{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim.downloader\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = gensim.downloader.load(\"glove-wiki-gigaword-50\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_embedding = lambda word: model[word]\n",
    "first_word = \"king\"\n",
    "second_word = \"queen\"\n",
    "word_one_embedding = get_embedding(first_word)\n",
    "word_two_embedding = get_embedding(second_word)\n",
    "embedding = word_one_embedding - word_two_embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "semantic_difference_db = {\n",
    "    \"apple-banana\": {\n",
    "        \"id\": 1,\n",
    "        \"word1\": \"apple\",\n",
    "        \"word2\": \"banana\",\n",
    "        \"embedding\": embedding.tolist(),\n",
    "    },\n",
    "    \"apple-banana\": {\n",
    "        \"id\": 1,\n",
    "        \"word1\": \"apple\",\n",
    "        \"word2\": \"banana\",\n",
    "        \"embedding\": embedding.tolist(),\n",
    "    }\n",
    "}\n",
    "\n",
    "if len(semantic_difference_db) > 1:\n",
    "    vectors = [word[\"embedding\"] for word in semantic_difference_db.values()]\n",
    "    variances = np.var(vectors, axis=0)\n",
    "    low_variance_dims = np.argsort(variances)[:3]\n",
    "    result = {\n",
    "        \"variance\": variances.tolist(),\n",
    "        \"top_variance_dims\": low_variance_dims.tolist(),\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db = {\n",
    "  \"hello-world\": {\"id\":1, \"embeddings\": [1, 2, 3]},\n",
    "  \"goodbye-world\": {\"id\":2, \"embeddings\": [4, 5, 6]},\n",
    "  \"hello-moon\": {\"id\":3, \"embeddings\": [7, 8, 9]},\n",
    "}\n",
    "\n",
    "len(db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add word to db \n",
    "def add_word(word):\n",
    "  if word in db:\n",
    "    print(\"word already exists\")\n",
    "    return\n",
    "  db[word] = {\"id\": len(db), \"embeddings\": [1, 2, 3]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "test=[\"hello\", \"world\",]\n",
    "w = '-'.join(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'hello-world': {'id': 1, 'embeddings': [1, 2, 3]}}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w in db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Range of embeddings: -5.4593 to 5.3101\n"
     ]
    }
   ],
   "source": [
    "vectors = model.vectors\n",
    "\n",
    "# Find the minimum and maximum values\n",
    "min_val = vectors.min()\n",
    "max_val = vectors.max()\n",
    "\n",
    "print(\"Range of embeddings:\", min_val, \"to\", max_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"Key 'kjkashdiuagf' not present\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m word \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkjkashdiuagf\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mmodel\u001b[49m\u001b[43m[\u001b[49m\u001b[43mword\u001b[49m\u001b[43m]\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/gensim/models/keyedvectors.py:403\u001b[0m, in \u001b[0;36mKeyedVectors.__getitem__\u001b[0;34m(self, key_or_keys)\u001b[0m\n\u001b[1;32m    389\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Get vector representation of `key_or_keys`.\u001b[39;00m\n\u001b[1;32m    390\u001b[0m \n\u001b[1;32m    391\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    400\u001b[0m \n\u001b[1;32m    401\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    402\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key_or_keys, _KEY_TYPES):\n\u001b[0;32m--> 403\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_vector\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey_or_keys\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    405\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m vstack([\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_vector(key) \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m key_or_keys])\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/gensim/models/keyedvectors.py:446\u001b[0m, in \u001b[0;36mKeyedVectors.get_vector\u001b[0;34m(self, key, norm)\u001b[0m\n\u001b[1;32m    422\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_vector\u001b[39m(\u001b[38;5;28mself\u001b[39m, key, norm\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m    423\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Get the key's vector, as a 1D numpy array.\u001b[39;00m\n\u001b[1;32m    424\u001b[0m \n\u001b[1;32m    425\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    444\u001b[0m \n\u001b[1;32m    445\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 446\u001b[0m     index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_index\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    447\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m norm:\n\u001b[1;32m    448\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfill_norms()\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/gensim/models/keyedvectors.py:420\u001b[0m, in \u001b[0;36mKeyedVectors.get_index\u001b[0;34m(self, key, default)\u001b[0m\n\u001b[1;32m    418\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m default\n\u001b[1;32m    419\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 420\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mKey \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m not present\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mKeyError\u001b[0m: \"Key 'kjkashdiuagf' not present\""
     ]
    }
   ],
   "source": [
    "word = \"kjkashdiuagf\"\n",
    "model[word]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model[\"Japan\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "common_category_words = [\n",
    "    {\"id\": 1, \"item\": \"apple\", \"category\": \"fruit\"},\n",
    "]\n",
    "\n",
    "new_word = {\"id\": 2, \"item\": \"banana\", \"category\": \"fruit\"}\n",
    "\n",
    "def add_common_category_words(new_word: dict) -> dict:\n",
    "  try:\n",
    "    for word in common_category_words:\n",
    "      if new_word[\"item\"] == word[\"item\"]:\n",
    "        return {\"data\": {\"Word already exists\"}}\n",
    "      \n",
    "    common_category_words.append(new_word)\n",
    "    return {\"data\": \"Success\"}\n",
    "  except:\n",
    "    return {\"data\": {\"Something went wrong\"}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'id': 1, 'item': 'apple', 'category': 'fruit'},\n",
       " {'id': 2, 'item': 'banana', 'category': 'fruit'}]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_category_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "common_category_words = [\n",
    "   \n",
    "]\n",
    "\n",
    "vectors = [word[\"embedding\"] for word in common_category_words]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = {\n",
    "    1: {\"id\": 1, \"item\": \"apple\", \"category\": \"fruit\"},\n",
    "    2: {\"id\": 2, \"item\": \"banana\", \"category\": \"fruit\"},\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'dict_values' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mid\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'dict_values' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "db.values()['id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "japan = model[\"japan\"]\n",
    "germany = model[\"germany\"]\n",
    "france = model[\"france\"]\n",
    "spain = model[\"spain\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectors = ['japan', 'germany', 'france', 'spain', 'italy', 'canada']\n",
    "\n",
    "# Extracting vectors\n",
    "vectors = np.array([model[country] for country in vectors])\n",
    "\n",
    "# Calculate the variance along each dimension\n",
    "variances = np.var(vectors, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.71291775, 0.15429573, 0.28167078, 0.5604816 , 0.12800254,\n",
       "       0.13497332, 0.17403843, 0.09592731, 0.38199902, 0.1408784 ,\n",
       "       0.02114136, 0.14096975, 0.33009112, 0.16774349, 0.13681199,\n",
       "       0.37320152, 0.19205116, 0.35523164, 0.02965586, 0.17145623,\n",
       "       0.5522394 , 0.18279208, 0.09120435, 0.21490712, 0.21804202,\n",
       "       0.13848749, 0.07703515, 0.05367562, 0.05377205, 0.20962723,\n",
       "       0.04383082, 0.01584739, 0.35106146, 0.09532548, 0.07464921,\n",
       "       0.11549121, 0.04268583, 0.42523637, 0.09548012, 0.04159508,\n",
       "       0.68399495, 0.163478  , 0.12226149, 0.03736075, 0.333157  ,\n",
       "       0.09128287, 0.06994296, 0.2708255 , 0.09994069, 0.09874613],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the variance\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.bar(range(1, 51), variances)\n",
    "plt.title('Variance of GloVe Dimensions for Country Words')\n",
    "plt.xlabel('Dimensions')\n",
    "plt.ylabel('Variance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top dimensions with the highest variance: [ 1 41  4]\n"
     ]
    }
   ],
   "source": [
    "# Identify dimensions with the highest variance\n",
    "top_dims = np.argsort(-variances)[:3]  # Get indices of top 3 dimensions with highest variance\n",
    "\n",
    "print(\"Top dimensions with the highest variance:\", top_dims + 1)  # Adding 1 to match human-readable indexing (1-50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Famili relations\n",
    "aunt = model[\"aunt\"]\n",
    "uncle = model[\"uncle\"]\n",
    "man = model[\"man\"] \n",
    "woman = model[\"woman\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top dimensions with the highest variance: [12 31 26]\n"
     ]
    }
   ],
   "source": [
    "vectors = ['aunt', 'uncle', 'man', 'woman']\n",
    "\n",
    "# Extracting vectors\n",
    "dict_vectors = {\n",
    "    'aunt': model['aunt'],\n",
    "    'uncle': model['uncle'],\n",
    "    'mother': model['mother'],\n",
    "    'father': model['father'],\n",
    "    'man': model['man'],\n",
    "    'woman': model['woman'],\n",
    "}\n",
    "\n",
    "vectors = np.array([model[fam] for fam in vectors])\n",
    "\n",
    "# Calculate the variance along each dimension\n",
    "variances = np.var(vectors, axis=0)\n",
    "\n",
    "# Plotting the variance\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.bar(range(1, 51), variances)\n",
    "plt.title('Variance of GloVe Dimensions for Country Words')\n",
    "plt.xlabel('Dimensions')\n",
    "plt.ylabel('Variance')\n",
    "plt.show()\n",
    "\n",
    "# Identify dimensions with the highest variance\n",
    "top_dims = np.argsort(-variances)[:3]  # Get indices of top 3 dimensions with highest variance\n",
    "\n",
    "print(\"Top dimensions with the highest variance:\", top_dims + 1)  # Adding 1 to match human-readable indexing (1-50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Line3DCollection at 0x7ff447a01f60>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JKJbcWWmaxt/b/o7737ofarMaEkjwrfXfwuNbH0d6gvCU40XiiTLYhVEufXM8U23z8/NobW2FQqHAli1bQm7N5UoDTsgfYq7gy4cn0pBIJMwMkacPkVarhd1uR3t7O5RKJRMRJSUlRe1vFMn2bl8WEJ6mePPz88jMzER8fLzgLCB6Z3tx1xt34djwMQDAmrw1+NO1f8Kmok3RXZgfiMQTRdA0DZ1OB6PRCKVSyblvDuA6PZJ23NLSUpSXl4f1oeYq4jnfoyaHQw2nUwNg8VbqSMPTh+i9995DRUUFbDYbVCoVBgYGIJfLFwyzRgrRTP15IyKKotDS0oINGzYwWnNCcGe1Oqz4xfFf4PHjjzPNAz+57Ce4d9O9iJMJwwXYF0TiiRJIlDM7O4vJyUmmW4krEHJpb2+HRqPBunXrkJWVFfZ1YznVFsmNwWJxNRbExRVCJnOfiRLi/SNEVFpaCqfTCb1eD61Wi8nJSfT29iI+Pt6NiOLj43lbC5+ptmDBfs/Ex8dDLpcvcGeNBhEdGz6Gu16/C73qXgDAVRVX4Q/X/AHLMoWpgO4JkXgiDE/ZGz58cwBX1xrgku7fsmULZxsFF8TjdDoxMDAAiqKgVCqRkZHhs+OKa0Rq0yfE4yvNJpQ0DbCwi0wmkzEEA7hqg2SYdWxsDN3d3ZzNEHmDkIgHgFvd1VdqLlLurLOmWTxw+AH8rfVvAFzNA09d9RRuWnmToN5Ti0EkngjC22wO18RD0zRGR0fR2+s6Ca1du5bT02m4dgtGoxGtra2QSqVITExET08PbDYb0tPTmYHJ1NTUmPoQecM5qZyFaTahRTyLtS/L5XJkZWUxEbPdbmeGWYeGhmA0GkOeIfK1HiESj7fDkecwtD931nCIiKZp/KPtH7jvrfuY5oG71t+Fx7c+joyEjLBfY6QhEk+E4Gs2h0vfHLvdjs7OTszNzWH9+vX4+OOPOd/kwol4pqam0NnZiaKiIixbtozZ8MxmMzQaDdNxBYDZxJRKJWdimZFNtZFW6oXEIzQEOzdDlKNJV2Q4M0TeILT2bqICH8iafClvh+PO2qfuw12v34Wjw0cBAKtzV+NP1/4JjcWN3L3ICEMkHp7Bns3xJnvDFfHMzc2hra0NKSkp2LJlC6PjxXUaL1zxUaKQwLZ1SEpKQlJSEtNxRcQySaGbiGWSris+6wtcYbGONqFsrGRjDCfCCGeGiI/1cA2SEg8Fi3kR+SMim9OGJz54Aj9//+dM88CPLv0RdjfuFnzzwGIQiYdHeLOk9txwwiUemqYxPDyMgYEBVFRUoLS01C2ainbEYzab0draCoqisHnzZiQlJS16/bS0NKSlpTGFbp1OB41G41ZfICSUkZERVlqHD9C0E1brEADvxCO0VBvALRH6myEaGxsDRVHMDFFmZiZSUlLcujCB8OfYuITT6eR0oDUQIvpw8kPc+/a96NW4Uubby7fjDzv+gLLMMk7WEW0I6xN7nsCfJbUnwiEem82Gjo4OzM/PY8OGDcjIyODs2r4QDPEQy+y8vDxUV1cv8PUJBJ5imUSjTKPRoL+/HxaLBWlpaQwRpaWl+d0kIrHp22yjoGkbJJJ4KBTFXh8jpIgH4G89i80QnTlzBhKJxC0tBwiLePgeaGUTkdqkxg/f/yH+2fVPAEBuUi72XLoHIwdGMN4xjrJLROIR4QWeDQSL5YalUmlIk+NarRZtbW1IT0/Hli1bvHYV8dH6HIhkDk3TGBgYwPDwMGOZzRU8NcpIWkej0WB8fJw5TRMiioar57n6TjkkkoUpGiFFPHwTjyc8Z4iID5FWq2VSqwDQ09PD/A0jOUPkDU6nk/euS5qm8fzp5/H9d78PtdnlUPtfa/4LP9ryI6THp2Prf2/FiqoVvK4hkhCJh0OwLan58s2haRpDQ0MYGhrC8uXLUVJSwks05QtSqZRxhfQGq9WK9vZ2mM1mbNq0CampqZz+fk94pnXIaVqtVmNwcJAZhFQqlRHZQADfUjlsXCgRz2Jg+xCVlpbCZDLhww8/RGJiYlRmiLyB7/buAe0Avvv2d/Hu2LsAgJVZK/HUlU9hY8FG5jFGo9GnR1YsQiQeDhCMJbUnSB0mkM4i9qbe0NCA9HT/Gkx8CHr6i6K0Wi1aW1uRmZmJuro6v7UXPjY6b6dptpmaXq+HXC6H0+lk5oe4nD8hWGyGR0gg7w+hECHgWkt5eTkA7zNESUlJbkTEx9+QDb4OLFaHFXtP7cWvP/o1rE4rEmQJeKjxIdxdf7db8wBN00zL+vkCkXjCRLi+OWyFYH9vbrVajfb29oA2dfa1I9FcwG5wWCwKiySkUimzOZWVlTEzQxKJhPGwIW2/SqUS6enpnJxs/c3wABd2qm0xeHa0+ZshOnPmDDo7O5GSkuI2zMp1swkfEc+J8RO45+17mOaBK5Zegd9u/S2WZXhXHjCZTGLEI8KFUCypPbEY8VAUhcHBQQwPD2PFihUoKioKKpriO+Ihs0M6nc5rg4OQQIZWKytdkYjVamXmh7q6uuBwOJhuK6VS6dZtFQxIxOPNDoFASBs9IJz1LDbD422GiBARaTbxHGYNN1rhkng0Zg0eff9R/KPzHwCAnKQcPHHZE7ix6ka/r9toNCIlJYWTNQgBIvGEgHAsqT3BJh5PWCwWtLW1wWazhVQv4Tvi0ev1aG1tRVJSEjZv3szMDgUCkl6MJDz/RvHx8ViyZAmWLFnCtP0SIhoeHnaLmMgg62JwOg2w288C8J1qEyMe3wh2k4+Pj0deXh7jnms2mxki6u7uhsPhcItq/c0Q+QIXqTaapvHC6Rfw/Xe/j1nzLADgjtV34McX/xiZCZmL/n6LxSISz4WMQGZzgoEv4iGtyLm5uaivrw8pfcBXjcfpdGJsbAw9PT0oKytDWVmZYDauUMFu+y0uLma6rTQaDaanp9HX14f4+Hg36wBvREtcR+XybMjlvjcUodwvobmPhhtdkGYT9mGCtG6zux69zRDxtaYB7QB2H9mNY6PHAAArslbg6W1PY1NhYLYFRHdRrPFcgAhmNicYkOsQgqAoCv39/RgdHQ27FZmPVBvginR0Oh1nitdCBLvbatmyZUyRW6PRYGRkBF1dXUhJSXEbZJXJZAFJ5Qgt4hES8XCpWhDsDJEvH6JQIx6b04anTj2FX374S6Z54MHGB3F3/d1QyALPDhiNRgAQI54LDZ6W1FyRDgERCmVP+Tc2Nob9RuN6jsdgMODMmTNwOp246KKLQjaTiyZCvR+eRW6bzcak5dhCpwkJnwCIDY02QHjEw6dOWyAzRN58iEKRzDk5cRLfOfwdpnng8qWX47dbf4uyjOAHQI1GIxISEiKm4B4JiMSzCEgDwQcffID6+npehtmkUilmZ2cxNDSE/Px8rFixgpM3GZcRz+TkJDo7O5GZmQmKomKSdLiEQqFg9MlommYGWWdmBgEAU1NyzM+fc/Vkn6SFtNkLaS1AZC0RPGeI2O337BkiEjlZrdZFZ4g0Zg1+9P6P8PfOvwNwNQ/suXQPbloRum2BwWCIyiA0nxCJxwc8Z3OMRiMvKRKKohh/mlWrVmHJkiWcXZuLGg9FUejp6cHZs2exdu1a2O12TExMcLTCyIIvEzu20Kler4LJBCxdejFstjSvQqdC2kCEKMgZrfWwm0mAczNE/f39mJ+fxwcffOBzhoimabzY8yIefvdhqEwqAMDtq2/Hjy/6MZSJyrDWZTQaF9U4jDWIxOMF3mZzQpW28QeTyYTW1lbQNI2amhpOSQcIv6uNpP5ommYEPs+ePctL3eh8AE3TTCu1UrkWCQmlbkKnZAhyfn4eEokEfX19zCBrtIROhRbxCGk9JL06MTGBzMxM5Ofne50hMigM+PXpX+P45HEAruaBp7Y9hcZCbmwLSCu1UO4LFxCJxwO+ZnO4NmybnJxEV1cXCgsL4XQ6g2pFDhThpNpIV51n6o+rFu3z6UNEYLdPgqIMAGRQKEqZ77OFTsvLyzE9PY3+/n7QNO0mdEoiosWETrmEkDZ6QHjuo8C55gLPGSKD2YBfn/g1nul4BlbKijhJHG5ZegvuWn0XchNzOVM8ON/kcgCReBgsNpvDVcTjdDrR09ODqakpxpvmxIkTvEQRoaSW2AKfNTU1KCgoCPuaQgKfaz/X0bYUUqnvg4RcLkdcXByqqqoAuAudTkxMRFToVCSexeFtTScnTuKet+9Bj7oHAHB5yeXYc/EeZFKZ0Gq1OH36NNNwwlbeDuW1nW/Do4BIPAACk73hIuIxGAxoa2uDVCrF5s2bmUYFPtJ45LrBrNlqtaKtrQ1Wq9VnV12sEw+fIDM8i2m0ed4/T6FTo9EIjUYDjUbjJnRKIiIuGzuERjxCqzkB7qoiWosWP37/x/ifjv8BAGQnZmPPZXvwhRVfYO4jmSFiG+KRGSJPQ7xA7r1Y4zkP4cuS2hMymSwscpiYmEB3dzdKSkpQWVnp9uHia94mmOtqNBq0tbVBqVRi3bp1PmsOXBFPNDY7vn/nuYgndHFQiUSClJQUpKSkLBA6PXv2LHp7e5GYmMiQULhCp0KzmRbaeoBz1tcv9ryIh449xDQP3LbqNvzk4p94bR5gN5wUFhYyBwpCRMPDwwHNEAFixHNewXM2ZzEFglDJweFw4PTp05iZmUFtbS2TH+bi2oshEJJgC3xWVVWhuLjY733gQw3hfMFidtdsBKO3xxY6dTgczOblTeg0LS0tqLqC0CIeIabaxk3jeOztx/De2fcAAMuVy/HUtqewpWhLwNdgHyiIMobBYHCzePecIUpISIBEIjkviUdYf+EIgTQQBDMQGkrEMz8/j5MnT8JkMmHLli1eSQeIXsRjt9vR0tKCkZERNDQ0BKQqzaX+WzQ2vEjUeIJNtQUDuVyOnJwcLF++HJs2bcKWLVtQWFgIq9WKrq4uvP/++2htbcXIyAjm5+cDOniIxOMdNqcNT378JO5quwvvnX0P8bJ4PLL5EXzw1Q+CIh1vkEqljL17XV0dLrnkEtTU1DA+RB9++CH+8Y9/4Mtf/jI++uijkPeHZ555BqWlpUhISMDGjRvx8ccf+338iy++iBUrViAhIQGrV6/GgQMHfD72rrvugkQiwd69e4Ne1wUV8bBlb0LxzQn0j0/TNMbHx9HT04PS0lKUl5f7/TDxGfH4uq5Op0NraytSUlKCEvgUazzeQVFW2GyjAPwbwBFwtdmHK3QqNOIRSo3no7Mf4Z7D96Bb3Q0AuKjwIjz9madRkcmPIoXnDJHT6URrayuamprw/vvv44MPPsDJkydxxRVX4IorrsDnPve5Re/TCy+8gN27d2Pfvn3YuHEj9u7di+3bt6O3t5dx8GXjxIkTuPnmm7Fnzx5ce+21eO6557Bz5040Nzdj1apVbo/9v//7P3z44YcLmo8CxQVDPOH65gQa8TgcDnR2dkKr1QasZcZnxOPNO2dsbAy9vb0oLy/HsmXLgroPIvF4h9U6BICCVJoKuTzP72P5un+hCJ0KjXhCkafhElqLFj85/hM82/4sACArMQu35t6KB69+MKIFfplMhvr6etTX10Ov16O6uhoNDQ1455138Oc//xk33njjotf4zW9+gzvvvBN33HEHAGDfvn1444038Oyzz+Khhx5a8PinnnoKV111FR544AEAwM9+9jMcPnwYv//977Fv3z7mcRMTE7j77rtx6NAh7NixI6TXd0EQTyiW1J4IhBx0Oh3a2tqQmJiIzZs3B2zRG6lUm8PhQHd3N2ZnZ0MW+Ixl4uFzgz3nOlohmI3cU+jU6XRibm7OTeiUvEfVajUjdBpNUBTFu6OoN9A0jf/0/gcPHXsIM6YZAMAtNbfgJxf9BO0ftUf1vhiNRuTm5uK6667DddddF9BzbDYbmpqa8PDDDzPfk0ql2LZtG06ePOn1OSdPnsTu3bvdvrd9+3bs37+f+X+KonDLLbfggQceQE1NTfAv5lOc18QTjiW1J/xFPDRNY3R0FH19fSHZBESiucBgMKC1tRVxcXHYvHlzyC25sUw8AH/RxjnX0cA62qJBTjKZbIHQ6dDQEGZnZ92ETklEFIp3TbiIRo3nzNwZ7D6yG0dGjgBwNQ/s3boXFxVfxNSBo008wUZbs7OzcDqdjE8RQV5eHnp6erw+Z2pqyuvjp6ammP9/4oknIJfL8Z3vfCeo9XjivCWecFNrnvA1a0McOOfm5lBfXw+lMnhdpnBbtX2BENrZs2fR1dWFpUuXoqKiIqwPNlckSaLQQKNCoSOYjjahELdCoUBqaiosFgvWrl3rNsg6OuqqV7EHWX21+3KJSNZ47E47ftf0O/zi5C9gcVoQL4vH/Rvvx73r70W83PW+ZO8f0YLJZBKEF09TUxOeeuopNDc3h/0+OC+JJ9DZnGAgk8lgt9vdvjc3N4e2tjakpKRgy5YtIcveSKXSBdfmAjRNQ6/XQ6vVYu3atV4LisGCi4jHbDajpaUFer2e8bRRKpWc2BRHC4H48LAhlHQcW6XDc+7E0zKALXSamZnJy6EhUnM8ns0DlxZfit9s+w0qM90PDmQ90fx7EXXqYJCdnQ2ZTIbp6Wm3709PTyM/P9/rc/Lz8/0+/v3338fMzAxKSkqYnzudTtx3333Yu3cvhoeHA17feUU87NmccC2pPcGOeNizLxUVFSgtLQ07muI61WYymTAyMgK73Y4tW7ZwVhgNl3jUajVaW1uRl5eHlStXMsR4+vRp2O125oStVCo5l4rht8YTexEP4LurTSKRIC0tDWlpaVi6dOkCodPu7m4kJye7DbJyIXTKd6ptzjLHNA/QoKFMUOLxyx7HzdU3e70P4dSFuQDpVAw24lEoFKivr8eRI0ewc+dOAK57e+TIEezatcvrcxobG3HkyBHce++9zPcOHz6MxkaX2Oktt9yCbdu2uT1n+/btuOWWW5gGhkBx3hAP15bUniCSOTabDR0dHTAYDNiwYQMyMjLCvjbXxDMzM4OOjg6kpaUhPj6e024cQjzBdkPRNI0zZ85gcHAQ1dXVKCgogM1mQ1JSEuNpQ1qBNRoNhoaGIJfLGRJSKpW8CKlyAYdDDadTCwCIjy+P8mqCQ6B/R0+hU7vdzgyycil0yhfx0DSNl/texkPHHsK00XWq/2rNV/GzS36GrETfTTZCmCsKVTJn9+7duO2227B+/Xo0NDRg7969MBqNDEnceuutKCwsxJ49ewAA99xzDy699FI8+eST2LFjB55//nmcOnUKf/7znwHArT5IEBcXh/z8fEZ3MFDEPPGQ2ZzJyUnMzMxg5cqVvJxOpFIpzGYzPvjgA2RkZGDz5s2cdd9wWTcZGBjAyMgIampqIJVKcebMGQ5WeA6h3FuHw4GOjg7odDo0NDQgPT19wev11gpMrKbJCZuLtBwf0QbpaIuLK4JMtnhKREgtzKGuJS4uDrm5uUz6lq1L1tHREbLQKR/3Zlg3jN1HduPt4bcBAJWZldi7bS8uLr540edypTAdDoxGY0g1ni9+8YtQqVR49NFHMTU1hdraWhw8eJBpIBgdHXUj1c2bN+O5557DI488gu9///uorKzE/v37F8zwcIGYJh52A4Hdboder+flA03TNNRqNXQ6HaqrqwOa8A8GXBCP1WpFa2sr7HY7I/A5PT3N+UZL3qiBbhAGgwEtLS1ISEgIalCVPVBXXl4Om83GFL4jkZYLBsGk2YQGrjZ6roROuYww7E47nml+BntO7oHZYYZCpsD9Dffjuxu+yzQPLIZoRzx2ux02my3k5oJdu3b5TK0dO3Zswfduuukm3HTTTQFfP5i6DhsxSzyeszlxcXG8dIZZrVa0t7djfn4eKSkpWLp0Kee/I1x1arbAZ319PZNr56N2RDapQD6QU1NT6OjowNKlS1FZWRnWBqdQKJCXl4e8vDzBpeXOEU/gU+1CiXj4KOb7EzolltK+hE652ug/Pvsx7nn7HnTNdgEALim+BL/d+ltUKoM7HER7oNVgMACA6McTbfiazeGjJVmtVqOtrQ1ZWVlYsWJFyOy+GEIlCHbdxJvAJx8zN+T6/q5LURT6+/sxNjaGNWvWLJgN4GINoaTl+Nrsz83wBEY8sdBcwCW8WUqTQdahoSEmlaRUKpm5mVChs+rw4+M/xrNt55oHfn7pz/HllV8O6XWSg220YDQaAYjEE1X4m83hkngoisLg4CCGh4exYsUKFBUVMQNZfCAU4U12kwOpm3i7Ll8Rj6/12mw2tLa2wmazYdOmTRFR1Q00LUfA9WZLajzB2CEIJeKJRr1JLpcjOzsb2dnZAFxZBaIvZ7Va0dHR4ZZGDcT2maZp7O/fj+8d/R7TPPCVmq/gsUse89s8sBiinWojjQXRrjNxjZghHl+W1ARcEY/FYkFbWxuzcZLcKl9DnkDwqTadToeWlhakpaX5bXLgw8LAH/HMzc2htbUVGRkZfj19+IavtNzExATTIEI2tXDnUWja+alOW+A1ngst4lkMbKFTtVqNyspKpmsuEKHTEd0I7nvnPrx15i0AQEVmBfZu3YtLSi4Je23Rbi4gttfR/htxDcETz2KW1ASEGML5IKlUKrS3tyM3N9etVkKuz5cPTaCRCVvgM5D5IS4tDAjYNR42xsbG0NPTw8lcE5dgp+WcTicMBgMKCws565az2UZA0zZIJAlQKIqDWpcQIATiYYOmaSQnJyM1NdWv0GlmZiZSM1LxwsgL+OXHv2SaB3Zv2I3dDbuRIOfGpVUoEc/5BkETTzCzOWSzCKUYSGoSo6OjWLlyJQoLCxc8hi97anLtxYjH4XCgq6sLGo0mYGkevlJt7NqR0+lkjO5CFR6NJNiuj2QehTQphNItd66xoBwSSWAblNAinmjPqbDhuR5fQqfvDr6Ln777UwyZXNFmfVY9nrj4CdQvrec0Qol2xENUC4R0OOACgiQetm8OOZEFYtQGBP9GMZvNaG1tBUVRTBuyr+tHK+IhLcnx8fFBqV7zJehJrkukbyQSSVjCo5GCt/dQXFycz265M2fOMIOTJM3jee+DlcoRGoQW8SzWZWdwGPB46+P4f23/DzRoZMZn4sG6B3FZxmWYm5rDeyPvcSp0KoSI53xzHwUESDyeltSB6iSRN0cwUcn09DQ6OzuRn5+PFStW+CUskrbi443oj3jYAp/BtiTzqXqt1WrR29uL/Px8VFdXC+rU7A/+iNhft9z4+DhOnz6NlJQUtzZgi2UQQPAzPELZ7IVEPEQRw9t7iaZpvNL/Cr539HuYMrrUkr+88st47JLHkJ2UzTzGU+iUpmk3fblghU6FQDznW0cbIDDiYbuDAsEpwgbTUk1RFHp7ezExMYGamhosWbJk0eewU3lcvxG9RVNOpxM9PT3MxLEv22x/IGTJ5eZCrtfd3Y2VK1eiqKiIk+sKEZ7dcuy0XE9PD+x2O1JTmz99dFHA91loqTahEI+vz/2IbgT3v3M/Dp05BAAozyjH3m17cWnJpW6P40Po1Ol0RlVBXSQeHsGVb04gxGM0GtHW1gbAJRERaOGOHVFx3a3lSRAmkwmtra1MCsuziydQsDvQuNhciPQNRVFYvXq111qYkBHuPfCWluvrmwBFAUNDDpw58wGzqXlLy3G5Fq4gJOIhhEw+aw7KgT80/wGPn3gcJocJcdI47G7Yjfsa7guoeYALoVMhRDxiqo0HcOmbsxjxTE5OoqurC4WFhaiqqgrqDUUey5dTKLm2Wq1Ge3s7CgoKsGLFirC9c8h1w/3wsKVvFAoFJ6cwUisSysYXDCQSCRISKFCUy61y48bPw2iUMG3bp0+fRnJyMkNCbHdPoUU8QkmTks+WRCLBqclTuOfte9Ch6gAAbCncgr3b9qIqKzgxSjZCETqNdnOBSDw8YLHZnGDhi3jYaavVq1eHNEkvkUh462wjH/y+vj6Mj49j1apVAaX/Ar1uuGTpKX3z7rvvcrJ5xirpEJDBUbk8BwqFEgoFmOl8sqmp1WomLUeK3jabTTDkI6S/AUVRMDlNePDdB/GX1r+4mgcSMvHzS36Or9R8hfN1BiJ0SvalYIROuYSYauMQgc7mBAtvxEMsn+VyeVhpK3J9PiIeYgI3Ozvrt7MuWAQib+MPFEUxZMiWvoll+2su1+1PKoe9qZGiN6kPqdVqAEBXV1dAaTk+IRTioWkarw68iu/1fA8auwYA8KXqL+HxSx9nmgf4hjeh0/b2dphMJjQ1NUEmkwUkdMoljEYjo/BwPiEqxCORSHjxzfEknomJCXR3d6OkpASVlZVhpxT41IMDgNraWk7Dal/DnoHAarUyCg6NjY1up65YJh4uEahUDrvoXVRUhOHhYWg0GiQmJi6aluMbQiCeUf0o7n/nfhwcOggAKMsow95te3FZyWVRWxMROo2Li8PSpUuRnZ0NvV4PjUbDCJ0mJCQwTQqZmZmc2aSwIabaOAYfHyxCDA6HgxlqDLUjzBu4bE+maRpDQ0MYGhrCihUr0N3dzYtKcCgkMTc3h5aWFmRmZnqVvuFDiicS4Pr+hmqHIJFIEB8fj7KyMpSVlTFpOY1Gg97eXthsNiYtF6hWWaiIlNW0NzgoB/7Y/Ef8/MTPmeaBG/NuxNM3Pc2Z8kC4IOk2qVSKjIwMRvOPLXR65swZdHZ2MkKnmZmZnNm4h+I+GguIenMBl5DJZDCZTDh58iQUCgW2bNnCaTjMVcRDBD6NRiM2btyItLQ09Pb28hJFBEOWbEmeyspKLF261KctshjxnIt4giUez3vnLy1HtMrYlg9cpuWiFfE0TTXhnsP3oF3VDgDYXLgZj216DJYxi2BIB/A9kO5P6LS7uxsOh4OTw4MomSNw0DQNi8WC6elplJWVoby8PKKDnoGCCGmmpaWhsbGRCc/5bFwIZM1OpxPd3d1QqVSLSvJwoQEX7fROuKBpGlZraMTjD55pOYqimBQPH2m5SBOP3qrHzz74Gf7c+mfQoJERn4HHLnkMX131VejmdDgtPR2xtQSCQDtC2UKnpNWeRLHDw8NMgwKJiBITEwM2UhQjHg7B5Zvd4XCgs7MTer0eubm5qKzkxwkynIiHpmmMjo6ir6/Pq5AmnyoDi5EEmRuSSqUBSd9wFfFEg3y4itTs9rOgKCMAGRSK4MwBg9ns2Skeb2k5q9UatIVAqGsJBzRN4/WB1/HA0Qdw1nAWAPDF6i/i8UsfR05SDvMYobR2E4Si/chWwCCHB19Cp4SIvBkXkgaH87GrTVh/5RCg0+lw4sQJ2O12FBUV8dodFGpU4nA40NbWhqGhIaxfvx7Lli1b8GHni3gWu+7s7CxOnjyJjIwMNDQ0BJSaFFNt7MaCUkilkXM7JWm5FStWoLGxERs3bkROTg50Oh2am5tx/PhxdHV1YXJyElarddHrRYJ4xvRjuPnVm/GV176Cs4azWJa+DK/c+Ar+cvVfGNIBoltv8gUujOCI0OmyZcuwbt06XHLJJaiqqoJcLsfIyAiOHz+Ojz/+GAMDA1Cr1W57jMlkCqm54JlnnkFpaSkSEhKwceNGfPzxx34f/+KLL2LFihVISEjA6tWrceDAAeZndrsdDz74IFavXo3k5GQUFBTg1ltvxdmzZ4NeF0HMptrYEQQp0g4ODsJkMvH2O0Npp56fn0draysSEhL8CnxGmnjYzQ2+FLl9IVaJh8tN7VwrdWjRNRdrWSwt19PTg6SkJL9pOT6Jx0E5sK9lH35+4ucw2o2Ik8bhng334IGGB5AYt3CsIdoqAZ4gaiJcN0LJZDJkZWUxSu5s40ISxR45coRR4g72MP3CCy9g9+7d2LdvHzZu3Ii9e/di+/bt6O3tZWaW2Dhx4gRuvvlm7NmzB9deey2ee+457Ny5E83NzVi1ahVMJhOam5vxwx/+EGvXroVWq8U999yD6667DqdOnQrpHsRkqs1ut6OzsxNzc3NYv349M7THp1kbEHzEQ9q5S0tLUVFRsah3TqRSbXa7HR0dHZifn2eaG4IBX2uNJYTa0Qbwp1wQSlqOL+JpnmrGPW/fg7YZ16hAY2Ej9m7di+rsap/PERrxsEc++ISncaHZbMbw8DBeeeUVmEwmbNu2DVu3bsW2bdtwzTXXoKyszO/1fvOb3+DOO+/EHXfcAQDYt28f3njjDTz77LN46KGHFjz+qaeewlVXXYUHHngAAPCzn/0Mhw8fxu9//3vs27cP6enpOHz4sNtzfv/736OhoQGjo6MoKSkJ+jXHXMRDivOpqanYsmWLW26Ub+IJNOIhHjXT09MBt3PzNZzqSRLz8/NoaWlBUlISGhsbveaWF0OsRjwAd5v+OTsEfuqJXCCQbjmKoqDVapGSksJJB+i8bZ5pHqBoChnxGfjZJT/DLatugXQRvyIhzBSxEYpYcbggUewdd9yBL3/5y8jJycFLL72EtrY2vPrqqzAajXjwwQd9Pt9ms6GpqQkPP/ww8z2pVIpt27bh5MmTXp9z8uRJ7N692+1727dvx/79+33+Hp1OB4lE4mYpHwxihnhomsbw8DAGBgZ8ulwKIeIJVeCTz4iHXHdychKdnZ0BRWCLXfNCl8w510odvA9PNF63r7Rce3s71Go1xsfHF03LLYbXB17H/e/czzQPfGHFF/D4pY8jN3lhescbLtSIxxcMBgMAoLGxEdu3b8f3vve9RZ8zOzsLp9O5QBYsLy8PPT09Xp8zNTXl9fFTU1NeH2+xWPDggw/i5ptvDjpbQhATqTYy92IwGLBhwwafLBuJiMff9aenp9HR0RGyCCmfEc/p06cxMTGBtWvXes3zBoNwiYemaZw5cwb9/f2M7XRWVhbS0tJ4/ZBztdlTlBU22wiA6NZ4wgFJy8nlclRVVSElJWVBWo7MoWRlZfntlhufH8cD7zyANwbfAAAsS1+G3277La5YekVQaxIa8XClIRkqDAYDc2AQCux2O77whS+Apmn88Y9/DPk6go94NBoN2trakJGRgc2bN/uVpYhExGOz2RZ8n61ptmrVKuTn54d0bb7qJoODg5BIJAukb0JFOMTjdDrR2dkJrVaLNWvWwGazQaPRMKKM5MSdlZUlWEdTq3UIAA2pNA1yeXgkHm2QFmZPwUzixKrVajEyMsJ4E5G/T0JCAhyUA39q/RMe++AxGO1GyKVy3Lv+Xjyw0XvzQKBrEQpCaaXmEqSVOph7kp2dDZlMhunpabfvT09P+9yX8vPzA3o8IZ2RkRG88847IUc7gICJh911VVVVheLi4oDsryNd47FYLGhtbYXT6QxrY+eDeLRaLXQ6HdLS0rBhwwbOfIRCJR6z2Yzm5mbI5XJs2rSJ+XuSoTsy6zA1NYW+vj4kJiYiKysr4vpli+FcY0Fo6Uoh1cd8pf0803Lz8/NQq9U4e/Ysent7MeYcw+9Hfo8enSt9s6lgE57a9pTf5oHFILR2ai5aqcMBUS0I5p4oFArU19fjyJEj2LlzJwDXfT1y5Ah27drl9TmNjY04cuQI7r33XuZ7hw8fRmNjI/P/hHT6+/tx9OhRpiMvVAiSeKxWK9rb22E2m4Pquop0jWd2dhbt7e3IycnBypUrw9oYudaBI9I3SUlJKCgo4NS8LpS1qtVqtLa2YsmSJVixYgUkEolb9Mg27SotLYXD4WBsBYh+GenIysrKCvoDScDFph9ORxuBUDbYQOpNZA4lPT0dOUU5+Nnxn+HPHa7mgRRZCm4ruA03lt2IREMi9Ao9UlNTQ3p9Qky1CSHiCRa7d+/GbbfdhvXr16OhoQF79+6F0WhkutxuvfVWFBYWYs+ePQCAe+65B5deeimefPJJ7NixA88//zxOnTqFP//5zwBcpPP5z38ezc3NeP311+F0Opn6j1KpDKlBSXA1HqLWnJWVhbq6uqA2zEhFPDRNY3BwEGfOnEF1dTUn9s9cSeY4nU50dXVhdnYW9fX1GBkZ4fyEHUzEQ9M0RkZG0N/fjxUrVqC4uJj5vj/I5XLk5OQgJyeHkSAhlgJDQ0OIi4tjoiGlUsm5K6w/hDvDEwsRjze8MfAG7n/nfkwYJgAAN624CXsu3YMUybn6kK+0XCAQGvFEO+IxGAwheQB98YtfhEqlwqOPPoqpqSnU1tbi4MGDTAPB6Oio2+vavHkznnvuOTzyyCP4/ve/j8rKSuzfvx+rVq0C4BoLefXVVwG4FPTZOHr0KC677LKgX1tUIx72BkZRFAYHBzE8PIzq6moUFhYGfcPlcjlDDHycKGUyGex2O5qammAymUKagfEFLiIek8mElpYWyGQyRvpmdHQ0asRDSFCtVrvNWwV7HbYESXFxMTNYR5SBu7q6kJaWxkRDvk7cXL0nzrVSB9/RxvVawkUgn5Xx+XF8753v4fXB1wEApeml+M3W32Bb6TbmMUlJSSgsLHSThyH2AYmJiW7dcr4OCUKs8URzPaGqFgDArl27fKbWjh07tuB7N910E2666Savjy8tLeV8DxFEqs1isTDeL5s2bQpZFI+8SZxOJy8nYLPZzOjBsQU+uUC4EY9KpUJ7ezuTyiL3go/aUSCEYbFY0NLSAsCVQ+ZaJZw9+W2xWJhoaGxsDBKJxKeaMzepNu7FQaMFf8TjpJxM84DBboBcKsc96+/BAxsfQFKc904rdlpu2bJlTMpUo9Ew9tJs1Wb2ISHaqS1PRNv22mAwCKqjjUtEnXjIhpmbm4v6+vqwCIM8l2viIemiwcFBxMXFoba2lvMTq6+OuUDWRtJ+NTU1KCgocPs5H945i11Tq9WipaUFOTk5qKmp4f3UmJCQgIKCAhQUFLjJxoyPj+P06dNISUlBVlYWI4ESDhwONZxO7ae/tzykawhpfsnXWlqnW3HP2/egZdp1eNhYsBFPbXsKK7NXBnV9dsoUgNsQ6+joqJtqs91u58VMLVREO+I5X03ggCgSD03T6O3txcjIiNcNMxQQ4zMu6zwOhwMdHR3Q6XRYvnw5RkZGeNk0QolM7HY72tvbYTAYfKb9uLAw8IS/iGd0dBS9vb0BdyJyDU/ZGNKurdFooFKp4HQ60d7eztSHgrVCJ2k2haIYUmnop1EhEA8hYvZaDDYDfn7i5/hjyx9B0RTS49Pxk4t/gttX376o8kAgSExMRGFhode0nE6nQ1xcHBwOx6JpuUgg2hGYSDw8QCKRIC4uDo2NjZzdXIlEwmmDAZGXSUxMxObNm2EymXDmzBlOru2JYImHrC05OdnvfBMfqTZvaUEypDo9Pb2on08koVAokJ+fj/z8fExMTODs2bNIT09n5OnZ9YfMzMxFNxou6jtCaS4g6yDEc2DwAO5/536Mz48DAG6suhG/uOwXyEvO83mNcOCZlmtvb4dcLgdN04um5SKBaDcXnK+WCECUU23l5eWcb4pcEQ9J0yxbtgzl5eWQSCSwWCy8dc0FQxBnz55FV1eX29p8gQ9dNc9rWq1WtLS0gKIoNDY2Bh1FRApSqRRyuRxLly7F0qVL3eoPfX19jIgmiYa8dRSxZ3jCgVAiHgCYNE7iB4d+gNcGXgPgah548oonceWyKyO+ptTUVKbz0TMtB8Ctdsf3gLEQUm3nowkcIIAaD9cIl3iIE+fMzAzq6uoYa1tybb7UBQIhHoqi0Nvbi4mJiYDFR6VSKex2O1fLBOBOPHNzc2hpaUFWVhZqamoEVRz2BjZherZsk42O3bJNOuUyMzMRFxfHi+totOBwOvC66nX8+1//ZpQHvlP/HXxv0/d8Ng/wCc+N3l9aLphuuVAR7eYCo9GIJUuWRO3384mot1NzjXCIx2g0orW1FTKZDFu2bFlwoiLkwEdxeDFSs1qtaG1thcPhwObNmwPuduGzuYDYPlRWVmLp0qWCOMWHCm8imp4t26mpqZDJugHEfqqtbaYNd791N1pnWgEAG5ZswNPbnkZNTk3U1uQvwginWy6c9USz2UFMtcUQQiWeqakpdHZ2oqioCMuXL/f6ASCnHz6Kjv4iHq1Wi9bW1pCiCj6aCwBXpKNSqRZEhecLpFIps4lVVFTAarVidnYaU1OulE97+xwyMzuZtFwwZl3R7Goz2Ax4/OTj+EPzH0DRFJKkSfjZpT/D12q/xknzQDgI5r4s1i0HwK12F0r61+l0RlUv0GQyicQTKwiWeNjpq8UEPtlzQpEgHrbL6vLly1FSUhL0hsV1c4HNZsPZs2dhs9mCirx8IZKbcDi/Jz4+HllZdkxNOSCRJGL16q3QaucwMTHBtGyz0z5CGoQkeHPoTdx/5H6MzY8BAHZW7MR18dfhc2s/F3XSAcKrqbDTcjRNMy314aTlhFDjEbvaeEC0U23BCnyyIx6u4UkQi039Bwoumwv0ej2am5uZ2sf5OtzmC+ekcsqRkZGJjIxMLFu2DHa7nTltd3d3w+l0ujUpeLtPkYx4zs6fxYPHHsQr/a8AAJamLcWTW5/EJUsuwYkTJwSTIuVqo5dIJD7TcgMDAzCbzQEpXYjEwx8u2IhndnYWbW1tyMvLQ3V1dUARDB9zQgRs4iHSN3K5POypf64iHtJJV15eDqlUCo1GE/Y1o4FwSJgoFnjWd+Li4tysi41GIzM31N/fj4SEBGaTy8jIiFiNx0k58f/a/h9++sFPMW+bh0wiw931d+OhxoeQFJcEs9nMvKeFAL4kc/yl5cbGXNEfW1uOpOWi2VxA9AlF4okRLEY8NE1jYGCA0YQLVuCTb4tqlUqFtra2kMzk/F03VNA0jb6+PoyNjTGddHzov8UCAnEdlUgkSElJQUpKCkpKSuB0OhcUwePi4pCcnIz5+Xm/BmvhoH2mHd85/B00TzcDANbnr8fTVz6NVTmrmMcISUEBiJwtgmdajlg+EDsOclCwWq1RfZ8bDAaRePhApFNtNpsNbW1tMJvNIWvC8aWALZFIYLfb0draypmSA7luqB8