src/notebooks/create_action_embeddings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Actions are words\n",
    "\n",
    "The notebook expects pre-created train, val, and test DataFrames stored in the pickle format in the `data/new_datasets` folder. \n",
    "\n",
    "## Text manipulation\n",
    "### Action representation\n",
    "A action is set of beat elements. A beat element is defined as `<hand><y><x><rotation>` matching pattern `[LR][0-2][0-3][0-9]`. Action of multiple beat elements is created as concatentation in lexicographical with `_` separator. Action is therefore defined as `<hand><y><x><rotation>(_<hand><y><x><rotation>)*`.\n",
    "\n",
    "Lexicographical ordering ensures all sets of the same beat elements are represented as the same word."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Tuple\n",
    "import pandas as pd\n",
    "\n",
    "def load_datasets(storage_folder) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\n",
    "    return [pd.read_pickle(storage_folder / f'{phase}_beatmaps.pkl') for phase in\n",
    "            ['train', 'val', 'test']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "storage_folder = Path('../../data/new_datasets')\n",
    "train, val, test = load_datasets(storage_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>l_lineLayer</th>\n",
       "      <th>l_lineIndex</th>\n",
       "      <th>l_cutDirection</th>\n",
       "      <th>r_lineLayer</th>\n",
       "      <th>r_lineIndex</th>\n",
       "      <th>r_cutDirection</th>\n",
       "      <th>word</th>\n",
       "      <th>prev</th>\n",
       "      <th>next</th>\n",
       "      <th>part</th>\n",
       "      <th>...</th>\n",
       "      <th>word_vec</th>\n",
       "      <th>word_id</th>\n",
       "      <th>prev_l_lineLayer</th>\n",
       "      <th>prev_l_lineIndex</th>\n",
       "      <th>prev_l_cutDirection</th>\n",
       "      <th>prev_r_lineLayer</th>\n",
       "      <th>prev_r_lineIndex</th>\n",
       "      <th>prev_r_cutDirection</th>\n",
       "      <th>prev_word_vec</th>\n",
       "      <th>prev_word_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>snippet</th>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">250f</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Expert</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
       "      <th>3.251</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>R100</td>\n",
       "      <td>0.251</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.038360</td>\n",
       "      <td>...</td>\n",
       "      <td>[-0.266, -0.4497, -0.3628, -0.2546, 0.04242, 0...</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.2656, -0.1178, 0.3367, 0.0794, 0.1754, -0.1...</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.499</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>R031</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.251</td>\n",
       "      <td>0.041286</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.2208, -0.3235, 0.1553, 0.0716, 0.1685, 0.02...</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[-0.266, -0.4497, -0.3628, -0.2546, 0.04242, 0...</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               l_lineLayer  l_lineIndex  l_cutDirection  \\\n",
       "name difficulty snippet time                                              \n",
       "250f Expert     0       3.251            1            0               0   \n",
       "                        3.499            0            3               1   \n",
       "\n",
       "                               r_lineLayer  r_lineIndex  r_cutDirection  word  \\\n",
       "name difficulty snippet time                                                    \n",
       "250f Expert     0       3.251            1            0               0  R100   \n",
       "                        3.499            0            3               1  R031   \n",
       "\n",
       "                                prev   next      part  ...  \\\n",
       "name difficulty snippet time                           ...   \n",
       "250f Expert     0       3.251  0.251  0.248  0.038360  ...   \n",
       "                        3.499  0.248  0.251  0.041286  ...   \n",
       "\n",
       "                                                                        word_vec  \\\n",
       "name difficulty snippet time                                                       \n",
       "250f Expert     0       3.251  [-0.266, -0.4497, -0.3628, -0.2546, 0.04242, 0...   \n",
       "                        3.499  [0.2208, -0.3235, 0.1553, 0.0716, 0.1685, 0.02...   \n",
       "\n",
       "                              word_id  prev_l_lineLayer  prev_l_lineIndex  \\\n",
       "name difficulty snippet time                                                \n",
       "250f Expert     0       3.251      90                 0                 2   \n",
       "                        3.499      53                 1                 0   \n",
       "\n",
       "                               prev_l_cutDirection  prev_r_lineLayer  \\\n",
       "name difficulty snippet time                                           \n",
       "250f Expert     0       3.251                    1                 0   \n",
       "                        3.499                    0                 1   \n",
       "\n",
       "                               prev_r_lineIndex  prev_r_cutDirection  \\\n",
       "name difficulty snippet time                                           \n",
       "250f Expert     0       3.251                 2                    1   \n",
       "                        3.499                 0                    0   \n",
       "\n",
       "                                                                   prev_word_vec  \\\n",
       "name difficulty snippet time                                                       \n",
       "250f Expert     0       3.251  [0.2656, -0.1178, 0.3367, 0.0794, 0.1754, -0.1...   \n",
       "                        3.499  [-0.266, -0.4497, -0.3628, -0.2546, 0.04242, 0...   \n",
       "\n",
       "                              prev_word_id  \n",
       "name difficulty snippet time                \n",
       "250f Expert     0       3.251         26.0  \n",
       "                        3.499         90.0  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore the distributions\n",
    "#### Word frequency distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In the `train` set, there are 2489 unique words.\n"
     ]
    }
   ],
   "source": [
    "print(f'In the `train` set, there are {len(train.word.value_counts())} unique words.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Beat Saber</th>\n",
       "      <th>$~x^{-1}$</th>\n",
       "      <th>$~x^{-1.5}$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.000000</th>\n",
       "      <td>R021</td>\n",
       "      <td>11.758441</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.693147</th>\n",
       "      <td>L011</td>\n",
       "      <td>11.727311</td>\n",
       "      <td>11.306853</td>\n",
       "      <td>10.960279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.098612</th>\n",
       "      <td>R020</td>\n",
       "      <td>11.184880</td>\n",
       "      <td>10.901388</td>\n",
       "      <td>10.352082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.386294</th>\n",
       "      <td>L010</td>\n",
       "      <td>11.142934</td>\n",
       "      <td>10.613706</td>\n",
       "      <td>9.920558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.609438</th>\n",
       "      <td>R031</td>\n",
       "      <td>11.107330</td>\n",
       "      <td>10.390562</td>\n",
       "      <td>9.585843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         index  Beat Saber  $~x^{-1}$  $~x^{-1.5}$\n",
       "0.000000  R021   11.758441  12.000000    12.000000\n",
       "0.693147  L011   11.727311  11.306853    10.960279\n",
       "1.098612  R020   11.184880  10.901388    10.352082\n",
       "1.386294  L010   11.142934  10.613706     9.920558\n",
       "1.609438  R031   11.107330  10.390562     9.585843"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "zipf = train.word.value_counts().reset_index()\n",
    "zipf.word = np.log(zipf.word)\n",
    "zipf.index = np.log(zipf.index + 1)\n",
    "zipf = zipf.rename(columns={'word': 'Beat Saber'})\n",
    "zipf['$~x^{-1}$'] = 12 - zipf.index\n",
    "zipf['$~x^{-1.5}$'] = 12 - zipf.index * 12/8\n",
    "zipf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 9.0)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABE4AAANVCAYAAABmirtaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdd3hUVf7H8fedTHolEAihGKogvYlCqIKKrKCiIBaKsGBBbICiIrgqigVRESxIUVDkBxKxgCuuUgJIB2krLaH3QEgvc39/BLKBzCQhbSbJ5/U88+Rmzrnnfu/E7LP5cO45hmmaiIiIiIiIiIhIThZnFyAiIiIiIiIi4qoUnIiIiIiIiIiIOKDgRERERERERETEAQUnIiIiIiIiIiIOKDgREREREREREXFAwYmIiIiIiIiIiAMKTkREREREREREHCg1wYlhGEGGYXQ3DOMlwzAiDcM4ZhiGeen1RyHHXpBtLNMwjPAiKVpERERERERESjWrswu4BluA8KIe1DCMO4H7inpcERERERERESn9Ss2ME8DIdnwS+LHQAxqGPzDt0renCjueiIiIiIiIiJQtpWnGyVTgILDBNM1DAIZhmIUccyJQHVgOHAUGFnI8ERERERERESlDSs2ME9M03zVNc9Hl0KSwDMO4CXgcSAYeK4oxRURERERERKRsKTXBSVEyDMMdmEHm/U80TXOfk0sSERERERERERdULoMT4AWgEfBfYJKTaxERERERERERF1XughPDMK4HXrr07aOmaaY6sx4RERERERERcV3lKjgxDMMAPgc8gdmmaf7h3IpERERERERExJWVq+AEGAZ0AM4Co51ci4iIiIiIiIi4uNK0HXGhGIZRlf+tZzLaNM0zRTDmMDLDGHx9fVs1aNCgsEOKiIiIiIiISBHatGnTGdM0Qwp6frkJToCpQCCwEphdFAOapvkZ8BlA69atzY0bNxbFsCIiIiIiIiJSRAzDiCnM+eXiUR3DMO4C7gFSyVwQ1nRySSIiIiIiIiJSCpSXGSdjL31dBzQzDKOZnT61sh3faRjG6UvHi0zTTCvW6kRERERERETEJZWX4MTz0teOl155+TDbcQXgfJFXJCIiIiIiIiIur1w8qiMiIiIiIiIiUhDlIjgxTbO5aZpGbi9gTrZTamVr02wTERERERERkXKqXAQnIiIiIiIiIiIFUWrWODEMoznQ3EFzqGEYg656b5lpmieKtyoRERERERERKctKTXAC3AWMd9B2PTDrqve6AApORERERERERKTA9KiOiIiIiIiIiIgDpSY4MU1zQl4LvF71+uMaxx+U7dzo4rkLERERERERESlNSk1wIiIiIiIiIiJS0hSciIiIiIiIiIg4UJoWhxUREREREcmSkpJCXFwcCQkJpKSkYLPZnF2SiBQhNzc3fHx8CAgIwN/fH8MwnFKHghMRERERESl14uLiOHHiBIGBgYSEhODl5YXFYnHaH1YiUrRM0yQjI4P4+HjOnDlDUlISlStXdsrvuIITEREREREpVZKSkjh58iQ1a9bEy8vL2eWISDEwDAOr1UpQUBD+/v7ExMRw8eJFAgICSrwWrXEiIiIiIiKlSmxsLMHBwQpNRMoJNzc3goODiYuLc8r1FZyIiIiIiEipEh8f75R/dRYR5/Hz8yMxMdEp11ZwIiIiIiIipUpGRgZWq1YdEClP3NzcyMjIcMq1FZyIiIiIiEipo0VgRcoXZ/7OKzgREREREREREXFAwYmIiIiIiIiIiAMKTkREREREREREHFBwIiIiIiIiIiLigIITERERERERkTJiwoQJGIahBZSLkIITERERERERceiPP/7I+kPc3svX15c6derQr18/fvjhB2eX61SmafLTTz/x0EMPUb9+ffz9/fH09KRatWq0aNGCPn368MEHH7B161ZsNpuzy5V80ubnIiIiIiIiUmCJiYkcOHCAAwcOsGDBAnr06MHChQvx8fFxdmm5Cg8PJyYmhoEDBzJ79uxCj3fq1Cn69u3LihUrcrQdO3aMY8eOsXXrVr777jsAli5dyu23317o60rxU3AiIiIiIiIi+fLYY4/x+OOPZ31vmiYXLlxg48aNfPDBB0RHR7N06VIeffRRvvzySydWWrJSU1O59dZb2bZtGwDNmjVj8ODBtGjRgoCAAOLj4/nvf//LmjVr+OGHHzh9+rSTK5ZroeBERERERERE8qVy5co0btw4x/sRERH069ePpk2bcubMGebOnctbb71FWFiYE6oseTNmzMgKTQYOHMjMmTOxWK5cGSMiIoIhQ4aQkZFBZGQkNWrUcEapUgBa40REREREREQKrWrVqgwcOBDInImyceNGJ1dUciIjIwGwWq1MmTIlR2iSnZubG3369KFRo0YlVZ4UkoITERERERERKRK1atXKOk5JScm17/Hjx3n55Zdp3bo1wcHBWYuo3nPPPSxZsiTXcxMSEvj2228ZOnQozZs3JzAwEHd3d0JCQujUqRPvvvsu8fHxds/t3LkzhmEQExMDwJw5c3IseNu5c+druu9Dhw4BULFiRYKCgq7p3Kvt2LGD119/ndtuu43q1avj6emJn58f9erVY+DAgaxbt+6axouLi+PVV1+lSZMm+Pv7ExQURPv27ZkxY0a+F6iNjIykb9++1KxZEy8vLwIDA2nRogUvvvgiJ0+edHje1Tv8xMXF8cYbb9CqVSuCg4MxDIMpU6Zc0/04gx7VERERERERkSIRHR2ddVyzZk2H/ebMmcPjjz9OYmLiFe8fO3aMxYsXs3jxYnr37s28efPw9fXNcX7Pnj3tLsJ65swZVq5cycqVK5k2bRo///wzDRo0KPgN5ZOnpyeQuUDs2bNnqVixYoHG+eOPP+jSpUuO91NTU9m3bx/79u3jyy+/5IUXXuDNN9/Mc7zo6Gi6d+/Ovn37rnh/zZo1rFmzhgULFvD999/j7e1t9/wzZ85w77335visU1JS2Lp1K1u3buXjjz9m/vz59OjRI9da9u3bx2233caBAwfyrNvVKDgRERERERGRQjt58mTWgrA1atSgZcuWdvvNmTOHQYMGZfV78sknadSoEaGhoRw9epSvv/6a+fPn8/333zN48GAWLFiQY4z09HSaNGlCr169aN26NWFhYZimSUxMDIsXL2bBggUcPHiQu+66i61bt+Ll5ZV17qxZs0hISOC2227j2LFj9O7dm9dff/2K8e2FNblp2bIl27dvxzRNhgwZwldffYW/v/81jXH5vnx9fenZsyddu3alQYMGBAQEcOrUKXbu3MmHH35ITEwMb731FvXr12fw4MG5jte3b18OHDjA0KFD6du3L8HBwezatYv33nuPbdu28euvvzJ48GDmz5+f49zExES6dOnCjh07MAyDvn370qtXr6xZRevWrWPy5MkcOXKEu+++mzVr1jj8mQP06dOHw4cP8/jjj9O7d28qVqzIwYMHCxwylSjTNPUqglerVq1MEREREREpfrt27XJ2CeXK77//bgImYD722GPmX3/9dcVr9erV5gcffGDWrl3bBEwPDw8zMjLS7liHDx82fXx8TMDs37+/mZKSYrfftGnTsq65fPnyHO1///13rjX/+uuvpsViMQFzxowZdvtcd911JmAOHDgw9w8gHzZs2GC6ubll1RwQEGA++OCD5ieffGJu3rzZTE1Nzdc4p0+fNmNjYx22p6SkmN27dzcB87rrrjPT09Nz9Bk/fnxWHYA5Z84cu+N07tw5q8+///3vHH1GjhxpAqafn58ZFRVlt56zZ8+aDRs2NAEzIiIi11osFov5888/53b7eSro7z6w0SzE3/uacSIiIiIiImVO+As/ObuEEhf9Vs9iv8b06dOZPn26w/b+/fszatQohzMPPvjgAxITEwkJCWHGjBl4eHjY7ffYY48xa9YsNmzYwMyZM7nllluuaK9Xr16udXbr1o1evXoRGRlJZGQkQ4YMyePOCqd169Z8/vnnPProo6SmphIXF8e8efOYN28eAN7e3rRt25a7776bAQMGOFwHpVKlSrlex8PDg3feeYfmzZsTExPD1q1badWqlcP+d9xxBwMGDLA7zhdffEH9+vXJyMhg6tSpdO/ePav9zJkzfP755wC88sortGvXzu74wcHBvPvuu/Ts2ZPVq1ezd+9ehz+bAQMG5Pk4j6vS4rAiIiIiIiJSJL7//ns++ugjzp8/b7f98u4zd9xxBz4+PrmO1bFjRwDWrl2b53VPnz7N3r172bFjR9YrJCQEIGub4OI2ePBgduzYwT//+c8cwUhSUhJ//PEHTz31FHXq1MkKVPKSkpLCoUOH2LVrV9Z9ZU6gyJTXvT3yyCMO22rXrp21CO5//vMfMjIystp++eUXkpKSALjvvvtyvcblnxPk/rN66KGHch3HlWnGiYiIiIiIiOTL+PHjmTBhwhXvJScnEx0dzaJFi3jzzTeZPXs269ev5z//+Q9VqlTJ6nfhwoWsRUrnzJnDnDlz8nXNEydO2H0/KiqKDz/8kOXLl3Pu3DmH5585cyZf1ykK9erV47PPPmPatGls3ryZdevWsXnzZlatWpW1KOq5c+d46KGHyMjIsDsbJCEhgQ8//JD58+ezc+fOKwKNq+V1bzfeeGOe7b/99hvx8fEcPHiQunXrAlyxlXT2nZLy4uhnBdCsWbN8j+NqNONERERERERECszLy4sGDRrw0ksvsXjxYgB27drFc889d0W/U6dOFWj8yzMfspswYQIREREsWLAg19DE0fnFzWq1cuONNzJy5Ehmz57N/v37Wb9+PREREVl9nn766RxbJkdHR9OkSRNefPFFtm/fnmtoAnnfW+XKlXNtzx5snT17Nuu4oD+rq3dJyq5ChQoFGtMVaMaJiIiIiIiIFInu3bvTrFkztm3bxoIFC/j000+zdqjJHgKMGDGC4cOHF+gav/32G6+++iqQ+bjJqFGjiIiIoGbNmvj5+eHm5gZkrs3x2muvFfKOik6bNm1YtmwZzZs3Z9++fcTGxrJ8+XLuuuuurD4PP/wwBw8exDAMBg8ezP3330/Dhg0JCQnJ2vLYZrNl3WP2x3bsMQwj13ZH51/+WVmtVjZv3pznOJflFtRcrrk0UnAiIiIiIiJlTkkslCr2NWjQgG3btpGWlsaePXuyFi/NvvBpfHw8jRs3LtD4lxctrVChAuvWrctay+Rqec1EcQZfX1/69++fFejs3bs3q23Pnj2sXr0agBdffDHHFsmXXct9nTx5kho1ajhszz6zJPu2wJd/Vunp6QQEBHDdddfl+5plkR7VERERERERkSKTnp5u97hSpUpZf8QvX74cm81WoPF37twJQJcuXRyGJnDlOh325HcWRVELCwvLOrZY/vcn+eX7AujXr5/D8/O6r+zWr1+fa/uGDRuAzEAn+1omLVq0yDpetmxZvq9XVik4ERERERERkSJhmiabNm3K+v7q2Q69e/cG4MiRI3z11VcFusblMCYhIcFhny1btvDnn3/mOo6XlxeQuXNNYeX1yEx2jhZezR4y5XZvn3zySb6vNXv2bIdt0dHR/P777wB07dr1ikdp7rjjjqytoqdMmVIkn1FppuBEREREREREisS0adOIjo4GMmctZJ9dATB69Gi8vb0BGDlyZNajKY6sXr2aFStWXPFevXr1stou79KT3enTp3n44YfzrLVq1aoA7N+/P8++ebnnnnuYOnVqjsVer/bLL79k7Sbk5+dHt27dstou3xc4DjymT5/O999/n++6fvzxR7tbH6empjJ06NCstUyeeOKJK9qrVq3K0KFDgcxHiAYNGkRqaqrD68TFxTF16tR811XaaI0TERERERERyZdTp06xY8eOK95LSUnh4MGDLFq0iPnz5wOZj6BMmjQpx/k1a9ZkxowZPPTQQ8TFxdGlSxf69+9P7969CQ8Px2azcfz4cTZt2sTixYv566+/+Oijj+jUqVPWGAMGDOCHH34gISGBTp068cILL2Sto7JmzRomT57MiRMnuPnmm1m7dq3De2nXrh2///47GzZs4K233qJHjx5ZC9l6e3tTrVq1fH8uhw8f5sknn+SFF17gH//4BxEREdxwww0EBweTmprK3r17WbJkCQsXLsx6RGnixIkEBARkjdGiRQsaN27Mjh07+PTTT4mNjeXhhx+matWqHDlyhLlz57Jw4ULat29PVFRUvupq06YNAwYMYOXKlfTt25egoCD27NnDu+++y9atWwG49957ue2223Kc+84777B27Vq2bNnC/Pnz2bhxI8OHD+fGG28kICCAuLg49uzZwx9//MGSJUvw8vJixIgR+f7MShXTNPUqglerVq1MEREREREpfrt27XJ2CeXK77//bgL5fvn7+5tff/11rmN+9913ZoUKFfI13pw5c3KcP3jwYIf93dzczClTppjjx4/Pes+eI0eOmMHBwXbH6NSp0zV9Rr1798735+Pt7W2+//77dsfZsmVLrp9LkyZNzGPHjmV9P378+BxjZL/vAwcOmLVr13Y4XteuXc2EhASH93X+/Pl831utWrVyraUoFPR3H9hoFuLvfc04ERERERERkQJzd3enQoUKNGzYkFtvvZVHHnmE0NDQXM+5++67ueWWW/j8889ZunQpO3fu5OzZs1itVkJCQrjhhhvo1KkT99xzD/Xr189x/syZM+natSufffYZW7duJTU1ldDQUDp27MiIESO48cYbmTBhQq41VKtWjfXr1/Pmm2+yYsUKjhw5QnJycoE+g8jISP7++29++eUXoqKi2LlzJ0eOHOHixYt4enoSHBzMDTfcwC233MJDDz2U4xGmy5o3b87WrVt58803Wbp0KceOHcPf35+6devSt29fnnjiiay1WfKjVq1abNq0icmTJ/Pdd98RHR2NxWKhcePGDBo0iKFDh16xQO3VAgMDiYyMZPXq1cyZM4fVq1dz7NgxEhISCAgIIDw8nFatWtGjRw969iy7O1kZ5jUsYiOOtW7d2ryW1Y1FRERERKRgdu/eTcOGDZ1dhoiUsIL+7huGsck0zdYFva4WhxURERERERERcUDBiYiIiIiIiIiIAwpOREREREREREQcUHAiIiIiIiIiIuKAghMREREREREREQcUnIiIiIiIiIiIOKDgRERERERERETEAQUnIiIiIiIiIiIOKDgREREREREREXFAwYmIiIiIiIiIiAMKTkREREREREREHFBwIiIiIiIiIiLigIITEREREREREREHFJyIiIiIiIiIiDig4ERERERERERExAEFJyIiIiIiIiIiDig4ERERERERERFxQMGJiIiIiIiIlBlz585l+PDhtGrVCk9PTwzDYO7cuc4uS0oxq7MLEBERERERESkqL7/8MjExMYSEhFClShUOHz7s7JKklNOMExERERERESkzZsyYQUxMDKdOneKRRx5xdjlSBmjGiYiIiIiIiJQZ3bp1c3YJUsZoxomIiIiIiIiIiAMKTkREREREREREHFBwIiIiIiIiIiLigIITERERERERcQnVq1fHMIx8v2bPnu3skqUc0OKwIiIiIiIi4hL69u3LuXPn8t2/bt26xViNSCYFJyIiIiIiIlJoiYmJ1K1bl+PHjxMaGsqBAwfw9vbO0c9ms9GnTx8iIyMxDIO5c+fywAMPADB58uSSLlskT3pUR0RERERERArNx8eHcePGAXDixAmmTZtmt9/IkSOJjIwE4O23384KTURclYITERERERERKRJDhw6lTp06QGYokpCQcEX7pEmT+PjjjwF4+umnGTVqVInXKHKt9KiOiIiIiIiIFAl3d3f+9a9/8eCDD3Lq1CmmTp3K888/D8C8efMYO3YsAP369Su2x3JmzJjB6tWrAdi6dSsAn332GcuXLwcgIiKCoUOHFsu1pWwyTNN0dg1lQuvWrc2NGzc6uwwRERERkTJv9+7dNGzY0NlliAOmadKiRQu2bdtGxYoViY6O5s8//6RHjx6kpaXRpUsXli1bhoeHR7Fcf9CgQcyZM8dh+8CBA7UbTylV0N99wzA2mabZuqDX1YwTEREREREpeyYEOruCkjfhgrMrAMAwDCZOnEjPnj05e/YsTzzxBJGRkaSlpdG0aVMWL15cbKEJwOzZsxWMSJHSGiciIiIiIiJSpO644w46dOgAwJdffklcXBw1a9Zk6dKlBAaWw1BLSjUFJyIiIiIiIlLkhg8fnnXs7+/PsmXLCAsLc2JFIgWj4ERERERERESK1KFDh7IWhQVISkrC3d3diRWJFJyCExERERERESkysbGx3H777Rw9epQKFSpgtVpJT0/nlVdecXZpIgWixWFFRERERKTscZGFUsub5ORk7rzzTnbv3o2XlxdLlixh1qxZzJw5k/nz5/P888/TrFkzZ5cpck0040REREREREQKzWaz0b9/f6KiorBYLMydO5eIiAjGjRuHu7s7pmny0ksvObtMkWum4EREREREREQKbcSIEURGRgLw/vvv06dPHwDCw8MZMmQIAD/99BNRUVFOq1GkIBSciIiIiIiISKG8/vrrTJ8+HYDRo0czcuTIK9pfeuklPD09ARg7dmyJ1ydSGApOREREREREpMBmzZrFuHHjAOjfvz+TJk3K0ad69epZ2xOvWrWKpUuXlmiNIoWh4EREREREREQKZOnSpQwbNgyAzp07M3v2bAzDsNt37Nix+Pj4AJkzUEzTLLE6RQpDu+qIiIiIiIhIgfTo0YO0tLR89Q0NDSUhIaGYKxIpeppxIiIiIiIiIiLigIITEREREREREREHFJyIiIiIiIiIiDig4ERERERERERExAEFJyIiIiIiIiIiDig4ERERERERERFxQMGJiIiIiIiIiIgDCk5ERERERERERBywOruA/DIMIwhoA9yY7WvVS80rTNPsnMf5FqA9cNulrw2AikAKcBSIAmaYprm2OOoXERERERERkdKn1AQnwBYgvBDnHwRq2nnfHbj+0usRwzBmAY+applaiGuJiIiIiIiISBlQmoITI9vxSWAD8I9rOL/apa8HgUVkzjA5AniQOQPlGTJnsAwmM0x5uJD1ioiIiIiIiEgpV5qCk6lkhh4bTNM8BGAYhnkN528A/gUsM03z6vPWGIYxB1gN1AMeMgzjU9M0VxdB3SIiIiIiIiJSSpWa4MQ0zXcLef7NebSfMgzjOWDJpbfuIzNIEREREREREZFySrvqXOn3bMd1nVaFiIiIiIiIiLgEBSdX8sh2nHEtJyamXiziUkRERERERETE2RScXKlTtuPd13Ji9MVDfLroPjLijhZxSSIiIiIiIiLiLApOLjEMwwKMzfbWgms53wSmxu9h2DddOfnDk3DhSJHWJyIiIiIiIiIlT8HJ/4wG2lw6XmSa5qa8TjAMY5hhGBsNw9h4+b31Xh7ce/o3VnzWFpaMhHMHi6teERERERERESlmCk4AwzC6A29c+vYE8Fh+zjNN8zPTNFubptk6+/vn3dwYUTmYSdHfk/pRK1j8GJzZW8RVi4iIiIiIyNXmzp3L8OHDadWqFZ6enhiGwdy5cws0Vnh4OIZh2H1169atiCsXV1VqtiMuLoZhtAQWAm5AEnCvaZqni2LsuYEBbPLy4u2dCwjf9g00vgc6jIIqNxTF8CIiIiIiInKVl19+mZiYGEJCQqhSpQqHDx8u1HiBgYE8/fTTOd6vXbt2ocaV0qNcByeGYTQEfgECgDSgj2maUQUZy93B5J3dnh70rRbKy2dj6bVjEexYBA3+AR1HQ1jzAtcuIiIiIiIiOc2YMYP69etTs2ZNJkyYwKuvvlqo8YKCgpgwYULRFCelUrl9VMcwjDrAcqASmVsPP2Ca5tKCjlc7uB4dQm+y25ZksfBSSEXGhlQkwTBgz4/wWSeY1xcObyjoJUVEREREROQq3bp1o2bNms4uQ8qQcjnjxDCMmsBvQBiZG+I8YprmwsKMaTWsTL31U+bumsv7m98n3Zaeo8+Pfr5s8/TgnVNnaZSaCnt/yXzV7gwdx0B4+8KUICIiIiIiIkUsJSWFL7/8kiNHjhAQEECbNm1o27ats8uSElTughPDMELJDE2uu/TW46ZpflkUY1sMCwMaDaBVaCvGrBjDoYuHcvQ57O7OQ2FVePrceR6Ou5g55efAH5mvmu2g02io3QUMoyhKEhERERERkUI4ceIEAwcOvOK9Nm3a8M0331CnTh0nVSUlqVw9qmMYRiUyH8+pe+mtUaZpflLU12lUsREL7lzAnbXvtNuebhi8W7ECT1QJ4awl24/g0Br46m6Y0Q3+/gVMs6hLExERERERkXwaPHgwv//+OydPniQ+Pp7NmzfzwAMPsGHDBm655RYuXrzo7BKlBJSb4MQwjEAyF4JtdOmtV0zTfK+4rufr7svEDhOZGDERb6u33T6rfby5t1pV1nl5XtlwdCN83Rc+7Qi7loDNVlxlioiIiIiIuIzq1as73P7X3mv27NnFWs/48ePp3LkzlStXxtfXlxYtWjBv3jz69etHTEwMn3/+ebFeX1xDqXlUxzCM5oCjbWhCDcMYdNV7y0zTPHHpXE/gJ6DlpbbFwGLDMBrncslU0zT/LkTJANxZ506ahjRl9IrR7D63O0f7Gasbw0IrM+RCHI/HXsA9e+OJ7bDgYah8A3R4DhrdDRa3wpYkIiIiIiLikvr27cu5c+fy3b9u3bp5dyoG//znP/n222+Jiori2WefdUoNUnJKTXAC3AWMd9B2PTDrqve6ACcuHVcFsq+8evelV25igPBrK9G+6wKuY+4dc5myeQpf7foqR7tpGMwICmS9lxdvnz5DtfSMKzuc2gWLhsAfb2YGKE3uAzf3HOOIiIiIiIg4S2JiInXr1uX48eOEhoZy4MABvL1zzr632Wz06dOHyMhIDMNg7ty5PPDAAwBMnjy5pMsukJCQEAASEhKcXImUhNIUnLi0PScu0vmd37G6WXB3s+DuZlz11YLV0pXGbiH8N+Nz0sj5LNx2L0/uC6vK+DNnuS0xKedFzu6DyMdI+vUNjjR6lHP1+mD18MbDzYLVzvUuH1vdDNwtFiwWLTgrIiIiIiLFw8fHh3HjxvH4449z4sQJpk2bxnPPPZej38iRI4mMjATg7bffzgpNSpP169cDEB4e7txCpESUmuDENGcHONkAACAASURBVM0JwIQCnhsNFGtqkJZhI/psYj56VsawPolX2HysvgdytF50szCqSghr4+J5/lws3nYWiPVOOEK99S9z7M/JfJJ+J99mdCEFjzyvbLUY2QKWnCGL1WLB3WrBI9uxu+VSn2zHl8fwsFqwWnILbDKPPbIdZ4VIbgYVfT24rqJvfj5eEREREREpBYYOHcp7773H/v37efvtt3n00Ufx9f3f/+efNGkSH3/8MQBPP/00o0aNclapWfbv309aWhp16tTB3f1/M/t37txJWFgYFSpUuKL/7t27efHFFwFKZegj184wtXNLkfCsWs+sOnDKNZxhw6PiCjxCfsUw7C/+GpZq8NGpY9RPS8t1pFNmEJ+l92Rexi0k4XUNNThf/Sp+jL+zEe3rVnJ2KSIiIiJSSuzevZuGDRs6uwxx4Ouvv+bBBx8E4K233uL5558HYN68eTz88MOYpkm/fv345ptvMIyi//ftGTNmsHr1agC2bt3Ktm3b6NChA7Vr1wYgIiKCoUOHZvUPDw8nJiaGgwcPXjGDZMKECUyaNImuXbsSHh6Oj48Pf//9Nz///DPp6emMHTuWiRMnFnn94lhBf/cNw9hkmmbrgl5XwUkRufbgJJPFOwbvat9gcT9vt92wudHvjJUXE/bmOWXmrOnPF+l38GVGd+LxueZanOn+NjV4sWdDAry0douIiIiI5E7BiWszTZMWLVqwbds2KlasSHR0NH/++Sc9evQgLS2NLl26sGzZMjw88p41XxCDBg1izpw5DtsHDhx4xW48joKTFStWMG3aNLZs2cLJkydJTEykUqVKtG3blscff5xbb721WOoXxxSclHIFDU4AsCTiVfU73AN2OOzie/E6Xjp9gTuN7XkOd8H0YVbG7cxMv504/ApWkxNUCfDkjbua0O2GKs4uRURERERcWH7+eGoyp0kJVeM6/hr4l7NLyPLzzz/Ts2dPAAYMGEBkZCRxcXE0bdqUlStXEhgY6OQKpTRScFLKNW3e0vx++SrSMmyXXuYVx+mXjlOzHWfvk5puY8fFX1h3fhYZpNq9hifBtEu9gwHnVtA6KSrPmuLx5mvbrXyRcQcnM/yL+paLTa9mYYy/8wYq+nk6uxQRERERcUEKTuxzpeAEoGPHjqxatSrr+5o1a7J27VrCwsKcWJWUZs4KTkrN4rCuzsNqoValwi50Wo+9sbcxZuUY9p3fl6M1hXOs8PyaG25/jBZVJuG2ajLsXAzYD7/8SGKY5XuGef6K2Wow6TeNIM2nMmnpJmm2zMAmPcMkNePK4/RLgU724+xBT/pVAZCjMCi38c4mpHLwjP2tu5ZsO8bqfWeY0KsRdzatWizPPYqIiIiISPEaPnx4VnDi7+/PsmXLFJpIqaQZJ0WkdevW5saNG4tkrKT0JN7Z8A7/9/f/OezTJrQNb0a8SZWkOFj1HmxfAGZG7gO7eULLARDxNARWL5JaC8pmM5n7ZwyTlu4hIdVx3d0aVuH1uxoTGli6Fr0VERERkeKjGSf2udKMk0OHDtGuXTuOHj0KgNVqZffu3dStW9fJlUlp5qwZJ5aCnijFx9vqzSs3v8LkzpPx97D/iM2GExu494d7WZF0DO7+BJ7cBC0HgiWXxVUzUmDD5/BBc1jyJJw7WEx3kDeLxWDAzeH88kxHOtYPcdhv+e6TdH9/BfPXH0Ihn4iIiIiI64uNjeX222/n6NGjVKhQAavVSnp6Oq+88oqzSxMpEM04KSJFOeMku2Pxx3h+5fNsPb3VYZ8HGz7Is62excPNA84fhqgPYPOXmUFJbgw3aNoXOjwHleoVceX5Z5omizYf5bUfd3EhyfHWy+3rVuTNu5tSs2Lp2jFIRERERIqWdtVxXcnJyXTr1o2oqCi8vLz49ddfmTVrFjNnzsQwDLZs2UKzZs2cXaaUUppxInaF+YUx6/ZZ/LPJPzEcbEg8b/c8Hvz5QQ5eOAhBNaDnu/D0drh5BLjnEjKYGbDtG5jaBv5vMJzcWUx3kTvDMLi3VXV+fbYjtzcKddgvat9ZbpuykpmrD5JhU+AnIiIiIuJKbDYb/fv3JyoqCovFwty5c4mIiGDcuHG4u7tjmiYvvfSSs8sUuWYKTkoBq8XKyJYj+fzWzwnxtv9Yy55ze+j3Yz++3/d95iMt/qFw2xvw9F8Q8Qx45LYtsQk7v4Pp7WD+g3DM8eyW4lTZ34tPHm7F9AdbUsnBjjpJaRn868dd3PfJGvaduljCFYqIiIiIiCMjRowgMjISgPfff58+ffoAEB4ezpAhQwD46aefiIrKe4dQEVei4KQUaVu1LQt7LaRj9Y5225PSk3g56mXGrh5LfGp85pu+laDbhMwApdML4JXHful7foTPOsG8++DwhiKtP796NKnK8mc7ck/Lag77bD50njs+WM3U/+wlLcNWgtWJiIiIiMjVXn/9daZPnw7A6NGjGTly5BXtL730Ep6emf84Onbs2BKvT6QwFJyUMsFewUztOpUxbcZgtdjfTfqnAz/R98e+7DyT7dEbn2DoMjYzQLnlFfCpmPuF9v4bvugGc3pB9OoivIP8CfLxYHLf5swa3IYwBzvqpGbYePfff9N7ahQ7jl4o4QpFRERERARg1qxZjBs3DoD+/fszadKkHH2qV6/O8OHDAVi1ahVLly4t0RpFCkOLwxaR4locNje7zu5izMoxxMTF2G23Wqw83fJpHr7hYSzGVRlZagJsnAlRH0LCqbwvVrMddBoNtbuAYX+tleISn5LOpKV7+Gqd/fsEcLMYDO9Ym5G31MPL3a0EqxMRERGRkqbFYV3H0qVL6dWrF+np6XTu3JlffvkFDw8Pu31PnDhBnTp1SExMpEWLFmzatAmjhP+2kNLNWYvDKjgpIs4ITgAS0hKY+OdEluxf4rBP+2rteaP9G1T0tjPLJC0JNn8FUVMg7mjeF6zWGjqOhvq3lXiA8ueBszy/aDvRZxMd9qkd4svbfZrSOjy4BCsTERERkZKk4ESkfNKuOlIgvu6+vBHxBhMjJuJjtb+DTtTRKO794V7WHlubs9HdG9oOg5Fb4B9TIKhm7hc8uhG+6QefdoBd34Ot5NYXaVu7Isue7sjwjrWxOMhsDpxO4L5P1zJhyU4SUtJLrDYREREREREpmxSclBF31rmTBXcuoGGw/fTtTNIZhv86nCmbppBmS8vZweoJrQfDk5vhrulQsW7uFzzxFywYkLkTz18LwZZRBHeRNy93N8be0ZDFj7enQai/3T6mCbPXRHPblJWs2nu6ROoSERERERGRsknBSRlyXcB1zL1jLgNuGGC33cTkix1fMGjZII5cPGJ/EDd3aP4APLEe+nwBIXlMgzq9GxYNgaltYMs8yLATyhSDZjWCWDIigme61cfdzf70kyOxSTz8xXrGLNzGhaSSqUtERERERETKFgUnZYyHmwej24zm41s+poJnBbt9tp/ezn0/3Mey6GWOB7K4QZN74bE10PcrCG2a+4XP7YfvH4ePWmYuOpueUoi7yB8Pq4WnutXjxyc70KxGkMN+CzYeofvkFfyy80Sx1yQiIiIiIiJli4KTMqpj9Y4s7LWQtqFt7bbHp8UzesVoJqyZQFJ6kuOBLBa4oRcMXwkPLMhcHDY35w/Bj8/Ahy3gz08zF58tZteH+vPdY+146Y6GeLnb/0/61MUUhn+1iSe+3syZ+OIPdURERERERKRsUHBShlX2qcyn3T/lqZZP4WbY36J30d5F3P/j/fwd+3fugxlG5k46Q5fDw4sztyfOTdxRWDoGpjTN3PI4Jb6Ad5E/bhaDf3aszbKnOnJTbcc76vy0/TjdJ68gcstRtKOUiIiIiIiI5EXBSRnnZnFjaJOhzL59NmG+YXb7HLhwgP4/9mf+nvl5hwmGAXW6wiNLYdDPULtz7v0TTsGv42BKE1j5LiRfKNB95Fd4JV++HnoTb9zdGD9Pq90+sYlpPP3tVobM2cjxC8U/I0ZERERERERKLwUn5UTzys35v17/R/frutttT7Wl8safb/DMH89wISWf4UZ4exjwPQxZDvVuy71v0jn4z2uZAcrvEyHx3DXeQf5ZLAYPtr2Ofz/TkS7Xhzjs9589p7h18kq+/vMQNptmn4iIiIiIiEhOCk7KkQCPAN7r9B6v3PwKnm6edvv8dug37v3hXjaf3Jz/gWu0gQcXwLAV0OAfufdNvgArJmUGKL+Oh/ji2y44LMibmYPaMKVfcyr4uNvtczElnRcX/8UDM9YRczah2GoRERERERGR0knBSTljGAb31b+P+T3nUzeort0+JxJOMPiXwUzfNp0MW0b+Bw9rDvfPg8fWQuM+gP1tggFIjYeoKZkByrIX4WLx7HhjGAZ3tajGr892omfTqg77rTtwjtumrGTGqgNkaPaJiIiIiIiIXKLgpJyqW6Eu3/T8hr71+9ptt5k2pm2dxtB/D+VEwjWGGlVugHtnwogN0OwBcLAwLQDpSbDu48xFZH96Ds4fvrZr5VMlP08+fqAlnz7cihB/+7NtktNsvP7TbvpMX8PfJy8WSx0iIiIiIiJSuig4Kce8rF6Mu3kckztPxt/D326fjSc3ct8P9/HH4T+u/QKV6sHd0+HJTdByIFjsPy4DQEYKbJiRuY3xkifh3IFrv14+3NYolOXPdOK+VtUd9tl6+Dw9P1zFh7/tJTXdVix1iIiIiEjhaIdEkfLFmb/zCk6E7td1Z+GdC2lRuYXd9vMp53nyP0/y1vq3SMlIufYLBNeCXh/CU1vhxmHgYH0VAGxpsPlL+Kg1fDccTuexTXIBBPq48859zfhqyI1UC/K22yctw2Tyr3/Ta+pqth85X+Q1iIiIiEjBWSwWbDb9A5dIeWKz2bBYnBNhGEpqi0br1q3NjRs3OruMQkm3pTN923Q+3/45Jvb/u2gQ3IC3O75NrcBaBb/QxROw5iPYOBPSEvPobECju6DjaKjSqODXdCAhJZ13fvkvc9ZG4+hXwWLAPzvW5plu9fFyz+WxIxEREREpEYcOHSIoKIiAgABnlyIiJSQhIYHTp08THh5+zecahrHJNM3WBb22gpMiUhaCk8vWH1/P2FVjOZV0ym67t9WbF9u+SO86vTGMXBaAzUvCGVj7Maz/HFLzsaZIg39Ax1EQZn9mTGFsjD7HmEXbOXDa8c46tSr5MqlPU26sFVzk1xcRERGR/IuNjSUxMZFq1ao5uxQRKSEnT57EYrEQEhJyzecqOHERZSk4AYhNjmVc1DhWHFnhsM8dte5g3E3j8PPwK9zFkmLhz09h3bTM7YrzUrc7dBoDNW4s3HWvkpyWwYe/7eXTlbnvrNOtYRXCgrwI8nYn0MeDIG93Kvi6E+jtQZCPe+b73u5Y3fQknIiIiEhxyMjI4MCBA1SqVIkKFSo4uxwRKWbJyckcOnSI6667Dk/PXJZ+cEDBiYsoa8EJZC6+M2/3PCZvmkyaLc1unxr+NXi749s0rtS48BdMjstcIHbtVEg8m3f/Wh2h4xgIj4DCzHy5yo6jFxizcDu7jscVahx/TytBvu4EXQpUAr3dLwUrlwKWS6FL5nHm94He7rgrcBERERHJU2pqKjExMfj4+ODv74+vry8Wi6VwM6JFxGWYpkl6ejoXLlzg3LlzhIaGFvjxPAUnLqIsBieX7Tq7izErxxATF2O33WpYearlUwxoNACLUQR/9KcmwMZZsOZDiD+Zd/+aN2eugVKna5EFKGkZNj5beYAPlu8lNaNkFx7z87T+L2S5OmC5FLoE+3pQwdeDYJ/MrwFeVv2fBBERESl3MjIyiIuL4+LFiyQlJWnBWJEyxs3NDT8/PypUqIC3t/2NPfJDwYmLKMvBCUBCWgIT/5zIkv1LHPZpH9ae1yNep5J3paK5aFoSbJkLq6dA3JG8+1drlRmg1L+9yAKUfacuMmbhdjYfcu2ddawWgyAfD4J9M4OWy4FKsK87FXw8soKWCllt7vh5KmwREREREZGyT8GJiyjrwcllP+z/gdfXvU5iuv3dcCp6VWRih4m0C2tXdBdNT4VtX8OqyXDe/qyXK1RpkrmIbMNeUATbVWXYTOasiea9f/+XhNSMQo/nKtzdjP+FKpe+Xp7NUquSL02qBVI7xA83i8IVEREREREpvRScuIjyEpwAHIo7xOiVo9l1dpfddgODwY0HM6LFCNwt7kV34Yw0+GshrHoXzu7Lu39IA+gwChrfA5bCbyOclJrB+uhznI1P4XxiGueT0riQmEpstuPzSWmcT0wjLjnN4fbGpYm3uxs3hAXQOCyAxtUCaVwtkHqV/bTwrYiIiIiIlBoKTlxEeQpOANIy0vhg8wfM2TXHYZ+mlZoyqeMkqvtXL9qL2zJg52JY+S6c3p13/+A60OFZaNoP3IowyMlFhs0kLintUpCSeilYyTyOTUzjQrb3z1/6PjYxlQtJrh+4eFotNKiaGaY0uRSm1K/ij4dVYYqIiIiIiLgeBScuorwFJ5etOrKKl6Ne5lzyObvtfu5+jL95PLfXur3oL26zwX9/ghVvw4ntefcPqgkRz0DzB8F67VtYlQSbzeRicjrnk1KzZrWcT7x0nJgZrpxPTOVcYhqxCamcS0glNjGVRCc/QuTuZnB9qD9NqgXSKCyQJtUCuT7UHy/3ws/0ERERERERKQwFJy6ivAYnAKcTTzN29Vj+PP6nwz731LuH59s8j4+7T9EXYJqw91dY+TYc2ZB3f/8waP8UtBoI7gVfmdmVJKdlcD4xLStIueJrQs6g5WxCKqnpxbvqvNViUK+KP43DAmgdXoE7m4Xh42Et1muKiIiIiIhcTcGJiyjPwQmAzbQxc8dMpm6ZSoZpf/ZDrcBavNPxHa4Pvr54ijBNOPAHrHwHYqLy7u9bGdqNgNZDwNOveGpyUaZpkpSWwbmE1ByBy7HzSew8FseOoxeIS04vsms2CPVn/rCbCPLxKLIxRURERERE8qLgxEWU9+Dksq2ntvL8yuc5lnDMbruHxYNRbUZx//X3F+9WuNFRmQHKgd/z7usdDDc/DjcOA6/A4quplDFNk8Pnkvjr6AV2HLvAjqOZr9jEtAKP2eq6Cswb2laP8IiIiIiISIlRcOIiFJz8T1xqHK+ueZV/x/zbYZ+uNbryr/b/ItCzmIOKIxszA5S/l+Xd1zMQ2g6Hmx4Dn+DirauUMk2TYxeS+evIBXYeu5AZqhy9wJn41HyPcesNVZj+UCttcywiIiIiIiVCwYmLUHByJdM0WbR3EZPWTyI5I9lun1DfUN7q8BatqrQq/oKOb8sMUHb/kHdfDz9oMxRuHgF+IcVfWylnmiYn41LYcTQzSLkcqJyMS3F4zoNta/L6XY2Ld9aRiIiIiIgICk5choIT+/af38+oFaPYd36f3XaLYeHRZo8yrMkw3Cwl8PjGqd2Z2xjv/A7MPBZHtXpD68HQbiQEVC3+2sqYUxeTidp3hucX/kVqRs7P+rnu9XnylnpOqExERERERMoTBScuQsGJY8npyby78V2+/e+3Dvu0rtKaNzu8SahvaMkUdWYfrJ4M2+aDg8Vss7h5QIuHIeLpzC2N5Zr8sO0YT36zxW7b232a0rdNjRKuSEREREREyhMFJy5CwUnelscs55U1r3Ax9aLd9kDPQF5r9xpdanYpuaJio2H1+7BlHtjyWPTUYoVm/aHDsxBcu0TKKyu+WH2Q137cleN9N4vB5wNa0bVBFSdUJSIiIiIi5YGCExeh4CR/jscf5/lVz7PllP0ZCAAPNHiAZ1s/i6ebZ8kVduEoRH0Am+dAuv01WbIYFmhyH3R4DkKKaWvlMmjiz7v5bOWBHO97u7vxzbCbaF4jyAlViYiIiIhIWafgxEUoOMm/dFs6n2z7hM+2f4aJ/f/+rq9wPe90eodagbVKtriLJ2HtR7DhC0hLzKOzATf0ho6jIbRxiZRXmtlsJs8u2Erk1pxbVQf7erDw0ZupHeLnhMpERERERKQsU3DiIhScXLv1x9czdtVYTiWdstvubfVm7I1juavuXSW/+0rCWVj3Mfz5GTh4tOgK1/eEjqOgWsvir60US0238cjsDazedyZHW41gbxY91o7K/l5OqExERERERMoqBScuQsFJwcQmxzIuahwrjqxw2KdHrR68ctMr+Hk4YTZCUmxmeLJuGiSfz7t/3W7QcQzUbFv8tZVSF5PT6PfpOnYdj8vR1rhaAPOH3Yyfp9UJlYmIiIiISFmk4MRFKDgpONM0+XrP17y38T3SHCzQWt2vOu90eofGlZz0SExyHGyYAWunQuLZvPvX6pj5CE94Byjp2TKlwKmLydwzbQ1HYpNytHWoV4kvBrbBw2pxQmUiIiIiIlLWKDhxEQpOCm/32d2MWTmG6Lhou+1Ww8rIliMZ2GggFsNJf1SnJsCm2RD1IcSfyLt/jZug02ioc4sClKscOB1Pn+lriE3MGZbd3aIa793XDItFn5mIiIiIiBSOghMXoeCkaCSmJTLxz4l8v/97h33ah7Xn9YjXqeRdqQQru0paMmz5ClZPgbgjefcPa5k5A+X6HgpQstlyKJb+n68jOc2Wo214p9qM7dHQCVWJiIiIiEhZouDERSg4KVo/HviR19a+RmK6/Z1tKnpVZGLERNpVa1fClV0lPRW2fQOrJ0NsdN79qzSBjs9Bw95g0aMoAL/tPsmwrzaRYcv5v0Xj77yBwe1LeGclEREREREpUxScuAgFJ0XvUNwhxqwcw86zOx32Gdx4ME+2eBJ3i3sJVmZHRjrsWAgr34Wze/PuX+n6zF14Gt0DbloIdf76Q7zw3V853jcM+Kh/C/7RNMwJVYmIiIiISFmg4MRFKDgpHmkZaXyw+QPm7JrjsE+TSk2Y1HESNfxrlGBlDtgyYFdkZoByalfe/YNrQ8Sz0Ox+cHNy+ONkU5b/zZTlOUMnDzcLcx65kZvrVHRCVSIiIiIiUtopOHERCk6K16ojq3g56mXOJZ+z2+7n7scrN79Cj1o9SrgyB2w2+O/PsPJtOL4t7/6BNSHiaWjxEFg9i78+F2SaJi8u/otv1h/O0ebvZeX/Hr2ZBqEBTqhMRERERERKMwUnLkLBSfE7nXiaF1e/yLrj6xz2ubvu3bxw4wv4uPuUYGW5ME3Y+2tmgHJkQ979/atC+6eg5UDwcJF7KEHpGTYenbuZ5btP5mirEuDJh/e3IMjHAy93C55Wt6yvnlaLduARERERERG7FJy4CAUnJcNm2pi5YyZTt0wlw8yw26dWYC3e6fgO1wdfX8LV5cI04eAKWPEOxKzOu79vCLR7EloPAU+/4q/PhSSlZvDgjHVsPnT+ms7zsFrwtFrwcne7Iljxsrrh7eFGgJc7Ad5W/L3c8fe0EuDtjr+XlQCvS18vfX+5XUGMiIiIiEjZoODERSg4KVnbTm/j+ZXPczT+qN12D4sHo9qM4v7r78dwte1/Y9bAyndg/3/y7utdAW56AtoOA6/A4q/NRcQmpNLnkzUcOJ3glOsbBgR5u1Orki+1Q/yoE+JHnRBf6lT2o2awD+5u2hFJRERERKS0UHDiIhSclLy41Dj+tfZf/BL9i8M+XWp04bX2rxHo6YKhw5GNmYvI/r00776egZnhyU2Pg09w8dfmAg6fS+Se6Ws4fTHF2aVcwWoxqFnR51KY8r9ApVqQN76eVnzc3TRbRURERETEhSg4cREKTpzDNE2+2/sdb61/i+SMZLt9qvhU4a0Ob9E6tMC/J8Xr+PbMGSi7l+Td18MP2gyBm58Ev5Dir83Jdh2Lo9+na7mYku7sUvLNMMDH3Q1fTyt+ntarvrrh7+VO8xpB3NGkKt4ebs4uV0RERESkzFNw4iIUnDjX/vP7Gb1yNHtjc25nC2AxLDza9FGGNR2Gm8VF/1g9tRtWvQc7FoFpy72v1RtaDYL2IyEgrETKc5b9p+OZ9vt+Ys4mkJJuIzktg+T0DFLSMo9T0m2kpOfxebmgIB93HrixJgNuDic00MvZ5YiIiIiIlFkKTlyEghPnS05P5t2N7/Ltf7912KdVlVa81eEtQn1DS7Cya3RmH6x+H7bPB1seMy3cPDK3MI54BoJqlkx9LshmM0nNsGWGKekZWYFKcloGyWk2ElLTiUtK42JyOheT04lLTuNichpxSemZX5PTr/g+IdX+wsPFwWox+EfTqjwSUYum1YNK7LoiIiIiIuWFghMXoeDEdfwW8xvj1ozjYupFu+2BnoH8q92/6FqzawlXdo1iYzIDlC1zwZaWe1+LFZrdDxHPQsU6JVNfGZaeYeP4hWT2nY5n/6l4DpxJYP+pePafTuBMfPGtudImvAKPtK/FrY1CcdM6KSIiIiIiRULBiYtQcOJajscf54VVL7D51GaHffo36M9zrZ/D082zBCsrgAtHYc2HsGk2pNtfxyWLYYHG90LHURDiQtsxlyEXEtPYfyY+K0g5cDozWDmfmEp8SjrJaYV/bKh6BW8GtQvn3lbVCfLxKIKqRURERETKLwUnLkLBietJt/0/e/cdHlWZt3H8eya9UQKhd0Iv0jtB7Kjguoq9Ytm1N0BUdFdsFAt2sVesrL3uq/Teey/SCQmQRvo87x8TXJSZTEgyZ2Yy9+e6vEjm/GbmdhcDufOc5yliyqopTFk5BYP73+dtarZh4qCJtKjewuZ05ZB1AOa/CIvfhkJvx/Ra0P4CV4FSr5Mt8cSlqNhJTkExOflF5OQXkZ1fRE5+ccmvRczbms63K/dSUOy9YAlzWHRvWpMz2tXh9HZ1aZkUb8O/gYiIiIhI1aLiJECoOAlci/cvZszsMaQeTXV7PSY8hgd6PcDfkv+GZQXB7RE56bDgFVj0OuRnep9vcy6kjIKG3XyfTcokNSuPDxfs5KMFv5OeU1Dm57WoHcfpJSVKj6Y1CQ9z+DCliIiIiEjVoOIkQKg4CWyH8w7zyNxHmLF7hseZIc2G8HDfh0mITLAvWEXkHoGFU1wlSt4R7/PJZ7gKlCZ9fJ9NyiSvsJhvVuzl7bnb2bDf/Z48L43YHgAAIABJREFUnlSPiWBAcm1qxEYQEeYgMtxBRJhFRJij5B+L2Mhw+rRIJLlOkPyeFhERERHxARUnAULFSeAzxjB1w1SeWfIMhR42W20Y35BJKZPolBREt7fkZ8HiN2HeS3A0zft8s4GuAqV5CgTDCpsQYIxh/tZ03pqznV83uF8ZVV5hDot/D+vA1X2aVurrioiIiIgECxUnAULFSfBYn76e0bNGsyNzh9vr4VY4d3S7g+s6XIfDCqJbIQpyYOl7MPd5yN7vfb5xb0gZDcmnq0AJINsOZvPRwp38sm4/uw7lVtrrTry4M5f0aFxpryciIiIiEixUnAQIFSfB5WjhUZ5c+CRfb/3a40y/Bv14YsAT1I6pbWOySlCYB8s/gDmTIXO39/kGXV0rUNqcqwIlgBhj2JyazX/XHeDX9QdYvusIFfly7bDgxcu7cV7n+pUXUkREREQkCKg4CRAqToLT99u+57EFj5Hj4ZSaxOhEnhrwFP0a9rM5WSUoKoBVn8DsZ+DwDu/zdTu6TuFpdwE4gmilTYhIy85n+oZUfl2fyqzNBzlaUHzSrxHusHj9mu6c1rauDxKKiIiIiAQmFScBQsVJ8NqVuYtRs0axNn2tx5nrO1zPHV3vICIswsZklaS4CNZ8AbOehvTN3udrt4GB90HHiyAs3Pf55KTlFRaz9PfD7D58lIJiQ2GRk8Ji1z8FxYbCYicrdx1h3tb0E54bGe7g3et70q9lkK2kEhEREREpJxUnAULFSXArLC7kheUv8O7adz3OdKrdiQkpE2icEKT7RDiLYd3XrgIl1XNJ9IeazV0FSudLITzS9/mkUhUUOfnHB0uYvvHgCddiI8P48MbedGtS0w/JRERERETspeIkQKg4qRrm7JnDQ3Me4lDeIbfX4yPieaTvIwxpPsTmZJXI6YRNP8LMibBvhff56o1hwN3Q5SqIiPZ9Pqk0eYXFXPfOIhZsO/H3c7XocD65uS/tG1TzQzIREREREfuoOAkQKk6qjrTcNB6Y/QAL9i3wOHNh8oWM6TWG2IhYG5NVMmNgy/+5CpTdi7zPJ9SHfndC9+sgMoj/vUNMdn4RV725kBW7jpxwrVZcJM9ccgoNa8RQPSaCajERREeE+SGliIiIiIjvqDgJECpOqhancfLOmnd4aflLFJkitzPNqjVj0qBJtE1sa3O6SmYMbJ8FsybBjtne5+OSoO/t0PMGiErwfT6psIyjhVz6+nw27M/yOhsV7qB6TAS14qPo26IWV/VpQoukeBtSioiIiIj4hoqTAKHipGpaeXAl98+6nz3Ze9xej3BEMLLHSC5vezlWVTjK9/f5rgJl66/eZ2NqQp9bodfNEFPD99mkQg5m5XPplPlsS3N/glRpTm2TxHX9mpHSKgmHowr8PhcRERGRkKLiJECoOKm6sgqyGDd/HD/t+MnjzODGgxnXbxw1oqtIgbB7Kcx+Gjb+4H02qpqrPOl7G8Qm+j6blNveI7kMf20+e47kluv5LWrHcU3fplzUvREJ0UF4wpSIiIiIhCQVJwFCxUnVZozhyy1f8tTCp8grznM7Uze2LuMHjqdHvXL/9xh49q1yFSjrvgG8fK2IiHPdvtPvDoivY0s8OXm/p+dwyZT5HMjML/drxEWG0bZ+NZomxtKkVixNa8XSJDGOZrViqRUfVYlpRUREREQqTsVJgFBxEhq2HdnGqFmj2HR4k9vrDsvBPzr/g5s730y4I9zmdD6UugFmPwNrvgDjLH02PNq1gWz/u6BaA1viyclJzczj9Vnb2JyaTUZuIZm5hWTkFnIkt5BiZ8X+TGhYI4bBbZMY3KYOfVvWIjayCv13ICIiIiJBScVJgFBxEjryi/N5evHTfLLxE48z3ep0Y0LKBOrF1bMxmQ3St8LsZ2HVJ+B0v2nuH8IioetV0P9uqNnUnnxSIcYYcgqKOZxTwM9r9/Pe/B3sOlS+23oAIsMd9GlRi1NbJ5FcJ564qDBiI8OJiwwnNiqMhOhwosJ1io+IiIiI+JaKkwCh4iT0/LrzVx6Z+wiZBZlur1ePqs64fuM4rclpNiezweHfYe5kWP4hFBeUPusIh86XwcB7oVZLe/JJpSh2Gn7bkMp783YwZ0uaT96jbb0E+rSoRZ8WifRqXovEuEifvI+IiIiIhC4VJwFCxUlo2p+zn/tn3c+y1GUeZy5rcxkje44kKqwK7v2QsQfmvQBL34Ui93u//MFyQMeLYeB9UCfIj3AOQZsPZPHe/B18tXwv2fleVhtVQNt6CQxIrs2lPRvTqq6OuxYRERGRilNxEiBUnISuImcRU1ZN4fVVr+P0sP9H65qtmZQyiRY1WticzibZqTDvRVj8FhR6O+7WgvbDYOBIqN/ZlnhSeQqLnew+nMvv6TnsPHSU39OP/ZPDtrScCu+RcrxezRO5qk9TzulQj8hwR6W9roiIiIiEFhUnAULFiSzev5gxs8eQejTV7fWY8BjG9BrDhckXYlmWzelskpMOC1+FhVMg3/0tTH/SeggMGgUNu/s+m/hcRm4hczanMX1jKjM2HiQtu/wn9xyvdnwkF3dvzOA2SXRpUkP7ooiIiIjISVFxEiBUnAjAkbwjPDz3YWbsnuFx5pxm5/BI30dIiKzCtyHkHoFFr8OCVyD3sPf5lqdDyiho2tf32cQWTqdh7d5Mpm9MZd3eTLLzi8gpKOJofvEfHx85WnjSrxsV7qBrkxr0aVGLvi1q0bVJTa1GEREREZFShUxxYllWDaAn0Ou4X+uXXJ5pjDn1JF6rLXAHcBbQEMgFNgOfAq8aY7xs1nAiFSdyjDGGqRum8sySZyh0uv/GsGF8QyamTKRzUhW/VSU/y3X7zvyXIOeg9/lmA10FSvMUqKqrcuQPqVl5LNp+iAXb0lmw7RBbUrNP+jXiIsPo27I2g9okMahVEk1qxfogqYiIiIgEs1AqTrYDzTxcLnNxYlnWdcCrQLSHkfXAecaY7SeTT8WJ/NWGQxsYNXMUOzJ3uL0eboVze9fbub7j9TisKv4T84Kjrg1k5z4P2fu9zzfu7SpQks9QgRJCDmbl8/2qvXy4cGe5ShRwbS47+pw2nNa2biWnExEREZFgFUrFyQ6gacmnB4DFwPkln5epOLEs6yzgByAMSAOeBOYD8cBVwLUlo+uBXsaYMv/NXcWJuHO08ChPLXqKr7Z85XGmb/2+PDnwSWrH1LYxmZ8U5sGKD2HOZMjY5X2+QVdXgdJ6CDiqeLkkfzDGsHD7IT5c8Ds/r91PYfHJ/zl1Rrs6PHJ+B61AEREREZGQKk5GAtuBxcaYnSWPHQvvtTixLCscWAe0ArKBHsaYjX+ZGQs8VvLpv4wx48qaT8WJlOaHbT8wbsE4cjycOJMYnciTA56kf8P+Nifzk6ICWPUpzH4GDpdhcVfdjq5jjNtfAA5tDBpKDmbl89OafSzY5rqlJz2noMzPjQx3cMugltxyakuiI/T7RkRERCRUhUxx4s5JFicXAV+UfPqwMeZxNzMOYAOucuUwUMcYU1SWLCpOxJtdmbsYPWs0a9LXeJy5vsP13NH1DiLCImxM5kfFRbBmGsx+GtI2eZ+v3dpVoHS8GMLCfZ9PAooxhq0Hs5m/7RDzt6YxZ3MamXnev0S3rZfARzf2plZ8lA0pRURERCTQqDhxKUtx8hFwRcmnDY0xez3MPQQcK1XONMb8X1myqDiRsigsLuTF5S/yztp3PM50rNWRiSkTaVytsY3J/MxZDOu/gVlPwwHPxdIfajaHgfdC58sgPNL3+SQgFRU7Wbk7g5mbDvLr+gOs3ev5COzbBycz8uw2NqYTERERkUBR0eIklDYNGFDy62ZPpUmJ6W6eI1IpIsIiuLfHvbx2xmskRie6nVmTvobh3w3nh20/2JzOjxxh0OFC+MdsuGyqa2+T0hzeDt/cAS92g0VvuPZOkZATHuage9Oa3Htma767YwAvXt6VetXc7/u9aMchm9OJiIiISFUREsWJZVnxwLEf36/zMr7huI/b+yaRhLr+Dfszbdg0+tbv6/Z6TmEO98++n4fnPszRwqM2p/MjhwPangc3TYcrp7lO1ylNxi74YSQ8fwrMf9l1eo+EJMuyGHpKA369bxA3Dmh+wvXyntIjIiIiIhISxQnQEDh2punu0gaNMYeAY999hdC9EmK32jG1ee3M17in+z2EW+736/hqy1dc+t2lbDi0we31KsuyoNUZMOJnuPZbaDaw9Pns/fDzgzC5E8x5DvKz7MkpAScuKpwxQ9oSEfbnY6wP5RSQnp3vp1QiIiIiEsxCpThJOO7jsvzY8dhMfGlDlmXdbFnWEsuylhw8eLDc4SR0OSwHIzqO4L0h79EwvqHbmR2ZO7ji+yv4aP1HBPOeROViWdA8Ba77zlWiJJ9R+vzRNPi/f8NzHWHGBMg9YktMCSzhYQ5a1D7xy7dWnYiIiIhIeYRKcRJz3MdlOcvy2I8lY0obMsa8bozpYYzpkZSUVO5wIp2TOvP50M85p9k5bq8XOgsZv2g8d06/kyN5IVoGNOkDV02Dm36DNueWPpt3BGY86VqB8utjkJNuT0YJGMl1TixONqs4EREREZFyCJXiJPe4j8tyBMexMytzS50SqUQJkQlMTJnIuH7jiAl339nN2DWDi769iMX7F9ucLoA07A6Xfwz/nAPt/8b/7sJzIz/TddTx5E7wy1jIOmBbTPEvd8WJVpyIiIiISHmESnFy/IYHpd5+85cZ/S1bbGVZFhe2upBPzvuE1jVbu51JPZrKjb/cyMsrXqbIWWRzwgBSrxNc8h7cugA6XQJWKV/OCnNg3ovwfGf48X7I2GNfTvGLVnVVnIiIiIhI5QiV4mQPcGxziEalDVqWlQjElny6y5ehRDxpUaMFU8+bymVtLnN73WmcvLbyNW74+Qb25+y3OV2AqdMWLnoDbl8CXa8Ch/uNdgEoyoOFr8ELXeDbu+HwDttiir3c36qjTYNFRERE5OSFRHFijMnmfyWItyOG2x73sbeji0V8Jiosiof6PMTkwZOpFlnN7cyy1GVc9M1F/LrzV5vTBaBaLeGCl+GOZdDjBggr5a684gJY+g680A2+uhXSt9qXU2zRvHYcjr/cxXUgM5/MvEL/BBIRERGRoBUSxUmJOSW/trIsq0Epc6e6eY6I35ze5HSmDZtGtzrd3F7PLMjk7ul38/iCx8kv1nGr1GwK5z8Ld62E3rdAeLTnWVMMKz6Cl3rAtBshdb19OcWnosLDaFYr7oTHdbuOiIiIiJysUCpO/nPcxyPcDViW5QCuLfn0MDDT16FEyqJeXD3eOvstbjnlFhwe9vL4dOOnXP795Ww7ss3mdAGqWgMYMh7uXg3974KIE7+J/oNxwurP4ZU+8OnVsG+VfTnFZ9xuEHtAxYmIiIiInJxQKk6+BjaXfHy/ZVlt3Mw8ABzbkXOyMUZruiVghDvCubXLrbx51pvUia3jdmbz4c1c+t2lTNs0DWOM25mQE18HzhwH96yBlFEQ5f62pz+s/wamDISpl8HupfZkFJ/QPiciIiIiUhmsYPnmyrKsLkCXvzz8TsmvG4Hxf7n2kzHmT7tmWpZ1FvADEAakAU8A83GdonMVcF3J6HqgV8neKGXSo0cPs2TJkrKOi1TIkbwjPDzvYWbsmuFx5pxm5/BI30dIiEywL1gwyD0Ci96ABS9D7mHv8y1Pg5TR0LSv77NJpfpy+W7u+XTlnx4b3CaJd67v5adEIiIiIuIPlmUtNcb0KPfzg6g4+Tfwr5N4ymBjzAw3r3Md8CrgaeOD9cB5xpjtJ5NPxYnYzRjDxxs+5pklz1DgLHA70zC+IRNTJtI5qbPN6YJAfhYsedt1THHOQe/zTQfAoFHQfBBYlvd58bs1ezI4/8U/b1XVqGYMc+4/zU+JRERERMQfKlqchNKtOgAYY94FuuIqT7YCebj2M1kI3Ad0P9nSRMQfLMviinZXMPW8qTSr1sztzJ7sPVz747W8tfotnMZpb8BAF5Xg2vvkrlVwznhIqF/6/O9z4P0L4K2zYNMvECSlcyhrkXTivja7D+dytKDID2lEREREJFgFzYqTQKcVJ+JPRwuPMn7ReL7c8qXHmT71+/DUwKeoHVPbxmRBpCgfln8IcyZDxk7v8/W7uPZMaXMuOEKugw4aAyb8xu7DuX967NvbB9CpUXU/JRIRERERu2nFiYgQGxHLuP7jmDBwAnEeTo9ZsG8BF31zEXP26JRtt8KjoOcNcOcyGPYS1Gxe+vy+FfDplfDaAFgzDZzF9uSUk9LK3ck6B7VBrIiIiIiUnYoTkSrk3Bbn8vnQz+lUu5Pb64fyDnHL/93CM0ueobBYh0a5FRYB3a6G25fA39+A2u4O4DpO6lr4YgS83BtWfAzFug0kkLSqe+LmyJt1JLGIiIiInAQVJyJVTOOExrx3zntc3/F6jzPvrn2Xa368hl2Zu2xMFmTCwqHzJXDrAhj+HtR1X0b9IX0zfPVPeKk7LH0Pitxv2Cv2Sk5ydySxihMRERERKTsVJyJVUERYBPd2v5cpZ0whMTrR7cya9DUM/24432/73uZ0QcbhgA5/g3/Ohss+hgZdS58/vAO+vRNe6Oo69rgwz5aY4l5y3ROLk60qTkRERETkJKg4EanC+jXsx7Rh0+jXoJ/b6zmFOYyZPYaxc8ZytPCozemCjGVB23Phpulw1TRo3Kf0+czd8MNIeP4UmP8yFOTYk1P+JNnNHic70nPIL9KeNCIiIiJSNipORKq42jG1efWMV7m3+72EW+FuZ77e+jWXfncpGw5tsDldELIsSD4DRvwE134LzVNKn8/eDz8/CJM7w+xnIV8bk9qpWnQE9apF/+kxp4HtaSqyRERERKRsVJyIhACH5eD6jtfz/pD3aRTfyO3MjswdXPH9FXy0/iN0THkZWJarNLn2Wxjxi6tMKc3RNPj1UXiuI8yYALmH7ckpbledbNHtOiIiIiJSRipOREJIp6ROfDb0M4Y0G+L2eqGzkPGLxnPnb3dyOE/f2JdZk96u23dumg5tzit9Nu8IzHjStQLl13GQk25PxhDmrjjRyToiIiIiUlYqTkRCTEJkAhNSJjCu3zhiwmPczszYPYOLv7mYxfsX25wuyDXsBpdPhX/OhQ4XApbn2fxMmP0MTO4IPz8EWQdsixlqWrnZIFYrTkRERESkrFSciIQgy7K4sNWFfHL+J7Su2drtTGpuKjf8fAMvLX+JImeRzQmDXL2OMPxduG0hdL4UrFK+1BYehfkvwfOd4YfRkLHbtpihwt2RxHO2pPHKjC0cyNSpRyIiIiJSOkt7GVSOHj16mCVLlvg7hshJyy/O55klz/Dxho89znSr043xA8dTP76+jcmqkPStMOc5WPkxeCuhHBHQ9UoYcA/UbGZLvKruUE4B3R77r9trDgsGt6nD7acl07VJTZuTiYiIiIgdLMtaaozpUe7nqzipHCpOJNj9tvM3Hpn3CBn5GW6vV4usxrh+4zi96ek2J6tCjuyEOZNh+QdQXFD6rBUGp1wGA+6F2sn25KvC+o//jT1HckudGXpKA0af3YbGibE2pRIRERERO6g4CRAqTqQq2J+znzGzx7D0wFKPM5e2uZSRPUYSHR7tcUa8yNwH816AJe9AUenfzGM5oMPfIWUk1GlnT74q6IMFv/PwV2u8zkWGObiidxP6tEikVd0EmibGEh6mu1pFREREgpmKkwCh4kSqimJnMa+vep3XVr2G0zjdzrSq2YpJKZNoWaOlzemqmOyDrv1NFr8JBWXYrLTdUEgZBfVP8X22Kmj6hlTemL2NeVvLfpJRZJiDrk1qcMupLTm1TR0fphMRERERX1FxEiBUnEhVs2T/EsbMHsOBo+5Pe4kOi2ZMrzH8vdXfsaxSTo8R744eggWvwsIp4OFWqT9pfY6rQGlU7q/9IW3XoaN8vnQ305bu9nr7zvFOa1uHsee1o4WbzWZFREREJHCpOAkQKk6kKjqSd4RH5j3C9F3TPc6c3exsHun7CNUiq9mYrIrKy4BFr8P8lyH3sPf5FoNh0Gho2s/32aqgomInny7ZxXP/3URatpc9Z0pEhFmc2b4u9arFUCs+kqT4KGonRFI7Popa8VHUjo8kKjzMx8lFRERE5GSoOAkQKk6kqjLG8MnGT3h68dMUON1/c9kwviETUiZwSpJuIakU+dmw5C2Y9yLkHPQ+37S/awVKi1NBq39OWnZ+Ea/P3Mrrs7eRV+j+9rSTkRAdTlJ8FEkJUbSum0Db+gm0rVeNTg2rExmu/VJERERE7KbiJECoOJGqbuOhjYyaNYrtGdvdXg+zwri96+2M6DgCh6VvDitFYS4sfQ/mPg9Ze73PN+rpKlBanaUCpRwOZuXz89r9bNifyaYD2Ww+kMXho4WV9vq14iK5bXAyV/VpqgJFRERExEYqTgKEihMJBUcLjzJh8QT+s/k/Hmf61O/DkwOeJCk2ycZkVVxRPqz4CGY/Bxk7vc/XP8VVoLQ5Dxz6Br28jDF8t2ofT/2wnr0ZeZX2uk0SYxl1dhvO71xf+wOJiIiI2EDFSYBQcSKh5MftPzJu/jiyC92fBJMYncgTA55gQMMBNier4ooLYdWnMPsZOLTN+3yd9jDwPuhwITi070Z55RYUM2XWVl6bubVSbuU55qJujZh0cWccDpUnIiIiIr6k4iRAqDiRULMraxf3z7qf1WmrPc5c2/5a7up2FxFhETYmCwHFRbD2S5j9NBzc4H2+VitXgdJpOISF+z5fFZWdX8TS3w+TmplHWnYBadn5pGfn//FxWnYBh3LycZ7EH6s3DmjO2PPb+y60iIiIiKg4CRQqTiQUFToLeWn5S7y95m2PMx1qdWBiykSaVGtiY7IQ4XTC+m9g1tNwwHOB9YcaTWHgvXDKFRAe6ft8IajYaTh81FWk7EjLYf2+LFbvyWD6xlQ8/XH78PntuWFAc3uDioiIiIQQFScBQsWJhLJ5e+fx4OwHSc9Ld3s9NjyWh/s+zPktzrc5WYgwBjb9BDMnwt5l3uerNYIBd0PXqyEi2vf5hHV7Mxn/0wZmbTrxlCTLghcv78r5nRv4IZmIiIhI1afiJECoOJFQl5abxkNzHmLe3nkeZ4a1HMZDvR8iNiLWxmQhxBjY+hvMmgQ753ufj68L/e6EHtdDZJzv8wkv/baZp3/ZdMLjsZFhzBh5KnWqqcgSERERqWwVLU503IKIVIraMbV59YxXubf7vYRb7vfR+GbrN1z63aWsT19vc7oQYVmQfDpc/yNc+x00H1T6fPYB+OUhmNzJteFsXqY9OUPYbYOTubL3ibetHS0o5svle/yQSERERES8UXEiIpXGYTm4vuP1vD/kfRrFN3I7syNzB1f+cCUfrvsQrXjzEcuC5gPh2m/ghv9C8pmlzx9Nh1/HuQqUGeMh97A9OUOQZVmMu6AjZ7Sre8K1H9bs90MiEREREfFGxYmIVLpOSZ34fOjnDGk+xO31QmchExZP4I7f7uBwnr5J96nGveCqL+DmGdDWyx4zeUdgxlPwXCf4v0chJ82OhCEnzGHx4LltT3h85a4j7D581A+JRERERKQ0Kk5ExCfiI+OZMHAC4/qNIyY8xu3MzN0zufibi1m8f7HN6UJQg65w2Ufwz7nQ4ULA8jxbkAVznnWtQPn5IcjSSojK1iIpnrb1Ek54/CetOhEREREJOCpORMRnLMviwlYX8sn5n9CmZhu3M6m5qdzw8w28tPwlipxFNicMQfU6wvB34bZF0PkysMI8zxYehfkvweTO8MMoyNhtW8xQMKRj/RMe+2H1Pj8kEREREZHSqDgREZ9rUb0FH533EVe0vcLtdYNhyqopjPh5BPuy9Y2jLZJaw9+nwB1LoNs14HC/oS8Axfmw6HV4vgt8cycc2m5fzirs3E71Tnhs2c4j7MvI9UMaEREREfFExYmI2CIqLIoHej/AC4NfoHpUdbczy1OXc9G3F/F/v/+fzelCWGILGPYi3LkCet4IYVGeZ52FsOw9eLE7fHkLpG22L2cV1KpuAsl14k94XLfriIiIiAQWFSciYqvBTQbzxdAv6F63u9vrWQVZ3DPjHh5f8Dh5RXk2pwthNRrDec/AXSuhz23gYV8aAEwxrJwKL/WEL0bAgXX25axizu144qqTr1bsZX9Gnk6dEhEREQkQlv5iVjl69OhhlixZ4u8YIkGj2FnM66tf57WVr+E0TrczyTWSmZQyieSayTanE7IPuvY3WfwmFGR7n297PqSMggZdfJ+tClm/L5Mhz892e6157TjGDGnL2R1OLFdEREREpOwsy1pqjOlR3udrxYmI+EWYI4xbTrmFt89+m7qxdd3ObDmyhcu/v5wvNn2hn77bLT4JznwU7l4Ng+4HD7dX/WHDd/D6IPjoEtilU5LKqm29BFrUjnN7bXtaDnd8vJwdaTk2pxIRERGR46k4ERG/6l63O9OGTeO0xqe5vZ5XnMej8x9l5MyRZBZk2pxOiE2EwQ/CPavhtIchJrH0+c0/w1tnwPsXwI659mQMYpZlcX7nE0/XOaagyMkXS3WakYiIiIg/qTgREb+rHlWdyYMn81Dvh4h0RLqd+eX3Xxj+zXBWpK6wOZ0AEF0dUka6VqCc+RjE1Sl9ftsMePdceHsIbP0NtGLIo5tSWtC0VqzH67+s02axIiIiIv6kPU4qifY4EakcGw9tZPSs0WzL2Ob2epgVxu1db2dExxE4LHW/flOYC8vehzmTIWuv9/mGPWDQaGh1FliW7/MFmbzCYr5duZdlOw/z8aJdJ1yfPvJUmnu4pUdERERESqc9TkSkSmmT2IaPz/uYi1pd5PZ6sSnm+WXPc/N/b+bg0YM2p5M/RMRA73/AXSvg/MlQo0np83uWwNRLYEoKrPsGnO43BA5V0RFhDO/RmKf+3plTGtc44fova7XqRERERMRfVJyISMCJjYjl3/3+zaSUScRHxLudWbhvIRd/ezGzd7s/kURsEh4FPa6HO5bK56wPAAAgAElEQVTBBa9AYsvS5/evgs+uhtf6w+ovwFlsT84gclb7EzdL/mXdAT8kERERERFQcSIiAeyc5ufw+dDP6Vy7s9vrh/IOceuvtzJp8SQKiwttTid/EhYBXa+E2xfDRW9BUtvS51PXwbQb4OVesGIq6P+/P7g7fnjZzsOkZuX5IY2IiIiIqDgRkYDWKKER7w55lxEdR3iceX/d+1z141XszNxpYzJxyxEGnS6GW+bDJe9DvU6lz6dvga9ugRe7w5J3oCjfnpwBLLlOPC2S/ryfiTHw6/pUPyUSERERCW3aHLaSaHNYEd+bt3ceD85+kPS8dLfXY8NjGdtnLENbDrU5mXhkDGz6GWZNhD1Lvc9Xawj974ZuV7v2UQlR43/cwGszt57w+GU9G9OnRS3O6ViP6IgwPyQTERERCT4V3RxWxUklUXEiYo+03DTGzhnL3L1zPc4MazmMh3o/RGyE5yNexWbGwLbpMHMS7JznfT6+LvS7A3qMgMjQO01m2c7D/P0Vz/87taoTz7OXdKFTo+o2phIREREJTipOAoSKExH7OI2T99e+z/PLnqfIFLmdaVqtKRNTJtK+Vnub04lXO+bAzImwfab32dha0Pc26HkTRFfzfbYA4XQa+jz1K6lZnm9dCnNY3HpqS24bnKzVJyIiIiKlUHESIFSciNhvTdoaRs0cxe7s3W6vRzgiuLf7vVzZ7kosy7I5nXi1axHMmgSbf/E+G10det8Cff4JMTV9ny0APPH9Ot6Yvd3rXMMaMYwZ0pZBbZJIiArX73URERGRv1BxEiBUnIj4R3ZBNo8teIwftv/gcSalUQqP9X+MxOhEG5NJme1dDrOehg3feZ+NTIBeN0Lf2yGutu+z+VFOfhGXvb6A1Xsyyvyc2Mgwujapwc0pLRnUOsmH6URERESCh4qTAKHiRMR/jDF8vfVrnlz4JLlFuW5nkmKSGD9wPL3q97I5nZTZgbWuAmXtl4CXP5siYl37n/S7AxJOPL63qjDGsH5fFnuO5JKdX8gbs7azbl9mmZ6b0jqJEf2b0bVJTarHRPg4qYiIiEjgUnESIFSciPjf9oztjJ41mg2HNri9bmFxU+ebuOWUWwh3hNucTsosbTPMfgZWfQamuPTZsCjodg0MuBuqN7Innx8VFDl5efoWXp6+hSJn2f78tizo3LA6jwxtT/emWnUlIiIioUfFSYBQcSISGPKL83lu6XN8tP4jjzNdkrowIWUCDeIb2JhMTtqhbTDnOVjxMTgLS591RECXy2HAvZDY3J58frQlNZvHv1/HjI0Hy/yc2Mgwvr6tP63qJvgwmYiIiEjgUXESIFSciASWGbtm8PDchzmSf8Tt9YTIBMb1G8cZTc+wOZmctCO7YO7zsOx9KPZ8ygwAVhh0vgQG3ge1W9mTz4+mb0xl6sKdbNifyYHMfAqKnKXOt6gdx9e39ychWrfuiIiISOhQcRIgVJyIBJ4DOQcYM3sMSw54/m/zktaXMKrnKKLDo21MJuWStR/mvQhL3obCo16GLehwIaSMhLodbInnb8YYfli9n/E/rWfXIfd7/QC0rZfAG9f0oHFirI3pRERERPxHxUmAUHEiEpiKncW8sfoNXl35Kk7j/qfxyTWSmZQyieSayTank3LJSYP5L8GiN6Ag2/t82/MhZRQ06OL7bAEgv6iY71ftY/bmNL5cvsfj3Fnt6zL+os4kxkXamE5ERETEfipOAoSKE5HAtuzAMu6ffT/7c/a7vR4dFs3oXqO5uNXFWJZlczopl6OHYOEUWPgq5JXhyN5WZ0HKaGjc0/fZAsSW1GwueGkOOQXuN9ltkRTHd3cMIDZSmyWLiIhI1VXR4sRRmWFERAJVt7rd+GLoF5ze5HS31/OK8xg3fxwjZ44ks6Bsx72Kn8UmwuAH4O7VcPojEFur9PnNv8BbZ8B7w2DHHHsy+llynXieHn6Kx+vbDubwxPfrbUwkIiIiEnwqdcWJZVkOIAmIAwqBI8aYrEp7gwCmFSciwcEYw2cbP2Pi4okUOAvczjSIa8CElAl0qRMat3ZUGQU5rv1P5r4AOane55v0g0GjoMVg15m9VdjUhTv597drPW4e+851PRncto7NqURERETs4ddbdSzLagEMAQYB3YHGQNhfxrKANcBC4BdgujHG/XcrQUzFiUhw2XhoI6NnjWZbxja318OsMG7rchsjOo4gzPHXL2sS0ApzYdkHMHcyZHre4+MPDXu49kBpfXaVLlD2Z+TxzrztTJl54u/52vFRfHlrP20YKyIiIlWS7cWJZVlRwFXAjUCv4y+V8rTj3yQD+BR4xRiz+qTePICpOBEJPkcLjzJx8USmbZ7mcaZ3vd48OfBJ6sTqp/FBpygfVkyFOc/CkZ3e5+t1chUobYeCo+reyfr+/B088vXaEx6vXz2aL27pR8MaMfaHEhEREfEh24oTy7JigDuAkUAt/lyUZADLgVTgUMk/MUAiUBNoA7Q+7jnH3vQX4BFjzOLy/gsEChUnIsHrpx0/8ei8R8kudH9CS82omjw+4HFSGqXYnEwqRXEhrP4cZj8D6Vu8zye1cx1j3OFCqIKrjYwxXPvOYmZtOuj2epPEWPon1+Kfg1rStFaczelEREREKp8txYllWVcCE4D6uMqPIuC/wBfAfGPMhjK8RjVcK1TOAC4DmpRcMsAnwChjzN5y/DsEBBUnIsFtd9Zu7p91P6vSVnmcubr91dzd7W4iw3R8a1ByFsPaL2HW03CwDBuiJraEgfdB50sgLML3+Wx0IDOP81+cw8GsfI8zEWEW1/Rtxr1ntiYuSqfuiIiISPCyqzg5tpvcVmAy8KkxJq28b1rymgNx3e5zOa59UR41xoyryGv6k4oTkeBX6Czk5eUv8/aatzG4/9rYLrEdkwZNomm1pjank0rjdMKG72DWJNjvuSj7Q40mMOAe6HIlhEf5Pp9NNh3I4rLXF3Aop/Rtxzo0qMbHN/ehWnTVKo9EREQkdNhVnGwAHgemGmPcb8lf3gCW1Rx4CNhqjHmqMl/bTipORKqO+Xvn8+CcB0nLdd8Px4bHMrbPWIa2HGpzMqlUxriOKJ45EfaU4et3tYbQ/y7odg1EVI19QNbuzeDy1xeQmVdU6lyHBtX49B99idfKExEREQlCdhUnjsouTPzxHr6k4kSkaknPTeehuQ8xd89cjzPDWg7jwd4PEhehfSCCmjGwbTrMnAQ753mfj6sD/e6AHiMgKt73+Xxs4/4snvhhPSt3HSEjt7DU2c6NqnN5ryZc0qMxYY6qewKRiIiIVC1+PY5Y/kfFiUjV4zROPlj3AZOXTabI6f4n8k2rNWViykTa12pvczrxiR1zYdZE2DbD+2xMIvS9DXrdBNHVfR7NDqlZeXw4/3demr4FZyl/PWhbL4E7T2/FaW3rEB1R9TbQFRERkapFxUmAUHEiUnWtTVvLqFmj2JW1y+31cEc493a/l6vaXYVl6afwVcKuxa49UDb/7H02ujr0/qfrn9hE32ezwYyNqdz43hKKSmtPgGrR4VzVpym3n5ZMbKRu4xEREZHA5NfixLKsycCbxpg15X6RKkLFiUjVll2QzeMLH+f7bd97nBnYcCCPD3icxOiq8c2zAHtXuAqUDd95n42Mh543Qt/bIT7J99l87OsVe7jrkxVlmm1QPZpHL+jIme3r+jiViIiIyMnzd3HixHWc8FLgLeBjY0xmuV8wiKk4Ean6jDF8s/Ubnlj4BLlFuW5nkmKSGD9wPL3q97I5nfjUgXUw+2lY8x/wcOLSH8JjXPuf9L8TEurZEs9XZmxM5d15O1izJ4O07NJP3wE4p0M9runXlHb1qlEzTsd2i4iISGDwd3FSBDj4398i84AvgLeNMTPL/cJBSMWJSOjYnrGd0bNGs+HQBrfXLSxu7HQjt3a5lXCHbl+oUtI2w+xnYdWnYIpLnw2Lgm5XQ/+7oUZje/L50OIdh3ji+/Ws2HXE66xlwQWnNGD8RZ21B4qIiIj4nb+Lk7rAdSX/tCl5+NgLbse1CuU9Y8zecr9JkFBxIhJaCooLeG7pc3y4/kOPM12SujAhZQIN4hvYmExscWg7zHkOVkwFZ+kn0eCIgC6Xw4B7ILGFPfl8xBjD3C3p/GfZbr5bvY+CotIPw7uwa0OeveQU7f0jIiIifhUwm8NaltUPuAEYDhw7n9EATuBnXCXKt8YY90dTBDkVJyKhaeaumYydO5Yj+e5/Cp8QmcCj/R7lzKZn2pxMbJGxG+Y+D0vfg+L80metMOg0HAbeB0mt7cnnQzvScnjkm7XM2nSw1LnuTWvy4Llt6d5Ue/+IiIiIfwRMcfLHC1pWLHApcD0woOThY2+SBrwPvGOMWVepb+xnKk5EQteBnAM8MOcBFu9f7HFmeOvhjO45mujwaBuTiW2y9sO8F2HJ21B41MuwBR3+BimjoG4HW+L5ijGGz5fs5tFv15JTUPqtS5f2aMwTF3YkPMxhUzoRERERl4ArTv704paVjGsVytXAsbXqx95wEfAm8KkxJttnIWyi4kQktBU7i3lz9Zu8svIVnMb97QvJNZKZlDKJ5JrJNqcT2+SkwfyXYdEbUJDlfb7t+ZAyEhp09X02H9pzJJdPFu3k3Xk7yMrzvLD0poHNeei89jYmExEREQnw4uSPN7EsBzAUeBU4dsTAsTfOAT4CJhljtvk8jI+oOBERgGUHlnH/7PvZn7Pf7fWosChG9xzN8NbDte9DVXb0ECx6HRa8AnkZ3ueTz4RBo6Fx8J/GNOGnDbw6Y6vH689f1oULujS0MZGIiIiEuoAvTizLao9r1clVQO1jD+Pa++TYel0DFAFPGGPG+TSQj6g4EZFjMvIz+Ne8f/Hrzl89zpzZ9Ez+1fdfVI+qbmMysV1eJix+w7UK5Wi69/nmKZAyGpoNcB1NE4ScTsMLv23mpd+2UOQ88e8YUeEOpt3Sj44N9XtfRERE7BGQxYllWQnA5cAIoOexh0t+XQm8gWuVSQfgxpLZKFwFyk3GmLcrPZSPqTgRkeMZY/h80+dMWDSBAmeB25n6cfWZmDKRLnW62JxObFeQA0vegXkvQPYB7/NN+rr2QGl5WtAWKGnZ+Yz9cg0/rXW/+mr0OW24vGcTasZF2pxMREREQk1AFSeWZQ3Ctbrk70AM/ytLsoCPgTeMMUvdPK8R8CXQHVhpjAm6m71VnIiIO5sOb2LUzFFsy3B/J2KYFcatXW7lho43EOYIszmd2K4wF5Z/CHMmQ+Zu7/MNu7sKlNbnBGWBYozhzk9W8O3KvW6vx0aG8eiwDgzv0djmZCIiIhJK/F6cWJbVELgO1yk6zY89XPLrQlyrSz4xxpR6zIBlWWcAvwBHjTHxpc0GIhUnIuJJblEuExZNYNrmaR5netXrxVMDn6JObB0bk4nfFBXAyqkw+1k48rv3+bqdXJvIthsGjuA6leZoQREXvTqf9fsyPc4kxkXSq1ki/ZNr0bN5IslJ8Tp9R0RERCqNX4sTy7J+AM7EtVfJsbLkEPAhrtUla0/itZKBTYAxxgTdj11VnIiINz/v+JlH5z1KVqH701ZqRtXk8QGPk9IoxeZk4jfFhbD6C5j9NKRv8T6f1BYGjoSOf4cgWqG069BRhr40hyNHC8s0HxcZxiU9GzPyrDbERYX7OJ2IiIhUdf4uTo4/c3MGruOFpxlj8svxWg2AqbiKk8HlDuUnKk5EpCz2ZO9h9KzRrDq4yuPM1e2v5u5udxMZpr0fQoazGNZ+CbOehoPrvc8ntoSB90LnSyEswvf5KsGSHYe4+YOlHMpxv+ePO7XiIhneozHt6ifQqGYs0REOosLDiAp3EBXuoEZsJJHhWpkiIiIipfN3cbIPeA940xhThh+VVV0qTkSkrAqdhbyy4hXeWv0WBvdfg9sltmPSoEk0rdbU5nTiV04nbPweZk6E/Z7LtT/UaAID7oEuV0J4lO/zVVBeYTELtqXz/vzf+W1DaoVfz2FB48RYOjeqQa9mNenVvBat68brqG8RERH5E38XJ+HGmKJyv0AVouJERE7Wgn0LeGD2A6Tlprm9Hhsey9g+YxnacqjNycTvjIHNv7gKlD1l+LMloQH0vwu6XwsRMb7PV0HGGL5ZuZdXZ2xlw373t66VV4+mNXnz2h7UiNWKLREREXHx++aw4qLiRETKIz03nbFzxzJnzxyPM+e3OJ+xfcYSFxFnYzIJCMbAthkwaxL8Ptf7fFwd6Hc79LgBooJjn/Xs/CLW78tkzuY0Fm0/xPxt6RV+zbM71GXK1eX+u5GIiIhUMf5ecRIG9C/5dKUxJsPLfA2gM4AxZla53zgAqTgRkfJyGicfrPuAycsmU+R0v4ivSUITJg6aSIdaHWxOJwFjx1xXgbJtuvfZmEToeyv0uhmiq/s+WyXadCCLez5dwdq9nk/hKYspV3fn7A71KimViIiIBDN/FydnAT/hOkmnSRmOHI4FdgI1gdONMTPK/eYBRsWJiFTU2rS1jJo1il1Zu9xeD3eEc0+3e7i6/dXawyGU7V7iKlA2/eR9Nqo69P4H9LkFYhN9n62SGGNYtTuDdfsy2ZKazdaD2RzOKSC/yElBsZP8Qie5hcWlbjQbFe7gyQs78beuDQlz6L8XERGRUObv4uQN4AZgijHmljI+52XglpN5TjBQcSIilSG7IJvHFz7O99u+9zgzsOFAHh/wOInRwfONsPjAvpWuAmX9t95nI+Oh543Q93aIT/J9Nptk5xexencGv6zbzztzd7idqZMQRe8WtfhHSgs6Ngyu1TciIiJSOfxdnCzHdevN5caYz8r4nIuBz4Dlxpju5X7zAKPiREQq0zdbv+HxBY+TW5Tr9npSTBJPDXyK3vV725xMAk7qetcxxmv/A8ZZ+mx4DPS4HvrdCdXq25PPBsYYrn5rEXO2uN9oGSAhKpwf7x5Io5qxNiYTERGRQFDR4sRRwfdvVPLrtpN4zo6SXxtW8L1FRKqsYS2H8dn5n9EusZ3b6wdzD3LTLzfxwrIXKHQW2pxOAkqddnDxW3DbYtexxFaY59miXFjwCjzfGb67F47stC+nD1mWxTOXnEKzWp5Lkaz8It6cvd3GVCIiIlJVVLQ4ObZl/8ksWzk2W6OC710hlmVFWJZ1vWVZP1iWtdeyrHzLsnIsy9piWdZUy7LO9Gc+EZFm1Zvx4bkfclW7q9xeNxjeWP0G1/90PXuy99icTgJO7WT42ytw5zLofh04IjzPFhfAkrfgha7w9e1w6GR+/hGY6laL5ps7BnBuJ88bwn61Yg/5RcU2phIREZGqoKK36uzEtXLkSmPMJ2V8zmXAVGC/MaZBud+8AizLagx8D3TyMvoZcLUxxvPucyV0q46I+NLMXTMZO3csR/KPuL2eEJHAv/v9m7OanWVzMglYGbth7guw7D0oyit91nJAp+Ew8D5IamNPPh+atzWNST9vZPnOE/97uaxnY576eydtsCwiIhJC/H2rzjLAAi4/iedcUfLrygq+d7lYlhXOn0uTtbg2uO0PnAE8gOuUIIBLgMl2ZxQR+atBjQfxxdAv6Fmvp9vrWYVZ3DfzPh6d/6jHfVEkxFRvBOdOhLtWuTaFjShlbw/jhFWfwsu94bNrYf8a+3L6QL+Wtfny1v4M6Xji6pNPFu9i3Hfr/JBKREREglVFi5OvSn4937Ksq70Nl8ycj+t2nS8r+N7l9Tf+V5osBLoYY942xswzxvxqjBkPdAcySmb+YVlWHX8EFRE5Xt24urxx5hvc0fUOwjzsY/HFpi+44vsr2Hx4s83pJGAl1IWzn4C7V7tWlEQmlDJsYN1X8Fp/+PgK2LPMtpi+cHXfpm4ff2fuDran5dicRkRERIJVRYuTD4EtuFadvGNZ1kuWZSX/dciyrFaWZb0CvIOrNNlR8rE/9Dvu4yeMMUV/HTDG7OB/+RyAjq0QkYAQ5gjj5s43884571A/zv2pKFuObOHy7y/ns42fUZHbMaWKiasNpz8C96yGUx+EaC9bjW38Ht4YDB9eBDsX2pOxkvVpXouODau5vTZr00Gb04iIiEiwqlBxUlI6XIhrdYYDuAXYaFnWPsuyllmWtdSyrH3ABuAfJTPZwIXGGH8dAxF53Mel7Ya3xcNzRET8rmudrnw+9HPOaHKG2+v5xfk8tuAx7pt5Hxn5GW5nJETF1IRT73etQDn9XxBbq/T5Lf8Hb58F7w2F7bMgiMo4h8PizWvc3942t5Sji0VERESOV9EVJxhj1gK9gMW4Vp5YQF2gC9C15ONjjy8BehpjVlX0fStg43EftyhlrqWH54iIBITqUdV59tRnebjPw0SFRbmd+e/v/2X4t8NZnrrc5nQS8KKrwcB7XQXK2U9CvOfTaABXafLeUHj7HFeZEiQFSr3q0Uy7pe8Jjy/Ylk6xMzj+HURERMS/KnSqzgkvZlmn49rDpBuQVPJwGrAU+NYY81ulvVk5WZZVG9dqkurAAmCAMab4LzNNcG1eWwOYZYwZ5O11daqOiPjT5sObGTVzFFsztrq9HmaFcWuXW7mh4w2EOdzvjyIhrjAPln8AcyZD5m7v8w26QcooaDMEAvyEmsJiJ10e/YWcgj8fRfzmNT04o31dP6USERERu1T0VJ1KLU6ChWVZQ4FPgFhgFfAcrlUlMUAPYBRQG9gKnGOM2eLhpf6g4kRE/C23KJeJiyfyxaYvPM70qteLpwY+RZ1Y7XktHhQVwMqPYc6zcHiH9/m6nSDlPmh3ATgqvJDVZ65/ZxHTN564r8kv96TQum5pG+aKiIhIsFNxUk6WZbUB7gFuxnUb0fEygYnAK8aYw2V5PRUnIhIoft7xM4/Oe5Sswiy312tE1eDx/o8zqLHXxXQSyoqLYPXnMPsZSC/DKU2120DKSOjwdwgL932+k/TGrG088cP6Ex5PrhPPj3cNJCIscEsfERERqZiKFich+bcEy7IigKtxHU3sbn1xNeDKkuulvc7NlmUtsSxrycGD2p1fRALD2c3O5vNhn3NK0ilurx/JP8Ltv93OhEUTKCgusDmdBI2wcOhyOdy2EC5+G+q0L30+bSP85yZ4uScs+wCK/bUHvHtDT2lATMSJt6ltSc3mowW/+yGRiIiIBIvK3uMkAWgOJABeb6I3xsyqtDcvI8uy4oAfgBSgGHgeeBvXvicRuPZnGQ2cV/KU540xd3t7Xa04EZFAU+gs5NUVr/Lm6jcxuP9a3y6xHRNTJtKsejN7w0nwcTph4w8wayLsW+l9vnoTGHA3dL0Kwt1vXmy3hdvSuen9JWTmFf3p8eoxEcwdcxrxUYG3UkZEREQqLiBu1bEs63rgduAU3K/gcMcYY2z/G4plWZOAkSWf3mCMedvD3EfAFSWfDjPGfFva66o4EZFAtWDfAh6Y/QBpue6PX40Jj2Fsn7EMaznM5mQSlIyBzf91FSi7F3ufT6gP/e+CbtdCZKzv83mx9PfDXPzavBMOBXpm+Clc1L2Rf0KJiIiIT/n1Vh3LshyWZX0CvInr+GEH/zt6uCz/2MqyLAsYUfLpZk+lSYkxx308wuOUiEiA61O/D9OGTWNgw4Fur+cW5fLQnId4YPYD5BTm2JxOgo5lQeuz4Ib/wjVfQ9MBpc9n7YOfxsDznWHu85CfbU9OD7o3rcmFXRue8Pg3K/f6IY2IiIgEgwqtOLEs6yZgSsmnB3Hd8rIEOAQ4vT3fGDOz3G9eDpZl1QP2lXz6qTHmMi/zB4A6wAZjTLvSZrXiREQCndM4+XDdhzy37DmKnEVuZ5okNGHioIl0qNXB5nQS1H6fB7MmwdbfvM/G1IQ+t0HvmyG6uu+zubF4xyGGvzb/hMfnjTmNBjVi/JBIREREfMmvt+pYljUP6IPrKN8Bxpj0cr+YDSzLqo2r4AH4jzHmIi/zh4CawBpjTKfSZlWciEiwWJu+ltEzR7Mza6fb6+GOcO7udjdXt78ahxWSe4hLee1eArOehk0/ep+Nqu4qT/rcCrGJvs92HKfTMHDidPYcyT3h2j8HtWTEgGbUSYi2NZOIiIj4jr9P1WkPGOCxQC9NSqQDGSUf97Esy+MeK5ZldcJVmgBs83UwERG7dKjVgc+GfsbQFkPdXi9yFvH0kqe57dfbSM8Nhi/tEjAa9YArPoF/zIZ2XvbMyc9wrVKZ3An++whk23c6ncNhMfSUBm6vvTZzK+c+P5uZm3RanoiIiLhUtDg5Vjysq2gQOxjX8prvSz5tAPzL3ZxlWTHAi8c9VOrGsCIiwSYuIo4nBz7JEwOeICbc/a0Jc/bM4eJvL2bBvgU2p5OgV78zXPoB3LoAOg2H0lYuFWS79j6Z3Al+HAOZ9uw1clWfJm6PJwZIyy7g2rcXcdZzM5kycyt5hcW2ZBIREZHAVNFbdVbjWnVymt37lZSXZVmtgWVAXMlDPwLv8ufjiO8C2pRcXwt0NcYUlva6ulVHRILVjowdjJ41mvWH1ru9bmFxQ6cbuLXLrUQ4ImxOJ1VC2haY8yys/ASMlxIiLNJ1hPGAe6BGE5/G+mnNfm6fuowiZ+l/F2qZFMfr1/SgZVK8T/OIiIiIb/h7j5N/4Vq18aQxZmy5X8hmlmUNhv9n777Do6zSN45/30kHQiD03qQJBOk1AcQGK4piwS42XBcQWbqCigoSQLGhsvaCa69gQQUSeu9Fem+hk0DanN8fE/yxMCVkkinJ/bkuryFznvc9j3vhMtxz3nP4L46NX91ZDvQ0xuz2dE8FJyISzDKyM3h52ct8suETlzVx5eJITEikSomLTyQRyZVjO2DuZFjxCdjdfh8BtlBo2hs6DoIydQqspT83HmToV2tIOZ3utq5+hWh+GRiP44A+ERERCSb+Dk5KAiuBskA7Y8y6PN/MxyzLKo3jmOHuQGMc+5lkA4dwBCZfAl8YY5wfPXEBBSciUhgk7UniqblPcSz9mNPx6LBonm7/NNfWvNbHnUmhcmIvzH8Vln0AWWfd11o2aHwLJAyGcvXd1+aR3W5Yuec4467dDGcAACAASURBVGZsYMkO57/3Ab7/VweaVitVID2IiIhIwfFrcJLTQD0ce4CUB0YB04wxR726aRBScCIihcWhtEOMSB7B4gOLXdb0qtuLYa2HudwfRSRXTh2EBa/BkvcgM9VDsQWX3+gIUCq6Peguz+x2w5fLdvPWnG1sT7m4n0c71WF4twYFMreIiIgUHH+vODl32kwxHMHJuZsdBtI8XG6MMQW39tbHFJyISGGSbc/m3bXvMmXlFLJd7ElRJ6YOEzpNoG7puj7uTgqd1COwcAosngrpJz3X1+8OCUOgSvMCa+mZH9bxwfwd//NezTLFmDW4sx7XERERCTL+Dk7seb7YEZw4384+CCk4EZHCaOWhlQxNGsr+1P1OxyNCIhjaaii31rtVf5kU7505DovedoQoZ497rr/sKkeAUr1tvrdy6NRZ2oz9gws/Jn31aDta1ozN9/lERESk4Pg7OHk/zxcDxpg+3lwfSBSciEhhdSL9BM8ueJaZO2e6rLmq+lU80/4ZYiJifNiZFFrpp2DJOzD/dUhL8VxfM94RoNRKgHwM8G57awGLd/zv08fNq5fi63+2V1AoIiISRPy+x4k4KDgRkcLMGMOXf31J4pJE0rOdnz5SqXglxieMp1n5Zj7uTgqtjFTHBrLzXoXTBzzXV2sDCUPhsq75EqB8s3wPg75Y5XTshqaVGXNjI0oVC/d6HhERESlYCk4ChIITESkKNh/bzNCkoWw5vsXpeIgVwj+b/pOHmjxEiK3QPI0p/pZ5FlZ87DjK+OQez/WVmzlWoNTv7lWAkpVt55rJSWw77Hzj2noVSvDJQ20oHx2Z5zlERESk4HkbnNjysxkRESnc6pauy7R/TOPWerc6Hc822by+8nUenvkwB1MP+rg7KbTCIqH1wzBgBdzwGpSu6b5+3wr4753wVkdY9y3Y87YlW2iIjaHXuj4C+a+Dpxn435XoSygREZHCLV9XnFiWFQm0BCriOGnnO2NMLrbHD35acSIiRc1vO37jmfnPcCrzlNPxUhGleL7D83Sq1snHnUmhl50Fa7+CpIlwZLPn+rL1If7f0LgXhIRe8nSJv2xkyuytLsfrVSjBzc2r0qxaKVrVjMVm0/4nIiIigSQgHtWxLKsq8AJwOxB23lATY8z68+oeBPoCJ4BrTCH6ikbBiYgURftO72NY0jBWHl7psubuhnfzRIsnCA/RXhCSz+zZsP57R4ByaJ3n+tK1HAFK3O0Qemm/H79bsZfnp28g5bTzPX7OqV8hmvs71KR7k0rERIW5rRURERHf8HtwYllWS2AGUAY4/ysWw8XBSXlgF45wpZsx5jevJg8gCk5EpKjKsmcxZeUU3lnzDgbnf6Y0jG1IYkIiNWNq+rY5KRrsdvjrZ5iTCPtdh3h/i6kGHQfCFXc7HgO6BDPW7OexT5d7rAu1WXSuX55ujStyfdNKRIRqzx8RERF/8fdxxNHARqAScBAYAyQDq3ESnORc8y1wA/CaMWZgnicPMApORKSoW7R/ESOSR3D4zGGn41GhUTzZ5kluqHODjnKVgmEMbPndEaDsWey5ProStB8ALe6H8GK5nMJw73uLSd6ci2OSc1gWXHN5Bcb3itMpPCIiIn7g781h++EITY4A7Ywxbxpj1nq45g8cK1PaeDm3iIgEkDaV2vDVDV8RXyXe6fiZrDM8Ne8pRswdQWqm81NKRLxiWVD3anjwN7j3B6jp/Pfi307th19HwCtxjhN70p3v1/O/U1i8dNsVtKxROtdtGQO/rjvIDa/PIz0rO9fXiYiISGDwdsXJAqA1MNoY88J579txveKkM/AnkGKMKZ/nyQOMVpyIiDgYY/h4/ce8vPxlsuxZTmuqRVdjQsIEGpVt5OPupMjZuQCSJsDWPzzXRpWGto9B60cgqpTbUmMM21JSWbbzGIdPpfP18j0ujy2+0KjrL+euNtWJDNPjOyIiIr7g70d1jgClgARjzLzz3ncXnFwBLAcyjTEReZ48wCg4ERH5X+uOrGPonKHsOrXL6XioLZSBzQdyz+X3YLO8XQAp4sGeZY4A5a+fPddGlHSEJ+3+BcVic3X7bLshafNhvlq6h5/X7sfu4eNVlVJR/OfellxeuWSu7i8iIiJ55+/g5CyOjV7bGGOWnve+u+CkPTAXOG2MKTSfFhSciIhcLDUzlRcWvsCP2350WdOhSgde6PACZaLK+LAzKbL2r4bkibD+B3CxmfHfwopDqwehfX8okftFskdOpzNr02Fe/3MzO46kua0dcm19Hu1UhxAdYSwiIlJg/L3Hybmd0WpewjVNc14PeDm3iIgEuOJhxRkbP5axHccSFRrltGbe3nnc8uMtLNi3wMfdSZFUKQ5u+wgeWwhNbgN3q50yU2H+qzC5Cfw8DE7uy9UUZUpEcEuLqswe0oVHO9VxWzvh1008+OESjqVmXMq/hYiIiPiQt8HJufP4rryEax7C8RWPPiGLiBQRPer04MseX9IwtqHT8ZQzKfSd2ZfJyyaTac/0cXdSJJVvAL3+A/2WOo4ltoW6rs06C4vegleawk9PwLGduZ5m2HX1efOu5lzVsILLmtmbDtNpwiy+X7kXb1YCi4iISMHw9lGd+4D3gQygsTFmS877Th/VsSwrERicM3a9MSYXDxoHBz2qIyLiWUZ2BpOXT+bj9R+7rIkrF0diQiJVSlTxYWdS5B3bCfMmw4pPINvD6g9bKMT1hvhBUMb9ipLzLdt5lHvfXUxqhuuTdRpVLsnEW5vSsFKheZpZRETE7/y9x4kNWAk0BvYCjwM/4AhSTM77m4EuwLCcV4B5xpiEPE8cgBSciIjkXtKeJJ6a+xTH0o85HY8Oi+bp9k9zbc1rfdyZFHkn9joez1n2gWOliTuWDRrfAvH/dqxgyYWF247wwAdLSHMTnlgWfNCnNZ3qlbuExkVERMQVvwYnOQ3UAuYBFXGEJZk4NowF2AOUA86dnmMBu4B2xpj9Xk0cYBSciIhcmkNphxiZPJJFBxa5rOlVtxfDWg9zuT+KSIE5fQjmvwZL3nXsdeKWBZffAPGDHXuoeJCWkcXYGRv4bPFust0cv9Pziso8c0MjShULv8TmRURE5Hx+D05ymqgATAV6eCidATxgjDnk9aQBRsGJiMily7Zn897a93hj5RtkG+ffwNeJqUNip0Tqla7n4+5EgNQjsHAKLJ4K6Sc919frBp2GQJUWHkt3Hknl+ekbmLn+oMua2OLhPNixFjc1q0LlUgoQRURE8iIggpPzmrkc6Am0BMoDIThO3lkBfGeMWe7m8qCm4EREJO9WHlrJsKRh7Et1fmpJREgEQ1oO4bb6t2FZOrZV/ODMcUd4snAKnHH+iNn/qNMVEoZAjXZuy4wx/LL2ABN+3cS2FPcrW1rVLE2v5lXp1aIqYSHe7u8vIiJSdARUcFKUKTgREfHOyYyTPDP/GWbunOmypmv1rjzb/lliImJ82JnIedJPOR7fmf8apKV4rq8Z7whQaiU4Ni9x4fCpdK5/LZmDJ9M93vK6RhV58+7mChFFRERyScFJgFBwIiLiPWMMX23+ivGLx5Oe7fwvkBWLV2R8/HiaV2ju4+5EzpOR5thAdt4rcPqA5/pqbRwBymVXuQxQdh5JZcyP6/ljo+cnmsfd3IQ7Wle/xKZFRESKJgUnAULBiYhI/tlybAtDkoaw5fgWp+M2y8Y/m/6Th5s8TIgtxMfdiZwn8yys/ATmToYTuz3XV27mCFDqdQOb88dtdh9No9+05azac8LlbWKiwkge1oWSkWEua0RERMTB38cRe/VVhzFmlzfXBxIFJyIi+ets1lkmLJnAF3994bKmVcVWjOs4jgrFK/iwMxEnsjJg9eeQPAmObfdcX6Gx4xjjy28EJ+GfMYYFW4+wYNsRvli62+kjPAn1yjH02vo0qlxSj+2IiIi44e/gxPkRCLljjDGhXlwfUBSciIgUjJk7Z/L0/Kc5lXHK6XipiFI81+E5Olfr7NvGRJzJzoK1X0PyREj5y3N92XqOAKXxLRDi/GORMYY+Hyxh9qbDTscbVirJy7c3pUHFkt50LiIiUmj5Ozix5/liR3BSaNZXKzgRESk4+07vY1jSMFYeXumy5q6GdzGoxSDCQ8J92JmIC/Zs2PADJE2Eg2s915euBfGDIK43hF78e3j30TS6TJxNlt3557aYqDB+6t+RarHFvO1cRESk0PF3cHJfLsqKAw2AXkAlYC7wLoAx5sM8Tx5gFJyIiBSsLHsWU1ZO4Z0172Bw/mdXg9gGJCYkUiumlo+7E3HBboe/foakCbBvhef6mGrQ4XFodg+ERf7P0Kt/bOalma5XsZQqFsaAK+tyZ5vqRIYVmu+mREREvBY0m8NalhUOvAE8ALxojHnSJxP7iIITERHfWLR/ESOSR3D4jPPHFqJCoxjZZiQ31rlR+z5I4DAGtvwBSYmwe5Hn+hIVocMAaNEHwovl3MLwdtI2Jv66yeXKE4DqscWYcldzGlfRsd0iIiIQRMHJ3xNaVjLQHrjGGPOHTycvQApORER85+jZo4yaN4qkPUkua7rX6s6otqMoEV7Ch52JeGAMbE9yrEDZkey5vlhZaN8PWj0EEdEAbE9J5bPFu5iatM3lZSE2iy/6tqVFjdj86lxERCRoBWNwcgfwKfCDMaanTycvQApORER8yxjDpxs+5aVlL5Fpz3RaUy26GokJiTQu29jH3Ynkwq6FjgBly++eayNLQdvHoE1fiCoFwPwtKdz//hIysl1vOde3U20e7FiL8tGRLmtEREQKu2AMTpoDS4EDxpjKPp28ACk4ERHxj/VH1jM0aSg7T+50Oh5qhfJ488e5t9G92Cybj7sTyYW9yxybyG6a4bk2oiS0fsQRohQvw8rdx3n6h3Ws2n3c9SWhNm5tWZUrG5SndtkS1ChTTI+xiYhIkRKMwUlHIAnIMMYUmq8/FJyIiPhPamYqYxeN5YetP7is6VClAy90eIEyUWV82JnIJTiwxhGgrP8eXGyA/Lew4tDqAWjXH6IrsPHASR54fwn7Tpz1OE256Aiuj6tEvy6XUaZERP70LiIiEsCCMTiZDAwAdhtjavh08gKk4ERExP9+3Pojzy98nrSsNKfjZaPKMrbjWNpVbufjzkQuwaGNkDwJ1n4FxvVjOACERkKL+6H9AFafKk7vqQtJy8jO1TRtasXy2cNtsdm0+kRERAo3b4MTn61ZtiyrhGVZTwH9cXyNkosHekVERHKvR50efNHjCy4vc7nT8ZQzKfSd2ZfJyya73BdFxO/KN4Be/4F+S6HZ3WALdV2bdRYWvQWvXkHcimf49f4a3N6yGrnJQhZtP8qYn9bj6y/RREREgo1XK04sy/ozF2U2oDRQDwgHLOAU0NwYszXPkwcYrTgREQkcmdmZTF4+mY/Wf+SyJq5cHOPjx1M1uqoPOxPJg2M7Yd5kWPEJZGe4r7VCoGlv9jV5jFdW2Pl6+R63RxcDdGtckVfvaEZYiPYAEhGRwsmvj+pYlmXHsXrkUtZ4bgfuMsYszPPEAUjBiYhI4Enak8SoeaM4evao0/ESYSV4uv3TXFfzOh93JpIHJ/fBvFdh2fuOlSbuWDZo3IsTLQfww76SLNx2lOlr9rssv69dDZ69UadPiYhI4eTv4GQ2Hncvw45jhck2YA4w3RiTledJA5SCExGRwHQ47TAj5o5g0f5FLmt61e3FsNbDiAqN8mFnInl0+hAseB0WvwOZqZ7rG94ACUPIrtCENmP/IOV0utOyp/7RkIfia+dzsyIiIv4XdJvDFlYKTkREAle2PZv3173P6yteJ9s43zizdkxtEhMSqR9b38fdieRR2lFYOAUWvQ3pJz3X1+vG3qb96DfHYsUu58cXD+hal8e71iVEG8aKiEghouAkQCg4EREJfCsPrWRY0jD2pe5zOh5uC2dIqyHcXv92LEt/cZQgceY4LP4PLHwDzhzzWG7qXMnnUb0ZvrSE0/FGlUvy9j0tqFq6WH53KiIi4hcKTgKEghMRkeBwMuMkz85/lt92/uaypmv1rjzb/lliImJ82JmIl9JPwdL3YP5rkHrYY/m6sCa8kNqD+fZGXLhdXfXYYkwf0JHoyLACalZERMR3FJwECAUnIiLBwxjD15u/Zvzi8ZzNdr7JZsXiFXkx/kVaVGjh4+5EvJSRBss/hHmvwCnXG8Kes8xel9eyejLbfgXnByj9ulzG4Gv16JqIiAQ/f28Oe2+eL3bDGOP6/MgApeBERCT4bD2+lcFzBrPl+Ban4zbLxqNNH+WRJo8QYgvxcXciXspKdxxhPHcynNjlsXy1vRavZ/Vkpr0FBhuhNovP+7alRY1YHzQrIiJScPwdnJw7jjg/GWNMaD7fs8ApOBERCU5ns84yYckEvvjrC5c1LSu0ZFz8OCoWr+jDzkTySXYmrPovJE+CY9s9lm+wV+ONrJ7MsLfBjo1Nz19HRKiCQxERCV6BEJzkN2OMCbo/nRWciIgEt5k7Z/L0/Kc5lXHK6XhMRAzPtX+OLtW7+LgzkXySnQXrvoGkiZCyyWP5Vnsl3si6kZ9t8bzcuyWd65cjMizoPqKJiIj4PTipAZQG3gZaAWuBD4HFwEEcD8qWzxm7D2gCLAH6Ai63fTfG7MxzU36i4EREJPjtO72P4cnDWXFohcuaOxvcyaCWg4gIifBhZyL5yG6HDT84ApSDazyW77SXZ0r2jUy3OnFbmzqM7N6A0BCbDxoVERHJH/4OTsKB+UAzYDQw1ri4oeU413EE8DywDOhgjMnI8+QBRsGJiEjhkGXP4q1VbzF19VSMi6dRG8Q2IDEhkVoxtXzcnUg+MgY2/QxJibDPdVh4zl5ThreyenCozm28em9bPb4jIiJBw9/Byb+BCcB/jTF35vKaacDtwHBjzIQ8Tx5gFJyIiBQui/cvZkTyCA6dOeR0PCo0ihGtR9Dzsp44vhsQCVLGwNY/YM4E2L3QY/lBU4pZsb25re8obJElfNCgiIiId/wdnCwDrgC6G2N+zeU11wC/ACuMMYXmjEcFJyIihc+xs8cYNW8Uc/bMcVnTvVZ3RrUdRYlw/QVSgpwxsCMZM2cC1o4kj+XHrBhSGj9E+Sv7EVNaJ++IiEjg8jY48fYB1To5r86/jnPu8AXXioiIBKTSkaV57crXGNZqGGG2MKc1M7bP4NYfb2VtylofdyeSzywLaiVg3f8jPPAb9jpd3ZaXNieou2YSZnJjln04DJPmcvs6ERGRoOZtcHJubXL9S7im3gXXioiIBCzLsrj78rv5tPun1ChZw2nNntN7uGfGPXyw9gPspiAOnBPxseptsN3zDTw8C1O/u9vSUlYqLba/RcakRpjfx0DqER81KSIi4hveBicbcAQgT1iW5XGHsJyaQeddKyIiEhQalmnIF9d/wQ11bnA6nmWymLRsEo/9/hgpZ1J83J1IAanSHOuOz+DReayJ6YLduP7eKyI7FWvuJJjcGH59Ek4d9GGjIiIiBcfb4OSjnNeWwHTLsqq6KswZ+wnH0cTmvGtFRESCQrGwYrzQ8QXGxY+jWGgxpzXz9s3jlh9uYf6++T7uTqQAVWxM3X5f82LtD/g2uyPZbgIUMtNgwevwShzMGAon9viuTxERkQLg7eawNmA20BFHGJIFzAKW4Nj3xAAVcIQlXYBQHCtU5gKdjSk865m1OayISNGy6+QuhiQNYf2R9S5rHmj8AP2a9XO5P4pIMEo5nc7iZUvJmjOJbtmzCbOy3V9gC4Nmd0HHJ6B0TZ/0KCIicj6/nqqT00Bx4FPg3NplVzc899XEj8BdxpjTXk0cYBSciIgUPZnZmbyy/BU+XP+hy5q4snGMTxhP1WiXizJFglJmtp3OT37Eo6E/clvIbCKsLPcXWCHQtDd0HARlL/NNkyIiIvj/VB2MManGmJ5AD2AGcAZHSHL+P2eAn4EbjDE3FrbQREREiqawkDAGtxrMlK5TiI10fhzr6pTV3Prjrfyy/RcfdydSsMJCbERXrM2orAeIT3+Fd7O6ccaEu77AZMPKT+GNVvDVg3BI292JiEhw8HrFyUU3dDy+UweIxRGaHAW2GmM8rOMMblpxIiJStB1OO8yIuSNYtH+Ry5qb697MsFbDKBbmfH8UkWAz6POVfLNi798/l+EED4XO4J6QmZSwznq+QcMekDAEKjUtwC5FRKSo8/ujOuKg4EREROzGzntr3+P1Fa+T7eL7gloxtZiQMIH6sfV93J1I/lu28xi93rx4I+RSnKJP6C/0CfmVklaa5xvVu84RoFTN82daERERlxScBAgFJyIics6qw6sYljSMvaf3Oh0Pt4UzuNVgetfvjWW5OZ1EJAj8seEgE3/7iw37T140Fk0a94b8Rr+oX4jKunj8IrW7QKehUKN9AXQqIiJFVcAEJ5ZlxQC3AO2AikAxoI8xZud5NZWBUsBZY8y2fJk4QCg4ERGR853MOMmz85/lt52/uay5stqVjOkwhpiIGB92JpL/jDH8J3kbL/68EbuTj5bFOMvYaovpkfoVIWkpnm9Yo4NjBUrtzqBwUUREvBQQwYllWf8ExgHR597CcbpOE2PM+vPq7gQ+Ac4CVY0xR72ePEAoOBERkQsZY/h689eMXzyes9nO93uoUKwC4xPG06JCCx93J5L/pi3axZPfrcHVx8vitkxer7+aTinTsJ3a7/mGVVs5ApS61yhAERGRPPN7cGJZ1lPAszjCknRgLdAC58GJDdgDVAD6GmPe8WryAKLgREREXNl6fCuD5wxmy/EtTsdtlo1H4x7lkbhHCLGF+Lg7kfy1bt8Jnv9pAwu2HXFZUzwki6eqrOCWM18SdmqP55tWauoIUOr/A2xeHwopIiJFjF+PI7YsKw5HaALwGVDJGNPKVb0xxg58iSNkucqbuUVERIJFnVJ1+Owfn3F7/dudjtuNnSmrpvDgbw9yIPWAj7sTyV+NKsfw0YOtaVylpMua1OxQRuxqRcPDY9nSbjzE1nZ/0/2r4PO74a0OsOYrsBfqwxpFRCTAeBvZ98cRgiwC7jbGHM/FNQtyXuO8nFtERCRoRIZG8lTbp5jceTLR4dFOa5YdXMYtP97CrF2zfNydSP4KC7Hx9j0taVMr1m1dFqFcNasaO++YDTe/A+UauL/xofXw9YPwRhtY+RlkZ+Vf0yIiIi54G5x0xvFIzhsm98/87Mh5reLl3CIiIkGna42ufN3ja5qVb+Z0/ET6CQbMGsC4ReNIz073cXci+adKqSg+79uObx9rT+f65dzW3v/hCs42vBn+uQBu/RAqNHF/8yOb4btH4bXmsOwDyMrIv8ZFREQu4NUeJ5ZlpQKRQEtjzIrz3rfjZI+TnLGmwAog0xgTkefJA4z2OBERkUuRZc/irVVvMXX1VAzO/yyuX7o+iZ0SqR3j4TEGkSAwd3MK0xbvZMYa54+jNapcksRb4mhUOQaMgb9+gTmJsG+555uXrAodB0KzeyAsMp87FxGRYOfXzWEtyzoJFAeuNMbMOe99d8HJNcAvQIoxpnyeJw8wCk5ERCQvlhxYwvCk4Rw6c8jpeFRoFCNaj6DnZT2xdKqIFALJmw9zz7uLXY5fH1eJ0T0up3x0pCNA2fonJE2AXQtcXvO3EhWg/QBo2QfCi+dj1yIiEsz8ujkssDfntc4lXHNlzut2L+cWEREJeq0qtuKrG76iU9VOTsfPZJ1h9PzRDEsexumM0z7uTiT/xdctx/M9G7sc/2n1fm54bR7fr9zrWIt1WVfo8zPc9xPUSnB/89MH4bcnYXITSJ4EZ0/ma+8iIlI0ebvi5E2gL7DAGNPhvPedrjixLKsSsA6IAcYbY0bmefIAoxUnIiLiDWMM0zZOY9LSSWTaM53WVC1RlcSERJqU87D/g0gQ+HjBDkZ9v85jXUK9csRVieGaRhWIq1oKdi1yrEDZMtPzJJGloO0/oU1fiCrtfdMiIhKU/P2oTjPgXFrQ3xgzJef9i4ITy7JqA98CTYBMoJ4xZmeeJw8wCk5ERCQ/bDiygaFJQ9lxcofT8VArlAHNB3Bfo/uwWd4uHBXxr/fnbWfsjA1kZufu82iX+uV48h+Xc1n5ErB3uWNVycafPF8YHg2tH4Z2/4LiZb3sWkREgo1fg5OcBl4F+uEISqYDXwEf5Px8H47jiq8DegFhOZc9Z4x5xquJA4yCExERyS9pmWmMXTSW77d+77KmQ+UOPN/xecpG6S+BEtwOnjzLxwt28t687aRlZOfqmmsbVaBfl7o0qRoDB9ZC8kRY9x242Gj5b2HFoOUD0L4/RFf0vnkREQkKgRCc2IB3gPtx/6fVuR3t3jbG/NOrSQOQghMREclv07dN57mFz5Gamep0vExkGcZ2HEv7Ku193JlI/ttzLI0R36wheXNKrq+Zcldzujep5Pjh8F+OFShrvgTjIYAJiYAW90GHxyGmqhddi4hIMPB7cHJeIzcBI4EWLkrWAmOMMV/ly4QBRsGJiIgUhN0ndzMkaQjrjrjeC6JP4z70b9afMFuYyxqRYGCM4ee1B3j+p/XsO3E2V9d8+EBrOtUr9/9vHN0Gc1+GldPAnuX+YlsYXHEndHwCYmt50bmIiASygAlO/r6hYwPYVkB5IARIAVYYY7bl60QBRsGJiIgUlMzsTF5d8SofrPvAZU2Tsk0YnzCeatHVfNeYSAGx2w3bUlLZd/wMf248xLTFu8jIsrusv6N1NZ69oTHhoeft+3N8N8ybDMs/hux09xNaIRB3O8QPgrJ18+nfQkREAoW/N4d9KeeXs40xP+T5RoWAghMRESloc/fO5cm5T3L07FGn4yXCSjC63Wi61erm485ECtaR0+m8P28Hr8/a4rLmX13qMOTaBhcPnNwP81+Dpe9B1hn3E1k2aHQTxA+GCpd72bWIiAQKfwcn5x4gvcEYMz3PNyoEFJyIiIgvHE47zMi5I1m4f6HLmpsuu4nhrYdTLKyYDzsTKXhr956g15vzSXey+iQi1MbsIZ2pFBPl6f0DhgAAIABJREFU/OLTh2HB67DkHcg47XmyBtdDwhCofIWXXYuIiL95G5x4e47hkZzXvV7eR0RERHKhXLFyvH312wxsPpBQK9RpzbdbvqX39N5sOrrJx92JFKzGVWL4vl8HqpS6OBxJz7Iz6be/XF9cohxc/SwMXAOdhkFEjPvJNv4EUzvBp7fB7iVedi4iIsHM2+Bkc85rZW8bERERkdyxWTYebPIgH3T7gColqjit2X5iO3dOv5NpG6aR3/uZifhTg4olmTW4M02rlbpo7Ktle/h9/UH3NygWC11GwhNr4MpREBXrvn7zr/DuVfDRjbBjnhedi4hIsPI2OPkcxzHDt+dDLyIiInIJmpZryhc9vuDamtc6Hc+wZzBu8Tgen/U4x88e93F3IgUnPNTGR31aExN18UlSz09fj92ei7AwMgYSBjtWoFz9HBQv775+22z4oDu81w22/gkKJEVEigxvg5M3gRXAXZZlPZQP/YiIiMglKBlekgkJE3i2/bNEhkQ6rZm1exa3/HgLSw9oLy4pPGKKhfHva+pd9P6OI2nM25qS+xtFlIAOA2DgauiWCNEeFlLvmg8f3wTvXAV//aoARUSkCPB2c9jqQFngPaAJMAv4FFgFHAOyXV8NxphdeZ48wGhzWBER8betx7cyJGkIm49tdjpus2w8Gvcoj8Q9QogtxMfdieQ/Ywy9py5k0fb/PWkqOiKUxU9eRVR4Hn6fZ6XDymkw9yU4nouPqhXjHJvINrgebN5+JykiIgXB36fq2IFzN7DO+3VuGGOM813tgpCCExERCQRns84ycelEPt/0ucuaFhVa8GL8i1QsXtGHnYkUjB9X7aP/Zysuet9mwe+DOlG7XIm83Tg7E1Z/AcmT4OhWz/XlL4f4fzuOM1YwKSISUAIhOMkrY4wpNH+qKDgREZFA8sfOPxg9fzQnM046HY+JiGFM+zFcWf1KH3cmkr+ysu30enM+q/accDo+olsD+naqk/cJ7Nmw7ltImgCHN3quL3OZI0BpciuEXLwHi4iI+J6/g5P78nwxYIz50JvrA4mCExERCTT7T+9nePJwlh9a7rLmjgZ38O+W/yYiJMKHnYnkr00HTnHt5CS3Nc/d2Ii729bAsqy8TWK3w8YfHQHKgTWe60vVgI5PwBV3Qqj++xIR8Se/Bify/xSciIhIIMqyZ/H26reZunoqduN8oWj90vVJ7JRI7ZjaPu5OJP+M+GYNny12vydJq5ql+fyRdthseQxPwLEZ7F+/QlIi7F3mub5kFegwEJrfA2FReZ9XRETyzCfBiWVZ7+HYv+QpY8z+vE5WmCk4ERGRQLbkwBKGJw/nUNohp+NRoVGMaD2Cnpf1zPs38iJ+lJFl55U//uKNWe73I+ndqhov9orzfkJjHMcSJ02AXQs815eoAO37Q8sHILy49/OLiEiu+So4ObcJbBNjzPoL3rcDcee/XxQpOBERkUB37OwxRs8bzew9s13WdKvZjVHtRhEdHu27xkTy0Y6UVAb8dwWrXex5AvDz4/E0rFQyHyedC3MSYfscz7XFykC7f0GrhyEyH3sQERGXvA1O8uPMtKD+WsqyrPKWZY2wLGuBZVkHLctKtyxrn2VZiyzLmmBZVjt/9ygiIpIfSkeW5tUrX2V46+GE2ZxvWvnzjp+59cdbWXM4F3s4iASgmmWL8/2/OjDwqroua576bi1Z2d6ccXDhpB3hvh/gwZlQ9xr3tWlH4I8xMLkxzBoHZ47lXx8iIlIgcrvi5BRQDOhijEk6732nK1GChWVZdwOvAqXdlH1vjOnp6V5acSIiIsFk49GNDJkzhB0ndzgdD7VC6d+8P/c3uh+blR/fs4j43tnMbBqM+sXpWHzdsrxxV3NKRhbAyTf7VkDSRNj4k+fa8Gho/RC06wfFy+Z/LyIi4rNHdZYDTYGPgAeNcewuF8zBiWVZjwJTcKyYOQC8CcwDjgMVgdpAD+CEMeZWT/dTcCIiIsEmLTONcYvH8d2W71zWtK/cnhc6vkDZKP2FToLTkdPptHj+d6djoTaLz/u2pUWN2IKZ/OA6R4Cy7lscH5ndCCvm2P+kfX+Irlgw/YiIFFG+Ck5GAc/i+H/8g8BWIBPonPPeUiD1Euc2xpiul3hNvrAsqzmwCAgF/gR6GmNOuagNN8ZkeLqnghMREQlWM7bNYMzCMaRmOv+jPDYylnEdx9G+SnsfdyaSP35ff5CHPnL+OS081Ma3j7WnUeWYgmsgZTMkT4LVX4DJdl8bEgHN74WOAyGmasH1JCJShPgqOIkE/gDyY78Pg2OVhzHGhOTD/S6ZZVmLgVbAfuByY8xxb++p4ERERILZ7pO7GZI0hHVH1rms6dOoD/2b9ScspAAebRApYB/M286zP63H2UffqxqW5537WhV8E0e3wdyXYeVnYM90X2sLgyvugI6DILZWwfcmIlKI+SQ4yZkoFLgVuAqoAkQAnXAEIcu49BUnGGO6XOo13rIsqzWO1SYAQ40xE/LjvgpOREQk2GVmZ/Laitd4f937LmualG3C+ITxVIuu5sPORPLHHxsO8ugny8jMvvjz74wB8Vxe2Uen3BzfDfNegeUfQXa6+1orBOJug/h/Q1nXG96KiIhrPgtOXEwedHucWJY1CRiU82MNY8yunPdLA2WBI8aYo5d6XwUnIiJSWMzbO4+Rc0dy9KzzPw6LhxVndNvRdK/d3cediXhvw/6TdHsl+aL3i4eH8OfgzlQoGem7Zk7uhwWvw9L3IDPNQ7EFjW6ChMFQoZFP2hMRKSwC4TjiYNM253WfMWaXZVn3W5a1FjgK/AUcsSxrq2VZoyzLKu6/NkVERPyjQ5UOfH3D17Sr5PwJ3dTMVIYlD2PUvFGkefzLnkhgaVipJL1bXbxiKjUjm4c/Wpq/xxR7UrISXPsCDFwDHZ+A8BJuig2s+wbebA//vQv2rfRZmyIiRZ23K05q5PxyrzEmK39aKliWZR0DSgHzgU1AHzfla4DrjDH7PN1XK05ERKSwsRs7H6z7gNeWv0aWiz/ma5asyYROE2gQ28DH3Ynk3bbDp7lucjIZLkKSjx9sTXzdcj7uCkg7CovehkVvwtkTnuvrXgMJQ6GaD/ZnEREJYn59VCfYWJZlA7JwbE6bjmOflhRgOPA9cBqIA8YA1+ZcNh+IP3cEsysKTkREpLBafXg1Q5OGsvf0XqfjYbYwBrcczB0N7sCyLB93J5I3P67aR//PVjgdCw+x8dkjbWlRo7SPu8px9gQseQcWvAFpRzzX1+oEnYZCzY4F35uISBBScHIJLMsqAZx/7HAG0NoYs+qCuhBgBnBNzlu3GmO+cnK/R4BHAKpXr95i586dBdK3iIiIv53KOMWYBWP4ZccvLms6V+vMc+2fo1RkKR92JpJ3437ewNtztjkdq122ODMHdSLE5scwMCPVsf/JvFch9ZDn+urtodMQqN0FFGKKiPzNJ3ucWJaVZFlWgZyAY1lWKcuyxliW9XhB3P8CZy/4+aMLQxMAY0w2MPi8t+5wdjNjzFRjTEtjTMty5fywnFNERMRHosOjSUxI5Nn2zxIZ4nzzzNm7Z9Prx14sObDEx92J5M3ArvXo3qSi07FtKanM3ZLi444uEF4c2veHgauh2wQoWcV9/a758PFN8M5VsOkXnJ69LCIilyy3m8N2BH63LGuOZVk35BxN7BXLsmpalvUcsB14Eojx9p6e5OzDcn544vJrM2PMGuDc3iZ6cFRERIo8y7K4ue7NfH7959QrXc9pzaG0Qzz020NMWTmFLHtQbH8mRVhUeAhT7mrBf+51/iXk/K1+Dk7OCYuCNo/AgBVw/WQoVd19/d6l8Nnt8HY8rP8e7D7c8FZEpBDKbXAyEkgF4oFvgQOWZU2xLOsqy7KiczuZZVn1Lct6zLKsecDWnPvGAMnAfy+t9Tzb7eLXzuzKedVyEhERkRy1S9Vm2j+m0bt+b6fjdmPnzVVv8uCvD3Ig9YCPuxO5dFdfXoFJtza96P0/NxwioB5rD42Aln2g/3Lo+SaUucx9/YE18MW9jpN41nwF9mzf9CkiUsjkeo8Ty7IqAk8BDwHhwLkLDY5jfJcDB3Ec63sciARigdJAPaAFUPLc7XJe1wBPG2O+8/ZfJLcsy/oW6JnzY3tjzAI3tYtxrDZJNca4Ox9Om8OKiEiR9MeuPxg9bzQnM046HS8ZXpLnOjzHldWv9HFnIpdm04FTXDs56aL372xTnbE3NfFDR7lgz4Z130LSRDi8wXN9bB2I/zfE3QYhYQXfn4hIgPD55rCWZVUA/oXjGN/zH7T0dKNzYUkW8CvwhjHG9Q5zBcSyrCeAl3J+vMcY84mb2iM4wp+/jDH13d1XwYmIiBRVB1IPMCxpGMsPLXdZ07t+bwa3GkxESIQPOxPJvWy7ocOLf3Lg5IVb4kH3JhV5/Y7m2Py5Uaw7djts/AmSJsCB1Z7rS1WHjk/AFXc5VrGIiBRyfjtVx3KcN9gR6ILjEZ62QHEX5TuARcBM4AdjjN8eGLUsqwaOfVUs4FtjzM0u6joDs3J+fMcY87C7+yo4ERGRoizLnsXU1VN5e/Xb2I3z/RTqla7HhIQJ1C5V28fdieTOxwt3Muq7tU7HypaI4MtH21GrrKuPuwHAGNj8G8xJdOxz4knJKtDhcWh+r2MfFRGRQiqgjiO2LKs0jv1AyuBYgXIc2GWMScu3SfKBZVnTcJyUY4AbjTE/XjBeEse+K3E5b7Uyxrj900fBiYiICCw5sIThycM5lOb86NSo0CiGtx7OTZfdhKXjUiXAGGMY+tVqvly2x2VN1dJRTO8fT0yxAH7UxRjYNgvmTHCctONJ8fKO03taPgARbp9OFxEJSgEVnAQLy7KqAUuACkAm8DrwA3AKR1gyHMe+LACvG2P6e7qnghMRERGH42ePM2r+KGbvnu2y5rqa1zG63Wiiw3O9x7yITxhj6D11IYu2H3VZU6tscaYP6EixcK8Pmix4O+ZBUiJsm+25NioW2v0LWj8MkQV+4KWIiM8oOMkjy7KaAd8B7s5z+w/wWM4xxm4pOBEREfl/xhg+2/gZE5dOJNOe6bSmSokqJCYkElcuzum4iL8cS83g5jfnsz0l1WXNAx1qMbrH5T7syku7lzj2QNn8q+fayBho86jjn2KxBd+biEgBU3DihZxHch4DegF1cOzRchCYC7xljLl4a3UXFJyIiIhcbOPRjQyZM4QdJ3c4HQ+1QunXrB99GvfBZtl825yIG8YYJvy6iSmzt7qs+aFfB+KqlvJhV/lg30pHgLLxJ8+14SWg1UPQrh+UKFfwvYmIFBAFJwFCwYmIiIhzaZlpvLj4Rb7d8q3LmnaV2jE2fixlo8r6sDMRz7YdPs1tby8k5XT6RWNVS0cxa3BnwkKCMPQ7uB6SJ8Lab/B4OGZolGP/kw4DILqiT9oTEclPfg1OLMvalofLDHAWOAFsARYCnxtjjuS5kQCg4ERERMS9GdtmMGbhGFIznT/+EBsZy9iOY+lQpYOPOxNxLyvbToNRv5Blv/hzc99OtRnRraEfusonKZsh+SVY/TmYbPe1IRHQ/B7oMBBKVfNNfyIi+cDfwcmF5w0aHMf8OuNs7NzkGcALwAsmSJfAKDgRERHxbPfJ3QxNGsraI86PfAW4v9H9DGg2gLCQAD61RIqcjQdOct3kZKdj/a+8jCeuqofNFsQnRR3dDnNfhpXTwMW+RH+zhcEVd0DHJyBWx4uLSODzd3Dyfs4v44BmOb8+AqwEDuf8XA64gv8/onglsBYoCTTGsbcIOWNvGWP+leeG/EjBiYiISO5kZmfy2srXeH/t+y5rGpdpTGJCItVK6lttCRwv/ryRt+Y43/OkT4eaPN2jkY87KgDHd8P8V2HZh5B98eNJ/8MKgSa3Qvy/oVw997UiIn7k9z1OLMu6F5gK7AGeAKYbY+wX1NiA64GXgSo4Tqp5L2esVc71TXGEJ/HGmFwcOB9YFJyIiIhcmnl75zFy7kiOnnV+7GvxsOKMbjua7rW7+7gzEefOZGTTddJs9p0463R8yLX1+VeXy3zcVQE5dQDmvwZL34PMNA/FFjTqCQlDoEIhCI9EpNDx94qTZjj2KDkAtDDGpHioLwcsAyoAHYwxS3PeLwOsBioCHxlj+uS5KT9RcCIiInLpUs6kMDJ5JAv2L3BZ0/OynoxoPYJiYcV82JmIc5sPnuLql10fvPhcz8bc07aGDzsqYKkpsOANWPwfyDjlub7B9ZAwGCo381wrIuIj3gYn3m4BPggIBcZ5Ck0AjDGHgXFAWM61594/AryBYw+Ujl72JCIiIkGibFRZ3rr6LZ5o8QShVqjTmu+2fMftP93OxqMbfdydyMXqVohm8wvdKF3M+R48z/+0niNOTuAJWsXLwlVPw8DV0HkERMa4r9/4E0ztDJ/cArsX+6RFEZGC5m1wkpDzeilLLZbkvF4YkJx7PEdnnImIiBQhNsvGA40f4MNuH1KlRBWnNTtO7uDO6Xfy6YZPCdJ95KUQCQuxMXNQJ6IjLg770rPs/PPT5X7oqoAVi4XOw2HgWug6GoqVcV+/ZSa8ezV82AO2J4P+uxWRIOZtcFI+5zXyEq45V1vugveP5byGeNWRiIiIBKW4cnF82eNLutXs5nQ8057Ji4tfZMCsARw/e9zH3Yn8r7IlIvj4oTZOxxZvP8qq3YX092hkScdmsAPXwDUvQIkK7uu3J8GH18P73WDLHwpQRCQoeRucnHs857pLuObcp6ELH+05t+7vMCIiIlIkRYdHMz5hPGPajyEqNMppzezds+n1Yy+WHFjidFzEV66oVoppDzsPT577ab2Pu/Gx8OLQvh88vgq6TYCSzleL/W3XAvjkZninK2z6WQGKiAQVb4OTWTj2JXnCsqz2nooty+qA4+Qdk3Pt+ZrmvO73sicREREJYpZlcVPdm/jvP/5LvdLOjzg9lHaIh357iDdWvkGWPcvHHYr8v3a1y9CgYvRF7y/deYzrJiexbKfzU6MKjbAoaPMIDFgJPV6BUh42xt27DD7rDW/Fw7rvwG53Xy8iEgC8PVWnEbAcxwaxmcBbwCfASmNMVk5NKHAFcA/QFwgHMoCWxpi1591rFo49UyYaY4bluSk/0ak6IiIi+S89O51JSyfx2cbPXNY0L9+c8QnjqVhc26SJf2w9fJquk+a4HC9dLIyH4mvzUHwtIkIL+VPp2Zmw5itInghHtniuL9cA4gdD45vBVsj/txERv/HrccQ5DdwNvI9jb5JzN8sGTub8HMP/71tiAXagjzHm4/PuUQf4JefH+40x87xqyg8UnIiIiBScP3f9yej5ozmRfsLpeMnwkozpMIau1bv6uDMRh74fL+XXdQfd1iTUK8eHfVphWZaPuvIjezas+xaSJsLhDZ7rY+tA/CCIux1CnJ9YJCKSV34PTnKaaI/jOOGmHkpXA/8KxmDEEwUnIiIiBetA6gGGJw9n2cFlLmtur387g1sOJjL0UvatF/Hewm1HuOfdRWRmu/9s/cmDbehYt6yPugoAdjtsmg5zEuHAas/1papDxyfgirsgNKLg+xORIiEggpPzmmkNXAU0BkrnvH0MWAf8YYxZmG+TBRgFJyIiIgUv257N1NVTeWv1W9iN870R6pauy8SEidQuVdvH3UlRt3L3cUZ+s4b1+0+6rVv9zDWUjCxiqyqMgc2/OQKUvbn4zBxdGTo8Di3uc+yjIiLihYAKTooyBSciIiK+s/TAUoYlD+NQ2iGn45EhkQxvPZyb695cNB6LkICy51gany7axZuztzodb1o1hi8fbU94qLfnNAQhY2DbbEiaADtzsQi9eHnH6T0tH4SIEgXenogUTgpOAoSCExEREd86fvY4o+ePZtbuCw/q+3/X1byO0e1GEx1+8aknIgUtPSuby0f/Srb94s/bT1xVj8evquuHrgLIjnmOAGWb6/+G/xYVC+0eg9aPQGRMwfcmIoWKt8FJvsfclmWFWZbVyLKs+Jx/GlmWVcTWIoqIiEhBKxVZile6vMLINiMJt4U7rfllxy/c+uOtrDq8ysfdiUBEaAgPdazldOyd5G2cOpvp444CTM0OcO938ODvUO8697VnjsKfz8PLTeDPFyCtkB/zLCIBJd+CE8uy/mFZ1q/ACRybwM7O+Wc1cMKyrF8ty+qeX/OJiIiIWJbFHQ3uYNo/plErxvlfUPee3sv9P9/PO2vecbkvikhBeeLqerSrXeai90+lZ/Hdyn1+6CgAVWsFd34OfZOgYQ/3teknICkRJjeBmU/D6cO+6VFEirT8OI44HPgIuPXcWy5Kz030BXCfMSbDq4kDjB7VERER8a+0zDTGLxnPN5u/cVnTtlJbxsWPo2xUETrVRPzOGMNNU+azcvfxi8Y2PncdkWEhfugqgB1cD8mTYN034CnsDI2Cln2g/QAoWck3/YlI0PH7HieWZX0J3IwjMMkCfgcWAgdy3qsAtAGuBkJxBChfGWNu92riAKPgREREJDD8vP1nnl3wLKmZqU7HYyNjeaHjC3Ss0tHHnUlRtnDbEXpPvfiAyTLFw/n1iQTKltDRuxdJ2QJzX4JV/wWT7b42JBya3QMdBzqONBYROY9fgxPLsroB03GEIXNxrCTZ4aK2JvA+0Cmn/h/GmF/yPHmAUXAiIiISOHaf2s2wpGGsSVnjsub+RvczoNkAwkK0FZsUPLvd0Hrs76ScvnjRddXSUSQP7aIToFw5tgPmvgwrPgW7h31hbKHQ9A6IHwSxOpJcRBz8vTlsn5zXtcDVrkITgJyxa4Fzn2Ae8HJuEREREaeqRVfjw24f0qdxH5c1H6z7gHt+voddJ3f5sDMpqmw2iztaO18JsefYGUZ+u9bHHQWR0jWhxyvw+Epo3RdCI13X2rNgxcfwWgv45hE4vMlnbYpI4eVtcNIWx+qRSbnZsySnZiKOR3jaejm3iIiIiEthtjAGtRjE21e9TWxkrNOadUfWcdtPtzF923QfdydFUf8r63JTsypOxz5bvIt3krf5uKMgE1MVuifC46uhXT8IK+a61thh9efwRhv44j44oGBKRPLO20d1zgDhQCtjzPJcXtMcWAqkG2Oi8jx5gNGjOiIiIoEr5UwKT859kvn75rusubHOjYxsM5Ji7v4yJpIPrnl5Dn8dPO107Iu+7Whdy3nQJxdITYGFU2DRVMg45bm+/j8gYTBUaV7wvYlIQPH3ozpncl5jLuGakhdcKyIiIlKgykaV5c2r3mRQi0GEWqFOa77f+j23/3Q7G49u9HF3UtT8/HgCDSpGOx277e0FrN930scdBaniZaHraHhiDXQeAZEe/kqyaTr8pwt80gt2LfJNjyJSKHgbnGzNeb3pEq7pmfO6xcu5RURERHLNZtno07gPH3X7iKolqjqt2XFyB3dOv5NPN3yKtycPirgSYrP4vl8Hl+PdX00m267ff7kWVRo6D4eBa6Hr01CsjPv6Lb/De9fAhz1gexLov3UR8cDb4GQ6jv1KHrUsq4enYsuyrgMew7Evih4mFhEREZ9rUq4JX/b4km61ujkdz7Rn8uLiFxnw5wCOnT3m4+6kqIgIDeGXgfEux8f/opVPlyyypOM0nYFr4JoXoEQF9/XbkxzhyXvXOcIUBSgi4oK3e5yUxrFypBSOMOQj4F1gybnNYi3LCgda4jhF5z4gBDgK1DXGFJpPI9rjREREJLgYY/huy3eMWzyOM1nOnyAuH1WeFxNepFXFVj7uToqKnUdS6TRhttOxZ3pczv0davm2ocIk86zjhJ25k+HkHs/1lZtDwhCo3w10NLRIoeLtHideBSc5DXQBZgAROMITADtwMufnGP5/ZYsFpAPdjDGzvZo4wCg4ERERCU7bTmxj6JyhbDrm/NhSC4tH4h7h0aaPEmpzvj+KiDcm/LqRN2ZtdTr23v0tubKBh5UT4l5WBqz6DOa+BMd2eK6v0AQS/g0NbwSbtwv0RSQQ+D04yWkiDpgKtPZQuhh42BizxutJA4yCExERkeCVnp3OS0tfYtrGaS5rmpdvzovxL1KpRCUfdiZFxSMfLeW39Qedjo29qQl3tqnu444KoewsWPMlJE+CI5s915et7ziFp9HNEKLQVCSYBURwcl4zLYGrgcbAuXPUjgJrgZnGmEKbLCg4ERERCX5/7vqT0fNHcyL9hNPxkuElGdN+DF1rdPVxZ1LYZdsNd72zkIXbjjodj69blo8fbOPjrgopezas/w6SJsKh9Z7rY2tDx0HQtDeEhBV8fyKS7wIqOCnKFJyIiIgUDgdSDzA8eTjLDi5zWXN7/dsZ3HIwkaGRPuxMCrtdR9JImDDL5XjV0lFM7x9PTDH95T1f2O2waQYkJcL+VZ7rY6pDx4HQ7G4IjSj4/kQk33gbnOihPREREZHzVCxekXeveZfHrngMm+X8o9Lnmz7nzhl3svW4830pRPKiepliTLglzuX4nmNn6PfZch2VnV9sNmh4PTwyB+78Eqp62AT6xC6YPgheaQoL34SMNN/0KSJ+l6sVJ5ZlFchDlcaYXQVxX3/QihMREZHCZ9nBZQz7P/buOzyqelvj+LsnvRFq6NJBEKQFkBZEBJF2PBIQsQJWRASVomADRAELKqKoqChSg3qodqlK77333kJ6m33/wOOVw56EkuyZzHw/z8PzXPZaCe89x8t1Vn57/RYP0olk690TwX7BGtRwkDpX6SyDWziQS1btP6suH//psv51r4ZqXqWYjYl8hGlK+xZJi8ZIB5bm3B9WTGrytBTdSwoKz/t8AK6ZLa/qGIaRda1/QDZM0zS9ZssSgxMAALxTfFq8Xl72sn479JvLnjbl2uiVJq+oQGABG5PBm2X32k6NkgU0/5nmNifyMQf+kBaPkfa4/r/7v4UUkm55Smr0mBQcmffZAFw1uwYnzmv9A7Jhmqbplwff1y0YnAAA4L1M09S0HdP01qq3lO5Mt+wpHV5ao2JGqXax2jang7fKyHKqypAFlrU+LSvr+Tu2U76VAAAgAElEQVSq2ZzIBx1efXGJ7E7r/x4uERR5cXhyS28ptHDO/QBsY9fg5KFr/QOyY5rmpLz4vu7A4AQAAO+34+wODVg8QPvi91nW/Qw/9anbRz1r9nS5HwW4GicTUtXkjd+U6bz839mnPNpITSoVdUMqH3Rs48UTKNtm59wbGC416CU1floK55UqwBNwq46HYHACAIBvSM5I1qhVo/Ttrm9d9jQq2UhvNHtDxUL50ITr99yMDZq19vBlz2+7MUqfP5zDQlPkrpPbpCVvS5tnSWYOh/L9Q6T6D0tN+0oFStkSD4A1btUBAACwUWhAqF5r8prGxIxReID1QsgVx1Yodk6slh65ggWTQA5e7lDD8vlv20/KaXESBXkoqrrU+TPpqVVSnfskI5vNA5kp0oqPLt7CM7e/dN5r7sUAfA6DEwAAgGvQtkJbzeg4QzcXtb4+9mzqWT35y5N6a9VbysjKsDkdvElkaIB+ebaFZe3l2ZttTgNJUtHK0l3jpb5rpfo9JEeA696sdGn159L7daX/PCWd4RpzIL9hcAIAAHCNykaU1Zd3fqmeNXu67Jm0dZIeWPCADl7gp824dpWjwlWmUMhlz/+z/qgS0zLdkAiSpELlpY5jpWc2SA0fl/yDXfc6M6V1k6Vx0dKsR6VTO2yLCeD6MDgBAAC4DgGOAPWv318TWk9QkeAilj1bzmxRlzldNHfvXJvTwZu0qHr5zpyE1Ezd99kKN6TBJSJLS+1GS89slJo8LQWEue41ndKmGdKHjaQZD0nHN9mXE8A1YTlsLmE5LAAAOJ1yWkOXDtWyo8tc9nSq1ElDGg1RaECojcngLcoPnmf53M9haPfrd8owDJsTwVLSGWn5h9KKT6T0hJz7q7WTYgZIpevlfTbAB7EcFgAAwEMUDSmq8beP13P1n5O/4W/ZM3vPbN0z9x5tO7PN5nTwBuO617V8nuU01WfqOpvTwKWwIlKrl6X+m6RbX5SCC2bfv2O+9GlLaXJn6eByezICuGIMTgAAAHKRw3Do4ZoP6+t2X6tMeBnLnv0X9uu++fdp8tbJ4vQvrsbt1YsrMsR6Eem8jcc0fO5WmxMhWyGFpFsHSf03S7e/KoUWzb5/9y/S53dIX3aQ9i6S+PsB8AgMTgAAAPJAzaI1NbPjTLWr0M6ynuHM0KhVo/T0b0/rXOo5m9MhvwoO8NOXPRq4rE9cuk+fLObWFo8TFCE16y/12yjdMVIKL5F9//4l0ledLg5Rdv3CAAVwM3ac5BJ2nAAAACumaeo/e/6jkStGKiUzxbInKiRKb8a8qQYlXH8gBv7pfHK66gz72WV91+t3KsCPn5F6rIxUad3X0tKx0oXDOfeXqntxB0q1dhJ7bICrdr07Thic5BIGJwAAIDt74/dq4KKB2nHO+gpSQ4YevflRPVn7Sfk7rPejAP+073SSWr610GV967A7FBrIP0seLTNd2jhNWvK2dG5/zv3Fa0oxz0vV/yU5GIwBV4rBiYdgcAIAAHKSlpWmd9e8q2+2feOyp25UXY1qPkolw0vamAz51cmEVDV8/VfLmsOQtg5rq+AAP5tT4aplZUqb46TFb0lnduXcX7Sa1Pw5qWZnyY/hGJATbtUBAADIJ4L8gjS44WB9cNsHKhhkfcvGupPr1HlOZ/1y4Beb0yE/iooI1hMtKlnWnKb0yCR+sJcv+PlLtbtJT62QYr+Qom7Kvv/0Dum7x6Rx0dLary+eXAGQZxicAAAA2OzWsrcqrmOcootb//ArIT1B/Rf21/A/hys1M9XmdMhv+reuovAg61MHS3ef1oRFLIvNNxx+Us27pSeWSvd8I5Wsk33/uX3S7D7SB/WkVZ9d3J0CINfxqk4u4VUdAABwtbKcWfpk0yf6eMPHcppOy57KBStrTMwYVS5U2eZ0yE9OXkhVw5HWr+xI0rLBt6l0wRAbEyFXmObFK4oXjZYOr8y5P6Kk1KSvVP9hKTA0z+MB+QU7TjwEgxMAAHCt1pxYo8FLBut40nHLerBfsAY2HKjYKrEyuFEDLjidpiq+ON+yVqt0pGb3aco/P/mVaUr7FkuLx1y8qjgnYcWkxn2kBr0uXoUM+Dh2nAAAAORz9YvXV1zHON1W9jbLempWqob9OUzPL3peF9Iv2JwO+YXDYejrXg0ta5uOxOvHLSdsToRcYxhSxRbSw3OlHj9IlVpl3590SvrlFWlsrYunVVLO25MT8FKcOMklnDgBAADXyzRNTd8xXWNWjVG603rZY6mwUhoVM0p1onLYfQCf9e3aw3p2xgbL2te9Gqp5lWI2J0KeOLzm4gmUnQty7g0qIDV8TGr8lBRaOO+zAR6GV3U8BIMTAACQW3ac3aGBiwdqb/xey7qf4ac+dfuoZ82echgcIMbl/j1+mdYdtD5lsG1YW4UEckWx1zi2UVrylrR1tqQcPtsFhF18fafJ01J4lC3xAE/A4MRDMDgBAAC5KTkjWaNXjdasXbNc9jQq2UhvNHtDxUI5QYBLbTkar/bvL3VZ3zrsDoUGWt/Eg3zq5PaLA5TNsyQXy6b/5h98cYFs02ekAqVsiQe4EztOAAAAvFBoQKhebfKqxsSMUXhAuGXPimMrFDsnVksOX8GySPiUm0pFqlNt1x+IO7y/VPwA1ctE3Sh1/kzqs1qqc7/kyGYwlpkqrfhYeq+2NLe/dO6AfTmBfIgTJ7mEEycAACCvHE44rEGLB2nj6Y0uex6s8aD61eunAL8AG5PB0z01Za3mbTxmWbuxRIQWPNOcm3a81bkD0tJ3pfXfSFnWO5P+5vCXbu4mNX9WKlLJnnyAjXhVx0MwOAEAAHkpw5mhD9d9qM83fy7TxR6DGkVqaEzMGN1Q4Aab08FTmaapusN/1vnkDJc9LIz1cvFHpD/el9Z8efGkSXYMh1QzVmr+3MUTLICXYHDiIRicAAAAO/xx9A8NWTpEp1NOW9ZD/UM19Jah6lipo83J4KlM01SFF+Zn29Pv9irqd3tVmxLBLRJPSn98IK2aKGUk5dBsSDU6Sc2fl0rebEs8IC8xOPEQDE4AAIBdzqSc0ZBlQ7TsyDKXPZ0qddKQRkMUGhBqYzJ4KqfTVLWXFigjy/W/+8/u01Q3lyloYyq4RdIZafl4aeUnUtqFnPur3im1GCCVrp/32YA8wuDEQzA4AQAAdnKaTn299WuNXTNWmWamZU+5AuU0Oma0ahSpYXM6eKJTCWlq8Pov2fZserWNIoLZk+MTUs5fHJ4sHy+lnMu5v1IrKWaAVK5x3mcDchmDEw/B4AQAALjD5tObNXDxQB1KOGRZD3AEqH/9/rq/+v0sAYWOx6fqka9WafMR1ycNnm9TVX1uq2JjKrhVWsLF13f++EBKtn4F8BLlm18coFSIkfg7BfkEgxMPweAEAAC4S2J6ooYvH675+1zvsYgpE6PhTYercHBhG5PBU32/7oj6TV+fbc/+N9vblAYeIT354gLZZe9Jicdz7i/b6OIApfLtDFDg8RiceAgGJwAAwJ1M09TsPbP1+orXlZKZYtlTLKSY3mz+phqWbGhzOniiO99bom3Hst9xsXro7SoaHmRTIniEjFRp/WRp6Vgp3vok2yVK1b04QKl6p+Rw5H0+4BowOPEQDE4AAIAn2Be/TwMXD9T2s9st64YMPVLrEfWu01v+Dn+b08HTlB88L8eet7vU1t31SvOql6/JTJc2TpOWvCOd25dzf/GaF68xrvEvyeGX9/mAq8DgxEMwOAEAAJ4iPStd76x5R99s+8ZlT51idTQqZpRKhZeyMRk8jdNpauCsjYpbczjH3u3D2yo4gA/EPicrU9o8S1rylnR6Z879RateHKDUjJX8GM7CMzA48RAMTgAAgKdZeGihXlr2ks6nnbesRwRG6LUmr6l1udY2J4OnOZ2YpugR2d+4I0k7RrRVkD/DE5/kzJK2zZYWvyWd2Jxzf6EKUvNnpZu7Sf6BeZ8PyAaDEw/B4AQAAHiiE0knNHjJYK0+4frfU7pU7aKBDQYq2D/YxmTwNBlZTlUZsiDHPk6e+DinU9q5QFo8Rjq6Luf+yLJS02ekug9IAfwdA/dgcOIhGJwAAABPleXM0qebPtVHGz6S03Ra9lQuWFljYsaocqHKNqeDJ8nMcuqpKWv145YT2fYtHtBSNxQJtSkVPJJpSrt/lRaPlg6tyLk/vITUtK9Uv4cUyD87sBeDEw/B4AQAAHi6tSfWatCSQTqeZH3VaLBfsAY2HKjYKrEsAvVxK/edVdcJf2bbs3RQS5UpxAdgn2ea0r7FF0+g7F+Sc39oUalJH6nBI1JQRN7nA8TgxGMwOAEAAPlBfFq8XvnjFf168FeXPa3LtdarTV5VgcACNiaDp0nPdKrqUNev7txarZi+7MHV1viHg8ulRaOlPa7/fvlbcEHplt5So8elkIJ5nw0+jcGJh2BwAgAA8gvTNDVjxwyNXjVa6c50y55SYaU0KmaU6kTVsTkdPMninaf04OcrXdbrlyukbx5pxM4TXOrImotLZHfMz7k3qIDU8LGLQ5SwInmfDT6JwYmHYHACAADym53ndmrAogHaG7/Xsu5n+OmpOk+pZ82e8nPwwdhX5XTyRJJ2v36n/P0cNiVCvnF808VXeLbOlpTD586AMKlBT6nx01JEcVviwXcwOPEQDE4AAEB+lJKZolErR2nWrlkuexqVaKSRzUcqKjTKxmTwJKZpqsIL2Z8e2PhqGxUIDrApEfKVk9ulJW9Lm+MkFwuq/+YfLNV/WGrSV4osbUs8eD8GJx6CwQkAAMjPftj/g4b9MUwJGQmW9UJBhTSi2QjFlImxORk8xYq9Z3TPJ8uz7alULEwLnolRoD+nT2DhzB5p6TvShmmSMzP7Xr9Aqc59UrN+UqHytsSD92Jw4iEYnAAAgPzuSOIRDVw8UBtPbXTZ80CNB9SvXj8F+gXamAyeIm7NYT0/c0OOffvfbG9DGuRb5w5Iy8ZK6yZLWdZ7lv5m+Em1u0nNn5OKVLInH7wOgxMPweAEAAB4gwxnhsavH6+JmybKdLGToHrh6hrTYozKFShnczp4gpMJqWr4eva3phQI9teGV9pwrTWyd+GotOx9ac0XUmZq9r2GQ6rZ+eIAJaq6PfngNRiceAgGJwAAwJv8efRPvbj0RZ1OOW1ZD/UP1dBbhqpjpY42J4MnOJeUrrrDf86xb+/IdnI4GJ4gB4knpT/HSSs/kzKScu6v3kmKGSCVvDnvs8ErXO/ghJcPAQAAcJnGpRorrmOcmpVuZllPzkzWi0tf1ItLXlTSlXzQgVcpFBaoza/dofa1Smbb9/S0dTYlQr4WHiW1Hib133xxIBJUIPv+bbOlCc2lKd2kw2vsyQifxomTXMKJEwAA4I2cplNfb/1aY9eOVaaLZY7lCpTTqJhRuqnITTangyfI6fTJjhFtFeTPdda4CinnpZWfSss/lFLO5dxf6TYpZqBUrnHeZ0O+xKs6