analyze_dataset.ipynb

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 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configuration for train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from omegaconf import OmegaConf, DictConfig\n",
    "# [TODO] Set the root path Clarity dataset\n",
    "root = \"\"\n",
    "path_scene_train = root + \"/metadata/scenes.train.json\"\n",
    "path_scene_dev = root + \"/metadata/scenes.dev.json\"\n",
    "path_listeners = root + \"/metadata/listeners.json\"\n",
    "root_wav = root"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = OmegaConf.load(path_scene_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "config_dev = OmegaConf.load(path_scene_dev)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hearing Loss Audiogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['L0001', 'L0002', 'L0003', 'L0004', 'L0005', 'L0008', 'L0009', 'L0010', 'L0011', 'L0012', 'L0013', 'L0014', 'L0015', 'L0017', 'L0019', 'L0021', 'L0022', 'L0023', 'L0024', 'L0026', 'L0027', 'L0028', 'L0029', 'L0031', 'L0032', 'L0034', 'L0035', 'L0036', 'L0038', 'L0039', 'L0040', 'L0041', 'L0042', 'L0044', 'L0045', 'L0047', 'L0048', 'L0049', 'L0051', 'L0053', 'L0054', 'L0055', 'L0056', 'L0057', 'L0058', 'L0059', 'L0061', 'L0062', 'L0063', 'L0064', 'L0065', 'L0066', 'L0067', 'L0068', 'L0069', 'L0070', 'L0071', 'L0072', 'L0073', 'L0074', 'L0076', 'L0077', 'L0078', 'L0080', 'L0081', 'L0082', 'L0083', 'L0084', 'L0085', 'L0086', 'L0088', 'L0089', 'L0090', 'L0091', 'L0092', 'L0093', 'L0094', 'L0095', 'L0096', 'L0097', 'L0098', 'L0099', 'L0100'])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "json_file = path_listeners\n",
    "with open(json_file, \"r\", encoding=\"utf-8\") as fp:\n",
    "    listener_audiograms = json.load(fp)\n",
    "listener_audiograms.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "len(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2500"
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "len(config_dev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
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       " 'S00987',\n",
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       " 'S00990',\n",
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       " 'S00992',\n",
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       " ...]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[conf['scene'] for conf in config]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and Save SNR for each training scene "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_SNR = {}\n",
    "for conf in config:\n",
    "    target_SNR[conf['scene']] = conf['SNR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       " 'S00893': 0.6268,\n",
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       " 'S00903': -10.4184,\n",
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       " 'S00906': 1.3356,\n",
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       " 'S00908': 3.7607,\n",
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       " 'S00910': -9.9862,\n",
       " 'S00911': 4.8537,\n",
       " 'S00912': 4.778,\n",
       " 'S00913': -7.3749,\n",
       " 'S00914': 4.6738,\n",
       " 'S00915': 5.4153,\n",
       " 'S00916': -4.3283,\n",
       " 'S00917': 3.1763,\n",
       " 'S00918': -3.547,\n",
       " 'S00919': 4.5967,\n",
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       " 'S00922': -5.4731,\n",
       " 'S00923': 1.1474,\n",
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       " 'S00927': 1.4624,\n",
       " 'S00928': 3.7566,\n",
       " 'S00929': 3.4788,\n",
       " 'S00930': -4.6569,\n",
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       " 'S00932': -7.3049,\n",
       " 'S00933': 3.1172,\n",
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       " 'S00935': -6.1351,\n",
       " 'S00936': 5.3034,\n",
       " 'S00937': -7.2367,\n",
       " 'S00938': 2.0509,\n",
       " 'S00939': -9.3165,\n",
       " 'S00940': -10.2882,\n",
       " 'S00941': -4.2212,\n",
       " 'S00942': 2.6713,\n",
       " 'S00943': -2.6781,\n",
       " 'S00944': -6.9819,\n",
       " 'S00945': -10.2109,\n",
       " 'S00946': 1.3159,\n",
       " 'S00947': -2.5092,\n",
       " 'S00948': -1.3144,\n",
       " 'S00949': -7.282,\n",
       " 'S00950': -9.1278,\n",
       " 'S00951': -0.3756,\n",
       " 'S00952': -6.7864,\n",
       " 'S00953': -7.5535,\n",
       " 'S00954': -6.2122,\n",
       " 'S00955': -1.0485,\n",
       " 'S00956': -6.1183,\n",
       " 'S00957': -2.7414,\n",
       " 'S00958': -3.8042,\n",
       " 'S00959': -3.6207,\n",
       " 'S00960': -6.5102,\n",
       " 'S00961': 0.9245,\n",
       " 'S00962': -8.9522,\n",
       " 'S00963': -11.225,\n",
       " 'S00964': 2.7026,\n",
       " 'S00965': 0.223,\n",
       " 'S00966': -0.237,\n",
       " 'S00967': -1.3918,\n",
       " 'S00968': -3.0804,\n",
       " 'S00969': -5.7893,\n",
       " 'S00970': 1.8004,\n",
       " 'S00971': 2.533,\n",
       " 'S00972': -2.7388,\n",
       " 'S00973': -4.3427,\n",
       " 'S00974': 5.4956,\n",
       " 'S00975': -2.5726,\n",
       " 'S00976': 5.4918,\n",
       " 'S00977': -4.9245,\n",
       " 'S00978': -7.558,\n",
       " 'S00979': 0.2625,\n",
       " 'S00980': 1.9499,\n",
       " 'S00981': -0.5524,\n",
       " 'S00982': 5.2482,\n",
       " 'S00983': -3.1933,\n",
       " 'S00984': -9.1926,\n",
       " 'S00985': -8.4581,\n",
       " 'S00986': -8.5224,\n",
       " 'S00987': -1.4367,\n",
       " 'S00988': -8.544,\n",
       " 'S00989': -8.6135,\n",
       " 'S00990': 3.6305,\n",
       " 'S00991': -11.1543,\n",
       " 'S00992': 0.0039,\n",
       " 'S00993': -11.8314,\n",
       " 'S00994': -1.0892,\n",
       " 'S00995': -3.1424,\n",
       " 'S00996': -11.2283,\n",
       " 'S00997': -11.5298,\n",
       " 'S00998': -3.1619,\n",
       " 'S00999': -7.8231,\n",
       " 'S01000': -8.7156,\n",
       " ...}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_SNR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_SNR = DictConfig(target_SNR)\n",
    "OmegaConf.save(target_SNR, \"./scenes.train.snr.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_dev_SNR = {}\n",
    "for conf in config_dev:\n",
    "    target_dev_SNR[conf['scene']] = conf['SNR']\n",
    "target_dev_SNR = DictConfig(target_dev_SNR)\n",
    "OmegaConf.save(target_dev_SNR, \"./scenes.dev.snr.json\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and Save time for each target's start and end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_time = {}\n",
    "for conf in config:\n",
    "    target_time[conf['scene']] = [conf['target']['time_start'], conf['target']['time_end']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from omegaconf import DictConfig\n",
