challenging-america-word-ga.../nb_nn.ipynb
2023-04-28 00:39:34 +02:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import lzma\n",
"from itertools import islice\n",
"import regex as re\n",
"import sys\n",
"from torchtext.vocab import build_vocab_from_iterator\n",
"from torch import nn\n",
"from torch.utils.data import IterableDataset\n",
"import itertools\n",
"from torch.utils.data import DataLoader"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
"# torch.cuda.is_available()"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
"# torch.cuda.device_count()"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
"# torch.cuda.current_device()"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [],
"source": [
"# torch.cuda.device(0)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"# torch.cuda.get_device_name(0)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[80], line 18\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[39myield\u001b[39;00m get_words_from_line(line)\n\u001b[0;32m 16\u001b[0m vocab_size \u001b[39m=\u001b[39m \u001b[39m10_000\u001b[39m\n\u001b[1;32m---> 18\u001b[0m vocab \u001b[39m=\u001b[39m build_vocab_from_iterator(\n\u001b[0;32m 19\u001b[0m get_word_lines_from_file(\u001b[39m\"\u001b[39;49m\u001b[39mtrain/in.tsv.xz\u001b[39;49m\u001b[39m\"\u001b[39;49m),\n\u001b[0;32m 20\u001b[0m max_tokens \u001b[39m=\u001b[39;49m vocab_size,\n\u001b[0;32m 21\u001b[0m specials \u001b[39m=\u001b[39;49m [\u001b[39m'\u001b[39;49m\u001b[39m<unk>\u001b[39;49m\u001b[39m'\u001b[39;49m])\n\u001b[0;32m 23\u001b[0m vocab\u001b[39m.\u001b[39mset_default_index(vocab[\u001b[39m'\u001b[39m\u001b[39m<unk>\u001b[39m\u001b[39m'\u001b[39m])\n",
"File \u001b[1;32mc:\\PROGRAMY\\Anaconda3\\envs\\scweet\\lib\\site-packages\\torchtext\\vocab\\vocab_factory.py:99\u001b[0m, in \u001b[0;36mbuild_vocab_from_iterator\u001b[1;34m(iterator, min_freq, specials, special_first, max_tokens)\u001b[0m\n\u001b[0;32m 97\u001b[0m counter \u001b[39m=\u001b[39m Counter()\n\u001b[0;32m 98\u001b[0m \u001b[39mfor\u001b[39;00m tokens \u001b[39min\u001b[39;00m iterator:\n\u001b[1;32m---> 99\u001b[0m counter\u001b[39m.\u001b[39;49mupdate(tokens)\n\u001b[0;32m 101\u001b[0m specials \u001b[39m=\u001b[39m specials \u001b[39mor\u001b[39;00m []\n\u001b[0;32m 103\u001b[0m \u001b[39m# First sort by descending frequency, then lexicographically\u001b[39;00m\n",
"File \u001b[1;32mc:\\PROGRAMY\\Anaconda3\\envs\\scweet\\lib\\collections\\__init__.py:637\u001b[0m, in \u001b[0;36mCounter.update\u001b[1;34m(self, iterable, **kwds)\u001b[0m\n\u001b[0;32m 635\u001b[0m \u001b[39msuper\u001b[39m(Counter, \u001b[39mself\u001b[39m)\u001b[39m.\u001b[39mupdate(iterable) \u001b[39m# fast path when counter is empty\u001b[39;00m\n\u001b[0;32m 636\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 637\u001b[0m _count_elements(\u001b[39mself\u001b[39;49m, iterable)\n\u001b[0;32m 638\u001b[0m \u001b[39mif\u001b[39;00m kwds:\n\u001b[0;32m 639\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mupdate(kwds)\n",
"Cell \u001b[1;32mIn[80], line 4\u001b[0m, in \u001b[0;36mget_words_from_line\u001b[1;34m(line)\u001b[0m\n\u001b[0;32m 2\u001b[0m line \u001b[39m=\u001b[39m line\u001b[39m.\u001b[39mrstrip()\n\u001b[0;32m 3\u001b[0m line \u001b[39m=\u001b[39m re\u001b[39m.\u001b[39msub(\u001b[39mr\u001b[39m\u001b[39m'\u001b[39m\u001b[39m\\\\\u001b[39;00m\u001b[39mn\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39m \u001b[39m\u001b[39m'\u001b[39m, line)\n\u001b[1;32m----> 4\u001b[0m line \u001b[39m=\u001b[39m re\u001b[39m.\u001b[39;49msub(\u001b[39mr\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39m[^a-zA-Z] \u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39m \u001b[39;49m\u001b[39m'\u001b[39;49m, line)\n\u001b[0;32m 5\u001b[0m line \u001b[39m=\u001b[39m line\u001b[39m.\u001b[39mlower()\n\u001b[0;32m 6\u001b[0m \u001b[39myield\u001b[39;00m \u001b[39m'\u001b[39m\u001b[39m<s>\u001b[39m\u001b[39m'\u001b[39m\n",