HoLBCQAA8GZbTm/RgMUDdCjhkGXd3+Gv/vX664EaD7DXwgdtOhyvjuOWuqyP7nyzujYoa2MieIW0BGnVxIuv8SSdyrm/XDOpxQCpQguJv4fwDwxOPASDEwAA4O0S0xM1YsUIzds7z2VP89LNNaLZCBUOLmxjMniCQ2eT1Xz07y7rI+6qqftvYaEwrkF6srR2krTsPSnhWM79ZRpefOWnSmsGKJDEjhMAAADYJDwwXG80e0Mjmo5QiH+IZc+SI0sUOztWK46tsDkd3K1s4VDd29D1qZKh32/WqB+225gIXiMwVLrlSemZDVL7d6TIG7LvP7xSmtJF+uRWadtcyem0JSa8FydOcgknTgAAgC/ZH79fAxcP1Laz2yzrhgw9UusR9a7TW/4Of5vTwV2cTlMVX5yfbc/4++qpXQ5LZYFsZWVIG6ZJS96Wzu3Lub9ya+n+uLzPBY/FiRMAAADYrnxkeU1uN1n3V7/fsm7K1KebPlWPH3roaOJRm9PBXRwOQxtebqPKUeEue3p/s1YHzyTbmApexy9AqveA1Ge1dPenUtFq2fdXvt2eXPBaDE7+YhjGaMMwzH/8utXdmQAAADxZoF+gBjUcpHG3jVPBoIKWPetPrVfsnFj9fMD1rSvwLpGhAfrl2RaKqVrMZU/MmN91MiHVxlTwSn7+0s1dpd7LpS6TpOK1Lu8Ji5LqP2R/NngVBieSDMOoK6m/u3MAAADkRy3KtlBcxzg1KNHAsp6QnqBnFz6rYX8OU2omH5Z9xaQeDdQtm5t0Gr7+q1gbgFzhcEg33SU9sUTqNlUqVff/a02elgKsdzIBV8rnByeGYfhJ+lSSv6STbo4DAACQLxUPK65PW3+qPnX6yM/ws+yZuXOm7p13r3ad22VzOriDYRh6s/PNan+z630mL3632cZE8HqGId3YTnr0d+n+WVLVtlJ0T3enghfw+cGJpH6S6kvaKukzN2cBAADIt/wcfnq89uP6ou0XKhlm/WF59/ndunfevZqxYwanDXzEh93ruaxNXXlQ249fsDENfIJhXNxr0n26FOR63w5wpXx6cGIYRgVJwySZkp6QlOHeRAAAAPlf3ai6mtlxpm6/wXohY1pWmoYvH67nFj2n+LR4m9PBHdYMdb2cs+3YJZq8/ICNaQDg6vj04ETSx5JCJX1hmuYSd4cBAADwFpFBkXrn1nf00i0vKcgvyLLn5wM/q8ucLlp/cr3N6WC3IuFBWv9ya5f1od9vVr9p63TkfIqNqQDgyvjs4MQwjAcktZF0WtJAN8cBAADwOoZhqGu1rprafqoqRVay7DmWdEwP//CwPtn4ibKcWTYnhJ0KhgbqocblXNa/X39UTd/8TftOJ9mYCgBy5pODE8Mwikp656/fPm+a5hl35gEAAPBmVQpV0dQOUxVbNdaynmVm6YN1H+ixnx/TyWR29XuzfrdXVfEC1ieQ/qvlWwt1JjHNpkQAkDOfHJxIGiupqKSFpmlOcncYAAAAbxfiH6JXGr+it1q8pYiACMuelcdXKnZ2rBYfXmxzOtilUFig5vRpporFwrLtqz/iF8WnsH4QgGfwucGJYRh3SLpPUrouLoS9nu/1mGEYqw3DWH3q1KlcyQcAAODN7ih/h2Z2mqnaxWpb1s+lndNTvz6lUStHKT0r3eZ0sENUgWD99tyturNmiWz7ar/2k02JACB7PjU4MQwjVBcXwkrSm6Zp7rie72ea5iemaUabphldrFix6w8IAADgA0qHl9YXbb/Qo7UelSHDsmfytsm6f/792h+/395wsM1H99fXzCcaZ9szbeVBm9IAgGs+NTiRNFxSeUm7JL3h3igAAAC+K8ARoL71+uqTNp+oaEhRy55tZ7ep69yumr1nts3pYJcG5QtryiONXNYHf7uJV3YAuJ3PDE4Mw4iW9Mxfv+1tmmaqO/MAAABAuqXkLYrrGKdmpZtZ1lMyUzRk6RC9sOQFJWVw24o3alK5qBY+f6vLeu3XftKuEwn2BQKA/+EzgxNJAyT5SdomqahhGN3+95ekmv/ov+0ftYJuSQwAAOADioQU0YetPtTz0c/L3+Fv2TN371x1ndNVW85ssTkd7FC+aJgmPhTtst7+/aU2pgGASxmmabo7gy0Mw/he0r+u8cvrmqa5PruG6Ohoc/Xq1df47QEAACBJW85s0cBFA3UwwXq3hb/DX/3q9dMDNR6Qw/ClnwH6hvbvL9GWoxcsa+O611WHm0vZnAiANzAMY41pmq6nszng/9sAAADAY9xU5CbN6DhDHSp2sKxnOjP11uq31OfXPjqbetbmdMhrw/5V02Wtz5R1Sk7PtDENAFzkM4MT0zTvMk3TyO6XpNf+8SUt/1HL9rQJAAAAck9YQJjeaP6GXm/2ukL8Qyx7lhxZotjZsVpxbIXN6ZCX6pcrpO+fauqyvv7QeRvTAMBFPjM4AQAAQP7SqVInzegwQ9ULV7esn0o5pUd/elTvr31fGU5uXvEWdcoW1PC7rE+ePD9jg81pAIDBCQAAADxY+cjymtxusu6vfr9l3ZSpTzd9qh4/9NCRxCM2p0Neub/RDZbPj8an6sQFLscEYC8GJwAAAPBogX6BGtRwkD5s9aEKBRWy7NlwaoO6zO6iH/f/aHM65AXDMPRKxxqWtUYjf9WO41xPDMA+DE4AAACQL8SUiVFcpzg1LNHQsp6QkaDnFz2v1/58TSmZKTanQ27r7uLUiSR1Gsf1xADsw+DkH0zTfPUfC2EXujsPAAAALhUVGqVPWn+ip+s+LT/Dz7Inbmecus/rrl3ndtmcDrkpyN9PMVWLWdbSMp0a9cN2OZ2mzakA+CIGJwAAAMhX/Bx+euzmx/Rl2y9VMqykZc/u87t177x7NWPHDJkmH67zq3e71nZZ+2jhHn35x377wgDwWQxOAAAAkC/ViaqjmR1nqnW51pb1tKw0DV8+XM8tek7xafE2p0NuKBIepOUvtHJZHzZ3q3aeSODkCYA8xeAEAAAA+VZkUKTebvG2XrrlJQX5BVn2/HzgZ3WZ00XrTq6zOR1yQ4nIYA1tb30ltSS1eXex2oxdrKPn2WsDIG8wOAEAAEC+ZhiGulbrqqntp6pSZCXLnmNJx9Tjhx6asGGCspxZNifE9XqwcXndXbe0y/ruk4nq+eUqGxMB8CUMTgAAAOAVqhSqoqkdpqpL1S6W9SwzS+PWj9NjPz+mE0knbE6H6xHo79A799TJtmf78QT9vPUEO20A5DoGJwAAAPAaIf4hernxy3q7xduKCIiw7Fl5fKVi58Rq0aFFNqfD9Xonm2WxkvToV6v11k87bEoDwFcwOAEAAIDXaVO+jWZ2mqnaxaw/aJ9PO68+v/XRqJWjlJ6VbnM6XKu765XR211qq2U162uKJenD3/fodGKajakAeDsGJwAAAPBKpcNL64u2X+jRWo/KkGHZM3nbZN0//37tj99vbzhcs871y+iLHg31cJPyLnuiR/yiz5fu0/7TSfYFA+C1GJwAAADAawU4AtS3Xl992uZTFQuxPqWw7ew2dZ3bVbP3zLY5Ha7H83dUU/kioS7rw+ZuVbv3l2jdwXM2pgLgjRicAAAAwOs1KtlIcZ3i1Lx0c8t6SmaKhiwdoheWvKCkDE4p5AfhQf76tnfTbHuS07P06FerbUoEwFsxOAEAAIBPKBxcWONajdOA6AHyd/hb9szdO1dd53TVljNbbE6Ha1E4LFCbX7sj257Tien6eNEefb/uiI6cT7EpGQBvwuAEAAAAPsNhOPTgTQ9qcrvJuiHiBsuegwkHdf/8+zVpyyQ5TafNCXG1woP81bdVlWx73lywXf2mr1fLtxZq4Y6TNiUD4C0M7jnPHdHR0ebq1RwDBAAAyC+SMpL0+vLXNWfvHJc9zUo304imI1QkpIiNyXC1TNPUn3vOaO3Bc3rrp5059u9/s70NqQB4CsMw1pimGX2tX8+JEwAAAPiksIAwjWw+UiObjVSIf4hlz9IjSxU7J1bLjy23OR2uhmEYalK5qPrcVkX3NiybY/+v207YkAqAt2BwAgAAAJ/WsVJHzew4U9ULV7esn045rcd+ekzvrX1PGc4Mm9Phaj3TqqqKhgdm29Nr0mq1eXeRdp9MsCkVgPyMwQkAAAB8XrkC5TS53WQ9UOMBy7opU59t+kwP//CwjiQesTkdrkaJyGB917upnm1dVd0auD59svNEop6eut7GZADyKwYnAAAAgKRAv0ANbDBQH7b6UIWCCln2bDy1UV1md9GP+3+0OR2uRtnCoerbqore7Hyzht9V02XftmMXdOJCqo3JAORHDE4AAACAf4gpE6O4TnFqWKKhZT0hI0HPL3per/7xqlIyud7W03VrUFZFwly/utNo5K9asOmYuDQDgCsMTgAAAID/ERUapU9af6K+dfvKz/Cz7Jm1a5bunXuvdp7L+RYXuE+An0PTHrtFt1Yr5rLnyW/W6t2f+e8RgDUGJwAAAIAFP4efHr35UX3Z9kuVDCtp2bMnfo+6z+uu6dunc2LBg1UpHqEvezRU6xrFXfa8/9tuZWY5bUwFIL9gcAIAAABko05UHc3sOFOty7W2rKdlpWnEihF6duGzik+LtzkdrsaAO6plW997OsmmJADyEwYnAAAAQA4igyL1dou39XLjlxXkF2TZ88vBXxQ7J1ZrT6y1OR2uVNXiEbq7bmmX9TbvLlbbsYv1wrebFJ/M1dMALmJwAgAAAFwBwzDUpWoXTW0/VZULVrbsOZ50XD1+7KEJGyYoy5llc0Jcibe61NbMJxq7rG8/nqCpKw/qqSkMwABcxOAEAAAAuApVClXRlPZT1KVqF8u603Rq3PpxevTnR3Ui6YTN6ZATh8NQg/KF1b3RDdn2Ld19WueT021KBcCTMTgBAAAArlKIf4hebvyy3rn1HUUERlj2rDq+SrFzYrXo0CKb0+FKxNYvk2PP3I3HbEgCwNMZbP/OHdHR0ebq1avdHQMAAAA2O5p4VIMWD9L6U+td9txf/X71r99fgX6BNiZDdkzT1Ns/7dTHi/Yo0+n6M1GNkgUUFOBQ00pF1bdVFQX687NnIL8xDGONaZrR1/z1DE5yB4MTAAAA35XpzNT49eP12abPZMr636+rF66u0TGjVT6yvL3hkK3k9EwdPZ+i299ZnGPvfY1u0Ov/rmVDKgC56XoHJ4xLAQAAgOvk7/BX33p99WmbT1UspJhlz7az29R1blf9Z/d/xA8vPUdooL8qR0Vo7D11cuz9ZsVBGxIB8DQMTgAAAIBc0qhkI8V1ilNMmRjLekpmioYuG6oXlr6gxPREm9MhOzFVi8lh5Ny3/fiFvA8DwKMwOAEAAAByUeHgwhp32zgNbDBQ/g5/y555e+ep69yu2nJ6i83p4ErhsECNuKuWAv2y/4jUduwSfbxoj6atPKhj8Sk2pQPgTuw4ySXsOAEAAMD/2npmqwYuHqgDFw5Y1v0d/upXr58eqPGAHAY/0/QEqRlZ2nMqUaYpdfhgaba9BYL9NeXRW1SzdKRN6QBcC3acAGhbPOEAACAASURBVAAAAB6qRpEamt5hujpV6mRZz3Rm6q3Vb6n3r711JuWMzelgJTjATzeVilTN0pHq0bR8tr0XUjP1xbL9tuQC4D4MTgAAAIA8FBYQptebva6RzUYq1D/UsmfZkWWKnROrP4/+aXM6ZOeuOqVz7Jm19jDLfgEvx+AEAAAAsEHHSh01o+MMVS9c3bJ+OuW0Hv/5cY1dM1YZzgyb08FKrdKRalyxSI59Gw7H25AGgLuw4ySXsOMEAAAAVyI9K13vrX1PX239ymXPzcVu1qjmo1QmooyNyWAlNSNLCzYf0/bjCTp1IU3frjti2de8SlHVKFVAT7aopIKhgTanBJCd691xwuAklzA4AQAAwNVYfHixhi4dqnNp5yzr4QHheqXJK2pbvq3NyZCd8oPnZVu/qVQBzX26mQzjCu42BmALlsMCAAAA+VBMmRjFdYpToxKNLOuJGYkasGiAXv3jVaVkcu2tp3g8pmK29S1HL2jbsQSb0gCwA4MTAAAAwE2iQqM0ofUEPVPvGfkZfpY9s3bNUre53bTz3E6b08FKi6rFcuxZuvuUDUkA2IXBCQAAAOBGfg4/PVLrEX3Z9kuVCitl2bM3fq/unXuvpm+fzg0ubtaoYhF1rpf97pmR87fblAaAHdhxkkvYcQIAAIDrdSH9gl7941X9fOBnlz2tbmil15q8psigSBuT4X8dOpus/WeSNHL+dm07duGy+qPNK0iSIoIDdHv14qpRqoDdEQH8heWwHoLBCQAAAHKDaZqK2xWnUStHKS0rzbKnRFgJjWo+SvWK17M5Hf7XpsPx6jhuabY9gX4Off5wAzWrUtSmVAD+ieWwAAAAgBcxDENdqnbRtPbTVLlgZcue40nH1ePHHvp4w8fKcmbZnBD/VKtMzid/0rOc+mLZPhvSAMgLDE4AAAAAD1S5UGVNbT9VXat2taw7Tac+XP+hHv35UZ1IOmFzOvxTg/KFcuxZuvu0DUkA5AUGJwAAAICHCvYP1kuNX9I7t76jiMAIy55Vx1cpdk6sFh5aaG84/O2plpUV6Jf9R6u0TKfSMjkdBORH7DjJJew4AQAAQF46mnhUg5cM1rqT61z23Ff9PvWv319BfkE2JoMk7TudpGW7Tys5PVMZWabG/Ljjsp6vejZUzBVcZwwgd7Ec1kMwOAEAAEBey3Rm6qMNH+nTjZ/KlPW/x99Y+EaNjhmtCpEVbE6Hfyo/eJ7l8xtLRKhaiQj1u72qKhQNszkV4JtYDgsAAAD4CH+Hv56u+7Q+a/OZokKiLHu2n92ue+beo+93fy9+SOo+tcsWtHy+/XiC/rP+qLpO+JNXd4B8gsEJAAAAkM80LNlQcZ3i1KJMC8t6SmaKXlr2kgYvGazE9ESb00GSWt1oPdj6r1MJaVq9/5xNaQBcDwYnAAAAQD5UKLiQPrjtAw1qMEgBjgDLnvn75qvr3K7afHqzzekQfQU37ZxMSLUhCYDrxeAEAAAAyKcMw9D9Ne7X5HaTVa5AOcueQwmH9MD8B/Tl5i/lNJ02J/Rdt1Qoop5NK8hhuO758o8D9gUCcM0YnAAAAAD5XI0iNTSjwwx1qtTJsp5pZurtNW+r96+9dTrltM3pfJPDYejljjW0/pU2WvBMc7WsdvltOgfPJLkhGYCrxa06uYRbdQAAAOAJ5uyZoxHLRyg5M9myXiS4iEY2H6kmpZrYnMy3jV+4W6N/uPyK4mH/uumS3xuGoRolC6jeDQVlGNkcVwFwxbiO2EMwOAEAAICnOHjhoAYsHqCtZ7Za1g0Z6lGzh/rU7eNyPwpy16GzyWo++vcr7n+4SXm92ummnBsB5IjriAEAAABc4oYCN2jynZP1UI2HLOumTH2++XM9vOBhHU44bHM631Q0POiq+r/8Y79OXmB5LOAJGJwAAAAAXijAL0DPN3he41uNV+HgwpY9G09vVJc5XfTDvh9sTud7ggMcKhZxdcOTXSe5ShrwBAxOAAAAAC/WvExzxXWMU6OSjSzriRmJGrB4gF7941UlZ1jvRcH1MwxDvZpVuKqvSUjNzKM0AK4GO05yCTtOAAAA4MmcplOfb/5c49aNU5aZZdlTIbKCxsSMUbXC1WxO5zt+3XZCf+45o7TMS6+GXrbntPaeuvSWnX/VKaX3utW1Mx7gla53x4l/boYBAAAA4JkchkOP1HpE0cWjNWjxIB1NOnpZz774feo+r7sGNBige6rdw60ueaBV9eJqVb34Zc8HzNxw2eAkJd16wAXAXryqAwAAAPiQOlF1NLPTTLUp18aynu5M1+srXlf/hf0VnxZvczrfZfVaTnqWU7tPJujI+RTxpgDgPryqk0t4VQcAAAD5iWmamrVrlkatHKXULOvbW0qEldCbzd9U/eL1bU7ne37aclyPfb3GZb1a8QhNfDhaZQqF2pgK8A5cRwwAAADgqhmGodiqsZrWYZoqF6xs2XM86bh6/thTH234SFlOXhvJS+HB2W9R2HEiQa/P22ZTGgD/xOAEAAAA8GGVClbS1PZTdU+1eyzrTtOp8evH65GfHtHxpOM2p/MdRcNzvqp49YFzNiQB8L8YnAAAAAA+Ltg/WENvGap3b31XEYERlj2rT6xW7JxY/X7wd5vT+YYqUeGqHBWebU9SGtcTA+7A4AQAAACAJOn2crdrVsdZqhtlfQVufFq8+v7eV2+seENpWWk2p/NuhmHo614NdXfd0qoSFa5KxcIu60lOz5LTyY5KwG4sh80lLIcFAACAt8h0ZurjDR/rk42fyJT154VqhappTIsxqhBZweZ0vqPa0AVKy3Re8mzlkFaKigh2UyIgf2I5LAAAAIBc5e/wV5+6fTTxjomKComy7NlxbofumXuPvtv1HVfl5pH/HZpI0vZjCW5IAvg2BicAAAAALDUo0UBxneLUokwLy3pKZope/uNlDVoySInpiTan801WwxQAeYtXdXIJr+oAAADAW5mmqSnbp+jt1W8rw5lh2VMmvIzGtBijmkVr2pzOez321Wr9tPXEJc/KFwlVwdBAy/4gf4eaVCqq3i0rKcCPn5ED/3W9r+pkf1k4AAAAAJ9nGIbuq36f6kXV08DFA7X/wv7Leg4nHtYD8x9Q33p99dBND8lh8MH9ekWGBFz2bP+ZZOlMssuvWbHvrE4kpGrkv2vlZTTAp/C3GQAAAIArUr1IdU3vMF3/qvQvy3qmmal31ryj3r/01umU0zan8z6hgX7X9HVzNhxl7wyQixicAAAAALhioQGhGtFshN5o/oZC/UMte5YdXabY2bH64+gfNqfzLnVuKHhNX5eQmqn0LHahALmFwQkAAACAq9ahYgfN7DhTNxW5ybJ+JvWMHv/5cb2z5h2Xe1GQvXa1SqpdrRLX9LWpGQxOgNzCcthcwnJYAAAA+KKMrAy9t/Y9Tdo6yWVPraK1NCpmlMpGlLUxmfc4Fp+iY/GpLus9v1yl88mXDqeWDb5NpQuG5HU0IF+43uWwnDgBAAAAcM0C/AL0fIPnNb7VeBUOLmzZs+n0JnWd01U/7PvB5nTeoWRkiOrdUMjlr7DAy+/8GP/7bjckBbwTgxMAAAAA1615meaK6xinW0reYllPzEjUgMUD9Mofryg5w/WtMLh6GRb7THaeSHBDEsA7MTgBAAAAkCuKhRbThNYT9Ey9Z+RnWN8I8+2ub9VtXjftOLvD5nTe64bCly/pZSMDkHsYnAAAAADINQ7DoUdqPaJJd05S6fDSlj374vep+7zumrp9Ktfm5oLBd9542TNu1QFyD8thcwnLYQEAAIBLXUi/oGF/DtOP+3902dOybEsNbzpckUGRNibzLtuPX1DbsUsueRbgZ6hEZPDfvy9RIFgPNi6vjrVL2R0PcDuWwwIAAADwSAUCC2hMzBi92vhVBfsFW/b8fuh3dZ7dWWtOrLE5nfcI8r/8taiMLFOHzqb8/WvV/nN6euo6Ld97xg0JgfyNwQkAAACAPGMYhjpX7axpHaapSqEqlj0nkk+o54899dH6j5TlzLI5Yf4XGmi9T8bKr9tO5GESwDsxOAEAAACQ5yoVrKQp7abonmr3WNadplPjN4xXr5966XjScZvT5W9REUEqWzjkinrjUzLyOA3gfRicAAAAALBFsH+wht4yVGNvHasCgQUse9acWKPYObH6/eDvNqfLvwzD0Hvd6qpkpPXrUP+UnsnSWOBqsRw2l7AcFgAAALhyxxKPafCSwVp7cq3LnntvvFfPRT+nIL8gG5PlX6Zp6lh8qjKzLn7G+33HSb0ye8slPe1qldD4++q7Ix7gNiyHBQAAAJDvlAwvqYl3TNQTtZ+QIcOyZ+r2qbpv3n3aG7/X5nT5k2EYKlUwRDcUCdUNRUItT6DM33RcaZnskQGuBoMTAAAAAG7h7/DXU3We0sQ7JioqNMqyZ8e5Heo2t5u+2/WdOC1/dQL9rT/uTVlx0OYkQP7G4AQAAACAWzUo0UBxHeN0a5lbLespmSl6+Y+XNWjxICWkJ9gbLh8rGBpo+XzNgXM2JwHyNwYnAAAAANyuUHAhvX/b+xrccLACHAGWPQv2L1CXOV206dQmm9PlTzVLWS/gTc1gQSxwNXxucGIYRn3DMIYahvGDYRiHDMNIMwwjyTCMPYZhTDEMo627MwIAAAC+yDAM3Vf9Pn3T7huVL1DesudI4hE9uOBBfb75czlNBgDZ8fdz6NWONS57npHFf27A1fCpwYlhGIskrZY0XNIdkspICpQUKqmipHslLTAMY65hGJFuCwoAAAD4sOpFqmt6h+n6V6V/WdYzzUy9u+ZdPfnLkzqdctrmdPlLleIRlz1jcAJcHZ+6jtgwjN2SKkk6ISlO0mJJBySZkqIl9ZNU5a/2xZJamuaVjbG5jhgAAADIffP2ztPw5cOVlJFkWS8SXEQjm41Uk9JNbE6WP6zcd1ZdJ/x5ybOIYH/VKn31Pyc2DKla8QJ6LKaiSljc2AN4quu9jtjXBidzJU2WFGeaZqZFPUzST5L++7fuA6ZpTr6S783gBAAAAMgbhy4c0oDFA7TlzBaXPT1q9tDTdZ5WgJ/1fhRfte7gOf17/B+5+j3LFQnVz/1buLy1B/A01zs48al/0k3T7GCa5jSroclf9SRJT/zjURd7kgEAAABwpWyBsvr6zq/18E0Pu+z5YvMXeuiHh3Qo4ZB9wfKBIH+/XP+eB84ka/2h87n+fQFP5VODkythmuYmSWf++m1ld2YBAAAAcFGAX4Cei35OH93+kQoHF7bs2XR6k7rO6aoF+xbYnM5zVSwWppCA3B+enElMy/XvCXgqBifW/nu+L8utKQAAAABcolnpZprVaZZuKXmLZT0xI1EDFw/Uy8teVnJGss3pPE9wgJ+G/esm+TmMXP2+mU7fWfkA+Ls7gKcxDKOupP9eeL7NnVkAAAAAXK5oSFFNaD1BX2z+QuPWjVOmxZv43+3+TutPrdeYmDGqVriaG1J6ji7RZdXmphLaevSCnNew43L8wt1atvvMJc8yndzMA9/B4ORyQ//xP093WwoAAAAALjkMh3rV6qXoEtEatHiQjiQeuaxnX/w+dZ/XXc9FP6d7b7xXhpG7py7yk8iQADWuVOSavvbbtZf/Z5uRxYkT+A5e1fkHwzDukXT3X79dLem7HPofMwxjtWEYq0+dOpXn+QAAAABcqnax2prZcabalm9rWU93puuNlW/omd+f0flUFppeC3+L13wysjhxAt/B4OQvhmHcLGniX79N1sWriLMdo5qm+YlpmtGmaUYXK1YszzMCAAAAuFxEYIRGx4zWa01eU7BfsGXP74d+V+ycWK0+vtrmdPmfv9/lg5Mh3212QxLAPRicSDIMo7yk+ZLCJDklPWSa5nZ3ZgIAAABw5QzD0N1V7tb0DtNVtVBVy54TySfU66deGr9+vDKdl+9FgbUAP+uPjScvpNqcBHAPnx+cGIZRUtLPkkr/9ehx0zTj3BgJAAAAwDWqWLCiprSfom7VulnWnaZTH234SL1+7KXjScdtTpc/lS0cavn8wFluLYJv8OnBiWEYRXVxaFL5r0f9TdP8zI2RAAAAAFynIL8gDblliMa2HKsCgQUse9aeXKvYObH67eBvNqfLfzreXNLyeSYLYuEjfHZwYhhGQUk/Sbrpr0cvmaY51o2RAAAAAOSiVje00qxOs1Qvqp5lPT4tXs/8/oxeX/660rLSbE6Xf0QVCFbV4uGXPc9yMjiBb/DJwYlhGOGSFkiq+9ejUaZpjnBjJAAAAAB5oERYCU28Y6KeqP2EHIb1x59pO6ap+7zu2nt+r83p8o/iBS5fupvp5GYd+AafG5wYhhEiaY6kW/569IFpmoPdGAkAAABAHvJ3+OupOk/pszafKSo0yrJn57md6javm77d9a1yuFzTJ1ldScyJE/gKnxqcGIYRKGmWpFv/ejRR0jNuCwQAAADANg1KNNCsjrN0a5lbLespmSl65Y9XNHDxQCWkJ9gbzsP5W9yscyYxXScupOrEhVSdTUp3QyrAHoYvTVMNw4iT1Pmv3/4p6QldvH7YJdM0r+iC8ujoaHP1au6EBwAAADydaZqasn2K3l79tjKcGZY9pcNLa0zMGNUqVsvmdJ7pyclrtGBz9rcQVSwWpvH31dONJawX8gLuYhjGGtM0o6/1633qxIn+f2giSY0lbZC0KYdfAAAAALyIYRi6r/p9mtJ+isoXKG/ZcyTxiB5c8KA+3/y5nCa7PPwsXtX5X3tPJanv1HU2pAHs5WuDEwAAAACQJN1Y+EZN7zBdd1W+y7KeaWbq3TXv6omfn9DplNM2p/MsEcH+V9S380Si4lOsT/EA+ZVPDU5M0zSu9pe7MwMAAADIO6EBoRredLhGNR+lsIAwy54/j/2pzrM7a9mRZTan8xy33Vj8invTMzmhA+/iU4MTAAAAALDSrmI7zewwUzWL1LSsn009qyd+eULvrH5HGVm+d6KidY3iev3fNVWzdAFFRQT9/cuw+FEzt+3A2/jUcti8xHJYAAAAIP/LyMrQB+s+0BdbvnDZU7NITY1uMVplI8ramMwzNX7jVx2LT73k2bLBt6l0wRA3JQIux3JYAAAAAMglAX4Bejb6WX18+8cqHFzYsmfzmc3qMqeL5u+db3M6z+OwOHLi5MQJvAyDEwAAAAD4H01LN9WsTrPUuGRjy3pSRpIGLRmkl5a9pOSMZJvTeQ6r23Z4VQfehsEJAAAAAFgoGlJUH7f+WP3r95e/YX2rzPe7v9c9c+/R9rPbbU7nGSwHJ6yDgJdhcAIAAAAALjgMh3rW7KlJd05S6fDSlj37L+xX93nd9c22b+RrOyQt5ia8qgOvw+AEAAAAAHJwc7GbNbPjTLUt39aynuHM0Jsr31Tf3/vqfOp5m9O5DydO4AsYnAAAAADAFYgIjNDomNEa1mSYQvytb41ZeGihOs/prFXHV9mczj2slsOu3HfWDUmAvMPgBAAAAACukGEY+neVf2ta+2mqWqiqZc/J5JN65KdH9OH6D5XpzLQ5ob0OnLl8MW5Cqnf/7wzfw+AEAAAAAK5SxYIVNaX9FHWr1s2y7jSd+njDx+r1Yy8dTzpuczr7WBw4kb/V4hMgH2NwAgAAAADXIMgvSENuGaKxLceqQGABy561J9eq8+zO+vXgrzans8cDjctd9owdJ/A2DE4AAAAA4Dq0uqGVZnWapXpR9SzrF9IvqN/v/TRi+QilZaXZnC5v+VkcOeFWHXgbBicAAAAAcJ1KhJXQxDsmqnft3nIY1h+zpu+Yru7zumvv+b02p8s7VrfqMDeBt2FwAgAAAAC5wN/hryfrPKmJbSYqKjTKsmfnuZ3qNq+bvt31rUwveKXFsDhxksXkBF6GwQkAAAAA5KLoEtGa1XGWbi17q2U9JTNFr/zxigYuHqiE9AR7w+Uyy1d1vGAgBPwTgxMAAAAAyGUFgwvq/Zbv64WGLyjQEWjZ88P+H9RlThdtPLXR5nS5x8/iEyWDE3gbBicAAAAAkAcMw1D36t01pf0UlS9Q3rLnSOIRPbTgIU3cNFFO02lvwFxg/aqOG4IAecjf3QEAAAAAwJtVK1xN0ztM15sr39R3u7+7rJ5pZmrs2rFacWyFRjYfqaIhRd2Q8tpYLYdNzcjShdSMPP+zwwL9Lf98ILcZ3rCQyBNER0ebq1evdncMAAAAAB5s/t75GrZ8mJIykizrhYMLa2SzkWpauqnNya7NJ4v3aOT87W75swsE+6tH0wrqd3sVy5MvwH8ZhrHGNM3oa/16XtUBAAAAAJu0q9hOMzvOVK2itSzrZ1PP6olfntDbq99WRlben9q4Xg43DiwupGbqvV936bftJ92WAb6BwQkAAAAA2KhsRFlNajtJPWr2cNnz5ZYv9eCCB3XowiEbk129sCD3b39Yse+suyPAyzE4AQAAAACbBfgF6Nn6z2rC7RNUOLiwZc/mM5vVZW4Xzds7z+Z0V65JpSLyd/OekfRMttEibzE4AQAAAAA3aVK6iWZ1mqUmpZpY1pMykjR4yWANXTpUyRnJNqfLWbkiYfr4/vqqXSZSEUH+ef4r0P/yj7BZTvZ2Im+5/1wVAAAAAPiwoiFF9dHtH2nSlkl6f+37yjQzL+v5z57/aMOpDRrTYoxuLHyjG1K6dnuN4rq9RnFb/qyvlx/QS99vvuSZkwtPkMc4cQIAAAAAbuYwHOpRs4e+uvMrlQkvY9mz/8J+dZ/XXd9s+0a+ejuq1VtBHDhBXmNwAgAAAAAeolaxWprRcYbuLH+nZT3DmaE3V76pvr/11bnUczancz+rW3ycTE6QxxicAAAAAIAHiQiM0KiYURrWZJhC/EMsexYeXqjYObFadXyVzency89qcOKjp29gHwYnAAAAAOBhDMPQv6v8W9M6TFPVQlUte04mn1SvH3tp3LpxynRevhfFG1nMTXhVB3mOwQkAAAAAeKiKkRU1pf0Udb+xu2XdlKkJGyeo14+9dDzpuM3p7Gf5qg4nTpDHGJwAAAAAgAcL8gvSC41e0Hst31NkUKRlz9qTa9V5dmf9euBXm9PZy89iOyyDE+Q1BicAAAAAkA/cdsNtiusYp/rF61vWL6RfUL+F/TRi+QilZqbanM4eVq/qZPGuDvIYgxMAAAAAyCdKhJXQxDYT1bt2bzkM649z03dMV/f53bXn/B6b0+U9q1d1OHCCvMbgBAAAAADyET+Hn56s86Qmtpmo4qHFLXt2ndulbnO7adbOWTK9aLLAqzpwBwYnAAAAAJAPRZeIVlzHOLUs29KynpqVqlf/fFUDFg9QQnqCzenyhsXchFd1kOcYnAAAAABAPlUwuKDea/meXmz0ogIdgZY9P+7/UV3mdNGGUxtsTpf7rF7V+WnrCTckgS9hcAIAAAAA+ZhhGLr3xns1pf0UVYisYNlzJPGIHlrwkD7b9JmcptPmhLnHanAiSWmZWTYngS9hcAIAAAAAXqBa4Wqa1n6a7q5yt2U9y8zSe2vf0+M/P67TKadtTpc7ShUMsXx+7Lx33iIEz8DgBAAAAAC8RGhAqF5r8prGxIxReEC4Zc/yY8vVeXZnLT2y1OZ01696yQjL52w5QV5icAIAAAAAXqZthbaa0XGGahWtZVk/m3pWT/7ypN5a9ZYysjJsTnftDMNQiQLBlz3nZh3kJQYnAAAAAOCFykaU1aQ7J6lnzZ4ueyZtnaQHFjyggxcO2pjs+oQG+V32jLkJ8hKDEwAAAADwUgGOAPWv318TWk9QkeAilj1bzmxRlzldNHfvXJvTXRurBbEmkxPkIQYnAAAAAODlmpRqorhOcWpSqollPTkzWS8seUFDlg5RckayzemujtW9Ok7mJshDDE4AAAAAwAcUDSmqj27/SM/Wf1b+hr9lz+w9s3XP3Hu07cw2m9NdOasTJ+w4QV5icAIAAAAAPsJhONSjZg99dedXKhNexrJn/4X9um/+fZq8dbJHvgJjMTdhxwnyFIMTAAAAAPAxtYrV0syOM3VnhTst6xnODI1aNUp9f+urc6nnbE6XPYMTJ7AZgxMAAAAA8EHhgeEa1XyUhjcdrhD/EMuehYcXKnZ2rFYdX2VzOtccnDiBzRicAAAAAICPMgxDd1W+S9M6TFO1QtUse06mnFSvH3tp3LpxynRm2pzwcpa36ojJCfIOgxMAAAAA8HEVIyvqm/bfqPuN3S3rpkxN2DhBPX/sqWOJx2xOdymrHSfcqoO8xOAEAAAAAKAgvyC90OgFvd/yfUUGRVr2rDu5Tp3ndNYvB36xOd3/Y8cJ7MbgBAAAAADwt5Y3tFRcxzjVL17fsp6QnqD+C/trxPIRSs1MtTkdO05gPwYnAAAAAIBLlAgroYltJqp3nd5yGNYfG6fvmK7u87trz/k9tmaz3HHC5AR5iMEJAAAAAOAyfg4/PVn7SX1+x+cqHlrcsmfXuV3qNreb4nbG2Ta8sDhwwo4T5CkGJwAAAAAAl+oXr69ZnWbptrK3WdZTs1L12p+v6flFz+tC+oU8z8OJE9iNwQkAAAAAIFuRQZEa23KshjQaokBHoGXPTwd+Utc5XbX+5Po8zcKtOrAbgxMAAAAAQI4Mw1C3G7tpSvspqhhZ0bLnSOIRPfzDw/ps02dyms48ynH5M06cIC/5uzsAAAAAACD/qFa4mqa2n6rRq0Zr1q5Zl9WzzCy9t/Y9LT+2XG80e0PFQovl6p9v9arOwp2ndPh8yt+/Dwv0V8MKhVUsIihX/2z4JoPJXO6Ijo42V69e7e4YAAAA+L/27jxMrrLM+/j3l5CEhEAghD1BFhEYIOygQISwyQ7vkAjIKiIgoL4oMy8joCDqgOiAMDKyCGFfEllFIkvYZV9kexWi7GELewghC/f88TxFVTq1dbq6qyr9+1xXX+ecOvc55+4+V3dX3edZzKzHTHxhIifdfxLTZk0ru3/owkP52eY/Y9TwUQ275n7nP8i9k6fWjBvUvy8XHbwJG680tGHXtvYk6dGI2Gh+j3dXHTMzMzMzM5svO6y8A+N33BtQhQAAIABJREFUHc/IYSPL7n93xrsccfsRnPbwacyaM6sh1yzXVaec6TPn8Jvbnm/INa13c+HEzMzMzMzM5tvwRYczbsdxHLz2wRVjLn72Yva7eT9e/vDlLl9vmcUWrjv2uTc/6vL1zFw4MTMzMzMzsy7p16cfR294NOdsdw5LLrxk2Zhn33mWsTeO5cZ/3Nila319oxH071vfR1kPTGGN4DFOGsRjnJiZmZmZmcHUT6Zy/L3Hc9+U+yrG7Lbqbhy36XEM6jdovq7x11fe509Pvc47H8/8/LVPZs3hpidfnytu2OABPHL8tvN1DVtwdHWME8+qY2ZmZmZmZg0zbOAwzt72bC559hLOePQMZsfseWJu+McNPPn2k/zyq79kzSXX7PQ11h2xOOuOWHyu1976aMY8hRO3ObFGcFcdMzMzMzMza6g+6sOBax3IJTtdwvDBw8vGvPjhi+z7p3259NlLaURPCDHvqLHuYGGN4MKJmZmZmZmZdYu1h63N+F3Hs9PKO5XdP+uzWZz68KkcNeko3p3xbpeuVW62HddNrBFcODEzMzMzM7NuM7j/YE4ZdQonb34yAxcaWDbm7lfvZswNY3jo9Yfm+zrlZin2mJ7WCC6cmJmZmZmZWbeSxB5f3IOrdrmKNYauUTbm7U/e5pBbDuGsx89i9mfzjosyP1w2sUZw4cTMzMzMzMx6xMpDVubSnS5l3zX3Lbs/CM598lwO/vPBTJk2pVPnVpm+Om5wYo3gwomZmZmZmZn1mAF9B3DsJsdy1tZnsfiAxcvGPP7W44y5cQy3vXRb3ect11XHrBFcODEzMzMzM7Met9WIrZiw6wQ2Wmajsvs/mvkRR995NCfffzIzZs+oeb6yg8O6yYk1gAsnZmZmZmZm1hTLLLIM529/PkeudyR9VP7j6dXPXc0+N+3D5PcmVz1X2emIG5Kl9XYunJiZmZmZmVnT9O3Tl8PXPZwLv3Yhyy6ybNmYye9PZp+b9mH8c+MrtyIpO61O4/K03suFEzMzMzMzM2u6DZbZgAm7TmCbFbcpu3/GnBn89P6fcsxdx/DhzA/n2V+2q06jk7ReyYUTMzMzMzMzawlDBgzh9K1O5/hNj6d/n/5lY2556RbG3jCWJ956Yq7XyzY48Rgn1gAunJiZmZmZmVnLkMRea+zF5TtfzipDVikbM+XjKRw08SDOe/I85nw25/PjOnLZxBrBhRMzMzMzMzNrOasPXZ0rdr6CPVfbs+z+OTGHMx8/k8NuO4y3pr9VocVJ9+ZovYMLJ2ZmZmZmZtaSBvUbxImbnchpW57G4H6Dy8Y8+PqDjLlhDH95/Z559oXbnFgDuHBiZmZmZmZmLW2HlXZg/K7jGbnUyLL73/v0PX5w1/cYsPQfgdmfv+4WJ9YILpyYmZmZmZlZyxu+6HDG7TCOQ9Y5BJXtmAP9l7yXQSv9D+o3tYezswWZCydmZmZmZmbWFvr16cf3N/g+52x3DsMGDisb03fgayyy8pkstNjj7qhjDeHCiZmZmZmZmbWVryz/FSbsOoHNV9i87H71ncnAFa5ioaWv4uNZH/dwdragceHEzMzMzMzM2s6SA5fk7G3O5piNjmGhPguVjek75FH2v3l/Zn82u+x+s3q4cGJmZmZmZmZtqY/6cOBaB3LpjpcyfPCIsjFjvzS2YmHFrB4unJiZmZmZmVlbW2vYWly+05XM+mC9uV6f/dFa7L363k3KyhYULpyYmZmZmZlZ21u0/2BmTNmLT6aMJT7rz2ezhvDp63silZ+Bx6xebq9kZmZmZmZmbS8VSMTsDzbk409WRH1mEJ8NanZatgBw4cTMzMzMzMzaXmm7kpi5lKcitobp1V11JA2XdKqkZyVNk/S+pMcl/VjSEs3Oz8zMzMzMzOrjHjnWXXptixNJOwBXAIt32LVe/jpU0u4R8WiPJ2dmZmZmZmZmLaFXtjiRNBKYQCqaTAd+AmwBbAWcDswBVgD+KGn5JqVpZmZmZmZmdao0CGyEO+1Y1/TWFidnAIuQCiQ7RsTdJfvukvQYcAmwLPAz4OCeT9HMzMzMzMy6KsLdeKxrel2LE0kbAqPz5rgORRMAIuJSYFLePEDS0j2Vn5mZmZmZmc2fcgUStzexrup1hRPgX0vWf18l7oK87Avs1n3pmJmZmZmZWSOUa1jirjrWVb2xcLJFXk4HHq4Sd0eZY8zMzMzMzKxFlRvnxGUT66reWDj5l7x8PiJmVwqKiCnAhx2OMTMzMzMzsxZVrsXJ1Gmf9ngetmDpVYUTSQOAYXnz1ToOKcSM6J6MzMzMzMzMrFHKjXHyycw5PZ+ILVB6VeEEWLRkfVod8YWYwd2Qi5mZmZmZmTVQua46laYpNqtXb5uOeGDJ+sw64gttugaW2ynpUODQQqykp7uQmzXXMGBqs5Ow+eJ71958/9qX71178/1rb75/7cv3rglWObVhp/L9a1+rd+Xg3lY4+aRkvX8d8QPKHPe5iDgXOBdA0iMRsVHX0rNm8f1rX7537c33r3353rU337/25vvXvnzv2pvvX/uS9EhXju9tXXU+Klmvp/tNIaaebj1mZmZmZmZmtoDpVYWTiPiUYtOq4XUcUoh5pXsyMjMzMzMzM7NW1qsKJ9mzebmapIpdlSQtDyzW4Zhqzu1qYtZUvn/ty/euvfn+tS/fu/bm+9fefP/al+9de/P9a19duneKiEYl0hYk/Rz4Ud7cLCLurxD3DeCyvPntiDi/J/IzMzMzMzMzs9bRG1ucXFOy/q0qcQfn5Rzghu5Lx8zMzMzMzMxaVa8rnETEo8AdefMgSaM6xkjaF9gmb14cEW/1VH5mZmZmZmZm1jp6XeEk+7/Ax0BfYKKkEyRtJumrkn4NXJTj3gSOr3QSScMlnSrpWUnTJL0v6XFJP5a0RPd/G9ZZkhaXtJ2k4yRdJ2mKpMhfdzY7P6tM0oaSjpc0UdIrkj6V9LGkf0i6XNIOzc7RypM0SNKY/PfydknPS3pP0ixJUyXdJ+lESSs0O1frHEm/LPkbGpK2anZONrcO96fa14vNztVqk7S0pP+QdL+kN/P/wimSHpR0mqSvNDtHA0l3duJ3r/B1ULPztnlJ6ifpm5L+lH/XCu8/J+f3n9s1O0crT9IASYdLuk3S25JmSnpd0i2SDpDUqVpIrxvjpCB/yLoCWLxCyGvA7rmFSsOPt+aQ9AKwUoXdd0XEVj2XjdVL0l3AV+sIvQnYNyI+6OaUrBMkbQQ8XEfox8CREXFRzUhrOknrAw8BpQOtj46IO5uTkZUjqd43ei9FxErdmYt1jaT9gDOBag/nro+IPXooJasgP4zbspOHfSUiHuiGdGw+SRpBem+5To3Qq4H9I2Jm92dl9ZC0GnA9sGaVsHtIn9ffq+ecFWeVWdBFxERJ6wDfA3YBViSNZ/ICcC1wZqUfoqSRwARgEWA6cCpwO+nnuXs+5wrAHyVtGBFTuvnbsfqpZP1N0oe5XZqUi9Wv0BLhTdLv3t3AS0AAG5Faka0G7AzcIGl0RHzWjEStotdJ3SQfJd2710l/c1cg3bdvkP6mXijp7Yj4U7MStdok9QXOI/3fewtYurkZWR3+Bzi7yn6/4W9hkg4n3T8Bb5Du533A+8CywCrArsCsZuVoc/km6X9aNcsAt+X151w0aS159tXSoskzwH8BfwMGAhsD/wYMBb4OvAMc0fOZWkeSliL9bq2YX5oAXAy8CixHes+5LzAKuE7S1hExp+Z5e2uLk66QNAkYTXrTv3VE3N1h/37AJXnzwog4GGsJko4hFccejoiX82uFXwK3OGlRkv4IXApMiIjZZfYvAtwCbJZf2j8iLu3BFK0KSX1r/UOStAlwL9APeDwiNuiR5Gy+SPoh8CvgWeA6irPVucVJiyn5H3dSRJzYzFxs/kjaAHiQVKicBOwRER9ViO3vp97tQdIPgF/nzeMi4hfNzMfmJmkMMD5vPghs0fE9qKSVgCeAIcBnwHIeG7P5JJ0FHJU3y/7vy58JT8ubdc2g21vHOJlvkjYkFU0AxnUsmgDkD2yT8uYBkvwkrkVExK8i4g+Foom1h4jYJSKuLFc0yfs/Bg4veWlsz2Rm9ainih8RD1H8u7m+pMHdm5XNL0krAz8ltfg6HD/hNutuvyMVTV4H9qxUNAFw0aStHJiXn1F84GqtY7OS9Z+Xew8aES8CF+bNPsCmPZCXVZFbxO6XN18CTi4XFxGFhz8Ax9ZzbhdOOu9fS9Z/XyXugrzsC+zWfemYGUBEPEVqJgnwxWbmYvOt9MPAgKZlYbX8DhhEalF5T7OTMVuQ5dZ4G+fN0yPi/WbmY40haV1gZN6cFBGvNDMfK6t/yfo/q8RNrnCMNcdqFMcgvbXGw7ub83JVSevVOrELJ523RV5Op/pgh3eUrG9RMcrMGqlfXtZs4WCtJfdHLUwDPzUi3qkWb80haX9ge2Aq8O9NTsesN9irZP2qwoqkJSStJmloE3KyrjuwZN0Doremv5esr1IlbtUKx1hzLFmy/maN2NL9NSehcOGk8/4lL5+v1G0AIA8I+2GHY8ysm+QZPhbLm/+/mblYfSQtLGkVSYcC91OcKeKMJqZlFUgaRhoYD+AYF7fazlhJz+RpNKcpTeV+maSdm52YVfXlvJwSES9LOkjS08C7wHPAO/lenpDH+7IWlwcd/UbenAZc08R0rLIrgMIsjT/KXUDmImlF0kDAAHdHxNM9lZxVNK1kfUiN2NLZcWt+Xu+1s+rMD0kDgGF589U6DnmVdBNGdFtSZlZwfMn6VRWjrKkk7QLcWCVkHMXBuqy1nEH6H3inp4xuSx3fFK6Sv74h6Q5gn4io9XTOel7hvr0o6QKKH9JKrUIad2ispB08m2PL24E0ow7A+IiY3sxkrLyImJpbWV5JKmA+Jul0UquSgaRZHf+N9OH7H8C3mpWrzWUyaey1ftRuRVK6f8WKUZlbnHTOoiXr0ypGzRvjQQ7NupGkvSiOP/QIaUpxay+TgW0i4pse3LD1SPoaaeq+mcw9ELO1vumkYvKhpDeJ65O6xZ0AvJZjRgO3Slq07BmsKST1ofjEdENS0WQqcAiwFOnD26bAn3PMOsD4fJy1rgNK1sc1KwmrLSJuBDYAziH9fl0I/AW4HTiVNKbJ8cDGETG50nms5+QJI27NmyMl7VMuLj/IKx1Oo+b/P/9h7ZyBJev1vLH/tMxxZtZAkkZSHKh5OmkqYs+z3rruIr35WIf0tGZPUv/ulYFLJB3UvNSsHEmDSAPCApwSEe7D3V5WiIi9I+K8iLgnIp6IiEkR8TNSa4bbc9w6wI+bl6aVMQhQXh9Aeu+5bUT8PiKmRsSMPCPZzsAtOW4z5p7IwFqIpMUpThrxAuABtluYpH7A/sAeFH8XSy1GeqiwR0/mZTX9hOKMfxflroxfkLSQpOGS/p001fQc0qxWUMfndRdOOueTkvV6Rk0uzArxSdUoM5svklYC/gQsQvrDd2BE/K2ZOVl1EfFRRDydvx6NiGsi4iDga6QBvS6U5A9vreVkYCXgeeA/m5uKdVa1WVgi4kPS9O3v5pcOl+RZIVrHjA7bF0fEXzsG5Vkjjil5qewTVmsJe1P8fHCxH/S0rjxm0G3AcaRuqv8FrA0sTGqdsCVwE7AmcIEkj8/WIiLiEVILvZmkLjs/BV4kFVNeodha6PsUC2IVp3kvcOGkc0p/oPV0vynE1NOtx8w6QdJypKZ4K+SXDouICU1MybogIm4HfpM3fyJpjWbmY4mkjUhvLACOiIiOH+SszUXEexTHhRpM6hJiLSBPQlD6OzexSuxTQGFsk40rxVnTFWbTCeDiZiZiNZ1IcQyMQyPihxHxTER8GhHTIuLuiNgFuDzHfF/Srk3J1OYREZeRWjZfRXHCFki/e3eRuqheT7Fw8l6tc3pw2E6IiE8lTSVVHYfXcUghxnOzmzVQnt3jVuCL+aWjI+L8JqZkjXE9aYrbPqSm5r9objpGGviuL2mmqmGS9i4Ts3bJ+taSls3rE6u1drCW8kzJej3vb6znvAKsVrJezcvA8qTxT6zFSFqN4ixJ90TEP5uZj1UmScDBefP5iLigSvixFGdJOpjqA+BbD8oF5b3zjEjLkbrjTMnjoCBpp5LwZ8qcYi4unHTes6Tq42qSFqo0JbGk5SlOjfpsTyVntqDL/YNvAdbKL50QEW4euWB4u2T9C03LwkoVmpSvSZqasZYTStbXB55oeEbWHdxdoHU9Q7FwMs90qB0U9s/pvnSsCw4sWffMZK1tGWBoXn+sWmBEvCLpLWBpwK1lW1DuzlhuRtxRJesP1jqPu+p03r15OYjqTSG3KnOMmXWBpMHAzaQPZACn5gEObcGwQsm6uzia9Zy1StY9lW1rubtkfdUasYX9r1WNsh6XWzDsnzenkwamtNZV+mC8Xx3xhZiyD9St9eRWKIVWtO9SnImnIhdOOu+akvVq83UXmnfNAW7ovnTMegdJA0nNHwvNXM+KiGObmJI13tiS9aealoV9LiL2iAhV+wJOKjlkdMk+tzZpA7kVX+HN43TSlO7WOq6h2CKo4mw5krai+IT87kpx1jSjgRXz+rURUXMgSmuqd4AP8vqXJVXspSFpHWCJvOnuV+3jUNLA9wDnR8SnVWIBF046LSIeBe7ImwdJGtUxRtK+wDZ58+KIeKun8jNbEOVZHv5AsSXX7ykOWGktTtL+ubVQtZivA4flzQ9wwdmsyyTtWuMN/2KkJ9+FD9x1vXm0nhMRLwFX5s09yg0+me/jb0peOqcncrNOOaBkfVyzkrD65NmObsqby5Omt51Hfqh3VslLHt+kRUiq2OVb0nakWZIgjQ11cl3n9CxYnSdpJPAX0hSo04FTgNtJY8bsTvpA1xd4E9ggItzstUVIWg9Yr8PLF+bl30n3stTEiHij2xOzqiRNAPbMm/cDh1Ocd72siHi6u/Oy+kh6AlgZuJb0JPR50gjni5D6A48BdszhAXwzItz/u01IOpHim8rREXFn87KxUpJeJE25eA3pb+cLpPctSwBbkIqVhS5yfwM2y7PsWAuRNAJ4mDTuwizgv0nF5Y+AkaTBKb+Uw/87Ir7bjDytvDyt7RukWateBb4QEVXfw1jzSfoSaXyTRfJLN5OKXpNJXXM2IH3mWz3vfwZYPyJm9WymVo6kD4EHSA8HngE+JbX62pM0mK9I70W3i4iH6jqnCyfzR9IOpIHyFq8Q8hqwe26hYi2iwxv8evhDQAuQ1Ok/VLkLgbWAXDhZt47Qd4HvRsTlNSOtZbhw0rpy4aSegZYnAfv7QU/rkrQ+cB3F7h7lnEeaNtzjLLQQSQdQHAz2PyPiR83Mx+onaTSpxdfSNUIfA/aICM+k2iIkTaNY9Crn76T/ew/Xe07PqjOfImJi7tP2PWAX0j+yOaSnOdcCZ/qpjZkZkFribQNsTRqEchnStO4zganAk8BE4HJPX2vWUAcCWwKbkgYOHQYMAT4mPeB5gPR7d3vTMrS6RMTj+X3nEaQnpquSPhS8SZqE4HcR4bFNWpNn02lTEXGHpDVIY1fuBKxNarE3B3iLVDAZD1ztgmXL+RawPbAJaSriRUnvOZ8CJgCXdLZrqlucmJmZmZmZmZlV4MFhzczMzMzMzMwqcOHEzMzMzMzMzKwCF07MzMzMzMzMzCpw4cTMzMzMzMzMrAIXTszMzMzMzMzMKnDhxMzMzMzMzMysAhdOzMzMzMzMzMwqcOHEzMzMzMzMzKwCF07MzMzMzMzMzCpw4cTMzMxqknSQpMhfKzU7n3pIOjbne2+zc2mkkvtw4nwc21fS3/Px+3dDemZmZgscF07MzMxsgSNpOeC4vHliE1NpKRExB/h53jxV0iLNzMfMzKwduHBiZmZmC6LjgMHAfRFxW7OTaTGXAf8AlgO+2+RczMzMWp4LJ2ZmZrZAkbQs8O28+etm5tKKcquTM/LmMZIGNjMfMzOzVufCiZmZmS1ojgD6A+8BNzU5l1Z1FTAbWBLYr8m5mJmZtTQXTszMzGyBIakPcHDeHB8RM5uZT6uKiLeBW/PmIc3MxczMrNW5cGJmZmYNIWmopBMlPSLpPUkzJL0iabykneo8x9qSLpb0aj7+ZUmXStog7x+XZ4R5scIptgBWyOt/qHKdrUpmp9lKyUGSbpf0hqQ5kq4rie8jaWtJv5J0n6SpkmZJel/SE/n1FWt8b3fm692Zt5fPxz0n6ZP8M5skaWw9P6sa1zq15Pu7QlK/MmET8nITSV/s6jXNzMwWVAs1OwEzMzNrf5JGkwoVS3TYNRwYA4yR9Adgv4iYUeEc+wEXAKUf8kcA+wJfl/Ttcsd1MDovA3iwzvQXBiYC21eJ+THwkzKvDwHWzV/fkbRfRFxb64KSNgOuA5bqkMdoYLSkX0fEMXXmX3revsC5FFvdnA18NyI+KxN+f8n6jsBZnb2emZlZb+AWJ2ZmZtYlkkYCN5OKJnNIH9a3BTYGvgk8nUP3BC6ucI7NgHGkoskM4BRgS2BT4EjgTVJBYGSNdEbl5XMR8UGd38KppKLJTaQiz0bA15i7xcpCwOv5e9sf2BzYENgD+CUwDRgEXC5pzRrXWw64HhBp9p9R+ZrfydcA+KGkberMHwBJA0itSApFk5Mj4sgKRROAvwGFn9GWnbmWmZlZb+IWJ2ZmZtZV5wIDSK08xkTEdSX7HpF0JXALqUAwVtKuEXFjh3P8FugLzAK2j4h7SvY9JGkCqYXE+pWSkCTgy3nzsU7kPxI4JSL+o0rM+cBJETGrw+uPAddLOgt4gNRN6Eek4kolXwJeATaPiFdKXn9U0iTgSdLP8yjg9nq+AUmLkYoxW5Huw/cjomoLkogISY/nYzas5zpmZma9kVucmJmZ2XyTtDGpVQjAJR2KJgDkrjkHkmZxAfheh3NsCqyXN8/pUDQpnOMt4Oga6SwBLJLX36zrG0gmAydUC4iIF8sUTUr3vwqcljd3y0Wcar7boWhSOM9zpC48AF+tcQ4AJC0F3EEqgMwidYeqt9vNW3k5QpIfqJmZmZXhwomZmZl1xXYl6+dXCoqIF4Db8uao3K2kYNuS9UuqXOsm4J0q+0vHC3m3SlxHV0bE7NphRZIWk7SypLXygLZrA9Pz7sWAlasc/gHQscVNqUfycqikxWvk8QXgXmCDfP3dI+Lyur6JpPBz6svcPz8zMzPLXDgxMzOzrlg7Lz8DHq4R+0BeDiB1V+l4jjnAE5UOjoiq+4ElS9bfr5FLqb/WEyTpC5LOyjP6fAD8kzR+y1P569yS8GFVTvVclXFHYO6iz6JV4tYE7iP9LN8DtouIm6vE17rWIhWjzMzMejE3yTQzM7OuGJqXH1aaLafEG2WOg+JMPB9GxMwa53i7yr4oWV+4xnlKvVcrQNKOpIFXB9V5zoFV9k2vsg9SEaqgb5W4r5esHxURf6mZ1bxK86zYFcnMzKw3c4sTMzMza4SoHUKtcT/qOUc1pa1MhlaMmtecajslLQlcTiqaTANOBL4CLA0MiAhFhIDSWXBqfa+N8OecD8Bv8uxGnVX6c+pMKx0zM7New4UTMzMz64pCV48hkmq18limzHFQbPExRFL/GueoNg7HyyXrS1SM6ryxQGGskf8TESdFxAMR8XaHFjKdKdY0wgPATsDHpK5Bt0tap5PnKPycPurE9M1mZma9igsnZmZm1hVP52UfYKMasYXZdz4Fnit5/Zm87Etxdp15SKq6PyI+Bl7Mm1+qFDcf1srLdyPitipxtb7/hsszEHUsnqxd/ai5rJ6XT1eNMjMz68VcODEzM7OuuLVk/eBKQZJWojgDzz0R8WnJ7ttL1vevcq2dmXsA2HIezMsN65gSuF6FMeEWllT2vZOkQVTPvdtExN0UiydLAZMkrVX9KJA0hGKB6cFqsWZmZr2ZCydmZmY23yLiYeChvHmgpJ06xuSphy+kWIA4s8M57geezJuHSRpV5hxLAafXkVKhkLMYsEYd8fV4Pi8HMfeArMDnLWHOB5Zv0PU6LRdPdiYNPFtv8WQTimOx3NKN6ZmZmbU1F07MzMysq75N6n7TB7he0m8kjZa0oaQDSNMUb5Vjx0fEjWXOcSRpNpl+wC2SfiFplKSNJX0HeBQYQXE64koDyd4AzM7r21SI6ayrSd8fwIWSTpG0jaSNJB1Iaq2xD2lq4KaJiLsoFk+WJhVP1qxyyLZ5+QFwRzenZ2Zm1rZcODEzM7MuiYgnSV1F3iO1KvkeMAl4BLgIKAxY+gfggArnuJfU1WcWaSrh/wDuJrVmORtYDjgCeCofUnbq44h4mzTbDMC+Xfi2Ss/5KvAdUmFnYeD/AbeRCkLjgA2Bq4CfNOJ6XRERdwK7UCye3FGueJK7Me2dN6+uYyppMzOzXsuFEzMzM+uyiJgErAb8lNQ65ANgJvAaqWCyS0SMqfYBPSIuIg2wehkwpeT4q4EtIuJ8Uhcc8vkr+W1eflnSKvP9Tc2d24XAKOA64G1Sged1YCKwV0TsTY1pjXtKRNxBsXiyDKnlScduS6OAFfP6bzEzM7OKFFGppauZmZlZa5E0GVgVuCwi9qsQI+CvpJYuv4iI43owxbYg6SJS659bI2L7ZudjZmbWytzixMzMzNqCpI1JRROAByrFRXoqVOg2c5Skod2dWzvJrXC+kTd/3MxczMzM2oELJ2ZmZtYSJH2xyr4lgfPy5kzSmCIVRcS1pDFSFgN+0KgcFxDHkcaiuSoiKhagzMzMLFmodoiZmZlZj7hZ0mvANaSuNh8ASwCbkWbdWS7H/SIPAlvLYaQBUKuNh9Kr5KmT/wmcRJpC2czMzGrwGCdmZmbWEkrGL6nmHODIiGiJgVjNzMxswefCiZmZmbUESaOA3YAtSa1LliLNVPMGcC9wXp622MzMzKzHuHBiZmZmZmZmZlaBB4c1MzMzMzMzM6vAhRMzMzMzMzMzswpcODEzMzMzMzMzq8CFEzMzMzMzMzOzClw4MTMzMzMzMzOrwIUTMzMzMzMzM7MmFEmVAAAAB0lEQVQK/hdwchdHwQ/7lgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams.update({'font.size': 28})\n",
    "\n",
    "ax = zipf.plot(figsize=(18, 14), linewidth=5, )\n",
    "ax.set_ylabel('log(frequency)')\n",
    "ax.set_xlabel('log(rank)')\n",
    "ax.set_ylim(0, 14)\n",
    "ax.set_xlim(0, 9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The word distribution is pretty much linear in loglog space. It closely follows Zipf's law for word distribution in a language."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png) Image source: [[Wikipedia]](https://commons.wikimedia.org/wiki/File:Zipf_30wiki_en_labels.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multiple beat elements per beat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7ceb3d90d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(train.word.str.len() // 5 + 1).value_counts().sort_index().plot.bar(logy=True, figsize=(16, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of beat elements per beat also follows logarithmic distribution with dips at odd number of beat elements. This is because of the urge to have symetric moves if slicing more than one beat element per beat."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train, val, test overlap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "146"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(val.word.unique()) - set(train.word.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(test.word.unique()) - set(train.word.unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate sentences\n",
    "Generate sentences from the `train` set, one song per line. Making it easy to train and evaluate word vectors with `gensim`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_sentence(x):\n",
    "    x.name = 'word'\n",
    "    x = x.reset_index('time')\n",
    "    x = x.drop_duplicates('time')\n",
    "    x = x.word.str.cat(sep=' ')\n",
    "    return x\n",
    "\n",
    "def word_df2text(df: pd.DataFrame):\n",
    "    return df.droplevel(2).word.groupby(['name', 'difficulty']).apply(create_sentence).str.cat(sep='\\n')\n",
    "\n",
    "train_text = word_df2text(train)\n",
    "val_text = word_df2text(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10758879, 22011654)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_text), len(train.word.str.cat(sep=' ')) # should be ~half the size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(storage_folder / 'train_text.cor', 'w') as wf:\n",
    "    wf.write(train_text)\n",
    "with open(storage_folder / 'val_text.cor', 'w') as wf:\n",
    "    wf.write(val_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create test analogies\n",
    "Similarly as in Test set developed by Mikolov et al.[1], where the word vectors are evaluated on automatically generated analogies:\n",
    "```\n",
    ": capital-common-countries\n",
    "Athens Greece Baghdad Iraq\n",
    "Athens Greece Bangkok Thailand\n",
    "Athens Greece Beijing China\n",
    "Athens Greece Berlin Germany\n",
    "\n",
    ": currency\n",
    "Algeria dinar Angola kwanza\n",
    "Algeria dinar Argentina peso\n",
    "Algeria dinar Armenia dram\n",
    "Algeria dinar Brazil real\n",
    "```\n",
    "- For example `Athens Greece Baghdad Iraq` from `capital-common-countries` analogy means:\n",
    "    - Athens to Greece are like Baghdad to Iraq\n",
    "\n",
    "We would like to create analogies in action vector space using translation, rotation and hand swaping as follows:\n",
    "```\n",
    ": single-hand-translation\n",
    "L030 L000 L130 L100\n",
    "L030 L000 L230 L200\n",
    "L130 L100 L030 L000\n",
    "L130 L100 L230 L200\n",
    "\n",
    ": single-hand-rotation\n",
    "L008 L000 L018 L010\n",
    "L008 L000 L028 L020\n",
    "L008 L000 L038 L030\n",
    "L008 L000 L108 L100\n",
    "\n",
    ": single-hand-hand-swap\n",
    "L000 R000 L121 R121\n",
    "L000 R000 L122 R122\n",
    "L000 R000 L123 R123\n",
    "L000 R000 L124 R124\n",
    "\n",
    ": single-hand-doublebeat\n",
    "L032 L032_L002 L132 L132_L102\n",
    "L032 L032_L002 L232 L232_L202\n",
    "L132 L132_L102 L032 L032_L002\n",
    "L132 L132_L102 L232 L232_L202\n",
    "...\n",
    "```\n",
    "\n",
    "#### Reasoning\n",
    "For example `L030 L000 L130 L100` from `single-hand-translation` analogy means:\n",
    "- The relation between coordinates `(3, 0)` and `(0, 0)` (while leaving the same hand and rotation) should be the same as the relation between coordinates `(3, 1)` and `(0, 1)`\n",
    "- The coordinate difference is independent of the absolute coordinate\n",
    "\n",
    "For example `L000 R000 L121 R121` from `single-hand-hand-rotation` analogy means:\n",
    "- The relation between left and right hand at coordinate `(0, 0)` and rotation `0` should be the same as the relation between left and right hand at the coordinate `(2, 1)` and rotation `1`\n",
    "- The hand relation is independent of position or rotation\n",
    "\n",
    "For example `L008 L000 L018 L010` from `single-hand-rotation` analogy means:\n",
    "- The relation between rotation `8` and rotation `0` in left hand at coordinate `(0, 0)` should be the same as the relation between rotation `8` and rotation `0` in left hand at coordinate `(1, 0)`\n",
    "- The rotation change is independent of position\n",
    "\n",
    "For example `L032 L032_L002 L132 L132_L102` from `single-hand-doublebeat` analogy means:\n",
    "- The relation between a single beat element action of left hand at coordinate `(3, 0)` and rotation `2` and double beat element action with additional beat element at coordinate `(0, 0)` and rotation `2` should be the same as the relation between a single beat element action of left hand at coordinate `(3, 1)` and rotation `2` and double beat element action with additional beat element at coordinate `(0, 1)` and rotation `2`\n",
    "- The coordinate difference between single and double actions is independent of the absolute coordinate\n",
    "\n",
    "#### Remarks\n",
    "- The analogy dataset contains a both-hand versions of `single-hand-translation` and `single-hand-hand-rotation` called `double-hand-translation` and `double-hand-hand-rotation`.\n",
    "- Where reasonable, the analogy sections are split into \"only x change\", \"only y change\", \"both x and y change\" for better performance understanding\n",
    "\n",
    "[1] Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In Proceedings of International Conference on Learning Representations (ICLR)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Word creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Tuple, Any\n",
    "\n",
    "def subword_tuple2string(tuple: Tuple[Any]):\n",
    "    return ''.join([str(x) for x in tuple])\n",
    "\n",
    "\n",
    "def word_tuple2string(tuple: Tuple[Any]):\n",
    "    for subword_id in range(0, len(tuple), 4):\n",
    "        if not (tuple[subword_id + 0] in 'LR'\n",
    "                and 0 <= tuple[subword_id + 1] < 3\n",
    "                and 0 <= tuple[subword_id + 2] < 4\n",
    "                and 0 <= tuple[subword_id + 3] < 9):\n",
    "            return None\n",
    "    return '_'.join(\n",
    "        [subword_tuple2string(tuple[subword_id:subword_id + 4]) for subword_id in range(0, len(tuple), 4)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analogies helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import product\n",
    "\n",
    "def add_valid_translations_one_hand(lines, translation):\n",
    "    def create_word_tuples(hand, position, rotation, translation):\n",
    "        tuple_from = hand, *position, rotation\n",
    "        tuple_to = [a + b for a, b in zip(tuple_from, translation)]\n",
    "        return tuple_from, tuple_to\n",
    "\n",
    "    add_valid_translation(lines, translation, create_word_tuples)\n",
    "    return lines\n",
    "\n",
    "\n",
    "def add_valid_translations_one_hand_doublebeat(lines, translation, rotation):\n",
    "    def create_word_tuples_multiplication(hand, position, rotation, translation):\n",
    "        tuple_from = hand, *position, rotation\n",
    "        tuple_to = *tuple_from, *[a + b for a, b in zip(tuple_from, translation)]\n",
    "        return tuple_from, tuple_to\n",
    "\n",
    "    def create_word_tuples_demultiplication(hand, position, rotation, translation):\n",
    "        return create_word_tuples_multiplication(hand, position, rotation, translation)[::-1]\n",
    "\n",
    "    for hand in 'LR':\n",
    "        add_valid_translation_only_positions(lines, hand, rotation, translation, create_word_tuples_multiplication)\n",
    "        add_valid_translation_only_positions(lines, hand, rotation, translation, create_word_tuples_demultiplication)\n",
    "    return lines\n",
    "\n",
    "\n",
    "def add_valid_translations_two_hand(lines, translation, hand_translation):\n",
    "    def create_word_tuples(hand, position, rotation, translation):\n",
    "        left_from = hand, *position, rotation\n",
    "        right_from = 'R', *position, rotation\n",
    "        tuple_from = *left_from, *[a + b for a, b in zip(right_from, hand_translation)]\n",
    "        tuple_to = [a + b for a, b in zip(tuple_from, translation * 2)]\n",
    "        return tuple_from, tuple_to\n",
    "\n",
    "    for hand, rotation in product('L', range(-8, 9)):\n",
    "        add_valid_translation_only_positions(lines, hand, rotation, translation, create_word_tuples)\n",
    "    return lines\n",
    "\n",
    "\n",
    "def add_valid_translations_one_hand_demultiply(lines, translation, rotation):\n",
    "    def create_word_tuples(hand, position, rotation, translation):\n",
    "        tuple_to = hand, *position, rotation\n",
    "        tuple_from = *tuple_to, *[a + b for a, b in zip(tuple_to, translation)]\n",
    "        return tuple_from, tuple_to\n",
    "\n",
    "    for hand in 'LR':\n",
    "        add_valid_translation_only_positions(lines, hand, rotation, translation, create_word_tuples)\n",
    "    return lines\n",
    "\n",
    "\n",
    "def add_valid_translation(lines, translation, create_word_tuples):\n",
    "    for hand, rotation in product('LR', range(9)):\n",
    "        add_valid_translation_only_positions(lines, hand, rotation, translation, create_word_tuples)\n",
    "\n",
    "\n",
    "def add_valid_translation_only_positions(lines, hand, rotation, translation, create_word_tuples):\n",
    "    for example_position in product(range(3), range(4)):\n",
    "        example_from, example_to = create_word_tuples(hand, example_position, rotation, translation)\n",
    "\n",
    "        for question_position in product(range(3), range(4)):\n",
    "            question_from, question_to = create_word_tuples(hand, question_position, rotation, translation)\n",
    "            add_valid_analogy(lines, example_from, example_to, question_from, question_to)\n",
    "\n",
    "\n",
    "def add_valid_analogy(lines, example_from, example_to, question_from, question_to):\n",
    "    \"\"\"Add analogy to `lines` if all words are valid\"\"\"\n",
    "    if example_from != question_from:  # skip identical analogies\n",
    "        analogy = [word_tuple2string(x) for x in [example_from, example_to, question_from, question_to]]\n",
    "        if None not in analogy:\n",
    "            lines.append(' '.join(analogy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analogy creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from importlib import reload\n",
    "import logging\n",
    "reload(logging)\n",
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n",
    "\n",
    "\n",
    "def create_analogies(path: Path):\n",
    "    \"\"\"\n",
    "    Shape of a beat element is [hand][y][x][rotation]. A word is of shape [beat-element][_beat-element]*.\n",
    "    \"\"\"\n",
    "    lines = []\n",
    "\n",
    "    def new_section(name: str):\n",
    "        logging.info(f'Generating analogies for section {name}')\n",
    "        lines.append(f': {name}')\n",
    "\n",
    "    new_section('single-hand-translation-x')\n",
    "    for dx in range(-3, 4):\n",
    "        if dx == 0:  # remove identity translation\n",
    "            continue\n",
    "        translation = '', 0, dx, 0\n",
    "        add_valid_translations_one_hand(lines, translation)\n",
    "\n",
    "    new_section('single-hand-translation-y')\n",
    "    for dy in range(-2, 3):\n",
    "        if dy == 0:  # remove identity translation\n",