ecr8sFotbKpKPTrlYQCh2CDKZDNnZ2UwDhtlsRmdnJ6xWK5qbmyGTyZhoSKlUht1FZbQb8fgJV/OAk3YiTZGGH1/8Y9yx+g7IpO6ndyEST6RTW2w7DpKWI92MFosFPT09mJycZKKhtLS0iN0zMdUWQyAK0p4gnWGZmZlB2y2wwZV9ARt2ux09PT2gKApbtmzhdGgs1IiHKCOkpKSgsbHR7X7xMZQaCYS7YYRrhwC4TttJSUlITk5GSUkJdDodYyfgTWU7mI344NBB3HfkPqZ54HPLP4dfXPYL5Kd4b5gRiWch5HI5c1BQq9VYtmwZKIpaNC3HNUjKViSeGIFMJoPFYmH+n+0Hw8W8Cdeptvn5eTQ3NzNvYK4nlUOJTqamptDR0YHS0lJUVCx02eSrRVvIcDr1sNtd5lfhzPAQSCQSxrcmMzMT5eXlsFqtTO2hvb0dNE0jMzOTiYZ81fomDZN48OiD2N+/HwBQklaCJ694EtvLtvtdg9CIR4i2CImJiUhPT0dBQYGbSy6RXCJpOdK2zdUQq8ViAUVRIvHwAb5TbXa7HZ2dndDpdNiwYQMyMjLCvj6XEQ9b+qawsBDvvvsu56e+YEiCXf9as2YNYxzliViNeIDQmwtIfUcuz4FcnsHLGuLj47FkyRI3K3C1Ws20BCclJTHRUHp6OiAB/tr+V/z0+E+ht+khk8iwq34XHmp8CMlxi89/CI14hG597c0ll6TlBgcH3brlyBBrqJ9lo9EIACLxxAoI8ej1erS2tiIpKQmbN28OyZ7V1/XDjXgoimJyx6RgTywR+CCeQNZLlK6NRuOi9S8uiEdI3VSBgGupnMVeu2ftwW63M1bgp0+fRp+uD/vO7sNp/WkAQH1+PZ6+8mmszlkd8BqERDxEKVtoEY+/9bDTcsA5Xygu0nIGgwFSqVSwuofh4rwkHpPJhI8++ghlZWUoKyvj9MMVbsTjOTtEZjzIGzwapm0Gg4FR4Q7E4C5WI55w3gehNBb4Qij3Li4uDrm5uUjOTMa/TvwLz/Q+AyftRLIsGbcU3ILPFnwWceo4qKAKOOUjpAiDvO+FQjw0TQetUML2hQo3LUdUC4Ty9+Ea51Wqzel0Ynx8HGazGevXr2ccKrlEOBGPP+kbvohnsYhnZmYG7e3tKC4uxvLlywP6m/BhtSB0nLNDiEzE4w2Hhg7hvnfuw6jeJQlzw/Ib8IvLfoHcxNwFKR+iW5aVleWzZVtoEQ8gHOIJlwjDTcsZDIbzmniE8VfmAEajESdPnoTNZkNCQgIvpAOEFvHQNI3h4WGcOnUK5eXlWL169YKTFJ/E4+2ETZxL29raUFNTg6qqqoDf5LEa8QDh13iioUo9ZZjCba/fhpv234RR/SiKU4vxws4X8Pdr/44lKUsYK/DKykps2rQJmzZtQl5eHtO4cvz4cXR1dWFqasrN5VZIxEPe90JbD1cDpCQtt3z5cmzatAmNjY1YsmQJjEYj2tracPz4cXR0dGBiYgJnzpwJSyD0mWeeQWlpKRISErBx40Z8/PHHfh//4osvYsWKFUhISMDq1atx4MABt5/TNI1HH30US5YsQWJiIrZt24b+/v6Q1kYQ9VQbF5sYEfgsLi5GdnY2Ojo6OFrdQgQb8TgcDnR1dUGj0SwqfcPHcKq3rjbiqqrX6306ly52zVglnlBA0xSrxhN+R1ug946iKTzb/ix+/P6PmeaBb6/7Nh7e/LDf5gFPOwG9Xg+1Wo2xsTF0d3cjNTUVSqUyqu6enhBaqo0cLvlaj6+03OjoKD772c8y0c4rr7yCyy+/3NVMEgBeeOEF7N69G/v27cPGjRuxd+9ebN++Hb29vcjNzV3w+BMnTuDmm2/Gnj17cO211+K5557Dzp070dzcjFWrXMPGv/zlL/H000/j73//O5YtW4Yf/vCH2L59O7q7u0NWVRHGXzlEENfLzs5OrF69GlVVVYiLi+PNrA0IboDUaDTiww8/hMViwebNmxfVW+PLLZR9TZPJhA8//BB2ux2NjY1Bkw7AHfFotVrMzc1FLG0X6mnabp8ERZkAyBEfXxqRtXSqOrHt39uw+8hu6G16rMtbh3e/8i4eu/SxgDrWCIgVeHl5OTZs2ICLLroIxcXFsFgsGBkZgclkYk7aZrM53JcVMki9SUgRj1Qqjch6SFqutLQUjY2NGBgYwI033giFQoGHH34YWVlZ2LJlC4aHhxe91m9+8xvceeeduOOOO7By5Urs27cPSUlJePbZZ70+/qmnnsJVV12FBx54ANXV1fjZz36GdevW4fe//z0A1yFp7969eOSRR3D99ddjzZo1+Mc//oGzZ89i//79Ib9m4Rx5goTZbEZraytomnZTSZbJZHA4HLz93kDJgdROgpG+4YN42BEP0acrKCgIS44nXOIhMjyjo6NMKpA9vR+MxUAkcK6+UwqJJHx/Fn/3zmg34omTT+B3Tb+Dk3YiVZGKR7c8iq+v/foC5YFQwLYCn56expkzZ5CamrrACpzoykVKq0xoHW3RtL3OyMjAypUrMTIygrfeegujo6M4fPiwX+V8wKU00tTUhIcffpj5nlQqxbZt23Dy5Emvzzl58iR2797t9r3t27czpHLmzBlMTU1h27ZtzM/T09OxceNGnDx5El/60pdCeo1RJ55QNjGVSoX29navAp8ymYzpSOHjjSOTyWC1Wn3+nD0Ls2rVqqAcBPmKeGiaxtDQEAYHB7Fy5UoUFhaGfc1Q1+lwONDW1gaj0YiGhgYoFAoYDAao1WrGYoCkgrKzsyMqUeILXHa0EXh7TW+deQv3HbkPI/oRAMD1ldfjicueQEEqN/JJ3iCXy1FaWsoUwEnLdm9vL2w2GzIyMhgiSkpK4u1vIaQOOyD6KgrsGk9JSQm+9rWvLfqc2dlZOJ3OBfN3eXl56Onp8fqcqakpr4+fmppifk6+5+sxoSDqxBMM2Ju6rw2UbV3AxxvH36Zrs9nQ3t4Ok8kUkhYcH8RDSH1kZAQNDQ0B54r9IdSIx2Qyobm5GQkJCWhsbATgOlkSCfuysjLYbDbGfrqtrQ0AmI0vKysr7HmsUNbNhVSOP0wZpvDQuw/h5d6XAQDFqcX49RW/xtXlV/Py+wg8mws8rcBNJhMzl+LLCpwrRHuj94QQbK/P1+FRIIaIx2q1or29HRaLxe+mTt4sDoeDl+KprxqPXq9HS0sLUlNTA5qF8QauicdsNqO52aVOvHHjRs78c0IhHrVajdbWVrc0nzdNPXYqiBh6qdVqjI+PM9EQIaFIRUNcRzzk3lE0hf9p/x/8+PiPobPqIJVIXc0DjQ8jRcH/puMvvSWRSJCcnIzk5GQUFxfD6XRCp9NBrVYzVuCBeNoECqERT7TXE0pXW3Z2NmQyGaanp92+Pz097TNNR1Kuvh5P/p2ennbL3kxPT6O2tjao9bERdeIJ5M0ajMAnKQjy1WDgjRwmJibQ3d0d9sAql8RDNvq8vDwYDAZOT6fBEs/o6Ch6e3uDtqFgG3qRaEitVjNEBICpCwUSDYX6d7FYBgFwo9FG0Kfrw63v3YpPJj8BANTl1eHpK5/G2ty1nP2OxRBMOzVR0VYqlQDOTemTbjmJRML8PJQ6ndBqPHzY2wcDo9EYtPmjQqFAfX09jhw5gp07dwJwvY4jR45g165dXp/T2NiII0eO4N5772W+d/jwYSYjsWzZMuTn5+PIkSMM0ej1enz00Uf41re+FfTrIog68fgDmX8ZGBgIyvqZL88ccm1Cat6kb8IBF8TDtstesWIFCgsLMT4+zun9CFR4lH1/6uvrmU0rVCgUCjcts0hEQxRlgc3mqrlwEfGY7CbsG9iH/2/s/4ODdiAlLgWPXvQo7lx7JyfNA8EgnDkedjswRVGMrpynFTjRlVuMVIRW44lmcwHgSksXFxcH/bzdu3fjtttuw/r169HQ0IC9e/fCaDTijjvuAADceuutKCwsxJ49ewAA99xzDy699FI8+eST2LFjB55//nmcOnUKf/7znwG4Puv33nsvHnvsMVRWVjLt1AUFBQy5hQLBEo/dbmdmTYIV+OSzs40MkBLpG4qi3KRvuLh2qHA6neju7sbs7CwzM0QiEy7nbgKJeIiXj9Vq5ez+eK7BXzRETuAkIgq1NmS1DgGgIZOlQy4P72Bx+Mxh3PfOfRjWDQMArqu4Dk9c/gQKU8Nr9ggVXA2QSqXSBX8L0qTQ1dUFp9PJaJZlZWV51R+LdmrLE0KIeEIZIP3iF78IlUqFRx99FFNTU6itrcXBgweZ5gDSSUqwefNmPPfcc3jkkUfw/e9/H5WVldi/fz8zwwMA3/ve92A0GvGNb3wDc3NzuOiii3Dw4MGwnJEFSTw6nQ6tra1ISUkJSeCTL7M2cm2bzYYTJ04gJycHK1eu5OwNGk7EY7FY0NLSAgBudtlkNoLLiIe8cX1tXAaDAc3NzUhJScGmTZt8pka5POGyoyFvQ5NpaWlITEyEw+EIasM910q90B4iUEwbp/HQsYfwn97/AABy43Px44Yf46sbvhrS9bgCX8oFCoXCzQrcYDBAo9FgZmaGsQInB4LMzEymE1VIxBPtiCcc5YJdu3b5TK0dO3Zswfduuukm3HTTTT6vJ5FI8NOf/hQ//elPQ1qPN0SdeNhvfJqmMT4+jp6enrDqJXwRD03TUKlUMJvNWLlyJYqLizkXIA2FIEgNLDs72ysRck085DV727iIdXdJSQkqKyujkj4hQ5NkcJL43Jw9exYWiwXHjx9365TzV/8Kp7GAoin8reNv+NH7P2KaB75V9y3sSNqB8qLykF8fV4hEeksikSA1NRWpqalYunQpo1mmVqvR19cHq9WKjIwMxMXFgaIowcj4RDsCE7vaIgSHw8GkidatWxeW1hofxONwONDZ2Qm1Wg2FQoGSkhJOrw+ERjxjY2Po6enxWwPj2riNTTwE7Hocl9bdXID43CgUCvT392PFihVQq9UYHR1loiFCQp7dWaEST/dsN75z+Dv4eNKlk1WXV4entj2F2rxaNDU1CWJzjcYm72klQFq2JyYmYDQa8cEHHzDREBdW4KFCbKfmF4IgHoPBgNbWVsTFxWHz5s1h5Q4B7onHaDSipaUFCoUCa9asQXt7O2fXZiMY4iFyQVNTU4sSNddt2p7EQ1EUurq6MDs7G9SsUDQ2X4lEsiAaIrUh0p1FSEipVAat0Waym/DLD3+Jp5uehoNyNQ/8cMsP8Y3ab0S8eWAxCCG6SEpKQlJSEmiahkajQXFxMWMFztaVi2T7PBDdiIfMUInEwyPUajU+/vhjJjXDxR9bLpdzRjxE+qaoqAjLly+HyWTitWMukGtbrVbG02fz5s2LmkVxLepJPvwURcFqtaKlpQU0TbvVloQKz/sQHx/v1p2l0+lYG18XUlN7IJEADseSRTfqt4ffxu4ju5nmgc9WfBa/vPyXXpsHor3hA8IgHgKaphe0bJMUKbt9nvw8KyuLV2kliqKiKqIqRjw8IzU1FWvXrvWqnBoquLCnpmka/f39GBkZcZO+Idfm40MbSC1Gp9OhubkZSqUSq1atCigdwHXEQw4Her0enZ2dyMzMDHgtQoZUKkVmZiYyMzNRXl4Oo3ECPT3zACTo7tZDJvvArVOOpIFmjDN46NhDeKn3JQBAYUohfn3Fr7GjYofX3yMUZW8hEY+3epOnFbher2dqdcQKnPwtMjIyOI1QnE4nZ67FoUAkHp4RHx/PKekA4afa/EnfkM2Vjw+tVCr12wZOBlUrKipQWloalH8OH6m2lpaWsJpAiN1xpBDsGmnaZbimUBTj4ou3MZP7w8PD6O7uRkpqCt6dfxdPdT0Fnc3VPHBX3V34weYfIFXhXy5JCBu+0IjHH3Gw2+e9WYHb7XZkZmYyRJSYmBizSgo0TYvEwzf4eOOHQzyklduX9A0hHj7aLUmrticoikJvby/Onj2Luro6pjAbKLhsLiAGcgCwYsWKkIbcYgXnGgsq3KKhiooKtJ5txb1v34vmWZckUUVSBR5Z+wi2lG1BgsR/ulGMeBYi2I2eWIHn5uYyG7VGo4FKpUJ/fz/i4+PdWraDTZtFs7nAZDKBpmmReGINcrncr4K0L5AJ+PLycixbtsxnhxjgemNy3XHjLSVms9nQ2toKm80W8iAmV6k2p9OJjo4O6HQ6AODE5TXag3r+QFxH2VI5ZrsZv/roV9h7ai8clAPJccn4QeMP8MVlX4ROq2OiIXannC/r6WhDSMQTzhyPRCJBSkoKUlJSUFJSAqfTCa1WG7QVOBvRjHiMRiMAiMQTawg24mF3iC0WURAtOD4aDDwJQq/Xo7m5Genp6Vi3bl3IxU4umgssFguam5shk8nQ2NiIY8eOhXUPaJpmamU2mw1SqZT54hPB3AfPVup3Rt7BvW/fyzQP7CjfgV9d8SsUpbr053KyclBRUQGLxcJ0yo2MjDDW1OQEDggn1SYU0ufyACKTydxats1mM9RqNdM0wm5i8KVqEc0BUqPRCLlcLjhfKi4RdeLh4wMYTHMBmfgnhnKLdYgFe/1gwCaeyclJdHZ2hi086nndUDA3N4eWlhZGqYGQb6hkRkgHONfJR77IeoniQjSH+IgdggW5+NqBr+HFnhcBAAUpBfj1Fb/GtRXXen1eQkKCm/X03NwcNBoNo+oskUgwMzODhISEqEZDQop4KIribWYnMTERRUVFKCoqcutcJHNcRONPqVQiLS2N+bxEi5SNRiOSkpIEpeTANaJOPHwg0HZq4vmSm5u7wFDOH/gSISWE1tvbi7GxMc66/cIhHtLQUFlZiaVLlzIbVah1IzbJyGQyJoqjKApOp5OZXifrJeTDBQkFs8nStANW6xkAwM5X7sbAvB5SiRTfrP0mHtnyyKLNAwRSqZQ5WVdUVDBWFeRfuVzu1ikXyRZeoRFPJDZaz85F0rKt0WjQ0dEBmqaRmZkJm83Gq5OxPxgMhvM6zQYIhHi4njNZLNXGnrIPpUDOV8RDNMasVis2bdrE2ZsvlPtL7KnHxsa8ph+DvSbpYCP3zdPPnp1mI+RDiIh9r9mP43OjslqHYbVK8Ps//A4DyQewZkkT9unWo/4sBfrYr4GkJNDJyUBiIuikJCA5GXRiovu/SUnMfyM+HpBIkJiYyLh+KpVKRj5maGgIXV1dSE9PZ9JyycnJvBKDkIgnWlptni3bRGV7dnYWp0+fxujoKHNwiJQVeDg6bbECQRAP1/BHPET6Zm5uLmjVa/b1uY545ufnMTDgKmZv2rSJ07RDsBEP2566sbHR64cgGOJh13PIc/1teGQDYrvJsqMhchINJRoKdM0KRSEGP/4LXnvlVgC7MAgDfo1DuA6vYgdeQjbUAV2H+b1SqYuskpKwWSqFPC0NsrQ0JCclofDT7zsUCpilUpgA6ADokpKQoFQiKScHSdnZkKamLiA2ck0kJABBt4sLh3iEYIsgkUiQlpaGtLQ0jI+Po6amBg6HAxqNBj09PbDb7cjIyGCiU76swAnxRPt+8IkLiniINI9CoQhJ9ZqA64hnenoa7e3tyMnJwfz8POe57mCaIYg9dXx8vF8n1UCJh6TNSCollA+TZzTE/vIWDXFxcpZKE1G//gr812VdONi6FGfnUvAybsTLuBFSCYXNuf24Nu9jfFb5PpZLBwGTCZJPv8D+99P2eAlFAQYDJAYDkgDAi199HIBEAKG4FtESyTkSCvDf/Pl5KDIyIC8sPPczX8SWmBg0sQWDaItyeoKiKMTHx0OpVDIt2yaTiWkaGRwchEKhcLMC5ypNKkY8EUIkUm3T09Po6OhAcXFx2NI8XEU8NE1jYGAAw8PDWL16NeRyObq7u8O+ricCrcd4s6f2hUDIjJ0uC5V0POEtJUdIKNxoyBPVW9Kx99V00DSN1lYjDhyQ48035Whvl+H4dBWOT1fhIdyC5cuduOYaB3bscGD9egpu2RiH4xwRGY2QmM3o+vhjlGRnI00uh+TT78FodCctsxkSo5H5f8pggHN+HrTBAInJBJnVCrnVCikhNpp2XePTVtxAUBrk/aCDJDYmFekrJcl+bJRtCNgg71v2ethW4KRlmzSNkJZt0kKvVCrDsgIXiSdGwSYetvTN6tWrfXqPBwMuIh6Hw4H29nbMz88z6ggajSYibdreEKw99WJkxo5KuCIdb2sA3FNy/qKhUA83EglQV0ehrs6GH/zAhtFRCd58U44DB+R4/30Z+vpcX3v3xiM7m8JVVzmxY4cDl13mQHKyHEhLA52WBgCgAegMBliXL4czREdWsump1WpoVSpY5+agjI9HVmIilPHxSKQoF5mxIy9PgjObMTc+jgSaRhJFuR7jhQAlFsu5+0CuxwMuAUAlJLjqYYES3KckFkitDYmJQID1GfI+8VfPYbfIV1ZWwmw2M00KIyMjTFNJKEaEYnNBjIIQD3HBtFgsaGxs5OyPGW7EYzQa0dzcjISEBDQ2NjJvSq411Qj8RZSh2lP7uuZiTQR8wleDAlmTxWIBTdOw2+1hRUMlJTS++U07vvlNO3Q64O23XST01ltyzM5K8c9/SvHPf8YhIYHGZZe5SOiqqxzIy+PGDZa96eFT4VqNRoMZtRq9Wi3i4uKQlZe3aAqot7UVOTk5KCz044BKSMkzhciK4BYQmwfBsSM3T4KTmM3Mr5JaLIDFAqiDq58FCjohYXFCS06GMz4eK2ZnkdjcDGlKinvkxmoYYRNbYlKSWwu9pxEhUdlWKpWLWoGf78rUgECIh+uNiTganjhxAunp6WhsbOS0TTWciIcYpRUXF2P58uVur53PNm1v1w3Hntob8bBTa6SBIFoFUs9oiLhflpaWAgBntaH0dODGGx248UYH7HbgxAkZDhxwEdHIiBQHD8px8KDrvbd+vYuECgqSUVXF3X0h1gJFRUVu0dDAwAAsFgtTEM/KynIriAfUXCCVAikpoD/dCDkX+6EowGxG8/HjKMvLgzI+3juxeYvcWKTnRnCsn7NTjxKLxRXBaTSLLqsqhJdCx8czJJaSlISCT/+bSkyETSaDRSaDSSKBWqFAXEYG4pVKJGZlIS4j41zklpSEnIEBpMpkwMCAKwL89PuIolo21zh/XgkLU58WbgsLC1FREbplsS+EQhA0TWNoaAhDQ0M+jdL4UkTwdt1A7an9XdPTCI5NOkLJ1wOuNCIxqMvLy1vQrs3V8GpcHHDppU5ceqkTv/iFFd3dUhw4IMcbb8jR3CzDqVOuL2ALli6149praVxzjQONjU7O9hS3aAhgCuIajQZDQ0NQKBTMz4XQSQapFEhOhi09HVRJCagQ048+QdMuUvJWR/NBcA6dDjNnzqBIqVxIbGaz+3OMRld9DYDEagWsVki02gXLUAAINIa5j/zH//f/eVxEcY6E2P/6+G8TgJfefBMfd3XBIpVi7ebNuGP/fr/RlMViwX333Yfnn38eVqsV27dvxx/+8Afk5eUBANra2vCLX/wCx48fx+zsLEpLS3HXXXfhnnvuCfDVuXBeEQ9FUeju7sb09DQAcG5NTRBsxONwOBiNs40bNyLt03y/t+vyYf/ruV4u7KnZZMZHEwEXILNIk5OTWLduHdM6v1i7NhfDqxIJUFNDoabGhgcesGFyUoKDB10kdPSoBCMjcXjmGeCZZxTIyKCxfbsD11zjwNatDvh4e4QEEg0VFxe7RUP9/f0wm81wOByw2+0LoqFIgzcSZHf7ZWUFFLEZDAZ0NTUh+9JLF38wTbvSg6Qm5o/gPCI3mM2AwQC7Xg+nTgfd5CTmJiaQIpUiXS5HelwcpGazqyMSAGw215cXYvOGJAC3fvoFADhyBF/+xjfw3HPP+XzOd7/7Xbzxxht48cUXkZ6ejl27duFzn/scPvjgAwBAU1MTcnNz8c9//hPFxcU4ceIEvvGNb0Amk2HXrl0BrQsAJLQApHKdTmfYU8Jmsxmtra0AgNraWrz//vvYsmULL90hp0+fBgBUV1cv+ljSnqxQKFBbW+u3yGi1WnH06FF85jOf4TRi6O/vh9VqRU1NDWf21B999BGKi4uxZMkSQZIOETQ1Go2oq6sLOI3oGQ2xPx5ctWsfOfIRVKpaHDuWjoMHZdBozl0vLo7GJZe4uuSuvtqBoiL+Pp4fffQRUlNTYbPZMDc35xYNZWZmRlQy5vjx41i9enXA7rV8Qq/Xo729HRdddFFEfy9FUejs7MTXvvY1SCQSjIyMIEupxFe/8AXseeQR4NNUotu/Pv5bMz6Ol//5T9zwmc8gKyEBMBoxNzEBZW8vxsfHvX72dTodcnJy8Nxzz+Hzn/88AKCnpwfV1dU4efIkNm3a5HXd3/72t3H69Gm88847Ab/W8yLi8SZ9w7X9NRuL+eYQzM7Ooq2tLaD2ZHJdgPuZBolEAqfTic7OzqDtqf2BoijmPgiJdIgrqlwuR0NDQ1BzUXwOrxIkJDhw1VVmfOlL8XA6gY8/luGNN1zR0OCgFEeOyHHkiBz33QfU1jpx9dWuaGjNGorTURqpVIrs7Gzk5uYyis5qtRp9fX2w2WxutaFw/W0Wg5DmeKIlECqVSrFmzRosWbIEX/ziF3H77bfj+PHjrgyOUun6ChD7n30WD7z+Or5+6BDzvRSHA9KEBHz00Ue44YYbFjynqakJdrsd27ZtY763YsUKlJSU+CUenU4XcFMSQUwTD1v6xrMNmE/ikclkfm0X/K3LH9jEwyUoisLs7CySkpI4sacmqUCtVov09HTeN6VgYDAY0NLSgszMTEbQNBzwNbxK7pdMBjQ2OtHY6MRjj1nR3y/BG2/E4cABGT76SIbWVtfXnj3xKCqiGBK6+GInwjXIZMvUsBWdybAksZ0eGBhg/G34ioaiJZnjDdG26iAmcAkJCW4kEAympqYW6DwSXcApL8PL5DkKhWKBmkteXp7P55w4cQIvvPAC3njjjaDWJwjiCWXTYtdNvJ3g+SYeX+TgdDrR1dUFtVodtCQPH8Sj1+sxOjrKnP7D/UCRNFRhYSFGR0dx4sQJpKSkIDs7Gzk5OUhLS4saCanVarS3t6OkpCRsRW9v4Gp41V92u7KSxr332nDvvYBKJcGhQ64uuXfekWN8XIq//EWBv/xFgdRUGlde6UrHfeYzDmRmBv96fNUS2cOSpDbkLxoKxSPKE4JodPgU0bREAPy3Uz/00EN44okn/D6flAL4RmdnJ66//nr86Ec/wmc+85mgnisI4gkW5FSbkJDgU/qG71Sbt2ubzWa0tLRAKpVi8+bNQftpkG4qrohnamoKHR0dTBjMFelQFIW8vDzk5+fDZrNBrVZDpVKhubkZEomEIaGsrKyIqS2Pj4+jt7cXK1euxJIlS3j/fb5ScoSMFouGAtlkc3JofPWrDnz1qw6YzcCxYzK8+aZLPWF6WoqXX47Dyy/HQSajsWXLuZTcsmWBa+gFsg5v0RAR0hwYGEBCQgJDQqEIaZI5KyFFPNFay2K21/fddx9uv/12v9coKytDfn4+ZmZm3L5PdOd8DdGTz/Pc3JzbgXl6enrBc7q7u7F161Z84xvfwCOPPLL4C/NAzBHP1NQUOjs7vc7BsBHpiEej0aC1tRW5ublhpXi4GCIl9tRnzpzBmjVrYLPZmE6/cK7prYlAoVAw6r7E60SlUmFwcBAdHR3IzMxkNi0+Gj2I7ND4+Djq6uqCzjVzBX/Dq+x2bdKGHuzfODERuPpqJ66+2gmKsqKpScqoJ3R3y/Dee3K8954cDz8MrFzpak645hoH1q2j4OutGEr3pKd0jMPhYDrlent7YbPZkJmZyUztBxINsVvZhYBo2l4D8Es8OTk5yMnJWfQajY2NmJubQ1NTE+rr6wEA77zzDiiKwsaNG70+p76+HnFxcThy5AhuvPFGAEBvby9GR0fR2NjIPK6rqwtXXHEFbrvtNvz85z8P9uUBiCHioSgK/f39GBsbw+rVq5m+cl+QyWS8+WmwIx6apjE6Ooq+vj5UVVWhpKSEs2uHArY9NZHiGR8fD5nMyGk0EPkbttfJ8k8n6mdnZzE7O4v+/n4kJiYyJJSZmRn2RkPSmnq9Hg0NDYLRt/IXDel0OuZ9abfbmXRcMPdCKgU2bKCwYYMNjz5qw9CQhImEPvhAhu5u19evfx2PvLxzdaFLL3WC7XPIRdu+XC4POxoiqUehEE+0Gx38EU+gqK6uxlVXXYU777wT+/btg91ux65du/ClL32J6WibmJjA1q1b8Y9//IMpV3zta1/D7t27GVO8u+++G42NjUxjQWdnJ6644gps374du3fvZmo/MpksIEIkEATxLPbmZ0vfBOpTw5cKAPvaFEWhq6sLKpUK69evR2YoiXYPhBPxeNpTs6V4Quma9zytB9u5lpSUhJKSEuZkrNFoMDs7i87OTjidTmRlZSEnJwfZ2dlBK4XbbDamfb6hoSFkpfFIgBCLSqVCR0cHKioqkJqaytnwalkZjW9/245vf9sOjQY4fNhFQocPu1Jyf/ubAn/7mwJJSTSuuMIlZrp9u5Pzuoq3aIjUhoitQGZmplunHAC3aFAIiGbEQ1EUTCYTJ4eof/3rX9i1axe2bt0KqVSKG2+8EU8//TTzc7vdjt7eXphY+nu//e1vmceyB0gJXnrpJahUKvzzn//EP//5T+b7S5cuxfDwcMBrE8QcD03TsH2qsusJnU6HlpYWpKenMwrOgaC9vR3JyckoLy/ncqkAXAOY3d3dzGZXV1cXdqcYwbvvvotVq1Yxk+eBwps9NcHU1BTOnDnjFi4vBk8PHS5PgMRwS6VSYXZ2Fnq9Hunp6czJeTFlX6PRiJaWFqSlpaGmpiaqaZFAMTExgd7eXkY9gcCzXZvtWRSuurbVChw/LmNUtcfH2TUmGtXVWtxwgwyf+5wMlZX8bgOkdkEOHzqdDomJicjKykJqaiq6u7tx+eWXC4J8hoaGYLVaA5rT4xp6vR5FRUVQqVQLzBfPJwgi4vGFsbEx9PT0oKKiAqWlpUG9KflMtZlMJpjNZmRmZnK+8YUS8fiyp2ZfM1i3UD6HQtmGW8R+mKTkhoeH3dI3WVlZbvdXq9Wira2NNzkkrkGkkkZHR1FbW7ugBuWrNsSF11B8PLB1qxNbtzrx619b0d4uZXTk2tpk6O5Worsb+PnPgYoKCjt2uFJyDQ3OQIWcA4ZEIkFKSgpSUlIWREPEALG9vX1BNBQNRLOd2viptpxQ0sZ8QRDE47l5OJ1OnD59GjMzM1i3bl3Qp3+Av+aCsbEx9Pb2QiaTYfXq1by07AZKPIvZUxME0ynHjnQiNRQaHx/vpuyr1WoxOzuLvr4+WK1WZGZmIicnBxRFYWBgAFVVVQHPRkUTRPl7dnYWGzZsWDRFzK4Nkb8XV8OrEgmwdi2FtWttePhhG8bHJXj66TM4fXo5TpxQYGBAiqeeUuCppxTIynJZO1xzjQOXX+4AH0LJcrmcKZQbDAacOnUKGRkZjJgriYZIbSiSNZdoptpMJhMUCkXQHbGxBkEQDxukJVkikaCxsTHkk49MJoPdbudsXWz7gOrqavT29vKmAxcISbDtqRerewV6zUh46CwGqVTKbDikQUGlUmF4eBgWiwUJCQkwm83M8KpQCtKecDqdaG9vh9lsRkNDQ9CpWPK6+BheBYCiIhrXXDOMH/0oB05nMo4ccSknvPWWHGq1FP/6lxT/+lcc4uNd1g7XXOOydliyhPuUHDnkLF26FEuXLmXqgWq1GqdPn4bD4XDrlOM7GqIoinMX4EBhMBiiqpkXKQiGeCQSCSNemZ+fj+rq6rA2FS4jHqvVitbWVjgcDqZOwueM0GIkEag9NcFiDq/hNhHwBYlEgsTERBgMBgDAhg0bYLFYGCkiAMjKymLSctHaLDxBGh8kEgk2bNjAybq4Gl5lg3S1paUBN9zgwA03uKwdPvzQJeFz4IAcw8NSHDokx6FDrq1i3Tonk5JbuZIbCR/PLjK5XI7c3FzGctpoNEKtVkcsGopmxGMwGM77NBsgEOIhcyeDg4NBScz4A1fEQ5obMjIysH79eshkMsZQjI+2y8WIJxh76kCuyWcTQbiw2+1oa2uDw+FAQ0MDk37Iz89nWpNJXairqwvp6elMl1xycnJUyNNsNqO5uRmpqam8NT6EO7xK4K2dOi4OuPhiJy6+2Ik9e6zo6ZEyJHTqlAzNza6vn/0sHqWl51q1N292IlR+9Tc8yq4NeUZD3d3dcDqdbp1yXDT5RLOdmrRSC+HgxycEQTyA64ZzJV4JcEM8Z8+eRVdX14LmBvYHnus3qL828GDtqQl81XgCnc+JBkwmE1pbW5GUlIS6uroFG7hEIkFGRgYyMjJQUVEBi8XCdMkNDg5CoVAwJBQppWW9Xo+Wlhbk5+f7HW7mGv4aFHy1a5O1+VujRAJUV1Oorrbh/vttmJpyWTscOCDHsWMyDA9L8cc/KvDHP7qsHa680kVC27Y5EMzHOJjPkWc0ZDAYoFarMTU1hb6+PiQlJTF206FGQ9GUzDGZTJxIEAkdgiAeiUSCtWvXcjp3Ew7xUBSFvr4+jI+Po7a2dsFgFHlTOp1OziVhvEUn7PpSMPbU7Gv6cwsVGunMzc2htbUVS5YsCXgDT0hIQHFxMaMtRtp2T58+DZvN5paS46r1nQ2ikF5WVua1szBSWExdm+2hFCzy82ncfrsdt99uh9EIHD3qIqGDB2WYnZXixRfj8OKLcYiLo3HRRefUE4qL/f+uUOeJJBIJUlNTkZqaitLSUtjtdqZTLpxoKJpdbQaD4by3vQYEQjx8IFTi8bSD9pZv5UtFmlybfd1w7Kl9XVMITQS+MD09zUSZoapAkCnqnJwc5lQ8OzuLs2fPoqenBykpKUw0xIWo6dmzZ3H69OmI6cQFA1/R0Pj4OHMgsdlsbo8L5LSfnAxce60D117rgNMJfPKJDAcOuGaG+vpkOHpUjqNH5XjgAWDNmnM6crW1C+tCXGUO4uLiAoqGsrKy/DamRDvVJtZ4YhihEA9JlaSlpaGurs5nNEMKt3w0GLBJIlx7agLSXEC+yLqFRDo0TWNkZARDQ0NYvXp1UPIb/sA+FS9btsxN1HR0dBRSqdRtZiiYe0zsL4aHh1FbWxtS238kQYhmZGQEIyMjqKurQ3x8fNjt2jIZsGmTE5s2OfHTn9rQ3y9hdOQ+/FCG9nbX1xNPxKOgwFUX2rHDZe0QH8+PJYK3aIjUhrq6uuB0OpkuuaysLLf25Wg2F3AhlxMLEAzxcL0ByuXyoIhhcnISnZ2dWLZsGcrLyxddD1+SPIR4uLCnZl+TvbEAcMvzRxsklUikh3xZg3MBT1HTubk5pi7EFjXNycnxG13SNI3e3l5MT09j/fr1SE1N5W3NXIHMfU1NTS1YM5ft2pWVNCor7fjOd+yYnXVZO7z5psvc7uxZKf76VwX++lcFUlJobNvmwObNyVixgt+OxLi4OOTl5SEvL88tGpqcnERvb69bNBTNGo8Y8cQ4Ao1IaJpGf38/RkdHsXbt2gXmSeFeP1hIJBLMzc1hdHQ0bHtq9jUBl4K2UqkUVOeaw+FAe3s7rFYrNm7cyEv9xRekUimUSiWUSqWbqKlKpXITNc3JyXErVBM3V9IQE80p+0BBURS6u7sxNzeHDRs2LCBVPtq1ASA7m8ZXvuLAV77igMUCvPuujImGpqak2L8/Dvv3L4FMlo/GxnN1obIy/iR8/EVDnZ2dsNvtGB4ehtlsXhAN8Y0LhXgEodUGuDYgLjdyk8mE9957D9u3b/d5siftuiaTCevWrQsqxH3vvfdQU1PDaXqFoiicOHECJpMJGzdu5KTDj2wa3d3dmJ2dBYCo+OV4g8ViQUtLC+Lj47FmzZqorsUTpG2XdMpRFMW4b549exYSiQR1dXWCmR3yBzLMarFYsG7duqA3Us9oiL1lhDK86rom0NLikvB55RUafX3uB44VK86R0Pr1vq0duAZN03j33XexZMkSzM/PQ6/XIyUlhYmG0tLSeD243X333cjLy1vU7C3WIZhPOh+pNsB3hwqpnyQnJwc0hOkJriMeq9WKlpYW2O125OTkcEI67I2ipqYGALymlkghPpInd1JPy8nJwYoVKwQVhQEL23b1ej0mJyfR19cHiqKQlpaGsbEx5OTkCHruwm63Myre69evD4ko/XkNhRoNSaVAfT2F+nobbrttFD09VgwOrsSBA3IcPy5DT4/r6ze/iUdurru1A5/dxqQeWlxcjKSkJNjtdqjVamg0GnR0dICmabdOOa6jIbHGE+MgZOOtUDg9PY329nYsXbo05PoJlzUevV6P5uZmpr5AJvVDhb8mAuKXU1lZycjRzMzMoK+vD8nJyUy3V3p6Om+bKbEHiHbrcaAgG+nMzAwKCgqwbNkyxnOGLWqak5MDpVIpGLVsq9WK5uZmJCQkYM2aNZysi6vhVTYoikJhoQ2f+Ywd3/qWHVot8PbbLgmft9+WY2ZGir//XYG//12BxEQal19+ztohN5fbhA0hVvL64uLikJ+fzwwtz8/PQ61Wu3VIchkNcWWJIHSct8TDzscTsJ05V69e7dMCNtDrcxHxEHvqsrIylJWVYWRkJCxC81Qi8NdEkJSUxOhjkZOdSqVi7LvZmylXabDR0VEMDAwssAcQMjQaDdra2lBaWsoMEnuKmqpUKvT29sJqtUKpVDKdctGq/5jNZjQ1NSE9PR01NTW8RZShDK96rsWzfTkzE7jpJgduuskBmw344AOXhM+bb8oxNibFgQNxOHAgDhIJjQ0bzqlqL18evoSPPzdUtqo66ZAktSESDZFOOaVSGVI0JM7xRBh8SO+zW6pJEXt+fp5x5gwH4SojeNpTk004FFsE9jVDHQpln+xItxcpslssFreUXCgNAKSjanJyEuvWrXPzdBcypqam0NXVherqaq+NHmxRU+LAqVKpMD09jd7eXiQnJzMEzmcUyYbBYEBTUxPy8vJQVVUVUQUFYPHhVc+UnL+5GYUCuPxyJy6/3Ilf/cqKzk4pQ0ItLTJ8/LHr60c/ikd5OcXUhTZudCKUsxJZYyDRoUKhWBANzc7OYmJiAqdPn0ZqaipzCAl0XsxoNMZEh2S4EAzx8AFCDkajkUk5sJ05w712qAThzZ6aIFTi4VKJgN3tVVVVBaPRCJVKhampKfT29iIlJYXZTAP5QJHXS7rAYkUSZHh4GENDQ1i7dm1AplxsB07SLUVScqTOwp4Z4qMxgRgCLl26FMuWLYtqGjNQryGn0xnQOiUSYPVqCqtX2/DQQzZMTJybF3rvPRkGB6X43e8U+N3vFFAqKWzf7mpQuOIKBwLdy8m6gr1v7GiorKzMLRpqb293i4aysrJ87kGiZM55AJlMBrVajaGhIRQVFWH58uWcpRxCTbX5sqdmXzdY4uFbiYC9mbIHMJubmyGVSplIyFt9gzRNyOVyNDQ0xEQXmOe8S6hzRZ71AZ1OB5VKhTNnzqCzsxMZGRkMEXEhajo7O4v29nZUVlaiuLg4rGtxDV/RkMVigUajQV5eHmw2W1ANCoWFNL7+dTu+/nU75ueBI0dcJHTokBwajRT//rcU//53HBQKGpde6iKhq692oKDAd12INCOF+7fwjIb0ej3UajXGx8eZaIhdGyJNDVxEPBqNBnfffTdee+01SKUuG+unnnrKbwrPYrHgvvvuw/PPP+9mee0tHa5Wq7F27VpMTExAq9WGlL0QTDu1P/vrUK939OhROBwOrFq1ipN5GDY6OzsRHx+PysrKgJ/jz56aYHp6GoODg9i8efOi1/NsIoj0UCjbtE2lUjH1DUJEdrsdLS0tyMzM9Pl6hQaKotDZ2Qm9Xo9169bxdvo0m82M66pGo0F8fDwTRWZmZgZ9r0hKUIiyPb5gsVjQ1NSE1NRUrFixAsC5yIgglHZth8Nl7XDggKtB4cwZ9+fW1Z1r1V61yr0upNfr0dbWhosvvji8F+cH5PBGuuUAoKurCwCwZ88eHDp0CA0NDSFf/+qrr8bk5CT+9Kc/wW6344477sCGDRvw3HPP+XzOt771Lbzxxhv429/+hvT0dOzatQtSqRQffPDBgsfu3LkTNpsNb775pkg8bDgcDnR2dmJ6ehqVlZUoKyvj5LpsdHd3QyqVMh+YxbCYPTUBKVJfdNFFfq/n6aETbSUCclpTqVRQqVTQ6XQAwHTQcaGJxjfIXJfT6URdXR0nKdlAQERNycyQw+FwEzVdrEg9NjaG/v5+rFmzJqCUoBBAmh+USiWqq6uZ94a34VV2o0www6sAQNNAb+85y+9PPpGCps+9D0tKzrVqX3SRE0bjHLq6urBlyxbuX7TX9bmiob/+9a/497//jd7eXqxatQqf+9zncPXVV2PDhg1BdSMSzcBPPvkE69evBwAcPHgQ11xzDcbHx70ewHU6HXJycvDcc8/h85//PACgp6cH1dXVOHnyJDZt2sQ89o9//CNeeOEFPProo9i6datIPAQmk4lJ7ZB+/MLCQg5W6I7e3l44nU6sXLnS7+PY9tS1tbWLbgxES+qSSy7xe02SKw/mQxgpjI+Po6enBwUFBUydIy4uzu1EL5SWYwIyzMpl63EoIHIuhIT0ej1SU1MZEmITOE3TGBoawujoKOrq6mKmYcNoNDLND4upj3u2a4cbDU1PS3DokBwHDriETM3mc787PZ3GJZcYsWrVGXzrW6WI9O10OBxQKpV48skn8cknn+DQoUN4+OGH8cADDwR8jWeffRb33XcftFqt23UTEhLw4osv4oYbbljwnHfeeccriSxduhT33nsvvvvd7wJwHba3bt2Kjz76CENDQ7j88stDJp7zqsZDTNKWLFmCFStWoKWlhVen0MWIMhh7avZ1/dV4hGxnQNM0BgYGMD4+jnXr1jH2DRRFudkU2O12t5RcpCILXyDDxNnZ2VEfZmXLuZAiNUnJeYqaklpbrGjFAcD8/Dyam5tRWFgYkCaiv+FVz4g/kGgoL4/GrbfaceutdphMwLFjrpTcm2/KoVJJ8dprKXjttdX41a9c1g4kGlq6lP/zOZnfu/3227F79244HI6gD+NTU1MLZL/kcjmUSiWmpqZ8PkehUCwgkLy8POY5VqsVN998M371q1+hpKQEQ0NDQa3LE4IhnnA2UKK429/f72aSxqX9tScW62oL1p6aIBC3UCGSjtPpRFdXF/R6PRoaGtyG4NibZVVVFXOiJ4XWtLQ0ZnA10ioAWq0Wra2tKCkpQVlZmaDuKeAqUhcUFKCgoMCtzZ0oLGdkZECr1UImkwm+G0qn06G5uRmlpaVYtmxZ0M8PdHiVPS/kj4SSkoBrrnHimmuccDqtOHVKihdftOLQoXiMjCTj2DE5jh2T48EHgVWrnIyqdm0tPxI+RqMRAJgDqlwuZ+bnHnrooUVldE6fPs39oj7Fww8/jOrqanz1q1/l5HqCIR7gnFxFMCAbnlqtxoYNG9xYm0/i8dfVFoo9Nfu6nsRD8txC9dCx2WxMu3BDQ4PfCMbzRG+1WpnmhKGhIcY5NNQiezAg3j/Lly/nxG6db0ilUqSnp2NkZASJiYmorq6GXq+HSqVi/GYIwYfqvskXCMETtQouwMXwKoFMBmzcSKG4WIWvfGUaqanrcPCgqznh5EkZOjtdX7/6VTyWLDlXF7rkEie40rU1mUyIj4/3Oqx933334fbbb/f7/LKyMuTn52NmZsbt+0R30NfAfH5+Pmw2G+bm5tz2z+npaeY577zzDjo6OvDSSy8BOGckmJ2djR/84Af4yU9+EujLBCAw4gkWZrOZmbJvbGxcMNgYjYgnVHtqAk9C80wpCI10jEYj42FUU1MTdG0kPj6eUQFgF9m7urqYIjuJhrhMyREFBS69f/iGN921jIwMlJSUwOFwMDNDHR0djKgpEYONZjqTuLPySfChDq96grRTV1TQ2LXLjl277FCrgbfecjUnvP22HJOTUjz7rALPPqtAcjKNrVtdJLR9uxNZWaGn5AwGg8+2enIYWwyNjY2Ym5tDU1MT6uvrAbhIg6IobNy40etz6uvrERcXhyNHjuDGG28E4Kphj46OorGxEQDwn//8B2azmXnOJ598gv/6r//C+++/j/Ly8qBfa8wSj0ajQWtrK3Jzc3226kYy4gnXnpp9XXYnD1v+RkgnWMB1im1ra0NhYSEqKirCJkRP59D5+XnGsK27uxvp6ekMCYU690LqUBMTE6ivr+dEjDUSWEx3TS6Xu/nN6PV6zM7OYmRkBF1dXVFLZxJdvurq6oi2eQc6vOrZoODNiycrC7j5Zgduvtll7fD++zKmS25yUopXX43Dq6/GQSqlsWmTEzt2uOaFKiqCIyFCPOGguroaV111Fe68807s27cPdrsdu3btwpe+9CWmo21iYgJbt27FP/7xDzQ0NCA9PR1f+9rXsHv3biiVSqSlpeHuu+9GY2Mj09HmSS5E6b66ujr2mwsCSbXRNI2xsTH09vaiqqoKxcXFPj9EMpmM09kgz2uTUxQX9tQE3jxRhBblAC7jvO7ublRVVfFyimVPgpeXl8NisTApucHBQcTHxzMkFWhaiaIodHV1QafTYcOGDTEjxkjqhRkZGQHNQ0kkEqSnpyM9Pd3t3s3OzuLMmTNMh2F2djavoqZktmjVqlVR1eVjR0Ps2TdvXkMOh8Pv/U1IAK680okrr3TiN7+xorX1nIRPR4cMJ07IceKEHD/4AbB8uZPRkVu/nsJit5koU4f7Wf/Xv/6FXbt2YevWrcwA6dNPP8383G63o7e3FyaTifneb3/7W+ax7AFSviCYdmrAdUP8FeyJmdXMzAxqa2sXjSrOnDkDnU6H2tpajlcKRtG5traWsafmwlPGbrfjyJEjuOyyy5gJaiGRDruFd82aNVGxe3Y6nUxaSaVSuaWVsrOzvTZykA5Du93OWD7HAkgXWH5+/qKtx4HA6XQyQ7+zs7Nuoqah6vB5A1FvFvpskafraldXFxITE1FWVhZ0u/boqITpkHv/fRkcjnN/q5wcCldd5cA11zhx+eUOr9YOL7/8Mn73u9/h1KlTXL08wSJmiMdisaC1tRUURaGuri4g1d/R0VGoVCom18kliAaT0+nkxJ4aONe1duTIEWRlZSE3N1cQ7cYEhPi1Wi3q6uoEoaJL0kpkcNVoNDJSNDk5OUhOTmZkexQKheAM5/yBb901MvRLCFyn03FijUEGWgM5HAoFRLHCYDBg7dq1zBxgqMOrOh1w+LArHXf4sBw63bn7mJBA4/LLXeoJV13lQF6e63f84x//wIsvvohjx47x8hqFhJggHvIBzMrKCqqAPTExgYmJibDkJ7yBpmmcPn2aOfVzIcfDbiIg7cYqlQoGgwHp6ekMCUWrZZZM9TscDkFHDESKRqVSQaPRICEhATabDenp6cyGEguIhu4a2xpDrVYDCF7UdHh4GGfOnImpgVY26dTX1zPvba6GV+12l7UDqQuNjp57jkRCY/16l6q2Xv9P9PS8jAMH3uD+RQoMgiIeb/bXZNZjMakZb5iamsKZM2eYzgwuQOoEMzMzoCgKV155ZdjX9PTQYb+ZLRYLQ0IajQZJSUlMbSNSMvsmkwmtra1ISkrC6tWrBac64AukmyoxMRFWqxUA/+rQXIDUz2pqasLyjAoHFEUxkeTs7OyCSDIpKcntvUdSsGNjY1i3bl3IwqqRBiEdo9GI+vp6n9kFzwYFTxJarF2bgKaBrq5zEj7Nze6fpZSUKdx5Zz6uuw646CKEZO0QCxAs8bC7xGpra0OqJQSqexYoSMqGpmlUVVXh1KlT+MxnPhPWNdnDb4s1EZCW2ZmZGczOzi6qDM0F5ubmGDUILmoMkcLMzAw6OzuZiIGtDq1SqWAymRiPoezsbMEMXwpVd40dSWq1Wqa5g8wMDQ4OMh2dQkjBBgKKotDR0QGTyeSXdHw9l01Coabkzp6V4OBBFwkdOULD6Tx3GMrMBHbsAK67DrjqKgRs7RALECTxWK1WtLa2MmmdUDcFjUaD9vZ2XHbZZWGvjW1PvWrVKthsNrz77rvYvn17yJtxOEoE7Cn2mZkZ2Gw25jTK1cwLGbCsqKhASUlJ2NeLFMbHx9HX14dVq1YtkA8hMJlMbhtpNCJJNmJJd81T1JTYGZSVlaGgoECwaVg2wiEdb9fyFw0RAlqMiL773R/i7NlVyM39Ol5/Hfg02wmAGOK5SOi664AYmHf2C0ERD+lWamlpQUZGBlatWhVWTl6n06GpqQlXXHFFWOvytKeWSCSw2Wx45513cOWVV4YUaXDpocMWllSpVJifn2dmXnJzc4MmbiJBNDQ0FFMDlsTVdWxsLKjNm23YRuYTCIlnZWXxXheiaRq9vb2Ynp6OuYihq6sLWq0WeXl50Ol0bqKmOTk5SE1NFVyUTEjHbDZj3bp1nDfvhBoNffvb30ZhYSH27NkDhwM4eRJ49VXglVeA/n73x65b5yKg668H1q5F2JbfkYagiGd8fBytra0oLy/npIvHYDDg5MmTIddhfNlTA67o7O2338YVV1wR1Bs3Eh464dSFSIpTpVKhrq4upnL1p0+fhkajCavjjqIoJiU3OzsLk8nECJpmZ2cH1E0Z7O8js0X19fWcX58v+CrIs0VN1Wo1kw4mM0PRbu6gKArt7e2wWCy8kI6338f+8tegcMcdd6C2thaPPPKI2zVc1g7nSOjkSdf3CIqLz5HQpZe6oiOhQ1DEo9VqYTQaOTthm83mkNNhxK55bm4O9fX1C9R/aZrGoUOHcNlllwU8++CviYAvBFMXcjgcaG9vh9VqRV1dHWczHXyDz3Wz243n5uaYduNAbb/9wel0um2CsZCiAs6t22q1+t282eng2dlZmM1mt5mhSJMsRVHMoDeRiYn07wfgMxr6whe+gB07djA2BL4wMwO88YaLhN56C2Ap2SAtzVUPuv564OqrXXUiIUJQxENRFOx2O2fXI+mwbdu2BXXSYttT+zMEO3ToEC666KKAJuCDaSLgC/7qQqmpqYyraizNuthsNsZ/ac2aNbxuJna7nSEhcppnp+SCSbmydddqa2sF22HnCafTidbWVsYsL5h1ExKfnZ1l6mrk/qWnp/N6EIs26fhaE/mamZlBXV0dvvzlL+Mvf/lLwNcwm4EjR1wk9NprwPT0uZ/JZMAll7hI6LrrgBAEwXnDeU08TqcThw8fDiodFog9NcHbb7+NjRs3LuqFIkQ7A3ZdaGpqCkajEQqFAiUlJcjLyxNMl5c/ECmZ9PR01NTURFTLjk3inrbf2dnZfqOuxXTXhAqHw4GWlhZIJBLU1taGdThhi5qqVCrQNO3W6s5lCoxEaDabDevWrRME6bChVquxY8cOlJSU4H//93+RGWKYQlHAJ5+4SOjVV4FP3bQZrFoFdHRwsGAOcF4TD0mHXXLJJQFtpIHaUxMcPXp00SI2l00EfICIOJaUlCA+Pj6q80LBQKfToaWlBQUFBZyoRoQDbwoAKSkpzP1jF9iD1V0TCux2O5qbmxEXF4e1a9dySpZs9YnZ2VlmaJqtPhHq39fpdDJSSUIknbm5OVx77bUoKirCSy+9xCnhDg66COjVV4H33wecTvfaUDQhKOLhyv6ajcOHD2PTpk1+o5Jg7akJ3n33XaxatcrrjFEkmgjCBbEGqKmpWdA4QSbYVSpVROaFggEhy/Lycs68XbgEu8A+OzsLuVzOqEIPDQ3F3EwUidDIADHfZMkWNVWr1VAoFCHZphPSIWMZQiMdvV6P6667DllZWfi///s/XmuqGg1w4ADAkY9b2Djvieedd97BunXrfEYlbHvqdevWBdUNdfz4cVRVVS1ohvBsIhAa6RCiJcO5i0VsfM8LBYOJiQn09PREdao/GFAUBa1Wi/HxcczMzEAikTCCpjk5OYJvKLBYLGhqamL8liIdobFFTVUqFWw2W0ApTaGTjsFgwM6dO5GUlITXXnstZroZuUJsVJDDgD9PnlDtqQm8uZAK2SkUONetZzKZ0NDQsGgKUiqVQqlUQqlUYvny5UxdaGxszM0jh6RE+ILngGWsiE8SR1m1Wo2qqioolUqoVCpGvTk1NZW5f5G2/V4MJpMJTU1NyMrKQnV1dVTWJpPJ3GzTSUpzcnISPT09SElJYQ5CpMuQ3QCxbt06wTXKGI1GfP7zn0dcXBz2799/wZEOILCIBwCjqcUVjh8/juXLly+YYA/Hnprgo48+QnFxMSMSKsQmAjaI5I9cLsfatWvDPgVGSkeOzBbNzs4GHZVGG/5012w2G1PXUKvViIuLc5t5iWb9x2g0oqmpCXl5eYJNC5IuQ/JFokmDwQCZTCZI0jGbzfjCF74Ai8WCgwcPLtqYdL7ivCeekydPorS01M39MFx7aoJPPvkE+fn5KC4uFnwTgcFgQEtLCzIzM3kpavNVFyIdSWTKPFZmi4BzNbRAdNdISoncPz5tvxfD/Pw8mpqaUFRUhPLycsG9l72BpDS7u7ths9lA0zQyMzOZaEkIpn9WqxU333wztFot3nrrrZhxv+UDgiMe8qbhCh9//DEKCgpQVFTkJjzKRbqmubkZSqUSJSUlgm4iIN5BJSUljOQPn/DWakz8hYLZRG02G1pbW5n2XaHl6X0hXN01bxJIxLo63C6vxaDT6dDc3IzS0lIsE9LgxyJwOp2MgG9dXZ3bzJVWq0VCQgJDQpmZmRGPJm02G2655RZMTEzg7bffjplUMV8474mnqakJ2dnZWLJkCTNAtm7dOk7mVFpbW5GSkoKSkhKGcIRGOuPj4+jt7cXKlSsj6nlP4E9Hzl9dyGw2M86uq1atinonXaAgumszMzOcpQWtVqtbSjM+Pt6ty4urTVSr1TKSVbEkCktIB4DX+SK2Y+3s7GzEo0m73Y477rgDAwMDeOeddwSlOh4tnPfE09raysyncGVPDbg2mM7OTuh0OhQVFSE3N1dQHUo0TWNgYADj4+Oora0NeSiNawRSF9Lr9WhpaUFeXh6qqqoER+a+EAndNbYytKftdziDl8S7aPny5WGlnyMN9lBrXV3dogcUmqYxPz/PkJBer0daWhoTDXEtaupwOHDnnXeio6MDx44d86mWfqFBcMTjz/46FHzyySfQarUoLS3lbNCQNBFYLBZMTU1BpVJBr9eHpQjNJYh3vF6vR11dnSDy297grS6UmpoKrVaLZcuW8WL3zBdI+67NZouYQysZvCQpJTJ4GWyXIZmLqq6ujkpUHCoI6UilUtTW1oYUFVutVjfXVTJzRRQUwom0nU4n/vu//xsff/wxjh07FlP3lm+ct8RD0zSGh4fR19eHrKwsrF+/noPV+e5cI+mQmZkZaDQaRkwyNzc3otLwpC4CuNIOkZ6zCRUURWFwcBDDw8OIi4uD0+kMqS4UDdjtdjcpmWjVokg0OTs7y9h+k5RcRkaG15Tc1NQUurq6sHr16pg6jXNBOp4gDQqEyK1Wq5tZYDARLEVR+M53voP33nsPR48ejZh9eazgvCQekvKYnZ1lTi01NTVhXTMYJQLi70IUoUmbbG5urs8NgAsYjUa0tLQww36xVBcZHh7G8PAw1qxZA6VSGVJdKBoQqu4aqWsQIqIoyk3QNC4ujpklEprb6WJwOByMiC9XpOMN3kRNCQn5EzWlKAr3338/Dh48iGPHjqG0tJSX9cUyBEc8bPvrUMC2p66rq8PExASMRiPWrFkT8jXZQ6FAcJ1rFEVBo9FgZmbGTQwxNzc37FCeDY1Gg7a2NhQVFaGioiJmUlRsE7R169Z5nWsgEiokmhSKjlys6K6xtdBUKhWMRiMSExNhNptRU1MTUymgSJGOJ+x2u5vrKlvUNDs7m4lyKYrCww8/jP379+Po0aOoqKiIyPpiDecV8XjaU8tkMgwPD0Or1aKuri6ka3LpoUPTNHQ6HWZmZjAzM+PWZpyTkxNyioYMKVZVVcVUYdjpdDJmYuvWrQsoleGtLkSIPJI6cvPz82hubkZ+fr5gByx9ob+/H6Ojo0hJScH8/LzbST4jI0Owr4WkNMkAdLSiS2+ips899xyWLFmC2dlZvP322zh27Biqqqqisr5YwHlDPN7sqQFgbGwM09PTIdV4+FQiIIrGhIQMBgOTTw7UJIs9L7JmzRqvYqVCBfGjoWk65FqUr3khcg/5qguRtuPS0lKUlpYKdqP2BHm/jI2NYd26dUhLS3Mj8mjYfgcKPtWxw4XFYsG+ffuwb98+jI2NoaCgADfccAOuvfbaoIwiLyQI410VBvzZUwP+tdoWuy6f8jcSiQQpKSlISUlBWVkZzGYzs4H29fUhJSUFubm5yM3N9TowSFEUuru7odVqsWHDhpiSkTGbzWhpaWHUjkPdRDx15AiRj4+P4/Tp07zUhUgHWKy1HdM0jf7+fkxOTmL9+vXM+0UulyMvLw95eXlMRK5SqTA4OIiOjo6gD0N8gJCOQqEQVB2NID4+Hna7HSaTCR999BHUajVef/11fPOb38T//M//4Iorroj2EgUHwUU8TqcTDocj4Mf6s6cGgOnpaQwODmLz5s0BXZM0EURT/obI6pNTaHx8PENC6enpjKI2Ud4V0vzQYpifn2eM9lasWMHbveWjLuRPd03IoGma0bqrr68PuNXfZDIx70GtVst0apLieiQ+F2zSWbt2reDqaDRN4+mnn8avfvUrHD58GPX19W4/o2lacGsWAmKWeAK1p56dncXp06dx8cUXL3rNcJoI+AK7O0mlUgFwrTMpKUmQxlb+QBogli5dGtEZHc90kkQiCbouRHTX1q5dG1MpTRIZk8NZqFEL6dQk95Bt+61UKnlJydntdjQ1NSE+Pl6wpPPHP/4Rjz32GA4dOoSNGzdGe0kxg5gknmDsqbVaLdra2nDZZZf5vSaXTQR8QavVoqWlBYmJiXA4HLDb7W4dckImITIvUl1dzah5RwPB1oXYdZHFvIuEBoqimOaN+vp6ziJjcg9JVG6xWNxSclzUNAjpkDZ1oX0eaZrGX//6V/zwhz/EgQMHsGXLlmgvKaYgOOJZzP46WHtqvV6PTz75BFu3bvX5GKHbGQCulGFXVxcqKipQUlLCSH+QNm2j0QilUsl0yAkp/TYyMoLBwUHBzYuQBg8y+Os5L5SUlMS57lqkQFS9iTYhnwO45B6ybb89PXKCgc1mc5uNEiLp/O///i8eeOABvPbaa4seakUsRMwQT6j21EajER988AE+85nPeP05TdNMhCWE1JonaJrGyMgIhoaGsHr16gVupwQmk4khIZ1Oh7S0NIaEojVwSf5mU1NTqK2tFbwMPKkLER058n5YuXIlcnNzBffe8AW2EVqk3TfZqtBqtTpoewybzYampqaI2WwHC5qm8e9//xv33nsvXnnlFb8HWhG+ERPEE449tcViwbFjx7B9+3avnWFC9tAhNg4qlQp1dXVIS0sL6HlsNWO1Wo2kpCSGhEI5gYYCkubR6/WcqYFHCmTjNplMSEtLg1arZepCpM1YaJ1VBKRNncj3RLMd2psEDbGt9haVC510AOCll17Cf//3f+PFF1/E1VdfHe3lxCwERzw0TcNmszH/z7anDkUHy26348iRI9i2bRvzIRRiE4EnHA4Hkyqpq6sLOW9OCutEvkcmkzEkxJcvid1uR1tbG3PiFrLOmie86a5Fa14oWNhsNrS0tAiy7Zid1iSiuqmpqQyZx8fHo7m5WdCk88orr+DrX/86/v3vf+O6666L9nJiGoImHi7sqSmKwltvvYXLL78c8fHxMdFEYLFY0NLSgvj4eM5sHIBzJ1AytEpRFLN5Zmdnc7JRkbULTbssEJC