"File \u001b[1;32mc:\\PROGRAMY\\Anaconda3\\envs\\scweet\\lib\\site-packages\\regex\\regex.py:278\u001b[0m, in \u001b[0;36msub\u001b[1;34m(pattern, repl, string, count, flags, pos, endpos, concurrent, timeout, ignore_unused, **kwargs)\u001b[0m\n\u001b[0;32m 272\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Return the string obtained by replacing the leftmost (or rightmost with a\u001b[39;00m\n\u001b[0;32m 273\u001b[0m \u001b[39mreverse pattern) non-overlapping occurrences of the pattern in string by the\u001b[39;00m\n\u001b[0;32m 274\u001b[0m \u001b[39mreplacement repl. repl can be either a string or a callable; if a string,\u001b[39;00m\n\u001b[0;32m 275\u001b[0m \u001b[39mbackslash escapes in it are processed; if a callable, it's passed the match\u001b[39;00m\n\u001b[0;32m 276\u001b[0m \u001b[39mobject and must return a replacement string to be used.\"\"\"\u001b[39;00m\n\u001b[0;32m 277\u001b[0m pat \u001b[39m=\u001b[39m _compile(pattern, flags, ignore_unused, kwargs, \u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m--> 278\u001b[0m \u001b[39mreturn\u001b[39;00m pat\u001b[39m.\u001b[39;49msub(repl, string, count, pos, endpos, concurrent, timeout)\n",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"def get_words_from_line(line):\n",
" line = line.rstrip()\n",
" line = re.sub(r'\\\\n', ' ', line)\n",
" line = re.sub(r'[^a-zA-Z] ', ' ', line)\n",
" line = line.lower()\n",
" yield '<s>'\n",
" for t in line.split():\n",
" yield t\n",
" yield '</s>'\n",
"\n",
"def get_word_lines_from_file(file_name):\n",
" with lzma.open(file_name, encoding='utf8', mode=\"rt\") as fh:\n",
" for line in fh:\n",
" yield get_words_from_line(line)\n",
"\n",
"vocab_size = 10_000\n",
"\n",
"vocab = build_vocab_from_iterator(\n",
" get_word_lines_from_file(\"train/in.tsv.xz\"),\n",
" max_tokens = vocab_size,\n",
" specials = ['<unk>'])\n",
"\n",
"vocab.set_default_index(vocab['<unk>'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def look_ahead_iterator(gen):\n",
" prev = None\n",
" for item in gen:\n",
" if prev is not None:\n",
" yield (prev, item)\n",
" prev = item\n",
"\n",
"class Bigrams(IterableDataset):\n",
" def __init__(self, text_file, vocabulary_size):\n",
" self.vocab = vocab\n",
" self.vocab.set_default_index(self.vocab['<unk>'])\n",
" self.vocabulary_size = vocabulary_size\n",
" self.text_file = text_file\n",
"\n",
" def __iter__(self):\n",
" return look_ahead_iterator(\n",
" (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file))))\n",
"\n",
"train_dataset = Bigrams(\"train/in.tsv.xz\", vocab_size)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[tensor([ 33, 0, 226, 35, 0, 6421, 6420, 219, 5781, 1]),\n",
" tensor([ 0, 226, 35, 0, 6421, 6420, 219, 5781, 1, 113])]"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"next(iter(DataLoader(train_dataset, batch_size=10)))"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [],
"source": [
"embed_size = 100\n",
"\n",
"class SimpleBigramNeuralLanguageModel(nn.Module):\n",
" def __init__(self, vocabulary_size, embedding_size):\n",
" super(SimpleBigramNeuralLanguageModel, self).__init__()\n",
" self.model = nn.Sequential(\n",
" nn.Embedding(vocabulary_size, embedding_size),\n",
" nn.Linear(embedding_size, 1000),\n",
" nn.ReLU(),\n",
" nn.Linear(1000, 500),\n",
" nn.ReLU(),\n",
" nn.Linear(500, vocabulary_size),\n",
" nn.Softmax()\n",
" )\n",
"\n",
" def forward(self, x):\n",
" return self.model(x)\n",
"\n",
"model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size)\n",
"\n",
"vocab.set_default_index(vocab['<unk>'])\n",
"ixs = torch.tensor(vocab.forward(['is']))"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(8.4503e-05, grad_fn=<SelectBackward0>)"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"out = model(ixs)\n",
"out[0][vocab['is']]"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"loss_track = []\n",
"\n",
"device = 'cpu'\n",
"model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
"data = DataLoader(train_dataset, batch_size=6000)\n",
"optimizer = torch.optim.Adam(model.parameters())\n",
"criterion = torch.nn.NLLLoss()\n",
"\n",
"last_loss = 1_000\n",
"trigger_count = 0\n",
"\n",
"model.train()\n",
"step = 0\n",
"for x, y in data:\n",
" x = x.to(device)\n",
" y = y.to(device)\n",
" optimizer.zero_grad()\n",
" ypredicted = model(x)\n",
" loss = criterion(torch.log(ypredicted), y)\n",
" if step % 100 == 0:\n",
" print(step, loss)\n",
" step += 1\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" if loss > last_loss:\n",
" trigger_count += 1 \n",
