example_word_and_phoneme_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example: word and phoneme classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Classify phonemes using data augmentation and mel spectrograms.\n",
    "\n",
    "The previous attempt get around 62% accouracy on phoneme classification with:\n",
    "* No data augmentation\n",
    "* Curriculum learning like strategy (train on most frequent classes first and less frequent after)\n",
    "* Validation set mate of random split of TRAIN+TEST folder \n",
    "\n",
    "Actually the last point was very important in terms of model generalization because i this notebook we're going t use the \"TEST\" (hold out) as validation set.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Notebook settings\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## fastai imports\n",
    "from fastai.basics import *\n",
    "from fastai.data_block import ItemList\n",
    "from fastai.vision import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import FastAI Audio modules here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from exp.nb_AudioCommon import *\n",
    "from exp.nb_DataBlock import *\n",
    "from exp.nb_DataAugmentation import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other library imports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If this cell fails, you probably haven't installed the correct system/Python dependencies. You might need to re-run the \"System setup\" cell again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from pdb import set_trace\n",
    "from IPython.display import Audio\n",
    "from pydub import AudioSegment, silence\n",
    "import torchaudio\n",
    "from torchaudio import transforms\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Options\n",
    "pd.set_option('display.max_colwidth', -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to do **phoneme classification** using the TIMIT dataset.\n",
    "\n",
    "The copyright on TIMIT isn't clear, and the data is hard to download, so we assume that the user has access to this dataset.\n",
    "\n",
    "**IMPORTANT:** the dataset should be located in the standard fast.ai location:\n",
    "> $HOME/.fastai/data/timit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working directory: /home/ste/.fastai/data/timit\n"
     ]
    }
   ],
   "source": [
    "path = getFastAiWorkingDirectory('timit')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What's inside:\n",
    "*All these informations are taken from README file inside the dataset*\n",
    "\n",
    "    The dialect regions are:\n",
    "         dr1:  New England\n",
    "         dr2:  Northern\n",
    "         dr3:  North Midland\n",
    "         dr4:  South Midland\n",
    "         dr5:  Southern\n",
    "         dr6:  New York City\n",
    "         dr7:  Western\n",
    "         dr8:  Army Brat (moved around)     \n",
    "     \n",
    "      Dialect\n",
    "      Region(dr)    #Male    #Female    Total\n",
    "      ----------  --------- ---------  ----------\n",
    "         1         31 (63%)  18 (27%)   49 (8%)  \n",
    "         2         71 (70%)  31 (30%)  102 (16%) \n",
    "         3         79 (67%)  23 (23%)  102 (16%) \n",
    "         4         69 (69%)  31 (31%)  100 (16%) \n",
    "         5         62 (63%)  36 (37%)   98 (16%) \n",
    "         6         30 (65%)  16 (35%)   46 (7%) \n",
    "         7         74 (74%)  26 (26%)  100 (16%) \n",
    "         8         22 (67%)  11 (33%)   33 (5%)\n",
    "       ------     --------- ---------  ---------- \n",
    "         8        438 (70%) 192 (30%)  630 (100%)\n",
    "         \n",
    "      \n",
    "      Sentence Type   #Sentences   #Speakers   Total   #Sentences/Speaker\n",
    "      -------------   ----------   ---------   -----   ------------------\n",
    "      Dialect (SA)          2         630       1260           2\n",
    "      Compact (SX)        450           7       3150           5\n",
    "      Diverse (SI)       1890           1       1890           3\n",
    "      -------------   ----------   ---------   -----    ----------------\n",
    "      Total              2342                   6300          10\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### File Format description:\n",
    "\n",
    "    /<CORPUS>/<USAGE>/<DIALECT>/<SEX><SPEAKER_ID>/<SENTENCE_ID>.<FILE_TYPE>\n",
    "\n",
    "     where,\n",
    "\n",
    "     CORPUS :== timit\n",
    "     USAGE :== train | test\n",
    "     DIALECT :== dr1 | dr2 | dr3 | dr4 | dr5 | dr6 | dr7 | dr8 \n",
    "                 (see Table 1 for dialect code description)\n",
    "     SEX :== m | f\n",
    "     SPEAKER_ID :== <INITIALS><DIGIT>\n",
    "          \n",
    "          where, \n",
    "          INITIALS :== speaker initials, 3 letters\n",
    "          DIGIT :== number 0-9 to differentiate speakers with identical\n",
    "                    initials\n",
    "                              \n",
    "     SENTENCE_ID :== <TEXT_TYPE><SENTENCE_NUMBER>\n",
    "          \n",
    "          where,\n",
    "              \n",
    "          TEXT_TYPE :== sa | si | sx\n",
    "                        (see Section 2 for sentence text type description)\n",
    "          SENTENCE_NUMBER :== 1 ... 2342\n",
    "                    \n",
    "     FILE_TYPE :== wav | txt | wrd | phn\n",
    "                   (see Table 5 for file type description)\n",
    "\n",
    "Examples:\n",
    "     /timit/train/dr1/fcjf0/sa1.wav\n",
    "                         \n",
    "     (TIMIT corpus, training set, dialect region 1, female speaker, \n",
    "      speaker-ID \"cjf0\", sentence text \"sa1\", speech waveform file)\n",
    "      \n",
    "\n",
    "      /timit/test/df5/mbpm0/sx407.phn\n",
    "      \n",
    "      (TIMIT corpus, test set, dialect region 5, male speaker, speaker-ID\n",
    "       \"bpm0\", sentence text \"sx407\", phonetic transcription file)\n",
    "      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Take a look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/ste/.fastai/data/timit/TRAIN'),\n",
       " PosixPath('/home/ste/.fastai/data/timit/TEST'),\n",
       " PosixPath('/home/ste/.fastai/data/timit/DOC'),\n",
       " PosixPath('/home/ste/.fastai/data/timit/README.DOC'),\n",
       " PosixPath('/home/ste/.fastai/data/timit/models')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path.ls()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!cat /home/ste/.fastai/data/timit/README.DOC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset contains \n",
    "* TRAIN: both X and Y data\n",
    "* TEST: both X and Y data\n",
    "\n",
    "**IMPORTANT**: the dataset provide it's own TEST set with labels.\n",
    "According to the *file description* we've to understand if the test set is actually an **HOLD OUT** set (unseen speaker/dialect) or not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample format\n",
    "\n",
    "All the timit samples are provided with the following structure:\n",
    "\n",
    "* WAV: The actual sound \n",
    "* WRD: Word annotation\n",
    "* PHN: Phoneme annotation \n",
    "* TXT: The complete transcription of the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "baseSample = str(path) + '/TRAIN/DR5/MBGT0/SA2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "REPRESENTATION<br />\n",
       "                <audio controls=\"controls\" >\n",
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EOMRTxD7DLAJfwYNBkMHMAdGBYoBf/6H+tf2uPZ59jz3EPir9kf0OPJR8oDzYPXa9wn5Gfpk+jL5bvnA+UT7kv4SAbkDvALQAP7+Pf1w/n4AzwHgAGb9hPoG+U/5KPum+wb6kffCA04VehSTDSwAiPbvAZsRSRnrFoMOsQePBQUDbgDCA9MJOAprCFcCWPr8+fD5W/nD+cP5N/w3/af6r/Pn7qfwuPQb+5/+qfuU+lL5E/de+S79GwKxBygKPwpWBmwFPQWiBvgMcA70DtgOCAytCkwHSgXCBcUDbQP1A2QBffyO+Iz29fOP9RP41Pf39+j0CPPF84n0a/iT+zH9nP0Y/OL79fu2/Gb++/6KAJ0B1gGtAQT/0vyT+537ff3x/bn7kfkd94z2Dff69UD27gM0F20Xfw+EACr2RQGoEXUaXxqhEqMMDAlgAw3/lAE5CRUMSAsnBZb7jvh89mz1A/eF+NL7v/10+pny1+yS7QLybvkq/rz8NPyX+tf38/h9+ysBDQhxCooMZw23B/MH4AcTCMIN9xBFExkSXgs3BfkA5gB6A4YFqQRs/0X6xfQL89Xzv/Oo9sf3x/aP9S7zffPY9NT37/vb/qcADADI/jT+FP0B/3oB3wLPA04C6QA7AL/9Pf3i/Nz7d/zG+nj4PfZE9Dr0/vPs8uD3/wtyGXoUaAmh+XX31AcSFlsd1xraEgUNEwfQANv+XwYCDdUNwApFAcD5X/fS9Hb0mPYZ+XP8FP0N99rupevn7NnyTvv1/X39Ef2a+a73Fvmp/I8EtgpKDCEMagtuCfAIfQwuDGQMsRCMD1oOkwz0BnoDXgJuAP3/lADL/WX5gvVR8o3yR/Pl9PH2HfYt9JL0vvXh9lv60v2J//8ASAGKABwBfQFLAbMCAgP5AbMBygAfABr/hv2m+2f65vhi9+v1LfQ089LyNPMu8ezysQYhGJkWYw4U/mj4vgW9E04dzR0FF64RwAuJAyr/zwR3C8UNDgw0A8P6Zfd29J3yLvPI9Iv4//vk9/PvF+zi6lvvnvdn+2P9FP8L/fn61vkl/JYD0wpKDu4Ngw3yDOULrw7xDYoMfA5EDtUORw0pCJkEUQGZ/dL7pvwY/MD5m/YI8qrvWPAb8oL1PPel9Qf2GvfR9wj7JP7WAHYDJwT4AzcE/gOJAxcEwgOdAs8BrQADAA3/av29+nL4mPbb9CT0HvPG8dbw2u917zzu8PiFEWIbNxbwCZf6Mf71DA0ZziEbIIwZSBMmCRkAewDnB+UMUA23CHb/Zfiy8xPvCe/s8OLyDfjE+FTzpO6P6qPpD/Ce9/78zQAoAZv/sv0L/QMAPwchDjkRPBL3ENUN8Q6GDhcPfw9RDIANrAxWB9sDlwBm/tn73fkW+Q32zPPl8i7xaPDc8BXz/fXx9sf3m/h0+W38zv7fAWMFUwbeBuEGBAaiBQEF6wRKBFEChwAk/vX8k/xr+vb4q/Xl8jjyRfC077rule2O7mXup+6j+0ITJR2ZGIYMDf+QAvoPextMJKQkth6pFjUK7v73/18HGwsfC6UFN/3R9hbw4ump6YXsWO8k9Hz0w/BV75/sSuq97tH1G/0IBIwFlgS/A8kCtQSnCbIOmxKZFuUW4BJ/DwoPqQ53DJMLHwkACNEF6QAa/sP52/QO9D7zk/FX8ijyCfFR8Zrw7/FT9vb4k/s0/uf//wDMArIEqwaqCXEK0wmRCTkHUwXyAzsBef/P/an7sPpr+SD3EfVa8sPv1O2S7A3tDe4j7hnueO3w7hH/BxldIbodORHbA74HuhE1HLQmhChlJBMbRQnP+1H8GwPkBqgGVAFr+nz0Ruze4/fjp+YO69/xZ/H571fyRfBy7jjwlvRm/r4HzwvPDEoMawolCqkMdg4GEo8YqBqDFyITFw47CuEGJQIoAskCpgGi/9/60vM57m/sM+w57snxNPQX9kP2qfMI9CD36fr6/1oE0QbtCAYJ8wcQCDwICQmaCiUKDQjFBe8BUP20+RP33vWr9n/2OvUP8g3uVuzb6lPqTewv7vDvYfHD8NH2Jw25IBMl0iAZEuAJog6AFYofKyh0KK4k+BZuAQH2gvfJ/PwA3v8P+tj05+5b5cbfnOBy43nrRfFo8EHzU/ay9ZX2A/fQ+kcEagyCEVQUJRQAEvoQZg92DYgQvxaoGG0WLBF7CEoDv/1I+U76evtR+zv70fbj7wTsKuru6vbu+/JZ90r86PxO+3T7KPyf/icE5Ad3C7IOnA2QCykJuAUHBZkFBAVXBEcC2/0c+Zb0w/CH77Dwd/Ee8k7xp+6Y7HPrieqC7NnwhvNG9nT7hg0EIukofibXGr4P6w54Em8a2iMMKIQnhRsaBTTzNu0M8RD3lPkm+cX1BvCE5qrdOdyK33roffH78zz3QfvJ/M/9GP0d//oFQQ1CE3AXzhgdGD4WnhL4DJEKEQ3ODzwRLRDBCDcEffyZ8lTxKPKA9Kf4Bvj09CLxIOxm7IjuS/Eg+Pr/IQPbA20D4gHAAXkEXAbmCeUNnA6vDqML3gXcAkIAR/5A/jH9zPvg+YL1TvF17p/sNu2R77rwp/D28JrwZe/j77Px+PM/9zX6iAaeHE0qiC7DKIsavhFgDgcPDhd9Hw0ljCM/E4D8M+z55VHnIOxe8Z/0r/Uo8cbmV9/W3OLgXevo8o74xf9dBKsGggavBJgGKAurDx8UohdTGb4ZFxg8Eo0K5AeFCAkI5AeGBIQATf2S9hLxCe/K7mfyxfV29bX0XfMx8jHzSvUp+A7+JATaBlwHlQYBBcgF2gZfB4cJUQvJCwwLEweDApL/g/x0+kX5m/dc9lb1C/NL8E/ule117h/wgfAy8Q/yufLI83b1qPZ4+Cv8SwpIHrgroDCAKlEethQUDeYK5A+MFwsg6B8jEob+9+0K5B7h7uJ06NDv2PX79GjuO+fx4Wnj4umq79H30wHACdgOuw5kDA4MkwyGDdgPexJqFRoYJxhIEyEMhQajA1gBdv7Y/FH8TvuL+Bf1w/FI8AnxgPJB83z0y/Ys+dn6wPvV/D3/dwIqBasGlQdcCAwJ/QjRB9oGCgdZB58GWgSXAST/evyn+er25fTM8zHzXfIl8f/vCfDN8ObxIvKH8sjzIfWb9lX4FvkM+wH+lwkfHH0pEjDqLDcihhdtDdQGSQgkDkoXpBuhE+UDY/MJ5kTeMtyA4JDp4vPT+KL2+fAO6i/n0um77U31ZQCRCjkSbhRJErsQHQ9tDQ4Nsg1GDxMSwxPjEaYNiwniA/f/H/sN9tT1cvau9sT3x/YX9hD2yPPo8i7ztfQM+bL9VQBaAicE/gQEBQoFqQTUBY4HOQhyCJgHyAUkBXYDpwDx/jH9nfsl+nX3N/Xy87byCPLD8ZfxEvJt8030qfTo9O709PVW9934ZPp9/Ib+nggdGWEmWS48LeAkDRsgD1kFfQJABUcO6BWXE8oIs/oN7IPgj9mC2cvht+6R+Cv8+fmD82XusuyC7MDxC/y7B4QSJBdqFh4VMhKlDhUMHwo/CqAMcw4RDlcMawn6BfgCXfwX9v7ymvEe8kf0CvZr+cb7KPsZ+fT1JPQj9qr58f0YA2wHLAqBCtQHFwRHAhICGAMnBWMGNgeVBzcFUQFQ/fb5pPjn99H2YvY29kP1SvSs8mfxkPH48oD09PXL9gf3t/ee+G75vfqZ/Fb/kQkUGKgkqCzILKQm0B0WEZ8FfP+F/4IGFA7hDm4Jf/+Q8hzm19uP16fcxufS8nf6C/0F++r3ifRq8RvzqvlUAxEOcRT+FkcYnBbpErsOHwrKB90H7Qe+B64HeQaCBesDLf9h+Tr14vFY8IfwS/Id99b7/v3Y/gj9RfqH+ZH50/qD/jQD/Qd0C0ELAAmfBrYDsAEfAfwAVwJaBG0ECwNYAMb8TvoG+Hn1n/SA9IP0qfQR9InzCPS49MH1/fY/93j3Bvj390n4Bvkc+iT9ggeQFogkAy5FL2YqzCBbEowEevvK+e8AAwpdDgINfAVr+cLrd91a1VrX5eB47Wj4u/6nAD3/7Pql9azzU/Zm/qEIARAuFaUYohhUFsoRVwzdCP0GbAVQBAgDVwIOA/IDowFz/nT6cvbZ8kXvx+0c8Vb2Svx6AfICGAJFAH38jvng+Az79gBPB4ALQQ0+DMQIjwRSAEH9Y/2l/zgCJARHA8YA1f3N+cj1PvN98ifzcPS89AH1LfU39e71qPbn9qH3HPhf+Dz4zffn9wj8lAgkGOMmSDASMqUtPSJlERwBNvbh9LP8TAbSDMUOsAnu/m7vfd5x1DjUSNyp6LL0yP1EA1oELwA7+pj2x/dp/hcGmQyXEsUWARhdFucRhg0MCxwJQwcKBdYBKQCr/x3/3v3M/Cv8EvtV+NLzp/AM8LDxmPUo+n/+qQLbBAsEVAFA/rn8N/7sAFoEIwi6CqkLIgogBuIBIP/+/Wz+Gv9g/77/B/+G/Ab5uPW/81rzV/OT8xv0n/Q39cX16/WF9nj38/gZ+sz6bvol+kj7fQMgEM0doyn5L+wwgyqOHDULrfsL88jzGfrAAXUI3AppBzf9We0S3yrXN9ff3QLoR/OM/W0EAQbvAsX+g/xp/poCCgdwDEkRcRTsFGUSBA8CDbwLrQrmCFMFRwKM/7n8Qvp7+BP59vpO/Pz6fvgU9k30qfNT9Hn2s/p//+ICFARRA70BDAHgADIBwwIUBZ4HAAk2CCMG4gMxAjsBawBg/6j+Hv5t/KT5pfZ59MzzyPPr83P0GvXR9Sf20fV29Yz1Zvah9wz5uvmU+rD7dwFtDF0YPCToK5EvBy02IwYUNASF+HP0zvZt/PsCKQjmCKwDdfch6Sve3thS2lrg3+mi9B7+jQMxBBsCsv+7/wgCFwVCCc8NexEME64RLQ9nDbkMigxnDLYKAwhwBP3/SPve9g71Zvb5+Mz6Qfvf+vz4J/Zj8xXysvPk91D9KwI6BW8GaQb4BL8CSAF3AVoD1AV4Bw0IeAfxBREE7AHB/0r+pv0O/ZP7Vfkm9771tfTr89jzY/RQ9Qr2Nvb39Yz1tfVQ9hr3A/hV+e/6Z/wPAGUJpxSZIAIpki0mLXQmuxmKCjH91PVv9V75Ef+TBGwHzgWm/cnw0eTT3L7alN2Y5BDu6vei/zoDfQO2AdwAPgKlBKQHNQvFDhYR+hAaD1oNxgzoDHkNEQ3fCuoHcwMn/sH42PRa9Kv2p/mp+6b8+Pu6+Rf2pvKE8STzYvcF/c8BQwUgBzAHuAVwA7MB6QFwAzoFjAYgB+EGBwZ5BGECmgAw/1b+FP3p+l/4PfaS9Ibz+PIx80r0qPWS9sf2efbO9ZL1efXB9Zj2RfiH+mD9/gPFDmUaJiWDKx0uzyrOIfwTpQWU+hT2VvcF/N8BmAb6B00ES/q77bjiE9wZ27neNeak7+34j/93AjUC8wDvAGoCyATwB3oLxQ6LECYQmQ6WDZMNRw4KDzQO6QsZCGADbf0v+Mv0DvWu94f6oPw6/dL7wfiA9PLwwO+28XL2evyjATAFOQcNB3wFSgPGARICnwOlBSYH5we7B/EGfAVgA4EB9/+v/g79lPoJ+Ov1efSs86nzF/Tr9Mv1WfZG9qv1FPUR9UP18fXn9q74gfp9/IQAzQnMFGMgVyjeLD0s3SXeGWoLmf6O9wD37PqRAP4F5ghJB4//RPM753HeUts43VPj0evx9F38dwAJAav/Cv9YANkC/gWHCeIMTQ+lD4kOag1jDW0OFxCVEP4OZAuPBukAxvpT9hf1PPez+qb92/7V/YH6F/ZB8TzuNu6d8Xj37v3/AlYGqAfqBvEEpgJuAdYBiQOSBUwHHAhJCMcHXAY0BPwB3v/b/ZP7H/kQ96v1CvUK9VP1nPXR9dj1hvXh9DT08vNj9B31Q/aL91X50/qj/HcAoQmdFAsg4yfkK8gq5yMdGFgKzv44+fP5u/6DBO0IIgqPBpD90/Bb5c3dWNy535rmm+4z9pb78f1w/ZD7Pvuv/WoBuAWnCeIM7g4tD0AOcw3SDW8PiBH9EeEPoAtGBhUB4vu9+I74Avst/rv/F//f+zb3d/Ke7g3tYu6w8t34yP65AoMEyATfA4MCYQFLAaYCzwQTB4UIAAmuCBYI9wYdBewCsACf/nD8Nfp4+GX3Kfdi91z3q/ZK9dLzwvIi8ijyJPNA9Vn35viE+cr5vfnK+XH6lv3EBiUTVCBqKecsxSrkIr8W8wi8/TL5zPtXAscIPgw4C6gFwPvd7r7jXt2n3c7iIeoi8Yj24Pnj+ij6qvjN+BX84ACiBRMJGwuzDIMNDg6cDgQQ3RF0E8QSJA9rCaYDaf9j/ZP8Ef4/AGQCCwIB/6T5n/Mm7yDtt+1I8ID0kfkE/o4AEgHKAK0AygBRAQICegNKBUwH5gjHCeYJbgl1CHUGbQMyAGb9ufv5+tb6O/t0+0T73fk895nzp/Ay77Tvz/GJ9Ij3uvlr+pH5mPfx9Vz1ZvYp+aL+WAmDFv0iKyoHK7cm7x2EEs4GfP7c/GQBpAg6DlwPhAuNA2L4suvL4Uvdg98p5t3tLfT994H5DPk59wf1efRP96P8YQIpB98KLQ6xEOcR1xE5EUkQXA+cDa0KZQcnBbwELQVTBSQF7gMiAlr+sPml9Onwze+X8MzyOvWL93v5o/rv+pr62fpX/In+BQEkA3MFcgc5CSgKPwp0CT8I6gYaBSgDcQGUAHEAUgCb/yH+5ft4+Yz2BPTW8R/xtvFq80P1ovZG92L3tfaY9Wb0yPPY9Mv2zfms/OYBRAuJFyoikSeHJiEhcBg6DiEFdACWAlUKhBL7FY4SBgm//J7vdeSw3ifg0+eH8ZH4kfos+J/zEO8H7K/rDO9Q9jD/xAZ3C5wNjA7uDk0O+wzWC4oLJQzZDCQNQQ2vDQsODg3KCa8Ey/5Y+u32FPao9tD41vpI+0X5dvVn8efuQu8S8q72KPwvAXAEQwULBI0C1gGzAtIEiAe0CQwLJQvNCaQHBwXvApcBcQAg/2r9+Puq+p35a/hP9+f1ifQx81TyEvLJ8lr0b/au9xb4I/cX9kr1r/Vc91X6xf0IBNsOyhqSI2IlCyHLGTARjgnxBEYFhwu6E6IYBRYrDP7+wvJ36ajkoeX663P0Yfqz+u718O6c6aPnR+mo7Wn0zPzrAwkILAntCDkJoQmRCTwJhwk+CxQODRHgEjITHxIwDxkKRAPS/RL7sPtT/roAIgKBAKz8Rva67z3r6+qr7qX0jfqD/h8Axf+J/S77LPrc+/f/AQWHCSUM2QywC5EJ1AYxBNICtgKGAwEEowN9AtMAbP5L+wb4afW581fzr/OS9Gz1M/ae9jD26/SA853yz/LI8z322vgF/FP+qP+2A+sMNBgRIDchWBzPFfcOUQq3CKYLyhKPGR0aXxKPBGj31O6s6xrtAvJY+Gf8CPsR9Pjqe+Sx4w/o6u6G9Rv7U//pACIA7v6r/9wC5AaaCQkLJQxNDgoR4BLaEiARjw5OC0YH1QNOAq8DfwY8CAEGlwH/+pX1SPFo77rw0vMA+If63flJ9970g/Qg9u346/y9ADEEQAbIBj0G/gVpBkMHoQdJBwoHBwcdB2wGwgS8AoQAN/74+wL6/Pie+J745Pc29if0z/KH8vvyxfOA9FP1TfXl9Av0tfRp9pT5avx8/nwFDRK3HBQh8xvQE5MNJQr/CjQO+RShHNke8RcpCeD5sPE78fT0/Pke/p//Wvyy823pQOIH4m3omvCb9uP4xvnd+VX4zvaI90H8KAN4CPYKfQsuDOgNnA+oDwsO8gweDWcNvAyXCwwLyQq6CFYF9gDG/Ir7w/pb+uP4P/cE9nP0nfKq8d/xU/R192j5cfpL+1f8If4WALkBxQMEBjMI1AhYCHUHFgcgB+EGNgZZBYkEzwNLAj8A5f3/+536ePkT+MH2EPbR9Xb1uPS280fzEfMn8wjzPvME9CD2X/go+n76VfqqAEwQ2hu8Hx0ZPQ9LCtoIOwwcEt4ZvCCzH0gUVwN/9o/0bvqRAMkDYANJ/1/3E+1P5dfj7OiX8TD3dfdd9B7y8vG88aPybPYb/R4EuwcDCEkHNgfHCM0K5QsRDcgOChHtEQcQQQ35Ck8JOQfSBHYDEQQLBPgCLf//+dH1ifOZ88zzGvUa9y/4e/dm9Qj0xfS397n7Hf96AQUD7gM6BOUDwgPrBBMHmwjwCCwIQwcKBnAEyQJ9AXQADABW/wf+3/uU+aj3BPYd9c70f/VD9vH1rPR6807y5vGW8nz0kfea+dn64/ns+r4H6xanHDMZeQ2LB4UIjA1uFGwaah/NHeMT5QRC+Zr5oAFcCAYJCwTJ/MH1WO5z6Rvqoe6l9L73J/Vx8Mftp+5O8P/xWfX8+lEBjwTMA5MCIgJdA3UGDAqpDTwRDBO6EcUNwwmLCP8JpgugC7MKtAgwBlQCSv3J+zL60Pph+yL5tfbb9KX05fOZ86X0MPbd+Hr6Z/vp+zT9+/6wAFQC+APCBSMHaQdfBgQFUAUXBl8GbwaVBeIDKwIWAGb+GP19/CH85vrB+Jj2xfXh9bj1pfWc9MLzgPNK8+vzvPQa9sr3nvgv+Fb3BgD/E9Yc/RnrDXQCRwT4C/8VbhxBH+wdThOFBsP6RPtJBr8NvwwwBfz7hvUF8RDuPO7Z8Xn1Wffo9HjvNuy77bHv/PDl8xn5Y/+mAsAAyP1t/bv/cwRCCnkO+hCFEX8OFQpvB+MIMQ2LENsPtgzBCG0EygDI/rv+ugD5AJQA4vuS9k3zrPL+84/16vek+Qz5aPgw9s725vg0/cMACAPfA/IDowOsA1EDywQzBhMIDQgKB0oFZAPTAU4BhwCBANH/tf6g/KH5Q/cq9lD2XPeL91n32PWG84PyavKT87X09/aL92v4sfcd9/j7gg/2G4MYOg47AjgCiQ3VFmgd1hzaGgkSuwYGABr/lwmCDzgLqQNe+672AfUu8nrxOPL79KX1r/Q78PHsHe1o78DvqfMy+Sr+LwAU/sP6uftD/ycFsAqfDgcP5Q3DC1wItAglDK4PmBFsD0QL0QfbBNwCVAIxAzsCoAE0/ij60faf9UP18fVT9nX3NvdD94z1zvWS9jL6hv2BAH0B3wEiAaABdwK8BNcGCQl+CAoHJwXfA8IDZgSDBMIDQQJxADT+ivw1+1H6gfmO+Gj3xPZD9rX1svSZ9Dr0wvR59Mj0KvVy9q73DPmb+OD4UP3HEcYdNxdqDIQAmgJzDxcamh57HH0Xfw9EA9MAqgAVDJIP7QhLAOD5U/Uh9dnxGPJd8qL14fUR9M3vCuxJ7HHvVPFv9tX8/v58/pT68PiQ/IMDrQn7DVoOgwzZCdkJgQkYDM4PtBBsDzEMeAiiBtUEDgMCAjsBNQEAALn8FvmP9RT1UvfR98H3m/fr9bX1Hfbn9+b5av2r/77/m/87APwAOgTFBfQGKQcgB4wGxQV5BXYFJAUUBeUDPgKzAFn/7v1h/Fv7Yfpb+Sz4vvb09AT1UPU29gH28fVd9MX0HvS+9fr1T/j/+d/7oPoo+u8AhhikHTsVNgh7AIUGcRVIHc0dRxjgEiwKUQH1AR4EnA0rC8MBkPsG+C31wvS68GfxoPG19cX1/vI578vrPO048WP0NftFACr/oPx++SL7ewAgCOUNTxARDnoK+gehCVELBA+VEVwPRAyECQoGPQTJArYBBQEsAED/g/2t+RT21fOG9HL3x/g8+Vb3uPR89Ez2e/kL/bj/hwHQAPMASwKfAzAGtAdcCOYIfgjnB8QGnwVABUAERARRA7MBAAAX/i784/o1+hX6Gfkc+HX2yPRp9Bf1MPbX9gD3XPYg9mn21PY/9yb54PnG+3r8R/1R+0r8DQdSHb0eyhMEBnEBrgh6Fn4dghvsFQQP5wUL/i8AVAO6CTAF+Pzt+Bb3BfNx8WXuS/A78vH2gveG837wi+3g7iT0gfm3AFoEqgCm/fL7Q/8UBfILvg92D4YNKAzpCQYKXgqwC4wN0gzWCucHYAO1/7z96/zF/Wz+v/0f+uf17PK88azzM/f5+jj6/PnT+RP3L/ht/Jn+qQImBxYImQV8BTAFsgNMB7oKAguTC1UJwgRBAtAAYQH/AYMCCQHY/Rv7ePh19kz3I/h1+HX4B/ef9YPzmfMh9WL3tPmN+2H6UvnT+Jv48Pl3/B7+pf9v/6z+h/uG/VkHKB5aIDsUTQVCACAHrRPzG/EYMhPcC5wD6fr1/cYA8QS6AJf57fZi96PyEvDX7I7uivIp+F77Cvfy85DxxvB19uL9OgTgCC4EjgAR/+8BFgcxDFMPMw8FDdUMLwpvCCkIbAdSCMoI8AfuBZABMfwP+Sb4Avob/Jz8Avqv9d/yOPLb80b3lPo9/T8Atvw0/RX8ivyKAHkGtwj/CYQKOQjuBGMG/Qf9B/YKBgpvB5wEEgLx/mD+U/61/pD9GP26+cv2zvUd9eT1A/jj+OP4vvdW9uv1LfYy+a37QP6s/qn9rPzP+/z70v2c/if/wf+y/9T/uf3p+z39+gdOHnEd6hA6A3z+xQMzD0oXCBbqEIcK/f919un65f2dAR3/IvrE+Mf46PJV8MHtD/AE9Vv7UP9h+274B/Yn8xD4mgDnBmcLxAfPA0sBNQKGBYEJqQxaDYcL9gpcCD0FwgMVAmoC5QMXBRsEygBE+z/39/Vy91j69fw+/IT5afaC9eT1p/gI/In+zv+5AuwBTQVhAp0BcwOJBS8J+AyQDRIKWQa5A+YBRAIAB6UFnARYAVD9W/qX+n75Rfoc+sP6fvkT+T/4afaS9pv30Pgo+5D8gPxq+4v5jfq2+5n+EgFbAZQA+/5H/qz9z/xA/r/9R/4d/5L+LvvW+hT+nhEyHj4UpAnb/UECfAaOEkUUhRG5C74H7fjw+Bv9nP46/p36a/np+ib5n/Q78bHugPP69kn/YQCm/dL7PPiS9Xr8HgOxCJoJaQcKBeICZgQ9BQQH1wgyCacI1wjeBVED9gAa/7/+DwKyA60B5P78+uD3jvet+Rj7Dvzl/Ln7Zfm9+WT6ZPtN/wICwAGDBFEDVwIuAggEUwSYBlgJSAnLBgAI8QS2AXACwwF6AR4DXgK7/xH9V/tS+Wv4qvol+wL7w/si+k/4ofgD+cr5EvvG/A795fy2/DT86ftQ/cv+if8fAF4AQ/+o/h7+6/w9/WP96Px5/ZD86/z/+fL6hwBeFaEbKBTKBxwAwwFDB2UR7RKrEHQMogYy+K35V/s+/ND60Pkf+mr8H/py9kHxDO/i8g33vv9EAnQBRv/D+mj3c/wPAlkH1wg/CUYHQAWfBPIDnAOMBOcFwQYWCLsGsgPn/4P8GPvL/V4BgwPiAcv9Jfq091j4ivqA/Jz9FP6Q/Q79C/w6/c7++/3NAJkFBAb0BesFVALvAPID6gZyBsoItwkBBPwA7wAR/oz/yQGXAEn/r/71/On5E/lr+Wj4O/pn/IT70vuK+/z4dfh7+S77jfxH/rj+/v2c/YP+4v18/tv/ef8ZAJcAy//u/sv9sPsf++n6RPzJ/OX73/w3/XQLLRpwFnwOIQUFAkcE0wuiELEQ7wtVCtL90ffs+s35Jvnq91X4JftO/On53vad8VHyj/S2+o0BtgO2AxwBR/x0+5//BQPhBZ4HcghWB2wG7gRXA0sBhwFaAvgDIAbUBQUDj//4+yj7n/3h/+wAdAAE//L8Z/tU+3T7Ufum/K/9Ov6C/wUB2QDY/uYAZQD5AWcDhgVaBDQFFAWsBCcEkgb7BEoD+wLTADsBOAGzAB3/8f2K/KP76fr8+7P6qvo++m75Qvqn+gL7zPqn+sP6dPsF/HP9N/3F/bL9U/7x/nz/dAB+AFz/DwDL/sX+3v5t/YP8zPup+/j8sPtd/Ef8RwMcFKgaVROECp0CXgLbBQUM8Q+MDUgLkgUF+/D4CfrN9w32q/Ww+fX8K/2g+0/30vLv8ir2MfyNAsUFYwVBAqL+Ov4vALABnwOrBV8H2gcdB7gF9QKY/5n+ef/cAbUEjAXCA8YAvP2s/PL8kP37/nb/6v++/8L+y/1O/MP62fqT+7X98f/TAFsBVAGl/6wCswF0AgsEuQP4BCoGwQYwBhQEOgR9Al4AXgLQADUBfgAq//H9K/zZ+7P7Gfof+7r6UfoM+6f6zPoG+nH6dPrM+jH8JP0r/ev9rP3S/d79J/8vAEUAFgBOAD3/JQC7/4P+0vzJ+o75K/tb+v78lvxq+5j/uQu8FzkanRVVCEoDKQApB2cMcw6DDUgKigAo/FX5ifWo9bPyn/Xj+Nj8M//S/fP3n/SW8tH1wvxHAgEGmwYqBM8B5gC+/7cABQEOA3MFbwfqCA0IYwSKAHb9Lv2i/ygC6AQHBXMDxgCS/vj8mfxk/A79Ov5T/3cA6v9W/sb7yvle+db6Ov27/7AB4gJOAisD4gN2BJMEJwURBagG0Qc8CPcFTQOtAM7+cP4z/6v/Ov9A/gX9yftB+/L6l/rs+RX6nfpe+wX8IfwI+yj6lPk4+hj7Ef0k/sj+/v7Y/v7+sv8GAP8AWADAAOAA5P/b/4n9kPv2+ar5+flx+6D7Lv3DABsM3xTJFeoPPAiGBdUEpwh0C+kLoApcCFQC2/7G+9r4tfUe9Ib1Nvid+0D+Hv5F+jP36/X99pH6yP62AWoD9QONA7YCowGzAPf/XgAxAvEEyAb9B9oG2AMlAXb/IP/q/0gBsAJEAygDZwIJATP/N/0F/MD7v/wx/iD/fP+1/mb9vPsP+9n6w/tg/ff/DwL7AvsDVAONA1EDUATeBGwGCgdpBwoGxQToAmcBjgCl/xr/QP4O/nD92PwF/Pb6Evqd+W75HPqa+kT7vPvA+5r7s/um+0T8mfxX/QH+1f6i//T/DABg/yT/Hf9g//f/jP8H/6L9C/xB+2H6a/qH+rP6UfypAkcO6RQrFpkOlAi7BdQF/QbXCOAIOQn9BkcEsAL3/h/79PYE9JzzTPak+cX90f/x/f/6pPgG+Ij40/ke/Oj+sACsAhsESgQhA3cBov+C/5oAhwJmBWIH+gfIBuUEDgMlAeH/jP8ZAA8BFQILAwgDxgHe/3P9o/ug+oT6KPtk/Iz9B/4X/pn9Qf1k/C78v/y1/YL/3wEEBJkFRgbhBbsFBwXCBEAEEQQRBCcEqQMhA88BYQCv/hv97/tL+w/7DPtb+6b7k/tx+yj74/rZ+tz6V/sl/Nz85f2J/jP/af9c/zP/UP9j/w8AoADsAB8B5gCRAOH/5P4L/g79o/xX/Dv8PvyK/C3/1QTTCdUMSwpGBpME1QOABOEETQWvBUcE2wPrAwEESgPTATD/g/w7++b6sPt//tYA1P+D/u/8Xfzc+5b77/s7+4r6+frc/CD/dwHWAUUBJQCl/+7/lwAbArYCkALsARUC7wKTA5MDDgOgAS8A1/8GAP8AWwFBAX4Aov9Z/1P/Kv+s/jr+gP06/bz97v7B/0UAVQDX/7L/q/8cAG4AgQBSAEUAVQAPAb0B2QHiAUsBpwAsACkAYQBoAGUAdwAWACIAFgDu/9H/XP8z/9j+0f5Z/2D/af9p/xH/1f58/lr+U/4L/uX9v/2y/d79Dv5m/mb+bP6J/i3+Hv4n/gT+6P2p/Xn9Wv1N/b/9Af/2AMkDVgZsBTEEWgOgApADRAR/BhQFVAOQApMCsgPLBFMFJAPQAJX/BP9s/9YBIgJYAKX+K/09/ST9C/7o/U78Rfr2+Z37g/1A/6v/+/7Y/ej9ov7b/0sAu/8X/wf+5P5xAIcBHgIuAmcBkQDKAFsByQGKAU4BtwBuALAAWwHsAY0BXgGwAAwAFgCBAIQAhwBoAFUAOwA1AJEAOADk/2D/IP8E/x3/1/8SAD8AZQBrACwAUgApABwAtf9s/3b/Wf++//3/UgA1AAkA5/+M/3z/n/9s/zD/Nv/0/g3/9/4q/xT/u/5m/iT+B/71/Sf+Mf43/kf+U/5D/lb+QP7I/ZP9Qf0u/S79if3F/pEA2wN5BVoE3wOAAsMClgM6BQoGUQNOArkByQLSBPoFXQWHAsYACQB2/+8AAgJhAM7+v/2D/fX9bP7Y/nP9zPug+kT7CP1A/jD/3v5a/if+dv4w/8j/Vv+S/uj9y/3o/vr/5gCHAaoBUQH2AF4BSwFOATgB0wCOAI4A6QCNAQ8CSwI4An0BBQHsALoApACHAF4ADwD0/w8ASwBYABkAq/8H/5L+mf7L/uH+Wf9Q/0b/f//x/0IA8f8pAJv/if/I/wYAqgCOAPYA3AC6AO8A0wBrAA8A1P+r/5X/u/8AAMH/AwDF/5X/mP8K/9v+c/5d/iT++/0U/vH9If4q/ir+FP4O/gf+2P2v/d79k/1t/W39Dv46/7AAKAPiA7MCcALJAdwCqQPxBMUEkAJwAmoCvAMnBRQF5QOmAfYA0wCkAJQBXgHu/0n/wv67/tX+y/5T/gj9ivzG+yj8JP3I/Vb+Y/6c/mD+Xf73/if/5P6s/iT+C/6S/nb/kQAYAVQBDwHpABUBOwGBAS8B7ACgAJoAJQHGAVQCegIYAp0BbgE4AR8B5gB+AAwAzv/U/w8AQgA1APH/af8N/9v+kv6i/rj+uP64/gf/M//e/2gA4wCnAMMAewBbAPMAFQGQAQwBIgHTAOwATgEVAcYAEgCo/z3/Af+b/4//Kv9v/+j+lv6o/kr+B/5j/V39lvx3/Mz8o/w6/Wb9rP1X/Sf9ufxk/HD8CP1T/qv/qgFRA9sDtgMIBFMEzwQkBTQFXQTZAlcDxQPIBHwFMAX7A4oCegLmAU4BQQE7AM7+U/4t/hH+BP68/Uf9d/wx/Kb7yfs3/F38vPw9/ab9pv06/rv+xf7u/vv+kv5m/vT+mP8/AO8AjQGBAaoBKAJUAlQCCwKzATsBKwGHAQICZwKTAocCagIxAvUBswESAWsA0f9W/wT//v4z/zb/J/8q/wH/nP5d/jT+6/28/bz9xf3i/Vb+5P5j/6v/9//0/9v/4f8AABkATgDNAG4BtgJtA2QDBARBAvsCdwIrAj4CmgDjAIL/dAASAYoA8wCS/yD/r/6y/Vr+4vxk/N/8nfu5/OL8wvzJ/Gr8v/zM+3P8G/xU+8z7cftX/Mv9Af8fAdwCIQVKBQoFYwX1A6wEzwRdBZMEuQNwBA4EQAV1BmAF0gOWApABWwDU/z8Av/6c/UP+4v31/fH9J/4F/fX7//v/+pf6tvoY+337Xfy8/Wb+v/5m/5v/pf+M/3L/pf95/yIAGAHZAawCVAPuA9gD2APoAw4DKALJAWEBHAFnAekBzwHGASIC3AE+AdAAAACo/sj9g/0b/fv8bf3P/f79ef7h/sL+dv5g/uX9ff2A/bz9BP6P/oX/SADgAHcBzwHAAaMBjQE1AewA7AAJAS8BwAFhApYCqQLJAsMCzAK/A3oDFQL8AY//9/+o/7v/RQA0/sX+K/2c/Uf+YP2//Wf8vPzy/L/8n/74/TT+hf+P/nn/M/+y/lr+tf1s/pz9jP7I/qz+jP+S/+H/if/Y/v797/zy/OH+UQJfBtEH3QcHBxEFwgW+BSMGtgP8ANT/Tf5SADUCdAK2AU4B6QDk/n/+QP5B+yj6KPu2+gj7jfy1/S79f/5OAL/+cP6l/oD9gPzI/e7+kv4pAC4CZwJUA78EDgTsAvUC1QKEAYQBEgJLAaAB/wJ9AxsDAgNHAlsAbP/R/mD9avw3/Av8FfwO/Rr+Vv6W/uj+Wv7x/d79qf1X/cj9r/5s/24AswGWAgUDcANnA7kCFQKKAe8ApwDDAOkA/AB0AdkB5gHJAYQBvQDR//f+Xf7V/an91f3o/Tr+0f42/2b/fP9N/9v+Y/4x/hH+Kv6v/kD/+v/AALkB5QIqBIIFvwSWBL8CXgHsAP3/pAB8/7v/Lf8E/xYA5P8cAEb/dv7+/bb8Mf3l/OL8Kv5z/lP/DwA4AFgAJQB3AJL/Sf+1/gf+Af4X/sL+Ef+C/8X/qP/O/4L/Tf+f/hH+mf28/Ir8BP4CAWkGVQr2CUgKMAbbBOID8gIuAmP9pvxY+rD6rP6P/6oAdAD/AAMAhv18/rn8Z/pa/Tr9jfzl/Vn/hwBuAfQENAOXAEEBMP99/Vr+M//b/Yz+SwEVAcwBZgToAxsD2wP7ApQAzv8lAIb+BP97AL7/b/8fACUAbP9v//H+z/wx/A78dPsF/CH9bf18/nsAdAIeAxgDpgJRAqACVwKwAekAdwCtAO8ASAFqAV4BRQG6AL0AAwCs/pb9C/3f/Pj8av2s/Qf+Rv9bAP8AGAHNAKcAkQCtAGUA1P92/zr/gv9SANMAJQFLAUUBWADR/6cASAFBAXsAJP9j/sX+xf97AM0A4wCkAFUAu/82/zb/uP8fANf/4f68/Vf9J/5s/34AswBCAPT/GQCRAPYAIgEJAdwAmgBrAFIAigDjABwBKAHjAFUA8f/R/9v/ov8w/6X+Xf5m/sv+Hf9W/3n/sv/3/0UAYQB0AF4AWABoAFsAQgBCAF4AewCkAJoAawBLAE4AZQBoACwA1P+C/5X/6v9rAJcAtwBlAGgAYQCRAF4AOADU/1n/r/6c/lD/af89/3n/dv+o/yUAlAC3AK0A3ACaABkA6v/0/zsArQB0ABYAgv+M/97/RQCXAF4Awf+F/3//hf+1/8j/yP+Y/9H/7v8WAGgAygD5APYA/wDKALcAlABFANv/Tf+c/ir+yP2v/U39FP0n/fv8cP1a/bL9sv2m/b/9NP37/aQAjQPEBvcGsQg/Bw0IKQmfBsQG5gFSAMX9GPv/+3H5YfoM+3r7z/3v/H//5gDk/4AC3AAX/7v/kv5W/+793v5z/k391gD8AAwBsAJtAg8C1gFXAhUB7v/jAJ//uP6J/zb/af/GAEsCBQIoAtkCwAGjAYQBSADI/p/93/yQ+2T7wPuE+7b8qf1W/o//VQBkAR4CqQLVAkQCSwLsAa0BzwE1ASIBGAEiAUUBUQFYAeAApwBCAFz/xf4k/mD9Af2v/Jz8lvw9/QH+r/7I/4QAHAHAAUEChwJkAkECyQEyAekAbgADANT/ov+f/6j/y//3/xIAWABOADUADAC1/4z/Vv8a/wT/8f77/if/fP/B/w8AZQCkAMAA3ADGAJEAWwAfAM7/jP9Z/0n/U/+C/77/CQBSAK0A4wAJAS8BHwFFAUUBEgHjAH4AJQC+/8v/bP9J/xH/0f7Y/s7+Hf8q/2D/n/+i/+T/AAAvAI4AsADZAOMA0wDpAOMA7wDcANYAnQBeAEgAEgDn/6v/lf98/2P/ef95/4//m/+4/7v/sv+u/4n/Rv8H/4P+Af6f/Tf96/ys/G38mfyz/F39if0L/nb+Cv/q/7oAegFXAzoFvgbOB2UILwgWCDkINgc9BSsD0AAe/mf8K/tP+XL4i/iX+OP4Gfqw+/X8y/6UAPMAQQEeAjsCMQIuAp0BigAiAGUAJQAZAKQA0ADzAJ0BHgISAhgCVALiAR8B0AD9/xH/2/6i/hT+1f0n/mP+pf5s/9T/yP81AI4ARQD3/77/Rv+4/pL+Wv7l/QT+g/7x/pj/dAASAbkBoAJHA3YDlgODAwsDZwLPAfYAAABN/7L+BP6T/WD9Tf1w/cX9Dv5A/pz+Af9A/3b/sv/F/87/BgAyAEUAkQDjAC8BkAHvATsCZwK2AqwCdwIbAqoBFQGOAPr/U/+l/jf+3v3C/cj99f1T/q/+M/+f/wYAcQDAAOMA7wDjAMYAmgBrAFsAEgAJAAAA/f9FAGEAigC3ANwA6QDgANMAdwAfALv/Hf+l/uj9UP3J/Cv8yfuE+zX7Z/ut+xL8kPxH/ev9ef5j/6X/KQBxAHoB/wEbAwcFMAaeB2sIvQlrCc0JzQksCGYGDgSzAXn+O/wy+sr3+vWM9f70F/WP9iz4ZPob/K/+gQDcAcUDwgRgBTQFrwQxBDEDZwLvAewAhwAfADIAewB+AF4BhwGgAdYBpgFRAcAAUgCM/03+gP3l/Cv8K/wu/Hf83/yM/a/+f/90AFEBzwE4AloCPgLsAWEB8wBeAKX/QP/o/uj+Ef9s/wAAXgAFAdkBYQK/AvUC5QJkAvUBWAF0AKv/2/4U/k39Ef3v/PL8hv1H/tj+if9VAP8AqgFEAm0CLgIYAp0BXgHWAGsADwCV/4z/YP92/6X/4f9FAFUAVQBrACIAHwDb/zD/uP4H/qz9Mf3P/KP8GPwS/On7z/sV/D78oPwb/b/9Zv7O/o//6v9IANYALwHcAdwCkAMOBNgEcgYmCBMJwAl0CQYJXAiVB84FVAPDAP79NfuU+I/29/Qe9OvzOvTe9Gn2zfjA+4/+nQCpAnAECgZlB/0H0QfaBtgF6ASWA2oCegGXAPH/Pf8U/xr/UP/x/ykAFgC7/4L/f/8n/4z+yP3P/C783/vi+xv8SvwF/e794f4sAFQBnQKZA3wEOgUKBcsEnAT1AxgDCALsACwATf/x/nP+Tf5g/qz+Df9T/4n/FgBLAD8AWwD3/6X/Zv9J//f+kv6l/qL+uP5A/4z/7v+KAOkAMgFbAZABcQE+AQ8BYQCM//H+C/4Y/UH8ZPvD+mf6a/pn+qD6O/vJ+5b8av0t/uj+pf84ALMAOwEIAqkCnwMkBAcFywbQCOwJXgrNCs0KegoDCl8I7gUYAzsAK/0V+lz39PRB81ryqvG88R7zFPXd97b6Pf27/0cCDgUdB18I+QiqCC8ImwdyBicFwgOAAl4BWADL/3n/mP/u/97/5//X/7v/5/+y/wr/8f3i/Bj8UfvQ+m76Gfp++lT7Z/zP/Vb/JQERA+sEbwZPB/oHlAhFCHgHVgavBOwCYQHO/9v9UfyN+9b6hPrc+iL7zPv1/Cf+BP/h/8MASAGtAfwBwAFqAUsB7ACXAB8AyP9y/y3/FP+S/g7+rP0b/bb8R/zP+2f7H/sY+y77d/sb/ID8G/2G/e79if4t//T/pwAoAeIBZwJUAzoFpAfjCRULnQuDDCENhg3vDCULkQgtBb0BbP6z+lL3J/Sm8bTvNu4m7pfv3PGP9DP3Mvqf/R8BzwSxB5EJjQrcChILwAqqCUUImAboBBgDagGdABIAxf98/wr/0f7O/v7+Tf/+/iT++/wF/E77p/qB+uz5S/mU+Rn6k/uT/X//egErAy0FAAdJCIsJ+Qm0CdQIJgeoBd8DAgJlAE3+bfz2+jj6JfoZ+n76CPuE+5P8nP27/q7/SADQAOkA9gD5AOMA/wCtAAkAdv+7/kr+5f1q/ev8GPy5+3r7h/vJ+9z7GPw3/F38qfy5/N/8Af0h/YD9yP0E/9AA0gL7BM4HEgt9DWMPNhFMEpESjhFgD1cMhQj+A1b/qvoN9mrxBu7u65zqYOpj66jt7PBm9Fz4rPzpALgEqAcoCuILTgxdDNwLwwoMCQcHbAUEBL8CpgEPAQkB/ADzADgBcQFRAfYAUgBA/6/93Ps1+pT4A/eJ9fT0RvWi9cv2zfiT+w3/IgIEBeoHvQoLDT0OrA4RDpAM/AqhCK8FrAK7/yH95voZ+bH39/ZP9+f3lPih+d/6V/zu/Sr/BgCaABgBhwHDAdMBmgFYASsB3ACEAO7/XP/k/oz+Gv5z/cn8ffxa/E78Jfzf+6P7k/uH+1T74/pO+iL6fvrW+gz7yfu8/boA2AQpCTsM/g4sElcVkxfuF8IWOxTEEGoM2gb/AC77TfUy8KXr5ufp5QDms+c96jbt4PCC9SL7lADVBE8I7Aq5DMgN8Q00DcYLBgocCAcGNwTMAh4CQQJRAg8CFQKTAlEDrwMhA+wBPwBT/ij84Pl49wf19fIo8v/x+PE38wH2aPkL/QwB3gQjCOILKg/HEE8R6hCfD4YN9grkBxQEmgCG/bb6i/i79uf1J/Z/9ib3PPi9+Vv7Bf2W/lb/3v+nABIBWAFkARwB4wCaAGEA7v9p/0n/MP8g/7j+Kv7+/RH+QP46/qL9/vyD/Gf8zPsI+xL68/hV+BD4LPjH+Lf58vtN/yQF6QnyDJsQvRSVGPoamxotGRUWPBL4DIgGLABx+aPyoe3i6FjlguP946fm/ukg7VTxwfZq/YoCcgb2CUcMkA0hDqkNrwyUCikITAavBAUDjQFuAbMCOgMeA5MDnwTFBb4FtQQ+A/8AY/6N+7f48fXS8vzwNfDt70HxZ/Ny9sz66/7vAsEG/wrSDtcQvhGuEW8QxQ7sC/AInwXiAWP+jft4+cT3YvZM9oX2+vau93L44Pln+3D8NP2v/V3+2/5D/8H/5P8DABwA2/8JAEgAUgBOAE4AWADX/6v/u/98//7+7v2//Pz7o/rq+GX3fPZD9lD2/fa0+Mj9fATHCHAMsRCKFVkawBwcHfoaeRcPE/UMeQab/7f3K/Gv6+/mguMd4sfjiuct683uHvOa+T8ANAUmCa0L6AxNDdIM/AsvClkHvATfAqoBYQCr/wUBPgODBB0F/gWuBwAJ1AhsBxQF3AE6/nH6UvcO9EHxh+8Z79PvRfG280n4Zv3AAUAF0AhkDGMP3hDXEHMPlg0CC+oH8QQbAvT+Evwc+u341/dS98H3yvhh+Yv5Bvo4+238JP1Q/XP9qf0O/nz+7v5Q/3//Y/9///r/MgAyAEsAaABoAOr/MP/e/rX+uf0C/F769vh791D2JPUO9Yz2//rcAYgGDAp5DvMTXxocHp0eNR0NGm0WXxDdCHoBrflq8h3sA+ZD4mrg7uH55RHq1+1B8nX4bgANBuwJDgxgDegNAg0VCzUJXwbfA60BQgBD/xH/sADbA/oFJgfEBywJ/woMCxMJHQZRAnn++fkz9oPzGPF+7+PuQu878S30uvj+/fwBIQUDCAwL4g3IDp8OJw0rC+0I/gVaA3cBdv+D/Xf7Tvq3+WH5/Pnc+kH7//qH+i77Ffx9/HP8ZPyN/Kz8pvxB/bn9Sv7C/gf/U//B/xkArQDpAAIBhACV/5b+2/37/D77xPjH9ir1SvQO9UL57wBTBpcJjA0vE+0Z7x4rIFEfexvuFgQQfggcAVX5NfGy6jzkhuCp3rXgseVW65rv+/O6+c8B8wcYDPUNdg6ZDaAL/Qg5B7wEWgJFAD3/yP4z/4cBxQUcCZoKoAoJCxUMnQsZCRQFUgBO+4X2EfPA8MHux+1P7i/wDvNy9uP6jgB/Bb0IGQpbCzENzA3ZDK0KGQicBc8C1gC4/6L+hv2W/Gf8Yfz4+xj8GP3S/S79kPuR+oT6s/q6+nH6VfqK+uz6pvvC/DH+Zv8vANwAUQF9AV4BlwHcARgBMP/4/Mb7jfqB+Hn2efQk89/z/ff/ADkIWgxGEJAV/BxfI9oj9SDQG7kWzg74BEH7oPLx6rfl2d9u3Lrbbd/K5invAfXK+Rf/6gbvDOEP9w9zDqAMBgq7BVQCHABQ/5L/DACXANYBOgWaCu4OpRCSDyENdwvjCBcEHv4Z+CTzCe/e6z3qGOp27KTwOvXN+b/9BQJ/B4YMrA4nDrYMzAukCp4IjAXfAkEBMgDb/jT+Lf7o/gMAwABIAN7+pv1U/QH9zPtS+Rr3bPaP9tH2NvcJ+OD57Pt2/bj+NQAPApkDdgRqBDoD4gEcAWgAJP/1/Cj6Uvio9gH1ifPv8p/1hv05B7wNYhKzFbIZoB7IIDEfDRolE7wLFQIv+brwpurZ5/Pl1+MA5B/mIO1W9fv8VAH7A3kGIgmwCcAJ/QaGBAUCU//S/OX70vzAALUEIAinCYcL3g7HEXgSdRC5C4wG/wD8+mD1e/By7TPsQOwz7ebvY/T/+TgANwWYBxwJigpxC5QKgQi7BdkC0wAa/9L9C/5Q/zUBDgNABO4ESgXOBaIFFwQ4AXb9O/oj+PH1NPR28+jzLfWo9oH4tvpX/SUA3AG2AgIDtgKwAlQCpgE1AM7++P3o/CX8ZPtk+lj6t/mq+Cb3GvZv9tD63gQYDFkP/RGnFJIZnR0MHUYaDBUjEMQHTf679q3wJu1G7PXoZ+c757LqmvHE+DH9Pf+OAGAD1QPFA1oDJQIbAksBJP+v/ur/rwOqCHoMRA4IDgcP1xBpECEOvQlABFb/Yfln83jvx+1Y7ljwlvJK9Pf2CPw7AagFRQihB2kGCgbPBKMCswDn/4n/Y/9Z/7L/7wGcBS8Iugl0CZIHjwUUBPwBr/7y+qH3AfW588LywvLx9Ab4WPry+xj9af4/AMABHgKtAdwAfP+4/tX+kv5T/vT+IP+7/q/9v/yj+6b7kfrH94P0YfLy8kf9EAh8DlIS7BUZG1chCiJwIAYbDhdKDpcBKvZc7dPnDOgc5rrkPOQl5w3u9/b4/bAB/wK7BR0FgAKOANv+XP+kAEn/Vv5Z/88DkQoHEQ8VXhXJFK0UixG8DN4GnQAi++v0q+256L3nj+p777L1Mvqy/XcBaQWUCOMJxwgaBjcDSwAu/Ob5UfrY/CIAhwJjBDMHrQq/DcsOJA7ZCtsFHwFK/Gz3/vPs8Zfx+PF38qnzy/Zt+03/NQH8AYcBvQASAPH+Dv6G/Qj97/wR/an9ov5oAJMC6ALfAbX/Ef1L+wn59/XZ8vPvQu/F9GcC8gzsE7UZdx5PJKQmlSISHf8Vuw7DATH0+Olv46vix+Xj5g7qeO068+z6nQFNBLIEBAToAqb94/gX9m/2S/ox/k4AZgSLCYgQNxfHG/kbNhlLFSoPJwbY/XL26fEs7/HrfenS6u3vm/Yt/kcElQbzB84HXQULA97/mfw4+jz55Pca9zj6SABMBgUMnA7ID3IQRg9HDLEIJATx/sD5svUb8sDwK/Lb9F/4S/so/DT9JP87AKv/kv43/Yf7Z/qX+YH5tvuS/rMA6AIhBC4EVwRqBHMDzQDl/EX5PfWg8gPwpe277cPvDfbrBUsTJhuNIB0jmiYZJ20gFxhADgcH9fsv7//njuSB5hDtUvAn9BD3O/oe/qoAu/9K/OD4Q/fy8vPvyfBD9cX9bAbZCykRFRZsGgMdlxxZGCMRNQrMAhL6c/MJ8B/w5fJ89Er1u/cS/NT/NAPLBMMCq/+g/Bz5Jvd19jn3kfmi/dwACAPtBukLcw8wEWkP7AukCNgErQDY/eL7bvp0+Z35MvmH+Wf7Hv03/vv9qfuE+XL4VfgT+IH4Cfqj+3P9Af8vAFQCmQS+BVMFuQNxAeT+pv1z/Ob6gfnd9x33UPai9W/1TfWy9WL2cfqVB1UTFxnpHLAd9R/MIBMblxTCDUwIGQBK9a7uEevI663wd/Lu85z0+/RG9yn5e/iY9uT1pfY99Wb08faW/AoFigx8EKQTsxWvFh4XdBUdER8LAQb8AP/60fZA9bv2sPkf+vD4Ivlb+jv70vuW/LD7Dvx3+pT4qvkI+4n9egG8BK4GeQZSCLAJWApRCgkIMAYEBS4CDAAa/6X/UP/F/mr9qfuq+g/6Xvmw+Sb5wfc89zn4E/kC+gX8uP50ACsBzQCqAKABAgJYATIB9P9c/1D+r/2f/Qv9rPyE+/P5fvj39aX0zPML89nyafYIA4QSfBryHkcgXCJyIy4eehWADTAGqP6T8yfsauls62rxovXL9m/3EPeB+CL5TPe58wLycPJd8vzxKvbu/UII0RBxFZ8X4RhDGMUWLBN9DYIG7AC//Kr4PfYD9z76Gv6s/r/8PvsM+5D75vqk+Pf2lfVj9Xn2Zfni/TsCYwbjCW4Kfgm6CG4JXgkHB40DMgGBAHsAMgD5ABUCqQJ9ASD/hvyg+i/58Pfa9uf10vSM9Tn4k/tA/hYAzAHiAjgChwCP/wAAHwDk/hT+yP3+/WP/AACgAKoAKv6N/HH5B/c99IPyR/J08RjxnfJE+00P1hyJIn8kfCNWJMUgkxf1DZwF5P9c9jztY+qo66zywPm6+jz55Pav9VP1ifPW7+HsxO0y8FTxM/bL//IKEhUAGiAaNhkRFyIUVhAuC4wEy/5B/Pn6HPol/Gn/OwIYAsj9LPm+9gf1MfM+80r0dvT69Tz5av0BBOoI2QpnC3oKEwgHBfgDEQTcAgICGAGNAY0D1QT0BdQGDgXzAJn8nfnq9hf1c/SA9Jj1wfZM+K37f//GASICoAE/AGz+dv34/KL9uP70/pj/WwDvAZ0CtgKjAtwAc/w8+cj1OvTy8nfxV/E+8t/y0vQF+7wNRR79IsQjRCCHH2of1ReyDioGcQDw+Uvx6u538O71Ifzp+p72+/IS8dzxO/FG7lPr9Oth8ED08Pn+Aw4OmhUwGGAW/BTpEwkSFw8cCl0Ehf9m/tv/WwBRAXoC5gFD/8r5ZvXl823zD/Jb8ZDzBPW9+Pv9YQJpB80JTAiSBoIFfwWNA+gC+AJXAo0CtgOoBUUIrgiOBycF7AFE/dD5TPgT9z31RPS49O327PlU/CH+4f8SAOj+tf1U/T39bf3e/Rr+Ef9eALkBgwOiBJMDhwEn/7X9p/os+Eb2svTC84bzgPNj9Sr2Q/f2+eEEyBeqHtMe5h3WHFcfkRwiE8YKnwOo/2X3jfHv8azz2vek+L/zVPG38H3yufIo8P3scuxb8PH1nfn2ANMJJhAWE6oTxhNLFWcUPxH8Cw0HJAMJAeYBowKtAPf/6/4I/Q/6A/eo9fH0d/Ii8YzzE/cS+jf91P80A2kG8Qa+BaUFsQZ5BYMD7AIOAxEEQwVTBsgGRgZWBcUDIgIz/2H7e/ks+KX2U/W79Rz4O/pk+xL8+/yD/tj+F/6//cX9rP21/ST+ef8VASIC8gLSAjUCmgEa/8v+Z/xV+G/2gvWJ9Hz0tvP79CT1TPa39yICvBfPH1UdmhzQHOsiASJnFIQJMgEq/j32wO8+8Z/zm/Zr+B7zHvPl9Cb35PWd8GzrR+uR71P27fjk/pIHnw3LELoT+BWJGMwV2A+BCQEG+AOzASgBuQEz/6z+if86/6/9Xvpy9x31TvFo70vx9/ZE/Ln7FP04AqsGEwlCCOoGZgblA5oCLwFtAhcE4gOsBMIF3gWoBjMGLQUfASH8HPkN+JX3FPbF9PH2SPlu+oD7F/5SAOT/6P1Q/T39cP2c/dv96P5FAOAA2QE3A3YD1gFbAMX+WvzK+av3xPYQ9tT1Y/VA9Gn1n/Vy9qD6qAZ9FzMbaRnBGtAc6yGEHjgUUQt9A4L/2vhK9ZX2c/Vg9cX0d/Li85/1UPaT8k/tVuvh7BXxOvXK91r9lgSBCWoN7RIeF1MXKRPLDj4Mlwp4CO4EowL1Aef/y/8SAaQAz/3Z+WX3/vQo8hLylvSR9/T2y/Y4+/kADgXrBcIE9AQUBR0FKgRABL8EcwPSAoYEfwbdB0wHOgbyAzgByP5m/bb78/gE9nP16vZJ+An5qvrf+6/8bfxk/Hn9Gv6D/Wf8c/xw/qv/pwCgAXcCIgKjAVgBnQCP/i78OPly91b2UPVj9E30efQx9JL25gFFFP0bXRhqF2sbvCFgIEgVKAvMA7X/zfm19Vj4UviM9RH1xfV++K35qviP9JvuF+w27RzwPvM99Kj3Pf89BoEKdg8IFR4WUhFEDdkMmQyBCa8EYQHWATsC+QFqA3AE9QFq/cz6fvrH99LzTvGT8bL0ZffN+Jb8+QD7Ar8DBAZlBxEFDgN6Az4D9QFeAUsCtQR5BlAGGgYwBz8H0gM1AAH/Bf1Y+bX2fPaY99T3u/c/+c/7av2A/Fr82/03/bz7rfvl/NX9pv2v/pcANQJtAksBGAL8ART/pvz5+s35f/dg9Q71TfXR9aj1OfiZAzITOhgFFUsVpRrJHwMd9hLNCYMDTgAS+9H3rflJ+K/0VvUG+aD80vv8+Er1qvEM8TjxPvEr8n3yJPWE+14CHQdkC9EPrhDeDV0NIA+yDTMIRwMbAq8DwgOAAigDqQSAAz8Af/+XAKL9x/eS9GP0U/Q39b73sfg++qP8Kv8xAwEGTQWAAksC2ASpA2ECNAM3A/UClgPbBbQHEAeiBQUD7wGdAd7+gPuL+Tb4Sfcj94H49vmU+rr6LvtK/AH+yP1U/C78Svwb/PX8U/6u/4n//f9uAUsCpgI+AY//c/6c/H76/PgD+I/2PfXL9R/5DwFECwcRnhEvEnMW3Ro2GpcT0ArxBLAB5f2w+p35pPgQ9sH1Z/pN/03/tvtS+KH31Pd59uLzRPJB8n3zxPZR/FsBnwRyBxkKxgv7DN4NhgyOCJwEZwO8AzED3wF3AZACiQMuA2cDagREAxH/K/s7+un5u/ex9lj4HPlh+SL7af6UAXQCxgFFAfIBZwNBAu8ALwHGAGEAowFzBMUFpQRjBM8EfASyA8YBM/+z/Mz6Ivo1+lj6nfkf+Tv6Bfz1/Er9Y/28/NL7DvzP/EH9G/2s/P78bP7k/6oAJQDL/1n/if5w/Ur8nfpx+QP4xPgo+rL9EQR6CjcOGhCeEhEXJBitFcUPRQmPBfMAAf3v+gD5qPYd9Z735fzY/pb9CPuX+pP7kfrK98j0AvMx83z0i/eH+6j+sAEdBWUIFQvfDPUM0wq0B9sFdgWMBKwCwwDQADgCPgO2A4MEhgTlAiUA6P5z/nr8qvj09b72Yfm3+af5wPtz/vf/DAGdAnMDZwLMAXoBjQExAq0BGAELApADcASTBE0FCgX1AgUChwG7/yv9jfvv+jj6bvnd+cz6w/vA+6n7nPzr/Tr9C/z1+5n8d/wb/I38pv3o/R7+Zv5A/77/zv5K/b/8ZPsc+t34Zfj8+CL7zQDRB4AMKg8TERgVXRg9FwYT3g2hCfEE+v9j/bz7gfkj9xP3fvpm/Yb9s/t6+lH6T/lZ91z1hvM+8lHywvQ5+Kn7Af8+AokFMwiXCkEMsAtuCQ0HQwYwBv4EnAMkA98DnATuBPcFiAYdBXQCKQAd/2r9w/qC98X1OffQ+An52fmT+6/9af7X/0gB+QBFADUAQgD/AKABKAJOAjcDCgXnBUkGhQYnBXYDWgKEAbL/JP3i+077d/os+sD6ffu8+x/7CPvv+xv8Nfud+nH6AvtL+7P7vPy//fv9ef7o/qX/Ov/x/dL83PuH+gz6nfny+mP9UQJrCGcN+g8vEt8UDhc+FV8RgwyRCNIDY/+D/HH7SPrg+Hj4Avse/U39d/sC+hb5ePdm9f7z4vLV8mPz3vWk+YP9KAEhBKsGqgjgCacK8wnnB/EFAQUEBbgEnAQhBeEFfAalBtQGkga8BKMBhv6A/N/60Pio9jP2HPhV+Uj6VPyQ/d7+MP9N/+f/Pf/3/mb+lv6aAGEBrAJaBIkF9wYgB38H7QbYBOwC8wBy/0f+evy8+5r7h/tq+xj8wvyQ/Hr77/qt+hz6ePmU+Wj5ZPo4+x78nP2M/7j/j/92/9T/nP46/cb7+fo1+Zr5WPpa/H4AFAZoCi0PexFlEwYUDxS0ET4NfgiJBTgB7v5q/fL8gPx6/Gr8cP2D/QX99vmI9871J/SD8ory9fJN9GL2D/q8/VQBCATUBYgGIAcWB3UGnwXlBPUDZgSiBd4G5wfqCC8JiAhlB0YG+wMMAeL9GPsy+V/4vveV9zn4e/ki+6L97v7B/2z/Df9d/nD+r/5//lr+2//QAOUCNAXqBgQH3galBssFiQS/A4cBU/8n/vX9jP2A/Zb9Af1k/Df8//up+9/60/nE+FX4RfnZ+b36/Pum/ML9IP/n/5oALwCM/3b+vP2J/YD8+Puw+/D5nfp6/GD+LwEEBoEKlg6rD5IPixAAEcgOBQufBoIFRwOOANT/3v/I/wT/xvzl/JD8DPsZ+FD1HvSf8+XyPfTL9bf3p/mg/DP/gQErA/ID6AONA20DbQToBE0FiQWYBr0IcQlSCccJCQntBiEECALWANX+c/yw+kv6w/qn+nH67/qa+7b7BfzW+5D9jP3f+/j9lv6v/kEBxgEFAxEE8QT6BZIFXAbrBYYDvANUAxICWwFYASwA8f7e/i3+Af00/C77d/qE+RL6zflF+bD6cfpn+g78Wvy2/Av9pv37/Qv+4f7o/oP+Pf+D/gv+YP3+/Rj8nfsY/Hr7R/zC/mQBtAfFDccSyA7oDR0RRg8VDAMJfAQBBEUB+v9m/1QBswKc/Qz6evtx+iD4TfX+82TyevKy9Hz28/mz/Cv9uP5lAJACWgPMA+4DlwGdAhoGIAcWCPkIHAr5CXIIPwiFB+sFWgI0/oD9Q/6f/fz7zPtq/Cv7IvpL+nr6MvqL+AP5fvoo/KL+1f44AfgE2wTJA4wFVgblBJYEUAV2BN4ExQXhBLkDvwOsAlIAtf+4/hj8K/sr+xL6SPkP+tD6TvqH+kv7kPvi+6n7gPsl/LP8xvwU/Yz+DADR/1IAhwC9ANH/ef4O/mD9Dvzm+rr6qfvy+2f8zPzV/L7/hQhrEy4VAQ62DPsMRwzwCNgEPQQkBe8CRQFBA4EIjAUL/Bf2g/Sv86PzAfQK9fr1MPeb+Gr7Af6s/Q78//oS+239dwIpBxAH4QVfBwwJPAlyCF8IdQhWBqYDowNGBi0H/wL7/gv+jP3Z+6P6Bfsu+0/5Uvdf9+z6FP77/Pz5//pj/sv+mgE6BUQE/wIdBesECwRvBgYIqAXrA/IDsgT3BX8F7wA9/Vb+pf5E/Ir7R/zf+/z5qviL+XT7z/sC+hn5jfpa/DT9y/0t/i3+8f2y/Rr/PwB7APT/Df8R/7z9Y/3r/hv7a/pO+gL77/vC/Mn8V/ve/TIKFRZzF1MQUQvOBmgI+Qh1BoIHdAlsB6kDnwPaBv8CdPro8nXuBfGJ9fP4xvqj+qT4NvdJ9zz4Zfjg+X76Ivsz/1kGagvZC0II/gTJAyoEMAXEB7YKwApCCMQGTwd8BrMCJP69+un5qvoe/HP+FP+D/Jf4gva49pj3Bvpn+9j8JQAJABsCYAU9BCUCdABqAW0CRgVYCUUJLwgwB8gEcwOTAmoBB/9N/kD/5P7x/q/+J/0S+kz3SffR91L4ZPu8/Tj7Xvq8/Ar/s/sZ+j78Q/7C/t7+0AALA3EBOv5H/B79g/3D+/L7kPs6/hH+Dv3C/AX7C/ylBgIWCxdGD6AK2ASFBkwIBwe0Bz8KTgqvBPgCYAU+Aeb6v/TG8NjzlPkR/XP9DPs/92b0G/QK9cT2bfsX/gv+TgAxBFYGjAV6ApoASwExBJsHbgvrDeULlQfCBJ8DdwKzAcYBxgFbAb0AjgBLAK/9Jvkq9mz21PeX+d/8LwC+/5z+3/or/Gz+xf33/lQBvALhBMEGDQc9BSoEvwLTAf4DcwW8BBEFogTsASf/UP6Q/S78RPud+0r8vPwL/A/7WPrK+EX4WPnA+mH8Sv3+/Cv9V/0F/QX9If5W/sj9bP4a//f+3v0R/az8jfzM/Mj9ov2J/Zz8xgLXEvoZdRCLCMkBXgKFBwMKZAsoDJoLuAaaASsCwf+H+0j5TPb39pr7Hf8t/1v6C/Sw8HryiPbN+Mb8DwCM/sb8gP06/5cAVQBLAPUBOgUjCHoKdwtMCGcDCALlA94F5AZfB7EGjwR3AYL/tf9y/xT9LvvS+7z86Pyp/GP9mfxl+dr4cfku/fYAhAA4AFD/Dv7V/kUB7gM0A9UCzwMxBCoFtQSJA/8C/wEvAZ0BxgKWAmUAfP5g/bb8oPy2/L/8Dvyd+h/6yfpn+4r6L/p6+p363PoY/G39ef00/Jb7ivt3/Mb8xvw9/RT9UP0X/s/9Af4O/YcCyhHuGAEQ0QaqAAgEwwq7DqIORwxICnkFGwIEBCsCzv/r/aP6uvlL+0T9zPy6+D30ufFa9AP42vh3+sD6bvjR9+b5UP2y/9f/q//0/6AB8gMAB44J3QeWBMgEBweuCPkIeAjaByoGNASAAx4EJwTAAUP/Sv52/S79Mf3l/Pv8xvq0+Ev6Pvof+1385f3P/AX8nPw9/cv/zwEvAYQBrALoAt8CvAMEBGADRwMLBDcEtgOQAmQBswDb/2//cv8w/1b+r/x9+yv74/qd+jv6o/p0+sb5XvqK+pT6Tvol+pf67PrJ+mH7ufvM+yH8GP0b/Yz9WvwvAE0N1RihEhYIXP8vAH4J6hH5E/cQzAv3BbkBfQO5A/UC4gKKABf+Tf1n/JD74Pje9IfySvRf+Hr6avvQ+cX0NPLl85T4Ef57AG4ALf/l/dv9oAAhBagGqwVDBrQH0AjqCPcHqAcaB/cFFwakB6QI6gbJA/kAr/71/fH+swDZAaL+AvtL+Sz5Ffug+/X7Pvua+eb5VPuD/e79bf3o/Xn+zv9bAWcCDgNOAmQBAgJNA3kEVwTCA+wCZwHgAP8AUQGKASkAu/7+/d/8Hvzy+//7hPvD+nr6bvqE+of5GfkT+e349vhb+SL6l/oo+//6UfvQ+pT61P/+DXoW6g9QBY38MP+wCc0SHhYsEycNnwXiAQ4DUARMBvAHOgbZAu7+3/v/+vn5HPjn9mz3LPks+kT7LPnr84rwMvEz9jT8Zv/k/4P9KPqO+C77twBmBP4FLQctB/4FZgRaBKUGgQgDCQMJKQn9CHIH2wURBPkBBQG5AfgDZgV3AoP+Pvsi+Vv5k/st/ir/Zv1e+vr3q/f5+Mb76P73/yr/Af5d/f799/73/5cB3AI0A98CvAKKAnQB/wBRAdkBdAJ9AvwB7wDx/vv8VPz4/OX9U/7Y/cz81vo/+UL4Ivki+uD5S/oo+vz4A/ki+fP5wPlF+pP9FglYEjAPkgZ5/QX8HgPPDJETrRRGEVgJuQJ0AMoAOgT2CT4MngmGA/j8cfmw+S77r/zf/A78FfoZ+rf51/Yt9FTz+/SF+MD78f0O/vX72vgT+I36yP1BAW8FoQd/BhEDPwDNAOUDggclCh8LwAnIBr8EFASpA9IDuAR/BqIGcwR9AQ3/1f37/Xn+8f6c/lf9Avxe+wz7O/of+lT7jfw6/Wr9c/09/fj8Hv0e/k3/cQAYAbMBoAHWAAYABgDKAM8BWgKKAvUBpABG/5z+rP7k/vv+M//R/r/9G/z5+qr6l/rJ+rr6Mvr/+Y75t/l6+mv6uvmX+tT/ZAo6DhgLUQM0/ML8nQJkCvcQHxPHEHgJZALP/Yb9/wKKCmkO8gyCBSf9Ofix9xj7+/4VAS8AEf2j+nv3f/Wr9e32PPlx+p36/Por+1f7RPuT++n7kPv+/GgA5QP+BTAFKwOzAfMAGAKiBMEHwAm3CX4IogVkAu8AyQE6BcQHbwdzBSUCQP/F/b/95P7h//r/Rv/x/UT8p/qj+rb7k/z7/Mz8mfz1/Dr9qf0E/g7+z/0R/iT/MgDTACsBCQGgAMj/Q//L/6QAUQGgAUUBMgCf/p/9gP1D/sj+rP4q/iv9o/tI+sP5xvl6+gj7qfvJ++z6LPp0+lz/5AdUC4oKYAXx//f+QQEEBqkL0Q+VEJkM1AbZAHb9m//eBIsJZwsjCEcC/vyH+XT5Yfvu/ej+Xf4n/S/6x/cK92j3CfnA+Zr5vfnw+a36K/wn/gr//v0O/ST9fP4fAWcDggXBBvQFdgT1AoMCWgN2BTYIQgksCK8FCwN0AmoCwwKfA6MDNAMFAnEAM/8X/m39av2D/Rj9O/wS/HD8yfwh/bn8K/wO/On7gPyi/a/+Zv+r/77/Tf/F/sX+XP+KAE4BkAF3AcoA9P9g/zb/bP+b/6X/pf/q/1n/rP0I/U78KPua+lj6Tvqj+h/70vs7/Lz70PpF+hH9kANiCIcKwQhpBVoDfQJaA7UFhAn+DJANpgtsB6ACYQDgABsDnwUaBpwEAgLe/lr8DPsb+2f7hPvA++P6ZfmI+Gj4nfl6+lv61vkf+cT4S/lq+2D+WwBFAUsBygBFAN7/swDMAiEFuwZfBxYHyAVzBEcE6AS1BcUFLQW1BDEENAOQAloCCAKBAb0Aif8t/vL8o/wb/cX9uf0R/Xf8Evzv+zH8o/xU/cX92/3o/cv93v1D/lb/hADzAM0AXgD0/97/9P9VAPMACQHNAEIAov8B/5L+kv67/sL+R/52/c/8NPy2+4f7kPt3+yv75vrD+tP60Prv+k77pv3ZAmwHNQp6CrEIGgdmBQQE6AM9BccHlwlVCrcJggdzBe4DuQLVAhICmgDh/5n+mf1E/YD9ff3V/Pz7W/pl+Nf2LfZ492X5/PpR/N/8pvzp+5r7ZPxz/Yn+uP/KAJQB+QF6AqwD5QTOBWMGdQbuBcUE1QO/Az0E0gQOBRoF9AQXBOwC8gEiAYEA0f9J/+T+F/5d/RH9Pf1m/Uf9FP3c/GT8C/z8+3P8J/2s/Tr+yP4K/xH/JP+C/9T/+v8DAP3/DAD3/wYAWwCkALMAdwAfAK7/JP+v/nn+c/5//k3+C/6p/R79ivxB/CX8BfzZ+8n7sPuT+0v7Mfvv+w3/VAP9BlsJ0wlFCSYIZgbVBCQEjwR8BSAGZgbuBVAFJAUkBRQFnwRBA40Bgv8I/Sj7evoi+zf8If2Z/UT9bfw4+zj63fmq+dD5bvr2+iL7O/v4+5P9af/WANMBdwKHAu8BdAFqAcABegKNA6IEQwUXBa8EjwSDBEQEzwOTAzQDdwKHAewAigB3AHsArQCnABkAOv9z/uL9Sv2//Kz88vwn/T39Xf2y/f79NP5W/lb+N/74/eL9Kv55/vv+ov97AB8BTgErAewAlAA1ANf/mP9//2b/YP9N/yT/JP/3/uv+u/5K/q/93PxB/PL72fvy+z78mfzP/NL85fw3/fH+oAFwBJUGngfaB4UHogZgBYwEWgRgBHwEYATiAzoD9QIbA58DGwTrA2cDVALQAOH+If0e/Nb7Jfyc/NX8vPxO/AL8G/xE/FH8c/ym/Kb8VPzy+wL8lvyv/QH/ZQBqAd8B/wEeAi4CPgJLAqACEQMoA+ICfQJXAmECmgIYA4kDlgM+A7ACJQKEAewAmgCEAGUA+v9p/8X+Ov7e/dL9+/00/kf+FP7P/VD9ufx3/OX82/27/lD/q//X/7j/m/+r//r/GQAJAP3/3v/F/5//ef9//7j/4f+4/2z/F//F/qL+tf64/q/+j/5D/uj9k/1E/SH9Mf1X/W39Xf0n/TH9Tf4vAFoCYASiBWMGjwYdBjcFUASvA2ADQQM+A9kCTgLvAewBZAIYA3ADowNkA50CYQH9/5n+jP3o/NL8/vwL/dX8pvy5/Pv8RP2J/d79BP7u/Zb9NP3l/NL8Lv34/Rf/AwCkAP8AagGzAeYBCwI1Ak4COAILAtMBgQFFAUsBoAEPAmcCnQKzApMCRALpAYoBDwG6ADIAm/8H/3n+B/7i/RH+Wv6c/r/+rP5d/hf+9f0R/kf+ov7Y/vT+zv7R/vv+Rv+P/97/DwAJAAAA2/+F/zb/J/8B//v+Gv86/0D/U/9Z/1b/Pf8X/+v+pf5m/hr+0v2m/X39Wv1B/Sf9JP28/RH/7ADJAlcEbwUEBvQFbAWsBPUDNwPDAnACTgIFAqoBigGzAUQC1QJEA3oDQQOaApcBewBN/zf+dv0u/S79N/0n/Rv9N/1j/an9B/5W/on+cP5A/vH9ov1d/Ur9r/1j/if/1P90ANwAIgFbAZQBxgHcAcYBswGKAUgBGAECATIBdwHPARICRwJXAlECGALTAXcBCQGRAAYAfP/3/oz+R/4t/kP+hv7F/gf/IP/x/pb+Q/4x/lP+mf7I/t7+9/7+/iT/Q/9v/4z/vv/R/7j/mP9p/0n/JP8X/x3/Df/3/hf/Lf8R//T+2/6o/nn+R/4e/vv9wv2T/Wb9Ov37/Ef9Xf4JAAsC3wNdBUkGjAYjBo8FpQTCA+8CagIbAs8BdAE4ARIBZwHTAWoC8gJBA+8CTgJhAUsADf/o/T39+/wB/R79R/1z/Yb9pv3l/Ur+rP7b/uv+zv6c/ir+vP2J/Zn99f2Z/lb/FgC6ABUBVAGXAcABzAHDAbMBvQGUAXoBWwFOAVQBfQG5ARgCZwKQAo0CVwLvAV4BzQAJAGb/wv4x/tX9lv19/Zn97v1z/u7+Ov86/97+jP5s/l3+Y/5d/nD+pf7+/k3/b/9//4n/jP+P/6L/q/+b/4L/af86//v+4f6o/nz+Xf5A/i3+Ef7e/b/9kP1g/ST96/zY/LL9F/87AVEDTQWrBl8HXwe7BtgFxQTMA/ICZAICAtMBfQEiARgBNQGKAR4CjQLMAnAC2QHvAN7/f/5N/Xf8Jfw7/Ib89fw6/YP9wv0E/mD+r/7h/vv+/v7V/nn+FP61/ab9y/1g/i3/JQDmAIEB+QE1AlcCOwIiAvwB3wHWAcABvQGaAYQBgQGmAfkBWgKjAr8CswJhAuYBKAF7AL7/Gv+W/i3+6/2m/Vr9JP0Y/Tr9ff3V/R7+R/5D/iT+9f3i/fH9FP5d/sv+Q/+b/9f/AwAMAAkA+v/0/9f/qP92/z3/6P6G/h7+uf1z/TT9C/3v/Ln8lvw+/BL8Evwu/ff+mgFABN4GpAh4CWsJdQgdB3MFBATvAncCOAIIArABagE4AUEBdwH5AYMC3AKWAtYBswAk/1T9rft3+un5HPrQ+sb7lvxQ/bL9Ef5m/qX+y/70/gr/Cv/F/nD+Dv7e/Qv+n/6u//MANQI0A/gDPQQqBLIDHgN0Au8BlAFnAVQBWAFbAWoBpgHyAWECtgLsAuUChwLiAfYA9P/0/gf+hv1Q/WP9k/3C/ev9BP4e/ir+JP4O/v79yP2W/Vr9K/3+/Av9UP3P/Wz+Df+b/xIAUgBYAFsAOAAGANH/hf89//H+if4h/qL9J/28/HD8Ifz4+7n7dPs4+3r7Bf1s/+gCPQaRCakLjQwVDH4KYgjuBdsDZwLmAa0BtgF9AVsBOwE+AZcB/wFOAmQCwwG6AA3/+Py6+qr4bPca9773LPnc+l38qf1a/t7+Af/7/uH+y/7k/v7+Df8N/xT/Vv/3//MAdwInBKsFwQY8BxAHSQbhBG0D7wHpAE4AJQBbAJ0A7wAVAW4BjQG5AZQBSAHTABkAKv8H/gj9NPzM+9/7evxX/UP+Df+V/+T/6v+u/2P/B/+//nn+YP5j/mz+kv7I/hf/gv/h/xYADwDI/1b/sv4a/pD9NP0O/f78FP00/Rv96/yD/BX8mvsu++b6o/qB+oH6Avsr/WEAGgXHCegNuBCREfcQlg5EC3sHAQRkASUAzv87AMMASwGqAZoBlAF3ARIBaAAg/4b9bfsA+Z72qfS/8x70xfVS+EH7z/2+/5cA3ACKAMX/J/+4/u7+j/97AGoBWgJRA3YEogUNB08IFgk8CYEIIwc0BcwCrQDF/qz9cP34/fT+9/+aAMYApAAGAFD/Vv6G/bP8HvzM+6371vs7/Pv8If5y/8YA2QFqAmoC8gEyAXQAxf9m/1D/m/8WAIQA5gDmAKAAMgCi//v+N/5K/XD8pvsb+9n60/ou+5b7Jfym/Nz8wvwr/GH7bvrG+W75lPns+af6kPuc/XoBOgZkDCMRDxVgFn0VnhInDkIJYATKAAr/+/4JAF4BBQJXAr0B0wCP/x7+V/xY+hD4HfYb9GTyMvH58FfyF/X8+Bj9igCsApMDBQMCAqAAlf9Q/wYA2QE0BLEGqgj2CZ0K0Aq2CnQKoQkpCCAGqQMPAYz+RPzm+k761vr4+239kv7b/o/+YP07/Mz6/PmH+cr5tvou/N79kv8iAW0CmQN5BBcFJwXLBOgD7wL5AUEB7wDvACIBnQHPAcwBIgH3/1D+g/zs+rf58PiB+IX41/iH+VH6G/vf+2H81fzy/Pv8mfwl/GH7CPvQ+kT7BfwU/SH+9/7U/14BUAXmCVkQthRDGFMY6BVyEW4LnwV7AGb9rPzL/bj/JQECAdv/RP3D+lz4q/Y39U30WvP18p3yffIb85L0x/fZ+6cAiQT6Bl8HIwb7A0ECPgF9AToD4QU1CfULhg1wDQIMoQkzBwEFagOjAff/9f3/+0767fhC+FX4CflF+rD7nPzM/DT8Jftr+jj6EvvP/AH/YQGjA2wFiAbXBrUGFAafBT0F+wSWBNgD5QLvAR8BjgAiAJX/9/74/bn8Qfu3+VL4Vvca96H3p/jj+fz6pvsu/GT8rPwY/Yz9N/7L/pL/y//u/3b/9P5W/hf+Mf6D/tH+/v6c/nz+BP8kAzkI9A+NFRQZ9BiEFFAODQYz/9n5x/fd+EH83v81AukBZv+t+933ZvUt9Jzz6PM09OH09/Xe9gz5jfur/xEE9wdLCvkJaQe/A50AXP9rAAIDDQfDCm0NFw4rDJQI7gO7/wj9rfvc+w78s/s1+zv6wPmO+dP5mvqK+yX8Hv1Q/XP93v3x/f3/2QHhBBAHLAhPCCMH0QWlBKYDZwM6Ax4DVwOaAtYBLABa/uX8w/sx+/b6TvrN+QD5p/j8+Jf50/rM++j8sv3x/dv9gP0R/Wr9NP6u/ygB+QE1Am4BSADV/jH9xvut+gb6Ivpx+gL7UfsI+z774vxXA+MKMRVJG1sdORo5EgkKlwGp/LP6RPxhALwE2gbIBa0ACfoq9Dvwx++K8MnxAvO58y31iPdF+j3+oAFQBaEH1wd1BhUDWwC1//IB6gaKDAcQ9BCpDmQKWQVxAD39hPt6+6/8nP3S/U786flY+Jj3ZfiR+QL6KPqO+Sz6MfyJ/6MD8QZ4CVgKsAm+BzoF3AIrAugCJAUzB44HOgbZAo//0vwo+5f6a/ph+nT6Nfrg+af5fvk1+kT78vzx/Q7+Tf2K/G38v/2C/3cBmgKAAswBswCJ//H++/03/jH+If6p/Rj7H/lc9qj1gvZY+Hr6Ivut+lX6k/02BhARdB0dI2kjORx1EVkHGv9B/Mb88wDhBf0HBAab/4X2A+/46q/qE+3g7vPvlPAF8tv1GPvvANsFmwihCf0HIQX4AkECOgXvCiARPhW2FMEPtAgPAu79Ffy8+wL8jfuw+mH5ofdc9nz16/V494v4+fhV+Nf3OPlU/VcDSwoKDzAQ3g5bCjkHOgREBJIFPwexCPAHUwVxAeX8vfkp+MH3TPjk92j3/fZG9x/5SPtw/cj+ov4B/lT9J/2P/msAGwNmBesF6wTyAb/+nPzA+zr9Wv5N/9v+o/zM+n74f/aS9Ur02/Q69Uz29/c2+Gv5WPpm/T8HWxQ0IX8r3Ch/ImQVawoEBZcBCwOpA1oDYQJq/Rr3+fAU6sPnS+dr57/oiujo6gzxXvmNA34KqQywC/oHvgbUB40KpQ+hExgWzBYcE3YNjwbh/5z8lPrQ+Nf2g/O58bPxN/Px9bH23vWi9JbzbPXT+HD9NwO9CDoNixDHEWURwg5HDXoKQgnOB+4F9ASaAlsBwv6m+0n4afTm8UHxWvLV9O33p/ro/Br+FP9//8H/4AACArIDTQXUBRoGeQVjBF0DSwEt/4386fnQ+Cb4+fia+TX6OPr5+Jv3n/Wy9Bjz4vPV9LX2RfoC/Fr+zv6J/igBbgqhG7YpITWkL+Ej/BN5BvgCHwAvAF3+Xvoa91rySe2N6VvlXON840Pj9uRU6A/w5fyECa4SjRUvEtUN7Av1DYsS+xXyFgsW3RLCDkgJSwLZ+3/1xvCh7QHrgOpf7Cjw/vSF91n33vXx9HL3Zv0eBP8KRg8mEmsUThVIFU4TxQ8PC8sGMQPmADr/sv2m/OP52/X48SnuiO2a71fzYvjG+2z+hABeAkoEeQXxBTYGGgZyBiMHBwe7BvgEdwLI/wX8FvkT9yP2ZvaF9oz2m/Yn9gP3f/cp+Cz4GvdW9wf33fj8+pD8AwDn/xUBpf/R/+UD4BOwJpMytzfHJqkXTAeJ/Yn/Qfyh+ef2PvIr8Y7uwuo+6BbkqeFN4k/kYOrU9Z8DSRKIGUoYQhRgDz0OfhEcFMMUvRPYD5YNbgoEBBv9LfTR7BXoW+Wz5l3qFvBM9q35Pvpx+fP4wPt9AaQHPg3hEDUTKBZHGOUV8BJwDDoGywTvADUACv/E+Jj3LfTJ8MrvGu3j7o3y4fVn+mb+KwHFBLsHkQlCCrQIvgdJCDkILAioBrYDhAGi/WH6WPh/9bj0rPR29E31fPQB9Ar1uPbm+FH7Qfxd/Yz9Ov1t/fL8y/1//v3/cQCnAC3+Y/5EAsYTSyi0Lt8xkx7ODoAD6fmy/Sj8MPeo9vLzRPKU8O7ro+mq5nzjKeTf6UrztgEKD/IWMxkCFNsOCw71DUYQRRLFD0QOJQx4B6MDc/yv88HslOYy5Tjob+3V80L5CPxd/MP7Yfz9/xcE0AghDoUR0BN3FdAUbhSoD4gHRwOf/jT9hvyT/FH6mPby8lLwJfAy8Bvy/fXg+cL9fQFmBcoISwryCvwJfgiIB74G7QZjBj0EWwHu/YT6XPc99Qv0rPMb9DD1n/UK92/3Q/dr+Fj5Ffu4/r7//wECAukAuP/C/SH9y/12/sv+sv4o+8b6QP5aDksmBCsnLKAdwwvYBLP7ufyY/376+fjq9pbzJPO97/TrredV5Hvlu+xS9w4FMBHfFAYTLQ/AC1cMEQ2QDQQQSQ9BDGsK+gW6AGf62fEE6y/nTugT7lP0/Pi2+1T8xvs+/Jb+uQLaBiwKnw5PErMTrRMzEToNBAfvAvwBswBlAPj9//q09zrzJfHZ8FHxn/Mz9un57v04AZ8EMweYCCIJ+QjzCNoImwi3B6IF7wJ//338Bvqr9zP2DvVw9OX0gvWu9hr3Uvcv+MH3Yflw/J/+lAELA7kBswGG/kH90vxX/FD9Dv5m/f784Pm6+gsCGhrSKegpSyZhFYoKiQNz/F3+bP6X+qT5WvQe8nfyHe686KvjT+NW63P0tf5eC6ES/BIgEIoMTgs3DX8ONw98D2MNowsWCIQB0Pqv8yntCOn/6MHthvO394r6C/yp+8b7JP7sAYIGswpQDqsRzRLTEscRIQ1YCZEI3AEAAMX//Pgp+Y74sPJb8SXx1vDJ8mb1D/pK/lsBogXKBywJUQvmCsoJTwkmCBoGMQQbAuv+Avyt+fT2EfWy9P7zn/TR9bX2PPjH+Ob4l/kl+oD86/6XAckCtgEuApb9dPsh/Mb7qfzu/v79zPvz+RL6SwFcGnkreihCJYMXTgqZBCr///s0/n38tPg99bzxqvAT7uDmouEW5aXsy/V3AVQLqxCXEjAQEQ3WCysNXQ58Dz0Q1Q5qDIIGfP7R91HxHezS6U3rGe9R8+r3Pvr4+2r8pvz7/qwCrgcCDSMRfhPKE54SFhGbEK4IbQNuAEH7kfln+vf3OvS586fwIO7D8Kz0m/dn/K0BvwTBB5EKuQs7C/YJWwkzCLsGqAUkA2EAG/2d+ef2JPVd9FrzNPTF9bX2+ven+Cn58/id+ez7y/4MAU4CgANIAZX/Ov4u/Ln7Evxw/Yb8o/uX+nX4pvzIDdknPSvjJUsfuA8wBrABuf3J+1b+ZPyR9xT03PAZ7hLo3uCP4ifrbfM3/SAIEQ6lECYRyw7cDBsN3g2JDgQQHRFjD/YJdADn953xb+yG6lbsL+/48WD1WPhV+qb8Wv15/pQB3gVxC94Q1hRBFh8TVg/vCxkJaQcOA0IAO/ws9772TfTD8SLxP/Be8BjzUve8+4cAjATIBs0IyQqKC8kL4wqaCUIIJAWKAj8AevyU+X/3MPUn9RH1WvTb9Hn1dfYz92X4Zfki+g78tf7GAC4C3AK2AdX+6P0V/Gf6sv3V/ZP8Pvza+PP3DwCTF8QtrykmJRAb7wqrBdv/g/zf+xT9XvsK9iHz3PCZ6uTio97X5HjvGfimAjgLcw7kENQQnA9QDjEO+A1TDm8QoRHuDsgGAvxE89fs0umT6uftyfA08+f1XPj8+nn9hv4vAC4EZQkED6ETqRZRFoURGA0JCToG2ASzAmD+SPrR9y30z/Iy8SzvLO/s8R32NfpJ/88DYAUQB3sJwwrDC+wLpArjCLsG+wSNAUr+/Poa9/f0uPRT9ZL1uPXI9WP1bPb695v4WPqA+3P8fP/8AAsCkwLPAej+Af2s/Bv86/1c/8n8dPoK9/b4+gUXI+ct1ySeI/YRXAdmBJn8Xvtw/Dv8t/lM9tjzCfB650Tf+N8O6p/0lv1SCHQMBw9pEFwPnA9KDmcN1QzCDXwQMBF9DOkBoffK7s/pbemb7K3whvNA9eH2bvkh/BT+y/8VAoIGOwyVEb8V4hYlEzEOpAktBqwCBgAE/+/8Af7m+kb3Q/WF7pjuA/BO8lj5n/7iAb8EXwbhBoIHqggZCSIJGQpPCdEHPQWRAAH9lPg39cL0tfQw9ef1nvYD97X2Nvdc93L3Jvlq+5n9JQH/AkQCDAH0/nf8vPvJ/O79qP86/yj8DPor/M4HDSTYKrwh2CHkD5IGeQTV/FT7Avwr++r38fT18v3ufedt4D3iBO2u9nn/wwlXDJwO7g9GD2wQ2w9tDhENfQ3VDokOpwmu/xT2q+0e6WrpRu1F8fjz5/Xn9nv5wvwa/8MBcwT5CO4NXxJaFckVvRIFDWIIEQQ7ArkBOv62/EL66PRz9JbzS/D28FfzdvX5+S8A2wNZBZgH8wcmCMoJ/Ar/CocJmwf7BHoBqP6T+yb4efVp9HP0jPWb9gD3Nvcm99H3qvj8+fj7Wvwx/aQAtwCEAVcCUP+4/rb8ffv4+yv86Pxt+/b45vnvAi4fhDA5JKUiWxScA3AEHv7y+x7+Pf6N+gr2XfO374Po+98O30HpjPWi/rQJtQ1zDbIO+AxdDiAQaQ9mDnMOVg8nD2cLyQFv9ynug+g16GPso/Ho9LX25PYm+Dv7tf3QABEETwiDDV8SVBXoFaQSQQy4Bu4EzAEN/3z/vPtX+4f6WfYn9rPyrfGp8hT1AvpD/3YDQAUNBpUGuwa+B90I8AgfCS8IGgYLBFsACP26+W/2XPX09S32EPer9wf3ZfdM+Lr4xvrf++/7Bf3L/UsA7AHmAMMA7v5X/Mn63Pud+rr63PzK+P32wPlBAlQevzIMKCInlRkQBsUDo/ya+Zr7Lv2U+oL2/vMc72Hnrd7Q3Krmn/PF/Z0K2A/LDyYR5A6lDvoPUA+JDr8O7g/FDyUMowK392Xt7Obj5evpp+8x9EP3/ffw+Gf7g/1bANIDvQg6DvMSxRZmF+YTNA5JB0cCNQF0AWz+Af1b+2b00vRj9Azxd/H78sX1w/mf/+4EkgZPB54HuwdcCF4KYQuqCasH8QW9Adj+O/y6+AT24fTe9CD2aPeb91z38Pfn94f52fsn/Vf9Qf0k/jP/1gB3AZ0BjP8h/bz8yfrK+eX75fss+Qb41PZk+pwOhSw5LgIn+iM6DRcEYQAs+oH6pvyA/Ej5efVE8rXsHeNu3FPhHe1c+OUEJA/FDwoRChCvDbsOJA8kDugNuA5WD0cOewcU/aDybena5ZfnNu2z8uT22vgf+eP63PxG/20CLQZ0C6gQjRQUFzsW6hCECREF2QK4/sL+4v1J+Ab4Hfa/8/7ymfL48cXzT/ip/c8CcgZcCF8IAAikCOAJNQpoCbEI9wX7Ak4A8vyn+er2hvRX9CT1Wfbk9yn4sfhl+en5h/vM/Oj8cP1//q/+4wCmAYQA2/8q/lj6H/lV+uz5YfuW+6T4ovYG+PICyR/1MqwpjSnnGbgF+wMo/Cn54vv4/C/6J/YC88ruO+cf3mvdeObC8ir+pgurEDMRWBJcD7IO9A8dD3wOmQ4nD9UOKAt3Aff2qOwD5iPlielx77X0I/jz+OD5Wvyf/t8BQAXTCcIOOBNzFi4XeBP7DHIGgALh/4b+3v3w+XT6APhR87j2YPM870P1nvYm+RgB6wVsBoUH7Qi+B2wH2QnZCRkI9wZvBdkBJ/5H/Cz5J/Un9Wz1KvYg9xz4Yvgv+DL5mvrs+1T9lv3C/U3+XP+dAO8AlAAK/2b9VPt++Wv6d/p0+lH6xPgH97r6EgoIKA8y9icyJ3IRyQL/AF76lPpa/ST+2flX9ODwFOxV5MPdV+Dr6p72NQI9DtQQjhGCETcO/g0ED/cOFw9sD5IPIQ7qB3P9PvMU6uDlzeYg7NPxU/b2+Or4UfqA/CT/owK1BsALQxBRFJwW3BVMEXoKtQVhAYD8GP3P+8r4i/k29/H2I/bi8x32j/SY94L/BQGvBGsJmwi1BqgHJgibBmwG7QblBNICuQHR/q37tPjB9s71pfVi91j4dfgv+Xv5t/kx+6P8rPxQ/Yb9vP02/4EA8wDI/7v+Lv1n+qf5bvli+B/6Tvoz9xP4qvpJBsolvDTsKIYqthW2AqoBzPrm+n/+Zv87+2n0HO9z6q7kqt3W3iTpWvTu/ooMvhDNEX4Suw4+DXwOeQ/XECYS+hGrDyYJyP2D8xHrquYV58jrdPBj9IL3wffw+Ej7lv0SAUoF7wp5EPIUUxezFkwS/AtlB5cB2P7h/pb7lvv2+iP4AfV68zTylPBQ9Ob4tvyaAQ0GLwjaB6gH3QctB6sHxAjwBy0HJwXvAQ7+Yfoj+CP2pfXh9pj3APgZ+c34APl++hX6/Ps6/RT9cP46//T+TgCgAL/+FP/I/cz6vfq6+hD44/kP+jb4bPfh9lf7dwq5Kqk0iimtKKsQkAHO/+n6Pf2dACUBevsF80bt0uhG4yLdpt+W6vT0DwBaDecQsRH3EOIMCQtwDZIQjhPsFMATyw/kBrb7evKZ62Ho8uhc7YfwUPRP90z3X/iL+Zr7Nv+sBMAL4xH/FUQXdBU2EGgKnwWwAhICMP+v/tX9+fmR+Xn1JfLv8KrwovQp+aL9JQIEBecFMwZMBn8GkgekCAYJEwkzCIkFNQJQ/q36e/hM9xb3I/ho+Cz4Dfg/+P339