vui_notebook_jp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Voice recognition for Japanese \n",
    "\n",
    "---\n",
    "\n",
    "This notebook is based on Udacity AI nano degree's Voice User Interface.  \n",
    "I tweaked a few part of utility scrits for Japanese, record 1670 sentences ( 171+ mins )   \n",
    "And tried to train model.\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aind2/anaconda3/envs/aind-vui/lib/python3.5/site-packages/matplotlib/font_manager.py:279: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  'Matplotlib is building the font cache using fc-list. '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1230 total training examples.\n"
     ]
    }
   ],
   "source": [
    "from data_generator import vis_train_features\n",
    "\n",
    "# extract label and audio features for a single training example\n",
    "vis_text, vis_raw_audio, vis_mfcc_feature, vis_spectrogram_feature, vis_audio_path = vis_train_features()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code cell visualizes the audio waveform for your chosen example, along with the corresponding transcript.  You also have the option to play the audio in the notebook!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2df4506f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Shape of Audio Signal** : (54785,)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Transcript** : ぷろろーぐ"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio controls=\"controls\" >\n",
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/V/8j/xv/S/83/zP/o//z//v8LACcANAA1AC8ALQAcABAAHwARAA0ADQD8//r//f8DAAAA9P/6/+X/3//0//X/4//U/8v/0f/R/9j/yf/X/+b/8//5/wAAEgAIAP7/DwALAPv/CAAEAAkADwAgADEANQA5ACcAGwAgAAoACQAIAAcAEgAMAAMABQAKAP//9v/q//T//P/3//b/9f8KAPz/CwANAAcA/f/2/+z/8/////v/CQAMAAsALQAmACsANAAwACwADgAAAOL/6v/l/+D/2v/e/9b/6P/h/+D/+f/h/9T/2P/N/9n/4P/2/+v/7v/z//P/EgAQABcAFgAXAAkADAD+/wUABwDz////9v8QABIA9/8NABYABwD9/wkADgAmACsANQAzAB4AGgAaAAcA8v/m//j/FQAXACwAQgBGAC8ANAATAAsA+P/w//v/6P/f/8z/1P/U/97/6f/y/+n/8f/v//L/5f/p/+j/4//d/9z/8v/l//D/+/8JABAABgD8/+D/3P/f/83/w//A/9r/1//N/8L/xP/L/9P/5P/0/+////8DABUAFwAeACsAMwAvAC0AOwAuACsAHAATABQAJAAlACgAIAAQABIA8//2//z/7f/y//r/AwD1/woADAAGABsAMQAqABMAJgATACQAEQAMABYA+/8HAA4ACgAbABYAFwAQAAwADAAMAB0AHQAVAPv/+P/o/+b/+f/8//b///8JAPv/AgAKAPz/6f/o/+X/3//p//n/9v/y/+r/9/8DAPr/AQD+//T/AAALAAQA/f/p/+//8//a/97/0f/g//z/6//6//j/7v/n/97/0v/L/9T/z//T/9P/5f/x/+T/5P/+/xYABwAEAAYA+f/o//3/AADz//T/9P/5//j/9f/3/w8AFQAIABsALQA8ADoAPwBWAEoAQwBSAGcAYABVAFAAVABXAFkATQBXAFsARwBDADMAOAAsACgAGgAfACoAIwALAAkACgDv/+f/0v+8/7H/rf+z/7//vP+y/6z/mf+j/4v/h/+b/6r/rf+7/8//yf/P/9L/wP+d/5D/e/+H/5z/p/+w/9D/3P/v//L/8//6//r/DwAlACsASQA9ACYALgAeADEAQAA+AEoAOwBJAFEAUQA/AEMAWwBKAD0AMgAyACwAKwAsACQADwAGACcAEwAUACMAEgAFAAEABgAFAPD/3//d/+T/6f8EACIAFwAOAAwA+P/r/+v/2f+7/7T/uv++/8T/1f/Z/9v/3f/c/9D/0//e/9b/zf/R/+T/4f/Q/8b/7f/y//H/FQATABwAOgA0AC4ANQBHAEEAJAAVAAoABQADAAsAEQAYABwABgAWAA8ADwARAA8AKAAcAAkA6f8IAP3//v+C/1b+3f6OAC8BYQBIAAsATv8RAAgBlwCQ/wgArwCPAL8AYwCg/3v/ZQDAABsA3/+i/3X/7/91ABkAgP+r/83/yP/p/9j/cv93/5sA7QBcAJX/Zv/A/2AA9ACPAKz/bv/d/1wAfgBUAK3/Sf/e/44AigAgAN//sf/t/5QAkADc/z//ff8nAHQAOwDM/2P/hP8HAEwA5v97/2z/mP/m//X/s/+C/4D/u//+/wYA4v/N//7/UQBgACcA7v++/wQASwBCAAsA4//s/x4AYgBfAA0Aw//6/0YAWAAzAPD/z//e/yQAKADy/9b/6/9AAEoATAD6/83/AgBQAHEAJQDX/7f/8P8+AEkAIwD2////GgA5AB4A1v+2/8z/6//q/+//3v+t/77/+P8UAP7/4P+y/6//2v/4/wcA2//X/9f/+P8BAAMA5//Z//j/FgAfAPf/FgA5ADgAJwAkACgAJgAfABsALQAmAAcA5P/P/9v//f8OAPX/6/8CAAMA5//z//z/AwDz/wUAAwAIACIAAQDm/+j/DAAEAND/vv/J/97/DAAiAPX/1f/l/wIA/v/1/+b/yP+4/87/3v/2/w8A///f/9f/6f/L/8L/w/+8/7f/zv/q/93//f8tAEkAUQAgAPX/+f8WACcADwAOAB0AOwA9AGAAXQA2AEsAUwA9ACkAFwAHAAUACAAVABoAMAAoABkAHgAtADwAOABaAF0ATQBhAEQAIQAMAB8AGAD+//P/2P/g//P/AQD4//T/5//3//n/BAAMAPj/+f/2//j/+f/V/8v/1v/S/+P/7f/k/+L/+/8WAAYA6f8AAPn/3f/G/7j/uf+3/9z/4//S/9H/xv/O/+D/4v/u/9j/w/+4/6b/vv/P/+j/4//7/xMAGAAiACEAIQAsACsAMgAdAPf/+f/e/9z/5f/6/xsACwACABAAJgAdAAgACQAJAPT/7P/x/+3/3//h/+j/BQAQABUAJQAnADcALQAqAB0A///t/9v/7/8CAP7/8P8FABcAJwA9ADsARQBQAFEAIAD//yEA3v9p/2j/xP9uAB0BKQGHAM7/df94//L/fgC7AKwATADR/7T/ov+p/+7/SwCHAF0A9P9n/0j/dP8lAJgAcQAnAML/f/9l/5P/BABUAJEAagDw/13/FP9j//j/bgCEABsAkv8+/1j/qv8FAEsAOwAJAPH/2//M/8H/4P8rAF4ATQADAK7/fP+V/+z/JQAcAAAAxP+c/67/0P/l/9T/3f/t//D/+v/3/+T///8jADoAQQBIAB4ALwA1ACwAMAAdACwAHgAGAPD/4v/g//P/EAAsAEkAMwAJABsALQApAC0APgBDAEUARgAzADgAMwAvADQAIwAcACUAIQAYAAwAFgD+/+//5f/7/w4AFAAaAA0AAgDp/+v/7v/+/xcABwDk/+f/2//f//b/BwAZABQA9P/S/8H/s//L/+7/5P/T/8//wv+//77/zP/v/9//wv+4/7//vP/w/wgABQD//wEA7P/h/+L/5f/f/+L/yf+v/7n/wv/Z//z/GgAiAAEA8f/9/woA/v/4//X/8f8GAPv/7P/6/ycARgBLAF0AQwA+AEoAMgA3ADIAIQAlACQAJwAPAOb/4/+5/8D/+/8gAC0AGgAFAB0ANAAoABwAIAAiAEEAGAAdACAAJAA9ADUAUgA4AC4ARwBXAEwALAALANv/1P/c//X/EgDx//H/CgANABgAGQD7/+X/2f/Q/6//kP+Y/5z/tf/a//L/6v/h//L/AgD9//r/+P/w/8//4v/Y/7z/yP/P/+b/EAAQAAsAAAD///z/5P/L/8n/1v/b/+//+v/z/+P/0f/d/+D/7//p/+P/6v/x/w4ALQA1ACsAGwAiAB4AGAAQACUALAArACIAGAArAFYAagBhAEUAKAAXAB4AQgBFAEwARQAyABwAKwAqAAkA+f/h//L/3f/p/83/uf+6/7z/7//w/9z/0v/R/83/xf/M/9H/0v/P/9z/8f/z/+X/3v/T/8H/t/+u/5n/ov+s/8f/5P/r/wAAHQAfABoAJQAfAPz/2//4/wIAAADx/wUAIQApACUAKAAxACgANQA9AD8AMgBJAFMARABRAEUAFAAQABoABwAVADMAGwABAOj/8v8JABsAHAABAP7/7P/i/+//9f8XAB8AMAA4ADEANQA6AB0AHQAnACgALgAdACAAIgAUAB4ACgAEAOv/4f/t/9//4f/O/8b/lP+U/6z/wv/I/+b/7f/p/8f/sv/K/8X/wP+q/7v/tv/F/67/tf/O/9H/4//S/+H/5P/6//D/DAAmAD0APQAqADgALwApAAkAEwD3/+j/5P/f/+j/7//o//j/AwDp/+P/1//T/9D/4P/6/+v/5P8jADwAOAAkAD0AUwBPAFAATAA7ACEALQA4ACgAQwBFAFIAUwBiAHUAewBeAEQAZQBZAFUAUABGADYADwAEAAYADAABAPj/2v/k//P/8f/y/wQACgDq/+D/2f/L/6D/qP/M/93/4P/6//n/1/+7/6b/s/+h/6H/sP+o/5//nP+2/6z/q/+y/8P/xv/C/7//wf+q/6L/q/+7/9f/6f/y//v/8v8HAP//6f/6//7/FgAbABwA/v8MAEAAXgBsAHYAfgBUAEQAPwBaAIsAowB4AHYAbgBPAF0ARQBwAHMAZgBEACEAAQDy/xkAKQA0ADUAOgA6ADgAFADz/+b/5P/k/7r/xf/C/83/v/+3/73/zf+6/7j/s//Q/8j/wf/W/9z/0P+//6b/tv/H/7r/tf+w/6v/vf/T/97/1P/Q/67/hv+Y/7b/yf/r//z/9f/5/+X/IAAuABMAGAAkADcAIAAEAOP/9P8SADIARgBBAEkAKgAcACAALABIAE4AWQB3AGgAVwBmAF4AVgA3ADIARgAfACAAQQA2AB4AKQA9ADkAMgATABQAAwD+/wkAGAARAPb/5f/c//H/AAD6//P/DQABAOn/7//h/8v/vv+e/5r/n/+V/4n/hf+R/3n/gv+a/6f/t//R/9f/xv/M/+P/6f/o/9v/+/8OAA0AEgAEAAEA9v/+/wcAAAAJABsAGAAIAOv/8v8QABUAHAAqAEAATwBOAF4AaABpAHIAYQBAAEsAXABXAGsAfABwAE8ARwBTAFMAOAA9ADsAKwA0AEkARAA2ABcAAwDq/83/p/+o/6r/lf+f/8r/5v/E/9P/1P+7/6//qf+Z/5f/kf+X/5j/qv/S/9//1/+6/7//n/+Y/6D/m/+U/4T/hv9u/3n/gf+y/9L/1//v//f//f/p//H/2f/k/97/zP/k/w8AMwBEADUAPwA/AFgAVABBAEgAPQArAE8AZAB6AKAAmwCOAIsAqQCfAI8AgwB+AJUAjQB2AG0AYABNAGQAYgA0AD8ASQA3ACUAIAAfAAoA/f/f/9X/x/++/9j/uv+6/7//v//j/+X/wP+b/5H/g/90/4X/wP/Y/8//4v/c/8X/uf+e/6T/u//Q/+7/wP/G/8//1f/Y/+7/5f/Q/6//rP+7/8n/9f8WAPv/6v/o/wEA+v8AAA0AGgAnAB8AHwAhABkABwAKABAAEQAjABwAOQA6AD8ATwA/ACUAJAA8AFIAWwBWAGoAOgAXAA8A//8CABQAGAApAC0AFAD4/+j/6//t/+H/5v/l/+X/1v/Q/xUAEAAbACIAHQAQAAEA5P+//8r/5v/t/9v/2//E/8D/sP+t/8H/s/+6/8P/uf+5/7D/rP/J/+L//f8GAAUADwAUABwAGgBJAFcARgA0AEMAYABoAFIAOQA+ACIA/f/1//f/9//w/xQAKAAfABwADwADAN7/4P/l/9n/3v/W//b/BwAuADsARwBXAEoASwBAADUAEAAWACMA8v/t/wIABgAYACQAHwADAOv/4P/L/9T/3//T/9z/1P+s/5//qP+u/8H/1f/y////GQAWAB0ACAAPABMABgD//wwACgD1/+3/6f/7//H/8v/8/xQA8v8EAA8ADgDr/9v/zP/K/7D/jf+0/63/v//l/wYAEgACAAwAEAADABIABQANABgAEQAiAC0APQA+AD4AJgAjAEwAQwA6AC8ALABiAGAAZwBqAFMAOQAoAC0AFQAYACkAFQD//wcAIQAzADgALAAeAAkA4f/X/+b/7/8SACEACAAdACcAJgA0ADQAIAAcABAA6P+2/7L/tv/A/8X/yv/L/9D/wf+7/6f/nP+p/43/fP94/4P/if98/5T/k/+W/6z/of+Z/4n/f/+f/5n/m/+5/+j/1v8AAB4AEgAVABwAOgAwADMALQASACMAIgAkADwAMwAmACQAOwAZABUAJwBJADoAGAAgAAsABwAgADkAUgBpAH4AiwB8AFAASQBNAHAAawB1AGwARwA2AFMAbwB9AG8AUgA1ABsACwAUABUAGgAsADsANwACAAUA6//1//n/2f/L/8v/2//X/5z/lv+G/43/t//W/+X/3f/D/9f/1f++/6v/s/+5/+3/9v/Z/7H/nf+t/5T/p//U//j/DQD5//3/yv+Z/4z/qv/S/+3/AAD+//r/8f/m//f/9v/2/xMA9f/H/73/yP/k/zkAVABmAEcAJwAeAAkADQAoADwA/f/j/+T/sv+M/4f/r/+9/9L/4//x/xEAKAA/AEoAXAA7ADUAMwANAAsAFAAkAC4ARwBbAJIAqACZAI4AjAB6AHUAgwB1AGkAcgBZACQAGQAsAC0ALwAwAO7/9f/6/+X/0f+6/9T/t//D/9z/3P/N/+f/4//L/8X/yP/H/8b/vP/R//3/7v/Q/9D/4f/a/9T/2v/X/+v/7v/u/9v/4//f/87/2//e/8j/v//a/9X/1/+4/6j/m/+f/67/qf/P/83/2f/t//j/8v/Q/9L///8EADgAOgBDAFcAVwB/AIgAkgCHAJQAgQBpAEcAOgBNADkAMAA4ACkAAQD//9z/8//2/xAAGQAOABgAFwAiABAAHQAWAB8AGQAUADQAHQACAD4AVABbAGwAdQBBABMA/P/o/8f/t/+f/5f/hv+K/6r/qP+c/5H/lP+1/7n/9f/p/97/yf+q/5n/q/+u/6f/qv+T/6H/t//S/9r//f8KAPj/3f/R/8j/wP/o/yYAPAAvADAAEgD2//D/4f/z/woACwAXAAoAAAAOACMAOgA6ADcALgAxADIAKAAqAF4AdwBfAGcAcABkAGwAgAB4AFwAVABqAGIAUABJAFoAaQBHACMA8f/O/7n/zP/Q/77/vv/g//r/DQAJABwADQADAAQA1v/b/9T/8P8DAA4A7//i//f/zf/B/8D/4P/9/+L/vP+p/6D/b/9M/2v/m/+e/5D/lv+L/5P/iv+Q/4D/Yf94/4P/lP97/5j/x//a/9v/6P8YADkAQQBbAIkAfgCHAHQAgAB4AE0AWgBkAEkARwBmAI0AsQCbAJEAewBIACIAIAA1ACoAWABcAD0ANQAkABYAIQAqACEATgBDAEMAQABTAF4AWQBRAE0AOABEADwAFgAMAA8ABgDw/7//nP+U/5P/s//W/9r/3//B/5P/av9H/0j/Uf91/4X/gf+O/3P/ef9z/1//Uf9s/37/f/+W/6X/w//Z/+7/7P/Y/+P/BwAQABEAOgBBAB8AJQAzACUAFgA2ACQA/P8KACgAQwBLAFMASwA/ADYAPQBNAFIAgQCXAIsAcABkAGYAYQBzAJYAlwCUAHoAXQBVAEYAHwD//woA/v/M/8T/uf/E/8n/t//S/7b/wf/M/8r/1P+v/5//nf+k/5f/hP+n/7n/v//O/8X/rv+7/8L/vf/V/wMA5//S/8n/y//p//r/9f8KAEMATABRAFgAQQAnAEoALAAYAAYA8v/q/9n/zf/W//r/9//4/w4ALwAfAAcAGQAjAA8ABgD2/xUAMQAuACYAJQAbACwAOwAkACYAPwAqAPH/7f/8/wcAEgAgAB4ANABEACoACwAgABsAKgAmAA4A+v/e//X/vv/J/6L/m//E/wUAFADv/wkA3f+x/4f/k//K//X/FgAjAEoARgAZABEADwAZADcANQA6ACgADwD6/+///v/Z/8T/1v/c/87/zf/Y/+f/9//t/8n/2v++/6X/wP/Z/wUAIQAaACoALAAyAFMAQgAjADIARgBTAF0AUABlAGcAcABrAF4AUwA/ACkAAQDV/7b/uf/C/9r/zv/0/wcA8P/y//7/9//h/8T/tP/A/8T/6P/5//H/9v/u/9H/tv+2/83/1P/w/w4AAgDx//3/2//N/9L/xP/W/wYAKQAgADEAIgATABUABwAPAPn/AQD9//r/+P8BAAcAAgAOAP7/FQDv/wUAIwDz/wIADgAMAPz/8v8DAB8AOwBrAH8AgwB8AIAAWAA5AB8AFgAHALz/of+e/5T/ov+o/7P/uf+7/73/wv/O/9b/4f/R/8P/1v/e/9P/xP/d/+v/8/8RAA4ACwD5//H/BgARAPf/7P/V/87/zP/e/wMAHwAiAAQADgAXABMA9//7//7/CQAnADMAJAApACgAGgAOAOr/3//x//7/FwAsAB4AGgAuADUAKQA7AEEALwAhABAAAgDz/xMAKQA+AEsAVwBrAFQARwAlABEADAAAAM//tP+r/4j/kv+k/8X/wP+9/8f/2P/P/7D/xf/i/9f/0v/T/xEADwABAOL/EQAfACwALQAbACgACwD+//f/9P/n//z/8v/v//L/7v/0//b/BgAMAA0A9P/i/+L/2v/r//H//v8SAA8AGAAlABgAGAAoAC8AHgATAA4AEwBDAFAAZQCCAHIAQgBBAEcAJgAqAB0AKAAqAEUAKQAlACIAGgARAPf/9//l//r/GAAUAPz/0f/H/9n/0v/k/+n/2//5/97/6f8AAOz/z/+5/6n/qf+9/7v/wf/g/w8ABwAjAA4Aw/+k/6P/jv+n/73/uf+9/9T/uv+5/8//0v/T/9T/6v/c/7v/uP+e/5r/vv+t/7H/w//h/+f//P8DADgAXQBJAEkASwBNAEQASwBkAGIAagBgAF0AYABgAE4ARgBdAG0AcwBHAFoAWgAwAC4AOQAyAEUAYABmAGwAegByAGQAUgA1ACkAJQA3ADQAKAD3/9X/zf/Q/8X/3P/J/8v/vf+P/4b/av9k/3T/aP9f/5H/lv+d/6D/o/+3/83/0f/S/6n/cP9v/2X/bf9c/3H/d/+F/5z/s//W/+T/BAAPABcAIgAjACgATgBvAHQApwCVAH4AbABxAIIAaABZAG4AhwCFAIMAiABwAEgALQAHAAgAEgAfACQASwBPADsAKgAPAA0ABgAcABAADAAjADYASgBQAE4APABRAGwAegBbAFwAVwBrAFoALQAqACAADgAJAPz/3//V/63/lv+U/6D/l/+a/4D/a/9X/zD/Kf8r/yj/Qf9k/3n/Zv93/37/Zf+f/8f/3/8IACsAEwAsABAA7f/l/9X/uv/I/9z/7/8DAPP/6f/e/8r/uP/K/9b/6f/i/wMACAD7/wQA6v/c/97/AgAxADAANABwAIEAeQCgAJgAmwCWAJYAlgCIAIUAbQBpAFsAWwBVAG8AkgCRAIUAYwBvAF8AIAD0/+f/5f/o/+3///8TADgAQQA3ADgAIQAJANf/v/+z/9T/yv/b/+//+v/p/9v/9v8DAPL/5//v/9n/uv+u/5//if9Z/yz/MP9K/0X/S/9K/1X/a/+B/5X/lv+c/5z/iv+S/6D/o/+0/77/yv/2/w0AGgArACEAJAALACIANAAlACYAEQAJAAYA+////w8ASwB/AKMAswClALIAkQCYAJcAfQCAAHcAkgCZAKAAqgC1ALkAlwCWAJ8AWgA0ADwARQBdAGwAagBkAC0AGgACAM7/w//c/+v/CAAZABgACgAAAPz/EwAOAB0AHQDt/6r/ff+E/3X/Yf9r/2X/Yv8//z3/UP9X/3L/n/++/67/qf+m/4H/bP9v/2f/Y/+M/77/1P/m//b/9f/l//X/1f/D/5H/iv+e/5j/3v/t/wsACAABAA8AJAAbAB4AQAA6ADMAQABEAFUAagBUAGIAXwBlAGwATwBGAEsATwBzAK0AuwDBAK4AlwBrAEAAFgAkADcAJwBBAEEAQQAJAAIAHQD3/9X/oP+H/3X/gP+k/7L/1f/S/+X//P8kACYAKAArADwASQABAAcAGwD5/9L/uf+d/6D/uP/B/8b/u//P/8D/rf+4/8T/vf/S/9r/tv/Q/9X/7f/s//L/AAANAAgA/v8KAAQAFQATAA8AIQBCAFgALwAiAFMAcwBeADcAJwAjAOv/3P/b/+j/8//e/9H/zP+V/3n/nf+l/8z/1v8FACEA7//k/9//2P/J/7//3P8RAAcAEgAxAFoAdQB2AIoAigB6AHgAagBOACQAIwAiAPv/6//n/xwAHwA8AFUAZAB8AGwAfQB2AGkAWgBAACgAHwAiAD4AMAAlADcAGgDs/+r/8P/V/9P/2f/Q/6r/tf+q/4v/Y/9g/43/cf9p/4//cP9l/3b/gP+K/6j/zP+j/6z/nf+P/4r/Yv9q/4X/tf///0AAagB4AK0AmwBpAEsAFQAIANH/7/8JACEAaQA9AFAAPQArABkA7f/o//3/CAAIAAIAFQAmACcASQBOAFoAVAA7ACIAKwAyACwATABXAEUAQgBEACsAGwACABcAMwA1AEsAQQAXACAA+f/u/+j/5f/y/+v/9//n/9b/tP/I/7//rf/K/9T/8P/s/9f/wf+9/8f/qv+e/5n/r/+6/6//2v/P/6L/pP+O/3v/a/95/37/fv9z/3j/lf/U/wIAKwAkAB4ADwD9/w0A+////+D/+P8FAA8ALwAoACkALQAqAC8AFwAhABUA+f8IABkAQABDAHAAhwCsALcA1gDhAM8AzACsAKIAlAB3AGoAZABjAFsAYgB5AFoAYgCBAIUAdwB6AIcAiAB2AG0AfwBLAC4APAA2ACMACgDC/6f/jf9Y/z7/HP8s/zj/KP85/0b/Of9T/1f/av+W/5v/n/+h/5H/sf/X/8X/vv+n/3z/U/8//zr/OP9F/3D/oP+O/6n/q/+x/5H/o//T/9f//P8BAP3/AQACABUAPgBfAHAAeQB2AG0AWABJAEUATgBoAG4AjACsAIkAfgBmAHUAcgBrAFoAbACbAMcAxgCTAIQAdAAwAA0A6v/+/yYALwAtAE4AZAB1AHcAWgBBACUAEwDv//v/HwAtADMAMwArACsADAACAPj/5v/W/7X/kP9o/0X/Rf9n/5L/tf/s//H/vv+P/3H/a/9w/2n/VP9v/5f/mf+m/4T/g/9y/1r/Uf9O/5H/pP/I/+D/CQAQAOP/8//o/+f/4//W/7X/q//F/7r/3/8NACoAMgAzACwABAD2//f/CgAPADYAWABzAFAAUgBWAIAAcgCPAJ0AiwCMAJYArADMAPcA6QDOALUAiwB9AHoAUwBOACsANQBAAEQAPgA1AEsASABNAEMAMwAuACAA+P/t/8H/sv/a/+P/4f/P/7H/j/+G/4L/hv+M/4H/mP95/4D/cv9L/1H/WP9R/1v/YP+F/5v/k/+D/5X/of+U/7b/p/+t/77/2/8NAE8ARwA4ACkAJgAwAFEAQwAaAC0AJwArABkAFwALABEABQAVADIASwCRAK0AkABsAEsAJwAiACgAKwA/AGUAcQCAAG0ASAAyACYASABlAFIAOQAzABcA+P8LAAMA1v+x/7f/5f/d//n/AwDu/+X/uP+o/6r/lP+K/7z/3v/5/w0AJwA1ADgAKgAiAAkAEgAcACMAMAAyACkAIAAIAPX/z/+//7D/vf/O/73/v/+0/6P/pP+Z/8L//P/0/+T/yf/a/8//yP/W/9n/yf/U/87/v//R/87/4v/w/9//xf/I/+X/6//b/+X/AQD/////DAALABkAFgAgACIADwAUABkAGAAPAAkA/P8dABwALgBdAHcAiACDAJAAgQBUAEQARwA6ACoADAAFAAMAAQAGAB8APABIADoAMQArAAwA/P/U/9f/+P8UADEARQBsAHgAVABfADwA8v/G/7D/of+L/4v/lf/F/+7/1/+4/5j/pP+q/8r/2f+7/6//qv+r/7L/p/+f/7j/6//1/+P/wf+z/9D/xv/J/83/9P/8//T//f8BAB4ASABCAEkALgA2AEAAIAALAAQA///y/9n/tv+z/87/1/8BAB0ACwAGACMAQABCAEgASQBLAE0AaAB4AH8AkgCaAKMAlgB5AEsAKQAqACEAAgD1/+H/6//y/9P/uP/s/wIA9v////v/+/8QAAkAAAD1/wEAIwBMACcAIABBABQACgDu/+H/0f/M/////f/s/+H/2//Z/97/xP+8/7r/gv93/33/bv9g/3n/cP+S/8b/3//c/67/q/+x/73/vv+//9r/yP+o/7T/vf/O/9H/4/8BABcAJgBBAEIASABTAHEAYgBhAE8ASABaADQAKwA4AB4ALAA6AEcAVgBoAHIAbgBuAHUAgQB4AGEASwA4ADoAJQAsAEMAKQBTAHUAaQBMADUAQwAoABAABAAYAOj///8bAAkACgDr//v/7f/S/7n/mP91/3D/YP9a/1z/Zv+Q/4b/if+p/7L/q/+P/37/ev+L/4b/j/+R/6b/xf/o/+D/3f/4/wgA//8NACwAIgApACcADwAHAPv/CwAbACwAGgAKACYAQwBJAEUAOwA2ADMAOAAqACkAUAA8AE4AXABgAIUAjgB2AIEAhAA/ADIATABOAGYAbgBuAHMAVAA8AD8AJAAmADQAGQD7/+P/0/+4/6b/p/+7/8f/1//g/8r/w/+v/5r/h/97/47/iv9z/1//Xf9K/1H/Z/+q/8T/y/+8/5z/rP+y/7n/zf/Z/8L/0P/N/73/zf/i/wAADgD7/+L/4f/t/+H/2//m//L/EAAPADoASgBCAFcAUABGAGMAZAB8AIEAigCsALwA5ADHAKEAlgB7AE8AFwAOABwAKAAPABoATABmAGIAZgB0AF4AeQBMAEEATQBfAHgASgAxABgA6P+v/6P/tP/L/97/zf+//6j/oP+c/4f/iP+b/4X/Zf9l/1z/bP9m/3b/gv+Q/4v/k/+N/4H/bf9d/0//TP9X/5f/1v/t/w8AEwAdABYAAwAFAOz/8f/0/+v/1v/a/wEAGgAsAEUASABKAF4ARwA/ABEAAgAPAAIACAAYABsAGwBLAGMAkwCmAIkAcwBbAEkANgAVACoAWwBdAGMAXgBtAGIAYQByAGgAhQB/AHAAcQByAJkAiwBvAG4AeQBbAFgAMQAqACQAEQAHAPj/6v/X/9n/qv+N/63/zv/f/+7/7P/j/8X/t/+7/5T/pP+m/3r/dv9s/1n/b/+H/4//hf+R/4r/e/9C/0f/Rv8y/1//ff+l/7L/m/+H/6z/sP+s/6L/hf+3/8T/xf/9/zgAMwA/ADMA9v/k/9//1v/q//f/DwApABwAMQAoADoAUwA8AF4AbQBTAGUAjwC0ANQA4wDyAN4AlwB9AFYAMAAfADsAOQBGAGcAZACSAJ0AmQCXAGkAOwA6ACkABwDm/xMAQwBCAGAAcABoAC4AAwDa/8b/4f/Q/8//wP+u/7b/oP+Z/6X/pf+p/6L/nv+C/5z/xv+//+b/9f8QAPr/6P/x//L/7v/I/87/zf+s/7X/qf+O/4r/gf9o/3f/iv+B/5H/gf+5/5z/RP8t/zD/Q/9q/4b/l//P//z/MgAQAP7/BwAJACQAQwBBADQAGgAVADEAGAA1AEoAagB0AHwAjQCFAGwAbABrAFwATQBWAHEAcgB/AHUAZwBzAHwAkwCjAKIAuQCtALgAuwCmAJwApQCUAHYAlgCbAHUAdABkAHoAdgBsAFsALwASAOj/9P/p/8f/vf+0/9n/8v/j/9v/tP+y/6L/lf+T/3v/bv+D/5T/ov+u/3X/Q/9k/1v/Rf9B/1j/bf+T/5f/Yf9v/1z/T/9u/5j/s//B/6z/v//X/+v/+//w/97/4P/w/8b/r/++/9L/z//i/8T/1f////D/2f/8/00AVABLAEUAKwAqABkA9/8DAAwA8/8XADwANQAXACYAUwBWAFcATwBIAFAAPwAuADIAQgBHADkACwDr/wkA8//r/+P/yf/L/8T/2//C/6j/kv+J/5//rf/D/9T/5v8HAOv/1//B/8f/0f/u/xQALABCAFsAPwAlAAQA/v/w/+v/CgAmACAANQA0ACUAJAADAAcABAD5/97/5f/t/+H/1f/p/xMAEgAHAA0A9//b/+///P80AEcAOgBBACUAIgBPAHEAdQCQAKYAigCBAHMAXABfAFkAcwB3AFQARwA8ABcANwA5ADgAQwBTAHYAaQBHADUAIgASAA8ABQABABcAEwAPAPP/yv/Z/+P/6/8cABIACwANABgAFgDa/73/yv/H/9D/AAARAPr/8v/a/97/5//k/wMA8v8KADIAAQAMADYAMQAoAC0APgBEAEoALAAAAOv/xf+1/6X/oP+9/7f/sv+q/4X/a/94/4//cf9y/4b/if+c/7T/mf+b/9r/9v8HAAoA7f8UAC8AAQD3/woAIAD9/+b/x//T/8z/z//1/x0AWgBSAHMAcQBNACUADQD1/+//KQAcAAYANQB0AFoASwA/ACMA0f/S/wkA+v8HAPT/7//S/6X/kf+B/3n/fP+N/3v/d/+D/0z/iP+y/6z/zf/H/6r/uv/H/8H/wP+p/6f/sf+t/7v/3//l/8X/vv+j/4z/pf+n/+H/9v8JAAwAGwAuAAgADQAgACMAGwAxAC0AOQA5AE4AbACDAFsAHAAsAAAA+P/n//D/+v8GACQACQAWABoABAAsADEABAAGADEANwAZACMAKAAqACQAEQD7/xoAKgALACMAJQBTAFYAOgBKADUAQAAfAOH/2f/k/8n/v/++//D/DAABAOr/7P8RAPn/DgAQABsAMAASABcADQASABYAAwAqAEsAegB5AFgAUgBfAGQATQBZAH0AfwB5AG4AWwBOAEUAOQBRAGgAWABtAJgArgCgAJEAyQDKAKEAvQCwAJEAgAB8AJQAoACZAF4AeACRAIEAlQByAHwAhQA4AE8ASwBFAC0ACwD+/7L/2P/U/9j/3f/D/8f/uf+h/2H/L/8a/+z+q/6X/qj+0/78/v3+Ev8b/xj/Hv8D//v+zf7O/oj+jP6z/tn+9f7z/jH/R/9o/3b/lv+s/5z/lv+S/4//bP9m/6L/sf/W//b//v9RAFUAOQBjAFYALAAvAEUAcwCGALMA0gDxAAoB9AAFARUBFAEsARsBDAH0AOcA4gDoANsArADBANQA2AD0AOAApgChAKMAbQBHAA4ACAAFAMz/r//l/8b/j/+e/73/sv99/1n/QP9G/z3/NP8i/zD/H/8P/yD/Ff8W/wj/Df/h/v7+LP84/23/Zv9z/0H/T/9L/yT/3/7z/hL/C/8j/2D/g/+E/5//mv+l/8H/0f/v/+P/6/8jADYAOwBPAEwAUgB+AJsAuACrAJYArACjAL4A8gDkAP0AAwEWAS0B/QD4AAkBHwE8ATYBMgEqAQUB6wDeALgAuACnAIQAlAB9AFMAGQARAA4AFwA6ADoAgQCBAGEAZQBQABgA4//e/6j/nv+n/5//4f/U/+T/4f+o/57/kf9a/zb/L/9A/2v/iP+I/3n/8v/+//L/AgDw/+b/uv+l/7j/yv+5/7//1f/b/9T/5v/q/73/0P+9/9X/7f/Y/93/4P/S/7z/oP+x/93/FgAaAAwAIQAgAFoAWAA/ABgAwv/P/xwABgATADYAQQBWAHoAcQBiAJoApQCiAKUAegBoAGcAQQD6//j/HwBAAE8AhwCOAHsAcQBHADsAMwAYABEAvP/Z/+b/6//P/6f/AADi/8b/vv9r/2P/s//G/5j/gf+a/6L/TP86/x3/Jv9R/zv/N/9r/3f/c/9//4D/h/+j/4H/bP9z/07/Yf9L/0z/XP9o/7H/4f/f/zUAJABBAHgAlQCFAKAAuACAAEEAdgBzAGcAbQBGAHsAhAB6AJcAtwCPAJwAtACNAGEAVQBQAHAAeQClALcAswCfAIEAewCTAH0AcQA4AC0APAAiACUACwAMAO//2/8CAA4A8P/Z/9n/3P+k/3P/Wv9c/0//U/+E/3f/Vf8x/w7/FP8m/xT/3v4O/xL/7P4Q/yv/Xf9n/0P/SP92/3r/ef9//6r/o/+k/8f/zf/q/83/2v/8/xUASAB2AJ8AxQDKAJAAgwCnAKEAvQD3AO0A3gD0APgA/gDrALEAhACLAKAAnQCJAKgAwACnAI8AlgCmAJAAkgBwAC8AFAAXAA8ACQAVABwAHwAHAP3/9/+//6b/sf/E/8H/oP/F/8T/2v+1/6X/u/+j/8r/1//0/wgACQDs/+3/7v/p//L/BwASAN7/0P/B/+X/4P/9/xEAEAAVAA8ABwDq/8z/rf+t/6X/n/+0/77/kf+i/8b/sv+2/8r/0v/A/8j/sf+t/6H/mP+B/5X/1P8BAPH/4v8DAAUADAAXABwAGQApAEAAVQBPAHQAjgB1AFUAQAA7ADYAFwAMACYAKwA7AEEANwBLAEkALQA0AFUAawB3AJIAngCeAIkAggB3AFwAYQB5AF0AHAAqAEAAUABYADcAQgA+AC4AKQArAAQA//8GAPn/5P/L/8n/qv+N/1n/Mv80/w//Ef8y/1H/bv9+/47/kv+H/53/sP+K/5b/g/9R/2X/Uf9I/0T/Wv9m/3T/cv9//4n/c/9v/3H/f/+Y/5H/gf+y/9D/z//R/+z/EgArABQAKgBQAFQAgwB0AF8ASABhAF8AdQCTAHcAdABjADEATwBfAAIABQAYABcAFwBYAHEAkAB+AIYAhAAzADYAKwAhACQANABNAGgAWQAoACoANgBEABEAAwDq//L/CADz//v/EABWAEUACwASAA8A+P/X/9H/rv/K/9j/3//s/wIA4v/O/9r/1//i/8//xP/G/8X/wv/O//z/AAABABAA+/8AAB0AIAAgABEACgAIAND/n/+L/4v/pv+w/9f/xP/P//v/CQALAAMANgBGADEAEgApAFkAdABtAFYAUwA3ADgAJgAJAAwAHgAqAEYAZQBPAGYAdwCRAJcAdwBzAHMAYwBnAEoAQgAsABYAIQBCAEQAOQBBAEwAZgB0AFMAIwA0ABEA7f/x//3/EwAOAA0A+v/7/wUAAwAOAA0A9v/1/+z/wf+i/33/hP92/2z/hf9w/2j/aP9v/33/tf+4/6z/tv+s/7T/eP9//3X/Zv91/0v/UP9h/4z/n/+r/7r/xv/S/8n/5v/h/9L/5f8GABoAIgA4AEoAKwAqACYANQBNACwAKgBEAFAAdwCDAKYAxgCmAKUAmwCtALwAsgCvAKwArgCZAK8AugCqAKYAhwCEAIAAUQBAAFMAWABDACwARwBAADMANwAQAAkA9//L/6j/pf+W/4H/iP+S/5r/gP9o/2X/Pf85/1D/Q/82/zb/Rf9O/2D/Xf9K/zz/Pf9L/zr/Mv9a/3z/cf9s/0P/Uf9e/4H/dv9o/3H/e/+F/3X/cf9p/2D/jv+i/8v/6/8TAC4AKgBEAEoAHwAvAGYAXgBsAKAAlQCyALcAoQCkAIQAZQBnAHoAcACYAKEAjwCfAKAAkACJAJIAowCkAIAApACqAJEAhgB0AFYAJQAsADkAQABLAEgAXwA7ABUAJgAxACUAKQAjABoAMAAeACAACADY/8L/uP+G/3f/j/+C/3X/n/+j/47/vP+u/5j/f/9t/2//VP9C/zn/J/8l/0T/UP9P/2v/i/+S/7j/sf/P/8n/yf/g/93//v8WAA8AIQBAAEsARgBVAGkAZwBzAFkAXABSADcAQgBaAGMAlwCOAI0AdgBpAGYATwBYAE4AaQBzAIIAfwCPAIsAjwCnAJgAlACUAMEA1gDVAO4A8gDUAKYAmQBaACoAGgAhAB4A///Y/9b/y/+8/7v/l/+U/3L/Yf9o/3b/Zf8v/yP/J/8l/z7/P/87/0z/Vf9u/4X/iP+P/5P/r//O/8//w//R/8f/ov+O/6f/y//o/+z/9v8cACIAMwAHAN//1f+p/4j/nf+r/8X/8v8jADYAYABzAHsAeQCKAHgAXQBLAEYASABBAFkAcAB1AIsAsACoALoAwACqAJsAiQCSAIsAiwCRAIYAfAB5AGIANwACAOr/0v/b/+T/1P/a/9T/vv++/9L/sP+i/57/nf+z/+z/AwDW/9P/wv+x/7j/o/+R/5H/hP9u/1f/V/9u/2P/c/+E/6D/kv+P/3r/c/96/23/cf9z/4r/uf+6/73/3//8/wQABQAHABcABAAFACwAMgBCACcAIwA8AD8ALgATACUADwD7//z/6P/n/xkANwBGADwAUQBkAE8AQwA8ACkAMQA6AGUAbABlAGcAWQBrAGwATgBIAEsAPQBBAE4ATAAxADkAQwAcAO//5/8DAP//+P8UAAgAEgAYAAkA8v/q/+//y/+q/5b/i/97/0n/Sf9k/4D/p/+l/5z/jv+L/2r/bv9q/0r/Rv94/5H/z//n//3/BwAOABYAHAARABYAGAAVABkAKgAkACsAPQBQAGgAcgB7AHAAVgBRAEYAPgA6AFAAYgBWAFcAZgBnAIAAkgCYAGcAUwBtAG8AaQCEAJwAogCGAG4AVQA9ABsA+P8cAB4AHQA6ADIAGgAeAA0A9v/u/+n/7f/w//T/+//U/9P/2//J/7X/xv/h/9j/1//J/9r/0//i/9j/zv/t//n/9P+8/6f/tv/C/77/wf/V/9n/1f/D/6v/rv+k/5f/iP+Y/8D/xP/J/9X/6P/Y/+z/4f8sABEAFQAMAAAACQD0/yQAEgAaACAAKgAfAAwAEQARAPf/8f/p/9P/wP/h/+n/6v/7/w8AFgAEAA0AFgAaAAcAEwABAAcA+f/l//D/2v/m/+X/1f/2/yQAKgAiADsARAAyAC8ALAAmAB0ADQD+//r/+f8XACEAIgAnACMA9//u//b/2f/t//D/6v/5//r/8P/6/xQAFAAlAC8ANQAxAD8AVgBnAF4AXQBfAEcALgD4/+P/nf9s/2D/RP9I/zT/Wf98/5n/pP+p/9L/9P/o/wcA7f/y/wMACQAaACUAHwAtADoAJwAyADwAQQAtABgADQD3/+f/0v/L/7n/sP/L/9L/yf/e//L/AQD7/w4ADAAZAAwABAAfAAkACgA1AE4AVwBPAFQAcQBtAGQAVgAdAP//6v/P/+T/DgAgACoAJAAeACQAFwAMAP//zf/B/5//lP+R/5r/tf+q/8z//v8RAAcAFgDp/87/2v/Z/8X/6f85AGkAkACYAIUATwBXAEQALwAeACgAOgA7ADEABgD8//L/+//u/9H/1//Z/7T/of+b/8//7//9/xgADAAvAEUAOAAbAO7/7P/g//v/DwA8AFQAWQBuAGAAdABQABgA9v/r/9//2//o//b/+v8BAAYA7v/t////4f/T/9P/uv+q/7z/wf+1/6L/tv/N/9n/+//1/wAAEAAUABkAGwAKABUAIgAlADkAUgBnAHoATQAwAD8AIwAyACEABAD+/+D/zf/E/83/6f/9////KABLAEIACwD7/+P/4//F/6b/n/+l/7f/wP/J/+r/AAAQABMAKQAwABYAHAAsAB0ADQAZAP//BQD///H/5v8CAA8A/P/v//b/8v/Z/97/4P/u/wIA/P/g/9z/zf+2/8r/0v/U/+b/6//l/8P/1f/E/6D/qP+6/93/8v8MABwAQwBTAF0AYgBVAD8ADAAGAOT/6f8AAPH/8f/1/wQA7f/M/9P/6v/O/8//zv++/8P/v/++/8X/zf/O//D/6f8FABoAKgBKAFkAbwBvAIIAiAB/AFIALQAlABEAAAARAD8AJAApACoAFgAYABkAFwAWACAALQAZAAAACgD///j/7//x//f/2P/W/9D/z//N/8b/6f/T/97/7v/i/9P/tv+z/67/vv/I/9P/2f/J/8P/4f/n/9D/uP+k/7H/q/+5/8j/3P/j/9P/x//L//H////x//T/CQAWAPb/5v/t/+n/+/8PABoAKwBVAGEAZgBhAGsATwBDAEQANQBDAD0AWwB/ALQAuwCVAH0AXgBUACMAHgAzAD4AJQAoACEA/v8KAA4ABAAGAPD/9/8NAAUAEAAXAD0ASAA0AC4AKAAKAOf/BQAfABQACADY/7n/x//T/8z/wv/E/+D/4v/f/+X/4v/K/7H/uP+3/8f/xv/P/+j/+v/g/8v/uv/J/8H/qv+z/6z/uP++/8b/wP+6/73/wP++/5f/rP+f/6X/vv/I/97/+/8iACQAIwAyADcAGQD//xIAIwA+ACoAMQBYAG8AcABcADwAPwBIAE4APgBTAIQAgwCHAJAAnACYAIYAjACoAM0AtQCvAKQAigCKAHAAYwBxAH0AXwA5ADYANAAgAB0ADwDo/8H/u/+k/5j/g/9j/1L/Mv9P/zb/Of9B/07/Qf86/0//WP9c/zf/VP9T/1L/Vf9D/2v/eP9v/5n/uP/B/8X/2v/b//T/BQAQAOv/4//O/8z/9P/y/xYANwBDAGwAdQBiAIYAlwCPAH0AWQBgADoAKgA4ADUASQBdAF8AWABiAGsAcACEAJIAlgCHAIMAkgBpACoAAwD1//j/9f/w/wgA/f/0/+n/w/+f/5L/kv+j/5n/sf+q/5j/qP+x/7H/xv/A/8z/v/+u/8X/6//U/9T/x/+1/+H/6P/z//r/8//o/+z////4/9//1//W//3/BwD7/xsAPgBBACgAKQAmAP7/3/+7/5j/pP+X/7P/xv/a/+//7f/r/83/2P+//9T/4P/W/8z/1P/i//b/BAASAAgAFAAiAP3/AgAPABMABAAKABAACgAVAAsARwBjAFQAUgBNADkARgBYAGMAfACBAJUAlgCvAKwAegBlAEoAOgAZAA8AKAAdADEAPgBKAFEATABHAE4ANwA0ADMALwBQAEsAegB1AGYAZQBZAEAAFAANAOL/2v/S/7D/t/+w/5r/n/+D/4P/bP91/37/af9i/27/ZP+E/6L/mf+c/5j/m/98/4f/j/+I/47/mv+4/9n/3v/U/+b/6f/1/xUAEQASABcAFAAmAB8AJAAmACQALwBKAEYARQA4AB4ATwBMADwAZAB1AF8AaAB4AJkAvgCYAIwAcwCBAFwALwArAE0AQwBNAF8AWwBxAFIAGQAWABkABQD8/+j/2//Q/+b/2v/H/8f/xf+S/3v/jP+W/6f/nP+g/67/sv+1/8j/y//R/8X/l/+m/8j/0f/K/8H/3v/z//z/AAD4//z/AQD1/93/2//Z/9r/y//S/8f/3v/q/wUABgDy/xIAAgD+//r//P8CAPH/2v+8/8v/y//A/8H/vP/T/+L/6//w/9z/xv/O/+j/7v8JABYAFwAmAD8AMQAQABwAFgD///H/2P/p/+3/8v8VAC4AJgArAD4AOgAkABoAMgAcAA0ADQALABAADgAUAEUAYABLAEgARgA9ADEANQBLAEoAWgBWAE8AUwBFAEQAXgBmAIsAiwCUAIAAZQBWACkACADp/8v/wf/V/8//yv/W//v//P/t/+7/5//R/7z/z//4/+P/1//Q/7//wv+u/8H/3P/Y/77/vv+9/4f/d/9k/2j/kf+U/6T/dP91/2P/Tf9t/3P/cP+b/6z/oP+9/77/4P/8/w0AEwAXABMAGQAaAEQATAA3ADoAOQBLAFEAZQB5AIoAgwB8AFoAOABaAE0AUgCAAKAAtAC9AK4AsACeAIIAcwBUAEcAXABsAIQAjgCOAJAAkQB8AFUAUQAyABcA+v/g/8T/z//V/8r/3//d/9b/yP+9/7X/qf+u/8f/zP/A/87/x//E/7f/p/+W/5L/hP9x/1//cv+b/8b/4P/u/+n/2f/L/6z/nv+K/37/gf+E/4v/lf+//8T/4f/a/8z/rf+b/5X/jv+X/63/2P/y/+7/9//3/+P/8f///+X/8v/8/w8AEgAbAAwADwAoADoAVABdAHAAgwB0AFgASQA7AEwAWACBAIgAgwCRAIQAcQBbAHIAagBVAG8AeABuAHkAgQB0AGIAVQA4ACYAJQAfADYARQAlACUAIQAMAPX/2v/D/8j/3v/j/9v/1v/B/67/k/90/6P/lv9+/4//pv/B/7//wv/L/7v/w/+z/6j/mP+P/6n/nv9+/3z/hf9m/2D/e/+G/4z/jf+N/5H/nP/I/8X/yP8NACIALwA8AFQAdwBzAGwAWABdAF8ASQA9AD0ACgALAC8AOAA/AFYAawBfAEAANABQAFIAXgBdAE4AWQBiAFAAQgBJAEkAVQBeAFwAcABUAFUAaQBNAEoAPgArADUAKAAWACAAFAD7/9v/1/++/6X/of9w/2T/W/9R/1D/dP+G/33/gv+a/6r/k/+V/5r/mP+c/7X/rv+7/8L/of+1/8r/1f/U/9r/7f/j/+T/1//P/9L/4v/w//H///8WABsADAAeABIAHwAyADoASgBzAG4AZABqAFkATgB2AJgApwC3AMEAnACIAIAAfABmAGgAcACKAKoApACcAJgAjwB1AFQAHAAgAAgA+//q/+b//f/6//j/AgAdABwALgARAPj/8f/1////+v/3/9z/vv+R/4j/h/+N/5j/av9t/3v/ev9z/33/iP+G/4f/gP9v/2n/ef+d/7L/1//m////DQD4/+P/sf+L/3v/nv+l/67/yv/Z/8z/3P/5/wsAIQAPAAEA/P/5/+P/BAAbACQAQgA3ADYANgA9ACUAGAAaADgAQwBGAFwAbABmAGIAdgB3AIgAdABwAHAAbAB9AGgAYAB6AGoAYQBVAFEAQwAkABsAHQAeABcACgAIAPr/AwASAPT/7f/r/+///f/5/wUACgD4/+f/4v/K/8X/sv+M/3n/Z/9R/0n/Q/9R/33/eP+C/43/i/+O/5z/if+C/43/oP+w/6L/tv+x/8b/5f/w//L/4//L/73/v/+3/7j/r//L/+b//P/9/wEADQD6/+v/8f/2/9v/AgAVAAMAJwA5AFEAWwBWACcAEAALAAkAHAA3AFgAZwCJAKMAoACdAKgApwCTAKMArgC4ALgAwACUAHoAiQB5AE8ASQBXAFEAQwBFAE8ARwB2AIUAkACWAJUAjABfAD0ACQDc/7P/sv+t/6j/u//E/7z/l/+F/27/YP84/zv/aP9D/yr/KP8x/z3/NP9A/0r/UP9k/2j/bP94/5r/mv+9/7//zf/Z/7r/vv/H/+D/BAAQAAoA8//l/+b/3v/g//7/IAA3AEkAewCAAG4AYABQAEkAQwBLAF4AYQBwAG0AewCCAHIAhwCMAJEAgwB8AH4AcQBqAGoAagBQAE0ASwBlAHUAbABOACUAIAAJAPH/2//c/93/4P/l/+T/BAAOAAMA+f///x4AIQAXABcAHAALAOr/8v/k/83/3//a/9X/9P/w/+D/3P/m/9b/3P/h/83/2v/F/53/k/+T/5v/sf/Q//D/4//Z/8X/uP+w/6f/o/+H/4b/j/+g/7f/wP/R/9r/2v/i/93/w/+t/5//lv9y/4H/pv+7/8b/yv/l//H/9v/p//D/9f/2/+3/4v8MACQAQgBJAEQAUgBYAHAAcABuAHUAmACkAKAAqwCnAIcAegBfAD4ARQAtAC8ASABTAE0AVgBRAFYAaACJAJgAgQCAAIMAdABKAEgASAAeABMAAQDb/8P/yf+3/6r/mP+X/7D/pP+m/3r/cv93/2b/XP9R/2f/Wv9u/3j/h/97/4H/nf+s/5b/rv+r/6j/wf+//7//xP/E/9j/AQDs/xEALQAlADUAJQApAEIAXABYAF0ASABHAEoASQBKAF0AhwCcALEAqwCSAIUAVwAcAAAA8//y//P/EgD4/xIAFgAcACsANgA2AC0AIQALAPr//f8BAN7/0P/o/9j/1//U/7D/nP+V/7L/n/+w/7H/ov+2/7D/t/+q/6X/uf/J/87/4v/s/+//4P/E/9H/2f/t/wMACgD6//L/9/8IABwAHAAUABwAIQAhADEAQQArABgAGAALAAkAAAAAAAwAGgAfABIAHAAgAB0AGwAVABkAIAA8AGMAdQBwAHQAXABaAFYASQBAADQAHwAIAPb/9f8BABgACQD0/xAADgANAPz/2P/y/////v8CAAUAAgD0//P/DwAHAPb//v8VAAIA6//x/+b/5f/t/+//9v/y/9r/xv+w/5//n/+z/7z/z//h/77/zv/A/7r/0f/x/+j/zP/t/9H/2P/3//3/7P/p/9X/3//8/+n/5//b/9v/xv/j//D//f/y/+v/4v/g/+n/AwAyACoAHgApADYANQA8ADEALgA4AEEAQQBJADsANQBLAE8AWwBaAF0AYwBZAGAAewB5AG4AhgCDAHEAcACHAHMAVQA1AP//+//q/87/zv/b/93/yP+s/5P/df9l/2P/Y/9s/5T/uv+t/6j/q/+z/7f/u/+w/9L/AgAZABkAEgD+//f/1P/M/8j/t/+y/6j/vv/N/77/4P/d/8P/0P/W/9D/2f/o/87/3v/n//n/CQAYABsAHgAyACsANwA0ADYALgAXABQACAADAAMABwATADQANgA/AF8AZgBdAF0AWwBYAHEAbwBhAGcASgAqABsAEQANABoACgAcACMALQAxAC8ANQBCADMALAAyAAEA6P/A/5//k/+R/6f/sf+m/5//uP+j/4j/fv9r/1b/UP9C/1L/Zf9q/4//s/+9/9X/7P/Z/+n//v8MAP//AgAKAAYACgASABoAHQA6ADQAFwAFACYAMQA3ADsAYACVAKcAowCmALkApgCCAGIAcgB9AJEAjgCmAKwAoQCSAG0AWABEACkAFwAAAPT/CgD2/wEAFgAPAAIA+P/m/9n/0//O/9j/yv/M/9f/4f/7/+//8f/0/+//5v+2/6D/qv+Y/33/ev+R/4z/hP+D/4v/cP9d/27/b/+O/57/s//G/9f/s/+z/7//7f/u/9j/4P/d/9D/1f/n/wsAIwAYACYAKwBJAFsARQAtAB0AGwAMAAQAFQATABQAJwAqACAAEwAlACMACgArADsAPQBGAEYAMAD7/+X/5//U/9X/+P8WAC8AVAB3AJwArQDCAMMAoQCtAK4ApACLAIYAYAA0ACkAHgAYAAwADAD//wAA8P/j/+b/4P/q/9L/pf+l/7L/uP/K/+D/5//f/8j/rf+L/4L/e/96/6L/p/+n/7j/uv+v/6b/mP+L/43/gf9u/3H/bP91/3v/hf+R/7D/tP/C/8b/rf+5/7v/zv/M/9n/7f/3/wgACwAPACAAOwBLAFcAaABmAHIAkACAAIgAmgCfAJkAjgCLAH0AYQA/ADIACgD//xkAKwA6AD4ARQBxAIYAcACBAHUAVQBEABYA9//g/wIAGgA2ACgAOQAvAPb/4P/R/9T/wf+//7v/1P+t/6j/n/+E/6T/r/+4/9L/9f/d/9H/3f/S/9f/zf+3/4v/iP+c/7n/tv/M/+7/s//E//P/DgAtADcASwBHADwAMgASABEABQAHAPz/CABOAHgAdAA7AE8AoACTAGEAWgAoAAkAAgD+/+j/yv/H/9z/HwBKAE8ARgBAAC0AGAAQAAEA5//o/+H/0v///xoA/P/j/wgARgA+ACQAKgAOAPP/zv+7/7f/sf+q/9v/1P/I/8P/wP/C/6//x/+5/8D//P8HAPL//f///+L/v/+u/7b/xf/Y/9j/BQAbABcAKQAoAEIAOAD//+L/vf+p/7H/uP/P//z/CgAYAFMAXgBoAFcANgAbAAkA6f/i//b/BwAcABEAQwBGAEkAVwBMAD8AGgAcACMAKQA7AB4AJgAPAC0ARACDAHMAYQB5AIQAkABiAEUAIwAHAOj/0P+6/8f/3//K/83/wf+2/9P/uP+7/9X/2P/V/7//wP+x/5n/p//B//v/6v/M//H/4v/Z/9r/5P/s/8P/uP/Y/9z/uf99/3T/W/9k/37/i/+T/7//0//k/wAA9P/q//T/z//I/7//w//f/x8AQQArAEkAOgAlACIAEAD6//L/4P/f/+r/CwAzACsANAA+ADAAXQBpAFMARQBIAFMAUAA8AC4AHwAPABgAJwA6AE4AUAA7AFAAcwB0AG8AfwCDAHQAfwB8AG4ATAA9AEAAOAAeABoASQBCADEAJgAbABsABwD4//j/0/+w/57/lf9//6H/r/+W/5v/tf+f/6L/lf+a/4b/h/+P/4z/mP+T/6P/pf+V/37/kv+M/5z/vf/g//j/9P/X/8P/qv+8/8j/yf/x/yIANwA3ABIABAANAAIAAwAVACoALwAjABwAKgAxAFUAdwBtAHsAoACKAIAAjACFAIUAcwCPAIsAeABrAFQAJwAJAOr/6//u//P/JAAdAAMACgAdAC0AJgAaAAcABAD4/9b/sv+h/5r/uf/K/8n/8P/r/+H/6P/C/5X/cP9l/2j/hv+W/7v/1P/m////HwAGAPD/CADd/8n/tf+t/8H/v//j/+v/HQA9AGIAXwBbAFsATABGABYAHAAVAAUA//8bADwAPwBdAH4AewCCAHYASQBAACQACQD4/9T/1//A/67/vP/i/+j/0//+/wwAMQAwACoAKwAFAO7/8//u/+f/7P/V/7//qP+e/6r/uf/W//r/GwAgACgAFAABAAAAAADg/8P/x//B/7//0v/7/wsAEwAlAAcA/v8BAO3/2//d/+f/zv/q/wwA/f/4/wEADAD8/wAA+f/r/+X/0f/y/xYAEwA7AEwALwAoACwAGwAIABEAKgA4ADYALAAdAB8AMQBAACsAMwAQAPz/9P/q/wAACgATAPr/CgD6/+H/1/+z/4n/ff+I/4X/vP/Q/9D/8P8EAA8AFwAoAEAARAAnAB4AIQAZAAcAFQA8AF8AdAB4AGkAYgBdADoAMgAnAAUA/f/e/8//v/+t/47/l/+s/63/0v/W/77/uP/E/7H/uv/h//b/AwARAEQAQwAmAAMA9v/4/wkAFQAmAC0AIwAfAAkA7v/V/93/7v/p/+L/CQASAOv/xf/n//X/9v/5/+f/6P/f/9n/+P/3/x4AKAAXAOz/9f8FAOj/5f8EABcAFwAQABMAIgApADoANwATAP3/+v/h/+L/7//z/wsAJgAFAPb/6v/2//X/5v8BAAsAEQALABkASABcAEgAWwBlAGQAewCEAIIAhACLAHYAbABKADoAUABWAFcAdABpAFoAVAA4ABYA/v/4/+j/yf+2/6//p/+V/33/c/9l/1X/QP9C/1H/Xf9y/2f/Xv9S/03/P/8t/0v/X/+O/7z/3//d/9j/2v/W/9//9//5////FQAPAO7/4////w8ALQAuACUAIwAXAAUA1v/p/9r/3v/r//f/BwAeAB8ANABGAGQAbwBpAIYAjQCOAJUAjgB6AGEASgA/ACMAJQAtAEcAYQBdAF8AYABaADkAGgA4ACsAFAASAA4ADgALAP//8f/o/+3/+v8HAAIABwDf/+H/8f/b/9f/0v/I/8j/wv/O/+z//v8MAAcADwAfACYAEQD4//D/0P/c/8P/tP+r/7H/m/+L/5P/nf/b/9j/9P8KAAQAGgAiABAAAgAKAA4A+P/l/wQA9/8PADMAPgBBADcAHgD+/9v/yv+v/7D/wf+2/8b/1f/h/9b/0//c/+z/BwAjAEQAMwAmAAoAEgACAPz/+P/4/wgAGwAxAF0AeABoAIYAoACTAIgAcABMAD4AIwDt/+T/5//s//D/EQARABIADQAMAPP/4P/m/9X/tv/E/9b/1P/o/wgALAAxACAALQA+ABsAAQDj/87/s/+m/3v/bP9z/1n/Rv9M/3b/hP+Q/5z/0v8DAPr//f/t/93/0P/i/+7/5//7/xUAPQBdAFwATQBFADAAKgAQAPX/1//N/6r/kv+A/47/v//q/wkAIABCAFEASABPAEAAFwDk/8f/0//v//7/EABNAHgAmwCmALEArwCsALQAwwDDALAArgB/AFQAOAAdAPv/CwAZAAUACAAKAPH/9/8RAPT/1//f/9f/yv/W/8v/x//T/9j//v8RABUAFgAlAA8A3f+9/8D/uP+2/7H/u/+k/6n/s/+4/7L/tP+4/8L/z//b//D/3f/I/8//w/+5/6f/l/+E/4b/iP+W/7z/v//K/8z/zv/V/+L/3P/y/yEAIwAVAB0APABeAG4AfACiAJEAgQCRAJcAawBbAEwANAAcAC0AOwA4AE8AYwB7AHEAcgBLAD8AQwBBADsAJQArAC4AJAAdACcAPQBIADgAPgBqAEUACADi/7f/fv9e/33/j/+Y/5X/m/+J/4L/rP/I/+L/7f/f/83/u/+v/6j/jv9u/3r/Z/9w/4r/nf+o/5D/lv+Q/6v/r/+t/8j/4P/Y/9j/2f/2//v/9f/2/w8APABPAGkAdgBxAGsAZwA1ACgAAwD6/wEACQA1AFQAiQCZAJwAjwB8AEMAAwDp/9L/3f/p/+z//P8KACgAMgA1AEcAQwAqACYATgA2AEQAOwAzAC0AKQAiAPv/BAAFAP//9f/n/9X/6f/0//r//P/W/8j/tP+7/8f/sP/V/+f/BAATAB0AQwBKAEQAJgAfABsADwAkADAALgA2ABUA4//N/8P/v/+0/6j/vf/t//P/2//d/87/sP+x/73/s//H/97/6P/J/8//8P/3/wUA+v/k/+n/1//A/6r/1v/7//z/CgAnACkAGwAoADMAIgAcAB8AFAAEABAAHwABAO3//v/1//X/BAD8/+v/+v8IADkAaQBtAHQAfABeACQABADx//L/+v/3/w8AGQAuADYAJQAWAOv/yv+5/7v/zf/q//H/BwAWAAcA9f/p//b/9//f/9X/0//L/8v/xP/g/+X/1v/m/wMAFAALADIAPwAmACoAKwAtACgAIgArACoALAAjAPf/0//Z/+X/0f+2/8j/3P/X/8v/0v/O/87/0v/N/9H/8P8QABwAQgBPAHQAggBqAGIATQAfAPf//f/u/9b/5P/v/+//4f+9/8f/5v/e//j/9//6/9v/rv+h/6L/s/+8/8f/4v/3/xcANwBMADwARgA1AC0AIwAJAP7//f/8//z/8//z//n/LQAqADcAYgBiAFcAIAAOAAEA4v/p/wIA///6/wkAAQD//woAHQAcAAoA///u//b/JgAiAEUAOQAWAP//9P8BAO7/BwDX/+H/6P/k/+7/8/8QAPz/DAAhACUAKQAXABwAAwD8//j/9/8FAPD/yP/Q/9//2//Z/+H/0P+u/7b/uv/P/+v//v/z/+7/+//4/+j/8f/n//P/9f/g/9z/5v/i/+T/7v8KAAsAAwAYAA8ABgD5//j/6//e/+r/8v8UAEEAVwCGAHsAdQB5AGIAbwBoAEUAIAANAOH/1f/Y/8n/v//b/+b/+/8LABAAIgAJAA4AHAAOAN//1//u/+P/3//y/+j/5f/g/9v/1//Q/93/4//e/7z/y//C/6v/hv+R/7D/t//F/7v/1//b/wcAKgA0AEMANAAcAAMA5f/A/67/q/+3/8z/9v8LACUAOAAuAC4AQABDAEIASgAzACsAIgAUABoALgBbAHAAggCBAHEAXwBbAEYANQBFAAsA/v8OAA8A//8VAEAAQwBTAGIAYwBJADkAJgAQABUADwABAO7/AQAHAOn/xf+3/5r/fv9z/3v/h/+S/6v/qP+x/7P/z//T/8j/6P/k/+b/zv/c////BQALAAsACwDz/wQA9P/d/9T/w/+x/5//tP/N/93/zP/+/zAASABKAFYAegBjAEkALQAYAPj/6//S/8j/0P/D/9L/5v/q/xUAJwAtAC0AHAAMAAwACQAZADMALgAnADQAMwAcACsAJAAdAB4ABAD3//j//v8NABoAAQACAAAA2//C/8b/zf/R/9L//f8cAB0AKAApADAAQwBAADUANgBLAFkATQAzADYAQwAZAAoADgD9//f/CAAZAB0A/f/n/+n/3P/p//v/3f/a/8r/uf+//8H/r/+f/43/jP+Q/5H/kv+0/87/9f8IACgANgBLAEgAPgBAAEMAMwANAPH/x/+//7n/nP+c/7z/xv/k////DgAHABcADADy//j/8v/6/wwAIQAPACIAIAAyAB4AEAALAAAA/v/w/+P/0//U/+n/6v/g/+T/3v/R/+X/AAAMABMAJQAvAC0AIwAPAAYA5P/O/7n/rf+r/8n/6v8TAB0AMABAAEcAKAATABoADAABAAUAFQAKAAkAFAAwACwARgB5AGoAVwBLAE8ARQA1ADEAPQBUAEoASgBQADgAOwBKAE0ASwBfAEkAVwBOADQALQAmABUABQD///P/6v/R/7T/h/9r/2z/ef+J/6z/zv+5/6L/qP+R/4P/f/9v/2n/T/9A/1//lf+1/+P/BQANAPf/5//O/7L/vf+7/8n/u//R//T/GwAvAEEAUABLAFgARwApAAoABwDs/8f/x//m//P//v8OAE0AWQBYAGQAUgBAADcAKQArAD0AWgB8AHkAigCIAH8AdQBeAF0APgA+AEYASwAfAPX/8//+//T/+/8AAAEAGwAFAAYACgADAAAA7f/w/wYA8v/I/7z/tv+q/5//p/+f/6j/0/8AABMAEgAaABQA3P+e/3v/X/9R/0v/Wv9d/3P/lP+l/6r/wf/f/9b/6f8FAO7/AwARAP//CgD6/+7/6/8HACoARgBkAHUAegCMAJMAigB/AFgAUABIAE0AYQBlAGQAdwB/AHcAjQCKAH8AbgBQAGIAPwANAPn/4v/U/8n/5f/Y/97/6//2/wcA//8FAAAABgDw/+X/4f/P/8b/yf/f/8//+/8hACgAIQAqAAYA5f/U/7L/jf+Y/33/bv9z/3b/gP94/4v/rP+4/6j/vP+6/6D/nv+g/5j/jf+x/7X/z//f/+n/9/8JAB4ALgA8AFMAUAAnABAABQAMAA0AMwBMAEIARAA+ACgAFgAXADAAQQBHAGcAZABAAD0AOQAZACoAQABXAGgAcgCNAH8AcQBmAFAAPgA4ACcA+//q/+D/3v/a/9v/2v/c//L/EgA9AGYAggCAAGYALgD2/83/wf+1/7v/v/+u/9X/5v/s/w0AIwATANv/s/+s/53/ef9p/17/Tv9Q/1n/gP+V/7r/r/+5/7b/r//I/7v/1//u/9f/vv+T/4r/if96/27/hf+R/7b/2P/p////GgAwADAAFAD9//j/9/8NADMATABdAIAAiACFAHUAdQBnAFUAUwBWAGYAbACGAKUAmgCXAK4AzADUANcAzgC4AK0AhgCGAIgAlACVAKMAnwCMAGsARAAwACUADAAIAAwA/P/s/+7/vP+S/2T/W/9S/0//bP9z/3r/jf+b/6j/tf+5/8D/vf+m/43/hP9n/zX/Mv8y/yP/Nf9J/07/UP9R/1T/av+W/7f/zv/n/+//1v/Q/7n/yP/H/8j/6P8BABoAMgBHAEwAWwBRAEQAOwBEAEYATwBSAEkAXABjAHUAjwCdAJkAigB+AGEASABaAFoAUwBkAH0AYwBaAFAAMwBMAEwAXAB2AHoAjwCWAHMAXgBWAE0ASQA/AEUAOgBBAC4AEQAXAA4A9//q/+v/1P++/6L/jf+D/37/kv+W/6f/vv/O/8n/xf+6/6X/kf+G/4j/iv+P/3//fv+O/5f/nv+q/6j/o/+f/47/gv98/47/pP+q/7H/4//v/9j/0P/X/8X/rv+t/7X/tP/F/+j/BwAQABsAKgA1ADkAQQBHAFUAUAA4ACsAOQBDAEcARQBZAFMAVgBcAEgAXgB1AGQAZwBuAGcAWABUAF4AXwCCAIYAaQBjAF4AXABoAGUAgAB/AHwAZgBeAEoAQwBGAEMATgBGAEkAIgAXABMABgDl/7L/ov+u/6X/hf+V/53/lv97/3X/d/9Z/2D/Q/8e/y3/LP83/1b/Zv+F/5P/lP+U/6b/kv+J/4D/ev9+/3b/eP+Z/8z//f8nAEgAYwBXADgAKgAMAPP/9P8FAB4AFQAtAFQAaACDAJ4ArwCzAKYAkgCOAJAAcQBzAH0AhACKAIgAmQCoAKoAxgDDALwAngB+AHcAagBZAFgATwBOAF0AXgBfAGUAaQBLADgAJgDw/+z/5f/Y/8v/uv+r/6v/i/9x/3P/bf96/2L/Yv9s/1v/WP9a/zn/LP81/xz/Kv8t/zj/O/8+/z7/YP9z/3z/nv+e/5v/kP9s/1n/eP+A/5r/vf/E/9H/zf/O/93/v/+8/93/4//+/yUARQBnAFgAcAB1AFoAjwC4ANAA5QDXANcA0QDhAPgA/AAGAQUBAgHJALwAsAB/AHEAOwApAD4ANwAbACAAFAD9/+v/7f/l/+b/AgD+/wMA+//2//L/2f/X/93/5P/e/+P/1v/H/7n/p/+z/63/n/+W/5X/hf+M/5H/mf+x/9b/6P/d/9P/2f++/6T/q/+z/7T/sP/J//H/DQDt/+v/5f/X/9f/0v/O/9T/1v/k/xAAIQAkAA0ADAA4ABoAFQAjACAADQAAAAMAHAArAC4AJgAfADIAIgAcACAAKwAiACoAMQBTAG4AagBUAFoAWQBBACkAEgANABAADQAZADoAVQBjAGMAcABQACgAFAAmACsANwBJADcANwAtAA4A7v/W/+3/5P/z/wAA9//g/8P/zv/B/6f/g/+C/4v/n/+r/8n/6P/W/8v/uf/D/+//6f/3/wAA+P/j/8v/xv+W/4z/nf+k/6j/y//W/9///f/4//r/9////xgAAADt//v/GQAuAB0AFAAdAB4ACwANABgAFQAiACMA9//k/+b/2v/U/+T/DAAMAAMAFgAWACwAMgAtABoAGQAVAAwACADp/+X///8fACMAHgAjACYAKAAQAPL/1//j/8b/sv/I/9T/1f/x////+f//////AwAsADcAOABBAEkAVABcAGgASwA9AC0AOQAuADQAQwBGAFkAWwBeAEYAJAASAAwA4P/G/9H/sP/E/+b/+/8JABEAKwAtAAIA7f/b/7j/tf/C/8r/x//W/wcACAD+//n/6//I/7H/rP+n/6f/u//W/97/2//R/+H/5f/S/8f/u/+//7j/v//Q/+7/9P/7/w4AIQARAA0A4/+//7T/lf+S/4v/o//P/9f/BwA0AEkAQgA1AEQAKAAVACgAQQA/AEYAVwBYADEAGwAzAEIAUQBvAFgASQBJAEgAQwAuADUAPwBAAFAAaQCHAJYAmwCYAHQAYABVAEMAJQD+//T/5P/u/9v/zv/c//z//v8AABAAEQDs/87/uv+j/4n/ff+T/6D/nP+O/5z/mf+p/8T/r/+x/7n/xf+1/7H/sP+7/87/0v/V/8D/rv+Z/4j/h/+e/7D/r/+z/7f/r//G/9b/4f/+//3/CQAHAAkACgD//wwAHAA5AEMAVwBlAGkAVABUAEYATwBpAHkAfgB5AHAAXAA0ACgAJQAWAA4AIQAyADQAOABCAEQAOAAyACAAKwBEAEcAPABRAFoAUwBTADkANwAwACMAMwA4ADEAFgD6//H/wf+w/8n/zP/M/77/wv+h/3T/Zv9b/2r/a/9k/2r/eP+K/4f/l/+c/57/ov+9/83/vP/C/8r/vv+//8D/zP/T/8r/yv/l//D/9f/2/w4AGQAuAEIAUQCBAJAAjwB/AHoAWwBOAFIAWQBdAFYATwBLAEQATABQAFwAdgCMAIwAfgBuAG4AXABFAEQASwBgAGAAUgA8AEoAQQAtAAwA4/+2/6n/nf+W/63/r/+6/9T/5//t/+n/9v/y//f/3//h/87/rP9w/3f/cP9j/3H/jv+z/73/1v/T/8b/xP/a/83/vv+7/6f/pv+h/5r/lf+c/57/vP+3/8T/z//Y/97/z//V/8//2v/5/xoAGQAgADMAOgAyAEMAQwBaAGwAbACLAIEAgwBuAF0ATAAkACUALgA2AEoAcgB5AJcAnwCvAIgAZQBtAFMAVgBsAGoAcgBxAFgAVwBNADcAJAALANT/uP+e/4n/j/+S/7H/4f/o//X///8AAP7/8f/n/+//9P/z//j/9v/g/7n/tP+Q/4P/if98/3H/Xf9K/0z/b/9u/3b/iv+i/6j/nP+d/6b/n/+A/3r/c/+C/4T/gP+a/7j/y//H/+n/5P/Z/+f/DQAxAFAAeACWAMMA0gDnAOoA1wC4AKQAiQBlAD8AJAASAPn/+//+/xAAHgAdADMANQAnADcAUgB2AIwAlQCIAFoAQgAzADwAQQA8AFQAegB7AF0AWQAuABsAJAALAPL/9P/h/83/x//Q/9j/yf/B/8L/x/+7/6H/o/+H/4H/gP9+/4D/gf9//4r/uP/Q/+b/FwAMABYAQABFAFEAbwBuAFoAUAA9ADsASgAoAAUA9f/d/+f/1v/o/+b/6//e/9f/xP+5/6P/nv+w/8j/7//+/wIAIwBEADoALgAwAB0ABQDr/+n/8f/2//f/9f/x//H/7f/V/9r/2v/K/8L/zv/Z//n/AwDw/9P/2//F/8H/sv+8/9v/6v/0/+z/AwDx//X/9//r/+v/9/8iABkALQBEAFEAVABtAHgAegBjAFwAPAAVACsAGQAGAAwAIgAyADcARwBfAGYAXABbAFkAPwApAP//6P/K/7X/xP/H/9H/5v/a//L/7v/X/9f/uv+h/4r/gv+P/5T/lv+o/6T/rf+3/6n/yP/b/+f/9P/5//n/AQD6//H/1//s//r//f8GAB4AMwAwADkANQBDAD0AMgAqACEA9f/w//D//v8KACgAJwAeACkAIQAbAAsABAAFABUAAgAPAB8ALgAwAEkAVwBUAGQAVwBIADcAKAAaAAYA/f/t/+f/2f/s//f/9v/n/9//2f/Y/9D/6P/+/w0ADAAMAPL/1//P/8j/v/+b/6z/zv/p/xoANgBYAFYAHwANAAUA9//i/+f/7/8FAC8AMAAuAD0AUAA4ABQA+v/i/8n/sf+//7//uP+1/7L/s//P/9P/0v/K/8b/sP+3/7//yv/n//T/HQAhABwALQA9AD8ALwAgAAIA6v/j/+X/BgALAA8AAQD4/xEAHQAvAD4ANwArAD4ASwBbAFoAdABzAHgAcABeAFwAVABTAGEAZABpAIUAagBeAC8AAQDa/7X/pf+h/7v/xv/w/wEAEQAhABYA/f/d/8f/qv+K/4j/nv+k/7D/uP/E/7X/oP+V/33/ef9l/2L/XP93/43/o/+z/7P/4//W/7j/r/+h/6D/q//H/9P/6f/y/+L/+/8JAPP/2f/V/9P/5v8pADUAZQCdAKoAkwCGAF8ARQBGADkAOwA9AEoAXgBTAFEAUABFAEEAQQBdAHkAkQB8AHIAZgBSAF4AVwB3AGsAXwBkAGIAZQBLACwAIQAWAP3/+f8MAAcA7P/J/7f/mP9v/2H/U/9X/3H/kf+k/73/2//0////+v/s/9b/qv+H/3f/df95/5L/sf/s//r/7P/c/9b/u/++/8T/v//H/+P/AgADAAwAJQAeAAAA6v/l//n/FAAUACMAGgADABQACwD0/+L/3v/p//D/IgBBAGcAkwCsALEAogCgAIIATwBPAEMAMQAhADoATgBQAFcAOwA1ACIADwAHAPr/+P/p/7v/sf+//6T/pP+5/8H/tP+6/8L/2v/u//b/GQAgACYAMgAyAPb/6v/S/87/1P/8/ywARwBdAGQAUAAbAAEA8v/e/+n/7f/u/+T/zv/F/7D/n/+s/+T/CQAFABwAIAD9/+z/3P/h//P/2v/T/9v/zf/a//L//f8HABwAGgARAAYA9f/l/9z/0/+1/9D/5v/z/+j/+f/6/+n/7P/6/xcAIABKAEoALAA3ACUA/v/L/8T/uf+1/87/CgASAEUAWwBTAEQAOgBAADEANwAyAB8APwBKAD0ASgBWAEAAJwAKAO7/0//K/9v/1P/Q/7r/p/+w/7n/yP/r//b/FwA7AEYAWgBQAEwALQAXAAgA6//M/8P/w//A/7z/vP/A/8n/2f/b//D/6f/m/9j/7P8PAAoACADz/+7/6P/V/8H/1f/a/+f//f/9/wsADAABAPz/+P/s/+r/9P8ZAEYAVABIAEMASgBIAEsAWQBoAFYAPwAsACUAAADp/+v/2P+//6n/u//T//X/JwAtADEAJwAQABAA9//2//7/5f/N/8T/0P/d/+H/2v/Y/9j/x//I/8b/1v/X//n/DQAOADcAPgBbAGEATgA/AAkABQD4//H/+v/k/8D/pP+y/7P/t//Y/9P/xv/I/8T/nf+K/6b/0/8HACIAUgBtAGkAYABlAF0APQBIAFAASQBdAG0AZQBSACgAAwD3/+j/1//N/9T/3P/e/9f/4f/3/wMAFAAMACUAIgDy/87/1P/W/9X/1f/T/87/5v8AAPr/+/8GAAsAEAAsAEAALgAsACsAJAAeABUAHQAbAAsABwDw//P/DwAAAP7/EgDz/9j/wP+x/8D/xv/O/+X/4////wIA/P/y/+j/+P/w//D/5v/k//L/3P/O/9f/2v/v//n/8f/x/wIAFwAWADAAWABhAFYAVwAoAA0A+//T/9D/2P/S/9P/3//j/+T/5f/R/7r/s/+x/8H/0f/f//n/FAAaAEAAMwAmADoAJQARAAkAEQALABgAAgDz/+H/9v8MAPH/6P/n/+D/4P/0//7/AwAnACMAQAA6ADkAMAAhABoAAQALAP//BgD0/+r/BQACAPr/8//9////9//3//X/+f/6/xcAPgBRAG8AhACJAH0AhAB+AG4ATgA5ADAAGAAcABwAKgArAEgAOAAtACMA/P/n/9//2P/K/9L/yf+v/6n/n/+C/5P/jv+N/4v/gv+J/6r/xv/S/+P/3v/e/9L/0v/p//D/BQAFABUABwALABMAGAAOABgAAADT/7v/tP/M//X/AwD+/w4ABgAfABwAFgAiACAACAD0//z/AQAMABsAJgAtAEMAVABSAFAAWgBsAHIAXgBdAFcAVgBIACoAGQAgABkAAQD+//L/3f/j/9b/6P8CAAkADgAOAAUA8P/I/8f/4P/i/+L/7P/m/9n/3P/X/83/y/+y/6D/rv/D/9r/5f/n/+z/4//a/9L/2//c/9L/1f/X/9f/1P/Q/8P/ov+A/5P/pP+8/9b/8/8AAP7//f/1/wYALAA7AEIASwBCAD4AQwBBAEUASABHAFkAUQA8ACwAPgA+AC8AHwAdACYAIwAwACUANQBBAD8AOgArAA4A8v/5/9L/4P/E/7P/rP+9/+D//v8jAB8AJQAZAAcA8v/g/7H/sv/C/83/2//s//v/7v/v//D/CAAmADIAMAAiAB4AJwARAPn/8f/X/87/uv+r/6//wv+7/7X/wv+7/8L/xv/p//3/FgBAAFUATgBOAD0AMAASAA0AEwALACUALgAxADYASwAlAEgAdwAsAAsATAA/AAIANgAIACQAWAA0AD4AOwBaAIMAdwAZAB4AHwDb/9P/pf+c/6r/rv+o/9X/w//P/93/0P/k//P/8f/m/8n/6f/v/7v/y//i/9D/xf/T/8j/u//B/8D/tf+W/6v/vv+t/6j/yP/K/7//0f/C/9z/6P/x//3/CgAUAB4AMQBOAFgAPQAzADEAFQAtADgALgAaAB8AHQAdABsAGgA1ADAAUABMABUABwDn/9n/0P/T/9L/AQAyAFAAUwBDADQAIQAEAPv/9v8IABYAJgAlAC0AMwAlAB8ALgArACwANgBIAE0ASgBYAFQAOgAtACEAKAAfABEAHQAVAPn/AwDw/+P/7P/V/9f/yP/o/93/zP+1/6D/lv91/3X/j/++/8P/wP+x/7j/r/+f/6f/qv+X/5L/mP+r/7L/wP/V/+j/4v/g/+n/7P/t//r/DgD+//7/AAD8//j/8f8DABMAGAAbACoAMwA7ABgAEQAfACIAKQAnAC4AHQAQAAEA/f8GACsAPwA3AEUASwBCADAAKQAkABYADgALAPv/GwAbABIAIwA1ABsA/P8LABQADQAGABQACwAEAP//5//n/9T/yv/d/9f/zv/Z//f/BwAhADcAQwAmAAsABwDq/9b/y//g//f//v8bAEMATgBhAH8AeABhAFIALwAzAD0ANwA8AD0ALgAdABAABwAGAAcA9////xgAFgAIAAcACwAIABgADQAPAAwA+P/3////6//f/9r/4v/U/8X/u/+4/6f/kP+H/5D/jf91/1z/VP9V/1f/Wv9l/3//p//I/+n/7P/z/+X/4P+//8//xP/A/9r/4v/0////FwArAEIASgBGAFAAXwBoAF0AbQBlAFEAVgBmAGEAcgCKAJ4AnwCCAGwAVgBHACgAGgD2//H/AQADABkAEgATAAcA8v8DABMAIwAwADEAIAAiACEAEQAIAO7/4//T/9r/4v/Z/9H/zv/B/7v/qv+W/4//hP+N/53/t//T/+n/9v/v//v/8v/r/+X/DwAhACQAHQANABsAHwATAA4ACwAJAAwA9f/w/wIA+P/f/8v/2f/i/9//1v/X/97/8P8GAB4AEQD8/+f/7//o/+H/AQAJACMAJAAlADoAMAAVAPf/8f/4/+v/y//C/97/7v/2/wAA/P8NABMAAgANAAsAGAAdABgAJQAgAB0AJAAjAC4ALwAvAB4AIQAbAA4AEwANAA4ABwAXABkAAwDt/+7/6f/z/wkAFQAEAPz/9f8BAAIA//8cABkAKQAxAD8ANQBAACcAHwAhAP3/8//j/+v/+f8CAAsADwAIAAEACQD7//j/5//k/+X/6//q/+P/6//j/+T/4f/g//P/+v/x/+H/BQAcABQACgAKABYAIgAkACcANAAyAEQAQwA5AC0AHwAVAA0A+v/x/9H/rv+c/4n/cv97/5r/lf+p/8n/6P8HAAEA7//n/8P/of+Y/7H/qf+n/7X/r/+v/7v/zP/t/wYADAAYABUAJwAtACcAMABRAD0AKQAaABYAJgAYAEIAPwA/AEAAUgBQACkAEQDl/8z/0v/E/9j/8P8LADAATwBhAGcAaABvAHEAPwA6ADUALAApADUASgBDADUAHgAVAAcA7f/z/+z/0v/d/97/t/+2/8L/rv8eAPP/9v8HAOP/8f8DAPr/2P/H/6X/ef+o/4n/n/+H/3P/zv+b/+r/0v/P//v/0/8JAOj/7P/a/+P/7P+9/+H/9f/g/+7/xv/K/7r/uf/S/8X/9//8/wkAQgBiAEsAZwCCAHYAZgBVAG0AjwCpAMEA1wC8ALEAfABeAFsAUwBDAEwARABnAI8AcwBNACsALgAYAAEA8//j/8D/n/+k/3v/hP+J/5v/sP+o/7f/s//F/8P/of+c/67/xf/U/+f/0v/h/9X/4P/K/7z/xv+6/8b/y//f/9v/6////+7/4v/f/+n/4P/X/+H/1f+6/8f/1f/V//f/KwBZAHIAZABtAHYAewB4AGoAdgBvAHcAfACAAHUAggBuAFQARgBMAD0AJwAdABcAHgAVAB4AJAAfAAIA3v/L/7n/kf+A/2b/df9h/1L/ff+s/8X/yv/W/+P/3f/S/9D/1v/Q/8b/yP/U/+v/DAAFAPr/HAD3//D/5v/5/xEADgAmABgAFAANAAEABgD2/97/0v/I/6v/oP+T/5n/lv+r/7r/wf+w/7L/vv/T//f//P8PABoACwAbADsAWwBZAGEAUgBEAD4AOAA9AE4AUABRAFsAYABSAGMAXAA/ACcADAAQAAcAAQAPABYAEQApAEEASwBTAF4AbwBaAFoASABTAFAAOABLAC0APQAlABYAEgAFAAkA8P/r/9v/2P/R/9z/1P/c/+D/7P/r/8r/u/+d/33/af+H/5j/uf/H/5//if+M/4T/e/97/4X/kf+c/6r/x//c/+f/AAAKACIAJwAcABUAIgAKAAUADgAMABIAEQAOABgAHwAyACoALAAkABAAFgAgABoAFQA0AEoAUwA+AEwASQAzADEAPAA7ADMAOABKAF0AVABYAFUASQBIADUAPgAwABAAIAAKAAIAJAA0ACYAIgAgAAwA+P/h/8z/t//M/8T/0f/X/9z/4v/r//v/8/8AAA8AAADp/8v/tP+b/6n/xP/Q/9b/2f+//6//pP/L/9T/xf+8/7L/of+J/4//lv+V/6r/tP+e/5//nv+a/5b/n/+r/6j/y//K/+H/BwARAAoALQAqACgALQAhADkAUQBNAE8ATQBGADoAPwA+ACwAIwAGACAAQgBUAGcAewB6AHcAYgBoAEwASQBVAGsAcAB2AHQAYgBUAE0ARQA0AE4AaQBoAGAAbgByAFIANwAkABwABQDi/+T/2f/d/9D/2f/l/8//vv+u/6v/iv99/27/bv9g/2L/Z/9a/1X/b/97/2v/ev93/2H/hf+P/5//tf++/7b/ov/D/8//y/+//8j/3v/s//X/AgAiACsAOgBAAFEAVABSAFYAWQBBADsAPgBCAE4AVwBuAHEAfgCFAIcAggB/AJAAhwB7AHUAZABqAFoAVwBWAGQAUAA+AEQAMgAkABoA/f/f/9b/w//N/+P/4P/i/wIADwAgADAAKQAnAAAA3P/G/77/p/+i/5b/of/F/77/w//A/8D/uP/A/7r/tf+z/6//t/+9/77/1P/l//f/8v8FAAMA+//l/8f/v/+8/9n/2P/b/9v/zP/T/9T/1P/m/+X/2//e//H/3P/k/+v/4v/o/+X/6v/u/+z/9v8WAAcAFgATABkALQA4ADIAJwAuADwAOAAlAC0AQQBCACIAFgAcAA4ACAATACoALAAvACMAFwAVAAcAIAAgAB0AKgBEAEgATABTAE4AOgAwAEAAIgAvACsAKQA5ADMANAAuAB0ADADn/9H/xP+8/8T/2//7/wgADQADAAMA5P/V/9T/0v/W/9H/x/+5/9L/1f/M/9L/4f/h/9X/2f/d/83/yP/O/7T/mP+0/8v/1P/+/xsAIwAzACYAIQAQAAsABwAGAAgABQATAEgAUwA5ADMAFgD//9v/0//h/+v/AgD4//7/8P/k/+f/4//s//7/GQAdABsAFQAXACMAFwAUABUADwAWABcAGwAgABMADgAkABIA+//w/9H/0v/E/9L//v8sAEgAWABkAHIATwA0AB0AHAAWABQAKgAhACYAIwAcABUAIAAeAPf/8v/t//X/AgD4/+//8P/j/9r/4P/c/83/zf/P/9D/0v/d/97/yv+z/7X/rP+1/7X/s/+5/6f/sP+q/6X/tf+7/7z/yP/c/9n/4v/x//D/4v/P/97/9f///xsAOwBYAG4AYwBdAF8ASwA3ABsAJQAeABkAGAAUADwAUwBJAE4AYQBtAGcAaABoAEkALgANAO//xf+//8v/xP+p/8z/4P/h/8f/zP/b/9r/7P/o/+f/9f/u/+T/3P/g/+L/6P/p/+//CgABAAkABgACAA0A/v/n/9P/w//D/7//u//U/+v/3f/u/+r/AwAeABMAJQAQAAcADgD9//r/8/8EACIAIwAsADYATABMAE8AVgBQAFAATgBFACkAIAASABgALQA1AEoASQBRAEsAQQAyABsA+P/y//P/7f/2/+T/6v/c/7v/wf/M/8v/3/8BACEACAANAB8AAwDu/+L/yf+c/43/hP+B/5X/lf+o/73/zf/U/9//3f/J/9L/2f/m/8X/yP/V/87/1f/s/xQAOQBuAG4AYABqAEIAKQAYABsANQAsAD4AVABqAHMAcQB6AGoAbwBcAEwALAAZAAkA8f/o/+D/wf/J/9z/0//b/+L/7P/e/9n/2v/Z/+7/6v/k/wcABwAIAA8A+v/s/9L/1v/Y/8f/1v/R/87/yP/G/9b/0//C/73/tv+5/9P/3v/h/+L/+P8YAA0ABQAhAC8ADgD9//j/6P/T/+L/9f8CABMAFQAlACwAGgAcAAoAAwAMADUAQgBQAGsAYABnAGQASQBLAGUAZABNAC4AHgAFAOv/AgAMABUAQAA4AC8AJwD0/97/2v/R/9X/yv/M/8X/2f/0//b//P/+//X/6f/Q/83/wv+0/8f/5/8RAP//7P/b/7//tv+v/7r/qP+m/47/df95/3L/Zf90/4j/mf/F/9D/0P/I//T/BQAlACQAJgAlACMAOQAfAA8A7//6/wEABQAhACIAJAAkADYAUQBUAEMASQBVAFQAYAA/AB4ANwA/AE0ARQBYADsANAADAAQAGgAhADYALABOAGcAhgCiALsArQCiAKMAiQB8AHwAaABeAFkAPgAkAAgA6v/e/9X/yv/E/67/pf+K/4n/iP+Q/5H/x//O/9f/3//L/9b/tP/I/97/zP+0/6L/ov+G/3f/lf+7/8H/4v/S/7j/tP/g/+T/7f/o/9//0P+4/7X/t/+u/5n/bf9i/37/av94/3n/iP+c/5T/mv+h/6z/x//t/yIAWABsAG0AfACPAJ0AogC2ALAAkgCCAHoAbABvAH4AeACDAGIAPwA7ADMASABgAFMAWgBaAGEAcAB4AHUAbgBdAE8AVQBQAGUAYAA4ACsAKgASAO//0P/N/7v/u//U/+f/DwAxAEoAPgAbAAoA2//A/8b/x//V/8n/uv+c/3T/VP83/zL/K/9b/5j/pf+w/7v/tv+V/6j/pv+N/33/ef9r/2b/ff+d/77/yf/J/7//xv/F/8n/7/8EAPv/DwAvADMAWQBfADoAKgAuADwATQA6AB0AGAASABkAIAAXACsATgB8AJkAhwB8AH8AcwB9AHwAdQBuAG0AfgCPAHkAXABHAEEAUABeAEEADAAFAAgALABLAEYANgAhABMAKgAJANr/s/+Q/6n/qf/D/6//oP+8/8T/zv+1/7H/rv+I/4P/lP+c/6r/ov+j/7b/wv/Q/+L/3f/g/+H/6f/g/wMAAQD8//z/w/+1/7H/xP/j//j/EwA1AEsASQAqABwAJAA3AC4AHgAOAOD/3P/M/+j/CgAVAB0AIQA4AEMARABPAE4AJwAxAEQANwAzABMALQBEAFwAXQBZADAA//8UACkAEgADAAsA9v/s//T//f/i/8v/4v/v/+v/2P/b/9j/y//Q/97/2//K/9b/4P/r//H//f8NAAwADAD5/8f/r/+s/5r/n/+3/8P/vf/J/9v/1//H/8T/uf/P/8H/0f/t/wQACAAVADsAOgAeABEAEQAJAAQAFQAcABgAFwAbABwAGQAnAC0APQA4ADIAIgAZAAsAGgAzADIARwBJAFYASABWAGAAWgBQAEMAKQD7//f/1//X//T//P8WABQAJQAcACkAIgAQAAgA6//t/wMAIQA0AE0AWQBGAD0AEgDr/9D/m/+P/5n/of+u/5//of+4/8T/zf/C/9H/0//h/+b/4v/v//r/+v/n/+v/0v/c/+D//P8TACsANgAVAPv/2f/L/6//qf+h/8n/0f/Q/+n/BgAcABUAGwAXAB0AFAAmADAAQgA5AC4ACADo/+f/yP/I/9H/3f/v/wsA/P/s/9v/5f/g/+r/8f/2/xEAEgAzADQAKgAEAPz/9/8CAAIAGwA7AD0ANwAuABMA8//j/9n/1//v/wkAJgApADUAMQAlADMAFQD///z/CgD8/+r/8/8QACQAQABaAEwASAA/ACYAFQAOAAoAHAAoAC0AJgAhABgAIgApADkASgBGADYAPgBCAC0AIAABAPT/zf/J/9X/wf+r/5X/jv+D/2z/af97/6X/0f/0/x8ANgAsAEUAJgAVAB0AAwDz/+H/7v/1/wAAAAD1//P//v/8/+r/5P/a/8j/3P/n//v/HwAZABUAJgApABsAHgASAPv/4P/q/9P/t/+g/2b/S/9K/zT/Qv9V/13/d/+T/5z/nv/U////WACDAK0AtwCzALcApgCVAKMAsACqALMAoABgACIA2P98/0//Lf8i/yn/P/9u/6n/7f86AGIAjADOAPMACAEDAREBAwHUAL4AlQBnADUABgDc/77/mP9x/4n/mP+M/4n/m//C/8z/8P8cAEEAZQBpAHsAbAA/AC0AGQDY/7X/dP9H/xX/+P4G//X+9f4R/zf/Vf9q/4L/kv+a/8H/8/8oAGEAcwCgAJAAhQBYAD4ALwAgACwAKwAsADgAQgBGAFIAQAA6ADcALQAoACIALwA5AFQAagBzAHMAZwBUAEwALQAdAAEA2f/Z/8j/uf+q/6T/of+g/77/1v///x0AOQBKAF0AdQBzAG8AYwBzAFwATwBaAD4AMQAEAPn/3P/B/7z/rf+F/3r/iP+O/4f/qf/J/8X/zf/a/+r/3//g//n/9P/V/9//5f/f/8//wv+5/8b/0f/g//H/BwAXADEAQwBBAE4ATAAqAAEAAADz/+7/BwAHAPL/4v/z/83/s//C/87/2f/U/9P/1v/I/8n/zP/Y//f/HAApADgAWABpAIUAhAB8AIgAfwCHAGwAYQBNAEYAIAD7//n/5f/M/8X/7//0/wwAOwBBADwANwAhAAQA8P/0//z/FgA6AGEAggCCAHoAXAAlAA4A+v/W/9j/4v/L/67/tv+q/6f/rP+3/7X/qf+T/5b/ov+E/5H/l/+W/5n/oP+c/5n/qP+q/7H/oP+m/7v/sP+5/8X/sP+3/7D/v//H/8r/zf/g/9j/wv/J/9D/zv/b/+H/2v/v/+7/9f8KAA8ALwBJAF8AVQBjAHIAhwClAMAA5wDeAMoAtACVAHgAgAB2AIEAjQCVAKIAkgCPAIUAbgBbAF8AVAA2ADAAPgA1AFQAXQBSAFUAWQBTADUAHAAKANj/sf+F/5D/kf+U/53/tv+t/8L/v/+5/7f/r/+1/6r/mv+L/4//gv+M/3L/gP9t/2z/a/96/5b/k/+R/4D/cv9m/1D/Rf9y/4T/jP+a/7H/2v/z/wQAGQAzAEsAWwBVAD8ALwAnABwAKQA1ACQAJgAdADkASABAAGMAiQCjAI8AlgCsAJ0AmAB8AGwAVgBMAGEAaQBoAF8AXABPAEMAMAAnACkAJgAmADoAMQA4AEsAVgBjAFsAQQAcABIAFgAHAO//8P/n/8z/p/+i/4P/gf+F/4//mP+B/47/kf+N/3b/Wf9Q/1T/gf+X/6r/n/+Z/53/l/+u/6f/sP+7/6f/qv+t/7r/vP+0/8T/xP/O/9v/5P8HABwAHAAhAEAARQBHADUAJAAUAAsA8P/e/+D/BAAKACUANwA2ADYATgBcAFkAWQBQAH0AeAB1AIAAhACKAJIAngCfAJYAgQBuAFsAaQBkAE0ANAAjAA8A8f/f/8n/4f/w//b//f8CAN7/yv/S/8P/1f/c/+X/5P/i//P/8f/m/+T/1f/N/9z/3f/q/+//9f8NAAEA6v/q/9//zf+k/6v/pv+D/3T/jP+m/6z/uf/B/8j/zv/V/+X/8P8AABMAGQAbACEALQA2ADgAKQAZAA0AKAA2ADAAHAAJAAEA5P/0/+T/8P/3/xIAFAApAC0AHAAiABYADAD4/xEAJAAsADYANgA3ADgAPABSAEIATABeAEwAUgBQAFcAVgBHAEkATAA4ACoAOAAnABEABwD9//H/4f/R/8X/x/+z/5r/o/+P/5f/oP+c/5r/l/99/2j/ZP9e/2v/b/+J/5D/jv+c/4P/gf99/4b/gv+Y/8D/x//F/8P/xv/D/8//8/8EAA0AMgA5AEEAUAA+AEoAVQBVAHEAhwCJAI4AlQCRAIIAewBnAFMAPgBVAGEAYgB1AHkAdwCOAJMAlgCVAIkAjACfAKEAqwC5AKUAiABaACoAAwDJ/87/vP+x/7//uf+1/7f/xP+8/8z/uf/A/8f/pf+K/4X/jv+M/5n/pv+Y/57/hP9t/0z/Pf86/zn/Qv9U/3j/hv+K/5r/pf+4/9P/7P/u/woAEQAfABkABAALABEAKgBWAFkAPwAzACwADQD2/wEA/f8aADAAKAAxAE0AWQBfAGwAeAB/AIUApAClAL8AzQDMAM8AygC5AK4AfgBnAFoAQgA5AA0AAgDv/9b/wf+0/7f/yP/W//L/8f/y/+r/2v/j/+v/4v/H/63/lv96/2D/bf97/5L/n/+1/8T/t/+q/5f/f/+Q/5D/iv+i/8j/0f/E/8r/yP+6/7D/qv+3/6n/uv/U/9D/zv/F/+j/4//r/+f/8f/b/9X/8f/y/wgA/f/9//T/FQAeADEAQABDAE4AWABaAFIAZgBlAGkAbAByAHcAYwBYAGcAYQBiAHUAhwCRAJ8AsQCuAK4AqQCGAFsARwAkACgAIgAfACcAJQALAPX/7v/0/+n/2f/a/9X/4//3/+r/9/8DAAIABwD7//v/6P/F/5X/df9c/0v/SP9k/3b/m/+6/7r/vf+u/47/dP9k/1D/Sf9a/23/fP+S/5X/m/+x/8T/4v8PACQAPQBhAF8AVQBDAEsAOwAtADYAQwBbAHAAeACHAIoAdQBwAGYAaQBfAFAAOwBBAEIALAAtADcAOAA1AEwATQBDABgA/P8CAPv/9P/x/+r/8f/u//L///8NAAwA6v/2/9L/tP+u/5b/iv97/5T/oP+z/7H/uP+4/5P/sf+l/6//q/+3/8L/yP/j////BQAPAB0ACAAHAN7/7v/U//n/8P+YAbICrQFoA94BtALCAmYCeAOyAaT/w/+u/mT+ZP5s/lv9q/zz/F385ftW+yb8U/w9/HL9vf2S/gX/UP+uAWYBRgF8AxADtAOfBDYEiwP1AlECDAJyAWoABgA4AVH/wv+9/6f/Ov/F/Sn+y/0c/h/+tP62/nf/j/5W/uf/Zf/qAJL/WP7H/mH/QP+GAN8BBQDEAPcAlwG6AZ4CjQEcABYBgAA6A4IBfgItAwQCpADyAML+G/xh/1r+wgBm/gf+Mv9G+7D+Af+s/9395v1Y/8T/ywA3/wsDRwPeAQMC1QGlAFz9gADFAd4CHANVA8ADxf8jAXD+yPwJ/uf7Twa6/jkA6gGkAZT+XP64/5D3l/4O/6L+BQOlAigCJv6K/UX8lf0r/qv+yv0sAbkChgIJAe//WP1tAEoEBP0wAaz+2f3TArX92wMxAZ7+pACw/uD/tfv0//b9S/skA3UBwAE//Xf/Rf41/Gr+cAAN/fL/yP8WBlQCXwAQ/27/2QHK+pUIyf8E/aYENQBeAPYACAOaAGcAwwH2AE7+tADq+7UAFQOZ/qAEHwP8AGb+8P+R/SH9MAPt/wAD0v/MArP/XgJ0/vT9rP82/Xj/FABg/1ABfgCq/Wf7RgB7ApD7tgF5AUIC8v8Z/xcAuf39+0wDuf7nAI3/NgHS/hkBr/1S/VwA5/my/rP+OP1i/4IBRP9GAZwD4f//AQ//hv2H+8P8FALdBoQDRALfAf3/bAAK/0IC4fosAk/9WAP5/6AEiQFv/ssCafzqAhr8WQHG/TgBLgHW/4wBIQHz/fABQABj/QsBw/1dBND+TgT+/vIAZwG6/p8BJAFSAG7/gwFC+5ABkAGv/0kA6AFX/w77aABTAV8DQf37ACkAkvwFAEv9wv3LAPz+tAFDAOcAZQEe/Oj9I/4I/53+HQBQAn3+sAC9AOT/rQDB/ED/J/8c/bH+GwED/0P/ZwM6AI4Btf2C/vb/8P3p/lsDvwAl/hIBN//FAXb/UgFFAAT+ygANBL0BOv32AxoCjgCBAEMAjP/1+6b/v/5n/2gDRQWgBQ8BtP9c/gX8Uvx4/jwBJv7zBHYG8gLXBCwBA/8H+uT7+v8v/m//JQXhAncCjwMf/5z9rviv+/n+wfmAAhUGCP67AcMDPP3lAcT74/up/qv+vAE+Ay8Adf5w/SD+YgAA+9gDUgDD/dv9pv55/tz+2QC2/eYElf9KAw0BDv2Y/Nj+uP+A/rkD9gPw/4sBngEx/MH/LftIAcH+RP/xApsA3QA7ARgBzf2nAHn+qABiAHsCXQY1AsoGBAGFALn/Lv9b/CkCMwA//0oDpAIJ/jv/qAII/9n6WAAuASP7NAE4AX8ADQE8A10A3fzUABf7WP4KAIoCQACkAAgBav0u/DD9Kv8f/voDGwKaAWgAs/08/1sAT/8a/k4BSQBcAm7/jQCx/wQBKv/wAf/9f/wr/vv++f1f/9sCXwBmAQ0BVv+r/XX9Kv++/Z8BhgEhAwABkgBD/4z+0fxv/nMB+v76AT4B4gFBASEBJgE6/mIA6P98AcAAbgHE/+P/mwJeAYQAtQEs/7/+m/7B/cr75f9t/xsBRwKq/pr+ff9O/m79pP4gAyn/OwAkAu4CqAS5ABABoP2L/kX8Lv3CBDADawT5AhcBWf2p+1r+s/x+/Bb8/wIKASgEIQKkAX0Aef5sAQP9e/yR/Fj+N/6AA1QESwNZALQAWv8cATn+Ef9r/7z3yQD2/84ADAD6/xwC9P4EBG7/Z/83/An9oP8XARcBEwOO/7D+qwBJ//H/6v8pAGz+gAGvAsMAVgDJ/jH/OAIAAhMCwAALAUr+m/+6/5f+PgEu/+QA9gBXAdQAYQHl/UP8PgHe/XP9RwIxAVP/5gKzAb3+s/5AAHL8BgAaAbYBhwD+/isC1/0jABf/Kf4Y/EUAJgFY/jAAPACcAp4ATf/dABMAjgAGAAP/nwCk/3//7gH2/+AAKABCAZr/wP+d/9794QAG/3QA6wDr//UAqf9t/1j+pgBL//oAAgIlAor/DgFoAMUDhQEsAD0Bgf0j/rP+of6eAFQA2AGfAqgC9P1KAOT8H/xs/5X8RP9yAFD/rgAh/w4Blv+u/7UAov9T/179TwH8ABX/EgFQ/+EAtv9d/1f/nP8iABr/nv+jAKT/kP8BAB8A+f/VAD0A/P6jAdgBjQE2/xj/Dv8uAFIA8P6qARkANQNVAI8ArgAMAPT9Tf4iAID+QgELASoB0AELAVIAIP6a/3/9VgGL/0v9ngFPAJAB0/9IAcb/u/7O//r8wf4tAJYA+QG0AIEAtgLU/hsAowAPAYcAwQAd/xb+Zv++/bz9A/4CAYcA+v/V/0MA0PzO/Zz9j/3X/7YAjgKLAEEBcwBn/63+Rv9yAjcCaAIeA/wDUgQDAlMBPwA0/+r/cv+2AQQAXgFkAFIAgADZAF8B3P2R/iX/yv32/AD+S/4//0YAWP+O/pT/2v+M/bH/Uv7Q/qz/gwA6ATMBcwH0APD96v1B/7T+AAGhA20CkALkAML/1v1m/G39tf7O/uz/pQAVAdX+8f07/jT7rvwc/K/7L/0r/iL/agBbAO8C2gHKAL//zf+J/vv9rgDYAc8DDgOOBt8ESgRtBakCcwOHAScC3wFZARIDuAT3A/oCkwJ+AHYAngBoAAX/4v5D/wMAAv4q/5j+k/6e/rj+yv5g/6MAaQLaA9cDIgWCAyoC/QDp//H/v/68/80AHwFlAeMA5v4l/E36ZPmh+Fj4p/gt+Eb4Dfjb9wb4sfeD9gT2tfVm93D5aPoN/aT9tP5I/0YBoAGwAMIBBAARAYcBlgOkBGUGjgcYCLQHfQgSBzIGtQUdBesFLwWDBgQIqAezB1YHDgbKBGkE8gNLBBgD5QMjBeIFmwaDCHUIZQeXBgwFXgMaAvUCMgKgAMoA5gBcADj/FP4W/K/5HPiB9tv1YvVa9qf1nfTW9Lj09PS08+vyoPNj9Kb1U/dT+Mv5afo7+UL55feY+GD6KvpV+3b9Vv6i/xMA6P4XALP+7f72/cH/9wBvAtoERQUzBjIHbwf3BrwGOwewBuoHdgg+CQ4LjAuvDq8QnhGyEuwROxDRDCcM6AsMDAUNHg0DC8MI3ga5BEAC7P68+xr5Zva89N/0hPQ19Ar0uPLJ8jXzTfSf9GH1vfYW+Hb5mPqc+2L88PzE/Bf9ev0+/l7/Rv/l/ar8tvsX+pv4//Y99in1aPUr9an2zfYG9yf4hPhQ+Jf5iPpK+iL97vxh/vYADgPABlMJXgumC0oLhQpGC5ENGhD+EcQVMhq9HRUfbhsoGKAVqxLvD9sL0AkKCDYGWAQvAYv+D/vF9ZvvxOp46IzmYeYO5WDnnOpV7ArvtfCY83L2APgH+sT9ewK4BvkIBQszDc8Nfw3+C+0JNghUBuMD8gGy/wX+hvtj92L1p/PJ8UrwE+998IjxovHs8T/0ePSy9fL1ivdS+iT9nQBrA3YIswzHEPwPHg5UDosPRRNVFZ4YRB2SIKojjyKHH0gcWRYjEgQMawicBe0C9//1+2L5PPQK7/LnuOF83fLZY9mH2G3a7N2u4UPmZeme7RrzS/gm/WwAmwWICxwRshQ3FjIXSRg7GCMWLhTCEb8OugslCK0EOAHj+/n2+vK377TtV+2H653qJ+s+7QTup+4q8tHym/Sk9qT5q/0CAeMERQhYCtAN7hATFF4XVhypHwwfgBs9GAYbgR9PJQ4l0h4kGYkUFBPmDo8IxAFV+770re+j7MbqSuhL4kjbntXn0zrVM9ZP1uLYmNyy4e3ns+7T9lT9NQEsBesJ3hC+GBgcmR3rGyUchxwIG5YZFRZgElgMbgZlAvj+UPwC937xnOuz5+fm2+VX5mXm6eY+6GDpNe208cf1Yvig+ov9LQBoBJUI6A0pEQETBBXuF3IegyOkJqEjpR6aHMscPiBWJPYifR7rFk0QiA6eCt8F/Py/8yLtJujJ5SfkrOHf3E/XQ9PT0j/VRddK2RXbuN5C5kPuBfce/mcEuQl/DXkSAxkLHygjPSNFIVAfQR1BG5gWxxBCC1sFHwBY+6f3d/Xn8Mfq+uQl4bPfxuBz4uDibuUk56fpl+0x8oH37/rZ/lQBOAVSCvYO9RGpE70UJxcJHOIggSN5IbweGB8KI5YnticXItMamBTKEPcOgQyjByEB5viv8VntLuka5ojfOdjt09TSyNT/1o/ZHdzW3ofhH+XZ6330+fz2Ag0GgQoPEuEaAyO0JIcjbyAwHgsexBunGeATKw7RBzMCRv/5+3L4bvGC6WLjF+C04HfioOPf4jPiMONS5PfplO668+j28feL/LMAMgkQD2QRxhDxD6gTXRgrIJ4j5iRXIXAeeCCmI1Eo8iSHHRoULA2VDBEMQgvCBmT+Tvbk7RrqpedK5K7fTdhf1PjTwNg93YDgu+EY4qjkseeN7gr3MABKB0gKDA2NEU0Y1x51IZ0grh01GiwYPBelFnwUvw46BxMAXPyV+6b5j/Xi7ennDuX+46DmCOgY6HznWuaa6ZHu1/TG+AX7rfv3/XQBXwY5DU0PJBFdEiAVMxi3G+YcXRtzGmIYQhmkGsobNxuqFYQN8QYoAi4DaQO1Afv8V/UO70bp3eYn5R3lXOTT4m7iH+Op5vjqxe6u8mj0r/bC+HH7DgHJBPwHAAvyDM4Ohg9kDv0M2QtLC0wMDw2gDNEJZwbMAg0At/8RAYIA5P6I/PT4yPfh99b4xPlD+XL4/vaK9t/3//ip+Rb5Ofhk+UD7zP6GAVcCQALPAbYCagRGB8sILArTCiYKoAnACM0IEAmiCG8H0AVpBQgFnwPZAa//UP09/Oj7gfw9/fn93v1t/CL7MvoL+gz76Puq/LD9xP5W/+3/CwD0/yEAagAeAcEB6AJbA2IDlAMIBLIEPQV+BfoEkATBA0ADlANrA4cC7wA0/7r9/fzj/Pz8Rvze+vP4bPfa9j/3FfeB9un1WvUi9hj3E/jX+Ar51fnu+i78Of2m/mb/pv/k/2EAdAGnAhYEqwSQBMwE2ATBBH8EdwSaBAEFmgX3BSUGtwVCBScESwM7A7sDBAUeBgIHSgecBioFBwPwABT/r/3T/f/+DgETAyQEHwTQAqYATP47/fL9HADkAisFRgfWBxEIsQdDBxgHbgQHARX9dfuK+tX7Hv4hAYwC5gC6/PP0h+6e6sHsUvGM97D+uf6Q+ov0iPB97rPvIPWJ+LX9xP8F/9v9Q/tY/RH8JPtA/Xn8L/0d/4oAnwI7BPwGZgfiBUkFdwJ5AdEDlQWMCacOKA/5DZUKogfGBN0DbQUVB8cJRQobCsMIDQgGB84DsgF6/i79qPys/HX+w/7M/3wBTwLeAgcBBP+J/Y37X/za/rcCcgYqB3UGMgPaAJf+Dvyk/Er9XQBAAbIBfP+Y+sP6ivcw9S/02vB68DjxffBR8WL0NfXz9Fr2WvZY97z3//fu+vT8sACE/gIA3gKn//4DjwPmA2QH1gVUCGgJeAo6DSENBxBpE/0UxRIzDRkJ8QckCkIN6Q1QDBgLggg4BHkA5vzh+ZD2lPKc7+7vMPER8FXuw+4K8gb0EvRb8oryTfiO/p0E3ggsCo8Myg0hEKERvBGEET4OJwyLCz4LZwqgCBkGaALr/kn6kPOc7ZDpF+hH6vLqp+vR68rrNO267TvvCe838AzzefVR+qT9YAGGBgMJ3wpVCwoNYQ25D2oTEBeUHZ8hPiZUKMUjex16E5cPlw7FDFUMBQkQCNED7P9W+xnz4+t84Z/aFNeX1RDYotiR3F3gQOTG6I7oD+rC7a/14/9YBwsRhRpLJGsqsSpUKUQm6yPOH4sa/BaNEUoNdwgRA7H+8PZ373flV95b2tPVGdV2043VF9lx26veEeH65cjrYPGQ95v9gQWtDYYUsxpXHiIjOiaAKyMxqTQqOt46UT5POc0zoSq+Id0cehMTDa8Dyvym9vbuu+b42zHRbce3vmG8TrrAuy6+ycIuza/Vut885XjsuvZxAQwNTxZhIMQrBjkARcxKXEtwRdU6iy2SHrASMgeS/nb2y+9p7MzmPuD5143P/cijwyfBpcDlwzjKx9A82p3jeO2d9vb9qAWyC00TlxopIicosSx5MgY3eT1cQLNFzEdISn1FqjrSLhwiFR7fFMEM8wDM9ibyneoK5DjY6cyLw5C5WbXusQ6zULjHvd7J5dPW4K7r8vTDAHYJ+BNMG4IjWC5mOjVGR0xwS+dECzmQK30dpw3c/1Hyweld5KjghN5g2efUd84ryXbFF8N4xKnGlMse0qraWOfQ8uP+cgdCD+oWWxycIi8lHynUK7cvWjRDOAg80z+VQzlFrUPHOQgurB+AFkUOcQT5+grx0Oze6APkR9yl0ZXJ/cByul62wbN7uP++ucoK2DnlMvTO/4kMdhWyHMMiVCd1MJM4wD/bQrQ/5jt4M+EoFRtpCcD6f+wZ4+zbwtUY04TQ2tBIzxjNTcvYycPLcc7I0gbZrN/v6Z/0zwD0C5UTCxuvIBYnASqGKtopSioKLmEynjWYOs4+bkQQRP06TC9AIBoYqgtZ/tDwnOdL563koeFb2urUD9FByanBxblVuHa8WMG7y/rXfemN+o0HYhRjHz8qky3kLN0s2DCKNj41aC+kKFEkEh8nFLMEzfbt613jS9ra0SLP2s9I0rnRDdGh0gbXq9sW3QffU+Nm6hTz9/n1AisN5BYFH7Ii3CWKKA4rNyweLOUs4y6zNC84zjvPOGUzNSyUIkkZ5QscAKf0Leye5MDdLdo418zVWtHxyvnGH8Q/xYPGssn70j/eq+yp940DJhHlH8kr1zCcM+40ADc6NDAsPyPMGhYVEA24A0f7m/Sh7r/mtd5h2NTU6tFazirMg8x/0TLXnNxK4ofonPDn9mv8hwFdCLEOjhPnFsUbgCKDKR8veDPHNqE7oD+HQR0/QDbgLrAlvB+uFWAKdgDt9xjzreqt41HcL9dg0uDKScXYwGLA9cJyxpTO+tif5oHz7P2NCZ8ViSKZKokugTFbNB01WjDeJ7AfoxcdDusC4Pi58k/uSOgL4Qzctdpc2uTXYtOP0TXT8NaI2h/dX+K+6WPxCfh8/VEFygwZEwQVexfdGlggzyVaKQguJTXVPLdBfUDSObszpCx0JfcYZwztAef6jfQ87DfmlOFW3gzYv9CBzJzLFst3ySDJrc8y247nLvAN+c0EABRlIIQmySkBLcUwjS+oKAwgEhnFEqIIX/2p9Pbwbe606KLjeuGP4xHkROCd21fZGtsM3Q3eY9/G467qA/GO9rr7vAJaCccNQw/6EKwUGBr+HrAiCSdDMJQ50kBdP7A5fjQZMGEqUR2KD4YDWf3M9r3u9ef34x7iWt2J1pHSOtLe0aLPK83M0h/e/enz8Of2KgK0ES4fCCQjJhkpMiwzKpUgNhasDiMIyf7Z9PruuO5T7rnq++Zg5yjrS+wY6N7iyuG94zvlH+QJ5A3otO4g9L74lv0LBBsJcgqBCgkM1A8tFOgXOhsZJccvOzqjPF85YzadNKQwESVbFq0IcwHZ+qvyY+v/5hnmWeRZ327c3NtZ23LY09Ms1bDdAOje7gz08v2fDW4cUSN6JbInbinrJZwaEQ3hAnj6YfEI6ffkvufA6p/reuuR7W/ynfPI74Dp++az6HHq5OpB63zvdvYn/I4AVgQeCZkLOgtACRQJFAsMDWkOURKsG6MmBjBfMUAwsC81MKwsHSE8E00IRwIG/GvzNOzo6CXpkOci5P3iC+TG5WbjZOCI4tPpafGk9d749gGEDw4bQSEWI1ElESYiILkTFAXY+Bzut+Nf2yTZDN2A4dvluumZ8Nr3tvl29gXxy+5+7xLvou327Y7y0/mWAIcGewstEMYQgA+6DIcL0go4CXgHfAsxFPEe9CYhKEcqxyy5Lssp7R1pEEgG/P5R9u/t1+dY5vjnN+cX6BTq3uxi7ofreOrB7TL0Mvh2+WX9xQeaFfgdHiH1Iq0kgSS9G0kNMP5r8Erkn9ml0gvS19WI2tXg5+iG8vb5Lfsi+H/11/SF9Fvyru9O8TD3Zv4ABZAKORAOFOcTCBI1EG0Pmg1ECX4Gtwr4EsAbmB5XHhYhYyacKJYi/BYRDGoFa/5K9Snsrebp5RzmaeZd6SPuePIS8+TxMPSH+ib/v/76/MUADAzWFnob3hs3HhMicSDzFgYIzPli7PTeXdTCzp7PZdKD1g7erOkL9nz82vzE+hn74/tf+a/0W/EC9Aj6JgC8Bd4LGRKnFe0VJhRNE7QRXQ2iCFkG0wsZE7wYtxn4GTUfwCQ9Jb4cKBHMCPUCs/vc8Hnoz+QF5XDl/uWu6gLx2vVG9jv2cvqxADIDIwH/AJ8HrxIKGT8ZNhnEHJUfFRoeDYj+f/It557aFtGPzQDQXdP017zg8ewg+NP7Gvs4+4r9H/7e+eH0YPQt+aD+QwKhBhUNXRP8FdsUHhXMFcgUXBBzC14OtRP2F2YXfBXfGVUffSBzGXIP9ggxBJP9gfKT6XzliuRp5ETjX+dA7hj0FPcf+Nj9QQURCS8IUwcTDZYWQR1jHE8bWxwGHSgXoAlV+xzv/+MJ2ALPkMtgzrzSQdfN3zjrYPaG+0X7Ifu4/Kz9vPpz9g/1Ifn8/nID9AciDSYTMhZ6Fa0UcRTqEhoPWQqWDL8RnRbfFhUWpxoJIJUhNRrmEPIJQwQN/NHvvubH4uPhROGH4Jjly+0R9an4Gft6AQcJwgykCvAJuw7dFkgbfxp9GpIdPx8ZGfoMDADg9OHogtqDz5HK7cvazpXSDNtD52vzmvlZ+9H8Tf9qAKD87ffa9Zv4BP0/AFgExQl2EFcUIhXnFEMV1BP0EIUMDQzREAMU4xYpFg0ZNB6oIFgdgxTtDFoGR//K9FLpO+NP4LjgiuAd4pDp2vDb9wr7Mf/8BhkMZA0yC1wOaBXlG6UcIBoNHEgd8hrDD3MB0PUw65PfnNOgzErMQNBs1Jra2+RE8KX49/p3+4P9bf+z/Vr4ifXN9zf9zwBvA9MHGw4IFBIVEhViFH0TqRFfDWUM+g6JEtoVixYHGbQcmx4DHNgUWw2MBSr+VPS+6tLj7t/E34HgFuPl6C7w1PeD/eoBvgfJDcAQphDrEDkU/RgcG10ZtBagFfQS7QzjAdL1hOvl4fXZrNNO0TTTWddQ3dTky+2Q9RT68Ptp/H7+vf6f/O/6YfoO/qsBawS/B0IK4g02Dw8QPRAeD1sP+gyaEOYT4BchG6gZSR2LHkIeUhnvD2QJhQE7+h/x+eg55V7iteK54hzmE+wp8Qb3Z/tDAQQHowo8DaoP1RROGQwctByjHKocBxroEmkImfw78VfmWNsT1B/QcdDu00HZsuEF6tbxEPdA+pr8rvz5/Kz6b/ki+lD7a/+qAfgF4wldDdYPtQ9AEJwO8w4cDSMOOBJKFDkZuhkiHFofgx6KHKkUKw3tBZ/9lfWK7IbmgOKt4Sfi8uN26KzsMPPX+Nr+JwVgCeUNbhGAFkwboR4UIYsgECC3HNEV3guu/jLywuZr3LbUYtCLz3HTktlC4Wnp9e929Qr5lvpc+sr5hfg8+AL5uPo7/30DJAjbC20O4RAtEf8P/AwpCo0JXghiC5MOMxNKGnwcnSAYIQ8gER0gFNcKMgBU9+vuPOfk4freCOFu4xToJe358Wn5hf66A9cHywr9Dm4SrxY1Gzge3B9iH4AdIRmlEesGAfos7czhBtma0wjRJNKm1jDd4+Vd7SzzufYj+W76t/nl+Nv3vvjz+u79XQK6BvoKLg7YD1MQYA+LDTkKvweFBr4IPg5+EpUY3RugH7gitCFNHl4WpAyXApL46O/l6N/j5ODy4AbksOha7ivymPY3+0AAcgXsB+UK8g7BFLYa7h5xIWIj0yJpHlEWhgqD/dXvduLs1zbRa89R0eXV6dyw5Qzu7vMr90T4tfh19wD2BvXi9Yj5bf0fAnsHqwzTEIkSDxE4D7QMoAjLBS0DpgTSCg8QyheQHAYhACXdIyQgHhhIDigDo/jB7tnnuOMJ4YriQeVM6l3wtfNw9yj6Hf3qAMIDSQe+DHETfBoiIbgkkSaXJbkgsBdBC1f9ke9Z4zDZXtPg0TLUqdke4MHnfu7F8uP0X/Qm9NvyDvLn8dfzjvmi/3wGNAsoD3ASuRKzEAoMdAf5AiQA0f6AAKYIMhG0GyMi4yVSKcAnoSPFGCcMkv8j9THtpOY25M7jF+cs6/7uJvT+9Rv3XfZz9SD4XfuRAJ8FpQ2vF2QiuypZLTssFyaKHCAPQf9d7xjhxdYU0YnR4tUL3X3ktOrZ8PrzXPV18yHwKe316x7u1vEH+WMA4AfxDoET5RYDFhMSvgsGBZT/lvxE/PD9bgT6DcYZcSUNKiUsTih8IhAa/AzL/9zyResJ52rmH+jC62rxHvWw9+33Jfbd9GHxAvBI8dL2zwBjDF0Z5CQiL8ozNDO9KwMfBw8i/MHqntxH1ITR+9Js2ELgq+n/8Nr07/Sz8Wnub+q35x/ngOlY7yD4iwI2DOkTjxi9GdkXsxJIC9QCd/tv9+33hfvTA4APuBvMKEgtgi6AKZAh0RfhCPf6h+4K6QTos+mM7bXxZPfw+l37JflT8/vu/OkO6I3qPvFA/VQKpxnUJ940zTtVOu4w9SC0D5P8+upr3MHTZtLg1SndIuUd7dnyhPSX8t3tNOk85ZniquLX5n3vo/pSBoAPABbiGRIa5xb/D1wH1//a+QD3R/jA/C0FzRDbG2InWy17LUEoeB2AE9oHIP0p8wrtnOwq8Jn1N/nd+2T8S/p39VTtLebI4bPgqORV7IT5cQlcGrwoiDJ3N2M1Ni68IFMRPAFV8tXnFuH739PhteWb6SfsY+0b7BLp1eTU4A/fht9G4+Dpa/K4/J4GpQ5FEzwUEhP1D3kLqgYOAUT+nfxR/nICpQctD7gWsR2TI2MmGCShHhMV3wwNBlYAcPyR+Wz5mPu0/ZP98vqW9nDxeOvl5FXfVd594a/pG/TQ/7MMiRiVIu0nsykAJxIivBqGESMIWP4v95nxM+4x7GnqP+l859TlQuPK4CXfxd5A4bLkJeoz8B72zfz+AasGAAmlCSkJSwjcBwQHEAd9BrgHmAimCeYLhwxnD6wS/xXlGyQddB2SGlwU2xDzChwHFAPi/9P+cv3w+0L45/Sf8OfsxenL5bbl8+bn6rbw9vUV/ukFSg4EFfIYzxugG7AaWRcQE5YObAiNAv770/Uc8YzsD+mR5r3kaeRj5MfkN+Zq6KrrWe/68ov2ZfrR/dgA8QJ1A5AEgwW9BxkLsQ21D/YP3Q5rDHkI3wTAAXoArwFfBMAIGA00EesTIhQ0EvMOJAuKB3oEtgEiAKn/aADxAdsCJgNsAoYADf7n+qL3xfTX8grye/LZ8xf2X/kS/XgAvwKmAysDDgJmAGL+PfwE+rv4z/jS+W37y/zd/UX/bADyAHYADf8n/Vf7GPnh9lz1IPUf9/r57fzF/9QBVQN1A8UBKf8E/Jj5mvgs+af7n/8dBFgIVQuZDJkMdAuFCSYH1ARqA48DIgXfB+EKhw2oD+MQ2BCcDwgNawkzBeAAbf3++sT5uvlp+rj7Of08/or+o/26+/b48fVG8y/xbPD+8BjzuvbK+gD/XQKVBCQGUgZEBR0DaABn/g/9dvxj/LX8nf0T/18AEQHgAN7/Sf4i/Lv5c/cN9ov1Ivad98b5wfyz/4gCgQRbBV4FnASHA1MCPgHZAFUBxgIVBbMHCwrxCzsNnw0IDSgLuAgNBrsD6AGaABgAKgAfAWsCPgNXA2wCxgCS/rD7/fh79uv0a/QA9Zf2DPnb+7b+MQHBAsEDtAP+AgcCwgDB/wf/wv46//r/KwFDAt8C8wLdASkAwf0P+1z4Jvbd9L/01vXJ91r6vPwa/9IAuAHxAZYBCgF/ADgApQDSAYoDfQVRB64IVwk3CS0IWgbsA3QBQ//E/Uv9af0n/jf/iwDbAYMCggLWAbQAgP9E/iP9mvyu/K39+P6hAGICDARlBREGvQWEBLkC2AAs/879Kv1N/R7+vf9vAdMCngO/A1UDJQPpAez9ZvqT9bTzM/U99cP44/oJ/eb/vP7l/Tj7rfho+I73lPlg/N//DAQbBoUHfQcHB4sG8AUlBicGTgb1BQkFPARMA8oB2f/Z/cP8mPzi/BT9lvwS/NP6Rfoe+tP6VP1fAB4E5wZGCLkI8gf2Bn4FwAMFA1UDOgX1B0EK4AqGCXwGEwJy/VP5HvZ99IX0A/at+IL7xv0V/+7+pv2n+yD5oPev9tL2A/iT+fD8BAAZAycF2AQxBAsC/v89/iP9pP2b/vsAlwPyBAsFiwO4/9j8afwG/OP9Kf+3/4sAvP9G/0v+8Pwo/Tb93/2r/RD9qfwQ/aH+2wGaB4sN4hSlGP0ZvRfoElcOfAhEBcICSwIRA4kDaQR4As//+fsD99ny3ewC6enkO+Pg5G3nRO0A8pT44v6DBKAIOwk7CawIxgmgC6IMrw0sDbcMIgtTCCwFYQGl/p/76vjP9SDzNPEo8G3wMfFi8mvziPSr9fH2iPdq+C/6oPzV/5wCpAQoBoYHAAkiCjALNwt9DFcN+w5bECMRpxOyFuIaqxy7G4AXVBJeDLEF4/8Z+nT2wfQl85DyQ/Ap7UzpZOXU4+Tif+M65EbnLuwR8376xAB8B4YNsBOcFy0ZEhkGGAIXsRTnEaoN7wgXBI7+9vn19NXwVO376v7pcemB6Qzpw+nI6m7s0O6h8NjzHPfb+n3+awGYBHYHOwp6DEIO3A54D3IPUxAzEuMTAhXmFQIXsxpWHcgcExv0FP0Q0gqOBEX/NPkr9kTzYvFg8BfuiOtK54fk4eLn4Z3izeJE5zbtuvVK/rkFYA6VFkkewCEAIfocbhcNEs4LEAalALT8o/oS+Sn44PUZ80Lv++sj6anmH+VU5ADm++gP7UrxR/X8+cP++wL5BUsHNwh7CLcJUAppCnAKcwoMDlcSIhivGksc9xwSHtIehhoqFokObwp6Bn0Cqv+F+6T5i/dA9fjyXO4w6vnkMeJl4bDgVOK948nqAvOy/EIFfwzJFKEbZiBwH/MaIhQFDv0IpQN+/xL7nPhi91H2Y/XK8tzwNe7L7LHqTOjc5g3mkei46w3wW/Tu+Er+9ALNBr0IwwlfCq4K5AobCvYIxwd6CecMGROoF1Yb6B4wIfEjkiDVG6QTFQ0kB88AP/zW9xn3M/dK9+72LfTX8Orrd+eg5FjivOKd4zfpvPB/+mMEfAyqFZIclSE7IRYcshRmDNkE5P0c+O3zBvLo8cnyqvNx89PyFvEZ73bsuOmk5wnnJOlI7OXwR/X3+fr+BANzBvUHlQi0CLEIOAhqBzMGVgaACCcNMROrF5scOR+QI5cj4CA+GtIRYQz3Bc8BSPwA+Xf4JPhY+AH2DvMV8LnrB+k45eLkQOX550jt4fPX/YwGcxBxGSYhLCXEIuIbyBI7CZwA4vfc8K7s9OtF7Trv+PCj8sLzRPOT8Yvus+ta6fjokOm07Gfxm/cl/sYDJwjFCtwLcgtqCg4IlwXrAgkB+QG2BJ0Kzg+XFSgbXSAGJdcjiiBKGMUR7wr7A1z+7/g4+Fr4Kfnk+An3Bfbq8l/vduqA5uXl0uV46YTti/Um/ycJvxPWG9YiXiRDIbYZMBByBsn7sPLN6nrnJ+ct6VDsQu9p82/1EfZ/82DwEu4g7O3rqOtE7gzzpvm4ADsGAwtMDigQHxDADeYJZgUuAfj+Jv91AoYH2wzHEzsawCGcJLwjVh4OF5MQ+wdxANz4TvUq9QT2bvfO99n4mPm494n0/e75653pTukG64Tu2/YOACAMXhfGII8m+yTCH9QVSwut/x3ziOmE4pXhnuIC5rzqW/AS9pb4Y/i19dXye/DW7ZPrWeuo7k/1jfzbA8EJCA9nEg4UBhNmD7IKQgTt/6z8e/2eADEF/QzQFBYfMCTmJVoicBxIFngM3QJh+NnyAvEj8dPyBPRs94n6gfuY+m728fNg8DXu+uwN7rzz9vqQBbEPhRoFI5wmKyWEHhUVnwhy+hLtUeKc3Gvasdvb39fmUO9R9jj60vr3+Yr3hfQk8AbtZOxr7yT1w/s/AyoK6hBaFTgXYxahEvkMNgZJABP9q/yb/pwD7gp4FS8egyMMJFQhoR3kFSMMrf889jbw1ewN7CPs+u/v9DP5WPui+jP6i/hf9vTzIPI19Gn4WwCoCfQT0xyUIaciaB+uGe4P4AJS9APozN+D22naW9wI4rrpu/F997b67vst+x75+fT28NvuRO/d8oD3xv1aBEALjxGhFRoX2RRUECEKYAQ9ABb++v0lATMHjRAYGS0fESLrIRcgIRoBEf8Ehfqi8kXt4+k66Dbqo+7p8yr4ZPr6+4D8zPv9+Qf4M/hx+nP/GQZIDjwW1RuuHuUdrBobE+sHufow7rfkgd3M2QnZ3NzF4/vrdvO2+KH89P19/cr6Avf68/zxlvIf9db5wf8pBoYMnREKFb0VjxO8DpIJQwUIA9IABgEABIULIhTsGfwcsx3HHnQcfBVcCon/dPcJ8Rzr8OZA5srpR+5U8uj1oflt/a/+7v5T/qP/GQKwBJYIgw28E3UXNxicFsgTXQ9zBl37vO995/Lhmd3T2+bchuK16Yfw8fXO+dv85v37/Hf6u/dw9ib2pfc2+pT+JQR/CUMOOxESEykSWg/oC+4IaQbqAwIEgQcIDhAT2RVvFwQa6xvXGCwRhAc0AMr5sPJj667mgObx6PTraO+/81L5gv1SADECWgREB1oIBQqoDNwRdhWTFckTFBJlEMoK/QCi9ffsIee74b3cqNp73aHjrOkl7/Tz//jk/Ij+7f19/D/77/lP+eH5iPxDAEIEJQgSDOsPCxIKEeMOrgxzC0sIagWpBRQK5A/jEVwS0BOMGPwZ6RUaDaIF2gDj+iPz6Oo55z3nhegA6k3tN/Ne+aH99wC2BNYJwwy4De4OHhLjFbsVgxIyDyIOOQvVA3j5zfAp7NDntOIZ3srdquG15jbr5+5l89f3Qvuv/MX8r/zo+3b7uvtW/dT/KgLKBOsHtgubDt0O2g38DMYMYQtQCGgHoAqvDwgSbBGnEUEV2hdaFQsO1AZ5Atb99/bP7sPpEej852Doturi74X1+fm8/fgCQQk5DtsPYxG9FJ4YyxhMFK4Peg04C4sEtvo58u7tpur15QfhsN+i4rDmAurX7PPwLPVP+Nb58vpB/Kr8K/xy/Iv+eAF/A2sEbAaZCb4MMw0FC+IJtgpFC5AJzwdDCt8P7BPUE3USjRSeF4wWvw+FB6UC2v7++LPwmuqS6FLoG+i76MfswfJ394j6Gf/9BdoMvQ+3ELoTtxg7G9UX+hHDDjsNZQiy/rf0He8u7CjotOLw37DhleW86DLr7u6A89P2i/jT+e37Tf0K/bX8FP4sAVEDCQTdBKAH3AoTDIUKxQiCCeIK3ArGCGcJBA5iE4YVIBTpE2sWeBeJE8ALygS4ADr8gfUL7tjppehv6GHoD+qW7o/zc/dB+xkBOQh7DeEPYhIoFw0cGRxJFxkSsA+YDNIE2/kT8VnszegI5Mjfi9/h4v/m+OlQ7ejxOPZj+GH5mfoX/F38lvvm+0T+EAGrAqoDyQVMCdcLDwwhChQJmQknCkYJnQejCaIOkxOCFboUtxUkGEwYqROYCwYFeQAx+8jzSuxJ6CvnW+es5/Pptu7i8234Mf1aA98Jww5lEbQUJxkzHOAa5RWeEfgOlwrNAV737O/H6wXoNOPs3+Hgm+SE6H3rEe+r81z3Qfkt+lj7Wfw9/Kn7gfy2/toAYwK6A10GfAm3Ch4KPQgRCDUJNwm2B60GRwpjEPYUZBVfFEIWDxk/GNgRVgmlA3v/tvmH8Wnqe+e95sTmNec26nLvaPS5+Mb9fgSKCncNNg9iE4kZ3Rx8GvAVjxOPEqgNPQMu+O3waOzv5nzgS90N3xfj1eai6jPwJ/Yn+gf8ev0i/4X/3P3a+0L8NP6r/wQAJwG5BB4JSAsTC9YJLgm/CVQJwwelBboGxgtyEVwUERQoFfQYIxuhF10P+gd7A0b+0fVJ7ATn3eWU5bHkkeXA6j3xPvYB+l3/1wbkC6EN7A+CFX8b6Bs/FyATXRKTEFAIafz58jnuP+qb4y3e393P4T3mhukb7mD08/mf/C79af6Z/9f+Lvxj+g78uP7c/5UA3gLIB7gL6wt7CtEIxAiiCOwG9QSkBMMIpA5/E8EVvBb2GRcdyBwbF3IO6gcqAuv6TvHg6Jjl5eSq5GbkO+fG7Qj0e/iu+woBrQcoCxMMsA0hEyEZvRl1FikUTBTGEQMJtf2z9BPvoOjl4PTbONxp4DTkzOfc7cb1U/zk/qj/EgHGATMAovxK+u36n/xI/ej9/wDoBQMKwQo1Cj4Kigk2CM4FYASgBTEIQgyWEI4UDxjjGkYdRB3eGUcTzAsBBTn9efQr7M3mxOSG42PjRuVC6krwvPSa+I795gOrCH0KmwyiEcIXYxpBGAEWJhX2EgAMdgH79/XwtOrh497ead5S4ePknuhx7on2zvxy/3r/DQD2AGv/Dfzq+Nr45/qX/A3+1ABUBfMITgruCU8JCggbBeMCzgGJAggFmQgfD1AV0xngHD4fuCEGIIAZaBBMCKgBAPlk75bnIeRn41Di9+JQ5iHsL/FU9Jn4Uv4uBPAG1QdGCzYRhxbIF4QWwRZxFvUSHgvIAS/69vJ963Pkr+DG4KTib+Wd6RvwIPek+0/+w/8yASsBfv4x/AD7b/u6+9D7tP0gARMF5AZZB5cHTgdKBk4D5QEMAloD8wbLCn4RQxcZG6UeyyClIsYfHxlvEcMJIQPh+c3w6unH5UDk4eE44qPklui77GnvaPQB+pj/SwPEBU4KNQ9ZFEkXSRj2GF8X0BNnDbcFVv4L9u/tHeeN4xjj/+Mh5nXpJO+S9aT67f1T/z4AEQDE/mL9Mvxk+776J/tG/QEAfQIEBCEFDQYvBl8FawMwApcCTgNOBpUKeRDtFo8abx6aIfEjEiO1HS4XxA9oCLX/5vVI7tjnoOMW4JzesuDo4i/mFOlx7lr1Afvv/4MDngiDDfAQSRR6FvkYSxlfFjcS8wybByEAwfdM8LPqgucC5U/kGOaG6UDurvI590z7Ef5q/zn/df8c/939dfw6+xf8nP3F/sX/ugBfAiMDaAOAAysDTgPdA0kFWAnrDjYUCBmVHHIggiOVJMsiHR7bFy8QIQie/8D2p+4A56fhcd4h3YLdSt6A4a/lOusO8dL2ZP3iAl0IngzGEIsVaBi+GnMa9BhSFlUR0gs2BJ/88/Qe7qbpRuYm5U3lWuez60DwAvVl+HX7Ef5Z/9r/vP6G/Xj81fuR++/7Bf1z/uH/KwESAlIDiQT9BOwELgUHBxgKEA9vEwgYORx0H/EiZCTpI28gXBp8E1QL8AKd+SzwhOik4YHdkNru2TjbXd3D4Vrm2+xo87/5SACaBToLrA+FE/cW5BgEGw8buRl4FnMRFAylBD39kPW27onpbOXu4zrkfOZx6m3uJfNU9y/7Bf46/9H//f4p/lv9afwG/Mj7lvzP/SX/qwDZAa4DWgU/BiYHWAhsCigNmxElFnsaPx5WIAAjOiRUI50f6xi1EbUJNwHk9z7ukubH3zrbTNhg1/zYTdtl303kM+vU8t35mABkBo4M5hFlFXMYcxogHbQdSRv+FxITEw7YBlb+P/YQ7xnqveV4463jiOVY6XzsF/Hf9er5ufxc/YX+pP6W/mr9tfuM+/r7ef1s/pX/uQG2A80FpAZjB2MIsQlTC8sMkxCwFAIZ5RwnHwkifiMdIwIgIxo+E+gKEAKN+BzvB+en31fa8Nbw1SbXoNmo3aLijekn8aP4pP+gBW0LtBDnFPMXXRo4HBUdUBxpGgAXGhJDDOME9vxL9ZTuSekO5eTi2OKA5JTndOtX8Mz0/fgM/L/9DP9f/xT/7v01/RD9ZP0p/kH/IwFwA6IFEwdsCIIJUwrkC/IMxQ5DEuUVBxrVHP8eBCHfIXkgQhxsFpwPvwfi/gr14OtE5KHdf9gy1czUkdZ32YHdFOOe6rTyEvq5ABMHqQ0bE8QWFhnQGhMdgh5UHmIc8xhwFDYO4Ab7/vv2cO+T6ILj4uBR4F7hY+Pf5tzrivGp9mj6Wv0z/zkAywCdAAYAJP+q/jj/nQB9AjEECAaDB6UI0wlACpELhgwLDYUPIxPvF78bMx2AHs4fXCAIHm4YeBGxCdYBL/hf7g/myd4G2VfUltIL1DzXP9uw3+nmze96+Pv/BQbCDC0T9RfhGpQc4x4gIUshrx+MHKsYXhPvC4ADe/pT8qjqz+MC32DccdwV3k7hMuY17LvyHPiu/CAAfgLrA9wDVANeAqQBfAGMAcMCLwS0BSMHmwedCCQJPglWCsIKIwwwD6MS7xazGSgcfx78H7Mf9Ru4FrcP/wfm/jf03+ox4jzbSNVb0RnRyNKX1qzaFeHJ6dPyjPuUAugJIxEAFwsbRx1/H+EgCCFzIOkeax0NGgUUmQxtBF78WvPD6ZThi9td2AzXJNjp24nhCumB8Bn4Sf/pBFgISgmKCeYImAd3Be4C5gEBAucC7APMBCAGxAaKB8QHsQhdCpMKqwykD6QTrReLGTEcOh6GH+QdGRniE9wM2gQf+kPvkuZN3orX+NBEzhvPy9FF1lDb5uPv7dv3sABJCLUQrxeQHFcf/yDVIg0jbiLLIJEe4hvDFisQWwju/0D3Q+3J4yvc79ZE1IHTBdYP21LibOtt9Kv9RAUEC1oOUA8aD6AMSwmgBccCUAFGAMgAKwJDBEkGywYSB9EGdgYGBwIH5wftCncOFhP1FvQaQB/dIRAiix7BGdcSiAk2/gjx1uXF22rTBs1zyZnKEc5J1L7bNeV08Lr6RgRyC9IRcBclG3od6R25HsQfaSDqIB8g1h7kG0QWjA7YBET62u4R48TYI9FszQHN+c+Z1uTf4ev+9/QCAQwNEk4VSRW7EhUOXQjcAh3+TPs3+lL7cv4OAkIGlApjDZ4OFA1MCYEGeAT6ApkCXgMWBzMN9BN/GnkfoiKVIjsfRRiiDg0DNfXz5ljaG9G1y4vJ3sqcz1jYpOMk7zL6pQOvC3QRahSCFU0VYhQfEwcSdxLvEzwWpBg4GtoanBjzE60MwgJ39wLrb99e1tXQT88I0SXXuuCk7Fn5mgT6DRIUEhewFugSOA1vBvf/E/pz9s31aPfb+hL/QgRKCVQNOQ/lDuwMXAjlAn/+o/vw+yn/CATuChkSSRpmIv4mJifYIZYZkA+MA6r1R+Yo2vXRxM1TzTnQiNeQ4G/qe/M1/M0ERgqjDMsLvgoMC9IKRgrECdkLmhCMFYYZQxwtHrkdmBnuEdkHy/zZ8KXk09pc1ZXUFdc33Grk9u52+tUEsQypEXgTaRJtDsAIugKs/Jr3dPQf9Fn2Jfq0/jkDtwcvC/wM0AxuCqgGlwGC/ID4jvdi+N37HQOtC7IVSh00I1sotilMJn0cvBDhBD75Ku2P4DLZOtbe1m/ZfN305PHsm/OK99b6if4wAGH/hPyF+7/9LQHmBPAIjw8jGIcfpCP5JNkkCCKAGnYPewNn+C3uUeSI3Vzb4d334q3o/O9P+HgABwZeCMMISAdfBNb/cvtk+Eb2rfVQ9jv5uv0/AkAGqwjGCnILeAr4B1EEsQDz+8v33PS88xj2f/syAqEK/xMSHCUiryTPJakkIh/aFT0KTAAi+P3wuunq4+fifeQy51rptuvf7oLwffAK79Hue+9l7yXvOvDU9Yj+PgfoDj0WOR9AJ/kqSyozJ4wi7Rr9EOIGSf4Y9zPw0OrX6B7q1+yR7qLvz/Gt9P/2c/es9sz1F/VV9SX21ve6+VL7Mv1l/9sCUgYtCIsIOAg8CJkH3AWkA1sBn/77+6n6qfrj+nP6hvoc/gwF2AwhFMMYhBwRICQh4R+6GowTeAuwAir7t/QB8ObrQ+mQ6A7pTeuU7FLsMevf6avpiOmk6V7qu+z+8ST52gHZCuwTJB3BJFEqmiwdLJ4pIyTfG0sS7wnFAon7rPSu7wnu+O257QztMexP7C/sW+tV6ubpwOql6/PsIPAV9Zz6Qv9DAwwHvAqsDVwOSg10C7gJkgfMBIQC9wDY/wb/CP+9/4QAkQCO/5f+iP0U/af9y/53AM0ELQysEzEY/Re/F8YZ7RkcFG0JWgBi/Pr4cPLq6+XpR+t461TpeOhO653tzusH6THqAvBC9XT3gfpCArsNtBa6GyYgGibjKgAqSSXHILccGRYkDBkDtf0j+uH04+5F7Onrr+pK597jg+Ku4dTg++Ce457ouu238tP48ABFCFQMlQ50EIYRnBBaDhoM4AnpB78FPgS8BOMFmgWIA8cBbAELANj7a/fR84TxwO/Y7dTuxvId+NL81QFfCyEVnBzcH+ogjCDLG+MVsQ6zCBwD+PyT+EX2r/dV+Ar3lPWg8zbyXu+Y7Nfpfecu54HoOu0w9FP8tARSDHYUkxuxIPgiQCLlH80b/xYrEfoLnwelAw0A6fzV+mz4sfRq7x7q7OXA4rHfkt3a3fTgT+Zm7Wn1Jv3lA+kIHwxDDjkPDQ6KCsMGrgQwBDUEGQQoBPcEJgY5BvoEJAPfAOn8ifdY8/bxu/Gj8K/w7/FB9UT5AP1RAeoDFQbOBqIKcxBfFekXwRY+GDsa2Rp1FwMSdw6EC5AHXwJs/5D+dfxv+Mv0JvQS9UnzLO//60zs8e0B7pztQu9S9Fj5Q/2kAZMH+Q2rEfMSxRNBFWoVyRJmDiIL3Qn5B7sEUAEI/xv9Ifth+cX3rvXF8g7w7e3n7HvsGuyA66vrKO2L75HyWfVP98/49vrh/YsAVAKbA3YEMAU1BnQH9AfaB8kH8Af9B6MHiQdgB10GqgPiAEH/Gv5v/Kz4CPfJ9jP3QfgN+J761Pzc/jcFiwrTEFMUCBYfGl4Z6hmUGMoWlRXNEcUQcQ9HDwcOpQo3B74Cjf6G+BTyMeyO5dThsd8X4OvhGORm6W3uUfNb+Kb8pQG3A8cEBgcwCcILhAzMDZEQAhPFFCIVaBUlFBEQfAvzBcwARPnL8X7sgOcs5XrjweN75IHkKeZf5w7pSOpu6nXr1uzc74rzG/gj/oYD/wjsDfoS7xbwGIoZMBijFpsUjRIGEOUMWAooCEkGkwSfAksAjv2T+kn4Hfaf9BTz+/IE9AL1DPdo+Fn+aAXYCiYPpRCkFqYZShn5GFgYaRpdGEgVUhTTFFEU7A9tCy4IwASI/774x/Mr713rzOes5PzkUOW35qDnaOj46qfsMe/98JfyD/V091/6dP2fAEQEYgdrCVAL8wywDQsNDwxjCwAKgwf2BF4DwQD7/az7ZPkS96XzBPEn77ztkexo68vqK+ua7NLuI/Hq8/r26/nm/Lb/NANlBkwJnAt5DfIPqxIjFf4V/xX4FXcVFRQBEh0Qlw0gCugGfQQaA00BXP+7/e779/rY+dr5gfm/+Ib4rvm4/RUBJQRZBVMIigtwDYwOsg5BECEQ9A5CDfwMvwxgC1oI5gUWBAsCM/9v++D4efZa9MHxZvAl8Afwee/x7nDvAPBh8DzwbfAC8Z/x8/ET82z0X/Ve9lD3bfkJ+9b73vz3/Yf/KQCKAMEBsgLbAs8CmQPiBKEFSQX8BH4EwQMuA8AC0wFNACj/5f6r/gT+2v1Q/Rn85fqX+ir7h/t3/En+YwCxAnsFbQhACioLtwv4CzcMegzJDJgMRAxVDM4M1gzFDFsM6QoSCS0H4wW3A0IBnf+L/qL9lfzR/CH9cv0T/gn/DAAVAI4AEAEHAV8A//9iAB4A3f/X/xMANQBKAIkAiAAOAIH/2/6S/SX8v/qZ+Vj4Ofev9kr2F/YD9kj2OPYb9jz2O/ak9Qz10/Sp9LH0wfRo9Rr2IvdO+IP5vPrY+0L9OP4P/xkAmwFZAswCeAO1BLsFMQbPBkMH7Qf3B8kHwAd5B1YHuwY3BhQGzAVjBacE8gN7A/wCIgJjAQcBCAG/AJUAHQHqAe0CcgM/BOoEXAVxBU8FZAUtBUAFcwXHBWoGUQcNCHQIgghHCKkHcwYSBYgD6AE4AMX+sv0R/bL8WvwT/JL7Q/sl+yn7KPtB+zL7CvsS+zz7w/sQ/HP82fw1/ZX9o/2w/bP9QP2a/K37FPui+gD6SvnL+JP4UPj/9+b37ff89+v3uvfm9xr4fPjM+Cz53Pl4+jD76vvU/ND9wv7K/xkBggJ/A4oEnAXxBgIIbwi/CEUJkgmECUsJMgk1CdEINgiOBwQHVQYYBd4DwQLoAQkBEgBw/wz/x/6H/oz+8P5n/9T/UwAkAQ8C9AKmA2oELAX0BX0GuAYmB3sHiAczB98GhgbPBbQETgMeAsUARP/j/ZX8S/vl+b/4H/im9z/3FPcw93f3pfcb+AH5YfrN+xH9aP7F/wUB8wGBAtwC9AKvAlsCxwFCAbAADgAv/yj+K/0s/Er7Ufop+T/4lPe59lf2OPZi9qr2Afeg94f4tfnd+h/8YP2z/rn/rACiAc8C6AOrBF8FJAYIB8sHIAiICNcItggnCGsHwwbyBT8FZQR2A9wCjwJVAuUBoAGCAVEB7ACZAF4AMwAZAC8ATgCAAPwAqAFfAg4DtwNKBM4EDwUXBQQFwwSuBGgE2gNjA+oCewL9AXEB0gAWAGH/uf7F/cb8p/u1+u75SfnJ+F/4a/id+Nv4O/mN+fP5avri+mz7I/wg/ev9Zv7w/rn/ugCcAWMC+wIpAy0DKgMBA4cC4gFJAXoAo//R/kL+5f0k/YH8E/zH+5f7Z/tp+6j7Bfw5/KX8T/03/uv+gv9FANUAawHzAYAC8AJQA5ADugPIA6sDvAPTA64DcwM5A/ICwwJ0AhUC9wHaAboBigFwAYkBaAFLATcBNQE/ATgBQgFXAWEBVgFTAV4BXgFpAUsBMgEWAe8A5gDAALYAsgCYAI0AagA+APf/o/9F/+b+mv57/mT+aP5j/ln+NP4f/gv+0P3G/c39rf2G/YT9nP2j/Z39mP2m/eX9Hf4h/hz+LP4y/jv+Ov5i/pT+g/6S/s7+Ff95/9b//v8wAGwAmgDQAB4BUwFzAawByQH0AXUC7gIHA+UCqAKfApMCYAI3AgQC1AFoAf8AsACYAJ4AcgBMACoAJAANAOL/3P/n/9X/u/+D/13/V/8t/yz/NP9O/0L/Hv8E//D+9f7t/uj+9f7e/sP+3/4M/zr/bP+y/8f/wP/4/zoAOAD4/9f/2//U/83/6f/x//f/8f/n/+b/8v/V/6j/ff+Q/7j/x/+y/6v/2f8IADAAVwBzAF8AMgA6AE8AaQBxAHAARQARABsAUwCFAH8ApADgABcBIAH+APkA8wDRAJEAgACAAK8AzwDFAIUAQAAwAPv/uP+R/4v/YP9A/zD/H//o/v7+Sf9s/4T/iv+n/+r/PgCHAJcAuQD5ACABbQG4ARACMAIdAhwCTQJpAp0CdQIaArYBYAEFAY0ALQCe//n+Zf4U/tT9jv1B/dL8f/xd/I/8rPy9/P78GP1J/VH9hf3g/Sr+hf7J/tX+2f4C/1f/c/+A/4j/k/+N/57/wP+4/5T/ef+B/4H/hv+D/6r/2/8PAFcAkQDSAP0AEwEjAS8BQQFtAZgBxAERAkcCgAK0AvACNANrA4UDjANFAycDbwPyAh8DDgPsAmgCrwEmAbYA7v+e/1P/l/8RAPv//f+K/xL/nv6G/kT+bP61/rz+H/9R/5r/mv9a/yn/6f7P/pX+c/5U/kP+XP6K/sj+wf6a/i3+tv1X/V79mP3Z/Qz+H/5F/lf+Vv5U/nP+kv6Q/gX/kP8bAHQAngDrAOQAIAGSAdwBBgL0ATgCrQIGAwQDygKDAlAC9QGWATYB2QCiAHwAaAA+AB0AAQCY/w3/t/62/rH+uf7I/gr/DP8B/+r+6P7h/uf+Bv8L/yv/XP+Q/7n/z//a/8z/sv+2/7//3/8fAGEApADJAN0A1gDXANUAzwD1APsAEQEiASkBHAETAckAnACPAK8AwwDaAOoA5gC4AJcAngCmAJMAbABYAFAATgBDAEkARgAnAPn/vP92/y3/8P6v/pj+jf61/rn+yP7e/sH+w/6z/sb+8v4M/x7/Kv9B/zv/NP8//1L/WP9D/xT/JP81/0H/Zf9s/3//bv+A/6z/2f/7/xoAQwBxAKcA2QDaAOEAAwEaAScBRQFxAYUBrgHZAc4B5AHVAc4B0AHDAcwBwQG1AbgBpAGGAW4BTAEvAfUA3wDSALsAgQAmANr/ff83/+D+lv5N/iT+Cv4E/vf91/29/cP9sf2U/Zr9i/2X/an9xf38/Rj+Lf5L/oX+q/7I/t3+Cv8x/2H/gf+G/5b/u//n/wwAXQCXAMUA2wAKAS0BPgFZAWEBhwGgAboB0AHfAeQB7wHlAdsB1gHKAc0BqAGFAWkBSgEuASwBNAEcARYB+ADLAJ0AYQBJACQA5P/N/7n/rv92/2//Vv83/zb/Gf8m/zf/L/8a/xb/Fv8P/wP/B/8i/zX/PP8w/0b/UP9P/1H/YP9g/17/Tf9E/1D/X/9g/17/eP+W/7H/wf/C/8//6f/t/+7/9//6/wcAEwAeADIASwBqAJAApgCwALoAsgDQAN4A9wASARIBKAETAf4A4QDMALQApACOAHIAQwAyACYA/v/P/6b/e/9T/0L/MP8o/xH/Cf8O/xH/EP8R/yb/P/9J/1f/Yv9q/4L/e/+H/5//tv/A/9T/4//s//P/DQAfAD0AYQBlAIQAlACHAJoAoACjAJUAlQCeAKEApwC1AMYA0QDeAOEA6wDiAO8A5QDPANcAwwCuAKMAmQCJAG0AcABSADgA1f8JAMQA5f9p/87+lP2y/Bz9xP21/vD/AwEKAc4BkwLF/3f+2f2j+537WP0E/jL/PgFoAVIBAwEWABT+sv9qACj/EQGKAQ4AKwCjAKP+G/+n//7+yP8cAQQBcAHHASYAWv8F/xf+Fv5H/0z/bf8MAQoBxQCxAZcBuQDtADABMAESAm4CUwIXAgYCPgE3ATwBmQHmAQkCNQKOATwBgQA6AIX/5v7D/q/+rP7w/tv+RP44/n39T/2Q/b395f1J/lP+Vf6I/o/+t/6r/vX+Jv8s/9r/9P+uACkCtQFiAVkB6v/l/sX+GP7n/Tr+rP5q/93/dAAuABQA7v9I/4//LACFAAgBYgEnAUABQQH/AA4B5gBQAU4BbAHkAdYBqAFyAdQAbABtADgAWwBlADMAPgAOAOL/9f/o/wQAQwBdAEkAGAARAM7/aP83/xj/xv4N/3j/pv/W/5n/ev9M/07/n//+//3//P8uABQAv/+i/3f/gv/f/wwAIACXALwAmQBPANj/L/8F/3b/Vv+E/0MAKgAaAKkApwCJALUAgAA1AIoA4wDdAEsBrAFOAREBCAGxAMMABAHrACcBPgEHAdwAvAARAGb/9f5k/uL9+P0X/v39Q/5E/uv90v3K/X79iP2p/Vf9sf0c/jL+z/4k/8X+C/9u/z3/uf+NAIwAxgAsAb8AcgC+AP4AyABpAZ4BPAFnAeQB8wENAisC/wFpAUMBRQFOASUB8ADSAI8AOACBAIcAUAAoAEsA0f/6/5UA2v/b/8v/Gv9n/1sAWQAFAUoBowBqADEAX/8s/6v/v/9v/10AgABW/+j/yv///p3/OP+0/n//kP+h/97/ev+9/qn+i/5P/gv/R//H/i//pv85///+XP8I/9n+Lf8t/3D/GwBcAJYAAQG1AJcA1QDyAOUA1gGsAqwClQJfAukB8AFtAsUBagHBAXwBOQE9ARIBpwCKAAwANf/P/uz+nv4K/sj9ev06/U79G/0B/YP9wf2x/ZL9b/3h/WD+mv6J/s7+Kv80/1//wP9FANUA1ACWANUAIwEZAWYBdgF7AawBogFqAbEB0QGeAYwBWQEWASEBRgEuAdoAuQBgAPf/4//S/67/cP9K/wL/KP9h/6D/y/+1/57/i/9w/4r/GQAvAHYA5wDTAAMB4QBjAKEAQgD3/yIAbwBsAKcAEgEkAMb/FACh/y//5P/Y/0v/ov/a/zX/B/+A/5j/xf60/r/+P/5z/iv/9f5b/9D/mP9z/+H/8P8WAIQArgCkAPAAZwF+AcwBEAL0AeMB9AEwAoACdQJPAiUClQGbAbMBDgF+AJcADQB1/47/XP8W/wX/wf7S/U39UP2//Lb87vyv/F38qPyf/If8Nf18/VL9cv2s/QD+gP4W/1X/T/+g/+P/SgDjAC4BXwF9AVcBiAELAmgCEAMJA7cCXwIoAtoBEQIrAvoBxAFpATUBMQEcAb8ALwDc/zD/8v5B/wT/6f7X/pL+ef6q/jT/Sf+K/93/8P8wAMcAMgE6AaEB1QGTAa4BTgJ9ApoCgwLmAT0B3wDiAH8AOADI/9/+bv4d/u39xv3O/YD9if2L/Wv9d/13/V79dv3F/X/+jv5C/87/CwAgADQAewDMANoAeQFtArcC+QJIA8cCrgIiA3oDeQOHA2EDuAI7AjoCsQEsASQBvwC9/yL/c/7m/b79KP1u/Pr77fu1+2r7dvuT+8H7r/uY++z7uvwn/VD9cP3z/ZD+9P6K/0cA0QBEAV4BRwF0Af4BaQKlApECaQJZAjkCEgIqAlUCLgLjAUECVQJtAkECuQFDAa8AbQCUAAsBVQHUAGgA6P9//0H/O//F/sz+If+t/rf+8P4L/wn/9P76/hz/4f9QAHgAdwC6ALcA4wBoAWoBoQHHAWYBKwEeARIBpAAkANb/jP8k/w//Bv/H/p7+L/6J/XX9Yf2l/Zf9tv21/df9Cf5O/qb+6f6i/0EA2AB/AZYBqQH2AXkCygL8AnYDUgNZAxwEkwSFBDkEBwRXAxAD0wJCAvAB3gEuAeL/h/9a/4r+m/3D/DP8A/zH+0P78vrJ+sv6jfpk+qP6CPtO+6z7B/yM/AP9cf0Z/tf+f/8bAFkAogAzAUsBoQHcARgCTwKLAp4CaQKiAq4CTALbAbEBogEHAh8CEwItAuEBVgHpAN0A4QDdALoAVABWAH0AfABbAFUABADQ/63/Sf+W//X/EADg//H/LgBCAIUAnACUALIA5QAnAUUBPQFgATgB1wB+AF4ANADs/3//WP8s/+D+xv6C/pj+Tv4C/pL9t/3K/er9Gf4y/kb+UP4d/nf+C/95/5b/tP/y/10AfQCYAOYAHAFvAcwBWwK5AvECwQLdAlkDDgRyBFgEvwNWAw8DkQIlAkcClQKRAkQB1f/0/qH+Sf5N/X78Ovzx+3b7wPqT+uj6rvrT+Xb5APro+rv79PtX/O38d/3W/Sj+DP8QAI8AhgCGABYBGwK8AtQC3ALhAp0CYQJ5ArECGQPoAlMC1AH8ARcCsAErAdwAEAFXAZYB0wHKASMBOgC6/9T/RADKAMQAqgCSAG0APAA3AP3/2/8KAB4AOACIAL0AdgAjAPP/4//6/1QAqgDtAMAADgCf/2j/eP+D/xz/v/53/mn+WP5V/mj+X/48/pH9L/16/f39fv6a/tn+5/4v/0X/Mf9N/xEAcACyAMwAAQFSAU0BlAFDAWEBrAEkApUCzwKZAuICkwNgBAUFvwS3AyEDiwJVAtMChAOYA5YCSAEXAFr/Df80/hb9a/zk+2z7+vqh+nz6EPoK+Vr4tPh6+T764PoU+1X7/vu2/ID9jv6Q/97/AgBuAEcBpwJwA5MDVgP3AtkC/QKUA+EDzwNBA0wCvQHnAfYBuAFuAboAGQCp/23/o//v/4QALgD9/woAXgDUAMIApQCnAOgAHAGqAUkCzALIAmUCJQIdAnwCqQI6AtMBhQHgAHkATgAHAJH/xf7t/W/9vf0F/uz9fv38/Jb8Zfxf/DP8Y/xa/GD8lPzz/Ir9D/5b/pX+Av+J/yEAWwC3AKEAzAAaAWsB9AEUAsIBaAFPAWUBpAE6ApACfgIiAigB1wAQARIB1wD7AOABAwMIBP4DDgONAm4CAgKmAckBLAJZAkICrQHxAEcAZf84/l39yfyH/Fb8MvwY/PT7Zvua+h366/nq+UD6Kvuu+xr8hfz5/Jn9Ff6C/sj+a/8+AAoB3AGTAg8DSwMNA8ECtQIBA0sDJQM4A0ADJwPaAmkCHwKfAfEALAC4//P/OwBXAOb/p/94/2kAUgFJATMB1ACjAIMA9gCEAVkCwwJ2Ah0C/AEjAjoCmwGmAFcAYgDr/8f/8/9d/7f+xP33/Mr88vwT/cH8ePws/CL8oPzc/Oj8+fyb/HD8GP3J/S7+s/5T/7L/6v8+AJ0ALAGqAZoBqwHuAVAC6AIyA3MC7AGrASgBIwErAToBTwFOAWkBQAFiAecAWQDI/2D/lf8PAFUAGgH5Ak4EIQQuAw4CDAKOAsoCrwLVAj8DDQPcAhECwQB//8j9VvwI/F/8kPw1/KL73PoZ+nb55/hw+Ir4Mfn1+a/6gft4/D79tf0B/mX+Nf9SAHABTgL2AooDIgRTBGkEmASEBDAEBATnA9oDowM7AwEDkQIDAmcBrgDL/1X/Hf/a/qn+c/4x/h7+Iv7I/tL/igB6AGIAkADKABYBbwHjAUYCSAIcAl8CvQLIAiYCNwF/AI8ApwA/AOb/kv/O/hf+j/0c/fr8vfxn/EH8QPxX/Kb85Pzo/B39LP0G/WP9+v2s/kb/0/8kAF0AxAA/AdEBDAJAAo0CwQL1AvgC/gIQA8wCigL8AZgBcgGZAZkB3AAHAJv/sf+w/6r/eP96/4n/mP+f/2r/Bf97/+cAXQL6AvsCowKUAgQDMgP6Ao4CXwJdAhkCOAFiAOj/AP+3/aP8yvt2+zv7zPrE+W75zvmq+UX5C/lh+SH6r/os+zX8rf3q/uL/iwAVAcoBjQLqAmMDYQQqBUIF9gTDBDoFfAUvBYUE2wOBA9MCDAK3AZIBJwHw/6P+AP7Y/QP+u/1D/fz8HP1g/T7+Y//p/8D/d/9y/wUAMgEdAqwC+gIBAyUDGwNGA5EDRANqAtIB1wHIAYQB2ADu/+z+z/3e/Cz8+/uY+wD7pPra+l/77vvO+0r7WPvG+1X8Kv0F/vr+7f9+AOsAaQHcAUwC5AJ0A58DoAO7A94D/APyA78DhgMtA3gCygGNAWMB4wD8/yH/g/5V/nv+Lf7E/Zb9uf0F/jP+Pf5//q/+3v7x/+kBnAPpAygDkQIcAwIEFwShA6kD7wPlAxEDPgLnAZEBGgDn/U38mvvE+0z7Vfpm+cH4SviT92f3Avjw+Kf5Nfr++lv8Ov7M/5QAIwG9AXICGAPmA+sE2AVbBkQGSAZVBl0GDAZEBTgEPQOdAuABjwGBARcB+/9d/ij9kPxP/GL8QfwN/L/75fuN/CL90P06/qv+gf+6AM0BkwIHA5ADEAROBGUEhwTtBBMFvwQGBGADyAITAiwBVwBk/z/++/ww/Mr7Wft9+ov56vjR+B35ffn0+YX6N/vN+1T8+vz//WH/cwAzARkC7gLaA8QETwWLBYgFmwV5BWkFCgViBMwDfQM2A70CJwJMAUYAtf8F/0L+mf0R/d/86vy7/FL8QPwO/Cv8v/xm/eL9jf63/1sBbwMcBUYFFwUtBXsFhAUTBdkE7AQ3BdcE+QMEAzMCGwEq///8c/u5+uj56/iD+BL4aPfr9or2X/bq9uL3rPiG+eb6sfxA/lX/PACMAdkClwM/BB4FJQZeByQIaAgkCI8HnQaTBdcEGARyA9ICaQLZAcAAXP8K/vP8DPxN++H67/o7+5L7zfve+0H8qfwX/Zb9QP4n/00AeQHFAikEewV5BpMGfgaKBqsGQgbbBYgFPQW+BOsDDgPCASoAg/7s/J373/o/+lL5NvjV95/3Z/da93j39/fa+OH53PoN/C39e/7y/1UBigKcA3kEAAXcBcEGJQc9BysH1wY4BmEFbgTOA1cDkQKaAbAA6/81/4n+m/28/Ar8k/s7+xv7Qftz+7P7xfv2+1f85PyQ/VT+Wf8oAL0AdAE3AqoDcwW5BswGpgbnBhwHuQbeBUUF+gSJBIkDQQJXAYAA7/6i/ND6F/rA+SD5Hfh99zT37PbK9t32cvd8+Kj51fot/Ar+EgCWAXoCMwMABNUE6wXkBpcHCwhGCCUImwe6BucFQAWKBKEDegJhAUgAaf/B/vT9GP1A/Hr76vq1+sb60vrz+v76G/tj+/r7wfyS/TP+Af/w//kADQIAA8EDKARHBIQEHwUrBu0GpQbPBfcEMARdA3gCngHKALn/Wf4d/V/81Psm+/v5tvj398j3Dfiu+Fj53vmZ+pz7n/yq/dH+5v8LASQCKQMKBLsERQW7BScGWAY7BhkG5QWbBXYFDAU0BDEDPAJjAcEAIAAm/zn+g/0C/ZX8P/zc+7v7vvvA++P7SPzG/CD9t/1E/un+Vv+U/9H/VwDXADcBkAEQAhwDbQRZBUsFsQQ/BAQEggPjAo0CewJYAvwBdAGoAIX/7/0l/Mv6ivrF+qX6Fvrg+Sv6bfqj+tj6Qfu9+1f8af3M/jMAMQGIAbUBIwLnAmIDawOQA9oDQQSrBOYE7QSTBK4DiwK8AVAB6gBpANX/nP+6/9f/m/8z/8T+Hf6G/R79Gv1N/aj96/0z/oX+z/4F/1X/ov/d/1YA7ABCAUcBbQGhAQwCcgKbAsACpwJlAlMCZgI4AqkBBAGCAAAApv9T/9/+R/6x/S/9CP0h/SD9Fv38/PL89vzw/ED95P2T/uX+Cf9j//z/2QBWAcIBHgJcAnoCdgJgAkkCOAI+Av0BbAF8AZUBWAH8ANEAoQB/AEkADADq/4z/OP8d/w//2v6z/qL+ov5e/lv+dP51/qb+9v4q/0r/tP/b/7j/nf+G/53/uv8cAI0A5QASAScBQgH1AJgAZgCHAK4AtwCtAK8AvADEALUAbAA9ADgAEwDK/6v/iv+c/4b/c/96/2v/Vv8T/+f+/P4y/1b/jv/z/z4ATgAhAOL/rv/G//r/EQD4//D/NgClANQAtwCNAHYAgwCUAKQA0AACAe8AyQDEALQAcQARANv/y//T/8b/g/9Y/yf/EP8n/y7/4/6L/pv+8P4j/zX/PP9c/3z/kv+1/8P/9v9OAIcAfwBjAIEAyQDyAAsBGQEgATYBXgFlAUkB+gDYALYAjABdABEA7v/u/zAALADu/3f/Xf9y/33/eP9L/wz/Av8Y/zP/S/81/zr/Vv+D/77/FwCNAMMArgCdALsA9QASARQBBQEDARsBJQG/AD8A8v/B/7P/fP9C/w//Fv8u/wn/4v6R/pD+s/7a/sH+n/6r/sr+/f4N/+/+F/92/9b/LABDAI4AnQDIAO8A7QDIAL4ACQFqAXoBTQFHAUEBBQHHALgAxQC3AEoA2v+B/0r/YP+e/7//i/9Q/1H/Pf8S/9n+6f5H/8T/+/8WADsAUQCiAKIAjQBwAFwAowD+AFgBIgGrAHEAsgAtAWwBCwF9AAQAHABOADwAIwDl/5D/Fv8X/2z/sP+g/0D//v7u/t3+lP4t/gf+Vf7Z/mX/1v8IAOr/xf+h/6r/7P8+AHMAiQCCANAANgFNAdkAgwCiAJ8AnACCAIIAjQBJAPz/p/+p/+T/DADt/5b/Rv8t/zz/Vf+E/5T/zv+p/6H/wv8fAHkASAAkAA0ANABuAMAA2gCjAGMATwCSAAABPAEkAekAqAC1AMQAygCuAFIADwDK/5z/XP/u/qn+yv7z/tv+uP7m/hX/Tv98/6f/jf9G/wL/0f4O/2n/6P8mABQAGQAkADQAhgC7AMcApQByAFAAGQAgAFIAjQC7AMoAzQCOACwA8//f/8D/Zv8m/zX/qf/a/7b/j/9w/3z/tv/z/yUAYACLAJ8AjQCDAIAAgAB6AGIAPwBMAF8AmgDlAL0ApAB3AIcAegBcADwA9f/j/8T/wv/y/wcASwBiACMAoP8//wP/KP+J/77/sv+J/3//uf8AACMAEQDs/63/pP/D/wwAggCmAHsAIgA1AGoASwAuAAEA3v8ZADQACADW/7H/CwAqACwACgCW/zH/5f7i/hn/P/97/4z/ff9u/zz/Zv/L/yMAagBNAOj/w/+v/yAAWgBkABkAm/+p/wYAkgC9AJoAgwByAI0AjQBRAPf/9P/0/xUAJABGADAAKQAYAM3/hv9d/5n/8P8rAPz/tP+Q/93/LAA/AEUALgA3ADUAdAChAI4AJwD7/wAAYgCWAGsA9f+g/wIAoQDAAHoANAAYACsANAANANb/wv/a/wEACQDn/7//sv/I/9f/2f+8/5f/ev+W/7D/uv/G/9H/8f8BAOz/kf9F/0L/gP/q/5sA2wCVACEAJAAbANf/Y/9J/4r/zP8GAP//EwAYAEUAPQAxAOv/zv8LACMASAA/ACIAEwAGAAMAwv/G/xwAQAAvAPr/tv+1//X/dwC4AIsA+v+v/5v/yP82AGMAOADb//f/cAChAMsAoACCAFMAVAD7/5r/bv/P/z8ASQAmAMH/g/+a/87/3v/W/+T/JABZAHMASQDk/9T/6//1/4L/R/9y/6X/1v/J/83/3/9WAMgA9wDxAK4AZgAMAOb/1P/L/6X/Wv/P/qT+9f7b/7AA7gCmAOn/kf+T/43/0P/q/zQAJQDV/7//gP88///+Sv/p/2YAhQBvAFYAMQAkAF4ApwC/AE8Ayv+Y/9n/8f/r/9r/7/8wAEEAeAC4AOEArgA0AOH/xP+7/8r/CgAzAEEAPgA5AEsAEQDx/8z/ov/U/+3/OQA8ABcAHAAzAFMAUgAVAJD/Y/95/5T/o//m/9//8f8GAFkAGgDO/3//df/I//b/EAC+/5f/fv+i/7//vP/Q/wcASgAeAOH/sP+S/3//ZP9l/1T/qv8VADkAAAAbAFYAigByAAEAp/+f//j/WQBUABEAAQA0AEcAegB2AEcA2/+0/xgAawDFAAIBzQAzALH/s//8/xQALwBoAJIAkgCWAMUA7wADAfIAdQAFANH/6//g/8b/4f/Y/+v/+//y/wcACQAPAL//p//S//f/JQAZAB0AGQAVAB0AAwDF/73/wP+g/zX//P4T/wT/8f49/7r/9P/g/4//YP9N/6n/EwAtAPb/0P/R/+//LgBKAGMAaQBRAB4A5P/E/97/7v8LACEAIgAlABYA9/8IABkAJQBFAIQAzgDBAJAATgD4/7v/3P8qAEAACgDr/wMAYQCvAKgAeABHAEIAKgAAAOH/1//C/77/2//R/7f/Vf8f/x3/N/9S/4P/vv/l/9T/rf+r/+f/UQBOAPX/if9r/5P/CgC3AOYAjwANANz/AQA6AGYAbACAAHoAXQASANL/7/8bAGsAsACxAGoARQBPAG0AaABeAAoA5//T/8z/u/+m/9j/BgAkACUANgA8ACIA9P/W/6b/fv+B/4v/aP95/77/w/+o/4P/pv/a/+z/7f/F/6v/qv/H/93/+//x/8P/lf94/2v/hv+a/9D/EADl/7T/qP/j/yAAWQCTAHkATAAaAPf/wf+v/+//QwBqAIoAfAB4AJcAuADDAKAAfQBKAB4ABwDy/9n/z//V//7/DAAdAEsAbACBAHQAeQCAAG8AMAAGAPj/5v/m/w8ARgBYAEQAIwD5/wsAEQD0/9j/wP+z/4z/jf+o/6r/tP/J/9f/5f/z/wUAGwAkAPr/1/+0/5n/qf+V/4X/fv+w/97/3//p/wQAKgAiABAA/f/g//D/FwAxAEMAWQBPACgAIgAiACAAJgD9//P/8//s/9//1//n/wUAGwAtACkABQD8//L/+v8CAOz/yv/Z/9L/vP+n/6j/6P/U/8r/zf/V/9n/3//w/+X/6//s//f/5//z/+P/0f/Y/9P/4P/n//b/CwAjAC4AKAAjADMAMwAsADsAUABvAF8AYgBoAHkAfwBmAGoAeABcADwAQQBeAFQAHwAkAB8A8f/R/+f/5//N/7b/x//O/83/zv/v/+7/4//g/8H/wP+e/5n/jP+h/63/tv+s/67/z//8/xEAMQBBACAAGwAiACEA/v/s/9L/xf/P//P/7f/0/woADwAUAOz/8//z/8v/x//S/9r/4f/7/w8AEwAJACMANQBGAFgAVQBsAFcAVQBKAEQAOgAPABoADAAMACcAMgAdAC4AKgAiABwABgAHAPj/4P8LAAUA9f///wwAAwD0/wwAFwAYABIAFwASAAMAAwDo/9P/v//M/7r/yv/L/83/4f/s////BwDl/7r/yv+8/7r/vf/B/7r/wv/E/8j/xv/Q//j/+P/1/xYAJQAcAEEARgBHACsAFQD5//b/KABIAFAATQBlAHUAZQBeAEIANQAgAAwABwAIABkAAwAXAAgAAAAMAPz/9f/c/9D/sv+//8v/2P/N/+7/AAD3//X/AQAZABYA///t/+D/0f+1/7P/wv/Z//X/7v/P/7j/sP+r/6T/rv/R/9z/1v/9/wsAAADo//H/+f/Z/+L/3f/1/wcAEgAfABUAJwAqACcAIAA6AC4AJwAAAAkADQAQADEAMAA9ACgAPAAcAAIA+P/3/+b/3//Z/+D//f8QADAAAwAAABgAFQD8//n/GwAZAAMAAgD8/+3/8f8DAA4AFAAOABMAFgD3/wAA+f/4/w4AAQAKABEALgBeAHYAcQBqAGwAYABPAEgAPwAWAAkABgDa/9b/xv/F/7v/qP+1/6X/uv+//+X/+P8JAB4AFwAAAAAAFgAJANz/3P/W/7T/tf/Z/+7/+v8WAAsA/v/1/+P/1/+p/5j/n/+M/3X/rf/N/+f///8uAEoATgBfAGkAWwA7ACcAJgDy/+L/BAD7/+n/5f/u/9P/2f/c/9P/zP+8/8X/xf/h/woAEwAMACQARgA+AEoATwBVAEAALAAdACYALAAoACwAQABFAEYAPwA1AC4AKQAZAAgAFwA1ADcARQBUAEAANwAhAPv/3v/F/6r/p/+b/4z/w//N/9//IAAXAAgA8P/0/8j/tP+9/7n/qf+w/+r/JwAyAEIAXQBmAEUAJwAIAOn/u/+o/6H/mv+y/9n/3v/I/+L/5f/J/6L/q/+o/4n/h/+X/6T/vP/F/8v/4f/g/9r/6v/L/7T/v//R/+L/9v8sAEEAVwBwAH8AeQB2AI0AgwBYAFYAXgBOAFQAVgBCAEsANgAuABQA//8kAB0AEgAcACAAOQA3AEMATwAxADEAOwBNAEMANABaAGYATgBfAGsASAArAB8ADgD1/73/mP9w/2P/bP9x/2P/fP+T/5H/kv+W/5D/Wv9Y/17/cf9q/4b/pP+h/8f/8v////7/BAAaABgABQDx/9//z//X/9T/0f/h////BwD9/xYAMwBcAEYASQBsAFwAXgB0AIEAWgBSAHgAZwBSAFgAZQBKAFEAXQBPACkALABGADQALgA8ADoAPABMAE4AVQA1ACAAEgD5/+z/6/8CAPH/4//p/+3/4f/o////4f/J/97/CADu/9//3f/M/83/6/8AAPv////2/wEA9f8VABoA+P/X/+P/5P+t/47/nP+M/2z/g/+R/5X/gv+T/43/dP+N/6r/uP+m/8b/zv/B/73/2f/m/7v/uf+i/5H/m/+7/+H/9f8TAEYAcABwAIYAnwCAAI8AkACQAIEAiACAAEIANgA5ADAAGwAcAEAARAA+AEwAOQAxADUAQQAvADUAWQBnAG8AaACHAJ0AmACEAIoAeQBtAEgAKwA6ADgAIgAEAPL/xv/L/7b/xv/D/8b/uf/C//b/9//r/9f/1v+y/5//hv9//2v/fv+k/43/hf+X/4r/gf9n/0b/S/8U/yz/S/9f/4P/sP/f/9f/zf/2/xoAFQDv/+b/7f/s/+v/2//6/wkAJgAVACgASABCAEYALwAyADQATABcAG0AfgCBAIwAgQBfAFEASgBAADgALQA4ACsAGQBDAFkARwBFADAA///I/8j/v//B/8X/z//l/+L/9v8NAAEAAgAbAA8A8v/6/xIADAD8/+7/8P/t//7/EwAkABoALgAuACwAJAAKAAMA2f/N/+T/DgARACUAIwAAAPj/EQALAAMA7//q/+D/8f8ZADoATQBLAGoATAAZACcAIgAHAAMAEwAJAPf/5P/s/8j/mf+t/7X/of95/47/if91/3r/g/+R/37/mf+l/8L/z//2/+b/1v/q/+P/3P/Y/+f/6P/6/+f/BwAJABAAIwAcABgA9v/f/87/0f/l/wIAAQAWADkAWABjAHcAiABvAG0AeAB4AE0APQBFAD8AQgBdAHIAYQBAADgAKgARAPL//P8MABgAGwApABMAHwA4ACoAMgA3AD0AHAANAP3/4//Q/9n/8//7/xcALAAqAA8ADgAJAAAA/f/7/+r/xf+w/7X/wf+f/5v/vf+X/4z/qv+9/7b/s/+7/9H/3v/d//X/8f/C/+b/9//P/8//zf/I/8H/0v/b/7z/uP/V/8z/uf+q/9P/0//Q/+v////9/wcAGgAdAPb/+v8HAPr/2P/s/wYA/f8TABwAGgASAAwAIAAmAAoAMgA4ACEAKwAsACMARQBcAGAAcwB2AJMAmwCQAJIAhQBsAFMAUwBLACQAMQA4AD0AMQAlAEEALQAiADMALgAbAD0AYQBqAFwAYgBRAC0ADgABAND/vP/M/9v/2P/C/8v/sP+G/3r/kv+U/2n/dP93/3b/df+B/57/gP+X/7T/vf+i/7z/0//L/+f/9v/r/8v/wP/B/8n/1//8//b/8f8CABIA+//f//T/9P/1/+X/BgDw/+X///8TACgALgBUAEYAVQBHAGUAdgBnAGsAegB6AH0AfABUADsARQBUAC0ALgBCAC0AGAAUACQAHQAJAAYAAQDy/wEAGAAFAOv/6v8AAPr/+P8MAAAA7f/n/+X/5P/M/+f/3f/V/+L/8/8BANf/wv+4/6T/mv+l/5v/kv+i/8X/x/+2/9X/5P/W/9f/4/8AAAYABwAJAOX/2f/p/+3/zf+9/8n/zf/Q/+P/+P/v/+P/AgAGAM3/yP/Y/8//yf/y/xQAGQATACAAIwAUAA0AJgAuACwAVQBfAF8AdQCKAJcAbwB/AIsAWwA+AFYAbABuAG8AhwBwAEUAPABNADgABQADAPP/0f/n/xAA///9/xMAKwAsACkAKQD1/8//2P/r/+n/zf/Q/9T/zP/T/+z/9P/m//f/+v/a/8D/wf+5/7P/zP/X/7j/r//X/+r/6//6//r/8P/d/9//5v+8/8r/3//x/9T/4P/2/9r/tP+3/8b/v//K/+P/4f/W/+b/5P/H/6H/r/+z/6H/qv/S/+r/+f8OAAgA/v/z/wcA7P/T/93/CgAMAPb/BQAVABgAGQAxAD8AVwBcAGUAUQApAFUAWQA0AEEAQAA3ACcAIgA4ADMAKgA2ADMAGQAlAEMAOAA7AFMAUwAuADsARAAyACQALgBNADcANQBEAD0ASABYAF0AUQBDADMAMAAHAAoAKQD7/+7/CAAHAOv/2P/d/6r/cv94/5H/fP96/4D/hf91/4f/mv+U/23/ev+O/4T/hf+t/7n/kf+R/5r/hv9r/2f/Vf9S/1n/hv+s/6r/3P/+//v/AAACAP//8P/1/wEA7v/z/xsAIgAjADMAUgB/AHUAgACOAFcARwBvAHEAWABYAGYAewCFAI8AwwDCALgAxADZAMAAyQDNALYAqwCqAK4AmgCTAJAAiABqAGEAUAAwADIAQABFABUA9/8HAO3/wP+f/6P/nf+s/4v/lv+V/5r/tP+R/3X/Xf9t/0f/Qv9R/1b/WP9e/2n/V/98/6j/uv+k/7X/uf+o/5b/n/+Z/4T/h/+f/5j/pP+6/93/5v/b//j//v/s/+X/6//y/+z//P8MAPT/8P8fACIADQAbADoAUwBuAIUAiwB1AHEAfwBtAGsAcgCRAJEApwCwAJ0AlACeALMAqQCgAJsAmACBAKMAswCEAGEAYABpAEUAGAAHAPH/3f/p/+H/4f/a/9//1v+8/7j/wf+w/5v/lv+k/6D/h/+j/6T/gf94/3n/WP9A/0P/U/8//1z/kP+p/7X/v//n/+v////z//f//P/p//H/8P/2/+7/5/++/6//rf+e/4f/lf+N/4b/jv+o/7z/v//L/+j/+//a/+7/AQDk/+7/MgBOADoAQQBnAH8AdAB8AHgAaABWAGkAZgBhAHYAmgCMAGwAgwChAGQATAB0AFkAUAA+AFgAbgBrAH8AfgCLAJoApwCVAHMAYgA+ABMAGgArAC4AJQAiACkAJAAUAAwAAgDb/8P/xf+q/43/jf+R/4L/eP+G/2j/TP9R/2H/Mf8e/yL/Pf82/0D/Z/9P/1f/XP9w/2z/cP+E/4j/cP93/6f/tv+5/8v/9f/l/+7/FAAPAPz/IQBGAEsASwBfAIYAmQClAMIAmwB3AGoAcABaAGwAmACvAK0AvADjAMsA0wDeAN4A0gDFAMQAngBdAEgAKQAcACMAPAA6ACkAQgA9ACoAAQAPABoA9P8EABUAGgD9/8X/n/+a/4j/hP9f/0z/Wf92/3P/a/+D/53/gf98/4L/fP+B/5L/n/+S/4b/sf/U/8L/xf/t/+f/2//e/9j/x/+z/6//nf98/3//lP+k/57/qv+//7P/pf+v/+H/6f8EAC0APwBMAGEAYQBKAE4AUQA0ADAAMgAeAAcAHQBCAE0APQBJAHMAcgBuAIkAlgB3AHkAigB/AHQAhwCDAFoAVwBPAGMATQBGAE0AGwD5/wcAKgAnABMAKwAvADAAOAAqABUAFAApABEAAwD6/wMA+P/T/+L/5//f/8z/6//u/+H/zv+5/47/Y/9i/13/Q/80/z3/R/9P/3r/pv/A/9r///8FAPH/7P/f/+j/0f/c/9f/xP/C/7X/0P/a//f/DAAlACwAMgAzACIAIQAeACMAEQD9/wUACAAGAOj/8//1/y4AbgBVAEcAYgBOAC4ANAArABEAFgA3AFEARABGAG8AiwBxAGYAUgA3ADMAJwAkAPL/5P8NAOr/zv/J/7v/yf/E/+z/EwAPAAcAGgAjACMALQAbABgA8//8//X/2P/i/+v/9//7/wIABgD//+r/0v+7/7L/vv/P/9j/1f/m/+r/4P/x/xEA///7//7/7f/U/+7/DQD5/9f/BgAzAC4ALQAbAAgA4f/V/9f/wP+e/8n/zv/O/8P/1//q/8r/w//e/+T/3f8BAAkAEgA0ADcAJgD4/9f/wP+k/43/l/+R/4n/wf8PACsANgBgAIEAgACAAHcAVAA4AD4AMQAjADgARgBfAHcAbwB8AGQAVQBLACoA+v/R/97/3P/d//L/CQANAAYAEQAEAPf/8P/Q/7T/ov+o/7j/wf/g/wUABgD5//r/8f/Z/9r/5//W/9X/3//z//v/8//w//T/z//L/8P/tv+4/9j/0f/H/6//p/+y/6L/wP/H/+D/0P/Z/xwAPgA+ADwARwBKAFsAYwB2AIcAgQB4AE8AUABiAEgAQgA/AEwARQBFAFAARQA/AGcAXwA0ABwA9v/1/9X/vv+5/6//nv+t/+D/FgAmADcAMQAXABgAEAANAAIAIQAIAPb/0f/f/9r/vv+z/8H/3v/F/97/+P/i/9P/zf/L/7n/uf/J/9r/9v8gAEMAHwApAEAAKQAFAPD/xf/C/7H/nv+c/4j/kf+q/7v/z//l/wQACQDo//j/+//b/73/qv+u/7r/xP/F/8T/qP+q/5j/sv/R/+7/9f///y0AOABBAEUAWgBZAGMAVABbAG0AaQCFAG8AXgBuAH0AcQBvAH4AeABcAGAAcgBHADsAQQA7AD0ARwCLAH0AMAAyACYAGgAhABQABwATABAAHwAdACMAHQAUABcAAwD//+z/1//i//L/xf+k/63/lP+E/3//cv9x/3T/lP+o/4v/kv+R/3P/cf9o/1H/L/8z/0//Xv9t/53/wf/A/7b/zf/2//D/2//z//b/3P/N/8b/yv/M/9n/2//3/yAAUgB1AHIAcwBjAGoAUAA/AC0APwBEAEkAXABuAIkAowCpALIAsgCXAJcAggBoAFYARABDAEIANwBGAEYARgA4AFAAWgBXAGAAXgBjAHMAbABwAEYAMAAyACEA8P/W/+j/4/+//9H/4//M/73/t/+u/5j/jv+r/5P/gv+F/57/nv+x/+H/6f/x/9r/2//I/6j/lP94/2H/UP9y/3f/bf+M/7j/zf/e/+H/2f/d/+L/4v/i/+L/DAAAAP3/FgATAPv/8/8SACIAIgD1/w8ABwDx/+X/9/8IAPD/9P/m/8v/zf/h/+b/9P8XAEkAcwB4AGkAYQBhAFoAVwByAGAASgBHAEsAJgASAB4ADgAPAB4AMQAwAGQAhQCFAIAAaQBTAEIAMQAbAPn/3v/z//n/5f/b/+//7f/W/9P/wP+//5//nv+Y/63/mP+I/4v/if+q/7j/1v8DACUAQABYAE8ALQAbAAwA9//l//D/AQAIAPr/9/8dAEUAOwATACIABgDO/9D/5f/k/9b/5v8LABUAFAAOAAgAGwAmADYANQAiACAAHwAwACkAGgAbABsAFwD5/93/v/+8/8T/uP+1/8//z//Q/+r/4P/N/8z/2v/D/4//k/+H/37/eP+j/7H/t//P/+b/DwAaADkAVQBeAGQAhACEAHMAcQBsAHQAUQBJAFoARwBaAFgAPwA7ACMA9//h/9L/4//h/+j/EgA3AE8ATQBuAHIAbABaADYAEgAAAAoABAABAAAADAAFAO7/4v/Y/+T/zv/m/+T/5v/i/+b/6P/s//r/7v/X/8P/4P/h/8j/pf+q/6b/nv+l/4X/bv+W/6T/s//L/83/5//v/+L/9v/v/+7/BAAmAAwA6v/W/+f/0f/X/7v/t//E/8//5P/M/8v/8/8XAO//6P/k/9f/3v/2/wQA5f/J/8//+P8MACgAZgB6AHUAiQCgAIsAbQBCADgANgAtADYAKwAhAB0AKgAxADoAUgA6ACYASQBmAEQAJwAiAAgA6f/X/9//2//d/+f/AQAkACgANQAuAD0AOgBZAIAAgACDAHwAcQBXAEcAJgD//wYAAwDr/+f/3v/c/+f/1f/b/8b/uP/C/7v/q/+8//L/6//T/+P/2P/O/8H/sf+b/5n/r//K/8X/uP/e/+D/4f/e/8X/vf+d/6D/pP+d/4f/l/+S/5n/rv+//8H/rv/E/9L/zP+8/7//tv/C/9P/x//W/+L/8/8TAAsA/v8DAA8AKABGAEwAZgCLAJcAqQC6AMYA2QDEAKoAvADmAMkAkQCHAGkATgA8AC0AFgAfAEwAcQB2AG4AbABGAA4A8v/d/9b/wv/Q/9z/z//Z/+f/6v/I/57/tf+v/6z/3v/a/+z/AQALABYAAAAHAAIA+v/s/8H/kf9j/zL/Iv8i/yb/WP+A/5z/tP/L/9L/yv/D/77/v/+x/8r/7f/y/xcAIQAYACMAJQAWAAYABAACAPX/5v/4/zMAPAA/AGkAbABHAD4ASAAtACEAKwA2ADwASQBlAGcAVgAtADQAOwAGAPr/FAAcAAoA/v8iABoAIAA6ABAAEAAqAEQAQAA7AFMASgAnABwAMgAHAPv/FQAcAA0A/P8hABUA/f/h/9T/zv+t/4P/ff9f/1H/av90/33/mP+2/8X/2f8aADkAQgBDACsAGAAXAD8AHQARAPf/8f/t/+D/2//N/8j/yP/l/8z/nf+L/5v/iv+P/5v/tv/D/7//3P8UAAsAFgARAPX/5v8JAAoA+/8jAB4AIQA2AFgAaQBrAGIAWQBmAGQAbwBOAEMASQA5ADoAKQA0AFkAYgBLAGMAbgBWAEQAOgAtABsAAgDa/8n/3P8HACUAIAAxADEALgAzADcAJAAjACYANgA5ACUAHgAMAPX/yv+9/6//o//A/8r/wv+m/6r/nP+E/4z/lv+w/8D/pP+Z/5D/kv+c/6L/rv+l/63/sf+//7//v//A/7P/q/+u/6T/rv/G/8H/tP/C/9//6f/6/w8AEgAQAAwA6P/y//z///8AAPD/4//Y/+H/8v8lAEIANwA1ADwAUwBYAHAAgwByAIEAfwCZAKAAqAC4AK0AnwCXAJQAfwBfAEQAMQAuAD0ATQBbAGAAewB9AH8AUgBNAEgAKgAzABwA+P/c/9H/uv+6/8n/3//f/97/BgAVABcACQAJAOH/wv/D/6//l/+N/4H/mv+k/5T/sP+j/6H/vv+y/6T/rv+0/57/n/+d/4//hv9//4f/e/+K/63/5f/n/9n/3f/c/8P/o/+i/5P/of/R//z/3P/m//r/EgAiAB4AOwA6AD4ATQBnAIAAlAChAJ4AmwB0AFMAPwAnADAALwAYABcAKAAjAAgAJgBIAF8AYwB5AIYAcwBcAEwAWQA0AC0AGgDr/+H/4P/X/8z/zP+8/8T/qv+Y/7P/4//q/9z/+f8TABAAKQASAP3//v8DAAQA9P8BAAkAKwAaAB8AJQAcAA4A9v/2/+3/9v/6/wwAEAAhABsABQAGAOL/wf+d/4X/ZP9U/2//hP+o/6v/p/+c/4j/iv+h/7P/pv+9/8v/0//R/9D/0//T/+X/8P////z/5P/b/8j/1f/o/9T/6f8DAO7/BAAKABUABAAGACkATABgAH0AtgDHAMYA6wDhALkArQCfAKQAlACYAJwAfABgAGoAfQB2AGgAXQBcAFAAXQBeAF4ASAA7ADwAIQAaACQADwDn/8n/qf+O/3j/av9l/0T/Rf9c/1f/T/9Z/2T/d/+A/6L/jf+M/5H/cf9m/2n/dv9//5P/lv/B/9r/4//5/wwADAAIAA4A/v/l/+H/8f8IABQAHAA7AEcAUwBNAEQAKAA5AFIAOgAaABYADQD1//P/FQAlAFQAgwCZAJkAmQC0AJQAbgBcAFEASQAzAE0ASwAwACcAHwAKAOr/1P+8/4//Yf95/5H/lv+y/9n/8P/u/wMABAADAP3/BQACAPn/8f/q/+j/zP/W/+T/1v/o/wUAAAD4/xYAJAAAAN//7v/9/xkAKgArACwAGgAvACUAAwDt//X/6P/Z/+z/9f/v//v/FwAHAPz/8f/h/+f/xf+z/77/1//5/yMAFQADAAwAFAAJAP//6v+4/8v/wv++/8H/0//l//X/GAAsAEUAWgBmAGwAXQA/ACIAAAD///D///8KAP7/DAAcAB4A7f/h/+r/3v/B/8//x//N/9f/BgA4ACMAGwAMAA0A9//+/wAA7//t/wgAFQANAP3//f8UABsAOQBQACgAGgARAAsA9v/u/+b/xv++/9f/7P/n/9r/w/+//5X/lf+e/5r/pf/U/+L/6f8IAA4A9P8BAB0ADQD8//X/FQAjADIATQBXAFMAcQCWAJAAhgBuAFcAJgAbABYAJAAKAAwAHwAYAA0AGwAtABkADgARAAYA8f8AAA4AEQAKACcAIAACAAUAAQAFAOz/3v/l//r/6//p/9//2f/d/+H/3f/F/9f/2f/A/67/v//N/+X/+v/y/+r/CQAWAPb/8/8GABQADAAaABkAKQALAPD/CADv/93/8v8CANz/wv/A/8L/uP+x/7j/sv+t/6r/l/93/3L/h/+z/7T/z//o/+z/9f8GABgADQAmAEMAKwD9/wYAGAD2/xMALQBHADUASABeAFEATwBYAFEATQBtAJAAbgBiAHgAZQBcAFUAcwBzAGIAZABgAFYAPAA1ADMAJgAQAPf/5f/S/+H/0f/d/+H/2v/a/+X/8f/v/9n/sv+4/7z/4v/n/+3//P/s/+P/2v/u/8b/rf+d/3j/Xf9O/z3/SP9z/4L/i/++/9L/xP+5/6//zP/N/7T/x//l/9r/xf+4/7f/uf/b/8n/ov/A/9X/1f/x/0IAZgBxAHkAkAChAKEAngCiAIwAagCXALwAxgDRAOcA2wCrAJAAfQB2AGwAYgBmAGMASQA0ACsAFQAyAEMANAA9ADQAQgAsAC0AOgA1AA4A4f/X/8//tP/M/+X/4v/5//T/3v+6/6P/gP9p/2f/b/+B/4z/mP+b/3v/Xv9t/1v/WP9p/3z/iP+R/4j/lf+X/5f/sf+m/6D/of/B/8r/3f/n//L/4v/1//v/1v/n/+n//f/u/+L/1//k/+v///8TAC8AMgBGAFQAWABfAFwAUgBLAGgAWgBbAGMAcABmAGwAjACXAJMAlgCyAJ4AmgB9AGIASwA6ADIAJgAhABsAHAAFAPL/BwD1/9j/9P8bAB4AKABKAFIAGQDs//D/AAD2//j//v/0//H/9//k/8b/uP+Q/2j/S/9S/1z/Y/+G/5f/rf+1/6//xP/S/8//zv+9/7L/u//S/9j/0//D/77/xv/M/97/+f/6/wYAHQAiABoAFAAZACoAOQAyAD0ANABQAG0AcwBwAHwAfQBcAGEAawBKACsAIQA1AC4AJgAlAB4ALwAgABgAEQAWACgAOwA5ACoAKAACAN3/z//V/7v/w//h/77/qP+u/8//xP+//7//rf+u/7H/w//W//r/EQA1AB8ACwAFAPD/3f/o/+7/9P/1/wYAKQAfABQAJwAqABcAHwAaACkAEQAOAA8AIgA7AFcAdwBaAEIAFQAEANT/vf+//6b/of+i/7P/xP/I/7v/v//W/8r/wP/F/9n/0f/V/9n/9//9//j/DQA1AEoASQBAADkAKwAUAPD/2f/S/8//2////wMAFQAaAAUADwAKACgAGwAoAFIAcAB4AJIAjQBcAEAANwAyABEADQAIAAQA/f/4//v/7P/0/wMA4P+5/6n/jP+Q/3X/fv+O/5v/r//B/8L/vf/V/7z/sP+y/8P/yf/L/8f/zP+7/7H/2P/t/woAIgA7AGQAYgBiAFYAQgAqAD4AQAA/ADkATQBrAGoAbgBfAE4ANAAnAA0ABwAKAAIA8//a/8n/q/+w/7T/yf/n//T/FQARAC8ATgBaAFcAUgBMAEwATgBGAFkAUwA8ACkAHQAxADwAPgA4ABwADAD3/9b/vf+4/5j/kP9+/2r/av+F/5X/gf+D/4H/c/9m/2j/i/+K/27/d/+p/8X/zf/1/xwADwD5/+n/xf+1/7n/r/++/9z//v8RABEABAD6//P/6//1//T/BwAnACcAPABCAD8AQQBAAEIAUABeAGIAYwBvAHEAVwBsAFoAXwBlAG4AfAByAIIAcwBbAFwAVABDADUAMQA7ACgAKwBAAEQAQwA0ADQAJQAIAPr/8P/e/7//1f/V/8//5v/e/+f/5//T/8//2v/U/9H/3v/5//3/+P/+/+v/7f8DABwADAASABgADADw/+7/xf+v/6z/pP+a/57/jf+M/5D/h/+E/4j/lP+c/6z/sP+7/7L/yv/V/+D/7/8BAB4AMwArADMAQwBBACkAJwAQAOr/5//j/87/yv/p//H/+v8TADYARwAqAB0AGgAJAPX/CgAOABcAMAA9AD0ATAA0ABgA///8//j/EgAYABoATgBZAGYAXwBbAFkAbwBxAG4AfgBwAF4AZQBYAD4ANQApABIADQASABMACwD6/+z/3P/F/7f/mv+S/57/nv+q/8P/0v/g//P/CgAUABUAFAABAPz/+/8GAAAADQAbABoA+//f/8z/zv/G/6//u/+w/6P/t/+y/5r/o/+z/5//m/+0/6f/x//V/9b/1v+0/77/uv/J/8T/4/8WABkANQBHAFYAcQBlAFEARgAxADoARQA7ADMAGgD3/+f/3f/p/wQAFAArAEcAWgBvAHUAYgBXAEUAPgAtAD0AKQAyADEABAD0//n/5f/W/+D/2P/k/9r/4v/S/8v/1P+7/8P/4P/t////8//9//v/8P/g/8H/rf+o/7L/ov+x/7n/tv/X//3/CQAYACYAKAAnABIAGwAfAC0AIgACABEAGwAcAB8AOgBHAFAAOwBPAE8ATQA5ABgAFgAVAPb/4//u/xEAGgAZABwAGAAQAB0ADwAUABYAAgAVABYADwAEAA4A/P/7/+f/2v/B/5v/yP/G/8v/zv/Q/9X/uf+9/77/yP/C/9D/8P/u//7/+P/s//X/9v/m/8//5f/1//b/AAATAA0ADAARABMADgANABEADQAMAAMA+P/+/93/0f/D/83/+f8QABsAIQAwAEkAUABMAEsAXwBDABwALgAzACUANgBDAEgALgAqAB0A9f/u/9n/yP/I/7j/yP/H/8D/0v/P/7X/tf+t/6D/qv+t/8P/4v/x/wUAHgApACMAEwAcADUAMAA/ACsAJQAlABkABwD///v/CAAaAAgAFAARABAA9//v/+T/6f/1//z//f/+/y4AKAAmABMA///z/+r/BAANAA0AHQAdAAwA/v/a/87/zv/M/8H/uP/h/+T/0f/a/9z/xv+x/7b/wP/J/9T/5//0//P/8f/y/wIACwAnADIARwBQAGEAWQAvAEMAOQAqAEcAUgBSAEwAYwBZAD0AMgAuACcABgDt/+7/zv/V/+j/AQADAOX/+P/3//7/GAAcABAACgAMAOb/zP/U/+f/xf+o/7v/yf/C/7H/vv+4/6//tv+x/67/vf/j//P/+v/4//7/6P/h/+n/6f/R/93///8GAA4AKQBKAFIAbACCAI0AgQCUAIAAfABxAIcAsQC4AMYAvgCiAHwAYwBFABUA6v/1/+f/1v/F/7v/q/+h/5//qf/I/8f/wP+x/6v/q/+l/7T/wf/L/9//3v+7/8T/s/+g/7T/uv+w/5//k/+f/5//pP+n/6P/vf/X/+v/6P/9//3/4f/c/+z/7v/z//z//f/7/9//5v/X/97/3//s/woACgAbAEwAbABpAIsAgQBgAEkASQA2ACgARABRAEoATQBMAFUASQAzADMADAD7/xMALAAvADwAKgA6ADoAKQAYAAcAAgD+/wMABQAEAAkABgDo/97/3f/J/7X/wv/V/+T/AgATACIAJQAmABoACwAfACsAMABLAF8AWQA/AC4ALwAfACoAKQAxAAkA8v/n/87/vP+q/7f/dP9n/1b/VP9s/3z/qP/h/+3/3//S/8X/vP+w/9n/3//l//H/8P/8/9z/qP+e/7X/tv+2/7f/o/+k/67/p//H//H/BgAuAEIAOwAeAAsAAwABABoAJAAZACoALAAtADgAXgBhAHgAgQB6AIUAjwCEAHUAdwByAFEASABOAD4AOgBMAFIAUwBEAC0AHAAYAPf/3//8/wwAEgAKAAwAIQAOAPL/+P/d/7n/sv+8/7r/v/+6/9b/3//y//j/+P/7/+//7f/p/+X/3/+9/5v/if+K/3L/dP+g/6f/mP+q/8j/5//k/9b/x//A/63/xv/Z/+P/+//l/+H/1v/l//r/HgA+AE0AUgBbAGoAVgBBAEgAZgCPAIUAZAB0AGcAbQBLACoAMQAsADcANAA7ABUAEgD8/+D/3//X/+D/4P///wMA+v8GAAUACAAhABIA+P/O/9r/6/+//8j/6f/z/+7/4//T/9P/yP/B/63/tv/O/8j/4P/N/7f/qf+C/5f/ov+X/8D/AAAhABQABgD+/wIAAAANACcAPQBHACAA9//1//L/+//r//T/EQAaACUANABGAFIAPAAuACQAEADe/+L//v8TABoALwBZAFUATAA8ACUACAAHAOX/zv+//6f/kv+P/6z/wv/q/wAAEQAHAAIAGwA4AGQAeQCZAIcAbwB+AHcAWQBJAIIAjwBvAEgAQgAkAOv/y//P/9n/5P8IAAMAEwA9AFYATwAAAOv/AwAHAPL/+P/r/+P/8v/t//T/9f8KANv/of+w/6//sv/W//v//v8PAAMA6P/d/8n/vf/I/7//yf/l/8T/m/+R/33/af9L/zb/Ov89/1X/Xf9x/3n/i/+t/77/tv+2/8n/1P/q/wEAGQBCAGIAVAAvADcASwBMAGQAYgB4AH4AdwB2AI0AnAB+AKAAjAC3AKoApgB1AMr/LgCqAOcAKQEDAcgAowBfAOH/uv/n/9D/of9I/yz/F//6/hP/N/8y/2X/v//0/wcAHgBHAGsAhgCNAJIAjACxAL0AvwC5AIwAbABPACgAAgC9/3f/Rv8o/+/+8/4u/1z/R/9x/4//o//N/9v/5f/5/yIASwByAG0AaABSADAAFQD+/+n/vP/G/8L/rv+//7f/n/+e/57/m/+R/43/nf+z/9r/1P/X/9n/7/8JAPn/yf/d//z/DwA7AD8AMwAqADEAHwAYAAcAIgAmAA8ABAARADMAHgAEABoALQAYAC0ANQBBAEgAYwB+AGwAdgBpAEkATQAzAB0AJQAwACUAHQApACUAKQA1AEAAQABCAFEATABDADgAJgAzAC8AKgAXAAsAEQD+//T/5v/e/97/xP+2/8f/0/+7/6j/rP+f/5f/p//J/7z/sf/K/7f/tP+c/8f/6f/Y/9L/7f/k/+H/z//M/9H//P/k/9n/1P/R//j//v8GAAIA9v/W//b/1P+j/6f/rP+1/8L/6/8OABoAGAAcACoAOAAoACwAEwAQABAAJAA8AEEAcABsAFwAawBUAFYAOwA+AEgAOwBQAFMAVQBGAEYASQA4ADMASwA6ABUAIQDt/9H/0v/m/+j/AgD//wwAMwAyADIANgAuAAcA/v/w/93/3f/w/8r/yf/b/8b/3f/k//P/+f8TADUAJgAZAOP/vP+S/2P/ZP9k/2D/aP+L/6r/qv+n/5z/o/+p/5f/kv+Y/6b/xP/A/6T/tv/P/9P/5P8UADQAPABHAFsAVABVAGEAWgBiAHIAaQBOADUACwDk/9L/1f/a/+j/AAAeABkACQD+//7/CwATAEIAYgB2AIgAlQCQAIMAiwCIAHIAcQBoAFQANQAtAEUAIwAWABQAIAATAAkA+f/q/9b/3P/g/8j/xP+y/8D/tf+b/3j/c/9//4X/j/+c/6v/qf+o/6//yP/h//T/EQAYAAoAGQAeABcA+f/s/+7/2v/R/8b/3f/o//j/GgArACsAKQA+ACkAEgD9/wsAKAAqADIAKQAnADQAOwA3AE8AcQB8AGEAZgBaAGAAWAA7AFEAUABPAFoAUwBbADYAHAD+/+b/yv+9/8v/vf/R/7H/qv+u/6P/lv+D/4//kP+b/7T/uv/E/7H/sf+y/6v/sP+9/8j/2//x/+b/5P/b/9n/zf/a/9b/2v/W/8n/uf+1/8L/uf+6/9T/8//9/xEAIwBAADUAUABXAFcAYABPAE0AUQBLAEYAVwBzAG8AhgCNAHcApgCOAGMARgAhABwA/v/+/xEABwALABgA+f/s//v/9f/k/wQAAwDu/+D/7P/9//b/8v/a//P/7v/t/+z/2P/l/9z/1v/V/9T/wv+1/9b/6v8BAAgAFwAGAAsA+P/Y/8j/xf/q/+L/+P/+/wUA8//K/83/vf/G/9X/0v/G/7r/rv+c/6z/w//L/+T///8MAPT/BgAnAB8AOABCADkAMAAeACUAIwA+ADcAGwAlAB8AKgAVABYABwDy/+L/1f/S/+f/BwAUAB8AEgAhACEABAD0/+//9P/+//3/+//3//D/+v8HACMARABEAFMATgBPAE0AFwAFAP//6v/1//L////3//n/KAA0ACcAIAAPAOn/y/+o/6j/wv/M/+n/+P/8/xwAIAAvABQADQD9/+T/2f/N/9b/3P/w//3/AgD//wkAEQD6//r/AwALAAwA9v/r/+z/6f/h/+r/8v/x//r/9//0/+3/9//y//L/9P/b/+H//P/x/+v/BgAlADwARQBKAD8ARABDACsAFQDy/93/4f/f//n/DwBHAFcARABIAD8ANAAoABAA8/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type=\"audio/x-wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Markdown, display\n",
