Google Revenue Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Google Revenue Prediction\n",
    "\n",
    "## Kaggle Challenge Description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What are we predicting?\n",
    "We are predicting the **natural log** of the sum of all transactions **per user**.\n",
    "\n",
    "$$ y_{user} = \\sum_{i=1}^n transaction_{user_{i}} $$\n",
    "$$ target_{user} = ln(y_{user} + 1) $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleansing \n",
    "\n",
    "Simply change csv_file to either \"test_v2.csv\" or \"train_v2.csv\" to determine which csv to use. Using \"test_v2.csv\" will make it easier to manage for your system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "from sklearn import model_selection, preprocessing, metrics\n",
    "import json\n",
    "import os\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.io.json import json_normalize\n",
    "import numpy as np\n",
    "import lightgbm as lgb\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading csv file\n",
      "Loading csv file\n",
      "Loading done.\n",
      "Wall time: 4.55 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "data = 'data'\n",
    "def load_csv(data, csv_file, nrows):\n",
    "    JSON_COLUMNS = ['device', 'geoNetwork', 'totals', 'trafficSource']\n",
    "    print(\"Loading csv file\")\n",
    "    df = pd.read_csv(os.path.join(os.getcwd(),data,csv_file),\n",
    "                    converters={column: json.loads for column in JSON_COLUMNS},\n",
    "                    dtype={'fullVisitorId': 'str'},\n",
    "                    nrows=nrows)\n",
    "\n",
    "    for column in JSON_COLUMNS:\n",
    "        column_as_df = json_normalize(df[column])\n",
    "        column_as_df.columns = [\"%s.%s\" % (column, subcolumn) for subcolumn in column_as_df.columns]\n",
    "        df = df.drop(column, axis=1).merge(column_as_df, right_index=True, left_index=True)\n",
    "#      Convert feature type\n",
    "    df[\"totals.transactionRevenue\"] = df[\"totals.transactionRevenue\"].astype('float')\n",
    "    \n",
    "    return df\n",
    "\n",
    "# Load both the training csv and testing csv\n",
    "train_df = load_csv(data, \"train_v2.csv\",10000)\n",
    "test_df = load_csv(data, \"test_v2.csv\", 2000)\n",
    "\n",
    "print(\"Loading done.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Columns and Types\n",
    "Initially we should dissect the data and learn what columns are found inside the csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "channelGrouping                                       object\n",
       "customDimensions                                      object\n",
       "date                                                   int64\n",
       "fullVisitorId                                         object\n",
       "hits                                                  object\n",
       "socialEngagementType                                  object\n",
       "visitId                                                int64\n",
       "visitNumber                                            int64\n",
       "visitStartTime                                         int64\n",
       "device.browser                                        object\n",
       "device.browserSize                                    object\n",
       "device.browserVersion                                 object\n",
       "device.deviceCategory                                 object\n",
       "device.flashVersion                                   object\n",
       "device.isMobile                                         bool\n",
       "device.language                                       object\n",
       "device.mobileDeviceBranding                           object\n",
       "device.mobileDeviceInfo                               object\n",
       "device.mobileDeviceMarketingName                      object\n",
       "device.mobileDeviceModel                              object\n",
       "device.mobileInputSelector                            object\n",
       "device.operatingSystem                                object\n",
       "device.operatingSystemVersion                         object\n",
       "device.screenColors                                   object\n",
       "device.screenResolution                               object\n",
       "geoNetwork.city                                       object\n",
       "geoNetwork.cityId                                     object\n",
       "geoNetwork.continent                                  object\n",
       "geoNetwork.country                                    object\n",
       "geoNetwork.latitude                                   object\n",
       "geoNetwork.longitude                                  object\n",
       "geoNetwork.metro                                      object\n",
       "geoNetwork.networkDomain                              object\n",
       "geoNetwork.networkLocation                            object\n",
       "geoNetwork.region                                     object\n",
       "geoNetwork.subContinent                               object\n",
       "totals.bounces                                        object\n",
       "totals.hits                                           object\n",
       "totals.newVisits                                      object\n",
       "totals.pageviews                                      object\n",
       "totals.sessionQualityDim                              object\n",
       "totals.timeOnSite                                     object\n",
       "totals.totalTransactionRevenue                        object\n",
       "totals.transactionRevenue                            float64\n",
       "totals.transactions                                   object\n",
       "totals.visits                                         object\n",
       "trafficSource.adContent                               object\n",
       "trafficSource.adwordsClickInfo.adNetworkType          object\n",
       "trafficSource.adwordsClickInfo.criteriaParameters     object\n",
       "trafficSource.adwordsClickInfo.gclId                  object\n",
       "trafficSource.adwordsClickInfo.isVideoAd              object\n",
       "trafficSource.adwordsClickInfo.page                   object\n",
       "trafficSource.adwordsClickInfo.slot                   object\n",
       "trafficSource.campaign                                object\n",
       "trafficSource.isTrueDirect                            object\n",
       "trafficSource.keyword                                 object\n",
       "trafficSource.medium                                  object\n",
       "trafficSource.referralPath                            object\n",
       "trafficSource.source                                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering and Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create 4 additional features from *date* feature: year, month, day, weekday. We will use those to better understand our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_time_features(df):\n",
    "    df['date'] = pd.to_datetime(df['date'], format='%Y%m%d', errors='ignore')\n",
    "    df['year'] = df['date'].apply(lambda x: x.year)\n",
    "    df['month'] = df['date'].apply(lambda x: x.month)\n",
    "    df['day'] = df['date'].apply(lambda x: x.day)\n",
    "    df['weekday'] = df['date'].apply(lambda x: x.weekday())\n",
    "    return df\n",
    "add_time_features(test_df)\n",
    "temp_df = add_time_features(train_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group date by date, year, month, day, weekday and aggregate by count/sum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_agg = train_df.groupby('date')['totals.transactionRevenue'].agg(['count', 'sum'])\n",
    "year_agg = train_df.groupby('year')['totals.transactionRevenue'].agg(['sum'])\n",
    "month_agg = train_df.groupby('month')['totals.transactionRevenue'].agg(['sum'])\n",
    "day_agg = train_df.groupby('day')['totals.transactionRevenue'].agg(['sum'])\n",
    "weekday_agg = train_df.groupby('weekday')['totals.transactionRevenue'].agg(['count','sum'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Target Variable Exploration\n",
    "\n",
    "We need to sum up the transaction revenue on the user level. Then graph a scatter plot of the natural log.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df[\"totals.transactionRevenue\"] = train_df[\"totals.transactionRevenue\"].astype('float')\n",
    "gdf = train_df.groupby(\"fullVisitorId\")[\"totals.transactionRevenue\"].sum().reset_index()\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(range(gdf.shape[0]), np.sort(np.log1p(gdf[\"totals.transactionRevenue\"].values)))\n",
    "plt.xlabel('Customer', fontsize=12)\n",
    "plt.ylabel('TransactionRevenue', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scatter plot above confirms the 80/20 marketing principle that states 80% of the profits come from 20% of the customers.\n",
    "\n",
    "From this analysis, this would mean that we have a distinct dataset between customers with TransactionRevenue and customers without TransactionRevenue.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sum of transactionRevenue, our target, through the time.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dunW5//77O393zTXXpL6+PjU1NSkpKemsFwqFlJSU5NChQ0etd/z72z76+bf3KS0tPWKfjvrRztFR/6jS0tK0t7cftafjMWzY4OPa/mRRUXFqb7dAH2AO6GAWTk6uOx3MAh3MAh9lJkjMAR/qqVnoMkC744478thjj2Xw4A9Dn5KSkqxbt67ofps2bcrBgwfz2c9+NgMHDszEiRPT2NiYIUOGdD56WSgUUlZWlhEjRnS+7D/5TWBXWVl5RH379u2prKzM0KFD8/7776e9vT39+vXr3D75zR1h27dvz4gRI9LW1pa9e/dmyJAhGT58eJqbm/PJT37ysGONGDEizc3NR5zjaD2NGjUqhUIh69evP6LX47Fjxwc5dKhwXPuc6CoqTk1Ly/u93Qa9zBzQwSycnFx3OpgFOpgFPspMkJgDPtQxC6WlJd1+s1KXj3A+++yz+ad/+qe88MILnf91FZ4lyb/+679m3rx5OXjwYA4ePJjnnnsuF154Yb71rW9l9+7daW1tzeOPP54JEybk/PPPz1tvvZUtW7akvb09Tz/9dKqqqnLGGWekvLy8c2XMhoaGVFVVpX///hk3blwaGxuTJPX19amqqkqSVFdXp76+PknS2NiYcePGpX///qmurk5DQ0OSZP369SkvL8/pp5+eqqqqLF++PO3t7dmyZUs2b96c8847L1VVVVm5cmX27duXnTt35oUXXsjFF1+cSy65JOvWrcvOnTuzb9++rFy5svPcAAAAAJx4urwD7VOf+lROO+204z5wdXV1NmzYkMsvvzz9+vXLxIkT85WvfCWf+MQn8qUvfSltbW2ZOHFiLr300iTJnXfemZtuuikHDhxIdXV1amtrkySLFi3KvHnz8sEHH2TMmDG59tprkyS33XZbZs+enQceeCAjR47M3XffnSS55ZZbMnv27NTV1eXUU0/NokWLkvzmcdH58+enrq4uAwYMyMKFC5MktbW12bBhQ+cCAwsWLMgpp5ySsWPHZurUqZk+fXra2tpy8803Z/jw4UmSmTNn5tprr01ra2umT5+esWPHHvffBwAAAIDfDSWFQqHos4Q/+clP8uCDD+bzn/9850v7k+QrX/lKtzd3ovII55HcgktiDviQWTg5ue50MAt0MAt8lJkgMQd8qCcf4ezyDrQHH3wwgwcPzvvvG04AAAAATj5dBmj79u3LD37wg57oBQAAAAD6nC4XERg1alRef/31nugFAAAAAPqcLu9Ae+eddzJ9+vScccYZGTBgQGd9+fLl3doYAAAAAPQFXQZoX/3qV3uiDwAAAADok7oM0P7gD/6gJ/oAAAAAgD6pywDtoosuSklJSQqFQkpKSpIkFRUVWbNmTbc3BwAAAAC9rcsA7bcXEDh48GCefvrpvPXWW93aFAAAAAD0FV2uwvnbBgwYkGnTpuX555/vrn4AAAAAoE/p8g60Xbt2df5cKBSycePG7Nmzp1ubAgAAAIC+4rjegZYkw4YNy6233trtjQEAAABAX3Bc70ADAAAAgJNN0Xegvfnmm9mxY0eSZOPGjfnmN7+ZJ598skcaAwAAAIC+4JgB2nPPPZcvfelL2bx5c7Zt25b/+B//Y/bv358nn3wy3/3ud3uwRQAAAADoPccM0B588MEsXbo0f/iHf5inn346n/3sZ/PNb34z3/nOd/LEE0/0ZI8AAAAA0GuOGaDt27cvo0ePTpK89NJLqaqqSpIMHDiwc0EBAAAAADjRHTNA6wjJCoVCXn755YwbN67zd7/+9a+7vzMAAAAA6AOOuQrn2WefnUceeSQHDhxIWVlZLrjgghQKhTzyyCM599xze7JHAAAAAOg1xwzQ5s6dm3nz5qWlpSWLFi1KaWlpbr/99qxduzYPPfRQT/YIAAAAAL3mmAFaRUVFvvOd7xxWu+mmmzJv3rz069ev2xsDAAAAgL7gmAHa0QwdOrS7+gAAAACAPumYiwgAAAAAAAI0AAAAACjqYwVo7777blavXp329vZs3bq1u3sCAAAAgD6jywBt1apVueqqq3L77bdnx44dqaury49//OOe6A0AAAAAel2XAdr999+fZcuW5bTTTktlZWWWLl2a++67ryd6AwAAAIBe12WA1t7ensrKys7Pn/3sZ1NSUtKtTQEAAABAX9FlgDZw4MBs3bq1MzRbv359ysvLu70xAAAAAOgLyrra4Gtf+1r+03/6T2lpacl/+A//IZs3b863v/3tnugNAAAAAHpdlwHav/t3/y7Lli3Lyy+/nEOHDuX888/P0KFDe6I3AAAAAOh1XQZor776apLk93//95Mk77zzTt55552MGTOmezsDAAAAgD6gywDtpptu6vy5tbU1LS0tOffcc/OjH/2oWxsDAAAAgL6gywDtH//xHw/7/H//7//N8uXLu60hAAAAAOhLulyF86M+//nPdz7WCQAAAAAnuo/9DrQkKRQK2bhxY/bv39+tTQEAAABAX3Fc70ArKSnJ0KFD8z/+x//ozp4AAAAAoM847negAQAAAMDJpMsAbfv27fnhD3+YXbt2HVafN29etzUFAAAAAH1FlwHaf/2v/zUDBw7M5z73uZSUlPRETwAAAADQZ3QZoG3bti0rVqzoiV4AAAAAoM8p7WqD008/Pb/+9a97ohcAAAAA6HO6vAOtsrIyl19+ecaPH59TTjmls+4daAAAAACcDLoM0M4444ycccYZPdELAAAAAPQ5XQZoX/nKV7J///5s2bIl55xzTg4cOJCBAwf2RG8AAAAA0Ou6fAfaK6+8ki9+8Yv58pe/nObm5nzhC1/I//t//68negMAAACAXtdlgHbXXXflu9/9boYMGZIRI0Zk4cKFWbBgQU/0BgAAAAC9rssAbf/+/Tn77LM7P1dXV6e9vb1bmwIAAACAvqLLAK2srCy7d+9OSUlJkuTNN9/s9qYAAAAAoK/ochGB66+/Pn/+53+e7du356tf/Wqef/75/M//+T97ojcAAAAA6HVdBmh//Md/nNGjR+f555/PoUOHcuONN2b06NE90RsAAAAA9LouA7SvfvWr+bM/+7NcffXVPdEPAAAAAPQpXb4D7cILL8zdd9+dCRMm5Dvf+U5aWlp6oi8AAAAA6BO6DNC+9KUvZdmyZfmbv/mb7N69O1dddVVuvPHGnugNAAAAAHpdlwFah/379+fgwYMpFArp169fd/YEAAAAAH1Gl+9Ae/jhh/PEE0/k4MGDmT59epYtW5bf//3f74neAAAAAKDXdRmgbdy4MfPmzcvnP//5nugHAAAAAPqULh/h/F//639l4MCB+eEPf5iDBw/m5Zdf/tgHv/feezN58uTU1dXl4YcfTpKsXbs2U6ZMycSJE7N48eLObV977bVMmzYtNTU1ufXWW9PW1pYk2bp1a2bMmJHa2trccMMN2bt3b5Jkz549ue666zJp0qTMmDGjc3GDgwcPZtasWZk0aVKuuOKKbNq0KUlSKBRy1113pba2NpMnT85LL73Uee6HHnootbW1qampycqVKzvry5cvz+TJkzNx4sQ89thjnfVjfQcAAAAATjxdBmhPPPFE5syZk7/927/N+++/n//yX/5Lli1b1uWBf/azn+WFF17IU089lX/4h3/Io48+mtdffz1z587NkiVL0tjYmI0bN2b16tVJklmzZmX+/Pl55plnUigUOs9x++235+qrr05TU1POPffcLFmyJElyzz33ZNy4cVmxYkWuvPLKLFiwIEny6KOPZuDAgVmxYkXmzp2bOXPmJEmeeeaZbNq0KY2Njbn//vszZ86ctLW1ZcOGDXnqqafS0NCQpUuXZuHChdm1a1e2bduWxYsXZ+nSpamvr8/jjz+eN954I/v37z/mdwAAAADgxNNlgPboo4/m8ccfz+DBgzNs2LA88cQTeeSRR7o88Pjx4/O9730vZWVl2bFjR9rb27Nnz56cddZZOfPMM1NWVpYpU6akqakpb7/9dvbv358LLrggSTJt2rQ0NTWltbU1L774Ympqag6rJ8mqVasyZcqUJMmll16aNWvWpLW1NatWrcrUqVOTJBdeeGF27tyZrVu3ZvXq1Zk8eXJKS0szatSojBw5Mi+//HLWrFmTCRMmpLy8PMOGDcv48eOzatWqrF27NhdddFGGDBmSQYMGpaamJk1NTdmwYcNRvwMAAAAAJ6YuA7TS0tIMHjy48/PIkSM/9iqc/fv3z3333Ze6urpcfPHFaW5uTkVFRefvKysrs23btiPqFRUV2bZtW957770MHjw4ZWVlh9WTHLZPWVlZBg8enJ07dx71WO+++26am5tTWVl5XPWP02tHHQAAAIATU5eLCAwZMiSvvfZaSkpKkiRPPfVU/s2/+Tcf+wQ333xz/uqv/irXX399Nm/e3Hmc5DfvJSspKcmhQ4eOWu/497d99PNv71NaWnrEPh31o52jo/5RpaWlaW9vP65ej8ewYYO73ugkVFFxam+3QB9gDuhgFk5OrjsdzAIdzAIfZSZIzAEf6qlZ6DJAmzt3bm655Zb86le/yr//9/8+5eXluf/++7s88KZNm3Lw4MF89rOfzcCBAzNx4sQ0NTUddvdaS0tLKisrM2LEiM5FAJJk+/btqayszNChQ/P++++nvb09/fr169w++c2dX9u3b8+IESPS1taWvXv3ZsiQIRk+fHiam5vzyU9+8rBjjRgxIs3NzUec46PnbmlpyahRo1IoFLJ+/foue/3tnj6uHTs+yKFDhePa50RXUXFqWlre7+026GXmgA5m4eTkutPBLNDBLPBRZoLEHPChjlkoLS3p9puVunyEc/To0WloaMiTTz6Zhx56KE1NTfnMZz7T5YH/9V//NfPmzcvBgwdz8ODBPPfcc7nqqqvy1ltvZcuWLWlvb8/TTz+dqqqqnHHGGSkvL+9cGbOhoSFVVVXp379/xo0bl8bGxiRJfX19qqqqkiTV1dWpr69PkjQ2NmbcuHHp379/qqur09DQkCRZv359ysvLc/rpp6eqqirLly9Pe3t7tmzZks2bN+e8885LVVVVVq5cmX379mXnzp154YUXcvHFF+eSSy7JunXrsnPnzuzbty8rV65MVVVVzj///KN+BwAAAABOTEXvQHv++edz2mmn5bzzzsvo0aOTJK+//npuv/32/OAHPyh64Orq6mzYsCGXX355+vXrl4kTJ6auri5Dhw7NTTfdlAMHDqS6ujq1tbVJkkWLFmXevHn54IMPMmbMmFx77bVJkttuuy2zZ8/OAw88kJEjR+buu+9Oktxyyy2ZPXt26urqcuqpp2bRokVJkmuuuSbz589PXV1dBgwYkIULFyZJamtrs2HDhs4FBhYsWJBTTjklY8eOzdSpUzN9+vS0tbXl5ptvzvDhw5MkM2fOzLXXXpvW1tZMnz49Y8eOTZLceeedR/0OAAAAAJx4SgqFwlGfJbzrrruyYsWK7N+/P9/4xjfyhS98IXfddVd+8IMfZOrUqbnjjjt6utcThkc4j+QWXBJzwIfMwsnJdaeDWaCDWeCjzASJOeBDPfkI5zHvQHv22Wfz1FNPpaWlJQsWLMh3v/vd7NmzJ9/73vfyh3/4h93aFAAAAAD0Fcd8B9rv/d7v5bTTTsvo0aPz6quvZvTo0XniiSeEZwAAAACcVI55B1pp6YfZ2pAhQzJ//vyUlXW5aCcAAAAAnFC6XIUzSQYNGiQ8AwAAAOCkdMxU7N133803v/nNI37uMG/evO7tDAAAAAD6gGMGaDNmzDjqzwAAAABwMjlmgPaVr3ylJ/sAAAAAgD6pyxebvfnmm/nf//t/Z9euXSkUCp31v/mbv+nWxgAAAACgL+gyQJs9e3bGjh2bCy+8MCUlJT3REwAAAAD0GV0GaPv27bNgAAAAAAAnrdKuNjjrrLPS3NzcE70AAAAAQJ/T5R1ohw4dyqWXXpoxY8akvLy8s+4daAAAAACcDLoM0CZMmJAJEyb0RC8AAAAA0Od0GaBdccUVefvtt/Ozn/0sbW1tGT9+fM4666ye6A0AAAAAel2X70D7p3/6p/zpn/5pfvzjH+e5557L9OnT8+Mf/7gnegMAAACAXtflHWj33ntvvv/97+fss89Okvzyl7/MrFmz8sUvfrHbmwMAAACA3tblHWitra2d4VmSnHPOOWlvb+8Qt0QpAAAgAElEQVTWpgAAAACgr+gyQDvllFPyz//8z52f//mf/zkDBw7s1qYAAAAAoK/o8hHOWbNm5frrr+9cOOCtt97Kvffe2+2NAQAAAEBf0GWANm7cuPyf//N/8sorr+TQoUO54IIL8olPfKInegMAAACAXnfMAK2hoSGXXXZZHn744cPqmzdvTpL85V/+Zbc2BgAAAAB9wTEDtC1btiRJfvGLX/RYMwAAAADQ1xwzQLv55puTJH/yJ3+SL37xi4f9rr6+vnu7AgAAAIA+4pgB2j/+4z+mra0tCxcuTKFQSKFQSJK0tbXl29/+di6//PIeaxIAAAAAessxA7TXXnstL7zwQnbs2JHvfe97H+5QVpa/+Iu/6IneAAAAAKDXHTNAu/HGG3PjjTfmsccey4wZM5Ik7e3tKRQKKSvrcvFOAAAAADghlHa1wejRozN16tQkyZtvvpnq6uq8/PLL3d4YAAAAAPQFXQZoCxcuzB133JEkOeecc/Lggw92fgYAAACAE12XAVpra2vGjBnT+XnMmDE5ePBgtzYFAAAAAH1FlwHawIEDs2bNms7P69aty6BBg7q1KQAAAADoK7pcDeDWW2/NjTfemLKyspSUlKSkpCTf/va3e6I3AAAAAOh1XQZo559/flatWpVf/OIX6devX0aNGpUBAwb0RG8AAAAA0Ou6DNAOHjyY1atXZ+/evUmSjRs35le/+lVmzpzZ7c0BAAAAQG/rMkCbOXNm/uVf/iUtLS353Oc+l1deeSXjx4/vid4AAAAAoNd1uYjAa6+9lieeeCJ/8id/krlz5+YHP/hBdu/e3RO9AQAAAECv6zJAq6ysTFlZWT71qU/lF7/4Rc4555y8//77PdEbAAAAAPS6LgO0QYMGZfny5fnMZz6TFStW5Oc//3l+/etf90RvAAAAANDrugzQ5s+fn9deey1/9Ed/lNLS0lxzzTX5z//5P/dEbwAAAADQ67pcROBTn/pUvv71rydJFi5cmJaWlpxxxhnd3hgAAAAA9AVd3oH27LPP5hvf+EY++OCD1NbW5vLLL88jjzzSE70BAAAAQK/rMkD7zne+kz/7sz/LypUrc8EFF+QnP/lJGhoaeqI3AAAAAOh1XQZohUIh//bf/tusXbs2VVVVGTx4cAqFQk/0BgAAAAC9rssArbS0NI2NjfnpT3+aP/qjP8rq1atTUlLSE70BAAAAQK/rMkD7b//tv2XZsmWZOXNmKioq8sADD+TWW2/tid4AAAAAoNd1uQrnuHHj8t3vfrfz8w9/+MPu7AcAAAAA+pQuA7SXX345d999d3bv3n3Yu8+WL1/erY0BAAAAQF/QZYA2f/78TJs2LZ/73Oe8+wwAAACAk06XAVpZWVn+8i//sid6AQAAAIA+p8tFBM4555z8/Oc/74leAAAAAKDP6fIOtH/5l3/Jn/7pn+b0009PeXl5Z9070AAAAAA4GXQZoM2cObMn+gAAAACAPqnLAG38+PHZtWtX9u3bl0KhkPb29vzqV7/qid4AAAAAoNd1GaDde++9efDBB5Mk/fr1S2tra84++2yPcAIAAABwUuhyEYGGhob85Cc/SU1NTVauXJk77rgjZ599dk/0BgAAAAC9rssAbejQoamsrMynP/3pvP7667n88svzi1/8oid6AwAAAIBe12WAVlZWll/96lf59Kc/nfXr16etrS0HDhzoid4AAAAAoNd1GaBdf/31+e///b/nC1/4Qp599tl84QtfyEUXXfSxDv7Xf/3XqaurS11dXRYuXJgkmTNnTiZOnJjLLrssl112WZ599tkkydq1azNlypRMnDgxixcv7jzGa6+9lmnTpqWmpia33npr2trakiRbt27NjBkzUltbmxtuuCF79+5NkuzZsyfXXXddJk2alBkzZqSlpSVJcvDgwcyaNSuTJk3KFVdckU2bNiVJCoVC7rrrrtTW1mby5Ml56aWXOs/90EMPpba2tvPx1Q7Lly/P5MmTM3HixDz22GMf628BAAAAwO+mLgO0tra2PPLIIxk0aFDq6+vzt3/7t/nGN77R5YHXrl2bn/70p3nyySdTX1+fV199Nc8++2w2btyY73//+2loaEhDQ0MmTJiQ/fv3Z+7cuVmyZEkaGxuzcePGrF69Okkya9aszJ8/P88880wKhUKWLVuWJLn99ttz9dVXp6mpKeeee26WLFmSJLnnnnsybty4rFixIldeeWUWLFiQJHn00UczcODArFixInPnzs2cOXOSJM8880w2bdqUxsbG3H///ZkzZ07a2tqyYcOGPPXUU2loaMjSpUuzcOHC7Nq1K9u2bcvixYuzdOnS1NfX5/HHH88bb7zx/++vDwAAAECf12WA9tt3gw0cODCf+cxnUlJS0uWBKyoqMnv27AwYMCD9+/fP6NGjs3Xr1mzdujVz587NlClTct999+XQoUPZsGFDzjrrrJx55pkpKyvLlClT0tTUlLfffjv79+/PBRdckCSZNm1ampqa0tramhdffDE1NTWH1ZNk1apVmTJlSpLk0ksvzZo1a9La2ppVq1Zl6tSpSZILL7wwO3fuzNatW7N69epMnjw5paWlGTVqVEaOHJmXX345a9asyYQJE1JeXp5hw4Zl/PjxWbVqVdauXZuLLrooQ4YMyaBBg1JTU9N5bgAAAABOPGVdbfAHf/AHeeCBBzJu3LgMGjSosz5mzJii+51zzjmdP2/evDkrVqzIY489lp/97Ge57bbbcuqpp+bLX/5yfvSjH2XQoEGpqKjo3L6ysjLbtm1Lc3PzYfWKiops27Yt7733XgYPHpyysrLD6kkO26esrCyDBw/Ozp07j3qsd999N83NzamsrDxq/bzzzjuiXlJSckSvGzZs6OrPeJhhwwYf1/Yni4qKU3u7BfoAc0AHs3Byct3pYBboYBb4KDNBYg74UE/NQpcB2iuvvJJXXnklf//3f99ZKykpyXPPPfexTvDLX/4yX/7yl/P1r389n/70p3P//fd3/u6aa65JfX19ampqDrurrVAopKSkJIcOHTpqvePf33asu+IKhUJKS0uP2KejfrRzdNQ/qrS0NO3t7Uft6Xjs2PFBDh0qHNc+J7qKilPT0vJ+b7dBLzMHdDALJyfXnQ5mgQ5mgY8yEyTmgA91zEJpaUm336zUZYC2dOnSjBgx4rDaL3/5y4918Jdeeik333xz5s6dm7q6uvz85z/P5s2bOx+9LBQKKSsry4gRIzpf9p8kLS0tqaysPKK+ffv2VFZWZujQoXn//ffT3t6efv36dW6f/OaOsO3bt2fEiBFpa2vL3r17M2TIkAwfPjzNzc355Cc/edixRowYkebm5iPOcbSeRo0alUKhkPXr1x/RKwAAAAAnpmO+A23Xrl3ZtWtXrrvuuuzevTu7du3K7t27s3379tx0001dHvidd97JjTfemEWLFqWuri7JbwKzb33rW9m9e3daW1vz+OOPZ8KECTn//PPz1ltvZcuWLWlvb8/TTz+dqqqqnHHGGSkvL+9cGbOhoSFVVVXp379/xo0bl8bGxiRJfX19qqqqkiTV1dWpr69PkjQ2NmbcuHHp379/qqur09DQkCRZv359ysvLc/rpp6eqqirLly9Pe3t7tmzZks2bN+e8885LVVVVVq5cmX379mXnzp154YUXcvHFF+eSSy7JunXrsnPnzuzbty8rV67sPDcAAAAAJ55j3oH2ta99Lc8//3yS5POf//yHO5SVdd5BVszf/d3f5cCBA7nzzjs7a1dddVWuu+66fOlLX0pbW1smTpyYSy+9NEly55135qabbsqBAwdSXV2d2traJMmiRYsyb968fPDBBxkzZkyuvfbaJMltt92W2bNn54EHHsjIkSNz9913J0luueWWzJ49O3V1dTn11FOzaNGiJL95XHT+/Pmpq6vLgAEDsnDhwiRJbW1tNmzY0LnAwIIFC3LKKadk7NixmTp1aqZPn562trbcfPPNGT58eJJk5syZufbaa9Pa2prp06dn7NixH+dvDQAAAMDvoJJCoVD0ZVxz5szJHXfc0VP9nBS8A+1InmEnMQd8yCycnFx3OpgFOpgFPspMkJgDPtST70A75iOcHYRnAAAAAJzMugzQAAAAAOBkJkADAAAAgCIEaAAAAABQhAANAAAAAIoQoAEAAABAEQI0AAAAAChCgAYAAAAARQjQAAAAAKAIARoAAAAAFCFAAwAAAIAiBGgAAAAAUIQADQAAAACKEKABAAAAQBECNAAAAAAoQoAGAAAAAEUI0AAAAACgCAEaAAAAABQhQAMAAACAIgRoAAAAAFCEAA0AAAAAihCgAQAAAEARAjQAAAAAKEKABgAAAABFCNAAAAAAoAgBGgAAAAAUIUADAAAAgCIEaAAAAABQhAANAAAAAIoQoAEAAABAEQI0AAAAAChCgAYAAAAARQjQAAAAAKAIARoAAAAAFCFAAwAAAIAiBGgAAAAAUIQADQAAAACKEKABAAAAQBECNAAAAAAoQoAGAAAAAEUI0AAAAACgCAEaAAAAABQhQAMAAACAIgRoAAAAAFCEAA0AAAAAihCgAQAAAEARAjQAAAAAKEKABgAAAABFCNAAAAAAoAgBGgAAAAAUIUADAAAAgCIEaAAAAABQhAANAAAAAIoQoAEAAABAEQI0AAAAAChCgAYAAAAARXRrgPbXf/3XqaurS11dXRYuXJgkWbt2baZMmZKJEydm8eLFndu+9tprmTZtWmpqanLrrbemra0tSbJ169bMmDEjtbW1ueGGG7J3794kyZ49e3Lddddl0qRJmTFjRlpaWpIkBw8ezKxZszJp0qRcccUV2bRpU5KkUCjkrrvuSm1tbSZPnpyXXnqp89wPPfRQamtrU1NTk5UrV3bWly9fnsmTJ2fixIl57LHHOuvH+g4AAAAAnHi6LUBbu3ZtfvrTn+bJJ59MfX19Xn311Tz99NOZO3dulixZksbGxmzcuDGrV69OksyaNSvz58/PM888k0KhkGXLliVJbr/99lx99dVpamrKueeemyVLliRJ7rnnnowbNy4rVqzIlVdemQULFiRJHn300QwcODArVqzI3LlzM2fOnCTJM888k02bNqWxsTH3339/5syZk7a2tmzYsCFPPfVUGhoasnTp0ixcuDC7du3Ktm3bsnjx4ixdujT19fV5/PHH88Ybb2T//v3H/A4AAAAAnHi6LUCrqKjI7NmzM2DAgPTv3z+jR4/O5s2bc9ZZZ+XMM89MWVlZpkyZkqamprz99tvZv39/LrjggiTJtGnT0tTUlNbW1rz44oupqak5rJ4kq1atypQpU5Ikl156adasWZPW1tasWrUqU6dOTZJceOGF2blzZ7Zu3ZrVq1dn8uTJKS0tzahRozJy5Mi8/PLLWbNmTSZMmJDy8vIMGzYs48ePz6pVq7J27dpcdNFFGTJkSAYNGpSampo0NTVlw4YNR/0OAAAAAJyYyrrrwOecc07nz5s3b86KFSvy53/+56moqOisV1ZWZtu2bWlubj6sXlFRkW3btuW9997L4MGDU1ZWdlg9yWH7lJWVZfDgwdm5c+dRj/Xuu++mubk5lZWVR62fd955R9RLSkqO6HXDhg1HHL/jOxyPYcMGH9f2J4uKilN7uwX6AHNAB7NwcnLd6WAW6GAW+CgzQWIO+FBPzUK3BWgdfvnLX+bLX/5yvv71r6dfv37ZvHlz5+8KhUJKSkpy6NChlJSUHFHv+Pe3ffTzb+9TWlp6xD4d9aOdo6P+UaWlpWlvbz9qT8fq9Xjs2PFBDh0qHNc+J7qKilPT0vJ+b7dBLzMHdDALJyfXnQ5mgQ5mgY8yEyTmgA91zEJpaUm336zUrYsIvPTSS/mLv/iLfO1rX8sVV1yRESNGdL7sP0laWlpSWVl5RH379u2prKzM0KFD8/7776e9vf2w7ZPf3Pm1ffv2JElbW1v27t2bIUOGZPjw4Wlubj7iWCNGjDhm/eP01FUdAAAAgBNTtwVo77zzTm688cYsWrQodXV1SZLzzz8/b731VrZs2ZL29vY8/fTTqaqqyhlnnJHy8vLOlTEbGhpSVVWV/v37Z9y4cWlsbEyS1NfXp6qqKklSXV2d+vr6JEljY2PGjRuX/v37p7q6Og0NDUmS9evXp7y8PKeffnqqqqqyfPnytLe3Z8uWLdm8eXPOO++8VFVVZeXKldm3b1927tyZF154IRdffHEuueSSrFu3Ljt37sy+ffuycuXKVFVVHfM7AAAAAHBi6rZHOP/u7/4uBw4cyJ133tlZu+qqq3LnnXfmpptuyoEDB1JdXZ3a2tokyaJFizJv3rx88MEHGTNmTK699tokyW233ZbZs2fngQceyMiRI3P33XcnSW655ZbMnj07dXV1OfXUU7No0aIkyTXXXJP58+enrq4uAwYMyMKFC5MktbW12bBhQ+cCAwsWLMgpp5ySsWPHZurUqZk+fXra2tpy8803Z/jw4UmSmTNn5tprr01ra2umT5+esWPHJskxvwMAAAAAJ56SQqHgZVw9zDvQjuQZdhJzwIfMwsnJdaeDWaCDWeCjzASJOeBDJ8w70AAAAADgd50ADQAAAACKEKAB/H/t3X2QVXXBB/DvsosQMI4vA2JlMYMBhYOaNqMz1PoCgdIKwjQxQ5ExCgWlKYpoFkjTDAEBI/SepZX+YYECKk5jEwyI4yBTaS+aY5JBCoRMILnLsnueP56HLWL3ysvuvbeHz2eGP/be+zvn9+N859w937lnLwAAAJSgQAMAAACAEhRoAAAAAFCCAg0AAAAASlCgAQAAAEAJCjQAAAAAKKGu0hMAAAAAoH1P//71rFz/cnbvbcqZp/bI+PqBuXRo/0pP66SjQAMAAACoQk///vXcv/aFHDjYmiTZvbcp9699IUmUaGXmFk4AAACAKrRy/ctt5dkhBw62ZuX6lys0o5OXAg0AAACgCu3e23RMj9N1FGgAAAAAVejMU3sc0+N0HQUaAAAAQBUaXz8wp9QdXt2cUtct4+sHVmhGJy9fIgAAAABQhQ59UYBv4aw8BRoAAABAlbp0aH+FWRVwCycAAAAAlKBAAwAAAIASFGgAAAAAUIICDQAAAABKUKABAAAAQAkKNAAAAAAoQYEGAAAAACUo0AAAAACgBAUaAAAAAJSgQAMAAACAEhRoAAAAAFCCAg0AAAAASlCgAQAAAEAJCjQAAAAAKEGBBgAAAAAlKNAAAAAAoAQFGgAAAACUoEADAAAAgBIUaAAAAABQggINAAAAAEqoq/QEAAAAgOr19O9fz8r1L2f33qaceWqPjK8fmEuH9q/0tKCsFGgAAABAu57+/eu5f+0LOXCwNUmye29T7l/7QpIo0TipuIUTAAAAaNfK9S+3lWeHHDjYmpXrX67QjKAyFGgAAABAu3bvbTqmx+H/KwUaAAAA0K4zT+1xTI/D/1cKNAAAAKBd4+sH5pS6w6uDU+q6ZXz9wArNCCrDlwgAAAAA7Tr0RQG+hZOTnQINAAAA6NClQ/srzDjpuYUTAAAAAEpQoAEAAABACQo0AAAAAChBgQYAAAAAJSjQAAAAAKAEBRoAAAAAlKBAAwAAAIASFGgAAAAAUIICDQAAAABKUKABAAAAQAkKNAAAAAAooa7SEzgZdetWU+kpVCX/LyRywL/IwsnJcecQWeAQWeA/yQSJHPAv3brVlCUPNUVRFF2+FwAAAAD4L+UWTgAAAAAoQYEGAAAAACUo0AAAAACgBAUaAAAAAJSgQAMAAACAEhRoAAAAAFCCAg0AAAAASlCgAQAAAEAJCjQAAAAAKEGBxnFbvnx5xowZkzFjxmTBggVJkk2bNqWhoSEf/ehHs2TJkiPGzJo1KytXrmz7eefOnZk6dWrGjRuXiRMnZtu2be3u649//GPGjx+fUaNG5Utf+lIOHjyYJHnuuecyYcKENDQ0ZNq0adm1a1cXrJRSypmDjsYfsnTp0ixbtuwEV8TxOtEs7N69O2PHjm37d8UVV+TCCy9sd18dnRMOkYXyqYbj/uyzz2b8+PFpaGjIZz/72fzjH//ootVSSjVk4eGHH87w4cPbttHePul6lc7CsYynPCqdicR1QzUoZw7aG//v/K5YWZ1xDblt27ZMmjQpY8eOzac+9als37693X397W9/y6RJkzJ69Oh87nOfy/79+w97/mc/+1lmz559dBMv4Dg89dRTxSc+8YmiqampOHDgQDF58uRizZo1RX19ffHqq68Wzc3NxZQpU4p169YVRVEUr7/+ejFt2rRi2LBhxYoVK9q28+lPf7p48MEHi6IoigcffLC46aab2t3fmDFjil//+tdFURTFHXfcUTzwwANFa2trUV9fXzz99NNFURTFY489VkybNq0rl81/KHcOOhq/d+/e4o477iiGDRtW3HPPPV24YjrSWVk4pKWlpfjkJz9ZrF69ut39tXdOKApZKLdqOe4jRowoXnrppaIoimLhwoXFN77xja5YLiVUSxbmzZtXrFmzpotWydGoliwc7Xi6XjVkwnVD5ZU7B64bqldnZeHWW29tO+f/+Mc/LmbOnNnu/qZOnVo8+uijRVEUxfLly4sFCxYURVEUjY2NxcKFC4sLLriguP32249q7j6BxnHp27dvZs+enVNOOSXdu3fPwIEDs3Xr1rz3ve/NOeeck7q6ujQ0NOSJJ55IkqxZsyZXXnllrrrqqrZtvPHGG3nhhRcyceLEJMmECRPyxS9+8Yh9bd++PY2NjbnggguSJOPHj88TTzyRPXv2pLGxMZdcckmS5PLLL8/GjRtz4MCBrl4+/6ecOehofJL88pe/zIABA/KZz3ymi1bK2+mMLPy7FStW5B3veEcaGhqOeK6jc0IiC+VWLcf98ccfz7nnnpvm5ubs2LEjp556ahetmI5USxaef/75PPzww2loaMitt97q04gVUC1ZOJrxlEc1ZMJ1Q+WVMwelxvtdsfI6Kwutra158803kyRvvfVWevbsecS+mpubs3nz5owaNSrJ4e8TmzdvTmtra2677bajnrsCjePyvve9r+2NaevWrVm7dm1qamrSt2/fttf069cvO3bsSJJcf/31+fjHP37YNv7617/mne98Z+bPn58JEybkxhtvTPfu3Y/Y186dOw/bbt++fbNjx46cfvrp6dWrVzZu3Jgkeeyxx9Lc3Jw9e/Z0+nppXzlz0NH4JBk3blymTp2a2trazloax6gzsnBIS0tLvvOd72TmzJntPt/ROSGRhXKrluPevXv3vPjii6mvr88zzzyTMWPGdMr6OHrVkoW+fftm+vTpWb16dc4+++zMmzevU9bH0auWLBzNeMqjGjLhuqHyypmDUuP9rlh5nZWFm266Kffdd18+/OEP54c//GFuuOGGI16zZ8+e9OnTJ3V1dUkOf58YPnx4Zs2a1W7x1hEFGifkpZdeypQpUzJr1qycc845qampaXuuKIrDfv5PBw8ezB/+8IdccsklWbFiRa688sp27z1ubW1td7s1NTW555578t3vfjfjxo3Lvn37ctppp3VYvtB1ypED/jucSBYO2bBhQwYMGJDBgwe3+3xH5wQqpxqO++DBg7Np06ZMnz49N9988wmshhNR6Sx885vfzEUXXZSamppcf/312bBhwwmuiONV6SwczXjKq5KZcN1QPcqRA/47nGgWbr/99sybNy8bNmzI3Xffnc9//vMpiuKw17S3nRO5blCgcdy2bNmS6667LjNnzsy1116b/v37H/bHOHft2pV+/fp1OL5v377p3bt3Lr/88iTJxz72sTz33HPZsWNH2x+GvOGGG47Y7t///ve27dbV1eUnP/lJHnnkkVxzzTVpbW3Naaed1kUrpj3lygHV70SzcMiTTz6Zq6++uu3nYzknUH6VPu5NTU158skn2x6/5ppr8uKLL3bS6jgWlc7Cvn37ct9997U9XhSFTxhUSKWz0NF4KqcaMuG6ofLKlQOq34lm4Y033sif//znjBgxIkkyatSo7Nq1K3v27DnsiybOOOOM7Nu3Ly0tLUe13bdTd9wjOam99tprmTFjRpYsWZJLL700SXL++efnlVdeyV/+8pe8+93vzqOPPpoJEyZ0uI33vOc96d+/f9avX5/6+vr86le/ytChQ3PWWWdl1apVh722R48e2bJlSy666KKsWrUqH/nIR5Ikd955Z+bOnZthw4blRz/6UUaPHp1u3fTC5VLuHFC9OiMLh/zmN7857JefYzknUF7VcNzr6upy9913p3///jnvvPOydu3afPCDH+zchfK2qiELvXr1yg9+8INceOGFOf/88/PTn/40I0eO7NyF8raqIQsdjacyqiUTrhsqq9w5oHp1RhZOP/309OjRI88++2wuvvjibNmyJb17984ZZ5xxRBYuvvjiPP7442loaMgjjzxyQtcNCjSOy7333pumpqbMnz+/7bGJEydm/vz5+cIXvpCmpqbU19dn9OjRJbezbNmyzJkzJwsXLkyfPn0O296/W7RoUe666668+eabGTp0aCZPnpwkmTt3bubMmZO33norgwcPzte+9rXOWyRvq9w5oHp1VhaS//27eP379y/5mo7OCZRXNRz32traLFmyJF/5ylfS0tKSs846y3tBBVRLFpYuXZq5c+emsbExAwYMyIIFC054bRybasjCsYyn61VLJlw3VFa5c0D16ows1NTUZPny5fnqV7+axsbG9O7dO8uWLWv3tXPmzMns2bPz7W9/O2effXYWL1583HOvKf7zJlEAAAAAoI3PrAIAAABACQo0AAAAAChBgQYAAAAAJSjQAAAAAKAEBRoAAAAAlFBX6QkAAFAe27Zty8iRIzNo0KAkSWtra3r37p3Jkyfn6quvLjl2+fLlGTJkSEaMGFGOqQIAVBUFGgDASaRnz55ZtWpV28/bt2/Pddddl9ra2owaNarDcc8880zOPffcckwRAKDqKNAAAE5i73rXu3LjjTfm3nvvzaBBgzJv3rzs378/u3btysyRx+wAAAIKSURBVJAhQ7J06dL8/Oc/z+9+97ssWLAgtbW1qa+vz6JFi7J58+a0tLTkAx/4QO6666706dOn0ssBAOgS/gYaAMBJbsiQIfnTn/6Uhx56KOPGjctDDz2UX/ziF9m2bVvWrVuXSZMm5bzzzsusWbMycuTIfO9730ttbW1WrlyZ1atXp1+/flm0aFGllwEA0GV8Ag0A4CRXU1OTnj175rbbbstTTz2V73//+9m6dWt27tyZf/7zn0e8ft26ddm3b182bdqUJGlubs6ZZ55Z7mkDAJSNAg0A4CT3/PPPZ9CgQbnlllvS0tKSq666Kpdddllee+21FEVxxOtbW1tz5513pr6+Pkmyf//+NDU1lXvaAABl4xZOAICT2CuvvJJvfetbmTJlSjZu3JgZM2a0fSPnb3/727S0tCRJamtrc/DgwSTJ8OHD88ADD+TAgQNpbW3Nl7/85SxevLhiawAA6Go+gQYAcBJpbGzM2LFjkyTdunVLjx49csstt+Syyy7LzTffnBkzZqRXr17p06dPPvShD+XVV19NklxxxRVZvHhxmpubM3369Hz961/Ptddem5aWlrz//e/P7NmzK7ksAIAuVVO097l8AAAAACCJWzgBAAAAoCQFGgAAAACUoEADAAAAgBIUaAAAAABQggINAAAAAEpQoAEAAABACQo0AAAAAChBgQYAAAAAJfwPfxC9SpWwRR8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,7))\n",
    "plt.ticklabel_format(axis='y', style='plain')\n",
    "plt.ylabel('transactionRevenue Sum', fontsize=12)\n",
    "plt.xlabel('Date', fontsize=12)\n",
    "plt.scatter(time_agg.index.values, time_agg['sum'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Count of transactionRevenue our target, through the same time period.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,7))\n",
    "plt.ticklabel_format(axis='y', style='plain')\n",
    "plt.ylabel('Frequency', fontsize=12)\n",
    "plt.xlabel('Date', fontsize=12)\n",
    "plt.scatter(time_agg.index.values, time_agg['count'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze engineered time features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sum of transactionRevenue over each year**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,7))\n",
    "plt.ticklabel_format(axis='y', style='plain')\n",
    "plt.xlabel('Year', fontsize=12)\n",
    "plt.locator_params(nbins=2)\n",
    "plt.scatter(year_agg.index.values, year_agg['sum'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Insight:** There is a large increase from 2016 to 2017."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sum of transactionRevenue over monthly period**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Insight:** There is a spike in revenue in December, April and August.\n",
    "December makes sense as it is holiday season. What drives April and August sales?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(month_agg.index.values, month_agg['sum'])\n",
    "plt.locator_params(nbins=12)\n",
    "plt.ticklabel_format(axis='y', style='plain')\n",
    "plt.xlabel('Month', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sum of transactionRevenue daily**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(day_agg.index.values, day_agg['sum'])\n",
    "plt.locator_params(nbins=10)\n",
    "plt.ticklabel_format(axis='y', style='plain')\n",
    "plt.xlabel('Day', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Insight:** No insights here, useless feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sum of transactionRevenue on weekdays**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(weekday_agg.index.values, weekday_agg['sum'])\n",
    "plt.locator_params(nbins=7)\n",
    "plt.ticklabel_format(axis='y', style='plain')\n",
    "plt.xlabel('Weekday', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Insight:** Weekends bring less revenue than weekdays as it makes sense because Google Office has more visitors during the weekdays - remember it is still a workplace."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare data for modeling\n",
    "\n",
    "### Analyzing which Columns are constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['socialEngagementType',\n",
       " 'device.browserSize',\n",
       " 'device.browserVersion',\n",
       " 'device.flashVersion',\n",
       " 'device.language',\n",
       " 'device.mobileDeviceBranding',\n",
       " 'device.mobileDeviceInfo',\n",
       " 'device.mobileDeviceMarketingName',\n",
       " 'device.mobileDeviceModel',\n",
       " 'device.mobileInputSelector',\n",
       " 'device.operatingSystemVersion',\n",
       " 'device.screenColors',\n",
       " 'device.screenResolution',\n",
       " 'geoNetwork.cityId',\n",
       " 'geoNetwork.latitude',\n",
       " 'geoNetwork.longitude',\n",
       " 'geoNetwork.networkLocation',\n",
       " 'totals.visits',\n",
       " 'trafficSource.adwordsClickInfo.criteriaParameters']"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "const_cols = [c for c in train_df.columns if train_df[c].nunique(dropna=False)==1 ]\n",
    "const_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should also remove the constant columns, as they will bring no bearing to the weights on the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_to_drop = const_cols\n",
    "#+ [\"trafficSource.campaignCode\"]\n",
    "train_df = train_df.drop(cols_to_drop,axis=1)\n",
    "test_df = test_df.drop(cols_to_drop, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove columns that primarily contain *null* values. (more than 50%)\n",
    "\n",
    "**Uncomment when running in production**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns that primarily contain null values: \n",
      "totals.bounces\n",
      "totals.totalTransactionRevenue\n",
      "totals.transactionRevenue\n",
      "totals.transactions\n",
      "trafficSource.adContent\n",
      "trafficSource.adwordsClickInfo.adNetworkType\n",
      "trafficSource.adwordsClickInfo.gclId\n",
      "trafficSource.adwordsClickInfo.isVideoAd\n",
      "trafficSource.adwordsClickInfo.page\n",
      "trafficSource.adwordsClickInfo.slot\n",
      "trafficSource.isTrueDirect\n",
      "trafficSource.keyword\n",
      "trafficSource.referralPath\n"
     ]
    }
   ],
   "source": [
    "null_columns = [c for c in train_df.columns if train_df[c].count()<=len(train_df) * 0.5]\n",
    "# train_df.drop(null_columns, axis=1)\n",
    "# test_df.drop(null_columns, axis=1)\n",
    "\n",
    "print(\"Columns that primarily contain null values: \")\n",
    "for c in null_columns:\n",
    "    print(c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_features = ['device.isMobile','year', 'month', 'weekday']\n",
    "train_df = pd.get_dummies(train_df, columns=categorical_features)\n",
    "test_df = pd.get_dummies(test_df, columns=categorical_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train and Test datasets must be aligned using an outer joing in order to have the same amount of features. It is an important step for training and inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df, test_df = train_df.align(test_df, join='outer', axis=1)\n",
    "\n",
    "# replace the null values added by join for 0\n",
    "train_df.replace(to_replace=np.nan, value=0, inplace=True)\n",
    "test_df.replace(to_replace=np.nan, value=0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Developing the Model: LGBM\n",
    "To discover which is the best model to predict the log revenue of customers. We can do a training analysis based on popular models such as LightGBM, ADABOOST and XGBOOST. All these models can be found in scikit-learn.\n",
    "\n",
    "First, we need to build the training and testing dataset respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "channelGrouping\n",
      "device.browser\n",
      "device.deviceCategory\n",
      "device.operatingSystem\n",
      "geoNetwork.city\n",
      "geoNetwork.continent\n",
      "geoNetwork.metro\n",
      "geoNetwork.country\n",
      "geoNetwork.networkDomain\n",
      "geoNetwork.region\n",
      "geoNetwork.subContinent\n",
      "trafficSource.adContent\n",
      "trafficSource.adwordsClickInfo.adNetworkType\n",
      "trafficSource.adwordsClickInfo.gclId\n",
      "trafficSource.adwordsClickInfo.page\n",
      "trafficSource.adwordsClickInfo.slot\n",
      "trafficSource.campaign\n",
      "trafficSource.keyword\n",
      "trafficSource.medium\n",
      "trafficSource.referralPath\n",
      "trafficSource.source\n",
      "trafficSource.adwordsClickInfo.isVideoAd\n",
      "trafficSource.isTrueDirect\n"
     ]
    }
   ],
   "source": [
    "train_df[\"totals.transactionRevenue\"].fillna(0, inplace=True)\n",
    "cat_cols = [\"channelGrouping\", \"device.browser\", \n",
    "            \"device.deviceCategory\", \"device.operatingSystem\", \n",
    "            \"geoNetwork.city\", \"geoNetwork.continent\", \n",
    "            \"geoNetwork.metro\", \"geoNetwork.country\",\n",
    "            \"geoNetwork.networkDomain\", \"geoNetwork.region\", \n",
    "            \"geoNetwork.subContinent\", \"trafficSource.adContent\", \n",
    "            \"trafficSource.adwordsClickInfo.adNetworkType\", \n",