    "target_time = DictConfig(target_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "OmegaConf.save(target_time, \"./scenes.train.time.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_time_copy = OmegaConf.load(\"./scenes.train.time.json\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and Save for each training scene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scene_name_train = []\n",
    "for conf in config:\n",
    "    scene_name_train.append(conf[\"scene\"])\n",
    "OmegaConf.save(scene_name_train, \"./scenes.train.scene_name.json\")\n",
    "scene_name_train_copy = OmegaConf.load(\"./scenes.train.scene_name.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scene_name_dev = []\n",
    "for conf in config_dev:\n",
    "    scene_name_dev.append(conf[\"scene\"])\n",
    "OmegaConf.save(scene_name_dev, \"./scenes.dev.scene_name.json\")\n",
    "scene_name_train_copy = OmegaConf.load(\"./scenes.dev.scene_name.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2500, 6000)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(scene_name_train_copy), len(scene_name_train)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check training Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_start = []\n",
    "time_end = []\n",
    "name = []\n",
    "scenes = []\n",
    "for conf in config:\n",
    "    scenes.append(conf['scene'])\n",
    "    name.append(conf['target']['name'])\n",
    "    time_start.append(conf['target']['time_start'])\n",
    "    time_end.append(conf['target']['time_end'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import soundfile as sf\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "  'S00818',\n",
       "  'S00819',\n",
       "  'S00820',\n",
       "  'S00821',\n",
       "  'S00822',\n",
       "  'S00823',\n",
       "  'S00824',\n",
       "  'S00825',\n",
       "  'S00826',\n",
       "  'S00827',\n",
       "  'S00828',\n",
       "  'S00829',\n",
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       "  'S00837',\n",
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       "  'S00841',\n",
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       "  'S00843',\n",
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       "  'S00857',\n",
       "  'S00858',\n",
       "  'S00859',\n",
       "  'S00860',\n",
       "  'S00861',\n",
       "  'S00862',\n",
       "  'S00863',\n",
       "  'S00864',\n",
       "  'S00865',\n",
       "  'S00866',\n",
       "  'S00867',\n",
       "  'S00868',\n",
       "  'S00869',\n",
       "  'S00870',\n",
       "  'S00871',\n",
       "  'S00872',\n",
       "  'S00873',\n",
       "  'S00874',\n",
       "  'S00875',\n",
       "  'S00876',\n",
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       "  'S00878',\n",
       "  'S00879',\n",
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       "  'S00881',\n",
       "  'S00882',\n",
       "  'S00883',\n",
       "  'S00884',\n",
       "  'S00885',\n",
       "  'S00886',\n",
       "  'S00887',\n",
       "  'S00888',\n",
       "  'S00889',\n",
       "  'S00890',\n",
       "  'S00891',\n",
       "  'S00892',\n",
       "  'S00893',\n",
       "  'S00894',\n",
       "  'S00895',\n",
       "  'S00896',\n",
       "  'S00897',\n",
       "  'S00898',\n",
       "  'S00899',\n",
       "  'S00900',\n",
       "  'S00901',\n",
       "  'S00902',\n",
       "  'S00903',\n",
       "  'S00904',\n",
       "  'S00905',\n",
       "  'S00906',\n",
       "  'S00907',\n",
       "  'S00908',\n",
       "  'S00909',\n",
       "  'S00910',\n",
       "  'S00911',\n",
       "  'S00912',\n",
       "  'S00913',\n",
       "  'S00914',\n",
       "  'S00915',\n",
       "  'S00916',\n",
       "  'S00917',\n",
       "  'S00918',\n",
       "  'S00919',\n",
       "  'S00920',\n",
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       "  'S00922',\n",
       "  'S00923',\n",
       "  'S00924',\n",
       "  'S00925',\n",
       "  'S00926',\n",
       "  'S00927',\n",
       "  'S00928',\n",
       "  'S00929',\n",
       "  'S00930',\n",
       "  'S00931',\n",
       "  'S00932',\n",
       "  'S00933',\n",
       "  'S00934',\n",
       "  'S00935',\n",
       "  'S00936',\n",
       "  'S00937',\n",
       "  'S00938',\n",
       "  'S00939',\n",
       "  'S00940',\n",
       "  'S00941',\n",
       "  'S00942',\n",
       "  'S00943',\n",
       "  'S00944',\n",
       "  'S00945',\n",
       "  'S00946',\n",
       "  'S00947',\n",
       "  'S00948',\n",
       "  'S00949',\n",
       "  'S00950',\n",
       "  'S00951',\n",
       "  'S00952',\n",
       "  'S00953',\n",
       "  'S00954',\n",
       "  'S00955',\n",
       "  'S00956',\n",
       "  'S00957',\n",
       "  'S00958',\n",
       "  'S00959',\n",
       "  'S00960',\n",
       "  'S00961',\n",
       "  'S00962',\n",
       "  'S00963',\n",
       "  'S00964',\n",
       "  'S00965',\n",
       "  'S00966',\n",
       "  'S00967',\n",
       "  'S00968',\n",
       "  'S00969',\n",
       "  'S00970',\n",
       "  'S00971',\n",
       "  'S00972',\n",
       "  'S00973',\n",
       "  'S00974',\n",
       "  'S00975',\n",
       "  'S00976',\n",
       "  'S00977',\n",
       "  'S00978',\n",
       "  'S00979',\n",
       "  'S00980',\n",
       "  'S00981',\n",
       "  'S00982',\n",
       "  'S00983',\n",
       "  'S00984',\n",
       "  'S00985',\n",
       "  'S00986',\n",
       "  'S00987',\n",
       "  'S00988',\n",
       "  'S00989',\n",
       "  'S00990',\n",
       "  'S00991',\n",
       "  'S00992',\n",
       "  'S00993',\n",
       "  'S00994',\n",
       "  'S00995',\n",
       "  'S00996',\n",
       "  'S00997',\n",
       "  'S00998',\n",
       "  'S00999',\n",
       "  'S01000',\n",
       "  ...])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(time_start) - np.array(time_end), scenes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# S00001\n",
    "# target: T005_JYD_04274 time_start': 86680, 'time_end': 196489\n",
    "\n",
    "# S00001_hr.wav\n",
    "# S00001_interferer_CH0.wav\n",
    "# [V] S00001_interferer_CH1.wav\n",
    "# S00001_interferer_CH2.wav\n",
    "# S00001_interferer_CH3.wav\n",
    "# S00001_mix_CH0.wav\n",
    "# [V] S00001_mix_CH1.wav\n",
    "# S00001_mix_CH2.wav\n",
    "# S00001_mix_CH3.wav\n",
    "# [V] S00001_target_anechoic_CH1.wav\n",
    "# S00001_target_CH0.wav\n",
    "# [V] S00001_target_CH1.wav\n",
    "# S00001_target_CH2.wav\n",
    "# S00001_target_CH3.wav\n",
    "\n",
    "# target/\n",
    "#     T001_A08_03103.wav\n",
    "\n",
    "# interferers/noise/\n",
    "#     CIN_dishwasher_001.wav\n",
    "\n",
    "# interferers/music/00...99\n",
    "#     1009600.low.mp3 \n",
    "\n",
    "# interferers/speech/\n",
