iplwinpredictor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "eab49b45",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "75ee36e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d921237",
   "metadata": {},
   "source": [
    "# Loading datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e362ae1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "match = pd.read_csv(\"matches.csv\")\n",
    "delivery = pd.read_csv(\"deliveries.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "96d7f863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>05-04-2017</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>06-04-2017</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>SPD Smith</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>07-04-2017</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>08-04-2017</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>08-04-2017</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id    Season       city        date                        team1  \\\n",
       "0   1  IPL-2017  Hyderabad  05-04-2017          Sunrisers Hyderabad   \n",
       "1   2  IPL-2017       Pune  06-04-2017               Mumbai Indians   \n",
       "2   3  IPL-2017     Rajkot  07-04-2017                Gujarat Lions   \n",
       "3   4  IPL-2017     Indore  08-04-2017       Rising Pune Supergiant   \n",
       "4   5  IPL-2017  Bangalore  08-04-2017  Royal Challengers Bangalore   \n",
       "\n",
       "                         team2                  toss_winner toss_decision  \\\n",
       "0  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
       "1       Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
       "2        Kolkata Knight Riders        Kolkata Knight Riders         field   \n",
       "3              Kings XI Punjab              Kings XI Punjab         field   \n",
       "4             Delhi Daredevils  Royal Challengers Bangalore           bat   \n",
       "\n",
       "   result  dl_applied                       winner  win_by_runs  \\\n",
       "0  normal           0          Sunrisers Hyderabad           35   \n",
       "1  normal           0       Rising Pune Supergiant            0   \n",
       "2  normal           0        Kolkata Knight Riders            0   \n",
       "3  normal           0              Kings XI Punjab            0   \n",
       "4  normal           0  Royal Challengers Bangalore           15   \n",
       "\n",
       "   win_by_wickets player_of_match                                      venue  \\\n",
       "0               0    Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal   \n",
       "1               7       SPD Smith    Maharashtra Cricket Association Stadium   \n",
       "2              10         CA Lynn     Saurashtra Cricket Association Stadium   \n",
       "3               6      GJ Maxwell                     Holkar Cricket Stadium   \n",
       "4               0       KM Jadhav                      M Chinnaswamy Stadium   \n",
       "\n",
       "          umpire1        umpire2 umpire3  \n",
       "0     AY Dandekar       NJ Llong     NaN  \n",
       "1  A Nand Kishore         S Ravi     NaN  \n",
       "2     Nitin Menon      CK Nandan     NaN  \n",
       "3    AK Chaudhary  C Shamshuddin     NaN  \n",
       "4             NaN            NaN     NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "06aa9d2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(756, 18)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "472bc6ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  inning         batting_team                 bowling_team  over  \\\n",
       "0         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "1         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "2         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "3         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "4         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "\n",
       "   ball    batsman non_striker    bowler  is_super_over  ...  bye_runs  \\\n",
       "0     1  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "1     2  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "2     3  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "3     4  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "4     5  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "\n",
       "   legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "0            0            0             0             0           0   \n",
       "1            0            0             0             0           0   \n",
       "2            0            0             0             4           0   \n",
       "3            0            0             0             0           0   \n",
       "4            0            0             0             0           2   \n",
       "\n",
       "   total_runs  player_dismissed dismissal_kind fielder  \n",
       "0           0               NaN            NaN     NaN  \n",
       "1           0               NaN            NaN     NaN  \n",
       "2           4               NaN            NaN     NaN  \n",
       "3           0               NaN            NaN     NaN  \n",
       "4           2               NaN            NaN     NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "486b5635",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179078, 21)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4db2fae7",
   "metadata": {},
   "source": [
    "### Grouping by match_id and inning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a7e03e78",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_score_df = delivery.groupby(['match_id','inning']).sum()['total_runs'].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e2f2f558",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_score_df = total_score_df[total_score_df['inning'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "904e9ff1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>total_runs</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
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       "      <th>8</th>\n",
       "      <td>5</td>\n",
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       "      <th>1518</th>\n",
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       "      <th>1520</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1524</th>\n",
       "      <td>11414</td>\n",
       "      <td>1</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1526</th>\n",
       "      <td>11415</td>\n",
       "      <td>1</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>756 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      match_id  inning  total_runs\n",
       "0            1       1         207\n",
       "2            2       1         184\n",
       "4            3       1         183\n",
       "6            4       1         163\n",
       "8            5       1         157\n",
       "...        ...     ...         ...\n",
       "1518     11347       1         143\n",
       "1520     11412       1         136\n",
       "1522     11413       1         171\n",
       "1524     11414       1         155\n",
       "1526     11415       1         152\n",
       "\n",
       "[756 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_score_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae103d66",
   "metadata": {},
   "source": [
    "### Merging by match_id "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2290c1ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_df = match.merge(total_score_df[['match_id','total_runs']],left_on='id',right_on='match_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7ac66d80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "      <th>match_id</th>\n",
       "      <th>total_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>05-04-2017</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>06-04-2017</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>SPD Smith</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>07-04-2017</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>08-04-2017</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>08-04-2017</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>11347</td>\n",
       "      <td>IPL-2019</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>05-05-2019</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>HH Pandya</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>Nanda Kishore</td>\n",
       "      <td>O Nandan</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>11347</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>11412</td>\n",
       "      <td>IPL-2019</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>07-05-2019</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>AS Yadav</td>\n",
       "      <td>M. A. Chidambaram Stadium</td>\n",
       "      <td>Nigel Llong</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>Ian Gould</td>\n",
       "      <td>11412</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>11413</td>\n",
       "      <td>IPL-2019</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>08-05-2019</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>RR Pant</td>\n",
       "      <td>ACA-VDCA Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11413</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>11414</td>\n",
       "      <td>IPL-2019</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>10-05-2019</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>F du Plessis</td>\n",
       "      <td>ACA-VDCA Stadium</td>\n",
       "      <td>Sundaram Ravi</td>\n",
       "      <td>Bruce Oxenford</td>\n",
       "      <td>Chettithody Shamshuddin</td>\n",
       "      <td>11414</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>11415</td>\n",
       "      <td>IPL-2019</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>12-05-2019</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>JJ Bumrah</td>\n",
       "      <td>Rajiv Gandhi Intl. Cricket Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>Ian Gould</td>\n",
       "      <td>Nigel Llong</td>\n",
       "      <td>11415</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>756 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id    Season           city        date                        team1  \\\n",
       "0        1  IPL-2017      Hyderabad  05-04-2017          Sunrisers Hyderabad   \n",
       "1        2  IPL-2017           Pune  06-04-2017               Mumbai Indians   \n",
       "2        3  IPL-2017         Rajkot  07-04-2017                Gujarat Lions   \n",
       "3        4  IPL-2017         Indore  08-04-2017       Rising Pune Supergiant   \n",
       "4        5  IPL-2017      Bangalore  08-04-2017  Royal Challengers Bangalore   \n",
       "..     ...       ...            ...         ...                          ...   \n",
       "751  11347  IPL-2019         Mumbai  05-05-2019        Kolkata Knight Riders   \n",
       "752  11412  IPL-2019        Chennai  07-05-2019          Chennai Super Kings   \n",
       "753  11413  IPL-2019  Visakhapatnam  08-05-2019          Sunrisers Hyderabad   \n",
       "754  11414  IPL-2019  Visakhapatnam  10-05-2019               Delhi Capitals   \n",
       "755  11415  IPL-2019      Hyderabad  12-05-2019               Mumbai Indians   \n",
       "\n",
       "                           team2                  toss_winner toss_decision  \\\n",
       "0    Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
       "1         Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
       "2          Kolkata Knight Riders        Kolkata Knight Riders         field   \n",
       "3                Kings XI Punjab              Kings XI Punjab         field   \n",
       "4               Delhi Daredevils  Royal Challengers Bangalore           bat   \n",
       "..                           ...                          ...           ...   \n",
       "751               Mumbai Indians               Mumbai Indians         field   \n",
       "752               Mumbai Indians          Chennai Super Kings           bat   \n",
       "753               Delhi Capitals               Delhi Capitals         field   \n",
       "754          Chennai Super Kings          Chennai Super Kings         field   \n",
       "755          Chennai Super Kings               Mumbai Indians           bat   \n",
       "\n",
       "     result  dl_applied                       winner  win_by_runs  \\\n",
       "0    normal           0          Sunrisers Hyderabad           35   \n",
       "1    normal           0       Rising Pune Supergiant            0   \n",
       "2    normal           0        Kolkata Knight Riders            0   \n",
       "3    normal           0              Kings XI Punjab            0   \n",
       "4    normal           0  Royal Challengers Bangalore           15   \n",
       "..      ...         ...                          ...          ...   \n",
       "751  normal           0               Mumbai Indians            0   \n",
       "752  normal           0               Mumbai Indians            0   \n",
       "753  normal           0               Delhi Capitals            0   \n",
       "754  normal           0          Chennai Super Kings            0   \n",
       "755  normal           0               Mumbai Indians            1   \n",
       "\n",
       "     win_by_wickets player_of_match  \\\n",
       "0                 0    Yuvraj Singh   \n",
       "1                 7       SPD Smith   \n",
       "2                10         CA Lynn   \n",
       "3                 6      GJ Maxwell   \n",
       "4                 0       KM Jadhav   \n",
       "..              ...             ...   \n",
       "751               9       HH Pandya   \n",
       "752               6        AS Yadav   \n",
       "753               2         RR Pant   \n",
       "754               6    F du Plessis   \n",
       "755               0       JJ Bumrah   \n",
       "\n",
       "                                         venue         umpire1  \\\n",
       "0    Rajiv Gandhi International Stadium, Uppal     AY Dandekar   \n",
       "1      Maharashtra Cricket Association Stadium  A Nand Kishore   \n",
       "2       Saurashtra Cricket Association Stadium     Nitin Menon   \n",
