Question and answer models with BERT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "da3a1029",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import BertForQuestionAnswering\n",
    "from transformers import BertTokenizer\n",
    "import torch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc57d1b9",
   "metadata": {},
   "source": [
    "## Load model and tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "028f069f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = \"bert-large-uncased-whole-word-masking-finetuned-squad\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4b390535",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-large-uncased-whole-word-masking-finetuned-squad were not used when initializing BertForQuestionAnswering: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n",
      "- This IS expected if you are initializing BertForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "model = BertForQuestionAnswering.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9f197397",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = BertTokenizer.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a8809f3",
   "metadata": {},
   "source": [
    "## Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1de63ce3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# example question and text containing the answer\n",
    "question = \"When was the first dvd released?\"\n",
    "answer_document = \"The first DVD (Digital Versatile Disc) was released on March 24, 1997. It was a movie titled 'Twister' and was released in Japan. DVDs quickly gained popularity as a replacement for VHS tapes and became a common format for storing and distributing digital video and data.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8a1dbded",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoding = tokenizer.encode_plus(text=question, text_pair=answer_document)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b9eb74fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'input_ids': [101, 2043, 2001, 1996, 2034, 4966, 2207, 1029, 102, 1996, 2034, 4966, 1006, 3617, 22979, 5860, 1007, 2001, 2207, 2006, 2233, 2484, 1010, 2722, 1012, 2009, 2001, 1037, 3185, 4159, 1005, 9792, 2121, 1005, 1998, 2001, 2207, 1999, 2900, 1012, 22477, 2855, 4227, 6217, 2004, 1037, 6110, 2005, 17550, 13324, 1998, 2150, 1037, 2691, 4289, 2005, 23977, 1998, 20083, 3617, 2678, 1998, 2951, 1012, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n"
     ]
    }
   ],
   "source": [
    "print(encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "38a3dc21",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = encoding['input_ids']\n",
    "sentence_embedding = encoding['token_type_ids']\n",
    "tokens = tokenizer.convert_ids_to_tokens(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e95390f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[CLS]'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.decode(101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f0175e5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[SEP]'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.decode(102)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "96eec17d",
   "metadata": {},
   "outputs": [],
   "source": [
    "output = model(input_ids = torch.tensor([inputs]), token_type_ids = torch.tensor([sentence_embedding]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8fa56dd",
   "metadata": {},
   "source": [
    "## Model output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bfb899ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(20)\n",
      "tensor(23)\n"
     ]
    }
   ],
   "source": [
    "start_index = torch.argmax(output.start_logits)\n",
    "end_index = torch.argmax(output.end_logits)\n",
    "\n",
    "print(start_index)\n",
    "print(end_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "84348666",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "march 24 , 1997\n"
     ]
    }
   ],
   "source": [
    "answer = ' '.join(tokens[start_index:end_index+1])\n",
    "print(answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c22ad39a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8a28ebb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "s_scores = output.start_logits.detach().numpy().flatten()\n",
    "e_scores = output.end_logits.detach().numpy().flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cceac747",
   "metadata": {},
   "outputs": [],
   "source": [
    "token_labels = []\n",
    "for (i, token) in enumerate(tokens):\n",
    "    token_labels.append('{:} - {:>2}'.format(token, i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "62b8f2df",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lnewb\\AppData\\Local\\Temp\\ipykernel_13176\\3367475488.py:2: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha=\"center\")\n"
     ]
    },
    {
     "data": {
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FgowQQgghcosEGSGEEELkFgkyQgghhMgtEmSEEEIIkVskyAghhBAit0iQEUIIIURukSAjhBBCiNwiQUYIIYQQuUWCjBBCCCFyiwQZIYQQQuQWCTJCCCGEyC0SZIQQQgiRWyTICCGEECK3SJARQgghRG6RICOEEEKI3NKysTMgRC3S4/S77N/WLYw5a5PFTe9z7jFz5hkz8dL+jZ01IYSoKaSREUIIIURukSAjhBBCiNwiQUYIIYQQuUWCjBBCCCFyiwQZIYQQQuQWCTJCCCGEyC0SZIQQQgiRWyTICCGEECK3SJARQgghRG6RICOEEEKI3CJBRgghhBC5RYKMEEIIIXKLBBkhhBBC5BYJMkIIIYTILRJkhBBCCJFbJMgIIYQQIre0bOwMiOqgx+l32b+tWxhz1iaLm97n3GPmzDNm4qX9GztrQgghRCzSyAghhBAit0iQEUIIIURukWlJNFlkThNCiPyzQDQy1113nVlppZXMQgstZDbeeGPzyiuvJH7/gQceMN26dbPfX3fddc2TTz65ILIphBBCiJyRuSBz3333mVNOOcWcd9555vXXXzd/+MMfzI477mj++9//Rn5//Pjxpm/fvubwww83b7zxhtlzzz3tMWnSpKyzKoQQQoickbkgc8UVV5iBAweaAQMGmLXWWsvceOONpm3btub222+P/P7VV19t+vTpY04//XSz5pprmmHDhpkNNtjAXHvttVlnVQghhBA5I1MfmTlz5piJEyeaQYMGFc41b97cbLfddmbChAmR93AeDY4PGpxRo0ZFfn/27Nn2cMyYMcP+nTt3buFw/0eRdL2Se7NMu9x78QWJ+sv3kq41dr4rvTfuetozV3K9kvJ03027v9rKs9rTrtZ85TXtas1XlmlXa77ymnb4WlwaaTQLgiAwGfHVV1+Z5Zdf3pqLNtlkk8L5M844w4wdO9a8/PLL9e5p3bq1ufPOO615yXH99deboUOHmm+//bbe94cMGWKvhRk5cqTV/AghhBCi+pk1a5bp16+fmT59umnXrl3TWbWEtsfX4KCR6dy5s7nilRmmZZv/mxmf0nNxc8Ur0+2KlHHD/k9AYoUKRF1PupZ2b5Zpx12DYtJ2IPWOHj3abL/99qZVq1ZFXcvrMxdTJknlUen1cssz6f7Gehd5TTvLOlTtz7wgy7Oxn1l1yOS+Dj1++i6mHDIVZJZZZhnTokWLepoU/u/QoUPkPZwv5ftt2rSxR5i584yZ/7+KBhQShxsMXCWMup50Le3eLNOOuwbFpB2G88Vey+szl1ImSeVR6fVSyzPp/sZ6F3lNO8s6lJdnzjLtannmLNNWHVqwdaiqnH0xE/Xo0cM8++yzhXPz58+3//umJh/O+98HZqRx3xdCCCFE0yVz0xJmn0MOOcRsuOGGpmfPnuaqq64yM2fOtKuYoH///taP5qKLLrL/n3jiiWbLLbc0l19+udlll13Mvffea1577TVz8803Z51VIYQQQuSMzAWZ/fff33z33Xfm3HPPNd98841Zb731zFNPPWWWW245e/2zzz6zK5kcm266qXXUHTx4sDn77LPNaqutZlcsrbPOOllnVdQgLkovviYEVsQ+W676UgghRPWxQJx9jz/+eHtE8cILL9Q7t99++9lDCCGEECIJbRophBBCiNwiQUYIIYQQuUWCjBBCCCFyiwQZIYQQQuQWCTJCCCGEyC0SZIQQQgiRWyTICCGEECK3SJARQgghRG6RICOEEEKI3CJBRgghhBC5RYKMEEIIIXKLBBkhhBBC5BYJMkIIIYTILRJkhBBCCJFbJMgIIYQQIrdIkBFCCCFEbpEgI4QQQojc0rKxMyDywcRL+9u/c+fONU8++aQZN6yvadWqVWNnSwghRBNHGhkhhBBC5BYJMkIIIYTILRJkhBBCCJFb5CMjhBAZId8yIbJHGhkhhBBC5BYJMkIIIYTILRJkhBBCCJFbJMgIIYQQIrdIkBFCCCFEbpEgI4QQQojcIkFGCCGEELlFgowQQgghcosEGSGEEELkFgkyQgghhMgtEmSEEEIIkVskyAghhBAit0iQEUIIIURukSAjhBBCiNwiQUYIIYQQuUWCjBBCCCFySyaCzCeffGIOP/xws/LKK5uFF17YdO3a1Zx33nlmzpw5ifdttdVWplmzZnWOo48+OossCiGEEKIGaJlFou+//76ZP3++uemmm8yqq65qJk2aZAYOHGhmzpxpLrvsssR7+d75559f+L9t27ZZZFEIIYQQNUAmgkyfPn3s4VhllVXM5MmTzQ033JAqyCC4dOjQIYtsCSGEEKLGyESQiWL69OlmqaWWSv3e3XffbUaMGGGFmd12282cc845iVqZ2bNn28MxY8YM+7dVC2NatjCmdYv/O+/+zp07t87/UdeTrqXdm2XacdeKTdv/btT5tGtJ15viM1eSdiX5bqz6l9e0K61DleTL/52o81k/84Isz8Z+z7Vahxr7mRujDpVKsyAIApMxU6dONT169LDaGExHcdx8882mS5cuplOnTubtt982Z555punZs6d56KGHYu8ZMmSIGTp0aL3zI0eOlFlKCCGEyAmzZs0y/fr1s4qPdu3aZSPInHXWWeaSSy5J/M57771nunXrVvj/yy+/NFtuuaV15L311ltNKTz33HNm2223tYIQDsPFamQ6d+5sNjzpRtOyzcJW0jul5+LmilemmznzjBk3rK/9Xu9z7rF/o64nXUu7N8u0465BMWk7kHpHjx5ttt9+e9OqVauiryVdb4rPXEnaleS7sepfXtOutA5Vkq9K6kFD5GtBlmdjv+darUON/cwLsjwfP30Xs8wyy5QsyJRkWjr11FPNoYcemvgd/GEcX331ldl6663NpptuarUtpbLxxhvbv0mCTJs2bewRZu48Y+b/r6IBhcThOhFXCaOuJ11LuzfLtOOuQTFph+F8OdeirjfFZ64k7UrynXbvhIv7FwbPJ5980owZ0rdB6m7a9WpNu9I6VEm+qqXdZJl2tbznWq1D1fLMC7IOlUpJgkz79u3tUQxoYhBiMCkNHz7cNG9e+krvN9980/7t2LFjyfcKUc1MvLSusMEspdxGLIQQTZlM4sggxGBKWnHFFa1fzHfffWe++eYbe/jfwQT1yiuv2P8//PBDM2zYMDNx4kQbh+bRRx81/fv3N7179zbdu3fPIptCCCGEyDmZrFrCFow5iGOFFVaoc8255DATZUk2zj3QunVrM2bMGHPVVVfZeDP4ueyzzz5m8ODBWWRRCCGEEDVAJoIMfjRpvjQrrbRSQagBBJexY8dmkR0hhBBC1Cjaa0kIIYQQuUWCjBBCCCFyiwQZIYQQQuQWCTJCCCGEyC0SZIQQQgiRWyTICCGEECK3SJARQgghRG6RICOEEEKI3CJBRgghhBC5RYKMEEIIIXJLJlsUCCFEraCdyoWobqSREUIIIURukSAjhBBCiNwiQUYIIYQQuUWCjBBCCCFyi5x9RebIWVIIIURWSCMjhBBCiNwijYwQOaMxNVzSrgkhqg1pZIQQQgiRWyTICCGEECK3SJARQgghRG6Rj4wQQpSJfIaEaHykkRFCCCFEbpFGRgghqhBpe4QoDmlkhBBCCJFbpJERuUazViGEaNpIIyOEEEKI3CJBRgghhBC5RYKMEEIIIXKLBBkhhBBC5BYJMkIIIYTILRJkhBBCCJFbJMgIIYQQIrdIkBFCCCFEbpEgI4QQQojcIkFGCCGEELlFWxQIIaoCbTchhKgqjcxKK61kmjVrVue4+OKLE+/57bffzHHHHWeWXnpps+iii5p99tnHfPvtt1llUQghhBA5J1PT0vnnn2++/vrrwnHCCSckfv/kk082jz32mHnggQfM2LFjzVdffWX23nvvLLMohBBCiByTqWlpscUWMx06dCjqu9OnTze33XabGTlypNlmm23sueHDh5s111zTvPTSS6ZXr15ZZlUIIYQQOSRTQQZT0rBhw8yKK65o+vXrZzUuLVtG/+TEiROtbXy77bYrnOvWrZu9d8KECbGCzOzZs+3hmDFjhv3bqoUxLVsY07rF/513f/kN//+o60nX0u7NMu24a8Wm7X836nzataTrxfx2OfemPXOl+a703rylnfaeXrqwb+H/0aNHm2fP3df6qRRThyptN1ml3ZTbTdr1ausf0+pfXvvePI83jVGHSqVZEASByYArrrjCbLDBBmappZYy48ePN4MGDTIDBgyw56NAE8N1XyiBnj17mq233tpccsklkfcNGTLEDB06NDK9tm3bNtDTCCGEECJLZs2aZZUeWGjatWuXjUbmrLPOihUoHO+9957VpJxyyimFc927dzetW7c2Rx11lLnoootMmzZtTEOBgOT/FhqZzp07mytemWFatvk/ye+UnoubK16ZbubMM3YlBPQ+5x77N+p60rW0e7NMO+4aFJO2w812tt9++3qrQpKuJV0v5rfLuTftmSvNd6X35i3thqgj5aZdyW+r3ZTXbkotk8buHystr2rte/M83izI8nz89F1MOZQkyJx66qnm0EMPTfzOKqusEnl+4403Nr///rv55JNPzBprrFHvOr40c+bMMT/99JNZYoklCudZtZTkZ4NQFCUYzZ1nzPz/VTSgkDhco3CVMOp60rW0e7NMO+4aFJN2GM6Xcy3qeim/Xcq9ac9cab4b6t68pN2QdaTUtCv5bbWbytpN2vVq6R8reeYs811pHaqF8WZB1qFMBZn27dvboxzefPNN07x5c7PssstGXu/Ro4d9iGeffdYuu4bJkyebzz77zGyyySZl/aYQQgghaptMnH1xzn355Zetbwsrl/gfR9+DDjrILLnkkvY7X375pdl2223NXXfdZf1gFl98cXP44YdbMxF+NdjHWK6NEKMVS0IIIYRYYIIMpp57773XOuLivLvyyitbQcb3ZcEGisYF5x7HlVdeabU2aGS4b8cddzTXX399FlkUQgghRA2QiSDDaiViv6RF/g0vmFpooYXMddddZw8hhFgQaGsEIfKNNo0UQgghRG7RppGiQdCsVgghRGMgjYwQQgghcosEGSGEEELkFgkyQgghhMgtEmSEEEIIkVuarLOvnFOFEEKI/NNkBRkhxIJFkwchRBbItCSEEEKI3CJBRgghhBC5RYKMEEIIIXKLBBkhhBBC5BYJMkIIIYTILVq1JBodrWYRQghRLhJkhBANggRSIURjINOSEEIIIXKLNDJCNCGkNRFC1BrSyAghhBAit0gjI4QQOaRatWvVmi9Ru0iQEUKIJoaEDVFLSJARVY06XCGEEE1SkBn3l76mXbt2TWoA1KAvRL5QmxVNkYkx9X7GjBllpVezgowQQghR7UiYrRwJMkIIIXKBBv2GLa+JNVKeEmSEELmnVjpkkU9U/xoXxZERQgghRG6RRkYIIUTNI61J7SJBRgghhMgpExtJQKsmwVCmJSGEEELkFgkyQgghhMgtMi0JIYQQOTGjiPpIkBFCVD0aSIQQcci0JIQQQojcIo2MEEKIopF2TFQbEmSEEKJGhQ2QwCFqHQkyVTZj0WxHCCGEKB4JMmUgYUOI/KD2KkRtI2dfIYQQQuSWTASZF154wTRr1izyePXVV2Pv22qrrep9/+ijj84ii0IIIYSoATIxLW266abm66+/rnPunHPOMc8++6zZcMMNE+8dOHCgOf/88wv/t23bNossCiGEEKIGyESQad26tenQoUPhf2zTjzzyiDnhhBOsliUJBBf/XiGEEEKIOBaIs++jjz5qvv/+ezNgwIDU7959991mxIgRVpjZbbfdrCYnSSsze/ZsezhmzJhREJ7c4f6PIup66xbRf8NplJP2Sxf2LZwbPXq0efbcfa3jYTFpx+Ur/L1y8tUQ92aZdrXmK69pV2u+sky7WvNVjWkX0weW+7vVmnae85VFHfLHlwVVnnH5S6NZEASByZidd97Z/mXFQBI333yz6dKli+nUqZN5++23zZlnnml69uxpHnroodh7hgwZYoYOHVrv/MiRI2WWEkIIIXLCrFmzTL9+/cz06dNNu3btshFkzjrrLHPJJZckfue9994z3bp1K/z/xRdfWOHk/vvvN/vss48pheeee85su+22ZurUqaZr165Fa2Q6d+5spk2bZgvCaT623377yCWXUdd7n3NPQYI8pefi5opXpps584xdtpl2b7HXGzJf4OdtQedrQaRdrfnKa9rVmi89c3WkXUwfWO7v5jXtxspXQ4xHcysYb7LMd/hexu9lllmmZEGmJNPSqaeeag499NDE76yyyip1/h8+fLhZeumlze67725KZeONN7Z/kwSZNm3a2CMMheIXavj/pO874cDB/xxx95eSdiX3xuXLfa+x8rUg067WfOU17WrNV5ZpV2u+qintUvrAUn83r2k3Vr4acjxqVcF4k2W+3b1JeWswQaZ9+/b2KBaUPQgy/fv3LyuDb775pv3bsWPHku8VQgghRO2TqbMvpqGPP/7YHHHEEfWuffnll9ZsdNddd1k/mA8//ND6teBPgwYHH5mTTz7Z9O7d23Tv3j3LbAohhPBQNGSRJzIVZG677TYbU8b3mXHQQCZPnmyde9yS7TFjxpirrrrKzJw50/q54FMzePDgLLMohBBCiByTqSCDhiWOlVZayZqeHAguY8eOzTI7QgghhKgxtNeSEEIIIXKLdr8WQgixwJD/jWhoJMhEoIYmhBBC5AOZloQQQgiRWyTICCGEECK3yLQkhBBCZIjcFbJFGhkhhBBC5BYJMkIIIYTILTItCSGEqAlkwmmaSCMjhBBCiNwijYwQQgghIrVbedBwSZARQgghqhSZy9KRaUkIIYQQuUWCjBBCCCFyiwQZIYQQQuQWCTJCCCGEyC0SZIQQQgiRWyTICCGEECK3SJARQgghRG5RHJkcoXgCQggh8sDEBTheSSMjhBBCiNwiQUYIIYQQuUWCjBBCCCFyiwQZIYQQQuQWCTJCCCGEyC0SZIQQQgiRWyTICCGEECK3SJARQgghRG6RICOEEEKI3CJBRgghhBC5RYKMEEIIIXKLBBkhhBBC5BYJMkIIIYTILRJkhBBCCJFbJMgIIYQQIre0NDVGEAT274wZM+zfuXPnmlmzZtn/W7VqVe/7SdcruTfLtKs1X3rm/KRdrfnSM+cn7WrNl555Rm7SDl9z47Ybx5usIPPzzz/bv507d27srAghhBCijHF88cUXL/r7zYJSRZ8qZ/78+earr74yiy22mGnWrJmV8BBqPv/8c9OuXbt630+6Xsm9WaZdrfnSM+cn7WrNl545P2lXa770zJ/nJu3wNcQRhJhOnTqZ5s2bN12NDA+/wgor1DtPIUUVcjHXK7k3y7SrNV9Zpl2t+cpr2tWaryzTrtZ85TXtas1XlmlXa77ymrZ/rRRNjEPOvkIIIYTILRJkhBBCCJFbal6QadOmjTnvvPPs31KvV3JvlmlXa76yTLta85XXtKs1X1mmXa35ymva1ZqvLNOu1nzlNe20e5uss68QQgghmg41r5ERQgghRO0iQUYIIYQQuUWCjBBCCCFyiwQZIYQQQuSWmhVkZs+ebY/G5IUXXjC//vpro+ZBZMP06dPN5MmT7cFnkY822Vh8++235ptvvjF54+OPPza///57WfeWe1+e0dqZ0rnjjjsq7kNrSpAZPXq02Xnnnc2SSy5p2rZtaw8+c27MmDGJ93799ddmxIgR5sknnzRz5sypc23mzJlm6NChdRo137nvvvvMXXfdZaZNmxaZ5g477GA++eSTemkNHz7c/PnPfzbXXnut+f777+tcJ32e47bbbjMrr7yy/V3CN8fx+OOPm3PPPdf8+9//tv8/99xz9nn79Oljbr755sK5888/3xxzzDHmuOOOM5dffrmZMmWKvfbKK6+Yq6++2gwaNMgefOZcGuTzgw8+MC+++KL5z3/+U+/6m2++aY444gjz/vvv2//5y+8fdthhNj9R8Kzhsowqry+++KLO9/71r3+ZAw880GyxxRZmp512MhMmTIjNN2n079/f3Hvvvfb/v//972attdYy3bp1M2effbZ9rqTyuvXWW+33l1pqKfvX/8w7SxrISJMtNKLg/KefflpSHdtmm23sPVGQDvVo0qRJsfWLNnH88cfb8ksirczohMaNG2fz+sADD5iJEycWOvRK2uR7771njjrqqNg2uccee5hDDjnE1g/g99dcc02zyiqr2CWdYQYMGGC3L4mDDewgqX6tscYa5oYbbohN44cffjD77ruvWXHFFW39mTdvnm0HHTt2NMsvv7zZdNNNbV+TVMd4R2+99ZZ5+umn7cFnl7ckfvzxR1tXyP8vv/xS7/pNN91k+4Viywt4XpevuDZ5//33m3feeadQj4cNG2aflSW1RFk/4YQTEgd4yuijjz4qtA2EXdKkvtFuKC/6OOo7+V177bXNbrvtZusw9wJC4iOPPGKfkYPPaYIj/Sr1Oq4PYxLKeymlz+eZqbdR8A4py//+97913ueHH35oy/Lggw82gwcPNo8++qhJI6lNnn766eaWW26xfS394S677GLfwbPPPlu4v9w6ltYmzzrrLPP888/bdgCU0yWXXGLrely5HHnkkYntsiiCGuGOO+4IWrZsGRxwwAHB8OHDgyeffNIefO7bt2/QqlWr4K677oq895VXXgkWW2wxWlqw8MILB6uuumowadKkwvUXX3zRXmvevLm99tFHHwU9evQIFllkkaBt27ZBixYtgrXWWitYf/316xzNmjULWrduHay77rr2/88++yxYaaWVgsUXXzzYaKONgqWWWsr+3i233GJ/5/PPPw+6detm01tuueXsb/KZY8cddwwefPDBYO7cuYV83XjjjfaZyUu7du2Cv//97/Y5jjjiiOCoo44KFlpooWDFFVe0+eZ7/OW7HTp0sJ+XX355m8cuXboEPXv2tAefObf55psH3377bWx5P/LIIzZ/fJe0evfuHXz11Vf22j//+U/73FwnD/zfvn37YLvttgu22WYb+/1HH300mD59euH46aef7DuifD7++GN7Lqq8ll122eAPf/hD8Nhjj9nfGjVqlE1v9913D84880z7mxwHH3xw8OWXX9bJ87Bhw2z57LPPPrYMLr744mDppZcO/vKXvwQXXnih/dypU6fI8uIdbLnllvZ9n3XWWcHzzz8f/Oc//7EHnwcNGmTrw6WXXhpZXq4OUR48wznnnBP8/vvvRdUxypJ833DDDbbc3UGerr322mCnnXYK7r33XpvOrFmz7PORDu+GNKl/P//8c736xV/3m6uttpotj6+//rroMuOg/vLeSMP9pqtTJ554YmZt8vzzz7fX9t5776Bjx442Ly5fxxxzjC03yvitt94qHPzeww8/HPz1r38NXnvttUJaf/vb3wrthDRoF2n1i3oSVV6HHXZYsM4669g0qS977LFH0L17d/t+x48fH6y33nrBMsssE9sme/XqFSyxxBKFcnQH5wYPHhzMmzcvtk2OHj268D55t7QB3jtceeWVtu5yPVxeQ4cOtXkhb3vttVedg7Qoy1122cX+H9Um+S1X/1w7uuKKK2y7v+qqq+xvcu6CCy6o1yZ5L+SH36HcSJ+//Oaiiy5q6wB/KSP6JPdc+++/vy2TjTfeOPjjH/9oz/MMtC0OPnPuoIMOCmbOnFlyH/b+++8HK6ywgr0e1efTjg899NDg5JNPrnOQTv/+/W1eTzjhBJsW7fzUU0+17ZjrpLn11lsHc+bMsfWiTZs2to7wTIwTXKcORpVXWps89dRTC++sc+fO9tl4d5QT5bHvvvvavqqcOpbWJmnbrjyXXHJJ28ZWXnll27d07drVXmOc4pp/8H3qk/u/HGpGkKGw6NTjuO6662zBR8EAS4dDQc+YMcN2hFSM119/3V7v06ePvfb2228HJ510UrDmmmva71MRf/vtN/si6NiGDBlSOM4777xCpT3ttNPsuQMPPDDYdNNN7aANdDJUbgYioEGSl++++87+T7r8v8kmmwS77babbZwIBFRWBlCEp5tvvtl+97nnnrONi+d0IJjQCSAUkM/jjz/eNjLYYostbHpU6jA0YvJJpY+DhsizkdcpU6bYxkKl/fTTT21+KSee/5577rGV8+yzzy7c6wZYN/j5g6AbKPg/qrwoD/JNxwI0UBqynzb38B0O8sXgRWdCY/rHP/5hv/fmm2/ahj1ixIjCvZtttpntAKLK69lnn7V5OuSQQyLLgw75kksusQKCP3i6g3fMcz3wwANWcGWgJ2+zZ89OrWN+uYQ7H3eNv8D7pAOmPtCBM9DwP8JXuH59//339l7qAkIHAyyDPYM2AzkdWlKZMbCTNgMRgygDAWXw3nvvWSGCPB133HGxdeiyyy4LVllllToCrTu22mqrYOedd7ZpRLVJ2rt7Zs7xrm+99dZCHfDLxT9cm3T33n777bbdnHvuucETTzxhBwOuMyDH1S/qBe0wqrwoi3//+9/2u9988439/jPPPFO4f9ttt7W/F1XHeD98f7/99rPCPEIpB59vuukm+3u8p6jy4nB16NVXX7Xvg4F3ww03DH744QcrwF5//fU2/XB5ueeir2Bw9g9XXgyy/B/VJp2gBwgh999/f533TNoMYE7A8NskEzT6mXfeecc+G/We56feM2mjnKkjDiZrvBPguajfDMBPPfVUnYkBn+lz6Ze33377OhMAdyDgx/VhtD3yRt6j+nzuo1+jnvoH30fA4zr9CTC54bvUtXfffdcKAeSbtoKwiwDkw730I1HlBUltcqeddgp22GEH+x2g7rrx5YMPPrDvAUGMSXCpdSytTdL+3RjKM9M3MKl28Cw8F0oHdzCpIf8Ibe5ckxZkkGoZgOOgMtPgwjMODjojZkJcd1x00UW28iGF8rJcx/fLL7/Yz//6178K32WmzEuiM/SlWc7xXafZoEH6nRogyND5AS/+5ZdfLlzj3hdeeMFWLmC2QAdLJ+4Gf2aXDp6DDsGB9Eyn6SDvfIdKSaeFZL/GGmtElhfnST+sZXIHlc+VCcyfPz84+uij7cyWtCdMmGDvpzwoB1fZgQbKczPY8nwcaDVcmjRazkWVF4ME30E4cGm5z67MKEM6CzRYNDyngSAf/I5fXv6MIqm83PeZjUYRHiSTBk+g80TQpNOhY0yqYwxQDB7M0nx4HjpGv44xkIwcObLwHZ4HYXf11VevV79cvulUgU76vvvusx04ZcYgwG+MHTs2ssyotwxadIzwxRdf2HfP87j8McuPI06gdeXlrkW1Sd6vX560f5cvNHZ0urzPTz75xB501OSHAZ77nEaGd+C3ISBtBv6k+sUzR5UX5UPdjWuTlA/pR9Ux6ujpp58e2yaTyiuqjvEemADxDvhNntmVp19eTmPDvQy2PsX0YdRdygl4Br+tu3xTDxFMwm2S88zkgQGV834d5TqCioP+hPJCSHTl6frHUtpkeAIQ7sNoE2PGjLH3R/X5fI+8MsFJa5P0lwgJDiZL1Lm1117blgHCSDjf1K+o8jrjjDNsmSBsRbXJtm3b2j7OtUkmSlyfNm2a/Z+yJJ0s2qTT8gBtg+/57xLtJnlBcHeaQr/MKqFmfGSwmyb5KGAfXHTRRe3OmuGjWbNm9UIkY+vD/o+fC9uKOxZZZBF7YPN2YINk1218RrCBY/P0IX347bff6twH2KedvXWxxRaz25r792HrdrZj7sWPhd/heRZaaCFrOwZsjNg9P/vss8L9LVq0qLOTKHnEpsz3eN6uXbvW8+FxYLNu1aqV9UOIOsJbrJNXfAewXWMrdenyPfLp5+OZZ56xecCWvuqqq5ott9zSbLXVVoVy2myzzey5qPLC9s737rnnHvv/+uuvb52qfbB5Y5vfZ599zBNPPGH9SPBDAOzsvXv3trZqysK3jZNX/F2iygvWW289W75RTozct/HGG5uePXta/xT/oCzD9WuZZZaxPiLULfxFKLO4OvbPf/7TbLfddubLL7+0PlFRuLLDL6B79+6F86uvvrr1OcAfIFy/HK5+8b7/+Mc/mqeeesrmeeDAgfY87wbCZUbdJL1ll13W/k9+eWf4arjfDvvo+PB7nTp1sv4i4YO8hv02/DbZsmXLOtfat29v2zfg49WlSxdbptjq+bzSSivZa/we4MMCPCfp+dCGOZ9Uv6iHceW19dZbF94bdZ/67sO9UXWMusC9cW2SOsFvRpUXR7iO8f9DDz1knx1/BnxnosoLdt11V7PwwgvbPpR2495hMX0Y9Zc+jOegb7j++uvr+MTweZ111rHvLNwmyRf9J23SvVP/3dKunB8M4DNDWbmdkslXVJ0G8ol/Bt+ljocPyjOuD+P5XRlE9fnUQ94dPk6nnXZapH+JKzP6DOqUg36CeknfQB+Mf0oY/MiiyuvBBx+074Ayj2qTSyyxhHn77bcLbXLWrFm2vFq3bm3/py9w/iul1rG0Nkm+HKSBPxz9nIO2RJl16NDB9qXOr7NBCGoEpFAkXdSFqOpQqXHwGfsj0iHSbBSo1jF9+JKmA/VfWHJHRYv6zDFx4kRrqwRmNHxGAkf65D7nI8PsAQnbB5U/0jb5x18AFSYzAWyj3Mv/vnrOB7U9KkTU4cwsMXswk8Q2jaoVNSIaBGYUSMioSJ157dhjj7X5RIp2Ggfg80MPPWQ1Jqg942A24ZdJeIbGu3DlyYzU9+0ZN26cVeFSjsxinQbBzf6SygvtALM/fgPJHq0S38MWjnrS+SWhsgyD/Zfnpcz4fcqe2RcaNVStzu4bVV5Ore38KNDkMSvjcFo98uHPvn14D1HNjZkJpjjynFbH+F3MiUceeaQ1G/mzP3yiqOuUjT9jphyYgVEXwvULjRh5wlwQx5///Gdrv6YOhsuM2TnXnGocM6I/e6YtOB+dqDZJvSffcW2S346qY7RJN6uOA1MP5ju0UGgxnWbQlRdlgXmB6/iu+HCe70TVL54nrn75dYR6g9YDUyJ1HLMRvkKki4Ykqo4x88Zs4rQbPmjw0BI4TVEUaGqj6hhtj3upP1F9nCsvtHmUE5pl/CvoQ4rpwzDJ8T2eA/8VNAaUPVpw6gx5clqXMJjaMH3sueee1leHNAYMGFC4Tj1Bu0CfRn3FpI22zcH/vI+wFsiZOtAaxtVv+rC4IZD2wrO68orr82m/1BPySdunHFwdo75cffXVNg++VpP65rS/+FKhUaJvuvvuu23ZkydMs3HwPNStqDbZq1cve42xAPO787txcJ3fdeblUupYqW3y8ccft1o2x0svvWTbG6DJIs+Ywl2ZVULNCDKA+hhhhQqMKp2Dz9jyscMyeEeBzwIq2DizAZ0LnUAcqNfohBzYIrGTOh8B33eGzsEH/xk6NhorA6lzCHOqPOyjvhrOh85w4MCBtgNicEONiG3SDYrkm2eiA6eyMMigWndqZzoBBhN+i8rNwWfnoIz9Pg78dJxJIgwNytlSo6DyHn744fYzFRhTAM6f5LOY8mJAmDp1qv1Lh+AaEM9ImcUNMnTSdC677rqrHdxQJTP40mnzfnGeY3COKi8gXQZhOjU6L8xCHHxmQHb+SlHwfvBXiIIOksEraWB2dYyOgd9i0OLdUX4InL6d3jmPOzBN8V7D9YuDeofpJY6kMqOzJw2ei3ZGuWGicFAXEdLi2iTp0dFHwTPQZqkDUSBAOpNsFPiKMUhggmCgdD5hbpDxDyYCPviOIDBG1S8GiTvvvDNIAgdO/H+crwy/yQBP/cKkgCAdVcdwdMV86Ey6vDcOPvN9rsWVhz+xiQJzF/UkTpBx5eXAjMIgWWwfhqDm2j0DIe+Z32OC6JtZwjjTPfnCHwYTCf0WQgICIH0S6bhJDiZW5x8HCFX0n85Eym9z8Jlz1M0ff/wx8rd5FudvE4Y2xu/Gtclwn0+bYMLAc/C+EeToe93htwvADwVBKVwX3QKMpIUWSW1yySWXtOXozGrkwxfyeEe0G8qz1DqW1ibxv4sz8wF1Aed8B+YuJoG0gSS3kGJoMptGolJDBYe6q6FBRYgK2Vc9orpEVYxa06kYk/jpp5/sUlW3DJG0MLGsttpqJecHFR+qTlSBqBZR4fH8vXr1qqPqA9SyLJd1SxVR+/Xo0aOgul0QoF52y/ZQhbPsvFiovixnpMx4tvHjx9tyC5seiiWtvMoFNTXmP0ygUVBXXn/9dWtSK6aOsUST8sLU6NTIcVCnWEqK+rkh6pcPanGWylJeO+64o9l+++1NNXLNNdfY8vrb3/5mzY5JYL5DNc7zhOsX5yuFOoZ5inofrmP8DsthX3rppTptcpNNNrHq+7BJ1wcTAmnHtV2uY57E1FYMmA4xk7Pk2ZkmymHs2LGJbRITIOEZWOqNuYv+6+6777Z1lvrEec6Rf98c5sP9hF0IlxnLkbMgqs/HfEsbxgwcNluF4f265el+m8QMmFZexTBlyhTbJnn+cDqV1LFKoG7i7lDpTtdRNBlBRghRHeDnQCfr/FRKge6KjpgOsaHTzpIs80WZJE2WGHQ7d+4cOTASuwnfiziBnWBle+21Vx0fNwe+Q/h74FtT6r2VkvbbYsFAncb3CoGsVAEFwZTJBT5ECHD44sW161Qq0ueImgWVPDbrODBr+XbfMJh+MF2Vei0t7bR7nYret7P7sGQzzjSQljZxWFhSifoe0+Ftt91W5zqrW1Dp4jPgllX7z4QdHJV9lD34119/tarbuOvkG/t0OfdyPcnchv8BviCYF8K2c3ymWHaadJ1luVGwRBffB2zhmN8oE8y7ftwOTAv40rBkG/iL2QgbP74AfAe/AcAsgykM8ximSI64tDFp4ffBEnMg3/jnUKcJXYA6HhOF893ifmKhUDf4Ln5G/K5blUJ9RAVP/eB5+U23wof7MGdgisFkRr7LeWbqbHgVDPCbSeY/TFQ8U9J1fjO8nNbFbmJliTtXStrF/G7SdcoLM3wU5T4z7xVfGsy8+Cvhh+R444037DvBPIWZD19An/B1f4Uj7Zd3hnkEE5x7d8XcG84X9dLPVzhtt8Io7l5WBxb7219++aWtx/369bPuAOF8Y3YjXfom+hD6D7+N48/IvcTFwb/Grb6iTyFmknNNwCyF2chPHz808oRfDP0mZngXnykqphXuDOFnKxYJMiISlgTG2dMrvZ7lvZXcn5Y2dnNkf3w/nBMsvi/AQMH/ccGi8JeIC76Vdn3y5MmF4Fyl3svghP8X16MC8TEIcw3HRwZf/BXoNIu5npY2HR3XrrnmGuu7g0CEPxc+JAgGpEcHhj9DOHCic47GJo+/CgM9PgDEy0DYwJmc70aljeO28zGLCsyFn4sLwhYV7AzfBTrmDTbYwH5GCMSOj3MlPhWky9+owHIMsPh3lPPMdOTkCaExKvAhsVo44oKw8SwcUUHt3NLZ8NJaf4myK69wsDIXYI8jHPAT53F3LS7QGe2BA38o/3AB2ihXjqhnxmeNIyrYKL+NnxaOtoDQynvhGmXqgtn997//tW2Ed4PfC0vccUTmOkuCXRvyr1MXyAPvLCoIIPfS5qPuJW03oYnLF3WRfJE2fix+2gjqbkl2+F73rtyEJfzbzf/3rsg33yFdyoDYPE5wwH8L6J/waSFdfgOBgrTwAYwKBumCvyKM4BdFmSAMIrzwXfLN7wBCEe/8T3/6k/XLwamdZ3CT46iYVvj8JMUuS0KCTBMlKmibfxAfI2lQx4GTihnunDiQzFmNEHede+PSdteT7uVaVIArDpzecMTjO+EZKJ71ONnFpc1M28WoiToYoHznP4Jo0UlwH43SrUqJChblAuJFBd9Ku45Wg/RJu9R76UgYvLk3KhAfzpBOMcvMjzJi8GaATbueljaOg+5etwrPzcjAOWVCOHAigxedGoMCz0R9cdFjgbJ1zvnhtCkrZn8MFlGBuRAmeJdxAS4ZHJ1jNiu86JCJVOvyzAyVGWZUYDmeB0fXcp45HN8kKu4JR1IQNgSuqKB2CE2UAzFvwrGbENR4RmbcUcHKuB8hi3oYDviJtokBjLKOC3TmHHF951cO/5nSnjkq2Ci/7QKOAm0Oodc5A3M/Qg5CMPUIwdqHe9E0QPg697KQgkjkUUEAuRchMerecNpJ+UpLO3wvmgyuu8CcSfneY4897ETDaR3RvvKu3Kov+g3qsmuv1H/qs8t3OBgkEwAEEQRJ6rjrI/zrOGgD3wkvfnAr2yAqphWrvpKchZOQINNEcTOtqM4jaYbmDnc93Dm5mbRLI+q666Di0nXXo+6lobj0kzq+qKBO/rW4fMd1qFEBxwBVKI2aDgI1bFywKBpoXPCtDz/8MPE6gzaDjku7lHspL6KAunvDgfjcqhwfloEyI2UATrrOTC4pbTo2/15mdGiXogIQhgMncp7VPC54F//7amvKmzxEpU2ZI4C4oHXhwFxuZQdEBTtjUHcdsuuAXVA8rmH+casYw4HlyBeCUDnPjOkKbZrLm8OttqI+IaDGBWFDMGKAiApqx8wdQQSTja++d/ciLCIMRQUrw0SDwBoV8DPtXq6jyWRlUdgU5AZNhN/wKh13L5qspN/2V/gQSJDJjINrLH+nzNCeEaTTxwlYEL7ONbQatK2oIIBcd3UkLe2kfKWlHb7XXadtp+W7c+fOtk/yoe65+sW94WXrbkuIqGCQTFSoC9RpJilEkPZBiHZ1m3vDgf4Q4mlLwOTBXxHqlqXHrYRNQ4JME4WOGB8PF/k0fNBBM3D7syz/8KM4hkEoYOYep3WhsselTQNEPRl3L/ZgXyUchkHEaV3czNMdzMIxBcWl7Z4prkwYVKOemZm32ycnnDYaAdINR6J1sLSVwYfOIe66i3sSTrvYe9Fk+PeiMcKHhFmbH1HYh0HRmVnirvMeMIPEpR0uL3xa/AGLtP1Bn/8RzIB7WebrOkZmrf4ATEfpCzJ+2px/+umnC/u2+OkCaVK//d/FP8pBnlyHG76f8mLgcZFTKX/fl4N8+5F7S3lmwFeBcvW1OH7kU4RiBGf8HZyfhX+dEBRoi9CK4lcVvh4Vu8ldY+aOxhPBAQHCv47GAI0js3RXVsXeC5ggGFj9Jd7uOtoursU9c9JvU78w0bg64QuVblBHuERoiIs4jLY2fN2P4Ex5heNDcZ10o+511109iMoXcWicBiMqbVc/w/em/Xbz/wnt5BvBwxdEAIGSsnNaE6Ko+9B2XbtCe+eHR0BzyPtF+CJ2DdoeJ7gS14rfc9HH6cOpyz7479CuomKmYa5G6xcXMy0NCTJNFGbNDEJxENslyRcc9X3cdSqsM9OUmra7N8mPJSlgFGpTJ8iUmi8acdIzo+6Nu04n62zYpQZwQ+AIb/ngQ2dGxxSVdtq9CFF+cMJwIL44QQUY7JLyzYzOmR6i0o7S5vgweNGZOfzAiWgOiDdERx0FHbKblYZh5oe/AMJPVGAuhA9f6xEOdoYq3w/sx4CBlgsQEvCPcuajMPjV+NqcUp4ZmEFzf1TgQ0dcEDZHVFA7/3o4dlPYQTwpWFk44Gcp9yKIIuCieWKzTf+3maAkPXPcb1O/iOdC20Rw9YWhsCY1bOpwGlyn3fWv8z8DMwJFVBDApHvdddpsXL5cLKG4tPndqHuLyfci/2vvlBEaFB838cD0x8GkE9MoGjtXvghgUcEgMYNxL+lj0kII5BkxK3KOeosgg1bxlFNOsemgccPUxTmELwSVqJhpHGgM42KmpVH+QnWRa44++ug6YfHDEKY7KZ4LcV+IoxAFW7YTn2bIkCGR1wm77cK4R93rwsNHwVb1xC0hRk4U/fr1szF5WNYXlTaxHly4+jAnn3yyDTkfB8/DlhJRHHHEETYk/e67717v2hlnnGFDfI8bNy7y3muvvda89tpr5uWXX468ftJJJ5kbb7zR5r3Uew8++GAb4pwluD7E4yCWBDGDiDkRRd++fW24f8KiR7Hvvvva3w3HZnJp846JqxEHZeWH6yeMvYPnveqqq+yWElEQGj4upssBBxxgY5BccMEF9n9C4PsQnj+cls8GG2xQJxaL/5mQ9/fdd19sPeG3kyJaJD0zUId22mknG++G+kgo93B6lO+dd95p7r33XhuzxA/hD8QBYVk1MVj69+9f7zptiPdKG+b3w0uYKXPeG1suEA/FXxI7YMAAs/nmm5sDDzwwcquOpHt5brbluPjii224ev+5eE7qcdwzx/32IYccUrjO9gjEKnHQB1CPiIVz6aWX1ttagWXnxNuinoN/nVD8xFlhmw1iuoTj1/D+v/vuu8IWMeG0N9poIxsmgKXn4XyRttu2gvYZTpv3w/ej7oX999+/8Ezh3x4+fHhhCxi2MWALGB/KlfPnnHOOjWvF/25rDZZPMy7QHsjjX//6VztGENOHpfs80z/+8Q/b7z/22GP23foxqeh7KU/eL9dJm3pGv8V14nNtuOGGNt8NFTPNoTgyQtQwlQbia6y0syQtMFdUsLPGIi3wITE8CGgZF4StoYLahSk14GcY8kxQQAQt9hUq5ZlL+W0GYt513KSrsagkXw3xTPPmzbNt0xcmmNT4E8QsgkFmRU0LMuuuu6558sknbTCoUmEWd+utt8Z2ZmxayOwqLoJj0m8fe+yxVjsQF4SKzonN7aIiLKY9E9IwUjWbh5WTbyGEECJP1Mzu11Gwi2zUrqQOJPo4MwZmANTTcRx11FE2Wmc5vz1ixIjYHVudajFuB9y0Z7rwwgsTdzdNy7cQQgiRJ2pakEmjEmVUlvdWa9pCCCFEtVHTgswWW2xR9l4cbKxWiU2wkt9ujHSFEEKIPFLTggy+JEkOewcddFDsTrGTJk1K9K1hdYG/+qCU38ZRLW71C5x99tlmqaWWKjldYIfjpN1t0/JdCfj24BhW6rVK0q2UStPG32natGmZpN1YuN2/y72eBD5crCorB/y7klbaVZJ2GknvMq08ssxXJflOy1dSeac9c9q7qoRK6l+WdbuSeyvl3wm/nWU/VOkzN1jbKGvRtqhJ2BOGvT/iIFx2eNNAH+KG+MG9ir2WlnbavWnfIc6EvzdRKWmnlUklz1zJ9UruTSqPak67knvT3mMl+c7yPVdreVb6zEnvo9JnTkq7Wp+5MfuhY1L69azKs5jrxVLTGhlRGmmOxGlOykn+N2m+OUlpF+PXk/SdJMfttLTTyqSSZ67keiX3pjmyV2valdyb9h4ryXeW77lay7PSZ056H5U+c1La1frMjdkPjUjp17Mqz2KuF4sEGVE0WToKywlZCCEWPEEN9L2K7CsazJE4ybcn6Vol6Rbj75TkuJ2WdlqZ4O9Ubr6T8px2vZJ70xzZb7rpJrPccsuVdT0tbXy4iJFUTr7T/LuS0k57j2n5TnqXaeVVyTOn5SutHiTlOy1fSeWd9sxp7yrpfVRaP5PSrqRup5V1JflKSzut/t6U8NuV9L1pv532zGnPlVYHm1RAvCS1WBxRhYs6k+JwIdc//fRT8/DDD9u4LjvssEOd7/7222/1IiviwPvqq6+apZdeus55nJkIe54Uej9JrUf01J49e9ow0ZUSlW8hhBAir9SERoYotqWGyWY/CkKU+97cp556qjn88MNtZFyEj4033tjODFiVcsUVV9hgcuzfwv4dBJX74IMPrPDCvhXs34PQ4e9rsvfee9u/eHVzzf3vmD59euHzlVdeGTmrGTx4sE2ze/fu5qmnnjIdOnSok3YxIJxNnjzZhpRnf5BwvnlmIYQQIo/UhCADbGxXjPrs3XffNccff7zVsISVUfx/xx13FNJDZfbGG2/YjbLYHAwhgM3a2EzLbbTltBycAzbLY7MvJ6iQ5tdff233sHj22WftNTbOAjYSdLCJWhiELCegcZ1N+NiMi43ARo0aZTcuK8YU9Oabb5r33nvPCmN//vOfC+fZMI6N+STICCGEyC1BDcBW5NOmTSvqu2xf365dO7vd/I8//hj89NNPhYOtxT/99FP7vf322y8YMmSI/czSM66x9fqYMWPsObY3d8vGwtuq+wdbla+++urBPvvsY7cw//333wt54f6+ffvabc4ffvhhm/6NN94YvPXWW/bgXp6Na3DYYYcVtqbn76qrrmo/p0G6bLnO7/n5fu+994Illlii5PIWQgghqoWaEGRKoW3btsGUKVMir6277rrB1VdfbQUXhJ3x48fb86+99lqw3HLLWWHgk08+sed8geDdd98NFllkESt0xK3HX2aZZYL333+/3nnOLbXUUsFGG20UPPHEE3Wurb322sEFF1wQbLDBBoVzr7zySvDII48Ejz/+ePDggw8Gd9xxR+ozk+8HHngg+O233yLzLUQ1MmfOnGCbbbYJPvjgg8bOSs3w3HPPlXwPE75iuPPOO20fE2b27NnB8OHDg7FjxxadVl7gmebOnVvvPOco61p85mqkZkxLxYLfy9SpU615Jgzmo379+pmTTz7ZbLvttmaTTTYp+NNg2mFL83/961/1IudihuI618Lga4MPz++//27ef/99s8Yaa9S5zjlMSO+8845ZeeWV63l8Y6rCs9ux0UYblfzMOCtj/sInKJxv8vX999+X5KD82WefmRVXXDHyt95+++165/AnivJsx+8HeB8ffvih6d27tzWVORPfoosuavbbb7869zzwwANm1qxZ5pBDDrH/E9/gm2++sefat29fMC+GHbfxL8KUh2/QpptumlheLl9z5swpbGPvQ1kRkRJTYdR1v8y22WYbM3z48MRoyzBx4kTTo0ePxO+k0aJFC2vGXHbZZeuc5/1SNr/88kuqIzt1jffLs/uwY/rf//536x/28ccfmwkTJthnwjRJvd1jjz3q5Ycd3sPtq5R848Tvm1/LSTuJKOf+6667rqg6AtTV559/PrIOYMKlDsfVkbjnisuX/6623HJLaw6mbTjTclS7i2LHHXe0EcsHDBhg21A4evkll1xi/eb2339/+z/ma0zr+OYRVfwPf/hDbB0hzT59+tR7j6zsI7/4G2Lipj8s9Zkx6S+zzDJml112sdfPOOMMc/PNN9vrRBFOa1/F1N2459p1111tHxX1HjH3R9Vd3Aq22267xGeOW6SCOwF9devWrROf6dFHH63z/yfeRsPcz7tdYYUVEvs4F/kX4r4bR1JfEaYUn04H7ytcrnHUjCBD5aTDptI57rrrLnPeeefZUNl77rmn+dvf/mZOOOEE69TL4LfuuuvWWea3+uqr2xdDxaTBOhBq9tprL+swS+P/8ssvbYV+6KGH7Dl+5/HHH6/XCdDR0AmwpQD5ojEzYLMCCV5++WU7INIBvPDCC+aiiy4yt956a6ECn3LKKbay+E7BPuTh0ksvtRWaykQ+ed6w3wydfFy+EQR8B2VX6eIclP1tEnhWOnKf9dZbzzZE53/EX98R213jLx0DZUWnzv9TpkyxgwjlRCdGPsNQsY844ggruNx7773mlVdesc/u0qQx0vEhGB566KEFx+2ddtqpTh6SnMMRLg877DAzfvz4Oufdb9C5jx071hx88MG2HDjHlhZRHQzBvagbbsCIauROQCV9Otbrr7/eDgjFQoeJ8BS3AJF3SR5533GO7Axw1HHKzX9/rpyuvfZaW49OOukk6/C+8847286SNsdu66uttlq932Uwcs+cVN5J+UbIue2222w78WFAcvhpL7nkkkU7/uOrRv32y8TtDJ9WR8hz8+bN7eDKIB+u49RtBBkGXnzRis0T75Kw7zj/x70rJk2nnXaa7cuoi7QXBC+/bSXlGx9BBIOhQ4daQZv76R/pdxg87r77bvvd0aNH24NFB/fff7857rjjrFASV0fifpuBEt9A2jb1NDxhA4SJ8Lvwnxmh44Ybbij09Qic8Nprr9m6x6QnDuoRApKru66/Q7ggXcaBuLrPZ/o56nK4niaVN+1ikUUWsWNK3DNHLVLxBSXqF+8kzg+Scgr3tVG43/DzS7//l7/8xVx++eV2guN+m99KW9VK+VEmcfUgPJ4APp1Mpui39913X5PGyJEjbb6KFWRqxrTUp0+f4OKLLy78//bbbwctW7a0fimXX3550KFDh+C8886r58Pi+5wU428ybty4YLvttgvat29v/WY222yz4Omnn7bXMC39+9//tp+feeYZ63/CtcMPP9zec8kllwSdOnUq/C6fOYffzMsvvxwsu+yyNt1tt93WHnzmHNeiOP/8822ed9hhh2CPPfawJqQBAwYUle9u3bpZHyDycdddd1lzlTu23nprex3z2iGHHGL/du7cOdhrr73ssfzyy1v/nVatWlmTGyHFKefjjz/e5gOTGNfWX399q1LGHBd1HHzwwcGOO+4YfP7553VMXk899ZTN18cff1zvOf785z9bfyRMcTw/3+VdYy6knG677bbg0EMPtffzbjBL3HLLLfZ5Pvroo+C6666z+Q/7I/F5tdVWs/5Im266adC7d+/gySefDN54443gzTffrHMsvvjiwYsvvlgnX349ijuS6hfXt9xyS2vq4/Muu+xi8+L7VMWx77772vpP+pgiMY+644orrgj23HPPoEWLFsGkSZPs9ymP7t27B/PmzQvuv/9+Wza77rqrfXeYRnkX//nPf4J//etfQc+ePW3dWXPNNQu+Wlw/8cQTbf059thjbb2jjvTq1Ss4+eST7bHJJpvYc2eddVZsvl0ek/KNSZZ0evToERx55JGF9N0RBlOrO2j3Sy65ZHDAAQcU0uUz50h/6aWXrlcmro5QP6L81lw9oR35/U0Y0g6biovh2muvte0z6V0508U//vGPYPfdd7dtDX+5s88+O3j11VcT8+3eIUycONG2WfLKccIJJwRt2rQphKP/05/+ZMscJk+ebPvTqDqCuZ60eY+Y52n37iDvhKHH5/Cf//xnsN566wWPPfaY3YJg+vTphQPzetIz+/6LZ5xxhu07eMfUG/KS9K7po/y66/qZd955xz53Ut2nXMk753yfSu7h4Jnp/1zfyME7YSygb0t6Zt7JCiusEAwePDh49NFH7bHbbrvZsqAP22mnnWxfQFlE1V/8NTfeeGP7d8aMGbb87r77buuKQL+L6wHv5Y9//GO9vpd2SR2+/vrrC3WEMuVd8VxJ5bnWWmsl9hVR8BwXXXRRwe80Df89FUPNCDIIKjRiB42agcxBg6AzjhtU3UEap59+erD//vvXqZwcadChx3UCvlOtq8hhfvnll+Cmm24qdNI333yzPRcHjYzG4Bg9erQVMOgA0ggLcVEOyjQ+Oo2wkzKfeTYGM3xv+MugQ8M98MADg8suu8x2EmnQwSAYhCsuf8kHQlWYLbbYwgp3adDx0ckh2IQdt0k7apDhHJ0AHTOO0HHQSdF4fRAkED6+/fbbOufp/PFFSoM8cS8DFB0QnSPCB2XEO6AOJeWHgzQQON3/HLxHBF0GqCRHdjp0OjNAcHD+XDjF0xHH+YchKJJPOuMw5557bqxgXWy+eR9bbbVV5IHAncTee+8d/O1vf6t3nnN0xGnO/VF+a8A52k1SR9uxY8fEd5ZEWr7CUG+GDRtm3xFCDX3NhRdeGJlv398OvvzySzvBo34gRDNJYOBkUOQd0G8C9YH3FFVHmOzQ/3L9tNNOs/l1B/kYOXKk9ZMJC/XucIslkp6ZQff111+356iPTL5g6tSpNt9J75rfiKu7lFlS3efeKJ9KBA03YWKscP9z0Dfy3Az0ac9833331Uubc/iG8UwIF2ussUa9Z3ITRjdx9mGShbDhxgTaVlT9jOpfR40aZSfYSeXJ+JDUV0TxwgsvRPoSxYFgFOVvVfOCjD+TAISYv/zlL4X/md1TiZO45557bEeARMrL4i+NmRk4FdRBo0SLQMPzDyqHq1jhToD0mO01JKQZ3syLciBvUUTlmxlB0oZhaU7KxcBAzszE1/pw8D6cI6ffwSBM0sF06dLFOswhOHHQUDh36qmnpv5mkuM2HUhYEAHO8bsbbrihbUhx/P3vf7dakJkzZ9Y5zyyJTgMBsFxBxueLL76wWqdVVlnFdn4IcUkwuP/www9lObIz+Ln6ye85p1AGCgYSJgF0cuF3dc0119i8RTnkco7fSiMp35XAABc1CHGOa8U498fVE4S3G264Ifa3EebRVs2fP7/kfKflywct5NFHH23f34orrmiFR/JGPxBuJ65+40TNBIQZP/UTTRpaECZNDJyUDQcD/M8//1zoG0k3qY6gIfn1119jn4vBLO4gvaRn7tevnxXC0G4z0XCrVOlHGNCT3jVtK67uojVKqvvUbfquOBC4kiabSc9M/x3XbihPnom8hIVXV395l1ETxrffftte8ye34b6X+hElaNOvc29SedJ/JtWDxqBmBBkaMR7ibsCmQN1SafdyUYsB0jzmAwQPJ6lfeeWVdpBEtetXeDoilkfTQVDBNt988zqStW+WOu6442wamHDCnQCVg++gckd1HSU8kC8EMD9fDI6uEYYhvf/+9791zpHvsMCUlu8wvpc9jTzq9zmXtnSb8mN2F9b8uN+nI3UzeZdvtEnMyNCAoRLl+wiXHHSkzPB5v2nQUXMPv7P99tsXzjNToqOkw/bT4TPn6NgQmHhPzz//vO0w0Z4xuPAsHHwHFSx5ZmdYX5XO7InZELMyBJ1iBRnyGRZkfKjLdOblklQeaJOoH079TkgAzjGz69+/vx0oGOgwyd177722k6NOM1HgM4I+quwwnCtGexYGoRWTni/c0IFiRpw1a5b9vxgBgT4BgSIM57iWVia8z7h6QhtFyEcbQXq+WYwDDSXlsvLKK9sJUSna3bR8UU/4Td4LgxShHRhsXZmQb7SjDPjhfJNn+iYmIWhSwwMhQg59HYOV04C4fggNcFIdSZvoVfLM9En0r5htfMGCvFIPk941zxpXd/mcVPeZmNCeqcsIVc4M445KwOxz5pln1jvPOSbCPBPvCA1J+Jm4xlhBXv/rjQF85hyTMdf38i7DfS9mIEyJYTA1Yq5KKk+EoGLqQRj6dsyxjL3kj+d07blSamKLAjjmmGPMW2+9ZR1ucSzCmY3Q/s5xFgc2HLtw4vSdFnHSxAkNxzznjIsjEytTcMDFEQyvc5zi+B7bBJx11lkFJ08fPOivueYa6zCMo6kLcofjHgHxWAVFPnBSxQFu++23NwceeKB1tCO/Ll84YRG4z+WLa2GnWucMhhOrvxqJgHnkFUczBw6McflmRQ35inJQxqmX38ZJlP06wk7KOLviiBfHbrvtZp01cWDG2Q3HXJzgcLa+7LLLrGMmDsqsjsLhF6dQnhvnURweu3btah2ACeiHExrvIm11gg8O3c5xm7IC8oATM46SVH3nvc+qD8qF8uvVq5c955fT/4R+ew6H6iRYUcHKN54JRz/Spm4kQf7Ib9HObf8Dh/Bhw4bZ983ntHxFlQfbdeDMiFM8Dpes0MA5nQjQtIP77rvP1inq7pAhQ2wbAfZIwWGUQJH8JUikX0duv/12Gz2aepcEdZ53S/vDUZDVazh04qDJb9KmqP++QzjtmPqDs2IctB0cw2kjOI+6fLGC7ZZbbrFtNK6OUCasKqEOR9UT6nV4FaCD66wsSoJ2l0RSvsgLbYMy4BlwovThe7w/nEEpSz/frDaknvCe4/LPSkbaX/gZWEGZVEdwaE9ykufdkiect+lTYe2117b34Ayc9MzdunVLLK+0d43jcFTdpc4lPRf9VRjfaZZnYvUnztBRK3hef/312GdmwQD9Lc/mVqPivMxiA9LE0Zn80efx/fAz0W/jJP3xxx8XFhN8/vnntn3Qf1NuPBMR6cmH3/fi2IsjOvXBrc6lzXE//T7lFFeef/rTn8xmm20WWV6MbfTpUdBP8Q5waKc/p9z79u1r+4lKqRlBhpdOwb744ovWg50BGE90B4XLAMVqmAsvvNAKDwgXCD+8eAQaGhCDJp0qncWgQYNsQfOCWUVCA2eZbFSjwgucCkPHHeWhHoaOAs9slhOzNJrVNnH52mqrrezzhWG1UzHQyOLyTV4ZLFj1wAoFVkHQKbmGScWl4l999dW2kwEayYknnmgbBR16HKzoYDCnLOmo6JRYfs457iVqMiuyWBHD8+KljlDD6gh+w4cOAy95GjUDWKXQcfHcdBqw5ppr2qX3CAR0MEmkDVL+6iUGYOpRmoDCb9I5lLqfFss/qdOsgOBzHHS6SUt+o0CgjFoFxMqD8IoC6gt1xHXWlCd1hPqUBpsLPvLII3YVEZMQ3j/lxpJZNqWjTiAMk6ZrF3SCDMgIvknQ+SII+fmiI3adcyX1pLEgzAMb+ZWTb/oB2nq4ntG3IYA4wafYpbV+HXH1N26iRx/J6jgGMSfwsjcdS68JccF7Tntu6gOTA/pN6g11hD5s8803L+pdR9XdKNxzUQZJUG9Zao9AyXJw+mSEAJ6LeswYlPTM/AbPhCAA9I+MI0ymIe2ZEEieeeaZOvczQeb50vpeJvqs/vLryLHHHlvYxLGUthPXV/iwuozVdjwfjBkzxgpTlIUTXMsmqDHwKI9a6fH9999btWmS0yIqN7y1Ad8EHMxwdMVchDo4zXcCk0WxfjCozrFho/IkT0n5cvbOcknKd7EOyklOynGk+V0kgUr11ltvtZ95n6hRUY2iDsbkUwyVOG4nQVDCl156qd55zvkO59VGVuVRKb5fF2Zc3j1Qd3jncQ7hDRHMsVrKJPzbSUclxJkwMaE6B+Ykc3ASaU7ymHDwNfSdPvmMeQ5zA2ZmTLqsuMIk5x84wNNn0B9TX1w9wPkUE3VjgSkZZ+Zw/TznnHOsKSzpmdN83iphiQr63jQw77NKKgy+QknO/aX6dJZCzcSRcbh9jsIQ1wGNDNI7WpewiQLNAyq/Aw44wP6PlI06kpkyqrPTTz/dStqo59GchGPQANoUZpSYFaJABYgWhoM4LszsUW+yrh71Xly+kIRLxQVbQu2PuS0u38zkUSeimuS3MGsBijq0IPyP+YtyS9qOPQriZzCD5l6kePajYoaD6QqpPymIF0GuCAgImHuYhTFzYAbGu0GjlQQqzv79+9vZEDMWYsswa8HMxizJzfZJ1wXIwgTIbB91bZIKnJkW5RmemZAW6lPUwmFTElo3tBbkKQlmTGgV0X4xSyE/zIKj4rSUQlp5RMEMmYBuzLQwkybNtsg3z00ZMOsiMCHqbAIhMnNOgu+gAWAWTx108UKYPftB0sIzwDjTiA8z1rhgZsxI08rEryfEzkHrxMwbszPmnTgwuaaZHOL6LZ4ZLZu/LxuxiGiPtNc0zQVpx9XvtLgnaNFor7z3KHNwEtT5KM2xA7MJJhFfG8Rn2hKaA/pHPzaTD/0QMW54X9RlB1og12fFvWu0vtSruOCF1KUk6PdxS3B9Ac9JOfH+ebcueCJaF+LsAM/BeEMdjntm3i39DGUclS+eNan+oj3jPT0bEXSRthHue3GzoO7SpxSj4Yr7bawduBZgOfBBs4IbQpypCK1fOEYNYxGauooJmgjM6phRJDkt8jlM1LK5OKdZlkAiCeN8h5Oa7/yHVofvsDzt0ksvtStSfErNFyBZ+45eLNklVoGfb38PqKh8cz3OQdnFgUhzUo4D50xiXThnTWYv/J7vfBZ38J24WTqOtmngnBvnuE054fRI+bo9qACHPlbQMEt3DoJuFszqLsqH2Bu8k/DSWzRYfN+VN3Fo3LuAb775JnFGyywZBzy+g4Mwf4mdwrJWnJzRGlRCUnngMBm3IsOtzGKJrr+s1j9YNYP2EmdQ8u7Khpg/OJimQdo4xhIvBCdDt+ySpfO0pziHcNpZEhMmTLCz+agQAy7mSVKZEGPDryc4QeJ0Sj0hX0nLwmnzpInzJDPRo446yrYxnpPQEElEhTwgP9QHNBdx78Ed4XzzXNRhVv1QP5PinqQtw08iykneP3D8djG3fFzcqHBspnBf5+JKhTVzzOqT3jXPjGMtjqa8Oz/WUNoWL+SN90fbdGEx+MxvEiuM33RO0bwfFw6D52RxSdIzUxfoy8gjn6lT7uDetPrLu3aOu3vssYd1MHcH7yHc93If9YKFA2karqjfdn03B5od/93imM8WFTjBx0Ea4bpHf0GYhUo1jk1OkIERI0bYTte9IAYgzBh0urfffrt9ScUsnwsffhyM8EFFTVu9EpevYpfs+g2c/Dz00EO20iXlmUqNJzpmpfAqBYQrIKbEoEGDbMVmZQGVkcBL4eXHxb4HOqS0eD48Ox0AnTkrB9hbyuWlmI0uUXO7jg+hhFVr/tLZpABZaepg0nPLQx10HgwydP50HMSUobzcao00QQbTBmnQKTCQMwCyCsANEOTrqquuKrm8iykPhKVKIHijE7T88iQUAUJyMbBqhTrnq5kZZBAIGAxYGcGAwrJ3loKzJNdvp3EbxEYFM3NHWpmEgwCG60klJockKgl5gCAWlW/qM4Md/UFS3JNKTBJR8VL8SRSrZJgQMFnDxMDBhIlztJuope4O2hJLicPlyeDJ8ya9a77vzJOlgvAWt7KIiR7LwV3MGxfMEIGVcmST36Rn5jtM0OL60bT6Sx118XSKAfcKt7KN56LswuXJGEDbivrtqHfrH/SrfsiTMH6dSzrKoUkKMg4qkC8IUCmR3HlhVDSCuzGYu7X+DEpRyz45l7a8sBTC+SpHkPEHz7h8s0ySylpKfBtmTcTHYAaephkp15aaNksn7kUaCEJuYGLm7QYVBBA68yR/JI4oWz+CKB0VES6JwkvjdlAezMgoT6C80VSQd9JPE2ToyF1kU1dGCI3OJ4nYNeHAWKWQVB7FxHqhbcT5JnG/Eyr88qSMme1VCuVMB0nZMltE0+Nru8rZILaYMqnEb4164u6lbriBlHvThJG0kAd//etfI+9D6KduJuWbGXBS3JO0ZfhJpE306G+YMCGQusGP+nHSSSfZCWRUbCYHghbLoPFDo9/B54+JH2VLPJikd42PIBqGciB/UbFe0MByDe2gP+FBSEF4IU88b9Izk+ekoIpp9Zd6FCfMD0jpe9M0XFG/zTukD2DcYZLsv1vaDMEVG4ua85EpBWzvvv2dlRHAnkTskYN/DMs78bLGbovfQtQGYdhYsSuG95hIWw7rw1Jw5w/AgW8Ldtnwpn7lQN6i8o09F9soPjTFgh0dWzD2VmcPjiPKlspKHpYaYkvFpygOt1IF/x2WKDp/CFZJpS3ndfZjVmHhE8T92LTx4+Ac+UnyR+L9Y/sOr/IiL9zL/lb4N3G/W2LP+6Js8G0AbPz4erCvDd/FLyoJns/3C8A/hvqEXRmww/ubwpVKUnnELZf04flYucdSX/zI8F9y+5GR96gN8PA3CS8NdrAa4sgjj7Q2cz4nwUoJ/KIacoPYYsoEn6xy/dbYf4l+gXtZ4vrSSy/Z8sIPJG2hKCtfkvZlo/7hg8R3HNQV3gurHJP8AHnWpBVXgwcPtiue3Mac+AeyQsotsS51NR9+cPjKuVUp+BixpxzPxoo26g79LAfvCn8pVuyE/Q9ZbYWfBu8FvxPeHfe6Padod3Hvmr3L6DMIb0Fewmkn+f5RfynLsI8a5+hPeS5/xQ3vgD477pkB3xr6eEIe4DfkfFZKrb8sj6ZfOeecc+pdS/NjoU6Stlsd5cA/j/xQf8O/7d6vW+5d8UqjCPDHKTUEBdSMIJO29MsNCDjyUfmdg1S4U6FDIC0aLn9xrsM5iwqNc2D4NxBWiJ9BhxwWXBASfOjsyYfbAZuOnoGZXY/Dm6bRgSEsuE3TiJMTJryxXdxGd3EOfixD5PeTHJTTnJSj4Dld3BWEHd/BC+GFgYvKmiTIuBgNYdyu12mwpBsHW99xm+Wl++yzj23EOOxynTzibIdzsdu0k+8xSODc6Bz5cC7G4Zvl+CyVp4NmeSsOdQh2dEp0KOHdhMlH0maRDpzr6GjpgHjvxO2hQ3G7eSNIVLLsPKk8GLzSYJnpjz/+aOs09YA6iaCHIzjLvhn0cGx17w5B8Mwzz7TpR4HjKfdSN/gcB2lRVyi/KKffJJI2iC2mTHBKT6onSRB3B6EdQRfhg/aF8y8DV9pOwNQ7BhImUX7IA+ofz0MdZHKDMzBtkD4FoQDBC4dWHIP9fDP4U9ZMjAiJkOQs7DshM4iRZjFLax1hB1Ken/aOEIfzKkuP6Vt5H25hRTHw27wjyoABlr4LgcFtFpn0rjlHn8Q78fFjwcRB347AzfP4fQGTP9ffU870B84pFkdx4gQxdhAjyX9mH5Ye8zw4ukfVz7T6y+/ivDtmzBjrLM11NsgE8hHue3lOYsTQnnguBHcccykDxjacwhkb6cfo45J+GyEZgTLKkd3fWdvHCW9ucsPz045cqA3GZhaBJL2Pmo8jwwBQDHi8U/jMlMPe8Xhs80JZY8+MiwGbGC7ErKBCIFlTAfwOlc6bTofZAS87rrEzm6fhkU83IDEw0Mkx46FzZFbC6hheLjt1kw86UwY45zHvg0RMZ+Z+EwGI2YWTlKloVGz+D+ebysIsj+elwtPZIVCFZ2sMWjREKiedIZ1o2ioUfi+p0+MaglB4ll3qLL0S4oK7IcDQMOlgWCXhBGDeLcIkq5LolBks/Fk5749OnI4iCuIzkF545YGDjpLBiYZO+fAeGAgIHuUCfiFE8jvVAAEdGdTpCBHIGSQYpKlLlCUdICvxKI9KY64gzDEDRgClHBGaGJDTiJoxhoOZVVJPkuA9c7jVKvQ7CEnM7NHwukCdaThNV1hrgOaIicCIESPs6joGd86h0Qjnm+elr6Pu8t6SSAv2mAR9Fat16CfoSxmg0b7Sx/EZjRCDVZyWrljoS8GfNCS9aw7qIgM35RPum5LiQnEvAiBCJX2lqwP0D/RBjA0IaP5qLTekkic+xz1zkkbD31U66jx/XcwfH1bTpZUJ9ZeJEqtY6U8YJ8BpuKgnSeUJrOBjV/Qo4tpVOOinHy8NKCfqaVwf2SQEmWKh8Bhw1ltvvciCpsIxe2LWxBbs4AKNIWjQIPxOCEEBNSIVO2mJLIM/SzwRVHxQBTOAIYQw+6GzZnbF9+hUaLRocFxlK0V4Q7UIDIDhfPMZtSIDTVwQNiouggsdU1pkWh/KiWrF4Ebn5rQK7neR5ukMUHESUdiZjVCJMxjyjigvN9uKyhcDfxJ0Lrw3GnvSMtmkAFlcC6uDgbwxCypnWXwS/B6qXQQplm4WM1gXS7HlUQwsl3ziiSfsIMpf3i/mOPLOzNQFNnRCWBq8y7B6nTyipWJ5LZ0rge8YuBjQEWri1PE+CIVJMPMstkyKDaTWEPghD5