    "#         irm_02484.wav "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "mix, sr = sf.read(os.path.join(root_wav, \"train/scenes/S00001_mix_CH1.wav\"))\n",
    "target, sr = sf.read(os.path.join(root_wav, \"train/scenes/S00001_target_CH1.wav\"))\n",
    "target_nonreverb, sr = sf.read(os.path.join(root_wav, \"train/scenes/S00001_target_anechoic_CH1.wav\"))\n",
    "target_speech, sr = sf.read(os.path.join(root_wav, \"train/targets/T005_JYD_04274.wav\"))\n",
    "start, end = time_start[0], time_end[0]\n",
    "\n",
    "mix = np.mean(mix, axis=-1)\n",
    "target_nonreverb = np.mean(target_nonreverb, axis=-1)\n",
    "target = np.mean(target, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.plot(*args, scalex=True, scaley=True, data=None, **kwargs)>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "channel = 1\n",
    "plt.plot(target_speech)\n",
    "# plt.plot(mix[start:end, channel])\n",
    "# plt.plot(target_nonreverb[start:end, channel])\n",
    "# plt.plot(target[start:end, channel])\n",
    "plt.plot(mix[start:end])\n",
    "# plt.plot(target_nonreverb[start:end])\n",
    "# plt.plot(target[start:end])\n",
    "plt.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbb2a7ad190>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6jO2nrildFaIKxSktVQsWLEBkZCR8fX0RExODPXssT8NdvXo1oqKi4Ovri5YtW2L9+vUGjw8dOhQqlcrg1qtXL4MyWVlZGDJkCPz9/REYGIjhw4cjLy9P8udGpDSpRy0VaKTNnu5OXli6D5tOZIru4rpVaGLQ9o/DJa6V9E5n5mHVvou4kpPv0H4EAH7IR0f1MahRed83RCVkD6p++OEHJCQkYMqUKdi/fz9at26N+Ph4ZGZmmiy/c+dODB48GMOHD8eBAwfQr18/9OvXD6mpqQblevXqhStXruhvK1asMHh8yJAhOHr0KBITE7Fu3Tps27YNI0eOlO15EhEBAIpsG6OkhAKt/QFQ2UkLKgBLvf+Hld4fYZTHOglqRuTeZA+qZs2ahREjRmDYsGFo1qwZFi5cCD8/PyxZssRk+blz56JXr16YOHEimjZtig8//BDt2rXD/PnzDcr5+PggPDxcfwsKCtI/dvz4cWzYsAFfffUVYmJi0LlzZ8ybNw8rV67E5cuXZX2+RFLS6QSbs3sTyUdAR/Vx/V9t1KfRQV082+9Nr5X42PMLpSpG5BJkDaoKCwuRkpKCuLi40gOq1YiLi0NycrLJbZKTkw3KA0B8fLxR+S1btqBmzZpo0qQJXn75ZVy/ft1gH4GBgWjfvr3+vri4OKjVauzevVuKp0YkO22RDj1mb8VTX5j+rDhTZVuGRawe6n047/u00tVwmh7qFMSoT+j/DlQZrgf4lOdWZ1eJyKXIOlD92rVrKCoqQlhYmMH9YWFhOHHihMlt0tPTTZZPTy9d1b5Xr17o378/GjRogDNnzuDtt99G7969kZycDA8PD6Snp6NmzZoG+/D09ERwcLDBfsoqKChAQUFps31ubq5Nz5VIav9k5OHM1Vs4c9W5C9lK2S7m4abjbGLVx0SV+9J7lsw1cS3d1AeVrgKRS3PL2X+DBg3S/79ly5Zo1aoV7rnnHmzZsgXdu3e3a5/Tpk3D+++/L1UViSq9r3ecRYL6vNLVsJkKOtyjvqJ0NYjIDcna/RcSEgIPDw9kZGQY3J+RkYHw8HCT24SHh9tUHgAaNmyIkJAQnD59Wr+P8gPhtVotsrKyzO5n0qRJyMnJ0d8uXLhg9fkRyUkQ2WbkqkOuvt90QOkq2OV1z1UObX/H1IzACuJpz01KV4HIpckaVHl7eyM6OhpJSUn6+3Q6HZKSkhAbG2tym9jYWIPyAJCYmGi2PABcvHgR169fR61atfT7yM7ORkpKir7Mpk2boNPpEBMTY3IfPj4+8Pf3N7gRUeUz2vNXh7a/etOxNAXO4KqBOJG7k332X0JCAr788kt88803OH78OF5++WXcunULw4YNAwA899xzmDSpNLnea6+9hg0bNmDmzJk4ceIEpk6din379mHMmDEAgLy8PEycOBG7du3C+fPnkZSUhMceewyNGjVCfHw8AKBp06bo1asXRowYgT179mDHjh0YM2YMBg0ahNq1a8v9lImoEuOgfkZsVHnJPqZq4MCBuHr1KiZPnoz09HS0adMGGzZs0A9GT0tLg1pdGtt16tQJy5cvx7vvvou3334bjRs3xtq1a9GiRQsAgIeHBw4fPoxvvvkG2dnZqF27Nnr27IkPP/wQPj4++v0sW7YMY8aMQffu3aFWqzFgwAB8+umncj9dIsmIbU1QVfZrONnM3HtGEASoHHxDPabeAeBhh/ZB5K6cMlB9zJgx+pam8rZs2WJ035NPPoknn3zSZPkqVargzz//tHrM4OBgLF++3KZ6EpHzBeKm0lWodMwF7J9vPYNXujZyaN9DPJOsFyKqoLigMhEp6hvv/yldBQCQLMmqyo27v+ZsPOXwPkqSgRJVRgyqiNyccwYdy9fH2Fp9VrZ922IbFxdm9n4iBzGoIrdwMv0mHp63HZtPmF4zkiSkq7gpASzZkHoFtgyyroipEzRFpp+/psjxJK7bT13FlpP8/FLFxqCK3MJL36cg9VIuhi3dq3RVXI5WV3rBm/HnScdbG/Kzje6K0h43LlfBrNhzAc1V50WX/+XgJfkqIzOxOdBKTP7lqE3l52z8x+DvfE0Rnl28B0O/3ovcfI1N+yJyJwyqyCVl5OZj6z9X9QFCzh1+EZuz43TpupfzN5/Gln+uKlgb9+aLQtFli8wErxUxpcKKPWk2lS8/NquwTEtXXr5WkjoRuSIGVeSSOk5LwvNL9uDPo6bXaiTzrt4ssF7IHue2y7NfF/dPRuWanZh6KUfpKhC5LQZV5JJKGgG2c/Cw6/hGfO6htQfcs2usi8dho/vGLrdtuR13nv0HAK+uOABBEDBpzWF8veOc0tUhcituuaAyUWXgzhOxxv1wEMFKV8JGj6h34jXPn43uv1VYubqrdIKAnWeuY8We4vVPh93fQNL9u/HbmsgqtlQRVTAOj+hRIJrLK9AqPoB5nvd8k/dfvHEHe89nObk28rmQdRur9120WOamxOOeKt4oMyLTGFQRuahMEwvzns68qXjwIYcWU/5Eq6l/oUDrmmkKnlyYLPsxjl3OxZWcO7Ifp8snm/Hdrn9lP05Z1sL05bvT8L2T60QkBwZVRC7qpe9TDP5OvZSDuFnb0GnaJoVqJL/MXJkG2ctBwha9C1m30efT7Yh1wmtrb7VDcUPaitx1M1+Dt38+gnfXpnKWrxv6avtZ5g8sg0EVuZ3USzmYv+mUy7ZqSKVsIsZdZ6/rEyfmFcg8xocrNItS5+o2k/fbk1Lh6GXXmXF3/vptk/e/47VM9D6WeH0MXC3NVWXpjBRqdSb/T65vz7ksfPT7ceYPLINBFbm8P4+mI+tWaf6gh+f9jRl//YOvtleemUmDFu1SugpUjt8d0+k+6qtt/9VetvVIEAQkHsvA5Wz5uwLNuV1mcH5Jjip/mA62THnI4yB0y5+SvF7kWpzRXe1uGFSRyxv1XYrJ+0+kV9z8QTm3jbtByl54y2eslhJnZxma5LkMzjwrvx2+ghHf7kOn6cp18+aW6YabtOaIXftQ36g8P3oqC22RDnvPZ1X4XgJHMKgickEPfGz5gjpn4ykcSDM9xkXlaPedgrkcXLHncZTn74hVH5P1GGXP+A47crPl3NZgduI/OHftliT1Wbn3giT7MYWLNruvGX/9gycXJqPJuxvwn9/l/Uy4KwZVRC4o18SU9vIBx+Of7cTWCrYkjcMBoUwCIE2wYo6jccbba49gbtIp9JlrPuv9n0fT8W3yeVH7q8itwGS/r7af1f//y+3nUMAxcEaY/JMkIQgCdp3NQtNa1RHo5610dSokUxfeTccznF8RC3LzNdhZZi1CEufctTz9/21d7BgA9t3No3VHY75bxlw3urO5auBMJAW2VJEkfj10GYO/3IUes03PiCLH7TwjLlhR8pL1wtd7jVJB2MJVL7emlp5xpHHp6s0CLNv9r34m57lrpYPA2TtG5L7YUkWSKFn4WLbFfAnJZ42DKqmvv4Ig4N/rd1DDzu33/StPLiNXVKSz/+w/89VunMy4iT3nsjB3UFu7WqfKUlkIR09l3ER1Xy+H9m+vG7cKEVTVsOWaY6rcl9Erx5fSCFuqyCGCIEBbpFPk17Wrtmq4I02RDrvPXsecjafwwjfOzzkTqz7q9GMq6WRG8Zilkh8jZdnzUTIXlF3JuYMes7eh47QkO/bquLYfJiIzN59dfm5MW6TDn0fTcT2PP5jFYFBFDnlh6V5Ef7RR/oSUJpi6jNzM12DPuSzoHGhFsMVX28/iic934pYCz98eBdoiDF+6F0t3GE53/3DdMQxctAtzk06Z7OqSW2vVGQCuOfvPnF0mWg5tJQjAvKRT+P3wFYP7pOIKA87FdluTa1q0/SxGfZeCxxbsMH7QjT6vzsKgihyy+eRV5NzRYLsd08DlMODznXjqi2RZp4SX9dHvx7Hv3xv4RuSsKmdIvZxjtotl9b6LSDqRiam/GU6H/jZZ2XXX3vJaCcByN5aSqqAAm08aJvUsm5DWXgVaHWYm/mMwi+qn/ZYXOzbFVc+bGH8dc63JFmRoQ2pxa+rFG0z0KQaDKnJptrZc/JNRPItq7cFLMtSmuLtm2h/HjVrC8jWuM7X46x3nsa5My0dZrt6iJkDA2BUHMO2P40pXxcAs74UY9rXrLsXh6JgsJU3/44TSVSCSDIOqSkanE3Dmap7bDBZVupqL/z6HTSdKf0mP+i4FX2w9iw0mxsK4kp8PyBNUyu3IxRz8dugyvth61uixIp3gNpmcOf6klDt16VZWiccy0P+zHfj3uv352B6auQXnJUo+684YVFUimbn5aPj2enSfuRWL/3bdJSTEjof67dBlWWcbzvjzJD5cdwwvLN1n9Fhmbr7hHQpFf4cuus5CvFIoLDLf4tdj1la0fv8v5FvIxeQqoj/aiJw7xksNycUZ3X9KjLUj5xjx7T7sT8vG66sPGT1m6aut7Lvu7NVbeO+XVOkr52YYVFUir608qP//3I2nlKuIFWVbgSwlMwSAZxfvNv2ABN//8zeftnkbjYWgQA6HLmSLKnerQIvHFuzAZ1vOWC0rKDg+x9IX+Nlrt5Cv0eH4lVznVcgB/2SYHiTuLvWXyq6zxYlJZ3stwHyvuRCcNImEbJdtYs3Rssr3cJR/JZlhnUFVpZJ6yTmtGo40IQNARplWoDX7LXdjKTW7ydRl4Ye9aWj8zh/6gZ1KKhsW5dzRYNG2szh0IVtU64mSLRIV6XJrLjTtbWEpGclJeELtDbZX7EnDl4kH8bjHDjzssRseeabH+5HyTL1dbOq+rUgfYDsxqCLRNEU6bDqRgdx8yxfmHrO3YeS3+7DnXJZdx7F1MPXUX49id/np7TI3tty4VYhfD102uO/Nn44AgEMZxaV2u1CL1u//hblJzm+Z9IHt3V/uMtZPDLnHEul0AkZ9tw8frXPOwrbhKtsTu/qjeOLIEoPhBhXnNSYqjxnVK6kiOy5esxP/0XcfxTWtia+ev89kuUKtDn8dy8BfxzJwfnpfm4/z/a40m8ov3XkeS3eet+tYYl3LK8CTC5P1f3+6yfauQbFOZzre+lZyQT9fZvkTZ+vvIU+LjLskkpS7nqnHj8H7+M/YLNSHqloDM5WQ7njN1Lan3TjsOxKt8hdBMPj9bv67x51nMVYE5X/UXLxxG4fLjNtkz611bKmqBPaez0LkW7/jZpkWoNuFtg/2XZ1Smj9n4/FMDPt6jyT1K6Ep0mHPuSxFEolas2DzaZxz0syWO4VSjEuwfjXdeeYaPvnzBLQyjQPzVTmex8mUU2bGKrkauUO/Fj92wTzv+UjymYh3ij4zXUjCi2CKrrFd23VTHxRddvLaypVZ39V1/t9mi4+Xf4/vOW9f70RFwqCqEijbwmJNoVaHC1niWjc2n7xqb5VM+uC3Y3jqi2RpgiqJf1FpiyzvMK/A9Weklff0l7uxYPMZrLAhUerlbHkTAN4ScR6VnLkqCIL+13xHteVuN7lbqtRC6efkYd0mWY8FALmCn0R7Mn9eXD1VSUX0iwM5/dJEXisqEwZVbmR24j9448dDFsedXM8rwNyNp3DJjoufIAh48JPNeODjzdh5xjhDutzDXb7bpWxWb0cs2aHchd6U67cKEPnW75jx10mrZcUG0QDw2Rbx3Z72vF3e/vmIHVs5Tmyw+PC8v/HC0uIkoA97mJl5etebPx7G0csVK+WFPcoPcE9YddAgbUr577MXv9mLizd4sXaGC1m3DWaFA8Cl7Du4XahF9m3rLc3zZBwG4a6cElQtWLAAkZGR8PX1RUxMDPbssdxttHr1akRFRcHX1xctW7bE+vXr9Y9pNBq8+eabaNmyJapWrYratWvjueeew+XLhoOGIyMjoVKpDG7Tp0+X5fk5y9ykU1i17yKOXjY/JXvcDwcxe+M/GPhFceuU2Cn3Jfu/klM88271PlNLZbhRh7p7DLsxSYpxJQfSsgEAm05kWi4IYLOIMmLI1Y3oDNNEZvU+ejlXdAvtyYyb6Pvp345Uq0JKu34bczb+gyIzA3Q2Hs9Ewg/G+ZLIMZez7+BAmuFkg8xyef7OXbuF+6dvQrPJf6LNB4l2Hed2obZCTTixlexB1Q8//ICEhARMmTIF+/fvR+vWrREfH4/MTNNf5Dt37sTgwYMxfPhwHDhwAP369UO/fv2QmlqcVOz27dvYv38/3nvvPezfvx9r1qzByZMn8eijjxrt64MPPsCVK1f0t7Fjx8r6XJ3FUi6Qv08XtzCVrNN0Il18Tpw5FnJXfbX9LK7lyTNGxh3Y2opWqNUh+cx1t8gAfiozT5L9LN9j2wQDa8pmsi/L3m61nDsaTP4lFfvLXVguZd/Bb+VmclYUUg787uZhb6Bj+HoJUOHTTaexcm/x+8XU63k5R/pu5iKdgEXbzhgFFhVNvqbIZFDTafomPP7ZTpy0kIZGioHozSb/iQaT1psNmis62YOqWbNmYcSIERg2bBiaNWuGhQsXws/PD0uWLDFZfu7cuejVqxcmTpyIpk2b4sMPP0S7du0wf/58AEBAQAASExPx1FNPoUmTJujYsSPmz5+PlJQUpKUZfqlXr14d4eHh+lvVqlXlfrqKk+MHwjs/H8FHv7vWWmxWKfx5nvxLKgZ/uQvv/Gx7hmFXXhzXXN0KtTrsPivtIFVTmewdMf2P4/g2+V/0/2ynwf1Dvtxl876cneS1PEdzwTlTR7Xh4POSPGhStZCK9dP+i/jv+hN4vNzrX5Fk5OYj6r0NaDBpPeJmbdUvl5SeU5r779CFbJzOzEORTpD1fezIWC13JmtQVVhYiJSUFMTFxZUeUK1GXFwckpNND55OTk42KA8A8fHxZssDQE5ODlQqFQIDAw3unz59OmrUqIG2bdvik08+gVZrfgB0QUEBcnNzDW4V3TEL3Yin7k7rX7knDct2S9sC4Qx7zmdhgR0Z0aWy8u7g7x9TTHWjWnZKgpQKcjHXSNT1k834/YhhUkcpM7MbLQtkh9NmWuTOX7d9/I69A/aleB4A8OZPh80+9trKA/rPtisE6E97bjb5G8fZDRnmXv+KpGyy5NOZeZi/+TTuFBah47Qk/f1LdpxD3KytaPLuHxi0yPYfFGLJPanFVckaVF27dg1FRUUICwszuD8sLAzp6aZneaSnp9tUPj8/H2+++SYGDx4Mf39//f2vvvoqVq5cic2bN2PUqFH473//izfeeMNsXadNm4aAgAD9LSIiQuzTtJu2SIdfD102yCAujjTfRou2mV+yJPVSLgq0RXh3rfuu5fTJn9YHabuihFXuN57kco40wYI5H8ic4HKAepvVMt3VpUldd565bqGkefYsfWSKpRmyvxy8jD6fbseRizkuk/cpUGUc0LjiYPR3fj6CJxfudOvxgWVpinTIvGn42SxZhUJbSbvn5ObWs/80Gg2eeuopCIKAzz//3OCxhIQEdO3aFa1atcJLL72EmTNnYt68eSgoML0A76RJk5CTk6O/Xbggfpq5vR74eDNeXXEAMf9Nsl5YZqb6v/MLdS7ylUzOZKltw7Z2D+laSbJuyTue7wOvr62WWew9U///SWvsm6W4q3zmfxnN2fiP045ljReMxxb+k5GHzNx8k11QSo1zXrY7DXvP30Cyk16n63kFsg/q/tBJGffLq6xj1WUNqkJCQuDh4YGMDMMBpxkZGQgPDze5TXh4uKjyJQHVv//+i8TERINWKlNiYmKg1Wpx/vx5k4/7+PjA39/f4Ca3Kw78uhc7CNBS8//ag8WDc1ftu4Aes7aaLONOszjWHzFeU0zJLkB3VU0lTbO9lO8cqd+GgiDgr6Pp+qWUnNVJ9k9GHgABnnAsF5uYbj2NC7VEeJtZsij1co5diYjlZu7U/XroMr5NPi/JMTakpiP6o414245xl2Ll3tFi43Hnjl2r7GQNqry9vREdHY2kpNKWGJ1Oh6SkJMTGxprcJjY21qA8ACQmJhqULwmoTp06hY0bN6JGjRpW63Lw4EGo1WrUrFnTzmfjPCn/ZuHdtUfMLn479ddjaP3+X7hSbobM7UL7vqjf+PEwzprJFu6sr2UpmttfWbbf6L5P/jyJsSsOiMq54gre+FH5rr/XPNeYfazQShJUOWiKdAbrTR6/kqsfM3Q9rwAr96TZnDD2sy1nMPK7FDz1hfjEuFL5ymsGDvuM0K+LV5Y3NHjKYzNaqxz/MXDoQjZ+O+Qaixd/6jXf5P3mAipXXYno1RUHMPmXo0izYxxeeTPv5pBbIfGs2bKUnIGX7wYzn+Uge/dfQkICvvzyS3zzzTc4fvw4Xn75Zdy6dQvDhg0DADz33HOYNGmSvvxrr72GDRs2YObMmThx4gSmTp2Kffv2YcyYMQCKA6onnngC+/btw7Jly1BUVIT09HSkp6ejsLD4wpmcnIw5c+bg0KFDOHv2LJYtW4bx48fjmWeeQVBQkNxP2WEDPk/G97vSMP0P0zPujlzKQV6B1mCRUp1OQOol6QfXO6OhavIvqWg+5U/Z9v/bocv43wZxeYiA4laMfRIut2AuNUB52bcLscpkfjDnqqMyTvxaQs4LgDndZ241ys3W59PidQWfW7IHb605gndsTBr6+Rbz4wnlFudxAH6qAsR7GM9unOC5Ch97fYlffCbjHpVts6falAvEcu5o8PMB15iB1URt+n1ty+dSKTqdgImrDxm0UJn7wWsLc6lMfj98Bb8fliYYVnJM3YLNxZ+x05k38diCHU6f7akU2YOqgQMHYsaMGZg8eTLatGmDgwcPYsOGDfrB6GlpabhypfQN1KlTJyxfvhyLFi1C69at8eOPP2Lt2rVo0aIFAODSpUv49ddfcfHiRbRp0wa1atXS33buLJ4q6+Pjg5UrV+LBBx9E8+bN8Z///Afjx4/HokWL5H66kjp7Vfy06cc/22HXr+7DF7PNPlZ+Npdcvk3+12LuLSmU5O0yZdXeC+g+c4v+1+ePKRfxhA1L+1hjLjXApew7+PtUaQBjb7I9Z7OU56asIEgzi9HSUhglwdYfRywvb6Ip0mHvefP5iZxx6Uk8Zjm49kEh+nns0P/dWZ2KhB8Ommxl7VBonM29p4kgzdVdyHLeDDF7hzJsPpmJ1SkXMfkX+dclzCvQYvTy/Ri9fL9dPQ97y/0YdIXRG2OWH8ChC9kYdnclgorO0xkHGTNmjL6lqbwtW7YY3ffkk0/iySefNFk+MjLS6oejXbt22LVLvqmiUjhy0fryFWorbeBlk+YdErE/Ux6dv8PsY0otGSIHnYX3zBt3p6e/90sqvnmhA3456JxEkPdPL16vbfmIGNQJrOKUY0ohfs42DO5QD9P6t7RY7nlP1wkSLSX3rIEcVFWZnsAiBUEQcObqLYz41nzQ44d8HPQZAW9VaZdJc9V5vHHgEtYcuITz0/salB98e5nRPlRuMq1kqtc3GKmZYLGMpR9B9rqjKT231/IKMPmXVAy6rx72/XsDl7Pv4JMnWpnc7ma+cXAjVwvQnTLdoRtS09GnZS14eagxO/EfdGxYA50bh1jcvvwKCkoHVTfzNfrZhpWFW8/+c1dXbxbgkfnWl6+wNq5AzLADW9Zqq8h2nLY+m6ck+3mhDNOpO/9vEyLf+t3kmoz7zt9wyq9gscRcnJ3VDbjRSuuONZtOZGDIV7v0XS2tVGfwtucyVEVx61eM6jhSfF92uJ6WzE06hTgzE0FKdFCfMAioAOApT8vb2Gukx2/Y4TMWtWG+m7e8MEjXHd7TIwURKuuv6593F1dOz8lH30+344e94t9zN/M1BjnB9qfdwPe7Srf/4LdjWH8kHc8t2YNPk07hx5SLaDX1L/3jq8osMm4qgLInqa8YZY+VsOoQ3v/tKH5MuYD5m0/jmcWW15o01dig9ALVLcuc08qCQZUCxE51tjpY08rjtwu1diU2rOx0MgzuLPnlbWoqviDALZazMWXa+uN4+fsU6wXt9KKF1h0xXli6DztOX9ePofrV5z2M9Pwd47AcAPCDz4cO19GUsrP7LC3/JFb5Ra8FB0Zyv+21AnVU1zHR6wfR2+z2Nd3TYK+XPX5FKCwvFzPqu+L31dgV+3H0ci7e/El8y3nLqX+hw3+TcDozD19tP2sUBJWf5AMAN8tMdig79CHrlvH4qSOXnLNQ9oo9F7D1H3FrTbrLd31FyQFmDoMqBeRrxL2prE2btvb4wbuL6pLryLpl3M3kKgka7fHFtrP4I1XZX8OA7eewES5gsdcnMtUG2OD9lk3l1bD8nTBmxQFHqmOSh5Vjyulpz8343nua1XKbT2RaHAtnTdysrfjo9+M4fsVwooPYbjFtkc4peZ6+TT6PfI3pH1brrYwXLOEOP8x+PXQZ9777BzakusasVDkwqFLAT/uNZ8J8v+tf/H74Cg5eyNbfV6jV6Zt0TTXtWvux6qyB5mJ8m3zebdaCknM6d+qlXJN5biwtGUTSa46z6O4hfaBSopHa9BiulqqzJu9f4j3D4v6uiFjyw0WzEJhlbkZgWeVXRci5rYFOJyBfU4Q3fjyEv46m25U24FqeuDF0tqbqEGPVvgtYWa77fPIvRzFw0S5k5to/tu+Jz52fHsRWr644AJ0AvPS9cfqbisIpA9XJOlPLwew5n4UXv9mHqY82N7kIaPZtDdYdvowezcKMHgPgEmv2bUi9guu3CvVjhkKr+aBDg2B4erhGPG/qS1Pu9dIm/3IUz8VG6v9ed/gKck0MhnV15n5ZuwMvB5Nv2us/Xovt2q7kXGuLdPgnIw9ebhdC2ad8uNT6g7/wQOMQnL16C5ey72DVvovw8lDhg8daYHCHeqL3K6ar7ELWbVT3dfwS+drKA7iQdRurX+oETZEOb/xoet3GQxey8fA862NtgeKJTpeyb6NXi1r6++QIAOX0XfJ59GgWjvAAX6WrIikGVS4u6UQmfLzUJn9ZrdiTpkjeIFuU/0Xy9Fe78Wr3xkjoca9CNTL03/WlucAyHPiVaKuyAYm7LvR63382Knr811bK19Ikl1bq0txytiw4nZuvxX9+P4asWxr8tP8ifvEuqhT9DKZa6LefMhxgrykSMGnNEQzuUA85dzSYl+T4GDageBmxA+/1cHg/JbOJD13Mxj2h1RzeHwD9RKd1YzujRZ0ASfbpbO/9chQLNp/Brre7K10VSVWCj6X701WwcX2uFAju/7d0vMa5a7eKc8M4oREg6r0N8h9EZqammjuTs1JfSKWJyvb3fTBKu4W/3H7O5NCBUua7wTxMrL1XEX207hi+KpMU2ZV8vuWMPo2KI05nlqYoMLcShrtIz5V3IXYlMKhyA+48kNkUJTsuBEGwmFSv2eQ/9evBVVbuku/IlCKdgKOXc6yOs/FS2Rdk1FXZnxV6utdXNm/znYjB3NY8pv4bx3yGoZvasGWvk/oomqr+dXj/crIlx9GRizk4dkXasYmmlr4SK+XfG5hWZlWMxGMZknTRzZZgNinJh0EVVSrvrE1Fs8l/Yn+a/TOKSB4qCWaj/Xf9cfT99G88u3i3LIuBv+tpnHRTrLZqw5xxgmD950VzteNBz1zvz+Cj0uJr708QVKblK0SViz98JlnYsoR7BNmPzP9b8tbT5LPW89sBxd35yWeuQ1MmXcCAz3fii62mJybY6+kvd2FbmRQLr644UKFn0rkjBlVUafyYchHL7w7el2rcRUXTQnUWHdQnrReUwSNqx1ZBWLD5NBbf7frZeeY6JqySfnFqT4UGuJdnKsx5yXMdBqi3GdxXFYazBhdbmWVoiju1XFpa0khO41YexOAvd+F/f8i7luHOM9eNAseXvt/vFukUKgsGVeR0Sq1A//pqw4vsLTebLeMM63zeVezYHdSmFxA35TmPPzHBc5X+b02RYDT9fo0MiwmrFQgwuqoPii4703shvFGarDJabZhouJ3a9hUW5Py4lm05czcbUq/gZPpNHEi7oc9cvmSHMuO5Wk6pfJnLXRWDKnI6FVTI1xQh8ViGXYuGSmHzyatoPuXPSrculWXKtkg845mEUR6/WS3nj1v4wOsbjPVci9Yq5y7DZEurTbCVgKGFWtwFeKn3x6KPCQDPe/yp/78UAZG1xKSOCFG5b1D10vf7ET9nm0G6G5VCvxjlWFqL7MOgipwuPTcf7/ycihHf7sNrKw8qXR26S0zAEAB50z9M8lphtYy/qrSLp7X6jJzVMWJLS1WQynLAPszzT4uPl2U8c9D8xbuOSvyafmLIGSa4U9eiGJUjexhZwqDKDfx51LFFZV1RydTwRAcXzCXpiLkgLPKeJXs9rBPK/M+5l7FmNgwc9xSZxsAf1qfFz/H6TPRxbQ1UrC2WXNECH2eQY5IEuQcGVUQEQFw3T4xa3oG4cqqrErcwrSWhKvEL6b4jcqbgVK9vrJbxQaHo46oM/m/94v6WhdZBL2gZVNnoVoEWDSatV7oapBAGVUQEwH26Luy9yP/t85okx2+ksr5mHQB08Tgiqlx/D+tLk9jynMuWFdNa5msmYItTp+CU73N4zoODoMXS6gQ0nyK+W5cqHgZVRATAfbp5ygZ/tVXXIWaA/T0q6WYCjvFcK9m+xCof