       "3                       Holkar Cricket Stadium    AK Chaudhary   \n",
       "4                        M Chinnaswamy Stadium             NaN   \n",
       "..                                         ...             ...   \n",
       "751                           Wankhede Stadium   Nanda Kishore   \n",
       "752                  M. A. Chidambaram Stadium     Nigel Llong   \n",
       "753                           ACA-VDCA Stadium             NaN   \n",
       "754                           ACA-VDCA Stadium   Sundaram Ravi   \n",
       "755         Rajiv Gandhi Intl. Cricket Stadium     Nitin Menon   \n",
       "\n",
       "            umpire2                  umpire3  match_id  total_runs  \n",
       "0          NJ Llong                      NaN         1         207  \n",
       "1            S Ravi                      NaN         2         184  \n",
       "2         CK Nandan                      NaN         3         183  \n",
       "3     C Shamshuddin                      NaN         4         163  \n",
       "4               NaN                      NaN         5         157  \n",
       "..              ...                      ...       ...         ...  \n",
       "751        O Nandan                   S Ravi     11347         143  \n",
       "752     Nitin Menon                Ian Gould     11412         136  \n",
       "753             NaN                      NaN     11413         171  \n",
       "754  Bruce Oxenford  Chettithody Shamshuddin     11414         155  \n",
       "755       Ian Gould              Nigel Llong     11415         152  \n",
       "\n",
       "[756 rows x 20 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc5eddc4",
   "metadata": {},
   "source": [
    "### Currently playing IPL Teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "72872f0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "teams = [\n",
    "    'Sunrisers Hyderabad',\n",
    "    'Mumbai Indians',\n",
    "    'Royal Challengers Bangalore',\n",
    "    'Kolkata Knight Riders',\n",
    "    'Kings XI Punjab',\n",
    "    'Chennai Super Kings',\n",
    "    'Rajasthan Royals',\n",
    "    'Delhi Capitals'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d3a5973f",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_df['team1'] = match_df['team1'].str.replace('Delhi Daredevils','Delhi Capitals')\n",
    "match_df['team2'] = match_df['team2'].str.replace('Delhi Daredevils','Delhi Capitals')\n",
    "\n",
    "match_df['team1'] = match_df['team1'].str.replace('Deccan Chargers','Sunrisers Hyderabad')\n",
    "match_df['team2'] = match_df['team2'].str.replace('Deccan Chargers','Sunrisers Hyderabad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d6d137ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_df = match_df[match_df['team1'].isin(teams)]\n",
    "match_df = match_df[match_df['team2'].isin(teams)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a3718d4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(641, 20)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f70bef0",
   "metadata": {},
   "source": [
    "### Selecting matches where DL was not applied"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "99012da1",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_df = match_df[match_df['dl_applied'] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26d78a93",
   "metadata": {},
   "source": [
    "### Selecting required features and merge with delivery dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4f8e4297",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_df = match_df[['match_id','city','winner','total_runs']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "07057180",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df = match_df.merge(delivery,on='match_id')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0088d27",
   "metadata": {},
   "source": [
    "### Selecting only 2nd innings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8b1b2ffd",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df = delivery_df[delivery_df['inning'] == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b051dee5",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df['current_score'] = delivery_df.groupby('match_id').cumsum()['total_runs_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "92517832",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df['runs_left'] = delivery_df['total_runs_x'] - delivery_df['current_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6e537850",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df['balls_left'] = 126 - (delivery_df['over']*6 + delivery_df['ball'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dc6fd3d0",
   "metadata": {},
   "outputs": [
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       "<p>72413 rows × 27 columns</p>\n",
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      ],
      "text/plain": [
       "        match_id       city               winner  total_runs_x  inning  \\\n",
       "125            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "126            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "127            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "128            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "129            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "...          ...        ...                  ...           ...     ...   \n",
       "149573     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "149574     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "149575     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "149576     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "149577     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "\n",
       "                       batting_team         bowling_team  over  ball  \\\n",
       "125     Royal Challengers Bangalore  Sunrisers Hyderabad     1     1   \n",
       "126     Royal Challengers Bangalore  Sunrisers Hyderabad     1     2   \n",
       "127     Royal Challengers Bangalore  Sunrisers Hyderabad     1     3   \n",
       "128     Royal Challengers Bangalore  Sunrisers Hyderabad     1     4   \n",
       "129     Royal Challengers Bangalore  Sunrisers Hyderabad     1     5   \n",
       "...                             ...                  ...   ...   ...   \n",
       "149573          Chennai Super Kings       Mumbai Indians    20     2   \n",
       "149574          Chennai Super Kings       Mumbai Indians    20     3   \n",
       "149575          Chennai Super Kings       Mumbai Indians    20     4   \n",
       "149576          Chennai Super Kings       Mumbai Indians    20     5   \n",
       "149577          Chennai Super Kings       Mumbai Indians    20     6   \n",
       "\n",
       "              batsman  ... penalty_runs batsman_runs  extra_runs  \\\n",
       "125          CH Gayle  ...            0            1           0   \n",
       "126     Mandeep Singh  ...            0            0           0   \n",
       "127     Mandeep Singh  ...            0            0           0   \n",
       "128     Mandeep Singh  ...            0            2           0   \n",
       "129     Mandeep Singh  ...            0            4           0   \n",
       "...               ...  ...          ...          ...         ...   \n",
       "149573      RA Jadeja  ...            0            1           0   \n",
       "149574      SR Watson  ...            0            2           0   \n",
       "149575      SR Watson  ...            0            1           0   \n",
       "149576      SN Thakur  ...            0            2           0   \n",
       "149577      SN Thakur  ...            0            0           0   \n",
       "\n",
       "        total_runs_y  player_dismissed  dismissal_kind    fielder  \\\n",
       "125                1               NaN             NaN        NaN   \n",
       "126                0               NaN             NaN        NaN   \n",
       "127                0               NaN             NaN        NaN   \n",
       "128                2               NaN             NaN        NaN   \n",
       "129                4               NaN             NaN        NaN   \n",
       "...              ...               ...             ...        ...   \n",
       "149573             1               NaN             NaN        NaN   \n",
       "149574             2               NaN             NaN        NaN   \n",
       "149575             1         SR Watson         run out  KH Pandya   \n",
       "149576             2               NaN             NaN        NaN   \n",
       "149577             0         SN Thakur             lbw        NaN   \n",
       "\n",
       "        current_score  runs_left  balls_left  \n",
       "125                 1        206         119  \n",
       "126                 1        206         118  \n",
       "127                 1        206         117  \n",
       "128                 3        204         116  \n",
       "129                 7        200         115  \n",
       "...               ...        ...         ...  \n",
       "149573            152          0           4  \n",
       "149574            154         -2           3  \n",
       "149575            155         -3           2  \n",
       "149576            157         -5           1  \n",
       "149577            157         -5           0  \n",
       "\n",
       "[72413 rows x 27 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57de55a3",
   "metadata": {},
   "source": [
    "### Finding the wickets remaining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b2b7528c",
   "metadata": {},
   "outputs": [
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       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>204</td>\n",
       "      <td>116</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>200</td>\n",
       "      <td>115</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     match_id       city               winner  total_runs_x  inning  \\\n",
       "125         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "126         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "127         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "128         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "129         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "\n",
       "                    batting_team         bowling_team  over  ball  \\\n",
       "125  Royal Challengers Bangalore  Sunrisers Hyderabad     1     1   \n",
       "126  Royal Challengers Bangalore  Sunrisers Hyderabad     1     2   \n",
       "127  Royal Challengers Bangalore  Sunrisers Hyderabad     1     3   \n",
       "128  Royal Challengers Bangalore  Sunrisers Hyderabad     1     4   \n",
       "129  Royal Challengers Bangalore  Sunrisers Hyderabad     1     5   \n",
       "\n",
       "           batsman  ... batsman_runs extra_runs  total_runs_y  \\\n",
       "125       CH Gayle  ...            1          0             1   \n",
       "126  Mandeep Singh  ...            0          0             0   \n",
       "127  Mandeep Singh  ...            0          0             0   \n",
       "128  Mandeep Singh  ...            2          0             2   \n",
       "129  Mandeep Singh  ...            4          0             4   \n",
       "\n",
       "     player_dismissed  dismissal_kind  fielder  current_score  runs_left  \\\n",
       "125                 0             NaN      NaN              1        206   \n",
       "126                 0             NaN      NaN              1        206   \n",
       "127                 0             NaN      NaN              1        206   \n",
       "128                 0             NaN      NaN              3        204   \n",
       "129                 0             NaN      NaN              7        200   \n",
       "\n",
       "     balls_left  wickets  \n",
       "125         119       10  \n",
       "126         118       10  \n",
       "127         117       10  \n",
       "128         116       10  \n",
       "129         115       10  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery_df['player_dismissed'] = delivery_df['player_dismissed'].fillna(\"0\")\n",
    "delivery_df['player_dismissed'] = delivery_df['player_dismissed'].apply(lambda x:x if x == \"0\" else \"1\")\n",
    "delivery_df['player_dismissed'] = delivery_df['player_dismissed'].astype('int')\n",
    "wickets = delivery_df.groupby('match_id').cumsum()['player_dismissed'].values\n",
    "delivery_df['wickets'] = 10 - wickets\n",
    "delivery_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91bcc190",
   "metadata": {},
   "source": [
    "### Calculating Current runrate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4c0b90d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# crr = runs/overs\n",
    "delivery_df['crr'] = (delivery_df['current_score']*6)/(120 - delivery_df['balls_left'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a9bb3b0",
   "metadata": {},
   "source": [
    "### Calculating Required Runrate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4411da1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rrr = required runs/overs remaining\n",
    "delivery_df['rrr'] = (delivery_df['runs_left']*6)/delivery_df['balls_left']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4502bf03",
   "metadata": {},
   "outputs": [],
   "source": [
    "def result(row):\n",
    "    return 1 if row['batting_team'] == row['winner'] else 0\n",
    "delivery_df['result'] = delivery_df.apply(result,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "755b9345",
   "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>match_id</th>\n",
       "      <th>city</th>\n",
       "      <th>winner</th>\n",
       "      <th>total_runs_x</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>...</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "      <th>current_score</th>\n",
       "      <th>runs_left</th>\n",
       "      <th>balls_left</th>\n",
       "      <th>wickets</th>\n",
       "      <th>crr</th>\n",
       "      <th>rrr</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>206</td>\n",
       "      <td>119</td>\n",
       "      <td>10</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.386555</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>206</td>\n",
       "      <td>118</td>\n",
