{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring Global Continuous Bag of Words (CBOW) Optimization\n",
    "\n",
    "---\n",
    "\n",
    "Elliott Forney - 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "#pip3 install 'git+ssh://git@github.com/IBM/regenerator@0.2.0' --user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy\n",
    "import collections, itertools\n",
    "import math\n",
    "import random, re\n",
    "\n",
    "import regenerator\n",
    "import munch\n",
    "import nltk\n",
    "\n",
    "import scipy as sp\n",
    "import scipy.spatial.distance as spdist\n",
    "\n",
    "import torch as th\n",
    "import torch.nn.functional as thfunc\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "matplotlib.rcParams['figure.figsize'] = (9, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Tokenizer:                                                                                    \n",
    "    def __init__(self):\n",
    "        #self.nltk_word_tokenizer = nltk.tokenize.WordPunctTokenizer()                              \n",
    "        self.nltk_word_tokenizer = nltk.tokenize.TreebankWordTokenizer()                            \n",
    "        self.nltk_sent_tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\n",
    "                                                                                                    \n",
    "    def __call__(self, text):                                                                       \n",
    "        return [self.nltk_word_tokenizer.tokenize(sent)                                             \n",
    "                for sent in self.nltk_sent_tokenizer.tokenize(text)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = Tokenizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_stream = regenerator.Stream.from_txt('wikipedia.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Stream>:\n",
       "'Anarchism\\n',\n",
       "\"Anarchism is an anti-authoritarian political and social philosophy that rejects hierarchy as unjust and advocates its replacement with self-managed, self-governed societies based on voluntary, cooperative institutions. These institutions are often described as stateless societies, although several authors have defined them more specifically as distinct institutions based on non-hierarchical or free associations. Anarchism's central disagreement with other ideologies is that it holds the state to be undesirable, unnecessary, and harmful.\\n\",\n",
       "'Anarchism is usually placed on the far-left of the political spectrum, and much of its economics and legal philosophy reflect anti-authoritarian interpretations of communism, collectivism, syndicalism, mutualism, or participatory economics. As anarchism does not offer a fixed body of doctrine from a single particular worldview, many anarchist types and traditions exist and varieties of anarchy diverge widely. Anarchist schools of thought can differ fundamentally, supporting anything from extreme individualism to complete collectivism. Strains of anarchism have often been divided into the categories of social and individualist anarchism, or similar dual classifications.\\n',\n",
       "'The etymological origin of the word anarchism is from the ancient Greek word \"anarkhia\", meaning \"without a ruler\", composed of the prefix \"a-\" (i.e. \"without\") and the word \"arkhos\" (i.e. \"leader\" or \"ruler\"). The suffix -ism denotes the ideological current that favours anarchy. The word anarchism appears in English from 1642 as \"anarchisme\" and the word \"anarchy\" from 1539. Various factions within the French Revolution labelled their opponents as anarchists, although few such accused shared many views with later anarchists. Many revolutionaries of the 19th century such as William Godwin (1756–1836) and Wilhelm Weitling (1808–1871) would contribute to the anarchist doctrines of the next generation, but they did not use the word anarchist or anarchism in describing themselves or their beliefs.\\n',\n",
       "'The first political philosopher to call himself an anarchist () was Pierre-Joseph Proudhon (1809–1865), marking the formal birth of anarchism in the mid-19th century. Since the 1890s and beginning in France, the term libertarianism has often been used as a synonym for anarchism and its use as a synonym is still common outside the United States. On the other hand, some use libertarianism to refer to individualistic free-market philosophy only, referring to free-market anarchism as libertarian anarchism.\\n',\n",
       "..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_stream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "token_stream = (\n",
    "    raw_stream\n",
    "    .map(str.lower)\n",
    "    .map(str.strip)\n",
    "    .map(tokenizer)\n",
    "    .unchunk()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Stream>:\n",
       "['anarchism'],\n",
       "['anarchism', 'is', 'an', 'anti-authoritarian', 'political', 'and', 'social', 'philosophy', 'that', 'rejects', 'hierarchy', 'as', 'unjust', 'and', 'advocates', 'its', 'replacement', 'with', 'self-managed', ',', 'self-governed', 'societies', 'based', 'on', 'voluntary', ',', 'cooperative', 'institutions', '.'],\n",
       "['these', 'institutions', 'are', 'often', 'described', 'as', 'stateless', 'societies', ',', 'although', 'several', 'authors', 'have', 'defined', 'them', 'more', 'specifically', 'as', 'distinct', 'institutions', 'based', 'on', 'non-hierarchical', 'or', 'free', 'associations', '.'],\n",
       "['anarchism', \"'s\", 'central', 'disagreement', 'with', 'other', 'ideologies', 'is', 'that', 'it', 'holds', 'the', 'state', 'to', 'be', 'undesirable', ',', 'unnecessary', ',', 'and', 'harmful', '.'],\n",
       "['anarchism', 'is', 'usually', 'placed', 'on', 'the', 'far-left', 'of', 'the', 'political', 'spectrum', ',', 'and', 'much', 'of', 'its', 'economics', 'and', 'legal', 'philosophy', 'reflect', 'anti-authoritarian', 'interpretations', 'of', 'communism', ',', 'collectivism', ',', 'syndicalism', ',', 'mutualism', ',', 'or', 'participatory', 'economics', '.'],\n",
       "..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "token_stream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Stream>:\n",
       "['strains', 'of', 'anarchism', 'have', 'often', 'been', 'divided', 'into', 'the', 'categories', 'of', 'social', 'and', 'individualist', 'anarchism', ',', 'or', 'similar', 'dual', 'classifications', '.'],\n",
       "['anarchists', 'enthusiastically', 'participated', 'in', 'the', 'russian', 'revolution—despite', 'concerns—in', 'opposition', 'to', 'the', 'whites', '.'],\n",
       "['beyond', 'the', 'specific', 'factions', 'of', 'anarchist', 'thought', 'is', 'philosophical', 'anarchism', 'which', 'holds', 'that', 'the', 'state', 'lacks', 'moral', 'legitimacy', ',', 'without', 'accepting', 'the', 'imperative', 'of', 'revolution', 'to', 'eliminate', 'it', '.'],\n",
       "['they', 'saw', 'it', 'as', 'a', 'part', 'of', 'overthrowing', 'state', 'and', 'capitalism', '.'],\n",
       "['he', 'also', 'argued', 'that', '``', 'ill', 'intentions', 'will', 'cease', 'if', 'repressive', 'force', 'disappears', \"''\", 'is', 'an', '``', 'absurdity', \"''\", '.'],\n",
       "..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_sent_len = 5\n",
    "max_sent_len = 96\n",
    "sample_frac = 0.02\n",
    "\n",
    "trimmed_token_stream, _ = (\n",
    "    token_stream\n",
    "    .filter(lambda sent: min_sent_len <= len(sent) <= max_sent_len)\n",
    "    .random_split(sample_frac)\n",
    ")\n",
    "    \n",
    "trimmed_token_stream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(len(sent) for sent in trimmed_token_stream[:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(len(sent) for sent in trimmed_token_stream[:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nsent\n",
    "#len(trimmed_token_stream)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nword\n",
    "#len(trimmed_token_stream.unchunk())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Vocab:\n",
    "    pad, unk, num = '<pad>', '<unk>', '<num>'\n",
    "    special = pad, unk, num\n",
    "\n",
    "    special_idx = range(3)\n",
    "    pad_idx, unk_idx, num_idx = special_idx\n",
    "\n",
    "    def __init__(self, words, max_size=None, num_regex=r'^[\\−\\-\\+]?[\\$¢€£¥₹₤]?\\.?\\d+([\\.,:/]\\d+)*$'):\n",
    "        self.num_regex = re.compile(num_regex)\n",
    "\n",
    "        words = filter(lambda word: not self.num_regex.match(word), words)\n",
    "        \n",
    "        if max_size:\n",
    "            counter = collections.Counter(words)\n",
    "            words = (word for word, count in counter.most_common(max_size - len(self.special)))\n",
    "\n",
    "        words = sorted(words)\n",
    "\n",
    "        def idx_factory():\n",
    "            idx, idx_factory.idx = idx_factory.idx, idx_factory.idx + 1\n",
    "            return idx\n",
    "        idx_factory.idx = 0\n",
    "\n",
    "        vocab = collections.defaultdict(idx_factory)\n",
    "        for word in itertools.chain(self.special, words):\n",
    "            vocab[word]\n",
    "\n",
    "        self.vocab = dict(vocab)\n",
    "\n",
    "        self.reverse = {idx: word for word, idx in self.vocab.items()}\n",
    "        \n",
    "    @classmethod\n",
    "    def from_corpus(cls, corpus, *args, **kwargs):\n",
    "        return cls((word for sent in corpus for word in sent), *args, **kwargs)\n",
    "    \n",
    "    @classmethod\n",
    "    def from_file(cls, filename, *args, **kwargs):\n",
    "        with open(filename, mode='r', encoding='utf8') as fh:\n",
    "            words = [line.rstrip('\\n') for line in fh]\n",
    "            \n",
    "        return cls(words, *args, **kwargs)\n",
    "\n",
    "    def save(self, filename):\n",
    "        with open(filename, mode='w', encoding='utf8') as fh:\n",
    "            fh.writelines(word + '\\n' for word in self)\n",
    "\n",
    "    def __getitem__(self, word):\n",
    "        #if word.isdigit():\n",
    "        if self.num_regex.match(word):\n",
    "            return self.num_idx\n",
    "\n",
    "        return self.vocab.get(word, self.unk_idx)\n",
    "\n",
    "    def __iter__(self):\n",
    "        '''Return iterator over words in order of indices.\n",
    "        '''\n",
    "        return (word for idx, word in sorted((idx, word) for word, idx in self.vocab.items()))\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]\n",
      "[False, False, False, False, False, False, False, False]\n"
     ]
    }
   ],
   "source": [
    "rgx = re.compile(r'^[\\−\\-\\+]?[\\$¢€£¥₹₤]?\\.?\\d+([\\.,:/]\\d+)*$')\n",
    "a = (\n",
    "    '123', '0.2', '.23', '0.23:0010', '000', '$1000', '$1,000,000.00',\n",
    "    '+100', '-100', '+$1,000', '0.00.00:00', '-€100', '+.3', '.333', '+0.1',\n",
    "    '1/4', '+$100,00/2.00', '−0.3', '€15.4'\n",
    ")\n",
    "print([bool(rgx.match(x)) for x in a])\n",
    "\n",
    "b = '0.', 'abc', 'x23', '0...', '1--2', '-$', '0..2', '1//4'\n",
    "print([bool(rgx.match(x)) for x in b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#max_vocab_size = 65_536\n",
    "max_vocab_size = 32_768\n",
    "\n",
    "vocab = Vocab.from_corpus(trimmed_token_stream, max_size=max_vocab_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab['£2,343']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32768"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab.save('global_cbow_vocab.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pad>\n",
      "<unk>\n",
      "<num>\n",
      "!\n",
      "#\n",
      "$\n",
      "%\n",
      "&\n",
      "'\n",
      "''\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "head global_cbow_vocab.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cantor\n",
      "canucks\n",
      "canvas\n",
      "canvases\n",
      "canyon\n",
      "canyons\n",
      "can’t\n",
      "cao\n",
      "cap\n",
      "capabilities\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "head -5000 global_cbow_vocab.txt | tail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "token_index_stream = trimmed_token_stream.map(\n",
    "    lambda sent: [vocab[word] for word in sent])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Stream>:\n",
       "[28228, 20758, 1822, 13677, 20800, 3438, 9056, 15474, 29437, 5264, 20758, 27367, 1846, 14990, 1822, 38, 21009, 27011, 9415, 5974, 44],\n",
       "[1824, 10256, 21639, 14849, 29437, 25643, 1, 1, 20982, 29760, 29437, 32027, 44],\n",
       "[3676, 29437, 27655, 10962, 20758, 1823, 29563, 15638, 22175, 1822, 32004, 14168, 29431, 29437, 28001, 16680, 19428, 17036, 38, 32227, 845, 29437, 14777, 20758, 25067, 29760, 9883, 15705, 44],\n",
       "[29505, 25964, 15705, 2456, 680, 21628, 20758, 1, 28001, 1846, 5013, 44],\n",
       "[13720, 1643, 2311, 29431, 679, 14683, 15345, 32120, 5348, 14650, 24776, 11774, 8793, 9, 15638, 1796, 679, 1, 9, 44],\n",
       "..."
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "token_index_stream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "token_rev_index_stream = token_index_stream.map(\n",
    "    lambda sent: [vocab.reverse[word] for word in sent])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Stream>:\n",
       "['strains', 'of', 'anarchism', 'have', 'often', 'been', 'divided', 'into', 'the', 'categories', 'of', 'social', 'and', 'individualist', 'anarchism', ',', 'or', 'similar', 'dual', 'classifications', '.'],\n",
       "['anarchists', 'enthusiastically', 'participated', 'in', 'the', 'russian', '<unk>', '<unk>', 'opposition', 'to', 'the', 'whites', '.'],\n",
       "['beyond', 'the', 'specific', 'factions', 'of', 'anarchist', 'thought', 'is', 'philosophical', 'anarchism', 'which', 'holds', 'that', 'the', 'state', 'lacks', 'moral', 'legitimacy', ',', 'without', 'accepting', 'the', 'imperative', 'of', 'revolution', 'to', 'eliminate', 'it', '.'],\n",
       "['they', 'saw', 'it', 'as', 'a', 'part', 'of', '<unk>', 'state', 'and', 'capitalism', '.'],\n",
       "['he', 'also', 'argued', 'that', '``', 'ill', 'intentions', 'will', 'cease', 'if', 'repressive', 'force', 'disappears', \"''\", 'is', 'an', '``', '<unk>', \"''\", '.'],\n",
       "..."
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "token_rev_index_stream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_index_stream, valid_index_stream = token_index_stream.random_split(frac=0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# len(train_index_stream), len(valid_index_stream)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('global_cbow_train_idxs.txt', mode='w') as fh:\n",
    "    for sent in train_index_stream:\n",
    "        fh.write(' '.join(map(str, sent)) + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28228 20758 1822 13677 20800 3438 9056 15474 29437 5264 20758 27367 1846 14990 1822 38 21009 27011 9415 5974 44\n",
      "1824 10256 21639 14849 29437 25643 1 1 20982 29760 29437 32027 44\n",
      "3676 29437 27655 10962 20758 1823 29563 15638 22175 1822 32004 14168 29431 29437 28001 16680 19428 17036 38 32227 845 29437 14777 20758 25067 29760 9883 15705 44\n",
      "29505 25964 15705 2456 680 21628 20758 1 28001 1846 5013 44\n",
      "13720 1643 2311 29431 679 14683 15345 32120 5348 14650 24776 11774 8793 9 15638 1796 679 1 9 44\n",
      "12227 12128 38 28511 2456 29560 24952 14849 15551 29760 21651 38 18479 1210 29437 23691 20758 17230 19439 8141 44\n",
      "2824 14994 4932 8937 18174 11849 20758 24740 21009 24938 3470 38 32004 29437 24740 3470 1 33 1 34 1 2456 11724 44\n",
      "1666 29499 29476 16676 7119 26087 10612 1846 2296 3776 1 38 21581 6670 775 680 22815 31039 17341 32215 2823 13636 16996 29760 17668 24013 20758 5724 1 38 21191 20758 23087 7067 5724 8876 14849 27452 7352 38 1846 29437 1 8034 20758 26561 5727 44\n",
      "27452 23999 19744 18479 16937 29760 2823 4737 8963 27452 28927 21738 38 28511 2456 29560 15562 32215 5375 1060 44\n",
      "12733 2296 20800 8597 16843 29422 4228 44\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "head global_cbow_train_idxs.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('global_cbow_valid_idxs.txt', mode='w') as fh:\n",
    "    for sent in valid_index_stream:\n",
    "        fh.write(' '.join(map(str, sent)) + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29437 824 2902 11763 3641 23224 8448 24240 927 11763 2413 8756 14849 11389 44\n",
      "14849 2 38 29437 26983 1928 26818 679 1 11716 9 18254 29437 8049 20758 11226 29437 5232 38 3422 29437 11479 1928 5561 14849 29437 26983 11389 10363 29760 12333 26963 22688 44\n",
      "1 24278 12147 29437 15165 1846 16843 29431 32509 8163 14094 16352 1 29744 3392 29457 25232 25360 11204 14849 680 1 11405 44\n",
      "29488 15638 1643 18736 14849 29437 4078 20758 30981 1846 29395 22757 11763 18841 14849 367 14849 24911 29760 29542 25025 38 25360 11204 8093 14053 26794 1846 8794 38 32008 1 1 2311 29431 1 26842 25004 14094 23170 19331 1846 3362 28309 20758 14094 29747 44\n",
      "14849 29437 12590 679 1 9 38 29437 11234 13636 3438 20673 29760 6241 2379 29450 1846 23049 13886 29607 2297 1269 29450 38 9751 1 29450 32215 680 1 26260 44\n",
      "29437 11479 15638 4865 29437 15144 1 1846 19732 29032 22381 32226 2 14353 20758 1796 14988 27 2393 38 2 14353 14650 29437 14988 31737 2394 20873 29437 31867 1846 20432 756 29760 12817 3446 680 16087 30859 19326 44\n",
      "30645 29447 14010 1 38 1 20758 9860 28511 2456 26347 38 1 38 30935 38 1 1846 22532 13677 680 14010 27436 1846 2296 1643 20800 23023 14849 31759 2456 1 1 44\n",
      "2513 15638 30492 28198 1846 30100 2634 29278 2379 44\n",
      "1 31737 21092 11843 14849 2 38 31993 11525 10158 27375 1846 29579 12938 1275 11944 29437 1733 10158 27950 6478 33 1 34 44\n",
      "29437 679 17441 9 1846 679 5473 9 31927 1643 6888 27064 17124 38 4710 14849 2 29437 24116 818 10437 5535 29437 1 21039 27357 29431 679 17441 9 15638 3667 679 1 9 1846 679 1 9 14849 29437 8641 1846 679 5473 9 15638 3667 679 5471 9 1846 679 5849 9 38 1846 14849 2 29437 29329 6804 20758 29437 2579 20758 27600 16788 823 5535 15705 27357 29505 31927 20282 17561 17124 2634 1536 44\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "head global_cbow_valid_idxs.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import time\n",
    "\n",
    "#start = time.time()\n",
    "#with open('global_cbow_train_idxs.txt', mode='r') as fh:\n",
    "#    sents = [[int(word) for word in line.strip().split()] for line in fh]\n",
    "#time.time() - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sentinel = (-1).to_bytes(4, byteorder='big', signed=True)\n",
    "#int.from_bytes(sentinel, byteorder='big', signed=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#nbytes = 4 # 32 bit max\n",
    "#sentinel = (-1).to_bytes(nbytes, byteorder='big', signed=True)\n",
    "\n",
    "#with open('global_cbow_train_idxs.bin', mode='wb') as fh:\n",
    "#    for sent in train_index_stream:\n",
    "#        for word in sent:\n",
    "#            fh.write(word.to_bytes(nbytes, byteorder='big', signed=False))\n",
    "#    fh.write(sentinel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#start = time.time()\n",
    "\n",
    "#with open('global_cbow_train_idxs.bin', mode='rb') as fh:\n",
    "#    sents = []\n",
    "#    cur_sent = []\n",
    "\n",
    "#    while data := fh.read(nbytes):\n",
    "#        if data == sentinel:\n",
    "#            sents.append(cur_sent)\n",
    "#            cur_sent = []\n",
    "#        else:\n",
    "#            int_data = int.from_bytes(data, byteorder='big', signed=False)\n",
    "#            cur_sent.append(int_data)\n",
    "#time.time() - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32768"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab = Vocab.from_file('global_cbow_vocab.txt')\n",
    "len(vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TokenIndexStream(regenerator.Stream):\n",
    "    def __init__(self, filename):\n",
    "        def load_token_indices():\n",
    "            with open(filename, mode='r') as fh:\n",
    "                for sent in fh:\n",
    "                    yield th.tensor([int(word) for word in sent.strip().split()], dtype=th.int64)\n",
    "            \n",
    "        super().__init__(load_token_indices)\n",
    "        \n",
    "    def buffer_shuffle(self, size=8192, refill_frac=0.5):\n",
    "        def generator_func():\n",
    "            it = iter(self)\n",
    "            split = int(size * refill_frac)\n",
    "\n",
    "            def refill():\n",
    "                return list(itertools.islice(it, split))\n",
    "            \n",
    "            buff = refill()\n",
    "            while True:\n",
    "                buff_new = refill()\n",
    "                if not buff_new:\n",
    "                    break\n",
    "\n",
    "                buff.extend(buff_new)\n",
    "                random.shuffle(buff)\n",
    "\n",
    "                for item in buff[split:]:\n",
    "                    yield item\n",
    "\n",
    "                buff = buff[:split]\n",
    "                    \n",
    "            for item in buff:\n",
    "                yield item\n",
    "                    \n",
    "        return self.from_func(generator_func)\n",
    "    \n",
    "    def buffer_sort(self, size=12_000, shuffle=True):\n",
    "        def generator_func():\n",
    "            it = iter(self)\n",
    "            reverse = False\n",
    "\n",
    "            while True:\n",
    "                buff = list(itertools.islice(it, size))\n",
    "                if not buff: break\n",
    "\n",
    "                if shuffle:\n",
    "                    random.shuffle(buff)\n",
    "\n",
    "                buff.sort(key=len, reverse=reverse)\n",
    "                reverse = not reverse\n",
    "\n",
    "                for item in buff:\n",
    "                    yield item\n",
    "\n",
    "        return self.from_func(generator_func)\n",
    "    \n",
    "    def batch(self, size):                                                                     \n",
    "        def generator_func():                                                                       \n",
    "            it = iter(self)                                                                         \n",
    "                                                                                                    \n",
    "            while True:                                                                             \n",
    "                batch = list(itertools.islice(it, size))                                            \n",
    "                if not batch: break                                                                 \n",
    "                                                                                                    \n",
    "                yield th.nn.utils.rnn.pad_sequence(                                                 \n",
    "                    batch,               \n",
    "                    padding_value=Vocab.pad_idx,                                                    \n",
    "                    batch_first=True)                                                               \n",
    "                                                                                                    \n",
    "        return self.from_func(generator_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TokenIndexStream>:\n",
       "tensor([29488, 29437,  2809, 32395,   669]),\n",
       "tensor([ 8232, 15638, 30443, 32215,   669]),\n",
       "tensor([14094,     1, 26874, 14899,   669]),\n",
       "tensor([ 7318, 20758, 29437, 10557, 30762]),\n",
       "tensor([   33,     1,     1, 31362,    34]),\n",
       "..."