    "            continue\n",
    "        translation = '', dy, 0, 0\n",
    "        add_valid_translations_one_hand(lines, translation)\n",
    "\n",
    "    new_section('single-hand-translation-other')\n",
    "    for dydx in product(range(-2, 3), range(-3, 4)):\n",
    "        if 0 in dydx:  # previously used translations\n",
    "            continue\n",
    "        translation = '', *dydx, 0\n",
    "\n",
    "        add_valid_translations_one_hand(lines, translation)\n",
    "\n",
    "    new_section('single-hand-rotation')\n",
    "    for rotation_change in range(-8, 9):\n",
    "        if rotation_change == 0:  # remove identity rotation change\n",
    "            continue\n",
    "        translation = '', 0, 0, rotation_change\n",
    "        add_valid_translations_one_hand(lines, translation)\n",
    "\n",
    "    new_section('single-hand-hand-swap')\n",
    "    for hand_id in range(2):\n",
    "        hand_from = 'LR'[hand_id]\n",
    "        hand_to = 'RL'[hand_id]\n",
    "\n",
    "        for example_position in product(range(3), range(4), range(9)):\n",
    "            example_from = hand_from, *example_position\n",
    "            example_to = hand_to, *example_position\n",
    "            for qustion_position in product(range(3), range(4), range(9)):\n",
    "                question_from = hand_from, *qustion_position\n",
    "                question_to = hand_to, *qustion_position\n",
    "                add_valid_analogy(lines, example_from, example_to, question_from, question_to)\n",
    "\n",
    "    new_section('single-hand-doublebeat-y')\n",
    "    for dy, rotation in product(range(-2, 3), [0, 1]):\n",
    "        if dy == 0:  # remove identity multiplication\n",
    "            continue\n",
    "        translation = '', dy, 0, 0\n",
    "        add_valid_translations_one_hand_doublebeat(lines, translation, rotation)\n",
    "\n",
    "    new_section('single-hand-doublebeat-x')\n",
    "    for dx, rotation in product(range(-3, 4), [2, 3]):\n",
    "        if dx == 0:  # remove identity multiplication\n",
    "            continue\n",
    "        translation = '', 0, dx, 0\n",
    "        add_valid_translations_one_hand_doublebeat(lines, translation, rotation)\n",
    "\n",
    "    new_section('single-hand-doublebeat-other')\n",
    "    for dx, dy, rotation in product(range(-2, 3), range(-3, 4), range(9)):\n",
    "        if 0 in dydx:  # previously used multiplication\n",
    "            continue\n",
    "        translation = '', dy, dx, 0\n",
    "        add_valid_translations_one_hand_doublebeat(lines, translation, rotation)\n",
    "\n",
    "    new_section('double-hand-translation-x')\n",
    "    for dx, right_dx, right_drotation in product(range(-3, 4), range(-3, 4), range(-8, 9)):\n",
    "        if dx == 0 or right_dx == 0:  # remove identity translation\n",
    "            continue\n",
    "        translation = '', 0, dx, 0\n",
    "        right_translation = '', 0, right_dx, right_drotation\n",
    "        add_valid_translations_two_hand(lines, translation, right_translation)\n",
    "\n",
    "    new_section('double-hand-translation-y')\n",
    "    for dy, right_dy, right_drotation in product(range(-2, 3), range(-2, 3), range(-8, 9)):\n",
    "        if dy == 0 or right_dy == 0:  # remove identity translation\n",
    "            continue\n",
    "        translation = '', dy, 0, 0\n",
    "        right_translation = '', right_dy, 0, right_drotation\n",
    "        add_valid_translations_two_hand(lines, translation, right_translation)\n",
    "\n",
    "    # Generates mostly unused beat combinations, yet doubles the testing time\n",
    "    # new_section('double-hand-translation-other')\n",
    "    # for dy, dx, right_dy, right_dx, right_drotation in product(range(-2, 3), range(-3, 4),\n",
    "    #                                                            range(-2, 3), range(-3, 4),\n",
    "    #                                                            range(-8, 9)):\n",
    "    #     if 0 in (dy, dx, right_dy, right_dx):  # remove previously used\n",
    "    #         continue\n",
    "    #     translation = '', dy, dx, 0\n",
    "    #     right_translation = '', right_dy, right_dx, right_drotation\n",
    "    #     add_valid_translations_two_hand(lines, translation, right_translation)\n",
    "\n",
    "    new_section('double-hand-rotation-x')\n",
    "    for rotation, right_dx, right_drotation in product(range(-8, 9), range(-3, 4), range(-8, 9)):\n",
    "        if right_dx == 0:  # remove identity translation\n",
    "            continue\n",
    "        translation = '', 0, 0, rotation\n",
    "        right_translation = '', 0, right_dx, right_drotation\n",
    "        add_valid_translations_two_hand(lines, translation, right_translation)\n",
    "\n",
    "    new_section('double-hand-rotation-y')\n",
    "    for rotation, right_dy, right_drotation in product(range(-8, 9), range(-2, 3), range(-8, 9)):\n",
    "        if right_dy == 0:  # remove identity translation\n",
    "            continue\n",
    "        translation = '', 0, 0, rotation\n",
    "        right_translation = '', right_dy, 0, right_drotation\n",
    "        add_valid_translations_two_hand(lines, translation, right_translation)\n",
    "\n",
    "    # Generates mostly unused beat combinations, yet doubles the testing time\n",
    "    # new_section('double-hand-rotation-other')\n",
    "    # for rotation, right_dy, right_dx, right_drotation in product(range(-8, 9),\n",
    "    #                                                              range(-2, 3), range(-3, 4),\n",
    "    #                                                              range(-8, 9)):\n",
    "    #     if 0 in (right_dy, right_dx):  # remove previously used\n",
    "    #         continue\n",
    "    #     translation = '', 0, 0, rotation\n",
    "    #     right_translation = '', right_dy, right_dx, right_drotation\n",
    "    #     add_valid_translations_two_hand(lines, translation, right_translation)\n",
    "\n",
    "    with open(path, 'w') as wf:\n",
    "        wf.write('\\n'.join(lines) + '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform and save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-06-14 14:26:37,719 : INFO : Generating analogies for section single-hand-translation-x\n",
      "2020-06-14 14:26:38,015 : INFO : Generating analogies for section single-hand-translation-y\n",
      "2020-06-14 14:26:38,216 : INFO : Generating analogies for section single-hand-translation-other\n",
      "2020-06-14 14:26:39,236 : INFO : Generating analogies for section single-hand-rotation\n",
      "2020-06-14 14:26:40,083 : INFO : Generating analogies for section single-hand-hand-swap\n",
      "2020-06-14 14:26:40,597 : INFO : Generating analogies for section single-hand-doublebeat-y\n",
      "2020-06-14 14:26:40,700 : INFO : Generating analogies for section single-hand-doublebeat-x\n",
      "2020-06-14 14:26:40,851 : INFO : Generating analogies for section single-hand-doublebeat-other\n",
      "2020-06-14 14:26:44,311 : INFO : Generating analogies for section double-hand-translation-x\n",
      "2020-06-14 14:27:06,852 : INFO : Generating analogies for section double-hand-translation-y\n",
      "2020-06-14 14:27:16,229 : INFO : Generating analogies for section double-hand-rotation-x\n",
      "2020-06-14 14:28:23,634 : INFO : Generating analogies for section double-hand-rotation-y\n"
     ]
    }
   ],
   "source": [
    "create_analogies(storage_folder / 'beat_analogies.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train word embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim\n",
    "\n",
    "def create_train_model(corpus_file, model_type: str='fasttext', **kwargs):\n",
    "    kwargs = {key: int(val) for key, val in kwargs.items()}\n",
    "    kwargs['size'] = 2 ** kwargs['size']\n",
    "    workers = 12\n",
    "    # change `workers` to suit our machine thread count\n",
    "    if model_type.lower() == 'fasttext':\n",
    "        model = gensim.models.FastText(corpus_file=str(corpus_file), **kwargs, workers=workers)\n",
    "    else:\n",
    "        model = gensim.models.Word2Vec(corpus_file=str(corpus_file), **kwargs, workers=workers)\n",
    "    \n",
    "    return model\n",
    "\n",
    "def create_eval_function(corpus_file: Path, model_type: str):\n",
    "    def eval_model(**kwargs):\n",
    "        model = create_train_model(corpus_file, model_type, **kwargs)\n",
    "\n",
    "        res = model.wv.evaluate_word_analogies(storage_folder / 'beat_analogies.txt')\n",
    "\n",
    "        return res[0]\n",
    "    \n",
    "    return eval_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bayes_opt import BayesianOptimization\n",
    "root = logging.getLogger()\n",
    "root.setLevel(logging.ERROR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate random projection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00039284155390659095 +-1.3113229875053778e-08\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "accuracies = []\n",
    "for size, _ in product(range(4, 9), range(2)):\n",
    "    acc = create_eval_function(storage_folder / 'train_text.cor', 'word2vec')(iter=0, size=size)\n",
    "    accuracies.append(acc)\n",
    "\n",
    "accuracy = {}\n",
    "acc_desc = stats.describe(accuracies)\n",
    "accuracy['random'] = acc_desc.minmax[1]  # get maximum\n",
    "print(f'{acc_desc.mean} +-{2 * acc_desc.variance}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Word2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|   iter    |  target   | cbow_mean |    hs     |   iter    |    sg     |   size    |  window   |\n",
      "-------------------------------------------------------------------------------------------------\n",
      "| \u001b[0m 1       \u001b[0m | \u001b[0m 0.03727 \u001b[0m | \u001b[0m 0.8506  \u001b[0m | \u001b[0m 1.397   \u001b[0m | \u001b[0m 1.102   \u001b[0m | \u001b[0m 0.6442  \u001b[0m | \u001b[0m 4.587   \u001b[0m | \u001b[0m 1.554   \u001b[0m |\n",
      "| \u001b[95m 2       \u001b[0m | \u001b[95m 0.05513 \u001b[0m | \u001b[95m 0.4353  \u001b[0m | \u001b[95m 0.722   \u001b[0m | \u001b[95m 8.599   \u001b[0m | \u001b[95m 1.07    \u001b[0m | \u001b[95m 5.677   \u001b[0m | \u001b[95m 5.111   \u001b[0m |\n",
      "| \u001b[95m 3       \u001b[0m | \u001b[95m 0.07131 \u001b[0m | \u001b[95m 1.159   \u001b[0m | \u001b[95m 0.1     \u001b[0m | \u001b[95m 20.0    \u001b[0m | \u001b[95m 0.1     \u001b[0m | \u001b[95m 4.0     \u001b[0m | \u001b[95m 1.0     \u001b[0m |\n",
      "| \u001b[0m 4       \u001b[0m | \u001b[0m 0.05211 \u001b[0m | \u001b[0m 0.3587  \u001b[0m | \u001b[0m 1.751   \u001b[0m | \u001b[0m 19.83   \u001b[0m | \u001b[0m 0.3967  \u001b[0m | \u001b[0m 7.908   \u001b[0m | \u001b[0m 6.857   \u001b[0m |\n",
      "| \u001b[0m 5       \u001b[0m | \u001b[0m 0.03508 \u001b[0m | \u001b[0m 0.3534  \u001b[0m | \u001b[0m 0.8144  \u001b[0m | \u001b[0m 19.54   \u001b[0m | \u001b[0m 1.509   \u001b[0m | \u001b[0m 4.019   \u001b[0m | \u001b[0m 6.921   \u001b[0m |\n",
      "=================================================================================================\n"
     ]
    }
   ],
   "source": [
    "bool_ = (0.1, 1.9)\n",
    "pbounds = {\n",
    "    'size': (4, 8),         # log int\n",
    "    'window': (1, 7),       # int\n",
    "    'iter': (1.1, 20),      # int  : Number of iterations (epochs) over the corpus.\n",
    "    'sg': bool_,            # bool : skip-gram if `sg=1`, otherwise CBOW.\n",
    "    'hs': bool_,            # bool : If 1, hierarchical softmax will be used for model training.\n",
    "                            #        If set to 0, and `negative` is non-zero, negative sampling will be used.\n",
    "    'cbow_mean': bool_,     # bool : If 0, use the sum of the context word vectors. If 1, use the mean, only applies when cbow is used.\n",
    "}\n",
    "\n",
    "word2vec_optimizer = BayesianOptimization(\n",
    "    f=create_eval_function(storage_folder / 'train_text.cor', 'word2vec'),\n",
    "    pbounds=pbounds,\n",
    "    random_state=1,\n",
    ")\n",
    "\n",
    "word2vec_optimizer.maximize(\n",
    "    init_points=2,\n",
    "    n_iter=3,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train FastText"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|   iter    |  target   | cbow_mean |    hs     |   iter    |   max_n   |   min_n   |    sg     |   size    |  window   | word_n... |\n",
      "-------------------------------------------------------------------------------------------------------------------------------------\n",
      "| \u001b[0m 1       \u001b[0m | \u001b[0m 0.1482  \u001b[0m | \u001b[0m 0.8506  \u001b[0m | \u001b[0m 1.397   \u001b[0m | \u001b[0m 1.102   \u001b[0m | \u001b[0m 4.814   \u001b[0m | \u001b[0m 2.44    \u001b[0m | \u001b[0m 0.2662  \u001b[0m | \u001b[0m 4.745   \u001b[0m | \u001b[0m 3.073   \u001b[0m | \u001b[0m 0.8142  \u001b[0m |\n",
      "| \u001b[95m 2       \u001b[0m | \u001b[95m 0.1915  \u001b[0m | \u001b[95m 1.07    \u001b[0m | \u001b[95m 0.8546  \u001b[0m | \u001b[95m 14.05   \u001b[0m | \u001b[95m 4.227   \u001b[0m | \u001b[95m 4.634   \u001b[0m | \u001b[95m 0.1493  \u001b[0m | \u001b[95m 6.682   \u001b[0m | \u001b[95m 3.504   \u001b[0m | \u001b[95m 1.106   \u001b[0m |\n",
      "| \u001b[0m 3       \u001b[0m | \u001b[0m 0.04696 \u001b[0m | \u001b[0m 1.9     \u001b[0m | \u001b[0m 0.1     \u001b[0m | \u001b[0m 20.0    \u001b[0m | \u001b[0m 9.0     \u001b[0m | \u001b[0m 2.0     \u001b[0m | \u001b[0m 1.9     \u001b[0m | \u001b[0m 4.0     \u001b[0m | \u001b[0m 7.0     \u001b[0m | \u001b[0m 0.1     \u001b[0m |\n",
      "| \u001b[0m 4       \u001b[0m | \u001b[0m 0.02028 \u001b[0m | \u001b[0m 0.1     \u001b[0m | \u001b[0m 0.1     \u001b[0m | \u001b[0m 1.1     \u001b[0m | \u001b[0m 3.0     \u001b[0m | \u001b[0m 5.0     \u001b[0m | \u001b[0m 1.9     \u001b[0m | \u001b[0m 8.0     \u001b[0m | \u001b[0m 7.0     \u001b[0m | \u001b[0m 1.9     \u001b[0m |\n",
      "| \u001b[95m 5       \u001b[0m | \u001b[95m 0.2052  \u001b[0m | \u001b[95m 0.1     \u001b[0m | \u001b[95m 1.9     \u001b[0m | \u001b[95m 20.0    \u001b[0m | \u001b[95m 3.0     \u001b[0m | \u001b[95m 2.0     \u001b[0m | \u001b[95m 1.9     \u001b[0m | \u001b[95m 8.0     \u001b[0m | \u001b[95m 1.0     \u001b[0m | \u001b[95m 1.9     \u001b[0m |\n",
      "=====================================================================================================================================\n"
     ]
    }
   ],
   "source": [
    "bool_ = (0.1, 1.9)\n",
    "pbounds = {\n",
    "    'size': (4, 8),         # log int\n",
    "    'window': (1, 7),       # int\n",
    "    'iter': (1.1, 20),      # int  : Number of iterations (epochs) over the corpus.\n",
    "    'sg': bool_,            # bool : skip-gram if `sg=1`, otherwise CBOW.\n",
    "    'hs': bool_,            # bool : If 1, hierarchical softmax will be used for model training.\n",
    "                            #        If set to 0, and `negative` is non-zero, negative sampling will be used.\n",
    "    # 'sample': (0, 1e-5),   # float: The threshold for configuring which higher-frequency words are randomly downsampled,\n",
    "    # 'negative': (0, 20),   # int  : If > 0, negative sampling will be used, the int for negative specifies how many \"noise words\"\n",
    "    'cbow_mean': bool_,     # bool : If 0, use the sum of the context word vectors. If 1, use the mean, only applies when cbow is used.\n",
    "    'min_n': (2, 5),        # int  : Minimum length of char n-grams to be used for training word representations.\n",
    "    'max_n': (3, 9),        # int  : Max length of char ngrams to be used for training word representations. Set `max_n` to be lesser than `min_n` to avoid char ngrams being used.\n",
    "    'word_ngrams': bool_,   # bool : If 1, uses enriches word vectors with subword(n-grams) information.\n",
    "                            #        If 0, this is equivalent to :class:`~gensim.models.word2vec.Word2Vec`.\n",
    "}\n",
    "\n",
    "fasttext_optimizer = BayesianOptimization(\n",
    "    f=create_eval_function(storage_folder / 'train_text.cor', 'fasttext'),\n",
    "    pbounds=pbounds,\n",
    "    random_state=1,\n",
    ")\n",
    "\n",
    "fasttext_optimizer.maximize(\n",
    "    init_points=2,\n",
    "    n_iter=3,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select the best hyper params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'cbow_mean': 1.159242413818441,\n",
       "  'hs': 0.1,\n",
       "  'iter': 20.0,\n",
       "  'sg': 0.1,\n",
       "  'size': 4.000000001257426,\n",
       "  'window': 1.0000000001826281},\n",
       " {'cbow_mean': 0.1,\n",
       "  'hs': 1.9,\n",
       "  'iter': 20.0,\n",
       "  'max_n': 3.0,\n",
       "  'min_n': 2.0,\n",
       "  'sg': 1.9,\n",
       "  'size': 8.0,\n",
       "  'window': 1.0,\n",
       "  'word_ngrams': 1.9})"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdf = pd.DataFrame(word2vec_optimizer.res)\n",
    "accuracy['search word2vec'], wordvec_params = rdf.loc[rdf['target'].idxmax()].to_list()\n",
    "\n",
    "rdf = pd.DataFrame(fasttext_optimizer.res)\n",
    "accuracy['search fasttext'], fasttext_params = rdf.loc[rdf['target'].idxmax()].to_list()\n",
    "\n",
    "wordvec_params, fasttext_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Or set really good ones if there was not time for search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "word2vec_params = {\n",
    "    'cbow_mean': True,\n",
    "    'hs': False,\n",
    "    'iter': 3,\n",
    "    'sg': False,\n",
    "    'size': 5,\n",
    "    'window': 1,\n",
    "}\n",
    "\n",
    "fasttext_params = {\n",
    "    'cbow_mean': True,\n",
    "    'hs': False, \n",
    "    'iter': 3,\n",
    "    'max_n': 3,\n",
    "    'min_n': 2,\n",
    "    'sg': False,\n",
    "    'size': 9,\n",
    "    'window': 1,\n",
    "    'word_ngrams': True,\n",
    "}   # ngrams\n",
    "\n",
    "fasttext_params = {\n",
    "    'cbow_mean': True,\n",
    "    'hs': False,\n",
    "    'iter': 3,\n",
    "    'max_n': 3,\n",
    "    'min_n': 2,\n",
    "    'sg': False,\n",
    "    'size': 8,\n",
    "    'window': 1,\n",
    "    'word_ngrams': False\n",
    "}   # without ngrams\n",
    "\n",
    "\n",
    "model = create_train_model(storage_folder / 'train_text.cor', 'word2vec', **word2vec_params)\n",
    "accuracy['good word2vec'] = model.wv.evaluate_word_analogies(storage_folder / 'beat_analogies.txt')[0]\n",
    "\n",
    "model = create_train_model(storage_folder / 'train_text.cor', 'fasttext', **fasttext_params)\n",
    "accuracy['good fasttext'] = model.wv.evaluate_word_analogies(storage_folder / 'beat_analogies.txt')[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Performance on train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>best top1 accuracy [%]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>random</th>\n",
       "      <td>0.052743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>search word2vec</th>\n",
       "      <td>7.131165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>good word2vec</th>\n",
       "      <td>8.520661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>search fasttext</th>\n",
       "      <td>20.518696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>good fasttext</th>\n",
       "      <td>50.467409</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 best top1 accuracy [%]\n",
       "random                         0.052743\n",
       "search word2vec                7.131165\n",
       "good word2vec                  8.520661\n",
       "search fasttext               20.518696\n",
       "good fasttext                 50.467409"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data=accuracy.values(), index=accuracy.keys(), columns=['best top1 accuracy [%]'])\\\n",
