Workathon Prediction Challenge.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "625ecfab",
   "metadata": {},
   "source": [
    "### Problem Statement\n",
    "\n",
    "What is Workathon?\n",
    "\n",
    "Workation is the best way to work at a remote location with a recreational and rejuvenating vacation for the team. In other words, A workcation (sometimes written as workation) is a type of vacation where employees work remotely from a different location, such as a vacation rental, hotel, or even their own home.\n",
    "\n",
    "In this competition, one has to use the knowledge of machine learning, deep learning, and model building to **predict the price per person for your next workstation trip.** The data has more than 18000+ rows of different packages with the details like start location, hotel type, cost per person, destination, Itinerary, and many more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6eef249c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0cb7255c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Uniq Id</th>\n",
       "      <th>Package Name</th>\n",
       "      <th>Package Type</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Itinerary</th>\n",
       "      <th>Places Covered</th>\n",
       "      <th>Travel Date</th>\n",
       "      <th>Hotel Details</th>\n",
       "      <th>Start City</th>\n",
       "      <th>Airline</th>\n",
       "      <th>Flight Stops</th>\n",
       "      <th>Meals</th>\n",
       "      <th>Sightseeing Places Covered</th>\n",
       "      <th>Cancellation Rules</th>\n",
       "      <th>Per Person Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e788ab76d9d8cf1e6ed2f139645ca5d1</td>\n",
       "      <td>Best of Shimla and Manali Holiday from Delhi</td>\n",
       "      <td>Standard</td>\n",
       "      <td>New Delhi|Shimla|Manali|Chandigarh</td>\n",
       "      <td>1N New Delhi . 2N Shimla . 2N Manali . 1N Chan...</td>\n",
       "      <td>New Delhi|Shimla|Manali|Chandigarh</td>\n",
       "      <td>30-07-2021</td>\n",
       "      <td>Not Available</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Not Available</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Not Available</td>\n",
       "      <td>Not Available</td>\n",
       "      <td>11509.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>178f892630ce3e335a5a41d5d83937fd</td>\n",
       "      <td>Kashmir Valley vacation</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Srinagar|Pahalgam|Srinagar</td>\n",
       "      <td>1N Srinagar . 2N Pahalgam . 1N Srinagar</td>\n",
       "      <td>Srinagar|Pahalgam|Srinagar</td>\n",
       "      <td>08-12-2021</td>\n",
       "      <td>The Orchard Retreat &amp; Spa:4.6|WelcomHotel Pine...</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>IndiGo|IndiGo</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Dal Lake | Avantipura Ruins | Mughal Gardens ...</td>\n",
       "      <td>Cancellation any time after making the 1st pay...</td>\n",
       "      <td>22485.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>f060f2954840503cc2fdaf495357b7df</td>\n",
       "      <td>Might of Mewar- Udaipur and Chittorgarh</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Udaipur|Chittorgarh</td>\n",
       "      <td>2N Udaipur . 1N Chittorgarh</td>\n",
       "      <td>Udaipur|Chittorgarh</td>\n",
       "      <td>26-04-2021</td>\n",
       "      <td>The Ananta:4.4|juSTa Lake Nahargarh Palace:4</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>IndiGo</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Lake Pichola | Jag Mandir Palace | Saheliyon ...</td>\n",
       "      <td>Cancellation any time after making the 1st pay...</td>\n",
       "      <td>12421.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32a19a6c171e67448f2346da46c619dc</td>\n",
       "      <td>Colorful Kerala ( Romantic Getaway )</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Munnar|Kumarakom|Allepey|Kovalam and Poovar</td>\n",
       "      <td>2N Munnar . 1N Kumarakom . 1N Allepey . 2N Kov...</td>\n",
       "      <td>Munnar|Kumarakom|Allepey|Kovalam and Poovar</td>\n",
       "      <td>27-08-2021</td>\n",
       "      <td>Elixir Hills Suites Resort &amp; Spa-MMT Holidays ...</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>IndiGo</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Mattupetty Dam | Echo Point | Tata Tea Museum...</td>\n",
       "      <td>Cancellation any time after making the 1st pay...</td>\n",
       "      <td>35967.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>107b068aa0ca03bc6248966f594d105f</td>\n",
       "      <td>A Week In Bangkok &amp; Pattaya</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Pattaya|Bangkok</td>\n",
       "      <td>4N Pattaya . 3N Bangkok</td>\n",
       "      <td>Pattaya|Bangkok</td>\n",
       "      <td>12-12-2021</td>\n",
       "      <td>Dusit Thani Pattaya - MMT Special:4.5|Amari Wa...</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>Spicejet|Go Air</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Coral Island Tour with Indian Lunch, Join Spe...</td>\n",
       "      <td>Cancellation any time after making the 1st pay...</td>\n",
       "      <td>25584.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Uniq Id  \\\n",
       "0  e788ab76d9d8cf1e6ed2f139645ca5d1   \n",
       "1  178f892630ce3e335a5a41d5d83937fd   \n",
       "2  f060f2954840503cc2fdaf495357b7df   \n",
       "3  32a19a6c171e67448f2346da46c619dc   \n",
       "4  107b068aa0ca03bc6248966f594d105f   \n",
       "\n",
       "                                   Package Name Package Type  \\\n",
       "0  Best of Shimla and Manali Holiday from Delhi     Standard   \n",
       "1                       Kashmir Valley vacation      Premium   \n",
       "2       Might of Mewar- Udaipur and Chittorgarh       Luxury   \n",
       "3          Colorful Kerala ( Romantic Getaway )      Premium   \n",
       "4                   A Week In Bangkok & Pattaya      Premium   \n",
       "\n",
       "                                   Destination  \\\n",
       "0           New Delhi|Shimla|Manali|Chandigarh   \n",
       "1                   Srinagar|Pahalgam|Srinagar   \n",
       "2                          Udaipur|Chittorgarh   \n",
       "3  Munnar|Kumarakom|Allepey|Kovalam and Poovar   \n",
       "4                              Pattaya|Bangkok   \n",
       "\n",
       "                                           Itinerary  \\\n",
       "0  1N New Delhi . 2N Shimla . 2N Manali . 1N Chan...   \n",
       "1            1N Srinagar . 2N Pahalgam . 1N Srinagar   \n",
       "2                        2N Udaipur . 1N Chittorgarh   \n",
       "3  2N Munnar . 1N Kumarakom . 1N Allepey . 2N Kov...   \n",
       "4                            4N Pattaya . 3N Bangkok   \n",
       "\n",
       "                                Places Covered Travel Date  \\\n",
       "0           New Delhi|Shimla|Manali|Chandigarh  30-07-2021   \n",
       "1                   Srinagar|Pahalgam|Srinagar  08-12-2021   \n",
       "2                          Udaipur|Chittorgarh  26-04-2021   \n",
       "3  Munnar|Kumarakom|Allepey|Kovalam and Poovar  27-08-2021   \n",
       "4                              Pattaya|Bangkok  12-12-2021   \n",
       "\n",
       "                                       Hotel Details Start City  \\\n",
       "0                                      Not Available     Mumbai   \n",
       "1  The Orchard Retreat & Spa:4.6|WelcomHotel Pine...  New Delhi   \n",
       "2       The Ananta:4.4|juSTa Lake Nahargarh Palace:4  New Delhi   \n",
       "3  Elixir Hills Suites Resort & Spa-MMT Holidays ...  New Delhi   \n",
       "4  Dusit Thani Pattaya - MMT Special:4.5|Amari Wa...  New Delhi   \n",
       "\n",
       "           Airline  Flight Stops  Meals  \\\n",
       "0    Not Available             2      3   \n",
       "1    IndiGo|IndiGo             0      5   \n",
       "2           IndiGo             0      4   \n",
       "3           IndiGo             0      5   \n",
       "4  Spicejet|Go Air             0      5   \n",
       "\n",
       "                          Sightseeing Places Covered  \\\n",
       "0                                      Not Available   \n",
       "1   Dal Lake | Avantipura Ruins | Mughal Gardens ...   \n",
       "2   Lake Pichola | Jag Mandir Palace | Saheliyon ...   \n",
       "3   Mattupetty Dam | Echo Point | Tata Tea Museum...   \n",
       "4   Coral Island Tour with Indian Lunch, Join Spe...   \n",
       "\n",
       "                                  Cancellation Rules  Per Person Price  \n",
       "0                                      Not Available           11509.0  \n",
       "1  Cancellation any time after making the 1st pay...           22485.5  \n",
       "2  Cancellation any time after making the 1st pay...           12421.5  \n",
       "3  Cancellation any time after making the 1st pay...           35967.0  \n",
       "4  Cancellation any time after making the 1st pay...           25584.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the Dataset\n",
    "train = pd.read_csv(\"~/Downloads/Talent/train.csv\")\n",
    "test = pd.read_csv(\"~/Downloads/Talent/test.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b7732f4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((21000, 15), (9000, 14), (30000, 15))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine the Dataset\n",
    "combined = pd.concat([train, test], ignore_index = False)\n",
    "\n",
    "train.shape, test.shape, combined.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d19c1933",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the Date Component into Date, Month and Year\n",
    "combined[\"Travel Date\"] = pd.to_datetime(combined[\"Travel Date\"], \n",
    "                                         dayfirst = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "38d589bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Day\"] = combined[\"Travel Date\"].dt.day\n",
    "combined[\"Month\"] = combined[\"Travel Date\"].dt.month\n",
    "combined[\"Year\"] = combined[\"Travel Date\"].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e8c09b4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y = \"Month\", x  =\"Per Person Price\", \n",
    "            data = combined, orient = \"h\")\n",
    "plt.show()\n",
    "\n",
    "# Jan, April, may, July, Sept, Oct and Nov "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8bc154a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Jan, April, may, July, Sept, Oct and Nov\n",
    "\n",
    "combined[\"Season\"]=  combined.Month.apply(lambda x:\"Peak_Month\" \\\n",
    "                     if x in [1, 4, 5, 7, 9, 10, 11]\n",
    "                    else \"Normal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "30066785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Season', ylabel='Per Person Price'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = \"Season\", \n",
    "            y = \"Per Person Price\", \n",
    "            data = combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ad1bf1e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ITExEZmamk1uMO+6L/0vPvig9x6YUf9tghD4gCMI7dL1jdCAwkjuM25FauEBaK4SLoYOxOzZBEAQR3oT9jtGhBmd5D5ZZnbvdpoe/ULnF0DExMbodAHHX491330X//v0xaNAgZGRk8F6o/Px8yeNCOJeU2CXWv39/5OXl8Z6p/Px8DBgwwOmYOH9GRgbS09P5uoSuMjlXldhlJY5HnE8q3kC4sMipFTioLwlCP4TUTNC8efMwY8YMTJ48Ga+//rqiPGrOBAndXsH0aQnrmdc/GlFmz3mqalnM2Fa/oHxuPwssEd4PXKrrgOe21m8IGQq+MGE/CUlISMAbb7yBkSNHOg1WExISsGLFCknXF7cGSIr4+HiUlpY6ldWkSRMsXboUEydOxG+//eaytig+Ph4AeFWHuG5x/eLypNxlUvHKOc68gZxagYP6kiCUQzNBAvbv34933nkHd955p9ah6IooM2CJYBS9OJSmF7+UDLZCgYsXL2Lq1Kkus3XuXF9yAyCgfiAjVdbs2bP5/OLzJSUlTq4yKVeV2GUlLE/KXSYVr5zjzBvIqRU4qC8JQl/o+ukwjitXrmD48OF49913kZeXp3U4igimT8vVIabO7ahQ84Xt379f9tyvv/4qebygoEDS9eUtLMvi8OHDXuXh6m7VqpWky0qqPM5x1blzZ7fx+urCIqdW4KC+JAj9ERKDoEmTJuHBBx9E3759PQ6CwsUdxlFdB9UcYtUCq4DWC7ODRV1dHe8BU/tOMVf3nDlzJF1WcrAsi7lz57qdreLSeePCIqdW4KC+JAh9ovvbYQUFBSgqKsK8efMUpSd3GOEvnAfM06AiWHXv27dP0mUlR11dHS5fvuxx0OatC4ucWoGD+pIg9ImuZ4LOnDmDyZMnY9u2bYoXDxrZHcbh7BALShWSCOvSuy/M4XBgxIgRuHr1qtd5u3XrBofDgYMHD6o+G9StWzd0795d0mUlh9lsRoMGDTzOenrrwiKnVuCgviQIfaLrQVBRUREuXLiA1NRU/lhdXR12796NxYsXo6qqCmaz8yggXNxhHGpOoYeaL2z69Ol48cUXvcpjNpudPGBKZ2MCAVc35yITu6zkYBgGM2fOxIwZM9zG660Li5xagYP6kiD0ia5vh91///04dOgQiouL+Ve3bt0wfPhwFBcXuwyAtEZNdxhHdV394+9KXhxK04tf1eqNBwLC3Xffjdtvv13yXNOmTSWP22w2SdeXtzAMg6SkJK/yc3UD0i4rqfI4x1VycrLbeH11YZFTK3BQXxKE/gipfYIAoHfv3ujatasu9wlSC7n9b9QkFPYJAoCysjIMGTLEZT8grfcJSkhIAMuytE9QmEF9SRDKoX2CCEn0MG6trKwM6u7YgYLzgDEMg6ioKFitVuTk5PBeMPFxOdcX5wobNmwYYmNjwTAM0tLSkJiYiMmTJyMzMxMmk4k/lp2dDavVyufPzMyE1WpFbGwsX9fkyZP5BfziusX1i8uTcpdJxSvnOPMGcmoFDupLgtAXITcT5C00ExR8QmVWiCAIgggd1Pj+1vXC6FCDE5wCJBUNBbjr9eGHH2LDhg1o3bo1Tp8+jYiICDRo0AAPPvggduzYgb59+2Lz5s0A4HRsx44dmDRpEnr06MGXWVhYiLfeeos/f9ttt2HPnj3o2bMnvvnmGwDAtGnT+DyFhYVYuHChy3ExwnRcDOK61YRrZyBjCEaZgYDiIgjjouuZoNzcXMyePdvpWLNmzXD+/HnFZRjdHTb9wUhEeTGUra5lsWBz7e95IxDlg0OsvhxgweYaAKE7E+RpRo1bxyNcz8P9rHRNjhTc+h8AGDNmDK9RkFoXBNTfehSm42LQak1JMNa16HWtDMVFENpBa4IAdOrUCefOneNfhw4d0jokXREVAURFMF69ruf1Lp/zS8NGq4RQbyA+ptTdJQWXR5heXJYQcTouBq3cU8HwX+nVqUVxEYSx0f1XWUREBJo3b651GF5jRHeYkFDziEnhzi2mFKXuLjFr1qyRPC70hwHXfVPu6lbTPRUM/5VenVoUF0EYH90Pgo4fP46WLVvCYrHg7rvvxty5c3HTTTfJpg83d1hNHWBRyR0mrpfDqB4xpdTV1WHu3LlePSUnN1NUV1eHRYsW8ZqYxYsXu90AUU33VDD8V3p1alFcBBEe6Pp22N133433338fW7duxbvvvovz58/j3nvvdbo1IIbcYYTasCyLy5cvB8w1ZrfbcebMGd435Q413VPB8F/p1alFcRFEeKDrmaCBAwfyP3fu3Bk9evTAzTffjJUrVzr5wYSEmzssUqNNsyNDyCMmhcPhwMiRI3HlyhW/y2IYBnFxcSgvLw/Inkmpqan8ZzY1NdXtQEhN91Qw/Fd6dWpRXAQRHuh6JkhMgwYN0LlzZxw/flw2jcViQcOGDZ1eWsC5tYLxEg44tJr6lvKIhdKrQYMGeOaZZwLSF2azGTNnzoTJpPzXyWQySaY3m83Izs4GwzC8V8qdHkZN95RcXf7EEIwyAwHFRRDhQUgNgqqqqnD06FG0aNFC61Ak0cQdVlv/2Ls3r+t5vcvn/FKleUHFnVtMKUrdXWIyMzNhs9lcjgv9YcB135S7utV0TwXDf6VXpxbFRRDGR9f7BD311FN46KGH0LZtW1y4cAF5eXnYtWsXDh06hHbt2ikqg3aMDj6huk8QUO8WGzp0qOR6HvGeQADcOrlonyB9lRkIKC6C0I6w3yfop59+QmZmJjp27IiHH34YUVFR2Lt3r+IBEKEOlZWVuvaHucNqtcJms8FkMqFt27YA6rdl4JxjnH+LW2jvzskl5e5KS0vjnWJiT1h0dDRycnLc+sO4coXpuLi0ck8Fw3+lV6cWxUUQxkbXM0GBwIgzQSzL4tKlS7p6ND2UZ4MIgiAI/RH2M0GhAsuyqhrVGYahv/z8wNP1KiwsxIgRI1BYWIj8/HwMGDAAeXl5yMjIQEZGBgoLC1FYWIhBgwahX79+yMvLc8rDlZGRkYH09HSkp6cjIyMDeXl56N+/P/r164f8/HzJ+uTikMLXfP6UHWzUqler9hEEoS90PxN09uxZTJ8+Hf/+979RUVGBDh06YPny5UhNTVWUX42RpFrOMLk6J/3ZjEgFmx3U1LJ46xPH73lMiPTRG3a9POCtT+r3KwmlmSB310u41iI+Ph6lpaUuA6X4+Hg4HA5cunSJP9a4cWOUlpaiSZMmWLp0KSZMmOB2PysAWL9+PSwWi+TaDk9rPuTOK1kr4mvZwUatemk9DUGEBmE/E1RaWoo//OEPiIyMxL///W989913ePXVV9GoUSOtQ9MVkQr9YcJBT6Rf3jCuPA0bHSSETqaSkhLJmaKSkhKnARBQ/1kF6j1Os2fP9jgAAoDZs2fLOqA8uaF8zedP2cFGrXrJu0UQBIeuv8YWLFiANm3a8E/SAMCNN96oXUAKUMujRf4w35GLVexk8gWWZXH48GFFaQ8fPozvvvvOxQEl9pCJ3VBy7ihP+aTaqLTsYHup1KqXvFsEQQjR9SDok08+Qf/+/ZGRkYFdu3ahVatWePLJJ/H444/L5tHCHaaFM0xIrUb+sFoD+MMqKysRExMj62QKNuLH6B0OB+bOneuSjotvzpw5knF6ysedc+edkis72F4qtXxY5N0iCEKMrm+H/fjjj1i6dCluvfVWbN26FRMmTEBOTg7ef/992TzkDiN8Qc7JpDYOhwOXL1+WdUPt27dPMk5P+YQuMm/LDraXSi0fFnm3CIIQo+uZIIfDgW7duvF/xSYnJ+PIkSNYunQpHnvsMck8WrjD1HKGCRH6wyI08odFhKg/TNh3XMxyTia1MZlMuOGGG3DlyhVJN1T37t0l4/SUT+gik/NOyZUdbC+VWj4s8m4RBCFG1zNBLVq0wB133OF07Pbbb8fp06dl82jtDlPLoyUccNTWMYpUFzUCZUaNX8qM+ldtXWj6w6QGa1q5l8T+MJPJhJkzZ8q6oUwmk2ScnvIJXWTelh3svlHLh0XeLYIgxOh6JugPf/gDjh075nTsv//9r+52jOacYdzPasM9pu5dHu1mO7RG7npxTqY1a9b4vDiaYRh06tRJ0eLopKQk3HnnnXx9Yg+Z+DjnhhLHqTSfu7yeyg62l0qterVqH0EQ+kTXM0FTp07F3r17MXfuXPzwww9YvXo13nnnHUyaNEnr0JxgGIafZaC/JvWPu+tls9mQkJAAoH4/IKnrGR8f77JNQ+PGjQHU+79mzZrFl+EuhlmzZjnVl5CQwN+mkzsuFac3+fwpO9ioVa9W7SMIQn/ofrPETz/9FM899xyOHz+O9u3bY9q0aW6fDhNjVG1GZWWl14+lOxwOlJeXAwDi4uJcbsX4g9VqDWh5WlJYWIi33noLkyZNwrFjx1BQUICePXvim2++AQB+zdmcOXNQXV2NtLQ03H///XyeHj16oLCwEAsXLkRNTQ0AIDIyEl26dMGXX34JlmUxbNgwjB492qW+Hj16SMYhPO7pvKd8/pQdbNSqV6v2EQShHDW+v3U/CPIXIw6C9GaRB0Jrx2iCIAhC/4T9jtGhgtruMCKwCK+f0CnF+b+E3q/09HQXP1hGRgby8/MxYsQI5OfnOznG3CHlKBM7xeTKknJfuUsfTLzxcMnFHQyPl1SfehtbMPMFuyyCIDyj+5mgG2+8EadOnXI5/uSTT+Ktt97ymN+I7jBhfaP+olxfUVPLYuVH+D0fAuIOW/lR/ccnlGeChP2ZkJCAixcvIiEhASzLoqSkxCV9fHw8li1b5uQHYxiGX2jL/UolJCRgxYoVkovl5RxlJpMJa9euhcViwZgxY/jyhWVJua8AyKYPJt54uOTiDobHq6ysDEOHDoXD4eD71Gq1BqQdgcgX7LIIwgjQTBCA/fv349y5c/xr+/btAICMjAyNI9MHkRH1gxmlr+v5lOeRf2nY8CDBDXouXrwoOQDi0oj9YEINA4c7L5Wco8zhcLg4xcRlSbmv3KUPJt54uDzFHciYc3Nz+b2AuD51h69xBDJ+cpoRhPro/musadOmTu/nz5+Pm2++Gb169dIoIveo4dDSgzfset31hJI7TIxrf3pGqR+soKDAxUvlyVHGOcWkypLygxUUFEhu8ChVdyDxxsMllbagoAAsywbc42W323HkyBGnY4cPH4bdbkdKSopf7QhEvmCXRRCEcnQ/CBJSXV2NDz/8ENOmTZN9FD3c3GG1dUCUBt4wrm4OesxYmrq6OicvlVJHmdSgpq6uDnPmzHEZPMmpPsR1BxJvPFxyaaXi9tfjJedQA4C5c+di3bp1Tk8x+uoTC6SHjJxmBKEdur8dJuTjjz/GpUuX+EeLpSB3GKE3hF4qfx1l5eXlXmk9guXE8sbD5U2b/fV47du3T/YPn8uXL2Pfvn1Ox3z1iQXSQ0ZOM4LQjpCaCVq+fDkGDhzodnfXcHCH6cEbJq47lNxhYioqKmCz2YJWfrdu3fjPoL+Osri4OFy5ckXxbTth3YHEGw+XN2321+PVvXt3NGzYUHIg1LBhQ3Tv3t3ndgQiX7DLIgjCO0JmJujUqVPYsWMHxo8f7zZdOLjDxN6wmt+9YEpeHN7kkXuFqjtM6hUszGazk5dKqadKauNJs9mM559/3uWc2WyWLE9cdyDxxsMll9ZsNru0xV+Pl8lkwowZMyTPSfWdrz6xQHrIyGlGENoRMjNBK1asQGJiIh588EGtQ3FBS3cY94i69/kAQNe7I6gGd/1WrVqF9evXK5plSUpKUrQ42mazucxcenKUcU6x1atXu5Ql5Qez2WxgWVYyfTCdWN54uKTScnEH2uOVkpKCTp06OS2OTkpKQteuXf1uRyDyBbssgiCUExIzQQ6HAytWrMCoUaMQEaG/cRu5w0Ib7vqNGDHCySkVHx8vmT4+Pt7FD8bNMAivvzsvldhfxeUzmUwuTjFxWVLuK3fpg4k3Hi5PcQcy5tzcXP6acH3qDl/jCGT85DQjCPXR34hCgh07duD06dMYO3as1qFoCucMA+DVHiIsy6KqqoqfdWAYBhaLJSADNq6cUF0PJCQ6OhrZ2dm8UwoAFi5cCAC89ysmJgaTJ0+G1WpFTk4Of/7BBx/Ejh070LdvX2zevBkAkJOTI9sv4ro4R5nNZuM39ROWLyxLnJc7Lpc+mMjF4k1apfm9wWq1wmazufRpINoRiHzBLosgCGXofsdofzGSO4ycYQRBEES4QDtG6xjyhRkDsTcsIyMD6enpeOihh9C/f3/k5eVhxIgRvDusX79+Li4qT84uzgcl9IoJXWNCV5S37ihP6cUuNCVlq+n3CiT+tC+c0GP79RgTER7oeiaotrYWubm5WLVqFc6fP48WLVpg9OjRkk95yBGskaTavjBxnY8+DChdHlVbC/xzI7zOp6S8UJ8JEvZpfHy8rCpDzPr162G1WlFZWenW2SX0QQm9YtzPJpMJDocDTZo0wdKlSzFx4kTF7ihPrinheW6tycWLF92WrabfK5Ao9W6Fu59Lj+3XY0yEPgj7maAFCxbg7bffxuLFi3H06FG8/PLLeOWVV7Bo0SKtQ9OciAggIoJR+PI1n+fyjITSARAA3kXlydklPC/8e0PoCuPyCX1kStxRnlxT4vNKylbT7xVIlMYYCm0JJnpsvx5jIsIHXX+dFRYWYvDgwfxj8TfeeCPWrFmDAwcOaByZM2p5s5wdV6pUKYmw7lB2hgG+x3/48GFs27YNBQUFLuc4ZxcAt44wISzLOj1y78kd5ck15c5PJle2mn6vQKLUuxXufi49tl+PMRHhha4HQT179sTbb7+N//73v+jQoQO++eYb7NmzB6+//rpsHrXcYVr6wgCgrg6I1MgZVkfOMADAq6++KjnI4Jxd/t5plnNHeXJNzZkzx6OfTFy2mn6vQKLUuxXufi49tl+PMRHhh65vh02fPh2ZmZm47bbbEBkZieTkZEyZMgWZmZmyecgdRqiFu0FOUVER7Ha7z44wQN4d5ck1tW/fPo+uLnHZavq9AolS71a4+7n02H49xkSEH7qeCVq7di0+/PBDrF69Gp06dUJxcTGmTJmCli1bYtSoUZJ51HKHqe0LA5ydYWYNnWFmgzjDAP+8YcKFzmK6desGh8OB4uJinxxhgLw7ypNrqnv37h5dXeKy1fR7BRKl3q1w93Ppsf16jIkIP3Q9CHr66afx7LPP8l9SnTt3xqlTpzBv3jzZQZDFYoHFYlEzTN6bpSZ1dQDDKLvdUlsr/tm/2zTCP9y0aLteePrpp/GPf/zDZdDAObtYlvXounOHJ6eUuGzuuMlkkjzvrmy5Ms1mM1iWdWqjnpxWnvrCU/v01JZgosf26zEmIvzQ9SDo2rVrkrJIX/+yDiRa+sKA64+oq5XPqAi9YevWrVOcLykpCX379sVPP/3k1tnlzhEmhGEY3nelxB3lyTXlzk8mV7aafq9AotS7Fe5+Lj22X48xEeGFrtcEPfTQQ5gzZw42b96MkydP4qOPPsLChQvxl7/8RevQyBdmEKS8YUrycC4qT84u4XmxWR247hxLSEhw8pEpcUd5ck2JzyspW02/VyBRGmMotCWY6LH9eoyJCB90vVlieXk5XnjhBXz00Ue4cOECWrZsiczMTLz44ouIiopSVIaRtBmcO4xzgXmTj0vP3SoUvvdnECeeBYuOjg7ZQWFhYSEWLlyImpoa1NXVobq6Gn/84x/x/fff47bbbsOXX34JlmUxbNgwjB492iUfAEybNg09evRwKfett95y8ooJXWM7duzApEmT0KNHDz4t915JzO7SC88DUFS2VJnexqUFSmMMhbYEEz22X48xEdqjxve3rgdBgcBIgyAOPTrEOEJ9B2l35OfnY82aNYiJicH06dP5QcuCBQtQUVGBzMxMl8ERN/gRDnTE56X+41cyEPFUvqc65FCSR6puQNkgy5/YCH1A145QAxoEBYBgdqLQ6q7mDAgNgoKL1HUtKyvDkCFD+PU18fHxWLZsGZ544gl+p2mGYbBu3Tpep8GpAIRqDE4J4E4VoERdIVRsSJUvV46n9WtK8ki1TamWw5t6CH1C145QCzUGQbpeGA243hJLTk7GG2+8gbvuukvr0FBZWam6P0zMwAzA7OVVrK0Ftqyv/3lAhv8usbpa4N/r/StDT0hd19zcXKcFxiUlJZg9e7aTaoNlWcyePRsLFy50UgEI1Rhr167FqFGjJFUB3BOPUudYlnU6JlRsSJUvV47cU5UcSvLItY1DSV2+xEboA7p2hJHQ/SBo/PjxOHz4MD744AO0bNkSH374Ifr27YvvvvtOV9uqq6mPENZlMgMRkd7OQF3/Mo+I8CW/qDTB4CDUNRqAaxvsdjuOHDnikk6ouRAe27Ztm6SyglMCdO7cWVYVAEBSXcH9zP0rVbencjzpCJQoDNzpOKTi8EX5QegXunaE0dD17bCKigrExcVh06ZNvD8MALp27YpBgwYhLy/PJY+UNqNNmzZBmU4rLS3V/EmGARlAdIx3g5jaGhafrqn/eVCm/4OgygqWn1kyGmvWrMFf//pXlJeXK85jMpnAMIzk7ssMwyAuLg5Xr151Om82m9GlSxcwDIPi4mKfd5o2m83o2rUrWJbFN99841JH165dJXUELMtixowZLnUL8wCQTOMuDinlh6d6QnVhvdGha0eoTdhb5Gtra1FXV+dyvzkmJgZ79uyRzEPaDCKQHDx40KsBEFB/i0hukMCyLC5fviypCrDb7YrVFXJwygEpZYc7HYEShUEg1BqkSghd6NoRRkTXt8Pi4uLQo0cPvPTSS7j99tvRrFkzrFmzBl9//TVuvfVWyTxqaTMAbdQZgH70GRxG0mgAzv3bo0cPxMXFeT0TBEByU09uJujKlSsuqoCuXbsCgCJ1hRycckBK2eFOR6BUYeCvWoNUCaELXTvCiOh6JggAPvjgA7Asi1atWsFiseDNN9/EsGHDYJb59rdYLGjYsKHTSw04fYQaL+EgQw+zz8IY1OwHNfrXZDJh5syZXvXHU089JXtbwGw2Y+bMmZIqjOzsbEldgNlslv28i+GUA9nZ2bK6DanYPCk6GIbxSmegpDylsRH6gK4dYUR0Pwi6+eabsWvXLly5cgVnzpzBvn37UFNTg/bt22sdmi6oq61f4+PVS+QS8zq/6FVXKx+fEUhJSUGnTp1cjiclJUke69u3L4YOHSr5ZTF06FAkJyc7nReqAjiNgPCczWZzOZaUlCRbvlw5nnQESvKI00ihVPnhTWyEPqBrRxgNXS+MlqK0tBTt27fHyy+/jL/+9a8e09M+QeoSLvsEJSQk4O2336Z9gmifoLCDrh2hFmG/MBoAtm7dii1btuDEiRPYvn07+vTpg44dO2LMmDFah0b+MIMidV2tVisyMzPBMAxiY2ORk5MDq9WKyZMnIzY2FgzDIDMzE1arFUD94Ck7OxuJiYmw2WxITExEdnY2/2UhPC88LndOfMxqtbot31MdcijJI9W2nJwc5OTkKK7Ll9gIfUDXjjASup8JWrduHZ577jn89NNPiI+PxyOPPII5c+bwXzaeMKI2w+FwoKyszKs8Qn9YVFQUqqurAfjvDhOXEcruMIIgCEI/kDYjABhxEKS322FGuAUmFo0uWLAA165d42d4gPqdcuPj4/Hbb78hIiICL7zwgqQLjPNpeZKjBlqWqkc8yWUJItCE4u8JIQ0NggKAWoMgNdcH0SAoMHDXrKqqChMnTsTFixeRkJAAh8OB0tJSPh3DMJI7JDdq1Ajvv/++yxofbp2McK2OcA2P1HsjrqGprKzEmDFjeMVCQkICVqxYofu4idAlFH9PCHkM7w7bvXs3XnnlFRQVFeHcuXP46KOPkJ6ezp/nXEzvvPMOSktLcffdd+Ott96SfFJHa7TyiN2bqcwdVlcDfFXwex4bYI70r966WuCrNf6VoTXCa8YNWoUOLA65vxMuXbok6QLj9lARerWEri+p90Z0bQljBkInbiJ0CcXfE0JbNB0EXb16FV26dMGYMWPwyCOPuJx/+eWXsXDhQuTn56NDhw7Iy8vDAw88gGPHjiEuLk6DiJURbH+W2B1mVqS9uP5Fbo5UmsdNaQbwhQnj9nVCdNWqVS4uMDFi15fUe6O5ts6ePcs7z4QUFBToOm4idAnF3xNCezQdBA0cOBADBw6UPMeyLF5//XXMnDkTDz/8MABg5cqVaNasGVavXo0nnnhCMp+UO0wNhF+oavrEHLUAolSrzrne39Han6Y1L730kt9lsCyLxYsXS7q2Fi9erDi9HmBZFosWLZLcVbqurk63cROhSyj+nhD6QLePyJ84cQLnz59Hv379+GMWiwW9evXCV199JZuP3GGE2