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_stream = TokenIndexStream('global_cbow_train_idxs.txt').buffer_sort()\n",
    "train_stream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TokenIndexStream>:\n",
       "tensor([    1, 15638, 30443, 32215,   669]),\n",
       "tensor([29437, 22692,  8333, 31737,    44]),\n",
       "tensor([29457,   680, 18368,  3451,    44]),\n",
       "tensor([29542, 10755,  2456, 11724,   669]),\n",
       "tensor([26293, 29447, 31840, 27112,    44]),\n",
       "..."
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_stream = TokenIndexStream('global_cbow_valid_idxs.txt').buffer_sort()\n",
    "valid_stream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1896212"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_stream)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99848"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(valid_stream)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TorchDataset(th.utils.data.IterableDataset):\n",
    "    def __init__(self, stream):\n",
    "        super().__init__()\n",
    "        self.stream = stream\n",
    "        \n",
    "    def __iter__(self):\n",
    "        return iter(self.stream)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.TorchDataset at 0x7ffac771de80>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = TorchDataset(train_stream)\n",
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.TorchDataset at 0x7ffabf1bee20>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_data = TorchDataset(valid_stream)\n",
    "valid_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ContinuousBagOfWords(th.nn.Module):\n",
    "    def __init__(self, vocab_size, embed_dim=128, width=5, max_norm=None):\n",
    "        super().__init__()\n",
    "\n",
    "        self.vocab_size = vocab_size\n",
    "        self.embed_dim = embed_dim\n",
    "        self.width = width\n",
    "        self.pad_width = self.width // 2\n",
    "\n",
    "        self.embedding = th.nn.Embedding(\n",
    "            num_embeddings=self.vocab_size,\n",
    "            embedding_dim=self.embed_dim,\n",
    "            padding_idx=Vocab.pad_idx,\n",
    "            max_norm=max_norm)\n",
    "\n",
    "        self.center_mask = th.nn.Parameter(\n",
    "            th.ones(self.width, dtype=th.float32),\n",
    "            requires_grad=False)\n",
    "        self.center_mask[self.pad_width] = 0.0\n",
    "\n",
    "        self.back_proj = th.nn.Linear(\n",
    "            in_features=self.embed_dim,\n",
    "            out_features=self.vocab_size)\n",
    "\n",
    "    def forward(self, idxs):\n",
    "        assert idxs.ndim == 2\n",
    "        batch_size = idxs.shape[0]\n",
    "        max_len = idxs.shape[1]\n",
    "\n",
    "        word_vectors = self.embedding(idxs)\n",
    "        assert word_vectors.shape == (batch_size, max_len, self.embed_dim)\n",
    "\n",
    "        ## if self.pad_method == 'zero':\n",
    "        padded_word_vectors = thfunc.pad(\n",
    "            input=word_vectors,\n",
    "            pad=(0, 0, self.pad_width, self.pad_width),\n",
    "            value=0.0)\n",
    "\n",
    "        folded_word_vectors = padded_word_vectors.unfold(1, self.width, 1)\n",
    "        assert folded_word_vectors.shape == (batch_size, max_len, self.embed_dim, self.width)\n",
    "        \n",
    "        #summed_word_vectors = (folded_word_vectors * self.center_mask).sum(-1)\n",
    "        summed_word_vectors = folded_word_vectors @ self.center_mask\n",
    "        assert summed_word_vectors.shape == (batch_size, max_len, self.embed_dim)\n",
    "\n",
    "        preds = self.back_proj(summed_word_vectors)\n",
    "        assert preds.shape == (batch_size, max_len, self.vocab_size)\n",
    "\n",
    "        return preds\n",
    "    \n",
    "    def save(self, filename):\n",
    "        with open(filename, mode='wb') as fh:                                                  \n",
    "            th.save(self.state_dict(), fh)\n",
    "    \n",
    "    @th.no_grad()\n",
    "    def nearest(self, idxs, n=10):\n",
    "        word_vectors = self.embedding(idxs)\n",
    "\n",
    "        # FIXME: just pull the weights out of the embedding layer?\n",
    "        full_embedding = self.embedding(th.arange(1, self.vocab_size))\n",
    "\n",
    "        dist = th.cdist(word_vectors, full_embedding)\n",
    "\n",
    "        # plus one because we start after `<pad>`\n",
    "        nearest_idxs = th.argsort(dist, dim=1) + 1\n",
    "\n",
    "        return nearest_idxs[:, :n]\n",
    "\n",
    "    @th.no_grad()\n",
    "    def nearest_cosine(self, idxs, n=10):\n",
    "        word_vectors = self.embedding(idxs)\n",
    "\n",
    "        # FIXME: just pull the weights out of the embedding layer?\n",
    "        full_embedding = self.embedding(th.arange(1, self.vocab_size))\n",
    "\n",
    "        dist = th.as_tensor(\n",
    "            spdist.cdist(\n",
    "                word_vectors.numpy(),\n",
    "                full_embedding.numpy(),\n",
    "                metric='cosine')\n",
    "            ).abs()\n",
    "\n",
    "        # plus one because we start after `<pad>`\n",
    "        nearest_idxs = th.argsort(dist, dim=1) + 1\n",
    "\n",
    "        return nearest_idxs[:, :n]\n",
    "    \n",
    "CBOW = ContinuousBagOfWords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, train_data, valid_data, max_epochs, learning_rate=0.01, learning_rate_decay=0.95,\n",
    "          batch_size=64, stop_epochs=10, load_workers=20, use_gpu=True):\n",
    "    \n",
    "    def collate(data):\n",
    "        return th.nn.utils.rnn.pad_sequence(                                                 \n",
    "            data,               \n",
    "            padding_value=Vocab.pad_idx,                                                    \n",
    "            batch_first=True)\n",
    "    \n",
    "    train_loader = th.utils.data.DataLoader(\n",
    "        train_data,\n",
    "        batch_size=batch_size,\n",
    "        pin_memory=use_gpu,\n",
    "        num_workers=load_workers,\n",
    "        drop_last=False,\n",
    "        collate_fn=collate,\n",
    "        shuffle=False)\n",
    "\n",
    "    valid_loader = th.utils.data.DataLoader(\n",
    "        valid_data,\n",
    "        batch_size=batch_size,\n",
    "        pin_memory=use_gpu,\n",
    "        num_workers=load_workers,\n",
    "        drop_last=False,\n",
    "        collate_fn=collate,\n",
    "        shuffle=False)\n",
    "\n",
    "    if use_gpu:\n",
    "        model.cuda()\n",
    "\n",
    "    # all parameters with `requires_grad` set\n",
    "    parameters = list(filter(lambda param: param.requires_grad, model.parameters()))\n",
    "    \n",
    "    # cross entropy loss, ignore padding tokens\n",
    "    loss_func = th.nn.CrossEntropyLoss(\n",
    "        ignore_index=Vocab.pad_idx,\n",
    "        reduction='sum')\n",
    "\n",
    "    # RProp optimizer\n",
    "    optimizer = th.optim.Rprop(lr=learning_rate, params=parameters)\n",
    "    #optimizer = th.optim.Adam(lr=learning_rate, params=parameters)\n",
    "    \n",
    "    # exponential learning rate decay\n",
    "    #lr_scheduler = th.optim.lr_scheduler.ExponentialLR(optimizer, learning_rate_decay)\n",
    "\n",
    "    # performance metrics\n",
    "    train_losses = []\n",
    "    valid_losses = []\n",
    "\n",
    "    # best model state for early stopping\n",
    "    best_train_loss = float('inf')\n",
    "    best_valid_loss = float('inf')\n",
    "    best_epoch = 0\n",
    "    best_state = None\n",
    "\n",
    "    # for each training epoch\n",
    "    for epoch in range(max_epochs):                                                                                                                                                                     \n",
    "        # enter training mode\n",
    "        model.train()\n",
    "\n",
    "        # zero the gradients\n",
    "        # we accumulate over all batches, so this is per-epoch\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # training pass\n",
    "        epoch_train_losses = []\n",
    "        for i, batch in enumerate(train_loader):\n",
    "            # zero the gradients\n",
    "            #optimizer.zero_grad()\n",
    "\n",
    "            # move batch to requested device\n",
    "            if use_gpu:\n",
    "                batch = batch.cuda()\n",
    "\n",
    "            preds = model(batch)\n",
    "            loss = loss_func(                                                                       \n",
    "                preds.reshape(-1, model.vocab_size),                                                \n",
    "                batch.reshape(-1))\n",
    "\n",
    "            loss_f = loss.item()\n",
    "            epoch_train_losses.append(loss_f)\n",
    "\n",
    "            loss.backward()\n",
    "\n",
    "        optimizer.step()\n",
    "\n",
    "        train_losses.append(sum(epoch_train_losses))\n",
    "\n",
    "        # back to evaluation mode\n",
    "        model.eval()\n",
    "\n",
    "        # validation loss\n",
    "        with th.no_grad():\n",
    "            epoch_valid_losses = []\n",
    "            for i, batch in enumerate(valid_loader):\n",
    "                if use_gpu:\n",
    "                    batch = batch.cuda()\n",
    "\n",
    "                preds = model(batch)\n",
    "                loss = loss_func(\n",
    "                    preds.reshape(-1, model.vocab_size),\n",
    "                    batch.reshape(-1))\n",
    "\n",
    "                loss_f = loss.item()\n",
    "                epoch_valid_losses.append(loss_f)\n",
    "\n",
    "        valid_losses.append(sum(epoch_valid_losses))\n",
    "\n",