JAk4pJjHqIEBNVX/x8M8CgTWBSgragoUH7Qt9JXXODFH0Q/ZSbEDDTjwqiicaRMAtMHKPgfgZgJjg8D9A/oLWgn3RCRhRoDqkzThNeLs6U7ptr6PPpv5lsOiGSyRh9G4Myk+a4Z4akfiyt/kZFOR9bZN/roK+J0nAl/TYCIYIbAh7vC7MeQgj1FoEPTUu5gky5GpmacfZl+RcOVlEB23AWdNfwcPdX5/jgkMcqCZaAsXKAPS5weGNHapy78KgOp+8HmMKz3m1mFgaHqihnSVYFcK2UjeJKJSrfCwIcDpP2xEkLzlUJSY7b7O/hOxWST/bailomGQW7arOCqZTdXBubNEf2YqCuslyTpaE4YuM0yKq3cjZG9HG7Xx900EF2lR5Ohqx4uuSSS4LddtvNrobAMZdl0Tiy0iYWRJlUWk/okwi1wOoVDlYHsnIkjaiQB3HB8Vjuykq6Ypev4sjZ0JvXOuIcSHHkxcmYciZEBavvwgd7lpE33gM7aIdXGeE4z+o1lje7fY74zModriVB+bgVTw0NjsdRDrfsB8VS5qRnrmQVYqV9byVQ5mxW6p7fbT7JZpQELq10cUo51IxGBnUYs8GwX4oDLQcmAVSdSI3YccOOTmGQFrHfo6ZF3RUlKWL7RKuBYxYSZ5wakVkKmiB+23fgQ5ODaYk9XQghzm9g5kF7A6j+cDyOU+OFwW6KUx4Oe4SqLieQWilOyuG9lnznvqSAd4BWLKq8KHdmymmBqorBd9zmwAyCmYjZhPNHYqaW5o/kgw0cHytmL1HOkw899FBZecURD3NNOCAeJoRiHHLLLQ9mjmkmB7RQvA8cftEWMNMsdjuFNDMgeUIl7vLEfkrMzFD5Uw9oI8xwqU/MytHIUS5oUmjHaaSF248rE2aovt9aKfWE9EgfDZILaofDKumgeYgyC/iggfL3ZWMWi9YIjW1auaMFifMDpB9Dm9hQ9cmHd4LPBpo4f7bNuyNP9JFoVMIOqA7fB4u+0vlg0S/Sd1MW7P3jQ9+L1pigd3HvGi0UpmDqUZS/R5xPBxAMEG1LXDA92iZawzhTI2Y1/5nLMZ/7z8R4gEYE7TDmZp/p06fb+kb74HPY98z3g4wLHBvuw6LKE98mzjN+ouHhfVEG+FJiSYiyHgBaKTQyrr8nry5oH8hH5n+DIkJCVAPFJ4XCoYApPAZ5VJVUBr9SUxQ0fjpVDgYUbNRUdDrVKKfEYtWINADsivgUuEiGdEg0AFYhMBiWumkaAgdp4U8DVDY6CzpBno1nZAWG2xSs2I0GfWEQhz8cAOOclMP79/jqQ+crE65ivg2Ziu86ZjpsrtEIaSSsmIgjabPKKHMNHRHvlGehLtCASvFHKmWPq7QNAaNAvcsAgDCDqY2yoL4waDoHUTqMUvdiKqY8GLB5/iSITooZw5kkitlPrFgzYBgEGTpG53fAIIAJFtMSn5kAcA1hIclRmd9F6GSPLr8eOh8J12HGlQlm3VL91hwMmLQ7Vq65/oDfw8cHXxnyVCzhfdmiVoj5MNjH+QHybDgh0/6jfOJKjd7twyCLuZE+jkkUQgYmCgZQzHiYgcrxwaI+YCLhXYTNubwDhELqRNy7pu8N+8UU6ytFG6Rt0k9H+dfQJjgYnKOEpLAQ78znTICT2hC/g8AZfiaXZw78kfz83HHHHdZUz+IDPjsfHYf/mUUT4ejRTtCmr+F9JZUn/k5ExEZYp0/g3SOY4dDuzPGl+nTyG9TtJi3I0MExgDNjCYONFska51IcLuOgk0EYQJBwjr5oS9LsybxUKjuVFkfEuBkHkjRClO93UYkjJJoPHLbczJIB1G0IRjkQUp9ZBH4McRsN8oxxjrmcR5igc49zUub3fOi4+G3ujbOzog0BnKe539llaXzcR0OkY+A5ihWgwuDMRr7Djtt0emgXSvVHyho6TPLDwEcZsCqCjpvBgI4cOzydDw6c5ZBUHqWuhnLaG4T2hsAJEm4CQR5xoGfwor3w/LwjVvYxWUEILUbTmLb7OlrOpDJhMlBuPaEOR/ll4K/GpAuBtVhIBwEBjQT5ZzZMuwiXAeXEQMqAmuQH6Ajv7l6sA3QcpJHkQFqJDxYO0rwL+jjnU4d/IgIG5YBwEPeu+X2nBS/W18RBX07ddKEGwuBojcYODQvCSXgVaalCfCn1N7xx6KdF9L3+knQ0rIyHYUEb4YKyjvtt+iLGLjbKRPhBS0taaCudMFXJgpxiV6bWIagRsNknBT3iGt9JguixlfiS4IsSFYQoqw3ssE/6QYsIhoUfgb8xZVTIaOzLBHIqBvyE/EBtDqKbJoWjLgYit/7666+R19i8E/8I/AwcfCY6Kb4GaWCPxaZ+0UUXFWy4jiz9kcqFAFS+vwqBtAiI53yuCIZGhOhySSqPYqC+DB061JaXC+xFe2JzVX8rAx98TLgnDZ6TvBH+HTu7/86BIHD4zhCIEV8LfDHYCiBuawVHWrj9tDKppJ4Q5dkFlvPhHFsdpIEfC0EACQKHj9A222xjywB/P7a/iAqtT9BEAjUm+QGmBa1rCKi7BI/Ej8JteVKpDxZRr+nv8F0kkjQHn3kv9I8EnKOM+Fzq1gpJpAXTox7gM1dsf+pgA2MiJydFM06rv2yFQdTdMIxhXKskenQpW1XQ9tm6pZTtaxqamokjg8c8Xvys9ogCDQTfSVq9EedtXSzlmBQq2dQvrDok6JYfHAmNC9qTMJSDC5CHDdvFOYgCVR8rCsJwLi0gHirsKJgxMIPhHYQ3EXMw83bb3Dv4jGc8ZRPWBIVhlo1Ggxk3aTFbcDNuAkkxS0PljSnSmd74PRfgLo1SNgQsBt6VX57MaJl1u5VmmDddTJFySCoPDrdCLw60mcw+8XXA7APMVNEQYebBXyQc54VnwKcFM0yaNoq0aAOYJTnCecJUwewbMy4aUGf2SYIZptOOsqKHlS1oSJiBoxlJKxPyjX9GOfUE7SwaWDS1rk+ifV533XW2DH3/sbCPBt93IQ/QfIZDHpBv9w7C92FOol4SAC7KDzBpqXFDQTmWssox7IOFnw3aCH9TUNpHOCZROGYT/QPvO+pdF+MrFUVaMD3SQgNRanA40nExjMqtv9wffhbgPPU6qv91fS/3oXUJaww55/yAkn7bh3aftpFplj6dlqBGYBMsZmt4ivubrfGZFUvM+tjPpSFWb2RBOZv6EaofzQWgNWGW7K9IYIYVtdcNEjehvoFy8DeeDMP+U2gC2ICMfXA4KGtWaLm9gOJwe3NEbXjmPpMOm9kxs2Em5g6qZtIqr1JhFQQrjdyGjF9//bWdtfozKWaQUVsThKlkQ8A4yBuzaX6fd4hWjr1c/HfJyp6GIlweaaB9Q1sSBk0R7yqqDjF7Y+ZXLMwAaatsBommBG1gv3797NYBTuMTXumQRKnh9qPKpNx6krYpqr8HURjqUNK+bMyQozQuaIooHzRYLiQ+//vtigMoT57BrQByRyWgXWX7BLaRYLsO6q9/JHHzzTdHaheKJeld09+zEizc9zjNYhKMC2hl4vaPYqsBNDLlkLb6Me6Z0FR37drV5oE+8i3v/VEv2M7B3yw4qt5xnXrOiiq2e+BA0017RTMa/m3GEfoktH1oa9wGmlFHHFyjz/Q1d2h3GJvpO9mMN7yHXbHUjCDjOgBeEg2dAuJwqvDwxl/sPk0HySDEfj58hyWejUU5m/qx34VTqSII7LrrrvV20XV7//ggHLHUEQGF52Y/IASKqIMO75hjjrHfd42Y3+Rc0p4tgFoYNTp/Mblx8JllpU888YQ1xdGYNtpoI7uslkaFoElD4Vy5AhQgBKLudCYqOnBU9HSodD6VUMmGgHGwLBHB1HU2dBz+YIUQziBfLpWWB+8/bH5hwKQDolN0O/a6w7U7zEGl5pEOlU03qRvk0Qczjz9RKXX3dbcDMEJWlnUkbVNU/ygV2jntOjxhQwBksAvv7uwf1CHKNm7jv0pA6KRsWQ4dtVt6liS9a9oVJmlMH7RX+lQGbnaNHjduXGK69EP0V2z6iNAQNsWxrxJtAHMfg3Sxgzo4cxmThPAO0Bxxz+SElChBpVmzZnYsOfXUUxP7Xp6bMYPvuvuYOBDygLoU/m3MufwuQgcbStK+udcJqQghnEsyaTF58CdDLMunzbml4owHbEhcDjXj7OvAKYlli6h0eTTU06iHw85e5a7eyAocJ3E+xFEPlfKgQYOsSpmw0ahb3fLCMKyyYmkhzpGsCvDV+6j1naq8EgdlKMdJGVUsamIX2ttfhcFqL9SKOCSjAscEhlMjaeP9zrtBpZm0yisJ1MwEeYpz3MY5MM40lLZ8mjJmtQRqVsxzrAzjd3C2RL2PU1y5kAZOjKxSq3SFUinlkQar4dzyWd95jwiu1EEct/0lnS7gYnjFXNwKNLdKEPMaecTs6PJYydYMYfxw+8WUSSX1JKuQB5hGKR/y6Rw+ce7FDIwTPO0uDpYz4whaajCzYuD945weZfZqCEo157p3zcIByoV+lTwyRmAm4ZxbHJHU1rkeF0zPBUuNgjqWtCihnNWPPJNbtYZbAM/S3gtfQbujT6JOJ/W9LIxhVRT9LysCi1mx5sqT1bulLABxkD7vye3fxPjGuEM+gf4fM3NScMNYgibGoEGDrFSKBsDNvFCPhx0MFzSo75yZCAdKdnXF8Y2ZeVqQq0qo1EE5CcoYp+AwaJu4hkRP4DOkfbROzI4BDYfbkRmtj1ObpmmAinXcZodaZhbMbNEu8Xf11Ve3M6titpFHK+S0JajQCcwFBEpzqvtqo1JHdmafzLpQLbsAb3xmhsssH+fFckF1zwzyscceq7OjuAMtVzGB5Bq6TCqtJ4CJiOBozET9IwzOy/6RNuNlp2H6MmawaGL47MqIZ4o7mIWXE8ysGKgPlZqnsjDnoi10JnccbDFPA4Hs6HvKDaaH5mLs2LGNPnaU0/cCmpC052/IBSC8K9+FA437bbfdVvgfi0TarvJx1IQgU8oqHFRomG6wT5azeiMr6ITcNug8CyspUHefcsopkQ2llGcGKt6CjkRLx4iq2/ef4DPn6CAYFBgE+ct3eWZAjYsgV03+SD6Yv5yqnHToDOhU6TAZ4LMAkwqrOBoT6ieDxt57723r5p///Gd7LmngLEZ4+vTTT+vVZUyJvBOuIRzyF4gAS1ksCCqpJ3y3XL+Mclbs+VGy/d8J+3Uwd3UmbEzK+FwAA305g5rPk08+adt2OeayLM25pfpK+dx///125djw4cOtD1LYp6ghIiXTJzo/lSR/xTB33nln7LHaaqvV63upz6z2ou/F7WLVVVe1+Q/7MqX5M6VFqY+jXJ/OJiPIUCDFVgAc+nh52CSjtiGoxWd2s69inSQbCpbs0QExg8I5jYPP+CRR1vgt4Wx91113WeEFnwgaF98J+zQ1tj+SD4OuLxQyc8dWjlCGA1sWUG8r9WFoSIodOIvJc9RWFWggcMZn4GWAdQNuKc6+jVlP0N6U65cRNeN1Aycz3vBgysHMFo0mn5OWV9Me8X8AhCWc+fEXxJ8ObUUl0B+hTUpyMi4X6oATkJjkuC0MmOHzbirxlUoizVEbjSy+J+WAhpnJCX5ZLm36QCZDUeEMwjBx8o9FFlnEpsH4hvYu3PfyO0wa6XvxYSJcB9r+sC9Tmj9TuQtAyvXpbDLLrxHIWHYcXv6ZBIHHsDG6bQjY9j5uG4IFBT4o2EX5i88Btk78ZghyFN5sstRnjlqmlzXYlVnyyHJVlpK7cyyzY7mi29TSwXPiE8QSPJZfZgV2XbfUmWWf2IjxTSLSZDHB8Mi7v9ySkN0clRC3VN1RSWCthgKfJpYNE2SRAGfY2wmahX9FJX4RUW56vAuCoRE11UXFJrIq/kPsOEz0UbdJX1ZUUk+ox24jR1df2ASTYHH4pSX5ZUSFPCC4HT4i5IfPSbsjJ/VhREJ2y/gJ7of/HT6FLphZJeDzwHYPBMWLioJbCfgA4qOBXxr9BEvZ8QMhLH6amychLRz4Z7DE2PeVSoL0k8BXzgX8KzVSMn5RLJNmywo/pAH1Az8TxqgkokJrTJkyxYZCYDsG+tm4vrfcwJpAED2eGd/TKP/FOAgjgg8VPp2E0AhHemYsIyBfOdSEs29SdNowPC4OfkSsxfGomG0IFgRUaEKLU6EJvU4DQdBisMfhkE6s3Gd2EOackOVNHRogYbndVg2EnidqLE67xEMoxonTH9TD+68kxaWII247B59KI69WAs7hxDly2waEIV/h+sizEO8lKtq27+CK0D5w4MA6QjkCC1FDEZQQhvnMAMw2Djizut2wwzEtqqWeMEjSvxAVFQd2IqTiGMokBWEoSRCK2peNzp9ozzj54pyfhItUy29EOcb6cWv4joteXOlu67w/BLi4KLiVgIMz7xvhi1g8DNT0lW77DmIcxcFCCeqnvxM0IMwwAFeyLUMlkZIpb/r18GIMFqAQSToqflcxvPbaa+aggw6y7zWpPPlO3G7jxdCQUeorpSY0MnhQF4tbqQAILnSgpazeyAqCLjGzpdP0VxCxHfu1115b0TMvSErZFI0VBDRmt1LijDPOsB7srB5jn5Wk0OGVQHm6YFQEeiM4FfvfEHArae+eYgb1coUNBEyCbzFQRoFHPzO+xoLZE9oRAoOFNSEIG3S6aBDDAwUDeVx5OK0EnT57urjgf06wd3WDZyekPwMXAw8BtNCQEDQuSyqpJwhbbkM8Vnv99a9/tc9H/WaCUsmMN23A4F2wgiRuo1n/fVQSzCwMK+1K2XqhFCg3N2FAe04AT94Fkwa2L0gCbSka3vBsnxVQaELREiTBKhomvFGTFoSOckGIjNIq0o7K2Sblmv/1vdSVTz75JLH/pY6gjWPFE+WDUFOqAEo9TNpwM24z4WJgooIGqdiVmzWhkSkF1NUILpVI4VnAfkN05nR8/s6xVEg6iLQokNVCKZuiMTCgPkVYYyZH9FSWhTL7pAI35PLWhgTTF6rRqEG9XOiQMRnEzbapD0STDXekCwraC4JHOBo2xO36zjJfhFJmbkkw6KKV8dskQiJ1gsGK5dnUF3aud+EVKC9f8Kk2MH/x3GgLUPczkKLip10QwbiY3afjZrxEN077bX+JNaYVBkY2VkSjkyRIFbsZaxSYMQj5cMEFF0RuoFhJn4tmCY1MqVo/QBODGTS84SQaC4TjpHAJmNsQlKhrDb2XEnWANHmfLsI5giB7DTEJYHl0KebogQMHWk0VeSZfcX2Ty3fSbuPhiNCVEt75Og3qChOYNKG/yQoy1QpxZJghsO7fF2SI88DsLG5H0Tzjb8p35plnWtMBjRppnA64XNVqQ/sjlTKolwsdCQMXs6QouIaAuCBCzEdBvAk6fbQCxZiGmPUzcNKBMYhUAlpUZ25FE4a/BIITZpe0bQoas56EKdYvI41wXB20NggqDLa8A0L7P/LII7Z8qKsM4JxHOEQgiNPApMU9ScPNtqOEjUrNotQjt62KD0II55LSRgDEpwbhyoeJI9qyJO0HwhObQhLXK06bgM8Upi23Gzr1gvbix1WKgt+nvWNGddoQ+n2EGoTRtPoVzk+zZs2soMDEELNkKW4E4d3G2R6lISGvaIuK9elEO41JuVhBpiZMS7UA6j0GcyRkt1U6AwBCDJ13LYIWio6IQYHZnBsYachZqaij/JGYQdIZ0onQIYX9kcLsu+++1rTXkIJMeCfbqM64sYQYZ2bBtITA5WbbOCnGmYb4TOdM/W0IGBTwG0CQYrClQ8xaiCm1nqB9YTaM8MDntLrPQMUgmTbglerkyXtyAz5CD1oWngHHVX6zEnNIElml6wtDYXATiNuvzYFAh2kKH6ewCS/NXIuQQ98cJ8QwuUDjxW7nzp+J8qau0Kclme1oR7w3NIzOnwWHabQipJdGQ2ln586da5+DiQcWgCyc6NEEluLPRiDNYsrAIY1MlYBDHrZfOkJmF5hW+Iuaj3Nuq/Vqp5QIpcyqacCYTJgNMFtE1YrK9Oyzzy5EnGxoaCR09s4fyWm/MFswADE7SevcuJ/ZT5QKnVUHtQYDN4Mug0ZYxc5Mn9UqWZlrMR3gE8Nggp0djUh4w8BqqCeYyPBL4LtpUVuZhWM6o/6krVgr1cmT38ffjgEWExz+f6yWIm8IX3nS7jaE1o9rOIljmnQmPaJR42OFsJE0icBvD9MUPoxRcC8roW655ZaCPwfaDJxpMd8gAMfBNTTwYT8Q7sekyuBfLEEQ2LIqVtuHsIXgiVnpH//4hxWKqNMIUWh0GnLF2YJAgkyVwWDOAM5MgwG+7N1AG4lwyG5WbtAwXYhv/AToeJgJ4QPDEnKemdmkM6uwMoEZPQ6W1eiPlDSoV2o3r1Yw5yCg0aGXutNvXsnabw3VOYNrmg9RseBTwOCHWp42d+ihh9rVXrQrzFq0KfKfNOOuxLQEaOzYcoQ2gHYZAZRtLChDlp+X25+gHUOwDGv98OVA65fWT1I2OFzzDpnpu21g0u5DWGKFKxriqEkLPn6YmakP4XfLircks1Ul5jK/L7ryyiutZgfNCs+GkI9ZKanvRTvidhtHeAnvNp43ZFqqMjCzJDmuVTu+ehmpnwEgak8OZiLMEDGn4R/kg8NgljBDpQMJOyPTIdHxpoGARR6b0qCOxnD//fdvMs/bEPUkDQYYZt6lEtbgMBcln5j/MCGhlXEwaOH463zRMHn4MPgxyDN5wsm0EpjZH3zwwXZgZBBF6+SWPxNbJm11UFJ/EuUQXgo40qN9KBU0WfirOGEgPGkhP0zEwoIMWsS4/evSzGUIMsUsZWaxAX3sCSecUNjTDC0f9YB3jSk6bj8kTFhoGxt7tW5DIY1MTswwlawmaCzo7FHdhp3W6DRZ9UP8Hj43tId8GszgUEkzY2RTUTpdzFz4InGgEUoCVTNq6Yb0kal2Tj75ZGtKw+TXVKi0nmRFQzp5OgiQhhb4sssuKztfaJCpJ5SNr8FC8MPXiFUrjYVz2kZTxGquYp223SaJaLeiQEvJggzKzW3QiCkLXyWW6fNbYZz/FA7ZaER8TQhaGJYqIzgR5iEJ3jmTwb59+9Y5j5keYRJtYlzfW9bGjFWMNDKNSLE7befNXpkUoRQ4R4RS7NWojBe0IMPsEH8kVLB0HCwRdv5IxcSRYeZKLJOmNKhTPqjmmZ2ilg+r2PMoaGddT7IizckzboJEP4I5FJ8O4hX5AeLQ4uCsWokgg7kiyq8DZ2ZW9jQWYadt/IeKde5HyEiKWk15Ua4Ib26lD20DU3k4crnDOXejQ0Dg851aMZf16tXL+gOlgTYN81UYtHCkndT31hrSyIjMiIpQygyX2QrqTdShmGiYPUSF9y4nQu6C8EdiFsYycVblNJVBPez71JDLdqudvPmt8a7QHiF0hf0jMIEgcPDOCPKGcAb4sWDmrWSmjvaF1UE41voaGdoKgzp+I41BJc79mJYw2/nB5aKCu+EL48f8KWaZMX0fWr9yI+JiUqLvuSLU35AmKwoxB8f1vZicagkJMlXG1KlTbYNgZoOkHmdHzQM0bhoVsQmiIpQm2ZAbMxx/Gk15UBfVb5JGiGEVGSYEwvk7PxVW0iDQoAHFNEr7whzCQM2KJxzvKzGXMeiPGDHCtnf29MEnBv8czE2kHd5bJw9O2+wPRHvGqR8zDYIDpiT2/kJbg38RZcn1UsGBmP7dCT2UFWkjXPLu0t49GiBWtK644opWi+OEFQRvzE0IWnF9b2NuJ5AFMi1VCTh4EScD5zYGQ7zQaWxUPOy0SNZ5gwbKCgoaTlSE0gUZpbYh/ZGyjJchGpc8+K1hkuZggAprXIhb4vY7cgHanDkDPxhWSfEdTBL4r2FeIg0iSscNnsWC8zttGpMxkxgmYwz2TGYaS4ip1Gmbe8MxgXgm+mjKFGGo3H4M8x5pswIS0xuaE0xLhDOgbmGeSnNHcHFwPvxf/8q2HhzUBwSwuL631pAgUyUwa0HaR5r2Q2mzUoTONY+CTCl7cjArSgtsVQm17o8kmk49Yals3GpAzAZoCPBnCm8IiH8Ewo5zemUlT9Jmi6VCmbCiD/MFmmVMcWgX0IjkNdgoZRWGfhpzGU7VpIdQGBfnKykUA5ozt0kxfjqEOKD+sfqLFUlRgkypk6hFStgPKc/ItFQlUIlxpGQm5as/aQhURLfRZS2BahuHSiJsshqEWQTPjBoa9bcfDl8IUdxqQEyfaGWY/KCBAYQbBm5MSfjDsAybHbnRxCBsNMSGpFnuMt3YwUYRAl1kWjRYxOdBWMNfjjKMM5OfeOKJRW3Rgjae94lpj6Xb/EY5G0c2WRBkROOz6KKLBh988EHh84cffmg/v/rqq8FSSy0V1CJDhw4NVllllWDEiBHBwgsvXHjme++9N+jVq1fmvz9lypTgqaeeCmbNmmX/nz9/fua/KfJHtdUT+ofnn3++3vnnnnvOXvv555+DI444ImjdunXQvHlze/B54MCBweTJk4PNN988aNasWdCuXbtgySWXtJ8322yz4PPPP68oX3369Amuu+66eudvuOGGYKeddgoam08//TR44okngvvuu6/Q16bxyy+/BAMGDAhatGhhy4mjZcuWwWGHHRbMnDkzOPTQQ4MZM2aUlZ911103uPrqq4PPPvvMvovx48fb86+99lqw3HLLRd6z1157BdOnTy98TjqaEk0nulWVg0rY39XWqUBRESc5l+YZnpdVDqxa8mdFaKXc3iNZ+SNhxyc2CE572M8BDdCpp56a2e+KfFGt9QQHVMxIRMZmxQ0H5gjyhc8FphxC5pN/50/DZ9oaGgR8a/CfQYOCtoTP9DU4A1cCjqZRfRUmLq41Nmg+WIbNCqZiV55h1mf5NquA8GPhIP4L56gDmJ7SAt/FgfkILRnaZzavdEHt0LaxOi4K/HKcWZPPSUeTorElKfF/vPPOO8Gyyy5rZzXMnvbdd99gzTXXtJL51KlTg1pkoYUWCj755JN6Wqh33303WGSRRTL73YMPPjjYcccd7QzU/11m3WuttVZmvyvyRbXWEzQBxxxzTNCmTZs6GhfOoUFIa3Ovv/56vfNoAdCKVkLbtm2Dt99+u955zlWadqXceuutwdprr23LiYPPt9xyS+p9Sy+9dKz2a5lllqk4X19//bV9H/PmzSuce/nll4P33nsv8T60gmiYnJawqSNn3yqBuAT4iBBeGgkfnxhmV9h2y4nUmQewzRNnpkuXLnXO4/gWNyNpCJjx4I8U3hqBWRpLIIWo5nqSthowCYL7ueW4PviMdOrUqaJ8VbLLdJbEhfJngQWLK/BxiQM/lai9qQio1xA+LPhGcvi4uC9J4NpKYMN333236uMaLRAaW5ISQTBnzpxgm222KdpuWyuMGjUqWHzxxYOLL77YzuYuvfTSgm3/mWeeyex3m6I/kiidWqwntLmePXvaZ3DwGZ+0hx9+uKK0X3zxRavx2WKLLYIhQ4bYg8+cGzduXNBYoDkZOXJkvfOcQ+OSBP3yfvvtF/z666+Fc2hBOLftttsGjQlawQkTJjRqHqoFCTJVAo2tqQkyQAe33XbbBe3bt7fqZ5wOn3766Ux/E8fDwYMHFwaojz76yKp26Zz22WefTH9b5IdarCdLLLFEwQnYmVncZxx//aMc3njjjaBfv352kO3Ro4d1lG3sfo3JUlQecHzmWprJv1OnTlbgQajh4PPyyy8fTJo0KWhMHn30Ueu4/c477wRNHS2/rhJQcxJoKW5/DtFwsEwVJ04CgxE0iq0QUNHi+Eh8iaa0GaRoWvWklND0le6GXS0khfInuq6LfhwHJqS77767sACBOF8sUPD3SGoMiCFE3n7//XcbSC+cH+ppU0GCTBU1NlbxYO+M2neoFvfvYZUEm9WFA3ctCFixgT8S8XrwR2KwqmV/JFEeqiflQYBL4rf4LMg4MsWG8icgXtinJy/wTEmBGQ+pEUG0GCTIVAlNcf8eQnTjTMl29ETfZJZDtNEswdGxT58+1gFRTnKiKdYTHHtHjRpV2MKAQGxom4oJDJcE2oEzzjjD3H///Xa5d9TvLiiKDVmR1reyfxTOvocddlid8+xhRJA8IgaLKqCxbVuiafPDDz8EN910U7DllltaWz229QsuuCD4+OOPM/vNpuqPJEqjFusJwf1WW20161y//vrr24PPa6yxRsVhHo499lgbMuLBBx+0/m633357MGzYsGCFFVawQS/zSJcuXYJ///vf9c6/9NJLwUorrRQ0JvSX3377bb3z06ZNs9eaEtLIiKqBwF733HOPne2waSYq4SyQP5JoqvWEwH50+fh8uK0E0J5g4mW35CeeeKLstDHdYB7HVIwZib2EWCLMlgi0a3bDzhvs/4bmKrzhJFvHED4iaefsrOF9ffPNN3YpuM9XX31l/bfw/2kqKI6MqBpV/muvvWZt1+woGxW7oaFAQEJYGjNmTJPxRxKlU4v1hIi0L730Up39kJZeemkrrG222WYVpY1zKXulAYKMczbdfPPNIzdAzAPE3cGxOyzIcK7SuDvlcs011xTMYuyXtai3KSfmu3Hjxplu3bqZpoQEGdGosJvryJEjbYh1wqQTBPDxxx8322yzTaarUXDaBIIQ+mj3a1HL9QQN088//1zvPI7MrHypBISYjz/+2GpmGEjxlSG4G+H9l1hiCZNHBg4caE466SQ70XJ90rPPPmt9gRprmwq3YzaaNXy4Wni+TbxDtjzgfJOisW1boulCfAaCZe25557BAw88EPz222+NnSUhan7bBcLz4+NBmHsOgqqts846wSGHHFJR2ldccYXdBBFGjx5t27bbRuGqq64K8gjlc8YZZ9hncdtB4FPEhreNzVZbbWV9DIV8ZEQjwsZ2bOCW19maEHmDTQ9ZlouWhNgqzoTGqiWW8zbkZoNs4zBx4kTrJ9O9e3eTZ9BY4StDrBZWsaHZqjbmzZtn3nnnHbvlCzFmmhISZIQQoomBMz0DMyYyArwhbIh8gclr3XXXtbueI8T07t3b7iHFXlyY5xsjPldjIUFGCCGaIK7rr8TfxzmeFsOf/vSnsn9H1Gf55Zc3jzzyiNlwww1tXCACNeJzyCoxYuPgkNxUkCAjhBBNiNtuu806jKKVAUwlzO6JtF0q4dU8cSAssWRZNOzS8KlTp9rd2Y888kiribnqqqusw/Uf/vAHM2PGDNNU0KolIYRoIpx77rl22ThbomyyySb2HOYIYuYQsv/8888vKT0GTdE4EKLiP//5j90u46mnnjI33HBDIcJypVGa84Y0MkII0URgOxDMQX379q1znoB1CDfTpk2rGrOVSGbIkCFWA9OxY0crvBAiACdkYh+xkAIBtanQvLEzIIQQYsFAPBR8KsIQ8K8hImljtlpnnXWs2YODzwRtE9kIMpTtkUceaf1h3EoqtDFnnXWWaUpIIyOEEE0EtC4suw5HJT7ttNNsSPvrrruuwc1W7B6O6apUs5UQxSJBRgghmggIGeyHROj9Xr162XNsC4J/TP/+/QuxZcrZgmFBma2aMpQvGhi0XWkrxv7UhFaJSZARQogmwtZbb13U9/BtYQlvKRDY8tVXX7WroHzw3WCrAoLxicpglRh70rE/VtKKsWZNbJWYBBkhhBBVbbYSIgkJMkIIIarabCVEEhJkhBBCVLXZSvwfp5xyStHfvaIJCYsKiCeEEKJiCI8vsuWNN96o8//rr79ul82vscYaBX8kll+znL4pIUFGCCFEg0HY/A8//NBuYshu0Sj9FRiv4YVFNC6LLbaYufPOOwu7Xf/4449mwIABZosttjBNCZmWhBBCVMz3339v/vjHP9rBFsGFvZxWWWUVc9hhh9mB9vLLL2/sLNbcppHPPPOMWXvtteucnzRpktlhhx3MV199ZZoKiuwrhBCiYgh6h0Mvzr1sYOjYf//97V5AomFhU8jvvvuu3nnO/fzzz6YpIdOSEEKIikE78PTTT9vdmH2IK/Ppp582Wr5qlb322suakS6//HIbp8etEjv99NPN3nvvbZoSEmSEEEJUzMyZM+toYhw//PBDYR8g0XDceOONNkZPv3797B5a0LJlS3P44YebSy+91DQl5CMjhBCiYnbeeWe7WmbYsGHWCfXtt982Xbp0MQcccICZP3++efDBBxs7izUrQH744Yf2c9euXc0iiyximhoSZIQQQlQMTqbbbrut2WCDDWycmN133928++67ViPD7swMskJkgQQZIYQQDcL06dPtbtdvvfWW+eWXX6xQc9xxx5mOHTs2dtZEDSNBRgghREXgo9GnTx/rtxHeNFKIrNHyayGEEBXBsmt8YoRoDCTICCGEqJiDDjrI3HbbbY2dDdEE0fJrIYQQFcOeP7fffrsZM2aMXb0UXj3TlDYxFAsWCTJCCCEaZNUSzr1u80If7bUkskTOvkIIIYTILfKREUIIIURukSAjhBBCiNwiQUYIIYQQuUWCjBBCCCFyiwQZIYQQQuQWCTJCCCGEyC0SZIQQQgiRWyTICCGEECK3SJARQgghhMkr/w8V6bzUGDV2UgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x=token_labels, y=s_scores)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha=\"center\") \n",