ohlqL5gJNfWmTNnYLfbZc/rNW4idAnF3xNCH+jWHXb+/HkAQLNmzZyON2vWDKdOnZLNp6Y7TIiaHjGh28qk0RUU1huqvrCKigrYbDa/y2nQoAEqKip89n0BxnJttWnTBikpKbIDoW7duukybiJ0CcXfE0If6HYQxCGewmRZ1u20psVigcViCXZYbuH8WWqg1QyvlC8sXHnxxRcxY8YMv8rw5GUaP368ovR6gPOgjRs3zmVgaDabdRs3EbqE4u8JoQ90ezusefPmAK7PCHFcuHDBZXYonKmrBepqWAUvQZ4apXncvAzmC/P1P8nhw4e7uMCkyha6vqTeG8211apVK8lZNpvNpuu4idAlFH9PCO3R7UxQ+/bt0bx5c2zfvh3JyckAgOrqauzatQsLFizQODpXoqOjsWnTJv5ntfDlMfWvXB/aCUu4a+bPPkHcbUmbzYZt27ZJ7hOUkJCAWbNm8fsCSb33tLhcWL6S9HrAZrNh69atTvsEhULcROgSir8nhLZoOhN05coVFBcXo7i4GED9Yuji4mKcPn0aDMNgypQpmDt3Lj766CMcPnwYo0ePRmxsLIYNG6Zl2JKQRyz04K5Zo0aNeO9VTk4OpkyZgtjYWD5NZmYmhg0bBpPJhCZNmgAAIiIiMHXqVEkXGOfTEjq9hK4vqfdGdG1FR0fzjjWr1YqcnJyQiJsIXULx94TQFk13jN65cyf69OnjcnzUqFHIz8/nN0tctmyZ02aJSUlJiuswmjaD2+XY2715HA4HysvLAQBxcXEwmQI3/rVarQEtjyAIgiBImxEAjDYI0psyAwhdbYYcnO+qpqZ+IVVkZCSmTZuGY8eOYc2aNYiJiUF6erqTF0ycX+ghE/rEPKUn15GxCZdrHS7tNCp6uX40CAoARnOH0SAo8AivHQCMHTvWRZ/RuHFjyXVCYj+R0F2UkJAAoH77fqFHTC49uY6MTbhc63Bpp1HR0/ULe3fYxo0bsWzZMhQVFeHixYs4ePAgunbtqlm87tDCHXbLY4zifYIcNSx++OD3fCMBk5/aDEct8MP7xhg/C6/dkCFDJP1hwgEQcF2zIfYTid1FHEKPmLv05DoyLuFyrcOlnUYl3K6frt1hV69exR/+8AdkZGTg8ccf1yBC3wimS0tYNmNmYYr0Zi1O/Re3KZLxexDEstf3fwlVdxiHMP5//vOfXuUV+okAuHWIeUpPriPjEi5eq3Bpp1EJx+unW3cYAIwcORIAcPLkScVlhpM7jNXIG8bX/TtGegzVl12fOVeW0lugnM9IarBEriPjES5eq3Bpp1EJ1+tnuEd6yB1GqE1dXR3sdruku0gufVFREex2O7mOwoBw8VqFSzuNSrheP91ulugrRneHCb1hjIZXjzGAO4xD2Ke+YDab+bVqYneRXPrk5GQ4HA4UFxeT68jghIvXKlzaaVTC9foZbhAUTu4wLacm6+uuv51jJHcY9xSXN3CuLJZlXdxFcumzsrIk05PryHiEi9cqXNppVML1+hnudlg44aitf+pL6YvP50Ue2ZfB3GEcjz76qFfphX4isbvI2/TkOjIu4XKtw6WdRiUcr5/hZoK0Qgt3mK+PqNc/Km+Mx9sDgfDaAcB//vMfl8fk4+PjUVJSwr/n9gkS+4nE7iLAeZ8gT+mNtMiccCZcrnW4tNOohNv10607DABKSkpQXFyM7777DgBw7NgxFBcXu5jl9YBa7jA97m2px5i8QXjtYmJieN9VbGwsYmNjYbVaMXnyZAwbNgwMwyA2NhaZmZmSfiKhuygnJ4d3kgk9YnLpyXVkbMLlWodLO41KuF0/XbvD8vPzMWbMGJfzs2bNQm5urqI6SJsRfEJ9x2iCIAhCf6jx/a3pTFDv3r3BsqzLKz8/HwAwevRoyfNKB0AE4QuFhYVIT09H//79+c+i3Pm8vDyMGDEC+fn5yMjIQHp6OtLT05GRkYHCwkIUFhZixIgRyMvLw4ABA5zKKywsREZGBp9WSH5+vkt6uVhHjBjB5xe/F6cV1ucurZZwceXn5/scn17bRhCEviB3WABQyxsGOM8EmcY0AbzY+ZmtYcGu+A0AwIxpAsafXaNrWDh+LytUZ4KkrltlZSVGjx7Nr/9hGAbr1q2D1WoFAJfzHNwaISHx8fFgGMZpfZHJZMLatWthsVgwZswY/lxCQgJWrFiB6OholJWVYejQoXA4HHx6rn4hYsfP0qVLMXHiREnnT2VlpVN9wti09gMJEbZJzrfmTRl6ahtBEN5h+Jmg3bt346GHHkLLli3BMAw+/vhj/lxNTQ2mT5+Ozp07o0GDBmjZsiUee+wx/Pzzz9oFLAPnnho8eLC6ColIBoyXLw5v87mU46d2Qw9IXbeCggKnAQ7Lspg9ezb/XnxemE5MSUmJywJrh8OB2bNnO/l5gOuOHgDIzc3lH9Pn0kshdvzMnj3bxfkjlVYcmzitlgjjFPvWfClDT20jCEJ/6NYddu3aNdjtdrzwwgvo0qULSktLMWXKFPz5z3/GgQMHNIrYM8EeBAnLZ1kWDLQZjAi/9EPVHSaO++zZsygoKHBJd/jwYdjtdjRr1gxr1qzxu97Dhw/j8OHDLscLCgrQokULHDlyRLL+lJQUp1jFjh9hmWJHmVS7pNJq6QcSt4nDm/jC0X1EEITv6OZ2GMMwLhZ5Mfv370f37t1x6tQptG3bVjKNlDusTZs2QZ1OKy0t1eQxQmZME5hilU/msTUsHO/8CgAw/bWpX7fDHNcc/K01I1BQUIBXXnkFRUVFkudvuOEG3HrrrTh48GBQ4zCbzZLqjYYNG2LdunUwmUxgWRYzZsxAcXGxW00Ht5O1w+HwGDeXVis/ENcmuR23lcQn1y9at40gCN8w/O0wbykrKwPDMGjUqJFsGnKHEb5w9uxZ2QEQUL+dQ7AHQABkBzWXL1/Gvn37AMg7fqTKKioqUhS31n4grk1yu3UriS9c3UcEQfhOyGyWWFlZiWeffRbDhg1zOyLUwh2mljcMEHmutLx6BnCHCfvy5ptvRmpqqq5ngrp37w5A3vEjVVZycjLq6uoUzQRp6Qfi2mS32yXXVymJL1zdRwRB+E5IDIJqampgs9ngcDiwZMkSt2m1doep6dHS2h3GfVUZwR3G+XHGjRsnObB48cUX0bRpU4wdOzZom0OazWZMmzYNr7zyisu5559/HiaTySlWT54yoaNMrl3itFp9poRtkhoEKokvXN1HBEH4ju5vh9XU1GDIkCE4ceIEtm/fbogNDwNGDVv/2LsXLw5v87mUU6OLpWQBpVWrVrDZbC7Hk5KS0LVrV7Rq1QqZmZl+15OUlIRhw4a5HLfZbHjggQfQqVMnyfrFsYodP0lJSZLOH7l2cejFDyTnXvMmvnB0HxEE4Tu6XhjNDYCOHz+OL774Ak2bNvW6XCPvE6QXaJ8gafu8nvcJEnrN9LSXDu0TRBAEh+EXRrtzh9XW1uLRRx/FgQMHsGrVKtTV1eH8+fM4f/48qqurtQzbBbW8YXqlsrIyJP1hUtctOjoakydPRmxsLBiGQWZmptMARHw+LS2Nd4NJ+cY4d1haWhpMJhOfLjo6mneUWa1W5OTk8F/UVqsVNpvNKb0UYseP1WqVdf5I1cfFpic/kLBNcr41b8rQU9sIgtAfunWH5ebmon379pL5vvjiC/Tu3VtRHUZzh7Esi0uXLunO7Buqs0EEQRCEPjH8TJA7d9iNN94oeY5lWcUDICPCMAz9ZasCeXl56NevH/r164f09HQnB5XQS8X5uMS+MDknmJTTSokrS1ynOy+WN2mV4o2jTEl+f+omQgu6foSe0c2aoGBhtDVBgPO6IPPoZCDSmw0T6+DILwYAmEZ3BRNp9j2QGgfq8usfvQ61mSB31+zChQsYMWKEU/r4+HheZsqtN0lISADLsk7rg+Lj4wGAPyZc6yO1VkVYntwaGGE+T2t5vEmrFG/WHinJT+t7wge6foQ/GH4myJ07DKh3KN12221o0KABGjdujL59++Lrr7/WJlg3aOYOA4BIE5hIs1cvDm/zuZTjxeBLb7i7ZlOmTHFJX1JSgrVr17p4qcQLpEtKSpyOCd1VUk4rJa4scT53Xixv0irFG0eZkvzkAQsf6PoReke37jAA6NChAxYvXoybbroJFRUVeO2119CvXz/88MMPPj0ppgZqDIJc/WHaEMr+MLl4t23bht9+k9aBrF69mtdWeENBQQE6d+7s4rQqKCjgb/EKEXu/pHxa4nStWrWSdW9JpVWKN44yqXL9cXmRByy0oetHhAK6uR2mxB3GTY3t2LED999/v2SacHKHAfW3tEyxUYrTszV1qHu3fldk8+Opft0Oc1yr5m+thTJr165F48aNUVdXh0GDBnnUUfhCXFwcrl27prhsk8mErl27gmEYt44wzos1Z84czJw5U1FapQ4tbx1l4nL9cXmRByy0oetHBALD3w7zhurqarzzzjuwWq3o0qWLbDpyhxG+8tlnnwVlAAQA5eXlXpXtcDhgt9s9OsI4L9a+ffsUp1Xq0PLWUSYu1x+XF3nAQhu6fkSooHttxqeffgqbzYZr166hRYsW2L59O5o0aSKb3ujuMEDsD9NwHCuoO9T8YcI+5OL+05/+hCVLlgRtJujq1atu1RVCTCYTkpOTAcCtI4zzYnXv3t2jT8xbh5a3jjJxuf64vMgDFtrQ9SNCBd3PBPXp0wfFxcX46quvMGDAAAwZMgQXLlyQTW+xWNCwYUOnl5pwHq1gvoSDDa39YRxqtDtYfchhNpsxdepUt+01m72/fWg2m/H888+7XCuz2cz7wMSYTCZkZ2cr9mWZTCbFaZV+ZpSml0vn7fFA5SW0h64fESrofhDUoEED3HLLLbjnnnuwfPlyREREYPny5VqHpR9qHGBr6rx6cXibz6WcGmWzGqFEv379ZGcahw0bJum28oTNZkNycrKL08pms8Fms7l1Zcn5tMTpAHn3llRapXjjKFOanzxg4QFdPyIU0P3tMDEsyzotfNYD0dHR2LRpE/+zmnD79PiCERY1+4q7a/b6669L7hPE3T7jniBTuk8Ql89msznlFZfH7RMkPCeVD6h/3Ficztu0ShGXOWvWLH6fICXlyrXbl7r1tlM64R66foTe0a077OrVq5gxYwb27t2LU6dOwW63Y/z48fjpp5+QkZGhZdguhLs7LBRxd8041xdHbGwsJk+ejOjoaCcvVU5ODiZPnsw7wzhfGHdM7ASTclopcWWJ63Tn/PImrVK8cZQpyU8esPCBrh+hd3TrDnv77bcxbNgwfP311/xfEXfddReef/553HXXXYrrMJo7DNCPP0y4GFqNnbIJgiCI8MHwj8i7c4dFR0dj48aNOHv2LKqqqvDzzz9j06ZNXg2AjIpe/GHCxdChNgDKz89H//79nbxgQscR5w4bNGiQIkeXHEo9X3LeMCkPmb8eL19jDTTBagdBEIRSNJ0J2r17N1555RUUFRXh3LlzbjdLfOKJJ/DOO+/gtddek9QayKHmTJCaDjGhPyxiVG8gwvNTS2xNHere3wkAMD/W27eNEmvrULuyvoxQ8oUJr01VVRWGDh3K72QbHx+PZcuW8etcGjdujNLSUj5v48aNsXLlSllHl5wTyVMa4Xkpb1hlZSXGjBnDawcSEhLw9ttvO3m7vPV4yaG248lfHxlBEMbH8DNBnDZj8eLFbtN9/PHH+Prrr3X/VIFmDrEIM5jICAUvsTdMSR7nl5LBlh4RXptZs2Y5qSVKSkqcfFjCARD33pOjS8qJ5CmNJ2+Y8Dx3Tuzt8tbjJYfajid/fWQEQRCBQNOnwwYOHIiBAwe6TXP27FlkZWVh69atePDBB1WKzH+CPQjSyh8Wqr4wYaxHjx51OS/0YUmxevVqWUeXlBPJUxo5zxeXjnONuYvTW4+XHGo7nvz1kREEQQQKXT8i73A4MHLkSDz99NPo1KmTojxS7jC1EH7RqrpoudYBKNeH+V/X72i9MFtNWJbFokWLMHfuXMmZS5ZlsXjxYsydOxcA3KaZM2cOFi9eLCtiZVkWc+fO9WnnamEcSv1gntoTyNu6cvWpVT9BEIQQXW+WuGDBAkRERCAnJ0dxHnKHEcHCbrfLOrqETiRP3iSuDDkVRV1dnc+D90D5wYLlePLXR0YQBBFIdDsTVFRUhDfeeAN2u92rvwS1cIdxqOkQ08wfFqK+MKf+8pHU1FRZR5fYieQuDVeG3W6XnA0ym81o0KCBTwOhQPnBguV48tdHRhAEEUh0OxP05Zdf4sKFC2jbti0iIiIQERGBU6dO4W9/+xtuvPFG2Xxau8M4gu3S0sofFqq+MH8HawzDIDs7W9bRJXQiefImcWXIecMYhsHMmTN98pQFyg8WLMeTvz4ygiCIQKLbQdDIkSPx7bff8jtKFxcXo2XLlnj66aexdetWrcOThFMxbNq0Sd0Zkto6sDW1Cl5ib5iSPM4v1AbesK4Gwmsjtb5M6MOSYtiwYbKOLiknkqc0cp4vLh3nGnMXp7ceLznUdjz56yMjCIIIFJreDrty5Qp++OEH/j2nzYiPj0fbtm159xFHZGQkmjdvjo4dO6odqiI4FYPacPv2eAO3X1C4ILw2ubm5GDJkiNM+QUIfVnx8vIsPzJOjS2rA4imN8LyUN8xms2Hr1q1O+wSJvV3eerzkUNvx5K+PjCAIIhBoOhN04MABJCcnIzk5GQAwbdo0JCcn48UXX9QyrJBAwz0ueSorK1FRUYGKigpdxKMUq9WKzMxMMAzDe8GEPqzJkyfz7rCoqCjeGyZEiRPJUxpP3rDo6Gjk5OQ4ecjE3i5vPV5yqO148tdHRhAEEQg03TFaDYzoDgOcd4zWA6G0ezRBEAShfwy/YzRB6BEppxXn78rLy8OAAQMwdepUDBgwAHl5eZL+K3cuMLGfLC8vTzaOjIwMpKenO7nD3MWq9ByhLmp70ujaGxe6toFF1+6w0aNHY+XKlU557r77buzdu1dxHWrNBKnpDQNE7rDH/gREeF7exdbUou6DzwAA5pF/qtdg+ENtLWrfry8vlGeChNcOAMaNG+fktJowYYKTvkIKsf9KzgXG+bLEfrIPP/wQiYmJ/HuxNwyoXxO0YsUK/laRO9+X2i4wQh61PWl07Y1LuF1bw88EKXGHDRgwAOfOneNfn332mYoRKkczbxgARHjh/vodX7xhrh4x3W4z5RXCa7dq1SpZp5U7xGk9ucDEfrKpU6c6vRd7w8RlidN4c45QF7U9aXTtjQtd28Cje3eYxWJB8+bNVYooMKgxCNLKHSYkVD1iYoSxb9iwQdZp5Q65tGIXmNzE66+//opt27ahX79+OHv2LAoKCiTTFRQUoG/fvgAg6/tyd45cXOqitidNbQ8coR50bYOD7v+U37lzJxITE9GoUSP06tULc+bMcbptIEYrd5hm3jCgfu+eqEh16+Tq/R2jPNIcjLvDDocDc+fO9Vj2a6+9hvvuuw+LFi1yq9RYtGiR5DnOtyVVD7m41EdtT5raHjhCPejaBg9dL4weOHAgVq1ahc8//xyvvvoq9u/fj/vuu89pkCOG3GGEP7hTOfhT5uXLlz2WXVdXh1WrVsFut7tNZ7fbYbfbZX1f7s6Ri0s91Pakqe2BI9SDrm3w0PVMkHB2ISkpCd26dUO7du2wefNmPPzww5J5tHKHqekNA8TuMO/1CgFBUG8oecTECPuSW9AcSEwmE2644QaUl5e7nQ0ym80YPnw4jh496nYglJqaCpZlUVxcLOn7cjgcsufojwL1UNuTprYHjlAPurbBw6dB0NWrV9GgQYNAx+KRFi1aoF27djh+/LhsGovFAovFomJUrnBOLbXQahpUyiMW6gSjL00mE2bOnIkZM2a4nRV46qmnEBERgezsbIwbN07yi9NsNiM7Oxssy2L8+PEusWdlZbk9R1Pm6sH1ufhayKXz99rI1UfXPvShaxs8fLod1qxZM4wdOxZ79uwJdDxuuXjxIs6cOYMWLVqoWq8SNPOGAUCtF+6v3/HFG+bqEat1E1ToILx2Uk4rJYj9V8LjQheY3H9WTZs2xf333w+g3q1ls9kk09lsNrRs2dKt70ttFxghj9qeNLr2xoWubXDwaZ+gf/3rX8jPz8enn36Kdu3aYezYsXjssce8vhhCd1hycjIWLlyIPn36ID4+HvHx8cjNzcUjjzyCFi1a4OTJk5gxYwZOnz6No0ePIi4uTlEdtGO0OoTyPkFCpPZ0oX2CCH+gfYKIQBFu11a3+wQ99NBD2LBhA37++WdMnDgRa9asQbt27TBo0CBs3LgRtQpnCNy5w8xmMw4dOoTBgwejQ4cOGDVqFDp06IDCwkLFAyAjwrIsKioqdPdIemVlZUj5w+SQcloJ/V1paWkwmUzo1KkTTCYT0tLSJP1Xci4w7vyUKVN4PxlXhjgOrt7Y2Fg+DuF/eO58X2q7wAh51Pak0bU3LnRtA0/AdoxetGgRnn76aVRXV6NJkyaYMGECnn32WcTGxgaieJ8x2kyQ3maAhBhlNoggCILQHt3OBHGcP38eL7/8Mm6//XY8++yzePTRR/Gf//wHr732mosCgyBCDaEzzJ2bS+gJk8sjTpufn897wdLT0zFo0CD0798f+fn5svUIy8vPz8eAAQOQn5/vlUvIH4eVJy+au3i9rSsQ+Qh5qE8Joh6fZoI2btyIFStWYOvWrbjjjjswfvx4jBgxAo0aNeLTHDlyBMnJyaiurpYtx5M7DACOHj2K6dOnY9euXXA4HOjUqRPWrVuHtm3bKoo12CNJLZ1hkY894rW6gq2pRe0HGwAAESMfCYg/rOb9+vJCfSZI7BAbO3YsvybH3Xocbv1PQkICWJZFSUmJUx6uLGFahmEkbx8yDIN169bBarW61MOtAaiqqsLQoUP5cho3boySkhKPawT8WZsi1V6pPHJrFnxdyxBuayDUgPqUCBV0OxM0ZswYtGzZEv/v//0/FBcXIysry2kABAA33XQTZs6c6bYcT+6w//3vf+jZsyduu+027Ny5E9988w1eeOEFXf3ChoozjPxhnpFziAHu3VxCTxg3ABLmkUor97cHy7KYPXu2ZD1cebm5uU7lcHV6cgn547CSa684j5zbyFfnEbmSAg/1KUFcx6dvsHPnznlc6xMTE4NZs2a5TePJHTZz5kz86U9/wssvv8wfu+mmm7wLVkXCxRkmxCj+MMA5/n/+858u5+W8Xe5Ys2aN7KyPHIcPH4bdbkezZs1cXEFr1qyR3XjPnUvIH4eVOK9cHjm3kdibptR5RK6kwEN9ShDO+DQIEg6AKioqUFNT43Q+ENNWDocDmzdvxjPPPIP+/fvj4MGDaN++PZ577jm3a43UdoeFpTNMHMPvGMUfBkjrM+rq6vDmm2/CZFI+gerr7tNz5sxBhw4dvC5PyiXkj8PKnY9MeH7OnDmSdXDeNCV1SZ33Nh8hD/UpQbji0+2wq1evIisrC4mJibjhhhvQuHFjp1cguHDhAq5cuYL58+djwIAB2LZtG/7yl7/g4Ycfxq5du2TzkTuMCCYHDx5U5IPyl/Lycp/qkXIJ+eOw4vK6E7oWFRVh3759knVw3jRvnUfkSgo81KcE4YpPM0HPPPMMvvjiCyxZsgSPPfYY3nrrLZw9exbLli3D/PnzAxIY95/u4MGDMXXqVABA165d8dVXX+Htt99Gr169JPOp7Q4LS2eYEIP4wwBR38qQkpIChmE8+qD8JS4uDh06dPC6HimXkD8OKy6v3W6XnA3i8nTv3l2yDs6bduXKFa+cR+RKCjzUpwThik+DoH/96194//330bt3b4wdOxZ//OMfccstt6Bdu3ZYtWoVhg8f7ndgTZo0QUREBO644w6n47fffrtbXYeW7rBwcYbJxWAUfxggLVI1m83IycmRdHO5K4dhGK9ndF544QU0bdrUpR5Pglcpl5A/DithXqk2cOdNJpNkHUJvmqe6pM6TKylwUJ8ShCs+3Q4rKSlB+/btAdSv/+GeTunZsyd2794dkMCioqJw11134dixY07H//vf/6Jdu3YBqSMQhIozjPxhnhFeSyl3l5y3yx2ZmZmK03IkJSWha9eukq6gzMxMdOrUSTKfO5eQPw4rufaK88i5jcTeNKXOI3IlBR7qU4JwxqeZoJtuugknT55Eu3btcMcdd2DdunXo3r07/vWvf7k8Ku8OoTsMAE6cOIHi4mLEx8ejbdu2ePrppzF06FCkpaWhT58+2LJlC/71r39h586dvoQdFBiG0Wz2g9ufx1e4/YKIeoTX0mazYevWrU77BAlvldlsNmzbts3jPkFcHnFauSfGTCaT01OVwnq48tLT0/l9gkwmExo1aoSSkhKXGMWIy5o1axa/T5A3eYXtFeeRitfdcU/4mo+Qh/qUIK7j8z5B33zzDYD6NThLliyBxWLB1KlT8fTTTysux507DAD+8pe/4O2338bLL7+Mzp074//+7/+wYcMG9OzZ05ewCUIxQneXJ28X5wnLycnB5MmTXfJIpc3MzOS9YLGxsYiKigLDMLDZbPxGieJ6OFeQ1WqFzWaDyWSCzWbD5MmTFbmE/HFYSbVBKo+c28hX5xG5kgIP9SlBXCcg7rDTp0/jwIEDuPnmm9GlS5dAxBUwjOYOY1kWly5d0tVfb9yCaDV2zCYIgiDCA13uGO1wOPDee+9h0KBBSEpKQufOnZGVlYUrV67gzjvvDEaMuoczu1dUVATdpM4wjO7+cuMWRIfaAEh83aR8SpwLjHN8yXnEuLRyXi6xd2vq1Kno168f8vLyAuLzUsMBpde4QhXqI4LQHq9mgliWxUMPPYTPPvsMXbp0wW233QaWZXH06FEcOnQIf/7zn/Hxxx8rrtyTO0zuS/Xll19WfNtNjZGk0Oelhj/LyR82cphidQVbU4PaD9cAACJGZIKJ9GOjxdpa1HywGkDoOsOE/bhu3To8+eSTTj4loP7Wr1CfAbh6xAD3Xq6EhAQA9YoCqae7GjdujNLSUr98XsF2QHlTF7mpPEN9RBCe0d1MUH5+Pnbv3o3//Oc/OHjwINasWYOCggJ888032LFjBz7//HO8//77isvz5A47d+6c0+u9994DwzB45JFHvAnb2EREgImMVPzi8CaPZDkGcoYB9ZoMsU9J6FgSosSZJfZyib1bQkpLS2XzeeMCC6YDypu6yE3lGeojgtAHXn2TrVmzBjNmzECfPn1czt1333149tlnsWrVKjz22GOKyvPkDmvevLnT+02bNqFPnz7kD9OBP8wIzjBh3Bs2bHDyKRUUFLi9tcl5xOScWUIvl1L88XkF0wHlTV3kpvIM9RFB6AevBkHffvutk8xUzMCBA/Hmm2/6HZQUv/zyCzZv3oyVK1e6Tae2OwzQ2h9WC0RFqVsnV+/v6GmRtq+IBzxKFBPunFmBjEupCywYDihv6iI3lWeojwhCX3h1O6ykpATNmjWTPd+sWTN+aj/QrFy5EnFxcXj44YfdpiN3GOELvigw3DmzAoU3LrBgOKC8qYvcVJ6hPiIIfeHVTFBdXR0i3KwFMZvNqA3SDsLvvfcehg8f7nHxoNruMEBrf5hGa3ME9YaqM0zYj550FFJ069ZN1pkVKLxxgQXDAeVNXeSm8gz1EUHoC6++QVmWxejRo2XdXMLbUIHkyy+/xLFjxxQtHtTSHQaEjz/MaM4wcT+azWawLCs7sDGbzW6dWYGMS6kLLBgOKG/qIjeVZ6iPCEJfeHU7bNSoUUhMTHS63SR8JSYmKl4U7Q3Lly9Hamqq7jZi5NDeH1aj+MXhTR7JcgzgDBNeN7FPyWazSfrDODiPGODZy6UUf3xewXRAeVMXuak8Q31EEPrBq5mgFStWBLRyT+4woP521vr16/Hqq68GtO5Aoqk/7Pe9eryF2y8onBG7wqR8SkJ/GIcSZ5bYywV43ifIH59XsB1Q3tRFbirPUB8RhD7wyR0WKDy5wwDwjypnZmZqFaauYFlWd4+kB3uXbDWQ8ikJ/WGc40vKIyaVX+jlysnJQU5OjpN3izPBp6WlYcqUKX77vILtgPKmLnJTeYb6iCD0QUDcYXrGaO4w4S7HeiFUd4wmCIIg9Ivudowm3KOmQ4wIDHLXrLCwEOnp6ejXrx/v+BIidoEp8YUpdURxvjKhp0xJfVLvfSFYZYSLKytc2knoC/rc+YamM0Ge3GFXrlzBs88+i48//hgXL17EjTfeiJycHEycOFFxHWrOBKnhEBPWYRk5Fojwzv/F1tSg+sN6L1bUiLG++8Nqa1D1QX05oTwTJHXNKisrMXr0aJSUlDil/fDDD5GYmOjkfeLW+CjxhSlxRFVWVjr5yhISEvD222/z5crV5613zF39/jqtpMoAEBauLHKCEVpg1M+d4WeCPLnDpk6dii1btuDDDz/E0aNHMXXqVGRnZ2PTpk0qR6pTInz0fv2Of+4wP+SrOqegoMBlAATUfx6582IXmBJfmBJHlNhXJi7XXX3eeMeU1B/IMsLFlRUu7ST0BX3ufEdTC6Ynd1hhYSFGjRqF3r17AwD++te/YtmyZThw4IDu1sWICdbiZT14w7i6OfS2UNsbxLGfPXsWa9ZIPzn366+/Yu3atU7eJw4lvjBPjqizZ8+ioKDA5bhUuVKeMaXeMTkC4bSSKoN7uMHorixyghFaQJ87/9C1Crxnz5745JNPMHbsWLRs2RI7d+7Ef//7X7zxxhuyebRwh3Go7hDTyhvG1f07Rnm8t6KiAm+++abb9VzLly+HyeT7BKqcI4plWSxatCigu05746MKhNNKrgwppYjRXFnkBCO0gD53/qPrhdFvvvkm7rjjDrRu3RpRUVEYMGAAlixZgp49e8rmIXcY4Stnz57FwYMHPabzZ6Ai54g6c+YM7Ha7z+V6U5cUgXBayZXhb2yhADnBCC2gz53/6Hom6M0338TevXvxySefoF27dti9ezeefPJJtGjRAn379pXMo4U7jEMNh5guvGGiukPVHQY49+fNN9+M5ORkjwMhhmF8fvpPzhHVpk0bpKSkBHQg5I2PKhBOK7ky/I0tFCAnGKEF9LnzH90OgioqKjBjxgx89NFHePDBBwEAd955J4qLi/GPf/xDdhCktTuMQw2flpbTnEZzhwH1bcrJycHYsWNlBzl//etfsXz5cp+t8XKOKIZhkJ2djXHjxgXslpg3PqpAOK3kypDysBnNlUVOMEIL6HPnP7q9HVZTU4OamhqX9Rdmszkotu5AoLpDrNZH79fv+OcOq3ETWOggvmatWrWS3Z28adOmePTRR528TxxKfGGeHFGtWrWS9JVJlSvlGVPqHZMjEE4rqTI4D5vRXVnkBCO0gD53/qHpTJAnd1ivXr3w9NNPIyYmBu3atcOuXbvw/vvvY+HChRpGLY/aDjFunx5f4fYLCmekrpnNZsOWLVtcHpN/7bXX+POc94nbt0eJL0yJI8pmszn5ysTluqvPG++Yu/r9dVrJlREOrixyghFaQJ8739G1O6ygoAB33XUXhg8fjjvuuAPz58/HnDlzMGHCBC3D1hQ97kRdWVlpqJ2yo6OjMXnyZMTGxvLH0tLSkJiYyJ/nvE+cC0yJL0yJI0roK+M8ZcJy5erz1jvmrv5glBEurqxwaSehL+hz5zvkDgsx9OgOExLKu0cTBEEQ+sHwO0YbAfKFGRfhtRU7wTIyMpCeno6MjAze5yXlCJPy+eTl5fE+MnFZct6f/Px89O/fH+np6cjPz1eUJ1Tx1bnmT12BdK8RRDgR6r8zunaH/fLLL5g+fTq2bduGS5cuIS0tDYsWLcKtt96quI5gjyTV8IXJ1RczMssrfQVbU4PKD+s31ooekeW7N0xMbQ0qPqgv10gzQcK+TkhIwMWLF5GQkACWZZ3WC3GPzIsdYYCrL+vy5csYMWIEn7dRo0a4dOkS/z4hIQErVqxwms4uKyvDkCFDZAfZUnlCFTkvWzBcSGLfUiDcawQRTgTbWWb4mSB37jCWZZGeno4ff/wRmzZtwsGDB9GuXTv07dsXV69e1SBaHRIRCSYyyouX2BvmTV75l5E9YhzcoOfixYsuC6a5wYnYESbl85kyZYpTXuEASJhOSG5urttZRiO5guS8bMFon/j6BMK9RhDhhBGcZbp1hx0/fhx79+7F4cOH0alTJwDAkiVLkJiYiDVr1rjsi6AH1HBo6cUdJsQoHjEx4r5Wipwva82aNYq2dygoKOC9P3a7HUeOHPEqT6gidiBxBMOFJOVb8te9RhDhhFGcZbrdLJHzfwmn1sxmM6KiorBnzx7ZQZDa7jDVfWFCamuBKO03hjSiR8xfpDZTVLq/VV1dHRYvXoy8vDzMmTPHqzyh6griXEdyg81AupDkfEvBrJMgjISRnGW6XRh92223oV27dnjuuedQWlqK6upqzJ8/H+fPn8e5c+dk85E7jDACRUVF+Oyzz1BeXu5VnlB1BXEOJLmBYiBdSEodZ+RfIghpjOQs0+1MUGRkJDZs2IBx48YhPj4eZrMZffv2lb19xqG2O0wNX5gQ3bjDhBjEIyamoqJCcgdnNejWrRv+9Kc/IT8/X/FAqFu3biE76OccSHa7XXI2KJAuJKWOM/IvEYQ0RnKW6eRbVJrU1FQUFxejrKwM1dXVaNq0Ke6++25069ZNNo+W7jC1HVpMXS3YGuVTjmJlRiDj4DCKR8xfpHxZ3NNOSvJmZWXBbDZj5syZePbZZxXnCZUpaDFCB5LUDE0gXUhyvqVg1kkQRsJIzjJdD4I4rFYrgPrF0gcOHMBLL72kcUTX4dxT3M9qwj2W7gvco/KEPNy1XbVqFdavX694cTTny+IWQ7MsC4ZhkJmZia1bt+K3335zm99ms/Hen5SUFHTq1Mnj4mhhnlCFcyBxfcYRDBeSuC6GYfh+5t6Tf4kg5JH6HQrF3xlN1wRduXIFxcXFKC4uBnDdHXb69GkAwPr167Fz507+MfkHHngA6enp6Nevn4ZRO8O5p2JiYkJq9Et4hru2I0aM4F1gCQkJiI+Pd0kHgJf9cu4em83mlG/o0KF4/fXXnfI2atTI6b2U9yc3N9ftZ8tIriBhn4n7M5h1ce418fUiCEIeqf/jQg1NZ4IOHDiAPn368O+5tTyjRo1Cfn4+zp07h2nTpuGXX35BixYt8Nhjj+GFF17QKlzN4f469mUvBpZlUVlZierqakRFRSE6OjqggzaLxQKGYQyzHkgI5+V56623MGnSJADAwoULUVNTg8jISDz44IPYsWMH+vbtix07dmDSpEl8PwjzcQ6ttLQ07N69G2lpabj//vudysrJyXHpQ6vViszMTKxZswYxMTFIT0/H5s2b3eYJVYR9LdWfwapr0qRJvHtNeL0IgpBH/DsUir8z5A4LIfTsDTPSTtEEQRCE9qjx/R0Sa4JCCW7GBUDAZ1uI4CO8fsXFxS4zEgBcZoQAoEuXLtizZw969uyJ77//Hrfddhv27NkDm82Gjh078uVwMzh1dXWorq5GZmYmRo8e7TamwsJCt3H06NHDbV4uxmnTprmk5coWliN1LFAEs2yCIAhv0XQmaN68edi4cSO+//57xMTE4N5778WCBQvQsWNHPg3Lspg9ezbeeecdlJaW4u6778Zbb73F7yLtCbVngoLpEhOW3eCx6WAiohTnZWuqcfWDBfV5R06v1134CVtbjavv15dplJkgKV8Y91QXd+9bziEmBcMwaNy4MUpKSnjHmPj8unXr+MX/YqRcWsI43Pl6KisrMWbMGH5be7FjTMr7A7j6zgI1xR1szxBBEMbC8O6wXbt2YdKkSdi7dy+2b9+O2tpa9OvXz8kN9vLLL2PhwoVYvHgx9u/fj+bNm+OBBx7wahM5I8JE+OD44vIGyBnmzSAsFOEGOEKHldCT42kABMBpoCT19wY3yJdDzqWlxNcjzCuVVsr7E0wXkBE8QwRBGAtNb4dt2bLF6f2KFSuQmJiIoqIipKWlgWVZvP7665g5cyYefvhhAMDKlSvRrFkzrF69Gk888YQWYSsm0B4tvXnDjOgM89UX5g+HDx+G3W5HSkqK03E5l5YQOV/P2bNnUVBQ4JKec4wBcPH+SPnOAuUCMopniCAIY6GrNUFlZWUAwD+CfOLECZw/f97pkXiLxYJevXrhq6++khwEqe0OE6OaS6y2RntvWO31DRdD8dFIPTFnzhysX7+efyzck0tLiNjXw7IsFi1aJLkxY11dHRYtWiS5Vk1qk8JAuICM5BkiCMJY6MYdxrIspk2bhp49eyIpKQkAcP78eQBAs2bNnNI2a9aMPyeG3GFEKFJeXo59+/bx7z25tISIfT1nzpyB3W6XTW+32xW5s6TK9gUjeYYIgjAWupkJysrKwrfffos9e/a4nBP/lcjtTimF2u4wMcF0iTl7wyIDVq7PCGIwijNMK19YXFwcunfvzr/35NISIvb1tGnTBikpKbIDodTUVADw6M6SKtsXjOQZIgjCWOhiEJSdnY1PPvkEu3fvRuvWrfnjzZs3B1A/I9SiRQv++IULF1xmhzi0dIeJCapHq67GS29YteTP/sbAQc4w/3jhhRf4W2GAZ5eWELGvh2EYZGdnY9y4cS6DHLPZjOzsbLAs6+L9kfKdBcIFZCTPEEEQxkLTQRDLssjOzsZHH32EnTt3on379k7n27dvj+bNm2P79u1ITk4GAFRXV2PXrl1YsGCBFiF7RC2XGPdouk95P9Bn3+kBX31h/pCUlISuXbu6HJdzaQmR8/W0atUKNpsNq1evdjoudIyJvT9SvrNAuYCM4hkiCMJYaLomaNKkSfjwww+xevVqxMXF4fz58zh//jwqKioA1P8HP2XKFMydOxcfffQRDh8+jNGjRyM2NhbDhg3TMnRZyCUW2kj5woQOK3cOMSlMJhOfTurzwDAMZs2aJZtfzqWlxNcjzCuVVsr7E0wXkBE8QwRBGAtNN0uUGySsWLGC30WX20dl2bJlTpslcounPWEkbQa3mzHLsk5PwHmTNxjuMIvFYshBn6edmgHaMdpbaMdogiCUosb3N7nDQhC9OcSMsls0QRAEoR8Mv2O00WBZFhUVFaioqFBtoz3Ce4TXqbCwEBkZGcjIyEBhYaFk+vz8fAwYMAB5eXkYMWKEbDoxhYWFGDFiBPLz853+FefPz89Hv3790L9/f+Tl5XmMR0mdnvIqSae0LCny8/PRv39/pKen+5Rf7/jTN3rFiG0iCE/o3h22ceNGLFu2DEVFRbh48SIOHjwouYhUDjVngoLpDZOrp+FjcxXrK9iaKlz+YGZ9vpFzwET6/hQdW1uNy+/PABB6M0HC/ouPj+e1FmK3FlC/gefQoUOdnphS4r2Scn5x/wrzl5WVISMjQ7IMqXjcodTNpSSdP56vsrIyDBkyhP9DID4+Hvn5+YbYQgEwpgPNiG0iQh/DzwQpcYddvXoVf/jDHzB//nwNI9Uv9Q4xi+IXn8+LPJIvg3jDhP4vKZ9Vbm6uy2PmSrxXUs4vofuLy5+bmytbhrd+LaVuLiXp/PF85ebmOs2ElpSUGMoTZkQHmhHbRBBK0LU7DABGjhwJADh58qTa4flFMF1aenCIhbI3zF28nFurVatWsNvtOHLkiEsaT94rT84vLn+LFi0ky5eLxx1K3VxK0vnj+ZLrszVr1hjCE2ZEB5oR20QQStHFZokcYneYL2jpDlPNGyakthqI0mDauvb6hotGetSZc2vNmTMHc+fOlU3ncDgkvVdKnV8OhwOvvfaa4njmzZsn+/SdUjeXknQAfPZ8ORwOzJkzR/ZcqHvCjOhAM2KbCMIbdLMwWsod5gvkDiP8xW6347PPPnM7gHY4HJLeK6XOL4fDocjdxcXjzq+l1M2lJJ0/nq99+/ahvLxc9nyoe8KM6EAzYpsIwht0MxPkzh3mDVq6w4LpDRPi7BDTaG2OoN5Q84Z58oOlpqbiT3/6E1auXCk7EDKZTEhJSXH5bCl1fplMJjAMo2gglJqa6vYzrNTNpTSdr56v7t27Iy4uTnYg1K1bt5D+o8SIDjQjtokgvEEXM0GcO+yLL75wcof5gsViQcOGDZ1eWsC5tILxchpw1NWAralS/OLwJo9kORLesFB6ycG5tcxmM2bMmCGbzmQySXqvOB+W0AMml184WPcUj7tbEnIOLimnmKd0SsuSa9PMmTNlz4W6J8yfvtErRmwTQXiDrt1hoYZa3jAh3GPqXuf7QPrLKhwQ+sHWrVvndE7o1kpJSUGnTp1cFvp68l55cn5x+R944AF89tlnbhdHC+Nxh1I3l5J0/ni+5PosMzPTEJ4wIzrQjNgmglCKrt1hQP3jtcXFxfjuu+8AAMeOHUNxcTHOnz+vVdiykDcsNJDygwHSPqvc3FyXWR0l3isp55fQ/cXld/eIvLd+LaVuLiXp/PF85ebmOn3+jeYJM6IDzYhtIggl6N4dlp+fjzFjxrikmTVrltsvEA4jaTM4/xfg3WPpnGuMu9QMw8BisQRsoGa1Wj3e/tErntxaQP1nsKCggPeCKfVeiZ1fQveXMH9+fj5Wr14NhmHwxz/+Ed98843beJTUqcQp5imdP56v/Px8rFmzBjExMZg+fbrhPGFGdKAZsU1EaEPusABgpEGQ3pxhHKG2Y7Qcwi+BY8eO8V/i6enp2Lx5MwB5CemCBQtQUVEhKURVMtByF4/c4Ikrt6amBpGRkW7L9jWGUEXPX+i+xKbn9hCBg66zMzQICgBqDYKEszSBNLQLoUFQ4OGuW1VVFSZOnIiLFy86qTQA8HvsAK4qi8rKSowePZpPzzAM1q1bB6vVyp8fM2YMvxuvUhWGlHZDqDMQl+uubF9jCFX0rIDwJTY9t4cIHHSdXVHj+1vThdGe3GE1NTV4/vnn8dlnn+HHH3+E1WpF3759MX/+fN0t2qusrFTFG8aRMupNmBS6v+pqqnBwZQ4AIHnUmzD74QzjcNRUwf57maGM8LpxA1fhAAhw3h2bUwqMGjUKQP2OzsL0LMti9uzZ/KyLUEcglV8OKe2GMK+4XHdl+xpDqCKlgNBLW32JTc/tIQIHXWdt0HQQxLnD7rrrLtTW1mLmzJno168fvvvuOzRo0ADXrl2D3W7HCy+8gC5duqC0tBRTpkzBn//8Zxw4cEDL0N0SLI2EsFwmIsqnwYw50hKQQVAoazOEiBUkSuBUFkC9DkLM4cOHYbfb0axZM1kvlzslgZx2g9MZdO7cGQUFBW5jEyowpNIq1XGEGnpWQPgSm57bQwQOus7aoavbYb/++isSExOxa9cu3h0mZv/+/ejevTtOnTqFtm3bupyX0ma0adMm6LfDSktLVX2iInnUm4iKVdaeupoqHPi/JwAA3cYvC8ggqPraZX52KRxJSUmBw+FAcXGx5Pm4uDh06NABRUVFsvmlVBgsy2LGjBkum9dxmM1mxMbGut2ZmSsbqN881G63S6ZLTU01lBaB67vi4mKnTSjNZjO6du2qaVt9iU3P7SECB11neQxvkRejxB1WVlYGhmHQqFEjyfOkzSDUwG63yw6AAKC8vFx2AMTll1ISeNJu1NXVuR0ACcs+c+aM7AAICH2NhRg9KyB8iU3P7SECB11nbdGNNkOJO6yyshLPPvsshg0bJjsq1EqboYYyQ6jLMGmly/gdUwhrM4R4UmjIkZqairq6Op9nguRUGJ60G0pmgoRlp6SkyA6EQl1jIUbPCghfYtNze4jAQddZW3QzE8S5w6TWWAD1i6RtNhscDgeWLFkiW44etBnB0kgIBxpaT48K6w9FbYYShYYcnMpi8uTJstfhhRdeQFZWFsxms2x+qbyetBsMw+D555+XPS8sm2EYZGdnS6Y1m82G0yLoWQHhS2x6bg8ROOg6a4suBkGe3GE1NTUYMmQITpw4ge3bt4f8fj+BwFFThTovXhze5HH3cgjKNApK/7PhVBatWrVCZmamy/mkpCR07dqV1xHI5ZeDyyf1n+LQoUORnJwsO3slLrtVq1aSaZXqOEINcd/pSQHhS2x6bg8ROOg6a4emC6PF7rBbb73VJQ03ADp+/Di++OILNG3a1Ks6aJ+g4GO0fYISEhKcHin3Zp8gk8mEtWvX0j5BGqLn/VZonyBCDrrOrhh+YbQnd1htbS0effRRHDhwAKtWrUJdXR2fprq6WsvQXSBvWGjCXbdGjRohJycHiYmJyMnJwbBhw8AwDGJjY5GZmckvtM/JyXH6jyk6OhqTJ09GbGwsGIaBzWbjB0Dc+ZycHNn8ckRHRyM7OxuJiYmw2WxITExEdnY2n1dYbmxsrNuyfY0hVBH2nbDP9IAvsem5PUTgoOusDbp2h508eVLWLP/FF1+gd+/eHuswkjaDm7XgXGDe5OPSR0VF8QPIQPnDhF/MNAAkCIIgAoHhd4z2NP668cYbFW9gFw5wsxYVFRW6tDyH8m0xKQoLCzF37lxUVVUhLS0NrVu3RkFBAWw2G0aPHi3p9gKAhQsXoqKiAjU1NZIuMQ6hmFVOmuqLPwyAi3/IWyeRURxGcu0wSvsIgvAPXW2WGAyCPZJUYy2QGFobFByE1xIARo8ejdLSUv49tzbIZDLh/fffx5QpU5zW7CQkJIBlWRfvmNAlxlFWVoahQ4e67AckXK/jy7qg+Ph4MAyDixcv8mkBeLXWwChrE+TaYZT2EYTRMfxMkCd3GADk5uaioKAAZ86cQVRUFFJTUzFnzhzcfffdGkZ+HbWdYWIGj3oLERHKd4CuranCpvfrZywGP/YWIvzcPbq2tgqbVk7yqwy9ILyWQ4YMcRoAAddnLh0OB6ZOnSrp9hIjdolx5ObmSm6IKOcHU+oPEw7AuLQsy3rlJDKKw0iuHUZpH0EQ/qNrdxgAdOjQAYsXL8ZNN92EiooKvPbaa+jXrx9++OEHr58UCzZqObSE9ZjNUYiI9O2v2IhIi895OYziEAOc4//nP//pNu2vv/6quFzOJZaSkgKgfkfnI0eOyKYvKChA586dffaHCdNyaZQ6iYziMJJrh7hfQ7V9BEEEBl3dDlPiDuOmx3bs2IH777/f5bza7jC1nWFiBj/2FqJjrZ4T/k5tTSU2LB8PAHhk3P/5PQiqvFbGzywR8sTFxWH9+vUA6meZLl++7DH91atXJWeLGIbBDTfc4FGfIYeck8goDiO5dphMJtxwww24evVqSLePIMIFwz8iL8aTO6y6uhrvvPMOrFYrunTpIpmG3GGEHikvL8e+ffuwb98+jwMgLr2cP4xlWZ8HQIC8k8goDiO5djgcDly+fDnk20cQROAICXfYp59+CpvNhmvXrqFFixbYvn07mjRpIlmO2u4wNZxhYoQOMbPGDjGzQRxigHO/BpqGDRuie/fu/M9KZoKuXLki+XRkIGaCpJxERnEYybWDmwm6cuVKSLePIIjAoZuZIHfusD59+qC4uBhfffUVBgwYgCFDhuDChQuS5WjpDlPLoUUOseD3q5yby1c435fJZMKMGTPcpjWbzR79YO7Oi9OK/WXeuopCzWEkF6/JZMLMmTNDvn0EQQQOXQyCPLnDGjRogFtuuQX33HMPli9fjoiICCxfvlyDSPVHbW0VamsqvXhdXy9VW+NtXolXrfEcYgDw6KOPuj3ftGlTxV+anEuMIyUlBZ06dZJNb7PZkJyc7LM/TJjWZrN55SQyisNIrh3ifg3