    "        if valid_losses[-1] < best_valid_loss:\n",
    "            best_train_loss = train_losses[-1]\n",
    "            best_valid_loss = valid_losses[-1]\n",
    "            best_epoch = epoch\n",
    "            best_state = copy.deepcopy(model.state_dict())\n",
    "\n",
    "        print('epoch:', epoch)\n",
    "        print('best epoch:', best_epoch)\n",
    "        print('learning rate:', next(iter(optimizer.param_groups))['lr'])\n",
    "        print('train loss:', train_losses[-1])\n",
    "        print('valid loss:', valid_losses[-1])\n",
    "        print('=======')\n",
    "                \n",
    "        if (best_epoch + stop_epochs) <= epoch:\n",
    "            print(f'terminating after {stop_epochs} of no improvement')\n",
    "            break\n",
    "            \n",
    "        #lr_scheduler.step()\n",
    "\n",
    "    if best_state is None:\n",
    "        raise RuntimeError('best state not set!')\n",
    "\n",
    "    model.load_state_dict(best_state)\n",
    "\n",
    "    if use_gpu:\n",
    "        model.cpu()\n",
    "\n",
    "    return munch.munchify({\n",
    "        'train_losses': train_losses,\n",
    "        'valid_losses': valid_losses,\n",
    "        'best_train_loss': best_train_loss,\n",
    "        'best_valid_loss': best_valid_loss,\n",
    "        'best_epoch': best_epoch,\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "cbow = CBOW(vocab_size=len(vocab), embed_dim=32)#, max_norm=math.sqrt(32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0\n",
      "best epoch: 0\n",
      "learning rate: 0.01\n",
      "train loss: 10178248619.263428\n",
      "valid loss: 522515790.4626465\n",
      "=======\n",
      "epoch: 1\n",
      "best epoch: 1\n",
      "learning rate: 0.01\n",
      "train loss: 9918670694.95752\n",
      "valid loss: 507141938.6359863\n",
      "=======\n",
      "epoch: 2\n",
      "best epoch: 2\n",
      "learning rate: 0.01\n",
      "train loss: 9626374658.73999\n",
      "valid loss: 489507894.90356445\n",
      "=======\n",
      "epoch: 3\n",
      "best epoch: 3\n",
      "learning rate: 0.01\n",
      "train loss: 9291170774.88379\n",
      "valid loss: 469126501.06225586\n",
      "=======\n",
      "epoch: 4\n",
      "best epoch: 4\n",
      "learning rate: 0.01\n",
      "train loss: 8903784501.391602\n",
      "valid loss: 445865610.16845703\n",
      "=======\n",
      "epoch: 5\n",
      "best epoch: 5\n",
      "learning rate: 0.01\n",
      "train loss: 8461869488.496582\n",
      "valid loss: 420255390.5041504\n",
      "=======\n",
      "epoch: 6\n",
      "best epoch: 6\n",
      "learning rate: 0.01\n",
      "train loss: 7975461329.395264\n",
      "valid loss: 394146121.8166504\n",
      "=======\n",
      "epoch: 7\n",
      "best epoch: 7\n",
      "learning rate: 0.01\n",
      "train loss: 7479521723.265869\n",
      "valid loss: 372648052.22021484\n",
      "=======\n",
      "epoch: 8\n",
      "best epoch: 8\n",
      "learning rate: 0.01\n",
      "train loss: 7070873536.835205\n",
      "valid loss: 360378813.0341797\n",
      "=======\n",
      "epoch: 9\n",
      "best epoch: 9\n",
      "learning rate: 0.01\n",
      "train loss: 6836720030.668213\n",
      "valid loss: 349786619.15527344\n",
      "=======\n",
      "epoch: 10\n",
      "best epoch: 10\n",
      "learning rate: 0.01\n",
      "train loss: 6633855668.796387\n",
      "valid loss: 338952673.03222656\n",
      "=======\n",
      "epoch: 11\n",
      "best epoch: 11\n",
      "learning rate: 0.01\n",
      "train loss: 6426298141.292603\n",
      "valid loss: 329838306.90649414\n",
      "=======\n",
      "epoch: 12\n",
      "best epoch: 12\n",
      "learning rate: 0.01\n",
      "train loss: 6252141569.286011\n",
      "valid loss: 321520737.6455078\n",
      "=======\n",
      "epoch: 13\n",
      "best epoch: 13\n",
      "learning rate: 0.01\n",
      "train loss: 6093511193.135376\n",
      "valid loss: 314846720.8713379\n",
      "=======\n",
      "epoch: 14\n",
      "best epoch: 14\n",
      "learning rate: 0.01\n",
      "train loss: 5966205980.695068\n",
      "valid loss: 309798039.1236572\n",
      "=======\n",
      "epoch: 15\n",
      "best epoch: 15\n",
      "learning rate: 0.01\n",
      "train loss: 5869756995.7717285\n",
      "valid loss: 305606500.0686035\n",
      "=======\n",
      "epoch: 16\n",
      "best epoch: 16\n",
      "learning rate: 0.01\n",
      "train loss: 5789575920.330688\n",
      "valid loss: 301917420.7269287\n",
      "=======\n",
      "epoch: 17\n",
      "best epoch: 17\n",
      "learning rate: 0.01\n",
      "train loss: 5718411178.980713\n",
      "valid loss: 298459693.6907959\n",
      "=======\n",
      "epoch: 18\n",
      "best epoch: 18\n",
      "learning rate: 0.01\n",
      "train loss: 5651183198.059204\n",
      "valid loss: 295117800.8208008\n",
      "=======\n",
      "epoch: 19\n",
      "best epoch: 19\n",
      "learning rate: 0.01\n",
      "train loss: 5586530108.439819\n",
      "valid loss: 291876730.692749\n",
      "=======\n",
      "epoch: 20\n",
      "best epoch: 20\n",
      "learning rate: 0.01\n",
      "train loss: 5523920273.508423\n",
      "valid loss: 288925406.12731934\n",
      "=======\n",
      "epoch: 21\n",
      "best epoch: 21\n",
      "learning rate: 0.01\n",
      "train loss: 5466987930.717407\n",
      "valid loss: 286194946.61279297\n",
      "=======\n",
      "epoch: 22\n",
      "best epoch: 22\n",
      "learning rate: 0.01\n",
      "train loss: 5414375881.496338\n",
      "valid loss: 283621936.8691406\n",
      "=======\n",
      "epoch: 23\n",
      "best epoch: 23\n",
      "learning rate: 0.01\n",
      "train loss: 5364773803.50293\n",
      "valid loss: 281114693.7988281\n",
      "=======\n",
      "epoch: 24\n",
      "best epoch: 24\n",
      "learning rate: 0.01\n",
      "train loss: 5316419113.846436\n",
      "valid loss: 278807645.5338135\n",
      "=======\n",
      "epoch: 25\n",
      "best epoch: 25\n",
      "learning rate: 0.01\n",
      "train loss: 5272091403.539307\n",
      "valid loss: 276577082.7001953\n",
      "=======\n",
      "epoch: 26\n",
      "best epoch: 26\n",
      "learning rate: 0.01\n",
      "train loss: 5229241680.962402\n",
      "valid loss: 274501364.10717773\n",
      "=======\n",
      "epoch: 27\n",
      "best epoch: 27\n",
      "learning rate: 0.01\n",
      "train loss: 5189109464.50708\n",
      "valid loss: 273269081.75097656\n",
      "=======\n",
      "epoch: 28\n",
      "best epoch: 28\n",
      "learning rate: 0.01\n",
      "train loss: 5165149861.021484\n",
      "valid loss: 271591103.4798584\n",
      "=======\n",
      "epoch: 29\n",
      "best epoch: 29\n",
      "learning rate: 0.01\n",
      "train loss: 5132487779.691528\n",
      "valid loss: 270378848.2155762\n",
      "=======\n",
      "epoch: 30\n",
      "best epoch: 30\n",
      "learning rate: 0.01\n",
      "train loss: 5108943165.40979\n",
      "valid loss: 269135138.102417\n",
      "=======\n",
      "epoch: 31\n",
      "best epoch: 31\n",
      "learning rate: 0.01\n",
      "train loss: 5084622919.11377\n",
      "valid loss: 267978862.52539062\n",
      "=======\n",
      "epoch: 32\n",
      "best epoch: 32\n",
      "learning rate: 0.01\n",
      "train loss: 5062215590.296265\n",
      "valid loss: 266930866.0784912\n",
      "=======\n",
      "epoch: 33\n",
      "best epoch: 33\n",
      "learning rate: 0.01\n",
      "train loss: 5041841171.974731\n",
      "valid loss: 265947010.31933594\n",
      "=======\n",
      "epoch: 34\n",
      "best epoch: 34\n",
      "learning rate: 0.01\n",
      "train loss: 5022611464.351685\n",
      "valid loss: 265129079.63964844\n",
      "=======\n"
     ]
    }
   ],
   "source": [
    "result = train(cbow, train_data, valid_data, batch_size=64, max_epochs=35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "cbow.save('global_cbow.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_loss = plt.figure()\n",
    "ax_loss = fig_loss.add_subplot(1, 1, 1)\n",
    "ax_loss.plot(result.train_losses, linewidth=2, label='Training')\n",
    "ax_loss.set_xlabel('Epoch')\n",
    "ax_loss.set_ylabel('Training Loss')\n",
    "ax_loss.autoscale(tight=True)\n",
    "ax_loss.grid();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_loss = plt.figure()\n",
    "ax_loss = fig_loss.add_subplot(1, 1, 1)\n",
    "ax_loss.plot(result.valid_losses, linewidth=2, label='Validation')\n",
    "ax_loss.scatter(\n",
    "    (result.best_epoch,), (result.best_valid_loss,),\n",
    "    linewidth=3, s=200, marker='x', color='red', label='Best')\n",
    "ax_loss.set_xlabel('Epoch')\n",
    "ax_loss.set_ylabel('Validation Loss')\n",
    "ax_loss.autoscale(tight=True)\n",
    "ax_loss.grid();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([27583, 30281, 20630,  2113, 29437, 15638,  1846,  5232,  9908,    38,\n",
       "        17920,    44,     2,     1,  1732,  4934, 20863,     1])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_words = ['space', 'trump', 'obama', 'apollo', 'the', 'is', 'and', 'cat', 'elliott', ',',   \n",
    "              'maggie', '.', '99', 'bksdhjfbhyjk', 'america', 'canada', 'omega', 'pytorch']     \n",
    "\n",
    "test_idxs = th.as_tensor([vocab[word] for word in test_words], dtype=th.int64)                  \n",
    "test_idxs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======= euclidean =======\n",
      "space\n",
      "['space', 'topology', 'utility', 'oscillator', 'discipline', 'controls', 'centers', 'phonology', 'lobby', 'setup']\n",
      "trump\n",
      "['trump', 'rockwell', 'clough', 'bloch', 'nixon', 'finley', 'dolan', 'poe', 'parry', 'obama']\n",
      "obama\n",
      "['obama', 'trump', 'wordsworth', 'lieutenant', 'poe', 'stanhope', 'gaye', 'cabinet', 'latimer', 'jing']\n",
      "apollo\n",
      "['apollo', 'mercury', 'frontline', 'atlas', 'mint', 'babylon', 'hermes', 'liberty', 'invincible', 'nomad']\n",
      "the\n",
      "['the', 'america’s', 'its', 'charters', '“the', 'another', 'today’s', 'caicos', 'their', 'manly']\n",
      "is\n",
      "['is', 'boasts', 'constitutes', 'contains', 'achieves', 'occupies', 'utilizes', 'makes', 'resembles', 'retains']\n",
      "and\n",
      "['and', 'utilizing', 'enabling', 'capturing', 'showing', 'and/or', 'towards', 'while', 'sending', 'sandwiched']\n",
      "cat\n",
      "['cat', 'snake', 'nymph', 'sinister', 'dog', 'sweet', 'lark', 'elephant', 'haunting', 'sage']\n",
      "elliott\n",
      "['elliott', 'roberts', 'vance', 'jarvis', 'cullen', 'olson', 'emery', 'engel', 'henderson', 'pollock']\n",
      ",\n",
      "[',', '—', '-', 'forbids', ';', 'faced', 'unites', 'lost', 'uses', 'lacks']\n",
      "maggie\n",
      "['maggie', 'jake', 'kate', 'lisa', 'bonnie', 'katie', 'betty', 'polly', 'peggy', 'annie']\n",
      ".\n",
      "['.', '(', ';', '—', 'classifies', '-', 'involving', '—the', 'vs', 'adopting']\n",
      "99\n",
      "['<num>', 'mid-june', '4–5', '1–5', '1977–78', 'thirty-six', '8–10', '1989–90', 'innsbruck', '1855.']\n",
      "bksdhjfbhyjk\n",
      "['<unk>', 'schmidt', 'ata', 'bose', 'beer', 'ness', 'dupont', 'conte', 'hoffmann', 'bhaskar']\n",
      "america\n",
      "['america', 'korea', 'rhine-westphalia', 'hebron', 'aisles', 'palmerston', 'kwazulu-natal', 'smithfield', 'raleigh', 'lanarkshire']\n",
      "canada\n",
      "['canada', 'australia', 'taiwan', 'libya', 'namibia', 'nicaragua', 'burma', 'europe', 'brazil', 'thailand']\n",
      "omega\n",
      "['omega', 'alpha', 'beta', 'mu', 'gamma', 'sigma', 'acs', 'psi', 'fraternity', 'rsa']\n",
      "pytorch\n",
      "['<unk>', 'schmidt', 'ata', 'bose', 'beer', 'ness', 'dupont', 'conte', 'hoffmann', 'bhaskar']\n",
      "(18, 10)\n"
     ]
    }
   ],
   "source": [
    "print('======= euclidean =======')\n",
    "nearest_idxs = cbow.nearest(test_idxs).numpy()\n",
    "for word, idxs in zip(test_words, nearest_idxs):\n",
    "    print(word)\n",
    "    print([vocab.reverse[idx] for idx in idxs])\n",
    "print(nearest_idxs.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======= cosine =======\n",
      "space\n",
      "['space', 'topology', 'oscillator', 'phonology', 'diversity', 'spaces', 'centers', 'notation', 'geometry', 'propulsion']\n",
      "trump\n",
      "['trump', 'clough', 'rockwell', 'nixon', 'finley', 'bloch', 'dolan', 'poe', 'obama', 'lichtenstein']\n",
      "obama\n",
      "['obama', 'trump', 'wordsworth', 'lieutenant', 'poe', 'stanhope', 'gaye', 'latimer', 'cabinet', 'jing']\n",
      "apollo\n",
      "['apollo', 'babylon', 'invincible', 'hermes', 'mercury', 'voyager', 'argus', 'terminator', 'mirage', 'bleach']\n",
      "the\n",
      "['the', 'america’s', 'its', 'today’s', '“the', 'caicos', 'their', 'graders', 'arnhem', 'another']\n",
      "is\n",
      "['is', 'boasts', 'constitutes', 'contains', 'achieves', 'occupies', 'utilizes', 'makes', 'resembles', 'retains']\n",
      "and\n",
      "['and', 'enabling', 'utilizing', 'sandwiched', 'showing', 'prompting', 'towards', 'sikorsky', '/', 'utilising']\n",
      "cat\n",
      "['cat', 'nymph', 'snake', 'haunting', 'comb', 'sinister', 'sauce', 'stalker', 'pet', 'lobster']\n",
      "elliott\n",
      "['elliott', 'roberts', 'vance', 'olson', 'cullen', 'emery', 'engel', 'mayer', 'jarvis', 'olsen']\n",
      ",\n",
      "[',', '—', 'forbids', 'faced', 'unites', 'uses', 'lacks', 'lost', '-', 'classifies']\n",
      "maggie\n",
      "['maggie', 'jake', 'kate', 'lisa', 'katie', 'bonnie', 'betty', 'polly', 'annie', 'peggy']\n",
      ".\n",
      "['.', '(', ';', '—', 'classifies', '—the', 'preserves', 'brings', 'fiba', '-']\n",
      "99\n",
      "['<num>', 'mid-june', '4–5', '1–5', '1977–78', 'thirty-six', '8–10', '1989–90', 'innsbruck', '1855.']\n",
      "bksdhjfbhyjk\n",
      "['<unk>', 'schmidt', 'ata', 'bose', 'ness', 'conte', 'hoffmann', 'bhaskar', 'bala', 'rossi']\n",
      "america\n",
      "['america', 'korea', 'rhine-westphalia', 'aisles', 'hebron', 'palmerston', 'kwazulu-natal', 'smithfield', 'lanarkshire', 'whittier']\n",
      "canada\n",
      "['canada', 'australia', 'libya', 'namibia', 'taiwan', 'nicaragua', 'burma', 'europe', 'brazil', 'thailand']\n",
      "omega\n",
      "['omega', 'alpha', 'beta', 'mu', 'gamma', 'sigma', 'acs', 'psi', 'fraternity', 'pd']\n",
      "pytorch\n",
      "['<unk>', 'schmidt', 'ata', 'bose', 'ness', 'conte', 'hoffmann', 'bhaskar', 'bala', 'rossi']\n",
      "(18, 10)\n"
     ]
    }
   ],
   "source": [
    "print('======= cosine =======')\n",
    "nearest_idxs = cbow.nearest_cosine(test_idxs).numpy()\n",
    "for word, idxs in zip(test_words, nearest_idxs):\n",
    "    print(word)\n",
    "    print([vocab.reverse[idx] for idx in idxs])\n",
    "print(nearest_idxs.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(31.5490, grad_fn=<SumBackward0>)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(27.5680, grad_fn=<SumBackward0>)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(30.4884, grad_fn=<SumBackward0>)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(32.0000, grad_fn=<SumBackward0>)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(cbow.embedding(th.tensor(vocab['23']))**2).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "emb_mat = cbow.embedding.weight.detach()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32768, 32])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb_mat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb_mat[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-0.4857, -0.1680, -0.2881,  1.7850,  0.1005,  0.0575, -0.6526, -0.5757,\n",
       "         0.5585,  0.9130,  0.9822, -0.2928, -0.4914,  1.4985,  2.0124, -0.5009,\n",
       "        -0.0243,  0.2896,  0.1180, -1.0195, -0.7109,  1.1856,  0.9750,  0.5419,\n",
       "        -0.2264, -0.7722, -1.5106,  1.0604,  0.6314, -0.0260,  0.9343, -1.1601])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb_mat[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([0.0000, 4.9247, 5.6569,  ..., 5.6569, 5.6569, 5.6569]),\n",
       " 5.656854249492381)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb_l2 = th.sqrt((emb_mat**2).sum(1))\n",
    "emb_l2, math.sqrt(emb_mat.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(0.), tensor(5.6569), tensor(5.5210))"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb_l2.min(), emb_l2.max(), emb_l2.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(emb_l2.numpy(), bins=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(emb_mat[:, 0], emb_mat[:, 1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(emb_mat[:, 1], emb_mat[:, 2]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(emb_mat[:, 2], emb_mat[:, 3]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "means = emb_mat.mean(0)\n",
    "u, d, v = th.svd(emb_mat - means)\n",
    "\n",
    "mags = d**2 / (emb_mat.shape[0] - 1.0)\n",
    "w = v.T / d\n",
    "\n",
    "pcomps = emb_mat @ w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(mags.shape[0]), mags);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(pcomps[:, 0], pcomps[:, 1]);\n",
    "\n",
    "words = ('elliott', 'fred', 'frank', 'john')\n",
    "idxs = [vocab[word] for word in words]\n",
    "plt.scatter(pcomps[idxs, 0], pcomps[idxs, 1], color='red');\n",
    "\n",
    "\n",
    "words = ('omega', 'mu', 'alpha', 'beta', 'gamma')\n",
    "idxs = [vocab[word] for word in words]\n",
    "plt.scatter(pcomps[idxs, 0], pcomps[idxs, 1], color='orange');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(pcomps[:, 1], pcomps[:, 2]);\n",
    "\n",
    "words = ('elliott', 'fred', 'frank', 'john')\n",
    "idxs = [vocab[word] for word in words]\n",
    "plt.scatter(pcomps[idxs, 1], pcomps[idxs, 2], color='red');\n",
    "\n",
    "\n",
    "words = ('omega', 'mu', 'alpha', 'beta', 'gamma')\n",
    "idxs = [vocab[word] for word in words]\n",
    "plt.scatter(pcomps[idxs, 1], pcomps[idxs, 2], color='orange');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uhLBBdkEnmXMzlcTFsVGI8h7fqlTxlkWKyKUTJyTb2pyg91meqWijLfc+dQI5IHAWkEtYdEcnXsPIi+D2G5rfn239kgm36NdfO+Lua9zxBF7c98qaFNJtKFRI35C2sVYS4eO+pukEfP/kyYa3RV4Ed964ptFCGgdbEzTvvum2FzRHVqLYzEchbi1R0PvMi2ijLfMLCn5JUKnOY/eBU9rXXObkGif/YsHB7h0bm0RnVIExrxSeOV7GyLUrQ839TOQAQIAFdVX4HDl9PtBDxYRtqi2oNqXdE7YJ8UKhQvqGtI21woSP6YQZ9po2E2292JzUbeYEebcduXal9lj5u1tMBbbHXn0zki+Iv/324pW5RqoqyLre/77Htq9viZoAaLFwt2GmctVrxH09/zG5PLfQ9Ji40TnbIYk6ir7j5Qofk2DyeqjosI30BdWmUKSQThLcl0ZIDzE6XMJDt21CqViAACgVC9ZXfpNTZWydOIx14wexdeJwrWhz+3oUnHzTdq4I0d0H1E4qYa9pmmj72NGz2LzneUxOlZv2675nT6Jc7yZxT+rebQDg4A/eCH2PLkdOnzceq5FrVzZt++zx17VX7Y8fPRu6T6b3MFOptogKb0ot6H0DwL5PXo+iZ47P8iV5LF8S75rL+5phqT6gVohsQoz31PAPSdR9fkzPqTteef+MAA/efdYxOlzCC+M34eWJW/DC+E3Geh0drE0hnYYRFdJXxPEhMRXNPnTbJjx026bAK8846YsgK/OZShX37J/GnudO4YFbN1oVCE9OlZs6j8LwGsyFDd8zEaU7yDZd4n29oPftPbG6+xzUlu31IPHjFQ9hqb7JqTLevmTurFKoCT7/e3Hx++UACO36cYWdjrQs8U2kGaHs1dEOJBtQqJC+Ie5iuPvAKauTop+45mxBE21dLsxWA7tCzvlO6lEwXREnLfD0+7OUZypW79XL1onDGNu+3njyLs9UmuplZq8Ed9wsX5LHH//uppY2chfvsQhL9Zk6jvz79+jOzVYnePfzs278oLHjKohSveMoqoeKLX4xlRdpijDFHWKYVYdkkl2Y+iF9gW2KRPc40xVru9pyt/zykNV2QeF974koyn4G+ZPE9fPw7tP9kyexa/9047miiBTgaudO2Dbu3zgsklQcXILR4RIeuHUjnHzzsXTy0nQsglJ9gN1xdl8hSgoyTFQMDTrG/dq9Y2PgPifFnUFVcPKNv6Xtd8vFJqVGSBCMqJC+IK6HStBi2a5c/Cs/txcEbstp0NW5rSOtqVvDFXm2CIBlmn3atmGV1jQuKmFRiyg0iQv/0/p+DysytSmAVfXHB0Xi/ARNgy44eSsfmHamVZL6E3GIIUkKhQrpC+IuhkH3p+kT4W1HjoJr3hV0IgqoqWzgGs0BaGk1jpryUQDEc5Z3W3j3HTqTWKSkjULt/c5emWsRQNUF1XKyDUrnBQkKL1FPwLoUi864Le0UpC0mcWb7PtO2DSCLDwoV0hfEXQxNjxsadFJb/P3tyLZ4zbuC9mUmIP0hQNMEXl2tQJy6lFnPrBi3hbdbV8gFJ4dKdcF4f1AUpDxTwQfu+5aVo+vocAnHXn0z9G8Z5wTs/o29dVZxSLtodXKq3OKt42L7PtO2DSCLDwoV0hHaXfUfdzE0Pc4Nt6eBqR3ZS8HJ4yNrV+Dojy80TOD8bqYmTGKrVCw0DdIzDdyLWvDqx00DRPEGSYuCk68btJmFShje2ougmUGTU2U8czy4LiPoMxdm8pe06LQdRaumKJnAPuLYSYdm0p9QqJC204mq/7iLYScWURsRcPsNJTxzvNzYVudmakIntgQ1Yzcvpqv0eaWMV822nJup4K4ta2PVqAw6OVyeU7HTYrv2T0d8RTPVeWV0dg1LkQU5tppM/l4+/zZe+XkF52Yq2pk6UWpB0p51BZg/MwrRvrvtTk+R/oZChbSddiygOmwXQ110xxt5sH2M7b6HRSzyIkY7dPcYBb2+m5LwigQFtAidoMhLGpGQ/d97rUWkODkgLNhRXYgmUu7esrYpEmE7gbhYcLB86UDotqZOorB0zLyqiZxd+6dRHHSgVG2u0upiAW+8pX/sCz96s+nxcV43bLskKbmgzwwhnYLtyaTtZKnqP04bc9zWZ5c7b1wTeP+8UoEFizavf+T0eaMJm/seZq+0mpW5qYqhAMdVGxRa3VOBcJGC+uOCXFa9DA06LWMGbF1ed+/YiBfGb7I6yer+tjY1GRdmq1D1/2cq1cbfK0kjk20tSDucZMNatgnpBBQqpO1kyYrbFN3Z89ypyI/xigC//b6XvaObcPeWtbH2d3WxYOVDYRJ95ZkKNu95HmNPn2iJFLh2/wACHVc7gduGHYYu2uEdB2DCWxxtI2x0betj29e3eLG0myiiwEZUhH1W/cQdSxH1dQgJgkKFtJ0sXZWZTujuILcoj3GjHWNPn2iKdow9faLluUauXdk0n8YG9xiFvf7wg88H1oXoZusAwPKlAxgdLlk5rrYb9wRog+7E5xqT6YSEk5Om4mj35BsURdId89HhUuyZQnEYGgyfG+UlTFTEjQzazAXykjQCSYgfChXSdpIMC0yboCiOyfwtKCK057lTLSLALch0sZlH48c73ND0+isKjjZSYot7Mu628Za3DdsG3YlvcqqMe588oRVk/toPt95nZraKnCFAYjrmb0X4G/opOLlGiisvgq0fWBkY2YkT5QoSFWk7xJqiJnSiJWnDYlrSEbJS9T+2fT3uMXSJ+OfIuCfPoNZn03N5xUOcGTquN8nkVBkzs1da7i84eYjo60JscU/G7WordnISGKnxery4n42hQcdKePkLje979qSxGHVBocl51/u31D3EyQlmr8xh3fjBWO60OlzDPZ0rsKkYuLqgsPuAvgMpDmnWigV18mWpJo30B4yokJ7DvZK7bvwgPnDft3BdhDz46HDJmIIRQBuuHh0u4fYbSk1Xw7YeJ0C8BbpSncfuA6cw9vQJXLzSKnJuv6EUaPRmg2t2dt27Cto5OJb1rVpKxQL2ffJ6Y5Hs0KCjveqP4l/jntxthGClOo/7nv0B7n3yROC2xYIDyNWi2PJMBbv2T+P+ydpJOKi+pVhwMDToQOrvr1i4+jOgcM/+aVw3fhDDDz7f+Ky6ERATUaJwYcSpFYsTNclSTRrpDxhRIT2F/0rOPygNCPd32L1jo9aRNahrxuRxUjTM2fGKobhX4UEnqYM/eMN6xk8Q80o1tcg2UPpog8ujOzfXWqJfPNuynT+VM/Z0a0rm7UtzDRHoZXS4hD/8Tye14syPK4JshaCNKZzueCoAj9c9UPxt5DrjNi+TU2WMPXWiKbJ0YbYaaCwXRpRWee+2KwoOnLw0/S2CasV0UZOxp04YJ1EDtb/FI5bTowmxRVQCR8puMTIyoo4dO9bt3SBdYOvE4cCTvt+N1YQ35B5mdmYSI4DZvn3rB1Y2jLyKgw7evtQ6a8YlrjOsbZqkk7hzf0aHS1bzjVzTNuCq6V4Up9lXJm7B5j3Ppxp5iIo3reMXERcvzxn3zftZXXffQa0wFAAvT9zS+N0vHvyv70W3rZehQQcP3LrRKHLCvmtB76ndTtSk/xCR40qpEd19jKiQniLJkEEvbs2MzWIcdBI0nVC9UYoLs1XkDVWbBSeP228oYf93X4vceZM1kQJc7SSynW/kXqXDU29jK1JKxQImp8q4qPGH6STe9nZ/BCII72fVpOUUgOEHn28IiijmiWEpsQuzVew+UNtvnYiIKlK8UZOs1KSR/oA1KqSnsBkyGIVOFfjNa0SI29kzcu3KyBGVYsGxNknrJO7xtJlv5FJdULGKgrdtWFVrrU5QUJwWF2ar+LfP/iDyFGq37iPIA+bCbLVRLxWlUNXmsz1TqWpbh91hhLZ0s5OP9D8UKqSnCCpmjJoHn5wqI9fFk/3ypbWA5n3PnozkXOrkBLt3bEw0SLBd5EQwOVXuyL49fvRsx4cgBjEbYzCiW1vln8vkJ06hqq1o17UOm4YR6nDTPUmnNNMgjpigUCGJ6PQC43chdaMKUa/owlpaO0F5phK5ddntphkdLoVawTtd+HbPK9XUCtxOsifT4lGpzuPgD94I3e7cTCWSeaLtaAH3uYN+N5FGkSwN4kgYrFEhsQvfOjEVWUeS4YPeaclRvU3SJi8SKfXkPSkMP/i8RY2KwMkpq3k7aVKpzieextxpRIK7nNqNTb3R6mIh0rRv77ZhReP+6IupU80d7Kh77bjrSKeGlpLehUJlkZNEbGR5gQl7X1kwn4oazXG9VS5embOqy+imLX4viRQgPZFi6gJL/rzxIhdeUT85Vda2Fuue22Ry6HZ0+UmyjtAgjoTB1M8iJ4nddZYXmN0HTgW+r141nzLN7SHpkKRiSYBEU5KDWDpQW6rDZksFpWJHh0uY+tLNeHTn5tBxFroUq/v90aVkkgzupEEcCSOViIqIfBzAvweQB/DnSqkJ3/1LAXwDwA0Afg5gp1LqFc/9awH8PYDdSql/l8Y+ETuiig1veDdn8P+IssCEhYvjhJMnp8rGlmL3fY1tX99ixEWIApCTeIJD4erogzTwpmpmKlXjuAbvbCl/VOOe/dPY89ypJr+UoNSp//u2bcMqPHO8HBopCRuc6d+vXfuncc/+aZQ0rwHQII40kziiIiJ5AH8G4LcBfBjAnSLyYd9mvw/gglLqgwAeAfAnvvv/FMB/SbovJDpRrmb8RW86kRJlgQkrort/8iR27Z+OXGQXFA1y39focAnvWMbMJ2klK9o1ym5cmK0a66687c06vCMp7vF93x47etYq4hq0juj2y31v5ZkKnjlexu03lDIxtJRkkzRW6o8BeEkp9WMAEJFvAvgEahESl08A2F3/+WkAXxERUUopERkF8DKAiynsC4lI0MA9P6aFMC+CBaUiO1CawsX3PnnCePVoUwMTlHryvq8ks3LcFEFGzmmEBH7uTd+bMPdaE/5C220bVmkN/rZtWNUYPxC0b3954g1MP3BzpH1oF3TVzR5pCJUSAK+70+sAbjRto5SaE5G3ALxLRC4B+AMAvwXg/57CvpAAwrpgwr6YpoVwXim84rH5tiXo+aI+ziYlNTToNL2vJFOD3SvILPl4uDg5dLzTh6RL1LEKg04OQ8uXBn4edd+buN1vfrPBI6fPa7c7cvq81fdsplLVzn7qNN3qZCTBdLuYdjeAR5RSb4dtKCKfE5FjInLs/Hn9l4KYCUqzuBNcddNsvZjCu1J//qjELZbzP842JeWfzDu2fX2s4kk34jS2fX3L1OEs8I5lDhyDZT9pLwUnj7u3rA31uHFx8tLyt3LygiUD0f5+1XmFbRtWBfqm5ERw/+TJpqLWuELb/x0LqlGx9XOxKeBvN0maC0j7SEOolAGs8fx+Tf027TYiMgBgBWpFtTcCeFhEXgFwD4B/KyKf172IUuqrSqkRpdTIqlXBLo6klTS+gKYTu0K8RSaKIZWLAC1pqaCUVFhnw7/4wMpIr+19rtHhEvbdcT2GBp3Qx3aSC7NVVBcUMuiw35e4OsP9bOwd3YQXxm+yEis7P7oG+z55faM+Y2jQAZT9vCOX6oLCkdPn8dBtm5omd3uZVwqP1d183YuVuB8R/3sLqlHxdxCZ6GS3oKkLKcudjIuZNFI/3wPwIRFZh5og+TSAf+nb5gCAzwL4bwDuAHBY1cY2/5/dDURkN4C3lVJfSWGfiI80voCjwyVj7UicL7I/7WRK2bgIgLu2rG0RHKbXXlCqafKsjld+brffpqnMUYYbdpoMOuz3HQLgTz+1WRuF1NV/+dn/3dcwcu3Kxmdr857nY3einZupND6Pk1Nl3PvkidD0kQIim/PpatiCalSAVvM5HZ1qRw5K75jSVGyV7i6JIypKqTkAnwdwCMA/AHhSKXVKRB4UkR31zf4CtZqUlwB8AcB40tcl0UjLq8B0VVSMGFVwr2h21YXPXVvW4pc0XTjuFV+pWMAjOzdj7+imlm2SvDcbgaWL4vgZ274+8ZfJ9cogvYMCWjpqvJ/tZU7OGOEAapGQe/ZP47rxg7hu/GDgpG6gJhJMEbwVntcZHS5hwVKpRhEpeRFtdDKoRgVoTs/q6GQ7clB0OcqIAtI5RPXgZdfIyIg6duxYt3ejp9BV9xecfOQ2QNdwym865uSkMYMmzr7oGBp0mvwfojyfe5XoFiWWDEXCUSIheRHceeMa7B3d1FS8Wxx0oBRCTzKkt4halBzkCfLQbZuM0cgoz++eMHUeQE5esPOja/CXJ95o22dRAG2Uct34Qa3gcbcP+p6ZvpvtImxf2fXTHUTkuFJqRHcfjSQWCVG6e8KeZ/eBUy0LYXVBWVvn23YaDC4ZsHq+oJkmbujbVL1vE553cXP8+797tukEZjOnhfQeUTunXN8RP+0oxtSldKrzSvv6aeKNUkYxfwyzDOikEAhL79jOEiOdg0JlEZHWF/CtENfXMNLeDrCrFdF5Sbg/2+TzXdj6S6KSRjFmeaaCsadOADGdc5PiTYH4o5hh5o9BLcqdng0WxTuKZAMmxYk1bu7ddgKribS38xJ2QtDdHyWfT0gcVhcLKDjJl9vqgurarCdvF3XUTrsgEdDpjhpvF1K/OOEGzVLqBxhRIVaE1ZVEuSKxTbfMXpnD/ZMnceT0+ZZ0lSmPHGYuFVR4m7WuHdIfOHnB2Pb12PPcqbZMVrZBJHkX2MUr87hn/zR2PTltfC5Tp93ocEk7uRnoTkdNP6V3FoNJHYtpiRVpF8Pphp/ZFAEWnDxuv6FkLFgEoC329W5jM6YeAPI5wXxWBr+QniUnwDuXOV0rts7nBEqpjqSLTG38QG121+NHzzZFZL3fW/+Fh+62fjnxpolpbQ76W2QRFtOSxJjCswLE+jLormiOnD4fuphXqvMti517u9teqMtNhXUQ+Qty81ITKUsHcqlOxCWLj4UudoQNWXak5QCsGHQwM1sN9TMKwvVN8TM5VcYzx8stX82PrF0BoHXqs1uL415wpBUlSKOjJ2tdQYvBpI41KsSKtHxYgrD9YpmW0HMzFew7dEZrmPWLypzVc1+8XNvOXagpUkgvM/Wlm43F714WUOteW10s4M4b10R2jHYx+amYalpe+NGb9ZRY8326Whxd91SU2oywae02pPEcadOJtbnbUKgQKzphhJT0i7W6WAgcdGgy5lo3fhCb9zyPsadP0AuFZBoRs+miiSjfq/JMBc8cL+P2G0qBRnUm4lzdR2nv9z5PVNFgM0YkTPhkcRbQYjCpo1AhVqRVKe9dCIYffB6b9zzfWBTChqoF4X4xgxZl74LiX+RmKtWudFNI/R9nCHaHXjvuSiHS92Ryqhx5plalOo8jp89j+oGb8ejOzZEES7uv7r3PE1U0hIkoG+GTxTRLP3Yx+aFQIdbYTlk24V8ILsxWMVOpNhYF90rO+4UbNLR0Dg06jSvLvAgq1fmaEd3slcB9cBeUuOPt00bV/7Fmtzv04nF3hw/mLaZO7nnulPVQQC/lmQq2ThwGgIZg8X7fALQMNBTP4/yRiLCre/9zOTlpmUzujxJEFQ1hIspG+GQ1zZJ0bc46LKZd5HSyMCxMHLhXcm5x7uRUGf/22R+0bFdw8njg1o0AmovwbNI2Nk6ZhCThvb+0BP/4T8GCOQnlei2WTcGrm1aJY2zoL2D1rwvu2uF3g9YVvo4OlzD21LTRLNE7HNE7KsBb3O4VDUFWBCbREGb0ZiN8aBbXHShUFjGd7r+3EQfnZiqYnCobPRcA4PYbrrrQRomK2DplEpKEdooUlyif3cmpsnbshQ2V6jzuffIEdu2fbrmQCXKD9rtAT06VawU2ASMQFYDlS2opKvf1/LOTvGtUVNEQNkbERvikNYqERIM+KouYuP33NlEY3TZBI95dBMBAXgLrRYYGHUx96WZcN34w+A16ntO/n5NT5cRD4gjJOjU3XEktzanzIjIN+QOuDgXNx2x5Nkkbd41KMyKc1uBWEg/6qBAtcQrDgqIwgH4woLvNR9auCBUqCggtar0wW8XkVNm4iHkRAI/s3MyFhiw6cgCWOflUh2bq5mUFRSddcRLXlyXIigBI12GW0ZLsQqGyiIma4wXMBWd7njuFS9WFxn06Q7ajP76QeJ+9+2Gz9Cm0Dj2bnCrj3idPpLYvhGQSac9kb/+FTJQJ5GnRruLVfrLW7ycoVBYxcQrDTNEWmwUx7lWVjig5ercT4dxMBcVBB29fmkt1X2wiO4R0mnZ1NPlFgt/VOW383y+B2QGX9CdsT17ExOm/73YbXly8LdE659okuB0LQGubpQ3FghPrcYR0Gicn2gsZtz3WtgW6VCzg7i1rQ/1dCk4e/+IDK5u+HwrAM8fLfTchOC36cZIyhcoiJ2r/vckFMY6LZdYQAAbbllDctspHPH4TtuJjplJFcbD3jx/pf96xbCBwjbA1l5u9MoeRa1fiods2Na0dy5fkG8LdvXB65ecVbSp5z3OnIu17nBN4r530s2jxnwYUKiQSpijM7h0bWxYo90RtY0yVBRRg9HmwwfW3OFf3fYgSt2lHLQEhaWPzObX55F+YreK+Z0/i2KtvNs3TunhlHm9Vqrhry9rGhVNQutn2BBznBN6LJ/0sWvynAWtUSCi6FkBT+7KuYn6dZRtx2sRtiUyCm6Pv9OsS0gmCLjqudgTaqf1KdR5PvPhay3dFAXj86FmMXLsy0NgNQOMEHNapE3QCN0WI4jym22TR4j8NKFRIIFFM4UwV81HN1dIqTp1XCsWCw0GDhGjISfSC2yABrpuCHPf5vN16Y9vXGz2PyjOVpvtM61OcE7hpzcryST9OJ2cvwNQPCSSNUGKUoWgFJ4+7tqzFUAo1G6ViAbt3bOy5wXOEdIKlA9GXf1Ox7ORUOVb6MihCU56pYN34Qew7dKZuXGeHbiJyzvA6phO469MU5TFZoF8nKVOo9CidKvJKI5Tor2sJ4qHbNmHv6CZMfenmRGLF++V85zIWqhLixzZF48XUFhynBqLg5HHnjWsC1wS3NmRuQcGJcMXhn4isi9wEncBNPk2C8AGL3aRfJykz9dODxJnRE9dqOmko0f+6j+zcbD0Y7YFbN0YyktINNRt76kTq7ciELFYO/uANHDl9vjEocF4plGLMzSr51qDHj54NTPdW5xWGBh0MLhnAuZnWLiA/QRORgVokJ+gEbroQU2jPHLQ06UfTOgqVHiRqkZdO2Iw9dQJ7njuFmdlqoHCJYgrnFyWmgWJBIsX7Htz/wwaq6Wb5AMCH/x//hSKFLBru3rIWj794Fu2s474wW22keNzvsX9kRlT2jm7CyLUrQ7/nM7NVPHDrxlBjOa/Xi0lwLCgVOJ8sZyjED0p90Xq/fVCo9CBR0zE6YVNdUI0FJ6xA1n2OsCGEfjGku0qqVOcDu3G878H98r8VUgz78sQtLbdNTpUxm6TXmJAeolQsYOTaldj/3ddQ7ULHmWt66H1lJyeA6Gd36dYcb5uyjuKgYxdh9WSIbCPC/vUrLFXkFSYrCg4uXplrvM92T6FfjFCo9CBR0zE29STeiIz7JfSHd4OG++nEkGm5nFfKeAXmvgfdJFMT140fbEr3tMvKm5Cs4k4n72YE0U27ei9oAPP30bvmmFI0Tc+vYLUeVOcV9jx3qtExFBQR9q51OvIiWFCq6QLNvzbpokBZb2PuNShUehDbL5+7YBQHHauK/HMzFeOVRdhVQpTi2lKxgOveVcALP3qz6Xbve7BZuLyUZyoYe/oEoMB0D1l0jA6XsMvQwtspSsWC1l/J9VLSfSvddSNs/Sg4udDIqhfXDC4oImxzMbSgVEvE1nZtynIbc69BodKDRPnylWcqcHICJy/aEKyX1cVC4JfQ2/bnf+0VEfxKyjMV7RXM/MICdu2fjh0RCXt/hPQzUf2K0sTthjHVapjWBzeCGrbvcwvK+oLLxY1omIpLbQSHLkptK0Cy3Mbca4jqQQfNkZERdezYsW7vRibZOnFY+4UvFhwsXzqgzakCtWjGQ7dtwq7906FFcX7R4+QFOZHQHDMhJH2GBh1MfenmSOnSuDg5YG6hNW27fEkeF6/Ma1O6BSeHK/MK84ZIZ6lYwOCSHH7404uhrx+laFegr19zMUV5XNw10S9yTGuszWOJGRE5rpQa0d1HH5U+w6T236pUG8MHpx+4GfvuuF7bax92FZDTFMdV51VHRErByadiBEdIv5CTWhs/UIu03n5DSetLktrcLRGtIePFKzVxpDvxV6oLRpEC1CKsNiLF9PwmwtayoPuD/Ed0pmpOTjA06PSVd0mWYOqnz7AttNWFQyenypi9Mmd87oKTb+vVmo4hT7h36UAOt/zK+5tanglZzCwo4J56unTbhlXY/73XtCfznADzKvkMquq8wmNHzyZ6jk5g48ZqqvULExm2nZAkPZj66TN04V+bL58pbOw1Udu2YVXHFym/OCo4edx+Q6nFdIqQxU5aM7J6kZzUHKjfqgT7Qvmh/0l2CEr9MKLSZ8RV+6bCstWeSv6tE4cj7YsAuGvL2oaoiLqQClrbESvVeRw5fR4vjN8U2lpIyGJisYoUoNa6PP3Azdr7JqfK2PPcqUZkVqS2vWtpYJoET7IDhUofEsdC2cZELqjaXddVpAA8c7zciOZEFRamhbc8U8F14wetnoMQkg2GInbtRCFouODY0yea1iY3AEtjtt6BQoUAsKttMbUHFgsOdu/YqJ3h4zU+cv/ZdicwrUNIf1As1DqTol5guClnNyprYvbKHNaNH8SKggMRNEaDXLw8F2hbQGO23oBdPwSAoZI9L7h4ubYADD/4PN7SiBQnL9i9YyNGh0tYMIiKct1IzsWd8BnWgUCRQkh/sHtHrTMpatfR2Pb12Du6CWPb1wdOWb4wW4VCzSXW/bk8U7HydoprzNapCfaEQoXU8Y8HHxp0AFX74ivUFgJdA/LyJQONq5Ggdr/7nj3ZIlYoRAjpfwadHPYdOoN14wcjf+e9BpPtWi3iGLO5UeFyfZKzm0aiWGkPTP1kEN0U4iOnz7e9Mt1b27J14rBVPtlra61r93PRhViZ2iGkP1i+JI/f/UipxTrAyQmqCyp2wXt5poLNe563dr2OigDYtmGV9r6gjqCoE+xJMihUMobOAt/bEtypArC4NtHLnJyx9sS/WFGkENIfXLwy32JdkBfBkoFcwwwuLu0SKcDVgv+Ra1c2raf3T55smv7uX3dN62N5poJ14wfZ6pwyTP1kDJv5E96ZO+3CJhzqH4R437MnQ6Mw3tAoXWYJ6V/mlUosUjqBfz2dnCo3iRTddkHrI1NB6UOhkjFsIxntnsxpKq4tFvQ20bYTRfc8dwpAbTF4+5LZBZcQsrgoOFdPR8uX5AO2TJ/yTKVRGHvvkyeM9TDuuqtbH/104oJyscDUT8awnYDa7smcUY3jbIWTG3HZd+gMqgHzPwghi4tK9Wq5/myESIxbB5MEwdXUdFBK2l13/etjmLAhyaBQyRhBBakuNnMs0iCKcVzUEfP8AhNCTNjKjlKxgNkrc4mN5GxeT4CmddfffGAzY43Eg6mfjOFvEy4VC7h7y1rtpOMotLvn31Q576dYqNWl8AtMCEmCu5a0y+3WizsOxLTu6lJBnbqgXAwwopJB4ljgB6HrJEqzc2hyqoxnjocLHycnDeOnse3rW6ytAx+rsegnhCxeZirVxB1BAmBFwQl9nkd2buZE5S7C6cmLAFNYsuQZOBgH/7AvG9xBYAAw9tQJq9yyO0QsDYYGaxNWWR5DSDTyIljmJG83zhqP7twcmG5fviSPS9UFzCuFvAjuvHEN9o5u6vBe9j9B05OZ+lkE2AwcjIo77Ctq2NWN5uw+cMq6AC4tkVIsOHjg1o34009tTucJCelDnFyrWX1OgC9/6vq+EynuO33otk2NVJKfi1fmGwW280rhsaNncf/kycSvTQt+eyhUFgGmepAkdSL7Dp2JnYqpVOfbauJkYqZSxX3PnsSxV98MnBtCyGLlnUvz2guIBQXcs3+68zvUZhTQcJOdfuBmPLpzM0r1dTFojXjixdcSvS4t+KNBobIIaEehV6927VSqNQdNZn4IaeUXl/srYmKD66EyOVXG6HAJL4zfhFKxELhGJHXVDrLgJ62kIlRE5OMickZEXhKRcc39S0Vkf/3+F0XkuvrtvyUix0XkZP3/+AUTxIiukyhO55AXdu0QQnqJoAiJP6IRdiEWdQq0n3ak4/uZxF0/IpIH8GcAfgvA6wC+JyIHlFJ/79ns9wFcUEp9UEQ+DeBPAOwE8DMAtyqlzonIPwdwCADLpBPiHaa1ouBABJiZrWJ1sRBavW7L2Pb1fRkKJoT0D3kRLCjVGO7qH5roxTtUMMwXasmANCIwcTA9v/cCMGgo4mIjjYjKxwC8pJT6sVLqCoBvAviEb5tPAPh6/eenAfyGiIhSakopda5++ykABRFZmsI+LVr8uc+ZShUXZqup50FHh0u4e8vaxM9DCCG25EXwofcst95+QSm8PHELXhi/CXtHNzUiyybKlhb5lepCorV024ZVLREe3ew01rDUSEOolAB4K4teR2tUpLGNUmoOwFsA3uXb5nYA31dKXU5hnxYN/srxPc+dCnS1DcuDRqlE3zu6CY/u3MzCVEIWGU4XqhsFtc6jV342a/0Yf4p6dLgUWJvnpnTcdHnQ4NS4NSWu75S3ykUA3H5DKXB2WqU635iVttjIhOGbiGxELR10c8A2nwPwOQBYu5ZX8oDeyM0GUx7U1hjOH5JkYSohiwvPWJ6OIQLs2j9tvd4IWh2z3TXOhL9I9lLIG7VZc/3r5eyVuRYRogAcOX2+8btpjb4wW02UcupV0hAqZQBrPL9fU79Nt83rIjIAYAWAnwOAiFwD4D8B+IxS6kemF1FKfRXAV4Ga4VsK+93z2E4s9mMqhDWp+N0HTjW+aMVBB29fmmu0MEaZ70MIIXGxsV0SXJ3bowA8dvQsDv7gDTxw60aMDpdC10xvWshmfRXAKBx0hphB66VXnATVyLh1NIuJNAJ43wPwIRFZJyJLAHwawAHfNgcAfLb+8x0ADiullIgUARwEMK6UeiGFfVlUxKkQ9w/Wsnm+mUq1kSu9MFtNPqmUuSJCSMrkRbTRlguz1UZ9R5BQ8Fs22Kyvrg+LHzdyE8UQ03sBGZSe0u1Xv5vHJRYq9ZqTz6PWsfMPAJ5USp0SkQdFZEd9s78A8C4ReQnAFwC4LcyfB/BBAF8Sken6v/ck3afFQpwWYQXzfJ9OtRz34NQGQkjGCfI2ces7TNdIeZEWywbb9VAnHKJGu/0iaXS4ZHTK9e/XYii8TaUkSin1LaXUP1NKfUAp9cf1276klDpQ//mSUuqTSqkPKqU+ppT6cf32vUqp5UqpzZ5/P01jnxYDusp0Jydw8uaQRVDFe1ilexAiV6eZMmBCCMkabvejH7dI138Bp+vM0aETNGHRmGLBCfW12r1jo3Y9vnh5rkmELAbzuEwU05J4uB/s3QdONSzpqwsKy5fk4eQUZn2FYGFutP4JoMVBxzp0qRRweW4Bj9Z9WianyvRZIYRkHl2UWdeZY8JfsAsE15gUnDx279hoVWeyzMm1iBB3FAjq+70YzOMoVDpMO0x8Ll6ea/79yjycvODuLWtx5PT5SK81Ony1RW7znueN23mL1lwq1Xnc++QJ7No/jdXFAooW49MJIaTb+Atio6RuvN067vpenqlo18hiwWmIlKBzgb8D04+NOV0/uYdTqHQQ2/bfKOw7dEZb3FqdVzhy+jxeGI83lWByqhwoMkxXGm6euDxTQV4zhZUQQuIiYlfjphMJQfg7aaJEI9xt/eu78uxHKUSIlGcquGf/NPY8dwoP3LrRSii5rzu2fX2LqEk6yy1rcChhB2lHLjHoC5Uk9Be0T6ViwWrWxXzC7iBCCHEp1aO0NkRdebyDCYFo0Qh3W9367oqUF8ZvakRRtk4cxj37p7VCxO1QsrF9cF+3HbPcsgYjKh2kHbnEoFxoktBf0D5xzg8hpJO4EYJdbVx3vBFu2zXOG7kwrZllQ8TFRKU6j7xIYBeTrkuon4SJH0ZUOohJOCQRFGPb18PRpFicvMQO/U1OlZEzREwENXfIpNNDk+LkxfrqihDS21Sq87gngittktdx00Am+/y8iDZyYVrHBcD9kydx75MnrOte5pVq6fhxV9x+jJiEQaHSQXSiwslFFxRec599h87gY+uGmtroli/JY98dre12ts9937MnjWpe1f8Fqf1OUJ1XEEHsdmpCCNHhRkYeuLW1PdjJC35pmT4RMbZ9vbadWQF4/OjZSGumK0a86ZxHdm7GK/UBi4tJpABM/XQe/yc5YmBCV4TlT/0kKQ2JalTkH6P+2NGz8V88IjOzVTyyc3NTO/Xl6nxLWzYhhNjirf0A0DI+xG0y8DdDjA6XjOmiKEuym9bp93ROFChUOsi+Q2dQnW/+yFbnVaTZDTZCwhu+DMPfIhd1do87Rt19ridefK1j0ZbVxQJGh0s49uqbeOLF1yLZVRNCiJ+g2o+tE4db1hjX8dZdQ8NqS3Svd/sNpcg2EosNCpUOkkYxre223u1M/fq66EzUtr4V9TqRsJRROyjPVPDP/vBbuDLP7iJCSDJ0LcQ2F3EXZqsNARNl/XNt+4FmLxbSCoVKB0nDmMfWLTYngnXjBzG4JI+LV65GYLzhSlM7XRSxcvFKzc5594FTsSY5J4UihRCSFLdwdtf+aew7dAbbNqzCM8fLsS/iwvxeCk6+IVLS9tbqR1hM20F0s3SiGPNMTpXx9qW58A1RU/YKaBIpLm5qyBSdcXv/bajOqyYLf0IIiUo3ewjzOcHbl+aahvo9dvSs8SLOhiCR4u3a0V3gBXlr9fuUZBMUKh0kqTGPyYV20MlZCwsXN5ypwzUosn1OihRCSK+ysKC066oO9yLOXb+jWiQI0GT+Zlo7dReRi2FKsgmmfjpMkkpuUwSkUl3A34/fhK0Th62LYd1alSDrZddgickVQkg76eYaE+W18yKhM3kKTh7LnJw2Rb+6WGikyoMu8HQXkUHO5qZzSjtmy3UDRlR6iDDDONtCWwEaH9igCM/ocAl3bVnb1bAsIYRkhXmlmqIYpjVU58FScPLYtmEVxp46ERqF1pUDRG3G6KcIDCMqPURYBMSmvVgA3LVlbZMYCVLYe0c3YeTalY2JoIQQspjxRzGC1lB/NMOUvvcyNOhony9qM0acCExWoVDpIfwGRP5QnslwzckBcwuIFPrThQwBMBVECFn02ESvdQLGZlbRLb/yfu3tUackt2O2XLegUOkxgtS7qRf/Pe+sFcfaovNXue/Zk3jotk24a8taPH70LMUKIWTRkhPB5FRZuxa7F3lljwGc69FiE/U2reNhF6p+VhQcbYopyWy5bkGh0kOEFUalpaCDQoYvjN/USAWdm6lgRcHBLy5VE9n2E0JILzGvFMaeOgGg2e/Ef5HnGsCVZyoYe+oElgyEl4WWZyrYOnFYu87bNmNMTpVx8UqrlUWc2XJZgEKlRzBFOYCrX5Q0DOWAcMHj/7KsGz8Y6fkJIaTXqS7UPKS8a2HQiJPqgkJV42vlR4DGOh7XAE43rgUA3rFsoOfqUwB2/fQMQVEOl6SGcgACK8JNgqdoGIdOCCH9zEyl2mS8FjV6rZtR65cXQQZwJkz7MdOj89AoVHoEm7ROUkO5yakyxp46Yaw/Kc9UcN34QWze83zjixnFLTcuS/ICJ88maUJI9vC2/UaNXvsN5Exrb1QBFGZl0Wsw9dMj2KZ1khjK2bTOAbWrCDc/a/uYJFyZV/jQe5bjpZ9eZBEvISRzVKrz2H3gFCTi9ZTrAu5iMu2MKjCidghlHUZUeoQ00jphRFHtbn62U61uP6RIIYRkmJlK1TgwtuDkWqLC3vXbneHjDj80bWdL0uh61mBEpUeI2ppmwuSPsu/QmchCYKZSRdHQAkcIIaTGyuVLG4Zv/vXb3yjhnWBfClnngzpBk0TXs4aooDGPGWVkZEQdO3as27vRE3g/yMVBB29fmks1VTM06OBSdaElxHj7DaWmMemEENKr6Ipcoz7+5YlbALSuyTOzVe1z+9NCfkxzhtKKnHR6TpCIHFdKjejuY0SlD/EaDnm/YKawZBLc5/QbG40Ol5r8VnpPDhNCSE0wbNuwKpHRpVtj4hcXQWtyWFq9nRb5NnYYnYQ1Kn2GdxAVkOwqYChC2/G8UnDy0hJ6fGH8Jrw8cQtKPVptTgjpP/IRql63bViFvaM1V+44eGtMgnxW/JgKaL31LDrSqBu0scPoJIyo9AHeEF2uHtlIg6gRmOq8wheenMau/dMt9S9xBxouX5LH7JV5RmQIIYkpODkAEikl/czxMkauXWm0tg+iWHAgUpvxE2Ud9BfaRknfB9n725K1OUGsUelxdHnKrODkBVBoe/syIYTYIALEOeXlY1wA3r1lbUudnk2tS14Ed964BkdOn29J39uStFbFFLEJq5tJAmtU+hBvHUoUnLxg+ZKBjnTq6Cyc4xBnkSCEED9xl5E464+upsXb0aND14gQZ5dNtSq2BbK6mpxu+rCwRqUH8deh2JIXwc6PrsH0Aze3ac/aA0UKISSLOHkxnkRNq5ZCPSWEWh1gsVCrBcxLLSX1xIuvpRIh96dpvOcNhWZHXf92zxwvN+2/ALj9hu61O1Oo9CA2BVlusZi3ZGxeKTxzvIzJqXKkYrJuU8srE0JIdigVC9j50TXIxxjvcXluAY/s3IypL92M3Ts2ouDkGxdkUS/MTK/uL8a1LZDVbaeAWDU6acEzQA8SVtBUcPL48qeu186OcD+YvRSlqFQXur0LhBDSwK3VOHL6fKwUt1cgROkEAmpRHDciUyoWcNeWtVau5bYFslkrpAVYo9KTmOb+AFedDAEEtq+VAp6DEEKIHteGAUh28j43U8HkVNlqHQ5zqvV6VplqT2znxdlu10koVHoQ08Apt8rbzUWacD/IWe0WIoSQrLJ8yUBDBARdNIYhgsZwVx15ESwoZeUKa2OXbzuoMIsDDSlUepCwuT9BoUT3Azc6XMKxV9/EEy++1vE0UFI7akII6RbejskkF3wLClgwrL0C4M4b12Dv6Ka4u9mC7by4tObKpQmFSo8SpKCDwpHeqMszx8tNIsUVEEODTlvs9l3u2rK24RFACCG9hL8RIdeGvgSFq0ZzaQoE20GFWRtoyGLaPsSUSywVC4FRFzcHOvWlm9tmeT/o5LB3dBNeGL8Jr0zcgru3rO2pDiRCyOLGvbhzU+wXr7Qnfd5Ny/qsQaHSh4xtXx9aBR5W2b1twyrj8xcLTs11Ngaz1QVcN34QWycOY3KqjL2jm/Cjh34Hj+7c3JYrE0IISRP3Im7Pc6faXuPXzU6bLMHUT5/hOg9WqvNNdtHLnByOvfpm6EwghZp98sXLc9rnd9vy4jrjuvincR579U3QaZ8QknXGtq/H5FTZOj2epCavm502WYIRlT6iZXKy59txYbaKx46ebbgSBhXQlmcqRot9V+GPDpcCoy42VKrzuPfJE7h/8iQeO3o20XMRQki7GXRyGB0uhaZkCk4ej+7cjLu3rE3UOFCeqeC68YMYfvD5FgfZxQSFSp8wOVXGvU+eiByKjFof4lX4T7z4WqTH6phXiiKFEJJ5nLzg/3nbrwAIT8k8dNsmHHv1zUhr291b1hprAy/MVjH29IlFK1YoVPoAN5ISp814QSmjBbMff51LL7nbEkJIXPIi2HfH9Y2OySCGBmuzex6PIFJKxUKjycAkVqrzatEW17JGpQ+IasHsJSeCX1qmn6Y8NOhgcMlAZnrpCSGkG8wr1RApY0+dCEznXJit4gtPTlunfJycYPbKHNaNHww1kFusxbUUKn1Akg/vvFKYqVSRA+CdqFNw8njg1o1NwmRyqoytE4cbwoUQQhYDgquNClWLqn/bxoBiwcHFK3ONwtzyTCWw+HaxrrtM/fQBpg9vTtA0vCrIs2QBzdu6xnAuk1NljD19omlEOCGELAYUgHv2T6e27rnFtsuXDrQMNTSJFO+MoSi4F5jrPLYQvQYjKn1A2OwfL0F50+VLBzD9wM0AWqMnM7NXYk0JJYSQXmRH8Qi++L5vYLXzM5yrvhsP/+QzODCzLfHzegcL7to/bfWYoUGnJcJtg1u/6J4b/LYQvQKFSg/ghhxNtSKm2QwAmsTG2Pb1gTlQN4Wk+3AHUSoWcG6mguKgg7cqVfqhEEJ6mh3FI5i45isYzF0GAFyz5DwmrvkKAEQSK2GDBVcUHKMVBHDVtyouuvpF1/G2l4QKUz8Zx+uN4qZc7nv2ZEv4bnS4hBfGb8LLE7c0Pti6x23bsMrY5eOmkKIW545tX4+XJ27BA7dupB0+IaTn+eL7vtEQKS6Ducv44vu+Eel57rxxDVbXL+T2HTrTtG5PTpVx8YreWNMlafFsmAN5r5CKUBGRj4vIGRF5SUTGNfcvFZH99ftfFJHrPPfdV7/9jIhsT2N/+okgRRzE7gOt9s6V6jyOnD6Pu7asbREr3tbjqHlYd19sC80IISTLrHZ+Ful2E/u/+1rTxeI9+6dx3fhBXDd+ELuenA5NpyctnjU9vteKchMLFRHJA/gzAL8N4MMA7hSRD/s2+30AF5RSHwTwCIA/qT/2wwA+DWAjgI8D+H/Vn4/UsVXE3oKpzXueD3SW3Tu6CY/s3IxSsaAtno0aFTk3U8HkVJkFtoSQvuBc9d2RbjcRdOEWZkPl962Kg83ct14gjRqVjwF4SSn1YwAQkW8C+ASAv/ds8wkAu+s/Pw3gKyIi9du/qZS6DOBlEXmp/nz/LYX9yjRhdScuppoSryL215QE5TzdxwWN8Y5q5FYcdBoFWoQQ0us8/JPPNNWoAMDswlI8/JPPdOT1Syn5VpnqF3upPgVIR6iUAHi91F8HcKNpG6XUnIi8BeBd9duP+h7bW0cwBlEqsU0dPV5FHKWmxEZJl0JMh7w4OYFSaPsUUUII6RRuwWw7un6CMHVrJiHoorRX6JliWhH5nIgcE5Fj58+f7/buJCJK3cnocAkP3bbJmKYB7AujhgYdqw/s2Pb1xoJbb1aoWHCw75PXB0ZwCCGkFzkwsw2/evpr+OWTz+FXT3+t7SIFQOoipV9II6JSBrDG8/s19dt027wuIgMAVgD4ueVjAQBKqa8C+CoAjIyM9HTFZtRK7DBFHGa7DFx1mrVhdLiEe0z9/Qp4ZeIWALXI0L999gdWz0kIIcRMqVgIXOdtywX6kTQiKt8D8CERWSciS1Arjj3g2+YAgM/Wf74DwGGllKrf/ul6V9A6AB8C8N0U9inTpF2JrSuY8hNVqZsGY7n76KavZqsL2u0IIYTYEVbgamtT0a8kFipKqTkAnwdwCMA/AHhSKXVKRB4UkR31zf4CwLvqxbJfADBef+wpAE+iVnj7VwD+b0qpvi92SLsS200Pmbp1wpR6nH1MMgix9ly5lucP6zXK0aKFENJjCGppcvdnP8WCE3ohGdemol9IxZlWKfUtAN/y3fYlz8+XAHzS8Ng/BvDHaexHr9COSmz3sWGFtyZ0YcWHbtuEPc+dagzMWjpwVdcmNQy6/YZrMHLtypbXPPbqm3jMYPNPixZCSK+hAFyeW0DR4EK7fGntNOx3EbepQ/S6ifdzWkhUxFbULDAyMqKOHTvW7d3IJHE+sP4uJKAmcG6/oYRnjpe1M4T2HTqTyDfFaw09OVXG7gOnWJRLCFmUFJx84Ky2rROHteut28ZsO+sty4jIcaXUiO6+nun6IcG4hm/ukKtHdm7G2Pb12HfoTOjUTFNY8YkXXzOGG5MaBnmvBMaeOkGRQghZlAha7R38aZ2gVPxiSAtxKGGHaGdoTufLMvbUCUDQsGgO8moxhRVNxm/l+tyKMPIiWDog2oJb71wh2u4TQhYrptXPuy4HlQuYOjR7bZ5PEBQqHaDdo7Z1ilp38jdNzbRpb/YisJsHNK8Uqgs1Uzjv/njrZvrpy0QIIWnh7wLV2VRMTpUh0IudXpvnEwRTPx2g3aG5KCd73bY27c0upi+Fieq8wpKBnNGwrp++TIQQEpWhQSd2F+i+Q2e067HAzoW8V2BEpQNENXiLmiaKEhEpDjott/nDikFCJE6S5uKVefzx7+rfw9j29Rh76gTTP4SQvsd/oec14oxTGmA6hyikE63PChQqHcBmsKBLnDSRrurbyQkWAMz7BMDbl+YwOVVueS43rDg5VTa70qKm/t125SjoUk7u6wLArv3TsUQQIYR0gh3FI4lm/zh5wc6PrsGR0+e1giSOsDCdW0yGnWFktc2ZQqUD2AwWdDGlie598gR27Z/WfnhMhVa6lt/qgmqknHQfyLB0VNxu9nMzlaYvwYqCAxFgZraK1cUCRQohJLPsKB5pmqZ8zZLzmLjmKwBgL1YUMHLtSuwd3ZTafkU5t4TR7lrKJNBHpUPYKtV14wdDT9q2PfJBz2Xq229XZGNo0MGl6gKnLBNCeo6/2/B7uGZJ6zDc16+swq+e/pr183j9o9IirShIkFdL2vusI8hHhRGVNhLnA2RTb2Lq3rF9rryIsbh3hcE9MQkFJw+lWr0CCCGkF1jt/CzS7Sba0eUYNrTWlqi1lJ2EXT9tIu4QKdsOHJsPj8kkyOSPcm6mAsO4IGsEwN1b1rZ0+bxFQzdCSI9yrvruSLeb6GaXo2sKajIATXtYbppQqLSJuC3J7oBB90RvGjRo8+HxP5crGoImI8/EKJT1s3d0E14YvwkvT9yCF8ZvwuhwKRMfdkIIicPDP/kMZheWNt02u7AUD//kMwBqhbJ3b1mLIU1XpYuudiRMPKSFzYVz2sNy04SpnzaRJIzmDeWZ5vDYfnhMYUHTcyad4WMSJLqiL0II6QXcgllT18/cvGoMUx10clgykMdMpYq8COaVaszk8a7FnSxeDbpw9ncdsetnERGlJTkI90Pi7eBZ5gQHwsJqY8I+kHEFRcHJY9uGVU1TQLdtWNVoxysOOhQqhJCe5MDMNmOHjzeZPltdwKX6tOS3KlWtSAHsxENa2F44p1XvkjYUKm0izbYxoDYm3OXCbLVJeXuFSXHQwduX5hoGaiaVbvpA+kXMioKDX1yqwuTH5hoYleqixDttuTxTaVxluPtNCCG9jI0794JC48LStAaHiYc0PU3SunDuFqxRaROm+pA4H7Qg5e3PPV6Yrba4vEa16x8dLjVqTJYvHTCKFKD2hXUF2JHT5xkxIYRkih3FI/i7Db+HH2+6FX+34fewo3gk9nMJgJcnbkHUngPdGhxUvBq3GcNElutPbKCPSg9g8kMRRLPPd7ePosxtfF2AmhALs98nhJBO4jdqA2pFsOOvfz6Sq6yL6yky/ODzsSLEj+7c3BStvnhlrjHh3qVYqBXk6mwikniaZNV11iXIR4VCpQcwGfEUY3qe2BrGBb22n6iiiRBC2k1aRm0uBSeHS9VaGj7qmTMnwNKBZqPNnNTcvm2fy43o9CNBQoWpnx5AF7ZzcoKLV+ZiPZ9ryW8TRrT1dVEAZq/MwckFB0WdvCBkE0IISUSx