8Frq9iz7OoppBTO3pwVecwEA73gtt7oPezVUXUEz1XnZ9k/kbAyqiAiA+KCqvipd5pqI97Lnb/js7sW/vCaqNHzn9V+0UZ1GAwnrbE+LXiicl2y2bP3qihi0bv51l7/tcqH3HKz3eRv+Mk+AIHIWBlVEBEB8UPWj9/sy18S0Jz22YLnXRwgoN7C7j8cek+W/956GBzxSsdZnMr7ynilZPexJMTDe80eT9/tC3CLetrQiDvFMuvs/92h5BIAwVbbSVSCShKfSFSAi1yD2wm3LYG0pfeK1CAAwFj+LKi9XPe3pJvVRaUzev8DrU1Hb11dnojhIUomuw+dec0Tt29y+zNVZDi1VZ3FKqOu04xHJhS1VRATAts6eGlAmsAIM81Qpwbas6pbLdvc4YPHxsuqgtCsvXGV90e/eHntF7dcVJijM8l6odBWIJMGgysm4NAq5KltaYEIUaq0ClB9QX8OGLOCz7+aXGiBihp81c70XAACqIB/hKstjtEKRLXq/HijCeM8f0VF9zJHqEREYVDndwQvZSleByCRbWmAe9jC9+HEH1XEM8dgoVZVMUjpXli3Hf9xjh2THba/+Bz4oxHHfF6yWXeBtevC+KQ95HMRrnmuw0vsjBCMXTVRpeFS90/qGRGSEY6qICIBtLUBjPddipvYp/d/d1SlYXGYw+Fmhlt31GOSxCWuL7kc+fPT33au6YPf+KpIXPcQlleygPmm9kAlzvBaIzq8lteaqczgqNFDk2ERSYUuVk3H1AnJVozytL2ZszuJys+uaOBAETff6Cm96rtT/PcFzFf7yedPu/clB7Ky9YtJ96Cd6rZJsX6YoFVABwO8+76CL+hDcadYiUXkMqogIHijCaM9fbdrGG+Znh031+tah+nRTH9T/f6yNyTbrq9LRSZ3q0PGtGeWxTnTZJz22yliTiqW3eg/Wek/Gl17SpcAgciZ2/zmZwF9h5II87Mi99I/v85ir7Y/Z2ickr0+oA3mLtvokSFcRM2xZR7AkFQRZ11x9Hq3U5wAA92gvIVMIwk34KVwrIvHYUkVEdiW0BIDXPNdIXJNiVVW2dK+VjLly3g+WINVNpx2rMikJqAAgyWcikn3GKFgbItuxpYqI7GqpKt3W+qK99uiqPogWqnPWCwL4y+dNvKkZgWO6+rLUpbw4G/JLkf2qqfKVrgKRTRhUERHe8/zO7m0/9ZonYU1KLfX+2Kby//P6UpZ6kLJq4xquIBgCO1bIDfBd6mSc/UeuxhNaDPLcYvf2fc2svUckhZ2+r+Jn78l2d1ETORODKidjTEWuxpGuPyJnaKM+i7O+z2C21wKlq0JkkVOCqgULFiAyMhK+vr6IiYnBnj2Wf9muXr0aUVFR8PX1RcuWLbF+vWHCO0EQMHnyZNSqVQtVqlRBXFwcTp06ZVAmKysLQ4YMgb+/PwIDAzF8+HDk5eVJ/tyI3F079SnrhYhcwOMeO/Cl1wxUg7LrP5J0tEUV60ed7EHVDz/8gISEBEyZMgX79+9H69atER8fj8zMTJPld+7cicGDB2P48OE4cOAA+vXrh379+iE1tTTvzMcff4xPP/0UCxcuxO7du1G1alXEx8cjP790UOOQIUNw9OhRJCYmYt26ddi2bRtGjhwp99MlcjtTPb9RugpEovXw2I9U3xcx2mMtIlVXUAM5qK9KV7paZKes24VKV0FSKkGQd5RPTEwM7rvvPsyfPx8AoNPpEBERgbFjx+Ktt94yKj9w4EDcunUL69aVJtfr2LEj2rRpg4ULF0IQBNSuXRsTJkzA66+/DgDIyclBWFgYli5dikGDBuH48eNo1qwZ9u7di/bt2wMANmzYgD59+uDixYuoXbu21Xrn5uYiICAAOTk58Pf3l+JUAAC2HT2PMd+ZbqkTLKwXb+5FMreNlPuy9phU+5KyXpbe1Ob3Z9tzrCi2eb+GemrxeZeIXN1FIQRfaB/GJSEEXtAiSJWHf4UwZAqBuCP4oJ46E41VF5EPb+zXNYYHdLgNX6judoXfEqpAA0/FF++uDH4Z3wMNwoIk3adc128xZJ39V1hYiJSUFEyaNEl/n1qtRlxcHJKTk01uk5ycjIQEw+R98fHxWLt2LQDg3LlzSE9PR1xcnP7xgIAAxMTEIDk5GYMGDUJycjICAwP1ARUAxMXFQa1WY/fu3Xj88ceNjltQUICCgtLcOLm54leit8WNlS/jsC8XK60odIJ0QZ0zAkRT+/JVmc+MTuSO6qqu4UOvpUpXg0RI3DINDQa+onQ1JCNrUHXt2jUUFRUhLCzM4P6wsDCcOGF6pff09HST5dPT0/WPl9xnqUzNmjUNHvf09ERwcLC+THnTpk3D+++/L/KZERVTq+z5Jctfv0REAHDsSg56KF0JCTFP1V2TJk0yaCHLzc1FRESE5Md5o+gVTNC8ZHS/+fYG0xdge5qlbd2XVHWydT/FXK9Otm+jzHOwdV/PeibiJU/xa9kRuaoDukZQQYdzQi1sL2qJYNVNXBRCcVSojxyhGqoiHzdRBaGqHDRRXUCgKg/bi1qiEF7QwAMCVMiHN3RQ86ePk6wf1FXpKkhK1qAqJCQEHh4eyMjIMLg/IyMD4eHhJrcJDw+3WL7k34yMDNSqVcugTJs2bfRlyg+E12q1yMrKMntcHx8f+Pj4iH9ydoqqHYRDF3NkPw6RWBeFUKWrQGSTg7p78Hjh+2ihOo8jQkOooYNOxLyrXFQFAOQJfjgn1LJSmpyhbnBVpasgKVln/3l7eyM6OhpJSUn6+3Q6HZKSkhAbG2tym9jYWIPyAJCYmKgv36BBA4SHhxuUyc3Nxe7du/VlYmNjkZ2djZSUFH2ZTZs2QafTISYmRrLnZ4+29aQdkEdEVJks03ZHv8IPIUCNI0JDABAVUJFr8vOuWB1msj+bhIQEPP/882jfvj06dOiAOXPm4NatWxg2bBgA4LnnnkOdOnUwbdo0AMBrr72GBx98EDNnzkTfvn2xcuVK7Nu3D4sWFa/0rlKpMG7cOHz00Udo3LgxGjRogPfeew+1a9dGv379AABNmzZFr169MGLECCxcuBAajQZjxozBoEGDRM38k5Oqck4wIyJy2FxtfyzQPqZ0NYjMkj2oGjhwIK5evYrJkycjPT0dbdq0wYYNG/QDzdPS0qBWl/7K6NSpE5YvX453330Xb7/9Nho3boy1a9eiRYsW+jJvvPEGbt26hZEjRyI7OxudO3fGhg0b4Ovrqy+zbNkyjBkzBt27d4darcaAAQPw6aefyv10rVJV0mn7RLbaWdQMl4QQPOm5TemqkAt4pnAS/ta1VLoaRBbJnqfKXcmV5+LDdcew+O9zku2PyFGtVafxi89ku7efo+2PcZ5rJKxRscGF7yBZ1xznfZ+2WnaltqtD6xeSa1tb1AnjNa9wUeUK6Pz0vpLvU8k8VXyHOpmaDVXkYg4JjXBA18ju7edoB0hYG/tM1T6vdBVIJp9pH8U4zRgGVOQW+C51MhUHVZEL2mZnt8oybXfIlYk+VdcAADCkcJKVkoaLQh/WNUDr/EWy1Imcb662v9JVIBKtYg27dwMMqagikWPQcELhS9ioa4eb8AMA7BAR8HlAh9XaLnjScxv+o3kGOagmeb3IOTrkL0BTdRr+0dVFJgJRBA+lq0QkGoMqJ2NLFbkm29+X9+R/J8sFb42ui83bqKHDRO0ofKh9BrkuFlAt1vbGcM8/lK6G28hEEDJ1TD1D7ondf07GmIpckUawLTh6ufA1xVoQjuvqmXlE5XIBFQB8qH1W6Sq4jeSiZkpXgcghDKqcjDEVuaJvi3raVP4PnWES3QLBsNH7p6LODteprNj8eQCA+drHMEnzotHj2agu6fGs6VUw3anHA4A0XSjuCN6yHmNw4Tuy7t+aFzSvK3p8IkcxqHIyNZuqyAXloiqmaQbbvb22XKvVaV1dR6tk4ApqIDJ/GWZoB+Lw3SzaSjohmGstk8/zmrdEr/m5qaiNXcdI1jVHTP58jCt8xa7tHbFU2xN34Gu9IJELY1DlZEypQBXR4qLeBn8XytI1aPrDk6qLlOFYruecUEt0S/c0rfXcXuZkIBhrddK2NIoxVTvU6cckkhqDKmdjSxW5KLGtIKYs0PbDPG0/bC5qjVRdJFYUdZewZpY9XPhfk/fvdyD3lqv5tah4XVNVmdQR5gwqfNeuYzxf+KZd20lhZOF4xY5NJCXO/nMyhlTk7nYUNTe6rwDemKl9yinHFxv6DSl8G81U/6KBOh0zvL6QtU7mrC3qBAA4o6uFe9RX7N7PLp34Adw3BT+bA+Sm+UuMut526Zqio/q4Tfux11+6+5xyHCK5saXKyTimilyV2HfmEI2yg5nFugNfpAhN8FPRA/LsX8Sg8XGaMQCA1zSjTT6+VCtugkBJiPSqZqyo8rYEVcMKJ5ocyzS88HU8W/gWthS1Fr0vosqOQZWTMaYiV+VI958p53Vhku7PXgLUyBe8JN/vZaGG6LKpZgbX2zqOaIOuA5rkL7VpG2sKzXRY3EIVbNe1wm5dU0mPR1SRMahysmo+7HElcoRQyTvRC2C5hUyAbcMMKvv5JJISgyonC6gi/S9mIilcRYCk+5O65cuUw3fXB7RGysBhomakZPsCgC+1fSTdnwaekp57Z7yORBUFgyonY/cfuao1RQ/oZ5m5i38F53cxri7qKsl+SrKHO5L+wJRTQh2bzotGUK71/C3Ni3i84H3Fjk8kNQZVTsagilxVETysDoQeVTjOOZWxqPRDlCNUVbAelpmaJVnWRl1bAIBOxNfwNUFcK+IzhZMAqHAbvhhY8J6obYoUvAysLHoIB4TGih2flHV/I/HjEt0FgyonU3H8AlUSzu42ernrPWYfU2LckPXnb71OF3Sh+FLbBxt17UQd829dS/3/dwviBphbC+o45ork8tnT0UpXQXIMqpyMLVVUWcj5Vt91d0bayqJu+vve7BVltvweXfFjVwV/GWtlyENlmKhzrvZxg7/FBCufFz2K/2ifgSDjV3UOLLf2cUyV63vpQfM/KFxZgF/FG2PMoMrJVJU4qnoyWtr14KjyerrwHbTLX2g2VUF5CZqXMVf7OPoXOm/8zhJtL4O/P9X2N/hbTKhiLqDZLFHuqHVFMTgn1LJY5gaqSXKsiiq2YXEX1of9Wihy/J7NwvBWb/M/KMi5GFSR0zzcurbB3wk97lWoJmQvV/lJoIMaWRDf6pQFf8zWPokLThzY/qeug8HfRXash1g+qFr2YgwAYJyZZKK2+o/mGatlfix6UJJjOeLhVpYDvxIfmQhsfL3U6NlM+tf951c64ceXYrF8RAxOfNgLz3asL/kxxKjEv9NdEoMqJ6vMCyp7qFSY3r94zMf7jzbH2Idca222Na90go8nPxKW2NIVpFJVvG6jedp+ku1LY8cqYfc3CsGW17siR6LWoyuwPlDYnnpKLaZBsKhyD94banRfncAqWPRcewztFClpndrWC0L7yGCoVCr4esmxgDi5I15BnKwyD1Tv2DAYgzrUQ+r78Xi+U6TLdYW2qxeEYx/0Qss60uZrqkjUMo6v+bUoFoMLbV8CJ6SaN5YMbS9DjYw5ur7hQV3p2Jfbgo/V8qaC2MiQqtj+RjcTpW1TJLjW588ikd8VdYOq4LE2tTG4Qz2jxyY/LH79RClMeaQZJsY3QWh166+zI2IaVLwZdO6MQRU5xUNRNeHpUfx2c+Ws8h5qFQQOzDVLDZ31QnZ6VTMWyTrLaQhM2fduDzwUJb57p3fBNJuPYatjOtNdQS/ZmJLCXMtgRLCfrVUy4k6z+moHGK9NaIpKpcLcQW0xrX9Lo8fUTugm+G54BzSv7Y91Yztj2P0NMLpbI+x5uzsGd4gAIO2Qh3rBfhjaKRLPxSrT7UimMahysgYhrptXR07Na1se//LJE62cVBPTyv4Qrh9cOV8jMSrCTLDjQn3s1jk+sNdSUFKcL8pYepnuNjFBTVKRuFQKYizUPiLZvpztoaiaeL2n9YBEEJR9fz7QOBS/v/oAWpRp7VapVPioX0v8Oa6LpEMetr3RDVMfba7/sUquga+GkzWzElxUVNYuH+Eif4lKrVuTUPRvWwe/j31Af1/TWtUVqYs7MNX956HgQMG3+yg368lSgFl+EH1JS0VZYoKqSzAeI2SLPbom+v8nFbU1eGyrTpoZhM6gUqkw5qHGeMVCLrLyukfVBAC80FncUkZy8lCr0CS8uqxDHpT8HFIpBlXkFOa+TL56rj1e73kvOjcKcUo93u1bnN9o2P2RSHk3Dl8P64BZA9tU2mDXVmW7/0oG/s4d1MZkWblbtd7p0xQju7h+fp65g9rgv4+XdkdpheKv3X1CE3ObmFQy888WZV+DvYJhAJqgeVn0fr7Txtl8bDm82r0xPh5gvlW7bEPVwmej8ee4LnjaxPgqR5V8j7iSFhwL6hIYVJFTmPuBFtcsDGMeauy0QesvPtAQp/7TG1MeaY4a1eQdQOquLujMt46Uzez9Vu8oHJzcAw+3qm22vFgndMYtOdbUq+H4uCJb/FF0n13b1Q2qYvD+bluwCJ0L5uKiYFsr1P0S/PD48G4KhRcKX7dpBuF1G9JXyMnXywNP3Wf+vVI2jPfyUJttHTL3Q0CsFx8Qlx+NKh8GVeQUYmY9Drs/UtY6bBhX3MXnxTEIFk3RPm/2sdwyF2KVCgj08zZbVs6ZggAcyj10VeRaemXdQhW7j1fWTfgZBVQ/F90vajagrcp/6hYX9cG9+d9gk8hlb9yN2DFVHRvWwJ53uuO17lx3kKTFq4sL2f5GN8x8sjW8PEq/Cvu1qY1fRt9vsvyURyxPEe4eVRN/vPaAxTLOonR3/6gHGyIqXNyvbbnGut4b5h6ZqQsgzdIR9gQutnCkdfN9zXM2BzG6cikIbJ09F2RhSY7xmtFoXrDYpv2JYaoLttCO11fJj++Cp8UHgNbeE4nju+CnlzshzN8XNav7om+ZpKJR4e4zljLSya20cqju67qzwB3BoMqFRAT7YUB0Xfz8SnEQ9UDjEMwZ1BatIwJNlr8vMhi/jemMV8382pr3dFs0reUazfZSTAF3hI8LtE79Oa6L0lVwqhSd+Onjzp5VeBVBiClYYNM2OgdDC2vPUIAaQwvfcOgYFVFfE9nUD07uYbKstcHajcOqI7p+kP7viKDS76VvXuhgahO9/w1oiZBq3ujWxLHJA1Jw/zm4wLdWzre7Uv5KQ0Za1AnAgfd64Jthlt90zWr5o2XdAIRUM90F4+et7C+BesF++OaFDnj1oUZ4tLXj427cnUqlcovASqr8RTO1T0qyH1tEBFvvols5siOA4m641vmLRO9b54Svyy26NsgTpJsJWxFSYJhiqdvZFlW8PXBoSk8c+yAeYf6Wz3vN6r7YNak7lgy1b2wdGWpbL8h6ITck67dEVlYWhgwZAn9/fwQGBmL48OHIy8uzuE1+fj5Gjx6NGjVqoFq1ahgwYAAyMjL0jx86dAiDBw9GREQEqlSpgqZNm2Lu3LkG+9iyZQtUKpXRLT09XZbnKYegqt4GyerK5rfa/XZ3/PNRb/3j/r6uudL35te74sF7Q5HQs4moxHuVIdt8EzfqYrDG2uuVB+laJ++LFPcFLOaz0L5MS0UOquH5wjdF7dvRlqpZTzk/hYEtrYXuRqoFjAOqeIn7AaoCPD3UDk+q2TDuAXxtITCbN7it2cfk0q+N6R+9dQKlGUdYmcgaVA0ZMgRHjx5FYmIi1q1bh23btmHkyJEWtxk/fjx+++03rF69Glu3bsXly5fRv3/p6u4pKSmoWbMmvv/+exw9ehTvvPMOJk2ahPnz5xvt6+TJk7hy5Yr+VrNmTcmfo7OU/RiH+fvCu8wadXWDjN/4HSLFrZUlJ2fnTVE6gSjZz1orkKda3FfVMyIWtS1/URSbr6l8HcW3AhUfT2zW97GasQCAKZrSCQNiBlTP0fY3um+W9glM1wxC94JPRB3bnSi1gLGjosL90S3K/LVIidW7Yu8xvdTNz690smk/AVVc8we+M8nWP3T8+HFs2LABe/fuRfv2xetyzZs3D3369MGMGTNQu7ZxZJyTk4PFixdj+fLleOihhwAAX3/9NZo2bYpdu3ahY8eOeOGFFwy2adiwIZKTk7FmzRqMGTPG4LGaNWsiMDBQnifoQiJNZGkf293xzL3R9YOQ8u8N/d9jujXCjjPXcCAt2+F9y6FrE2mC5orZYeLajgnS5BIadF8EWtQOQOOwaoh6b4PJMvbG+va2VNm6SPdmXVs0zv/WYCHj8SKWN9mlM564cge+WFj0qE3HN026T8VuXRRi1Cck258cQqv74OrNAlmPsXxEDJ7+crdd20o9mcbc+oQ1rXSJlvdMx3pYsPmMFFVyW7K1VCUnJyMwMFAfUAFAXFwc1Go1du82/UZKSUmBRqNBXFxpormoqCjUq1cPycnJZo+Vk5OD4GDjlpk2bdqgVq1a6NGjB3bs2OHAs3FtIdV88Oe4Lvj7zdJFVqX40JW/+Lwe3wTBEo1lMMXRX2gqVfE4LnKM2DFVZV+v7hZ+eYsjzc9zlUqFlnUD4OvlgaQJD+Knlw1/aTcIqQqVSoXU9+Nt3re9Y82sLdFkika+37uK+7PoPnyj7YFxha8oXRWz7HnNbNXpHvN5xxxdH3X/e6YH8j/Vvq7RfR/2a4F7w5wzLKEkYXBFJltQlZ6ebtTd5unpieDgYLNjm9LT0+Ht7W3UuhQWFmZ2m507d+KHH34w6FasVasWFi5ciJ9++gk//fQTIiIi0LVrV+zfv99sfQsKCpCbm2twc6aGDq4J2CS8OuqWmcmiszGq6tok1GiQr6+Xh0N1cilKtKm7kVoilwky17w/0EJCRjmIWWD3ntBqBjO9ercIxw93B6nbc9EqsvPr0tExOGIvRO4yKF0HFaZoh2GtrrPSVTHL1CvmzG+QjQkPGt1ny0zu4KreJsdJmRoHWb4btWz2fyn9b0BLDDKxXFNFY/O3xFtvvWVyEHjZ24kTzmnaTU1NxWOPPYYpU6agZ8+e+vubNGmCUaNGITo6Gp06dcKSJUvQqVMnzJ492+y+pk2bhoCAAP0tIsK5L/6ILpYz9PZrWwdA8Yw/OSwd1gEe5b78PzIxEJSxieO876Z3CPLzwm9jXOPCohb5woqZXSe3mtV98JeJi441z8bWt7k7o6xbMNxWqlmS5tQNqoLfxnTGew9bzkfnbtwh+FO6huEBvvhtTGe0jghEx4bBeKxNbXwzzLZZh7OeaoNRDxpfVz41MRDev8yPpXb1A22ubwlTQVv/dnVw4L0eGHhfPUSF+2PlyI7YOrGr3cdwdTb/XJswYQKGDh1qsUzDhg0RHh6OzMxMg/u1Wi2ysrIQHh5ucrvw8HAUFhYiOzvboLUqIyPDaJtjx46he/fuGDlyJN59912r9e7QoQP+/vtvs49PmjQJCQkJ+r9zc3OdFlh5e6jxVHvLx3ql6z1oWScA7cr88rZEii+F+jWqontUTSSdKH0dFV4E3m4D2tURXVaqAfa9W4Tjj1TjFtY1r3TCjL9O4q3eUQYJSe+LDMKJ9Ju4ma+V5Pi2EJuJumyxsmdJ7mWGhDLv6Nd7NnG4e8QeX2r7OvV4v43pjKCq4rvbywd5zWr5w7+KJ3adzZK6ai7pIYe7oEuFmwi+nbWUVomWdQPMJn42pWODYBy6kK0ftqFWq0zO3nu0dW0s2HQaJzNu6u/z9/XC8hEx8FSrERXuj7Wj7zc7zsoeZd/HHRuaHhRfUdj8zRQaGorQUOvJz2JjY5GdnY2UlBRER0cDADZt2gSdToeYGNMLg0ZHR8PLywtJSUkYMGAAgOIZfGlpaYiNjdWXO3r0KB566CE8//zz+M9//iOq3gcPHkStWsZJ5Er4+PjAx0eZteAea1Pb6oXc00NtccaIETuCn+kDWmHQol22bygRub6yVozoiPo1xHevPtOxPj7586TDx/1sSDv0nrsdJ9JvGtzfok4AlprIQaZSqfBcbH23HOhZ0Rsw4wo+Rh78cPyDXpibdAoLt8r/GoltPSxRvgXojV5N8NkWaeopZevSDp24VAjj4hqjy73iE23OHtjGzhoZiwj2w8JnohHk54WBd78TQ11orVDBxOsxvse9CPP3Rfem9s0sLDvGq42ZhNNifD88Bmeu5uFKTj6+2Xm+0i0FJNvPvaZNm6JXr14YMWIEFi5cCI1GgzFjxmDQoEH6mX+XLl1C9+7d8e2336JDhw4ICAjA8OHDkZCQgODgYPj7+2Ps2LGIjY1Fx47FYyFSU1Px0EMPIT4+HgkJCfqxVh4eHvpgb86cOWjQoAGaN2+O/Px8fPXVV9i0aRP++usvuZ6uyzH1oTOnpInY1C+IR9vURtKJTIM8WWKsfinWeiEZxDQIRlrWbYP7zE0XNiegihcGd4jAij0XHKqLSqUSNS7tvsgg7D1/A093qIdTmTetlpdbv7Z1gaOmHzP3pRxsJgGteK7dBHpDKB7IW8XbA2/1jiq+6KyS95jWPsP/Bsagfrb52WPhAb4ud1rnah/HP4Jxq3z5mcYAMC7OthxbUk/n79WiuHfkq+fa43LOHTRzwuB1R/h6eeCFzg1ElQ2t7mPQUiWlzo1D0LlxcYA2Mb6J01PrKE3WNvRly5ZhzJgx6N69O9RqNQYMGIBPP/1U/7hGo8HJkydx+3bpRXD27Nn6sgUFBYiPj8dnn32mf/zHH3/E1atX8f333+P777/X31+/fn2cP38eAFBYWIgJEybg0qVL8PPzQ6tWrbBx40Z061Y6O66iE9tNl/JuHIItdDE82ro26gb56detE5PEs2ktf9xnR54sKVrXJz/SDA1Dq+F/G1x7ynZZ378Yg3PXbqFJWHXM+MvxFjJLHmgcgu2nrlksY66b46zOdLc9ALSrF4SEHvdiVuI/DtVPFAW+owUYdqXYe6Ho2DBYsu64tHJBVdnuv4+faIWocH+bflzJJaHwJRwUGqGN6jR+NjM43ZUvu3EOLNztSny91Hila3GqnekDWmLSmiMYLjIIE6v8YvWVLaACZE7+GRwcjOXLl+PmzZvIycnBkiVLUK1a6aKykZGREAQBXbt21d/n6+uLBQsWICsrC7du3cKaNWsMxlNNnToVgiAY3UoCKgB44403cPr0ady5cwfXr1/H5s2bK1VABYgPqmpU87E4VkClUiG6fhCq381U/V7fZibHG5Tlr+BCmdV9vfBy13sc3o8zx475eHogKtzfKWM2XnrQ9LkR83TLj9kpX19za1C6qpFdGopOwaGCAF8vdZm/7esSmze4HQbJNFOybH1KxmjqlI+psEvXDGeF2lij6wLBzCXHBapZ4aVOjUe9uwsx1w3yw3fDYyTL7QcUL0g99P5Iyfbnrrj2nwuQ41rq6JfUqw+ZTh5ar4Yfkic9hMEdzCdrtPfi6uyBoM7gKk9pyiPNcOC9HqLqk1GtqfwVspHB8jMSXYHf7tPU6bOQQqv7YPoAcZn/XWnZpv0uutzNC/c3MPi3srDnR5+nzIvKbxjXhRnVwaCqwhIzm2v5i6YnDACwOH6gfPDTMLQqercobU2094MldgZaWWWX65HyEmSpKl1tWKW+pMWgdd0AR6vkkGH3N0BQVW/UCjCdEqHsmDmNh/nWm86NxD93KQ2T6aKpUqlEzbASoMLzZfJFBfrJf/GQouvOns+UKVtELuVjj/cfbW73Z/fdvk3xx2sP4N2+0v4QuCe0mvVCMnGlYJpsx6CqgrL2VfrnuC7o1Mh8Rl9bPNU+Ap8/Ey3JvmxVdgkQZ3UhvNPH+hf4mrtrZg26LwI/v9IJK+4mnbQm0oZZitbUr2EcHDUIqYp5g9tixQjD+sx6qo3V/RXCEy3qKDNY18+7zIB/ia85rUXMdJo3uI1BksTo+kGy56myzvrxpev+k+e5npvWxyBYtZVarULTWv6ixnqK8fMrnfDBY80R37xijKOSwsd3W1ZHd3N8WEVlwKCqgnK0GVaJfFTlW8CUmkEIWG4lENOF1q5e0N2yKrStFwQ/b3HjzPq3q4vxNs56MiXQz8to0GiJR1rXNpoRGV4mQ7m5p5egeQVR4c5ZzsLVNKlb0+D9qVKp4GXjmn62kqLFwhXGKlkae1ZyTqsrOA6zrLb1gvBcbKSiQxFcYXJBWU/dF4FDU3piYnyU0lVxCwyqXEDbeuISeorx8ROtMKpLQ8Q0sDz7ztp3hvWPdWmJ8rsyaFVwgJgZhA80Lm1tK1uP6gokh5SCh1qF1+IcH/BtapkLRx0X6sPbQ56li2y5hCmR+FPwlrY7aPmLMfouxCEx0iwmbZKbZOv90MTqDZXBgqfb2bWd2M/AI61qw9dLjZ4OzmDkWCnxGFS5AGvZ1G3d16Q+TRX5pTX54WYY+1AjNLRzPII9NZ7UW55B1c92jLR5m5JApmygp5SQaj5Wz6e3mZYWS2+dejX88HxsfYzp1sjp06WnPtIMj7Sujfjm5lM7yOHnInNjrux//p0ahWDvO3H46eVOmPpoc5NlpGixaFFH2bF8ppi6QNcN8qvQS5eY07eVcUJqSy2UC5+JRlR4dcwzsdSMKUFVvXFkajy+eFaZ4RmVkXv+nK9g3D2XR/u7LUpiE89JqeyaVZ7q0iChbf0gbPvnqt2tGi1tHFj+2ZB2aFSzGo5M7YmqIrv65HZPaDWcyswz+7i5d521xo33H5O+VUFM+DD0/gYYKn7VDiPl17a0henWV8eCHi8PtcGCz7bSqq0nXH2rdxSC/LzxcOta6DVnu93HcoSXpxoos/JSjWreyLmjMSpny6oHlVWvFuH6pKRimRsG4Ki6QVXQu0U4vtx+Tpb9uyu2VCmgXb1Apatg9oLa5d5Q+Pt62rQ8hCMXBlGVsiCgSnFeqpe73oOAMjOyZj3VGqMebIhfx9h/Ff7p5U6iW536tCz+xVnd10uyQbOm3N/IcCzUI62NV6Iv8dHjLTCwffFAeSnUELEO3cdPiEsXUJ6cPyuej62PBxqH6IN/W3mqVaiqQJejtTFVx8Ies7qP6r5eeD2+icEak+7AFVp7yTyVCtj8etcKmQbHUQyqFPBMmVlEzvDfx1uKLvvNsPuQ8l4Pqy08cgzVqB9s3y/VN3tF4c1ehoMoQ6r5YFLvpnZ3RQLFweJ3w2PQqKbzp1f3b2t6AeiEHk0M/h5rJp8YUHwO/vdEK5vH7Jn6nhxSOEmfONASKbuypfL+Yy3w3fAYu1uEa1pJdqsUrYdhvU7qlD/3b2uGG9/pHsO6XEZULdefDFLNx1O2FjB3x7OiAGcH93WDTOcmMkWlUin2YXmqfV2j+74b3gHvmxlzohzDF1DqHDkAMEvk4rCmXlupl54AgDRBuszL1iyzkD9NEQoFBT5elj+H5cdcZUHe1qhUXaTVMpcEES1MDLLMWvNKJ0VzZIl29zVkQ5UxBlWVlKMfBlsXWBbD00MN73IB3QONQx3KYyMF41NleFV48YGGzqqKET9vT8lXgTfX7STnRLIcofT91DDU+L0l97jD34s6yLp/WzQIqYqvh94najFuUxyd6WXO90Vxdm2XiSCTOdPIWDsJZ4I7AxOVGnONEbUkKzmuhUPvj0T2HQ262jD2ShQRn9GvnmuPF7/dJ+1x3dgr3e7B3KRTsh5D7kSXc7QD9P8P9/fFw61qwdfLAwFVvHAl5w6aW8jwL4XxmtHo67HHpm3kOiPdmtREtyjrLYOmLmjb3+iG2oHiW6adQQtPzHiyNZ5cmFx8B6/Dbo+