    "    .sort_values('best top1 accuracy [%]') * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remarks\n",
    "Analogy evaluation is done on top1 accuracy. Word2vec embeddings achieve significantly better performance than random (~2000 embedded words => 1/2000 = 0.05 %, empirically shown above as 0.052743), yet are over 5× outperformed by FastText. This can be explained by the sub-word insight the FastText model gets as the semantic information is directly represented by the syntax of a word. This is especially prominent in the multi beat element actions which are composed of the individual beat element actions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try performance on `val` set\n",
    "`val` set was not used for the creation of beat vectors in the hyper parameter search, therefore we can use it for the evaluation.\n",
    "\n",
    "`val` set may achieve higher accuracy on the analogy task. Any words with less than 5 occurences are cut. This leaves less hard analogy pairs in the analogy evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-06-15 21:50:03,404 : INFO : resetting layer weights\n",
      "2020-06-15 21:50:15,951 : INFO : collecting all words and their counts\n",
      "2020-06-15 21:50:15,952 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
      "2020-06-15 21:50:16,028 : INFO : collected 1329 word types from a corpus of 205224 raw words and 403 sentences\n",
      "2020-06-15 21:50:16,028 : INFO : Loading a fresh vocabulary\n",
      "2020-06-15 21:50:16,033 : INFO : effective_min_count=5 retains 697 unique words (52% of original 1329, drops 632)\n",
      "2020-06-15 21:50:16,035 : INFO : effective_min_count=5 leaves 203913 word corpus (99% of original 205224, drops 1311)\n",
      "2020-06-15 21:50:16,041 : INFO : deleting the raw counts dictionary of 1329 items\n",
      "2020-06-15 21:50:16,043 : INFO : sample=0.001 downsamples 62 most-common words\n",
      "2020-06-15 21:50:16,044 : INFO : downsampling leaves estimated 90382 word corpus (44.3% of prior 203913)\n",
      "2020-06-15 21:50:16,048 : INFO : estimated required memory for 697 words, 2000000 buckets and 256 dimensions: 1775956 bytes\n",
      "2020-06-15 21:50:16,056 : INFO : resetting layer weights\n",
      "2020-06-15 21:50:22,125 : INFO : training model with 12 workers on 697 vocabulary and 256 features, using sg=0 hs=0 sample=0.001 negative=5 window=1\n",
      "2020-06-15 21:50:22,290 : INFO : worker thread finished; awaiting finish of 11 more threads\n",
      "2020-06-15 21:50:22,301 : INFO : worker thread finished; awaiting finish of 10 more threads\n",
      "2020-06-15 21:50:22,320 : INFO : worker thread finished; awaiting finish of 9 more threads\n",
      "2020-06-15 21:50:22,386 : INFO : worker thread finished; awaiting finish of 8 more threads\n",
      "2020-06-15 21:50:22,405 : INFO : worker thread finished; awaiting finish of 7 more threads\n",
      "2020-06-15 21:50:22,430 : INFO : worker thread finished; awaiting finish of 6 more threads\n",
      "2020-06-15 21:50:22,447 : INFO : worker thread finished; awaiting finish of 5 more threads\n",
      "2020-06-15 21:50:22,453 : INFO : worker thread finished; awaiting finish of 4 more threads\n",
      "2020-06-15 21:50:22,483 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2020-06-15 21:50:22,494 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2020-06-15 21:50:22,497 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2020-06-15 21:50:22,503 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2020-06-15 21:50:22,505 : INFO : EPOCH - 1 : training on 244294 raw words (107954 effective words) took 0.3s, 408031 effective words/s\n",
      "2020-06-15 21:50:22,685 : INFO : worker thread finished; awaiting finish of 11 more threads\n",
      "2020-06-15 21:50:22,798 : INFO : worker thread finished; awaiting finish of 10 more threads\n",
      "2020-06-15 21:50:22,802 : INFO : worker thread finished; awaiting finish of 9 more threads\n",
      "2020-06-15 21:50:22,825 : INFO : worker thread finished; awaiting finish of 8 more threads\n",
      "2020-06-15 21:50:22,829 : INFO : worker thread finished; awaiting finish of 7 more threads\n",
      "2020-06-15 21:50:22,834 : INFO : worker thread finished; awaiting finish of 6 more threads\n",
      "2020-06-15 21:50:22,847 : INFO : worker thread finished; awaiting finish of 5 more threads\n",
      "2020-06-15 21:50:22,867 : INFO : worker thread finished; awaiting finish of 4 more threads\n",
      "2020-06-15 21:50:22,873 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2020-06-15 21:50:22,882 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2020-06-15 21:50:22,888 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2020-06-15 21:50:22,904 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2020-06-15 21:50:22,906 : INFO : EPOCH - 2 : training on 244294 raw words (108181 effective words) took 0.3s, 415241 effective words/s\n",
      "2020-06-15 21:50:23,111 : INFO : worker thread finished; awaiting finish of 11 more threads\n",
      "2020-06-15 21:50:23,158 : INFO : worker thread finished; awaiting finish of 10 more threads\n",
      "2020-06-15 21:50:23,201 : INFO : worker thread finished; awaiting finish of 9 more threads\n",
      "2020-06-15 21:50:23,223 : INFO : worker thread finished; awaiting finish of 8 more threads\n",
      "2020-06-15 21:50:23,246 : INFO : worker thread finished; awaiting finish of 7 more threads\n",
      "2020-06-15 21:50:23,254 : INFO : worker thread finished; awaiting finish of 6 more threads\n",
      "2020-06-15 21:50:23,309 : INFO : worker thread finished; awaiting finish of 5 more threads\n",
      "2020-06-15 21:50:23,320 : INFO : worker thread finished; awaiting finish of 4 more threads\n",
      "2020-06-15 21:50:23,327 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2020-06-15 21:50:23,332 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2020-06-15 21:50:23,338 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2020-06-15 21:50:23,341 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2020-06-15 21:50:23,342 : INFO : EPOCH - 3 : training on 244294 raw words (108118 effective words) took 0.3s, 397022 effective words/s\n",
      "2020-06-15 21:50:23,350 : INFO : training on a 732882 raw words (324253 effective words) took 1.2s, 266373 effective words/s\n",
      "2020-06-15 21:50:23,354 : WARNING : under 10 jobs per worker: consider setting a smaller `batch_words' for smoother alpha decay\n",
      "2020-06-15 21:50:23,445 : INFO : Evaluating word analogies for top 300000 words in the model on ../../data/new_datasets/beat_analogies.txt\n",
      "2020-06-15 21:50:23,450 : INFO : precomputing L2-norms of word weight vectors\n",
      "2020-06-15 21:50:23,454 : INFO : precomputing L2-norms of ngram weight vectors\n",
      "2020-06-15 21:50:25,721 : INFO : single-hand-translation-x: 37.6% (896/2380)\n",
      "2020-06-15 21:50:26,150 : INFO : single-hand-translation-y: 36.8% (543/1476)\n",
      "2020-06-15 21:50:26,768 : INFO : single-hand-translation-other: 9.8% (226/2300)\n",
      "2020-06-15 21:50:29,375 : INFO : single-hand-rotation: 75.8% (7807/10296)\n",
      "2020-06-15 21:50:32,342 : INFO : single-hand-hand-swap: 50.2% (5870/11704)\n",
      "2020-06-15 21:50:32,470 : INFO : single-hand-doublebeat-y: 86.1% (62/72)\n",
      "2020-06-15 21:50:32,622 : INFO : single-hand-doublebeat-x: 67.3% (70/104)\n",
      "2020-06-15 21:50:32,962 : INFO : single-hand-doublebeat-other: 71.1% (236/332)\n",
      "2020-06-15 21:50:33,186 : INFO : double-hand-translation-x: 34.4% (73/212)\n",
      "2020-06-15 21:50:34,478 : INFO : double-hand-rotation-x: 49.1% (691/1408)\n",
      "2020-06-15 21:50:34,921 : INFO : double-hand-rotation-y: 0.0% (0/50)\n",
      "2020-06-15 21:50:34,922 : INFO : Quadruplets with out-of-vocabulary words: 88.7%\n",
      "2020-06-15 21:50:34,931 : INFO : NB: analogies containing OOV words were skipped from evaluation! To change this behavior, use \"dummy4unknown=True\"\n",
      "2020-06-15 21:50:34,932 : INFO : Total accuracy: 54.3% (16474/30334)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New model achieved   51.41 % accuracy on the train data.\n",
      "New model achieved   54.31 % accuracy on the validation data.\n"
     ]
    }
   ],
   "source": [
    "model = create_train_model(storage_folder / 'train_text.cor', 'fasttext', **fasttext_params)\n",
    "train_accuracy = model.wv.evaluate_word_analogies(storage_folder / 'beat_analogies.txt')[0]\n",
    "root.setLevel(logging.INFO)\n",
    "\n",
    "val_accuracy = create_eval_function(storage_folder / 'val_text.cor', 'fasttext')(**fasttext_params)\n",
    "print(f'New model achieved {train_accuracy * 100:7.4} % accuracy on the train data.')\n",
    "print(f'New model achieved {val_accuracy * 100:7.4} % accuracy on the validation data.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try performance by hand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0261429138481617"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.spatial import distance\n",
    "distance.cosine(model.wv['L000'], model.wv['R125_R217'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.27005821466445923"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance.cosine(model.wv['L000'], model.wv['L001'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-06-17 14:57:35,482 : INFO : storing 1932x256 projection weights into ../../data/new_datasets/word2vec.model\n"
     ]
    }
   ],
   "source": [
    "model.wv.save_word2vec_format(str(storage_folder / 'word2vec.model'), binary=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-06-15 21:50:35,638 : INFO : saving FastTextKeyedVectors object under ../../data/new_datasets/fasttext.model, separately None\n",
      "2020-06-15 21:50:35,659 : INFO : storing np array 'vectors_ngrams' to ../../data/new_datasets/fasttext.model.vectors_ngrams.npy\n",
      "2020-06-15 21:50:40,850 : INFO : not storing attribute vectors_ngrams_norm\n",
      "2020-06-15 21:50:40,850 : INFO : not storing attribute vectors_norm\n",
      "2020-06-15 21:50:40,851 : INFO : not storing attribute vectors_vocab_norm\n",
      "2020-06-15 21:50:40,852 : INFO : not storing attribute buckets_word\n",
      "2020-06-15 21:50:40,928 : INFO : saved ../../data/new_datasets/fasttext.model\n"
     ]
    }
   ],
   "source": [
    "model.wv.save(str(storage_folder / 'fasttext.model'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the model\n",
    "Test if it can still create vectors for out of vocabulary words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<gensim.models.keyedvectors.Word2VecKeyedVectors at 0x7f8d167bc690>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gensim.models.KeyedVectors.load_word2vec_format(str(storage_folder / 'word2vec.model'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_model = gensim.models.KeyedVectors.load(str(storage_folder / 'fasttext.model'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(256,)"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_model['R125_R217_R000_LLLL'].shape   # fabricated word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(82000, 256)"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = np.array(['L000', 'L11100'] * 41000)\n",
    "loaded_model[v].shape  # query multiple words at the same time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate results table\n",
    "Models are trained on the validation dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working on word2vec random\n",
      "Working on word2vec CBOW\n",
      "Working on fasttext skip-gram\n",
      "Working on fasttext CBOW\n"
     ]
    }
   ],
   "source": [
    "hyper_params = {\n",
    "    'word2vec random': {\n",
    "        'iter': 0,\n",
    "        'size': 8,\n",
    "    },\n",
    "    'word2vec CBOW': {\n",
    "        'cbow_mean': True,\n",
    "        'hs': False,\n",
    "        'iter': 3,\n",
    "        'sg': False,\n",
    "        'size': 5,\n",
    "        'window': 1,\n",
    "    },\n",
    "    'fasttext skip-gram': {\n",
    "        'cbow_mean': True,\n",
    "        'hs': False, \n",
    "        'iter': 3,\n",
    "        'max_n': 3,\n",
    "        'min_n': 2,\n",
    "        'sg': True,\n",
    "        'size': 8,\n",
    "        'window': 1,\n",
    "        'word_ngrams': False,   # does not convert model into Word2Vec as written in the Docs,\n",
    "                                # but consistently increases the performance. To force FastText\n",
    "                                # to become Word2Vec, one must set `max_n` = 0\n",
    "    },\n",
    "    'fasttext CBOW': {\n",
    "        'cbow_mean': True,\n",
    "        'hs': False, \n",
    "        'iter': 3,\n",
    "        'max_n': 3,\n",
    "        'min_n': 2,\n",
    "        'sg': False,\n",
    "        'size': 8,\n",
    "        'window': 1,\n",
    "        'word_ngrams': False,\n",
    "    },\n",
    "}\n",
    "\n",
    "res_dict = {}\n",
    "for name, params in hyper_params.items():\n",
    "    print(f'Working on {name}')\n",
    "    model = create_train_model(storage_folder / 'val_text.cor', name.split()[0], **params)\n",
    "    rs = model.wv.evaluate_word_analogies(storage_folder / 'beat_analogies.txt')\n",
    "    res_dict[name.capitalize()] = {x['section']: 100 * len(x['correct']) / (len(x['correct']) + len(x['incorrect']) + 1) for x in rs[1]}\n",
    "\n",
    "res_dict['Size'] = {x['section']: sum((len(x['correct']), len(x['incorrect']))) for x in rs[1]}\n",
    "res_df = pd.DataFrame(res_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Word2vec random</th>\n",
       "      <th>Word2vec cbow</th>\n",
       "      <th>Fasttext skip-gram</th>\n",
       "      <th>Fasttext cbow</th>\n",
       "      <th>Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>single-hand-translation-x</th>\n",
       "      <td>0.19</td>\n",
       "      <td>8.72</td>\n",
       "      <td>27.27</td>\n",
       "      <td>37.56</td>\n",
       "      <td>2592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>single-hand-translation-y</th>\n",
       "      <td>0.24</td>\n",
       "      <td>8.37</td>\n",
       "      <td>36.58</td>\n",
       "      <td>42.80</td>\n",
       "      <td>1672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>single-hand-translation-other</th>\n",
       "      <td>0.12</td>\n",
       "      <td>4.37</td>\n",
       "      <td>4.25</td>\n",
       "      <td>8.18</td>\n",
       "      <td>2468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>single-hand-rotation</th>\n",
       "      <td>0.10</td>\n",
       "      <td>5.23</td>\n",
       "      <td>66.98</td>\n",
       "      <td>77.86</td>\n",
       "      <td>11228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>single-hand-hand-swap</th>\n",
       "      <td>0.15</td>\n",
       "      <td>1.53</td>\n",
       "      <td>49.76</td>\n",
       "      <td>44.52</td>\n",
       "      <td>13284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>single-hand-doublebeat-y</th>\n",
       "      <td>0.00</td>\n",
       "      <td>7.41</td>\n",
       "      <td>66.67</td>\n",
       "      <td>75.31</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>single-hand-doublebeat-x</th>\n",
       "      <td>0.00</td>\n",
       "      <td>2.15</td>\n",
       "      <td>45.16</td>\n",
       "      <td>62.37</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>single-hand-doublebeat-other</th>\n",
       "      <td>0.00</td>\n",
       "      <td>5.49</td>\n",
       "      <td>56.96</td>\n",
       "      <td>69.62</td>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>double-hand-translation-x</th>\n",
       "      <td>0.00</td>\n",
       "      <td>16.15</td>\n",
       "      <td>26.09</td>\n",
       "      <td>32.92</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>double-hand-translation-y</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>double-hand-rotation-x</th>\n",
       "      <td>0.11</td>\n",
       "      <td>8.61</td>\n",
       "      <td>48.24</td>\n",
       "      <td>52.66</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>double-hand-rotation-y</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total accuracy</th>\n",
       "      <td>0.13</td>\n",
       "      <td>4.24</td>\n",
       "      <td>49.67</td>\n",
       "      <td>53.03</td>\n",
       "      <td>32722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Word2vec random  Word2vec cbow  \\\n",
       "single-hand-translation-x                 0.19           8.72   \n",
       "single-hand-translation-y                 0.24           8.37   \n",
       "single-hand-translation-other             0.12           4.37   \n",
       "single-hand-rotation                      0.10           5.23   \n",
       "single-hand-hand-swap                     0.15           1.53   \n",
       "single-hand-doublebeat-y                  0.00           7.41   \n",
       "single-hand-doublebeat-x                  0.00           2.15   \n",
       "single-hand-doublebeat-other              0.00           5.49   \n",
       "double-hand-translation-x                 0.00          16.15   \n",
       "double-hand-translation-y                 0.00           0.00   \n",
       "double-hand-rotation-x                    0.11           8.61   \n",
       "double-hand-rotation-y                    0.00           0.00   \n",
       "Total accuracy                            0.13           4.24   \n",
       "\n",
       "                               Fasttext skip-gram  Fasttext cbow   Size  \n",
       "single-hand-translation-x                   27.27          37.56   2592  \n",
       "single-hand-translation-y                   36.58          42.80   1672  \n",
       "single-hand-translation-other                4.25           8.18   2468  \n",
       "single-hand-rotation                        66.98          77.86  11228  \n",
       "single-hand-hand-swap                       49.76          44.52  13284  \n",
       "single-hand-doublebeat-y                    66.67          75.31     80  \n",
       "single-hand-doublebeat-x                    45.16          62.37     92  \n",
       "single-hand-doublebeat-other                56.96          69.62    236  \n",
       "double-hand-translation-x                   26.09          32.92    160  \n",
       "double-hand-translation-y                    0.00           0.00      0  \n",
       "double-hand-rotation-x                      48.24          52.66    882  \n",
       "double-hand-rotation-y                       0.00           0.00     28  \n",
       "Total accuracy                              49.67          53.03  32722  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('precision', 2)\n",
    "res_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convenience functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'cbow_mean': 1.9, 'hs': 0.1, 'iter': 2.869, 'max_n': 3.0, 'min_n': 2.0, 'sg': 0.1, 'size': 8.0, 'window': 1.0, 'word_ngrams': 1.9}\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "def create_params_dict(keys_str, values_str):\n",
    "    keys = re.split('[ |]+', keys_str)\n",
    "    values = re.split('[ |]+', values_str)\n",
    "    d = {key: float(val) for key, val in zip(keys, values) if key != ''}\n",
    "    d['word_ngrams'] = d['word_n...']\n",
    "    del d['word_n...']\n",
    "    return d\n",
    "\n",
    "params = create_params_dict('cbow_mean |    hs     |   iter    |   max_n   |   min_n   |    sg     |   size    |  window   | word_n... |',\n",
    "                  '1.9      |  0.1      |  2.869    |  3.0      |  2.0      |  0.1      |  8.0      |  1.0      |  1.9')\n",
    "\n",
    "print(params)"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}