4KBYcFIzanOpVBegEG8464LG9HIhgkgBeqemJG0oVHoAXb3LO5YNtIQMXZy8oFhwAvOo80oZc57e3v59h87g9htKjdceGnSMYuTCbBWQq6FL1wPG/b9ULGDnR9cYvWEIISQN3EhzWkZtWaCXakrShqmfHiWoduTRnZsbaZ2w1I0/52nybfGmitxcZ5Q0j5MT7Pvk9Yl9WgAgnxN8+ZPX46ljZ/HCj95M9FyEkP4l7RqVuAgQeTzJ0KCDwSUDma0pSRvO+ulDTB/6UrHQ9GEOM1rzt8LpRITOGGh0uGRdaAvUpjbvPnAqFSv9hQXV2If7J082tUATQohLmFFbJxAAd21Zi5FrV2LsqRMtXZk6Ck4eD9y6sa+FSRQoVHqIsEiGk5OW0KD7Qb/3yRPaGT/eVrig1mJdz3/U4tmZuvlR0oiK913sHd2Ex4+eZbcRIX3MjuKR2GIjyKit3fjN3vY8dyq0W2ho0KFI8UGh0iPYiIl3LBsINHHzq3lX2Oh8Wvzoirji2OKnYaWfF2lqtcvVbaoJIf2HP31zzZLzmLjmKwDQNQFig66VOGiWmsnBllCo9Aw2YiK0r99fw1r/PWz+kKmIy/1C3bN/Ovh16wwNOk3Ot3EjK0qpptekSCGkf/ni+77RVGMCAIO5y/ji+76RaaESJQqdxB9lMUCh0iPYDDMU1CIvOkW+79CZli6h6rzCvkNnAlM4pmFa3hCmjaW0kxc8cOvG0Pdgw0L4JoSQPiHtFuNO4Y9CT06VcfFyq6WEbTdP1g3b2gnbk3sEm/55BRit8oPmSpjslR/duRkvjN/UIlLGnj7RFL0JEykCYN8d1zfmErnW0IQQEkYvthj7xYe77vkbIIYGHSvzzbQt9XsNCpUewdZ4zSRIguZKRJlLpIvMhKHQPAWa84AIIbY8/JPPYHZhadNtswtL8fBPPpP6a6Xh8CSoTbjftX8aWycONyIhunVvcIm+rtBP0Ly3xQBTPz2CbW2HSZCETXO2He9tk4LSsXXiMLZtWBXL8p8QsnjJQotxFAby0og4u5EP08WZaS33p3lM28Vdj3sNCpUewhUSpg9+UK7TK3SS5DiDvjQ5gXHScnmmgsfpd0IIiUGnWozTKMv3R5wr1XnkAzoThx98HjOz1caaDDSv8eWZirEOcLFY6jP102OYQoh5kdBc5+hwCWPb12N1fdLxvkNnIuc4x7avh5NvDZA6OcG/vHGt9j4X9uYQQhYj80oZ00oXZqtNdSd7nmtNjyu0pqUWk6U+hUqPYQr1LSjVkYKs0eES9t1xPZYvuVovIwB2fmwN9o5uwr47rsfQoGP9fIQQ0u+UigWrC7VKdd5oM6HqzxNWR9iPMPXTY5hSLzYhQFNB1j37p7Hv0JlIqSBvikcBeOZ4GSPXrgSAxhh0QgjpRwpO3ropwI18JJ1ztpi9VihU6vRKj3pYUWwQQYVXbnQFuFrPYjomYRXoNl9gAZpysmkMKySEkHZTLDjYvWOjcc0qFhwsX1obJlgcdKAUsGv/NIr1yfNhs36KBQeX5xZirfH9CoUKWu3pdSftrBC1KDaK1bx3+GDQMQnyZLFBd2UwOlwKnfRMCCHdRsR8YVVw8ti9Y6N2DfWndApODnMLqqn41n08kLzxoZ+gUEFwj3oWPxy2rcT+L4qN1bwrNoKOiSn9tKLg4J8uzQW+jgDYtmGV9j7bOUAiAF3zCSHd4MJsVVtHIgBuv6HUdDEZvJYJdn70Ghw5fV4rSLJ47ukWFCpIHiHIKkEdQiYx4da6BB2TR3ZubhEUTk5w8UqwSAGa61n8X0TdhFFdWx5FCiEkaygAR06fb/wedv6oVOdx5PT5RVt3EgV2/SDYtbWXCeoQenTnZq1tvpsHjepk+45lA9aOtTpHxcmpMrZOHMY9+6ebJoxSkxBCegXvmmtz/sjqxbC7Hq8bP9hw1+0mjKggWYFqlgnqEAqrdTEdk20bVmHrxGGcm6lgRcFBcdDBuXq7cxS8X1B/iorihJDeYEfxSM84xnYCrzjZtmEVHgsxuXS3j9rM4R8M6xb4ppEuymLNJoUK0nNtzRpJbPN1x2TbhlV45ni58XxJ7PBzIo1Jz+G5XD1BKSxCSHvZUTyCiWu+gsHcZQDANUvOY+KarwBA34iVUoATtx8Bmi5uvWkgHe5aHFUYuINhvRHsmUoVY0+dMD4mClms2aRQqWNboNpLJBVg/mOydeJwagMF55UK7SIKQgB8+VPXs62ZkC7xxfd9oyFSXAZzl/HF932jL4SK251o241415a1Tetl0LrmdRLXratBwsA0GLa6oFIRE1ms2aRQ6XPSFGBpf1Bds7k4kZElAzncs386lWmnhJDorHZ+Fun2LLF8SR5//LubsGv/tDHV7EZHwroRBTWRMnLtykZafHWxgOKgY+wO+vKnrm+sy1GFQZgfVlKSmIq2CxbTLiKSFki164OqEymuACkVC/jQe5a33H95ruZ+y8QPId3hXPXdkW7PCgLg1IMfx+hwybimFQtOU5uwt3mgWHCaZpopAPu/9xrGnjrRNJ7k7UtzLbPPXFHjvXiM2swRtA7n5errxV3vx7avD2y06AYUKouENOb8mPxP0iIv0ugiemTnZrwycQvGtq/HSz+92NbXJYRE5+GffAazC0ubbptdWIqHf/KZLu2RHd4Tvemk7JquuSf7XfunAQCP7NyM5UtbOxyr86rFcba6oLB8yUBTd+QjOzdj7+impu2iCoMgwTCvFLZOHMb9kydjr/e6rs5uzxUS1YPFiCMjI+rYsWPd3o2ewpRnDZof4a9Ev3h5zlhAWyw4EEFjXHnSmRZuLQ3darPDjlNH8MW//QZW/+JnOPfOd+PhX/sMDmzs/VoEEp9e6/oR1MQGcLV2z7W5f6tSbarj8xe5AtFm/Liv9/LELaHbRe362bzn+cBmBp3/FJDteUEiclwpNaK9j0JlcbBu/KD2g2v6Ium+pCZ0z2ESGLb1KE5O8I5lA8ZJojYIarUsbpqIxGfHqSOY+KuvYHDuavHk7MBSjH/88xQrpKcoFpyWk3zBybdEDZKuYUD7hEGU9dmLrXDSvV67u2KDhApTP4uEqPnOKC3DbquxF1M488ufuh53b1kb+pzVBZVIpAC1K4qcoCVPTKLzxb/9RpNIAYDBucv404OPYMepI13aq8XDjlNH8Hf/4ffw4z+5FX/3H36PxzyAYsEJLLLXRSJ0JpSmotV5pVrWNicvLSdTJydtq+vwpmeiEKfOMI2ygaRQqCwSouRBJ6fKkdItbqux94Pr/yLlRRqLwcEfvBHzXUSnUl2wdswlZlb/Qt/JMaAWMPFXX+GJs4240axrfnEeOShc84vzPOYBvHWpivf80pLIj/ObUOZEL3fcmg1vDcfOj65B3n9B1Obro9HhEl4Yv8koVvwvH7cgNshXpVOwPXmRYOup4qpnE0ODDn5RaZ3po+v7d3/2mxmR3uPcO9+Na36hN7AanLuML/7tN5gCahOmaBaPuR6lgH/8pyuRH+d1ib3v2ZPa9I57std5TOkKbN2TuX/d1d0WN5ViMva8/YaSceBhFLLgq0Khsoiw8VQJSvkUnDweuHVjowLej+6DG9d1lmSLh3/tMy01Kl5MEReSHNOx5TFPD2+0IWiYq6n7xXTSdtMk3gu1sadOAIKGsElqUd9uZ/Us+KpQqJAmglSy+yU1ucHqPrjtUN0iwEBOmNLpIO6V+58efAQDqrU4+dw7s+2d0cuYolk85ukwNOjggVs3hhqwuREWr6nbtg2rcOT0+UA/J7/o8bcxu9skcZVtp7N6FmbhJapREZGVIvJtEflh/f8hw3afrW/zQxH5bP22QRE5KCKnReSUiEwk2ReSDiaVXPIMMoxS79IO1a0UsO+O6yMXkpFkHNi4DV+4ZRdmB3zeGQNL8fCvZds7o5d5+Nc+w2PeJu7eshZTX7oZABrmaKbaFAAtpm6PHT2bWjo7q5OUs+CrkjSiMg7gO0qpCREZr//+B94NRGQlgAcAjKDWiHFcRA4AuAzg3ymljojIEgDfEZHfVkr9l4T7RBJgo56jhBrD7Kfj4Iqm0eGSse2atAc3skI/lc7BY94+9o5uamn1DWo91kVD0sJ7UdeJduAodHsWXiIfFRE5A+DXlVJviMj7AfyNUmq9b5s769v8L/Xf//f6dk/4tvv3AP4PpdT/O+x16aOSnKAvgve+FT4jN9N2QV+m+ydP4okXX0tl0rHf72D4wecTtzETQhYfxYKD5UsH2l7g7zdfc3LSVKMCNK9rJqO5brvDtpsgH5WkEZX3KqXcXtOfAHivZpsSgNc8v79ev827g0UAtwL496YXEpHPAfgcAKxdG+7DQcyEjRV3/wVtB7R28+gKwianynjmeDmRSHENlkoaMdSDfoWEkAxw8YrZaTstTN03gDkiHdQO3M9CJYhQoSIifw3gfZq7/tD7i1JKiUjk04aIDAB4AsD/qpT6sWk7pdRXAXwVqEVUor4OuYrtFyGsfz7uc7gMDTp4q1KFKZpq4+r4VpsXGkJIfxK1GN/JhxfwO3nB8iUDLXb8wNUI9K7904ER6Cy0A2eNUKGilPpN030i8o8i8n5P6uenms3KAH7d8/s1AP7G8/tXAfxQKfWozQ6T5AS10nkr2k0h0aAvjP8+07YCYHCJ2SLfXxdjSjMlnStECCFBCNBYc469+iYeP3q2KZXjpnZ0EV+XsCi2d33LGSz6O9kOnDWS1qjsA/BzTzHtSqXUF33brARwHMBH6jd9H8ANSqk3RWQvgP8BwCeV0vQ8GmCNSjJMMyz8udSgwVaA3rzNzfuGDTIshQiMR3dubroSMeVsAWDs6ROBVzqm90EI6S/cGWEzs1UUQyK2NuRF8KOHfqfptjiFrqY11x3mGlZnt9hrVJJa6E8A+C0R+SGA36z/DhEZEZE/BwCl1JsA/gjA9+r/HqyLlGtQSx99GMD3RWRaRP5Vwv0hFujai3UncwWzDbPuOYDaHA1v+94vLrV+Ad3nyBvaAPMiTV/IPc+dCkwz7bvjesM7vfo+CCH9TV4EOz+2BkrVvvMXZqtYNpDD8iWt65Qtd964puU217r+5Ylb8ML4TVbiwRRZnqlUjSIlL9K1duCskaiYVin1cwC/obn9GIB/5fn9PwL4j75tXkfbpyEQHbr2YlN0ww1pmq4eXPM3U9TCfzUjAG6/oVase4/B4dYb9pycKhu/yO6XP+i5wshJ6z4SQnqPeaWw/7uvNbUQz1ZrgXrdxGQ/OUFD5ORFcOeNa7B3dFMq+xYnRb2gVKxJx/0InWkXKd6++MmpMu598oQ2LxpU0Oo+hymsqUMBOHL6fOO5dY/zGrkFDb7y5mzDUkkmFhRTQ4T0A3kRo8+JTXfPgtKvd2l4msTxk1rMNSl+OD15kWMzgCuMqNXo7vY2DrdBz+3dzpSKCqNULFCkENIHpOHTVJ6pNE2Bd9dHbzrbPyneBp2769CgY9y+0xb1WYdCZZETZwCXn6jK393exprZ9NzFQu1L7tpe7zt0BrffUEKA+3ULTk4wtn09rfgJIQ28QiTMosEWXVTmgVs3ai+uigVn0dek+KFQWeSYIhYLSll/UbZtWKW93cnVfAW8eK8UbEKqpqjL/+X697dc6TxzvIxlA3Yf6UEnh+VLB7Br/zQuXp6zegwhpP/xCpEgK4d14wexdeJwaHTFFJUB0HKh9ujOzZh+4OaWdXByqty4KLN5zX6DNSqLnDRGeP/liTe0t7/nnTXxoRMjYb4CLqa5QqYrHVsUpJG3brc7JSEkewQZuLkCJazRwLRueQmKyth0Ddmulf0MhcoiJ+kI78mpsvFEf26mYhxmFcUm2mvp7zo7JslG50VSHZJICOkt8iLY+dE1xhlkCrW08rYNq/DM8XLgeuFdt3RR4ihOs7rH01KfQmXRE2USsg7brhw/UW2idaZvOoYGHVyqLhi3Kzj5toiUgpNDpWrtWUgIiUha3XlOrua3EjaDzE0nf2TtChz98YXAbc/Vi3D9kY+giyr/+miKnJjWq8VkqU+hQhKN8A76ssxemcO68YNa8RM15RQ0M8il4ORxy6+8H3954o3GtksHcrg8d1VAVKrzbWlHzkWp4iWEdA8BDv7gDasLlkp1Hv/1R2+GrheriwXtGmV6nJOXlqi1KXKSp6U+hQpJRlAO1zVqc68s7tk/jVKxgG0bVmkLWINSTkGCyJ3FoQvTekWKSzvakS9eYSqJkHbi/966FxymE7mJ6rwKtawPel0/7rq1K4Lp5PIlAy0Xh6Y1bl6plkjwYmtfZtcPSYTJjt+P+2Uvz1Tw2NGzLXUtQ4PBLXmmq4dSsdCwsj5y+nyktI5rUV0sOC3dSYSQbLO6WMArE7fgy5+6PpaHUlL8lgpRIhy6qe9Ba1yYjUO/w4gKSUQUO/4gBjVXGF5sin6j5my9FtVuEVt5pkJbfUJ6AO8IDaA2EyxKpCQJOgfbKO6zOlEStMYlSc/3A4yokNRxzdiiECYykpjDmfBu7w4ae2XiFrx/RfDzCBDoKkkIaT/+7+/gErvr7oKTt16j8jlpOUm6RpF+vGsUYB5kZ0rb2KxxixVRKdgOd5qRkRF17Nixbu8Ggb4bJ8ifIIhSzDkaQftiIifAv7xxLY6cPl8bqlgfSGZDwckhJ8K6FEK6hAB4ZOfmprVi3fhBq/qzR3duBoCWtcK2yN7JC/bdcb2V/4kbpXXraJKucf2MiBxXSo3o7mPqhyRCV6lenVeRTvwuSY2M/GmoZQEtwwsKTZNWo+wr25AJ6S53bVnbskbYpp29j9t94FSjXs52CajOKysPk8WerkkTRlRSJI0pm71G0FVMXM+SvAgWlEp8DD9w37dSGVRGCMkOd29Zi72jm1put4mo5kVw541r8Jcn3kjkSC1Ao76NpENQRIU1KimR1pTNXsOmUj0q80qFHkOb2RcUKYT0F05eMHLtSu19NjUi80ppuw6jspg8TLIAhUpKpDVls9cwDQ10IyEvjN+ER3du1m5jU9CmO4a2ojBPEzZC+go37WLCWxT/yM7NideAgpMzrm+dYLEPI3ShUEmJqJbw/YJNpbppm907Nlr5l/iPoa0ovPPGNbHfFyEkm5RnKlYn7dHhEhYSRlUvVRdideKkITAWa5ReB4tpUyKNKcS9ik3RmGkbbzGbiZxIkxW/rSh089iPHT2r3X5o0IFSnJ5MSK9hU3g/OVVGLqJrrZ/VxULkoti0ph1zGOFVGFFJiaAUCDGjc2j0469ZKRo8THSicO/oJmPq6ZZfeb/WYp8Qkn2CUuuuWEgiUuKu32mVASzWKL0OCpWUoFlPPIIiTrr8cqU6D6UQSRSa/jY2lvusciEku5hO2jZDTP0UC04q63daAsO0Ni6GKL0fpn5ShH3z0THZRj902ybjkK+3KlU8snNzpFZw3d8mbIhY1GFnhJD2YDJjM52040Qd3qpUMf3AzZEf5yetMgCbsSGLBQoV0lV0s4Jc0eG6OvqJmzf2v0aQQZQrlkz7ANRqXAaXDODcTCVxLpzYs+PUEXzxb7+B1b/4Gc698914+Nc+gwMbt3V7twjiu1LrcMWJO3HdPxk96KRt+m4PDTr4RWVO+11NK1KRlsAIWhsXGzR8I5HopKldkIGTa9ykM36yeZ6Ck8ftN5RaFj+gFgLevWMjRodLmJwq456AyItrPnX/5Elj0S5Jjx2njmDir76CwbnLjdtmB5Zi/OOfp1jpcZYO1FqB36pUtWtLlLXH9J1/6LbaemG6L2gti/r6FBjRCDJ8o1Ah1gR9+dspVoKiGiaXSi9bJw5rH+/O3fAvKEDzVczM7JXAuT53b7k6M4i0l7/7D7+Ha35xvuX219+5Cr/6b77WhT0iaZH2WhI0aweIFqmwWfsoTpJBZ1qSCt0wtXMNnEzGTU+8+Froc5gERHmm0nj+lyduaYxt93sXXJlbCPR7eeLF1xZlJX43WP2Ln0W6nfQOaa8lo8OlRjemm+rxtgq/MH4THqkPKNy1fzrQ7yRs7aPnSXuhUCHWdKtdbnKqbKz/CKsLuX/ypPE+nfjRDllcUJgLyLvP1+cSkfZz7p3vjnQ76S3SXkuCBIZOXOzaP43rNCZtYWvfYnUm7xQUKsSaTrbLuc6O140fDOzOCbPIDoq46ESOaUEKkkN5Ea2PDkmfh3/tM5gdWNp02+zAUjz8a5/p0h4tHobq/kXtbNlfXSykahsfJDB04sL9nvsjImFrHz1P2guFCrGmU6Z23isdIFgkhNnkB0VcdAMT44iuLb88FMu3gUTnwMZtGP/45/H6O1dhAYLX37mKhbQd4sJsFXkRfPA9y9syR6vg5LFtw6pUUyhBAiNMRHgjImFrHz1P2guFCrGmU6Z2tid9m0LaoAVVJ7Cue5f9wpITYOsHVuL7Z99iIW0HObBxG37133wNv/wHz+FX/83XKFI6yLxS+OFPL6beip8XMZowJkmhBAkMGxHhipnR4RJuv6HUWE/yIrj9hqsWCXQmby/0USGRiOJfErcK3iZcWioWmkSK6bXuvHGNtm146wdWavfl6I8vhL62y9KBPP7+jX9iJIWQhHz5U9djdLhkTPPGvRAI8yIx2R+4uGJmcqqMZ45frZWbVwrPHC9j5NqVTWsiu37aA4UKaQtJBnMFGbEBV0PEWycO49xMBcVBB29fmkN1obWy3xUzT7z4GuaVCvVfiXKlWKnOU6QQkgK7D5wCYP7uC2prShyfE9PFlVdclGcqLe633oiIzYBAOpO3D/qokLYQ5F3itgGb0HkWhLlU6rB5LT8fuO9bdJglJGUKTg6VavAAUNeE8fGjZ7V1aUHf5zQ8noIiwOvGD2r3SQC8PHGL1fOTYOijQjpOkip4XS3MIzs345W614nNMEHb1/JjKs4ddPRflWLBYbcPIaGEF99WqvM4cvq8sXg+6Ptsinjsee5UlJ00wmLZ7sLUD2kLSQdzBYVRbQVInEXElCoC0HKlV3Dy2L1jIwDgC09OY4GBGEK02KZIdSkYl6Dvs2lNuDBbxf2TJ0OL7sNS1RwQ2F0YUSFtoZ1V8DYCJMpr+X0bRq5diR899Dt4ZeIW/Oih38HItSvxzPFy0+IpQKPqf3S4hKBskcAckfHv86M7N6NYcKz2G0CkbUlv04aO4MyRFzGmWIK+z0FrwuNHz4a2N4cZtnWq45HooVAhbaGdX2ydCHLygmLBifxaNtbXJmOoJ158LdQQCgCWOTkoTei74OSummhJbWG8Z/80rszZF+jOVKrW25Lept9Lp7xW934Ugovwg0SMAkLbm21S1f5xGxQpnYOpH9I20qiCDypwS6MV0Kaa37SIzSvVCA+PbV9vnLJsKiJcuXwpxravx9hTJxodSwAwG1J0SEi/4R0QairCD2J0uITdB04ZhXtYujhpqpq0FwoVklnC8sZpXNHYXEkVBx1cmNUvgK6oeWH8psCF0vQa+w6daRIphPQapWIBs1fmjN+RMARo6uaJWwuye8dG7No/Hbm+BQBrUDIOUz8ks3Ri0FdYNf/kVBlvX5oLfI5zMxVMTpW1NQQFJ99I77Qg8Y2sCMkCgquf4VzMGhrvdzBJynh0uIS7tqxtSbLqBIe/Lg0AHrptU9N3dekAT49ZgREVklk6Megr7ErKNuKhS/sUC06jK0jngNnvNQekv/F251yYrcLJC965ZAAzlaqxc8eP+12L62LtZ+/oJoxcuzLwue6fPNnUwedGam+/oYRLnrTrTKVqbVJJ2guFCsksncgbh9W72Igi04L8T/VIjP81ciKhpnJOXvCOpQOxwunFgsMiW9JWBp1cSy1VdV5h+dIBLF86EBgp9Jo3jm1fj2OvvqkVDkA8gRCUFp6cKmsN5SrV+YYlgf92b70a6Q50piWZJQ23yaSYHHaBWiuljYutG1kJc7l0yQmwoGB9VerFPT