Guided Project - Answering Questions using SQL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "db = 'chinook.db'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "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>name</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>album</td>\n",
       "      <td>table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>artist</td>\n",
       "      <td>table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>customer</td>\n",
       "      <td>table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>employee</td>\n",
       "      <td>table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>genre</td>\n",
       "      <td>table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>invoice</td>\n",
       "      <td>table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>invoice_line</td>\n",
       "      <td>table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>media_type</td>\n",
       "      <td>table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>playlist</td>\n",
       "      <td>table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>playlist_track</td>\n",
       "      <td>table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>track</td>\n",
       "      <td>table</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name   type\n",
       "0            album  table\n",
       "1           artist  table\n",
       "2         customer  table\n",
       "3         employee  table\n",
       "4            genre  table\n",
       "5          invoice  table\n",
       "6     invoice_line  table\n",
       "7       media_type  table\n",
       "8         playlist  table\n",
       "9   playlist_track  table\n",
       "10           track  table"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def run_query(q):\n",
    "    with sqlite3.connect(db) as conn:\n",
    "        return pd.read_sql(q, conn)\n",
    "\n",
    "def run_command(c):\n",
    "    with sqlite3.connect(db) as conn:\n",
    "        conn.isolation_level = None\n",
    "        conn.execute(c)\n",
    "\n",
    "def show_tables():\n",
    "    q = '''\n",
    "    SELECT\n",
    "        name,\n",
    "        type\n",
    "    FROM sqlite_master\n",
    "    WHERE type IN (\"table\",\"view\");\n",
    "    '''\n",
    "    return run_query(q)\n",
    "\n",
    "show_tables()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "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>genre</th>\n",
       "      <th>tracks_sold</th>\n",
       "      <th>percentage_sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rock</td>\n",
       "      <td>561</td>\n",
       "      <td>0.533777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alternative &amp; Punk</td>\n",
       "      <td>130</td>\n",
       "      <td>0.123692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Metal</td>\n",
       "      <td>124</td>\n",
       "      <td>0.117983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R&amp;B/Soul</td>\n",
       "      <td>53</td>\n",
       "      <td>0.050428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Blues</td>\n",
       "      <td>36</td>\n",
       "      <td>0.034253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Alternative</td>\n",
       "      <td>35</td>\n",
       "      <td>0.033302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Latin</td>\n",
       "      <td>22</td>\n",
       "      <td>0.020932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Pop</td>\n",
       "      <td>22</td>\n",
       "      <td>0.020932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Hip Hop/Rap</td>\n",
       "      <td>20</td>\n",
       "      <td>0.019029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jazz</td>\n",
       "      <td>14</td>\n",
       "      <td>0.013321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                genre  tracks_sold  percentage_sold\n",
       "0                Rock          561         0.533777\n",
       "1  Alternative & Punk          130         0.123692\n",
       "2               Metal          124         0.117983\n",
       "3            R&B/Soul           53         0.050428\n",
       "4               Blues           36         0.034253\n",
       "5         Alternative           35         0.033302\n",
       "6               Latin           22         0.020932\n",
       "7                 Pop           22         0.020932\n",
       "8         Hip Hop/Rap           20         0.019029\n",
       "9                Jazz           14         0.013321"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "albums_to_purchase = '''\n",
    "WITH usa_tracks_sold AS\n",
    "   (\n",
    "    SELECT il.* FROM invoice_line il\n",
    "    INNER JOIN invoice i on il.invoice_id = i.invoice_id\n",
    "    INNER JOIN customer c on i.customer_id = c.customer_id\n",
    "    WHERE c.country = \"USA\"\n",
    "   )\n",
    "\n",
    "SELECT\n",
    "    g.name genre,\n",
    "    count(uts.invoice_line_id) tracks_sold,\n",
    "    cast(count(uts.invoice_line_id) AS FLOAT) / (\n",
    "        SELECT COUNT(*) from usa_tracks_sold\n",
    "    ) percentage_sold\n",
    "FROM usa_tracks_sold uts\n",
    "INNER JOIN track t on t.track_id = uts.track_id\n",
    "INNER JOIN genre g on g.genre_id = t.genre_id\n",
    "GROUP BY 1\n",
    "ORDER BY 2 DESC\n",
    "LIMIT 10;\n",
    "'''\n",
    "\n",
    "run_query(albums_to_purchase)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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p3rw5AMuXL+fOnTvqfTxnZ2fq1q3L7du36d+/P4qiYGNjw65duwo1ric9s87X15fVq1eT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9977bf0eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "genre_sales_usa = run_query(albums_to_purchase)\n",
    "genre_sales_usa.set_index(\"genre\", inplace=True, drop=True)\n",
    "\n",
    "genre_sales_usa[\"tracks_sold\"].plot.barh(\n",
    "    title=\"Top Selling Genres in the USA\",\n",
    "    xlim=(0, 625),\n",
    "    colormap=plt.cm.Accent\n",
    ")\n",
    "\n",
    "plt.ylabel('')\n",
    "\n",
    "for i, label in enumerate(list(genre_sales_usa.index)):\n",
    "    score = genre_sales_usa.loc[label, \"tracks_sold\"]\n",
    "    label = (genre_sales_usa.loc[label, \"percentage_sold\"] * 100\n",
    "            ).astype(int).astype(str) + \"%\"\n",
    "    plt.annotate(str(label), (score + 10, i - 0.15))\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "employee_sales_performance = '''\n",
    "WITH customer_support_rep_sales AS\n",
    "    (\n",
    "     SELECT\n",
    "         i.customer_id,\n",
    "         c.support_rep_id,\n",
    "         SUM(i.total) total\n",
    "     FROM invoice i\n",
    "     INNER JOIN customer c ON i.customer_id = c.customer_id\n",
    "     GROUP BY 1,2\n",
    "    )\n",
    "\n",
    "SELECT\n",
    "    e.first_name || \" \" || e.last_name employee,\n",
    "    e.hire_date,\n",
    "    SUM(csrs.total) total_sales\n",
    "FROM customer_support_rep_sales csrs\n",
    "INNER JOIN employee e ON e.employee_id = csrs.support_rep_id\n",
    "GROUP BY 1;\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "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>employee</th>\n",
       "      <th>hire_date</th>\n",
       "      <th>total_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jane Peacock</td>\n",
       "      <td>2017-04-01 00:00:00</td>\n",
       "      <td>1731.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Margaret Park</td>\n",
       "      <td>2017-05-03 00:00:00</td>\n",
       "      <td>1584.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Steve Johnson</td>\n",
       "      <td>2017-10-17 00:00:00</td>\n",
       "      <td>1393.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        employee            hire_date  total_sales\n",
       "0   Jane Peacock  2017-04-01 00:00:00      1731.51\n",
       "1  Margaret Park  2017-05-03 00:00:00      1584.00\n",
       "2  Steve Johnson  2017-10-17 00:00:00      1393.92"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_query(employee_sales_performance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NGDEC2dnZ6NGjB9566y0cOnQIADBz5kxYWVkhLi4OsbGxuHnzJpYuXYrY2Fgk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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9977c092e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "employee_sales = run_query(employee_sales_performance)\n",
    "\n",
    "employee_sales.set_index(\"employee\", drop=True, inplace=True)\n",
    "employee_sales.sort_values(\"total_sales\", inplace=True)\n",
    "employee_sales.plot.barh(\n",
    "    legend=False,\n",
    "    title='Sales Breakdown by Employee',\n",
    "    colormap=plt.cm.Accent\n",
    ")\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "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>country</th>\n",
       "      <th>customers</th>\n",
       "      <th>total_sales</th>\n",
       "      <th>average_order</th>\n",
       "      <th>customer_lifetime_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USA</td>\n",
       "      <td>13</td>\n",
       "      <td>1040.49</td>\n",
       "      <td>7.942672</td>\n",
       "      <td>80.037692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Canada</td>\n",
       "      <td>8</td>\n",
       "      <td>535.59</td>\n",
       "      <td>7.047237</td>\n",
       "      <td>66.948750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>5</td>\n",
       "      <td>427.68</td>\n",
       "      <td>7.011148</td>\n",
       "      <td>85.536000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>France</td>\n",
       "      <td>5</td>\n",
       "      <td>389.07</td>\n",
       "      <td>7.781400</td>\n",
       "      <td>77.814000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Germany</td>\n",
       "      <td>4</td>\n",
       "      <td>334.62</td>\n",
       "      <td>8.161463</td>\n",
       "      <td>83.655000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>2</td>\n",
       "      <td>273.24</td>\n",
       "      <td>9.108000</td>\n",
       "      <td>136.620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>3</td>\n",
       "      <td>245.52</td>\n",
       "      <td>8.768571</td>\n",
       "      <td>81.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>2</td>\n",
       "      <td>185.13</td>\n",
       "      <td>6.383793</td>\n",
       "      <td>92.565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>India</td>\n",
       "      <td>2</td>\n",
       "      <td>183.15</td>\n",
       "      <td>8.721429</td>\n",
       "      <td>91.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Other</td>\n",
       "      <td>15</td>\n",
       "      <td>1094.94</td>\n",
       "      <td>7.448571</td>\n",
       "      <td>72.996000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  customers  total_sales  average_order  \\\n",
       "0             USA         13      1040.49       7.942672   \n",
       "1          Canada          8       535.59       7.047237   \n",
       "2          Brazil          5       427.68       7.011148   \n",
       "3          France          5       389.07       7.781400   \n",
       "4         Germany          4       334.62       8.161463   \n",