    "            \"trafficSource.adwordsClickInfo.gclId\", \n",
    "            \"trafficSource.adwordsClickInfo.page\", \n",
    "            \"trafficSource.adwordsClickInfo.slot\", \"trafficSource.campaign\",\n",
    "            \"trafficSource.keyword\", \"trafficSource.medium\", \n",
    "            \"trafficSource.referralPath\", \"trafficSource.source\",\n",
    "            'trafficSource.adwordsClickInfo.isVideoAd', 'trafficSource.isTrueDirect']\n",
    "for col in cat_cols:\n",
    "    print(col)\n",
    "    lbl = preprocessing.LabelEncoder()\n",
    "    lbl.fit(list(train_df[col].values.astype('str')) + list(test_df[col].values.astype('str')))\n",
    "    train_df[col] = lbl.transform(list(train_df[col].values.astype('str')))\n",
    "    test_df[col] = lbl.transform(list(test_df[col].values.astype('str')))\n",
    "\n",
    "num_cols = [\"totals.hits\", \"totals.pageviews\", \"visitNumber\", \"visitStartTime\", 'totals.bounces',  'totals.newVisits']    \n",
    "for col in num_cols:\n",
    "    train_df[col] = train_df[col].astype(float)\n",
    "    test_df[col] = test_df[col].astype(float)\n",
    "\n",
    "train_set,test_set = model_selection.train_test_split(train_df, test_size=0.2)\n",
    "\n",
    "train_Y = np.log1p(train_set[\"totals.transactionRevenue\"].values)\n",
    "val_Y = np.log1p(test_set[\"totals.transactionRevenue\"].values)\n",
    "\n",
    "train_X = train_set[cat_cols+num_cols]\n",
    "val_X = test_set[cat_cols+num_cols]\n",
    "test = test_df[cat_cols + num_cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "channelGrouping\n",
      "device.browser\n",
      "device.deviceCategory\n",
      "device.operatingSystem\n",
      "geoNetwork.city\n",
      "geoNetwork.continent\n",
      "geoNetwork.metro\n",
      "geoNetwork.country\n",
      "geoNetwork.networkDomain\n",
      "geoNetwork.region\n",
      "geoNetwork.subContinent\n",
      "trafficSource.adContent\n",
      "trafficSource.adwordsClickInfo.adNetworkType\n",
      "trafficSource.adwordsClickInfo.gclId\n",
      "trafficSource.adwordsClickInfo.page\n",
      "trafficSource.adwordsClickInfo.slot\n",
      "trafficSource.campaign\n",
      "trafficSource.keyword\n",
      "trafficSource.medium\n",
      "trafficSource.referralPath\n",
      "trafficSource.source\n",
      "trafficSource.adwordsClickInfo.isVideoAd\n",
      "trafficSource.isTrueDirect\n",
      "totals.hits\n",
      "totals.pageviews\n",
      "visitNumber\n",
      "visitStartTime\n",
      "totals.bounces\n",
      "totals.newVisits\n"
     ]
    }
   ],
   "source": [
    "for x in train_X:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds.\n",
      "[100]\tvalid_0's rmse: 1.69471\n",
      "[200]\tvalid_0's rmse: 1.69315\n",
      "Early stopping, best iteration is:\n",
      "[153]\tvalid_0's rmse: 1.68773\n"
     ]
    }
   ],
   "source": [
    "## TODO: Build LightGBM training function.\n",
    "def train_with_gbm(train_X,train_Y,val_x,val_y,test_X):\n",
    "    params = {\n",
    "        \"objective\" : \"regression\",\n",
    "        \"metric\" : \"rmse\", \n",
    "        \"num_leaves\" : 30,\n",
    "        \"min_child_samples\" : 100,\n",
    "        \"learning_rate\" : 0.1,\n",
    "        \"bagging_fraction\" : 0.7,\n",
    "        \"feature_fraction\" : 0.5,\n",
    "        \"bagging_frequency\" : 5,\n",
    "        \"bagging_seed\" : 2018,\n",
    "        \"verbosity\" : -1\n",
    "    }\n",
    "    \n",
    "    lgtrain = lgb.Dataset(train_X, label=train_Y)\n",
    "    lgval = lgb.Dataset(val_x, label=val_y)\n",
    "    model = lgb.train(params, lgtrain, 1000, valid_sets=[lgval], early_stopping_rounds=100, verbose_eval=100)\n",
    "    \n",
    "    pred_test_y = model.predict(test_X, num_iteration=model.best_iteration)\n",
    "    pred_val_y = model.predict(val_x, num_iteration=model.best_iteration)\n",
    "    return pred_test_y, model, pred_val_y\n",
    "\n",
    "# Training the model #\n",
    "pred_test, model, pred_val = train_with_gbm(train_X, train_Y, val_X, val_Y, test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eBAQEZOlnMpnIyLj7P0K5c+dm+PDhVKlSBTc3NywWCydOnKBChQrWtpmZmdy4cYNz586xcOFC7O3tGTt2LJmZmfeN688+j1PNmjWZPHkyrVu3xsHBge7duxMQEJDlY7gP4+rVZMxmy2ONTZ48F5f8JCQk5XQYkg3Kne1S7myb8me7lDvbpdzlnMKFizBy5FjGjRtFSsodcuWyZ8SIzylfvgLR0RNo37419va5KFv2FXr16pfT4doUFVbkmVetWjWSkpIIDAzMcj0oKIjevXuzcuVKTCYTo0ePznL/5ZdfJikpidDQUD7//HNCQkL4+OOPSU9PB+CNN96ge/fu1j+Hh4czevRoWrVqxVtvvYWdnR01a9YkJSWF27dv3xPXX/u8/vrrj2Wtfn5+nDx5ktatW5OZmckbb7xxT8FIRERERESeT1Wrvs6MGXPvud6nT/9/7Dt//nwVxR7AYLFY9FpaRLLQjhXbpDdAtku5s13KnW1T/myXcme7lDvb9bzkLjvfWDE+oVhERERERERERJ55KqyIiIiIiIiIiGSTCisiIiIiIiIiItmkwoqIiIiIiIiISDapsCIiIiIiIiIikk0qrIiIiIiIiIiIZJMKKyIiIiIiIiIi2aTCioiIiIiIiIhINqmwIiIiIiIiIiKSTXY5HYCIiIiIiMjTJjp6Ajt2bKVAgYIAlCr1IkOHjmDatGh++GEvRqMBN7dShIYOxNnZmZSUFEaNGs7p06cwm8107dqTunV9cnYRIvKv0I4VkUeUlJRE9+7d/7bNgAEDuHjx4t+2CQoKIiYm5qHmfFDbSZMmsW3bNmsbEREREXk8jh8/SkREFHPmLGTOnIUMGzaS9evXcOrUSWbN+oZ585bg5ubGlCkTAJg16yvy5MnLggXLmTDhC8aPH018fFwOr0JE/g3asSLyiBITEzlx4sTftomJifnH4svj8Mknn1j/fODAgcc2buHCjo9tLPl3ubjkz+kQJJuUO9ul3Nk25c92PYncpaRmkHTzDmlpaZw+fYqFC+dx8eIFSpYsRY8evSldugzdun2Cvb09AO7uFVm1ahkAu3fvJDw8EoAXXniBGjW82L59C++91/6xxykiTxcVVkQeUWRkJPHx8XTv3h0/Pz9mz56NwWCgUqVKDB48mAULFhAfH0/nzp1ZsGAB+/fvZ/bs2aSkpJCWlkZUVBSvv/66dbzLly/Tp08fbt++jdFoJCwsjKpVq94z7/Llyxk1ahQ3b95k0KBB+Pn50b9/fzw9Pfnll18AePfdd1m4cCEDBw7k9OnTALRt25bWrVs/0ho7Rm4m/vqd/+EpiYiIiNieteP8SQKuXEng9der06lTV0qXfplFi+YzYEAvZs1agMFgAODmzZvMmTODd95pCUB8fBxFixazjuXiUpSEhPicWIaI/MtUWBF5RGFhYXTo0IGePXsSEhLC0qVLcXZ2JiIigilTptCvXz8WL17M9OnTKViwIIsXL2batGkUKlSI5cuXM336dKZNm2Ydb/ny5fj4+PDRRx+xe/duDh06dN/CSv78+Vm1ahU7duxgypQp+Pn5ZYlp/vz5LFu2jAMHDpCYmMjq1auJi4tj3Lhxj1xYEREREXleubjkx8WlPHPnzrZe69mzG3PnziQ1NZGSJUvy+++/8+mn3fH0rEGXLh0xGAxYLBaKFMlPkSJ3d9LkzWuPnZ12Rd2PnontUu7uT4UVkWw6ePAgvr6+ODs7A9CmTRsGDBiQpY3RaGTq1Kls376ds2fPcuDAAYzGrJ828vb2pkePHpw4cYJ69erRvv39t4s2aNAAgLJly3L9+vUHxlWuXDnOnj1Lx44dqVu3Ln379v1flikiIiLyXElISOLMmdOcOfMrjRu/BYDFYsFstnDzZirffbeDIUMG0LZtB9q2DeLKlWQAihYtxqlTZ7FYHAD4/feLlC37CgkJSTm2lqeRi0t+PRMb9bzkzmg0PPKnEVRYEckms9mc5WeLxUJGRkaWa7du3aJVq1Y0b96cGjVq4O7uzoIFC7K0qVatGuvXr2fnzp1s2LCBVatWMXv2bP4vk8kEYN1++iDOzs6sX7+evXv3smvXLgICAli/fj0FChR46LXNDGv40G1FREREnhUpqXf/Lmc0Gpg4cSxVqlTF1bUEq1Ytt77cGjiwD0OHRlGzZq0sfevUqceaNavo02cA8fFxxMT8QHBwx5xYhoj8y1RYEXlEdnZ2ZGRk4Onpybx58+jWrRtOTk4sXboULy8v4G4RJDMzk3PnzmEwGPj444+xWCz07duXzMzMLOONGTOGYsWKERwcjJeXFwEBAdmKy2QykZGRwa5du1izZg0TJ07kjTfeYN++fVy6dOmRCitXryZjNluyFYfknOflLcKzSLmzXcqdbVP+bNeTzl2ZMmX57LNQ+vX7DLPZjItLUcLDoxgzJhKLxcK0aVOYNm0KAMWLuzJy5Fg6duzCuHEjad++NWZzJt26fUKJEm5PLEYReXqosCLyiAoXLoyrqysjRoygS5cuBAUFkZ6eTqVKlYiIiADAx8eHzp07M2PGDCpUqECTJk0wGAzUqVOHQ4cOZRkvKCiI3r17s3LlSkwmE6NHjwbu/irlokWLEhgY+FBx1a9fH39/f5YsWcLmzZt56623cHBwoHnz5ri7uz/ehyAiIiLyjGvUqCmNGjXNcm38+CkPbJ83b14GDx7+pMMSkaeQwWKx6LW0iGShHSu2SW9ebZdyZ7uUO9um/Nku5c52KXe263nJXXa+sWL85yYiIiIiIiIiInI/KqyIiIiIiIiIiGSTCisiIiIiIiIiItmkwoqIiIiIiIiISDapsCIiIiIiIiIikk0qrIiIiIiIiIiIZJMKKyIiIiIiIiIi2aTCioiIiIiIiIhINtnldAAiIiIiIiJPg+joCezYsZUCBQoCUKrUiwwbNhKApKQkQkI6MWDAEMqXrwjAlSsJREVFcPXqVSwWM+3aBdOoUdMci19EcoYKKyIiIiIiIsDx40eJiIiicmWPLNf37dvD5MnjuXz5UpbrX301lYoVX+Wjjz4mISGetm1bUb26J4ULF/k3wxaRHKajQGITkpKS6N69+9+2GTBgABcvXvzbNkFBQcTExDzO0B67QYMGcezYsZwOQ0REROS5kpaWxunTp1i4cB4dOrRh0KBQLl++DMCyZUsYMmT4PQUTszmT5ORkLBYLKSkpmEwmjEb9K5bI80Y7VsQmJCYmcuLEib9tExMT84/FF1swYsSInA6BwoUdczoEySYXl/w5HYJkk3Jnu5Q726b82a7HmbuU1AxOnbzI669Xp1OnrpQu/TKLFs1nwIBezJq1gPHjo+/br0uXELp378SOHVu5ceM6ISGf4exc6LHFJSK2QYUVsQmRkZHEx8fTvXt3/Pz8mD17NgaDgUqVKjF48GAWLFhAfHw8nTt3ZsGCBezfv5/Zs2eTkpJCWloaUVFRvP7669bxLl++TJ8+fbh9+zZGo5GwsDCqVq2aZc6goCDKly/Pjz/+SGpqKgMHDqROnTr8+uuvDB8+nNu3b3Pt2jU6d+5MYGAgSUlJ9O3bl99//52SJUty+fJlpkyZQvHixRkzZgwHDhwgMzOTFi1a8P777xMSEkKzZs1o1KgRAC1atCAyMpKRI0cSEhKCl5cX06dPZ+PGjWRmZlKnTh1CQ0Pp2rUrgYGB1KtXj/Hjx/PLL7/w9ddfEx8fz4cffsjixYvp1asXV65cAaB79+7Ur1//kZ53x8jNxF+/8z9mTURERMQ2rB3nj6trCcaOnWy9FhgYxJw5M7l06Q9cXUvct9+wYYNp27YDAQGtiI39nR49ulCp0qtUrPjqvxW6iDwFVFgRmxAWFkaHDh3o2bMnISEhLF26FGdnZyIiIpgyZQr9+vVj8eLFTJ8+nYIFC7J48WKmTZtGoUKFWL58OdOnT2fatGnW8ZYvX46Pjw8fffQRu3fv5tChQ/cUVgCSk5NZtWoVJ06coFOnTmzfvp1ly5bRrVs3vL29iY2NpXnz5gQGBjJ16lRKly7Nl19+ybFjx2jTpg0AS5cuBWDVqlWkpaXRsWNHXn31Vfz9/Vm7di2NGjXi3LlzpKamUrFiRevcu3fv5vjx4yxfvhyDwUBoaChr1qyhXr167N+/n3r16vHjjz9y+fJlMjMz+f7776lXrx5btmyhRIkSTJ8+nRMnTrBmzZpHLqyIiIiIPG+uXr3IyZMneeeddwCwWCyAhWLFnKy7Y0wmI05OeXFxyc+1a9c4evQnFiyYj52dHS4ulXjjjTqcOfML9ep55+BKnn7aKWa7lLv7U2FFbMrBgwfx9fXF2dkZgDZt2jBgwIAsbYxGI1OnTmX79u2cPXuWAwcO3HPW1dvbmx49enDixAnq1atH+/bt7ztf69atAahQoQIuLi6cOnWK/v378/333/PVV1/x66+/cvv2bQD27t3L2LFjAahcuTKvvPIKAPv27ePEiRPs378fgNu3b3Pq1Cneffddhg0bRnJyMuvWraN58+ZZ5t63bx9Hjx6lRYsWAKSkpODq6krbtm3p2rUrycnJALi7u/Of//yH3bt3ExQURJEiRRg/fjxxcXH4+Pg8E8ejRERERJ60xMQ7DB8eSenS5XF1LcHKlct4+eWymEz5SEhIAiAz08yNG7dJSEjCYrHDxaUoy5atpkGDRty4cYP9+w/QoEFTa3u5l4tLfj0fG/W85M5oNDzypxFUWBGbYjabs/xssVjIyMjIcu3WrVu0atWK5s2bU6NGDdzd3VmwYEGWNtWqVWP9+vXs3LmTDRs2sGrVKmbPnn3PfCaTKcvcdnZ2fPrppxQoUABfX1+aNm3KunXrrG3vvtnIKjMzk9DQUBo2bAjAtWvXyJcvH/b29vj6+rJ9+3Y2bdrEV199dU+/4OBgPvjgAwBu3ryJyWQiX758mM1mNm/ezOuvv06RIkXYv38///nPf3jttdcwmUxs3LiR77//nh07djBr1iw2bNjwSB9SmxnW8KHbioiIiNi6lNQMypQpy2efhdKv32eYzWZcXIoSHh71wD4Gg4FRo8YzceLnzJkzE6PRQFDQ+3h4vPYvRi4iTwMVVsQm2NnZkZGRgaenJ/PmzaNbt244OTmxdOlSvLy8gLuFjczMTM6dO4fBYODjjz/GYrHQt29fMjMzs4w3ZswYihUrRnBwMF5eXgQEBNx33g0bNlClShWOHTvGzZs3eeWVV9i7dy8bN26kWLFi1oJNZmYm3t7erF27lvLly3Pq1ClOnz6NwWCgZs2aLF26FF9fX9LS0mjbti0RERF4eXnh7+9PZGQkTk5OlCiR9exuzZo1mTx5Mq1bt8bBwYHu3bsTEBBAixYtqFu3Ll9++SWDBw+maNGidOrUCU9PT0wmE9988w2xsbEMGDCAunXr4uvrS3JyMgUKFHjo5331ajJm871FInm6PS9vEZ5Fyp3tUu5sm/Jnu55U7ho1akqjRk0feH/58rVZfi5X7hWmTp3x2OMQEduiworYhMKFC+Pq6sqIESPo0qULQUFBpKenU6lSJSIiIgDw8fGhc+fOzJgxgwoVKtCkSRMMBgN16tTh0KFDWcYLCgqid+/erFy5EpPJxOjRowGYNGkSRYsWJTAwEIDY2Fhr0WXChAmYTCZ69OhB27ZtcXBwoHz58pQoUYILFy7QvXt3BgwYQLNmzShVqhRFihQhd+7cvPfee5w/f56AgAAyMjJo0aKFtRhUrVo1kpKSrPP9lZ+fHydPnqR169ZkZmbyxhtvWGPx8fFh9uzZVKtWjbx585Keno6vry8A77zzDr169aJZs2aYTCZCQ0MfqagiIiIiIiIiD89gud/ZBREhKCjI+tt5Hsa3336Lm5sb1apV448//qB9+/Zs3br1kY7gPC20Y8U26c2r7VLubJdyZ9uUP9ul3Nku5c52PS+50zdWRHJQmTJlCA8Px2w2YzQaGTZsmE0WVUREREREROThqbAi8gDz589/pPaVK1dm5cqVTygaEREREREReRrpdbqIiIiIiIiISDapsCIiIiIiIiIikk0qrIiIiIiIiIiIZJMKKyIiIiIiIiIi2aTCioiIiIiIiIhINqmwIiIiIiIiIiKSTfp1yyIiIiLyWK1YsYRVq1ZgMECJEm706xeGs3MhVq5cxroEFRkQAAAgAElEQVR1q0lNTcXdvQL9+w/G3t6e69evExkZTlzcJQwGA337DqJyZY+cXoaIiMhD0Y4VeW4FBQXRp0+fLNeio6OJjo7+235Lly5l3bp1jz2WmJiYR+43adIktm3bZh1DREQkp508eYJFi75h2rRZzJ+/FDe3UsyY8SW7dm1nxYolTJz4BfPnLyU1NYUlSxYCMH78aDw8qvLNN8sYMmQ4gwf3JyUlJYdXIiIi8nBUWJHn2qZNm9i6desj9Tl8+DBpaWlPKKJH88knn1C/fn0ADhw4kMPRiIiIQPnyFVi8eBWOjo6kpqaSkBBPwYJObNq0nvfea0+BAgUxGo306TOQxo2bkpGRwQ8/fE+zZgEAlCvnjptbSWJifsjhlYiIiDwcHQUSmzRu3Di+++47nJ2dcXFxwc/PD6PRyNy5czGbzVSqVInw8HAcHBzYsWMHEydOxGw2U7JkSYYNG0aRIkUA6Nq1KxEREVSvXh0nJ6cscxw9epSRI0eSkpKCs7MzERERxMbGsn37dvbv38+VK1fYsmULy5Yt4/bt23h6erJgwQI8PDwYMmQI3t7e1KhRg0GDBvHHH39gZ2fHZ599Rt26dYmOjuann37i0qVLtG/f3jrn1atXCQ4O5tNPP6VBgwZZ4pkzZw6LFi3CZDLh6+tLaGgo/fv3x9PTk19++QWAd999l9atW7N//37GjRsH3N2F4+DgQOfOnR/6+RYu7JitvEjOc3HJn9MhSDYpd7ZLubsrJTWDpJt3ALCzs2P37p2MHj2cXLns+eijjxkwoDfXr1+jV68eXL2aQJUqr9GtW08SE29gsVhwdna2jlW0aDHi4+NzaikiIiKPRIUVsTnbt2/n0KFDrFu3jjt37hAQEIC3tzfr169n8eLFODg4MG7cOGbOnEmbNm0YMmQIixYtws3Nja+//pphw4YxefJkAKpXr86NGzeIjIxk7Nix1jnS0tIICwtj2rRpuLq68v333zN48GDmzJmDn58fnp6eBAQEsGDBApKSkjhy5AgFChTgwIEDeHh4sH//fvr27cugQYOoWbMmH3zwAbGxsQQGBrJ69WrrHBs2bABg48aNJCUl0blzZ0JCQu4pqhw9epSFCxeyYsUK8uTJw0cffcTx48et98PCwpg/fz7Lli3j1q1bTJgwgeTkZBwdHVm3bh3z5s17pGfcMXIz8dfvZCs/IiLyfFo7zp+kv/xct64Pdev6sGbNKnr16oHJZOTgwRhGjRqHvb0DkZHhTJ/+Be3adQAMWcayWCwYjdpYLSIitkGFFbE5P/zwA02aNMHe3h57e3saNGiAxWLh/PnztG7dGoD09HQqVqzI0aNHqVKlCm5ubgC0adOG6dOnZxmvV69e+Pv7ZzkSdO7cOWJjY+natav1WnJycpZ+BoOBWrVqERMTw+HDhwkODubgwYP4+vpSvHhxHB0d2b9/P5GRkQCULFkSDw8Pfv75ZwCqVKmSZbzw8HCKFClCw4YN71nzn+Pmz3/3reicOXMe+Hzy5ctHvXr12LJlCyVLlqRkyZIUK1bsb5+piIjI4+Dikp/z58+TkJBA9erVAXj//XaMHTuSsmXL8tZbTXjppeIAtG7dkqlTp1KuXCnAQq5cmdbdo4mJ1yhb9sV/ZTeQdhzZLuXOdil3tku5uz8VVsTmGI1GzGZzlmuZmZk0adKEsLAwAG7dukVmZiYHDx7M0s5isZCRkZHlWp48eYiKiuKzzz6jUaNGFCxYELPZjJubG99++611/CtXrtwTi4+PD/v27eP48eN8/fXXLFmyhB07duDr62ud7//On5mZCUDu3Lmz3OvUqRO7du1i0aJFtGvXLss9Ozs7DIb/9zYvLi6OPHnyPPAZtWzZki+//BI3NzdatGjxwHYiIiKPU0JCEqdPn2fo0EHMnr0QJycnNm5cR+nSL9O0aXPWrFmHr29j7O0dWLduI2XLunP9+h28vWsza9Z8goLe58yZ05w+fYaXX65EQkLSP0/6P3Bxyf/E55AnQ7mzXcqd7Xpecmc0Gh750wgqrIjNqVWrFjNmzCAwMJC0tDR27txJ8+bN2bJlC127dqVQoUIMHTqUUqVKERgYSHh4OBcuXMDNzY0lS5bg5eV1z5jVq1encePGLFq0iC5dulCmTBkSExP58ccfqV69OitWrGDt2rXMnz8fk8lkLY7Url2b8ePHU6hQIfLnz0+FChWYN28e8+fPB6BmzZosX77cehTo8OHDDB06lFOnTt0TQ4UKFfDx8SEwMJAGDRpk2WVSvXp1+vTpQ48ePXBwcKB3795069YtS3+TyURGRgZ2dnZUr16dy5cvc/HiRQYNGvTIz3hm2L27ZkRERP5OSurdFxceHq/RocOH9OjRGZPJjiJFijBy5FiKFXuBmzdv0rFjEJmZmbzySnn69h0IQO/e/Rk1KpKgoNYYDAYGDx6Go6O+9yUiIrZBhRWxOT4+Phw5coSAgAAKFixI0aJFKVOmDCEhIQQHB2M2m6lQoQKdO3fGwcGBYcOGERISQnp6Oq6urowYMeK+4/bq1Ytdu3YBYG9vz6RJkxgxYgSpqak4OjoyevRo4G5hZ/z48eTPn5/GjRvzwgsvULlyZeBuIeXMmTO89NJLAAwaNIghQ4awcuVKACIjIylatOgD1/bSSy/Rrl07hg0bxtSpU/H392f69OlUqlSJ9u3b895772E2m3nzzTepVasWa9assfatX78+/v7+rFy5EgcHB958801u3LiBvb39Iz/jq1eTMZst/9xQnirPy1uEZ5FyZ7uUu/sLCGhFQECre65/+GFnPvzw3o+pFypUmDFjJvwboYmIiDx2Bsv/Pasg8pQ7cuQI586dIyAggPT0dNq0aUNUVBTly5fP6dCeChaLhfT0dD744AMGDhxIpUqVHnkMFVZsk/4Fz3Ypd7ZLubNtyp/tUu5sl3Jnu56X3GXnKJA+ty42p3Tp0qxbt47mzZvTokUL3nrrLRVV/iIhIYHatWvj4eGRraKKiIiIiIiIPDwdBRKb4+TkxMyZM3M6jKdW0aJF7/lor4iIiIiIiDwZ2rEiIiIiIiIiIpJNKqyIiIiIiIiIiGSTCisiIiIiIiIiItmkwoqIiIiIiIiISDapsCIiIiIiIiIikk0qrIiIiIiIiIiIZJMKKyIiIiIiIiIi2WSX0wGIiIiIyIOtWLGEVatWYDBAiRJu9OsXRt68eRk3bjQnTvwHiwUqVqxE7979cHDIzfXr14mMDCcu7hIGg4G+fQdRubJHTi9DRETkmaUdKwJAUFAQffr0yXItOjqa6Ojov+23dOlS1q1b99hjiYmJeaxjTp48mR9//PGxjunn58eFCxeyXIuJieG1117D39+f5s2b06hRI6Kiorh169Zjnfv/6tSpE3FxcU90DhER+fedPHmCRYu+Ydq0WcyfvxQ3t1LMmPElc+fOIjMzk7lzFzN37iJSU1OZP38OAOPHj8bDoyrffLOMIUOGM3hwf1JSUnJ2ISIiIs8wFVbEatOmTWzduvWR+hw+fJi0tLQnFNHjc/DgQTIzM/+VuV599VW+/fZb1qxZw7p167h+/TpDhw59onPOmDGDYsWKPdE5RETk31e+fAUWL16Fo6MjqampJCTEU7CgE1Wrvk5wcEeMRiMmk4lXXnHn8uVLZGRk8MMP39OsWQAA5cq54+ZWkpiYH3J4JSIiIs8uHQV6BowbN47vvvsOZ2dnXFxc8PPzw2g0MnfuXMxmM5UqVSI8PBwHBwd27NjBxIkTMZvNlCxZkmHDhlGkSBEAunbtSkREBNWrV8fJySnLHEePHmXkyJGkpKTg7OxMREQEsbGxbN++nf3793PlyhW2bNnCsmXLuH37Np6enixYsAAPDw+GDBmCt7c3NWrUYNCgQfzxxx/Y2dnx2WefUbduXaKjo/npp5+4dOkS7du3t8559epVgoOD+fTTT2nQoIH1+oULFwgJCaFcuXKcOHGCwoULM2nSJJycnNi9ezeTJ08mIyMDNzc3hg8fzq5duzh+/DhhYWH4+/tz5swZJk6cyNmzZ2ncuDF79+6lSJEidOzYkU8++YT8+fMzZMgQbty4Qd68eRk0aBBVqlShf//+3Lhxg/PnzxMaGmqN5+zZs3Tp0oUxY8bck5tcuXLRt29ffHx8GDx4MI6OjkRFRbFv3z4MBgPNmzenc+fOxMTEMG3aNHLlysWFCxfw8/Mjb9681kLX9OnTKVKkCN988w3ffvstd+7cIVeuXIwbN44yZcrg5+fHvHnzOHDgAN9//z2JiYnExsZSu3btbBV1Chd2fOQ+8nRwccmf0yFINil3tutJ5S4lNYOkm3ews7Nj9+6djB49nFy57Pnoo48pWbKUtd3ly5dYunQRffsOIjHxBhaLBWdnZ+v9okWLER8f/0RiFBERERVWbN727ds5dOgQ69at486dOwQEBODt7c369etZvHgxDg4OjBs3jpkzZ9KmTRuGDBnCokWLcHNz4+uvv2bYsGFMnjwZgOrVq3Pjxg0iIyMZO3asdY60tDTCwsKYNm0arq6ufP/99wwePJg5c+bg5+eHp6cnAQEBLFiwgKSkJI4cOUKBAgU4cOAAHh4e7N+/n759+zJo0CBq1qzJBx98QGxsLIGBgaxevdo6x4YNGwDYuHEjSUlJdO7cmZCQkCxFlT+dPHmSqKgoKlasSI8ePVi7di1vvfUW48aNY968eRQsWJDFixczduxYRowYwYoVKwgJCaFSpUo0adIEi8XC/v37KVy4MAcOHMDPz4+zZ89SuXJl3n33XTp37kzDhg356aef+OSTT/juu+8AcHJyYtq0aQBERkZy+fJlwsPDiYqKomrVqvc9wuTi4kKBAgU4d+4cx44d49KlS6xZs4a0tDSCgoJ45ZVXyJMnDz///DPr16/HycmJWrVq0a9fP1auXMmAAQNYv349LVu2ZOvWrcyfP5/cuXMzadIkFixYwODBg7PMd+TIEdatW4fJZKJx48YEBgbi7u7+SP+96hi5mfjrdx6pj4iIPF5rx/mT+/8v2rRs2YyWLZuxdOlSQkN7smXLFoxGI8ePH6dHjxA6dAjinXeaEhcXh8FgyFLssbc3UbBgXhXvHkDPxXYpd7ZLubNdyt39qbBi43744QeaNGmCvb099vb2NGjQAIvFwvnz52ndujUA6enpVKxYkaNHj1KlShXc3NwAaNOmDdOnT88yXq9evfD3989yJOjcuXPExsbStWtX67Xk5OQs/QwGA7Vq1SImJobDhw8THBzMwYMH8fX1pXjx4jg6OrJ//34iIyMBKFmyJB4eHvz8888AVKlSJct44eHhFClShIYNG9533YULF6ZixYoAlCtXjsTERH7++WcuXbpEhw4dADCbzRQsWDBLP0dHR0qXLs2pU6fYv3+/Nc58+fJRs2ZNbt++ze+//26dt2rVqhQsWJDffvvtvnF+8sknVK5cmerVq983zr8+HwcHB2JiYggICMBkMpEnTx6aNWvGvn378PPz45VXXqF48eIAODs74+3tDYCrqys3b97E0dGRcePGsX79es6dO8f3339PhQoV7pnrtddew9HR0fqcExMT/zY2ERF5eh058gtXr17Fw6MqAHXrNiQ8PJzffrvIwYMxjBs3ms8+60vDho1JSEjCbLbHYrHw3/9eoECBu/8fePHiJWrXLkBCQlJOLuWp5OKSX8/FRil3tku5s13PS+6MRsMj7+BXYcXGGY1GzGZzlmuZmZk0adKEsLAwAG7dukVmZiYHDx7M0s5isZCRkZHlWp48eYiKiuKzzz6jUaNGFCxYELPZjJubG99++611/CtXrtwTi4+PD/v27eP48eN8/fXXLFmyhB07duDr62ud7//O/+d3T3Lnzp3lXqdOndi1axeLFi2iXbt298zl4OBg/bPBYLCO9frrr1t3lKSmpt73o7E+Pj7s3buX3377jaFDh9KhQweMRiO+vr73xPhPcQ4aNIipU6eyc+dOfHx87ukLcOXKFZKSkihVqtQ9ufrr2Lly5cpyz2QyZfn50qVLBAUF0b59e+rWrUuRIkU4ceLEQz2bRzUz7P4FLRER+fekpGZw9eoVhg4dxOzZC3FycmLz5o2ULv0yx44dZeLEsUyYMIXy5Sta+9jZ2eHtXZtvv11FUND7nDlzmnPnzvLaa3//AkBERESyT4UVG1erVi1mzJhBYGAgaWlp7Ny5k+bNm7Nlyxa6du1KoUKFGDp0KKVKlSIwMJDw8HAuXLiAm5sbS5YswcvL654xq1evTuPGjVm0aBFdunShTJkyJCYm8uOPP1K9enVWrFjB2rVrmT9/PiaTyVoYqF27NuPHj6dQoULkz5+fChUqMG/ePObPnw9AzZo1Wb58ufUo0OHDhxk6dCinTp26J4YKFSrg4+NDYGAgDRo0eKgPs3p4eBAWFsbZs2cpXbo0X3zxBXFxcYwaNSpLnPXq1ePjjz/m1VdfxdnZmVy5crFjxw569epFvnz5cHNzY/PmzdajQFeuXKFcuXL3nbNKlSoMHTqUfv364enpec/9tLQ0xowZQ0BAAHny5KFmzZqsXr0aX19f0tLSWLt2LR9//PE/rg3g2LFjvPjii7z//vukpKQwefJkXnjhhYfq+6iuXk3GbH70gozkrOflLcKzSLmzXU86dx4er9Ghw4f06NEZk8mOIkWKMHLkWHr37gFYGDUq0tq2cmUPevfuR+/e/Rk1KpKgoNYYDAYGDx5m3ckoIiIij58KKzbOx8eHI0eOEBAQQMGCBSlatChlypQhJCSE4OBgzGYzFSpUoHPnzjg4ODBs2DBCQkJIT0/H1dWVESNG3HfcXr16sWvXLgDs7e2ZNGkSI0aMIDU1FUdHR0aPHg3cLeyMHz+e/Pnz07hxY1544QUqV64M3C2knDlzhpdeegm4u7tjyJAhrFy5Erj7jZKiRYs+cG0vvfQS7dq1Y9iwYUydOhV/f/97ji79lYuLC1FRUXz66aeYzWaKFSvG559/DsAbb7xBeHg4o0eP5vXXX8disVgLIZ6enpw+fZp8+fIB8PnnnzN06FCio6PJlSsX0dHR2NvbP3DeGjVq4OXlxcSJE6lfvz7Hjx/H398fuLu7p2bNmvTt2xe4e/zq3Llz+Pv7k56eTrNmzXjzzTcf6tdL165dm0WLFtG0aVMsFgs1atTg9OnT/9hPRERsW0BAKwICWmW5tmjRyge2L1SoMGPGTHjSYYmIiMj/z2DJzjkBeWocOXKEc+fOERAQQHp6Om3atCEqKory5cvndGhiw7RjxTZp14PtUu5sl3Jn25Q/26Xc2S7lznY9L7nTN1aeQ6VLl2bKlCnMnj0bi8XCO++8o6KKiIiIiIiIyL9EhRUb5+TkxMyZM3M6DBEREREREZHnkjGnAxARERERERERsVUqrIiIiIiIiIiIZJMKKyIiIiIiIiIi2aTCioiIiIiIiIhINqmwIiIiIiIiIiKSTSqsiIiIiIiIiIhkkworIiIiIiIiIiLZZJfTAYiIiIg8Tb77bgMLF87HYDCQO3duPv20D+XLV2TmzK/Yvn0LRqMRd/cKhIYOBPJz5UoCUVERXL16FYvFTLt2wTRq1DSnlyEiIiL/Eu1YEbmPQYMGcezYsQfe79SpE3FxccTGxjJw4EAALly4gLu7O3v37s3S1s/PjwsXLvzPMT2ucURE5MF+//0cX3wxiXHjopkzZyHBwR8ycGAohw//yLZtm5k16xvmzVvCrVu3WLFiCQBffTWVihVfZe7cRYwbF83YsaO4evVKDq9ERERE/i3asSJyHyNGjPjb+zNmzAAgJiaG2NhY6/VcuXIxePBg1qxZg6Oj4xON8UkqXNh2Y3/eubjkz+kQJJuUu5yXkppBrlz29Os3mCJFigBQvnxFrl27Snp6OmlpaaSmpmI0mkhLS8Pe3h4AszmT5ORkLBYLKSkpmEwmjEa9uxIREXleqLAiz72QkBCaNWtGo0aNAGjRogXnz5/niy++4MUXX6RPnz7cvn0bo9FIWFgYVatWxc/Pj3nz5hEZGcmFCxeIiIigY8eOFC1alFq1ajF69GiGDx+eZZ6YmBimTJnC/PnzAejfvz+enp54enrSvXt3ypQpw5kzZ6hYsSKvvfYaq1atIjExkalTp/Lyyy8DMGXKFE6ePImDgwMRERGUL1+eK1euMGTIEC5fvozBYKB3797UqlWL6OhofvrpJy5dukT79u1p27btQz+TjpGbib9+5zE9YRER27B2nD/Fi7tSvLgrABaLhejoCdSpUxcvL29q1PCiZcu3sbPLRalSL+Lv3xKALl1C6N69Ezt2bOXGjeuEhHyGs3OhnFyKiIiI/ItUWJHnnr+/P2vXrqVRo0acO3eO1NRUKlasCMDy5cvx8fHho48+Yvfu3Rw6dIiqVata+4aFhTFlyhTCw8Otx3T69+9Ps2bN2Lt3L7Vr136oGE6dOsXIkSMpX748jRo1omjRoixZsoQpU6awZMkS63GjF198kVGjRrFr1y769+/P6tWrGTFiBC1btqR+/frEx8fTtm1bVq9eDUBaWhobNmx4nI9LROSZ9ufOodu3b9O/f3/i4i7z9ddfs3nzd1y5EseePXuwt7dnwIABzJw5lcGDBzNy5FA6d+5E27ZtOXfuHEFBQdSp40WVKlVyeDXyMLRbzHYpd7ZLubNdyt39qbAiz7169eoxbNgwkpOTWbduHc2bN2fPnj0AeHt706NHD06cOEG9evVo3779P47n6OjI8OHDrUeCHkaRIkWsxZwXXngBb29vAFxdXbN8V+Xdd9+1xhwaGsrNmzf54Ycf+O2335g8eTIAGRkZ1uNJ+ku9iMijSUhI4vLly/Tr9xkvvfQS48dPJTXVwPr1G/HxeZM7dyzcuZNKw4ZvM2HCGK5du8ahQ4f4/PNoEhKSyJevMNWqebJjxx6KFy+d08uRf+Dikp+EhKScDkOyQbmzXcqd7Xpecmc0Gh750wgqrMhzz97eHl9fX7Zv386mTZv46quvrIWVatWqsX79enbu3MmGDRtYtWoVs2fP/scx69SpYz0S9CeDwYDFYrH+nJ6eniWGvzKZTPcd96/XLRYLdnZ2mM1m5s6di5OTEwDx8fEULlyYrVu3kjt37od4AveaGdYwW/1ERGxZSmoGt2/fokePLjRp8hYfftjZeu+VV8qza9cOGjVqislkYvfuHVSqVBlnZ2dcXIqyc+c2GjRoxI0bN/jppyO8/bZ/Dq5ERERE/k0qrIhw9zhQZGQkTk5OlChRwnp9zJgxFCtWjODgYLy8vAgICMjSz2QykZGRcd8x/zwSlJCQAICzszOxsbGkpqZy584dDh069NBHhf60du1aOnTowJYtW3j55ZfJmzcvNWvWZOHChXTr1o0zZ87Qrl07tm3b9ohPIKurV5Mxmy3/3FCeKs/LW4RnkXL39FixYilxcZfYvXsnu3fvtF7//POJzJ07i/btW2Nvn4uyZV+hV69+GAwGRo0az8SJnzNnzkyMRgNBQe/j4fFazi1CRERE/lUqrIhwd2dKUlISgYGBWa4HBQXRu3dvVq5ciclkyrIDBeDll18mKSmJ0NBQPvnkkyz3/jwS1LFjRwDKlStHvXr1eOuttyhRogTVqv1/7N1/XM/3/v/xWz9UjkIo1ic2tnMoZ/rYyY/YkmR+bYhDy6/ms7FPCuNgrCTJj21ixJnjx0HNz5FMZmYax2cUU9ZxjtnG6sR2KFOK9Ov9/v7h633Wiqkhb+7Xy2WXy6vn6/V8Ph+v1yOsx/v5fPWHaseZmZnJgAEDqFevHgsWLABuvOclIiKCF198EbhRDDLn30gkIlKbRo4czciRo6s8N2XK9Crbf/vb37F8+ap7GZaIiIg8wCyMP92bICKCVqyYK616MF/KnflS7syb8me+lDvzpdyZr0cldzV5x4rlPYpFREREREREROShp8KKiIiIiIiIiEgNqbAiIiIiIiIiIlJDKqyIiIiIiIiIiNSQCisiIiIiIiIiIjWkwoqIiIiIiIiISA2psCIiIiIiIiIiUkMqrIiIiIiIiIiI1JB1bQcgIiIiD569ez9i48Z4LCwssLOz4/XXp/Dkk79l8eK3ycg4AUCnTl0YN24CVlZWpn5Hj6bw5z8vZd26jbUVuoiIiMh9pRUr8tAYOXIkU6ZMqdAWGxtLbGzsbftt3bqVpKSkux5LamrqXR3zp+5FzCIiN/3rX5n8+c9LiImJZd26jQQF/Q9vvjmV7du3kJeXR1zcFtat28TJkxkkJ+8DoLj4OitX/pmIiBmUl5fV8h2IiIiI3D8qrMhD5eOPP+bTTz+tVp+0tDRKSkruUUT3hjnGLCLmo04dG954YyZNmjQBoE0bd3788RKDBwcQFTUfS0tLrlzJp7CwgPr1GwCQmprC9etFhIdH1mLkIiIiIveftgLJAyEmJoa9e/fi6OiIk5MTvr6+WFpasn79egwGA23btmXWrFnY2try2Wef8e6772IwGGjevDlRUVGm//kPDg5m9uzZeHp60rBhwwpzZGRkMH/+fK5fv46joyOzZ88mOzub5ORkUlJSyM3NZd++fXzwwQdcu3aNjh07smHDBjw8PIiIiMDLy4sOHToQFhbG999/j7W1NZMmTcLb25vY2FhOnDjBDz/8wIgRI0xzXrp0iaCgIF5//XX8/PxM7efOnSMkJIRWrVrx7bff4u7uTvv27dmxYwf5+fksX76cJ5988hdjdnJyYvfu3eTl5ZGVlcXUqVNp1KgRc+fOpbi4GEdHR6Kionj88cerlY/Gje1/RTalNjk5OdR2CFJDD0rurheXAS489pgLAEajkdjYxTz7rDd16tQB4L33YklI2Err1m54eLQHwNvbB29vH9LSvqit0EVERERqhQorUuuSk5M5fvw4SUlJFBUV4e/vj5eXF7t372bz5s3Y2toSExPDmjVrCAgIICIigk2bNuHq6srq1auJiopi6dKlAHh6epKXl0d0dDQLFy40zVFSUpWwR/cAACAASURBVEJ4eDgrVqzAxcWFQ4cOMXPmTNatW4evry8dO3bE39+fDRs2UFBQQHp6OvXr1+fo0aN4eHiQkpLCtGnTCAsLo3PnzowePZrs7GwCAwNJTEw0zfHRRx8BsGfPHgoKChg7diyhoaEViio3nT59mvnz59OmTRt69eqFs7MzW7ZsYdmyZWzZsoUpU6b8YszPPfccu3fvpmHDhqxYsYKSkhJ69+7Nu+++S7t27dizZw+TJ09m+/bt1crJK9GfcPFyUU1TKiJmbFfMAAr+/3FRURFz50Zy8eIFYmL+s60yOHg8Y8YE89Zb0SxcOJ/w8Nm1E6yIiIjIA0CFFal1hw8fpk+fPtjY2GBjY4Ofnx9Go5GsrCyGDh0KQGlpKe7u7mRkZNCuXTtcXV0BCAgIYOXKlRXGmzx5MgMGDKiwJSgzM5Ps7GyCg4NNbYWFhRX6WVhY0KVLF1JTU0lLSyMoKIhjx47RvXt3HnvsMezt7UlJSSE6OhqA5s2b4+HhwZdffglAu3btKow3a9YsmjRpwvPPP1/lfTdp0gR3d3cAmjVrhpeXFwAuLi6cO3fujmK+6ebcmZmZ1K9f3/R1nz59iIiIoKCgAAeHB+PTcBF58Dk5OfD9998TGvq/PPnkk2zatAE7OzuOHz9Oo0aNaNmyJQCBgUOJjo6usNqmYcPfYG1t9cCswLkfHqV7fRgpf+ZLuTNfyp35Uu6qpsKK1DpLS0sMBkOFtvLycvr06UN4eDgAV69epby8nGPHjlW4zmg0UlZW8SWJdevWZd68eUyaNIlevXrRoEEDDAYDrq6u7Ny50zR+bm5upVh8fHw4cuQIJ0+eZPXq1WzZsoXPPvuM7t27m+b7+fzl5eUA2NnZVTg3ZswYDh48yKZNmxg+fHiluWxsbCp8/dPfqgHcccw/nfvnz/HnMYqI3ImsrH8TFDSCPn368T//M5aCglIKCkpJTv4b//jH35k/PwZLS0s++CCBdu2eISenwNQ3L+8aZWXlFdoeZk5ODo/MvT6MlD/zpdyZL+XOfD0qubO0tKj2qxFUWJFa16VLF1atWkVgYCAlJSUcOHCA/v37s2/fPoKDg2nUqBGRkZG0aNGCwMBAZs2axblz53B1dWXLli106tSp0pienp707t2bTZs28dprr9GqVSvy8/P54osv8PT0ZPv27ezatYv4+HisrKxMhYeuXbuyaNEiGjVqhIODA25ubsTFxREfHw9A586d2bZtm2krUFpaGpGRkZw+fbpSDG5ubvj4+BAYGIifnx9Nmzat1nO505h/3icvL8+0suejjz7CxcWl0vtmfsma8KpX2YjIw+96cRnbt2/lwoUf+NvfDvC3vx0wnVu0KJbc3FxefnkYlpYWtGv33/zv/4bWXrAiIiIiDwAVVqTW+fj4kJ6ejr+/Pw0aNMDZ2ZlWrVoRGhpKUFAQBoMBNzc3xo4di62tLVFRUYSGhlJaWoqLiwtz586tctzJkydz8OBB4MbqkCVLlphe6mpvb89bb70F3CjsLFq0CAcHB3r37k2zZs14+umngRuFlG+//ZYnnngCgLCwMCIiIkhISAAgOjoaZ2fnW97bE088wfDhw4mKimL58uUMGDCg0talW7nTmH/eZ/HixcyZM4eioiIaNGjA4sWL72i+n7p0qRCDwfjLF8oD5VH5FOFh9KDlbuTI0YwcObrKc1OmTL9t32ee8SQ+fuu9CEtERETkgWRh/PneBpH7LD09nczMTPz9/SktLSUgIIB58+bRpk2b2g7tkaXCinl60H44lzun3Jkv5c68KX/mS7kzX8qd+XpUcqetQGKWWrZsybJly1i7di1Go5GBAweqqCIiIiIiIiJmQYUVqXUNGzZkzZo1tR2GiIiIiIiISLVZ1nYAIiIiIiIiIiLmSoUVEREREREREZEaUmFFRERERERERKSGVFgREREREREREakhFVZERERERERERGpIhRURERERERERkRpSYUVEREREREREpIasazsAERGRh5HRaGTu3EhatXqKYcNGUl5ezuLFb3PiRBoAnTt3JSRkIpmZ3/HqqxGUlRkAMBjKOXv2DHPnvk23br61eQsiIiIicge0YkXM3siRI5kyZUqFttjYWGJjY2/bb+vWrSQlJd31WFJTU+/qmD914cIFxowZc8/GF5G7IzPzOyZODObAgf2mtr17P+Jf/8pi/frNrFu3iRMn0vjss/20bNmKnTt3sm7dRtat20iHDp3x8+ulooqIiIiImVBhRR4KH3/8MZ9++mm1+qSlpVFSUnKPIro3mjZtyqpVq2o7DBH5BQkJW3nhhYF07+5najMYyikqKqK0tJSSkhJKS0uxsbGp0O/LL9M5cGA/U6fOuN8hi4iIiEgNaSuQ1KqYmBj27t2Lo6MjTk5O+Pr6Ymlpyfr16zEYDLRt25ZZs2Zha2vLZ599xrvvvovBYKB58+ZERUXRpEkTAIKDg5k9ezaenp40bNiwwhwZGRnMnz+f69ev4+joyOzZs8nOziY5OZmUlBRyc3PZt28fH3zwAdeuXaNjx45s2LABDw8PIiIi8PLyokOHDoSFhfH9999jbW3NpEmT8Pb2JjY2lhMnTvDDDz8wYsQI05yXLl0iKCiI119/HT+///xgde7cOV599VUcHR2xs7Nj9erVvP322xw9epTy8nIGDRrEyy+/fMtn07FjR0aNGkVycjK5ubm3jOnChQtkZWVx/vx5hgwZQnBwcLXy0rixfQ0zKrXNycmhtkN4pF0vLqPgShGTJ78BwLFjKaZzffq8SHLyfgYO7EN5eTkdO3bi2We9K/RfvnwJY8eOo149/RkUERERMRcqrEitSU5O5vjx4yQlJVFUVIS/vz9eXl7s3r2bzZs3Y2trS0xMDGvWrCEgIICIiAg2bdqEq6srq1evJioqiqVLlwLg6elJXl4e0dHRLFy40DRHSUkJ4eHhrFixAhcXFw4dOsTMmTNZt26dqVDh7+/Phg0bKCgoID09nfr163P06FE8PDxISUlh2rRphIWF0blzZ0aPHk12djaBgYEkJiaa5vjoo48A2LNnDwUFBYwdO5bQ0NAKRZWbvvvuO1avXo2rqyubNm0CYMeOHZSUlPDKK6/w+9//nitXrlR6Nr6+FbcFzJkz55YxnT592nRPfn5+DB8+nPr1699xbl6J/oSLl4uqkU0RAdgVM4CCW5xbu3YVjo4N2bXrE4qLi5kx409s2vQ+gYE3irJ///uX5OVdpmfP3vcvYBERERH51VRYkVpz+PBh+vTpg42NDTY2Nvj5+WE0GsnKymLo0KEAlJaW4u7uTkZGBu3atcPV1RWAgIAAVq5cWWG8yZMnM2DAgApbgjIzM8nOzq6wYqOwsLBCPwsLC7p06UJqaippaWkEBQVx7NgxunfvzmOPPYa9vT0pKSlER0cD0Lx5czw8PPjyyy8BaNeuXYXxZs2aRZMmTXj++eervO/GjRub7uPIkSOcOnWKlJQbn2pfu3aN06dP891331V6Nj93u5g6deqEjY0NjRs3pmHDhhQUFFSrsCIiNffTVUN2dnWwt7fFycmBzz8/SHh4OC4ujQAYOvSP7N27FyenG38/HT58gMGDB9G0aYNaiFp+Da0UM2/Kn/lS7syXcme+lLuqqbAitcbS0hKDwVChrby8nD59+hAeHg7A1atXKS8v59ixYxWuMxqNlJWVVWirW7cu8+bNY9KkSfTq1YsGDRpgMBhwdXVl586dpvFzc3MrxeLj48ORI0c4efIkq1evZsuWLXz22Wd0797dNN/P5y8vLwfAzs6uwrkxY8Zw8OBBNm3axPDhwyvN9dPry8vLmTp1qqkI8+OPP1KvXj1iYmIqPZufu11Mtra2pnYLC4tK1/6SNeFVF4VE5PZubgUyfX29lMLCYnJyCmjV6rfs2PEhTz7ZlrKyMvbs+YTf/a4NOTkFODk5cORIKpMmTSMn51ZrXuRB5OTkoJyZMeXPfCl35ku5M1+PSu4sLS2q/WoEFVak1nTp0oVVq1YRGBhISUkJBw4coH///uzbt4/g4GAaNWpEZGQkLVq0IDAwkFmzZnHu3DlcXV3ZsmULnTp1qjSmp6cnvXv3ZtOmTbz22mu0atWK/Px8vvjiCzw9Pdm+fTu7du0iPj4eKysrUyGia9euLFq0iEaNGuHg4ICbmxtxcXHEx8cD0LlzZ7Zt22badpOWlkZkZCSnT5+uFIObmxs+Pj4EBgbi5+dH06ZNb/kMOnfuzNatW+nevTslJSUMGzaM2bNnV/ls3NzcKvW905iq69KlQgyG6hVjpPY9Kv/YmasJEyazaNHbDBs2GEtLKzw9OzB8eJDp/Llz/+Kxxx6rxQhFREREpCZUWJFa4+PjQ3p6Ov7+/jRo0ABnZ2datWpFaGgoQUFBGAwG3NzcGDt2LLa2tkRFRREaGkppaSkuLi7MnTu3ynEnT57MwYMHAbCxsWHJkiXMnTuX4uJi7O3teeutt4AbhZ1Fixbh4OBA7969adasGU8//TRwo2jx7bff8sQTTwAQFhZGREQECQkJAERHR+Ps7HzLe3viiScYPnw4UVFRLF++nAEDBlTaugTw0ksvkZWVhb+/P2VlZQwaNMhUMPr5s/npKpSaxCQi919YWKTpuEGDhsyePe+W13766f/dh4hERERE5G6zMFZ3j4DIXZKenk5mZib+/v6UlpYSEBDAvHnzaNOmTW2HVutq+9loxYp50ooV86XcmS/lzrwpf+ZLuTNfyp35elRyp61AYlZatmzJsmXLWLt2LUajkYEDB6qo8v/p2YiIiIiIiJgHFVak1jRs2JA1a9bUdhgPJD0bERERERER82BZ2wGIiIiIiIiIiJgrFVZERERERERERGpIhRURERERERERkRpSYUVEREREREREpIZUWBERERERERERqSEVVkREREREREREakiFFRERERERERGRGrKu7QBEROTRZDQamTs3klatnmLYsJEUFhayYEEUWVmZGI1Gevfux4gRL/Pdd2eZPTvc1M9gKOfs2TPMnfs23br51uIdiIiIiIhoxYqYmZEjR5Kamnrf5z137hy+vv/5AS47O5uJEyfi5+dHr169CAwM5Pjx4/c8jjFjxnDhwoV7Po/IvZaZ+R0TJwZz4MB+U9vq1e/h5NSU+PitrFoVR2Lidk6ezKBly1asW7fR9F+HDp3x8+ulooqIiIiIPBC0YkWkmi5fvsywYcOYMGECS5YsASA9PZ3x48eTmJhIkyZN7tncq1atumdj/1Tjxvb3ZR65+5ycHGo7hNu6XlxGwZUiEhK28sILA2natJnp3MSJUygvLwfg0qVcSktLqFev4vfil1+mc+DAfuLiNt/XuEVEREREbkWFFXlgGY1GFi5cyKeffoqVlRUBAQEAbNu2jQULFnDlyhXCwsLw9fXl66+/Zs6cOVy7do0ff/yRsWPHEhgYSGxsLBcuXCArK4vz588zZMgQgoODSUhI4NChQ+Tn55OdnU3Xrl2JjIwEYOXKlezZs4fy8nKeffZZpk6dWiGuLVu28MwzzzBkyBBTW/v27Zk+fTpFRUUAdO7cmd///vfk5OSwbds21qxZw4cffoiVlRVdu3Zl6tSp/PDDD4waNYrk5GQAYmNjARg/fjxeXl707NmT9PR06tWrx8KFC3F1dcXX15e4uDiOHj16y/hjYmLYu3cvjo6OODk54evry6BBg6r17F+J/oSLl4uqnTORX7IrZgAFwOTJbwBw7FiK6ZyFhQXW1tZERc3kwIH9PPecDy1aPF6h//LlSxg7dlylgouIiIiISG1RYUUeWB9//DFpaWns2rWL0tJShg0bRnFxMb/97W/ZsWMHn332GcuWLcPX15cPPviAcePG4eXlRXZ2Nv379ycwMBCA06dPs2HDBgoKCvDz82P48OHAjVUmSUlJWFlZ0bt3bwIDA7lw4QInT55k27ZtWFhYMHXqVD788EP+8Ic/mOI6ceIEzz77bKV4X3jhBdPx5cuXGTNmDJ06deLgwYMkJyezfft26tSpw/jx49m8eTPdunW75b3/+OOPtG/fnqioKOLj44mOjmbFihUVrqkq/vPnz3P8+HGSkpIoKirC39+/whYmkQfBT1fV2NnVwd7etkJbbOy7XL16lQkTJrB1axwTJkwAIC0tjYKCfIYNG4Kl5cO3k/VBX20kt6bcmTflz3wpd+ZLuTNfyl3VVFiRB9axY8fo06cPNjY22NjYsHPnTkaOHImfnx8ATz31FJcvXwZg+vTpHDp0iL/85S98/fXXXLt2zTROp06dsLGxoXHjxjRs2JCCggLgxioTe/sbn3o3b96c/Px8jhw5QkZGhmmFx/Xr13FxcalQWIEbn6zfNG3aNE6fPs21a9d46aWXeOWVVwDw8PAAICUlhX79+lG3bl0ABg8eTGJi4m0LK7a2tgwcOBAAf39/Fi1aVOmaquI/fPhwhWd281mJPEhycgpMx9evl1JYWExOTgGpqUd48smnaNLECQBv7x4cOJBsun779p307NmHS5eu1krc95KTk0OF5yLmQ7kzb8qf+VLuzJdyZ74eldxZWlpU+9UIKqzIA8va2rpCAePcuXNcu3YNKysroGJx4/XXX6d+/fp0796dvn37kpSUZDpna2trOrawsMBoNN6yvby8nKCgIEaPHg3AlStXsLKyMhVwAJ5++mnS0tJMK1/efvtt4MZWnp8WdOzs7AAwGAyV7q2srKxCLDfbrK1v/JG0tLQ03Z/BYDDd809VFb+lpWWV81XXmvDnf/UYIlW5Xlx2y3PJyfs4eDCZqVPfpLS0lOTkfXTo0Ml0/sSJNCZNmnY/whQRERERuWMqrMgDq0OHDsTFxfHSSy9RVlbGq6++SmFhYZXXfv755+zZs4emTZuyYcMGANNLMKujc+fOLF26lKFDh2Jra0tISAj+/v507NjRdE1gYCCDBg0iISEBf39/LCwsyM3N5cSJEzzzzDNVjvnee+8REBCAtbU127dvp3PnztSvX5+8vDx+/PFH7O3tOXToEN27dwegqKiI5ORkfH19SUhIwNvb+47i79KlC6tWrSIwMJCSkhIOHDiAm5tbtZ/DpUuFGAzGX75QHijm/ilCaOgkFi6cx6hRN96n5O3dnSFDAk3nz537F4899lhthSciIiIiUiUVVuSB1bNnT06ePMmgQYMwGAyMGjWKPXv2VHnt+PHjGTZsGLa2trRp04b/+q//4ty5c9We09fXl6+++oqhQ4dSXl7Oc889h7+/P+fPnzdd06hRIzZv3kxMTAxr1qyhvLycOnXq0L9/f0aNGlVpzO7du3Pq1CkGDx5MWVkZzz77LCNGjMDa2ppXX32VP/7xjzRr1oynn366Qr+PP/6YxYsX4+zszFtvvXVH8fv4+JCeno6/vz8NGjTA2dm5wsoWkQdNWFik6djBwYHZs+ff8tpPP/2/+xCRiIiIiEj1WBh/uhdBRB4IrVu35vTp09Xul56eTmZmJv7+/pSWlhIQEMC8efNo06ZNtcbRihXzZO4rVh5lyp35Uu7Mm/JnvpQ786Xcma9HJXd6x4rII65ly5YsW7aMtWvXYjQaGThwYLWLKiIiIiIiInLnVFgReQDVZLUKQMOGDVmzZs1djkZERERERERuxbK2AxARERERERERMVcqrIiIiIiIiIiI1JAKKyIiIiIiIiIiNaTCioiIiIiIiIhIDamwIiIiIiIiIiJSQyqsiIiIiIiIiIjUkAorIiIiIiIiIiI1ZF3bAYiIyKPFaDQyd24krVo9xbBhIyksLGTBgiiysjIxGo307t2PESNeBuD//u9vzJ0bSdOmzUz9//znVfzmN/VqKXoRERERkYq0YkXMxsiRI5kyZUqFttjYWGJjY2/bb+vWrSQlJd31WFJTU+/qmNVRUFBASEhIrc0vUlOZmd8xcWIwBw7sN7WtXv0eTk5NiY/fyqpVcSQmbufkyQwATp7MIDBwBOvWbTT9p6KKiIiIiDxItGJFzMrHH39M79698fPzu+M+aWlpdOzY8R5Gdf/l5+dz6tSpezZ+48b292xsubecnBxqO4QqXS8uo+BKEQkJW3nhhYEVVqBMnDiF8vJyAC5dyqW0tIR69W58D548mYGVlTX79++jXr16jB07jv/+72dq5R5ERERERKqiworcFzExMezduxdHR0ecnJzw9fXF0tKS9evXYzAYaNu2LbNmzcLW1pbPPvuMd999F4PBQPPmzYmKiqJJkyYABAcHM3v2bDw9PWnYsGGFOTIyMpg/fz7Xr1/H0dGR2bNnk52dTXJyMikpKeTm5rJv3z4++OADrl27RseOHdmwYQMeHh5ERETg5eVFhw4dCAsL4/vvv8fa2ppJkybh7e1NbGwsJ06c4IcffmDEiBGmOS9dukRQUBCvv/56pWLPunXr2LRpE1ZWVnTv3p2pU6eSm5t7y/EBxo8fD4Cvry9xcXEcPXqUQ4cOkZ+fT3Z2Nl27diUyMpLo6GguXrxISEgIM2bM4NVXX8XR0RE7OzuKi4sJCQmha9euGI1GevXqRXx8PE2bNr3jfL0S/QkXLxfVKNciVdkVM4ACYPLkNwA4dizFdM7CwgJra2uiomZy4MB+nnvOhxYtHgegfv0G9OzZCx+fHmRkfMmMGX9i3bqNODvf+feziIiIiMi9pMKK3HPJyckcP36cpKQkioqK8Pf3x8vLi927d7N582ZsbW2JiYlhzZo1BAQEEBERwaZNm3B1dWX16tVERUWxdOlSADw9PcnLyyM6OpqFCxea5igpKSE8PJwVK1bg4uLCoUOHmDlzJuvWrcPX15eOHTvi7+/Phg0bKCgoID09nfr163P06FE8PDxISUlh2rRphIWF0blzZ0aPHk12djaBgYEkJiaa5vjoo48A2LNnDwUFBYwdO5bQ0NBKRZWMjAw2btzI9u3bqVu3Lq+++ionT55k1apVtxz/VtLT00lKSsLKyorevXsTGBhIeHg4o0aNYvny5Zw7d47vvvuO1atX4+rqyvbt29m5cyddu3bliy++oEWLFtUqqojcKz9dTWNnVwd7e9sKbbGx73L16lUmTJjA1q1xTJgwgVWrVpjO+/k9x7Ztz/DVV1/Stu3g+xr7/fCgrjaSX6bcmTflz3wpd+ZLuTNfyl3VVFiRe+7w4cP06dMHGxsbbGxs8PPzw2g0kpWVxdChQwEoLS3F3d2djIwM2rVrh6urKwABAQGsXLmywniTJ09mwIABfPrpp6a2zMxMsrOzCQ4ONrUVFhZW6GdhYUGXLl1ITU0lLS2NoKAgjh07Rvfu3Xnsscewt7cnJSWF6OhoAJo3b46HhwdffvklAO3atasw3qxZs2jSpAnPP/98pXu+Oa6Dw42/eNatWwdw2/FvpX379tjb25v65OfnU69exXdMNG7c2PTM+vTpw+LFi7l27Ro7duxg0KBBtx1f5H7JySkwHV+/XkphYTE5OQWkph7hySefokkTJwC8vXtw4EAyZ89+z44dHzBy5GgsLCwAKC4upaiorMJYDwMnJ4eH7p4eFcqdeVP+zJdyZ76UO/P1qOTO0tKi2q9GUGFF7jlLS0sMBkOFtvLycvr06UN4eDgAV69epby8nGPHjlW4zmg0UlZWVqGtbt26zJs3j0mTJtGrVy8aNGiAwWDA1dWVnTt3msbPzc2tFIuPjw9Hjhzh5MmTrF69mi1btvDZZ5/RvXt303w/n//mux/s7OwqnBszZgwHDx5k06ZNDB8+vMI5a2tr0w+CABcuXKBu3bq3HN/CwqLCMyotLTUd29ramo4tLCwqjfHz2H7zm9/g7e3N3r17SUlJYdasWZWu/yVrwisXi0R+jevFZbc8l5y8j4MHk5k69U1KS0tJTt5Hhw6d+M1vfkNCwge0aPE4Pj49+Prrr/jnP//Bm29G3r/ARURERER+gQorcs916dKFVatWERgYSElJCQcOHKB///7s27eP4OBgGjVqRGRkJC1atCAwMJBZs2Zx7tw5XF1d2bJlC506dao0pqenJ71792bTpk289tprtGrVivz8fL744gs8PT3Zvn07u3btIj4+HisrK1NxpGvXrixatIhGjRrh4OCAm5sbcXFxxMfHA9C5c2e2bdtm2qqTlpZGZGQkp0+frhSDm5sbPj4+BAYG4ufnV2G7jaenJ1OmTGH8+PHY2trypz/9iXHjxt1y/JycHNNvGcrIyCAnJ+e2z9Ta2rpSwemnBg8ezKRJk+jevXuFwsydunSpEIOhcgFHHmzm+ilCaOgkFi6cx6hRAQB4e3dnyJBALC0tWbAghsWL32HNmr9gZWVNVNT8Su9XEhERERGpTSqsyD3n4+NDeno6/v7+NGjQAGdnZ1q1akVoaChBQUEYDAbc3NwYO3Ystra2REVFERoaSmlpKS4uLsydO7fKcSdPnszBgwcBsLGxYcmSJcydO5fi4mLs7e156623gBuFnUWLFuHg4EDv3r1p1qwZTz/9NHCjkPLtt9/yxBNPABAWFkZERAQJCQkAREdH4+zsfMt7e+KJJxg+fDhRUVEsX76cAQMGsHLlStq2bcuIESN46aWXMBgM9OzZky5duvDkk09WOX7fvn3Zu3cvffv2pW3btri7u9/2mTZu3BgXFxdGjhzJ/PnzK53/wx/+gIWFBYMHP3zvoZCHQ1hYpOnYwcGB2bMrfx8DtGnjzl/+svY+RSUiIiIiUn0Wxqr2FYjcRenp6WRmZuLv709paSkBAQHMmzePNm3a1HZoDyWj0cjXX3/NG2+88Ysvxr0VrVgxT+a6YkWUO3Om3Jk35c98KXfmS7kzX49K7vSOFXkgtWzZkmXLlrF27VqMRiMDBw5UUeUeWr9+PatXr2bJkiW1HYqIiIiIiMhDTytWRKQSrVgxT4/KpwgPI+XOfCl35k35M1/KnflS7szXo5K7mqxYsbxHsYiIiIiIiIiIPPRUWBERERERERERqSEVVkREREREREREakiFFRERERERERGRGlJhI7Za8wAAIABJREFURURERERERESkhlRYERERERERERGpIRVWRETkvjEajURHz2LjxngACgsLCQ+fxsiRQxkxYgjvv7+uUp/vvz9Pnz6+fPXVP+9ztCIiIiIiv0yFFbkrCgoKCAkJqVafGTNm0KNHD5KSkli6dCk+Pj6sXbuWAQMG3LbfV199xahRo+jfvz/9+vUjLCyMa9eu/ZrwHzi+vr6cO3eO/fv3s2TJktoOR+SuyMz8jokTgzlwYL+pbfXq93Byakp8/FZWrYojMXE7J09mmM4XFxczZ85MyspKayNkEREREZFfZF3bAcjDIT8/n1OnTlWrz44dO8jIyMDGxoYePXqwdu1aWrZsyejRo2/bb9KkScybN4/27dtjMBiYPXs2S5YsYcaMGb/mFh5IPXr0oEePHrUdhshdkZCwlRdeGEjTps1MbRMnTqG8vByAS5dyKS0toV49e9P5RYveok+fF4mL++t9j1dERERE5E6osCJ3RXR0NBcvXiQkJIQzZ87g6OiInZ0dsbGxvPnmm1y4cIGLFy/i5eXF3LlzCQ4Oxmg0MmTIENzd3blw4QIhISHExMQwcOBATp8+TV5eHmFhYZw9exYbGxumT5+Ol5cXubm5XL9+HQBLS0tCQ0M5f/48ALm5uYSFhfH9999jbW3NpEmT8Pb2JjY2FoDx48cDN1aExMXFcfToUXbs2EFeXh7du3cnICCAGTNm8OOPP2JnZ0d0dDRt2rQhMTGR9evXYzAYaNu2LbNmzcLW1rbCM/D19aVfv358/vnnWFtbM27cOP7617+SlZXFG2+8Qd++fcnNzSUiIoJ///vfWFhY8Kc//YkuXbqQl5fH1KlT+fe//82TTz5JcXExAAkJCRw9epQFCxaYYnZ1dSU1NZVly5YRHx/PyJEjcXd35/jx4xQXFzNlyhTi4uI4c+YML7/8Mi+//HK189m4sf0vXyQPJCcnh9oOoZLrxWUUXCli8uQ3ADh2LMV0zsLCAmtra6KiZnLgwH6ee86HFi0eB2DXrkTKysro399fhRUREREReWCpsCJ3RXh4OKNGjTJt71m9ejWurq4kJSXh5ubG0qVLKSkpoV+/fvzjH/9gxYoVtG7dmp07dwKQmprKypUrcXV1NY25ZMkSWrRowfLlyzl9+jQRERF4eXkxY8YMgoODcXZ2plOnTvTo0QMfHx8A5syZQ+fOnRk9ejTZ2dkEBgaSmJh429gvXLjARx99hLW1NWPHjqVXr14MHz6cgwcP8t577xEaGsrWrVvZvHkztra2xMTEsGbNGsaNG1dprCZNmpCQkMCMGTNYuXIlcXFxpKWlMW/ePPr27cvcuXMZPHgwPXr04OLFiwwbNozExESWLl2Ku7s7q1at4tixY+zZs6daz99oNLJt2zaWLVtGdHQ0H374IT/++CMDBw6sUWHllehPuHi5qNr9RKqyK2YABb9wTUTEHKZMmUF4+DTWrVvNs892IzFxO8uXr7ovMYqIiIiI1JQKK3LXNW7c2FQgeeGFF8jIyGDdunWcPXuWvLy8O34fyrFjx1i4cCEArVu3ZsuWLQAMGjSI559/niNHjnD48GGmT5/Oiy++SFhYGCkpKURHRwPQvHlzPDw8+PLLL287j7u7O9bW1qY5Fy1aBEC3bt3o1q0b77//PllZWQwdOhSA0tJS3N3dqxzL29sbABcXF5ydnbG2tsbFxYUrV64AcPjwYc6ePcvSpUsBKCsrIzs7m6NHjxITEwNAhw4daN68+R09o6rm9fDwoG7duvzXf/2XaV6R2vbTlTR2dnWwt7fFycmBQ4cO8bvf/Y6mTZsCDvj7D+CTTz6hvLyY4uIiQkNfBW5sE4qOjmDatGkP7fa4B3G1kdwZ5c68KX/mS7kzX8qd+VLuqqbCitx1dnZ2puP4+Hj27t3L0KFD6dKlC19//TVGo/GOxrG2tsbCwsL09ZkzZ7CwsGDPnj2EhITQs2dPevbsyahRo/D39ycsLKzS2EajkfLyciwsLDAYDKb20tL/vAjzp/HeLLDc7HvmzBnKy8vp06cP4eHhAFy9etX0Toifq1OnTpVj3WQwGFi/fj0NGzYE4OLFizRu3BgLC4sKsVtZWVU5/s1rysrKqjWvSG3LyfnPmpXr10spLCwmJ6eAHTs+xMrKiqlT36S0tJSdO3fRoUMnAgKGM3bsBFOfP/7xRcLDo2jTxr3CWA8LJyeHh/K+HgXKnXlT/syXcme+lDvz9ajkztLSotqvRtBPYHJXWFtbV/phH+Dzzz8nICCAF198kb///e989dVXFQoct+Pp6cnu3btp3bo1Z86cYcyYMezYsYO4uDieeeYZvLy8ADh16hRubm4AdO7cmW3btpm2AqWlpREZGUlOTg6pqakAZGRkkJOTc9s5AwICOHz4MMuWLWPWrFn89a9/JTg4mEaNGhEZGUmLFi1M72upjs6dO7Nx40bGjRvHt99+y/Dhw9m/fz9eXl7s3LmTNm3akJGRwb/+9a9KfR0dHfn2229p3rw5+/fvr2L0u2dN+PP3dHx5tFwvrvx3w02hoZNYuHAeo0YFAODt3Z0hQwLvV2giIiIiIr+aCityVzRu3BgXF5dKv5knKCiIyMhIVq5cib29Pe3bt+fcuXN3NOaECRMIDw+nf//+WFtb8/bbb9OgQQNWrlzJO++8Q3h4OHXq1KFly5am7TthYWFERESQkJAA3HiprrOzM3379mXv3r307duXtm3b3nIrT0REBOHh4WzcuJG6desSHR3NU089RWhoKEFBQRgMBtzc3Bg7dqxpPl9f3zvemhAeHk5ERAQvvvgiAG+//Tb29vZMmDCB6dOn069fP1q1alXlVqAJEyYwZ84cli1bxrPPPntH89XUpUuFGAx3trJIHhzm8ilCWFik6djBwYHZs+f/Yp9t23bdw4hERERERGrOwnin+zJE5JGhwop5MpfCilSm3Jkv5c68KX/mS7kzX8qd+XpUcleTrUCW9ygWEREREREREZGHngorIiIiIiIiIiI1pMKKiIiIiIiIiEgNqbAiIiIiIiIiIlJDKqyIiIiIiIiIiNSQCisiIiIiIiIiIjWkwoqIiIiIiIiISA2psCIiIiIiIiIiUkMqrIiIiIiIiIiI1JAKKyIick8ZjUaio2excWM8AIWFhYSHT2PkyKGMGDGE999fV6lPUtJOpk2bdJ8jFRERERGpPhVWHnEFBQWEhIRUq8+MGTPo0aMHSUlJLF26FB8fH9auXcuAAQNu2++rr75i1KhR9O/fn379+hEWFsa1a9d+Tfj31datW3nuued466237uq4586dw9fXF4DY2Fi6du3KgAEDGDBgAL169WLx4sW/OMbIkSNNx61bt76r8Yn8GpmZ3zFxYjAHDuw3ta1e/R5OTk2Jj9/KqlVxJCZu5+TJDACuXMnnnXfmsWRJDGCspahFRERERO6cdW0HILUrPz+fU6dOVavPjh07yMjIwMbGhh49erB27VpatmzJ6NGjb9tv0qRJzJs3j/bt22MwGJg9ezZLlixhxowZv+YW7pukpCTmz5/Ps88+e0/neemllxg/fjwA165do2/fvnh6evLcc8/dss/Ro0fvaUwiNZWQsJUXXhhI06bNTG0TJ06hvLwcgEuXciktLaFePXsAkpP30aSJEyEhEzl8+FCtxCwiIiIiUh0qrDzioqOjuXjxIiEhIZw5cwZHR0fs7OyIjY3lzTff5MKFC1y8eBEvLy/mzp1LcHAwRqORIUOG4O7uzoULFwgJCSEmJoaBAwdy+vRp8vLyCAsL4+zZs9jY2DB9+nS8vLzIzc3l+vXrAFhaWhIaGsr58+cByM3NJSwsjO+//x5ra2smTZqEt7c3sbGxAKZCg6+vL3FxcRw9epQdO3aQl5dH9+7dCQgIYMaMGfz444/Y2dkRHR1NmzZtSExMZP369RgMBtq2bcusWbOwtbWt8Ax8fX1p164dp06dYuPGjRw6dKhSn1WrVvH3v/+d2bNnEx4ejqOjI/Pnz+f69es4Ojoye/ZsmjdvzsiRI2nQoAHffPMN7777LqNHj+b3v/89OTk5bNu2jdmzZ/PNN9+Qm5tL69atWbRo0W3z85vf/IZ27drxzTff4OXlRWRkZKX+CxcuBGDIkCF88MEHAERERHDixAngxiqYxx9/vFrfF40b21frenlwODk51HYIXC8uo+BKEQCTJ78BwLFjKabzFhYWWFtbExU1kwMH9vPccz60aHHje3TgwD8C8NFHu+5z1CIiIiIiNaPCyiMuPDycUaNGmbb3rF69GldXV5KSknBzc2Pp0qWUlJTQr18//vGPf7BixQpat27Nzp07AUhNTWXlypW4urqaxlyyZAktWrRg+fLlnD59moiICLy8vJgxYwbBwcE4OzvTqVMnevTogY+PDwBz5syhc+fOjB49muzsbAIDA0lMTLxt7BcuXOCjjz7C2tqasWPH0qtXL4YPH87Bgwd57733CA0NZevWrWzevBlbW1tiYmJYs2YN48aNqzSWt7c37777Lt98802VfUJDQ0lNTSU0NJT27dvzxz/+kRUrVuDi4sKhQ4eYOXMm69atA25sxVm2bBkAly9fZsyYMXTq1Iljx45Rp04dtmzZgsFgICgoiIMHD9K2bdtb3uP58+dJS0sjKCiI9PT0KvuHh4cTHx9vKqoAdOnShaioKN566y02b97MG2+8cUffDze9Ev0JFy8XVauPyE27YgZQcAfXRUTMYcqUGYSHT2PdutW88spr9zw2EREREZG7TYUVMWncuLGpQPLCCy+QkZHBunXrOHv2LHl5eXf8PpRjx46ZVlG0bt2aLVu2ADBo0CCef/55jhw5wuHDh5k+fTovvvgiYWFhpKSkEB0dDUDz5s3x8PDgyy+/vO087u7uWFtbm+a8ufqjW7dudOvWjffff5+srCyGDh0KQGlpKe7u7lWO5eHhAdwoFP1Sn8zMTLKzswkODja1FRYWmo7btWtX5dgdOnSgYcOGbNiwgbNnz5KZmVnlM928eTOffvopBoMBKysr/vd//5c//OEPAHfUH8DPzw+Ap556ii+++KLKa0TupZ+vnLGzq4O9vS1OTg4cOnSI3/3udzRt2hRwwN9/AJ988kmFPg4OdtjYWD8QK3Dul0fpXh82yp15U/7Ml3JnvpQ786XcVU2FFTGxs7MzHcfHx7N3716GDh1Kly5d+PrrrzEa7+xFktbW1lhYWJi+PnPmDBYWFuzZs4eQkBB69uxJz549GTVqFP7+/oSFhVUa22g0Ul5ejoWFBQaDwdReWlpaZbw3Cyw3+545c4by8nL69OlDeHg4AFevXjW91+Hnbm4PupM+BoMBV1dX06qd8vJycnNzq4zrp1/v37+fpUuXMmrUKAYNGsTly5erfKY/fcfKT91p/58+DwsLizvO20+tCX++2n1EbvrpViBT2/VSCguLyckpYMeOD7GysmLq1DcpLS1l585ddOjQiZyc/6xzKSi4TklJWYW2h5mTk8Mjc68PG+XOvCl/5ku5M1/Knfl6VHJnaWlR7VcjqLDyiLO2tqasrKxS++eff05AQAAvvvgif//73/nqq68qFDhux9PTk927d9O6dWvOnDnDmDFj2LFjB3FxcTzzzDN4eXkBcOrUKdzc3ADo3Lkz27ZtM20FSktLIzIykpycHFJTUwHIyMggJyfntnMGBARw+PBhli1bxqxZs/jrX/9KcHAwjRo1IjIykhYtWlRZtLipU6dOv9inVatW5Ofn88UXX+Dp6cn27dvZtWsX8fHxt30uR44coU+fPgwePJjs7GxSU1NNz+JO3K6/lZUVZWVlFQpMv8alS4UYDPqNLObGXP6xCw2dxMKF8xg1KgAAb+/uDBkSWMtRiYiIiIjUjAorj7jGjRvj4uJS6TfzBAUFERkZycqVK7G3t6d9+/acO3fujsacMGEC4eHh9O/fH2tra95++20aNGjAypUreeeddwgPD6dOnTq0bNnStH0nLCyMiIgIEhISgBsv1XV2dqZv377s3buXvn370rZt21tu5YmIiCA8PJyNGzdSt25doqOjeeqppwgNDSUoKAiDwYCbmxtjx441zefr60uPHj0qjNOmTZtb9rnJxsaGJUuWMHfuXIqLi7G3t7+jX8E8ZMgQpkyZwu7du6lTpw7PPPPMHT/TX+rfo0cPBgwYYHp+Ig+asLBI07GDgwOzZ8+/7fV9+75I374v3uOoRERERER+PQtjTfYJiMhDTStWzJO5rFiRypQ786XcmTflz3wpd+ZLuTNfj0ruarIVyPIexSIiIiIiIiIi8tBTYUVEREREREREpIZUWBERERERERERqSEVVkREREREREREakiFFRERERERERGRGlJhRURERERERESkhlRYERERERERERGpIRVWRERERERERERqSIUVEREREREREZEasq7tAERE5OFlNBqZOzeSVq2eYtiwkRQWFrJgQRRZWZkYjUZ69+7HiBEvA5Cd/S8WLJhDfn4edevWJTw8iscff6JW4xcRERER+SVasSJ3ZPr06SQkJFSrz6ZNm9i0adM9iujemTFjBufPnwdgzJgxXLhwoUbjpKamEhAQQP/+/enXrx9vv/025eXl1R5n6dKlfPHFFzWKQaQ2ZWZ+x8SJwRw4sN/Utnr1ezg5NSU+fiurVsWRmLidkyczAIiKCmfAgMG8//4H/M//vEZ4+DSMRmNthS8iIiIicke0YkXumcDAwNoOoUZSU1MJCQkBYNWqVTUao6SkhD/96U9s2rSJ5s2bU1JSwoQJE9iwYQOjRo2q1ljHjh2jU6dONYqjpho3tr+v88nd4+TkUKvzXy8uo+BKEQAJCVt54YWBNG3azHR+4sQppgLjpUu5lJaWUK+ePTk5F8nKysLP73kAvLy6EhOzgK+/Pk3r1m3u/42IiIiIiNwhFVakSkajkQULFnDgwAGcnZ0pLy+nY8eOJCYmsn79egwGA23btmXWrFls2bKFrKwsZs6cCcCCBQto1qwZBQUFAIwfP55du3bx3nvvYWFhwdNPP82cOXMoKSkhKiqKb775hvLycsaMGcMLL7xQKZYVK1bw4YcfYmVlRdeuXZk6dSo//PADwcHBtGrVim+//RYXFxfeeecdGjZsyN/+9jeWLl1KWVkZrq6uzJkzB0dHR3x9fWnXrh2nTp1i48aNxMXFceTIEfLz83F2dmbx4sUkJCRw8eJFxo4dy4YNGxg8eDBxcXEcPXqUQ4cOkZ+fT3Z2Nl27diUyMhKAmJgY9u7di6OjI05OTvj6+tKjRw8KCwspKrrxA6aNjQ1hYWFcvXqVrKwsgoKCSE5OxtLSktTUVFatWkV0dDRTpkzh2rVrWFpaEh4eTmZmJidPniQ8PJxly5ZhZ2dHZGQkeXl52NnZMXPmTNzd3Zk+fTp169bln//8J1euXGHy5Mns3LmTr776Cj8/P6ZPn16t/L8S/QkXLxf9iu8geVTtihlAwf8/njz5DQCOHUsxnbewsMDa2pqoqJkcOLCf557zoUWLxzl16p80adIES8v/LKR0cnImJ+eCCisiIiIi8kBTYUWqtHfvXv75z3+SlJREQUEB/fv3p6ioiN27d7N582ZsbW2JiYlhzZo1vPTSSwwcOJA333wTS0tLPvnkE7Zs2cLmzZsBuHDhAvPnzychIYFmzZoxdepUDh48yIkTJ2jbti1vvfUWhYWFvPTSS3h4eNC8eXNTHAcPHiQ5OZnt27dTp04dxo8fz+bNm+nWrRtff/014eHhdOrUiQULFrBs2TLGjRtHTEwMcXFxNGjQgM2bN7Nw4ULmzp0LgLe3N++++y5ZWVmcPXuWzZs3Y2lpybRp0/jwww8ZO3YsmzdvZuXKlTg6OlZ4Junp6SQlJWFlZUXv3r0JDAzk/PnzHD9+nKSkJIqKivD398fX15cGDRrw2muvMWjQIFq2bEmnTp3o3bs3np6eALi6upKamoqXlxeJiYkMGjSIbdu24ePjw6uvvsrf/vY3jh8/ziuvvML27dsJDQ2ldevWvPTSS0RERODu7s63335LSEgIe/fuBeDixYts2bKFHTt2MGPGDPbu3YutrS3e3t6EhITg4FC7Kxnk0fHzVTN2dnWwt7et0B4b+y5Xr15lwoQJbN0ax7PPPou1tVWFa6ytLXF0tK/1VTj3y6Nynw8j5c68KX/mS7kzX8qd+VLuqqbCilTp6NGjPP/889SpU4dGjRrh7e2N0WgkKyuLoUOHAlBaWoq7uzuNGjWiTZs2pKamUqdOHVq2bImTk5NprPT0dJ555hmaNbuxHeCdd94B4M9//jPXr19n+/btAFy7do1vvvmmQmElJSWFfv36UbduXQAGDx5MYmIi3bp144knnjBtkRk4cCBTpkyha9eu/PDDD6btNgaDgQYNGpjG8/DwAODxxx/njTfe4IMPPuC7777jxIkTtGjR4rbPpH379tjb39gi07x5c/Lz8zl8+DB9+vTBxsYGGxsb/Pz8TNcHBwcTEBDA4cOH+fzzzxkzZgwTJ07k5ZdfZvDgwXz44Yf893//NykpKURGRtK0aVPGjx/PqVOn6NatGyNGjKgw/9WrVzl58iQzZswwtV27do3Lly8DN4pGAC4uLv+PvTsPq6rc////3ICgJQcVcUo5EXYUydkUzAREzRnnIQUzk1LQMqVEtgiGI2GpmB6HcEhFc8Ac+ppznpw6WdmAlmOaypBDOCCw9/794a/9ieMQYohbX4/r8rr2Xnut+37t9SaveLvWvXj66adxdXUFoEyZMly6dEmNFblvMjKy8r3Pzs7l8uXrZGRksW/fHjw9q1O+/I2/I5o3D2THjm0EBrYjPT2d9PTfMRgMAJw7l4ajo/NN4z2M3Nweje/5MFLtbJvqZ7tUO9ul2tmuR6V2dnaGu14aQY0VuSWDwZBv0UgHBwdMJhNt27bFaDQCN37R/2OthKCgIDZu3EiJEiXo2LFjvrEcHBysvygBnD9/HrjR9IiPj8fb2xuAzMzMfE2QP/b5X3l5edZx/2CxWLC3t8dkMtGgQQNmz54NwPXr17ly5Yp1PycnJwC+//57RowYwUsvvcQLL7yAnZ3dXy6S+cexfz4/dnZ2t8z4zTff8MMPP9C3b186dOhg/TNhwgReeukl2rRpw3vvvcemTZto3rw5Tk5ONGzYkA0bNrBjxw42btzImjVrSEpKyncuHB0dWbt2rXXbuXPnKFOmDAAlSpSwbv/zuSmM+cbW93S8PLqyr+fd8fNt2zazc+c2IiJGk5uby7Ztm3n22SZUqFCRJ56oxtatn9Gy5Qvs27cHg8GAp2f1+5RcRERERKRw1FiRW/L19bXe5nPt2jV27dqFp6cnmzdvZvDgwZQrV46YmBjc3d0ZOnQogYGBTJ8+HYvFwsiRI/ONVbt2bWJjY8nIyMDNzY0JEybQpEkTfHx8WLZsGXFxcaSnp9O5c2eSk5PzXTni4+PDrFmz6NWrFw4ODqxatQofHx8Ajh8/TmpqKl5eXqxatYrmzZtTt25djEYjx48fx8PDgw8++IC0tDQmTZqUL9OXX35J48aN6dOnDxcuXGDHjh20bn2jmfBHg6YgmjZtyty5c+nTpw85OTns2LEDLy8vXFxcSExMpGHDhtSseWN9iB9++AEvLy8ASpUqRfPmzZk6dSozZswAYMqUKVSsWJH+/fvTpEkTunTpki+Ps7MzTz75JGvXriUoKIgvvviC6OhotmzZcrfl/Uu//XYZs1lPY7E1tvCvCOHhw3n33QmEhPQCoHnzAHr0uLHQdUzMeCZPjmPhwvk4OjrxzjuT8625IiIiIiLyIFJjRW6pZcuWfPfdd3To0IHy5cvj6emJs7Mz4eHh9O/fH7PZjJeXF6GhoQCULFmSBg0akJOTw+OPP55vrIoVKxIVFcXAgQMxm83Uq1ePrl27cu3aNWJiYujQoQMmk4mIiAjc3d1JS0sjNDSUtWvXEhAQQGpqKt26dSMvL49mzZrRr18/zp07h4uLC9OnT+eXX36hRo0axMXF8dhjjzFhwgTeeOMNzGYzFStWtN569Gft2rUjPDzcenXNM888w+nTpwHw9/cnNDSUefPm/eV58vf35+uvv6ZLly64uLhQoUIFnJyc8PDwYNKkSYwePZrLly9jMBioU6cO0dHR1mPbt2/PgQMHrLcnBQcHM2LECFavXo29vT2TJ08G4Pnnn2fs2LFMnjyZ+Ph4YmJimDdvHiVKlOC9997LdzWQyIMoKirG+trZ2ZnY2Im33K9aNXcSE+fcp1QiIiIiIn8Pg+Wv7n8QeQCdPn2akJAQtm3bVqw5vv76a06cOEGXLl3Izc2lV69eTJgwwXqVyu2YTCbee+89XF1dGTBgwH1KW3C6YsU22cIVK3Jrqp3tUu1sm+pnu1Q726Xa2a5HpXZaY0XkPvPw8CAxMZGkpCQsFgudO3f+y6YK3FiEt2zZssyaNes+pBQREREREZGiUqjGSm5ubr6FMkXut6pVqxb71Spw44k78+fPv+vjUlJSiiCNiIiIiIiI3G8FWhXwv//9Lx988AE5OTn06NGDRo0asXHjxqLOJiIiIiIiIiLyQCtQYyU+Pp569eqxZcsWypQpw4YNG/jwww+LOpuIiIiIiIiIyAOtQI0Vk8lE06ZN2b17Ny1btqRq1aqYzeaiziYiIiIiIiIi8kArUGPFbDZz8OBBduzYQdOmTfnpp5/Izc0t6mwiIiIiIiIiIg+0Ai1e+9prrzFixAi6d+9OtWrVaNGiBVFRUUWdTURERERERETkgVagxkrr1q1p3bq19f3mzZuxt7cvslAiIiIiIiIiIragQLcCZWRkEBoaygsvvEBmZiahoaGkp6cXdTYRERERERERkQdagRorsbGxtGzZEicnJ1xcXKhZsyZGo7Gos4mIyC2sXJlMnz5deemlFxk7djS//36J69eziYyMJDi4J/369WTChFiuX88u7qgiIiIiIg+9AjVWfv31V3r27ImdnR2TVJZoAAAgAElEQVQlSpQgIiKCs2fPFnU2eUhkZWURFhZ2V8dERkYSGBjI+vXrmT59Ov7+/iQlJREUFHTH4w4dOkRISAidOnWiffv2REVFcfXq1XuJL/JAOXDgvyxZsohp02axYMFSfH2fY8qU8Sxc+CEmk4mFC5NZuHAZ169fZ/HiBcUdV0RERETkoVegNVYMBkO+xytfvnxZj1uWArt06RKpqal3dcyaNWs4ePAgjo6OBAYGkpSUhIeHBwMGDLjjccOHD2fChAnUr18fs9lMbGws06ZNIzIy8l6+wiPH1bV0cUeQ/5F9PY+s369x6FAqjRo1pkKFigD4+bVg8uQ4OnXqSq1a1bGzu9Ev/9e/anD8+LHijCwiIiIi8kgo8OK1I0eOJCsri+TkZD7++GPatm1b1NnkIREXF0d6ejphYWEcPXqUsmXLUrJkSWbMmMHo0aNJS0sjPT0dX19fxo8fz+DBg7FYLPTo0YNatWqRlpZGWFgYCQkJdO7cmcOHD3Px4kWioqI4duwYjo6OjBo1Cl9fXzIzM8nOvnH7g52dHeHh4fz6668AZGZmEhUVxZkzZ3BwcGD48OE0b96cGTNmADB06FAAWrRowaJFi9i/fz9r1qzh4sWLBAQE0KtXLyIjIzl//jwlS5YkLi6OmjVrkpKSwsKFCzGbzXh7ezN27FicnJzynYPJkyfzxRdfYGdnR8uWLQkPD+fatWsYjUYOHz6MwWBg4MCBdO7cmdWrV7N//34mTZoEQHBwMOHh4QDEx8djNpt5+umniYyMvOU5+Pzzz5k+fTp5eXlUrVqVd955h7Jly95VzQbGfUb6hWuFL7r87dYlBJEFeHs/w8qVyZw7d5ZKlSqzceMn5Obm8tRT1fHw8CAjI4tz586yYsUy3npLT28TERERESlqBX7cckpKCmazmd27d9OrVy969OhR1NnkIWE0GgkJCbHe3jNv3jyqVq3K+vXr8fLyYvr06eTk5NC+fXt++OEHZs+eTY0aNVi7di0A+/btY86cOVStWtU65rRp03B3d2fmzJkcPnyY6OhofH19iYyMZPDgwVSoUIEmTZoQGBiIv78/AO+88w4+Pj4MGDCAU6dO0adPH1JSUu6YPS0tjY0bN+Lg4GBdwLlv377s3LmTWbNmER4ezooVK0hOTsbJyYmEhATmz5/PkCFDrGP8+uuvfP7552zYsIFr164RGRnJ9evXmTFjBmXLlmX9+vWcP3+eHj16ULNmzTvmOXHiBNu3b8fZ2ZnY2NibzkGNGjVISEhg0aJFuLi4kJyczLvvvsv48eMLWT15kLi5OdOyZXMuXBhKdPTbGAwGunXrRpkyZahUqQwAaWknGTo0nJCQYDp3blfMieVuuLk5F3cEKSTVzrapfrZLtbNdqp3tUu1urUCNlbfeeospU6bQuXPnos4jDzlXV1drg6RDhw4cPHiQBQsWcOzYMS5evFjg9VC+/PJL3n33XQBq1KjB8uXLAejatSutW7dmz5497N69m1GjRtGxY0eioqLYu3cvcXFxAFSrVo26devy7bff3nGeWrVq4eDgYJ1z6tSpAPj5+eHn58dHH33EyZMn6dmzJwC5ubnUqlUr3xgVK1bEycmJ3r17ExAQwMiRI3FycmLv3r1MmDABgHLlyhEYGMj+/fspXfr2t+F4eHjg7Ox823Owfft2zp49S0hICABmsxkXF5cCnVN58GVkZHH16hWqV/dmzpxF//+2dMzmaeTm2rNhwwbGjo1h+PC3aN26DRkZWcWcWArKzc1Z9bJRqp1tU/1sl2pnu1Q72/Wo1M7OznDXSyMUqLGSmpqKxWLBYDAUKpjIH0qWLGl9vXjxYjZt2kTPnj1p2rQpP/30ExaLpUDjODg45Pt5PHr0KAaDgU8//ZSwsDBatWpFq1atCAkJoUuXLkRFRd00tsViwWQy3bSGUG5u7i3z/tFg+ePYo0ePYjKZaNu2rfUpWVeuXMFkMt2U9eOPP2b//v18/vnn9O7dm8WLF98xz58/u1Oe/z0HJpOJBg0aMHv2bACuX7/OlStX7nQqb2m+sfVdHyNFK/t6HgCZmRm8/voQPvpoBY8/XpqFCz+kZcvWfPHFLqZMieO99xKpWbPWX4wmIiIiIiJ/lwI1VipUqED79u2pW7cujz/+uHW7HrksBeHg4EBeXt5N27/44gt69epFx44d+e677zh06FCBF0Vu1KgRGzZsoEaNGhw9epRBgwaxZs0aFi1aRIMGDfD19QVuNAW9vLwA8PHxYeXKldZbgQ4cOEBMTAwZGRns27cPgIMHD5KRkXHHOXv16sXu3btJTExk7NixfPjhhwwePJhy5coRExODu7u7db0WgB9//JF33nmHxYsX4+vry48//sjx48eteYxGI+fPn2fr1q3MmDGDjIwMjh49isVi4fTp0xw+fLjA5yA5ORmj0cjx48fx8PDggw8+IC0tzbpeS0H99ttlzOaCNbnk/nJ3f5J+/foTGvoSZrOZOnXq8eabb/HSSy9isViYNCnOum/t2nUZMeLtYkwrIiIiIvLwK1BjpX79+tSvX7+os8hDytXVlSpVqtz0ZJ7+/fsTExPDnDlzKF26NPXr1+f06dMFGnPYsGEYjUY6deqEg4MDU6ZMwcXFhTlz5hAfH4/RaKREiRJ4eHhYb9+JiooiOjqa1atXAzcW1a1QoQLt2rVj06ZNtGvXDm9v75tu5flDdHQ0RqORpUuXUqpUKeLi4qhevTrh4eH0798fs9mMl5cXoaGh1vlatGhBYGAg9erVo0OHDpQqVYoGDRrQvHlzGjduTExMDB07dsRkMvHaa6/h7e1NTk4Oq1atok2bNnh4eNCwYcMCn4MKFSowYcIE3njjDcxmMxUrViQ+Pr5A51RsR7duvejWrVe+bcuWrX5kLs8UEREREXmQGCwFvfdCRB4ZumLFNqmxYrtUO9ul2tk21c92qXa2S7WzXY9K7YpsjZWOHTvecvu6devuajIRERERERERkYdJgRorY8aMsb7Ozc1lw4YNVKtWrchCiYiIiIiIiIjYggI1Vho3bpzvfdOmTenduzeDBw8uklAiIiIiIiIiIrbArjAHXbhwgfT09L87i4iIiIiIiIiITSnUGitnzpyhZ8+eRRJIRERERERERMRW3PUaKwaDgXLlyuHp6VlkoUREREREREREbEGBbgVKSUmhcePGNG7cmGeffRZPT0+GDRtW1NlERERERERERB5od7xiZezYsaSlpfHVV19x/vx56/a8vDxOnTpV5OFERERERERERB5kd2ysdO/enZ9//pnDhw/zwgsvWLfb29tTr169Ig8nImKrPv10PcuXL7W+v3LlMunpaSxfnkJi4nucPHkCi8VCmzbt6dfvpeILKiIiIiIi9+SOtwLVrl2brl27smzZMrp06WL906lTJ9zd3e9XRpFbysrKIiws7I77REZG8uuvv95xn+DgYPbt2/d3RiuQy5cvU79+fdLS0vJt379/P126dCEtLY1Bgwbd9vjvvvuOqKgoAFasWMH69euLNK/cnbZtO7BgwVIWLFjKvHmLKFfOleHD3yI5+SPc3CqyePEK5s5dRErKKr7//mBxxxURERERkUIq0OK1Z8+eJTY2lqtXr2KxWDCbzZw+fZodO3YUcTyR27t06RKpqal33Gffvn1/2XwpLqVLl6ZVq1Zs2LCBl19+2bo9JSWF7t27U7FiRebOnXvb42vXrk3t2rUBOHDgAI0bNy7yzFI4H320gLJly9K5czcsFgsmkwmA337LJDc3h8cfL13MCUVEREREpLAK1FgxGo0EBQWxadMmevfuzdatW2ndunVRZxO5o7i4ONLT0wkLC6NFixYkJSVhMBjw9vZmzJgxLFmyhPT0dEJDQ1myZAl79+4lKSmJ7OxscnJymDBhAg0aNLCOd+7cOUaOHMnVq1exs7PDaDTedMtbcHAwtWvXtq47ZDQa8fPzIzMzk+joaM6dO4fBYGDEiBHUrFmToKAgdu3aBcDzzz9PZGQk7dq149///jf29vZ07dqVKVOmWBsr169fZ8eOHbz99tucPn2akJAQtm3bxrp165g3bx729vZUrVqV+Ph4vvnmGxITExk8eDDbtm1j7969uLm5cfHixZv2dXJyuqtz6+qqX/TvRfb1PLJ+vwbAxYsXSU5ewvz5i4EbT1ZzcHBg3Lgx7Nixleef98fd/Z/FGVdERERERO5BgRorBoOB0NBQLly4wFNPPUXHjh3p1q1bUWcTuSOj0UhISAjDhg0jPDycFStWULZsWWJjY0lMTOTtt98mOTmZOXPm4OLiQnJyMrNnz6ZcuXKsXLmSOXPmMHv2bOt4K1euxN/fn1deeYXPP/+cr7766pZrCeXm5rJ8+XK2bdvGtGnT8PPzY/z48XTr1o3AwEDS09N58cUXSUlJoXLlyvz000/Y29tjMpnYv38/7dq1Y9euXcTGxvLUU0/x+++/c+zYMZ566im2bNmCr68vLi4uZGVlWed8//33WbFiBa6urkyePJljx45ZP2vatCktWrSgcePGPP/88wQGBt60r5eX112d24Fxn5F+4VohqiIA6xKC+KN6n3yymuef9+OJJ6rm2yc6+h1GjozEaHyLBQvmMXDgq/c/qIiIiIiI3LMCNVYef/xxANzd3fn5559p2LAhdnYFelKzSJH78ssvCQgIoGzZsgD06tWLyMjIfPvY2dkxc+ZMtm3bxvHjx9m/f/9NP8O+vr4MHTqU1NRU/Pz86Nev3y3ne/755wF4+umnuXjxIgC7d+/m2LFjTJ8+Hfi/J2c1b96cPXv24ODgQEhICBs2bCArK4vMzEw8PT0B6Ny5M+vXr2fYsGGsXbuWl1566aY5AwIC6NOnDy1btuSFF17Ay8vrtuvC3Gpfuf/c3JwB2LlzK0aj0fp+165d/Otf/6JixYqAM126BPHZZ59ZP/+75hXbo9rZLtXOtql+tku1s12qne1S7W6tQI2VOnXq8MYbb/D666/z6quvcuLECRwcCnSoSJEzm8353lssFvLy8vJtu3LlCt27d6dTp048++yz1KhRgyVLluTbp2HDhmzYsIEdO3awceNG1qxZQ1JS0k3z/XFbjcFgyJdh4cKFlClTBoD09HRcXV0xmUwkJibi6OjI66+/zqeffsq6deto1qyZ9diuXbvy8ssv8+KLL3LixAl8fX1vmtNoNHLo0CF27txJREQE4eHhVKpU6Zbn41b7BgUF3ekUShHIyMji999/5+TJk1Sr9jQZGTeuYVmz5hPs7e2JiBhNbm4ua9eu49lnm1g/vxdubs5/yzhy/6l2tku1s22qn+1S7WyXame7HpXa2dkZ7npphAJ1R0aPHs23336Lh4cHo0ePZvfu3SQkJBQqpMjfxcHBgby8PBo3bsyiRYsYMmQIZcqUYcWKFTRp0gTAegvOiRMnMBgMvPbaa1gsFt566y3rAqJ/mDJlChUrVqR///40adKELl26FDiLj48PS5cuZciQIRw5coS+ffuydetWvL29OX78OE5OTnh6etKkSRNmzZrFpEmTrMdWqVKFypUrM336dDp16pSvYQM3rn5p164dixcv5tVXXyU3N5fU1NR8jZU/vuft9r3bxsp8o9ZQuhfZ12809n799RSuruXzNaLDw4fz7rsTCAnpBUDz5gH06NGnWHKKiIiIiMi9K/AaK3Z2diQnJ9O1a1dcXFx46qmnijqbyB25urpSpUoVxo8fz6uvvkpwcDC5ubl4e3sTGxsLgL+/P6GhocydOxcvLy/atm2LwWCgWbNmfPXVV/nGCw4OZsSIEaxevRp7e3smT54MwLRp06hQoQJ9+tz+l1+j0Uh0dDQdO3YEbjRpSpe+0eVs2LAh167dWK/Ex8eHjz/+mGeffTbf8d26deOtt95i8+bNN43t4ODAsGHDePnll3FycsLV1ZVJkyZx5MgR6z5NmzZl6tSpODs733Lfu/Xbb5cxmy13fZzk5+XlzfLlKfm2OTs7Exs7sZgSiYiIiIjI381gsVj+8renVatW8eGHH3L9+nWWL19Ohw4dGD58OD179rwfGUXkPlNjxTY9KpdnPoxUO9ul2tk21c92qXa2S7WzXY9K7QpzK1CBVqD96KOPWL58OaVLl8bV1ZXVq1ezcOHCQoUUEREREREREXlYFKixYmdnZ72tAaBy5crY29sXWSgREREREREREVtQoMZKmTJlSE1NtS6q+cknn+Di4lKkwUREREREREREHnQFfirQ66+/zi+//EKzZs1wcnLigw8+KOpsIiIiIiIiIiIPtAI1Vjw9PVm7di0nTpzAZDLh4eFBiRIlijqbiIiIiIiIiMgD7Y63Ao0ZM8b6+tKlS3h6evKvf/1LTRUREREREREREf6isfL9999bXw8cOLDIw4iIiIiIiIiI2JI7NlYsFsstX4uIiIiIiIiISAGfCgRYnwgkIiIiIiIiIiI33HHxWrPZzKVLl7BYLJhMJuvrP5QpU6bIA4qIPIiOHj3Ce+9N4cqVy9jZ2RMRMZqaNb14+eV+5ORcx8HhxlpUrVu34cUXQ4o5rYiIiIiIFJU7NlZ++uknfHx8rM2UJk2aWD8zGAykpqYWbTqRu5SVlcWoUaOYOXPmbfeJjIwkPDycJ5544rb7BAcHEx4enu9n/u/YVx4O2dnZvPlmGKNGjcHXtxm7du1g3Dgj8+d/xJkzp1m/fgsODgV66JqIiIiIiNi4O/6f/6FDh+5XDpG/xaVLl/6y4bdv3z7CwsLuUyJ5GO3fv5cqVari69sMgGbN/Khc+QlSU3+gVKnHGDFiKBcunKdRo8a8+moYTk4lizmxiIiIiIgUFf2TqjxU4uLiSE9PJywsjBYtWpCUlITBYMDb25sxY8awZMkS0tPTCQ0NZcmSJezdu5ekpCSys7PJyclhwoQJNGjQwDreuXPnGDlyJFevXsXOzg6j0Ui9evVumnfFihVMnDgRuHFFTJMmTbh27RpGo5HDhw9jMBgYOHAgnTt3ZvXq1ezfv59JkyYB/3fFC8C///1vSpYsydGjR6lRowbvvvsujo6OLFiwgGXLlmFvb09AQAARERFkZmYSHR3NuXPnMBgMjBgxgqZNm7Jnzx7i4+MBcHFxISEhgXLlyt3VeXR1LV2o8/8oyL6ex6lTJ3F1dWXixHEcOfIzpUs7M2TIMK5evUKDBg15/fWRODmVZNw4I7Nnz+T110cUd2wRERERESkiaqzIQ8VoNBISEsKwYcMIDw9nxYoVlC1bltjYWBITE3n77bdJTk5mzpw5uLi4kJyczOzZsylXrhwrV65kzpw5zJ492zreypUr8ff355VXXuHzzz/nq6++umVj5bHHHiMlJYVDhw4RGhrKli1bmDFjBmXLlmX9+vWcP3+eHj16ULNmzTvm//rrr/n000+pUKECPXv25D//+Q/ly5dn6dKlrFq1ilKlSvHKK6/w/fffM3/+fLp160ZgYCDp6em8+OKLpKSk8MEHHxATE0OdOnWYO3cuP/74I82aNbur8zgw7jPSL1y7q2MeFesSgsjLy2PPni+YPv3feHs/w65dO4iIeJ2VK9fRrJmfdd/g4JeJiopQY0VERERE5CGmxoo8lL788ksCAgIoW7YsAL169SIyMjLfPnZ2dsycOZNt27Zx/Phx9u/fj51d/gdl+fr6MnToUFJTU/Hz86Nfv363nK979+4A1KxZE1dXV44dO8bevXuZMGECAOXKlSMwMJD9+/dTuvTtrwZ5+umnqVSpEgCenp5cunSJ48ePExAQgLOzMwALFiwAYPfu3Rw7dozp06cDkJeXx6lTpwgMDCQ8PJyWLVsSGBjIc889dzenTgrAw6Ma1atXx9/fF4CuXTsyZcp4tm3bSM2aNXn22WcBOHeuFE5Ojri5Od+3bPdzLvl7qXa2S7Wzbaqf7VLtbJdqZ7tUu1tTY0UeSmazOd97i8VCXl5evm1Xrlyhe/fudOrUiWeffZYaNWqwZMmSfPs0bNiQDRs2sGPHDjZu3MiaNWtISkq6aT57e/t8czs4OOR7gtYfGUwmEwaDId9nubm51tdOTk7W13/s5+DgkO9x52lpaZQqVQqz2czChQutT+dKT0/H1dUVLy8vAgIC2L59O/Hx8Rw8eJDBgwf/5Tn7s/nG1ne1/6Mk+3oe3t4N+OWXSezatZ+aNb345psDWCxw7Voe48dPIDFxDg4OJZg9ey5+foFkZGTdl2xubs73bS75e6l2tku1s22qn+1S7WyXame7HpXa2dkZ7nppBDVW5KHi4OBAXl4ejRs3ZtGiRQwZMoQyZcqwYsUK61N77O3tMZlMnDhxAoPBwGuvvYbFYuGtt97CZDLlG2/KlClUrFiR/v3706RJE7p06XLLedetW8czzzzDd999x5UrV/jnP/+Jj48PK1euxGg0cv78ebZu3cqMGTPIyMjg6NGjWCwWTp8+zeHDh+/4nRo1asTIkSMZOnQoTk5OjBgxgiFDhuDj48PSpUsZMmQIR44coW/fvmzdupUBAwYQGxvLSy+9RJkyZdi6detdn8fffruM2Wz56x0fUa6u5Zk48V0SEiaRnX2NEiUcGT8+ntq163DmzK+8/HI/TCYT9es3YsCAQcUdV0REREREipAaK/JQcXV1pUqVKowfP55XX32V4OBgcnNz8fb2JjY2FgB/f39CQ0OZO3cuXl5etG3bFoPBQLNmzfjqq6/yjRccHMyIESNYvXo19vb2TJ48GYBp06ZRoUIF+vTpA8DVq1fp3LkzdnZ2JCQkUKJECcLCwoiJiaFjx46YTCZee+01vL29ycnJYdWqVbRp0wYPDw8aNmx4x+/k7e1Nv3796N27N2azmVatWtG0aVM8PT2Jjo6mY8eOwI0mUOnSpXnzzTcZNWoUDg4OPPbYY8TFxf3dp1mAevUaMHfuwpu2h4W9TljY68WQSEREREREioPB8r/3K4jII09XrNimR+XyzIeRame7VDvbpvrZLtXOdql2tutRqV1hbgWy++tdRERERERERETkVtRYEREREREREREpJDVWREREREREREQKSY0VEREREREREZFCUmNFRERERERERKSQ1FgRERERERERESkkNVZERERERERERApJjRURERERERERkUJSY0VEREREREREpJDUWBERKYCjR48QHh7KgAEvMnBgMIcOpVo/y8rKon//3hw69GMxJhQRERERkeKgxoo8dEaNGsXq1avv6phly5axbNmye5p33759BAcH39MY8mDKzs7mzTfD6Ns3hKSkpbz00kDGjTMCsGfPfwgN7c8vv5ws5pQiIiIiIlIcHIo7gMiDoE+fPsUd4YHi6lq6uCM8ELKv55H1+zX2799LlSpV8fVtBkCzZn5UrvwEAB9/vJzo6HcYM2ZUcUYVEREREZFiosaK2DyLxcKkSZPYsWMHFSpUwGQy0bhxY1JSUli4cCFmsxlvb2/Gjh3L8uXLOXnyJGPGjAFg0qRJVKpUiaysLACGDh3KunXrmDVrFgaDgdq1a/POO++Qk5PDuHHj+PnnnzGZTAwaNIgOHTrclOXChQsMHDiQ9PR06tSpw9ixY3F0dMTHx4dnnnmGjIwMVq5cyfz58/nkk0+wt7fnueeeIyIigrCwMPr06YOfnx9Tp07lxx9/ZN68eaSnp/Pyyy+TnJzMm2++SWZmJgBhYWEEBgZy8uRJYmJiuHjxIiVLlmTMmDHUqlWLUaNGcfHiRU6ePElERAQtWrQo8DkdGPcZ6Reu/Q3VsW3rEoLIAk6dOomrqysTJ47jyJGfKV3amSFDhgEwdeqM4g0pIiIiIiLFSo0VsXmbNm3ixx9/ZP369WRlZdGpUyeuXbvGhg0bSE5OxsnJiYSEBObPn0/v3r3p3Lkzo0ePxs7Ojs8++4zly5eTnJwMQFpaGhMnTmT16tVUqlSJiIgIdu7cyTfffIO3tzeTJ0/m8uXL9O7dm7p161KtWrV8WU6fPk1iYiL//Oc/GT58OMuWLaN///5cuHCBQYMG0aRJE3bu3Mm2bdtYtWoVJUqUYOjQoSQnJ+Pn58fevXvx8/Pjv//9L+fOncNkMrFr1y78/PzYvHkzTzzxBHPmzCE1NZVPPvmEwMBA3n77baKjo6lVqxZHjhwhLCyMTZs2AVCmTBlmz55932vyMHFzc8bJyZ69e3ezaNEi6taty5YtW3j77TfYvn07jo6OANjb21GmzGO4uTkXe16xTaqd7VLtbJvqZ7tUO9ul2tku1e7W1FgRm7d//35at25NiRIlKFeuHM2bN8disXDy5El69uwJQG5uLrVq1aJcuXLUrFmTffv2UaJECTw8PHBzc7OO9fXXX9OgQQMqVaoEQHx8PAAffPAB2dnZrFq1CoCrV6/y888/39RYadSoEU8++SQAHTt2ZPXq1fTv3x+AunXrArB3717at29PqVKlAOjWrRspKSlERUUxePBgLl++DECNGjX44Ycf+PzzzwkODqZ8+fJMnTqVtLQ0/P39CQsL48qVK3z//fdERkZaM1y9epULFy4AUKdOnb/vRD+iMjKyKFXqH7i7P0mVKk+RkZFF3bpNyMvL49tvD/Hkkx4AmExmLl68SkZGVrFldXNzLtb5pfBUO9ul2tk21c92qXa2S7WzXY9K7ezsDHe9NIIaK2LzDAYDFovF+t7BwQGTyUTbtm0xGm8sMHrlyhVMJhMAQUFBbNy4kRIlStCxY8d8Yzk4OGAwGKzvz58/D4DZbCY+Ph5vb28AMjMzcXFxuSmLg8P//SdlsVjyvS9ZsqR1rP+Vl5dH5cqVMZvNfPbZZzRo0IDy5cuzd+9efvjhB+rXr4+9vT2ffvopu3btYvv27Xz44Yd8/PHHODo6snbtWutY586do0yZMvnmvFvzja0LddzDJvt6HgA+Pk1JTHyfQ4dSqVnTi2++OQAYqFy5SvEGFBERERGRYqfGitg8X19f620+165dY9euXXh6erJ582YGDx5MuXLliImJwd3dna8m6zcAACAASURBVKFDhxIYGMj06dOxWCyMHDky31i1a9cmNjaWjIwM3NzcmDBhAk2aNMHHx4dly5YRFxdHeno6nTt3Jjk5GXd393zHf/XVV5w5c4ZKlSqRkpLC888/f1NeHx8fZs2aRa9evXBwcGDVqlX4+PgA0Lx5c2bNmsWYMWOoUKECgwYNonHjxtjb2/PRRx9x6tQpIiMjad68OQEBAVgsFp588knWrl1LUFAQX3zxBdHR0WzZsuWezulvv13GbLb89Y6PCFfX8kyc+C4JCZPIzr5GiRKOjB8fj5OTU3FHExERERGRYqbGiti8li1b8t1339GhQwfKly+Pp6cnzs7OhIeH079/f8xmM15eXoSGhgI3ruJo0KABOTk5PP744/nGqlixIlFRUQwcOBCz2Uy9evXo2rUr165dIyYmhg4dOmAymYiIiMDd3Z20tDRCQ0OtV4xUr16d0aNHk5GRgY+PD927d78pb0BAAKmpqXTr1o28vDyaNWtGv379APD39ycpKYmGDRvy2GOPkZubS0BAAACdO3fmzTffpGPHjtjb2xMREcE//vEP4uPjiYmJYd68eZQoUYL33nsv31U38veoV68Bc+cuvO3nK1euu49pRERERETkQWGw/PkeChERdMWKrXpU7nt9GKl2tku1s22qn+1S7WyXame7HpXaFWaNFbsiyiIiIiIiIiIi8tBTY0VEREREREREpJDUWBERERERERERKSQ1VkRERERERERECkmNFRERERERERGRQlJjRURERERERESkkNRYEREREREREREpJDVWREREREREREQKSY0VEREREREREZFCcijuACIiD7qjR4/w3ntTuHLlMnZ29kREjKZmTS8WL07i00/XYzKZaN26LS+/HIrBYCjuuCIiIiIich/pipVHVFZWFmFhYXd1TGRkJIGBgaxfv57p06fj7+9PUlISQUFBdzzu0KFDhISE0KlTJ9q3b09UVBRXr169l/h/i0GDBpGWlnbLz1atWkVQUBBBQUE888wztGvXjqCgIGJjY+953tOnT/PMM89Yx3/hhReIjIwkMzMTgO+++46oqKh7nufUqVOMHj36nsd51GVnZ/Pmm2H07RtCUtJSXnppIOPGGdmz5z9s27aZ+fM/YtGi5Xz99Vds27aluOOKiIiIiMh9pitWHlGXLl0iNTX1ro5Zs2YNBw8exNHRkcDAQJKSkvDw8GDAgAF3PG748OFMmDCB+vXrYzabiY2NZdq0aURGRt7LV7hnc+fOve1n3bp1o1u3bgC0aNGCOXPmULVq1b9t7goVKrB27VoALBYLU6dOZdiwYSxdupTatWtTu3bte57jzJkznDp1qlDHurqWvuf5bV329Tyyfr/G/v17qVKlKr6+zQBo1syPypWfYNWq5bRq1YZSpUoB0K5dRz77bCOBga2KM7aIiIiIiNxnaqw8ouLi4khPTycsLIyjR49StmxZSpYsyYwZMxg9ejRpaWmkp6fj6+vL+PHjGTx4MBaLhR49elCrVi3S0tIICwsjISGBzp07c/jwYS5evEhUVBTHjh3D0dGRUaNG4evrS2ZmJtnZ2QDY2dkRHh7Or7/+CkBmZiZRUVGcOXMGBwcHhg8fTvPmzZkxYwYAQ4cOBW40NxYtWsT+/ftZs2YNFy9eJCAggF69ehEZGcn58+cpWbIkcXFx1KxZk5SUFBYuXIjZbMbb25uxY8fi5OSU7xz8Mebly5eJjo4mLy8PJycnJk6cyJNPPnnbc7dv3z7i4+Mxm808/fTT1obL/2atXLkyU6ZMYf/+/ZhMJrp27cpLL71003gGg4GhQ4fy3HPPcejQIS5dukRiYiKLFy8mODgYFxcXfv75Z95//30yMjKYPn06eXl5VK1alXfeeYeyZcuye/duJk2ahMVioUqVKiQkJBAXF8fp06eJjY1l7Nixd/XzMTDuM9IvXLurYx426xKCyAJOnTqJq6srEyeO48iRnyld2pkhQ4aRlpZGw4bPWvd3c6tARkZ68QUWEREREZFiocbKI8poNBISEmK9vWfevHlUrVqV9evX4+XlxfTp08nJyaF9+/b88MMPzJ49mxo1alivsti3b99NV3FMmzYNd3d3Zs6cyeHDh4mOjsbX15fIyEgGDx5MhQoVaNKkCYGBgfj7+wPwzjvv4OPjw4ABAzh16hR9+vQhJSXljtnT0tLYuHEjDg4OhIaG8sILL9C3b1927tzJrFmzCA8PZ8WKFSQnJ+Pk5ERCQgLz589nyJAhtxxv4cKFDBgwgLZt27JmzRq++eabOzZWAE6cOMH27dtxdna2NoH+14oVK4AbV/rk5OQwcOBAnnnmGSpVqnTTvo6Ojvzzn//k2LFjuLq65vusRo0aJCYmcv78eUaNGsWiRYtwcXEhOTmZd999l7FjxzJy5Ejmz5+Pl5cXCQkJrFmzBqPRSGJi4l03VeT/uLk54+Rkz969u1m0aBF169Zly5YtvP32Gzz11FO4uDyGm5szAC4upXB0LGF9X5yZxTapdrZLtbNtqp/tUu1sl2pnu1S7W1NjRXB1dbU2SDp06MDBgwdZsGABx44d4+LFiwVeD+XLL7/k3XffBW40A5YvXw5A165dad26NXv27GH37t2MGjWKjh07EhUVxd69e4mLiwOgWrVq1K1bl2+//faO89SqVQsHBwfrnFOnTgXAz88PPz8/PvroI06ePEnPnj0ByM3NpVatWrcdz8/Pj3HjxrFr1y5atGhBQEDAX35XDw8PnJ3v/JfKnj17SE1NZe/evQBcvXqVw4cP37KxAjeuXClZsuRN2+vUqQPAt99+y9mzZwkJCQHAbDbj4uLC4cOHqVixIl5eXgCMGDECuNH8knuTkZFFqVL/wN39SapUeYqMjCzq1m1CXl4eJpOFY8d+ISMjC4CjR3+hbNny1vfFwc3NuVjnl8JT7WyXamfbVD/bpdrZLtXOdj0qtbOzM9z10ghqrEi+X+YXL17Mpk2b6NmzJ02bNuWnn37CYrEUaBwHB4d8T0Q5evQoBoOBTz/9lLCwMFq1akWrVq0ICQmhS5cuREVF3TS2xWLBZDJhMBgwm83W7bm5ubfM+0eD5Y9jjx49islkom3bthiNRgCuXLmCyWS6be42bdpQv359tm/fzoIFC9ixY4e12XM7f85wu6wmk4mIiAhat24NwPnz53n88cfJyMi4abycnByOHz9O9erVOXv27C3nMplMNGjQgNmzZwNw/fp1rly5Qnp6er7znpWVxZUrV+6Y/6/MN7a+p+MfBtnX8wDw8WlKYuL7HDqUSs2aXnzzzQHAQI8efUhKmkunTl2xt7dn48Z1tGvXsXhDi4iIiIjIfafGyiPKwcGBvLy8m7Z/8cUX9OrVi44dO/Ldd99x6NChfE2DO2nUqBEbNmygRo0aHD16lEGDBrFmzRoWLVpEgwYN8PX1BSA1NdV6dYWPjw8rV6603gp04MABYmJiyMjIsF5xcfDgwVs2I/48Z69evdi9e7f11pcPP/yQwYMHU65cOWJiYnB3d7eugfK/3njjDTp06EDv3r3x9PRk4sSJBfq+fyhbtuwts/r4+LBixQoCAgLIycnhxRdfJDY2lieeeCLf8WazmRkzZlC3bl3c3d1vaqz8oW7duhiNRo4fP46HhwcffPABaWlpxMbG8ttvv3HkyBGqV6/OvHnzAHj++edvWeOC+O23y5jNBWuoPexcXcszceK7JCRMIjv7GiVKODJ+fDx169bj2LEjDBrUn7y8XJo186NNm/bFHVdERERERO4zNVYeUa6urlSpUuWmJ/P079+fmJgY5syZQ+nSpalfvz6nT58u0JjDhg3DaDTSqVMnHBwcmDJlCi4uLsyZM4f4+HiMRiMlSpTAw8PDevtOVFQU0dHRrF69GrixqG6FChVo164dmzZtol27dnh7e9/2Vp7o6GiMRiNLly6lVKlSxMXFUb16dcLDw+nfvz9msxkvLy9CQ0Ot87Vo0YLAwEDrGK+99hpRUVHMnDmTEiVKEBMTc1fn8nZZe/fuzcmTJ+nSpQt5eXl07dqVJk2acPr0adLT062Pqf4j4x/n5Hbc3NyYMGECb7zxBmazmYoVKxIfH4+TkxPx8fG89dZb5Obm4u7uzpQpU8jJySErK4uIiAji4+Pv6jtJfvXqNWDu3IU3bQ8JeZmQkJeLIZGIiIiIiDwoDJaC3uchIo8MXbFimx6V+14fRqqd7VLtbJvqZ7tUO9ul2tmuR6V2hVljxa6IsoiIiIiIiIiIPPTUWBERERERERERKSQ1VkRERERERERECkmNFRERERERERGRQlJjRURERERERESkkNRYEREREREREREpJDVWREREREREREQKSY0VEREREREREZFCUmNFRB4Zn3++g1atmlvf79ixlZdf7kdwcE8iIl7n0qWLxZhORERERERskRor8kAIDg5m5MiR+bbNmDGDGTNm3PG4FStWsH79+r89y759+/7WMW/l1KlTjB49GoDvvvuOqKioIhtf4NSpX5g5833AAsChQz/y3ntTGD9+CosXr6BaNXfmzPmgeEOKiIiIiIjNUWNFHhj/7//9P7Zs2XJXxxw4cICcnJwiSlS0zpw5w6lTpwCoXbs248ePL7LxH3XZ2dmMGzeGoUOHW7dt2vQp7dsHUblyFQBefvlVXnwxpLgiioiIiIiIjXIo7gBi+xISEti0aRNly5bFzc2NFi1aYGdnx8KFCzGbzXh7ezN27FicnJzYvn0777//PmazmWrVqjFu3DjKly8PwODBg4mNjaVRo0aUKVMm3xwHDx5k4sSJZGdnU7ZsWWJjYzl16hTbtm1j7969ZGZmsnnzZj7++GOuXr1K48aNWbJkCXXr1iU6OhpfX1+effZZoqKiOHPmDA4ODgwfPpzmzZszY8YMvvnmG86ePUu/fv2sc/7222/079+fN954g5YtW+bLs2DBApYtW4a9vT0BAQFERESQmZl52/HT0tI4efIkv/76Kz169GDw4MHExcVx+vRpYmNjadOmDYmJiSxevJjg4GBq167NV199xfnz5zEajfj5+ZGZmUl0dDTnzp3DYDAwYsQImjZtWqDxx44de1c1dXUtXcifhgdL9vU8sn6/Rnz8eIKCuuLp+bT1s1OnTuLp+TSjRr3J2bNn8fSsztChbxZjWhERERERsUVqrMg92bZtG1999RXr16/n2rVrdOnSBV9fXzZs2EBycjJOTk4kJCQwf/58evXqRXR0NMuWLaNq1arMmzePcePGMX36dAAaNWrExYsXiYuL491337XOkZOTg9FoZPbs2VSpUoVdu3YxZswYFixYQIsWLWjcuDFdunRhyZIlZGVl8fXXX/OPf/yD/fv3U7duXfbu3ctbb71FVFQUPj4+DBgwgFOnTtGnTx9SUlKsc2zcuBGATz/9lKysLEJDQwkPD7+pqXLw4EGWLl3KqlWrKFWqFK+88grff/89c+fOve34hw8ftuZr2bIlffv2xWg0kpiYyNixY2+69Sg3N5fly5ezbds2pk2bhp+fH+PHj6dbt24EBgaSnp7Oiy++WODx79bAuM9Iv3Dtro970KxLCGLh6o+xt3egQ4cgzp49Y/0sLy+PL77YxbRpH1C2bDk++GA6U6bEMXFiQjEmFhERERERW6PGityT3bt307ZtWxwdHXF0dKRly5ZYLBZOnjxJz549gRtNglq1anHw4EHq1KlD1apVAejVqxdz5szJN96bb75JUFBQvluCTpw4walTpxg8eLB12+XLl/MdZzAYaNq0Kfv27ePAgQP079+fL7/8koCAACpXrkzp0qXZu3cvcXFxAFSrVo26devy7bffAlCnTp18440dO5by5cvTunXrm77zH+M6OzsDN65eAe44fpMmTXB0dMTV1ZUyZcqQlZV1x/P6/PPPA/D0009z8eJF67k+duyYtRGVl5dnvdXnbsd/lGzevJHs7GxeeaUfubm5XL9+nVde6Ue5cuUICPCjZk0PAPr1603//v1xc3Mu5sT3xtbzP8pUO9ul2tk21c92qXa2S7WzXardramxIvfEzs4Os9mcb5vJZKJt27YYjUYArly5gslk4ssvv8y3n8ViIS8vL9+2UqVKMWHCBIYPH84LL7yAi4sLZrOZqlWrsnbtWuv4mZmZN2Xx9/dnz549fP/998ybN4/ly5ezfft2AgICrPP97/wmkwmAkiVL5vts0KBB7Ny5k2XLltG3b998nzk4OGAwGKzv09LSKFWq1B3Hd3Jysm43GAw37fu//tj/z/OYzWYWLlxovU0qPT0dV1dXtmzZctfjP0pmzUqyvj579gwhIb2YN+8j/vOfz5k58326d++Li0sZUlLWU6OGFxkZttuUcnNztun8jzLVznapdrZN9bNdqp3tUu1s16NSOzs7w10vjaDGityTpk2bMnfuXPr06UNOTg47duygU6dObN68mcGDB1OuXDliYmJwd3enT58+jB07ltOnT1O1alWWL19OkyZNbhqzUaNGtGnThmXLlvHqq6/y1FNPcenSJf773//SqFEjVq1axbp161i8eDH29vbW5sVzzz3H1KlTKVeuHM7Oznh5ebFo0SIWL14MgI+PDytXrrTeqnPgwAFiYmI4fPjwTRm8vLzw9/enT58+tGzZkooVK+bLN3LkSIYOHYqTkxMjRoxgyJAhdzU+gL29/U2NpTvx8fFh6dKlDBkyhCNHjtC3b1+2bt162/3vdvw/m2+8+UodW5R9/fbfv1mz5mRkpBMeHorFYqFixcpERo65j+lERERERORhoMaK3BN/f3++/vprunTpgouLCxUqVOCpp54iPDyc/v37Yzab8fLyIjQ0FCcnJ8aNG0d4eDi5ublUqVLltk/CefPNN9m5cycAjo6OTJs2jfHjx3P9+nVKly7N5MmTgRuNnalTp+Ls7EybNm2oVKkStWvXBm40Io4cOcKTTz4JQFRUFNHR0axevRqAuLg4KlSocNvv9uSTT9K3b1/GjRvHzJkzCQoKYs6cOXh7e9OvXz969+6N2WymVatWNG3aFE9Pz7sa39PTk6ysLCIiIujevftfnmuj0Uh0dDQdO3YEYMqUKZQufftO6p/Hj4+P/8vx/+y33y5jNj98V71UrlyFzZt3Wd936dKdLl3++tyLiIiIiIjcjsGiewbkHnz99decOHGCLl26kJubS69evZgwYQI1a9Ys7mhyDx7WxsrD7lG5PPNhpNrZLtXOtql+tku1s12qne16VGqnW4HkvvPw8CAxMZGkpCQsFgudO3dWU0VEREREREQeGWqsyD0pU6YM8+fPL+4YIiIiIiIiIsXCrrgDiIiIiIiIiIjYKjVWREREREREREQKSY0VEREREREREZFCUmNFRERERERERKSQ1FgRERERERERESkkNVZERERERERERApJjRURERERERERkUJSY0VEHmqff76DVq2aA5Cbm8uUKePp168H/fr1YMaM9zCZTMWcUEREREREbJkaK3LPsrKyCAsLu6tjIiMjCQwMZP369UyfPh1/f3+SkpIICgq643GHDh0iJCSETp060b59e6Kiorh69eq9xP9btGjRgtOnTxd3jJvs27eP4ODg4o7x/7F393E93/vjxx+f+nRhcpHUaNlhbEmuv6jYKGQuUuSog1XYcJxiy1nnlE8qLRdHMsQ4fZnkMiYXoWEbdkFlB3OxmGUsFyvlZJGuPp/3749+Pl+tmBprH573283t9vH+vF+v1/P9fn643d7P2+v1etebnJwfWb58MaAAsG1bMoWFhSQlJZOYuIkzZ07x2WcH6jdIIYQQQgghhEGTwor4zW7dukVWVlat2mzfvp20tDQ8PDzYuXMna9asYcKECezcufOh7YKDgwkODmbXrl2kpqaiVqtZsmTJbwlfPKVKSkqIjp7FtGnB+mN/+csbREfPw8jIiJ9/vsXt20U0btykHqMUQgghhBBCGDp1fQcgDF9MTAx5eXkEBgaSnZ2NpaUl5ubmxMfHM3PmTHJzc8nLy8PFxYU5c+YwdepUFEVh9OjRdOjQgdzcXAIDA4mLi2PEiBGcP3+ewsJCNBoNFy9exNTUlNDQUFxcXMjPz6ekpAQAIyMjgoKCuHr1KgD5+floNBquXbuGWq0mODiYvn37Eh8fD8C0adOAytklSUlJZGZmsn37dgoLC3Fzc8PX15ewsDBu3ryJubk5MTExtG/fnh07drB27Vp0Oh2Ojo5ERkZiZmZW47344YcfmDJlCgsWLKBTp04sWLCAzMxMtFot3t7ejB8/npCQEHr27ImPjw8Afn5+vPbaa3z77bcsXryYH374gcGDB/PVV1/RvHlz3nzzTd5++20aNWpEREQEhYWFPPfcc2g0Gjp37kxoaCiFhYVcvnyZkJAQTE1NmTdvHmZmZrRp06ZOObWysqhTu/pWUlpB0c93AYiNnYOXlzdt275c5Ry1Ws2KFfGkpGzB3t6BLl261UeoQgghhBBCiKeEFFbEbxYeHo6/v79+ec+qVauws7Nj9+7dODg4sHTpUsrKyhg2bBhnz55l5cqV2Nvb62enZGRkkJCQgJ2dnb7PJUuW8OKLL7J8+XLOnz9PREQELi4uhIWFMXXqVGxsbHBycmLAgAG4uroC8N577+Hs7MyECRPIyclhzJgx7Nix46Gx5+bmsnfvXtRqNZMnT+b1119n3LhxHD58mBUrVhAUFMSWLVvYvHkzZmZmxMXFsXr1av72t79V6+unn34iMjKSuXPn0rVrVzZt2gRUzs4pKyvjzTffpGPHjowaNYr4+Hh8fHy4evUqN2/eZOzYsQwZMgRFUUhPT8fKyorMzEz69+/PDz/8QKdOnRg9ejSTJ09m0KBBnDx5krfffpt9+/YB0LRpU1auXElZWRn9+/dn7dq1tG3bFo1GU6ecvhmzn7z/3q1T2/qUGudFEZCSshVjYzUeHl5cv36t2nlTp05j0qSp/OtfMSxcOI/w8Nm/f7BCCCGEEEKIp4IUVsRjZWVlpS+QeHh4cOrUKRITE7l48SKFhYWPvB/KsWPHWLhwIQD29vYkJycD4O3tzaBBgzh69ChHjhwhNDSU4cOHo9FoSE9PJyYmBoBWrVrRpUsXvvnmm4eO06FDB9RqtX7MRYsWAdCvXz/69evH+vXruXz5sn52SXl5OR06dKixr7fffptOnTrRo0cPAI4ePUpWVhbp6ekAFBcXc/78ecaOHcusWbO4cuUKO3fuxMvLCwsLC9q0acP58+dJT08nICCAY8eO0bBhQ5ydnSkuLubHH39k0KBBAHTt2pUmTZpw8eJFADp37gzA+fPnsbGxoW3btgCMHDnymVsqZW3diAMH9lJSUsJbb71BeXk5paWlvPXWG0RERNCsWTP9TJ4xY3yIiYnB2rpRPUf9+DxN1/KskdwZLsmdYZP8GS7JneGS3BkuyV3NpLAiHitzc3P953Xr1rFv3z58fHzo3bs33333HYqiPFI/arUalUql/3t2djYqlYq0tDQCAwNxd3fH3d0df39/Ro4ciUajqda3oihotVpUKhU6nU5/vLy8vMZ47xVY7rXNzs5Gq9UyZMgQwsPDAbhz584D3yKj0WhYvnw5hw4dwtXVFa1WS0hIiL4YcvPmTRo2bIhKpWLEiBHs2bOHtLQ0Vq9eDYCrqytfffUVFy9eJCoqCn9/f4yMjHBzc6vxvt27vvuvQ6VSVTnX2Nj4Ybf5gVaHD6pTu/p2bynQihVr9MeuX7+Gv78vq1atJzFxFWfPnmbevDiMjIzYujWFzp27c+NGUT1G/fhYWzd6aq7lWSO5M1ySO8Mm+TNckjvDJbkzXM9K7oyMVLXeGkE2rxW/mVqtpqKiotrxr776Cl9fXzw9PSktLeXcuXNVChwP06NHD/bs2QNUFlUmTZqElZUVSUlJHD16VH9eVlYWDg4OADg7O/PRRx8BkJOTw/Hjx+natSuWlpZ8//33AJw6dYobN2786phHjhxh1qxZODk5ceDAAQoKClAUhaioKNauXVtj+86dOxMVFUV0dDTFxcU4OzuzZcsWysvLuXPnDmPHjuXkyZNA5cybzZs307JlS55//nmgcpbM5s2badeuHZaWlpiYmHDw4EF69+6NhYUFdnZ27N+/H4CTJ0+Sn5/Pyy9X3T/E3t6e/Px8zp07B6C/ntoqKLjNjRtFBvfn3v4qDzJuXADPP9+S8ePHMn78GIyNjfnrX4PqdI+EEEIIIYQQAmTGingMrKyssLW1JSwsrMrxgIAAoqKiSEhIwMLCgm7duj3yK4mnT59OeHg4np6eqNVqFixYQJMmTUhISCA2Npbw8HBMTExo06aNfvmORqMhIiKClJQUoHJTXRsbG4YOHcq+ffsYOnQojo6OD1zKExERQXh4OBs3bqRBgwbExMTQrl07goKCCAgIQKfT4eDgwOTJk/Xj9e/fnwEDBuj76NmzJ05OTixevJiQkBAuX77MyJEjqaiowNvbGycnJwBatmxJy5YtGTlypL5t27ZtURSFXr16AdCrVy8uXLhAw4YNAYiNjSUqKor4+HhMTEyIj4/H1NS0yjWYmJiwaNEiQkJCUKvVD7zWZ0nLlrYcOPAFUHl/3n03tJ4jEkIIIYQQQjxNVMqjrs0QQjwWiqKQl5eHn58fu3fvrlYc+SMoKLiNTif/NRiaZ2V65tNIcme4JHeGTfJnuCR3hktyZ7ieldzJUiAhDMC+ffvw8vJixowZf8iiihBCCCGEEEKIRydLgYT4nQ0ePJjBgwfXdxhCCCGEEEIIIR4DmbEihBBCCCGEEEIIUUdSWBFCCCGEEEIIIYSoIymsCCGEEEIIIYQQQtSRFFaEEEIIIYQQQggh6kgKK0IIIYQQQgghhBB1JIUVIYQQQgghhBBCiDqSwooQQgghhBBCCCFEHUlhRQjx1Pr880O4u/cFQKfT8cEHS3jjDR/8/X2ZOTOE//73v/UcoRBCCCGEEMLQSWFF/CahoaGkpKTUqs2mMype9QAAIABJREFUTZvYtGlTvcfxW2PJy8vj3XffZdiwYXh6ejJlyhRycnIe2qaoqIjAwMA6jSdqJyfnR5YvXwwoAOzZs4vz58/x4YfrSUpKxs7OjmXL3q/fIIUQQgghhBAGT13fAYhnz5gxY+o7BL26xlJcXIyfnx8TJ04kNjYWlUrFrl27mDBhAmlpaZiYmNTY7tatW2RlZf2WkH8XVlYW9R1CrZWUVlD0893KzyUlREfPYtq0YGbPDgegTZuX+Nvf3sbU1BQAe/sObN++td7iFUIIIYQQQjwdpLAiakVRFObPn8+hQ4ewsbFBq9XSq1cvduzYwdq1a9HpdDg6OhIZGUlycjKXL19m1qxZAMyfP58WLVpQVFQEwLRp00hNTWXFihWoVCo6derEe++9R1lZGdHR0Vy4cAGtVsukSZPw8PB4pDiA3yWWPXv20KxZM3x9ffUxeXp6YmpqSllZGaWlpcycOZPc3Fzy8vJwcXFhzpw5xMTEkJeXR2BgIMuXL68xVjMzM/bu3cvSpUt57rnncHBwQKvVMn/+fE6ePMmcOXMoLS3F0tKS6Oho/vSnP+Hn50eTJk24cOECvr6+nD17lri4OADi4+MxMzNj8uTJj5znN2P2k/ffu3X8ldSP1Dgviv7/59jYOXh5edO27cv67zt27Kz//PPPP5OY+L+MGDHqd45SCCGEEEII8bSRwoqolX379vHtt9+ye/duioqK8PT05O7du+zZs4fNmzdjZmZGXFwcq1ev5i9/+QsjRoxg5syZGBkZsX//fpKTk9m8eTMAubm5zJs3j5SUFFq0aEFISAiHDx/m5MmTODo68q9//Yvbt2/zl7/8hS5dutCqVauHxgFw4cIFtmzZ8sRjycrKwtHRsdr9GTx4MAC7d+/GwcGBpUuXUlZWxrBhwzh79izh4eH4+/uzfPnyh8Y6d+5ctm3bhrW1NdOnT8fCwoKysjJmzJjB4sWL6dy5M2lpacyYMYNt27YBYG9vz7Jly7hz5w7u7u7cvn0bCwsLdu/eTVJS0hP9XfxRWFs3YsOGDTRs2IAJE97gypUrqFQqrK0b6c/58ccfeeedQHr16smUKW+iUqnqMeLH7/5rFYZFcme4JHeGTfJnuCR3hktyZ7gkdzWTwoqolczMTAYNGoSJiQnNmjWjb9++KIrC5cuX8fHxAaC8vJwOHTrQrFkz2rdvT0ZGBiYmJrRp0wZra2t9XydOnKB79+60aNECgNjYWAA++OADSkpK9AWD4uJiLly4UKWwUlMcABkZGb9LLEZGRvolJTXx8PDg1KlTJCYmcvHiRQoLCykuLqZp06b6cx4U69dff023bt14/vnnARgxYgSffPIJly5donHjxnTuXDnzYsiQIUREROhn3dw73rBhQ/r168eBAwdo1aoVrVq10vf1tLtxo4itWz+ipKSEYcOGU1FRrv+8cOESfvzxMhERYYwd68/YsX7k59+u75AfK2vrRty4UfTrJ4o/HMmd4ZLcGTbJn+GS3BkuyZ3helZyZ2SkqvXWCFJYEbWiUqlQFEX/d7VajVarZciQIYSHV+5lcefOHbRaLQBeXl7s3bsXExMThg8fXqUvtVpdZbbAzZs3gcq3t8TGxupnhOTn59OkSZNfjQP43WL5+eefa9wsV6PRMH78eNLT09m3bx8+Pj707t2b7777rkq8D4s1MzMTnU5Xre+ajimKor8+c3Nz/fFRo0axYsUK7Ozs8Pb2rtbu16wOH1TrNvWtpLQCgP/93/+bnXP9+jX8/X1JTNzI+fPnmDnzXaKi5uLs3Lu+whRCCCGEEEI8ZeStQKJWXFxcSEtLo6ysjFu3bvHFF18AcODAAQoKClAUhaioKNauXQvAgAEDOHbsGF999RXu7u5V+urUqRMnT57kxo0bAMydO5dPP/0UZ2dn/Zt68vLy8PT05Pr1648Uh5OT0+8Sy+DBg7l69Spbt/7f5qfbtm0jMzOTP/3pT3z11Vf4+vri6elJaWkp586dQ6fToVarqaioeGis3bt35/Tp0+Tl5aEoCnv37kWlUvHSSy9RWFjIqVOnANi7dy+2trZVZsHc06NHD3766ScyMjIYOHBgrXIMUFBwmxs3igzqz72Nax/k3/9ehqIorFy5jPHjxzJ+/FjCwt6t9b0RQgghhBBCiPvJjBVRKwMHDuT06dN4eHjQvHlz2rZtS6NGjQgKCiIgIACdToeDg4N+o1Rzc3O6d+9OWVkZDRs2rNLX888/j0aj4c0330Sn09G1a1e8vb25e/cuUVFReHh4oNVqCQkJ4cUXXyQ3N5fJkyezc+fOGuMAaN++/e8SC0BiYiJz584lMTERlUqFnZ0dH374IaampgQEBBAVFUVCQgIWFhZ069aNK1eu0KNHD2xtbfHz82PdunU1xmpmZkZ4eDgTJ07E1NQUOzs7GjdujKmpKe+//z7vvfced+/epUmTJrz//oNfF+zu7k5hYeFDlyw97Vq2tOXAgcqi26JFy+o5GiGEEEIIIcTTSKX8cn2CEKJe/fe//9UXXYyMjIiJidG/+edRKIpCeXk5EyZMYObMmTVusvtrCgpuo9PJfw2G5llZ9/o0ktwZLsmdYZP8GS7JneGS3BmuZyV3ddljRZYCCfEH07RpU37++Wc8PDwYPnw4t2/f1m9w+yhu3LhBnz596NKlS52KKkIIIYQQQgghHp0sBRLiD0alUuk3tK0LGxsbjh079hgjEkIIIYQQQgjxIDJjRQghhBBCCCGEEKKOpLAihBBCCCGEEEIIUUdSWBFCCCGEEEIIIYSoIymsCCGEEEIIIYQQQtSRFFaEEEIIIYQQQggh6kgKK0IIIYQQQgghhBB1JIUVIYQQQgghhBBCiDpS13cAQghRk3379rJx4zpUKhXm5ua88867vPRSOxYvjuX48a9p0KABffr0ZeLEyRgZSY1YCCGEEEIIUT/kaUTUiZ+fH++++26VY/Hx8cTHxz+03ZYtW9i9e/djjyUjI+Ox9RcaGkpKSkqN3+Xl5fHuu+8ybNgwPD09mTJlCjk5OXUey8/PT//Zy8urzv08Sv+G5McfL/HBB0uIi4snMXEjAQETmTkzhHXr1vDTTz+xdu1mPvxwAwUF+WzfvrW+wxVCCCGEEEI8w2TGiqizjz/+mMGDBzNw4MBHbnP8+HF69er1BKN6coqLi/Hz82PixInExsaiUqnYtWsXEyZMIC0tDRMTk1r3mZmZqf+8c+fOxxlutf5rw8rK4jFH8uhKSiswMTHln/+cRfPmzQFo374DN28WkJV1loEDB2FmZgbAa6+5snFjEqNG+dZbvEIIIYQQQohnmxRWnkFxcXHs27cPS0tLrK2t6d+/P0ZGRqxduxadToejoyORkZGYmZlx8OBBFi9ejE6no1WrVkRHR+sfdqdOncrs2bPp0aMHTZs2rTLGqVOnmDdvHiUlJVhaWjJ79mxycnL47LPPSE9PJz8/nwMHDrB161aKi4vp1asXGzZsoEuXLkRERODi4kLPnj3RaDRcu3YNtVpNcHAwffv2JT4+npMnT3L9+nXeeOMN/ZgFBQUEBATwzjvvVCn23L59mxkzZpCfnw9AYGAgAwYMwM/Pj6CgIJycnLhy5Qr+/v589tlnABw6dIj169dTXl7O1KlTGTp0KHv27KFZs2b4+v7fQ7ynpyempqaUlZVhbGzM3LlzOXr0KCqVCk9PTyZPnkxGRgb//ve/MTc3Jzs7G3t7exYuXMiCBQsAGD16NFu3bsXe3p7z588THx9Pbm4uly9f5urVq4wePZqpU6ei1WpZsGABmZmZaLVavL29GT9+/CP3Xxtvxuwn7793a9XmcUmN86JlS1tatrQFQFEU4uPf59VX+9Ku3St8+ukBXF0HYGJiwoEDH1NQkF8vcQohhBBCCCEESGHlmfPZZ5/xn//8h927d3P37l1GjhyJi4sLe/bsYfPmzZiZmREXF8fq1avx9fUlIiKCTZs2YWdnx6pVq4iOjmbp0qUA9OjRg8LCQmJiYli4cKF+jLKyMsLDw1m5ciW2trZ88cUXzJo1i8TERPr370+vXr0YOXIkGzZsoKioiBMnTtC4cWMyMzPp0qUL6enp/OMf/0Cj0eDs7MyECRPIyclhzJgx7NixQz/G3r17AUhLS6OoqIjJkycTFBRUbQbNgQMHeOGFF0hISCArK4tdu3YxYMCAh96nu3fvsmXLFgoKChg1ahQ9e/YkKysLR0fHaucOHjwYgA0bNnD9+nV27dpFWVkZfn5+vPLKKzRo0IATJ06QlpaGjY0NPj4+fPnll4SHh7Nu3boaix7nz5/X35+BAwcybtw49uzZA8D27dspKyvjzTffpGPHjgC17v+Pztq6EVA5Syg0NJTc3J9YtWoV5ubmvP/++wQFvUXjxo0ZOnQoly9f1J8vkHthwCR3hktyZ9gkf4ZLcme4JHeGS3JXMymsPGOOHDnCkCFDMDU1xdTUlIEDB6IoCpcvX8bHxweA8vJyOnTowKlTp+jcuTN2dnYA+Pr6kpCQUKW/GTNm4OXlxSeffKI/dunSJXJycpg6dar+2O3bt6u0U6lU9O7dm4yMDI4fP05AQADHjh3Dzc2Nli1bYmFhQXp6OjExMQC0atWKLl268M033wDQuXPnKv1FRkbSvHlzBg0aVO2au3XrxqJFi8jNzcXV1ZXAwMBfvU8jR45ErVbz/PPP07VrV7755huMjIwwNTV9YJuMjAxGjhyJsbExDRo0YPjw4Rw9epT+/fvz8ssv06JFCwDatm3LrVu3Hjq+k5MTpqamWFlZ0bRpU4qKijh69ChZWVmkp6cDlUWH8+fP065du1r3/0d340YRP/30E//8ZzCtW7dm0aLllJaquHbtKp6ePkyc+DcA9u//mOeft+XGjaJ6jviPwdq6kdwLAyW5M1ySO8Mm+TNckjvDJbkzXM9K7oyMVLXeGkEKK88YIyMjdDpdlWNarZYhQ4YQHh4OwJ07d9BqtRw7dqzKeYqiUFFRUeVYgwYNmDt3LsHBwbz++us0adIEnU6HnZ2dfs8QrVarX4ZzP1dXV44ePcqZM2dYtWoVycnJHDx4EDc3N/14vxxfq9UCYG5uXuW7SZMmcfjwYTZt2sS4ceOqfNe6dWvS0tL44osvOHjwIB9++KF+tsu9MX55XcbGxvrPOp0OExMTOnbsWOOmthqNhvHjx1e7r/fHe29PEKgsKv3y2n6ppvO1Wi0hISH64tHNmzdp2LAhJ0+erHX/v2Z1ePUC1e+lpLSC4uI7TJs2hSFDhjFx4mT9d19++TlHjnzB/PmL/v+soo2MG+dfb7EKIYQQQgghhLwV6BnTu3dv9u/fT1lZGbdv3+bQoUMUFRVx4MABCgoKUBSFqKgo1q5dq58hcuXKFQCSk5NxcnKq1mePHj0YPHgwmzdvBuCll17i1q1bfP311wBs27ZN/wYhY2NjfbGhT58+fPnllxgZGdGoUSMcHBxISkrC1dUVAGdnZz766CMAcnJyOH78OF27dq3xuhwcHIiMjGTZsmXk5uZW+W79+vXEx8czZMgQIiMjuXnzJrdv38bS0pLvv/8eoMqMG4A9e/agKApXr17lzJkzdOrUicGDB3P16tUqS2u2bdtGZmYmf/rTn3B2dmbHjh1otVru3r1LampqjffrfsbGxtWKOg/i7OzMli1bKC8v586dO4wdO5aTJ08+tv7vV1Bwmxs3iurlT9HPd9m2bQu5udf5/PNDjB8/Vv/ntdf60bSpJX5+vrz1lh8DBgzCze3RN08WQgghhBBCiMdNZqw8Y1xdXTlx4gQjR46kSZMm2NjY8NJLLxEUFERAQAA6nQ4HBwcmT56MmZkZ0dHRBAUFUV5ejq2tLXPmzKmx3xkzZnD48GEATE1NWbJkCXPmzKG0tBQLCwv+9a9/AZWFnUWLFtGoUSMGDx5MixYt6NSpE1BZOPj+++9p3bo1UDkTJCIiQj9LJCYmBhsbmwdeW+vWrRk3bhzR0dEsX74cLy8vEhISGDFiBDNmzGD48OEYGxsTEhJC48aNeeuttwgNDWXbtm3V9lx57rnn8Pb2pqKigujoaJo1awZAYmIic+fOJTExEZVKhZ2dHR9++CGmpqb4+vpy6dIlvLy8KC8vZ/jw4bi7uz/0VdADBgzAy8vrga93vt9f/vIXLl++zMiRI6moqMDb2xsnJ6dH7v/+WS1/dH5+E/Dzm1Djd2FhEb9zNEIIIYQQQgjxYCrlt64ZEAblxIkTXLp0iZEjR1JeXo6vry9z586lffv29R2a+AMpKLiNTif/NRiaZ2Xd69NIcme4JHeGTfJnuCR3hktyZ7ieldzJHiviV7Vp04Zly5axZs0aFEVhxIgRUlQRQgghhBBCCCHqSAorz5imTZuyevXq+g5DCCGEEEIIIYR4KsjmtUIIIYQQQgghhBB1JIUVIYQQQgghhBBCiDqSwooQQgghhBBCCCFEHUlhRQghhBBCCCGEEKKOpLAihBBCCCGEEEIIUUdSWBFCCCGEEEIIIYSoI3ndshDid6MoCnPmRPHSS+0YO9YPrVbL++8v4OTJ4wA4O/chMPBtVCpVPUcqhBBCCCGEEI9GZqzUQlFREYGBgbVqExYWxoABA9i9ezdLly7F1dWVNWvW4OXl9dB2586dw9/fH09PT4YNG4ZGo6G4uPi3hP+7uXLlCv3796/X/m/fvs3s2bPx8PDAy8sLPz8/zp49C0BGRgZ+fn4AaDQaTp8+/cB+/Pz8yMjIqHbc3t7+V+O8P9+P2/33ICUlhdDQ0BrPe5Q4fy+XLv3A229P5dChT/XH9u3by48/Xmbt2s0kJm7i5MnjHDz46UN6EUIIIYQQQog/FpmxUgu3bt0iKyurVm22b9/OqVOnMDU1ZcCAAaxZs4Y2bdowYcKEh7YLDg5m7ty5dOvWDZ1Ox+zZs1myZAlhYWG/5RKeCTqdjkmTJuHk5MSOHTtQq9Wkp6czadIk9uzZU+XcOXPmPLE4du7cqc+3gJSULXh4jOD551voj+l0Wu7evUt5eTk6nY7y8nJMTU3rMUohhBBCCCGEqB0prNRCTEwMeXl5BAYGkp2djaWlJebm5sTHxzNz5kxyc3PJy8vDxcWFOXPmMHXqVBRFYfTo0XTo0IHc3FwCAwOJi4tjxIgRnD9/nsLCQjQaDRcvXsTU1JTQ0FBcXFzIz8+npKQEACMjI4KCgrh69SoA+fn5aDQarl27hlqtJjg4mL59+xIfHw/AtGnTAOjfvz9JSUlkZmayfft2CgsLcXNzw9fXl7CwMG7evIm5uTkxMTG0b9+eHTt2sHbtWnQ6HY6OjkRGRmJmZlblHqSlpbFmzRpKSkooKytj7ty5dO/enW+//RaNRgNA+/btAThz5gyzZ89m69atFBcX06tXLzZs2ECXLl2IiIjAxcWFnj17PvBaTp48yfXr13njjTfo2rVrtf4BUlNTWbVqFcbGxtjZ2REbG8vx48e5fv0606dPx8ioclKWs7Mz8+bNQ6fTVbkePz8/goKC6NWrFwsXLuSTTz7B2NgYX19fAgIC9OcVFBQQEBDAO++8w8CBA/XHMzIy+Pe//425uTnZ2dnY29uzcOFCYmJiquT7p59+YvHixeh0Olq1akV0dDTNmzevEst3331HaGgoWq2WHj168Pnnn3PgwAGuXr1aLV8WFhY1/kavXLlCSEgIxcXFdOnS5Vd/0w9iZVVz/3VRUlpB0c93mTHjnwAcO5au/27IkOF89tmnjBgxBK1WS69eTrz6at/HNrYQQgghhBBCPGlSWKmF8PBw/P399ct7Vq1ahZ2dHbt378bBwYGlS5dSVlbGsGHDOHv2LCtXrsTe3p6dO3cClQ/hCQkJ2NnZ6ftcsmQJL774IsuXL+f8+fP6gkNYWBhTp07FxsYGJycnBgwYgKurKwDvvfcezs7OTJgwgZycHMaMGcOOHTseGntubi579+5FrVYzefJkXn/9dcaNG8fhw4dZsWIFQUFBbNmyhc2bN2NmZkZcXByrV6/mb3/7m74PnU7H5s2bWblyJc2aNeOjjz4iISGBlStX8s9//pPQ0FD69OnD8uXLycjIwNHRkby8PIqKijhx4gSNGzcmMzOTLl26kJ6ezj/+8Q80Gs0Dr6WsrIy9e/cCMHz48Gr9AyxevJgtW7ZgZWXFv/71Ly5evMi3335L+/bt9UWVe/r16wfA999/X+3+fPzxxxw/fpzU1FTKy8sZO3YsQ4cOBSqXgE2ePJmgoKAqRZV7Tpw4QVpaGjY2Nvj4+PDll18SHR3Nl19+SUJCAg0aNGDy5Mls2rQJOzs7Vq1aRXR0NEuXLq3ST2hoKG+//Tb9+vUjMTERrVYLwOzZs6vlKyQkpMY8v/fee3h7ezN69Gh27NhBcnLyQ38XD/JmzH7y/nu3Tm1/KTXOi6IHfLdmzf9iadmU1NT9lJaWEhb2dzZtWs+YMW88lrGFEEIIIYQQ4kmTwkodWVlZ6QskHh4enDp1isTERC5evEhhYeEj74dy7NgxFi5cCFTuh3HvQdjb25tBgwZx9OhRjhw5QmhoKMOHD0ej0ZCenk5MTAwArVq1okuXLnzzzTcPHadDhw6o1Wr9mIsWLQIqiw39+vVj/fr1XL58GR8fHwDKy8vp0KFDlT6MjIxYvnw5n332GT/88AOZmZkYGRlx8+ZN8vLy6NOnjz72bdu2oVKp6N27NxkZGRw/fpyAgACOHTuGm5sbLVu2xMLC4qHX0rlzZ4AH9g/g5ubGmDFjGDhwIK+//joODg6kp6dXm2nzKHkYMmQIpqammJqa6othAJGRkTRv3pxBgwbV2Pbll1+mRYvK5S1t27bl1q1bVb4/deoUnTt31v9efH19SUhIqHJOYWEhV69e1Rd/Ro0aRVJSkj62X+brypUrNcaSmZlJXFwcAJ6enoSHh9fqPjwp1taN9J/NzU2wsDDD2roRX311mPDwcGxtmwHg4/Nn9u3bh7X11PoK1eDdf6+FYZHcGS7JnWGT/BkuyZ3hktwZLsldzaSwUkfm5ub6z+vWrWPfvn34+PjQu3dvvvvuOxRFeaR+1Gp1lTegZGdno1KpSEtLIzAwEHd3d9zd3fH392fkyJFoNJpqfSuKglarRaVSVVnqUl5eXmO89wos99pmZ2ej1WoZMmSI/kH8zp07+hkT99y5c4c///nPeHp60rNnT+zt7dmwYQMqlapKTMbGxvrPrq6uHD16lDNnzrBq1SqSk5M5ePAgbm5u+vFrupb7Y35Y/+Hh4Zw7d47Dhw8TEhJCUFAQHTt2ZOPGjSiKUuXeLlq0iN69e9f4xplf5uHKlSs0a1b5sD9p0iQOHz7Mpk2bGDduXLW29xdxfhkrUG35kaIoVFRUVDlmbGz8wN9MTfm6P5+/dK8flUpVbdZOfblx4//mrJSUlHP7dik3bhTx0ksvs337Ltq2daSiooK0tP288kr7KueLR2dt3UjunYGS3BkuyZ1hk/wZLsmd4ZLcGa5nJXdGRqpab43wx3jqMhBqtbraAzHAV199ha+vL56enpSWlnLu3LlqD9MP0qNHD/2GqtnZ2UyaNAkrKyuSkpI4evSo/rysrCwcHByAyv1CPvroIwBycnI4fvw4Xbt2xdLSUr/M5dSpU9y4ceNXxzxy5AizZs3CycmJAwcOUFBQgKIoREVFsXbt2irtLl26hEql4q9//av+fK1Wi6WlJba2thw6dAiA3bt369v06dOHL7/8EiMjIxo1aoSDgwNJSUn6ZU0Pupb7Paj/iooKBg0ahKWlJVOmTMHLy4usrCx69OiBlZUVy5Yt0xdpvvjiC1JSUmjXrl2N96Rnz57s37+f8vJy7t69y1tvvUVubi4ADg4OREZGsmzZMv2x2rg3C+feLJPk5GScnJyqnNOoUSNatWrF4cOHgcq9Y+6pKV8P0rt3b3bt2gXA/v2Vy2vqYnX4IFLjvB7Ln5LS6v9m7pk+fQZFRUWMHTuK8ePHYmNjw7hxAQ88XwghhBBCCCH+aGTGSi1YWVlha2tb7c08AQEBREVFkZCQgIWFBd26dXvgUo1fmj59OuHh4Xh6eqJWq1mwYAFNmjQhISGB2NhYwsPDMTExoU2bNvrlIBqNhoiICFJSUoDKTXVtbGwYOnQo+/btY+jQoTg6OlZbynNPREQE4eHhbNy4kQYNGhATE0O7du0ICgoiICAAnU6Hg4MDkydP1o/Xv39/XF1dcXBwYMiQIahUKl599VX+85//ABAbG0tYWBiLFy+uUhixsLCgRYsWdOrUCagspHz//fe0bt36odfySzX1r1armT59OhMnTsTMzAwrKyvmz5+PSqXigw8+YN68eXh4eKBWq7G0tCQhIYHmzZuTnZ1drX93d3fOnDmDt7c3Op0Of3//Km/zad26NePGjSM6Oprly5c/Um7vad68OdHR0QQFBVFeXo6tra3+bURLlizBxsaGMWPGsGDBAmbOnMnixYuxt7fXz0qpKV8PEhERQUhICMnJyXTs2JGGDRvWKtZ7Cgpuo9M92qyr2tJoovSfmzRpyuzZc5/IOEIIIYQQQgjxe1Apj7pmRQjxRC1btgwfHx9sbGzYv38/qamp+jc9/d6eZGFFPDnPyvTMp5HkznBJ7gyb5M9wSe4Ml+TOcD0ruavLUiCZsSLEH4StrS0TJ05ErVbTuHFj/awWIYQQQgghhBB/XFJYEeIPwtvbG29v7/oOQwghhBBCCCFELcjmtUIIIYQQQgghhBB1JIUVIYQQQgghhBBCiDqSwooQQgghhBBCCCFEHUlhRQghhBBCCCGEEKKOpLAihBBCCCGEEEIIUUdSWBFCCCGEEEIIIYSoIymsCCGEEEIIIYQQQtSRur4DEEIYnrS03SQnb9T//c6d2+Tl5bJ9+16aNbOqx8iEEEIIIYQQ4vf1RGasFBUVERgYWKs2YWFhDBgwgN27d7N06VJcXV1Zs2YNXl5eD2137tw5/P398fT0ZNiwYWg0GoqLi39L+L+bK1ctOj5eAAAgAElEQVSu0L9//3rt//bt28yePRsPDw+8vLzw8/Pj7NmzAGRkZODn5weARqPh9OnTD+zHz8+PjIyMasft7e1/Nc778/043Rs7JSUFJycn8vPz9d89yr3Jyclh5syZjzWmlJQUQkNDqxz74osv8PLywsvLi27duuHu7o6Xl1et/w39noYM8SAxcSOJiRtZtSqJZs2sCA7+hxRVhBBCCCGEEM+cJzJj5datW2RlZdWqzfbt2zl16hSmpqYMGDCANWvW0KZNGyZMmPDQdsHBwcydO5du3bqh0+mYPXs2S5YsISws7LdcwjNBp9MxadIknJyc2LFjB2q1mvT0dCZNmsSePXuqnDtnzpwnFsfOnTv1+X5S7ty5Q2RkJMuXL3/kNteuXSMnJ+eJxXTPa6+9xmuvvQZUFqiCgoJwcnJ64uM+LuvXJ2JpacmIEaPqOxQhhBBCCCGE+N09kcJKTEwMeXl5BAYGkp2djaWlJebm5sTHxzNz5kxyc3PJy8vDxcWFOXPmMHXqVBRFYfTo0XTo0IHc3FwCAwOJi4tjxIgRnD9/nsLCQjQaDRcvXsTU1JTQ0FBcXFzIz8+npKQEACMjI4KCgrh69SoA+fn5aDQarl27hlqtJjg4mL59+xIfHw/AtGnTAOjfvz9JSUlkZmayfft2CgsLcXNzw9fXl7CwMG7evIm5uTkxMTG0b9+eHTt2sHbtWnQ6HY6OjkRGRmJmZlblHqSlpbFmzRpKSkooKytj7ty5dO/enW+//RaNRgNA+/btAThz5gyzZ89m69atFBcX06tXLzZs2ECXLl2IiIjAxcWFnj17PvBaTp48yfXr13njjTfo2rVrtf4BUlNTWbVqFcbGxtjZ2REbG8vx48e5fv0606dPx8iocvKSs7Mz8+bNQ6fTVbmeew/8vXr1YuHChXzyyScYGxvj6+tLQECA/ryCggICAgJ45513GDhwoP54RkYG//73vzE3Nyc7Oxt7e3sWLlxITExMlXz/9NNPLF68GJ1OR6tWrYiOjqZ58+ZVYsnMzOT999+npKSEn3/+mbCwMAYOHMiVK1cICQmhuLiYLl26VGnz+uuvc/78eVJTUxk+fHiV7+7cuUN0dDQXLlxAq9UyadIkPDw8iImJ4cqVK8yePZuvv/6axYsX07ZtW/7+979jYWHB7NmzOXHiBCtWrCAhIYGVK1eya9cujI2N6dOnDyEhIVy/fp233npL/2/g/rHnzJlDQUEBsbGxGBsb1/hvafHixSiKQnBwMAChoaH07duXzz//HDMzM06fPs2dO3eYOnUqI0aMeOC11JaVlUWNx0tKKyj6+a7+74WFhWzevIHVq9fVegwhhBBCCCGEeBo8kaVA4eHh2NjYEBYWxg8//EBsbCxr1qzh0KFDODg4kJyczL59+zh27Bhnz55l5cqVQOXMhXnz5mFjY0NCQgIODg76PpcsWcKLL75IWloaCxYsYPHixUDlEqKpU6cyaNAgZs2axdmzZ+natSsA7733Hs7OzqSmprJ06VJmzpxZZTlITXJzc9m+fTszZsxg9uzZvP766+zevZtp06axYsUKLly4wJYtW9i8eTM7d+7EysqK1atXV+lDp9OxefNm/YP2W2+9RUJCAgD//Oc/effdd9m+fTt2dnYAODo6kpeXR1FREV9//TWNGzcmMzMTgPT0dF577bWHXktZWRl79+5l7NixNfYPlQ/oH374ISkpKbzwwgtcvHiRb7/9lvbt2+uLKvf069cPK6ual3R8/PHHHD9+nNTUVLZu3UpKSgo3btwAKpeATZ48maCgoCpFlXtOnDhBREQEaWlpXLt2jS+//JLo6Gh9vm1sbIiIiGD58uWkpqbSvXt3oqOjq/Wzfv16YmJi2L59OzExMSxZskSfb29vb3bu3En37t2rtDExMWHevHnMnz+/2m9gxYoVODo6kpKSwoYNG1i5ciU5OTmEh4fTsWNHIiMj6devH0ePHgXgu+++4/jx40DlMh5XV1cOHz7MZ599xrZt29i+fTuXL19m8+bNAFX+DdwTHx9Pbm4uCxYseGBRBWDUqFGkpqaiKAp3794lPT2dAQMGAJVLlZKTk1m7di0LFizgxo0bD7yW2nozZj/D/76z2h9zs6q12F27UnjttX688ILdA3oSQgghhBBCiKfbE9+81srKSv+A7+HhwalTp0hMTOTixYsUFhY+8n4ox44dY+HChUDl3hnJyckAeHt7M2jQII4ePcqRI0cIDQ1l+PDhaDQa0tPTiYmJAaBVq1Z06dKFb7755qHjdOjQAbVarR9z0aJFQGWxoV+/fqxfv57Lly/j4+MDQHl5OR06dKjSh5GREcuXL+ezzz7jhx9+IDMzEyMjI27evEleXh59+vTRx75t2zZUKhW9e/cmIyOD48ePExAQwLFjx3Bzc6Nly5ZYWFg89Fo6d+4M8MD+Adzc3BgzZgwDBw7k9ddfx8HBgfT09GozbR4lD0OGDMHU1BRTU1N27typ/y4yMpLmzZszaNCgGtu+/PLLtGjRAoC2bdty69atKt+fOnWKzp07638vvr6++oLU/WJjYzl48CAff/wx33zzDXfu3AEqZ7LExcUB4OnpSXh4eJV2nTp1YtSoUURGRlZZKnbkyBFKSkr096q4uJgLFy7QsGFD/Tn9+vUjMTERZ2dn2rVrx8WLFykoKODzzz9n6dKlrFu3jmHDhtGgQQOgsiCyY8cOfZHq/iLX559/zs2bN/noo4/0v7UHadWqFS+88ALHjh3j2rVr9OvXT58zb29vTExMaNGiBd27d+c///nPA6+lVatWDx2nNqytG+k/Hz78KeHh4VWOifoluTBckjvDJbkzbJI/wyW5M1ySO8MluavZEy+smJub6z+vW7eOffv24ePjQ+/evfnuu+9QFOWR+lGr1ahUKv3fs7OzUalUpKWlERgYiLu7O+7u7vj7+zNy5Eg0Gk21vhVFQavVolKpqix1KS8vrzHe+x96FUUhOzsbrVbLkCFD9A/td+7cQavVVhnnzp07/PnPf8bT05OePXtib2/Phg0bUKlUVWK6f6aCq6srR48e5cyZM6xatYrk5GQOHjyIm5ubfvyaruX+mB/Wf3h4OOfOnePw4cOEhIQQFBREx44d2bhxI4qiVLm3ixYtonfv3lWO3X9P7j9+5coVmjVrBsCkSZM4fPgwmzZtYty4cdXa3l/E+WWsQLXlR4qiUFFRUa2fsWPH4uTkhJOTEy4uLrz77rtV2tzr/5czcQCCgoLw9vZm9+7dVcaNjY3F0dERqFxC1qRJE/2sFIBu3boRGhrKkSNH6NWrF1ZWVnz88cdUVFRga2tbLXZAH/v9vymAF154geDgYKKjo9m8eXONcd5v1KhR7N69m2vXrumXr0HV/Op0OtRq9QOvpbZWh9dcHLt/KdDPP//M5cuXadXqZW7cKKr1GOLxs7ZuJLkwUJI7wyW5M2ySP8MluTNckjvD9azkzshI9cCtER7Y5kkEolara3wg/uqrr/D19cXT05PS0lLOnTtX4wNpTXr06KHfUDU7O5tJkyZhZWVFUlKSfokGQFZWln4JkbOzMx999BFQuWzi+PHjdO3aFUtLS77//nugcpbEvaUsDxvzyJEjzJo1CycnJw4cOEBBQQGKohAVFcXatWurtLt06RIqlYq//vWv+vO1Wi2WlpbY2tpy6NAhgCoP93369OHLL7/EyMiIRo0a4eDgQFJSEq6urg+9lvs9qP+KigoGDRqEpaUlU6ZMwcvLi6ysLHr06IGVlRXLli3TF2m++OILUlJSaNeuXY33pGfPnuzfv5/y8nLu3r3LW2+9RW5uLgAODg5ERkaybNky/bHauDcL58qVKwAkJydX28S1sLCQS5cu8fbbb9O3b18+/fRTfey9e/dm165dAOzfv5/S0tJqY5iamjJv3jz98jOovLebNm0CIC8vD09PT65fv46xsbH+d6xWq+ncuTPr1q2jV69eODs7s3LlSvr166fvY8+ePZSUlFBRUcG2bdtwdnau8Trbtm3L6NGjadCgARs2bPjV+zJ48GCOHj1Kfn5+lb1j0tLSUBSFq1evcurUKf7nf/7ngddSWwUFt7lxo6jan/v3V7l6NQcrq+a/OutGCCGEEEIIIZ5mT+SJyMrKCltb22pv5gkICCAqKoqEhAQsLCzo1q2b/iH610yfPp3w8HA8PT1Rq9UsWLCAJk2akJCQQGxsLOHh4ZiYmNCmTRv98h2NRkNERAQpKSlA5aa6NjY2DB06lH379jF06FAcHR2rLeW5JyIigvDwcDZu3EiDBg2IiYmhXbt2BAUFERAQgE6nw8HBgcmTJ+vH69+/P66urjg4ODBkyBBUKhWvvvoq//nPf4DKZSxhYWEsXry4SmHEwsKCFi1a0KlTJ6DyQf3777+ndevWD72WX6qpf7VazfTp05k4cSJmZmZYWVkxf/58VCoVH3zwAfPmzcPDwwO1Wo2lpSUJCQk0b96c7Ozsav27u7tz5swZvL290el0+Pv7V3mbT+vWrRk3bhzR0dG1egMPQPPmzYmOjiYoKIjy8nJsbW31byNasmQJNjY2jBkzhj//+c8MGzYMtVqNs7MzJSUlFBcXExERQUhICMnJyXTs2LHKUp77derUiYCAAFJTU4HKWSxRUVF4eHig1WoJCQnhxRdfpFGjRhQVFRESEkJsbCz9+vXj2LFjtG3bFmtrawoKCvSFLzc3N7Kyshg1ahQVFRW8+uqrvPHGG/z0008PvN6oqCjGjBmDu7u7folUTczNzenatSuvvPJKleMlJSWMGjWKsrIyoqOjsbS0fOC1PAkODo4kJ+94In0LIYQQQgghhKFQKY+6FkcI8btTFIU7d+7g6+tLYmIi1tbWQOXbgXr16oW3t/cTGbeg4DY6nfzXYGielemZTyPJneGS3Bk2yZ/hktwZLsmd4XpWcveHWQokhHg8Tp8+Tf/+/fHx8dEXVYQQQgghhBBC/HHI5ghC/IF17txZ/+rt+82fP78eohFCCCGEEEII8UsyY0UIIYQQQgghhBCijqSwIoQQQgghhBBCCFFHUlgRQgghhBBCCCGEqCMprAghhBBCCCGEEELUkRRWhBBCCCGEEEIIIepICitCCCGEEEIIIYQQdSSFFSGEEEIIIYQQQog6ksKKEKJWPv/8EO7ufes7DCGEEEIIIYT4Q5DCingkRUVFBAYG1qpNWFgYAwYMYPfu3SxduhRXV1fWrFmDl5fXQ9udO3cOf39/PD09GTZsGBqNhuLi4t8S/lNDo9Fw+vTpehs/J+dHli9fDCj1FoMQQgghhBBC/JFIYUU8klu3bpGVlVWrNtu3byctLQ0PDw927tzJmjVrmDBhAjt37nxou+DgYIKDg9m1axepqamo1WqWLFnyW8J/asyZM4dOnTo98XGsrCywtm6EtXUjGjVuAEBJSQnR0bOYNi34iY8vhBBCCCGEEIZCXd8BCMMQExNDXl4egYGBZGdnY2lpibm5OfHx8cycOZPc3Fzy8vJwcXFhzpw5TJ06FUVRGD16NB06dCA3N5fAwEDi4uIYMWIE58+fp7CwEI1Gw8WLFzE1NSU0NBQXFxfy8/MpKSkBwMjIiKCgIK5evQpAfn4+Go2Ga9euoVarCQ4Opm/fvsTHxwMwbdo0APr3709SUhKZmZls376dwsJC3Nzc8PX1JSwsjJs3b2Jubk5MTAzt27dnx44drF27Fp1Oh6OjI5GRkZiZmVW5B0eOHGH+/PkoioKtrS1xcXEANV5/ZmYmK1euxMTEhCtXrtC/f3+ee+45PvnkEwASEhJo3rw5Li4uuLu7c+LECRo2bMjChQuxs7MjLS2NNWvWUFJSQllZGXPnzqV79+74+fkRFBSEk5MTcXFx7Nu3D0tLS6ytrenfvz+9evUiKCiIl19+maysLKysrFiyZAlNmzatVb7fjNlP3n/vApAa50UREBs7By8vb9q2fbluPyIhhBBCCCGEeBopQjyCnJwcxc3NTcnJyVFeeeUVJScnR1EURUlNTVU++OADRVEUpbS0VBk4cKBy+vRpRVEU5ZVXXtG3v9f2/uNRUVHK/PnzFUVRlHPnzik+Pj6KoijKtm3blC5duiju7u5KeHi4cvDgQX0/06dPVz788ENFURTlxx9/VPr06aPcuHFDWbp0qbJ06dJq423btk1xd3dXysvLFUVRlEmTJinr169XFEVRDh06pEyfPl357rvvlDFjxiglJSWKoijKwoULleXLl1e5/tLSUsXFxUX59ttv9eckJSU98PrT09OVbt26KdeuXVOKi4uVrl27Kps2bVIURVFCQ0OVxMRE/b1ISUlRFEVRkpKSlClTpiharVbx9/dXCgoKFEVRlK1btypTpkxRFEVR3njjDSU9PV359NNPlTFjxiilpaVKYWGh4ubmpmzbtk3JyclR7O3tlbNnzyqKoihBQUFKUlLSo6ZZb+J7+xSPGTsUjxk7FEVRlPXr1yuhoaGKolT+Frp27VrrPoUQQgghhBDiaSQzVkStWVlZYWdnB4CHhwenTp0iMTGRixcvUlhY+Mj7oRw7doyFCxcCYG9vT3JyMgDe3t4MGjSIo0ePcuTIEUJDQxk+fDgajYb09HRiYmIAaNWqFV26dOGbb7556DgdOnRArVbrx1y0aBEA/fr1o1+/fqxfv57Lly/j4+MDQHl5OR06dKjSx/nz53n++edxcHAA4O9//7v+uwdd/yv/r717D4uq3P///xwYEPfGDFFSSz9ZGqKVWp5QU0HBOAhqJmkC2TfdukF2+tmWCIIHPGwxz5aRblLTNMtzkdnBDoqHMkPLQ+IhPASKqYiAMLN+f/hzPpJiSBaNvh7X1XXNWrPue73v9R7GmXf3veahh6hTpw4Abm5ueHt7A1C3bl3Onz8PQJUqVejRowcAPXv2ZNq0aTg4ODB37lw+/fRTDh8+zPbt23FwKL1qb8uWLQQEBODs7IyzszNdu3a1Pefu7m6Lv1GjRpw7d+6G16c8Vqx4l8LCQoKCulNSUmx7PHXqTGrWrPW7+5dbo1atapw6lVfZYUgFKHf2S7mzb8qf/VLu7JdyZ7/ulNw5OJhwd3e9qTYqrMhNc3FxsT1evHgxGzZsoE+fPrRr144DBw5gGOW7sanZbMZkMtm2MzMzMZlMpKWlERUVhZ+fH35+fkRERNCzZ0/i4uKu6dswDCwWCyaTCavVattfXFx83XivFFiutM3MzMRisRAQEEB8fDwA+fn5WCyWUudxcnIqFWteXh75+fls3LixzPE7OTmV6sPR0fGaa+Dg4GDr12q14ujoSH5+Pr179yYkJIRWrVrh6enJkiVLrml39XivdvUSJpPJVO58XG1BvL/tcWFRCW+8sci2ffLkCSIiwnjzzaU33a+IiIiIiMjtRjevlXIxm82UlJRcs3/z5s2EhYUREhJCUVER+/btK/ML/6+1bNmS999/H7hcVBk4cCDu7u4sWrSI9PR023F79+61zRRp27Yt7777LgBZWVns3LmT5s2b4+bmxsGDB4HLM0hOnTr1m+fcsmULo0ePpk2bNmzcuJHc3FwMw2DMmDEsXLiwVLsGDRqQm5trO8f8+fN5++23f9f4AQoKCvj0008BWLlyJR07duTIkSOYTCYGDx5si+3XhZ527drx0UcfcenSJS5cuMCmTZtKFX5+r9zcC5w6lcepU3nknS+4Zf2KiIiIiIjcbjRjRcrF3d2dunXrEhsbW2p/ZGQkY8aMISUlBVdXV1q0aMGxY8fK1WdMTAzx8fGEhIRgNpuZMmUK1atXJyUlheTkZOLj43FycqJBgwa25TtxcXEkJCSwcuVK4PJNdT08PAgMDGTDhg0EBgbStGnTa5byXJGQkEB8fDxLly6latWqJCUl0bBhQ6Kjo4mMjMRqteLl5cWgQYNs5/P19aVLly4kJyfz0ksvUVxcTP369ZkyZQoZGRnXHX/9+vXLfW0//PBDpk+fjoeHB//5z39wc3PDy8uLgIAATCYTHTp04JtvvinVpnPnznz77bf07NmT6tWr4+Hhcc3Ndv8oderUZePGL/+Uc4mIiIiIiPzVmYyKrBMQkVvC09OT/fv333S7b7/9liNHjtCzZ0+Ki4sJCwtj4sSJNG7c+JbElZt7AatVbw325k5Z93o7Uu7sl3Jn35Q/+6Xc2S/lzn7dKbnTPVZE7hANGjRgzpw5pKamYhgGPXr0uGVFFRERERERESk/FVZEKlFFZqsA3H333SxYsOAWRyMiIiIiIiI3SzevFRERERERERGpIBVWREREREREREQqSIUVEREREREREZEKUmFFRERERERERKSCVFgREREREREREakgFVZERERERERERCpIhRURERERERERkQpSYUVEbsoXX2zCz69jZYchIiIiIiLyl6DCSjnl5eURFRV1U21iY2Pp0qUL69evZ9asWXTu3JnU1FRCQ0Nv2G7fvn1EREQQEhJCUFAQcXFxXLx48feE/6c5duwYvr6+ldr/hQsXGDt2LMHBwYSGhhIeHs73338PwLZt2wgPDwcgLi6O3bt3l9lPeHg427Ztu2a/p6fnb8Z5db5vJ1lZPzF37gzAqOxQRERERERE/hLMlR2AvTh37hx79+69qTarVq0iIyMDZ2dnunTpQmpqKg0aNGDAgAE3bDds2DAmTpxIixYtsFqtjB07lpkzZxIbG/t7hnBHsFqtDBw4kDZt2rB69WrMZjNbt25l4MCBvP/++6WOnTBhwh8Wx5o1a2z5tkfu7q62x4VFJeSdL6CwsJBx40YzdOgwxo6Nr8ToRERERERE/jpUWCmnpKQkcnJyiIqKIjMzEzc3N1xcXJg9ezajRo0iOzubnJwcvL29mTBhAkOGDMEwDJ5++mmaNGlCdnY2UVFRvPLKK/To0YP9+/dz9uxZ4uLiOHToEM7OzowcORJvb29Onz5NYWEhAA4ODkRHR3P8+HEATp8+TVxcHCdOnMBsNjNs2DA6duzI7NmzARg6dCgAvr6+LFq0iO3bt7Nq1SrOnj2Lj48PYWFhxMbGcubMGVxcXEhKSqJx48asXr2ahQsXYrVaadq0KYmJiVSpUqXUNUhLSyM1NZXCwkIuXbrExIkTeeyxx/jhhx+Ii4sDoHHjxgDs2bOHsWPHsmLFCi5evEjr1q1ZsmQJzZo1IyEhAW9vb1q1alXmWHbt2sXJkyfp378/zZs3v6Z/gHXr1jF//nwcHR257777SE5OZufOnZw8eZKYmBgcHC5PyGrbti2TJk3CarWWGk94eDjR0dG0bt2aqVOn8vHHH+Po6EhYWBiRkZG243Jzc4mMjOTFF1+ka9eutv3btm3j9ddfx8XFhczMTDw9PZk6dSpJSUml8v3zzz8zY8YMrFYr9erVY9y4cdSsWfOaWBo3bszXX39NUVERo0aNokOHDhw4cIDx48dz8eJFzpw5w6BBg+jbty95eXm89NJL/PTTT9SrV4+ff/6ZOXPmUKdOHaZMmcL27duxWCz06tWL55577qZf7/8v6SNyfim4fJ1fCSUPSE6eQGhoLx58sNFN9yciIiIiInLbMqRcsrKyDB8fHyMrK8t46KGHjKysLMMwDGPdunXGq6++ahiGYRQVFRldu3Y1du/ebRiGYTz00EO29lfaXr1/zJgxxuTJkw3DMIx9+/YZffr0MQzDMN577z2jWbNmhp+fnxEfH2989tlntn5iYmKM//73v4ZhGMZPP/1ktG/f3jh16pQxa9YsY9asWdec77333jP8/PyM4uJiwzAMY+DAgcZbb71lGIZhbNq0yYiJiTEOHDhg9O3b1ygsLDQMwzCmTp1qzJ07t9T4LRaLERERYeTm5hqGYRgrVqww/vGPfxiGYRjBwcHGV199ZRiGYcyZM8fw8fExrFar0bFjR+P8+fPG559/bnh7exspKSmGYRiGn5+fkZeXd8Ox9O/f33bu6/VvGIbh6+trnD592jAMw5g8ebLxww8/GPPnz7fFdT1bt2619d2/f39j69atxgcffGA888wzRlFRkXHhwgUjJCTEyMnJMfr3729s3LjR6NWrl5GWlmbr40r+tm7dajRv3tw4efKkYbFYjKeeesr45JNPSl3/06dPGx06dLDl/o033jCGDh16TVz9+/c3Ro4caRiGYfzwww9G+/btjaKiIiMpKcnYsmWL7Ro1b97cMAzDmDRpkvGf//zHMAzDyMjIMLy8vIysrCxj6dKlxsSJEw3DuPx67N+/v7Fjx44yr0dZnh+/wQgevtoIHr7aMAzDeOutt2zxZWVl2eIQERERERG502nGSgW4u7tz3333ARAcHExGRgZvvvkmhw4d4uzZs+W+H8qOHTuYOnUqcPm+HcuXLwegV69e+Pv7k56ezpYtWxg5ciTdu3cnLi6OrVu3kpSUBEC9evVo1qwZ33333Q3P06RJE8xms+2c06ZNA6BTp0506tSJt956i6NHj9KnTx8AiouLadKkSak+HBwcmDt3Lp9++imHDx9m+/btODg4cObMGXJycmjfvr0t9vfeew+TyUS7du3Ytm0bO3fuJDIykh07duDj40OdOnVwdXW94VgeffRRgDL7B/Dx8aFv37507dqVbt264eXlxdatW6+ZaVOePAQEBODs7IyzszNr1qyxPZeYmEjNmjXx9/e/bttGjRpRu3ZtAB588EHOnTtX6vmMjAweffRR2+slLCyMlJSU6/Z15fp7eXlRq1Yt9u/fz8iRI/nyyy95/fXXOXDggO21tXnzZttr55FHHuGhhx4CID09nb1797J161YALl68yP79+2nZsuVNXZNfW7HiXQoLCwkK6k5JSbHt8dSpM6lZs9bv6ltunVq1qnHqVF5lhyEVoNzZL+XOvil/9ku5s1/Knf26U3Ln4GAqdWuE8lBhpQJcXFxsjxcvXsyGDRvo06cP7dq148CBAxhG+W7saTabMZlMtu3MzExMJhNpaWlERUXh5+eHn58fERER9OzZk7i4uOGXqrEAACAASURBVGv6NgwDi8WCyWQqtdSluLj4uvFeKbBcaZuZmYnFYiEgIID4+Mv3zcjPz8disZQ6T35+Pr179yYkJIRWrVrh6enJkiVLMJlMpWJydHS0Pe7cuTPp6ens2bOH+fPns3z5cj777DN8fHxs57/eWK6O+Ub9x8fHs2/fPj7//HNGjBhBdHQ0Dz/8MEuXLsUwjFLXdtq0abRr167UvquvydX7jx07Ro0aNQAYOHAgn3/+OW+//TbPPvvsNW2vLuL8OlbgmuVHhmFQUlJyTT+/HpvVasVsNvPiiy9y11134ePjQ2BgIOvXr7cde73XmcViYcSIEbZC0JkzZ/j73/9+3fPdyIL4/yskFRaV8MYbi2zbJ0+eICIijDffXHrT/YqIiIiIiNxu9KtA5WQ2m6/7hXjz5s2EhYUREhJCUVER+/btu+bLdFlatmxpu6FqZmYmAwcOxN3dnUWLFpGenm47bu/evXh5eQGX7xfy7rvvApCVlcXOnTtp3rw5bm5uHDx4ELg8S+LUqVO/ec4tW7YwevRo2rRpw8aNG8nNzcUwDMaMGcPChQtLtTty5Agmk4nBgwfbjrdYLLi5uVG3bl02bdoEYPviD9C+fXu++uorHBwcqFatGl5eXixatIjOnTvfcCxXK6v/kpIS/P39cXNz4x//+AehoaHs3buXli1b4u7uzpw5c2xFmi+//