    "ax.grid(True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ae6b46ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lnewb\\AppData\\Local\\Temp\\ipykernel_13176\\2800425266.py:2: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha=\"center\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x=token_labels, y=e_scores)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha=\"center\") \n",
    "ax.grid(True) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6173bdc",
   "metadata": {},
   "source": [
    "## Question Answering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5ec7600e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sunset Motors is a renowned automobile dealership that has been a cornerstone of the automotive industry since its establishment in 1978. Located in the picturesque town of Crestwood, nestled in the heart of California's scenic Central Valley, Sunset Motors has built a reputation for excellence, reliability, and customer satisfaction over the past four decades. Founded by visionary entrepreneur Robert Anderson, Sunset Motors began as a humble, family-owned business with a small lot of used cars. However, under Anderson's leadership and commitment to quality, it quickly evolved into a thriving dealership offering a wide range of vehicles from various manufacturers. Today, the dealership spans over 10 acres, showcasing a vast inventory of new and pre-owned cars, trucks, SUVs, and luxury vehicles. One of Sunset Motors' standout features is its dedication to sustainability. In 2010, the dealership made a landmark decision to incorporate environmentally friendly practices, including solar panels to power the facility, energy-efficient lighting, and a comprehensive recycling program. This commitment to eco-consciousness has earned Sunset Motors recognition as an industry leader in sustainable automotive retail. Sunset Motors proudly offers a diverse range of vehicles, including popular brands like Ford, Toyota, Honda, Chevrolet, and BMW, catering to a wide spectrum of tastes and preferences. In addition to its outstanding vehicle selection, Sunset Motors offers flexible financing options, allowing customers to secure affordable loans and leases with competitive interest rates.\n"
     ]
    }
   ],
   "source": [
    "sunset_motors_context = \"Sunset Motors is a renowned automobile dealership that has been a cornerstone of the automotive industry since its establishment in 1978. Located in the picturesque town of Crestwood, nestled in the heart of California's scenic Central Valley, Sunset Motors has built a reputation for excellence, reliability, and customer satisfaction over the past four decades. Founded by visionary entrepreneur Robert Anderson, Sunset Motors began as a humble, family-owned business with a small lot of used cars. However, under Anderson's leadership and commitment to quality, it quickly evolved into a thriving dealership offering a wide range of vehicles from various manufacturers. Today, the dealership spans over 10 acres, showcasing a vast inventory of new and pre-owned cars, trucks, SUVs, and luxury vehicles. One of Sunset Motors' standout features is its dedication to sustainability. In 2010, the dealership made a landmark decision to incorporate environmentally friendly practices, including solar panels to power the facility, energy-efficient lighting, and a comprehensive recycling program. This commitment to eco-consciousness has earned Sunset Motors recognition as an industry leader in sustainable automotive retail. Sunset Motors proudly offers a diverse range of vehicles, including popular brands like Ford, Toyota, Honda, Chevrolet, and BMW, catering to a wide spectrum of tastes and preferences. In addition to its outstanding vehicle selection, Sunset Motors offers flexible financing options, allowing customers to secure affordable loans and leases with competitive interest rates.\"\n",
    "print(sunset_motors_context)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "68b000d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def faq_bot(question):\n",
    "\n",
    "    context = sunset_motors_context\n",
    "    input_ids = tokenizer.encode(question, context)\n",
    "    tokens = tokenizer.convert_ids_to_tokens(input_ids)\n",
    "    sep_idx = input_ids.index(tokenizer.sep_token_id)\n",
    "    num_seg_a = sep_idx+1\n",
    "    num_seg_b = len(input_ids) - num_seg_a\n",
    "    segment_ids = [0]*num_seg_a + [1]*num_seg_b\n",
    "    output = model(torch.tensor([input_ids]), token_type_ids = torch.tensor([segment_ids]))\n",
    "    answer_start = torch.argmax(output.start_logits)\n",
    "    answer_end = torch.argmax(output.end_logits)\n",
    "    if answer_end >= answer_start:\n",
    "        answer = ' '.join(tokens[answer_start:answer_end+1])\n",
    "    else:\n",
    "        print(\"I don't know how to answer this question, can you ask another one?\")\n",
    "    corrected_answer = ''\n",
    "    for word in answer.split():\n",
    "        if word[0:2] == '##':\n",
    "            corrected_answer += word[2:]\n",
    "        else:\n",
    "            corrected_answer += ' ' + word\n",
    "    return corrected_answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a0ac072c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' crestwood'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faq_bot(\"Where is the dealership located?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6447006d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' ford , toyota , honda , chevrolet , and bmw'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faq_bot(\"what make of cars are available?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ef088983",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' 10 acres'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faq_bot(\"how large is the dealership?\") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2be40658",
   "metadata": {},
   "source": [
    "## RoBERTa and DistilBERT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a8bbd3c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of RobertaModel were not initialized from the model checkpoint at roberta-base and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "from transformers import RobertaTokenizer, RobertaModel\n",
    "model_name = \"roberta-base\"\n",
    "tokenizer = RobertaTokenizer.from_pretrained(model_name)\n",
    "model = RobertaModel.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2bd15ee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import  DistilBertTokenizer, DistilBertModel \n",
    "model_name = \"distilbert-base-uncased\"\n",
    "tokenizer =  DistilBertTokenizer.from_pretrained(model_name)\n",
    "model = DistilBertModel.from_pretrained(model_name)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "llms_course_env",
   "language": "python",
   "name": "llms_course_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}