NjeYxqFJA2S/DuYPayH4800tVOPbN5uPpYTSOSQpiauXv5Jwp9YL9ys2ic62rgo+nBxqGVsXZq7dkPY6cX6Q5KO3yUKlUmG9n/h57FcILp3R10Fh9SfQ25zzqo57uouR1qVHN+oQAcyJELhAtl7OC6ZlpLV0wtQPZr+Sawu4/Y+z+U9hjbUwPSCbzGjt54Pi0/i0N1jZ0Flue55qXpZnhZ5GM/X9dm4SiblAVJPRQbuFeW59dLkwPKE4uauZQPcQuDixAwNai4tmWK7TdHDqmVC4IYTijK829dFUoDqbKdmXyOmxo4TPtUNXbA4ufb690VURjS5V5DKrIpYj55RNU1Rt/v9kN+9/rIX+FUDz769Nyyfbm3B1ILmdL4/IR4tYLBIBAv9LWDXe8aIVU88Hfbz6EVyUeHyancx6RJu+/5sCA8e+Gd0AVG1YkeEXzGkYWjsdU7fN2H1MMb0+16C7gUZrx+v9/qn3c6PFqvszOXVavFrVwZGo8ujd1v/UG3fG7Rm4Mqsil1AoQNx6kbpAfgkXkTZJLv7Z18M9HvWVtafSvYtg7v2HcA7Idqywpm/R7FHws3c5kZuvTXucVL0s9bHELVfCX7j4UQN7PwksPil9M97RQOou3AKUB1If9WqB3i3A83sZ8brXKSs7cdnIoabR+8m4KlY4N7csBVxExqFKAs/uhTQUqrtoX/uVz7XF/oxr4YaT4VhpnMHW6zC3zIhUfT8MWC2clcLS2bqQtTgl1MapwPAYXvmO2jEaQZz1BuQkqZevtzJlXfVsaL6diSsln4mPNQPxd1BxrizrrH3u2Y318/kw07lEg7xvJo0FIVRye2hPLX3St72slcaB6JdAkvDo+eaIVQqr5YNjSvQBct9m2Uc1qWOaCH1ClxhDcFxmEvedv2LSNvYOVd03qjvPXbyGmYQ0Tj6rwdh/78nH9qbvP4uMqFxmhYb4Wzqtf01riA2cp1gYUq0l4dVFHq1ndBw/eG4rPdj+Gz4qKM76Xb0Pr3CgEUx5phibhrp/kkkwr+97zZ3euAQZVCqhZ3fkZmp9sH4FCrc7px62InNnKZ0trxPIXY7D99DU8HVPPrmOFB/giPMD8e9N0sCUNpYP8zo1CgDTljn9ock/cKtQipJqPcpVwEpVKhWEiB+MTuRsGVQro2DAYE+ObKLL8CVVcnRqFoFMjrplmj4XPRuPof8y1nMgf8gX4eRmsXVmROLNFjeTVITIYe85n4fG2xqtfUDGOqVKASqXC6G6NEN/c+dP0yT6eahWiwqujblAV1HdiLqDyFyRPNxjQumpUrMn7RxaON3m/4AIdgNV8PPG6ZpSZR+2vnans8GTe6z3vRVVvD7z3sGNpKUgeXw1tj8+GtMOUR/j6mMOgqpLyVLvXS18y00+pJIIqlQrrX30AWyd2g6eCC4m2rBOAexS4UNuSoqqDmYHuJwT7uiWd5YIQhg80z0q2v4ahVbF0qOssf+MMM59s7VD3+JiHGuPw1HjcG8bxVq7I39cLfVrWsnsJpcqA3X+ViLenGs92rI9bBVpEBLvWUhbW/PhSLL7ZeR6jbJjaLTVXmPasVquQNKEr1uy/iCAnppS4hoqfEXtifBOodycB+Y7tRwXgi2ejndoS3ayWPwZ1iJD1GGLyVMU0rIHfDl926Dhyr/NIJCcGVZXMh/1aKF0FuzQMrYb3H3PPusuhfzvHxzSEVvfBxRt3RJWVMw9SBlxjEdnR3RoBVRoDG8SVN9ci4+ulRpyTu/bXvyZ9DjMvDxU0RQIeMrMO4SJtX4z0/N3o/vKtmlx0lyoTBlVEldTi5+/De2tTMV7BpWEAYFjhG2itaA0ss3VEldyLT5eoZWGmpiPWjr4fP+y9gNd73ovbhUVmF2j+r3aIyaCqPA5Up8qEQRWRC5PzV36T8OpY9ZLpQeVyyBWMB/gXCJ44JdRFG6fVwooqxuPBzL0CMi6FKMqAdnXxT0YeYiVOddEmIhBtIgIBAPIl0SCqmNxrtDJRJVORfuVnozpeLJxgcF/3wpkAXGiB1hYDjO6qG+Sa4w89PdR47+FmiGvmnDXjMgXX6KYlcmUMqojIaTbqog3+viiEKlQTMzyMG+/NdX+ZG1OlfIII+z3cyvxyNFt1rfT/v6BzsdeNyEUwqCIissDWDtjIGu6Tm+rbFwxTPswd1NZC6dIzIQDw8+a0eqLyGFQRkSKuC26Si6hOtPUyZQRUcZ/M6OUHu9uSzsAdEtESORuDKiJyqptCcXfaPl0ThWsi0n0vKl0DWYX72zeLUGwnp9ID+omciUEVETlV38L/Yq62P97QjFS6KuJ42Njy5MwVtyWQ0FPZlBpEFYmsQVVWVhaGDBkCf39/BAYGYvjw4cjLy7O4TX5+PkaPHo0aNWqgWrVqGDBgADIyMgzKqFQqo9vKlSsNymzZsgXt2rWDj48PGjVqhKVLl0r99IjIDmlCGGZrn0AOKuaC4jWcmOneUSoV0Kqu7dnyMxAECEC2YH38mJvFmEQOkTWoGjJkCI4ePYrExESsW7cO27Ztw8iRln+djh8/Hr/99htWr16NrVu34vLly+jfv79Rua+//hpXrlzR3/r166d/7Ny5c+jbty+6deuGgwcPYty4cXjxxRfx559/Sv0UichOrpqqwFGuOtTo62H3Ibq+cVqEqHB/fD30PvwhIiv704VvY0tRa4zXvAIBzkt0SuQuZEv+efz4cWzYsAF79+5F+/btAQDz5s1Dnz59MGPGDNSuXdtom5ycHCxevBjLly/HQw89BKA4eGratCl27dqFjh076ssGBgYiPNz0UhALFy5EgwYNMHNmcQ6cpk2b4u+//8bs2bMRHx8v9VMlqvCGxLj2YshK6N2iFrBD6VqI161JTXRrUhORbxlnQe9mZima8nbqWmCnrni5qGpwofxiRC5Ctpaq5ORkBAYG6gMqAIiLi4Narcbu3btNbpOSkgKNRoO4uDj9fVFRUahXrx6Sk5MNyo4ePRohISHo0KEDlixZAqHMaMjk5GSDfQBAfHy80T7KKigoQG5ursGNiIAcwQ+juii3kLWrur9RiNJVUJyYlioOVKfKRLagKj09HTVrGv768fT0RHBwMNLT081u4+3tjcDAQIP7w8LCDLb54IMPsGrVKiQmJmLAgAF45ZVXMG/ePIP9hIWFGe0jNzcXd+6YXkB22rRpCAgI0N8iIuRd8Z1IjCfbF78PW9sx7kUq5wTzCSErs5Z1lHtNXAW7/4gM2dz999Zbb+F///ufxTLHjx+3u0JivPfee/r/t23bFrdu3cInn3yCV1991e59Tpo0CQkJCfq/c3NzGViR4p6Mrouo8OpoXFPJnE5CpRpsfFpXG43Ul62Wq1LJk18KgmAyM2pc0zAs253m/AoRuQCbg6oJEyZg6NChFss0bNgQ4eHhyMzMNLhfq9UiKyvL7Fio8PBwFBYWIjs726C1KiMjw+w2ABATE4MPP/wQBQUF8PHxQXh4uNGMwYyMDPj7+6NKFdODY318fODj42PxeRE5m0qlQqu6gUpXo1J14cypOwvzLw9SuhouT4DpMVVdm4Tix5di8cRC88MtiCoqm4Oq0NBQhIZaX/cpNjYW2dnZSElJQXR0cUbiTZs2QafTISYmxuQ20dHR8PLyQlJSEgYMKF7Y9OTJk0hLS0NsbKzZYx08eBBBQUH6oCg2Nhbr1683KJOYmGhxH0RkjqrCztQz5X/PdAU+VroW7sK4qUqlUqF9ZLACdSFSnmyz/5o2bYpevXphxIgRWLhwITQaDcaMGYNBgwbpZ/5dunQJ3bt3x7fffosOHTogICAAw4cPR0JCAoKDg+Hv74+xY8ciNjZWP/Pvt99+Q0ZGBjp27AhfX18kJibiv//9L15//XX9sV966SXMnz8fb7zxBl544QVs2rQJq1atwu+/G896ISLr1K6aJ0AGbLEWRxBgEFPtu28G2pstTVQ5yJqnatmyZYiKikL37t3Rp08fdO7cGYsWLdI/rtFocPLkSdy+fVt/3+zZs/Hwww9jwIAB6NKlC8LDw7FmzRr9415eXliwYAFiY2PRpk0bfPHFF5g1axamTJmiL9OgQQP8/vvvSExMROvWrTFz5kx89dVXTKdARKIc1jVQugqyWT7CdE+Bo8LqRRn8/Wjr4h/Pox5sKMvxiFyRbC1VABAcHIzly5ebfTwyMtIgFQIA+Pr6YsGCBViwYIHJbXr16oVevXpZPXbXrl1x4MAB2ypMRFTBdWxQQ/9/R8fKlZ39V/67fMaTrfFC5wacJUmViqxBFRERuRYp5xxY2pe3pxptIgIlPBqR6+OCykRkkZS5iOYMbKP/f2h11xy7pFKpHHvO0UMlq4vcHEmVIUBgniqichhUEZFFKgnbNvzK5HaaO7Atwv19AQC9W5hPmeJ2GnZVugZOUZnSbBCJxe4/IpLcwPYROHDhBv7JyDNbpl4NP/yV0AXnr91yuXE3UgaSFRlbqogMMagiIsn974lWEAQBDSatt1jO39fLJZKblsUwQZzyyT/LD1QnqozY/UdEslCZGLDjDpddtVoFLw9+NZbX6Z4a1gsRVXL85iAiiypjF08jRddadE1PRNdVugpELo9BFRFZ5FcJFw728qh8gaQ1pmYKVsaAm8gSBlVERGSzx1rXZlBFVA4HqhOR01Tz4VdORTB3UBv0bBaO66ml97nDeDkiufEbjoic5p7Qanj1oUYIcdHEn3re1ZSugUt7rE0dpatA5JLY/UdETpXQswmei41UuhqWPTIXCGsJDFhs02YfawbKVCHlmcqYsE93r/MrQuTCGFQREZVX4x7g5b+Blk/YtNl6XQeZKiQPR1NLTdYMK7MvdgASsfuPiJwm0M9L6SpUemoV8EDjEOTc0eCeUMe6Ob2rBQPau38wpiJiUEVElkkxv6tWgC9+erkTfL0qdnoGd5gLp1Kp8O0LHfT/d2xfUtSIqOJgUEVEFkkxbT7M3xe1A6tIUBvXdl4IU7oKojgaTBGRaRxTRUQkEaFSf6Wy/4+oMn8DEBFJpkhg6w9RZcegiogsuuYRqnQVyAXENQtDVW8PdLm39P1QNowU2FJFxDFVRGTZV9Vfwv1KV8INVPR2Kn9fLxyc0hOe6tJnyqFZRIbYUkVEFuWog+ze9v5GNQAAz3SsL1V1SEFeHmqzg9x1XsxCT8SWKiKSzddDO+DctVu4N4wX3IrqXc0whKqy0T2oidJVIVIcgyoiMnLeNwqR+Scc3o+3pxpNwqtLUCNyRSqo8H1RDwBAd4XrQuQK2P1HREaue4YrXQUiIrfDoIqIjKjKzORiokgiInEYVBERSUCtqnwpBd7p21TpKhC5FAZVRERkl0da11a6CkQuhUEVEZGdXi58Tf9/HTOqE1V6DKqIyKIwfx+lq+CydBU+5ad4QuXr/SQywqCKiIwIZYKFqY82V7Am5C64TA0RgyoisqJmdV+lq+AWKuNAdSIyxKCKiEgif47ronQViEhBsgZVWVlZGDJkCPz9/REYGIjhw4cjLy/P4jb5+fkYPXo0atSogWrVqmHAgAHIyMjQP7506VKoVCqTt8zMTADAli1bTD6enp4u59MlqjBU7MqxC7PHE1VusgZVQ4YMwdGjR5GYmIh169Zh27ZtGDlypMVtxo8fj99++w2rV6/G1q1bcfnyZfTv31//+MCBA3HlyhWDW3x8PB588EHUrFnTYF8nT540KFf+cSIix3CgegkOVCeSce2/48ePY8OGDdi7dy/at28PAJg3bx769OmDGTNmoHZt4/wmOTk5WLx4MZYvX46HHnoIAPD111+jadOm2LVrFzp27IgqVaqgSpUq+m2uXr2KTZs2YfHixUb7q1mzJgIDA+V5gkRERERlyNZSlZycjMDAQH1ABQBxcXFQq9XYvXu3yW1SUlKg0WgQFxenvy8qKgr16tVDcnKyyW2+/fZb+Pn54YknnjB6rE2bNqhVqxZ69OiBHTt2WKxvQUEBcnNzDW5ERJbEt+AaiURUSragKj093ai7zdPTE8HBwWbHNqWnp8Pb29uodSksLMzsNosXL8bTTz9t0HpVq1YtLFy4ED/99BN++uknREREoGvXrti/f7/Z+k6bNg0BAQH6W0REhMhnSkSVVWRw6ffOKu2DCtaEiFyBzUHVW2+9ZXageMntxIkTctTVSHJyMo4fP47hw4cb3N+kSROMGjUK0dHR6NSpE5YsWYJOnTph9uzZZvc1adIk5OTk6G8XLlyQu/pEVIFM1g5VugqK4pAqIjvGVE2YMAFDhw61WKZhw4YIDw/Xz8YrodVqkZWVhfBw003m4eHhKCwsRHZ2tkFrVUZGhsltvvrqK7Rp0wbR0dFW692hQwf8/fffZh/38fGBjw8zRxOReGUDiXxU7u8PgSPViWwPqkJDQxEaGmq1XGxsLLKzs5GSkqIPejZt2gSdToeYmBiT20RHR8PLywtJSUkYMGAAgOIZfGlpaYiNjTUom5eXh1WrVmHatGmi6n3w4EHUqlVLVFkiIiIiW8k2+69p06bo1asXRowYgYULF0Kj0WDMmDEYNGiQfubfpUuX0L17d3z77bfo0KEDAgICMHz4cCQkJCA4OBj+/v4YO3YsYmNj0bFjR4P9//DDD9BqtXjmmWeMjj1nzhw0aNAAzZs3R35+Pr766its2rQJf/31l1xPl4gqpdKUCn1actA6UWUnW1AFAMuWLcOYMWPQvXt3qNVqDBgwAJ9++qn+cY1Gg5MnT+L27dv6+2bPnq0vW1BQgPj4eHz22WdG+168eDH69+9vMmVCYWEhJkyYgEuXLsHPzw+tWrXCxo0b0a1bN1meJxHR/wa0UroKRKQwWYOq4OBgLF++3OzjkZGRRv3wvr6+