V9hEEERh07w6T4pZbbsGIESOQm5vrMS3tE6QetE+Q6z5BYpcYR7D2CUpISAAA2icItE8QQYQ6hl8Y7ckddvXqVcyYMQN79+7FqVOnYLfbMX78ePz000/IyMjQMnQecoYZB+G1jImJwZQpU2Cx1O+jlJaWhszMTJhMJieXl9DtlZOTg8mTJ8NqtSIqKkrSJcZhtVphs9lgMpmQlpYm6fXy1R/GOdC4tN46iYziMJJrh1HaRxCE/+jaHVZZWYlhw4bh66+/xm+//YaEhATcddddeP7553HXXXcpqsNI7jDg+myFN3vyBNMdJiyD3GEEQRBEoDD8jtGexl/R0dHYuHGjStGEBsJdjfVAKN8CKywsxIIFC1BRUYGYmBhMnz4dPXr04H1cAPgnDcXv33rrLURHR+P06dPo1KkTXnvtNY91CV1Vwvdcee48Vp4cYoHCXZzh5tgK57ZLQf1BGBFdbZYYDNQYSaq5Lkhv64FCcRDEsizKysrwxBNPOK35iY+Px7JlyzBhwgR+nQ23ZxW3zkfo5hKyfPly2UesxWtQli5diokTJ/Kzm4DzGh7x7RlPa4MChbs4w23tDK0bcob6g9ACw88EKXGHCXniiSfwzjvv4LXXXsOUKVPUDdYNWvnDxg1fikgF3rCamiosXz2xPs+wpYj00xdWU1uF5asm+lWGllRWVmLIkCEux0tKSjB79mynAY5wkbPUe47s7Gx8/PHHkufEriphHcK65DxWnhxigcJTnOHk2CK/mDPUH4RR0b07jOPjjz/G119/rftHWYPtzxKWH2GOQqSX2ovISIvXecSEui/s5MmTsucOHz7sU5nXrl3D2rVrXTZblHJYydUh5bES53eX1h88xRlOji2j+NMCBfUHYWQ0HQRt2bLF6f2KFSuQmJiIoqIiJ3fY2bNnkZWVha1bt+LBBx90W6aUOyzYCAcCanrEamurERWl/q2o2tpq/mctvWl6Y/ny5XjkkUcQEVH/a8WyLBYvXuxVGVyeuXPnAoDb/A6Hg0/rzy1YpXEKYzPqAni5vgiHtktB/UEYHV1slsgh5Q5zOBwYOXIknn76abcbzHGQO4zQklWrVvE/yzms3CH0WHnK73A4AuK8UhpnODi2jOJPCxTUH4TR0b07bMGCBYiIiEBOTo6ictR2hwHq+sOEfqsIjbxhESHsC2NZFs8//7zPt708MXz4cP5nOYeVO8QeK3f5TSYTUlJS/P58K40zHBxbRvGnBQrqD8Lo6GYQxLnD9uzZwx8rKirCG2+8AbvdrnjK1WKx8BvcaQHnz1IDraahpXxhoURWVhYmTJgQ8HL/+te/8rfCgOtOqvHjxysuQ+yxcpffZDIFxHmlNM5wcGzJ9UU4tF0K6g/C6OjidpicO+zLL7/EhQsX0LZtW0RERCAiIgKnTp3C3/72N9x4443aBawTamqrUFNTqeB1fY1UTY3SPG5eIe4La9Gihew54SykN8TGxkr6xqQcVklJSZJfHlIeK3F+d2n9wVOc4eTYMoo/LVBQfxBGRtfusIsXL+LcuXNOx/r374+RI0dizJgxso/SC6F9goKL0fcJEvvAhPv6CKF9gowF7YvjDPUHoQVh7w5LSEhAUlKS0ysyMhLNmzdXNABSi3D2h4XiXpsMw6BRo0aYMmUKYmNjwTAMYmNjee8X5+PifuaOi91cbdu2BQB06tTJ7doIsavKarXy76VcX+7ySznEAoW7OMPNsUV+MWeoPwijomt3mBQ33ngjpkyZonizRCO6wy5duqSbR9NDcSaIIAiC0D+GnwliWVbyJTcAAuo3utPTbtFqwzAM/RUWQPLz8zFgwADk5+ejsLAQI0aMQH5+PjIyMpCRkYHCwkKn9FyawsJCFBYWuqTjzufl5aF///7o168f0tPTXcpREo+veIrRXXpvy5er05vyjEQ4t50gQhFyhwUIrdYFTXt0iSJ1BgBU11bhtX8+CQCY+ugSRCnMJ6amtgoLfy8nVGaCpK5PWVkZhg4dCofDAYZh0LhxY5SUlIBhGP42X0JCAlasWIHo6GindRFSa4Xefvttfg2NmPj4eOTn57sdwArjMZlMWLt2LaxWq1ft9BQj1xap9ErWekilB+BUJ+B+jZNRoXUzBBFYDD8TNG/ePNx1112Ii4tDYmIi0tPTcezYMac0o0ePBsMwTq977rlHo4jl4fxhgwcPVlUlERlhQVRktLKXYNAT5U0+0UvpoEtPSF2f3Nxcfu8T4WBB+HcB50kCXP1JQo+Y2LUlpqSkhC9HDmE8DocDs2fP9rqdnmIUxyDlhPKm/LVr17oc86Y8I+FtXxIEoT0h4Q4bMGAAVqxYwb+PitJmk0ClqOkP02IiLxTdYeI47XY7jhw5oihvQUEBOnfuLOnwEuJpA8Y1a9bI+pak4jl8+DDsdjtSUlIUxSnnGRNSUFDAx+CtE0oqfUFBAX8bW0w4OabIr0UQoUlIuMMsFguaN2+uqEwt3GGAdv6wmrpqWKDu7aiautB2h127do13cymhrq4Oc+bM8bteOdeXw+GQjWfu3LlYt24dTCb3k7ZK/V91dXVYtGgR5s6d65UTSq58T6qNcHBMkV+LIEIXXWyWyCHlDgOAnTt3IjExER06dMDjjz+OCxcuyJZB7jDCEwcPHvR6cFxeXu6VA0wOKd/Svn37ZOO5fPky9u3b57Fcbzxldrsd+/bt88oJ5YsHzV15RoL8WgQRuuhGmyHnDhs4cCAyMjLQrl07nDhxAi+88ALuu+8+FBUVSeoxtHCHAdr5wyLN6t8aFNYZKu4wYZ/16NEDDRs29Gog1LBhQ1y5ckWxA0yObt26uXweu3fvLhtPw4YN0b17d4/leuMpS01NRffu3b1yQvniQXNXnpEgvxZBhC66mQni3GFr1qxxOj506FA8+OCDSEpKwkMPPYR///vf+O9//4vNmzdLlmOxWNCwYUOnl9pwPq1gvYSDDi2m2aXcYXp/CfvMZDJhxowZittrNpsxc+ZMv/tazvXlLp7nn3/e460wQLnLyWw2Izs7WzYWuXLkjpvNZrfxhYNjyts+IwhCP+hiECTnDpOiRYsWaNeuHY4fP65SdPqmprYK1TWVyl4C51e1N/lEr1B3hwFASkoKOnXqpCitzWZDcnKypMNLiJwTjCMzM1PWtyQVT1JSErp27aooRkDeMybEZrPxMXjrhJJKb7PZYLPZFLvQjAr5tQgiNNG1O0yKixcvolWrVnjnnXfw2GOPeUxv9H2CtMaI+wRxfi4gcPsESe3RI4b2CQptaJ8ggggsht8nyJM77MqVK3jqqadQWFiIkydPYufOnXjooYfQpEkT/OUvf9EydBfU9IfpaX9LPcXiDqnrY7VaYbPZYDKZkJmZicmTJ/N+LqErjPsiE/qTpJxiQtdWWloaX09sbKxTOXII4+Fi8BZPMYpj8NYJJZVeXKcnF5pRIb8WQYQeunaHVVRUID09HQcPHsSlS5fQokUL9OnTBy+99JLixYZGc4cBNBNEEARBGB/DzwR5cofFxMRg69atuHDhAqqrq3Hq1Cnk5+fT0xZEQJDyhgndV0ocYkrTpKeno3///rwTTOyYys/PR//+/TFo0CC3ni9x7Hl5eZKuKmHdwn89udC8cY6pBfm4CIIIFuQOCzBqrA0SzgTNGvSWVw6w6toqzP50kk95pcoIxZkglmVx4cIFPPbYY2BZ1mk9kHBNixKHmLdpGIbBBx98gClTpvBrR15//XWMHDnS6daiuzVEwrVDHMI1KMK6ufVN3L9y6YTtVrKWSC1onQ1BhC+GnwlS4g4DgKNHj+LPf/4zrFYr4uLicM899+D06dMaROwZtR1iUREWWCKiFb/E/jBv8kqVEYpUVlY6DTqEX/hC95USh5i3aViWxdSpU50cU1OnTnVZW+XOPSV0jHmqW+gic5dO7Pzy5BxTC/JxEQQRTHTvDvvf//6Hnj17Yty4cZg9ezasViuOHj0aEn8NBmsQRO4w/9i/f79P+ZQ4xJSk+fXXX/mfWZZ1ei8uS+yeknOeca4qT3UrTacklmBDPi6CIIKNrm6H/frrr0hMTMSuXbt4d5jNZkNkZCQ++OADRWVIucPatGmj2u2w0tJSVX1aswa9hbho5U8RVdVWYubH4wEAc9L/D5YI7weT5ZVl/O2wcCMuLg7Xrl1zq49o2LAhrl69GhDNRkpKCubNmweGYeBwODBkyBDZna5NJhNuuOEGjztbc+m8jVEYS7BhWRYzZsxAcXGxU4xmsxldu3YlHxdBhAGGvx0mRuwOczgc2Lx5Mzp06ID+/fsjMTERd999Nz7++GPZMsgdRgQTJQ6xy5cvB2QABNTP/HDuKXeOMaD+9+Xy5csetRZcOm9jFMYSbMjHRRCEGujaHXbhwgVcuXIF8+fPR15eHhYsWIAtW7bg4YcfxhdffIFevXq5lKOVO4xDDYcYucN8x+FwYOTIkbhy5YpP+ePi4nD16lW3A41AecaAes8X9/l15xgDrs/wlJeXu73NpXTGyF0swYZ8XARBqIFuZoKk3GHcf36DBw/G1KlT0bVrVzz77LMYNGgQ3n77bcly9OAO4wiWV4vcYb6/GjRogGeeecandpvNZjz//PNu+zxQnjGurOzsbL4sT84zk8mEmTNnenSNcem8iVEcS7AhHxdBEGqgi0GQnDusSZMmiIiIwB133OGU/vbbb9ft02FqU11bharaSsUvsT/Mm7xSZYQiXbp08SmfEoeYkjRNmzZ1ckw1bdpUtiyxe0rOeca5qjzVrTSdkliCDfm4CIIINrp3h9177724+eabnRZG/+Uvf0FMTAxWr17tsQ6j7xOkNUbeJ0iJQ4z2CQoutE8QQYQvhl8Y7ckdBgBPP/001q5di3fffRc//PADFi9ejH/961948sknNYxcnmA7xISDLMI3GIZBs2bNkJmZ6eINE7qvlDjEMjMzFaWJjY0FwzDIzMzk3VLCfzMzM8EwDKKiomQ9XxxCx1haWpqLq0pYt81mc/pXLp2w3UqcY2pBPi6CIIKJrt1hHO+99x7mzZuHn376CR07dsTs2bMVz4QYzR2mp1mgtWvXolGjRrQ+gyAIggg4hp8J8uQO4xg7diyOHz+OiooKFBcX62YQEO4E63ZfsHHnopI6J+Xh4lxg6enpKCws5N1f3HtP7i05V5lU3Vw5cv4vQh8E0rdGvjQiVAj1z6qmM0Hz5s3Dxo0b8f333yMmJgb33nsvFixYgI4dO14PUOZL9uWXX8bTTz/tsQ61ZoLUWAsEOM8EvdZnMSxmzwqLqroqTP0iy6s8SsoKtfVALMuirKwMEydOxMWLF13WmEitPwHgsr5GTKNGjVBWVsav6WncuDEYhpFdUyO3Fke8bgcAxowZw2sjuLVFtDZGf1RWVjpdK3/WUdE6KCJUCPZn1fAzQZw2Y+/evdi+fTtqa2vRr18/XL16lU9z7tw5p9d7770HhmHwyCOPaBi5K2o7wwDAYrbAEqHgJRj0KM6joKxQo7KyEkOGDJF1UUl5qqQ8XGIuXbrktKi5tLTUrXtLztkl9nsJ0wHXdSXk0NIf4mvlzzUiXxoRKhjhs6rpZolbtmxxer9ixQokJiaiqKiI12Y0b97cKc2mTZvQp08f3HTTTarF6S3BHASRN8x3Tp486fRe6KIC4OKpKigo4H/2F869Ja5HCq5uT/4vcmjpA85xJsYX3xr50ohQwSifVd3sGA24ajPE/PLLL9i8eTNWrlwpW4aUO0wNhAMCtdxh1Y5qREPdafJqRzX/s5qOtGDBsiwWL14sOeAIlPqCK2vRokWKb5N6qpuLmxxa2sJdB6nrxV1zpb41riy543StCb1gpM+qLjZLBKS1GWJWrlyJuLg4PPzww7LlkDuM8AbORWW32wM66JHCbrdL+rB8gRxa+oBznMnhjW+NfGlEqGCkz6puZoI4bcaePXtk07z33nsYPny424VXWrnD1HCGAc7esCiT+t4wYZ2h5A1jWRYvvvgivv32W6fjnIvK4XCguLg4IL4vOVJTUwHAxYflC+TQ0gec40xuIOSNb418aUSoYKTPqi4GQZw2Y/fu3U7aDCFffvkljh075nHhlcVigcWi7eJdzqcVbPTiDQsVnnzySUyYMMHpGOeiYlkW48ePdzpnNpsBBOa2GOfekqpHLj3LsrKDJXJo6QPuOowfP97lc+Ktb01YltRxutaEXjDSZ1XzfYKysrKwceNGfP7552jfvr1s2uXLlyM1NdVn71OwiY6OxqZNm7Bp0ybVZkeq6qpQVavgVVflfR4FZYUa7du3x5AhQyRdVFKeKpvN5pVfyx2ce0tcjxRc3TabTfY8ObT0A3dNxfjiWyNfGhEqGOWzqulM0KRJk7B69Wps2rSJ12YA9VoA4QzD5cuXsX79erz66qtaheoRTpehJtx+PcHOYxQYhsGIESPw+eef83v0CL+8bDYbtm3b5nKOO+bPPkHu6gGc9wkSpt+6davLPkHi8gjtsdlsTtfKn2sk9zkkCL1hhM+qpjNBS5cuRVlZGXr37o0WLVrwL/EtL+5x4czMTI0i1Q8a7m3pQmVlpa7iUYI7F5XUOSkP17Bhw3gXWGxsLKZOncq7v2JjYzFlyhS37i05Z5fY7xUdHY2cnBy+HKF3LFTWYoUL4mvlj2+NfGlEqGCEz6qmO0arAbnDgkuo7RpNEARBhAaG3zGaIPSCnP9GyuvFHcvLy8OAAQOQn58vW47YEca5pbi8eXl5LuXKOXjcec2k6lPaRiXtd+cFUuoO8qV9/tQZ6k6jYEJ9QxD16N4dduXKFTz77LP4+OOPcfHiRdx4443IycnBxIkTFdUR7JGkWs4wDuFM0OLe82AxK39MvqquClk7Z/yed67PCoyqumpk7XwOQGjOBImvWVVVlaT/RujFEa7XAeCkSDCZTFi7di0sFotTOUuXLsXEiRP5++UsyzrpNIQIy5Vy8HjymonrU+JF83ROiRdIqTvIUzpvHESBjCscob4hQgXDzwQpcYdNnToVW7ZswYcffoijR49i6tSpyM7OxqZNmzSM/DpaOMM4LOYon71ffjnEvBh46RHxNZPz30h5w4SuLw6Hw4HZs2e7lDN79myn93IDIHG5Ug4eT14zqfo8edE8nVPiBVLqDvKUzhsHUSDjCkeobwjiOrp3hxUWFmLUqFHo3bs3AOCvf/0rli1bhgMHDuhqbQygjktLa3eYuN5Q84cBzjGfO3dO0n/TuXNnj44vIYcPH8Z3333nVM7hw4d9ik/s4JFy9HAPC8jV58mL5umcuP1SXiCl7iBP6bxxEClJaxSnUTCgviEIZ3SxWSKHlDusZ8+e+OSTTzB27Fi0bNkSO3fuxH//+1+88cYbkmWo7Q7TwhnGoYU7jKuXIxQfiRSyZMkSl2MOhwNz5871epAZyN2mOQfPnDlzJB09SjZwdOdFY1lW1mXGtV+uPO6cEneQJ8eQXPukHERKfEVK4wpHjOR7IohAoZtBkJw77M0338Tjjz+O1q1bIyIiAiaTCf/3f/+Hnj17SpYzb948zJ49W62wiRBHrNEA6gcBaol35eAcPPv27XPrplJShtw5u90ueU6u/WIvkFTZwjRt27aVdWt5ap+4HEDe0+VLXOGIkv4L174hwhfdDILk3GFvvvkm9u7di08++QTt2rXD7t278eSTT6JFixb8dL4Qtd1hajnDOLR2h4nrDSV/GIewD5OTk/HNN984zeKYTCbccMMNKC8v1+yWI+fg6d69u6Sjx5sypLxoZrMZXbt2BeDqMuPaf+XKFbdeICXuIE+OIbn2STmIlPqKjOI0CjRG8j0RRKDQxSPynDvsiy++cHKHVVRUYMaMGVi4cCEeeugh3HnnncjKysLQoUPxj3/8Q7Isi8WChg0bOr3UgnNpBfMlHHBoNXUt5Q8LpZewD5944gmXfjSZTJg5cyZMJu9+PbxN7w7OwWMymSRdPGaz2WN9XBlS/iqGYZCdnS1ZNtd+qTxcejlHkPi4p3Ry7ZPKp6ROpXGFI9Q3BOGKrt1hNTU1qKmpcfnP3mw2B9X27Q1aOMM4quqqffZ++eUQq6t2E5X+EV6z9u3bS/pvkpOTvfKGJSUlwWazOZWTlJTk0xeL2MEj5zVzV58nL5qnc+L2S3mBlLqDPKXzxkGkJK1RnEbBgPqGIJzRdJ+gJ598kneHCfcGErrDevfujd9++w2LFy9Gu3btsGvXLkycOBELFy5UtFcQ7RgdXEJxnyAxSvbJoX2CaJ8go0B9Q4QKht8nSIk7rKCgAHfddReGDx+OO+64A/Pnz8ecOXMwYcIEDSMnjISc/0bKGyZ0faWlpcFkMsFms8FqtbqUY7VanRxhQp8YlzctLc2lXCkHjyevmbg+JV40b9ov5wVS6g7ylM4bB1Eg4wpHqG8I4jrkDgsxWJbFpUuXdPFo+tq1a9GoUSNaS0AQBEEEHMPPBBHewzCMbv5yU0MTEizy8/PRv39/pKenu3VxiY8LHWLc8fT0dPTv39/JISaXLy8vj69Xyj3mCX8cX2qSn5/vdds8oWYbA1mX3q6NEaE+JnxF9+6wX375BdOnT8e2bdtw6dIlpKWlYdGiRbj11lsV1aHFTFCwfWJO/rD7Z3jnD6utRtbnv28qd98MWCK8e8y+qq4aWf+pzx9K64GE16SqqgpDhw7lH3+Pj4/nv6yVrg3i1uA88cQT/DofhmGwbt06WK1WAJDMJwW3pojLJ4c/a3fUpKysDEOHDoXD4VDcNk+o2cZA1qW3a2NEqI+Ni+Fngjy5w1iWRXp6On788Uds2rQJBw8eRLt27dC3b18nv5jeUNMnZjFHITpC+Us46LF4kY/PH6LeMOE1mTVrltP+PyUlJZIuLk8OsdmzZzstdGZZ1mmjTql8UnDuMU/44/hSk9zcXL69StvmCTXbGMi69HZtjAj1MeEPunaHHT9+HHv37sXhw4fRqVMnAPWag8TERKxZswbjx4/XImyvCMYgSEt/WKh6w4SxHj161OX86tWrec0D4NkhJucGO3z4MOx2O5o1a+a1e8xutyMlJUXyvJzzSYnjS03sdjuOHDnidMxT2zyhpu8qkHWRpyv4UB8T/qKbHaMBV3cY5wATTm2azWZERUVhz549koMgtd1hUqjpE6uuq0FMpMVzwgDWx6GHxdmBQigj5ZBzaHlizpw5im/XCpk7dy7WrVvnsi+WO+eTlONMKxeUu/6Sa5sn1PRdBbIu8nQFH+pjIhDoZmG0lDvstttuQ7t27fDcc8+htLQU1dXVmD9/Ps6fP49z585JljNv3jz+MWSr1UpbwRM+wzm0lMhKhZSXl8Nut3ud7/Lly9i3b5/Lcc75JC6vrq4Oly9fdrnVJnZpqcW+fftk/+iQa5sn3LU90G0MZF1qxh2uUB8TgUA3M0FS7rDIyEhs2LAB48aNQ3x8PMxmM/r27YuBAwfKlqO2O0yKYPvEnPxh5siAlu0JYX2h5A0T9plS5BxanmjYsCFuueUWF1+Xknzdu3d3Oe7O+dSgQQMXx5lWLqju3bujYcOGkgMhubZ5Qk3fVSDrIk9X8KE+JgKBLmaC5NxhQL0Msbi4GJcuXcK5c+ewZcsWXLx40UWxwaGlO0yKYLi1tPSHhao3zNNgjWEYl1s1cg4tTzz//POSvi4l+aRuF7lzPkk5zrRyQZlMJsyYMUPynFzbPKGm7yqQdZGnK/hQHxOBQNfuMCFWqxVNmzbF8ePHceDAAV2pI8So6ROrqqtGZa3yV1Xtde9XlRf5+Pwh6g0TXhNukb2QYcOGubi43DnEOFeXmKSkJHTt2tXF0eQJLp8c/ji+1CQlJcWlfz21zRNq+q4CWRd5uoIP9THhL7p3h61fvx5NmzZF27ZtcejQIUyePBmpqanYsGGDojqMtmM0oB9/WCjtEySkrKwMQ4YMCfg+QeI9cWifINonSG/XxohQHxsXw+8TpMQddu7cOYwcORK33XYbcnJyMHLkSKxZs0bDqLVFuOmf1lRWVqKiokL1x/T9xWq1IjMzEwzDIDY2FpMnT5Z0cblziHGursmTJyM2NpY3uwu/7KXypaWl8fWK3WOe8MfxpSZWqxU2m82rtnlCzTYGsi69XRsjQn1M+AO5w0IMvcwCCQnVGSGCIAhCvxh+JijUYVkWFRUVITkbQlyHu467du1Ceno6+vXrx7vACgsLkZGRgYyMDN5LxHnHBg0a5HRcjNhnJFXW1KlT0a9fP0ydOtUlrzhtOCPVl+HoivK13eHaXwThCU1ngpYuXYqlS5fi5MmTAIBOnTrhxRdf5B+B5zQE77zzDkpLS3H33XfjrbfeklzYKkcwR5LCWRm1ZkOcvGH9cmDx4hH5qtoaZG1/sz7vAzmwRPj+eH1VXQ2yttWXFeozQXKzawzDoFGjRigtLQUAJCQk4I033sDIkSOdBr0JCQlYsWKF0zS8eJ3C0qVLMWHCBH57/4SEBLz00kt48skn+TzLly9HmzZtUFlZiTFjxjilFZcfTkj15cSJE8NuDYiva19ozQwRqhh+Jqh169aYP38+Dhw4gAMHDuC+++7D4MGD+W33X375ZSxcuBCLFy/G/v370bx5czzwwAMoLy/XMmzdYDFHeukNuz7osUR4l9fVIabu/kRawLIsPwAC6r1EU6dOdZn1k/IViX1Gs2fP5t9zx3JycpzyZGdnu+SVKz+ccNeX4dQ3vjqyyK1FEPJoulniQw895PR+zpw5WLp0Kfbu3Ys77rgDr7/+OmbOnImHH34YALBy5Uo0a9YMq1evxhNPPKFFyLKotVhZS2+YkFB1iEnBzUQq4ddff5U8XlBQwPuKpHxGUp6x2tpap/fXrl3Du+++i40bN7otP5zw1Jfh4ory1ZFFbi2CcI9udoyuq6vD+vXrcfXqVfTo0QMnTpzA+fPn0a9fPz6NxWJBr1698NVXX8kOgtR0h6npCJOiuq5WVW+YuG4OIznEfKWurg6LFy/GnDlzJH1GSlm/fr1s+YsWLcK8efPCZhM4OTeUXDqjuqJ8dWSRW4sgPKP5wuhDhw7hhhtugMViwYQJE/DRRx/hjjvuwPnz5wEAzZo1c0rfrFkz/pwU5A4jtKKoqAj79u2T9BkFArvdHlY+JDk3lBiju6J8dWSRW4sgPKP5TFDHjh15LcaGDRswatQo7Nq1iz8v/kuFZVm3f72o6Q4LtiNMCmdvmHaXT1h3KDnExLAsixdffBHffvut32V169YN3bt3l/QZBYLU1NSwGtTLuaHEGN0V5asji9xaBOEZzQdBUVFRuOWWWwDUf4ns378fb7zxBqZPnw4AOH/+PFq0aMGnv3DhgsvskBCLxQKLRf1bRJxHS020nMqWcoiFKk8++SQmTJjgVxlmsxlZWVkwmUzIysrC+PHjfSpn6NCh+Oc//+ny17vZbPbJRxbKcA4oT31pdFeUXD94arev+QginND8dpgYlmVRVVWF9u3bo3nz5ti+fTt/rrq6Grt27cK9996rYYTXUdMRJkVVXY2X3rCa63lrvcvr6hCrcRNZaNG+fXsMGTJEUdqmTZtKHrfZbLyvSMpnJOUZi4hw/hskNjYW48aNk1xjJSw/nJDry3BzRfnqyCK3FkG4R9OZoBkzZmDgwIFo06YNysvLUVBQgJ07d2LLli1gGAZTpkzB3Llzceutt+LWW2/F3LlzERsbi2HDhmkZNg/DMJrOgHD79PiUd7vveY0GwzAYMWIEduzYwXvAuOPifYJee+01yX2CxAMXm82Gbdu24bfffkNCQgJmzZrlcZ+gRYsW8Xm3bt3qlDacF59L9SW3T1A49Y24H5S229d8BBEOaDoT9Msvv2DkyJHo2LEj7r//fnz99dfYsmULHnjgAQDAM888gylTpuDJJ59Et27dcPbsWWzbtg1xcXFahq0ptDN1cIiOjuY9YED9ACgzMxNTpkzhF9nn5OQgMTGR945FRUXxx8UzgWKfEZdOWNYtt9zCb/zZqVMnfo1GdHS0S9pQXXMVCKT6MhxdUb46ssitRRDykDssxNCbO2zt2rVo1KgRrS8gCIIgAorhd4w2IuHmE4uOjg65AZDwGkk5lbhj+fn5XvuWPDma9OzA8jYWPcVOEAThC7p2h23cuBHLli1DUVERLl68iIMHD6Jr165e1aH2TFCwfWLC8t8aMM6jvqKqtgaTti6vT99/nF++ML7MuhpM2lJfZih6w4R9mJCQgIsXL/JOJQC8Z8lkMsHhcCj2LXlyNOnZgeWtX4p8VARBBBvDzwR5coddvXoVf/jDHzB//nwtw9Qt9e4w9y9XX5j/LyN5w7iF0JxTSehZ4vZWUepb8uRo0rMDy1u/FPmoCIIwArp1h3Xq1AkjR44E4J3bSU8Ew6mlB3dYqHvDpPqQZVkUFBQ4HROm8eRb8uRo0rMDy1u/FPmoCIIwCppvlsghdof5ipruMCnU9InVu8OiglqHXL0cRnrc1p2eweFwyPqWPDmalPrEtHA6eeuXIh8VQRBGQvOF0XLuMF8hdxgRDBwOh6xvyZOjSalPTAunk7d+KfJREQRhJDSfCZJzh/k6EFLTHSZFsH1ienCHhbo3rKKiAjabzas8JpMJKSkpkp8lT44mpT4xLZxO3vqlyEdFEISR0HwQJOcOW7ZsmU/laeUOkyLYTi2tbjsYyRsmxGw2A5C+LcY5waT63JOjSalPTAunk7d+KfJREQRhJDS/HSaGc4eFKmr6xOrdYe5frr4w/1+h7g3jrtGQIUOcnEo2m83Js8ShxLfkydGkZweWt34p8lERBGEUdOsOA+ofXz59+jR+/vlnAMCxY8cAAM2bN0fz5s01i9sdavrEuL16FKff6l16o8JdoxEjRuDzzz93cSpxniVunyClviVPjiY9O7C89UuRj4ogCCOg6SCIc4edO3cOVqsVd955p5M77JNPPsGYMWP49Nw6jlmzZiE3N1dRHdxjvGo/JRYsKioqUFtb6zmhSly+fBk1NaE7MzR27Fi8++67GDt2LKqrq52O9e7dGzt37uTPcee9KU+cR3ieYRiP6dXE21j0FDtBEMaD+94O5nYwhneH/fTTT7RYkyAIgiBClDNnzqB169ZBKdvwgyCHw4Gff/4ZcXFxAVm0yT1tdubMGUMIWf2B+qIe6od6qB/qoX64DvVFPdQP9XjbDyzLory8HC1btoTJFJwlzJo/HRZsTCZTUEaQDRs2DOsPsxDqi3qoH+qhfqiH+uE61Bf1UD/U400/WK3WoMaiu6fDCIIgCIIg1IAGQQRBEARBhCU0CPISi8WCWbNm6WZDRi2hvqiH+qEe6od6qB+uQ31RD/VDPXrsB8MvjCYIgiAIgpCCZoIIgiAIgghLaBBEEARBEERYQoMggiAIgiDCEhoEEQRBEAQRltAgyEuWLFmC9u3bIzo6Gqmpqfjyyy+1DkkR8+bNw1133YW4uDgkJiYiPT2dF9JyjB49GgzDOL3uuecepzRVVVXIzs5GkyZN0KBBA/z5z3/GTz/95JSmtLQUI0eOhNVqhdVqxciRI3Hp0iWnNKdPn8ZDDz2EBg0aoEmTJsjJyVHNPZWbm+vSTqGQl2VZ5ObmomXLloiJiUHv3r1x5MgRpzKM0A833nijSz8wDINJkyYBMO7nYffu3XjooYfQsmVLMAyDjz/+2Om83q7/oUOH0KtXL8TExKBVq1b4+9//HhCXkrt+qKmpwfTp09G5c2c0aNAALVu2xGOPPcbLrDl69+7t8hnhHI+h0g+e+gLQ3++CFp8JAJL/XzAMg1deeYVPE3KfCZZQTEFBARsZGcm+++677HfffcdOnjyZbdCgAXvq1CmtQ/NI//792RUrVrCHDx9mi4uL2QcffJBt27Yte+XKFT7NqFGj2AEDBrDnzp3jXxcvXnQqZ8KECWyrVq3Y7du3s3a7ne3Tpw/bpUsXtra2lk8zYMAANikpif3qq6/Yr776ik1KSmIHDRrEn6+trWWTkpLYPn36sHa7nd2+fTvbsmVLNisrK/gdwbLsrFmz2E6dOjm188KFC/z5+fPns3FxceyGDRvYQ4cOsUOHDmVbtGjBXr582VD9cOHCBac+2L59OwuA/eKLL1iWNe7n4bPPPmNnzpzJbtiwgQXAfvTRR07n9XT9y8rK2GbNmrE2m409dOgQu2HDBjYuLo79xz/+EdR+uHTpEtu3b1927dq17Pfff88WFhayd999N5uamupURq9evdjHH3/c6TNy6dIlpzR67wdPfcGy+vpd0OozwbKsU/vPnTvHvvfeeyzDMOz//vc/Pk2ofSZoEOQF3bt3ZydMmOB07LbbbmOfffZZjSLynQsXLrAA2F27dvHHRo0axQ4ePFg2z6VLl9jIyEi2oKCAP3b27FnWZDKxW7ZsYVmWZb/77jsWALt3714+TWFhIQuA/f7771mWrf9FM5lM7NmzZ/k0a9asYS0WC1tWVhaoJsoya9YstkuXLpLnHA4H27x5c3b+/Pn8scrKStZqtbJvv/02y7LG6QcxkydPZm+++WbW4XCwLBsenwfxf/R6u/5LlixhrVYrW1lZyaeZN28e27JlS/46BaMfpNi3bx8LwOmPvl69erGTJ0+WzRNq/cCy0n2hp98FPX0mBg8ezN53331Ox0LtM0G3wxRSXV2NoqIi9OvXz+l4v3798NVXX2kUle+UlZUBAOLj452O79y5E4mJiejQoQMef/xxXLhwgT9XVFSEmpoapz5o2bIlkpKS+D4oLCyE1WrF3Xffzae55557YLVandIkJSWhZcuWfJr+/fujqqoKRUVFgW+sBMePH0fLli3Rvn172Gw2/PjjjwCAEydO4Pz5805ttFgs6NWrFx+/kfqBo7q6Gh9++CHGjh3rJBoOl88Dh96uf2FhIXr16uW0uVz//v3x888/4+TJk4HvADeUlZWBYRg0atTI6fiqVavQpEkTdOrUCU899RTKy8v5c0bqB738LuihLwDgl19+webNmzFu3DiXc6H0mTC8QDVQ/Pbbb6irq0OzZs2cjjdr1gznz5/XKCrfYFkW06ZNQ8+ePZGUlMQfHzhwIDIyMtCuXTucOHECL7zwAu677z4UFRXBYrHg/PnziIqKQuPGjZ3KE/bB+fPnkZiY6FJnYmKiUxpxPzZu3BhRUVGq9OXdd9+N999/Hx06dMAvv/yCvLw83HvvvThy5Ahfv9R1PnXqFB+/EfpByMcff4xLly5h9OjR/LFw+TwI0dv1P3/+PG688UaXerhz7du396WZXlNZWYlnn30Ww4YNcxJfDh8+HO3bt0fz5s1x+PBhPPfcc/jmm2+wfft2PkYj9IOefhe07guOlStXIi4uDg8//LDT8VD7TNAgyEuEfyUD9QMK8TG9k5WVhW+//RZ79uxxOj506FD+56SkJHTr1g3t2rXD5s2bXT7oQsR9INUfvqQJFgMHDuR/7ty5M3r06IGbb74ZK1eu5Bc7+nKdQ60fhCxfvhwDBw50+ssrXD4PUujp+kvFIpc3GNTU1MBms8HhcGDJkiVO5x5//HH+56SkJNx6663o1q0b7HY7UlJSZOMMtX7Q2++C1p8JAHjvvfcwfPhwREdHOx0Ptc8E3Q5TSJMmTWA2m13+Mr1w4YLLiFXPZGdn45NPPsEXX3yB1q1bu03bokULtGvXDsePHwcANG/eHNXV1SgtLXVKJ+yD5s2b45dffnEp69dff3VKI+7H0tJS1NTUaNKXDRo0QOfOnXH8+HH+KTF319lo/XDq1Cns2LED48ePd5suHD4Perv+Umm42zBq9E1NTQ2GDBmCEydOYPv27U6zQFKkpKQgMjLS6TNihH4Qo+Xvgh764ssvv8SxY8c8/p8B6P8zQYMghURFRSE1NZWf0uPYvn077r33Xo2iUg7LssjKysLGjRvx+eefK5oqvHjxIs6cOYMWLVoAAFJTUxEZGenUB+fOncPhw4f5PujRowfKysqwb98+Ps3XX3+NsrIypzSHDx/GuXPn+DTbtm2DxWJBampqQNrrDVVVVTh69ChatGjBT+MK21hdXY1du3bx8RutH1asWIHExEQ8+OCDbtOFw+dBb9e/R48e2L17t9Ojwdu2bUPLli1dbgUEGm4AdPz4cezYsQMJCQke8xw5cgQ1NTX8Z8QI/SCFlr8LeuiL5cuXIzU1FV26dPGYVvefCcVLqAn+Efnly5ez3333HTtlyhS2QYMG7MmTJ7UOzSMTJ05krVYru3PnTqdHF69du8ayLMuWl5ezf/vb39ivvvqKPXHiBPvFF1+wPXr0YFu1auXyaHDr1q3ZHTt2sHa7nb3vvvskHwO988472cLCQrawsJDt3Lmz5OOP999/P2u329kdO3awrVu3Vu3R8L/97W/szp072R9//JHdu3cvO2jQIDYuLo6/jvPnz2etViu7ceNG9tChQ2xmZqbkI9Kh3g8sy7J1dXVs27Zt2enTpzsdN/Lnoby8nD148CB78OBBFgC7cOFC9uDBg/xTT3q6/pcuXWKbNWvGZmZmsocOHWI3btzINmzYMCCPQ7vrh5qaGvbPf/4z27p1a7a4uNjp/4yqqiqWZVn2hx9+YGfPns3u37+fPXHiBLt582b2tttuY5OTk0OqHzz1hd5+F7T6THCUlZWxsbGx7NKlS13yh+JnggZBXvLWW2+x7dq1Y6OiotiUlBSnR8z1DADJ14oVK1iWZdlr166x/fr1Y5s2bcpGRkaybdu2ZUeNGsWePn3aqZyKigo2KyuLjY+PZ2NiYthBgwa5pLl48SI7fPhwNi4ujo2Li2OHDx/OlpaWOqU5deoU++CDD7IxMTFsfHw8m5WV5fSoYzDh9n2JjIxkW7ZsyT788MPskSNH+PMOh4OdNWsW27x5c9ZisbBpaWnsoUOHnMowQj+wLMtu3bqVBcAeO3bM6biRPw9ffPGF5O/CqFGjWJbV3/X/9ttv2T/+8Y+sxWJhmzdvzubm5gbkUWh3/XDixAnZ/zO4faROnz7NpqWlsfHx8WxUVBR78803szk5OS775+i9Hzz1hR5/F7T4THAsW7aMjYmJcdn7h2VD8zPBsGyAttwkCIIgCIIIIWhNEEEQBEEQYQkNggiCIAiCCEtoEEQQBEEQRFhCgyCCIAiCIMISGgQRBEEQBBGW0CCIIAiCIIiwhAZBBEEQBEGEJTQIIgiCIAgiLKFBEEEQRAA5efIkGIZBcXGx1qEQBOEBGgQRRBgwevRoMAwDhmEQGRmJm266CU899RSuXr0a8Lq4ehiGQVxcHLp164aNGzcGvB418KUtbdq0wblz55CUlKRSlARB+AoNgggiTBgwYADOnTuHH3/8EXl5eViyZAmeeuopn8piWRa1tbWy51esWIFz585h//796NKlCzIyMlBYWOhTXUJLtBZ405bq6mqYzWY0b94cERERKkdKEIS30CCIIMIEi8WC5s2bo02bNhg2bBiGDx+Ojz/+GED9oObll1/GTTfdhJiYGHTp0gX//Oc/+bw7d+4EwzDYunUrunXrBovFgi+//FK2rkaNGqF58+a47bbb8PbbbyM6OhqffPIJAODs2bMYOnQoGjdujISEBAwePBgnT57k844ePRrp6emYN28eWrZsiQ4dOgAAlixZgltvvRXR0dFo1qwZHn30UT5PVVUVcnJykJiYiOjoaPTs2RP79+93if8///kPunXrhtjYWNx77704duyYx35z15Ybb7wReXl5GD16NKxWKx5//HHJ22FHjhzBgw8+iIYNGyIuLg5//OMf8b///Y8/v2LFCtx+++2Ijo7GbbfdhiVLlniMiyAI/6FBEEGEKTExMaipqQEAPP/881ixYgWWLl2KI0eOYOrUqRgxYgR27drllOeZZ57BvHnzcPToUdx5552K6omMjERERARqampw7do19OnTBzfccAN2796NPXv24IYbbsCAAQOcZnz+85//4OjRo9i+fTs+/fRTHDhwADk5Ofj73/+OY8eOYcuWLUhLS3OKa8OGDVi5ciXsdjtuueUW9O/fHyUlJU6xzJw5E6+++ioOHDiAiIgIjB071qs+E7aF45VXXkFSUhKKiorwwgsvuOQ5e/Ys0tLSEB0djc8//xxFRUUYO3YsP5P27rvvYubMmZgzZw6OHj2KuXPn4oUXXsDKlSu9io0gCB/wyjlPEERIMmrUKHbw4MH8+6+//ppNSEhghwwZwl65coWNjo5mv/rqK6c848aNYzMzM1mWZdkvvviCBcB+/PHHHusCwH700Ucsy7JsZWUl+9JLL7EA2M8++4xdvnw527FjR9bhcPDpq6qq2JiYGHbr1q18rM2aNWOrqqr4NBs2bGAbNmzIXr582aW+K1eusJGRkeyqVav4Y9XV1WzLli3Zl19+2Sn+HTt28Gk2b97MAmArKip8agvLsmy7du3Y9PR0pzwnTpxgAbAHDx5kWZZln3vuObZ9+/ZsdXW1ZB1t2rRhV69e7XTspZdeYnv06CEbF0EQgYFuWhNEmPDpp5/ihhtuQG1tLWpqajB48GAsWrQI3333HSorK/HAAw84pa+urkZycrLTsW7duimqKzMzE2azGRUVFbBarfjHP/6BgQMHYtKkSfjhhx8QFxfnlL6ystLp9lDnzp0RFRXFv3/ggQfQrl073HTTTRgwYAAGDBiAv/zlL4iNjcX//vc/1NTU4A9/+AOfPjIyEt27d8fRo0ed6hHOXrVo0QIAcOHCBbRt29brtijtk+LiYvzxj39EZGSky7lff/0VZ86cwbhx4/D444/zx2tra2G1Wt2WSxCE/9AgiCDChD59+mDp0qWIjIxEy5Yt+S/lEydOAAA2b96MVq1aOeWxWCxO7xs0aKCortdeew19+/ZFw4YNkZiYyB93OBxITU3FqlWrXPI0bdpUtp64uDjY7Xbs3LkT27Ztw4svvojc3Fzs378fLMsCqH+SSwjLsi7HhAMR7pzD4fCpLXKxiomJiZE9x9X97rvv4u6773Y6Zzab3ZZLEIT/0CCIIMKEBg0a4JZbbnE5fscdd8BiseD06dPo1atXQOpq3ry5ZF0pKSlYu3YtEhMT0bBhQ6/KjIiIQN++fdG3b1/MmjULjRo1wueff47+/fsjKioKe/bswbBhwwAANTU1OHDgAKZMmRK0tijlzjvvxMqVK1FTU+MyG9SsWTO0atUKP/74I4YPH+5vqARBeAkNgggizImLi8NTTz2FqVOnwuFwoGfPnrh8+TK++uor3HDDDRg1alTA6ho+fDheeeUVDB48GH//+9/RunVrnD59Ghs3bsTTTz+N1q1bS+b79NNP8eOPPyItLQ2NGzfGZ599BofDgY4dO6JBgwaYOHEinn76acTHx6Nt27Z4+eWXce3aNYwbNy5gsftKVlYWFi1aBJvNhueeew5WqxV79+5F9+7d0bFjR+Tm5iInJwcNGzbEwIEDUVVVhQMHDqC0tBTTpk3TOnyCMDQ0CCIIAi+99BISExMxb948/Pjjj2jUqBFSUlIwY8aMgNYTGxuL3bt3Y/r06Xj44YdRXl6OVq1a4f7773c7M9SoUSNs3LgRubm5qKysxK233oo1a9agU6dOAID58+fD4XBg5MiRKC8vR7du3bB161Y0btw4oPH7QkJCAj7//HM8/fTT6NWrF8xmM7p27cqvYRo/fjxiY2Pxyiuv4JlnnkGDBg3QuXPngMxiEQThHoblbqgTBEEQBEGEEbRPEEEQBEEQYQkNggiCIAiCCEtoEEQQBEEQRFhCgyCCIAiCIMISGgQRBEEQBBGW0CCIIAiCIIiwhAZBBEEQBEGEJTQIIgiCIAgiLKFBEEEQBEEQYQkNggiCIAiCCEtoEEQQBEEQRFjy/wHOTLjFwdZcrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y = \"Day\", x  =\"Per Person Price\", \n",
    "            data = combined, orient = \"h\")\n",
    "plt.show() # 5, 8, 10, 11, 15, 18, 19, 27, 29 and 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "debd7985",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Jan, April, may, July, Sept, Oct and Nov\n",
    "\n",
    "combined[\"Day_Season\"]=  combined.Day.apply(lambda x:\"Peak_Days\" \\\n",
    "                     if x in [5, 8, 10, 11, 15, \n",
    "                              18, 19, 27, 29, 30]\n",
    "                    else \"Normal_Days\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8f8256af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Day_Season', ylabel='Per Person Price'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = \"Day_Season\", \n",
    "            y = \"Per Person Price\", \n",
    "            data = combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "68da214b",
   "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>Uniq Id</th>\n",
       "      <th>Package Name</th>\n",
       "      <th>Package Type</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Itinerary</th>\n",
       "      <th>Places Covered</th>\n",
       "      <th>Travel Date</th>\n",
       "      <th>Hotel Details</th>\n",
       "      <th>Start City</th>\n",
       "      <th>Airline</th>\n",
       "      <th>Flight Stops</th>\n",
       "      <th>Meals</th>\n",
       "      <th>Sightseeing Places Covered</th>\n",
       "      <th>Cancellation Rules</th>\n",
       "      <th>Per Person Price</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>Day_Season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e788ab76d9d8cf1e6ed2f139645ca5d1</td>\n",
       "      <td>Best of Shimla and Manali Holiday from Delhi</td>\n",