via+qn7pv2y/j3+IP9j/6j+ef/b/3D+B/8X/vL7hPtS+dr3OPmO+Uz4KfeB+If6/AmAKrky7yjJJ6UPUQEPAMb8MP81AvICQfxq8nbso+gp5CveluDl6gv05P78C1MPUA+4DnQKWAniDJgRvxXfFnoUbw/LBm38TfTX7lbrZuuI7gzxN/Sr9l/2IPbH9ub46/yJA9YK/RBbFKAUSBPYD5QKGgcOBVECMQOpA5oA5P6O+Yb0+fBe71vxdvSh+az9pf+aAd8CbQPuA1YFJgfKCFsK3wpbCeEGBQPI/rD74/lY+SL5Zfn8+C/4tPcs90z3dfg1+RL7ff2D/Uf+jP7b/cL9U/4MAMH/Ef+7/5/9JfoC+nT6h/nJ+qr6xPdQ9h/5MgEsG4AzjCoMJ8sYpgLzAM7+pf8OA5kFvQC19mXvwulh55ziRN/p5iXx/PirBQUNCwywCzIJTAa3CecPdxVwGJYWchHQCrYBOPlA9PLwe+5e8Gfyr/Px9YL1xfNz8wf1kfjL/kAGbQyYEFkRrhCWDncLqgndBy0HNgfhBWYFBQJd/Hj4cPQP8tzx3vSe9636CP1a/Ur+gv8VAQ4DXQXEB44J6Qm9CTYI/gRUApL/sv3r/CH8yfuN+iz51Peo9rj2t/cm+MP5ufs3/ML8CP0k/XP8If2P/ln/zv87AGP/nPxa/Pz6+fit+qb7kfrm+TL5Yfm7//UVji4lKf0i3hj1A+kAoAAVA1YGuwd9A5v4hO/S6SHp7eVD4jjo+fAD98MAvgftBmYGIQXPAwAI4Q6tFVwZCxeREbkLJwQU/fP5A/iP9a/10fXx9AH1jPNU8dnwUfLY9fj7UQJZB9AKZwvpCo0KjgnQCXoLvAt0CnQJ2gb+BBsCPvxt+/P3mPWb9xz4/Pjw+br6ZfkS+tX8Wf9RAugEOgaMBrgGhQbrBaIExQMeAz4B5/8U/4n97Pv/+bf4PPgQ+CD4DPkG+t358PmB+gb6Jfqg+6z8Zv5J/4n/2/68/Lb85fsI+zv8c/yZ/cb7Xvr5+Z373QjNJDAtZiE/HJQJbP/iAQsENQlOC5EIef5j8y3rIekk6xjnRedb78X0WPlxAJoBjgCXALX/nwPZCj8SQxhDGGUSdAwmCPgCNQASAeH/N/2A+2j4J/YO9FvxsPA48bnyCvdd/J//5gEeAxEDIQRzBRAHdwo7DF0MVw3/C24JdQYhAzsBhf9s/979JfzD+zL5m/fX9oX2PPhL+if9Ov90ACgB0wFtAuUC3wO4BGwFYAURBBEDzwHh/zr+rP25/Mb7evuB+hn6xvn2+BP56vix+Cn5sPlL+078Hvy5/b/8rfrW+x/7nftd/UP+Ov52/WT8RfpK/MkD9huyLFchwRr8C/r/CAN/BusNzg80DSQDxfUN7Ybpm+5r7j3rKe8L9F30LPfp+fD5mvoS+2n/aQaZDQwTLxRTEOwJQgh1COcHJQqUChYHVwJg/Uv6e/je9vT12/Uq9QH1P/fj+Jf4Yfmz+sD76/0YAa8EQwekB/0I3QnHCNoIZQjBCJsIBggHBzEE7AFA//L8O/wu+4H6Z/pr+i/6Avos+sb6Tvvl/FP+1P87AW4B1gHiAeIBJQJhAoMCOAIoAdv/Pf9w/p/9pv1t/WH8d/t3+t341/hC+pf6Ivtk+0X5uvh7+D/4fvki+1r8Z/wO/Pb5p/ln+4L/3g6nJQ0k9BggEJACrATWCbQRaRg4FbYMN/3r89Htd/Gb9031lvNU87nxbu+k707xwvM39Wj3Hv3fAkMHrggvCCcFYAP0BncLSQ+CEQoQEgtmBTEC3wFHA4MDWgJLABj8YvhS94L3/fZy9zL4P/iU+Hj5Z/sl/AL8sv0/AKkDjwVsBhwI1wbeBRoHUgfKB64HewelBsUDSAG9ABkAwv77/dX9JP3y+//67/qd+uP5AvpB+x78v/xm/ST+2/0U/fj9FP8iAOYAcQFYAYoA1//e/mP/UP/3/oX/Hf83/i79Bfz8+pr6e/np+Zf6nfo++in5u/cm97723fh//5sQNhscEzQMUQKdABoHLg2WFz0ZlhXjCOX9O/rd+ZMCTQV3Aif+wfiZ9JrxEfMz9er3Dfct9VL3hPlC+tD6cft3+o376/6TAu0G0Qf0BYAENAMkA2kGkQqQC3oKiwcIBKwCXgIYA48EHgSUAfH+FP3v+/j7Xfwo/Kn7W/vW+xL8Wvz4/E787/zl/Xn+9/9bAVQCkAEVAUEB3wFkAwgEnwR8BG0D+QGHAbABZwE1AdMARQAK/yT+ov0e/b/8Ffwx/Av8//v8+/L7TvwO/Bv8Tvzr/ET9G/0H/qz+f/5z/lr+ef5W/gT+5P4R/3n//v5H/p/9kPxz/JD9m/9pByYQKAw6Boz/mf2WAg0G5QtHDowNzgWs/AX87/yGAzAHvgXJAyH+nfrE+Fv7bP76/2UACPzM+oH6zPob/TT9nP2j/Ib8mfzC/SIAq/+u/6L+Dv58//wA1QJgAwgDOwE7AMYAagFwA4YEHgTiAigBawCkAMYBZAICA2cC1gAcADr/0f9oAKAAgQDB/x3/U/6Z/sj+2/70/on+g/5W/on+8f4U/yT/tf6S/qz+Pf/U/zIAdAAZAJv/Wf+M/wAAfgCgAHEA9P+J/2//uP8MAA8A6v+V/yr//v5G/3b/wf98/zP/5P6v/uj+Pf95/03/Gv/Y/pL+tf67/sL+5P4U/4n/QQF9AoACpgHh/6v/mgBOAoYD8gPiA30CUQFOAJQAbQKpA+sDrAIfAQkA4f9lANMAoAFYAZ0Apf/L/tv+Zv/3/7X/Ef9a/hf+YP5m/qL+u/6W/jH+/v1A/sv+b/9y/0P/J/8B/2n/7v+dANAAnQA4ANv//f9LAM0AKwEFAeAAhAB0AKcA0wDsAO8A0wBrAGUAYQBrAIQAWwAvAAwA1//u/xYAFgDk/7X/ov+f/8X/3v/q/8v/lf9s/3z/qP/L/+H/uP+S/1n/U/+V/8H/5//L/67/if+C/6X/wf/6/+7/0f+y/67/wf/b//f/AwDq/8v/rv+4/7j/zv/L/7j/m/9s/2P/ef+Y/ykAwwC3AGgA7v/k/zsAqgD8ADUBVAH2AKAAZQCUAAIBKwE7AQ8BzQCBAG4ArQDZAPMA2QB0ADsAAwAMACkANQAcANv/if9G/1P/dv+M/4z/Y/8t/yT/Nv9W/3b/jP98/3L/XP9g/5L/u//U/87/u/+y/7v/1/8PAC8ALwAfAAwACQAiAEsAbgBuAFgAQgAiADgASABeAHQAWwBVADgAKQAvAEUASwA1ACUAAAD6/wMACQAcAAkA+v/X/8X/vv/U/+T/3v/O/7X/n/+i/6X/uP/B/77/tf+l/6j/q/+7/8H/vv++/7X/wf+7/9H/2//X/8v/wf/F/9T/3v/h/9T/xf++/7j/uP/F/9T//f9FAFIAOAAiABkALABYAIcAnQC6AKoAhwB+AHsAlwDAAMYAswCkAIoAfgCKAIoAkQB0AFUAOwAlACkAKQAiAAwA3v/L/6v/qP+y/6v/qP+M/3n/YP9j/3z/gv+P/4X/ef95/3n/kv+o/7X/tf+4/7L/u//I/+H/+v8JAAYAAwAAABIAJQApADIALwAyADIANQA4AEsAUgBCADgAJQAsADIAPwBCAC8AJQAfABIAHwAZAB8ADAD9//H/5P/e/+H/4f/h/9T/wf++/77/u//B/7v/uP+4/7v/vv/B/8v/wf/I/8H/vv/L/8v/4f/k/+7/7v/X/97/2//x/+r/6v/k/+T/4f/u/+7/8f/n/+H/zv/I/87/1P/k/+r/9P8JAB8AIgAiABwAHAAyAEgAXgBlAHEAaABrAGgAaABuAIQAhACHAHEAYQBbAFUASwBIACwAJQAfAAwAAwD9/+r/0f/B/7X/sv+u/7X/q/+f/5//kv+P/4//pf+1/67/m/+P/5v/rv/B/8H/0f/x/+7/1P/F/8H/3v/6/wAAHwAiACIAIgAPAPr/DwAsAC8ANQA1AEgARQA7ACkAIgAlADUAPwA4ADIAKQAlACIAIgAfACUAEgAMAP3/7v/0//T/9P/u/+r/0f/O/8X/u//F/9H/5P/e/9H/wf++/8H/u//L/9f/3v/u/+7/8f/k/+7/6v/x//T//f8DAAAAAAD6//3/+v/9//r/9P/6//3/9P/u/+r/8f/x//H/8f/0/+7/6v/n/+7/9P/0//r/5P/e/xYAJQAPACIAOwAcADgAGQDk/wwAawBVAFIAWwBeACkAOwAGAOr/LABLADUASAA4AC8AGQD3/+T/6v/6/wAADAAGAPr/9//e/77/vv/L/9f/6v/0/+f/1//R/8v/vv/B/8j/4f/u/+f/3v/n/+H/8f/k/97/8f/0//3/AAADAAYACQAGAPr/AAAGAAwAEgAWABkAJQASAAkADAD6/wYABgAMAAYABgAJAAMA9P/6//H/+v/b/xYA/f++/xkA0f/O/y8Asv/F/x8Atf/k/wMAvv/q/xkAwf/0/wMA3v/3/wkA5P/9//T/5P/3/wAA9P8PAAAAAAAWAAwADwAWAAwAKQAvACIAJQAsADIAOwAyADsAPwBSAEIAOwA/ADsAQgBCAC8AMgApAC8ALAAlAB8AGQAcABIAFgAPAB8ADwAAAOr/4f/b/9H/1//F/8j/1P/R/9f/0f/U/9H/yP/L/8H/vv/B/+f/9P/u//f//f/3/wMAAAD3//r//f8pAA8ACQAlACIAGQD9/wwABgAAAB8ALwApACwAHwD6/7v/zv8DAEsAWwBVAKv/jP81ALj/DwDV/jgArQB+AAYAOwAJAMj/Pf8R/wr/Y//3/y8AcQCnALoAmgBFAOH/tf+S/4//kv/O/+T/HwCzAH4ADwAPADgAAAC7/6j/9/8PAAYAFgA1AKoAVQDI/wYAoABbANv/6v8yAEgAHwAMAOT/AABVAGEAQgApABwALAAfAOH/mP/I/+T/y/+1/wYAXgBbAIEAPwAMABkA3v+r/5L/q//3/+7/9/8MAAMAFgAlAPT/BgA/AAAA6v/k/7X/0f8GAAAAHAAyAEUAWABlAI4AZQAiADUA1/+b/3z/Sf+S/3QA2QA4AekBxgEyAVsAPf8e/oz9FP0R/d79qP6u/8YALwGXAcMBagEVAcAAOwDI/zP/9P5W/3n/ef/h/3EAjgCzAKcAXgBCAB8Awf+J/1b/Wf+C/67/3v/h/zsAdABLAGsAOADn//f/3v+C/87/CQAGAGUAmgCqAOYAswAZAMv/yP+l/+r/RQCOANwA7wCwAIcAOwD0/9f/UP8k/2//b/+l/+7/7v8yAF4AUgBYAFUASAAcAPT/1/+1/67/9/8pAB8ASwA1ABIAQgAiAAMAHwD0/77/1/8MACkAhADWAMoA6QCEAPT/SwAGAIX/n/9y/2D/YP95/8H/QgB0AEgAXgB0AKABTgEfAOr/MP86/xYAXP8n/1b/F/+y/14AqgDGAP8AFQGHAD8A/AFgA/wBVv/1+xX8f//cAcABUgAn/2b+v/6l/0gApwAAAI/+UP0x/Ur+1/+9AIEAhf/h/hf/lf8WAAYAlf8X/3b+J/7x/gkAaAAfAIn/kv8AAFIApwDGAIoA9P+y//r/sABBAf8A4ADvACIBzAEoAhUCigH2ALAA+QAVAcoAhAA4AB8ANQBVAJEAewDn/1z/YP+r/8X/dv/0/sL+2P4N/3n/5//9/8j/jP+f/wkARQAiAOH/hf98/8H/DAApAAAAEgA1AH4AigCBACkA6v+4/2z/Zv9g/1P/Zv9p/2//f/+r/8v/1/+S/2P/UP9A/xT/Af/7/sX+6/4U/xr/Af8n/yT/Pf9G/w3/9P7u/gH/Gv+o/mz+Zv52/kD+Dv4X/o/+v/6M/kf+Kv4h/u79sv2f/cv96/7TAfgELQYjB5gIoQlkClsJrgelBuEEAgPZAGP/jP+l/7L+cP6J/mb+af4n/kT9qfya+/n5pPkZ+hz66fnD+yH+Ov/B/3cAXgGmAjQDRwJaAvwBkAG5AeYBTgKHApYCFQOmA4ADVwPDAsYB7ADq/wr/g/7Y/fv86PwY/Zb9+P0B/nP+9P5N/1b/ef/R//3//f8iACsBRwJLApYC7wLZAjED4gLpAaoBDAFbALv/Sf/F/+7/mP/q/+f/qP/q/87/BP+D/gH+n/2T/Xb9uf3r/TH+Af9p/9f/awBIABIADwD0/n/+j/58/uj9Wv0e/Sr+6P7e/rv+Pf6D/pn+Vv6A/dz8/PsY/Cj8nfuj+1T7o/tU/EH99gDxBnEJegrfC2kO1BD6DwsNBQvKB/gEPgIq/oP9Mf3y+nH6fvpU+5b8TvzA+wj7DPrp+RP5UvgA+e34Xvle+4P94f9qAr8DuATeBdQGdQefBmYFgARBA88CwwIoAgsCqgFUAbAB7wGjAcYAtf+G/j39O/y5++/6Qvos+rb6w/s6/aj+3v/zAN8BnQK2A68EjAT1A4YDWgNkAyQD5QKwAvIBowGBASUBAgFFAAT/Y/7i/Zn9av0x/ST95fw0/QH+bP64/uv+6P4q/1z/cv+M/3L/Ov8a/yf/XP9Q//f+zv7C/pL+fP52/lb+5f1z/TT9+/zM/H38Ifzi+5r7VPsx+xj7KPsi++z6Ivvi+xH/YAXACeULxQ2lEHQUOxV7Ek8QVAwGCGMEM/9K/Nz63vay9K/0UPYA+TL5Nfnz+Tv6vPvM+536h/rs+eb5V/sr/VD/TgErAzoFTweXCe8KaAoyCRAIiAbhBAIDAgGM/0D+V/0x/ZD9Af7S/Z/99f2//V397/wh/Hr7wPqj+lT7C/wb/V3+xf+9AToDjAT6BX8H0QdyB18H2gbFBa8EJAP1AcMAVv+i/sj97/zV/AL8tvtK/CX8O/zc/Cv9hv0O/uT+fP+f/yIAsADZAAwBHAH2AMYAdwDB/yf/6P5d/qL9av1z/UH9K/1j/ab9ef00/RT9xvxz/Cv8cfvJ+sn6dPpI+rr6LvuA+637Ify5/TIBTwdRDGYO+g8GE2QWGBe2FCYRAgxZBz4D3v2E+Tn2w/Fi7yLwg/Ky9DD1X/aU+Az7vP1W/qn9+/0X/lb+n/8JAQUCCwPLBAQHAwn2CnoLfgo/CRkImAYkBCsBF/56+wL6JvmB+Kf4MvmE+YT6gPwO/rj+tf64/hT/Zv+P/3//fP/9/60AvQF2A7UERgW4BX8GIAfkBlYGrwUkBMYCUQEMAPf+ef0V/FT7w/oY+wz75vqt+1T8r/wO/lD/IgDZAF4BJQKzAvsCCwN6AgsCfQGnAP3/QP8L/tL8Hvy2+z77//oC+zv7o/sx/Pj8jP3Y/QT+2/3V/ej9R/2A/PL7fftB+yL7mvso/PX7TvyJ/ZX/yASXC7IPYRMIFoYXfBhXF3cUJA4qBav/pPnS8ofvpetU6OXpB+1I8ev1sPmp/UgAHgMzBrsF9ARTBBUCpgGpAh4DnAODBAQGVgcDCWQKWwkAB9sE9QHL/uX7+fiy9anzsvN29ED2CflL+6b9swB9AyoFCgb6BQEFzAMLA1ECOwG6AJ0AwwDcAWoDOgS/BPgEgwS2A50ChAGl/5n9Pvw4+wj7h/sC/KD8g/33/j8A7ACgAaoBFQEYARUB4wDQAMAAswDDADIBgQEyAdMAUgAq/z3+V/07/Az7O/rw+R/6sPrM+8n8ov3F/gYAygBFAfYAHwAt/1b+VP0u/Aj7IvpL+U/5ofkM+mH63/oS++z7Tf4LBP8LxxCuEgIVLRnjG5IamRb0D9oH3AFh+7bzEO746SLnBufP6f3uufPz92H8AADfAw0HQwdyBjoFnAMoA68DJAQeBPgErgYpCFIJLAo8CWYGTQNLAPX8p/lZ9mfzNPLS8o/0Q/eq+i3+DwHSA6IGqgi3CJIHmQXfA6ACOwEWALj/zv9SACIBGwL1AqwCvQH8AAAAc/4R/S78YfzG/Cv9yP5xAK0ByQJnA6kDCwNEAisBYP+P/gv+Pf3F/fH+yP+RAKYBlgKTAj4CUQGo/y3+lvzJ+tP5ePl4+fP5PvsL/az+IgD5AMMBEgKEAbAAef/l/VH8G/tR+nH5PPnK+Mr4OPn5+eb6W/vJ+338d/y7/icFBBA7FtUXiBsiH4cf4x3bF/UNqgFS+Bnwfua/4VDgkN5p4S7ogfGB+vwA5AZxCjgM0g2pDDgKtQZ9A0oDyQOiBB0HPwmRCmgKsAkpCDoExf6I+Hryju5v7LvrHe328GL2K/xqApQIHg1pD1AP+w2KCywIRwRoAMv9Hvwr+9L7R/0K/14A9gBhAQkBIgDO/hT9wPv2+tP69fvF/T8A4gIUBpsHwQgcCVkHlQV6A7v/zPzM+qr5APnA+fz7tf0fAG0CUQMUBAgEoAKaAL/+CP0++2T6W/q2+vL7R/2s/gkAwwAPAdkAaADU/6X+YP22/O/7O/v2+qr6mvr5+r360/o++kX6l/rT+i77jft9/E39/f9fBn4S1BvdHM4isSXPHkAZ0RD1Aej01eod4rvZn9kY4Orjsup59oQAkQiDDv0RdRFUDZoKVgd9AtkACQHAAR4EyAbQCgINsws1CdUDdv2L9+bwpeuz6NzoGuzT8Dn4BQFMCP4NjhEpE7QSSQ+dCmwFbgAO/Zf68/l6+2P9hf8vATUCZwLsADT+DPvX94b1RPT39IX3G/u+/4wEoQj/C68N3g0uDW4LmAdmBGoB/v1O/BL70/lr+u/6vfqE+9z89fwY/c/91f1p/hYA0AD8AZkDGwRmBFoERAOtAWn/ufyU+tD44Pcc+Gj53PrG/Mv+YQCQAZABgQAK/7n8ZPpC+B33DfZs9iD3yvhx+x793v/8AEgBUAQwBzkRSRswGoceph64GDAY9xBQBXH62u6H5mTeW9zx4L7iHujZ8Cb5kwM+C3UQsRKoD2MOpgyYCB0HJAUoA/sC5QO1BgMIggfYBZoApvuY97/yke8A7Ubsgu4C8nv4AwBJBqkLZg6uDyMQgw6gC38HCANv/5D8rfvp+7P8n/1g/Wr9g/3P/Nn7Z/rN+C/4XPjQ+eL8KQCpA9cGmgnlC7MMlgwYC1UJhQZnAtMADv5X/Nj8//rW+kH7G/tE+2H7+Pt6+8P7+PyZ/Yz/VAGzAjoE6ATrBG0EXQONAfH+Yfw++nj4/fdF+DL57/ph/Cr+9P/zAK0B0AC4/mr9qvpV+Cn4j/Yg9+r4cfkY+7/9j/4AABwBzwPQCu4WHRs/Hb8gDBxjGOMUUQo6/6/0auqp4ULds99y44fnA+989p/+3gbvDMcQGhC8DYoMWwkzBzkHfAa4BS0FkgVQBuQFuAStAPb6Rvb48QnvL+697pDx8fRb+SIAbwZqC3MO1Q7SDaAL1AggBvsCJQDi/cn7RPv/+9X8JP0B/ZP8rfsY+xj7OPuz+3r8n/3O/6MC6wS1BnUHyAalBYYEdgM+AjIBXgASAX0C7AKyBVMGfARNBEgBg/zp+qH49/SZ9LX1Vvbj+az+uQFDBRkIkQg/CDYH6wT/Acj+oPtI+fr3yvfK+Ej6yfvc/Af+1f6y/rX+F/4x/OP6Gfra+Mr59vrs+qn8dv3C/bL+Zv6i/Rj9qfuG/HYEaxRvGyQgzSX4H3scTRipCxkA+POX58Dd69jA3OHic+k08nj5YQFPCDoNfhFJD1QLngmfBXwEyAbqCK0KlAqhCc4H1QS2AZP7FPRJ7qbpG+gY6njvGvdd/XADtwlaDoURDRLFD/gLEAepArX/lv7k/sj+qP7R/rX+R/6j/G76jvct9H3yUfMa9tz6/f/IBJoJhg3eD5sQyA+gDCAH+QFQ/Xv52vcg94j3Vflt/Mj/OwKlBS0GgATFA0EB3v4h/kf91fyG/TH+lv70/8YASwCl/4P+Xf3V/NX8Zv1//m//1/8PAFUA9P/F/rL9RPyd+tP5tPmB+Uv6z/tt+637+Pzl/BH9Mf71/Tr9Jfyt+wn6Ufrc+4n+yAacFnEejyLgJg4h1BopE3UGwPoM7wzmat+t3D3hgOjt7/r2wvwLAnkEBwccCjYI8QYvCLEH4whHDHMPqBCiDl4L8QXL/wz6XfM57r/qmenL67TvnvaG/rwDmwcMCisLlwoMCcoH9AU3BGQDWgOABGMFYAU6BEEBgP2B+dH1nPP18m3zCvVf+Pv8RQE6BWUIwAn9CMcH0QUIBDEDqgFkAVgBWAE1AjoD3wNBA0cCyP+f/Wf89vqz+nf7ivxE/a/+OwAoAQsC1gECAUIAM/+4/s7+cv8ZAIcAugCRADIAM/8O/l38Vfr8+Bb4hfhO+of7Qf2o/ir/hf8E/6z93/un+pr5yvgs+sb6d/yM/y3/xf+o//v+xgJaDRcaMh0aIlEgehYjEvYITf7t9mXuK+jw40PkHupe8Dn2L/q2/CH+if53AgQFjAVlCU4LmgtmDrsQHxK4EJMLfAVN/t33evOd8JTvi+8f8T7zTPb1++7/HgIXBAsE8gNgBHwFkgcZCcoJ8wkWCaEHiQUOA67/ivvU9xH17vPV9J72D/nS+9v9rv/8AC4CZAOjA9gDogRqBJUFdQaoBo4HVgaJBHQCAwBQ/rL9uf2W/az+Ov40/gf/Gv71/f79NP3y/HP9Af5j/0gBbQIkA3YD0gLsAQIB8f/3/vv9vPzl+5D7cft3+4r7o/v8+gz72fvl+5z9U/4k/tv+mf2T/NX83/vD+0r8FfzY/DH96/37/Bj8k/yj+2sA3wwTG/gfDSQ6Ib8VAQ8UBSv7dvVe8LTtQ+tc7KTw/vTa9/r3tPdA9pX1Yfqy/hEDzQqVDyMRpxLTEs0ROg54CG0Ctvyb+BT2TPZf98H3lPib92L2X/f399r4VPuZ/RIAZAP9BmEKdAz+DMYLWAnxBYMC0AA2/4D92Py2+xL75vo1+hz6CfrD+a35S/r/+yT+zQBqA3kFCgcpB9QGkgZzBZMEpgOjAv8BHAGBAE4ARQC3AJ0AKwEZANv+8f0o/GT8AvwV/EH9sv24/mb/QgDZAPYAGAGwANwA5gDpAEEB/AB0ACr/lv0e/Mb63fnm+FL5AvqK+jT8nP2Q/Qf+dv7f/Jz8bfw1+1f70vy//V3+wf98/sz8JfzW++H/vAvtGisegCH2HdoQSwuHAjH7t/j09ZX1ufPu8yT15/XB9WryVPF38MnwE/iW/hEEZwwjEMUPOg9gDTsLnghGBokEOgMVApQAgQDu/j771/hG9VTyevKD83L2W/o0/gIBswI9BHkE6AQUBSEF0QUNBmMGXAfXBoYFnwMvAfX9Z/u3+VX5tPl++j77VPy8/PL8Y/34/cL+2/8JAWECNAQwBUkG1AZMBmYFHgTDAqoBPgHZAncCsAI3A5v/Gv+i/TX6Z/rd+eD5Nfum/AT+Q/+nADUAcQC9AD8ACQHGAVcCyQKdArkBAwBw/q/8D/s++t350/mU+un7XfwO/YP9PvzS+xX7OPoP+7n7BfzI/b/+Hf+7/8X+9f3P/Ib9TgFBDCYaYho4HVYYZAvgB6cAzPzf/BH9rP0M+//6pPj69d/zm+5v7sPvQfI++v3/+APqCOoIMwYOBYwEtQSrBTYI5gnJCqAKdQfeBOkAivtb+Wz3LPcA+cn6avz4/Kb8V/uH+nT6jfrv/PH/XgJABdoGMwe1BtsEAgMSAlEBXgExAsMCCAMrApoAwv6s/On6VfrJ+u/7UP2i/rv/9/+l/w8ATgDk/zIBlwGQArIEHQUgBzoGxQXCBPkBigJ+AOT/9P/F/sL+NP3C/KD77/oM+7P6LvwL/UP+wf9LAMMA0ADWAGsAXgBVAEgA+v+Y//f+B/6m/d/84vuj/PL8wvy//Sv9cPxQ/bn7FfxX/HT7wPvM+4r8qfyW/YD9ffyp/aL/mQQtECsU5hL5Ex8L1wauBqIE7gWVBp8G/wEH/+L8LPik90r0HvI69FD19vha/Dr9Xf3l/G37nfv0/hgCSgTUBqEH6gYKBwcGvwTPBFoDjQK2AloCVALfAcoAdv6Q/HH7s/qz+zv8wvyD/cz8rPz+/Hn9Gv6s/p//UgASAZ0BVwIbA9kCgwJkAjgCPgK2AtICVwKgAaQAzv+J/zUAy/+S/xwA3v1W/vH++P2l/wUBHADDAGoBj/81AdwBugB9AvkBOwECAcAAlwAiAKQAyP+F/+T/JP86/zb/6/5D/nb+if7r/Z/+hv7P/S3+9f2y/fj9Mf6G/Zb91f3r/Cf+7v6l/vT/Ov+o/kr+if1T/pL+1f4N/+v9xf2c/aD8Lv2Q/bn9kv5N/+wAigL9BgYJYgfgCLsFsgSIBv4FvgdZBxcGJAMiAAYA6/21/qz+JP19/e/7pvuj+5b70vug+wL8SvyJ/Vb+u/4U/9j+M//k/80AqgG5AqYC/AEiAt8BbQJHAz4DQQODApQB2QCzAMoAdABeALX//v64/jr+J/7L/QH+z/2c/VD+R/6C/4QA0f9lAK7/Q/9FASgBYQHVAgIBEgGEAZQApgEiAqMBFQGtAU4BlwA4AhkAWwCKAP7+GQDk/+r/if+W/mz+vP1g/hH/MP+i/6L/wv7h/mD/Wf9oAKcAUgBYAAwAuP+P//H/UP86/1n/c/7b/qj+Af49/gT+If6P/uH+cP5d/vv9r/3S/YP+zv7L/nb/6/0t/tH+NP67/4n/Y/+V/2z/qP8lAIQBLgICA5MDKwOjAzQDvANEBD0ETQS8A/8CRwI7AnQB7wAlAeT/qP+J/5/+xf5//u79z/0L/uL9+/18/hH+C/5W/lr+1f5Q/7L/if+4/77/uP90ANAA7ABkARUBCQEiARgBdAFbAYoBXgEJAfYApwBeAGUALADn//3/lf+Y/0gAqP8g/3//cP7o/oz/ov81AE4A0f9G/6L/+v9rADIBEgHzAMYAdAB0AKQA/ADjAO8AxgBFACwAHwDX//H/FgC+/5v/vv82/1P/Q//3/hr/IP8q/xr/IP/h/rL+3v6s/vT+Gv8n/zb/Cv9J/zD/af+r/8v/HAAcAOH/+v8AAA8ABgA1AAAA9P+y/7j/zv8SABIA4f/U/4n/Vv+M/8j/WADO/2P/U//L//f/AwBOAO7/KQBoADsA/AC3AKQA5gDZABUB/AASAf8AIgH5AK0A1gC3ANMA5gCHAG4ADADu/yIAOwBCAPH/yP92/5j/n/+F//H/u/+M/4z/Y/+P/7L/y//O/wwA2/+u//T/9/8pAFgAVQBSADIAGQAMADsAYQBLAFsAHwASAO7//f8MAAwAPwDX/9f/u/+Y/97/3v/0//H/u/+S/6L/uP/U//f/6v/u/8v/yP/O//T/GQAWACUAEgAWAO7/HwAiACkAMgAcABwA9P/x//f/FgAWABkAAADk/97/7v/q//T/9/8MAOr/4f/b/+H//f8GABwAEgApAAAA+v8ZACkAMgAvACwAHwAiACUAMgBCAD8AQgAvAC8ALwAiADsANQAvACUAIgAiACIAJQAcABwAEgAJAAwAAAAJAP3/AwAGAAMADAADAAkACQAWABwAHAAlACIAHAAZACUAHwAsAC8AIgApACwAHwAlACIALwA1ACwAJQAiACIAIgAiACUAIgAiAAYAAwD3/w8AHwAfABYA/f/0//f/9//9/wwACQAMAPr/8f/3//f/DAAfABkAGQD6/wAACQAcACIAIgAiACIADAAPACUAHwAlACIAIgAiAAwABgAZACUAIgAiABwACQAJAAAAEgAPABwADAADAP3/AAAAAAwADAAPAAYA+v/0//r/AAAPAA8ACQD9//r/+v/9/xIAGQAGAAwA/f8JAAAAEgAfABwAGQAGABIACQAfACIAIgAcABYADwAJAAwADAAMAAwADAAGAAYABgD9/wMAAAAMAAMA9//6//f/AAD9/wMABgD9/wAA9P/6//3/BgAGAAMA/f/3//f/AAD9/wYAAAD3//T/9P/9//f//f8PAAYAAAD9//3/AAAMAAkABgAJAAYAAAD9//3/AAD9/wAA/f/3//H/8f/0//r//f/6//3/9//x//T/+v8DAP3/9//0//f/9P/6//r//f/0//3/AAD9//T//f/9/wAAAAADAAMA9//6/wAAAAADAPr/9//9//r/+v/9//3/AAD3//r/9P/9//3/+v/6//H/+v/3//f/9P/6//r/AAD3//f/+v/0//T/9//9//r/+v/x//3//f8AAPr//f/3//T/+v/9//T/9P/6//3//f/3//T/+v/6//T/9P/0//T/8f/3//T/9P/3//T/9P/0//T/8f/0//T/8f/6//T/+v/3//T/9P/x//H/8f/0//H/9P/x//r/9P/x//H/9P/0//T/9//x//f/+v/0//T/8f/0//3/9P/0//H/8f/6//f/8f/3//T/9P/0//H/9P/3//H/8f/0/+7/9P/6//H/9P/0//T/8f/x//H/9P/x//T/8f/u//T/8f/0//H/9P/x//T/8f/x//T/8f/0//H/8f/x//H/8f/x//H/9P/x//T/9P/0//f/9P/x//H/9P/0//f/9P/3//T/AAD9/wAA9/8DAPr/9P/0//3/+v/x//f/+v/9//f/9P8AAPr/+v/3//r//f/3/wAAAAD0//T//f/3//f//f/0//r/+v/x//3/9//3//3/9P/6//T/+v/3//T/8f/3//3/AAD9//3/9P/3//r/8f8GAAAAAAD6//3//f/9//r/AAD6//T/+v/9/wAAAAD9//3/+v/9//3//f/9//3/AAAAAP3/+v/6//3/+v/9//r/AAAAAP3/9//9/wYAAwAJAAMA/f/6/wAAAwADAAAAAwAGAAAAAAAGAAMAAAAAAAAAAAD9//3//f8JAP3//f8JAP3/AwAGAP3/BgAGAAMABgAAAAMAAAD9/wYAAwAMAAkABgAAAAMADAADAAwABgAAAAMA/f8GAAMABgAJAAkACQAJAAAA/f/9/wAAAAAAAP3/AAD9//3/+v/9/wkABgADAAwAAAADAAkACQAJAA8AEgAAAPr/AAD6/+7/9//0//f//f8DAAAABgAAAAYAAwAGAA8AOABFAB8ADwASAPT/1P/F/+T/zv/e/+7/DwAGAB8AFgAcAAYADwAGAAwA9P/9/wAACQD6//3/BgAMAAMABgAAAAAA9//6/wAA/f8AAP3/BgAGAAAAAAAAAAwAAAAGAAAAAAAAAAMAAAD9//r//f8AAAAAAwAGAAAAAwAGAAkACQADAAAAAAD9//f/9P/x/+r/8f/x//T/+v8DAAkACQADAAAADAAJABIAIgAlABYADAAMAAMA5P/x/+7/5//b//H/7v/u//H/9P/u/+r/8f/3//H/6v/0//f/9//3/wAA+v8DAAMABgD9/wMA/f/x//r/8f/3//T//f8AAAAAAwAAAAMACQAMAP3/BgD9/wAABgADAAAA+v/0/wAABgD6/wAA+v8AAAMAAwAGAAMACQD3//3/BgD3/wAADAAJAPT/BgD3/wwA9/8DAP3/AwD9/wwACQAAAAwAFgAGAAMAAAAJAAAAAwAJABYABgAlAAAA9P8GAPH/AwAfAAkAHwAWAAAAEgADABYA9//0//r/HwAGABIAAwAJAOH/AAAGAAYA/f8iAAMA+v8JACUAAAD6/y8A7v8WABwAFgD0/wwAHwD9/+T/HAAyAP3/+v8cAPf//f8AABYA8f/6/xYAFgDn//r/AAAMAAkAHwADAAkAAwD3//3/CQApAP3/GQAPAAMAyP8SAAwAOADq/ykA8f/6/7v/NQADACkA2/8pAPH//f/k/0gAIgAfANf/LwDB//f/5P8fADIAJQAAAAkA5P/b/+r/RQAiAP3/AwD6/9f/9/8lACwA/f8AACIA3v8DABwAOADe//T/CQApANv/DwAJACIAAAADAP3/5//L/zUAMgDq/xkAHAAJAOT/HwAJAPr/6v8yAAMA3v/3/yUA7v8MAPr/DAD9//T/AwAZABkA6v8pAAMA9P/e/wAAHwAPAPr/IgAGAPT/y/8vAAMA+v8JADsA6v8DAPr/GQD6/wAA7v8vAOr/CQDq/xwABgADAAYANQDx/w8A5P/9/+r/9/8iAAYA/f/h/xIAKQD0/wAAIgDx//T/HwASAOH/AAAvACkA1P/0//f/8f/6/zsAKQD6/87/FgDh//f/EgA4APf/BgAAABkA8f8JACIA9//n/zsA/f/u/wYAJQDe//f/LAAZAMj//f8iAO7/5P9CABwA5//k/yIA/f/e/yIANQDx//r/EgD0/+H/EgAfACIA4f8sAP3//f/x/yIADwD0//r/PwADAOr/BgASAOr/HwBCACUA6v8AAAkA6v/n/xYAEgD9//r/LAD0//f/GQAfAPT/HwAfAAwAEgAfABwADAAMAAkA8f/k/wYAAAAJAP3/EgAPABIAGQDx/+7/AwAPAPr/EgAlABYACQAfAPT//f8GAAwABgAAAAYAAAAAAA8ABgAJAAMACQD3//r/+v/0/wAABgADAAMABgADAAwAEgAlAB8AFgAWABIAJQAcABkAAAAAAAYAHwD0/wMA9/8WAAkA8f/3//r/BgAPAAAAAwAJAAYAAAAGABkAHAAZAAMADAADAAkAEgAMAP3/BgAMAAAABgAMAAAA/f8SAAkADAAJAAwAAwAGAAMAFgAMAAAAFgASAP3/DAAGAA8AAwAGAAMAAAAAAAwABgAfAAkACQAMAAMAAAAMAAYA9//9/wAA/f8DABIAAADq//f/+v/9/wMADAAJAAYAGQAWAAYAAwAGAAYA/f8iAPr/HAAMAP3//f8DAAMA7v/9//H/+v8AAA8AGQAPABIADwAJAA8AAAD9/wYAAAAJABkAIgAWAAAAAAAPAAMABgAPABYA9/8JAAMA9P8AAPf/BgAGAAYABgADAAYADAAGAAkA/f8GAPr/AwD3/wwABgASAAYADwAAAAwA9P8GAAMAEgAJABIA8f8MAAwA9/8GAAAAAwADAP3/+v8AAAYAAAD9/wkAAwAJABIAAAD3/wAABgAMAPr/+v/6/wkAAAD0/xIADwD3/wkAAAAMAAAADwD9/wMABgD9/wkA+v/9/wwA9/8SAP3/GQAMABIAFgADACwABgAAAAYAAwAWAPr/9P8SAAMACQAiAPT/FgAGAAAACQDx/w8A+v/6/wwA9P/x/wMA+v/6/wMABgD9//H//f8JAPH//f8GAAYA+v/6/wMABgAAAAYABgADAA8ABgADAAAA/f8AAAAABgADAAYAAwAJAAkADwADAPT/AwD9//3/+v8AAPf/9/8AAAAA/f/9/wMA8f8JAAkAAAD3/wwABgAAAAMA/f8DAAwA+v8PAAYACQAGAAMAAwDx/wYA/f/9/wkA+v/9//T/AwD0/wAA/f8AAPT/BgAGAAYA9/8GAAkAAwD3/wkA/f8DAAAA+v/9/wAAAAADAAMAAwAGAPT/+v8GAAAA9//9/wYA9//3//f/AAD9//3//f/6//f//f/0//r/BgADAAAAAwADAAMA+v8DAAMA9/8GAP3/+v8JAPr/BgADAAYABgD6/wYA9//0/wkA+v8GAAAAAAD9/wYAAwAGAPT/9/8GAP3/AAAMAP3//f/6/wMABgAMAP3/DwAGAAwA/f/9/wwADwAGAAYAAADu/wkAAwD6//H/AwADAAMACQD9//r/AAAGAPr/AAAGAPr/+v8MAAYAAAAMAAMACQD0/wAABgAGAAAAAAD6//H/9/8JAAAABgAAAAYAFgAGAPf//f8AAAwAAwAGAAAABgAGAAwABgD6/wMA9P/9/wYACQD0/w8AHAAJAAkABgAAAPf/AAD9//f/DwASAAMAAAADAPr/+v8AAPr/9//0//T/+v8GAA8AHAAJAAMAAAD6//H/AwAJAP3//f/9//f/DAAAAAYA/f/0/wAA9//6/wwA+v8GAAAA9/8AAAMA9P8DAAkADAD9/xYADwD6/wwA9/8AAAYADwAPABYA9/8PAAMAAADx/wAA7v/9/wkA/f/0//H/AADu//3/8f8SAAkAkAE4Amn/SADR/6v/yP/k/8j/9/+V/8j/n//I/2P/Pf+i/x8A1/8pAH4AQgAGAL7/4f8PACUAEgDb/wAAXgAlAO7/LwBCANH/zv/0/+r/MgAWAAMADAAGAO7/xf/e/y8A2/+1/+H/HwAPAPr/AADx/wAA4f/h//f/HAAPAP3/DwA1AOr/+v8PACkACQD6/w8AEgApAA8A1//0/x8ADwAJAB8ANQAsAAYABgAZAAwAHAAlACkANQApAAAAAwAsABwALAAvADIAOAAlAOT/1P/B/8H/wf/k/+T/6v/U/8v/1//b/7j/vv/e//H/+v8ZACkANQAcAAAACQAfAAAAAAAfAAAACQAfAO7/DwDq/wYA7v8MAPT/4f8DAAMAzv/0/wYA7v8DAIQAHAF7AJQAmgAyAFIALwApAD8A7v/h/8j/q/+r/zb/Pf+S/2//ef8MAPH/5/+7/7X/0f/9/0UAOAA4AJoATgBLAPYAswA1AF4AfgBeAJ0AhABOAB8ALADX/8H//f/L/5j/kv+S/4X/bP/B/5j/tf/U/8v/DwA4AF4AOABIAEUARQAiADsAHwDu/yIAEgDe/wAAm/+y/5//vv/R/7j/pf/B/1P/hf+l/2//j/95/zP/Sf9J/0b/Sf9s/zD/6/4U/+T+y/6c/p/+U/4U/oP+Lf6Z/gT/r/7C/hf/Y/5K/jT+1f03/h7+bP7k/jb/pwA+AXoCXQXrBZIGoQeuBsgFIwZdBYAE1QTVA2cCvQG6ADb/pv2z/Lb70Por+8b7k/sI/Ef8nfvZ+6P87/zL/Sr//f/gAKABBQMOAy4D0gNXA4YDOgTuBJkEyASvBOgCSwKaApoADAA7ACH+B/5H/hH9Lv3r/AL86fsL/KD8dv0e/tj+Y/+M/0sA9gASAWcB9QHMAQ8C2QJqAv8B1gHDADUAsv8d/13+xf0I/e/7o/vW+k760/kc+Q/6Rfol+5n8MfwF/C78XfzG+8v9jP1H/RT+ff31/M/96QGDDXMPlxKpFkoN7ArGCzwHNgeECWMGxQOTAz4Cpv2E+f70mvEQ713yuPb5+Az7d/un+Pr3h/nM+vj8ZQC5AioFaAhRCqcK3QiMBjcElgPuBF8H5Ad1BzcFFQLu/uj8KPsl+i/5L/kM+mf7O/w7/P/6vfna+ND5FfxW/9YBNwPoA6wEFAW4BdgFhQb+BZ8FEwctB84GJwY6AwMAsv5X/an8ov0O/db7SPu2+lj6hPrv+lv74vuM/TD/9gBOAqYCOwL5ATgCgwKAAxcEzwPvAuwB4ADB/8L+Hv0Y/O/6S/p++gn6bvme+Pr3hfjX+KD6avsi+7b8Mf0L/T3/yP/R/vf/u/9p/+8AkgbgEscSVBTlF04LngngCWoEwgMzBoACbP+F/zf+Fvjo85HwWO7K7R7z+fgb+0H9N/7s+537qP73/y4CywVFCDUKjQwrDUsL9wdwBPUB+QA4AuIDLgOmATP/3PtV+bv3Fvfq9ln37fhX+4P93v7F/ir+pv3i/aj/cAJpBdcGIwd1B4IG1wY8B0YH2wS1BGQDOAAxA14AvP34/fz6Nfl++rD6mvoM+1T7IvvC/Ef+YP8/ACIBqgF6AskDuASGBNUDKwOHAh4C9QF0AYcAv/4x/Uf8jfvp+sD6uvkG+S/53fgm+cb54PkM+kT74vxE/KX+yP6W/rL/ZQAyAEgAEgFs/8X/GAL5CpAUtxFHF9QRywYTCfEE8wDZAu8Clv40/dv9PvpW9ebxOe9y7jjxDfgk/Rr//AC9AD3+hf9eAhEDnwVYCCwKSAtBDD4LdQcuA9f/nP2i/bj/lwBZ/+X9d/vw+IL3gve391z4gfoU/d7/MQK8AukBJQFoACUBHgNABeQGjgdyBioFsgWsApABWgNuAY4AZwGaAK/8ef6Z/Jf4sPvM+536ff24/pb9kv6u/1P/WADcAXQC+wLoA00E3wNtA1oCMgElAHL/Sf/F/mz+qf1X/Bj7WPoG+sr5fvqX+hv73Ps7+xX8V/xh/OL9Af6v/RH/Kv4PAB8ArP5v/5/9GP21/b/8kv6/BO0S/xOnE3kXVQkAB7gGegKwAf4DJQJp/mz+7/y390TyeO/d7sDvfPXJ/O7/sADTAQwA+P3jAM8CYARDBxIKdAupC0ELLwhRA3z/1fw+/ML99//h/zH+Qfy3+Zv3JvcJ+Dj5CPvP/fwAJAM0BMUDGwIvARgBOAL4BFYGvgaMBjcErwM+AdH/ov9J/3EBDwK9AO8AqP4s+oD8sPpk+mn+Pf7C/nEAWABv/yUAGQDh/2oB2QKmA5MEOgTZAtAAOv8q/tj95f0X/u79lv2Z/Mz7D/sv+sb5GfrT+rD7oPy8/K/83Pxd/Df9Ef4x/Qf/Af/x/er/jP7f/Iz97/uQ+/796AQFFmYXERdcGTIKngc9BhsCWAHMAhICqf2p/cb7q/X571/sMOzz7hH1R/2mATECmQPpAXL/GwI6BB0GMgk0DKwNXQ2jC0YHagJE/Tj6yvkl+3n9Af7P/KP6a/hm9hT2wffw+ZP8u/9zAwEG5AaSBoYECANLAv8CAQW4BkwHLQYEBZoCBgBa/hH9avwL/HD9Ov3e/Wn+pvyc/Lb8zPwt/nEAYQF6Al0DVwMFA9kCTgLiAcYBrQGqAZ0BCQGY/0f+2PzW+2T7xvsx/I38g/xa/I38YfyN/Jz8Dv2p/cL9+/19/az+Lf7C/ZL+7/uT/FT94vxw/cv97/vA+7n74PnG+tj8EQRDGIsaFBnKHCUMvQi0B6kCKwHsAu8B/vyT/aP6UPQ27mDpN+rk7ejzNP0SApYC3gT7A/wAOgOPBeQGSAr1DYwPdg6wC6UGagEC/HX42vfE+Pn6Wvzp+0X6Yvj09Ub1gvfM+hf+8gHCBdcHvQhrCGwGlgTvAgUDogSJBVkGHQVHAvT/k/wP+zj69vny+1IAPwBFAPkBSPuv/Vr+2fsiAckCowIOBK8EcAKKAeYA1f6y/6AABQFeAuwBigDr/o38dPsP+/L6gPxq/eX9if61/Rj9WvyN+zf8RP1g/Xb+Lf7P/d7+7v2Q/Q79K/tn+8n8G/xa/Sv86fqz+of5SPp3/OsDwhdBHucath5pD3EJjgheAgUBpADKAHD8bfuL+R7zkuya517oI+1n8xT9IQMBBLgGzgbCAyEFJgczCPwKkg7UECcPmgtTBav/4PkK9lb14fXR9+D5cfrD+QD51/Yj9kX4CPxCAKUEmAi2Cg8LVQpJCMIFdgM7AqACRANwA7wCTgFw/TL6HPlD9xn51fy1/7j/CwPcAAv+4gFN/rL/qQJRA78ErwVNBYACRQHr/q/9sv73/gwAswD6/2D/ef0F/B/73/qt+xv9hv6i/9H/Ov80/mb9Pf2Q/Vr+Ef86/5n+UP40/Y38YfxL+3H7+fqw+qD8jfs+/DH8DPo++nv5wPni+8wCoxZ5IrodfSCnEyYJOQk1AocAfP/F/4380/nw+LDyduu05a7lleum8ob8fATFBS8IgQmMBlwGeAgiCWoLuw7KEU8QsAvSBOv99/eZ87DyavNZ9Zv3ePnj+db5Yvg299P42Py5Ad4GfQs3DSsN0ws8CbEGyQOdARUB/AB9AQgCQP83/Ab5Lfah98334/pm/d7+WAHmAHACnQKNAtwCnwNWBcgFIAZgBXoDagFy//j9jP34/Pv8Sv1B/Wb9/vxE/CX8zPuZ/M/9J/87AKQAwwBhANv/B//+/mP+NP6M/iH+sv0h/B/74/pn+m37jfoC+2f7kfqK+077/Pq8+177DvxU/Yz/Rw21IdsgWh/dGygK8Aj0BPT+lv7l/FT7OfdM99/z6u385x/lwuk48fn5GwSLB5QIMQzZCo4IVQp3CrMKfQyyDw0Q2QshBRv9B/cb8ofwJfHM8kD1vveX+Tj7evs++tP6y/1RAogHzwxJD8gODg1CCgAH+AP5AP7+B/52/d79ufxr+tr3uPQO9aj2wfh2/X0DUwb3BSIKXQU0BAQHQQFKBWwGjAR2BawDSwBR/Lb6LPgJ+Bn6Nfu8/TP/JP89/+7+N/5H/oz/rQDfAR4DbQM7AmQBUP+M/SH9Mfw0/OL8Xf3/+237bvpC+a35Ufpr+mT7evuW+0H9ufzc/CH81vrp+8P75f3JAtASIiYBItgg6xiuB8cHnQB9/If7O/qw+fr1svXT8VDr4+Wo5DTrnPRA/iAIHwuNDKsPwgyRCqMLhwqgCmoMBA9wDvMI1gDK+CjytO147ZHvs/EO9eb4EvuA/QH+NP1g/gUBzgV9C+QPpBEwENwMDAnlBPwA4v34+1j6D/ph+sP51/ei9RT0XPVc9yz6egHKCPYIagtjDScGsQirB40BjwW1BNICBQPZAaD8GflZ9130wfXw+GT7Zv41AJcAWwHzAB8A4ACNAYMCsgP4BL8EdAJCAEr9S/uH+mv6h/q2+3f8oPso+wz66viF+An6wPry+13+v/5y/+v+dv1m/Vr8//vP/FD+BQPEEPQjvCF+HW0XiQWsBPf/sPrj+kL5P/lG9vH1CPMA7ezn4OYg7cH2WADQCaYMHg3LD2MNegoSC3EJ8whLCjsM7AuMBuT+u/aa8EDtju1h8AvzmPZ++gX9u/9+ACUAvQC8AuQG/wuoD+cQ4Q7mCgQHdwLL/vX78PnK+Hv4NflI+Wv4uPbr9dH2uvi2/OAAeQWNC5YNRg/uDa0J9wZRA6kCxgB9AQkBLf4U/Sb5tfbY9ET0qPVI+V39zQCpA9UEjwS8A5oCIgJ9AhUDcAPCA98CwwA3/qn7T/kJ+Hj4Rflr+or7K/w7+5H6w/px+v/6tvs4+5n96P4z/9f/pv2M/Wr8/Pv7/aoA6A4zI5IjOx/6GWsI3gTX/yn5kfg89+r3r/Uz9rLzP+7o6H7mJ+zL9c7/CQoXDioPFhKcD9YLcQs/CasHTwiKCqoKCgbV/oj28O/h6/HrCe8i8l/2D/tw/nEBuQJHAoACwgMQBwUMuw8gETAPXgu4BqMBjP32+ef3zvae9uT34/hr+En3APd794T5ff3GAY8FoQhXDWISjw86DocLwAExA+8AcPytAC79uvl++cH2cPSv9Eb1b/YV+87/SgMgB18HGgbYBUoEWgMbBGQDOwLDAUsAif4+/Jf5Zfcw9s72Sfi0+Vf7rfte+8P7v/wY/Vf9wv1K/ev+cv++/4oAf/5a/Rj9mvvC/KYCRRQNJPIfLx20EsgE7gP4+5T4YvcQ91L3b/Yj98XzJu5U6T7pRfBk+mYEKAwtDjAQsRG1DSILGQq4BmMFwQYZCRkIEQOW+xH0lO4K7M7tOPFz9Bb5ff0JAawDYwQIBLwD7gRJCI0MVg9ZD2oMeAh2A6L+7PoD+Gb2q/Vf9nj4VfkP+Zv4qvj/+T39jgC/BO0HkQhnDIkOkAxuC70ImgKXAUsBBP6UAA3/Pvp0+RD3TfT39gD4X/hh/NH+gQG1BCcF4QS/BMwDUQM9BJ8EbQPiAer/7v0h/I36Nfk/+AD4dfid+fL6IvtI+qr6LvsS/B79Tf3V/Tf+BgDGAEIALACS/h796Pwh/RwAswtUHhoiKB00GCYJxgK+/0z4X/f69aX2dfaV9pL1pvFJ7Pvp+u0g9wkBLwp/DrIPxxGyD8ALpwqVB4kEeQQtBhoHJwRT/iz3ivC17P3sP/B68/P3+PyUAOID9AXhBUAFSgW+Bg8Kcw09DqkMRQmGBFb/PvtJ+PT15fRN9Sb3Kfns+db6Lvv2+sz8DwDMAj8HrQmjDH8O2QtqC/QGLgTfA5//rv/1/S77RfpJ+D/3SfY/9z/4Rfpz/fT/dAILBGMEbAWMBQEF/gT7AyED5gHZAJX/c/2w+8D5vfjQ+Pb4zfmt+hj7RPuj+/z76fuj/JP9xf1A/hf/Df/e/24AJ/4R/jH9ffsL/SH8MP9+CAMbryJOHagZWwv7A2EBuvmX+AP3kvZv9lD21PW58k/tAere7Er1fP/tCCQOTQ8mEd4PFQz8CkIIwgTuA0oFkgaTBHL/TPiH8Tbt0ezK73bz1PeN/HcAfQMaBpsG7gWSBVMGVQk+DJkNtgwyCbgEov8r+0n4M/a89DP1x/bm+I36H/u8+9n73Pz6/1cDiAb5CKAKZAw6D+8KnggdBnn9HwAa/vz6if6U+rP6L/rw+Lr4CflF+sP7if5kAggEnwWCBawEnwNhAhsCCwIoAZ0AmP+c/i79h/vW+kv5KfkG+sn6jfsh/CH8evv8+6n7KPx5/dL8G/1H/Yn+Y//u/34AZv68/VD90vyl/7sF2BdGItAdzhrCDjoEXgKw+a72TfXe9GD1Cvbq9l30i+/Y687sj/RK/tQHHg67D/ARiBFQDZMLiwjJAwsCswIxBIYDlf9h+cLy3e0a7Yvv3/Jl95b8vQCPBJIHawi3B64GMAYACK0K3wtqC/MIrATR/5D7Fvio9ejz6PPY9W74zPqG/DH9Xf3V/hwBMQMTB6EIvQlXDEcNXgs8CB0GXf6v/fH9Zfnl/Fv76fkl+/D50/nc+mT7qfzh/sABmQNmBbsFzwT7A2cCegFqAWsAuP9c/1b+gP1w/Ff7Ufpx+eD5ZPob+//7evx6/B78o/zo/D39Dv5X/Sf95P5a/g3/xf8t/n/+Wv3J/ML9rv+oB2YYkyC0G48ZLg0LBDUC4Plc9wH2DvUt9Vb2sfZN9Cjwj+xl7vH19/4mCIkNPQ9VEUAQkAxkCwAIMQMbAoMCfQO2Ag3/Rfno8rHu8O2K8Lbz/fcr/ekArwTKB4EI2gebBi0G0Qe9CekKjQozCBQEgv9X+w34bPXr8xf0M/a9+CX7Wv0L/lb+3v+9AJwDmwbRB6cKigpICbQI4AcdB+8BZQDL/Uj5If10+vz4Tvun+I36jfwr/XD+wf97AH0BhgMKBfsErARqA7YCDwLZACwAIP9q/ZD8MfzP+zH7Mfvf+sb6Jfut+zH8Xfz1+9L7UfwO/Yn+kv7Y/bn9lvy//Wb+Kv6W/p/95f37/HP+YQCzC80dZx94HE0XBgm4BVUAuvgK92P0+PNE9BD2TPay89ruEewD8En4hwEsCu4NIxAAErsPpg1BDBAH/wJ3AeIBoAJbAVD9HfcM8e3ta+7s8F30NfmG/bkB5wUWCf8JmAgQBxMHTAj5CeMK0AnUBnQCHv6H+jb3nPQ685nz5/XH+P/70v2D/sX+rv/JAYYEygcMCX4KqgrwCPYIjAXMAUUBEgDb/UD+hvz5+TX7zfgZ+fn6avuc/RwASwH1AkcE8gPrAzcDSwJhAuYByQG2AcYAUP/e/YP8pvvf+rP6d/vD+4D8R/03/Rj9jfyw+x/73/pk+yf9YP4k/sj+bf3P/F3+mf2P/mP+CPxO/KD7+PzlAikTgyG8H6EcGRN4BwQFK/3g95/1ifPS89X0j/YU9qbxbOx26yjxd/pQBIQLEQ/gEZESGhCvDqkLrwUeAoEBZALfAsYAvPsE9U/vEO0Q7nHwU/Ru+ST+0gLUB4cKTgrdCCMHbwcDCWsKMgvdCToGyQGf/ZT56/Uk8wLyFfOv9T/5oPxD/sv+XP9hAFoCXQWuB2gJlAofCg8K2ggEBewBif7J+436k/tT/iH+V/2s/Jr5GPta/Fr8QP+aAE4CLQWVBgQGEQWjAhwAkv+i/yIAigDL/yf/j/52/Xr89fsf++n6Avym/cv+UP+v/tL8kPu9+rD6h/up+6P8Ef7o/dL9v/7v/AX9cP1n+8b8RPxU+/L8wwCcDkQfwiKBHrkWOAqPBIX/Bvlc9mb0ufI08+T1QPb+8gbuyOqn7jn3MgEMCowOWRG9ElkRBBAUDrcIdgNYAUgBEgKmAbX9GvfZ8P3sy+zN7lTyE/ez+5oAJwZCCmoLZAqBCMcHxAiECqkLLgsTCIADAf/M+hD3v/Oq8YTxTfPk9tD6bf2s/mD/OwC9AVcEAAfgCOYJPwpuCksKzgcOBAwBcPx0+mT6VflR+hz6ZPrS/Ib9ef58/x7++v+QAR4DJwZ1BiMG0QWWBA4D2QE1APj9Xf0R/cz8cP3v/HP8PvzJ+3P8r/1j/jD/AAAAADUADADh/pz9//u6+lX6kfo4+6n7HvxK/FH88vyc/Gr8wPvQ+hL7nfsB/0YHkRMnITAjLBz5FBkJcAKy/Tn3Q/WW84PzEfSi9fv0DPG47GzrNfDT+BsCawpADxkSnROXEnkQgw0NCFQDxgHTATgC3ACA/Br2LPC77FPsJu6w8Zv2qfsvAdQGegpkC1sKzQhYCFIJ/wobDFcLaAhHBL7/S/v99lTzBfHN8LbyRvYo+vL8c/52/04AnQHrAzoGWAj2CS4Lkwu9ChYIcASBAHb9o/p7+dn58Pjm+fz5Zfl0+sn6mvv+/M7+VAGcBPYI+QrcC1QLuwYtBYcC9P8ZABH+Pf3S/Bj8ffsf+/P55vjN+TH7tf3vAIoCjQN6A98CZwK5AfkAOABp/9H+rP4q/tz8GPsp+df3bPfz90v5Nfpb+7/8Evxa/bX9N/yZ/Rj9v/7nBr4PghtjItocFRY6DaYDb//d+cj1AfRK84bzzvRZ9VHyju5G7DbumPUU/m8GBQ1mEJESSBPEEWAPUQv+BewCKwISAk4BQP7m+Bvz1+4Q7bvtD/CQ8xD4Hv25AtEHVQqECs0J6ggyCbAKAgwYDGsKCgcIA+j+mvpv9gXzH/Fe8Ynzwfbw+Qv8Zv2S/v3/FQKPBN4G0AiHCs8LGwwSC4EIsgSRACH92fqq+Q/5yvhu+FL4iPgi+Qb63Pv7/Hb+4gHCA/EGSwnmCNAIRgeSBeIDowIiAZL/ef4Y/Vf8lvun+j76KPvP+xH9Tf8t/xUB7AHWAbYDkwOvA9gDfQPVApYC2QHQACIA6/5T/sX9Zv1q/Rv9J/1t/dL9Ov7h/h3/UP9Q/3L/0f/q/wkAqP9Q/8v+8f03/Tf8jfu6+rr6yfpx+xv8Lvz4+y773Prs+rL+jwTDCt0StRibGTUV/Q8WCYACEf8S+tf3UvfR9n/2YPUR9EXxP+/W70TzIvmS/yMG3AoRDiYQrg/+DcYLwQj6BbwEEQTsAukAbf0G+bj0ufFh8I3wXfKG9UX5kP3DAQEFvgZvB6gH8AfQCPwJxgq9Cl4J8QavA/r/Hvxr+J/1HvQ09JL1e/dr+QL7NPxa/c7+rQDGAgoFPAcsCYEK0wooCjwIaQVXAo//g/0V/P/6Ufqw+fz4wfjB+Pb4sPnW+q/8J//cAVoEXwZpB5gHTAeMBpIFgwRKAxgC0wC1/2n+Bf13+w/6H/nB+Ej5a/rv+6z9XP/gABgCOgP4A20EpQTbBOUE2ARgBJ8DSwLAADb/uf16/KP7IvsM+0v74vuv/Hn9Sv4N/97/3ADJAdkCtgNHBHkEUAT1AyQDIgIYAQ8ADf9N/sX9UP3+/Lz8kPy8/Pv8g/1H/gr/BgD5AMkBigLsAjEDFQPDAn0CCAKXAQkBjgD9/97/lAB8/+7+kv7+/G39n/0u/V3+0f7r/qL/qgDZACIBKAG6APYAQQFbAb0BjQFFAbcAPwDI/2b/4f6W/nP+j/7k/jb/Sf95/1D/fP+l/wMAawCqANwA5gDzAPYAugCkABkA1P+b/4X/if95/1b/Cv/0/sj+mf7C/oP+sv6y/q/+rP5p/g7+ff0I/bz8nPxK/ab9Ef6//mz/OgS5C/gLqQyjDOQGXQXoAwYAq/83/iH8kfo+/Er8Rfrq+Jj2IPYK97D5kv53AHoDjwXUBVkHQggWBxEFRwRtA50C1QNEA7ABUP9R/GH63fib+O344/gC+un7J/4SAM8BagLpATgCxgLYA1kFCgb+BTAFNwQCA4QBzv/C/en7uvrD+or7N/zi/Oj8v/wb/bX9wv7u/+8A6QEOA0cENAV8BeEEqQM4AgkBdAD9/4n/6/4t/oz9K/3y/Kb8avxK/Jb8if3r/lUAcQEeAmcCgwKmArYCrAJkAgsCowFRAR8BtwDu//T+8f1B/fX8If12/dL9Sv64/kb/6v9lALoA6QD5AFEByQE4Am0CVALWAT4BvQApAKX/Kv+o/mb+Tf5g/pb+uP6//rL+1f4g/5j/LwCtAAUBOwGKAbkBsAGQATIB0wB3ADsAHwDn/7j/Zv8X/9X+r/6o/q/+y/4B/0n/vv8yAHcAxgDgAM0A4wDsAOwA7ADmALcAlABbACwA9P+M/1D/Df/0/gH/JP9G/2//jP+u/9T/GQAyAFsAfgCUALcA3ADsAOAAvQB7ADsABgDx/9T/qP+C/3b/Vv9p/4n/gv+M/5j/sv/U/xwASwBhAIQAgQCHAJQAigCEAHEAQgApABwADAD6/9f/rv+J/4z/fP+S/5j/qP++/97/9/8cADsAOwBIAEsAUgBeAGEAYQBVADsAKQAiAAkA9P/e/77/wf+y/7v/wf++/8j/xf/R/+r/+v8ZACIAKQA/AEUAVQBVAFIAPwAlACIADAAMAPr/6v/h/9f/1P/I/8v/zv/O/87/5P/x/wMAIgAiACIALAA4ADgANQA1ACIAIgAcAAwACQADAO7/6v/X/9T/1//U/9v/6v/u/+r/AwAyAEEBqgFFAaYBhwDB//H/IP+//pL/6/7L/oX/lf9T/+H/UP/e/ln/Sf+u/5cAugDgAAkB5gDpAOYAhwAsAAYAxf/e/w8AKQADANv/dv9D/zP/XP9T/5X/u//L/y8AWAB+AIcAWAAiACUAIgBFAGgAbgBLAD8AHwAAAOT/vv98/2//cv+b/9T/+v8MAAkA9//x/wkAEgAiACkAJQBOAFgAYQBuAE4AJQDx/9T/yP/B/97/zv/R/9T/2//k/+T/5P/R/+H/9P8JAC8AXgA/AEsARQAfACIADAD6//H/8f/q//T/HwAPABwA8f/n/87/u//b/9T/6v/9/wAAGQAiACwAGQAZAAYA5/8ZAAYAFgApABkAAwAMAPT/6v/u/97/5/8AAAAADwAcAO7/8f/x/+r/8f/3//3/8f8DAAMAEgAsABkAGQAWAPT//f/6//3/8f/3/+f//f/3/wAACQADAPf/9//u//3/BgAGAAYACQD0//r/9/8DAPr/CQD6/wkAAwASABkAGQAJAAAA+v8AAAAAJQD9/wMA9//n//H/9P/9//T/AADx//3/+v8GABwADwADAAMA8f/9/wAA9/8DAAMABgAMACkAFgAlABYAAwD6//r/6v/6/+r/9P/x//T/DAADAA8A/f/9//H//f8AAAkAGQAAAAMAEgD0/w8A7v/u//H//f/6/x8AEgAAAAMA9P/q/wAA8f/0/wMA+v8lACIANQAcAAMA4f/b/9H/7v/x//T/BgAJAA8ALAASABwADADh/+f/7v/3/xkAIgAiAB8AAADq/+f/xf/X/9v/6v8MACUALABCABwADwDn/+r/3v/0/+7/EgAZAB8AIgAiAAwAAADq/9v/7v/q/wwAJQAWABwADAD6//r/9//x//H/8f/x/wAAAwAcABYAAAADAPH/9P/x//T/AAAAAPT/DwAGAAkAJQAcABwAGQAMAP3/DwDn/wAA8f/q/+7/9P/x/w8AAAAPAAYA9/8DAAMAAwAfAP3/GQAWAAMAEgASAPr/9P/x/9f/6v/x//H/DAD6/wYAAwAAAAAAAwAAAA8A7v8cAAMAJQAGACUA9/8fAAAA9P8GAO7/5//q/+r/8f/3//3/AwDx//T/1//u/97/7v/x/wkAGQAWAC8AIgAZAAkA9//q/+H/1//u//f/DAADABkABgAAAPH/9P/x//H/9/8AAAYACQAJAAkACQAMAAAA9//3/+r//f8GAAwACQAMAAAADAD3/wAA+v8AAPH//f/u//f/6v8JAAAAFgADABYADwAPABYAAAAMAPf/9//3/wMA+v8GAPf/8f/u/+H/4f/x//H/9//3//3/CQAAAA8A/f/6/+7/9P/x/wMADAAJAAwA9//x/+7/5P/k/+H/6v/0//f/EgAPACIAFgAiAPf/9P/x//T/8f/0/+7/8f/0//T/CQD0/wMAAwD0//f//f8DABIACQAMAAkA/f/3//H/6v/x/+r/5//k/+f/9/8DAAkABgD6//T/9//3/wkA9//0//H/8f/3//H//f/6//r/8f/3//r/AAADAAYAGQASABYAHwAJAA8AEgAWAAkADAAMABYADwAJACIAHAASAAwACQAJAP3/AAD9//r/9P/3//r/8f/x//T/9P/3//3//f8AAPr/9P/6/wwABgADABYAHAAWABwADAAfABkAHwAMABIACQAMAA8AEgAMAAkAGQAlABkAKQAPAB8ACQD6//f/9P/x//T/7v/x//H/9//9/wMADAAJAAwACQAMAAYACQADAP3/+v/9/wMAAwAJAAYACQD9/wkADAAfAAkADwAPAAkABgAAAAkA/f8JAAMA/f8DAPf/DwAAAAkA/f8DAAwAAwAAAAYADAAJAA8ADwD9//H/8f/x//r//f8DAAYABgASAB8AJQAWAB8A+v/0//H/9P8AAAAA/f/9//3/DAAMACIAHAAMAAYABgADAAkADwAMAAMA/f/9/wMACQAGAPf//f/6/wMADwAfAEIAlwDZAPkA3ACKAFgAAwAiANH/ef8X/+T+Cv9y/77/yP+4/7L/1P8cAFsAOAD0/6j/if+i/97/AAAMAPr/DAA4AI4A4wDpAOYAqgB0AHEAYQBYACwA/f/O/8H/y//k/+H/5P/X/9T/5//e/+r/0f/O/8X/vv++/7v/yP/X//T/DwAsACkAKQAvADUAVQA/ABwAAADb/+T/5/8AAAMA8f/x//r/HwAyAEIAKQAcAPH/9//3/wAADADx//r/8f8MACIAKQAsAAAA+v/k/+H/9P/n//H/y//b/+T//f8PABYADwADAAkAHAApACUAIgASAPH/+v/6/wYA/f/9//f/8f8JAP3/HwADAAkADAAAAAkA7v/3/+T/8f/x//r/+v/6/wwA+v8cAA8AHAAZAAMADAAAAPr//f/3//3//f8GAAwAAAAJAAwAAAAJAP3//f/0//3/BgAGAAMA9P/3/wYACQAcABkACQAPAPH//f/u//r/+v/x//H/2//h/9f/6v8AAPr/AwD6//T/BgD3/yIAAwAMAPf/8f/9//H/+v8GAA8AHAAGAA8A/f8AAAYA+v8AAPr/7v/3/+7//f8DAAMACQDx/wMA9P/9//3/+v8AAPT//f/0//r/AwAGAPr//f/3/wAA+v/6/wkACQAAAP3/AAAGAAwADAADAPH/9//0/wwADAADAAMA9P8GAAkAHwAfAAkA9P/u//H/9P8DAPr/AADu//3/CQAMAB8AAwAJAP3/9P8GAAkAHwAPAAwA/f/3/wMAAwAJAPT//f/0//T/8f/6/wkA9/8AAPT/AAAMAAMACQD6/wMA8f/9//r//f8AAAkA+v8JAAwACQAcAAYADwD9/wMA8f8DAPr/EgAMAAMACQD6/wwAAwAPAAMAAAD0//H/9//0/wwA9/8AAP3/8f8MAAMAIgAMAAYAAADx//T/+v/0//3/+v8JAPT//f/0/wMACQAZABIA9/8AAPH/DAAcABYAHwAAAAAA+v8DACIAHAASAPf/+v8GAAwALAAAAAkA9P8JAAkACQAAAOf/8f/x/wkADAASAAAAAwD0/w8AHAAMAAYA6v/3/+7/AwAGAPr//f/n/wMAAAASAA8A9/8MAAAAHAAiABkAHwAGAAkADwAAAB8ADwAJAA8AAwAJAAkACQAJAP3/AwD0/wMA9//0/wMAAAAAAPH/8f/x/+f/9P/u/+f/7v/e/+r/6v/3/wAADAAAAP3/9/8WABYAHwAiABkALAADABwACQASACkACQAvAP3/HAD9/wMADAAAACIA+v/x//H/5//0//T/+v8AAN7/3v/b//f//f8GAP3/6v/n//T/9/8WABYAAwD6//T/FgAiACUAGQAAAAkA+v8MAA8ADAAiAAAAEgAWAAkAHAD9/w8A8f8SAPr/AwD9/+7/9P/3//T/9//x//r/+v/3/wAA9/8AAP3//f/6//T/AwAGAAkAHwASAAkA/f/0/wwABgAfAA8AAAD9/+7/AAAJABIACQD9//T/7v8DAAkADAAJAAwAAwAMAA8AHwAZAB8AJQASAAYA7v/q//H//f8AAOr/3v/O/8H/2//u/wkACQD0//H/7v/0/wMA+v/9//H/9//9//r/FgAfABwAOAAvACwAKQAJABYAHAAfACUACQD6/+f/9P/0/wYA9//k/9v/yP/h//H/9P/u/+7/8f/6/xIADAAAAPH/7v/k//3/8f8GAPH/9/8DAA8AJQAWABwA+v8AAAYADwAGAAkABgAZACIAIgAiACIAEgAMABwAAADu/9v/zv/L/+H/6v/x/+7/6v/9/wYAJQAGAAYA9P8GACkAHwAlABwABgD3//3/KQAWAAkA7v/q/+r/9/8PABIABgD0//H/8f/6/wkAAwDu/+T/5P/b//f/7v8GAP3//f/3/wMADwAlABIAHAD0/87/6v/e/x8ADAAiAAkA5//x//T/HAAvACIAEgDk/+r/7v8WABYADADq/9H/6v/x/ykAHwAvAP3/9P/0/+r/HwAGABwACQDn//T/2//3//3/FgAWAPT/6v/L/9f/7v8fACIADADu/97/5//3/xkAJQAcAAYA9P/9/wkAKQAiACIAAwD3/+7/CQADACwA/f/x/+f/3v/x/wAAEgAPAPr/AwD0/w8AFgAJAA8A9P/x/+7/+v8SABIAHAD6/+7/9P/6/xkAGQAGAAAA4f/9//f/IgApAAkAAADU/+r/7v8WACIACQAAAN7/7v/9/wMAHwAJAPf/7v/0/wAA/f8PAPT/AAAJABwAEgAGAPr/9P/0//3/JQAWAAMA/f/k//r/8f8fAAwAFgAWAPT/AAD0//T/9//3/xkAGQAiABwABgAJAPT/+v8DAAkA9//e/8j/y//h//T/AAD0//H/6v/q//r/BgAPAAYA/f8GAAYA+v8JAAAACQAcAAYAJQAAAP3/CQD6/yUADAAcAAkAAAAfACIAIgAPAPT/9P/u/xYADwDq/x8ACQDx/9f/y/8sAB8A9//n/9v/AwD6/+H/7v/n/w8AAwD6/wYACQAiAA8ADwAMAA8ACQD3/+7/8f/6/wwACQAMAP3/AwAAABwAGQAfAAMA/f8DAAwACQAMAAYA6v/u/+f/9P/3//r/CQD0//T/8f/x//T/5/8AACIABgADAPH//f8DAAwAHwAWABIAAAD0/wYAFgAcAAkA9P/e//f/8f8MAAYAAAAGAPr/GQAWABkAIgAJABIA+v/x//T/8f8ZABIACQAAAOH/5P/U//H/BgASABYACQD6/wAA/f8GABIA/f8AAOf/7v/6/wAAKQAcACkAEgD6/wwA/f8lAB8AKQAfABIAAwD0//H/9P/x//r/CQAAAP3/9P/9/wkABgAJAAkA9P8GAPT/DAAfAAYAEgDx/wMABgAJAB8A/f/9//H/9/8DAPr//f/9/+T//f/3/xkAFgAGAA8ACQD9/wYAAwAPABwAEgAZAO7//f/3//r/DwASABwAAwADAO7//f8DACIACQAZAAwAAAAfAO7/BgDx/+T/8f/b//H/8f/u//3/9P8DAAYA/f8SAPr/GQAZAB8AJQAiACIABgD6/wMA6v/q/+f/AAApACUAAwDR/97/HwApAC8AAwDq//r/8f8cABYA/f/u/87/8f8MABIA7v/F/97/CQA1AAAA3v8JADIAOwAsAAAAAAAAABIACQASAOT/2//h/w8AKQAGANT/u//3/yIAJQD6/8H/9P8cACUAHwD3//3/DAD6/wwA/f8fABwAGQASAPf/AAADAAwACQAGAO7/6v/6/wwAKQASAAYA+v/6/wkA+v/0/+H/7v/u//3/DAD3//H/8f/6/yIAIgAiAAMAAwAAAAMAIgAiABwABgADABwAIgAiABwACQAMAAwADAADAP3/5//k/+H/6v/u/+7/9P/x//T/9//6/wYAAAAJAPT/8f/h/97/9P/u/wwAAAAGAP3/AAAGAA8AKQAPAP3/AAD3//3/DAAMACIADAAJAAkADAAcABIAHwAGAP3/5//e/+7/9P8DAAkAAAAGAAMAFgAWAAkABgAAAPr/9P/x/+T/7v/9//3/CQAJAAkABgAGAAMA+v8AAP3/DAAWABYAJQAPAP3/9//3//f/7v/0/+f/5P/x/+f/9P/x/wAA+v/3/wAAAAAPABYAFgAPAAAA8f/x//T//f/6//f/5//3/+7/8f8fABkAKQAJAOT/5P8AACwAMgAcAAAA8f8GABIAJQASAP3//f8SABIAAADu//3/HwAvAAMA9//x/wwAHwADAPf/2//n//T/+v8AAO7/9P/u//f/BgADAAAA8f/k/+r/7v8iAFUADwDL/7X/AwBuAF4A4f/B/wMASwBFAPH/3v/9/wwABgADAPf/8f/3/wMADAAAAPr/6v/u//H/8f/x/+r/7v/q//H/9/8PACIAHAAcACkAGQAZACIAIgAiACUADwAAAAAABgAMAB8AHwAGAPr/7v/3/wMABgDu/+T/4f/u//T/AAAMAAkAAAADAAwACQAGAAkABgAJAAkAEgAJABwADwD9/wAA9/8DAAkAFgAcAAkAAAD9//3/BgAMAAkADAD9//H/9P/3/wYAHAAMAP3/7v/0/+7/+v8DAAkA/f/x//T/7v8AAAwAHwAJAAYABgAAAAwABgAfABIA/f8AAP3/AAAJABIAEgADAAkAFgAlACIAIgAlAAwABgASACUAEgAAAPH/4f/u//T/9//9//3//f8DAPf/+v/6/+f/9P/u//3/9//x//H/AAAPAAwAAwD6//H/9P8DAA8ACQAJAAAA+v8MABwAJQAfAA8AKQAPAB8ACQD3//H/+v/6//H/5P/q/+T/6v/9//f/+v/0//H/8f/0//H/6v/e/+r/8f/9/wYADAD9//r/9P/0/wAAGQASAPr/8f/x/wAAFgAlAA8A9P/x//H/+v8JAP3/7v/b/97/5//0/wMADAD6//f/AAAfABIADwD6/+r/8f8SACUAJQAcAAwAAwAMACUAGQAAAPT/7v/x//H/BgD6//r//f/x//H/9/8AAAYA8f/x/+7/8f/x//f/8f/0//3//f8AAAkABgAAAAYAHwAGAAMAAwD0/wAA/f8JABYADwAMABIAIgAPAPf/8f/q//H/AwAPAAYA9P/x//H/CQAlACIAJQASAP3//f/6/wMAAwDu//3//f/6//H/8f/6/wwAFgADAAAACQAJAAkADAAWAPr/5//u//H/9/8fAAwADAAGAP3/BgAWABwAHAAlABIACQD0/wAADAADAPf/9P/0//H/+v8JABIAAwD6//T/8f/x/+r/7v8GAAwABgADAAwABgAMACUAIgAiABYABgASABwAAADn/9f/5P/u//3/9P8AAPr/8f/x//T/DAAMAAwAGQASAAMA/f/x//H/AAD9//3/CQAMAAAA9//n//f/9P/0/wYADAD9//f/8f/x//T/AAAJACIAAAD6/+7/AAADABYAEgAJAAMAAwD6/wAABgAJAAMACQAZAAwACQAJAO7/7v/q//H/9P8JAPf/9P8AAAkABgAJACIAAAADAPT/+v/3//r/HAADAAYA9P/x/+r/9P/6/wAA9//x//T/8f/9/wAA8f/3/+7//f8fAAkAAwAJAPT/9P/x/wYAAwD9/wYA7v8DAAYADAAlABYACQAJAAMACQAAACkAEgAcABkABgD3//H/5//x/wYAEgAiACIAGQAAAP3/DwAGAAMAHwAPAAkADwAAAOr/+v/9/wwAEgASAP3/8f/x//f//f/9//H/8f/0//3/EgAsADUAIgAsACwAIgAlACIAIgAiACIAHwADAAYAAwD6//f/BgAGAPT/8f/x/+7/8f/x//r/AAAGAAMABgAMABkAIgAlACIAHwAGABwAIgApAC8AJQASAA8AHwAfADUAKQAGAPr/9P8PACUAIgAAAOr/7v/x/wkAJQAZAPr/9//6/wMADAAcAAYA5//q//f/FgAvACwAHwAJAAkAAAAJACkAJQAiACIAAwD0/wkAAwAlAB8AJQAJAAwAHAD9/wAADwAJABwAIgAGAP3/BgADAP3//f8JAAkA/f8AAA8AAAAGABIAAAAJAP3/9P/q/+7/9/8AABwAIgAiACIAIgASAAkADAAfAAwABgD3//T/7v8GACkAKQA7ACkAIgAiABYACQAWAAMABgD9//r//f8GAAYA9P/0//T/+v8cABIA+v/9/+f/7v/9//3/+v8ZAA8A/f8SAA8AAAASABIA+v/x/wYA+v/3/yIAGQD6/wkAEgAMAAMAHwAfAAAADAAJAPT/9P/u/+T/8f8AAPf//f/3//f/GQAiACIAEgAJAPf/+v/6//3/AwAJAA8AKQAPABwADAAJAA8ABgAAAP3/BgD9/wMAIgADAPr/8f8ZAAkA/f8GAPf/+v8PABIA9//x/+f/2//x/wAA8f/n//f/7v8DAB8ADAADAPH//f8GAPr/9P/x/+7/AwAMAAYA6v/k//T/8f8cAAwA6v/u/+T/AAD9/wYAAADu//r/DAASACkAAAD3//f/BgAAAAMACQAMAAkAJQASAPT/9P/x//r/DAAAAPT/7v/x//f//f/3//H/7v/u//r/AwAJAP3/+v8GAAkACQASAAMAEgAcABwAFgD9//H/5P/3/w8ADAD9//T//f8DAAYACQAfAP3/9P/x//T/8f/6/x8AHwADAAMAAAD0//r//f/u//r/5P/h//H//f8JAAkAHAAGAPr/AwD0/wkADwAGAPT/7v/u/+r/+v8GAAwA/f/x/+7/6v/u//r/AwAJAPf/5P/b/+7//f8AABwAEgAJAAAA/f8JABwAJQAMAPr/8f/u/9v/4f/n/+7/8f/3/xwAIgAPAAkA/f/q//H/5//0//f/7v/3//r/BgAfAAMA/f8AAAkABgAMAAwABgD6/w8AHwAZACIAGQD9/wAA+v/3//H/9//h/+7/5P/u/+T/2//b/9v/1//e/+r//f8MAAYADAAiAA8AHAAWAB8AIgAlACkACQADAAwADAASAAwA9P/q//T//f8WAAAA8f/u/+r//f8JAP3/7v/0//H/7v/0//H/7v/e//3/AAD9//f/8f/x//f/AwAMAAMA9P8JACUAHwAiAAYAHAAWABwAJQAWAAYAAAADAAkADwD0/+r/9//x/wYAAAD3/+f/6v/x//f/AAD0//H/7v/u//H/8f/u//r/AAAGAA8AHwAGAA8ADAASABkAJQAZAAAAAwAPAAYAIgADAPr/8f8AAPf/9P8JAAAAAAD3//H/9P/x//f/9P8JABIAAAD6//3/CQAPAAwADwD3//3//f/6/wMA+v8cABYAHwAiACIAGQAMAAYAAwDu/+H/1P/L/+7/8f/x/+r/FgAlAB8AJQASAAYA/f/3//T/AwADAOr/6v8AAO7/EgAWABkADwADAPr/AAAGAAMAAwD9//f/AwAAAPT/8f/0//r/+v8GAP3/AwD6//T/CQD9/wMAHAD9/wMA8f/0/+r/7v/x//3/FgAvACIAJQAZAAAAHAAJAPT/+v/3//H/7v8AAPf/+v8PAB8A+v/6/+f/3v/x//H/8f/6/wwABgAAABYADAAPABwAFgD6//T/5//0/wAADAD9/+7/9P8AAAwABgAPAAMA+v8fABIACQADAPH/CQAfAAkACQD6//T/8f8JAAkAAAAMAAMA/f8GAAkAHwAfABkAFgDx/wAA/f8GAAwAHwAlABwAIgAMAAwADwAJABIAGQAcAAAA9P/x//f/GQAGAAAADAD0/w8AAADq//H/9/8JABYAJQAfACUAKQAlACIA9/8PABYACQAfAAMAIgAJABwAFgD9/wYACQAcABIACQAMAPT/AwD0/xIAAwAGAAAA9P/0/+7/9//6/xIAEgD3/w8AAwAMAPr/DAD9/+f/8f/x//T/GQAlABkA9P8DAAYAAAAZABwACQD0/+7/9//0//f/DwAGAAMABgD3//r/DAAJAP3/DwAPAAYA7v/0/+7/9/8GACIAHwAZAAAA+v8JAAYAAwD9//3/+v/9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type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "AudioItem REPRESENTATION"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Audio file\n",
    "baseSample_ad = AudioData.load(baseSample + '.WAV')\n",
    "display(AudioItem(baseSample_ad))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>start</th>\n",
       "      <th>end</th>\n",
       "      <th>phn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3750</td>\n",
       "      <td>h#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3750</td>\n",
       "      <td>4060</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4060</td>\n",
       "      <td>5960</td>\n",
       "      <td>ow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5960</td>\n",
       "      <td>6649</td>\n",
       "      <td>nx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6649</td>\n",
       "      <td>9566</td>\n",
       "      <td>ae</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   start   end phn\n",
       "0  0      3750  h#\n",
       "1  3750   4060  d \n",
       "2  4060   5960  ow\n",
       "3  5960   6649  nx\n",
       "4  6649   9566  ae"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Phoneme file\n",
    "baseSample_phn = pd.read_csv(baseSample + '.PHN', delimiter=' ', header=None, names=['start','end','phn'])\n",
    "baseSample_phn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>start</th>\n",
       "      <th>end</th>\n",
       "      <th>wrd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3750</td>\n",
       "      <td>6649</td>\n",
       "      <td>don't</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6649</td>\n",
       "      <td>11553</td>\n",
       "      <td>ask</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11553</td>\n",
       "      <td>12960</td>\n",
       "      <td>me</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12960</td>\n",
       "      <td>14360</td>\n",
       "      <td>to</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14360</td>\n",
       "      <td>21013</td>\n",
       "      <td>carry</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   start    end    wrd\n",
       "0  3750   6649   don't\n",
       "1  6649   11553  ask  \n",
       "2  11553  12960  me   \n",
       "3  12960  14360  to   \n",
       "4  14360  21013  carry"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Phoneme file\n",
    "baseSample_wrd = pd.read_csv(baseSample + '.WRD', delimiter=' ', header=None, names=['start','end','wrd'])\n",
    "baseSample_wrd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\"0 47514 Don't ask me to carry an oily rag like that.\\n\"]\n"
     ]
    }
   ],
   "source": [
    "# Phoneme file\n",
    "with open(baseSample + '.TXT') as f:\n",
    "    lines = f.readlines()\n",
    "    print(lines)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parsing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TEST PASED\n"
     ]
    }
   ],
   "source": [
    "def extractInformationsFromAbsolutePath(s: str):\n",
    "    items = list(filter(lambda x: len(x)>0,s.split('/')))\n",
    "    USAGE = items[0]\n",
    "    DIALECT = items[1]\n",
    "    SEX = items[2][0]\n",
    "    SPEAKER_ID = items[2][1:]\n",
    "    SENTENCE_ID = items[3]\n",
    "    return USAGE, DIALECT, SEX, SPEAKER_ID, SENTENCE_ID\n",
    "    \n",
    "def test_extractInformationsFromAbsolutePath():\n",
    "    USAGE, DIALECT, SEX, SPEAKER_ID, SENTENCE_ID = extractInformationsFromAbsolutePath('/TRAIN/DR7/MPAR0/SI2206')\n",
    "    assert 'TRAIN'==USAGE\n",
    "    assert 'DR7'==DIALECT\n",
    "    assert 'M'==SEX\n",
    "    assert 'PAR0'==SPEAKER_ID\n",
    "    assert 'SI2206'==SENTENCE_ID\n",
    "    print('TEST PASED')\n",
    "\n",
    "test_extractInformationsFromAbsolutePath()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>relPath</th>\n",
       "      <th>USAGE</th>\n",
       "      <th>DIALECT</th>\n",
       "      <th>SEX</th>\n",
       "      <th>SPEAKER_ID</th>\n",
       "      <th>SENTENCE_ID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDX</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>MPAR0:SI2206</th>\n",
       "      <td>/TRAIN/DR7/MPAR0/SI2206</td>\n",
       "      <td>TRAIN</td>\n",
       "      <td>DR7</td>\n",
       "      <td>M</td>\n",
       "      <td>PAR0</td>\n",
       "      <td>SI2206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPAR0:SX316</th>\n",
       "      <td>/TRAIN/DR7/MPAR0/SX316</td>\n",
       "      <td>TRAIN</td>\n",
       "      <td>DR7</td>\n",
       "      <td>M</td>\n",
       "      <td>PAR0</td>\n",
       "      <td>SX316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPAR0:SI1576</th>\n",
       "      <td>/TRAIN/DR7/MPAR0/SI1576</td>\n",
       "      <td>TRAIN</td>\n",
       "      <td>DR7</td>\n",
       "      <td>M</td>\n",
       "      <td>PAR0</td>\n",
       "      <td>SI1576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPAR0:SA1</th>\n",
       "      <td>/TRAIN/DR7/MPAR0/SA1</td>\n",
       "      <td>TRAIN</td>\n",
       "      <td>DR7</td>\n",
       "      <td>M</td>\n",
       "      <td>PAR0</td>\n",
       "      <td>SA1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPAR0:SX46</th>\n",
       "      <td>/TRAIN/DR7/MPAR0/SX46</td>\n",
       "      <td>TRAIN</td>\n",
       "      <td>DR7</td>\n",
       "      <td>M</td>\n",
       "      <td>PAR0</td>\n",
       "      <td>SX46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              relPath  USAGE DIALECT SEX SPEAKER_ID  \\\n",
       "IDX                                                                   \n",
       "MPAR0:SI2206  /TRAIN/DR7/MPAR0/SI2206  TRAIN  DR7     M   PAR0        \n",
       "MPAR0:SX316   /TRAIN/DR7/MPAR0/SX316   TRAIN  DR7     M   PAR0        \n",
       "MPAR0:SI1576  /TRAIN/DR7/MPAR0/SI1576  TRAIN  DR7     M   PAR0        \n",
       "MPAR0:SA1     /TRAIN/DR7/MPAR0/SA1     TRAIN  DR7     M   PAR0        \n",
       "MPAR0:SX46    /TRAIN/DR7/MPAR0/SX46    TRAIN  DR7     M   PAR0        \n",
       "\n",
       "             SENTENCE_ID  \n",
       "IDX                       \n",
       "MPAR0:SI2206  SI2206      \n",
       "MPAR0:SX316   SX316       \n",
       "MPAR0:SI1576  SI1576      \n",
       "MPAR0:SA1     SA1         \n",
       "MPAR0:SX46    SX46        "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def extractTimitDatasetInformation(path):\n",
    "    '''Put in a dataframe all the information about samples'''\n",
    "    import glob\n",
    "    files = glob.glob(str(path)+ '/**/*.WAV', recursive=True)\n",
    "    ret = pd.DataFrame(files, columns=['relPath'])\n",
    "    #Remove path and extension\n",
    "    ret['relPath'] = ret['relPath'].apply(lambda x: x.replace(str(path),'').replace('.WAV',''))\n",
    "\n",
    "    def extractIntoSeries(s):\n",
    "        USAGE, DIALECT, SEX, SPEAKER_ID, SENTENCE_ID = extractInformationsFromAbsolutePath(s)\n",
    "        return pd.Series({\n",
    "            'USAGE': USAGE,\n",
    "            'DIALECT': DIALECT,\n",
    "            'SEX': SEX,\n",
    "            'SPEAKER_ID': SPEAKER_ID,\n",
    "            'SENTENCE_ID': SENTENCE_ID,\n",
    "            'IDX': SEX + SPEAKER_ID + ':' + SENTENCE_ID})\n",
    "\n",
    "    #IMPORTANT: Apply multiple transformations at one\n",
    "    ret = ret.merge(ret['relPath'].apply(lambda s: extractIntoSeries(s)), left_index=True, right_index=True)    \n",
    "    ret = ret.set_index(['IDX']) # Add index\n",
    "    return ret\n",
    "                \n",
    "all_files_df = extractTimitDatasetInformation(path)\n",
    "all_files_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of files: 6300\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of files: {len(all_files_df)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Overview  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USAGE\n",
       "TEST     1680\n",
       "TRAIN    4620\n",
       "Name: relPath, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of files in train and test set\n",
    "all_files_df.groupby(['USAGE'])['relPath'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determine if the test set is an HOLD OUT set.\n",
    "We'll check if in the TEST there are unseen data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#A,B contains the tuples of SEX','SPEAKER_ID','SENTENCE_ID\n",
    "A = list(all_files_df[all_files_df['USAGE']=='TRAIN'].groupby(['SEX','SPEAKER_ID','SENTENCE_ID']).indices.keys())\n",
    "B = list(all_files_df[all_files_df['USAGE']=='TEST'].groupby(['SEX','SPEAKER_ID','SENTENCE_ID']).indices.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of items in B present in A: 0\n"
     ]
    }
   ],
   "source": [
    "nItemsBinA = len(list(filter(lambda x: x is True, [item in A for item in B])))\n",
    "print(f'Number of items in B present in A: {nItemsBinA}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**IMPORTANT**: yes, this is an **hold out set**, so let's use it as validation set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Word classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create data bunch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_phn_file(idx:str, phn_file, sig, sr, ext, delimiter=' ')->pd.DataFrame:\n",
    "    df = pd.read_csv(phn_file, delimiter=delimiter, header=None)\n",
    "    df.columns = ['START', 'END', ext]\n",
    "    df['SIG'] = df.apply(lambda x : sig[x['START']: x['END']], axis=1)\n",
    "    df['SR'] = sr\n",
    "    df['IDX'] = idx\n",
    "    df['IDXN'] = pd.Series(map(lambda x: idx+':'+str(x), range(len(df))))   \n",
    "    df = df.set_index(['IDXN'])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ste/.fastai/data/timit/TRAIN/DR7/MPAR0/SI2206.WAV \tExists: True\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>START</th>\n",
       "      <th>END</th>\n",
       "      <th>PHN</th>\n",
       "      <th>SIG</th>\n",
       "      <th>SR</th>\n",
       "      <th>IDX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDXN</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>MPAR0:SI2206:0</th>\n",
       "      <td>0</td>\n",
       "      <td>3212</td>\n",
       "      <td>h#</td>\n",
       "      <td>[tensor(3.0518e-05), tensor(0.), tensor(3.0518e-05), tensor(6.1035e-05), tensor(0.0001), tensor(-6.1035e-05), tensor(0.0001), tensor(0.), tensor(-9.1553e-05), tensor(3.0518e-05), tensor(0.0001), tensor(6.1035e-05), tensor(9.1553e-05), tensor(6.1035e-05), tensor(0.0001), tensor(-3.0518e-05), tensor(3.0518e-05), tensor(3.0518e-05), tensor(0.0001), tensor(0.0002), tensor(9.1553e-05), tensor(9.1553e-05), tensor(0.), tensor(0.0001), tensor(6.1035e-05), tensor(6.1035e-05), tensor(3.0518e-05), tensor(0.0002), tensor(6.1035e-05), tensor(0.0001), tensor(3.0518e-05), tensor(0.0001), tensor(3.0518e-05), tensor(6.1035e-05), tensor(3.0518e-05), tensor(9.1553e-05), tensor(9.1553e-05), tensor(-3.0518e-05), tensor(0.0002), tensor(0.0001), tensor(0.0002), tensor(-3.0518e-05), tensor(0.0001), tensor(6.1035e-05), tensor(0.0002), tensor(9.1553e-05), tensor(9.1553e-05), tensor(9.1553e-05), tensor(0.), tensor(0.), tensor(3.0518e-05), tensor(6.1035e-05), tensor(0.), tensor(3.0518e-05), tensor(0.), tensor(3.0518e-05), tensor(6.1035e-05), tensor(3.0518e-05), tensor(6.1035e-05), tensor(0.), tensor(0.0001), tensor(9.1553e-05), tensor(9.1553e-05), tensor(3.0518e-05), tensor(0.0002), tensor(6.1035e-05), tensor(9.1553e-05), tensor(0.0002), tensor(0.0001), tensor(9.1553e-05), tensor(9.1553e-05), tensor(6.1035e-05), tensor(0.0002), tensor(9.1553e-05), tensor(0.0001), tensor(0.0002), tensor(9.1553e-05), tensor(0.0002), tensor(0.0002), tensor(0.0001), tensor(0.0002), tensor(9.1553e-05), tensor(9.1553e-05), tensor(9.1553e-05), tensor(0.0001), tensor(0.0002), tensor(0.0002), tensor(0.0002), tensor(6.1035e-05), tensor(0.0001), tensor(9.1553e-05), tensor(9.1553e-05), tensor(9.1553e-05), tensor(0.0001), tensor(9.1553e-05), tensor(0.0001), tensor(0.0002), tensor(3.0518e-05), tensor(0.0001), tensor(9.1553e-05), ...]</td>\n",