    "from data_generator import vis_train_features, plot_raw_audio\n",
    "from IPython.display import Audio\n",
    "%matplotlib inline\n",
    "\n",
    "# plot audio signal\n",
    "plot_raw_audio(vis_raw_audio)\n",
    "# print length of audio signal\n",
    "display(Markdown('**Shape of Audio Signal** : ' + str(vis_raw_audio.shape)))\n",
    "# print transcript corresponding to audio clip\n",
    "display(Markdown('**Transcript** : ' + str(vis_text)))\n",
    "# play the audio file\n",
    "Audio(vis_audio_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='step1'></a>\n",
    "## STEP 1: Acoustic Features for Speech Recognition\n",
    "\n",
    "For this project, you won't use the raw audio waveform as input to your model.  Instead, we provide code that first performs a pre-processing step to convert the raw audio to a feature representation that has historically proven successful for ASR models.  Your acoustic model will accept the feature representation as input.\n",
    "\n",
    "In this project, you will explore two possible feature representations.  _After completing the project_, if you'd like to read more about deep learning architectures that can accept raw audio input, you are encouraged to explore this [research paper](https://pdfs.semanticscholar.org/a566/cd4a8623d661a4931814d9dffc72ecbf63c4.pdf).\n",
    "\n",
    "### Spectrograms\n",
    "\n",
    "The first option for an audio feature representation is the [spectrogram](https://www.youtube.com/watch?v=_FatxGN3vAM).  In order to complete this project, you will **not** need to dig deeply into the details of how a spectrogram is calculated; but, if you are curious, the code for calculating the spectrogram was borrowed from [this repository](https://github.com/baidu-research/ba-dls-deepspeech).  The implementation appears in the `utils.py` file in your repository.\n",
    "\n",
    "The code that we give you returns the spectrogram as a 2D tensor, where the first (_vertical_) dimension indexes time, and the second (_horizontal_) dimension indexes frequency.  To speed the convergence of your algorithm, we have also normalized the spectrogram.  (You can see this quickly in the visualization below by noting that the mean value hovers around zero, and most entries in the tensor assume values close to zero.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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smIeRFQqtfAIGFQTfTf4jKJeMbX5GZH7iXH0knprGFy7JCsq+C7Ol3ud7YczV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F3fJx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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2dedae8320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Shape of Spectrogram** : (247, 161)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from data_generator import plot_spectrogram_feature\n",
    "\n",
    "# plot normalized spectrogram\n",
    "plot_spectrogram_feature(vis_spectrogram_feature)\n",
    "# print shape of spectrogram\n",
    "display(Markdown('**Shape of Spectrogram** : ' + str(vis_spectrogram_feature.shape)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mel-Frequency Cepstral Coefficients (MFCCs)\n",
    "\n",
    "The second option for an audio feature representation is [MFCCs](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum).  You do **not** need to dig deeply into the details of how MFCCs are calculated, but if you would like more information, you are welcome to peruse the [documentation](https://github.com/jameslyons/python_speech_features) of the `python_speech_features` Python package.  Just as with the spectrogram features, the MFCCs are normalized in the supplied code.\n",
    "\n",
    "The main idea behind MFCC features is the same as spectrogram features: at each time window, the MFCC feature yields a feature vector that characterizes the sound within the window.  Note that the MFCC feature is much lower-dimensional than the spectrogram feature, which could help an acoustic model to avoid overfitting to the training dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aQDnbL59mLaGYubf7X5bxXto2QvnC7gBtUyM0ZGp50FpBGXKwdgeNbLleCKxX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2dedad17f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Shape of MFCC** : (247, 13)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from data_generator import plot_mfcc_feature\n",
    "\n",
    "# plot normalized MFCC\n",
    "plot_mfcc_feature(vis_mfcc_feature)\n",
    "# print shape of MFCC\n",
    "display(Markdown('**Shape of MFCC** : ' + str(vis_mfcc_feature.shape)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you construct your pipeline, you will be able to choose to use either spectrogram or MFCC features.  If you would like to see different implementations that make use of MFCCs and/or spectrograms, please check out the links below:\n",
    "- This [repository](https://github.com/baidu-research/ba-dls-deepspeech) uses spectrograms.\n",
    "- This [repository](https://github.com/mozilla/DeepSpeech) uses MFCCs.\n",
    "- This [repository](https://github.com/buriburisuri/speech-to-text-wavenet) also uses MFCCs.\n",
    "- This [repository](https://github.com/pannous/tensorflow-speech-recognition/blob/master/speech_data.py) experiments with raw audio, spectrograms, and MFCCs as features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='step2'></a>\n",
    "## STEP 2: Deep Neural Networks for Acoustic Modeling\n",
    "\n",
    "In this section, you will experiment with various neural network architectures for acoustic modeling.  \n",
    "\n",
    "You will begin by training five relatively simple architectures.  **Model 0** is provided for you.  You will write code to implement **Models 1**, **2**, **3**, and **4**.  If you would like to experiment further, you are welcome to create and train more models under the **Models 5+** heading.  \n",
    "\n",
    "All models will be specified in the `sample_models.py` file.  After importing the `sample_models` module, you will train your architectures in the notebook.\n",
    "\n",
    "After experimenting with the five simple architectures, you will have the opportunity to compare their performance.  Based on your findings, you will construct a deeper architecture that is designed to outperform all of the shallow models.\n",
    "\n",
    "For your convenience, we have designed the notebook so that each model can be specified and trained on separate occasions.  That is, say you decide to take a break from the notebook after training **Model 1**.  Then, you need not re-execute all prior code cells in the notebook before training **Model 2**.  You need only re-execute the code cell below, that is marked with **`RUN THIS CODE CELL IF YOU ARE RESUMING THE NOTEBOOK AFTER A BREAK`**, before transitioning to the code cells corresponding to **Model 2**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#####################################################################\n",
    "# RUN THIS CODE CELL IF YOU ARE RESUMING THE NOTEBOOK AFTER A BREAK #\n",
    "#####################################################################\n",
    "\n",
    "# allocate 50% of GPU memory (if you like, feel free to change this)\n",
    "from keras.backend.tensorflow_backend import set_session\n",
    "import tensorflow as tf \n",
    "\n",
    "# gpu specific\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.per_process_gpu_memory_fraction = 0.5\n",
    "set_session(tf.Session(config=config))\n",
    "\n",
    "# watch for any changes in the sample_models module, and reload it automatically\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "# import NN architectures for speech recognition\n",
    "from sample_models import *\n",
    "# import function for training acoustic model\n",
    "from train_utils import train_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='model0'></a>\n",
    "### Model 0: RNN\n",
    "\n",
    "Given their effectiveness in modeling sequential data, the first acoustic model you will use is an RNN.  As shown in the figure below, the RNN we supply to you will take the time sequence of audio features as input.\n",
    "\n",
    "<img src=\"images/simple_rnn.png\" width=\"50%\">\n",
    "\n",
    "At each time step, the speaker pronounces one of 28 possible characters, including each of the 26 letters in the English alphabet, along with a space character (\" \"), and an apostrophe (').\n",
    "\n",
    "The output of the RNN at each time step is a vector of probabilities with 29 entries, where the $i$-th entry encodes the probability that the $i$-th character is spoken in the time sequence.  (The extra 29th character is an empty \"character\" used to pad training examples within batches containing uneven lengths.)  If you would like to peek under the hood at how characters are mapped to indices in the probability vector, look at the `char_map.py` file in the repository.  The figure below shows an equivalent, rolled depiction of the RNN that shows the output layer in greater detail. \n",
    "\n",
    "<img src=\"images/simple_rnn_unrolled.png\" width=\"60%\">\n",
    "\n",
    "The model has already been specified for you in Keras.  To import it, you need only run the code cell below.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Japanese Language\n",
    "\n",
    "あいうえお かきくけこ さしすせそ たちつてと なにぬねの はひふへほ まみむめも やゆよ らりるれろ わをん -> 46  \n",
    "ぁぃぅぇぉゃゅょゎっ -> 10  \n",
    "がぎぐげご ざじずぜぞ だぢづでど ばびぶべぼ > 20  \n",
    "ぱぴぷぺぽ > 5\n",
    "ゔ > 1\n",
    "ー > 1  \n",
    "無音 > 1  \n",
    "合計 > 84  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 13)          0         \n",
      "_________________________________________________________________\n",
      "rnn (GRU)                    (None, None, 85)          25245     \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 85)          0         \n",
      "=================================================================\n",
      "Total params: 25,245\n",
      "Trainable params: 25,245\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# index is from 1 to 85\n",
    "# model_0 = simple_rnn_model(input_dim=161, output_dim=85) # change to 13 if you would like to use MFCC features\n",
    "model_0 = simple_rnn_model(input_dim=13, output_dim=85) # change to 13 if you would like to use MFCC features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As explored in the lesson, you will train the acoustic model with the [CTC loss](http://www.cs.toronto.edu/~graves/icml_2006.pdf) criterion.  Custom loss functions take a bit of hacking in Keras, and so we have implemented the CTC loss function for you, so that you can focus on trying out as many deep learning architectures as possible :).  If you'd like to peek at the implementation details, look at the `add_ctc_loss` function within the `train_utils.py` file in the repository.\n",
    "\n",
    "To train your architecture, you will use the `train_model` function within the `train_utils` module; it has already been imported in one of the above code cells.  The `train_model` function takes three **required** arguments:\n",
    "- `input_to_softmax` - a Keras model instance.\n",
    "- `pickle_path` - the name of the pickle file where the loss history will be saved.\n",
    "- `save_model_path` - the name of the HDF5 file where the model will be saved.\n",
    "\n",
    "If we have already supplied values for `input_to_softmax`, `pickle_path`, and `save_model_path`, please **DO NOT** modify these values. \n",
    "\n",
    "There are several **optional** arguments that allow you to have more control over the training process.  You are welcome to, but not required to, supply your own values for these arguments.\n",
    "- `minibatch_size` - the size of the minibatches that are generated while training the model (default: `20`).\n",
    "- `spectrogram` - Boolean value dictating whether spectrogram (`True`) or MFCC (`False`) features are used for training (default: `True`).\n",
    "- `mfcc_dim` - the size of the feature dimension to use when generating MFCC features (default: `13`).\n",
    "- `optimizer` - the Keras optimizer used to train the model (default: `SGD(lr=0.02, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)`).  \n",
    "- `epochs` - the number of epochs to use to train the model (default: `20`).  If you choose to modify this parameter, make sure that it is *at least* 20.\n",
    "- `verbose` - controls the verbosity of the training output in the `model.fit_generator` method (default: `1`).\n",
    "- `sort_by_duration` - Boolean value dictating whether the training and validation sets are sorted by (increasing) duration before the start of the first epoch (default: `False`).\n",
    "\n",
    "The `train_model` function defaults to using spectrogram features; if you choose to use these features, note that the acoustic model in `simple_rnn_model` should have `input_dim=161`.  Otherwise, if you choose to use MFCC features, the acoustic model should have `input_dim=13`.\n",
    "\n",
    "We have chosen to use `GRU` units in the supplied RNN.  If you would like to experiment with `LSTM` or `SimpleRNN` cells, feel free to do so here.  If you change the `GRU` units to `SimpleRNN` cells in `simple_rnn_model`, you may notice that the loss quickly becomes undefined (`nan`) - you are strongly encouraged to check this for yourself!  This is due to the [exploding gradients problem](http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/).  We have already implemented [gradient clipping](https://arxiv.org/pdf/1211.5063.pdf) in your optimizer to help you avoid this issue.\n",
    "\n",
    "__IMPORTANT NOTE:__ If you notice that your gradient has exploded in any of the models below, feel free to explore more with gradient clipping (the `clipnorm` argument in your optimizer) or swap out any `SimpleRNN` cells for `LSTM` or `GRU` cells.  You can also try restarting the kernel to restart the training process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "61/61 [==============================] - 81s - loss: 1399.4398 - val_loss: 1293.4748\n",
      "Epoch 2/20\n",
      "61/61 [==============================] - 80s - loss: 1266.4105 - val_loss: 1322.6320\n",
      "Epoch 3/20\n",
      "61/61 [==============================] - 80s - loss: 1267.1186 - val_loss: 1302.5386\n",
      "Epoch 4/20\n",
      "61/61 [==============================] - 80s - loss: 1265.6240 - val_loss: 1302.3172\n",
      "Epoch 5/20\n",
      "61/61 [==============================] - 80s - loss: 1264.0021 - val_loss: 1291.4078\n",
      "Epoch 6/20\n",
      "61/61 [==============================] - 80s - loss: 1265.6585 - val_loss: 1298.5010\n",
      "Epoch 7/20\n",
      "61/61 [==============================] - 81s - loss: 1264.6088 - val_loss: 1293.7898\n",
      "Epoch 8/20\n",
      "61/61 [==============================] - 79s - loss: 1263.7061 - val_loss: 1276.0984\n",
      "Epoch 9/20\n",
      "61/61 [==============================] - 80s - loss: 1264.9539 - val_loss: 1301.5428\n",
      "Epoch 10/20\n",
      "61/61 [==============================] - 79s - loss: 1264.6921 - val_loss: 1303.0824\n",
      "Epoch 11/20\n",
      "61/61 [==============================] - 80s - loss: 1266.1517 - val_loss: 1277.6379\n",
      "Epoch 12/20\n",
      "61/61 [==============================] - 80s - loss: 1263.6507 - val_loss: 1309.8461\n",
      "Epoch 13/20\n",
      "61/61 [==============================] - 79s - loss: 1264.5694 - val_loss: 1311.4329\n",
      "Epoch 14/20\n",
      "61/61 [==============================] - 79s - loss: 1265.0792 - val_loss: 1337.0320\n",
      "Epoch 15/20\n",
      "61/61 [==============================] - 80s - loss: 1263.8076 - val_loss: 1292.2759\n",
      "Epoch 16/20\n",
      "61/61 [==============================] - 80s - loss: 1264.9611 - val_loss: 1275.9492\n",
      "Epoch 17/20\n",
      "61/61 [==============================] - 79s - loss: 1265.1311 - val_loss: 1309.0298\n",
      "Epoch 18/20\n",
      "61/61 [==============================] - 80s - loss: 1264.4605 - val_loss: 1291.1675\n",
      "Epoch 19/20\n",
      "61/61 [==============================] - 80s - loss: 1266.0259 - val_loss: 1278.2728\n",
      "Epoch 20/20\n",
      "61/61 [==============================] - 80s - loss: 1263.0709 - val_loss: 1264.1823\n"
     ]
    }
   ],
   "source": [
    "train_model(input_to_softmax=model_0, \n",
    "            pickle_path='model_0.pickle', \n",
    "            save_model_path='model_0.h5',\n",
    "            spectrogram=False) # change to False if you would like to use MFCC features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='model1'></a>\n",
    "### (IMPLEMENTATION) Model 1: RNN + TimeDistributed Dense\n",
    "\n",
    "Read about the [TimeDistributed](https://keras.io/layers/wrappers/) wrapper and the [BatchNormalization](https://keras.io/layers/normalization/) layer in the Keras documentation.  For your next architecture, you will add [batch normalization](https://arxiv.org/pdf/1510.01378.pdf) to the recurrent layer to reduce training times.  The `TimeDistributed` layer will be used to find more complex patterns in the dataset.  The unrolled snapshot of the architecture is depicted below.\n",
    "\n",
    "<img src=\"images/rnn_model.png\" width=\"60%\">\n",
    "\n",
    "The next figure shows an equivalent, rolled depiction of the RNN that shows the (`TimeDistrbuted`) dense and output layers in greater detail.  \n",
    "\n",
    "<img src=\"images/rnn_model_unrolled.png\" width=\"60%\">\n",