WLBgARYsWGBx3zt37jT72BtvvIE33njDtsoSETmguq+X0lVQFEdUEXHtPyIiIiJJMKgiIrITF6khorIYVBERERFJgEEVERERkQQYVBER2Skr9D4AQJrOeu6+iq5uYBXrhYgqOFln/xERVWQa70A0z1+MAnjhtNKVUcjGhC64ma9FTX9fpatCpDgGVUREDriFyt1C06hmdaWrQOQy2P1HREREJAEGVUREdqpXw0/pKhCRC2H3HxGZwPzYYjSvHYD5T7dFrYDK3QVIRMUYVBEROeDhVrWVrgIRuQh2/xERERFJgEEVERERkQQYVBGRkUIVcw4REdmKQRURGfk1ZASO6+rhHc0LSleFiMhtcKA6ERnJ8QxB78LpAID/KFwXIiJ3wZYqIiIiIgkwqCIiIiKSAIMqIjIiMPknEZHNGFQRERERSYBBFREREZEEGFQRERERSYBBFREREZEEGFQRERERSYBBFREREZEEGFQRERERSYBBFREZia4frHQViIjcDtf+IyIjz8XWRxUvD3RsyOCKiEgsBlVEZMTLQ42nY+opXQ0iIrfC7j8iIiIiCTCoIiIiIpIAgyoiIiIiCcgWVGVlZWHIkCHw9/dHYGAghg8fjry8PIvbLFq0CF27doW/vz9UKhWys7Pt2u/hw4fxwAMPwNfXFxEREfj444+lfGpERERERmQLqoYMGYKjR48iMTER69atw7Zt2zBy5EiL29y+fRu9evXC22+/bfd+c3Nz0bNnT9SvXx8pKSn45JNPMHXqVCxatEiy50ZERERUnkoQBEHqnR4/fhzNmjXD3r170b59ewDAhg0b0KdPH1y8eBG1a9e2uP2WLVvQrVs33LhxA4GBgTbt9/PPP8c777yD9PR0eHt7AwDeeustrF27FidOnBD9HHJzcxEQEICcnBz4+/vbeAaIiIhICUpev2VpqUpOTkZgYKA+8AGAuLg4qNVq7N69W9b9Jicno0uXLvqACgDi4+Nx8uRJ3Lhxw+y+CwoKkJuba3AjIiIiEkuWoCo9PR01a9Y0uM/T0xPBwcFIT0+Xdb/p6ekICwszKFPyt6VjT5s2DQEBAfpbRESE3fUkIiKiysemoOqtt96CSqWyeLOli82VTJo0CTk5OfrbhQsXlK4SERERuRGbMqpPmDABQ4cOtVimYcOGCA8PR2ZmpsH9Wq0WWVlZCA8Pt7mSJcTsNzw8HBkZGQZlSv62dGwfHx/4+PjYXTciIiKq3GwKqkJDQxEaGmq1XGxsLLKzs5GSkoLo6GgAwKZNm6DT6RATE2NfTUXuNzY2Fu+88w40Gg28vLwAAImJiWjSpAmCgoLsPjYRERGRJbKMqWratCl69eqFESNGYM+ePdixYwfGjBmDQYMG6Wf+Xbp0CVFRUdizZ49+u/T0dBw8eBCnT58GABw5cgQHDx5EVlaW6P0+/fTT8Pb2xvDhw3H06FH88MMPmDt3LhISEuR4qkREREQAZMxTtWzZMkRFRaF79+7o06cPOnfubJArSqPR4OTJk7h9+7b+voULF6Jt27YYMWIEAKBLly5o27Ytfv31V9H7DQgIwF9//YVz584hOjoaEyZMwOTJk63myCIiIiJyhCx5qiqCnJwcBAYG4sKFC8xTRURE5CZyc3MRERGB7OxsBAQEOPXYNo2pqkxu3rwJAEytQERE5IZu3rzp9KCKLVVm6HQ6XL58GdWrV4dKpZJsvyURNFvAnIvnXRk8787Hc64MnnfnM3fOBUHAzZs3Ubt2bajVso1yMoktVWao1WrUrVtXtv37+/vzg6cAnndl8Lw7H8+5Mnjenc/UOXd2C1UJ54ZwRERERBUUgyoiIiIiCTCocjIfHx9MmTKF2dudjOddGTzvzsdzrgyed+dzxXPOgepEREREEmBLFREREZEEGFQRERERSYBBFREREZEEGFQRERERSYBBlZMtWLAAkZGR8PX1RUxMDPbs2aN0lVzS1KlToVKpDG5RUVH6x/Pz8zF69GjUqFED1apVw4ABA5CRkWGwj7S0NPTt2xd+fn6oWbMmJk6cCK1Wa1Bmy5YtaNeuHXx8fNCoUSMsXbrUqC4V+TXbtm0bHnnkEdSuXRsqlQpr1641eFwQBEyePBm1atVClSpVEBcXh1OnThmUycrKwpAhQ+Dv74/AwEAMHz4ceXl5BmUOHz6MBx54AL6+voiIiMDHH39sVJfVq1cjKioKvr6+aNmyJdavX29zXdyBtXM+dOhQo/d+r169DMrwnNtu2rRpuO+++1C9enXUrFkT/fr1w8mTJw3KuNL3ipi6uDox57xr165G7/eXXnrJoIxbnXOBnGblypWCt7e3sGTJEuHo0aPCiBEjhMDAQCEjI0PpqrmcKVOmCM2bNxeuXLmiv129elX/+EsvvSREREQISUlJwr59+4SOHTsKnTp10j+u1WqFFi1aCHFxccKBAweE9evXCyEhIcKkSZP0Zc6ePSv4+fkJCQkJwrFjx4R58+YJHh4ewoYNG/RlKvprtn79euGdd94R1qxZIwAQfv75Z4PHp0+fLgQEBAhr164VDh06JDz66KNCgwYNhDt37ujL9OrVS2jdurWwa9cuYfv27UKjRo2EwYMH6x/PyckRwsLChCFDhgipqanCihUrhCpVqghffPGFvsyOHTsEDw8P4eOPPxaOHTsmvPvuu4KXl5dw5MgRm+riDqyd8+eff17o1auXwXs/KyvLoAzPue3i4+OFr7/+WkhNTRUOHjwo9OnTR6hXr56Ql5enL+NK3yvW6uIOxJzzBx98UBgxYoTB+z0nJ0f/uLudcwZVTtShQwdh9OjR+r+LioqE2rVrC9OmTVOwVq5pypQpQuvWrU0+lp2dLXh5eQmrV6/W33f8+HEBgJCcnCwIQvGFS61WC+np6foyn3/+ueDv7y8UFBQIgiAIb7zxhtC8eXODfQ8cOFCIj4/X/12ZXrPyF3idTieEh4cLn3zyif6+7OxswcfHR1ixYoUgCIJw7NgxAYCwd+9efZk//vhDUKlUwqVLlwRBEITPPvtMCAoK0p93QRCEN998U2jSpIn+76eeekro27evQX1iYmKEUaNGia6LOzIXVD322GNmt+E5l0ZmZqYAQNi6dasgCK71vSKmLu6o/DkXhOKg6rXXXjO7jbudc3b/OUlhYSFSUlIQFxenv0+tViMuLg7JyckK1sx1nTp1CrVr10bDhg0xZMgQpKWlAQBSUlKg0WgMzmVUVBTq1aunP5fJyclo2bIlwsLC9GXi4+ORm5uLo0eP6suU3UdJmZJ9VPbX7Ny5c0hPTzd4/gEBAYiJiTE4z4GBgWjfvr2+TFxcHNRqNXbv3q0v06VLF3h7e+vLxMfH4+TJk7hx44a+jKXXQkxdKpItW7agZs2aaNKkCV5++WVcv35d/xjPuTRycnIAAMHBwQBc63tFTF3cUflzXmLZsmUICQlBixYtMGnSJNy+fVv/mLudcy6o7CTXrl1DUVGRwRsDAMLCwnDixAmFauW6YmJisHTpUjRp0gRXrlzB+++/jwceeACpqalIT0+Ht7c3AgMDDbYJCwtDeno6ACA9Pd3kuS55zFKZ3Nxc3LlzBzdu3KjUr1nJeTL1/Muew5o1axo87unpieDgYIMyDRo0MNpHyWNBQUFmX4uy+7BWl4qiV69e6N+/Pxo0aIAzZ87g7bffRu/evZGcnAwPDw+ecwnodDqMGzcO999/P1q0aAEALvW9IqYu7sbUOQeAp59+GvXr10ft2rVx+PBhvPnmmzh58iTWrFkDwP3OOYMqckm9e/fW/79Vq1aIiYlB/fr1sWrVKlSpUkXBmhHJa9CgQfr/t2zZEq1atcI999yDLVu2oHv37grWrOIYPXo0UlNT8ffffytdlUrD3DkfOXKk/v8tW7ZErVq10L17d5w5cwb33HOPs6vpMHb/OUlISAg8PDyMZhJkZGQgPDxcoVq5j8DAQNx77704ffo0wsPDUVhYiOzsbIMyZc9leHi4yXNd8pilMv7+/qhSpUqlf81KnqOl5x8eHo7MzEyDx7VaLbKysiR5Lco+bq0uFVXDhg0REhKC06dPA+A5d9SYMWOwbt06bN68GXXr1tXf70rfK2Lq4k7MnXNTYmJiAMDg/e5O55xBlZN4e3sjOjoaSUlJ+vt0Oh2SkpIQGxurYM3cQ15eHs6cOYNatWohOjoaXl5eBufy5MmTSEtL05/L2NhYHDlyxODik5iYCH9/fzRr1kxfpuw+SsqU7KOyv2YNGjRAeHi4wfPPzc3F7t27Dc5zdnY2UlJS9GU2bdoEnU6n/3KMjY3Ftm3boNFo9GUSExPRpEkTBAUF6ctYei3E1KWiunjxIq5fv45atWoB4Dm3lyAIGDNmDH7++Wds2rTJqHvUlb5XxNTFHVg756YcPHgQAAze7251zkUPaSeHrVy5UvDx8RGWLl0qHDt2TBg5cqQQGBhoMKuBik2YMEHYsmWLcO7cOWHHjh1CXFycEBISImRmZgqCUDz1tV69esKmTZuEffv2CbGxsUJsbKx++5JpuD179hQOHjwobNiwQQgNDTU5DXfixInC8ePHhQULFpichluRX7ObN28KBw4cEA4cOCAAEGbNmiUcOHBA+PfffwVBKJ5SHxgYKPzyyy/C4cOHhccee8xkSoW2bdsKu3fvFv7++2+hcePGBtP7s7OzhbCwMOHZZ58VUlNThZUrVwp+fn5G0/s9PT2FGTNmCMePHxemTJlicnq/tbq4A0vn/ObNm8Lrr78uJCcnC+fOnRM2btwotGvXTmjcuLGQn5+v3wfPue1efvllISAgQNiyZYvB9P3bt2/ry7jS94q1urgDa+f89OnTwgcffCDs27dPOHfunPDLL78IDRs2FLp06aLfh7udcwZVTjZv3jyhXr16gre3t9ChQwdh165dSlfJJQ0cOFCoVauW4O3tLdSpU0cYOHCgcPr0af3jd+7cEV555RUhKChI8PPzEx5//HHhypUrBvs4f/680Lt3b6FKlSpCSEiIMGHCBEGj0RiU2bx5s9CmTRvB29tbaNiwofD1118b1aUiv2abN28WABjdnn/+eUEQiqfVv/fee0JYWJjg4+MjdO/eXTh58qTBPq5fvy4MHjxYqFatmuDv7y8MGzZMuHnzpkGZQ4cOCZ07dxZ8fHyEOnXqCNOnTzeqy6pVq4R7771X8Pb2Fpo3by78/vvvBo+LqYs7sHTOb9++LfTs2VMIDQ0VvLy8hPr16wsjRowwCuJ5zm1n6pwDMPjMu9L3ipi6uDpr5zwtLU3o0qWLEBwcLPj4+AiNGjUSJk6caJCnShDc65yr7j5xIiIiInIAx1QRERERSYBBFREREZEEGFQRERERSYBBFREREZEEGFQRERERSYBBFREREZEEGFQRERERSYBBFREREZEEGFQRERERSYBBFREREZEEGFQRERERSYBBFREREZEE/g9Cvk0Tv/90VgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(mix)\n",
    "plt.plot(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf_listener = OmegaConf.load(os.path.join(root_wav, \"metadata/listeners.json\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['L0001', 'L0002', 'L0003', 'L0004', 'L0005', 'L0008', 'L0009', 'L0010', 'L0011', 'L0012', 'L0013', 'L0014', 'L0015', 'L0017', 'L0019', 'L0021', 'L0022', 'L0023', 'L0024', 'L0026', 'L0027', 'L0028', 'L0029', 'L0031', 'L0032', 'L0034', 'L0035', 'L0036', 'L0038', 'L0039', 'L0040', 'L0041', 'L0042', 'L0044', 'L0045', 'L0047', 'L0048', 'L0049', 'L0051', 'L0053', 'L0054', 'L0055', 'L0056', 'L0057', 'L0058', 'L0059', 'L0061', 'L0062', 'L0063', 'L0064', 'L0065', 'L0066', 'L0067', 'L0068', 'L0069', 'L0070', 'L0071', 'L0072', 'L0073', 'L0074', 'L0076', 'L0077', 'L0078', 'L0080', 'L0081', 'L0082', 'L0083', 'L0084', 'L0085', 'L0086', 'L0088', 'L0089', 'L0090', 'L0091', 'L0092', 'L0093', 'L0094', 'L0095', 'L0096', 'L0097', 'L0098', 'L0099', 'L0100']\n"
     ]
    }
   ],
   "source": [
    "print(list(conf_listener.keys()))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torch_171_daniel",
   "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": "3.8.16"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "7c9cb1bcb746fd43c70a7667c29c966ffe14df3c625afb960b30bce79fd091af"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}