       "      <td>16000</td>\n",
       "      <td>MPAR0:SI2206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPAR0:SI2206:1</th>\n",
       "      <td>3212</td>\n",
       "      <td>3648</td>\n",
       "      <td>n</td>\n",
       "      <td>[tensor(0.0002), tensor(0.0002), tensor(0.0002), tensor(0.0004), tensor(0.0004), tensor(0.0004), tensor(0.0005), tensor(0.0004), tensor(0.0003), tensor(0.0003), tensor(0.0002), tensor(-0.0003), tensor(0.0008), tensor(0.0011), tensor(-0.0012), tensor(0.0018), tensor(-0.0004), tensor(0.0007), tensor(0.0002), tensor(3.0518e-05), tensor(9.1553e-05), tensor(0.0009), tensor(-9.1553e-05), tensor(0.0006), tensor(0.), tensor(0.0011), tensor(9.1553e-05), tensor(0.0008), tensor(0.0005), tensor(0.0010), tensor(0.0009), tensor(0.0005), tensor(0.0012), tensor(0.0009), tensor(0.0014), tensor(0.0008), tensor(0.0011), tensor(0.0008), tensor(0.0008), tensor(0.0007), tensor(0.0006), tensor(0.0009), tensor(0.0005), tensor(0.0005), tensor(0.0003), tensor(0.0002), tensor(0.0003), tensor(0.0002), tensor(0.0002), tensor(6.1035e-05), tensor(-0.0002), tensor(-0.0004), tensor(-0.0003), tensor(-0.0004), tensor(-0.0005), tensor(-0.0007), tensor(-0.0010), tensor(-0.0013), tensor(-0.0013), tensor(-0.0013), tensor(-0.0014), tensor(-0.0016), tensor(-0.0019), tensor(-0.0022), tensor(-0.0023), tensor(-0.0025), tensor(-0.0025), tensor(-0.0026), tensor(-0.0028), tensor(-0.0028), tensor(-0.0030), tensor(-0.0029), tensor(-0.0028), tensor(-0.0027), tensor(-0.0026), tensor(-0.0028), tensor(-0.0028), tensor(-0.0028), tensor(-0.0025), tensor(-0.0020), tensor(-0.0017), tensor(-0.0016), tensor(-0.0016), tensor(-0.0017), tensor(-0.0016), tensor(-0.0016), tensor(-0.0011), tensor(-0.0011), tensor(-0.0009), tensor(-0.0011), tensor(-0.0013), tensor(-0.0013), tensor(-0.0015), tensor(-0.0012), tensor(-0.0008), tensor(-0.0005), tensor(0.0002), tensor(0.0006), tensor(0.0016), tensor(0.0021), ...]</td>\n",
       "      <td>16000</td>\n",
       "      <td>MPAR0:SI2206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                START   END PHN  \\\n",
       "IDXN                              \n",
       "MPAR0:SI2206:0  0      3212  h#   \n",
       "MPAR0:SI2206:1  3212   3648  n    \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           SIG  \\\n",
       "IDXN                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             \n",
       "MPAR0:SI2206:0  [tensor(3.0518e-05), tensor(0.), tensor(3.0518e-05), tensor(6.1035e-05), tensor(0.0001), tensor(-6.1035e-05), tensor(0.0001), tensor(0.), tensor(-9.1553e-05), tensor(3.0518e-05), tensor(0.0001), tensor(6.1035e-05), tensor(9.1553e-05), tensor(6.1035e-05), tensor(0.0001), tensor(-3.0518e-05), tensor(3.0518e-05), tensor(3.0518e-05), tensor(0.0001), tensor(0.0002), tensor(9.1553e-05), tensor(9.1553e-05), tensor(0.), tensor(0.0001), tensor(6.1035e-05), tensor(6.1035e-05), tensor(3.0518e-05), tensor(0.0002), tensor(6.1035e-05), tensor(0.0001), tensor(3.0518e-05), tensor(0.0001), tensor(3.0518e-05), tensor(6.1035e-05), tensor(3.0518e-05), tensor(9.1553e-05), tensor(9.1553e-05), tensor(-3.0518e-05), tensor(0.0002), tensor(0.0001), tensor(0.0002), tensor(-3.0518e-05), tensor(0.0001), tensor(6.1035e-05), tensor(0.0002), tensor(9.1553e-05), tensor(9.1553e-05), tensor(9.1553e-05), tensor(0.), tensor(0.), tensor(3.0518e-05), tensor(6.1035e-05), tensor(0.), tensor(3.0518e-05), tensor(0.), tensor(3.0518e-05), tensor(6.1035e-05), tensor(3.0518e-05), tensor(6.1035e-05), tensor(0.), tensor(0.0001), tensor(9.1553e-05), tensor(9.1553e-05), tensor(3.0518e-05), tensor(0.0002), tensor(6.1035e-05), tensor(9.1553e-05), tensor(0.0002), tensor(0.0001), tensor(9.1553e-05), tensor(9.1553e-05), tensor(6.1035e-05), tensor(0.0002), tensor(9.1553e-05), tensor(0.0001), tensor(0.0002), tensor(9.1553e-05), tensor(0.0002), tensor(0.0002), tensor(0.0001), tensor(0.0002), tensor(9.1553e-05), tensor(9.1553e-05), tensor(9.1553e-05), tensor(0.0001), tensor(0.0002), tensor(0.0002), tensor(0.0002), tensor(6.1035e-05), tensor(0.0001), tensor(9.1553e-05), tensor(9.1553e-05), tensor(9.1553e-05), tensor(0.0001), tensor(9.1553e-05), tensor(0.0001), tensor(0.0002), tensor(3.0518e-05), tensor(0.0001), tensor(9.1553e-05), ...]   \n",
       "MPAR0:SI2206:1  [tensor(0.0002), tensor(0.0002), tensor(0.0002), tensor(0.0004), tensor(0.0004), tensor(0.0004), tensor(0.0005), tensor(0.0004), tensor(0.0003), tensor(0.0003), tensor(0.0002), tensor(-0.0003), tensor(0.0008), tensor(0.0011), tensor(-0.0012), tensor(0.0018), tensor(-0.0004), tensor(0.0007), tensor(0.0002), tensor(3.0518e-05), tensor(9.1553e-05), tensor(0.0009), tensor(-9.1553e-05), tensor(0.0006), tensor(0.), tensor(0.0011), tensor(9.1553e-05), tensor(0.0008), tensor(0.0005), tensor(0.0010), tensor(0.0009), tensor(0.0005), tensor(0.0012), tensor(0.0009), tensor(0.0014), tensor(0.0008), tensor(0.0011), tensor(0.0008), tensor(0.0008), tensor(0.0007), tensor(0.0006), tensor(0.0009), tensor(0.0005), tensor(0.0005), tensor(0.0003), tensor(0.0002), tensor(0.0003), tensor(0.0002), tensor(0.0002), tensor(6.1035e-05), tensor(-0.0002), tensor(-0.0004), tensor(-0.0003), tensor(-0.0004), tensor(-0.0005), tensor(-0.0007), tensor(-0.0010), tensor(-0.0013), tensor(-0.0013), tensor(-0.0013), tensor(-0.0014), tensor(-0.0016), tensor(-0.0019), tensor(-0.0022), tensor(-0.0023), tensor(-0.0025), tensor(-0.0025), tensor(-0.0026), tensor(-0.0028), tensor(-0.0028), tensor(-0.0030), tensor(-0.0029), tensor(-0.0028), tensor(-0.0027), tensor(-0.0026), tensor(-0.0028), tensor(-0.0028), tensor(-0.0028), tensor(-0.0025), tensor(-0.0020), tensor(-0.0017), tensor(-0.0016), tensor(-0.0016), tensor(-0.0017), tensor(-0.0016), tensor(-0.0016), tensor(-0.0011), tensor(-0.0011), tensor(-0.0009), tensor(-0.0011), tensor(-0.0013), tensor(-0.0013), tensor(-0.0015), tensor(-0.0012), tensor(-0.0008), tensor(-0.0005), tensor(0.0002), tensor(0.0006), tensor(0.0016), tensor(0.0021), ...]                                                                                                                                          \n",
       "\n",
       "                   SR           IDX  \n",
       "IDXN                                 \n",
       "MPAR0:SI2206:0  16000  MPAR0:SI2206  \n",
       "MPAR0:SI2206:1  16000  MPAR0:SI2206  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows: 54\n",
      "TEST DONE\n"
     ]
    }
   ],
   "source": [
    "def extractItems(path, idx, relativePath, ext, verbose=False)->pd.DataFrame:\n",
    "    base_path = str(path) + relativePath\n",
    "    wav_file = base_path + \".WAV\"\n",
    "    wav_exists = os.path.isfile(wav_file)\n",
    "    if verbose: print(wav_file, f'\\tExists: {wav_exists}')\n",
    "    #Sanity check\n",
    "    if not wav_exists: return none\n",
    "    #Proceed\n",
    "    sigTwoChannels,sr = torchaudio.load(wav_file)\n",
    "    sig = sigTwoChannels[0] # MONO NO STEREO - convert to \"one dimension\"\n",
    "    phn_file = base_path + '.' + ext\n",
    "            \n",
    "    ret = process_phn_file(idx, phn_file, sig, sr, ext, delimiter=' ')\n",
    "    \n",
    "    return ret\n",
    "\n",
    "#    sig,sr = torchaudio.load(str(phn_file.replace('PHN', 'WAV')))\n",
    "#    df = process_phn_file(phn_file, sig, sr, delimiter=' ')\n",
    "#    df['Source'] = phn_file\n",
    "#    final = final.append(df)\n",
    "            \n",
    "def test_extractItems_PHN():\n",
    "    row = all_files_df.iloc[0]\n",
    "    ret_df = extractItems(path, row.name, row['relPath'], 'PHN', verbose=True)\n",
    "    display(ret_df.head(2))\n",
    "    print(f'Number of rows: {len(ret_df)}')\n",
    "    assert 54==len(ret_df)\n",
    "    print('TEST DONE')\n",
    "\n",
    "test_extractItems_PHN()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ste/.fastai/data/timit/TRAIN/DR7/MPAR0/SI2206.WAV \tExists: True\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>START</th>\n",
       "      <th>END</th>\n",
       "      <th>WRD</th>\n",
       "      <th>SIG</th>\n",
       "      <th>SR</th>\n",
       "      <th>IDX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDXN</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>MPAR0:SI2206:0</th>\n",
       "      <td>3212</td>\n",
       "      <td>6050</td>\n",
       "      <td>not</td>\n",
       "      <td>[tensor(0.0002), tensor(0.0002), tensor(0.0002), tensor(0.0004), tensor(0.0004), tensor(0.0004), tensor(0.0005), tensor(0.0004), tensor(0.0003), tensor(0.0003), tensor(0.0002), tensor(-0.0003), tensor(0.0008), tensor(0.0011), tensor(-0.0012), tensor(0.0018), tensor(-0.0004), tensor(0.0007), tensor(0.0002), tensor(3.0518e-05), tensor(9.1553e-05), tensor(0.0009), tensor(-9.1553e-05), tensor(0.0006), tensor(0.), tensor(0.0011), tensor(9.1553e-05), tensor(0.0008), tensor(0.0005), tensor(0.0010), tensor(0.0009), tensor(0.0005), tensor(0.0012), tensor(0.0009), tensor(0.0014), tensor(0.0008), tensor(0.0011), tensor(0.0008), tensor(0.0008), tensor(0.0007), tensor(0.0006), tensor(0.0009), tensor(0.0005), tensor(0.0005), tensor(0.0003), tensor(0.0002), tensor(0.0003), tensor(0.0002), tensor(0.0002), tensor(6.1035e-05), tensor(-0.0002), tensor(-0.0004), tensor(-0.0003), tensor(-0.0004), tensor(-0.0005), tensor(-0.0007), tensor(-0.0010), tensor(-0.0013), tensor(-0.0013), tensor(-0.0013), tensor(-0.0014), tensor(-0.0016), tensor(-0.0019), tensor(-0.0022), tensor(-0.0023), tensor(-0.0025), tensor(-0.0025), tensor(-0.0026), tensor(-0.0028), tensor(-0.0028), tensor(-0.0030), tensor(-0.0029), tensor(-0.0028), tensor(-0.0027), tensor(-0.0026), tensor(-0.0028), tensor(-0.0028), tensor(-0.0028), tensor(-0.0025), tensor(-0.0020), tensor(-0.0017), tensor(-0.0016), tensor(-0.0016), tensor(-0.0017), tensor(-0.0016), tensor(-0.0016), tensor(-0.0011), tensor(-0.0011), tensor(-0.0009), tensor(-0.0011), tensor(-0.0013), tensor(-0.0013), tensor(-0.0015), tensor(-0.0012), tensor(-0.0008), tensor(-0.0005), tensor(0.0002), tensor(0.0006), tensor(0.0016), tensor(0.0021), ...]</td>\n",
       "      <td>16000</td>\n",
       "      <td>MPAR0:SI2206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                START   END  WRD  \\\n",
       "IDXN                               \n",
       "MPAR0:SI2206:0  3212   6050  not   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    SIG  \\\n",
       "IDXN                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      \n",
       "MPAR0:SI2206:0  [tensor(0.0002), tensor(0.0002), tensor(0.0002), tensor(0.0004), tensor(0.0004), tensor(0.0004), tensor(0.0005), tensor(0.0004), tensor(0.0003), tensor(0.0003), tensor(0.0002), tensor(-0.0003), tensor(0.0008), tensor(0.0011), tensor(-0.0012), tensor(0.0018), tensor(-0.0004), tensor(0.0007), tensor(0.0002), tensor(3.0518e-05), tensor(9.1553e-05), tensor(0.0009), tensor(-9.1553e-05), tensor(0.0006), tensor(0.), tensor(0.0011), tensor(9.1553e-05), tensor(0.0008), tensor(0.0005), tensor(0.0010), tensor(0.0009), tensor(0.0005), tensor(0.0012), tensor(0.0009), tensor(0.0014), tensor(0.0008), tensor(0.0011), tensor(0.0008), tensor(0.0008), tensor(0.0007), tensor(0.0006), tensor(0.0009), tensor(0.0005), tensor(0.0005), tensor(0.0003), tensor(0.0002), tensor(0.0003), tensor(0.0002), tensor(0.0002), tensor(6.1035e-05), tensor(-0.0002), tensor(-0.0004), tensor(-0.0003), tensor(-0.0004), tensor(-0.0005), tensor(-0.0007), tensor(-0.0010), tensor(-0.0013), tensor(-0.0013), tensor(-0.0013), tensor(-0.0014), tensor(-0.0016), tensor(-0.0019), tensor(-0.0022), tensor(-0.0023), tensor(-0.0025), tensor(-0.0025), tensor(-0.0026), tensor(-0.0028), tensor(-0.0028), tensor(-0.0030), tensor(-0.0029), tensor(-0.0028), tensor(-0.0027), tensor(-0.0026), tensor(-0.0028), tensor(-0.0028), tensor(-0.0028), tensor(-0.0025), tensor(-0.0020), tensor(-0.0017), tensor(-0.0016), tensor(-0.0016), tensor(-0.0017), tensor(-0.0016), tensor(-0.0016), tensor(-0.0011), tensor(-0.0011), tensor(-0.0009), tensor(-0.0011), tensor(-0.0013), tensor(-0.0013), tensor(-0.0015), tensor(-0.0012), tensor(-0.0008), tensor(-0.0005), tensor(0.0002), tensor(0.0006), tensor(0.0016), tensor(0.0021), ...]   \n",
       "\n",
       "                   SR           IDX  \n",
       "IDXN                                 \n",
       "MPAR0:SI2206:0  16000  MPAR0:SI2206  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows: 14\n",
      "TEST DONE\n"
     ]
    }
   ],
   "source": [
    "def test_extractItems_WRD():\n",
    "    row = all_files_df.iloc[0]\n",
    "    ret_df = extractItems(path, row.name, row['relPath'], 'WRD', verbose=True)\n",
    "    display(ret_df.head(1))\n",
    "    print(f'Number of rows: {len(ret_df)}')\n",
    "    assert 14==len(ret_df)\n",
    "    print('TEST DONE')\n",
    "\n",
    "test_extractItems_WRD()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>relPath</th>\n",
       "      <th>USAGE</th>\n",
       "      <th>DIALECT</th>\n",
       "      <th>SEX</th>\n",
       "      <th>SPEAKER_ID</th>\n",
       "      <th>SENTENCE_ID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDX</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>MPAR0:SI2206</th>\n",
       "      <td>/TRAIN/DR7/MPAR0/SI2206</td>\n",
       "      <td>TRAIN</td>\n",
       "      <td>DR7</td>\n",
       "      <td>M</td>\n",
       "      <td>PAR0</td>\n",
       "      <td>SI2206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPAR0:SX316</th>\n",
       "      <td>/TRAIN/DR7/MPAR0/SX316</td>\n",
       "      <td>TRAIN</td>\n",
       "      <td>DR7</td>\n",
       "      <td>M</td>\n",
       "      <td>PAR0</td>\n",
       "      <td>SX316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              relPath  USAGE DIALECT SEX SPEAKER_ID  \\\n",
       "IDX                                                                   \n",
       "MPAR0:SI2206  /TRAIN/DR7/MPAR0/SI2206  TRAIN  DR7     M   PAR0        \n",
       "MPAR0:SX316   /TRAIN/DR7/MPAR0/SX316   TRAIN  DR7     M   PAR0        \n",
       "\n",
       "             SENTENCE_ID  \n",
       "IDX                       \n",
       "MPAR0:SI2206  SI2206      \n",
       "MPAR0:SX316   SX316       "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_files_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 6300 entries, MPAR0:SI2206 to FJEM0:SX184\n",
      "Data columns (total 6 columns):\n",
      "relPath        6300 non-null object\n",
      "USAGE          6300 non-null object\n",
      "DIALECT        6300 non-null object\n",
      "SEX            6300 non-null object\n",
      "SPEAKER_ID     6300 non-null object\n",
      "SENTENCE_ID    6300 non-null object\n",
      "dtypes: object(6)\n",
      "memory usage: 344.5+ KB\n"
     ]
    }
   ],
   "source": [
    "all_files_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows: 86\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 86 entries, MPAR0:SI2206:0 to MPAR0:SI946:8\n",
      "Data columns (total 6 columns):\n",
      "START    86 non-null int64\n",
      "END      86 non-null int64\n",
      "WRD      86 non-null object\n",
      "SIG      86 non-null object\n",
      "SR       86 non-null int64\n",
      "IDX      86 non-null object\n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 4.7+ KB\n"
     ]
    }
   ],
   "source": [
    "def createItemsDataFrame(path, files_df, ext):\n",
    "    ret = pd.DataFrame()\n",
    "    for i,row in files_df.iterrows():\n",
    "        tmp_df = extractItems(path, row.name, row['relPath'], ext)\n",
    "        ret = ret.append(tmp_df)\n",
    "        #print(len(ret))\n",
    "#    ret = files_df.apply(lambda row: ret.append(extractWords(path, row.name, row['relPath']), ignore_index=True), axis=1)\n",
    "#    files_df.apply(lambda row: print(row), axis=1)\n",
    "    return ret\n",
    "\n",
    "def test_createItemsDataFrame():\n",
    "    tdf = createItemsDataFrame(path, all_files_df.head(10), 'WRD')\n",
    "    print(f'Rows: {len(tdf)}')\n",
    "    tdf.info()\n",
    "    assert 86==len(tdf)\n",
    "\n",
    "test_createItemsDataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7min 17s, sys: 7.03 s, total: 7min 24s\n",
      "Wall time: 1min 14s\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 54387 entries, MPAR0:SI2206:0 to FJEM0:SX184:6\n",
      "Data columns (total 6 columns):\n",
      "START    54387 non-null int64\n",
      "END      54387 non-null int64\n",
      "WRD      54387 non-null object\n",
      "SIG      54387 non-null object\n",
      "SR       54387 non-null int64\n",
      "IDX      54387 non-null object\n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 2.9+ MB\n"
     ]
    }
   ],
   "source": [
    "%time all_words_df = createItemsDataFrame(path, all_files_df, 'WRD')\n",
    "all_words_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store pre computed result...\n",
    "# WARNING: this is a 11GB file..\n",
    "#all_words_df.to_pickle(str(path/'models/all_words_df.pkl'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#all_words_df = pd.read_pickle(str(path/'models/all_words_df.pkl'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Words count: 54387\n"
     ]
    }
   ],
   "source": [
    "print(f'Words count: {len(all_words_df)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>START</th>\n",
       "      <th>END</th>\n",
       "      <th>WRD</th>\n",
       "      <th>SIG</th>\n",
       "      <th>SR</th>\n",
       "      <th>IDX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDXN</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MPAR0:SI2206:0</th>\n",
       "      <td>3212</td>\n",
       "      <td>6050</td>\n",
       "      <td>not</td>\n",
       "      <td>[tensor(0.0002), tensor(0.0002), tensor(0.0002), tensor(0.0004), tensor(0.0004), tensor(0.0004), tensor(0.0005), tensor(0.0004), tensor(0.0003), tensor(0.0003), tensor(0.0002), tensor(-0.0003), tensor(0.0008), tensor(0.0011), tensor(-0.0012), tensor(0.0018), tensor(-0.0004), tensor(0.0007), tensor(0.0002), tensor(3.0518e-05), tensor(9.1553e-05), tensor(0.0009), tensor(-9.1553e-05), tensor(0.0006), tensor(0.), tensor(0.0011), tensor(9.1553e-05), tensor(0.0008), tensor(0.0005), tensor(0.0010), tensor(0.0009), tensor(0.0005), tensor(0.0012), tensor(0.0009), tensor(0.0014), tensor(0.0008), tensor(0.0011), tensor(0.0008), tensor(0.0008), tensor(0.0007), tensor(0.0006), tensor(0.0009), tensor(0.0005), tensor(0.0005), tensor(0.0003), tensor(0.0002), tensor(0.0003), tensor(0.0002), tensor(0.0002), tensor(6.1035e-05), tensor(-0.0002), tensor(-0.0004), tensor(-0.0003), tensor(-0.0004), tensor(-0.0005), tensor(-0.0007), tensor(-0.0010), tensor(-0.0013), tensor(-0.0013), tensor(-0.0013), tensor(-0.0014), tensor(-0.0016), tensor(-0.0019), tensor(-0.0022), tensor(-0.0023), tensor(-0.0025), tensor(-0.0025), tensor(-0.0026), tensor(-0.0028), tensor(-0.0028), tensor(-0.0030), tensor(-0.0029), tensor(-0.0028), tensor(-0.0027), tensor(-0.0026), tensor(-0.0028), tensor(-0.0028), tensor(-0.0028), tensor(-0.0025), tensor(-0.0020), tensor(-0.0017), tensor(-0.0016), tensor(-0.0016), tensor(-0.0017), tensor(-0.0016), tensor(-0.0016), tensor(-0.0011), tensor(-0.0011), tensor(-0.0009), tensor(-0.0011), tensor(-0.0013), tensor(-0.0013), tensor(-0.0015), tensor(-0.0012), tensor(-0.0008), tensor(-0.0005), tensor(0.0002), tensor(0.0006), tensor(0.0016), tensor(0.0021), ...]</td>\n",
       "      <td>16000</td>\n",
       "      <td>MPAR0:SI2206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPAR0:SI2206:1</th>\n",
       "      <td>6050</td>\n",
       "      <td>7580</td>\n",
       "      <td>good</td>\n",
       "      <td>[tensor(0.0001), tensor(0.0002), tensor(9.1553e-05), tensor(0.), tensor(0.0002), tensor(0.0005), tensor(0.0005), tensor(0.0013), tensor(0.0027), tensor(0.0022), tensor(0.0006), tensor(0.0015), tensor(0.0046), tensor(0.0160), tensor(0.0185), tensor(0.0075), tensor(-0.0016), tensor(-0.0064), tensor(-0.0105), tensor(-0.0190), tensor(-0.0179), tensor(-0.0099), tensor(-0.0025), tensor(-0.0009), tensor(0.0038), tensor(0.0095), tensor(0.0070), tensor(-0.0009), tensor(-0.0056), tensor(-0.0042), tensor(-0.0037), tensor(-0.0018), tensor(0.0065), tensor(0.0133), tensor(0.0117), tensor(0.0047), tensor(-0.0011), tensor(-0.0062), tensor(-0.0112), tensor(-0.0134), tensor(-0.0097), tensor(-0.0020), tensor(0.0037), tensor(0.0069), tensor(0.0087), tensor(0.0068), tensor(0.0014), tensor(-0.0037), tensor(-0.0065), tensor(-0.0065), tensor(-0.0053), tensor(-0.0013), tensor(0.0038), tensor(0.0072), tensor(0.0067), tensor(0.0034), tensor(-0.0004), tensor(-0.0045), tensor(-0.0065), tensor(-0.0055), tensor(-0.0018), tensor(0.0016), tensor(0.0038), tensor(0.0048), tensor(0.0043), tensor(0.0017), tensor(-0.0018), tensor(-0.0037), tensor(-0.0038), tensor(-0.0029), tensor(-0.0010), tensor(0.0020), tensor(0.0037), tensor(0.0037), tensor(0.0020), tensor(0.0001), tensor(-0.0011), tensor(-0.0026), tensor(-0.0030), tensor(-0.0020), tensor(0.0007), tensor(0.0020), tensor(0.0028), tensor(0.0024), tensor(0.0013), tensor(0.0002), tensor(-0.0024), tensor(-0.0034), tensor(-0.0022), tensor(-0.0012), tensor(0.0008), tensor(0.0008), tensor(0.0027), tensor(0.0031), tensor(0.0009), tensor(-0.0018), tensor(-0.0022), tensor(-0.0020), tensor(-0.0017), tensor(-0.0002), ...]</td>\n",
       "      <td>16000</td>\n",
       "      <td>MPAR0:SI2206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                START   END   WRD  \\\n",
       "IDXN                                \n",
       "MPAR0:SI2206:0  3212   6050  not    \n",
       "MPAR0:SI2206:1  6050   7580  good   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    SIG  \\\n",
       "IDXN                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      \n",
       "MPAR0:SI2206:0  [tensor(0.0002), tensor(0.0002), tensor(0.0002), tensor(0.0004), tensor(0.0004), tensor(0.0004), tensor(0.0005), tensor(0.0004), tensor(0.0003), tensor(0.0003), tensor(0.0002), tensor(-0.0003), tensor(0.0008), tensor(0.0011), tensor(-0.0012), tensor(0.0018), tensor(-0.0004), tensor(0.0007), tensor(0.0002), tensor(3.0518e-05), tensor(9.1553e-05), tensor(0.0009), tensor(-9.1553e-05), tensor(0.0006), tensor(0.), tensor(0.0011), tensor(9.1553e-05), tensor(0.0008), tensor(0.0005), tensor(0.0010), tensor(0.0009), tensor(0.0005), tensor(0.0012), tensor(0.0009), tensor(0.0014), tensor(0.0008), tensor(0.0011), tensor(0.0008), tensor(0.0008), tensor(0.0007), tensor(0.0006), tensor(0.0009), tensor(0.0005), tensor(0.0005), tensor(0.0003), tensor(0.0002), tensor(0.0003), tensor(0.0002), tensor(0.0002), tensor(6.1035e-05), tensor(-0.0002), tensor(-0.0004), tensor(-0.0003), tensor(-0.0004), tensor(-0.0005), tensor(-0.0007), tensor(-0.0010), tensor(-0.0013), tensor(-0.0013), tensor(-0.0013), tensor(-0.0014), tensor(-0.0016), tensor(-0.0019), tensor(-0.0022), tensor(-0.0023), tensor(-0.0025), tensor(-0.0025), tensor(-0.0026), tensor(-0.0028), tensor(-0.0028), tensor(-0.0030), tensor(-0.0029), tensor(-0.0028), tensor(-0.0027), tensor(-0.0026), tensor(-0.0028), tensor(-0.0028), tensor(-0.0028), tensor(-0.0025), tensor(-0.0020), tensor(-0.0017), tensor(-0.0016), tensor(-0.0016), tensor(-0.0017), tensor(-0.0016), tensor(-0.0016), tensor(-0.0011), tensor(-0.0011), tensor(-0.0009), tensor(-0.0011), tensor(-0.0013), tensor(-0.0013), tensor(-0.0015), tensor(-0.0012), tensor(-0.0008), tensor(-0.0005), tensor(0.0002), tensor(0.0006), tensor(0.0016), tensor(0.0021), ...]   \n",
       "MPAR0:SI2206:1  [tensor(0.0001), tensor(0.0002), tensor(9.1553e-05), tensor(0.), tensor(0.0002), tensor(0.0005), tensor(0.0005), tensor(0.0013), tensor(0.0027), tensor(0.0022), tensor(0.0006), tensor(0.0015), tensor(0.0046), tensor(0.0160), tensor(0.0185), tensor(0.0075), tensor(-0.0016), tensor(-0.0064), tensor(-0.0105), tensor(-0.0190), tensor(-0.0179), tensor(-0.0099), tensor(-0.0025), tensor(-0.0009), tensor(0.0038), tensor(0.0095), tensor(0.0070), tensor(-0.0009), tensor(-0.0056), tensor(-0.0042), tensor(-0.0037), tensor(-0.0018), tensor(0.0065), tensor(0.0133), tensor(0.0117), tensor(0.0047), tensor(-0.0011), tensor(-0.0062), tensor(-0.0112), tensor(-0.0134), tensor(-0.0097), tensor(-0.0020), tensor(0.0037), tensor(0.0069), tensor(0.0087), tensor(0.0068), tensor(0.0014), tensor(-0.0037), tensor(-0.0065), tensor(-0.0065), tensor(-0.0053), tensor(-0.0013), tensor(0.0038), tensor(0.0072), tensor(0.0067), tensor(0.0034), tensor(-0.0004), tensor(-0.0045), tensor(-0.0065), tensor(-0.0055), tensor(-0.0018), tensor(0.0016), tensor(0.0038), tensor(0.0048), tensor(0.0043), tensor(0.0017), tensor(-0.0018), tensor(-0.0037), tensor(-0.0038), tensor(-0.0029), tensor(-0.0010), tensor(0.0020), tensor(0.0037), tensor(0.0037), tensor(0.0020), tensor(0.0001), tensor(-0.0011), tensor(-0.0026), tensor(-0.0030), tensor(-0.0020), tensor(0.0007), tensor(0.0020), tensor(0.0028), tensor(0.0024), tensor(0.0013), tensor(0.0002), tensor(-0.0024), tensor(-0.0034), tensor(-0.0022), tensor(-0.0012), tensor(0.0008), tensor(0.0008), tensor(0.0027), tensor(0.0031), tensor(0.0009), tensor(-0.0018), tensor(-0.0022), tensor(-0.0020), tensor(-0.0017), tensor(-0.0002), ...]                      \n",
       "\n",
       "                   SR           IDX  \n",
       "IDXN                                 \n",
       "MPAR0:SI2206:0  16000  MPAR0:SI2206  \n",
       "MPAR0:SI2206:1  16000  MPAR0:SI2206  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_words_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Run  this for phonemes...\n",
    "#%time all_phonemes_df = createItemsDataFrame(path, all_files_df, 'PHN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wise\n"
     ]
    },
    {
     "data": {
      "text/html": [
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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = all_words_df.iloc[7]\n",
    "print(tmp.WRD)\n",
    "display(Audio(tmp.SIG, rate=tmp.SR))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join data and create \"src\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Join files with words\n",
    "src = all_words_df.merge(all_files_df,left_on='IDX',right_on='IDX')\n",
    "assert len(src)==len(all_words_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USAGE\n",
       "TEST     14553\n",
       "TRAIN    39834\n",
       "Name: SIG, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Optionally subsample and ensure good distribution among train and \"valid\"\n",
    "#src = src.sample(5000, random_state=1) # Uncomment this to reduce dataset\n",
    "src.groupby(['USAGE'])['SIG'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>WRD</th>\n",
       "      <th>FREQ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'em</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>1168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abbreviate</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abdomen</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abides</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          WRD  FREQ\n",
       "0  'em         6   \n",
       "1  a           1168\n",
       "2  abbreviate  7   \n",
       "3  abdomen     7   \n",
       "4  abides      1   "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wrd_freq = src.groupby(['WRD'])['SIG'].count().reset_index(name='FREQ')\n",
    "wrd_freq.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add word frequency measure to src\n",
    "src = src.merge(wrd_freq,left_on='WRD',right_on='WRD')\n",
    "assert len(src)==len(all_words_df) # does not pass if src is reduced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    54387.000000\n",
       "mean     400.091584  \n",
       "std      535.387063  \n",
       "min      1.000000    \n",
       "25%      8.000000    \n",
       "50%      89.000000   \n",
       "75%      650.000000  \n",
       "max      2202.000000 \n",
       "Name: FREQ, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "src.FREQ.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54387\n"
     ]
    }
   ],
   "source": [
    "# Keep high frequency words\n",
    "#src = src[src.FREQ>=8]\n",
    "print(len(src))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([0.0002, 0.0002, 0.0002,  ..., 0.0004, 0.0003, 0.0002]), 16000)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create merged column...\n",
    "src['SampleAndSr'] = src.apply(lambda row: (row.SIG,row.SR),axis=1)\n",
    "src.iloc[0]['SampleAndSr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_valid\n",
       "False    39834\n",
       "True     14553\n",
       "Name: SIG, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create validation set column from \"test\" hold out\n",
    "src['is_valid'] = src['USAGE'].apply(lambda x: not (x=='TRAIN'))\n",
    "src.groupby(['is_valid'])['SIG'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6102"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Count unique words\n",
    "len(src.WRD.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Useful statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shortest item: 0\n",
      "Longest item: 26160\n"
     ]
    }
   ],
   "source": [
    "src['LENGHT'] = src.SIG.apply(lambda x: x.shape[0])\n",
    "print(f'Shortest item: {src.LENGHT.min()}')\n",
    "print(f'Longest item: {src.LENGHT.max()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7faccf975d68>"
      ]
     },
     "execution_count": 46,
     "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": [
    "src['LENGHT'].hist(bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**WARNING** there are zero lenght items that should be filtered..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shortest item: 231\n",
      "Longest item: 16000\n",
      "Number of items: 54296\n"
     ]
    }
   ],
   "source": [
    "#Discarding too short and too long sounds\n",
    "min_duration = 50 # in samples\n",
    "max_duration = 16000\n",
    "src = src[src.LENGHT>min_duration]\n",
    "src = src[src.LENGHT<=max_duration]\n",
    "print(f'Shortest item: {src.LENGHT.min()}')\n",
    "print(f'Longest item: {src.LENGHT.max()}')\n",
    "print(f'Number of items: {len(src)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classify words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine tune spectrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = src.sample(5,random_state=1)\n",
    "ads = [AudioData(src.iloc[i].SIG,src.iloc[i].SR) for i in range(len(samples))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "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\n",
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      "text/plain": [
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      ]
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      "text/plain": [
       "Image (1, 128, 25)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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      "text/plain": [
       "Image (1, 128, 12)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": "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\n",
      "image/png": "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\n",
      "text/plain": [
       "Image (1, 128, 21)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": "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\n",
      "image/png": "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\n",
      "text/plain": [
       "Image (1, 128, 14)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for ad in ads:\n",
    "    display(Image(tfm_spectro(ad)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specrogram should be configured properly: let's define a new function **tfm_spectro_tuned** with the parameters we wants, using partial application"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "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\n",
      "image/png": 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\n",
      "text/plain": [
       "Image (1, 128, 25)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
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\n",
      "text/plain": [
       "Image (1, 224, 49)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#n_mels = 128 should be ok\n",
    "display(Image(tfm_spectro(ads[1],n_mels=128,to_db_scale=True))) #original\n",
    "tfm_spectro_tuned = partial(tfm_spectro,n_mels=224, ws=200, n_fft=3200, to_db_scale=True)\n",
    "display(Image(tfm_spectro_tuned(ads[1]))) #improved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "Image (1, 224, 29)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
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\n",
      "text/plain": [
       "Image (1, 224, 49)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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jJkEdCN2R9K9B0T9q/wAb+HNSi/srTLMadBcvJDZup3Kp/hD9gO2BxjFNNKN1ufOcSQ4nq0uTKowbte83u7TTXVatx6LZ666XPEvhn4s+H7ix1bSYYrqNLm+limjk3xNHNIDk7sEqcAgDgEA96KxPF/7SXjTxRb2sdtbQWMltE8Xmw/NujLqwXBGBgqMkdeOmMUVqqkV1fyObA5ZnFTCx+t0KPOr93pd2202s/mzg9VMB1S5NsqiM3D+WEXAC7jjA7DFSa7pcmj6rNYSRsgUqyByM7GUMp4PdSD+PamWlpcaxfPHEY1ZkkmYscKFVWdv0U8Vq+IYf7QspdfunAka/WNliUf6oxKUcgnI3AZHGDhumMVja6bPqnU9nUjG/S3z6fkzBoooqTpP/2Q==\n",
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\n",
      "text/plain": [
       "Image (1, 224, 23)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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LlHyAPQuvHtgc4zW7cpNJM+RqZpw3h4YipiF7SfPUSg7te40n/dXxbvWx3Xwbudd8appmo6mLt7MywytJA23H7yQfN3KjKkivL/27NKmPj3RtTiRRFD4TsEYJyAXluiD9MoRXtFn410HR/DVqLBbix1EQeW1jDHu3EyqFPHGCVKg/WvJv20tXi8N+JLDwrcQCX7b8PdNiDKc7JI7uZw2fTG4fiK86o5SxkHJWXvJL0W5+a8IVsXV8SaOIjR9nFrERjDb3Uoc0r7PdWa0fkeI6ZcSXFhrEgOMaTEjD1Cz24/oKoapfzarqdzqlz/rLmd5ZP95mJP8AOu21X4dHQrbVINLlNwH8GQai5Xpj7ZArEe2AW/GuHvYpIb2aGWPaySsrL6EHpXTK60P6HwOKwuLnOdF329dv+B+B22nfFjXta8O6T8Ork266daOxngnA8u5CqpjViRkcqeR659q9Aj8eXMV2moK6yxvaxp5sUbIqxIbgoAD1ABGG/iGD3rwmwuZLO9iuon2tHIrBiucYPp3+leow+KLb+1RY6rM4sjoDTKZyC5YTSDPH3flJ+T+HG3jFb0ZLqeFm+T4OEl7OkrPnk0lu5NOT829Pu7KxvaD40165ufD01jc6tc26QwSTi0aEpIwkYsHL8nI6gdOvWsP9sHWTr3j7RdRDAq3hW2CYORjzJsVy+h6ho4tNJgjit1e3Zt95cai8KqSNxG0HqOQT0OVFaf7SLyv4p0czsS48OW4bK4OfMlzx2qKqjNKXVfqefQyfD4bivC14xs1GutrOzs+ny8znvDfjnVdLs9aE900r3nh3+zIfN5CRGaH5QO2FVse/NYE88t1O9zO5aSRyzse5JyTTKKyvc+4pYahRqzqQik5Wv8lZfgAJByDV+T+1ddmX7Jp7YhiKIsKHCoCSQT3Pzcnqc88mu1/Zs+Hdn4+8aXEurW8T2Wl2RuJvtKboixZVVXHoQW57EZ7YPv8A4T+HngDS45tJs7GzDRwFmVQMHdnBz/ED/e6nvzTXLZts+L4k46y3h/FSw8qcqlSCTdtkpd/lZ/NfP5GW11LT4l1BrYojNhWkQEE+wPXHH0yPUZ3Piz40tPHPioatYRlYYrVII8jsCx7+7V9CXnwW8KeKtJn0uOExzQrNHDutlkePdbE/K5HTIDBvXK+mPCvjt8L0+Eni238MAsWksPtDljn700qr/wCOqtOTjG8Uy8l4syPP81jSjeOIipWi/wCVqLb+633nFUUUVB9weufsr+MLXwm+uC5k2G9NrCknmbQnMrEk84BAK+5YDvXp/hXWrNdRuoJVVWxwmScpzhtxPzA+vB9QDkV89fDlJpJZUhLqftdu++MjepTzJBjdxncgHPrX0F+zBZ6F4+8I3OtXcIS/W+MUwDqwwoXGCCScg9W5JyeeCdlOnSpc0j8f4+wmAyyGKzSqm1PkjK3fRRfpZO/y7mLpfjWTR/Fc2nW97MY7BJ44Iw/yyKILY/Ng7mOO/ozDGdmfOP2rPHNt49+Mt7qNnJujs7aGz3A8F41+cDPTDlh+Fe0ap4G0G08SyW0ekAR/bCPMMZw4NvGrjDfLnYoGR7joXI+Z/H99DqfjvW9St3Vo7jV7mVGU8ENKxBH50qyjK00dnBtHK8xzpZjRg1OFG13b7bje9ur5b+WpkUVPaafcXsF1cwLlbSASzH0UyIn/AKE61ASCSQMc9KyP1NSi20uh1/wa1Wx0/wAUxw6k6rFJKhAdHPmPhlWPK/dzvJycj5elfTfwDs/CFzod5r3hVVKy3I82QO2JSOAxDcjhce/fJr5F0bRtS1gXLab5rCyha4+RTxggcc8HHPr8tdn8IJviL4Te91fwzY3bWVxZq0wWBiHOW8s4742t0zwe/FaNSqUuTa/U/POO+Gv7fy6rGliPZzfKrN2i7O9nqtWtvNH0Lqnhy51/xvu05yB5w3wPJgA7cFuME9l74I6DrXzH8bfCR8EfES58OGFU8i0tMlMYd/s8e9uAOS+4njrmu50D9pnxJ4O8Xi31aQzJHe4mvGQ71iyGClO5BPPfr6kHzLxz4m1jxX4uuvE2tlTc3TJI2FGMbF28cg/Lj2NE+bRX0X3nNwbkPEOTZo44mUfq6pJRtvKV1r6JX311F8MRB9F8RSecymPRkYBTjdm9tlwfUYOfwrHdHjcxyKVZThlIwQa2dAv9OWw8QtewMrXOlqLWOCNtiyfbLd8HH3V2qwyT6DqRVXV/Emoatqt1qrbYjc3DymJFG1NzE4HHQZqNLH6BRdX61V93T3dX/hWi79bvvodV8JfGN3Y+KiXtR9muILe3uUjBAwgVA5I54ALnkZ2kZxX0V4B8Vafe6NLZ2ulQAD901t5WxlIL5+U9sn+fpXyr4LvrXTr17q6DouxlefyvMVB5bkfJ0ZtwVgG4+Su5+GHxAbSNcm0tZ3t7GC0muWMysHHLkDBJLcSDBzkgetbx5Zw5ZHw3GfDNPOKU5QjaUUn1s7Pye+y7nUfGfwtY/EO8vJ7GxS1urdN8czg4kPHyA7Rk4WT5QSRgZHK48b8dRG28VXdk0cK/Z2WL9yMAhVAHHY4HIHAPFdhr3xXudW02OyutTdBIJ1hvbdf3kRMpBVsnGCnl8jn3w1ef6oJBqVwJrtbh/ObfOjZEjZ5YHuCec96VVxtoe9w1gMZl+HVGs/dgrRWrtsm7vvbZ6r01GQXU1vFNDE2FnjCSD1XcrfzUVHRRWB9PZJ3P/9k=\n",
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\n",
      "text/plain": [
       "Image (1, 224, 42)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "image/png": 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\n",
      "text/plain": [
       "Image (1, 224, 28)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Try with more examples...\n",
    "for ad in ads:\n",
    "    display(Image(tfm_spectro_tuned(ad)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataBlock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6102,\n",
       " [\"'em\",\n",
       "  'a',\n",
       "  'abbreviate',\n",
       "  'abdomen',\n",
       "  'abides',\n",
       "  'ability',\n",
       "  'able',\n",
       "  'ably',\n",
       "  'abolish',\n",
       "  'aborigine'])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create classes for all data!\n",
    "classes = list(sorted(all_words_df.WRD.unique()))\n",
    "len(classes), classes[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "ils = AudioList.from_df(src, path, cols=['SampleAndSr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Augmentation\n",
    "tfms = get_audio_transforms(spectro=True,\n",
    "                            mx_to_pad=max_duration, #1 sec window\n",
    "                            white_noise=True,\n",
    "                            modulate_volume=True,\n",
    "                            random_cutout=False,\n",
    "                            pad_with_silence=False,\n",
    "                            pitch_warp=False,\n",
    "                            down_and_up=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([functools.partial(<function tfm_add_white_noise at 0x7facee6c5d90>, noise_scl=0.005),\n",
       "  functools.partial(<function tfm_modulate_volume at 0x7facee6c5e18>, lower_gain=0.1, upper_gain=1.2),\n",
       "  functools.partial(<function tfm_pad_to_max at 0x7facee6d31e0>, mx=16000),\n",
       "  functools.partial(<function tfm_spectro at 0x7faccdb71598>, n_mels=224, ws=200, n_fft=3200, to_db_scale=True)],\n",
       " [functools.partial(<function tfm_pad_to_max at 0x7facee6d31e0>, mx=16000),\n",
       "  functools.partial(<function tfm_spectro at 0x7faccdb71598>, n_mels=224, ws=200, n_fft=3200, to_db_scale=True)])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Replace the spectrogram transform with tfm_spectro_stft\n",
    "tfms[0][-1]=tfm_spectro_tuned\n",
    "tfms[1][-1]=tfm_spectro_tuned\n",
    "tfms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = (ils\n",
    "        .split_from_df('is_valid')\n",
    "        .label_from_df('WRD', classes=classes)\n",
    "        .transform(tfms)\n",
    "        .databunch(bs=64)) # NOTE: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "REPRESENTATION<br />\n",
       "                <audio controls=\"controls\" >\n",
       "                    <source 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0pu7XLhwwZfIftBD39LzGgPUDV0YVB/qIU4dGxboEk8VmhiUFnYUrBKGC/T/R/Uw7ffjQ9+O5nXuiffDAx8RYxqxHH4d7hQFCowF1/2h9274hAAFBn0GUAkFBh0C6gJA/7/5Svjd+5H8JfwdAs8D9gKWAvwA6ft996H3tvSD8VzyUPKD8ZDxsfLn8EHva/F08rHyC/V6+PX7kfwi/an8cPsx+ML0ZfM48urvtO1I6XnlfOQG56vsRPbDA9cM4BFGFJET9h1oRyF1cX7RY1lIGDY/FpbrfMGspsWigqRqqIC4Ptlb+p4LSB8HMI4v1ChiJnElFx/VHC8jjyMqHUkTUwh5/Hvsz9UxvoO3p7/xxgDOht1u9PAEBxEnHp4mdyexIGEXahBhD68Rqg+/DAoMjwhUAH33t/Df59TfkeHb7Ln7AgcGFQsfjyPGIRsawg9CAi755PFa72j2vv2uAtUFvwh9CjUGLwQtAZ38T/of+v78o/5gAB0CtAC4/1L9sPrs9ib0PvR08rHy0vMC9ETyqPG08RHv7e7/8O3yX/W/+Vv+Ov0Z/D38j/n49nT2IPbe873uReqy4i7iuOio8QT7zATLDKQN8guyFH80SmQ7fOhvr1O6QDorOQFZ1H6x0aLOn7afu63oyXLrRAVRGFgtozQ9Mlgt1ydBIVEcJx5UH3IZiBK8CbEBZfOn3n/I1Lxuvi7DGMqh2FfsNP9cDUIdvChzLEcnRRysEowN/gv4CUEGpQXnA4QAUv3F+yX4IPL57sbvQfPy+JAA2weJDiIUixXxE8UOXwhOAtP+KP9A/yQAJgOMBREG2ARTBNUB2vwK+Tj2R/VQ9mL4E/ph/Fj/XQHtAaUBr/5n+rz2xvOD8dLvcu+x7vDtwO1B72jyYvQU9nr4BPua/cv9HPs196f1YvRa7+7q6OiL55nqw/BS+TwA3gZ9CikGNQbcFiM+r2qHd2hmOk4LPr0kL/VjyeupyJ0pmmWeCLR31m74RhCTIv02BTylN8UtdCQ5HC0YWhkwF8sQOgyMBdf98+xu2YLHj7+8wH/ERc+d4b/13gZnFSYmUi8lLswjDBfsDUoHhwO0ADT/bACs/2AAsQEhAW38v/Ua9OrzLPZw+5kBkgf1CvsQcxUiFLwNjAWBAR/+Q/7E/yQAfgJHBKUFawTbA34ChfzO+KT2O/W/9TX3a/n7+SL9CAFFAZAAN/5A+yn3DvT58rrv5O0/7JbrS+yx7sbzcfeA+qH7WPv+/OD6QfcR88Dx//De68brHe9o8hD3iPtOAt4CT/6F/JEP0jz7aW9372kzWJJFQSn4+uvMA6pfmD+TkJhmsYzXxfe/EOEkBjicPqs5si8LIwMaURhFGIgWuxF9Dj4Hgv147S/ax8ievmW9McLVz5TkAfiMCVoZZSlAMQEudyNbFdQJrgK7/gr9wvxn/tD/SgMpBvAE3P93+df1ZfM79TT7qACcBNsHEA4lE7sRTw0FBi0BB/4Q/5YCYwNfBCAFWQYLCPkFIQGn+W70SvCx7pzx5/St9xD7oP8yA5MDJgPE/2T7ffdW9LHyGvBs7SfsZusL7fnuOPIX9Vz2iffj+fX76fvy+Ib0SvBv7CnvevRW+K37rP8yA3UBbfwhAf8el08wcAdq+lbaScE24BHZ4cW9f6URmneYVac8yjXvTQYSGbwspTurOdAxsydvHoUXPBemGK8V+xARCgT/EfO44O3PqcK2vhnCactn34/1PgdYFmsjxS0cLTgk5RfRCu0Bkfyh++b8T/5RAREGYgdoBU4C7/1f+fbzGvRZ91v+zAQaB6oLSQ8fDSAJRAUyA7L9y/2ZAfAEEQpoDZ4L9gYvBCQAAfix8uTtcusk7eHyAfih+3gANQLAAMT/4/1k+9H3v/W58wjy+fLh8pPw1e517pbv2PFW9LP1zvTs9u/5Yvhr9b/1E/rR+wT7dv3kABgA4Pqz9TL0IgwdQLVsHm69VhlJ1TuNHIHui8Txqwqg7ZkfpTDKPvTtBV4QTSVwNRUzuSkvI2AbKxVMFugakx6QGwcRTgIg9srm59GdwlW+E8BbyMjby/X8CDMWriE1KTUpciEPFmgJeADC/Kn8kACbCPUOSgtxBj4HXAVP+ibwk+ze62/wZ/oyA8gJHA71DkcMMguzCIEB+P7E/wUCXAUpCssM4wiQBFQA3ftK+N7zSvBp7hHvRPKD9bb4Q/qA+nf5v/kl/ML8NPuP+dr4lfek9ln3CPYp87fw0u8R7z7wF/EU7l3uffNf+b79IQGuBmsIRAnhBYj/FvnS88Pw7AkvRsRr72HRRFw/OjP1Cojgs78/sqakZZ4Cto7iHQYFCgQSPSoWL+0kQh0CHvEXbQ+RE4od9iW3HvgNJABl883htcpew+/DKMUJzxvko/6hDnIZKSF6IskgtRfdDokGOQFgAIAFDRPEFp0TGQ/4CRQBKfOT7P3l3+N+52LwT/7bBwQOlQ6ADS4MtgftBc8DcgJjAykGQQpWC60K1QXs/rb4+fKf8LHuQe8F7wLwufP79Rn4zvja+J74Vvh0+q37dv2X/qP++/2I+1L5C/XA8Rrw4e6672bvUe6B7vj2fgLGBucH3gahBkEGxgYCB1AFKQZHCLsRASoaPb07QC3VILgSJfy46KrZXc+Qyr7L4NeE7SkCNQoQDjMW4hivFY4UfBZ2FE8RxBKmFD8WVRN0CV79wPHT557dYtl32hndW+PM7Yz6tARfDNcQUhCFD5gN/gu8DRIVRRj6FDoUHhX7EDUGXv2M9hHvqOnu5vHpnPFZ97z6+P7VBY8IwgdTCOMIpAnICZgJgAkgCfYG+QGC/bD6ofeJ82Lwxu978MDxgPIR84b0j/UQ9zr59fup/D38hfxw+0z7vPqY+sv5kvjs9n3z9vN981/xEfNn+rQAVACj/gf+BP/8AE/+d/ny+AsITyz+SVhQLEMDOWEuwRMx+HPjCNdbyOC8XsOV2MbvVvhY+w4DngdoCaoLKxGREzMWxBqHHqomtiqhJaYY4wi8+vbr1uIr24/WaNc32y7i0uvd9+z+nABaAucDugZxCkwS5RsRIWYhTSGKIQ8erxWMCbb8RPIM6anlx+Me567rYO0y8LP1BPtM/34CzAQCB7YL0Q5tD9oPuQ41CuQEIQG2/KH3JvSH8Dnu/O3J7kHv//CG9Lz2a/md/KP+oP9sAJwAW/5e/YL9tACZBXgEKP8r+jr5pPY79ZX3kvgN+Fb4dPpD/pMDdwjbB7YHpAmhCvILBw28DeMMbgvICfAI1AmPCAsEEP+p/E/6JfjO+Eb5d/kH+jT7/vzE/0oD5wPPA6gEgAX5BQIHXwjnB94GEQaQBKsDGgPhAVj/tvwQ+4/5/vgW+VL5s/lw++P9nABEBSMIOwieBwsIPgdlBm4HyQXPAxoDjQFY/0P+i/6k+hn4ifeA9pX3E/qd/C79+P48AIj/VAAIAagAZP8E/wr9WPv1+9H7BPub+SL5tvj++Mj6rfup/Ar9Ov2a/X/+cP+I/0D/K/6a/b79r/5Y/xD/BP+L/nb98vwf/mAAOQHAABgAYABRAR0CWgLGAtIC0gJpASQAGABHBDIL7A2nDNoLFgywCSkGOwR+AuQAKP+m/Tr9xP/SAkoD0gL5AXUB/ACcANgA8ACxAQgB0P/0/+QARQH8AMAAlP/f/tP+3/5M/5T/xP8o/7v+BP8E/3D/xP98/zT/cP+g/5T/9P9IAJAAwAAhAWkBaQGxAZkBRQE5AeQAwAC0APwACAEIAVEBRQEtAXUBaQE5AfwAzACoANgA8ADAAKgAtACcAHgAeACoAGwAbABUACQADAAYADwA3P/E/5T/iP9A/5T/fP9Y/yj/HP/4/vj+EP8Q/wT/BP8E/xD/EP80/0z/NP9M/xD/BP8E/9/+3/74/gT/7P74/gT/EP8E/xz/HP8c/1j/oP/E//T/GACEAJAAtACoAOQAzADkAOQA2AD8AOQAzADkAPAA/ADMANgA5ADMANgA2ADMAGkBjQG9AbEBRQEhAeQAhACcAFQAJADQ/3D/rP98/6z/0P+U/9D/0P+4/6D/lP+U/5T/cP9w/5T/rP/E/6z/0P/Q/8T/uP+g/4j/fP9k/4j/oP+g/7j/3P/o/xgAbACcAJAAqADMAJwAkACQAHgAPAAkAAwA6P8AANz/3P/o/wAA9P9IADAAPAAwADAAJAAMAOj/3P/Q/9D/0P/Q/8T/rP/E/8T/oP+g/3D/iP98/3z/oP+4/9D/JAAwADwAMAA8ACQAAADc/wwAAADQ/9z/uP/Q/9D/3P/E/9z/0P/o/wAA6P8AABgAMAD0/yQADAAAAPT/DAD0/wwA6P/0/9z/0P/c/9D/0P/Q/9D/0P/E/9z/AAD0/wAAPAAkABgAMAAkACQA9P8AAAwA3P/Q/9z/0P/Q/9D/3P/c/9D/3P/Q/9z/6P/Q/+j/3P/c/9z/xP/c/9D/0P/Q/9D/0P/Q/9D/0P/Q/9D/0P/Q/9z/AADQ/9z/9P8AANz/AADo/+j/6P/Q/9D/6P/c/+j/AADo/9D/3P/Q/9D/9P/c//T/3P8MAOj/6P/0/wAA3P/o//T/0P/c/9D/6P/c/+j/0P/c/9D/0P/c/9z/3P/c/9D/3P/Q/9z/0P/o/+j/0P/c//T/6P/Q/+j/3P/0/+j/3P/o/+j/3P/c/9D/9P/c/9z/6P8AANz/9P8AAOj/AADc//T/6P/c/9z/9P8AANz/3P/Q/+j/3P/Q/9z/3P/Q/9z/3P8MAOj/AAD0/9z/9P/0/9D/6P8AANz/3P/Q//T/9P/Q/+j/3P/c/+j/3P/Q/9z/3P/o/9D/6P/c/9z/3P/o/+j/6P8MANz/6P8AAOj/6P/c/+j/6P/o/wAA3P/Q/9z/6P/c/wAA9P8AAOj/9P/0/9D/9P/c/+j/3P/Q/9z/3P/o/wAA3P/c/9D/9P/o/9z/9P/0/9z/9P/Q//T/DAAAAOj/6P/o/+j/6P/0/+j/xP/c/9z/0P/c/wAAAAD0/wAA6P/0/wwA6P/c//T/AAAM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type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "AudioItem REPRESENTATION"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Original sound (AudioData)\n",
    "ils.get(99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch = data.one_batch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 224, 161])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Shape of first X.\n",
    "#Important: it's a spectrogram image:\n",
    "#The original 1D sound is \"augmented\" and transformed into a 2D spectrogram\n",
    "batch[0][0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "image/png": 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9sbjxt5Qw329hZaZ6nz+zST059DBryU1B1gwW31n5A4NwBUhffk2aOfqFY5iHyYW6zz4qMdvRcqd5KOjsoojF+rkhkeuAnp/Y9RRLaygYpV/Kb00IpvIAMVUJtO/N+m225iF2++PY6OOihPJgCd9VnKwM4EnXmxIO2WBYdWWWMCezkEM69o9PFm+6oTH+a4+FW5e0xJcO5gZ+ZYhoW0/FZd+X4xYlSo0piVutGOaOOKPU7ErP96kNHOUmmLVNEOfuWMcckeFNjQyJ3Hji6cysuh9jSCu4p9mxROTPzqYRVJdMASViCJ59j+4O+N6m9EZLmJczK6w/6OA7i9NZY4H74WBI5iEjKaGNn75gFxX6wbojCmnZWDxVRiWnlt49kKcbsmWSVUDb2uLDrkebitRa2cP3HJVDmCxe/uEHxNaRWiG2MrnN7sxGNFPjLESIMMzHNgbHh2HWIF0itZ5hXYP3R7dbFDG29q8o6CfvIfUIsLUwwUVLhGR3QIIn7JW825UivSVU7uz07+Qm/b6L06ZCYiLvDza/m9S6HG0ZaHfeiscxTcXauLDBcJ23BY1sN38qm0CB5oPU9bG1lRO59sTG4TobZIrrZgKjaonX9h8sSLVZxtgclbBf22hpXPhitWFYHGFVIyQ/LQz7F2eOflW4cgJmjQVypeSqJDkK3bMFFCCCi1oeEEXEsJO6nFNtow3+F7BEbvxxRuZB3kmcVt6sXN8XJcymlAU1LN5ZiWKIkwtMtWW0adWUuZRiCUekcUq4Lpo1rCvLpLP9XitPqoVqZ1lzd1rZ/Ic7dkYOZ5Y05Mr+MwyfKZxEAzuEXSIcMnEmE6JlBLmmRTXVD+PMoYvWIFtj2bA6xogulSSlFOV9Z+FHtctQV+TNhrycEW47/MmaXHtcr9atacLv0I88yN9cnHpBtwanGouz5EwaFSqUqbdkVtIlSmtLiIuqTJ8dUddgmabbdrguIVVBmYzZcoFShas9qRazVA7UlWRAoF+O5ZfRZZZicyuEgxoOcdNTbzKpBX+PWkZSJs68zRJX5nbjsj62CDOoL+cSLDnql26ahfH7iOuVahOPbA6zgLvdI6drcuMJ+4LULsnRg7ybOLBhdanr0sB31tIae6LeGXAgZ4jZAvaiEMPKExsh14JU9XEmuOuQ260BI6p7UKcQbFAogHx3QZxBnEGuS0ISrdMS5yUm8xSY/VhDhGqfSQ243UB105NqCIcjckaGRJw50viA1MJwUk1dEBQYFd6bS4+z42yJu+vxfaa62qPLooSNR/qBvJ4TW4/fx2Mp57emDB/kby5WrI4RN2ttzLMx/r5cj1mJw3UFYp+SuT415ekXjtQIqXH4R2fHPqrauIDrItpUU8Igs9mEmk6vXhEXlnikVsi1ufrstVg3JQcb1RwBBLmG+joaSwTgNx2pVcLuXndnSPaALAyOlRpzt7lmKgXhzaLnygC9acY9JdzhYsa9uCQvreSUK4fWFWnZkGZl9qVc54MlfHdxIy6QkcIjiEGxPFOdz+16myU59PiD4no1dzwXUoOxEawWZA/+2VMAtOuQfW9ImHEkczmzLLm4xrEkE1tMCdVcZK6V+q4gWEo9cazxNV9e2rjBrEJbU7R6Y61BFzOy78q5WekmB+hXQq506kEj4PclJuzL+EKJa3Vzh/SZ+PwFaR4KnlLQmZWWUiXIYbCifsyGOXyQdxI3IqJHhqlcuVKolmmCTXYHg2TtD4Q9VLtIriwuzJWBRHXeWDZ9fjIdXHYHi7WSMTDkZcs4xefXa3JtSpZmilY6dTNygOZqmFp7LqplzgLxr74g1zZ41D2eoUHxdwaakCGhd1s7t1qmbHhYyvHYNaBQb9TccSxZ+Qja7YcJ/R1bjyudkdxW5Vox5tch20PZ/xbh4oP8jcXJvjMFDB68uWEdyzPjQ55y6Sj0VLuMv7NYLLZmzXINeWbMVvF8Aa7gAIeBXAdz+qUXPFpC/eQDUqOl5KPQZIaFkBpTOLePb5zDaCnBLFecBXZPgmXQdWAcqMqbO3JVhpmKwg8LSK0peZwDKtSbPJV+Uq0TONUtF8ius89pHZyuDN7fhAmVHU9mBvDohyMy6EHeWpxeXh3HNAuyeKzruVIzxAl5t0NjpLmK+IvNVEIZb2QODhUYlgE3cl3HaO09StY69xPsK562RvEWIc0yrkoWm9UZ9UqaV4w0wTmYBXIDuD/+I3JtityfCDilPyuF5n2Hdt1U8hnj1zRXO64T4lwhCr7XqYOCu+eOT1dweW1Ai2DciaJM5wIwrCvcfkDv7vDjbM2DvLW4dH1jfC0pI0MskKcAWrLVGA17FyPStjQv7tDrm3LjmMjQx+m6OPfWfRitYXDgjGNmmI8TeFbwZsxW24zzSmoVKrNQ3XllcVyAYW6JS9jC9rPlNHjVryzD3T8qJJdlqB5n//mhDM23CnU2Nz1TXOfol/bQTG66VAPi6Zx0cYk7PyukR4WzMOmkhHHhkV1Hur45JikP8tZieMIQIEYrQ3gx3j9lombLq2LZztbw1Xc2QORKnFZu+NjiizMB7wgff2DdEu+ON7gtTFlabrqaKw9txIdk2Wqb0KAcThxaK8MC4hJyk5m9zmyfenJjtcK4UPDK4bx0XA7mRscuhu9Li63NuCaRWkjzjN8Lh3MHohPCeywvDSeFVnhtidZEyhmzeQZgmAkyztRsdn/b9+j3XqxEo4oeDpYZqrEf2CxxRlVJhe85n8yNOngxn+60FLTMiEXMweaF89lqQt1oYeW3eV/reoB1K2ILdTNYR9ApoUqo11K7y8SlEmdAUE5/cU2/FrRShrkzCycwFK7KvNtN5yQRy+QHoM74kEgN0GSqW2FYguQjkmYsL6XWqD3yrJpIlkTB3x6sm5LsGuPzF/b6V6//du/QfwRSmq3WO06X15aBlkHvseMRFxYj9ic14f33juRAozsVLAPOljTkzYbhtLXeqjOyzVxbuy413qBhqkgUhpUybwZErJXWNANU2QrW88iwysSFIk2CbAqndaY7FdI8Q1CGldUTp8H3MisjxYX6JtE0kbhQfBupb0oyBBOCJ5f29jBzyEfvkxaGIkqNs90rlzdI6YfbPEx5f9f97d+l33Nx/vQE3e3ty8ypgEytSxHueqiCQekxbsL87NwK2CPaZACJiuuTwZ2KZYlza51JQTXnJhTwgLXe/D4T9havNSGSs7npJiQkKLmGqo7oPJEaxQXl8udnpJma4i2B2hg00+JehiqC75TQ6TQ66lym8ok0y4SQ8IMd30C1vMG3nYOQzhekypA6I5FmfP4Cv+0meP+DfH/i+ODZ5Mag1O3qwla169BZM41jxpmjP59NQAZJ4Dtzq65PuOGeEs4caWbUcblyxLmf4Pu+U6pNT9hBnmUWVU/OhpZZ1D2uysS5MmsG/DyibSJUke2HQlpYJj2sFdcmxCs6T4WVtcI/fYLvx9qihQDOZ+qQ0DZTVYnuVMhBIcsUTkxz1QGGZWWchEmnWiiA7PuJEWxEiz/Iu4sbzt+kNpM0zthiicrMerButSJVJfvdbHE9+MGKvq4vUPjhaCLM9TrcIcI9LpcchHqb8c+vqO4sGzZL6NBKacOAc5k0U+ZNT11HqE15+rWiTbJMembxIwq+jWXUs4bTNdXW4tPUGOWHCHiXkSYRXKY/LXzaiQkSZtjH2sC1rYUO1jo8sr9Kb50SF8F99rGNDjzIO4sb471RjLv6mEwYqTi2dszZjG/87jlhD/4A7XUy19cNE4Yw/ODTqewh28JFWB+BCPVNJH71tQEPHNQuoknQOjMPPeKUNM+0IZr7rIs7bRXXJMukG1NMzUJdR7NoIZBOZjQ3hQWsMkuYsymRCxkRZViWEc4M4QBuEPyguM8+AkrrsLL55nEwyg6Yzfr3yvB0Bafrv5Ob9PsuLi6OQNSpdRdK/WzeWhHaCbqal6K03dCwV/xBmX+9IxyOtL6Sof/QwAy5Ebi9Qwu2D7WaYrguHYlaEJ9pfUS84hcD8zDgnCLzyKrqCC5T1ZFZPaCVUjWRqkpQZ2b1AElo68HCg7piWNfU1x2pkgKuULQoofcZ74r79pZIhX22IninDM/WxTVbx8XvU/kuxAiYytyNH5TuUYUuHmjhvg9x48oIsJbVSDoEFHZSndipXDq2zpqbTL1R+NO/oL6JE4O+72E4qSa0c3rxcuJ4seKx4u6MFCnOQJPDSca5TF1HGhdJ0RGaSO0jKTtCSFQuI0kIIeNEwSl1iEjnCT4bCLVQgridUR/ngkeMvWdIHlVIWZAqQxbcINS3ibCD+rq3Tkg0F50DhE03gShkuTROG7XSzzB3qHP49YM1fFdx+T6zWbjHRioGEhi3beqYEWfFtS3txWBuzwnV5R6S8RX6ThkWVuweg31X+Ktl5JgZ99otBJLgRanrRFNFGm9K2DSR1g8M0VP5ROMj7iDUIRJ8QpxS+4TbC95lqrtomXwrpEUzdUJUQA+ebghodqTsEG/DShKhvukJO6X67oph4Qzaf0gWM5ZQAuycNRhdsSRlmJU48eRBCd9V3ETHBiDujXVaqbG9cSPY1PdlJPQPPqP+5pr6ukd+8gPk29foMKBODHLfGGfLGEu5wdZAuFISiScz/HpNaoEkOCwJqUJi5npy55nVA41L9F1lyhki9Y3QVJE6mMI3PuI7wYlSXe6NJq4W+tPaWomlm+O2nn4I5CTENE482bn4mz31RolffElsHX5QwtaYHuRuNxXiydnccamLpsZokHX1wFn9ruLUMU2a0XUGpVeOm5pud7aTLoPfZ/w+Mzyek371V4TXd3RPF6RXr4x5AahvIzlQAAIlISgJixtsgCiuqkKkCZLsNfNqwIniRJFtoPaJyiXSbUXjE60fOPtVovHmmgFqn5AoZBXczdY2c3pDfAPTw9RcOfpDIHeeoQ9ocki02RS529NeGAghB7Pk4eLOeuN7I890UdG7Laji+jwNZclhmJaGP8jbi1OBcQ1Dur014ELpfiCgtxvCNoGD6i5SbQZDwwBcXDGsiwsvg+Nj0jFB7jEEjUtqVmafDXCaE+oUEgzqaHw0ZRLFH4TgMkES9WuPd5kgmfm3Byqf8EUJgyRyrfTRk85WttHJ2YTeFA4ozL9V8rZCDp7hENDBWTISgZxpvzVaOwO5Znh1CUDe7vG9JWDp4hJStvmSsmleuv5hl8n3IFPb7r5YIbq0pXZ7qqs9uQ74u55wtZug/+niktgeExtRxV/fTSslAKulZavFueLqUu0msAEqZHV4l4nJ4ckGVC2KVm2FIJngEi/+ZIGXTF0qxsFZUbsbKvYfLSY41rgPeSzDPP43t/g7h986ZBeQg8MfxPrKwZP+/C/xP/4hiJ2j9n3Z+t5TbRNhexwA83f3QBLpgSTz+xAnysScBSOSucRvqXD9Pb8gzQOuG+C7l2+wL4zdFMAy4tuNxY4lDuTsBJwUi5IJN3sre+x2SGnVRXU4UYbkSTjSMk0n158oIooXZfdBcddi3Q4nis4S3aFi96RsjipAVkmU9RUgf/El1cYR9kLYmDKGfVG4uoKciI9X5gWGjHt8PlUB6quOcFtW06aMu9xM8SRypB5+kLcXJxnyfn/8jepEjOQGRZZL0ouX5oKHSLq+sTeWJYXZ88by6Xy3PbpiwWD/Zddx2CXku7IvRQvCJUOfPFmFYbB//Wogq8V6wyOzQg4lnkZ7agCNltBIk4jbiv6kLIMsi3FGYks3CN1/8YeELbgO6iuh2gr1reIPTJl6moVC/JRIj9dTf9h/9RL38so+0zt0s7GHM4E21QM/4fcgbmS9H0WSTr1XfzDuGLBe8P3XuLIDWD34956VP1hsWN9Gy04F67gEIWwjfteTXr2ajuNLt6LPgSF7UjQlnM16tLjpatWT1JER3MzixqQOevudOMVfB4a5Tdr53mLPcWTU9bB/WpVrgtkrxe+hvVKqvVqt7+wMDWIPXZcYTtsjgPX5C+J3z62l19TT1lLfq9GLPHjjdxbn7/V7pTKFM/5mxW97cI7w8Ud2+ruDAAAgAElEQVQ2szG+qdcjVtBDPl0RPvpw+nv9ene0EGIsqn4/4G7N4o6urLpTwl4YkhWTU3R0uaKtIl3yZISmHehiIKkgXknZEbMj3JnCqgrthRi6JmfCLuM7xfeG0PadgWlHF9rcWKY+e9Vbkd0BT86Nh7BTXB8Zlr+rWf6DZ8a7XVp1/gA44YGf8N0l+G6c6/T4p48tfit0ve7W0DXpvTPuF7XDIdlEHlbYTuvGVi2ouWb59jX6wXJaIQFmpUY08kjb0V4bSqXPni55dHB0ORB8ohsCXQoElznEQJ9t3K5LgS556iuhT5bpNpdKdwrqPdXGxudGEvTqVqeBJ9uPZ1yH9cutzRpnyOsZ/pAIWyP+jO3vuti8WhCXFUEtAarvsiHMHyzhO0uotoUSd7lAl3PUCf5gjKR6twPN9D9+UgaC7Ob4/XG3CGLxlFY2R+JWK9KrV4h+Zm77EJGCzB4z4nGF7Px5T73x9Mk6GnSOfTJt33c1XQ5kFfZ9RZ88qXemkNHTXiiHFJB94VEMSlo3hKs9EptipQyg4Hul85YNdydCqkAurm0Q69lj4qwi3B6mQfZcAQ7C++/BrCX+1RdoG4gzj+SKXAv1TUS6/sESfg/i6svSmmoa8rwxF3aIRoqpmfT6gqGAHLTUxNyuf4OlNVdGZun6hKyXSNPghmy94t0BN3Ibqilp2Jfyy+sd83/2lxyGwJA8YePZxYqYPN2u4pACMTkO+5pDqmDv2XY1fTQiokOsCLeOYWldnmFdI68uqV7c4G8OZTZEaW5SYeNXhqVZwvj8hdEBF8oTd3FLuNha+OBK3/lsTXpUYl9n0LRxAL667dHt/k2izQd5K3Huq5cASPDkNpArwe1tnlZK8pFqMbdVVmjJ3hYfurY16FfBC7rDgM5b/PkZfh8Ju4zebnD7oSCrHe7x+VR3y/OKm3/yhxYTDp7mStjFmi56uK04pIquq4j7wD5WhI1nv68ZBk93Imz72uLBBsjCsHSWyX/xFfLqknFHXvt8ZwP3nY0fTMSW7z8tSodtpb+9g+AnDsM8r4mL4wqNcdA/ByOWTxeXD9Rw34O49OKlJSTeF+RJobnI2Ta+U9pZAzYLDEYdd7tBVqupsJ1LG0vrgK6XhJsD1W1PurpBdp3RxoVAXsxwuwE3n9M9arn9xNH3gRQ9y68yh1gxDIHmwrOPFWkXkK1nP1SErZBuazQLcQG7rmL26ghQnQrnOZFevLSZ4wbkF3+JOiwbHnkKnWd4ujru0xMpqy0K7jHYpN39XX5vLOiufOn6PGjhu4oD8B+9D94ZA0MQpLipYd3Y/LBYGy415nvUO/JmA2dr3GArtu7fDJ3V8PwV4cWNza1stqTaIU2NtgHXR9yjc/q1pz9Rht7ABesvDuyGimHwzJ4rhxgMqnXn2Pe2Z6W68mVUUznsy9B7mXW5D0uDMqrQ2P48dVCN/DYR/I8+ZVgV5oZsoN10vkS9txmUAuyd2Lf2/ZTcoNgCSXhwx9+DOMDiHmcIGvUYNjApce4Jz54gGerbI3HmuOUpnc4JnVJdbG2WZNEifTQizItL4ue/AbBxUi8wEmaqousFcSbkBvLBkwdPXAT2fUWOjsXLzL6vIEF1JwzRk2uluRB84RfMdxXdiRSUt/yOQkgq3DPlSuvNYECMAeLTtY0cpNLdmbcMJw25DRazCiBivWTnj5n9UKBt4wqLByV8Z3HhvWcMJ421r6R8qdFAqqkVdDlHEjSvdraIuqqtXgYMpw2+U+u99spw1iLXm9/d9+a9Nf1Ld0KDIy3qwsqlluFm6NcWG5INWt/HAB7CDmJ0pMay3RBsSbbfOoYl1NdKteMY640X14/zMlY499uhbAyAYVWVhTnRGPmrQJx70jxQ38QjqfquJ3z2MfnCQA2uMLmOcn897oO8nTh9dEqc+WlvrzpBhwHZd4VGTQhdxn3xnd20ujKaEGBY+ALzStQ3kWHtid89/52mvrQFBh+8MWd5T24MdiVR8Ftnk28Jg1plK3D3vUddQbEUSxjn1lOWJISt7T05+aKjutXjHuQiY/swfGKzI7kJVrLpylhrAjl0RuWhxsYQ557q9ThEr/gX18THK/LhYEPw28F644WN68ESvru4PLelg3lWvbEKTDcbi5+GiN9bJgggs3ZSstQI/pDx6zX1d7cMpbWXa0d4/z3cYoFbrdB7+03cfjDIVdm26TsIB+totK97cm9WUT3k5AuyBRuEKgxbY//Y7+13/n/9U9pr6/LIPSWs7qyPHJ+dIgr9aWXKWvAaYZfQ7Q632SN7q2HGWdnZpwZYiF9/Q1yV2DOD2x6QaN2k8OEHx2TlQd5aXK492UOcV6YYAgyDARV0xBNGXGv9VFnMp9hwWvv6o4/h9eVxSL5x5CenyAfPcOenxmxVFh7K7db4XcZi8q7sEHbGY01vVjG2jpxkAthqNgau1BiKZqT6RZTw8Qe0FwOhU7Spp4trrxKuh+5RCxm6tWd+URA6UWkuO1s5drtB77Y2Z1xD/8wQNeNuu3FcVTK2UiIr7nZHPluPYLgHeQcxQiS1L3pi4S/UFi4ZmNNvB9x7Tw2Z0thqL//4kSFoYmY4b9H3n5q7DgaxT6uWvJ6RVwtbzJ1BDj3x2+fHBEeguh2pOJT9pytkEMhinDPRENC2vNv+VQ8pG3h1zFwPf/CU+uWdhQbO4la3WjH75o5qpwwrbxZxJsy/2RcEdcJ9/q2NiV5cki6vCVvL8vdPLBN3BTuYK7HkJB03yet2R55XD+74exA3LYep7/FUjwsTSwDudh15OTeCpJIZ8/SRuco+klrH/pOCx5vPjUqjLXHfsjaemphht7caXu2mIm+1M25qgO3TgOsFSWJdkCTIULLeZPGjZFNCKS4boF8H5HaL3xt7qvuDT3Ena/Kf/pL2KpNq61ePfDOpEVyXSK8vkDBubkzUFwdQ6E4MeS03m+mLCp9+ZMiZaPR5IkaH91CsfndxYKiYHI4rW0eZmOlzRttA2KWJ9HJkbnA765/uHweDT6U0Db5r5UhtsAw0KenCcHm5ctNIgdGy2cfEuVlBkhWZLS4rZaMk+N76vyk53GA94LG7YfMfCb2+pX9vhZ5YoX3x5c5mZaJZ3P17M3IDrouEzz5588v46jkuKnFunxOfv8A/eWIJ03unxk+t2XgLTwoV2IMSvrNYsfpg2na/3OBWq2m8k7J6NtweLK5LyrCyGV0ub5BoPILVLpO3WyZul2zHlN0BGWzru31O+RA9sr0S3WR5x+EnclmUI+B6R3VjGfEweMIOtALJYm64rnAxk66u6M6qqYzEv/h3tqVpsJjucGYxsLvr6D9+NIEq3GpFen1R9qQcM2tZzPBdZjip8V1Gyx7odDa3PYAP5EjvLE7LnhKj5T2WHOT9p7aj+OMP0Lay+ePtwZAyUYkLh++V9OrCFmTXQnNdUNCjBVW1pTw5TwgV/+jc/lRWieVa0KBILzTXlnCMREuMjFkKMgiz10p9C3lwVBsl1RZKNK8O5NVsuqhuZUAD/+MfApRN8lp24RVY1+7A4bEtCQcmkK4/ZDSUzwe0rgh3A8PC4btyYWo0eW7X2/k9yDuJy5XDH2KhdZMpxkmnc0SVdLZAm8r6yd1gO3+TEhvb0C5OcDGDg+bbWzto2RbqO5vSo7IhegBZLQ0aL5gSVvZv2DlO/9VLU8goNJdqMyhFIX0Pi+8S81cZPXhj33dWvvF/8SVp1U6fEWeglSM+XiE//xnNdbRjdDotiUSVYS5IXePXa3Q5J3z0oVk7uTct6B3hYmvcNHcdUle4qAZ8fX31sEznexCXmoIDHFchjEo4ryDbSq5cOeOznjWw2yOlsFtfD/iPPrC3Jch/9SX+yRN8l/FdRvaDEQotCts9oGX9Ag7GRdgqEDYlRgzmOk++OJgljEKzyTYrcupYfrlDeldoPwzhLOdnDMswrUXLtQEx4iJweG9O2PSEg1Ltykx1gny+sqz32WP46D3ysiE/Wk9Zt+9HsK9Dv/rWFP52hywX+EMizozixD/wpr+zuNQ644n5bSVsHG7IpLYQInlHPpmTtzvb/BSgen5DfO+U7J1Zmb4n/eA9m0/ZD7id9YzToj5awiEaMluOvV5RqDdw9Y+eFsUSwr/8C6TEhLMX5hu37zvCN5e4gyvlFKXaKnd/9NQ2f27MteZgyU+aeYaVscT6TmmurdsR9tA/XthKiScrhkcL0qwiLWqjSo73Rl5FymQg6MWVWcztYIRJpydHZX2QtxZn67qCsa3+1naisE8F2qTWW13a1sux0Jw//5L+tEFDmV47P6M/b2004GZnJRmFPAvIweBbenWDv+0LQMBKJzIIrlf2j00rqztwz55M6BjbDq/0ayWfLfGd9bIBmhtl99T4BPOLMkQllhCl2ti1+jNLKurnG+o7pblW+pPSelzXhqYpLcpUO3zP78R6vlPS7S1pYbEgDtJPP6XaPgSF7youB0FrZ5wtZe8HYpNnfnMocVcmN9XEvJArSyo0RoalKc785YDMZgyLYt6ubmztRFZS5ZDOVoyl21v8zXZalBN2Nt2XazFuGkyx4tP11E/efjy3hT0N7D9eGWLmF7+yzPiQp41NE+OsFjxg2X/SnRgRfP71b2hfDyy/6YltqTm2to6WbG26XAlhd+TRkZwJ77/HOAaRZ2GqImw/mtlMy4O8k7hxEU4OzlyyQnjvma1cLe7NbTu08Uc8YYEx+SdPjGg8Ke2ffwcxmkWq3MRN4zu1DknXT5moXl5Ng+ntVbayy6Jk5mUiTr2bgv7dY2c7TZxaQXuwB4BcFK1iwjP6x4+sJijHVWJxVmhBhp72i0uab2+NGzHZ+9WL7Wc+RFI1rhwTowRWJX76lFD258XWlyQIDmeO6vJhhcS7iq2Q8HYjwi5beWXWEl7doru9DZPf3JWbWtp6417jZ4+ME7pLxO9eEF++LrzUDvEeCcHQ1aPyRrMa6foGo5GDxTcH6htILQWlbTAu46+xk4wLmbLm/tQKyf7ROS4e0dRT5+LsBNdr4cq+RwtSYrz068/pn63IFQW8ajVQvzkgvbFxzV8YgNWfnYII3XmDG5OeytGfVgZdWwjy7XGO+kHeTpyRAikSlbAxxlNyJv36c1uwE5X08s1dHeqsj5tOWuvD7gf8T39kKGrFNjfNWgiB6tsrs0jL+RtMDVBc8W6gvczkRgtBkTAsXNmiWZTIU9DdwjC3ODF/8j6utwfDDdbnHlnAfGcMrKZ89lmjEvonT9i+X08MXDlYkZ3X12jlyRXUv/zarnHWkmcVw8KR2wq3soy6X3qrkTaQXl/8Ld6e/zjEjau3fJcIF3dGaDn2THMZIh8skRjJxUerM/Jdu9s93ftW7B1nPaSu0bYmfvGlJQlnC/yTx8cheYVmk4knja1qcKYUkqFbW3jgSoLgEpAN7JCbsi9lXeM7ChLHFDh89glcXhva+7ZYs17LuouCxv7kqU3ceaG6S6RGqC979HBgOJvZ6rEXL60dWFekeV22RwXkvSfT1ns/PMwcf1/ifJdxKVsS8vV3toT66spocL3DdWNLz1my8vTxlNmm2oAEvL6aWAtCl8s6V7XN6cU6DacNeroiPzIGAxeVapNIjadbSSm3AArDWsitn6g7/N5ArIvvzLoZ97Wj2pobbm6y8R4+OzHUT6+Eby6n2Rh/YELu9GetzaIIVLcDcSaEX36BW8yt3TeGGs5GXOMiTOOe8fHK1q5V9yxsdYSOPcjbifP7jAwZ9/KKvNtN7Aj6gw+RpsHvCoFk7ag2yTB0HIEDboB0dWWjoosFfp9tJ9zmjrhq8D/+YZlXcWhTEVe2/0OiTp2VYSlItvWvkq211q8DrrfuS3Nrrvns/3pxPPE+UW2M/q29jIRtojtrcPM5vlPiV1/bYNQ202wSfeFRjAuj/CVjiUhrMaou57Z00dsuZnWgtdEPj+txp713ngJmAP/4/O/qXv3eivP7iBvytKttrBUO5zN0MZuoQHLlqF9vSSdHsiDJNkY5LpYZZ4pFIW+3DOtAfLIyK9VYDznNj2tsxzLNmJS4wSxeapXuxE9bo8JezSW/vDBcoirV5Y7ZpSF2wqanuh1sRuW/+OnEjWhKnGlf9fSrIxDXdyUpmQWDeS0WVgedlb7yYl5aiga+HUtXw7JsKRVbPC4ZdL38O7lRv8/i/LZD7u/sHdmo5p68aNHXl9Of5MvviDM/bbuUqFR3Cf7oD6wf3Db4/TC5qmHuiDNPdVcYEG53k7sDU+LurJp6wMPScHy5KkCDsgV+HL7f/Vc/wY2Ywj/7C2Zfb6dBdL/tGObC9v0av4v40xOqvY2pVt9cGmtXMNdabzL+YCAEBPTv/QBimjZ+Tt+D2L++M6sdW5kWBlUv7+yaZw/u+F3Fudsd7u5erUsszkmNIy0q0saSFN8l0vUNBngoyUyfqW4Hdp8sjgDYzYGwL+WQAvevrg0smr55bjtBnj01zpt9ZP/IFT5rYZiLzY0EWwkWZ5BmQr+yOt/dB+aiRyXXf/2LaaRTvSe12Guzkn/8CdUm0a89erOZALuSlflL22AfS2G9ezxDdgfGdRFQOjkx4/vCq5i0WNHSIfnmBS5Bbh/ogt9VXH75mnxxhX90TvjoQ8soP3qf7G2FhH/6hPCDTwmbHqlsVqS66aeMM9z1HE4LS2rXw8UVzY3dqByEsIu4m51lwCXL5vzEhtG/vWRYyQTbysF6yGYZlTQzREycm4Xq14I/GKKGP/kH5TOs7ZjWtdF0VEJcVhyetNQ3Pd2JoB8+sziurvGDUn95WUo45mqHpUM3d0foWF1RbbMNM+0S1V2cthGEfWLk+ZZkCduDvJu4vNsZm8LTR6QnpwBG/1H6qPmDJwzvn+L2A24xo7qLZdulxWayPRDnpfNwfUO6uGT+tXVLVCC8vC2kkrmAYyEvWxCIv/mK1NhyGg1mhdoLGzz3B0iNEmc68cfEFqqtEVTefbZA/rOfmbueB4albX+XqPQrT7/y+NuOYSkcPlpZInKyJtwl9OLqyKl9OBa8XVTbxewczWWHXFwTbjvcYTAkuFhhXp3A43OLCR+Q1e8s02OcTmYMZ22xQLadUwMM5y3DuqZ/uiDvD1TfXdsekt4yTFumbYCGdH2DhED+N38GmFLlL74inSxwveJPT5HEtPN4FN/ZylkyzF7Hsr5stHLlRWI/17emhIczYfvJ0up6rbMuTYb6zjgPYytI1xNbQ1O7AXS1oL46IOvVRC0cdoaNlLMT68T0CjFRff7COkabQ+HakQItKyskFu1Ue3yQdxPn5nNwnjQLDCtP2NscyRiAD0tProS7D2q064h/9QX7Z7WtmUiZ4f2y0ajQZvDzvzcdXBJoLswMScFJWQFraO7w0Yf2mlCU0EGaubJI2wK0EV8IoE5ZPO+RbJvh9+djT9k6J75TFt+WklJl0325tt6x7xSd1fjnV+THJxbvHZR6U9bJnixs190O9OaW+N1zeP8pMsSyNLyspR1nY5x7Y8vBg7y9uPTHP8afrA1/1ziqVzv8LlLfRvxhXMCtdGfH2Kcr5Q5/23H3gVGBSAL/o8/YfjSfNkT5XvHnp8S5x0Ul397ZqOWQjQ973uIPtl4st5nUCJsPA+qtJSbZ3PcIRnVJqP75n09ghTgXtAK/T6BKfafU/+yXVFtb9D0BJlxBSquSr67pz2eI2vB7cx0nGjjEtpamW0OIHz4+Qb0jzSsr1YwbDcZRhd5alg/ybuK2H7ZI2xwxgn/2K/zLa+oXd9RlMY7EsmkdcH//p+YCK8Fdbzg8ctQbWw/Rf3RGt3bIp9aa84Pd+FTWjLlZi+uSEVh6h2z3hIMyrIAq069g997xBks2QEPYlcy7x4CzlRR0DuSgNK92iJorl6Zh/o3xEWrXl41M1vqTfY+slvSnwcjcdwPV7VCmAz05wOz5fpqD2T+pwHviwjgLJSsyJEPpVM5mV24fUDTvKq47Nd7AcYqOnIi/+Qq+fUF4eYtL4wJre8P+49VEGKnDQL+CxXc91S7SnVfkGtKyFK8HG55XZ+yv+sOPcH2CX3/BsKqI33xrlnCpSJUZTpT+kQ1dzZ/bKjPXH2FaYQ/dP/lj4kxoL3Wa1JPffAsK7eeXpJ98zET86R1hZ5ZUHcastV7SL41Z1m+OO0py7QzU8PKa/Nn7gGXj2oRpYaQkTAkH47WpbxPpm+/+jm/Z75+4YSFo31svd3cPJdw05Lnh5nLl0ELH1q89YWf8fZysyA3Uf/Y1rkvT5suRq2Zs8I+LDPfvL0iLinw4MCwc4cMPqPaZtMi4oKRW8ac9koSzP700qH9vdUIVAyrcfVgxLIXl14dpQz3PngCQfvVXDMuK9Okzs6Ina5pLpbkcjkt/gmeYYzXC11fGXV0YKNQJWlf0j2xyb1iItRpLkdoPihbAb64cs2+3tk7tQd5JXGogX17j95H6qjeycIDzE/YfG3QpNdbblRBIjTB7XbB16xnqtKBODJniD+aqwFy2hGAx4/WGYeXZP6ktEaqFu59/ZAlDm3De6m/r5R4Nyuv//BGptZ5tXI4D7mad+jX064rUWKy4/+z0jXmV3YdzXIR0vmb1zUDYRVID+dEpcuhtGXdtg0rx+QuDny28FblPF8SZwz86t6RmUZEqs+p2rmEabhqrAA/ybuLUWxE5XO+pnt+g5yeEDz9AK8/h3BNnBXncA//gJ6jA7OuNdQsqbxD7Tz8mN5Z8tNeGjIGyjvVnP56GqFIN+0cO/6NPEYXNR55u7XBtoqoS/iCczfe4xcDdJ0JeJOJMywinFhgV9Gtl9yxYzzkK/ckRch821icOB2U4b2m+vSPN7OHp3lugF1fk+jhfDWap+4XgeysfIaAfvzcBdA2Am/Cd7bKzYfp7u/se5J1kuhVyc4dubLeHLgyClRphmNkQUnUH3bO5JRs5l35qwnWQz1bEmae5STQXnXUi/v5PSZXQP5ojUdHVwizZSshLqzN2Z8L+icOHRPCZsBPOmh3zZUd3lm2DU2NuOte57KuDtMpm2eoynxKYKNrCt5dkD811Ylh6tPLEmYFVu7NAur21PnYEf3ZG+PRjJCpxIVQ7i4v9QUmLeuqjG5ws2hy1Qtgm/J2xSYzx44O8vTjUoPJ6uoLTlZUxFi39o/lE09G+PjB7lUmtWM/1fEG1s4m6sLMYMM0c819dMjJn7T9emeVqCxfhrDZAQQuUUYI4V/oT8F4JPpEaZVV1LJqevEo07WAb6BuFOuO7QhsyS4UGxOZTfH+E8qdXry1T/npj8yezUNp50C+dgRjEkNfpxx+x/8kzGxOYGezL9ZH60uhOfA9hG6l2it+UHSzbjvb5Fle2wnePZ/9v3+2D/AeKkwycn7L7ZM3wbI3sOuJJw+bjmtAZXs/94nNOfm0cM/V1JM68MRV885zm2upmqRbSX/wlcRFwUTk88vdGL42nurks5ZDaE/bRSj2Fb9AJxJNM7SLeZapVRxWSQfjnGdckcgVxqbg6cfLrHXiluVTm3x4mxi0bdFLyv/1zQ/so99ZcgC8xb7VJdI9b9o/CxD8jUdHKE15v8LcdYafUv3nN/EWH7Dvr9BwG8p/+Ep1bBWCEiD3I24stWKwrdk8DhycNvHhNnHsOj4X6NhtQ4YNn+JfXVHeJsB2MoaBL5N2OxYuIG0dD/5OfGvj1NtKvxiEjc+vu7kDz6xcGWl2XXlxBreQsxOTQ2uKsIXmW8446JAM3NAlfJbpTIZ5EnFPCn38JXjn5fCD82RdTJp6enk0tweo2Gcyr08L+JaRnp4ba6RPDwhFnBu3SADgYlhXa1sjX39FcGzg2/Ktfk1czQxadG/vs4ZmxkqXmoXn8rmL7jsVIKbsTN3ULhpVteg975e7vndP98Al14XL+f9h7sx5LkjNN7zHz/fhZY4+M3PfK2lgki002u8XuVg8EQbrQn9Jv0Y0wFyMNMOhuTmumh9MLq8iqYi3J3NfYI87qu7uZLj6PkzOCIEHMAgsqxAfwhhmZFRFux9zs+973eZ2sRrUH8+AgFY6fheS6jPC844U8HHNGQ1DY8ZT61Wt0JW0e48rYy8kUptESK1ZL4mda+HT8CqWkP+kGcnFpInB6FRYof3AN5Yq5afGLOy1oU1GNxLzs3LlJ8HKMdfUbvrSS16euoO56NL406IOT9nznKequQ7Ue00ymdA4q9A/uYeZzinXhLmabIeXHtymGLs76+nne8bdQ7fxN0UQImBLw5rUQrxpLMDFkKw7ppo/aFdedO06XWI+m77eTCUvRd+Qwr3Wbvs5S3HBm93QyqCLxJruptHSM0RS5h3/iMK8CysLFc1pU76DGDypcR5rXUVRiGwGca89w/J7H9Jor/bsgoOpLAE5xcUDz8AkmEDm/arHDxVDEDMXQaUeSsiueNcWtVlSxi+50CB8fsrgmBq5y4KIrUXynmx51pKhvXTgntX4LpQFMJL2wJgD1o3fxDufLP40OMmmL9BR2Phcc2mQugCPtsNgJUHFnaYE0gSK90l8+9MaX+a9qCa9uJpMOrCUYt6/jWmFSl7XPDeNCXnO2jZHVnRrXMTja4FQQejXWtM1jtyG9YCiH8r2aPKeM5QNQ9l2c0YgmcJYWVdVIK+asJWNcUXTXPR9/LjdsXYkgQl3YpH7+UqitYUjjK/xJKfrGUJrnVd8/R8N9C6WtkoYsWm61yeUuphvglEqCZibJMqJVdWN4uScRE41F+x7Zqqa6tNY+PLmBpustJaFFv7m5BWNxr11ZElOdwtB/Xre7pEanDv7cMMlClLaklYexSKi2NihlKQdyiwbIV1y0tphBLU3tdkeSvmZ7DLh58U0WnaEdP7Z/7ot6xy2Ead17VYuVM2tw8gYzFBFGE8jiVkYAUGdIO6eUufiZ2ui8/vDSwJuQaSPcluxCjLsQhYjpRnhJ+5A3VuXM2GLe9PoaVRfSnTfauipWVPEbnotxofM6Ba0oL64sBareJCd+PBaZVi2y/skNj/lCWqggEkwAACAASURBVB7zNJTdsFHLXbHcqLFt+HaypbEWdND8V8LSs4uCslCOgjejwxauRPs9nX2tmxrqQBP/7VeUXQVa4U8KmtDF3dqUM5+S1hRKlNi6EptpcJzhZeeL8G1Lw5tX5lkKZtnThKcWZ39Msdmhsy9zqnKji3PvtiixjaHZGAikaFULusO82Yn81o5pPIXz/ABzdZtixZOdSIHePyG9OpQWT61xcnk9VgufpnbIT0NqozG1pqwdaqPxegVF5aK0hOrUlawmZeX86V6/urShOoWljmQe7OaifHFz+4Zxjexy/qQUlMloSNVVlD1HiGKOorxzQb72T94nOCkod4Z4icXLDJ0XM/SzPYLTcynX25ZWLVQI5LymWp9FvF9Tv94lHzlEX74SeHjPJbk+FNpW1VD3AqwSXZ+7KAUw1M5Zo+OqpV5B8vFVFle7NIFeyvjr/QOSLZfeixKdqxbdYdELhyZ3CF971LWsljz1KUsXz2tIcx9s67hLXEzqyi5XWorLK8s0TydrxHKgwdudoivw52eZdbTgJvBfnmC1ori5KWffroaW6J9sSS8wXwtxJxn5qtd6nA3mi/uYy9sEz47/736v5/X/ofTZKOwMUOSlFn/a4M3kE17Finr/QDjVoSJfabeRulkahYwDNDLgR8ku6J9kwvTzYXbVJd3QS5+xlo4IVU/hn6Q4qSxCXUnLhkKz9ruGsvBQCszCoypcqsqhTHxMrfAW4J26eKcuqlb4C0s5lJ3RnzWgWz5hqGgePkE1Fn9cvvE6R7Ij2iQVE5OjlrNpE4u+shjKBehM9Fp2NeHrhXiOXZf0ckz97MUf+5l970qfSefPxm3RYUXn+ZSq7+FeuSQItSAQDV1rw3TWVsEV/7GTy2y1WgkJZgZVQ7xXocqacCJTjmIIxbCFFeWtUpqWLa013uLNGdLJFbrUxM8WNJmDrTTuzMGkLtUsQM1dbOngzyzdZ9B/KB+ezkG1jDULjlLKvkMdSh6K884tdAPeSSKy/4YlTqS+dREvs/jHLY1Vt2PIwKHqKvlATWvyzYg6AvVqD1Uaql98KFzD83rr0m7rIda17Hrh3kLI9isO2a0NmWrcurbc5YyraG7uyHhrUeHNLd7CUgxdvHmNm1qCF6c0/VBmycgruu6IODaYGTqHbXPYBWeaEIwtwdS2lH3QZet+W7joxJHosdwh3PUIxhpVCBdn/TcLVu5neHMIPn/WXiJAPXghUCVP2imzeyviDkxzwXyUyI5cQb4ZiDn//hP5QJ3JtFxF40PvWU7w/IRsxX1z6WkMs8s+urK416/+sZ/Z9660v5CH72RQS4uOemtIMdTkq+K6yy73JDW9PU8VowAMOCdzgqklOpEIiXLgEswEhFT1fZjOlygP40votZtZggf7AFgP7Ez+jXivoP8sFxl+Deqbxzipwj/Vy1f1+mc1/hR0LoapbDticjMiPjBLRJtVYOZz6kgtz3/Zqki7rC+0B29uBQWSQx1InJq+dIFgavETu1TPWAf8p4fYKKDqSkuH9VXQimJFER2WlBeGf/SH9n0rHR2V+NMabyFN5Cb2qWOPKm5vukqRbLqER+USi9GEQvNvXrwiHDfEr8Tcnq5Jry/bjjGOIv3xFVQtDjYshK2ypX69C4BxLHZnAy8xYkifZJIkn0lau67Bn4I/balcj2Y4uUWX0jJJNh2qnqL3NEH3euLka9+Q0ouUy07dkcAd2wnAQu9VSTC1Sw9zHWvKSyM6hw3RoXiMz/SCyQc7lBvdZQfBDDpYR1OH4D/cpep5382T+x6VDh4dEuzNCObtkD9wW/mUfIF1oBgpvPuvZIew7UXGdbB1jTcrcY6muGlL12rs0jq6uCBN6+hYXrM28pZGc0Buqd0A6wrJYPbOECe3uEn739ZiUOruSsj25P2h4EMKhS4Mdac1UE1T1MUt/IVZKiR1bfHnhs6hOO+8RYOJZMF4/+lLOkeN7MoTibmoYpf46QxvnNMEDt68ghY9ciaYdXJoQlemMA7YqsKcCxjeunT98hXNNw8F09a84fgZr839UNJmaY6O0I3QtJRl+UBVbbCuI9nBDtQdjXHAfzWmiuX2PHic4y0U05vxMsfO6ffRlcKZ59ShxnYj8qHGzSAY27ZRLO2e8FCID1U7ajtLW0IJZN30I6EmnLSav3du4SUQnlT0P9uXHmFSCXizAVsUxI9m+AtJmhIvNOiTmSSW6nY+bqT95E2Er9M5EhJtE7UBjGH4f/19ntcfUG8IDIFq0RsaEwjNPzoql2mazu0bS5IqtoUF3bstYTsDQanpGvLBGThTIJW6BOefvsKfWfKVtk2jHdI/v4M3R/ptbUDi2aKLD2qSH10GLYusWAvQRcusaVtJxpOF0H1dUPUDmWe3oor02pDwtMGdFlQXRoIlnooHGQsqCDBf3ic8LMhXfVFS+xqaRsaBjW0pte0v5/PfE0wtw0/2cZKSOhQXn50vzhOdvoVaLsKqo4SmpcT0gwX/1QTV8lnq9Z7EKJjWf/tyn+TGkHLo0kSewI8SS9WVWAdz7SK6lDOZrcplW8S4Cmd9lflFl97LBmdzA6cClVecBfmEBynJhgtWZPr5yMGpoPeyECpXDdZVQlB4sEcTaurXu2QXZOSXrrtE+ylomF6PCMcGXh8ss+n01UsAeK9OKM/IXD3phxpPzP71SiyLt7DYuiY6qrChjz6QcPHOkaGZzQSWeV5vVfoMZm48OfPoos0HLhVqIeTWYNIa0lswpK4tZpGQjxzJhzMyyO+cNDQh+NOaYiPCn1uCeavlc2Tob3xg2KccKAZ/94Dk46sEk5pqq4d1hGtjfFe+voBky12eT4MXp0viF7RRD3mxXLzJhgNGJGnOwYQm8ij7is5eLvkpR5IaUG30sD//AeWVtSWD+kzG5pSWquNS9T28BYTtz+4fJkw+XKXe26cOFb2nGe7W5vICc15/eGk96EvzuZ2/BseSwuQltJl00HvZ4O1NZAdsLyfVn71H44ut0z2a42Q1neeJtDUOFxhf4y8M0VGJc+/2shdpPDC9kMaH4ofXSTYdogeHLC4EgqPzId8ICGYWf6JINxWNJz275M46vVdF+0o2+LMGNewv+3XFSOHmcqmyi4SmI4vZeyoUWrN/CBaKFY/FpZD5lVD6n7S02I/fJzypJIrM07i5oftMbkn6eEy2qtGdjsCW/vFzqutbS57hef3hpc10BptrcnvVoF4ftYvH0qx05Xb6+S71k2dLFp8ysNjxhUhgxXSuyxr9cl+kWc9fC8O6sni/fcz8zkgU1T2Z5Vb9ABTMrvjUHUX97AXFsDUs+Yqyp+k+Sxg8FTW18aCO5dbt3X8tsPT9Bd6ixvQiiYLdHFB3IJjIbdgsEhpfmtr13r6gPW5fbU1NmrIravJg0sgrV8Pseoy/OxO94Bn78l9+B9qBKKQJRehg2p053wi+m6f2PStt6xrju7htpodNEuJncwaPM4rVULSAVYXzzi2Zr7Ye3CpuhQKlxb16WfKQw7C9eGjKWEtvLQrJVkTKX3WlcV2suKi6XZRnihZfEe8b6rCNoDCW6CBfjhObQNDE1S1Rtpgv7oO1WN8VtIgSC2ivbXhjBbAUTNsR4Z1LpJf7wkI8mx/7is6zCf5cxBZlX2FDr819Vkv8iH7vFuXOSNoy3Y7I0YCid54h8W2UVh+/jwlduUC4YJsG8/k3eHsTmkjjzxpst8Ps3ZUlm0/nErwYTBq81JDd3kAfjqkvrAjq9668Gq2jmP/8Gk3bGzSeJGzmIy1K5v8C96ws9P6PB21EGOTrEbPrEW4i0xLrWeLf7TG7Fi3bPNbTWFdTDgN0Vsv39qvPBWf87i1JZ2plaOl2SNkTeJObyfkVoPnmIU5SyY3bhWynu/welJWuQHK9L2gTC6YbSAvn2hU5q56/jt+69PEHXbLtEC9pWpKV9NrKSyN5nU5LVFGRrUiOm3VAl5L0FBylYp0cONR7+xRrkvCUr4eUPTm3LbbOVKWgaxEqVF1FeCKvTV2D8+4ddGVJf3ZTZr+NeEDykSbeM3IxspB8sE0xUmJDCALq0AFjSTc9ytUQXSiwlujEMr8ppiv/RMxZRV9ICsHEEL9Y0Pvt3nJOXA2CdioD+UrrPWmEyW07Aemqg7soxQ8TenIEGXYFxmTPV+Hbls42lIhS03r5qXa3t0g3A7xFja7aHSNUNF8/kIWTVbi5zI69Wbk0nlexbm0CCuOLxq/usGwIO3kb3eBBOG5aKKUlvdxHlzC/KBMWN7fLV3XvhcyUnVwzue61hnqLLQqqnvBjioEm3ZTbrO506D+YSfhhapaEsDqSpPrwOMd++hXF9XWZJ//sQ+YXfbq7shtWHdUG/dR48wY9TWgiRbEWEp0Y6tjFyWTxnaOCv53Sto1oUE1LHNjaxI76FAMxrVutMKOukLXevSPigiQjnMjidPZEKaPjWBajEfikMrKbgex2biaJTU4pr1dvXqFLSfks+87SmedPLf5CznO6lAmO/k+f4S4UVRfc1mqg45iiL+c2E8g0JTi1ZH/xLtQG44lrsBi98TjHz+a4B1MAJjcC3Nwyux6Rryqiz17gFnbZA/QWFf5RQv30OVbB9JrH4GFCFTt4C4s+nv7xn9b3tLSTyQJxJyn+3GDWRm2ooCJfbVOQRhFOYRl/MCKcGqoLK8RP59AY6lev5Zbsydc6paIJNW4iZz+JYoBgUuMmlmBSy1hunOItLPGzxfIM5maW3qsafyrnOy+15KveG2eegdHDQgz0P7lDHSmq/pusPaeE+SWXxa0BXiK71RkhwSkt6sU+5WUBYEpTXYK+60jy7NzMiKZyLxfY08sDUALkLIagp6lMdRrk5zacN6u/hdKjRzXhxNB885DouMJ0fdC6dawpmtij7Hn4c0s+0vQ/OyTbDgWFkcv50c0NBIINdlNI11w6B8Kd0bUsAP84JZwY/HFBdNxQbPWIDxvsb78SjFuo6L0o6Tw8wZ0XqPqscSwrLJhYwlMZr7mpIl8RWVa+6rU7rfQYq1hM/PFuSdl7I5jwEkv17hXGt0P46QfSEx1X1HHrUXFdvIXs4ON3OtSxSzMeY3/6gUA2PSh3Bm3khMLp9+VIco4LfuvS3QcTyXT72YetQkahyhpdQjBrSLZ96o4maF+/BL5AI8OQ4v3LuNeu4E9rVCcimNT4E0G5DR6ny0mGU8rry0sakksdyr4mueAR3xd/hj+VSYv37z+juDRCFY30DB3xgbjbWziVZfiw4PgDH29BO14TxbZqIDqtl/4VAKcy5CNH/r8fvSsUsDWfdFMxv9LBm1vcRSX/fgbqvds4mRAn0i21lITlGwFeYtGFpFwpK+fc/Ke3CWYNep7/kR/Z96/06Q9XyFcUkzuScqkrg0py4gND96tj0nVN40O4nxCdGE4/GolA4fZVJrd86vU+wYtTqGrCR4fEB62z7eGuzIsduYzUr3dpAk26psmHbQ/xeIx77Qrx74+k/3jrGrOrPvUoEiNTe5wrb2wJkm1ekK++ofmD7HwA0fO5SLZmot4u+x7FSKFqOPmg34ZrC5q47Cq6ezXFaohqRK9YbHTQRS0C3aB1Cd66ThNo/KmIaTHSG7QasjUXXVhUVf/RH9r3rfSZRbPsyaRCpyXV9pDhv+zC8Zg6FlaNSnJG/7wro6tamsTZmqLu+tjZXAIKn7+k+9UR4cSAbWfGrpwJQSYVTaCgbc2UP7jG4t1N6mcvcVOo1rvkK4p0O3iTIlpCthlIP/JCjAksdcwy8akJWmHEl/cxriI+aOg+mArsPYbOUUO61eaaNGBdEWlET04pBg5uKi68xteooqF3X7zQdaTJbqxiFYTPTugcG4KTnHy1RZc4SiLLovOpyduWDscGN5EeWdURuPj0Rkh6dxM17It+r7SiKI4C0RZ6CudkTtORUVr53hXKnRHOaER5aUT8IoXVUQtDAidvqP/qR5KYZERebzVMrwUsdhyqv/6I+KDBupq6A0VfLbF0/sJQh4rO09nyFdwEtIHZssjPRmzKytnSfHmfzqMTrAPxgxPqruSiLC44qFrhJZbmwWN0YwlP/mtWd/P1A/yp7ODpuiv21CfPcFOD+vwBZV/Op35iKHtKYJrn9Val+//hCb1XDeHESIulbsg2FNOrLs1LuQEGkxqUYvau+HqrrpLWhSP6w/mlgKrnYS9vMbkRwL/8jnK7L7deBeHrGemmR3RQtDfgUuK+VhTlQLHY9hj89oB8TfqA1oH+40z+7mmFm1vMl/fxJ7VYQmlDd5B//+z85s2tKGmgtXlCeWGAcaCKIdtQeHOFvzA4a6t0dnOUlV1VWYttMceDZwLGXDoE19epI0Xzk3uYQFpI4WEhITz++ejubUs3N7aJH4/pvsjldRVHlD2Zati6ltttYdAPxcFmtTR++cn7qEp2jKoLbt5AYyn78uDytf/Ce6HbJM9PvsEpLcGnj6hi8f4aVxQs9ZNn5EOZMbspuEczUTaHDvHrDP3hOwQvx/gThZtC0XdwcpZmfefmNVa+mFG3eSvqx+9J5MTIWwpzm8AS7wnQM/nZDdR//pxsXbWoOsF9AET/7jPC05KqK7K15E+uUfQ1s6shmJb4alpDlHPesX7b0vOrHarVGHdeEMwM+eVWJaKE6awrIS+oXk9mti3kaHKni5uJwVwZiZHAldGYCgKKnl6myB/8fIX+kwSlWlnWZNoe/mWKohqZ0VotvT63sMw/2CCYWhY7Ls7TfZKrPZpRTHQsr1CrITw+Q/paZh9uyG62vNVG0vie1biZEmN9rhg8EaZ20o4Ty4FlsS2LUCcFOgxxLl3AKmmCe7OadEM8yMYDfy6m+Krviwj4XE741qUbT1Gs+uRbMeG4YXbVx2o5I9JIcEzZ1Yx/fnG568grGbyFGNg7R4bm4JBsJ5aR1q1rArdMAQWLy1CshugLW9TxWdaJLJh43+Cllvm7a/INVZCPhKQ1+pc9ihWFnS8o+g7pxQ7RcUPnoMZLDKPfp/hTS/9lTbLlcPzRYEl3qDsaP7H4v/oKJ5NXdedA+jdubpb9R+NDsSrfp3nyAhUGLO5tyJ85ksN3Rhfz54bec4PxIdl0xXPSnK/Cty3t5gbrKPIVAYxn6wonlxuzreul1i7Z1jL7bX0eWJZ2gN6vnuKMRhR9h2BqyC/2lmct0Q8aTu55EkwTgf35D0SlYmD02SnBuCHZ0iKlat194UlFcWVVVN4rcsnJVjRu1uDmDZ39Euc34v3wptJ0zlfFouDcu03R0xKU/dEdnFJEuv7CCs540Sx7mBiZReva4qytYqtaEuiLBl0pki2X+KDBKSE6qhh8M5Eb+4ai82qBszgP03nb0sG0zedw5VVZ9SzRobx6VBwLtiOTdoeyFjeRNKczzqCXwOzPr2Hmc5zSEp42pBuutDHcVi+ooVixND3JOT76qIOuFG4ioojwMKXsi+ihCeR/TlZzci9EV7D4wQ5+619pfE3dcUTpff0yVUcxvyqutyaSxvPpD1coB+J1mdzutKM2y/yyLMxiJAny7sUd3Ex6if60ZvqzS9h3rqFry+JyhD+VPmT8eEx0ZNBFw+LmgPBUGtbZdox6tvvdPsHvQWldGdykwU8M4YlAK8PTFgV8dISXWdxE4h3Kls0CiBU0UHRfl8wvOeg7N+g9nNJEmmKoiE6blt5vcRaaJhAiqjezJDsyC+6+MnLuzGuMz1LTB8KDKUYQnlqyNYf+g5mcSTXUoUZlBdVqTNVVonwpofEtwdSQbsg8OHo+p4qVUPtLgWwWQ1dUQ6WFpsFNIBgr6tBhccEhuRTjT2qSLU18IND1+d0VVv5pD2Ut6bpeKoCydXfJ+D6vP7x0MfRw5wXxi4Tw4QFOoSiGemlAd1NRuRhfXpNO2SqVQ9ktw69fU0cwuztE5RWLLUeaxC8XIi41is6+XF6Mq+i/rKljw+hhxep/fEXy81tUo0jOh3sGqy26FJ9KE1oGT0qsgvG7og90svYDcmFVLkxGdj9/KqLc3lfHMrNuRHjhpYIx1q3QINl0KHtiH7WjPl5iWblfiaEqlEmJbmTurQz0XtWCiysrMEJzMK4iOEV4Nufe47cunQ81epaRbXc4/bOLBBPpqfVeN+gP7uKmDfmKpokkhdMpW0P8GaEhSZdwzJOfbsiERYM+nhIfyk7jzyxuIi0QqxRYRfRyTv3yFfOLDk3ooEvo7FdYVy48vNoXx11aE8wNs2ty8/bGuZxh10LKvoM3t0THNYOnhUj2HzzGKvAnlpN3Q0b3E5SFfOgQnCiKFfne44OKo5+uEo4twb/9tQRzZ4KVq2JZ3EVf4i/cwpK9s40uakwrZN349Zzubon54NZ3+gC/D6WND8ndNU7vuKRbmvBEHHW9B1PSy32UtZJ26VnqSLAZZzxDq8Fe28FbyM0x3RDigjJQXtsgOC7o7LftlBPZZfIVBzdRVCtCXzq7LYcnlrojeA03s8z+1TsoC4vLkXAG49YcX9aC9UV2485RTR1r/KMEN1HoH9wDxA5ajBQYER+km4q1L0uqvvx8TtYwv6KEZYi8rjuHBm9eYXy1DAOf3HTFVRg66BN59Zaxxt09xf3lpxRr5zvh25YOppbJTZdiRS4cTilm9/xCjyZQpJu+3GRrBY70xawr7RfjwfSdAfF+Q/fhBONLz86fQ7od0HRcvNTgt/g4q2XhBqcK3RrRrZI+5OqXKcm201oyFfPLDk6uWOxo8tYoZTxQJxPKgSIYy/Ql/mKXZNMh3+7S2bfLmC/VGqTG73RxU+kHOmlNE1iCsWV6PaTqG6bXWrDmwtJ7mjC/HIqbb2pB054jNU7eQF3jFJCta8yqHA/OGYVvX3r0j68pRvILd7M2WsGH6VUXLzHML2n8mcWbaXShmF0NsdrSOWwwLix2NINP92m+frCEivef1UvaQhVr4v2Csi/nQje3BBOL+0hulW4ukvozMqpTQL4qt/HgRPqRTQDeXP4+YUDVAedkweCrCTYKKAfgTwrWP5lIImndjuKaVurVtmGqgUxP1r7I3jS11xT2Zx9SdRTOk13SDRFZdA6EvaiMeJ7D3TnNxXWCqdyM7f0n8g+cD0zeunR+c0NUzYnCn1mqvtBYy4HCzWryNbkcdHYV/lhTDuS33n8wE9FDjEQp9HoEpwJV6jybCQsmF5EBRtonujTCKMwtzdERKJHzKwun9yKZ0uSKpiMmqNEDUVHrSl7XbgJHf7Ejypw0p+kFjH+8IX2+l4eYz79BWYRx48mCNr6MBa1ncXJJEp1fCRk8LfDHMoZcXI7I1xTN9QuizPEU0dMxK1+lBCdyvGi+fkC+HtF/3ubofXBbRpfnLJq3Lp1se7iJIjySS4dEPMjCmFwPaWLxW6zcL4h3xdyjKwUPn+Ol0i+cfLxN+fFtBk9KdGWZvD8kenSMqowIDFyFLqBzX0gIZ6Ah+7MPiE5rSUoaKdxEXqGqauVlQxdvzpKI1Xsp4gonB1tWTO50yFdkS5v+4jogi6+73yxvwOIvlv+eaizuQpEPNckFn9WvG4JT2S3LvqVcCWUOXVrsi9d4u6dEJ0ZGfjevyc/xD19gHUh3ImY3Y5z8fGLytqX9hVniepUVHo2TKaJDS76qUJUSpXNW038m8WG6hOLP7uEtLN5cvCTl0MU/SfEy6dOZYYyuTBtkY+m9aqh2Vkg2nWUATbITEr1ORI3iiempji3hkcZqmen6szcejt6zFAz0nxvq2zviDQlk91xc0K0NVNP7/QTjiJPPS6yIMFLN5GZA3RGy7OKipv/NhI1PE5FkhRZdGpwMes8zTJ6T39wgX9EEU8P0ow10aWh+8SHWEfZO0Vc4xfkifNvS3f/0mM5xq43zITqRRRmdCIsv2teM3+tTjvxWIKAITy3jW74QutrXkZMbis2Y8Kik6kkqfNX3MT4YV9P9N7/l6KOY9IJa7h51pDGffS2qnHYM2PiidHFy8RdbBzAw33E5vdel98ow/N2YqusuL0feQs6OuiMhiOp0ilOKFrH/rCY6kinQ/ApUA7M0z0/vDVlcipZYYeNrobd+9hD18fsYT/SNw2/mZGua4Djj5N0QXcvPYLV8wM7r7UpnP7om7rWeouyK+jicWOY7Dv7UsvP3cyZ3RTdYjDzqGEYPcso+OG1oTTCzRLsJdUdejXVs8Q6mJFvytIuRi61KihFUPUu4n6KCQF6P16+KtL8S66l1LcVA0TmUHazqiVq6GIlYIhjXHP1khfAgZfAkF9VOK6pQfdFxVVc38RIhLcS/28XNLCvfVFQDg3Ut80ua7itDMdRMr2s6B4IgXsbG3r7K7HqMMm06QV7T+Apn94R004ro1dPyoTyXcr116fklT4xO7e+yGCjC44piRaio+WZEuSLRXY2vaDzwjgUZ13+eEx/ILdl89jVWKYoVnyaw2NMxjS+CgnzQClAdwEK+3UFf3pHGd1m18KQz05LcZkcPckllCml3KQhmlnzVle/ts6/R//DbFrQk50wb+DiVJdsK8VKxb5784hL5iqbzTHzC7kJ6kcMvJhLEOLT0n+etF7qRnmDHk7xmY+nuNtQDOSvOfnqFpmMZPK1F2q/VedTst1BaAOMWf24JxyK/j758RRPKZWFxwQEtlFWnlNuvygqCiUX/wxdMr7lka623NzfML4pO7/R/fIdgbgimYijnJ+/jZuDNFEcfyg5ZxZpmfUixIjPjKlZ4M83wUYWTVCJwzWFyXQQHTiFS/KXPeDgQ/4pq4eqzBf70TXuo7GqSCxIlkdwY4i40ndeKYALZlZ649gJL2fdQjfyjEqojRiynMvS/HuMezTE+Yg9oFPHffEk+kiPEmRD2vP7w0vFhw/CLEwaPS7zEEB821PsHy7iIfEXhLBwx+BRGNIJphrewOHeuk24L1sO5c5PwKCO5AP5EM74rxHynkNdicrmDPxHhQr5hMP2Isq84+UGfuiPTEGWhs2eJXs7B2lY+ZQT5NoZ8RdP9x2fCzg5D2NkST/NE+0FcHgAAIABJREFUmsv1/gHeaU50XJGtSqp7HcsNfnHBITpUrH5TtumiimDetE1whT+B2VVflN6RQxNBHbmYjo8ZxtIb1BAetzG0HSXAznMWzVuX7v7qKRye4P3dp2SrDt2HU9AObqZwW8xz/5FqJVYaN7VM/ptr8hraP24vBpZm1EE/eEEdW9Y/r6mGhvmOS9nT9F+ICEHXYkI3kWF6p7eMfNWV/Fm8KyGN2ZUe6v4zursVdSSv5/7LmsZTmIvrwri+fZV8p4ebwcrvC9k1t7eoVkJ0aaQ53oh6W7URswDZqos/q+i8XJCuOzitOnzjNxn56hlDRxZ+uuFSrEUY3xG/y9iw8WlBvd6n8WH4+8V39+S+R6WTn16jeucyIJeA5qvf49y9QXQkY7zuK8vGP81EKtWe3ZItkWbZdow1elCiqgZ2NtG1Ilt5M37LVxXuLz/FW8gNXCJb5fLhLSzxfkN0IL2/eFcmK/6kQl3ZwfubT4SlnUL3ywO81Irt04NyLWax7eGlFveXn8rUZWXA9KrP/LLMc53Komr5npWBYiTtpCp24cEzyp5Cl6I7HN8WeuwZecJ44pepOxr3d0/w53LurDsO3lMJA1Jlff46/hZK50OHYtXHvXZl2bpIrg9Z+2RG1ReFyuTdHtGRqI/PjElWQ/7zu/hThZPVOLsnLG6PCE4U2aYiOHGoYitjt7/8oVgnOzK2U5Vu802kzbHyTYF14eijCKeEbCNgfneEc/MabmYJTw310+eE4wYna1pccEPZV8SvZLsuVoSxna8pWewLSx0p/GnbHG9Hd8q2SabXLgm+2Ld0Xmfka7Ljyu4pI0qAOlCkf36H3otGAiZHDjbN8BYwuzM4Z1Z/C6Xd3JAPHcwgXqLh0nUHvZCHm627JFtagmfGIj41LdQ8HzkEY8vJex3KG1tkqw6DpwangO4L2yqlLYcfhWRrjiA3kgZnoQlmsiDyocb/z19hPBkB9p/XzK448vBvrRGdGvyFwf7sQ+KnM6JHR/L6/ucvZbqRlGItGFmyWxuiCfQVnWMZ/a38vsLLZFSojCz88NUcE3lC+HLE/+LkcgnSJfjTks6BlcuOgelVj8Gne3ipId4tMbcuER80ZKsab3KOAXnb0tFxJTubkt3DWV+n6ipmH64THrXnqE1LuqWpes4S3xvMDE4lFK18VXH8QWd5a+2+aui9qlpltfz9ZFtjAtEihseKOlCC5kA40KqRr43/9kuML6KGbM0V0kFXc3qvg6oazCDGTSUGonNkOP1wiFkk1F3L9LqP8YSF4xQC14y/OsDNDP3nJU6mCMY1erqQ3BQfQc7FmsHzWjLvSovOajb+4YjouJYjwgDqZy/of3aIm4iFMDwRyZf65ul38uC+T6WdrEY10PR94v2G6s4OTSBk08FTWWBNTxjV2aojlxUr0v7gtKbzzb7Eya5Cd79mfklThxpvImQtq6DuNZR9SxNYGf+9MCwuqtbiaSlvb7d6QitnwbkQE6pY2jZWK4qR4vBP1zj6yRB/bknuruMlMiOe/08fYXy5jBhXRnl1pOk/mrN4b4t85BA+OUbXcPJ+QLM5pO4HEi+xbym7mvhvv8JfiMIHV5NfGpBsuehffUHdEfOUqhuOfhhTDUKqrsSvqV73u36G/78v7YxTwrGhGHpEB5ncBB1ah50jaN7mDJ8ruwwK8qGiiTTp3U3Qb/KDk4uG8TvgjJNlbAQavIUQ8b29CdC2aVxpc1Qdl+HDDBTs/dU6/RfywdCNnNG6u6WIBrbkohPvV+Qjh6YdsyXb0ps0npzxzo4V6punlH1p1VjfQ5eQXLCM3+nRBPKz1W1UmUkSwtOazkFFvhHSRA51pHAv72A8OPqTVSYfb1MMRZCRbDjoGop3L30nD+77VFolGd3nCbq0lAMfbypnnGAqO4s3B3eq8RbQe15I6EzLfCn6DosLcptxcphe9zC9mmrUsP/XmxhPTETOQjN62KAqRfLOhjz00OAWlvjhKd684ugHHYKxItmxjG95OIXEwfqJwf3lp7JbRyLnir7apRgqwqOCYGoE0lm1uXXtedWf1NhGONy6BDVb0H1taLqNCDOMHA3yNUs4NbjXrhA9PKIJNcZThAcZ8UFDtT3Caki3FemmfEAX2x7lQP6NbP085fNtSx/99WVoLPFnL8nXPPTjVwAMvjyVhzmzREcCEfI+f9xKnViazIuRmH6cArINC45FlZrJPSOjvWcNvSea2SWH8FgxvuVhHFHn+JMalRek2wHpBUtnTy4CyY6osOtQpPTqx+8tc1bWvsyZ/OklMd+PM8qeZuWbAn/aytFy0UUGRym2KFpSV02ztcro1wdgZfJSdR1Z2D70v5nQrPZohl3ctCF+MuPgTyS6tum4bfAkFAPpaSYXzuypSsQX5/VWpfNVJQR6LemcXNgUiv1OfynJd1NZHPbqhSU30CktwaShjmHl64JixVKuGpS2xC81Nmrwxwpv0RDMzFJRXazIwnUXmnzNo1kfMLvmiMm9tvQfyytVWbnl1hHMbnQJprLA5hcD5pcdUJBd7JFuKPzjhPg1rP/DPtGhwimhXO2gPn6fqqOIHp+gy5rsxipOpln9XUa6rgUpDHA8plgNmd+SaLP0Sp/0gmVyw21FDKKHrGP5GYzPMo73HJ7+9qWdDOY3ejRbI7qvS+pRBzeD2TWfbE1Sj+pIzk3HPx5Rd6DuWbJ1RfzFa4xnmV/yKdcabNhgjYhJ9UIca/64YHJbMXxYMr8qfMByAMFEka5pVCWXFjeTReiUFjdtrQCJxV/IAS+YGsJTGSNWsbz+lYW6C8VGLEKGiyPWf1sI5UHD4Y+7Akg6OqEeRm0yqCK9EFL2FcNHQqStb2xTDlzKrmJ822d6zV3yEf3dGe6iNfs7EB8YvLns/MoKN+e83q708HHJ4oKDfviSuuPgzHPcVMQLxap4dqtYvrgO5TVkEQNQ+v7Om63AN6jcwTnxyLYs/li3UbMNdWxpQi2pTDOhPITHsqPoWQoWOru04lZFMBYze+91xfCrOfF+wfGHDm7SPnAtgoWqKy2j6P5+m7Xs4B8n1JEifDGhGCmMA/W9q2SbAd2vD1E1TG4IJSx+NJZme1rReAgCpWi5MzN5rXM6JZjY9oLWZurNLfGewZ/aNxjl8/qDS0e/eU4Tgb1+kcOPPE4+GhGdNtRhS0qtzlgtrROvvbUCzC7Lbtfdq6FRdF46rPxOUW7IDtN9bSjWInQhbRZlITqyWEeMUliwaUZ4LM3lsqsYPkhxU4j3K0HyJjl15JJfKvEyEUuoWnam6VWH8BjwXLq7Nbq2HPx8RBNCenOlPf8hwlULzUoXf6JoIrEpTD5cZfC0oViLSDc1+bqhuytJptFRO28e9pa7oLdQTK6LzCtbl5t5eHTerH7b0lSltFaudqn6lmRHNHKSlq6oxR7cjutaf4cjr1MvsRjfEu4nqFJz8ZdzuVH3CvrPDOPbrex/pjj+wMVuFhz/rMYfazov56Ahf/8Sa18WTK85GE9Rd1yc3BJ9+pR8RXP45xsUQwcvLvHnjai2fTkOGE8SmsqdEdHrBHdeCfTcEfGBrmXHLHuihB7f67H+RSnN8J4gknt//4D5JZ/koozfTu9Ktp2Ti4jWdCOKoahsQC4oqpEGfB0oMOdju7ct3UymeHPxc7iZtF9mVx28OfgTqLsWp5CFaRUEY2k6r39W4RayMzYdD1UpjKupehCGFYc/AfvenKKnKQeWcmhZHS1YvzCRuettSXFKtzz2fxJQDuQS5C0qQa6FIbqSD0V0WGGMJjzIqDvSq1QWBk+FLpttBRz9eMDJ+x2sK22cZWJ9JQ1vZeVYMbvkYTx5rasG0p/dlFFkxxC/1IL2KAWgBDB+T5DJm5+kVH2LN7eMHlWCi/Mh3+p8l8/ve1Ha/OIjcaf1FYOHht5LI2mctg1AtOJHdkoxDYVjg3UswUnO7LJD/AqyrRBVKU7vdcBCHJSEl+dcXz8hGsu723gW32mI/ZL5dSH8jx6W1KEi227wJwKldI7ndP/+Pum72ziF9AZnV3zMaUA1CLCeHA1WvywxDvRelMwvSmM5uQj+VNF//iY+1rRMazeTI0JySYmR60he6eNbniBA5g79Fw2jhxXxgcGfG8GHrIiKJ9sIqCMrLZ/Pd/Hm4rVZbLv/z7/h8/p/Lb330whv1uCmsPLvHrD6H6RPaDxRvfgThZvYZTSEP29QtWL8TpdyaFn/9azdRVmGXpe1g9aWzWjOwY+FAWN9S2MVszzAhpIW6iY1+VrrY35hMA6MP95EjQZUXUfsnZlifg3iFw6Tm5Kx3H0O+YrL4pJuLzyi9bMaho8aOrs5dfjG8hmeWPyXY+FyO5bOnmLt8wzVWKq+3KTjXcXsikN4lDP4ekLn+YygRcBBu5s2UA5h9pOL9J8J0P3Mh31ef3jpcigE/vDUoAZ9bJouz0NNYBk8a3Bz6H6xR9UB/yQTpp+xYBU6ySlWRJRgfJhfazjdG5C87hE7Jd0PT/B/cYxVlsBpGJ920alD1VGMb0eUfUtw7HB6Vwv3L1I0K338aU22YfAS6cf1XshD9yeajU8XpBsaXcBixyU6lFGdmyh6v58wvdWhHCg6+yKy6BzV1Gs94pcpTiFCjSZy8BM5z4Wnlu4r8cpgDOaL+6iiZu3zBdVACF3+3BCcCAK5jDXxXsnpe2+g7ef1h5cOjxV7f+pQdxTNIKa5sUN0aKl6FhNYogMxtDfrA6yr4MtHhMeKld+O6b6y7P+3Gy0toZL2TWhAW0a/08RuwTurh/x3F++DhmGQETyWV3cdqWXecfeVBOSs3M/FJ5zkhK9nmEhuw1jwFtIaGTwyHH/QbW/lBje1DJ4WFENNMLHUw4h0Q1H2ZOSHhYOPPXZ/0aMcBXitwb7sObiLBm8m1tCzCZGephT/w8cUFwfs/7SHceXcKyNC6D03LZMxoIkb/Ol5n/BtSxsXms1SDEmxRzUM5GLQXvrOBvmTu6IWyf7VhxLVcH3A2m9mzK4LHClfccRY/sLj9rV9xu8ZkjpgUQXcjXbRcYVW4lFxM7mZgjBmOkcNG78pOX4vwi0MKsk4+XgNJ2ldgPqsUVwvhQzBxOLNGwaPU07eDak70N1tmF8OBYbkSXRtsqOpO5Zsw3J61yc8fgNmKgcu3d2GfKgxj57jLSyHf7nF+JZH1XPINoRFc5a33PjgJQYnqwknDe7MYfWr7Dt6dN+f0smNCltpwuOKqudRdR3KniY8VnhTjXEV6ZYSPaArypoqVtSRbn0ilujIcvKBookNV/73CTf6x1y+c8CX4236fkasS0zi8Wo+xHgQHUoLqI4UvZeG4KQg3fQoRiI8yN7ZZn5Z0TuT6rXz3vAgY3FR3n/9ZzkmEGecGOyh+0/PyFc1vZcGfwrFUObP8SuF17abVj89oQkkld64iqojPJrqz98XW+c1uUWXbXaz8WxL+JLWT7LpML8WUXU0o2/APZp/d0/ve1KaWhE/9lhc9Kl6DumaphjISKv/1JJuuNQdkek7uTjnAOKXmTjdZpr1/7hHvVWiM8XRjweseAl/tvGY//nmv+avRveprEO45zJPQ2kUt0jiqit0g2JViJtuBsH912KWd2H7f3sBiE20ijVHP+pRdS3RscU7nFP0HPI1n+jQ4C1AdSKsgsFnR2z+c4J1IDyWxRgdCBLu6E9WRaqmoPsqp4rlMjW/1NIifLG1GlcRnKg3ZK+2S1CsKKLjmmKgWP/lC9IbK9/Zw/u+lF75zKH/1JBsCwuwjhXptrwqey8LipHsWta1rH+WLJPZq75H/4tjnAyoaryoovtCc/JjaclcDk74eaj5k/AZiQlwPpwy7KYUm/XSeWcdaCKPxlf0XhSs3K9oLq7jzw2dPYtZ6RFMLMEExrcdFpfBKRTd1w2z91fJ1xTRQS49QRfMwRGdI8P4x+u44xRdwdoXKfmVksUVGD4qSXZkYaHA/fo50EaXdaRB7Y814ZElnDSsf1bgzcXasLgYyuWtAe9vPqFzbChubp4TGL6F0nUs8V4oSLcs/sRSrslCml0NqGLpvYlmT/R5xlEsdjyaB4+lDaIVVeZRDiBaS3mw2MBXNcdNwlAbKuvQCUpmaYiOJVD7DHY+vxJSnsU9+JrZjS7evGLjV2NUURPvG6GFhe2ZbA7RQSbs6YH0KI2nyFct9t51hp8cMLui2f+LNbw5HP2wIwQH12ICgW06eZvTsjpc8gW9heDwvJQlLD7Yn+NPYe23cxYXNWVXMXgqv5v+b/awWi1Z2Of1h5fO14SY1X9qaCJDOJVmdLSXUke0SUgyN9WLgu5ugfGh7Cvca1fw5pbjX1wkfByQXSupK4dff32dr9Id/pfZPXYbn+O6x9GLEeaLAbZR5GuyCLwE5pc1TmFpAk22JoDKya2I+Z0B0w9Wmdxw8BaW8KS1XEZygzU+6AKykSNRZgrKYQC+R9WzpNswfFKRbVrcQw9dKca3PfrPDcHc0n2RoYwYnapIzotlD9JNSx0qJjd9FreGDB/V2E++pOpJTkr34RT30kXKS6t4s0LQJuf1VqWNA9FeyuCbCcoospHGmzg4+2OiE0lyx0qO8PzeqmQi1zLyKi+uMHxSML+k2PrnEq9TUmUe2//e4e9e3uHfHrxHYn3GVQccQcox86hjy8rXC3qvJGw73dLs/WkgtkwPsjXxn0xuOhRrlukNzdrvcoJTRdW3MJ7R2Tesf1ERH9QUI4j2FW4qJiTVtJMSpaiGhsFjwd15M0u6oek9TVD/+DmLdzfEHz2UW7g/BxNKrzJfk4uLasd3qpIPwekPRyTvbzO9EeKczM9ZNN9CabRkhmSXejipYvCspP8Q0vcuEL8UrO7gaUV8aJhec5hddnETy/BJhWoMwaNDmo7l8COfOveI+jlHP1RUjcO0CMlbM/PahSlVz6ILhZsq0p2I+OmCYALptqXqG7FRbgrtId4tlli4qistktEDWWT5h5eF1LDqMrviiuT/xHB6r0Ox1SM8kYvE6V0X6xuiYyO8xTXpTZ6+K+2myQ1XAhR3LNGpZXS/QOeKdFuk/25qOPqBR/OXP6T30tJ/JsCkyU2PdEthDo/PmdXfQmldsoxL7b4U1EcwM8wuu5y+26FzZIhezon/zaeAvLKiU5FhTW51mP5kBycT7aE69ahKl7rX4DkN8yzkV8ltvpxcwNEGN1OEx5rOniLZdDChSzC2ywuBt6gphxankvPn4IkRyHoDJ++FNIHCmynyVY/xXc3ionCu174omF/W1B3F0UcBG5+k8prtgco0Ti5gpmzT0H9mWFxS6A/uithibGlCy+SGZnHRJzwRKoPTTmPKgWVyIyA6qolfpgQzi24NXyZJBIx0Xm9VuvsCgof76MLQOTRMr3qM74ihJ18VgPj03SG2ruk/bzCBpffZHoc/8t6Ez1Ti30WDfR3RfeoynXS4vXbI//roBzx4tsXJpEtnz7L2eUX/Wb1MhqpixeARjB40nN4N2taJYXYzpvu6oP/C4M0E4Lm44LTwdYPxLMWKwc3B+7tPMZ4Y59MtS9XzMK5MYeKXDvHX+yTboqru/etPqGOLKuSWPvzdGG8uu9nsuowI174Ui6sQHKQ7ML7roYua3pMFG78WXnfx339M1TtfhG9buv+8YvHRRerYYXzHIZgJsjc6kYcILZfwL35IdFCAVVQXVykHhv6zCn8qt0V/BrqQB7a4VcHUo+sVaG1RjqUuHKyG8W2vXbgWE0hSVO9FQR1q8jUJxR5+OaHoK8q+hz+tCSbivGtCMV6lG87S1jl4UsNPP0A1cPKu26I95KISjGHwpCG5tyVjvueS4QxgeiHeQnwqwURGh1hF1bX4k5qqb/AWlo3fVnQOarm0XOpBYzGhh5vC6TveMgbtvP7w0qfv+JQ9zWLbkQvDb8dEh4rhJ/uEJxZvVhKd1ihrGd+NxD75f7Z3Zj+SZNd5/90bS0Zk5F770lXVM9093T09+5AmQQmWTRuGYcE0DAP2g/8Y/UGGHywbtgXBWkhRFIfN0XB6pvetqmuvyj1jj3v9cLKLelYJGJCo89xLVkVkxD3nfN/vKyq8iUZZyDqioEmWLe1nImRY2Rhga4ZX4wWKwkG5hlY3pv/DnHjNMtmUHLms91u7pD81Egv22mJ9V7rdhqZ2kszJXHIuXLo/xbiicwxONY2nwwvldLpoWPzKEi8Kbat+IpIu40tKQOdFgfrsfbyR4uR7TZxMwsLDU0N4XhKeyDgqXvFQFUy3FMMbHv4on2PyHAb3Wsw2ggtwp7rStF66NErMOsrKk6zo1Vn6+5TyxSv5JX/9FOMqytClrCvCU8vRj5qsfFEwuOlTBvJUqwLLwtczMQApi9fKeXPYo3gTwUmNonTYWBugzHxb0vqtAiVZ8qgNCirf0tjPKVq1i9Ca4x+1cVKBeGIhWQuZrQss3ZvA4OMeeg7T1IWi9d9+xWRHzrdebPCHOcMbLtaBk088RreaNPcMWUeRLsLxZyHdB2NpcGJLeDLf6JxqjC9SrslOiD/PUTQuZE1pnhpvDN5VvuKlS0eHhqSrqZ8YokPLyWchReTidLsiMrj9Dqcfu5y/7wl1X0FRB28io5FgaKgChS4VJ583SJbg5KxFEOY4xz6NXdnBFs+aeE5F9xF0H4woIyHg1+Z02OPPRXgwuF1jcMvHnwqQaHJd/Mv+VIDnupDZXrQvZ9CspWi8mBCeGsnP++g9qrpl+W8GVL7m5PM66ZKl8zwj78gMsKwpwY6EcpONbrdIF4QQUTQUaU/M+s48MT5Z1PQeFkKKTSzZgpwD209mtF8V3/El/N0vHR1meInFyQ1LvxyS9STadfSvbomtMs7IO0bwH8hTLDyzZAsyL9SlRI25M8VkxxAdWEwm3Xb3obyyvLEiPFEcDVvM1pSE72SK0XWX+nFJ68E5ZUMUNVlH9rNlTYmxyYWyrvBHpaQG3PKI9pWA21OL8RRnn7bxpzKGia9FIr1/8Ah/VJJ35HO7YwFpKisqHMEkiyjBzFnaxp3n8rWExVjVJBeviODsnoc/FXVR2rPzM3CJG1+57S5berZao/PNiOE7HtZ3cWPh+xlXEfYNKssvLtZkByZbDkv3J0JGjWG2LGjdzlODCS1pTxG0M7LMI+sokhVD96Ggf/P9iLxtSTab9L6VJ1sZabINYU97E8g7ogHMm4qsJTPF8NTImORcYOhF47fsm7wF003F4KZL47CS2V+hcN5/j9m6hzuTQfXrP27TegnBsMIpYHYNwRT7MhIKz2TLsnx/RhlZZusaZZQotufNh5MYnExoskUDjn/YYXCr9l1ev9+L0smCRuUl6aIoS9rPK4KBofurE6brGpQiPFJyPmpXzDYM2UKAG8se2c3s3N6Z4Uw16bKc1F13bulU8jrrPsnxxtK1nt/zSBY17RcVTmo5/biGP2KO3TA4OTQOK7yZJXqj5olLisEtRxLXV4ykh7qy+tOFNEaDmy5Zd55G9UEXZaD9ssIfQbomVNnzOy5Lv87QmaLqSMh4tJ/SelWIl3pNjgVVDVovRFHjD2Htb2PGO750633poouWuhrR/BOU1qXl/HuLgv5YCcmbms6jKbZeo2jAwb/blBFJqvDPHLypon/bJ13w5uZvQ+PAUHlaNhWlotiNKHKX7rOC9hOJkZhseHgTcfOVISgz586sOMTrggkpA4UTi+Mt6UmC/MrPRxe0BfMW51s3rP7ZPm4My18m9B5Xc/KrpbEHxVLBeEfUL6qChQcZKpeclrIuxIjmrkWlGp2LizA4jlEWjv+ZprErr2g3laT3tb/oUwUOk23F6ccBbgr1I8HKufGVgOGypbtPMtKe/ELP33fl4N7wGXzQRhmYvCORDd5Y5FKNXUiX5OIs/bLPdEOS1AWxISOOrf9dYN7UqXwt45GpZbapWHhQXITftF8UjLcdqlDhTTXjd8TR13omT8tkRVEFiuHdJrNN2fuqAqJ9C54BpUiWFEXkEh5ntF5KU+HNLE5Y4sYwXXOYrQjzpvnCwdSErADQeJMT7TnzICGPqu7hDzRlo2LhQULRsiRLou7BUdT2hvL69uSzr/7NiHhZsfibK2X1ZUv7ByOK5lzb5wtdv7bbx8ll2GsWZUamLBdIOCcRs5DdPSDtCf3emxQXUqtgf0xjVzHeEtDR4LZIqAa3PMr6XCofaIpIGp3mSzmb+RPLyt8Oae7O7QVWVDZZV0irbioCU3KNdTR5G6KHJ1SBQ3hmSJYtyaLG9GvUBsKxSRcVs1VN96nkougC2q8y3Lhk8StBi0nnb2i9tPjnDpPtgLJTkrWhfmqIt5pwdIqpGaJD2Scf/agt/JvOFRrusqWLtRZWiWpYF3IzWkfT/mZA1gHXlxuiNrSExzIfU0a4LfY9ga1bDc4XD7FafB7FQoSbWmbXjCDVukY2EoCtmbnPQ/az4UnBbF1ROxO93vEPO7R/c35BOagNLe5MkSx5WC1ntXDfxewdSKbdWoe87TLdkJiHvAPBkcN0UzHdkA1OsiJaxehQom/HWzWM7xC+6FMFlnTRkqyGlCFE+5JOgGspI0vr2z5p1+Hwv96bp92LT3p8s8JNLMN3rm7Cy5b29oe0nxuKSIQC0VFJudSkinzSZUOReMzWFAs/P8L4oi1MlwxFyzK5LjSsKgSbZfgj8aCcfVgXbEhLiFvhkWayrWi/KlGZxp9YBu85LHwrM7bg3LL4oCRZVeQdRbrdwY1lIC0wSsXgtky2G3uWjZ8m2Cxj6aucsuERL4oUSxl18aQF2apYLZJ944pPpDaQ9NKTzwJs6FPWhQI2vuaStxTB0BIdGfREuuxkqw3A5LolOJYbXZKcRARbXTXHly49+nSFzpMZeQuifUNwGDO4XceZZqAt7qlHfK0E36P52ojPZKYIj4Vc+jbt3el2cXJ7YZqf7IAzcgnOFdGB7H4nGy7BiXh300VD/fmA8XWf5V8MyNpzatcUTj/26T3M6D00pAvSwFShlTViIjFg5b8XOG/hAAAX0UlEQVT8jGTRJV4Sx1zjoKL93BKcKWoDOT74I0UZWfy+luCczNJ7nNLalc798I96OJli5X5B1gUURIeZANN3hU473vFo7OdC8TqQn88bS8REeDLXW17VpUpXPvTvNqgNLI2DnP0ft4lXFKN7Pfy+Q/MVEBjKdkhtVFE0FGu/KHETS7wiF8EbW1hdZLap6D4u52IDS/1IsfrLhDKEhW8K0iVYfFDijyuwinIhkhDHtQbTTY03lk4161kGt3xa3w6FkHUsR4GioYj2EsZbLqPrPqPrmuiooP5qzOnH0tWv/XRG73Eme+a/zwnOFMtfFrjzwbdOSlq/OUNV8mWp9RXDdz3yrsEfvlX2OCx8k8uKrquo/foFnUdCpci6MnjXuQy862dXy+PLln6Lv1398yOyjkeyInO2eFHT2LUUkXQlg9t1Jpsu4Zmh/nJEuiBrM11a8rZicqdH3jacfegSr1nCY83kXsZoJ8BNIPzLbwFo/PVTRu/IuObk8wh/bEmWXLwxhCeyyvNGcsP1P+niTRXGlzVb3oLpVkhVk/lk3jWMt3wOftwjX6oomz4mcNCloX5sMY5i8auM83seKCH6T65HxO/2SFYtnWcVvUcl6aIMNBe+llBvJzOUkTzl85Zl9qObWAdarwoRPcRClI1XFfXD7Du+hL/7pRtvUoqWJX1ngeAsF9rV8wplLOObIgxQ2jK8LZ2pm1ninTYL3xSymUhFYhUepjipIuvKgV6XUItyyrqs4YrvvYc/hPyj61SBSKcmOwJnz9qabEGeMr3HgpVTVr4c3pR50I6ibFgm25KvVz+RfArjweyaQYUl03Ufd5ihCkP30QxlLMbXZB1LvCLo4fN7inhFBA3hsXTH3uSt2jvAnWTEyy5OavAmFl0ohu+6zDYV4f2XdB/n5C1FsiyjHn/37Lu8fr8XpQ9/WEcXcPKJT/9ugD9U+BND0ZJXoz+yuIc1qlDWVWlXkzcdCSocyWzPyWB4q443UfhDRXikSZYNeeJRPzFkXcv5+wH+2HJ+N8BqWPhyjBvLNqRoQrJRUkZw+nFwsel4G6YdnP0DUmoidoPxtkd4Io2MP9DYxCVeVThH5xjfQZWGs498Tj718EfiTdG5dP+dJzFWQbxWExP7E/FYD991KDqB2Au+PiRZVfhj+ZKVdUt5a5N42ZOAoNWKxr6l2j/8rq/h73zpvCNbhrxjGd4RZclox6WIRASaNxW1vqL50sEfy4YgayviFUWtbxnckY5z+J68utovDItflyijUAOf4U0HN1ZMt99GNshNZWsOtYGaG+vn4xstRvXaqKL1xT6d5xVlHTrPEspIuuXeY3l6heeG9ouKZMmycr+gduLgZFBcX2G66aPHCbNNgRx1n1SoUoiyjV3IOz6mZijqiuhA5qDJoqIKIV71iY4rJp+tk86Js9IYGfKuT7yq5hsfkbDpm9e/y+v3e1HaHyqiw4qyYdC5Il+s8CfScPgTS/OgpIhg6e9Tioac1eDtPFH+kbe+ZTdWREc5px+7YKD1TONNBKxZtio6T1P8kcjmk9WAZFnGI0XHoEpFtlRSP7L4/+cLbCPk7J5DVbOkSz7eVOCVpx/5OLkh+h/3ad8/xNQN53c9nESA6mXdZXJNM/h8CTtPAZhcc4gOJJQx6ypmqy5omK0p4hUfVcrTTpVzC2pqGdxywbVEx/Ikt4Fhui7K7fqRpfFaSBUm8L/Dy/f7Ubr3qKSIZAa38ksju1tkxDHdlBsuWysYvlMjXbSUdUlVCvq/ncc19wztp7D0m5Ks65GuVgR92ao035RMthFxw5JP73FB1pNxTRUagnMLzQJvoCGs0DmYP/yEfLVJ0bTkvYqzD1y6jwt6j0sRGaz4TP/DZ4w+X4N5xkjZEEFD2ZBvxuhdjTt0SJdFaNF5mjN8t0bWm8diTDX+BMbXNcGJuApbr2RFqQsjIY5Th3hZIOtoUQglyxYvFvD75Lpglq/qcqWD//lLGnsJ0a6Eyyz+WnzAZR3yruX8rosTlYQDQ1WTxX7RkvhXfyZntdmapvdgymxZZPtY8XaUAQxuuWKOeqWZrjv0b3tUgSU8M+hsnvapLWs/L3H6HuHAMLwRkHU8/LHCnTokayXDdz3iBQfjiipncNNhtCM3XP1QGggQG2frtUi+Fh5YgiPJPMm6LkVT4Y8VWU9R31dgZM0Yb9ZlpVfKWKd2OKH7qCB6o8VV54EeuVgts8fphvx+lIHpxpX7/bKlAWabIRt/OeHsQ4GXF5FIsIIzRRla7HGNyYZzsfxXpTQolSfntGTFYl1N0VTEK5bwwJGUpEBRSf413Sc5TiZjFuNC+5sh4Ymi9XSCKRzKSLPyC3m0lqGi+XiAKiE4UfjnDmU0583U7Pw1LscBVYr4NdqTRKhaX/6Nxq4lb0iwTtqzTDck6rbzRL5M1gUvli8DyJ48XtLUzhLKTshs1aX9siI6MJiaxZtoYWAncn5Oe0INu9qYXL60u7py4RhTldg83URex61Xht63lrWfW6oQ6geWvG3xpoqyrigiRfO1xR8pDv8govLlwL7yRUa81aIMoYgsxrN4wwx/Il4UN1Ec/LjH4tcFw9tNyDVpWzNbdShC4btYX8YotZGl9QI6TytR+xwIb9DJZQzUeq7JmopgKKvH2aaSM+FdSQot62BqMgD3h5bKFwY1RkZC0UFG44mIONzEoicpg9t1BnfFYtr7u2P5PyMR27Zfl3hjRXI7ZXRTvpBXdbnS5dExvZ++wTkZEh0Inb79ogID4y1NEYkaBgth36CLea6bL4qa5T/bZflXGbPNCjuPXnAKUZpYd84b0jDdicgjhZMr2s+MdKJLArH0hjIYLhryaq+NLK9+0kEZaL3M6TwVx10VWrypxZtq3Fg+Q9A3jN+FvKmpQsDIMcKuZnSfljgp1A9F5+hP7EXiQO9xIRuUrCTdaFDWobVbEL/bk5+tW1DVoHr2kvW/nIBV0mW/VfNbhamZC1vsVf3j64JhcfRvNkkXRdlyfk/M740DQ1VTZD1Rs9T3Y1qvfusBthpmH65jappoV3zF1oHZWu0i9KbxSrByWMvolpzB6scFC9+UDG9DvG7wBwp/ZmjsS8oTCrKliqJpmW74TLcCgSUFML5hab2w+DNDtiCv0mJZ7gRlwIvBHyjMTAK7/bFl4euM3iPD6IbGm1V4E0twOKX7pGS2VadoOJJSuuOR9hxauzIIv8hwqbkY39L52WuJ0tBgUwdvrHHTK1HrZUsDZDeWKRqKeFUI+OlmQXAOyYKmflLhzhStlznTnUg6ZiNmdye1nH7kUQaa2kDM8mUkKe+dp3KD+BOLM9WcfaRxE4U/tHjTguinj0X+70quXVHXhOcyonFTQ/TaofEanMIyvKFp7lVUgTyJJ9ckIdQfaYn1cizNvRydwWTHsP6zmM5XHufvOyzdn+IUhta3Q/K2Ifi/XxKvK/b+bZfmV0dM1x2h/Tes0BxiS7LoojIRVNgffYyTlqAt/T/awTqwcr9AZRpdqquAxX+C0jqKOP0wYPnLFOtZxjfAGcnrd7Yhkqveo0rk7dc0RcOSLlu686Fx0bakXU3WFTNU2RSQZO+vd6kf2wvMrjtT+AMY3YTZRghK03oOtTOH5psKN5G/ZxWc3XOpDS3dpznnHyiK9ty3MtOER2LN1KWlsWcZvOehRx7eKGPpK4n46t8J6TzPSXdyzj5pEC/7DD7qUDvXTH/yGcm1gtlOyfD76xQRmJqIb1svEvp3HAa3QSdahuybAelSgKpkcO+PK/w/+zW1gcY49gL+flX/+NL2znWKJjh/8WswiqJpaL4SDnTZEcnTeMuhiDTpoihjVAnTDY/ZJtQPpEExnpwH3akmODec/uttrILphvhKdAHj2yXWtcxWHM7/+DZlqFj5ZUH/tiN4NkdCFt8azWerHmXD4MTyCmy8FuW3LgTn2/12SlmH+r5muh1hHUXzpWa2qci6rnyR1hVZR8624ZllvO2gZw7OTBMv67mX2eAPFdOtgHirxHiw/rOKxQclla/o3/Gw2tJ9kjFb8Rj/5+/JeGc6z0q5qkuVU/8v//5PdAVBtMzwtiY8ciTDJARrpRlo7EP34YRkKWB8u2TxSzEhOansfZ1MRhW9JxWtV4b+XQddKKpQNin+SJ5eZcvSfaCpwnn+3Ab0vs05/8CVSFijsa782ejEcP6hxtQsreeK2sSycP+cdLmOG8PkuksV+riJQJnSJU287BL0ZWY42VGoQqMAXYoSJ12SG7wModbXxJtGNjaRS+9xSdpzSLYrWk9cKk/g8F5imW4BRlHVPIpIMdkBN1G0X1T4U4P3eP+7vYq/46X9oSXrWU4/reEPNYtfl/gjRNKUQtUrqXw4/AMxPlGrGL8jHfLGn/cBWHiYy021pCWOtiaAdS+2dJ+UZAuWxr4hOHDQxTx3ToswdnA7wJspsuWK8Q0uGC+nH803N4Vc9Mk1h2ythZNLDG6yLPtqiYWV/XNZh8EdWP/TXdl4ZEILK94uNayMYcq2IV0UA/5b/0y84opR6tRj9WcjBu/Lq3ZyTaMqResFzDYtaFlP6gLOPxCGz1VdrrSTW/LlUsYRAZx+7OKPLdG+JV+q0CPZl862hLbvH/hg3u6DA/yhvBp1AfGKwk0lxjVZFha2O6uEon9eURtyQc6P1y2Nw4rJtiRH+aeOzBMnVlLgK4U/UNiapVwuJOy77pB25aZqvYC8JyOTZFmhKrmhrGupFtusfJETnoqq241h6csUbwLdxwneUNPYU/I0e25ovS7IWwLW7D2wnH3WQufy+f2RmLBW/9drqtBKJMW+zBzdWJFd8QkvXfr0c1C5liV+aHAy0f+1X+XgGhq7kuvr9zXhqaX3wBIdyKahaEn2SLzsEh1YagOo701xUjnD1YYizQ8P5VxVP6lIlhWt3Qp/pBi9Iz7mom1oP5MP5GZWiFtn82DEiQYjN27R0Eyui9S/9bqcq3KEXajzeSzEa835Jy3ylsN0y4jy+sigMzE5YS3NF7D0ZUKynVMbVpzf9WnuVXSfFMw2NMM7QhhTBrpPU/yR4uAnO7gzRdG2tHYz4lVFEUlnf1WXK21CQ+3EIV0x1N84dJ6Jkfz48xruuQcGhu9pvFiaCy82IrN/E1PWNc3XltFNcd+1X5UM7rXwplA/FlV2suzhjy2TWyX1wwwnEd5heGJJFyxb//2Y2rlDcy8nONVkLXlNR4cVnaeV3PRPferHluaLGU4q+SJ5y8GJNfGaqKh1BXnXsPBtxmxdid8lkDTSvKU4/TSirFvyrs/kOpx9FEKpma25OJkl+tP71L/aI+uKh0a2KZbxVkB0YBjfFJGGKmC64RMdimCi/epqZXLZ0ou/lDiw+oFgOcbbDsO7Jf7I0vsGykgsmxgY3YAykJndbLMuc7i/2qfyIVuwHP3AIe1qnBTZYrRFGtX/QAa6o3dDomMjyenLCutaysUGtT6gZC+bLCu8iSVZ0FhHUQZK1DQtxfkHDTExzW0dnUdyoHMTWPwqx4SG6bpPFVqGN1zajxxe/CdJbncyyW1OFlyKjmG2aWk8d3Fyi1UKZ2uT/r/YwbhQP5hvY4Yi/ZpuaKxvaRwU1I+E5OBklmhPUd+bfWcX7/elnNudP/oTcElWYO3/9dHaZ/KuovlKYRwh1ruxxnqQ9yzG1VQ1TVFXJKvgqibG0/gjRbpsiQ5AV5D15FwXHlvSFWg/dIjXJQ/F+HKGjA7g/EOfogFl4JItABqqUBxzXiprtvE7whsc35Ku3ZsKXb/zvESnEkmbLji4M5FdeTPF7Jph/W8yzj4V9XX3iaBMvFQI/taxBH0R51Z1SK41KSIRXFhHyZO8KVMAL4asa/GHDpPr0HlumK06VHXwMhf3yVV3fJnSRz8ImG5B0Tac/mCB8ZZL7cRBFyIIbb4R95yTiWImvpMR9A2THdnJxoua3sOSxqGhfvB2RjdPgdJCQOg8VMTroj/MO/IUqc3ziYuGNA7xmqhg/DH4I1j4akTe0Jx8rmUBbaHyLbVzQX2Iid2l+ySj9VIG3Z1nBuOKF7hqGEGEHLhUrYqTT0V+VfmAEsrr4tcpbixEsfG7huZeJUrvtpC6mnsl4Zmcf6M3DuMdTb5YUfkSuhicWvq3r3jBly3tzmSbERw5zNYU41uVeIS3tbAE5zgObyJNhjVKmo0TccCF54bhTZesrVi+n1E0DXnbCtF/o0AXzFEhEJ5avLEEZIMANht7MvB1ciFvdZ4J/Pzlf2wzfA+KtigGiqbMJf2JZbYhlNaiqcSz4sDi1xmTTT0nPsxXhR9Iw6RyTdmwVIEiXtF4U83i1wW1l2e0X1bkkcKdKfq3Zb5ZtCpG7wpHMW/KpqSaR966I4ezj8Qbk3X/wfjnqv7Rpd8GWneeGUGBZHKTzbbLeXKTc4H29UeKYNfHKphtCl9wsq3xppbuw4S05xK9EdJV71E1F5OKYbz5SjG+YVHGEi9qWi8T/GFJvC7zuLwt+XLepKR+qMi3ciHnJ5r2Y3nydB+J68748ndqfct0W3SD/Ts18X6U0j13Hr8dtItqu36g8YeiiHZS6N/2mH6wijcpmW4p2k8hW5TNSXjoUtUs7mweaZFCul6RrsgXtGxV5B05Lxv/SsBw2dJlA/r3FPGyvPZaz+Qcp5sFOof+XUFsNA5LyjpEe5bxjizvz39QkPbkIlSBw3jbYbZh8Mcw3nHwD7yLCIa8NQ+77iisKx4TZSE4VdQPLaqUc+LRDwJWvpigBsJ4aT9VrPztEBADlHiaFdNbOWgo2xXGU+RtaO5aeo9yVKVwY3l1n33gs/KrimTFML7xNulJZGPTdQc9T2zK2wqdKbIFQ+uFdMLW1dQPJbzb62ucWOMkCncizVxZlyf+VV2u9PL9knyxJBiIe61xWNF8k2My2W5Y1xIdWqbrroDNj8q5DVNBpbCesF38YQZK4OU6FwhR6zksPCiItwu8KQTHej7kFXulk5Ss/dWIvK1wMtntZh3LdLtOcKLFYOQo9n/c5fxDy2TTFW/zPN21qimcmRiTWq8MwaAifDWk81C68u6zgnjD4E4rMAonFvGrNxPf8+CuZbxdozYEd2YJTxX+SKMrWPxqwvm9YE6AyKkfSaaJGwsvWxnZxgTn3+0F/H0oVxkLriXoV6RdBy+Go+/XqD+DhYcFs2WXZFk65eZry2TDpbknts/efZe8LeKFg3/eIjizhCfgZobR9wuSYY3OixKVSwesjMzVRjsu6ZLl9JMIbyYbFn8sjcDgrkMeSTwFwGxdbg7jwfCDEv/MwVqov/BxEnkSlXXF7JoCq2kuLFEbG6qa5uj7Hv5QzE+t5wJdN55AO/O2Qi2kzNZC8rbl+t/FJCuSGOrNKsY3GlQ1mG6BzgNm1yzBqaK1V5E3ZHzUelWRdq9ixS5bev8PXVTicPKJR9aWV3K6JK/U/m2PwV3I2xZ/InPCsG84+8QKQAiJUUhWDHlLsBheIoxAfeaRty37fxASnAiMsgog7TqC9ThTTHYstbGhcVjhpMIJtPPzXrxZEe2JmGH5i5jwSAsc00K+WOFNRNmdL1bowpK3Del6weAu+JOK8Nyg87lpPdBUgaJ+VjF4T1P54qPhtCaRFueKeD1gvOWgKsv5HY+ypi7OmMoK9zrryXm2++1Edtc3nCs94T9B6eBcEe47oAXhm7Y1/lCL9F0kgxIss2kxgWFyzYHFjKAPC1/HQmBIFNG+SKym65rmXknvayXsv2WDkwhb2psKMHN0pyQ6EGl80tPMlh3GN8UhZ2sVeVNhtWV8ywgTMXCIDi0qdqgfKryBIzkiBxXBoUsZCd8w3PVwE8XZPZ8i0tRG0kSMd2R+2Pj5S4qmufiZlu5L47T4m5zBTYe8IwE+xofzj6zYCSZyM5aRzBlHtyyHf9iiCuzFOfOqLld6+dcZWc/SeWrIesKa0YXo9lZ/NkJZiXGtmgZvKMRVk7jEq3JeC48zOR8Cq7+Iab2W1/r5R3LRcCyzbUnH9EeW/HqKM9NUntgvB3ct0y2LOxFksX/o0Xxj8EYOwbFmcjdnuunjJpbamdystf6cQbOiyRYqsp7wbxa+rdj885h0WXwkeQv8vpAZwlND8uk2WEXvUcbSbwpazxNQMLjlE867ZgFlWkzdUPkQb5fEq6KJ3PiLGJ1LCqg/0vQeyNjoqi5X/x+REhI/pWOy9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "Image (1, 224, 161)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 224, 161])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Corrisponding label: tensor([[[-59.2357, -80.0000, -80.0000,  ..., -80.0000, -80.0000, -80.0000],\\n         [-58.2857, -80.0000, -80.0000,  ..., -80.0000, -80.0000, -80.0000],\\n         [-58.3054, -80.0000, -80.0000,  ..., -80.0000, -80.0000, -80.0000],\\n         ...,\\n         [-62.8059, -59.4403, -59.6048,  ..., -80.0000, -80.0000, -80.0000],\\n         [-69.3624, -71.3416, -54.0287,  ..., -80.0000, -80.0000, -80.0000],\\n         [-55.1246, -69.4214, -62.5651,  ..., -80.0000, -80.0000, -80.0000]]])'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Augmented sound transformed to spectrogram...\n",
    "img=batch[0][0]\n",
    "display(Image(img),img.shape,f'Corrisponding label: {batch[0][1]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**IMPORTANT**: as we see the phoneme is \"paddded\" with silence to the predefined value (max_duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"'em\",\n",
       " 'a',\n",
       " 'abbreviate',\n",
       " 'abdomen',\n",
       " 'abides',\n",
       " 'ability',\n",
       " 'able',\n",
       " 'ably',\n",
       " 'abolish',\n",
       " 'aborigine']"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Take a look at the classes to ensure that are what we've setup\n",
    "data.classes[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = cnn_learner(data, models.resnet50, metrics=accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adapt_first_layer(src_model, nChannels):\n",
    "    '''\n",
    "    Change first layer of network to accomodate new channels\n",
    "    '''\n",
    "    # save original\n",
    "    original_weights = src_model[0][0].weight.clone()\n",
    "    new_weights = original_weights[:,0:1,:,:]\n",
    "\n",
    "    # create new layes\n",
    "    new_layer = nn.Conv2d(nChannels,64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
    "    new_layer.weight = nn.Parameter(new_weights)\n",
    "\n",
    "    # Replace layer and put to gpu.\n",
    "    src_model[0][0] = new_layer\n",
    "    src_model.cuda();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First layer shape: torch.Size([64, 1, 7, 7])\n"
     ]
    }
   ],
   "source": [
    "nChannels=1\n",
    "\n",
    "# Alter existing model\n",
    "adapt_first_layer(learn.model,nChannels)\n",
    "print(f'First layer shape: {learn.model[0][0].weight.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    },
    {
     "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": [
    "lr_find(learn)\n",
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 26:46 <p><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5.229638</td>\n",
       "      <td>6.012459</td>\n",
       "      <td>0.193999</td>\n",
       "      <td>03:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.375930</td>\n",
       "      <td>5.903974</td>\n",
       "      <td>0.266534</td>\n",
       "      <td>03:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3.943572</td>\n",
       "      <td>6.366159</td>\n",
       "      <td>0.324616</td>\n",
       "      <td>03:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3.682126</td>\n",
       "      <td>6.220423</td>\n",
       "      <td>0.350699</td>\n",
       "      <td>03:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.221303</td>\n",
       "      <td>6.029784</td>\n",
       "      <td>0.384213</td>\n",
       "      <td>03:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2.842394</td>\n",
       "      <td>5.804327</td>\n",
       "      <td>0.419310</td>\n",
       "      <td>03:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2.361724</td>\n",
       "      <td>5.706207</td>\n",
       "      <td>0.432868</td>\n",
       "      <td>03:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2.077004</td>\n",
       "      <td>5.674556</td>\n",
       "      <td>0.437341</td>\n",
       "      <td>03:20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "learn.fit_one_cycle(8, slice(2e-2))\n",
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('pass1_all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.freeze_to(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 31:16 <p><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.285257</td>\n",
       "      <td>5.757097</td>\n",
       "      <td>0.433969</td>\n",
       "      <td>03:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.472304</td>\n",
       "      <td>5.756268</td>\n",
       "      <td>0.435345</td>\n",
       "      <td>03:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2.378742</td>\n",
       "      <td>5.631007</td>\n",
       "      <td>0.443053</td>\n",
       "      <td>03:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.218575</td>\n",
       "      <td>5.635602</td>\n",
       "      <td>0.445393</td>\n",
       "      <td>03:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2.088931</td>\n",
       "      <td>5.501657</td>\n",
       "      <td>0.474572</td>\n",
       "      <td>03:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.899984</td>\n",
       "      <td>5.499947</td>\n",
       "      <td>0.475053</td>\n",
       "      <td>03:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.676688</td>\n",
       "      <td>5.485618</td>\n",
       "      <td>0.483036</td>\n",
       "      <td>03:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.668107</td>\n",
       "      <td>5.497147</td>\n",
       "      <td>0.482348</td>\n",
       "      <td>03:54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "learn.fit_one_cycle(8, slice(1e-3))\n",
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('pass2_all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.unfreeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 35:49 <p><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.838075</td>\n",
       "      <td>5.527787</td>\n",
       "      <td>0.474296</td>\n",
       "      <td>04:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.993158</td>\n",
       "      <td>5.582695</td>\n",
       "      <td>0.457505</td>\n",
       "      <td>04:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2.059170</td>\n",
       "      <td>5.388886</td>\n",
       "      <td>0.483243</td>\n",
       "      <td>04:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.905824</td>\n",
       "      <td>5.417671</td>\n",
       "      <td>0.479114</td>\n",
       "      <td>04:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.747095</td>\n",
       "      <td>5.353645</td>\n",
       "      <td>0.495286</td>\n",
       "      <td>04:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.620091</td>\n",
       "      <td>5.335407</td>\n",
       "      <td>0.503407</td>\n",
       "      <td>04:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.413612</td>\n",
       "      <td>5.296753</td>\n",
       "      <td>0.508705</td>\n",
       "      <td>04:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.343631</td>\n",
       "      <td>5.283715</td>\n",
       "      <td>0.508293</td>\n",
       "      <td>04:28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(8, slice(1e-3))\n",
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('pass3_all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not so good result: should work on it ;-)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fin."
   ]
  }
 ],
 "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": "3.7.0"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}