       "      <td>10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.474576</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>206</td>\n",
       "      <td>117</td>\n",
       "      <td>10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.564103</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>204</td>\n",
       "      <td>116</td>\n",
       "      <td>10</td>\n",
       "      <td>4.5</td>\n",
       "      <td>10.551724</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>200</td>\n",
       "      <td>115</td>\n",
       "      <td>10</td>\n",
       "      <td>8.4</td>\n",
       "      <td>10.434783</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     match_id       city               winner  total_runs_x  inning  \\\n",
       "125         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "126         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "127         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "128         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "129         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "\n",
       "                    batting_team         bowling_team  over  ball  \\\n",
       "125  Royal Challengers Bangalore  Sunrisers Hyderabad     1     1   \n",
       "126  Royal Challengers Bangalore  Sunrisers Hyderabad     1     2   \n",
       "127  Royal Challengers Bangalore  Sunrisers Hyderabad     1     3   \n",
       "128  Royal Challengers Bangalore  Sunrisers Hyderabad     1     4   \n",
       "129  Royal Challengers Bangalore  Sunrisers Hyderabad     1     5   \n",
       "\n",
       "           batsman  ... player_dismissed dismissal_kind  fielder  \\\n",
       "125       CH Gayle  ...                0            NaN      NaN   \n",
       "126  Mandeep Singh  ...                0            NaN      NaN   \n",
       "127  Mandeep Singh  ...                0            NaN      NaN   \n",
       "128  Mandeep Singh  ...                0            NaN      NaN   \n",
       "129  Mandeep Singh  ...                0            NaN      NaN   \n",
       "\n",
       "     current_score  runs_left  balls_left  wickets  crr        rrr  result  \n",
       "125              1        206         119       10  6.0  10.386555       0  \n",
       "126              1        206         118       10  3.0  10.474576       0  \n",
       "127              1        206         117       10  2.0  10.564103       0  \n",
       "128              3        204         116       10  4.5  10.551724       0  \n",
       "129              7        200         115       10  8.4  10.434783       0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c6d1c5bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = delivery_df[['batting_team','bowling_team','city','runs_left','balls_left','wickets','total_runs_x','crr','rrr','result']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95e5db38",
   "metadata": {},
   "source": [
    "### Shuffling the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "eaed600d",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = final_df.sample(final_df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "204bc531",
   "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>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>city</th>\n",
       "      <th>runs_left</th>\n",
       "      <th>balls_left</th>\n",
       "      <th>wickets</th>\n",
       "      <th>total_runs_x</th>\n",
       "      <th>crr</th>\n",
       "      <th>rrr</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3298</th>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>121</td>\n",
       "      <td>89</td>\n",
       "      <td>6</td>\n",
       "      <td>142</td>\n",
       "      <td>4.064516</td>\n",
       "      <td>8.157303</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          batting_team    bowling_team    city  runs_left  balls_left  \\\n",
       "3298  Delhi Daredevils  Mumbai Indians  Mumbai        121          89   \n",
       "\n",
       "      wickets  total_runs_x       crr       rrr  result  \n",
       "3298        6           142  4.064516  8.157303       0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cf84abe",
   "metadata": {},
   "source": [
    "### Handling Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7ed0e626",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ff52e0c",
   "metadata": {},
   "source": [
    "### Selecting only non-null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3e665c65",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = final_df[final_df['balls_left'] != 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0df12517",
   "metadata": {},
   "source": [
    "### X and y matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "03480695",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = final_df.iloc[:,:-1]\n",
    "y = final_df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cc4b3f5",
   "metadata": {},
   "source": [
    "### Train and test datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ec19b976",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fbb92de",
   "metadata": {},
   "source": [
    "### Converting columns with string values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6ccb5e7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "trf = ColumnTransformer([('trf',OneHotEncoder(sparse=False,drop='first'),['batting_team','bowling_team','city'])]\n",
    ",remainder='passthrough')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f996e5d5",
   "metadata": {},
   "source": [
    "### Importing Classification Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a112be8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "236ede25",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline(steps=[\n",
    "    ('step1',trf),\n",
    "    ('step2',LogisticRegression(solver='liblinear'))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "600a9339",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;step1&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;trf&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                sparse=False),\n",
       "                                                  [&#x27;batting_team&#x27;,\n",
       "                                                   &#x27;bowling_team&#x27;, &#x27;city&#x27;])])),\n",
       "                (&#x27;step2&#x27;, LogisticRegression(solver=&#x27;liblinear&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;step1&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;trf&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                sparse=False),\n",
       "                                                  [&#x27;batting_team&#x27;,\n",
       "                                                   &#x27;bowling_team&#x27;, &#x27;city&#x27;])])),\n",
       "                (&#x27;step2&#x27;, LogisticRegression(solver=&#x27;liblinear&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">step1: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;trf&#x27;,\n",
       "                                 OneHotEncoder(drop=&#x27;first&#x27;, sparse=False),\n",
       "                                 [&#x27;batting_team&#x27;, &#x27;bowling_team&#x27;, &#x27;city&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">trf</label><div class=\"sk-toggleable__content\"><pre>[&#x27;batting_team&#x27;, &#x27;bowling_team&#x27;, &#x27;city&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, sparse=False)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;runs_left&#x27;, &#x27;balls_left&#x27;, &#x27;wickets&#x27;, &#x27;total_runs_x&#x27;, &#x27;crr&#x27;, &#x27;rrr&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('step1',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('trf',\n",
       "                                                  OneHotEncoder(drop='first',\n",
       "                                                                sparse=False),\n",
       "                                                  ['batting_team',\n",
       "                                                   'bowling_team', 'city'])])),\n",
       "                ('step2', LogisticRegression(solver='liblinear'))])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "46c60946",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = pipe.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4d3c5f1",
   "metadata": {},
   "source": [
    "### Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c3ddc5a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8030695914219637"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "438f455b",
   "metadata": {},
   "source": [
    "### Predicting for the whole match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "652f59c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def match_progression(x_df,match_id,pipe):\n",
    "    match = x_df[x_df['match_id'] == match_id]\n",
    "    match = match[(match['ball'] == 6)]\n",
    "    temp_df = match[['batting_team','bowling_team','city','runs_left','balls_left','wickets','total_runs_x','crr','rrr']].dropna()\n",
    "    temp_df = temp_df[temp_df['balls_left'] != 0]\n",
    "    result = pipe.predict_proba(temp_df)\n",
    "    temp_df['lose'] = np.round(result.T[0]*100,1)\n",
    "    temp_df['win'] = np.round(result.T[1]*100,1)\n",
    "    temp_df['end_of_over'] = range(1,temp_df.shape[0]+1)\n",
    "    \n",
    "    target = temp_df['total_runs_x'].values[0]\n",
    "    runs = list(temp_df['runs_left'].values)\n",
    "    new_runs = runs[:]\n",
    "    runs.insert(0,target)\n",
    "    temp_df['runs_after_over'] = np.array(runs)[:-1] - np.array(new_runs)\n",
    "    wickets = list(temp_df['wickets'].values)\n",
    "    new_wickets = wickets[:]\n",
    "    new_wickets.insert(0,10)\n",
    "    wickets.append(0)\n",
    "    w = np.array(wickets)\n",
    "    nw = np.array(new_wickets)\n",
    "    temp_df['wickets_in_over'] = (nw - w)[0:temp_df.shape[0]]\n",
    "    \n",
    "    print(\"Target-\",target)\n",
    "    temp_df = temp_df[['end_of_over','runs_after_over','wickets_in_over','lose','win']]\n",
    "    return temp_df,target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "aca3ed23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target- 178\n"
     ]
    },
    {
     "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>end_of_over</th>\n",
       "      <th>runs_after_over</th>\n",
       "      <th>wickets_in_over</th>\n",
       "      <th>lose</th>\n",
       "      <th>win</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10459</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>43.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10467</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>50.8</td>\n",
       "      <td>49.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10473</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>57.6</td>\n",
       "      <td>42.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10479</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>69.4</td>\n",
       "      <td>30.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10485</th>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>59.3</td>\n",
       "      <td>40.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10491</th>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>46.9</td>\n",
       "      <td>53.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10497</th>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10505</th>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>27.2</td>\n",
       "      <td>72.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10511</th>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>25.1</td>\n",
       "      <td>74.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10518</th>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>86.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10524</th>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>19.1</td>\n",
       "      <td>80.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10530</th>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>15.7</td>\n",
       "      <td>84.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10536</th>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>86.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10542</th>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>88.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10548</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>20.3</td>\n",
       "      <td>79.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10555</th>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>28.9</td>\n",
       "      <td>71.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10561</th>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>55.5</td>\n",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10567</th>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>70.6</td>\n",
       "      <td>29.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10573</th>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>89.4</td>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       end_of_over  runs_after_over  wickets_in_over  lose   win\n",
       "10459            1                4                0  56.2  43.8\n",
       "10467            2                8                0  50.8  49.2\n",
       "10473            3                1                0  57.6  42.4\n",
       "10479            4                7                1  69.4  30.6\n",
       "10485            5               12                0  59.3  40.7\n",
       "10491            6               13                0  46.9  53.1\n",
       "10497            7                9                0  41.0  59.0\n",
       "10505            8               15                0  27.2  72.8\n",
       "10511            9                7                0  25.1  74.9\n",
       "10518           10               17                0  13.7  86.3\n",
       "10524           11                9                1  19.1  80.9\n",
       "10530           12                9                0  15.7  84.3\n",
       "10536           13                8                0  13.5  86.5\n",
       "10542           14                8                0  11.6  88.4\n",
       "10548           15                5                1  20.3  79.7\n",
       "10555           16                8                1  28.9  71.1\n",
       "10561           17                8                2  55.5  44.5\n",
       "10567           18                6                1  70.6  29.4\n",
       "10573           19                8                2  89.4  10.6"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df,target = match_progression(delivery_df,74,pipe)\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2158a8ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Target-178')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3/qSV5/be1v7GuctqdKXOqpWzhJuTWWdf9CnNOLZOM4+u15bLJqexboNNNtXPVUqdwmvq4YLVlT8weyqnBNKe6MRYdVn1sRac2uZyT71cpTS7fl/l9A+yMBmAdOPSJSkiwvjnzX74QXrkEesz4a7RiAYAAADu0I7Lx1R/8QhdTow1rX9T42n1imhkbSg3cjgcWnhqmwZvm66/b/MUcNsCVTWyYidVCA13bzg32RN9UjOPrtOMY+u17fLRu3oNL5tNDXKVvt58zpslNHVDAulAkj1Zr/z9nT7et8jlnmLZcuv3hq+qdHB+C5MBSBdee0167z3n9WrVpHXrJO6pSFdoRAMAAAB34FjMBdX+Y5hOXDM5mSNj7umQcu2sDWURu8OuWcc26I1tM7T3yqlb7rfJpscK19GbFTooIiiPBQnvza6o45p5bL1mHF2X4jiBlHjZbGqUu6w6Faqh9gWrK08WLk8CJOnTfYv00uYppqOMJCnEN1Az672sJnnLW5wMQJp15IgxGzo+3rm2ZInUuLH1mXBPaEQDAAAAt+li/FXVW/ymdkefNK33LtFUH9/XLUPPSJaME45TDq3U8B0/61jshVvu97F566mIRnq9XLs0NQ/Z4XBo53+az7uiT9zV63jbvNQ4d1l1Cq+p9gWrKVcAX5sAZhac3Kouqz9WdOI107q3zUufVuuuZ4o/YHEyAGnSk09KU6c6r7dqJc2da30e3DMa0QAAAMBtiE2KV9Olb2v1+f2m9Y6FaujHOi/KOxM9IhqfnKgvDvypUTt/1bn46FvuD/D21Qslmmlg2TYK89A8WIfDoR1RxzTj6HrNOLZOe1x8U+FWvG1eeiBPOXUKr6l2Basx3xa4TTujjqv18vd1OOacyz19S7XQe5Ufy1R/ngK4yd9/S/fdJ93civTykrZulcrz9ER6RCMaAAAAuIUke7Ie/muc5pzYbFpvmLuMFjR6TQHemfPW9quJcfpo3wK9u3uuy5OO/xXsm0X9S7dSn1IPKsg3i9vzORwObbt8VDOOrdOMo+u17zbGipjxsXmrSd5y6lSoph4qeJ/HmulAenc2LkrtVo7VGhff2JOk1vmr6Ps6vS35MwJAGtSsmfTHH87rvXpJ33xjfR6kChrRAAAAQAocDoeeXv+NJkQuM61XDA3XigfeUIhfoLXB0qCL8Vf17u65Gr9voa4lJ9xyfy7/YA0u21bPlngg1Zv4DodDWy4d+af5vE4Hrp65q9fx9fJW07wV1KlQDT1UsJqy+2VN1ZxAZhWXnKCe677SD0fWuNxTKTRccxr0V6GsYRYmA+BxixZJzZs7r2fJIu3fLxUoYH0mpAoa0QAAAEAK3tg2QyN3/mJaK5w1p1Y3Ga78gdmtDZXGnYy9pJE7f9HXB5cqyZF8y/0FA3NoWPmH1b1oA/l4ed/1+zocDm2+dFgzjq7TzGPrdfAum89+Xj5qlreCOoXXVNsCVRVK8xlwC4fDobd2ztaw7bNc7skbEKrfGryi6mERFiYD4DHJycZIjq1bnWtDhkgjR1qfCamGRjQAAADgwmf7/1Dvjd+a1sL8smlV02EqFZzf2lDpSOTVsxq+fZa+O7xKDt36S4mSQfn0VsWO6liohrxstzcb1uFwaOPFyOvN50MpzJ1Nib+Xr5rnq6BOhWqqTYGqnHAHLPTjkTXqvvZLxdsTTesB3r6aWus5dQyvaXEyAJabMkXq1s15PWdO6eBBif5fukYjGgAAADAx69h6dfprvGkDNdDbX0vuH6yaOYt7IFn6s+PyMb2xfaZ+Ob7xtvZXDi2sUZU6q0W+SrLZbE51h8Oh9RcOasYxo/l8JOb8XeXy9/JVi/yV1KlQDbUuUEXBvjSfAU9Zc36/2q34UGdTuPh0VMXOGlS2remfCwAygGvXpFKlpGPHnGsffyy98IL1mZCqaEQDAAAAN1l+dreaLX1HCfYkp5q3zUtzGvRTi/yVrQ+Wzq07f0BDtk3Xn2d23tb+erlKaXTFzqqfu7TsDrvWXTh4/eTzsdgLd5UhwNtXLfNVVqfwmmqVvzIXoQFpyOGr59R6xfvaGXXc5Z4ni9TXVzV6yd/b18JkACzx7rvSgAHO68WLSzt3Sn6Z81LojIRGNAAAAPAf2y4dVf0/Ryg68Zpp/duaz6hbsQYWp8pY/jy9Q4O3Tdf6Cwdva3/9XKV0KOacjsdevKv3y+Ltp1b5jeZzy3yVlc034K5eB4D7RSfGqsuqj7Xg1DaXe+rnKqWf6/dVTv8gC5MBcKsLF6SICCkqyrk2Y4bUsaP1mZDqaEQDAAAA/zh89ZzqLB6uU9cum9bfqfSIBpRtY22oDMrhcOjXE5v0+rYZKZ5+vFuB3v5qXaCyOhWqqRb5KymrD81nIL1Isier7+bv9Mn+RS73RGTLo7kN+6s0c/qBjOGVV6SxY53Xa9aU1qyRGMmTIdCIBgAAACSdj7+iun+8qX1XTpnWXy75oMZW7cps0lSWbLfr+yOrNGz7rLu+aPBfWX381SZ/VXUKr6kH81VUoI9/KqUE4Amf7FuklzdPkd1FKyLEN1Az672sJnnLW5wMQKo6dMiYDZ1ocmHpihVS/frWZ4Jb0IgGAABApheTFKcHlozWOhejIh4Jr61pdZ6Xl83L4mSZR0JykiZELtOIHbN1Ou7ybX9cNp8AtS1gNJ+b562oLD7MjwQykgUnt6rzqvG6khRnWve2eemzaj30v+L3W5wMQKp57DHphx+c1x96SPrlF8vjwH1oRAMAACBTS7Qnqd3KsZp3cotp/YE85fR7w1e5GMsisUnx+njfIo3ZPUeXEmJM9wT7ZlHbAlXVsVANNc9XUQHeNJ+BjGzH5WNqs+IDHU7hqYm+pVrovcqPyduLbxgC6crGjVL16s7r3t7Sjh1S6dLWZ4Lb3Ekv947+NE9KStLrr7+uokWLKkuWLCpWrJhGjBghu91+fY/D4dDw4cOVP39+ZcmSRY0aNdLOnbd3gzYAAABwrxwOh55e/43LJnSV7EX0c/0+NKEtFOjjrwFl2yiyzVi9Xq6dQnwDJRmP4D9ZpL5+a9BPZ9t/rqm1n9dDBavRhAYygfKhhbSu2ZuqnbOEyz1j985X+7/G6mqi+clpAGmQwyG9+qp57amnaEJncnd0InrUqFEaO3asJk+erHLlymnjxo3q0aOHRo4cqZdfflmSNGbMGI0aNUrffvutSpYsqZEjR2rFihXau3evgoJuffstJ6IBAABwLwZt+VHv7J5jWiuaNZdWNx2uvFlCrQ2FGyTak3Tq2mXlz5JdPl7eno4DwIPikhPUc91X+uHIGpd7KoWGa06D/iqUNczCZADuyrx5UqtWzutZs0oHDkh581qfCW7lthPRa9as0UMPPaRWrVqpSJEi6tixo5o1a6aNGzdKMk6fjBs3TkOGDNHDDz+s8uXLa/LkyYqNjdX3339/978iAAAA4DaM37vAZRM6l3+wFjUeSBM6DfD18lF41pw0oQEowNtP02r31vDyD7vcs/XyUdVYNFQbXMz8B5BGJCdLAwaY1/r3pwmNO2tE16tXT3/++af27dsnSdq6dav++usvtWzZUpJ06NAhnT59Ws2aNbv+Mf7+/mrYsKFWr15t+prx8fGKjo6+4QcAAABwp346skZ9Nn9nWsvq4695DV9V8SC+AAKAtMZms2lYhQ76vnZv+XuZj006HXdZDf8cqZlH11mcDsBtmzLFmAF9szx5pH79rM+DNOeOGtEDBgzQo48+qtKlS8vX11dVqlRRnz599Oijj0qSTp8+LUnKkyfPDR+XJ0+e67Wbvf322woJCbn+o1ChQnfz6wAAAEAmtuT0Tj2x9nM55Dx1zsfmrZ/r9VG1sGIeSAYAuF2PFqmjJfcPVi5/80e7ryUnqNOq8Rq18xfdwZRRAFaIjZXeeMO8Nny4dBvjepHx3VEj+qefftJ3332n77//Xps3b9bkyZP1/vvva/LkyTfss9lsN/zc4XA4rf1r0KBBioqKuv7j2LFjd/hLAAAAQGb298XDarfyQyXak03r39Z6Rs3yVbQ4FQDgbtTJVVLrm41QuZCCLve8vm2GnljzueKSEyxMBiBFH30knTjhvF6qlNSrl/V5kCbdUSP61Vdf1cCBA/XII4+oQoUKeuKJJ9S3b1+9/fbbkqS8/8x6ufn089mzZ51OSf/L399fwcHBN/wAAAAAbkfk1bNqsfxdXUmKM61/UOVxPV6krsWpAAD3oki2XFrVZJgeTOGbiNOOrNL9S0brzLUoC5MBMHXunPRPb9DJO+9IvuYjd5D53FEjOjY2Vl5eN36It7e37Ha7JKlo0aLKmzev/vjjj+v1hIQELV++XHXq1EmFuAAAAIDhbFyUmi99R2fizJsQ/Uq31CulW1qcCgCQGkL8AjWnQX+9UKKZyz1rzu9XjUVvaNuloxYmA+Bk5EjpyhXn9bp1pYcesj4P0qw7akS3adNGo0aN0u+//67Dhw9r9uzZ+vDDD9W+fXtJxkiOPn36aPTo0Zo9e7Z27Nih7t27KzAwUI899phbfgEAAADIfK4mxqnV8vd14OoZ0/rjhevq3cqPWpwKAJCafLy89XG1bvr4vm7ytpm3L47GXlCdxcP12/FNFqcDIEk6cED67DPz2nvvSS5G9SJzsjnuYML/lStX9MYbb2j27Nk6e/as8ufPr0cffVRDhw6Vn5+fJGMe9Jtvvqkvv/xSly5dUs2aNfXpp5+qfPnyt/Ue0dHRCgkJUVRUFGM6AAAA4CQhOUltVryvRae3m9ab5a2gOQ36y8/bx+JkAAB3WXRqmzqv+lhRibGmdZtsGlP5EfUv3crlHVUA3KBLF2n6dOf1Dh2kmTOtzwPL3Ukv944a0VagEQ0AAABX7A67uq39Qt8dXmVar5ajmJbeP0TZfAMsTgYAcLfdUSfUZsUHOujiaRhJ6l60gb6o3lP+3sykBdxu3TqpVi3ndR8faedOqWRJ6zPBcnfSy72j0RwAAACAJw3Y8qPLJnTxbHn0e8P+NKEBIIMqE1JA65q9qYa5y7jc8+2hFWq69G2di4u2MBmQCTkc0muvmdeeeYYmNEzRiAYAAEC68OGeeXp/z++mtTwBIVrYeKByB4RYnAoAYKUw/yAtajRQT0U0crln5bm9qrloqHZGHbcuGJDZzJ0rrVjhvJ4tmzR0qPV5kC7QiAYAAECaN+3wKvX7e5ppLcgnQPMbvqZi2XJbnAoA4Al+3j76qvpT+rBKV3m5mAd9KOacai8apvknt1gbDsgMkpKkAQPMawMGSLn5nAzmaEQDAAAgTVt0apu6r/3StObr5a1f6r+iKjmKWBsKAOBRNptNfUu30G/1+ynIx3wk05WkOLVe8b7G7ZmvNHY9FpC+TZok7d7tvJ4vn9S3r/V5kG7QiAYAAECatfFCpB7+a5ySHMlONZtsmlrrOd2ft5wHkgEA0oJWBapoddPhKpI1l2nd7nCo79/f6ZkNE5SQnGRxOiADiolxPXpjxAgpa1Zr8yBdoRENAACANOnAldNqufw9xSTFm9bHVe2qLoVrW5wKAJDWlA8tpPXNRqherlIu93x9cKmaL3tHF+KvWJgMyIA+/FA6fdp5vWxZqXt3y+MgfaERDQAAgDTn9LXLar5sjM7FR5vWB5Zpo5dKPWhxKgBAWpUrIFiLGw9St6L1Xe5Zdna3ai0apj3RJy1MBmQgZ85I775rXhszRvLxsTYP0h0a0QAAAEhTohNj1XL5e4q8eta03q1ofY2u1MXiVACAtM7f21eTaj6jMZUekU3mlxgeuHpGtRYN0x+ntlucDsgARoyQrl51Xm/YUGrVyvo8SHdoRAMAACDNiE9O1MMrx+nvS4dN6y3zV9bXNZ6SzWbeYAAAZG42m02vlW2j2fX7KKuPv+meqMRYtVj+rj7dt8jidEA6tnev9KX55dF6912Jz81wG2hEAwAAIE2wO+zqtvYL/Xlmp2m9ZliEptd9Ub5ePPYJAEjZQwWraVWTYSoUGGZaT3bY9cKmyeq9cZKS7M4X4gK4yeDBUrLJ75UuXaQaNazPg3SJRjQAAAA8zuFw6JXN0/TT0bWm9VJB+TS34avK6hNgcTIAQHpVKXthrW82QrXCirvc89n+xWqx7F1dSoixMBmQzqxeLf38s/O6r680apT1eZBu0YgGAACAx723e64+2rfAtJYvS6gWNh6gnP5BFqcCAKR3ebOEaukDQ/R44bou9yw+s0O1Fw3T/iunLUwGpBMOh/Tqq+a155+XIiKszYN0jUY0AAAAPGrKoZUasPVH01qIb6AWNBygwllzWZwKAJBRBHj7aWrt5zSyYieXe/ZeOaWai4ZqqYvxUECm9csvxonomwUHS6+/bnkcpG80ogEAAOAx809uUc91X5nW/Lx89GuDV1Qxe7jFqQAAGY3NZtOQcu00s97LyuLtZ7rnUkKMmi0do68OLLE4HZBGJSZKAwea1wYNknLmtDYP0j0a0QAAAPCIdecPqONf45XssDvVbLJpWu3n1TB3GQ8kAwBkVB0K1dBfTYaqQJbspvUkR7Ke2TBBfTZN5RJD4JtvpH37nNcLFpReftn6PEj3aEQDAADAcnujT6rV8vcUmxxvWv+kWjd1DK9pcSoAQGZQNUdRrW/2lqrlKOZyz0f7Fqjtig8UlRBrYTIgDblyRRo+3Lz21ltSliyWxkHGQCMaAAAAljoZe0nNl43RhYSrpvXXy7XT8yWaWpwKAJCZ5A/MruUPvK7OKXzTc/6prarzx3BFXj1rYTIgjXj/femsyX/7FSpITzxhfR5kCDSiAQAAYJmohFi1XP6ujsScN60/FdFIIyp0tDgVACAzCvTx1491XtSw8g+73LMr+oRqLHxDK87utjAZ4GGnThmNaDPvvit5e1ubBxkGjWgAAABYIi45Qe1Wfqitl4+a1tsWqKrPq/WUzWazOBkAILOy2WwaXqGDfqjzggK8fU33XEi4qiZL39akyOUWpwM8ZPhwKdZkLM0DD0jNm1seBxkHjWgAAAC4XbLdrifWfK5lLk6U1clZQj/UeUE+XpywAQBY75HCtbX8gTeUNyDUtJ5oT1bPdV/p1b+/V7Ld+ZJdIMPYvdu4pNDMu+9KHBjAPaARDQAAALdyOBx6efMUzTy23rReJji/5jTor0Aff4uTAQDw/2qERWhD8xGqkr2Iyz3v7/ld7f8aqyuJ16wLBlhp4EDJ7Jstjz0mVa1qfR5kKDSiAQAA4Fajd/2qT/f/YVorkCW7FjYaqBz+2SxOBQCAs4KBYVrZ5A09XLC6yz1zTmxW3T/e1JGYcxYmAyywYoX022/O635+0siR1udBhkMjGgAAAG4z8eAyvb5thmkt1DdQCxsPVKGsYRanAgDAtaw+AZpR7yUNLvuQyz3bo46pxsKhWn1un4XJADdyOKRXXzWvvfCCVLSotXmQIdGIBgAAgFvMPbFZ/9swwbQW4O2rOQ37q1xIQYtTAQBwa142L42q1FlTaz0nPy8f0z1n46PVeMkoTT200uJ0gBvMnCmtNxmjFhoqDRlieRxkTOZ/mgIAAAB3KDoxVpsuHtLGi4e08WKkfjuxWckO5xmDXjabfqzzgurlKuWBlAAA3L6uRespIiiP2q34UGfjo53qCfYkPbn2C+2OPqmRFTvJy8Z5P6RDCQnSoEHmtcGDpRw5rM2DDMvmcDgcng7xX9HR0QoJCVFUVJSCg4M9HQcAAAAmYpLitOXSEW28eEgbLkRq48VI7b1y6rY+9qvqvfR08fvdnBAAgNRzJOac2iz/QNujjrnc075gNU2t/Zyy+gRYmAxIBR9/LL30kvN6eLi0d68UwH/TcO1Oerk0ogEAAJCi+OREbb18VBsvRmrjBeO0887o47LfxaeRb1booKHlH3ZDSgAA3OtK4jU9vuYzzTmx2eWeyqGF9VuDftx/gPQjKkoqXlw6f965NmWK9MQT1mdCukIjGgAAAHcl0Z6knVHHbzjpvD3qmBLtyff82s8Uv1+fV+spm82WCkkBALBest2uQVt/1Ht7fne5J29AqH5t8IpqhEVYmAy4S0OGSKNHO69XqiRt3ix5MW4GKaMRDQAAgFtKttu158pJbbwQeX2u85bLRxSXnJjq79UlvJam1e4tb76YAQBkAJMil+uZDRNcfqM2wNtXk2o+o0cK17Y4GXAHjh+XSpSQ4uKca4sWSU2bWp8J6c6d9HK5rBAAACATsDvsOnj1rDZeiNSGi8ZJ582XDismKd4t7+dj81aF0EKqnqOYmuWroPYFq3GBEwAgw+hRrKGKZ8uj9ivH6kLCVad6XHKiHl39iXZHn9Cw8g/zdyDSpmHDzJvQzZrRhIZbcCIaAAAgg3E4HDoSc96Y6XzxkDZcjNSmi4cUlRjrlvfzstlUNriAquUopmo5iqp6WIQqhhZSgLefW94PAIC0IvLqWbVZ/r52RZ9wuadzeE1NqvmMAn38LUwG3ML27VLlypLdfuO6zWaM5Khc2ROpkA5xIhoAACATORl7SRsv/v9J540XD+l8/BW3vV/JoHyq/k/TuVpYMVXJXlhZfbhNHQCQ+RTLlltrmg3XI6s+0fxTW033TD+6TpFXz+nX+q8of2B2ixMCLgwc6NyElozLCWlCw004EQ0AAJCOnIuLvuGk88aLkTp17bLb3q9o1lz/OelcTFWzF1WIX6Db3g8AgPQo2W5X/y3TNG7vApd78mfJrt8a9NN9OYpamAwwsWSJ9MADzuv+/tK+fVJ4uPWZkG5xIhoAACADuJwQo03/aThvvHhIR2LOu+39CmTJrmo5iql6WDFVy1FM9+Uoqpz+QW57PwAAMgpvLy+NrfqEygQXUO+N3yrJ4XyJ4clrl1R/8QhNqfWsOobX9EBKQMYp6NdeM6+9/DJNaLgVJ6IBAADSgCuJ1/T3pcPGSecLRuP5wNUzbnu/XP7B/zSci6r6P03nfFl4XBgAgHu19MxOdfjrI11KiHG5560KHTWkXDvZbDYLkwGSfvhBeuwx5/UcOaSDB6XQUMsjIX27k14ujWgAAACLXUtK0NbLR4yTzhcOaePFSO2OPimH3PNpWahv4A0nnavlKKpCgWF88QsAgJvsv3JabZa/r71XTrnc81jhOppQ82ku94V14uOl0qWlw4edax9+KPXta3kkpH80ogEAANKIhOQkbY86Zlwm+M9J5x1Rx5XsMLkcJhVk8wnQfTmKGhcJ5iim6jmKqVi23DSdAQCw2OWEGHVeNV5/nN7hck/NsAj9Uv8V5c0Sal0wZF5jx0qvvOK8XqSItGePMSMauEM0ogEAADxkb/RJrTq3TxsvGiedt14+qgR7klveK8DbV1WyF1H1f045V8tRTKWC88nL5uWW9wMAAHcmyZ6sPpun6tP9f7jcUygwTHMa9FOl7IUtTIZM5/JlKSJCunjRufb999Kjj1oeCRkDlxUCAABYLDYpXo+u/kS/ndjsltf39fJWpdDCN5x0LhtSQD5e3m55PwAAcO98vLz1SbXuKhOcXy9vnmr6RNSx2Auqu/hNTav9vB4qWM0DKZEpvPOOeRP6vvukLl2sz4NMiRPRAAAAqaDbmi805fDKVHktb5uXyoUUvOGkc4XQQvL39k2V1wcAANb749R2dVo1XlGJsaZ1m2x6u1IXvVamNSO1kLqOHpVKljRmRN9syRKpcWPrMyHD4EQ0AACAhb6NXH7XTWibbCodnO/6JYLVwyJUKTRcgT7M6AMAICNpmq+C1jZ7U22Wv68DV8841R1yaODWH7U7+oS+rN6Lb0Aj9Qwdat6EbtmSJjQsxYloAACAe7Ar6riqLxyq2GSTT+5NRGTL8/8nncOKqWr2IgryzeLmlAAAIK24GH9VHf/6SEvP7nK5p27Okppdv69yBdAXwT3aulWqUkW6uf3n5WXUypf3TC5kGFxWCAAAYIHYpHjVWDRUO6OOm9bDA8NuOOlcNXsR5fDPZnFKAACQ1iTak9R747f6+uBSl3uKZM2lOQ36qXxoIQuTIcN58EFp4ULn9Z49pQkTrM+DDIdGNAAAgAWeWve1JkQuM63NqPuSOobXtDYQAABINxwOh8bvW6hX/v5OdhetmWw+AfqxzgtqVaCKxemQIfzxh9SsmfN6lizSvn1SwYLWZ0KGcye9XC+LMgEAAGQo0w6vctmEfqFEM5rQAAAgRTabTS+XelBzG/RXkE+A6Z6rSXFqs+IDfbhnntLYOUKkdXa79Npr5rW+fWlCwyNoRAMAANyhfdGn9OyGiaa1KtmL6L0qj1qcCAAApFct8lfWmmZvqmjWXKZ1hxzq9/c0/W/DN0pITrI4HdKt77+XtmxxXs+Z03WDGnAzGtEAAAB3IC45QZ1XjdfVpDinWpBPgKbXfVEB3n4eSAYAANKrciEFtb75W6qfq5TLPd8cXKZmy97RhfgrFiZDuhQXJw0ZYl4bOlQKCbE2D/APGtEAAAB34JXN07T18lHT2tc1nlLxoLwWJwIAABlBTv8g/dF4kHoUbehyz/Kzu1Vz0TDtjjphYTKkO598Ih01+Xw1IkJ65hnr8wD/oBENAABwm6YfXavPDyw2rT1T/H51KVzb4kQAACAj8ff21YSaT+u9yo/JJpvpnoNXz6jWH8O08NQ2i9MhXbh4URo1yrz29tuSH0/uwXNoRAMAANyGg1fO6Kl1X5vWKoaGa2yVJyxOBAAAMiKbzab+ZVrp1wavKJuLSwyjE6+p5fJ3NSlyucXpkOaNHi1dvuy8XqOG1LGj5XGA/6IRDQAAcAvxyYnqvGq8rpjMhc7q46/pdV9UFh9OlwAAgNTTpkBVrWoyTOGBYaZ1u8OhZzdM1I7LxyxOhjTr8GHp44/Na++9J9nMT9kDVqERDQAAcAuvbflBmy8dNq19Ua2nSgXntzYQAADIFCpmD9f6Zm+pds4SpvUEe5LG7V1gcSqkWa+/LiUkOK+3bSs1aGB9HuAmNKIBAABSMPvYBo3ft9C01qNoQ3UtWs/iRAAAIDPJkyVES+4frK5F6prWfzq6VlcTnZ/aQiazebM0bZrzupeX9M471ucBTNCIBgAAcOHw1XPque4r01rZ4AL6uNqTFicCAACZUYC3n6bUek5PFqnvVLuaFKfpR9d6IBXSDIdD6tfPvPbUU1KZMtbmAVygEQ0AAGAiITlJXVZ/rMuJsU61LN5+ml7vJWV1cYEQAABAarPZbOpb+kHT2oTIZdaGQdryxRfSsmXO64GB0vDhVqcBXKIRDQAAYGLwtp+0/sJB09on93VTuZCCFicCAACZXeXsRVQ1exGn9dXn92t31AnrA8HzDhyQ+vc3r/XvL+XLZ20eIAU0ogEAAG4y98RmfbBnnmmta5G66lGsocWJAAAADL0iGpmuT4xcbm0QeF5ystS9uxTr/ASfChRw3aAGPIRGNAAAwH8ci7mgbmu/NK2VDMqnz6v1lM1mszgVAACA4bHCdRTg7eu0PuXQSiXakzyQCB4zdqy0apV5beJEKSjI2jzALdCIBgAA+EeiPUmPrv5EFxOuOtX8vXw1ve6LyubLXGgAAOA5oX5Z9XDB6k7rZ+OjNffE3x5IBI/YuVMaMsS89uyzUrNm1uYBbgONaAAAgH8M3TZTq87vM619dN8TqpS9sMWJAAAAnPUq1sh0nUsLM4nEROnJJ6WEBOdasWLSe+9Znwm4DTSiAcCVNWukRo2kcuWkYcOkmBhPJwLgRgtObtU7u+eY1rqE19L/Iu63OBEAAIC5RnnKqGjWXE7r809t1YnYix5IBEuNHi1t3uy8brNJ334rZctmeSTgdtCIBgAz8+ZJ9etLy5dLu3ZJI0ZIzZtL0dGeTgbADU7GXtITaz83rUVky6OvavRiLjQAAEgzvGxe6mlyebLd4dDkQys9kAiW2bRJGjnSvPbKK8bXsUAaRSMaAG62ebPUubNxA/F/rVplNKOjojyTC4BbJNmT9diaT3U+/opTzc/LR9Prvqhg30APJAMAAHCte7EG8jL5RvnEyOVyOBweSAS3i4szRnIkmVxKWaaM6wY1kEbQiAaA/zp6VGrVyvUYjrVrpaZNpUuXrM0FwG1G7Jit5Wd3m9Y+qPK4quYoanEiAACAWysYGKbmeSs6rR+8ekYrzu3xQCK43dChxhO7N/P2lqZMkQK4VBtpG41oAPjX5ctSy5bS6dMp79uwQWrSRLrI7DUgvfvz9A6N3PmLae3hgtXVu0RTawMBAADcgV4RjUzXJxxcZmkOWGDVKun9981rQ4ZI1apZmwe4CzSiAUAybhvu0EHaufP29m/eLN1/v3T+vHtzAXCb09cu6/E1n8kh50dXi2TNpQk1n2YuNAAASNPa5K+qXP7BTuszj61XVEKsBxLBLWJipG7dJLORK1WqGI1oIB2gEQ0ADof09NPSkiXmdVeNqK1bjWb02bPuywbALZLtdnVd85nOxDnPfPf18tZPdV9UqF9WDyQDAAC4fX7ePnqiSF2n9WvJCfrhyGoPJIJbvPaadPCg87qfnzGSw8/P+kzAXaARDQBvvmn85W2mQgXj9HN4uHl9+3apceNbj/MAkKa8vetX/XnG/AmIMZUeVY2wCIsTAQAA3B2X4zkil1sbBO7xxx/SZ5+Z1956Sypf3to8wD2gEQ0gc/v2W6MRbSZ/fmnePKlyZWn5cqmoiwvLdu2SGjWSTp50U0gAqWn52d0atmOWaa1NgarqU+pBixMBAADcvbIhBVUrrLjT+saLkdp26agHEiHVREVJPXua12rXlvr1szYPcI9oRAPIvBYvNkZymMmWTfr9d6lgQePnRYoYzegIF6ck9+41mtHHj7sjKYBUci4uWo+t/lR2k/l6hQLD9G3NZ5gLDQAA0h3Xp6KXWZoDqezll82/xgwMlCZPlry9rc8E3AMa0QAyp+3bjcsJk5Kca97e0syZxkno/ypUyGhGlyhh/pr790sNG0pHOXUApEV2h11PrPlcJ69dcqp527z0Y50XlMM/mweSAQAA3Jsu4bWU1cffaf27w6sUn5zogUS4Z7/+ajSbzbz7ruuvS4E0jEY0gMzn5EmpVSspOtq8/sUXUvPm5rUCBYxmdOnS5vXISKMZffhwqkQFkHre2/27Fp7eZlobVbGz6uQqaXEiAACA1BHkm0WdC9VyWr+YcFW/HN/ogUS4J+fPS//7n3ntgQek556zNg+QSmhEA8hcrlwxmtDHjpnXBw+Wnnoq5dfIl09atkwqW9a8fviw0Yw2u9UYgEesOrdXQ7ZNN621yFdJr5ZpZXEiAACA1NUzoqHpOuM50hmHw2g0nz3rXAsOliZOlLxo5yF94r9cAJlHUpLUubO0ZYt5/bHHpJEjb++18uQxmtEVKpjXjx41Zkbv338XQQGkpgvxV/TI6k+U7LA71fJnya7JtZ6Vl41PiQAAQPpWN2dJlQrK57S++PROHYk554FEuCs//miMijTz0UdSeLi1eYBUxFddADIHh0Pq3VtasMC83rCh8Z3lO7mkLFcuackS51nS/zp+3HjdvXvvOC6A1OFwONR97Zc6HnvRqeZls+mHOr2VKyDYA8kAAABSl81mU89izqeiHXJoUuQKDyTCHTt50vi61UybNlK3btbmAVIZjWgAmcOYMdJXX5nXypSRZs+W/J0v97ilnDmlP/+U7rvPvH7qlNGM3rXrzl8bwD0bu3e+5p7827T2ZvkOapC7jMWJAAAA3OfJovXlbfKk16TI5Uq2Oz8dhjTE4ZCeflq65HyxtnLkML6evZODU0AaRCMaQMb3ww/SoEHmtTx5pHnzpOzZ7/71c+SQFi+WatQwr585Y4zp2LHj7t8DwB1bd/6ABmz50bTWJE95DSr7kMWJAAAA3CtvllC1zl/Faf1o7AX9eYavR9K0iRONr03NfP65lDevtXkAN6ARDSBjW7FC6t7dvBYYKM2dKxUpcu/vExoqLVok1a5tXj93zmhGb9167+8F4JYuJcSoy+qPleRIdqrlCQjRd7WflzeXvAAAgAyoV0Qj0/WJkcutDYLbd/iw1KePee2RR4y7joAMgK/AAGRce/ZI7dpJCQnONS8v4xKIatVS7/1CQqSFC6V69czrFy5I998vbd6ceu8JwInD4VCvdV/pSMx5p5pNNk2r/bzyZAnxQDIAAAD3a5GvkvJlCXVan318oy7EX7E+EFJmt0s9ekhXrzrX8uaVPvnE+kyAm9CIBpAxnTkjtWxpPl9LksaPNy57SG1BQdL8+cZcaDMXL0oPPCBt2JD67w1AkvTJ/kWafXyjae2N8u30QN7yFicCAACwjo+Xt7oVqe+0nmBP0rTDqzyQCCn65BNp2TLz2tdfS2FhlsYB3IlGNICMJzZWattWOnTIvN6vn+ubiFNDtmzS778bp5/NXL4sNWkirV3rvgxAJrXp4iH1//t701qj3GU0tNzDFicCAACwXk8X4zkmRC6Xw+GwNgxc27tXGjDAvNazp9S6tbV5ADejEQ0gY0lOlh57TFq/3rzesaP07rvuz5E1qzF/ulkz83p0tFFbxYkEILVEJcSq86rxSrAnOdVy+QdrWu3ezIUGAACZQomgvGqQq7TT+rbLR7XpoosDO7BWUpLUrZsUF+dcCw+Xxo61PhPgZnw1BiBjeeUV6ddfzWu1a0tTphjzoa2QJYuRpUUL8/qVK1Lz5saFigDuicPh0P82fKPIq2dN61NrP6f8gdktTgUAAOA5ri4tnBC5zNIccOG996R168xrkyZJwcHW5gEsQCMaQMYxbpwx+9lM8eLSb78ZzWErBQRIs2e7fqQqJsZoVC9dam0uIIP58sCfmn7U/BP5QWXbqnm+ihYnAgAA8KyOhWoo2Nf565/vj6xWbFK8BxLhum3bpGHDzGsvvuh6zCOQztGIBpAx/PyzcRraTFiYcYFgzpzWZvqXv780a5bUrp15PTZWatVKWrzY0lhARrH10hH12fydaa1uzpIaUaGjxYkAAAA8L9DHX48Wru20Hp14TbOOuRhlCPdLSJCefFJKTHSulSghvfOO9ZkAi9CIBpD+rV0rPf64ZHbphr+/cRK6eHHrc/2Xn580fbrUoYN5/do1qU0baeFCa3MB6dyVxGvqvOpjxdudP5HP4ZdNP9R5QT5e3h5IBgAA4Hm9ijUyXZ8QudzaIPh/I0ZIW7c6r3t5SZMnS4GB1mcCLEIjGkD6dvCg0cA1u+DBZpO++06qU8f6XGZ8faUffpC6dDGvx8VJbdtKv/9ubS4gnXI4HHpuwyTtu3LKtD6l1rMqlDXM4lQAAABpR7UcxVQhpJDT+vKzu3XgymkPJMrk1q2T3n7bvPbaa8a9RkAGRiMaQPp14YIxX/n8efP6e+9JHdPYI/m+vkZz/PHHzesJCVL79sYpbgApmhS5XNOOrDKt9SvdUq0KVLE4EQAAQNpis9nUs1hD09pETkVb69o1qVs3yW53rlWoIA0fbnkkwGo0ogGkT3Fx0kMPSfv3m9d793Y9M9rTfHyMR666dTOvJyYaIzx+/tnaXEA6sjPquF7YNNm0VjMsQm9XcvHkAQAAQCbTtWg9+ZqMKvv20Aol2ZM9kCiTGjxY2rvXed3HR5oyxRgrCWRwNKIBpD92u9HEXWV+ElJt2kgffWSM5kirvL2liROlXr3M60lJUufO0owZ1uYC0oGYpDh1+usjXUtOcKqF+gbqxzovytfLxwPJAAAA0p6c/kFqV6Ca0/qpa5e14JTJrGKkvmXLpHHjzGvDhkmVK1sYBvAcGtEA0p9Bg4yL/8xUq2bMYfZOB5eTeXlJX30lPfOMeT05WXr0UePXA+C6FzZO1u7ok6a1SbWeUZFsuSxOBAAAkLb1imhkus6lhRa4ckXq0cO8Vr26NHCgtXkAD6IRDSB9+eIL6d13zWuFC0tz5khZs1qb6V54eUmff26MEjGTnCx17SpNnWptLiCNmnJopb49tMK09lLJ5mpX0Pm0DwAAQGbXJE95FQp0vsR57om/deZalAcSZSL9+kmHDzuv+/sbIxt9eJIPmQeNaADpx++/u27YhoZK8+dLefNaGilV2GzSxx9LL79sXv93FMmkSdbmAtKYPdEn9fxG898H9+UoqncrP2pxIgAAgPTB28tLPYo1cFpPciRr6uG/PJAok5g/X/r6a/Pa229LZcpYmwfwMBrRANKHTZukLl3Mbxj29ZV++SV9/yVus0ljx0r9+5vXHQ6pZ0/Xn8QAGdy1pAR1/mu8YpLinWrBvln0U50X5e/t64FkAAAA6UOPog1lk/M9OhMil8nhcHggUQZ38aLrO4EaNHB9EAnIwGhEA0j7jhyRWreWYmLM65MmSQ0bWpvJHWw2Y+xISjPC/vc/Y5QHkMn02TxV26OOmda+qfG0IoLyWJwIAAAgfSmSLZceyFPOaX1P9EmtOb/fA4kyuJdekk6dcl7PmtX4GtaLlhwyH/6rB5C2Xb4stWwpnT5tXh85Unr8cUsjuZXNJo0eLb3xhus9zz9vjPIAMokfj6zRVweXmNaeK95EncJrWpwIAAAgfXJ9aeEyS3NkeLNmSdOmmdc++EAqVszaPEAaQSMaQNqVkCA9/LC0a5d5/amnpMGDrc1kBZtNGjFCevNN13teeskY5QFkcPuvnNbT678xrVUKDdeHVTPQN6IAAADcrF3B+5Tdz/ly95+OrtWVxGseSJQBnTkjPfusea15c+MpVyCTohENIG1yOIxG89Kl5vXmzaXPPjOathnV0KHSqFGu66+8YozyADKouGRjLvTVpDinWjafAE2v+5ICvP08kAwAACB9CvD2U9cidZ3WY5LiNf3oOg8kymAcDqMJff68cy00VJowIWN/DQvcAo1oAGnT8OHS1KnmtYoVpenTjUsKM7rBg1NuNg8YYIzyADKg/n9/ry2Xj5jWvqzeUyWD81mcCAAAIP3rVayR6TrjOVLB1KnSL7+Y1z7+WCpQwNI4QFpDIxpA2jNpkjGawkyBAtLvv0vBwdZm8qRXX5U+/NB1fcgQ1/++gHRq5tF1+nT/H6a1pyIa6TGTkzwAAAC4tUrZC+u+HEWd1tec36/dUSc8kCiDOHbMGKFo5uGHM9bdRsBdohENIG1ZvNj1zKygIGnePKlgQWszpQV9+0rjx7uuDxtmjPJwOKzLBLhJ5NWz6rX+a9Na+ZCC+qjqkxYnAgAAyFg4FZ3KHA6pVy8pKsq5liuX9PnnjOQARCMaQFqyfbvUoYOUlORc8/aWZs40xnJkVi++aMzFduWtt4xRHjSjkY4lJCepy6qPFW1yWU6gt7+m131JgT7+HkgGAACQcTxauLYCvJ1HHU459JcSkk2+HkPKvvxS+sP8aT598YWUO7e1eYA0ikY0gLThxAmpZUspOtq8/tVXUrNm1mZKi557zvh34eq76e+8I732Gs1opFsDtv6gjRcjTWufVeuuMiHM1QMAALhXoX5Z1aFgDaf1c/HRmnvybw8kSscOHpT69zevde1qjOUAIIlGNIC04MoVqVUr6fhx8/rrr0s9e1qbKS17+umUb1t+/31jlAfNaKQzvx7fqHF7F5jWnixSX92KNbA4EQAAQMbVK6Kh6TrjOe5AcrLUvbsUE+NcK1Ag5fGKQCZEIxqAZyUmSp07S1u3mte7duUiPjM9ekiTJ0teLv4Y/+gjY5SH3W5tLuAuHYk5p+5rvzStlQ7Or0+rdbc2EAAAQAbXMHcZFcvmPDJiwamtOhF70QOJ0qFx46S//jKvTZggZc9uaRwgraMRDcBzHA6pd29pgfkJSDVqlPLJ38zuiSek774z5meb+fRT6fnnaUYjzUu0J+mRVZ/ocmKsUy3A21fT676obL4BHkgGAACQcXnZvNSzmPOpaLvDoW8PrfBAonRm1y5pyBDz2jPPSM2bW5sHSAdoRAPwnHfekb7+2rxWpoz088+Sn5+1mdKbRx+VfvjBdTP6yy+l//2PZjTStCFbp2vthQOmtfFVn1SF0HCLEwEAAGQO3Ys2kJfJwZ+Jkctld/A1hEuJidKTT0rx8c61okWl996zPhOQDtCIBuAZ338vDR5sXsuTR5o3j8eYblenTtL06ZKPj3l9wgRjxnZysrW5gNsw7+QWvbfnd9Pao4Vr66mIxhYnAgAAyDwKBObQg/kqOa1HXj2rFWf3eCBROvH229KmTc7rNpv07bdSUJDlkYD0gEY0AOutWGHMODYTGCjNnSsVKWJppHTv4YelWbMkX1/z+uTJUrduUlKStbmAFByPvaAn13xuWiueLY++rN5LNkbzAAAAuFWvYo1M17m00IXNm6W33jKv9e0rNeCCbcAVGtEArLVnj9SunZSQ4Fzz8pJ++kmqVs3yWBlC27bS7Nmux5lMm2Zc/kgzGmlAkj1Zj67+VBcSrjrV/Lx8NL3uSwryzeKBZAAAAJlL6/xVlMs/2Gl95rH1upwQ44FEaVhcnDGSw+xrqjJlpJEjrc8EpCM0ogFY58wZqUUL6dIl8/rHH0utW1ubKaNp1Ur67TfJ39+8/tNP0iOPGDPNAA8atn2W/jq317Q2tmpXVclRxNpAAAAAmZSft4+eLFrPaT0uOVE/HFnjgURp2LBh0s6dzuve3sZTqFk4SAGkhEY0AGvExEht2kiHD5vX+/eXnn/e0kgZVvPmxngTV58EzZolde5sfiodsMCiU9v09q7fTGsdC9XQc8WbWJwIAAAgc2M8x21Yvdr1JYSDB0vVq1ubB0iHaEQDcL/kZOmxx6QNG8zrnTpJY8ZYmymja9JE+v13Y+a2mV9+kTp2NL/lGXCjU9cuqeuaz+WQw6lWNGsufVPjaeZCAwAAWKxMSAHVzlnCaX3TxUPaeumIBxKlMTExxp07DufPYVW5svT665ZHAtIjGtEA3MvhMC5s+M389KPq1JGmTDHmQyN1NW4szZ8vZc1qXp8zR2rf3phzBlgg2W7X46s/07n4aKear5e3ptd9SSF+Lr55AgAAALfiVHQKBgyQDhxwXvfzM76edXVPD4Ab0PkB4F7jxhmzn82UKCH9+qsUEGBppEylQQNp0SIpKMi8Pn++ccnhtWvW5kKmNHLnbC09u8u09l7lx1QtrJjFiQAAAPCvzuE1ldXH+a6Z7w6vUlxyJh7rt3ix9Omn5rURI6QKFazNA6RjNKIBuM+sWVK/fua1nDmlefOMf8K96tSR/vhDCna+CVuSUWvd2njcDHCTpWd26s0ds01rDxW4Ty+VbG5xIgAAAPxXkG8WdQmv5bR+KSFGvxzf5IFEaUBUlNSzp3mtVi3jriMAt41GNAD3WLNG6trVfIZWQIAxqqN4cetzZVY1a0p//imFhprXlyyRWrWSrl61NBYyhzPXovTY6s9M50KHB4ZpYs3/MRcaAAAgDXA5nuPgMktzpBl9+0rHjjmvZ8kiTZ4seXtbnwlIx2hEA0h9Bw4Y4x7MZg/bbNJ330m1a1ufK7OrVs1oRufIYV5fvlxq0UK6csXaXMjQ7A67nlj7mU7HXXaq+di89VPdF5XDP5v1wQAAAOCkds4SKhWUz2l98ZkdOnz1nAcSedCcOdKkSea1MWOkkiWtzQNkADSiAaSu8+elli2Nf5p5/32pQwdrM+H/Va1qnH52NRLlr7+k5s2NR9CAVPDOrjn64/QO09rblbqolsnt7AAAAPAMm82mXhGNTGuTDi23NownnT8vPf20ee3++6Xeva3NA2QQNKIBpJ64OKldO2n/fvP6Cy8YjzbBsypVkpYulXLlMq+vWSM1ayZdvmxpLGQ8K8/u0RvbZ5jWWuavrFdKt7A4EQAAAG7lySL15WNzHjkxKXKFku12DyTygN69pTNnnNeDgqSJEyUv2mnA3bjj3zknTpxQ165dFRYWpsDAQFWuXFmbNv3/0HqHw6Hhw4crf/78ypIlixo1aqSdO3emamgAaZDdLj35pLRqlXm9bVtp3DhjNAc8r3x5adkyKU8e8/r69VKTJtLFi5bGQsZxPv6KHl39iewmc+ILZMmuybWelZeNT+ABAADSmjxZQtS6QBWn9WOxF7T4jPmTbhnKjz9K06eb18aNkwoXtjQOkJHc0VeAly5dUt26deXr66v58+dr165d+uCDDxT6n8uv3n33XX344Yf65JNPtGHDBuXNm1dNmzbVFWaOAhnbwIHSDPOTj6pWTfr+ey5ySGvKljXmQudzngEnSdq0SXrgAddjVgAX7A67nlzzuU5cu+RU87Z56ce6Lyqnf5AHkgEAAOB29CrW0HQ9w19aeOqU9Pzz5rXWraUePazNA2QwNofD5KiSCwMHDtSqVau0cuVK07rD4VD+/PnVp08fDRgwQJIUHx+vPHnyaMyYMXrmmWdu+R7R0dEKCQlRVFSUgoODbzcaAE/6/HPXf1kXKSKtXev65C08b/9+Y87Z8ePm9QoVpMWLpdy5rc2FdOu93XP12pYfTGujKnbW4HIPWZwIAAAAdyLJnqzw317SqWuXb1j39fLWyXafZsxDBQ6H1KaN9PvvzrUcOaQdO1wf4gEysTvp5d7RiejffvtN1apVU6dOnZQ7d25VqVJFX3/99fX6oUOHdPr0aTVr1uz6mr+/vxo2bKjVq1ff4S8DQLowd64x+9lMaKg0bx5N6LSuRAnjZHR4uHl9+3apcWPzGWnATdac369BW38yrTXLW0EDy7axOBEAAADulI+Xt7oXbeC0nmhP1rTDLsYxpneTJpk3oSXps89oQgOp4I4a0ZGRkfr8889VokQJLVy4UM8++6xeeuklTZkyRZJ0+vRpSVKem5pOefLkuV67WXx8vKKjo2/4ASCd2LRJ6tLFmA99Mz8/6ZdfpDJlLI+Fu1CsmNGMLlLEvL5rl9SokfGoGuDCxfiremTVx0p2OP+ZkDcgVFNrP8dcaAAAgHSiZwrjOe7g4fr04fBhqU8f81rnzsbXvQDu2R19NWi321W1alWNHj1aVapU0TPPPKOnn35an3/++Q37bDddRuZwOJzW/vX2228rJCTk+o9ChQrd4S8BgEccOWLMyIqNNa9PmiQ1NP/EBWlUkSJGMzoiwry+Z4/x/+mJE5bGQvrgcDjUY92XOhp7wanmZbPp+zrPK3dAiAeSAQAA4G4UD8qrhrmdDxZtjzqmjRcjPZDITex2qWdPyexuszx5jNPQAFLFHTWi8+XLp7Jly96wVqZMGR09elSSlDdvXklyOv189uxZp1PS/xo0aJCioqKu/zh27NidRALgCZcvSy1bSi6edNCoUdJjj1kaCakkPFxatswY12Fm/36jGf3Pn/vAvz7au0C/ndhsWhtarr0a5ylncSIAAADcK5eXFkYuszaIO336qbR0qXnt66+lsDBr8wAZ2B01ouvWrau9e/fesLZv3z4VLlxYklS0aFHlzZtXf/zxx/V6QkKCli9frjp16pi+pr+/v4KDg2/4ASANS0iQHn7YGNVg5qmnpEGDrM2E1FWwoNGMLl3avH7woHG54fnzlsZC2rXhwkG9ttX8csL785TT6+XaW5wIAAAAqaFDoRoK9s3itP7DkTWKTYr3QKJUtm+fNGCAea1HD+PyQgCp5o4a0X379tXatWs1evRoHThwQN9//72++uor9e7dW5IxkqNPnz4aPXq0Zs+erR07dqh79+4KDAzUY5yOBNI/h8NoNLv6bnHz5sZjSy5G8SAdyZ/f+P/5pqdgrjt4UOrY0fjGBDK1ywkx6rLqYyXak51quf2D9V3t5+TtxVxoAACA9CjQx1+PFXY+WBideE0zj633QKJUlJQkdesmXbvmXAsPl8aOtT4TkMHd0VeG1atX1+zZs/XDDz+ofPnyeuuttzRu3Dg9/vjj1/e89tpr6tOnj55//nlVq1ZNJ06c0KJFixQUFJTq4QFYbNgwaepU81qlStKMGZKvr7WZ4D558xrN6AoVzOvLl0svvGB8gwKZjsPh0Kxj61V1wRAdijnnVLfJpu9qP698WbJ7IB0AAABSS69ijUzXJxxcZmmOVPf++9Latea1iROlEO43AVKbzZHGrjqNjo5WSEiIoqKiGNMBpCUTJ0q9epnXChY0/gIvUMDaTLDG+fNSkybS1q3m9fHjpRdftDYTPGrLpcPqs/k7LT+72+WeIeUe0siKnS1MBQAAAHdwOByqvGCwtl12vidmX+sPVCIorwdS3aPt26X77pMSE51rL7wgffyx9ZmAdOpOerk8Kwvg1v74Q3rmGfNaUJD0++80oTOynDmlRYuMx9PM9Olj1JHhnY2L0v/Wf6OqC15PsQldP1cpDS/fwcJkAAAAcBebzeby0sKJ6fFUdEKC9MQT5k3o4sWld96xPhOQSdCIBpCybdukDh2M+Vk38/GRZs2SKla0PheslTu3NGeOlDWrc81ulzp3lm66zBYZR0Jykt7f/btKzO2nrw8ulUOuH6bK7R+s7+v0lo+Xt4UJAQAA4E6PF6krPy8fp/XJh1cqyeSukDTtrbfMn/b08pImTzb/mgdAqqARDcC148elli2lK1fM6199JTVtam0meE7FitK0aeaXUUZFGTdKX7pkfS64jcPh0G/HN6ncvNf06pbvFZ1ocpHLf9QIi9CqpsNUMDDMooQAAACwQph/kNoVvM9p/dS1y5p/ysUIv7Ro/Xrp7bfNa6++KtVxvpgRQOqhEQ3AXHS01KqVdOKEef2NN6QePazNBM976CFp1Cjz2v79xslos9PzSHd2XD6mZkvf0UMrP9SBq2dS3Js/S3ZNqfWs1jQdruLpcUYgAAAAbindX1p47ZrUrZuUbHKCu1w56c03rc8EZDI0ogE4S0yUOnUyxnKYeeIJ/pLOzAYOlB57zLy2eLH0yivW5kGqOh9/Rc9vmKRKCwZp8ZkdKe4N8PbV6+XaaW+r9/VE0frysvFpBQAAQEbVJG95hZs8+Tb35N86fe2y9YHu1JAh0p49zus+PtLUqZK/v/WZgEyGrxgB3MjhkJ57zvXlc40bS998Yz6eAZmDzWb8N1Cjhnn944+lL7+0NhPuWaI9SeP2zFeJOa/o8wOLZXe4ngMtSZ3Da2pPq/f0VsVOyuYbYFFKAAAAeIqXzUs9TC4tTHbYNeXQSg8kugPLl0vjxpnXhg6VqlSxNA6QWdkcjlt8pWmx6OhohYSEKCoqSsHBwZ6OA2Q+o0ZJr79uXitbVlq1SgoNtTQS0qhTp6Tq1c3Ht/j4GN/MaNzY+ly4Y/NObtErm7/T3iunbrm3avYiGlf1CdXPXdqCZAAAAEhLjsScU9Hf+jpdXl0yKJ/2tHpPtrR4YOnKFalSJenQIedatWrS6tWSr6/1uYAM4k56uZyIBmCIi5MGDHDdhM6bV5o3jyY0/l++fNKvv0pZsjjXkpKkjh2lgwetz4XbtjvqhFosG6NWy9+7ZRM6T0CIJtR4Whuav0UTGgAAIJMqnDWXmuQt57S+78oprT6/zwOJbsOrr5o3of39pcmTaUIDFqIRDUBas0aqXFl6913zemCgNHeuVLiwpbGQDtx3n/HJm5mLF6U2bYyLL5GmXIy/qpc3TVGF+QO14JSLWfD/8PPy0cAybbS/9QfqGdGIOdAAAACZnOtLC5dbG+R2LFjgemzgqFHGU78ALMNXk0BmFhsr9esn1a0r7d1rvsfLS/rpJ6PhCJjp1EkaNsy8tnu39Oij5jdTw3JJ9mR9um+RSsztp/H7FirZYU9x/8MFq2t3q/f0duVHFORrcvIdAAAAmU67gtWUwy+b0/r0Y2t1JfGaBxK5cOmS1KuXea1+falPH0vjAKARDWReK1cac7I+/NC4oNCVTz6RWre2LhfSp6FDjVEcZubNM8a+wKP+OLVdlRcM1gubJutiwtUU91YMDdeS+wdrVv0+KpYtt0UJAQAAkB74e/uqa5G6TusxSfH66ehaDyRy4aWXpJMnndezZpUmTZK8va3PBGRyNKKBzCYmRnr5ZalhQ+nAAdf7fH2ljz6SnnvOumxIv7y8jBEdVaua1z/4wPhkD5bbf+W02q74QM2WvaOdUcdT3JvLP1hfVu+lzc1HqXEe59l/AAAAgJTSeI5lluZw6eefpe++M6+9/74UEWFtHgCSJB9PBwBgoWXLjEeTIiNT3letmtE0LF/ekljIIAIDjcsLq1eXTp92rj/zjFSypDEKBm53OSFGI3f+ovH7FirRnvJoFF8vb71UsrneKNdeIX6BFiUEAABAelUxe7iq5SimjRdv/Npy7YUD2hV1XGVDCnoomaSzZ42vPcw0a+a6BsDtOBENZAZXrkjPPy81bpxyE9rfX3rnHePyQprQuBsFC0q//GL8t3SzxESpfXvpyBHLY2UmyXa7vjzwp0rO7a8P9sy7ZRO6TYGq2tnyXb1f5XGa0AAAALhtvYo1NF336Kloh8NoNJ8/71wLCZEmTJBsNutzAZBEIzrzOndOSkrydApYYfFiqUIF6fPPU95Xq5b099/GLF8fHpbAPahZU/rmG/PauXNS27bS1ZRnFOPuLD2zU1UXDtGzGybqXHx0invLhRTUokYD9VuDfioRlNeihAAAAMgoHi1cRwHevk7rUw7/pYRkD/Ubpk0zDsaY+fhj4+AMAI+hEZ1ZPfmk0ZycOTPli+qQfkVHS//7n9S0aconUAMCjBlZf/0llSljXT5kbF27SgMHmte2bTPqdru1mTKwyKtn9fDKsbp/yWhtu3w0xb05/LLpk/u6acuDo9U0XwWLEgIAACCjCfELVMdCNZzWz8df0ZyTm60PdPy49MIL5rV27YyvQQB4FI3ozGjFCmnBAmnPHqlTJ2Oe68KFNKQzkoULjdEaX3+d8r66daWtW6V+/bgxGKlv1CjpoYfMa7/+Kr3xhrV5MqAridc0cMuPKvP7q5p9fGOKe71tXnqpZHPtb/2BepdsJh8vfs8DAADg3qSZSwsdDuM+pKgo51rOnNKXXzKSA0gDaERnNg6HNGTIjWubNkkPPmjMD1692jO5kDouX5Z69jT+/zx2zPW+LFmkceOk5cuNy+MAd/DykqZONZ6+MDN6tPT999ZmyiDsDrsmHlymEnP7aczuOUqwp/zo44P5Kmp7i3f00X1PKod/NotSAgAAIKNrmLuMIrLlcVpfeHqbjsdesC7IV19JixaZ1778Usqd27osAFyiEZ3ZzJ9vjGAws3y5cUK2dWvjlCzSl99/l8qVkyZNSnlfgwbGaISXX+YUNNwvKEj67TfjFIKZnj2ldeuszZTOrTy7R9UXvqFe67/WmTiTEx//USoon+Y1fFXzGw1QmZACFiUEAABAZmGz2dTT5NJCu8OhbyNXWBMiMtJ4ytfM449LDz9sTQ4At0QjOrOZPPnWe37/XapcWXrsMWn/frdHwj26eNGY+d26tXTypOt9WbNKn3wiLV0qFS9uXT6gSBFp9mzJ1/kiE8XHG/Pajh+3OlW6cyTmnLqsGq8Gf76lzZcOp7g31DdQ46o+oe0t31GL/JUtyQcAAIDMqVvR+vIyGXsxMXK57A433wuTnCx17y7FxDjX8uc3LigEkGbQiM5svv9e+vZbqXDhW+/94Qfj8rpnnqFJlFb9+qtxCnrq1JT33X+/tH271Lu3MS4BsFq9etIXX5jXTp82ZknHxlqbKZ24mhinN7bNUOnfX9X0oymfHvey2fR8iSba3+ZDvVzqQfl6+ViUEgAAAJlVgcAcapGvktP6oZhzWnZmt3vf/KOPpJUrzWsTJkjZs7v3/QHcETpSmY23t9Stm7R3rzR+/K3nJCUnG7OWihc3HnU5f96anEjZ+fPGifV27YwmnivZshnNv8WLpaJFLYsHmOrZU3rlFfPa5s3GSQYuTb3O7rBr6qGVKvV7f43c+YvikhNT3N8kT3ltffBtfVqth3L6B1mUEgAAAEjh0sLIZe570wMHpMGDzWv/+59xdxKANMXmcKStr/qjo6MVEhKiqKgoBQcHezpOxhcTYzSk333XuOjuVoKCjIZ0374S//94xqxZ0vPPS2fPpryvaVPp669v7/Q7YJXkZGOMzIIF5vXhw6VhwyyNlBatPb9fL2+eqvUXDt5yb/FsefRBlcfVpkBV2bgJHAAAAB6QaE9SwV9e1Nn46BvW/b18dar9p8rulzX13/S558yfuixa1Lj3KojDGYAV7qSXy4nozC5rVmnQIGO4/6BBUmBgyvuvXDEaRcWKSR9+KF27ZklMyGg8d+4sdeyYchM6OFj65htp4UKa0Eh7vL2lH3+USpc2rw8fLs2YYWmktOR47AU9vvpT1f5j+C2b0MG+WfRe5ce0o+UYtS14H01oAAAAeIyvl4+eLFrfaT3enqgfjqxO/TeMjTVGj97MZpMmTaIJDaRRNKJhyJ5dGj1aOnhQeuEF80vF/uvCBeNkdIkSxuiOxJQfGcc9cDikn34yZkHfqkHXooW0c6fUq5fxFzCQFoWESHPmSDlymNe7dTNGdWQisUnxenP7LJWc21/f3+ITdZtsejqisfa3/kD9y7SSv/ct/rwGAAAALNAropHp+oSDy1L/zWbNkqKjnde7d5caNkz99wOQKmhE40Z58xq3yu7dKz355K2bmSdOGJcZli1rXG5od/ONuJnNmTPGCehHHkl5PndoqHEJ5e+/SwULWpUOuHvFi0szZ0o+JpfpXbtmXF546pT1uSzmcDj0w+HVKv37qxq+42ddS05IcX/D3GW0+cGR+qrGU8odEGJRSgAAAODWSgfnV52cJZzWN186rC2XDqfum02YYL7+3HOp+z4AUhWNaJgrWlSaPFnavl16+OFb7z9wwLg8r2pVoxmatkaPpz8OhzRtmtHg//nnlPe2aWOcgu7WjVPQSF8aNza+8WXm+HGpfXspLs7aTBbacOGg6i1+U4+t+VTHYi+kuLdI1lyaWe9lLb1/iCpnL2JNQAAAAOAOuby08ODy1HuTAwek5SavV6GCVK1a6r0PgFRHIxopK1fOeORl/Xrj8rtb2brVuIisXj3zvxhwa6dOSe3aSV27Shcvut6XI4f03XfSr79K+fNbFg9IVc8+K/XubV5bt0566qkM942tk7GX1H3tF6qxaKhWn9+f4t5sPgEaXbGzdrd6Vx0K1WAONAAAANK0zuG1lM0nwGl92pFVirvF03+3beJE83VGVAJpHo1o3J7q1aVFi6QlS6SaNW+9f/VqqVEj6cEHpU2b3B4vQ3A4jFPoZctKv/2W8t727Y1T0I8/zl+0SP/GjpUeeMC8Nm2aNGaMtXncJC45QaN3/qqSv/fT5EMrU9xrk009ijbUvtbva1C5hxTg7WdRSgAAAODuZfMNUJfwWk7rlxJiNPvYxnt/g6QkYyzlzfz8jMNcANI0GtG4M40bS2vWGKdwy5e/9f6FC41HYzp1kvbscX++9Or4ceMkeffu0uXLrvflzCn9+KNxSj1vXqvSAe7l6ytNn27MjTYzeLDxZ0465XA4NPPoOpX5/TUN2TZdMUnxKe6vm7OkNjR/SxNr/U/5smS3KCUAAACQOlxeWhi57N5ffMEC87tkHnpICgu799cH4FY0onHnbDapbVtpyxZjNESxYrf+mJkzjTEfPXtKR464PWK64XAYlyyUKyfNm5fy3k6djFPQXbpwChoZT44c0pw5UojJBXwOh3H6f9s263Pdo78vHlajP0eq06rxOhxzLsW94YFh+rHOC1rZZKjuy1HUooQAAABA6qoVVlylg53HR/55ZqcOXT17by/u6pLCXr3u7XUBWIJGNO6et7fRHNqzR/r8cylfvpT32+3SpElSyZLSyy9LZ85YkzOtOnrUGF3y1FNSdLTrfblySTNmGCdGc+e2Lh9gtdKlpZ9+krxM/mqKiTG+AXb2Hj9xtciZa1F6ev3Xum/h61pxLuWnQQK9/TWiQkftafW+uhSuzRxoAAAApGs2m83lpYWTIlfc/QufOSPNneu8XqiQ1KTJ3b8uAMvQiMa98/U1Lhw7cEB6913jZGNKEhKk8eOliAjp9ddTHkWRETkc0pdfGqNNFi1Kee+jj0q7dkkdO1qTDfC05s2lDz80rx05InXoIMWnPNrCk+KTE/Xe7rkqMfcVfXNwmRxK+aLFrkXqam/r9/RG+fbK4sMcaAAAAGQMTxatJx+bt9P6pEPLlWy3392LTplizIi+WY8exkE5AGkejWiknsBA6dVXpchI6Y03pKxZU94fEyONGmWM9hgzRoqNtSanJx0+LDVtajTur1xxvS9PHmn2bOn774250EBm8tJLxpMCZv76S3ruOeMbOmmIw+HQL8c3qty8AXptyw+6khSX4v6aYRFa03S4ptZ+XgUDmWUHAACAjCV3QIjaFKjitH489qL+OL39zl/w37GWN7PZjEY0gHSBRjRSX0iINGKE0ZDu08e4vTYlly5JAwcaJ6Q/+8w4MZ3R2O3Sp58ap6D//DPlvU88YZyCbtfOkmhAmmOzGb9fGjQwr0+aJI0bZ2mklGy/fFRNl76t9ivH6uDVlEcOFciSXVNrPafVTYerVs4SFiUEAAAArOdqPMddXVq4erW0d6/z+gMPSEWK3PnrAfAIGtFwn9y5pbFjpf37jYsDzOa+/tfp01Lv3sac2KlTpeRka3K628GD0v33Sy+8YJwCdyV/fuOytilTbj3eBMjo/PykWbNcf1LZv780f76lkW52Li5az22YqMoLBuvPMztT3Bvg7as3yrXX3tbvq2vRevKy8dcvAAAAMrbm+Soqf5bsTuu/ntikc3Ep3JNkhksKgQyBr4ThfuHh0jffGKd8O3e+9f5Dh6Qnn5QqVZJ++SXNPYJ/2+x26aOPpIoVpeXLU97bo4e0c6fUurU12YD0IGdO45sz2bI51+x26ZFHpN27rc8laeqhlSoxt5++OPCn7Lf4M6pLeC3tafWeRlTsqKw+ARYlBAAAADzLx8tb3Ys6P+WYaE/Wd4dX3f4LXbkiTZ/uvJ49O08SA+kMjWhYp1Qp6aefpM2bpRYtbr1/506pfXupZk1p8WL350tN+/dLDRsao0lSmn1dsKBxqnPiRCk01Kp0QPpRvrwxK91mc65FR0tt2kgXLlga6dvI5Xpy7ReKSkx5rv19OYpqZZOh+rHuiyqcNZdF6QAAAIC0o2exhqbrEyKXyXG7h86mTzd/urhrVymAgx5AekIjGtarUkWaN09asUKqV+/W+zdsMC74e+ABad069+e7F8nJ0gcfGKeg//or5b1PPy3t2CE9+KA12YD0qk0b6Z13zGsHD0qdOkmJiZZEWXVur57ZMDHFPXkDQjWp5v+0vtkI1ctVypJcAAAAQFoUEZRHjXKXcVrfGXVcGy5G3t6LMJYDyDBoRMNz6tc3mtHz5kmVK996/5IlUq1axqM3O3a4O92d27PHaKz37y/FxbneFx4uLVokffWVcbEjgFt79VVjZI+ZpUull192e4QjMef08MpxSrAnmdb9vXw1qGxb7Wv9vroXa8gcaAAAAEApXFp4cNmtP3j3bmnNGuf1++4zxnkCSFf4KhmeZbMZYzo2bTLGdpQseeuP+fVX48TxE09Ikbf5HVR3SkqSxowxmulr16a897nnjCZ606aWRAMyDJtN+vJLqXZt8/rnn0uffuq2t7+aGKeHVnyos/Hml6p0KFRdu1u9q9GVuijIN4vbcgAAAADpTYdCNRTiG+i0/sOR1YpJSuEQl8RpaCCDoRGNtMHLy7jIcOdO42LDggVT3u9wSN99Z8ydfv556eRJa3LebOdOqU4daeBAKT7e9b6iRY0T3Z99JgUFWZcPyEgCAqSff5YKFTKvv/yyW+bJ2x12PbH2c229fNS0PqBMG82s10dFs+VO9fcGAAAA0rssPn56rHAdp/UrSXGaeXS96w9MSJCmTHFeDwiQHn00FRMCsAqNaKQtPj7Gdzb375fGjpVy5kx5f1KScRKyeHFpwADp4kVrciYmSqNGSVWrGjOsU/Lii9K2bVLjxtZkAzKyvHml336TAp1PVCg52ZgXvX9/qr7l0O0z9cvxjaa1NgWqalTFzqn6fgAAAEBG0yuiken6hMhlrj9o7lzp3Dnn9Y4dpdDQ1IgFwGI0opE2BQRIffoYozdGjJCCg1Pef+2a9O67xsnjkSOlq1fdl23bNmNW9euvG9+hdSUiQlq+XBo/XsqWzX15gMymcmVp6lTz2uXLxuWGly+nylt9f3iVRu381bRWPqSgptV+Xt5e/FUKAAAApKRq9iKqFBrutL7y3F7tiz5l/kGM5QAyHL56RtoWFCS98YbRkH71VaNBnZLoaGN/sWLSRx+lfGngnUpIkN58U6pWTdq82fU+m03q29doWDdokHrvD+D/Pfyw9NZb5rW9e6VHHjGemLgH6y8cVM91X5vWcvoH6bcG/ZgHDQAAANwGm83m8lT0xMjlzosnTkgLFjivFyvG19lAOkYjGulDWJhx4vnAAenZZ40RHik5d844UV2ypDRx4j03pPT331KNGtLw4cZYDldKlpT++kv68EPz0QEAUs+QIUbD2czChVL//nf90idiL6rdyg8Vb3f+/e7r5a1Z9V5mJjQAAABwBx4rXEd+Xs5fy08+tFJJ9uQbF7/9VrLbnV+kZ0/jjikA6RK/e5G+FChgzITevVt6/HHj9HFKjh0zHtspX16aMcP8L7KUJCRIQ4caTeitW13v8/IyTmxv2WJcXgjA/Ww24xtN1aqZ1z/6SPra/ERzSmKT4vXQyg916tpl0/rn1XqqQe4yd/y6AAAAQGYW5h+k9gWdP3c/HXdZ805u+f8Fu934PP9mXl5S9+5uywfA/WhEI30qXlz67juj8du27a33790rde5sNKwWLJAcjlt/zMaN0n33GY//p3SiukwZafVq48R2Fh7TByyVJYv0yy9Svnzm9eefN2a13yaHw6Ee677UpouHTOt9Sj3o8pFCAAAAACnrVayR6foNlxYuX26M57zZgw8ah9MApFs0opG+Vawo/fqr0Qhu1OjW+//+W2rRQmrYUFq1ynxPXJw0eLBxIeGOHa5fy9tbGjTImBdds+ZdxQeQCgoUMP4cMJshn5QkdeggHTJvLN9s5M5fNP3oOtNa87wV9V7lx+4lKQAAAJCpPZC3nApnzem0/vvJLTp17ZLxEy4pBDIsGtHIGGrXlpYskRYtcv2Y/n+tXCnVqye1amWcqv7XunVS1arS229LyckuP1zly0tr10qjR9/6AkUA7le9ujRpknntwgWpTRvjMtMUzDq2XkO3zzStlQrKpx/rviAfL+97TQoAAABkWl42L/Uo6nzZYLLDrimH/pIuX5ZmzXL+wFy5pNat3R8QgFvRiEbGYbNJTZtK69cbf3GVuY0ZrvPmSVWqGBeevfKKMd95927X+318pDfeMMZ23E7DG4B1HnlEev1189rOncZceRffYPr74mE9ueYL01p2v6ya07C/Qv2yplZSAAAAINPqUayhbHK+72li5HI5pk0znlK+2ZNPSn5+FqQD4E40opHx2GzSww9L27cbN+0WLnzrj/npJ2ns2JQvM6xUyWhyjxgh+funWlwAqejNN6X27c1rc+caY3ducvraZbVd+YFik+Odat42L82o+5JKBOVN7aQAAABAphSeNaea5i3vtL7vyild/eoz8w9iLAeQIdCIRsbl7S1162ZcVPjxx1KePHf3Oj4+RnNr/Xrj9DSAtMvLS5o61fjGkZl335WmTLn+07jkBLVfOVbHYy+abh9/35N6wOSTZAAAAAB3z+wC8EqHLylo2y7nzbVr394TzwDSPBrRyPj8/aUXXpAOHjRmOoeG3v7HVq0qbdokDR3KY0BAepE1q/Tbb1Lu3Ob1p5+WVq+Ww+HQ/9ZP0NoLB0y3PVe8iZ4v0dSNQQEAAIDM6aEC9ynML9sNaz2XHzTfzGloIMOgEY3MI2tWadAgKTLS+GdgoOu9fn7SqFHGhYQVK1qXEUDqCA+XZs82/wZSQoLUvr2++HOSph7+y/TD789TTh/d94SbQwIAAACZk7+3r7oWqff/P09IVte/DjtvzJpV6tzZumAA3IpGNDKf7NmNk9EHDxonpX19b6xXry5t3mzMkr25BiD9qFNH+uor89rZs6r9VH9ljUt0KkVky6MZdV+Sr5ePmwMCAAAAmVeviIbX/3e7jceUIybBeVOXLlJQkIWpALgTjWhkXnnzGrOj9+2TRo6UevSQfvhBWr1aKlfO0+kApIZu3aRXXzUtVT5ySZM/Xyub3XF9Ldg3i+Y06Kcc/tlMPwYAAABA6qgQGq7qOYpJknotizTfxFgOIEOhEQ0UKSINGSJNnCg98ohxOSGAjOPtt6XWrU1LHTYc0/BZ2yVJXjabfqzzgsqEFLAyHQAAAJBp9YpopMLnrqrpjtPOxdKljYsKAWQYNKIBABmbt7c0bZrsLp50GDp7h7qsPqz3Kz+uFvkrW5sNAAAAyMQeCa+t/604YlpL6tFDstksTgTAnWhEAwAyPEdQkAaPeEzns/mb1qd8vUF9osMsTgUAAABkbiHe/nrmr6NO64neNs1/oLwHEgFwJxrRAIAM76O9CzQmfqc69K2nRG/nUxV+8YmytWsnnThhfTgAAAAgs1q8WGFnLjktz61SQJ9d2e6BQADciUY0Mj2Hw6F90ac07+QWXUm85uk4AFLZgpNb1W/LNEnSijJ59HyP6uYbT52S2rWTrvHnAAAAAGCJCRPMlxtFaOGp7ToWc8HiQADciUY0MrXlZ3er3uI3Ver3/mq1/D3lnv2cRu/8VUn2ZE9HA5AK9kSfVJfVH8vucFxf++b+4hr3YCnzD9i4UerZU/rPfgAAAABucP689MsvTssnQ7NoQaV8csihbw+tsD4XALehEY1MadPFQ3pw6Rg1+nOkVp/ff309LjlRQ7ZNV81FQ7XtkvOcKgDpx8X4q2qz/H1Fmzzp8OrjVXW2QS3zD/zxR2nUKDenAwAAADK5776TEhOdlr9tUFTJ3ka7amLkctkddquTAXATGtHIVPZEn1Snvz5StYWva+HpbS73bb50WNUWva4RO35Woj3JwoQAUkOiPUmdVo3XgatnTOsjqz6q3L/Ol0qWNH+BN96Qfv7ZjQkBAACATMzhcDmWY2KjiOv/+3DMOS09s8uqVADcjEY0MoUjMefUc+1XKjfvNc08tv62PibRnqxh22ep+sI3tOXSYfcGBJCq+myeqiVndprWuhapq9fKtJZCQ6U5c4x/mnniCWnLFndFBAAAADKvDRukHTuclpeVya2DeYNuWJsQucyiUADcjUY0MrQz16L08qYpKjm3vyYdWn7DnNjbtfXyUVVfOFRDt81UQjKno4G07rP9f+iz/YtNa7XCiuvrGk/JZrMZCyVLSjNmSN7ezptjY6W2baUz5qeqAQAAANyliRNNl2c2Le+09vOxjbqUEOPuRAAsQCMaGdLlhBi9vm26Iub21fh9C5Vwi/EaOfyyqURQXpf1JEey3to5W9UWvq5NFw+ldlwAqWTJ6Z16adMU01rBwByaXb+vArz9biw0aSJ99JH5Cx47JrVvL8XFpXJSAAAAIJOKjZV++MF5PThYIV26Oi3H2xP1/eFVFgQD4G40opGhxCbFa8yuOSo2p69G7fxVMUnxKe7P5hOgoeXbK7LNWG1r8bYGlmkjr39PSprYHnVMNRcN1eCtPyk+2flSBQCes//KaXVc9ZGSTS4zCfT212/1+ylvllDzD37+eenZZ81ra9ZIzzxjzLEDAAAAcG9mzpSio53XH3tMT5RvbvohEyKXuzkUACvQiEaGkJCcpM/2/6GIOa9o4NYfb/nYjr+Xr/qWaqHINmP1ZoWOCvELVIC3n96u/IjWNR2h8iEFXX5sssOut3f9pqoLhmj9hYOp/UsBcBcuJ8SozfL3Xf7en1zrGVXJUcT1C9hs0vjxUqNG5vUpU6T337/nnAAAAECm5+KSQvXqpdLB+VU3p/OF4n9fOqy/Lx52by4AbkcjGulast2uqYdWqvTv/dV747c6HXc5xf3eNi89FdFI+1t/oA+rdlWugGCnPdXCimlj85F6vVw7edtc/xbZFX1Ctf8Yptf+/l7XkhLu9ZcC4C4l2ZP1yKpPtPfKKdP6mxU6qGN4zVu/kK+vcTojIsK8PmCANHfuPSQFAAAAMrn9+6UVK5zXK1aU7rtPktQropHph3JpIZD+0YhGuuRwOPTL8Y2qtGCQnlz7hQ7FnLvlx3QJr6WdLcfo6xpPq1DWsBT3+nv76q2KnbSh2VuqFBrucp/d4dB7e35XlQWDtfrcvjv+dQC4d69t+UELT28zrXUOr6k3yrW//RcLC5PmzJGCnb9JJYdDevRR09u9AQAAANwGF5cUqlcv4ylFSZ0K1VQ2nwCnLdMOr+IQGJDO0YhGurP49A7VXDRU7VeO1c6o47fc3zJ/ZW1uPko/1n1RpYLz39F7VclRROubvaXh5R+Wj83b5b69V06p3uIR6rf5O8XeYi41gNQz4eAyjd0737R2X46imlTzGdlSmPtuqkwZ6ccfJS+TvyKvXpXatJHO3fqbXwAAAAD+IylJmjzZed3PT3r88es/zeYboEcK13LadjkxVrOPb3BnQgBuRiMa6ca68wf0wJLRarr0bW24GHnL/fVyldKKB97Q7w1fTXk27C34eftoWIUO2tj8LVXJ7vp1HHLow73zVWn+IK08u+eu3w/A7Vlxdree22h+oiJfllD9Wv8VBfr4392Lt2ghvfeeee3wYaljRymB0xgAAADAbZs/XzplMk6vfXvjycT/6FWskelLcGkhkL7RiEaat+PyMbVb8aFq/TFMS87svOX+KtmLaF7DV7XigTdUP3fpVMtRKXthrWv2pkZW7CRfL9enow9cPaOGf47Uy5umKCYpLtXeH8D/O3T1rDr89ZES7clONX8vX/1S/xUVCMxxb2/St6/Uo4d5bcUKqXdvY1wHAAAAgFtzdUlhz55OSzXDiquMyRPNS87sVOTVs6mdDIBFaEQjzYq8elZPrPlMFecP0q8nNt1yf8mgfJpe9yVtbP6WWuSvfOeP498GXy8fDSnXTpubj1K1HMVc7nPIofH7Fqri/EFadmZXqucAMrMridfUdsUHOh9/xbQ+sebTqhHm4sLBO2GzSZ9/LtWrZ17/5hvp44/v/X0AAACAjO70afOLv8PDpSZNnJZtNpvLU9GTOBUNpFs0opHmnIy9pOc3TFKpuf313eFVcijlE4eFAsP0TY2ntbPlGHUKrykvm/v/sy4fWkhrmg7XO5Uekb+Xr8t9kVfPqvGSUeq9cZKuJnI6GrhXyXa7Hl/zmXa4mA8/pNxDeqxI3dR7Q39/adYsqXBh83rfvtLChan3fgAAAEBGNGWKlOz8NKN69DC/m0XSE0Xrmd7V9O2hFUq221M7IQAL0Ij+v/buO7yp6o/j+CfdtLRlFcpeyt57g6AIAorIRgTFPREHOHGDiKD+nKgMB+ICRcGBsjeyl+y9Z1sonbm/P66MkpvSluYmbd+v58kjnu9p8qHGNP3m3HPgM04kxmnomm90za9D9NH2v5RiWPyQukRUcITeqddfWzuP1qCKbRSQznYZnhDg56+h1bpodYfX1aTwNenO/XDbX6rx21D9fXiDTemA3On5dd/plwOrLGtdSzXQKzW7Z/+DFi0qTZ8uhYW51pxOqVcv6Ztv2KYDAAAAsGIY1ttyOBzut8KTVDQkUjeXrOcyvj/+pP48vC47EwKwCY1oeN2Z5AS9tmGaKvzyuEZt/lXnUtM/ACwiMJ9erdldO7qM0WOVOyjEP8impNaqRpbUwuuHa3Sdvgrxd786es/Z47p+zgjdt/xzxSbH25gQyB2+3LVAIzf/YlmrVaCMvmzygOeuiKhVS/r6a/PN8uViYqS+fc0tPFZwijcAAACQxqJF0tatruPXX+/+ysP/DKrYxnL8851zrz4XANvRiIbXJKQm6d0tv6vCL4/rhfU/KDb5XLrzQ/wD9VSVTtrZZayer3GrwgPz2ZT0yvz9/PRE1U5a22GEmheplO7ccTtmq8bMYfrjEJ/gAhm19Pg23b38M8taVHCEprcaovyBIZ4Nccst0uuvu68vXiw1aiQNHCgdPOjZLAAAAEBO4e6QwkGDrvilN0bXUsl8BV3Gpx9YpWMJsVebDIDNaETDdinOVI3fMVeVfn1Sg1d9qWOJ6f/wCHD464FrrteOzmM1qm5fFQ4Otylp5lWKKK557V7QO/X6K186K7X3xZ9Qh7lvatCycTqddNbGhEDOs+/sCXVdMFZJzhSXWpBfgKa1HKyyYVH2hBk2TOrXL/05kyZJlSpJb7whJbA3PAAAAPKw2Fjpu+9cxwsVkrp2veKX+/v5aWCFVi7jyc5Ufbl7YTYEBGAnGtGwjdNw6vu9y1Rj5lANWv6p9sWfSHe+Qw7dXq65/u30lj5seKdKhLp+CuqL/P389FjlDlrXcYRaRVVJd+74nfNUY+ZQzTiw2qZ0QM5yNiVBtywYoyMJMZb1TxrepeZRle0L5HBIn31mro5Oz9mz0nPPSVWrSj/8wP7RAAAAyJu++06Kt9ia8vbbzYPBM+CuCm0sxz/fMVcG77OBHIVGNDzOMAz9fnCtGvzxgnouek9b4g5d8WtuKVlfazu+oS+bPqiK4cVsSJn9rgmP1px2z+n9+gMUFuD+B+yBc6fUef5oDVjysU6xOhq4wGk4NWDpx1p9ardl/ckqnTSwQmt7Q0lSSIg0bZp5QGHp0unP3b1b6tFDatNGWs0HTgAAAMhjrmJbjvMq5C+q64pWcxnfFHtAy0/syGoyAF5AIxoetfDYFrX++1V1nDfKbTPpUtcVraYlN7ykn1oNUc0CZTwf0MP8HH56qFJ7re84Um2LVU937he7F6jajKc1ff9Km9IBvu3lDVP14z7rw/9uKlFHI2v3tjnRJRwOqXdv6d9/pZdflkJD058/f75Uv750zz3SkSP2ZAQAAAC8adMmaelS1/EGDczDwDOBQwuB3IFGNDxizand6jTvLbX86xUtOLblivMbFqqgWdc9o7/bPqsmRa61IaG9yucvqr+ue0YfN7xL+QPcH6h2OOG0blkwRv0Wf6ATiXE2JgR8y7d7luiVDdMsa9UiSuqbZg/J388HfoSFhkovviht2XLlvaMNw9zW49prpbfekhIT7ckIAAAAeEM2rIY+r1uphooMdF38MWXPEp1N4VwWIKdwGD62oU5sbKwiIyMVExOjiIgIb8dBJm2NPaQX1/+gb/dafOppoVpESb1Wq4e6lmogh8Ph4XS+Yc/ZY7pn+WeadXhDuvOKBkfoo4Z3qVvphjYlA3zDPyd2quXfryghNdmlVjgov5bf+Koq5C/qhWQZsHSp9Nhj0vLlV55bsaI0erS533Qeef0DAABAHpGUJJUsKR0/nnY8Xz7p0CEpMjLTd/nQPxP04ba/XMYnNL7XO1v2AZCUuV6uDywnQ26w7+wJ3bP8U1Wb+XSGmtDlwqI0qcn9WtdxpG4t3TDPNKElqWxYlP5oM0yfNbpHEYH53M47mhir2xa+o16L3tOxhFgbEwLeczD+lG5ZMMayCR3g8NcPLR7z3Sa0JDVpIi1ZIn35pVSiRPpzd+yQbr1VuuEGaf16e/IBAAAAdvjlF9cmtCR1756lJrQkDXJ3aOHOeVm6PwD2oxGNq3IsIVZDVn2la399Qp/tmKtUw5nu/GIhkXq//gBt6TRad5Rv6RuX1nuBw+HQoIpttPGmN9WxeO105363d5mqzXxa3+1dyonAyNXOpSSp64IxOnjulGX9gwYD1aaY6yElPsfPzzwFfOtW6YUXzMMN0/P331KdOtIDD0jHjtkSEQAAAPCobNyW47x6hcqrToGyLuMLj23RltiDWb5fAPbJm11AXLXY5HgNX/+DKvzyuMZu+U2JTtfVi5cqEBiqEbV7aUeXMXqoUnsF+QfYlNS3lQotrBmtn9LExvepgMV+V+cdT4xTr0X/U/eF7+rIuRgbEwL2MAxDg5aP04qTOy3rj1Rqr3uvaWtzqqsUFia98op5oGGvXunPdTqljz82948eO9a8lBEAAADIifbvl/74w3X8mmukVq2u6q7dHVo4nlXRQI5AIxqZci4lSaM3z1D56Y/rlQ3TdOYKhwKE+gfr2Wq3aNfN72hYtZsVls5BfXmVw+HQgAqttPGmUepSsl66c6fuX6FqM5/W5N2LWB2NXGXEpun6Zs8Sy9oN0TU0pu7tNifKRmXLSlOmSAsWSPXrpz83JkYaMkSqWVOaMcM84BAAAADISSZONBdaXO7OO6/6bJS+ZZsp2C/QZXzSrgVKdqZc1X0D8DwOK0SGJDtTNH7nPL2yYZrby+YvFeQXoPuvaadnq92iYvmytv9TXmQYhibvWaxHV36hk0ln0p17c8l6+rjhXSqer6BN6QDPmLZvhbotfMeyVim8uJa2f1kFg8LsDeUpTqc0aZL07LPS4cNXnn/jjdKYMVK1HLAlCQAAAOB0miufd+1KO+7nJ+3dax5geJX6LHpfU/a6LmL5qeXjuqVUg6u+fwCZw2GFyDZOw6nJuxep6oyndf+K8VdsQvs5HBpYvpW2dh6td+vfQRM6kxwOh/qVa66NN72pW6/wA3T6gVWqNuNpfbFrAaujkWOtPbVH/Zd+ZFkrEBiqX1o9kXua0JL5BvzOO839o4cNk4KC0p//xx9SrVrSo49KJ0/akxEAAADIqrlzXZvQktSxY7Y0oSX323NwaCHg+2hEw5JhGPrlwCrV+e1Z9VvyoXacOXLFr7mtdENt6PimJjS5T2XDomxImXtF5yugH1sM1pRmD6tIcLjbeaeT4zVg6cfqPH+0DsTTpELOcuRcjLrMf1tnUxJdav4OP33X/FFViijuhWQ2CA+XRoyQNm+WunVLf25qqvS//5n7R7//vpTCJYcAAADwUR44pPBybYtVUzmLnsPMg2t0KANXcAPwHhrRcDH3yCY1/+tl3Tz/ba2P2XfF+TdG19KK9q/qhxaDVTUyez7hhLk6ulfZptp405vqUbpxunNnHlyj6jOHavyOuayORo6QmJqsbgvHal/8Ccv62Hq364biNW1O5QUVKkg//ijNmSPVrp3+3JMnpUceMef9+ac9+QAAAICMOnXKfG97uaJFpc6ds+1h/Bx+urOC66GHqYZTk3YtyLbHAZD9aETjgn9O7FT7OSN03ezXteT4tivOb1rkWs1t97x+v26oGhSuYEPCvKloSKS+a/Govm/+qKKC3e+1E5Mcr0HLP1XHuaO09+xxGxMCmWMYhu5fMV6L3bzO3HdNWz18bXubU3lZmzbSypXSuHFS1BWuKNm0ydw7uksXc4sPAAAAwBdMniwlul7tqDvukAJdDxi8GgPLt5JDrgcfjt48Q4fPnc7WxwKQfWhEQ5ti9uu2Be+o4Z8vaNbhDVecX6tAGf3S6gktun64WhetakNCSFL3Mo21qdMo9SnbNN15fxxepxozh2rc9tmsjoZPGvPvTE3cNd+y1qZoVf2v/gA5rvI07RzJ31+65x5p2zbpySev/Gb911+l6tWlIUOk06dtiQgAAAC4ZcO2HOeVCSui9tGuV1CeSDqje1d8zu/CgI9yGD72f2dmTlrE1dl95phe2vCjvty9UM4MPA0q5i+mV2t2V6+yTeTn4DMMb/pp/z+6f8V4HUmISXdeu2LV9Vmje1QuP3t2wzfMOLBaXea/LUOurzkV8hfV8vavqHA6+6LnKecb0tOnX3lukSLSq6+ajWx/f89nAwAAAC61erVUr57reLNm0qJFHnnImQfXqNO8tyxrExrfq4EVWnvkcQGklZleLt3EPOjwudN65J9JqjTjCU3ateCKTeiS+Qrqk4aDtLnTKPUp14wmtA/oWqqBNt00Sv3LtUh33t9HNqrGb0P14bZZchpOm9IB1jbG7Fefxe9bNqHDA0I0vdUTNKEvde210s8/S7NmmSuf03P8uPTAA1LdutLs2fbkAwAAAM4bP9563AOroc/rWLy2upduZFl7bNWXbFkJ+CBWROcxb276Ra9smKb4VIt9my5TOCi/nql2sx689gblCwiyIR2y4tcDq3TfivE6eIXTgdsUrarPGt2jiuHFbEoGXHQ8MU6N/3xRO88cdak55NCvrZ/UTSXq2B8sp0hJMfePfvFF6YT1AY9pdO0qjR4tVazo8WgAAADI4xISpBIlzMMKL5U/v3TokPlPDzmWEKsaM4fqaGKsS+36YjX0x3VDWUwHeBgrouGWQ7piEzp/QIiG1+imnTeP1RNVO9GE9nGdS9bTxpve1J3l07/saO7Rzar12zN6b8vvrI6GrZJSU9R94buWTWhJeqtOH5rQVxIQID34oLldx+DB5r+n56efpGrVpKFDpVjXN+UAAABAtpk2zbUJLUm9enm0CS1JUSERGtfIetX1X0c26KNtf3n08QFkDo3oPObhSu1VPF8By1qwX6CGVO6onV3G6qWatykiMNTecMiyAkFhGt/kXv3W+mmVCi3kdl58aqIeW/WlWv/9mrbFHbYxIfIqwzD0yMpJmnd0s2V9YPlWGlLlJptT5WAFC0pjx0rr10s3XeH7lpQkjRolVapkHhyTmmpPRgAAAOQtNh5SaOWWUg00oHxLy9rTa6bwuy/gQ2hE5zGhAcF6ofqtacb8HX66p+J12t7lbb1d73ZFhbAlSk7VoURtbej4pu6peF268xYe26Javw3TmH9nKtXJ6mh4zvvb/tS4HdZ7Fjcrcq0+bniXHA6HzalygSpVpBkzpN9+M/+cniNHpLvvlho2lBYssCcfAAAA8oZdu6S//3Ydr1pVatLEthjv1OtvuSgrPjVRA5d+zO+9gI+gEZ0HDarQRuXDoiRJvcs01eZOb2lco7tVKrSwl5MhO0QGhWpco7v1Z5thKpPOf9OE1GQ9sfprtfz7Ff0be9DGhMgr/jy0ToNXfWlZKxNaWNNaPq5g/0CbU+UyHTpI69ZJ774rFSiQ/tzVq6VWraSePaXdu+1IBwAAgNxuwgTr8UGDJBsXnBQICtPnje6xrC0+vk1jtsy0LQsA9zisMI+afXijCgWHqU7Bct6OAg+KSz6noWum6KPt6e+LFewXqFdq3qYhVW5SgJ+/TemQm22JPajGfw5XTHK8Sy0sIFiLrh+u2gXLeiFZLnbihDR8uPTxx1fehiM4WHrySWnYMI/v2wcAAIBcKjVVKldO2r8/7XhAgHTggFS0qO2RHlwxwfL33yC/AK288TXVKFDa9kxAbsdhhbiittHVaULnAeGB+fRhwzs1u+2zF1bBW0l0Jmvo2ilqNuslbYzZ73YekBGnks7q5vljLJvQkvRV0wdpQntC4cLS++9La9dKN9yQ/tzEROn11839oydNkrhUEQAAAJk1a5ZrE1qSunTxShNakkbV6aOK+Yu5jCc5U3TH0o+V7EzxQioA59GIBvKA64pV17qOI/VIpfbpzltxcqfq/f6c3tj4s1KcHGyGzEtxpqrnwve0Ne6QZf31Wj3VtVQDm1PlMdWrS3/8IU2fLl17bfpzDx2SBg409+9bvNiWeAAAAMglvHxIoZX8gSGa2OQ+OeS6LcjqU7v1+safvZAKwHk0ooE8In9giN6rP0Dz272gayw+IT4vyZmi59Z9p8Z/vqhNrI5GJg1Z/ZX+OrLBsta3bDM9U+1mmxPlUQ6HuRJlwwZp9GjpSltdrVghNW8u9e0r7dtnT0YAAADkXMeOST9bNHVLlJBuvNH+PJdoEVVZT1S5ybL22saf9M+JnTYnAnAejWggj2lZtIrWdhyhIZU7Wn5KfN6qU7vVbNbLWndqr43pkJN9sv1v/W/rn5a1RoUr6rNG98hh44ElkBQUJD3xhLRtm3TvvZLfFX7sf/ONVLmy9NJLUrz11ioAAACAvvpKSk52HR840Nwj2sterdVd1SJKuoynGk7dsfQjJaQmeSEVABrRQB4UGhCst+vdroXXv6jK4cXdzotJjtdN80bpQPxJG9MhJ5p7ZJMe/meSZa1kvoL6qeXjyhcQZHMqXFC0qPTJJ9KqVVKbNunPPXdOevllsyE9ebLkW2caAwAAwNsMw/22HHfdZW8WN0L8g/RF0wfk73Bte22OPagX1v3ghVQAaEQDeVizqEpa3eENPV21s/zcrFQ9cO6UOs17S7FuDp4Ddp45qtsWvqsUw3Vf8Xz+Qfq51RMqnq+gF5LBRe3a0uzZ0tSpUoUK6c/dv1/q18/csmPFCnvyAQAAwPctXy5t3Og63qaNVLGi7XHcqV+ovJ6v3tWy9va/M7Xw2BZ7AwGgEQ3kdfkCgvRmnT5afP1LlpcuSdLa03vVc+H/OGEYLmKT49Vl3midTDpjWZ/Y5D7VL1Te5lRIl8Mh3Xqr+cvDyJFS/vzpz1+yRGrUSBowQDp40J6MAAAA8F0+eEihO89Vv0X1CpZzGTdkaMDSj3UmOcH+UEAeRiMagCSpcZFrtLLDa7quaDXL+h+H1+nBfybI4DJ9/CfV6VSfxR9oU+wBy/qLNW5VzzJNbE6FDAsJkYYONfePvusus0Gdni++kCpVkl5/3dy+AwAAAHnP2bPSlCmu45GR0m232Z/nCgL9AvRF0wcU5Oe6b/XOM0f19JpvvJAKyLtoRAO4IMQ/SFNbDna7MvqzHXM1YtN0m1PBVw1bO0UzD66xrHUv3UjDa3SzNxCyJjraXNWyYoXUokX6c8+elZ5/XqpaVfr+e/aPBgAAyGt++EGKi3Md79tXypfP/jwZUD2ylF6r1cOy9tH2v/TnoXU2JwLyLhrRANIoEBSmmW2eUnRIAcv6c+u+0+Tdi+wNBZ8zcec8jf53hmWtbsFymtjkPvlZHAwCH1a/vjR/vvTtt1KZMunP3bNH6tlTat1aWr3annwAAADwvhy0LcelhlS+Sc2KXGtZG7T8U51OOmtzIiBvoksAwEXZsCj92vpJhfoHW9bvXDZO845utjkVfMWiY1t034rxlrViIZH6ueUQhQWE2JwK2cLhMBvM//4rvfqqFBqa/vwFC8wG9qBB0uHD9mQEAACAd2zdar7/u1zt2lK9evbnyQR/Pz9NanK/5e+4++NP6rGVX3ohFZD30IgGYKl+ofL6tvnD8rPYNzbJmaKu88doc4z13sDIvfacPaZbF7yjJIuDK4P9AvVTy8dVOqywF5IhW+XLZ27BsXWr1L9/+nMNQxo/3tw/+s03peRkezICAADAXuOtF6No0KArnzfiA64Jj9ZbdftY1r7YvUA/7f/H