       "5  Czech Republic          2       273.24       9.108000   \n",
       "6  United Kingdom          3       245.52       8.768571   \n",
       "7        Portugal          2       185.13       6.383793   \n",
       "8           India          2       183.15       8.721429   \n",
       "9           Other         15      1094.94       7.448571   \n",
       "\n",
       "   customer_lifetime_value  \n",
       "0                80.037692  \n",
       "1                66.948750  \n",
       "2                85.536000  \n",
       "3                77.814000  \n",
       "4                83.655000  \n",
       "5               136.620000  \n",
       "6                81.840000  \n",
       "7                92.565000  \n",
       "8                91.575000  \n",
       "9                72.996000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_by_country = '''\n",
    "WITH country_or_other AS\n",
    "    (\n",
    "     SELECT\n",
    "       CASE\n",
    "           WHEN (\n",
    "                 SELECT count(*)\n",
    "                 FROM customer\n",
    "                 where country = c.country\n",
    "                ) = 1 THEN \"Other\"\n",
    "           ELSE c.country\n",
    "       END AS country,\n",
    "       c.customer_id,\n",
    "       il.*\n",
    "     FROM invoice_line il\n",
    "     INNER JOIN invoice i ON i.invoice_id = il.invoice_id\n",
    "     INNER JOIN customer c ON c.customer_id = i.customer_id\n",
    "    )\n",
    "\n",
    "SELECT\n",
    "    country,\n",
    "    customers,\n",
    "    total_sales,\n",
    "    average_order,\n",
    "    customer_lifetime_value\n",
    "FROM\n",
    "    (\n",
    "    SELECT\n",
    "        country,\n",
    "        count(distinct customer_id) customers,\n",
    "        SUM(unit_price) total_sales,\n",
    "        SUM(unit_price) / count(distinct customer_id) customer_lifetime_value,\n",
    "        SUM(unit_price) / count(distinct invoice_id) average_order,\n",
    "        CASE\n",
    "            WHEN country = \"Other\" THEN 1\n",
    "            ELSE 0\n",
    "        END AS sort\n",
    "    FROM country_or_other\n",
    "    GROUP BY country\n",
    "    ORDER BY sort ASC, total_sales DESC\n",
    "    );\n",
    "'''\n",
    "\n",
    "run_query(sales_by_country)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ewD/+8Q+WLVumrHz6oIiICNRqdYkveF9fX9auXQsUrUzbvXt3pkyZglarpVmzZpw8eZJ1\n69bh7u6Oo6NjqRkpExIS6NOnD5aWlvj6+paYtvzatWv06dMHW1tbWrVqxfbt25V9o0ePZvz48bz2\n2mtYWFgQEhJSqr6xsbH4+/tja2tL8+bNlUtS165dy7hx4zh16pQy0+fDli1bhqWlJRs3blR+7bi4\nuLB8+XLatm372HNx69YtevbsibW1NQ4ODowYMQIoe5l4gFWrVtGsWTPs7Ox44403iI2NVfJVq9Ws\nWLGCZs2aYWVlxcyZM7l9+zZdu3ZFq9UyYsQICgsLlfS//vorHTp0QKvV0r17dy5duqTsa9KkCYsX\nL6Z9+/aYm5uj1+tZtGgRrq6uWFpa0qpVK4KDg0udD0mqCg8uWdCvXz9l+YaUlBTGjBmDi4sLtra2\nvPnmm2RnZ9O/f39iYmKwsLDA0tKyzOn7p06dyujRo/noo4+wsbEBoEOHDmzduhUo+lx6eDK8B1sb\n9+7dS5s2bbC0tMTNzY3ly5eXW3Z+fj6TJk3CxcUFV1dXJk+eTEFBAfB/LSNLlizBwcEBFxcXdu7c\nyb59+2jevDl2dnYsXLiwxLlYuHAhTZs2xd7enuHDhyurhhd/vqxduxYPDw969epFXl4eb731FnZ2\ndmi1Wjp37qzM6PygxYsXKytBF5s4caKyPte6devw8vLC0tISLy8v5Tw97OzZs4wcORJLS0ugaHLF\nByf8mzRpEu7u7lhZWfHSSy+VWPbiYadPn6Zbt25otVo6dOigrFxdmfrUCTW+xFwdV7wC7aBBg8Rn\nn30mhBBi9erVwtfXVwghxN27d4VarRY6nU55TM+ePcWaNWuEEEUrmWo0GrF+/Xqh1+vFZ599Jlxd\nXcWECRNEfn6+OHDggLCwsBBZWVlCiKKVTS0tLcXx48dFfn6+mDhxoujevbsQQoisrCzh5uam5BUa\nGirs7OzE1atXlcdaW1uLU6dOCSGEyMvLK3U83t7eStmhoaHC3t5eBAcHK3V9eNXVB73yyiti9uzZ\n5e5/3LkYMWKEmD9/vlK3EydOKOlUKlWJFXYPHTok7OzsRGhoqMjPzxd/+9vfRI8ePUqkf+ONN0Rm\nZqa4cuWKMDU1FX5+fuLu3bsiPT1dtG7dWmzYsEEIIcT58+eFg4ODOHv2rNDr9WLDhg3C09NTWQ3W\n09NTdOjQQURHR4vc3Fxx/fp14ebmJuLi4oQQQkRERJSomyRVlQdXuI6MjBRt2rQRs2bNEkII0b9/\nfzF8+HCRlpYmCgsLxdGjR4UQRasZu7m5lZtndna2MDIyEiEhIeWmKeu9rlarlVWTnZyclPdnamqq\nuHDhQrllz5gxQ3Tp0kUkJiaKxMRE0bVrVzFz5kwlvbGxsZg3b54oLCwUq1atEnZ2dmLkyJEiKytL\nhIeHi4YNG4q7d+8KIYT46quvRJcuXURMTIzIz88Xf/3rX8WIESOEEEWfLyqVSgQGBoqcnByRm5sr\n/v3vf4vXX39d5ObmCr1eL86fP6+s6PygiIgI0ahRI5GZmSmEEEKn0wknJydx5swZkZWVJSwtLcUf\nf/whhBAiLi5OXLlypczzNnbsWNGmTRvx/fffK+kftHnzZpGSkiJ0Op1Yvny5cHR0VD6HZ8+erazu\nHRUVJWxtbZUVsA8ePChsbW1FYmJipepTF8igo5KKPxQuX74srK2tRWJiYqWDjubNmyv7Ll26JNRq\ntUhISFC22drairCwMCFEUeBQ/CYTQojMzExhbGwsoqKixLZt20p88QohxHvvvSc+//xz5bGBgYHl\nHsu9e/eEsbGxEuAIIcT06dPF6NGjlbo+Kuho1qyZ+Pe//13u/sedi4CAAPHee++JqKioUo99eJn4\nd955R3z88cfK/czMTKHRaERERISSvji4EkKIjh07isWLFyv3P/zwQzF58mQhhBDvv/++8iFYrEWL\nFsqHuKenp1i3bp2y7+bNm6Jx48bi4MGDoqCgoNzjlaSn5enpKSwsLIRWqxWenp5iwoQJIjc3V8TG\nxgojIyORlpZW6jGPCzqio6OFSqUS169fLzdNWe/1B9+DHh4eYuXKlSI9Pf2xZXt5eSlfnkIIsX//\nftGkSRMlvZmZmdDr9UIIITIyMoRKpRJnz55V0nfs2FHs2rVLCCFEq1atxOHDh5V9MTExQqPRCJ1O\np3y+FAcoQgixdu1a0a1bN3Hx4sVyj7WYt7e32LhxoxBCiAMHDoimTZsKIYp+zGm1WvHzzz+LnJyc\nR+aRm5srFixYIDp16iRMTExEs2bNxL59+8pNr9Vqlbo9GHQsWrRIBAQElEjbt29fsWHDhkrVpy6Q\n3StPqE2bNgwYMIAFCxZU+rEPLvlevMS8nZ1diW0PLpn+4ECtRo0aodVqiYmJISIigtOnT2NjY4ON\njQ1arZYtW7Zw//79Mh/7sJiYGGxsbDAzM1O2eXh4KAvlPY6trW2JLo7KWrJkCXq9npdffpnnn3+e\n77///pF19fDwUO43atQIW1vbEnV1cHBQbjds2LDUeS4+pxERESxbtqzEeYuKiiqxxP2Di8p5eXnx\n1VdfMXv2bBo3bszIkSOf6rgl6VF27dpFcnIyd+7c4ZtvvsHU1JR79+5hY2OjNONXhlarRa1WP9Vr\n9qeffiIoKAgPDw98fX3LHVgORe9Vd3d35b6Hh0eJ95atra0yDqT48+/h9+6D79U///nPynu1devW\naDSaEp9xD75XAwIC6Nu3L8OHD8fV1ZVp06aVO9ZjxIgRSjfF1q1bGTlyJABmZmZs27aNFStW4OTk\nxMCBA7l+/XqZeZiamjJt2jTOnj1LUlISQ4YMYciQIUoX0NKlS2ndujVarRatVkt6ejqJiYml8omI\niODHH38s8Zl04sQJYmNjK1WfukAGHU9h9uzZrFq1qsQXX6NGjQCqdIn0B5d9z8zMJCUlBWdnZ9zc\n3OjZsyfJyckkJyeTkpJCeno6//rXv5T0jxrk5ezsTHJyMllZWcq2yMhIZRn7x+nduze//PJLufsf\ndy4cHBxYuXIl0dHRfPfdd4wfP77cK1acnZ1LLEmflZVFUlJSuSvOPoqbmxuffvppifOWmZnJsGHD\nlDQPn7fhw4dz7NgxpQ7Fq9ZKUlUTZczX6ObmRnJycpnjyB43kLNhw4Z06dKFn376qdw0jRo1KvU+\nfTDfjh07snPnThISEvD392fo0KHllu3i4lLivRoREYGzs/Mj61ged3d39u3bV+K9mpWVhZOTk5Lm\nwToYGRkxY8YMwsPDOXnyJHv27Cl31eYhQ4YQEhJCdHQ0v/zyixJ0ALz66qscOHCAuLg4WrRowbhx\n4x5bV3Nzcz755BOysrK4c+cOx48fZ8mSJezYsYOUlBRSUlKwtLQs9/kNCAgocZwZGRlMnTr1ietT\nW8mg4yl4eXkxbNgwvv76a2WbnZ0dLi4ubNq0Cb1ez9q1ax97JUdZL8IH7d27l5MnT5Kfn8+MGTN4\n5ZVXcHFxYcCAAdy4cYNNmzZRWFhIQUEB586dq3AU7OrqSteuXZk+fTp5eXlcvHiRNWvWVPja8SlT\nppCenk5gYKAyuDU6OpoPP/yQy5cvP/Zc7NixQwnYrK2tUavVqNVFL8mHl4kfMWIE33//vbLs/Cef\nfMIrr7zyRJfrjRs3ju+++05ZLTgrK4u9e/eWCL4edOPGDYKDg8nPz8fExISGDRsq9ZSkmuDo6Ei/\nfv0YP348qampFBYWcuzYMaCo5TQpKanMgKTY4sWLWbduHcuWLSM5ORmAsLAwZfB2+/btCQ8PV95f\nDw4cLygoYMuWLaSnp2NkZISFhYWyAnRZZQ8fPpx58+aRmJhIYmIic+fOfeL5KN577z0++eQT5fMl\nISGB3bt3K/sf/uwMCQnh8uXL6PV6zM3N0Wg05b5X7ezs8PHxYfTo0Tz33HO0aNECgPj4eHbv3k12\ndjYajQZzc/MSK14/aN68eZw7d46CggLy8vL46quv0Gq1tGjRgoyMDDQaDba2tuTn5/P555+TkZFR\nZj5vvfUWe/bs4cCBA+j1enJzczly5AgxMTGVqk9dID85K+nhyH7mzJlkZ2eX2L5q1SoWL16MnZ0d\nV69epVu3bpXK8+El4keOHMns2bOxtbXlwoULbNq0CSiKrA8cOMAPP/yAs7Mzzs7OTJs2jby8vAof\nz9atW7lz5w7Ozs4MGjSIuXPnVnjOEa1Wy8mTJ9FoNHTu3BkrKyteffVVrK2tlcvGHnUuzp49S+fO\nnbG0tOSNN97g66+/xtPTEyi9THyvXr2YO3cub775Ji4uLty5c4cffvihQufwYR07dmTVqlVMmDAB\nGxsbmjdvzvr168t9bF5eHtOmTcPe3h5nZ2cSEhKeqFtNkh7nUa/bjRs3YmxsTMuWLWncuDH//Oc/\nAWjRogUjRozgueeew8bGpsyW1S5dunD48GEOHTqEl5cXdnZ2/PWvf+W1114DoFmzZsycOZNevXrR\nvHnzUleybNy4kSZNmmBtbc3KlSuVlZDLKvuzzz6jU6dOtGvXjvbt29OpUyc+/fTTCh/zg/cnTpyI\nv78/ffr0wcrKiq5duyo/Fsp6bFxcHIMHD8bKyoo2bdrg6+v7yIBn5MiRHDp0iL/85S/KNr1ez/Ll\ny3FxccHOzo6jR4+yYsWKcus+evRo7O3tcXFx4dChQwQFBWFmZkbfvn3p27cvzZs3p0mTJpiZmZX7\nI8nV1ZVdu3Yxf/587O3t8fDwYOnSpej1+krVpy6Qa69IklRn5OXl0aNHD/Lz88nPz8ff35/58+eT\nkpLCsGHDiIiIwNPTkx9//BErKytDV1eSpIfIoEOSpDolOzsbMzMzdDod3bp1Y9myZezevRtbW1um\nTp3KokWLSElJKTHfgyRJtYPsXpEkqU4pvtoqLy8PvV6PVqtl165dBAYGAhAYGMjOnTsNWUVJksoh\ngw5JkuoUvV5Phw4dcHR0pGfPnrRu3Zr79+8rl0g7OjoSHx9v4FpKklQWY0NXQJIkqTLUajUXLlwg\nPT2dvn37EhISUqmBxJIkGY5s6ZAkqU6ytLSkf//+nDt3jsaNGysTRsXFxZWYbOpBMhiRJMOSQYck\nSXVGYmIiaWlpAOTk5PCf//yHDh068PrrryvLtK9fvx5/f38D1lKSpPLIq1ckSaozLl26RGBgIEII\n9Ho9o0aN4qOPPiI5OZmhQ4dy7949PDw8+PHHH7G2ti71eJVK9djJ+CRJqj4y6JAk6Zkhgw5JMizZ\nvSJJkiRJUo2QQYckSZIkSTVCXjIrSdIzz9PTs8TKqFLt5uHhwd27dw1dDekJyDEdkiQ9M8ob0yHH\netQt8vmqu2T3iiRJkiRJNUIGHZIkSZIk1QgZdEiSJEmSVCNk0GFAR44cwdPTEz8/P/z8/MjIyDB0\nlSRJkiSp2sigw8ACAgI4fPgwhw8fxsLCAkAOkJKkWsDDyQmVSlVtfx5OTgY5rvXr1+Pt7W2QsiVJ\nXjJrYA8GGL6+vrz88svExsby4YcfMnHiRPLy8vD392fatGmsX7+ePXv2kJOTg0ql4tdffyU7O5vA\nwEASExN57rnnWLNmDUFBQSxevBidTsfMmTPp06ePAY/w6R09epQ5c+ag1+sxNjbm888/p0uXLoau\nllTPRcbFEeXsWm35u8ZEVVvejyKEqDUL3+l0OoyMjAxdDakGyZYOA9u4cSN+fn6MGTMGlUrFm2++\nyYYNG2jZsiUhISGcOnWKAwcOkJeXB4CbmxtBQUG4uLhw6dIlVq5cSd++fQkODmbNmjUIIVi6dCnB\nwcEEBwezePFiAx/h00lKSmLWrFns3r2b4OBgdu7ciZmZ2SMfI1uKpPomKiqKQYMG4eDggL29PX//\n+9+ZM2cOo0aNUtJERESgVqvR6/UArFu3Di8vLywtLfHy8mLr1q1cu3aN999/n1OnTmFhYYGNjQ0A\n6enpBAQE4ODgQJMmTfjiiy+UfNevX0/37t2ZMmUKWq2WZs2acfLkSdatW4e7uzuOjo5s2LBBSZ+f\nn89HH32Eh4cHTk5OjB8/Xvn8OnLkCG5ubixevBgnJyfGjBlDUlISAwcORKvVYmtri4+PT02cUslA\nZNBhYMXdK2vXrgWgY8eOANy+fZv+/fvTs2dPrl27Rnx8PABt27YFwNnZmZSUFG7cuEHXrl2V/BIT\nE7l69SqX5SvOAAAgAElEQVS9e/emT58+ynLfddXevXsZNWoUjRo1AqBRo0a0b9+eNWvW0KNHD3x8\nfAgNDQWgffv2BAQEsHjxYkaPHs348ePp0aMHc+fOZeLEibz00kvKSqTr1q1TWpYOHjwIwOjRo3n/\n/ffx9vZm7ty55Ofnl2gl6t27NzqdrmZPgPTM0+v1DBgwgCZNmhAREUF0dDTDhw8HKNViUXw/Ozub\niRMnsn//ftLT0zl58iQvvPACLVu25LvvvqNLly5kZGSQnJwMwIQJE8jIyODu3buEhISwYcMGvv/+\neyXfM2fO8MILL5CcnMzw4cMZNmwYv//+O7du3WLjxo1MmDCB7OxsAD7++GNu3rzJxYsXuXnzJtHR\n0Xz++edKXnFxcaSmphIZGcnKlStZtmwZbm5uJCUlER8fz/z586v1fEqGJYOOWkQIgVpd9JSsWLGC\nadOmERISgpeXl/Lr/eEPmZYtW3Lq1Cnl8XZ2drRr145Dhw4RHBysfCHXVTExMTj9r+9769ateHt7\nM27cOPbs2cPRo0fZuXMnc+bMUdKuXLmSjz/+GIC+ffty9OhRtmzZwjvvvMOJEydYvXo1AMOHDyc4\nOJiDBw+yZMkSpbw//elPHDt2jL1792JiYkKTJk24ffs2N27coGnTprIpWKpxZ86cITY2lsWLF9Ow\nYUNMTExK/NAoj5GREZcuXSI3N5fGjRvTqlWrMtPp9Xq2bdvGwoULMTMzw8PDgw8//JCNGzcqaZo0\naUJAQAAqlYphw4YRExPDrFmz0Gg0vPrqq5iYmHDz5k0AVq1axZdffomVlRWNGjVi2rRpbN26tUS9\n5syZg0ajwdTUFI1GQ2xsLHfu3MHIyIhu3bo95RmTajMZdNQiDwYUr732Gh988AHDhg3D1NS03LRj\nx45l3759+Pr6Mm7cOFQqFZMnT1auiJk0aVKN1b86ODk5ER0dDcCIESPYvHkzMTExhIWF4efnx5tv\nvkl6ejoALVq0oEGDBspji1uFnJycaNOmDSYmJkpQV3zOXn/9daKioko9pmHDhgCMHDmSrVu38sMP\nPzBixIjqP2BJesi9e/fw8PBQXrsVYWZmxrZt21ixYgVOTk4MHDiQ69evl5k2MTGRwsJC3N3dlW0e\nHh7K+w6gcePGyu3i94adnV2JbZmZmSQkJJCdnU3Hjh2xsbHBxsaGfv36kZSUpKS1t7dHo9Eo96dO\nnYqXlxd9+vShadOmLFq0qMLHKdU9ciCpAfn4+JTovzx8+LByu2/fvvTt27dE+sDAQOX2zJkzlds7\nduwoka5fv37069evqqtrEP3792fw4MEMHToUS0tL8vPzUalUvPTSS/z4448ASpdHeU3NZQ2cW7hw\nIUePHiU3N5fu3buX+RiAHj16sHDhQmVQriTVNDc3NyIjI9Hr9SUCj0aNGildGgCxsbElHvfqq6/y\n6quvkpeXx6effsq7777LkSNHSr0X7Ozs0Gg0RERE0LJlS6BofIiLi0ul62pnZ4eZmRnh4eFKC+XD\nHi6/UaNGLF26lKVLl3LlyhWl29PX17fS5Uu1n2zpkGo1Ozs7Zs+ejb+/P71792bChAl89NFHvPba\na/j4+NCrVy8WLlwIlPwwK+92sQEDBtCjRw8+/fRTtFptuY9RqVS0a9eO559/vlqOT5Ie5+WXX8bJ\nyYlp06aRnZ1NXl6eMkbj6NGj3Lt3j7S0NOV9ABAfH8/u3bvJzs5Go9Fgbm6uBCyNGzcmKiqKgoIC\nANRqNUOHDuXTTz8lMzOTiIgIvvzyyxKDVB9W3mBtlUrFuHHjmDRpEgkJCQBER0dz4MCBcvMKCgri\n1q1bAFhYWGBsbFypVh2pjhGSJD3SP/7xD3Hu3DlDV0OqAuV95JW13d3RUQDV9ufu6Fjhet+7d0+8\n8cYbwtbWVtjb24uJEycKIYT44IMPhLW1tWjWrJlYvXq1UKvVQqfTidjYWOHj4yOsra2FVqsVvr6+\n4urVq0IIIfLz88WAAQOEjY2NsLe3F0IIkZKSIt566y1hb28v3N3dxbx585Sy161bJ7y9vZX7N2/e\nFGq1ukT93NzcxIkTJ4QQQuTm5opPPvlEPPfcc8LKykq0bt1afPPNN0IIIUJCQoSbm1uJx3755ZfC\n09NTmJubCzc3N/HFF1889nzIr666S64yKz2W0BeA0IHQA6LovxCgUoHKGNQaVOr62VM3a9Ysbty4\nUWIgnFR3yVVm6wf5fNVdMuh4xomCDMhPKfrLS4HCTNDlQGFO0X9dTlHA8Qh6jRkpDqaoVKaoVcX/\nG6BWW2BspMVIrcXIyBq1yqSGjkqSyiaDjvpBPl91V/38eSqVIoQOchMgJxZy7kNePOSlgih4+ryN\nNIAeIXLQiZxy06lVjTAy0mKs1mJkpMVIbY2xkRaVSlPuYyRJkqT6QwYd9ZQQesiOgay7kBUFufdB\nFFZPWUbGwOPz1oss9IVZFPDg9M8qjI3s0Ri7YGLsgrGRAyqVHEQmSZJUH8nulXpEFGZB5p3//UWA\nPq9Gyi2wdCTdovwWjspQoUFj7IyJxgMTYzfU6oZVkq8kgexeqS/k81V3yZaOOk7oCyD9BqSGQ/Y9\nw9RBXXWzdAoKyC+MIL8wgqJWEAdMNB400DRFrX70miuSJElS7SZbOuogIURRgJF6BTJugP7px2U8\njTwbdzIbplVzKWpMNJ40NGmFxtgwS4JLdZ9s6agf5PNVd8mWjjpE5KcWtWikXYGCdENXRyFqZCIf\nPfkFt8kvuI2RWksDk1aYmjSVV8RIkiTVIbKlow4QOfch8TRk3DR0VcqUY/8c2SZJj09YxVRoMDVp\nSgOT1hgbaWu8fKnukS0d9YN8vuou2dJRi4ns2KJgI/O2oavySEJdeprxGimXAnLzr5KbfxVjI0fM\nGryIibGzQeoiSZIkPZ68NrEWyktNRhcZBHe31PqAA0CUsbZJTSvUxZGetZe0rL0UFCZUSxmHDh3C\n19eXnj17MmjQIFJSUp44L29v73L3rV+/npYtW9KrVy/eeOMNZY2Mp+Hr64tery+xbfTo0dy+fZv9\n+/ezb9++py6jvnFzc0OlUlXbn5ubm6EPESh6HVTXYobr169/5GtdevbIlo5apCAznZQ/LpMTH42t\nWwMaGbpCFSQMH3MoCgpjSCvchYlxE3ILXsbB2qJK8k1MTGTu3Lns3bsXMzMzbt68SX5+/hPnV9Yi\ndA+aOnUqY8aM4YsvvuC3335j4MCBT1zW48p7eDXj2iwqKoqAgADu37+PWq3m3Xff5W9/+xtz5sxh\n1apVODg4ADB//nz+9Kc/PXVZ//73v6ui2mV67733Kpy2SZMmrFmzBj8/vypNWxMe91qXni2ypaMW\n0BcWknwtjJiTB8iJjwYgKToPYVQ3wo7aFHQUu3gnh/e+PMyafZfIyn36loK9e/cSEBCAmVnRZbtN\nmzalcePGDBo0CD8/P1q0aMEXX3xBbm4uI0eOpHfv3owYMQKdTkd2djZDhgzB19eXd955B4CCggLe\nffddXnzxxTJX4Czur05NTaVBgwZA0WqcPj4+dO/eXXlMly5dGD16NC+//DJ79+4FSraiFC8PLoRg\n0qRJdO3aldWrV5coa/369axduxaAGTNm4O3tTe/evUlPrz2DlYsZGxuzfPlywsPDOXXqFP/617+4\ndu0aAFOmTOH8+fOcP3/+qQMOSZKqhww6DCwrLoqYE7+REXGjaBG1YnpBjq5ujE8QKv3jE9UgIWDH\nUVMKdXp2nrjJX7/8DwfO3X2qPGNjY3FyKn2p7k8//cSuXbtwd3fnvffeY/Xq1fj7+3Pw4EF8fHzY\nvn07K1eupG/fvgQHB7NmzRoAkpOTWbBgAUFBQXz33Xel8l2yZAnt2rXj9OnTvPrqqwghWLp0KcHB\nwQQHB7N48WIAEhISmDdvHiEhIcyfPx8o+cvywdsjR47k+PHjrFu3rswum9DQUO7cucOxY8c4ePAg\nlpaWT3XOqoOjoyMvvPACAObm5rRq1Yro6KJAvb4OLAwICCAyMpKBAwdiaWnJ0qVL2bNnD23btsXG\nxgY/Pz+uX79eblqAoUOH4uTkhFarpWfPnly5cqVSdUhKSmLgwIFotVpsbW3x8fFR9i1atIimTZti\naWlJ27Zt2blzZ7n5XLt2jT59+mBra0urVq3Yvn27sm/v3r20adMGS0tL3NzcWL58eaXqKFUdDyen\nMrsEPcr4DKwsGXQYSEF2Jvd/P0Zi2Cl0uWXP5lnU2lEXJsSqXR/2oTdduRWTqdxPzcrjm50XmLnu\nBIlpTzZzqpOTk/Ll9rCxY8eyYMEC7OzsuHr1Kl999RV+fn5s2LCBhIQE/vjjD7p27VriMQ4ODtja\n2uLk5ERaWuk5TqZOnUpYWBimpqZcvXqVxMRErl69Su/evenTpw/3798HwNbWFhcXF8zMzDA2Luot\nLe/L94UXXkCtVuPh4UF8fHyp/Tdu3ChVz9rs7t27hIaG0rlzZwD+9a9/8cILLzB27Ngyz2ldtWHD\nBtzd3fn1119JT0/H39+fESNG8PXXX5OQkEC/fv0YMGAAhYWFpdJ+9NFHAPTv359bt24RHx/Piy++\nyF/+8pdK1WHZsmW4ubmRlJREfHy8EuBCUavfiRMnSE9PZ9asWbz11lvK6/NB2dnZ9OnTh7feeovE\nxER++OEHxo8fr7RUjR07llWrVpGens7ly5drTffQsygyLo4oZ9dSf5FxcU+dd70NOo4cOYKnpyd+\nfn54e3srvwQq4/79+yxYsAB49MC/yhBCkH73BrEn9pOb+OgnUOghR+dSJeVWp9rU0qHTq9hxrOz+\nngs345nwzSEOX4isdL79+/dn8+bNZGYWBTO3bt0iLi6OefPm0bt3bzp16gRAy5YtmTp1KocPH+bk\nyZO8//77tGjRglOnTgFlBwQPD/AsplKpmDp1Kt999x12dna0a9eOQ4cOERwcTGhoKFDUYhITE0N2\ndjY63f+tBpyfn8/FixdL5BcWFoZOpyMiIoLGjRuXKu/BetZ2mZmZDB48mH/+85+Ym5szfvx4bt++\nTWhoKI6OjkyZMsXQVaxyxa+dbdu2MWDAAPz8/DAyMuKjjz4iJyeHkydPlkpb7O2338bMzAyNRsPM\nmTMJCwsjIyOjwmVrNBpiY2O5c+cORkZGdOvWTdk3aNAg5fU0ZMgQmjVrxpkzZ0rl8euvv9KkSRMC\nAgJQqVS0b9+eQYMGKa0dJiYmhIeHk5GRgZWVldKiJdUv9TbogKKmxsOHD7NkyRJWrFihbK9oM2zj\nxo2ZPn06UDWDoXR5ucT/foyU62GIcr5oHpYcnV/rWzsEuscnqiGnr7gTk5Rd7v6s3AK+/Ol35m85\nTWpmxdemsbOzY8aMGQwYMAAfHx/+8Y9/YGJiwvz589m6davSsvHuu+/y888/07t3b3r37s2FCxcY\nO3Ys+/btw9fXl3HjxgHld4E87NVXX+XEiRMUFhYyefJk/Pz88PPzY9KkSUq9Zs+eTc+ePZk2bRoA\ngYGBdO/evUTTtUqlYvv27XTv3p3AwECMjY1Lldu+fXs8PDzo3r17rR3TAVBYWMjgwYMZNWoU/v7+\nANjb2yvHM27cOM6ePWvIKlarmJgYPDw8lPvFV8KU1xKn1+uZNm0aTZs2xdramiZNmqBSqUhMTKxw\nmVOnTsXLy4s+ffrQtGlTFi1apOzbsGEDHTp0QKvVotVqCQ8PLzPviIgITp8+jY2NDTY2Nmi1WrZs\n2aK0ivz0008EBQXh4eGBr68vp0+frnD9pLqjXl+9UhxcpKWlYWVlha+vLy+//DKxsbF8+OGHTJw4\nkby8PPz9/Zk2bRpz587l8OHDZGdnY2lpyerVq/nss8/YuHHjU/cXZyfEknT5LPr8yi3CptcLcvQu\nmPHHU5VfnUQFVpitCQWFRvxyomKDRk9dieVKRBIf+HegS+uKjZ0p/sJ/UHZ26QBn48aNpbbt2LGj\nxP2jR48qtw8fPlxiX2BgoHJbpVJx7tw5APr160e/fv1KpDU2NmblypUlto0dO5axY8eW2PZwGYAy\nePS5555Tts2bN69UutpmzJgxtG7dmokTJyrb4uLicHR0BODnn3+mbdu2hqpetXgwQHR2duby5csl\n9t+7dw9XV9dSaQG2bNnCnj17OHz4MO7u7qSlpaHVaiv1mdaoUSOWLl3K0qVLuXLlivJZ6uXlxbvv\nvktwcDBdunQBoEOHDmXm7ebmRs+ePdm/f3+ZZXTs2JGdO3ei0+n45ptvGDp0KJGRlW+VlGq3eh10\nbNy4kaNHj3Lz5k3279/P0aNHefPNN+ncuTN5eXmEhIQARV8mkydPZsaMGcyYMYPAwMAyf5E+CaHT\nkXI9jIx7t544j+SYfBq6NkSlq5qVXKuaELWjpeNImDtJlfh1npaVz/wt/6VvJ0/eG9AejXHda/h7\n1i5HPHHiBJs3b+b555+nQ4cOqFQq5s+fz5YtWwgNDUWtVuPp6Vkll7q6urpW6rLWJ8m/oho3bszt\n27fx8/Nj6NChLFq0iODgYLy9vfnqq69o0KCB8qXv6OiopAXIyMjA1NQUrVZLVlYW06dPr/TrJigo\niJYtW+Ll5YWFhQXGxsao1WqysrJQq9XY2dmh1+tZv359qYCo2IABA5g+fTqbNm1i+PDhCCEICwvD\n3NwcLy8vtm/fzoABA7C0tMTCwgIjo6pbSFKqPerep2wlBAQEcOTIEUJDQ/nkk09QqVR07NgRgNu3\nb9O/f3969uzJtWvXlIF1X375JS+++CLdu3d/6vIL83KIOxvyVAEHgL5QkKuvvWM7BIZdcA4gJ8+E\n3aeeLCjbf+4u09ccJSm9dgZ1j/Jgi8mzoFu3buh0OkJDQ7lw4YJyeeyGDRu4ePEioaGh7Ny5s8wx\nK5V17949hBDV9nfvXsVXhZ4+fTpz587FxsaGX3/9lU2bNjFhwgTs7e0JCgpiz549ykDi4lZbGxsb\nli9fTmBgIO7u7ri4uNC2bdsnGiz8xx9/0Lt3bywsLOjWrRsffPABPj4+tGrVig8//JBXXnkFR0dH\nwsPDy/3sNDc358CBA/zwww84Ozvj7OzMtGnTlPluNm7cSJMmTbC2tmblypVs2bKl0vWUar96u/bK\nkSNHOHjwIHPnziU7O5v+/fujVqs5ePAgarWav//97wwePJgePXrg7e3N5s2buX37Nt9//z3r168H\nivogZ8yYwYYNG/D29ubYsWMVLj8vLYWECyfQ5VXNF5naWIWLa3yta+0QKmOSnQ0/n8juk8/xy4mn\nu2JBa27K9JGdaeVuW0W1kmobufZK/SCfr+qlUqmIci7dEucaE/XU571et3Rs2rQJPz8/evfuzdSp\nU0vse+211/jggw8YNmwYpqamQFF/9vXr1/Hz8ys1+r0yzZFZcfe4fza4ygIO+F9rh6h9rR3CWGPo\nKpCW1ZB9ZzMfn/AxUjLz+HTtcY6EVfwXqCRJklRx9balw1BSb4aTdqtyE+9UlNpYjYtLHCp9brXk\n/yR0plak2hm2DluDvThwLrVK8xzp15IRfq2qNE/J8GRLR/kWLFjA/PnzS/3A8vb2JigoyEC1Kpt8\nvqpXdbZ0yKCjigghSA7/nczoO9Vajr2HCQ31tWeJ+0IzW9K0hrt6JTHdgumrTSnUVf3LuPeL7vzt\njRdRG2gVXanqyaCjfpDPV/WS3Su1nNDrSQw7Xe0BB0BSdCFC3aDay6koYWTYC6B2nbCvloAD4OD5\nSL76+Xf0evnhJkmSVBVk0PGUhF5HQuhJsu9H1Uh5+kI9eVT8UrvqJtSGCzqiE204EV69010Hh97j\ny59+RycDD0mSpKdWr+fpqG5CpyM+9ORjpzOvaokxhbg4m6LSV26iseogDHgt/c/HrRCi+mfNDAkr\nunRy8uBOGMmulnrJw8PjmZvzpC57cEZWqW6RQccTEnq9QQIOAH2BnjyVGw0w/NgOoTZMY9mtGAfO\n/1Fz03QfuRiFEDBliAw86qO7d+8augqS9EyQ3StPQAhBUvg5gwQcxYrGdpgYrPxiQmWYl9COozW/\nHs3RS1Es/fEsOl3tWeBOkiSpLpFBxxNIvXGRrJgIg9ZB97/WDkMTBvjVf/mOM9fuVXyFzKp0/HI0\n3+4ONUjZkiRJhlS8UOSDf25ulfsekt0rlZR+9wbpd28YuhoAJEXrcHY2QaXPN1gdRA33g+uFih3H\njAHDjWc58HsEjjaNGOLTwmB1kCRJqmmFhYWl1jWq7PpEsqWjEjJjIki5Hmboaih0BXryDdzaIWq4\noeP3G65E3M+q2ULLsPHgFY5erJkrliRJkuoLGXRUUF5qMkmXzxm6GqUkxegRKsNNRS5UNXcpaaFO\nzU/Haselq41UKvKWnSYlzHDjeiRJkuoaGXRUgC4/j4SwkyBq3wDCwnwd+Wp3w1WgBls6Tlx2536K\n4Re8c1Ib8c75DPJORXHugz3k3n/6dV8kSZKeBTLoeAwhBIlhp9HlGv7LrjyGbO0Q1EwglpdvzM6T\nhp+XpKWRhsFH4sm9kQRAXnwW5yb8ii7fcFPBS5Ik1RUy6HiM1BuXyE2ON3Q1HqkwX0e+kWHGdghV\nzQQdh0PdSM003IBZgC5GJvQKukd+bMmWjdSwOK4tPfFEeR45coQZM2Yo9+fMmcPRo0fLTBsWFsb3\n338PoPyvCF9f33LL/OmnnxgyZAhxcXEsWLCgstUvtwxJkqSyyKDjEbLvR5N+97qhq1EhSTECoar5\ni5EEumovIyvXlF9PZ1d7OY8yQJjywk+3KUwvu7XlzvoLJJx4ssuoKzoTZvv27Rk9ejQAa9eufar8\nVSoVJ0+eZMWKFWzevBlHR0emT59e4TwrUoYkSdLDZNBRjsLcHJIunzV0NSqsME9HgVHNj+2oiaDj\nt7MuZOcZrvvirVwNLjtuoM9/xLEKCP34APlVNObkyJEj9O/fn9dffx1vb2+ys7OVFopVq1Zx6dIl\n/Pz8CA8PJygoCB8fH7p3786BAwcAWLlyJV27dmXKlCll5n/t2jX+8Y9/sGPHDkxMTIiIiGDUqFEA\ndOnShXfffZcXX3xRye+7776ja9euTJs2TWnV+PXXX+nUqRNjxoyhoKAAgIsXL9K9e3e6du3Kli1b\nABg9ejTjx4+nR48ezJ07l4kTJ/LSSy+xbt26KjlX1cXNza3UnARPMi+BJEn/R87TUY7k8HPoCwsM\nXY1KSYwVODkYoxI19wUtqrms1MxGHDhnmInAjICxSSo4VLHp5vPiswj79CAvfTuwSso3NTXll19+\nYcGCBRw6dAhLS0tUKhXjxo1j48aNHD58GCEEEyZMIDg4GJ1OR79+/ejVqxdr167l1KlTnD59mtDQ\n0pOZHThwgClTpmBtba1sK26tSE5OZsGCBeTl5TFhwgR69erF+vXrOXXqFGfOnOHMmTMALFy4kGPH\njpGUlISfnx8AM2bMYOvWrTg5OeHt7c2wYcMA6Nu3L99++y2tWrVi27ZtLFmyBD8/P95+++0qOVdP\ny8PJici40lciPTwnAVR+XgJJkv6PbOkoQ2bUHXIMOMX5kyrM1VFQw2M7BNUbmO053Zj8wpq/ashM\npWL83QI4dLdSj7t/8BaRP16ucPoGDRqQm5ur3M/NzaVhw4YAtG3bFgBnZ2dSU1NLPE6IokuHExMT\nuXr1Kr1796ZPnz7cv3+fhIQEZQGzjh07llnu+++/z4kTJ9i/f3+pfQ4ODtja2uLs7ExaWhpJSUm4\nuxe1or3wwgtKOrVaTcOGDXF1dcXe3h6AlJQU3NzcMDY2pkmTJsTHx5c4FicnJ9q0aYOJiQlqA63b\nU5bIuDiinF1L/EmSVPVkS8dDCnOySa5FE4BVVlKcCid7IxDV3+0hVEZA9c2bEZ9iRUhYzS3qVqyx\n2ojhoenkXkt8oseHzz+CzcsumHtqH5u2WbNmSkuEXq/nwoULfPzxx4SFhZUYJ1EcZBQr3mdnZ0e7\ndu3Yv38/KpUKna7oeY+MjESv13P+/Pkyy9VoNGzbto1XX30VT09PGjRoUGY6IQS2trZERUUhhCAs\nLKzEvuzsbJKTk0lISABAq9USGRmJk5MTt2/fxsHBoUR9hRAlbkuS9GyRQcdDksLPIepYt8qDCnIK\nyTdyx6TwTrWXJYyqd8G5X07YotenVWsZD2turOFPR+6TG/3kXTq67AIuzzrMK+sHPTatjY0NgwYN\nonv37qhUKgIDA0t0eZTHzc2NIUOG8MUXXzB58mT8/PxQqVS0adOGb775hrfffptu3brh4+NTbh7W\n1tZs3LiRUaNGsXLlSmX7w4NCjYyMCAgIoFu3bnTp0gWNpujy7KlTp9KjRw86duyIo6MjUHT1zYgR\nI9Dr9UyYMAEjI6MS+ZV3W5KkZ4NKyJ8biszou3Vq8Gh5NA2NcbKPqPbWDp2pFal21ZN3ZLwts9ZX\nT97l6WxkQqd99yhMzX184grosPxPuAxoWSV5GZpOp8PIyIgzZ87w/fffs2LFCkNX6YmoVKoyW1hU\nKlWpLhXXmKhyx3TIj02pPivr/QBlvycq+36QLR3/oy8sIOXGRUNXo0oU5BSSb+yGScHdai1HGBkD\n1TOQ9KdjlkDNda30wxTPn29RmFd1gdqVhcdw6NkEjblpleVpKN988w07d+6koKCA9etrOBqUJKne\nqD0juQws9dYV9PmGn/GyqiTHqUFlVK1lFAUdVe/6PUcu3q65gGNkvgnu22+gr8KAA4quZvnjX/+t\n0jwNZdKkSYSEhHDixAmaNm1q6OpIklRHyaADyMzK5vd7GWDlYOiqVJn87EIKjKv3Shahrp6gY8fR\nmmkZUCN4N0WNxc4/qm087J2NoWTeTamezCVJkuoYGXQA4VfvkJWn40ISxDV0QW1mYegqVYmkOCOq\n8ykW6qpvSQm96cLNmOpfQK2hSsUHkTpU/6neAbeiQM+VBWVPay5JkvSseeaDjsTkVGLv/9+lkbEZ\nBZzPaECOlQsqY8MtGV8V8rMLKNBU3yylwqhqXz56vYodx6r/JWmnNmLcxUzyT0dXe1kA8cF3SP49\npp7v+4oAACAASURBVEbKkiRJqs2e+aDjyrXSv3SFUHEtqYBrets63+WSdF9NdT3NQlW1+Z6+6kZ0\nYvWusdLUWMPI44nkXX2yOTie1B/f1o+xHZIkSU/jmQ46EpNSSU4pf8Bidr6eC0lw38y1zna55GcV\nUqCpnrEdQl118ywUFBrxy4nqvcT3JSMT+u6NIi+q5iccSzgWQerFujfLbW0TFRWFn58fbdq04fnn\nn+frr78GimZC7dOnDy1atKBv376kpdXs/C6SJFXMMx10/HHrXoXSxaTncz6jIbnWLqiq6YqN6pR8\n35jqeKpFFU7udOSiG4lpVTM/Rln6qkzp9MudKpuD40n88e0Zg5VdXxgbG7N8+XLCw8M5deoU/+//\n/T+uXbvGwoUL6d27N9evX8fPz48FCxYYuqqSJJXhmQ060jOyuJ+QXOH0QsDVxAKuCfs61+WSl1VQ\nLa0doopijtw8DbtPVV8wMLzABM/tf6DPNdxKtQD3D98m7Uq8QetQ1zk6Oirrv5ibm9OqVSuioqLY\ntWsXgYGBAAQGBrJz505DVlOSpHI8s0FHRVs5Hpadr/tfl4sL6obmVVyr6pMcrwGqeNrpKsruP+fd\nyMiu+qnnVQjGpaqx+uUP0NeOGSRla0fVuXv3LqGhobzyyivcv3+fxo0bA0WBSfFCc5Ik1S7PZNCR\nnZNLVMzTfSjFpBdwPtOsznS55GXmV3lrh1A9/Rd5RnYD9p6p+ktkG6rgg3t61Aeqfw2ayoj7z00y\nbla8hU0qW2ZmJoMHD+af//wn5ubmpdZxkeu6SFLt9EwGHbfuRFXJ2gnFXS43hD2qOtDlUtWtHYKn\nX3J+7xkXcvOrdgCprdqIcZezKTgVVaX5VgkBEVvq7irGtUFhYSGDBw9m1KhR+Pv7A9C4cWPu378P\nQFxcnLK6rSRJtcv/Z+/Ow5usswWOf9+kSfe9pTuFglDKTgEXdmSbEVFUkBkELqLoMMPIuOsIgs64\n3ivi6B0RFUQZvAKyuYAIZd8pIrLJWqBAW7rvaZL3/lEaKbTQ5k2aLufzPHls3qQnB6HNyW85vyZX\ndJSVmTlz9qJDYxaYLCRnQrp3dL2eciktKMPswNEOVdFWdGTm+vJjsmN3ksTpDYzdnknpoQyHxnWk\n8yuPYHbCdFJT8fDDD5OQkMATTzxhuzZixAgWLFgAwGeffWYrRoQQ9UuTKzpSL2ZgsWj/hF5l7FxT\nvZ9yycow4qjRDq0jHSt3hGJ24N9Fot7I79emUnq2fm+XNOebuPDtUVen0SBt27aNRYsWsWHDBrp2\n7Uq3bt1Ys2YNzz33HOvWraNt27asX7+e559/3tWpCiGqUD/fGZ3oXGqaU+NXTLn4uIfSxt+Mmlu/\nPnGX5Jswh0bjVmbfQtqrqRpOmL2YGci2XxxXHAxW3Gm94hRlxa7doXIzzdoZCPZJR/n2SxjV0dXp\nNDi9evXCYql6Ou7HH3+s42yEELXVpIqOouISMrPq5lNwQamF5FKFaP9owqzZWIsL6+R1ayL7sjuh\n/trjqKr9b/Bfbw3Eqjrm72J0mZHAlcex1pMdKtcyeOuI7mDFM/M45pOpWIBiwJRyAWNspKvTE0KI\nOtOkplfOp9b9NrrzuSaSC7wpDYhC0Tv3qPmaKs4zYTZGa45j70jHqYuh7P1Ve8GhoPJIro7AerQl\n9mpBcQbiby0gzrAdw/6NmM9WPuslb1WSizITQgjXaFJFh7OnVqqjqnD4chnHaYbiH+qSHK6VneGp\n6ftVRQ92rulYttlb02sDuCsKf061ol9bv7bE6o0Q211HQkIKIefXYd2/F7Wo6sZneas3OmQXlRBC\nNBRNpujIyc0nv8C5h4ndTH6pheRMhQzvaHSe2t94tSjOK8WiYbRD1dt3Au+hMxEcPptv9+sCBOl0\nPHaoiLJt9WdLrG+UG21vL+EWv724/7wB84mbF0PmCxkU7/mlxq+xadMmWrRowcCBAxk5ciQmk+mG\nz8/NzWX58uU1jn8jffr0cUgcIUTT1mSKDleNclTlfK6Jnwq9KQ2IRNG5bsol67KH3d+r6o21/x4V\nlm6xr1ip0ELvxkM7sij9xfUdJxU3iO6iJ6HzRSIur0fdtxNrXu0aneX/sL1Wzx8/fjwbNmzgjjvu\nYOnSpTd8bnZ2Nl9//XWt4ldHmm0JIRyhySwkvZiW6eoUKrFY4fBlM77uzbjFrww1r26PWgcozjVh\nCY5CX5Z68ydfo3yko3ZrOvYdj+HMJfsX1HbVG+n1w3lKM4vtjuEI3qF6olqXoE85jOVwtoY9PFC0\nNdmu7+vcuTO7du3izjvvxGQyMWLECJ555hlmzZpFSkoKFy5cIC4ujnXr1jFw4EDeeust5syZw+ef\nf86mTZvYuHEjL7/8Mi+99BJbt26lU6dOFBQU8OmnnzJt2jQOHDiA1Wpl0aJFREdrX/8jhBDQREY6\nCouKKapmXt3V8kstJGfpuOwTjc6j7qdcsrPsW9uh1nJRrNmiY9kW+9cvDNS5c/uKM5S5quBQVMI7\nuJGQeJmowo2wbxuWy9maw5adT8NUi2Z1FWtANm/ezJdffsmrr77Kli1bSEpK4tKlSwC0bdvW1rti\nyJAhbNiwgdDQ0EqjFYqicOnSJX766Sc2btxI7969bY+98cYbJCUlMWPGDObOnVvpdYUQQosmMdKR\n4YA3B2c7l2Pigs6b+CB/3PPSUK2ObQ1enaKcK6MdptqNdqg6N6C0xs/ffiiGS1n2nbHygNlIyMrj\nWCx1/8bn7q8jup0Z46VjWH5N0zSqUZ3CrckY/3hXjZ77+eefs337dhISEoiOjqZr165A+cjH6dPl\n60gSExOv+76rC46KAiIlJYUOHToA0KVLF9asWQOUFx0bNmzAbDbTrl07+/9gQghxjSYx0pFxOcfV\nKdSIxQqHLps5rmuG4hdSZ6+bneVV6+9Ra7EWpbTMjRXbbrzosSoKKg/n6wn++jhqHRccIW3caNcz\nlxbWreiTN2O54Lw1QUXb9tf4uePHj2f9+vX861//ok2bNuzduxeA/fv306JFCwB0uvIfa4PBgNlc\nXib5+/vbRkIOHjwIQGxsLIcOHQLg559/BiArK4tNmzaxefNmXnnlFRnhEEI4VKMf6VBVlYzMhlF0\nVMgvsZBcoqN5QDSh5iysJc7ddVOUXYolKBK96UKNv6c2RUfSTzFkF9TujBUjCo9esGLeeqpW36eF\nm6eOmI5WPHNOYj5zjroZa4Ki3b+gmi0obrWbsnrmmWeYMGECZWVljBgxgoiIiEojGuHh4WRlZTF6\n9Gg++ugjYmJiGDx4MK1atSIyMpLw8HC6dOlCv379SEhIwGAwEBgYiI+PD4MGDaJTp062WLKQVAjh\nCIrayD/K5OTms9HOxXr1gV4H8UFuTp9y8Q50J9jneI2fXxwaR5Hx5otzC0vceW5eAIUlNZ+YCNDp\nGH+4iNKf62bHUWCsgbCIXNRjB1ELXbNmJGbxW3h2bFPnr2uxWNDr9Xz11VecPn2a5557rs5zqEuK\nolQ5eqMoCucjKy+Yjb5w3ram5WqPPfaYjACJRq2qnweo+meitj8PDhvpWL9+Pf/4xz9QVZXg4GA+\n/vhjAgMDNccdMGAASUn2d25sKFMr1amYcvHzCKO1j8lpu1wKs0sJCIxAX1bDRY01/OS7dm8UhSU1\n7z7aXO/GvTsyKT3j3L83nUEhupMOn9IzmH89idWxBw/XWvG+wy4pOv7+97+zY8cO3Nzc+Oqrr+r8\n9YUQTYtDio7Lly/z6quv8t133+Hl5cWJEycoLa35IsMb0Tqse7mBTa1UJ6/E7PQpl9wcX4K8a/bu\nq+pu/veSU+DF2r01bwTWWW+kz7pUSi87bzrJN0JPRIsidCcPYT2Y55SFofYoTj4C/3Vvnb/uG2+8\nUeevKYRouhyykPS7775j/PjxeHmVL0hs3bo1a9asYcCAAfTs2dN2+uPEiRP505/+RJ8+fXj11VcB\nWLBgwXXP27NnD4mJiTz44INkZ5fvPFm7dq3teV988UWNc8vOqd1agvrubI6Jn4p8MQVEgs6x64AL\nskqwGCJq9Fy1BrXgN7siMJXVrFV6f507vVaeocwZBYcOojq7kdAljYjsJNi3A2s9+3dRsv+IDNkL\nIRo9h7xrXbx4kYiIym9WY8aMISkpiR9//JG3337bdn3YsGFs2bKFb7/9ttrnvfrqq6xatYpPP/2U\n8+fLW13369ePpKQkduzYwYcfflijvIqKSzCV1ZfPso5jsaocumzmlD4cnV+wQ2Pn5vrU6Hk3KzrS\nc/zY+FPNplXusxiJX3oCS1FZjZ5fU55BelrfbqFd5EG8j/yI+fARsNp3XoyzWbLzKDtd+yZtQgjR\nkDhkeiUiIoLU1Mq/ML///nvee++98t0jGRm26xV9ASpGRap6Xk5ODlFRUQC0aVM+z71nzx5eeeUV\nysrKOHLkSI3yyq1lS+qGJrfYzL5iPc0Dogg1ZztkyqUgsxT/gHD0pks3fJ6q3PhT+cptIVisNy86\nJubrMX5/HEd+xg9LcCPYKx3z4cOwz1xnu1C0Kj2egjFOun8KIRovhxQdv//97xk1ahRjxozBx8eH\nEydO8Nxzz3Hw4EFKSkoqdTu8do3GG2+8webNmys9LyAggAsXLuDv78/x4+U7Kt5++20++eQTIiMj\nbYXIzeTl2d9yuyE5m1NGqs6XdkEBGPIuaf40n5vjS5DXTYqOG5wwey49mB1HblxwGBR49IKKZYtj\ntsQafBRiOljxSP8V84mL9WatRm2YzshIhxCicXNI0RESEsL06dMZPnw4AEFBQYwbN44+ffrQs2dP\n2y6WqhaFDh8+nL59+9KjRw8CAgIAeOmll7j77rtp06YNsbGxAIwcOZIRI0bQpUuXGu+KcfWpsnXJ\nYlX55bKZAM8wWvmYsObZf9ZMQWYpAf5h6Mqq37KqKlaqG55YttUPVa1+zUSATsf4I0WUHtC+JTa4\ntRvNgrOxHvkFNbm0QRYbFUwyvSKEaOQadZ+OpC37Gv0US3ViA42EmDKxltrXd8InxIMgz1+rfTwn\nOgZLFYXF8fNhvLa4+rf+GL0bI3dlUXrK/tb0eneFmE7glX8K86kUu+PUN+4dbiH2y7dv/kRhN+nT\nIcTNNYg+HfWNqqoUFDadkY5rpWSbSNX50S4oEDc7plwKLpcQ0KoZOlPVR8ir1YwpLNnsAVRd6HVw\nMzDgx4uUpts37eUf7UZ4TD7Kr79gPVDYoEc1qlIm0ytCiEau0RYdxSWlWCz1c6dCXTFbVQ5eNhPg\nGU4r71Ks+bWbcsnNDSDQs5qiQ71+eeaBk1EcT6264Oirc6fDqjOYCmp3BoviBtGd9Piaz2I+ehw1\nvdpZnQbF6m1Abe5HWaiRMj+FUkMpJZY8ovOz8fDV3lRPCCHqo0ZbdJSW1v6AscYq58oulxYB0QSX\n1XzKJf9yCf5xoejKMq57TKXy9larVWHZlqp3YN9rdSd8+QksNezZAeDdzI2oVkXozxzG8ktOgxzV\nUIM9MUf7YA42YvK2YnIrprgsl6L8dEoLrkwvFV65XVGceVGKDiFEo9WIiw7H9nxoDM7kmDiv96Nd\nYABuuWmg3rwIyMsPJMCjctGhooNrdq/sOhrDuYzrp00mFLrh8e2vNRudUFQiOhgINFzEfOQI7LPW\n6+2uqgJqhA/mSG/MgXpMXhZKdUUUl+ZQlJdGWfGVUZ/cK7caKM66RGCLBKflLIQQrtR4iw6TFB1V\nMVtUDl62EOgZTpxHCdb8rBs+Py+jBL9rRjtUN2Ol55SZ9SzfWrk8cAMmp6lYNp28aU4eAXqi25kw\npB7FciyjXo1qqG56rDG+mMM9KfNXMHmUUUIBxcXZFOWmYS0rLZ/vybpy06go68ZblYUQoiFrtEWH\nySTTKzeSXWxmX7EbLQOjCTJdxlpaUu1z8woCCXC/qujQGyo9vuVgDBm5v52x4qvTMfHXYkqTb/wG\nGtrWQIh/BtbDh1D3lblsVEP1MmCJ8cXczJ0yPwWT0USxJY/iokyKc9PLT/c1AdfPMjmcFB1CiMas\n0RYdMr1SM6ezTZzT+9EuMBC33EtQxdanvPQS/OJC0JWVn3BbXnSUlwglJgMrt/9WsETq3Bi1N5uS\nE1V/7Dd46YjuaMUz8zjm06l1VmhYAz2xRPtgCTFg8lEpdSumxJxHUUEGJRU9TYqu3FyoOFt77xIh\nhKivGm/RIdMrNWa2cGXKJaLaKZf8wiD8jVeKDp0bFUXHj8kx5BWVL1ho72Zk4PoLlKRdv7YjsKUb\nYWG5qEcPou4vcfgUiqqANdwHa6Q3ZYFumLzMlOqLr1pfcWUkJu/KrZ4yO+H0YCGEqC8abdFhkqKj\n1ipNuZRexmr6bQQjN60E37hgdGWZqHo3oJT8Yg++21W+WLK3zp3Oq89gyv9tWktnVIjpqOBdcgbz\n8VNYNbahUN10WKN9sYR7Uhagw+RhpoRCikuyKMpJw1J2Jd/sK7cGyFpWWqPnbdq0iR9//NF2WnNV\nUlJSmDlzJvPnz+eJJ55gzpw5jkpTCCHs0niLjjIpOux1OttEqt6f+MBA9FdNueQXBuNvzETV6QH4\nfncUxaYcRqjuRC47gfnKlljfSDciYwtRThzEerCgVqMaqqcb1hi/8vUV/gqlRhMl1nyKizIpyk1H\ntZihjDpZX+EKlrKar0Wq6liB6p7TmAqOSZMm8c033xAWFsbPP/8MwKxZs5g3bx7NmjUD4LXXXmPY\nsGGuTFMIUYVGW3RIl2JtTBaVny9bCPKKoKV7Mdb8bHLTSvCLCwKdnqw8H35MzmN8kQHPb34FPUR1\n0eNPKuYjx1Avq9Vuk7UGuGOJ8sUSasTkq1LqVvLb+or8zPK/vGLKb02MbbSmBlRVpVOnTnTs2JHD\nhw/z2Wef0alTJ2bMmMGmTZto166d7bl9+vRhy5YtLFiwgM8++4zCwkJee+01Bg0a5Iw/hlNNnDiR\nqVOnMn78+ErXn3zySZ588kkXZSWEqIlGW3QIx8gqMpNVZCAuMJrA0svkFXngHmDmux3NmHQxF8+D\nZ4m63Yzb2cNYDmfZRjWsYd5YorwxB7lh8rJQoi+h2JRDcV4apqIriyryr9yEjaWG0ysVMjIyWLBg\nAXv27OGzzz7jmWeeYe/evWzatInFixezbt064LcRjzFjxvBf//Vf5OXlMWrUqAZZdPTu3ZuUlOvP\n3JHzUISo/xpt0VGDkWdRC6eyTRj1/sSXKChpRdy6/RyB+jOYE9Io8gdTjIESvCkuLe9fYanoetqA\n11e4gmqp3X6e1q1bYzAYiIqKIicnh7Nnz9KpUycAEhMTbUVHhe+//5733nsPVVXJyGhcc1Tvv/8+\nn3/+Od27d+d//ud/8Pf3d3VKQohrVN23WoirGI1u+AS6owtRSTZkkfTLGTYHn+bHWIVdbUM4FOrN\naSOklZWQn5/zW8Ehak1RavcjefW6DlVViY2Nta1zSE5OrvQYwBtvvMGaNWtYuXJljdaENBRTpkzh\n1KlT/PTTT4SHh8s0ixD1VKMd6RD28fQyoveEUkMRuUoWl6znybNe6eFtLb8FWYI4df5iFd8dAoTg\n7u+Ov68Pvp5GvI06PJUyjJYiDCW56Aovo+amopTad9Jso6fTVgiEhYWRmJhIv3796Ny5s+16RYEx\nfPhw+vbtS48ePQgMbDxnvISGhtq+fvTRR7n77rtdmI0QojqNtuhQaDyf4pxBp9Ph4W1A8bBQ7FZA\nlprBBes5TOqVNQUaunaVlpaSXlrK9efT6oEwUMLwDPTE39cbXw8jXgbwpLwwcSvJQVeQjpp7AaUW\niyobi5qOdPTr149+/frZ7sfGxvLpp58CVLmNdvPmzQBMnz6d6dOnOyBT11JVtdIajkuXLhEeHg7A\n119/TYcOHVyVmhDiBhpt0SE1x28MBjeMXnqs7iYK9XlkqJe4ZE5FVa780nbBYSfFxcUUFxdzfdNv\nAxAF+ii8/bzx9/HC18OAlxt4YMJoLkRfnP1bYWJpXFujazu90hT98Y9/ZOPGjWRmZtK8eXNmzZpF\nUlISP/30EzqdjhYtWjB37lxXpymEqEKjLToa03x1bXh4GHDzUigzlpCnZHNJTSXbcqXNt8pvBUYD\n+N9TWFhIYWFV0zAeQHMUYyy+Pj74eXvi42HAy03FQy3FUFaAW3EWSn465F0Ea30+q7YyRd9ofyQd\n5j//+c911yZOnOiCTIQQtdVof8O5Gw03f1IDpigKnt5GFA8rJW6F5CiXuWA5R7F6pY12w3mftZuq\nquTl55OXX9W+W2+gJTqPVvj5XilM3N3w1FvLCxNTPvqiLJT8i6j56Sj1ZLulu2/jWWchhBDXarxF\nh7vx5k9qINzc9Lh7uaF6mCnS55HBJdLMFzBXDFs0gQLDXlarlZzcPHJyqzpwxRfwxc07obww8fLA\n212Pp86Cu7UEoykPXVEmSt4lKLhcJ/m6+wXVyesIIYQrNOKio2GOdLi7GzB46SgzlpCvyyFdvcBl\nc3r5dMjV0yPCYcxmM1nZOWRV2U8kEAjE4GvA388HP0+P8h05OjNGSzEGUx76wkzIvQDFOZpz8fAL\n1hxDCCHqq0ZbdHjU85EORQEPL3d0HlZKDUXkKJlctJ6n0HplquDK9tTyJ7sqS1GhrKyMy5nZVD3e\nEQwE33CrsFJ4GSXvApQU3PB13KXoEEI0Yo226HA31p+iQ68v356qupspdssnkwwums9RxpWdFzI9\n0ijcdKswN98q7B4Qet13CyFEY9F4iw4XjXQYjeXbU83upRTocslQL5FuvujS7ami/rjZVuHOga3q\nPikhhKgjUnRo4OFpxM0TTMZiW/fOXOuVeX2ZHhF2CAkJcXUKwkFiIyI4e+n68rJ5eDgpF6vq6CtE\n49doiw5Hrumwde90t1DsVki2ksEF8zlKudIxU6ZHhAPodDoCAgJcnYZwkLOXLnE+Mvq669EXzrsg\nGyHqh0ZbdOj1Ory8PCgqql0rbYOhfHuqxb2MQn359Eia5QLWimELKTCEkwQGBqLTSUdSIUTj1WiL\nDgA/H+8bFh0eHgbcPK9079Rlc8maSra1iu6dQtSBiIgIV6cghBBO1aiLDl9fLy6lZ5Z37/Qyonj+\n1r3zouU8ReqVFttXr78QwkWaN2/u6hSEEMKpGnXR4R4Ml4tOccmcKt07Rb0XGxvr6hSEEMKpGnXR\n4eftw3lziqvTEKJGpOgQQjR2jXrVWohnCAZdw2yHLpoWX19fAgPlsDchROPWqIsOnaIjzDPM1WkI\ncVMyyiGEaAoaddEBEO1z/T55IeobKTqEEE1Boy864vzjXJ2CEDclO1eEEE1Boy86Wvi2QK/oXZ2G\nENVSFIXWrVu7Og0hhHC6Rl90GPVGYnxiXJ2GENWKi4vDx8fH1WkIIYTTNfqiA2SKRdRvHTt2dHUK\nQghRJ5pE0dHKT44LF/WXFB1CiKaiSRQdEd4ReOo9XZ2GENcJCgoiOlp2WAkhmoYmUXToFJ1MsYh6\nSUY5mh43NzcURal0i4mRdWeiaWjUbdCv1iGoA4eyDrk6DSEqkaKj6TGbzcydO7fStccee8xF2QhR\nt5rESAfALQG3yBSLqFeMRiNt27Z1dRpCCFFnmkzR4aZzo31we1enIYRNt27dMBqNrk5DCCHqTJMp\nOgA6B3d2dQpC2PTu3dvVKQghRJ1qUkVHjG8MQe5Brk5DCCIiIrjllltcnYYQQtSpJlV0AHQK6eTq\nFISgV69erk6hwZo0aRJhYWF06vTbz3J2djZDhgyhbdu2DB06lNzcXBdmKISoTpMrOjqHyBSLcC03\nNzduv/12V6fRYE2cOJG1a9dWuvbGG28waNAgjh07xsCBA3n99dddlJ0Q4kaaXNER6B7ILf4yrC1c\np2vXrnLWiga9e/cmMDCw0rWVK1cyYcIEACZMmMCKFStckZoQ4iaaXNEB0CtChraF68gCUsdLT08n\nLCwMgPDwcNLT012ckRCiKk2y6Gjh14Job2k9LepeRESE9OaoA4qiuDoFIUQVmmTRAdArUkY7RN27\n66675A3RCcLCwkhLSwPg0qVLNGvWzMUZCSGq0mSLjviAeEI8QlydhmhCIiIiSExMdHUajYKqqqiq\nars/YsQIFixYAMBnn33GPffc46LMhBA30mSLDkVRuCPiDlenIZqQ4cOHo9M12R85h/njH//IHXfc\nwa+//krz5s2ZP38+zz//POvWraNt27asX7+e559/3tVpCiGq0GQOfKtKp+BOJJ1PIr8s39WpiEYu\nMjJSRjkc5D//+U+V13/88cc6zkQIUVtN+mOXm86N/lH9XZ2GaAKGDx8uazmEEE1eky46ALqGdiXc\nK9zVaYhGLCoqim7durk6DSGEcLkmX3ToFB3Dmg9zdRqiERsxYoSMcgini4mJQVGUSreYmBhXpyVE\nJU16TUeFFn4taBfYjiPZR1ydimhkOnToQJcuXVydhmgCzp8/z9y5cytde+yxx1yUjRBVa/IjHRWG\nNB+CXtG7Og3RiBgMBv7whz+4Og0hhKg3pOi4ItA9kNvD5RAu4TjDhw8nJER6wQghRAUpOq7SJ7IP\nvgZfV6chGoHIyEgGDx7s6jSEEKJekaLjKu56d+6Jk06GQhtFURg7dix6vUzXCceLjYi4bsGoIxYq\nVxc3NiLCAVkLUU4Wkl6jtX9rejbrye703a5ORTRQvXr1onXr1q5OQzRSZy9d4nzk9QdWRl8475S4\nLdIvXVfUREdHc+7cOU2vJ5omKTqqMLj5YE7mnSSzJNPVqYgGJiAggPvuu8/VaQjhMGazWXbFCIeR\n6ZUqGHQG7ou7D50i/3tEzel0Oh599FG8vb1dnYoQQtRL8q5ajSifKPpG9nV1GqIBueeee2RaRQgh\nbkCKjhvoE9mHKO8oV6chGoCOHTsydOhQV6chhBD1mhQdN6BX9IxuPRpvgwyXi+oFBQUxceJEaXUu\nhBA3IUXHTfi7+zOm9RjpViqqpNfrZR2HEELUkBQdNRDjG8PdLe92dRqiHho5ciRxcXGuTkMI+Zvn\nNgAAIABJREFUIRoEKTpqqEtIF+4Iv8PVaYh6pG/fvtJ1VAghakGKjloYFDOINgFtXJ2GqAe6desm\nh7kJYaeYmJgqu5/GxMS4OjXhZNIcrBZ0io77W93PJ4c/Ib043dXpCBdp06YNDz/8MDqd1OxC2OP8\n+fPXNRwDaTrWFMhvzVpy17szPn48IR5yemhTFBMTw5QpUzAYDK5ORYgGoaozXUTTJUWHHXwMPkyI\nn0CwR7CrUxF1KCQkhKlTp+Lp6enqVIRoMCrOdLn6JpouKTrs5Gv0ZUL8BILcg1ydiqgDvr6+PPHE\nE/j7+7s6FSGEaLCk6NDAz+jHhHYTCHQPdHUqwokCAwN5+umnadasmatTEUKIBk2KDo38jf5MiJ9A\ngDHA1akIJwgNDeXpp58mPDzc1akIIUSDJ0WHAwS4B/Bf7f5LFpc2MpGRkTzzzDOEhMjfqxBCOIIU\nHQ4S4B7ApIRJxPrGujoV4QBt2rTh2WeflTUcQgjhQFJ0OJCnmyfj2o6jQ1AHV6ciNOjevTt//etf\nZZeKEEI4mBQdDuamc+OB1g/QP6q/q1MRtaQoCr/73e945JFHatSHY+3atfTt25eBAwfy9NNPY7Va\nmT9/vu3xAQMGYLVanZmyEOIq0um0/pOOpE7SP6o/zTybsfzUcsqsZa5OR9yEt7c3Dz/8MB061GyU\nKjMzk9dee40ffvgBDw8P3nzzTebNm8cXX3zBxIkTAWrdBElVVWmcpFGLFi3w9/dHp9NhMBjYvXu3\nq1MSdUg6ndZ/UnQ4UUJQAsEewSw9uZSM4gxXpyOq0bJlSyZPnkxQUM17rnz77beMHz8eDw8PAKZN\nm4anpyf+/v4MHDiQF198EVVVefbZZ9myZQuPP/44EydOZPfu3Tz33HOYzWYeeeQRJkyYwIABA+jZ\nsycXL15k4cKFzvpjNgk6nY6NGzcSGCjb2IWoj5r89MqmTZuYPn267f6sWbNYvXo1w4cPZ8CAAdxx\nxx0kJyfbHh8yZAhvvfVWjeOHeYUxuf1kejbr6dC8hWPceeedPPPMM7UqOAAuXrxIZGSk7b67uzv9\n+/enY8eObNiwgUGDBgEwfvx4tmzZwoIFCwCYMWMGq1evZsuWLXzxxReUlZWPgt13331ScDiAqqoy\npSVEPdbkiw64fhj83Llz3H///SQlJbF161batm0LQFZWFiEhIWzZsqVW8Q06A79v8XvGthmLt8Hb\nYXkL+3l6evL4448zevRo9Hp9rb8/IiKC1NRU2/3S0tLr1oEoikL79u0xGo221zhw4AAjRoxgwIAB\npKenk5GRgaIoJCYmavsDCaD8//ngwYPp0aMH8+bNc3U6opGQtSKOI9MrVfDy8mLbtm2MGDGC4OBg\nvL3LC4VVq1bxwAMPsG7dOi5evEhERESt4t4ScAtTOkxh5emV/JrzqzNSFzWQkJDA2LFjNfXf+N3v\nfscDDzzA2LFj8fT0ZPbs2YwcOZLFixfbnlPVGo1u3bqxdOlSPD09sVgs6PV6VFWVE2sdZNu2bURE\nRJCRkcHgwYNp164dvXv3dnVaogGJjYjg7KVL112XtSKOIb/pqhATE0NsbCwDBgxgyJAhpKeXH2O/\ndu1ahg0bxn333cfy5cvtiu1t8OaPbf7IXbF3YdQZHZm2uAk/Pz8eeeQRnnjiCc0Nv0JDQ3nhhRcY\nOnSobdRi8uTJ9OjRg/vuu4+tW7dWuSh05syZDB8+nIEDBzJmzBig9gtORfUqPgiEhoYycuRIWUgq\nak0OqHOuJj/S4eHhQUlJie1+SUkJfn5+vPTSS7z00kv83//9H++++y5///vf2blzJw888AAWiwWj\n0ciUKVPsft0eYT2ID4znx3M/ciDzgCP+KKIaiqLQp08fRo4ciZeXl8PiDhs2jGHDhlW69t///d+2\nrzds2HDd1z169GD9+vWVvufq5wn7FRUVYbVa8fHxobCwkB9++IGXX37Z1WkJIa7S5IuOW265hZ9+\n+gkAq9VKcnIyU6ZMwWw24+bmRmhoKFarle+++44ZM2bYtkNOmDCB3NxcTR0rfY2+jGw1kh5hPfgu\n5TsuFF5wyJ9J/CYqKoqxY8fSqlUrV6cinCwtLY2RI0eiKApms5mxY8cyZMgQV6clhLhKky86goKC\nuP/+++nduzeKojBhwgT279/PqFGj8PT0xGg0Mn/+fJ555hn+8Y9/2L6vV69erFq1inHjxmnOIdon\nmkcTHmX/5f2sP7eeQnOh5phNna+vL0OHDmXgwIF2LRQVDU/Lli1tHyCEEPVTky86oHwx0LULgkaM\nGFHp/qJFiyrdnzx5skNzUBSFbqHdSAhMYMvFLexJ24PJanLoazQFPj4+DBkyhP79++Pu7u7qdIQQ\nQlxFio56xsPNg8Exg+kd0ZvdabvZlbaLInORq9Oq93x8fBg8eDADBgyQYkMIIeopKTrqKU83T/pF\n9eP28NtJzkhm+6Xt5JnyXJ1WvVNRbPTv39/WHVQIIUT9JEVHPWfUG7kt/DZ6NOvBz5k/s+PSDtKL\n012dlsu1atWK3r170717d4xG2XoshBANgRQdDYRep6draFe6hnblQuEFDlw+wMHMg01q6sXX15fb\nbruN3r17Ex4e7up0hBBCk5iYGM6fP1/pWnR0NOfOnauXcR1Bio4GKNI7kkjvSIbEDOF47nEOXD7A\nrzm/YlEtrk7N4RRFsXWV7NKli+xEEUI0GlWdiuuILqfOiusIUnQ0YHqdnvjAeOID4ykyF3Ek6wgn\nck9wOu80JZaSmweop3x9fUlISKBDhw60b9/e1oZeCCFEwyZFRyPh5eZFYrNEEpslYlWtpBamcjL3\nJCdzT5JamIpVrb8nbyqKQmxsLB06dKBDhw7ExsbKWSRCCNEISdHRCOkUHTE+McT4xNA/qj8llhLO\n5J3hQuEF0orSSCtOI6c0xyW5KYpCaGgoMTExtluLFi3w8fFxST5CiIapuoPZ6iNn5dqQ/h9UkKKj\nCfDQe9imYSqUWkptBUhaURpZJVkUlhVSYC6gqKwIFdXu19PpdPj5+eHn54e/vz8BAQFERUURExND\ndHS0bG0VQmhWcTDb1aIvnK/m2a5VVa6gPV9nxXUmKTqaKHe9O819m9Pct/l1j1lVK0XmIgrLCstv\n5kLKrGVYVStW1Yrqo9IpqhM6nQ69Xo+bmxu+vr74+/vj5+eHj4+PnJwqhBDiOlJ0iOvoFB0+Bh98\nDNVMeYQBCXWakhBCiEZAVusJIYQQok5I0SGEEEKIOiFFhxBCCCHqhBQdQgghhKgTUnQIIYQQok5I\n0SGEEEKIOiFFhxBCCCHqhBQdQgghhKgTUnQIIYQQok5I0SGEEEKIOiFFhxBCCCHqhBQdQgghhKgT\nUnQIIYQQok5I0SGEEEKIOiFFhxBCCCHqhBQdQgghhKgTUnQIIRqFNWvWEB8fT5s2bXjzzTddnY4Q\nogpSdAghGjyr1cpf/vIX1q5dy6FDh1i8eDFHjx51dVpCiGtI0SGEaPB2797NLbfcQmxsLAaDgTFj\nxrBy5UpXpyWEuIYUHUKIBi81NZWYmBjb/ejoaFJTU12YkRCiKlJ0CCGEEKJOKKqqqq5OQgghtNi5\ncyczZ85kzZo1ALzxxhsoisJzzz1X6XmKorgiPSGapKrKCyk6hBANnsVioW3btqxfv56IiAh69uzJ\n4sWLadeunatTE0Jcxc3VCQghhFZ6vZ7333+fIUOGYLVamTRpkhQcQtRDMtIhhBBCiDohC0mFEEII\nUSek6BBCCCFEnZA1HUIIcQN79uyhR48emuOkp6dTUlJiu9+8eXO7Y2VlZd3w8aCgILtjV9i6dSvH\njx9n4sSJZGRkUFBQQMuWLe2O99RTT/Hwww/Tvn17zblV+OCDDxg7diwBAQEAZGdns3jxYqZMmaIp\nrsVi4dtvv+XMmTOYzWbb9SeffFJT3IbEarWyc+dO7rjjDofGlTUdQghxjcOHD7N48WIWL15MQEAA\ne/futTvWqlWreOqpp7hw4QLNmjUjJSWFdu3acejQIbtjtmzZEkVRqt6SqCicOnXK7tgAs2bNYu/e\nvRw7doxff/2VCxcuMGrUKLZt22Z3zI8//pj58+djNpuZOHEif/jDH/D399eUZ5cuXfjpp58qXeva\ntSv79+/XFPf3v/89Hh4edOzYEZ3utwmBl19+2e6YHTt2rHLLtqqqKIrCzz//bHdsgIyMDN58800O\nHz5cqbjdsGGD3TEd8f/yWjLSIYQQwJkzZ2yFhtFo5MyZM+zdu5cWLVpoijt9+nR27tzJoEGD2L9/\nP0lJSXzxxReaYp4+fVrT99/M8uXL2b9/P926dQMgMjKS/Px8TTEfeeQRHnnkEY4dO8b8+fPp1KkT\nvXr14tFHH2XAgAF2xbRYLLY37Yr7JpNJU54A58+f11wEXOubb75xaLxrjR07lgcffJBvv/2WDz/8\nkM8++4zQ0FBNMe+8806WLVvGfffd57AeN1J0CCGavNtvvx2TycTo0aNZsWIFcXFxtGzZUnPBAWAw\nGAgODsZqtWK1WhkwYADTpk3TFPPo0aPEx8eTnJxc5eMVxYK9jEYjiqLY3mgKCws1xatgsVg4evQo\nR48eJSQkhM6dO/POO+8wd+5cvvzyy1rHGzZsGA8++CCPPfYYAHPnzmXYsGGa8xw6dCg//PADQ4YM\n0RyrQmxsrMNiVSUzM5NJkyYxZ84c+vXrR79+/TRPC86dO5d33nkHvV6Pp6enrcDLy8uzO6YUHUKI\nJi8sLIxffvmFtLQ0MjIyiIuLc9gnu4CAAAoKCujbty9jx46lWbNmeHt7a4r5zjvv8NFHH/HUU09d\n95iiKJqG1AFGjx7NY489Rk5ODvPmzePTTz/l0Ucf1RTzb3/7G6tXr+bOO+/kxRdfpGfPngA899xz\ntG3b1q6Yb775JnPnzuXf//43AIMHD+aRRx7RlCeUF6H33nsvqqpiMBgc8mZbYefOnUydOpUjR45g\nMpmwWCx4e3trjm0wGACIiIjg22+/JTIy8qZrf25G6+hWVWRNhxBCALm5uXz99dcsXryYEydOkJ2d\nzdq1a21vjvYqLCzEw8MDVVVZtGgRubm5jB07luDgYAdl7hzr1q3jhx9+QFVVhg4dyuDBgzXFmz9/\nPqNHj66y4MrNzdW8vsORWrZsycqVK6tdh6FF9+7d+fLLLxk1ahR79+5l4cKF/Prrr7z++uua4n7z\nzTf06dOHc+fOMXXqVPLy8nj55ZcZMWKE3TEr/s2ePn2a6dOnc+7cOS5evKjpZ0KKDiGEuEZaWhpf\nffUVX375JWfPnuXcuXOuTqlKrVq14plnnuHxxx+3XRs+fLjm9QOnT58mIiICDw8PAIqLi0lLS9M8\n3ZSamkpKSkqlHSF9+/atdZzRo0fz1VdfVVsUaF2P0bdvXzZu3FhpEamjdO/enb1799KpUydbns5Y\nsOkIf/rTn9DpdGzYsIEjR46QnZ3NkCFD2LNnj90xZXpFCCGuERYWxkMPPcRf/vIXzp49a1cMX1/f\nG35KdsRQvcFgICkpiV27djF37lyMRiOpqama444aNYrt27fb7uv1ekaNGqXpzeb555/nyy+/JCEh\nAb1eD5RPBdlTdMyZMwdw3uLMuLg4+vfvz+9+9zvc3d1t1x2xZdbLywuTyUSXLl149tlniYiIwGq1\n2h3vrbfe4tlnn2Xq1KlV/nt777337I69a9cukpOT6dq1KwCBgYGaF+pK0SGEaPJeeeUVRo8eTXx8\nPKWlpQwbNowDBw7g5ubGf/7zH7sWAVbMh0+fPp2IiAjGjRtnG66+ePGiQ/L28vLi//7v/3jrrbfo\n06cPS5Yscch0gNlsxmg02u4bjUbNbzbLly/n2LFjld7E7RUREQE4b3Fmy5YtadmyJSaTySG7Ya72\n+eefY7FYeP/995k9ezbnzp1j2bJldserOGOoe/fujkrRxmAwYLFYbP+mMjIytI/+qEII0cQlJCSo\nVqtVVVVVnTt3rtq/f3/VbDarhw8fVnv06KEpdqdOnWp0zR5dunSxfb1u3Tq1bdu2amhoqOa4gwYN\nUleuXGm7v2LFCnXgwIGaYg4bNkzNz8/Xmpqqqqrq4+Oj+vr62m4V9yv+6yj5+fkOy7kh+uKLL9S7\n775bjYqKUl988UW1TZs26ldffaUppox0CCGavIotogBr165lzJgx6PV62rVrR1lZmabY3t7eLFq0\niDFjxqAoCosXL9a8e6XCK6+8Yvt60KBB/PDDDyxYsEBz3A8//JCxY8fyl7/8BVVViYmJYeHChZpi\nenl50aVLF+68885Kox32DP87Y1fF1X755RfGjRtn2/0REhLCwoULHdJNtap1KP7+/nTv3p2XXnqp\n1guM77777huObq1atcquPKG890diYiLr169HVVVWrFih+fRmWUgqhGjybrvtNj7++GPCwsJo27Yt\n+/bts7X8jo+P5+jRo3bHPnPmDE888QTbtm1DURR69erFu+++65AeIFDe+vv48eOVulDas06iKgUF\nBQD4+PhojvXZZ59VeX3ChAma4iYnJ7N161YURaF379629Qda3HHHHfzzn/+0NS3buHEjL774YqV1\nLvZ69tln0ev1/PGPfwTgyy+/pKioiPDwcLZu3crq1atrFW/Tpk0AfP3111y6dImHHnoIgMWLFxMW\nFsbs2bM15WuxWEhLS6u0+FdLC38pOoQQTd6uXbuYMGECGRkZ/O1vf+Oll14C4LvvvuPzzz9n8eLF\nLs6wah9//DFz5szh/PnzdOnShZ07d3L77bdr7tNRWlrKsmXLrjt7ZMaMGVpTdqhXXnmFJUuWcN99\n9wGwYsUKRo0aZfv7s1fnzp05cODATa/Zo1u3btc1dau41rFjRw4ePGhX3IpdMTe7Vhv/+te/mDVr\nFmFhYej1eoe0bJfpFSFEk7dt2zZb8ytFUZg9ezYhISH07t1bc8FRUlLCJ598wqFDhyqNRnz66aea\n4kL5Lo49e/Zw2223kZSUxNGjR3nxxRc1x73nnnvw9/cnMTHRIQs/AY4fP84LL7xw3dkgWs6JWbRo\nEQcOHLBt7X3++efp0qWL5qIjLi6OV199lXHjxgHwxRdfEBcXpylmBYvFwu7du229Lvbs2YPFYgHA\nzc3+t+TCwkJOnTply/P06dOaO8nOmTOHY8eOObSnjBQdQogmr6o1AmfOnOGf//wnM2fOZMyYMXbH\nHjduHPHx8axdu5YZM2awaNEizfPiFTw8PGxvuKWlpcTHx3Ps2DHNcc+fP8+aNWs0x7naxIkTmTVr\nFn/7299ISkpi/vz5mraKQvmZMCUlJZX+H0RFRWnO9dNPP+Xll1+2jaD06dPHIUUilI9OPfzwwxQU\nFKCqKn5+fnz88ccUFhbywgsv2B139uzZ9O/fn7i4OFRVJSUlhblz52rKNSYmxuFN22R6RQghqpGV\nlcWgQYOqPeOkJioaP1U0gyorK6NPnz7s3LlTc34jR45k/vz5vPvuu2zYsIHAwEDKysr47rvvNMWd\nPHkyU6dOpWPHjppzrJCYmMi+ffsqTSFUXKutip4UZ8+eZc+ePQwePBhFUVi3bh09e/bk66+/dlje\nzpKbmwvg0Df10tJS2/qj+Ph4u0ep3nnnHQAOHTrEsWPHuOuuuxzWr0RGOoQQohpBQUFVHh9fGxVn\nYgQEBPDLL78QHh5Oenq6I9Jj+fLlAMycOZMBAwaQm5vrkAPPtm7dyoIFC2jZsiXu7u4Omct3d3fH\narVyyy238P777xMVFWVbqFpbFT0pEhMTGTlypO16//797c4PnLsTpMLVO46u5oj1Mvv27bOtw6lY\nfzJ+/Phax6kY+WvevDnNmzev1K9Eax8YKTqEEKIaSUlJBAYGaooxefJksrOzefXVVxkxYgQFBQXV\nvvHUhsVioX379rZPtv369dMcs8L333/vsFgV5syZQ1FREe+99x7Tp09nw4YN1e5ouRmtO16q8/TT\nTwPV7wRxhKu3S5eUlPDNN984ZLpt3LhxnDx5ki5dulTq+GpP0fHyyy8DsGTJEkaNGlXpsSVLlmjK\nU6ZXhBBNXlW9E7KysoiMjGThwoXEx8e7KLMbu+eee/jXv/6laQvjjaSnp1da9Oms17FXy5Ytq/zk\nrWVxKjhnJ0h1SktLGTp0KBs3btQUp127dhw+fNihB9TdaKeNvWSkQwjR5F17hoeiKAQHBzukiZcz\nt59mZ2fTvn17evbsWSlXrdMAq1at4qmnnuLChQs0a9aMlJQU2rVrx6FDh+yOuXfvXv75z39ed+Cb\nlimbq4uAkpISlixZovk4d3DOTpDqFBUVcf78ec1xOnTowKVLl2wt4rX4/vvv+e6770hNTeWvf/2r\n7XpeXp6mHTYgRYcQQjjtDA9wzvbTCq+++qpD41WYPn06O3fuZNCgQezfv5+kpCS++OILTTHHjh3L\n22+/TceOHR12euu1WzmnTZtGYmKi5ukrZ+wEqXD1qJrFYiEjI4Pp06drjnv58mUSEhLo2bNnpX9n\n9hSgkZGRdO/enSVLltCmTRugfDuvI5qNSdEhhBBO5IztpxWuXsdx+fJlgoODHTK8bjAYCA4Oxmq1\nYrVaGTBgANOmTdMUMyQkhBEjRmjO7WpXD/NbrVb27t1baRTFXsOGDeP48eMO2QlyratH1SreyLWO\nHkD5YmJHSUhIYNGiRZhMJttW4bNnzzJx4kSGDx+uKbYUHUII4UR33HEHBw8edOj20507d/L8888T\nFBTE9OnTGTduHJcvX8ZqtbJw4ULNO1gCAgIoKCigb9++jB07lmbNmmmeapo5cyaTJk1i0KBBld7A\nK3ph2OOpp56yfe3m5kaLFi346quvNOUJXLfl9uTJk/j7+9OxY0eaNWumKfZLL73E559/XunauHHj\nrrtWW45cSPzss89SUFBASkoKvr6+QPnUytNPP83TTz/NnDlz7I4tC0mFEMKJEhISOHHihEO3n3bv\n3p3XXnuN3NxcJk+ezPfff89tt93G0aNH+cMf/sD+/fs15VxYWIinpydWq5VFixaRm5vL2LFjNXWm\nHDt2LMeOHaN9+/a26RVFURzWdMuR7rrrLnbs2MHAgQNRVZWNGzeSmJjI6dOnmTFjhq1TqT2uXYhp\nNpvp1KkThw8ftiuer69vlaNbFf/O8vLyah3zlltu4ddff70ursViIT4+nuPHj9uVK8hIhxBCOJUz\ntp+azWaGDBkClC9Ive222wAcssvGYrEwfPhwkpKS0Ol0DtueunfvXod0S71aZmYms2bNqnTg24wZ\nMzS37TabzRw5csS2TTYtLY3x48eza9cu+vbta1fR8frrr/Paa69RXFyMn58fUF4YGI1GJk+ebHeu\nzjhxV1GUKgsZvV6vefrOMat5hBBCVCk2NpbY2Fg8PT1tv8w1/+K+aiGmp6dnpce0xtbr9eh0OlvH\nTEe544477P40X50xY8YQGhrKsmXLWLp0KaGhoTz44IOa4547d65SX45mzZpx7tw5goKCbM3eauuF\nF14gNzeX8ePHk5eXR15eHvn5+WRmZvL6669rztmREhISWLhw4XXXv/jiC82FrUyvCCGEEzlj+6le\nr8fb2xtVVSkuLsbLywso/+RcUlJCWVmZppzvuece9u/fz+DBgyut5XjvvffsjtmuXTtOnjzp0Gmm\nDh068Msvv1S6puWk1gpTpkzh7NmztsZYy5YtIzo6mrfffts2CmQvR+TnbKmpqdx33314enqSmJgI\nlI9UFRcXs3z5ck3n20jRIYQQTtS5c2c2bNhw3fbTTz75xNWpVau6TqFaplpSUlKqvK5lu/KTTz5J\nz549GT16NABLly5l9+7d/Pd//7fdMaG8eFu2bBnbtm0DoFevXtx///0O2Rk0YcIE/vKXv9CjRw/N\nsZxtw4YNtuI4ISGBO++8U3NMKTqEEMKJKjpZdu7cmf3796PT6ejcubPtbIz65OzZs07pOnpty3ZH\n8fX1pbCwEJ1Oh6IoWCwW28iMvYsonS0+Pp4TJ04QGxtrG63SOuLTkMhCUiGEcCJnbD91lnvvvde2\ns+L+++9n2bJlDomr1+tp27atw4saZyyihPIts8899xzp6emoqqppJ8i11q5d64AMGy4Z6RBCCCc4\nceIEaWlpdOnSpdL205SUFO666y7bXHl90rVrV9t226u/doS+ffuyf/9+h7ZsV1WVRYsWcfr0aaZP\nn865c+e4ePEiPXv21JRr69atWb16tUMOYqvKgQMH2LJlCwB9+vShc+fOTnmd+khGOoQQwgmmTZvG\n66+/bnuDrdh+evDgQV588UVWr17t4gyvd/WaBUceHAbOadk+ZcoUdDodGzZsYPr06fj4+PDnP/+Z\nPXv2aIobFhbmtIJjzpw5zJs3z9YU7aGHHmLy5MlMnTrVKa9X38hIhxBCOEGPHj2qffOrrzsYbrQr\nxhHTCykpKRw/fpxBgwZRVFSExWKxdby0R0WjratHZRyxXuaJJ57g0qVL3HvvvQ7rnlqhU6dO7Nix\nw1aMFhYWcvvtt8uaDiGEEPbLycmp9rHi4uI6zKTmLBaL02LPmzePjz76iKysLE6ePElqaiqPP/44\n69evtzumwWDAYrHYRmUyMjIccphcXl4eXl5e/PDDD7ZriqI4pOhQVRW9Xm+7r9fraUqf/aXoEEII\nJ+jevTvz5s3j0UcfrXT9448/rpfrOZztgw8+YPfu3dx6661Aeavt9PR0TTH/+te/MnLkSNLT0/n7\n3//O0qVL+cc//qE51/nz52uOUZ2JEydy6623MnLkSABWrFjBpEmTnPZ69Y1MrwghhBOkpaUxcuRI\njEZjpQZLJpOJ5cuXEx4e7uIM69att97Krl27bFMhZrOZbt26aZ5WOHr0KOvXr0dVVe68805NazHe\neustnn32WaZOnVrlmhYtzdGulpyczNatW4HyhaRdu3Z1SNyGQEY6hBDCCcLCwti+fTtJSUm2rpl3\n3XUXAwcOdHFmrtGvXz/b2SPr1q3jf//3f7n77rs1x42Pj7e15i4sLOTNN9/kueeesyu2FOLCAAAg\nAElEQVRWRcHSvXt3zXldq6SkhA8//JATJ07QsWNHpkyZ4pAj7RsaGekQQgjhdFarlU8++cS2TmLo\n0KE88sgjdsW6ePEir7/+OidPnqR9+/bMmDGDefPm8T//8z/cf//9dh+9fuDAgWq3r/773//mT3/6\nk11xAR588EEMBgN9+vTh+++/p0WLFrz77rt2x2uopOgQQgjhNCtXruT8+fP8+c9/BqBnz55kZGSg\nKApvvfUWDzzwQK1jDh48mF69enH77bezZs0aVqxYwW233cbs2bM1TVvFxcWxZMmS69bcvPzyy6xe\nvbrSkfS1dfWOJbPZTM+ePTXFa6jklFkhhBBO89ZbbzFixAjbfZPJxL59+9i4cSP//ve/7YqZkZHB\nzJkzGTp0KLNnz6asrIxFixZpXiezZMkSRo0axY4dO4DynSaPP/44mzdvZuPGjZpiX306bVOcVqnQ\ndP/kQgghnM5kMhETE2O737t3b4KCgggKCqKwsNCumKqqkp2dbdtqGhwcTG5uru1+UFCQXXETExNZ\nsWIFI0eO5IMPPmDevHkArFmzplK/DnscOHAAPz8/W/7FxcX4+fk5tMV6QyDTK0IIIZymdevWnDhx\nosrHWrVqxcmTJ2sds0WLFuh0uir7WyiKwqlTp2odEyArKwuAw4cPc++99zJo0CDef/99W+8Pe4sZ\n8RspOoQQQjjN2LFj6d+//3X9SubOncvGjRtZvHixizK7XsuWLW1bZSveGhVFsY1G2FvMiN9I0SGE\nEMJp0tPTbe3Eu3XrBsC+ffsoLS1lxYoVhIWFuThDUZek6BBCCOF0GzZs4NChQwC0b9++yfYraeqk\n6BBCCCFEnZDdK0IIIRosi8VCWloaZrPZdq158+YuzEjciBQdQgghGqR//etfzJo1i7CwMNsOE0VR\n7D7PpWL3SnVk94p2Mr0ihBCiQWrdujW7du0iODjYIfEqdq+oqsrZs2cJDAxEVVVycnJo3rw5p0+f\ndsjrNGXSkVQIIUSDFBMTg7+/v8PinT59mlOnTjFo0CBWr17N5cuXyczM5JtvvmHIkCEOe52mTEY6\nhBBCNCjvvPMOAIcOHeLYsWPcddddlTqGPvnkk5riX31Oyo2uidqTNR1CCCEalPz8fKB8wWjz5s0x\nmUyYTCaHxY+MjOQf//gHDz30EACLFi0iMjLSYfGbMhnpEEIIIa6SlZXFrFmz2Lx5M4qi0LdvX2bM\nmCELSR1Aig4hhBAN0uDBg1myZAkBAQEAZGdnM2bMGNauXeuQ+IWFhXh7ezsklignC0mFEEI0SBkZ\nGbaCAyAwMJD09HTNcbdv305CQgLt2rUDyk+InTJliua4QooOIYQQDZRer+fs2bO2+ykpKbYD27T4\n29/+xtq1a21bcTt37szmzZs1xxWykFQIIUQD9c9//pPevXvTr18/VFVly5YtfPTRRw6JHRMTU+m+\nXq93SNymTooOIYQQDdKwYcNITk5m586dALz77ruEhIRojhsTE8P27dtRFIWysjLmzJljm2oR2sj0\nihBCiAZJVVXWrFlDcnIyw4cPp6ioiN27d2uO++GHH/LBBx+QmppKVFQUP/30E//7v//rgIyF7F4R\nQgjRIP3pT39Cp9OxYcMGjhw5QnZ2NkOGDGHPnj2a4m7bto1evXrd9JqoPRnpEEII0SDt2rWLDz74\nAA8PD6B894ojmoRNnTq1RtdE7cmaDiGEEA2SwWDAYrHYdqxkZGTYTpu1x44dO9i+fTsZGRm2VusA\neXl5WCwWzfkKGekQQgjRQP31r39l5MiRpKen8/e//53evXvz4osv2h3PZDJRUFCA2WwmPz/fdvPz\n82Pp0qUOzLzpkjUdQgghGqyjR4+yfv16VFXlzjvvdMguk5SUFGJjYx2QnbiWFB1CCCEapEmTJjF1\n6lS6dOliuzZz5kxmzpxpV7xp06bx7rvvcvfdd1fZZGzVqlX2piqukKJDCCFEgxQdHU1wcDBPPfUU\n48ePB6Bbt24kJyfbFW/fvn0kJiayadOmKh/v16+f3bmKclJ0CCGEaJC6detGUlISDz30EM2bN2fO\nnDn06NGD/fv3uzo1UQ1ZSCqEEKJBUlUVf39/Vq9eTWhoKP379yc3N1dz3G3btjF48GDatGlDXFwc\nLVu2JC4uzgEZC9kyK4QQokEaMWKE7euZM2eSmJjI7NmzNcedNGkSs2fPJjExUc5ccTCZXhFCCNGg\nnDhxgrS0tOs6hG7dupWIiAhatWqlKf6tt97Krl27NMUQVZPpFSGEEA3KtGnT8PPzu+66v78/06ZN\n0xx/wIABPPPMM+zYsYPk5GTbTWgn0ytCCCEalLS0NDp27Hjd9Y4dO3LmzBnN8StGOfbu3Wu7pigK\nGzZs0By7qZOiQwghRIOSk5NT7WPFxcWa4yclJWmOIaomRYcQQogGpXv37sybN49HH3200vWPP/6Y\nxMREu+Nefd4KlI9uhISE0Lt3b1q2bGl3XPEbWUgqhBCiQUlLS2PkyJEYjUZbkbF3715MJhPLly8n\nPDzcrrizZs267lpWVhZr165l5syZjBkzRlPeQooOIYQQDVRSUhK//PILAO3bt2fgwIFOeZ2srCwG\nDRoki0kdQIoOUa+lpKTQsmVLzGazpiOrhRBCi65du0qnUweQ3+L1RP/+/QkKCqKsrMzVqTjMN998\nw6233oqPjw+hoaGMGzeO1NTUWsep6uAlIYSoK0lJSQQGBro6jUZBFpLWAykpKezevZvmzZuzatUq\n7r//foe/hsViqdPOekuXLmXSpEl89NFH3HvvveTm5vLCCy/Qu3dvfvrpJ/z9/Z2eY13/mYUQDVvH\njh2v+5CTlZVFZGQkCxcudFFWjYuMdNQDCxcuZPDgwYwfP54FCxbYru/evZuIiAiungFbvnw5nTt3\nBsrPHXjjjTdo/f/s3XlYlFX/P/D3sIgiyCL7MIALsqiIaOYeGqCWolYgSkjk9mj2lEsGfbUkM7T6\nWfaYT2ahmIrZJq64m2mRmVsuoMaOgiiIgijCnN8ffLm/IIsg9wyg79d1zXVxL3POGZj58Jn7Pkvn\nzrC0tERQUJA0lCwtLQ06OjqIjo6Go6Mjnn32WQBAYGAgbG1tYWZmBm9vb5w/f14qOy8vD6NGjYKJ\niQmefvppLFiwAIMGDZKOJyYmws/PD+3bt4ebmxu+//77Wl/T3Llz8e6772LcuHEwMDCAlZUVvv76\naxgZGUnTFMfExGDgwIGYPXs2LCwsEBkZCbVajblz58LS0hKdO3fGjh07qpR769YtTJ48GXZ2dlCp\nVFiwYIH0+6mpPCKi+tq+fTu2bdsmPbZv346kpCQcO3YMrq6uTd28x4OgJte5c2exYcMGcfHiRaGv\nry+uXbtW5di+ffuk7YCAAPHRRx8JIYT47LPPRL9+/cSVK1dESUmJ+Ne//iXGjx8vhBAiNTVVKBQK\nERoaKu7cuSPu3r0rhBBizZo1oqioSJSUlIhZs2YJT09Pqexx48aJ8ePHi7t374rz588LlUolBg0a\nJIQQoqioSKhUKhETEyPUarU4deqUsLS0FBcuXKj2ehITE4WOjo5ITU2tduy9994T/fv3F0IIsXbt\nWqGnpye++OILUVZWJu7evSv++9//Cjc3N5GVlSXy8/PFkCFDhI6OjigrKxNCCDFmzBgxffp0UVxc\nLHJzc8XTTz8tvvrqq1rLIyKi5oNJRxP79ddfRZs2bcTt27eFEEJ4enqKzz77TDo+f/588eqrrwoh\nhLh165Zo27atyMjIEEII4ebmJg4cOCCde+XKFaGvry/KyspEampqrf/4K+Tn5wuFQiFu3bolysrK\nhL6+vrh06VKVuiuSju+++04MHjy4yvOnTZsm3n///WrlHjlyROjo6Ih79+5VO/bll1+KLl26CCHK\nkwRHR8cqx4cOHSpWrVolbe/Zs0dKOrKzs4WBgUGVZCI2NlYMGTKk1vKIiKj5YJ+OJrZu3Tr4+fnB\nyMgIABAQEICYmBi88cYbAIAJEyZgwIAB+PLLL/HTTz+hV69esLe3B1B+C2Xs2LHSqA4hBPT19ZGT\nkyOVX3EuAKjVarzzzjv44YcfcP36dSgUCigUCly/fh137txBWVlZlfNVKpX0c1paGhISEmBubi7V\nVVZWhpCQkGqvycLCAgBw9epVODo6Vjl29epV6fiDdQDAlStXquyr/Pz09HTcv38ftra2UhuEEHBw\ncKi1PCIiaj6YdDShu3fvYvPmzVCr1dI/0pKSEty8eRN///03unfvDjc3Nzg6OmLnzp2IjY3FhAkT\npOc7ODggOjoa/fr1q1Z2WloagKojPzZu3Iht27bhwIEDcHBwQEFBAczMzCCEgKWlJfT09JCZmYnO\nnTsDADIyMqTnqlQqeHt7Y/fu3Q99XS4uLrC3t8f333+PuXPnSvuFEPjxxx/xwgsvSPse7LRla2tb\npd6K11HRhtatW+PGjRu1jmjhSBciouaLHUmb0M8//ww9PT1cuHABp0+fxunTp3HhwgUMGjQIMTEx\n0nkTJkzA8uXL8euvvyIgIEDaP23aNLzzzjtIT08HAOTm5mLr1q3ScfHAFCy3b9+GgYEBzMzMUFRU\nhIiICOmftI6ODl544QUsXLgQxcXFSExMrNJbe+TIkbh48SLWr1+P0tJS3L9/H8ePH0diYmKNr+3j\njz/GBx98gE2bNuHevXvIzs7GpEmTcPv27TpXgQwMDMTnn3+OrKws5OfnY+nSpdIxGxsb+Pn5Ydas\nWbh9+zaEEEhOTsbhw4drLa+iQ23F74iIiJoOk44mtG7dOrz66qtQKpWwsrKSHq+99ho2btwItVoN\nAAgKCsLhw4fx7LPPSrc3AOCNN97A6NGj4efnBxMTE/Tv3x/Hjh2Tjj/4rX/ixIlwcHCAUqlEt27d\n0L9//yrH//Of/+DmzZuwtbVFaGgoJkyYAAMDAwCAkZER9uzZg02bNsHOzg52dnYIDw9HSUlJja8t\nMDAQ3377LZYtWwYLCwt069YN9+7dw9GjR+sc7z5lyhQMGzYMPXr0QO/evasNH163bh1KSkrg7u4O\nc3NzBAQEIDs7u9byMjIy4OTkBKVSWes5RESkHZyRlGoVHh6OnJwcrFmzpqmb8sgWL14MKyuragtD\nERGR9jHpIElSUhJKSkrQvXt3HDt2DM8//zyio6MxatSopm4aERE9BtiRlCS3b9/G+PHjcfXqVVhb\nW+Ott95iwkFERLLhlQ4iIiLSCnYkJSIiIq1g0tEMbNy4EcOHD5e2f/vtN3Tp0gXt2rXD1q1bce3a\nNQwePBgmJiZ46623mrCljTN//nxYWlrCzs6uqZtCRI+gW7duVYaoh4WFwdzcHH379m1wWRkZGWjX\nrl21of3NkY6ODpKTk5u6GQ8VGRkpTdhYMV1AxSjI5oJJx/9ycnKCoaEh2rVrB1tbW4SFheHOnTt1\nPueXX3556AyYYWFhMDAwgImJCUxMTODh4YF33nkHt27dks6ZMGEC4uPjpe13330X//73v3Hr1i34\n+/vjq6++gpWVFQoKCvDxxx837oU2kYyMDCxbtgyJiYm4cuWK1uv/5ZdfoKOjU20I7pkzZ6Cjo4Oh\nQ4dqvU3U/G3cuBFPPfUUjI2NoVQq8fzzz+Po0aONKjMyMhITJ06UqYXyq/yP60Fnz57F4MGDAQBH\njhzB/v37ceXKFSQkJDy03A4dOuDAgQPStkqlwq1btzQ+od/06dMRGhpabf/p06fRunVraZHMumhr\n0kEdHR0YGxujXbt2sLS0hK+vLzZv3tygMiq3tTlOlsik438pFArs2LEDt27dwokTJ3D8+HF88MEH\ndT5HCFGvP+rbb7+NgoIC5ObmYs2aNUhISMCAAQNQXFxc4/lpaWlwd3evdbshysrKHul5cktLS4OF\nhQXat29f43FttNPS0hK///478vPzpX0xMTFwcXHReN3U8ixbtgyzZ8/G/Pnzce3aNaSnp+O1117D\ntm3bmrppsqntc1efuJaamgonJye0bt1a7mbJKjQ0FD///HO1eLt+/XqMGjUKpqamDy1DW1djFAoF\nzpw5g1u3biEpKQmhoaGYOXMmFi1apJX6K2g0Hmt9tZdmysnJSezfv1/afuutt8SoUaOEEELk5eWJ\nsLAwYWdnJ8zNzcXYsWNFUVGRaNOmjdDV1RVGRkbC2NhYXL16tVq5r7zyiliwYEGVfbdv3xa2trbi\niy++EEKUL1Q2cOBAIYQQnTp1Erq6uqJNmzbC2NhYjB8/Xujr64tWrVoJY2NjsX//fqFWq0VUVJTo\n1KmTsLCwEOPGjRP5+flCiP9bXfabb74RDg4O4plnnhFCCPH777+L/v37C1NTU+Hp6SkOHToktcfb\n21ssWLBADBgwQBgbG4thw4aJGzduSMd//fVX6bkODg4iJiZGCCHEvXv3xJw5c4SDg4OwsbER06dP\nr3Fl13379km/K2NjYxEWFlZrO+Pi4kTXrl2FmZmZGDJkSJVVbJ2cnMTHH38sunfvLoyNjcWkSZNE\nTk6OGDFihGjXrp3w9fUVN2/erPHve+jQIWFvby+mT58u/d7LysqEUqkUixYtkhaNE0KICxcuCF9f\nX2Fubi5cXV3F5s2bpWM7duwQPXv2FO3atRMODg5i4cKF0rGK1xQTEyMcHByEpaWlWLx4cY3toeat\noKBAGBkZiR9//LHWcx78bFe8xyosWbJEKJVKYWxsLFxdXcWBAwdEfHy8aNWqlWjVqpUwMjKSVnm+\ncuWK8Pf3F+bm5sLZ2VmsXr1aKmfhwoUiICBAvPzyy8LY2Fh4eHiIixcviqioKGFlZSUcHR3F3r17\nq7R90qRJwtbWVtjb24v58+cLtVothCiPNQMGDBCzZs0S7du3rxabKuoLCQmp8TVXxMlvvvlGtG7d\nWujp6QljY2Ppc7Bt2zbh6ekpTE1NxYABA8SZM2eEEEKEhIQIHR0dYWhoKIyNjcXHH38sfV4qVpD2\n9vYW8+fPF/379xdGRkbC399fXL9+XQQHB4t27dqJPn36iLS0NKktdX1OH+Tq6iq+/fZbabusrEzY\n2dmJbdu2CSGEOHbsmOjXr58wNTUVdnZ2YubMmeL+/fvS+QqFQvzzzz9SO7/55hvpWOX43dB2Pahy\nPRV++OEH0bp1a5GXlyeEePh7peJvV7HoZ8Xvd82aNcLNzU0YGxuLTp06VVlYs+K9u3TpUmFjYyMm\nTpworl+/LkaOHClMTU2Fubl5tQU/HxWTjv9VOelIT08XXbt2Fe+9954QQojnnntOBAUFiYKCAlFa\nWioOHz4shCj/Q6lUqjrLrSnpEEKIiRMniqCgICFE+Zu2YjXXirZUXj32wTIauqR9VlaWaN++vYiP\njxdClCcB7du3F9evXxdClH+IOnfuLC5fvizu3r0rvL29RUREhFSesbGx+O6770RpaanIy8sTp0+f\nFkII8eabb4rRo0eLmzdvisLCQuHv7y/eeeedGn8PD/6uamrnxYsXRdu2bcX+/ftFaWmp+Oijj0Tn\nzp2lD7+Tk5Po16+fyM3NFVeuXBFWVlbCy8tLnD59Wty7d08MHTq0xlVvK9f/+++/i6effloIIcTO\nnTvF8OHDxddffy0lHUVFRUKlUomYmBihVqvFqVOnhKWlpZT8/PLLL+Ls2bNCCCH+/vtvYWNjI+Li\n4qq8pqlTp4p79+6J06dPCwMDA5GYmFhjm6j5io+Pl1Zsrk1NSUfFezwpKUmoVCqRnZ0thBAiLS1N\nJCcnCyFq/qc+aNAgMXPmTFFSUiK95w4ePCid36ZNG7F3715RVlYmJk6cKBwdHcWHH34oSktLxerV\nq0WHDh2kssaMGSOmT58uiouLRW5urnj66afFV199JYQojzV6enriiy++EGVlZTV+SahP0lFRVuW4\ndeLECWFlZSX+/PNPoVarxbp164STk5MoKSmRnls5rj34T9Hb21s4OzuLlJQUcevWLeHu7i6cnZ3F\ngQMHpNddseL2wz6nD1q8eLHw8fGRtuPj44WVlZUoLS0VQgjx119/iT/++EOo1WqRlpYm3N3dxfLl\ny6XzH5Z0VPweGtquB9WUdNy/f1/o6elJ8fth75Xako6dO3eKlJQUIYQQhw8fFoaGhuLkyZNCiPL3\nrp6enoiIiBAlJSXi7t27IiIiQkyfPl2UlZWJ0tJSceTIkXq9hofh7ZVKxowZA3NzcwwePBhDhgxB\nREQEsrOzsXv3bqxatQrt2rWDrq4uBg0a1Oi67OzskJeXV+txUcflvFWrVmHx4sWwtbWFvr4+3n33\nXfzwww9ShyGFQoHIyEi0adMGBgYGWL9+PZ5//nkMGzYMAPDss8+id+/e2Llzp1RmWFgYOnXqBAMD\nAwQGBuLUqVMAgNjYWPj6+iIwMBC6urowMzODh4cHAGD16tX49NNPYWJigrZt2yI8PByxsbH1/h08\n2M7vvvsOI0eOxNChQ6Grq4u5c+eiuLgYv/32m/Sc119/HRYWFrC1tcWgQYPQt29feHh4oFWrVhg7\ndixOnjxZZ519+/ZFfn4+Ll68iHXr1lW7t759+3Z06NABEydOhEKhQI8ePfDCCy/g+++/BwAMHjwY\nXbt2BVDeqS4oKAi//PJLlde0cOFCtGrVCh4eHujRowdOnz5d798JNQ83btyAhYWFtIJzQ+nq6qKk\npARnz55FaWkpHBwc0KFDhxrPzczMxO+//46lS5dCX18fPXr0wOTJk6usfTRo0CD4+PhAR0cHAQEB\nyMvLQ3h4OHR1dREUFIS0tDTcunULOTk52LVrFz799FO0bt0aFhYWePPNN6t8LpVKJWbMmAEdHR1p\nmQM5rF69Gv/617/Qu3dvKBQKhISEwMDAoEp/j7riGlAeh5ycnGBsbIwRI0bA2dkZQ4YMkV53xef7\nYZ/TB4WEhODw4cNSf7Jvv/0WEyZMgK6uLgDAy8sLffr0gUKhgIODA6ZOnVrlc11fDW1Xfejp6cHC\nwgJ5eXn1eq/UZsSIEXBycgJQ/n7y8/PDr7/+Kh3X1dVFZGQk9PX1YWBgAH19fVy9ehUpKSnQ1dXF\ngAEDHvk1VHk9spTymIiLi8OQIUOq7MvIyIC5uTnatWsna11ZWVlV1lFpiIYuaZ+WlobNmzdL96KF\nECgtLcWzzz4rnWNjYyP9bGhoiMLCQgDlr79Tp07V2pCbm4s7d+6gV69e0j61Wt3ge5+V23nlypUq\nS9krFAqoVCpkZWVJ+6ytraWf27RpU227ot11CQkJwYoVK3Do0CGsWbMGGzZskI6lpaUhISFB+tsI\nIVBWViYlJ3/88QciIiJw9uxZlJSUoKSkpMoifA+2sfLvklqO9u3b4/r161Cr1Y+UeHTq1AmfffYZ\nFi5ciPPnz2PYsGFYtmxZlc9ZhStXrsDc3ByGhobSPkdHR/z111/S9oPvcwsLC6nfRZs2bSCEQGFh\nIbKysnD//n1p1WpRfjUbDg4O0vMf1vn9UaWlpWHdunX4z3/+I9V9//79BnUcr+/nu7bPaW0dYFUq\nFQYNGoT169fjtddew5YtW3DkyBHp+KVLlzB79mwcP34cxcXFKC0trRLb6quh7aqP0tJS5Obmwtzc\nvF7vldrs2rUL77//Pi5evAi1Wo3i4mLpCyRQ3udNX19f2p43bx7ee+89+Pn5QaFQYMqUKXj77bcf\n+XVU4JWOSmr6h6lSqZCXl1dltEmFR+0ZXFhYiH379km9wBvKwcEBu3btQl5eHvLy8pCfn4+ioiIp\n0DzYNpVKhYkTJ1Y5//bt2/UafqtSqXD58uVq+y0sLGBoaIhz585J5d68eRMFBQUNei2V22lnZ1dl\nKXugPOmpnJjI4eWXX8bKlSvx/PPPV+sEp1Kp4O3tXeV3devWLaxYsQIAEBwcjDFjxiArKws3b97E\ntGnTWsSQP2qYfv36wcDAAFu2bKn1nLZt21YZ4Xb16tUqx4OCgvDrr79K7+mKgP1g3Ki46llUVCTt\nS09Pf6RFClUqFVq3bo0bN25I79+bN2/izJkz0jmaGtGgUqnwP//zP1U+O4WFhRg3bpzs9db2Of3i\niy9qfU5oaCjWrVuHH3/8ER07doSnp6d0bPr06XBzc8M///yDmzdvYvHixbV+rh/8u1decPJR2vUw\nW7Zsgb6+Pvr06fPI75WSkhK89NJLmDdvHnJzc5Gfn48RI0ZUeY0P/n3atm2LTz75BP/88w+2bt2K\nZcuW4eDBg4/8Oiow6XgIGxsbjBgxAjNmzMDNmzdRWloqXZKytrbGjRs3akxIalJSUoK//voLY8eO\nRfv27fHKK688UpsauqT9yy+/jG3btmHPnj1Qq9W4e/cufvnll3p9AwkODsb+/fvxww8/oKysDHl5\neTh9+rSU+b755pvIzc0FUH71Zs+ePfV+HQ+2MzAwEDt27MDBgwdRWlqKTz75BK1bt0a/fv3qXWZ9\nODk54fDhwzWOTho5ciQuXryI9evXo7S0FPfv38fx48eRlJQEoDxhNDMzg76+Po4dO4aNGzfW+Zqo\nZWrXrh0iIyPx2muvIS4uTvr2Gx8fj/DwcACAp6cndu7cifz8fGRnZ2P58uXS8y9evIiDBw+ipKQE\nrVq1Qps2baQrJtbW1khNTZXeK/b29ujfvz8iIiJw7949nDlzBt98880jfTu2sbGBn58fZs2ahdu3\nb0MIgeTk5Cpza9RHWVkZ7t27Jz1qW026silTpuDLL7+UVrouKirCzp07pX+Q1tbW1ea6eNTPS22f\n08TExFqf8+KLLyI9PR3vvfdetSG0t2/fRrt27WBoaIjExET897//rbUcT09P/PTTTyguLsbly5fx\nzTff1LtdMTExtd5me1B+fj42bNiAmTNnIjw8HGZmZg1+r1T8fiuuylbcMty1a9dDY/WOHTvwzz//\nAACMjY2hp6f3yLcbK2PS8b/qysK//fZb6OnpwdXVFdbW1lJwcXFxwfjx49GxY0eYm5vXusT6Rx99\nBBMTE1hYWOCVV17BU089haNHj6JNmzb1asuD2w1d0t7e3h5xcXH48MMPYWlpCctJpL0AACAASURB\nVEdHR3zyySdV+oDURqVSYefOnfjkk09gbm6Onj17St+alixZgs6dO6Nv374wNTWFn58fLl68WGtZ\nD3udXbp0wfr16zFz5kxYWlpix44d2LZtG/T09Or1e2mI/v3713ip28jICHv27MGmTZtgZ2cHOzs7\nhIeH4969ewCAlStXYsGCBTAxMcEHH3wgfYurrU3NcZw81c/s2bOxbNkyfPDBB7CysoKDgwO++OIL\njBkzBkD5bToPDw84OTlh+PDhCAoKkp577949hIeHS5Ph5ebmIioqCgAQEBAAIQTat2+P3r17Ayif\nDyQlJQV2dnZ48cUXsWjRomq3eutS+X22bt06lJSUwN3dHebm5ggICKg1NtVm06ZNMDQ0hKGhIdq0\naYPOnTtXq+dBvXr1wurVqzFz5kyYm5ujS5cuiImJkY5HRERg0aJFMDc3x7Jly6qV15DPSm2f07qS\nI0NDQ7z44ou4cuUKgoODqxz75JNPsGHDBrRr1w7Tpk2r8rd8sG2zZs2Cvr4+bGxsEBYWhpdffrne\n7crIyMDAgQNrbWNFP5B27drB2dkZ0dHRWL58Od577z3pnNjY2Hq/VyrabWRkhM8//xwBAQEwNzfH\npk2bMHr06FrbAZTfcvLx8YGxsTEGDBiA1157Dc8880ydz6mPRq+9kpmZiYkTJyInJwc6OjqYMmUK\n/v3vfyM/Px/jxo1DWloanJycsHnzZpiYmDS6wUT0+Js0aRK2b98Oa2vrKrcGAOD//b//h7feegvX\nr1+X7p1HRUUhOjoaenp6WL58Ofz8/Jqi2UR1Gj58OJYvX/5Ezw3U6KQjOzsb2dnZ8PT0RGFhIXr1\n6oW4uDisWbMG7du3x7x587B06VLk5+djyZIlcrWbiB5jR44cgZGRESZOnFgl6cjMzMTkyZORlJSE\nv/76C+bm5rhw4QImTJiAP//8E5mZmfDx8cGlS5d4lYmoGWr07RUbGxupQ46RkRHc3NyQmZmJuLg4\n6b5ZaGhonR2yiIgqGzhwIMzMzKrtnzVrVrWlAOLi4hAUFAQ9PT04OTnB2dm5yu1GImo+ZO3TkZqa\nilOnTqFv377IycmRhjvZ2Njg2rVrclZFRE+YrVu3QqVSoXv37lX2Z2VlVRkGqlQqqwyzJqLmQ7Z5\nOgoLC/HSSy9h+fLlMDIyqneHOoVCwR7/RFSn4uJifPjhh9i7d2+jymG8IWpaslzpKC0txUsvvYSQ\nkBCpR6y1tbU0WVV2djasrKzkqIqInkD//PMPUlNT0aNHD3To0AGZmZnw8vLCtWvXoFQqpeHjQHm/\nj0eZ44KINE+WpOPVV1+Fu7s73njjDWmfv78/1q5dC6B8bPLDhucQEVVWMZsmUD7lfHZ2NpKTk5GS\nkgJ7e3ucPHkSVlZW8Pf3x3fffYeSkhKkpKTg8uXL6NOnTxO3nohq0uik4+jRo9iwYQMOHDiAnj17\nwsvLC/Hx8Xj77bexd+9euLi4YP/+/dKEOkREDzNhwgT0798fFy9ehIODA9asWVPleOXbJO7u7ggM\nDIS7uzuee+45rFy5kiNXiJqpRg+ZbXQDeI+ViLSE8YaoaXFGUiIiItIKJh1ERPREUNo7QKFQ1Puh\ntHd4eKHUILy9QkRPDMabJ5tCocDI//mp3udvX/wC3y8y45UOIiIi0gomHURERKQVTDqIiIhIK5h0\nEBERkVYw6SAiIiKtYNJBREREWsGkg4iIiLSCSQcRERFpBZMOIiIi0gomHURERKQVTDqIiIhIK5h0\nEBERkVYw6SAiIiKtYNJBTYpLTRMRPTn0mroB9GS7kpXR4KWmiYioZeKVDiIiItIKWZKOSZMmwdra\nGh4eHtK+yMhI2Nvbw8vLC15eXoiPj5ejKiIiImqhZEk6wsLCsHv37mr7Z8+ejRMnTuDEiRMYPny4\nHFURERFRCyVL0jFw4ECYmZlV2y+EkKN4IiIiegxotE/HihUr4OnpicmTJ6OgoECTVREREVEzp7Gk\nY8aMGUhOTsapU6dgY2OD2bNna6oqInrM1NRPbN68eXBzc4OnpydefPFF3Lp1SzoWFRUFZ2dnuLm5\nYc+ePU3RZCKqB40lHZaWllAoFACAKVOm4M8//9RUVUT0mKmpn5ifnx/OnTuHU6dOwdnZGVFRUQCA\n8+fPY/Pmzbhw4QJ27dqFGTNm8NYuUTMlW9IhhKjyQc/OzpZ+/umnn9CtWze5qiKix1xN/cR8fHyg\no1Mesvr27YvMzEwAwNatWxEUFAQ9PT04OTnB2dkZx44d03qbiejhZJkcbMKECTh06BBu3LgBBwcH\nREZG4uDBgzh16hR0dHTg5OSEVatWyVEVERGio6Mxfvx4AEBWVhb69esnHVMqlcjKymqqphFRHWRJ\nOjZu3FhtX1hYmBxFExFVsXjxYujr60tJBxG1HJwGnYhajLVr12Lnzp04cOCAtE+pVCIjI0PazszM\nhFKpbIrmEdFDcBp0ImqWHuwnFh8fj48//hhbt26FgYGBtN/f3x+bNm1CSUkJUlJScPnyZfTp06cp\nmkxED8ErHUTU7NTUT+zDDz9ESUkJfH19AZR3Jl25ciXc3d0RGBgId3d36OvrY+XKldLIOSJqXhSi\niceWKRQKDm97gikUigavMsv3Cz0qxpsnG+NN0+PtFSIiItIKJh1ERESkFUw6iIiISCuYdBAREZFW\nMOkgIiIirWDSQURERFrBpIOIiIi0gkkHERERaQWTDiIiItIKJh30WHOwVUGhUNT74WCrauomExE9\ntrj2Cj3WMrIzsc3503qfP+rSLA22hojoycYrHURERKQVTDqImjEHpV2Dbg8pFAo4KO2autlERDXi\n7RWiZizjylVsnujWoOcErrugodYQETUOr3QQERGRVsiSdEyaNAnW1tbw8PCQ9uXn58PPzw8uLi4Y\nNmwYCgoK5KiKiIiIWihZko6wsDDs3r27yr4lS5bAx8cHSUlJGDp0KKKiouSoioiIiFooWZKOgQMH\nwszMrMq+uLg4hIaGAgBCQ0OxZcsWOaoiIiKiFkpjfTquXbsGa2trAICNjQ2uXbumqaqIiIioBdBa\nR1KFQqGtqoiIiKgZ0ljSYW1tjZycHABAdnY2rKysNFUVERERtQCyJR1CCAghpG1/f3+sXbsWABAT\nE4PRo0fLVRURERG1QLIkHRMmTED//v1x8eJFODg4YM2aNQgPD8fevXvh4uKC/fv3Izw8XI6qiIiI\nqIWSZUbSjRs31rh/3759chRPRE+YSZMmYfv27bC2tsaZM2cAlM/9M27cOKSlpcHJyQmbN2+GiYkJ\nACAqKgrR0dHQ09PD8uXL4efn15TNJ6JacEZSImp2GjL3z/nz57F582ZcuHABu3btwowZM6rc6iWi\n5oNJBxE1Ow2Z+2fr1q0ICgqCnp4enJyc4OzsjGPHjmm9zUT0cEw6iKhFqG3un6ysLKhUKuk8pVKJ\nrKysJmkjEdWNSQcRtUic+4eo5WHSQUQtQm1z/yiVSmRkZEjnZWZmQqlUNkkbiahuTDqIqFmq79w/\n/v7+2LRpE0pKSpCSkoLLly+jT58+TdFkInoIWYbMEhHJacKECTh06BBu3LgBBwcHREZGIjw8HAEB\nAYiOjoajoyM2b94MAHB3d0dgYCDc3d2hr6+PlStX8tYLUTPFpIOImp2Gzv0TERGBiIgITTaJiGTA\n2ytERESkFUw6iIiISCuYdBAREZFWMOkgIiIirWDSQURERFrBpIOIiIi0gkkHERERaQWTDiIiohbA\n0U4JhUJR74ejXfNbDoCTgxEREbUA6VevIKnr6Hqf73IuToOteTS80kFERERwUNo16EqKg9KuwXXw\nSgcREREh48pVbJ7oVu/zA9ddaHAdGk86nJycYGJiAh0dHejr6+PYsWOarpKIiIiaIY0nHTo6Ojh0\n6BDMzMw0XRURERE1Yxrv0yGEgFqt1nQ1RERE1MxpPOlQKBTw9fXFU089hdWrV2u6OiIiImqmNH57\n5ejRo7C1tUVubi58fX3h5uaGgQMHarpaIiIiamY0fqXD1tYWAGBpaYmxY8eyIykREdETSqNJx507\nd1BYWAgAKCoqwp49e9CtWzdNVklERETNlEZvr+Tk5GDs2LFQKBQoLS1FcHAw/Pz8NFklEdETydHB\nAekZGfU+30GlQlp6ugZbRFSdRpOODh064NSpU5qsgoiIAKRnZCA1fnO9z3caHqjB1hDVjNOgU62c\nnFQNmhJXoVDAyUnV1M0mIqJmitOgU63S0jKRe7Nhw5wtTadoqDVE9DhzclIhLS2z3uc7OtojNbX+\nt5OoeWDSQURETa6hX3L4Badl4u0VImpRoqKi0LVrV3h4eCA4OBglJSXIz8+Hn58fXFxcMGzYMBQU\nFDR1M4moBkw6iKjFSEtLw+rVq3Hy5EmcOXMGpaWliI2NxZIlS+Dj44OkpCQMHToUUVFRTd1UegI5\n2DasH5yD7ZPXB463V4ioxWjXrh1atWqFoqIi6OjooLi4GEqlElFRUfjll18AAKGhofD29saSJUua\nuLX0pMnIzsQ250/rff6oS7M02JrmiVc6iKjFMDMzw5w5c+Dg4AClUgkTExP4+PggJycH1tbWAAAb\nGxtcu3atiVtKRDVh0kFELUZycjI+/fRTpKWl4cqVKygqKsKGDRugUCiqnPfg9sPYO9o36LK4vaO9\nnC+L6InB2ytE1GIcP34cAwYMgLm5OQBg7Nix+O2332BtbS1d7cjOzoaVlVWDys1Kz8J7f7xX7/Mj\nn45sUPlEVI5XOoioxXBxcUFCQgLu3r0LIQT2798Pd3d3+Pv7Y+3atQCAmJgYjB49umkbSkQ14pUO\nImoxevTogYkTJ6JXr17Q1dVFz549MXXqVNy+fRuBgYGIjo6Go6MjNm+u/3TgVD9OjiqkpTdg8i4H\ne6SmcfIuqopJBxG1KG+99RbeeuutKvvMzc2xb9++JmrRkyEtPRPqc5/U+3ydrnM12BpqqXh7hYiI\niLSCSQcRERFpBZMOIiIi0opmm3Q0dNw8x84TERE1b822I2lDx80DHDtPRETUnDXbKx1ERET0eGHS\nQURERFqh8aQjPj4erq6u6NKlC5YuXarp6oiIiKiZ0mjSoVarMXPmTOzevRvnzp1DbGwsEhMTNVkl\nERERNVMaTTqOHTsGZ2dnODo6Ql9fH0FBQYiLi9NklURERNRMaTTpyMrKgkqlkrbt7e2RlZWlySqJ\niIiomWoWQ2YVCkWN+x9lCGxtZdGjsTSd0uDnNPRvsH3xCxotf9SlWRotX9MC111o8HNqeg1CCDma\nQ0T0yDSadCiVSqSnp0vbmZmZUCqV1c5rqmCoUCjw8/ZD9T5/7EjvBrVVoVAgNb7+q106DQ9scPkN\nXYCJ/3jkpVAokNS1/suou5yL49+AiJ5YGr298tRTT+Hy5ctIS0tDSUkJNm3aBH9/f01WSURERM2U\nRq906OrqYsWKFfDz84NarcakSZPg5uamySqJiIiomdJ4n47hw4cjKSlJ09U8kRwd7KHTdW6Dzici\nImoqnJG0BUtNy4AQot6P1LSMpm4y0RPLwcGhQQtYOjg4NHWTiWTXLEavEBE97jIyMhrccZ3occMr\nHURERKQVTDqIiIhIK5h0EBERkVYw6SAiIiKtYNJBREREWsGkg4halIKCAgQEBMDNzQ1du3bFH3/8\ngfz8fPj5+cHFxQXDhg1DQUFBUzeTiGrApIOIWpQ33ngDzz33HC5cuIDTp0/D1dUVS5YsgY+PD5KS\nkjB06FBERUU1dTOJqAZMOoioxbh16xZ+/fVXhIWFAQD09PRgYmKCuLg4hIaGAgBCQ0OxZcuWpmwm\nEdWCSQcRtRgpKSmwsLBAWFgYvLy8MHXqVNy5cwc5OTmwtrYGANjY2ODatWtN3FIiqgmTDiJqMUpL\nS3HixAm89tprOHHiBNq2bYslS5ZAoVBUOe/BbSJqHph0EFGLYW9vD5VKhd69ewMAXnzxRZw4cQLW\n1tbIyckBAGRnZ8PKyqopm0lEtWDSQUQthrW1NVQqFS5evAgA2L9/P7p27Qp/f3+sXbsWABATE4PR\no0c3YSuJqDZc8I2IWpTPP/8cwcHBuH//Pjp27Ig1a9agrKwMgYGBiI6OhqOjIzZv3tzUzSSiGjDp\nIKIWpUePHvjzzz+r7d+3b18TtIaIGoK3V4iIiEgrmHQQERGRVmgs6YiMjIS9vT28vLzg5eWF+Ph4\nTVVFRERELYBG+3TMnj0bs2fP1mQVRERE1EJo9PaKEEKTxRMREVELotGkY8WKFfD09MTkyZO56iMR\nEdETrlFJh6+vLzw8PKRH9+7d4eHhgW3btmHGjBlITk7GqVOnYGNjw9ssRERET7hG9enYu3dvvc6b\nMmUKRo0a1ZiqiIiIqIXT2O2V7Oxs6eeffvoJ3bp101RVRERE1AJobPTKvHnzcOrUKejo6MDJyQmr\nVq3SVFVERETUAmgs6Vi3bp2miiYiIqIWiDOSEhERkVYw6SAiIiKtYNJBREREWsGkg4iIiLSCSQcR\nERFpBZMOIiIi0gomHURERKQVTDqIiIhIK5h0EBERkVYw6SAiIiKtYNJBREREWsGkg4haHLVaDS8v\nL/j7+wMA8vPz4efnBxcXFwwbNgwFBQVN3EIiqgmTDiJqcZYvXw53d3dpe8mSJfDx8UFSUhKGDh2K\nqKioJmwdEdWGSQcRtSiZmZnYuXMnJk+eLO2Li4tDaGgoACA0NBRbtmxpquYRUR2YdBBRizJr1ix8\n/PHHUCgU0r6cnBxYW1sDAGxsbHDt2rWmah4R1YFJBxG1GDt27IC1tTU8PT0hhKj1vMoJCRE1H3pN\n3QAiovo6evQotm7dip07d6K4uBi3b99GSEgIbGxspKsd2dnZsLKyauqmElENeKWDiFqMDz/8EOnp\n6UhOTsamTZswdOhQfPvttxg1ahTWrl0LAIiJicHo0aObtqFEVCMmHUTU4oWHh2Pv3r1wcXHB/v37\nER4e3tRNIqIaNCrp+OGHH9CtWzfo6urixIkTVY5FRUXB2dkZbm5u2LNnT6MaSUT0oGeeeQZbt24F\nAJibm2Pfvn1ISkrCnj17YGpq2sStI6KaNKpPR/fu3fHzzz9j2rRpVfZfuHABmzdvxoULF5CZmQkf\nHx9cunSJnbuIiIieYI260uHi4gJnZ+dqvcjj4uIQFBQEPT09ODk5wdnZGceOHWtUQ4mIiKhl00if\njqysLKhUKmlbqVQiKytLE1URERFRC/HQ2yu+vr7IycmRtoUQUCgUWLx4MUaNGqXRxhEREdHj46FJ\nx969extcqFKpREZGhrSdmZkJpVLZ4HI0TaVSYexI7wadT0RERI9Gttsrlft1+Pv7Y9OmTSgpKUFK\nSgouX76MPn36yFWVbNLT0yGEqPcjPT29qZtMRETUYjUq6diyZQtUKhUSEhIwcuRIjBgxAgDg7u6O\nwMBAuLu747nnnsPKlSs5coWIiOgJ16ghs2PGjMGYMWNqPBYREYGIiIjGFN/iOahUcBoe2KDziYiI\nHldce0WD0ng7hoiISMJp0ImIiEgrmHQQERGRVjDpICIiIq1g0kFERERawaSDiIiItIJJBxEREWkF\nkw4iIiLSCiYdREREpBVMOoiIiEgrmHQQERGRVjDpICIiIq1g0kFERERawaSDiIiItIJJBxEREWkF\nkw4iajEyMzMxdOhQdO3aFd27d8fnn38OAMjPz4efnx9cXFwwbNgwFBQUNHFLiagmTDqIqMXQ09PD\nsmXLcO7cOfz+++/44osvkJiYiCVLlsDHxwdJSUkYOnQooqKimrqpRFQDJh1E1GLY2NjA09MTAGBk\nZAQ3NzdkZmYiLi4OoaGhAIDQ0FBs2bKlKZtJRLVg0kFELVJqaipOnTqFvn37IicnB9bW1gDKE5Nr\n1641ceuIqCaNSjp++OEHdOvWDbq6ujhx4oS0Py0tDYaGhvDy8oKXlxdmzJjR6IYSEVUoLCzESy+9\nhOXLl8PIyAgKhaLK8Qe3iah50GvMk7t3746ff/4Z06ZNq3asc+fOVRIRIiI5lJaW4qWXXkJISAhG\njx4NALC2tpaudmRnZ8PKyqqJW0lENWnUlQ4XFxc4OztDCFHtWE37iIga69VXX4W7uzveeOMNaZ+/\nvz/Wrl0LAIiJiZGSESJqXjTWpyM1NRVeXl4YMmQIjhw5oqlqiOgJcvToUWzYsAEHDhxAz5494eXl\nhfj4eLz99tvYu3cvXFxcsH//foSHhzd1U4moBg+9veLr64ucnBxpWwgBhUKBxYsXY9SoUTU+x87O\nDunp6TAzM8OJEycwZswYnD9/HkZGRvK1nIieOAMGDEBZWVmNx/bt26fl1hBRQz006di7d2+DC9XX\n14eZmRkAwMvLC506dcLFixfh5eXV8BYSERHRY0G22yuV+3Bcv34darUaAJCcnIzLly+jY8eOclVF\nRERELVCjko4tW7ZApVIhISEBI0eOxIgRIwAAhw8fhoeHB7y8vBAYGIhVq1bB1NRUlgYTERFRy9So\nIbNjxozBmDFjqu1/4YUX8MILLzSmaCIiInrMcEZSIiIi0gomHURERKQVTDqIiIhIK5h0EBERkVYw\n6SAiIiKtYNJBREREWsGkg4iIiLSCSQcRERFpBZMOIiIi0gomHURERKQVTDqIiIhIK5h0EBERkVYw\n6SAiIiKtYNJBREREWsGkg4iIiLRCr6kbQNSSOdjaweVcXIPOJyJ6UjHpIGqEtCtZTd0EIqIWg7dX\niIiISCuYdBAREZFWNCrpmDdvHtzc3ODp6YkXX3wRt27dko5FRUXB2dkZbm5u2LNnT6MbSkRUl/j4\neLi6uqJLly5YunRpUzeHiGrQqKTDz88P586dw6lTp+Ds7IyoqCgAwPnz57F582ZcuHABu3btwowZ\nMyCEkKXBREQPUqvVmDlzJnbv3o1z584hNjYWiYmJTd0sInpAo5IOHx8f6OiUF9G3b19kZmYCALZu\n3YqgoCDo6enByckJzs7OOHbsWONbS0RUg2PHjsHZ2RmOjo7Q19dHUFAQ4uLqP6qIiLRDtj4d0dHR\neO655wAAWVlZUKlU0jGlUomsLPbyJyLNeDDm2NvbM+YQNUMPHTLr6+uLnJwcaVsIAYVCgcWLF2PU\nqFEAgMWLF0NfXx/jx4/XXEuJiIioRXto0rF37946j69duxY7d+7EgQMHpH1KpRIZGRnSdmZmJpRK\nZSOaSURUO6VSifT0dGm7rpijUChq3B/5dGSD6qytnLqMHemt0TqchgdqtHydrnM1Wr6l6RSNlg8A\n2xe/oNE6Rl2apdHyGzIZ4aOUH7jugmzl19SXUyEa0cMzPj4ec+bMweHDh9G+fXtp//nz5xEcHIw/\n/vgDWVlZ8PX1xaVLlx7pDUJE9DBlZWVwcXHB/v37YWtriz59+iA2NhZubm5N3TQiqqRRM5K+/vrr\nKCkpga+vL4DyzqQrV66Eu7s7AgMD4e7uDn19faxcuZIJBxFpjK6uLlasWAE/Pz+o1WpMmjSJCQdR\nM9SoKx1ERERE9cUZSYmIiEgruOAbNQtffPEFgoODYWpqCgDIz89HbGwsZsyYIUv5ZWVl2LFjB1JT\nU1FaWirtnz17tizlE1HLwXjTdFrslY4///xT9jKvXbuG9PR06SGHvLy8Oh9yOnLkCNasWQMAyM3N\nRUpKimxlz5kzB+fOnZOtvAetXr1aCgAAYGZmhtWrV8tW/qhRo7B27VrcuHEDt2/flh5E9cF4Ux3j\nTe0Yb2rXoq50nD9/HrGxsYiNjYWpqSmOHz8uS7lbt27FnDlzcOXKFVhZWSEtLQ1ubm6yvOl79eoF\nhUJR89AhhQLJycmNrgMAIiMjcfz4cSQlJSEsLAz379/Hyy+/jKNHj8pSvpubG6ZOnYrS0lKEhYVh\n/PjxMDExkaVsoPybQcUcMBXbJSUlspWfmZmJM2fOyFZeZd27d6+xo3TF65Gz3tzcXCxduhTnz5/H\n3bt3pf2Vh6yTPBhvasd4U7fHId5oKtY0+6QjNTVV+uC3atUKqampOH78OJycnGSrY8GCBUhISICP\njw9OnjyJgwcPYv369bKULWf2X5eff/4ZJ0+ehJeXFwDAzs5O1sx68uTJmDx5MpKSkrBmzRp4eHhg\nwIABmDJlCoYMGdLo8ocPH45x48Zh2rRpAIBVq1Zh+PDhjS63wrBhw7Bnzx74+fnJVmaF7du3y15m\nbYKDgzFu3Djs2LEDX375JWJiYmBpaam1+h93jDf1w3hTt8ch3mgq1jTrpKNfv34oKSlBYGAgtmzZ\ngo4dO6JDhw6yBgAA0NfXR/v27aFWq6FWqzFkyBC8+eabspSdmJgIV1dXnDhxosbjFR/axmrVqhUU\nCoWUARcVFclSbmVlZWVITExEYmIiLCws0KNHDyxbtgyrVq3Cpk2bGlX20qVLsWrVKvz3v/8FUD4T\n7uTJk+VoNoDy99KYMWMghIC+vr70raDyysiPytHRUYYW1s+NGzcwadIkLF++HM888wyeeeYZPPXU\nU1qr/3HGeFN/jDd1exzijaZiTbNOOqytrXH27Fnk5OQgNzcXHTt21Mh8H6ampigsLMTgwYMRHBwM\nKysrtG3bVpayly1bhq+++gpz5sypdkyhUMh2WTwwMBDTpk3DzZs3sXr1akRHR2PKlIbN7leXWbNm\nYdu2bXj22WfxzjvvoE+fPgCAt99+Gy4uLo0uX0dHB9OnT8f06dMbXVZN5syZg4SEhFovTcohISEB\nr7/+Oi5cuICSkhKUlZWhbdu2sgSaCvr6+gAAW1tb7NixA3Z2drLfq39SMd7UH+NN3R6HeKOxWCOa\nuZs3b4ro6Gjh6+srOnToIExNTcUff/whax2FhYWitLRU3L9/X6xdu1YsX75cXL9+XdY6tGHPnj1i\n7ty5Ys6cOWLPnj2ylh0dHS0KCwtrPHbz5s1HLjcgIEAIIUS3bt1E9+7dqz3kMmjQIFFWViZbeTXp\n1auXuHTpkvD09BSlpaUiOjpahIeHy1rHtm3bxM2bN8Xff/8tvL29hZeXKAsVhAAAGVxJREFUl4iL\ni5O1jicZ4039Md7U7nGIN5qKNS1qcrCcnBxs3rwZmzZtQnp6epX1XZq7Tp064a233sK//vUvad/I\nkSNluz+XkpICW1tbtG7dGgBQXFyMnJwcWS8NZ2VlIS0trcoQsMGDBzeqzKtXr8LW1hZpaWk1Hpfr\nUuIrr7yC5ORkjBgxAgYGBtJ+OYew9e7dG8ePH4eHh4fUmatnz544efKkbHWQ9jDe1I7xpm6MN7Vr\n1rdXHmRtbY2XX34ZM2fOlGWImbGxcZ2XvuS+LH7w4EH88ccfWLVqFVq1aiXr0tsBAQH47bffpG1d\nXV0EBATINtQvPDwcmzZtgru7O3R1dQGUX65tbBCwtbUFoPn7lB06dECHDh1QUlIiay/1ygwNDVFS\nUgJPT0/MmzcPtra2UKvVspT90UcfYd68eXj99ddrfM9+/vnnstRD/4fxpnaMN3VryfFG07GmWScd\n77//PgIDA+Hq6op79+5h+PDhOH36NPT09LBx48ZGv3EqelsvWLAAtra2CAkJgRACGzZswNWrV+V4\nCRJDQ0N89913+OijjzBo0CB8//33st7rKy0tRatWraTtVq1ayfpm//nnn5GUlFQla5fDg4FY/G+H\nKyFjxysAeO+99wAAhYWFAAAjIyNZyq3s22+/RVlZGVasWIFPP/0UGRkZ+PHHH2Upu2Idkd69e8tS\nHlXHeFN/jDd1a8nxRuOxptE3aDTI3d1dqNVqIYQQq1atEt7e3qK0tFScP39ePPXUU7LV4+HhUa99\njeHp6Sn9vHfvXuHi4iIsLS1lK9/Hx6fK/bYtW7aIoUOHylb+8OHDxe3bt2UrT9v+/vtv4enpKRwc\nHISDg4Pw8vISZ8+ebepmUTPCeFN/jDd1Y7ypXbO+0lExLAsAdu/ejaCgIOjq6sLNzQ3379+XrZ62\nbdtiw4YNCAoKgkKhQGxsrGy9ySu8//770s8+Pj7Ys2cP1q5dK1v5X375JYKDgzFz5kwIIaBSqbBu\n3TrZyjc0NISnpyeeffbZKt8+5Lysf+LECRw5cgQKhQIDBw5Ez549ZSt76tSpWLZsmTTG/9ChQ5gy\nZUqVS8SNVVNPdRMTE/Tu3Rvz589H+/btH7nsUaNG1flNdevWrY9cNpVjvKk/xpu6teR4o+lY06yT\nDgMDA5w9exbW1tY4ePAgPvnkE+lYcXGxbPVs3LgRb7zxBt544w0oFAoMGDAAGzdulK18oPwPmZ+f\nj0uXLkmzu3l7e8tWfqdOnZCQkKCxy3n+/v7w9/eXtczK3n//fXz//fd44YUXAJR3xAoICMD8+fNl\nKb+oqKjKpELe3t6yzy0wYsQI6OrqYsKECQCATZs24c6dO7CxscErr7yCbdu2PXLZc+fOBQD89NNP\nyM7OxssvvwwAiI2NhbW1deMbT4w3DcB4U7eWHG80Hmua+lJLXRISEoSLi4swNzcXixYtkvbv2LFD\nBAUFNWHLGm716tWiW7duwtTUVHh7e4vWrVuLIUOGyFb+3bt3xYYNG8TixYtFZGSk9GgpunTpIoqL\ni6XtO3fuiC5dushW/pgxY8T7778vUlJSREpKili0aJEYM2aMbOULIUTPnj1r3detWzdZ6ujVq1e9\n9lHDMd7UH+NN3R6HeKOpWNOsr3QcPXpUmnBGoVDg008/hYWFBQYOHIjY2FjZ6rl79y6++eYbnDt3\nrsoc89HR0bLVsXz5cvz555/o27cvDh48iMTERLzzzjuylT969GiYmJigV69esne+AoBLly4hIiKi\n2jz8cq3lYGdnh7t370pD8O7duwelUilL2UD53/K9996TvtkMGjRI1r8vUD6D4rFjx6SJjP7880+U\nlZUBAPT05PmoFRUVITk5GR07dgRQPnRRE7NBPokYb+qP8aZuj0O80VSsadZJR01z+aempmLx4sVY\nuHAhgoKCZKknJCQErq6u2L17N959911s2LBB6sErl9atW1d5g7u6uiIpKUm28jMzMxEfHy9beQ8K\nCwtDZGQkZs2ahYMHD2LNmjWyDM+qGJZlYmKCrl27wtfXFwqFAnv37pU+THIwMzPT+LDSr7/+Gq++\n+ioKCwshhEC7du3w9ddfo6ioCBEREbLU8emnn8Lb2xsdO3aEEAJpaWlYtWqVLGU/6Rhv6o/xpm6P\nQ7zRVKxpUZODVcjLy4OPj0+t6ws0VMWEKhWTrNy/fx+DBg1CQkKCLOUDwNixY7FmzRp89tlnOHDg\nAMzMzHD//n3s3LlTlvKnTp2K119/Hd27d5elvAf16tULf/31F7p3746///67yr7GiImJqfN4aGho\no8pvig6YBQUFACDrqpiV3bt3D4mJiQAAV1dXjXzTpP/DeFMd403NHrd4o4lY06yvdNTG3Ny8xqWb\nH1XFHPOmpqY4e/YsbGxscO3aNdnKB8rHnQPAwoULMWTIEBQUFMi6quGRI0ewdu1adOjQAQYGBrIv\nc2xgYAC1Wg1nZ2esWLECSqVS6kTWGI39kD+MNjtgVh4xUNm7774raz1//fUXUlNTUVpaitOnTwMA\nJk6cKGsd9H8Yb6pjvKnZ4xZvNBFrWmTScfDgQZiZmclW3tSpU5Gfn49FixbB398fhYWFtf5BH0VZ\nWRm6du0qZYzPPPOMbGVX2LVrl+xlVrZ8+XLcuXMHn3/+ORYsWIADBw489FtDQ3To0KHGbwiNvYdb\n8bueM2cOjh8/Lu0fNWqU7JPfVB72ePfuXWzfvl32y+YhISH4559/4OnpWWWmRiYdmsN4Ux3jTc0e\np3ijqVjTrG+v1DQOOS8vD3Z2dli3bh1cXV2bqGUNN3r0aPznP/+Bg4ODRuu5du1alY5Xmq5PLjdu\n3JB+vnv3Lr7//nvk5eXJFozd3NywY8eOKp2innvuOVy4cEGW8mty7949DBs2DIcOHZKtTDc3N5w/\nf15jK1c+yRhvGo7xpmaPQ7zRVKxp1lc6HlycSKFQoH379rJPpHPv3j38+OOP0mWkCnJepsrPz0fX\nrl3Rp0+fKu2X6x7f1q1bMWfOHFy5cgVWVlZIS0uDm5sbzp07J0v5x48fx+LFi6stwCTX5dQHJ7J5\n88030atXL9mCQFN0wLxz5w4yMzNlLbNbt27Izs6W1pAg+TDe1B/jTd0eh3ijqVjTrJMOTS/KU0HT\nw78AYNGiRRopt8KCBQuQkJAAHx8fnDx5EgcPHsT69etlKz84OBgff/wxunfvDh0dHdnKrVC5k55a\nrcbx48erBJvGGj58OC5duqTRDpiVvymXlZUhNzcXCxYskLWO69evw93dHX369KnSfs5I2niMN/XH\neFO3xyHeaCrWNOvbK9rSrVs3nD17Vmv1Xb9+He3bt5f1slXFMsc9evTAyZMnoaOjgx49ekidfxpr\nwIABOHr0qCxl1aTy7H16enpwcnLC3Llz4eLiIkv5P/30U7V9JiYm6N69O6ysrGSpo/Jy2Xp6erC2\ntpZtfo4Kv/zyS437NXHfnjSD8ebhGG8eTtPxRlOxpllf6dCW/v374++//9bI8K+EhASEh4fD3Nwc\nCxYsQEhICK5fvw61Wo1169bJ1qPc1NQUhYWFGDx4MIKDg2FlZSXrZeGFCxdi0qRJ8PHxqZL1Vkx+\n01gHDx6UpZzafPPNN/j9998xdOhQCCFw6NAh9OrVCykpKXj33XcREhLS6Drmz5+Pb7/9tsq+kJCQ\navsag8lFy8d483CMNw+n6XijqVjDKx0A3N3dcfnyZY0M/+rduzc+/PBDFBQUYOrUqdi1axf69u2L\nxMREjB8/HidPnpThFZTPHtemTRuo1Wps2LABBQUFCA4ObtQiY5UFBwcjKSkJXbt2lS53KhQK2WbZ\nu3HjBiIjI6sswPTuu+/K1v5hw4Zh3bp10rC1nJwcTJw4EbGxsRg8eLAs3zy9vLyqXLYtLS2Fh4cH\nzp8/3+iyH1ySu4KQeUlu0jzGm4djvHk4TcUbjceaRk+k/hhITU2t8SGHHj16SD+7urpWOVZ5+enG\nKC0tFd7e3rKUVRs51yWoiY+Pj3j//fdFcnKySE5OFosWLRLPPvusbOW7ublV2Var1dK+xv4dPvzw\nQ2FkZCR0dXWFsbGxMDY2FkZGRsLc3FyEh4c3qmx6/DDePBzjTe1aerzh7RX8XweyB4d/yaFyJ6g2\nbdpUOSbXPVZdXV3o6OigoKBAY7Ng9u/fH+fPn4e7u7tGyr969WqVTlDz58/Hd999J1v53t7eGDly\nJAICAgAAP/74o7Tyo6mpaaPKjoiIwNtvv43JkyfLvr4CPX4Ybx6O8aZ2LT3e8PYKNDv8S1dXF23b\ntoUQAsXFxTA0NARQfqnq7t27uH//fqPrAMp7xJ88eRK+vr5V7q3KNf+/m5sb/vnnH43NQDh79mz0\n6dMHgYGBAIAffvgBx44dq7K8eGMIIfDjjz9KndMGDBiAF198UdbOdZWnbCaqDePNwzHePFxLjTdM\nOvD/27v/mKjrPw7gz4NhLRcE2DAU9NCKohvCwciJQgbURjZutukWyzmy0oUjW7LpMtgsl1uBS8w8\nWauNrQ3LW9cW1Lhb/jg7hTMzZ61DPJpzd9QB6kmdXJ/vH4xPksrdt3sfn/vcPR+b293nttf7/Qf3\n9H3vz/vzfgP5+fmwWCy3PP7V3t6udNdCdqfd+kRt+3vzSumbiXrM8N5774XP50NCQgI0Gg0CgYAc\nZmpZs7Bu3Tq8+uqrKC4uVrorFMWYN8Exb4JTa95w0IHIP/4VSYODgxHfBfDf2yqr0RdffIHGxkZ4\nPB5IkhSRBZi5ublwOp1YsGCB/GtT5K8zig3Mm+kxb0Kj1rzhmg5E/vGvSKqpqZFXMK9evRqff/65\n8DYSExPx8MMPRzRwJElCR0cHBgYG8Oabb+K3337D5cuXhR03vXXrVpjNZuFnodysu7s7YrUpdjBv\npse8CY1a8yauZzqcTifcbjeWLFky5fEvl8uF6upq6PV6pbsY1OQx2f9+LdqKFStw+vTpiG2rvHHj\nRiQkJMBiseD8+fMYHh5GVVUVTp06JaR+pDcbmnTmzBkcPXoUALB8+XLk5+dHvE1SB+ZN6Jg3oVFj\n3sT1TEdDQwN27dol/1EnJCRg3bp1OHv2LLZt2waz2axwD4O7eWFSJA8Bi/S2yna7HQ6HAwUFBQCA\n1NRU+P1+YfWLioqwZs0a1NTURGSzIWDiZEyj0SjXrK2txUsvvYT6+nphbZB6MW9Cx7wJTq15E9eD\nDrfbfdtdAXU6HS5evDjzHfoPzpw5g+TkZHm1enJyMgDxm0aVlZXB5XLh119/RUVFBa5fv45AICCk\nNgAkJSUhEAjIQTY0NCT0zIUrV67gnnvuwTfffCNf02g0QkOgvb0ddrtd/k+lsbERS5cujfoQoJnB\nvAkd8yY4teZNXA86RkZG7vjZ2NjYDPbkvxP5RZyO0WjEgQMH4PV60d/fj0uXLuGVV15BT0+PkPqb\nN2+GwWCAx+PB9u3bcejQIezcuVNIbQD4+OOPhdW6E0mSkJiYKL9PTExEHN+9pH9h3oSOeROcWvMm\nrgcdRUVFMBqN2LBhw5TrBw8eVMX91ZnU1taGkydPoqSkBADw4IMPwuPxCKv//PPPQ6/Xo6enB5Ik\nwWQyCVmEtXv3bmzduhX19fW3nQ4Wta8AAKxfvx4lJSUwGAwAAJPJhLq6OmH1Sd2YN6Fj3gSn1ryJ\n60FHa2srDAYDOjo65C99b28v/H4/Dh8+rHDvostdd92FWbNmye/Hx8eF39PNzc1Fbm4ugImzHd59\n9100NjaGVXMySIqKisLuXzBbtmxBeXk5jh07BmDi187kPWMi5k3omDfBqTVv4nrQkZGRAZvNBqvV\nKh/AU11djZUrVyrcs+hTVlaGd955B2NjY/j222+xb98+rFq1Kuy6ly9fxq5du9Df34+8vDzs2LED\nRqMR7733HlavXh12/clH7m63adGHH34Ydn0A+PPPP7F//344nU7odDps2rRJ+JH2pH7Mm9Axb+5M\n7XkT14/MUuj+/vtvtLe3ywujnnrqKbz44oth162srMSyZcuwdOlSdHV1wWQy4fHHH0dLSwvmzp0b\ndv2cnBx0dnbeMn391ltvwWw2Tzml8b9as2YNkpKSsHz5cnz99ddYuHAhWltbw65LFK+YN3em+ryJ\n7HlypHYmk0nau3ev/L64uFhauHChpNVqpc7OzrDr33wqpiRJ0rx586RAIBB23Um9vb2SVquVbDab\nJEkTpz2+/PLLUnl5uTQ6Oiqkjccee0x+fePGDamgoEBIXaJ4w7wJTu15o545GVLE7t278dlnn8nv\n/X4/+vr6cO3aNaxfvx7PPfdcWPUlScLw8LC86jo9PR2jo6Py+7S0tLDq6/V6mEwmGAwGtLW1wWg0\nAgC6urqmPD8fjqSkJPm1mqY5iaIN8yY4teeN+npMM8rv9yMrK0t+X1pairS0NKSlpcHn84Vdf3R0\nFHq9fsqjXoWFhQAmnmu/cOFCWPW9Xi/mz5+PTz75BDU1NaioqMDevXvh8/ng8/nCDhngn70LAEzZ\nv0CKwHkLRLGMeROc2vOGazpoWosXL4bT6bztZ4sWLUJ/f/8M9+j/o9Vq5VXvk3/qGo1G/oKGGzJE\nJA7zJvZxpoOmVVJSctu9BT766CNhhyNF0sDAgNJdIKIQMW9iH2c6aFoej0c+P2ByGrKvrw9//fUX\nTCYTMjIyFO4hEcUK5k3s46CDQmKxWHDu3DkAQF5eHvcWIKKIYd7ELg46KGoEAgG43W6Mj4/L1yY3\n2yEiEol5owyu6aCo8MEHH6C5uRkZGRnyaY8ajQY//vhjWHW9Xu+0n4tYTU5E6sK8UQ5nOigqLF68\nGHa7Henp6ULrTq4mlyQJg4ODSE1NhSRJGBkZQXZ2Nhd+EcUh5o1yEpTuABEAZGVlISUlRXjdgYEB\nXLhwARUVFTCbzfj999/xxx9/4KuvvkJVVZXw9ogo+jFvlMOZDlLU+++/DwA4d+4cfvnlF1RXV0/Z\nuW/Lli1C2tHpdDh79mzQa0QUu5g3yuOaDlLU1atXAUws4MrOzobf74ff7xfeTmZmJnbu3Ina2loA\nQEdHBzIzM4W3Q0TRi3mjPM50UFzwer1obm7GkSNHoNFosGLFCuzYsYMLu4hIOObNnXHQQVGhsrIS\nnZ2duO+++wAAw8PDWLt2Lbq7u4W24/P5MHv2bKE1iUhdmDfK4UJSigpDQ0NyAABAamoqPB6PsPo2\nmw2PPvooHnnkEQAThyZt2rRJWH0iUg/mjXI46KCokJiYiMHBQfm9y+WSD04S4bXXXkN3d7f8iFx+\nfj6OHDkirD4RqQfzRjlcSEpR4e2330ZpaSnKysogSRKOHj2KAwcOCG3j5iOzgYngIaL4w7xRDgcd\nFBWefvppOBwOfP/99wCA1tZWzJkzR1j9rKws2Gw2aDQa3LhxA3v27JGnPokovjBvlMPbKxQVJElC\nV1cXHA4HnnnmGVy/fh0nT54UVn///v1oa2vDpUuXMG/ePPzwww/Yt2+fsPpEpB7MG+Xw6RWKChs3\nbkRCQgIsFgvOnz+P4eFhVFVV4dSpU0LqHz9+HMuWLQt6jYhiH/NGOZzpoKhgt9vR1taGu+++G8DE\nanKRm/bU19eHdI2IYh/zRjlc00FRISkpCYFAQF5BPjQ0JJ/+GI4TJ07AZrNhaGhI3gIZAK5cuYJA\nIBB2fSJSH+aNcjjTQVFh8+bNMBgM8Hg82L59O0pLS7Ft27aw6/r9fly7dg3j4+O4evWq/C85ORmH\nDh0S0HMiUhvmjXK4poOixs8//4yenh5IkoQnn3xS6Gpvl8uFBQsWCKtHROrGvFEGBx0UFerq6lBf\nX48lS5bI15qamtDU1BRW3YaGBrS2tmLVqlW33fznyy+/DKs+EakP80Y5HHRQVJg/fz7S09Px+uuv\n44UXXgAAFBYWwuFwhFW3r68Per0e33333W0/LysrC6s+EakP80Y5HHRQVCgsLITVakVtbS2ys7Ox\nZ88eFBcX4/Tp00p3jYhiDPNGOVxISlFBkiSkpKTAbDbj/vvvR3l5OUZHR4XVP378OCorK/HQQw8h\nJycHWq0WOTk5wuoTkXowb5TDR2YpKjz77LPy66amJuj1erS0tAirX1dXh5aWFuj1ep6BQBTnmDfK\n4e0VUpTT6YTb7b5lp75jx47hgQcewKJFi4S0U1JSArvdLqQWEakT80Z5vL1CimpoaEBycvIt11NS\nUtDQ0CCsnSeeeAJvvPEGTpw4AYfDIf8jovjBvFEeb6+QotxuN3Q63S3XdTodLl68KKydyV8dvb29\n8jWNRgOLxSKsDSKKbswb5XHQQYoaGRm542djY2PC2rFarcJqEZE6MW+Ux0EHKaqoqAhGoxEbNmyY\ncv3gwYPQ6/Vh17/5/ANg4tfGnDlzUFpaCq1WG3Z9IlIP5o3yuJCUFOV2u2EwGDBr1iz5S9/b2wu/\n34/Dhw9j7ty5YdVvbm6+5ZrX60V3dzeampqwdu3asOoTkXowb5THQQdFBavVip9++gkAkJeXh5Ur\nV0a0Pa/Xi4qKCi7uIopDzBvlcNBBcaugoIA7EBLRjGDeTOAjsxSXrFYrUlNTle4GEcUB5s0/uJCU\nYppOp7vltEev14vMzEx8+umnCvWKiGIR8yY43l6hmOZyuaa812g0SE9Px+zZsxXqERHFKuZNcBx0\nEBER0Yzgmg4iIiKaERx0EBER0YzgoIOIiIhmBAcdRERENCM46CAiIqIZ8T+rSRWImFlERQAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f996dc42e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "country_metrics = run_query(sales_by_country)\n",
    "country_metrics.set_index(\"country\", drop=True, inplace=True)\n",
    "colors = [plt.cm.Accent(i) for i in np.linspace(0, 1, country_metrics.shape[0])]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(9, 10))\n",
    "ax1, ax2, ax3, ax4 = axes.flatten()\n",
    "fig.subplots_adjust(hspace=.5, wspace=.3)\n",
    "\n",
    "\n",
    "# top left\n",
    "sales_breakdown = country_metrics[\"total_sales\"].copy().rename('')\n",
    "sales_breakdown.plot.pie(\n",
    "    ax=ax1,\n",
    "    startangle=-90,\n",
    "    counterclock=False,\n",
    "    title='Sales Breakdown by Country,\\nNumber of Customers',\n",
    "    colormap=plt.cm.Accent,\n",
    "    fontsize=8,\n",
    "    wedgeprops={'linewidth':0}\n",
    "    \n",
    ")\n",
    "\n",
    "# top right\n",
    "cvd_cols = [\"customers\",\"total_sales\"]\n",
    "custs_vs_dollars = country_metrics[cvd_cols].copy()\n",
    "custs_vs_dollars.index.name = ''\n",
    "for c in cvd_cols:\n",
    "    custs_vs_dollars[c] /= custs_vs_dollars[c].sum() / 100\n",
    "custs_vs_dollars.plot.bar(\n",
    "    ax=ax2,\n",
    "    colormap=plt.cm.Set1,\n",
    "    title=\"Pct Customers vs Sales\"\n",
    ")\n",
    "ax2.tick_params(top=\"off\", right=\"off\", left=\"off\", bottom=\"off\")\n",
    "ax2.spines[\"top\"].set_visible(False)\n",
    "ax2.spines[\"right\"].set_visible(False)\n",
    "\n",
    "\n",
    "# bottom left\n",
    "avg_order = country_metrics[\"average_order\"].copy()\n",
    "avg_order.index.name = ''\n",
    "difference_from_avg = avg_order * 100 / avg_order.mean() - 100\n",
    "difference_from_avg.drop(\"Other\", inplace=True)\n",
    "difference_from_avg.plot.bar(\n",
    "    ax=ax3,\n",
    "    color=colors,\n",
    "    title=\"Average Order,\\nPct Difference from Mean\"\n",
    ")\n",
    "ax3.tick_params(top=\"off\", right=\"off\", left=\"off\", bottom=\"off\")\n",
    "ax3.axhline(0, color='k')\n",
    "ax3.spines[\"top\"].set_visible(False)\n",
    "ax3.spines[\"right\"].set_visible(False)\n",
    "ax3.spines[\"bottom\"].set_visible(False)\n",
    "\n",
    "# bottom right\n",
    "ltv = country_metrics[\"customer_lifetime_value\"].copy()\n",
    "ltv.index.name = ''\n",
    "ltv.drop(\"Other\",inplace=True)\n",
    "ltv.plot.bar(\n",
    "    ax=ax4,\n",
    "    color=colors,\n",
    "    title=\"Customer Lifetime Value, Dollars\"\n",
    ")\n",
    "ax4.tick_params(top=\"off\", right=\"off\", left=\"off\", bottom=\"off\")\n",
    "ax4.spines[\"top\"].set_visible(False)\n",
    "ax4.spines[\"right\"].set_visible(False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "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>album_purchase</th>\n",
       "      <th>number_of_invoices</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>no</td>\n",
       "      <td>500</td>\n",
       "      <td>0.814332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yes</td>\n",
       "      <td>114</td>\n",
       "      <td>0.185668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  album_purchase  number_of_invoices   percent\n",
       "0             no                 500  0.814332\n",
       "1            yes                 114  0.185668"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "albums_vs_tracks = '''\n",
    "WITH invoice_first_track AS\n",
    "    (\n",
    "     SELECT\n",
    "         il.invoice_id invoice_id,\n",
    "         MIN(il.track_id) first_track_id\n",
    "     FROM invoice_line il\n",
    "     GROUP BY 1\n",
    "    )\n",
    "\n",
    "SELECT\n",
    "    album_purchase,\n",
    "    COUNT(invoice_id) number_of_invoices,\n",
    "    CAST(count(invoice_id) AS FLOAT) / (\n",
    "                                         SELECT COUNT(*) FROM invoice\n",
    "                                      ) percent\n",
    "FROM\n",
    "    (\n",
    "    SELECT\n",
    "        ifs.*,\n",
    "        CASE\n",
    "            WHEN\n",
    "                 (\n",
    "                  SELECT t.track_id FROM track t\n",
    "                  WHERE t.album_id = (\n",
    "                                      SELECT t2.album_id FROM track t2\n",
    "                                      WHERE t2.track_id = ifs.first_track_id\n",
    "                                     ) \n",
    "\n",
    "                  EXCEPT \n",
    "\n",
    "                  SELECT il2.track_id FROM invoice_line il2\n",
    "                  WHERE il2.invoice_id = ifs.invoice_id\n",
    "                 ) IS NULL\n",
    "             AND\n",
    "                 (\n",
    "                  SELECT il2.track_id FROM invoice_line il2\n",
    "                  WHERE il2.invoice_id = ifs.invoice_id\n",
    "\n",
    "                  EXCEPT \n",
    "\n",
    "                  SELECT t.track_id FROM track t\n",
    "                  WHERE t.album_id = (\n",
    "                                      SELECT t2.album_id FROM track t2\n",
    "                                      WHERE t2.track_id = ifs.first_track_id\n",
    "                                     ) \n",
    "                 ) IS NULL\n",
    "             THEN \"yes\"\n",
    "             ELSE \"no\"\n",
    "         END AS \"album_purchase\"\n",
    "     FROM invoice_first_track ifs\n",
    "    )\n",
    "GROUP BY album_purchase;\n",
    "'''\n",
    "\n",
    "run_query(albums_vs_tracks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
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}