1JyYm+pwvWqwuFK20JtGME/LGzQZ7ZECtVoOiKCQkJGD58uWCjCjfeOMN3H777fjHP/6BG2+8MdrLiXkIlniIPfWKFSvCVnY9dOgQLr74YiQmJoKiKDidTkGm1gAwXjRkzoWvDYTIfhASslgsbs0JoZziib22UqlEdXW14Dc/NkLVXfOsCyUkJDD3MFKzLhaLBU1NTUhPTxe0Zpw3WK1WnDp1CgqFAikpKZidnYXdbndLyUU7ojx8+DC+8pWv4C9/+QtuvvnmqK7lfIHgiIeiKLS1tXFmTw0Ab7/9NhoaGpCUlCTYeg5wbiq+rKwsoI49LsGW75mfn0dGRgajqB3I7AeRkYmUvTaX4Ep3zde8EJ91IZPJhKamJmRlZaG6ujqm7rvVakVTUxNSU1NRU1MDqVTq5ljLNmojUXlKSkpEX+OxY8fwhS98AX/4wx9wyy23xNT9FTIERzykwLhixQrOCtJHjx7F6tWrkZaWJljSIQOKNTU1C2R/Ig3i6zIzMwOtVouUlBSGhLx98Emrd6zJyAD86a5Foi5kNBrR1NSEvLy8mBMq9UY6vh7HTskpFArmHvJVoyQ4fvw4brzxRvz2t7/F1772tZi6v0KH4IgH4M7+mkhWNDc3Q6vVMsOW2dnZghE/JC3Hk5OTghxQJO2xMzMzbh984i00NjaGgYEBv63eQkWkdNf4qAvNz8+jqakJRUVFKC8vj6lNkaTX0tPTUVNTE/Daie03ISKHw8GQOddmgR9++CFuuOEGPP744/jv//7vmLq/sYDzlnjYTQTs8H1mZgZmsxlKpRJ5eXlh2RGEC6I1ZzKZUFtbK/iWY/LBJ/NC5P5WVFSguLhYcAVhfyC6a6tWrYp4hMludw+lLqTT6dDc3IzS0lIsW7YsQqvmBux6VDCk4wkyQE3uI9v2Ozs726uwbqBoamrCZz/7WfzkJz/Bd77zHZF0eMB5STykXdpXEwGpZ0xPTzN2BESEM1IT/8SwjniLCFnuxhPE4VWj0SArKwtarRZ2u93NqlrIr0dIumvebAlIJOStLkRSg+Xl5SgpKYnGkkMGIR3SwMHlhu7Z5BEfH8/cx2Bcf9va2rBjxw48/PDDuP/++0XS4QmCJJ5w7K+Dlb8xm81MUZ098Z+bm8tbBEK6vzIzM2OuC4kM89rtdkYZm0SUJBJikzlX2l1cgFhojI+Po66uTnBKCovVhebn59HW1haTtTSLxYJTp04x73k+N3QirEuIiKIot5Scr0NRZ2cnrrnmGtx77734wQ9+IJIOjziviCdcJQKSAiFS+ot54oQCtVqN9vb2mOz+IlEaGVD0VScjZK5SqTA3N4fU1FS3+xgNEGsAlUoVE7prngOXOp0OAJCXl4fy8vKo3cdQEEnS8YQ322/SsUlScgBw+vRpXHPNNfjmN7+Jn/zkJzH1uYxFnBfEQ+o4xPCNCyUCdlF9dnaWycPn5uaGLDszPj6O3t5erFy5MqZ87gFXepLYigcTpdlsNjf5Hi7uY7Ag8j3z8/MBW2wLCVNTU+js7ERhYSEsFgtTFyJNHpGaFwoFZrMZTU1NzGxXtNdpNpuZSKizsxM///nPsX79erz77rv42te+hieeeCKmMhCxCkESTzD2155KBHzI3zidTjcSksvlzOYZiEc9TdMYGBjA+Pg4amtrkZmZyen6+Mbc3BxaW1tRWFiIioqKkO8vuY+EiKRSKXMf+WqNdTqdaGtrg81m412lmQ9MTEygt7fXTdk72LpQtCA00vGEXq/HX/7yFzz++OOQSCRITEzE1Vdfjc9+9rO48cYbOet8feaZZ/CrX/0KU1NTWLt2LX73u9+hoaHB5+NffPFF/PCHP8Tw8DAqKyvxxBNP4JprrvH62Lvuugt/+tOf8Nvf/hb33nsvJ+uNBGKaeKJhZ0BRFNPZNTMzAwDM5qlUKhdsnk6nE11dXdDr9airq4upFAkAzMzMoLOzE5WVlWErSLBB5HtIatPpdLp5C3HxoSe6a1KpNOqCmaFgbGwM/f39qK2t9TlITVEUdDodk9oUio6c2WzGqVOnkJ2dzavTbDgYGRnBVVddhR07duCpp57CqVOn8Nprr+HDDz/E22+/zclB6IUXXsCtt96Kffv2YePGjdi7dy9efPFF9Pb2Ijc3d8HjT5w4gUsuuQR79uzBtddei+eeew5PPPEEmpubsWrVKrfH/t///R9+8pOfQKVS4YEHHhCJJ1wEQjxC8NAhxWBCQuzNMzs7m1E5BoDa2tqYO22Pj4+jr68Pq1at8voh4QrsPDy73d2XinEgCER3TcgYHh7GmTNnUFdXF/BslzchzmjYY8QC6UxMTGD79u3YunUr/vSnP/GWXtu4cSM2bNiA3//+9wBce0ZxcTHuvvtuPPTQQwse/8UvfhFGoxGvv/46871NmzahtrYW+/btc1v/xo0bcejQIezYsQP33ntvTBGPII+Ai71RhWJnIJVKoVQqoVQqUVVVxWifDQwMoLOzEwCQkpKCtWvXxhTpkO6vsbEx1NXV8Z4alEgkSE9PR3p6OioqKpjN8+zZs+jp6WHmMwLtNCQyMrGoGcd2PK2vrw/YCgNw3ceUlBSkpKRg2bJlbvNCg4ODEakLEdLJyclBVVWVIElnamoKO3bswMUXX4x9+/bx9v4gKiwPP/ww8z2pVIpt27bh5MmTXp9z8uRJ7N692+1727dvx/79+5n/pygKt9xyCx544AHU1NTwsna+IUji8QXPJgIhyd+wN0+lUom2tjakpqbC4XDg+PHjjABnbm6uoEmIoiicPn0aGo0GGzZsiEr3V3JyMpKTk1FaWurWaTgwMIDk5GRm80xNTV3w9ye6a0uWLEFlZaVg3h+BgKZp9Pf3Y3JyEuvXrw/73sfHx6OoqAhFRUVudSEShXNdFyKEL2TSmZmZwbXXXov169fjr3/9K6+R8OzsLJxO54IB5by8PPT09Hh9ztTUlNfHT01NMf//xBNPQC6X4zvf+Q73i44QYoZ4YsFDBzg3EV9VVcXMWhB3UPYJnqgmCKnDiu0BtGHDBkHM37A3T7vdzngLnTp1CnFxcW7yPTqdjhfdtUiAtHvPzs5iw4YNnM+QyeVy5OXlIS8vz60u1NfXx0ldiJBObm6uYHXj1Go1rrvuOqxcuRJ///vfY67mB7hUFZ566ik0NzcL8h4Hipi48+x6jkQiEWTqhKRIRkdHUVtb6zYRn5SUxGyGbAHOvr4+Qcy4AOdMxGQyGdavXy9I5YG4uDjk5+cjPz/frcmjo6ODeX8UFhaipKQkpj6UFEWhu7sbc3NzWL9+Pe+HEalUiszMTGRmZmL58uVManNiYgKnT58Oui5kMplw6tQpQYuVarVaXH/99Vi2bBmee+65iLy/icfV9PS02/enp6eRn5/v9Tn5+fl+H//+++9jZmbGTbXC6XTivvvuw969ezE8PMzti+AJgmwuYNtfC6GJYDGQjUOr1aKuri7gFAkxw5qenoZGo0FiYiJDQt7SSHyBeNEQ/SwhErs/kA0zKysLRqMRVquVsSOIphZfICAzRgaDAfX19RGTbPIFbzpybOkZb/JTTU1NyM/PF2xqU6fT4brrrkN2djb2798f0Xu8ceNGNDQ04He/+x0A19+7pKQEu3bt8tlcYDKZ8NprrzHf27x5M9asWYN9+/ZBrVZjcnLS7Tnbt2/HLbfcgjvuuANVVVX8viCOIOiIJxZIh9g8OxwONDQ0BPWmVigUKCgoQEFBARwOBzMrRIyxCAnxOSCo0+nQ0tKCgoICwW4c/kB01+rq6pCVlcV0ds3MzGB0dBTd3d3IzMxkUnJCSB8SOJ1OJrW5fv16QdT+POtCJKpsa2sD4F4XItprQiad+fl53HjjjUhPT8fLL78ccWLfvXs3brvtNqxfvx4NDQ3Yu3cvjEYj7rjjDgDArbfeisLCQuzZswcAcM899+DSSy/Fk08+iR07duD555/HqVOn8Oc//xkAkJWVtUBfkGQCYoV0AIESD7ueI2TSMZlMaG1tRVJSEurq6sIqVMrlciaNxFaBbm1thUQi4WXQktgClJeXY+nSpZxcM1Jg667V19czumvszq6ysjKYzWbmBN/X18eLDFIoIK32TqcT9fX1gozK2IPSpC5E7qPVagVN01AqlYJNbRqNRtx0001QKBR45ZVXolJP/eIXvwiVSoVHH30UU1NTqK2txcGDB5kGgtHRUbfP8+bNm/Hcc8/hkUcewfe//31UVlZi//79C2Z4Yh2CTLU988wz0Gq12LlzJ0pLSwWZ+iHT/EuWLOE1r00GLcmsEEVRzOk9nG6kiYkJ9PT0oKamxme+WagIVXeNpDbJxH98fHxEokpP2O125kARi4OtBoMBp06dQnJyMjODRVxCo12rJDCbzbjppptgs9nw5ptvIjU1NdpLEsGCIInnxRdfxJ///Ge8++67qKmpwc6dO3H99dcLJpwnjpsVFRURlaanaZrpRpqZmYHNZgva3I6maZw5cwYjIyNYu3YtJ9bikQRXumtEwZjIIEkkEmbj9KZAwRVsNhuam5sRHx+PNWvWxNxgq8FgQFNTEwoLCxkDumDrQnzDYrHg5ptvhk6nw6FDhwSnQi5CoMQDuDZIjUaD/fv34z//+Q+OHDmC5cuX4/rrr8fOnTujov1E0zRGRkYwNDQUdcdNthXBzMwMjEYj44fjqyWWoiimZbeuri7mToF86a6xFShUKhXsdrubfA9XaTCr1Yrm5mYkJSVh9erVgozk/cEb6XjCk9CByOrI2Ww2fPWrX8Xk5CTefvvtmNNFvFAgWOJhg5z0X331VfznP//BW2+9haVLl+K6667DDTfcEJEPMdm0VSoV6urqgpoojwRIQZ1YK3v64bDdTtetWyeoInsgiJTuGnG2JCRkNBqZ4d9Q5XsAd+fNWPNgAs6RTlFRUcB2HjRNM/5CMzMzsFqtzL3Mzs7mvNBvt9tx++23Y2hoCEeOHGFEVUUIDzFBPJ7Q6/V444038J///Icp1BESWrduHecfavZgZV1dneA3bVJQn5mZwdzcHFJSUmC32xEXFyfYQrY/WCwWt0ghkukpMvxLPHFC0T4jw5VZWVmCVGleDKSmU1xcjPLy8pCu4UtHji2FFM59cTgc+PrXv46uri4cPXqUV21BEeEjJomHDaPRiDfffBMvv/wy3njjDWRkZOC6667D9ddfj40bN4a9SRGxSZKTj7VCMJnmB1wnwuTkZKagnpKSIvhNkPgACUF3jV3LUKvVSEpKYkjIl7cQmXMR8nClP8zPz6OpqSks0vEGLutCTqcT3/rWt/DJJ5/g2LFjMed1dSEi5omHDbPZjMOHD+M///kPXnvtNSQkJOC6667Dzp07sXnz5qBJQ6/Xo6WlBTk5OVixYkXMpUfI+vPy8lBVVeU2K8SVuR2fIOsXou4a0T4j91ImkzEkRFreyaZdVFTksyYiZJD1E7dcvkDqQoSIgMDrQhRF4e6778b777+Po0ePcmrdIYI/nFfEw4bNZsORI0fwn//8B6+88gokEgk++9nPYufOnbjkkksWTTeRGZeysjIsXbo05jYNtVqNtrY2n+tnF4FVKhWzcRLds2iTrFarRWtrK5YtW4bS0tKormUxeGt5T09Ph1arRWlpKaeRQqRASGfp0qVYtmxZxH6vr7qQN4sMiqJw33334a233sLRo0cF/z4RcQ7nLfGw4XA48O677+LFF1/EK6+8ApvNhmuvvRbXX389Lr/88gVFTjINX1NTs0ApNhZw9uxZnD59OmCLbbbumUqlAk3TbrNCkSYhQvrLly9nhFZjBTRNMxbncrkcTqdTEMZswUCv16O5uTnipOMJb3WhxMREHDx4EDt37sTzzz+PV199FceOHYtJcr+QcUEQDxtOpxPHjx/HSy+9hP3792N+fh5XX301du7cicsuuwy7d+9GQUEBvvvd7wZswCUU0DSN4eFhDA8PY82aNQukNQK9Btvczm63u5EQ3zUuQpqrVq2KSdInkWZVVRUKCwthMBiY0/v8/DwyMjKY+ykkZXICvV6PpqYmlJaWRpV0vMFqtaK7uxsPPvggTp48CYlEgttuuw233347Nm/ezHnTCZeW1Xa7HY888ggOHDiAoaEhpKenY9u2bfjFL36BgoICTtcdC7jgiIcNiqLw4Ycf4qWXXsLLL7+M8fFxxMfH49FHH8Udd9wRFS+aUEHTNHp7ezE9Pc1Zuze7tXh6ehoWi8VtVojr7riRkREMDg5i7dq1IZFmtEGUsn1Fmmxlcq1Wi5SUFIaEhNDoQUhHyOlNmqbx2GOP4a9//SseeeQRtLS0MG6dHR0dnKlwcG1ZrdPp8PnPfx533nkn1q5dC61Wi3vuuQdOpxOnTp3iZM2xhAuaeAgmJyfx2c9+FgCwZcsWvPnmmxgfH8eVV16J66+/Htdcc43g5nbYcDqdjMJxONP8i4E9sGowGDiZbwHcddfq6upictJ8amoK3d3dAduE2+12ptFDrVZDoVC4eQtFmoR0Oh2am5sFTzq/+tWv8Pvf/x7vvPMO1qxZA8D1/v/kk0+wcePG/7+98w6K6urf+LM0CaKg0sSAKBLBACJtwRJMICKiFLERW5CRGIU3CvgTezTGHmOLkqh5iRONSBEjGo0IVlApShcbxSALIoJIXXbP7w9n78vSgrINOJ+ZHce75+49Z3eG595zvud5RPa9iSuyujnJycmwtbVFYWGhRB1QZIFeLzyEENjb22PkyJE4cuQIlJSUwOfzkZmZyTwJPXnyBI6OjnBzc8PUqVOl8oehPQS+X4QQWFhYSGwNoa6ujhGhqqoqqKmpMcUJ7yJ87+u7JksUFxcjLy8P5ubm77VpsbkpbPOqLoF9j7j3LQlER1CIIosQQrBv3z7s3r0bly9fhpWVldiu1djYCBUVFURGRsLDw4M5vnDhQlRWVuLs2bOtztHX10dgYCCWL1/OHNu4cSNiYmIYZ++WxMXFYdKkSaisrJTpG1tx0L02pYgBFouF6OhoDB48mBETOTk5jB49GqNHj8bmzZuRm5uLyMhIHD58GAEBAZg4cSLc3d0xdepUaGhoSE2E6urqcO/ePalsrPzggw8wdOhQDB06FA0NDYwIPXr0SMgBuiMhae67ZmNjI5NrHv9GUVERnjx5AgsLi/f2vZOXl2eKD5qvsT148ABcLpeZ3tTQ0BD59KZAdAwNDWX2rpsQgkOHDmHXrl24dOmSWEUHEF9kdXPq6+uxatUqeHt79zrRAajwAECHi3ssFgujRo3Chg0bsH79ejx+/BiRkZEICwvD8uXLMX78eLi7u8PNzQ3a2toSE6Hq6mrcu3cPGhoaUt9j1KdPH+jp6UFPTw9cLpdZx8jPz2833E7gBtHY2AgbG5tuUe3VkoKCAuTn58PS0lJk04MsFksoHVQwvVlYWIjs7OxWVkhdobKyEvfu3ZN50Tl69Ci2bNmCCxcudLi4313gcrmYNWsWCCE4fPiwtLsjFajwvAMsFgtGRkZYvXo1QkJCUFBQgKioKERERGDlypVgs9lwd3eHu7s7hgwZIjYRqqioQHp6OlPuKivTfsDbUKrm4XaCvUIpKSlQVFRkpo+ePn3KxGx3NzeI5mtS1tbWYjNbZbFY6NevH/r16wdDQ0NmerO0tBR5eXldik0XiM6IESNkdtMlIQTHjx/HunXrcO7cOYwbN04i1xVHZLUAgegUFhYiPj6+Vz7tAHSNRyQQQlBcXIzo6GhERUUhMTERlpaWTJyDKDegcjgcZGdnw9jYGEOGDBHJZ0oCPp/PxPaWlpaCxWJh8ODB0NHREWm4nbghhODhw4fgcDiwsrKS2ppUY2OjkH3Pu7hQdBfROXnyJFasWIGzZ8/C0dFRotcXdWQ18D/RefToERISEqTqbi9tqPCIGEIIOBwOE+dw7do1mJmZMXEOI0aMeG8REpQbv+8itrQR+K4NGDAAOjo6TFUXj8cTSbiduBEUQpSXl8PKygoqKirS7hKA/9n3CIRIsGbUVmLtq1evcO/ePRgZGcms6ABv98QsW7YMERERcHFxkfj1w8PDsXDhQvz8889MZPXp06fx4MEDaGtrt4qsTkxMhIODA7Zv385EVm/dupUpp+ZyuZgxYwbS0tIQGxsrtB40cODAbjnV3BWo8IgRQghevnyJs2fPIjIyEvHx8Rg5ciTjH9dZp2LBXXZJSUm3LTduz3dNkGApmEJqaGhgsnA0NTVlZhqOz+cjJycHVVVVYi1Z7yoC+x7BOhuPx2O+T3l5eWRkZMi8I0RMTAwWL16MP/74A25ublLrx8GDB5kNpBYWFti/fz/YbDYAYOLEiTAwMEBYWBjTPiIiAuvWrWM2kO7cuZPZQFpQUNDuhtyEhARMnDhR3MORKajwSAhBtZIgU+jy5cswMDBg4hxMTU3bnG4SVH69fv0alpaWMnOX/S501nftfcLtJIGgvF6QZSTqHBlxIRD1Fy9eoKSkBPX19VBVVYWenh60tLRk8i47NjYWPj4+OH78OLy8vKTdHYqYoMIjJV6/fo3Y2FgmU2jw4MGMCI0ZMwZycnIoLy9HQkIC9PX1MWbMGJn8Q/FvlJWVISsri7GQeRcEWThlZWV4/fo11NXVmXUMSWUi8Xg8JotJlKmnkqSiogL379+HgYEB5OTkmO9TTU1NKA9H2ly6dAnz58/H0aNHMWfOHGl3hyJGqPDIAG/evBHKFBo4cCAcHBwQFxcHc3NzhIeHy8yU07sgSt+1+vp6RoQqKyu7VNHVWZqampCeng4ej4cxY8Z0uwA94H+i01L4BXk4ZWVlqKioYHKaNDU1hcreJUVCQgJmz56NQ4cOYf78+TJVqUkRPVR4ZIy6ujocO3YMq1atApfLhYaGBlOi/T6ZQtJCUAjRlY2V7SGo6BLYzYgj3E4QtS0vL4/Ro0d3m++9OQLRMTY27nCvGpfLFcoWEpS9C0LZxF1xeOPGDcyYMQN79+7FokWLqOj0AqjwyBg3b96Em5sbAgICsHr1asTHxzOZQvLy8pg6dSo8PT0xYcIEmbwDl7TvWstwOyUlJUaE1NTU3uuPWGNjI9LS0pjUWVmtsusIgUv2v4lOS9qLyOhMKNv7kJSUBE9PT2zfvh1ff/01FZ1eAhUeGePkyZOorq7GV199JXScy+Xi2rVrTJwDl8vF1KlTmTgHWVjwJoQgNzeXKTcW1xRYe4gi3K6hoQFpaWmMDVF32V/UHIHomJiYdCkGmhCCqqoqZopTUHEoEKKu3vikpKTAzc0NmzZtwn/+8x8qOr0IKjzdkKamJqFMoTdv3mDKlCnw8PCAo6OjVEp9m/uuyUK5cctU0Obhdu0Zb9bX1yM1NRVqamoYNWpUtxSd8vJyZGRkdFl0WiIIZWvuTj5gwADmO33XYo/79+/D1dUVa9asQXBwMBWdXgYVnm4Oj8fD7du3ERUVhTNnzuDly5dwdnaGh4cHJk2aJJGnDsEifFNTk0xW3wnu3EtLS5lwO8HeFg0NDSgoKKC2thapqakYNGhQp/dXyRriEp22qKurYzasCrKFmhd7dPT9ZWVlwcXFBYGBgVizZk23/K4pXYMKTw+Cz+cjJSWFEaHi4mImU8jFxUUsvlCNjY24f/9+t1mEbx5uV1ZWhrq6OqipqaG6uhra2trdVnQEceGjRo0SWRhaZ2lsbBTKFurTp0+762y5ublwcXHBkiVLsGnTpm75XVO6DhWeHgqfz0dGRgaTKfT06VM4OTnBzc0Nrq6uIskUqq+vR1paGvr27QtTU9NuuQgvSA1VUFAAl8tl3J+1tLRkYt2sM7x48QIZGRn4+OOPJS46LWm+zlZeXg4Wi4W8vDyoq6vDxMQEHh4eWLhwIbZu3dotpzIpooH+8j0UOTk5WFhYYMuWLcjOzkZqaipsbGxw6NAhDBs2DJ6enggLC0N5eTne596jpqYGycnJUFNT67aVX1VVVcjOzsbw4cPh4OCAcePGQUNDAxwOBzdu3MDdu3dRUFCAuro6aXe1XQSiY2pqKnXRAcAUdJiamuKTTz6BmZkZnj17huDgYNjb26Nfv34wNzdHdXW1WK7/008/wcDAAMrKymCz2bh7926H7SMiImBsbAxlZWWYmZnhwoULQu8TQrBhwwYMHjwYH3zwAZycnPDo0SOx9L03QZ94ehmEEDx69AiRkZE4c+YM7t+/j/Hjx8PDwwPTpk3rVKbQ69evkZaWhiFDhnTJ9FSaCGx82suiabnB8l3WMCRFc9Hp6gZdcVJYWIhJkybBzs4OxsbGiImJQV5eHlavXo1NmzaJ7Drh4eFYsGABQkNDwWazsXfvXkRERCAvL6/NOPLExER88skn2LZtG6ZOnYqTJ09ix44djLEnAOzYsQPbtm3Db7/9hmHDhmH9+vXIzMxETk6OxNwzeiJUeHoxhBDk5+cza0LJycmws7NjNqzq6uq2+gMryAL6N981WUZQbtxZGx8ulyu0V+hdIgjEhWCKUNZFp7i4GM7OznByckJoaCgzvfb48WPU1tbC3NxcZNdis9mwsbHBwYMHAbydbtbT00NAQEC7UQY1NTWIjY1ljtnZ2cHCwgKhoaEghEBXVxdBQUEIDg4G8PYpWVtbG2FhYdTWpwvQqbZeDIvFwvDhw7Fy5UrcunULT58+xYwZM3Du3DmYmJjA0dER+/btQ0FBAQghOHHiBI4cOYKPPvqo24pOWVkZs8els95xioqKGDx4MEaPHo2JEydixIgRzPrWzZs3kZeXh1evXr3XlOX7IBAdMzMzmRYdDoeDKVOm4JNPPsHhw4eF1nRGjBghUtFpbGxEamoqnJycmGNycnJwcnJCUlJSm+ckJSUJtQcAZ2dnpn1+fj44HI5QGzU1NbDZ7HY/k9I5qPBQALwVIT09PXzzzTe4evUqnj17hgULFiAuLg6jR4/GqFGjsHTpUsjLy7/TTnhZgsPhICsrC6ampu9dbiwvLw9tbW2YmZnBwcEBJiYm4PF4SE9Px7Vr15CTk4Py8nLw+XwR9/4tpaWljOi0NX0kK5SVlcHV1RW2trY4evSo2NcAy8vLwePxWgmxtrY2OBxOm+dwOJwO2wv+fZfPpHSOHiE8FRUVmDt3Lvr37w91dXX4+vrizZs3HZ5TX1+PZcuWYdCgQVBVVYWXl1er6FoBL1++xIcffggWi4XKykoxjEC2EKSDLl26FHFxcVi/fj3Kyspgbm6O77//Hvb29ti2bRtyc3MldpffVYqLi5GTkwNzc3OR/cGWk5ODhoYGRo0aBQcHB5ibm0NOTg45OTm4du0aMjMzUVpaCh6PJ5LrlZaWIisrS6RjEAfl5eWYNm0aTE1NERYWJvMl9hTJ0yOEZ+7cucjOzsbly5cRGxuL69evw8/Pr8NzVqxYgXPnziEiIgLXrl3D8+fPMX369Dbb+vr6inRaoDuxdetWHDhwADdu3EBKSgpKSkoQGBiI+/fvY9y4cbC2tsbmzZuRmZkptrv8rlJUVISHDx9izJgxYktuZbFYGDhwIIyNjTFhwgRYWlpCWVkZjx8/xtWrV5Geno6SkhJwudz3+vzmoiPLkcmvXr2Cu7s7hg8fjhMnTkjMT1BDQwPy8vKtbh5LS0vbrfbT0dHpsL3g33f5TErn6PbFBbm5uRg1ahSSk5NhbW0NALh48SKmTJmCf/75p81poaqqKmhqauLkyZOYMWMGAODBgwcwMTFBUlIS7OzsmLaHDx9GeHg4NmzYAEdHR7x69Qrq6uoSGZsscOvWLWhoaGDkyJGt3quqqkJsbCyio6OZTCF3d3d4enrCwsJCJvZp5Ofno6CgAJaWllJJbm3LambgwIFMcUJnXB44HA6ys7NlXnSqqqowbdo0aGlp4cyZMxLfB8Vms2Fra4sDBw4AeFtcoK+vD39//3aLC2pra3Hu3Dnm2NixY2Fubi5UXBAcHIygoCAAbys6tbS0aHFBF+n2z8BJSUlQV1dnRAcAnJycICcnhzt37sDT07PVOampqeByuUKLhsbGxtDX1xcSnpycHGzevBl37tzB06dPxT8YGWTcuHHtvqempoa5c+di7ty5ePPmDS5cuIDo6GhMmTIFAwcOZCK+bWxsJL7Pp7lLtrW1Nfr16yfR6wtgsVhQVVWFqqoqhg8fzoTbPX/+HA8ePICamhq0tbWhqanZpr8dh8NBTk4ORo8eLbanNVFQXV2N6dOnY8CAAYiKipLK5tvAwEAsXLgQ1tbWsLW1xd69e1FTUwMfHx8AwIIFCzBkyBBs27YNAPDNN9/AwcEBP/zwA1xdXXHq1CmkpKTgl19+AfD2t1u+fDm2bNkCIyMjppxaV1cXHh4eEh9fT6LbCw+Hw2k1362goICBAwd2uKiopKTU6sml+aJhQ0MDvL29sWvXLujr6/da4eksqqqqmDVrFmbNmoXa2lr8/fffiIqKgpeXF/r27Qs3Nze4u7vD3t5e7HP+hBA8fPgQpaWlsLa2hqqqqliv9y6oqKjAwMAABgYGqK+vZ/YKPXz4sFW4XUlJCXJzc2Fubi7TolNTU4OZM2eiT58+iImJkZpB7OzZs/HixQts2LABHA4HFhYWuHjxIlMcUFRUJPQUPnbsWJw8eRLr1q3DmjVrYGRkhJiYGGYPDwD83//9H2pqauDn54fKykqMHz8eFy9epHt4uojMCk9ISAh27NjRYZvc3FyxXX/16tUwMTHBvHnzxHaNnoqKigo8PDzg4eGB+vp6XLlyBVFRUZg7dy4UFBSYTKHx48eLfA2AEIIHDx6gvLwc1tbWMhHp3B7KysrQ09ODnp4e43dWWlqKp0+fQlFREY2NjRg5ciQGDRok7a62S11dHWbPng0+n4/z589LPAqjJf7+/vD392/zvatXr7Y6NnPmTMycObPdz2OxWNi8eTM2b94sqi5SIMPCExQUhC+//LLDNsOHD4eOjg7KysqEjjc1NaGioqLDRcXGxkZUVlYKPfU0XzSMj49HZmYmIiMjAYCp3tLQ0MDatWtFuuO6J6OsrAxXV1e4urqCy+Xi6tWriIyMhK+vL5qamoQyhbrqas3n85GTk4OqqirY2Nh0q7tSJSUl6OrqQldXF8+ePWP8zR49eoTCwsIuh9uJg/r6enzxxReora3FpUuXpDadSel+9JjigpSUFFhZWQEA/v77b0yePPlfiwv++OMPeHl5AQDy8vJgbGzMrPE8efJEyKMrOTkZixYtQmJiIgwNDWW6nLU70DxT6MyZM6ipqYGrqyvc3d3h5OT0zqLB5/ORmZmJ2tpaWFpadhuDz5YI1n5Gjx6NQYMGgcfjCSWCslgsRoQGDBggtQKOxsZGzJs3DyUlJYiLi8OAAQOk0g9K96TbCw8AuLi4oLS0FKGhoeByufDx8YG1tTVOnjwJ4O0eDkdHRxw/fhy2trYAgK+//hoXLlxAWFgY+vfvj4CAAABv/Zva4urVq/j00097XVWbJODxeEhKSmKseyoqKjB58mR4eHjg888//9fpGx6Ph4yMDDQ0NMDS0lLm8oA6i0B0LCwsMHDgwFbvtwy34/P5TBCbOGKp24PL5WLhwoXIz89HfHy8TE8FUmSTHiE8FRUV8Pf3x7lz5yAnJwcvLy/s37+fWVQuKCjAsGHDkJCQgIkTJwJ4O00QFBSEP/74Aw0NDXB2dsahQ4fanZ6jwiMZ+Hw+kpOTGRF6/vw5Jk2axGQKtZzOEYTQ8Xg8jBkzRmL7RkRNcXEx8vLy2hWdlrSMpW5sbGwVbicOmpqa4Ovri5ycHCQkJNAnf8p70SOEh9Iz4fP5SE9PZzKFCgoKhDKFmpqasHTpUixduhQTJkzoltEMAPDPP//g4cOHnRadlhBC8ObNG0aEamtrmb1CmpqaInsC5PF4WLJkCVJTU3H16lW6iZLy3lDhoXQLCCHIzs5m1oRyc3OhpKQEHR0dxMbGQk9PT2YW3d8FgeiMGTNGZOskzTesVldXM+F2mpqa711wwePxEBAQgFu3buHq1audNlilUNpC+lvLexji8I1LT0+Ht7c39PT08MEHH8DExAT79u0T91BkChaLBVNTU3z77be4ePEisxemX79+MDMzw7Rp03D06FGUlpZ2G/84cYgOAPTt2xfDhg0Dm83GuHHjoKmpidLSUty8eZMJt6utre305/H5fAQFBeHatWuIi4ujokPpMvSJR8S4uLigpKQEP//8M1PoYGNjwxQ6tMXXX3+N8+fPIywsDGpqavD394ecnBxu3boFAPj111+Rnp6O6dOnQ09PD4mJifDz88POnTvb3bPQU6moqICdnR3s7Ozw66+/Ql5eHk+fPhXKFBo7dizc3d3h5ubWZqaQLPDs2TM8fvwYY8aMkdiaYWNjI/MkVFFRgb59+0JLSwva2trthtvx+XyEhITgzz//REJCAgwNDSXSV0rPhgqPCBG3b1xzli1bhtzcXMTHx4tvQDIIIQSnT5/GzJkzW5USE0Lw7NkzREdHIzo6GomJibCxsWGC7fT19WVChKQhOi3pTLgdn8/H+vXrER4ejqtXr+Kjjz6SSl8pPQ861SZC/s03ri3+zTeuPaqqqt5rIbq7w2KxMHv27Db3r7BYLOjr62P58uW4du0aioqKMG/ePPz9998wNzdnfLkeP34stem4oqIiqYsO0DrczsjICA0NDUhLS8OUKVOwaNEi+Pj44MSJE4iLi5OY6NCp6t4BFR4RIi7fuJYkJiYiPDz8X6MfejMsFgu6urpYtmwZrly5gufPn8PPzw83b96EtbU1xo4di+3bt+PBgwcSE6GioiI8efJE6qLTEnl5eWhpacHU1BQODg7w8/NDRkYGoqKi0NTUhB9//BF//fUXGhoaxN4XcUScpKamQktLC7///juys7Oxdu1arF69monIpkgeKjydICQkBCwWq8PXgwcPJNKXrKwsuLu7Y+PGjZg0aZJErtndYbFY0NTUhJ+fHy5evAgOh4Ply5cjLS0NY8eOhY2NDb777jtkZWWJLVOosLAQT548gaWlpUyJTktYLBYKCgrA4XBw584dnD17FqqqqliyZAm2b98u1mvn5ubi4sWLOHr0KNhsNsaPH48DBw7g1KlTeP78eZvnVFVV4dixY9izZw8+++wzWFlZ4b///S8SExNx+/ZtAMCiRYuwb98+ODg4YPjw4Zg3bx58fHwQHR0t1vFQ2kdmvdpkCWn7xgnIycmBo6Mj/Pz8sG7duvcaS29HENjm4+MDHx8fVFVV4dy5c4iOjsbEiRMxZMgQuLu7w8PDQ2SZQoWFhXj69KnUMoE6CyEEP/30E3bv3o1Lly7BxsYGADBhwgTs2bNH7E884ow4aUlvnaqWFajwdAJNTc1OBXDZ29ujsrISqampjG9cfHw8+Hw+2Gx2m+dYWVlBUVERV65cEfKNKyoqgr29PdMuOzsbn332GRYuXIjvv/9eBKOiAG8zhebNm4d58+ahurqayRRycXGBhoaGUKbQ+4hQQUEB8vPzu4XoHDlyBN9//z3++usvxlpKAIvFErvpqqSnqs+fPy+SflPeHTrVJkJMTEwwefJkLF68GHfv3sWtW7fg7++POXPmMBVtxcXFMDY2xt27dwG8/cPn6+uLwMBAJCQkIDU1FT4+PrC3t2fu1rKysvDpp59i0qRJCAwMBIfDAYfDwYsXL6Q21p5Iv379MHv2bISHh6O0tBR79uzBy5cv4enpCRMTEwQHB+PmzZvg8Xid+jyB6FhZWcm86Pz222/YsGED/vzzT4wdO1akn0+nqiktoU88IubEiRPw9/eHo6OjkG+cAC6Xi7y8PKENfD/++CPTtrlvnIDIyEi8ePECv//+O37//Xfm+NChQ1FQUCCRcfU2VFRU4OnpCU9PT9TX1yMuLg5RUVHw9vaGkpISkyk0bty4Nv3hCgoKUFBQACsrK/Tv318KI+gchBCcPHkSq1atwtmzZ+Hg4CDya9CpakpL6D4eCuUd4HK5SEhIQGRkJM6ePQs+nw9XV1d4enrCwcEBSkpK2L59O0aMGAFnZ2eZzqghhCAiIgL+/v6IjIzE5MmTpdofcUWcAMJT1Tt37pTcoChtQoWHQnlPmpqacOPGDURGRiImJga1tbXQ1dVFfn4+zpw5gwkTJki7ix0SExODxYsX49SpU5g2bZq0uwNAPBEnWVlZ+Oyzz+Ds7Ixdu3Yx15KXl+/U2i1FDBBKj+LgwYNk6NChpE+fPsTW1pbcuXOnw/anT58mI0eOJH369CGmpqbk/PnzQu/z+Xyyfv16oqOjQ5SVlYmjoyN5+PChOIfQLWlqaiK+vr5EWVmZaGtrk/79+5NZs2aREydOkBcvXpCamhqZeoWHhxMVFRUSFRUl7a9OiJcvXxJvb2+iqqpK+vfvT3x8fEh1dTXzfn5+PgFAEhISmGN1dXVk6dKlZMCAAURFRYV4enqSkpIS5v2NGzcSAK1eQ4cOleDIKM2hwtODOHXqFFFSUiK//voryc7OJosXLybq6uqktLS0zfa3bt0i8vLyZOfOnSQnJ4esW7eOKCoqkszMTKbN9u3biZqaGomJiSHp6enEzc2NDBs2jNTV1UlqWN2Cb7/9lmhoaJCMjAzC4/FIUlISCQ4OJoaGhqRv377Ew8ODhIWFEQ6HI3XRiY6OJioqKuTUqVPS/toovRQ61daDYLPZsLGxYXZk8/l86OnpISAgACEhIa3az549GzU1NYiNjWWO2dnZwcLCAqGhoSCEQFdXF0FBQQgODgbwdk5dW1sbYWFhmDNnjmQG1g3YvXs3Jk+eDFNTU6HjfD4f9+/fZzKFCgsL4eTkBHd3d0yZMgVqamoS9Y+Lj4/HnDlzcPjwYcybN08mvOsovQ9aTt1DaGxsRGpqqtBGOjk5OTg5ObXr+ZaUlCTUHgCcnZ2Z9vn5+eBwOEJt1NTUwGazO/SR640EBwe3Eh3g7W9gaWmJrVu3Ijc3F3fv3oWlpSX279+PYcOGwcvLC8ePH8fLly/Fbt1z/fp1eHt7Y9++fVR0KFKFCk8Poby8HDweD9ra2kLHO9pIx+FwOmwv+PddPpPSPiwWC2ZmZti0aRPS09ORnp6OCRMm4MiRIzA0NISbmxuOHTuGsrIykYtQUlISZs2ahZ07d2LRokVUdChShQoPhSIFWCwWjI2NsXbtWqSkpCA3NxeTJk3CiRMnYGRkBBcXF4SGhuL58+ddFqHk5GR4eXlhy5YtWLJkCRUditShwtND0NDQgLy8vJAdPND2RjoBOjo6HbYX/Psun0l5d1gsFgwNDbFq1SokJSXh8ePH8PT0xJkzZ2BsbIzPP/8c+/fvR1FR0TuL0L179+Dh4YF169YhICCAig5FJqDC00NQUlKClZUVrly5whzj8/m4cuWKkOdbc+zt7YXaA8Dly5eZ9sOGDYOOjo5Qm9evX+POnTvtfiala7BYLAwdOhQrVqzA9evXUVhYiC+++AKXLl2CmZkZHBwcsGfPHjx58uRfRSgzMxNubm5YuXIlgoKCqOhQZAfpFdRRRM2pU6dInz59SFhYGMnJySF+fn5EXV2dcDgcQggh8+fPJyEhIUz7W7duEQUFBbJ7926Sm5tLNm7c2GY5tbq6Ojl79izJyMgg7u7utJxaCvD5fMLhcEhoaCj5/PPPiaKiIjE3NycbNmwgaWlp5M2bN0Il08nJyURDQ4Ns2LCB8Pl8aXefQhGCCk8P48CBA0RfX58oKSkRW1tbcvv2beY9BwcHsnDhQqH2p0+fJh999BFRUlIiH3/8cbsbSLW1tUmfPn2Io6MjycvLk8RQKO3A5/NJeXk5OXbsGHF1dSV9+vQho0aNIqtXryZ3794laWlpREdHh4SEhFDRocgkdB8PhdLNqaysZDKFLl68iKamJvj6+uLQoUMiyROiUEQNFR4KpQfx+vVr7Nq1C99++y3k5eWl3R0KpU3o7RBF7Pz0008wMDCAsrIy2Gw2k0XUHhERETA2NoaysjLMzMxw4cIF5j0ul4tVq1bBzMwMffv2ha6uLhYsWNBuNHJvo3///vjuu+8kKjoVFRWYO3cu+vfvD3V1dfj6+uLNmzcdnlNfX49ly5Zh0KBBUFVVhZeXV6vqSQEvX77Ehx9+CBaLhcrKSjGMgCJpqPBQxEp4eDgCAwOxceNGpKWlYfTo0XB2dm6VuyIgMTER3t7e8PX1ZUqBPTw8kJWVBQCora1FWloa1q9fj7S0NERHRyMvLw9ubm6SHBalGXPnzkV2djYuX76M2NhYXL9+HX5+fh2es2LFCpw7dw4RERG4du0anj9/junTp7fZ1tfXF+bm5uLoOkVaSHeJidLTsbW1JcuWLWP+z+PxiK6uLtm2bVub7WfNmkVcXV2FjrHZbPLVV1+1e427d+8SAKSwsFA0naZ0mpycHAKAJCcnM8f++usvwmKxSHFxcZvnVFZWEkVFRRIREcEcy83NJQBIUlKSUNtDhw4RBwcHcuXKFQKAvHr1SizjoEgW+sRDERvi8I9ri6qqKrBYLKEESopkSEpKgrq6OqytrZljTk5OkJOTw507d9o8JzU1FVwuV+h3NjY2hr6+vtDvnJOTg82bN+P48eO0SKKHQX9NitgQh39cS+rr67Fq1Sp4e3vLdMR0T4XD4UBLS0vomIKCAgYOHNjhb6ykpNTqRqH579zQ0ABvb2/s2rUL+vr6Yuk7RXpQ4aF0W7hcLmbNmgVCCA4fPizt7vQoQkJCwGKxOnw9ePBAbNdfvXo1TExMMG/ePLFdgyI9FKTdAUrPRRz+cQIEolNYWIj4+Hj6tCNigoKC8OWXX3bYZvjw4dDR0WlVKNLU1ISKiooOf+PGxkZUVlYKPfU0/53j4+ORmZmJyMhIAGDsgTQ0NLB27Vps2rTpPUdGkQWo8FDERnP/OA8PDwD/84/z9/dv8xyBf9zy5cuZY83944D/ic6jR4+QkJCAQYMGiXMYvRJNTU1oamr+azt7e3tUVlYiNTUVVlZWAN6KBp/PB5vNbvMcKysrKCoq4sqVK/Dy8gIA5OXloaioiPmdo6KiUFdXx5yTnJyMRYsW4caNGzA0NOzq8CjSRtrVDZSejaj94xobG4mbmxv58MMPyf3790lJSQnzamhokMoYezuTJ08mY8aMIXfu3CE3b94kRkZGxNvbm3n/n3/+ISNHjiR37txhji1ZsoTo6+uT+Ph4kpKSQuzt7Ym9vX2710hISKBVbT0IKjwUsSNK/7j8/HwCoM1XQkKChEZEac7Lly+Jt7c3UVVVJf379yc+Pj6kurqaeV/wmzX/ferq6sjSpUvJgAEDiIqKCvH09CQlJSXtXoMKT8+CWuZQKBQKRaLQqjYKhUKhSBQqPBQKhUKRKFR4KL0SURqXtmTJkiVgsVjYu3eviHtNofQMqPBQeh2iNi5tzpkzZ3D79m3o6uqKexgUSreFFhdQeh1sNhs2NjY4ePAggLd7i/T09BAQEICQkJBW7WfPno2amhrExsYyx+zs7GBhYYHQ0FDmWHFxMdhsNi5dugRXV1csX75caD8ShUJ5C33iofQqxGVcyufzMX/+fKxcuRIff/yxeDpPofQQqPBQehXiMi7dsWMHFBQU8J///Ef0naZQehjUModC6SKpqanYt28f0tLSwGKxpN0dCkXmoU88lF6FOIxLb9y4gbKyMujr60NBQQEKCgooLCxEUFAQDAwMxDIOCqU7Q4WH0qtoblwqQGBc2tyItDkC49LmNDcunT9/PjIyMnD//n3mpauri5UrV+LSpUviGwyF0k2hU22UXkdgYCAWLlwIa2tr2NraYu/evaipqYGPjw8AYMGCBRgyZAi2bdsGAPjmm2/g4OCAH374Aa6urjh16hRSUlLwyy+/AAAGDRrUyiFbUVEROjo6GDlypGQHR6F0A6jwUHods2fPxosXL7BhwwZwOBxYWFjg4sWLTAFBUVGRUNTy2LFjcfLkSaxbtw5r1qyBkZERYmJiYGpqKq0hUCjdGrqPh0KhUCgSha7xUCgUCkWiUOGhUCgUikShwkOhUCgUiUKFh0KhUCgShQoPhUKhUCQKFR4KhUKhSBQqPBQKhUKRKFR4KBQKhSJRqPBQKBQKRaJQ4aFQKBSKRKHCQ6FQKBSJ8v+zDDAIRgUWDAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create 3d plot with the 2 vecotrs\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# Plotting the vectors\n",
    "ax.quiver(0,0,0, vectors[0][top_dims[0]], vectors[0][4], vectors[0][10], color='b', label=vectors[0])\n",
    "ax.quiver(0,0,0, vectors[1][top_dims[0]], vectors[1][4], vectors[1][10], color='r', label=vectors[1])\n",
    "ax.quiver(0,0,0, vectors[2][top_dims[0]], vectors[2][4], vectors[2][10], color='g', label=vectors[2])\n",
    "ax.quiver(0,0,0, vectors[3][top_dims[0]], vectors[3][4], vectors[3][10], color='y', label=vectors[3])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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17txZwcHBdrMuVqhQQZ06ddKaNWuUlpYm6Y8Rmw8//FAtWrRQ3bp1ZRiGVq1apS5dusgwDLv3KiIiQsnJyTn+7Avi+j7nNXPndTdOKuHo6KgWLVrIMAwNGDDAbPfx8VG9evXsjomVK1eqfv36Cg4OttuHhx9+WJKy/bzDw8NVq1Yt83njxo3l5eVlt04fHx9t375dp06dyve+fvHFF3J0dNSzzz5r1z5q1CgZhqEvv/zSkjpyc/r0acXHx6t///6qWLGi3Xb//Oc/64svvsj2mps/j7lJSUmRp6dnvvpu3LhRGRkZGjFihN21V4MGDZKXl9cdzR4aHR1tNzp///33S5Lde3orOX239ejRQ25ublq6dKnZtm7dOp07dy5fo9xt2rTR9u3blZmZqaysLG3btk2tW7c2l10fBbt06ZLi4+PN0bCCfOavu/HsAUnmz/Xm4zKn0e7CPN6A0oogBqBArk/KsGzZMknSiRMn9M0336hXr17mBeJZWVnmxAqurq6qVKmSfH199dNPPyk5OTnbOmvUqJGvbR87dsz8xa98+fLy9fVVu3btJCnbet3c3OTr62vXVqFCBf3+++/m80OHDikwMNDuF8mbHThwQIZhqE6dOnYhx9fXV3v37jUnMsjJ2bNndenSJdWrVy/bsvr16ysrK0vHjx/P177fyNPTU6mpqdnahw4dqg0bNmjDhg3m6WO5OXr0qCTlWFtwcLC5/LqoqCilpaXp008/lSR99913OnLkiHk8nD17VklJSXr33XezvU/R0dGSlOd7lR/X9zk/v6Tfe++9ds+9vb3l5uamSpUqZWu/8Zg4cOCAfv7552z7ULdu3Rz34ebtSNmPs1dffVV79uxRUFCQWrVqpUmTJt3yl/mjR48qMDAw277Wr1/fXG5FHXnVJ+V8/NSvX1/nzp0zQ/t1+f2ce3l55Sts51WHi4uLatasme19Koib39MKFSpIkt17eis57bOPj4+6dOlifodKf5yWWKVKFTPw56Vt27ZKTU1VfHy89uzZo+TkZHPCntatW+vUqVM6cuSItm/frmvXrplBrKCfeScnJ1WtWtWu7ejRo3JwcLAL/bmtszCPN6C04hoxAAUSEhKi4OBgffDBB/r73/+e46x5r7zyisaPH6+//OUvmjJliipWrCgHBweNGDEixym68zMalpmZqT//+c+6cOGCXnjhBQUHB8vDw0MnT55U//79s633eii8U1lZWbLZbPryyy9zXGde13IVleDgYMXHx+vkyZOqUqWK2V63bl0zMBT2bIKPPPKIvL29tWzZMvXp00fLli0zR0glme//U089pX79+uW4jsaNG99RDXv27JEk1a5d+5Z9c/pZ5XZMGDdcr5SVlaVGjRplmyb/uqCgoAKvs0ePHrr//vu1evVqrV+/XrNmzdLMmTP18ccfq2PHjrfcl/y4W+rIS34+59L/Hd8ZGRmFevuK66PcN7txlsEb5ec9vZXc9rlv375auXKlvvvuOzVq1Ehr1qzR0KFD8zWj4o3Xibm4uKhixYrmzJJNmzaVu7u7vv32Wx0+fNiuf0HdeGbD7Sju4w0oCQhiAAosKipK48eP108//aRly5apTp06atmypbn8o48+0kMPPaSFCxfavS4pKSnbiER+7d69W7/++quWLFliN33+hg0bbm8nJNWqVUvr1q3ThQsXch0Vq1WrlgzDUI0aNcyQk1++vr5yd3fX/v37sy3bt2+fHBwcsv1inx+PPPKIPvzwQy1dulRjxowp8OslmTfk3b9/f7a/wu/fvz/bDXtdXV315JNP6v3331diYqJWrlyphx9+WP7+/pL+2FdPT09lZmaakwgUptTUVK1evVpBQUHmqFBRqFWrln788Ue1b98+11/cb0dAQICGDh2qoUOH6syZM2revLmmTZuW6y+k1apV08aNG3Xx4kW7UbF9+/aZy62oIzc3Hj8327dvnypVqnTbU5136dJFcXFxWrVqlXr37p3vOm48/TcjI0OHDx+2OxYrVKiQ442gjx49avfagrjdYyQyMlK+vr5aunSpQkNDdenSpXzf86958+Zm2HJ1dVVYWJhZh5OTk1q2bKmtW7fq8OHDqly5svm9VdDPfE6qVaumrKwsHTp0yG4ULKfjQCq84w0orTg1EUCBXR/9mjBhguLj47PdQ8rR0THbX41XrlypkydP3vY2r/91+sb1Goaht95667bX2a1bNxmGocmTJ2dbdn07TzzxhBwdHTV58uRs+2QYhs6fP59nzR06dNCnn35qd01dYmKili1bprZt28rLy6vAdffo0UMNGjTQlClTtG3bthz73Oqv9i1atFDlypU1f/58paenm+1ffvml9u7dm+MsaFFRUbp69ar++te/6uzZs3Y/d0dHR3Xr1k2rVq0yR65udP0+abfj8uXLevrpp3XhwgW9+OKLhRqQbtajRw+dPHkyx/tUXb58OdvpdreSmZmZ7bTZypUrKzAw0O59v1mnTp2UmZmpf/zjH3btb7zxhmw2W4F/kb3dOnITEBCgpk2basmSJXbhZs+ePVq/fr06depU4HVe98wzzyggIECjRo3Sr7/+mm35mTNnNHXqVEl/XBvn4uKiOXPm2B3zCxcuVHJyst1xXKtWLW3btk0ZGRlm23//+9/bOj34Og8PjxxPt74VJycn9e7dWytWrNDixYvVqFGjfI8YOzk5KTQ0VFu3btXWrVvN68Oua926tb7++mtt27bN7h6Dt/OZv9n1427OnDl27W+++abd88I+3oDSihExAAVWo0YNtW7d2rxe6OYg9sgjj+jll19WdHS0Wrdurd27d2vp0qW3/Vdn6Y/TlWrVqqXRo0fr5MmT8vLy0qpVqwp0vcbNHnroIT399NOaM2eODhw4oMjISGVlZembb77RQw89pOHDh6tWrVqaOnWqxo0bpyNHjqhr167y9PTU4cOHtXr1ag0ePFijR4/OdRtTp07Vhg0b1LZtWw0dOlROTk565513lJ6erldfffW26nZ2dtbq1avNqaWfeOIJ3X///eapmmvWrNGxY8fy/MXK2dlZM2fOVHR0tNq1a6fevXubU1lXr14929TaktSuXTtVrVpVn376qcqVK6cnnnjCbvmMGTO0adMmhYaGatCgQWrQoIEuXLigXbt2aePGjbpw4cIt9+3kyZP6z3/+I+mPUbBffvlFK1euVEJCgkaNGqW//vWvBXy3Cubpp5/WihUr9Mwzz2jTpk1q06aNMjMztW/fPq1YsULr1q0zb2uQHxcvXlTVqlX15JNPqkmTJipfvrw2btyoHTt26PXXX8/1dV26dNFDDz2kF198UUeOHFGTJk20fv16ffrppxoxYkS2a3SKqo68zJo1Sx07dlRYWJgGDBhgTl/v7e2tSZMm3dY6pT9GrlavXq1OnTqpadOmeuqpp8zp2Hft2qUPPvhAYWFhkv4YiR03bpwmT56syMhIPfroo9q/f7/efvtttWzZ0m7yi4EDB+qjjz5SZGSkevTooUOHDuk///lPgd/LG4WEhGj58uWKiYlRy5YtVb58eXXp0iVfr+3bt6/mzJmjTZs2aebMmQXabtu2bc2JY26+oXvr1q01ffp0s991t/OZv1nTpk3Vu3dvvf3220pOTlbr1q0VGxurgwcP2vUriuMNKJWsnKIRQOkxb948Q5LRqlWrbMuuXLlijBo1yggICDDKlStntGnTxoiLi8t1muibp8W+cdmN09f/8ssvRnh4uFG+fHmjUqVKxqBBg8xput977z2z3/Wbkd7s+jTaN7p27Zoxa9YsIzg42LzpaMeOHbPddHTVqlVG27ZtDQ8PD8PDw8MIDg42hg0bZk7dnpddu3YZERERRvny5Q13d3fjoYceMr777ju7PgWZvv66pKQk4+WXXzaaNWtmlC9f3nBxcTGCgoKMJ5980vjss8/s+uZ2Q+fly5cbzZo1M1xdXY2KFSvmeHPXGz3//POGJKNHjx45Lk9MTDSGDRtmBAUFGc7Ozoa/v7/Rvn174913373l/lSrVs28Ma7NZjO8vLyMhg0bGoMGDTK2b9+e42uUy/T1Z8+eteuX2zHRrl07o2HDhnZtGRkZxsyZM42GDRsarq6uRoUKFYyQkBBj8uTJdrcE0P+/kXJO+3F9GvL09HTj+eefN5o0aWLe0LtJkybG22+/na2+m6dBv3jxojFy5EgjMDDQcHZ2NurUqZPnDZ0Lo46c5PU53bhxo9GmTRujXLlyhpeXl9GlS5dcb+h888/kVk6dOmWMHDnSqFu3ruHm5ma4u7sbISEhxrRp0+x+Dobxx3T1wcHBhrOzs+Hn52cMGTIk2w2dDcMwXn/9dfMGzW3atDF++OGHfH8vXf+M3vhdk5qaavTp08fw8fHJ8YbOOb1nN2rYsKHh4OCQ52cuJ+vWrTMkGU5OTkZaWprdsvPnzxs2m82QlOPnJj+f+dw+L4ZhGJcvXzaeffZZ45577jE8PDxyvKHznRxvQFliM4wCXHUKAACAQtGsWTNVrFhRsbGxxV0KgGLANWIAAAAW++GHHxQfH283+RCAsoURMQAAAIvs2bNHO3fu1Ouvv65z587pt99+K/TbTQAoGRgRAwAAsMhHH32k6OhoXb16VR988AEhDCjDGBEDAAAAAIsxIgYAAAAAFiOIAQAAAIDFuKFzIcjKytKpU6fk6ekpm81W3OUAAAAAKCaGYejixYsKDAyUg0Pu414EsUJw6tQpBQUFFXcZAAAAAO4Sx48fV9WqVXNdThArBJ6enpL+eLO9vLyKuRoAAAAAxSUlJUVBQUFmRsgNQawQXD8d0cvLiyAGAAAA4JaXLDFZBwAAAABYjCAGAAAAABYjiAEAAACAxQhiAAAAAGAxghgAAAAAWIwgBgAAAAAWI4gBAAAAgMUIYgAAAABgMYIYAAAAAFiMIAYAAAAAFiOIAQAAAIDFCGIAAAAAYDGCGAAAAABYjCAGAAAAABZzKu4CAKA0qj728wL1PzKjcxFVAgAA7kYlakTs66+/VpcuXRQYGCibzaZPPvnklq/ZvHmzmjdvLldXV9WuXVuLFy/O1mfevHmqXr263NzcFBoaqu+//77wiwcAAACA/69EBbG0tDQ1adJE8+bNy1f/w4cPq3PnznrooYcUHx+vESNGaODAgVq3bp3ZZ/ny5YqJidHEiRO1a9cuNWnSRBERETpz5kxR7QYAAACAMs5mGIZR3EXcDpvNptWrV6tr16659nnhhRf0+eefa8+ePWZbr169lJSUpLVr10qSQkND1bJlS/3jH/+QJGVlZSkoKEh/+9vfNHbs2HzVkpKSIm9vbyUnJ8vLy+v2dwpAqcGpiQAAlE35zQYlakSsoOLi4hQeHm7XFhERobi4OElSRkaGdu7cadfHwcFB4eHhZp+cpKenKyUlxe4BAAAAAPlVqoNYQkKC/Pz87Nr8/PyUkpKiy5cv69y5c8rMzMyxT0JCQq7rnT59ury9vc1HUFBQkdQPAAAAoHQq1UGsqIwbN07Jycnm4/jx48VdEgAAAIASpFRPX+/v76/ExES7tsTERHl5ealcuXJydHSUo6Njjn38/f1zXa+rq6tcXV2LpGYAAAAApV+pHhELCwtTbGysXduGDRsUFhYmSXJxcVFISIhdn6ysLMXGxpp9AAAAAKCwlagglpqaqvj4eMXHx0v6Y3r6+Ph4HTt2TNIfpwz27dvX7P/MM8/ot99+05gxY7Rv3z69/fbbWrFihUaOHGn2iYmJ0YIFC7RkyRLt3btXQ4YMUVpamqKjoy3dNwAAAABlR4k6NfGHH37QQw89ZD6PiYmRJPXr10+LFy/W6dOnzVAmSTVq1NDnn3+ukSNH6q233lLVqlX1r3/9SxEREWafnj176uzZs5owYYISEhLUtGlTrV27NtsEHgAAAABQWErsfcTuJtxHDMDNuI8YAABlE/cRAwAAAIC7FEEMAAAAACxGEAMAAAAAixHEAAAAAMBiBDEAAAAAsBhBDAAAAAAsRhADAAAAAIsRxAAAAADAYgQxAAAAALAYQQwAAAAALEYQAwAAAACLEcQAAAAAwGIEMQAAAACwGEEMAAAAACxGEAMAAAAAixHEAAAAAMBiTsVdAICSrfrYz/Pd98iMzkVYCQAAQMnBiBgAAAAAWIwgBgAAAAAWI4gBAAAAgMUIYgAAAABgMYIYAAAAAFiMIAYAAAAAFiOIAQAAAIDFCGIAAAAAYDGCGAAAAABYjCAGAAAAABYjiAEAAACAxQhiAAAAAGAxghgAAAAAWIwgBgAAAAAWI4gBAAAAgMUIYgAAAABgsRIXxObNm6fq1avLzc1NoaGh+v7773Pt++CDD8pms2V7dO7c2ezTv3//bMsjIyOt2BUAAAAAZZRTcRdQEMuXL1dMTIzmz5+v0NBQvfnmm4qIiND+/ftVuXLlbP0//vhjZWRkmM/Pnz+vJk2aqHv37nb9IiMj9d5775nPXV1di24nAAAAAJR5JWpEbPbs2Ro0aJCio6PVoEEDzZ8/X+7u7lq0aFGO/StWrCh/f3/zsWHDBrm7u2cLYq6urnb9KlSoYMXuAAAAACijSkwQy8jI0M6dOxUeHm62OTg4KDw8XHFxcflax8KFC9WrVy95eHjYtW/evFmVK1dWvXr1NGTIEJ0/fz7P9aSnpyslJcXuAQAAAAD5VWKC2Llz55SZmSk/Pz+7dj8/PyUkJNzy9d9//7327NmjgQMH2rVHRkbq/fffV2xsrGbOnKktW7aoY8eOyszMzHVd06dPl7e3t/kICgq6vZ0CAAAAUCaVqGvE7sTChQvVqFEjtWrVyq69V69e5r8bNWqkxo0bq1atWtq8ebPat2+f47rGjRunmJgY83lKSgphDAAAAEC+lZgRsUqVKsnR0VGJiYl27YmJifL398/ztWlpafrwww81YMCAW26nZs2aqlSpkg4ePJhrH1dXV