6mwl5C2knYZ7bkuxCYnCob60riOEuHEfR9NkH32c5AZ1rSk3TTu8DNYbsGVF5cv5MFS5E/U6li1/5p3D9Zu0oMiggJ0DCO8z97WA2A9/iEdUkQ0g6Kg47Rg9YVHt7v775DZ2I50cYl6DlNk9bZ+dN9mPohmaaTg77cPLnfHVOhNVw9OlwytlLqUKgZT41cu7LWkvz0CVTnW5foIOnzzmUOLs8tWEWYgtql47J0IIfLc2ydJnoKTh5K6T/DJsO2IOEQRSDY1rgEDT2888Y1LTPE2PmTDRhRIQTNxm9A62LrihTvFeHY9vVwIrQ6KNSmxO47dAbVeRW5S2KmUo0UYcrHbcMwcIUipadod1RtaNBp+Sy+ZaiNMhm2mcRImBOtFxvTRhedWaQAuGvLWuwd3UT32YzCiAoh0LdC+9Fe/cUQG26ha1T7FPelbPL2+w6dwXzK/iy9V822eMmLBEYr3AihrihWt53u9gdu3dhyEo9q2KbrunOFg/+5TVETG9NGl7Di+U5GcIk9FCqEwC4f7r/6cyMjftxi2LRRAO598gR27Z9GwcmhMrcApdAYnOgdvNaO/H5SbIuPSTA2RdbzSqFkSHMAzRHC+ydPNjpevM8d9PcyRUiiGqeNDpdw7NU3W4aA+ocIBpk/RrUxoBjpPShUCIE5d+2iW2xNC+GCquVU25EocU8c3qvgeaXw2NGzeOzoWQC1E8zgkjwuXrGfGRQXJy9asaZjQSk8unOz1lOG2GHbeu7WUgUda/fzu3d0E/aObsLkVLmpdipIVA4NOtg6cbglKhF1vMXkVBnPHC83XmteKTxzvIyRa1c2PY+upd+NmtD+vv9hjQohMOeuAXOu2rQQ5kXaIlJsmVeqIyJl0MlZixQAWFFwGmF6t8NiEQwETo2CkzPWgDRvl2+Ig4du22TdzbLnuVPWf88Ls1WrmpAwTGmb3QdONdWdmETTuZlKWye1k2yQKKIiIisB7AdwHYBXAHxKKXVBs91nAdxf/3WvUurrvvsPAPhlpdQ/T7I/hMQlzqBDU5g7SbTAyQugkPn5P0ODDmYimtH94tLV+px5pRodS4De2Zc0Yxpu6ccrqt3/bdIxQeaCQWmk2r5dHW0RNJ/LjykqaWtYuLpYSHVIKckmSVM/4wC+o5SaEJHx+u9/4N2gLmYeADCCWmrzuIgccAWNiNwG4O2E+0FIYqLmrk0LpG3bcqlYwLYNq3Dk9PmmxwO1WpRu13MUnDw+snYF/uuP3myqiQhqQw3Cr728Ax2PvfpmI3XVizg5yYy41BWQ+utAbr8h2mf9hfGbsHnP84ECwh2yaVvYCoSnXIPwii3WnfQ3SYXKJwD8ev3nrwP4G/iECoDtAL6tlHoTAETk2wA+DuAJEXkHgC8A+ByAJxPuCyEdx7RAhtVh6Fw3vV0NxUEHb1+a6+jJb/mSPJRSnvoXhe++cqFJkAiA228o4fGURIW3TsKd0eL3sck6d29ZCwBaa3ags0XEuu6aoDqQ0eFS43NnolioDeoLi3KsLhYiF7aaopLLnJw2wpMXwYJSjJosMpIKlfcqpd6o//wTAO/VbFMC8Jrn99frtwHAHwH4MoDZsBcSkc+hJmiwdu3auPtLSGoD0Ex4Iy26k65pmqu3kPHCbBX5nKBYcPBWpYrVvuhLwcmhUl2IfTLPSa1mZGa22hTJccP0gD7VoAAcOX0exUEn1hwiP/7JuV5r9d0HThlPjgJ0rGA4CCdXE1lbJw5r/xbFghM6+yYtTHUZYVPIg0S1k5PGYE2b1zZFE031XKaopG6/Oj0+g2SHUKEiIn8N4H2au/7Q+4tSSomI9bopIpsBfEAptUtErgvbXin1VQBfBWqzfmxfhxAvQW2OaYsV70k3TBjpChnnFxREgueMDD/4fCzBkBdp8cHYOnHYqr7m3EwFK+pX2UkIM/Xyu+D63YGj1rXUipxVqlOr37GsdhyCai2SDoi0iS7pZui4n7kgi/og/6C8CHZ+bE3jOYcM4jQnzXUxUdqTgdaopLvvbtG122rNCMriJVSoKKV+03SfiPyjiLxfKfWGiLwfwE81m5VxNT0EANegliL6PwEYEZFX6vvxHhH5G6XUr4OQNmG6urz3yRMA2jPO3SZ/bhIbptvdxTxuVKO6oFrqBmy9V5LUFXgxeXEA+r+T1/sDiFbHkxPgl5YNpD5V2i0oTuuY6FhRcHDxypy2I0cXZdAN89QRlKoBWlNED9y6sWX0g5MX7Lvj+pbiXW80bJlj31zq33fX26VcF1Xe1yCLh6TtyQcAfLb+82cB/GfNNocA3CwiQyIyBOBmAIeUUv9BKbVaKXUdgF8F8N8pUki7MS3M80rFbrHsNH67/7j4j4WN74QbBTF0vEYiyOI9rNZhcqocqe5jQdl3kkRhdbGAyakyZq/Mpf7cLm9Vqtj50TUtrdxuvZDOHTZMpDg5wbYNq5AL+UN6U0SjwyXs/OiaRrtzXgQ7P7pGKxy80bALs9WW75Y79HPd+EFsnTjcuM8kUIFkbdCkt0kqVCYA/JaI/BDAb9Z/h4iMiMifA0C9iPaPAHyv/u9Bt7CWkE4TdDL2LsqdpmhIpehutzkR2eA/Fjo/Cj8KwLFX30wlfbJtwyrrffPe7gq1tBl0crX2cEsKTh7bNqzCfc+ebIlsDYZEEUzeJjpWFws4cvq8dv7UkdPnW7a3iYwtGcg1FdgG4RWHuqJcv3AIq4kJms0Ttu/d/I6S7pFIqCilfq6U+g2l1IeUUr/pChCl1DGl1L/ybPcflVIfrP/7muZ5XqGHCukEYSfjblnP796xsWXAoamQMSiSMjToaJ/HfwIWANe9q4CtE4dx3fhBfOC+b+Ge/dNY5uSMosnFtuMnbGCje5LVXV1v27CqJYLgLdhMy9m2VCzg0Z2b8crELfj7P/pt7LvjeqtokWsCeOT0ee2+BM3PAWqTel+ZuAVDg8HH2n3PUbppbCJjF6/MWx9D9/l2HzgVKECC9sl7e5CQsdn3LI6HIO2FzrRkURHVrbNTjA6XsO+T1zdNbt33yeu1YXXTvudFMPWlm7XP408dKAAv/OjNhuhxr5IvzFYb7qem87VNMGVo0Gnsh4lzMxXt1fXYUyew/3uvtbzOR9auwOhwKbUTlVugue/QmYZIAoBHPrU5UGSJXC1EjZt+e+zoWWydOIxbfuX9Vo7Ips/lioLTIvJsImO2uEJpcqpsTJ3ZphDd24OEjM2+0xp/8cFZP2TREbc7od3YmlaZwvXu7brn2XfojHUrs/L9H5WCk290FI0Ol7B14rD2hO4OWfS/H5N3zH/90ZuYnCqnUrjqTdv4O8Aeum0T9n3yemN7tLu7Sf1eyjMVPHb0LAadXMPptzjoQCm0WOXr/EacnODilbnGPpZnKti1fxoKtZSh60Wia49fOpDTvrehQQeDSwZaOtRcEadDl0IM+m4FzeaJ09pP+h8KFbIo6WXbbZOdeZzi1LTJi7R0oQQNx4tSEKtQ+3uFDdsLwxtJMaUgXhi/qckMzTQYT8GufTiI2eoCKtUF/IsPrMT3z74V2Drv/bzOXplrqY1x92OmUkXByePuLWvxlyfeaIiSnNTe49KBXMtASa/A9BP0+fELh7Dv1rYNq1rM8Uwus+32PCK9AYUK6TtsF7detd0Ou2LV0c72We8+eEWK9++wwnOFn4RzMxXtiXDbhlVNJ+Qg3A4dm7oP72dk3fhB7fZu23SS4+um4vx47ef9n1fT/ngf6xcEbrBqplKFk5NGJCdMBJg+P0ODTqTvlluQq3M77qfvKEkX1qiQviKoo6Bdr6drs2wnbp2Ntw4lzLEzzboFE36RMvbUicbfYaZSxduXkrfwummG0eESxravb3iBHDl9Hrt3bMQrE7fg0Z2bA5/DbZc1mdaF1Vj4cb1dHt25OVLXkC0mAWRTqxEU6akuKAwuGcDLE7c0IkgmTBOKH7g13LXWi6n9WNe9RIgLIyqkr4g6FC0JnXK51RF0xaqLJpmiEEdOn08l0lLy1BdMTpWx68nplhbm6oJKlCbxRo3Cjn1YoWulOo9lTq5l2rWTE8xemcO68YMtUYawSJa73Z7nTjUiR2nMLJL6+/X/vZOmwAD7lGBaqdKos4AIAQBRXZ7QGoeRkRF17Nixbu8GySDrxg9qTwyCYCv6OJiKRHUDB9PEJEZ0jqRh81FsXEyHQgYkOjnBO5YNNIpBw4Yp+sWBDXkRfPlTV7ugwo795FQ51GJfADyyc3NTesrvABuUzrI5WZs+j1Exfabc/dEVntpa77fzs+qnW98Zkn1E5LhSakR3H1M/pK+IGrZPQjeuDoNSW2FGWzrCPEnc8P6+T17f5K/idkgXCw4gtXSKQu3/sInPD922KdSrxb8PXpEChB/70eFSqOma22XippBmKtUWm3r/8RsdLuGF8Zus0iXua6SB6f26+/PKxC14ZOfmpnTgXVvWBqb7utFBY0ohsZOHBMHUD+kr4hSaxiWozbJdBImROMIp6L7lS/L449+9Gk3QnZS3ThyOZE3vGpz5Bw4Gba/rRFlRcLSvmxPBuvGDWFFwIAHObe4oAJuIUhLhmUZ6BrD7TOnSgSPXrmyKGInAqni2XfRytx3pHhQqpK/o5ELYSVHkEiRG4ginoG6g2St2k5RtcfK1ic1RnGUvaRxeJ6fKuGiYreO2D4eJJ3cgos3EaNdULc7nybatOIy4n6ksds34jwmHDZIwKFRI39GpxbnTV4eTU2WtlweAxmtHFU5j29cbazlc35Kg92Pb9lwsONi9oxYZ2RVSO+JFVwi979AZ7SThKLieM2FCS2eqFrVg2v951EVx/J4m/YipnibOMaW/yuKCQoWQBHRKFLknN51IccVIVOHkLvZBhJ3IbVMbrkgBonu6+PchaQ2QjUuqi67eJmkXmenvdOzVN/GYYY7S7gOnevrE7Bdn/qMa5Zh2s9uOdAcKFUIyzuRUWWs1D7Q6wdoKJ5vaDCA4beQt4M0bIj0u3pOQKfJjsnUv+gb3JTGvKxXDW45tSCqWdH+n0eGSUajMVKqJojrdxibdZ3tMO2lBQLIBhQohGSYokgIAC0q1LM42YXGbE0dQ2sgvdOZVsEeK3+3V3QfvPgLA2NMnWlIgb1+aa/IR0c69yQugzHOCTG3a/n0xpdb86ARcGukIW4fbXjsx24gQ2yJ0erEsPihUCMkwYYJCodZ5Y/JSMV19By3qAoSeaE0Ooyb8JyFT5Ec3CLC6oLD7wKmW7iO33iEvtfqOoqerJUqHi41NvhedgNMd9137p3Hs1Texd3RT6HO6RInw6P6GWa3dCIuCRSlC70a3HekuFCqEZBibq0SvGLENi5sWe1vjrTChYxo4F3Yi9U8NdpmpVJuiKu7/3pO6O4jvkZ2bY5+cw06ouqGLgFm4PX70LEauXdmWLiH/iTnLtRs6AeZ+TvzpuDjPRS+W/oaGb4RkGNurxKheKkmNt4Lm3viNx9wTu80cJtP8HQAthb9xDO7CCJqJpDOeczEdd7dzKgp+U7kHbt1o9bdqx/FIC918qkd2bsYrlsZ5Yc8VNuuK9DaMqBCSYaKmAmzD4klbq4Ouak1pnbBoT5A/ivv+gn4Pu90GXVppXqnQq/6gSEyS/dEVLOdFmgSIu09Zr91Is0Mui/4wpH1QqBCSYaKmAqKExaMONgzbrzChYzqRu7eH+aP4xVa7ahW8x8V7LIKMyca2r8eu/dPaOp24+6MrWPb+70/tsHaD9CsUKoRkHBvDsLheKn4mp8pNnTflmQrGnj7R2I+g/QrD1MKcF8HkVDlSseXkVBmzmuhLmrUKUWo+RodLOPbqm3j86FljfY7u+YP+TjadWd6IFGs3rpLVomISDwoVQnoMd8Hd89ypRmRl6UCu6f6gRTloEd/z3KmWqEZ1XmHPc6ciLfS61zC1/c4r1RAAOvwFrCYPGK/7bRpE9evYO7qpabZO0AnSRgTZpmy8gxjd/V7MJ+gsFxWTeFCoENKjeOfgzFSqVotx2CJumkETZTbN/ZMnmyIL7msUDYMEARgjBzr/E1OkYfnSAW3KxvaE7X9MnJoT2yiTjQiyNbbzpnZYu0FDuH6EXT+E9CBxOzza3RkyOVVuSX+4ryFSa0mNgq6bI6xo1Ka7SLff/seYSKPmw6bwNagDyWWxpnaCyHpRMYkOhQohPUjcxTjscUVDe7Dpdj/7Dp0xGr/NzFYDTeH8lIoF7RWwSSi4t8cRY7YTnZ2cpCIMwt4DoG/DvXvLWrblhmBzbElvwdQPIT1I3A6PsMft3rERY0+daLKid3KC3Ts2Wu1XkFByX0P3+kEmcX7CikbjiDjbq+13LBtIRRjYFr4ylRMdFhX3H4yoENKDxDVsC3vc6HAJ+z55fdNV+75P6k3OdJiEktRf2/T6d0WIFIQZfsW5ora92p6JUKsTBE3L2gePbf8hymIAV9YYGRlRx44d6/ZuENJV4rZgtvNxuo4cAXDXlrWNmTftbh01tW8Hnaxsp0nbjhgghERDRI4rpUa091GoEELCiHLyz4KHRdKunxUFBxevzDW1aoeJHUJIfChUCCGJ2DpxONEQw3bQichMEuO8bos1QnqJIKHCYlpCSChZa/nshKlX3EJWGo4Rki4spiWEhJK1ls8sTwrO8r4R0otQqBBCQonbZdQushbhsdmHLOwbIb0IUz+EkFCizJHpRH1GlicFZ3nfCOlFKFQIIVbY1Gx0qj6jk6ZeUYUXDccISRemfgghqdGp+oxOmXrFmRtEwzFC0oURFUJIanSyPqMT9vJxJ/HS+p6Q9GBEhRCSGlnrDkoKC2MJ6T4UKoSQSExOlbF14jDWjR/E1onDTWmQrHUHJaXfhBchvQiFCiHEmrCajbTrM4JEUSfoN+FFSC/CGhVCiDU2NRtp1WdkweE1Sls2IaQ9UKgQQqzpZM1G3ELWtGFhLCHdhakfQog1nazZYCErIQSgUCGERKCTNRssZCWEABQqhJAIdNLMjIWshBCANSqEkIh0qmaDhayEEIBChRCSYVjISghh6ocQQgghmYVChRBCCCGZhUKFEEIIIZmFQoUQQgghmSWRUBGRlSLybRH5Yf3/IcN2n61v80MR+azn9iUi8lUR+e8iclpEbk+yP4QQQgjpL5JGVMYBfEcp9SEA36n/3oSIrATwAIAbAXwMwAMeQfOHAH6qlPpnAD4M4P+bcH8IIYQQ0kckFSqfAPD1+s9fBzCq2WY7gG8rpd5USl0A8G0AH6/f9z8DeAgAlFILSqmfJdwfQgghhPQRSYXKe5VSb9R//gmA92q2KQF4zfP76wBKIlKs//5HIvJ9EXlKRHSPJ4QQQsgiJVSoiMhfi8j/ofn3Ce92SikFQEV47QEA1wD4r0qpjwD4bwD+XcB+fE5EjonIsfPnz0d4GUIIIYT0KqHOtEqp3zTdJyL/KCLvV0q9ISLvB/BTzWZlAL/u+f0aAH8D4OcAZgE8W7/9KQC/H7AfXwXwVQAYGRmJIogIIYQQ0qMkTf0cAOB28XwWwH/WbHMIwM0iMlQvor0ZwKF6BOY5XBUxvwHg7xPuDyGEEEL6iKRCZQLAb4nIDwH8Zv13iMiIiPw5ACil3gTwRwC+V//3YP02APgDALtF5AcA/icA9ybcH0IIIYT0EVILbPQWIyMj6tixY93eDUIIIYSkgIgcV0qN6O6jMy0hhBBCMguFCiGEEEIyS0+mfkTkPIBXu70fAbwbAM3rksFjmBwew3TgcUwOj2Fy+v0YXquUWqW7oyeFStYRkWOmXBuxg8cwOTyG6cDjmBwew+Qs5mPI1A8hhBBCMguFCiGEEEIyC4VKe/hqt3egD+AxTA6PYTrwOCaHxzA5i/YYskaFEEIIIZmFERVCCCGEZBYKlZiIyEoR+baI/LD+/5Bhu8/Wt/mhiHy2ftugiBwUkdMickpEJjq799kgyTGs3/7HIvKaiLzdub3OBiLycRE5IyIvici45v6lIrK/fv+LInKd57776refEZHtHd3xDBH3GIrIu0TkiIi8LSJf6fiOZ4gEx/C3ROS4iJys/39Tx3c+IyQ4hh8Tken6vxMi8rsd3/lOoZTivxj/ADwMYLz+8ziAP9FssxLAj+v/D9V/HgIwCGBbfZslAP5/AH672++pl45h/b4tAN4P4O1uv5cOH7c8gB8B+OX65+cEgA/7tvm/Avjf6j9/GsD++s8frm+/FMC6+vPku/2eeuwYLgfwqwD+NYCvdPu99OgxHAawuv7zPwdQ7vb76cFjOAhgoP7z+wH81P293/4xohKfTwD4ev3nrwMY1WyzHcC3lVJvKqUuAPg2gI8rpWaVUkcAQCl1BcD3AVzT/l3OHLGPIQAopY4qpd7oxI5mjI8BeEkp9eP65+ebqB1LL95j+zSA3xARqd/+TaXUZaXUywBeqj/fYiP2MVRKXVRK/R2AS53b3UyS5BhOKaXO1W8/BaAgIks7stfZIskxnFVKzdVvXwagbwtOKVTi817PSfInAN6r2aYE4DXP76/Xb2sgIkUAtwL4Thv2MeukcgwXITbHpLFNfTF7C8C7LB+7GEhyDEmNtI7h7QC+r5S63Kb9zDKJjqGI3CgipwCcBPCvPcKlrxjo9g5kGRH5awDv09z1h95flFJKRCKrWREZAPAEgP9VKfXjeHuZbdp9DAkhvYuIbATwJwBu7va+9CJKqRcBbBSR/wHA10Xkvyil+i7SR6ESgFLqN033icg/isj7lVJviIibH/RTBvDrnt+vAfA3nt+/CuCHSqlHk+9tNunAMVyMlAGs8fx+Tf023Tav1wXxCgA/t3zsYiDJMSQ1Eh1DEbkGwH8C8Bml1I/av7uZJJXPoVLqH+pNBf8cwLH27W53YOonPgcAuB0onwXwnzXbHAJws4gM1Ttabq7fBhHZi9oH7p7272pmSXQMFzHfA/AhEVknIktQK7A74NvGe2zvAHBY1aruDgD4dL2TYB2ADwH4bof2O0skOYakRuxjWE95H0StmP6FTu1wBklyDNfVhQtE5FoAGwC80pnd7jDdrubt1X+o5Qi/A+CHAP4awMr67SMA/tyz3f+MWsHiSwB+r37bNagVPv0DgOn6v3/V7ffUS8ewfvvDqOV0F+r/7+72e+rgsfsdAP8dtY6BP6zf9iCAHfWflwF4qn7Mvgvglz2P/cP6485gEXabpXQMXwHwJoC365+9D3d6/7PwL+4xBHA/gIue9W8awHu6/X567Bj+T6gVIk+j1pAx2u330q5/dKYlhBBCSGZh6ocQQgghmYVChRBCCCGZhUKFEEIIIZmFQoUQQgghmYVChRBCCCGZhUKFEEIIIZmFQoUQQgghmYVChRBCCCGZ5f8PwvERYWsqhwQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(pcomps[:, 30], pcomps[:, 31]);\n",
    "\n",
    "words = ('elliott', 'fred', 'frank', 'john')\n",
    "idxs = [vocab[word] for word in words]\n",
    "plt.scatter(pcomps[idxs, 30], pcomps[idxs, 31], color='red');\n",
    "\n",
    "\n",
    "words = ('omega', 'mu', 'alpha', 'beta', 'gamma')\n",
    "idxs = [vocab[word] for word in words]\n",
    "plt.scatter(pcomps[idxs, 30], pcomps[idxs, 31], color='orange');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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