5kRA3kMjGoBbnUvW0/v1B1rWTifH66Z5b+nIuRh7Q8FrziQn6Ob5Y3QsMday/lmju9XEzeVuyKFKljQPKVy6VGpyhYMn4+KkYcOkrl1pRgMAAOQ2KSnSpEmu48HBUr9+9ufJovuvaafri9WwrN23/HMdS7D+XQdA9qARDSBdD1x7vZ6q0smytvvsMXWeP1pnUxJsTgW7OQ2n+i/9SOtO77WsD6vaRbeXv8JBd8i5GjeWFi+WvvrKbE6nZ+ZM6cUX7ckFAAAAe8ycab0V2623SoUK2Z8ni/wcfhrf+F5FBLoerHg0MVYP/DNeBgdyAx5DIxrAFY2s01s9yzS2rP1zcqf6LP5AqU6nzalgF8MwNGTV124vVetSsp5er93T5lSwncNhrnbZssVsNIeksw/4yJHS77/blw0AAACelUMPKbRSOqyw3qt3h2Xtx30r9M2exTYnAvIOGtEArsjPYR7s0LxIJcv6LwdWafCqL/nkOBcyDEOPrfpC7261birWjCytr5s+KD8HP07yjLAw6eWXzYZ0H+s99iSZe0sfPGhfLgAAAHjGoUPSjBmu42XLSm3b2p8nG9xRvqVuLml9wOJD/0zUwfhTNicC8oar6hyMGDFCDodDgwcPvjBmGIZeeukllShRQvny5VObNm20cePGq80JwMtC/IP0c6shujY82rL+/rY/NXbLbzangic5Dace+mei/rf1T8t6keBwTW/1hMItLmtDHlCmjDR5svT331JQkGv9+HGpb19zP0EAAADkXF98IaWmuo7feafklzMXpDgcDn3ScJAKB+V3qZ1Ojtfdyz9loRXgAVl+xVixYoXGjRunWrVqpRkfNWqUxowZo/fff18rVqxQdHS0brjhBsXFxV11WADeVTg4XL+1flpFgsMt60+unqwf9y23ORU8wWk4df+K8fpo+1+W9UA/f01tMVjl8kfZnAw+p21bafRo69q8edIrr9ibBwAAANnHMKTx413HHQ6zEZ2DRecroI8a3mVZ++3QWn22Y47NiYDcL0uN6DNnzqhfv3769NNPVbBgwQvjhmHonXfe0XPPPadu3bqpRo0amjRpkuLj4zV58uRsCw3AeyqGF9MvrZ5QiH+gS82QoduXfKglx7d5IRmyS6rTqbuXfaZP3bzxCnD465tmD6tl0So2J4PPevhh86AaK6+9Zq6aBgAAQM6zcKG0davr+A03mFfI5XA9yjRWn7JNLWtDVn+tXWeO2pwIyN2y1Ih+6KGH1KlTJ11//fVpxnft2qXDhw+rffv2F8aCg4PVunVrLV7MZu9AbtGkyLX6uulDcsjhUktITdbN89/W9jiLE5Xh81KdTt257BNN2DXPsh7o56/vWzyq20o3sjkZfJrDYR5gU66ca80wzEMOrU5ZBwAAgG/LRYcUuvN+/YGKDingMn4mJUF3Lhsnp+G0PxSQS2W6ET1lyhStXLlSI0aMcKkd/u+XzGLFiqUZL1as2IXa5RITExUbG5vmBsD3dSvdUG/X7WdZO54Yp45zR+l4Ilvy5CQpzlT1X/qhvty90LIe5BegqS0Gq2upBjYnQ45QsKA0ZYoUEOBaO3JEuv12670FAQAA4JtiY6Xvv3cdL1xYuuUW+/N4SKHg/Pqs0d2WtXlHN7s9MwdA5mWqEb1v3z499thj+vrrrxUSEuJ2nsORdpWkYRguY+eNGDFCkZGRF26lS5fOTCQAXjS4cgc9Uqm9ZW37mSO6Zf7bOpeSZHMqZEWyM0V9F3+gb/YssawH+wXqp5aPq7Obk6UBSVLjxtKbb1rX/v5bsvgQGwAAAD7q22+l+HjX8dtvl4KD7c/jQZ1K1tWgCm0sa8PWTtG/sQftDQTkUplqRK9cuVJHjx5V/fr1FRAQoICAAM2bN0/vvfeeAgICLqyEvnz189GjR11WSZ/3zDPPKCYm5sJt3759WfyrALCbw+HQ2Lr9dUvJ+pb1xce3acDSj7mUycclpaao16L/6ft9yyzrIf6Bmt5qiDqWqGNvMORMjz8ude5sXRs+3DzAEAAAAL4vD2zLcakx9fqpbFgRl/GE1GQNWPqxUpxc3QdcrUw1otu1a6f169drzZo1F24NGjRQv379tGbNGlWoUEHR0dGaNWvWha9JSkrSvHnz1KxZM8v7DA4OVkRERJobgJzD389Pk5s9pIaFKljWv9+3TEPXTLE5FTIqMTVZPRa9q2n7/7Gs5/MP0q+tnlT74rVsToYcy+GQJk6UrK5wcjqlvn2lY8dsjwUAAIBM2LhRWmaxUKVhQ6lmTfvz2CAiMFQTGt9rWVt+YodGbf7V5kRA7pOpRnR4eLhq1KiR5hYWFqbChQurRo0acjgcGjx4sN544w1NmzZNGzZs0MCBAxUaGqq+fft66u8AwMtCA4L1S6snVT4syrI++t8Z+nDbLMsavCchNUndFr6j6QdWWdbDAoL1W5un1S66hs3JkOMVLix9843k7+9aO3hQuuMOsykNAAAA35THVkOfd12x6nq00o2WtZc2/Ki1p/bYnAjIXTJ9WOGVPP300xo8eLAefPBBNWjQQAcOHNCff/6p8PDw7H4oAD6kWL5I/dbmaRUMCrOsP7Jykn5x0/CE/c6lJKnr/LGaeXCNZT1/QIh+bzNUrYtWtTcYco/mzaXXXrOu/f679NZb9uYBAABAxiQlSV9+6TqeL5/Uu7f9eWw2onYvVQov7jKe7EzVHUs/VmJqshdSAbmDwzAMw9shLhUbG6vIyEjFxMSwTQeQAy04+q+unzNCSc4Ul1qof7DmtXteDQpbb+MBe8SnJOqW+WP015ENlvWIwHz6vc1QNS1yrc3JkOs4nVKnTmbj+XL+/uZ+0c2b258LAAAA7v3wg9Sjh+v4HXdIkybZn8cLlh7fpuZ/vSynRcvs2Wq36PXaPb2QCvBNmenlZvuKaAB5W8uiVTSpyf2WtfjURHWeP1q7z7A/rLecTUlQ53mj3TahIwNDNeu6Z2hCI3v4+UlffCGVKOFaS02V+vSRTpywPxcAAADcy6PbclyqSZFrNbRqF8vayM3Ttez4dpsTAbkDjWgA2a532aYaUbuXZe1IQoxumjdKp5LO2pwKccnn1HHuKM05usmyXjAoTH+3fVaNCle0ORlytagoafJksyl9uX37pDvvlHzr4iwAAIC8a98+6Y8/XMevvVZq2dL+PF40vEY31SpQxmXcaRi6Y+nHik9J9EIqIGejEQ3AI4ZW7aJ7K7a1rG2OPahuC8ayt5aNYpPj1WHum1pwbItlvXBQfs1u+6zqFypvczLkCa1bSy+9ZF375Rdp7Fhb4wAAAMCNiROtFwncdZfkcNgex5uC/QP1RZP7FejnegD31rhDenbtd15IBeRsNKIBeITD4dAHDQaqY/HalvW5Rzfr7uWfyse2qc+VTiedVfs5I7X4+DbLepHgcM1u+5zqFCxnbzDkLc8+K7VrZ10bOlRatszePAAAAEjL6ZTGj3cd9/eXBgywP48PqF2wrIbX6GZZe3fr75pzZKPNiYCcjUY0AI8J8PPXt80fUV03Dc6vdi/Si+t/sDdUHnMq6axumDNSy07ssKwXDY7Q3HbPq1ZB10vOgGzl7y999ZVUrJhrLSVF6tVLOnXK/lwAAAAwzZkj7d7tOt6xo1S8uO1xfMXQql3UsFAFy9qdy8YpLvmczYmAnItGNACPCg/Mp19bPanSoYUt669t/Enjd8y1N1QecSIxTu1mv6F/Tu60rEeHFNDcds+remQpm5Mhz4qOlr7+2vqyzj17zANwuEoCAADAOzik0FKAn7++aPqAQvwDXWp7zh7XkNVfeSEVkDPRiAbgcSVCC2pm66cUEZjPsn7vis/156F1NqfK3Y4lxKrt7De0+tRuy3rJfAU1r93zqhpZ0t5gQLt20vPPW9emTZPef9/ePAAAADCvTJs61XW8WDGpUyf78/iYKhElNKJWL8vaZzvmaubBNfYGAnIoGtEAbFGjQGlNbTFYAQ7Xgx5SDae6L3xXa0/t8UKy3OdoQozazn5d607vtayXDi2see1eUKWIvHt5Hbxs+HDzAEMrTz4prVxpbx4AAIC87uuvpcRE1/E77pACXVcC50WPVr5RrYtWtazdvfxTnUw8Y3MiIOehEQ3ANu2ia+izRndb1uJSEtRp3lvaH3/C5lS5y+Fzp9Xm79e0IWa/Zb1sWBHNa/e8KoZb7NML2MXf3/xlp0gR11pSkrlfdGys/bkAAADyKnfbctx1l705fJifw08TGt+r/AEhLrVD507r4ZUT7Q8F5DA0ogHYakCFVm5PHT5w7pQ6zRut2OR4m1PlDgfjT6nN369pc+xBy3r5sCjNa/e8yucvanMywELJktKXX1rXduyQ7rmH/aIBAADssHq1tGaN63jz5lKVKrbH8WXl8xfVmLr9LGvf7Fmi7/cuszkRkLPQiAZgu+E1umlA+ZaWtXWn96rHwveU7EyxOVXOtj/+hFr//aq2xB2yrF+Tv5jmtXtBZcOibE4GpKNDB2nYMOvad99J48bZmwcAACAv4pDCTLm74nXqULyWZe2BFeN15FyMzYmAnINGNADbORwOjWt4t9oWq25Z//Pwej2wYoIMVkNmyJ6zx9T679e0/cwRy3ql8OKa2+55lQ4rbHMyIANeeUVq1sy69thj0tq19uYBAADIS86dM7dMu1z+/FKPHvbnyQEcDoc+a3SPCgSGutROJJ3RvSs+43dZwA0a0QC8Isg/QD+2eEzVI0tZ1j/fOVdvbPrZ5lQ5z64zR9X679e088xRy3rViBKa2+45lQwtZHMyIIMCA6UpU6RCFs/RxESpZ08pLs7+XAAAAHnBtGnS6dOu4717m81oWCoZWkjvNxhoWZt+YJW+2LXA3kBADkEjGoDXFAgK08zWT6l4vgKW9efXfa+vdi20N1QOsiPuiFr//Zr2nD1uWa8RWUpz2j6v4vkK2pwMyKTSpaVJk6xrW7dKDzzAftEAAACewLYcWda3bDPdVrqhZe3RVV9o39kTNicCfB+NaABeVSasiGa0ekphAcGW9buWj9OcIxttTuX7tsYeUuu/X9W+eOs3N7UKlNHsts+pWL5Im5MBWdS5s/TEE9a1r7+WJkywNw8AAEBut3OnNHu263i1alLjxvbnyWEcDoc+anCXooIjXGqxyed017JxbNEBXIZGNACvq1uonL5r/qj8Ha4vScnOVN264B1titnvhWS+6d/Yg2oz+zUdOHfKsl63YDnNbvusokJc3xABPu2NN6RGjaxrDz8sbeRDKQAAgGzj7oP+QYMkh8PeLDlUVEiExjWyXj3+15EN+nj73zYnAnwbjWgAPuGmEnX0gZs9tmKS43XTvLd0+NxpWzP5oo0x+9Xm79d0yM33okGhCvq77bMqHBxubzAgOwQFSd9+KxUo4Fo7d87cL/rsWdtjAQAA5DqpqdLEia7jgYFS//62x8nJupZqoP7lWljWnlw9WdvjDtucCPBdNKIB+Iz7rmmnoVW7WNb2nD2uzvNH60xygs2pfMf603t13d+v60hCjGW9ceGKmnXdMBUMCrM5GZCNypWTxo+3rm3aJD3yiK1xAAAAcqU//5T2W1x1evPNUlSU/XlyuPfq36GSFmfzxKcmauDST5TqdHohFeB7aEQD8Clv1O6p3mWaWtZWntylPovfV4oz1eZU3rfm1G5d9/frOpYYa1lvVuRa/XndMBWgCY3c4NZb3TecJ0yQvvzS3jwAAAC5DYcUZqsCQWEa3/hey9qi41s1dstvNicCfBONaAA+xc/hpwlN7lWLqMqW9V8PrtZjq77IU4c+rDq5S21nv6ETSWcs6y2jKuv3NkMVERhqczLAg956S6pf37r2wAPSv//amwcAACC3OHZMmj7ddbxkSal9e/vz5BLti9fS/de0s6w9t+47beTcI4BGNADfE+IfpJ9aPq5K4cUt6x9u+0tv/zvT5lTeseLEDrWb/YZOJVnvi9umaFX91uZphQfmszkZ4GHBweZ+0eEW+52fPWvuF33unP25AAAAcrovv5SSk13HBw6U/P1tj5ObvFWnryrkL+oynuRM0R1LPlKyM8ULqQDfQSMagE8qHByu39o8rajgCMv6U2sm6/u9y2xOZa+lx7fp+jkjdDo53rLerlh1zWj9lMICQmxOBtikYkXps8+sa+vXS4MH2xoHAAAgxzMM99ty3HWXvVlyofyBIZrY+D455HCprTq1W29s/NkLqQDfQSMagM+qkL+ofmn1hPL5B1nW+y/5SIuObbE5lT0WHdui9nNGKjbZesXnjdG19EurJxUaEGxzMsBmPXtK999vXRs3Tpoyxd48AAAAOdmyZeYB0Je77jqpQgX78+RCLYtW0ZAqHS1rr238WStP7rI5EeA7aEQD8GmNi1yjyc0esvxEOdGZrFvmj9G2uMNeSOY5845u1o1z31RcSoJl/aYSdfRTq8eVL8C6QQ/kOmPHSrVqWdfuuUfats3ePAAAADkVhxTa4rVaPVQ1ooTLeIqRqjuWfKSE1CQvpAK8j0Y0AJ/XtVQDja13u2XtRNIZdZw7SscSYm1O5RmzD29Ux7mjdDYl0bJ+c8l6mtpisELcrBIHcqWQEOm776SwMNfamTNSr15SgvUHNwAAAPjP2bPWV5NFRkrdutmfJxcL8Q/SF00ekL/Dte22KfaAXlz3gxdSAd5HIxpAjvBY5Q56rFIHy9qOM0d08/y3dS4lZ3+qPOvQenWa/5bOufl0/NZSDfR988cU7B9oczLAB1SuLH3yiXVt9WrpySftzQMAAJDTfP+9+SH+5fr1k/Jx+Hl2a1C4gp6rfotlbfS/M3PtNpNAemhEA8gx3q7bT7eWamBZW3piu/ov/VBOw2lzquzx+8G16jL/bSWkWpxeLalH6cb6tvkjCvIPsDkZ4EP69XN/2egHH0g//mhvHgAAgJyEbTls91y1rqpbsJzLuCFDA5Z+orNutmMEcisa0QByDH8/P33V9EE1LlzRsv7jvhV6avVkm1NdvV8PrNItC8Yo0WndhO5dpqkmN3tIgX40oQG9955Uvbp1bdAgaedOe/MAAADkBFu2SAsXuo7XqSPVq2d7nLwiyD9AXzS5X0EWv8vtOHNET6/5xgupAO+hEQ0gRwkNCNb0Vk+oQv6ilvUxW37T+1v/tDlV1v28/x91W/iOkpwplvXbyzXXl00fUICfv83JAB8VGmruFx0a6lqLiZF695aScvY2PQAAANlu/HjrcVZDe1yNAqX1as3ulrUPt/2lWYfW25wI8B4a0QBynKIhkZrZ+ikVCspvWX9s1Reavn+lzaky78d9y9V94XtKdqZa1geWb6WJje+nCQ1crlo1cysOKytWSMOG2ZsHAADAlyUnS5MmuY4HB5tbn8HjnqjSSU2LXGtZu2v5OMUkxducCPAOGtEAcqTKESX0U8vHLS9xchqGei9+XytO7PBCsoz5bu9S9Vr0P6UY1k3ouyu20eeN75G/Hy/TgKWBA6U77rCujR0rTZ9uaxwAAACfNXOmdOSI63i3blLBgvbnyYP8/fw0qcn9CvUPdqntjz+px1Z94YVUgP3ocADIsVoWraIvmtxvWTuXmqTO80Zr15mjNqe6ssm7F6nP4veV6uZgxQeuuV6fNBwkPwcv0UC6PvhAqlLFujZwoLRnj61xAAAAfBKHFPqEa8OjNapOb8vapF0L9PP+f2xOBNiPLgeAHK1X2aYaWdv6h/nRxFjdNO8tnUo6a3Mq977YtUD9l34kp2FY1h+p1F4fNBhIExrIiPz5zf2iQ0Jca6dOSX36mJeiAgAA5FWHDpkroi9Xvrx03XX258njHrj2erUrZn3w9r3LP9fxxDibEwH2otMBIMd7umpn3XdNW8vav7EHdeuCsUpM9X4zavyOuRq49BO3TejHK3fUu/XukMPhsDkZkIPVrCm99551bckS6fnn7c0DAADgSyZNklIttgO8806JbQBt5+fw0/jG9yo8wHUhxdHEWD2wYrwMN78vArkBrzoAcjyHw6H36w/UTSXqWNbnHd2su5aNk9PNVhh2GLd9tgYt/1SGrN9UPFWlk96u248mNJAVd98t9ba+MkKjRlmvAgIAAMjtDEMaP9513OGQBgywPw8kSWXCiujd+tZnnfywb7mm7FlicyLAPjSiAeQKAX7++rbZI6pbsJxlffKexXph3Q/2hvrPh9tm6b4VbvZlk/RstVv0Zp0+NKGBrHI4pE8+ka65xrp+xx3S/v32ZgIAAPC2BQukbdtcx9u3l8qUsT8PLhhYvpW6lKxnWXto5UQdjD9lcyLAHjSiAeQa+QND9GurJ1U6tLBl/Y1NP+vT7bNtzfTelt/10D8T3dZfrHGrXqvVgyY0cLUiIsz9ooOCXGsnTkh9+0opKfbnAgAA8BYOKfRZDodD4xoOUqGg/C61U0lndc+Kz9iiA7kSjWgAuUqJ0IKa2fopRQTms6w/8M8E/X5wrS1Zxvw7U4+t+tJt/ZWa3fVyze40oYHsUreuNGaMdW3BAumll2yNAwAA4DUxMdL337uOFy4s3Xyz/XngIjpfAX3U8E7L2syDazR+5zybEwGeRyMaQK5To0BpTW0xWAEOf5daquFUj0Xvac2p3R7N8OamX/TE6q/d1t+o1VMv1LjVoxmAPOnBB6Xu3a1rb7whzZplbx4AAABvmDJFOnfOdbx/fyk42P48sNSzTBP1LtPUsjZ41ZfafeaYzYkAz6IRDSBXahddQ581utuydiYlQZ3mjda+syc88tivb/xJw9ZOcVt/q05fPVP9Fo88NpDnORzSZ59J5cu71gxDuv126dAh+3MBAADYyeqQQoltOXzQ+w0GKDqkgMv4mZQE3bnsEzkNp/2hAA+hEQ0g1xpQoZVeqtHNsnbw3Cl1mveWYpLis+3xDMPQy+t/1PPrLC6B+8/Yurfryaqdsu0xAViIjJS+/VYKDHStHT0q9esnpabanwsAAMAOGzZIy5e7jjdqJNWoYX8epKtwcLg+bWT9AcHco5v1/tY/bU4EeA6NaAC52os1umlg+VaWtfUx+9Rj0btKdl79AWaGYeiF9d/rpQ1T3c55v/4ADa7S8aofC0AGNGwojRplXZszR3rtNXvzAAAA2IVDCnOcziXr6a4KrS1rQ9dO0ZbYgzYnAjyDRjSAXM3hcOiThoPUrlh1y/qswxt03/LxV3UisWEYembtt3p9489u53zc8C49VKl9lh8DQBY89ph0i5ttcF5+2WxIAwAA5CaJidKXFgemh4ZKvXvbnwcZNrbe7SoTWthlPCE1WQOWfqwUJ1f0IeejEQ0g1wvyD9CPLQarRmQpy/qEXfP02safsnTfhmHoydVf683Nv1jWHXLos0b36L5r2mXp/gFcBYfD3B+xTBnXmmGYW3QcPWp/LgAAAE+ZPl06YXEWTo8eUkSE/XmQYRGBoZrQ+D7L2rITO/TW5l9tTgRkPxrRAPKEyKBQzWz9tIrnK2BZf3H9D/py14JM3adhGBq86kuN2fKbZd0hhyY0vleDKrbJZFoA2aZQIfPU+IAA19qhQ+bJ8U4OgAEAALkE23LkaG2jq+sRN1fSDt/wo9ad2mtzIiB70YgGkGeUDiusGa2eUv6AEMv6oOWfavbhjRm6L6fh1MMrJ+q9rX9Y1v0cDn3R5H4NqGC9PzUAGzVtKr3xhnXtzz+lN9+0Nw8AAIAn7N1rvre5XKVKUosW9udBloys3VvXhke7jCc7U3XH0o+UlHr1ZxwB3kIjGkCeUrdQOX3X/BH5O1xf/pKdqeq28B1tjNmf7n04DaceWDFBH277y7Lu7/DT100f0u3lebMH+IwnnpBuusm69sIL0sKF9uYBAADIbhMnmtuPXe6uu8wty5AjhAYEa1KT++Vn8d9s7em9emXjVC+kArIHjWgAeU7HEnX0YYM7LWsxyfG6ae4oHTp3yrKe6nTqnuWfadyO2Zb1AIe/pjR7WL3LNs22vACygZ+fNGmSVLKkay011Ty85/hx+3MBAABkB6dTmjDBddzfXxowwP48uCpNi1yrp6t0tqyN2DRdy45vtzkRkD1oRAPIk+69pq2GVe1iWdsbf0Kd543WmeSENOOpTqfuWjZO43fOs/y6QD9/fdf8EXUv0zjb8wLIBkWKSN98Y/5CdrkDB6SBA9kvGgAA5EyzZ0u7d7uOd+okRbtu8wDf91LN21QzsrTLuNMwNGDpxzqXkuSFVMDVoRENIM96vXZP9S5jvXJ51and6r34f0pxpkqSUv7bj+uL3dYHGgb5BejHFoN1a+mGHssLIBu0bCm98op1bcYMacwYe/MAAABkB3eHFN51l705kG2C/QP1RdP7FeBwXUSxJe6Qnl33rRdSAVeHRjSAPMvP4aeJTe5Ty6jKlvUZB9fo0ZVfKNmZon5LPtDkPYst5wX7BWpay8fVpWQ9T8YFkF2GDZNuuMG69swz0tKl9uYBAAC4GidPStOmuY4XK+b+jAzkCHUKltPwGrda1t7Z8rvmHtlkcyLg6tCIBpCnBfsH6qeWQ1Q5vLhl/aPtf6nOb8/qu73LLOsh/oGa3mqIbipRx4MpAWQrPz/pyy+tL1NNSZF69TJ/oQMAAMgJvv5aSkx0HR8wQAoMtD8PstWwajerYaEKlrU7l41TXPI5mxMBWUcjGkCeVyg4v2a2eVpRwRGW9U2xByzH8/kH6ddWT6p98VqejAfAE4oVkyZPNpvSl9u717yM1erUeQAAAF9iGGzLkcsF+PlrUpP7Fezn+qHC7rPH9OTqyV5IBWQNjWgAkFQhf1H92vpJ5fMPytD8sIBgzWz9lNpF1/BwMgAec9110osvWtd+/ll67z178wAAAGTW6tXS2rWu4y1aSJWttyBEzlM1sqRG1O5lWRu3Y7Z+O7jG3kBAFtGIBoD/NCpcUd80e0gOOdKdlz8gRL+1flptilWzKRkAj3n+ebMhbeWpp6QVK+zNAwAAkBnuVkMPGmRvDnjcY5VvVKuoKpa1Qcs/1cnEMzYnAjKPRjQAXOKWUg30Tr3b3dbDA0L0R5uhalnU+g0AgBzG39/cV7FoUddacrK5X3RMjP25AAAAruTcOfN9zOXCw6UePezPA4/yc/hpQpP7FBYQ7FI7dO60Hl35hRdSAZlDIxoALvNo5Q4aXLmDy3hkYKhmXfeMmkVV8kIqAB5TvLj01VeSw+JqiF27pLvvZr9oAADge6ZOtf7AvHdvKSzM/jzwuAr5i+rtuv0sa1/vWaQf9y23ORGQOTSiAcDC6Dr9NLRqF/n915iqGVlas9s+q8ZFrvFyMgAeccMN0jPPWNd++EH66CN78wAAAFwJ23LkSfdWbKsbo2tZ1u5fMV5HznE1H3yXwzB8a4lPbGysIiMjFRMTo4iICG/HAZDHHT53WqeT43Vt/mj5+/HZHZCrpaRIbdtKCxa41oKCpGXLpDp1bI8FAADgYscO6RqLRTLVq0vr11tf6YVc40D8SdWYOVSnk+Ndal1LNdDUFoPl4DkAm2Sml0tXBQDSEZ2vgKpElKAJDeQFAQHS5MlS4cKutaQkqWdPKS7O/lwAAACXmzDBenzQIJrQeUDJ0EL6X/0BlrWf9v+jr3YvtDkRkDF0VgAAAM4rVUr6ws1BL9u2Sffdx37RAADAu1JTpYkTXccDA6X+/W2PA+/oV665upVqaFl7ZOUX2nf2hM2JgCujEQ0AAHCpm26SnnrKuvbNN+73YwQAALDDH39IBw64jt9yi1SkiP154BUOh0MfNbxTUcGuWyHEJMdr0PJx8rHdeAEa0QAAAC5ef11q0sS69sgj5t6LAAAA3sAhhfhP0ZBIfdzwLsvarMMb9OO+5TYnAtJHIxoAAOBygYHSlClSwYKutYQEc7/oM2fszwUAAPK2o0el6dNdx0uVkm64wf488LpupRvq9nLNLWvPrP1Wyc4UmxMB7tGIBgAAsFK2rPuDgP79V3r4YXvzAAAAfPmllGLRWBw4UPL3tz0OfMN79QeoRD7XBRTbzxzRuO2zvZAIsEYjGgAAwJ1bbpEGD7auTZpk3gAAAOwwdar04ovWtTvvtDcLfErBoDCNqN3LsvbyhmmKTY63ORFgjUY0AABAet58U2rQwLr24IPSpk325gEAAHmLYUhvvCHddpsUb9FQbNtWqlDB/lzwKf3KNlftAmVcxo8lxuqtzTO8kAhwRSMaAAAgPUFB0rffShGuJ5IrPt7cL9rql0IAAICrlZAg9e8vPfec+zkPPGBfHvgsfz8/jarTx7L29r8zdTD+lM2JAFc0ogEAAK6kQgVp/Hjr2saN0mOP2ZsHAADkfkeOmKudv/7a/ZyePc2V0oCk9sVr6YboGi7j51KTNHzDD15IBKRFIxoAACAjbrtNeugh69pnn0mTJ9ubBwAA5F7r1kmNGklLlrif88AD0ldfSQ6Hfbng896s3UcOuT4nxu+cp40x+72QCLiIRjQAAEBGjR4t1aljXbvvPmnrVlvjAACAXGj6dKlZM2nvXuu6n5/0v/9JH34oBQbamw0+r26hcrq9XHOXcadhaNiaKV5IBFxEIxoAACCjQkKk776T8ud3rZ05Y14em5Bgfy4AAJDzGYb01ltS167S2bPWcyIjpd9+kx5+2NZoyFlerdVdwX6uH1L8enC15h7hoG14D41oAACAzLj2WunTT61ra9dKQ4bYmwcAAOR8iYnSXXdJTz9tNqStVKxobtXRvr292ZDjlA2L0qOVrJ8nT635Rk7DaXMiwEQjGgAAILN695buuce69tFH5qppAACAjDh2TLrhBmniRPdzWreWli2Tqla1LRZytmeq36KCQWEu4/+c3Knv9y7zQiKARjQAAEDWvPuuVLOmde2ee6QdO+zNAwAAcp6NG6XGjaUFC9zPuftu6c8/pcKF7cuFHK9gUJier97VsvbM2m+VmJpsbyBANKIBAACyJl8+c+VzmOtKE8XGmiubhgyRJk82DzF0cgkkAAC4xG+/SU2bSrt2Wdf9/KQxY6Rx46SgIHuzIVd46NobVC4symV819lj+nj7315IhLzOYRjuNh/yjtjYWEVGRiomJkYRERHejgMAAJC+L7+U7rjjyvMiI6X69aUGDaSGDc1/li0rORyezwgAAHyHYZhXVj3xhPsPqsPDpW++kTp1sjcbcp3Juxep35IPXcYLB+XX9i5jVMBi+w4gMzLTy6URDQAAcLXuvDP9fR3dKVw4bWO6QQOpZMlsjwcAAHxEUpL08MPuDz6WpHLlpF9+kWrUsC0Wci+n4VTDP17QqlO7XWrDqnbRiDq97Q+FXIVGNAAAgJ3OnpUaNZI2bbr6+ype/GJT+nyDOsr1kkoAAJDDnDghde8uzZ3rfk6LFtLUqfzsR7aafXij2s15w2U8xD9QWzu9rdJh7D+OrKMRDQAAYLdNm6Q2bcyT77NbmTJpV03Xry8VLJj9jwMAADzj33+lLl2k7dvdzxkwQPrkEyk42L5cyDNumjtKvx1a6zI+sHwrTWhynxcSIbegEQ0AAOANBw9KEyZIixdLK1Z4pil93jXXpF05XbeuuZ8kAADwLbNmST16SDEx1nWHQxo5UnrqKc6OgMesP71XtX97VobStgEdcmhNhzdUq2AZLyVDTkcjGgAAwNsMQ9q/32xI//PPxdupU555PIdDqlIl7crpOnWkfPk883gAAODKPvhAeuwxKTXVuh4WJn39tXTLLfbmQp5019JxmrBrnst4h+K19FuboV5IhNyARjQAAIAvMgxp506zIX2+Qb1ypXTmjGcez9/fPOjo0pXTNWtKQUGeeTwAAGBKSTEb0B9+6H5O6dLS9OnmB8eADfbHn9C1vz6hhNRkl9qs657R9dEckInMoxENAACQUzid0pYtF1dMr1ghrV4tJSR45vGCgqRatdKunK5WTQoI8MzjAQCQ15w6JfXsKf31l/s5jRtLP/0kRUfbFguQpGfXfqsRm6a7jNctWE7/3Piq/Bx+XkiFnIxGNAAAQE6WkmIefnjpth5r10rJrqtXskW+fOYe0+dXTTdoIFWqJPnxiwgAAJmybZt5KOGWLe7n9O0rff65FBJiXy7gPzFJ8ar4y+M6keR6Rd5XTR9Uv3LNvZAKORmNaAAAgNwmMVFavz7tyumNG93vOXm1wsOl+vUvrppu0ECqUIFDlAAAcGfOHOm229I/D+K116Rnn+XnKbzq3S2/a/CqL13Gy4YV0b+d3lKIP9u4IeNoRAMAAOQF8fHmSulLV07/+6+5F7UnFCyYdr/pBg2kUqX4ZRoAgHHjpIceMq9qspIvn/TFF1L37vbmAiwkpaao6syntPPMUZfa6Dp99UTVTl5IhZyKRjQAAEBeFRcnrVqVduX0jh2ee7yiRdPuN92gAftdAgDyjpQU6cknpXffdT+nRAnzUML69e3LBVzBt3uWqPfi913GCwSGakeXsSoUnN8LqZAT0YgGAADARSdPms3pS1dO793ruccrVSrtyulmzaT8/DIDAMhlYmKkPn2k335zP6dBA+nnn81mNOBDnIZTTf4crhUnd7rUnqzSSW/V7euFVMiJaEQDAAAgfUeOSCtXXlw1vWKFOeYJ+fOblyz36eOZ+wcAwG47d5qHEm7a5H5Ojx7SxIlSaKhtsYDMmHtkk66b/brLeJBfgLZ0Gq1y+aO8kAo5DY1oAAAAZI5hSAcPpl01vWKFuZo6O/j5ST/+KHXtmj33BwCAt8yfL3XrJp044X7O8OHSiy+aP/8AH9Zl3mj9enC1y/jt5Zrry6YPeiERchoa0QAAALh6hiHt3p22Mb1ypRQbm7X7CwmRZs2SWrTI1pgAANhmwgTpvvuk5GTrenCwuQq6d29bYwFZtTFmv2r9NkxOi/bgqhtfV91C5ewPhRyFRjQAAAA8w+mUtm9Pu3J61SopPj5jX1+ggLRggVSjhkdjAgCQrVJTpWHDpNGj3c+JjpZ++klq3Ni2WEB2uGf5p/psx1yX8XbFqmvWdc/I4XDYHwo5Bo1oAAAA2Cc1Vdq8+eKq6X/+kdaskZKSrOeXLCktWSKVLm1rTAAAsiQuTurXT/rlF/dz6tSRpk/nZxtypIPxp3TNr0N0LtX1vdvvbYbqxuK1vJAKOUVmerlsVgQAAICr4+9vrnAeOFD64ANp2TLzl/YH3ewreOCAdOON2bf/NAAAnrJnj9S8efpN6FtvlRYupAmNHKtEaEE9UeUmy9rQNd8o1em0ORFyKxrRAAAAyH5BQdJ770ndu1vXN2+WunTJ+JYeAADYbfFiqVEjaf1693OefVb64QcpLMy+XIAHPFW1k6KCXVezrj29V1/vWeSFRMiNaEQDAADAM/z9pS+/lFq3tq4vXmwe5pSSYm8uAACu5Msvpeuuk44eta4HBUlffCG9/rrkR2sFOV9EYKiG17jVsvb8uu91LsXNlmtAJvBqCQAAAM8JCZF+/lmq5WZvwV9+ke6/X/KtY0sAAHmV02mucr7jDvdnHURFSXPmSP3725sN8LB7r2mra8OjXcb3xZ/Q/7b+4YVEyG1oRAMAAMCzIiOl336Typa1rn/+uTR8uL2ZAAC43Nmz5pZSI0a4n1Ozpnkwb7Nm9uUCbBLoF6ARtXtZ1t7YNF0nEuNsToTchkY0AAAAPK9ECemPP6TCha3rr74qffSRvZkAADhv3z6pRQtp2jT3czp3lhYtcv/BKpALdCvVUE0KX+MyHpMcr9c3/uyFRMhNaEQDAADAHpUrSzNmSKGh1vWHHpJ+/NHeTAAALF9uHkq4Zo37OU89Jf30kxQeblcqwCscDofeqtvXsvb+tj+164ybfdOBDKARDQAAAPs0biz98IN5kOHlDEPq21eaN8/+XACAvGnKFPNQ3cOHreuBgeYWUqNGWf/sAnKhFlGV1bVUA5fxZGeqnlv3nRcSIbegEQ0AAAB7dexo/lJvJSlJuvlmad06ezMBAPIWp9M8n6BPHykhwXpO4cLSX39Jd91lbzbAB4yo3Uv+Dte24Td7luifEzu9kAi5AY1oAAAA2G/AAGnkSOtabKzUoYO0e7etkQAAeUR8vNmAfuUV93OqVTO37GjVyr5cgA+pElFC91S8zrL21JrJMgzD5kTIDWhEAwAAwDueflp67DHr2qFDZjP6+HF7MwEAcreDB82tOL5LZ3uBDh2kxYulChXsywX4oOE1uiksINhlfO7Rzfrt0FovJEJORyMaAAAA3uFwSGPGSL16Wde3bJE6d5bOnrU3FwAgd1q5UmrYUPrnH/dzBg+WfvlFioy0LRbgq6LzFdBTVTpZ1p5e841SnU6bEyGnoxENAAAA7/HzkyZNktq1s64vWyb17CklJ9ubCwCQu/zwg9Sypbki2kpAgPTxx9LYseafAUiSnqjSScVCXD+Y2RizX5N2zfdCIuRkNKIBAADgXcHB0tSpUt261vWZM6V77pHYixAAkFmGIb32mtSjh3TunPWcggWlP/6Q7rvP3mxADpA/MEQv17zNsvbC+h8Un5JocyLkZDSiAQAA4H0REWbDuXx56/qkSdKzz9qbCQCQsyUkSLffLr3wgvs5lSqZV9+0bWtfLiCHGVShjSqHF3cZP3julN7Z8rsXEiGnohENAAAA3xAdLf35pxQVZV0fOVJ67z17MwEAcqbDh6U2baTJk93Puf56aelS6dprbYsF5EQBfv56s05vy9rITdN1LCHW5kTIqWhEAwAAwHdcc425MjoszLo+eLD07be2RgIA5DBr10qNGpkrnd158EHz503BgvblAnKwm0vWV4uoyi7jcSkJenXjNC8kQk5EIxoAAAC+pUEDc89oq8OiDEO64w5p9mz7cwEAfN/PP0vNm0v79lnX/f2l99+XPvhACgy0NxuQgzkcDr1Vp49l7aNtf2t73GGbEyEnohENAAAA39O+vTRxonUtKUnq2lVavdrORAAAX2YY0ptvSrfeKp09az0nMtJcBf3QQ/ZmA3KJJkWuVffSjVzGU4xUPbv2Oy8kQk5DIxoAAAC+qV8/afRo61pcnNSxo7Rzp72ZAAC+JzFRuvNOadgwsyFtpWJFcz/o9u3tzQbkMm/U7qUAh7/L+Pf7lmnZ8e1eSISchEY0AAAAfNcTT5g3K0eOSDfeKB09am8mAIDvOHZMatdOmjTJ/Zw2bcz9oqtUsS0WkFtdGx6t+69pZ1l7as1kGe4+DAJEIxoAAAC+btQoc3W0le3bpU6dpDNn7M0EAPC+DRvMQwkXLXI/5557pD/+kAoXti8XkMu9UKOrwgNCXMYXHNuiXw6s8kIi5BQ0ogEAAODb/Pyk8ePdX079zz/SbbeZe0cDAPKGGTOkpk2l3but635+0tix0iefSEFBtkYDcruiIZEaWq2LZW3o2ilKcabanAg5BY1oAAAA+L6gIOmHH6T69a3rf/4pDRokOZ325gIA2MswpDFjpC5d3F8NEx4u/fKLNHiw5HDYGg/IKx6v3FEl8hV0Gf839qDG75znhUTICWhEAwAAIGcID5dmzpSuuca6/tVX0tCh9mYCANgnKUm6917z7AB3+9CWLy8tWSLddJO92YA8JjQgWK/U7G5ZG77+R51JTrA5EXICGtEAAADIOYoWNff6LFbMuj56tLlSDgCQu5w4YW7R9Nln7ue0aGEeSli9un25gDxsQPmWqh5ZymX8cMJpjdky0wuJ4OtoRAMAACBnqVBB+u03c4W0lSeekL7+2t5MAADP+fdfqXFjaV46l/sPHCj99ZcUFWVbLCCvC/Dz15u1e1vWRm3+VUfOxdicCL4uU43oESNGqGHDhgoPD1fRokXVtWtXbdmyJc0cwzD00ksvqUSJEsqXL5/atGmjjRs3ZmtoAAAA5HF160o//SQFBlrXBw40940GAORss2ZJTZpIO3ZY1x0OadQo81Db4GB7swHQTSXqqE3Rqi7jZ1MS9fKGqV5IBF+WqUb0vHnz9NBDD2np0qWaNWuWUlJS1L59e509e/bCnFGjRmnMmDF6//33tWLFCkVHR+uGG25QXFxctocHAABAHta2rfTll9YHUaWkSLfdJq1caX8uAED2+PhjqWNHKcbNqsqwMPNDyaee4lBCwEscDodG1eljWRu3Y7a2xB60ORF8mcMw3O3wf2XHjh1T0aJFNW/ePLVq1UqGYahEiRIaPHiwhv53UExiYqKKFSumN998U/fdd98V7zM2NlaRkZGKiYlRREREVqMBAAAgr3jvPemxx6xrUVHS4sXuDzgEAPie1FTpySeld95xP6dMGemXX6RatWyLBcC9Pove15S9S1zGby3VQFNbPu6FRLBLZnq5V7VHdMx/n0oWKlRIkrRr1y4dPnxY7du3vzAnODhYrVu31uLFiy3vIzExUbGxsWluAAAAQIY9+qj03yIIF8eOSTfeKB0+bG8mAEDWxMVJXbum34Ru2lRavpwmNOBDXq/dU4F+/i7j0/b/o0XHtlh8BfKiLDeiDcPQkCFD1KJFC9WoUUOSdPi/N/jFLjvFvFixYhdqlxsxYoQiIyMv3EqXLp3VSAAAAMirRoyQBgywru3cKd10k8SCBwDwbXv3Si1aSL/+6n5O377S7NnSZX0HAN5VIX9RPXTtDZa1p9Z8o6vYkAG5SJYb0Q8//LDWrVunb775xqXmuGxvJsMwXMbOe+aZZxQTE3Phtm/fvqxGAgAAQF7lcEiffmo2nK2sXi116yYlJtqbCwCQMcuXS40aSevWuZ/z8svSV19JISH25QKQYc9X76rIwFCX8SXHt2na/n+8kAi+JkuN6EceeUTTp0/XnDlzVKpUqQvj0dHRkuSy+vno0aMuq6TPCw4OVkRERJobAAAAkGmBgdJ330mNG1vX//5bGjhQcjptjQUAuILvv5dat5aOHLGuBwdL33wjvfgihxICPqxwcLieqXazZW3Y2ilKdqbYnAi+JlONaMMw9PDDD2vq1KmaPXu2ypcvn6Zevnx5RUdHa9asWRfGkpKSNG/ePDVr1ix7EgMAAADuhIWZl3RXrmxdnzJFeuIJictDvWfVKvMDgUaNzH9Ony4lJHg7FQBvMAzp9delnj3dvw4ULSrNnSv17m1rNABZ82ilG1UqtJDL+La4w/p0xxwvJIIvyVQj+qGHHtJXX32lyZMnKzw8XIcPH9bhw4d17tw5SeaWHIMHD9Ybb7yhadOmacOGDRo4cKBCQ0PVt29fj/wFAAAAgDSKFJF+/10qXty6/s470ltv2RoJkrZsMZtN9etLkyZJK1aY/7zlFrPR1K+f9NNP0n+/WwDI5RITzb39n3/e/ZwaNaRly6QmTezLBeCq5AsI0qs1u1vWXlo/VXHJ/JzPyxxGJnYLd7fP84QJEzRw4EBJ5qrpl19+WZ988olOnTqlxo0b64MPPrhwoOGVxMbGKjIyUjExMWzTAQAAgKxbt05q2dL9IYWTJkl33GFvprxo715zX9eJEzO2LUr+/FLnzlKPHlKHDlKo616TAHK448elW2+VFi50P6djR/MqFvoCQI6T6nSq7u/Pan2M6zlwL1S/Va/Usm5UI2fKTC83U41oO9CIBgAAQLaZO1e68UYpKcm15u8v/fKL2exA9jt6VHrjDemjj6y//xkRFiZ16mQ2pW+6iaY0kBts3mx+2LRzp/s5jzwijRkjBQTYlwtAtvr94Fp1nDfKZTzUP1jbOr+tEqEFvZAKnpCZXm6WDisEAAAAcoQ2baSvv7Y+3Co1Vere3bzsG9knJkZ64QWpQgXp3Xez3oSWpLNnzQMoe/SQoqLMf373nTkOIOf56y+paVP3TWg/P+n996X33qMJDeRwNxavpXbFqruMx6cm6qUNP3ohEXwBjWgAAADkbt27m40NK/Hx5orbrVvtzZQbxcdLo0ZJ5ctLr72W/c3i+Hjphx+kXr3MpnT37uZl+2fOZO/jAPCMTz4xt9uJibGuR0RIM2ZIDz1kby4AHuFwODSqTh/L2uc752pTzH6bE8EX0IgGAABA7vfgg+4PxDpxwty+49AhezPlFklJ5vYb11wjDR0qnTqV/vygIOmxx6TDh6UlS6QnnpDKlMncY547J/34o9Snj9mU7tZNmjxZiovL+t8DgGekpkqPPy7df7/5ZyvlykmLF5uNagC5Rr1C5dWvbHOXcadh6Jm133ohEbyNPaIBAACQNxiGdM890uefW9dr15bmzZMiI+3NlVOlpkrffCMNH57+Xq/n+flJAwea8y9vPBuGtGKFueL5+++l3buzlik42Gxkde8udenCf0vA2+LipL59pV9/dT+naVPpp5+kokVtiwXAPrvPHFPlGU8qyZniUpvX7nm1KlrVC6mQndgjGgAAALicwyF9/LHZoLSydq3UtauUkGBrrBzHMKSff5bq1JH6989YE7pHD2nTJvNDAKvVzw6H1KiRubXHzp1mU3roUHOf6cxITDSz9e9vNrVuvln64gvp9OnM3Q+Aq7dvn9SiRfpN6D59pNmzaUIDuVi5/FF6pFJ7y9pTa76Rj62PhYexIhoAAAB5S3y8dP315rYQVs7vPezvb2+unGD2bOnZZzN+wGOHDtLrr0v16mXt8QxDWr364krp7duzdj+BgVL79uZ/21tukQoWzNr9AMiYFSvMD4IOH3Y/5+WXzYNNrQ6TBZCrnEw8o4q/PK7TyfEute+aP6oeZRp7IRWyS2Z6uTSiAQAAkPecPGmu1Nu82br+0EPS//5Hg+S85cvNBvTff2dsfvPm0htvSK1aZV8GwzBXrZ9vSmf1gMnAQPODiO7dzRXwhQplX0YA5v+j/fu7v7okOFiaOFHq3dvWWAC8a/TmGXpqzWSX8Yr5i2nTTaMU5B/ghVTIDmzNAQAAAKSnUCHpjz+kkiWt6x98II0YYW8mX7Rxo3TrrVLjxhlrQteuLc2YIS1YkL1NaMn8UKBOHem116R//zWb0i+8IFWpkrn7SU6WfvtNGjRIKlbMXLX92WfS8ePZmxfIawzD/ACqRw/3TeiiRaU5c2hCA3nQw5VuUJnQwi7jO84c0Sc7MvhBN3I8VkQDAAAg79q40VwZ7W4P4c8/l+66y9ZIPmHXLvNQwa++MptLV3LttdKrr5oNKD+b17oYhvnf8fxK6U2bsnY//v5S27bmSulbb5WiorI3J5CbJSZK995r7snuTvXq5n7R5crZFguAb/lq10L1X/qRy3iR4HBt7zxGkUGhXkiFq8XWHAAAAEBGLVwo3XCD9Qo+f3/pp5+kzp1tj+UVhw6ZK44//dRcOXwlpUqZDesBA8wtL3zBpk1mQ/qHH6QNG7J2H/7+Ups2ZlO6WzcOUgPSc/y4+f/JggXu53ToIH37rcTv+ECe5jScqv/781pzeo9L7dlqt+j12j29kApXi0Y0AAAAkBk//STddpvkdLrW8uUzt6Vo2tT2WLY5eVIaNUp67z3p3Lkrzy9SxNwz+oEHpJAQz+fLqn//vdiUXrcua/fh5ye1bn2xKR0dnb0ZgZzs33/ND+p27HA/5+GHpbFjpQD2fwUg/XV4g26Y47r9WYh/oLZ1flulLLbvgG+jEQ0AAABk1rhx0n33WdcKFTJXTletam8mTztzRnr3Xemtt6SYmCvPDw+XnnxSevxx8885ydatF5vSa9Zk7T4cDnPv6+7dzQ8uihfP1ohAjvLXX+b/C+5eO/z8zNeXhx+2NxcAn9dhzpv647DrB8R3VWitzxvf64VEuBo0ogEAAICsePll6aWXrGulS0tLlrg/4DAnSUyUPvlEev116ejRK88PCTGbScOGSYVzwUqlbdvMhvQPP0irVmXtPhwOc3/x803p3PC8ADJq3DjpwQel1FTreni49N135pYcAHCZtaf2qO7vz8lQ2pakn8OhNR3eUM0CZbyUDFlBIxoAAADIoHLDZlz8F8PQ639+oH5rfrec+2+RsurZ703FhuS3KV3W7R7ZyXUwJUX68kuz2b5375XvJCBAGjRIeuGF3Nto3bHj4kGHK1dm/X6aNzeb0t27m3tnA7lRaqr01FPmVhvulC1rHkpYo4Z9uQDkOAOXfqxJu1z3lr+pRB3NaP2UFxIhqzLTy7X5SGsAAADAhzkceuGGB/R7Jev9oKsc36NPf3xVwcmJNge7SoZhNltr1pTuuuvKTWiHQ+rbV9q8Wfr449zbhJakihWloUOlf/6Rdu4098pu2DDz97NokbllSenSUrNmZqMuI81+IKc4c0a69db0m9BNmkjLltGEBnBFr9bsoWA/14OOZx5co9mHN3ohEexAIxoAAAC4hNPPX491eUrLSlW3rDfev1Hv/jpafk43l6T7EsOQ/vjDbKz26GEeLHYlN99s7qH89dfSNdd4PKJPKV/eXO25fLm0e7c0erTUuHHm72fJEmnIEHNlaJMm0ttvm/cH5FT79plb0fzyi/s5ffpIc+ZIxYrZlwtAjlU6rLAGV7bevufptd/IaVgcII0cj0Y0AAAAcJnEgCDdc9sL+rdIWct6h61L9Oqsj8xGr69avFhq08bcozUjW060aWN+zc8/S7VqeTqd7ytbVnriCWnpUmnPHmnMGKmp9Ur5dC1bZh7wWL681KiReTDkrl3ZnxfwlBUrzOfu2rXu57z0kvnhVUiIbbEA5HzDqnVRoSDX7c5Wntylb/cs9UIieBqNaAAAAMBCbEh+Dezxsg6ER1nW+635XY8t+sbmVFdW9ehOff7Dy+aexfPnX/kLGjSQ/vxTmj07a43WvKBMGXPbjcWLzZWh77xjfn8za8UK6emnpQoVzO/7m2+ae1QDvuqHH6TWraXDh63rwcHS5MnS8OHmlj4AkAkFgsL0QvWulrVn132nxNRkewPB4zisEAAAAHlamsMKLVQ8vk8/fP20CibEWdafufFhfVPH+tJSO5U7eUCPL5ysWzbPy9gXVK0qvfaauecrDaSsOXBAmjrVPOhw4cKsr5CvW1e67TZz64P69aX8vn8YJnI5w5BGjpSefdb9nKgo6aefzD3RASCLElOTVXXGU9p19phLbUzd2/V4lY5eSIXMyEwvl0Y0AAAA8rQrNaIlqd6Bzfp6yvPKl+J6SGGqw08PdH1Gf7o54NDTomOP69HF36jnulkKyMh+imXLSi+/LN1+u+Tv7/mAecWhQxeb0vPnZ70p7ecnVa9u7k19/latGv+tYJ/EROm++6RJk9zPqV5d+vVXqVw522IByL2m7FmiPovfdxkvGBSmHV3GqmBQmBdSIaNoRAMAAAAZlJFGtCS13b5c46a+ZtnsTQgI0u29XtU/bg449ISC8TF6cOn3umPVDAVn5NLVYsWk55+X7rnHvJwennP4sDRtmtmUnjdPcl7lgUthYeZWHpc2p0uWzJ6swKWOH5e6dZMWLHA/58YbpW+/lSIj7csFIFdzGk41/nO4/jm506X2dNXOerNOHy+kQkbRiAYAAAAyKKONaEnqsW6W3vrtXctaTHCYevR7U1ujymVTMmv5E+N194ppGrTiJ4UnnbvyFxQoYO5L/OijZkMT9jp69GJTes6cq29Kn1eiRNrGdP36Unh49tw38qZ//5U6d05/3/KHHjL3SA8IsC0WgLxhzpGNajv7DZfxYL9Abe08WmXCinghFTKCRjQAAACQQZlpREvSg0u+09Pzv7CsHcpfWLf1f0sHI4pmR7Q0gpMT1X/1DD249AcVOhd75S8IDZUee0x66impYMFsz4MsOHbM3FP3++/NwyFTU7Pvvv38zC08Lt/Sg4YhMuLvv6Xu3aXTp63rfn5mA/qRR+xMBSCP6TzvLc04uMZl/I5yLTWp6f32B0KG0IgGAAAAMiizjWgZhl7+62MNWGX9ddsKl1b3fqMUky97VqcGpKaox/q/9Oiib1T8zIkrzk/yC9DkOh00cManUnR0tmSAB5w4kbYpnZyB7VUyKyzMXCl9vjHdqJFUqhSHUyKtceOkBx90/8FIeLi5FUdHDgwD4FkbTu9T7d+fkfOyVqVDDq3q8JrqFCznnWBIF41oAAAAIIMy3YiW5OdM1f+mj1KnLYss6ytLVFG/3q8pITAky7kchlNdNi/Q4wu/UvlTh644P9Xhp2nVr9M7Lfpqf2Qx7R7ZKcuPDZslJEhr1kjLll287XTdJzNbFC+edtV0gwZs6ZFXpaaa2/aMGeN+Ttmy5qGENWrYlwtAnnb3sk/1+c65LuPto2vqj+uG2R8IV0QjGgAAAMigrDSiJSk4JUkTvx+upnvXW9ZnXdNI99/6nFL9/DN3x4ahtjtW6Kn5X6jqsd0Z+pLfKjXT2y1v1/YiZS6M0YjO4Y4dk1asuNiYXr5cOnUq+x/H4Ui7pUejRmbTkS09crczZ6R+/aTp093PadLEXLVfrJhtsQDgQPxJXfvrEzqXmuRS+6PNULUvXssLqZAeGtEAAACXyWqz0dfRbLx6V/PcCE88q28nD1O1o7ss61NqtdewDo9keCuExnvX66n5X6jBgc0Zmj+/XF2NbtVf64pXcqnx3MhlDEPats1sSJ9vTq9Z45ktPUJDXbf0KF2aLT1yi337pC5dpLVr3c/p3VsaP17Kl8++XADwn+fXfafXN/7sMl67QBmt6vC6/Bx+XkgFdzLTy+VjbgAAACCL4oLDNKDHy5r61VMqHXPEpd573Z86GlZQY1r1T/d+ahzerqfmf6HWu1Zl6HFXlaist1oN0JKyrArKMxwOqVIl83b77ebY+S09Lm1O79hx9Y8VHy8tWGDezouOTtuYbthQYuFQzvPPP9LNN0uH0tnuZ/hw88YHDwC85OmqnfXJ9tk6nhiXZnzt6b36evci9S/f0kvJcLVoRAMAAABX4Vj+Qrqj5yv64aunVPhcrEv90SXf6mj+QvqqnusK5YrH92nIwq/c7jV9uc1R5TS6VX/9XbERTSJIISHm9glNmlwcO37cbEyfb04vXy6dPHn1j3X4sPTzz+ZNMp9/VatebEw3bizVrMmWHr7sxx+l/v2lc+es60FB0oQJUt++9uYCgMtEBIZqeI1uemTlJJfac+u+V48yjRXiH+SFZLhavEsAAAAArtKuQiV1V/fh+mbKswpNTnSpvzLrY50IjdRvVVpIkkrGHNVjiybrtg2z5W84r3j/uwsU15iW/fRL1VYyuBwV6SlSRLrpJvMmmVt6bN9+sSl9fkuPJNe9NzPFMKRNm8zbhAnmWL58F7f0ON+cLlOGD028zTCkkSOlZ591PycqytwPulkz22IBQHrurdhW7275XdvPpL3ibF/8Cf1v6596qmpnLyXD1aARDQAAAGSDtSUq68FbntGnU19VoDM1Tc1Pht75dbQkqdH+jeq75jcFp6Zc8T4P5y+k95r30Xc1b1CKP2/dkQUOh3Tttebt/JYeiYmuW3ps3371j3XunLRwoXk7r1gx1y09IiOv/rGQMUlJ0r33SpNcVxVeUK2a9OuvUvny9uUCgCsI8g/QiNq91GPRey611zf+rLsqtFbh4HAvJMPV4LBCAACQJ3BYIdzJ7udGtw1/a8yMsVd1H6dCwvVhkx76ol4nJQYGZ+k+eG4gU06cSLulx7Jl2bOlx+UcDqlKFdctPQIDs/+x8rrjx6XbbpPmz3c/p3176bvv+HAAgE8yDENNZw3XshOu5x8MqdxRb9e73QupcDkOKwQAAAC8ZGqNdoo6e0rPzJ2Y6a89E5RPnzfoqs8adVVccFj2hwPcKVxY6tjRvEnmdg47dqTd0mP16uzZ0mPzZvM2caI5FhJibulxvjHduLFUtixbelyNLVukTp3SP7zywQeld99lX28APsvhcOitOn3V6u9XXWrvb5ulhyu1V/n8Rb2QDFnFTxwAAAAgm33S6DYVPXNKg/75OUPzE/0D9WXdm/Rh0546GcrKRPgAh0O65hrz1q+fOZaYKK1dm7Y5vW3b1T9WQoK0aJF5O69gQalOHal2bfNWp455OGJw1q4QyFP+/lvq3l06fdq67ucnvfOO9MgjdqYCgCxpWbSKbilZXz8fWJlmPMmZoufXfa+vmz3kpWTIChrRAAAAQHZzOPRa20Eqcva0btk8z+20FIefvq95vd5r3keHIqJsDAhkQXCwuWq5UaOLYydPum7pceLE1T/WqVPSnDnm7byAALMZfWmDunZt86A9mD791FzpnOJmD/rwcGnKlIuHWQJADjCyTm/9enC1Ui874HnynsUaUuUm1S/EHvc5BY1oAAAAwAMMh5+e7DRYheJj1HLPGpf6L1VaakzL27WrUEn7wwHZpVAhqUMH8yaZW2/s3Om6pUdi4tU/VkqKtH69efvyy4vjJUpcXDV9vjl97bWSv//VP2ZOkZoqDR0qvf22+zllypiHEtasaV8uAMgGVSJK6O6KbfTJ9tkutadWT9bfbZ+Vg+2ccgQa0QAAAICHJPsH6v5bn9WIP97XzZvnK8Xhp3kV6mtMy9u1sVhFb8cDsp/DIVWsaN769jXHkpJct/TYujX7HvPgQfP2228Xx/LlMxuul27tUauWuSI4tzlzxtw+Zfp093MaN5Z+/lkqVsy+XACQjV6qcZu+2r1IZ1PSfrA55+gm/X5orTqWqOOdYMgUGtEAAACAB50NDtWjNz+tZ258WA5JZ4JDvR0JsFdQkNSwoXk77+RJacWKtM3p48ez7zHPnbu4ZcilKlRw3Xu6TJmcezDi/v1Sly7SmjXu5/TqJU2YYDbnASCHis5XQE9W6aSXN0x1qT295hu1j64lfz8/LyRDZtCIBgAAAGxwlgY0cFGhQtKNN5o3ydzSY9eui1t5rF1rNlePHs3ex92507xNvaSRUaCAuVr60gZ19epSSEj2PnZ2++cf6eabpUOH3M958UVp+HDzgEIAyOGeqHKTPt7+t44kxKQZ3xCzX1/sXqA7K7T2UjJkFI1oAECuUm7YDG9H8IjdIzt5OwIAAJ7jcJirlStUkPr0uTh++LDZkF679mJzessWyel0d0+Zd/q0NH++eTvP31+qUsV172lf2drixx+l/v3Nld9WgoKk8ePNLTsAIJcID8ynl2p00wP/THCpvbDue/Uq00ShAcFeSIaMohENAAAAAPBN0dFpD0OUzObrxo0XG9Pnm9Sxsdn3uKmp5mNs3ChNnnxxvFgx1609KlWSAmz61dowpJEjpWefdT8nKkqaNk1q3tyeTABgo0EV2+idLb9rS1zaq0EOnDuld7f8rmeq3+KlZMgIGtEAAAAAgJwjXz6pQQPzdp5hSLt3p105vXatud1HdjpyRPrjD/N2XkiIuZXHpQ3q2rWlyMjsfeykJOm++6SJE93PqVZN+vVXqXz57H1sAPARgX4BGlmnt25dMNalNnLzL7rnmrYqEpwLD6bNJWhEAwAAAAByNofDbL6WLy917XpxPCZGWrcubYN6wwYpISH7HjshQVq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      "text/plain": [
       "<Figure size 1800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(18,8))\n",
    "plt.plot(temp_df['end_of_over'],temp_df['wickets_in_over'],color='yellow',linewidth=3)\n",
    "plt.plot(temp_df['end_of_over'],temp_df['win'],color='#00a65a',linewidth=4)\n",
    "plt.plot(temp_df['end_of_over'],temp_df['lose'],color='red',linewidth=4)\n",
    "plt.bar(temp_df['end_of_over'],temp_df['runs_after_over'])\n",
    "plt.title('Target-' + str(target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3dfedb29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sunrisers Hyderabad',\n",
       " 'Mumbai Indians',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kings XI Punjab',\n",
       " 'Chennai Super Kings',\n",
       " 'Rajasthan Royals',\n",
       " 'Delhi Capitals']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "27c07510",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Hyderabad', 'Bangalore', 'Mumbai', 'Indore', 'Kolkata', 'Delhi',\n",
       "       'Chandigarh', 'Jaipur', 'Chennai', 'Cape Town', 'Port Elizabeth',\n",
       "       'Durban', 'Centurion', 'East London', 'Johannesburg', 'Kimberley',\n",
       "       'Bloemfontein', 'Ahmedabad', 'Cuttack', 'Nagpur', 'Dharamsala',\n",
       "       'Visakhapatnam', 'Pune', 'Raipur', 'Ranchi', 'Abu Dhabi',\n",
       "       'Sharjah', nan, 'Mohali', 'Bengaluru'], dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery_df['city'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9167efc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(pipe,open('pipe.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "961ba853",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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