" print(trigger_count, 'LOSS DIFF:', loss, last_loss)\n",
"\n",
" if trigger_count >= 1_000:\n",
" break\n",
"\n",
" loss_track.append(loss)\n",
" last_loss = loss"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [],
"source": [
"loss_track2 = [t.detach().numpy() for t in loss_track]"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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RkTwUFSzUKkOwqK6bpBERESmdooJFaa5gHQsiIiKZKCpYcCiEiIhIXooKFhwKISIikpeiggWHQoiIiOSlqGChURmHQhgsiIiI5KCoYKFWG4dCZG4IERFRLaWsYGEcCmGyICIikoXCggWHQoiIiOSkzGDBzU2JiIhkocxgwR4LIiIiWSgrWJSeDYMFERGRPJQVLFSsvElERCQnZQYLJgsiIiJZKCpYaDgUQkREJCtFBQsVh0KIiIhkpahgwaEQIiIieSkqWHCvECIiInkpKlgYdze9XqTDir1n5W0MERFRLaSoYGHchAwAnv0+Wb6GEBER1VKKChbGoRAiIiKSh6KChdoiV2TlF8nTECIiolpKUcFCY5EsOr6xGjquECEiIqoyigoW3l7Wp5NfVCJDS4iIiGonRQULH4316ew/my1DS4iIiGonRQULbxvBYsKS3TK0hIiIqHZSVLCwnGMBAPlFOhlaQkREVDspKlgQERGRvBgsiIiIyGMYLIiIiMhjFB8sBraOlLsJREREtYbig8WBczk4fjlP7mYQERHVCooPFmezbuCW/26QuxlERES1guKDBREREVUdxQWLpY8koFd8PdzZuYHk9o1HLsvUIiIiotpDccHipmZh+GZCD8SF1ZHc/sDCnUhleW8iIqJKpbhgYWRrU9O0C7lV3xAiIqJaRLHB4lzWDavbvDTWJb+JiIjIcxQbLApL9Fa3PbMsGULY6MogIiIij1BssCjWWQcLAHjvn7QqbgkREVHtUeuCxbx1x6q4JURERLWHgoMFhzyIiIiqmoKDhe0eCwDYf4bLTomIiCqDYoNFWIDW7n0Tl+6pwpYQERHVHooNFlOHtkSPuFCb9+UVllRxa4iIiGoHxQaLiEBfLH6ou837dHqB/WeykV/EgEFERORJig0WAOCtsX16eYUlGP7JZtw+b0sVt4iIiEjZFB0sNGrHlTaPXMxDVn4RACC/qAQZmflV0SwiIiLFUnSwcMX6NMOup33nrEefOetw+EKOzC0iIiKquWp9sCgqLf19ObcQALD28CU5m0NERFSj1fpgUViiM4ULAFCruFEZERFReTFYlOgx4/cDpu+dTMsgIiIiBxQfLBLj6jm8X6VS4ZsdGWXfg8mCiIiovBQfLLy9HJ+iZYzgSAgREVH5KT5Y1PHROLxfp5duVuZsiSoRERHZp/hg8crQVg7vL9ZLNytjrCAiIio/xQeLmFB/HHxjkN37Syy2V1ezx4KIiKjc3A4WZ8+exf3334969erBz88P7dq1w+7duyujbR7j7+OFt+5oa/M+y+3Vv952qiqaREREpEhe7hx87do19OrVC/369cPKlSsRHh6Oo0ePom7dupXVPo/JK7C94djK1AuS749eysO6tEto3yAY9RxsvU5ERETW3AoW77zzDmJiYrBo0SLTbU2aNPF4oyrDtfxim7enX8qzuu2hRbvQsK4fNk+5BQCQfDoL+85kYVyPWKi4bISIiMgut4ZCfvvtN3Tt2hX33HMPIiIi0KlTJ3z55ZcOH1NYWIicnBzJhxzCAnzcOv7MtRu4UaQDANw+bwte+/UAfvn3bGU0jYiISDHcChbHjx/H/Pnz0axZM6xatQpPPPEEnn76aSxZssTuY2bNmoXg4GDTR0xMTIUbXR5jE2IxPjEWXz3cHW0bBLn0mKEfbZJ8b9xHZOHmE/jvP2kebyMREVFNpxJCCOeHGfj4+KBr167YunWr6bann34au3btwrZt22w+prCwEIWFhabvc3JyEBMTg+zsbAQFuXaB9zSdXuD131KxdHuG02NPzh6Gxi//CQDo1yIcXz7QFfGvrgQAbJ7SDw3r+ldqW4mIiKqDnJwcBAcHO71+u9VjUb9+fbRu3VpyW6tWrZCRYf8CrdVqERQUJPmQm0atgsViEJfoBXDgXNlQDudbEBERSbk1ebNXr15IS5MOARw5cgSxsbEebVRV0Dop9W1k3qGz4chlbDhy2fT9hewCNAjx83jbiIiIaiq3eiyee+45bN++HW+//TbS09Px7bff4osvvsCkSZMqq32Vpo7WcalvoxvFOrv33TV/K37ec8ZTTSIiIqrx3AoW3bp1w/Lly/Hdd9+hbdu2mDlzJubOnYuxY8dWVvsqTc+mYS4dd9vHmx3e/87fhz3RHCIiIkVwaygEAG677TbcdtttldGWKtUrPgz16vgg83qRw+OOX77u8H6XZ74SERHVAorfK8SRfi0jKvwcrq+pISIiUr5aHSz0HkkFTBZERERGtTtY6CseCoQATmVex6ItJ1BgNtEz6eBFPLJ4F67kFTp4NBERkbLU6mCh80BngwAwaO5GzPj9IFpO+xvf7zLU9Jjw1W6sOXwJr/96oOIvQkREVEPU6mCh8UB9q6vXi1BQXFZta8rP+yX3H7mY6/Q5MvMKkVNge5M0IiKimqRWB4sXBrZAVJAvBreJqrTXuJZvWHWSfaMYtqqnXy8sQZc3k9B++j+V1gYiIqKqUquDRUyoP7ZNvQWfjeuC7VP7e+x5i0rKejAKivVIOZ2FDjP+weSf9lkdm3E13/S1J+Z8AMDFnAKbIYaIiKiy1epgAZTt9xEV7Cu53dWS37Z8svao6etinR4fJB0BAPxko0qn2my/kWJ9OTYwMXM26wbmrUtHwttr8JKNEENERFTZan2wsGdsQtn+Jz3iQt167K8p50xfF5bosT7tst1j1WbzPIorMJtUCIFes9dizirDXi4/stQ4ERHJgMHCjmKz7U9Hd2vk1mNPZebbve/tvw5JvlebJYuS8my5WspDoyhEREQV4nZJ79qisKSsJkX3JqFoEOKHs1k3Kvy8X2w8jvAALbo3CcUn69IlcyFOX72BEH8f0/cLNp/AtetFeHFQC6fP65liX0RERBXDYGGH+bBEaB0fzB3dEfd8ts0jz/2WRa+F0fBPNuPk7GEo1unhpVZh5h8HAQAjO0ajWWSgw+fUscuCiIiqAQYLO6bd1hoXsgsQHqiFr7cG3Rq7N8+ivOatS8cna9Px7aMJptsyrxehmZPHsceCiIiqAwYLGx7vG4fQOj747rEeVf7axsmXs1eWbceeV1Bi+nr53jPQ6YG7uzSUPM5Rh4UQAou3nkSb6GB0b1I1AakmE0KYVgsREZF7OHnTzKpn++DFgc3xbP/mNu+PCfWrsraY90BM+Go3zmffwNykI3ju+xS8+GMKUs9mS453NBSyPu0yZvx+EPd+7v5Qzr8Z11yaW7Jk60ncPm8LsvNrdgXRv1PPo9tba7DjeKbcTSEiqpEYLMy0iArEU7c0g5+Pxub9b4xoW2Vt2XXymuT7xFlrMTeprD7GbR9vRkGxDheyCwA4Lq514sr1crXh0Pkc3PnpVvSavdbpsa//dgDJp7Mwf8Oxcr1WdTFx6b+4kleI8Yt2unR8yuksPLF0DzIcrAQiIqpNGCzcUN16xwfP3Yges9bgxJXrlTLHYvfJq6avtx3LxPM/JOPa9SKHj8kvKnF4f03h6mTYkfO2YGXqBUxcuqeSW0REVDMwWLjBvErmf4a1krElBidL/0p+b1UadJUQLMxXxoz5cjt++fesVR0OMjiVWb5eISIipWGwcIN5sHi4VxMZWyL15/7zcDdXfLPjFAbP3YhTmdchhECujd1Vj13Os7rt9LWq7/I/dD4HW9OvVPnruoNrcoiIDBgs3GBeftu8YmZ1YKvr3hgMzIdJBry/AesOX8Kry1Nx+EIu+s5Zj+e+T0a76f/gxR9TJAW7vtmRYfWcKlT9eQ/5cBPu+98OnL7KeQxERNUdg4U7LK6pC8Z3RVx4HXnaYuFjs43PjPr/dwPe+P0g3vyzbPgi/VIeHlq8S3LcimTD3iY/7TmD1QcvOnwdW/NMth4r602ozHIaVdlbIkeAIiJSAgYLN7SKCpJ8379VJFY+01um1kh9t/O0zdsXbjnh1vMcvZSHG0U6pJzOcvkx9325w6XjKrotvCsXeyEE/rfpODYfrdqhE9YnIyIyYIEsN9St44Mdr/SHr3fZclR1dVsqUkF6vcD4RTux88RVm/dvPZaJ+77cjltaRuDOzg0RWsfH5nGWcguK0eXNJBSV6PGfYa0woXec1TElOj2OXb6O5pEBpgJVws0r9pb0TFMPzcnZw9x6rEQF3tbfUs5hx/FMzBjRBl4aZnciql34W89NkUG+CPbzNn2vUViw+Cv1gt1QYbT1mOHiPfHrPfgwSToEI+xMY/wt5RyKSgy7txov/EIIHL6QY9pJ9tXlqRg0dyO+3HTc9DjzYRxXftSeGi4pKtHj8a9340peoUvH3ygu27Tu6e/24psdGfh93zmPtIWIqCZhj0UFKSxX4ND5HJeP3XnyKnaelIYQdzoYlmw9iem/H8TwDtH4eEwnfL/bMJzz9l+HoRfAuawb+GrbKdefENIJthW16sBF6PTA/8Z3den4a9eLsMvs53H1evWpQrrjeCYahvqjQUjVVY8lotqJPRYV5Ik9JbrG1sVfT/dGQpNQLH0kwfkDaiBb8yM+XW+o0vl7ivVf9rNXHrYKFc5+0r+lnEPaBeslso4IIfDxmqNYn3bJ5v3nXChnbrTz5FU89nVZoazqkjn/zbiGUV9sd6mCKhFRRbHHooq1jArE4Qu5ktsWPtQNQb7e+P7xRJlaVXmEEDhupzKoJ+envLp8v83lseb+t+k4NqdfwefjukDrZZgns/bwJfx39REAtudkqN2I3panKMeK5KSDF/HxunS8f28HNA0PACCtoEpEVNnYY+Fh3hoV9k8faPq+X4twyf0LH+yGrx/pLrktyNdb8v27d7WvvAZWsvwiHT5dn47/rNiPcQt24Jllyej/3w34z4pUq2PNL7wv/ZTi9LlHfbEdeYW2S4Y7ChV5hSX436bjePPPQ1ifdhm/Jpf1kJwv3WvFHvfm0EiThcYiWWxJv4KV+887fgYh8O2ODKtN5lw14avdSDmdhRd+KPt5cuksEVUlBgsP0wsg0Ncb797VHpP6NcXUodLS33X9fdC7WbidRxtYToD8aWLN6clYvvcs3v07DUu3Z2DT0Sv4zcYwB2C9NfkPu8+49PwLN5ctny0s0WHZzgynwxUzLWp5pJ7NNi199XLSrWCrEFpeYQn+2HcO1y1CjmWPRbFOoMBsUufY/+3AE9/8a7Vb7Pv/pOH2eVtQUKzDtzsz8Mry/bjt480O2+VMjlklVXsTah0p1unx1baTSL/k3tAS0YFz2Thuo2ov1R4cCvEwYwXMe7vFmG777P4upk2qfLycZ7lAix6MLrF1PdjC6uG3lHOSi66rVqZewNP9mwEA5q07ho/WHEWgr+1/xhuOXEbf5uHYdPSy5Pavtp2CCsCMkW2dVlDdm5FlddsLPyRj1YGLGNouyuFj3/jjID5YfQTJrw+U9M78nXoBX248jnoBPogM8sXaw4b5Hb8mn8Wry617dsrDWWByZsnWk55ZtlsOqWezsevkVYxPbCxrhdvMvELUC9DK9vo10dXrRRj2kSEUV/W/G6o+2GNRBTrHhpi+tuwet6Vdg2DJ9yqVCt0bh5q+f3Wo/BugVdQzy5KR6WSnVFsOnc/B0Yu5KCzR4aM1hqWuuQW2h0fGL9yJ9Eu5OGdjuGNJ6cRQ8wuwvZoZlrevOmCoTvrX/guS220tL80tLEFeQQlKzIqDzfzjIC7kFODAuRxTqAAgOaaiNO5MDrHh34xrHmqJ+277eDNm/H4Qv+w9K1sb5q1LR5c3k/DVtpOytaEmOnutrDfO3Ro0pBwMFlUgItAXSx9JwIpJvVw6PibUH3d2bgAAuLtLQwDSZa2P9olD6oxB2Db1Fo+3tSb4+d+z+GLDcecHAhjw/kaH95sHvRNXbO9QWlCsd+m1LIOGuUUuVEC1nM9x4Fw2Jv+YUq5uZWNg0usFsm/Is+z1RpGuXL1SRgfOlW+eSUWV6PSYsyoNAPDarwdkaYMSeDAnUw3DoZAqclOzMLeOf//ejnhuQHNT3QHLOYQBWi8EaL2gUatsbkCmZJ9tOOaR5+n/3/U4drksTNzy3w02j8stKIafjwa/Jp+1ubrFmRK9Hm//ddjpcZbd/sYu5S3pV7B1an+3XtMYmB5Zsgvr0sqGgnILihHo643DF3Lw8s/78eLAFripWRj0eoGNRy/j1eWpmDyohcOQ5IpinR7tpq+CRq3CwTcGu9RTZ8nej3rZzgyknMnGW7c7H8oqjw+Sjnj8OWsjnV6U632varkFxTh5JR9tGwR5pHwAscfC4yYPauGx54oJ9Tf94qxXx/ZYb4eGwVa3ab3UmHdfZ0QF+eLD0R0R4u9t45FkHiocySkoQUGxDs8sS8Zz3ztfvWLpu52Ol8Ea2fuVdi67AAXFOuw4nokSnWu9J4fO50AIIQkVAHD3/G3YdPQyBs/dhOTTWbh/gWGfl/GLduLBRbtwNusGnv0+2enzn76aj+3HM+3en5lXhBK9QGGJHnmFJcgvKjFVWHWVvRD38i/78d3ODCQdcrxhniOzVx7Gg4t22vx5LtpystzPW9uZX5fLE8LlMHjuJgz/ZDPWH7ns/GByCYOFB3wzIQFv3dEWGybfjCdvbloprzHtttbo3jgUH4/pJLl93tjOVscWlugxrH19bH+lP0Z2bIAvxpVVjtw29Ra3JlV1bhRS7jYrxdXrRbjz063lfvx7/7j2F/Dkn/bZve+V5fsx6ovtpnklgOEvwvRLudDpBR79ajdmryzrFSks0eMfGzvVpl3MxbgFOyW35ReVYJODTduEEFi85QR6zlqDE1eu4/CFHPR+dx1Gf7FdsixWpxfYcTwT+UUlkrLshcU69J2zHjfPWY/NR6/g4cW7cCrTeahzdmHKKZ1bo9MLrD18EZlm5dePXMy1WrVj7rMNx7A+7TI2HrW+mFjOdXlvVRpGfrIZ03+rucMimXmF2H48UzLv4UpeIcYv3Im/Ux0vgS4vd4NFsU6PP/adw+Vc18roe4pxlZazpeDkOgYLD+gVH4axCbGIrVen0rrSooJ98cPERAzvEC25vX6wdYlmyxUB3ZuEYuGDXfHxmE6m47+ZkIAx3RuZjmkU6o9fnuxp9Vwfju5kdZs9NaDXs1x+SzmLg26UOq8Mv/xrmMj40dp0022Tf0rBgPc34rnvk7H64EWrIaJf/nVtCe/t87Y4vL/J1L8w/feDOJddgH7vrcfguZtM9+03CxYLNh/HqC+2o8OMf7DAbFnwpdxCXM4txNmsG7h/wQ6sPXwJzyxLlryGEMKq92Dp9gzkF5XghR9SsMNG74jxn9u3O07h4cW7MeITw3lsPXYFAz/YiKEfbbJ6jCVbE38thxY/WZeOlDPZWLz1pNPnc9fhCznYdybL5eMz8woxbsEO/FV6Ebx2vUgSFq5eL8LMPw4izaIIX7/31mP0F9ux2ixsvrcqDRuOXMbEpf9W7CTscHeI9ouNx/HUt3ud/nusLNV5Q8mMzHzMTTqCrHz3J7zLgcFCgRY/1N3qtltaRkpCSa/4MIw2WxLrrVGhc6OKLWs9PHNIhR5fXV3Irtq/oJxZXDoR1Bg27NUKMa5ecebIxfLXHPAx273129IiZcU66QUlv8h6AueZa9JaHqM+346+c9abNqozav3aKvz87xmM+mI7Nh65LPnr2rjwZWWqYT6I8S/PP/YZjjmVadiQLiu/CHOTjtjsJSm0MTG3KuYsncu6gRGfbMbguZsw4pMtkrojjsxeeRibjl7Bk9/8i7WHL6LTzNWY9mvZEuVpK1KxYPMJDJornbRs7N0xHz7acsx+L5VRiU6PvRnX3B7GAgC9mw8x9rBZ1nmpiIs5BVb/puzxdK44eeU65qw6LOlJK68752/B3KSjeMlBr2Z1wmChMLPubOfyRFHzrkovG8sTb2kZgSA/1+dn+HipER6ovHX/yaez5G6CxPTfD2LEJxUroOUpWm81sm8U41zWDav6K0aTbVRVtezd2nnyKs5m3UCqg5UgDyzcKfnrOjvfcDF2Vkp9+m8HMDfpKPrOWW8VLq4X2R8ucUSnF9hw5DKulmPJtLFN+86UnavxXJy5ZvYX6zsrDStXlm7PQPaNYmw+egUpTno/jJlpz6lrOH217AJuPqR1LuuGKVy99dch3PHpVjR7dSXWHnZvTovOhaGQG0U6HCtd9eTt4S7PIxdzkfD2Gtz9mavDmK