    "\n",
    "Use your research to complete the `rnn_model` function within the `sample_models.py` file.  The function should specify an architecture that satisfies the following requirements:\n",
    "- The first layer of the neural network should be an RNN (`SimpleRNN`, `LSTM`, or `GRU`) that takes the time sequence of audio features as input.  We have added `GRU` units for you, but feel free to change `GRU` to `SimpleRNN` or `LSTM`, if you like!\n",
    "- Whereas the architecture in `simple_rnn_model` treated the RNN output as the final layer of the model, you will use the output of your RNN as a hidden layer.  Use `TimeDistributed` to apply a `Dense` layer to each of the time steps in the RNN output.  Ensure that each `Dense` layer has `output_dim` units.\n",
    "\n",
    "Use the code cell below to load your model into the `model_1` variable.  Use a value for `input_dim` that matches your chosen audio features, and feel free to change the values for `units` and `activation` to tweak the behavior of your recurrent layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 13)          0         \n",
      "_________________________________________________________________\n",
      "rnn (GRU)                    (None, None, 200)         128400    \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, None, 200)         800       \n",
      "_________________________________________________________________\n",
      "time_distributed_1 (TimeDist (None, None, 85)          17085     \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 85)          0         \n",
      "=================================================================\n",
      "Total params: 146,285\n",
      "Trainable params: 145,885\n",
      "Non-trainable params: 400\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model_1 = rnn_model(input_dim=13, # change to 13 if you would like to use MFCC features\n",
    "                    units=200,\n",
    "                    activation='relu',\n",
    "                    output_dim=85)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please execute the code cell below to train the neural network you specified in `input_to_softmax`.  After the model has finished training, the model is [saved](https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model) in the HDF5 file `model_1.h5`.  The loss history is [saved](https://wiki.python.org/moin/UsingPickle) in `model_1.pickle`.  You are welcome to tweak any of the optional parameters while calling the `train_model` function, but this is not required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "89s - loss: 359.5376 - val_loss: 231.9518\n",
      "Epoch 2/20\n",
      "87s - loss: 88.6610 - val_loss: 207.4911\n",
      "Epoch 3/20\n",
      "85s - loss: 59.1090 - val_loss: 185.7374\n",
      "Epoch 4/20\n",
      "87s - loss: 43.9879 - val_loss: 153.1041\n",
      "Epoch 5/20\n",
      "86s - loss: 35.1108 - val_loss: 81.2763\n",
      "Epoch 6/20\n",
      "86s - loss: 29.3170 - val_loss: 42.4030\n",
      "Epoch 7/20\n",
      "87s - loss: 25.9211 - val_loss: 28.3801\n",
      "Epoch 8/20\n",
      "87s - loss: 23.1927 - val_loss: 23.4895\n",
      "Epoch 9/20\n",
      "86s - loss: 21.5432 - val_loss: 22.9955\n",
      "Epoch 10/20\n",
      "86s - loss: 19.6879 - val_loss: 21.8165\n",
      "Epoch 11/20\n",
      "86s - loss: 18.6427 - val_loss: 18.3402\n",
      "Epoch 12/20\n",
      "85s - loss: 17.7500 - val_loss: 19.1010\n",
      "Epoch 13/20\n",
      "86s - loss: 16.4413 - val_loss: 17.6379\n",
      "Epoch 14/20\n",
      "85s - loss: 15.8641 - val_loss: 15.3611\n",
      "Epoch 15/20\n",
      "86s - loss: 14.9459 - val_loss: 15.5049\n",
      "Epoch 16/20\n",
      "86s - loss: 14.4468 - val_loss: 16.3476\n",
      "Epoch 17/20\n",
      "86s - loss: 13.6960 - val_loss: 15.2989\n",
      "Epoch 18/20\n",
      "86s - loss: 13.3613 - val_loss: 13.9782\n",
      "Epoch 19/20\n",
      "85s - loss: 12.7873 - val_loss: 12.5904\n",
      "Epoch 20/20\n",
      "85s - loss: 12.0700 - val_loss: 12.4222\n"
     ]
    }
   ],
   "source": [
    "train_model(input_to_softmax=model_1, \n",
    "            pickle_path='model_1.pickle', \n",
    "            save_model_path='model_1.h5',\n",
    "            verbose=2,\n",
    "            spectrogram=False) # change to False if you would like to use MFCC features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='model2'></a>\n",
    "### (IMPLEMENTATION) Model 2: CNN + RNN + TimeDistributed Dense\n",
    "\n",
    "The architecture in `cnn_rnn_model` adds an additional level of complexity, by introducing a [1D convolution layer](https://keras.io/layers/convolutional/#conv1d).  \n",
    "\n",
    "<img src=\"images/cnn_rnn_model.png\" width=\"100%\">\n",
    "\n",
    "This layer incorporates many arguments that can be (optionally) tuned when calling the `cnn_rnn_model` module.  We provide sample starting parameters, which you might find useful if you choose to use spectrogram audio features.  \n",
    "\n",
    "If you instead want to use MFCC features, these arguments will have to be tuned.  Note that the current architecture only supports values of `'same'` or `'valid'` for the `conv_border_mode` argument.\n",
    "\n",
    "When tuning the parameters, be careful not to choose settings that make the convolutional layer overly small.  If the temporal length of the CNN layer is shorter than the length of the transcribed text label, your code will throw an error.\n",
    "\n",
    "Before running the code cell below, you must modify the `cnn_rnn_model` function in `sample_models.py`.  Please add batch normalization to the recurrent layer, and provide the same `TimeDistributed` layer as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 13)          0         \n",
      "_________________________________________________________________\n",
      "conv1d (Conv1D)              (None, None, 200)         28800     \n",
      "_________________________________________________________________\n",
      "bn_conv_1d (BatchNormalizati (None, None, 200)         800       \n",
      "_________________________________________________________________\n",
      "rnn (SimpleRNN)              (None, None, 200)         80200     \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, None, 200)         800       \n",
      "_________________________________________________________________\n",
      "time_distributed_2 (TimeDist (None, None, 85)          17085     \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 85)          0         \n",
      "=================================================================\n",
      "Total params: 127,685\n",
      "Trainable params: 126,885\n",
      "Non-trainable params: 800\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model_2 = cnn_rnn_model(input_dim=13, # change to 13 if you would like to use MFCC features\n",
    "                        filters=200,\n",
    "                        kernel_size=11, \n",
    "                        conv_stride=2,\n",
    "                        conv_border_mode='valid',\n",
    "                        units=200,\n",
    "                        output_dim=85)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please execute the code cell below to train the neural network you specified in `input_to_softmax`.  After the model has finished training, the model is [saved](https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model) in the HDF5 file `model_2.h5`.  The loss history is [saved](https://wiki.python.org/moin/UsingPickle) in `model_2.pickle`.  You are welcome to tweak any of the optional parameters while calling the `train_model` function, but this is not required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "26s - loss: 186.0613 - val_loss: 110.4133\n",
      "Epoch 2/20\n",
      "23s - loss: 50.0777 - val_loss: 72.4996\n",
      "Epoch 3/20\n",
      "24s - loss: 29.6262 - val_loss: 40.9511\n",
      "Epoch 4/20\n",
      "24s - loss: 23.1548 - val_loss: 27.6812\n",
      "Epoch 5/20\n",
      "22s - loss: 19.9526 - val_loss: 18.4811\n",
      "Epoch 6/20\n",
      "24s - loss: 17.6266 - val_loss: 17.3081\n",
      "Epoch 7/20\n",
      "23s - loss: 15.9014 - val_loss: 15.6515\n",
      "Epoch 8/20\n",
      "24s - loss: 14.6957 - val_loss: 14.7129\n",
      "Epoch 9/20\n",
      "24s - loss: 13.3844 - val_loss: 17.5702\n",
      "Epoch 10/20\n",
      "24s - loss: 12.6611 - val_loss: 13.2436\n",
      "Epoch 11/20\n",
      "23s - loss: 11.7400 - val_loss: 11.9438\n",
      "Epoch 12/20\n",
      "24s - loss: 10.7804 - val_loss: 11.6400\n",
      "Epoch 13/20\n",
      "23s - loss: 10.4195 - val_loss: 11.2064\n",
      "Epoch 14/20\n",
      "25s - loss: 9.5931 - val_loss: 11.2541\n",
      "Epoch 15/20\n",
      "23s - loss: 9.1449 - val_loss: 10.1959\n",
      "Epoch 16/20\n",
      "24s - loss: 8.4639 - val_loss: 10.0064\n",
      "Epoch 17/20\n",
      "23s - loss: 8.2517 - val_loss: 9.5402\n",
      "Epoch 18/20\n",
      "23s - loss: 7.7977 - val_loss: 8.6364\n",
      "Epoch 19/20\n",
      "24s - loss: 7.4751 - val_loss: 10.2149\n",
      "Epoch 20/20\n",
      "23s - loss: 7.2110 - val_loss: 8.1181\n"
     ]
    }
   ],
   "source": [
    "train_model(input_to_softmax=model_2, \n",
    "            pickle_path='model_2.pickle', \n",
    "            save_model_path='model_2.h5', \n",
    "            verbose=2,\n",
    "            spectrogram=False) # change to False if you would like to use MFCC features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='model3'></a>\n",
    "### (IMPLEMENTATION) Model 3: Deeper RNN + TimeDistributed Dense\n",
    "\n",
    "Review the code in `rnn_model`, which makes use of a single recurrent layer.  Now, specify an architecture in `deep_rnn_model` that utilizes a variable number `recur_layers` of recurrent layers.  The figure below shows the architecture that should be returned if `recur_layers=2`.  In the figure, the output sequence of the first recurrent layer is used as input for the next recurrent layer.\n",
    "\n",
    "<img src=\"images/deep_rnn_model.png\" width=\"80%\">\n",
    "\n",
    "Feel free to change the supplied values of `units` to whatever you think performs best.  You can change the value of `recur_layers`, as long as your final value is greater than 1. (As a quick check that you have implemented the additional functionality in `deep_rnn_model` correctly, make sure that the architecture that you specify here is identical to `rnn_model` if `recur_layers=1`.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 13)          0         \n",
      "_________________________________________________________________\n",
      "rnn_0 (GRU)                  (None, None, 200)         128400    \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, None, 200)         800       \n",
      "_________________________________________________________________\n",
      "rnn_1 (GRU)                  (None, None, 200)         240600    \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, None, 200)         800       \n",
      "_________________________________________________________________\n",
      "time_distributed_4 (TimeDist (None, None, 85)          17085     \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 85)          0         \n",
      "=================================================================\n",
      "Total params: 387,685\n",
      "Trainable params: 386,885\n",
      "Non-trainable params: 800\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model_3 = deep_rnn_model(input_dim=13, # change to 13 if you would like to use MFCC features\n",
    "                         units=200,\n",
    "                         recur_layers=2,\n",
    "                         output_dim=85) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please execute the code cell below to train the neural network you specified in `input_to_softmax`.  After the model has finished training, the model is [saved](https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model) in the HDF5 file `model_3.h5`.  The loss history is [saved](https://wiki.python.org/moin/UsingPickle) in `model_3.pickle`.  You are welcome to tweak any of the optional parameters while calling the `train_model` function, but this is not required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "163s - loss: 495.6770 - val_loss: 195.7849\n",
      "Epoch 2/20\n",
      "163s - loss: 151.3757 - val_loss: 142.4536\n",
      "Epoch 3/20\n",
      "162s - loss: 128.7162 - val_loss: 130.0719\n",
      "Epoch 4/20\n",
      "162s - loss: 110.4170 - val_loss: 107.9297\n",
      "Epoch 5/20\n",
      "162s - loss: 85.1040 - val_loss: 88.7280\n",
      "Epoch 6/20\n",
      "162s - loss: 62.7614 - val_loss: 79.7157\n",
      "Epoch 7/20\n",
      "163s - loss: 45.8115 - val_loss: 54.4053\n",
      "Epoch 8/20\n",
      "162s - loss: 35.1769 - val_loss: 39.0615\n",
      "Epoch 9/20\n",
      "163s - loss: 29.3062 - val_loss: 29.5970\n",
      "Epoch 10/20\n",
      "164s - loss: 25.4099 - val_loss: 25.4844\n",
      "Epoch 11/20\n",
      "162s - loss: 22.9058 - val_loss: 22.3367\n",
      "Epoch 12/20\n",
      "161s - loss: 21.1273 - val_loss: 20.7285\n",
      "Epoch 13/20\n",
      "163s - loss: 19.6896 - val_loss: 18.9932\n",
      "Epoch 14/20\n",
      "162s - loss: 17.9001 - val_loss: 16.7459\n",
      "Epoch 15/20\n",
      "164s - loss: 16.8519 - val_loss: 17.1642\n",
      "Epoch 16/20\n",
      "163s - loss: 15.6537 - val_loss: 15.8556\n",
      "Epoch 17/20\n",
      "162s - loss: 14.8766 - val_loss: 16.0015\n",
      "Epoch 18/20\n",
      "163s - loss: 14.4124 - val_loss: 14.8207\n",
      "Epoch 19/20\n",
      "163s - loss: 13.5589 - val_loss: 15.0334\n",
      "Epoch 20/20\n",
      "163s - loss: 13.2972 - val_loss: 19.5477\n"
     ]
    }
   ],
   "source": [
    "train_model(input_to_softmax=model_3, \n",
    "            pickle_path='model_3.pickle', \n",
    "            save_model_path='model_3.h5', \n",
    "            verbose=2,\n",
    "            spectrogram=False) # change to False if you would like to use MFCC features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='model4'></a>\n",
    "### (IMPLEMENTATION) Model 4: Bidirectional RNN + TimeDistributed Dense\n",
    "\n",
    "Read about the [Bidirectional](https://keras.io/layers/wrappers/) wrapper in the Keras documentation.  For your next architecture, you will specify an architecture that uses a single bidirectional RNN layer, before a (`TimeDistributed`) dense layer.  The added value of a bidirectional RNN is described well in [this paper](http://www.cs.toronto.edu/~hinton/absps/DRNN_speech.pdf).\n",
    "> One shortcoming of conventional RNNs is that they are only able to make use of previous context. In speech recognition, where whole utterances are transcribed at once, there is no reason not to exploit future context as well.  Bidirectional RNNs (BRNNs) do this by processing the data in both directions with two separate hidden layers which are then fed forwards to the same output layer.\n",
    "\n",
    "<img src=\"images/bidirectional_rnn_model.png\" width=\"80%\">\n",
    "\n",
    "Before running the code cell below, you must complete the `bidirectional_rnn_model` function in `sample_models.py`.  Feel free to use `SimpleRNN`, `LSTM`, or `GRU` units.  When specifying the `Bidirectional` wrapper, use `merge_mode='concat'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 13)          0         \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, None, 400)         256800    \n",
      "_________________________________________________________________\n",
      "time_distributed_5 (TimeDist (None, None, 85)          34085     \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 85)          0         \n",
      "=================================================================\n",
      "Total params: 290,885\n",
      "Trainable params: 290,885\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model_4 = bidirectional_rnn_model(input_dim=13, # change to 13 if you would like to use MFCC features or 161\n",
    "                                  units=200,\n",
    "                                  output_dim=85)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please execute the code cell below to train the neural network you specified in `input_to_softmax`.  After the model has finished training, the model is [saved](https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model) in the HDF5 file `model_4.h5`.  The loss history is [saved](https://wiki.python.org/moin/UsingPickle) in `model_4.pickle`.  You are welcome to tweak any of the optional parameters while calling the `train_model` function, but this is not required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "156s - loss: 368.9123 - val_loss: 121.9044\n",
      "Epoch 2/20\n",
      "157s - loss: 97.5584 - val_loss: 85.9281\n",
      "Epoch 3/20\n",
      "154s - loss: 70.2320 - val_loss: 64.6917\n",
      "Epoch 4/20\n",
      "157s - loss: 52.6111 - val_loss: 47.4896\n",
      "Epoch 5/20\n",
      "157s - loss: 40.5024 - val_loss: 37.8461\n",
      "Epoch 6/20\n",
      "155s - loss: 32.1479 - val_loss: 30.2981\n",
      "Epoch 7/20\n",
      "155s - loss: 27.1627 - val_loss: 26.2739\n",
      "Epoch 8/20\n",
      "156s - loss: 23.9423 - val_loss: 23.2824\n",
      "Epoch 9/20\n",
      "157s - loss: 21.4684 - val_loss: 21.4849\n",
      "Epoch 10/20\n",
      "157s - loss: 19.5476 - val_loss: 18.9249\n",
      "Epoch 11/20\n",
      "155s - loss: 18.1407 - val_loss: 18.5522\n",
      "Epoch 12/20\n",
      "156s - loss: 16.8407 - val_loss: 18.0094\n",
      "Epoch 13/20\n",
      "156s - loss: 15.6414 - val_loss: 15.1013\n",
      "Epoch 14/20\n",
      "155s - loss: 14.7722 - val_loss: 14.3125\n",
      "Epoch 15/20\n",
      "156s - loss: 13.8875 - val_loss: 14.1520\n",
      "Epoch 16/20\n",
      "154s - loss: 13.1283 - val_loss: 12.8444\n",
      "Epoch 17/20\n",
      "156s - loss: 12.2226 - val_loss: 11.9871\n",
      "Epoch 18/20\n",
      "157s - loss: 11.7166 - val_loss: 11.9071\n",
      "Epoch 19/20\n",
      "156s - loss: 10.9742 - val_loss: 11.2702\n",
      "Epoch 20/20\n",
      "156s - loss: 10.3629 - val_loss: 10.1736\n"
     ]
    }
   ],
   "source": [
    "train_model(input_to_softmax=model_4, \n",
    "            pickle_path='model_4.pickle', \n",
    "            save_model_path='model_4.h5', \n",
    "            verbose=2,\n",
    "            spectrogram=False) # change to False if you would like to use MFCC features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='model5'></a>\n",
    "### (OPTIONAL IMPLEMENTATION) Models 5+\n",
    "\n",
    "If you would like to try out more architectures than the ones above, please use the code cell below.  Please continue to follow the same convention for saving the models; for the $i$-th sample model, please save the loss at **`model_i.pickle`** and saving the trained model at **`model_i.h5`**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "## (Optional) TODO: Try out some more models!\n",