       "      <td>Standard</td>\n",
       "      <td>New Delhi|Shimla|Manali|Chandigarh</td>\n",
       "      <td>1N New Delhi . 2N Shimla . 2N Manali . 1N Chan...</td>\n",
       "      <td>New Delhi|Shimla|Manali|Chandigarh</td>\n",
       "      <td>2021-07-30</td>\n",
       "      <td>Not Available</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Not Available</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Not Available</td>\n",
       "      <td>Not Available</td>\n",
       "      <td>11509.0</td>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "      <td>Peak_Month</td>\n",
       "      <td>Peak_Days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Uniq Id  \\\n",
       "0  e788ab76d9d8cf1e6ed2f139645ca5d1   \n",
       "\n",
       "                                   Package Name Package Type  \\\n",
       "0  Best of Shimla and Manali Holiday from Delhi     Standard   \n",
       "\n",
       "                          Destination  \\\n",
       "0  New Delhi|Shimla|Manali|Chandigarh   \n",
       "\n",
       "                                           Itinerary  \\\n",
       "0  1N New Delhi . 2N Shimla . 2N Manali . 1N Chan...   \n",
       "\n",
       "                       Places Covered Travel Date  Hotel Details Start City  \\\n",
       "0  New Delhi|Shimla|Manali|Chandigarh  2021-07-30  Not Available     Mumbai   \n",
       "\n",
       "         Airline  Flight Stops  Meals Sightseeing Places Covered  \\\n",
       "0  Not Available             2      3              Not Available   \n",
       "\n",
       "  Cancellation Rules  Per Person Price  Day  Month  Year      Season  \\\n",
       "0      Not Available           11509.0   30      7  2021  Peak_Month   \n",
       "\n",
       "  Day_Season  \n",
       "0  Peak_Days  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3f4ce93d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check meals and Airline...\n",
    "combined[\"Flights\"] = combined.Airline.str.split(\"|\", \n",
    "                                                 expand = True)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7a31eed1",
   "metadata": {},
   "outputs": [],
   "source": [
    "flight = combined.Flights.value_counts()[:5].index.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "05d2f5ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Flight_Cat\"] = combined.Flights.apply(lambda x:\"Top5\" \n",
    "                                                if x in flight \n",
    "                                                else \"Others\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ef57ba1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Flight_Cat', ylabel='Per Person Price'>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = \"Flight_Cat\", \n",
    "            y = \"Per Person Price\", \n",
    "            data = combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e81c1a05",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Places Covered\n",
    "#combined[\"Places Covered\"].str.split(\"|\", expand = True).iloc[:,0:4]\n",
    "\n",
    "combined[\"Count_Locs\"]=  combined[\"Places Covered\"].\\\n",
    "apply(lambda x:len(x.split(\"|\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "91ec9231",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Meals', ylabel='Per Person Price'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = \"Meals\", \n",
    "            y = \"Per Person Price\", \n",
    "            data = combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "79bd6b1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(combined.Itinerary.str.split(\".\", expand = True)[0].str.split(\"\", \n",
    "                                                              expand = True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "4ae6f657",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1N New Delhi</td>\n",
       "      <td>2N Shimla</td>\n",
       "      <td>2N Manali</td>\n",
       "      <td>1N Chandigarh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1N Srinagar</td>\n",
       "      <td>2N Pahalgam</td>\n",
       "      <td>1N Srinagar</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2N Udaipur</td>\n",
       "      <td>1N Chittorgarh</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2N Munnar</td>\n",
       "      <td>1N Kumarakom</td>\n",
       "      <td>1N Allepey</td>\n",
       "      <td>2N Kovalam and Poovar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4N Pattaya</td>\n",
       "      <td>3N Bangkok</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8995</th>\n",
       "      <td>2N Munnar</td>\n",
       "      <td>1N Thekkady</td>\n",
       "      <td>1N Allepey</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8996</th>\n",
       "      <td>1N Srinagar</td>\n",
       "      <td>1N Pahalgam</td>\n",
       "      <td>1N Srinagar</td>\n",
       "      <td>1N Gulmarg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8997</th>\n",
       "      <td>3N Dharamshala</td>\n",
       "      <td>2N Dalhousie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8998</th>\n",
       "      <td>2N Jaipur</td>\n",
       "      <td>1N Jodhpur</td>\n",
       "      <td>2N Jaisalmer</td>\n",
       "      <td>2N Udaipur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8999</th>\n",
       "      <td>2N Shirdi</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0                1               2                       3\n",
       "0       1N New Delhi        2N Shimla       2N Manali            1N Chandigarh\n",
       "1        1N Srinagar      2N Pahalgam      1N Srinagar                    None\n",
       "2         2N Udaipur    1N Chittorgarh            None                    None\n",
       "3          2N Munnar     1N Kumarakom      1N Allepey    2N Kovalam and Poovar\n",
       "4         4N Pattaya        3N Bangkok            None                    None\n",
       "...               ...              ...             ...                     ...\n",
       "8995       2N Munnar      1N Thekkady       1N Allepey                    None\n",
       "8996     1N Srinagar      1N Pahalgam     1N Srinagar               1N Gulmarg\n",
       "8997  3N Dharamshala      2N Dalhousie            None                    None\n",
       "8998       2N Jaipur       1N Jodhpur    2N Jaisalmer               2N Udaipur\n",
       "8999        2N Shirdi             None            None                    None\n",
       "\n",
       "[30000 rows x 4 columns]"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.Itinerary.str.split(\".\", expand = True).iloc[:, 0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "7930f89f",
   "metadata": {},
   "outputs": [],
   "source": [
    "count1 = localdf[0].str.split(\"N\", expand = True)[0]\n",
    "count2 = localdf[1].str.split(\"N\", expand = True)[0]\n",
    "count3 = localdf[2].str.split(\"N\", expand = True)[0]\n",
    "count4 = localdf[3].str.split(\"N\", expand = True)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b09f6315",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_m = []\n",
    "for i in range(0, 11):\n",
    "    saved = combined.Itinerary.str.split(\".\", expand = True)[i]\\\n",
    "    .str.split(\"N\", expand = True)[0].\\\n",
    "    apply(lambda x:0 if x==None else x).astype(int)\n",
    "    list_m.append(saved)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d8285ca8",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Total_Travel_Days\"] =\\\n",
    "pd.concat(list_m, axis = 1).sum(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "e86181bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Total_Travel_Days', ylabel='Per Person Price'>"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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twPM8f+LECb5nz568UqnkY2Nj+Xnz5tWq1ALPU7kFQgghRIxu9/2b43kqHR5MBoMBWq0Wer2e8q0IIYQQkbjd929R5VgRQgghhLCMAitCCCGEkDpCgRUhhBBCSB2hwIoQQgghpI5QYEUIIYQQUkcosCKEEEIIqSMUWBFCCCGE1BEKrAghhBBC6ggFVoQQQgghdYQCK0IIIYSQOkKBFSGEEEJIHaHAihBCCCGkjlBgRQghhBBSRyiwIoQQQgipIxRYEUIIIYTUEQqsCCGEEELqCAVWhBBCCCF1RCb0AAghhJD6ojfZUGy0wWCxI1wth06jgDZEIfSwSANGgRUhhJAG6XqZGS9+dQL7LhS7b+uVpMPro9ohPkIt4MhIQ0ZLgYQQQu6I3mTDz0VGHMsvxc83jdCbbEIPyU1vsnkFVQCw90IxXvrqBFNjJQ0LzVgRQgipNdZng4qNNq+gqsreC8UoNtpoSZDUC5qxIoQQUitimA0yWOx+j5cHOE7InaLAihBCSK3czmyQ0MJVcr/HwwIcJ+ROUWBFCCGkVsQwG6QLVaBXks7nsV5JOuhCaRmQ1A8KrAghhNSKGGaDtCEKvD6qnVdw1StJh2Wj2lF+Fak3lLxOCCGkVqpmg/b6WA5kaTYoPkKN7LEdUGy0odxiR5hKDl0o1bEi9YtmrAghhNSKmGaDtCEK3BcdigeaReK+6FCmxkYaJpqxIoQQUms0G0SIbxRYEUIIuSPaEAqkCKmOlgIJIYQQQuoIBVaEEEIIIXWElgIJIYTcEb3JhmKjDQaLHeFqOXQaWhokhAIrQgghtcZ6r0BChEJLgYQQQmpFDL0CCREKBVaEEEJqRQy9AgkRCgVWhBBCakUMvQIJEQoFVoQQQmpFDL0CCREKBVaEEEJqpapXoC8s9QokdUdvsuHnIiOO5Zfi55tGyqPzQ9DAau/evRg2bBji4+PBcRw2btzoPma32/Hiiy8iJSUFGo0G8fHxeOqpp3D9+nWP3/Hwww+D4ziPryeeeMLjnNLSUqSnp0Or1UKr1SI9PR1lZWUe5+Tn52PYsGHQaDTQ6XSYOnUqbDbPJ87JkyfRu3dvqNVqNGnSBAsWLADP83V6nxBCCOvE1CuQ/HnXy8yYvO4Y+r39A0a+vx/9lv+AKeuO4XqZWeihMUnQcgsVFRVo3749xo8fj1GjRnkcM5lMOHr0KGbPno327dujtLQU06ZNw/Dhw/HTTz95nJuVlYUFCxa4v1erPbf6pqWl4dq1a9i2bRsA4Omnn0Z6ejo2b94MAHA6nRgyZAgaN26MnJwclJSUICMjAzzPIzs7GwBgMBjQv39/9OnTB0eOHMH58+eRmZkJjUaDGTNm1Pl9Q4hYUW2juwP1Crw7BNoBmj22Az3m1QgaWA0ePBiDBw/2eUyr1WLHjh0et2VnZ+Ohhx5Cfn4+mjVr5r49JCQEsbGxPn/PmTNnsG3bNhw8eBBdunQBAKxcuRLdunXDuXPn0LJlS2zfvh2nT5/G1atXER8fDwBYvnw5MjMzsXjxYoSHh2Pt2rWwWCxYs2YNlEolkpOTcf78ebz99tuYPn06OI6ri7uEEFGj2kZ3F+oV2PDdzg5Qeg54ElWOlV6vB8dxiIiI8Lh97dq10Ol0aNu2LWbOnIny8nL3sQMHDkCr1bqDKgDo2rUrtFot9u/f7z4nOTnZHVQBwMCBA2G1WpGbm+s+p3fv3lAqlR7nXL9+HZcvX66H/y0h4kK1jQhpeGgHaO2JpvK6xWLBSy+9hLS0NISHh7tvHzduHBISEhAbG4u8vDy8/PLL+N///uee7SosLER0dLTX74uOjkZhYaH7nJiYGI/jkZGRUCgUHue0aNHC45yqnyksLERCQoLPcVutVlitVvf3BoOhlv9zQsSBPtkS0vDQDtDaE0VgZbfb8cQTT8DlcuH999/3OJaVleX+d3JyMpKSktC5c2ccPXoUHTt2BACfy3Q8z3vcfifnVCWu+1sGXLp0KebPn+/vv0dIg0CfbAlpeKp2gO718aGJdoD6xvxSoN1ux5gxY3Dp0iXs2LHDY7bKl44dO0Iul+PChQsAgNjYWNy4ccPrvJs3b7pnnGJjY90zU1VKS0tht9v9nlNUVAQAXrNdf/Tyyy9Dr9e7v65evRrgf0yIONEnW0IaHtoBWntMz1hVBVUXLlzAnj17EBUVFfBnTp06Bbvdjri4OABAt27doNfrcfjwYTz00EMAgEOHDkGv16N79+7ucxYvXoyCggL3z23fvh1KpRKdOnVyn/PKK6/AZrNBoVC4z4mPj/daIvwjpVLpkZdFSENFn2wJaZhoB2jtCDpjZTQacfz4cRw/fhwAcOnSJRw/fhz5+flwOBz4y1/+gp9++glr166F0+lEYWEhCgsL3fWlfv75ZyxYsAA//fQTLl++jG+//RajR49Ghw4dkJqaCgBo3bo1Bg0ahKysLBw8eBAHDx5EVlYWhg4dipYtWwIABgwYgDZt2iA9PR3Hjh3Drl27MHPmTGRlZblnyNLS0qBUKpGZmYm8vDxs2LABS5YsoR2BhPyGPtkS0vDxAEBvef7xAtqzZw+PysfJ4ysjI4O/dOmSz2MA+D179vA8z/P5+fl8r169+EaNGvEKhYK/7777+KlTp/IlJSUef6ekpIQfN24cHxYWxoeFhfHjxo3jS0tLPc65cuUKP2TIEF6tVvONGjXiJ0+ezFssFo9zTpw4wffs2ZNXKpV8bGwsP2/ePN7lctXq/6zX63kAvF6vr/X9RYgYlFVY+Ys3yvljV27xF2+U82UVVqGHRAj5E34tNfFPfnSQb/7iFvdX+kcH+V9LTUIPLahu9/2b43kqHR5MBoMBWq0Wer0+YL4YIYQQIiS9yYbJ64753PHbK0nHVIHQ+i5OfLvv30znWBFCCCFEOGIpo8JScWLmdwUSQgghRBhiKKPCWnFiCqwIIYQQ4pMYyqjczqxaMFFgRQghhBCfqsqo+MJKGRXWZtUosCKEEEKIT2Ioo8LarBolrxNCCCGkRvERarw5uj1KK2wwWBwIV8sQGaJATLhK6KEBYK84MQVWhBBCCKkRSzvufKmaVXvpqxMewZVQs2pUxyrIqI4VIYQQsRBjHav6artDdawIIYQQ8qcUG23IvVKKyX0T0aFpBKwOF1RyKY7ml+LjnEvM1LECKmeuWBgLBVaEEEII8cloteP9cR2xat8vWLH7ovv2nok6vD+uIyqswtexYg3tCiSEEEKIT5EhCqza9wv2XSzxuH3fxWKsyrmECAZmiFhDgRUhhBBCfDLbnV5BVZV9F4phtjuDPCL20VIgIYQQQnwymB1/6ngw1XcT5ttFgRUhhBBCfApRSv/U8WBhqSQELQUSQgghxKcQuRSpiVE+j6UmRiFELnxgRU2YCSGEECIKGqUMU/okeQVXqYlRmNInCRql8AtfrDVhFv4eIYQQQgiTlDIJJBwwJCUOE1ITYHW4oJRJcMNggURSeVxoDaIJ82effYbU1FTEx8fjypUrAIB3330X33zzTZ0OjhBCCCHCKTbakLnmCK7rLR63X9dbkLn6SNBng3wRfRPmDz74AHPmzMG0adOwePFiOJ2VWy0jIiLw7rvv4rHHHqvzQRJCCCEk+AwWO0w2p0dx0D8K9myQL6w1Ya71jFV2djZWrlyJV199FVLp70lrnTt3xsmTJ+t0cIQQQggRDmuzQb5UNWHulaTzuF2oJsy1nrG6dOkSOnTo4HW7UqlERUVFnQyKEEIIIcJjbTaoJvERamSP7VCvTZhvV61nrBISEnD8+HGv27/77ju0adOmLsZECCGEEAZoQxRYNCIZPartCuyRGIVFI5KZaHpcRRuiwH3RoXigWSTuiw4VbGy1nrF64YUXMGnSJFgsFvA8j8OHD2PdunVYunQpPvroo/oYIyGEEEIEoDfZsGDLaTzQLBLj/7Ar8NjVMizcchpvjW7PTHAl2srr48ePh8PhwKxZs2AymZCWloYmTZrg//2//4cnnniiPsZICCGEEAEUG23YeaYIO88U1XichcCKpcrrd1THKisrC1lZWSguLobL5UJ0dHRdj4sQIlKsfGokhPx5rNWI8iVQ5fXssR2Ceg26o+R1h8OBpKQk6HS/Z+BfuHABcrkcLVq0qMvxEUJEhKVPjYSQP08MuwJvp/J6MAOrWievZ2ZmYv/+/V63Hzp0CJmZmXUxJkKICLHWr4vUP73Jhp+LjDiWX4qfbxrpMW6AqnYF+sLKrkDWZtVqPWN17NgxpKamet3etWtXTJ48uU4GRQgRH9Y+NZL6RbOTd4eqGlEvfXXCo+SCUDWifGFtVq3WgRXHcSgvL/e6Xa/Xu6uwE0LuPgaLHSEKKSb0SECHphGwOlxQyaU4ml+Kj3MuMZGLQeoGazktpH6xVCPKF9ZqbXE8z/O1+YGhQ4ciJCQE69atc1dedzqd+Otf/4qKigp899139TLQhsJgMECr1UKv1yM8PFzo4RBSZ365acQvxRVY/eMl/HixxH17amIUxqcm4F6dBvc2DhVwhKSu/FxkRL+3f6jx+K7pvXFfND3WJHiul5lrnFWLq6MZ1Nt9/671jNUbb7yBXr16oWXLlujZsycAYN++fTAYDNi9e/edj5gQImoapcwrqAKAHy+WgAOwfMwDgoyL1D3WcloIYWlWrdbJ623atMGJEycwZswYFBUVoby8HE899RTOnj2L5OTk+hgjIUQEjBaHV1BVJediCYwWR5BHROoLazkthAAirrwOAPHx8ViyZEldj4UQImI0i3H30IUq0L91NFrGhXvl050rMDCxU6wK1VUjwXZbgdWJEyeQnJwMiUSCEydO+D23Xbt2dTIwQoi40CzG3UMbosDsoW3w8oaTWLH7ovv2HolRWDIyhZnAhXYu3l1uGCworbDBYHEgXC1DZIgCMeGqoI/jtpLXJRIJCgsLER0dDYlEAo7j4OvHOI6jnYEBUPI6aaj0Jhsmrzvms+RCzyQdVtBOsQbD32PdK0nHxK5AMYyR1J2rJRXYd7EYMeEq9wzqDb0ZPRJ1aBqlqZO/UafJ65cuXULjxo3d/yaEEF8m9UmEi+e9dgVO6pMo4KhIXRNDzTIxjFFMWF5SLTJYcK3MjK0nC7yuPS10GijlUkQHcebqtgKr5s2bAwDsdjvmzZuH2bNn4957763XgRFCxKXYaMOENUcwoUcCJqQmwOpwQSmT4NjVMkxYcwSbJ/dg5kJM/hwx5NOJYYxiwfqSaoXVgRV7LvrckQwAix4L7sa6WiWvy+VybNiwAbNnz66v8RBCRMpgscNkc3rk3PwRvZE1HGLIpxPDGMVADMVgTXZnjTuSf7xYApM9uClKtS63MHLkSGzcuLEehkIIETN6I7t7iKF/XKhKhh6JUT6P9UiMQqjqjjbF33VuZ0lVaCar/8Ap0PG6VuvAKjExEQsXLsRf/vIXLF26FO+9957HV23s3bsXw4YNQ3x8PDiO8wrYeJ7HvHnzEB8fD7VajYcffhinTp3yOMdqtWLKlCnQ6XTQaDQYPnw4rl275nFOaWkp0tPTodVqodVqkZ6ejrKyMo9z8vPzMWzYMGg0Guh0OkydOhU2m+cT5uTJk+jduzfUajWaNGmCBQsW+EziJ+RuJIY3W1I3qvrHVX+8WeofV2F1IDM1AanVgqvUxChkpiagwkp11W6HGJZUtWr/H9oCHa9rtQ7ZP/roI0RERCA3Nxe5ubkexziOw9SpU2/7d1VUVKB9+/YYP348Ro0a5XX8jTfewNtvv401a9bg/vvvx6JFi9C/f3+cO3cOYWFhAIBp06Zh8+bNWL9+PaKiojBjxgwMHToUubm57pY7aWlpuHbtGrZt2wYAePrpp5Geno7NmzcDqGzJM2TIEDRu3Bg5OTkoKSlBRkYGeJ5HdnY2gMrdAP3790efPn1w5MgRnD9/HpmZmdBoNJgxY0Zt70ZCGhwxNGsldYcDMDglDhndW7jz6YrKrUIPy01vtmPqumM+c/6mrjuGL/7WReghioIYZqJjwpV4pHU0Wvmoq3a2wICYcGVQx1PrwKoudwUOHjwYgwcP9nmM53m8++67ePXVV/H4448DAD755BPExMTgiy++wDPPPAO9Xo9Vq1bhs88+wyOPPAIA+Pzzz9G0aVPs3LkTAwcOxJkzZ7Bt2zYcPHgQXbpUvpBWrlyJbt264dy5c2jZsiW2b9+O06dP4+rVq4iPjwcALF++HJmZmVi8eDHCw8Oxdu1aWCwWrFmzBkqlEsnJyTh//jzefvttTJ8+HRzH1dn9QohYsdRWgtQfvcmGWT7ybgB2Shn4Cgj+eJ1mISAQA9YaHPtSVVftFR911RYLUFetVkuBhw4dwquvvooXX3wR27dvr68xAagM4AoLCzFgwAD3bUqlEr1798b+/fsBALm5ubDb7R7nxMfHIzk52X3OgQMHoNVq3UEVAHTt2hVardbjnOTkZHdQBQADBw6E1Wp1z8odOHAAvXv3hlKp9Djn+vXruHz5co3/D6vVCoPB4PFFSEPGSlsJUn/EkHejC1VgdeaDOJ5fiomf/ITn1h7FhDVH8L/8UqzOfJCJgEAMxLDse8NgwasbTnolsOdcLMGrG07ihsES1PHc9ozVhg0bMHr0aKhUKshkMrz11ltYvnw5pk2bVi8DKywsBADExMR43B4TE4MrV664z1EoFIiMjPQ6p+rnqwqbVhcdHe1xTvW/ExkZCYVC4XFOixYtvP5O1bGEhASf/4+lS5di/vz5Af+/hBAiFmLIuwGAFbsvIKfam+2+iyUAx2HF2A4CjUp8WJ+JLq2weT3OVXIulqC0whbUCuy3PWO1ZMkSZGZmoqysDGVlZZg/fz4WLVpUn2MDAK8lNp7nAy67VT/H1/l1cU5V4rq/8bz88svQ6/Xur6tXr/odOyGEsE4MeTdF5dbKIMqHfReKmcoHEwOWZ6INARq8Bzpe1247sDp37hxmzZoFmaxykuuFF15AWVkZiot9Twf/WbGxsQB+n7mqUlRU5J4pio2Nhc1mQ2lpqd9zbty44fX7b9686XFO9b9TWloKu93u95yioiIA3rNqf6RUKhEeHu7xRQghYiaGHaBlZv+zZvoAx4l4hKtkCFFIMblvIlZldMb74zri48wHMblvIkIUUoQHubTGbQdWRqMRERER7u+VSiXUanW95QwlJCQgNjYWO3bscN9ms9nwww8/oHv37gCATp06QS6Xe5xTUFCAvLw89zndunWDXq/H4cOH3eccOnQIer3e45y8vDwUFBS4z9m+fTuUSiU6derkPmfv3r0eJRi2b9+O+Ph4ryVCQghpyMSQd6NRSP0eDwlwnIhHI40CqzI641i1fLpj+aVYldEZjTTBfT7WKoz773//C61W6/7e5XJh165dyMvLc982fPjw2/59RqMRFy/+nsF/6dIlHD9+HI0aNUKzZs0wbdo0LFmyBElJSUhKSsKSJUsQEhKCtLQ0AIBWq8XEiRMxY8YMREVFoVGjRpg5cyZSUlLcuwRbt26NQYMGISsrC//+978BVJZbGDp0KFq2bAkAGDBgANq0aYP09HS8+eabuHXrFmbOnImsrCz3DFNaWhrmz5+PzMxMvPLKK7hw4QKWLFmCOXPm0I5AQshdh/W8G41ChtTEKJ8VuVMTo6BRUIHQhkIpk+D9GlraSATIp6vVMysjI8Prtmeeecb9b47j4HTefoXTn376CX369HF/P336dPffWbNmDWbNmgWz2YznnnsOpaWl6NKlC7Zv3+6uYQUA77zzDmQyGcaMGQOz2Yx+/fphzZo17hpWALB27VpMnTrVvXtw+PDhWLFihfu4VCrF1q1b8dxzzyE1NRVqtRppaWl466233OdotVrs2LEDkyZNQufOnREZGYnp06e7x0wIIXcrHqgsbMWQiBA5pvRNAgCvxrxT+iYhIkT4PDBSN4qNNr/5dMFuuM3xVDo8qAwGA7RaLfR6PeVbEUJEi/XGvABQUGbG9+dvIjpM6VHEtM/9jRHLyBjJn3csvxQj399f4/GNz3XHA80iazx+u273/bvWLW0IIYTc3QI15tWbhK9jBQBxEWr0bRWNJhFq6EKVaBJZ+T0FVQ0La7tUaZGZEEJIrdxOgVAWcq3EMKtG/jzWqsPTjBUhpE7pTTb8XGTEsfxS/HzTyMzshdiwfD/qzf7HwkIpA7HMqpE/j7VdqjRjRQipMzRDUDdYvx9DAuyoY6GUgVhm1UjdYGmXKs1YEULqBM0Q1A0x3I9SCYeeib4LhPZM1EEqEX6LoFja7pC6w0p1+DuesbLZbCgqKoLL5fK4vVmzZn96UIQQ8aEZgrohhvtRKZXguT73wQXeq5TBc30SoZQK/5mdtYRmcveodWB14cIFTJgwAfv3e25trOqrV5s6VoSQhkNMMwR6kw3FRhsMFjvC1XLoNOwUthTD/Wh1ujDxk58woUcCJqQmuEsZHLtahomfHME3k1KFHiJzCc3k7lHrwCozMxMymQxbtmxBXFwcVR0nhAAAQpX+LyeaAMeD5XqZGS/+3wnsu8hm/pIYZlqMVgdMNidW7L7o83iFNbhNb32pSmh+6asTHsEVS213SMNU6yvd8ePHkZubi1atWtXHeAghIiWXcn5biMilwn8I05tsXkEVULnE9uJXJ7BibAfB33DFMNMihuAPYCuhmdw9ar0Q3qZNGxQX+17/J4TcvUoqbBifmoDUxCiP21MTozA+NQG3KoRPui4qt3oFVVX2XShGUbk1yCPyxtrWcV+qgj9fWAn+qrCS0EzuHrWesVq2bBlmzZqFJUuWICUlBXK55ycTatNCyN1JLZcifd1hn3k3U9cdw/89203oIaIsQH0lFuovAezPtGhDFFg2qp1HuxiVXIobBgv63N+YmXESIoRaB1aPPPIIAKBfv34et1PyOiF3N41Chg7NInzm3aQmRkEToPZRMGgC1Fdiof5SdSw2OAYqx/XtiQKvXLXe9zcWblCEMKDWV7o9e/bUxzgIISIXESLH8/2SMCQlDjHhKvcsRqHejMToUESECJ93o1HI/OaBsRD8AewXCNWbbJjzTR7aN4tAZmoL92N9NL8Uc7/Jw1uj29OsFblr1foq0rt37/oYByFE5LQhCsSGq5C96wL2/SFw6ZkYhcUjU5h4o40IkWNK3yQA8Kq/NKVvEhPBX6ACodkMJNiXVNjwxEPNsPrHSx4zlFX5dCUVwtfaIkQod/TxrKysDKtWrcKZM2fAcRzatGmDCRMmQKvV1vX4CCEioTfZ8OrGPI+gCgD2XSzBaxvzmAgIrA4XpBwwNCXOIw+syGCBlKs8LjQxFAh1uHis/vGS18xf1ffzhrUVYliEMKHWgdVPP/2EgQMHQq1W46GHHgLP83j77bexePFibN++HR07dqyPcRJCGCeGgKDUZIPR6qzMW/oDHoDR6kSpyYbocJUQQ3MzWOwIUUgxoUcCOjSN8Fhm+zjnEhMFQl0u3udyKlAZXDld1e9hQu4etQ6s/vGPf2D48OFYuXIlZLLKH3c4HPjb3/6GadOmYe/evXU+SEII+8RQMZzngY9yfqkxx2r2kDYCjMqTVi3He2M7+Fxme29sB4SrhV+uNNn8FwA12WgTE7l73dGM1R+DKgCQyWSYNWsWOnfuXKeDI4SIhxiKRvKA35kWFuZZNEpZjctsHIDlYx4QZFx/pFX7n3nUMhD8ESKUWhcIDQ8PR35+vtftV69eRVhYWJ0MihAiPmIoGhl4pkX4VixGi6PG4C/nYgmMFuHHKIbHmhCh1HrG6q9//SsmTpyIt956C927dwfHccjJycELL7yAsWPH1scYCSEiIIaikREBZloCHQ8GMSypiqkPH8sNt0nDVOvA6q233gLHcXjqqafgcFR+cpLL5fj73/+O119/vc4HSAgRD9aLRlIfvrrDenV4gP16YKRh4niev6O0ApPJhJ9//hk8zyMxMREhISF1PbYGyWAwQKvVQq/XU/sf0qDoTTbM+M//0Cou3Gs327kCAzNFI6+XmWucaYlj4M1Wb7JhyrpjNQZ/LJStqMLybJDeZMPkdcd87lRl7X4k4nC77993HFj98Q/t3r0bLVu2ROvWrf/Mr7orUGBFGqpfbhrxS3GFV+J1VdHIe3Ua3Ns4VMAR/u6GwYLSChsMFgfC1TJEhigQI3CZhT9iPfgDfpsN+r8TXrOTrMwG/VxkRL+3f6jx+K7pvXFfNBvPRzFgOYgOltt9/671UuCYMWPQq1cvTJ48GWazGZ07d8bly5fB8zzWr1+PUaNG/amBE0LESSxFI8WwPBQfocabo9szG/zpTTavoAqorFf24lcnsIKB2SAx5KqJhRheMyyp9a7AvXv3omfPngCADRs2gOd5lJWV4b333sOiRYvqfICEEHEQQ9HIQO1i9CabQCPzdL3MjJn/+R8G/b99GPPvAxj07j688J//4XqZWeihAQCKyq1eQVWVfReKUVRuDfKIvIklV411YnnNsKTWgZVer0ejRo0AANu2bcOoUaMQEhKCIUOG4MKFC3U+QEKIOIihaOTtVIcXmhjeyMrM/md79AGOBwOVhKgbYnjNsKbWgVXTpk1x4MABVFRUYNu2bRgwYAAAoLS0FCoVG9PUhJDgCzQDEKa6o9akdUoMy0NieCPTKKR+j4cEOB4MVSUhqgdXLJaEYJkYXjOsqfWVbtq0aRg3bhxCQ0PRvHlzPPzwwwAqlwhTUlLqenyEEJGQSzn0bdUYbeK1XrsCT1/XQy7lhB6iKJaH9Gb/gRMLs0EahQypiVE1tgbSKIQPogFxlIRgnRheM6yp9bP/ueeeQ5cuXZCfn4/+/ftDIqmc9Lr33nspx4qQu1iZyYYXB7XGgi2nPHrc9UiMwuyhbVHGwBJWqEqGfq0ao7WP4O/MdT1CGZhVCwkQlLAwGxQRIseUvkkA4LUDdErfJESEsPNmqw2hQOrPEEPtN9bUqtyC3W5Hy5YtsWXLFrRpI3yzUjGicgukofrlphGzv8mrcRZj4WPJgpdbuHTTCJuTx4ItpzzGWRX8KaQcEgQe44Ub5Viw+bTP5PCeiTrMGdYGSTHCtw8rKDN7VNlXyiQoKreiz/2NEUs7xRoUMZT/CIZ6Kbcgl8thtVrBccJP6RNyN2K5lozV4fK7K9DqcAV5RN7sLu+gCqjswbdgyykmSkKopBLMHd4G8zadQk614G/u8DZQSmqdGlsv4iLUeDQ51mOZrXPzSGaej6Tu0JJq7dR63nvKlClYtmwZPvroI8hkwk+bE3K3YL2WTKDmwEar8M2DxVASwgkeS749gweaRWJ8aoJ7NujY1TIs+fYM5gxlZ7WAltnuHvRY375aR0aHDh3Crl27sH37dqSkpECj0Xgc//rrr+tscISQSoG24LPQnkOrliNEIcWEHgle+Usf51yCloEkVzGUhDDZnNh99iZ2n73p8/jMAcKPUUxYnuUlDVOtA6uIiAiqrk5IkN3OFnyh3yxiwpX4OPNBZO++4JG8npoYhY8zH0RMuFLA0VXSqv3fR1q18MFfhdXpN0CtsFJgdbtYn+UlDVOtA6vVq1fXxzgIIX6UBdiCH+h4sPxz9wWfLW0kHIcVYzsINKrfiWGHU7hahvfGdsDqHy95Bajvje2AcDU7KRgszwaJYZaXNEx39Ap1OBz4/vvv8fPPPyMtLQ1hYWG4fv06wsPDERpKTS0JqWshCpnfWYxAW/SDobLNie/8pao2J0K/kVUVjaxph5PQ4wOAELkUXxy6gg7NIjHhtxyrqsd63aErTCTYA+zPBolhlpc0TLW+Gl+5cgWDBg1Cfn4+rFYr+vfvj7CwMLzxxhuwWCz417/+VR/jJKTesfzpWyoBPs54ENl7fCyzZTwIKQMbxcTQ5gRgv8Gx1elCRrcWXn0Bm2hVeLB5JKxO4XdXimE2iCqGE6HUOrB6/vnn0blzZ/zvf/9DVFSU+/aRI0fib3/7W50OjpBgYf3Tt0omxT/3nPa9zAYOi0cmCzSy34mhzQnA/mMNAEqZBFtOFngX3+yTJOCofieG2aBQpf+3N02A44TcqVp/zs3JycFrr70GhcLzRdO8eXP8+uuvdTawKi1atADHcV5fkyZNAgBkZmZ6HevatavH77BarZgyZQp0Oh00Gg2GDx+Oa9eueZxTWlqK9PR0aLVaaLVapKeno6yszOOc/Px8DBs2DBqNBjqdDlOnToXNxkZuC7lzYmh6a7I5a15mu1jMxG62MKUMPRKjfB7rkRiFMAbeyMTwWEs5Dtl7LvoMorP3XICUgTqCYpgNUkglSK3h+ZiaGAUFC9O8pEGq9TPL5XLB6fS+iF+7dg1hYXVfDfjIkSMoKChwf+3YsQMAMHr0aPc5gwYN8jjn22+/9fgd06ZNw4YNG7B+/Xrk5OTAaDRi6NChHv+PtLQ0HD9+HNu2bcO2bdtw/PhxpKenu487nU4MGTIEFRUVyMnJwfr16/HVV19hxowZdf5/JsElhqa35QFqRJUzUCPK4nAhMzXB680sNTEKmakJsDBQIFQMj7XJ7vRba8tkFz6IFkP/uDKzDeNreD6OT00I2JORkDtV64+Q/fv3x7vvvosPP/wQAMBxHIxGI+bOnYtHH320zgfYuHFjj+9ff/113Hfffejdu7f7NqVSidjYWJ8/r9frsWrVKnz22Wd45JFHAACff/45mjZtip07d2LgwIE4c+YMtm3bhoMHD6JLly4AgJUrV6Jbt244d+4cWrZsie3bt+P06dO4evUq4uPjAQDLly9HZmYmFi9eTO1pREwMn74D9bALtOwRDHqzHVPXHcOEHgnupOuqwpZT1x3DpxMeEnqIonisTQHKKQQ6Hgxi2F0ZqpRj7MpDNT4fN0/uIfQQSQNV6xmrd955Bz/88APatGkDi8WCtLQ0tGjRAr/++iuWLVtWH2N0s9ls+PzzzzFhwgSPtjrff/89oqOjcf/99yMrKwtFRUXuY7m5ubDb7RgwYID7tvj4eCQnJ2P//v0AgAMHDkCr1bqDKgDo2rUrtFqtxznJycnuoAoABg4cCKvVitzc3BrHbLVaYTAYPL4IW8Tw6VvCwe+yhkT41SGfwd0fX6csBH9ieKwDlVNgodxC1e7KXkk6j9tZ2l0ZqpKhY7MIrNh9ERM/+QnPrT2KiZ/8hBW7L6JjswgmGm6ThqnWz6z4+HgcP34c69evR25uLlwuFyZOnIhx48ZBra7fxM+NGzeirKwMmZmZ7tsGDx6M0aNHo3nz5rh06RJmz56Nvn37Ijc3F0qlEoWFhVAoFIiMjPT4XTExMSgsLAQAFBYWIjo62uvvRUdHe5wTExPjcTwyMhIKhcJ9ji9Lly7F/Pnz7/S/TIJADJ++pRIO41MTAMAroXl8agKkDERWKpkEqzI6Y8Wei147F1dldIZKJnxOS6hKhn6tGqN1vNarbMWZ63om3mzDVHL0SIzy6BNYpUdiFBPBH8D+7soKqwOZqQng4f2ayUxNQAUDy+ekYarVVeTQoUPYtGkT7HY7+vXrh/Hjx9fXuHxatWoVBg8e7DFr9Ne//tX97+TkZHTu3BnNmzfH1q1b8fjjj9f4u3ie9/g07aux9J2cU93LL7+M6dOnu783GAxo2rRpjeeT4BNDbaPGoUq8/t1Zj9pGVcsa6w/nY/no9kIPEXaXC+/XkHQtAYc5w4TvcWeyOjBrUGss2HLKI/jrkRiF2UPbwsTAm63J5j8gCNSWJ1hY310ZaGn6i791CfxLCLkDtx