3l5edk9AAAAACC/SkwQc3FxUUhIiGJjY822rKwsxcbGKiwsLM/Xrly5Uunp6XrqqaduuZ0TJ07o/PnzCggIuOOaAQAAACAnJSaISVJMTIwWLFigJUuWaO/evRoyZIjS0tIUHR0tSerbt6/GjRuX7XULFy5U165ddc8999i1p6am6vnnn9e2bdt05MgRxcbG6rHHHlPt2rUVERFhyT4BAAAAKHtK1DViPXv21NmzZzVhwgQlJCSoadOmWrt2rTmBx7Fjx+TgYJ8t9+/fr2+//Vbr16/Ptj5HR0f99NNPWrJkiZKSkhQYGKgOHTpoypQp3EsMAAAAQJEpUUFMkoYPH67hw4fnuGzz5s3Z2urVqyfDMHLsX65cOa1bt64wywMAAACAWypRpyYCAAAAQGlAEAMAAAAAixHEAAAAAMBiBDEAAAAAsBhBDAAAAAAsRhADAAAAAIsRxAAAAADAYgQxAAAAALAYQQwAAAAALEYQAwAAAACLEcQAAAAAwGIEMQAAAACwGEEMAAAAACxGEAMAAAAAixHEAAAAAMBiBDEAAAAAsBhBDAAAAAAsRhADAAAAAIsRxAAAAADAYgQxAAAAALAYQQwAAAAALEYQAwAAAACLEcQAAAAAwGIEMQAAAACwGEEMAAAAACxGEAMAAAAAixHEAAAAAMBiBDEAAAAAsBhBDAAAAAAsRhADAAAAAIsRxAAAAADAYgQxAAAAALAYQQwAAAAALFbigti8efNUvXp1ubm5KTQ0VN9//32ufRcvXiybzWb3cHNzs+tjGIYmTJiggIAAlStXTuHh4Tpw4EBR7wYAAACAMqxEBbHly5crJiZGEydO1K5du9SkSRNFRETozJkzub7Gy8tLp0+fNh9Hjx61W/7qq69qzpw5mj9/vrZv3y4PDw9FREToypUrRb07AAAAAMqoEhXEZs+erUGDBik6OloNGjTQ/Pnz5e7urkWLFuX6GpvNJn9/f/Ph5+dnLjMMQ2+++aZeeuklPfbYY2rcuLHef/99nTp1Sp988okFewQAAACgLCoxQSwjI0M7d+5UeHi42ebg4KDw8HDFxcXl+rrU1FRVq1ZNQUFBeuyxx/Tzzz+byw4fPqyEhAS7dXp7eys0NDTPdaanpyslJcXuAQAAAAD5VWKC2Llz55SZmWk3oiVJfn5+SkhIyPE19erV06JFi/Tpp5/qP//5j7KystS6dWudOHFCkszXFWSdkjR9+nR5e3ubj6CgoDvZNQAAAABlTIkJYrcjLCxMffv2VdOmTdWuXTt9/PHH8vX11TvvvHNH6x03bpySk5PNx/HjxwupYgAAAABlQYkJYpUqVZKjo6MSExPt2hMTE+Xv75+vdTg7O6tZs2Y6ePCgJJmvK+g6XV1d5eXlZfcAAAAAgPwqMUHMxcVFISEhio2NNduysrIUGxursLCwfK0jMzNTu3fvVkBAgCSpRo0a8vf3t1tnSkqKtm/fnu91AgAAAEBBORV3AQURExOjfv36qUWLFmrVqpXefPNNpaWlKTo6WpLUt29fValSRdOnT5ckvfzyy/rTn/6k2rVrKykpSbNmzdLRo0c1cOBASX/MqDhixAhNnTpVderUUY0aNTR+/HgFBgaqa9euxbWbAAAAAEq5EhXEevbsqbNnz2rChAlKSEhQ06ZNtXbtWnOyjWPHjsnB4f8G+X7//XcNGjRICQkJqlChgkJCQvTdd9+pQYMGZp8xY8YoLS1NgwcPVlJSktq2bau1a9dmu/EzAAAAABQWm2EYRnEXUdKlpKTI29tbycnJXC+GMqf62M/z3ffIjM5FWMndpSDvi1S23hsAAEqz/GaDEnONGAAAAACUFgQxAAAAALAYQQwAAAAALEYQAwAAAACLEcQAAAAAwGIEMQAAAACwGEEMAAAAACxGEAMAAAAAixHEAAAAAMBiTsVdAEq+6mM/z3ffIzM6F2ElAAAAQMnAiBgAAAAAWIwgBgAAAAAWI4gBAAAAgMUIYgAAAABgMYIYAAAAAFiMIAYAAAAAFiOIAQAAAIDFCGIAAAAAYDFu6AyUIgW5ubbEDbYBAACKCyNiAAAAAGAxghgAAAAAWIwgBgAAAAAW4xoxAAAAALdUkGvRuQ791hgRAwAAAACLEcQAAAAAwGKcmggAACzBLTaAwsWpgiUbI2IAAAAAYDGCGAAAAABYjCAGAAAAABYjiAEAAACAxQhiAAAAAGAxghgAAAAAWIwgBgAAAAAWK3H3EZs3b55mzZqlhIQENWnSRHPnzlWrVq1y7LtgwQK9//772rNnjyQpJCREr7zyil3//v37a8mSJXavi4iI0Nq1a4tuJwAAAMqY0n7PK+6Th4IqUSNiy5cvV0xMjCZOnKhdu3apSZMmioiI0JkzZ3Lsv3nzZvXu3VubNm1SXFycgoKC1KFDB508edKuX2RkpE6fPm0+PvjgAyt2BwAAAEAZVaKC2OzZszVo0CBFR0erQYMGmj9/vtzd3bVo0aIc+y9dulRDhw5V06ZNFRwcrH/961/KyspSbGysXT9XV1f5+/ubjwoVKlixOwAAAADKqBITxDIyMrRz506Fh4ebbQ4ODgoPD1dcXFy+1nHp0iVdvXpVFStWtGvfvHmzKleurHr16mnIkCE6f/58nutJT09XSkqK3QMAAAAA8qvEXCN27tw5ZWZmys/Pz67dz89P+/bty9c6XnjhBQUGBtqFucjISD3xxBOqUaOGDh06pL///e/q2LGj4uLi5OjomON6pk+frsmTJ9/+zgAAgLtaab+eCUDxKzFB7E7NmDFDH374oTZv3iw3NzezvVevXua/GzVqpMaNG6tWrVravHmz2rdvn+O6xo0bp5iYGPN5SkqKgoKCiq54AAAAAKVKiTk1sVKlSnJ0dFRiYqJde2Jiovz9/fN87WuvvaYZM2Zo/fr1aty4cZ59a9asqUqVKungwYO59nF1dZWXl5fdAwAAAADyq8QEMRcXF4WEhNhNtHF94o2wsLBcX/fqq69qypQpWrt2rVq0aHHL7Zw4cULnz59XQEBAodQNAAAAADcrMUFMkmJiYrRgwQItWbJEe/fu1ZAhQ5SWlqbo6GhJUt++fTVu3Diz/8yZMzV+/HgtWrRI1atXV0JCghISEpSamipJSk1N1fPPP69t27bpyJEjio2N1WOPPabatWsrIiKiWPYRAAAAQOlXoq4R69mzp86ePasJEyYoISFBTZs21dq1a80JPI4dOyYHh//Llv/85z+VkZGhJ5980m49EydO1KRJk+To6KiffvpJS5YsUVJSkgIDA9WhQwdNmTJFrq6ulu4bAAAou5gcBCh7SlQQk6Thw4dr+PDhOS7bvHmz3fMjR47kua5y5cpp3bp1hVQZAAAAAORPiQtiAFCaFeSv4hJ/GQcAoKQiiAEAAKDU4XRP3O1K1GQdAAAAAFAaMCIGAEAZxYgBABSf2xoRu3btmjZu3Kh33nlHFy9elCSdOnXKnBYeAAAAAJC7Ao+IHT16VJGRkTp27JjS09P15z//WZ6enpo5c6bS09M1f/78oqgTAAAAAEqNAo+IPffcc2rRooV+//13lStXzmx//PHHFRsbW6jFAQAAAEBpVOARsW+++UbfffedXFxc7NqrV6+ukydPFlphAAAAAFBaFTiIZWVlKTMzM1v7iRMn5OnpWShFAQAAABKTyqD0KvCpiR06dNCbb75pPrfZbEpNTdXEiRPVqVOnwqwNAAAAAEqlAo+Ivf7664qIiFCDBg105coV9enTRwcOHFClSpX0wQcfFEWNAAAAAFCqFDiIVa1aVT/++KOWL1+uH3/8UampqRowYICioqLsJu8AAABA0SrIaXsSp+4Bd5PbuqGzk5OToqKiFBUVVdj1AAAAlFhczwQgvwp8jdj06dO1aNGibO2LFi3SzJkzC6UoAAAAACjNCjwi9s4772jZsmXZ2hs2bKhevXrphRdeKJTCAAAA7hQjVADuVgUeEUtISFBAQEC2dl9fX50+fbpQigIAAACA0qzAQSwoKEhbt27N1r5161YFBgYWSlEAAAAAUJoV+NTEQYMGacSIEbp69aoefvhhSVJsbKzGjBmjUaNGFXqBAAAAAFDaFDiIPf/88zp//ryGDh2qjIwMSZKbm5teeOEFjRs3rtALBAAAAIDSpsBBzGazaebMmRo/frz27t2rcuXKqU6dOnJ1dS2K+oBsuGcKAAAoLfi9puy6rfuISVL58uXVsmXLwqwFAIBcMfsdAKA0KXAQS0tL04wZMxQbG6szZ84oKyvLbvlvv/1WaMUBAAAAQGlU4CA2cOBAbdmyRU8//bQCAgJks9mKoi7cAf5qDAAAANzdChzEvvzyS33++edq06ZNUdQDAAAAAKVege8jVqFCBVWsWLEoagEAAACAMqHAI2JTpkzRhAkTtGTJErm7uxdFTQAAoAA4JR0ASp4CB7HXX39dhw4dkp+fn6pXry5nZ2e75bt27Sq04oC7Bb/kALBCSfmuKSl1AsDdrMBBrGvXrkVQBgAAAACUHQUOYhMnTiyKOgAAAACgzLjtGzoDAAAAwK1wOnPOChzEMjMz9cYbb2jFihU6duyYMjIy7JZfuHCh0IpD6VaQD6VUtj6YAAAAKN0KPH395MmTNXv2bPXs2VPJycmKiYnRE088IQcHB02aNKkISgQAAACA0qXAQWzp0qVasGCBRo0aJScnJ/Xu3Vv/+te/NGHCBG3btq0oagQAAACAUqXAQSwhIUGNGjWSJJUvX17JycmSpEceeUSff16wU81ux7x581S9enW5ubkpNDRU33//fZ79V65cqeDgYLm5ualRo0b64osv7JYbhqEJEyYoICBA5cqVU3h4uA4cOFCUuwAAAACgjCtwEKtatapOnz4tSapVq5bWr18vSdqxY4dcXV0Lt7qbLF++XDExMZo4caJ27dqlJk2aKCIiQmfOnMmx/3fffafevXtrwIAB+t///qeuXbuqa9eu2rNnj9nn1Vdf1Zw5czR//nxt375dHh4eioiI0JUrV4p0XwAAAACUXQUOYo8//rhiY2MlSX/72980fvx41alTR3379tVf/vKXQi/wRrNnz9agQYMUHR2tBg0aaP78+XJ3d9eiRYty7P/WW28pMjJSzz//vOrXr68pU6aoefPm+sc//iHpj9GwN998Uy+99JIee+wxNW7cWO+//75OnTqlTz75pEj3BQAAAEDZVeBZE2fMmGH+u2fPnrr33nsVFxenOnXqqEuXLoVa3I0yMjK0c+dOjRs3zmxzcHBQeHi44uLicnxNXFycYmJi7NoiIiLMkHX48GElJCQoPDzcXO7t7a3Q0FDFxcWpV69eOa43PT1d6enp5vOUlJTb3S0AAAAAZZDNMAyjuIvIj1OnTqlKlSr67rvvFBYWZraPGTNGW7Zs0fbt27O9xsXFRUuWLFHv3r3NtrfffluTJ09WYmKivvvuO7Vp00anTp1SQECA2adHjx6y2Wxavnx5jrVMmjRJkydPztaenJwsLy+vO9nNYlUW7vFg9T7e7vbu5jqLY5uF8d6UlJ/Fnbibj5uSeHzfyeeiJB03JQG3Oyl8xfG5KCnKwue3pOxjSanzZikpKfL29r5lNsjXiNiaNWvUsWNHOTs7a82aNXn2ffTRRwtWaQk0btw4u5G2lJQUBQUFFWNFAAAAAEqSfAWxrl27KiEhQZUrV1bXrl1z7Wez2ZSZmVlYtdmpVKmSHB0dlZiYaNeemJgof3//HF/j7++fZ//r/01MTLQbEUtMTFTTpk1zrcXV1bXIJyYBkLO76S9eAACg6JT2/+fna7KOrKwsVa5c2fx3bo+iCmHSH6cZhoSEmBOFXK8lNjbW7lTFG4WFhdn1l6QNGzaY/WvUqCF/f3+7PikpKdq+fXuu6wQAAACAO1WgWROvXr2q9u3bF9t9tmJiYrRgwQItWbJEe/fu1ZAhQ5SWlqbo6GhJUt++fe0m83juuee0du1avf7669q3b58mTZqkH374QcOHD5f0xwjeiBEjNHXqVK1Zs0a7d+9W3759FRgYmOfIHwAAAADciQLNmujs7KyffvqpqGq5pZ49e+rs2bOaMGGCEhIS1LRpU61du1Z+fn6SpGPHjsnB4f+yZevWrbVs2TK99NJL+vvf/646derok08+0X333Wf2GTNmjNLS0jR48GAlJSWpbdu2Wrt2rdzc3CzfPwAAAABlQ4Gnr3/qqae0cOFCu2nsrTR8+HBzROtmmzdvztbWvXt3de/ePdf12Ww2vfzyy3r55ZcLq0QAAAAAyFOBg9i1a9e0aNEibdy4USEhIfLw8LBbPnv27EIrDgAAAABKowIHsT179qh58+aSpF9//dVumc1mK5yqAAAAAKAUK3AQ27RpU1HUAQAAAIuU9mnBgZKgQLMmAgAAAADuXIFHxCTphx9+0IoVK3Ts2DFlZGTYLfv4448LpTAAAAAAKK0KPCL24YcfqnXr1tq7d69Wr16tq1ev6ueff9ZXX30lb2/voqgRAAAAAEqVAo+IvfLKK3rjjTc0bNgweXp66q233lKNGjX017/+VQEBAUVRIwAAKMO4nglAaVTgEbFDhw6pc+c/vhBdXFyUlpYmm82mkSNH6t133y30AgEAAACgtCnwiFiFChV08eJFSVKVKlW0Z88eNWrUSElJSbp06VKhFwgAxYm/xAMAgKKQ7xGxPXv2SJIeeOABbdiwQZLUvXt3Pffccxo0aJB69+6t9u3bF02VAAAAAFCK5HtErHHjxmrZsqW6du2q7t27S5JefPFFOTs767vvvlO3bt300ksvFVmhAAAAAFBa5DuIbdmyRe+9956mT5+uadOmqVu3bho4cKDGjh1blPUBAAAAQKmT71MT77//fi1atEinT5/W3LlzdeTIEbVr105169bVzJkzlZCQUJR1AgAAAECpUeBZEz08PBQdHa0tW7bo119/Vffu3TVv3jzde++9evTRR4uiRgAAAAAoVQocxG5Uu3Zt/f3vf9dLL70kT09Pff7554VVFwAAAACUWgWevv66r7/+WosWLdKqVavk4OCgHj16aMCAAYVZGwAAAO4i3NIDKDwFCmKnTp3S4sWLtXjxYh08eFCtW7fWnDlz1KNHD3l4eBRVjQAAAABQquQ7iHXs2FEbN25UpUqV1LdvX/3lL39RvXr1irI2AABKJEYNAAC3ku8g5uzsrI8++kiPPPKIHB0di7ImAAAAACjV8h3E1qxZU5R1AAAAAECZcUezJgIAAAAACo4gBgAAAAAWI4gBAAAAgMUIYgAAAABgMYIYAAAAAFiMIAYAAAAAFiOIAQAAAIDFCGIAAAAAYDGCGAAAAABYjCAGAAAAABYjiAEAAACAxQhiAAAAAGAxp+IuAHePIzM6F3cJAAAAQJlQYkbELly4oKioKHl5ecnHx0cDBgxQampqnv3/9re/qV69eipXrpzuvfdePfvss0pOTrbrZ7PZsj0+/PDDot4dAAAAAGVYiRkRi4qK0unTp7VhwwZdvXpV0dHRGjx4sJYtW5Zj/1OnTunUqVN67bXX1KBBAx09elTPPPOMTp06pY8++siu73vvvafIyEjzuY+PT1HuCgAAAIAyrkQEsb1792rt2rXasWOHWrRoIUmaO3euOnXqpNdee02BgYHZXnPfffdp1apV5vNatWpp2rRpeuqpp3Tt2jU5Of3frvv4+Mjf37/odwQAAAAAVEKCWFxcnHx8fMwQJknh4eFycHDQ9u3b9fjjj+drPcnJyfLy8rILYZI0bNgwDRw4UDVr1tQzzzyj6Oho2Wy2XNeTnp6u9PR083lKSkoB9wiA1bgGEgAA3E1KRBBLSEhQ5cqV7dqcnJxUsWJFJSQk5Gsd586d05QpUzR48GC79pdfflkPP/yw3N3dtX79eg0dOlSpqal69tlnc13X9OnTNXny5ILvCAAAAFDM+OPk3aFYJ+sYO3ZsjpNl3PjYt2/fHW8nJSVFnTt3VoMGDTRp0iS7ZePHj1ebNm3UrFkzvfDCCxozZoxmzZqV5/rGjRun5ORk83H8+PE7rhEAAABA2VGsI2KjRo1S//798+xTs2ZN+fv768yZM3bt165d04ULF255bdfFixcVGRkpT09PrV69Ws7Oznn2Dw0N1ZQpU5Seni5XV9cc+7i6uua6DAAAAABupViDmK+vr3x9fW/ZLywsTElJSdq5c6dCQkIkSV999ZWysrIUGhqa6+tSUlIUEREhV1dXrVmzRm5ubrfcVnx8vCpUqEDQAgAAAFBkSsQ1YvXr11dkZKQGDRqk+fPn6+rVqxo+fLh69eplzph48uRJtW/fXu+//75atWqllJQUdejQQZcuXdJ//vMfpaSkmJNq+Pr6ytHRUZ999pkSExP1pz/9SW5ubtqwYYNeeeUVjR49ujh3FwAAAEApVyKCmCQtXbpUw4cPV/v27eXg4KBu3bppzpw55vKrV69q//79unTpkiRp165d2r59uySpdu3adus6fPiwqlevLmdnZ82bN08jR46UYRiqXbu2Zs+erUGDBlm3YwAAAADKnBITxCpWrJjrzZslqXr16jIMw3z+4IMP2j3PSWRkpN2NnAEAAADACiUmiAEA7i5MfwwAwO0r1unrAQAAAKAsYkQMAIAcMOIHAChKjIgBAAAAgMUYEQPAX/4BAAAsxogYAAAAAFiMIAYAAAAAFuPURKAIccofAAAAcsKIGAAAAABYjCAGAAAAABYjiAEAAACAxQhiAAAAAGAxghgAAAAAWIwgBgAAAAAWI4gBAAAAgMUIYgAAAABgMYIYAAAAAFiMIAYAAAAAFiOIAQAAAIDFCGIAAAAAYDGn4i4AAICidGRG5+IuAQCAbBgRAwAAAACLEcQAAAAAwGIEMQAAAACwGEEMAAAAACxGEAMAAAAAixHEAAAAAMBiBDEAAAAAsBhBDAAAAAAsRhADAAAAAIs5FXcBALI7MqNzcZcAAACAIsSIGAAAAABYjCAGAAAAABYrMUHswoULioqKkpeXl3x8fDRgwAClpqbm+ZoHH3xQNpvN7vHMM8/Y9Tl27Jg6d+4sd3d3Va5cWc8//7yuXbtWlLsCAAAAoIwrMdeIRUVF6fTp09qwYYOuXr2q6OhoDR48WMuWLcvzdYMGDdLLL79sPnd3dzf/nZmZqc6dO8vf31/fffedTp8+rb59+8rZ2VmvvPJKke0LAAAAgLKtRASxvXv3au3atdqxY4datGghSZo7d646deqk1157TYGBgbm+1t3dXf7+/jkuW79+vX755Rdt3LhRfn5+atq0qaZMmaIXXnhBkyZNkouLS5HsDwAAAICyrUScmhgXFycfHx8zhElSeHi4HBwctH379jxfu3TpUlWqVEn33Xefxo0bp0uXLtmtt1GjRvLz8zPbIiIilJKSop9//jnXdaanpyslJcXuAQAAAAD5VSJGxBISElS5cmW7NicnJ1WsWFEJCQm5vq5Pnz6qVq2aAgMD9dNPP+mFF17Q/v379fHHH5vrvTGESTKf57Xe6dOna/Lkybe7OwAAAADKuGINYmPHjtXMmTPz7LN3797bXv/gwYPNfzdq1EgBAQFq3769Dh06pFq1at32eseNG6eYmBjzeUpKioKCgm57fQBQlnCfPAAAijmIjRo1Sv3798+zT82aNeXv768zZ87YtV+7dk0XLlzI9fqvnISGhkqSDh48qFq1asnf31/ff/+9XZ/ExERJynO9rq6ucnV1zfd2AQAAAOBGxRrEfH195evre8t+YWFhSkpK0s6dOxUSEiJJ+uqrr5SVlWWGq/yIj4+XJAUEBJjrnTZtms6cOWOe+rhhwwZ5eXmpQYMGBdwbAAAAAMifEjFZR/369RUZGalBgwbp+++/19atWzV8+HD16tXLnDHx5MmTCg4ONke4Dh06pClTpmjnzp06cuSI1qxZo759++qBBx5Q48aNJUkdOnRQgwYN9PTTT+vHH3/UunXr9NJLL2nYsGGMeAEAAAAoMiUiiEl/zH4YHBys9u3bq1OnTmrbtq3effddc/nVq1e1f/9+c1ZEFxcXbdy4UR06dFBwcLBGjRqlbt266bPPPjNf4+joqP/+979ydHRUWFiYnnrqKfXt29fuvmMAAAAAUNhKxKyJklSxYsU8b95cvXp1GYZhPg8KCtKWLVtuud5q1arpiy++KJQaAQAAACA/SsyIGAAAAACUFiVmRAwAAAAoatxiA1ZhRAwAAAAALEYQAwAAAACLEcQAAAAAwGIEMQAAAACwGEEMAAAAACxGEAMAAAAAixHEAAAAAMBiBDEAAAAAsBhBDAAAAAAsRhADAAAAAIsRxAAAAADAYgQxAAAAALAYQQwAAAAALEYQAwAAAACLEcQAAAAAwGIEMQAAAACwGEEMAAAAACxGEAMAAAAAixHEAAAAAMBiBDEAAAAAsBhBDAAAAAAsRhADAAAAAIsRxAAAAADAYgQxAAAAALAYQQwAAAAALEYQAwAAAACLORV3AQCAwnFkRufiLgEAAOQTI2IAAAAAYDGCGAAAAABYjCAGAAAAABYjiAEAAACAxUpMELtw4YKioqLk5eUlHx8fDRgwQKmpqbn2P3LkiGw2W46PlStXmv1yWv7hhx9asUsAAAAAyqgSM2tiVFSUTp8+rQ0bNujq1auKjo7W4MGDtWzZshz7BwUF6fTp03Zt7777rmbNmqWOHTvatb/33nuKjIw0n/v4+BR6/QAAAABwXYkIYnv37tXatWu1Y8cOtWjRQpI0d+5cderUSa+99poCAwOzvcbR0VH+/v52batXr1aPHj1Uvnx5u3YfH59sfQEAAACgqJSIUxPj4uLk4+NjhjBJCg8Pl4ODg7Zv356vdezcuVPx8fEaMGBAtmXDhg1TpUqV1KpVKy1atEiGYeS5rvT0dKWkpNg9AAAAACC/SsSIWEJCgipXrmzX5uTkpIoVKyohISFf61i4cKHq16+v1q1b27W//PLLevjhh+Xu7q7169dr6NChSk1N1bPPPpvruqZPn67JkycXfEcAAAAAQMU8IjZ27NhcJ9S4/ti3b98db+fy5ctatmxZjqNh48ePV5s2bdSsWTO98MILGjNmjGbNmpXn+saNG6fk5GTzcfz48TuuEQAAAEDZUawjYqNGjVL//v3z7FOzZk35+/vrzJkzdu3Xrl3ThQsX8nVt10cffaRLly6pb9++t+wbGhqqKVOmKD09Xa6urjn2cXV1zXUZAAAAANxKsQYxX19f+fr63rJfWFiYkpKStHPnToWEhEiSvvrqK2VlZSk0NPSWr1+4cKEeffTRfG0rPj5eFSpUIGgBAAAAKDIl4hqx+vXrKzIyUoMGDdL8+fN19epVDR8+XL169TJnTDx58qTat2+v999/X61atTJfe/DgQX399df64osvsq33s88+U2Jiov70pz/Jzc1NGzZs0CuvvKLRo0dbtm8AAAAAyp4SEcQkaenSpRo+fLjat28vBwcHdevWTXPmzDGXX716Vfv379elS5fsXrdo0SJVrVpVHTp0yLZOZ2dnzZs3TyNHjpRhGKpdu7Zmz56tQYMGFfn+AAAAACi7bMat5mrHLaWkpMjb21vJycny8vIq7nKQh+pjP8933yMzOhdhJeBnAQAASqP8ZoMScR8xAAAAAChNCGIAAAAAYDGCGAAAAABYjCAGAAAAABYjiAEAAACAxQhiAAAAAGAxghgAAAAAWIwgBgAAAAAWI4gBAAAAgMUIYgAAAABgMYIYAAAAAFiMIAYAAAAAFiOIAQAAAIDFCGIAAAAAYDGCGAAAAABYjCAGAAAAABYjiAEAAACAxQhiAAAAAGAxghgAAAAAWIwgBgAAAAAWI4gBAAAAgMUIYgAAAABgMYIYAAAAAFiMIAYAAAAAFiOIAQAAAIDFCGIAAAAAYDGCGAAAAABYzKm4CwCsdGRG5+IuAQAAAGBEDAAAAACsRhADAAAAAItxaiKAYsFpogAAoCxjRAwAAAAALEYQAwAAAACLlZggNm3aNLVu3Vru7u7y8fHJ12sMw9CECRMUEBCgcuXKKTw8XAcOHLDrc+HCBUVFRcnLy0s+Pj4aMGCAUlNTi2APAAAAAOAPJSaIZWRkqHv37hoyZEi+X/Pqq69qzpw5mj9/vrZv3y4PDw9FREToypUrZp+oqCj9/PPP2rBhg/773//q66+/1uDBg4tiFwAAAABAkmQzDMMo7iIKYvHixRoxYoSSkpLy7GcYhgIDAzVq1CiNHj1akpScnCw/Pz8tXrxYvXr10t69e9WgQQPt2LFDLVq0kCStXbtWnTp10okTJxQYGJivmlJSUuTt7a3k5GR5eXnd0f4BAAAAKLnymw1KzIhYQR0+fFgJCQkKDw8327y9vRUaGqq4uDhJUlxcnHx8fMwQJknh4eFycHDQ9u3bc113enq6UlJS7B4AAAAAkF+lNoglJCRIkvz8/Oza/fz8zGUJCQmqXLmy3XInJydVrFjR7JOT6dOny9vb23wEBQUVcvUAAAAASrNiDWJjx46VzWbL87Fv377iLDFH48aNU3Jysvk4fvx4cZcEAAAAoAQp1hs6jxo1Sv3798+zT82aNW9r3f7+/pKkxMREBQQEmO2JiYlq2rSp2efMmTN2r7t27ZouXLhgvj4nrq6ucnV1va26AAAAAKBYg5ivr698fX2LZN01atSQv7+/YmNjzeCVkpKi7du3mzMvhoWFKSkpSTt37lRISIgk6auvvlJWVpZCQ0OLpC4AAAAAKDHXiB07dkzx8fE6duyYMjMzFR8fr/j4eLt7fgUHB2v16tWSJJvNphEjRmjq1Klas2aNdu/erb59+yowMFBdu3aVJNWvX1+RkZEaNGiQvv/+e23dulXDhw9Xr1698j1jIgAAAAAUVLGOiBXEhAkTtGTJEvN5s2bNJEmbNm3Sgw8+KEnav3+/kpOTzT5jxoxRWlqaBg8erKSkJLVt21Zr166Vm5ub2Wfp0qUaPny42rdvLwcHB3Xr1k1z5syxZqcAAAAAlEkl7j5idyPuIwYAAABA4j5iAAAAAHDXIogBAAAAgMUIYgAAAABgsRIzWcfd7PpldikpKcVcCQAAAIDidD0T3GoqDoJYIbh48aIkKSgoqJgrAQAAAHA3uHjxory9vXNdzqyJhSArK0unTp2Sp6enbDZbkW0nJSVFQUFBOn78OLMzIt84blBQHDO4HRw3KCiOGdyOknDcGIahixcvKjAwUA4OuV8JxohYIXBwcFDVqlUt256Xl9dde+Dh7sVxg4LimMHt4LhBQXHM4Hbc7cdNXiNh1zFZBwAAAABYjCAGAAAAABYjiJUgrq6umjhxolxdXYu7FJQgHDcoKI4Z3A6OGxQUxwxuR2k6bpisAwAAAAAsxogYAAAAAFiMIAYAAAAAFiOIAQAAAIDFCGIAAAAAYDGCWAkyb948Va9eXW5ubgoNDdX3339f3CXhLvL111+rS5cuCgwMlM1m0yeffGK33DAMTZgwQQEBASpXrpzCw8N14MCB4ikWd4Xp06erZcuW8vT0VOXKldW1a1ft37/frs+VK1c0bNgw3XPPPSpfvry6deumxMTEYqoYxe2f//ynGjdubN5INSwsTF9++aW5nOMFtzJjxgzZbDaNGDHCbOO4wc0mTZokm81m9wgODjaXl5ZjhiBWQixfvlwxMTGaOHGidu3apSZNmigiIkJnzpwp7tJwl0hLS1OTJk00b968HJe/+uqrmjNnjubPn6/t27fLw8NDERERunLlisWV4m6xZcsWDRs2TNu2bdOGDRt09epVdejQQWlpaWafkSNH6rPPPtPKlSu1ZcsWnTp1Sk888UQxVo3iVLVqVc2YMUM7d+7UDz/8oIcffliPPfaYfv75Z0kcL8jbjh079M4