JKVK1fSsGHD616TVq1a8dFHH1FcXExBQQEvvPAC2dnZwOXZI4mJicyZM8e272ZcmYVz7NgxAJYvX06bNm2ue+wHH3wAwO7duzl//jwPPfQQmzdvJiYmhq5du/LFF18Al4sn3t7erFu3DoD9+/fz448/YjKZaNu2Le+88w7FxcXk5+fTr18/du3addNx5+Ze4NSpPE6dyiPvfMFNtxcREREREblTaMZKObm7u1O3bt1rfpknMjKSMWPGkJKSgqurKy1atLB9if4tMTExxMfHExISgtlsZsqUKVSvXp2UlBSSk5OJj4/HycmJBg0a2JbvxMXFkZCQwMqVK4HLN9X18PAgMDCQDRs2EBgYSNOmTa9ZynNFQkIC8fHxLF26lKpVq5KUlETDhg2Jjo4mMjISq9WKl5cXgwYNsp3P19eXzp074+XlRUBAACaTiQ4dOvDNN98AkJycTGxsLDNmzChVGHF1daV27do88sgjwOVCysGDB7n//vtvOJZfu17/ZrOZmJgYnn/+eapUqYK7uzuTJ0/GZDLx6quvMmnSJIKDgzGbzbi5uZGSkkLNmjXJzMy8pn8/Pz/27NlDr169sFqtRERElPo1n/vvv59nn32WcePGMXfu3HLl9oqaNWsybtw4oqOjKS4upm7durZfI5o5cyYeHh707dsXuFxc6tmzJwDTp0/H0dGRoUOH0q9fP6pUqULjxo259957OXbsGFFRUcTGxtK9e3fq169PzZo1cXFx4ZlnnuHo0aP07NmTkpISevXqVWYhp6Lq1KnLxo1f3tI+RURERERE7JXJKO+6FRH5w1z5haLyFkHWrFnDfffdx+OPP86JEyfo378/H3/8se2XkH6v3NwLWK16a7A3d8q619uRcme/lDv7pvzZL+XOfil39utOyZ3usSJyh3jggQdITEzEarXi4ODAuHHjbllRRURERERERMpPhRWRv4DFixff1PGPPPKIbQmViIiIiIiIVB79L24RERERERERkQpSYUVEREREREREpIJUWBERERERERERqSAVVkREREREREREKkiFFRERERERERGRClJhRURERERERESkgvRzyyJyQxs2fMDSpYsxmUy4uLjw4ov/pnHjJpUdloiIiIiIyF+CZqzcInl5eURFRd1Um9jYWLp06cL69euZNWsWnTt3JjU1ldDQ0Bu227dvHxEREYSEhBAUFERcXBwXL178PeH/aY4dO4avr2+l9n/hwgXGjh1LcHAwoaGhhIeH8/333wOwbds2wsPDAYiLi2P37t1l9hMeHs62bduu2e/p6fmbcV6d74pauXIlI0eOvOEx77zzDuvXr6/wOX766QivvjqTV16ZzZtvLiUy8nlGjRpR4f5ERERERERuN5qxcoucO3eOvXv33lSbVatWkZGRgbOzM126dCE1NZUGDRowYMCAG7YbNmwYEydOpEWLFlitVsaOHcvMmTOJjY39PUO4I1itVgYOHEibNm1YvXo1ZrOZrVu3MnDgQN5///1Sx06YMOEPi2PNmjW2fP+Rdu7cSevWrSvc3snJmZdfHk3NmjUBaNy4CWfO5FJcXIyTk9OtClNERERERMRuqbByiyQlJZGTk0NUVBSZmZm4ubnh4uLC7NmzGTVqFNnZ2eTk5ODt7c2ECRMYMmQIhmHw9NNP06RJE7Kzs4mKiuKVV16hR48e7N+/n7NnzxIXF8ehQ4dwdnZm5MiReHt7c/r0aQoLCwFwcHAgOjqa48ePA3D69Gni4uI4ceIEZrOZYcOG0bFjR2bPng3A0KFDAfD19WXRokVs376dVatWcfbsWXx8fAgLCyM2NpYzZ87g4uJCUlISjRs3ZvXq1SxcuBCr1UrTpk1JTEykSpUqpa5BWloaqampFBYWcunSJSZOnMhjjz3GDz/8QFxcHACNGzcGYM+ePYwdO5YVK1Zw8eJFWrduzZIlS2jWrBkJCQl4e3vTqlWrMseya9cuTp48Sf/+/WnevPk1/QOsW7eO+fPn4+joyH333UdycjI7d+7k5MmTxMTE4OBwecJW27ZtmTRpElartdR4wsPDiY6OpnXr1kydOpWPP/4YR0dHwsLCiIyMtB2Xm5tLZGQkL774Il27drXt37ZtG6+//jouLi5kZmbi6enJ1KlTSUpKKpXvn3/+mRkzZmC1WqlXrx7jxo2zFTKuSE1NZdWqVTg4OPDoo48ybty4Us/v2rWLCRMmUFRUhJubG+PGjeP48eN8+umnbN26lVq1avHEE0+U+/Xs7u5KYVEJUJc6deoCYBgGs2dPp0OHjiqqiIiIiIiI/P+0FOgWiY+Px8PDg9jYWA4fPkxycjKpqals2rQJLy8vli9fzoYNG9ixYwfff/898+bNAy7PXJg0aRIeHh6kpKTg5eVl63PmzJnUr1+ftLQ0pkyZwowZM4DLS4iGDBmCv78/o0eP5vvvv6d58+YAjB8/nrZt27Ju3TpmzZrFqFGjOH369A1jz87OZtWqVQwfPpyxY8fSrVs31q9fz9ChQ3nttdf48ccfeeedd1i2bBlr1qzB3d2dBQsWlOrDarWybNky5s2bx9q1a3nhhRdISUkB4OWXX+bf//43q1at4r777gOgadOm5OTkkJeXx9dff81dd93F9u3bAdi6dStPPPHEDcdy6dIlPvjgA/r163fd/gFmzJjBf//7X1auXMm9997LoUOH+OGHH2jcuLGtqHJFp06dcHd3v+71+fDDD9m5cyfr1q1jxYoVrFy5klOnTgGXl4ANGjSI6OjoUkWVK7799lsSEhJIS0vjxIkTfPXVV4wbN86Wbw8PDxISEpg7dy7r1q3jscceu6ZoYrFYeP3113nvvfdYuXIlxcXFZGdn256/dOkSw4cPZ/To0axdu5ZnnnmG4cOH065dO3x9fYmJibmpogrA/0v6CJcq/1d3LSgoYPTokRw7lsXLL4++qb5ERERERERuZ5qx8gdwd3e3fcEPDg4mIyODN998k0OHDnH27Nly3w9lx44dTJ06Fbh8347ly5cD0KtXL/z9/UlPT2fLli2MHDmS7t27ExcXx9atW0lKSgKgXr16NGvWjO++++6G52nSpAlms9l2zmnTpgGXiw2dOnXirbfe4ujRo/Tp0weA4uJimjQpffNSBwcH5s6dy6effsrhw4fZvn07Dg4OnDlzhpycHNq3b2+L/b333sNkMtGuXTu2bdvGzp07iYyMZMeOHfj4+FCnTh1cXV1vOJZHH30UoMz+AXx8fOjbty9du3alW7dueHl5sXXr1mtm2pQnDwEBATg7O+Ps7MyaNWtszyUmJlKzZk38/f2v27ZRo0bUrl0bgAcffJBz586Vej4jI4NHH33U9noJCwuzFaSucHR0pEWLFvTu3ZsuXbowYMAA7rnnHtvzR44c4a677rJdk4CAABISEsjLy7upcV5PrVrVOHHiBNHRg3nwwQd5++0luLi4/O5+5Y9Tq1a1yg5BKki5s1/KnX1T/uyXcme/lDv7pdxdnworf4Crv3guXryYDRs20KdPH9q1a8eBAwcwDKNc/ZjNZkwmk207MzMTk8lEWloaUVFR+Pn54efnR0REBD179iQuLu6avg3DwGKxYDKZSi11KS4uvm68VwosV9pmZmZisVgICAggPj4egPz8fCwWS6nz5Ofn07t3b0JCQmjVqhWenp4sWbIEk8lUKiZHR0fb486dO5Oens6ePXuYP38+y5cv57PPPsPHx8d2/uuN5eqYb9R/fHw8+/bt4/PPP2fEiBFER0fz8MMPs3TpUgzDKHVtp02bRrt27Urtu/qaXL3/2LFj1KhRA4CBAwfy+eef8/bbb/Pss89e0/bqIs6vYwWuWX5kGAYlJSXX9PPqq6+ya9cuvvjiC1544QVbwe16fVzp59c5qoijR38mMrI/AQFBPP/8IPLyisnLK/7thlIpatWqxqlTv7+gJn8+5c5+KXf2TfmzX8qd/VLu7NedkjsHBxPu7q431+YPiuWOYzabr/uFePPmzYSFhRESEkJRURH79u277hfh62nZsqXthqqZmZkMHDgQd3d3Fi1aRHp6uu24vXv32pYQtW3blnfffReArKwsdu7cSfPmzXFzc+PgwYPA5VkSV5ay3OicW7ZsYfTo0bRp04aNGzeSm5uLYRiMGTOGhQsXlmp35MgRTCYTgwcPth1vsVhwc3Ojbt26bNq0CaDUL9S0b9+er776CgcHB6pVq4aXlxeLFi2ic+fONxzL1crqv6SkBH9/f9zc3PjHP/5BaGgoe/fupWXLlri7uzNnzhxb4eHLL79k5cqVNGzY8LrXpFWrVnz00UcUFxdTUFDACy+8YFuK4+XlRWJiInPmzCm1PKe8rszCOXbsGADLly+nTZs2pY45c+YMgYGBPPTQQ/zrX/+iffv27N+/3/b8Aw88wNmzZ8nIyADggw8+oG7dutx99904OjpWqMCyIN6fwqIS3nvvHbKzT/LFF5t47rl+tv/OnTt7032KiIiIiIjcjjRj5RZxd3enbt261/wyT2RkJGPGjCElJQVXV1datGhh+xL9W2JiYoiPjyckJASz2cyUKVOoXr06KSkpJCcnEx8fj5OTEw0aNLAt34mLiyMhIYGVK1cCl2+q6+HhQWBgIBs2bCAwMJCmTZtes5TnioSEBOLj41m6dClVq1YlKSmJhg0bEh0dTWRkJFarFS8vLwYNGmQ7n6+vL507d8bLy4uAgABMJhMdOnTgm2++ASA5OZnY2FhmzJhRqjDi6upK7dq1eeSRR4DLhZSDBw9y//3333Asv3a9/s1mMzExMTz//PNUqVIFd3d3Jk+ejMlk4tVXX2XSpEkEBwdjNptxc3MjJSWFmjVrkpmZeU3/fn5+7Nmzh169emG1WomIiCj1az73338/zz77LOPGjWPu3Lnlyu0VNWvWZNy4cURHR1NcXEzdunVtv0Y0c+ZMPDw86Nu3L2FhYfTu3ZuqVavSoEEDnnrqKT788EMAnJ2dmT59OuPHj6egoIDq1aszffp0ANq1a8e0adOoVq0aTz75ZLnjys29gNVqEB4+gPDwG/9KlYiIiIiIyJ3MZJR3XYqI3DGuFFbEvtwp0zNvR8qd/VLu7JvyZ7+UO/ul3NmvOyV3WgokIiIiIiIiIvInUmFFRERERERERKSCVFgREREREREREakgFVZERERERERERCpIhRURERERERERkQpSYUVEREREREREpIJUWBERERERERERqSAVVkREREREREREKkiFFRERERERERGRClJhRURuyDAMkpISWbp0cWWHIiIiIiIi8pejwoqUKS8vj6ioqJtqExsbS5cuXVi/fj2zZs2ic+fOpKamEhoaesN2+/btIyIigpCQEIKCgoiLi+PixYu/J/w/zbFjx/D19bVt79q1i8jISEJCQggODmbMmDEUFhZWqO+srCxGjRpV4dhiY2M5fvx4hdsfOXKYf/1rCJs2fVLhPkRERERERG5nKqxImc6dO8fevXtvqs2qVatIS0sjODiYNWvWkJqayoABA1izZs0N2w0bNoxhw4axdu1a1q1bh9lsZubMmb8n/Eqxb98+oqOjGT58OGvXrmX16tUYhsHo0aMr1N+JEyfIysqqcDzbtm3DMIwKt1+58h2Cg3vg49O1wn2IiIiIiIjczsyVHYD8dSUlJZGTk0NUVBSZmZm4ubnh4uLC7NmzGTVqFNnZ2eTk5ODt7c2ECRMYMmQIhmHw9NNP06RJE7Kzs4mKiuKVV16hR48e7N+/n7NnzxIXF8ehQ4dwdnZm5MiReHt7c/r0adusDgcHB6Kjo20zLU6fPk1cXBwnTpzAbDYzbNgwOnbsyOzZswEYOnQoAL6+vixatIjt27ezatUqzp49i4+PD2FhYcTGxnLmzBlcXFxISkqicePGrF69moULF2K1WmnatCmJiYlUqVKl1DVIS0sjNTWVwsJCLl26xMSJE3nsscf44YcfiIuLA6Bx48a24xcsWMBTTz1Fs2bNADCbzYwYMYLNmzf/5liys7M5evQox48f5+mnn2bIkCEkJSVx7Ngxxo4dS2JiIikpKaSlpWGxWOjQoQMjRozg+PHjREdH06hRI/bu3Yu7uzszZ87knXfeIScnh0GDBrFkyRLc3NzKnXtXVxfOny9g+PCXAdixY+tNv35ERERERETuBJqxImWKj4/Hw8OD2NhYDh8+THJyMqmpqWzatAkvLy+WL1/Ohg0b2LFjB99//z3z5s0DYM2aNUyaNAkPDw9SUlLw8vKy9Tlz5kzq169PWloaU6ZMYcaMGcDlJStDhgzB39+f0aNH8/3339O8eXMAxo8fT9u2bVm3bh2zZs1i1KhRnD59+oaxZ2dns2rVKoYPH87YsWPp1q0b69evZ+jQobz22mv8+OOPvPPOOyxbtow1a9bg7u7OggULSvVhtVpZtmwZ8+bNY+3atbzwwgukpKQA8PLLL/Pvf/+bVatWcd9999na7N27l6ZNm5bqx9XVlW7duv3mWPbv38+CBQtYsWIFKSkpnD9/nvj4eB5++GESExP54osv2LNnD++++y6rV68mOzubtWvXApdnygwYMID169dz1113sW7dOgYNGmTLwc0UVQCqVFHNVUREREREpDz07UnKxd3d3VZACA4OJiMjgzfffJNDhw5x9uzZct8PZceOHUydOhUAT09Pli9fDkCvXr3w9/cnPT2dLVu2MHLkSLp3705cXBxbt24lKSkJgHr16tGsWTO+++67G56nSZMmmM1m2zmnTZsGQKdOnejUqRNvvfUWR48epU+fPgAUFxfTpEmTUn04ODgwd+5cPv30Uw4fPsz27dtxcHDgzJkz5OTk0L59e1vs7733HgAmk+maWS9Xu9FY2rRpg7OzM+7u7tx9993k5eWVapuenk5GRga9evUCoLCwkLp16/L444/j7u5ui79Ro0acO3fuhtenPGrVqmZ77OLihKtrlVL75K9JObJfyp39Uu7sm/Jnv5Q7+6Xc2S/l7vpUWJFycXFxsT1evHgxGzZsoE+fPrRr144DBw6U+z4eZrMZk8lk287MzMRkMpGWlkZUVBR+fn74+fkRERFBz549iYuLu6ZvwzCwWCyYTCasVqttf3Fx8XXjvVJgudI2MzMTi8VCQEAA8fHxAOTn52OxWEqdJz8/n969exMSEkKrVq3w9PRkyZIlmEymUjE5OjraHj/88MPs3r2bTp062fZduHCBf//738yaNavMsQClCjK/PgeAxWIhMjKSAQMGAHD+/HkcHR355ZdffrPtzSoqKuH8+QLbdmFhMRcuFHHqVN4NWkllq1WrmnJkp5Q7+6Xc2Tflz34pd/ZLubNfd0ruHBxMuLu73lybPygWuQ2YzWZKSkqu2b9582bCwsIICQmhqKiIffv2lSpw3EjLli15//33gctFlYEDB+Lu7s6iRYtIT0+3Hbd3717bEqK2bdvy7rvvApd/JWfnzp00b94cNzc3Dh48CEBGRganTp36zXNu2bKF0aNH06ZNGzZu3Ehubi6GYTBmzBgWLlxYqt2RI0cwmUwMHjzYdrzFYsHNzY26deuyadMmANavX29r89xzz/H222+TkZEBXC72TJ48GVdXV5ydncscS1kcHR1tOWjbti1r1qwhPz+fkpISoqKi2LBhww2vt6Oj4zUFo/K4cKFiv2IkIiIiIiJyp9GMFSmTu7s7devWJTY2ttT+yMhIxowZQ0pKCq6urrRo0YJjx46Vq8+YmBji4+MJCQnBbDYzZcoUqlevTkpKCsnJycTHx+Pk5ESDBg1sy3fi4uJISEhg5cqVwOWb6np4eBAYGMiGDRsIDAykadOm1yzluSIhIYH4+HiWLl1K1apVSUpKomHDhkRHRxMZGYnVasXLy4tBgwbZzufr60vnzp3x8vIiICAAk8lEhw4d+OabbwBITk4mNjaWGTNmlCqMeHp6kpyczIQJEygoKKC4uJh27drZZsaUNZayPPjgg+Tl5TFixAiSk5PZt28fffr0wWKx8MQTT9CzZ88b/pxy586dGTRoEPPnz6devXrlytH1xMWNqXBbERERERGR25nJ+L1rBkTktpObewGrVW8N9uZOmZ55O1Lu7JdyZ9+UP/ul3Nkv5c5+3Sm501IgEREREREREZE/kQorIiIiIiIiIiIVpMKKiIiIiIiIiEgFqbAiIiIiIiIiIlJBKqyIiIiIiIiIiFSQCisiIiIiIiIiIhWkwoqIiIiIiIiISAWpsCIiIiIiIiIiUkEqrIiIiIiIiIiIVJAKKyIiIiIiIiIiFaTCioiIiIiIiIhIBamwIiIiIiIiIiJSQebKDkBE/nocHEyVHYJUkHJnv5Q7+6Xc2Tflz34pd/ZLubNfd0LuKjJGk2EYxh8Qi4iIiIiIiIjIbU9LgUREREREREREKkiFFRERERERERGRClJhRURERERERESkglRYERERERERERGpIBVWREREREREREQqSIUVEREREREREZEKUmFFRERERERERKSCVFgREREREREREakgFVZERERERERERCpIhRURAWDdunUEBgbi7+/PkiVLKjscKcOFCxcIDg7m2LFjAGzZsoXu3bvj7+/P9OnTbcft3buXXr160a1bN+Li4igpKamskAWYM2cOQUFBBAUFMWXKFEC5syczZ84kMDCQoKAgUlNTAeXPnvznP/9h5MiRQNn5OXHiBM8++yxPPvkkQ4YMIT8/vzJDFiA8PJygoCBCQ0MJDQ3lu+++K/OzSll/j1I5Pv30U3r16kVAQABJSUmA3jPtxYoVK2x/c6GhoTz++OOMGzdO+SsPQ0TueD///LPh4+Nj/PLLL0Z+fr7RvXt348cff6zssORXdu3aZQQHBxtNmzY1srKyjIKCAqNTp07GTz/9ZBQXFxvPP/+8sWnTJsMwDCMoKMj49ttvDcMwjNjYWGPJkiWVGfodbfPmzUZYWJhRVFRkXLp0yYiIiDDWrVun3NmJbdu2Gc8884xRXFxsFBQUGD4+PsbevXuVPzuxZcsWo02bNsbLL79sGEbZ+Rk0aJCxfv16wzAMY86cOcaUKVMqJ2AxDMMwrFar0aFDB6O4uNi2r6zPKjf6t1D+fD/99JPRoUMH4+TJk8alS5eMvn37Gps2bdJ7ph06cOCA4efnZ5w4cUL5KwfNWBERtmzZQtu2bbn77rv529/+Rrdu3fjwww8rOyz5lXfeeYfExEQ8PDwAyMjI4H/+53+oV68eZrOZ7t278+GHH3L8+HEKCwtp3rw5AL169VI+K1GtWrUYOXIkzs7OODk58eCDD3LkyBHlzk60bt2aRYsWYTabyc3NxWKxcP78eeXPDpw9e5bp06czePBggDLzU1xczI4dO+jWrVup/VJ5Dh06BMDzzz9PSEgIb731VpmfVcr6t1Aqx8aNGwkMDKR27do4OTkxffp0qlatqvdMOzRmzBiGDRtGVlaW8lcOKqyICDk5OdSqVcu27eHhQXZ2diVGJNczYcIEWrZsadsuK2+/3l+rVi3lsxI1atTI9qHjyJEjpKWlYTKZlDs74uTkxKxZswgKCsLb21t/e3YiISGBYcOGcddddwHXvmdeyc8vv/yCq6srZrO51H6pPOfPn8fb25u5c+fy5ptvsmzZMk6cOFGuvzt9hqlcR48exWKxMHjwYEJDQ1m6dKneM+3Qli1bKCwsJCAgQPkrJxVWRASr1YrJZLJtG4ZRalv+msrKm/L51/Tjjz/y/PPP89JLL1GvXj3lzs7ExMSQnp7OyZMnOXLkiPL3F7dixQrq1KmDt7e3bV9Z+blenpS3ytWiRQumTJlCtWrVqFGjBr1792bWrFn6u7MDFouF9PR0Jk6cyPLly8nIyCArK0u5szPLli1jwIABgD5vlpe5sgMQkcpXu3Ztvv76a9v2qVOnbMtN5K+rdu3anDp1yrZ9JW+/3n/69Gnls5J98803xMTEMGrUKIKCgti+fbtyZycyMzO5dOkSXl5eVK1aFX9/fz788EMcHR1txyh/fz0ffPABp06dIjQ0lHPnznHx4kVMJtN181OjRg3y8vKwWCw4Ojrq38C/gK+//pri4mJbYcwwDO69995yvW8qf5WrZs2aeHt7U6NGDQC6du2q90w7c+nSJXbs2MHkyZMBfd4sL81YERHatWtHeno6Z86coaCggI8++oiOHTtWdljyG5o1a8bhw4dt027Xr19Px44duffee6lSpQrffPMNAGvWrFE+K9HJkyeJiopi6tSpBAUFAcqdPTl27Bjx8fFcunSJS5cu8cknn/DMM88of39xkMowsQAACIlJREFUqamprF+/njVr1hATE4Ovry+TJk26bn6cnJxo2bIlH3zwAQCrV69W3ipZXl4eU6ZMoaioiAsXLrBq1SqSk5Ov+1mlrPdTqRw+Pj589dVXnD9/HovFwpdffsmTTz6p90w7sn//fu6//37+9re/AfrMUl6asSIi3HPPPQwbNoyIiAiKi4vp3bs3jz76aGWHJb+hSpUqTJ48maFDh1JUVESnTp148sknAZg6dSrx8fFcuHCBpk2bEhERUcnR3rkWLFhAUVGR7f/8ADzzzDPKnZ3o1KkTGRkZ9OjRA0dHR/z9/QkKCqJGjRrKnx0qKz+JiYmMHDmS1157jTp16jBt2rRKjvTO5uPjw3fffUePHj2wWq3069ePxx9/vMzPKmW9n8qfr1mzZrzwwgv069eP4uJi2rdvT9++fXnggQf0nmknsrKyqF27tm1bnzfLx2QYhlHZQYiIiIiIiIiI2CMtBRIRERERERERqSAVVkREREREREREKkiFFRERERERERGRClJhRURERERERESkglRYERERERERERGpIBVWREREROycp6cn3bt3JzQ01PZfXFxchfvLyMggISHhFkZY2ieffEJSUtIf1n9ZsrKyGDp06J9+XhERub2ZKzsAEREREfn9Fi5cSI0aNW5JXwcPHiQ7O/uW9HU9Xbp0oUuXLn9Y/2U5ceIEhw8f/tPPKyIitzeTYRhGZQchIiIiIhXn6elJenr6dQsrmZmZTJgwgbNnz2KxWAgPD6d3795YrVYmTpzId999R35+PoZhkJSURN26denbty95eXn4+/vTo0cPxo8fz/r16wHYtm2bbXv27Nns2rWLnJwcPD09mTp1Kq+99hofffQRVquVe++9l8TERO65555SMa1cuZINGzbw+uuvEx4eTtOmTdm1axdnzpyhT58+nD59mu3bt1NQUMCMGTPw9PQkPDycJk2a8M033/DLL78QGhpKTEwMAB9//DFz5szBarXy97//ndjYWB599NFS8TVq1Ijdu3eTnZ1Nq1atWLBgAfPmzeOTTz6hsLCQgoICXn75Zfz8/Jg9ezbHjx/n1KlTHD9+nHvuuYfk5GQ8PDw4fPgwCQkJnDlzBgcHB4YMGUJgYCDZ2dmMGzeOkydPUlxcTFBQEIMHD/7jky8iIpVOM1ZEREREbgORkZE4OPzfKu///ve/VK9enZiYGKZMmULTpk3Jy8sjLCyMhg0bYhgGOTk5LF++HAcHB1JSUnjjjTeYN28eMTExbNiwgUmTJrFt27Ybnvf48eOsX78es9nM6tWrOXDgACtWrMBsNrN8+XLi4+N54403frOPZcuW8d1339GnTx9ee+01Ro4cycSJE3nrrbcYP348AIcPH+btt9+moKCAPn368Mgjj1C/fn0SExNZtmwZ9erVIz09nX/+8598+OGH18R3pSi0YMECjh8/zpYtW1i8eDEuLi68//77zJo1Cz8/PwC+/vprVq9ejaurK4MHD2bZsmXExMQwfPhwevfuzbPPPsvJkycJDw+nY8eOjBgxgueeew5fX1+KiooYOHAg9evXJzAw8PekVURE7IAKKyIiIiK3gestBTp48CA//fQTo0aNsu0rLCzkhx9+oF+/flSvXp1ly5aRlZXFtm3b+Pvf/37T523evDlm8+WPlJ999hm7d+/mqaeeAsBqtVJQUPCbfVwpZtSrVw+AJ554AoD69euzfft223FhYWE4OTnh5OTEk08+yVdffcUDDzxA27ZtbW29vb2pUaMGe/bsuSa+q917771MmTKFdevWcfToUdvMnStat26Nq6srAE2aNOHcuXOcPXuWffv28fTTTwNQp04dPv74Yy5evMiOHTs4d+4cM2fOBODixYvs27dPhRURkTuACisiIiIitymLxUK1atVYs2aNbd/p06epVq0amzZtYsKECQwYMIAuXbrwwAMPsHbt2mv6MJlMXL1yvLi4uNTzf/vb32yPrVYrL7zwAv369QPg0qVLnDt37jfjdHZ2LrXt5OR03eOuLpAYhoGDgwNWqxWTyVTqOMMwKCkpuSa+q33//ff885//5LnnnqN9+/a0atWKsWPH2p53cXGxPb5yDa6c/+rzHTp0iFq1amEYBsuWLaNq1aoAnDlzhipVqvzm2EVExP7pV4FEREREblMNGjTAxcXFVlg5efIkwcHB7Nmzh82bN+Pj40O/fv14+OGH+fjjj7FYLAA4OjraChM1atTgxIkT5ObmYhgG77//fpnn69ChA++++y4XLlwAYObMmbz00ku3bDxr167FarVy7tw50tLS8PX1xdvbm6+++oqsrCwA0tPTOXnyJM2aNbumvaOjo60wtGPHDh5++GEGDBhA69at+eSTT2zjL4urqytNmzZl9erVwOXr2bdvXwoLC2nevDmpqakAnD9/nr59+/LJJ5/csrGLiMhfl2asiIiIiNymnJ2defXVV5kwYQLz58+npKSEf/3rXzz++OPcfffd/O///i/du3enpKSE9u3b224627x5c+bOnUt0dDRz5szhmWee4amnnqJWrVp07tyZ3bt3X/d8Tz/9NNnZ2fTp0weTyUSdOnWYPHnyLRtPYWEhvXv3Jj8/n379+uHt7Q1AYmIi0dHRWCwWXFxcmDdvHtWqVbumfcOGDalSpQq9e/dm3rx5fPTRRwQEBGC1WvHx8eHcuXO2olBZXnnlFcaOHcvixYsxmUxMmDCBWrVqMXXqVMaPH0/37t25dOkSwcHBhISE3LKxi4jIX5d+FUhERERE/vLCw8N59tlnefLJJys7FBERkVK0FEhEREREREREpII0Y0VEREREREREpII0Y0VEREREREREpIJUWBERERERERERqSAVVkREREREREREKkiFFRERERERERGRClJhRURERERERESkglRYERERERERERGpoP8PB5EJ3gH9fysAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgb.plot_importance(model, figsize=(15, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python37\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make prediction on validation data.\n",
    "val_predictions = model.predict(val_X, num_iteration=model.best_iteration)\n",
    "# Get min and max values of the predictions and labels.\n",
    "min_val = max(max(val_predictions), max(val_Y))\n",
    "max_val = min(min(val_predictions), min(val_Y))\n",
    "\n",
    "# Create dataframe with validation predicitons and labels.\n",
    "val_df = pd.DataFrame({\"Label\":val_Y})\n",
    "val_df[\"Prediction\"] = val_predictions\n",
    "\n",
    "# Plot data\n",
    "sns.set(style=\"darkgrid\")\n",
    "sns.jointplot(y=\"Label\", x=\"Prediction\", data=val_df, kind=\"reg\", color=\"m\", height=10)\n",
    "plt.plot([min_val, max_val], [min_val, max_val], 'm--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrics\n",
    "\n",
    "Mean Squared Error is used as a primary metric. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model validation metrics\n",
      "MSE: 2.84\n",
      "RMSE: 1.68\n"
     ]
    }
   ],
   "source": [
    "val_predictions[val_predictions<0] = 0\n",
    "mse = mean_squared_error(val_predictions, val_Y)\n",
    "rmse = np.sqrt(mse)\n",
    "\n",
    "print('Model validation metrics')\n",
    "print('MSE: %.2f' % mse)\n",
    "print('RMSE: %.2f' % rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Submission Dataframe\n",
    "\n",
    "For each visitor id, we predict the **natural log** of the sum of all transactions **per user**.\n",
    "\n",
    "$$ y_{user} = \\sum_{i=1}^n transaction_{user_{i}} $$\n",
    "$$ target_{user} = ln(y_{user} + 1) $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "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>fullVisitorId</th>\n",
       "      <th>PredictedLogRevenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0003809040884259760</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0006948037456238434</td>\n",
       "      <td>0.157802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0007761730641712778</td>\n",
       "      <td>0.071913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0007975147778360613</td>\n",
       "      <td>0.046703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0009160697318817252</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0013342493653818027</td>\n",
       "      <td>0.115989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0025299019359143707</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0027496395366185860</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0031439844570426262</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0033624623761408314</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         fullVisitorId  PredictedLogRevenue\n",
       "0  0003809040884259760             0.000000\n",
       "1  0006948037456238434             0.157802\n",
       "2  0007761730641712778             0.071913\n",
       "3  0007975147778360613             0.046703\n",
       "4  0009160697318817252             0.000000\n",
       "5  0013342493653818027             0.115989\n",
       "6  0025299019359143707             0.000000\n",
       "7  0027496395366185860             0.000000\n",
       "8  0031439844570426262             0.000000\n",
       "9  0033624623761408314             0.000000"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(test, num_iteration=model.best_iteration)\n",
    "visitor_ids = test_df[\"fullVisitorId\"].values\n",
    "\n",
    "submission = pd.DataFrame({\"fullVisitorId\":visitor_ids})\n",
    "predictions[predictions<0] = 0\n",
    "submission[\"PredictedLogRevenue\"] = predictions\n",
    "submission = submission.groupby(\"fullVisitorId\")[\"PredictedLogRevenue\"].sum().reset_index()\n",
    "submission.columns = [\"fullVisitorId\", \"PredictedLogRevenue\"]\n",
    "submission[\"PredictedLogRevenue\"] = submission[\"PredictedLogRevenue\"]\n",
    "submission.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep Learning Approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
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    "name": "ipython",
    "version": 3
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