1YbdiwAaNHj4ZKpYJMJsPy5cuxfPlyTJs2rR6H97srV65g586dAXsRxsXFoXnz5rhw4QIAIDY2FjabDaWlpR6zVkVFRejevbv7nBs3bnj9rps3b7pnqWJjY3Ho0CGP46WlpbDb7V4zWX+kVCqhVArfyoP4J4bu7a8NaYPXNp70CggWjUgRcFS/c7rgd+ciCw2O7S4eC7ac8gr+ci6WYMGWU0wU3yw1sZ+rJoY6VuEqOUw2p8fr5Y9YmfkjDc9tz80vWbIEmZmZKCsrQ1lZGebPn49FixbV59g8rF69GtHR0RgyZIjf80pKSnD16lXExcUBADp16gS5XO7eTQgABQUFyMvLcwdW3bp1g16vx+HDh93nHDp0CHq93uOcvLw8FBQUuM/Zvn07lEolOnXqVGf/TyI8HgCEX1nzYDDbsXDLKTzQLBKrMjrj/XEdsSqjMx5oFomFW07BwEDxTTE0OHa5eL877lgI/jQKqTsgqJ4bZLI5magHJobdlVVL/L6wssRPGqbbnrE6d+4c1q5dC5ms8kdeeOEFzJs3D8XFxdDpfD9564rL5cLq1auRkZHh/vsAYDQaMW/ePIwaNQpxcXG4fPkyXnnlFeh0OowcORIAoNVqMXHiRMyYMQNRUVFo1KgRZs6ciZSUFPcuwdatW2PQoEHIysrCv//9bwDA008/jaFDh6Jly5YAgAEDBqBNmzZIT0/Hm2++iVu3bmHmzJnIysqiHYENAOvLGkabE7vO3sSuszd9Hp85UPigRQwNjsUQ/IUopEhNjPIZAKYmRjERWIlhd6UYlvhJw3TbgZXRaERERIT7e6VSCbVaDYPBUO+B1c6dO5Gfn48JEyZ43C6VSnHy5El8+umnKCsrQ1xcHPr06YMvv/zSo6bWO++8A5lMhjFjxsBsNqNfv35Ys2YNpNLfL1Br167F1KlT3bsHhw8fjhUrVnj8ra1bt+K5555Damoq1Go10tLS8NZbb9Xr/53UPzEsaxgD1LEyMpAbFKqS+U26ZiUx3P9x4ccoAYepfZMggefSas/EKEzumwQJA9OpYthdCbCfYE8aplpdRf773/9Cq9W6v3e5XNi1axfy8vLctw0fPrzuRvebAQMGgOe9p+jVajX++9//Bvx5lUqF7OxsZGdn13hOo0aN8Pnnn/v9Pc2aNcOWLVsCD5iIiijac4igjpUYdmEpZBI80ioareLDvXYFnr1ugIKBnYt6iw2NQhV4NCUOmX/IsSoyWNAoVAGDxQZAI+gYxbCTFmB/Jpo0TLW6GmdkZHjd9swzz7j/zXGcz6rshLBMDMsa0t/qWNW0PMRCmxO92Y6XvjqBZaPa4aXBrWC0OBGmkuGGwYIXvzqBj57qLPQQYXc68dKjrTB3k/euwHnDk2FnoMFxuFqB1zac9LkRoGeiDosY6AsphmU2McxEk4bptgMrl0v4Cw4h9UEMzVrlUgnmDG2LhVtOeSy19UiMwpxhbSFnoI6VVi3HG39ph49zLnmMsWdiFN74SzuEM5BjJeEkmP1Nns9dgXM35WHhY8IHLVa7y+/uSqudjWsx6ztpxTATTRom4d8xCBGYQiZBz0Qd9l30vgj3TNQxsTwUopRh4dcn8ECzSIyvtgV/2XdnsOTxdkIPESq5FKurBVVAVZ4Qh2V/EX6M5gB9+MwM9OHTB5ghDXQ8mLQh7ARS1YlhJpo0TBRYkbue3enCpD73wQXeKzdoUp9EJpaHjBYHdp+9id017Ao0WhyIEXhzarnF7nempfKNTNi8loB9+BjYFRhoBpWFfDoxEEuCPWl4hP8oTojAHC4eEz75CR2q1Yjq0CwSEz45AgcDtY30Zv91gfQM1LEymP0npwc6HgxhAfrssbArUCmT+O0LqWRgBlUMdKEK9KyhjlVPhhLsScMj/FWEEIGVmx1+KzSXByh1EAwhAWYpQpTC1zYKUUoRopBiQo8Erx13H+dcYmKMUo7zu+zLwiaAMpPNb1/IMpPwxTfFYnKfRIDnvctW9EkUcFSkoatVYOV0OpGTk4N27dp5NTUmRKwCveGzUJBRIZH43RWokAg/ixGulPltwhzOwBIWxwHje7QA4P1mO75HCzAQVyFMJceTqw7X2NLmm0mpQg9RFEoqbLDaXZjUNxGz/rBL1Wi1w2p3oaSCktdJ/ajVlU4qlWLgwIE4c+YMBVakwVDLpOjbqjHaxGu9ZlpOX9dDLRM+sCo1B5jFCLBUGAxOnvfbhHnhCOH78CmlEqw9eAXtm0V61Ig6drUMaw9eYaJXYHSYEp2aR/qcQe2ZpEN0GPUevR1OnodcxiF790Wv18zkPolw+aiNSEhdqPVHyJSUFPzyyy9ISEioj/EQEnRyCYdXHm1dY20jFkoZhKvkSGd8FsNkc/pNXmchMdxoc2Bsl+ZY/eMlr1m18akJMAZoeRMstIT158k4DitqCPQBYBEDpTVIw1TrwGrx4sWYOXMmFi5ciE6dOkGj8awATH3ziNhIZRK8vvFUZbL6b0FL1YzV69+ewYIRwl+Aw9VydGwW4XMWo0diFBM1oioC7LirYCCwqrA6/RYx/deTwjdULzbaMH7NEUzokeA1qzZ+zRFsntyDmSUsvcmGYqMNBosd4Wo5dBp2yi+IobQGqVusPB9rHVgNGjQIQGXrGu4PCQk8z1PldSJK5VY7nvAzi1FuFb5MQLnF7rddTLnFLnj/MzHsuAtXy/D6qHb4+MdLXvfj66PaITzA/yEYDBZ7gM0Uwu8ABYCCMjO+P38T0WFKWB0ulJrsOHzpFh6+vzHiGGgXEyiQZ2EGldQdltoX1foqsmfPnvoYByGC4V3AF4eu+Jyx+uLQFcwa2EroIaLMZMfUdcdqXAr8dMJDQg8RHOB3x53wC6qASibF6mpBFVAZrHIAloxMEWZgfxCukvvdXclC/SW9yYYrt0zYcuK6V4CaoNMgRCEVfOYqIsAsrpaBWV5SN1hrX1TrwKp37971MQ5ChMMBaX5mrFiICDRKmd9ZDBba7vAA/tYzAY+mxCI6XOUOCG7ozYiPUIOFVOFyq6PG5aGciyUoZ6BRtC5UgdWZDyJ79wWPx7tnYhRWZz7IRP2lMpMd2bsv1Ji/tGREiuCBVXSYEj2TdD7b2tAmgIaFtfZFd3Q13rdvH/7973/jl19+wX/+8x80adIEn332GRISEtCjR4+6HiMh9YoDapzFAIA5Q9sIMCpPkgBNmBnIr4dGLoVKLsXWkwVevQIn902CRi787kqT1el3NihQZfZgWbH7gu/WQByHFWM7CDSq31XYag5Qf7xYggoGNgFoQxRYPCIZr2w46dVfc/GIZMEDP1J3WGtfVOvA6quvvkJ6ejrGjRuHo0ePwmq1AgDKy8uxZMkSfPvtt3U+SELqE8/D75sEC7uyeQATeyRAAviov5TAxGyQC7zfgGARA+UWwtUyvDe2g8/ZyffGdmAix6qo3Frz7soLxSgqtwoeFATKT2Ihf0lvsmHBltM++2su3HIab41uL/j9SOoGa+2Lan0VWbRoEf71r3/hqaeewvr16923d+/eHQsWLKjTwRESDIE+XbPw6ZsDEKaU+ix2KOWYWK2Exe7yGxBY7ML3XAyRS2vMp1t3iI06VmVmu99ZNRbaF4mhn2Gx0YadZ4qw80xRjccpsGoYdKEK9ErSYa+P5cBeArQvqvWz/9y5c+jVq5fX7eHh4SgrK6uLMRESVIHyk1jIX1JKJXC6gOw93sUOp/RJglIqfOV1Q4DWP4GOB4PJ4fSbT2dyCD/TEqqU+p1V0zDQGkgi4fwuTUsZWJtmbXmI1B9tiAKvj2qHl7464RFc9UrSYdmodkEPoGv9jhEXF4eLFy+iRYsWHrfn5OTg3nvvratxERI0Ei7AmwQDfU54eAdVAFvFDsMCBKCBjgcD7/KfTzd7iPD5dKEKmd+di0sZ2LnIAX47AbAgXCWHLlSBZaPaITpc6VWzjIXdlaTuxEeokT22A4qNNpRb7Aj77fEXRR2rZ555Bs8//zw+/vhjcByH69ev48CBA5g5cybmzJlTH2MkpF7JJJzfNwkWPn2b7E4cyy/D5L6JvpOuGSh2GKKQokdilFeOFVCZMMxCz0Ue8Hs/spCrZvJT2DLnYgkTj7VMwmH9oXyPJdWq/KX1h/Lx6pDWQg8RulAF1j/dDXM35Xncnz0So7D+6W5M7K4kdUsbwkaB2loHVrNmzYJer0efPn1gsVjQq1cvKJVKzJw5E5MnT66PMRJSr6I0Ciz99ozPN4kvD+fjrdHthR4iLHan3+UhCwNvtma7028RUxYqXZtsDqxI64CPcy55lTJYkdYBZgby6Qxmh98cK4NZ+DHanC5M7Jngs+H25D5JsDmFz6ezOFxeQRVQGZzO25SH5WMegFagsZGGjeP5O9vzZDKZcPr0abhcLrRp0wahoaF1PbYGyWAwQKvVQq/XU/sfhlwvM9e4Ps9CFenLxUa8utH7TQKofDNbPCIZLXTCvgaPXLqFjNWHPQKCqgD145xL+GT8Q3gwoZGgY7x004g53+T5TLLvmajDgsfaIqGxsPfjmQI9fi2zeC0HVs2gNolQoXWcsCHB2QIDHv9gf42P9dd/745WccJe384WGDDo/+2r8fi253sKPkYiLrf7/n3bM1YmkwkvvPACNm7cCLvdjkceeQTvvfcedDpdnQyYECHFR6jx5uj2KK2wwWBxIFwtQ2SIQvA2MVXsTt5vSQi7U/hFLF9J1X9se8VC0rXN6Wfn4sViJmZaQhUy5ncuGq0OvwVrjQwUWhXDZgrSMN12YDV37lysWbMG48aNg0qlwrp16/D3v/8d//nPf+pzfIQExfUyM178vxMe7ViE6jPlS6A3qgoG3shCFTK/FcNDFcInrwdaRmPhzbbC7n/nYgUDS6oapczvciULO2nDA/SmDHQ8mFhpHkzqxm0/s77++musWrUKTzzxBADgySefRGpqKpxOJ6RS4T+JEnKn9CabV1AFVLZCePGrE1gR5D5TvrBWAM8XmZTzWyB02ePC72YLCTBrxkKCPXj2dy5KOGBVRmefOVarMjoz0QkgUqPwu5kiUsNG4MJS82BSN267+M3Vq1fRs2dP9/cPPfQQZDIZrl+/Xi8DIyRYKitd++4zVVXpWmhVBfB8EaIAni8Gq8NvgVADA7NqKpkUqYlRPo+lJkZBJRM+sPrjzsVVGZ3x/riO+DjzQUzum4hj+WVM7FxUSCV4v4byH+/v+RkKBuqqxYSrsGRkCnpUe7x7JEZhycgUJpb5AzUP1ptsAo2M/Bm3PWPldDqhUHhevGUyGRwO4S+WhPwZZQEqWbNQ6VobUlmP5/vzNxEdpvy9wbHBgj73NxZ8Rg0IvMxWzsBuNgkHTO6TCMB75+LkPklMzLSYbQ6/O0BZ2Lkohlw1oLKw7qtD2oAHUG6uXGarup0FxUYbcq+U1lj+g6rDi9NtB1Y8zyMzMxNK5e8dwS0WC5599lloNBr3bV9//XXdjpCQeqYJsPzDxPIQALvThW9PXK/WK1CH1Pt8z8AEm0Yp9VuQMdAyXDDIJRwkHDAkJc6jtMYNgwUSrvK40CJCFFi+43yNS4ELGSgGW27xXxKinIFcNb3JhiulJlwsMiImXAWrw4UKmxOFejMSo0OhUkgFD1qMVrvfILrCKvyHuiqUB3b7bjuwysjI8LrtySefrNPBECIEjULmt/K6hoGk6xsGC17dcNJrlmDfxWK8uiEPb41pL/jShkYuxfqnu2L+plNejaLXP90VMgYq2HMuHnHhavxys8LzdgBxWhU4l/ALbVaHy+8OUKtD+NmgMJX/ZtZhDCSG60128Dzw7ckCjzyrnolRmNw3CXqTXfDAIEKtwBv/PVdjEL1khPB5iYB48sBYCf5u+9m/evXq+hwHIYKJCJFjat8kSACvgGBy3yREhAifGF5qsvldeik12QQPrKQSDrM3eNeI2nexBPM3ncaSkcLPtLgkHN7dcQ6ZPe6FTCpBudmOMLUcjcOUeHfHefyj//1CDzHgDlAWShmo5FK/bXcWM9B2x8HzWLn3ZzzQLBLjq5WtWLn3F7zCQHV4m9N/EM3CkmqgPLBsBjb3AGzt7Bb+YwUhAtOGKBCnVWFwShwy/7A8VGSwIE6rYuKiESg/iYX8pQq702/wx0KZAJPDiUfbN8GybWd9Ft9koQmzGMoEVFgdftvusFD+w+Z04Qk/ZStYCFrEUEal2GjzCqqq7L1QzEQeGGs7u4V/hRIisBsGC17ZcLLGbdnLxzwg+GxQoOKaLBTfFEPwJ4YmzHKJxO/StFwifOJ1hc1/AGoKcDwoRFC2QgxlVAwW/3le5QGOB8Pt7OymwIqQICqtsPkMqoDKT9+lFcIvs4UpZX5r8oQxUJAxNMBMSqDjwcADOFdYjlUZnX0m2AufYQWUmW1+m4KXmYXfgh8a4PnGQoHQ6j0r/+jHiyVMPNZVZVT2+pgRYqWMihiCP9Z2dgv/0YcQgYmh9UXFbw2Oq9dgqmpwzMIym1Im8VsjSikT/nJjsTvwRVZX3DBYUGSwotRkQ1G5FUUGC77I6gqrXfjHWq2QYeq6Y+jQLNJdx2pVRmd0aBaJqeuOQc3AZgqOg9/HmoHNlQGX0VhYZtOGKPD6qHZeNeqq+pQKvcQGiKOGHms7u4V/hRIisHCVzG+ZABZyWvQmO6auO4YJPRI8ygQcu1qGqeuOYc34B4UeIgxmG+YMbYuFW055zKz1SIzCnGFtUc7ATEtUiBK/6s3YerLAazaohU6DJlrhdzhJOKBDswifffhYCVoA+J1VY2E2SKv2P5MS6HiwcAAGp8Qho3uL3/M7GShKXEUbosCiEcle6RI9EqOwaEQyE8Efazu7hX/HIERgURoFvnymKw79cgtA5XZ3s92JIoMFXz7TFeFK4S/A4Wq536a3LEzHa9UKLNx62mMXVlXw9/p3Z5jIaXHwPFbu+8Vng+OV+37BawyMkQf7QQt4eDWKrnqsvzh0BbMGthJ6hIgOU6Jnks5n4nXPJB2iw5Q+fiq49CYbZvnYcQdUzgaxsONOb7JhwRbfr+uFW07jrdHtBR9jRIgcU/omAfB+zUwRYGc3BVbkrscDKDJYa5zFCGssfNASpgqQY8XArJrV4cLuszex++xNn8dnDRR+F5bV4b/BsZWBXYFiCFokEvi9HxnIr4fJ5sRzD98HF897va6fezgRJpsT2hABBwhx7LgrNtqw80wRdp4pqvG40GPUhijQRKvC0GqFf4sMFjQRYGe38FdjQgRWYXVgRQ19zwBgEQOVrs0OBxaOSMbsjXle0/ELR6TAzEBrqUC7hwIdDwYOHPM7xbgAQQvHQNCikEqw/nC+z+Bv/eF8vMZAjagysx0TP/nJ5/L5xE+O4Ku/d0ecwMUtxbDjTgxj1JtseH3bWTze8R53OkfVZpll285i8cgU2hVISDCZ7E6/u4dMDCSGO53A0m01TcefwqxBws9iBFqOZGG5Ugw7xVQyKTbkXsOE1AS8NLiVR87f17nXMGuw8I91iEKGvz98H87fMLpv4zgOTSLU6NuyMUIYSLA3mO1+l89ZCAjEsONODGMsqbBhZMd78HG1D01VH0ZKKoI7qyb8s9+PefPmYf78+R63xcTEoLCwEEBl/8L58+fjww8/RGlpKbp06YJ//vOfaNu2rft8q9WKmTNnYt26dTCbzejXrx/ef/993HPPPe5zSktLMXXqVGzatAkAMHz4cGRnZyMiIsJ9Tn5+PiZNmoTdu3dDrVYjLS0Nb731lldjaiI+JmuAmjwBjgcDz8PvMtsLDCwPhSqkfpcrQxnouSiGquZlFVbMHNQSr23M83iTqJydTEZZhRXNozR+fkP9iw5XwWx34t0d5716V3YfmYxogcuTAHA3XK4JCwGBLlSB/q2j0TIu3Kvn4rkCAxM77sRQEsLh4v3ORM8b1tbXj9UbpgMrAGjbti127tzp/l4q/f3i/MYbb+Dtt9/GmjVrcP/992PRokXo378/zp07h7CwMADAtGnTsHnzZqxfvx5RUVGYMWMGhg4ditzcXPfvSktLw7Vr17Bt2zYAwNNPP4309HRs3rwZAOB0OjFkyBA0btwYOTk5KCkpQUZGBnieR3Z2drDuClJPwtT+XwaBjgeDGLaOmxxOzPazK5CFquahSpnf5sGB6jMFQ7hageyd5/HioFbutjvhajnsTheyd17AlH5JQg8RNwwWvFZD78rXNubhrdEM9K4MEOgH2qIfDNoQBWYPbYOXN5z0mFnrkRiFJUFevqqJGHYFuly835loZ5B7gAp/FQlAJpMhNjbW63ae5/Huu+/i1VdfxeOPPw4A+OSTTxATE4MvvvgCzzzzDPR6PVatWoXPPvsMjzzyCADg888/R9OmTbFz504MHDgQZ86cwbZt23Dw4EF06dIFALBy5Up069YN586dQ8uWLbF9+3acPn0aV69eRXx8PABg+fLlyMzMxOLFixEeHh6ke4PUB7VcgkdaR6OVj0+NZwsMUMuFT2oJVHCRhYKMepMdz609imWj2uHF35awQlVSFBmsSFt5EO+P6yj0EKGWSbA680Fk777g8UbWMzEKqzMfhJqBWltOuDDlkftrnLFy8sJvAijz17vyQjHKGOhdWWK0Ys6wtli42bsp+OxhbVFSYUUzgWf+9CYbXq32OAOVhYlf25hHuwJvk8nm/4NlsDsBCH81DuDChQuIj4+HUqlEly5dsGTJEtx77724dOkSCgsLMWDAAPe5SqUSvXv3xv79+/HMM88gNzcXdrvd45z4+HgkJydj//79GDhwIA4cOACtVusOqgCga9eu0Gq12L9/P1q2bIkDBw4gOTnZHVQBwMCBA2G1WpGbm4s+ffoE584QMVa6jvsSoVbglUdb4+Avnhe3JloVHu/QBBFq4cfJAejbqjHaxGu9gr/T1/XgGKhtVFUS4tjVMvcYzXYpjl0tg8nmDLg0EwycBFix+4LXLMa+iyUAx2HRiOAuGfiikEjx8oaTPt9sZ2/MwxIGGhwbArQnCnQ8GFQKKQrKzHjURw/QgjIzosOFL7dAuwLrhjbANTrYNcuYDqy6dOmCTz/9FPfffz9u3LiBRYsWoXv37jh16pQ7zyomJsbjZ2JiYnDlyhUAQGFhIRQKBSIjI73Oqfr5wsJCREdHe/3t6Ohoj3Oq/53IyEgoFAr3OTWxWq2wWn8v9mYwGG7nv96gXC8ze3VHF6rreE2KDBaf5RYSGociSiN8YCWRArOHtsFrG/O8lgwWjkiG3SX8LIZK6n82SCUVfjbIYnf5nWmx2IW/HytsNW+myLlYErBPXzCIoXelRiHDh/vO+Lwvq5bahGaw2P0uTbOQYC+GXYGhAcrRBLudlvBXOj8GDx6MUaNGISUlBY888gi2bt0KoHLJrwpX7aM6z/Net1VX/Rxf59/JOb4sXboUWq3W/dW0aVO/5zc0epPNK6gCKj+NvfTVCehNwlfjLjXZkF1DuYXs3RdQysAYQ6RSzKlhyWDON6cQIhX+jczuctU4G7Riz0Umgj8xzLQYAvQ1Y+GNTPNb70pfeiRGMbE07W+3bw4ju321ajneG9sBx/JLMfGTn/Dc2qOYsOYIjuWX4r2xHZiY5RXDrsAKq8N/y68g56AyHVhVp9FokJKSggsXLrjzrqrPGBUVFblnl2JjY2Gz2VBaWur3nBs3bnj9rZs3b3qcU/3vlJaWwm63e81kVffyyy9Dr9e7v65evVqL/7H43c5Ut9D8zRD8yMgMQYXd6XemhYVegU4efsfoZKCWQUiAmZRAx4NBDLvZzAF6V5oZeD4GDFAZCKI1SlmNu9nW/HiJiQBVDL0C9Wa73/6agZ4LdU34R60WrFYrzpw5g549eyIhIQGxsbHYsWMHOnToAACw2Wz44YcfsGzZMgBAp06dIJfLsWPHDowZMwYAUFBQgLy8PLzxxhsAgG7dukGv1+Pw4cN46KGHAACHDh2CXq9H9+7d3ecsXrwYBQUFiIuLAwBs374dSqUSnTp18jtmpVIJpVL4tXyhiGEaWQzlFsQw02IM0Kw60PFgCJFL/fYUC5ELH1iJoWxFudnht3flJxMeEnqI0KrlfpfZwhnY7Wsw2/3OqhnMdsE3AVQ1in7pqxMeJRdYahQdrmKr5Zfwzyw/Zs6ciWHDhqFZs2YoKirCokWLYDAYkJGRAY7jMG3aNCxZsgRJSUlISkrCkiVLEBISgrS0NACAVqvFxIkTMWPGDERFRaFRo0aYOXOme2kRAFq3bo1BgwYhKysL//73vwFUllsYOnQoWrZsCQAYMGAA2rRpg/T0dLz55pu4desWZs6ciaysLNoRGIAYppHD1f634LNwAQ5RSP2OkYWZFjHsXOQBTO6TCMC7p9jkPklMFAiVAlg0IgWvbfS1vT0Fwj/SlTkt/t7IWChboZRKsDqzM36+WeFxe7xWhdWZnaFkIOevLMBMij7IMy01iY9Q483R7VFaYYPB4kC4WobIEIXgQV8V1mptCf/s9+PatWsYO3YsiouL0bhxY3Tt2hUHDx5E8+bNAQCzZs2C2WzGc8895y4Qun37dncNKwB45513IJPJMGbMGHeB0DVr1njUw1q7di2mTp3q3j04fPhwrFixwn1cKpVi69ateO6555CamupRIJT4x9oT3helVIKPMx5E9p4LXi1EPs54kIkLcKhKio99JIanJkbh48wHEcpAYMVx8DsbJGFg5+KtChssdheGVOspdsNggcXuRGmFDWgs7Bg5AFLweHVIa/DgUG62I0wtBwce0t+OC00ll+CRVtFoFe+jRMl1A1QMlCipzOnj8O3JAo8AtWdiFCb3TWIi5y9QLa0QBmYnAfY3IGlDFFg2qh2+P38T0WFK9/PxhsGCPvc3DvqsGsfzPAsf0u4aBoMBWq0Wer3+rpntul5mrnEaWeheXQBwvtCAhVtO+8wP6pmkw+whrXF/rLCP1bWSCrzsoyAjUFnteunIZNwjcE2eczcMAM/VWCCUB4+WMcLej2cLDZj6xTH888mOcDh5GH4rvimTcpj0+VG8l9YBrQR+rPNLKjB/8ym09lFa48x1PeYOayt4/aVrJRWwOHnM3eRda2ve8GSopJzgz8dLxUbM2ZhX42tmwYi2SNCFCjCy310prsArG71LawCVH0aWjEhBc53wtbYmrzvmM1e2V5KOiVpbwG/B3/+dwL6L9Rf83e77N9MzVqRhiI9QI3tsBxQbbSi32BGmkkMXyk4dKxf8J10L/7kWKLf5SV6/WIxyBhLsFRIJFm71XUhw2Xdn8BoDDY7D5VKsfKozfvy5GDHhKlgdLlTYnLihN2PlU50hZ2A6yGR3YqyfJsws7GazuXjM3+R7l+r8Tacw/zHh64H5La1xkY3SGhEhckztmwQJ4FXEdHLfJESECJ8qIYZaW+7d5xd97z4PdvBHgRUJKh5gYy3jDwLtDipnIOlaDFvwLQ6X336GMwcK/0bmAnBdb/ZZs6yFToOmDMyggoffvmezGQhQzf52qV4sZmJXoBg2U2hDFIjXqjDYRxHTeK1K8IAFEMcGJNaCPwqsSL0rLDPj8i0TNEopLHYX5FInzpSXo0WjEMQy8EYWcAs+A3kOYtiCL4Z+hjYXjxU11CwDgIWPJQsxLA884Lf8Bwu5G2LYSSuG0hp6kw2vbMxjeplNDBuQWCu0SoEVqVd6kw1GmxPZuy/4zMXQm4SfRlbJ/G/BV8mEvwCHBdiCH8ZA8BcWoLpxoOPBYA5Qs4yFmRYxBKhieKzVAUprqBkorcHaTIsvYtiAVFVo1dfyuRCFVoV/9pMGzWC2eyW4ApW5GPM25WEpAx3cOQ5+8xxY6MNncjjxt573ggPnkUfQM1GHiT0TYHIIHxCo5f6DPxbeyAI1Y2VhpkWj9F/+g4WyFZoAgX6g3W7BIIH/0hrC71sUxzIbazvufNEoZfji0BV0aBbp3u1b9ZpZd+gKljzeLqjjEf4VSho0Y4C+Z0YGkq6lAHRhCp95DrowJRN1gyosDjSJVGNS3/swa3BLGC1OhKlkMFrtiApVwsjABbjCVtlWovpSlrutRIAO9MEghpplEg5+ey5KGIgIKn6rvF7jY83AzJ/F6YLN4cLQaqU1igwW2BxOWJzC5/xpFP6fbyEBjgcLD+DbEwVeO+563y9wbZLfVFgdSPOz4SPYs7xsPGqkwQr0iYuFT2RSCYfXNtS8LXvpSOHzbqI0SvyqN3vlB1V++k5EE63wuWpGq5P5atxhcqnfoCWMgVk1hUxSY89FcBwWMLDjzmjxX3n9UwYea5PVARcPr5w0HoCLrzwuNLmU87tcKZcKP13O2o47Xxwu3u+Gj3nDgvuaocCK1KtAiY+BjgeDGEoZOHj2k67DA1TjDmcg78YJ+A1alowQ/n60OvyUCbhQDKuDgZkWpf/HmoXlysgQBZbvOF9j0LKIgdfMrQob/tbjXgxJiXOX/1DJpSjUmxGnVeNWhQ0JAk8KiSEPzOXi/eZOOl3B3fIh/LOfNGiRGgX6tWpcY7HDSI3w6/PlIihlYLazn3Qtl0oCfPoWfg1LDM2sA5b/YKAvJMcBfVs1Rhsfr+vT1/VM5CVaHS4cyy/D5L6JPpd9mQhQVTLoLXaf1eHH90iAhoEPI2LIAzMFSDMIlFtZ14R/1EiDFhOuwtxhbZFTbRq5iVaFMZ3uYaLXVHiAInzB3lHiS8Dt7QzMqt0yWjE+NQGAd97N+NQE3Kqw4t7Gwla6FkPQIoYyAVIJ8NLg1li4+ZTXkursYW3BccIXhTBa7X53ihmtwgcEarkUq3K8l7D2/VZWY/HIFGEG9gdiKLcQaAzB3qVKgRWpV3qTDdf1Fp8FGRMah0Krlgs+jayUSvx++mahV2CgpGoWltlCVXI8u/Yolo1qh5cGt4LR4kSoSoYigwUvfnUCn0/sIvQQRRG0iKFMgEoqxSs+Wiztu1iChZtPYwkDeYmNNEq8s/MC08vnRqvD/+YeBvLAQlUy9EzS+VwO7JmkQygD1x5FgNlyRZCv4cLfI6RBKzPZvWpYAb9f3JaMEL7cgt5sw4uDWmPBFs9P3z0SozB7aFvozTYBR1dJJZP6XVJlodZWqEKK7LEdfCbYZ4/tgFAGtuCHBAhaQhgIWuxOl98yAXYGdrP5XVK9yMaSqt3p8rt8zsL9GLD8BwMz0TabE3OHtcG8Td49QOcOawMbA2MsM9swsUeCz5I543sk/HYND17PRQqsSL2qsNX8iezHiyVMbMHXqhV47RvftbYWbDnFRJKr2eHALD/Bn9kh/P1o53ms2veLz1oyq3Iu4dUhrYUeImQSDlP6JAHwDlqm9EmCTCJ8cpCL52GxuzCkWpmAGwYLLHYnXLzwl22DCJZUDRaH39IaBgZa2gSaaWZhJtru4rHk2zM+e4Au+fYMXntU+BZLYSo5blXYvUrm3DBYwIFDaJCXK4V/1EiDVhHg00yg48Fgdfj/ZMtCkisHDgu2nKox+JszVPiLm9XhQlrX5vg455JX3s34HglM3I8OF4/G4QqftY0ahyvgCPLuIV9UMik+O3gZbeK1HjmI1/UW7Dxzg4legRoRLKmGqWT4Z1pHFOjNHrfHa1X4Z1pHJqrDs7aE5YvZ4fTbA3TGAOGv4RqlDB/l/OLzfuyRGIXlYx4I6niEf2aRBk0Mn8gC7XoJdDwYXLz//nEMxAPgAHz101WMT03Ai7/lWIWpZLhhsOCrn65h6iNJQg8RPIDicqvP2kbF5VY0DhN+M4WTd+HZ3onI3lW91pYOU/olwckLH6CKYUlVJZVAJZf4zO+c3CcRKgaClpIK/xs+SiqsSBB4w4cYlisNZrvfXDWD2R7UjVLCv6uRBk0ulaBnos6ruBxQ+UbBwhZ8bYBdf4GOB4MxwLIFC0munASY+khLr5m1quVKTiJ89MfzwL/3/uIxG8RxnHs26FUGljVUEiliwiQYnBKLzNQWHrNqMYx0ApBLOEzpW8OSat8kyBlYUuUBrKxhaXrlvl8YmfmT46mPj9RYaPWrv3cXeoiICHD9C3Q8GMoClMzRBzhe1yiwIvWqtMKG8T1aAOB9JBW2QGmFDRC4AJ48wK5AFoK/sACtWFhY1pBzEsze4jtXbeGWU0zswrI5ncjslgCFnINGKXPPqvVIjMJDzRvB5hT+07eTA+ZvPu0V/P2qt1Qu+wa5irQvVpcT8eEqDG0X77mkWm5FvFYFq0v4+9HicCK9awufS4GdmkXCwkB/TZVMgm73NvK4jfutCFi3extBJRP+2hOulvvdFchCOZpAvSlDgrxxRvirMWnQNCoZ0j8+jAk9EjySCo9dLcPkL47h6+eE/0RWVmH1uyuwzGQFIOx0vEoqwaqMzlix56JXTZ5VGZ2ZWNawOFw46qcgo4WBHCuphEN8pApzN3nPqs0b3hYur0XC4DPbnX77njFRDJaTgAcPnq92f/E8eL7yuNAkHOd3KVDCwKyaEy7MHtoWr2486XXtWTQyBQ4GAlSrzeF3V6CVgQ1IrJUoocCK1KvIEAU6NYv02fqiZ6IOkQx0Ro/QKLFo62mfSwbLtp3BawwsGVidLrxfQ0sbCTi8NlT4HXcmq8NvQcZA1ZGDQSmrrL/ka1Zt3qZTTBRkBA+/fc9YWMLiOA4v+7gfgco33KUM3I9yqYT5NlBKiRTzNp/yee1ZuPlU0Hvc+WJ3we+uQBaWzwH4LVESbBRYkXqlkkkwd3hbzN+U57UUOHd4Wyamuh0ul98ZAodL+JkWFw+/dYNYSF6P1Cjw9k7v3mwsvZGZbDW3Bsq5WMJEIi4P/xsVGHioYbQ5/baLMTJwP4qhDVSF3YlxfnbSslAPzCKCXYGlJpvfEiWlJhsSgjgeCqxIvSo22vDEhwewbFQ7zHJX45aiyGDFEx8ewJdPdxO8QCgfYIaAhVIGgZLTKxhIXg9UtsLGwFJgoL5mLPQ9C/RYsvBYGy3+28VUMNAupiJQG6gAx4OCB1bnXPLdFJyZmWj2dwWGKGR4clVlykn1EiWLtp7B10HeBECBFalXBosdxUYbJn7yk8/jLLyRiaGUQaDkdBbaSgQK/ljYuSiGHaChygCPdYDjwRAVqsS7u9huFxOolAsLGz7A+Z+JZuDSE/g1w0CvQImEQ4dmET5TTlIToyANcj4dA88s0pCJoYGnGGYIQuRSv2UrWKgbFOgNX8NAQCAPUJCRhR2gSpnEb/siJQPL52JoF6NR+H/NBNpJFgzlAcqoBDoeDDHhSr+7AmPClQKMypNMwvltaUOBFWlQdKEK9ErSYa+PF2WvJB10ocInrwfaihvsrbq+WBxOzB3eBvM3nfKZq2ZhoEyAlAMeaRWNVvHhXgHB2esGSIXfhIUykxVzh7XFgs3eO5zmDGNjB6jN5fLbvsjGQM6fGAICi8uJBSPaYs5G7/zOhSOSYWFgx50YZie1IQosG9UOL351wiO46pmkwxuj2gmeygEAURoFblVYMalv4h9STmSosNohk3CI0gR3jMI/aqRB04Yo8PqodnjpqxMewVWvJB2WMfKi1Chl/j/ZMnBxc/FAkd6CV4a0Bg8O5WY7wtRycOBRWGaGjoFPjUqpBC892gpzN3kHBPOGJ0POQGAVGaLE4q2nfe5weuO7s0z0M+RdwMKtNbcvms1Azp8YAgKnE3h751lkpiZ45Xcu23YGzz9yv9BDhFLmfwaVhdlJAIiPUGPF2A4oNtpQbrEjTCWHLlTBxPW7isMJZPtoAF9VyDaYhH/2kwYvPkKNbIZflLyLx+S+ifBVxHRy30TwDCRZaeRSGOQSLNl6ptoYdZjSLxEaBpYCXYBXfSigMiCYuymPiWbWNqcL+3+5hVbxWvdtVQUZ9/9SAhsDS1gBdwUK/3REiEKKHolRXknXQGUgzcIsL88D3+XdwHd5N3wen9pP+MCqzGTzu4RVZrIJN7hqtCHsXLOrKzPZ8cmBS5iQmoCXqrXT+mT/Jbw0qHVQx06BFQkKll+UZocTFrsTj1brjF7021ZdMwMVmgEge9cFr0TXfReLAQ5YMkL4oMUUYHu7iYGt48YAtbZYSLAPlNNnYmCMJrsTmakJXkFgamIUMlMTmHisK6wONI1U45/jOkImlaDcbEe4Wg6704VJa48ykTupVkihNzswuNq154bBAg4c1AwEqGJgdjjwj/4t8dPlWwAqdyib7U4UGSz4R/+WMDuC+1hTYEXuehKOw0f7fvG5O6dnoo6NLc92Z827hy4UM/FGFnBbNgPb2yNDFHh7B9u1tsSwA9RgtmPqumM19rhbM/5BoYeIMLUUn058yGeO1acTH2KifVGIXIaPcs7UWGh18QjhC62KgUoqRYHB4rPKfgudBnFBbMAMUGBFgkRvsqHYaIPBUvmpUadhZwaLB/tbng0Wh99egSwkC4epA2xvD3A8GGwBdrOxsBQohp2LYSo5TDanz+3tVceFppHJ8PKGkz5meUswZ+MpLB0pfBBtdvgvtMrKbDnrXABTVfaFv9KRBu96mdlrR0mvJB1eH9UO8RFqAUdWqdzM/g6nMJUM/0zr6LOh7D/TOjIxi6GWS/B4h3hkpiZ4Lb2s+fES1HLhAwJDgC73gY4HQ4nR6jfv5laFFfc2FnbnohhyrIz+ZnkvFsPIxCxvgDZQDCxXioHZHiBADfJjLfzVmDRoepPNK6gCgL0XivHSVyeQPbaD4DNXIUr2yy2opBK/DWVZaMIsB4ep/e7Hqxs9e8j1SIzCohEpEH4OI/BMCgszLWHqytkgXzl/Uo5DKANjvGW0YvbQtli4xXfZiltGK5pHaQQcIWAI9IEpwPFg0KoVWO5naXrBcOFn1cTAYnP6DVAtFFiRhqTYaPNZWA6oDK6KjTbBAyuVzH9ndJVM+MCKB/Dpgcs+d718euAyZg1sJfQQ4QS8giqgclfgaxtPMtGYVwwzLaFyKRb7yfljYQlLIZdiyhdH8c8nO8Lh5GH4bXZSJuUw6fOjeOeJB4QeIjSBPjAFOB4MdleAQqsM1CwTg0YB+pQGe0cyBVakXhlE0JtNwgFT+yZiSEocYsJV7mnkQr0Z9zUORZCL9vpkdToxvX9LLNjsXSB0zrC2sDKQiGsM0OCYhca8nIvH4hHJ2P9zCaL/8Fjf0JvR/T4dOAZKa4hhCStULsUH6R1x6Jdb7tdMhc2JG3ozPkjvCBmEf9GEyP1/YGKhW4ExQJpBoOOkkiVAn1JLkPuUUmBF6pUY2pzYXS40DlPhl5sVHrdzABqHKZn41KiUSb12NwGVOTgLNp/GghFtBRrZ7wLlJzERREs4FBmt3gc4DiUVVkSHCl9oVQxLWDIJh+tlNe/CasZA7iTHwV0c0lfRSE742C/g9Y+F66MYBAxQg5yrRo8aqVcKmcRvVXMFA5WFlTIJCsos2OLjTSKhcSjitMK/2VodLr+zGNYgfyLzJTxAs1YW8pccPA+bw+X7se6TBAcD1TfFsIRldfFM7cLy5VaFDQ6nC0PbxXuUhCgqt8LhdOFWhQ0tdMKOUSWT+F2aVjFwfRSDgDuSg7y5hwIrUq/sLhfmP9YGReVWaJQyd26Q0WpHdJgSDgZmg1wu71YIgHDr874E3LnIwCyGJkD+EgtNb128/8eahYBAHWAJS83AEpY5QDHYYO/C8kWtkMJocSJBF+K+9oSqZNAopbDZeSaKb1qdLr+FVq0MlP+ownLJHKVU4rdRtDLIm3sosCL1Sg4Odh7I3n3Ra6cYK/3jxFAxPEQp9VvHioVZDJeLx6IRyZjts+ltClwM5C+JISCQcMCUPjUsYfVJYiLnL2AxWAby6TQKGQxyO36+WeHOAzPbnb/lTmqgUQj/9me0OPwWWv10wkNCDxEA+yVzOABT+iYCPDxWR3om6jClb2LQM/6Ef2aRho3jMPcb3zvF5m3KY6KysBgqhofIpVid2Rk/V8sDi9eqsDqzMxOJuDaXCzf0Vh9lAqy4esuEGAaWVCtE8FjLOA4KGedzCUsh4yBjIDko0NJKGAO5QS4XDxePGkuUsBDoh6pkfgutslCfTm+yYc43eWjfNAKZ3Vt4fKib+00e3hrdXvCZqxClDHy5BYNTYpGZ2sKjNRDPVx4PJuEfNdKg+ZsNymFkNijQxYuFi5tMwoGv4U1iSt8kyBiYxnDxwL/2/lzjEtbsoW0EGJWn8EABAQOPtdnlRCONAnz1fC+eRyONAmaX8K8ZjoPf3EkGYj84ef/VuFmoERUiD1D+g4EPTCUVNjzxUDOfNaLGpyagpEL4kjnR4SpY7U6fG5DitSpEB7mlDdOZcUuXLsWDDz6IsLAwREdHY8SIETh37pzHOZmZmeA4zuOra9euHudYrVZMmTIFOp0OGo0Gw4cPx7Vr1zzOKS0tRXp6OrRaLbRaLdLT01FWVuZxTn5+PoYNGwaNRgOdToepU6fCZmOn+ziLykWwU0wpk+CRVtGY3DcRqzI64/1xHfFx5oOY3DcRj7SKhpKBBFK7i68xNyh79wXYGfj0zfPwu8zGQF44QhRS9Ez0nbHcM1HHRB0rGSR4dWMeXtmQh4mf/ITn1h7FxE9+wssb8vDaxjzIWLhsc8D4Hi3QMzHK4+bK6vAtwEC1BVgc/pd9LQy0i6lqZp1a7X5kqZm1w+nC6h8v+bz2rP7xEhxOBl7YAJpGadC3dQyaRKihC1WiSYQafVvHoKkAhWqF/3jmxw8//IBJkybhwQcfhMPhwKuvvooBAwbg9OnT0Gh+v7MGDRqE1atXu79XKDyj52nTpmHz5s1Yv349oqKiMGPGDAwdOhS5ubmQSisvpGlpabh27Rq2bdsGAHj66aeRnp6OzZs3AwCcTieGDBmCxo0bIycnByUlJcjIyADP88jOzq7vu0K0wkSwU0wK4OUhrXHoF88LRxOtCiM7NIHwb7XiyA2qCLClOdDxYHC6eMwd3gbzN3nXA5s7vC2cDASo/mry5AhQk8cXjUyKLcev44VBrfBy9fZFOZcwvf/9Qg9RFHlgRosDL311AstGtXMX/g1VyVBksODFr07gX092EnqIcAb4wORk4RPTb2LCVYgJ8uyUL0wHVlVBTpXVq1cjOjoaubm56NWrl/t2pVKJ2NhYn79Dr9dj1apV+Oyzz/DII48AAD7//HM0bdoUO3fuxMCBA3HmzBls27YNBw8eRJcuXQAAK1euRLdu3XDu3Dm0bNkS27dvx+nTp3H16lXEx8cDAJYvX47MzEwsXrwY4eHh9XEXiJ5a7n87MQv942QSDgVlZp/LbAmNQ9FUK/wLtcLq9Ju8zkJukBhq8tyqsKHC6sSkvomY5X4jk6LC6sD1UjM0SikSGgs7RjHM8pqcTkx5JAmvbczz2pSycEQyTAwUrGVtC35NY3hrdHusqlZpv2eiDm+Nbs/EGAPVgAp2jSgxEP5drRb0ej0AoFGjRh63f//994iOjsb999+PrKwsFBUVuY/l5ubCbrdjwIAB7tvi4+ORnJyM/fv3AwAOHDgArVbrDqoAoGvXrtBqtR7nJCcnu4MqABg4cCCsVityc3NrHLPVaoXBYPD4uqvwwMIRyehRbaq7qn8cGPiwY3Px+HDfL+jQLNJjKbBDs0h8uPdn2BiYxdCqZXhvbAccyy91Lw9NWHMEx/JL8d7YDggP8CYSDBzgtaRRJTUxioXVIYSqZJj+n+PVktg5VFidmP6f40zk04lhllfOSbyCKqByRm32xjzIOeHfWpSyyi34vvRM0jGxxK+RS72CKqByZ9uqnEvQMJBjFXCjAgOvGdaI5h7heR7Tp09Hjx49kJz8e9Lh4MGDMXr0aDRv3hyXLl3C7Nmz0bdvX+Tm5kKpVKKwsBAKhQKRkZEevy8mJgaFhYUAgMLCQkRHR3v9zejoaI9zYmJiPI5HRkZCoVC4z/Fl6dKlmD9//h3/v8VOKZdi9saTeKBZJMZX2068aOtpJuoGWRxOpHVpXmNyJgu5GCFyKdbUkOfAAUz04ePBY3xqAgDvMgHjUxPAMxBFa+RSrEjriOzdF7zGuCKtIxNvZKEB6oGFMpAHJoblSvD+t+Az8HREhb/2RReKUcHAEr8YEuxZI5rAavLkyThx4gRycnI8bv/rX//q/ndycjI6d+6M5s2bY+vWrXj88cdr/H08z4P7w9YVzsc2ljs5p7qXX34Z06dPd39vMBjQtGnTGs9vaAwWO3aevYmdZ2/6PP7CQDviIGwdFA4cvjh0BR2aRbq3t1cts31x6AoTDY5NdqfPCxvAzu5KjUKG9YfyPe7HqiB6/aF8zBkm/K5AHsA/qwVVQGUgKAGHJQw0ODY7nJg9tC0Wbjnl8Zj3+K0vpJmBQD/QciQLy5UWhxMVVqfPLfgVVifCVMLfjwazw+8Sf6D2RsFQYXP4LWJaYRN+jKwRRWA1ZcoUbNq0CXv37sU999zj99y4uDg0b94cFy5cAADExsbCZrOhtLTUY9aqqKgI3bt3d59z48YNr9918+ZN9yxVbGwsDh065HG8tLQUdrvdaybrj5RKJZRK4ev3CKXc4v/CUc5Ck1EOyOjWAtfLzB43N9Gq8GDzSCZ2OAW6n1i4H10uHlm9EjxqbXEch3itCj0TdUzUDTLaAjQ4ZiCh2WB24NnPc7FsVDu8+Ic8sCKDFWkrDzKR0BxoOZKF5UoOHD7K+YXp8h+hSin+mdYRBXrPa0+8VoV/pnVEqEr42SCjxem3iOkn49koYsoSpgMrnucxZcoUbNiwAd9//z0SEhIC/kxJSQmuXr2KuLg4AECnTp0gl8uxY8cOjBkzBgBQUFCAvLw8vPHGGwCAbt26Qa/X4/Dhw3jooconyaFDh6DX693BV7du3bB48WIUFBS4f/f27duhVCrRqZPwFzpWhatlWJHWAR/neC6z9UyMwoo0NnKDpFxlPoav/nFT+iRBykBgJYbEcLPdiQqbE9+eLPCYaancgp8As0P4MYqhUbRGWVk08tjVMveHEbNdimNXy2CyOZl4rMXQ4676DMsfsVL+Q6OUQSW311jElIXq8Fq13G8RU22AnMC7kfDPfj8mTZqEzz//HF988QXCwsJQWFiIwsJCmM2V0b3RaMTMmTNx4MABXL58Gd9//z2GDRsGnU6HkSNHAgC0Wi0mTpyIGTNmYNeuXTh27BiefPJJpKSkuHcJtm7dGoMGDUJWVhYOHjyIgwcPIisrC0OHDkXLli0BAAMGDECbNm2Qnp6OY8eOYdeuXZg5cyaysrJoR6AfapkUq3MueV2A910sweqcy1DLhP9EJpNIaq4RtecCZBLhXyZiSAznOA6ranisV+VcAsfAKMXQKFoqAVZnPojj1TYq/C+/FKszH0SQ2575ZOddmD20rc9NKXOGtYWdFz7HSizlP2oqYrpiz0Umyn/EhCv9bgKICb97V2RqInw47McHH3wAAHj44Yc9bl+9ejUyMzMhlUpx8uRJfPrppygrK0NcXBz69OmDL7/8EmFhYe7z33nnHchkMowZMwZmsxn9+vXDmjVr3DWsAGDt2rWYOnWqe/fg8OHDsWLFCvdxqVSKrVu34rnnnkNqairUajXS0tLw1ltv1eM9IH4VAZZeWEjOFEOvQHDwmxjOQMwSeIYguMPxSaOQol+rxmgdr/Vamj5zXc9Eo2i5VIIVuy/4DFDBcVjwWFuBRvY7GSfBmzvPYnxqgtdy5Ts7zuEFBvISxdBRQQzXHm2IonJZulqvwJ5JOrwxqp3gVddZJPwzyw+vlg7VqNVq/Pe//w34e1QqFbKzs/0W8mzUqBE+//xzv7+nWbNm2LJlS8C/R35nCJQbxEBypsnqhC608uIRHa6E0eJEmEqGG78V6WOhRpRSKsG6agn2VXkO6w5dwWtDhM8XEcMMgRTA7KFtMHtjntfS9MIRKUwUg7U6XH53ilkZ2HFXWmHDmM7N3LlBVcuVNwwWjOncDKUVNkDgemBi2M0mhj6lABAfocaKsR1QbLSh3GJHmEoOXaiCgqoaMB1YEfHTKKV+k9dDlMJf3LQaGdZldcWCzdWrceuwLqsreE74uRY778JrPgKCHr8FBHYG+seJYYbAxvOYszHPR92gyvpLC0YIPxsU6MMGCxsVQpRS6C32GvPpWHhdV7WLqWk3GwuzQQGLmDKQg1pFG0KB1O1i51EjDZJGLsUn4zvDyVcmalbNBvVIjELvpCgm6gappVK8suGkzyJ9C7acxpIRwm/Bl3MSLNpy2nc9sC2nmJixUkolSE2MqnEXlpKB5CCL3c9s0MViWOzCzwYFCkpY6GeoUcjwcQ35dDyAJQzUVSs3O/zvZpsg/G42lUzit5k1C5sASO1RYEXqlVzCIVKjxNxNp7xaX8wbngy5RPjkIFOAIn0sfLK1OFzYdfYmdtVQD2zmQOEDglKTDXOHtUXu5VuV3eZ/m528oTejc0IjlJmEb1huDDDbE+h4MIQqZAEKhAp/2TbbnThbWI5VGZ19Lp+z0LsyVCXzu5stlIHdlTaXC7OHtcFCr9nyKMwe1gY2l/Cva1J7wj+zSIPmBLyCKqCyqOW8TXlMfLINVIQvUJ5YMIghfykiRIGb5VZs9bE8dG/jUDQOE373UIgIlqYtDv9LWBYG+vCZbQ58kdUVP12+BaAqx8qJIoMFX2R1hckqfNmKqp20Nc2g+qnrHDQuJ/DGf8+gfbNIZFabVXv9uzOYNUj4TQCk9iiwIvWqwlbzrpeciyWoYKAgY8DcIAY+2YqhjhUH4D0fVc33XSyBC8AiBtoXhcilWJ35ILJ3X/BKXl+d+SATCc3lFv9LWJ8ysITVSKPErzU0Lm+h06BJhLDdFAAE3knLAB7A7rM3sbvGzhQUWImR8Fdj0qCJoSCjUhYgN4iBPIeAn76DPyQvYtg6Dg7MlzKoKhBa0xIWC0G0w0/9JQBM9ADVyKV+d9LOGyb8Y20UwUw0qT3hX6GkQRNDQcYyk83vJ1sWcoPAAVk974UEnFdD2Yk92ahjJYat41aHC7n5ZZjcN9HnUiALpQwknP8gmoG0RJj9zET/eLGEiRwrk8OJFwe3xsLNp7xmJ2cPawsTAz0Xw0Swk5bUHj1qpF6FKfzXkgljYIdTiFKGJ1cdrnHp5evnugs9RKilEjRrpMakvvdh1uCW7mRho9WO6DAlpAxEVqEqmd/8JRbeJCosDr+92ViYIeDhfwlL+OIfgCnAEj4LQXS52Y4KqxOPpsR55C8VGSwoKDNDw0A+nUYu9bsrkIVd06T2hL/SkQatwuHEnGFtsWDzKY/gqqr1RQUDnxrlEgk6NIvwufSSmhgFOQMtbSQSDgVlFq/WO6mJUZjSNwlNtCoBR1dJJZPg44wHkb3HM38pNTEKH2c8yMTW8UiNAjanq8bebJEs1OnhgS9qWML64tAVJhKaA/X4ZKEHaCONEu/szKtx5o+F5UqXi8fCEW0xu1pttcqCtclMNC4ntSf8s580aHqTHa9tyMM/n+wIh5NHudmOMLUcMimHSZ8fxaKRwl/cjBYbFo1IxuyNeV7B38IRKTCYrAKOrpLDxdfYzxBgIzFcKuHwzz3eyes/XiyBhOOwmIF6YBzgNzeIhfuRkwBpXZpj9Y+XvALU8akJTOxm41DZ0uSPLU6q9EzSMTB/CticLr/LlTan8Mu+UgmHG+VmvDKkNXhw7usjBx4lRgtiwoT/wERqjwIrUq8iNHKsGNcRRy7dQsxvtY2MNidu6M1YMa4jwEBV80iNEm/vOIdZg1rhFakE5WY7wtVy2J0uvLfzHP7Rv6XQQwyYGM5CTos5QD0wFsYoigR7Hlh/KN/njNX6Q/l4YZDwz0cXgIk9EgAe3jl/PRIgfMgSuEI9CxXsJTygC1Vj/8/F7tpvRqsDRQYLUhN1kAh/eSR3gAIrUq9CpFJc9bMtuykD27LNDiee65OE+Zu9i5jOGdYWZgaWKwPmtDBQtkIMb2RiSLAHB6R3a47rZb/ngXEchyZaFR5sHsnERgWNXIrPDlxG+2YRyExt4RH8fXbgMhM77gIlhgc6HgyaEDmulJSje6IOJpvTPWOVGB2KMpMFzaPChB4iuQPCP7NIg2YTwbZshUSC177xzsXIuViCBZtPMTHGQLW0WNiCL4ZegWIYo1oqhVohxbcnC7x6V07plwi1VPiEZrPDibF+litZ+DCilEn8LleyUEZFG6JAY1sIvj9/E9FhSlgdLpRbHSgqt6LP/Y2pN59ICX8VIQ2axe7EMT/b2y0MLL1YHDXnYuRcLIGFgS34YqgiLZf4rwfGwiYAMfRmc/I8sndd8Nm7EhywYLjws0F6U4A+fOOFL2KqlEowpW+Sz+XKKX2TmOhdCQBxEWo8mhyLYqMN5RY7wlRydG4eSUGViFFgReqVxe7Ee2M7+Pxk+97YDkwEVuUiKGIqkcJvgr3dJfz9KIZ6YDaXC5P7JgLgvXZhTe6bCDsDvdksDj+Noi8UMxHohyilfouYstAaKD4yBA4Xj0dTYj2WK4sMFsSGKxEfGSL0EN20IQoKpBoQCqwaAL3JhmKjDQZLZdK1TsPOizQiRIGV+37BhNQEvDS4lUez1v/LvcpEy4YwERQxVUulmLf5FB5oFonx1WYIFm05xUROi0YlQ/rHbNcDc7l4mGxODK5W2+iGwQKTzclEmYBA1bgDHQ8GtVzqd3ZSzUj9pWZRGkgkHMotDvdsUMvYMNzDUFBFGh7hryLkT7lRZsYtsx08AJvDBZvDhet6Cyw2J2IYSAy3u1yYPqAljlzybtY6fUBLJmYINAGKmGoYKGJqtDmx6+xN7Kqhp9jMgcLPWGnkUnRqHulzFqNnEhvFDpUyKT47eBlt4rWICf99K/t1vQU7z9zAa0PaCDi6SmJIulZIOEzukwjAe3Zycp8kKFgoD/8bCqJIsAn/CiV3TG+ywexwYdHW01672RaNSIHeZBN85kopkaDAYKlxV2BcuPB1WpwuHvOGt8W8Td5FTOcNT4aTgSJ9YpjFqLA7/W7Br2Bg2Vcu4fDKo60xb5Nnm5PKx7ot5AwEBAqp/1w1BQO5QS4Xj3itGkNT4jxmJ4sMFsRrVVTYktzVKLASMYPZjlc3nvS5m+21jSexdGSK4IGVC/4LMrKw466kwgaT1YnJfRPx4m/LlaEqKSqsDlwvrWx9kdBY2DGKYRZDb7LjubVHMaFHgtcW/OfWHsWa8Q8KPUTwAF7/9ozPJdXXvzuLOUOFn7G6VWH1m6t2q8KKexuHCjU8AECxyQazzYkHmkV4FLaMj1DhWqkJaoUUzQQdISHCEf5qTO6Y0U8j1JyLJTAyUNtIDM1aQ1UyPPt5LpaNaucuW8CBQ4XViRe/OoHP/9ZF4BFWLrP5Xa5kYJktXC33m9DMQq6aye7EzrM3sbPGJVXhi29qlHI89fGRGnPVvvq78LlqIYrf+2tW7fY1Wh04drUMH+dcwtcMjJEQoVBgJWIGEexmE0Oz1lC5FP9M64js3Rc8ApeeiVH4Z1pHhDIQtFTYncj8rQFv9VmMzFQ2ltnCFP4byrLQcNsYoEhpoOPBoFFI0bGG3pWs5PyFq+V+xxgeYEMIIQ2Z8Iv15I4FunixMEMghmatDp7HimpBFQDsu1iCFXsuwsELny9iMNkxdd0xdGgWiVUZnfH+uI5YldEZHZpFYuq6YwGD7GBw8Dwm9bkPqYlRHrenJkZhUt9EJu7HQIVUWSi0qgAwb3gyelS7H6ty/ljY72u1VzZX9zXGOcPawspAoE+IUIS/ipA7JobdbIoABRkVDBRkFEPdoDARLLOZ7S5M+OQnn0tYE9YcYWIJK2Ch1eAPyYvJ5YRSIsXkvknVcv6cUEo5mFioWWa2I+vTn7BsVDuPMRYZrEhbeRAfPdVZ6CESIhgKrMSM57FoRApe23jSazfbohEpAAMzBGUVNswe1gYLN5/yKsg4e1gbJopGGswBetwFOB4MYQGCaFaW2fwFfywss0kk8JsYzkBxeDhcwE2jCXERITDZnJByTsgkEsRp5bheZkJkqFLoISJcJUex0YaJn/zk8zgLgT4hQqHASsScPLB4a01FI0/j1SGthR4itGoFFn17Gu2bRXoUZKzchXUGrz0q/C4sTYAq0SxUkQYqK6+/5qPy+qIRKQKO6neB7icW7ke1TIr1h/PRoVmk16za+sP5mMvArsCYMBXe2HYOreLC3Ynh5b8lhp8tMGD56PZCDxG6UAV6Jemw10cfvl5JOuhCWViwJEQYFFiJWKAdTv8YcH+QR+TN7nJh99mb2F3DGF8cJPwyW0iAKtIhDCSvl9ucePrTn/DPcR3xilTi3t7ucLqQvuoQPmRg6UUM9yMH4KXBrTB/02mPmbWeiTrMG96WiaVAbYgCCx5LxotfnfAcY5IOb4xqJ3gJFaByjK+PaoeXvjrhEVz1StJhGSNjJEQoFFiJWKAddSzsuDMEWP4JdDwYOABT+yZBAvjoH5fExJttudmOq6VmDF/xo+/jDOwAlUu4yqa38F5mm9I3iYnim0abE1m/BagvS1t7BKiZqw9jJQMBKgDER6ixYmwHj8a8ulB2WlUBlWPMZnyMhAiBAisRCw1QFDLQ8WAIDbDLKtDxYCgz29AoVOHVP67IYEGjUAXKzMLngYmhnyEASMBjSLVq3DcMFjAQUwEATDYH5g5vi2XbznoFf3OHt4XJJnygX0UMjXnFMEZCgk34dzVyx1RyCXom6bDPR55DzyQdVHLhM3GVMgn6tmqMNvFad76ISi7F0fxSnL6uh5KBXYHhagVmf5NX4xLWIgaqw4cGSF4PZSB5XW91IHNN5a7A6n34Fm09g/882w33CDg+oDLnb/mO8zV2AlgwXPjHmhAibhRYiRjHo7IRKs97L2H1SQQn/KZA6E02vDioNRZs8e7NNntoW+gZmA2yOV1+q8NbncLngVmcTswd1hbzN3v3M5w7rC0sTuGXfU1Wp99dgSwsTdsDPNZ2Bh5rQoi4UWAlYlaXCyab02sJ64bBApPNCatL+DcJbYjv2aCciyVYsOUUE70CA5VTKGcgD6zM5MDff2u7U71u0NiVB/HBk52EHiLCAhR7DXQ8GMTQzJoQIm7CX+nIneOBj3J+qXEJi4WGsoFmg2wMzBAEzFVjIA8sRCH1WzcohIGlQI1c6ndpmoV+hmJoZk0IETe6ioiYiwfOFZZjVUZnRIcrYbQ4EaaS4YbBghe/OgEXA0uBYujNFhqgxx0L+UvqAKUM1AwELQ4Xj7nD2mD+ptMe92XPRB3mDWsLBwNPSKXMf14iCzl/hBBxo8BKxCx2B9b+rSsWbDnl8YbbIzEKa//WFSab8FvwxdCbTQJgUp/74ALvtVNsUt9EJhpqOlyuynw6eJcymNwnCQ4Gln1vVdhQYXViUt/7MGtwyz+0YnHg11IzNEopEhoLO8YQhQxT+1WWhPhjcNUzSYfn+yUhRCH885EQIm50FRGxKI0S8zef8qgiXbXj7o1tZzB3WFuhhwgJ5783Gwvb8MttTr897r5moMcdD8Bid/ksZWCxO8FD+HILoSoZ0j8+jAk9Etw7QE22yvvx45xL2PCc8PdjdLgKVrsTjybHIrN7C4/SGjFhSkT/YTcjIYTcCQqsRMzscGJsl+ZY/eMlj51YVX3PzA7hd2Hx8N+bTfjFIcBgsfvdzWZgoPimWibF2kNX0Cou3KuUwe6zRUzk08WGq9CpeaTP+7Fnkg6xjAQtTaM06CuXorTCBoPFgXCVDMlNtB73KyGE3CkKrMSMB1b/eKnGmjwsvNkCwBeHrvjszfbFoSuYNaiV0MMLWFyTheKbNpcTrw5pjTkb86q1YonCwhEpsLmED6K1IYrKXYtfnfBaZmOlFUuVmHAVBVKEkHpBgZWIuXj43XHHQK4wJByQ5mdWjYWlQDEU39TI5Xjzv2fxwqBWeLlar8D3dp3HCwOFD1ABcbRiIYSQ+kSB1R14//338eabb6KgoABt27bFu+++i549ewZ9HGLYcSeXSPzOWM0eIvysmtXlxKIRKXht40mv4puLR6bAysBsUFyEGs8/cj9e25DnteNu0chkxEWoBRydJ2pzQgi5m1FgVUtffvklpk2bhvfffx+pqan497//jcGDB+P06dNo1qxZUMcihl6BtypsfmesbplsSBBwfAAg5ST41/cXMXd4WzicvHs2SCbl8MGei3jm4fsEHmGl5lEaLPtLO+jNdvdskFYtRzxDQRUhhNztOJ7nGVgwEo8uXbqgY8eO+OCDD9y3tW7dGiNGjMDSpUsD/rzBYIBWq4Ver0d4ePifGsu1UhNe+upEjUtYr49qh3siQ/7U3/izTl3XY/S/DnjsFKuasfo45xL+82w3tI3XCjpGvcmGs4XlWLH7gndroL5JaBUbRjMwhBByl7vd92/hpzRExGazITc3Fy+99JLH7QMGDMD+/ft9/ozVaoXVanV/bzAY6mw890SGYPHIFLy6wfcSltBBFVBZ2LJDswifO8VYKWypDVGgWaMQPNou3qM1UFG5Fc0bhVBQRQgh5LZRYFULxcXFcDqdiImJ8bg9JiYGhYWFPn9m6dKlmD9/fr2NqXmUBq+Paodyi8O9PBSmkjERVAGAlAOm9KksyFi93MKUPkmQMpC8DlTmMD2aHOuRdN25eSQFVYQQQmqFAqs7wHGe0QDP8163VXn55Zcxffp09/cGgwFNmzat0/GwEkT5EhGiQFG5FUOrFbYsMlgglVQeZwUlXRNCCPmzKLCqBZ1OB6lU6jU7VVRU5DWLVUWpVEKpVAZjeEzShihwT2QILt6s8DzAcWgaSctshBBCGhYW2qCJhkKhQKdOnbBjxw6P23fs2IHu3YVv18GqqmW2FlEaxGtVaBGlwaPJsYil3WyEEEIaGJqxqqXp06cjPT0dnTt3Rrdu3fDhhx8iPz8fzz77rNBDYxotsxFCCLkbUGBVS3/9619RUlKCBQsWoKCgAMnJyfj222/RvHlzoYdGCCGEEIFRHasgq8s6VoQQQggJjtt9/6YcK0IIIYSQOkKBFSGEEEJIHaHAihBCCCGkjlBgRQghhBBSRyiwIoQQQgipIxRYEUIIIYTUEQqsCCGEEELqCAVWhBBCCCF1hCqvB1lVPVaDwSDwSAghhBByu6retwPVVafAKsjKy8sBAE2bNhV4JIQQQgiprfLycmi12hqPU0ubIHO5XLh+/TrCwsLAcZzQwwkqg8GApk2b4urVq9TO50+g+7Fu0P1YN+h+rBt0P9aN+rwfeZ5HeXk54uPjIZHUnElFM1ZBJpFIcM899wg9DEGFh4fThaMO0P1YN+h+rBt0P9YNuh/rRn3dj/5mqqpQ8johhBBCSB2hwIoQQgghpI5QYEWCRqlUYu7cuVAqlUIPRdTofqwbdD/WDbof6wbdj3WDhfuRktcJIYQQQuoIzVgRQgghhNQRCqwIIYQQQuoIBVaEEEIIIXWEAitS75YuXYoHH3wQYWFhiI6OxogRI3Du3DmhhyVqS5cuBcdxmDZtmtBDEaVff/0VTz75JKKiohASEoIHHngAubm5Qg9LVBwOB1577TUkJCRArVbj3nvvxYIFC+ByuYQeGtP27t2LYcOGIT4+HhzHYePGjR7HeZ7HvHnzEB8fD7VajYcffhinTp0SZrAM83c/2u12vPjii0hJSYFGo0F8fDyeeuopXL9+PShjo8CK1LsffvgBkyZNwsGDB7Fjxw44HA4MGDAAFRUVQg9NlI4cOYIPP/wQ7dq1E3ooolRaWorU1FTI5XJ89913OH36NJYvX46IiAihhyYqy5Ytw7/+9S+sWLECZ86cwRtvvIE333wT2dnZQg+NaRUVFWjfvj1WrFjh8/gbb7yBt99+GytWrMCRI0cQGxuL/v37u9uhkUr+7keTyYSjR49i9uzZOHr0KL7++mucP38ew4cPD87geEKCrKioiAfA//DDD0IPRXTKy8v5pKQkfseOHXzv3r35559/Xughic6LL77I9+jRQ+hhiN6QIUP4CRMmeNz2+OOP808++aRAIxIfAPyGDRvc37tcLj42NpZ//fXX3bdZLBZeq9Xy//rXvwQYoThUvx99OXz4MA+Av3LlSr2Ph2asSNDp9XoAQKNGjQQeifhMmjQJQ4YMwSOPPCL0UERr06ZN6Ny5M0aPHo3o6Gh06NABK1euFHpYotOjRw/s2rUL58+fBwD873//Q05ODh599FGBRyZely5dQmFhIQYMGOC+TalUonfv3ti/f7+AIxM/vV4PjuOCMjNNvQJJUPE8j+nTp6NHjx5ITk4Wejiisn79euTm5uKnn34Seiii9ssvv+CDDz7A9OnT8corr+Dw4cOYOnUqlEolnnrqKaGHJxovvvgi9Ho9WrVqBalUCqfTicWLF2Ps2LFCD020CgsLAQAxMTEet8fExODKlStCDKlBsFgseOmll5CWlhaUPowUWJGgmjx5Mk6cOIGcnByhhyIqV69exfPPP4/t27dDpVIJPRxRc7lc6Ny5M5YsWQIA6NChA06dOoUPPviAAqta+PLLL/H555/jiy++QNu2bXH8+HFMmzYN8fHxyMjIEHp4osZxnMf3PM973UZuj91uxxNPPAGXy4X3338/KH+TAisSNFOmTMGmTZuwd+9e3HPPPUIPR1Ryc3NRVFSETp06uW9zOp3Yu3cvVqxYAavVCqlUKuAIxSMuLg5t2rTxuK1169b46quvBBqROL3wwgt46aWX8MQTTwAAUlJScOXKFSxdupQCqzsUGxsLoHLmKi4uzn17UVGR1ywWCcxut2PMmDG4dOkSdu/eHZTZKoB2BZIg4HkekydPxtdff43du3cjISFB6CGJTr9+/XDy5EkcP37c/dW5c2eMGzcOx48fp6CqFlJTU73KfZw/fx7NmzcXaETiZDKZIJF4voVIpVIqt/AnJCQkIDY2Fjt27HDfZrPZ8MMPP6B79+4Cjkx8qoKqCxcuYOfOnYiKigra36YZK1LvJk2ahC+++ALffPMNwsLC3HkEWq0WarVa4NGJQ1hYmFdOmkajQVRUFOWq1dI//vEPdO/eHUuWLMGYMWNw+PBhfPjhh/jwww+FHpqoDBs2DIsXL0azZs3Qtm1bHDt2DG+//TYmTJgg9NCYZjQacfHiRff3ly5dwvHjx9GoUSM0a9YM06ZNw5IlS5CUlISkpCQsWbIEISEhSEtLE3DU7PF3P8bHx+Mvf/kLjh49ii1btsDpdLrfdxo1agSFQlG/g6v3fYfkrgfA59fq1auFHpqoUbmFO7d582Y+OTmZVyqVfKtWrfgPP/xQ6CGJjsFg4J9//nm+WbNmvEql4u+9917+1Vdf5a1Wq9BDY9qePXt8Xg8zMjJ4nq8suTB37lw+NjaWVyqVfK9evfiTJ08KO2gG+bsfL126VOP7zp49e+p9bBzP83z9hm6EEEIIIXcHyrEihBBCCKkjFFgRQgghhNQRCqwIIYQQQuoIBVaEEEIIIXWEAitCCCGEkDpCgRUhhBBCSB2hwIoQQgghpI5QYEUIIYQQUkcosCKENCgtWrTAu+++K/Qw7tj3338PjuNQVlYm9FAIIXeAAitCSL3gOM7vV2ZmZsCf37hxY72MrSp48fe1Zs2aevnbdemP/w+JRAKtVosOHTpg1qxZKCgoEHp4hNyVqAkzIaRe/PGN/csvv8ScOXNw7tw5921CNuDu3r27x/ief/55GAwGrF692n2bVqt1/9vpdLqDFxadO3cO4eHhMBgMOHr0KN544w2sWrUK33//PVJSUoQeHiF3FTavEoQQ0YuNjXV/abVacBzncdsXX3yB++67DwqFAi1btsRnn33m/tkWLVoAAEaOHAmO49zf//zzz3jssccQExOD0NBQPPjgg9i5c2etx6ZQKDzGolaroVQq3d9v27YNcXFx2LJlC9q0aQOlUokrV67gyJEj6N+/P3Q6HbRaLXr37o2jR4+6f+/YsWPxxBNPePwtu90OnU7nDtp4nscbb7yBe++9F2q1Gu3bt8f//d//1fr/8EfR0dGIjY3F/fffjyeeeAI//vgjGjdujL///e/ucwKNfcKECRg6dKjH73U4HIiNjcXHH38MAPi///s/pKSkQK1WIyoqCo888ggqKir+1NgJaWgosCKEBN2GDRvw/PPPY8aMGcjLy8MzzzyD8ePHY8+ePQAqgwAAWL16NQoKCtzfG41GPProo9i5cyeOHTuGgQMHYtiwYcjPz6/zMZpMJixduhQfffQRTp06hejoaJSXlyMjIwP79u3DwYMHkZSUhEcffRTl5eUAgHHjxmHTpk0wGo3u3/Pf//4XFRUVGDVqFADgtddew+rVq/HBBx/g1KlT+Mc//oEnn3wSP/zwQ52NXa1W49lnn8WPP/6IoqIiAAg49r/97W/Ytm2bx0zet99+C6PRiDFjxqCgoABjx47FhAkTcObMGXz//fd4/PHHwfN8nY2bkAaBJ4SQerZ69Wpeq9W6v+/evTuflZXlcc7o0aP5Rx991P09AH7Dhg0Bf3ebNm347Oxs9/fNmzfn33nnnVqNLyMjg3/sscc8xguAP378uN+fczgcfFhYGL9582ae53neZrPxOp2O//TTT93njB07lh89ejTP8zxvNBp5lUrF79+/3+P3TJw4kR87dizP8zy/Z88eHgBfWloacNz+zv3uu+94APyhQ4dua+w8X3lfLlu2zP39iBEj+MzMTJ7neT43N5cHwF++fDnguAi5m9GMFSEk6M6cOYPU1FSP21JTU3HmzBm/P1dRUYFZs2ahTZs2iIiIQGhoKM6ePVsvM1YKhQLt2rXzuK2oqAjPPvss7r//fmi1Wmi1WhiNRvffl8vlGD16NNauXese7zfffINx48YBAE6fPg2LxYL+/fsjNDTU/fXpp5/i559/rtPx87/NJHEcd1tjBypnraqWLIuKirB161ZMmDABANC+fXv069cPKSkpGD16NFauXInS0tI6HTMhDQElrxNCBFH1hl+F53mv26p74YUX8N///hdvvfUWEhMToVar8Ze//AU2m63Ox6dWq73Gk5mZiZs3b+Ldd99F8+bNoVQq0a1bN4+/P27cOPTu3RtFRUXYsWMHVCoVBg8eDABwuVwAgK1bt6JJkyYev1upVNbp+KuC1Kr8tNsZ+1NPPYWXXnoJBw4cwIEDB9CiRQv07NkTACCVSrFjxw7s378f27dvR3Z2Nl599VUcOnQICQkJdTp2QsSMAitCSNC1bt0aOTk5eOqpp9y37d+/H61bt3Z/L5fL4XQ6PX5u3759yMzMxMiRIwFU5lxdvnw5KGOu+vvvv/8+Hn30UQDA1atXUVxc7HFO9+7d0bRpU3z55Zf47rvvMHr0aCgUCgBwJ8Ln5+ejd+/e9TZOs9mMDz/8EL169ULjxo1ve+xRUVEYMWIEVq9ejQMHDmD8+PEexzmOQ2pqKlJTUzFnzhw0b94cGzZswPTp0+vt/0KI2FBgRQgJuhdeeAFjxoxBx44d0a9fP2zevBlff/21xw6/Fi1aYNeuXUhNTYVSqURkZCQSExPx9ddfY9iwYeA4DrNnz3bPAgVDYmIiPvvsM3Tu3BkGgwEv/P927tglmTiO4/hHyC0THQRPok0xJ20LhwbpKCrEsZD+gyCoLVoEC+WQtlquycWhpTFaGgIHN3FwFISsQEjIFu0ZAnmiHp6nniMp3i+45e7gPrd9ju/9fru7b7aNcLlcWl9f1/HxsZrN5uiHfEnyeDza2dnR9va2hsOhksmkHh4edH19rcnJSW1ubn4q1+3trZ6entTr9VSr1VQoFHR/f6+zs7MPZZdexoErKysaDAav8lSrVV1eXmpxcVGBQEDValV3d3evyjAAVgUCGIN0Oq2joyMVi0XFYjGdnJzo9PRUCwsLo3ssy9LFxYWmp6cVj8clSaVSST6fT/Pz81pdXZVpmkokEl+W27ZtdbtdxeNxZbNZbW1tKRAIvLlvY2NDjUZDoVDozb9kuVxO+/v7Ojg4UDQalWmaOj8//69xWiQSkWEYmpub0+HhoVKplOr1umZnZz+cPZVKKRgMyjRNGYYxOj81NaWrqystLy8rHA5rb29PlmWNxpwAXrien1krCwB48fj4KMMwZNu2MpnMuOMA3w6jQACAhsOhbm5uZFmWvF6v1tbWxh0J+JYYBQL4kcrl8qstDX4/YrHYuOP9k6WlpT++Qz6fd/RZrVZLoVBIlUpFtm1rYoLvbuAzGAUC+JF6vZ46nc6719xut2ZmZr440ce12231+/13r/n9fvn9/i9OBOBvKFYAAAAOYRQIAADgEIoVAACAQyhWAAAADqFYAQAAOIRiBQAA4BCKFQAAgEMoVgAAAA6hWAEAADjkFxvxt8cCeRZKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = \"Total_Travel_Days\", y = \"Per Person Price\", \n",
    "              data = combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f9168d31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    30000.000000\n",
       "mean         4.722133\n",
       "std          1.792381\n",
       "min          1.000000\n",
       "25%          3.000000\n",
       "50%          5.000000\n",
       "75%          6.000000\n",
       "max         14.000000\n",
       "Name: Total_Travel_Days, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.Total_Travel_Days.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c49b5763",
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = []\n",
    "for i in range(0, 10):\n",
    "    saved = combined[\"Hotel Details\"].str.split(\":\", expand = True)[i].\\\n",
    "    str.split(\"|\", expand = True)[0].apply(lambda x: 0 if x is None else x)\n",
    "    ratings.append(saved)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8fed16ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                             Not Available\n",
       "1                                 The Orchard Retreat & Spa\n",
       "2                                                The Ananta\n",
       "3         Elixir Hills Suites Resort & Spa-MMT Holidays ...\n",
       "4                         Dusit Thani Pattaya - MMT Special\n",
       "                                ...                        \n",
       "299995                                                    0\n",
       "299996                                                    0\n",
       "299997                                                    0\n",
       "299998                                                    0\n",
       "299999                                                    0\n",
       "Name: 0, Length: 300000, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(ratings, ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ba199ee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = []\n",
    "for i in range(0, 10):\n",
    "    ratings.append(combined[\"Hotel Details\"].\\\n",
    "                   str.split(\"|\", expand = True)\\\n",
    "    [i].str.split(\":\", expand = True)[1].\\\n",
    "                   apply(lambda x:0 if x is None else x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d58a3dac",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"ratings\"] = pd.concat(ratings, ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "515a941e",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined.ratings.replace(to_replace = [\"Four\", \"Three\", \n",
    "                                      \"Five\", 'Two', \n",
    "                                      'A member of The Leading Hotels Of The World'],\n",
    "                        value =[\"4\", \"3\", \"5\", \"2\", \"0\"], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "95fc70f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"ratings\"]=pd.to_numeric(combined.ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6718fcdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined.ratings.fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b3e14e04",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Count_Sightseeing\"] = combined[\"Sightseeing Places Covered\"].\\\n",
    "apply(lambda x:0 if x ==\"Not Available\" \\\n",
    "else len(x.split(\"|\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f42dbd02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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REcmHsedvhRBWNnW4hZWWlkKtVkOj0UitYURERCRvxp6/m1UfKyIiIiI5Y2JFREREZCJMrIiIiIhMhIkVERERkYkwsSIiIiIyESZWRERERCbCxIqIiIjIRJhYEREREZkIEysiIiIiE2FiRURERGQiTKyIiIiITISJFREREZGJMLEiIiIiMhEmVkREREQmwsSKiIiIyESYWBE1Y3v37sWECROwd+9eS4dCRERgYkXUbFVUVCAxMREXLlxAYmIiKioqLB0SEZHVY2JF1EwlJSXh8uXLAIDLly8jOTnZwhERERETK6JmKD8/H0lJSXrbkpKSkJ+fb6GIiIgIYGJF1OwIIbBkyRIIIfS2a7Vag9uJiMh8mFgRNTM5OTnIyMiolUAJIZCRkYGcnBwLRUZEREysiJoZPz8/uLm5GSxzc3ODn5+fmSMiIiIdJlZEzUxubi5KS0sNlpWWliI3N9fMERERkQ4TK6Jmxt/fHw888AAUCoXedoVCgd69e8Pf399CkRERERMromZGoVBg9uzZUCr1v742NjaYPXt2rYSLiIjMh4kVUTPk4+ODqKgoKYlSKBSIiopChw4dLBwZEZF1Y2JF1ExFR0ejbdu2AAB3d3dERUVZOCIiImJiRdRMOTg4YO7cufD09MScOXPg4OBg6ZCIiKyeQnA2QbMqLS2FWq2GRqOpc8g8ERERyYux52+2WBERUYuzd+9eTJgwAXv37rV0KGRlmFgREVGLUlFRgcTERFy4cAGJiYmoqKiwdEhkRZhYERFRi5KUlISioiIAQFFREZKTky0cEVkTJlZERNRi5OfnIzk5WVpLUwiB5ORk5OfnWzgyshZMrIiIqEUQQmDJkiV1budYLTIHJlZERNQi5OTkICMjAzU1NXrba2pqkJGRgZycHAtFRtaEiRUREbUIunU0bWxs9Lbb2NhwHU0yGyZW1GAcxkxEcqRbR7Ou7VxHk8yBiRU1CIcxE5GccR1NsjSLJla//PILRo4cCW9vbygUCmzatEkqq66uxgsvvIDg4GA4OzvD29sbjz32GM6fP6+3j0GDBkGhUOjdHn30Ub06xcXFiImJgVqthlqtRkxMDEpKSvTq5ObmYuTIkXB2doa7uztmzpyJqqoqvTpHjx7FwIED4ejoiA4dOuDNN9+0us6QHMZMRHIXHR0NFxcXAICrqyvX0SSzsmhiVV5ejm7dumHZsmW1yq5evYpDhw7h1VdfxaFDh7Bx40b89ttvGDVqVK26cXFxKCgokG6ffPKJXnlkZCSysrKQmpqK1NRUZGVlISYmRiqvqalBREQEysvLsWfPHqxbtw4bNmzA3LlzpTqlpaUYOnQovL29kZGRgaVLl2Lx4sV4//33TfiOyBuHMRNRc8HLfmQptpZ88uHDh2P48OEGy9RqNbZv3663benSpejduzdyc3Ph5+cnbXdycoKXl5fB/Zw8eRKpqalIT09Hnz59AADLly9HSEgITp8+jcDAQGzbtg0nTpxAXl4evL29AQCJiYmIjY3FwoUL4ebmhuTkZFRUVODLL7+ESqVCUFAQfvvtN7z//vuYM2dOi/8S324Y8+LFi1v8e0BEzUNSUhKuXLkCALhy5QqSk5MxdepUC0dF1qJZ9bHSaDRQKBRo1aqV3vbk5GS4u7vjvvvuQ3x8vPSFAoC0tDSo1WopqQKAvn37Qq1WY9++fVKdoKAgKakCgPDwcFRWViIzM1OqM3DgQKhUKr0658+fR3Z2dp0xV1ZWorS0VO/WHHEYMxE1B2xZJ0trNolVRUUFXnzxRURGRuqtKh0VFYW1a9di9+7dePXVV7FhwwY88sgjUnlhYSE8PDxq7c/DwwOFhYVSHU9PT73y1q1bw97evt46uvu6OoYkJCRIfbvUajV8fX0b+MrlgcOYiUjuOEEoyUGzSKyqq6vx6KOPQqvV4qOPPtIri4uLw5AhQxAUFIRHH30U33zzDXbs2IFDhw5JdQxdohJC6G2/kzq6L2l9l8DmzZsHjUYj3fLy8m7zauWJw5iJSO7Ysk5yIPvEqrq6GhMnTsTZs2exfft2vdYqQ3r06AE7OzucOXMGAODl5YULFy7Uqnfp0iWpxcnLy6tWq1NxcTGqq6vrrXPx4kUAqNWSdTOVSgU3Nze9W3OlG8Z8Mw5jJiK5YMs6yYGsEytdUnXmzBns2LEDbdu2ve1jjh8/jurqarRv3x4AEBISAo1GgwMHDkh19u/fD41Gg379+kl1jh07hoKCAqnOtm3boFKp0LNnT6nOL7/8ojcFw7Zt2+Dt7Y2AgABTvNxmYfz48VAqb/zbKJVKjBs3zsIRERHdwJZ1kgOLJlZlZWXIyspCVlYWAODs2bPIyspCbm4url+/jvHjx+PgwYNITk5GTU0NCgsLUVhYKCU3f/zxB958800cPHgQ2dnZ2Lp1KyZMmIDu3bsjNDQUANC5c2cMGzYMcXFxSE9PR3p6OuLi4jBixAgEBgYCAMLCwtClSxfExMTg8OHD2LlzJ+Lj4xEXFye1MEVGRkKlUiE2NhbHjh1DSkoKFi1aZBUjAm/2zTffQKvVAgC0Wi02bNhg4YiIiP6HE4SSxQkL+umnnwSAWrcpU6aIs2fPGiwDIH766SchhBC5ubliwIABok2bNsLe3l7cddddYubMmaKoqEjveYqKikRUVJRwdXUVrq6uIioqShQXF+vVycnJEREREcLR0VG0adNGPPPMM6KiokKvzpEjR0T//v2FSqUSXl5eYv78+UKr1TboNWs0GgFAaDSaBr9flpaXlycGDRok+vfvL90GDx4s8vLyLB0aEZHk2rVrYuzYsaJ///7ikUceEdeuXbN0SNQCGHv+VgjBYRLmVFpaCrVaDY1G06z6WwkhEB8fj4MHD+qNrFEoFOjVqxfnsSIiWdm7dy8++OADzJo1S7qCQdQYxp6/LTpBKDUfutE2txJCSKNtrKmvGRHJW2hoKBMqsghZd14n+fDz86szQ3dzc9ObCZ+IiMhaMbEio+Tm5tY5a3xpaSlyc3PNHBEREZH8MLEio+jmh7m1H5VCoeD8MEQkO3v37sWECROwd+9eS4dCVoaJFRmlrnlglEol54chIlmpqKhAYmIiLly4gMTERFRUVFg6JLIiTKzIaD4+PoiOjtbbFh0dzflhiEhWkpKSUFRUBAAoKipCcnKyhSMia8LEihokOjoarq6uAG50Wr91iRsiIkvKz89HcnKyNC2MEALJycnIz8+3cGRkLZhYUYPxsh8RyZEQAkuWLKlzO6dtJHNgYkUNkpSUhCtXrgAArly5wiZ2IpIN3Xx7NTU1ettramqk+faImhoTKzIam9iJSM44epnkgIkVGUXXlK5bgFmnpqaGTexEJAsKhQKTJ0+udTwSQmDy5MnsxkBmwcSKjKJrYjd0wGITOxHJgRACa9euNdhitWbNGv4AJLNgYkVG4ZI2RCR3/AFIcsDEiozCJW2ISO50fayUSv1Tm42NDftYkdkwsSKjsMWKiOROt0KEoRYrrhBB5sLEiozCFisiaq6EEOxfRWbDxIqMwmHMRCR3utHLho5THL1M5nJHidXq1asRGhoKb29vqTPgBx98gG+//dakwZF86JrYDfVdYBM7EcmBrvP6rdPCaLVadl4ns2lwYvXxxx9jzpw5ePjhh1FSUiLNcNuqVSt88MEHpo6PZMTHx6fW2oBRUVFchJmIZEHXsm5jY6O3nZ3XyZwanFgtXboUy5cvx8svv6z3z9urVy8cPXrUpMGR/IwfP15qtVIqlRg3bpyFIyIiukHXsl7Xdraskzk0OLE6e/YsunfvXmu7SqVCeXm5SYIi+frmm2+kZnatVosNGzZYOCLrtnfvXkyYMAF79+61dChEsqBrWdclUQqFgi3rZFYNTqw6duyIrKysWtt/+OEHdOnSxRQxkUzp1gq8GdcKtJyKigokJibiwoULSExMREVFhaVDIpKF6OhotG3bFgDg7u5eqwsDUVNqcGL13HPPYfr06Vi/fj2EEDhw4AAWLlyIl156Cc8991xTxEgyoBttU9d2jrYxv6SkJBQVFQEAioqKaiW9ZH5sQZQHBwcHzJ07F56enpgzZw4cHBwsHRJZEYW4gzPi8uXLsWDBAuTl5QEAOnTogPnz52Pq1KkmD7ClKS0thVqthkajqXPCTTnKzs7GY489Vmf5qlWrEBAQYL6ArFx+fj5iYmKkwSMAYGtri1WrVsHHx8eCkVmviooKREZG4vLly3B3d8eaNWt4QidqQYw9f9/RdAtxcXHIycnBxYsXUVhYiLy8PCZVLRxH28gHWw/liS2IRATcYef1M2fOALhx7drDwwMAcObMGWRnZ5s0OJIPjraRD91cPTe3VgFATU0N5+qxEF3/Q11SK4Rg/0MiK9XgxCo2Nhb79u2rtX3//v2IjY01RUwkUz4+Ppg0aZLetkmTJnG0jZmx9VBe2IJIRDdrcGJ1+PBhhIaG1tret29fg6MFici02HooL2xBJKKbNTixUigUuHLlSq3tGo2m1oGFWpb8/HysX79eb9v69et5ucMC2HooH2xBJKKbNTix6t+/PxISEvSSqJqaGiQkJODvf/+7SYMj+eDlDiLD2IJIRDezbegD3n33XQwYMACBgYHo378/AOC///0vSktLsWvXLpMHSPKgu9xxq5svd3C6BfOpq/UwIiKC0y1YgG6279WrV0MIwdm+iaxYg1usunTpgiNHjmDixIm4ePEirly5gsceewynTp1CUFBQU8RIMqC73KFbJ1BHqVTycoeZ1dVKqNVq2XpoQZztW144WStZyh1NEEp3rrlOEArcaCWJiorSO3ErFAqsWbOGv8zNiJO1ytfevXvxwQcfYNasWQYH+ZB5cLJWagrGnr+NuhR45MgRBAUFQalU4siRI/XW7dq1a8MipWZNoVCwhcTM/P39ERwcjKNHj9Yq69q1K1sPLSg0NJQJlQwYmqyVk1iTuRiVWN1///0oLCyEh4cH7r///jpPpgqFgiMDWyjd5SelUqn3GSsUCixZsgSLFy9mJ10zKisra9B2ImtR12St4eHh7H9IZmFUYnX27Fm0a9dO+pusDzuvy0d2dnad38M///wT2dnZ6Nixo5mjIrK8241e5g9AMgejOq/7+/tDoVCguroa8+fPR01NDfz9/Q3eqGXSdV6/9aCkUCjYed3Mzp0716hyopaKk7WSHDRoVKCdnR1SUlKaKhaSMYVCgcmTJ9e6BCyEwOTJk/kr0IxCQkJqjc7UsbGxQUhIiJkjIpIHf3//Ovv5sv8hmUuDp1sYO3YsNm3a1AShkJwJIbB27VqDLVZr1qxhB3YzysvLg1arNVhWU1ODvLw8M0dEJB91HYt4jCJzaXBidffdd+Ott97C+PHjkZCQgH/96196t4b45ZdfMHLkSHh7e0OhUNRK2IQQmD9/Pry9veHo6IhBgwbh+PHjenUqKysxY8YMuLu7w9nZGaNGjaq1xEpxcTFiYmKgVquhVqsRExODkpISvTq5ubkYOXIknJ2d4e7ujpkzZ6KqqkqvztGjRzFw4EA4OjqiQ4cOePPNN63my6prYjfUYsUmdvPSXZY1hJdlyZrl5OQYHC0L3Dh+8zhF5tDgxOqzzz5Dq1atkJmZiU8//RRLliyRbh988EGD9lVeXo5u3bph2bJlBsvfffddvP/++1i2bBkyMjLg5eWFoUOH6q1VOGvWLKSkpGDdunXYs2cPysrKMGLECL1r7JGRkcjKykJqaipSU1ORlZWFmJgYqbympgYREREoLy/Hnj17sG7dOmzYsAFz586V6pSWlmLo0KHw9vZGRkYGli5disWLF+P9999v0GturtjHSj50l2UN4WVZsmacyJhkQcgEAJGSkiLd12q1wsvLS7z99tvStoqKCqFWq8V//vMfIYQQJSUlws7OTqxbt06qc+7cOaFUKkVqaqoQQogTJ04IACI9PV2qk5aWJgCIU6dOCSGE2Lp1q1AqleLcuXNSnbVr1wqVSiU0Go0QQoiPPvpIqNVqUVFRIdVJSEgQ3t7eQqvV1vm6KioqhEajkW55eXkCgLTf5iQjI0P079+/1u3gwYOWDs2qaLVaMX36dIOfxfTp0+v9fyRq6fLy8sSgQYP0vheDBw8W+fn5lg6NmjmNRmPU+btBLVb79+/Hyy+/jBdeeAHbtm1rgjTvf86ePYvCwkKEhYVJ21QqFQYOHIh9+/YBADIzM1FdXa1Xx9vbG0FBQVKdtLQ0qNVq9OnTR6rTt29fqNVqvTpBQUHw9vaW6oSHh6OyshKZmZlSnYEDB0KlUunVOX/+PLKzs+t8HQkJCdIlSLVaDV9f30a8K5Yj2MdKNrKzs+ucqPfIkSP1/j8StXS6dRt1xyqu20jmZnRilZKSgtDQUHz44Yf45JNPMHz48AZf+muIwsJCAICnp6fedk9PT6mssLAQ9vb2aN26db11PDw8au3fw8NDr86tz9O6dWvY29vXW0d3X1fHkHnz5kGj0Ui35tqxmH2s5ON2SSyTXLJ2XLeRLMnoxGrRokWIjY1FSUkJSkpK8MYbb2DBggVNGRsA1GohEf+/cnx9bq1jqL4p6uhOYPXFo1Kp4ObmpndrjnR9F2xsbPS229jYsO+Cmd3u/599rMjaOTg4YO7cufD09MScOXO4TiCZldGJ1enTp/H888/D1vbGZO3PPfccSkpKcPny5SYJzMvLC0Dt1qCLFy9KLUVeXl6oqqpCcXFxvXUuXLhQa/+XLl3Sq3Pr8xQXF6O6urreOhcvXgRQu1WtJVIoFJg9e3ad23kyNx9/f3+4uLgYLHNxcWGSS4Qb6zZ+/fXXXLuRzM7oxKqsrAytWrWS7qtUKjg6OqK0tLQp4kLHjh3h5eWF7du3S9uqqqrw888/o1+/fgCAnj17ws7OTq9OQUEBjh07JtUJCQmBRqPBgQMHpDr79++HRqPRq3Ps2DEUFBRIdbZt2waVSoWePXtKdX755Re9KRi2bdsGb29vq1nKhX0X5CE3N7fetQJzc3PNHBEREekYtVagzo8//gi1Wi3d12q12LlzJ44dOyZtGzVqlNH7Kysrw++//y7dP3v2LLKystCmTRv4+flh1qxZWLRoEf72t7/hb3/7GxYtWgQnJydERkYCANRqNaZOnYq5c+eibdu2aNOmDeLj4xEcHIwhQ4YAADp37oxhw4YhLi4On3zyCQDgH//4B0aMGIHAwEAAQFhYGLp06YKYmBi89957+OuvvxAfH4+4uDjp0l1kZCTeeOMNxMbG4qWXXsKZM2ewaNEivPbaa1bVWjN+/HgkJSVJl0nHjRtn6ZCsjp+fH9zc3Az+qHFzc4Ofn58FoiIiIgDGT7egUChue1MqlQ0auvjTTz8JALVuU6ZMEULcGFb++uuvCy8vL6FSqcSAAQPE0aNH9fZx7do18cwzz4g2bdoIR0dHMWLECJGbm6tXp6ioSERFRQlXV1fh6uoqoqKiRHFxsV6dnJwcERERIRwdHUWbNm3EM888oze1ghBCHDlyRPTv31+oVCrh5eUl5s+f3+Ch7cYO15Sr5cuX6w1j/uyzzywdktU5e/aswakWdLezZ89aOkQii1u+fLkYOHCgWL58uaVDoRbC2PO3QggOITKn0tJSqNVqaDSaZteRPT8/HzExMXqTr9ra2mLVqlXw8fGxYGTWRQiB+Ph4HDx4UG8EoEKhwAMPPID33nvPqlpRiW5VUlKCMWPGQKvVQqlUYtOmTXpdWYjuhLHn7wbPvE7WSQiBJUuW1Lmd+bn56AYM3Dq7tI2NDQcSEAF4+eWXpfU0tVotXnnlFQtHRNaEiRUZRTeP1c2tVcCN5YA4j5X5+fj4YNKkSXrbJk2axIEEZPUOHjxYa73AI0eO4ODBgxaKiKwNEysyCuexIiK502q1mD9/vsGy+fPnS61YRE2JiRUZhfNYyUt+fj7Wr1+vt239+vXIz8+3UERElpeWllbnFEClpaVIS0szc0RkjZhYkdE4j5U8sL8bkWF9+/at1aquY2Njg759+5o5IrJGd5xYVVVVIT8/H7m5uXo3atm4Bpflsb8bkWG5ubm1vhc6NTU1PEeRWTQ4sTpz5gz69+8PR0dH+Pv7o2PHjujYsSMCAgLQsWPHpoiRZIRrcFmerr/brZdflUol+7sREVlYg2ZeB4DY2FjY2tri+++/R/v27dm3xgqFhoZy/S0L0vVr061AoKPVatnfjayav78/nJyccPXq1VplTk5O/NFBZtHgxCorKwuZmZm49957myIeIjLCrQuC6xQUFLDPG1mtnJwcg0kVAFy9ehU5OTm8skJNrsGXArt06YLLly83RSxEZAQOKScikq8GJ1bvvPMOnn/+eezevRtFRUUoLS3VuxFR0+KQcvn67LPPMGjQIHz22WeWDsUqBQQEIDg42GBZ165dERAQYN6AyCo1eK1A3TIat/bjEEJAoVDUOSKDbmjOawWSPGi1WowaNcpgcqVWq/Htt9/WWu6Gmh7Xp5OH/Pz8Wv0PAWDt2rW8TE6NYuz5u8F9rH766adGBUZEjaNUKjF9+nQkJCTUKnvmmWeYVFmIofXpli1bZuGorI+Pjw/uu+8+HD9+XNoWFBTEpIrMpsGJ1cCBA5siDiIykhACGzduNFj2zTffICwsjCMDzay+9el69eploaisU35+Pk6dOqW37dSpU8jPz4ePj4+FoiJrckc/bUtKSpCYmIgnn3wScXFxWLJkCTQajaljIyIDzp49i9OnTxssO336NM6ePWvmiKwbBxPIB1clIDlocGJ18OBB3HXXXViyZAn++usvXL58Ge+//z7uuusuHDp0qCliJKKbFBQUNKqcTIuDCeSDqxKQHDQ4sZo9ezZGjRqF7OxsbNy4ESkpKTh79ixGjBiBWbNmNUGIRHSzkJAQODk5GSxzcnJCSEiImSOybvWtT2dra8v16cxItyrBrZ+HjY0NVyUgs7mjFqsXXngBtrb/655la2uL559/HgcPHjRpcCRPe/fuxYQJE7B3715Lh2KVFAoFfH19DZb5+vqyf5WZ5eXl1Tka+vr168jLyzNzRNZLtypBXdv53SBzaHBi5ebmZnAhy7y8PLi6upokKJKviooKJCYm4sKFC0hMTERFRYWlQ7I6OTk59fax4uUO89K1khjCVhLz8/HxwaRJk/S2TZo0iaMCyWwanFhNmjQJU6dOxfr165GXl4f8/HysW7cOTz75JCZPntwUMZKMJCUloaioCABQVFSE5ORkC0dkfbgIs7zoWkMMXX5iKwmR9WlwYrV48WI88sgjeOyxxxAQEAB/f3/ExsZi/PjxeOedd5oiRpKJ/Px8JCcnSyNrhBBITk5Gfn6+hSOzLnVd1uDlDsvx8fFBVFSU3rbo6Gi2kliA7sf+zdatW8fjFJlNgxMre3t7fPjhhyguLkZWVhYOHz6Mv/76C0uWLIFKpWqKGEkGOIxZXnx8fNC5c2e9bZ07d+aJ3ILGjx8vTc6qVCoxbtw4C0dkfeo6Hmm1Wh6nyGzueIpmJycnBAcHIyAgANu2bcPJkydNGRfJDIcxy0t+fr7ezNIAcOzYMf4qt6BvvvlGb+b1DRs2WDgi66M7Tt2aQAkheJwis2lwYjVx4kRpmYZr166hV69emDhxIrp27coDSQvGYczyIYTA22+/bbDs7bff5q9yC8jPz0dSUpLetqSkJCa6Zubn5wcXFxeDZS4uLvDz8zNzRGSNGpxY/fLLL+jfvz8AICUlBUIIlJSU4F//+hcWLFhg8gBJHjiMWT6ys7Nx5MgRg2VHjhxBdna2eQOycrz8JB85OTkoKyszWFZWVsYWKzKLBidWGo0Gbdq0AQCkpqZi3LhxcHJyQkREBM6cOWPyAEk+dB10dUmUQqFAVFQU+/WY2e1O1DyRmxcvPxHRzRqcWPn6+iItLQ3l5eVITU1FWFgYAKC4uBgODg4mD5DkJTo6Gm3btgUAuLu71xoJRU3vdq2DbD00Lz8/P7i5uRksc3Nz4+UnMwoICEBwcLDBsq5duyIgIMC8AZFVanBiNWvWLERFRcHHxwfe3t4YNGgQgBuXCOv6h6aWw8HBAXPnzoWnpyfmzJnDZNoC/Pz8pNFnt1IqlTyRm1lubm69awUamlCZmoZCocDjjz9usOzxxx/njw4yC9vbV9E3bdo09OnTB7m5uRg6dKh0gO/UqRP7WFmJ0NBQhIaGWjoMq5Weni6NPruVVqtFeno6Px8z0rVYGUqu2GJlXkIIrF271mDZmjVr0KNHDyZX1OQa1GJVXV2NTp06wdHREWPHjtUbfREREcGDOZEZeHl5NaqcTIstVvKh6+9mCPu7kbk0KLGys7NDZWUlM34rx0WYLauuy4DGlpNp+fv719uvh1ORmA/7u5EcNPgIPGPGDLzzzju4fv16U8RDMsdFmC3P398fTk5OBsucnJx4IreAqqoqg9srKyvNHIl1Y+shyUGD+1jt378fO3fuxLZt2xAcHAxnZ2e98o0bN5osOJKfpKQkXL58GQBw+fJlJCcnY+rUqRaOyrrk5OTg6tWrBsuuXr2KnJwcdOzY0cxRWa/s7GycPn3aYNnp06eRnZ3Nz8NMdBMZHzx4UG/6C4VCgQceeIA/OsgsGtxi1apVK4wbNw7h4eHw9vaGWq3Wu1HLxdmliUjOdBMW33o53MbGhhMZk9k0uMVqxYoVTREHyZxudulbR6PV1NRgyZIlWLx4MQ9aZuLv7w8HBweDl2EdHBz4q9zMdJdmDbUi8tKs+fn4+GDSpElYs2aNtG3SpEmcyJjM5o56uV6/fh07duzAJ598gitXrgAAzp8/X+dSAtT8cbSNfOTk5NTZt62iooKfhZkZc2mWzOvWvm3sC0rm1ODEKicnB8HBwRg9ejSmT5+OS5cuAQDeffddxMfHmzxAkgcubiof586da1Q5UUuWn5+PDRs26G3bsGEDuyyQ2TQ4sXr22WfRq1cvFBcXw9HRUdo+duxY7Ny506TBkXxwcVP56NevH+zt7Q2W2dvbo1+/fmaOyLrpLs0awkuz5iWEwBtvvGGw7I033uA6mmQWDU6s9uzZg1deeaXWgd3f379JfikHBARAoVDUuk2fPh0AEBsbW6usb9++evuorKzEjBkz4O7uDmdnZ4waNarWr5fi4mLExMRInfBjYmJQUlKiVyc3NxcjR46Es7Mz3N3dMXPmzDqHWRM1FSFEndOdXL9+nScPM8vOzq730mx2drZ5A7JixozQJGpqDU6stFotampqam3Pz8+Hq6urSYK6WUZGBgoKCqTb9u3bAQATJkyQ6gwbNkyvztatW/X2MWvWLKSkpGDdunXYs2cPysrKMGLECL3XERkZiaysLKSmpiI1NRVZWVmIiYmRymtqahAREYHy8nLs2bMH69atw4YNGzB37lyTv2Y54uKm8rF58+Z6l7TZvHmzmSOybrw0Kx/8LEgOGpxYDR06FB988IF0X6FQoKysDK+//joefvhhU8YGAGjXrh28vLyk2/fff4+77roLAwcOlOqoVCq9Om3atJHKNBoNPv/8cyQmJmLIkCHo3r07kpKScPToUezYsQMAcPLkSaSmpuKzzz5DSEgIQkJCsHz5cnz//ffSr59t27bhxIkTSEpKQvfu3TFkyBAkJiZi+fLldU5IB9xoLSstLdW7NUcKhQLz5s2rcztHBJrPqFGj6l2EedSoUWaOyLrd7n+f3w3z6devX719QXmZnMyhwYnVkiVL8PPPP6NLly6oqKhAZGQkAgICcO7cObzzzjtNEaOkqqoKSUlJeOKJJ/QOVrt374aHhwfuuecexMXF4eLFi1JZZmYmqqurERYWJm3z9vZGUFAQ9u3bBwBIS0uDWq1Gnz59pDp9+/aFWq3WqxMUFARvb2+pTnh4OCorK5GZmVlnzAkJCXrzfPn6+jb+jbAQHx8f3HfffXrb7rvvPg5jNjOFQgFbW8Mzpdja2vJEbmb9+vWrt48VT+bmo1Qq8eabbxosW7BgAZd7IrNo8H+Zt7c3srKy8Nxzz+Gpp55C9+7d8fbbb+Pw4cPw8PBoihglmzZtQklJCWJjY6Vtw4cPR3JyMnbt2oXExERkZGTgwQcflIbbFhYWwt7eHq1bt9bbl6enJwoLC6U6hmL38PDQq+Pp6alX3rp1a9jb20t1DJk3bx40Go10y8vLu6PXLgf5+fk4deqU3rZTp05xtI2ZpaWl1dm3r6qqCmlpaWaOyLopFIo6O6jr+oiS+fTq1avWD8CgoCD06NHDQhGRtWnQBKH79+/H5s2bUV1djYceegiPP/54U8Vl0Oeff47hw4frtRpNmjRJ+jsoKAi9evWCv78/tmzZgkceeaTOfQkh9A54hg5+d1LnViqVCiqVqu4X1UzoJgitazsnCDWf9u3bN6qcTCsnJ6fODtOnTp1CTk4O+yCaWUJCAkaPHi0dnxctWmTpkMiKGN1ilZKSgtDQUHz44Yf49NNP8fDDD+v1tWpqOTk52LFjB5588sl667Vv3x7+/v44c+YMAMDLywtVVVUoLi7Wq3fx4kWpBcrLywsXLlyota9Lly7p1bm1Zaq4uBjV1dW1WrJaIt0EobcOXKipqeEEoWYWEBBQ76UnnsTNy8fHp1HlZHqtWrVCTEwMlEolYmJi0KpVK0uHRFbE6MRq0aJFiI2NRUlJCUpKSvDGG29gwYIFTRmbnhUrVsDDwwMRERH11isqKkJeXp70q71nz56ws7OTRhMCQEFBAY4dOyb1fQgJCYFGo8GBAwekOvv374dGo9Grc+zYMRQUFEh1tm3bBpVKhZ49e5rsdcqVbnFTQ2tw9e7dm3P1mBFnXpeX7777rlHl1DQ6d+6Mdu3aoXPnzpYOhayM0YnV6dOn8fzzz0udZp977jmUlJTg8uXLTRacjlarxYoVKzBlyhS9TrtlZWWIj49HWloasrOzsXv3bowcORLu7u4YO3YsAECtVmPq1KmYO3cudu7cicOHDyM6OhrBwcEYMmQIgBtfwGHDhiEuLg7p6elIT09HXFwcRowYgcDAQABAWFgYunTpgpiYGBw+fBg7d+5EfHw84uLi4Obm1uTvgaXpFje9dY4kIQQXNyWr1rVr10aVk+lVVFQgMTERFy5cQGJiIpe0IbMyOrEqKyvTa05VqVRwdHQ0y/QBO3bsQG5uLp544gm97TY2Njh69ChGjx6Ne+65B1OmTME999yDtLQ0vTm1lixZgjFjxmDixIkIDQ2Fk5MTvvvuO9jY2Eh1kpOTERwcjLCwMISFhaFr165YvXq13nNt2bIFDg4OCA0NxcSJEzFmzBgsXry4yV+/nAkhOCGlmd3u/ebnYV4dO3as99Jsx44dzRwRJSUloaioCMCNqxjJyckWjoisiUIYeRRWKpVYuXIl1Gq1tG3y5Mn44IMP9PoYcQ6d+pWWlkKtVkOj0TSrli4hBOLj45GZmak3OaVSqUTPnj3Zed2Mzp49iylTptRZvnLlSp7MzSg7OxuPPfZYneWrVq1ivzczys/PR0xMjF5/UFtbW6xatYr93ahRjD1/N2hUoKGD+VNPPSX9rVAoDM7KTs2frvP6rbRardR5nScP86hr1nVjy8m0dAuUG1pLkwuUmxdHL5McGH0pUKvV3vbGpKrl0nVev/nyKcDO65Zw8wCKOykn0+IC5fLB0cskB5yGloyi67xe13b+CjQftljJC/u8yYe/v3+dgwW6du3KH4BkFkysyGg+Pj6IioqSkiiFQoGoqCguaWNmly5dalQ5mRbXCpSXuhJZJrhkLkysqEGio6PRtm1bAIC7uzuioqIsHJH14fB+eWGLlXzk5OTg6NGjBsuOHj3KS4FkFkysqEEcHBwwd+5ceHp6Ys6cOXUOM6emY2iVgIaUE7VUdU1krFQq2ReUzKZBiVVNTQ1+/vnnWsvDkHUJDQ3F119/jdDQUEuHYpXYx4rIsLr6fCqVSvYFJbNpUGJlY2OD8PBwlJSUNFE41Bzs3bsXEyZMwN69ey0dilVinx6iuvn4+KBLly5627p06cK+oGQ2Db4UGBwcjD///LMpYqFmgEtFWJ63t3ejysm0mOjKS35+Po4fP6637fjx48jPz7dQRGRtGpxYLVy4EPHx8fj+++9RUFCA0tJSvRu1bFwqwvLYWVpe/Pz8avXp0VEqlZwg1Ix0E4EaSmaXLFnC74aFWNtVjgYnVsOGDcOvv/6KUaNGwcfHB61bt0br1q3RqlUrtG7duiliJJnIz89HcnKydHASQiA5OZm/BM3sp59+alQ5mVZ6enqd/dq0Wi3S09PNHJH14gSh8mONVzkatKQNwIO2teJSEfIxcOBAvQXCDZWT+dxuxQmuSGE+ulGBhw4d0nvfbWxs0LNnT44KtABDVzmmTp1q4aiaVoMTKx60rVNdawXe/EuQawWaR1ZW1m3L77nnHvMEQ7dd2JcL/5qPblRgTEyMwe388WdedV3lCA8Pb9Hfizuax+q///0voqOj0a9fP5w7dw4AsHr1auzZs8ekwZF86H4J3npgUigUnB/GzG53OYOXO8ia+fj4YNKkSXrbJk2axFGBZqa7mnHrZfKampoW39+twYnVhg0bEB4eDkdHRxw6dAiVlZUAgCtXrmDRokUmD5DkQaFQYPLkybW+DEIITJ48mb8EzWjmzJmNKifT0v24vNNyMr3r16/Xe5+anu4qh6FzRkvv79bgxGrBggX4z3/+g+XLl8POzk7a3q9fPxw6dMikwZF8CCGwdu1ag2Vr1qxp0b8+5ObAgQONKidqyfLz8/HVV1/pbVu/fj0H2ZiZn58f3NzcDJa5ubm16NGyDU6sTp8+jQEDBtTa7ubmxolDW7C6+lgBaPG/PuTm5MmTjSon07rdJSZegjIfIQQSEhIMliUkJPAHoBnl5ubWOQVTaWkpcnNzzRyR+TQ4sWrfvj1+//33Wtv37NmDTp06mSQokh9r/vUhN3fffXejysm0bve/z++G+WRnZ9e7CHN2drZ5A7Ji1twvt8GJ1VNPPYVnn30W+/fvh0KhwPnz55GcnIz4+HhMmzatKWIkGbDmXx9y89tvvzWqnExr06ZNjSon0+HkufJhzes2Nni6heeffx4ajQaDBw9GRUUFBgwYAJVKhfj4eDzzzDNNESPJgO7Xx8GDB/UOTgqFAg888ECL/vUhN1yEWV4uX77cqHIyHS4vJC8+Pj6Ijo7GqlWrpG3R0dEt/vL4HU23sHDhQly+fBkHDhxAeno6Ll26hLfeesvUsZGMWPOvD7kpKytrVDmZFicIlQ9/f3+4uLgYLHNxceEPQAuIjo6Gu7s7AKBdu3aIioqycERNz+jE6urVq5g+fTo6dOgADw8PPPnkkwgICEDv3r3r/EemlsXHxwf33Xef3rb77ruvxf/6kJuePXs2qpxMq127do0qJ9PJzc2t84dFWVkZuyxYgIODAx5++GEolUoMHz4cDg4Olg6pyRmdWL3++uv48ssvERERgUcffRTbt2/H008/3ZSxkczk5+fjxIkTettOnDjBYcxm9tdffzWqnEyL/XrkQ9dlwZCW3mFarioqKrB161ZotVps3brVKtYKNDqx2rhxIz7//HN8+umn+Ne//oUtW7Zg06ZNbOa2ErdbK5AnD/O5dOlSo8rJtI4dO9aocjIdhUKBIUOGGCwbMmQIuyxYgKG1Als6oxOrvLw89O/fX7rfu3dv2Nra4vz5800SGMkLV42XD870LS8PPfRQo8rJdLRaLf79738bLFu2bBkHdphZXWsFtvSrHEYnVjU1NbC3t9fbZmtry6UCrISuid3GxkZvu42NDZvYzczb27tR5WRavr6+jSon00lLS6t3Wpi0tDQzR2S9rPkqh9HTLQghEBsbC5VKJW2rqKjAP//5Tzg7O0vbNm7caNoISRZ0owKjo6NrlXFUoHlxSLm88POQj5CQELi5uRlMrtRqNUJCQiwQlXWqa7WOm69yBAQEmD8wMzC6xWrKlCnw8PCAWq2WbtHR0fD29tbbRi0XRwXKw+2WjuLSUubFxEo+lEolHn74YYNlERERUCrvaIYhugPWPPO60S1WK1asaMo4qBkwNCrw+PHjyM/Ph4+Pj4Wisj7FxcWNKifT4qhA+aipqcHXX39tsGz9+vWIi4ur1Z2BmoZCocDkyZNrtVoJITB58uQW/YOD6TsZRXdd3FDn9ZZ+vVxuCgoKGlVOpnW7jrgtvaOunGzevLnOkeo1NTXYvHmzmSOyXkIIrF271mCL1Zo1a1r0OYOJFRmlruvlADgq0My4hIq83G5kNEdOm8+oUaPqvNynVCoxatQoM0dkvXTnjFsTKCFEiz9nMLEio/j6+tbZhG5jY8ORT2bk6enZqHIyrV27djWqnExHoVDUGr2uY29v36IvP8mNNY8kZ2JFRklPT6+3iT09Pd3MEVkvV1fXRpWTaXXs2LFR5WQ6aWlpdc7sXVFRwekWzEg3kryu7S05yWViRUYJCQmpd3FTDmM2n9s1obfkJnY5ut2Q8ZY6pFyO+vbtW++lwL59+5o5Iuvm4+ODqKgoKYlSKBSIiopq8SPJmViRURQKRZ1fBh8fnxb960NubrfoORdFNy+NRtOocjKdnJycOmdX12q1/NFhAdHR0Wjbti0AwN3dHVFRURaOqOkxsSKj5OTk4PTp0wbLTp06xQOWGV25cqVR5UQt1eHDhxtVTqbn4OCAuXPnwtPTE3PmzIGDg4OlQ2pyRs9jRdbNz8+vzhmN3dzc4OfnZ4GorJOLi0udy3boysl8Wrdu3ahyMp3777+/UeXUNEJDQxEaGmrpMMyGLVZklNzc3HrX4MrNzTVzRNbLycmpUeVkWn/99Vejysl0brfIMhdhJnOQdWI1f/58KBQKvZuXl5dULoTA/Pnz4e3tDUdHRwwaNAjHjx/X20dlZSVmzJgBd3d3ODs7Y9SoUbUm7CsuLkZMTIy0LE9MTEytZUFyc3MxcuRIODs7w93dHTNnzkRVVVWTvXa54XQL8lFUVNSocjKtNm3aNKqcTOfnn39uVDmRKcg6sQJurEVXUFAg3Y4ePSqVvfvuu3j//fexbNkyZGRkwMvLC0OHDtXrYzJr1iykpKRg3bp12LNnD8rKyjBixAi9qQMiIyORlZWF1NRUpKamIisrCzExMVJ5TU0NIiIiUF5ejj179mDdunXYsGED5s6da543QQY43YJ86DqC3mk5mVZ2dnajysl0pkyZ0qhyahqfffYZBg0ahM8++8zSoZiF7PtY2dra6rVS6Qgh8MEHH+Dll1/GI488AgBYuXIlPD09sWbNGjz11FPQaDT4/PPPsXr1agwZMgQAkJSUBF9fX+zYsQPh4eE4efIkUlNTkZ6ejj59+gAAli9fjpCQEJw+fRqBgYHYtm0bTpw4gby8PHh7ewMAEhMTERsbi4ULF8LNzc1M74blcNX4xhNC1DnHTkNUV1fftvzatWuNeg4HBweO9DRSnz59sGXLlnrLyTzy8vJuW96pUyczRUPAjUXhk5KSoNVqkZSUhPHjx6NVq1aWDqtJyT6xOnPmDLy9vaFSqdCnTx8sWrQInTp1wtmzZ1FYWIiwsDCprkqlwsCBA7Fv3z489dRTyMzMRHV1tV4db29vBAUFYd++fQgPD0daWhrUarXewa9v375Qq9XYt28fAgMDkZaWhqCgICmpAoDw8HBUVlYiMzMTgwcPrjP+yspKVFZWSvfr63QsZ0qlEpMnT8Ynn3xSqywyMpKrxhuhoqIC4eHhTf48OTk5jX6eH3/8EY6OjiaKSJ5MlejerktAVVVVoxJdJrnGM2Z5ISZW5vXyyy9Lfdu0Wi1eeeUVLFu2zMJRNS1ZJ1Z9+vTBqlWrcM899+DChQtYsGAB+vXrh+PHj6OwsBBA7eU7PD09paH/hYWFsLe3rzUqx9PTU3p8YWEhPDw8aj23h4eHXp1bn6d169awt7eX6tQlISEBb7zxRgNetTxptVqsXr3aYNnKlSsxadIkJlfUrJgr0V2wYEGjHm8NSa6p1NVdwdhyMq2DBw/qdd8BgCNHjuDgwYPo1auXhaJqerJOrIYPHy79HRwcjJCQENx1111YuXKlNIPurb/khBC3/XV3ax1D9e+kjiHz5s3DnDlzpPulpaXNsqN3Wloarl69arDs6tWrSEtLs6rhtHfCwcEBP/74o0n2NXnyZIOjzdq1a4ekpKRG798a5pqhlufixYuNKifT0Wq1mD9/vsGy+fPnY/PmzS32x7isE6tbOTs7Izg4GGfOnMGYMWMA3GhNat++vVTn4sWLUuuSl5cXqqqqUFxcrNdqdfHiRfTr10+qc+HChVrPdenSJb397N+/X6+8uLgY1dXVt13wVqVSQaVSNfzFygyHMTeeQqEwWcvDpk2bMGDAgFrbN2zYYJL9WwNTJrrr16/HF198UWt7XFwcxo8f36h9M8k13u1+6PKSqvmkpaXVO0VPS/4x3qzSxcrKSpw8eRLt27dHx44d4eXlhe3bt0vlVVVV+Pnnn6WkqWfPnrCzs9OrU1BQgGPHjkl1QkJCoNFocODAAanO/v37odFo9OocO3YMBQUFUp1t27ZBpVKhZ8+eTfqa5eJ2ndPZed38nnzySb3706ZNs1AkzZMu0TXFLTY2tlbS7OjoiJiYmEbvm8mA8bp3796ocjId3YAnQ1r6gCdZJ1bx8fH4+eefcfbsWezfvx/jx49HaWkppkyZAoVCgVmzZmHRokVISUnBsWPHEBsbCycnJ0RGRgK48eFNnToVc+fOxc6dO3H48GFER0cjODhYGiXYuXNnDBs2DHFxcUhPT0d6ejri4uIwYsQIBAYGAgDCwsLQpUsXxMTE4PDhw9i5cyfi4+MRFxdnFSMCAdRqsWtoOZnehAkT9O4/+uijFoqEAGDp0qV695cvX26hSKwXW6zkQ6lU1nkp8I033mixlwEBmV8KzM/Px+TJk3H58mW0a9cOffv2RXp6Ovz9/QEAzz//PK5du4Zp06ahuLgYffr0wbZt2+Dq6irtY8mSJbC1tcXEiRNx7do1PPTQQ/jyyy/1JrtMTk7GzJkzpdGDo0aN0hu1YGNjgy1btmDatGkIDQ2Fo6MjIiMjsXjxYjO9E5bXu3fvRpVT0zLVJS26czf3nbzvvvu4zJMFCCEaVU6m1atXLwQHB+t1YO/atSt69OhhwaiankLwP82sSktLoVarodFomlVrV0pKCpYsWVJn+ezZszF27FgzRkTXrl2TRrVx5Jjl8fOwvA0bNuDDDz+ss/zZZ5/FuHHjzBgRlZSUYMyYMdBqtVAqldi0aVOzncfK2PN3y22LI5OKiIhoVDkRUVPr1q1bo8rJ9Fq1aoXo6GgolUpER0c326SqIZhYkVE2b97cqHIioqZ26zqwDS2nptG5c2e0a9cOnTt3tnQoZsHEioiIWoSMjIxGlZPpVVRUIDExERcuXEBiYqJJVjuQOyZWZBR3d/dGlRMRNbXbdYpu6Z2m5SgpKQlFRUUAgKKiIiQnJ1s4oqbHxIqMwhmNiUjuDh482KhyMq38/HwkJydLozGFEEhOTm7xl2SZWJFR/vzzz0aVExE1NRcXl0aVk+kIIQyOJNdtb8kTEjCxIqPcbiSHNYz0ICJ5y8vLa1Q5mU5OTg4yMjJqLXxdU1ODjIwM5OTkWCiypsfEiozy0EMPNaqciKipscuCfPj7++OBBx6oNcO6jY0NevfuLU303RIxsSKjnD9/vlHlRERNrbKyslHlZDoKhQKzZ8+udclPCIHZs2e36OWFmFiRUY4fP96ociKipna72e45G77lCSFadP8qgIkVGen3339vVDkRUVOzta1/+dvblZPp1NV5HQA7rxMBXNKGiOSPndflQ9d53dClQHZeJwJw5syZRpUTETU19rGSD39/fwQHBxss69q1a4vuvM52USIisighhEmWOuncuTN+/fXXesuvXbvWqOdwcHBo0R2vTamqqsrg9pae4DKxIqO4ubk1qpyIqC4VFRUIDw9v8uf59ddfG/08P/74IzvBGyE7OxunT582WHb69GlkZ2ejY8eOZo7KPHgpkIzy888/N6qciIjIGrDFiozC0TZE1FQcHBzw448/Nno/Wq0Ww4cPr7P8hx9+qDVhZUM5ODg06vHU8vFsSEbx8PBoVDkRUV0UCoXJLq+9//77mDNnTq3tH3zwAZydnU3yHHR7AQEBCA4OxtGjR2uVde3aFQEBAeYPykx4KZCMcuLEiUaVExGZQ69evdCuXTu9bR4eHujRo4eFIrJOCoUC8+bNq3N7Sx4AwMSKjFJdXd2ociIic/n3v/+td/+zzz6zUCTWzcfHB5GRkXrbIiMj0aFDBwtFZB5MrMgoGo2mUeVEROaiVqulvyMjI9GqVSvLBWPlYmNj4erqCuDG6PEpU6ZYOKKmx8SKjGJnZ9eociIiS7CGE7mcOTg44KWXXoKnpyfmzZtnFZ3/2Xm9hTPVxHtt2rTB1atX6y3nxHtERHSr0NBQhIaGWjoMs2Fi1cKZa+K9/Px8TrxHRERWj5cCiYiIiEyELVYtnKkm3ispKcGkSZPqLF+/fn2jO4haw7V3IiJq2ZhYtXCmmnjP0dERbm5uKC0trVWmVqvRvn37Rj8HERFRc8dLgWS077//3uD27777zsyREBERyRMTK2qQ6OhovfuPP/64hSIhIiKSH14KpAaJiYlBUlKSdJ+JFRFRy2GqKXpu3l9lZSUAQKVSmXRKHblO0cPEiu6YKTrFExGRfJhrih5TkOsUPbwUSERERGQibLEiqoepm8VN6ea45Bqjjima7OX8WQDN5/OQ6+UTkgdTTdGjU1FRgdGjRwMAvv32W5NOqyPXKXqYWBHVo7k0i+sOXHJliib75vJZAPL+POR6+YTkwVRT9Bji4OBgFf97vBRIREREZCJssSIy0r8HlEBlIywdhkQIoEp74297JSC3qzuVNQpM/6VVk+y7ZmSN/I5eAkDN//9tA0BOn8d1wOY7G0tHQWQV5HZoIpItlY2Ag8zOTfJuVG/CJNQW8jx62Vk6ACKyNF4KJCIiIjIRJlZEREREJiLrxCohIQEPPPAAXF1d4eHhgTFjxuD06dN6dWJjY6FQKPRuffv21atTWVmJGTNmwN3dHc7Ozhg1ahTy8/P16hQXFyMmJgZqtRpqtRoxMTEoKSnRq5Obm4uRI0fC2dkZ7u7umDlzJqqqqprktRMREVHzI+vE6ueff8b06dORnp6O7du34/r16wgLC0N5eblevWHDhqGgoEC6bd26Va981qxZSElJwbp167Bnzx6UlZVhxIgRqKmpkepERkYiKysLqampSE1NRVZWFmJiYqTympoaREREoLy8HHv27MG6deuwYcMGzJ07t2nfBCIiImo25Nj9U5Kamqp3f8WKFfDw8EBmZiYGDBggbVepVPDy8jK4D41Gg88//xyrV6/GkCFDAABJSUnw9fXFjh07EB4ejpMnTyI1NRXp6eno06cPAGD58uUICQnB6dOnERgYiG3btuHEiRPIy8uDt7c3ACAxMRGxsbFYuHAh3NzcDD5/ZWWltE4SAJSWlt75G0JERESyJusWq1tpNBoAQJs2bfS27969Gx4eHrjnnnsQFxeHixcvSmWZmZmorq5GWFiYtM3b2xtBQUHYt28fACAtLQ1qtVpKqgCgb9++UKvVenWCgoKkpAoAwsPDUVlZiczMzDpjTkhIkC4vqtVq+Pr6NuIdICIiIjlrNomVEAJz5szB3//+dwQFBUnbhw8fjuTkZOzatQuJiYnIyMjAgw8+KLUSFRYWwt7eHq1bt9bbn6enJwoLC6U6Hh4etZ7Tw8NDr46np6deeevWrWFvby/VMWTevHnQaDTSLS8v787eACIiIpI9WV8KvNkzzzyDI0eOYM+ePXrbJ02aJP0dFBSEXr16wd/fH1u2bMEjjzxS5/6EEHrrZRlaO+tO6txKpVJBpVLVWU5EREQtR7NIrGbMmIHNmzfjl19+gY+PT71127dvD39/f5w5cwYA4OXlhaqqKhQXF+u1Wl28eBH9+vWT6ly4cKHWvi5duiS1Unl5eWH//v165cXFxaiurq7VkkVE1JJxQWzT4ILYLZOsEyshBGbMmIGUlBTs3r0bHTt2vO1jioqKkJeXh/bt2wMAevbsCTs7O2zfvh0TJ04EABQUFODYsWN49913AQAhISHQaDQ4cOAAevfuDQDYv38/NBqNlHyFhIRg4cKFKCgokPa9bds2qFQq9OzZ0+SvnYj0CXHTTO7XLRdHs3TT+6X3Pt4hLohtGlwQu2WSdWI1ffp0rFmzBt9++y1cXV2lvkxqtRqOjo4oKyvD/PnzMW7cOLRv3x7Z2dl46aWX4O7ujrFjx0p1p06dirlz56Jt27Zo06YN4uPjERwcLI0S7Ny5M4YNG4a4uDh88sknAIB//OMfGDFiBAIDAwEAYWFh6NKlC2JiYvDee+/hr7/+Qnx8POLi4uocEUjN380nocqaeipSLTe/X6Y4md88upbr3t25yspKODk5WToMohZL1onVxx9/DAAYNGiQ3vYVK1YgNjYWNjY2OHr0KFatWoWSkhK0b98egwcPxvr16+Hq6irVX7JkCWxtbTFx4kRcu3YNDz30EL788kvY2Pzv4JycnIyZM2dKowdHjRqFZcuWSeU2NjbYsmULpk2bhtDQUDg6OiIyMhKLFy9uwneALO3mk/n0X1rXU5Pqw5N5y/UCAHtLB3ELAaD6//+2g7zWw64C8I6lg6AmJevE6na/ch0dHfHjjz/edj8ODg5YunQpli5dWmedNm3aICkpqd79+Pn54fvvv7/t8xGR6d08CKRmZI3Mj14yc/1/rXymHkxjD8BeVqnLDfIdMtSEi5OTLPDQJDPsFGo6pugYevNJ6N8DiqHiFSijVdb8r5XPFCdzvc/SFjx63SF2liZqWjw0yQw7hZqOKTqG3nwSUtkADkys7ghP5kRkLZrNBKFEREREcscWKxkr7xEFKGX2EQkBaP9/7LbSFpBbS4T2OpwPJVs6CiIislIyO2uTHqUtYGNn6SgMkNsYICIi68R+uaZjqglbmVgRERE1U+yXazqmmrCVfayIiIiITIQtVkRE1CA3zzFYdWOLpUJpdqpu+tsUKxLcbNT902CrlFf3ESEEav6/X66N0lZ2I4Sva6uxOesjk+6TiRURETXIzSsScBbxO2fqFQlslXawtZFfH1g7GU/X2hSYWBEZqbJGATn9MhcCqNLe+NteKb8BmjferyYix0WYBQDd+og2kNc6KnJ8v4haKCZWMqPXNFxTXXdFMuym98zUzezTf2ll0v3RneMizJZ180z6clwrUM5uXivQ1MsLkTwwsZKZm5vYnQ+vsWAkzR8X/iVqGjf3k5HrWoHy9b8ffHLrb0SmwcSKqB4ODg5GLfRtCRUVFdLw5W+//RYODg4WjqhupohNzp8F0Hw+D7nGRdRSMLGSmZubhsu7R8p0glAZq6mWWvpMtfCvKeY1aWoODg7NIs7GaC6fBWAdnwcRGcbESmb0moZt7JhYNQKb2Ymopbu5L+l19sttsOtN0C+XiRUREVEzdXO/3M2/mnY+Jmtjqn65TKyIiOiOyXGCUAFA1w5hB3nNfFF1+yrUzDGxIiKiO8YJQi3r5r6ko7pNgy27jzTI9ZpqqaXPVNNfMLEiIiJqpm7uS2prI8+Z15sLU/XLZWIlZ1oZTpcsxP/iUtrKb7pvOb5nRC0Mp74wDbnGRY3DxErGnA8lWzoEIqJaOPUFUd2Ulg6AiIiIqKVgi5XMsInddOQcGxGRqV3Xym8eKyEEav6/i4aN0lZ28ws2xXvGxEpm2MRORER3YnMW57GSA14KJCIiIjIRtlgRERE1U+w+Yjqmio2JFRERUTPF7iPyw0uBRERERCbCxIqIiIjIRJhYEREREZkIEysiIiIiE2FiRURERGQiTKyIiIiITISJFREREZGJcB4rIjMRQqCiosJk+7t5X6bcL3Bjvhm5rellas3l87CGz4KoJWFiRWQmFRUVCA8Pb5J962Y2NpUff/yxxU/k11w+D2v4LIhaEiZWREREBKD5tOQC8m3NZWJFZCamXtNLCIHKykoAgEqlMukBRs7reZlKc/k8rOGz4MlcPppLSy4g39ZcJlZ34KOPPsJ7772HgoIC3Hffffjggw/Qv39/S4dlEA9Y8tEUa3o5OTmZdH/WhJ+HfPBkTi0JE6sGWr9+PWbNmoWPPvoIoaGh+OSTTzB8+HCcOHECfn5+lg6vFh6wiIjIWM2lJReQb2uuQgghLB1Ec9KnTx/06NEDH3/8sbStc+fOGDNmDBISEmrVr6yslP6pAKC0tBS+vr7QaDRwc3Nr8nivXbvWZImVqTGxIrJOpm5Zb+qTeUtuWae6lZaWQq1W3/b8zRarBqiqqkJmZiZefPFFve1hYWHYt2+fwcckJCTgjTfeMEd4BvHXBxHJHS/LUkvCxKoBLl++jJqaGnh6eupt9/T0RGFhocHHzJs3D3PmzJHu61qszIUHLCIiIvNhYnUHbm2lEULU2XKjUqmgUqnMERYRERFZGJe0aQB3d3fY2NjUap26ePFirVYsIiIisj5MrBrA3t4ePXv2xPbt2/W2b9++Hf369bNQVERERCQXvBTYQHPmzEFMTAx69eqFkJAQfPrpp8jNzcU///lPS4dGREREFsbEqoEmTZqEoqIivPnmmygoKEBQUBC2bt0Kf39/S4dGREREFsZ5rMzM2HkwiIiISD6MPX+zjxURERGRiTCxIiIiIjIRJlZEREREJsLEioiIiMhEmFgRERERmQgTKyIiIiITYWJFREREZCKcINTMdNOGlZaWWjgSIiIiMpbuvH276T+ZWJnZlStXAAC+vr4WjoSIiIga6sqVK1Cr1XWWc+Z1M9NqtTh//jxcXV2hUCgsHc4dKS0tha+vL/Ly8jh7vIXxs5AXfh7ywc9CPlrKZyGEwJUrV+Dt7Q2lsu6eVGyxMjOlUgkfHx9Lh2ESbm5uzfpL0pLws5AXfh7ywc9CPlrCZ1FfS5UOO68TERERmQgTKyIiIiITYWJFDaZSqfD6669DpVJZOhSrx89CXvh5yAc/C/mwts+CndeJiIiITIQtVkREREQmwsSKiIiIyESYWBERERGZCBMrMpns7GwoFApkZWU1q323VAqFAps2bbJ0GERmMX/+fNx///2WDoNu8eWXX6JVq1aWDsOsmFg1cxcvXsRTTz0FPz8/qFQqeHl5ITw8HGlpaQB4cjW32NhYKBQKKBQK2NnZoVOnToiPj0d5ebnZYykoKMDw4cPN/rzNQWxsLMaMGWPpMAi1vzOenp4YOnQovvjiC2i1WkuHZzVu/hwUCgXatm2LYcOG4ciRI5YOrRa5/9BmYtXMjRs3Dr/++itWrlyJ3377DZs3b8agQYPw119/WTq0O1JVVWXpEBpt2LBhKCgowJ9//okFCxbgo48+Qnx8fK161dXVTRqHl5eX1Qxvbu6a+n9B7nTfmezsbPzwww8YPHgwnn32WYwYMQLXr1+3dHhWQ/c5FBQUYOfOnbC1tcWIESMsHVazw8SqGSspKcGePXvwzjvvYPDgwfD390fv3r0xb948REREICAgAAAwduxYKBQK6f4ff/yB0aNHw9PTEy4uLnjggQewY8cOvX0HBARg0aJFeOKJJ+Dq6go/Pz98+umnenUOHDiA7t27w8HBAb169cLhw4f1ymtqajB16lR07NgRjo6OCAwMxIcffqhXR9dykJCQAG9vb9xzzz1G7VvOdC2Hvr6+iIyMRFRUFDZt2iRdqvjiiy/QqVMnqFQqCCGg0Wjwj3/8Ax4eHnBzc8ODDz6IX3/9VdrfzY/z8/ODi4sLnn76adTU1ODdd9+Fl5cXPDw8sHDhQr04bm6t3L17NxQKBUpKSqTyrKwsKBQKZGdnA/hfk/3333+PwMBAODk5Yfz48SgvL8fKlSsREBCA1q1bY8aMGaipqWnqt9EiDF222LRpk7SupxACQ4YMwbBhw6QV7ktKSuDn54eXX37ZqH0AMPi/sHLlSrRt2xaVlZV6jx03bhwee+wxE79SedF9Zzp06IAePXrgpZdewrfffosffvgBX375JQDc9ntyq0GDBmHWrFl628aMGYPY2FgAwKlTp+Dk5IQ1a9ZI5Rs3boSDgwOOHj16R8/Z3Ok+By8vL9x///144YUXkJeXh0uXLhl1DAFu/P/7+fnByckJY8eORVFRUa3nWbBgATw8PODq6oonn3wSL774Yq3LuCtWrEDnzp3h4OCAe++9Fx999JFU1rFjRwBA9+7doVAoMGjQIFO+DY3GxKoZc3FxgYuLCzZt2lTrYAwAGRkZAG78gxYUFEj3y8rK8PDDD2PHjh04fPgwwsPDMXLkSOTm5uo9PjExUUpqpk2bhqeffhqnTp0CAJSXl2PEiBEIDAxEZmYm5s+fX6tVRqvVwsfHB1999RVOnDiB1157DS+99BK++uorvXo7d+7EyZMnsX37dnz//fdG7bs5cXR0lFokfv/9d3z11VfYsGGD1IwdERGBwsJCbN26FZmZmejRowceeughvVbHP/74Az/88ANSU1Oxdu1afPHFF4iIiEB+fj5+/vlnvPPOO3jllVeQnp7eqFivXr2Kf/3rX1i3bh1SU1Oxe/duPPLII9i6dSu2bt2K1atX49NPP8U333zTqOdprhQKBVauXIkDBw7gX//6FwDgn//8Jzw9PTF//vwG7evW/4WJEyeipqYGmzdvlupcvnwZ33//PR5//HFTvoxm4cEHH0S3bt2wceNGCCGM+p40xL333ovFixdj2rRpyMnJwfnz5xEXF4e3334bwcHBTfKczUlZWRmSk5Nx9913o23btkY9Zv/+/XjiiScwbdo0ZGVlYfDgwViwYIFeneTkZCxcuBDvvPMOMjMz4efnh48//livzvLly/Hyyy9j4cKFOHnyJBYtWoRXX30VK1euBHDjhzcA7NixAwUFBdi4caMJXrEJCWrWvvnmG9G6dWvh4OAg+vXrJ+bNmyd+/fVXqRyASElJue1+unTpIpYuXSrd9/f3F9HR0dJ9rVYrPDw8xMcffyyEEOKTTz4Rbdq0EeXl5VKdjz/+WAAQhw8frvN5pk2bJsaNGyfdnzJlivD09BSVlZXStjvdtxxMmTJFjB49Wrq/f/9+0bZtWzFx4kTx+uuvCzs7O3Hx4kWpfOfOncLNzU1UVFTo7eeuu+4Sn3zyiRBCiNdff104OTmJ0tJSqTw8PFwEBASImpoaaVtgYKBISEiQ7t/82f/0008CgCguLpbKDx8+LACIs2fPCiGEWLFihQAgfv/9d6nOU089JZycnMSVK1f0nvupp55q+JsjI7d+TjorVqwQarVab1tKSoq49VD51VdfCZVKJebNmyecnJzE6dOnG7QPQ/8LQgjx9NNPi+HDh0v3P/jgA9GpUyeh1Wob+Aqbj7o+CyGEmDRpkujcubPR35Nu3bpJZQMHDhTPPvusXv3Ro0eLKVOm6G2LiIgQ/fv3Fw899JAYOnSo9F4b85wtyZQpU4SNjY1wdnYWzs7OAoBo3769yMzMFEIYdwyZPHmyGDZsmN5+J02apPd96NOnj5g+fbpendDQUL3PztfXV6xZs0avzltvvSVCQkKEEEKcPXtW1ucDtlg1c+PGjcP58+exefNmhIeHY/fu3ejRo4fUfG5IeXk5nn/+eXTp0gWtWrWCi4sLTp06VavFqmvXrtLfCoUCXl5euHjxIgDg5MmT6NatG5ycnKQ6ISEhtZ7rP//5D3r16oV27drBxcUFy5cvr/U8wcHBsLe3l+4bu2+5+v777+Hi4gIHBweEhIRgwIABWLp0KQDA398f7dq1k+pmZmairKwMbdu2lVogXVxccPbsWfzxxx9SvYCAALi6ukr3PT090aVLFyiVSr1tus/nTjk5OeGuu+7S22dAQABcXFxM+jzN3YQJE/DII48gISEBiYmJ0iXshrj1fwEA4uLisG3bNpw7dw7AjdZmXadiaySEgEKhMPp7cie++OILHDlyBIcOHcKXX34pvddN+ZxyNXjwYGRlZSErKwv79+9HWFgYhg8fjpycHKMef/LkyVrH6lvvnz59Gr1799bbdvP9S5cuIS8vD1OnTtV73xcsWNBs3ndbSwdAjefg4IChQ4di6NCheO211/Dkk0/i9ddfl/oS3Oq5557Djz/+iMWLF+Puu++Go6Mjxo8fX6vjuJ2dnd59hUIhjdIRRqyE9NVXX2H27NlITExESEgIXF1d8d5772H//v169ZydnfXuG7NvORs8eDA+/vhj2NnZwdvbW+99vPW1arVatG/fHrt37661n5v76Rj6LOr7fG6lS8Bufm8NdZhu7PM0d0qlstb/n6H36erVq8jMzISNjQ3OnDlzR/u49X8BuNFnpFu3bli1ahXCw8Nx9OhRfPfdd3fyUlqEkydPomPHjkZ/T25m7Ofw66+/ory8HEqlEoWFhfD29gZg/HezJXF2dsbdd98t3e/ZsyfUajWWL1+OsLAwAPUfQ4w9dt/6Q+Hmx+mOLcuXL0efPn306tnY2Bi1f0tjYtUCdenSReq0bGdnV6uj8X//+1/ExsZi7NixAG5cS7+586Gxz7F69Wpcu3YNjo6OAFCrf89///tf9OvXD9OmTZO2GfOLw5h9y9mtB6f69OjRA4WFhbC1tZUGFzQFXctIQUEBWrduDQCyHapsSe3atcOVK1dQXl4uJT6G3qe5c+dCqVTihx9+wMMPP4yIiAg8+OCDDdpHXZ588kksWbIE586dw5AhQ+Dr69vo19Uc7dq1C0ePHsXs2bPh4+PT4O9Ju3btUFBQIN2vqanBsWPHMHjwYGnbX3/9hdjYWLz88ssoLCxEVFQUDh06BEdHR7N9N+VMoVBAqVTi2rVrRh1DunTpUutYfev9wMBAHDhwADExMdK2gwcPSn97enqiQ4cO+PPPPxEVFWUwLt0VDrkOouGlwGasqKgIDz74IJKSknDkyBGcPXsWX3/9Nd59912MHj0awI1LSDt37kRhYSGKi4sBAHfffTc2btyIrKws/Prrr4iMjGxwC0RkZCSUSiWmTp2KEydOYOvWrVi8eLFenbvvvhsHDx7Ejz/+iN9++w2vvvqq1IG+sftuKYYMGYKQkBCMGTMGP/74I7Kzs7Fv3z688soregebxrr77rvh6+uL+fPn47fffsOWLVuQmJhosv03RxqNRrrsobvdd999cHJywksvvYTff/8da9asqXVZfcuWLfjiiy+QnJyMoUOH4sUXX8SUKVOk71efPn1uu4/6REVF4dy5c1i+fDmeeOIJE75i+aqsrERhYSHOnTuHQ4cOYdGiRRg9ejRGjBiBxx577I6+Jw8++CC2bNmCLVu24NSpU5g2bZreiDbgxsADX19fvPLKK3j//fchhJAGypjruyknus+hsLAQJ0+exIwZM1BWVoaRI0cadQyZOXMmUlNT8e677+K3337DsmXLkJqaqldnxowZ+Pzzz7Fy5UqcOXMGCxYswJEjR2qNmk1ISMCHH36I3377DUePHsWKFSvw/vvvAwA8PDzg6OiI1NRUXLhwARqNpunfnIawUN8uMoGKigrx4osvih49egi1Wi2cnJxEYGCgeOWVV8TVq1eFEEJs3rxZ3H333cLW1lb4+/sLIW50/Bs8eLBwdHQUvr6+YtmyZbU6evr7+4slS5boPV+3bt3E66+/Lt1PS0sT3bp1E/b29uL+++8XGzZs0OtQWFFRIWJjY4VarRatWrUSTz/9tHjxxRf1OinW1XH1dvuWq/o64t7auVantLRUzJgxQ3h7ews7Ozvh6+sroqKiRG5ubp2PM/Q8t36GuGXgwp49e0RwcLBwcHAQ/fv3F19//XWtzuu3dro29rmbmylTpggAtW5TpkwRKSkp4u677xYODg5ixIgR4tNPP5U6nl+8eFF4enqKRYsWSfuqrq4WvXv3FhMnTpS21bcPIer+X9CJiYkRbdq0qdVxuiW6+bOwtbUV7dq1E0OGDBFffPGF3uCMhn5PqqqqxNNPPy3atGkjPDw8REJCgl7n9ZUrVwpnZ2fx22+/SY85ePCgsLe3F1u2bDHqOVuSW78Trq6u4oEHHhDffPONVOd2xxAhhPj888+Fj4+PcHR0FCNHjhSLFy+udVx58803hbu7u3BxcRFPPPGEmDlzpujbt69eneTkZHH//fcLe3t70bp1azFgwACxceNGqXz58uXC19dXKJVKMXDgwKZ4S+6YQohm3qGFiKiFGTp0KDp37ixN6UDUkg0dOhReXl5YvXq1pUMxCfaxIiKSib/++gvbtm3Drl27sGzZMkuHQ2RyV69exX/+8x+Eh4fDxsYGa9euxY4dO7B9+3ZLh2YyTKyIiGSiR48eKC4uxjvvvIPAwEBLh0NkcgqFAlu3bsWCBQtQWVmJwMBAbNiwAUOGDLF0aCbDS4FEREREJsJRgUREREQmwsSKiIiIyESYWBERERGZCBMrIiIiIhNhYkVERERkIkysiMjqxMbGYsyYMZYOg4haICZWRCQ7sbGxUCgUUCgUsLOzQ6dOnRAfH4/y8nJLh9Zkbn7Ndd2ISP6YWBGRLA0bNgwFBQX4888/sWDBAnz00UfSArkt0YcffoiCggLpBgArVqyotY2I5I2JFRHJkkqlgpeXF3x9fREZGYmoqChs2rQJAJCUlIRevXrB1dUVXl5eiIyMxMWLF/Uef/z4cURERMDNzQ2urq7o378//vjjD4PPlZmZCQ8PDyxcuBAAkJqair///e9o1aoV2rZtixEjRtR67L59+3D//ffDwcEBvXr1wqZNm6BQKJCVlSXVOXHiBB5++GG4uLjA09MTMTExuHz5ssEY1Go1vLy8pBsAtGrVCl5eXvj0008xdOjQWo/p2bMnXnvtNQD/u7z5xhtvwMPDA25ubnjqqadQVVUl1RdC4N1330WnTp3g6OiIbt264ZtvvqnnUyCihmJiRUTNgqOjI6qrqwEAVVVVeOutt/Drr79i06ZNOHv2LGJjY6W6586dw4ABA+Dg4IBdu3YhMzMTTzzxBK5fv15rv7t378ZDDz2EN954Ay+//DIAoLy8HHPmzEFGRgZ27twJpVKJsWPHQqvVAgCuXLmCkSNHIjg4GIcOHcJbb72FF154QW+/BQUFGDhwIO6//34cPHgQqampuHDhAiZOnNjg1/7EE0/gxIkTyMjIkLYdOXIEhw8f1nvdO3fuxMmTJ/HTTz9h7dq1SElJwRtvvCGVv/LKK1ixYgU+/vhjHD9+HLNnz0Z0dDR+/vnnBsdERHUQREQyM2XKFDF69Gjp/v79+0Xbtm3FxIkTDdY/cOCAACCuXLkihBBi3rx5omPHjqKqqqre/W/atEm4urqKNWvW1BvPxYsXBQBx9OhRIYQQH3/8sWjbtq24du2aVGf58uUCgDh8+LAQQohXX31VhIWF6e0nLy9PABCnT5+u9/mEEAKASElJke4PHz5cPP3009L9WbNmiUGDBum9pjZt2ojy8nJp28cffyxcXFxETU2NKCsrEw4ODmLfvn16zzN16lQxefLk28ZDRMZhixURydL3338PFxcXODg4ICQkBAMGDMDSpUsBAIcPH8bo0aPh7+8PV1dXDBo0CACQm5sLAMjKykL//v1hZ2dX5/7379+PcePGYeXKlZg8ebJe2R9//IHIyEh06tQJbm5u6Nixo97+T58+ja5du8LBwUF6TO/evfX2kZmZiZ9++gkuLi7S7d5775X231BxcXFYu3YtKioqUF1djeTkZDzxxBN6dbp16wYnJyfpfkhICMrKypCXl4cTJ06goqICQ4cO1Ytp1apVdxQPERlma+kAiIgMGTx4MD7++GPY2dnB29tbSpLKy8sRFhaGsLAwJCUloV27dsjNzUV4eLjUn8jR0fG2+7/rrrvQtm1bfPHFF4iIiIC9vb1UNnLkSPj6+mL58uXw9vaGVqtFUFCQtH8hRK1ReuKW9ey1Wi1GjhyJd955p9Zzt2/fvmFvxv/HpFKpkJKSApVKhcrKSowbN86oxyoUCuky5pYtW9ChQwe9cpVK1eB4iMgwJlZEJEvOzs64++67a20/deoULl++jLfffhu+vr4AgIMHD+rV6dq1K1auXInq6uo6W63c3d2xceNGDBo0CJMmTcJXX30FOzs7FBUV4eTJk/jkk0/Qv39/AMCePXv0HnvvvfciOTkZlZWVUlJyaww9evTAhg0bEBAQAFvbxh9qbW1tMWXKFKxYsQIqlQqPPvqoXusUAPz666+4du2alFimp6fDxcUFPj4+aN26NVQqFXJzczFw4MBGx0NEhvFSIBE1K35+frC3t8fSpUvx559/YvPmzXjrrbf06jzzzDMoLS3Fo48+ioMHD+LMmTNYvXo1Tp8+rVfPw8MDu3btwqlTpzB58mRcv34drVu3Rtu2bfHpp5/i999/x65duzBnzhy9x0VGRkKr1eIf//gHTp48iR9//BGLFy8GAKkla/r06fjrr78wefJkHDhwAH/++Se2bduGJ554AjU1NXf02p988kns2rULP/zwQ63LgMCNTv1Tp07FiRMn8MMPP+D111/HM888A6VSCVdXV8THx2P27NlYuXIl/vjjDxw+fBj//ve/sXLlyjuKh4hqY2JFRM1Ku3bt8OWXX+Lrr79Gly5d8Pbbb0tJjU7btm2xa9culJWVYeDAgejZsyeWL19usPXKy8sLu3btwtGjRxEVFQUhBNatW4fMzEwEBQVh9uzZeO+99/Qe4+bmhu+++w5ZWVm4//778fLLL0vTHuj6XXl7e2Pv3r2oqalBeHg4goKC8Oyzz0KtVkOpvLND79/+9jf069cPgYGB6NOnT63yhx56CH/7298wYMAATJw4ESNHjsT8+fOl8rfeeguvvfYaEhIS0LlzZ4SHh+O7776T+pARUeMpxK0dA4iIqMGSk5Px+OOPQ6PRGNXH604IIXDvvffiqaeeqtWKFhsbi5KSEmmuLyKyDPaxIiK6A6tWrUKnTp3QoUMH/Prrr3jhhRcwceLEJkuqLl68iNWrV+PcuXN4/PHHm+Q5iKjxmFgREd2BwsJCvPbaaygsLET79u0xYcIEaeb2puDp6Ql3d3d8+umnaN26dZM9DxE1Di8FEhEREZkIO68TERERmQgTKyIiIiITYWJFREREZCJMrIiIiIhMhIkVERERkYkwsSIiIiIyESZWRERERCbCxIqIiIjIRP4Pic3uNlj3u/QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = \"Package Type\", \n",
    "            y = \"Per Person Price\",\n",
    "           data = combined)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f887d95a",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Package Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Budget</th>\n",
       "      <td>466.0</td>\n",
       "      <td>14151.447425</td>\n",
       "      <td>8236.467588</td>\n",
       "      <td>1757.0</td>\n",
       "      <td>8901.250</td>\n",
       "      <td>13114.25</td>\n",
       "      <td>17927.00</td>\n",
       "      <td>64008.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deluxe</th>\n",
       "      <td>6006.0</td>\n",
       "      <td>18409.508825</td>\n",
       "      <td>10228.326587</td>\n",
       "      <td>1198.0</td>\n",
       "      <td>12119.000</td>\n",
       "      <td>16426.75</td>\n",
       "      <td>22774.25</td>\n",
       "      <td>171062.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxury</th>\n",
       "      <td>3278.0</td>\n",
       "      <td>23679.556437</td>\n",
       "      <td>11958.923869</td>\n",
       "      <td>2963.0</td>\n",
       "      <td>15471.625</td>\n",
       "      <td>22222.50</td>\n",
       "      <td>29170.75</td>\n",
       "      <td>107850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premium</th>\n",
       "      <td>5581.0</td>\n",
       "      <td>23965.407006</td>\n",
       "      <td>13272.618593</td>\n",
       "      <td>2967.0</td>\n",
       "      <td>15232.000</td>\n",
       "      <td>21767.50</td>\n",
       "      <td>29188.00</td>\n",
       "      <td>170763.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard</th>\n",
       "      <td>5669.0</td>\n",
       "      <td>16354.089081</td>\n",
       "      <td>9363.498521</td>\n",
       "      <td>791.0</td>\n",
       "      <td>10563.500</td>\n",
       "      <td>14729.50</td>\n",
       "      <td>19972.00</td>\n",
       "      <td>153795.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count          mean           std     min        25%       50%  \\\n",
       "Package Type                                                                    \n",
       "Budget         466.0  14151.447425   8236.467588  1757.0   8901.250  13114.25   \n",
       "Deluxe        6006.0  18409.508825  10228.326587  1198.0  12119.000  16426.75   \n",
       "Luxury        3278.0  23679.556437  11958.923869  2963.0  15471.625  22222.50   \n",
       "Premium       5581.0  23965.407006  13272.618593  2967.0  15232.000  21767.50   \n",
       "Standard      5669.0  16354.089081   9363.498521   791.0  10563.500  14729.50   \n",
       "\n",
       "                   75%       max  \n",
       "Package Type                      \n",
       "Budget        17927.00   64008.5  \n",
       "Deluxe        22774.25  171062.5  \n",
       "Luxury        29170.75  107850.0  \n",
       "Premium       29188.00  170763.0  \n",
       "Standard      19972.00  153795.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.groupby(\"Package Type\")[\"Per Person Price\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "33d243a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_ = {\"Luxury\": 5, \"Premium\": 4, \n",
    "         \"Deluxe\": 3, \"Standard\":2, \"Budget\":1}\n",
    "\n",
    "combined[\"Package Type\"] = combined[\"Package Type\"].map(dict_)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "95ee4df4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#combined[\"Destination\"].apply(lambda x: len(x.split(\"|\")))\n",
    "# Hotel Count\n",
    "combined[\"Hotel_Count\"] = combined[\"Hotel Details\"].\\\n",
    "apply(lambda x: 0 if x==\"Not Available\" else len(x.split(\"|\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a8b66409",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    30000.000000\n",
       "mean       129.369367\n",
       "std        137.097312\n",
       "min          2.000000\n",
       "25%         81.000000\n",
       "50%         81.000000\n",
       "75%         81.000000\n",
       "max        488.000000\n",
       "Name: Cancellation Rules, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#combined[\"Cancellation Rules\"].apply(lambda x: len(str(x).split())).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "805225dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "newdata = combined.drop(['Uniq Id', 'Package Name',\n",
    "                         'Destination', \n",
    "                         'Itinerary','Places Covered',\"Flights\", \n",
    "                         'Travel Date', 'Hotel Details',\"Start City\",\n",
    "                         'Airline', 'Sightseeing Places Covered',\n",
    "       'Cancellation Rules'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "18f3ec6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 15)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "59f6ed9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((21000, 15), (9000, 15), (21000, 15), (9000, 14))"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the Data in Train and Test\n",
    "\n",
    "newtrain = newdata.iloc[0:train.shape[0], :]\n",
    "newtest = newdata.iloc[train.shape[0]:, :]\n",
    "\n",
    "newtrain.shape, newtest.shape, train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "09c47caa",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Package Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>466.0</td>\n",
       "      <td>14151.447425</td>\n",
       "      <td>8236.467588</td>\n",
       "      <td>1757.0</td>\n",
       "      <td>8901.250</td>\n",
       "      <td>13114.25</td>\n",
       "      <td>17927.00</td>\n",
       "      <td>64008.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5669.0</td>\n",
       "      <td>16354.089081</td>\n",
       "      <td>9363.498521</td>\n",
       "      <td>791.0</td>\n",
       "      <td>10563.500</td>\n",
       "      <td>14729.50</td>\n",
       "      <td>19972.00</td>\n",
       "      <td>153795.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6006.0</td>\n",
       "      <td>18409.508825</td>\n",
       "      <td>10228.326587</td>\n",
       "      <td>1198.0</td>\n",
       "      <td>12119.000</td>\n",
       "      <td>16426.75</td>\n",
       "      <td>22774.25</td>\n",
       "      <td>171062.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5581.0</td>\n",
       "      <td>23965.407006</td>\n",
       "      <td>13272.618593</td>\n",
       "      <td>2967.0</td>\n",
       "      <td>15232.000</td>\n",
       "      <td>21767.50</td>\n",
       "      <td>29188.00</td>\n",
       "      <td>170763.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3278.0</td>\n",
       "      <td>23679.556437</td>\n",
       "      <td>11958.923869</td>\n",
       "      <td>2963.0</td>\n",
       "      <td>15471.625</td>\n",
       "      <td>22222.50</td>\n",
       "      <td>29170.75</td>\n",
       "      <td>107850.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count          mean           std     min        25%       50%  \\\n",
       "Package Type                                                                    \n",
       "1              466.0  14151.447425   8236.467588  1757.0   8901.250  13114.25   \n",
       "2             5669.0  16354.089081   9363.498521   791.0  10563.500  14729.50   \n",
       "3             6006.0  18409.508825  10228.326587  1198.0  12119.000  16426.75   \n",
       "4             5581.0  23965.407006  13272.618593  2967.0  15232.000  21767.50   \n",
       "5             3278.0  23679.556437  11958.923869  2963.0  15471.625  22222.50   \n",
       "\n",
       "                   75%       max  \n",
       "Package Type                      \n",
       "1             17927.00   64008.5  \n",
       "2             19972.00  153795.0  \n",
       "3             22774.25  171062.5  \n",
       "4             29188.00  170763.0  \n",
       "5             29170.75  107850.0  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Per Person Stats basis Package Type\n",
    "\n",
    "newtrain.groupby(\"Package Type\")[\"Per Person Price\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "ac432b00",
   "metadata": {},
   "outputs": [],
   "source": [
    "newtrain[\"Package_Count\"] = newtrain.groupby(\"Package Type\")\\\n",
    "[\"Per Person Price\"].transform(\"count\")\n",
    "\n",
    "newtest[\"Package_Count\"] = newtrain.groupby(\"Package Type\")\\\n",
    "[\"Per Person Price\"].transform(\"count\")\n",
    "\n",
    "newtrain[\"Package_Min\"] = newtrain.groupby(\"Package Type\")\\\n",
    "[\"Per Person Price\"].transform(\"min\")\n",
    "\n",
    "newtest[\"Package_Min\"] = newtrain.groupby(\"Package Type\")\\\n",
    "[\"Per Person Price\"].transform(\"min\")\n",
    "\n",
    "\n",
    "newtrain[\"Package_Max\"] = newtrain.groupby(\"Package Type\")\\\n",
    "[\"Per Person Price\"].transform(\"max\")\n",
    "\n",
    "newtest[\"Package_Max\"] = newtrain.groupby(\"Package Type\")\\\n",
    "[\"Per Person Price\"].transform(\"max\")\n",
    "\n",
    "newtrain[\"Package_Mean\"] = newtrain.groupby(\"Package Type\")\\\n",
    "[\"Per Person Price\"].transform(\"mean\")\n",
    "\n",
    "newtest[\"Package_Mean\"] = newtrain.groupby(\"Package Type\")\\\n",
    "[\"Per Person Price\"].transform(\"mean\")\n",
    "\n",
    "newtrain[\"Package_Median\"] = newtrain.groupby(\"Package Type\")\\\n",
    "[\"Per Person Price\"].transform(\"median\")\n",
    "\n",
    "newtest[\"Package_Median\"] = newtrain.groupby(\"Package Type\")\\\n",
    "[\"Per Person Price\"].transform(\"median\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "8e41cdfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 21000 entries, 0 to 20999\n",
      "Data columns (total 20 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Package Type       21000 non-null  int64  \n",
      " 1   Flight Stops       21000 non-null  int64  \n",
      " 2   Meals              21000 non-null  int64  \n",
      " 3   Per Person Price   21000 non-null  float64\n",
      " 4   Day                21000 non-null  int64  \n",
      " 5   Month              21000 non-null  int64  \n",
      " 6   Year               21000 non-null  int64  \n",
      " 7   Season             21000 non-null  object \n",
      " 8   Day_Season         21000 non-null  object \n",
      " 9   Flight_Cat         21000 non-null  object \n",
      " 10  Count_Locs         21000 non-null  int64  \n",
      " 11  Total_Travel_Days  21000 non-null  int64  \n",
      " 12  ratings            21000 non-null  float64\n",
      " 13  Count_Sightseeing  21000 non-null  int64  \n",
      " 14  Hotel_Count        21000 non-null  int64  \n",
      " 15  Package_Count      21000 non-null  int64  \n",
      " 16  Package_Min        21000 non-null  float64\n",
      " 17  Package_Max        21000 non-null  float64\n",
      " 18  Package_Mean       21000 non-null  float64\n",
      " 19  Package_Median     21000 non-null  float64\n",
      "dtypes: float64(6), int64(11), object(3)\n",
      "memory usage: 3.9+ MB\n"
     ]
    }
   ],
   "source": [
    "newtrain.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "6c7f3bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "newtest.drop(\"Per Person Price\", axis = 1, inplace = True)\n",
    "\n",
    "dummytrain = pd.get_dummies(newtrain, drop_first = True)\n",
    "dummytest = pd.get_dummies(newtest, drop_first = True)\n",
    "\n",
    "X = dummytrain.drop(\"Per Person Price\", axis = 1)\n",
    "y = dummytrain[\"Per Person Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "6ee7a307",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((21000, 20), (9000, 19))"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummytrain.shape, dummytest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "f79311d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modelling\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "from xgboost import XGBRegressor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "b7d3ca37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Base Models\n",
    "\n",
    "def base_models():\n",
    "    models = dict()\n",
    "    models[\"LR\"] = LinearRegression()\n",
    "    models[\"DTree\"] = DecisionTreeRegressor()\n",
    "    models[\"RF\"] = RandomForestRegressor()\n",
    "    models[\"GBM\"] = GradientBoostingRegressor()\n",
    "    models[\"XGB\"] = XGBRegressor()\n",
    "    return models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "02017c65",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to evaluate the models...\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "\n",
    "def eval_models(model):\n",
    "    cv = KFold(n_splits=5, shuffle = True, random_state = 1001)\n",
    "    scores = -cross_val_score(estimator = model, X = X, \n",
    "                             y = y, cv = cv,\n",
    "                            scoring=\"neg_mean_squared_error\",\n",
    "                            n_jobs=-1)\n",
    "    return scores\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "bcd1c59a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR 77063864.06682393\n",
      "DTree 85254159.80467983\n",
      "RF 73327036.22552547\n",
      "GBM 69691606.17061423\n",
      "XGB 65910325.23938173\n"
     ]
    }
   ],
   "source": [
    "# Lets use the functions to calculate the scores...\n",
    "\n",
    "models = base_models()\n",
    "\n",
    "result, names = list(), list()\n",
    "\n",
    "for name, mymodel in models.items():\n",
    "    finalscore = eval_models(mymodel)\n",
    "    result.append(finalscore)\n",
    "    names.append(name)\n",
    "    print(name, np.mean(result))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "74658d95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results...\n",
    "\n",
    "plt.boxplot(result, labels = names, showmeans = True)\n",
    "plt.xlabel(\"Models\")\n",
    "plt.ylabel(\"RMSE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "3d4e0606",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stacking Regressor\n",
    "from sklearn.ensemble import StackingRegressor\n",
    "GBM = GradientBoostingRegressor()\n",
    "XGB = XGBRegressor()\n",
    "RF = RandomForestRegressor()\n",
    "# ('lr', RidgeCV()),('svr', LinearSVR(dual=\"auto\", random_state=42))\n",
    "stack = StackingRegressor(estimators = [('GBM', GBM), \n",
    "                                        (\"RF\", RF)], \n",
    "                         final_estimator=XGB,\n",
    "                          cv = 5)\n",
    "\n",
    "predictions = stack.fit(X, y).predict(dummytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "2d299f6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.219962924506567\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(X, y,\n",
    "                                               test_size = 0.20,\n",
    "                                               random_state = 42)\n",
    "\n",
    "xgb = XGBRegressor()\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "pred = xgb.fit(xtrain, ytrain).predict(xtest)\n",
    "\n",
    "print((np.sqrt(np.log(mean_squared_error(ytest, pred)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8f0aebe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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