776hx48Z27Rw3yEnDhg11+vRp8/Htt9+ay0rNMWOgRGjVqpUxbNgw83lmZqYRGBhoTJ8+vRirwt1KkrF69WrzeVZWluHv72/MmjXLbEtKSjJcXV2NDz74oBgqxN3ozJkzhiRjy5YthmH8cYw4OzsbK1euNPvs3bvXkGTExcUVV5m4y1SoUMH417/+xfGCPF28eNGoU6eOsWHDBqNdu3bGc889ZxgG3zPI2cSJE40mTZrkuKw0HTOMiJUAGRkZ2rlzp8LDw802BwcHhYeHKy4urhgrQ0lx+PBhJSQk2B1D3t7eCg0N5RiCKTk5WZJUsWJFSdLOnTt19epVu+MmODhY9957L8cNlJmZqQ8//FBpaWkKCwvjeEGehg0bps6dO9sdHxLfM8jdgQMHFBgYqJo1ayoqKkrHjh2TVLqOGafiLgC3du7cOWVmZsrPz8+u3c/PT/v27SumqlCSJCQkSFKOx9D1ZSjbsrKyNGLECLVp00b33XefpD+OGxcXF/n4+Nj15bgp23bv3q2wsDBduXJF5cuX1+rVq9WgQQPFx8dzvCBHH374oXbt2qUdO3ZkW8b3DHISGhqqxYsXq169ejp9+rQmT56s+++/X3v27ClVxwxBDACgYcOGac+ePXbn4AM5qVevnuLj45WcnKyPPvpI/fr105YtW4q7LNyljh8/rueee04bNmyQm5tbcZeDEqJjx47mvxs3bqzQ0FBVq1ZNK1asULly5YqxssLFqYklQKVKleTo6JhtNpjExET5+/sXU1UoSa4fJxxDyMnw4cP13//+V5s2bVLVqlXNdn9/f2VkZCgpKcmuP8dN2ebi4qLatWsrJCRE06dPV5MmTfTWW29xvCBHO3fu1JkzZ9S8eXM5OTnJyclJW7Zs0Zw5c+Tk5CQ/Pz+OG9ySj4+P6tatq4MHD5aq7xqCWAng4uKikJAQxcbGmm1ZWVmKjY1VWFhYMVaGkqJGjRry9/e3O4ZSUlK0fft2jqEyzDAMDR8+XKtXr9ZXX32lGjVq2C0PCQmRs7Oz3XGzf/9+HTt2jOMGpqysLKWnp3O8IEft27fX7t27FR8fbz5atGihqKgo898cN7iV1NRUHTp0SAEBAaXqu4ZTE0uImJgY9evXTy1atFCrVq305ptvKi0tTdHR0cVdGu4SqampOnjwoPn88OHDio+PV8WKFXXvvfdqxIgRmjp1qurUqaMaNWpo/PjxCgwMVNeuXYuvaBSrYcOGadmyZfr000/l6elpnlvv7e2tcuXKydvbWwMGDFBMTIwqVqwoLy8v/e1vf1NYWJj+9Kc/FXP1KA7jxo1Tx44dde+99+rixYtatmyZNm/erHXr1nG8IEeenp7mdafXeXh46J577jHbOW5ws9GjR6tLly6qVq2aTp06pYkTJ8rR0VG9e/cuXd81xT1tI/Jv7ty5xr333mu4uLgYrVq1MrZt21bcJeEusmnTJkNStke/fv0Mw/hjCvvx48cbfn5+hqurq9G+fXtj//79xVs0ilVOx4sk47333jP7XL582Rg6dKhRoUIFw93d3Xj88ceN06dPF1/RKFZ/+ctfjGrVqhkuLi6Gr6+v0b59e2P9+vXmco4X5MeN09cbBscNsuvZs6cREBBguLi4GFWqVDF69uxpHDx40FxeWo4Zm2EYRjFlQAAAAAAok7hGDAAAAAAsRhADAAAAAIsRxAAAAADAYgQxAAAAALAYQQwAAAAALEYQAwAAAACLEcQAAAAAwGIEMQAAAACwGEEMAFAq2Ww2ffLJJ8VdRp42b94sm82mpKSk4i4FAGAxghgAoETp37+/bDabbDabnJ2d5efnpz//+c9atGiRsrKyzH6nT59Wx44di7HSW2vdurVOnz4tb2/v4i4FAGAxghgAoMSJjIzU6dOndeTIEX355Zd66KGH9Nxzz+mRRx7RtWvXJEn+/v5ydXUt5krz5uLiIn9/f9lstuIuBQBgMYIYAKDEcXV1lb+/v6pUqaLmzZvr73//uz799FN9+eWXWrx4sST7UxOPHDkim82mFStW6P7771e5cuXUsmVL/frrr9qxY4datGih8uXLq2PHjjp79qzdtv71r3+pfv36cnNzU3BwsN5++21z2fX1fvzxx3rooYfk7u6uJk2aKC4uzuxz9OhRdenSRRUqVJCHh4caNmyoL774QlLOpyauWrVKDRs2lKurq6pXr67XX3/drp7q1avrlVde0V/+8hd5enrq3nvv1bvvvmsuz8jI0PDhwxUQECA3NzdVq1ZN06dPL4y3HQBQiAhiAIBS4eGHH1aTJk308ccf59pn4sSJeumll7Rr1y45OTmpT58+GjNmjN566y198803OnjwoCZMmGD2X7p0qSZMmKBp06Zp7969euWVVzR+/HgtWbLEbr0vvviiRo8erfj4eNWtW1e9e/c2R+aGDRum9PR0ff3119q9e7dmzpyp8uXL51jfzp071aNHD/Xq1Uu7d+/WpEmTNH78eDNcXvf666+rRYsW+t///qehQ4dqyJAh2r9/vyRpzpw5WrNmjVasWKH9+/dr6dKlql69+m28owCAouRU3AUAAFBYgoOD9dNPP+W6fPTo0YqIiJAkPffcc+rdu7diY2PVpk0bSdKAAQPsQs/EiRP1+uuv64knnpAk1ahRQ7/88oveeecd9evXz269nTt3liRNnjxZDRs21MGDBxUcHKxjx46pW7duatSokSSpZs2audY3e/ZstW/fXuPHj5ck1a1bV7/88otmzZql/v37m/06deqkoUOHSpJeeOEFvfHGG9q0aZPq1aunY8eOqU6dOmrbtq1sNpuqVauW37cPAGAhRsQAAKWGYRh5Xm/VuHFj899+fn6SZAak621nzpyRJKWlpenQoUMaMGCAypcvbz6mTp2qQ4cO5bregIAASTLX8+yzz2rq1Klq06aNJk6cmGdQ3Lt3rxkKr2vTpo0OHDigzMzMHLdns9nk7+9vbq9///6Kj49XvXr19Oyzz2r9+vW5bg8AUHwIYgCAUmPv3r2qUaNGrsudnZ3Nf18PbDe3XZ95MTU1VZK0YMECxcfHm489e/Zo27Ztt1zv9fUMHDhQv/32m55++mnt3r1bLVq00Ny5c+9kN+22d3PdzZs31+HDhzVlyhRdvnxZPXr00JNPPnlH2wMAFD6CGACgVPjqq6+0e/dudevWrVDW5+fnp8DAQP3222+qXbu23SOvsJeToKAgPfPMM/r44481atQoLViwIMd+9evX19atW+3atm7dqrp168rR0THf2/Py8lLPnj21YMECLV++XKtWrdKFCxcKVDMAoGhxjRgAoMRJT09XQkKCMjMzlZiYqLVr12r69Ol65JFH1Ldv30LbzuTJk/Xss8/K29tbkZGRSk9P1w8//KDff/9dMTEx+VrHiBEj1LFjR9WtW1e///67Nm3apPr16+fYd9SoUWrZsqWmTJminj17Ki4uTv/4xz/sZmq8ldmzZysgIEDNmjWTg4ODVq5cKX9/f/n4+OR7HQCAokcQAwCUOGvXrlVAQICcnJxUoUIFNWnSRHPmzFG/fv3k4FB4J3sMHDhQ7u7umjVrlp5//nl5eHioUaNGGjFiRL7XkZmZqWHDhunEiRPy8vJSZGSk3njjjRz7Nm/eXCtWrNCECRM0ZcoUBQQE6OWXX7abqONWPD099eqrr+rAgQNydHRUy5Yt9cUXXxTq+wIAuHM2wzCM4i4CAAAAAMoS/jwGAAAAABYjiAEAAACAxQhiAAAAAGAxghgAAAAAWIwgBgAAAAAWI4gBAAAAgMUIYgAAAABgMYIYAAAAAFiMIAYAAAAAFiOIAQAAAIDFCGIAAAAAYLH/B/yJok7GOEWVAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top dimensions with the highest variance: [17 12  3]\n"
     ]
    }
   ],
   "source": [
    "gender = dict_vectors['man']-dict_vectors['woman']\n",
    "# Plotting the variance\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.bar(range(1, 51), gender)\n",
    "plt.title('Variance of GloVe Dimensions for Country Words')\n",
    "plt.xlabel('Dimensions')\n",
    "plt.ylabel('Variance')\n",
    "plt.show()\n",
    "\n",
    "top_dims = np.argsort(-gender)[:3]  # Get indices of top 3 dimensions with highest variance\n",
    "print(\"Top dimensions with the highest variance:\", top_dims + 1)  # Adding 1 to match human-readable indexing (1-50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cosine Similarity between 'aunt - uncle' and 'woman - man': 0.6922299265861511\n",
      "Cosine Similarity between 'mother - father' and 'woman - man': 0.6738762855529785\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vectors = {\n",
    "    'aunt': model['aunt'],\n",
    "    'uncle': model['uncle'],\n",
    "    'mother': model['mother'],\n",
    "    'father': model['father'],\n",
    "    'man': model['man'],\n",
    "    'woman': model['woman'],\n",
    "}\n",
    "\n",
    "# Compute difference vectors\n",
    "diff_aunt_uncle = vectors['aunt'] - vectors['uncle']\n",
    "diff_mother_father = vectors['mother'] - vectors['father']\n",
    "diff_woman_man = vectors['woman'] - vectors['man']\n",
    "\n",
    "# Compute cosine similarities between differences\n",
    "from scipy.spatial.distance import cosine\n",
    "\n",
    "similarity_aunt_uncle = 1 - cosine(diff_aunt_uncle, diff_woman_man)\n",
    "similarity_mother_father = 1 - cosine(diff_mother_father, diff_woman_man)\n",
    "\n",
    "print(f\"Cosine Similarity between 'aunt - uncle' and 'woman - man': {similarity_aunt_uncle}\")\n",
    "print(f\"Cosine Similarity between 'mother - father' and 'woman - man': {similarity_mother_father}\")\n",
    "\n",
    "# Plot the vectors for the three highest changing dimensions\n",
    "# Identify the top 3 dimensions with the greatest change\n",
    "top_dims = np.argsort(np.abs(diff_woman_man))[-3:]\n",
    "\n",
    "# Plotting these dimensions\n",
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "for i, word in enumerate(['aunt', 'uncle', 'mother', 'father', 'man', 'woman']):\n",
    "    plt.scatter(vectors[word][top_dims[0]], vectors[word][top_dims[1]], label=word)\n",
    "\n",
    "plt.xlabel(f'Dimension {top_dims[0] + 1}')\n",
    "plt.ylabel(f'Dimension {top_dims[1] + 1}')\n",
    "plt.title('Plot of Word Vectors in Top 2 Changing Dimensions')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Line3DCollection at 0x7ff444059f60>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot on the top_dims[0] 3d plot \n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# Plotting the vectors\n",
    "ax.quiver(0,0,0, vectors['aunt'][top_dims[0]], vectors['aunt'][top_dims[1]], vectors['aunt'][top_dims[2]], color='b', label='aunt')\n",
    "ax.quiver(0,0,0, vectors['uncle'][top_dims[0]], vectors['uncle'][top_dims[1]], vectors['uncle'][top_dims[2]], color='r', label='uncle')\n",
    "ax.quiver(0,0,0, vectors['mother'][top_dims[0]], vectors['mother'][top_dims[1]], vectors['mother'][top_dims[2]], color='g', label='mother')\n",
    "ax.quiver(0,0,0, vectors['father'][top_dims[0]], vectors['father'][top_dims[1]], vectors['father'][top_dims[2]], color='y', label='father')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.31739 , -0.14033 ,  0.32292 ,  1.072   ,  0.33008 ,  0.39406 ,\n",
       "       -0.016682,  0.076903, -0.74591 , -0.31521 ,  1.0033  , -0.12659 ,\n",
       "        0.063252,  0.64006 ,  0.70721 ,  0.84303 , -0.68832 ,  0.47214 ,\n",
       "       -0.66002 ,  0.73962 ,  1.1116  , -0.89428 , -0.90364 , -0.47281 ,\n",
       "        0.88529 , -2.0194  ,  0.30623 , -0.31662 , -0.44423 , -0.52139 ,\n",
       "        3.0287  ,  0.70315 ,  0.92315 ,  0.52263 , -0.62674 , -0.58995 ,\n",
       "       -0.15876 , -0.078332, -1.0794  , -0.71552 , -1.2764  , -0.85554 ,\n",
       "        1.2827  , -1.2134  ,  1.0125  ,  0.40329 , -0.16276 ,  0.99117 ,\n",
       "        0.031016, -0.35431 ], dtype=float32)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model[\"japan\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[40], line 16\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# create 3d plot with the 2 vecotrs \u001b[39;00m\n\u001b[1;32m      6\u001b[0m fig \u001b[38;5;241m=\u001b[39m go\u001b[38;5;241m.\u001b[39mFigure(data\u001b[38;5;241m=\u001b[39m[go\u001b[38;5;241m.\u001b[39mScatter3d(\n\u001b[1;32m      7\u001b[0m     x\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m0\u001b[39m, vectors[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maunt\u001b[39m\u001b[38;5;124m'\u001b[39m][top_dims[\u001b[38;5;241m0\u001b[39m]], vectors[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124muncle\u001b[39m\u001b[38;5;124m'\u001b[39m][top_dims[\u001b[38;5;241m0\u001b[39m]], vectors[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmother\u001b[39m\u001b[38;5;124m'\u001b[39m][top_dims[\u001b[38;5;241m0\u001b[39m]], vectors[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfather\u001b[39m\u001b[38;5;124m'\u001b[39m][top_dims[\u001b[38;5;241m0\u001b[39m]]],\n\u001b[1;32m      8\u001b[0m     y\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m0\u001b[39m, vectors[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maunt\u001b[39m\u001b[38;5;124m'\u001b[39m][top_dims[\u001b[38;5;241m1\u001b[39m]], vectors[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124muncle\u001b[39m\u001b[38;5;124m'\u001b[39m][top_dims[\u001b[38;5;241m1\u001b[39m]], vectors[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmother\u001b[39m\u001b[38;5;124m'\u001b[39m][top_dims[\u001b[38;5;241m1\u001b[39m]], vectors[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfather\u001b[39m\u001b[38;5;124m'\u001b[39m][top_dims[\u001b[38;5;241m1\u001b[39m]]],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     12\u001b[0m     line\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m(color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblack\u001b[39m\u001b[38;5;124m'\u001b[39m, width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m     13\u001b[0m )])\n\u001b[0;32m---> 16\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/plotly/basedatatypes.py:3410\u001b[0m, in \u001b[0;36mBaseFigure.show\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3377\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   3378\u001b[0m \u001b[38;5;124;03mShow a figure using either the default renderer(s) or the renderer(s)\u001b[39;00m\n\u001b[1;32m   3379\u001b[0m \u001b[38;5;124;03mspecified by the renderer argument\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3406\u001b[0m \u001b[38;5;124;03mNone\u001b[39;00m\n\u001b[1;32m   3407\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   3408\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mplotly\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpio\u001b[39;00m\n\u001b[0;32m-> 3410\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/plotly/io/_renderers.py:394\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[1;32m    389\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    390\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires ipython but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    391\u001b[0m         )\n\u001b[1;32m    393\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m nbformat \u001b[38;5;129;01mor\u001b[39;00m Version(nbformat\u001b[38;5;241m.\u001b[39m__version__) \u001b[38;5;241m<\u001b[39m Version(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m4.2.0\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 394\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    395\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires nbformat>=4.2.0 but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    396\u001b[0m         )\n\u001b[1;32m    398\u001b[0m     ipython_display\u001b[38;5;241m.\u001b[39mdisplay(bundle, raw\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m    400\u001b[0m \u001b[38;5;66;03m# external renderers\u001b[39;00m\n",
      "\u001b[0;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed"
     ]
    }
   ],
   "source": [
    "import plotly as py \n",
    "import plotly.graph_objects as go\n",
    "\n",
    "# create 3d plot with the 2 vecotrs \n",
    "\n",
    "fig = go.Figure(data=[go.Scatter3d(\n",
    "    x=[0, vectors['aunt'][top_dims[0]], vectors['uncle'][top_dims[0]], vectors['mother'][top_dims[0]], vectors['father'][top_dims[0]]],\n",
    "    y=[0, vectors['aunt'][top_dims[1]], vectors['uncle'][top_dims[1]], vectors['mother'][top_dims[1]], vectors['father'][top_dims[1]]],\n",
    "    z=[0, vectors['aunt'][top_dims[2]], vectors['uncle'][top_dims[2]], vectors['mother'][top_dims[2]], vectors['father'][top_dims[2]]],\n",
    "    mode='markers+lines',\n",
    "    marker=dict(size=12, color=['blue', 'red', 'green', 'yellow', 'orange']),\n",
    "    line=dict(color='black', width=2)\n",
    ")])\n",
    "\n",
    "\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(-0.3091900050640106, -0.35172998905181885, 'japanese')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create 3d plot with the 2 vecotrs\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# plot the sushi vector\n",
    "ax.quiver(0, 0, 0, sushi_vector[0], sushi_vector[1], sushi_vector[2], color='b')\n",
    "ax.text(sushi_vector[0], sushi_vector[1], sushi_vector[2], \"sushi\", color='b')\n",
    "\n",
    "# plot the japanese vector\n",
    "ax.quiver(0, 0, 0, japanese_vector[0], japanese_vector[1], japanese_vector[2], color='r')\n",
    "ax.text(japanese_vector[0], japanese_vector[1], japanese_vector[2], \"japanese\", color='r')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}