt2ig4OFjBwcEaPHiwIweLxeKUk/nfon3nxouKimSxWDRnzhx17dpV119/vaZPn663335bZ86c0Xfffec4bnBwsObMmVPiPMeNG6eNGzfq/fffL/Hctddeqw0bNuiHH37QoEGDNGzYsBrV/b0cVTX/bdq00TvvvKNp06YpMDBQ0dHRaty4saKiopxiLmX+z/f222+rXr16Tgv8Xc78v/zyy1q5cqXmz5+vtLQ0TZs2TQ8++KC++eabS3g3PVt5PwOlzUFp7395MaXFnx9zqfO/detWPfTQQ/p//+//KS0tTSkpKdqzZ4/GjBkjqfz5b9GihTZs2KCVK1fqgQce0KhRo7R169Zyz6u2qu75v5iy5r8iyvseKW/+L3Zed9xxh2644QYlJSXpxhtv1MKFC7V9+3Z99dVXlcoPdQP1EvUS9RL1EvVS7Ue9VPvqJR93J4CK6dSpkzZs2KCsrCzl5+crIiJC3bp1U+fOnR0xAwYM0K5du3TkyBH5+PioXr16io6OVqNGjSRJixcv1pdffqnjx48rNDRUkn0l/0WLFumdd97R448/rgULFjguHzx3qXJ0dHSJ7vG5Audc1zYmJkZxcXGy2WyOmFatWskYo/3796tz585Od4G4sNs7fvx4zZ8/X8uWLVN8fHyJ8w8KClLTpk3VtGlTde/eXc2aNdObb76pyZMnX9L7WdNUxfxL9oUb77rrLh06dEhBQUGyWCyaPn26I+ZS5/8cY4zeeustjRgxQn5+fo7xS53/M2fOaMqUKZo3b57j0uZ27dppw4YNeuGFF8pcSLAmKus9iI6OlmT/60pMTIxjPDMz0/E+RkdHO/6ae/5ffzIzM9WzZ09HzIV3F5Kkw4cPO45zqfOfnJysXr166U9/+pMk+xwFBQXp6quv1t/+9rdy59/Pz09NmzaVZP8+WbNmjV566SW9/vrrFXvjaonqnv+KKG3+K6Ks7xEfHx+Fh4fLZrOVmH+r1Spvb+9S97vw98P5YmJilJiYWOJuWsA51EvUS9RL1EvUS7UX9VLJ8ypNTauXuFKqhrHZbIqIiNCOHTu0du1aDR06tERMgwYNVK9ePS1evFiZmZm66aabJEmnT5+WJKfPyJ97fO6OI4mJiY5iJi4uTpLUo0cPLVu2TPn5+Y59vv76a8XGxqphw4aSpF69eunXX391ul3u9u3b5eXl5bg17bnjNm3aVCEhIZLs/yiPGzdOn376qRYvXuxUEJTHGFNjLkesSpcz/+eLiopScHCwPvjgA/n7+6t///6SLn3+z1m6dKl27type++912n8Uuf/7NmzOnv2bInvWW9vb6e7JNVkF3sPGjVqpOjoaC1atMgxlp+fr6VLlzr+Ae3UqZN8fX2dYjIyMrR582ZHTI8ePZSVlaXVq1c7YlatWqWsrCxHzKXO/+nTp0udo3PnV9b8l/V+1KWfbVfNf0WUNv8V0aNHD6fXluzfI507d5avr2+p8+/n56dOnTqV2G/RokXl5nz06FGlp6c7FZxAaaiX5NivLv1OPYd6yY56iXqptqBequX1UtWum45LdfLkSbN+/XrH3T2mT59u1q9fb/bu3WuMMebDDz80qampZteuXeazzz4ziYmJ5tZbb3U6xltvvWVWrFhhdu7cad59910TFhZmJk2a5Hj+8OHDJjw83Nx6661mw4YNZtu2beaRRx4xvr6+ZsOGDWXmduLECRMVFWXuvPNOs2nTJvPpp5+a0NBQ88ILLzjlHx8fb37zm9+YLVu2mKVLl5pmzZqZ++67r9zzfuCBB4zNZjNLliwxGRkZju306dPGGGNOnTplJk+ebFasWGF++eUXk5aWZu69915jtVqd7q5S07li/o0xZsaMGSYtLc1s27bNvPLKKyYgIMC89NJL5eZWkfk/53e/+53p1q1bhc/7YvNvjDHXXHONadOmjUlNTTW7d+82s2fPNv7+/ubVV1+t8Ot4soq8B1OnTjU2m818+umnZtOmTebOO+80MTExJjs72xEzZswYEx8fb7755huzbt0607dvX9O+fXtTUFDgiBk0aJBp166dWbFihVmxYoVp27atGTJkSLn5VWT+Z8+ebXx8fMyrr75qdu3aZZYvX246d+5sunbtWu6xJ0+ebJYtW2b27NljNm7caKZMmWK8vLzM119/7Yg5evSoWb9+vfnqq6+MJDN37lyzfv16k5GRUeH32JO5cv737t1r1q9fb5566ikTHBzs+J1z8uTJcnPcsmWLWb9+vbnxxhtNnz59HPuds3v3bhMYGGgmTpxotm7dat58803j6+trPv7443KPO3fuXOPr62vefPNNs3XrVjNhwgQTFBTkuAvVyZMnzR//+Efzww8/mD179pjU1FTTo0cPExcX53TuqFuol6iXqJeol6iXqJeol2pXvURTykOkpqYaSSW2UaNGGWOMeemll0x8fLzx9fU1V1xxhfnzn/9s8vLynI7x2GOPmaioKOPr62uaNWtmpk2bVuIWtGvWrDEDBgwwYWFhJiQkxHTv3t0sWLDgovlt3LjRXH311cZqtZro6Gjz5JNPljj2Tz/9ZPr162cCAgJMfHy8mTRpktMvitKUds6SzOzZs40xxpw5c8bccsstJjY21vj5+ZmYmBhz0003mdWrV18055rEVfM/YsQIExYWZvz8/Ey7du3Mv//97wrlV5H5P3HihAkICDCzZs2q8HlfbP6NMSYjI8OMHj3axMbGGn9/f9OiRYtSz62mqsh7UFRUZP7617+a6OhoY7VaTe/evc2mTZucjnPmzBkzbtw4ExYWZgICAsyQIUPMvn37nGKOHj1qhg8fbkJCQkxISIgZPny4OX78+EVzrMj8v/zyy6Z169YmICDAxMTEmOHDh5v9+/eXe9x77rnHJCYmGj8/PxMREWGuu+46pwLLGHsBV9r789e//vWiedcErpz/UaNGlfpaqamp5eaYmJhY6n7nW7JkibnyyiuNn5+fadiwoZk5c2aFzv+f//yn43ugY8eOTrc3P336tBkwYICJiIhw/O4bNWpUifNC3UK9RL1EvUS9RL1EvUS9VLvqJYsx/1tdCwAAAAAAAHAR1pQCAAAAAACAy9GUAgAAAAAAgMvRlAIAAAAAAIDL0ZQCAAAAAACAy9GUAgAAAAAAgMvRlAIAAAAAAIDL0ZQCAAAAAACAy9GUAgAAAAAAgMvRlAIAAAAAAIDL0ZQCUKuMHj1aFotFFotFvr6+ioqKUv/+/fXWW2+pqKiowsd5++23Va9evepLFAAAwE2olwB4CppSAGqdQYMGKSMjQ7/88osWLlyoa6+9Vg8//LCGDBmigoICd6cHAADgdtRLADwBTSkAtY7ValV0dLTi4uLUsWNHTZkyRZ9//rkWLlyot99+W5I0ffp0tW3bVkFBQUpISNCDDz6oU6dOSZKWLFmiu+++W1lZWY6/Ij755JOSpPz8fD366KOKi4tTUFCQunXrpiVLlrjnRAEAAC4R9RIAT0BTCkCd0LdvX7Vv316ffvqpJMnLy0svv/yyNm/erHfeeUeLFy/Wo48+Kknq2bOnXnzxRYWGhiojI0MZGRl65JFHJEl33323vv/+e82dO1cbN27U7bffrkGDBmnHjh1uOzcAAICqQL0EwNUsxhjj7iQAoKqMHj1aJ06c0GeffVbiud/+9rfauHGjtm7dWuK5jz76SA888ICOHDkiyb5GwoQJE3TixAlHzK5du9SsWTPt379fsbGxjvF+/fqpa9euevbZZ6v8fAAAAKoa9RIAT+Hj7gQAwFWMMbJYLJKk1NRUPfvss9q6dauys7NVUFCg3Nxc5eTkKCgoqNT9161bJ2OMmjdv7jSel5en8PDwas8fAACgulEvAXAlmlIA6oyffvpJjRo10t69e3X99ddrzJgx+r//+z+FhYVp+fLluvfee3X27Nky9y8qKpK3t7fS0tLk7e3t9FxwcHB1pw8AAFDtqJcAuBJNKQB1wuLFi7Vp0yZNnDhRa9euVUFBgaZNmyYvL/vSeh9++KFTvJ+fnwoLC53GrrzyShUWFiozM1NXX321y3IHAABwBeolAK5GUwpArZOXl6eDBw+qsLBQhw4dUkpKipKTkzVkyBCNHDlSmzZtUkFBgWbMmKEbb7xR33//vV577TWnYzRs2FCnTp3St99+q/bt2yswMFDNmzfX8OHDNXLkSE2bNk1XXnmljhw5osWLF6tt27a6/vrr3XTGAAAAlUO9BMATcPc9ALVOSkqKYmJi1LBhQw0aNEipqal6+eWX9fnnn8vb21sdOnTQ9OnT9fe//11JSUmaM2eOkpOTnY7Rs2dPjRkzRnfccYciIiL03HPPSZJmz56tkSNH6o9//KNatGihm266SatWrVJCQoI7ThUAAOCSUC8B8ATcfQ8AAAAAAAAux5VSAAAAAAAAcDmaUgAAAAAAAHA5mlIAAAAAAABwOZpSAAAAAAAAcDmaUgAAAAAAAHA5mlIAAAAAAABwOZpSAAAAAAAAcDmaUgAAAAAAAHA5mlIAAAAAAABwOZpSAAAAAAAAcDmaUgAAAAAAAHC5/w+0lI5VTAZbogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1200x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12,9))\n",
    "\n",
    "for i, ax in enumerate(axes.flatten()):\n",
    "    \n",
    "    ax.bar(\n",
    "        x=df[df['parts_id'] == parts_ids[i]]['date'],\n",
    "        height=df[df['parts_id'] == parts_ids[i]]['volume'],\n",
    "        color='lightgray'\n",
    "    )\n",
    "    ax.plot(\n",
    "        cv_df[cv_df.index==parts_ids[i]]['ds'],\n",
    "        cv_df[cv_df.index==parts_ids[i]]['CrostonOptimized'],\n",
    "        ls='--',\n",
    "        label='Croston'\n",
    "    )\n",
    "    \n",
    "    ax.plot(\n",
    "        cv_df[cv_df.index==parts_ids[i]]['ds'],\n",
    "        cv_df[cv_df.index==parts_ids[i]]['ADIDA'],\n",
    "        ls=':',\n",
    "        label='ADIDA'\n",
    "    )\n",
    "    \n",
    "    ax.plot(\n",
    "        cv_df[cv_df.index==parts_ids[i]]['ds'],\n",
    "        cv_df[cv_df.index==parts_ids[i]]['TSB'],\n",
    "        ls='-.',\n",
    "        label='TSB'\n",
    "    )\n",
    "\n",
    "    ax.set_xlabel('Date')\n",
    "    ax.set_ylabel('Volume')\n",
    "    ax.legend(loc='best')\n",
    "    \n",
    "    ax.xaxis.set_major_locator(plt.MaxNLocator(5))\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "71159b55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'CFE': -41.35299, 'CFE_max': 8.651390075683594, 'CFE_min': -44.09335708618164, 'PIS': 2060.1028, 'NOS': 12}\n",
      "{'CFE': -5.0277653, 'CFE_max': 9.537116050720215, 'CFE_min': -12.577536582946777, 'PIS': 269.7729, 'NOS': 26}\n",
      "{'CFE': -34.143932, 'CFE_max': 8.507474899291992, 'CFE_min': -34.601707458496094, 'PIS': 1423.5746, 'NOS': 14}\n"
     ]
    }
   ],
   "source": [
    "croston_errors = errors(cv_df['y'], cv_df['CrostonOptimized'])\n",
    "adida_errors = errors(cv_df['y'], cv_df['ADIDA'])\n",
    "tsb_errors = errors(cv_df['y'], cv_df['TSB'])\n",
    "\n",
    "print(croston_errors)\n",
    "print(adida_errors)\n",
    "print(tsb_errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5dd24fe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "models = ['Croston', 'ADIDA', 'TSB']\n",
    "errors = {\n",
    "    'CFE': (-41, -5, -34),\n",
    "    'PIS': (2060, 270, 1424),\n",
    "    'NOS': (12, 26, 14)\n",
    "}\n",
    "\n",
    "x = np.arange(len(models))\n",
    "width = 0.25\n",
    "multiplier = 0\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for attr, value in errors.items():\n",
    "    offset = width*multiplier\n",
    "    rects = ax.bar(x+offset, value, width, label=attr)\n",
    "    ax.bar_label(rects, padding=3)\n",
    "    multiplier += 1\n",
    "    \n",
    "ax.set_xlabel('Models')\n",
    "ax.set_ylabel('Error metrics')\n",
    "ax.set_xticks(x+width, models)\n",
    "ax.legend(loc='best')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9645e9dc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}