69fkZmvqnNjoyctwXz1h1zOMTpqit5hvd927FM/PLvGWx2MHRZHTBYyGRg60gAwNiERk6OdI87/zUjg3xNX9uqzvjp2M6SLeJdEa3A3TNd3Tq9KplflOSk9dIgcdYa9Jy9VjIsYs7Yc2DO2O2cnV8s2RzNnWH56b8ftHm7eQnzu+dvxQqzEu5956yXDB3M+P0gvtlxCn+nXjBVY3XE+Jf77ynnMH7hTpvd9kIIXLBTKt742lkWS4CdrW4RQmDfmSzJ0ue0i2XDHfd8thX3L9hh1RM0N+kIPkwqm5dj/GNitcX8m4yrhvco6eBF9Jy9Fk99awhw5hNZH16822EbLbnS83P7vC3o/98N2HE8E14a94JFiU6Px77abXeV2B+lPXmu/l+x12Nx8FwOfk029A7q9AJ95qxD//9uwMUcx9sBGJd577KxV09+UQk2HLnscm+KUW5hCZ7/IcU06bq64nJTmXw4uhO2n8hEz6b1PPq87owTmocAW78DfL0NG3V1bhSCf21UoLSlaVgdpHjwL/yXh7REYlw9jJRp3JUcu5ZfZHOow5kLOQXQ6wWW7jhl0fXt3jDEtetFVqHY/L/A7lPWhb7Mhw4AmKqdjusRi5m3t3X4es1eXYmxCY1Mf7EaL8iAoWbIu6vSsOrABZy4ch0fju6IER2icfB8DnaduIpNR6/gSl4hfn6ip1XxKLUK+HR9OtYfvozFD3eDv0/Zr+b/rNiPpdsdryyyNZx1vbAEc81CBQDTj9fy9Sf/mIKdJ66awuHK1AuSycDOrD18ERvSLmNEx7LhVlcmbxrD0S//noW3xr2/c1cduIh/Dho+JvaVTpq/mFOAunV8TN9bbiFgi/mk0bf/OoSiEj2mj2hjmqsTFqCVVEFOeHsNnri5KaYMbum4oTZ+DP/37V6sOXwJT/WLx4sWKwkPnsuBSgW0qh/k+HmrMQYLmfj5aNCvRYTnn7icvYnuzuC+p0tD/LjHenLgy0Nbulwx0c9bgxtmBZTu6dIQhSV6yZyB8YmNcbWGTFiqjd792/WLj6XjV/KsegkWbj7p1nN0mrla8v0ry/eb5nrYY+8i/fX2U06DBWC94d2mo5fRu1k4Vh24IPnr+ZllyZj5xyGrHq/4V1daPacQwLt/G4Y2Wr+2CgNbR+LzcV2gUqmchgp7bBV8Mw5PWP5/v16kw+KtJ9EyKtB0m72egNNX87H12BWczy7AiA7RiAsPMPVmmPfEmPdYFOv0eO77ZCQ2rQdfLw3OZ9/AU7c0M92fcibLatdnwDDfQuulRpiN0ur2hrE+23AMs1ceRj2zYFFYojf9oWSPsRcnv6gEX2w0rGoyX+W39vAlq3lo89cfw33dGyE6xM9uzY7cwhIUlegl2zmsKa24+/X2U5JgcaNIZwoyh2cOdtrm6opDIQrj7szmBxJjAQAvuVl/Y849HWzeHhHoa/N2W5JfvxVbXy6rHvrOXe3xn9uk5crVatdWmzQI8cNyG6taqHIZx37L+9hDF6Srbf6s4JI/Z6HCmbnlKI41bsFO6PQCl2wsk3R1GM1yaew/By9ibtJRPP3dXrfbYzRhifXQhV4AP+4+jS832a4Ea+vibqnPnHWY8vN+zE06isEfSlfemPfgGJZD5+FybiF++fcM/th3Hq8uT8ULP6bgvX+O4OC5svfe1usah8m6vpmEez/bhj0WvU+WvxZOXLmO64Ulpp4W8y0DjEuIU89m4+M1R1FQrEOJTm818fmdvw+j75z1pu+LzCatLth8wua8kd7vrsOT3+yR3GY5l+eV5futHgcY/rgyZz6Jt6BYh4zMfLsBz9jrtOnoZbz/Txp0eoH9Z7IxbUVquef+eAp7LBTgk/s64alvDb+A3O2wmDGiDZ7p38xqsyVX8snaF/qaqlV+cl/ZstRnBzTD2sOXcHPzcMnySEtaLw2iQ/zw7t3tEeTrDbVaZTWJ1Eutdmnbb41aVWPTfW01Z1Wa1cVCblZDBy7adfIqXq9AnYt1aZesbvtwTfnaYnTBxhwAIUSFJxOaX1st5wiYrx45fS0f931pmAvw4sDmVs/jqNT8dzszMPWXsovxzpNXcdf8raYaPJY7Gu8/k43hDiY060pXKhl3DQ7x90aJXmCGxTyd+eulF3HLHPHG77bf41UHLmLT0cuY8tM+vDa8jWnTSaOf9pzB1CEtrX7P+vtoTJvdFRTr8J8V0mG6oR9tQp6deiz3L9iB8ACtaQ6R+e/avMISfDCqo83HVQUGCwW4rX10WbBwM1moVCqnOzjaGyQxHwfuHV+2FfyzA5rj2QHNscyi4mSv+HrYkm5dj+DermXLXi27E9Uq185Jo1Y5nfgXE+onmQlP8qpuoaIiRn+xvUKPd6XkuydUtBims8mGJWZLjc13Bi608bgNDipdmocKSz/tOYMXf0xBgLbs98+yXY57qkos1r6ezzZsAuiM5c/rh932a8MYC89Zhgqj539IwcIHu2HqL2XB7viV6+jyZhJaRAYisWk9yaRavYDdUAHA5u9SoyMXnfc8VSYOhSiE8eLbzWwX1PJ4vG8cAGDasNZ2j3mqXzwASFaM+PlY9xZYLj39ZkIPp69vWdxLpVK5VJJco1Y5nCcSGaTFppdq56ZtREYVLbPd/D/W80PMmQ9pmNfMsBUsyrPnz9t/HcKLPxqWL5tfdJ1V69TphWTCaqCvt0t/sIyc57ll3RuOXMY/By7YDCdpF3OtCrBVpJ6Kl5sTYT2NwUIhkqcNxPoXb0ZMqH+FnuflwS2x9eVb8PBNTUy3GedhGBknG/n5aJD0fF+sfaGvZGKSUZ/m4fB2cwmZrSVnWi8Npg+3Djqvm93m5aTH4renbrJ5e5OwOm61z57nb7Xu6lWCBgpcPlybVcb+He/YmcAr6bGowC635oyTKi3ZKl9vLr9Ih5Nmy543Hrns0rDxNRfri7hq6zH7vQyWvnfSC+OIp2uCuIvBQiGC/b3R2AMXSZVKZVWL4vaODeweHx8RgLjwAJv3eWvUOPrWUHz9SHdseqmfS6+v9dLgo9L9UJpFlD1vuI1JoXX9y2Z9q1UqtKofaHWMkXnNDnPuTFod0CrS7n2D2kS5/Dzl9WDPxpX+GuZOzBqKLS+zl0dJUk57vv6J5bwEW5ZsO+Xx13XHze+tR7/31pu+33Y802qDvqrwb4brw3+u7jFki7s1QTyNwYKcUqlUpotqWICPk6Ot9W4W7lZPyogO0Tg8czBWPtPbrA3Wx5l3FXppVHa7/36301sBAD3jpVVK+zYPx3v3dLC5+dr/xndFaB3b5+9Oz+NDvRpb3dYi0n4oMrI13FRZBraOrBZbSG9lsPEoWxM6qeq4Mq/DE9ytCeJpDBbkknfuaoenb4nH8id7Veh5tKVDJjGhjrvYfb01kqBg6xJnvvTL3jLbqCBftDPbWn7FpLL2J792q1Vl0VeHtcLdXRpa9dr0Li2TbutV3r6jneT1f5yYKLl///SBpq/DArR4fXgbq+f44oEuNttvro4bwaJxPesg1zzSds8SAHwxruz1PxzdEZ+Pc9weW7P8K4MSK7kSVTYGC6oR6gVo8fzAFhWew/H1Iwno2zwcS2xslOaIrdwgJHud2A4WlmPKHWNC8NPERCx/sidCSodSPru/7CJaR2t7oZTxWPN2eKlVeGNkG9yX0EhSfbJdg2DJY8330LDVzol9m9otrmPOx0uNd+9ub3X7f4a1srqtrkXPys5X+2PabfYn5CbElVWA9VKrnfZWmBc3Mjym6no37u9hXQb/tvb1K+W1+jQPl3z/8hAnVRaJqoGq/P9oC4MFVanuTUKx5OHududl2GPrQhcfUTZ8YNxr4bP7O0uGFWxNrO7aOBSdzCrotTYrnWuc9NTbYiO3uNL5K92bGFbd1PHR4MibQ/BAYmMAkFQGdFRPwzJAdG8SipeHtLS5CZylED8f3Ns1xmquha35eDNHSitIalQqaOyEhenDW0t+ETn7Y2dw6XwS8/kXAb5e2DvtVscPdNP/3RJv8/YQP+vhqOkjrHuBPOFhi2Grpm7+uyWSQ+b1oirZpdce1rGgGsH8kvjH/92Ek5nXJXX7g0p7BQa3rY/Bbeuj8ct/lt7j/D+X+fXW2IV4d5cYBPl6QycE9p3JNu1F8PYd7dA0PAB3dGog2TgqKtgXSx9JcLo01nL1jHFoyDxwvDiwOdIv5Uk2zwKABnUNwwIvDW6BphEBmFZaTMfWihzz0syGc1RZLfl74dbm+L/+hp6HArNZ+/Z6K0L8vfHPs31MIcp8xYgQ1r0kFfVwL8PKpC8f6Irpvx0w7Smi9VJj77RbsXT7Kfx3tWGCm60w91CvxpJNtIzevas9Gtb1w33/c76RU8O6fjj+9lDEvfIXAPknxXmaSlXx2hZU/ew5dQ1X8grtTlqvbOyxoBrB/GLXtkEwbmtv2Oxozt3t0ap+EKaPsN3N70pol2wfX3rh0KhVGNKuPm5rH41XhrYyTZwM8ffBCwNb2OxxualZGNpaDIMYzbqzHUL8vTHXohpeoK+X6fWMBraJwtzRnUxVBo0SS4cr/H28MK5HLCYPaoEusXUlBcaMNGoVHusTZ/pepxdWm4WZz18wH5O117MBABFBvjZ34jQOS0V58BeZcd7Kra0j8f3jZTVQNBoV6tbxQWRw2Wv52OhmCfW3DjqJcfVwb7cY9IwPc755FAzDQubnG++gx8Lyva0JtDZCaXUR5Mu/eytCzuGQ6vuvisiMvf8i93SNwcpneqNhXdtzP1xZt2/eZeipSU/GyY3GGiBjujfC3mm3okNMiOS4QK2hhyPEzxsdYkKQ0CTU5sUrLMDH6oI+qV88fn6ip83VIiqVCi+YTbCso9XA11tt9timuL1T2TJi86e2N9/D0a8p409w7uiODo5yj/mupdHBZSHIuCW5+YRZHy817ugkXRZta1+H/q3KNv4b1c06kFky/ixWP9cHPzyeaHeO0Qu3NsftnRpgVGnIMx9Ke7BnY5vBx5Fe8fWw9JEE3GSxasmW+IgApL052K3nNzKf/1PdyNmVrwRyFsliJKQawV9bvqWW7nbzeirlT+oXj0FtoiRj8raGGNrHGHo41GoVVjzZ08GkScft+mliIt5ffURSgEfrpcEf/3cThDD0ctzcIgL3dGmIDjEhuL+HtOiZ+eva6pFwVVez4SkA6N8yAocv5Eq2Rh/UJhKrDtgvaDQ2oRF0emGaMGvZppzSPSYCLf6i/WBUR2xJv2LaDMxy51QApjkxACRBy5lmZvN2vNQq06ZWRveUBoo3bm+DkZ2i0TU2FKsPXoRGrcLgtlGYOrQlnv8hBX/uc32TtZuaheHY5TxsTr/i8Lim4XWg9bL//6N1/SAcPG97meOYbjEO9/MprwYhfjibdQOJcfWw7bjzolBNwupY7cZq+TM2+u2pXhjxyRaPtFPJ3C1O6EnssaAaoUeTeri9YzQmu7kLqys9Fk3C6qB3szAMa1/fYylfpVKhWWSg3Yv0TxMT8eLA5hjdrZHkMfafz/HrdW0cim8f7YEPRnXAoge7mW5v2yDYtNxWo1Zhzj0drEKFJcuhEGOhssFtHRQBK/0xW/78OsSEYPOUfmhYt6zH4d27O2Daba2x45X+eGVoS8RHBJj+0geAt+5oh9l3Wa9+eX14a0QEajGptKT8gFaRGNauPqaardQw722x7LF4ZWhLyXwUexdjZ0ttN5oVe2tczx+HZw5GVOmwjNZLg55Nw+Djpcaw9vVNPzOtlwbz7utsetydnewXnTPnKOO9Prw17u/RyLTV+2f3d7Z5nOUcnDHdYxAZpMXM29uih9lqoHoenCPz3j0d8NYdbTFvrO02WUpoYr0Vga2quDe3CEf7hiGVtgoIKNuyoLK9NLiFzZo25hLN3p+fn0jE+ETH/3fNuTIhvLKwx4JqBLVahbmjOzk/sNQdnRpg+d6zePJm578kVCoVvn4koSLNc1vXxqHoWsF9XWy5o1PDcj82IlCLS7mFpl4Uo28f7YGkQxcxokO028+pgnVgCvbzxiOlJeMf69MUj/VpiuwbxTh19TpGOqjy+lCvJniwZ2PT82nUKqsLl/nwyF2dG2LeurKqkH4+0l93lkM+zSICsOyxHpIlx7aCofncFB8vdfl21XUSFF8caAjQ9sKmr7caD/VqIrltcNuyi233xqHYefIqAEMPUPLpLADA+MRYzBjZFkIIqFQqCCHwxsg2aBMdhN9TzlvtVwEYltjuOnEVaw6X7cA6rkcsvt5eVk1z+9T+6DFrjen7egE+SGzq2kUwtp4/+rWMwLJdpyW3fzq2s2n3ZJXKsCx7eOncKleLt52YNRQ3vbNO0mPmTFVNTWjXIBi9m4XbnGBsVMesp7ZLbCi6xIa6XMVUzh4LBgtSpHfvbo9He8c5LPNdk7hTHKu8Nk3ph6ISvdW4e3igFmO6W9eOAIBRXWPw/e7TeGZAM5v3u1q8M9jPG8seS3R6nLMLyvQRbfDoV7vxeN84xIUHIOW1gejwxj8ADMHJkdXP9zV9PbpbDPKLdIgOdjwZtbzTACw70jo0NFxkgv28ER3iZ1oOba/wm/kuorYMaB1hChZhAVocf3soSvTC1Hth/DmqVCrT8NDvKbaHaSb2bYqJfZvi4zVH8ce+8/j+8R4I8feRBIsoi5+TO3OV/vdAV8RHBODbCQmSlTrmE6TDArSSybauXjJVKpXTuRr392iEpdvL9uUY37Mxvt15GoPbRuLX5HPILbC/w2hFuFK7piJzYOSsnMuhEFIkb40araODqkVZ6or4dGxnxIXVcblLuSK0Xhq3f5G9fWc7JD3f19QDAQDD2pX95VzVP/9bW0ci5bWBmDrEUDQs2N8b04e3xl2dG+JWB3u9WJp9V3t8NKaT0/aXd1OvIl3Z0t+YUD/8+tRNeHFQCzzaJw7DzLr5A+ysjLA3/8DIPJBElq7ksbUs2ZyxW958mMa8Zsr/9W+GVc/1Mc19+bB0ou7bd7Szei7h4s9l5u1t0SwyECqVCj3jw0yvZ7kvj+U12FEVWUvO3iOdxcar9QK02PlKf7x5eztMN6uS+89zffC42UorZ5z9MeDKUlDLeUTmGoX6V9uS9+yxIKrGhrarj6HtKm88uaI0ahXiI6S/5OeO7og/90v/+q3r74Mz11zvjq6IYItaIg9aDBl4lJu5wlg3onvjUNNEzh8et99TM8RiXkuv+HrYkp6JOzs7nqPhpVbhi3FdcD67AK2jgxweaxRbrw4OvTEYvt5q/LL3LADHy1FHdmyAga2jbK5KMs89T98Sb3eC6CiLpdJTh7ZE3xbhVnMuLOf9TOgdh9yCEnxuZ7dTaVukb5JxYqmRSmWYV/O/Tcfx33s6AigbArujUwP4emvQISYYDev6m2rJuMIw38iwxHvefZ2x4cgl05bpT98S71KxNVvB4r17OmDeunR8+UAXRIf4Yecr/dH97TU2Hi0f9lgQkUfZ6gb/YFRHtGsQjC8f6CpDi+xzNjzijLs9FrteHYDfn7pJUsAs0sbOvUbeGjXGdDdcfAe3icL8+7vgw9Ed8ebtbe0+BjAEvoFtojDezR1x/Xw0kl4aZ2dnb2O8OLOJlze3jLB5TJ/m4TYKxmnQr0UE/C3mw1j2HPl6azB1qHUpe1ssl/o+2rsJPjXrAVSrDLsT/zixJxpZ7LGjVqswrH1903J288nWzpjPcYgI0uLx0iJ7ACQTqM0ncLaqLw2Bxp4bf7Of891dGmLdizebKg9HBPl6tH6MJ7DHgogqTXjphTs+IgC//5/9XWblElrHx7Q8tTza2CmIZk9YgBZhAVpsMVtC6mx57+vD22Bg6ygkxIXC38fL4QRXI8sLc3m5OqRh7v17O0jOyXxY5utHumPcgp0AYHMHYUvG5apjbewPA5T14DjyydjOuPPTrabvvTRqSS+gvXkstjgbTjLnpVZj5sg2OHb5OrrG1sWxy2XLaX3NgsLrw9ugY0wIsvKLMbp7DH7ecxavLN8PAGgdHYSk5/siPMBxAN74Uj+sTD2Pb3dkYMeJqy63sbIwWBCRx/1nWCtkXM23KlpV3XxyXye8+OM+PNPf9uRTe1Y92wc/7Tnt0qojW5zNkTDn661BPzt/9Vt6/tbm2HniKoaXYwWPLeWZQmJ5nTbPTa3qB2HVs32wLu2S1Z43tvxvfFfsO5Nt2qPH0oejO2HZzgy8988Ru8/RuVFdnJg1FE2m/mXzfneChT3hgVpctgiobRsEYZxZ3ZQSfdlkDj+LlUTmYbFxWFmviY9GbTXUaIuPlxojOzbAlbwiBgsiUqYJvV2f5Can+IhArJjUy+3HtYgKxKvD7O8W60z3xqEIrePjsER4eTztZkBypjxTUx31lnipVWgRFYgWUa6t1qqj9UJi03p27w8L0OKpW5rh9NUb2H82G9Ehfkg6ZF18rSI1YlxhK4BZ1mJpGh6A6GBfhPj7OFw1Y77ix93Jzx0auteDVlkYLIiIqpifjwbbp/aXtdaAK9zpsZg6pCVSzmRZregw75yprDLT79xtuIgLIZB9oxhTft5nt9fGukel4u+B5ZDRXZ0bSnY8BgzzZTa81M/p63WOrQtvjQpxYe6Hzq6NQ7Hwwa5oXM+6uFhVYrAgIpKBO+P1chFu9FmYT040J9nkr5KrT6lUKoT4++DzcfYnCVuGpfpOapW4IsDXC5nXi8zaYfs4V+p7BGi9sH/6oHLvW3RLS9eXVVeW6v8vm4iIZOGJLdXN/5p3pShUZetSup/N5+O6YHS3GIxzo0y2pS8f6IrmkQGSVSaA6wW87PH11lSLn1V5sceCiIgqjXk4kXMr7x2v9MeF7ALTks5BbaIwqI2D/W+cUKkMBdlubW3oIXBlhUptwR4LIiKqNOZzLOSshBsZ5IsOMSEVfp5FD3VDw7p++HZCD8ntC802/6vt3AoW06dPh0qlkny0bNnS+QOJiKjGiQn1d36QE17VfIKqu/q1iMDmKbdYrVYx3y23hu8kUGFuD4W0adMGSUlJZU/gxdEUIiIl+XZCAjYcuYxxPco//8CoY8MQDGkbZVXVUslUFZ5lUbO5nQq8vLwQFVX+cSkiIqreesaHoWd8mEeeS61WYf79XTzyXFQzuD3H4ujRo4iOjkZcXBzGjh2LjIwMh8cXFhYiJydH8kFERKRUnVwoV65kKuFGMfiVK1ciLy8PLVq0wPnz5zFjxgycPXsWqampCAy0XUlt+vTpmDFjhtXt2dnZCApybdc9IiKi6i79Ui52nbyGe7vG1Ojlovbk5OQgODjY6fXbrWBhKSsrC7GxsXj//ffxyCOP2DymsLAQhYVlNdRzcnIQExPDYEFERFSDuBosKjTzMiQkBM2bN0d6errdY7RaLbTaim1NTERERDVDhepY5OXl4dixY6hfv77zg4mIiEjx3AoWL774IjZs2ICTJ09i69atuOOOO6DRaDBmzJjKah8RERHVIG4NhZw5cwZjxoxBZmYmwsPDcdNNN2H79u0IDw+vrPYRERFRDeJWsFi2bFlltYOIiIgUgHuFEBERkccwWBAREZHHMFgQERGRxzBYEBERkccwWBAREZHHMFgQERGRxzBYEBERkccwWBAREZHHVGgTsvIwbqaak5NT1S9NRERE5WS8bjvbFL3Kg0Vubi4AICYmpqpfmoiIiCooNzcXwcHBdu9XCWfRw8P0ej3OnTuHwMBAqFQqjz1vTk4OYmJicPr0aYf7xNdkSj9HpZ8foPxzVPr5Aco/R6WfH6D8c6ys8xNCIDc3F9HR0VCr7c+kqPIeC7VajYYNG1ba8wcFBSnyH4o5pZ+j0s8PUP45Kv38AOWfo9LPD1D+OVbG+TnqqTDi5E0iIiLyGAYLIiIi8hjFBAutVovXX38dWq1W7qZUGqWfo9LPD1D+OSr9/ADln6PSzw9Q/jnKfX5VPnmTiIiIlEsxPRZEREQkPwYLIiIi8hgGCyIiIvIYBgsiIiLyGMUEi3nz5qFx48bw9fVFQkICdu7cKXeTXDJr1ix069YNgYGBiIiIwO233460tDTJMTfffDNUKpXkY+LEiZJjMjIyMGzYMPj7+yMiIgKTJ09GSUlJVZ6KTdOnT7dqe8uWLU33FxQUYNKkSahXrx4CAgJw11134eLFi5LnqK7nZtS4cWOrc1SpVJg0aRKAmvf+bdy4EcOHD0d0dDRUKhVWrFghuV8Igddeew3169eHn58fBgwYgKNHj0qOuXr1KsaOHYugoCCEhITgkUceQV5enuSYffv2oXfv3vD19UVMTAzefffdyj41E0fnWFxcjClTpqBdu3aoU6cOoqOj8cADD+DcuXOS57D1vs+ePVtyjFzn6Ow9fPDBB63aPnjwYMkxNfk9BGDz/6RKpcKcOXNMx1Tn99CVa4Onfn+uX78enTt3hlarRXx8PBYvXlyxxgsFWLZsmfDx8RELFy4UBw4cEI8++qgICQkRFy9elLtpTg0aNEgsWrRIpKamiuTkZDF06FDRqFEjkZeXZzqmb9++4tFHHxXnz583fWRnZ5vuLykpEW3bthUDBgwQe/fuFX/99ZcICwsTU6dOleOUJF5//XXRpk0bSdsvX75sun/ixIkiJiZGrFmzRuzevVv06NFD9OzZ03R/dT43o0uXLknOb/Xq1QKAWLdunRCi5r1/f/31l3j11VfFL7/8IgCI5cuXS+6fPXu2CA4OFitWrBApKSlixIgRokmTJuLGjRumYwYPHiw6dOggtm/fLjZt2iTi4+PFmDFjTPdnZ2eLyMhIMXbsWJGamiq+++474efnJz7//HPZzzErK0sMGDBAfP/99+Lw4cNi27Ztonv37qJLly6S54iNjRVvvPGG5H01/38r5zk6ew/Hjx8vBg8eLGn71atXJcfU5PdQCCE5t/Pnz4uFCxcKlUoljh07ZjqmOr+HrlwbPPH78/jx48Lf3188//zz4uDBg+Ljjz8WGo1G/P333+VuuyKCRffu3cWkSZNM3+t0OhEdHS1mzZolY6vK59KlSwKA2LBhg+m2vn37imeeecbuY/766y+hVqvFhQsXTLfNnz9fBAUFicLCwspsrlOvv/666NChg837srKyhLe3t/jxxx9Ntx06dEgAENu2bRNCVO9zs+eZZ54RTZs2FXq9XghRs98/y1/Yer1eREVFiTlz5phuy8rKElqtVnz33XdCCCEOHjwoAIhdu3aZjlm5cqVQqVTi7NmzQgghPv30U1G3bl3J+U2ZMkW0aNGiks/Imq2LkqWdO3cKAOLUqVOm22JjY8UHH3xg9zHV5RztBYuRI0fafYwS38ORI0eKW265RXJbTXkPhbC+Nnjq9+dLL70k2rRpI3mtUaNGiUGDBpW7rTV+KKSoqAh79uzBgAEDTLep1WoMGDAA27Ztk7Fl5ZOdnQ0ACA0Nldz+zTffICwsDG3btsXUqVORn59vum/btm1o164dIiMjTbcNGjQIOTk5OHDgQNU03IGjR48iOjoacXFxGDt2LDIyMgAAe/bsQXFxseS9a9myJRo1amR676r7uVkqKirC0qVL8fDDD0s22avJ75+5EydO4MKFC5L3LDg4GAkJCZL3LCQkBF27djUdM2DAAKjVauzYscN0TJ8+feDj42M6ZtCgQUhLS8O1a9eq6Gxcl52dDZVKhZCQEMnts2fPRr169dCpUyfMmTNH0sVc3c9x/fr1iIiIQIsWLfDEE08gMzPTdJ/S3sOLFy/izz//xCOPPGJ1X015Dy2vDZ76/blt2zbJcxiPqcj1s8o3IfO0K1euQKfTSX5wABAZGYnDhw/L1Kry0ev1ePbZZ9GrVy+0bdvWdPt9992H2NhYREdHY9++fZgyZQrS0tLwyy+/AAAuXLhg8/yN98kpISEBixcvRosWLXD+/HnMmDEDvXv3RmpqKi5cuAAfHx+rX9aRkZGmdlfnc7NlxYoVyMrKwoMPPmi6rSa/f5aM7bHVXvP3LCIiQnK/l5cXQkNDJcc0adLE6jmM99WtW7dS2l8eBQUFmDJlCsaMGSPZ0Onpp59G586dERoaiq1bt2Lq1Kk4f/483n//fQDV+xwHDx6MO++8E02aNMGxY8fwyiuvYMiQIdi2bRs0Go3i3sMlS5YgMDAQd955p+T2mvIe2ro2eOr3p71jcnJycOPGDfj5+bnd3hofLJRk0qRJSE1NxebNmyW3P/bYY6av27Vrh/r166N///44duwYmjZtWtXNdMuQIUNMX7dv3x4JCQmIjY3FDz/8UK5/sNXdggULMGTIEERHR5tuq8nvX21XXFyMe++9F0IIzJ8/X3Lf888/b/q6ffv28PHxweOPP45Zs2ZV+1LRo0ePNn3drl07tG/fHk2bNsX69evRv39/GVtWORYuXIixY8fC19dXcntNeQ/tXRuqqxo/FBIWFgaNRmM1E/bixYuIioqSqVXue+qpp/DHH39g3bp1TreVT0hIAACkp6cDAKKiomyev/G+6iQkJATNmzdHeno6oqKiUFRUhKysLMkx5u9dTTq3U6dOISkpCRMmTHB4XE1+/4ztcfT/LSoqCpcuXZLcX1JSgqtXr9ao99UYKk6dOoXVq1c73X46ISEBJSUlOHnyJICacY5GcXFxCAsLk/ybVMJ7CACbNm1CWlqa0/+XQPV8D+1dGzz1+9PeMUFBQeX+46/GBwsfHx906dIFa9asMd2m1+uxZs0aJCYmytgy1wgh8NRTT2H58uVYu3atVbebLcnJyQCA+vXrAwASExOxf/9+yS8C4y/C1q1bV0q7yysvLw/Hjh1D/fr10aVLF3h7e0veu7S0NGRkZJjeu5p0bosWLUJERASGDRvm8Lia/P41adIEUVFRkvcsJycHO3bskLxnWVlZ2LNnj+mYtWvXQq/Xm0JVYmIiNm7ciOLiYtMxq1evRosWLapFF7oxVBw9ehRJSUmoV6+e08ckJydDrVabhhCq+zmaO3PmDDIzMyX/Jmv6e2i0YMECdOnSBR06dHB6bHV6D51dGzz1+zMxMVHyHMZjKnT9LPe0z2pk2bJlQqvVisWLF4uDBw+Kxx57TISEhEhmwlZXTzzxhAgODhbr16+XLHnKz88XQgiRnp4u3njjDbF7925x4sQJ8euvv4q4uDjRp08f03MYlxQNHDhQJCcni7///luEh4dXiyWZL7zwgli/fr04ceKE2LJlixgwYIAICwsTly5dEkIYlks1atRIrF27VuzevVskJiaKxMRE0+Or87mZ0+l0olGjRmLKlCmS22vi+5ebmyv27t0r9u7dKwCI999/X+zdu9e0ImL27NkiJCRE/Prrr2Lfvn1i5MiRNpebdurUSezYsUNs3rxZNGvWTLJUMSsrS0RGRopx48aJ1NRUsWzZMuHv719lSxUdnWNRUZEYMWKEaNiwoUhOTpb8vzTOpN+6dav44IMPRHJysjh27JhYunSpCA8PFw888EC1OEdH55ebmytefPFFsW3bNnHixAmRlJQkOnfuLJo1ayYKCgpMz1GT30Oj7Oxs4e/vL+bPn2/1+Or+Hjq7Ngjhmd+fxuWmkydPFocOHRLz5s3jclOjjz/+WDRq1Ej4+PiI7t27i+3bt8vdJJcAsPmxaNEiIYQQGRkZok+fPiI0NFRotVoRHx8vJk+eLKmDIIQQJ0+eFEOGDBF+fn4iLCxMvPDCC6K4uFiGM5IaNWqUqF+/vvDx8RENGjQQo0aNEunp6ab7b9y4IZ588klRt25d4e/vL+644w5x/vx5yXNU13Mzt2rVKgFApKWlSW6vie/funXrbP6bHD9+vBDCsOR02rRpIjIyUmi1WtG/f3+r887MzBRjxowRAQEBIigoSDz00EMiNzdXckxKSoq46aabhFarFQ0aNBCzZ8+uqlN0eI4nTpyw+//SWJtkz549IiEhQQQHBwtfX1/RqlUr8fbbb0suzHKeo6Pzy8/PFwMHDhTh4eHC29tbxMbGikcffdTqD7Ga/B4aff7558LPz09kZWVZPb66v4fOrg1CeO7357p160THjh2Fj4+PiIuLk7xGeXDbdCIiIvKYGj/HgoiIiKoPBgsiIiLyGAYLIiIi8hgGCyIiIvIYBgsiIiLyGAYLIiIi8hgGCyIiIvIYBgsiIiLyGAYLIiIi8hgGCyIiIvIYBgsiIiLyGAYLIiIi8pj/B2QC2eM6gTtrAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plt.plot(loss_track2)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [],
"source": [
"torch.save(model.state_dict(), 'model.bin')"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\PROGRAMY\\Anaconda3\\envs\\scweet\\lib\\site-packages\\torch\\nn\\modules\\container.py:217: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
" input = module(input)\n"
]
},
{
"data": {
"text/plain": [
"[('<unk>', 0, 0.1108938604593277),\n",
" ('was', 12, 0.0792110487818718),\n",
" ('had', 37, 0.07402306795120239),\n",
" ('is', 8, 0.04529397189617157),\n",
" ('has', 39, 0.03909718990325928),\n",
" ('would', 48, 0.038855526596307755),\n",
" ('said', 43, 0.022579118609428406),\n",
" ('will', 27, 0.02008220925927162),\n",
" ('went', 251, 0.013605386018753052),\n",
" ('did', 151, 0.013007525354623795)]"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"device = 'cpu'\n",
"model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
"model.load_state_dict(torch.load('model.bin'))\n",
"model.eval()\n",
"\n",
"ixs = torch.tensor(vocab.forward(['he'])).to(device)\n",
"\n",
"out = model(ixs)\n",
"top = torch.topk(out[0], 10)\n",
"top_indices = top.indices.tolist()\n",
"top_probs = top.values.tolist()\n",
"top_words = vocab.lookup_tokens(top_indices)\n",
"list(zip(top_words, top_indices, top_probs))"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [],
"source": [
"def prediction(word: str) -> str:\n",
" ixs = torch.tensor(vocab.forward([word])).to(device)\n",
" out = model(ixs)\n",
" top = torch.topk(out[0], 5)\n",
" top_indices = top.indices.tolist()\n",
" top_probs = top.values.tolist()\n",
" top_words = vocab.lookup_tokens(top_indices)\n",
" zipped = list(zip(top_words, top_probs))\n",
" for index, element in enumerate(zipped):\n",
" unk = None\n",
" if '<unk>' in element:\n",
" unk = zipped.pop(index)\n",
" zipped.append(('', unk[1]))\n",
" break\n",
" if unk is None:\n",
" zipped[-1] = ('', zipped[-1][1])\n",
" return ' '.join([f'{x[0]}:{x[1]}' for x in zipped])"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [],
"source": [
"def create_outputs(folder_name):\n",
" print(f'Creating outputs in {folder_name}')\n",
" with lzma.open(f'{folder_name}/in.tsv.xz', mode='rt', encoding='utf-8') as fid:\n",
" with open(f'{folder_name}/out.tsv', 'w', encoding='utf-8', newline='\\n') as f:\n",
" for line in fid:\n",
" separated = line.split('\\t')\n",
" prefix = separated[6].replace(r'\\n', ' ').split()[-1]\n",
" output_line = prediction(prefix)\n",
" f.write(output_line + '\\n')"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating outputs in dev-0\n",
"Creating outputs in test-A\n"
]
}
],
"source": [
"create_outputs('dev-0')\n",
"create_outputs('test-A')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "scweet",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.15"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}