    "### Feel free to use as many code cells as needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='compare'></a>\n",
    "### Compare the Models\n",
    "\n",
    "Execute the code cell below to evaluate the performance of the drafted deep learning models.  The training and validation loss are plotted for each model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aind2/anaconda3/envs/aind-vui/lib/python3.5/site-packages/matplotlib/font_manager.py:1316: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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jBpaqqvBiCYe/P+NqBT6XX5kM3d4+gJ90D3GO3FxDUtVcmM0m7u4Ryt09QsktKCL5YBrv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f094f885f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from glob import glob\n",
    "import numpy as np\n",
    "import _pickle as pickle\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "sns.set_style(style='white')\n",
    "\n",
    "# obtain the paths for the saved model history\n",
    "all_pickles = sorted(glob(\"results/*.pickle\"))\n",
    "# extract the name of each model\n",
    "model_names = [item[8:-7] for item in all_pickles]\n",
    "# extract the loss history for each model\n",
    "valid_loss = [pickle.load( open( i, \"rb\" ) )['val_loss'] for i in all_pickles]\n",
    "train_loss = [pickle.load( open( i, \"rb\" ) )['loss'] for i in all_pickles]\n",
    "# save the number of epochs used to train each model\n",
    "num_epochs = [len(valid_loss[i]) for i in range(len(valid_loss))]\n",
    "\n",
    "fig = plt.figure(figsize=(16,5))\n",
    "\n",
    "# plot the training loss vs. epoch for each model\n",
    "ax1 = fig.add_subplot(121)\n",
    "for i in range(len(all_pickles)):\n",
    "    ax1.plot(np.linspace(1, num_epochs[i], num_epochs[i]), \n",
    "            train_loss[i], label=model_names[i])\n",
    "# clean up the plot\n",
    "ax1.legend()  \n",
    "ax1.set_xlim([1, max(num_epochs)])\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Training Loss')\n",
    "\n",
    "# plot the validation loss vs. epoch for each model\n",
    "ax2 = fig.add_subplot(122)\n",
    "for i in range(len(all_pickles)):\n",
    "    ax2.plot(np.linspace(1, num_epochs[i], num_epochs[i]), \n",
    "            valid_loss[i], label=model_names[i])\n",
    "# clean up the plot\n",
    "ax2.legend()  \n",
    "ax2.set_xlim([1, max(num_epochs)])\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Validation Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Question 1:__ Use the plot above to analyze the performance of each of the attempted architectures.  Which performs best?  Provide an explanation regarding why you think some models perform better than others. \n",
    "\n",
    "__Answer:__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='final'></a>\n",
    "### (IMPLEMENTATION) Final Model\n",
    "\n",
    "Now that you've tried out many sample models, use what you've learned to draft your own architecture!  While your final acoustic model should not be identical to any of the architectures explored above, you are welcome to merely combine the explored layers above into a deeper architecture.  It is **NOT** necessary to include new layer types that were not explored in the notebook.\n",
    "\n",
    "However, if you would like some ideas for even more layer types, check out these ideas for some additional, optional extensions to your model:\n",
    "\n",
    "- If you notice your model is overfitting to the training dataset, consider adding **dropout**!  To add dropout to [recurrent layers](https://faroit.github.io/keras-docs/1.0.2/layers/recurrent/), pay special attention to the `dropout_W` and `dropout_U` arguments.  This [paper](http://arxiv.org/abs/1512.05287) may also provide some interesting theoretical background.\n",
    "- If you choose to include a convolutional layer in your model, you may get better results by working with **dilated convolutions**.  If you choose to use dilated convolutions, make sure that you are able to accurately calculate the length of the acoustic model's output in the `model.output_length` lambda function.  You can read more about dilated convolutions in Google's [WaveNet paper](https://arxiv.org/abs/1609.03499).  For an example of a speech-to-text system that makes use of dilated convolutions, check out this GitHub [repository](https://github.com/buriburisuri/speech-to-text-wavenet).  You can work with dilated convolutions [in Keras](https://keras.io/layers/convolutional/) by paying special attention to the `padding` argument when you specify a convolutional layer.\n",
    "- If your model makes use of convolutional layers, why not also experiment with adding **max pooling**?  Check out [this paper](https://arxiv.org/pdf/1701.02720.pdf) for example architecture that makes use of max pooling in an acoustic model.\n",
    "- So far, you have experimented with a single bidirectional RNN layer.  Consider stacking the bidirectional layers, to produce a [deep bidirectional RNN](https://www.cs.toronto.edu/~graves/asru_2013.pdf)!\n",
    "\n",
    "All models that you specify in this repository should have `output_length` defined as an attribute.  This attribute is a lambda function that maps the (temporal) length of the input acoustic features to the (temporal) length of the output softmax layer.  This function is used in the computation of CTC loss; to see this, look at the `add_ctc_loss` function in `train_utils.py`.  To see where the `output_length` attribute is defined for the models in the code, take a look at the `sample_models.py` file.  You will notice this line of code within most models:\n",
    "```\n",
    "model.output_length = lambda x: x\n",
    "```\n",
    "The acoustic model that incorporates a convolutional layer (`cnn_rnn_model`) has a line that is a bit different:\n",
    "```\n",
    "model.output_length = lambda x: cnn_output_length(\n",
    "        x, kernel_size, conv_border_mode, conv_stride)\n",
    "```\n",
    "\n",
    "In the case of models that use purely recurrent layers, the lambda function is the identity function, as the recurrent layers do not modify the (temporal) length of their input tensors.  However, convolutional layers are more complicated and require a specialized function (`cnn_output_length` in `sample_models.py`) to determine the temporal length of their output.\n",
    "\n",
    "You will have to add the `output_length` attribute to your final model before running the code cell below.  Feel free to use the `cnn_output_length` function, if it suits your model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 13)          0         \n",
      "_________________________________________________________________\n",
      "conv1d (Conv1D)              (None, None, 200)         28800     \n",
      "_________________________________________________________________\n",
      "bn_conv_1d (BatchNormalizati (None, None, 200)         800       \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, None, 400)         481200    \n",
      "_________________________________________________________________\n",
      "bidirectional_2 (Bidirection (None, None, 400)         721200    \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, None, 400)         1600      \n",
      "_________________________________________________________________\n",
      "time_distributed_1 (TimeDist (None, None, 85)          34085     \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 85)          0         \n",
      "=================================================================\n",
      "Total params: 1,267,685\n",
      "Trainable params: 1,266,485\n",
      "Non-trainable params: 1,200\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# specify the model\n",
    "model_end = final_model(input_dim=13, # change to 13 if you would like to use MFCC features\n",
    "                        filters=200,\n",
    "                        kernel_size=11, \n",
    "                        conv_stride=2,\n",
    "                        conv_border_mode='valid',\n",
    "                        units=200,\n",
    "                        output_dim=85)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please execute the code cell below to train the neural network you specified in `input_to_softmax`.  After the model has finished training, the model is [saved](https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model) in the HDF5 file `model_end.h5`.  The loss history is [saved](https://wiki.python.org/moin/UsingPickle) in `model_end.pickle`.  You are welcome to tweak any of the optional parameters while calling the `train_model` function, but this is not required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "171s - loss: 217.4489 - val_loss: 134.2593\n",
      "Epoch 2/20\n",
      "170s - loss: 91.2612 - val_loss: 91.2295\n",
      "Epoch 3/20\n",
      "166s - loss: 51.8411 - val_loss: 50.9931\n",
      "Epoch 4/20\n",
      "165s - loss: 34.4563 - val_loss: 33.0314\n",
      "Epoch 5/20\n",
      "163s - loss: 26.1011 - val_loss: 21.8671\n",
      "Epoch 6/20\n",
      "164s - loss: 21.5310 - val_loss: 17.7306\n",
      "Epoch 7/20\n",
      "162s - loss: 18.2225 - val_loss: 13.7399\n",
      "Epoch 8/20\n",
      "163s - loss: 16.1819 - val_loss: 12.2524\n",
      "Epoch 9/20\n",
      "163s - loss: 14.0644 - val_loss: 10.2115\n",
      "Epoch 10/20\n",
      "162s - loss: 12.3045 - val_loss: 9.5464\n",
      "Epoch 11/20\n",
      "162s - loss: 11.0151 - val_loss: 8.2371\n",
      "Epoch 12/20\n",
      "162s - loss: 9.8832 - val_loss: 7.3541\n",
      "Epoch 13/20\n",
      "162s - loss: 9.0148 - val_loss: 6.7663\n",
      "Epoch 14/20\n",
      "161s - loss: 7.9740 - val_loss: 5.8303\n",
      "Epoch 15/20\n",
      "161s - loss: 7.1619 - val_loss: 6.1320\n",
      "Epoch 16/20\n",
      "163s - loss: 6.3848 - val_loss: 5.4944\n",
      "Epoch 17/20\n",
      "164s - loss: 6.0190 - val_loss: 4.7443\n",
      "Epoch 18/20\n",
      "164s - loss: 5.3742 - val_loss: 4.5267\n",
      "Epoch 19/20\n",
      "163s - loss: 4.9303 - val_loss: 4.2951\n",
      "Epoch 20/20\n",
      "163s - loss: 4.4997 - val_loss: 4.8750\n"
     ]
    }
   ],
   "source": [
    "train_model(input_to_softmax=model_end, \n",
    "            pickle_path='model_end.pickle', \n",
    "            save_model_path='model_end.h5', \n",
    "            verbose=2,\n",
    "            spectrogram=False) # change to False if you would like to use MFCC features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Question 2:__ Describe your final model architecture and your reasoning at each step.  \n",
    "\n",
    "__Answer:__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='step3'></a>\n",
    "## STEP 3: Obtain Predictions\n",
    "\n",
    "We have written a function for you to decode the predictions of your acoustic model.  To use the function, please execute the code cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from data_generator import AudioGenerator\n",
    "from keras import backend as K\n",
    "from utils import int_sequence_to_text\n",
    "from IPython.display import Audio\n",
    "\n",
    "def get_predictions(index, partition, input_to_softmax, model_path):\n",
    "    \"\"\" Print a model's decoded predictions\n",
    "    Params:\n",
    "        index (int): The example you would like to visualize\n",
    "        partition (str): One of 'train' or 'validation'\n",
    "        input_to_softmax (Model): The acoustic model\n",
    "        model_path (str): Path to saved acoustic model's weights\n",
    "    \"\"\"\n",
    "    # load the train and test data\n",
    "#     data_gen = AudioGenerator()\n",
    "    data_gen = AudioGenerator(spectrogram=False, mfcc_dim=13)\n",
    "    data_gen.load_train_data()\n",
    "    data_gen.load_validation_data()\n",
    "    \n",
    "    # obtain the true transcription and the audio features \n",
    "    if partition == 'validation':\n",
    "        transcr = data_gen.valid_texts[index]\n",
    "        audio_path = data_gen.valid_audio_paths[index]\n",
    "        data_point = data_gen.normalize(data_gen.featurize(audio_path))\n",
    "    elif partition == 'train':\n",
    "        transcr = data_gen.train_texts[index]\n",
    "        audio_path = data_gen.train_audio_paths[index]\n",
    "        data_point = data_gen.normalize(data_gen.featurize(audio_path))\n",
    "    else:\n",
    "        raise Exception('Invalid partition!  Must be \"train\" or \"validation\"')\n",
    "        \n",
    "    # obtain and decode the acoustic model's predictions\n",
    "    input_to_softmax.load_weights(model_path)\n",
    "    prediction = input_to_softmax.predict(np.expand_dims(data_point, axis=0))\n",
    "    output_length = [input_to_softmax.output_length(data_point.shape[0])] \n",
    "    pred_ints = (K.eval(K.ctc_decode(\n",
    "                prediction, output_length)[0][0])+1).flatten().tolist()\n",
    "    \n",
    "    # play the audio file, and display the true and predicted transcriptions\n",
    "    print('-'*80)\n",
    "    Audio(audio_path)\n",
    "    print('True transcription:\\n' + '\\n' + transcr)\n",
    "    print('-'*80)\n",
    "    print('Predicted transcription:\\n' + '\\n' + ''.join(int_sequence_to_text(pred_ints)))\n",
    "    print('-'*80)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the code cell below to obtain the transcription predicted by your final model for the first example in the training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 13)          0         \n",
      "_________________________________________________________________\n",
      "conv1d (Conv1D)              (None, None, 200)         28800     \n",
      "_________________________________________________________________\n",
      "bn_conv_1d (BatchNormalizati (None, None, 200)         800       \n",
      "_________________________________________________________________\n",
      "bidirectional_7 (Bidirection (None, None, 400)         481200    \n",
      "_________________________________________________________________\n",
      "bidirectional_8 (Bidirection (None, None, 400)         721200    \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, None, 400)         1600      \n",
      "_________________________________________________________________\n",
      "time_distributed_4 (TimeDist (None, None, 85)          34085     \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 85)          0         \n",
      "=================================================================\n",
      "Total params: 1,267,685\n",
      "Trainable params: 1,266,485\n",
      "Non-trainable params: 1,200\n",
      "_________________________________________________________________\n",
      "None\n",
      "--------------------------------------------------------------------------------\n",
      "True transcription:\n",
      "\n",
      "ぷろろーぐ\n",
      "--------------------------------------------------------------------------------\n",
      "Predicted transcription:\n",
      "\n",
      "ぷろろーぐ\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "get_predictions(index=0, \n",
    "                partition='train',\n",
    "                input_to_softmax=final_model(input_dim=13, # change to 13 if you would like to use MFCC features\n",
    "                        filters=200,\n",
    "                        kernel_size=11, \n",
    "                        conv_stride=2,\n",
    "                        conv_border_mode='valid',\n",
    "                        units=200,\n",
    "                        output_dim=85), \n",
    "                model_path='./results/model_end.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the next code cell to visualize the model's prediction for the first example in the validation dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "the_input (InputLayer)       (None, None, 13)          0         \n",
      "_________________________________________________________________\n",
      "conv1d (Conv1D)              (None, None, 200)         28800     \n",
      "_________________________________________________________________\n",
      "bn_conv_1d (BatchNormalizati (None, None, 200)         800       \n",
      "_________________________________________________________________\n",
      "bidirectional_11 (Bidirectio (None, None, 400)         481200    \n",
      "_________________________________________________________________\n",
      "bidirectional_12 (Bidirectio (None, None, 400)         721200    \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, None, 400)         1600      \n",
      "_________________________________________________________________\n",
      "time_distributed_6 (TimeDist (None, None, 85)          34085     \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, None, 85)          0         \n",
      "=================================================================\n",
      "Total params: 1,267,685\n",
      "Trainable params: 1,266,485\n",
      "Non-trainable params: 1,200\n",
      "_________________________________________________________________\n",
      "None\n",
      "--------------------------------------------------------------------------------\n",
      "True transcription:\n",
      "\n",
      "はじめはげんざい ならくをおもわせるふかいがけをぜっさんらっかちゅうなのである\n",
      "--------------------------------------------------------------------------------\n",
      "Predicted transcription:\n",
      "\n",
      "はじめはげんざい ならくをおもわせるふかいがけをぜっさんらっかちゅうなのである\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "get_predictions(index=0, \n",
    "                partition='validation',\n",
    "                input_to_softmax=final_model(input_dim=13, # change to 13 if you would like to use MFCC features\n",
    "                        filters=200,\n",
    "                        kernel_size=11, \n",
    "                        conv_stride=2,\n",
    "                        conv_border_mode='valid',\n",
    "                        units=200,\n",
    "                        output_dim=85), \n",
    "                model_path='./results/model_end.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One standard way to improve the results of the decoder is to incorporate a language model.  We won't pursue this in the notebook, but you are welcome to do so as an _optional extension_. \n",
    "\n",
    "If you are interested in creating models that provide improved transcriptions, you are encouraged to download [more data](http://www.openslr.org/12/) and train bigger, deeper models.  But beware - the model will likely take a long while to train.  For instance, training this [state-of-the-art](https://arxiv.org/pdf/1512.02595v1.pdf) model would take 3-6 weeks on a single GPU!"
   ]
  }
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